From 9e1673c06b66576067772573627fb547b5c97856 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Wed, 22 May 2024 09:42:10 -0700 Subject: [PATCH 001/191] Adding planet IMF derivation --- changes/Power_Law_Function.ipynb | 608 +++++++++++++++++++++++++++++++ 1 file changed, 608 insertions(+) create mode 100644 changes/Power_Law_Function.ipynb diff --git a/changes/Power_Law_Function.ipynb b/changes/Power_Law_Function.ipynb new file mode 100644 index 00000000..fb713697 --- /dev/null +++ b/changes/Power_Law_Function.ipynb @@ -0,0 +1,608 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "23642516", + "metadata": {}, + "source": [ + "## Plotting Studied Power-Law Functions Over Small Mass Intervals" + ] + }, + { + "cell_type": "markdown", + "id": "ad57fd40", + "metadata": {}, + "source": [ + "#### Importing Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "d6503a21", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy.optimize import curve_fit" + ] + }, + { + "cell_type": "markdown", + "id": "4f72f6f7", + "metadata": {}, + "source": [ + "#### Creating the general power-law function for small masses" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d51b38dc", + "metadata": {}, + "outputs": [], + "source": [ + "#define function\n", + "def power_law(M, a):\n", + " return M**(-1*a)\n", + "\n", + "# M is a defined mass range according to a specified paper, and a is the associated alpha value" + ] + }, + { + "cell_type": "markdown", + "id": "3fe49d7b", + "metadata": {}, + "source": [ + "#### Defining different mass/alpha values based on published papers" + ] + }, + { + "cell_type": "markdown", + "id": "b7736fad", + "metadata": {}, + "source": [ + "***Masses are in solar masses!***" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "4145bc16", + "metadata": {}, + "outputs": [], + "source": [ + "#Moraux 2007 (0.03 - 0.6) a = 0.69 ± 0.15\n", + "M_mass = np.linspace(0.03, 0.6, 1000)\n", + "M_a = 0.69\n", + "M_aerr = 0.15\n", + "\n", + "#Suárez 2009 (0.02 - 0.50) a = 0.60 ± 0.13\n", + "S_mass = np.linspace(0.02, 0.5, 1000)\n", + "S_a = 0.60\n", + "S_aerr = 0.13\n", + "\n", + "#Lodieu 2009 (0.01 - 0.5) a = 0.5 ± 0.2\n", + "L_mass = np.linspace(0.01, 0.5, 1000)\n", + "L_a = 0.5\n", + "L_aerr = 0.2\n", + "\n", + "#Béjar 2011 (0.013 - 0.1) a = 0.7 ± 0.3\n", + "B_mass = np.linspace(0.013, 0.1, 1000)\n", + "B_a = 0.7\n", + "B_aerr = 0.3\n", + "\n", + "#Peña Ramírez 2012 (0.006 - 0.25) a = 0.6 ± 0.2\n", + "P_mass = np.linspace(0.006, 0.25, 1000)\n", + "P_a = 0.6\n", + "P_aerr = 0.2" + ] + }, + { + "cell_type": "markdown", + "id": "f2a24e63", + "metadata": {}, + "source": [ + "#### Plotting all of the curves together (w/o accounting for error)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "60cab6f2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plot all of the power law functions based on mass ranges\n", + "plt.plot(M_mass, power_law(M_mass, M_a), color='r', label='Moraux (a=0.69)')\n", + "plt.plot(S_mass, power_law(S_mass, S_a), color='b', label='Suárez (a=0.60)')\n", + "plt.plot(L_mass, power_law(L_mass, L_a), color='m', label='Lodieu (a=0.50)')\n", + "plt.plot(B_mass, power_law(B_mass, B_a), color='c', label='Béjar (a=0.70)')\n", + "plt.plot(P_mass, power_law(P_mass, P_a), color='y', label='Peña Ramírez (a=0.60)')\n", + "plt.title(\"Power Law Mass Functions for Planetary Masses According to Published Papers\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-a)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f5494d3d", + "metadata": {}, + "source": [ + "#### Accounting for error in a" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "1e6c80f4", + "metadata": {}, + "outputs": [], + "source": [ + "#Moraux\n", + "M_aup = M_a + M_aerr\n", + "M_alow = M_a - M_aerr\n", + "M_outup = power_law(M_mass, M_aup)\n", + "M_outlow = power_law(M_mass, M_alow)\n", + "\n", + "#Suárez\n", + "S_aup = S_a + S_aerr\n", + "S_alow = S_a - S_aerr\n", + "S_outup = power_law(S_mass, S_aup)\n", + "S_outlow = power_law(S_mass, S_alow)\n", + "\n", + "#Lodieu\n", + "L_aup = L_a + L_aerr\n", + "L_alow = L_a - L_aerr\n", + "L_outup = power_law(L_mass, L_aup)\n", + "L_outlow = power_law(L_mass, L_alow)\n", + "\n", + "#Béjar\n", + "B_aup = B_a + B_aerr\n", + "B_alow = B_a - B_aerr\n", + "B_outup = power_law(B_mass, B_aup)\n", + "B_outlow = power_law(B_mass, B_alow)\n", + "\n", + "#Peña Ramírez\n", + "P_aup = P_a + P_aerr\n", + "P_alow = P_a - P_aerr\n", + "P_outup = power_law(P_mass, P_aup)\n", + "P_outlow = power_law(P_mass, P_alow)" + ] + }, + { + "cell_type": "markdown", + "id": "041954f2", + "metadata": {}, + "source": [ + "#### Graphing Individual Power-Law Functions (Accounting for Error in a)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "dc7a858e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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NZxdgh5x92NnE7avAexXbRiBnG/YwXvIm4DWx5eyllFMDPb7HHbsQKqaezfoefkRodYDCMaAcwzoesNA16M+EpO2GZLq7P+juz3jomvkvQivNvxVYzRoKx+P5cX6inO0wsTZ7ve7+XXefQdj/ZR+/5e7HhsSI+Hg4J+rZWsYYX9aVev2kSDDc/UnCYO9TgN/mzF5DqI3aJWvazoSa562ryHnNl+O053lo/nsLYcNPdBBqWhLbZz3GwmkR/x9wLSFglONnhMz7qqydbLK+Ywg12q8j1G7OADYmZXL3R9z9jYQ/6v8Cvzazqe7e6+6fdff9CNn4yxnaP7gc7RT5rCUsp0A/9RKeAXayoYOhcn+zkSpn3bnbw0h9hFA7dGTcjpKuYeX09V9OaMGYkXWb6u5fyVfGUttIvtfEndPlhAP20wnbYDkujp9tm4MnSnxmd/+ru59E2IE+RKjhxd2fc/d3uvsOhFrI78cgsBz4Z8730Oru742vu93dTyVs+1cy2Ew/ZmIFwp+BV+TOs9Bv9XxC0J4df4f7Kf83n2VZ472yPEPWPsxC17HZVOY/UdPc/SFCDVsyzqngPsnMGgmJ8zeAXdx9IWE/XPB/QPjeX5qzjTW7e/Ld/h+hdrjc7gY3ELbv7cjpYhm74fyGcGa77eL2cRWD/4/N7v4Rd9+NsH192OJYC3f/hbu/gLAdOGFfPxxjtS8vtr8sJ/6O1Ehi+4iUuX8tZDnwRM72Nc3dk9Mn5+7Li24j+V4TXUQ4VnktcHPW9lvMeB53LCe0YMzJ+h7a3H3/uL5CMaCi4j76b8Dn3b1UzHMK/8Z/I1R+7ZSz/iMICXl2d93hbIfXAq8uY7ncdQ6JEYT/QtLlbch/38zy/fd3yprfSuh+VfREP5MiwYjeDpwQa0m38nAqs8uBL5rZtLhxfZhtx2lkm0bsLmShL/xHc+bfDbzJwiDmkwl9P4GtffJ/TGhqPJPQn6/kedjd/Ym4nk/lmT2N0Gy2GsiY2WcIfTOT93yLhb75A4SmNAh9mI83swNjjfkmws54uKd2uxt4g4VBY4sonM3ncwHweQunXDMze54NDipeSeg3m8+thD/Ex+L7HkcItpXocz6W6841jdD3dIOFQaP/M4zXXkLYdl4St7NmCwO7kgHTud9f0W2kiIsJLX+vpPh/IttlhL6z+Q7mC35mM9vOzF4ZD467Cf+x/jjvtVmfbT1h59lPOJvQXmZ2evy9GszscAtjrhotXN9iuodxI5sY/vY9bLGcJ5P/bDZTY9lXx2XfSp4TAOTj7s8SEpfvWxgs32BmSYL2C+CtZnZwPAD5EqH/77ICZWy00FfagIa4/dRFPLAwqP8jyfYQA/gbCd0OIOyTjrVwPvzphO4miSbCiQfa42tfSmidK+aHhPiwS3zNXDM7NT5+N2G//KacVs+C3N0J+5RXxsfZGmMZVwN9sXxbT59sZi+3MNjVGNye+y1c/+GE+Nt3Ef5jI9mXn2LhRALbE1pEyvVjwvZ3ooVB8TvaYOtjwX35CONvWcZy3XmMdP8KodvwJgsDcqfE/fkBNnja5ZXAwqz/Z9FtpIgrgUOB/yB/5c82xvO4I+7frga+YWZtcTva3cxeGNdXKAZUTDye+zvwPXf/YZ75p8Z9r8VE4YMUGHPl7n8jJAO/MbP94+96FKHl6Afu/sgIi3kOcIyZnRvLmxxX7lv0VaGr96fj/msO4Qx6yX/hHmD/GD+ayT+m+BQLpwdvJIzF2GZ8Sa66CCiV4O6PufviArM/QAg4jxNqlH4B/KTI6j5L+KNuJAy0zW0V+Q9CANlAqP29MmveecDv3P0qd19LSHwusKGngyv0GW70/KeG/SvhwONhQrNXF0Ob3E4GHrBwxqRvE/oJdxFqqH5N+JMvAf7J8He+/02ouVpP+F5+MYzXnksIAFfHMvyYwVMInwNcZKGpNHugMe7eQzjgfSmhlur7wBmxJnNUxnLdeXyL8HnXEA6O/lLuC+Mf+1RCU/pqwu/9UQb/098G/s3CmVe+Q+ltpND73ETo939noYPVPK/pdPe/uXu+EwZ8i8KfOUVo4XiG0Pz6QkLtGYTB1rfGbfj3hKbrJ9x9MyG4viG+7jlCbVlTfN3pwDIL3bHew9CBjpWUnOVlC2Hg402E/8MQ7v4gofb8ZsKBw4Fx2XKdTgjIDxH6hH8orvdawn/xN4R+wbsTzwBTwNWEg9CjCfukTgZbk2rdZkK/9VstnMntFkIr0EcA3P0aQpJ7L3AHIQklzttMOCj4JWGf9SbC9lTMt+MyV5vZ5vh+R8Z5byQcPD9jg2eSOrvUB3D3B9x9mwQ0q3yXFyjfnoSa0S2Ebej77n4dYXv/CuF/9Ryh1rhkOXL8jHCgsYywfZR96loPXSffSjjpwEZCLElqS3P3RbmGG3+HYyzXnW1E+1fYmgi9gjAe7QnCb3gBobsRhFOdAqw1szvL2EYKvU8nYf+wK9sesxR73Xged5zB4Aky1sflku5AeWNAuZ+jTO8g/J//J+v/vCVr/hsIXRQ3E5K0//Ui16sgdNH9ByHObSF8zh8TtssR8XDygqMIA93vifukmwjx77+LvPQLwGLCfvE+winKv5C1zs8R9i2PkP/kNb8gVAiuAw4jHNsWZdtWoIiIDLJw+sdfuPsF1S6LiIiMTGxl2Mvdx6qiRSYgM7uQMPB/OGedoioX3xCR+hCb6Q8ltJaIiEgdstAl9e2EVlCRMTdpukiJyPCY2UWEJtMPxWZ5ERGpM2b2TkL3pT+7+/WllhepBHWREhERERGRilELhoiIiIiIVIzGYEhBc+bM8YULF1a7GCJlueOOO9a4+9xql0NkIlEckHqiOFA7lGBIQQsXLmTx4kJn9hWpLWb2ZOmlRGQ4FAeknigO1A51kRIRERERkYpRgiEiIiIiIhWjBENERERERCpGYzBkZNyhpweamqpdkrrS29vLihUr6OrqqnZR6lZzczMLFiygoaGh2kURmfS6uqC5udqlqC+KA6OnOFD7lGDIyKxcCStWwKJF1S5JXVmxYgXTpk1j4cKFmFm1i1N33J21a9eyYsUKdt1112oXR2TSu/9+OPhgyOhoomyKA6OjOFAf1EVKRsYM+vqqXYq609XVxezZsxVURsjMmD17tmr+RGpEf79CwXApDoyO4kB9UIIhI5PJhMgiw6agMjr6/kRqR1+fEoyR0H5sdPT91T4lGDIymUyIKu7VLomIiFSJWjBEJB/1mpSRSbpI9fWBBlmN3C23wIYNlVvfjBlw1FGVW98oveMd7+DDH/4w++23X7WLIiJjQAnG6E3wMKA4MEkpwZCR6+uD3l4lGKOxYQPMnVu59a1eXZHV9Pf3k06nCz7Px91xd1KpwYbRCy64oCLlEZHapARj9Go0DCgOyKioi5SMnCJLXbrkkks44ogjOPjgg3n3u99NfxxL09raymc+8xmOPPJIbr755m2en3vuuRxwwAEccMABfOtb3wJg2bJl7Lvvvrzvfe/j0EMPZfny5UPe67jjjmPx4sVb1/+pT32Kgw46iKOOOoqVK1duU7bbbruNo48+mkMOOYSjjz6apUuXju2XISKj0t8P3d3VLoUMl+KAjDUlGDJySQuG1I0lS5Zw2WWXcdNNN3H33XeTTqf5+c9/DkB7ezsHHHAAt956Ky94wQuGPJ8yZQo//elPufXWW7nllls4//zzueuuuwBYunQpZ5xxBnfddRe77LJLwfdub2/nqKOO4p577uHYY4/l/PPP32aZffbZh+uvv5677rqLz33uc5x99tlj80WISMXoZD71RXFAxoO6SMnIKcGoO9deey133HEHhx9+OACdnZ3MmzcPgHQ6zWte85qty2Y/v/HGG3nVq17F1KlTAXj1q1/NDTfcwCtf+Up22WUXjiqjw29jYyMvf/nLATjssMO45pprtllm48aNnHnmmTzyyCOYGb3avkRqnhKM+qI4IONBCYaMnLsiS51xd84880y+/OUvbzOvubl5SP/a7Ode5GxhSbAppaGhYeupBdPpNH15utf993//N8cffzxXXHEFy5Yt47jjjitr3SJSPQoD9UVxQMaDukjJ6HR2VrsEMgwnnngiv/71r1m1ahUA69at48knnyz5umOPPZYrr7ySjo4O2tvbueKKKzjmmGMqXr6NGzey4447AnDhhRdWfP0iUnkKA/VFcUDGg1owZOTMoL292qWobzNmVO6UH8n6ithvv/34whe+wItf/GIGBgZoaGjge9/7XtE+swCHHnooZ511FkcccQQQTjt4yCGHsGzZsgoVPPjYxz7GmWeeybnnnssJJ5xQ0XWLyNjQIO/RGecwoDgg48KKNXnJ5LZo0SJPzvywjU2b4LrrYOZMGIMajIlqyZIl7LvvvtUuRt3L9z2a2R3uvqhKRRKZkIrGAeDqq8OZpE46KVx/VUpTHKgMxYHapi5SNcrM/p+Zfc/M7jWz1Wb2lJldZWb/bmbTq10+IFz/oqOj2qUQEZmQRhMHzOwnZrbKzO7PmnaOmT1tZnfH2ymVKqvG4YpINiUYNcjM/gy8A/grcDIwH9gP+DTQDPzOzF5ZvRJGZmGgtyKLiEhFVSAOXBhfl+ub7n5wvF1VqfIqDIhINjVo1qbT3X1NzrQtwJ3x9g0zmzP+xSpAV/MeFnffehYNGT5165RJYlRxwN2vN7OFY1i+IZRgDI/iwOgoDtQ+tWDUoDxBZUTLjAt36OmpdinqRnNzM2vXrtXOcYTcnbVr19Lc3FztooiMqTGMA++PXa5+YmYz8y1gZu8ys8Vmtnh1maOPFQbKpzgwOooD9UEtGDXIzN7m7j+JjxcAFwGHAQ8CZ7n7w9Us3zYUWcq2YMECVqxYQblBW7bV3NzMggULql0MkTFlZvsA3wQGgA8C/w2cBjwMnOnuS0aw2h8Anwc83n8DeFvuQu5+HnAehEHepVaaTutUtcOhODB6igO1TwlGbXo/8JP4+FzgcuAk4FRCgDixSuXaViqlqywNQ0NDA7vuumu1iyEite884GtAK/B34OPAW4GXA99lBHHA3Vcmj83sfOCPlShoQ4POWD4cigMyGaiLVO3by91/5O4D7n4FMKvaBRqisRG2bKl2KUREJppp7v4Hd/8l0Ovul3rwByBv16ZSzGx+1tNXAfcXWnY4MhmdUFBEhlILRm1aYGbfAQyYa2YN7p4Moaut0dQNDUowREQqL531+NyceY2lXmxmvwSOA+aY2Qrgf4DjzOxgQhepZcC7K1FQnbFcRHIpwahNH816vJjQRL7ezLYHfl+dIhWgtnERkbHwPTNrdfct7v79ZKKZ7QH8rdSL3f2NeSb/uJIFTKTT4WreAwOh16yIiBKMGuTuFxWY/hxwdrHXmtlPCH10V7n7AXHaOcA7gWRE2dkVO/95JhMiS39/iDIiIjJq7v6jAtMfBT40vqUpLjnbak8P6MQ+IgIag1F3zOwzJRa5kHG8uNJW3d0VX6WIiGyrjDhQFQoDIpJQglF/3lFsprtfD6wbp7IM0qlqRUTGS9E4UA26JJKIZFMXqRpkZpsKzQKmjHC17zezMwhjOj7i7usLvPe7gHcB7LzzzuWvXaeqFRGpmDGKA2MmldK1MERkkFowatMGYE93b8u5TQOeHcH6fgDsDhwcX/+NQgu6+3nuvsjdF82dO7e8tWcyOpOUiEhlbaCycWBMNTbC5s3VLoWI1AolGLXpYmCXAvN+MdyVuftKd+939wHgfOCI0RRuG42NsKlQZZuIiIxARePAWFOCISLZ1EWqBrn7p4vM+/hw12dm8909qfGq2MWVtlJkERGpqErHgbHW0KB6JhEZpARjghnPiytt1dAAGzboVLUiIpNUOg19fdDbG0KCiExuSjDqjJnd6e6HFpo/nhdX2kZXF0ydOi5vJSIyWZWKA9XU1aUEQ0Q0BqPu1GpQAXQmKRGRcVDLcUBhQERACUbNM7NZZjaz2uUoyQza26tdChGRCade4oDCgIgklGDUIDPb2cwuNbPVwK3A7Wa2Kk5bWOXi5dfUBBs3VrsUIiITQj3GgeZmhQERCZRg1KbLgCuA7d19T3ffA5gPXAlcWs2CFdTUFAZ6i4hIJdRdHNAZy0UkoQSjNs1x98vcvT+ZEK9jcSkwu4rlKqyxMbSN9/eXXlZEREqpuzigMCAiCZ1FqjbdYWbfBy4ClsdpOwFnAndVrVSluENnJ7S2VrskIiL1ri7jgLtOKCgiSjBq1RnA24HPAjsCRggwf2C8Tjk7EmbQ0aEEQ0Rk9OozDhDCgBIMkclNCUYNcvce4AfxVj+SC+7Nm1ftkoiI1LV6jQOZDGzeDHPnVrskIlJNGoMhldPcDOvWVbsUIiJSJU1NCgMiogRDKqmpKZxCZGCg2iUREZEqaG6G9evDWAwRmbyUYEjlmIXkoqOj2iUREZEqSKfDWaR0RW+RyU0JRo0zs5OKPa9JupSriEjF1FsccFcYEJnslGDUvv8t8by2NDbCmjXVLoWIyERSV3Egk9F1V0UmOyUY9ceqXYCiWlpg9epql0JEZCKr6TgwZYrqmUQmO52mtkaZ2U8BB3Y2s58AuPvbqluqMiSnqu3qCqP9RERkROo1DjQ3w9q1YSxGOl3t0ohINSjBqF0XxvtjCFdyrR/usGWLEgwRkdG5MN7XZBz45S/h9tvhTW8aOj0538eWLTB9enXKJiLVpS5SNcrd/+nu/wQ2Zz2GUJtVXevXw0knwbXX5p/f2Biqr0REZMRGEwfM7CdmtsrM7s+aNsvMrjGzR+L9zNGU79JL4be/zT8vlQpnLReRyUkJRu3rKfF8/M2YAUuXwpIl+ee3tMBzz41rkUREJrCRxIELgZNzpn0CuNbd9wSujc9H7IADYMUK6O3ddt6UKbBy5WjWLiL1TAlGjXP3o4o9rwozOPBAeOyx/PMbG6GzM9xERGRURhIH3P16IPea2qcy2NXqIuC00ZTrgAPCOIsnn9x23pQpg+MwRGTyUYIhI3PggfDEE4Wjh7vax0VEast27v4sQLyfl28hM3uXmS02s8Wri5wVcP/9w/3jj287L5UK4zA2bx59oUWk/ijBkJF53vOguxuWL88/f8oUdZMSEalD7n6euy9y90Vz584tuNzee4dEolBjdjoN63LbUERkUlCCISNz4IHhfunS/POnTg0dcNU+LiJSK1aa2XyAeL9qNCtraoIFCwonGK2t8Mwzo3kHEalXSjBqnJnNNLNpw1h+zM8cAsA++4TLtT70UP75qVRILtRNSkRkVOI+fPT7bfg9cGZ8fCbwu9GucJdd4NFH889ragpdpDQcT2TyUYJRg8xsBzO72Mw2AmuAB8zsKTM7x8waSrz8Qsb4zCFAuKDennvCvfcWX0anERERGTYz29nMLjWz1cCtwO2x8uhSM1tYxut/CdwM7G1mK8zs7cBXgJPM7BHgpPh8VPbcE55+OlxfNX85wpnNRWRyUYJRmy4BfuLu04HXAr8B9iVcGPF7xV44HmcO2Wr//eHBB8NYjHymTQuRZ2CgIm8nIjKJXAZcAWzv7nu6+x7AfOBK4NJSL3b3N7r7fHdvcPcF7v5jd1/r7ifG9Z3o7qMeIbHvvuH+/vvzz586tfBQPRGZuJRg1KbZ7n4dgLv/FjjW3dvd/dPAsSNYX1lnDoHyzx4ChASjt7fw9TAyGejpUTcpEZHhm+Pul7n71oFs7t7v7pcCs6tYriH23DP0iH3ggfzzp0wJA727usa3XCJSXUowatNqM3tL7Cr1AWAZgJkZY/yblXv2EAD22y/c33134WUaG+HZZytWPhGRSeIOM/u+mR0ZY8EO8fH3gbuqXbjElCmw++5w333555uF25o141suEakuJRi16W3AK4GrgSOB98fps4BPjmB9FT1zyFZtbbDbbnBXkVg3bVpoH9fZpEREhuMM4D7gs8BfCfHgs8D9wOlVLNc2DjggtGAU2s23tua/GJ+ITFxKMGqQuz/l7q9z9wPc/S1Z3ZvWuvtvRrDKip85ZKtDDw0JRk9P/vnpdIg6Ohm6iEjZ3L3H3X/g7ie7+4ExHpzs7t939wID36rj0EPD2aIefjj//ClTQk/ZLVvGt1wiUj1KMGqQmb3KzGbFx3PjGaXuM7PLzGxBideOy5lDtjr6aOjoKN5NqqUFli2r2FuKiEx0eeLAReXGgfF2+OHh/tZbCy+Tyai3rMhkogSjNn0x6+we3yX0t30p8Gfgp8VeOF5nDtnq8MPD6WhvuqnwMq2toQNue3vF3lZEZILLjQN3U2YcGG+zZ4fB3rfdVniZtrZQz9TXN27FEpEqUoJRm9JZj/dw92+6+wp3vxAoMfJ6nE2ZEtrH//Wv4stlMrBixfiUSUSk/tVPHACOOCI0ZBc6W1QmE5ILDfYWmRyUYNSm68zsc2Y2JT4+DcDMjgc2VrVk+Tz/+fDEE8VH8U2fHub39o5fuURE6lddxYEjjwxD8RYvLrxMW1u46rf7+JVLRKpDCUZtej8wACwlXGjvt2a2GXgnNXb2EABOPDGch/Dqqwsvk06HC+4988z4lUtEpH7VVRxYtCj0hr322sLLNDeHweA654fIxKcEowa5e6+7n+PuOwMHAnPdfZq7v8ndn6p2+bax3XZwyCHw178Wr5qaMSNUX6kTrohIUfUWBxob4Zhj4J//LL6Lb2mBRx5RK4bIRKcEo8a5+0Z3X1vtcpT04heHEXyPPFJ4mYaG0Ib+3HPjViwRkXpXL3HgxBPD6Whvv73wMq2toQVj/frxK5eIjD8lGHXGzO6sdhnyetGLQjeoP/6x+HIzZ8LSpRqLISIyQrUaB446KiQQV11VfLnWVnjoIbViiExkSjDqjLsfWu0y5DVjBpxwQkgwCp1GBEIrRm9vuLq3iIgMW63GgeZmeOlLwziMjUWGoU+dChs2wKpV41Y0ERlnSjBqnJnNMrOZ1S5HWf7t30L7+F//Wny5mTNDV6rOzvEpl4hIHaunOHDaaaEn7J//XHy56dPhwQc1JE9kolKCUYPMbGczu9TMVgO3Areb2ao4bWGVi1fYoYfCbrvB5ZcXb/vOZEJ3qmLjNUREJrF6jQN77w377Qe//W04cWAhzc3Q3R3OcC4iE48SjNp0GXAFsH28+vYewHzgSuDSahasKDN405vCGItiV/aG0KVq+XJYW/PjFkVEqqE+4wDwhjfA44/DjTcWX27WrHBiwc2bx6dcIjJ+lGDUpjnufpm79ycT3L3f3S8FZlexXKW97GUwfz5ccEHxVgyzkGTce68GfIuIbKtu48CLXww77AA//WnxMJBOh9PW3nsv9PcXXk5E6o8SjNp0h5l938yONLMd4u1IM/s+cFe1C1dUQwOcdRbcfz/cfHPxZZubQ3KxdOm4FE1EpI7UbRzIZOD00+G+++DWW4sv29oahu499tj4lE1ExocSjNp0BnAf8Fngr8DV8fH91OAVXLfxylfCjjvCt75VegTfrFnw5JO6NoaIyFB1HQde+crQivHtb5dunZgzJwzJW7NmfMomImNPCUYNcvced/+Bu5/s7ge6+wHx8ffdvbva5SupoQE+9KHQCfc3vym+rFlIMu65B7ZsGZfiiYjUunqPA01N8P73h8Sh1OWRUqlwcsG77oKOjvEpn4iMLSUYdaJWL6xU0HHHwRFHwA9/CKtXF1+2sTF0l7rrLo3HEBEpoBbjQEND4Ybqk06C5z0PvvvdcN2LYpqawrruvFNhQGQiUIJRP6zaBRiiuTncFzoPoRl87GPhhOhf/GLpS7a2tobrYtxzT/FzG4qITF61FQeABQvCGIp8zOCTnwxnifra10qva9q00IJxzz0a9C1S75Rg1I8/VbsAQzQ2hg62xc4vuHAh/Pu/h3MV/v73pdc5e3Zo7XjwwdIJiYjI5FNbcQDYfvviycCee8I73hGuv3rttaXXN3t2GItx//2qaxKpZ0owapCZbVNL5e6fLrXMuNt5Z+jqKr7MG94Ahx0GX/0qPPxw6XXOnQtPPQUPPaQkQ0QmrXqJA9OmQVtb8VBw1lmw777w+c+Hyx+VMncuPP20kgyReqYEozb9w8w+YGY7Z080s0YzO8HMLgLOrFLZBs2YEQZot7cXXiaVCl2k2trgox+FjRuLr9MM5s0Ll3ddulRJhohMVvURB4DddivcTQrCaWv/93/DdS8++tHQG7aUefNCkqFrZIjUJyUYtelkoB/4pZk9Y2YPmtkTwCPAG4FvuvuF1SzgVnvuWfrsT3PmhBaMlStDdOkucQKUJMl4/PHQXUpVWCIy+dRNHJg3L/SaLTY4e4cdQl3TY4/B2WeXPoN5EgZWroQ77gjD+USkfijBqEHu3hVPRfh8YBfgROAQd9/F3d/p7ndXt4RZZs0KCUSxsRgABx4I55wTzhT1yU+WH12efFJnlxKRSWcs44CZLTOz+8zsbjNbPNqyptOw116wfn3x5Y46Cj7+cbjhBvjc58qrO5ozJ7SO3HKLzmQuUk+UYNQ4d+9192fdfUO1y5KXGeyzT2jzLhUtTj45tGBcfz185jPlJRnbbQdr18Jtt+kE6SIyKY1RHDje3Q9290WVWNkOO4STC5Yalvdv/wbveQ9cdRV86UvldX+aOTP0lr3pptCiISK1TwlGDTOzcyq8vorWWm3V1ga77grr1pVe9nWvgw98AK6+Gj7ykdLRCMJpRXp7w9moVq0afXlFROpEpePAWEmnYf/9S1/vAuDtb4e3vQ2uvDI0aJfT/am1NYSaxYtDz9lS9VMiUl1KMGqQmaXM7MdA0xisvqK1VlvtsQdMmVJ8wHfizDNDJ9x//Qve+95wTsJSpk0LEeb228MZphRdRGQCG+M44MDVZnaHmb0rz3u/y8wWm9ni1aUulJpl7tzQklGqq5QZvO998J//CX//ezibeTlhoLExNGovXx7CRznJjIhUhxKM2vQHYJ27f7LaBSlbJgMHHxwSjHLGS7z61eG0Io88Am95S7iyUilNTYPjMhRdRGRiG8s48Hx3PxR4KfDvZnZs9kx3P8/dF7n7orlz55a9UjPYb79wX07j9JvfHAZ+P/ggnH463Hdfee8xZ064v+mmUN+kAeAitUcJRm1aBFwxBustWmsFI6+5AkL79UEHhTET5XSsPeEEuPDC0HH33e+Gn/609OtSqaHR5cEHFV1EZCIaqziAuz8T71fF9ziiUutuaoJDDglnJC+nofklLwm7/sbGcEG+Cy4o73UtLaG+6amnwqDxZ57RCQdFaokSjNp0PPAjMzuywustWmsFI6+52mqHHcIVlVavLm9vv8cecPHFcNxx8L3vwTvfWd6VmJLosmJFGDT+9NOKLiIykYxJHDCzqWY2LXkMvBi4v5LvMWsWPO95odtTOXVNe+0FP/sZvOhF8MMfhvEZjz5a+nWpVBii19ICd98dGrbXrtXlk0RqgRKMGuTuDwIvAb5W4fWOWa3VELvuGiLGypXlRZe2Nvjyl+ELXwgX2Hv960OUKdXGnkSXqVPD1ZiSQeCKLiJS58YqDgDbATea2T3AbcCf3P0vFX4PFiwYrGsqNwx88Yvwla+E+qI3vzlcPqnUtVlhcGyGO9x6azilrRINkeoy1z+wZpnZNHcvcYGJstc1FUi5++b4+Brgc8UCy6JFi3zx4lGcbOqJJ0IXptmzoaGhvNesXg3f/jb85S8hYrz//fDiF4dTlJTS2Rmux9HWBnvvHd7XbOTll7piZndU/OQFIlVWyTgwEqONAyMJAxs3hjqm3/wmnN/jrLPgta8NvWnL0d4ebm1toZF8zpzyQojUP8WB2qEEY5Iws90Y7M+bAX7h7l8s9ppRJxgQWjHuuiucYaq1tfzX3XUXfO1r8PDDoUXkXe+CE08MrRaldHSERKO1NVxpfO7cMAhdJjQFFpHKq0QceOaZ0IVp2rTQnalcDz8c6ptuvTV0uzrjDHjNa0I4KUdS59TUBLvtBttvX36SIvVJcaB2KMGoQWb2+2Lz3f2V41GOiiQYEC6/evfd4X727PKSBAhjKv7+d/jRj0I12MKF8IY3wMteVl6E6eoKl4DNZEKSssMOw4tuUlcUWGQimWhxYNOmUG/U3R2SheE0Lt99N5x3Xrje6rRpcOqp4ZJKO+xQ3ut7e0OryMBASDJ23hlmzFCrxkSkOFA7lGDUIDNbDSwHfgncCgzZFbv7P8ejHBVLMCB0wn388TByb8qUECWG89prroFLLgnnJGxrg9NOC7eddy79+r6+EF36+0Nb+c47h0RHrRoTigKLTCQTMQ709oYzky9bNvzWDAhD7X75y1Dv5A4veAG8/OXhvrGx9OvdQz1XZ2forrXTTqEn7vTp6k07USgO1A4lGDXIzNLAScAbgecBfwJ+6e4PjGc5KppgJDZvhiVLwliLtrbhRRj3cL2MX/4S/vGPUB31vOfBK14BJ51UXhesLVtCF6p0OlR/zZ+vqqwJQoFFJpKJHAfWr4cHHgjhYPr00IVpOFauhF//Gn7/+zCYe/r0MFTvJS8JIaGcRvK+vvD+vb0hOVmwIJyYsK1N4aCeKQ7UDiUYNc7MmggB5muEQdn/N17vPSYJBoREYe1aWLo0tJu3tAxvfAaEBOWqq+CPfwzdpxob4cgj4fjj4dhjQ9JQzMBAiC7d3YPJxvbbh0hV7khEqSkKLDJRTcQ4MDAQEoWHHgotCtOnD398RF9f6Db1pz/BddeF3fns2SEEHH88HH54ebvz7GQjnQ6hYPvtQ7KhMRv1RXGgdijBqFExoLyMEFQWAr8HfuLuT49XGcYswUi4w7p1oc18/foQCYZbfeQeTlHyl7+EVo3nnguvP+SQ0G5+5JHhNCLF2r/7+0PLRnd3WG727MGWjalT1XZeJxRYZKKZDHGgvz+cXfyRR8KZn5qbQ/ep4e52t2wJZyq/7rpwDdbOzrD7PvTQEAaOOgp22aX0evv7QyN3Z2d43toako1Zs0K5yumKJdWjOFA7lGDUIDO7CDgA+DNwqbtX9CJI5RrzBCPb5s3honnLl4c9fEvL8A/u3UN12N//Dv/8ZxjzASFhOOKIEGUOOyxEi0LrdR+MLgMDIZrMmxdu06aF8SNKOGqSAotMJJMtDriHeqanngr1RBBCwEjOy9HdDbffHq7BetttIbRAGG9x+OFw8MFw4IHh3B+lulN1d4eQ0NcXytjWFk5MOGtWKF9zs0JCLVEcqB1KMGqQmQ0A7fFp9g9kgLt723iUY1wTjERfX4gyy5cPXjSvuTnsyYfbMXblynB+w1tvDVFm/fowfd680FH3oIPC/d57Fx7w3dcXokty0b/GxjBQfM6ckHCMpFwyJhRYZCKZzHGguzv0on3yybDbTqVC3U5LS/knIcz29NMhBNx2W0g8NmwI06dNC4lGctt7b5g5s3TZOjtDdyr3EBJmzQohobU1hAS1clSP4kDtUIIhBVUlwcjW2xsiwbPPhiqt/v4QXVpaht+SMDAQ2uDvuSfc7r03rBfCCMM99gjRZZ99wv0ee+QfedjXF6JLd3dYp1mo0po1K9ySsinpGHcKLCKVV+040NUVkoxnnoE1a8JuN50OB/JNTcNvPXAP9Vf33jt4e+yxwat+z5sHe+0VwsDee4fHO+xQOLHp6wtl7OoaDAlNTaGH7axZIemYMiXUk40kOZLhURyoHUowpKBqB5YhkkHZGzaElon16wcjQrL3Hu5pZ1euDNHlvvvCgPOlS0NHXggRbOHCkGjsumt4vNtu4byG2aMG3UOy0dUVEqLEtGkhwsycGZKO5uaRRUMpmwKLSOXVUhxIBmOvXx/qnDZtCrvgVCrsYkdat7NlSzi5YRIGli4Np9IdGAjzm5tDCEhuu+4abrnhILucSdLhHm7pdEg2pk8PoSEJW0o8KktxoHYowZCCaimwbCMZmL1lS6jWWrsWenoGo01TU9hzD+eMUO6hmmzp0jCWY+nScIaqZ54ZXCadDucz3HXXMGJwwQLYccfBcxxmMmE9PT0huiRlMguvnTYtRJi2tsEI09SkFo8KUGARqbxajgN9fSEEbNoUwsC6dWEahF1usnsdyYkBu7rCZZsefjiEgSeeCElHMj4Ewm57xx0L37JPjpjURXV3D3avyi5nW1u4tbaGMic3JR/DozhQO5RgSEG1HFjy6u4OpyFpbw+RZsOGwVOBZCcejY3hVm5rQmdn6Az8xBNh4PiyZeHxihWD0QwGT3ebRJcFC8KA8nnzwujCWbNClVhuhIHBiw+2toZbEhkbG3Xa3DIpsIhUXj3FgeQgvqMjJB7r1xcOAw0NYfc63AP4jo4QDpIw8OSToQ7q6adDopNt+vQQCubPHwwDyTlDttsuDBbPZEI4SMJCb+/Q0NTUNBgakkHljY2Dn0F1U0MpDtQOXcpYJo6kymfWrNB2DUPbqtvbQwTYtCm0eCTt32bhlhzMNzQM7W41ZUoYm7HPPkPfLzm/4tNPh2Qj+37JknD18GypVDijVRJdsqPNjBkhgrS1bduVKulwnH1LkqSkzKrmEpFJLmkNaG4OYWDnncP07DDQ0RF2zZs3h3qopJ4nST4ymcHdaiazbT1USwvsu2+45dq8Oez+s28rVoQxHv/612Cik13eWbMGQ8HcuSFEJEP6Zs8OSUpfXwhbufVSEMra0jL0lpQ/+3OIjDdtdjKxZTKDrQJz5gxOT7oxJW3WnZ0hAdm8Odx3dw8ul0SYdDqsL/s2f364LcpTYbJ5cxjnsXJlSERWrRp8/sQTcMstIdrlamgYjC6zZoVxHDNmhFtbW6jOStrSk4QkiTJTpgyebiWp4spkBhMnJSIiMslkh4Fs2WEg6dW6ZUsIAVu2hEQkGbidMBvcpWYyg2EBwq45X11U8l7t7YPhIDssrFoVBp7feee2rSCJlpahicfs2SE0tLYOntAwObv7tGmDZ9xKEqckNCRjVaZMCWEj+7MkN5FK0KYkk1Nyqo98Z4qCEFV6e0PU6ekJj5PrYyS3zZvDctlJSCKJPPPnh65SyfPc5bZsCZFm3brQqrJ2bXicPF+9OowFWbcutJjk09Q0OK4jSTymTRtMRJL7qVPD/ezZIdmaNWsw2uRGmOSmhEREJqhSYcB9MAwk90kXrOTs5e3tYXq+16bTgwlIOh0O6BcuhN13L1ym3t6hISDf4yefhLvuCglQoV7u2UP+kvCQnYxMmTLYIN7SMtgFa+rUkLgkZ+lKeutm11dlJ1bJY5FcSjBE8kk66haKPIn+/sGOs319g4+TM0t1dQ22kHR3548GSWKw666DESmVGnoPIRlZuzZ0LN64cdvbhg3h/sknw/2mTYPdwAp9xqRNPbcLVnJLkpOkBSWJVkmrSjI/6QycRBtFHBGpc0nP2VLXtRgYGNz9Z98nYSBpKM+9hkYhSeP4ggVDQ0HyOKmn6u8P9VxJCNi0aTAMZN82bRq8WvrGjYOXdSomNywkz5P7pBUkaTBPzpCVhLPkJIrTpw+2lCShIfemeqyJSQmGyGgke8jm5vKW7+8PkSfffXZrSTLir6cnVJX19oYoluy987WawOB4knQ6LNPVFaLL5s2D1W5btoT7pEtYcjauZFTk8uWDz4slKNmy292Tx7nVY0mrSnJLqsySvgvZj5P5ukyuiNS4VKq8RCThPrjrzw0D/f2DXbayQ0JPT1gmOTFhtqSeaP78wWlmg0lJ7i25nFN7e+FQkPt8y5ZwBq1kXvb5TUpJQkJ2mMh9npvAJKEg6QmcPJ4xI1wUUWqfEgyR8TSa2v0kKmXfBgaGPs5uRUmiV/bj7Fuh5CFJXtyHRqGkT0B2VVzSSpM8zu1CtmrVYNeyjo7C3bwKyT7l8FveAt/5zsi+OxGRGpGM4xjpeIfs3X5uGMgXCpL6q6TBHQZ7xra2Dh3oXk59jtnQFpp8t+yQkISOJJwk09auHRpasi8lVcj224dGel0tvfYpwRCpF6ONSrncQyRKolPyuNgtu8ott9oteZ4slx31kqtN9fYOdhdLbknSktuXIDtadXQMHaQvIjJJJS0RlTqDeTm7/uxbsYb4QqGgWJ1Wor9/23CQHRa6utSdqp4owRCZrJKuVOn02F9rI0lmcu9zpxVazr38bmgiIlK2JGEZD8V2/+Xcm+nSUPVCCYaIjL0kmRERkUkrSWQUDiY+NTaJiIiIiEjFKMEQEREREZGKUYIhIiIiIiIVY17sai8yqZnZauDJapdjDMwB1lS7EGNkMn+2Xdx97ngVRmQyyIoDE3nfUi59B7X/HSgO1AglGDLpmNlid19U7XKMBX02ERkL+v/pOwB9B1I+dZESEREREZGKUYIhIiIiIiIVowRDJqPzql2AMaTPJiJjQf8/fQeg70DKpDEYIiIiIiJSMWrBEBERERGRilGCISIiIiIiFaMEQyYsMzvZzJaa2aNm9ok8899sZvfG27/M7KBqlHMkSn22rOUON7N+M/u38SzfSJXzuczsODO728weMLN/jncZRSaqMvaZZmbfifPvNbNDq1HOsTSR40a5Jmp8kfGlMRgyIZlZGngYOAlYAdwOvNHdH8xa5mhgibuvN7OXAue4+5FVKfAwlPPZspa7BugCfuLuvx7vsg5Hmb/ZDOBfwMnu/pSZzXP3VdUor8hEUub/7xTgA8ApwJHAt+thn1muiRw3yjVR44uMP7VgyER1BPCouz/u7j3ApcCp2Qu4+7/cfX18eguwYJzLOFIlP1v0AeA3QL0cgJfzud4E/NbdnwJQciFSMeX8/04FLvbgFmCGmc0f74KOoYkcN8o1UeOLjDMlGDJR7Qgsz3q+Ik4r5O3An8e0RJVT8rOZ2Y7Aq4AfjmO5Rquc32wvYKaZXWdmd5jZGeNWOpGJrZz/33D3q/VmIseNck3U+CLjLFPtAoiMEcszLW9/QDM7nhAoXjCmJaqccj7bt4CPu3u/Wb7Fa1I5nysDHAacCEwBbjazW9z94bEunMgEV87/r+z9ap2ayHGjXBM1vsg4U4IhE9UKYKes5wuAZ3IXMrPnARcAL3X3teNUttEq57MtAi6NO/85wClm1ufuV45LCUemnM+1Aljj7u1Au5ldDxxE6DMsIiNX7v+v5H61jk3kuFGuiRpfZJypi5RMVLcDe5rZrmbWCLwB+H32Ama2M/Bb4PQ6qwEv+dncfVd3X+juC4FfA++rg51/yc8F/A44xswyZtZCGGi6ZJzLKTIRlfP/+z1wRjyb1FHARnd/drwLOoYmctwo10SNLzLO1IIhE5K795nZ+4G/AmnCWS4eMLP3xPk/BD4DzAa+H2ti+tx9UbXKXK4yP1vdKedzufsSM/sLcC8wAFzg7vdXr9QiE0OZ+5WrCGeQehToAN5arfKOhYkcN8o1UeOLjD+dplZERERERCpGXaRERERERKRilGCIiIiIiEjFKMEQEREREZGKUYIhIiIiIiIVowRDREREREQqRgmGjIqZuZn9LOt5xsxWm9kfx7kc+5jZ3WZ2l5ntnjNvupldbGaPxdvFZjY9zjuuUFnN7CozmzGCshxnZkcP8zXzk3KYWYuZ/dzM7jOz+83sRjNrLfH6LcMtZ87rrzOzpyzrsqxmduVo1zuK8syNp6MVkTqgWJD3dYoFo6RYUL+UYMhotQMHmNmU+Pwk4OkqlOM04Hfufoi7P5Yz78fA4+6+u7vvDjxBuAprUe5+irtvGEFZjgOGFVSADwPnx8f/Aax09wPd/QDg7UDvCMqRV7xIVr7//gbg+XGZGcD8Sr3ncLn7auBZM3t+tcogIsOiWLCt41AsGBXFgvqlBEMq4c/Ay+LjNwK/TGaY2RFm9q9Ym/QvM9s7Tt/fzG6LNU33mtmeZjbVzP5kZvfE2prX576RmR1sZrfE11xhZjPN7BTgQ8A7zOwfOcvvARwGfD5r8ueARVm1W21xXQ+a2Q+THa6ZLTOzOfHxW7LK+yMzS8fpJ5vZnbHM15rZQuA9wH/GZY8xs9fGz3OPmV1f4Dt8DZDU0swnKzC7+1J3747v9+G4rvvN7EN5vp/WWI47Y63XqXH6QjNbYmbfB+4EdspThksJV20FeDXharWl1pv3NzOzr8Tv814z+3qcNtfMfmNmt8dbEsBeGL+rpNZxWnzbK4E3F/i+RKT2KBYoFigWSODuuuk24huwBXge8GugGbibUGvzxzi/DcjExy8CfhMf/x/w5vi4EZhC2LGen7Xu6Xne717ghfHx54BvxcfnAP+VZ/lXAlfkmX5FnHcc0AXsRrhq6TXAv8VllgFzgH2BPwANcfr3gTOAucByYNc4fVa+sgD3ATvGxzPylGVX4I6s5wcDq4CbgS8Ae8bph8V1TQVagQeAQ5LfId5ngLb4eA7hirsGLCRc+fqoAr/jdcCR8ftNA1fH15Ra7za/GTALWMrghTxnxPtfAC+Ij3cGlsTHfwCeHx+3Zm0vOwL3VXsb10033UrfUCxQLFAs0C3rphYMGTV3v5ewA3ojcFXO7OnAr8zsfuCbwP5x+s3A2Wb2cWAXd+8k7DBfZGb/a2bHuPvG7BVZ6Cs7w93/GSddBBxbongG5Ltcffb029z9cXfvJ9S4vSBn2RMJO/Tbzezu+Hw34Cjgend/In4P6wqU4SbgQjN7J2GHnWs+sDp54u53x/V/jbCDvt3M9o3lusLd2919C6FW6Zg8n+tLZnYv8DfCjnm7OO9Jd7+lQBkB+oEbgdcDU9x9WRnrzfebbSIE6gvM7NVAR1zHi4Dvxu/w94Tawmnx+znXzD5I+H374vKrgB2KlFdEaohigWKBYoEklGBIpfwe+DpZTeLR54F/eOg/+gpCzRbu/gtCrVEn8FczO8HdH2awZubLZvaZCpTrAeAQy+pnGh8fBCyJk3KDTu5zAy5y94PjbW93P4fCAWvoytzfA3ya0BR9t5nNzlmkk/i9ZL1mi7v/1t3fB1wCnBLfr5Q3E2rTDnP3g4GVWetuL+P1lxJqFC8vZ735frMYFI4AfkPoD50096eA/5f1Pe7o7pvd/SvAOwg1l7eY2T5x+WbCdyMi9UOxoADFAsWCyUQJhlTKT4DPuft9OdOnM9iH9KxkopntRhhs9x1CQHqeme0AdLj7JYQAdWj2imKNyHozS2pqTgf+SRHu/ihwF2Gnnvg0cGecB3CEme0ag83rCTU32a4F/s3M5sWyzzKzXQg1by80s12T6XH5zUDSdxQz293db3X3zwBr2LbP68OEWr9k+eeb2cz4uBHYD3gSuB44zcKZRaYCrwJuyFnXdGCVu/ea2fHALsW+nzxuAL7MtgcHedeb7zezcJaT6e5+FaE/9MFxHVcD78/6nAfH+93d/T53/19gMZAElb2A+4dZfhGpLsUCxQLFAiFT7QLIxODuK4Bv55n1VeAiM/sw8Pes6a8H3mJmvcBzhD60hwNfM7MBwpky3ptnfWcCPzSzFuBx4K1lFO/twP+ZWdJX9OY4LXEz8BXgQMKO+4qhH80fNLNPA1fHwNML/Lu732Jm7wJ+G6evIpw55Q/Ar+Pgtw8QBvntGd/7WuAehr5Bu4VTJu4RA93uwA/MzAiVAH8i9Fd2M7sQuC2+9AJ3vyvns/4c+IOZLSb0gX6ojO9nyIclBIdchdZ7INv+ZtOA35lZc/zM/xmX/SDwvdi0niF81+8BPhQDVT/wIGGgKMDx8bOLSJ1QLFAsQLFAGBx4IyJZLJwZZBWwvbtX7LSARd7vVYQm50+XXHiSsHCWlVPdfX21yyIik5NiQfUpFtQntWCI5PcAoVZozAMKgLtfkac/7qRlZnOBcxVQRKTKFAuqSLGgfqkFQ0REREREKkaDvEVEREREpGKUYIiIiIiISMUowRARERERkYpRgiEiIiIiIhWjBENERERERCpGCYaIiIiIiFSMEgwREREREakYJRgiIiIiIlIxSjBERERERKRilGCIiIiIiEjFKMEQEREREZGKUYIhIiIiIiIVowRDREREREQqRgmGiIiIiIhUjBIMERERERGpGCUYIiIiIiJSMUowRERERESkYpRgiIiIiIhIxSjBEBERERGRilGCISIiIiIiFaMEQ0REREREKkYJhoiIiIiIVIwSDBERERERqRglGCIiIiIiUjFKMEREREREpGKUYIiIiIiISMUowRARERERkYpRgiEiIiIiIhWjBENERERERCpGCYaIiIiIiFSMEgwREREREakYJRgiIiIiIlIxSjBERERERKRilGCIiIiIiEjFKMEQEREREZGKUYIhIiIiIiIVowRDREREREQqRgmGiIiIiIhUjBIMERERERGpGCUYIiIiIiJSMUowRERERESkYpRgiIiIiIhIxSjBEBERERGRihm3BMPM3Mz2iI9/aGb/PV7vPRmZ2ZvN7OoxWvd7zWylmW0xs9lj8R5Z73Wdmb1jLN9Dhs/MzjazC6pdjnplZsvM7EVjsN4/m9mZY7DeC83sC5Veb70ws+9nf34zO8bMllazTBPVWO5bzOwLZrbGzJ4bi/XnvNeY/MdldOrh+DMeW+1WgfWMSTwoV8kEYyz+JO7+Hnf/fCXXCWBmZ8VE5tyc6afF6RdW+j2LlMXNrD1uKFvMbMMYvtfC+H6ZZJq7/9zdXzwG79UAnAu82N1b3X1tBda5zMw64/e00sx+amatoy/tsMswbsEgHrC5mb0yZ/q34vSzxqssI+HuX3L3ESV++ZJGMzvOzFZkPd9aIRGf/5eZPWtm++dZ9joz6zKzzWa2yczuMLNPmFlT1jIzzOwnZvZcXO5hM/t41vxTzezu+Po1ZnatmS0s8hkWmdkfzWy9mW0wswfN7ItmNnMk38lImNk5ZnZJ9jR3f6m7XzSCdZmZfdDM7o/7rRVm9iszO7CC5R3yu1Vbzn5nvZn9ycx2yrPcu4Bud/90Ms3db3D3vStYlnPiNv/BnOkfitPPqdR7lShHEku2ZN3uGcP322abGM2+pcR77QR8BNjP3bev0Dqz4/zTZnaumaUrse5hlmGP0ktW7P2ui+95UM70K+P048arLCMxVsef+ZjZAjP7uZmtjdvJbWb28jLK2Oruj4/2/UcaDyplInaRegx4ffbBNnAG8HAVynJQ3FBa3X1GFd5/LGwHNAMPDPeF8SCm0Db3CndvBQ4FDgc+XWC5mjTCoPIwsLV2IW6zryVswxKZ2aeBDwEvdPdC29373X0aMJ9wEPEG4Cozszj/m0ArsC8wHXgl8XuOwfni+LrpwK7A94GBAuU5GrgOuAnYJ/63Twb6gIPyvaYOfBv4D+CDwCxgL+BK4GVVLNMQOfv0Skn2O/OBlcD/5S7g7ue5+39W6g2LfI4h+4OoWrFrRlbsqtdtOtcuwFp3XzXcF5bY9g6K29CJwJuAd46wfOOuREwu5mHCtpmsZzZwFLC6UmWrd2Y2C7gR6AH2B+YQ4tAvzOzfCrxmLPZxVTPiBMPMmmJt6zPx9q2cGsOPxhrHZ8zsbTmvHdLcbmYvj7WHG8zsX2b2vKx5uTWZpZrqnwPuA14Sl58FHA38PqcMv4q1mRvN7Hoz2z9r3imxRnJzrJX4rzh9Tqy13GBm68zshuH+OYt9nqQ2x8w+Ymar4vf31qxlp5jZN8zsyVjuG81sCnB9XGRDrEn5fxZac27Meu3RZnZ7fN3t8SApmXedmX3ezG6Kn/lqM5uTp+x7AUuz3uvvZa77i2Z2E9ABFG32c/engT8DB+R5/93N7O+xNmBNrBmYkTV/mYWa7ntjWS4zs+as+Xm3MzP7GbAz8If4/X0sTi+2jVxoZj8ws6vMrB34sIXWl0zWMq8xs7uLfNw/AM+3wVrvk4F7CdtwuZ/543Eb3WxmS83sxDj9CDNbbKFGfqVlteqZ2VHx828ws3ssq8YpbjePx/U9YWZvzldwy6o9t8FazzPN7KlYzk8V+dxli/+NdwDHunvJAy13b3f36wgJxP9j8AD5cOAX7r7e3Qfc/SF3/3WcdzDwhLtf68Fmd/+Nuz9V4G2+CvzU3b/s7ivj+z7l7v8T37vk75bzGY8ws5vj7/GsmX3XzBqz5u9vZtdY2OestNCF5GTgbEJlytZaZstpHTKzd5rZkvh7Pmhmh+Z5/z2Bfwfe6O5/d/dud++IraBfybP8kH1LnJbdBXab/aeZTSX8r3ewwZrxHcwsZaG16bH4XV1uYZ+dvV293cyeAv5uZs1mdklcdoOF/c12BX6nsrl7F/BrYL+sz9RkZl+P2/RKC10rpsR5ua1oyWdIvudX5XxfN5nZN81sHXBOgWLcDrQk+5l4PyVOT9Y100IMWm2h1eWPZrYg5722+f+a2R5m9k8L+7I1ZnbZcL4fy9NKnr2tJdtE/L7Wx/d+adaysyy0TD8T519ZZJsY0jJnZq80swfi732dme2bNa/oPj9ruRcB12S914VlrvvjZnYv0G4lDv7c/SHgBvLHrlL/cTez95jZI/H7+Z7Z1soRzOxtFv7H683sr2a2S5yexP574ud6fRnbSG5M/oiZ3ZFT3o+Y2ZVFPu7PCfuepGLtjcAVhIPpkp/Zgm9aOM7ZGH+/A+K8vMdfcV6xY8W8sTDPb1H2MVee1x5g4bhvU/zNkttpeRb/T2AL8HZ3f87dO939l8AXgW8kv298/b+b2SPAI1nTkv3pdDO7OP6eT5rZpy0ed1rp/132f3RU+4ARcfeiN2AZ8KI80z8H3ALMA+YC/wI+H+edTKgNOgCYCvwCcGCPOP9C4Avx8aHAKuBIIE2owVkGNMX5W1+X+9o8ZTqLkDG+CbgsTnsf8CPgC8CFWcu+DZgGNAHfAu7OmvcscEx8PBM4ND7+MvBDoCHejgGsQFmGlLvQ9Jzv4jhCLejn4vpPIewAZsb53yPUnO4Yv6ujY/kXxvVmcr+L+HgWsB44HcgQdgbrgdlx/nWE2ty9CAHtOuArBT7XkPcqc91PETL4DNBQbBsDdiK0jnw+6/XviI/3AE6Kn3kuIbH6Vs56bgN2iOVaArynzO1saxnK3EYuBDYCzyck6s3Ag8BLs5a5AvhIge/xQsI2eR7w3jjt8vj93QicVeozA3sDy4Edsn6b3ePjm4HT4+NW4Kj4eEdgLWHbSsV1r43rngpsAvaOy84H9i9Q/nOAS3K2ifMJ289BQDewb4HXbv1Ns6YdB6zI+Z/8mrDD3bnEstusL06/Hvjf+PgCwnb1VmDPnOV2A7oItUvHA61F9odTgX7guBL7zXK21WSbP4xQ+5eJ3+US4ENx3jTC/ugjcRubBhyZ+xvk+y4IrWFPE5Iri2XaJU9Z3wM8WeLzXMjgfuos4r4l336NwvvPIb9bnPYhQhxZEL+rHwG/zNmuLo7f+xTg3YTEvIXwPz4MaCtW9iKfKfs3aAEuAi7Omv8tQsXUrPi9/wH4coFt8LWE/U4KeD3QDszP+r76gA/E33hKof8TIWlMttmvAp+M08+J02YDr4nlnQb8Crgya9vM+/8Ffgl8isF91QsKfCfJd54pNZ2h29pZQC+h9j4NvBd4hhgfgT8Bl8XtoYHQGllomziHwX3LXvG7PCm+7mPAo0Bj1m+Yd5+f57Pl/mblrPtuQkza5jfLs93vR6gcevtw/uNZ6/kjMINQ2bUaODnOOy2Wa9/4+k8D/8pXhlLbSNbvlh2Tm4B1ZO2vgbuA1xT4zNcRKn2uJsa7+Bv8P2AFcd9Y7DMTKoDviJ/X4mdL/i+F9h8FYzhFYmGJfdlxFDnmynmdAfcA34nveQDhOPc/gel5lr8F+Gye6bvG3yz5nzoh+Z1F3M4Yul1dDPwu/pYLCa1HyTZ2FsX/d9cx+B8tax9Qydtouki9Gficu69y99XAZwkHmQCvI9Tw3e/u7RSusSF+MT9y91vdvd9Df7FuwoY5UlcAx5nZdEIz3sW5C7j7TzzUVHbH8h0Ul4fwg+1nZm0eajzvzJo+nxCkez30w/Ui5bgzZtobzOw7ZZa9l/C99rr7VYQMeO+Ysb4N+A93fzp+V/+K5S/lZcAj7v4zd+/zkEU/BLwia5mfuvvD7t5JONA9uMzylrPuC939gTi/t8B6rrQwTuVG4J/Al3IXcPdH3f0aDzWsqwljQV6Ys9h33P0Zd19HOCBIPsewt7MS2wjA79z9Jg814l2EA5S3wNaWs5cQkutiLgbOiOt9IaFbSrmfuZ+wo9vPzBrcfZm7J92reoE9zGyOu29x91vi9LcAV7n7VbHc1wCLCTtWCN2CDjCzKe7+rBfukpTPZz3U0txD2BEfNIzX5vNi4C9euCWhlGcIO20IB3c/B94PPGhmjyY1PR76uh5HSL4uB9bEWq5844BmEnbQ2a1MX43/8XYL3bnK3VaJy97h7rfE/8cywkF2suzLgefc/Rvu3hW3x1vL/PzvAL7q7rd78Ki7P5lnudmEoF4phfaf+bwb+JS7r8j6n/2bDa0tPsdDy1RnXPdsQvDtj9/dplGUNdnvbCIcaH4NQg0rYZ/xn+6+zt03E/ZJb8i3Enf/VdzvDLj7ZYTE+IisRZ5x9/+Lv3FnkfJcArzRwli3N8Tn2e+z1kPrWkcs0xcZul0V+v/2EroI7RC3oyEtUHmsyYpd/1Vi2cST7n6+u/cT9oXzge3MbD7wUsKB//oY2/5Z5jpfD/wp/pd6ga8TEs2js5YptM+v1LqXl/jN7jSz9fG9LwB+mrtAif944ivuviHu7/6R9TneTUhsl7h7H2E7PDhpxcjzXqW2ERgak7sJyV8Su/YnHMj+schnhsHYtTehS93Nw/jMvYQD5n0IB8NL3P3ZrHn59h/FYnixWFhK3mOuPMstJCRlZ8f9+v3Aj4GD3X1jnuXnkH+/+mzW/MSX435myHYWW4heD3wy7vuXAd9g8FgbCvzvCnzO4ewDRm00CcYOQHawejJOS+Ytz5lXyC6EJrpkZ7aBUGOwQ5HXFBV/pD8RMv057n5T9nwzS5vZVyw0aW8iZMEw+IO/hnDA9WRsUvp/cfrXCDUJV1tohv5EiaIc6u4z4u2DJZZNrI07kUQHoQZ6DiHrHEn//Nzfivh8x6zn2WfVSN6zUuteTmmnxe9pF3d/X74dupnNM7NLYzPoJkLwze3KVehzDGs7K2Mbyfe5LgFeEQ9MXwfckLXTzCv+yecSttU/5tnBFPzM7v4ooQb4HGBVXC75PG8n1NA9ZKEbSTKwbBfgtTnfwwsItUfthJ3Ze4BnLQx63adY+XOUuw31EWqLsjUQdoDZ3kA42PzsMMqQbUdCzRwx8fmSux9GOEC9HPhVTASJgfB17j6X0DJ5LKG2J9d6wkHc/GSCu3/MwziMKwi1deVuq8Rl97LQjeG5uOyXspbdiZGPySn3tWuzP08FFNp/5rMLcEXWtriEcLCQHSCz/2c/A/4KXGqhu81X48H4SJ0Wf7smQvL5TzPbnvCfbAHuyCrbX+L0bZjZGTbYdWMDoXaz2L4ir3hw+ShhG3jE3Ye8zsxazOxHsavEJkLL2AwzS5f4/36MUAN7m4UuQUO6LecxJyt2fb2cspP1/3f3jviwlbAdrnP39WWuJ9uQ+OLuA4TvsuKxq8C6y/ndDnX3me6+u7t/Oq5niBL/8VKfYxfg21nb1jrCb7kjeRTbRop8rouAN8XE+nTgci9deflb4ARC5c3PhvOZ3f3vwHcJvTJWmtl5ZtYWX1po/1EwhpeIhaUUOubKNQ9Y7+5bsqZlH/fmWkP+/er8rPmJQtvZHKCRbY+1827/Of+7XMPdB4zaaBKMZwg/eGLnOA1ChrZTzrxClgNfzNqZzXD3Fg814RB+7Jas5cs9+0MyaHObDZ/QhepU4EWEQZ0L43QD8FDjdyphg7qScDBCzCA/4u67EWroP2wF+vkVMdLPs4bQjWP3PPOKtaLAtr8VhN/k6TLfe7TrLlW+cn05rut57t5GqHGx4i/ZqtR2llvGottIvtd4GD9yM/Aqwk4637aXzyWEbXWbljZKfGZ3/4W7v4DwGzjwv3H6I+7+RsI2/L/Ary30eV4O/Czne5jqsa+9u//V3U8i7AQfInR7qrSnGPw+E7uybaL6MOH7f18ZyfwQFs4YcxihT/QQHmq8v0ToUrJrnvm3E4LnNn2p40HcrcCrSxRhONvqDwjf9Z5x2bOzll1O/v88lP5fFXtttmuBBWa2qIxlIXQr2boPiwfkg4UqsP8sUN7lhG4W2dtjc/wvkfu6WMv4WXffj1DT/HKyBpuOVKwR/S0huXkBYX/bSehilJRruofBvEPEmuTzCQnK7Jiw3E+RfUUJSezKtz/4CKF29ci4rRybFCN+jrz/Xw99wN/p7jsQasS/b8M781B7vB9J7FoOzLL8Y5CGFbviAfBOjEHsKrDuSsWuYv/xUpYD7875j0xx938VWL7oNhLlxq5bCOMnjiHEvpKxKx7M/pnQLSff8kU/s7t/J1b47E+oDPtonF5o/1E0hheKhRW0grAdt2VN2zVOz+dvwGts23G6ryN8luwxhYW2szUMtjwkRnTsVoF9wLCVm2A0WBhcl9wyhP5cnzazuRYGBH+Gwebcy4GzzGw/M2sB/qfIus8H3mNmR1ow1cxeZmbT4vy7CZl12sLAxrzdDPL4J6HJe5uzghCa5roJNXctZHXHMbNGC9eQmO6h2XQTIegkA4z2iDuiZHp/meVJjOjzxFqRnwDnWhgIl7YwmLuJ0F9zgMIDqK8C9jKzN5lZxsxeT+gvWqoJtBxjue5c0wjNlxvMbEfiDqlMpbazlQz9/gpuIyVcTKgpOJBQo12O7xC21evzzCv4mc1sbzM7IW4DXYQDomRbfYuZzY3bzYb4kn4GW1leErehZgsD3RaY2XYWBj1OjZ99C8PfvstxGfBWC4MAzcLJA/4TuDR3QQ9dPF4EfNTMPlRqxbH27oWEPqu3EbZPzOy/zezw+P9uJpwxaQOw1MxeYGEw9Ly47D6EQeK35H2T8Pu+zcLA3uQ1CxiarAxnW51G2J9sie/93qx5fwS2t3C60iYzm2ZmR8Z5K4GFeQJY4gLgv8zssPg972F5ulW4+yOEs2b9Mm4LjXG7eEOBxO4eYH8zOzh+l+ckM4rtP2N5Z9vQboY/BL5og4NW55rZqYW+KDM73swOtFAbu4kQfEe9jcbv51RCF7gl8X9zPvDNrN94RzN7SZ6XTyUcIKyOy72VPMnpMFxG6B54eZ550wj/8w0WWt+2xtZi/18ze60NDvRdH8tb9vfmoZvf08Bb4n7jbZSXvBJbcf9MOKCZaWYNZpYc9ObbJrJdDrzMzE600FL1kfjZCh1cD8dYrjtXsf94KT8EPmmDg/+nm9lrs+bni115t5ESLia0KvR5+d1nziaMp1mWZ17Bzxz3xUfG772dEL/6S+w/CsbwYrGwUmKlxz+B/437x4MIPQV+XuAl3wTagB+b2fbxNW8ktIx/1L1o9/rkPfsJ2+kX4+fcBfgwOV0nyzHafcBIlJtgXEX4wZLbOYQBqosJZ725D7gzTsPd/0wYIPd3QnPv3wut2N0XE/rWfZfwoR8lDFxJ/AehtWADYdzHleUU2INrPfTLzHUxobb0acLA3NwDidOBZRaa9d5D7JsI7EnISrcQaqq/7/GsMcMwos8T/Rfhu76d0Ez6v0Aq1iR8EbjJQtPhkHEFHq5V8XLCDnQt4QDp5e6e3UQ3ImO57jw+SxjotZHQBe63wyhnqe3sy4SEOelzXGobKeQKYrePWNtdTtnWxW013w6n2GduAr5CqOV4jlDjc3acdzLwgJltIZyC9A0e+l0uJ7TMnE04IFpOOPhNxdtHCDV76wjJ7/vK/Nxlc/e/Ap8g9FXeSNi/XEQY8J5v+XsI41n+x8zeU2C13zWzzYRg+y3gN4RBkkl3BY/vt4bw+U4CXuahuXsDIaG4L35ffyH8jl8tUJ4bCV0DjgUetsHuM9cxWKExnG31vwi1hpsJQXTr2T089KE+ibDPeI7Qt//4OPtX8X6tmW0zzsHdf0XYL/wirvtKBsek5Pogg10WNhC6Vr2K0K88d70PEwZF/i2WJ/dgJO/+08NZdn4JPB7/ZzsQts3fE7qdbib8z46ksO0Jg/83EbpT/ZMRBNssf4i/+SbCd3WmD45b+DhhP3FL/Cx/I0/fbHd/kNAv+mbC9ncg4RTGI+KhO9/fPH+//28RxgmsIXxXf8maV+z/ezhwa/ysvyeM5XtimEV7J2FfsZZQ6zycA/HTCcngQ4SBuh+CgtvEVu6+lLD9/B/hM7+CcGrhHkZpLNedR8H/eBnlvIIQ6y+N2+H9hDEtiXOAi+L39zqKbyPF/IyQGJfb8o6HsS+FkpFin7ktTltPiLNrCWNgoPD+o1gMLxYLK+nNhC5RzxD26Z9297/lWzAeG72AwRPArCUkB6d7GKdVrg8QkrDHCfvaXxAqm4erEvuAYUlGmotIhZjZY4Qm7bw7HhGpb2Z2AnCBh+6yInXPwimYVxHGlDxS7fJI/RvNGAwRyWFmryHUlhdstRORuncAMKa1fyLj7L3A7UoupFKKXjhGRMpnZtcRxp+c7nnOJCIi9c/Mvk3oUndmtcsiUglmtowwAPu06pZEJhJ1kRIREZlgzOw/CddCccLYvbcSTlhxGeEsbsuA1/nITh8rIlKUukiJiIhMIPHMZR8EFrn7AYSr/L6BcHKFa919T8LpiYd1+mcRkXKpi5QUNGfOHF+4cGG1iyFSljvuuGONhwvliUiI71PMrJfQcvEM8EnCVeshnLntOsLZsgpSHJB6ojhQO5RgSEELFy5k8eLF1S6GSFnMLPdCfSKTkrs/bWZfJ1zUshO42t2vNrPt4nUpcPdnk2t85DKzdwHvAth5550VB6RuKA7UDnWREhERmUDMbCbheje7Es7bP9XM3lL8VYPc/Tx3X+Tui+bOVWWwiAyfEowJxsx2MrN/mNkSM3vAzP4jTj/HzJ42s7vj7ZRql1VERMbEi4An3H11vCLyb4GjgZVmNh8g3q+qYhlFZAJTF6mJpw/4iLvfaWbTgDvM7Jo475vu/vUirxURkfr3FHCUmbUQukidCCwmXBH4TMJVj88Efle1EorIhKYEY4KJ/WuTPrabzWwJsGOl32fjRlixAvbfv9Jrnth6e3tZsWIFXV1d1S5K3WpubmbBggU0NDRUuygiNcndbzWzXwN3Eiqd7gLOA1qBy83s7YQk5LWjfa/FmzZxyLRppM1Gu6pJQ3Fg9BQHap8SjAnMzBYChwC3As8H3m9mZxBqsj6S7/znuYP7CtmyBTo6xqDQE9yKFSuYNm0aCxcuxBSQh83dWbt2LStWrGDXXXetdnFEapa7/w/wPzmTuwmtGRXTPTBAR38/0zI6nCiX4sDoKA7UB43BmKDMrBX4DfAhd98E/ADYHTiY0MLxjXyvK3dwXyoFA7pW9bB1dXUxe/ZsBZURMjNmz56tmj+RGtE1MEB7f3+1i1FXFAdGR3GgPijBmIDMrIGQXPzc3X8L4O4r3b3f3QeA84EjRvMeqRToIvAjo6AyOvr+RGpHnzvr+vqqXYy6o/3Y6Oj7q31KMCYYC/+6HwNL3P3crOnzsxZ7FXD/aN5HLRgiIgKwtre32kUQkRqjTpMTz/OB04H7zOzuOO1s4I1mdjDgwDLg3aN5k3RaLRiVcMstsGFD5dY3YwYcdVTl1jda73jHO/jwhz/MfvvtV+2iiMgYMGBzfz+9AwM0pFRnORK3bNzIhgq2As3IZDhq+vSKrW+0FAcmJyUYE4y730jY5+e6qpLvk0qBut2O3oYNUMnrWK1eXZn19Pf3k06nCz7Px91xd1JZBxkXXHBBZQokIjXLgC39/cxUgjEiG/r6mNvYWLH1re7pqch6FAdkNLQ3kBExCy0YasWoP5dccglHHHEEBx98MO9+97vpj5lia2srn/nMZzjyyCO5+eabt3l+7rnncsABB3DAAQfwrW99C4Bly5ax77778r73vY9DDz2U5cuXD3mv4447jsWLF29d/6c+9SkOOuggjjrqKFauXLlN2W677TaOPvpoDjnkEI4++miWLl06tl+GiIyaAZtU41RXFAdkrCnBkBExC2MwNA6jvixZsoTLLruMm266ibvvvpt0Os3Pf/5zANrb2znggAO49dZbecELXjDk+ZQpU/jpT3/Krbfeyi233ML555/PXXfdBcDSpUs544wzuOuuu9hll10Kvnd7eztHHXUU99xzD8ceeyznn3/+Nsvss88+XH/99dx111187nOf4+yzzx6bL0JEKmZKOs2qCtWay9hTHJDxoC5SMmJJglGixVRqyLXXXssdd9zB4YcfDkBnZyfz5s0DIJ1O85rXvGbrstnPb7zxRl71qlcxdepUAF796ldzww038MpXvpJddtmFo8oY+NHY2MjLX/5yAA477DCuueaabZbZuHEjZ555Jo888ghmRq8Gj4rUvCmpFOv7+hhwJ6Wz+9Q8xQEZD0owZMQGBsI4DF1Is364O2eeeSZf/vKXt5nX3Nw8pH9t9nMv0hcuCTalNDQ0bD21YDqdpi/PoMb//u//5vjjj+eKK65g2bJlHHfccWWtW0SqJwX0u7Olv582XXCv5ikOyHhQFykZsSTBkPpx4okn8utf/5pVq1YBsG7dOp588smSrzv22GO58sor6ejooL29nSuuuIJjjjmm4uXbuHEjO+64IwAXXnhhxdcvImMjBWzU9TDqguKAjAdVNciIaQzG6M2YUbkzPyXrK2a//fbjC1/4Ai9+8YsZGBigoaGB733ve0X7zAIceuihnHXWWRxxRLg+4zve8Q4OOeQQli1bVpmCRx/72Mc488wzOffccznhhBMqum4RGTvJOIydmpurXZS6MyOTqdiZn5L1FaM4IOPBijV5yeS2aNEiT878kGvTJrjhBnj+80sf1MqgJUuWsO+++1a7GHUv3/doZne4+6IqFUlkQioWBwCuXruWWQ0NOLCur48XzZxJWuMwilIcqAzFgdqmLlIyYv396iIlIiKQMmPAnc3qJiUiKMGQUVAXKRERSaTMKnpFahGpX0owZMQ0yHtk1C1xdPT9idSm1nSap7u7q12MuqD92Ojo+6t9SjBkxNxBlVXD09zczNq1a7VzHCF3Z+3atTRrIKlIzWlOpdjc30+3mraLUhwYHcWB+qCzSMmo6OKtw7NgwQJWrFjB6kqeOmqSaW5uZsGCBdUuhojk4cCmvj7mNjZWuyg1S3Fg9BQHap8SjBpnZjOBHYBOYJm711TVkBKM4WloaGDXXXetdjFEpI7UehzI1pxK8WxPjxKMIhQHZDJQglGDzGw68O/AG4FGYDXQDGxnZrcA33f3f1SxiFspwRARqbx6igPZpqbTrOzpYcCdlE5XKzJpKcGoTb8GLgaOcfcN2TPM7DDgdDPbzd1/nPtCM9spvnZ7YAA4z92/bWazgMuAhcAy4HXuvn40hTRTgiEiMkZGHAeqKW1Gnzub+vqY0dBQ7eKISJUowahB7n5SkXl3AHcUeXkf8BF3v9PMpgF3mNk1wFnAte7+FTP7BPAJ4OOjKWcmowRDRGQsjDIOYGYzgAuAAwhDI94GLKXCFU35ZMxY1durBENkEtNZpGqYmW2zdzazOcVe4+7Puvud8fFmYAmwI3AqcFFc7CLgtNGWL5VSgiEiMlbMLGVmqfi40cwOja3R5fg28Bd33wc4iBALPkGoaNoTuDY+r7hp8XS1OkuSyOSlBKMGmdnxZrYCeMbMrjazhVmzrx7GehYChwC3Atu5+7MQkhBgXoHXvMvMFpvZ4lJnuEinlWCIiIwFMzsNeBZ42sxOBW4Avg7ca2avKPHaNuBY4McA7t4Tu1lVvKIpn4ZUiq6BATbrQkkik5YSjNr0VeAl7j4XOA+4xsyOivPKGjVnZq3Ab4APufumct/Y3c9z90Xuvmju3Lkl3kNX8xYRGSP/Q2h5OBr4GXCGu58APD/OK2Y3wqDwn5rZXWZ2gZlNZQwqmgpJm7FKNVAik5YSjNrU6O4PALj7rwm1TBeZ2asIfWmLil2rfgP83N1/GyevNLP5cf58YFUlCmqmi+2JiIwFd3/O3Z8AnnL3pXHak5SO3RngUOAH7n4I0M4wukMNp6KpkLZ0muXqJiUyaSnBqE29ZrZ98iQmGycSaq32LPZCMzNCs/gSdz83a9bvgTPj4zOB31WioLqat4jI2EjGXxAGaCfT0oTT1hazAljh7rfG578mJBxjUtGUT0MqRVd/PxsVIEQmJSUYtekTwHbZE9x9BfBC4CslXvt84HTgBDO7O95Oia87ycweAU4qYz1lU/wQEam4dxETCXe/LWv6TpTYf7v7c8ByM9s7TjoReJAxqmgqpCFedE9EJh+dprYGufvfCkzfCHyxxGtvpPA4jRNHWbS8lGCIiFSWu99eYPoywilmS/kA8HMzawQeB95KqFS83MzeDjwFvLYihS2gLZNheXc3e7W0kNZF90QmFSUYdcbMznH3c6pdjmxKMERExk85ccDd7wYW5Zk1JhVN+aTN6HdnXW8vcxtL9eoSkYlEXaTqT9GLK403M+jtrXYpREQmlZqKA8W0pNM82dVV7WKIyDhTglFn3P0P1S5DtnQaFDtERMZPrcWBYqamUqzu7aVD18QQmVTURapGmdlLCKen3ZFwatpngN+5+1+qWa5cSjBERMZGvcSBYsyMlBnPdXezW0tLtYsjIuNECUYNMrNvAXsBFxNONwiwAPigmb3U3f+jWmXLlclAZ2e1SyEiMrHUUxwoZUYmw+NdXewyZYoGe4tMEkowatMp7r5X7kQzuwx4GKiZwJLJqAVDRGQM1E0cKCVjRq87a3p62K6pqdrFEZFxoDEYtanLzI7IM/1woKYO55VgiIiMibqJA+VoTad5rLNTV/YWmSTUglGbzgJ+YGbTGGwa3wnYFOfVjFQqnEWqvz+MxxARkYo4izqJA+VoSadZ1dPDxr4+ZjQ0VLs4IjLGlGDUIHe/EzjSzLYnDO4zYEW8OmvNSU5VqwRDRKQy6i0OlKM5leLxri4OVYIhMuEpwahhMZDURTDp6YHm5mqXQkRkYqmnOFDKtHSalT09bO7rY1pGhx8iE5nGYEhF6GJ7IiJSjJnRmEqxTAP3RCY8JRhSET091S6BiIjUuunpNCu6u9nS11ftoojIGFKCIaOWyUB7e7VLISIitS5pxXhMF1ASmdCUYNQ4M7u52PNa0NCgBENEZKzUQxwYjunpNE93d7NJrRgiE5YSjNqXO3S66FBqM/uJma0ys/uzpp1jZk+b2d3xdkolC6gEQ0RkTA0rDtQ6M2NKOs3DHR3VLoqIjBGdxqFGmdmx8eHU5LG7Xw+UukrRhcB3gYtzpn/T3b9e0UJGDQ2wfv1YrFlEZPIaRRyoeW2ZDCu7u1nb28tsnbZWZMJRglG73hrvZ8fHDlxPOBd6Qe5+vZktHNuiDZVKwcBAGOjd2Die7ywiMqGNKA7Ui7ZMhgfa23nB9OmkbEJ8JBGJlGDUKHd/K4CZ3Zk8TmaNcJXvN7MzgMXAR9w9b5uDmb0LeBfAzjvvPKw36O5WgiEiUiljEAdqypR4de8V3d3srAspiUwoGoNR+3KrdUZSzfMDYHfgYOBZ4BuFFnT389x9kbsvmjt37rDepLt7BCUTEZFSKhEHatKshgaWtLfT2d9f7aKISAUpwah9Hy/xvCR3X+nu/e4+AJwPHFGRkmUxA43XExEZE6OOA7UqY0bajKUKICITihKMGufuVxd7Xg4zm5/19FXA/YWWHammJti4sdJrFRGRSsSBWjazoYFnurtZqWZwkQlDYzAmGDP7JXAcMMfMVgD/AxxnZgcT+u0uA95d6fdtbIRNmyq9VhERmQxmNDRwX3s7MxoaaEqp7lOk3inBmGDc/Y15Jv94rN+3sRHWrg1nk1JsEBGpPjNLE07s8bS7v9zMZgGXAQsJlU2vK3TCj/HWlErR0d/Pg+3tHNzaiumsUiJ1TYeCUhFmIbno6qp2SUREJPoPYEnW808A17r7nsC18XnNmNnQwLM9PTytrlIidU8JRg0yszk5z99iZt8xs3dZDVfrmEFnZ7VLISJS/8zsXDN7/ihevwB4GXBB1uRTgYvi44uA00ZcwDEyO3aV2tzXV+2iiMgoKMGoTVsH8JnZp4HTgTuAk4Bzq1WoUsxgy5Zql0JEZEI4Hfi2mT1pZl81s0OG+fpvAR8DBrKmbefuzwLE+3n5Xhgrsxab2eLVq1ePoOgjlzGjJZ3mri1b6B0YKP0CEalJSjBqU3YrxauBV7v7RcCbgBdVp0ilNTfDunXVLoWIyISwwt0XEfb5m4FLzOwhM/sfM9ur2AvN7OXAKne/YyRvPJrrIVVCazpN58AA97e34z4hrikoMukowahNU8zsEDM7DEi7ezuAu/cCNXs1ouZmWF8TwwVFROqeA7j7I+7+eXffH3gd0AxcVeK1zwdeaWbLgEuBE8zsEmBlctryeL9qrAo/WnMaGniup4dH1e9WpC4pwahNzxK6Qn0dWJcVEGYDNdsxNZOBnh4N9BYRqYBtxtu5+73u/kl336PYC+MyC9x9IfAG4O/u/hbg98CZcbEzgd9VuMwVNaehgYc7OnhGg75F6o5OU1uD3P34ArM2AMeOY1FGpL09tGaIiMiIHTMG6/wKcLmZvR14CnjtGLxHxaTMmNPYyN1bttAYH4tIfVCCUUfcvR/oqHY5ikmnQzep2bOrXRIRkfrl7hU5ZYa7XwdcFx+vBU6sxHrHS8aMGZkMd2zezFHTpzM9o8MWkXqgLlJ1xszurHYZimlpgVU126tXRKT+1XocqLSmVIqp6TS3bdqk09eK1AklGHXG3Q+tdhmKaWqCjRuht7faJRERmZhqPQ6MhSnpNE2pFLcoyRCpC0owapyZzTKzmdUuR7mSywBu3lzdcoiITBT1FgfGytSsJGOTkgyRmqYEowaZ2c5mdqmZrQZuBW43s1Vx2sIqF6+khgZYs6bapRARqV/1HgfGytR0muZUips3bmS9mspFapYSjNp0GXAFsL277xlPSTgfuJJwTvOa1toKzzxT7VKIiNS1uo4DY6klnaY1k+HmTZtYqVPYitQkJRi1aY67XxbPGgWEM0i5+6VAzZ+fqaEBOjvD6WpFRGRE6joOjLXmVIpZmQyLN2/miY4OXfFbpMYowahNd5jZ983sSDPbId6ONLPvA3cVe6GZ/SQ2o9+fNW2WmV1jZo/E+zHvy5tKwdq1Y/0uIiIT1ojjwGTRkEoxt7GRBzs6eKC9nb6BgWoXSUQiJRi16QzgPuCzwF+Bq+Pj+4HTS7z2QuDknGmfAK519z2Ba+PzEfMBp3tZJ6zvKbhMays89dRo3kVEZFIbTRyYNNJmbNfYyNPd3dy6aRMd/f2lXyQiY05XrKlB7t4D/CDehvva6/MMADwVOC4+vohw0aWPj7R8fZv6eOCgW0m/dTfYa+e8yzQ3h+thbNkSkg0RESnfaOLAZGPxKt+b+vq4ccMGDm5tZV5TU7WLJTKpqQWjTozywkrbufuzAPF+XpH3eZeZLTazxatXr867TMOMBhrmN5JaXnyQRToNK1eOotQiIrLVZLvA3nC1ZTJMy2S4ffNmHlSXKZGqUoJRP2w83sTdz3P3Re6+aO7cuQWXa957KvZUR9F1TZ8Oy5aB9vEiIhUxLnGgnjWmUmzX2Mjyri5u2riRDTqVrUhVKMGoH38axWtXmtl8gHi/arSFad6nBXuqveiZOzIZ6OnRYG8RkQoZTRyYNJIuUykzbtq4kYfa2+lVTZfIuFKCUYPMbJtaKnf/dKllivg9cGZ8fCbwu5GXLmjeeyrWNYCvLH4O8qlT4bHHRvtuIiKTyxjEgUmnJZ1mXmMjT3Z1cePGjazq7tbpbEXGiRKM2vQPM/uAmQ0ZQW1mjWZ2gpldxGDCQM4yvwRuBvY2sxVm9nbgK8BJZvYIcFJ8PipT9m0BYODhLUWXmzoV1q+HjRtH+44iIpPKiOOADErF1ozGVIrbN2/mjs2b2dLXV+1iiUx4OotUbToZeBvwSzPbFdgATCEkhFcD33T3u/O90N3fWGCdJ1aygC0HTcNT0H/fJjLHzim6bHMzPPIILFpUyRKIiExoI44Dsq3mVIrtm5rY2NfHDRs3smtzM7tOmUJTSvWsImNBCUYNcvcu4PvA982sAZgDdLr7hqoWLEuqJY3v1kr//ZtKLtvWFs4mtWEDzJgx5kUTEal79RAH6tH0TIYBd57q7uapri72mDKFnZqbaVCiIVJR+kfVOHfvdfdnazGoDOzbRv8Dm/G+0oPnpk6FJUtA3V9FRIanluNAPUqZMbuhgekNDTzc2ck/NmxgWWenBoKLVJASjBpmZudUuwzFDBw4Azr6y2rFaG0NYzGee27syyUiMlHUehyoZxkz5jY2Mj2TYWlHB//YsIHHOjro0tXARUZNCUYNMrOUmf0YqOlLkQ4cMhPS0H/TurKWnzkT7r8fuoufeEpEZNKrlzgwEWTiQPDpmQyPdXZy3YYNPLhlC5s1GFxkxJRg1KY/AOvc/ZPVLkhRrQ2kD5pO303lXeiisRHM1FVKRKQM9REHJpCMGbMbG5nd0MAzPT3cuGEDt23axOqeHvoVtESGRQlGbVoEXFHtQpQjc+wcBh5up39Ze1nLz5wJTz8dbiIiUtCI44CZ7WRm/zCzJWb2gJn9R5w+y8yuMbNH4v3M0RRwU18fXRNw3ELKjJkNDcxraqJzYIDFmzZxXew+1a7uUyJlUYJRm44HfmRmR1a7IKVkTp4Haej748qyXzNnDtx7r66NISJSxGjiQB/wEXffFzgK+Hcz2w/4BHCtu+8JXBufj4i784YHH+Rjjz/O2t7eka6m5rWm08xraqI1nebRri6u37CBWzZu5NnubnomYHIlUilKMGqQuz8IvAT4WrXLUkpqThPpo2fT+6fnyjqbFEAmE05du3gxdHaOcQFFROrQaOJAPOPUnfHxZmAJsCNwKnBRXOwi4LSRls/MeNf8+Szr6uKtS5fy+ATfmWfMmNPQwLzGRnrduXvLFv6+fj13b97M6p4e+pRsiAyhBKNGufszwMuqXY5yNL5qPr66h76rV5X9milTIJUKSYYGfYuIbKsSccDMFgKHALcC27n7s3HdzwLzCrzmXWa22MwWr169uuC6T5s7l2/svjs9AwO89aGH+Mu68k74Ue9a0mnmxbEaG/r6WLx5M9dmJRs63a2IEoyaFmueal76BbNJ7T6Vnp8+hQ+UPxCurQ16epRkiIgUMpo4YGatwG+AD7l76fOJD77nee6+yN0XzZ07t+iye06ZwoX77MMeU6bw6See4PPLltExScYppMyYlskwr7GRWTnJxuJNm3imq2vSfBciuXQl7xpkZr8vNt/dXzleZSmHpYzGt+9C19kP0vv752g8bX7Zr50xI1zh+9ZbYdEiaGkZs2KKiNSN0caBePXv3wA/d/ffxskrzWy+uz9rZvOB8pudi9i+sZEf7b03P3rmGS587jlu3byZT+y8My+YPr0Sq68LSbIxjTA+pWNggHvb23GgJZVih6YmZjc00JZOk9FVw2USUIJRm/4fsBz4JaFZ26pbnNIyJ80lffl0ev7vMRqOm4PNaCj7tTNmwKZNcPPNIcmYRDFJRKSQEccBMzPgx8ASdz83a9bvgTOBr8T731WqsBkz/n3HHXn+9Ol86ckn+dCjj/KimTP5wI47smPT5LqUh5kxNZ1majoNQM/AAMu6uni0s5MUMLOhge3jdTda02nSVvMhXmTYlEbXpu2Bs4EDgG8DJwFr3P2f7v7PqpasADOj6RN74lv66PrKw/gwzxne1gZNTXDTTbBiha6TISKT3mjiwPOB04ETzOzueDuFkFicZGaPxPV9pdKFPri1lZ/vuy/v2WEHbtiwgdc88ADfWL6c9RP4TFOlNKZSzIoDxGc3NNA9MMCSjg5u3riRv8XuVE91dbGht1eDxWXCUAtGDXL3fuAvwF/MrAl4I3CdmX3O3f+vuqUrLL1HK03/vhvd33mc3kOepvH1C4b1+ilToKEhnMJ2zRrYd9+QdIiITDajiQPufiOFWzxOrGxJt9WQSvGO+fM5dfZsznv2WS5btYor16zhtDlzePN227F9Y+NYF6FmmRkt6TQtsXVjIHanWtLRwYA7BrRlMsxpaGBmJsPUdJopqRSmVg6pM0owalQMKC8jBJWFwHeA3xZ7TRnrXAZsBvqBPndfNLpSbqvhLTvRd+cGus99lNT2zWReOGdYr89kYLvtYPXqkGQccEB4rn2riEw2YxEHxtPcxkY+tcsuvGnePH763HNcvmoVl69axYtnzeK1c+dy4NSpk/7AOZXTncrd6Xbnqa4uHotN+RkzZmcyzGpooC2ToSWVoklJh9Q4JRg1yMwuIjSL/xn4rLvfX8HVH+/uayq4viEsZUz54n50vPceOj/5AFO+fgCZo2cPez2zZoUzTN15Z7gw3z77hG5UIiKTwRjHgXG165QpfG7XXXnfjjvy85Ur+d2aNfx53Tp2a27mVXPm8NLZs5mR0eEIhBaOZjOaswaC97uzZWCA1Z2dW1s50mbMzGSYlckwLZOhJZ2mOZXSeA6pGTbcvvIy9sxsAGiPT7N/IAPc3Ud0qB1bMBaVm2AsWrTIFy9enHfepk3wr39BoTMY+oZeOt53NwOPtdN89t40nFr+maVybd4MHR2wYAHsthu0to54VTKBmdkdY9EqJ1INYxUHhqtYHAC4eu1aZjU0DKs2vb2/n6vXreOKNWt4sKODNHBkWxsnzZzJC2fMoE3JRkn97nQPDNA1MEC/O05sDUmlmJ7JMCN2r2pOpWhOpUhNksRDcaB26F9cg9x9rAbfO3C1mTnwI3c/L3cBM3sX8C6AnXfeecRvZDMaaDnvEDo//gBdn19K//2baPrIHlhzetjrmjYtJBWrV8PTT8MOO8DChTrblIhMXGMYB6puajrNq+bO5VVz5/JwRwd/WbeOv61fz2effJLMU09xxLRpHD19Oke3tbFzc3O1i1uT0jljOSB0r+pxZ01vL8/09Gxt7TAzWlIp2jIZpsfuWEni0aBT5soYUYIxuTzf3Z8xs3nANWb2kLtfn71ATDrOg1BzNZo3s9YMU759ID0/eIKei5bTf+cGmj6xF5nDZw5/XRZOZ+sexmY8/TTMnBlaNGbPDmM3RESkvuzV0sJeLS18YMcdebCjg2vWr+f6DRv4+vLlACxoauLotjaOaGvj4NZWdaUqwsxoMqMpJ2lwd3rdWd/by8qYeCQyZrSm00xLp2nLZJgSx3c0plI0mmmch4yY/qmTiLs/E+9XmdkVwBHA9cVfNTqWSdH0gd1JHzGTri89TOd77yHzork0vWdXUguHf1W9JNGA0G3qzjshnYaddgotG21tGhAuIlJvzIz9p05l/6lT+dCCBazo7uZfGzfyr02b+N2aNVy+ejUAuzY3c3BrK4e0tnJQays7NDbqILgEM6PRjMY8rRX9MflY1dvLip6eIaeYN9g6AL01nWZqKkVTOk1TXFeDEhApQgnGJGFmU4GUu2+Oj18MfG683j9z5CymXn44PRcvp+fCp+j7+2oyJ82j8cydSe81skEVLS3h1tcXWjSWLYPm5jBWY9680LVKrb8iIvVnQVMTr5s3j9fNm0fPwAAPdnRw95Yt3Ll589bxGwDT02n2aWlhv6lTw31LC9sr6Shb2ox0zqDyRNLysam/n3V9ffTmXKPDgOZUKiQhqRRTM5nQ7SorAVESMnkpwZg8tgOuiH/0DPALd//LaFY43PMDWFOapncupOE1O9B7yXJ6fvU0fX9dRerANhpfswOZE+ZgLcPfJDOZ0F0KoLc3JBqPPhquqbH99uE0t8mF/EREpL40plIc3NrKwa2tnLX99vS780hnJ/e3t7OkvZ0lHR1c/Nxz9Mfl29Jpdpsyhd2am4fcz85kdLA7DFtbPgrMd3f63OkcGGBTfz+9OS0gEJKQplSKlng9j+S2NQGJ9xmzSTMQfbJQgjFJuPvjwEGVWl9raxhkvXlzaCkYjtSsRpo+uDuNZ+5M75+eo/c3z9B1zkPw5RSZ588i86J5ZJ4/C5s6/M2zoSGc4hZCy8aqVRC78tLaGpKN2bPDYyUcIiL1J23GPi0t7NPSsvVUht0DAzza2cmSjg4e7ujg8a4u/rZ+PZvWDJ40cXo6zcLmZnZqbmZBU9OQ2/R0WsnHMFlsoWgosoy70w/0utPR20tfTEryaYwDz5vNto4FmZJOk4nvk4kJSe4YE6lNSjBkRFIpOOigcKrazs5wFe7hsukNNL5pJxreuID+uzbSd80q+v6+mr6/r4GMkX5eG+kjZ5E5aiapfaZh6eHt/DOZoWea6u6Gp56Cxx8Pz5uaQmyaNSskHC0tGiwuIlKPmlKprWM4Eu7O2r4+Hu/s5PGuLh7v7GRZVxe3b9rEH3t7h7y+NZ3emmxs19DAdo2NW2/bNzYyK5NRDfsImBkZwmDyfN2wsvW5b73mx8b+fvrcGYin4E1kzHjhjBk6+1Ud0OGUjNjUqXDkkXDbbeGieCM9bayZkTl0BplDZ+D/tSf9d2+k/19r6btlfTgD1Q+egJY06f2nkT6gjfTz2kgd0EZqZqGG2/yamoa2WvT2hlPfrlgRunuZhc80a1YYSN7SEhKnpiYNHBcRqTdmxpyGBuY0NHBEzpVauwYGeKa7mxXd3SyP9093d/NwRwc39PTQnVPLnjFjXlbiMbehgVmZDLMbGgZvmQzTlYiMWCa2UhTrXLCmtxddva0+KMGQUWlrg6OPhvvuC92Rpk8fXdcjSxuZw2aQOWwGTR+AgXU99N+2nv57NtJ/3yZ6Ln6KpKOtzWsktUcr6T2mktpjKqk9WkktbMEay6vZaGgIt2w9PeFzrFgBAwMhsUinQzew6dPD502SjubmME9EROpLcyoVxmfkaX53dzb297Oyp2fwFk/xurKnh/u2bGFNb+82SQhAGpiVk3zMjIlHcgG86en01ufTMxkySkhkAlKCIaPW0gJHHAHPPQcPPQQbN4ZpU6eOvuY/NauR1Mnb0XDydgB4Vz/9SzYzcP8m+h9pZ+DRLfTcvh56447ewLZvIrVTC6mdppDaaQq2INyn5jeVHETe2Bhu2QYGQuLx7LOhi1USU9xDkjFtWvis06aF58k6Ght1FisRkXpjZsyIycDeLflPp+7utA8MsLa3l3W9vazt62NN8jg+X9vby6OdnWzo66OnyFlRWtPprUnHjJh0TIvXpmiNt2mZzNbHW6el0xqPIDVLCYZUhBnMnx8GUa9dC08+GbofQTjortT4BmtOkzlkBhwyY+s07xtg4KlOBh7ZwsCyDgZWdDKwvJPev62CjX1DVzAtQ2q7Jmy7JlLz4v32zdh2TdjsRlKzGqEtg6UGM6NUKnyGfBeU7esLY1A2bQrJR9LqkWhqCsnHlCmD942Ng60nDQ0a9yEiUm8sXqCuNZ1mlxJXG3d3uuK4gg19fWzo62Nj9i1O3xjnPdnVxeb+frb09zNQdM3QYDYkEWmN162YGs/a1BIHSk+N9y1ZZ3RKHifLTEmlSKs1RSpEhzZSUalUGDg9d24YVL1xI6xcGbod9fQMdjlKxkNUJOnIpEjvNpX0blO3mecbe7cmHAPPdeMru/CV3Qys7Kbvgc34ht5tV5g2bGYDNqsRmx3vZzWSmtWAzWzEpmewtgZoy5Bqa2BKW4aWlvy1SH19YaxHR0do4emP3buSMR8Q7qdMCQlM0v0qSUQymcEkJLmpwkpEpH6YWTiAT6fZPreJvAh3p2NggC39/VsTjuzb5r6+wcdZ09f09tI5MEBHfz8dAwP0DuOc8k1mW5OOlnQ6nNUpnrlp6+M4YHvItDzL5S6fzNe1MSYHJRgyZpqawgXv5s0LB9SdneFAe9MmWLcu3Hd3Dz3QTg6ok4Pq0R5M2/QG0tMbSO/flne+d/Xjq7oZWNWNr+3B1/Xi63rwtT0MrO8N90904Ot6oKfITnpKCmtr2Jp82LQMNr0BpmWwqWmapmZonpqGqeG5ZT32KRn602l6eozOzpCU9PUVvs5IOh2Sj6amwfvklnxv6fTgLfu59ukiUknpVIrVvb2kgEzWNQ0adF2DUTOzra0R241iPb0DAyHhGBigs7+f9njfkUyPjzv6+7d53jUwQNfAAJvj4+74PLkv1cKST4pw1q9Gs3Cf9bghmRYv1jdkuZioHD9jxii+DRkvSjBkXJgNXnl7zhzYbbcwvbcXurpCotHVBVu2hCSkvT0kIPkOspNEJDloTqUGk5HhJiTWnMZ2biG1c/5+tgl3h/Z+fH0Pvqkv3Db2xsdZ9xv7YHMfA0924Bv78M29xROTbC0h8WhoSdMwNYO1pKE5hU1JQ3Maa05Bc3jsjSl6GtN0N6XY2JhmoDFNf0MKb0pDU1hu6+OmNMRT/KbTIQnJ7aaVPS07OUmlCt/r2EFE/l9bG50DA3TFA9ct/f109vezob+f/jw7cDMjzeAVpNPxeSo+lsprSKVoSKXIX802csmVvrcmHbErWFdOEpIvMemOr+sZGKAnPk7W1eNOe3//1sc9cfmegQHm5J6ZRWqWEgypquTgttDF+pIuRsmtry90tUqSkuRxZ2d4PFBGdYrZYDKSe0vmZS8TXmPQmsFah/+X8b6BkJy09+Ht/Xh7PySPO/pgS5zX0T9kOTr78TU9eNcA3tkPXf141wB0D/2QRjhzSdETWqUtJBuNKawxRU+80ZDCG1OQMbwxhTfEaQ2psHxDeI03Dj4mLmdNKdLN8dZkpJtTZJq3vW9oCfeZKWG5VKORTlve7z3f7yEitaslnaYlqbnI0e9Obzxw7I0XWOvNOtDszj6wjAlJOdUxKTNShP1yKut5ygyDIfMsPpbK23ql71SKYV5vd8TW9Obp1iw1SQmG1LRk3EG5F/IbGAjjHPLdknnZSUt//7b3PT2DyyVjJorJHk+RK0xPkUqlsCkNWAvYvDC90C2V8zxZz9bn7oPJRmf2fT90DYT7zpz5vQN4d7inewDvybrvifM29g0+7hmcR/cAxdrBHeiLt+7yfiY8YyFhyRhkUtBgeGbo89z7GS+fw6Fnzy/zHUSk2tJmpNNpig+BHuTxQmv9hOSk352BPI/7shKX/pi8JNP7gZ6BgSHrqMR1EywmLwZbu35Z9i1Oy01q8i2Tb5rIRKMEQyaUpOa7kq2oAwNDb+7FH2ffZyc2yePkefZ97q3QdHdwN7b+dRvjrYy272KJUEn9A1hvSDiy7613APod63PoG4Bex/oHwvPeeN8fptMXnnvvAKk4nz7H+gbw5Hlcjqz5dPQy0O20P6OaK5GJzOL4jUofmCRXgx6ISYrH+63Ts6Z5zuNkma0JThx3kLx+ICY1W9eXrDu+Zutrs9eZvEecX0sXjstOeCzr3vLMz12GAstZseVLrC932Xzd7qQ2KcEQKWEkYzvGUkgyit9KLVdofu70wecpBgZSW6cXSqhyb4WmF1suec8hr+2HOWq8EJERSFocanWMh2e1snjWc896TvbznNfley0Fli05PTvpSp5nTSNnXm45trlllzHfcjnT8q4v63lbOl2zv6MMpQRDpM5kd50SERkOMzsZ+DZh2NYF7v6VKhdp0ku6X2VNqFZRRCqmhuplRUREZKyYWRr4HvBSYD/gjWa2X3VLJSITkRIMERGRyeGI/9/encfJVdX5/3+9q5d09pAFiAkkbAqIyhIxgiKIuOCCOjrIoICjwzju208dx+HHoDPiMriNyAAqICg4CIiKIw4uiLIlENaICwQStoRA9nR6qc/3j3MqqXSqu6u7q7uqO+/n43Effff7ubeq697PPefcC/wlIh6MiA7gcuCEOsdkZmOQEwwzM7OdwxxgednwijxuO5JOl7RI0qJVq1aNWHBmNna4DYb1avHixU9JerjH6JnAU/WIpwHszPsOjb//8+odgFmDq1S5f4fH8kTE+cD5AJJWVTgPDFUj/ZY0UizgePrTXzw+DzQIJxjWq4iY1XOcpEURsaAe8dTbzrzv4P03GwNWAHuUDc8FHutrgUrngaFqpN+SRooFHE9/Gi0e652rSJmZme0cbgf2k7SXpFbgbcC1dY7JzMYgl2CYmZntBCKiS9L7gV+QHlP7nYi4r85hmdkY5ATDBur8egdQRzvzvoP332zUi4jrgOvqHEYj/ZY0UizgePrTaPFYL1R6E6OZmZmZmdlQuQ2GmZmZmZnVjBMMMzMzMzOrGScYtgNJr5b0gKS/SPpUhemS9PU8/W5Jh9YjzuFSxf6fnPf7bkl/kPSCesQ5XPrb/7L5XiipW9JbRjI+M2tMgz13SNpD0q8lLZV0n6QP1TOesulNku6U9NN6xyNpmqQrJf0xH6cX1zmej+TP6l5JP5DUNgLx7C/pZklbJH18IMtaHUSEO3dbO9KTRf4K7A20AncBB/aY53jg56SXNi0Ebq133CO8/0cAu+T+1+xs+182369IjUXfUu+43blzV99uKOcOYDZwaO6fDPyp0u/OSMVTNv2jwPeBn9bz+ORpFwPvzv2twLQ6fl5zgIeA8Xn4h8BpIxDPrsALgX8HPj6QZd2NfOcSDOvpcOAvEfFgRHQAlwMn9JjnBOCSSG4BpkmaPdKBDpN+9z8i/hARz+TBW0gvqxorqvn8AT4A/AhYOZLBmVnDGvS5IyIej4g7ACJiPbCUdBFbl3gAJM0FXgtcOMQ4hhyPpCnAUcC3ASKiIyLW1CuePK0ZGC+pGZhAPy9srEU8EbEyIm4HOgexLzbCnGBYT3OA5WXDK9jxh76aeUarge7bu0h3eMaKfvdf0hzgTcB5IxiXmTW2mpw7JM0HDgFurXM8XwU+ARSHGEct4tkbWAV8N1fZulDSxHrFExGPAl8GHgEeB9ZGxPUjEM9wLGvDxAmG9aQK43o+y7iaeUarqvdN0jGkBOOTwxrRyKpm/78KfDIiuoc/HDMbJYZ87pA0iVQy+uGIWFeveCS9DlgZEYuHGENN4iGVFhwKfCsiDgE2AkNtZzCU47MLqYRgL+BZwERJbx+BeIZjWRsmTjCspxXAHmXDc9mx6LOaeUarqvZN0vNJRecnRMTqEYptJFSz/wuAyyUtA94CnCvpjSMSnZk1qiGdOyS1kJKLyyLiqjrHcyTwhvwbdznwckmX1jGeFcCKiCiV6lxJSjjqFc8rgIciYlVEdAJXkdomDnc8w7GsDRMnGNbT7cB+kvaS1Aq8Dbi2xzzXAqfkJ0wsJBWPPj7SgQ6Tfvdf0p6kH9R3RMSf6hDjcOp3/yNir4iYHxHzSSe690bENSMeqZk1kkGfOySJ1L5gaUScU+94IuKfI2Ju/o17G/CriBjqHfqhxPMEsFzSc/J8xwL31yseUtWohZIm5M/uWFK7meGOZziWtWHSXO8ArLFERJek9wO/ID2Z4TsRcZ+k9+Tp55GeHHQ88BdgE/DOesVba1Xu/xnADNKde4CuiFhQr5hrqcr9NzPbzhDPHUcC7wDukbQkj/t0RFxXp3hqrgbxfAC4LF9APzjUWIcST0TcKulK4A6gC7gTOH+445G0O7AImAIUJX2Y9LSodZWWHUo8NnSKcDU1MzMzMzOrDVeRMjMzMzOzmnGCYWZmZmZmNeMEw8zMzMzMasYJhpmZmZmZ1YwTDDMzMzMzqxknGDYkkkLS98qGmyWtkvTTEY5jf0lLJN0paZ8e06ZKukTSX3N3iaSpedrRvcUq6TpJ0wYRy9GSBvTSIUmzS3HkZ4tfJukeSfdKuim/4bav5TcMNM4ey/9G0iP5mealcdcMdb1DiGeWpP+tx7bNbOB8Lqi4nM8FQ+RzwejlBMOGaiNwkKTxefg44NE6xPFG4McRcUhE/LXHtG8DD0bEPhGxD/AQ6S3cfYqI4yNizSBiOZqBv9X0o8AFuf9DwJMR8byIOAh4F9A5iDgqyi9NqvS/v4b0PHryyXR2rbY5UBGxCnhc0pH1isHMBsTngh0djc8FQ+JzwejlBMNq4efAa3P/ScAPShMkHS7pD/lu0h+U30Qq6bmSbst3mu6WtJ+kiZJ+JumufLfmxJ4bknSwpFvyMldL2kXS8cCHgXdL+nWP+fcFDgM+Wzb6LGBB2d2tKXld90s6r/SDK2mZpJm5/+1l8f63pKY8/tWS7sgx3yBpPvAe4CN53pdKemven7sk3djLMfwboHSXZjZlJ+aIeCAituTtfTSv616llwz1PD6Tchx35LteJ+Tx8yUtlXQu6eVIe1SI4XLSG1AB3kx6W3l/6634mUk6Ox/PuyV9OY+bJelHkm7PXekE9rJ8rEp3HSfnzV4DnNzL8TKzxuNzgc8FPhdYEhHu3A26AzYAzweuBNqAJaS7Nj/N06cAzbn/FcCPcv83gJNzfyswnvTDekHZuqdW2N7dwMty/1nAV3P/mcDHK8z/BuDqCuOvztOOBtqBvUlvAP0l8JY8zzJgJnAA8BOgJY8/FzgFmAUsB/bK46dXigW4B5iT+6dViGUvYHHZ8MHASuBm4HPAfnn8YXldE4FJwH3AIaXPIf9tBqbk/pmkN7AKmA8UgYW9fI6/AV6Uj28TcH1epr/17vCZAdOBB9j2Is9p+e/3gZfk/j2Bpbn/J8CRuX9S2fdlDnBPvb/j7ty567/D5wKfC3wucFfWuQTDhiwi7ib9AJ0EXNdj8lTgfyTdC3wFeG4efzPwaUmfBOZFxGbSD+YrJH1B0ksjYm35ipTqyk6LiN/mURcDR/UTnoBKr6svH39bRDwYEd2kO24v6THvsaQf9NslLcnDewMLgRsj4qF8HJ7uJYbfAxdJ+gfSD3ZPs4FVpYGIWJLX/yXSD/Ttkg7IcV0dERsjYgPprtJLK+zXf0i6G/g/0g/zbnnawxFxSy8xAnQDNwEnAuMjYlkV6630ma0jnagvlPRmYFNexyuA/8rH8FrS3cLJ+ficI+mDpM+3K8+/EnhWH/GaWQPxucDnAp8LrMQJhtXKtcCXKSsSzz4L/DpS/dHXk+5sERHfJ9012gz8QtLLI+JPbLsz83lJZ9QgrvuAQ1RWzzT3vwBYmkf1POn0HBZwcUQcnLvnRMSZ9H7C2n5lEe8BPkMqil4iaUaPWTaTj0vZMhsi4qqIeC9wKXB83l5/TibdTTssIg4Gnixb98Yqlr+cdEfxh9Wst9Jnlk8KhwM/ItWHLhX3F4AXlx3HORGxPiLOBt5NunN5i6T98/xtpGNjZqOHzwW98LnA54KdiRMMq5XvAGdFxD09xk9lWx3S00ojJe1Namz3ddIJ6fmSngVsiohLSSeoQ8tXlO+IPCOpdKfmHcBv6UNE/AW4k/SjXvIZ4I48DeBwSXvlk82JpDs35W4A3iJp1xz7dEnzSHfeXiZpr9L4PP96oFR3FEn7RMStEXEG8BQ71nn9E+muX2n+IyXtkvtbgQOBh4EbgTcqPVlkIvAm4Hc91jUVWBkRnZKOAeb1dXwq+B3weXa8OKi43kqfmdJTTqZGxHWk+tAH53VcD7y/bD8Pzn/3iYh7IuILwCKgdFJ5NnDvAOM3s/ryucDnAp8LjOZ6B2BjQ0SsAL5WYdIXgYslfRT4Vdn4E4G3S+oEniDVoX0h8CVJRdKTMv6pwvpOBc6TNAF4EHhnFeG9C/iGpFJd0ZvzuJKbgbOB55F+uK/eftfifkmfAa7PJ55O4H0RcYuk04Gr8viVpCen/AS4Mjd++wCpkd9+eds3AHex/QY2Kj0ycd98otsH+JYkkW4C/IxUXzkkXQTclhe9MCLu7LGvlwE/kbSIVAf6j1Ucn+12lnRy6Km39T6PHT+zycCPJbXlff5InveDwDdz0Xoz6Vi/B/hwPlF1A/eTGooCHJP33cxGCZ8LfC7A5wJjW8MbMyuj9GSQlcDuEVGzxwL2sb03kYqcP9PvzDsJpaesnBARz9Q7FjPbOflcUH8+F4xOLsEwq+w+0l2hYT+hAETE1RXq4+60JM0CzvEJxczqzOeCOvK5YPRyCYaZmZmZmdWMG3mbmZmZmVnNOMEwMzMzM7OacYJhZmZmZmY14wTDzMzMzMxqxgmGmZmZmZnVjBMMMzMzMzOrGScYZmZmZmZWM04wzMzMzMysZpxgmJmZmZlZzTjBMDMzMzOzmnGCYWZmZmZmNeMEw8zMzMzMasYJhpmZmZmZ1YwTDDMzMzMzqxknGGZmZmZmVjNOMMzMzMzMrGacYJiZmZmZWc04wTAzMzMzs5pxgmFmZmZmZjXjBMPMzMzMzGrGCYaZmZmZmdWMEwwzMzMzM6sZJxhmZmZmZlYzTjDMzMzMzKxmnGCYmZmZmVnNOMEwMzMzM7OacYJhZmZmZmY14wTDzMzMzMxqxgmGmZmZmZnVjBMMMzMzMzOrGScYZmZmZmZWM04wzMzMzMysZpxgmJmZmZlZzTjBMDMzMzOzmnGCYWZmZmZmNeMEw8zMzMzMasYJhpmZmZmZ1YwTDDMzMzMzqxknGGZmZmZmVjNOMMzMzMzMrGacYJiZmZmZWc04wTAzMzMzs5pxgmFmZmZmZjUzZhIMSZ+Q9LCkAyX9ut7xVEvShyRdXe84dhaS7pN09DCsdzdJN0paL+k/a73+Hts6WtKK4dyGDY6kDZL2rnccvZF0sqTra7CePfO+NtUiLjMzG1saKsGQtEzS5nzielLSdyVNqnLxQ4CXA18HbhhCDL+R1J5jeErSVZJmD3Z9/WxrP+DvgdOGY/1l21kmqUPSzB7jl0gKSfOHc/tl2ztTUmc+tqXuE8O4vYskfa58XEQ8NyJ+MwybOx14CpgSER8b6soknSapOx+jdfmzet3QwxxwDDeN4Pbm5+/jHT3Gz8zf32UjFctgRcSkiHhwJLYl6XWSbpO0UdJqSZdJmttPfJdFxCuHuu2IeCTva/dQ12VmZmNPQyUY2esjYhJwKPBC4DPVLBQRJ0XEXyPiFRHxuf6X6NP7cwz7ApOALw9xfb05ADgpItb2nCCpucbbegg4qWz9zwPG13gb1bgiX5iUui/WIYbhMA+4PyJioAv28VnfnL+H04BvAz+UNH3wIY6sIXyHJ0o6qGz470jfX8skvQX4PvA1YCbwXGALcJOkXXpZpta/KWZmZhU1YoIBQEQ8CvwcOAhA0kJJf5C0RtJd5dVccqnDZyX9PldRub78br2k/5H0hKS1uRrLc6uMYQ1wDXBw2breKWlp3s6Dkv6xbNrRklbk6lorJT0u6Y2Sjpf0J0lPS/p02SYOBT6dly3dvX2XpEeAX+Xxf5+394ykX0ial8d/okdJQKeki/rYne8Bp5QNnwpcUj6DpNdKujPfMV8u6cyyaW2SLs13StdIul3SbnnaaflYrJf0kKSTqzm+Zes+U9KlZcOlY9Gch/v7fF9S9t1YnuM5HTgZKB2nn+R5l0l6Re4fJ+mrkh7L3VcljcvTSp/lx8o+y3f2Ev9F+XiWtvWKKtf9SUlPAN/t6/hERBH4Dikh3KH6jaRPSfprPjb3S3pT2bTTJN0k6cv5O/SQpNeUTZ8q6dt5/x6V9DlJTZIOAM4DXpz3aU2ev6/vyA7fYUk/k/SBHvHeLemNfezy9/LxLDmFHb+rfe3zvpJ+q/T//pSkK/J4SfpK/jzX5jhKvy/j8jF6RKn09DxJ4/O0mZJ+mr9fT0v6naSKv515//fN/RdJ+mY+Busl3Sppn952WtI/5X3qyOuJ0nHvMZ+A/wQ+l0skNkfEE8C7gQ3AR/J8p+X/ma9Ieho4Uz1KpSQdofS/vDb/PaJsWq//d9rxf3RIvwFmZja2NGyCIWkP4HjgTklzgJ8BnwOmAx8HfiRpVtkifwe8E9gVaM3zlPwc2C9PuwO4rMoYZgBvBv5SNnol8DpgSt7eVyQdWjZ9d6ANmAOcAVwAvB04DHgpcIb6rqP9MlLJxqvyRdincwyzgN8BPwCIiC+WSgHy/KuAH/ax3luAKZIOUKo3fSJwaY95NpIu5qYBrwX+qexC8FRgKrAHMAN4D7BZ0kRStbTXRMRk4AhgSR9xDFbFz1fSnqTP9xukY3QwsCQizid9zqXj9PoK6/wXYGFe5gXA4WxfYrY7aZ/nAO8CvqkKd4cj4rQe2/q/Ktc9nVTycXpfO54v4koXj3+uMMtfSd+tqcC/AZdq+2p9LwIeIN3p/iLw7XyRCnAx0EUqrTsEeCXw7ohYSvqMb877NC3P39d3pGTrdziv/+1l+/IC0vG8ro9dvhR4W1miMxm4dQD7/FngemAXYC7pu0Het6OAZ+f4TwRW52lfyOMPzsei9P8L8DFgBen7tRvpf7LakqqTcny7kH5H/r3STJIOA84hfc8mAB8FngGOqTD7c4A9gf8pH5kT0R8Bx5WNfhHwIOn/ZrttK5WG/Yz0/zsjb/9n+XevpK/f1dJ6Ruo3wMzMRolGTDCuyXftbgJ+C/wH6QLluoi4LiKKEfFLYBEpASn5bkT8KSI2ky60Dy5NiIjvRMT6iNgCnAm8QNLUPmL4uqS1pDr1M4Gtd2Aj4me5KlZExG9JFzIvLVu2E/j3iOgELs/Lfy1v/z7gPuD5fWz7zIjYmPfjH4HPR8TSiOjKx+Jg5VIMgHyX9Zq8jb4u2mBbKcZxwB+BR8snRsRvIuKefIzvJiUzLyvbrxnAvhHRHRGLI2JdnlYEDpI0PiIez/vZm7/Nd4JL3bP6ibmkt8/3ZOD/IuIHEdEZEasjYkmV6zwZOCsiVkbEKtKF4DvKpnfm6Z352G4gXdzVYt1F4P+PiC15nypZmP8XniBdqL6pUnW6iPifiHgsf25XkJKQw8tmeTgiLsj15S8GZgO7KZVAvQb4cP7OrQS+Arytt53q5ztSUv4d/jGwn1J7I/IxuCIiOnrbBuli/gHgFVQoaatinztJiduzIqI9Im4qGz8Z2B9Q/r96PCdb/wB8JCKejoj1pP+1t5UtNxuYl78LvxtAVbirIuK2/P97GWW/Sz28Abg2H98u4KvAZlIi1FOp9O7xCtMeL5sO8FhEfCMiuip8z14L/Dkivpen/4D0u1CejPf6u9rDQH4DzMxsjGvEBOONETEtIuZFxHvziW0e8NbyC1PgJaSTfskTZf2bSG0nyHdBz85VD9YBy/I82zV47uGDETGVlAiU7oKS1/caSbfkqhJrSElO+bpWx7aGj6UT+pNl0zeXYuvF8rL+ecDXyvb5aUCku6sl3wYeiIgv9LHOku+R7kieRoWLNkkvkvRrSatygvUetu3b94BfAJcrVfn5oqSWiNhIuhP8HuDxXB1k/z5i+GH+fEvdY1XEDb18vqQSlb9WuY6engU8XDb8cB5Xsjpf7FXa7lDXvSoi2vtZxy35GM2MiIW5ZGQHkk5RagRe+p4cxPbfya3HLiI25d5JpO9XC+lzKy3736S71RX18x0p2fodzkn9D4G352pFJ5G+S/25hPQ9PYkdS9r62+dPkP5PblN6atjf51h+BfwX8E3gSUnnS5pCKpmYACwuW9//5vEAXyKVPlyfqwF9qor4S3r73va0K/BIaSAnMI+w/Xem5Kn8t9LDJ2aXTYftf0966vkdJQ+X/770G/8gfgPMzGyMa8QEo5LlwPd6XJhOjIizq1j274ATSHdDpwLz83j1tkBJRNxDqpb1TSXjSFUQvgzsFqnayHXVrGsAyu+MLgf+scd+j4+IP0Cqh066o/6uqlYc8TCpsezxwFUVZvk+cC2wR06wziPvW75z+28RcSCpCsTryG06IuIXEXEc6eLmj6RqYQOxkXSBV7L7AJZdDvRWr72/u8yPkS6yS/bM42qhv3UPuDF4Jbk06wLg/cCM/J28l+q+k8tJDYNnln2/pkREqY1SpRh7/Y6U6bncxaQSnWOBTRFxcxWx/Yh0h/3B/L3dqr99jognIuIfIuJZpFLAc5XbRUTE1yPiMFKj6GcD/x/pgnwz8Nyy4zA1UvVDIpU+fiwi9ibd3f+opGOr2IeBWAHsVbaPBdL3p9LjiB/I499aPjIv8zds/xS9vr5nPb+jkL6nj1aYt081+A0wM7MxZLQkGJcCr5f0qlwi0abUULbPRzJmk0kXUatJF7H/McBtX0y6u/gGUh3kcaT2Dl1KjWWH/MjHPpwH/LNyo3SlBrlvzf2vAT5IKvHprYpNJe8CXp7vOvY0GXg6ItolHU5KzsjbO0bS83L7jXWkaiPdSu9/eEOuh72FVI1ooI+uXAIcpfRs/anAPw9g2cuAV0j6W0nNkmZIOjhPe5IKjaLL/AD4jKRZSo1Xz6DC3fJBGs51l5tIuohcBekhBOQHI/QnIh4nVfH7T0lTJBUk7SOpVOXpSWCupNayxXr9jvSxnZtJVWj+k+pKL0p3xV9OanvSU5/7LOmtZb8Nz+R5uyW9MJfAtJCS2nagO1LbhQtI7al2zeuYI+lVuf91Sg3HRfrudzPw73h/LgderfSAgBZSW4d2YIdkLJdufJz0/fo7SeMl7Q5cSGob9pUqt3kd8Oy8jmZJJwIHAj8dSOA1+g0wM7MxZFQkGBGxnFQK8WnSRcVy0p3HauK/hFTs/yhwP6mx80C23UFqwPivuW72B0lVPp4hXVxdO5D1DXDbV5Man16eq3fdS6ozD6lKwixgqbY9Seq8Ktb514hY1Mvk9wJnSVpPuiAubzS+O3Al6QJrKal9zKWkz+BjpLuhT5Pq4793gPv5S+AK4G5gMQO4wImIR0glMh/L219CalQNqfrYgbnayzUVFv8cqS3P3cA9pAcADPURxyOx7q0i4n7ShfvNpITgecDvB7CKU0iJ8/2k7/SVbKt68ytSm6EnJJWq3fT1HenLJTm2qpOsiFgUETtUf6tin18I3CppA+n/80MR8RDp4vuCvJ8Pk246lB5B/UlSNahb8v/a/7Gtvc1+eXhD3ua5UeN3qeT9fBup7cVTpNKb1/fWViW3O3kH6YlRT5E+v/HAkRGxutIyFdaxmlQS+THSsfgE8LqIeKrPBXc05N8AMzMbW1R9W0Uzs8GRdApwekS8pN6xmJmZ2fAaFSUYZjZ6SZpAuqN9fr1jMTMzs+HnBMPMhk1ux7CKVJXp+3UOx8zMzEaAq0iZmZmZmVnNuATDzMzMzMxqprneAVjjmjlzZsyfP7/eYZhVZfHixU9FxKz+5zQzM7Ph5ATDejV//nwWLertibZmjUVSz7dSm5mZWR24itQYJGmapCsl/VHSUkkvljRd0i8l/Tn/3aXecZqZmZnZ2OMEY2z6GvC/EbE/6aVzS4FPATdExH7ADXnYzMzMzKymnGCMMZKmAEeR3mJNRHRExBrSm9AvzrNdDLyxHvGZmZmZ2djmNhhjz96k9w58V9ILgMXAh4DdIuJxgIh4XNKulRaWdDpwOsCee+7Z60aKxS6KxU00N0+pcfhjW2dnJytWrKC9vb3eoYxabW1tzJ07l5aWlnqHYmZmZhU4wRh7moFDgQ9ExK2SvsYAqkNFxPnkNy4vWLCg15ekdHQ8QUfHE0yZsmCo8e5UVqxYweTJk5k/fz6S6h3OqBMRrF69mhUrVrDXXnvVOxwzMzOrwFWkxp4VwIqIuDUPX0lKOJ6UNBsg/105lI1ITUBxKKvYKbW3tzNjxgwnF4MkiRkzZrgEyMzMrIE5wRhjIuIJYLmk5+RRxwL3A9cCp+ZxpwI/Hsp2pAIRTjAGw8nF0Pj4mZmZNTZXkRqbPgBcJqkVeBB4JymZ/KGkdwGPAG8d2iacYJiZmZnZjpxgjEERsQSo1Dji2FptI1WR6rWJhlVp7dpb6OpaU7P1NTdPY+rUhTVb31C9+93v5qMf/SgHHnhgvUMxMzOzEeIEwwapAHTXO4hRr6trDa2ts2q2vo6OVTVZT3d3N01NTb0OVxIRRASFwraalxdeeGFN4jEzM7PRw20wbFBSPfh0QWmjy6WXXsrhhx/OwQcfzD/+4z/S3Z0SxUmTJnHGGWfwohe9iJtvvnmH4XPOOYeDDjqIgw46iK9+9asALFu2jAMOOID3vve9HHrooSxfvny7bR199NEsWrRo6/r/5V/+hRe84AUsXLiQJ598cofYbrvtNo444ggOOeQQjjjiCB544IHhPRhmZmZWc04wbJBERDd+ktTosnTpUq644gp+//vfs2TJEpqamrjssssA2LhxIwcddBC33norL3nJS7YbHj9+PN/97ne59dZbueWWW7jgggu48847AXjggQc45ZRTuPPOO5k3b16v2964cSMLFy7krrvu4qijjuKCCy7YYZ7999+fG2+8kTvvvJOzzjqLT3/608NzIMzMzGzYuIqUDVqqEtOd22PYaHDDDTewePFiXvjCFwKwefNmdt01vXOxqamJv/mbv9k6b/nwTTfdxJve9CYmTpwIwJvf/GZ+97vf8YY3vIF58+axcGH/7T5aW1t53eteB8Bhhx3GL3/5yx3mWbt2Laeeeip//vOfkURnZ+fQdtjMzMxGnBMMG4LuXIpho0VEcOqpp/L5z39+h2ltbW3btbMoH+6rKlwp6ehPS0vL1kfMNjU10dXVtcM8//qv/8oxxxzD1VdfzbJlyzj66KOrWreZmZk1DleRskFLF52uIjWaHHvssVx55ZWsXJnes/j000/z8MMP97vcUUcdxTXXXMOmTZvYuHEjV199NS996UtrHt/atWuZM2cOABdddFHN129mZmbDzyUYNmgRLsEYqubmaTV78lNpfX058MAD+dznPscrX/lKisUiLS0tfPOb3+yz7QTAoYceymmnncbhhx8OpMfPHnLIISxbtqxGkSef+MQnOPXUUznnnHN4+ctfXtN1m5mZ2ciQnwJkvVmwYEGUngDUU1fXOtasuZGpU4+kpWWXEY5s9Fq6dCkHHHBAvcMY9SodR0mLI6LS+1/MzMxsBLmKlA2BSzDMzMzMbHtOMGzQIor4ZXtmZmZmVs4Jhg1aRNElGIPgaolD4+NnZmbW2Jxg2JAUix31DmFUaWtrY/Xq1b5IHqSIYPXq1bS1tdU7FDMzM+uFnyJlQxLhBGMg5s6dy4oVK1i1qnZPjtrZtLW1MXfu3HqHYWZmZr1wgmFDUiz6TcsD0dLSwl577VXvMMzMzMyGjROMBidpF+BZwGZgWaSW1Q3DJRhmZmZmVs4JRgOSNBV4H3AS0AqsAtqA3STdApwbEb+uY4gASE0Ui1vqHYaZmZmZNRAnGI3pSuAS4KURsaZ8gqTDgHdI2jsivl2P4LbF0kSEq0iZmZmZ2TZOMBpQRBzXx7TFwOIRDKcPLsEwMzMzs+35MbUNTFJLhXEz6xFLJVKBiG4/ctXMzMzMtnKC0YAkHSNpBfCYpOslzS+bfH2dwupFENFV7yDMzMzMrEE4wWhMXwReFRGzgPOBX0pamKepfmFV5rd5m5mZmVmJ22A0ptaIuA8gIq6UtBS4StKngIarj5QaevvNymZmZmbmBKNRdUraPSKeAIiI+yQdC/wU2Ke+oe3IJRhmZmZmVuIqUo3pU8Bu5SMiYgXwMuDsukTUBz+q1szMzMxKXILRgCLi/3oZvxb49xEOp19u5G1mZmZmJS7BGGUknVnvGMpJTXR3t9c7DDMzMzNrEE4wRp8GecleIjVRLDrBMDMzM7PECcYoExE/qXcM5aRmIjbXOwwzMzMzaxBug9GgJL0KeCMwh/Ro2seAH0fE/9Yzrp5cgmFmZmZm5ZxgNCBJXwWeDVwCrMij5wIflPSaiPhQFetoAhYBj0bE6yRNB64A5gPLgL+NiGeGHmsz3d0bh7oaMzMzMxsjXEWqMR0fEcdHxOURcVPuLgdeCxxf5To+BCwtG/4UcENE7AfckIeHTGqmWOwgoliL1ZmZmZnZKOcEozG1Szq8wvgXAv3WR5I0l5SMXFg2+gTg4tx/Man6Vc34XRhmZmZmBq4i1ahOA74laTLbqkjtAazL0/rzVeATwOSycbtFxOMAEfG4pF0rLSjpdOB0gD333LPKcINisZNCYVyV85uZmZnZWOUEowFFxB3AiyTtTmrkLWBFRDzR37KSXgesjIjFko4exLbPB84HWLBgQVS3lIjoGOimzMzMzGwMcoLRwHJC0W9S0cORwBskHQ+0AVMkXQo8KWl2Lr2YDaysbayuImVmZmZmboMx5kTEP0fE3IiYD7wN+FVEvB24Fjg1z3Yq8ONabVMq0N3td2GYmZmZmROMncnZwHGS/gwcl4drQmrxo2rNzMzMDHAVqTEtIn4D/Cb3rwaOHY7tSC0Ui04wzMzMzMwlGA1P0s19DTeCQsElGGZmZmaWOMFofG39DNddetneFiK66x2KmZmZmdWZq0g1KElH5d6Jpf6IuBGo8tGxI69Y3EJT04R6h2FmZmZmdeQEo3G9M/+dkfsDuJH0ToyG5ATDzMzMzJxgNKiIeCeApDtK/aVJdQqpX8XilnqHYGZmZmZ15jYYja9niUVDlmCkht7r6h2GmZmZmdWZE4zG98l+hhuC1EpXlxMMMzMzs52dE4wGFxHX9zXcKAqFVpdgmJmZmZkTDKuN9KjaDorFznqHYmZmZmZ15ATDaigoFjfXOwgzMzMzqyMnGFZTTjDMzMzMdm5OMBqQpDdJmp77Z0m6RNI9kq6QNLfe8fWmUGilq2ttvcMwMzMzszpygtGY/j0ins79/wXcCbwG+Dnw3bpF1Y9CoY3Ozqf7n9HMzMzMxiwnGI2pqax/34j4SkSsiIiLgFl1iqlfUnqSVESx3qGYmZmZWZ04wWhMv5F0lqTxuf+NAJKOARq2DpJUIKLodhhmZmZmOzEnGI3p/UAReAB4K3CVpPXAPwDvqGdg1eju3ljvEMzMzMysTprrHYDtKCI6gTOBMyVNBZojYnV9o6pOodBCZ+dqWlt3rXcoZmZmZlYHLsFocBGxdrQkFwCFwgQ6O1fVOwwzMzMzqxMnGKOMpDvqHUNfCoVWurs3UixuqXcoZmZmZlYHTjBGmYg4tN4xVKOra329QzAzMzOzOnCC0eAkTZe0S73jGIhCodXVpMzMzMx2Uk4wGpCkPSVdLmkVcCtwu6SVedz8OofXr0JhIh0dTxAR9Q7FzMzMzEaYE4zGdAVwNbB7ROwXEfsCs4FrgMvrGVg1CoUWisV2P67WzMzMbCfkBKMxzYyIKyKiuzQiIroj4nJgRh3jGoACXV3P1DsIMzMzMxthTjAa02JJ50p6kaRn5e5Fks4F7qx3cNVobp5Ee/sj9Q7DzMzMzEaYX7TXmE4B3gX8GzAHELACuBb4dh3jqlqh0EZHx0q6uzfR1DSh3uGYmZmZ2QhxgtGAIqID+FbuRrEmOjtX0dQ0r96BmJmZmdkIcRWpUaLRX7BXSXPzZDZvfshPkzIzMzPbiTjBGD1U7wBKOjuf4a67juWZZ37Z53zprd6b6epaMzKBmZmZmVndOcEYPX5WzUyS9pD0a0lLJd0n6UN5/HRJv5T05/x30C/va26exqZNf2LTpgf6nbepaZwbe5uZmZntRJxgNCBJO5RWRMRn+psn6wI+FhEHAAuB90k6EPgUcENE7AfckIcHGx8TJz6X9vaH+p23qWkKHR2P0t29abCbMzMzM7NRxAlGY/q1pA9I2rN8pKRWSS+XdDFwaqUFI+LxiLgj968HlpKeRHUCcHGe7WLgjUMJcOLEg2hvf4iIYp/zSUJqcSmGmZmZ2U7CCUZjejXQDfxA0mOS7pf0EPBn4CTgKxFxUX8rkTQfOAS4FdgtIh6HlIQAu/ayzOmSFklatGrVql7XPXHiQRSLm+noeKzfnWlunkp7+zK6u9v7ndfMzMzMRjc/prYBRUQ7cC5wrqQWYCawOSLWVLsOSZOAHwEfjoh1vdeo2mHb5wPnAyxYsKDXxz9NmvQCADZuvI9x4+b2E0sTUhPt7Q8yceKB1e2AmZmZmY1KLsFocBHRmas9ral2mZyU/Ai4LCKuyqOflDQ7T58NrBxKXBMnPo9CYTwbNlT3YvHm5mm0tz9MV9f6oWzWzMzMzBqcE4wGJunMQSwj0tu+l0bEOWWTrmVbu41TgR8PLbZmJkx4LuvXV/d6DqlAodDGpk1/9HsxzMzMzMYwJxgNSFJB0reBcYNY/EjgHcDLJS3J3fHA2cBxkv4MHJeHh2TixOfR3v4gnZ1PVzV/c/MUOjpWsWVL/+02zMzMzGx0chuMxvQT4P6I+OeBLhgRN9H7S/mOHVJUPUyefAhPPvld1q79PTNnvr6qZVpaprNx4720tOxCU9OEWoZjZmZmZg3AJRiNaQFwdb2D6E9b2760tu7OmjW/qnqZQqGFQqGFDRvuIqJ7GKMzMzMzs3pwgtGYjgH+W9KL6h1IXyQxbdoxrFt3C93dG6perrl5Kl1da9i06QG3xzAzMzMbY5xgNKCIuB94FfClesfSn+nTX01EJ6tXXzeg5VpaZtHe/hDt7Q8PU2RmZmZmVg9OMBpURDwGvLbecfRn4sTnMmHCgaxadeWASiMk0dIyi02b7qO9/dFhjNDMzMzMRpITjAYWEaPipRGzZr2F9vYHWbfulgEtJzXR0jKTDRvucpJhZmZmNkb4KVINSNK1fU2PiDeMVCzVmD791Tz++Pk89ti3mDJlIdW+NRzS+zRaW2ewceMSIjppa5s3oOXNzMzMrLE4wWhMLwaWAz8AbqX3x842hEKhldmz/4GHH/4szzxzPdOnv2pAy0vNW6tLFYubmDDhOUhNwxStmZmZmQ0nV5FqTLsDnwYOAr5GejHeUxHx24j4bV0j68WMGa9lwoQDWb78y3R1rRnw8qm61G60tz/CunW30d29sfZBmpmZmdmwc4LRgCKiOyL+NyJOBRYCfwF+I+kDdQ6tV1Iz8+b9K11d63j44c8RURzEOkRr6yyKxXbWrPkd7e0r/BhbMzMzs1HGCUaDkjRO0puBS4H3AV8HrqpvVH2bMGE/5s79EGvW/IYnnvjOoNfT3DyFlpZd2LDhbtatu42urlHR1t3MzMzMcBuMhiTpYlL1qJ8D/xYR99Y5pKrtuutJbNq0lMceO4/m5mnMmvWWQa1HambcuN3o6lrH2rU30dY2j7a2vWlqaqtxxGZmZmZWS04wGtM7gI3As4EPlj1VSUBExJR6BdYfScybdwbd3Rt45JGzKRY72HXXkwb9ZKjm5ilETGLLlhW0tz9CW9vetLXt6UTDzMzMrEE5wWhAETGqq64VCi3svfcXeOihf2HFinNob3+IPfb4OIXCuEGtTyrQ0jKDiG62bHmI9va/Mm7cnrS17UFzc8PmWmZmZmY7pVF9IWuNq1BoZe+9v8Duu7+Tp566mqVL38HGjfcPaZ2lF/O1tMyks/MJ1q79PWvW/IEtWx6nWOysUeRmZmZmNhROMGzYSAXmzHkf++77dbq7N/DHP76TRx75Ap2dTw95vc3N02ht3RUosmHDEp555gbWr19CR8dTFItdtdkBMzMzMxswV5GyYTd16hEceOAVPPbYuaxadRWrV/+MXXf9W2bNOpHW1llDWndT03iamsYTUaSr6xk6Oh4HCrS0zGLcuNk0N0+jqWl8bXbEzMzMzPrlBMNGRHPzZPbc85PsuuuJPPbYeTzxxCU8+eSl7LLLccyY8QYmTz4MafAFaqlUYwowhYgixeJ6Nmx4EoBCYQKtrbvT2jqTpqZJg24LYmZmZmb9c4Jhg1QAgojigBKDtrb57L332WzZsoInn/wBq1f/lKef/jktLbsxffqrmDbtKCZOfB5S06Ajkwo0NU2iqWkSAMViBx0dK2hvfwgICoUJtLTsSmvrdAqFiTQ1TRhScmNmZmZm28hvSrbeLFiwIBYtWtTr9I0b/0h7+4O0tOw66MfQprd238jq1T9l3bpbgW6amqYydeqRTJ78QiZPPoTW1jmDXn/lbXZQLG6mWOwA0qN1m5om09w8nZaWXSgUxueupWbbtOEnaXFELKh3HGZmZjs7l2DYoE2Y8GyKxU62bFlOa+vMQZU6FAptTJ/+SqZPfyVdXetZt+4W1q69kbVrf8/TT18HQEvLLCZNOoSJEw9kwoT9GT/+OTQ3Tx503IVCK4VC69bhiCBiCx0dj9Levmy7+ZqaptHcPIXm5skUCm1I4ygUxtU04TEzMzMbS5xg2KBJBSZNOoimpols3vxHCoUJQ7rwb26ezPTpxzF9+nFEFGlvf5ANG5awfv0dbNhwF888c/3WeVtb5zBhwrNpa9srv+V7Hm1t87dWixrYfgipjUJh+5f3RXRRLG6gvf1pIsqfTCWamibkaliTc7uOVqTW/LfFCYiZmZnttFxFynrVXxWpcl1d69i06Y90dj5FoTCOpqYpNW/X0Nn5NJs2PcDmzQ+waVPqtmx5FOjeOk9z8wza2vaktXV2btid/o4bl/72TCIGI5V4dBLRQbHY2SP5ABCFwniamibkNh7jKRTaypKPFqRmCgXn97XkKlJmZmaNwVc4VhPNzVOYMuVwOjvXsGXLcrZseQwo5mpGk5CG/lVraZnO1KkvZurUF28dVyx2bq3a1N7+MO3tD7NlyyNs2HAHHR0rgWKPOKfR0jIrt7eYsbVrbi71T6e5eQbNzZN7jTmVeLQCrTRVqBWWEpAuisXNdHevo6Oji4jijjNSoFBoo6mpLScgbbkaVitSc4WuySUjZmZm1vCcYFhNtbRMo6VlGhMm7E9X11o6OlbS2fkkxeIWIFWrKhTG5QvpoX/9CoUW2trm09Y2f4dpEV10dj7Fli2P09HxBB0d6W9n52q6ulazYcMjdHauJqKj4rpTFaipNDdPpbl5Ck1NU2hunpr/lvon09Q0MVeTmpD7J+a2Gn03Eo8oEtG9tdF5RFfueitVFIVCS1lVrNat29k23LS1g6YewwUnKGZmZjbsnGDYsCgUWmhtnUlr60zgQLq72ykWN9LVtZ6urmfo6lqzNekoSdWGWrbesU8XyIO/IJaaczWp3XudJyIoFjfS2fkUnZ1Pb00+urrW0d29jq6utVv7t2x5jK6utXR3r6dnyciOmnKCkpKOVFVqW5eeUtVWoRvfS39bfmFgqnaWnoC1eWuSAt19JCbbH5PUtZQd6/JqWy1Ac1liUiAlJgXS51E+nP76Eb9mZmZWzgmGjYimplQVqKVlBjAfgGKxi4gtFItbKBY76O7eSLG4ke7uTXR3b8oX0eUXzaV+bb34TYlIpQvg6hKT9Ija9M6MSqUglUQU6e7eSHf3Wrq61ueYt3W9DXd1rWHLlhV5XDvFYjvl7UeqlUot2nJJUHnj8v7/pv7mrQnFtr/bV8XalmS09PJ32/zQlNfZVLb8tv7S+gqF8lKV0nzq8ZmVPkttN25b6c3g349iZmZmI8MJhtVNauTcTFPTxIrTS20ZUoPqrq1dsdhJsdhBxBYiSv0dOSFJ01MyIrZPUEq2H58uYre/oN12V377/nRBrK0lE+PGDe3ufdqX9rJucx/Dm3MyVurftt/lf7u7N1UcX/pbeypLVkpJSHm7kfL+bQnKtiSkPBHZPgHZNq7AlClHMHfu+4YhfjMzM6slJxjWsFJj6hZg4C+821Z1KP1NjaxLf4vb/U3Tu8q60rw79qcnRqVli8Vutq8qVUpqBqdUFarctpIYlXWVxqf+7Utudlxm2xOwusoSjy6g9ESsLdsdj1TK1JWPQSnR2zHh2zZceZ5t2yz93dxjnu6y7XSVxVAaXyQinGCYmZmNAk4wbEwaybYBqe1DMf9NXUpMyofTPNv6e06rPLxtuSLFYmmdxe3WVT5P+fa3X28aliIfl3EUChN6xF1KlrbFv20aVC4NGhnFYjstLbvVbftmZmZWPScYOxFJrwa+BjQBF0bE2XUOaUxIpQZN7AwPaNrWkHxb4rH9uB3/Dnx65XkKhXFDjN7MzMxGghOMnYRSZfZvAscBK4DbJV0bEffXNzIbTbavmlUaV59YzMzMrDH5+ZI7j8OBv0TEg5Fa+l4OnFDnmMzMzMxsjHGCsfOYAywvG16Rx21H0umSFklatGrVqhELzszMzMzGBicYO49KFVl2aLUbEedHxIKIWDBr1qwRCMvMzMzMxhK3wdh5rAD2KBueCzzW1wKLFy9+StLDZaNmAk8NQ2yjhfe/sfd/Xr0DMDMzM9C2J7jYWKb0ZrM/AccCjwK3A38XEfcNYB2LImLBMIXY8Lz/O/f+m5mZWXVcgrGTiIguSe8HfkF6TO13BpJcmJmZmZlVwwnGTiQirgOuq3ccZmZmZjZ2uZG3DcT59Q6gzrz/ZmZmZv1wGwwzMzMzM6sZl2CYmZmZmVnNOMEwMzMzM7OacYJhO5D0akkPSPqLpE9VmC5JX8/T75Z0aD3iHA5V7Pv+km6WtEXSx+sR43CqYv9Pzp/53ZL+IOkF9YjTzMzMGpcTDNuOpCbgm8BrgAOBkyQd2GO21wD75e504FsjGuQwqXLfnwY+CHx5hMMbdlXu/0PAyyLi+cBnccNvMzMz68EJhvV0OPCXiHgwIjqAy4ETesxzAnBJJLcA0yTNHulAh0G/+x4RKyPidqCzHgEOs2r2/w8R8UwevIX0RngzMzOzrZxgWE9zgOVlwyvyuIHOMxqN1f2q1kD3/13Az4c1IjMzMxt1/KI960kVxvV8lnE184xGY3W/qlX1/ks6hpRgvGRYIzIzM7NRxwmG9bQC2KNseC7w2CDmGY3G6n5Vq6r9l/R84ELgNRGxeoRiMzMzs1HCVaSsp9uB/STtJakVeBtwbY95rgVOyU+TWgisjYjHRzrQYVDNvo9l/e6/pD2Bq4B3RMSf6hCjmZmZNTiXYNh2IqJL0vuBXwBNwHci4j5J78nTzwOuA44H/gJsAt5Zr3hrqZp9l7Q7sAiYAhQlfRg4MCLW1SvuWqnysz8DmAGcKwmgKyIW1CtmMzMzazyK2JmqmJuZmZmZ2XByFSkzMzMzM6sZJxhmZmZmZlYzTjDMzMzMzKxmnGCYmZmZmVnNOMEwMzMzM7OacYJhQyIpJH2vbLhZ0ipJPx3hOPaXtETSnZL26TFtqqRLJP01d5dImpqnHd1brJKukzRtELEcLemIAS4zuxSHpAmSLpN0j6R7Jd0kaVI/y28YaJw9lv+NpEeUnz2bx10z1PUOIZ5Zkv63Hts2MzOzoXGCYUO1EThI0vg8fBzwaB3ieCPw44g4JCL+2mPat4EHI2KfiNgHeIj0Juo+RcTxEbFmELEcDQwowQA+ClyQ+z8EPBkRz4uIg4B3AZ2DiKOi/ILESv/7a4Aj8zzTgNm12uZARcQq4HFJR9YrBjMzMxscJxhWCz8HXpv7TwJ+UJog6XBJf8glC3+Q9Jw8/rmSbsulDndL2k/SREk/k3RXvnN/Ys8NSTpY0i15masl7SLpeODDwLsl/brH/PsChwGfLRt9FrCgrKRjSl7X/ZLOK118S1omaWbuf3tZvP8tqSmPf7WkO3LMN0iaD7wH+Eie96WS3pr35y5JN/ZyDP8GKN2xn01ZkhYRD0TElry9j+Z13Ztf8tfz+EzKcdyRS0BOyOPnS1oq6VzgDmCPCjFcTnp7N8CbSW/s7m+9FT8zSWfn43m3pC/ncbMk/UjS7bkrJTMvy8eqVAI1OW/2GuDkXo6XmZmZNaqIcOdu0B2wAXg+cCXQBiwh3cH/aZ4+BWjO/a8AfpT7vwGcnPtbgfGki+wLytY9tcL27gZelvvPAr6a+88EPl5h/jcAV1cYf3WedjTQDuxNenv1L4G35HmWATOBA4CfAC15/LnAKcAsYDmwVx4/vVIswD3AnNw/rUIsewGLy4YPBlYCNwOfA/bL4w/L65oITALuAw4pfQ75bzMwJffPJL1tXcB8oAgs7OVz/A3wonx8m4Dr8zL9rXeHzwyYDjzAthd5Tst/vw+8JPfvCSzN/T8Bjsz9k8q+L3OAe+r9HXfnzp07d+7cDaxzCYYNWUTcTboYPQm4rsfkqcD/SLoX+Arw3Dz+ZuDTkj4JzIuIzaSL51dI+oKkl0bE2vIV5XYT0yLit3nUxcBR/YQnoNLr6svH3xYRD0ZEN6n05SU95j2WdHF/u6QleXhvYCFwY0Q8lI/D073E8HvgIkn/QLp472k2sKo0EBFL8vq/RLpYv13SATmuqyNiY0RsIJUwvLTCfv2HpLuB/yNdpO+Wpz0cEbf0EiNAN3ATcCIwPiKWVbHeSp/ZOlLSdqGkNwOb8jpeAfxXPobXkkqOJufjc46kD5I+3648/0rgWX3Ea2ZmZg3ICYbVyrXAlymrHpV9Fvh1pLYEryeVchAR3yeVIGwGfiHp5RHxJ7bdpf+8pDNqENd9wCHlbQ5y/wuApXlUzwSk57CAiyPi4Nw9JyLOpPfkZfuVRbwH+AypWtISSTN6zLKZfFzKltkQEVdFxHuBS4Hj8/b6czKpZOWwiDgYeLJs3RurWP5yUunSD6tZb6XPLCcIhwM/IrWNKVX9KgAvLjuOcyJifUScDbybVIp1i6T98/xtpGNjZmZmo4gTDKuV7wBnRcQ9PcZPZVt7gtNKIyXtTWp4/XVScvJ8Sc8CNkXEpaRk5dDyFeW7489IKt21fwfwW/oQEX8B7iRd4Jd8BrgjTwM4XNJeOfE4kXQXv9wNwFsk7Zpjny5pHqkU5mWS9iqNz/OvB0rtCJC0T0TcGhFnAE+xY/uHP5FKgErzHylpl9zfChwIPAzcCLxR6SlTE4E3Ab/rsa6pwMqI6JR0DDCvr+NTwe+Az7NjolhxvZU+M6UnXk2NiOtIbWMOzuu4Hnh/2X4enP/uExH3RMQXgEVAKcF4NnDvAOM3MzOzOmuudwA2NkTECuBrFSZ9EbhY0keBX5WNPxF4u6RO4AlSe4oXAl+SVCQ9NemfKqzvVOA8SROAB4F3VhHeu4BvSCq1G7g5jyu5GTgbeB7pIv7q7Xct7pf0GeD6nIR0Au+LiFsknQ5clcevJD1F6yfAlbkh9AdIDb73y9u+AbiL7TewUenxufvmpGcf4FuSRLoJ8DNS25WQdBFwW170woi4s8e+Xgb8RNIiUnuYP1ZxfLbbWVKi0FNv630eO35mk4EfS2rL+/yRPO8HgW/malbNpGP9HuDDOWnpBu4nPTQA4Ji872ZmZjaKlBphmlmZ/JSolcDuEVGzR8T2sb03kaoffabfmXcS+YlbJ0TEM/WOxczMzKrnEgyzyu4jlRAMe3IBEBFXV2ibsdOSNAs4x8mFmZnZ6OMSDDMzMzMzqxk38jYzMzMzs5pxgmFmZmZmZjXjBMPMzMzMzGrGCYaZmZmZmdWMEwwzMzMzM6uZ/wdnQk8k5/xDlwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Setting up subplots\n", + "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(10,10))\n", + "plt.subplots_adjust(hspace=0.75, wspace=1.25)\n", + "fig.delaxes(ax[2, 1])\n", + "\n", + "#Moraux\n", + "ax[0,0].plot(M_mass, power_law(M_mass, M_a), color='r')\n", + "ax[0,0].fill_between(M_mass, M_outup, M_outlow, color='r', alpha=0.2, label=\"error in a\")\n", + "ax[0,0].set_title(\"Moraux Mass Function for Planetary Masses in Blanco 1\")\n", + "ax[0,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[0,0].set_ylabel(\"M^(-0.69 ± 0.15)\")\n", + "ax[0,0].legend()\n", + "\n", + "#Suárez\n", + "ax[0,1].plot(S_mass, power_law(S_mass, S_a), color='b')\n", + "ax[0,1].fill_between(S_mass, S_outup, S_outlow, color='b', alpha=0.2, label=\"error in a\")\n", + "ax[0,1].set_title(\"Suárez Mass Function for Planetary Masses in 25 Ori Group\")\n", + "ax[0,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[0,1].set_ylabel(\"M^(-0.60 ± 0.13)\")\n", + "ax[0,1].legend()\n", + "\n", + "#Lodieu\n", + "ax[1,0].plot(L_mass, power_law(L_mass, L_a), color='m')\n", + "ax[1,0].fill_between(L_mass, L_outup, L_outlow, color='b', alpha=0.2, label=\"error in a\")\n", + "ax[1,0].set_title(\"Lodieu Mass Function for Planetary Masses in UKIDSS Galactic Clusters\")\n", + "ax[1,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[1,0].set_ylabel(\"M^(-0.5 ± 0.2)\")\n", + "ax[1,0].legend()\n", + "\n", + "#Béjar\n", + "ax[1,1].plot(B_mass, power_law(B_mass, B_a), color='c')\n", + "ax[1,1].fill_between(B_mass, B_outup, B_outlow, color='c', alpha=0.2, label=\"error in a\")\n", + "ax[1,1].set_title(\"Béjar Mass Function for Planetary Masses in σ Orionis\")\n", + "ax[1,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[1,1].set_ylabel(\"M^(-0.7 ± 0.3)\")\n", + "ax[1,1].legend()\n", + "\n", + "#Peña Ramírez\n", + "ax[2,0].plot(P_mass, power_law(P_mass, P_a), color='y')\n", + "ax[2,0].fill_between(P_mass, P_outup, P_outlow, color='y', alpha=0.2, label=\"error in a\")\n", + "ax[2,0].set_title(\"Peña Ramírez Mass Function for Planetary Masses in σ Orionis\")\n", + "ax[2,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[2,0].set_ylabel(\"M^(-0.6 ± 0.2)\")\n", + "ax[2,0].legend()\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f63bb9fc", + "metadata": {}, + "source": [ + "### Creating a General Power-Law Curve Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "ed848735", + "metadata": {}, + "outputs": [], + "source": [ + "#combine all mass data\n", + "all_masses = []\n", + "for a in M_mass:\n", + " all_masses.append(a)\n", + "for b in S_mass:\n", + " all_masses.append(b)\n", + "for c in L_mass:\n", + " all_masses.append(c)\n", + "for d in B_mass:\n", + " all_masses.append(d)\n", + "for e in P_mass:\n", + " all_masses.append(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "df52ba0f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5000\n" + ] + } + ], + "source": [ + "print(len(all_masses)) #to make sure the correct amount of entries were appended" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "dfc306a1", + "metadata": {}, + "outputs": [], + "source": [ + "#combine all power_law output data\n", + "all_power = []\n", + "for f in M_mass:\n", + " all_power.append(power_law(f, M_a))\n", + "for g in S_mass:\n", + " all_power.append(power_law(g, S_a))\n", + "for h in L_mass:\n", + " all_power.append(power_law(h, L_a))\n", + "for k in B_mass:\n", + " all_power.append(power_law(k, B_a))\n", + "for l in P_mass:\n", + " all_power.append(power_law(l, P_a))" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "15d0484d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5000\n" + ] + } + ], + "source": [ + "print(len(all_power))" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "6a6629e9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#use scipy curve fit to create a general power law\n", + "popt, pcov = curve_fit(power_law, all_masses, all_power) #popt gives fit value for a\n", + "avg_a = popt\n", + "\n", + "#create an array with mass values using fitted a\n", + "avg_power = power_law(all_masses, avg_a)\n", + "\n", + "#plot combined mass and power law datasets\n", + "plt.figure()\n", + "plt.scatter(all_masses, all_power)\n", + "plt.plot(all_masses, avg_power, color='r')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "3739e5d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.64439915]\n" + ] + } + ], + "source": [ + "print(popt)" + ] + }, + { + "cell_type": "markdown", + "id": "109097ab", + "metadata": {}, + "source": [ + "#### Cleaning Up the Curve Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "7475e6d3", + "metadata": {}, + "outputs": [], + "source": [ + "#combining all mass and output data sets to fit\n", + "all_masses = np.array(all_masses)\n", + "all_power = np.array(all_power)\n", + "\n", + "#put combined mass data set into increasing order and reorder output array to match new indices \n", + "sorted_indices = np.argsort(all_masses)\n", + "sorted_masses = all_masses[sorted_indices]\n", + "sorted_powers = all_power[sorted_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "id": "acc33225", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#use scipy curve fit to create a general power law\n", + "popt, pcov = curve_fit(power_law, sorted_masses, sorted_powers,sigma=sorted_errors, absolute_sigma=True) #popt gives fit value for a\n", + "avg_a = popt\n", + "avg_aerr = np.sqrt(np.diag(pcov)) #gives the error in a\n", + "\n", + "#create an array with mass values using fitted a\n", + "avg_power = power_law(sorted_masses, avg_a)\n", + "\n", + "#plot combined mass and power law datasets\n", + "plt.figure()\n", + "plt.scatter(sorted_masses, sorted_powers, label='raw data')\n", + "plt.plot(sorted_masses, avg_power, color='r', label='curve fit')\n", + "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-a)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "bd3e02dd", + "metadata": {}, + "source": [ + "Based on the curve fit, our a value for the entire mass range is 0.60524093 ± 0.00211972" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "id": "0e0bc7ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.00211972]\n" + ] + } + ], + "source": [ + "print(avg_aerr)" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "id": "7037fa8c", + "metadata": {}, + "outputs": [], + "source": [ + "#calculate the low and high outputs based on error of a\n", + "avg_aup = avg_a + avg_aerr\n", + "avg_alow = avg_a - avg_aerr\n", + "avg_outup = power_law(sorted_masses, avg_aup)\n", + "avg_outlow = power_law(sorted_masses, avg_alow)" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "id": "1cb19a53", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ysrj44ovJz8/n888/54033uCWW24hOzubn376KWS6YPbt28dVV11F9+7d6dGjB6+++irgarV33nknffv2Zd68eUybNo2TTjqJ7OxsRo8e7S8Mrr/+enr37k1GRgZ//aub4fDRRx9l48aNnHnmmZx55pkAvP/++5x88sn06tWLSy+9lH379gHw3nvv0aVLF0477TRee+21kM9h8ODBbNmyhezsbD755BMGDBjAbbfdRv/+/XnkkUeYPXs2PXv2pHv37lx99dUcOHAAcLX02267jZNPPpnevXvz1VdfcfbZZ9O5c2eeeOKJkPe68MILOfHEE8nIyOCpp56K+F2tXr2aLl26MHLkSHr06MEll1zif9533XUXffr0ITMzk1GjRvlm1GLAgAHcdNNNnHLKKWRmZvLFF18AkJeXx9VXX02fPn3o2bMnr7/+OgBTpkzh0ksv5Ve/+hWDBw9m06ZNnHHGGWRnZ5OZmcknn3wSsbwhqej0b9W5nHjiiVoR/rBypaZ9/LFuOXCgQucbdYPly5dHnHbatGmalpZWYlrNtLQ0nTZtWoXvv3DhQs3MzNS8vDzdvXu3du7cWe+//35VVd22bZs/3bhx4/TRRx9VVdWRI0fqv//9b/+xstIFcuutt+rYsWP92zt27FBVVUCnT5+uqu5ZDBkyRA8ePKiqqtdff71OnTpVVVW3b9+uqqqHDh3S/v3769dff62qqh07dtStW7eqqurWrVv19NNP13379qmq6r333qt/+9vftKCgQNu3b68//PCDHj58WC+99FI9//zzS8m4atUqzcjI8G/3799fr7/+elVV/zVWrFihqqojRozQhx56yC/DxIkTVVX1pptu0u7du+uePXt0y5Yt2rp165DP3Zef/Px8zcjI8D/DwPw0atQopIyAfvrpp6qqetVVV/nfl++aqqrDhw/XN954w5+Pa665RlVVP/74Y38e//KXv+gLL7ygqqo7d+7U4447Tvft26eTJ0/Wdu3a+a/3wAMP6N133+1//nv27CklV6jvGFioIXRqQtf4GyQlUahqNX6jyhg3blyp2nR+fj7jxo2r8DU/+eQTLrroItLS0jjiiCMYOnSo/9i3337L6aefTvfu3cnNzWXZsmUhrxFJulmzZvG73/3Ov928eXMAkpOTufjiiwGYPXs2ixYtok+fPmRnZzN79mx+/vlnAF5++WV69epFz549WbZsWUhT0/z581m+fDmnnnoq2dnZTJ06lTVr1vD999/TqVMnjjvuOESE4cOHR/x8hg0bBsCKFSvo1KkTxx9/PAAjR45k7ty5/nS+59a9e3f69u1LkyZNaN26NampqezatavUdR999FGysrLo168f69atY+XKlaXSlMUxxxzDqaeeCsDw4cP59NNPAfjoo4/o27cv3bt358MPPyzxHi6//HIAzjjjDPbs2cOuXbt4//33uffee8nOzmbAgAHs37+ftWvXAnDWWWfRokULAPr06cPkyZMZP34833zzDU2aNIlY1lAk7ACu3NxcJn3xBYUXXUSvk07if2+5hZycnHiLZdRyfH/KSPdHSlnueFdeeSUzZ84kKyuLKVOmMGfOnAqnU9WQ90lNTSU5OdmfZuTIkdxzzz0l0qxatYoHHniAL7/8kubNm3PllVeG9BtXVc466yxefPHFEvuXLFlSYZfZRo0a+a9dHg0aNAAgKSnJv+7bDraTz5kzh1mzZjFv3jzS0tL8SjdSgvMiIuzfv58xY8awcOFCjjnmGMaPH1/imqHOUVVeffVVTjjhhBLHFixY4M83uMJi7ty5vP3224wYMYJbbrmFK664ImJ5g0nIGn9ubi6jRo1i99atAGzYvJlRo0ZViS3WqNt06NAhqv2RcMYZZzBjxgwKCgrYu3cvb775pv/Y3r17adOmDYWFhSW+3yZNmrB3796w6QIZPHgwjz/+uH97586dpdIMHDiQV155hS1btgCwY8cO1qxZw549e2jUqBFNmzZl8+bNvPvuuyFl6devH5999hk//vgj4FpDP/zwA126dGHVqlX89NNPAKUKhkjo0qULq1ev9l/7hRdeoH///lFfB2D37t00b96ctLQ0vv/+e+bPnx/V+WvXrmXevHmAy8tpp53mV/KtWrVi3759vPLKKyXOmT59OgCffvopTZs2pWnTppx99tk89thj/kJt8eLFIe+3Zs0ajjzySK699lp++9vf8tVXX0UlbzAJqfj9zfGDB92OBg0q3Rw3DHCdsGlpaSX2paWlMWHChApfs1evXgwbNozs7GwuvvhiTj/9dP+xv//97/Tt25ezzjqLLl26+Pdfdtll3H///fTs2ZOffvqpzHSB3H777ezcuZPMzEyysrL46KOPSqXp1q0bd999N4MHD6ZHjx6cddZZbNq0iaysLHr27ElGRgZXX32138wBMGrUKM4991zOPPNMWrduzZQpU/wumf369eP7778nNTWVp556ivPPP5/TTjuNjh07Rv2cUlNTmTx5Mpdeeindu3cnKSmJ6667LurrAJxzzjkcOnSIHj16cMcdd9CvX7+ozu/atStTp06lR48e7Nixg+uvv55mzZpx7bXX0r17dy688EL69OlT4pzmzZtzyimncN111/Hss88CcMcdd1BYWEiPHj3IzMzkjjvuCHm/OXPmkJ2dTc+ePXn11VcZO3ZshfLtJ5Thv6Yt0XbuiojreBs6VPnoI+X44xVQEYnqOkbdIJrOXVXXwduxY0cVEe3YsWOlOnaN2kdwB3Qk9O/fX7/88ssYSeSIpnM3IW38HTp0YM2aNVBY6HZ49r7KNMcNw0dOTo71Fxm1moQ09UyYMMGFJ/WZeurXp169epVqjhuGYYAbM/Dtt99Gdc6cOXPo3bt3jCSKnoRU/OD1oAfU+A8fPszYsWOrdNCNYRhGbSQhTT3jxo3j4MGDJWr8RUVFbN++HXA95FdddRWANdkNw6hzJGSN3+9THaD4gyksLKx8z7hhGEYtJCEVv78TN8CdMxS+FoBhGEZdIiEV/3nnnedWfDb+EDV+w6itTJkyhRtuuKFC51555ZWlBhaFuv7GjRujuu7q1avJzMyskEyx5NFHH6Vr167k5OTwxhtvcO+99wI1K6x1PEhIG/8777zjVnyKP8IJiA3DcIo/MzOTtm3bxlWOQ4cOkZJSORU1ceJE3n33Xf9ctL54PjNnzmTIkCF069at0nLWRhKyxl/Kxm+K36jhPP/88/To0YOsrCxGjBgBwJtvvknfvn3p2bMngwYNYvPmzaXO27x5MxdddBFZWVlkZWXx+eefl6p9P/DAA4wfP77UuaFCCL/yyissXLiQnJwcsrOzKSgoYNGiRfTv358TTzyRs88+m02bNgGwaNEisrKyOPnkk/nnP/9ZZt7uu+8+unfvTlZWFn/+858BF6Z44cKFAGzbto309HSgdDjiYcOGFVfkcC2WV199laKiIm655Rb69OlDjx49ePLJJ0vd97rrruPnn39m6NChPPTQQ/6WUqiw1nWNhKzxlxrAZYrfiJCbVq5kiRc/vqrIbtyYh487rszjy5YtY8KECXz22We0atWKHTt2AHDaaacxf/58RIRnnnmG++67j//7v/8rce7vf/97+vfvz4wZMygqKmLfvn0hY/CE4oYbbuDOO+8EYMSIEbz11ltccsklPP744zzwwAP07t2bwsJCbrzxRl5//XVat27N9OnTGTduHM899xxXXXUVjz32GP379+eWW24JeY93332XmTNnsmDBAtLS0vx5K4958+axdOlSWrRowYwZM5g+fTrnnXceBw8eZPbs2UyaNIlnn32Wpk2b8uWXX3LgwAFOPfVUBg8e7K/ZAzzxxBO89957fPTRR7Rq1YopU6YAcMoppzB06FCGDBnCJZdcEtGzSjRipvhF5BjgeeBo4DDwlKo+IiItgOlAOrAa+B9VjexLjZAJEyYwatQo8n01/ko2Fw0jlnz44YdccskltGrVCsAfinf9+vUMGzaMTZs2cfDgwRJKLfDc559/HnDhlZs2bRqx4v/oo4+47777yM/PZ8eOHWRkZPCrX/2qRJoVK1bw7bffctZZZwFuhq42bdqwe/dudu3a5Q+SNmLEiBKB23zMmjWLq666yh/fyJe38ggMR3zuuefy+9//ngMHDvDee+9xxhln0LBhQ95//32WLl3q76/YvXs3K1euDPmMjNLEUiMeAv6fqn4lIk2ARSLyAXAlMFtV7xWRPwN/Bv5UlTfOycnhs88+Y9ILL7gdVuM3IqS8mnms0DLCJd94443cfPPNDB06lDlz5oQ014QiJSWFwwHzTIcKNxwuhHCgbBkZGf5IlD527doVUZjlsvIWKGPwfQPDEaempjJgwAD+85//MH36dH9Me1Xlscce4+yzzw4rg1GamNn4VXWTqn7lre8FvgPaARcAU71kU4ELY3H/d955p9jGbzV+owYzcOBAXn75Zb97sc8csnv3btq1awfA1KlTyzx30qRJgKuN79mzh6OOOootW7awfft2Dhw4wFtvvVXqvPJCCAeGWT7hhBPYunWrX/EXFhaybNkymjVrRtOmTf0TkJQXCvq5557zT17jy1t6ejqLFi0CCOtldNlllzF58mQ++eQTv6I/++yzmTRpEoWeOfeHH34gLy+v3OsEEhzWuq5RLZ27IpIO9AQWAEep6iZwhQNwZBnnjBKRhSKycKsXVz8a1q5dazZ+o1aQkZHBuHHj6N+/P1lZWdx8880AjB8/nksvvZTTTz/dbwYK5pFHHuGjjz6ie/funHjiiSxbtox69er559AdMmRIyDDN5YUQvvLKK7nuuuvIzs6mqKiIV155hT/96U9kZWWRnZ3N559/DsDkyZP53e9+x8knn0zDhg1DynfOOecwdOhQevfuTXZ2Ng888AAAf/zjH5k0aRKnnHIK27ZtK/f5DB48mLlz5zJo0CDqe67Z11xzDd26daNXr15kZmYyevToqCYlDw5rXdcQDTOrTaVvINIY+BiYoKqvicguVW0WcHynqjYv7xq9e/dWnwdApKSnp7sO3g8+gNmzwfPfDSbW+TdqPt999x1du3aNtxiGUSlCfcciskhVS0WHi2mNX0TqAa8Cuar6mrd7s4i08Y63AbbE4t7+QVwHD1qN3zAMI4CYKX5xPTrPAt+p6oMBh94ARnrrI4HXY3H/EoO4zMZvGIbhJ5Y1/lOBEcAvRWSJt5wH3AucJSIrgbO87SqnxCCuchS/hWc2DKOuEbOqsKp+CpTl7zUwVvf10aJFC+clUVhYrqln7NixFprZMIw6RUKGbChBGBu/Reg0DKOukbCK3z80fP9+69w1DMMIIGF7Pf3xevbvh0aNQATMddOIgPm7d7MrCp/wcDRLSaFf06ZVdr3Kcs0113DzzTfX2ciURgIr/vPOO8+NaNy/H5o3N8VvRMyuQ4doXYVzOGz1jSCvJEVFRSQnJ5e5HQpVRVVJSipu3D/zzDNVIo9Re0lYU4/fnXP/fjcRS1LCZtVIAKZNm8ZJJ51EdnY2o0ePpqioCIDGjRv7R+HOmzev1PaDDz5IZmYmmZmZPPzww4CbFKVr166MGTOGXr16sW7duhL3CgyJ3LhxY8aNG0dWVhb9+vULGfr5iy++4JRTTqFnz56ccsoprFixIrYPw4g5CasN/e6cBw6Y4jdqNN999x3Tp0/ns88+Y8mSJSQnJ/vdjPPy8sjMzGTBggWcdtppJbYbNmzI5MmTWbBgAfPnz+fpp59m8eLFgIuqecUVV7B48WI6duxY5r3z8vLo168fX3/9NWeccQZPP/10qTRdunRh7ty5LF68mLvuuovbbrstNg/CqDYS1tTjd+f01fjDNIkNI17Mnj2bRYsW+ePlFBQUcOSRLoRVcnIyF198sT9t4Pann37KRRdd5I9m+etf/5pPPvmEoUOH0rFjR/r16xf23vXr12fIkCEAnHjiiXzwwQel0uzevZuRI0eycuVKRMQfGM2ovSSs4vdjit+o4agqI0eO5J577il1LDU1tYQdP3C7vDhTgaGNy6NevXr+sMnJyckhA53dcccdnHnmmcyYMYPVq1czYMCAiK5t1FwS1v7hd+c8cAAaNDBTj1FjGThwIK+88gpbtriwVTt27HAeaWE444wzmDlzJvn5+eTl5TFjxgxOP/30KpcvMDy0bxYro3aTsDV+vztnQYHbkZYGe/bEVyijVtAsJaXKPHF81yuPbt26cffddzN48GAOHz5MvXr1+Oc//1mubR6gV69eXHnllZx00kmAc9Ps2bMnq1evrirRAbj11lsZOXIkDz74IL/85S+r9NpGfIh5WOaqoCJhmceMGePcOS+6CH7/e7j6ali1KmTa2vAMjNhhYZmNRKDGhGWOJ353zgMH3K8356dhGEZdJ2EVv9+d0zefZxkzBBmGYdQ1Elbxt2jRwq2Y4jciwMx9Rm0m2u83YRW/nwgUv8Xkr9ukpqayfft2U/5GrURV2b59O6mpqRGfk7BePSXcOaFcG//o0aMtJn8dpn379qxfv56tW7fGWxTDqBCpqam0b98+4vQJq/hLjNwF58tfBnl5edUklVETqVevHp06dYq3GIZRbdQdU08UzSDDMIxEJmEVfylTj03GYhiGASSw4u/QoYNb8Y3ctRq/YRgGkMCKf8KECW7FFL9hGEYJElbx5+TkuAiFhw875V9O565hGEZdImEVP1Ds11pQYDV+wzAMj7DunCKSCgwBTgfaAgXAt8DbqrostuJVDn8Hb36+KX7DMAyPcmv8IjIe+Aw4GVgAPAm8DBwC7hWRD0SkR6yFrCj+sA2m+A3DMPyEq/F/qarjyzj2oIgcCXSoWpFiQF4eNGkCImDD8g3DqOOUW+NX1bfDHN+iqtEFyq9Gtm/f7lZ8Nf5yZuGyeD2GYdQVwpl6kkVktIj8XURODTp2e2xFqzz+uUrz8sIq/tGjR1eTVIZhGPElnFfPk0B/YDvwqIg8GHDs1zGTqoooKipyK74afzkTrlu8HsMw6grhFP9JqvobVX0Y6As0FpHXRKQBIDGXrpKUqPE3aABh5j41DMOoC4RT/PV9K6p6SFVHAUuAD4HGMZSrSvDX+AsKXKwem4zFMAwjrOJfKCLnBO5Q1buAyUB6rISqKlq2bOlWfGacJk3iJ4xhGEYNIZxXz3BVfS/E/mdUtfaEu8zPd7+m+A3DMCIaudsUOAdoByiwEfiPqu6KrWiVp4Q7J5jiNwzDILw75xXAV8AAIA1oBJwJLPKO1Wj8nbs+xd+4xndLGIZhxJxwNf5xwInBtXsRaY4L4fB8jOSqEvyduz4bvyl+wzCMsJ27gjPvBHOY2uTOuXev+zXFbxiGEVbxTwC+EpFJInKbtzyBM/9MiL14lcNf4/cp/rS0ctOPGTMmxhIZhmHEn3BePVOB3sDHwAHgIDAH6K2qU8o7V0SeE5EtIvJtwL7xIrJBRJZ4y3mVzUB5dOzY0a3k5bkJWRo1Kjf9pEmTYimOYRhGjSDsRCyqulNVX8L57j+rqi+p6s4Irj0F5w0UzEOqmu0t70QnbnT4p188fNgp/zA1fsMwjLpAOK+eDiLykohswXXmfunV4l8SkfTyzlXVucCOqhM1enJyckjyBWbbuzdsjd8wDKMuEK7GPx2YAbRR1eNU9VigDTATeKmC97xBRJZ6pqDmFbxGxBw+fNit7N1rNX7DMAzCK/5WqjpdVYt8O1S1yDP9tKzA/SYBnYFsYBPwf2UlFJFRIrJQRBZu3bq1Ardy+D179uxxNf5yQjMbhmHUBcJpwUUiMlFE+opIW2/pKyITgcXR3kxVN3sFx2HgaeCkctI+paq9VbV369ato72VH79nz549rsZfTmhmwzCMukC4AVxXAL8F/oYL2SDAOuBN4NlobyYibVR1k7d5EW7S9pgiIqhqScVfWBjr2xqGYdRYylX8qnoQZ56J2s9RRF7EhXpoJSLrgb8CA0QkGzcobDUQ82mv1DfHrs/G36AB7N8f69sahmHUWCps8BaRIeUdV9XLVbWNqtZT1faq+qyqjlDV7qraQ1WHBtT+Y8+ePc6+f8QR5SazQVyGYSQ6lenp7FNlUsSQEu6cAC1alJveBnEZhpHoVFjxq+pfq1KQWOF359yzx/02axY3WQzDMGoCCR2PHwI6dyOs8RuGYSQ6CR2PHwI6d3ftcr9hbPyGYRiJTkLH4y/BTi+8UNOm8ZXDMAwjziR0PH4ImHA9Px8OHrTpFw3DqPMkdDx+gEceeaR4Y+fOiEw9ubm5MZTIMAwjvsQsHn9NIScnp3hj586IavxXX311DCUyDMOIL2G9erzY+xWNxFmz2LEDOnZ0YRuKispMdvDgwWoUyjAMo3qpW6Eqd+xwNf6UsOWdYRhGwlL3FH/jxpCaGm9JDMMw4kbdUvw7d7p4PS0rMpWAYRhGYhCx4heRx8rbrhX4fPkrEd/fMAyjthNNjf/UMNs1H5/iP/LIsEnNpdMwjESlbpl6dnhzv0dg6hk5cmSMhTEMw4gPkQRpW4UbvdvWt66qv6CWjNwtgW/u3ggCtRWV4+5pGIZRmwlb41fVTp6i/y5gHUKHcqiR+MM2HDjgwjNbaGbDMOowlTH11Joaf4mwDVu3QvPm8RPGMAwjzkSj+P8dZrvGUiJsw5YtTvEnJ8dPIMMwjDgSseJX1X+Ut11r2LzZKf569cImtfl3DcNIROqWVw+4Gn9aWkR2fpt/1zCMRKRuKn6Adu3iK4dhGEacMMVvGIZRx4hK8YtIJxH5tYh0iZVAMcen+CMYvWsYhpGIhJtsfWbA+gXAh8CvgNdF5MqYShYrtm6FQ4egVauIklvoBsMwEo1wNf6OAet/An6pqlfh4vT8IWZSxQD/IK7Dh12tP8JAbSNGjIihVIZhGNVPOMUfODo3RVVXAajqNtyE67WGEoO4Nmxwij8pvKVLtdYMUDYMw4iIcJovS0T2iMheIFtEjgYQkfpArRoBVWIQ1/r1ztRTv378BDIMw4gT5QZpU9WylHsaMLrqxakmNmyAhg3hqKNgzZp4S2MYhlGtRDMRSwsRaQ6gqrtUdV7sxIoxGza4306dIkpuI3gNw0gkwnn1dBCRl0RkK7AA+FJEtnj70qtFwljgU/zp6REltxG8hmEkEuFq/NOBGcDRqnqcqh4LtAFmAi/FWLbY8d//Ou8e8+U3DKMOEk7xt1LV6arqn5VEVYtU9SWg9s5YXljogrUddVS8JTEMw6h2win+RSIyUUT6ikhbb+krIhOBxdUhYFXSMnDKxdWroU0bSAk7CRlgdn7DMBKHcIr/CuAb4G/Af4D3vfVvgVo3sqmEL/9PPzlTT6NGEZ1rdn7DMBKFchW/qh5U1Umqeo6qdlfVTG99oqoeqC4hq4oSvvyrVrnJWI49Nn4CGYZhxIFwXj0XiUgLb721iEwVkW9EZLqItK8eEWPE6tXut0vtjTdnGIZREcKZeiao6g5v/XFgCXAu8C4wOYZyxZ5166CoCI45JuJTzM5vGEYiEE7xB47cPVZVH1LV9ao6BSg3ypmIPOf5/H8bsK+FiHwgIiu93/jNel5YCBs3ug7eCDE7v2EYiUA4xT9HRO4SkYbe+oUAInImsDvMuVOAc4L2/RmYrarHAbO97ZiSm5tLeno6SUlJpKenk5qaWnzwp5+gbduI5t81DMNIFMIp/htwUThXAJcCr3kB264ljFePqs4FdgTtvgCY6q1PBS6MUt6oyM3NZdSoUaxZswZVZc2aNRQVFRUnWLHCBWszf37DMOoQ4bx6ClV1vKp2ALoDrVW1iar+RlXXVuB+R6nqJu/am4CYDp0dN24c+fn5JfYVFhYWb3z/vfvNzo74ms2bx886ZRiGURVEHKRNVXer6vZYChOIiIwSkYUisnDr1q0VusaacJE3V650vyecEPE1d+3aVSFZDMMwagoVnmxdRL6qwGmbRaSNd34bYEtZCVX1KVXtraq9W0c4W1YwyclhpgzIy3Ox+Tt2LD+dYRhGAlFhxa+qvSpw2hvASG99JPB6Re8fCSXs+WXx/ffQoUNUk7JkZGRUQirDMIz4UqF4/BGmfxGYB5wgIutF5LfAvcBZIrISOMvbjhkdI6nJf/89NG3qlH+ELF++vBJSGYZhxJeYxeNX1ctVtY2q1lPV9qr6rKpuV9WBXojngQGDw2LChAkTwif65hv327evf1cJl0/DMIwEI6Hj8efk5JAUbkL1H3+E/Hzo2tW/a//+/WGv3a5du8qKZxiGERcSPh7/4cOHwyWAZctcsLZwncEBbNy4sZKSGYZhxIeEj8cvIuETffWVG8TVtm3sBTIMw4gz4WYhuQL4LS4GfztAgPU475xnYyta1aCq4RN9/bX7PflkF7wNZ+cPZ/JJTk6OzHPIMAyjBlGu4lfVg8Akb0lcfvgBCgqgRw94+WUgMjt/WDOSYRhGDSRqP/4KDtyKGyWmWyyLoiJn7unWrUTAtsaNG4c91Xz6DcOobVRkAFcERvOaQ4npFsvj88+heXMIUORPPPFE2NPMp98wjNpGRRT/21UuRQwpMd1ieSxY4H779/fvuvXWWyM6NTc3N1qxDMMw4ka4AVylaveqenu4NLWS7dvh55+he3fwsrRx48aIzD3Dhw+PtXSGYRhVRrga/0cicqOIlIhnICL1ReSXIjKV4tg7tZ/PP4f09BKzckVi7jEMw6hNhFP85wBFwIsislFElovIKmAlcDnwkDcNY62lW7duxRtz5rhBXOed59/1j3/8I6KxAGEjgRqGYdQQwk3Esl9VJ6rqqUBHYCDQU1U7quq1qrqkOoSsLOV59mzevLl446efYMMGF7fHU/bLly/nuuuuC3sPc+00DKO2EM1ELIWquklVd8VQnphQnmfP9u1Bc8t8+CH84hfQvr1/18SJEyO6j9X6DcOoDUSk+EVkfIzliCnhPHtKmHtmzYKkJBgyxL8rIyOD66+/Pux9rNZvGEZtIJxXT5KIPAs0qCZ54sJtt91WvLF2rfPuOe00VwDgzD2R1voTxcnJMIzEJVyN/01gh6r+pTqEiRdjx44tueP1113Att69/btyc3Mjcu30pTUMw6iphFP8vXHx+Gs95XXwbt++nbaBkTlnzYIDB+DCC/27hg8fHrFrp/n1G4ZRkwmn+M8EnhSRvmHS1XjChW7YsGFD8UZ+vnPt7NULWrTw787JyWHgwIER3c8majEMo6YSzp1zOXA2cH/1iBM7wnXwljLPvPYaNGgAw4b5dzVv3pxZs2ZFdD+bqMUwjJpKWK8eVd0InF8NssSVsWPHlqzN//CDm4938GDw5uDdtWsXEFnUTrCOXsMwaiYRuXOq6t5YC1IdhLPzl6rN5+ZCs2YlbP0ZGRlRhXGwsM2GYdQ0wrlzvlHeUl1CVhXh7Py5ubklO3kXLHDunRdcACluzprly5dHZeu3sM2GYdQ0pLypCUVkK7AOeBFYQFAsflX9OKbSefTu3VsXLlxYJdcqz/zSsmVLtm3bVjLNgAHw17/CpEn+2bmaNWvGzp07ozLlRDQFpGEYRhUiIotUtXfw/nCmnqOB24BM4BHgLGCbqn5cXUq/OvGFbygReuHjj92ArmHDStn6IxnN68PCORiGUVMI59VTpKrvqepIoB/wIzBHRG6sFuliQLipGMeMGcPUqVOLd6jCE084t84AzyARYeLEiSVNQ+Vw+PBhxowZUyGZDcMwqpJyTT0AItIA59VzOZAOvAE8p6obyjuvKqlKU09ubm7YAVaqSnJycsnYO488AsceC1deCVu3AjBw4EBmzZpFSkoKRUVFEd3fTD6GYVQXFTL1eBOtfA70Av6mqn1U9e/VqfSrmpycnLDumLm5uaUV+UMPOb/+P/zBv2v27NkAJVsIYTAXT8Mw4k04G/8I4HhgLPC5iOzxlr0isif24sWGcO6Yo0ePBlwnrp/Vq+HNN+Hkk128fo/69euTk5NTMsJnGEz5G4YRT8LZ+JNUtYm3HBGwNFHVI6pLyKom3CjevLw8AHbu3FnywJNPurl5b74ZGjYEoLCwkDFjxrBs2bKoOnBN+RuGES8inogl0Yikkxco2Xm7fz/cey8ceST8v//n3z1p0iQgOpMPmPI3DCM+1FnFH24wl0+ZlwjeBrBwIbzxBgwcCP37+3eLSFQDuwLPMwzDqE7qrOLPyckh1fPLL4tBgwYBIfz1H3/czc37xz+6uP0e9evXZ9asWVHZ+8GUv2EY1UudVfwAzzzzTLnHZ8+eTW5uLhMnTqRevXrFBwoL4bbbIDkZ7rnHefvg7P3t2rVj2bJlpvwNw6ix1GnFH66TF+Dqq68G4ODBgyUPrF0L//u/0KED3HGHf/fGjRsZNGgQy5Yti3hwlw9T/oZhVAd1WvFD+LALBw8e9MfqnzZtWsmDH38M//oXnHoq3Fg8mNnXUtiwYUNJl9AIEBG/ickwDCMWhB25WxOoypG7oYikpu17Tu3atSs9ycodd8AvfwlPPQUvvujfPW3aNHJyckhLS6OgoCBquWrDuzEMo+ZS0SBtdYJIgq35plIs5eUDMGECLF4M11xTap7eMWPGkJ+fT0PP7z8azPRjGEYsMMUPTJw4kRQv3n5Z+Gz3EKImfvgw/PnP8N13MHYsXHKJ/9CkSZPIyMiolPJv3rx51OcZhmGURVwUv4isFpFvRGSJiMTOhhMFU6ZMCZvGZ7uHEMr/4EEXx2fpUvjd7yAgENzy5ctJS0ursPLftWuX1f4Nw6gy4mLjF5HVQG9V3RZJ+ljb+H1kZGRENGNW4DMrpZBTUtzo3hNPhLffhgcfdC2CgHND9hNEiNn9DcOIFLPxR8CyZcsiqlmnpaX510sp4kOH4NZb4d134fzzXVTPgFq+iLBhw4ao/fx9+FochmEYFSVeil+B90VkkYiMipMMIXnhhRfCpikoKChhdw9p87/vPjdZe0YGPPccdO7sPywibNy4MaoZvHwMHz7cJnA3DKNSxMvU01ZVN4rIkcAHwI2qOjcozShgFECHDh1OXLNmTbXJN2jQIH+s/fJo2LAh+fn5/u2QrYV+/eAvf3HTNj72GLz1VonDqlphd0/fRDCGYRihqFGmHlXd6P1uAWYAJ4VI85Sq9lbV3q1bt65W+WbNmhXRqNuCggLq16/v3w5ZiM6f79w8V61yET3vvx8CBnX5CouKmH5mz55tA74Mw4iaalf8ItJIRJr41oHBwLfVLUc4NmzYEJEHTmFhISJStrcPuKkaf/c7mDEDsrPh+efdgC+PgoICli9fXmG7vxUAhmFEQ7WbekTkF7haPkAK8C9VnVDeOdXl1ROKpKSkiD1punXrxrJly4ByBl9lZTm3z44dYdEi1/kbNCisWbNm7Nq1q8IymwnIMAyoQaYeVf1ZVbO8JSOc0o83kXT2+li+fDlJSUnk5uaiqqFj83/9tTP9/OtfruN38mS47jrXB+BRGaUPxS2Ahg0bmheQYRilsFg9ETBmzBj/xCyRElHtv00bN9K3b1/YvRteeMFN8lJYWFmRS2GtAMOoe9SYGn9tZOLEiaUjc4YhuPYfMkrnpk0u1MOtt8KWLXDDDa4lcP75LtZ/FeJrBYiIf1pJwzDqJlbjj5L69etTGGWNPDk5malTp5KTk1N27V8EzjwTrrjC2f+3bYOXX4Y333Rz/caAlJQUpkyZEtG8BIZh1D6sxl9FHDx4MOp4O0VFRQwfPpyUlBSmTZsW2lVUFT78EK6+2k3wsns3jBkDr7wCo0dDq1ZVlINiDh06xPDhwxER6tWrZ/0BhlFHsBp/BYk0rk8ofC2AK664gsMBcXxKIOJs/8OGQY8ebt8XX8DMmfDllyXi/8SC66+/nokTJ8b0HoZhxJayavym+CtBbm4uwwOicFaEgQMHhh8lfOyxcOmlcPLJ0KSJGxfw9tvwwQdQwWBv0RDYUW0YRu3BFH8MqUzt30dKSgqHDh0qP1HDhm6il/794YQT3L4ff3QB4T76CHburJQMkWKtAcOoHZjijzFVUfuPis6dnfdPnz7Qvr0z/SxdCnPnwmefOS+hasIKAsOomZjiryYiDfBWZSQlQffucM45LhzE0Ue7/T//DJ98Ap9+6loF1YiZhgyjZmCKv5qp9gIAnO9/164uDlBWFqSnu4Jh1y746ivXObxokXMVrWasVWAY1Y8p/jgRlwIAnMJv184VApmZrk+gSRN3bN065xn09dfwzTfV1jcQjI0mNozYYoo/zsStAPDRoIFzC+3Xz7UKOncGX0jpjRtdAbB0qftdty5+cmKtA8OoKkzx1xByc3MZOXIkRUVF8RWkSRPo2dP1C3TuDL/4BTRu7I7t2QM//ADffQcrVrglDuahYKxAMIzoMMVfA4l7KyCQ1FTXEujVyxUExxzjgsj5Ygbt3FlcGPz4o5tYZtMmN+I4zljoCcMIjSn+Gk5VjAWoUpKSoEUL1z/QtauLH3TMMc5rKMmL9LF/P6xe7TyIfv7ZFQY//+w6k2sQ1lIw6iqm+GsRNaolEEhysisMunaF445zncdt27rF13EMLs7QunWwZg2sX+/W1651LYRwg9TigHUyG4mKKf5aSo3pEygLEddx3K5dccugTRs46ig48kg44ojitEVFsHmzKwTWrnWdyps2ueW//43JPARVRWpqKs8884yZk4xahSn+BKEik8LEBV+B0Lq1izXUsaMrCI46yi2tWxd7FfnYvt0VAoEFgq9Q2LYt5oHpqgIrIIyahCn+BKbG9Q+EIyUFGjVy/QXHHONCTrRsWby0agXNmhX3JYBT+jt2uAB1W7a4ZevW4mXLFldw1ILCIRDrfzBiiSn+OkSNNw+Vh4hrCTRpUlwoHH20KwiaNy/+bd7ctSgCOXzYeR9t3epaCDt2lL3UYLNSOKxPwogUU/xG7WsZlEW9ek7pt2wJnTq5DucWLYoLhGbNXN/CEUeU7HQOZN8+VwBs316yQNi92y27dhX/5uVVY+Zih7m91j1M8RtlkjAFQiDJya7l0KiR61to3brYhOQrFAKXpk1LtyB8HDrkBrX5CoLAQsFXUPiWfftg717Iz6++vFYjFoCvdlGW4k+JhzBGzaKsP3KtNhkVFUFBgVvKG3Us4vocGjRwyr9ly+IWRJMmbjRz4NKkifNa8m2Xd39fIRBu2bfPFSy+9RjNsVwVLF++vOx5o6PEWiDxw2r8RoWpNR5GVUlSUnFrIjW1uJBo0cK1HNLSXCsjLa14Cd5OSyvZcR3MoUOuxZCf7wqCvDz3m5/v1n1L8Hbw/tpYYFcR1g/iMFOPERdq7GC06iYpyfVN1KvnCo0jjnBmp6ZN3RJYODRs6JbU1NDrDRsWh9IojwMHigsC3+JrBRUUlNzev7/ksVBLDW6JxIua7r5rit+o0dRqs1KsESkuOFJS3G/DhsWd1z4TVHAhEVhY+FooDRq4JXC9vNZHIIcPu8IkuEDIz3eFwoEDlfs9eLDWueNWFxUtYEzxGwlFnTQzVQVJSa7w8C3Jya5gaNzYtToCWx6+giFUYRG8v3790kukBUogBw+6giC4UPAtvmMHDri0vvS+9fKWstLVAh0IkJSUxPPPPx+V8jfFbxiY6Skm+Po9gpcGDUKbr8oqKOrXLzaFRbKkpFS8gAmksDB8QVFWoVFYWPYSfNy3fehQ6GMRtHY6duzI6tWrI86aefUYBlS6w89aGiE4fNgtsRwU5zN3JSeXLmh84zoaNizdMklNLTaPlbX4CpBQ6dLSXB+M71hwupQqVKFFRa5QCC4Q7r/fTZIErF27tkpuZYrfMKJg4sSJVRpiISHHUMQCVacY49EHFFjg+JbAAqh+/eKCxlcIhWqd+AqKspayjgfQoUOHKsmSKX7DiCOxHAxlZq0qwteiiTNJSUlMmDChaq5VJVcxDKPGMWvWLFS1Wpdu3brFO9t+unXrFpM8Dhw4sNrzkpqaGnXHbnlYjd8wjCqjLoRzSISBYVbjNwzDqGOY4jcMw6hjmOI3DMOoY5jiNwzDqGOY4jcMw6hjmOI3DMOoY9SKWD0ishVYE8UprYByZt+oVSRSXiCx8mN5qZkkUl6gcvnpqKqtg3fWCsUfLSKyMFRgotpIIuUFEis/lpeaSSLlBWKTHzP1GIZh1DFM8RuGYdQxElXxPxVvAaqQRMoLJFZ+LC81k0TKC8QgPwlp4zcMwzDKJlFr/IZhGEYZmOI3DMOoY9RqxS8i54jIChH5UUT+HOK4iMij3vGlItIrHnJGQgR56SIi80TkgIj8MR4yRkoEecnx3sdSEflcRLLiIWckRJCXC7x8LBGRhSJyWjzkjJRw+QlI10dEikTkkuqULxoieDcDRGS3926WiMid8ZAzEiJ5L15+lojIMhH5uFI3rO6JGqpqAZKBn4BfAPWBr4FuQWnOA94FBOgHLIi33JXIy5FAH2AC8Md4y1zJvJwCNPfWz63l76UxxX1lPYDv4y13ZfITkO5D4B3gknjLXYl3MwB4K96yVlFemgHLgQ7e9pGVuWdtrvGfBPyoqj+r6kHgJeCCoDQXAM+rYz7QTETaVLegERA2L6q6RVW/BGI4o3WVEElePlfVnd7mfKB9NcsYKZHkZZ96/0SgEVCTvSUi+c8A3Ai8CmypTuGiJNK81AYiyctvgNdUdS04fVCZG9Zmxd8OWBewvd7bF22amkBtkTMSos3Lb3GtsppIRHkRkYtE5HvgbeDqapKtIoTNj4i0Ay4CnqhGuSpCpN/ZySLytYi8KyIZ1SNa1ESSl+OB5iIyR0QWicgVlblhbZ56UULsC65tRZKmJlBb5IyEiPMiImfiFH9NtYtHlBdVnQHMEJEzgL8Dg2ItWAWJJD8PA39S1SKRUMlrDJHk5StcrJp9InIeMBM4LtaCVYBI8pICnAgMBBoC80Rkvqr+UJEb1mbFvx44JmC7PbCxAmlqArVFzkiIKC8i0gN4BjhXVbdXk2zREtV7UdW5ItJZRFqpak0MEhZJfnoDL3lKvxVwnogcUtWZ1SJh5ITNi6ruCVh/R0Qm1tB3E6ku26aqeUCeiMwFsoAKKf64d2xUokMkBfgZ6ERxh0hGUJrzKdm5+0W85a5oXgLSjqdmd+5G8l46AD8Cp8Rb3irIy7EUd+72Ajb4tmvaEs135qWfQs3t3I3k3Rwd8G5OAtbWxHcTYV66ArO9tGnAt0BmRe9Za2v8qnpIRG4A/oPrFX9OVZeJyHXe8SdwXgnn4ZRMPnBVvOQtj0jyIiJHAwuBI4DDInITrud/T1nXjQcRvpc7gZbARK9meUhrYDTFCPNyMXCFiBQCBcAw9f6pNY0I81MriDAvlwDXi8gh3Lu5rCa+m0jyoqrfich7wFLgMPCMqn5b0XtayAbDMIw6Rm326jEMwzAqgCl+wzCMOoYpfsMwjDqGKX7DMIw6hil+wzCMOoYpfqMEIqIi8kLAdoqIbBWRt6pZji5eJMLFItI56FhTEXleRH7yludFpKl3bEBZsorIOyLSrAKyDBCRU6I8p41PDhFJE5FcEflGRL4VkU9FpHGY8/dFK2fQ+XNEZK0EDL8VkZmVvW4l5GntuSMaNQBT/EYweUCmiDT0ts/CDUqqbi4EXlfVnqr6U9CxZ4GfVbWzqnYGVuFGAZeLqp6nqrsqIMsAXETRaLgZeNpbHwtsVtXuqpqJC1NRZcH2xBHqv7wLONVL0wyIW4BCVd0KbBKRU+Mlg1GMKX4jFO/iRj0DXA686DsgIieJi6G/2Ps9wdufISJfeLX0pSJynIg0EpG3vSBZ34rIsOAbiUi2iMz3zpkhIs29uCo3AdeIyEdB6Y/FxSz5e8Duu4DeAS2DI7xrLReRJ3xKUURWi0grb314gLxPikiyt/8cEfnKk3m2iKQD1wF/8NKeLiKXevn52hs6H4qLAV8Ntw0BhaeqrlDVA979bvau9a03KC/4+TT25PjKazFc4O1PF5HvRGQiLibNMcHn4qI8Xuat/xp4LYLrhnxnInKv9zyXisgD3r7WIvKqiHzpLb5Cpr8Ux8BfLCJNvNvOBHLKeF5GdRLv4cq21KwF2IeLK/8KkAosISCuOW7kcIq3Pgh41Vt/DMjx1uvjAkldDDwdcO2mIe63FOjvrd8FPOytjydEaApgKDAjxP4Z3rEBwH5cbPNk4AO8sAPAalz8ma7Am0A9b/9E4AqgNS5KYidvf4tQsgDfAO289WYhZOkELArYzsaFOJ4H3A0c5+0/0btWI1xc/2VAT9978H5TgCO89Va4UegCpONGcPYr4z3OAfp6zzcZeN87J9x1S70zoAWwguIBn828338Bp3nrHYDvvPU3gVO99cYB30s74Jt4f+O21O54/EaMUNWlOCVxOS7sRSBNgX+LyLfAQ4Av1O084DYR+RMuImIBTqkNEpH/FZHTVXV34IU8u3wzVfXNJjQVOCOMeELoaJ+B+79QF9u8CNdaCY7+ORCndL8UkSXe9i9w8Zzmquoq7znsKEOGz4ApInItTqkG0wbY6ttQ1SXe9e/HKdEvRaSrJ9cMVc1T1X24GvnpIfL1DxFZCszCKc+jvGNr1M0zURZFwKfAMKChqq6O4Lqh3tkeXGH6jIj8Ghf+BFzB/7j3DN/AtbSaeM/nQRH5Pe79HvLSbwHaliOvUU2Y4jfK4g3gAQLMPB5/Bz5SZ6v+Fa5VgKr+C1fjLgD+IyK/VBcy1lervUeqZuq7ZUDPQJu2t54FfOftCi4YQoXrnqqq2d5ygqqOp+xCpeTFVK8DbseZV5aISMugJAV4zyXgnH2q+pqqjgGm4WJIRRL3OAfXEjlRVbOBzQHXzovg/JdwrbGXI7luqHfmKe6TcJOzXEixCSsJODngObZT1b2qei9wDa7VN19EunjpU3HPxogzpviNsngOuEtVvwna35Rie/WVvp0i8gtch+ujuEKjh4i0BfJVdRquECkx57FXm9wpIr5a7gig3LlEVfVHYDFO8fq4HfjKOwZwkoh08gqEYbhabyCzgUtE5EhP9hYi0hHXaukvIp18+730ewGfnRoR6ayqC1T1TmAbpe3rP+BaTL70p4pIc2+9PtANWAPMBS4U5/XTCDcByidB12oKbFHVQnHzF3Qs7/mE4BPgHkoX4CGvG+qdifNAaqqq7+D6XrK9a7wP3BCQz2zvt7OqfqOq/4sLLOhT/MfjokoacabWRuc0YouqrgceCXHoPmCqiNyMm5fVxzBguLgolf/F2ev7APeLyGGcF8v1Ia43EnhCRNJwoWkjiaD6W+AxEfHZped5+3zMA+4FuuOU64ySWdPlInI78L5XOBQCv1PV+SIyCnjN278F59X0JvCK1wF6I66j9zjv3rNxYXQDb5Anzs30WK8w6gxMEhHBVbbexvWNqIhMAb7wTn1GVRcH5TUXeFNEFuL6W76P4PmUyCxOgQdT1nW7U/qdNQFeF5FUL89/8NL+HvinZy5KwT3r64CbvMKkCDdPrG+GtTO9vBtxxqJzGnUCz2tnC3C0qsZ83mIRuQhnRrk9bOI6gucBdYEWz7dsxAmr8Rt1hWW4GnW1TFavqjNC2P7rLCLSGnjQlH7NwGr8hmEYdQzr3DUMw6hjmOI3DMOoY5jiNwzDqGOY4jcMw6hjmOI3DMOoY/x/kXdfSGZ4kWwAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#fit plot including error\n", + "plt.figure()\n", + "plt.scatter(sorted_masses, sorted_powers, color='k', label='data created from all papers')\n", + "plt.plot(sorted_masses, avg_power, color='c', label='calculated curve fit')\n", + "plt.fill_between(sorted_masses, avg_outup, avg_outlow, color='c', alpha=0.2, label=\"error in a\")\n", + "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-0.605 ± 0.002)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "cf3ff96a", + "metadata": {}, + "outputs": [], + "source": [ + "#create an error array for all values in sorted_masses\n", + "power_errors = []\n", + "for m in M_mass:\n", + " power_errors.append(power_law(m, M_a) - power_law(m, M_alow))\n", + "for n in S_mass:\n", + " power_errors.append(power_law(n, S_a) - power_law(n, S_alow))\n", + "for o in L_mass:\n", + " power_errors.append(power_law(o, L_a) - power_law(o, L_alow))\n", + "for p in B_mass:\n", + " power_errors.append(power_law(p, B_a) - power_law(p, B_alow))\n", + "for q in P_mass:\n", + " power_errors.append(power_law(q, P_a) - power_law(q, P_alow))\n", + "\n", + "#sort errors based on sorted indices\n", + "\n", + "# convert sorted_indices to a NumPy array for indexing\n", + "sorted_indices = np.array(sorted_indices)\n", + "\n", + "# Sort errors based on sorted indices\n", + "sorted_errors = [power_errors[i] for i in sorted_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30d890fa", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0206cbc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1949c38b90e1d2de86545054791ba089c34b84e4 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Mon, 24 Jun 2024 13:29:04 -0700 Subject: [PATCH 002/191] Addition of BD IMF and updated name for exoplanet IMF --- changes/BD_IMF.ipynb | 778 ++++++++++++++++++++++++++++++++++++ changes/Exoplanet_IMF.ipynb | 608 ++++++++++++++++++++++++++++ 2 files changed, 1386 insertions(+) create mode 100644 changes/BD_IMF.ipynb create mode 100644 changes/Exoplanet_IMF.ipynb diff --git a/changes/BD_IMF.ipynb b/changes/BD_IMF.ipynb new file mode 100644 index 00000000..7930e27f --- /dev/null +++ b/changes/BD_IMF.ipynb @@ -0,0 +1,778 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "11fc535c-f15b-4386-a98d-5d8f4c1c1ebf", + "metadata": {}, + "source": [ + "## Plotting Studied Power-Law Functions for Brown Dwarf Stars (BDs)" + ] + }, + { + "cell_type": "markdown", + "id": "b35147ec-8425-4834-8d36-2d3802823277", + "metadata": {}, + "source": [ + "#### Importing Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "id": "de897cdf-e478-4d49-b16a-ea7a1e0d547d", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.optimize import curve_fit\n", + "import math\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score" + ] + }, + { + "cell_type": "markdown", + "id": "24c6f353-fbf2-412d-864d-6df254492717", + "metadata": {}, + "source": [ + "#### Defining a general power law function and mass range" + ] + }, + { + "cell_type": "markdown", + "id": "dc79ec2b-79ad-4e17-a010-701923320bf4", + "metadata": {}, + "source": [ + "All of the papers that we consider here are in agreement that the initial mass function (IMF) for brown dwarfs is best represented by a power law function, much like full-fledged stars and planets. The general power law equation is as follows:\n", + "\n", + "$ f(\\alpha) = M^{-\\alpha} $,\n", + "\n", + "Where M represents the mass of an object and $\\alpha$ represents the index of the function. \n", + "\n", + "We know that we can find a general power law to represent brown dwarfs in most star forming regions because, in a [2021 paper by M. J. Huston and K. L. Luhman](https://arxiv.org/pdf/2101.11497), a conclusion is made that there are not significant variations in the IMF of low mass stars and brown dwarfs based on star-forming conditions." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "2f67bd3c-cc80-4294-88e5-f257100893f6", + "metadata": {}, + "outputs": [], + "source": [ + "def power_law(M, a):\n", + " return M ** (-1 * a)\n", + "# M represents a mass value, and a is the associated alpha value of a specified paper\n", + "\n", + "# By NASA JPL, BDs have a mass range of 13-80 Jupiter masses (0.0124 - 0.0764 solar masses)\n", + "BD_masses = np.linspace(0.0124, 0.0764, 1000)" + ] + }, + { + "cell_type": "markdown", + "id": "1a4b6339-52d4-41a8-b890-5993559be0de", + "metadata": {}, + "source": [ + "We chose this mass range for brown dwarf stars because 80 Jupiter masses is approximately the lower mass limit for hydrogen burning, which brown dwarfs characteristically do not do. In addition, 13 Jupiter masses is the lower mass limit for deuterium burning, which is what defines a brown dwarf star. This mass range is supported by [NASA JPL](https://www.jpl.nasa.gov/images/pia23685-what-is-a-brown-dwarf)." + ] + }, + { + "cell_type": "markdown", + "id": "cbbf169d-5f2d-4fa2-83e9-20b977e299a1", + "metadata": {}, + "source": [ + "#### Defining different $\\alpha$ values and errors based on specified papers" + ] + }, + { + "cell_type": "markdown", + "id": "f5bcb285-3f28-406d-8377-8ea9376fe53e", + "metadata": {}, + "source": [ + "To create a general power law function for brown dwarfs we wanted to consider a plethora of functions already found. To do this, we found seven papers over a span of 25 years surveying different star-forming regions. The papers considered were as follows:\n", + "\n", + "1. \"L Dwarfs and the Substellar Mass Function\" ([I. Neill Reid, et al. 1999](https://iopscience.iop.org/article/10.1086/307589/pdf))\n", + "2. \"The Substellar Mass Function: A Baysian Approach\" ([P. R. Allen, et al. 2005](https://iopscience.iop.org/article/10.1086/429548/pdf))\n", + "3. \"The low-mass content of the massive young star cluster RCW 38\" ([K. Mužić, et al. 2017](https://academic.oup.com/mnras/article/471/3/3699/4044705))\n", + "4. \"Looking Deep into the Rosette Nebula’s Heart: The (Sub)stellar Content of the Massive Young Cluster NGC 2244\" ([K. Mužić, et al. 2019](https://ui.adsabs.harvard.edu/abs/2019ApJ...881...79M/abstract))\n", + "5. \"The Field Substellar Mass Function Based on the Full-sky 20 pc Census of 525 L, T, and Y Dwarfs\" ([J. D. Kirkpatrick, et al. 2021](https://iopscience.iop.org/article/10.3847/1538-4365/abd107/pdf))\n", + "6. \"A Self-consistent Model for Brown Dwarf Populations\" ([R. E. Ryan, Jr., et al. 2022](https://iopscience.iop.org/article/10.3847/1538-4357/ac6de5/pdf))\n", + "7. \"A Volume-limited Sample of Ultracool Dwarfs. II. The Substellar Age and Mass Functions in the Solar Neighborhood\" ([W. Best, et al. 2024](https://iopscience.iop.org/article/10.3847/1538-4357/ad39ef/pdf))\n", + "\n", + "Below we list the indexes of the power laws found in the specified papers and their respective errors." + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "384973ef-5396-4d20-af19-5899db1a5673", + "metadata": {}, + "outputs": [], + "source": [ + "#Neill Reid 1999\n", + "NR_a = 1.3\n", + "NR_aerr = 0.3 #made up because actual paper does not include errors or an exact numerical conclusion\n", + "\n", + "#Allen 2005\n", + "A_a = 0.3\n", + "A_aerr = 0.6 #error > actual value --> unconstrained\n", + "\n", + "#Mužić 2017 (RCW 38)\n", + "M1_a = 0.29\n", + "M1_aerr = 0.11\n", + "\n", + "#Mužić 2019 (NGC 2244)\n", + "M2_a = 1.03\n", + "M2_aerr = 0.02\n", + "\n", + "#Kirkpatrick 2021\n", + "K_a = 0.6\n", + "K_aerr = 0.1\n", + "\n", + "#Ryan 2022\n", + "R_a = 0.71\n", + "R_aerr = 0.11\n", + "\n", + "#Best 2024\n", + "B_a = 0.58\n", + "B_paerr = 0.16\n", + "B_naerr = 0.20\n", + "B_avgerr = 0.18" + ] + }, + { + "cell_type": "markdown", + "id": "38e02440-ed2d-4e91-8b1f-a8c54408da42", + "metadata": {}, + "source": [ + "#### Plotting curves together (w/o errors)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "4e520010-9702-45f2-877c-1fb5104fa7a1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting different IMFs based on literature\n", + "plt.plot(BD_masses, power_law(BD_masses, NR_a), color = 'r', label = \"Neill Reid 1999\")\n", + "plt.plot(BD_masses, power_law(BD_masses, A_a), color = 'orange', label = \"Allen 2005\")\n", + "plt.plot(BD_masses, power_law(BD_masses, M1_a), color = 'y', label = \"Mužić 2017\")\n", + "plt.plot(BD_masses, power_law(BD_masses, M2_a), color = 'g', label = \"Mužić 2019\")\n", + "plt.plot(BD_masses, power_law(BD_masses, K_a), color = 'c', label = \"Kirkpatrick 2021\")\n", + "plt.plot(BD_masses, power_law(BD_masses, R_a), color = 'b', label = \"Ryan 2022\")\n", + "plt.plot(BD_masses, power_law(BD_masses, B_a), color = 'm', label = \"Best 2024\")\n", + "\n", + "#organizing the graph\n", + "plt.title(\"Brown Dwarf IMFs Based on Literature\")\n", + "plt.xlabel(\"Brown Dwarf Masses (solar masses)\")\n", + "plt.ylabel(\"M^-a\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e10c5f12-486a-44c8-ab70-bee7cd9b7998", + "metadata": {}, + "source": [ + "Looking at the curves, it is clear that the Neill Reid conclusion of the $\\alpha$ value is outdated in comparison to the other papers. This is most likely due to outdated theory and information, as this paper was created in 1999 using simulations, versus actual data of brown dwarf stars. With this in mind, we will subtract it from our future considerations as the rest of the papers considered since then agree that $\\alpha$ should be less than 1. In addition, it is explained in the Mužić 2019 paper that NGC 2244 is unusually dense, which is leading to a larger index value than typical. For this reason, I omitted this paper as well from consideration. Lastly, in Allen 2005, the error for the function is larger than the actual $\\alpha$ value, hinting that the function is unconstrained and should not be considered. Thus, we omitted this paper as well.\n", + "\n", + "Below is the graph subtracting the Neill Reid, Allen, and Mužić 2019 conclusions." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "11fc4c9d-0be7-4d75-a35c-4b82fae451f0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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PPfecY9/bb7/d0Yn6Uu3bty91zRWhMr/PpWG322nXrh1vvvlmoY9HRkY63S/sO7tu3TqGDx9Or169mD17NhEREbi5uTFv3rwCHa+LUlTgyR9ii6ujOv+sRUESZESVsFqtwMXWiqJER0cDcODAgQKP7d+/n+DgYMeEbiaTiW7durFu3TqioqIcp2V69uxJdnY2X3/9NXFxcfTq1asi30qxTpw4wdq1a+nevbujRSavplmzZrFixQqCg4Np2bIlOp2ONm3asG7dOtatW+fUQfXChQusXLmS5557jmeeecax/dChQ1X2XgozduxYnnnmGc6ePcuXX35ZIcds164d7dq146mnnmLDhg306NGDjz76iBdffLFMx7NarU7fs++++44+ffrw2WefOe2XlJREcHCw0zYvLy9GjRrFqFGjsFgs3HTTTbz00ktMnz6dkJAQfHx8sNlsl52TJzo6mt27d6OUcvqlWtj3+lIhISF4enoW+W9Ar9cXCARVraig0KRJE3bs2EHfvn3L3Hry/fff4+7uzrJlyzCbzY7t8+bNK3EdAQEBhc5gXdJTbaX5WQvXkz4yotLl5OTwxx9/YDKZaNWqVbH7RkRE0LFjRz7//HOn/4h2797NH3/8UWBulp49e7J582ZWr17tCDLBwcG0atWKWbNmOfapComJiYwZMwabzcaTTz5ZoM7s7GzefvttrrnmGsd/wD179uTLL7/kzJkzTnXm/YV96V/Urp5NtUmTJrz99tvMnDmTbt26letYKSkpjoCbp127duj1+lINU87v4MGDHDhwwNEPB7TP8tLPcfHixZw+fdpp26XD2k0mE61bt0YpRU5ODgaDgZEjR/L999+ze/fuAq+df7jz4MGDOXPmjNOQ4YyMjCJPU+RnMBjo378/P/30E8eOHXNsj4uLY8GCBVxzzTX4+vpe9jiVycvLq9DTm7feeiunT5/mk08+KfBYZmamoz9bcQwGAzqdzqn15NixY4XO4Ovl5VVoYGnSpAnJycns3LnTse3s2bMsWbLksq+fV0NJf9bC9aRFRlS43377zTHfSHx8PAsWLODQoUP897//LdF/wK+99hqDBg2ie/fuTJ482TH82s/Pj2effdZp3549e/LSSy9x8uRJpyDQq1cvPv74Yxo2bFimUxSXc/DgQb766iuUUqSkpLBjxw4WL15MWloab775JgMHDnTav3v37hiNRg4cOODU4bNXr158+OGHjveSx9fXl169evHqq6+Sk5ND/fr1+eOPP4iJianw91JaDzzwQIUcZ9WqVUydOpVbbrmF5s2bY7Va+fLLLx2/RC7HarXy1VdfAdqpgGPHjvHRRx9ht9uZMWOGY7+hQ4fy/PPPM3HiRK6++mp27drF119/7dSRFqB///6Eh4fTo0cPwsLC2LdvH++//z5DhgxxtK698sorrF69miuvvJK77rqL1q1bk5iYyNatW1mxYgWJiYkA3HXXXbz//vuMGzeOLVu2EBERwZdffomnp2eJPpsXX3yR5cuXc8011/Cf//wHo9HIxx9/THZ2Nq+++mqJjlEab775ZoHa9Ho9TzzxRKH7d+7cmUWLFvHwww/TtWtXvL29GTZsGHfccQfffvst99xzD6tXr6ZHjx7YbDb279/Pt99+y7Jly5z6zxVmyJAhjn9DY8eOJT4+ng8++ICmTZs6BZO8OlasWMGbb75JvXr1aNSoEVdeeSWjR4/m8ccf58Ybb+T+++8nIyODDz/8kObNm5e4k25Jf9aiGnDRaClRCxU2/Nrd3V117NhRffjhh07DQ/OG57722muFHmvFihWqR48eysPDQ/n6+qphw4apvXv3FtgvJSVFGQwG5ePjo6xWq2P7V199pQB1xx13FHhOdHS0GjJkSIHtvXv3Vr17977s+8z//vR6vfL391edOnVSDzzwQKFDSfN07dpVAWrz5s2ObadOnVKAioyMLLD/qVOn1I033qj8/f2Vn5+fuuWWW9SZM2cUoGbMmOHYr7ghtBU5/Lo4FDH8uqifr1JKHT16VE2aNEk1adJEubu7q8DAQNWnTx+1YsWKy9Za2PBrX19f1bdv3wLPz8rKUo888oiKiIhQHh4eqkePHmrjxo0Fft4ff/yx6tWrlwoKClJms1k1adJEPfbYYyo5OdnpeHFxcWrKlCkqMjJSubm5qfDwcNW3b181Z84cp/2OHz+uhg8frjw9PVVwcLB64IEH1O+//16i4ddKaVMHDBgwQHl7eytPT0/Vp08ftWHDBqd9ivr5FjZkujB5P9/CLgaDochjpaWlqbFjxyp/f38FOA11tlgsatasWapNmzbKbDargIAA1blzZ/Xcc885fZaXfmfy++yzz1SzZs2U2WxWLVu2VPPmzSt06PT+/ftVr169lIeHhwKchmL/8ccfqm3btspkMqkWLVqor776qsjh10XVUdKftXAtnVJV3BtMCCGEEKKCSB8ZIYQQQtRYEmSEEEIIUWNJkBFCCCFEjSVBRgghhBA1lgQZIYQQQtRYEmSEEEIIUWPV+gnx7HY7Z86cwcfHp9IWHBNCCCFExVJKkZqaSr169Ypd163WB5kzZ864fF0SIYQQQpTNyZMni52hvdYHmbypxU+ePOny9UmEEEIIUTIpKSlERkY6LcBbmFofZPJOJ/n6+kqQEUIIIWqYy3ULkc6+QgghhKixJMgIIYQQosaSICOEEEKIGqvW95ERQghRkM1mIycnx9VliDrMzc0Ng8FQ7uNIkBFCiDpEKUVsbCxJSUmuLkUI/P39CQ8PL9c8bxJkhBCiDskLMaGhoXh6espEocIllFJkZGQQHx8PQERERJmPJUFGCCHqCJvN5ggxQUFBri5H1HEeHh4AxMfHExoaWubTTNLZVwgh6oi8PjGenp4urkQITd53sTz9tSTICCFEHSOnk0R1URHfRQkyQggh6qS1a9eyfv16AP7880/++uuvEj/3vffeY+PGjZVVmigFCTJCCCHqpNatW3P33XezadMm7r33Xlq3bu14bMKECYwYMaLQ573zzjssXryYK664oooqFcWRICOEEKLamzBhAjqdjnvuuafAY1OmTEGn0zFhwoRSHTMkJISnn36aa665hpdeeonAwEDHY++88w7z588v8JxNmzYxd+5cfvrpJ8xmc5HHnjlzJl27dsXHx4fQ0FBGjBjBgQMHnPbJyspiypQpBAUF4e3tzciRI4mLi3Pa5/7776dz586YzWY6duxY4HWeffZZdDpdgYuXl1epPouaTIJMGSkFW7bAhQuurkQIIeqGyMhIFi5cSGZmpmNbVlYWCxYsICoqqkzHHD16NFartUDri5+fH/7+/gX2v+qqq9ixYwcBAQHFHnft2rVMmTKFTZs2sXz5cnJycujfvz/p6emOfR566CF++eUXFi9ezNq1azlz5gw33XRTgWNNmjSJUaNGFfo6jz76KGfPnnW6tG7dmltuueXyb76WkCBTRjffDF26wLffuroSIYSoG6644goiIyP54YcfHNt++OEHoqKi6NSpk9O+DRs25O2333ba1rFjR5599lkA5s+fX2hLRt7jl55astvtvPrqqzRt2hSz2UxUVBQvvfRSkbX+/vvvTJgwgTZt2tChQwfmz5/PiRMn2LJlCwDJycl89tlnvPnmm1x33XV07tyZefPmsWHDBjZt2uQ4zrvvvsuUKVNo3Lhxoa/j7e1NeHi44xIXF8fevXuZPHny5T7OWkOCTBlddZV2vXCha+sQQojyUEphs6W75KKUKnW9kyZNYt68eY77c+fOZeLEiaU+zqhRo5xaMb755huMRiM9evQodP/p06fzyiuv8PTTT7N3714WLFhAWFhYiV8vOTkZwHH6asuWLeTk5NCvXz/HPi1btiQqKqpcnYg//fRTmjdvTs+ePct8jJpGJsQro1tvhWnTYO1aOHMG6tVzdUVCCFF6dnsG69Z5u+S1e/ZMw2AoXV+O22+/nenTp3P8+HEA1q9fz8KFC1mzZk2pjuPh4eGYkO3IkSNMmTKFl19+meuvv77Avqmpqbzzzju8//77jB8/HoAmTZpwzTXXlOi17HY7Dz74ID169KBt27aANsOyyWQqcPoqLCyM2NjYUr2XPFlZWXz99df897//LdPzayoJMmUUHQ1XXw0bNsDixfDAA66uSAghar+QkBCGDBnC/PnzUUoxZMgQgoODy3y85ORkhg4dypAhQ3jssccK3Wffvn1kZ2fTt2/fMr3GlClT2L17d6mGd5fFkiVLSE1NdYStukKCTDmMHq0FmYULJcgIIWomvd6Tnj3TXPbaZTFp0iSmTp0KwAcffFDEsfUFTl1dOnuszWZj1KhR+Pr6MmfOnCJfL6/lpiymTp3K0qVL+fPPP2nQoIFje3h4OBaLhaSkJKdWmbi4OMLDw8v0Wp9++ilDhw4t1Smv2kD6yJTDLbeAXg+bNkFMjKurEUKI0tPpdBgMXi65lHVW14EDB2KxWMjJyWHAgAGF7hMSEsLZs2cd91NSUoi55D/qhx56iF27dvHjjz/i7u5e5Os1a9YMDw8PVq5cWeIalVJMnTqVJUuWsGrVKho1auT0eOfOnXFzc3M65oEDBzhx4gTdu3cv8evkiYmJYfXq1XWqk28eaZEph/BwuPZaWLVKG730+OOurkgIIWo/g8HAvn37HLcLc9111zF//nyGDRuGv78/zzzzjNO+8+bNY/bs2SxZsgSdTufol+Lt7Y23t3OfIXd3dx5//HGmTZuGyWSiR48enDt3jj179hQZHKZMmcKCBQv46aef8PHxcRzfz88PDw8P/Pz8mDx5Mg8//DCBgYH4+vpy33330b17d67KG00CHD58mLS0NGJjY8nMzGT79u2ANpmfyWRy7Dd37lwiIiIYNGhQKT/Nmk+CTDmNHq0FmYULJcgIIURV8fX1Lfbx6dOnExMTw9ChQ/Hz8+OFF15wapFZu3YtNpuN4cOHOz1vxowZjiHY+T399NMYjUaeeeYZzpw5Q0RERKGT8+X58MMPAbj22mudts+bN88xcd9bb72FXq9n5MiRZGdnM2DAAGbPnu20/5133snatWsd9/OGmcfExNCwYUNA60w8f/58JkyYUOYVpGsynSrL+LcaJCUlBT8/P5KTky/7xS+LhAStZcZqhX37oGXLCn8JIYSoEFlZWcTExNCoUaNiT6UIUVWK+06W9Pe39JEpp6Ag6N9fu71okWtrEUIIIeoaCTIVYPRo7XrhQm3pAiGEEEJUDQkyFeCGG8Bshv37Yds2V1cjhBBC1B0SZCqAr68WZgC++MK1tQghhBB1iQSZCpI3keKCBXDJnEtCCCGEqCQSZCpI//4QGgrnzsGyZa6uRgghhKgbJMhUEKMRbrtNuy2nl4QQQoiqIUGmAo0bp13/9BNcuODaWoQQQoi6QIJMBerQAdq1A4tFW7JACCGEEJVLgkwF0ukutsrI6SUhhKha1157LQ8++GCx+xw7dgydTudYs6g6WLNmDTqdjqSkpBLtX5L3WZdIkKlgt92mrYi9YQMcPuzqaoQQonaYMGECI0aMcNr23Xff4e7uzhtvvAHADz/8wAsvvOCC6gp69tln6dixY4n2vfrqqzl79ix+fn6VUsuOHTsYM2YMkZGReHh40KpVK955550C+61Zs4YrrrgCs9lM06ZNmT9/vtPjM2fOpGvXrvj4+BAaGsqIESM4cOCA0z5z5szh2muvxdfXt1ThrDwkyFSwiIiLSxZIq4wQQlSOTz/9lNtuu40PP/yQRx55BIDAwEB8fHyKfI7FYqmq8kosJycHk8lEeHg4Op2uUl5jy5YthIaG8tVXX7Fnzx6efPJJpk+fzvvvv+/YJyYmhiFDhtCnTx+2b9/Ogw8+yJ133smyfMNw165dy5QpU9i0aRPLly8nJyeH/v37k56e7tgnIyODgQMH8sQTT1TKeymUquWSk5MVoJKTk6vsNRcsUAqUioxUymqtspcVQohiZWZmqr1796rMzExXl1Jq48ePVzfccINSSqlZs2Ypd3d39cMPPzjt07t3b/XAAw847kdHR6vnn39e3XHHHcrHx0eNHz9excTEKEBt27ZNKaWU1WpVEydOVC1atFDHjx9XSikFqNmzZ6uBAwcqd3d31ahRI7V48WKn15o2bZpq1qyZ8vDwUI0aNVJPPfWUslgsSiml5s2bpwCny7x585yOPWzYMOXp6almzJihVq9erQB14cIFx/H/+usv1bt3b+Xh4aH8/f1V//79VWJiYqHvc+nSpcrX11d99dVXJf48//Of/6g+ffo4vZ82bdo47TNq1Cg1YMCAIo8RHx+vALV27doCjxX2ngpT3HeypL+/jVUXmeqOG2+EgAA4eRKWL4eBA11dkRBCFE4pRYbd7pLX9tTrS90K8fjjjzN79myWLl1K3759L7v/66+/zjPPPMOMGTMKPJadnc2YMWM4duwY69atIyQkxPHY008/zSuvvMI777zDl19+yejRo9m1axetWrUCwMfHh/nz51OvXj127drFXXfdhY+PD9OmTWPUqFHs3r2b33//nRUrVgA4nTZ69tlneeWVV3j77bcxGo0cPXrUqa7t27fTt29fJk2axDvvvIPRaGT16tXYbLYC72HBggXcc889LFiwgKFDh5bsQwSSk5MJDAx03N+4cSP9+vVz2mfAgAHF9sVJTk4GcDqOK0iQqQTu7nDHHfDuu/DJJxJkhBDVV4bdjve6dS557bSePfEyGEq8/2+//cZPP/3EypUrue6660r0nOuuu85x6gm0zr4AaWlpDBkyhOzsbFavXl2gf8ott9zCnXfeCcALL7zA8uXLee+995g9ezYATz31lGPfhg0b8uijj7Jw4UKmTZuGh4cH3t7eGI1GwsPDC9Q0duxYJk6c6Lh/aZB59dVX6dKli+O1ANq0aVPgOB988AFPPvkkv/zyC7179y7R5wGwYcMGFi1axK+//urYFhsbS1hYmNN+YWFhpKSkkJmZiYeHh9NjdrudBx98kB49etC2bdsSv3ZlkCBTSe68UwsyP/8McXFwyfdDCCFEKbVv357z588zY8YMunXrhre392Wf06VLl0K3jxkzhgYNGrBq1aoCv6QBunfvXuB+/pFOixYt4t133+XIkSOkpaVhtVrx9fUt0fsoqqY827dv55Zbbil2n++++474+HjWr19P165dS/S6ALt37+aGG25gxowZ9M/r0FkGU6ZMYffu3fz1119lPkZFkSBTSdq1gyuvhM2b4fPPYdo0V1ckhBAFeer1pPXs6bLXLo369evz3Xff0adPHwYOHMhvv/1WbOdeAC8vr0K3Dx48mK+++oqNGzeWuHUnz8aNG7ntttt47rnnGDBgAH5+fixcuNAxeupyiqopT2HB6lKdOnVi69atzJ07ly5dupToFN3evXvp27cvd999t1OLEkB4eDhxcXFO2+Li4vD19S1Qz9SpU1m6dCl//vknDRo0uOzrVjYZtVSJ7rpLu/70U1DKtbUIIURhdDodXgaDSy5lGaUTHR3N2rVriY2NZeDAgaSmppbpfd9777288sorDB8+nLVr1xZ4fNOmTQXu5/WP2bBhA9HR0Tz55JN06dKFZs2acfz4caf9TSZToX1aSqJ9+/asXLmy2H2aNGnC6tWr+emnn7jvvvsue8w9e/bQp08fxo8fz0svvVTg8e7duxd4zeXLlzu1TCmlmDp1KkuWLGHVqlU0atSohO+ockmQqUSjRoG3Nxw6BH/+6epqhBCidoiMjGTNmjXEx8czYMAAUlJSynSc++67jxdffJGhQ4cWOEWyePFi5s6dy8GDB5kxYwZ///03U6dOBaBZs2acOHGChQsXcuTIEd59912WLFni9PyGDRsSExPD9u3bOX/+PNnZ2SWua/r06fzzzz/85z//YefOnezfv58PP/yQ8+fPO+3XvHlzVq9ezffff19sp9zdu3fTp08f+vfvz8MPP0xsbCyxsbGcO3fOsc8999zD0aNHmTZtGvv372f27Nl8++23PPTQQ459pkyZwldffcWCBQvw8fFxHCczM9OxT2xsLNu3b+dw7kRqu3btYvv27SQmJpb4/ZdasWOaagFXDL/O7667tKHYt9/ukpcXQgiH2jL8Os+pU6dUs2bN1FVXXaWSk5MLHX791ltvOT3n0uHXSin1xhtvKB8fH7V+/XqllDZE+oMPPlDXX3+9MpvNqmHDhmrRokVOx3nsscdUUFCQ8vb2VqNGjVJvvfWW8vPzczyelZWlRo4cqfz9/QsMv16yZInTsQobqrxmzRp19dVXK7PZrPz9/dWAAQMcj1/6Pvfu3atCQ0PVww8/XOhnN2PGjALDwQEVHR1doI6OHTsqk8mkGjdu7Kg5T2HHyP/einutS4+VpyKGX+tyi6u1UlJS8PPzIzk5ucQdsSrSP/9At27aSKYzZ7Rh2UII4QpZWVnExMTQqFEj3N3dXV1OtaXT6ViyZEmBmYRFxSvuO1nS399yaqmSdekC7dtDVpbW6VcIIYQQFUeCTCXT6eDee7Xbs2eDi+adEkIIIWolCTJV4PbbwddX6/SbO8mjEEKIakopJaeVahAJMlXA2xvGj9duf/CBa2sRQgghahMJMlXkP//RrpcuhUumGxBCCCFEGUmQqSItW0LfvlofmY8+cnU1QgghRO0gQaYKTZmiXX/6qTaKSQghhBDlI0GmCg0bBpGRcP48LF7s6mqEEEKImk+CTBUyGuH//k+7/f77rq1FCCGEqA1cGmRmzpxJ165d8fHxITQ0lBEjRnDgwAGnfbKyspgyZQpBQUF4e3szcuTIAit01iR33gkmE/z9N2zc6OpqhBBCiJrNpUFm7dq1TJkyhU2bNrF8+XJycnLo378/6enpjn0eeughfvnlFxYvXszatWs5c+YMN910kwurLp+wMLjtNu32m2+6thYhhKgpJkyYgE6nQ6fT4ebmRqNGjZg2bRpZLu5wmJOTw+OPP067du3w8vKiXr16jBs3jjNnzjjtl5iYyG233Yavry/+/v5MnjyZtLQ0x+Nr1qzhhhtuICIiAi8vLzp27MjXX3/tdIxPPvmEnj17EhAQQEBAAP369ePvv/+ukvdZrRW7ElMVi4+PV4Bau3atUkqppKQk5ebmphYvXuzYZ9++fQpQGzduLNExXb1oZGF27dIWktTrlTp61NXVCCHqipq+aOTAgQPV2bNn1YkTJ9SSJUuUr6+vmjZtmkvrSkpKUv369VOLFi1S+/fvVxs3blTdunVTnTt3dtpv4MCBqkOHDmrTpk1q3bp1qmnTpmrMmDGOx1966SX11FNPqfXr16vDhw+rt99+W+n1evXLL7849hk7dqz64IMP1LZt29S+ffvUhAkTlJ+fnzp16lSVvd+KVhGLRlarIHPo0CEFqF27dimllFq5cmWBFUGVUioqKkq9+eabhR4jKytLJScnOy4nT56sdkFGKaX699fCzIMPuroSIURdUdODzKWrX990002qU6dOSimlnnvuOdWmTZsCz+vQoYN66qmnlFJK/f3336pfv34qKChI+fr6ql69eqktW7Y47Q+oTz75RI0YMUJ5eHiopk2bqp9++qlUtf79998KUMePH1dKaatTA+qff/5x7PPbb78pnU6nTp8+XeRxBg8erCZOnFjk41arVfn4+KjPP/+8VPVVJxURZKpNZ1+73c6DDz5Ijx49aNu2LQCxsbGYTCb8/f2d9g0LCyM2NrbQ48ycORM/Pz/HJTIysrJLL5OHH9auP/0UkpNdW4sQou5SCtLTXXNRqux17969mw0bNmAymQCYNGkS+/bt459//nHss23bNnbu3MnEiRMBSE1NZfz48fz1119s2rSJZs2aMXjwYFJTU52O/dxzz3Hrrbeyc+dOBg8ezG233UZiYmKJa0tOTkan0zl+d23cuBF/f3+6dOni2Kdfv37o9Xo2b95c7HECAwOLfDwjI4OcnJxi96kTKitlldY999yjoqOj1cmTJx3bvv76a2UymQrs27Vr1yKbE2tKi4zdrlTr1lqrzOuvu7oaIURdUNhfv2lp2v9DrrikpZW89vHjxyuDwaC8vLyU2WxWgNLr9eq7775z7DNo0CB17733Ou7fd9996tprry3ymDabTfn4+DidvgEcLTja55OmAPXbb7+VqM7MzEx1xRVXqLFjxzq2vfTSS6p58+YF9g0JCVGzZ88u9DiLFi1SJpNJ7d69u8jXuvfee1Xjxo1rZAtbnlrTIjN16lSWLl3K6tWradCggWN7eHg4FouFpKQkp/3j4uIIDw8v9FhmsxlfX1+nS3Wk011slXnnHcjJcW09QghR3fXp04ft27ezefNmxo8fz8SJExk5cqTj8bvuuotvvvmGrKwsLBYLCxYsYNKkSY7H4+LiuOuuu2jWrBl+fn74+vqSlpbGiRMnnF6nffv2jtteXl74+voSHx9/2fpycnK49dZbUUrx4Ycflvl9rl69mokTJ/LJJ5/Qpk2bQvd55ZVXWLhwIUuWLMHd3b3Mr1UbGF354kop7rvvPpYsWcKaNWto1KiR0+OdO3fGzc2NlStXOr6sBw4c4MSJE3Tv3t0VJVeo226DJ56Akyfh++9h9GhXVySEqGs8PSHf4Jkqf+3S8PLyomnTpgDMnTuXDh068NlnnzF58mQAhg0bhtlsZsmSJZhMJnJycrj55psdzx8/fjwJCQm88847REdHYzab6d69OxaLxel13NzcnO7rdDrsdnuxteWFmOPHj7Nq1SqnP6LDw8MLBCGr1UpiYmKBP8rXrl3LsGHDeOuttxg3blyhr/X666/zyiuvsGLFCqfQVVe5NMhMmTKFBQsW8NNPP+Hj4+Po9+Ln54eHhwd+fn5MnjyZhx9+mMDAQHx9fbnvvvvo3r07V111lStLrxDu7tqyBTNmwBtvwKhRWkuNEEJUFZ0OvLxcXUXp6fV6nnjiCR5++GHGjh2Lh4cHRqOR8ePHM2/ePEwmE6NHj8bDw8PxnPXr1zN79mwGDx4MwMmTJzl//ny5a8kLMYcOHWL16tUEBQU5Pd69e3eSkpLYsmULnTt3BmDVqlXY7XauvPJKx35r1qxh6NChzJo1i7vvvrvQ13r11Vd56aWXWLZsmVOfm7rMpaeWPvzwQ5KTk7n22muJiIhwXBYtWuTY56233mLo0KGMHDmSXr16ER4ezg8//ODCqivWvfdqgebff2H1aldXI4QQNcctt9yCwWDggw8+cGy78847WbVqFb///rvTaSWAZs2a8eWXX7Jv3z42b97Mbbfd5hR0yiKv1efff//l66+/xmazERsbS2xsrKOlp1WrVgwcOJC77rqLv//+m/Xr1zN16lRGjx5NvXr1AO100pAhQ7j//vsZOXKk4xj5OxnPmjWLp59+mrlz59KwYUPHPmmualKrLiqn+071UR3nkbnU1Klax7e+fV1diRCiNqttw6+VUmrmzJkqJCREpeXrOdyzZ89Ch2Jv3bpVdenSRbm7u6tmzZqpxYsXq+joaPXWW2859gHUkiVLnJ7n5+en5s2bV2hdMTExCij0snr1asd+CQkJasyYMcrb21v5+vqqiRMnqtTUVKf3V9gxevfu7dgnOjq60H1mzJhR3EdXrVVEZ1+dUuUZAFf9paSk4OfnR3JycrXt+Hv8ODRtClYrbNoE+VoahRCiwmRlZRETE0OjRo1qbQdRpRTNmjXjP//5Dw/njagQ1VZx38mS/v6uFqOW6rroaLj9du32zJmurUUIIWqqc+fO8f777xMbG+uYO0bUfi7t7Csuevxx+Pxz+Okn2LMHihhxJ4QQogihoaEEBwczZ84cAgICXF2OqCLSIlNNtGwJedMhvPKKa2sRQoiaSCnFuXPnGDt2rKtLEVVIgkw1Mn26dv3NN3D0qGtrEUIIIWoCCTLVyBVXwMCBYLPBa6+5uhohhBCi+pMgU8088YR2PXcunDrl2lqEEEKI6k6CTDXTsyf07g0Wi4xgEkIIIS5Hgkw19Nxz2vUnn8Ala5kJIYQQIh8JMtVQ795w3XXaitgvveTqaoQQQojqS4JMNZXXKjN3LsTEuLYWIYQQorqSIFNNXXMN9O+vLVsgrTJCiLpuwoQJ6HQ6xyUoKIiBAweyc+fOCnuNZ599lo4dO152v08++YSePXsSEBBAQEAA/fr14++//3baRynFM888Q0REBB4eHvTr149Dhw45Hj927BiTJ0+mUaNGeHh40KRJE2bMmOFYaPJShw8fxsfHB39///K8xVpJgkw1ltcqM38+HDni0lKEEMLlBg4cyNmzZzl79iwrV67EaDQydOjQKq9jzZo1jBkzhtWrV7Nx40YiIyPp378/p0+fduzz6quv8u677/LRRx+xefNmvLy8GDBgAFlZWQDs378fu93Oxx9/zJ49e3jrrbf46KOPeCJv6Go+OTk5jBkzhp49e1bZe6xRKmExy2qlJqx+XZzBg7WVscePd3UlQoiarratfr1u3ToFqPj4eMe2EydOqFtuuUX5+fmpgIAANXz4cBUTE+N4fPXq1apr167K09NT+fn5qauvvlodO3ZMzZs3r8Cq0kWteH0pq9WqfHx81Oeff66UUsput6vw8HD12muvOfZJSkpSZrNZffPNN0Ue59VXX1WNGjUqsH3atGnq9ttvV/PmzVN+fn4lqqmmqIjVr6VFpprLa5X58kvYt8+1tQghah+lFLZ0m0suSqky152WlsZXX31F06ZNCQoKArSWiwEDBuDj48O6detYv3493t7eDBw4EIvFgtVqZcSIEfTu3ZudO3eyceNG7r77bnQ6HaNGjeKRRx6hTZs2jlafUaNGlaiWjIwMcnJyCAwMBCAmJobY2Fj69evn2MfPz48rr7ySjRs3Fnmc5ORkxzHyrFq1isWLF/PBBx+U9iOqM2TRyGquSxcYMQJ+/BGefBJ++MHVFQkhahN7hp113utc8to903pi8DKUeP+lS5fi7e0NQHp6OhERESxduhS9XvubfNGiRdjtdj799FN0Oh0A8+bNw9/fnzVr1tClSxeSk5MZOnQoTZo0AaBVq1aO43t7e2M0GgkPDy/V+3j88cepV6+eI7jExsYCEBYW5rRfWFiY47FLHT58mPfee4/XX3/dsS0hIYEJEybw1Vdf4evrW6qa6hJpkakBXn4Z9HpYsgSKCfNCCFGr9enTh+3bt7N9+3b+/vtvBgwYwKBBgzh+/DgAO3bscHSK9fb2xtvbm8DAQLKysjhy5AiBgYFMmDCBAQMGMGzYMN555x3Onj1brppeeeUVFi5cyJIlS3B3dy/TMU6fPs3AgQO55ZZbuOuuuxzb77rrLsaOHUuvXr3KVWNtJy0yNUCrVjBpEnz6KTz+OKxdC7l/bAghRLnoPfX0THNNJ1K9Z+n+lvby8qJp06aO+59++il+fn588sknvPjii6SlpdG5c2e+/vrrAs8NCQkBtBaa+++/n99//51Fixbx1FNPsXz5cq666qpS1//666/zyiuvsGLFCtq3b+/YnteiExcXR0REhGN7XFxcgVFRZ86coU+fPlx99dXMmTPH6bFVq1bx888/O1pplFLY7XaMRiNz5sxh0qRJpa65NpIgU0M8+yx89RWsWwe//gou6KgvhKiFdDpdqU7vVCc6nQ69Xk9mZiYAV1xxBYsWLSI0NLTYUzGdOnWiU6dOTJ8+ne7du7NgwQKuuuoqTCYTNputRK/96quv8tJLL7Fs2TK6dOni9FijRo0IDw9n5cqVjuCSkpLC5s2buffeex37nT59mj59+tC5c2fmzZvnOEWWZ+PGjU71/PTTT8yaNYsNGzZQv379EtVZF8ippRqifn148EHt9n//q62QLYQQdUl2djaxsbHExsayb98+7rvvPtLS0hg2bBgAt912G8HBwdxwww2sW7eOmJgY1qxZw/3338+pU6eIiYlh+vTpbNy4kePHj/PHH39w6NAhRz+Zhg0bEhMTw/bt2zl//jzZ2dmF1jFr1iyefvpp5s6dS8OGDR01paWlAVrAevDBB3nxxRf5+eef2bVrF+PGjaNevXqMGDEC0ELMtddeS1RUFK+//jrnzp1zHCdPq1ataNu2reNSv3599Ho9bdu2JSAgoBI/6RqmUsZTVSM1ffh1fhcuKBUQoA3HnjvX1dUIIWqamj78mnxDo318fFTXrl3Vd99957Tf2bNn1bhx41RwcLAym82qcePG6q677lLJyckqNjZWjRgxQkVERCiTyaSio6PVM888o2w2m1JKqaysLDVy5Ejl7+9f7PDr6OjoAkO1ATVjxgzHPna7XT399NMqLCxMmc1m1bdvX3XgwAHH44UN9867FEWGXxdOp1Q5xr/VACkpKfj5+ZGcnFwren2/8QY8+ig0aAAHD4KHh6srEkLUFFlZWcTExNCoUaMyd0wVoiIV950s6e9vObVUw0yZAlFRcOoUvPmmq6sRQgghXEuCTA3j7g6vvKLdnjkTzpxxbT1CCCGEK0mQqYFGj4bu3SE9HQpZlkMIIYSoMyTI1EA6Hbz9tnb788/hn39cWo4QQgjhMhJkaqhu3eCOO7TbDz4ItbvLthCiItXyMR6iBqmI76IEmRps5kzw9IQNG2DRIldXI4So7tzc3ABtkUMhqoO872Led7MsZGbfGqx+fW1yvGeegWnTYPhwLdgIIURhDAYD/v7+xMfHA+Dp6elYXFGIqqSUIiMjg/j4ePz9/TEYyj67tASZGu6RR+CTT+DkSa2F5oUXXF2REKI6y1sHKC/MCOFK/v7+pV5t/FIyIV4t8P33cPPNYDLB7t3QrJmrKxJCVHc2m42cnBxXlyHqMDc3t2JbYkr6+1taZGqBm26CgQPh999h6lTtWlqLhRDFMRgM5WrOF6K6kM6+tYBOB++9B2Yz/PEHfPedqysSQgghqoYEmVqiaVN4/HHt9kMPQWqqa+sRQgghqoIEmVrkv/+Fxo3h9Gl4/nlXVyOEEEJUPgkytYiHh3aKCeCtt7SOv0IIIURtJkGmlhk8GG68EWw2uOcesNtdXZEQQghReSTI1EJvvw3e3rB+PXz0kaurEUIIISqPBJlaKCpKmxwPtA7AJ064th4hhBCiskiQqaX+8x+4+mpIS4N775VFJYUQQtROEmRqKb0ePv1Um+33f/+Db75xdUVCCCFExZMgU4u1agVPP63dfuABOHfOtfUIIYQQFU2CTC03bRq0awfnz8ODD7q6GiGEEKJiSZCp5Uwm+Owz7VTTggXw88+urkgIIYSoOBJk6oCuXeGRR7Tbd9+ttc4IIYQQtYEEmTri+eehTRuIi5NRTEIIIWoPCTJ1hLs7fPklGI3a6tgyikkIIURtIEGmDunUCZ55Rrs9ZYq2uKQQQghRk0mQqWOmT9f6zCQlweTJcopJCCFEzSZBpo4xGuGLL7RTTcuWyVpMQgghajYJMnVQy5YX12J65BHYu9e19QghhBBlJUGmjrr/fujfHzIzYcwYyMpydUVCCCFE6UmQqaP0evj8cwgJgZ07tRmAhRBCiJpGgkwdFh6uhRmA996DX35xbT1CCCFEaUmQqeMGDYKHHtJuT5wIZ864th4hhBCiNCTICGbO1OaYSUiA228Hm83VFQkhhBAlI0FGYDZrM/16esLq1fDii66uSAghhCgZCTICgBYt4MMPtdvPPQd//OHaeoQQQoiSkCAjHMaNg7vu0mb7HTsWTp50dUVCCCFE8STICCfvvnuxv8ytt4LF4uqKhBBCiKJJkBFO3N211bH9/WHTJplfRgghRPUmQUYU0Ljxxfll3nkHvv3WtfUIIYQQRZEgIwo1fDg8/rh2e/Jk2LfPtfUIIYQQhZEgI4r04otw7bWQlgY33AAXLri6IiGEEMKZBBlRJKNRO60UFQWHDmmLS8pkeUIIIaoTCTKiWCEh8NNP4OEBy5bBf//r6oqEEEKIiyTIiMvq2BHmz9duv/46fPWVK6sRQgghLpIgI0rk1lvhySe123feCf/+69p6hBBCCJAgI0rh+edh2DDIzoYRI2SlbCGEEK4nQUaUmF6vnVZq1QpOn4ahQ7URTUIIIYSrSJARpeLrC0uXap2At23T1mSSkUxCCCFcxaVB5s8//2TYsGHUq1cPnU7Hjz/+6PT4hAkT0Ol0TpeBAwe6pljh0Lgx/PyztpzBL7/AQw+5uiIhhBB1lUuDTHp6Oh06dOCDDz4ocp+BAwdy9uxZx+Wbb76pwgpFUa66Cr78Urv93nvaYpNCCCFEVTO68sUHDRrEoEGDit3HbDYTHh5eRRWJ0rj5Zpg1S1vK4MEHoWFDbWkDIYQQoqpU+z4ya9asITQ0lBYtWnDvvfeSkJBQ7P7Z2dmkpKQ4XUTleewxuPtuUEqb+XfzZldXJIQQoi6p1kFm4MCBfPHFF6xcuZJZs2axdu1aBg0ahK2Y3qUzZ87Ez8/PcYmMjKzCiusenQ4++AAGDoSMDBgyRBaYFEIIUXV0Sinl6iIAdDodS5YsYcSIEUXuc/ToUZo0acKKFSvo27dvoftkZ2eTnZ3tuJ+SkkJkZCTJycn4+vpWdNkiV1oa9O0Lf/8NkZGwfr12LYQQQpRFSkoKfn5+l/39Xa1bZC7VuHFjgoODOXz4cJH7mM1mfH19nS6i8nl7w6+/QosWcPIkDBgAlzkLKIQQQpRbjQoyp06dIiEhgYiICFeXIgoRHAx//AH162unl4YOhfR0V1clhBCiNnNpkElLS2P79u1s374dgJiYGLZv386JEydIS0vjscceY9OmTRw7doyVK1dyww030LRpUwYMGODKskUxoqK0MBMQAJs2wS23QE6Oq6sSQghRW7k0yPz777906tSJTp06AfDwww/TqVMnnnnmGQwGAzt37mT48OE0b96cyZMn07lzZ9atW4fZbHZl2eIyWrfWTjN5eMBvv8G4cTL7rxBCiMpRbTr7VpaSdhYSFe+33+CGG7QWmQkT4LPPtPWahBBCiMuplZ19Rc0yaBB88w0YDDB/PkyZos03I4QQQlQUCTKiUo0cCV98oc0389FH8PDDEmaEEEJUHAkyotKNHQuffqrdfvttePJJCTNCCCEqhgQZUSUmTdJmAAaYORNeeMG19QghhKgdJMiIKvOf/8Abb2i3Z8yAZ5+VlhkhhBDlI0FGVKmHH9ZWzAZ47jl46ikJM0IIIcpOgoyoctOmwZtvardfflm7L2FGCCFEWUiQES7x0EPw3nva7ddfhwcflDAjhBCi9CTICJeZOlUbkg3w7rvaPDN2u2trEkIIUbNIkCkju1IsjIvDJs0I5fJ//6fN+KvTwYcfwsSJYLW6uiohhBA1hQSZMrpp927G7NvHWydPurqUGm/SJG3SPINBux45ErKyXF2VEEKImkCCTBkNCw4G4OljxziYkeHiamq+22+HH34Asxl+/hkGDoSUFFdXJYQQorqTIFNGk8LD6R8QQJbdzqT9++UUUwUYPhyWLQMfH1i7Fvr0gXPnXF2VEEKI6kyCTBnpdDo+adECb4OB9SkpvH/6tKtLqhV694Y1ayAkBLZuhWuugRMnXF2VEEKI6kqCTDlEubvzWuPGAEw/epTDcoqpQlxxBaxbB1FRcPAg9OgBe/a4uiohhBDVkQSZcrq7Xj36+PuTabcz+cAB7HKKqUK0aAF//QUtW8KpU1qYWbXK1VUJIYSobiTIlJNep+PTFi3w1Ov5MzmZD+QUU4WJjNTCzDXXQHKy1gH4q69cXZUQQojqRIJMBWjs4cGs3FNM044eZW96uosrqj2CgmD5crj1VsjJgTvugBdflFmAhRBCaCTIVJAp9eszMDCQLLudsXv3ki1T1FYYd3f45ht47DHt/tNPw113acFGCCFE3SZBpoLodDrmtWhBsJsbO9LTefLoUVeXVKvo9fDqq/D++9rtzz6DYcO0U05CCCHqLgkyFSjcbGZuixYAvHHqFCsSE11cUe0zZQosWQIeHtqcM927w+HDrq5KCCGEq0iQqWDDgoO5p149AMbv30+CnP+ocMOHa8Oz69eHffugWzdYudLVVQkhhHAFCTKV4I0mTWjh4cEZi4VJ+/ejpGdqhevcGf75RwsxFy7AgAHwwQfSCVgIIeoanSrjb9nvvvuOb7/9lhMnTmCxWJwe27p1a4UUVxFSUlLw8/MjOTkZX1/fKnvdbampXLV1KxaleL1JEx6JjKyy165LsrK0jr95w7L/7//gvffAzc21dQkhhCifkv7+LlOLzLvvvsvEiRMJCwtj27ZtdOvWjaCgII4ePcqgQYPKXHRt0snHh3eaNgXg8SNHWC+9UiuFu7u2YvasWaDTwccfw/XXw/nzrq5MCCFEVShTkJk9ezZz5szhvffew2QyMW3aNJYvX879999PsvzCdvi/evUYExqKDRi1Zw/nLmm5EhVDp4Np07RVs729tQUn8049CSGEqN3KFGROnDjB1VdfDYCHhwepqakA3HHHHXzzzTcVV10Np9PpmNO8OS09PTltsXD7vn2yhEElGjoUNm2CZs20hSavuQY++UT6zQghRG1WpiATHh5OYu7Q4qioKDZt2gRATEyMdGy9hLfRyOLWrfHQ6/njwgVeOH7c1SXVam3aaC0xN9wAFgvcfTfceSdkZrq6MiGEEJWhTEHmuuuu4+effwZg4sSJPPTQQ1x//fWMGjWKG2+8sUILrM4yDpVsteu23t582Lw5AM8eO8ZP0oGjUvn5wQ8/wMyZ2uR5c+dqi07GxLi6MiGEEBWtTKOW7HY7drsdo9EIwMKFC9mwYQPNmjXj//7v/zCZTBVeaFlVxqglpRTHnj3G8ReP0+b7NoSMCCnR8+47dIj3T5/G22Bg8xVX0NrLq0LqEUVbuRJGj9Y6/wYEwNdfg/RHF0KI6q+kv7/LPPy6pqis4deHHz3MqTdOYfQ30mV7F9yj3S/7nBy7nf47d7ImKYmmHh78fcUVBMg44Up38iTcfDP8/bd2/7HHtIUnq1HeFkIIcYlKHX6dX7t27Th58mR5D1PjNJ7ZGJ8rfbAmWdk7ei/2nMsvEumm17O4dWuizWYOZ2Yyeu9ebLU7R1YLkZHw558wdap2/7XXoGdPkOWwhBCi5it3kDl27Bg5dXAafr2bntYLW2P0N5KyKYWYJ0rWASPYZOLHtm0dnX+nHTlSyZUKALNZmyjvhx/A319rnenUCRYvdnVlQgghykOWKCgHj4YetJinLRJ58vWTJPyaUKLndfTxYX7LlgC8eeoUH54+XWk1Cmc33gjbt8PVV0NKCtx6qzYbcEbJ+m0LIYSoZsodZHr27ImHh0dF1FIjhYwIof799QHYN24fWSezSvS8W0NDeaFhQwCmHjrE/xJKFoJE+UVHw5o1MH26NpnenDnamk27d7u6MiGEEKVV7iDzv//9j4iIiIqopcZq8moTvDt7Y020sufmPdiybCV63pPR0UwMD8cO3LpnD9tyJxYUlc/NDV5+GZYtg7Aw2LMHunSBt94C++W7OwkhhKgmyhRkavlAp1LTm/W0WdwGY6CR1L9TOTTlUIk+I51Ox8fNm9PX3590u50hu3ZxMqtkLTqiYlx/PezYAYMHQ3Y2PPww9OunjXQSQghR/ZU6yFgsFm655ZbKqKVG82jkQeuFrUEPsXNjOfPhmRI9z02v57s2bWjj6clZi4Uhu3aRbLVWcrUiv7AwWLoUPvoIPD1h9Wpo1w4WLJDlDYQQororVZBJS0tj0KBBWOUXbaECrw+k8SuNATj8wGGS/koq0fP83dz4tX17wk0mdqWnM2zXLjJtJTs9JSqGTqd1+t2+Ha68EpKT4bbbYMwYyF2NQwghRDVU4iBz/vx5evfujcFgYLGMWS1S5KORhIwKQVkVe27eQ9apkp0qinZ357d27fA1GFiXnMyte/eSI501qlyzZvDXX/D882AwwKJFWuvMr7+6ujIhhBCFKXGQueaaa/Dy8uLHH3/ETWajLZJOp6PlZy3xau9FTlwOu2/YjS29ZK0rHX18WNquHe56PUsTEph04ICslu0CRiM8/TRs3AjNm8OZM9rK2uPGSeuMEEJUNyUOMkeOHGHgwIF4enpWZj21gsHLQNsf2+IW4kba1jT2jt2LspUskPT092dx69YYgK/i4njo8GHpXO0iXbvCtm1aB2CdDr78Elq3hiVLXF2ZEEKIPCUOMt9++y0vvvgin3zySWXWU2t4NPKg7U9t0Zl1JPycwJHHSj6D79DgYD5v1QqAd0+f5rljxyqpSnE5np7wxhuwYQO0agVxcXDTTTBqFMTHu7o6IYQQJQ4yN954I7/++ivTpk1jwYIFlVlTreHX3Y9Wn2uB5NRbpzj9Ycln8L0tLIx3mzYF4Lnjx3lRwoxLXXUVbN0KTzyh9Z359lutdeabb2RkkxBCuFKpRi316dOHFStW8Nhjj1VWPbVO6KhQGr3YCIBD9x0i4beSz+B7X4MGzGqsjYJ6+tgxZh4/Xik1ipJxd4eXXtLWaWrfHhISYOxYrf+M5EwhhHCNUs8j07lzZ1avXl0ZtdRaUU9EET4hHGyw5+Y9pGxOKfFzp0VF8XIjLQg9ERPDqydOVFaZooSuuAL++Qeee06bIfh//9NaZ2bNgjq4fqoQQrhUmWb2bd68eUXXUavpdDqaf9ycgAEB2DPs7By8k/S96SV+/vToaF7MDTOPHz3K6xJmXM5kgmeegZ07oXdvyMyE//5XCzkbNri6OiGEqDt0qhRDYiZNmlSi/ebOnVvmgipaSkoKfn5+JCcn4+vr69JabOk2tvfdTurmVMwNzHRa3wn3KPcSP//5Y8eYkXsO47XGjXk0KqqSKhWloRR88QU88oh2ugngrrvglVcgMNC1tQkhRE1V0t/fpQoyer2e6OhoOnXqVOyQ4CXVaHxqdQoyADkJOWzruY2MfRl4tPCg01+dMAWbSvz8GTExPJ/bV2ZGdDQzGjZEp9NVVrmiFBISYNo0yMvxISHw2mtwxx2gL/fyrEIIUbdUSpCZMmUK33zzDdHR0UycOJHbb7+dwGr+J2d1CzIAWSez2NZjG9kns/Hp6kOHFR0w+hpL/PyZx4/zREwMAA81aMAbTZpImKlG1q3TljvYt0+7f9VV8N572uraQgghSqakv79L9XfiBx98wNmzZ5k2bRq//PILkZGR3HrrrSxbtkwmbSsF90h32v/RHmOQkdR/Utk5eCfWtJKvXzU9Opr3codmv3XqFHcfPIhNPv9qo2dPbc2mV14Bb2/YtAm6dYM775S5Z4QQoqKVqkXmUsePH2f+/Pl88cUXWK1W9uzZg7e3d0XWV27VsUUmT+qWVLb33Y4t2YZfbz/a/9oeg5ehxM//PDaWSfv3YwdGhYTwRatWmOQcRrVy5gw8/jh89ZV2388Pnn0WpkzRRjwJIYQoXKW0yBR4sl6PTqdDKYVNVmsuNZ/OPnT4owMGXwPJa5PZNXwXtsySf47jw8P5tk0b3HQ6Fp07x/Bdu0iTlcmrlXr1tKUN/vpLG9GUnAwPPQQdOsCKFa6uTgghar5SB5ns7Gy++eYbrr/+epo3b86uXbt4//33OXHiRLVrjakJfLv50v739hi8DSStSmL3iN3YskoeZkaGhPBLu3Z46vUsu3CB3tu3E5udXYkVi7Lo0UObSG/OHAgO1vrPXH89DB9+sS+NEEKI0itVkPnPf/5DREQEr7zyCkOHDuXkyZMsXryYwYMHo5dTGmXm192Pdv9rh95Tz4U/LmhhJqPkYWZAYCBrOnYkxM2NrWlpdN+2jQMZGZVYsSgLg0Ebln3wINx3n3b/l1+gXTu4915tHSchhBClU+rh11FRUXTq1KnYUTI//PBDhRRXEapzH5lLXVhzgV1DdmHPsOPXy492v7Qr1WimI5mZDNy5k8OZmQQajfzcrh09/PwqsWJRHvv3a5Po/fSTdt/bW+tP8/DD2mKVQghRl1VKH5lx48bRp08f/P398fPzK/Iiyibg2oCLfWb+TGZHvx3kJJZ8zvsmHh5s6NSJK318SLRa6bdjB9/JMJlqq2VL+PFHWLsWunaFtDR4+mlo1gzmzQPpdiaEEJdXrlFLNUFNapHJk7ollR0DdmBNsOLVzosOyztgCiv5pHkZNhuj9+7ll9xpZp9r2JCno6NlrplqzG6HRYtg+nTIWxu0fXttCPfAgSA/OiFEXVMlo5ZE5fDp7EOntZ0whZtI35XOtl7byDqZVeLnexoM/NCmDQ82aADAjGPHGL13LxnyJ361pdfDmDHa6abXXgN/f20dp8GDoVcv+PNPV1cohBDVkwSZasqrjRcd13XEHGUm82Am267eRvqeki80adTreatpUz5p3hw3nY5vz52j17ZtnJYRTdWauzs8+igcPqyt3eTurg3d7t1ba5n5919XVyiEENWLBJlqzLOpJ53WdcKzlSfZp7LZds02ktYmleoYd9arx4oOHQh2c2NLWhpdt2zh75SUyilYVJigIHj9dS3Q3HMPGI2wbJnWl2bkSNi719UVCiFE9SBBpppzj3Kn01+d8O3hizXJyo7+O4j/tnQdeHv5+/P3FVfQ1suLsxYLvbZt49MzZyqpYlGR6teHDz+EAwe0xSd1OvjhB2jbFsaNgyNHXF2hEEK4lgSZGsAt0I0OyzsQfFMwyqLYO3ovJ98+WapjNMod0XRDUBDZSnHXwYNM3r+fTOk3UyM0bgxffAG7dsFNN4FS2ozBLVrAhAna3DRCCFEXSZCpIQweBtp824b6U+uDgiMPHeHQA4ewW+0lPoaP0cgPbdvycqNG6IG5sbH02LaNo5mZlVe4qFBt2sD338M//8CgQdoQ7c8/h1at4PbbZZZgIUTdI0GmBtEZdDR9tymNZzUG4PS7p9k1dBc5SSWfa0av0zE9Oppl7dsT7ObGtrQ0Om/Zwv9yh2qLmqFLF/jf/7RlD4YN04Zvf/21FnRGj4bdu11doRBCVA0JMjWMTqcjaloUbb5roy1psOwCW6/aSsah0i1J0C8wkK2dO3Oljw9JVitDdu1i+tGj5NhL3sIjXK9rV/j5Z9iyBUaM0E45LVqkLXtwyy2wY4erKxRCiMolQaaGChkZQqe/OmFuYCbzQCZbr9zKhVUXSnWMSHd31nbqxH/q1QPglRMn6LltGzFyqqnGueIKWLIEtm+Hm2/Wtn33HXTsCEOGaPPQ1O6pL4UQdZUEmRrMp5MPV/x9BT5X+mC9oI1oOvXeKUozWbNZr+eD5s1Z3Lo1/kYjm1NT6fjvvyySpQ1qpA4dYPFirVPw6NHaRHv/+582D83VV2tLIkijmxCiNpEgU8OZI8x0XNOR0NtCwQaH7z/Mvtv2YUsv3Wikm0ND2d6lCz18fUnJXeJg8v79pMuophqpbVv45htt2PY994DZDJs2wY03QuvW8NlnIHMjCiFqA1lrqZZQSnHqrVMcmXYEbODZxpO2P7TFs3npllG22u08f/w4Lx4/jgJaeHjwVatWdKnFn11dEBcH774Ls2dDUpK2rV49ePBB+L//A/nxCiGqm5L+/pYgU8skrUti7617scRaMPgYaPl5S0JuDCn1cdZcuMBt+/ZxxmLBADwZHc1T0dG46aURryZLTYU5c+Ctt+D0aW2bnx/ceSdMnQoNG7q0PCGEcKgRi0b++eefDBs2jHr16qHT6fjxxx+dHldK8cwzzxAREYGHhwf9+vXj0KFDrim2hvDv6U/nrZ3x6+mHLdXGnpv2cGTaEew5pesYcW1AADu7duXWkBBswPPHj3Pl1q3sTkurnMJFlfDx0dZwOnoU5s6Fli0hORneeAOaNNE6Cv/1l3QMFkLUHC4NMunp6XTo0IEPPvig0MdfffVV3n33XT766CM2b96Ml5cXAwYMICur5CtB10XmCDMdVnagwUPa6tcnXzvJtp7byIwp3WikIDc3FrVpw8LWrQk0Gh1zzrx24gQ2+U1Xo5lMMHEi7NkDv/4K11+vdQL+/nvo2VMb1v3VV2CxuLpSIYQoXrU5taTT6ViyZAkjRowAtNaYevXq8cgjj/Doo48CkJycTFhYGPPnz2f06NElOm5dO7V0qXPfn+PAnQewJlkx+Bpo/nFzwkaHlfo4Z7OzuevAAX5NTATgal9fPmvRgpZeXhVdsnCR3bu1fjRffgl5fytERMCUKXD33RBS+jOUQghRZjXi1FJxYmJiiI2NpV+/fo5tfn5+XHnllWzcuLHI52VnZ5OSkuJ0qctCRobQZXsXfHv4YkuxsW/MPvZP2l/qUU0RZjO/tGvHZy1a4GMwsCElhQ7//ssLx45hkfG8tULbtlr/mZMn4aWXtBBz9iw89RRERsKkSfDvv66uUgghnFXbIBMbGwtAWJhz60FYWJjjscLMnDkTPz8/xyUyMrJS66wJ3KPd6bimI9FPR4MOYufF8m/nf0n5t3QhT6fTMSkigt1duzIoMBCLUjxz7BhX/Psvm5KTK6l6UdWCg+GJJ+DYMe30UufO2lDtefO0U05du2r9azJKN5m0EEJUimobZMpq+vTpJCcnOy4nT5ZulejaSm/U0+j5RnRY1QFTPZM2G/BVW4mZEVPqjsBR7u782q4dC1q1IsTNjT0ZGVy9bRv3HzpEqtVaSe9AVDWTCW67TVugcv16bVFKk0lrlZk8GerXh4cekpW3hRCuVW2DTHh4OABxcXFO2+Pi4hyPFcZsNuPr6+t0ERcFXBtA151dCbk1BGxw/PnjbL1yK2m7SzcaSafTMSYsjH3dujE+LAwFvHf6NG3++Yefzp8v1ezConrT6bRZgb/8Ek6dglmzoFEjbT6at9+GFi20zsI//ACSY4UQVa3aBplGjRoRHh7OypUrHdtSUlLYvHkz3bt3d2FlNZ9bkBttFrWh9cLWGAONpG1LY0vnLZx49QTKVroAEuTmxvxWrfijfXsaubtzMjubEbt3M2TXLg7JuYdaJyQEpk2Dw4e1pQ+GDtWCzooVMHIkREfDM89op6WEEKIquDTIpKWlsX37drZv3w5oHXy3b9/OiRMn0Ol0PPjgg7z44ov8/PPP7Nq1i3HjxlGvXj3HyCZRPqGjQum6uyuBQwJRFsXRx4+yrdc20venl/pY1wcGsqtrV6ZHReGm0/FbYiJt//mHJ48elWUOaiG9HgYNgl9+0eakmT5dCzlnzsALL0DjxlorzaJFshSCEKJyuXT49Zo1a+jTp0+B7ePHj2f+/PkopZgxYwZz5swhKSmJa665htmzZ9O8efMSv0ZdH35dEkopYufFcvjBw9hSbehMOqKfjCbqv1HoTaXPugcyMrj/0CH+uKCtxh1lNvNW06bcGByMTqer6PJFNZGdra3A/dlnWgtNnsBArX/N5MnQvr3r6hNC1CyyREEuCTIll3U8i4P3HiTxN22uGM/WnrT4pAV+V/uV+lhKKX48f54HDx/mRO6f5P0DAnizaVPayNwztV5MjDbKad48rV9Nnq5dtUAzZoys7ySEKJ4EmVwSZEpHKUX8wngOP3CYnHM5oIN699aj8czGGH2NpT5ehs3Gy8eP89rJk1iUQg/cFRHBc40aEWYyVfwbENWKzQbLl8Onn8LPP0NOjrbd0xNuugnGjYPrrgODwbV1CiGqHwkyuSTIlE1OYg5HHj1C7Dxtzh5TfRPN3mlG8E1lOz10OCODaUePsuT8eQB8DAamR0XxYIMGeMhvsTohPl4b+fTZZ7Bv38Xt9eppw7zHjdMm5RNCCJAg4yBBpnwurLrAgbsPkHVEm7M+4PoAmr7bFK+WZTs99GdSEg8fPsyW3MUno8xmXm7cmDGhoeil/0ydoBRs3qyFmm++gdyuVAB07KgFmjFjoJhZFoQQdYAEmVwSZMrPlmnjxMwT2vDsbIXOqKPBQw2Ifjoao0/pTzfZlWJBXBxPxMRwMrf/TBcfH15u1Ih+AQHSIbgOyc7WhnF/8YW2eGXeqSeDAfr310LNDTeAh4dr6xRCVD0JMrkkyFSczCOZHH7oMAm/JABgijDR5PUmhI4JLVP4yLTZeOvUKWaeOEFa7hDta/39eblRI7r7lb6DsajZEhK04dpffgmbNl3c7u0NI0bA6NHakG7pWiVE3SBBJpcEmYqX8GsChx445Djd5NvDl6ZvNsW3W9k+33iLhZknTjD79GksuV/HoUFBvNSoEe29vSusblFzHDyoBZqvvnKeXC8wUJt4b/Ro6N1bOgkLUZtJkMklQaZy2LJsnHrzFMdfPI49U1urKXR0KI1mNsKjYdnOA5zIyuKF48eZd/YseVPojQkN5bmGDWnm6VlBlYuaRCmtdWbhQvj2W8i/Xmx4ONxyi9af5qqrtBmGhRC1hwSZXBJkKlfWqSxinooh7os4UKAz6WjwQAOinojCzd+tTMc8mJHBjGPHWBgfD4ABuC0sjCeio2khgabOstlg7Vot1Hz3nXMn4ehoGDVKu3TqJKFGiNpAgkwuCTJVI3VbKkcePULSqiQAjIFGGs5oSL176pVpdmCA7ampPH3sGEsTtD45OmBUaChPRUfLpHp1nMWizU+zcCH8+COk5VvztHFjbY6akSOhWzdtOQUhRM0jQSaXBJmqo5Qi8bdEjjx2hIy92oKR7o3caTijIWG3h6EzlO3P5H9TUnjh+HF+zg00ACODg3kqOpqOPj4VUruouTIztZFP33yjXWdmXnysfn248UYt1PTsKX1qhKhJJMjkkiBT9exWO7FzY4l5JoacOG08rWdLTxo+35CQkSHo9GULNDvS0njx+HG+P3eOvC/tsKAgnoqOppv8bAWQng6//Qbffw9Llzq31ISEaKOfRo6EPn1k9JMQ1Z0EmVwSZFzHlm7j9PunOTHrBNYLVgC8O3rT8IWGBA0JKvN8MXvS03np+HEWxcdjz93W28+Px6KiGBQYKBPrCQCysrTFK7//Hn76yblPjb8/DBumnYK6/nqQM5VCVD8SZHJJkHE9a7KVk2+d5NSbp7ClauORfK/ypeELDQnoW/YJ8A5mZPDy8eN8HR+PNfdr3NrTk0cjIxkbFoZZOkeIXDk5Wkfh77/XVuiOi7v4mNkM/frB8OEwdKi2ZIIQwvUkyOSSIFN95CTkcOLVE5x+77RjyLbvVb5EPRlVrhaaU1lZvHP6NB+fOUNq7sR6ESYTDzRowP9FRODvVrbRU6J2stlgw4aLLTX556kBbYXu4cO1S7t2MgJKCFeRIJNLgkz1k302mxOvnODsnLPYs7RA493Rm6gnowi5qex9aJKtVuacOcPbp05xxmIBtMUp74yIYGr9+jSWee7FJZSCPXu0lbl//llbAyq/6OiLoaZXL+lXI0RVkiCTS4JM9WWJs3DyzZOcmX0GW5rWkuLZypOo6VGEjglFbyzbqSGL3c438fG8fvIku9PTAW3o9tCgIO6vX5++sp6TKEJsrLbm088/a8O784+A8vWFAQNg8GAYOFAWtRSiskmQySVBpvrLScjh1LunOP3uaaxJWqdg90buNHi4ARETIzB4lW3MrFKK3xMTeff0aX5PTHRsb+XpydT69RkXFoa3sfSLXoq6ISMDVq7UQs0vvzj3qwFt4r1Bg7Rgc+WVIF8lISqWBJlcEmRqDmuKldOzT3PqjVPknNeGbRsDjNS7tx71p9bHHGEu87EPZGTw/unTzI+NdSxQ6WcwMDEigin16tFUZgwWxbDb4Z9/tNaa336Df/91ftzfX1ute9Agaa0RoqJIkMklQabmsWXYiP08llNvniLzsNa2rzPpCLs9jMiHI/FqU/axsilWK/NjY3n/9GkO5TtvcH1AAHdHRHBDcDBuMtpJXEZ8PCxbpk3A98cfkK/BD7jYWjNokLYOlLTWCFF6EmRySZCpuZRNcf7n85x8/SQpG1Ic2wMHBdLg4QblGrptV4o/8p12yvtHEObmxsSICO6MiKCJdA4WJWCzwd9/ay01hbXW+PlpE/D166fNWdOsmYyEEqIkJMjkkiBTOyRvSObkGyc5v+Q8eanDs6Un9afWJ2xcGEafsv/JG5OZyadnzzI3NpbY3NFOIK00omzi4rTWmt9+K7y1JirqYqjp21ebcVgIUZAEmVwSZGqXjMMZnH7nNLHzYx0jnQw+BsLHh1NvSj28Wpb9tFOO3c4vCQnMOXOGPy5ccGqlGRcezoTwcFrLFLCiFGw22LpVm2F4+XJYv15b8DK/jh0vBpuePUEaAoXQSJDJJUGmdrKmWIn9IpbT758m88DFvi4B/QKoP7U+QUODyrxIJRTdStPVx4cJ4eGMDg0lUCbaE6WUkQHr1mmhZsUK2LHD+XGzGXr00EJNnz7QubP0rxF1lwSZXBJkajelFBdWXuD0+6dJ+CWBvMWXzJFmwieFEzEpAvco9zIfP8duZ2lCAvNjY/k1IQFb7naTTscNwcFMCA+nf0AARjn1JMogLg5WrdKCzfLlcOqU8+Pe3lorzbXXapcrrpBgI+oOCTK5JMjUHZnHMjnz0RnOfnIWa6I2Hw06CBwYSMRdEQQNDULvVvbAEWexsCAujvmxsezMnWgPINxk4vawMMaFhdHO27u8b0PUUUrBwYNaoFm5UlsbKv9ClwA+PheDTZ8+2mkpCTaitpIgk0uCTN1jy7Jx/ofznP3kLElrkhzb3cLcCJ8QTsSdEXg2Ld+8MdtTU5kfG8vX8fGcz8lxbG/j6cnYsDBGh4bKkgiiXOx22LkT1qzRLmvXQlKS8z6+vlqw6dNHCzcdO4KhbPNHClHtSJDJJUGmbss4lMHZz84SOz+WnLiLgcO/jz/hE8IJvikYo3fZ/6S12O38lpjI57mnniz5/jld5evLmNBQbg0JIdxc9sn8hACt4/DOnbB6tRZs/vwTkpOd9/Hzg6uvhmuu0QJO167gXvYzq0K4lASZXBJkBIA9x07CLwmc/eQsicsSHUO49V56QkaGED4uHP9r/cvVQTgpJ4cl58+zID6eVRcu5HXXQQ9cFxDAmNBQbgoOltW4RYWw2WD7di3UrF6tBZvUVOd9TCbo0kULNddco4WcwEBXVCtE6UmQySVBRlwq60QWsfNjifsyzjFzMIC5gZmw28MIuyMMr9blG2Ydm53Nt+fOsSAujs35fruYdDquDwjg5pAQhgcHy8gnUWGsVm0U1F9/aSOj/vqr4PpQAG3bXmyxueYabV4bIaojCTK5JMiIoiilSNmUQtwXccQvjHcsWAng08WHsHFhhN4aiinMVK7XOZKZycL4eBbExbE3I8Ox3ajTcZ2/PzeHhDAiOJgQU/leR4j8lIIjR5yDzcGDBfeLjNRCTY8e0L07tGsnHYhF9SBBJpcEGVEStiwbib8mEvt5LIm/JaKseeeetP40oaNDCbkpBLfAsregKKXYm5HB9+fO8d25c+zKN/JJD/TODTU3BgcTIX1qRCWIj9cCTd5l61btFFV+np5a35ru3bV1orp3h9BQ19Qr6jYJMrkkyIjSssRbiF8YT9xXcaT+c/G0kM6oI6B/AKGjQwm+IRijb/n+bD2YL9RsTUu7+DrA1b6+3BAczPDgYFrIytyikqSlwebNWqhZv167nZJScL9GjZyDTYcOIGdFRWWTIJNLgowoj8yjmcQviid+YTzpOy+2oOjMOoIGBxE6OpSgIUEYvMo35jUmM9MRajZf0mOzuYcHw4ODGR4URHdfX5l8T1Qaux327YNNm2DjRu16717tNFV+7u5aJ+K8YHPVVVCvnmtqFrWXBJlcEmRERUnfl+4INfmXRdB76AkcGEjwTcEEDQ3Czb98f6qezMri54QEfj5/ntVJSeTk+ycaZDQyJCiI4cHB9A8IwEc6M4hKlpysre6dF2w2bSo4UR9A/fraKam8S5cuEBBQ9fWK2kOCTC4JMqKiKaVI35lO/EIt1GQdy3I8pjPq8L/On5CbQgi6IQhzePn6uqRYrSxLTOTnhAR+TUjggvVih2STTsd1AQEMDQpiUGCgTMAnqoTdDocOXQw2GzfC7t3a9ks1bXox1HTtqi2xIOuuipKSIJNLgoyoTEop0rancf6H85z74RwZey+OSkIHvlf7EnJjCME3BuPRuHxBw2q3sz4lhZ/Pn+en8+c5kpXl9HhzDw8GBQYyKCiIXn5+eMgUr6KKpKVpHYf/+efi5ejRgvvp9dC6tXPLTfv22nw3QlxKgkwuCTKiKmUcyODcknOcX3Ke1L+d+7p4tfciaGgQQUOD8O3mW67J95RS7M/I4OeEBP6XkMD65GTyDz7x0Ou51t+fQYGBDAwMpJl0GBZVLDER/v3XOdycOVNwP5NJ6zzcubPWYtOpkzbXjcxILCTI5JIgI1wl61QW5388z/kfzpO0NsmxMjeAW7AbgYMDCRoaRGD/QIx+5evrkmy1svLCBX5LTOS3hAROWyxOjzdxd2dQUBADAwPp7eeHt/StES5w5szFUJMXchITC+5nNGotN3nB5oortLDj41P1NQvXkSCTS4KMqA4s5y0k/p5IwtIEEn9PxJZ8sf1EZ9Th18uPoGFaa015F7RUSrEnPV0LNYmJ/JWc7NRh2KjT0d3Xl74BAfQLCKCbjw9uMhJKuIBSEBOjBZqtW2HbNu06IaHgvjodNGt2Mdh06qRdgoOrvm5RNSTI5JIgI6obe46d5PXJJCxNIGFpgtMIKACPFh4EDQoioH8A/r39MXiWr69LqtXKqqQkfk9MZFliIjGX9K3xNhjo7edHv4AA+gYE0NbLC52u7Ke9hCgPpeDkyYuhJu/69OnC94+MvBhuOnbUWm6io7XgI2o2CTK5JMiI6i7jUAYJv2qhJnlt8sVZhdHmq/Hv6U9A/wACBwTi1a78IeNoZiYrL1xgxYULrEpK4nxOjtPjYW5u9M0NNX0DAoiWzgqiGoiP10JN/oBz+HDh+/r6akstdOigdSZu31677+1dtTWL8pEgk0uCjKhJrMlWEpcncmHZBRKXJZJ9MtvpcVO4SQs1/QMJuD4AU2j5hnvYlWJnWhorLlxgZVISfyYlkXHJONpos5ne/v6OS2N3d2mxEdVCcrK2UObWrdpl505tAr9LsrlDkyYXg01eyGnUSBtNJaofCTK5JMiImkopRcaBDC3U/JFI0pok7BnOIcO7kzcB/QMIuC4Avx5+5Z5hONtuZ1NKiqPF5p/UVKyX/BdR32RyCjbNPTwk2Ihqw2KBAwe0ULNzpxZ0du6Es2cL39/bW2utyQs4ea03fn5VW7coSIJMLgkyorawZ2t9axKXJXLhjwukbU9zelznpsP3Sl/8r/PHv48/vlf5YnAvX7BJs1rZmJLCmqQk1iYl8XdqqlPHYYBwk4lefn6OYNPK0xO9BBtRzZw7B7t2XQw2O3fCnj2QnV34/g0aQJs2zpfWrWXkVFWSIJNLgoyorbJjs7mw/AJJq5K4sOoC2Sec/0fWu+vx7eFLQJ8A/K/zx6eLD3q38rWhZ9hsbEpJYW1usNmUkkL2Jf+FBBqNdPf15Wo/P3r4+dHVxwdPmZxPVENWqzZLcf5ws2MHnDpV9HOio53DTdu20KqVtmq4qFgSZHJJkBF1gVKKrJgsLqzSgk3S6iQssc5zyRi8Dfj19MO/tz9+Pf20YGMqX7DJstn4OzXVEWw2pKSQeUkfG6NORydvb6729aWHnx9X+/lR31y+pRuEqExJSVpfmz17tMvu3dp1bGzh++t0Wl+b/OGmTRto2VIm9isPCTK5JMiIukgpRcb+DEdrTdKaJKyJVqd99O56fK70wb+nFmx8u/ti9CnfRHk5djvb09JYn5zMhpQU1icnc+aSyflA60Cc12Jzta8v7by8ZFVvUe0lJhYMN3v2aKetCqPXQ+PGWotNy5YXL61ayYKaJSFBJpcEGSFA2RVpO9NIWp1E8p/JJP+VTM75S4Z2GMC7o7cj2Phd41fuUVFKKU5kZ2vBJjmZ9Skp7ExLw37Jfl56PV18fOjm68uVvr508/GhgdksnYhFjXDuXMFws2dP4bMW5wkNdQ42ebejomQUVR4JMrkkyAhRUF6LTfI6LdQkr0t2WsU7j0cLD/x7+uPb3Rffq3zxbOmJTl++cJFqtbI5JcXRYrMpJYUUm63AfhEmE93yhZsuPj74ydIKooZQCuLiYN8+2L//4vX+/dqEf0Xx8IAWLQq24DRrpj1Wl0iQySVBRoiSyTqVpQWb3Ev67vQC+xj8DPhe6esINr5X+uIW4Fau17Upxb70dP5OTeXvlBQ2p6ayKy2NS6ONDmjp6ekUbtp5eWGSP19FDZOWpg0Rzws2eSHn0CFt+HhhdDpo2FALNs2ba8Em7zoyEmpjf3oJMrkkyAhRNjmJOSSvTyZ5fTIpG1NI/ScVe+alJ4XAs6XnxWDT3Rev1l7lWtkbtNFRW1NTncLNsayCLUYmnY723t509vbmCh8fOvv40NbLC7OEG1EDWa1w7Jhz601e0Llwoejnmc3aZH/NmxcMOeHhNXe5BgkyuSTICFEx7Dl20nelk7IphZSNKaRsSiHzcGaB/Qw+Bny6+eB7pS8+XXzw6eKDuUH5+7vEWyz8nZLiCDd/p6ZywWotsJ+bTkdbLy+uyBdu2nt54VEb/2QVdYJSWj+cvGBz6BAcPKhdHz5c9EzGoE34lz/Y5L8ODKy691AWEmRySZARovJYzllI2Xwx2KT+nYotrWB/F7cwN3y6+ODb9WK4MYWVvyPxkcxMtqalsTU1lS2514mFhBsD0Do33HT28eEKHx86envjJeFG1HA2G5w4cTHY5L8+dgzsBRtRHQIDLwabpk21Vp2866Ag17fkSJDJJUFGiKqjbIr0PelasPknhdR/U7W+NgWzDeZIsyPU5F3cAsvX3yZvlNSW1FRHuNmSmsq5Qv5k1QFNPTzo4O1Ney8vOnh708HbmygZLSVqiexsiIkpPOQUtZp4Hl/fi6Emf8Bp0gTq16+akVUSZHJJkBHCtWwZNtJ2pJH6byqp/6SS+m8qGfszoJD/edwbu+PTxQfvTt74dPLBu6N3hbTcnM7Odmq52ZKaytkielX6G41OwaaDlxdt5NSUqGXS07XTUnnB5sgR7f6RI5cPOWazNj9O/pBz3XXaEg4VSYJMLgkyQlQ/1lQraVtzw82/qaT8k0LWkYKdeUFb8du7kzfeHb0d1x5NPMo9DDzeYmFHWpp2SU9nZ1oaezMyCiySCaAHmnt60iFfwGnv5UV9ab0RtVBmptaSkxds8oecY8e0TsmXeu89mDq1YuuQIJNLgowQNUNOYg6pW7Vgk7Y9jbTtaWQezCy05cbgbcCrgxfeHS+23Hi28Sz3IpkWu519GRkXA05uyDlfRG9KP4OB1l5etM1ttWnj6UlbLy/CTCYJOKJWslq1eXAuDTmPPgo9elTsa0mQySVBRoiay5pmJX1XuhZstmnhJn1XOvasgj0YdUYdnq088WrvhXc7b7zaeuHVzgtzZPlaTZRSxOa13qSnOwLOgYyMwrr+ANrCmW3yBZy2uSEn2FS+02RC1CUSZHJJkBGidrFb7WQeyCRtexqp21IdIefStaTyGPwMWqhpmxtw2mm3y9ux2GK3czAjg93p6ezJu05P50hmZoElGPKEurldbL3JDTctJeAIUSgJMrkkyAhR+ymlyD6V7WixSd+VTtquNDIPZKKshf8XZ6pnwqudc+uNZytPDB7lOz2VabOxPyODPenpjpCzJz2dmEIm9MsTZDTS0tOTVl5etMwNNy09PWno7o5BTlGJOkqCTC4JMkLUXXaLnYwDGVq42X0x4GQfzy78CXrwaOKBZ2tPvFppwcaztSeeLT0xepdvnac0q5V9+Vpudqensy8jgxPZRdQCmHU6muULNq1yr5t7eOAt606JWk6CTC4JMkKIS1lTrKTvSXe03qTv1gKONaHw01MA5iiz1genlZcWbnJvuwWV7xRVus3GwYwM9l9yOZCRQXYx/z1Hms2OgNPC05NmHh408/AgSlpxRC0hQSaXBBkhREkopbDEWsjYm0H6vnQy9maQsS+D9L3p5MQXPQe8W6hboQHHVK98I5dsSnEiK6tAwNmfkUF8MXPSm3Q6Gnt40Dw32DTLF3Lqm83oJeSIGkKCTC4JMkKI8spJzHGEmox9FwNO9omiTwvpvfR4NvPEo7kHni1yr5tr127+5WvFScjJ4UBuqNmXkcHBjAwOZWZyJDMTSzH/pbvr9TTNCziXBJ0IGTIuqhkJMrkkyAghKos1zUrG/gyncJOxL4PMI5mFLsuQxy3EzSnYeLbwxLO5J+5N3Ms1F45NKU5mZXEoM/PiJTfkHM3KKnSyvzxeeSHH05OmHh40dnence51pNmMUVYUF1VMgkwuCTJCiKpmz7GTdTSLjIMZZB7MdLq2nCl8aQQAdOAe7e4UcjyaeuDRxAP3hu7oTWUPE1a7nePZ2Y7Wm/wh51hWVpFDxgGMOh3RZrMj2ORdN/HwoLGHB37S8VhUAgkyuSTICCGqE2uqlczDmWQcuCTkHMjAllJMM45eW2gzL9jkXdybuOPRxAOjT9nDhMVuJyYry6n15mjudUxmZrGdjkGbADB/yGmSL+w0kNYcUUYSZHJJkBFC1ARKKXLO5Ti34hzIJPOIdrFnFNdmonU6vjTceDTRWnTcQtzK3P/FrhRnLRaOZGY6wk3e9ZHMzGI7HoPWmhNlNhPt7k7D3Eve7WizWYKOKJIEmVwSZIQQNZ1SCkuchawjWWQevhhuMo9kknUki5zzxYcJg7dBCzeNPXBv5I57w9xL7u3yzJGTZrUSk5XlCDhH8oWdmKysYjsfAxiABvmCzqWBJ9JsxiRBp06SIJNLgowQorazJlvJPJobbg5r4SYv6GSfzC504c383ILdCoQbx3W0OwbPsnVAtivF6exsjmVlcTwrS7vOvX8sK4sTJQg6OqCeyVRkyIk0m2VywFpKgkwuCTJCiLrMnm0n61husDmaSdaxLO0So10XtUZVfm5hF4OORyMPp6BjjjKXeaSVPXdBzryQkz/w5IWeLHvxp9QA/I1GonJDTWS+gJN3v4HZjFladWqcWhFknn32WZ577jmnbS1atGD//v0lPoYEGSGEKJo1xVog3GTGZDruF9sBOZdbqBvuUe6YI82Yo8za7bzrSDOmMBM6fen76CiliM/JKTToHM/K4mR2Nim2y9cH2oKdRQWdKLOZCJNJ+upUMyX9/V3t2+PatGnDihUrHPeN0oQohBAVxuhrxLu9N97tvQt9POdCjlPIuTTw2NPt5MTnkBOfQ+q/qYUeQ+emwxx5MeDkv50XdgobdaXT6QgzmQgzmehWxC+yFKuVk9nZnMzK4kR2tuP2ybzbua068Tk5xOfksCUtrdDj6IF6+QJOA7OZ+rmXeiaT49rdUL5FRUXFq/apwGg0Eh4e7uoyhBCiTnILcMMtwA2fTj4FHlNKYb1gJetEFtkns8k+ka3dzrs+mU326WxUjiLraBZZR4teAdzob3RqzTFHmjE3MGOuf/Fi8CoYInyNRtoYjbTx8ir0uEopEnJynILNpUHnVHY2VqU4lXt7YzGfR5DRSL28kJMXcPLdrm82E+zmJktBVKFqH2QOHTpEvXr1cHd3p3v37sycOZOoqKgi98/OziY732qyKSkpVVGmEELUOTqdDrdAN9wC3fDpWDDoANitdixnLI6Ak33ykrBzIhtrktVxSd+ZXuTrGf2NmOqbnANO7u287W5BzkPNdTodwSYTwSYTnXyKqFEp4iwWp6BzKjub0xYLZ7KzOZ17O8tuJ8FqJcFqZVd60XW66XRE5As29fLdrm8yOYKQl7TuVIhq3Ufmt99+Iy0tjRYtWnD27Fmee+45Tp8+ze7du/Ep4gtZWL8aQPrICCFENWVNtRYacLJP515OZWNPv3ynXwCdWYe5XsGA4xR6Ikzo3UrXH0YpxQWrldPZ2ZyxWLRwk3vJfz8+J+dyg8QcfA0Gwk0mIkwmIsxmx23HttzbQW5lnweoJqsVnX0vlZSURHR0NG+++SaTJ08udJ/CWmQiIyMlyAghRA2llMKWYnMKNnnXltMWx+2cc8XPp+Og0zoomyO0UGOqZ7p4O/dijjBjCjehN5cu8OTY7ZzNa8kpJvCkl2A0Vh633L5CEZcEnfDcAJR3O9xkqlVz7tSazr75+fv707x5cw4fPlzkPmazGbPZXIVVCSGEqEw6nQ6jnxGjnxGv1oX3hQFtqHn22XwB51R2gfBjOWNB5Shy4nLIicuB7cW/tjHQiLleISHnkvt5/Xfc9Hqi3N2Jcncv8phKKVJsNmItFs5mZ3PWYtFu57s+m51NrMVCgtVKTr7+O5cTaDQW2sITZjIR5uZGmMlEqMlEsJsbhlrSylOjgkxaWhpHjhzhjjvucHUpQgghqhm9WY9HQw88GnoUuY+ya0tBZJ/OxnLWQvZZ7Trvkv++ylFYE61YE62k7y66TwyAwddQaMgxhZswhV28dgtyQ2fQ4Wc04mc00sLTs9jjWux24vLCTV7QyQ05+bfFWizkKEWi1Uqi1cqejIziPysg2M2N0HwBJ8xkIjTf7bz7oSZTtZ6Hp1oHmUcffZRhw4YRHR3NmTNnmDFjBgaDgTFjxri6NCGEEDWQTq/TgkWYqdj9lNJCTPaZ7EJDTv779gw7thQbmSmZZB7ILL4APbiFuDlqMIWZcAvLvR+a73aYCbdQN0xGvTb3TTEtPKB1WL5gtRYacs5aLMRbLMRZLMTl5JCQk4MdHEPSd5fgc/M3Gp1DTl4Iyr3dyceH6MvUWFmqdZA5deoUY8aMISEhgZCQEK655ho2bdpESEiIq0sTQghRi+l0OtyC3HALcoN2Re+nlMKWanMKPE6hJ9aCJc6incpKyAE7jtNa6RTfygNgDDIWHnoKuR9kciPIzY22lzmm1W7nfE4OcTk5xOWFnEJux1ksxOfkYFWKJKuVJKuVg5mFB7X3mjZlaoMGl30/laFaB5mFCxe6ugQhhBCiSDqdDqOvEaOvEa+WRfffAW0oes65HEewscRZHJcC989poceaYMWaYCVjb/GnikAbnu4W6qa1+ISYcAtxc1wuvR8aYiLc+/L9SfNGa+WFmvwBJ+92nMVCY4+iT+dVtmodZIQQQojaQm/UY44wY44oQYCwKXIScooOOnE5jtYeS7wFbDjm4sk8eJnTW7kMPgYt5IQWH3o8Q0w0D3GnlX/xQc1VJMgIIYQQ1YzOoMMUqvWbKe7UFuR2YE7UTldZzmmtOXmXQu+fzwEb2FJt2FJtxc64nJ/eU19ka0/AgIAiJ0WsbBJkhBBCiBpMp9dhCjZhCjbhxeVbTZRdYU2yFh108t+P1+6rHIU9w0728WyyjxccBt7Mr5kEGSGEEEJUPp3+4tISni2KH/4NFyckLC70eLV13WknCTJCCCGEKFL+CQlp6upqCqq+M9wIIYQQQlyGBBkhhBBC1FgSZIQQQghRY0mQEUIIIUSNJUFGCCGEEDWWBBkhhBBC1FgSZIQQQghRY0mQEUIIIUSNJUFGCCGEEDWWBBkhhBBC1FgSZMooK+s46en7Ucrm6lKEEEKIOkvWWiqj06ff5+TJ19Hr3fHyaouXVwe8vdvj7d0BL6/2uLkFuLpEIYQQotaTIFNGdnsOer0Xdns6qan/kpr6r9PjZnOkI9R4ebXF07MVnp4tMBg8XFSxEEIIUfvolFLK1UVUppSUFPz8/EhOTsbX17dCj62UnczMo6Sn7yAtbQdpaTtJT99BVtaxIp6hw929YW6oaYWnZ0u8vLTbbm6BFVqbEEIIUZOV9Pe3BJlKYLUmk5a2yxFw0tP3kpGxD6s1scjnuLmF4unZEg+PZnh4NMHDo2nupQlGY9XULYQQQlQXEmRyuSLIFMViOUdGxj4yMvaRnr7PcTs7+2Sxz3NzC8kXbJo6go67e2Pc3ILR6XRV9A6EEEKIqlHS39/SR6YKmUwhmEwh+Pv3ctputaaRmXmAjIz9ZGYeITPzcO7lCDk58eTknCMn5xwpKRsLHFOv98TdPRp392jM5mjHbXf3hri7R2MyRaDTyeA0IYQQtZMEmWrAaPTGx6czPj6dCzxmtaYUCDfa9SEsljPY7RmOlp3C6HRumM2RjnBjNkdhNtfHbG6Qe10fozFQWnWEEELUSBJkqjmj0Rcfn074+HQq8JjNlkV29kmyso6TnX2crKy8y7HcbadQKoesrKNkZR0t8jX0endMpvqOYJMXci5ua4DJFI5e71aZb1UIIYQoNQkyNZjB4I6nZzM8PZsV+rjdbsViOXNJuDlJdvZpsrNPYbGcJifnPHZ7FllZR8jKOlLMq+lwcwvFZArPdwm75L62zWgMkBYeIYQQVUKCTC2m1xtxd4/C3T0K6FnoPjZbFhbLmdxwczHgON8/g1JWcnLiyMmJIz19R7Gvq9OZLgk5F2+7uYXkuwTj5haMXi9fQyGEEGUjv0HqOIPBHQ+Pxnh4NC5yH6XsWCzxWCyxTpecnLhLtsVhtV5AKUtuy0/xo7HyGI3+l4QbrVN03u1Lt+v1ntLiI4QQApAgI0pAp9NjNodjNodfdl+7PRuLJc4RbAqGn3Pk5JzPvU4AFFZrElZrEpmZh0pUj17vnhtugjAaA3FzC8RoDMh3u/Brvd5DApAQQtQyEmREhdLrzflOZxVPKRs5ORccw8vzAo7Fkj/sOG9XKhu7PatULT55dDpzvmBTePDRApEfRqO/07W0AgkhRPUkQUa4jE5nwGQKxmQKBlpddn+lFDZber6Ak4jVegGrNTH3dtHXSllRKhuL5SwWy9ky1GrEYCgYcPJfF/e4weAnfYGEEKISyP+sosbQ6XQYjd4Yjd54eDQq8fPyAlBJAk/eaS6rNdlxDTaUsmK1JmC1JpS5fr3eKzfc+GIw+GAw+GI0atcGg0++7RdvF76vl0xyKIQQuSTIiFovfwAqySmv/PJCkM2WXGjIyX99cR/na7s9AwC7PR2LJR2L5Uy535PB4F0gABUMPXkXr9z9tWu9Pu9+/m0yR5AQomaSICNEMfKHILO5fpmOYbfnXBJ2UrHZUrHZUnJvp2CzpV5yOyV3n4u3rdYUwAaAzZaGzZZWge/T7bJhpySB6NJrnc4kfYuEEJVKgowQlUyvd8vXF6jslFLY7VmXBJ2UYoJRSm5rkhZ67Pa82xevlbLkHjsnt7/RhYp4y/noMRg80es9L7n2KGRbcdfF7y8tSkLUXRJkhKghdDodBoMHBoMHJlNohRzTbrfkBpv0S8KOc+Bxvi4sFF0akLLzXsHxeE5OhZRcKJ3OeEmw8Sgi9LjnhqiL11pIyrtf3GP5r92lpUmIakKCjBB1mF5vQq834eYWUKHHtdutuYEnE7s9A5stI9915iX3i76+3L5gB0Apa25LVEqFvo/i6HTmYoJO/iBU3GOFhyS93px7cUenMzvd1+vN6HSGKnufQlR3EmSEEBVOrzei1/sD/pX2GkoplLKUMiRlYrdnFbi22Qrf7vx4JnnBSXv9bKzW7KILrFSGAoFHCzjO9wuGoYvbtW1FHSP/9qKPoW2XUCVcS4KMEKJG0ul0jl+uULEtSkWx23NKEITKEpLyPzcbuz3bMfmjdj8LUPkqsWG3p2O3p1fJ+y6ewSn0aD8TEzqdKbfFz+y4ffHafMl9U+7zC2679LkFt11uHzeZrqCWkyAjhBAlpNe75XYs9qnS19Van6z5Ak7+kKPdLmy7cxi6eLvg9qKP4bw9L1TZ81Vnyz0NmFGln0lp6HTGCghPbo7LxYB06W233JF6l7vtlnvswm7nHVNaukpKgowQQlRzWutTXojydnU52O3WQkOSUhbsdkvudXa+2xe3lX6fwrdpr1/wuXa7hbxpCvJoM3tbq0kLVknpCgSl4kNUSQNVSUJUwdtaGMx/DKPT6xmNQRiNrvluSpARQghRKtpyG0YMBi9Xl1IopWzY7TkVGJ6yc4+nXbRtBW8X91hePUXddj51CJDXB8ziio+w1Jo1+5D69e9xyWtLkBFCCFGr6HQGDAYD4O7qUkrsYvjKC2CF3S5riCppoCr+tbWWrZxLtmn39XqTyz47CTJCCCGEi9XE8FVdSFduIYQQQtRYEmSEEEIIUWNJkBFCCCFEjSVBRgghhBA1lgQZIYQQQtRYEmSEEEIIUWNJkBFCCCFEjSVBRgghhBA1lgQZIYQQQtRYEmSEEEIIUWNJkBFCCCFEjSVBRgghhBA1lgQZIYQQQtRYEmSEEEIIUWMZXV1AZVNKAZCSkuLiSoQQQghRUnm/t/N+jxel1geZ1NRUACIjI11ciRBCCCFKKzU1FT8/vyIf16nLRZ0azm63c+bMGXx8fNDpdI7tKSkpREZGcvLkSXx9fV1YYfUin0vh5HMpmnw2hZPPpWjy2RROPhdnSilSU1OpV68een3RPWFqfYuMXq+nQYMGRT7u6+srX5hCyOdSOPlciiafTeHkcymafDaFk8/louJaYvJIZ18hhBBC1FgSZIQQQghRY9XZIGM2m5kxYwZms9nVpVQr8rkUTj6XoslnUzj5XIomn03h5HMpm1rf2VcIIYQQtVedbZERQgghRM0nQUYIIYQQNZYEGSGEEELUWBJkhBBCCFFj1Zog88EHH9CwYUPc3d258sor+fvvv4vdf/HixbRs2RJ3d3fatWvH//73P6fHf/jhB/r3709QUBA6nY7t27dXYvWVqyI/m5ycHB5//HHatWuHl5cX9erVY9y4cZw5c6ay30aFq+jvzLPPPkvLli3x8vIiICCAfv36sXnz5sp8C5Wioj+X/O655x50Oh1vv/12BVddNSr6s5kwYQI6nc7pMnDgwMp8C5WiMr4z+/btY/jw4fj5+eHl5UXXrl05ceJEZb2FSlPRn82l35e8y2uvvVaZb6N6U7XAwoULlclkUnPnzlV79uxRd911l/L391dxcXGF7r9+/XplMBjUq6++qvbu3aueeuop5ebmpnbt2uXY54svvlDPPfec+uSTTxSgtm3bVkXvpmJV9GeTlJSk+vXrpxYtWqT279+vNm7cqLp166Y6d+5clW+r3CrjO/P111+r5cuXqyNHjqjdu3eryZMnK19fXxUfH19Vb6vcKuNzyfPDDz+oDh06qHr16qm33nqrkt9JxauMz2b8+PFq4MCB6uzZs45LYmJiVb2lClEZn8vhw4dVYGCgeuyxx9TWrVvV4cOH1U8//VTkMauryvhs8n9Xzp49q+bOnat0Op06cuRIVb2taqdWBJlu3bqpKVOmOO7bbDZVr149NXPmzEL3v/XWW9WQIUOctl155ZXq//7v/wrsGxMTU6ODTGV+Nnn+/vtvBajjx49XTNFVoCo+l+TkZAWoFStWVEzRVaCyPpdTp06p+vXrq927d6vo6OgaGWQq47MZP368uuGGGyql3qpSGZ/LqFGj1O233145BVehqvh/5oYbblDXXXddxRRcQ9X4U0sWi4UtW7bQr18/xza9Xk+/fv3YuHFjoc/ZuHGj0/4AAwYMKHL/mqqqPpvk5GR0Oh3+/v4VUndlq4rPxWKxMGfOHPz8/OjQoUPFFV+JKutzsdvt3HHHHTz22GO0adOmcoqvZJX5nVmzZg2hoaG0aNGCe++9l4SEhIp/A5WkMj4Xu93Or7/+SvPmzRkwYAChoaFceeWV/Pjjj5X2PipDVfw/ExcXx6+//srkyZMrrvAaqMYHmfPnz2Oz2QgLC3PaHhYWRmxsbKHPiY2NLdX+NVVVfDZZWVk8/vjjjBkzpsYsclaZn8vSpUvx9vbG3d2dt956i+XLlxMcHFyxb6CSVNbnMmvWLIxGI/fff3/FF11FKuuzGThwIF988QUrV65k1qxZrF27lkGDBmGz2Sr+TVSCyvhc4uPjSUtL45VXXmHgwIH88ccf3Hjjjdx0002sXbu2ct5IJaiK/38///xzfHx8uOmmmyqm6Bqq1q9+LSpPTk4Ot956K0opPvzwQ1eXUy306dOH7du3c/78eT755BNuvfVWNm/eTGhoqKtLc4ktW7bwzjvvsHXrVnQ6navLqXZGjx7tuN2uXTvat29PkyZNWLNmDX379nVhZa5jt9sBuOGGG3jooYcA6NixIxs2bOCjjz6id+/eriyvWpk7dy633XYb7u7uri7FpWp8i0xwcDAGg4G4uDin7XFxcYSHhxf6nPDw8FLtX1NV5meTF2KOHz/O8uXLa0xrDFTu5+Ll5UXTpk256qqr+OyzzzAajXz22WcV+wYqSWV8LuvWrSM+Pp6oqCiMRiNGo5Hjx4/zyCOP0LBhw0p5H5Whqv6fady4McHBwRw+fLj8RVeByvhcgoODMRqNtG7d2mmfVq1a1ahRS5X9nVm3bh0HDhzgzjvvrLiia6gaH2RMJhOdO3dm5cqVjm12u52VK1fSvXv3Qp/TvXt3p/0Bli9fXuT+NVVlfTZ5IebQoUOsWLGCoKCgynkDlaQqvzN2u53s7OzyF10FKuNzueOOO9i5cyfbt293XOrVq8djjz3GsmXLKu/NVLCq+s6cOnWKhIQEIiIiKqbwSlYZn4vJZKJr164cOHDAaZ+DBw8SHR1dwe+g8lT2d+azzz6jc+fONaYPXqVydW/jirBw4UJlNpvV/Pnz1d69e9Xdd9+t/P39VWxsrFJKqTvuuEP997//dey/fv16ZTQa1euvv6727dunZsyYUWCIW0JCgtq2bZv69ddfFaAWLlyotm3bps6ePVvl7688KvqzsVgsavjw4apBgwZq+/btTsMAs7OzXfIey6KiP5e0tDQ1ffp0tXHjRnXs2DH177//396dR0VV/n8Af18GGAaYEWXIGQwwAxFHVkVN2YpMSdEszaOdxLUUlVxwPSYcITF3xbI0D1biLpoLkUsOKZrxw+YIsSgokIrhLiiCDp/fHx7u13EEzUBEP69z5hzu8zz3mc/z3DvMh7tw/4+GDx9OUqmUsrKyGmWMT6MhPksPa6p3LdX33JSVlVFkZCQdO3aMzp49SwcOHCAfHx9ycXGhO3fuNMoYn0ZD7DNJSUlkZmZGq1evptOnT1N8fDxJJBI6fPjwMx/ff9FQn6cbN26QpaUlrVq16pmO53n1QiQyRETx8fHk6OhI5ubm1LlzZ/r999/FusDAQAoLCzNov2XLFmrbti2Zm5uTRqOhvXv3GtQnJCQQAKNXVFTUMxhN/arPuam5Hf1Rr0OHDj2jEdWP+pyXiooK6t+/P9nb25O5uTmp1Wrq27cv/fHHH89qOPWmvj9LD2uqiQxR/c7N7du36Z133iE7OzsyMzMjJycnGj16tPgl15Q0xD6zdu1acnZ2JgsLC/L09KSdO3c29DAaREPMzbfffksymYyuX7/e0OE3CQIRUeMcC2KMMcYY+2+a/DUyjDHGGHt5cSLDGGOMsSaLExnGGGOMNVmcyDDGGGOsyeJEhjHGGGNNFicyjDHGGGuyOJFhjDHGWJPFiQxjjDHGmixOZBhjj5Wbm4uuXbvCwsICXl5ejR1Oo8jLy4NKpUJZWVm99BcUFISJEyfWS18vssuXL+OVV17BuXPnGjsU9pziRIa9cIYNGwZBEMSXra0tevXqhZMnTzZ2aE8kOjpajN3U1BRKpRIBAQFYtmxZoz2AMioqClZWVsjLyzN6qF2NmnkfM2aMUd24ceMgCAKGDRvWwJE2nJkzZ2LChAmQy+WNHcpLRalUYujQoYiKimrsUNhzihMZ9kLq1asXSkpKUFJSgoMHD8LU1BR9+vSpc527d+8+o+geT6PRoKSkBMXFxTh06BAGDhyIuLg4dOvWrd6OCDyJqqoqAEBBQQH8/Pzg5ORU59POHRwcsGnTJlRUVIhld+7cwYYNG+Do6Njg8TaU4uJi7Nmz57lKxIgI9+7da+wwnonhw4cjMTERV69ebexQ2HOIExn2QpJKpVCpVFCpVPDy8sKMGTPw999/49KlSwCAwsJCCIKAzZs3IzAwEBYWFkhMTER1dTXmzp2LV199FVKpFF5eXkhJSRH7HTBgAMaPHy8uT5w4EYIgIDc3F8D9L34rKyscOHAAwP3TBxEREZg2bRpatGgBlUqF6Ojox8ZvamoKlUoFe3t7uLu7Y8KECUhNTUVWVha+/PJLAMDKlSvRoUMHcZ2dO3dCEAR88803Ytnbb7+N2bNnA7ifjPTr1w8tW7aEtbU1fH19xThrtG7dGjExMRg6dCgUCgU++eQTCIKAjIwMzJ07F4Ig1Bm/j48PHBwckJSUJJYlJSXB0dER3t7eBm1TUlLg5+cHGxsb2Nraok+fPigoKBDrq6qqMH78eKjValhYWMDJyQlxcXEA7n+JR0dHw9HREVKpFPb29oiIiBDXraysRGRkJFq1agUrKyt06dIFWq1WrC8qKkJoaCiaN28OKysraDQaJCcn1zquLVu2wNPTE61atXriPlJTU9G5c2dIpVKo1WrMmDGjzsTjxx9/RKdOnSCXy6FSqTBkyBCUlpaK9VqtFoIg4Oeff0bHjh0hlUpx5MgRo35q9u0tW7bA398fMpkMvr6+OHXqFNLT09GpUydYW1sjJCRE/DwAQHp6Onr06AGlUolmzZohMDAQJ06cEOsfN+dff/01XFxcYGFhgZYtW2LAgAFiXXV1NeLi4vDaa69BJpPB09MT27ZtE+uvXbuGjz76CHZ2dpDJZHBxcUFCQoJYr9FoYG9vjx07dtQ6f+wl1qiPrGSsAYSFhVG/fv3E5bKyMvr000/J2dmZ9Ho9Ef3vKd6tW7em7du305kzZ+jChQu0ZMkSUigUtHHjRsrNzaVp06aRmZkZnTp1ioiIVqxYQRqNRuzby8uLlEolrVq1ioiIjhw5QmZmZnTr1i0iuv90W4VCQdHR0XTq1Cn6/vvvSRAE2rdvX63xR0VFkaen5yPr+vXrR25ubkREdPLkSRIEgUpLS4mIaOLEiaRUKmnQoEFERFRVVUWWlpa0f/9+IiLS6XT0zTffUGZmJp06dYpmz55NFhYWVFRUJPbv5ORECoWCFi1aRPn5+ZSfn08lJSWk0WhoypQpVFJSQmVlZXXO+5IlSyg4OFgsDw4OpqVLl1K/fv0MnvS7bds22r59O50+fZr+/PNPCg0NJXd3d3EbLVy4kBwcHOi3336jwsJCOnz4MG3YsIGIiLZu3UoKhYKSk5OpqKiIjh8/TqtXrxb7HjVqFHXr1o1+++03ys/Pp4ULF5JUKhW3Y+/evalHjx508uRJKigooN27d1Nqamqt26Rv3740ZswYg7K6+jh37hxZWlpSeHg45eTk0I4dO0ipVFJUVJS4fmBgIH322Wfi8tq1ayk5OZkKCgro2LFj9MYbb1BISIhYf+jQIQJAHh4etG/fPsrPz6crV64YxVqzb7dr145SUlIoOzubunbtSh07dqSgoCA6cuQInThxgpydnQ3GdPDgQfrxxx8pJyeHsrOzaeTIkdSyZUu6efPmY+c8PT2dJBIJbdiwgQoLC+nEiRO0fPlyse/Y2FgxnoKCAkpISCCpVEparZaIiMaNG0deXl6Unp5OZ8+epf3799OuXbsMxjVo0CCjJ0UzRkTEiQx74YSFhZFEIiErKyuysrIiAKRWqykjI0NsU/PLftmyZQbr2tvb0xdffGFQ5uvrS+Hh4URkmDxcvXqVzM3NKSYmRkweYmNjqVu3buK6gYGB5OfnZ9Tf9OnTa42/rkRm+vTpJJPJiIiourqabG1taevWrUR0P6mKi4sjlUpFRMZJ1aNoNBqKj48Xl52cnOi9994zaufp6WnwJfwoNYlMaWkpSaVSKiwspMLCQrKwsKBLly4ZJTIPu3TpEgGgzMxMIiKaMGECvfXWW1RdXW3UdvHixdS2bVuqqqoyqisqKiKJRELnz583KA8ODqaZM2cSEZG7uztFR0fXOZ4HeXp60ty5cw3K6upj1qxZ5OrqahD7V199RdbW1mKi9nAi87D09HQCICaONYnMzp0764y1Zt/+7rvvxLKNGzcSADp48KBYFhcXR66urrX2o9frSS6X0+7du4mo7jnfvn07KRQKMel50J07d8jS0pKOHj1qUD5y5EgaPHgwERGFhobS8OHD6xzXpEmTKCgoqM427OXEp5bYC+nNN9+ETqeDTqfDH3/8gZ49eyIkJARFRUUG7Tp16iT+fPPmTVy4cAHdu3c3aNO9e3fk5OQAADp06IAWLVogNTUVhw8fhre3N/r06YPU1FQA908nBAUFGazv4eFhsKxWqw1OGfwbRARBEAAAgiAgICAAWq0W169fR3Z2NsLDw1FZWYnc3FykpqbC19cXlpaWAIDy8nJERkbCzc0NNjY2sLa2Rk5ODoqLi2udk6dhZ2eH3r17Y926dUhISEDv3r2hVCqN2p0+fRqDBw9GmzZtoFAo0Lp1awAQ4xk2bBh0Oh1cXV0RERGBffv2iesOHDgQFRUVaNOmDUaPHo0dO3aIp20yMzOh1+vRtm1bWFtbi6/U1FTx1FVERARiY2PRvXt3REVFPfZC8IqKClhYWBiU1dVHTk4O3njjDXFbAff3o/Ly8lrvvsnIyEBoaCgcHR0hl8sRGBhoMB81nnT7PLjftWzZEgDg7u5uUPbgfvjPP/9g9OjRcHFxQbNmzaBQKFBeXi6+f11z3qNHDzg5OaFNmzb4+OOPkZiYiNu3bwMA8vPzcfv2bfTo0cNge/zwww/i9hg7diw2bdoELy8vTJs2DUePHjUaj0wmE/tk7EGcyLAXkpWVFZydneHs7AxfX1989913uHXrFtasWWPU7t94MHmoSVo8PDxQWVmJrKwsHD16VPwCqmFmZmbUR3V19VONKycnB6+99pq4HBQUBK1WKyZVCoXCIL4HY4mMjMSOHTswb948HD58GDqdDu7u7uIFvTX+7Zw8yogRI7Bu3Tp8//33GDFixCPbhIaG4urVq1izZg2OHz+O48ePA/jfBcY+Pj44e/YsYmJiUFFRgQ8//FC87sLBwQF5eXn4+uuvIZPJEB4ejoCAANy9exfl5eWQSCTIyMgQk1mdToecnBwsX74cADBq1CicOXMGH3/8MTIzM9GpUyfEx8fXOh6lUolr164ZlP3bPupy69Yt9OzZEwqFAomJiUhPTxevB3na7fPgfleTUD1c9uB+GBYWBp1Oh+XLl+Po0aPQ6XSwtbUV37+uOZfL5Thx4gQ2btwItVqNOXPmwNPTE9evX0d5eTkAYO/evQbbIzs7W7xOpuaPjEmTJuHChQsIDg5GZGSkwXiuXr0KOzu7Jxo7e7lwIsNeCoIgwMTExOBumocpFArY29sjLS3NoDwtLQ3t27cXlwMDA6HVaqHVahEUFAQTExMEBARg4cKFqKysNDqiU19yc3ORkpKCDz74wCCW7OxsbN26VTwSFBQUhAMHDiAtLc3g6FBaWhqGDRuG/v37w93dHSqVCoWFhQ0Sa69evVBVVYW7d++iZ8+eRvVXrlxBXl4eZs+ejeDgYLi5uRklCsD9bTJo0CCsWbMGmzdvxvbt28U7V2QyGUJDQ7FixQpotVocO3YMmZmZ8Pb2hl6vR2lpqZjM1rxUKpXYt4ODA8aMGYOkpCRMmTLFKMl9kLe3N7Kzs43Ka+vDzc0Nx44dAxGJbdPS0iCXy/Hqq68a9ZObm4srV65g/vz58Pf3R7t27Z76qN3TSktLQ0REBN59911oNBpIpVJcvnzZoE1tcw7cv0D97bffxoIFC3Dy5EkUFhbi119/Rfv27SGVSlFcXGy0PRwcHMS+7ezsEBYWhvXr12PZsmVYvXq1wXtnZWUZXTDOGACYNnYAjDWEyspKXLx4EcD9OyJWrlyJ8vJyhIaG1rne1KlTERUVhddffx1eXl5ISEiATqdDYmKi2CYoKAiTJk2Cubk5/Pz8xLLIyEj4+vrWyxGNe/fu4eLFi6iursaVK1eg1WoRGxsLLy8vTJ06VWzn4eGB5s2bY8OGDdizZ49BLIIgGCRVLi4uSEpKQmhoKARBwOeff/7UR4YeRyKRiKfjJBKJUX3z5s1ha2uL1atXQ61Wo7i4GDNmzDBos2TJEqjVanh7e8PExARbt26FSqWCjY0N1q1bB71ejy5dusDS0hLr16+HTCYTbw//6KOPMHToUCxevBje3t64dOkSDh48CA8PD/Tu3RsTJ05ESEgI2rZti2vXruHQoUNwc3OrdTw9e/bEqFGjoNfrxfHU1Ud4eDiWLVuGCRMmYPz48cjLy0NUVBQmT54MExPjvx8dHR1hbm6O+Ph4jBkzBllZWYiJiXnq+X8aLi4u4p1TN2/exNSpUyGTycT6uuZ8z549OHPmDAICAtC8eXMkJyejuroarq6ukMvliIyMxKRJk1BdXQ0/Pz/cuHEDaWlpUCgUCAsLw5w5c9CxY0doNBpUVlZiz549Btvj9u3byMjIwLx5857pnLCmgRMZ9kJKSUmBWq0GAMjlcrRr187gqEVtIiIicOPGDUyZMgWlpaVo3749du3aBRcXF7GNu7s7bGxsxGswgPvJg16vf2z/T+qvv/6CWq2GRCJBs2bN0L59e8ycORNjx46FVCoV2wmCAH9/f+zdu1dMqjw8PKBQKODq6mqQVC1ZsgQjRoxAt27doFQqMX36dNy8ebNe4n0UhUJRa52JiQk2bdqEiIgIdOjQAa6urlixYoXB/MnlcixYsACnT5+GRCKBr68vkpOTYWJiAhsbG8yfPx+TJ0+GXq+Hu7s7du/eLf6Pm4SEBMTGxmLKlCk4f/48lEolunbtKv4vIb1ej3HjxuHcuXNQKBTo1asXli5dWmu8ISEhMDU1xYEDB8QjTHX10apVKyQnJ2Pq1Knw9PREixYtMHLkSPFW+IfZ2dlh3bp1mDVrFlasWAEfHx8sWrQIffv2/Vdz/l+sXbsWn3zyiXgL/bx58wxO79Q15zY2NkhKSkJ0dDTu3LkDFxcXbNy4ERqNBgAQExMDOzs7xMXF4cyZM7CxsYGPjw9mzZoFADA3N8fMmTNRWFgImUwGf39/bNq0SXzvn376CY6OjvD3939m88GaDoEePPbJGGPskb766ivs2rULv/zyS2OH8tLp2rUrIiIiMGTIkMYOhT2H+IgMY4w9gU8//RTXr19HWVkZP6bgGbp8+TLef/99DB48uLFDYc8pPiLDGGOMsSaL71pijDHGWJPFiQxjjDHGmixOZBhjjDHWZHEiwxhjjLEmixMZxhhjjDVZnMgwxhhjrMniRIYxxhhjTRYnMowxxhhrsjiRYYwxxliT9f8tsCuMO8o3ogAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting different IMFs based on literature\n", + "plt.plot(BD_masses, power_law(BD_masses, M1_a), color = 'y', label = \"Mužić 2017\")\n", + "plt.plot(BD_masses, power_law(BD_masses, K_a), color = 'c', label = \"Kirkpatrick 2021\")\n", + "plt.plot(BD_masses, power_law(BD_masses, R_a), color = 'b', label = \"Ryan 2022\")\n", + "plt.plot(BD_masses, power_law(BD_masses, B_a), color = 'm', label = \"Best 2024\")\n", + "\n", + "#organizing the graph\n", + "plt.title(\"Brown Dwarf IMFs Based on Literature\")\n", + "plt.xlabel(\"Brown Dwarf Masses (solar masses)\")\n", + "plt.ylabel(\"M^-a\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "20d30430-4eb1-4c19-9179-3138a2814c6e", + "metadata": {}, + "source": [ + "#### Accounting for error in $\\alpha$" + ] + }, + { + "cell_type": "markdown", + "id": "64da7c68-85ef-40f6-a538-331d7f799572", + "metadata": {}, + "source": [ + "At this point we wanted to explore the individual functions and their errors to make sure that they seemed reasonable. Thus, we used the specified errors to find upper and lower bounds for each function and plotted the information." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "9d984c93-387a-4586-ba16-ac579562ca24", + "metadata": {}, + "outputs": [], + "source": [ + "#Mužić 2017 (RCW 38)\n", + "M1_aup = M1_a + M1_aerr\n", + "M1_alow = M1_a - M1_aerr\n", + "M1_outup = power_law(BD_masses, M1_aup)\n", + "M1_outlow = power_law(BD_masses, M1_alow)\n", + "\n", + "#Kirkpatrick 2021\n", + "K_aup = K_a + K_aerr\n", + "K_alow = K_a - K_aerr\n", + "K_outup = power_law(BD_masses, K_aup)\n", + "K_outlow = power_law(BD_masses, K_alow)\n", + "\n", + "#Ryan 2022\n", + "R_aup = R_a + R_aerr\n", + "R_alow = R_a - R_aerr\n", + "R_outup = power_law(BD_masses, R_aup)\n", + "R_outlow = power_law(BD_masses, R_alow)\n", + "\n", + "#Best 2024\n", + "B_aup = B_a + B_paerr\n", + "B_alow = B_a - B_naerr\n", + "B_outup = power_law(BD_masses, B_aup)\n", + "B_outlow = power_law(BD_masses, B_alow)" + ] + }, + { + "cell_type": "markdown", + "id": "7ac4acb4-f29b-4735-b58d-be7cd78d9f76", + "metadata": {}, + "source": [ + "#### Graphing Individual Power-Law Functions (Accounting for Error in $\\alpha$)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "ac20c536-15c8-4fc5-885e-190d3fd888d1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Setting up subplots\n", + "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8,8))\n", + "plt.subplots_adjust(hspace=0.5, wspace=0.75)\n", + "\n", + "#Mužić 2017\n", + "ax[0,0].plot(BD_masses, power_law(BD_masses, M1_a), color='y')\n", + "ax[0,0].fill_between(BD_masses, M1_outup, M1_outlow, color='y', alpha=0.2, label=\"error in $\\\\alpha$\")\n", + "ax[0,0].set_title(\"Mužić Mass Function for BDs in RCW 38\")\n", + "ax[0,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[0,0].set_ylabel(\"$M^{-0.29 ± 0.11}$\")\n", + "ax[0,0].legend()\n", + "\n", + "#Kirkpatrick 2021\n", + "ax[0,1].plot(BD_masses, power_law(BD_masses, K_a), color='c')\n", + "ax[0,1].fill_between(BD_masses, K_outup, K_outlow, color='c', alpha=0.2, label=\"error in $\\\\alpha$\")\n", + "ax[0,1].set_title(\"Kirkpatrick Mass Function for BDs within 20 pc\")\n", + "ax[0,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[0,1].set_ylabel(\"$M^{-0.6 ± 0.1}$\")\n", + "ax[0,1].legend()\n", + "\n", + "#Ryan 2022\n", + "ax[1,0].plot(BD_masses, power_law(BD_masses, R_a), color='b')\n", + "ax[1,0].fill_between(BD_masses, R_outup, R_outlow, color='b', alpha=0.2, label=\"error in $\\\\alpha$\")\n", + "ax[1,0].set_title(\"Ryan Mass Function for BDs in Milky Way Disk\")\n", + "ax[1,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[1,0].set_ylabel(\"$M^{-0.71 ± 0.11}$\")\n", + "ax[1,0].legend()\n", + "\n", + "#Best 2024\n", + "ax[1,1].plot(BD_masses, power_law(BD_masses, B_a), color='m')\n", + "ax[1,1].fill_between(BD_masses, B_outup, B_outlow, color='m', alpha=0.2, label=\"error in $\\\\alpha$\")\n", + "ax[1,1].set_title(\"Best Mass Function for BDs within 25 pc\")\n", + "ax[1,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[1,1].set_ylabel(\"$M^{-0.58^{+ 0.16}_{-0.20~}}$\")\n", + "ax[1,1].legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cbc89037-9574-487a-9f75-e17fee2667db", + "metadata": {}, + "source": [ + "### Creating a General Power-Law Curve Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "c594d201-d695-414d-9d23-05d6e1f2171b", + "metadata": {}, + "outputs": [], + "source": [ + "#Combine all of the power_law output data for all 5 papers\n", + "all_power = np.concatenate([power_law(BD_masses, M1_a), power_law(BD_masses, K_a), \n", + " power_law(BD_masses, R_a), power_law(BD_masses, B_a)])\n", + "all_err = np.concatenate([np.tile(M1_aerr, 1000), np.tile(K_aerr, 1000), \n", + " np.tile(R_aerr, 1000), np.tile(B_avgerr, 1000)])" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "5b054e0c-c0b6-4556-b649-a959fba74da0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4000 4000\n" + ] + } + ], + "source": [ + "#make sure all entries are appended\n", + "print(len(all_power), len(all_err))" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "a1aaa018-16da-44e0-9473-2611ec50ab2c", + "metadata": {}, + "outputs": [], + "source": [ + "#create a mass array of the same size\n", + "all_masses = np.tile(BD_masses, 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "baafa6a8-0c11-4380-899a-a61834474854", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4000" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#make sure it is the right size\n", + "len(all_masses)" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "id": "56aa35b6-aa24-4695-9094-fe5857846bd5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#use scipy curve fit to create a general power law\n", + "popt, pcov = curve_fit(power_law, sorted_masses, sorted_powers, sigma = sorted_errors)#, absolute_sigma = True) #popt gives fit value for a\n", + "avg_a = popt\n", + "avg_aerr = np.sqrt(np.diag(pcov)) #gives the error in a\n", + "\n", + "#create an array with mass values using fitted a\n", + "avg_power = power_law(sorted_masses, avg_a)\n", + "\n", + "#plot combined mass and power law datasets\n", + "plt.figure()\n", + "plt.scatter(sorted_masses, sorted_powers, label = 'data')\n", + "plt.plot(sorted_masses, avg_power, color = 'r', label = 'curve fit')\n", + "\n", + "#label graph\n", + "plt.title(\"Power Law Curve Fit for BD Candidate Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"$M^{-0.584 ± 0.002}$\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "id": "011eac9f-7e9d-4e5d-8bfc-f0380920f575", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.5836496] [0.00202593]\n" + ] + } + ], + "source": [ + "#determine the index of the power law based on the curve fit\n", + "print(avg_a, avg_aerr)" + ] + }, + { + "cell_type": "markdown", + "id": "0c050ca1-499e-426a-b4ef-0e4e64fc80a2", + "metadata": {}, + "source": [ + "Based on the curve fit, our a value for BDs is 0.5836496 ± 0.00202593. The error is smaller than expected, which may take more looking into. " + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "id": "de5f6d54-b675-447c-bef7-a979715eb5f0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#account for error in plot\n", + "avg_aup = avg_a + avg_aerr\n", + "avg_alow = avg_a - avg_aerr\n", + "power_outup = power_law(sorted_masses, avg_aup)\n", + "power_outlow = power_law(sorted_masses, avg_alow)\n", + "\n", + "plt.scatter(sorted_masses, sorted_powers, label = 'data')\n", + "plt.plot(sorted_masses, power_law(sorted_masses, avg_a), color= 'r', label = \"curve fit for power law\")\n", + "plt.fill_between(sorted_masses, power_outup, power_outlow, color = 'r', label = \"error in a\")\n", + "plt.title(\"Power Law Curve Fit for BD Candidate Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"$M^{0.584 ± 0.002}$\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "id": "ecbe93a5-32d7-4648-bc99-c20b13db9ca2", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "only integer scalar arrays can be converted to a scalar index", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[154], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mall_power\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index" + ] + } + ], + "source": [ + "range(all_power)" + ] + }, + { + "cell_type": "markdown", + "id": "83430453-9da9-49e6-a1b1-4f12db8b63a7", + "metadata": {}, + "source": [ + "#### Cleaning Up the Curve Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "id": "d1ef4b5a-06b7-4295-9c5a-7e9d231020ec", + "metadata": {}, + "outputs": [], + "source": [ + "#combining all mass and output data sets to fit\n", + "all_masses = np.array(all_masses)\n", + "all_power = np.array(all_power)\n", + "\n", + "#put combined mass data set into increasing order and reorder output array to match new indices \n", + "sorted_indices = np.argsort(all_masses)\n", + "sorted_masses = all_masses[sorted_indices]\n", + "sorted_powers = all_power[sorted_indices]\n", + "sorted_errors = all_err[sorted_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "id": "d3af8ed8-a459-486c-9f44-c6a9f1008d59", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#use scipy curve fit to create a general power law\n", + "popt, pcov = curve_fit(power_law, sorted_masses, sorted_powers,sigma=sorted_errors, absolute_sigma=True) #popt gives fit value for a\n", + "avg_a = popt\n", + "avg_aerr = np.sqrt(np.diag(pcov)) #gives the error in a\n", + "\n", + "#create an array with mass values using fitted a\n", + "avg_power = power_law(sorted_masses, avg_a)\n", + "\n", + "#plot combined mass and power law datasets\n", + "plt.figure()\n", + "plt.scatter(sorted_masses, sorted_powers, label='raw data')\n", + "plt.plot(sorted_masses, avg_power, color='r', label='curve fit')\n", + "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-a)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "id": "400886ad-ad9a-47a9-8fa2-e513ef125d45", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[7.20652905e-05]\n" + ] + } + ], + "source": [ + "print(avg_aerr)" + ] + }, + { + "cell_type": "markdown", + "id": "9bc14680-1eb4-45ec-abed-4b399d7693a4", + "metadata": {}, + "source": [ + "#### Converting into a log function to match the curve fit" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "id": "76bfab00-53a2-4a18-9cee-b4f11cfdde98", + "metadata": {}, + "outputs": [], + "source": [ + "#take the log of each of the values in sorted_powers\n", + "log_power = []\n", + "for n in sorted_powers:\n", + " log_power.append(math.log(n))\n", + "\n", + "#take the log of each of the values in sorted_masses\n", + "log_masses = []\n", + "for a in sorted_masses:\n", + " log_masses.append(math.log(a))\n", + "\n", + "#make sure both are arrays\n", + "log_power = np.array(log_power)\n", + "log_masses = np.array(log_masses)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "id": "527b824d-29a7-4715-810b-01b626eae7fa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitted parameters: a = -0.5450, b = -4.1664664035223473e-10\n", + "RMSE: 0.5058\n", + "MAE: 0.4105\n", + "R-squared: 0.2151\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#define a linear model\n", + "def linear_model(x, a, b):\n", + " return a * x + b\n", + "\n", + "#curve fit\n", + "params, covariance = curve_fit(linear_model, log_masses, log_power)\n", + "\n", + "#get the fitted values\n", + "power_fitted = linear_model(log_masses, *params)\n", + "\n", + "#calculate residuals\n", + "residuals = log_power - power_fitted\n", + "\n", + "#calculate error metrics\n", + "rmse = np.sqrt(mean_squared_error(log_power, power_fitted))\n", + "mae = mean_absolute_error(log_power, power_fitted)\n", + "r2 = r2_score(log_power, power_fitted)\n", + "\n", + "#output the results\n", + "print(f\"Fitted parameters: a = {params[0]:.4f}, b = {params[1]}\")\n", + "print(f\"RMSE: {rmse:.4f}\")\n", + "print(f\"MAE: {mae:.4f}\")\n", + "print(f\"R-squared: {r2:.4f}\")\n", + "\n", + "#plot the results\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(log_masses, log_power, label='Data', color='blue', s=10)\n", + "plt.plot(log_masses, linear_model(log_masses, *params), label='Fitted line', color='red')\n", + "plt.legend()\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Linear Fit of Multiple Linear Functions')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "23e78450-e261-4fe7-b307-41204c800ed3", + "metadata": {}, + "outputs": [], + "source": [ + "linpower_fit = []\n", + "for l in log_masses:\n", + " linpower_fit.append(m*l + b)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "2d6df51d-54d3-48d2-bfcd-49f0dceed8bc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(log_masses, log_power, '.')\n", + "plt.plot(log_masses, linpower_fit)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4ddeb15-31d9-48ba-881d-992def181f89", + "metadata": {}, + "outputs": [], + "source": [ + "#find general error by calculating average range\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/changes/Exoplanet_IMF.ipynb b/changes/Exoplanet_IMF.ipynb new file mode 100644 index 00000000..0bd50ba4 --- /dev/null +++ b/changes/Exoplanet_IMF.ipynb @@ -0,0 +1,608 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "23642516", + "metadata": {}, + "source": [ + "## Plotting Studied Power-Law Functions For Exoplanets" + ] + }, + { + "cell_type": "markdown", + "id": "ad57fd40", + "metadata": {}, + "source": [ + "#### Importing Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "d6503a21", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy.optimize import curve_fit" + ] + }, + { + "cell_type": "markdown", + "id": "4f72f6f7", + "metadata": {}, + "source": [ + "#### Creating the general power-law function for small masses" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d51b38dc", + "metadata": {}, + "outputs": [], + "source": [ + "#define function\n", + "def power_law(M, a):\n", + " return M**(-1*a)\n", + "\n", + "# M is a defined mass range according to a specified paper, and a is the associated alpha value" + ] + }, + { + "cell_type": "markdown", + "id": "3fe49d7b", + "metadata": {}, + "source": [ + "#### Defining different mass/alpha values based on published papers" + ] + }, + { + "cell_type": "markdown", + "id": "b7736fad", + "metadata": {}, + "source": [ + "***Masses are in solar masses!***" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "4145bc16", + "metadata": {}, + "outputs": [], + "source": [ + "#Moraux 2007 (0.03 - 0.6) a = 0.69 ± 0.15\n", + "M_mass = np.linspace(0.03, 0.6, 1000)\n", + "M_a = 0.69\n", + "M_aerr = 0.15\n", + "\n", + "#Suárez 2009 (0.02 - 0.50) a = 0.60 ± 0.13\n", + "S_mass = np.linspace(0.02, 0.5, 1000)\n", + "S_a = 0.60\n", + "S_aerr = 0.13\n", + "\n", + "#Lodieu 2009 (0.01 - 0.5) a = 0.5 ± 0.2\n", + "L_mass = np.linspace(0.01, 0.5, 1000)\n", + "L_a = 0.5\n", + "L_aerr = 0.2\n", + "\n", + "#Béjar 2011 (0.013 - 0.1) a = 0.7 ± 0.3\n", + "B_mass = np.linspace(0.013, 0.1, 1000)\n", + "B_a = 0.7\n", + "B_aerr = 0.3\n", + "\n", + "#Peña Ramírez 2012 (0.006 - 0.25) a = 0.6 ± 0.2\n", + "P_mass = np.linspace(0.006, 0.25, 1000)\n", + "P_a = 0.6\n", + "P_aerr = 0.2" + ] + }, + { + "cell_type": "markdown", + "id": "f2a24e63", + "metadata": {}, + "source": [ + "#### Plotting all of the curves together (w/o accounting for error)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "60cab6f2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plot all of the power law functions based on mass ranges\n", + "plt.plot(M_mass, power_law(M_mass, M_a), color='r', label='Moraux (a=0.69)')\n", + "plt.plot(S_mass, power_law(S_mass, S_a), color='b', label='Suárez (a=0.60)')\n", + "plt.plot(L_mass, power_law(L_mass, L_a), color='m', label='Lodieu (a=0.50)')\n", + "plt.plot(B_mass, power_law(B_mass, B_a), color='c', label='Béjar (a=0.70)')\n", + "plt.plot(P_mass, power_law(P_mass, P_a), color='y', label='Peña Ramírez (a=0.60)')\n", + "plt.title(\"Power Law Mass Functions for Planetary Masses According to Published Papers\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-a)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f5494d3d", + "metadata": {}, + "source": [ + "#### Accounting for error in a" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "1e6c80f4", + "metadata": {}, + "outputs": [], + "source": [ + "#Moraux\n", + "M_aup = M_a + M_aerr\n", + "M_alow = M_a - M_aerr\n", + "M_outup = power_law(M_mass, M_aup)\n", + "M_outlow = power_law(M_mass, M_alow)\n", + "\n", + "#Suárez\n", + "S_aup = S_a + S_aerr\n", + "S_alow = S_a - S_aerr\n", + "S_outup = power_law(S_mass, S_aup)\n", + "S_outlow = power_law(S_mass, S_alow)\n", + "\n", + "#Lodieu\n", + "L_aup = L_a + L_aerr\n", + "L_alow = L_a - L_aerr\n", + "L_outup = power_law(L_mass, L_aup)\n", + "L_outlow = power_law(L_mass, L_alow)\n", + "\n", + "#Béjar\n", + "B_aup = B_a + B_aerr\n", + "B_alow = B_a - B_aerr\n", + "B_outup = power_law(B_mass, B_aup)\n", + "B_outlow = power_law(B_mass, B_alow)\n", + "\n", + "#Peña Ramírez\n", + "P_aup = P_a + P_aerr\n", + "P_alow = P_a - P_aerr\n", + "P_outup = power_law(P_mass, P_aup)\n", + "P_outlow = power_law(P_mass, P_alow)" + ] + }, + { + "cell_type": "markdown", + "id": "041954f2", + "metadata": {}, + "source": [ + "#### Graphing Individual Power-Law Functions (Accounting for Error in a)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "dc7a858e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Setting up subplots\n", + "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(10,10))\n", + "plt.subplots_adjust(hspace=0.75, wspace=1.25)\n", + "fig.delaxes(ax[2, 1])\n", + "\n", + "#Moraux\n", + "ax[0,0].plot(M_mass, power_law(M_mass, M_a), color='r')\n", + "ax[0,0].fill_between(M_mass, M_outup, M_outlow, color='r', alpha=0.2, label=\"error in a\")\n", + "ax[0,0].set_title(\"Moraux Mass Function for Planetary Masses in Blanco 1\")\n", + "ax[0,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[0,0].set_ylabel(\"M^(-0.69 ± 0.15)\")\n", + "ax[0,0].legend()\n", + "\n", + "#Suárez\n", + "ax[0,1].plot(S_mass, power_law(S_mass, S_a), color='b')\n", + "ax[0,1].fill_between(S_mass, S_outup, S_outlow, color='b', alpha=0.2, label=\"error in a\")\n", + "ax[0,1].set_title(\"Suárez Mass Function for Planetary Masses in 25 Ori Group\")\n", + "ax[0,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[0,1].set_ylabel(\"M^(-0.60 ± 0.13)\")\n", + "ax[0,1].legend()\n", + "\n", + "#Lodieu\n", + "ax[1,0].plot(L_mass, power_law(L_mass, L_a), color='m')\n", + "ax[1,0].fill_between(L_mass, L_outup, L_outlow, color='b', alpha=0.2, label=\"error in a\")\n", + "ax[1,0].set_title(\"Lodieu Mass Function for Planetary Masses in UKIDSS Galactic Clusters\")\n", + "ax[1,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[1,0].set_ylabel(\"M^(-0.5 ± 0.2)\")\n", + "ax[1,0].legend()\n", + "\n", + "#Béjar\n", + "ax[1,1].plot(B_mass, power_law(B_mass, B_a), color='c')\n", + "ax[1,1].fill_between(B_mass, B_outup, B_outlow, color='c', alpha=0.2, label=\"error in a\")\n", + "ax[1,1].set_title(\"Béjar Mass Function for Planetary Masses in σ Orionis\")\n", + "ax[1,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[1,1].set_ylabel(\"M^(-0.7 ± 0.3)\")\n", + "ax[1,1].legend()\n", + "\n", + "#Peña Ramírez\n", + "ax[2,0].plot(P_mass, power_law(P_mass, P_a), color='y')\n", + "ax[2,0].fill_between(P_mass, P_outup, P_outlow, color='y', alpha=0.2, label=\"error in a\")\n", + "ax[2,0].set_title(\"Peña Ramírez Mass Function for Planetary Masses in σ Orionis\")\n", + "ax[2,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[2,0].set_ylabel(\"M^(-0.6 ± 0.2)\")\n", + "ax[2,0].legend()\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f63bb9fc", + "metadata": {}, + "source": [ + "### Creating a General Power-Law Curve Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "ed848735", + "metadata": {}, + "outputs": [], + "source": [ + "#combine all mass data\n", + "all_masses = []\n", + "for a in M_mass:\n", + " all_masses.append(a)\n", + "for b in S_mass:\n", + " all_masses.append(b)\n", + "for c in L_mass:\n", + " all_masses.append(c)\n", + "for d in B_mass:\n", + " all_masses.append(d)\n", + "for e in P_mass:\n", + " all_masses.append(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "df52ba0f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5000\n" + ] + } + ], + "source": [ + "print(len(all_masses)) #to make sure the correct amount of entries were appended" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "dfc306a1", + "metadata": {}, + "outputs": [], + "source": [ + "#combine all power_law output data\n", + "all_power = []\n", + "for f in M_mass:\n", + " all_power.append(power_law(f, M_a))\n", + "for g in S_mass:\n", + " all_power.append(power_law(g, S_a))\n", + "for h in L_mass:\n", + " all_power.append(power_law(h, L_a))\n", + "for k in B_mass:\n", + " all_power.append(power_law(k, B_a))\n", + "for l in P_mass:\n", + " all_power.append(power_law(l, P_a))" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "15d0484d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5000\n" + ] + } + ], + "source": [ + "print(len(all_power))" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "6a6629e9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#use scipy curve fit to create a general power law\n", + "popt, pcov = curve_fit(power_law, all_masses, all_power) #popt gives fit value for a\n", + "avg_a = popt\n", + "\n", + "#create an array with mass values using fitted a\n", + "avg_power = power_law(all_masses, avg_a)\n", + "\n", + "#plot combined mass and power law datasets\n", + "plt.figure()\n", + "plt.scatter(all_masses, all_power)\n", + "plt.plot(all_masses, avg_power, color='r')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "3739e5d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.64439915]\n" + ] + } + ], + "source": [ + "print(popt)" + ] + }, + { + "cell_type": "markdown", + "id": "109097ab", + "metadata": {}, + "source": [ + "#### Cleaning Up the Curve Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "7475e6d3", + "metadata": {}, + "outputs": [], + "source": [ + "#combining all mass and output data sets to fit\n", + "all_masses = np.array(all_masses)\n", + "all_power = np.array(all_power)\n", + "\n", + "#put combined mass data set into increasing order and reorder output array to match new indices \n", + "sorted_indices = np.argsort(all_masses)\n", + "sorted_masses = all_masses[sorted_indices]\n", + "sorted_powers = all_power[sorted_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "id": "acc33225", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#use scipy curve fit to create a general power law\n", + "popt, pcov = curve_fit(power_law, sorted_masses, sorted_powers,sigma=sorted_errors, absolute_sigma=True) #popt gives fit value for a\n", + "avg_a = popt\n", + "avg_aerr = np.sqrt(np.diag(pcov)) #gives the error in a\n", + "\n", + "#create an array with mass values using fitted a\n", + "avg_power = power_law(sorted_masses, avg_a)\n", + "\n", + "#plot combined mass and power law datasets\n", + "plt.figure()\n", + "plt.scatter(sorted_masses, sorted_powers, label='raw data')\n", + "plt.plot(sorted_masses, avg_power, color='r', label='curve fit')\n", + "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-a)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "bd3e02dd", + "metadata": {}, + "source": [ + "Based on the curve fit, our a value for the entire mass range is 0.60524093 ± 0.00211972" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "id": "0e0bc7ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.00211972]\n" + ] + } + ], + "source": [ + "print(avg_aerr)" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "id": "7037fa8c", + "metadata": {}, + "outputs": [], + "source": [ + "#calculate the low and high outputs based on error of a\n", + "avg_aup = avg_a + avg_aerr\n", + "avg_alow = avg_a - avg_aerr\n", + "avg_outup = power_law(sorted_masses, avg_aup)\n", + "avg_outlow = power_law(sorted_masses, avg_alow)" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "id": "1cb19a53", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#fit plot including error\n", + "plt.figure()\n", + "plt.scatter(sorted_masses, sorted_powers, color='k', label='data created from all papers')\n", + "plt.plot(sorted_masses, avg_power, color='c', label='calculated curve fit')\n", + "plt.fill_between(sorted_masses, avg_outup, avg_outlow, color='c', alpha=0.2, label=\"error in a\")\n", + "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-0.605 ± 0.002)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "cf3ff96a", + "metadata": {}, + "outputs": [], + "source": [ + "#create an error array for all values in sorted_masses\n", + "power_errors = []\n", + "for m in M_mass:\n", + " power_errors.append(power_law(m, M_a) - power_law(m, M_alow))\n", + "for n in S_mass:\n", + " power_errors.append(power_law(n, S_a) - power_law(n, S_alow))\n", + "for o in L_mass:\n", + " power_errors.append(power_law(o, L_a) - power_law(o, L_alow))\n", + "for p in B_mass:\n", + " power_errors.append(power_law(p, B_a) - power_law(p, B_alow))\n", + "for q in P_mass:\n", + " power_errors.append(power_law(q, P_a) - power_law(q, P_alow))\n", + "\n", + "#sort errors based on sorted indices\n", + "\n", + "# convert sorted_indices to a NumPy array for indexing\n", + "sorted_indices = np.array(sorted_indices)\n", + "\n", + "# Sort errors based on sorted indices\n", + "sorted_errors = [power_errors[i] for i in sorted_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30d890fa", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0206cbc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 80f3e0dfcd2c599c9c4093f58fb1a16805e1c095 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Wed, 26 Jun 2024 13:59:08 -0700 Subject: [PATCH 003/191] Changes to BD_IMF --- changes/BD_IMF.ipynb | 298 ++++++++++++++++++++++++++++++++++--------- 1 file changed, 240 insertions(+), 58 deletions(-) diff --git a/changes/BD_IMF.ipynb b/changes/BD_IMF.ipynb index 7930e27f..41fc800b 100644 --- a/changes/BD_IMF.ipynb +++ b/changes/BD_IMF.ipynb @@ -18,10 +18,23 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 3, "id": "de897cdf-e478-4d49-b16a-ea7a1e0d547d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/caitlinbegbie/opt/anaconda3/lib/python3.9/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.1' currently installed).\n", + " from pandas.core.computation.check import NUMEXPR_INSTALLED\n", + "/Users/caitlinbegbie/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.4' currently installed).\n", + " from pandas.core import (\n", + "/Users/caitlinbegbie/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -55,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 5, "id": "2f67bd3c-cc80-4294-88e5-f257100893f6", "metadata": {}, "outputs": [], @@ -104,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 6, "id": "384973ef-5396-4d20-af19-5899db1a5673", "metadata": {}, "outputs": [], @@ -150,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 15, "id": "4e520010-9702-45f2-877c-1fb5104fa7a1", "metadata": {}, "outputs": [ @@ -195,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 16, "id": "11fc4c9d-0be7-4d75-a35c-4b82fae451f0", "metadata": {}, "outputs": [ @@ -243,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 17, "id": "9d984c93-387a-4586-ba16-ac579562ca24", "metadata": {}, "outputs": [], @@ -283,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 18, "id": "ac20c536-15c8-4fc5-885e-190d3fd888d1", "metadata": {}, "outputs": [ @@ -348,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 10, "id": "c594d201-d695-414d-9d23-05d6e1f2171b", "metadata": {}, "outputs": [], @@ -362,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 11, "id": "5b054e0c-c0b6-4556-b649-a959fba74da0", "metadata": {}, "outputs": [ @@ -381,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 12, "id": "a1aaa018-16da-44e0-9473-2611ec50ab2c", "metadata": {}, "outputs": [], @@ -392,7 +405,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 13, "id": "baafa6a8-0c11-4380-899a-a61834474854", "metadata": {}, "outputs": [ @@ -402,7 +415,7 @@ "4000" ] }, - "execution_count": 150, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -414,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 20, "id": "56aa35b6-aa24-4695-9094-fe5857846bd5", "metadata": {}, "outputs": [ @@ -453,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 21, "id": "011eac9f-7e9d-4e5d-8bfc-f0380920f575", "metadata": {}, "outputs": [ @@ -480,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 22, "id": "de5f6d54-b675-447c-bef7-a979715eb5f0", "metadata": {}, "outputs": [ @@ -512,28 +525,6 @@ "plt.show()" ] }, - { - "cell_type": "code", - "execution_count": 154, - "id": "ecbe93a5-32d7-4648-bc99-c20b13db9ca2", - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "only integer scalar arrays can be converted to a scalar index", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[154], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mall_power\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index" - ] - } - ], - "source": [ - "range(all_power)" - ] - }, { "cell_type": "markdown", "id": "83430453-9da9-49e6-a1b1-4f12db8b63a7", @@ -544,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 19, "id": "d1ef4b5a-06b7-4295-9c5a-7e9d231020ec", "metadata": {}, "outputs": [], @@ -562,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 23, "id": "d3af8ed8-a459-486c-9f44-c6a9f1008d59", "metadata": {}, "outputs": [ @@ -599,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 52, "id": "400886ad-ad9a-47a9-8fa2-e513ef125d45", "metadata": {}, "outputs": [ @@ -607,12 +598,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[7.20652905e-05]\n" + "[0.5836496] [7.20652905e-05]\n" ] } ], "source": [ - "print(avg_aerr)" + "print(avg_a, avg_aerr)" ] }, { @@ -625,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 25, "id": "76bfab00-53a2-4a18-9cee-b4f11cfdde98", "metadata": {}, "outputs": [], @@ -647,7 +638,7 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 44, "id": "527b824d-29a7-4715-810b-01b626eae7fa", "metadata": {}, "outputs": [ @@ -655,7 +646,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fitted parameters: a = -0.5450, b = -4.1664664035223473e-10\n", + "Fitted parameters: m = -0.5450\n", "RMSE: 0.5058\n", "MAE: 0.4105\n", "R-squared: 0.2151\n" @@ -673,9 +664,9 @@ } ], "source": [ - "#define a linear model\n", - "def linear_model(x, a, b):\n", - " return a * x + b\n", + "#define a linear model (no intercept since no coefficient in power law)\n", + "def linear_model(x, m):\n", + " return m * x\n", "\n", "#curve fit\n", "params, covariance = curve_fit(linear_model, log_masses, log_power)\n", @@ -692,7 +683,7 @@ "r2 = r2_score(log_power, power_fitted)\n", "\n", "#output the results\n", - "print(f\"Fitted parameters: a = {params[0]:.4f}, b = {params[1]}\")\n", + "print(f\"Fitted parameters: m = {params[0]:.4f}\")\n", "print(f\"RMSE: {rmse:.4f}\")\n", "print(f\"MAE: {mae:.4f}\")\n", "print(f\"R-squared: {r2:.4f}\")\n", @@ -710,21 +701,23 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 47, "id": "23e78450-e261-4fe7-b307-41204c800ed3", "metadata": {}, "outputs": [], "source": [ "linpower_fit = []\n", "for l in log_masses:\n", - " linpower_fit.append(m*l + b)" + " linpower_fit.append(linear_model(l, -0.5450))" ] }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 48, "id": "2d6df51d-54d3-48d2-bfcd-49f0dceed8bc", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -743,15 +736,204 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "69324962-a5fb-44b7-a734-8d17dbe84c98", + "metadata": {}, + "source": [ + "#### Attempting a more accurate error" + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "a4ddeb15-31d9-48ba-881d-992def181f89", + "execution_count": 54, + "id": "40a444f8-9de7-4d39-8b40-2fa6272acf78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.545\n" + ] + } + ], + "source": [ + "print(params[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "cff2e9d0-e55a-4dbb-83e1-ccf940f84d97", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.15556349186104046\n" + ] + } + ], + "source": [ + "#find the difference between fit and upper/lower bounds\n", + "m = params[0]\n", + "up_diff = R_a - m\n", + "up_differr = R_aerr\n", + "low_diff = m - M1_a\n", + "low_differr = M1_aerr\n", + " #the errors in up_differr and low_differr are the same because m is a fixed value in this case\n", + "\n", + "#calculate error using sqrt function\n", + "fit_err = np.sqrt((up_differr)**2 + (low_differr)**2) #uncertainty formula\n", + "print(fit_err)" + ] + }, + { + "cell_type": "markdown", + "id": "b71d92d4-1658-46b1-b4a5-31aa601a29fa", + "metadata": {}, + "source": [ + "#### Replot our function with our new " + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "93f528bc-5570-46b2-902c-1c8137fb9c46", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#calculate error margins with RMSE value\n", + "logerr_up = linear_model(log_masses, -0.5450 + fit_err)\n", + "logerr_low = linear_model(log_masses, -0.5450 - fit_err)\n", + "\n", + "#plot new errors with function\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(log_masses, log_power, label='Data', color='blue', s=10)\n", + "plt.plot(log_masses, linear_model(log_masses, *params), label='Fitted line', color='red')\n", + "plt.fill_between(log_masses, logerr_up, logerr_low, color='red', alpha=0.2, label='error margin')\n", + "plt.legend()\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Linear Fit of Multiple Linear Functions')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b90191d6-3b08-42c9-9b7e-b925f79c30cb", + "metadata": {}, + "source": [ + "This margin of error makes a lot more sense, as it encompasses many more values represented by the function. " + ] + }, + { + "cell_type": "markdown", + "id": "9412765c-6aba-4280-8c97-0f3c7a6f7daa", "metadata": {}, - "outputs": [], "source": [ - "#find general error by calculating average range\n" + "#### Graphing our new error in power law space" ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "a1ff5709-854c-46db-8dc5-822cfd65b212", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting different IMFs based on literature\n", + "plt.plot(BD_masses, power_law(BD_masses, M1_a), color = 'y', label = \"Mužić 2017\")\n", + "plt.plot(BD_masses, power_law(BD_masses, K_a), color = 'c', label = \"Kirkpatrick 2021\")\n", + "plt.plot(BD_masses, power_law(BD_masses, R_a), color = 'b', label = \"Ryan 2022\")\n", + "plt.plot(BD_masses, power_law(BD_masses, B_a), color = 'm', label = \"Best 2024\")\n", + "\n", + "#average our two fit parameters (one from power law, one from linear fit)\n", + "best_a = (avg_a + (-1*m)) / 2\n", + "\n", + "#finding upper and lower bounds using best_a\n", + "up_a = best_a + fit_err\n", + "down_a = best_a - fit_err\n", + "power_up = np.array(power_law(BD_masses, up_a))\n", + "power_down = power_law(BD_masses, down_a)\n", + "\n", + "#plotting our fit line and error margin\n", + "plt.plot(BD_masses, power_law(BD_masses, best_a), color = \"black\", label = \"fit for functions\")\n", + "plt.fill_between(BD_masses, power_up, power_down, color = \"black\", alpha = 0.2, label = \"fit error\")\n", + "\n", + "#organizing our graph\n", + "plt.title(\"Final Power-Law Curve Fit for Brown Dwarf Mass Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"$M^{0.56 ± 0.16}$\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "a0e7f65f-d511-4840-937f-4fd2c57a2bd7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.5643248]\n" + ] + } + ], + "source": [ + "print(best_a)" + ] + }, + { + "cell_type": "markdown", + "id": "706598df-cec8-495d-9fbc-cfd29c9193d3", + "metadata": {}, + "source": [ + "### Conclusions" + ] + }, + { + "cell_type": "markdown", + "id": "40494bed-857e-4527-b10b-d72856d034b2", + "metadata": {}, + "source": [ + "From this process, we have determined that the best IMF model for brown-dwarf mass objects is given by a power law, $M^{-\\alpha}$, where ${\\alpha} = 0.56 ± 0.16$. This fits within our expected output because, in average, brown dwarf mass objects have been represented with $\\alpha$ values between 0.5-1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7df524b5-524e-49f3-a183-c547afd36419", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 166e46c0afccab5eb86a6c29755b3b87d06603e6 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Wed, 26 Jun 2024 16:13:43 -0700 Subject: [PATCH 004/191] Deleting outdated doc --- changes/Power_Law_Function.ipynb | 608 ------------------------------- 1 file changed, 608 deletions(-) delete mode 100644 changes/Power_Law_Function.ipynb diff --git a/changes/Power_Law_Function.ipynb b/changes/Power_Law_Function.ipynb deleted file mode 100644 index fb713697..00000000 --- a/changes/Power_Law_Function.ipynb +++ /dev/null @@ -1,608 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "23642516", - "metadata": {}, - "source": [ - "## Plotting Studied Power-Law Functions Over Small Mass Intervals" - ] - }, - { - "cell_type": "markdown", - "id": "ad57fd40", - "metadata": {}, - "source": [ - "#### Importing Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "d6503a21", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from scipy.optimize import curve_fit" - ] - }, - { - "cell_type": "markdown", - "id": "4f72f6f7", - "metadata": {}, - "source": [ - "#### Creating the general power-law function for small masses" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d51b38dc", - "metadata": {}, - "outputs": [], - "source": [ - "#define function\n", - "def power_law(M, a):\n", - " return M**(-1*a)\n", - "\n", - "# M is a defined mass range according to a specified paper, and a is the associated alpha value" - ] - }, - { - "cell_type": "markdown", - "id": "3fe49d7b", - "metadata": {}, - "source": [ - "#### Defining different mass/alpha values based on published papers" - ] - }, - { - "cell_type": "markdown", - "id": "b7736fad", - "metadata": {}, - "source": [ - "***Masses are in solar masses!***" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "4145bc16", - "metadata": {}, - "outputs": [], - "source": [ - "#Moraux 2007 (0.03 - 0.6) a = 0.69 ± 0.15\n", - "M_mass = np.linspace(0.03, 0.6, 1000)\n", - "M_a = 0.69\n", - "M_aerr = 0.15\n", - "\n", - "#Suárez 2009 (0.02 - 0.50) a = 0.60 ± 0.13\n", - "S_mass = np.linspace(0.02, 0.5, 1000)\n", - "S_a = 0.60\n", - "S_aerr = 0.13\n", - "\n", - "#Lodieu 2009 (0.01 - 0.5) a = 0.5 ± 0.2\n", - "L_mass = np.linspace(0.01, 0.5, 1000)\n", - "L_a = 0.5\n", - "L_aerr = 0.2\n", - "\n", - "#Béjar 2011 (0.013 - 0.1) a = 0.7 ± 0.3\n", - "B_mass = np.linspace(0.013, 0.1, 1000)\n", - "B_a = 0.7\n", - "B_aerr = 0.3\n", - "\n", - "#Peña Ramírez 2012 (0.006 - 0.25) a = 0.6 ± 0.2\n", - "P_mass = np.linspace(0.006, 0.25, 1000)\n", - "P_a = 0.6\n", - "P_aerr = 0.2" - ] - }, - { - "cell_type": "markdown", - "id": "f2a24e63", - "metadata": {}, - "source": [ - "#### Plotting all of the curves together (w/o accounting for error)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "60cab6f2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#plot all of the power law functions based on mass ranges\n", - "plt.plot(M_mass, power_law(M_mass, M_a), color='r', label='Moraux (a=0.69)')\n", - "plt.plot(S_mass, power_law(S_mass, S_a), color='b', label='Suárez (a=0.60)')\n", - "plt.plot(L_mass, power_law(L_mass, L_a), color='m', label='Lodieu (a=0.50)')\n", - "plt.plot(B_mass, power_law(B_mass, B_a), color='c', label='Béjar (a=0.70)')\n", - "plt.plot(P_mass, power_law(P_mass, P_a), color='y', label='Peña Ramírez (a=0.60)')\n", - "plt.title(\"Power Law Mass Functions for Planetary Masses According to Published Papers\")\n", - "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", - "plt.ylabel(\"M^(-a)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f5494d3d", - "metadata": {}, - "source": [ - "#### Accounting for error in a" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "1e6c80f4", - "metadata": {}, - "outputs": [], - "source": [ - "#Moraux\n", - "M_aup = M_a + M_aerr\n", - "M_alow = M_a - M_aerr\n", - "M_outup = power_law(M_mass, M_aup)\n", - "M_outlow = power_law(M_mass, M_alow)\n", - "\n", - "#Suárez\n", - "S_aup = S_a + S_aerr\n", - "S_alow = S_a - S_aerr\n", - "S_outup = power_law(S_mass, S_aup)\n", - "S_outlow = power_law(S_mass, S_alow)\n", - "\n", - "#Lodieu\n", - "L_aup = L_a + L_aerr\n", - "L_alow = L_a - L_aerr\n", - "L_outup = power_law(L_mass, L_aup)\n", - "L_outlow = power_law(L_mass, L_alow)\n", - "\n", - "#Béjar\n", - "B_aup = B_a + B_aerr\n", - "B_alow = B_a - B_aerr\n", - "B_outup = power_law(B_mass, B_aup)\n", - "B_outlow = power_law(B_mass, B_alow)\n", - "\n", - "#Peña Ramírez\n", - "P_aup = P_a + P_aerr\n", - "P_alow = P_a - P_aerr\n", - "P_outup = power_law(P_mass, P_aup)\n", - "P_outlow = power_law(P_mass, P_alow)" - ] - }, - { - "cell_type": "markdown", - "id": "041954f2", - "metadata": {}, - "source": [ - "#### Graphing Individual Power-Law Functions (Accounting for Error in a)" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "id": "dc7a858e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Setting up subplots\n", - "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(10,10))\n", - "plt.subplots_adjust(hspace=0.75, wspace=1.25)\n", - "fig.delaxes(ax[2, 1])\n", - "\n", - "#Moraux\n", - "ax[0,0].plot(M_mass, power_law(M_mass, M_a), color='r')\n", - "ax[0,0].fill_between(M_mass, M_outup, M_outlow, color='r', alpha=0.2, label=\"error in a\")\n", - "ax[0,0].set_title(\"Moraux Mass Function for Planetary Masses in Blanco 1\")\n", - "ax[0,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[0,0].set_ylabel(\"M^(-0.69 ± 0.15)\")\n", - "ax[0,0].legend()\n", - "\n", - "#Suárez\n", - "ax[0,1].plot(S_mass, power_law(S_mass, S_a), color='b')\n", - "ax[0,1].fill_between(S_mass, S_outup, S_outlow, color='b', alpha=0.2, label=\"error in a\")\n", - "ax[0,1].set_title(\"Suárez Mass Function for Planetary Masses in 25 Ori Group\")\n", - "ax[0,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[0,1].set_ylabel(\"M^(-0.60 ± 0.13)\")\n", - "ax[0,1].legend()\n", - "\n", - "#Lodieu\n", - "ax[1,0].plot(L_mass, power_law(L_mass, L_a), color='m')\n", - "ax[1,0].fill_between(L_mass, L_outup, L_outlow, color='b', alpha=0.2, label=\"error in a\")\n", - "ax[1,0].set_title(\"Lodieu Mass Function for Planetary Masses in UKIDSS Galactic Clusters\")\n", - "ax[1,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[1,0].set_ylabel(\"M^(-0.5 ± 0.2)\")\n", - "ax[1,0].legend()\n", - "\n", - "#Béjar\n", - "ax[1,1].plot(B_mass, power_law(B_mass, B_a), color='c')\n", - "ax[1,1].fill_between(B_mass, B_outup, B_outlow, color='c', alpha=0.2, label=\"error in a\")\n", - "ax[1,1].set_title(\"Béjar Mass Function for Planetary Masses in σ Orionis\")\n", - "ax[1,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[1,1].set_ylabel(\"M^(-0.7 ± 0.3)\")\n", - "ax[1,1].legend()\n", - "\n", - "#Peña Ramírez\n", - "ax[2,0].plot(P_mass, power_law(P_mass, P_a), color='y')\n", - "ax[2,0].fill_between(P_mass, P_outup, P_outlow, color='y', alpha=0.2, label=\"error in a\")\n", - "ax[2,0].set_title(\"Peña Ramírez Mass Function for Planetary Masses in σ Orionis\")\n", - "ax[2,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[2,0].set_ylabel(\"M^(-0.6 ± 0.2)\")\n", - "ax[2,0].legend()\n", - "\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f63bb9fc", - "metadata": {}, - "source": [ - "### Creating a General Power-Law Curve Fit" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "ed848735", - "metadata": {}, - "outputs": [], - "source": [ - "#combine all mass data\n", - "all_masses = []\n", - "for a in M_mass:\n", - " all_masses.append(a)\n", - "for b in S_mass:\n", - " all_masses.append(b)\n", - "for c in L_mass:\n", - " all_masses.append(c)\n", - "for d in B_mass:\n", - " all_masses.append(d)\n", - "for e in P_mass:\n", - " all_masses.append(e)" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "df52ba0f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5000\n" - ] - } - ], - "source": [ - "print(len(all_masses)) #to make sure the correct amount of entries were appended" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "dfc306a1", - "metadata": {}, - "outputs": [], - "source": [ - "#combine all power_law output data\n", - "all_power = []\n", - "for f in M_mass:\n", - " all_power.append(power_law(f, M_a))\n", - "for g in S_mass:\n", - " all_power.append(power_law(g, S_a))\n", - "for h in L_mass:\n", - " all_power.append(power_law(h, L_a))\n", - "for k in B_mass:\n", - " all_power.append(power_law(k, B_a))\n", - "for l in P_mass:\n", - " all_power.append(power_law(l, P_a))" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "15d0484d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5000\n" - ] - } - ], - "source": [ - "print(len(all_power))" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "6a6629e9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#use scipy curve fit to create a general power law\n", - "popt, pcov = curve_fit(power_law, all_masses, all_power) #popt gives fit value for a\n", - "avg_a = popt\n", - "\n", - "#create an array with mass values using fitted a\n", - "avg_power = power_law(all_masses, avg_a)\n", - "\n", - "#plot combined mass and power law datasets\n", - "plt.figure()\n", - "plt.scatter(all_masses, all_power)\n", - "plt.plot(all_masses, avg_power, color='r')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "3739e5d8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.64439915]\n" - ] - } - ], - "source": [ - "print(popt)" - ] - }, - { - "cell_type": "markdown", - "id": "109097ab", - "metadata": {}, - "source": [ - "#### Cleaning Up the Curve Fit" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "7475e6d3", - "metadata": {}, - "outputs": [], - "source": [ - "#combining all mass and output data sets to fit\n", - "all_masses = np.array(all_masses)\n", - "all_power = np.array(all_power)\n", - "\n", - "#put combined mass data set into increasing order and reorder output array to match new indices \n", - "sorted_indices = np.argsort(all_masses)\n", - "sorted_masses = all_masses[sorted_indices]\n", - "sorted_powers = all_power[sorted_indices]" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "id": "acc33225", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#use scipy curve fit to create a general power law\n", - "popt, pcov = curve_fit(power_law, sorted_masses, sorted_powers,sigma=sorted_errors, absolute_sigma=True) #popt gives fit value for a\n", - "avg_a = popt\n", - "avg_aerr = np.sqrt(np.diag(pcov)) #gives the error in a\n", - "\n", - "#create an array with mass values using fitted a\n", - "avg_power = power_law(sorted_masses, avg_a)\n", - "\n", - "#plot combined mass and power law datasets\n", - "plt.figure()\n", - "plt.scatter(sorted_masses, sorted_powers, label='raw data')\n", - "plt.plot(sorted_masses, avg_power, color='r', label='curve fit')\n", - "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", - "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", - "plt.ylabel(\"M^(-a)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "bd3e02dd", - "metadata": {}, - "source": [ - "Based on the curve fit, our a value for the entire mass range is 0.60524093 ± 0.00211972" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "id": "0e0bc7ca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.00211972]\n" - ] - } - ], - "source": [ - "print(avg_aerr)" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "id": "7037fa8c", - "metadata": {}, - "outputs": [], - "source": [ - "#calculate the low and high outputs based on error of a\n", - "avg_aup = avg_a + avg_aerr\n", - "avg_alow = avg_a - avg_aerr\n", - "avg_outup = power_law(sorted_masses, avg_aup)\n", - "avg_outlow = power_law(sorted_masses, avg_alow)" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "id": "1cb19a53", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#fit plot including error\n", - "plt.figure()\n", - "plt.scatter(sorted_masses, sorted_powers, color='k', label='data created from all papers')\n", - "plt.plot(sorted_masses, avg_power, color='c', label='calculated curve fit')\n", - "plt.fill_between(sorted_masses, avg_outup, avg_outlow, color='c', alpha=0.2, label=\"error in a\")\n", - "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", - "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", - "plt.ylabel(\"M^(-0.605 ± 0.002)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "id": "cf3ff96a", - "metadata": {}, - "outputs": [], - "source": [ - "#create an error array for all values in sorted_masses\n", - "power_errors = []\n", - "for m in M_mass:\n", - " power_errors.append(power_law(m, M_a) - power_law(m, M_alow))\n", - "for n in S_mass:\n", - " power_errors.append(power_law(n, S_a) - power_law(n, S_alow))\n", - "for o in L_mass:\n", - " power_errors.append(power_law(o, L_a) - power_law(o, L_alow))\n", - "for p in B_mass:\n", - " power_errors.append(power_law(p, B_a) - power_law(p, B_alow))\n", - "for q in P_mass:\n", - " power_errors.append(power_law(q, P_a) - power_law(q, P_alow))\n", - "\n", - "#sort errors based on sorted indices\n", - "\n", - "# convert sorted_indices to a NumPy array for indexing\n", - "sorted_indices = np.array(sorted_indices)\n", - "\n", - "# Sort errors based on sorted indices\n", - "sorted_errors = [power_errors[i] for i in sorted_indices]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30d890fa", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c0206cbc", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From e5a7bbc6bb7182eea2c0a0a9090ab667e9cfdfa3 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 8 Jul 2024 13:42:36 -0700 Subject: [PATCH 005/191] New testing documents for brown dwarf changes --- changes/Test_BD_Cluster.ipynb | 430 +++++++++++++++++++++++++++++++++ spisea/imf/tests/test_bd.py | 300 +++++++++++++++++++++++ spisea/imf/tests/test_class.py | 58 +++++ 3 files changed, 788 insertions(+) create mode 100644 changes/Test_BD_Cluster.ipynb create mode 100644 spisea/imf/tests/test_bd.py create mode 100644 spisea/imf/tests/test_class.py diff --git a/changes/Test_BD_Cluster.ipynb b/changes/Test_BD_Cluster.ipynb new file mode 100644 index 00000000..b2bdb139 --- /dev/null +++ b/changes/Test_BD_Cluster.ipynb @@ -0,0 +1,430 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4257f594-b092-40bc-85cf-2397fe17724c", + "metadata": {}, + "source": [ + "# SPISEA: Making a Cluster with BD Candidates" + ] + }, + { + "cell_type": "markdown", + "id": "08de775e-f1bb-464b-8148-48b51e6d8822", + "metadata": {}, + "source": [ + "This is a document to generate a synthetic cluster in SPISEA with the addition of brown dwarf candidates. It will follow the Quick Start Guide closely, but with changes due to a lower mass limit and ability to consider smaller objects." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b7536665-00cf-49ad-9028-930f56cf3204", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary packages. \n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "60ee38e8-ba5c-4c23-b9a2-22773bd91a62", + "metadata": {}, + "source": [ + "### 1: Making an Isochrone Object for our Cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0cd4c0a3-39e8-4bbf-9eba-e1883b6dacec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone generation took 57.656324 s.\n", + "Making photometry for isochrone: log(t) = 7.70 AKs = 0.80 dist = 4000\n", + " Starting at: 2024-07-03 13:28:50.362836 Usually takes ~5 minutes\n", + "Starting filter: wfc3,ir,f127m Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.100 Msun T = 3021 K m_hst_f127m = 23.96\n", + "M = 1.336 Msun T = 6744 K m_hst_f127m = 18.16\n", + "M = 4.781 Msun T = 15818 K m_hst_f127m = 14.97\n", + "M = 6.899 Msun T = 17664 K m_hst_f127m = 13.11\n", + "M = 6.911 Msun T = 6971 K m_hst_f127m = 10.61\n", + "M = 6.988 Msun T = 3957 K m_hst_f127m = 9.32\n", + "M = 7.297 Msun T = 3738 K m_hst_f127m = 8.65\n", + "Starting filter: wfc3,ir,f139m Elapsed time: 18.77 seconds\n", + "Starting synthetic photometry\n", + "M = 0.100 Msun T = 3021 K m_hst_f139m = 23.57\n", + "M = 1.336 Msun T = 6744 K m_hst_f139m = 17.69\n", + "M = 4.781 Msun T = 15818 K m_hst_f139m = 14.59\n", + "M = 6.899 Msun T = 17664 K m_hst_f139m = 12.74\n", + "M = 6.911 Msun T = 6971 K m_hst_f139m = 10.18\n", + "M = 6.988 Msun T = 3957 K m_hst_f139m = 8.69\n", + "M = 7.297 Msun T = 3738 K m_hst_f139m = 8.01\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 38.82 seconds\n", + "Starting synthetic photometry\n", + "M = 0.100 Msun T = 3021 K m_hst_f153m = 22.93\n", + "M = 1.336 Msun T = 6744 K m_hst_f153m = 17.23\n", + "M = 4.781 Msun T = 15818 K m_hst_f153m = 14.22\n", + "M = 6.899 Msun T = 17664 K m_hst_f153m = 12.37\n", + "M = 6.911 Msun T = 6971 K m_hst_f153m = 9.74\n", + "M = 6.988 Msun T = 3957 K m_hst_f153m = 8.00\n", + "M = 7.297 Msun T = 3738 K m_hst_f153m = 7.28\n", + " Time taken: 60.42 seconds\n" + ] + } + ], + "source": [ + "# Define isochrone parameters\n", + "logAge = np.log10(5*10**7.) # Age in log(years)\n", + "AKs = 0.8 # extinction in mags\n", + "dist = 4000 # distance in parsec\n", + "metallicity = 0 # Metallicity in [M/H]\n", + "\n", + "# Define evolution/atmosphere models and extinction law\n", + "evo_model = evolution.MISTv1() \n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "red_law = reddening.RedLawHosek18b()\n", + "\n", + "# Also specify filters for synthetic photometry (optional). Here we use \n", + "# the HST WFC3-IR F127M, F139M, and F153M filters\n", + "filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f139m', 'wfc3,ir,f153m']\n", + "\n", + "# Specify the directory we want the output isochrone\n", + "# table saved in. If the directory does not already exist,\n", + "# SPISEA will create it.\n", + "iso_dir = 'isochrones/'\n", + "\n", + "# Make IsochronePhot object. Note that this will take a minute or two, \n", + "# unless the isochrone has been generated previously.\n", + "#\n", + "# Note that this is not show all of the user options \n", + "# for IsochronePhot. See docs for complete list of options.\n", + "my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n", + " evo_model=evo_model, atm_func=atm_func,\n", + " red_law=red_law, filters=filt_list,\n", + " iso_dir=iso_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5a6e081f-84d3-4efb-a6d5-59d5f60d845f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " L Teff ... m_hst_f153m \n", + " W K ... \n", + "---------------------- ------------------ ... ------------------\n", + "1.6794720325683452e+24 3020.5752857611837 ... 22.92776805171169\n", + " 1.7370492282378e+24 3026.596844621303 ... 22.89188358781423\n", + "1.8137828986723996e+24 3034.2260855891886 ... 22.845919008054395\n", + "1.8944919038459294e+24 3041.88871922475 ... 22.799687950466488\n", + "1.9794440999691394e+24 3049.612041774559 ... 22.753170128603966\n", + "2.0689567354242308e+24 3057.411643839487 ... 22.706329930464427\n", + " ... ... ... ...\n", + " 8.450906433033863e+30 3441.3110094585145 ... 6.433232663228037\n", + " 8.557948261819579e+30 3436.3422272496196 ... 6.420004996757628\n", + " 8.585406026317698e+30 3435.514440695956 ... 6.416813383458505\n", + " 8.699093176188767e+30 3430.8941764462447 ... 6.403203879003122\n", + " 8.881152106206099e+30 3423.152501093308 ... 6.381645353870332\n", + " 8.910673547171454e+30 3421.7638232666736 ... 6.378271217031613\n", + " 9.076365228968617e+30 3414.983593067963 ... 6.359238998015297\n", + "Length = 666 rows\n" + ] + } + ], + "source": [ + "# The stars in the isochrone and associated properties \n", + "# are stored in an astropy table called \"points\" \n", + "# within the IsochronePhot object. \n", + "print(my_iso.points)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1205c450-d7f2-4ad7-86b0-006762426d5c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 M_sun: F127M = 19.299 mag, F139M = 18.792 mag, F153M = 18.265 mag\n" + ] + } + ], + "source": [ + "# Example case:\n", + "# Identify a 1 M_sun star, print F127M, F139M, and F153M mags\n", + "idx = np.where( abs(my_iso.points['mass'] - 1.0) == min(abs(my_iso.points['mass'] - 1.0)) )[0]\n", + "f127m = np.round(my_iso.points[idx[0]]['m_hst_f127m'], decimals=3)\n", + "f139m = np.round(my_iso.points[idx[0]]['m_hst_f139m'], decimals=3)\n", + "f153m = np.round(my_iso.points[idx[0]]['m_hst_f153m'], decimals=3)\n", + "print('1 M_sun: F127M = {0} mag, F139M = {1} mag, F153M = {2} mag'.format(f127m, f139m, f153m))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a8169db9-486e-4d20-9a09-dfcd7ebbd3e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make a color-magnitude diagram from the isochrone\n", + "py.figure(1, figsize=(10,10))\n", + "py.clf()\n", + "py.plot(my_iso.points['m_hst_f127m'] - my_iso.points['m_hst_f153m'], \n", + " my_iso.points['m_hst_f153m'], 'r-', label='_nolegend_')\n", + "py.plot(my_iso.points['m_hst_f127m'][idx] - my_iso.points['m_hst_f153m'][idx], \n", + " my_iso.points['m_hst_f153m'][idx], 'b*', ms=15, label='1 $M_\\odot$')\n", + "py.xlabel('F127M - F153M')\n", + "py.ylabel('F153M')\n", + "py.gca().invert_yaxis()\n", + "py.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "7e9599c3-e7f7-4f1b-8eda-8148058b16c3", + "metadata": {}, + "source": [ + "### 2: Bringing in an IMF" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "493b8648-1c3b-4aab-858f-2f955195e634", + "metadata": {}, + "outputs": [], + "source": [ + "# Make multiplicity object Here, we use the MultiplicityUnresolved object, \n", + "# based on Lu+13. This means that star systems will be unresolved, i.e., \n", + "# that all components of a star system are combined into a single \"star\" in the cluster\n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "\n", + "# Make IMF object; we'll use a broken power law with the parameters from Kroupa+01\n", + "\n", + "# NOTE: when defining the power law slope for each segment of the IMF, we define\n", + "# the entire exponent, including the negative sign. For example, if dN/dm $\\propto$ m^-alpha,\n", + "# then you would use the value \"-2.3\" to specify an IMF with alpha = 2.3. \n", + "\n", + "my_imf = imf.Muzic_2017(multiplicity=imf_multi)" + ] + }, + { + "cell_type": "markdown", + "id": "0e42984d-bf09-4e71-a823-aa698672cfe6", + "metadata": {}, + "source": [ + "### 3: Generating Cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "0bf0fed8-2b70-41be-b950-83f1362af9f0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 177 stars out of mass range\n", + "Found 47 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "# Define total cluster mass\n", + "mass = 10**5.\n", + "\n", + "# Make cluster object\n", + "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c8de1a27-0b07-404c-a13d-8434e204017d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass isMultiple ... m_hst_f153m N_companions\n", + "------------------- ---------- ... ------------------ ------------\n", + "0.35277838228082964 True ... 21.056406061698077 2\n", + " 0.4109143920284334 False ... 20.80304487623113 0\n", + "0.44680797582475024 False ... 20.65082717236568 0\n", + "0.16814683886383167 False ... 22.19558068545299 0\n", + " 0.1998139027268053 False ... 21.941926401844015 0\n", + " 0.4740288564083312 False ... 20.534885068309187 0\n", + " 1.0126972980825961 True ... 17.971915603237292 2\n", + " ... ... ... ... ...\n", + " 5.3779990877122295 True ... 13.776442294620189 1\n", + " 0.6634904439575919 False ... 19.510526216999857 0\n", + " 1.35508336893066 True ... 17.17251870764391 1\n", + " 3.7213803186176304 False ... 14.933652308856155 0\n", + " 1.5278429001189309 False ... 16.86599227492448 0\n", + " 0.8112843101887811 False ... 18.792308025746074 0\n", + "0.14401644779125125 False ... 22.41409431495889 0\n", + "Length = 411 rows\n" + ] + } + ], + "source": [ + "# Look at star systems table\n", + "print(cluster.star_systems)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "6b10a958-a0f8-443c-be6b-e07c5de2d4dc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "system_idx mass ... m_hst_f139m m_hst_f153m \n", + "---------- ------------------- ... ------------------ ------------------\n", + " 0 0.09565348913151368 ... nan nan\n", + " 0 0.03322585947572763 ... nan nan\n", + " 6 0.08726287084485738 ... nan nan\n", + " 6 0.6433751483393655 ... 20.33081658819904 19.657228868526335\n", + " 7 0.06161747316330432 ... nan nan\n", + " 13 1.7263481600403079 ... 17.018762642329463 16.60938637243561\n", + " 14 0.2737246431039749 ... 22.090906238571662 21.4548720525631\n", + " ... ... ... ... ...\n", + " 396 0.03899827614913988 ... nan nan\n", + " 398 0.26985155635770386 ... 22.112746284229008 21.476822094207677\n", + " 400 2.0771742779244846 ... 16.6610738152462 16.27195342256348\n", + " 400 1.8654858596867614 ... 16.870042360441936 16.47226405265614\n", + " 403 1.3729396331088282 ... 17.601870317877122 17.140899115559176\n", + " 404 1.222648505597827 ... 18.015195949214007 17.53027863177404\n", + " 406 0.1534511179062777 ... 22.950623123620353 22.315542833099645\n", + "Length = 208 rows\n" + ] + } + ], + "source": [ + "# The companions table is accessed in a similar way\n", + "print(cluster.companions)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "095a963e-c0de-4bbc-a03f-471f3cdb7b91", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", + "# multiple systems on the photometry\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points\n", + "\n", + "py.figure(2, figsize=(10,10))\n", + "py.clf()\n", + "py.plot(clust['m_hst_f127m'] - clust['m_hst_f153m'], clust['m_hst_f153m'],\n", + " 'k.', ms=5, alpha=0.1, label='__nolegend__')\n", + "py.plot(iso['m_hst_f127m'] - iso['m_hst_f153m'], iso['m_hst_f153m'],\n", + " 'r-', label='Isochrone')\n", + "py.xlabel('F127M - F153M')\n", + "py.ylabel('F153M')\n", + "py.gca().invert_yaxis()\n", + "py.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d694ed41-c732-4093-9f0a-ad021fe8397a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/spisea/imf/tests/test_bd.py b/spisea/imf/tests/test_bd.py new file mode 100644 index 00000000..a29005c6 --- /dev/null +++ b/spisea/imf/tests/test_bd.py @@ -0,0 +1,300 @@ +import numpy as np +import time +import pdb + +def test_generate_cluster(): + from .. import imf + from .. import multiplicity + + # Make multiplicity object + imf_multi = multiplicity.MultiplicityUnresolved() + + # Make IMF object; we'll use a broken power law with the parameters from Muzic+17 + massLimits = np.array([0.01, 0.5, 1, 120]) # Define boundaries of each mass segement + powers = np.array([-0.71, -0.81, -1.6]) # Power law slope associated with each mass segment + my_imf = imf.IMF_broken_powerlaw(massLimits, powers, imf_multi) + + # Define total cluster mass + M_cl = 10**5. + + mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) + + # Make sure that the total mass is always within the expected + # range of the requested mass. + assert np.abs(M_cl - sysMass.sum()) < 120.0 + + # Check that enough companions were generated. + # Should be greater than 25% of the stars with companions. + mdx = np.where(isMulti)[0] + for mm in mdx: + assert len(compMass[mm]) > 0 + + return + +def test_prim_power(): + from .. import imf + + #mass_limits = np.array([0.1, 1.0, 100.0]) + #powers = np.array([-2.0, -1.8]) + mass_limits = np.array([1.0, 100.0]) + powers = np.array([-2.0]) + + imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) + + Mcl = 1e5 + (mass, is_multi, c_mass, s_mass) = imf_tmp.generate_cluster(Mcl) + + print('mass shape = ', mass.shape) + print('mass array:') + print(mass) + print('is_multi array:') + print(is_multi) + print('c_mass array:') + print(c_mass) + print('s_mass array:') + print(s_mass) + print('np.isfinite(mass):') + print(np.isfinite(mass)) + print('Mass Sum: ', mass.sum(), ' (should be {0})'.format(Mcl)) + + return + +def test_xi(): + from .. import imf + + import cProfile, pstats, io + #from pstats import SortKey + pr = cProfile.Profile() + + mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) + powers = np.array([-0.3, -1.5, -2.3]) + imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) + + ########## + # + # Test validity of returned values. + # + ########## + N_size = 10 + m = np.linspace(0.2, 20, N_size) + val_good = np.array([1.6206566 , 0.26895718, 0.10135922, 0.05639448, 0.03703704, + 0.0243668 , 0.01613091, 0.01137139, 0.00839526, 0.00642142]) + + val_test = np.zeros(len(m), dtype=float) + for ii in range(len(m)): + val_test[ii] = imf_tmp.xi(m[ii]) + + # print(repr(val_test)) + np.testing.assert_almost_equal(val_test, val_good) + + ########## + # + # Performance testing + # + ########## + t1 = time.time() + # pr.enable() + + # Run a time test + N_size = int(1e4) + m = np.random.uniform(1.1, 99.0, size=N_size) + foo1 = imf_tmp.xi(m) + + # pr.disable() + t2 = time.time() + + print('test_xi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) + + # s = io.StringIO() + # sortby = SortKey.CUMULATIVE + # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) + # ps.print_stats() + # print(s.getvalue()) + + return + +def test_xi2(): + """ + Test that xi() produces the correct probability for + a given slope. + """ + from .. import imf + + import cProfile, pstats, io + #from pstats import SortKey + pr = cProfile.Profile() + + # Negative slope + mass_limits = np.array([1.0, 10.0]) + powers = np.array([-2.0]) + imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) + imf_tmp.normalize(1e4) + + tmp = np.random.rand(100) + masses = imf_tmp.dice_star_cl(tmp) + + pdf = imf_tmp.xi(masses) + + sdx = masses.argsort() + + pdf_sorted = pdf[sdx] + + print('Testing negative slope') + assert pdf_sorted[0] > pdf_sorted[-1] + assert pdf_sorted[0] > pdf_sorted[1] + assert pdf.max() == pdf_sorted[0] + + # Positive slope + mass_limits2 = np.array([1.0, 10.0]) + powers2 = np.array([2.0]) + imf_tmp2 = imf.IMF_broken_powerlaw(mass_limits2, powers2) + imf_tmp2.normalize(1e4) + + tmp2 = np.random.rand(100) + masses2 = imf_tmp2.dice_star_cl(tmp2) + + pdf2 = imf_tmp2.xi(masses2) + + sdx2 = masses2.argsort() + + pdf_sorted2 = pdf2[sdx2] + + print('Testing positive slope') + assert pdf_sorted2[0] < pdf_sorted2[-1] + assert pdf_sorted2[0] < pdf_sorted2[1] + assert pdf2.min() == pdf_sorted2[0] + + + import pylab as plt + plt.clf() + plt.loglog(masses[sdx], pdf_sorted, 'k.') + plt.axis('equal') + # pdb.set_trace() + + return + + + + +def test_mxi(): + from .. import imf + + import cProfile, pstats, io + #from pstats import SortKey + pr = cProfile.Profile() + + mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) + powers = np.array([-0.3, -1.5, -2.3]) + imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) + + ########## + # + # Test validity of returned values. + # + ########## + N_size = 10 + m = np.linspace(0.2, 20, N_size) + val_good = np.array([0.32413132, 0.64549722, 0.4662524 , 0.38348249, 0.33333333, + 0.27290819, 0.21615424, 0.17739363, 0.14943557, 0.12842838]) + val_test = np.zeros(len(m), dtype=float) + for ii in range(len(m)): + val_test[ii] = imf_tmp.m_xi(m[ii]) + + # print(repr(val_test)) + np.testing.assert_almost_equal(val_test, val_good) + assert val_test.shape == m.shape + + ########## + # + # Performance testing + # + ########## + t1 = time.time() + # pr.enable() + + # Run a time test + N_size = int(1e4) + m = np.random.uniform(1.1, 99.0, size=N_size) + foo1 = imf_tmp.m_xi(m) + assert foo1.shape == m.shape + + # pr.disable() + t2 = time.time() + + print('test_mxi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) + + # s = io.StringIO() + # sortby = SortKey.CUMULATIVE + # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) + # ps.print_stats() + # print(s.getvalue()) + + return + +def test_theta_closed(): + from .. import imf + + import cProfile, pstats, io + #from pstats import SortKey + + mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) + powers = np.array([-0.3, -1.5, -2.3]) + imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) + + ########## + # + # Test validity of returned values. + # + ########## + N_size = 10 + m = np.linspace(0.2, 20, N_size) + val_good = np.array([[1., 0., 0.], + [1., 1., 0.], + [1., 1., 0.], + [1., 1., 0.], + [1., 1., 0.], + [1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.]]) + + val_test = np.zeros((len(m), len(powers)), dtype=float) + for ii in range(len(m)): + val_test[ii] = imf.theta_closed(m[ii] - imf_tmp._m_limits_low) + + np.testing.assert_equal(val_test, val_good) + + + + ########## + # + # Speed tests and performance profiling. + # + ########## + N_size = 10000 + m = np.linspace(1.1, 99, N_size) + + tmp = np.zeros((len(m), len(powers)), dtype=float) + + t1 = time.time() + # pr = cProfile.Profile() + # pr.enable() + + for ii in range(len(m)): + tmp[ii] = imf.theta_closed(m[ii] - imf_tmp._m_limits_low) + + # pr.disable() + t2 = time.time() + + print('Runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) + + # s = io.StringIO() + # sortby = SortKey.CUMULATIVE + # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) + # ps.print_stats() + # print(s.getvalue()) + + + return + diff --git a/spisea/imf/tests/test_class.py b/spisea/imf/tests/test_class.py new file mode 100644 index 00000000..7c15b3dc --- /dev/null +++ b/spisea/imf/tests/test_class.py @@ -0,0 +1,58 @@ +import numpy as np +import time +import pdb + +def test_Muzic(): + from .. import imf + from .. import multiplicity + + # Make multiplicity object + imf_multi = multiplicity.MultiplicityUnresolved() + + # Make IMF object; we'll use a broken power law with the parameters from Muzic+17 + my_imf = imf.Muzic_2017(multiplicity=imf_multi) + + # Define total cluster mass + M_cl = 10**5. + + mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) + + # Make sure that the total mass is always within the expected + # range of the requested mass. + assert np.abs(M_cl - sysMass.sum()) < 120.0 + + # Check that enough companions were generated. + # Should be greater than 25% of the stars with companions. + mdx = np.where(isMulti)[0] + for mm in mdx: + assert len(compMass[mm]) > 0 + + return + +""" +def test_CombinedBD(): + from .. import imf + from .. import multiplicity + + # Make multiplicity object + imf_multi = multiplicity.MultiplicityUnresolved() + + # Use the IMF class from imf.py + my_imf = imf.CombinedBD_2024(multiplicity=imf_multi) + + # Define total cluster mass + M_cl = 10**5. + + mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) + + # Make sure that the total mass is always within the expected + # range of the requested mass. + assert np.abs(M_cl - sysMass.sum()) < 120.0 + + # Check that enough companions were generated. + # Should be greater than 25% of the stars with companions. + mdx = np.where(isMulti)[0] + for mm in mdx: + assert len(compMass[mm]) > 0 + + return""" \ No newline at end of file From 61b3b3bd4dc619f4c64525f524d1315ab2bd0c11 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 10 Jul 2024 14:40:01 -0700 Subject: [PATCH 006/191] changes to evolution, imf, and testing document --- changes/Test_BD_Cluster.ipynb | 808 ++++++++++++++++++++++++++++++---- spisea/evolution.py | 8 +- spisea/imf/imf.py | 29 +- 3 files changed, 750 insertions(+), 95 deletions(-) diff --git a/changes/Test_BD_Cluster.ipynb b/changes/Test_BD_Cluster.ipynb index b2bdb139..277a3412 100644 --- a/changes/Test_BD_Cluster.ipynb +++ b/changes/Test_BD_Cluster.ipynb @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "0cd4c0a3-39e8-4bbf-9eba-e1883b6dacec", "metadata": {}, "outputs": [ @@ -51,49 +51,537 @@ "name": "stdout", "output_type": "stream", "text": [ - "Isochrone generation took 57.656324 s.\n", - "Making photometry for isochrone: log(t) = 7.70 AKs = 0.80 dist = 4000\n", - " Starting at: 2024-07-03 13:28:50.362836 Usually takes ~5 minutes\n", + "Changing to logg=4.00 for T= 32651 logg=3.99\n", + "Changing to logg=4.00 for T= 32840 logg=3.98\n", + "Changing to logg=4.00 for T= 33037 logg=3.97\n", + "Changing to logg=4.00 for T= 33144 logg=3.96\n", + "Changing to logg=4.00 for T= 33205 logg=3.95\n", + "Changing to logg=4.00 for T= 33358 logg=3.94\n", + "Changing to logg=4.00 for T= 33504 logg=3.93\n", + "Changing to logg=4.00 for T= 33651 logg=3.91\n", + "Changing to logg=4.00 for T= 33713 logg=3.91\n", + "Changing to logg=4.00 for T= 33775 logg=3.90\n", + "Changing to logg=4.00 for T= 33892 logg=3.89\n", + "Changing to logg=4.00 for T= 34002 logg=3.87\n", + "Changing to logg=4.00 for T= 34111 logg=3.86\n", + "Changing to logg=4.00 for T= 34182 logg=3.85\n", + "Changing to logg=4.00 for T= 34222 logg=3.85\n", + "Changing to logg=4.00 for T= 34332 logg=3.83\n", + "Changing to logg=4.00 for T= 34443 logg=3.82\n", + "Changing to logg=4.00 for T= 34546 logg=3.81\n", + "Changing to logg=4.00 for T= 34658 logg=3.80\n", + "Changing to 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logg=5.30\n", + "Changing to logg=5.00 for T= 34962 logg=5.25\n", + "Changing to logg=5.00 for T= 34906 logg=5.25\n", + "Changing to logg=5.00 for T= 34475 logg=5.21\n", + "Changing to logg=5.00 for T= 34277 logg=5.20\n", + "Changing to logg=5.00 for T= 34285 logg=5.20\n", + "Changing to logg=5.00 for T= 34658 logg=5.23\n", + "Changing to logg=5.00 for T= 34770 logg=5.24\n", + "Changing to logg=5.00 for T= 34253 logg=5.20\n", + "Changing to logg=5.00 for T= 33628 logg=5.15\n", + "Changing to logg=5.00 for T= 33312 logg=5.12\n", + "Changing to logg=5.00 for T= 33636 logg=5.15\n", + "Changing to logg=5.00 for T= 33643 logg=5.15\n", + "Changing to logg=5.00 for T= 33659 logg=5.15\n", + "Changing to logg=5.00 for T= 33651 logg=5.15\n", + "Changing to logg=5.00 for T= 33504 logg=5.13\n", + "Changing to logg=5.00 for T= 33466 logg=5.08\n", + "Changing to logg=5.00 for T= 33504 logg=5.08\n", + "Changing to logg=5.00 for T= 33558 logg=5.07\n", + "Changing to logg=5.00 for T= 33605 logg=5.08\n", + "Changing to logg=5.00 for T= 33651 logg=5.08\n", + "Changing to logg=5.00 for T= 33674 logg=5.07\n", + "Changing to logg=5.00 for T= 32870 logg=5.05\n", + "Changing to logg=5.00 for T= 30054 logg=5.01\n", + "Changing to logg=5.00 for T= 29819 logg=5.01\n", + "Changing to logg=5.00 for T= 35785 logg=5.39\n", + "Changing to logg=5.00 for T= 39683 logg=5.62\n", + "Changing to logg=5.00 for T= 39701 logg=5.62\n", + "Changing to logg=5.00 for T= 39655 logg=5.63\n", + "Changing to logg=5.00 for T= 39518 logg=5.63\n", + "Changing to logg=5.00 for T= 39373 logg=5.63\n", + "Changing to logg=5.00 for T= 39518 logg=5.65\n", + "Changing to logg=5.00 for T= 40031 logg=5.69\n", + "Changing to logg=5.00 for T= 40096 logg=5.70\n", + "Changing to logg=5.00 for T= 40495 logg=5.74\n", + "Changing to logg=5.00 for T= 41381 logg=5.82\n", + "Changing to logg=5.00 for T= 41976 logg=5.87\n", + "Changing to logg=5.00 for T= 42199 logg=5.90\n", + "Changing to logg=5.00 for T= 42228 logg=5.90\n", + "Changing to logg=5.00 for T= 42286 logg=5.90\n", + "Changing to logg=5.00 for T= 42345 logg=5.91\n", + "Changing to logg=5.00 for T= 42394 logg=5.92\n", + "Changing to logg=5.00 for T= 42462 logg=5.92\n", + "Changing to logg=5.00 for T= 42530 logg=5.92\n", + "Changing to logg=5.00 for T= 42619 logg=5.92\n", + "Changing to logg=5.00 for T= 42717 logg=5.93\n", + "Changing to logg=5.00 for T= 42825 logg=5.93\n", + "Changing to logg=5.00 for T= 42934 logg=5.94\n", + "Changing to logg=5.00 for T= 43053 logg=5.94\n", + "Changing to logg=5.00 for T= 43152 logg=5.94\n", + "Changing to logg=5.00 for T= 43271 logg=5.95\n", + "Changing to logg=5.00 for T= 43411 logg=5.95\n", + "Changing to logg=5.00 for T= 43561 logg=5.96\n", + "Changing to logg=5.00 for T= 43692 logg=5.96\n", + "Changing to logg=5.00 for T= 43833 logg=5.96\n", + "Changing to logg=5.00 for T= 43985 logg=5.97\n", + "Changing to logg=5.00 for T= 44147 logg=5.97\n", + "Changing to logg=5.00 for T= 44310 logg=5.97\n", + "Changing to logg=5.00 for T= 44484 logg=5.97\n", + "Changing to logg=5.00 for T= 44638 logg=5.97\n", + "Changing to logg=5.00 for T= 44802 logg=5.98\n", + "Changing to logg=5.00 for T= 44957 logg=5.98\n", + "Changing to logg=5.00 for T= 45144 logg=5.98\n", + "Changing to logg=5.00 for T= 45300 logg=5.99\n", + "Changing to logg=5.00 for T= 45478 logg=5.99\n", + "Changing to logg=5.00 for T= 45509 logg=5.99\n", + "Changing to logg=5.00 for T= 45646 logg=5.99\n", + "Changing to logg=5.00 for T= 45825 logg=5.99\n", + "Changing to logg=5.00 for T= 45983 logg=6.00\n", + "Changing to logg=5.00 for T= 46142 logg=6.00\n", + "Changing to logg=5.00 for T= 46313 logg=6.00\n", + "Changing to logg=5.00 for T= 46473 logg=6.00\n", + "Changing to logg=5.00 for T= 46623 logg=6.00\n", + "Changing to logg=5.00 for T= 46784 logg=6.00\n", + "Changing to logg=5.00 for T= 46946 logg=6.00\n", + "Changing to logg=5.00 for T= 47109 logg=6.00\n", + "Changing to logg=5.00 for T= 47261 logg=6.00\n", + "Changing to logg=5.00 for T= 47413 logg=6.01\n", + "Changing to logg=5.00 for T= 47566 logg=6.01\n", + "Changing to logg=5.00 for T= 47720 logg=6.01\n", + "Changing to logg=5.00 for T= 47863 logg=6.01\n", + "Changing to logg=5.00 for T= 48029 logg=6.01\n", + "Changing to logg=5.00 for T= 48173 logg=6.01\n", + "Changing to logg=5.00 for T= 48317 logg=6.02\n", + "Changing to logg=5.00 for T= 48473 logg=6.02\n", + "Changing to logg=5.00 for T= 48618 logg=6.02\n", + "Changing to logg=5.00 for T= 48753 logg=6.02\n", + "Changing to logg=5.00 for T= 48910 logg=6.02\n", + "Changing to logg=5.00 for T= 49057 logg=6.02\n", + "Changing to logg=5.00 for T= 49193 logg=6.02\n", + "Changing to logg=5.00 for T= 49329 logg=6.03\n", + "Changing to logg=5.00 for T= 49431 logg=6.03\n", + "Changing to logg=5.00 for T= 49465 logg=6.03\n", + "Changing to logg=5.00 for T= 49614 logg=6.03\n", + "Changing to logg=5.00 for T= 49751 logg=6.03\n", + "Changing to logg=5.00 for T= 49888 logg=6.03\n", + "Changing to T= 50000 for T= 50026 logg=6.03\n", + "Changing to logg=5.00 for T= 50026 logg=6.03\n", + "Changing to T= 50000 for T= 50176 logg=6.03\n", + "Changing to logg=5.00 for T= 50176 logg=6.03\n", + "Changing to T= 50000 for T= 50304 logg=6.03\n", + "Changing to logg=5.00 for T= 50304 logg=6.03\n", + "Changing to T= 50000 for T= 50431 logg=6.04\n", + "Changing to logg=5.00 for T= 50431 logg=6.04\n", + "Changing to T= 50000 for T= 50559 logg=6.04\n", + "Changing to logg=5.00 for T= 50559 logg=6.04\n", + "Changing to T= 50000 for T= 50699 logg=6.04\n", + "Changing to logg=5.00 for T= 50699 logg=6.04\n", + "Changing to T= 50000 for T= 50839 logg=6.04\n", + "Changing to logg=5.00 for T= 50839 logg=6.04\n", + "Changing to T= 50000 for T= 50957 logg=6.04\n", + "Changing to logg=5.00 for T= 50957 logg=6.04\n", + "Changing to T= 50000 for T= 51086 logg=6.04\n", + "Changing to logg=5.00 for T= 51086 logg=6.04\n", + "Changing to T= 50000 for T= 51215 logg=6.04\n", + "Changing to logg=5.00 for T= 51215 logg=6.04\n", + "Changing to T= 50000 for T= 51345 logg=6.04\n", + "Changing to logg=5.00 for T= 51345 logg=6.04\n", + "Changing to T= 50000 for T= 51475 logg=6.04\n", + "Changing to logg=5.00 for T= 51475 logg=6.04\n", + "Changing to T= 50000 for T= 51606 logg=6.04\n", + "Changing to logg=5.00 for T= 51606 logg=6.04\n", + "Changing to T= 50000 for T= 51737 logg=6.05\n", + "Changing to logg=5.00 for T= 51737 logg=6.05\n", + "Changing to T= 50000 for T= 51868 logg=6.05\n", + "Changing to logg=5.00 for T= 51868 logg=6.05\n", + "Changing to T= 50000 for T= 52000 logg=6.05\n", + "Changing to logg=5.00 for T= 52000 logg=6.05\n", + "Changing to T= 50000 for T= 52119 logg=6.05\n", + "Changing to logg=5.00 for T= 52119 logg=6.05\n", + "Changing to T= 50000 for T= 52240 logg=6.05\n", + "Changing to logg=5.00 for T= 52240 logg=6.05\n", + "Changing to T= 50000 for T= 52384 logg=6.05\n", + "Changing to logg=5.00 for T= 52384 logg=6.05\n", + "Changing to T= 50000 for T= 52505 logg=6.05\n", + "Changing to logg=5.00 for T= 52505 logg=6.05\n", + "Changing to T= 50000 for T= 52626 logg=6.05\n", + "Changing to logg=5.00 for T= 52626 logg=6.05\n", + "Changing to T= 50000 for T= 52747 logg=6.05\n", + "Changing to logg=5.00 for T= 52747 logg=6.05\n", + "Changing to T= 50000 for T= 52759 logg=6.05\n", + "Changing to logg=5.00 for T= 52759 logg=6.05\n", + "Changing to T= 50000 for T= 52857 logg=6.05\n", + "Changing to logg=5.00 for T= 52857 logg=6.05\n", + "Changing to T= 50000 for T= 52991 logg=6.05\n", + "Changing to logg=5.00 for T= 52991 logg=6.05\n", + "Changing to T= 50000 for T= 53125 logg=6.06\n", + "Changing to logg=5.00 for T= 53125 logg=6.06\n", + "Changing to T= 50000 for T= 53235 logg=6.06\n", + "Changing to logg=5.00 for T= 53235 logg=6.06\n", + "Changing to T= 50000 for T= 53358 logg=6.06\n", + "Changing to logg=5.00 for T= 53358 logg=6.06\n", + "Changing to T= 50000 for T= 53481 logg=6.06\n", + "Changing to logg=5.00 for T= 53481 logg=6.06\n", + "Changing to T= 50000 for T= 53592 logg=6.06\n", + "Changing to logg=5.00 for T= 53592 logg=6.06\n", + "Changing to T= 50000 for T= 53728 logg=6.06\n", + "Changing to logg=5.00 for T= 53728 logg=6.06\n", + "Changing to T= 50000 for T= 53864 logg=6.06\n", + "Changing to logg=5.00 for T= 53864 logg=6.06\n", + "Changing to T= 50000 for T= 53976 logg=6.07\n", + "Changing to logg=5.00 for T= 53976 logg=6.07\n", + "Changing to T= 50000 for T= 54100 logg=6.07\n", + "Changing to logg=5.00 for T= 54100 logg=6.07\n", + "Changing to T= 50000 for T= 54225 logg=6.07\n", + "Changing to logg=5.00 for T= 54225 logg=6.07\n", + "Changing to T= 50000 for T= 54363 logg=6.07\n", + "Changing to logg=5.00 for T= 54363 logg=6.07\n", + "Changing to T= 50000 for T= 54475 logg=6.07\n", + "Changing to logg=5.00 for T= 54475 logg=6.07\n", + "Changing to T= 50000 for T= 54588 logg=6.07\n", + "Changing to logg=5.00 for T= 54588 logg=6.07\n", + "Changing to T= 50000 for T= 54714 logg=6.08\n", + "Changing to logg=5.00 for T= 54714 logg=6.08\n", + "Changing to T= 50000 for T= 54853 logg=6.08\n", + "Changing to logg=5.00 for T= 54853 logg=6.08\n", + "Changing to T= 50000 for T= 54967 logg=6.08\n", + "Changing to logg=5.00 for T= 54967 logg=6.08\n", + "Changing to T= 50000 for T= 55093 logg=6.08\n", + "Changing to logg=5.00 for T= 55093 logg=6.08\n", + "Changing to T= 50000 for T= 55208 logg=6.08\n", + "Changing to logg=5.00 for T= 55208 logg=6.08\n", + "Changing to T= 50000 for T= 55322 logg=6.09\n", + "Changing to logg=5.00 for T= 55322 logg=6.09\n", + "Changing to T= 50000 for T= 55450 logg=6.09\n", + "Changing to logg=5.00 for T= 55450 logg=6.09\n", + "Changing to T= 50000 for T= 55590 logg=6.09\n", + "Changing to logg=5.00 for T= 55590 logg=6.09\n", + "Changing to T= 50000 for T= 55719 logg=6.09\n", + "Changing to logg=5.00 for T= 55719 logg=6.09\n", + "Changing to T= 50000 for T= 55834 logg=6.09\n", + "Changing to logg=5.00 for T= 55834 logg=6.09\n", + "Changing to T= 50000 for T= 55873 logg=6.09\n", + "Changing to logg=5.00 for T= 55873 logg=6.09\n", + "Changing to T= 50000 for T= 55963 logg=6.09\n", + "Changing to logg=5.00 for T= 55963 logg=6.09\n", + "Changing to T= 50000 for T= 56092 logg=6.10\n", + "Changing to logg=5.00 for T= 56092 logg=6.10\n", + "Changing to T= 50000 for T= 56234 logg=6.10\n", + "Changing to logg=5.00 for T= 56234 logg=6.10\n", + "Changing to T= 50000 for T= 56364 logg=6.10\n", + "Changing to logg=5.00 for T= 56364 logg=6.10\n", + "Changing to T= 50000 for T= 56507 logg=6.10\n", + "Changing to logg=5.00 for T= 56507 logg=6.10\n", + "Changing to T= 50000 for T= 56624 logg=6.11\n", + "Changing to logg=5.00 for T= 56624 logg=6.11\n", + "Changing to T= 50000 for T= 56754 logg=6.11\n", + "Changing to logg=5.00 for T= 56754 logg=6.11\n", + "Changing to T= 50000 for T= 56885 logg=6.11\n", + "Changing to logg=5.00 for T= 56885 logg=6.11\n", + "Changing to T= 50000 for T= 57030 logg=6.11\n", + "Changing to logg=5.00 for T= 57030 logg=6.11\n", + "Changing to T= 50000 for T= 57161 logg=6.11\n", + "Changing to logg=5.00 for T= 57161 logg=6.11\n", + "Changing to T= 50000 for T= 57293 logg=6.11\n", + "Changing to logg=5.00 for T= 57293 logg=6.11\n", + "Changing to T= 50000 for T= 57425 logg=6.11\n", + "Changing to logg=5.00 for T= 57425 logg=6.11\n", + "Changing to T= 50000 for T= 57570 logg=6.11\n", + "Changing to logg=5.00 for T= 57570 logg=6.11\n", + "Changing to T= 50000 for T= 57703 logg=6.11\n", + "Changing to logg=5.00 for T= 57703 logg=6.11\n", + "Changing to T= 50000 for T= 57850 logg=6.12\n", + "Changing to logg=5.00 for T= 57850 logg=6.12\n", + "Changing to T= 50000 for T= 57983 logg=6.12\n", + "Changing to logg=5.00 for T= 57983 logg=6.12\n", + "Changing to T= 50000 for T= 58117 logg=6.12\n", + "Changing to logg=5.00 for T= 58117 logg=6.12\n", + "Changing to T= 50000 for T= 58251 logg=6.13\n", + "Changing to logg=5.00 for T= 58251 logg=6.13\n", + "Changing to T= 50000 for T= 58398 logg=6.13\n", + "Changing to logg=5.00 for T= 58398 logg=6.13\n", + "Changing to T= 50000 for T= 58546 logg=6.13\n", + "Changing to logg=5.00 for T= 58546 logg=6.13\n", + "Changing to T= 50000 for T= 58695 logg=6.13\n", + "Changing to logg=5.00 for T= 58695 logg=6.13\n", + "Changing to T= 50000 for T= 58844 logg=6.14\n", + "Changing to logg=5.00 for T= 58844 logg=6.14\n", + "Changing to T= 50000 for T= 58993 logg=6.14\n", + "Changing to logg=5.00 for T= 58993 logg=6.14\n", + "Changing to T= 50000 for T= 59143 logg=6.14\n", + "Changing to logg=5.00 for T= 59143 logg=6.14\n", + "Changing to T= 50000 for T= 59293 logg=6.15\n", + "Changing to logg=5.00 for T= 59293 logg=6.15\n", + "Changing to T= 50000 for T= 59334 logg=6.15\n", + "Changing to logg=5.00 for T= 59334 logg=6.15\n", + "Changing to T= 50000 for T= 59457 logg=6.15\n", + "Changing to logg=5.00 for T= 59457 logg=6.15\n", + "Changing to T= 50000 for T= 59621 logg=6.16\n", + "Changing to logg=5.00 for T= 59621 logg=6.16\n", + "Changing to T= 50000 for T= 59800 logg=6.16\n", + "Changing to logg=5.00 for T= 59800 logg=6.16\n", + "Changing to T= 50000 for T= 59965 logg=6.16\n", + "Changing to logg=5.00 for T= 59965 logg=6.16\n", + "Changing to T= 50000 for T= 60159 logg=6.17\n", + "Changing to logg=5.00 for T= 60159 logg=6.17\n", + "Changing to T= 50000 for T= 60353 logg=6.17\n", + "Changing to logg=5.00 for T= 60353 logg=6.17\n", + "Changing to T= 50000 for T= 60534 logg=6.18\n", + "Changing to logg=5.00 for T= 60534 logg=6.18\n", + "Changing to T= 50000 for T= 60758 logg=6.18\n", + "Changing to logg=5.00 for T= 60758 logg=6.18\n", + "Changing to T= 50000 for T= 60968 logg=6.18\n", + "Changing to logg=5.00 for T= 60968 logg=6.18\n", + "Changing to T= 50000 for T= 61235 logg=6.19\n", + "Changing to logg=5.00 for T= 61235 logg=6.19\n", + "Changing to T= 50000 for T= 61504 logg=6.20\n", + "Changing to logg=5.00 for T= 61504 logg=6.20\n", + "Changing to T= 50000 for T= 61844 logg=6.21\n", + "Changing to logg=5.00 for T= 61844 logg=6.21\n", + "Changing to T= 50000 for T= 62287 logg=6.23\n", + "Changing to logg=5.00 for T= 62287 logg=6.23\n", + "Changing to T= 50000 for T= 63038 logg=6.27\n", + "Changing to logg=5.00 for T= 63038 logg=6.27\n", + "Changing to T= 50000 for T= 64239 logg=6.32\n", + "Changing to logg=5.00 for T= 64239 logg=6.32\n", + "Changing to T= 50000 for T= 65464 logg=6.37\n", + "Changing to logg=5.00 for T= 65464 logg=6.37\n", + "Changing to T= 50000 for T= 66696 logg=6.42\n", + "Changing to logg=5.00 for T= 66696 logg=6.42\n", + "Changing to T= 50000 for T= 67967 logg=6.47\n", + "Changing to logg=5.00 for T= 67967 logg=6.47\n", + "Changing to T= 50000 for T= 69263 logg=6.53\n", + "Changing to logg=5.00 for T= 69263 logg=6.53\n", + "Changing to T= 50000 for T= 71367 logg=6.63\n", + "Changing to logg=5.00 for T= 71367 logg=6.63\n", + "Isochrone generation took 150.174449 s.\n", + "Making photometry for isochrone: log(t) = 6.70 AKs = 0.80 dist = 4000\n", + " Starting at: 2024-07-10 12:22:04.577984 Usually takes ~5 minutes\n", "Starting filter: wfc3,ir,f127m Elapsed time: 0.00 seconds\n", "Starting synthetic photometry\n", - "M = 0.100 Msun T = 3021 K m_hst_f127m = 23.96\n", - "M = 1.336 Msun T = 6744 K m_hst_f127m = 18.16\n", - "M = 4.781 Msun T = 15818 K m_hst_f127m = 14.97\n", - "M = 6.899 Msun T = 17664 K m_hst_f127m = 13.11\n", - "M = 6.911 Msun T = 6971 K m_hst_f127m = 10.61\n", - "M = 6.988 Msun T = 3957 K m_hst_f127m = 9.32\n", - "M = 7.297 Msun T = 3738 K m_hst_f127m = 8.65\n", - "Starting filter: wfc3,ir,f139m Elapsed time: 18.77 seconds\n", + "M = 0.070 Msun T = 2928 K m_hst_f127m = 22.41\n", + "M = 1.175 Msun T = 4611 K m_hst_f127m = 18.72\n", + "M = 1.650 Msun T = 5355 K m_hst_f127m = 17.71\n", + "M = 1.973 Msun T = 6311 K m_hst_f127m = 16.34\n", + "M = 2.292 Msun T = 9669 K m_hst_f127m = 16.68\n", + "M = 2.648 Msun T = 11771 K m_hst_f127m = 16.64\n", + "M = 2.819 Msun T = 12291 K m_hst_f127m = 16.51\n", + "M = 3.075 Msun T = 13014 K m_hst_f127m = 16.33\n", + "M = 3.332 Msun T = 13709 K m_hst_f127m = 16.16\n", + "M = 3.482 Msun T = 14099 K m_hst_f127m = 16.07\n", + "M = 3.615 Msun T = 14438 K m_hst_f127m = 15.99\n", + "M = 8.977 Msun T = 23502 K m_hst_f127m = 14.07\n", + "M = 49.000 Msun T = 32344 K m_hst_f127m = 9.23\n", + "M = 50.757 Msun T = 31046 K m_hst_f127m = 9.25\n", + "M = 52.540 Msun T = 41937 K m_hst_f127m = 10.13\n", + "M = 54.406 Msun T = 48753 K m_hst_f127m = 10.85\n", + "M = 56.318 Msun T = 65464 K m_hst_f127m = 12.01\n", + "Starting filter: wfc3,ir,f139m Elapsed time: 44.06 seconds\n", "Starting synthetic photometry\n", - "M = 0.100 Msun T = 3021 K m_hst_f139m = 23.57\n", - "M = 1.336 Msun T = 6744 K m_hst_f139m = 17.69\n", - "M = 4.781 Msun T = 15818 K m_hst_f139m = 14.59\n", - "M = 6.899 Msun T = 17664 K m_hst_f139m = 12.74\n", - "M = 6.911 Msun T = 6971 K m_hst_f139m = 10.18\n", - "M = 6.988 Msun T = 3957 K m_hst_f139m = 8.69\n", - "M = 7.297 Msun T = 3738 K m_hst_f139m = 8.01\n", - "Starting filter: wfc3,ir,f153m Elapsed time: 38.82 seconds\n", + "M = 0.070 Msun T = 2928 K m_hst_f139m = 22.11\n", + "M = 1.175 Msun T = 4611 K m_hst_f139m = 18.14\n", + "M = 1.650 Msun T = 5355 K m_hst_f139m = 17.18\n", + "M = 1.973 Msun T = 6311 K m_hst_f139m = 15.86\n", + "M = 2.292 Msun T = 9669 K m_hst_f139m = 16.28\n", + "M = 2.648 Msun T = 11771 K m_hst_f139m = 16.25\n", + "M = 2.819 Msun T = 12291 K m_hst_f139m = 16.12\n", + "M = 3.075 Msun T = 13014 K m_hst_f139m = 15.94\n", + "M = 3.332 Msun T = 13709 K m_hst_f139m = 15.77\n", + "M = 3.482 Msun T = 14099 K m_hst_f139m = 15.68\n", + "M = 3.615 Msun T = 14438 K m_hst_f139m = 15.60\n", + "M = 8.977 Msun T = 23502 K m_hst_f139m = 13.70\n", + "M = 49.000 Msun T = 32344 K m_hst_f139m = 8.87\n", + "M = 50.757 Msun T = 31046 K m_hst_f139m = 8.88\n", + "M = 52.540 Msun T = 41937 K m_hst_f139m = 9.77\n", + "M = 54.406 Msun T = 48753 K m_hst_f139m = 10.50\n", + "M = 56.318 Msun T = 65464 K m_hst_f139m = 11.66\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 90.13 seconds\n", "Starting synthetic photometry\n", - "M = 0.100 Msun T = 3021 K m_hst_f153m = 22.93\n", - "M = 1.336 Msun T = 6744 K m_hst_f153m = 17.23\n", - "M = 4.781 Msun T = 15818 K m_hst_f153m = 14.22\n", - "M = 6.899 Msun T = 17664 K m_hst_f153m = 12.37\n", - "M = 6.911 Msun T = 6971 K m_hst_f153m = 9.74\n", - "M = 6.988 Msun T = 3957 K m_hst_f153m = 8.00\n", - "M = 7.297 Msun T = 3738 K m_hst_f153m = 7.28\n", - " Time taken: 60.42 seconds\n" + "M = 0.070 Msun T = 2928 K m_hst_f153m = 21.45\n", + "M = 1.175 Msun T = 4611 K m_hst_f153m = 17.51\n", + "M = 1.650 Msun T = 5355 K m_hst_f153m = 16.63\n", + "M = 1.973 Msun T = 6311 K m_hst_f153m = 15.37\n", + "M = 2.292 Msun T = 9669 K m_hst_f153m = 15.89\n", + "M = 2.648 Msun T = 11771 K m_hst_f153m = 15.87\n", + "M = 2.819 Msun T = 12291 K m_hst_f153m = 15.75\n", + "M = 3.075 Msun T = 13014 K m_hst_f153m = 15.57\n", + "M = 3.332 Msun T = 13709 K m_hst_f153m = 15.40\n", + "M = 3.482 Msun T = 14099 K m_hst_f153m = 15.31\n", + "M = 3.615 Msun T = 14438 K m_hst_f153m = 15.23\n", + "M = 8.977 Msun T = 23502 K m_hst_f153m = 13.34\n", + "M = 49.000 Msun T = 32344 K m_hst_f153m = 8.52\n", + "M = 50.757 Msun T = 31046 K m_hst_f153m = 8.53\n", + "M = 52.540 Msun T = 41937 K m_hst_f153m = 9.42\n", + "M = 54.406 Msun T = 48753 K m_hst_f153m = 10.15\n", + "M = 56.318 Msun T = 65464 K m_hst_f153m = 11.31\n", + " Time taken: 137.05 seconds\n" ] } ], "source": [ "# Define isochrone parameters\n", - "logAge = np.log10(5*10**7.) # Age in log(years)\n", + "logAge = np.log10(5*10**6.) # Age in log(years)\n", "AKs = 0.8 # extinction in mags\n", "dist = 4000 # distance in parsec\n", "metallicity = 0 # Metallicity in [M/H]\n", "\n", "# Define evolution/atmosphere models and extinction law\n", - "evo_model = evolution.MISTv1() \n", + "evo_model = evolution.MergedBaraffePisaEkstromParsec() \n", "atm_func = atmospheres.get_merged_atmosphere\n", "red_law = reddening.RedLawHosek18b()\n", "\n", @@ -119,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "id": "5a6e081f-84d3-4efb-a6d5-59d5f60d845f", "metadata": {}, "outputs": [ @@ -130,21 +618,21 @@ " L Teff ... m_hst_f153m \n", " W K ... \n", "---------------------- ------------------ ... ------------------\n", - "1.6794720325683452e+24 3020.5752857611837 ... 22.92776805171169\n", - " 1.7370492282378e+24 3026.596844621303 ... 22.89188358781423\n", - "1.8137828986723996e+24 3034.2260855891886 ... 22.845919008054395\n", - "1.8944919038459294e+24 3041.88871922475 ... 22.799687950466488\n", - "1.9794440999691394e+24 3049.612041774559 ... 22.753170128603966\n", - "2.0689567354242308e+24 3057.411643839487 ... 22.706329930464427\n", + " 6.591312905046435e+24 2928.195035574271 ... 21.454394826602254\n", + " 7.13135291244921e+24 2943.7437337117412 ... 21.3665241093368\n", + " 7.761969450327467e+24 2958.6936525140045 ... 21.272757288262653\n", + " 8.296060854703104e+24 2975.089258880875 ... 21.19905370697203\n", + " 8.830228238420424e+24 2992.264636608189 ... 21.13035092375595\n", + " 8.860779496066351e+24 3009.539168873201 ... 21.12685391099373\n", " ... ... ... ...\n", - " 8.450906433033863e+30 3441.3110094585145 ... 6.433232663228037\n", - " 8.557948261819579e+30 3436.3422272496196 ... 6.420004996757628\n", - " 8.585406026317698e+30 3435.514440695956 ... 6.416813383458505\n", - " 8.699093176188767e+30 3430.8941764462447 ... 6.403203879003122\n", - " 8.881152106206099e+30 3423.152501093308 ... 6.381645353870332\n", - " 8.910673547171454e+30 3421.7638232666736 ... 6.378271217031613\n", - " 9.076365228968617e+30 3414.983593067963 ... 6.359238998015297\n", - "Length = 666 rows\n" + "2.1036424651269878e+32 63037.64788294872 ... 11.22232056398674\n", + "2.1760653272980036e+32 64239.1816824387 ... 11.267570563986745\n", + " 2.250981517613893e+32 65463.61740672747 ... 11.312820563986737\n", + "2.3279407848949313e+32 66696.03248243559 ... 11.357320563986734\n", + "2.4080856466771184e+32 67967.29719504561 ... 11.40257056398674\n", + "2.4909896846856737e+32 69262.79294373626 ... 11.447820563986737\n", + "2.6240489308607902e+32 71367.42051224748 ... 11.521320563986743\n", + "Length = 1605 rows\n" ] } ], @@ -157,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "1205c450-d7f2-4ad7-86b0-006762426d5c", "metadata": {}, "outputs": [ @@ -165,7 +653,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1 M_sun: F127M = 19.299 mag, F139M = 18.792 mag, F153M = 18.265 mag\n" + "1 M_sun: F127M = 19.051 mag, F139M = 18.453 mag, F153M = 17.785 mag\n" ] } ], @@ -181,23 +669,23 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "a8169db9-486e-4d20-9a09-dfcd7ebbd3e1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -230,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "id": "493b8648-1c3b-4aab-858f-2f955195e634", "metadata": {}, "outputs": [], @@ -246,7 +734,7 @@ "# the entire exponent, including the negative sign. For example, if dN/dm $\\propto$ m^-alpha,\n", "# then you would use the value \"-2.3\" to specify an IMF with alpha = 2.3. \n", "\n", - "my_imf = imf.Muzic_2017(multiplicity=imf_multi)" + "my_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)" ] }, { @@ -259,7 +747,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "id": "0bf0fed8-2b70-41be-b950-83f1362af9f0", "metadata": {}, "outputs": [ @@ -267,8 +755,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 177 stars out of mass range\n", - "Found 47 companions out of stellar mass range\n" + "Found 60499 stars out of mass range\n", + "Found 11203 companions out of stellar mass range\n" ] } ], @@ -282,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "id": "c8de1a27-0b07-404c-a13d-8434e204017d", "metadata": {}, "outputs": [ @@ -292,22 +780,22 @@ "text": [ " mass isMultiple ... m_hst_f153m N_companions\n", "------------------- ---------- ... ------------------ ------------\n", - "0.35277838228082964 True ... 21.056406061698077 2\n", - " 0.4109143920284334 False ... 20.80304487623113 0\n", - "0.44680797582475024 False ... 20.65082717236568 0\n", - "0.16814683886383167 False ... 22.19558068545299 0\n", - " 0.1998139027268053 False ... 21.941926401844015 0\n", - " 0.4740288564083312 False ... 20.534885068309187 0\n", - " 1.0126972980825961 True ... 17.971915603237292 2\n", + "0.31585425987928323 True ... 18.92999676208373 2\n", + "0.11445929647600789 False ... 20.934530625744358 0\n", + " 0.645145955409843 False ... 18.42659654946194 0\n", + " 0.2125430430785249 False ... 20.08717879087111 0\n", + " 0.4443648340365779 False ... 18.982532572754547 0\n", + " 0.9427790018124477 True ... 17.5522451444909 1\n", + "0.20316227789117086 False ... 20.155331870086147 0\n", " ... ... ... ... ...\n", - " 5.3779990877122295 True ... 13.776442294620189 1\n", - " 0.6634904439575919 False ... 19.510526216999857 0\n", - " 1.35508336893066 True ... 17.17251870764391 1\n", - " 3.7213803186176304 False ... 14.933652308856155 0\n", - " 1.5278429001189309 False ... 16.86599227492448 0\n", - " 0.8112843101887811 False ... 18.792308025746074 0\n", - "0.14401644779125125 False ... 22.41409431495889 0\n", - "Length = 411 rows\n" + "0.07822412513422393 False ... 21.306060916420513 0\n", + "0.17640075706320751 False ... 20.342815617558465 0\n", + "0.09652737394053626 False ... 21.126211979448616 0\n", + " 0.1622394758046402 False ... 20.45232101696704 0\n", + "0.39015806512343487 False ... 19.2083064020004 0\n", + "0.15023739165143188 False ... 20.558755046061993 0\n", + "0.24854036705122345 False ... 19.857602142038818 0\n", + "Length = 123017 rows\n" ] } ], @@ -318,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "id": "6b10a958-a0f8-443c-be6b-e07c5de2d4dc", "metadata": {}, "outputs": [ @@ -328,22 +816,22 @@ "text": [ "system_idx mass ... m_hst_f139m m_hst_f153m \n", "---------- ------------------- ... ------------------ ------------------\n", - " 0 0.09565348913151368 ... nan nan\n", - " 0 0.03322585947572763 ... nan nan\n", - " 6 0.08726287084485738 ... nan nan\n", - " 6 0.6433751483393655 ... 20.33081658819904 19.657228868526335\n", - " 7 0.06161747316330432 ... nan nan\n", - " 13 1.7263481600403079 ... 17.018762642329463 16.60938637243561\n", - " 14 0.2737246431039749 ... 22.090906238571662 21.4548720525631\n", + " 0 0.0460428924123163 ... nan nan\n", + " 0 0.24553283737800713 ... 20.536185197536078 19.87454892207238\n", + " 5 0.4308071906008011 ... 19.720217136851623 19.036277872397545\n", + " 8 0.05926443486987895 ... nan nan\n", + " 10 0.37939808636570665 ... 19.922230480262495 19.247518348082274\n", + " 15 0.12172477389722892 ... 21.49272357482301 20.84357767155626\n", + " 16 0.4299286896336996 ... 19.72390856044099 19.040131407955787\n", " ... ... ... ... ...\n", - " 396 0.03899827614913988 ... nan nan\n", - " 398 0.26985155635770386 ... 22.112746284229008 21.476822094207677\n", - " 400 2.0771742779244846 ... 16.6610738152462 16.27195342256348\n", - " 400 1.8654858596867614 ... 16.870042360441936 16.47226405265614\n", - " 403 1.3729396331088282 ... 17.601870317877122 17.140899115559176\n", - " 404 1.222648505597827 ... 18.015195949214007 17.53027863177404\n", - " 406 0.1534511179062777 ... 22.950623123620353 22.315542833099645\n", - "Length = 208 rows\n" + " 122997 0.24062971315528156 ... 20.56327953828116 19.90217696791337\n", + " 122998 0.44959030611032147 ... 19.653329536810013 18.96646512002086\n", + " 123000 0.3746449017452262 ... 19.94002433187758 19.265866413907457\n", + " 123000 0.3396256601362069 ... 20.078397682202386 19.40756594570426\n", + " 123006 0.08879348065800587 ... 21.800450628386503 21.146929171115758\n", + " 123006 0.06195810103955034 ... nan nan\n", + " 123006 0.04465994487393095 ... nan nan\n", + "Length = 34087 rows\n" ] } ], @@ -354,23 +842,23 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "id": "095a963e-c0de-4bbc-a03f-471f3cdb7b91", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 31, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -399,9 +887,143 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "d694ed41-c732-4093-9f0a-ad021fe8397a", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "minimum primary mass: 0.07000163959328269\n", + "minimum companion mass: 0.01000292189962548\n", + "maximum primary mass: 55.80384055631119\n", + "maximum companion mass: 44.92501458090958\n" + ] + } + ], + "source": [ + "print(\"minimum primary mass: \" + str(np.min(clust['mass'])))\n", + "print(\"minimum companion mass: \" + str(np.min(cluster.companions['mass'])))\n", + "print(\"maximum primary mass: \" + str(np.max(clust['mass'])))\n", + "print(\"maximum companion mass: \" + str(np.max(cluster.companions['mass'])))" + ] + }, + { + "cell_type": "markdown", + "id": "fa4c660d-ea4e-491a-95db-278617ee1757", + "metadata": {}, + "source": [ + "#### Creating a histogram to compare Weidner Kroupa and Muzic" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "c2540680-d69f-427f-b0d1-55130826ad8a", + "metadata": {}, + "outputs": [], + "source": [ + "#generate clusters for each class\n", + "w_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)\n", + "m_imf = imf.Muzic_2017(multiplicity=imf_multi)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4d1c647e-8e1a-4a24-8b20-26a8b14f4660", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 58485 stars out of mass range\n", + "Found 10731 companions out of stellar mass range\n", + "Found 256 stars out of mass range\n", + "Found 83 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "# Define total cluster mass\n", + "mass = 10**5.\n", + "\n", + "# Make cluster objects for each\n", + "w_cluster = synthetic.ResolvedCluster(my_iso, w_imf, mass)\n", + "m_cluster = synthetic.ResolvedCluster(my_iso, m_imf, mass)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "eb3847e7-c184-4d49-a7ec-69d0a1e50fea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "primary masses in Weidner Kroupa cluster: 119280\n", + "companion masses in Weidner Kroupa cluster: 32991\n", + "primary masses in Muzic cluster: 1113\n", + "companion masses in Muzic cluster: 682\n" + ] + } + ], + "source": [ + "#show total numbers for each cluster\n", + "print(\"primary masses in Weidner Kroupa cluster: \" + str(len(w_cluster.star_systems)))\n", + "print(\"companion masses in Weidner Kroupa cluster: \" + str(len(w_cluster.companions)))\n", + "print(\"primary masses in Muzic cluster: \" + str(len(m_cluster.star_systems)))\n", + "print(\"companion masses in Muzic cluster: \" + str(len(m_cluster.companions)))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "4e2fe19b-9da8-4388-92bd-c1390444e8c4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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9ffj6+mL79u24dOkSGjVq9Mj4sn9YsrKyKsReuXJFZz1TTcnOzsYLL7wgbd+/fx83btyQctm1axeuXLmCPXv2SLNLAKpcZ/CoxdHl9evXD/369UNhYSEOHjyImJgYBAYGokmTJvDy8oK1tTWysrIqvO/KlSsAUGPj8az6AR4Ui35+fti0aRNycnJgZ2dXZWxZvz///DOcnJz+ct9xcXEwNDTEli1b0KBBA2l/VbfAV+d7Wd7w4cMxZMgQlJaWYvHixVXGrV27Fj4+PhViKvtDOWzYMAwbNgz5+fn4z3/+g2nTpsHf3x9//vknnJycYGpqiunTp2P69OnSTNfkyZPRt29f/PHHH098LocPH0Z2dvZjHw3Spk0bxMXFQQiB48ePIzY2FjNmzICxsTEmT54sq6/qjHlZUf2w7OxsAP/7t6Ts+1xYWKgTV92Cu7yn8Tvj5OSEFStWAAD+/PNP/Pjjj4iKikJRURGWLFnyl/KlR+NME9UZkZGREEIgJCQERUVFFdqLi4vxyy+/AABeffVVAA/+kDwsOTkZ6enp0p1oNan882d+/PFH3L9/X1qAW/aPePk7/5YuXVpjOSiVSnh7e0sLh8vuDPP19cWpU6dw9OhRnfhvv/0WCoWiwq38T8rX11cqDsv3Y2JionMbulxXr16t9PJCSUkJzpw5AxMTEzRs2BBA1TNiPXv2hIGBAc6ePYtOnTpV+qoOhUIBAwMDncskBQUFWLNmTTXP7vFef/11vP766xg+fPgjx0+hUFT42Tp+/PgjL4mampqid+/e+Pjjj1FUVISTJ09WiLG3t0dwcDDefvttnD59Gnfv3n2i87h58yZGjx4NQ0NDjB8/XtZ7FAoF2rVrh3nz5qFhw4Y6P79KpbLGLhvevn0bmzdv1tn33XffQU9PD6+88goASHesPvwwVQAV3leWG/D4mVng6f9utmjRAp988gnatGlToQ+qeZxpojrDy8sLixcvRmhoKDw8PDBmzBi0bt0axcXFOHbsGJYtWwZ3d3f07dsXrq6uGDlyJBYuXAg9PT307t1bunvO0dFR9j/a1bFhwwYYGBigR48e0t1z7dq1w6BBgwAAXbp0gaWlJUaPHo1p06bB0NAQ69atw++///6X+p06dSouXboEX19fNGrUCLdu3cK//vUvnfVS48ePx7fffos+ffpgxowZcHJywtatW/H1119jzJgxT7TepjLTpk3Dli1b0K1bN0ydOhVWVlZYt24dtm7dilmzZj3yzruqrFmzBkuXLkVgYCBefPFFqFQqXLp0Cd988w1OnjyJqVOnSpdNy579869//QtDhw6FoaEhXF1d0aRJE8yYMQMff/wxzp07h169esHS0hJXr17F4cOHpZkVufr06YO5c+ciMDAQI0eOxI0bNzBnzpxKH4XxVzVo0AA///zzY+P8/f3x6aefYtq0afD29sbp06cxY8YMODs769x5GRISAmNjY3Tt2hUODg7Izs5GTEwMVCqVtD7O09MT/v7+aNu2LSwtLZGeno41a9bAy8urysuDDztz5gwOHjyI0tJS3LhxA4cOHcKKFSuQl5eHb7/9Fq1bt67yvVu2bMHXX3+N/v37o2nTphBCYMOGDbh16xZ69OghxbVp0wZ79uzBL7/8AgcHB5ibm8PV1fWxuVXG2toaY8aMQWZmJlq0aIFt27Zh+fLlGDNmjHR3pFqtRvfu3RETEwNLS0s4OTlh586d0uX1h5X9HH7xxRfo3bs39PX10bZtW+nn9GE1/bt5/PhxjB07FgMHDoSLiwuMjIywa9cuHD9+XPYsHf0FtbkKnagyqampYujQoaJx48bCyMhImJqaig4dOoipU6eKnJwcKa6kpER88cUXokWLFsLQ0FDY2NiIIUOGiIsXL+ocz9vbW7Ru3bpCP05OTqJPnz4V9qPcXV9ld1ulpKSIvn37CjMzM2Fubi7efvttcfXqVZ337t+/X3h5eQkTExNha2sr3n33XXH06NEKd988fAdVeeXvntuyZYvo3bu3eOGFF4SRkZGws7MTr732mti7d6/O+y5cuCACAwOFtbW1MDQ0FK6urmL27NmipKREiim7Q2f27NmVnvej7tYqc+LECdG3b1+hUqmEkZGRaNeunc65PXw8OXfPnTp1SkRERIhOnToJW1tbYWBgICwtLYW3t7dYs2ZNhfjIyEih0WiEnp5ehbudNm3aJLp16yYsLCyEUqkUTk5O4p///Kf49ddfpRi5d8+tXLlSuLq6CqVSKZo2bSpiYmLEihUrKtydVdXPUVUe9b0vU9ndc4WFhWLixInihRdeEA0aNBAdO3YUmzZtqvDzsnr1atGtWzdhb28vjIyMhEajEYMGDRLHjx+XYiZPniw6deokLC0tpfMbP368uH79+iPzKrvDrOxlYGAgrK2thZeXl/joo4/E+fPnK7yn/B1tf/zxh3j77bdFs2bNhLGxsVCpVOKll14SsbGxOu9LTU0VXbt2FSYmJgKA9P0pO15ycvJj+xLif7//e/bsEZ06dRJKpVI4ODiIjz76SBQXF+u8PysrS/zzn/8UVlZWQqVSiSFDhkh3+z38M15YWCjeffddYWtrKxQKhU6f5e+eE6JmfzevXr0qgoODRcuWLYWpqakwMzMTbdu2FfPmzdO5646eDoUQj7nVgehvLioqCtOnT8e1a9eeylopIiKqH7imiYiIiEgGFk1EREREMvDyHBEREZEMnGkiIiIikoFFExEREZEMLJqIiIiIZODDLWtQaWkprly5AnNz87/0sQpERET07AghcPv2bWg0GujpVT2fxKKpBl25cqXCp78TERFR/XDx4sVHfvYpi6YaZG5uDuDBoMv59HoiIiKqfXl5eXB0dJT+jleFRVMNKrskZ2FhwaKJiIionnnc0houBCciIiKSgUUTERERkQwsmoiIiIhk4JomIqJ6qrS0FEVFRbWdBlGdZ2hoCH19/b98HBZNRET1UFFRETIyMlBaWlrbqRDVCw0bNoRarf5Lz1Fk0UREVM8IIZCVlQV9fX04Ojo+8mF8RH93QgjcvXsXOTk5AAAHB4cnPhaLJiKieub+/fu4e/cuNBoNTExMajsdojrP2NgYAJCTkwM7O7snvlTH/54QEdUzJSUlAAAjI6NazoSo/ij7D0ZxcfETH4NFExFRPcXPuCSSryZ+X1g0EREREcnANU1ERM8JrVaLu3fvPrP+TExMoFKpnll/lYmNjUV4eDhu3bpVZUxUVBQ2bdqE1NTUZ5YXPZ9YNBERPQe0Wi0Wffopiq9ff2Z9GtrYYOyUKbILpyVLluCDDz5Abm4uDAwe/Pm5c+cOLC0t0blzZ+zdu1eK3bt3L1555RWcPn0aLVq0qPKYb775Jl577bW/diLPSGXF2969e9G3b18EBQVhwYIF9fqS6++//46ZM2di3759uH79Opo0aYLRo0fj/fff14k7ceIExo4di8OHD8PKygqjRo3ClClTdM49KSkJEyZMwMmTJ6HRaDBp0iSMHj260n7j4uLw9ttvo1+/fti0adPTPEUWTUREz4O7d++i+Pp1DDA2hu0zuKPu2t272HD9Ou7evSu7aOrWrRvu3LmDI0eOoHPnzgAeFA1qtRrJycm4e/eutFh3z5490Gg0jyyYgAd3RZXdGVWbSkpKoFAoqvX4h61bt2LgwIH44IMPMH369EpjiouLYWhoWFNpPlUpKSmwtbXF2rVr4ejoiP3792PkyJHQ19fH2LFjAQB5eXno0aMHunXrhuTkZPz5558IDg6GqakpIiIiAAAZGRl47bXXEBISgrVr1+K3335DaGgobG1t8cYbb+j0eeHCBUycOBEvv/zyMzlHrmkiInqO2JqYwMHc/Km/nqQwc3V1hUajwZ49e6R9e/bsQb9+/dCsWTPs379fZ3+3bt1QVFSESZMm4YUXXoCpqSk8PT113h8bG4uGDRvq9DNz5kzY29vD3NwcI0aMwL1793Tag4OD0b9/f8yZMwcODg6wtrbGe++9p3NXldx+t2zZAjc3NyiVSly4cEH2WHz33XcYMGAAZs6cqVMwRUVFoX379li5ciWaNm0KpVIJIQQyMzPRr18/mJmZwcLCAoMGDcLVq1crnNPDwsPD4ePjI237+Phg7NixGDt2LBo2bAhra2t88sknEEJIMWvXrkWnTp1gbm4OtVqNwMBA6flGjzN8+HAsWLAA3t7eaNq0KYYMGYJhw4Zhw4YNUsy6detw7949xMbGwt3dHQMGDMBHH32EuXPnSnksWbIEjRs3xvz589GqVSu8++67GD58OObMmaPTX0lJCQYPHozp06ejadOmsnL8q1g01SNarRZZWVnIysqCVqut7XSIiKrNx8cHu3fvlrZ3794NHx8feHt7S/uLiopw4MABdOvWDcOGDcNvv/2GuLg4HD9+HAMHDkSvXr1w5syZSo//448/Ytq0afj8889x5MgRODg44Ouvv64Qt3v3bpw9exa7d+/G6tWrERsbi9jYWKldTr93795FTEwMvvnmG5w8eRJ2dnayxuCrr77CsGHDsGLFCoSFhVVo/+9//4sff/wR69evly7l9e/fHzdv3kRSUhISExNx9uxZvPnmm7L6e9jq1athYGCAQ4cOYcGCBZg3bx6++eYbqb2oqAiffvopfv/9d2zatAkZGRkIDg6udj9ltFotrKyspO0DBw7A29sbSqVS2tezZ09cuXIF58+fl2L8/Px0jtOzZ08cOXJEp7CdMWMGbG1tMWLEiCfOr7p4ea6eKL9eobprCYiI6gIfHx+MHz8e9+/fR0FBAY4dO4ZXXnkFJSUlWLBgAQDg4MGDKCgogI+PD0JCQnDp0iVoNBoAwMSJExEfH49Vq1YhOjq6wvHnz5+P4cOH49133wUAfPbZZ/j1118rzDZZWlpi0aJF0NfXR8uWLdGnTx/s3LkTISEhOHv2LL7//vvH9ltcXIyvv/4a7dq1k33+6enpGDt2LFasWIEhQ4ZUGlNUVIQ1a9bA1tYWAJCYmIjjx48jIyMDjo6OAIA1a9agdevWSE5Oxosvvii7f0dHR8ybNw8KhQKurq44ceIE5s2bh5CQEAAPZovKNG3aFAsWLMBLL72EO3fuwMzMTHY/wIPi58cff8TWrVulfdnZ2WjSpIlOnL29vdTm7OyM7Oxsad/DMffv38f169fh4OCA3377DStWrHjmi/s501RPPLxeYYCxMYr/fy0BEVF90q1bN+Tn5yM5ORl79+5FixYtYGdnB29vbyQnJyM/Px979uxB48aNcfToUQgh0KJFC5iZmUmvpKQknD17ttLjp6enw8vLS2df+W0AaN26tc5ToR0cHKTLUHL7NTIyQtu2bat1/o0aNULHjh0xa9YsZGVlVRrj5OQkFUxl5+To6CgVTADg5uaGhg0bIj09vVr9d+7cWWfBtZeXF86cOSM9MPXYsWPo168fnJycYG5uLl3ey8zMrFY/J0+eRL9+/TB16lT06NFDp638Yveyy3IP739UzO3btzFkyBAsX74cNjY21crrr+JMUz0jrSMoKKjdRIiInkDz5s3RqFEj7N69G7m5ufD29gYAqNVqODs747fffsPu3bvx6quvorS0FPr6+khJSanwsRfVnfUor/ziaoVCIX34sdx+jY2Nq323m7m5OX799Vf4+flJlyrLZrPKmJqa6mwLISrt5+H9enp6OmuTgOo/+To/Px9+fn7w8/PD2rVrYWtri8zMTPTs2RNFRUWyj3Pq1Cm8+uqrCAkJwSeffKLTplarkZ2drbOvrFgtm12qKsbAwADW1tY4efIkzp8/j759+0rtZd87AwMDnD59Gs2aNZN/4tXAmSYiInqmunXrhj179mDPnj06C5W9vb2xY8cOHDx4EN26dUOHDh1QUlKCnJwcNG/eXOelVqsrPXarVq1w8OBBnX3ltx/nSfqtDktLS/z666+wtLSEj48PLl++/Mh4Nzc3ZGZm4uLFi9K+U6dOQavVolWrVgAAW1vbCjNXlV26qmxsXFxcoK+vjz/++APXr1/HzJkz8fLLL6Nly5ayF4GXOXnyJLp164ahQ4fi888/r9Du5eWF//znPzpFWEJCAjQajXTZzsvLC4mJiTrvS0hIQKdOnWBoaIiWLVvixIkTSE1NlV4BAQHo1q0bUlNTdWbkahqLJiIieqa6deuGffv2ITU1VZppAh4UTcuXL8e9e/fQrVs3tGjRAoMHD8Y777yDDRs2ICMjA8nJyfjiiy+wbdu2So/9/vvvY+XKlVi5ciX+/PNPTJs2DSdPnqxWfk/Sb3WpVCokJCTAxsYGPj4+uHTpUpWx3bt3R9u2bTF48GAcPXoUhw8fxjvvvANvb2906tQJAPDqq6/iyJEj+Pbbb3HmzBlMmzYNaWlpFY518eJFTJgwAadPn8b333+PhQsXSs9Raty4MYyMjLBw4UKcO3cOmzdvxqeffir7nMoKph49emDChAnIzs5GdnY2rl27JsUEBgZCqVQiODgYaWlp2LhxI6KjozFhwgRp1mz06NG4cOECJkyYgPT0dKxcuRIrVqzAxIkTAQANGjSAu7u7zqthw4YwNzeHu7v7U/1MRl6eIyJ6jlx7Rmsd/0o/3bp1Q0FBAVq2bKmz4Nfb2xu3b99Gs2bNpNmCVatW4bPPPkNERAQuX74Ma2treHl5VflAyzfffBNnz57Fhx9+iHv37uGNN97AmDFjsGPHjmrlWN1+n4SFhQV27NiB3r17V7ir8GEKhQKbNm3CuHHj8Morr0BPTw+9evXCwoULpZiePXtiypQpmDRpEu7du4fhw4fjnXfewYkTJ3SO9c4776CgoAAvvfQS9PX1MW7cOIwcORLAg9mq2NhYfPTRR1iwYAE6duyIOXPmICAgQNb5/PTTT7h27RrWrVuHdevWSfudnJykO+NUKhUSExPx3nvvoVOnTrC0tMSECRMwYcIEKd7Z2Rnbtm3D+PHj8dVXX0Gj0WDBggUVntFUGxSi/EVQemJ5eXlQqVTQarWwsLCo0WNnZWVhaWQkRllbAwCW3riBUTExcHBwqNF+iKjuu3fvHjIyMuDs7IwGDRoAqB9PBKfa5ePjg/bt22P+/Pm1nUqtqOz3pozcv9+caSIieg6oVCqMnTLlb/fZc0TPEosmIqLnhEqlYhFTix51R9/27duf2Ud9PC2jR4/G2rVrK20bMmQIlixZ8owzevZYNBEREdWARz1o8YUXXnh2iVTh4Y+BeRIzZsyQFmOXV9NLUuoqFk1EREQ1oHnz5rWdwlNlZ2cn+6Ninld85AARERGRDCyaiIjqKd78TCRf2VPD/wpeniMiqmcMDQ2hUChw7do12NraVvujPIj+ToQQKCoqwrVr16Cnp/eXHn7JoomIqJ7R19dHo0aNcOnSJemhgUT0aCYmJmjcuDH09J78IhuLJiKiesjMzAwuLi7V/lBWor8jfX19GBgY/OVZWRZNRET1lL6+PvT19Ws7DaK/jVpdCH7//n188skncHZ2hrGxMZo2bYoZM2boLNYSQiAqKgoajQbGxsbw8fGp8OGLhYWFGDduHGxsbGBqaoqAgIAKH36Ym5uLoKAg6eFvQUFBuHXrlk5MZmYm+vbtC1NTU9jY2CAsLEznk5iJiIjo76tWi6YvvvgCS5YswaJFi5Ceno5Zs2Zh9uzZOh9COGvWLMydOxeLFi1CcnIy1Go1evTogdu3b0sx4eHh2LhxI+Li4rBv3z7cuXMH/v7+KCkpkWICAwORmpqK+Ph4xMfHIzU1FUFBQVJ7SUkJ+vTpg/z8fOzbtw9xcXFYv349IiIins1gEBERUd0malGfPn3E8OHDdfYNGDBADBkyRAghRGlpqVCr1WLmzJlS+71794RKpRJLliwRQghx69YtYWhoKOLi4qSYy5cvCz09PREfHy+EEOLUqVMCgDh48KAUc+DAAQFA/PHHH0IIIbZt2yb09PTE5cuXpZjvv/9eKJVKodVqZZ2PVqsVAGTHV8eVK1fEtKFDxZUJE8SVCRMefH3lSo33Q0RE9Hcj9+93rc40/eMf/8DOnTvx559/AgB+//137Nu3D6+99hoAICMjA9nZ2fDz85Peo1Qq4e3tjf379wMAUlJSUFxcrBOj0Wjg7u4uxRw4cAAqlQqenp5STOfOnaFSqXRi3N3dodFopJiePXuisLAQKSkpleZfWFiIvLw8nRcRERE9n2p1IfiHH34IrVaLli1bQl9fHyUlJfj888/x9ttvAwCys7MBAPb29jrvs7e3x4ULF6QYIyMjWFpaVogpe392dnalj363s7PTiSnfj6WlJYyMjKSY8mJiYjB9+vTqnjYRERHVQ7U60/TDDz9g7dq1+O6773D06FGsXr0ac+bMwerVq3Xiyt8iKIR47G2D5WMqi3+SmIdFRkZCq9VKr4sXLz4yJyIiIqq/anWm6YMPPsDkyZPx1ltvAQDatGmDCxcuICYmBkOHDoVarQbwYBbIwcFBel9OTo40K6RWq1FUVITc3Fyd2aacnBx06dJFirl69WqF/q9du6ZznEOHDum05+bmori4uMIMVBmlUgmlUvmkp09ERET1SK3ONN29e7fCkzn19fWlRw44OztDrVYjMTFRai8qKkJSUpJUEHl4eMDQ0FAnJisrC2lpaVKMl5cXtFotDh8+LMUcOnQIWq1WJyYtLQ1ZWVlSTEJCApRKJTw8PGr4zImIiKi+qdWZpr59++Lzzz9H48aN0bp1axw7dgxz587F8OHDATy4XBYeHo7o6Gi4uLjAxcUF0dHRMDExQWBgIABApVJhxIgRiIiIgLW1NaysrDBx4kS0adMG3bt3BwC0atUKvXr1QkhICJYuXQoAGDlyJPz9/eHq6goA8PPzg5ubG4KCgjB79mzcvHkTEydOREhICCwsLGphdIiIiKguqdWiaeHChZgyZQpCQ0ORk5MDjUaDUaNGYerUqVLMpEmTUFBQgNDQUOTm5sLT0xMJCQkwNzeXYubNmwcDAwMMGjQIBQUF8PX1RWxsrM6TctetW4ewsDDpLruAgAAsWrRIatfX18fWrVsRGhqKrl27wtjYGIGBgZgzZ84zGAkiIiKq6xRCCFHbSTwv8vLyoFKpoNVqa3x2KisrC0sjIzHK2hoAsPTGDYyKidFZ60VERETVJ/fvd62uaSIiIiKqL1g0EREREcnAoomIiIhIBhZNRERERDKwaCIiIiKSgUUTERERkQwsmoiIiIhkYNFEREREJAOLJiIiIiIZWDQRERERycCiiYiIiEgGFk1EREREMrBoIiIiIpKBRRMRERGRDCyaiIiIiGRg0UREREQkA4smIiIiIhlYNBERERHJwKKJiIiISAYWTUREREQysGgiIiIikoFFExEREZEMLJqIiIiIZGDRRERERCQDiyYiIiIiGVg0EREREcnAoomIiIhIBhZNRERERDKwaCIiIiKSgUUTERERkQy1WjQ1adIECoWiwuu9994DAAghEBUVBY1GA2NjY/j4+ODkyZM6xygsLMS4ceNgY2MDU1NTBAQE4NKlSzoxubm5CAoKgkqlgkqlQlBQEG7duqUTk5mZib59+8LU1BQ2NjYICwtDUVHRUz1/IiIiqj9qtWhKTk5GVlaW9EpMTAQADBw4EAAwa9YszJ07F4sWLUJycjLUajV69OiB27dvS8cIDw/Hxo0bERcXh3379uHOnTvw9/dHSUmJFBMYGIjU1FTEx8cjPj4eqampCAoKktpLSkrQp08f5OfnY9++fYiLi8P69esRERHxjEaCiIiI6jxRh7z//vuiWbNmorS0VJSWlgq1Wi1mzpwptd+7d0+oVCqxZMkSIYQQt27dEoaGhiIuLk6KuXz5stDT0xPx8fFCCCFOnTolAIiDBw9KMQcOHBAAxB9//CGEEGLbtm1CT09PXL58WYr5/vvvhVKpFFqtVnb+Wq1WAKjWe+S6cuWKmDZ0qLgyYYK4MmHCg6+vXKnxfoiIiP5u5P79rjNrmoqKirB27VoMHz4cCoUCGRkZyM7Ohp+fnxSjVCrh7e2N/fv3AwBSUlJQXFysE6PRaODu7i7FHDhwACqVCp6enlJM586doVKpdGLc3d2h0WikmJ49e6KwsBApKSlP9byJiIiofjCo7QTKbNq0Cbdu3UJwcDAAIDs7GwBgb2+vE2dvb48LFy5IMUZGRrC0tKwQU/b+7Oxs2NnZVejPzs5OJ6Z8P5aWljAyMpJiKlNYWIjCwkJpOy8vT86pEhERUT1UZ2aaVqxYgd69e+vM9gCAQqHQ2RZCVNhXXvmYyuKfJKa8mJgYaXG5SqWCo6PjI/MiIiKi+qtOFE0XLlzAr7/+infffVfap1arAaDCTE9OTo40K6RWq1FUVITc3NxHxly9erVCn9euXdOJKd9Pbm4uiouLK8xAPSwyMhJarVZ6Xbx4Ue4pExERUT1TJ4qmVatWwc7ODn369JH2OTs7Q61WS3fUAQ/WPSUlJaFLly4AAA8PDxgaGurEZGVlIS0tTYrx8vKCVqvF4cOHpZhDhw5Bq9XqxKSlpSErK0uKSUhIgFKphIeHR5V5K5VKWFhY6LyIiIjo+VTra5pKS0uxatUqDB06FAYG/0tHoVAgPDwc0dHRcHFxgYuLC6Kjo2FiYoLAwEAAgEqlwogRIxAREQFra2tYWVlh4sSJaNOmDbp37w4AaNWqFXr16oWQkBAsXboUADBy5Ej4+/vD1dUVAODn5wc3NzcEBQVh9uzZuHnzJiZOnIiQkBAWQkRERASgDhRNv/76KzIzMzF8+PAKbZMmTUJBQQFCQ0ORm5sLT09PJCQkwNzcXIqZN28eDAwMMGjQIBQUFMDX1xexsbHQ19eXYtatW4ewsDDpLruAgAAsWrRIatfX18fWrVsRGhqKrl27wtjYGIGBgZgzZ85TPHMiIiKqTxRCCFHbSTwv8vLyoFKpoNVqa3yGKisrC0sjIzHK2hoAsPTGDYyKiYGDg0ON9kNERPR3I/fvd51Y00RERERU17FoIiIiIpKBRRMRERGRDCyaiIiIiGRg0UREREQkA4smIiIiIhlYNBERERHJwKKJiIiISAYWTUREREQysGgiIiIikoFFExEREZEMLJqIiIiIZGDRRERERCQDiyYiIiIiGVg0EREREcnAoomIiIhIBhZNRERERDKwaCIiIiKSgUUTERERkQwsmoiIiIhkYNFEREREJAOLJiIiIiIZWDQRERERycCiiYiIiEgGFk1EREREMrBoIiIiIpKBRRMRERGRDCyaiIiIiGRg0UREREQkA4smIiIiIhlYNBERERHJUOtF0+XLlzFkyBBYW1vDxMQE7du3R0pKitQuhEBUVBQ0Gg2MjY3h4+ODkydP6hyjsLAQ48aNg42NDUxNTREQEIBLly7pxOTm5iIoKAgqlQoqlQpBQUG4deuWTkxmZib69u0LU1NT2NjYICwsDEVFRU/t3ImIiKj+qNWiKTc3F127doWhoSG2b9+OU6dO4csvv0TDhg2lmFmzZmHu3LlYtGgRkpOToVar0aNHD9y+fVuKCQ8Px8aNGxEXF4d9+/bhzp078Pf3R0lJiRQTGBiI1NRUxMfHIz4+HqmpqQgKCpLaS0pK0KdPH+Tn52Pfvn2Ii4vD+vXrERER8UzGgoiIiOo4UYs+/PBD8Y9//KPK9tLSUqFWq8XMmTOlfffu3RMqlUosWbJECCHErVu3hKGhoYiLi5NiLl++LPT09ER8fLwQQohTp04JAOLgwYNSzIEDBwQA8ccffwghhNi2bZvQ09MTly9flmK+//57oVQqhVarlXU+Wq1WAJAdXx1XrlwR04YOFVcmTBBXJkx48PWVKzXeDxER0d+N3L/ftTrTtHnzZnTq1AkDBw6EnZ0dOnTogOXLl0vtGRkZyM7Ohp+fn7RPqVTC29sb+/fvBwCkpKSguLhYJ0aj0cDd3V2KOXDgAFQqFTw9PaWYzp07Q6VS6cS4u7tDo9FIMT179kRhYaHO5cKHFRYWIi8vT+dFREREz6daLZrOnTuHxYsXw8XFBTt27MDo0aMRFhaGb7/9FgCQnZ0NALC3t9d5n729vdSWnZ0NIyMjWFpaPjLGzs6uQv92dnY6MeX7sbS0hJGRkRRTXkxMjLRGSqVSwdHRsbpDQERERPVErRZNpaWl6NixI6Kjo9GhQweMGjUKISEhWLx4sU6cQqHQ2RZCVNhXXvmYyuKfJOZhkZGR0Gq10uvixYuPzImIiIjqr1otmhwcHODm5qazr1WrVsjMzAQAqNVqAKgw05OTkyPNCqnVahQVFSE3N/eRMVevXq3Q/7Vr13RiyveTm5uL4uLiCjNQZZRKJSwsLHReRERE9Hyq1aKpa9euOH36tM6+P//8E05OTgAAZ2dnqNVqJCYmSu1FRUVISkpCly5dAAAeHh4wNDTUicnKykJaWpoU4+XlBa1Wi8OHD0sxhw4dglar1YlJS0tDVlaWFJOQkAClUgkPD48aPnMiIiKqbwxqs/Px48ejS5cuiI6OxqBBg3D48GEsW7YMy5YtA/Dgcll4eDiio6Ph4uICFxcXREdHw8TEBIGBgQAAlUqFESNGICIiAtbW1rCyssLEiRPRpk0bdO/eHcCD2atevXohJCQES5cuBQCMHDkS/v7+cHV1BQD4+fnBzc0NQUFBmD17Nm7evImJEyciJCSEM0hERERUu0XTiy++iI0bNyIyMhIzZsyAs7Mz5s+fj8GDB0sxkyZNQkFBAUJDQ5GbmwtPT08kJCTA3Nxcipk3bx4MDAwwaNAgFBQUwNfXF7GxsdDX15di1q1bh7CwMOkuu4CAACxatEhq19fXx9atWxEaGoquXbvC2NgYgYGBmDNnzjMYCSIiIqrrFEIIUdtJPC/y8vKgUqmg1WprfHYqKysLSyMjMcraGgCw9MYNjIqJgYODQ432Q0RE9Hcj9+93jaxpKv9xJERERETPm2oXTV988QV++OEHaXvQoEGwtrbGCy+8gN9//71GkyMiIiKqK6pdNC1dulR6iGNiYiISExOxfft29O7dGx988EGNJ0hERERUF1R7IXhWVpZUNG3ZsgWDBg2Cn58fmjRpovMxJURERETPk2rPNFlaWkpPvo6Pj5du6xdCoKSkpGazIyIiIqojqj3TNGDAAAQGBsLFxQU3btxA7969AQCpqalo3rx5jSdIREREVBdUu2iaN28enJ2dkZmZiVmzZsHMzAzAg8t2oaGhNZ4gERERUV1QraKpuLgYI0eOxJQpU9C0aVOdtvDw8JrMi4iIiKhOqdaaJkNDQ2zcuPFp5UJERERUZ1V7Ifjrr7+OTZs2PYVUiIiIiOquaq9pat68OT799FPs378fHh4eMDU11WkPCwurseSIiIiI6opqF03ffPMNGjZsiJSUFKSkpOi0KRQKFk1ERET0XKp20ZSRkfE08iAiIiKq02rkA3uJiIiInnfVnmkCgEuXLmHz5s3IzMxEUVGRTtvcuXNrJDEiIiKiuqTaRdPOnTsREBAAZ2dnnD59Gu7u7jh//jyEEOjYsePTyJGIiIio1lX78lxkZCQiIiKQlpaGBg0aYP369bh48SK8vb0xcODAp5EjERERUa2rdtGUnp6OoUOHAgAMDAxQUFAAMzMzzJgxA1988UWNJ0hERERUF1S7aDI1NUVhYSEAQKPR4OzZs1Lb9evXay4zIiIiojqk2muaOnfujN9++w1ubm7o06cPIiIicOLECWzYsAGdO3d+GjkSERER1bpqF01z587FnTt3AABRUVG4c+cOfvjhBzRv3hzz5s2r8QSJiIiI6oJqF01NmzaVvjYxMcHXX39dowkRERER1UXVXtPUtGlT3Lhxo8L+W7du6RRURERERM+TahdN58+fR0lJSYX9hYWFuHz5co0kRURERFTXyL48t3nzZunrHTt2QKVSSdslJSXYuXMnmjRpUqPJEREREdUVsoum/v37AwAUCoX0nKYyhoaGaNKkCb788ssaTY6IiIiorpBdNJWWlgIAnJ2dkZycDBsbm6eWFBEREVFdU+275zIyMp5GHkRERER1muyF4IcOHcL27dt19n377bdwdnaGnZ0dRo4cKT0pnIiIiOh5I7toioqKwvHjx6XtEydOYMSIEejevTsmT56MX375BTExMU8lSSIiIqLaJrtoSk1Nha+vr7QdFxcHT09PLF++HBMmTMCCBQvw448/VqvzqKgoKBQKnZdarZbahRCIioqCRqOBsbExfHx8cPLkSZ1jFBYWYty4cbCxsYGpqSkCAgJw6dIlnZjc3FwEBQVBpVJBpVIhKCgIt27d0onJzMxE3759YWpqChsbG4SFhaGoqKha50NERETPL9lFU25uLuzt7aXtpKQk9OrVS9p+8cUXcfHixWon0Lp1a2RlZUmvEydOSG2zZs3C3LlzsWjRIiQnJ0OtVqNHjx64ffu2FBMeHo6NGzciLi4O+/btw507d+Dv76/zLKnAwECkpqYiPj4e8fHxSE1NRVBQkNReUlKCPn36ID8/H/v27UNcXBzWr1+PiIiIap8PERERPZ9kLwS3t7dHRkYGHB0dUVRUhKNHj2L69OlS++3bt2FoaFj9BAwMdGaXygghMH/+fHz88ccYMGAAAGD16tWwt7fHd999h1GjRkGr1WLFihVYs2YNunfvDgBYu3YtHB0d8euvv6Jnz55IT09HfHw8Dh48CE9PTwDA8uXL4eXlhdOnT8PV1RUJCQk4deoULl68CI1GAwD48ssvERwcjM8//xwWFhbVPi8iIiJ6vsieaerVqxcmT56MvXv3IjIyEiYmJnj55Zel9uPHj6NZs2bVTuDMmTPQaDRwdnbGW2+9hXPnzgF4cJdednY2/Pz8pFilUglvb2/s378fAJCSkoLi4mKdGI1GA3d3dynmwIEDUKlUUsEEAJ07d4ZKpdKJcXd3lwomAOjZsycKCwuRkpJSZe6FhYXIy8vTeREREdHzSXbR9Nlnn0FfXx/e3t5Yvnw5li9fDiMjI6l95cqVOsWLHJ6envj222+xY8cOLF++HNnZ2ejSpQtu3LiB7OxsANC5JFi2XdaWnZ0NIyMjWFpaPjLGzs6uQt92dnY6MeX7sbS0hJGRkRRTmZiYGGmdlEqlgqOjY7XOn4iIiOoP2ZfnbG1tsXfvXmi1WpiZmUFfX1+n/aeffoKZmVm1Ou/du7f0dZs2beDl5YVmzZph9erV6Ny5M4AHTyB/mBCiwr7yysdUFv8kMeVFRkZiwoQJ0nZeXh4LJyIioudUtT+wV6VSVSiYAMDKykpn5ulJmJqaok2bNjhz5oy0zqn8TE9OTo40K6RWq1FUVITc3NxHxly9erVCX9euXdOJKd9Pbm4uiouLK8xAPUypVMLCwkLnRURERM+nahdNT1NhYSHS09Ph4OAAZ2dnqNVqJCYmSu1FRUVISkpCly5dAAAeHh4wNDTUicnKykJaWpoU4+XlBa1Wi8OHD0sxhw4dglar1YlJS0tDVlaWFJOQkAClUgkPD4+nes5ERERUP1T7Y1Rq0sSJE9G3b180btwYOTk5+Oyzz5CXl4ehQ4dCoVAgPDwc0dHRcHFxgYuLC6Kjo2FiYoLAwEAAD2a9RowYgYiICFhbW8PKygoTJ05EmzZtpLvpWrVqhV69eiEkJARLly4FAIwcORL+/v5wdXUFAPj5+cHNzQ1BQUGYPXs2bt68iYkTJyIkJISzR0RERASgloumS5cu4e2338b169dha2uLzp074+DBg3BycgIATJo0CQUFBQgNDUVubi48PT2RkJAAc3Nz6Rjz5s2DgYEBBg0ahIKCAvj6+iI2NlbnEuK6desQFhYmLVQPCAjAokWLpHZ9fX1s3boVoaGh6Nq1K4yNjREYGIg5c+Y8o5EgIiKiuk4hhBCPC+rYsSN27twJS0tLzJgxAxMnToSJicmzyK9eycvLg0qlglarrfEZqqysLCyNjMQoa2sAwNIbNzAqJgYODg412g8REdHfjdy/37LWNKWnpyM/Px8AMH36dNy5c6dmsiQiIiKqJ2Rdnmvfvj2GDRuGf/zjHxBCYM6cOVU+XmDq1Kk1miARERFRXSCraIqNjcW0adOwZcsWKBQKbN++HQYGFd+qUChYNBEREdFzSVbR5Orqiri4OACAnp4edu7cWelTtomIiIieV9W+e660tPRp5EFERERUpz3RIwfOnj2L+fPnIz09HQqFAq1atcL777//RB/YS0RERFQfVPuJ4Dt27ICbmxsOHz6Mtm3bwt3dHYcOHULr1q11nsxNRERE9Dyp9kzT5MmTMX78eMycObPC/g8//BA9evSoseSIiIiI6opqzzSlp6djxIgRFfYPHz4cp06dqpGkiIiIiOqaahdNtra2SE1NrbA/NTWVd9QRERHRc6val+dCQkIwcuRInDt3Dl26dIFCocC+ffvwxRdfICIi4mnkSERERFTrql00TZkyBebm5vjyyy8RGRkJANBoNIiKikJYWFiNJ0hERERUF1S7aFIoFBg/fjzGjx+P27dvAwDMzc1rPDEiIiKiuuSJntNUhsUSERER/V1UeyE4ERER0d8RiyYiIiIiGVg0EREREclQraKpuLgY3bp1w59//vm08iEiIiKqk6pVNBkaGiItLQ0KheJp5UNERERUJ1X78tw777yDFStWPI1ciIiIiOqsaj9yoKioCN988w0SExPRqVMnmJqa6rTPnTu3xpIjIiIiqiuqXTSlpaWhY8eOAFBhbRMv2xEREdHzqtpF0+7du59GHkRERER12hM/cuC///0vduzYgYKCAgCAEKLGkiIiIiKqa6pdNN24cQO+vr5o0aIFXnvtNWRlZQEA3n33XURERNR4gkRERER1QbWLpvHjx8PQ0BCZmZkwMTGR9r/55puIj4+v0eSIiIiI6opqr2lKSEjAjh070KhRI539Li4uuHDhQo0lRkRERFSXVHumKT8/X2eGqcz169ehVCprJCkiIiKiuqbaRdMrr7yCb7/9VtpWKBQoLS3F7Nmz0a1btxpNjoiIiKiuqPbludmzZ8PHxwdHjhxBUVERJk2ahJMnT+LmzZv47bffnkaORERERLWu2jNNbm5uOH78OF566SX06NED+fn5GDBgAI4dO4ZmzZo9cSIxMTFQKBQIDw+X9gkhEBUVBY1GA2NjY/j4+ODkyZM67yssLMS4ceNgY2MDU1NTBAQE4NKlSzoxubm5CAoKgkqlgkqlQlBQEG7duqUTk5mZib59+8LU1BQ2NjYICwtDUVHRE58PERERPV+qPdMEAGq1GtOnT6+xJJKTk7Fs2TK0bdtWZ/+sWbMwd+5cxMbGokWLFvjss8/Qo0cPnD59Gubm5gCA8PBw/PLLL4iLi4O1tTUiIiLg7++PlJQU6OvrAwACAwNx6dIl6e6+kSNHIigoCL/88gsAoKSkBH369IGtrS327duHGzduYOjQoRBCYOHChTV2nkRERFR/PVHRlJubixUrViA9PR0KhQKtWrXCsGHDYGVlVe1j3blzB4MHD8by5cvx2WefSfuFEJg/fz4+/vhjDBgwAACwevVq2Nvb47vvvsOoUaOg1WqxYsUKrFmzBt27dwcArF27Fo6Ojvj111/Rs2dPpKenIz4+HgcPHoSnpycAYPny5fDy8sLp06fh6uqKhIQEnDp1ChcvXoRGowEAfPnllwgODsbnn38OCwuLJxkmIiIieo5U+/JcUlISnJ2dsWDBAuTm5uLmzZtYsGABnJ2dkZSUVO0E3nvvPfTp00cqespkZGQgOzsbfn5+0j6lUglvb2/s378fAJCSkoLi4mKdGI1GA3d3dynmwIEDUKlUUsEEAJ07d4ZKpdKJcXd3lwomAOjZsycKCwuRkpJSZe6FhYXIy8vTeREREdHzqdozTe+99x4GDRqExYsXS5e/SkpKEBoaivfeew9paWmyjxUXF4ejR48iOTm5Qlt2djYAwN7eXme/vb299Dyo7OxsGBkZwdLSskJM2fuzs7NhZ2dX4fh2dnY6MeX7sbS0hJGRkRRTmZiYmBq9TElERER1V7Vnms6ePYuIiAipYAIAfX19TJgwAWfPnpV9nIsXL+L999/H2rVr0aBBgyrjFAqFzrYQosK+8srHVBb/JDHlRUZGQqvVSq+LFy8+Mi8iIiKqv6pdNHXs2BHp6ekV9qenp6N9+/ayj5OSkoKcnBx4eHjAwMAABgYGSEpKwoIFC2BgYCDN/JSf6cnJyZHa1Go1ioqKkJub+8iYq1evVuj/2rVrOjHl+8nNzUVxcXGFGaiHKZVKWFhY6LyIiIjo+SSraDp+/Lj0CgsLw/vvv485c+Zg37592LdvH+bMmYPx48frPC7gcXx9fXHixAmkpqZKr06dOmHw4MFITU1F06ZNoVarkZiYKL2nqKgISUlJ6NKlCwDAw8MDhoaGOjFZWVlIS0uTYry8vKDVanH48GEp5tChQ9BqtToxaWlp0ocPAw8+LkapVMLDw0P2OREREdHzS9aapvbt20OhUEAIIe2bNGlShbjAwEC8+eabsjo2NzeHu7u7zj5TU1NYW1tL+8PDwxEdHQ0XFxe4uLggOjoaJiYmCAwMBACoVCqMGDECERERsLa2hpWVFSZOnIg2bdpIC8tbtWqFXr16ISQkBEuXLgXw4JED/v7+cHV1BQD4+fnBzc0NQUFBmD17Nm7evImJEyciJCSEs0dEREQEQGbRlJGR8bTzqNSkSZNQUFCA0NBQ5ObmwtPTEwkJCdIzmgBg3rx5MDAwwKBBg1BQUABfX1/ExsbqrLlat24dwsLCpLvsAgICsGjRIqldX18fW7duRWhoKLp27QpjY2MEBgZizpw5z+5kiYiIqE5TiIenj+gvycvLg0qlglarrfEZqqysLCyNjMQoa2sAwNIbNzAqJgYODg412g8REdHfjdy/30/0cMvLly/jt99+Q05ODkpLS3XawsLCnuSQRERERHVatYumVatWYfTo0TAyMoK1tXWF2/ZZNBEREdHzqNpF09SpUzF16lRERkZCT6/aTywgIiIiqpeqXfXcvXsXb731FgsmIiIi+lupduUzYsQI/PTTT08jFyIiIqI6q9qX52JiYuDv74/4+Hi0adMGhoaGOu1z586tseSIiIiI6opqF03R0dHYsWOH9GDIx31+GxEREdHzoNpF09y5c7Fy5UoEBwc/hXSIiIiI6qZqr2lSKpXo2rXr08iFiIiIqM6qdtH0/vvvY+HChU8jFyIiIqI6q9qX5w4fPoxdu3Zhy5YtaN26dYWF4Bs2bKix5IiIiIjqimoXTQ0bNsSAAQOeRi5EREREddYTfYwKERER0d8NH+tNREREJEO1Z5qcnZ0f+Tymc+fO/aWEiIiIiOqiahdN4eHhOtvFxcU4duwY4uPj8cEHH9RUXkRERER1SrWLpvfff7/S/V999RWOHDnylxMiIiIiqotqbE1T7969sX79+po6HBEREVGdUmNF088//wwrK6uaOhwRERFRnVLty3MdOnTQWQguhEB2djauXbuGr7/+ukaTIyIiIqorql009e/fX2dbT08Ptra28PHxQcuWLWsqLyIiIqI6pdpF07Rp055GHkRERER1Gh9uSURERCSD7JkmPT29Rz7UEgAUCgXu37//l5MiIiIiqmtkF00bN26ssm3//v1YuHAhhBA1khQRERFRXSO7aOrXr1+FfX/88QciIyPxyy+/YPDgwfj0009rNDkiIiKiuuKJ1jRduXIFISEhaNu2Le7fv4/U1FSsXr0ajRs3run8iIiIiOqEahVNWq0WH374IZo3b46TJ09i586d+OWXX+Du7v608iMiIiKqE2Rfnps1axa++OILqNVqfP/995VeriMiIiJ6XskumiZPngxjY2M0b94cq1evxurVqyuN27BhQ40lR0RERFRXyL48984772DQoEGwsrKCSqWq8lUdixcvRtu2bWFhYQELCwt4eXlh+/btUrsQAlFRUdBoNDA2NoaPjw9Onjypc4zCwkKMGzcONjY2MDU1RUBAAC5duqQTk5ubi6CgICnHoKAg3Lp1SycmMzMTffv2hampKWxsbBAWFoaioqJqnQ8RERE9v2TPNMXGxtZ4540aNcLMmTPRvHlzAMDq1avRr18/HDt2DK1bt8asWbMwd+5cxMbGokWLFvjss8/Qo0cPnD59Gubm5gCA8PBw/PLLL4iLi4O1tTUiIiLg7++PlJQU6OvrAwACAwNx6dIlxMfHAwBGjhyJoKAg/PLLLwCAkpIS9OnTB7a2tti3bx9u3LiBoUOHQgiBhQsX1vh5ExERUT0k6hhLS0vxzTffiNLSUqFWq8XMmTOltnv37gmVSiWWLFkihBDi1q1bwtDQUMTFxUkxly9fFnp6eiI+Pl4IIcSpU6cEAHHw4EEp5sCBAwKA+OOPP4QQQmzbtk3o6emJy5cvSzHff/+9UCqVQqvVys5dq9UKANV6j1xXrlwR04YOFVcmTBBXJkx48PWVKzXeDxER0d+N3L/fdeZjVEpKShAXF4f8/Hx4eXkhIyMD2dnZ8PPzk2KUSiW8vb2xf/9+AEBKSgqKi4t1YjQaDdzd3aWYAwcOQKVSwdPTU4rp3LkzVCqVToy7uzs0Go0U07NnTxQWFiIlJeWpnjcRERHVD9X+wN6aduLECXh5eeHevXswMzPDxo0b4ebmJhU09vb2OvH29va4cOECACA7OxtGRkawtLSsEJOdnS3F2NnZVejXzs5OJ6Z8P5aWljAyMpJiKlNYWIjCwkJpOy8vT+5pExERUT1T6zNNrq6uSE1NxcGDBzFmzBgMHToUp06dktrLf96dEOKxn4FXPqay+CeJKS8mJkZnEbyjo+Mj8yIiIqL6q9aLJiMjIzRv3hydOnVCTEwM2rVrh3/9619Qq9UAUGGmJycnR5oVUqvVKCoqQm5u7iNjrl69WqHfa9eu6cSU7yc3NxfFxcUVZqAeFhkZCa1WK70uXrxYzbMnIiKi+qLWi6byhBAoLCyEs7Mz1Go1EhMTpbaioiIkJSWhS5cuAAAPDw8YGhrqxGRlZSEtLU2K8fLyglarxeHDh6WYQ4cOQavV6sSkpaUhKytLiklISIBSqYSHh0eVuSqVSulxCWUvIiIiej7V6pqmjz76CL1794ajoyNu376NuLg47NmzB/Hx8VAoFAgPD0d0dDRcXFzg4uKC6OhomJiYIDAwEACgUqkwYsQIREREwNraGlZWVpg4cSLatGmD7t27AwBatWqFXr16ISQkBEuXLgXw4JED/v7+cHV1BQD4+fnBzc0NQUFBmD17Nm7evImJEyciJCSEhRAREREBqOWi6erVqwgKCkJWVhZUKhXatm2L+Ph49OjRAwAwadIkFBQUIDQ0FLm5ufD09ERCQoL0jCYAmDdvHgwMDDBo0CAUFBTA19cXsbGx0jOaAGDdunUICwuT7rILCAjAokWLpHZ9fX1s3boVoaGh6Nq1K4yNjREYGIg5c+Y8o5EgIiKiuk4hhBC1ncTzIi8vDyqVClqttsZnqLKysrA0MhKjrK0BAEtv3MComBg4ODjUaD9ERER/N3L/fte5NU1EREREdRGLJiIiIiIZWDQRERERycCiiYiIiEgGFk1EREREMrBoIiIiIpKBRRMRERGRDCyaiIiIiGRg0UREREQkA4smIiIiIhlYNBERERHJwKKJiIiISAYWTUREREQysGgiIiIikoFFExEREZEMLJqIiIiIZGDRRERERCQDiyYiIiIiGVg0EREREcnAoomIiIhIBhZNRERERDKwaCIiIiKSgUUTERERkQwsmoiIiIhkYNFEREREJAOLJiIiIiIZWDQRERERycCiiYiIiEgGFk1EREREMrBoIiIiIpKBRRMRERGRDLVaNMXExODFF1+Eubk57Ozs0L9/f5w+fVonRgiBqKgoaDQaGBsbw8fHBydPntSJKSwsxLhx42BjYwNTU1MEBATg0qVLOjG5ubkICgqCSqWCSqVCUFAQbt26pROTmZmJvn37wtTUFDY2NggLC0NRUdFTOXciIiKqX2q1aEpKSsJ7772HgwcPIjExEffv34efnx/y8/OlmFmzZmHu3LlYtGgRkpOToVar0aNHD9y+fVuKCQ8Px8aNGxEXF4d9+/bhzp078Pf3R0lJiRQTGBiI1NRUxMfHIz4+HqmpqQgKCpLaS0pK0KdPH+Tn52Pfvn2Ii4vD+vXrERER8WwGg4iIiOo2UYfk5OQIACIpKUkIIURpaalQq9Vi5syZUsy9e/eESqUSS5YsEUIIcevWLWFoaCji4uKkmMuXLws9PT0RHx8vhBDi1KlTAoA4ePCgFHPgwAEBQPzxxx9CCCG2bdsm9PT0xOXLl6WY77//XiiVSqHVamXlr9VqBQDZ8dVx5coVMW3oUHFlwgRxZcKEB19fuVLj/RAREf3dyP37XafWNGm1WgCAlZUVACAjIwPZ2dnw8/OTYpRKJby9vbF//34AQEpKCoqLi3ViNBoN3N3dpZgDBw5ApVLB09NTiuncuTNUKpVOjLu7OzQajRTTs2dPFBYWIiUlpdJ8CwsLkZeXp/MiIiKi51OdKZqEEJgwYQL+8Y9/wN3dHQCQnZ0NALC3t9eJtbe3l9qys7NhZGQES0vLR8bY2dlV6NPOzk4npnw/lpaWMDIykmLKi4mJkdZIqVQqODo6Vve0iYiIqJ6oM0XT2LFjcfz4cXz//fcV2hQKhc62EKLCvvLKx1QW/yQxD4uMjIRWq5VeFy9efGROREREVH/ViaJp3Lhx2Lx5M3bv3o1GjRpJ+9VqNQBUmOnJycmRZoXUajWKioqQm5v7yJirV69W6PfatWs6MeX7yc3NRXFxcYUZqDJKpRIWFhY6LyIiIno+1WrRJITA2LFjsWHDBuzatQvOzs467c7OzlCr1UhMTJT2FRUVISkpCV26dAEAeHh4wNDQUCcmKysLaWlpUoyXlxe0Wi0OHz4sxRw6dAharVYnJi0tDVlZWVJMQkIClEolPDw8av7kiYiIqF4xqM3O33vvPXz33Xf497//DXNzc2mmR6VSwdjYGAqFAuHh4YiOjoaLiwtcXFwQHR0NExMTBAYGSrEjRoxAREQErK2tYWVlhYkTJ6JNmzbo3r07AKBVq1bo1asXQkJCsHTpUgDAyJEj4e/vD1dXVwCAn58f3NzcEBQUhNmzZ+PmzZuYOHEiQkJCOINEREREtVs0LV68GADg4+Ojs3/VqlUIDg4GAEyaNAkFBQUIDQ1Fbm4uPD09kZCQAHNzcyl+3rx5MDAwwKBBg1BQUABfX1/ExsZCX19film3bh3CwsKku+wCAgKwaNEiqV1fXx9bt25FaGgounbtCmNjYwQGBmLOnDlP6eyJiIioPlEIIURtJ/G8yMvLg0qlglarrfHZqaysLCyNjMQoa2sAwNIbNzAqJgYODg412g8REdHfjdy/33ViITgRERFRXceiiYiIiEgGFk1EREREMrBoIiIiIpKBRRMRERGRDCyaiIiIiGRg0UREREQkA4smIiIiIhlYNBERERHJUKsfo0JP7l5REa5evSptm5iYQKVS1WJGREREzzcWTfVQXmEhThw/jtL///BiADC0scHYKVNYOBERET0lLJrqoYLiYhjeu4fXGzRAE2trXLt7FxuuX8fdu3dZNBERET0lLJrqMRtjYziYmz/YKCio3WSIiIiec1wITkRERCQDiyYiIiIiGVg0EREREcnAoomIiIhIBhZNRERERDKwaCIiIiKSgUUTERERkQwsmoiIiIhkYNFEREREJAOLJiIiIiIZWDQRERERycCiiYiIiEgGFk1EREREMrBoIiIiIpKBRRMRERGRDCyaiIiIiGRg0UREREQkQ60WTf/5z3/Qt29faDQaKBQKbNq0SaddCIGoqChoNBoYGxvDx8cHJ0+e1IkpLCzEuHHjYGNjA1NTUwQEBODSpUs6Mbm5uQgKCoJKpYJKpUJQUBBu3bqlE5OZmYm+ffvC1NQUNjY2CAsLQ1FR0dM4bSIiIqqHarVoys/PR7t27bBo0aJK22fNmoW5c+di0aJFSE5OhlqtRo8ePXD79m0pJjw8HBs3bkRcXBz27duHO3fuwN/fHyUlJVJMYGAgUlNTER8fj/j4eKSmpiIoKEhqLykpQZ8+fZCfn499+/YhLi4O69evR0RExNM7eSIiIqpXDGqz8969e6N3796VtgkhMH/+fHz88ccYMGAAAGD16tWwt7fHd999h1GjRkGr1WLFihVYs2YNunfvDgBYu3YtHB0d8euvv6Jnz55IT09HfHw8Dh48CE9PTwDA8uXL4eXlhdOnT8PV1RUJCQk4deoULl68CI1GAwD48ssvERwcjM8//xwWFhbPYDT+mntFRbh69Wptp0FERPTUmJiYQKVS1Vr/tVo0PUpGRgays7Ph5+cn7VMqlfD29sb+/fsxatQopKSkoLi4WCdGo9HA3d0d+/fvR8+ePXHgwAGoVCqpYAKAzp07Q6VSYf/+/XB1dcWBAwfg7u4uFUwA0LNnTxQWFiIlJQXdunWrNMfCwkIUFhZK23l5eTU5BLLlFRbixPHjKI2OhomJSa3kQERE9LQZ2thg7JQptVY41dmiKTs7GwBgb2+vs9/e3h4XLlyQYoyMjGBpaVkhpuz92dnZsLOzq3B8Ozs7nZjy/VhaWsLIyEiKqUxMTAymT59ezTOreQXFxTC8dw+vN2iAJtbWtZ0OERFRjbt29y42XL+Ou3fvsmiqikKh0NkWQlTYV175mMrinySmvMjISEyYMEHazsvLg6Oj4yNze5psjI3hYG5ea/0TERE9VQUFtdp9nX3kgFqtBoAKMz05OTnSrJBarUZRURFyc3MfGVPZWp9r167pxJTvJzc3F8XFxRVmoB6mVCphYWGh8yIiIqLnU50tmpydnaFWq5GYmCjtKyoqQlJSErp06QIA8PDwgKGhoU5MVlYW0tLSpBgvLy9otVocPnxYijl06BC0Wq1OTFpaGrKysqSYhIQEKJVKeHh4PNXzJCIiovqhVi/P3blzB//973+l7YyMDKSmpsLKygqNGzdGeHg4oqOj4eLiAhcXF0T//0LnwMBAAIBKpcKIESMQEREBa2trWFlZYeLEiWjTpo10N12rVq3Qq1cvhISEYOnSpQCAkSNHwt/fH66urgAAPz8/uLm5ISgoCLNnz8bNmzcxceJEhISEcPaIiIiIANRy0XTkyBGdO9PK1gcNHToUsbGxmDRpEgoKChAaGorc3Fx4enoiISEB5g+t25k3bx4MDAwwaNAgFBQUwNfXF7GxsdDX15di1q1bh7CwMOkuu4CAAJ1nQ+nr62Pr1q0IDQ1F165dYWxsjMDAQMyZM+dpDwERERHVE7VaNPn4+EAIUWW7QqFAVFQUoqKiqoxp0KABFi5ciIULF1YZY2VlhbVr1z4yl8aNG2PLli2PzZmIiIj+nursmiYiIiKiuoRFUz2Rl5fHz8IjIiKqRSya6gGtVotV8+Yh/fffce+hJ5ATERHRs8OiqR64e/cuim/ehCgsxP3i4tpOh4iI6G+JRRMRERGRDCyaiIiIiGRg0UREREQkA4smIiIiIhlYNBERERHJwKKJiIiISAYWTUREREQysGgiIiIikoFFExEREZEMLJqIiIiIZGDRRERERCQDiyYiIiIiGVg0EREREcnAoomIiIhIBhZNRERERDKwaCIiIiKSgUUTERERkQwsmoiIiIhkYNFEREREJAOLJiIiIiIZWDQRERERycCiiYiIiEgGFk1EREREMrBoIiIiIpKBRRMRERGRDCyaiIiIiGRg0VTO119/DWdnZzRo0AAeHh7Yu3dvbadEREREdQCLpof88MMPCA8Px8cff4xjx47h5ZdfRu/evZGZmVnbqREREVEtY9H0kLlz52LEiBF499130apVK8yfPx+Ojo5YvHhxbadGREREtYxF0/8rKipCSkoK/Pz8dPb7+flh//79tZQVERER1RUGtZ1AXXH9+nWUlJTA3t5eZ7+9vT2ys7MrfU9hYSEKCwulba1WCwDIy8ur0dxu376NwuJiFJeW4oJWi7uA9LUwNESmVquzTURE9Ly5XlCAwqIi3L59G6ampjV67LK/20KIR8axaCpHoVDobAshKuwrExMTg+nTp1fY7+jo+FRyA4BtGRmVfl3ZNhER0fNm5vffP7Vj3759GyqVqsp2Fk3/z8bGBvr6+hVmlXJycirMPpWJjIzEhAkTpO3S0lLcvHkT1tbWVRZaTyIvLw+Ojo64ePEiLCwsauy4zyOOlXwcq+rheMnHsZKPYyXf0xwrIQRu374NjUbzyDgWTf/PyMgIHh4eSExMxOuvvy7tT0xMRL9+/Sp9j1KphFKp1NnXsGHDp5ajhYUFf6lk4ljJx7GqHo6XfBwr+ThW8j2tsXrUDFMZFk0PmTBhAoKCgtCpUyd4eXlh2bJlyMzMxOjRo2s7NSIiIqplLJoe8uabb+LGjRuYMWMGsrKy4O7ujm3btsHJyam2UyMiIqJaxqKpnNDQUISGhtZ2GjqUSiWmTZtW4VIgVcSxko9jVT0cL/k4VvJxrOSrC2OlEI+7v46IiIiI+HBLIiIiIjlYNBERERHJwKKJiIiISAYWTUREREQysGiqB77++ms4OzujQYMG8PDwwN69e2s7pVr3n//8B3379oVGo4FCocCmTZt02oUQiIqKgkajgbGxMXx8fHDy5MnaSbaWxcTE4MUXX4S5uTns7OzQv39/nD59WieG4/XA4sWL0bZtW+nheV5eXti+fbvUznGqWkxMDBQKBcLDw6V9HK8HoqKioFAodF5qtVpq5zjpunz5MoYMGQJra2uYmJigffv2SElJkdprc7xYNNVxP/zwA8LDw/Hxxx/j2LFjePnll9G7d29kZmbWdmq1Kj8/H+3atcOiRYsqbZ81axbmzp2LRYsWITk5GWq1Gj169MDt27efcaa1LykpCe+99x4OHjyIxMRE3L9/H35+fsjPz5diOF4PNGrUCDNnzsSRI0dw5MgRvPrqq+jXr5/0DzLHqXLJyclYtmwZ2rZtq7Of4/U/rVu3RlZWlvQ6ceKE1MZx+p/c3Fx07doVhoaG2L59O06dOoUvv/xS59M2anW8BNVpL730khg9erTOvpYtW4rJkyfXUkZ1DwCxceNGabu0tFSo1Woxc+ZMad+9e/eESqUSS5YsqYUM65acnBwBQCQlJQkhOF6PY2lpKb755huOUxVu374tXFxcRGJiovD29hbvv/++EII/Vw+bNm2aaNeuXaVtHCddH374ofjHP/5RZXttjxdnmuqwoqIipKSkwM/PT2e/n58f9u/fX0tZ1X0ZGRnIzs7WGTelUglvb2+OGwCtVgsAsLKyAsDxqkpJSQni4uKQn58PLy8vjlMV3nvvPfTp0wfdu3fX2c/x0nXmzBloNBo4Ozvjrbfewrlz5wBwnMrbvHkzOnXqhIEDB8LOzg4dOnTA8uXLpfbaHi8WTXXY9evXUVJSAnt7e5399vb2yM7OrqWs6r6yseG4VSSEwIQJE/CPf/wD7u7uADhe5Z04cQJmZmZQKpUYPXo0Nm7cCDc3N45TJeLi4nD06FHExMRUaON4/Y+npye+/fZb7NixA8uXL0d2dja6dOmCGzducJzKOXfuHBYvXgwXFxfs2LEDo0ePRlhYGL799lsAtf9zxY9RqQcUCoXOthCiwj6qiONW0dixY3H8+HHs27evQhvH6wFXV1ekpqbi1q1bWL9+PYYOHYqkpCSpneP0wMWLF/H+++8jISEBDRo0qDKO4wX07t1b+rpNmzbw8vJCs2bNsHr1anTu3BkAx6lMaWkpOnXqhOjoaABAhw4dcPLkSSxevBjvvPOOFFdb48WZpjrMxsYG+vr6FarnnJycClU2/U/ZXSkcN13jxo3D5s2bsXv3bjRq1Ejaz/HSZWRkhObNm6NTp06IiYlBu3bt8K9//YvjVE5KSgpycnLg4eEBAwMDGBgYICkpCQsWLICBgYE0JhyvikxNTdGmTRucOXOGP1flODg4wM3NTWdfq1atpJufanu8WDTVYUZGRvDw8EBiYqLO/sTERHTp0qWWsqr7nJ2doVardcatqKgISUlJf8txE0Jg7Nix2LBhA3bt2gVnZ2eddo7XowkhUFhYyHEqx9fXFydOnEBqaqr06tSpEwYPHozU1FQ0bdqU41WFwsJCpKenw8HBgT9X5XTt2rXCI1H+/PNPODk5AagD/1499aXm9JfExcUJQ0NDsWLFCnHq1CkRHh4uTE1Nxfnz52s7tVp1+/ZtcezYMXHs2DEBQMydO1ccO3ZMXLhwQQghxMyZM4VKpRIbNmwQJ06cEG+//bZwcHAQeXl5tZz5szdmzBihUqnEnj17RFZWlvS6e/euFMPxeiAyMlL85z//ERkZGeL48ePio48+Enp6eiIhIUEIwXF6nIfvnhOC41UmIiJC7NmzR5w7d04cPHhQ+Pv7C3Nzc+nfcY7T/xw+fFgYGBiIzz//XJw5c0asW7dOmJiYiLVr10oxtTleLJrqga+++ko4OTkJIyMj0bFjR+lW8b+z3bt3CwAVXkOHDhVCPLgtddq0aUKtVgulUileeeUVceLEidpNupZUNk4AxKpVq6QYjtcDw4cPl37XbG1tha+vr1QwCcFxepzyRRPH64E333xTODg4CENDQ6HRaMSAAQPEyZMnpXaOk65ffvlFuLu7C6VSKVq2bCmWLVum016b46UQQoinP59FREREVL9xTRMRERGRDCyaiIiIiGRg0UREREQkA4smIiIiIhlYNBERERHJwKKJiIiISAYWTUREREQysGgiIiIikoFFExHpUCgU2LRpEwDg/PnzUCgUSE1NrdWcasuUKVMwcuTIGj1mVFQU2rdvX6PHfN5s2bIFHTp0QGlpaW2nQqSDRRNRPZSTk4NRo0ahcePGUCqVUKvV6NmzJw4cOCDFPFz81EWxsbFQKBRo1apVhbYff/wRCoUCTZo0efaJ/b+rV6/iX//6Fz766CNpn5xxr00+Pj5QKBSYOXNmhbbXXnsNCoUCUVFRzz6xavL394dCocB3331X26kQ6WDRRFQPvfHGG/j999+xevVq/Pnnn9i8eTN8fHxw8+bN2k6tgqKioirbTE1NkZOTU6HoWLlyJRo3bvy0U3ukFStWwMvLS6dwqyvjXlxcXGWbo6MjVq1apbPvypUr2LVrFxwcHJ52ajVm2LBhWLhwYW2nQaSDRRNRPXPr1i3s27cPX3zxBbp16wYnJye89NJLiIyMRJ8+fQBA+kP/+uuvV5ix+eWXX+Dh4YEGDRqgadOmmD59Ou7fvy+r75KSEowYMQLOzs4wNjaGq6sr/vWvf+nEBAcHo3///oiJiYFGo0GLFi2qPJ6BgQECAwOxcuVKad+lS5ewZ88eBAYG6sSePXsW/fr1g729PczMzPDiiy/i119/1Yn5+uuv4eLiggYNGsDe3h7//Oc/pbaff/4Zbdq0gbGxMaytrdG9e3fk5+dXmVtcXBwCAgKkbTnjDgCZmZno168fzMzMYGFhgUGDBuHq1atV9pOcnIwePXrAxsYGKpUK3t7eOHr0qE6MQqHAkiVL0K9fP5iamuKzzz6r8nj+/v64ceMGfvvtN2lfbGws/Pz8YGdnpxO7du1adOrUCebm5lCr1QgMDEROTo7Unpubi8GDB8PW1hbGxsZwcXGRCrKioiKMHTsWDg4OaNCgAZo0aYKYmBjpvVqtFiNHjoSdnR0sLCzw6quv4vfff5faf//9d3Tr1g3m5uawsLCAh4cHjhw5IrUHBATg8OHDOHfuXJXnSvSssWgiqmfMzMxgZmaGTZs2obCwsNKY5ORkAMCqVauQlZUlbe/YsQNDhgxBWFgYTp06haVLlyI2Nhaff/65rL5LS0vRqFEj/Pjjjzh16hSmTp2Kjz76CD/++KNO3M6dO5Geno7ExERs2bLlkcccMWIEfvjhB9y9exfAgz/wvXr1gr29vU7cnTt38Nprr+HXX3/FsWPH0LNnT/Tt2xeZmZkAgCNHjiAsLAwzZszA6dOnER8fj1deeQUAkJWVhbfffhvDhw9Heno69uzZgwEDBqCqzyvPzc1FWloaOnXqJO2TM+5CCPTv3x83b95EUlISEhMTcfbsWbz55ptVnv/t27cxdOhQ7N27FwcPHoSLiwtee+013L59Wydu2rRp6NevH06cOIHhw4dXeTwjIyMMHjxYZ7YpNja20vcUFRXh008/xe+//45NmzYhIyMDwcHBUvuUKVNw6tQpbN++Henp6Vi8eDFsbGwAAAsWLMDmzZvx448/4vTp01i7dq1UnAsh0KdPH2RnZ2Pbtm1ISUlBx44d4evrK83KDR48GI0aNUJycjJSUlIwefJkGBoaSn07OTnBzs4Oe/furfJciZ45QUT1zs8//ywsLS1FgwYNRJcuXURkZKT4/fffdWIAiI0bN+rse/nll0V0dLTOvjVr1ggHB4dK35eRkSEAiGPHjlWZS2hoqHjjjTek7aFDhwp7e3tRWFj4yHNYtWqVUKlUQggh2rdvL1avXi1KS0tFs2bNxL///W8xb9484eTk9MhjuLm5iYULFwohhFi/fr2wsLAQeXl5FeJSUlIEAHH+/PlHHq/MsWPHBACRmZmps/9x456QkCD09fV13nfy5EkBQBw+fFgIIcS0adNEu3btquz7/v37wtzcXPzyyy/SPgAiPDz8sXl7e3uL999/X/z+++/C3Nxc3LlzRyQlJQk7OztRVFQk2rVrJ6ZNm1bl+w8fPiwAiNu3bwshhOjbt68YNmxYpbHjxo0Tr776qigtLa3QtnPnTmFhYSHu3buns79Zs2Zi6dKlQgghzM3NRWxs7CPPp0OHDiIqKuqRMUTPEmeaiOqhN954A1euXMHmzZvRs2dP7NmzBx07dkRsbOwj35eSkoIZM2ZIsyZmZmYICQlBVlaWNNPzOEuWLEGnTp1ga2sLMzMzLF++XJrtKdOmTRsYGRnJPp/hw4dj1apVSEpKkmaUysvPz8ekSZPg5uaGhg0bwszMDH/88YfUd48ePeDk5ISmTZsiKCgI69atk86pXbt28PX1RZs2bTBw4EAsX74cubm5VeZTUFAAAGjQoIHO/seNe3p6OhwdHeHo6Ci9pyzf9PT0SvvKycnB6NGj0aJFC6hUKqhUKty5c6fCmD486/U4bdu2hYuLC37++WesXLkSQUFBOrM4ZY4dO4Z+/frByckJ5ubm8PHxAQCp7zFjxiAuLg7t27fHpEmTsH//fum9wcHBSE1NhaurK8LCwpCQkCC1paSk4M6dO7C2ttb5WcvIyMDZs2cBABMmTMC7776L7t27Y+bMmdL+hxkbG8v+uSR6Flg0EdVTDRo0QI8ePTB16lTs378fwcHBmDZt2iPfU1paiunTpyM1NVV6nThxAmfOnKlQIFTmxx9/xPjx4zF8+HAkJCQgNTUVw4YNq7DY29TUtFrnMnjwYBw8eBBRUVF45513YGBgUCHmgw8+wPr16/H5559j7969SE1NRZs2baS+zc3NcfToUXz//fdwcHDA1KlT0a5dO9y6dQv6+vpITEzE9u3b4ebmhoULF8LV1RUZGRmV5lN2CaqywupR4y6EgEKhqPCeqvYDD4qPlJQUzJ8/H/v370dqaiqsra3/8pgOHz4cX331FX7++edKL83l5+fDz88PZmZmWLt2LZKTk7Fx40YA/1u837t3b1y4cAHh4eG4cuUKfH19MXHiRABAx44dkZGRgU8//RQFBQUYNGiQtIastLQUDg4OOj9nqampOH36ND744AMADx69cPLkSfTp0we7du2Cm5ub1H+ZmzdvwtbWtlrnTfQ0sWgiek64ubnpLGw2NDRESUmJTkzHjh1x+vRpNG/evMJLT+/x/xzs3bsXXbp0QWhoKDp06IDmzZtXOkNQXVZWVggICEBSUlKV63X27t2L4OBgvP7662jTpg3UajXOnz+vE2NgYIDu3btj1qxZOH78OM6fP49du3YBeLCYumvXrpg+fTqOHTsGIyOjCn+kyzRr1gwWFhY4derUY3N/eNzd3NyQmZmJixcvSu2nTp2CVqut9NEKZecVFhaG1157Da1bt4ZSqcT169cf2+/jBAYG4sSJE3B3d4ebm1uF9j/++APXr1/HzJkz8fLLL6Nly5Y6i8DL2NraIjg4GGvXrsX8+fOxbNkyqc3CwgJvvvkmli9fjh9++AHr16/HzZs30bFjR2RnZ8PAwKDCz1lZQQoALVq0wPjx45GQkIABAwborMO6d+8ezp49iw4dOvzlsSCqKRX/O0dEddqNGzcwcOBADB8+HG3btoW5uTmOHDmCWbNmoV+/flJckyZNsHPnTnTt2hVKpRKWlpaYOnUq/P394ejoiIEDB0JPTw/Hjx/HiRMnHnlHVpnmzZvj22+/xY4dO+Ds7Iw1a9YgOTkZzs7Of/m8YmNj8fXXX8Pa2rrKvjds2IC+fftCoVBgypQpOg8/3LJlC86dO4dXXnkFlpaW2LZtG0pLS+Hq6opDhw5h586d0h1khw4dwrVr16osZPT09NC9e3fs27cP/fv3ByBv3Lt37462bdti8ODBmD9/Pu7fv4/Q0FB4e3tXeXmtefPmWLNmDTp16oS8vDx88MEHMDY2/gsj+YClpSWysrIqvSwHAI0bN4aRkREWLlyI0aNHIy0tDZ9++qlOzNSpU+Hh4YHWrVujsLAQW7ZskcZs3rx5cHBwQPv27aGnp4effvoJarUaDRs2RPfu3eHl5YX+/fvjiy++gKurK65cuYJt27ahf//+aN26NT744AP885//hLOzMy5duoTk5GS88cYbUt8HDx6EUqmEl5fXXx4LohpT24uqiKh67t27JyZPniw6duwoVCqVMDExEa6uruKTTz4Rd+/eleI2b94smjdvLgwMDHQWVMfHx4suXboIY2NjYWFhIV566SWxbNkyqR2PWAh+7949ERwcLFQqlWjYsKEYM2aMmDx5ss7C5qFDh4p+/fo99jweXghemfILwTMyMkS3bt2EsbGxcHR0FIsWLZIWPgshxN69e4W3t7ewtLQUxsbGom3btuKHH34QQghx6tQp0bNnT2FrayuUSqVo0aKFtIC8KvHx8eKFF14QJSUl0rnLGfcLFy6IgIAAYWpqKszNzcXAgQNFdna21F5+IfjRo0dFp06dhFKpFC4uLuKnn34STk5OYt68eVIMKlnUX5mHx6My5ReCf/fdd6JJkyZCqVQKLy8vsXnzZp3v96effipatWoljI2NhZWVlejXr584d+6cEEKIZcuWifbt2wtTU1NhYWEhfH19xdGjR6Vj5+XliXHjxgmNRiMMDQ2Fo6OjGDx4sMjMzBSFhYXirbfeEo6OjsLIyEhoNBoxduxYUVBQIL1/5MiRYtSoUY89Z6JnSSFEFffcEhH9jQkh0LlzZ4SHh+Ptt9+u7XT+Vq5du4aWLVviyJEjNTKLSVRTuKaJiKgSCoUCy5Ytk/3gT6o5GRkZ+Prrr1kwUZ3DmSYiIiIiGTjTRERERCQDiyYiIiIiGVg0EREREcnAoomIiIhIBhZNRERERDKwaCIiIiKSgUUTERERkQwsmoiIiIhkYNFEREREJMP/Ad9EJdINElAwAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#define masses\n", + "w_masses = w_cluster.star_systems['mass']\n", + "w_comps = w_cluster.companions['mass']\n", + "m_masses = m_cluster.star_systems['mass']\n", + "m_comps = m_cluster.companions['mass']\n", + "\n", + "#define mass range of histogram\n", + "mass_ranges = [0.01, 0.08, 0.5, 1, 60]\n", + "\n", + "#create a histogram to compare\n", + "#plt.hist(m_masses, bins=mass_ranges, alpha=0.5, label='Muzic_2017', color='blue', edgecolor='black')\n", + "plt.hist(w_masses, bins=mass_ranges, alpha=0.5, label='Weidner_Kroupa_2004', color='red', edgecolor='black')\n", + "\n", + "#labels and legend\n", + "plt.xlabel('Stellar Mass (Solar Masses)')\n", + "plt.ylabel('Number of Stars')\n", + "plt.title('Comparison of Stellar Mass Distributions')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51924315-a841-4f2d-bddb-84a1641b8d82", + "metadata": {}, "outputs": [], "source": [] } diff --git a/spisea/evolution.py b/spisea/evolution.py index 6cf765d5..93c494cc 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1232,7 +1232,7 @@ class MergedBaraffePisaEkstromParsec(StellarEvolution): """ def __init__(self, rot=True): # populate list of model masses (in solar masses) - mass_list = [(0.1 + i*0.005) for i in range(181)] + mass_list = [(0.01 + i*0.005) for i in range(181)] # generates masses from 0.01 - 1 M_sun # define metallicity parameters for Geneva models z_list = [0.015] @@ -1306,9 +1306,15 @@ def isochrone(self, age=1.e8, metallicity=0.0): isWR[idx_WR] = True iso.add_column(isWR) + # Assume mass of brown dwarfs does not change over their lifetime + bd_idx = iso['mass'] < 0.08 + iso['mass_current'][bd_idx] = iso['mass'][bd_idx] + + iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) + return iso diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 758eda00..c7e55056 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -47,7 +47,7 @@ class IMF(object): If None, no multiplicity is assumed. Otherwise, use multiplicity object to create multiple star systems. """ - def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None): + def __init__(self, massLimits=np.array([0.01,150]), multiplicity=None): self._multi_props = multiplicity self._mass_limits = massLimits @@ -690,6 +690,33 @@ def __init__(self, multiplicity=None): IMF_broken_powerlaw.__init__(self, massLimits, powers, multiplicity=multiplicity) +#added for brown dwarfs +class Muzic_2017(IMF_broken_powerlaw): + """ + Define IMF from `Mužić (2017) +`_. + Mass range is 0.01 M_sun - inf M_sun. + """ + def __init__(self, multiplicity=None): + massLimits = np.array([0.01, 0.5, 1, np.inf]) + powers = np.array([-0.71, -0.81, -1.60]) + + IMF_broken_powerlaw.__init__(self, massLimits, powers, + multiplicity=multiplicity) + +class CombinedBD_2024(IMF_broken_powerlaw): + """ + Define IMF from several papers considering brown dwarf mass ranges + Derivation given in SPISEA/changes/BD_IMF + Mass range: 0.1 M_sun - 0.8 M_sun + """ + def __init__(self, multiplicity=None): + massLimits = np.array([0.01, 0.08]) + powers = np.array([-0.56]) + + IMF_broken_powerlaw.__init__(self, massLimits, powers, + multiplicity=multiplicity) + ################################################## # # Generic functions -- see if we can move these up. From ec4edb90e5d1ee8b69d2ae91304d15acf9b0ff66 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 10 Jul 2024 15:36:38 -0700 Subject: [PATCH 007/191] some histogram changes --- changes/Test_BD_Cluster.ipynb | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/changes/Test_BD_Cluster.ipynb b/changes/Test_BD_Cluster.ipynb index 277a3412..2dbd9d71 100644 --- a/changes/Test_BD_Cluster.ipynb +++ b/changes/Test_BD_Cluster.ipynb @@ -982,15 +982,15 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 51, "id": "4e2fe19b-9da8-4388-92bd-c1390444e8c4", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] }, "metadata": {}, @@ -1005,11 +1005,15 @@ "m_comps = m_cluster.companions['mass']\n", "\n", "#define mass range of histogram\n", - "mass_ranges = [0.01, 0.08, 0.5, 1, 60]\n", + "bin_edges = [0.01, 0.08, 0.5, 1, 60]\n", "\n", - "#create a histogram to compare\n", - "#plt.hist(m_masses, bins=mass_ranges, alpha=0.5, label='Muzic_2017', color='blue', edgecolor='black')\n", - "plt.hist(w_masses, bins=mass_ranges, alpha=0.5, label='Weidner_Kroupa_2004', color='red', edgecolor='black')\n", + "#setting up subplots\n", + "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8,8))\n", + "plt.subplots_adjust(hspace=0.5, wspace=0.75)\n", + "\n", + "#create histograms to compare\n", + "ax[0].hist(m_masses, bins=bin_edges, alpha=0.5, label='Muzic_2017', color='blue', edgecolor='black', histtype='bar')\n", + "ax[1].hist(w_masses, bins=bin_edges, alpha=0.5, label='Weidner_Kroupa_2004', color='red', edgecolor='black', histtype='bar')\n", "\n", "#labels and legend\n", "plt.xlabel('Stellar Mass (Solar Masses)')\n", From d2bc99b6b086a05f2d9fd99a0793bd69c7dd138b Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 15 Jul 2024 15:51:35 -0700 Subject: [PATCH 008/191] Adding notebook with issues in multiplicity --- changes/Compact_Multiplicity.ipynb | 480 +++++++++++++++++++++++++++++ 1 file changed, 480 insertions(+) create mode 100644 changes/Compact_Multiplicity.ipynb diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb new file mode 100644 index 00000000..d223c0f1 --- /dev/null +++ b/changes/Compact_Multiplicity.ipynb @@ -0,0 +1,480 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c61a66d4-bc64-4080-ad07-38ae7f00670b", + "metadata": {}, + "source": [ + "## Comparing Clusters with Compact Objects: With and Without Companions" + ] + }, + { + "cell_type": "markdown", + "id": "77645bcf-b0f4-456b-89fd-eb9730b23c71", + "metadata": {}, + "source": [ + "In testing the addition of substellar objects into SPISEA, we have come across a curious problem. When generating a cluster with an IMFR and allowing for companions, we end up creating substellar mass compact objects, which does not make much sense. This is happening before modification of the code to specify brown dwarves as their own phase." + ] + }, + { + "cell_type": "markdown", + "id": "31969dcc-b1c4-4601-a44f-c9ba09a73188", + "metadata": {}, + "source": [ + "#### Importing the Necessary Packages:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c0a4a637-c694-4d3d-9b1f-3b1b66cc8a19", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary packages. \n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "f8639685-3185-4e3e-88c6-98602bdaedc9", + "metadata": {}, + "source": [ + "### Cluster 1: Without Companions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", + "metadata": {}, + "outputs": [], + "source": [ + "# Create isochrone object \n", + "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", + " evo_model = evolution.MergedBaraffePisaEkstromParsec(),\n", + " filters=filt_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ac6b221b-6bd5-41c5-8a5e-94a0daed1dba", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "k_imf = imf.Weidner_Kroupa_2004()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1a68a281-791a-4821-8799-c2df8e936286", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 907596 stars out of mass range\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "k_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "k_out = k_cluster.star_systems" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3bdd27c9-3820-4fff-9ed6-da26fb997e08", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bkoyEhIRCjx09erTh5eV11XaiYmEGERxK06ZN5eLiYj5ef/11SZemtf7444965JFHJMlutsh9992nxMTEfN9e9OzZ0+55w4YNJcm8LGbdunW6cOGCBg0aZFefm5ubwsPDC1xIsE+fPvm2JScn6//9v/+nkJAQOTs7y8XFRaGhoZKkgwcPXrPPRW3HoUOH9Pvvv2vAgAF2051DQ0OvOfPncrVq1dLOnTu1c+dObdu2TcuWLZPNZlOHDh2ueVlXUfp67tw5bdq0SX379jWnVF/NokWLdN999+nxxx/Xhx9+KDc3tyL35dVXXzX7cvmjb9++duXypo1fPp1bku6++27Vq1dPX331VaHn+Pzzz2WxWPSPf/zD7v0JDAzUXXfdZb4/tWvXlre3t55++mm98847+b59vRYPD498P7MDBgzQxYsXtXnzZrPMo48+qoULF5rf/Hz99dc6cOCARo8efV3ne+yxx7RmzRqdPn1a8+fPV/v27Qu9C0x6erqefvpp1a5dW87OznJ2dlaVKlWUkZFh9zN+991368svv9QzzzyjjRs3KjMz064eHx8f1apVS6+99preeOMNff/993aXuQGAI2MMVLJjoMvXIcr7b3h4uCSpXr168vf3N8cLGzduVEBAgOrVq5evnmu9ttfj/Pnz+uqrr/TAAw+ocuXK+d7b8+fPa/v27ddd7+WMK2ZVt2nTRm5uboqPj5d06TLBiIgIde3aVVu3btW5c+d04sQJHT582O6y8wsXLmjatGm688475erqKmdnZ7m6uurw4cMFvtcF/bw88sgjslqtdrPePvjgA2VlZenRRx+97tfk7rvv1g8//KCRI0dq3bp1172WT2E/T5dfbvjEE09Ikt36jLNnz1aDBg3Url27Ip8rPDxctWrV0nvvvae9e/dq586dhV5eJl0a33Xs2FGenp5ycnKSi4uLnnvuOZ0+fVrJycmSLl066erqquHDh2vRokX5lj6QLr1GZ86c0cMPP6xPP/30mrPmUf4REOGm4+fnJ5vNVuAf0mXLlmnnzp121yBLMi/jmTBhgt3gycXFRSNHjpSkfB94vr6+ds+tVqskmf9ozauzefPm+epcsWJFvvoqV66c7w5PFy9eVOfOnbVy5UpNnDhRX331lXbs2GH+4bryH8gFKWo78i7TCQwMzFdHQdsK4+bmpmbNmqlZs2Zq2bKlHn74YX355ZdKTEzUc889V+hxRe1rSkqKcnNzVb169SK1Z/ny5bLZbHr88cev+9b0t912m9mXyx9XBlN5r11B0/mDg4PtLoG60qlTp2QYhgICAvK9P9u3bzffH09PT23atEmNGjXS5MmTVb9+fQUHB+v5558v0t1DAgIC8m3Le18vb9+YMWN09uxZLV26VNKlQUr16tV1//33X/Mcl3vwwQfl5uammTNn6rPPPtPQoUMLLTtgwADNnj1bjz/+uNatW6cdO3Zo586duuWWW+x+xv/zn//o6aef1urVq9W+fXv5+PioV69eZvBosVj01VdfqUuXLpoxY4aaNGmiW265RWPHji3SZXgAUNExBrJXmmOgBg0ayM/PTxs2bDDXH8oLiKRLN+3YuHGjsrKytG3btkLvXnat1/Z6nD59WhcuXFBMTEy+/uddbvV3/0Gf97OWdymUm5ub2rRpYwZEX331lTp16qSIiAjl5ubqm2++MS81uzwgGjdunKZMmaJevXrps88+07fffqudO3fqrrvuKrDvBY25fHx81LNnT73//vvmGosLFy7U3Xffrfr161/3azJp0iT9+9//1vbt2xUZGSlfX1916NAh393qClPYz9Pl466AgAD169dPc+fOVW5urvbs2aNvvvnmur+Ys1gsevTRR7VkyRK98847uv3223XPPfcUWHbHjh3mTWPmzZun//3vf9q5c6eeffZZSf/3s1arVi3Fx8fL399fo0aNUq1atVSrVi27NZgGDhyo9957T7/++qv69Okjf39/tWjRwu5yQlQsjrnUO25qTk5Ouvfee7V+/XolJiba/QHJu+vDsWPH7I7x8/OTdOkPQe/evQust27dutfVjrw6P/74Y/PbrqspKLzYt2+ffvjhBy1cuFCDBw82tx85cqTY25E3IElKSsq3r6Bt1yMoKEh+fn764YcfCi1T1L76+PjIycmpyAszL126VFOmTFF4eLjWr1+vRo0a3VAfribvtUtMTMwXXP3+++/me1AQPz8/WSwWffPNN+Yg8HKXb2vQoIGWL18uwzC0Z88eLVy4UC+88IJsNpueeeaZq7Yxb5B8ubz39fLBaO3atRUZGam33npLkZGRWrNmjaZOnSonJ6er1n+lypUrq3///po+fbqqVq1a6O9VamqqPv/8cz3//PN2fcjKytJff/1lV9bd3V1Tp07V1KlTderUKXM2UY8ePczbB4eGhpoLVP7000/68MMPFR0drezsbL3zzjvX1QcAqGgYA91YO4pjDGSxWBQeHq7Y2Fjt2LFDZ86csQuIwsPDFR0drW3btun8+fM3dHv76+Xt7S0nJycNHDiw0HUEa9asecP1G4ahzz77TO7u7mrWrJm5vUOHDnruuee0Y8cOnTx5Up06dZKHh4eaN2+uuLg4/f7777r99tsVEhJiHrNkyRINGjRI06ZNszvHn3/+KS8vr3znLuxLv0cffVQfffSR4uLidOutt2rnzp2aM2eOuf96XhNnZ2eNGzdO48aN05kzZxQfH6/JkyerS5cuOnHixDXvtFfYz9OVIeCTTz6pxYsX69NPP1VsbKy8vLzMGX3XY8iQIXruuef0zjvv2N1J9krLly+Xi4uLPv/8c7uZ9atXr85X9p577tE999yj3Nxc7dq1SzExMYqKilJAQID69+8v6dJr/uijjyojI0ObN2/W888/r+7du+unn34q0u8/yhcCItyUJk2apC+//FL/7//9P3388cfXvHtW3bp1VadOHf3www/5/jDdqC5dusjZ2Vk///xzgdNgiyLvj9+VwcHcuXPzlS3sG6aitqNu3boKCgrSBx98oHHjxpnn/vXXX7V169YiLZJXmJMnT+rPP/+86m1Zi9rXvLtAfPTRR3r55ZevGr5IlwKl+Ph4de/eXe3bt9eXX35pd6vW4nDvvfdKujS4ad68ubl9586dOnjwoPmNTEG6d++uV155Rb/99lu+S9cKY7FYdNddd2nmzJlauHChvvvuu2sec/bsWa1Zs8Zu6vqyZcvMhTUv9+STT6pz584aPHiwnJycNGzYsCK160pPPPGETp06pfDw8EIv7bNYLDIMI9/7/t///tf89q8gAQEBGjJkiH744QfNmjWrwFsi33777frXv/6lTz75pEivEQDcDBgDXX87imsM1L59e33yySd67bXX5O/vb3cJWXh4uE6fPq2YmBizbHEprP+VK1dW+/bt9f3336thw4aF3tXuRk2dOlUHDhzQ5MmT7f7Od+zYUZMnT9aUKVNUvXp13XHHHeb2NWvWKCkpKd/7YbFY8r3Xa9eu1W+//abatWsXuU2dO3dWtWrVtGDBAt16661yc3Mz7/Il3fhr4uXlpQcffFC//faboqKidOzYsauOayUV+vM0aNAgu3JNmzZV69at9eqrr2rfvn0aPny43N3di9znPNWqVdM///lP/fjjj3ah6pUsFoucnZ3tvvzLzMzU4sWLCz3GyclJLVq00B133KGlS5fqu+++MwOiPO7u7oqMjFR2drZ69eql/fv3ExBVQAREuCm1adNGb731lsaMGaMmTZpo+PDhql+/vipVqqTExER98sknkmQ3nXnu3LmKjIxUly5dNGTIEFWrVk1//fWXDh48qO+++04fffTRdbWhRo0aeuGFF/Tss8/ql19+UdeuXeXt7a1Tp05px44d5myIq7njjjtUq1YtPfPMMzIMQz4+Pvrss88KnLaZdxeIN998U4MHD5aLi4vq1q1b5HZUqlRJL774oh5//HE98MADGjZsmM6cOaPo6OjrusQsMzPTnP6dm5uro0ePasaMGZKkqKioYunrG2+8obZt26pFixZ65plnVLt2bZ06dUpr1qzR3Llz5eHhYVfew8NDsbGx6t27t3mXseIcmNWtW1fDhw9XTEyMKlWqpMjISB07dkxTpkxRSEiInnrqqUKPbdOmjYYPH65HH31Uu3btUrt27eTu7q7ExERt2bJFDRo00BNPPKHPP/9cb7/9tnr16qXbbrtNhmFo5cqVOnPmjDp16nTNNvr6+uqJJ57Q8ePHdfvtt+uLL77QvHnz9MQTT+jWW2+1K9upUyfdeeed2rBhg/7xj3/I39//hl6XRo0aFfht1OWqVq2qdu3a6bXXXpOfn59q1KihTZs2af78+fm+MWzRooW6d++uhg0bytvbWwcPHtTixYvVqlUrVa5cWXv27NHo0aP10EMPqU6dOnJ1ddXXX3+tPXv2XHOGFQDcLBgDld0YKG9ssWrVKj344IN2+8LCwuTr66tVq1apWrVqRbqbalF5eHgoNDRUn376qTp06CAfHx/zb+qbb76ptm3b6p577tETTzyhGjVq6OzZszpy5Ig+++yzIt2l7cyZM+bYLiMjQ4cOHdLy5cv1zTffqG/fvvney6ZNm8rb21vr16831/6RLgVEeXfJuvzyMunSF2YLFy7UHXfcoYYNG2r37t167bXXirykQB4nJycNGjRIb7zxhjmD2dPT065MUV+THj16KCwszFxe4Ndff9WsWbMUGhpapPcvOTnZ/HlKTU3V888/Lzc3N02aNClf2SeffFL9+vWTxWIxL+28Ea+88so1y3Tr1k1vvPGGBgwYoOHDh+v06dP697//nS+ge+edd/T111+rW7duuvXWW3X+/HnzTml579+wYcNks9nUpk0bBQUFKSkpSdOnT5enp6fdl6aoQMpseWygFCQkJBiPPvqoUbNmTcNqtRpubm5G7dq1jUGDBhlfffVVvvI//PCD0bdvX8Pf399wcXExAgMDjXvvvdd45513zDKX3zHgcnl3D7jy7kyrV6822rdvb1StWtWwWq1GaGio8eCDDxrx8fFmmcGDBxvu7u4F9uHAgQNGp06dDA8PD8Pb29t46KGHjOPHjxd4t4pJkyYZwcHBRqVKlfK1pSjtMAzD+O9//2vUqVPHcHV1NW6//Xbjvffey3fnrcJceRezSpUqGcHBwUZkZKSxceNGu7IF3cXsevp64MAB46GHHjJ8fX0NV1dX49ZbbzWGDBlinD9/3q7+y9+nrKwso0+fPoabm5uxdu3aQvuR915+9NFHBe4fNWqUceXHZ25urvHqq68at99+u+Hi4mL4+fkZ//jHP4wTJ07YlSvstXzvvfeMFi1aGO7u7obNZjNq1aplDBo0yNi1a5dhGIbx448/Gg8//LBRq1Ytw2azGZ6ensbdd99tLFy4sNB+5AkPDzfq169vbNy40WjWrJlhtVqNoKAgY/LkyfnuWJMnOjrakGRs3779mvXnudadTgzDKPAuZidPnjT69OljeHt7Gx4eHkbXrl2Nffv2GaGhocbgwYPNcs8884zRrFkzw9vb27BarcZtt91mPPXUU8aff/5pGIZhnDp1yhgyZIhxxx13GO7u7kaVKlWMhg0bGjNnzjQuXLhQ5H4AwM2AMdD/taU0xkB5AgMDDUnG7Nmz8+3r1auXIcl45JFH8u27ntf2yruYGcalu4c1btzYsFqthiS7v59Hjx41HnvsMaNatWqGi4uLccsttxitW7c2XnrppWv2JzQ01BzXWSwWo0qVKkbdunWNgQMHGuvWrSv0uAceeMCQZCxdutTclp2dbbi7uxuVKlUyUlJS7MqnpKQYQ4cONfz9/Y3KlSsbbdu2Nb755pt8fb3WGM0wDOOnn34y2xwXF1dgmaK8Jq+//rrRunVrw8/PzxxrDh061Dh27NhVX7O8Ni5evNgYO3asccsttxhWq9W45557zHHdlbKysgyr1Wp07dr1qnVfrrCfmSsVdBez9957z6hbt645npo+fboxf/58u7H5tm3bjAceeMAIDQ01rFar4evra4SHhxtr1qwx61m0aJHRvn17IyAgwHB1dTWCg4ONvn37Gnv27ClyP1C+WAzjiqXnAQAOr1mzZrJYLNq5c2dZNwUAAOCm9tlnn6lnz55au3atuVg2UBa4xAwAIElKS0vTvn379Pnnn2v37t1atWpVWTcJAADgpnXgwAH9+uuvGj9+vBo1aqTIyMiybhIcHAERAECS9N1336l9+/by9fXV888/r169epV1kwAAAG5aI0eO1P/+9z81adJEixYtKvTubEBp4RIzAAAAAAAAB1eprBsAAAAAAACAskVABAAAAAAA4OAIiAAAAAAAABwci1RLunjxon7//Xd5eHiwMBgAAOWYYRg6e/asgoODVakS33OVJcZPAABUDEUdPxEQSfr9998VEhJS1s0AAABFdOLECVWvXr2sm+HQGD8BAFCxXGv8REAkycPDQ9KlF6tq1apl3BoAAFCYtLQ0hYSEmH+7UXYYPwEAUDEUdfxEQCSZ06KrVq3KAAcAgAqAS5rKHuMnAAAqlmuNn7h4HwAAAAAAwMEREAEAAAAAADg4AiIAAAAAAAAHxxpEAFCCcnNzlZOTU9bNACoMFxcXOTk5lXUzUEwuXryo7Ozssm4GgMvwOQugMAREAFACDMNQUlKSzpw5U9ZNASocLy8vBQYGshB1BZedna2jR4/q4sWLZd0UAFfgcxZAQQiIAKAE5IVD/v7+qly5MgMwoAgMw9C5c+eUnJwsSQoKCirjFuFGGYahxMREOTk5KSQkRJUqsaoBUB7wOQvgagiIAKCY5ebmmuGQr69vWTcHqFBsNpskKTk5Wf7+/lwGUUFduHBB586dU3BwsCpXrlzWzQFwGT5nARSGr3MAoJjlrTnEP4qAG5P3u8P6XRVXbm6uJMnV1bWMWwKgIHzOAigIAREAlBAuKwNuDL87Nw/eS6B84ncTQEEIiAAAAAAAABwcaxABQCn67UymUjJK75bP3u6uquZlK7Xz3axq1KihqKgoRUVF/a16LBaLVq1apV69epWrdt2I6OhorV69WgkJCaV+blRcfAZWTCX1WXOteo8dO6aaNWvq+++/V6NGjYr13ACA/AiIAKCU/HYmUx1f36TMnNxSO6fNxUnx48Ov6x9ISUlJmj59utauXauTJ0/K09NTderU0T/+8Q8NGjSowqytVJrhSXR0tKZOnWo+r1q1qho2bKiXXnpJ4eHhJX7+otq4caPat2+vlJQUeXl52e0ry7AJjoHPwNJVWr/TsbGxioyMVGJiogIDA83tgYGBcnFx0YkTJ8xtJ0+eVEhIiNatW6fOnTtfs+6QkBAlJibKz89P0tU/w65XRESENm3aJOnSWll+fn5q0qSJHn30UfXu3ftv1Q0AFRUBEQCUkpSMbGXm5GpWv0aq7V+lxM93JDldUSsSlJKRXeR/HP3yyy9q06aNvLy8NG3aNDVo0EAXLlzQTz/9pPfee0/BwcHq2bNnCbe8cIZhKDc3V87O5e/PV/369RUfHy9J+uuvv/Tvf/9b3bt3N/+BCTg6PgP/vvL4Gdi2bVs5Oztr48aN6t+/vyTp4MGDOn/+vDIzM3XkyBHVrl1bkrRhwwa5uLioTZs2RarbycnJLnQqbsOGDdMLL7ygnJwc/fbbb1q1apX69++vIUOG6N133y2x814pJydHLi4upXY+ACgMaxABQCmr7V9FYdU8S/xxI/8AGzlypJydnbVr1y717dtX9erVU4MGDdSnTx+tXbtWPXr0MMumpqZq+PDh8vf3V9WqVXXvvffqhx9+MPdHR0erUaNGWrx4sWrUqCFPT0/1799fZ8+eNcsYhqEZM2botttuk81m01133aWPP/7Y3L9x40ZZLBatW7dOzZo1k9Vq1TfffKOff/5Z999/vwICAlSlShU1b97cDGekS98M//rrr3rqqadksVjsFuPcunWr2rVrJ5vNppCQEI0dO1YZGRnm/uTkZPXo0UM2m001a9bU0qVLi/TaOTs7KzAwUIGBgbrzzjs1depUpaen66effir0mKefflq33367KleurNtuu01TpkzJd0eZNWvWqFmzZnJzc5Ofn99Vv9lesGCBPD09FRcXV6Q2X83x48d1//33q0qVKqpatar69u2rU6dOXfWYBQsWqF69enJzc9Mdd9yht99+29yXnZ2t0aNHKygoSG5ubqpRo4amT5/+t9uJiofPwJvrMzDv/Bs3brRrd9u2bdW2bdt82++++265u7ub286dO6fHHntMHh4euvXWW+2CmWPHjslisSghIUHHjh1T+/btJUne3t6yWCwaMmRIkV7HwlSuXFmBgYEKCQlRy5Yt9eqrr2ru3LmaN2+e+Xr26dNHY8aMMY+JioqSxWLR/v37JUkXLlyQh4eH1q1bJ+nSjKq2bdvKy8tLvr6+6t69u37++ed8ffrwww8VEREhNzc3vf3227LZbIqNjbVr38qVK+Xu7q709HRJ0m+//aZ+/frJ29tbvr6+uv/++3Xs2LECX18vLy+1adNGv/766zVfBwDIQ0AEAJAknT59WuvXr9eoUaPsBu+Xy/tHhmEY6tatm5KSkvTFF19o9+7datKkiTp06KC//vrLLP/zzz9r9erV+vzzz/X5559r06ZNeuWVV8z9//rXv7RgwQLNmTNH+/fv11NPPaV//OMf5rT/PBMnTtT06dN18OBBNWzYUOnp6brvvvsUHx+v77//Xl26dFGPHj10/PhxSZcG1dWrV9cLL7ygxMREJSYmSpL27t2rLl26qHfv3tqzZ49WrFihLVu2aPTo0ea5hgwZomPHjunrr7/Wxx9/rLffflvJycnX9VpmZWVp4cKF8vLyUt26dQst5+HhoYULF+rAgQN68803NW/ePM2cOdPcv3btWvXu3VvdunXT999/r6+++krNmjUrsK5///vfmjBhgtatW6dOnTpdV3uvZBiGevXqpb/++kubNm1SXFycfv75Z/Xr16/QY+bNm6dnn31WL7/8sg4ePKhp06ZpypQpWrRokSTpP//5j9asWaMPP/xQhw4d0pIlS1SjRo2/1U6gOPEZeMmNfAa2b99eGzZsMJ9v2LBBERERCg8Pz7c9L+TJ8/rrr6tZs2b6/vvvNXLkSD3xxBP68ccf850jJCREn3zyiSTp0KFDSkxM1Jtvvnldr2NRDB48WN7e3lq5cqWkS2Hb5SHXpk2b5OfnZ9a9c+dOnT9/3pwVlZGRoXHjxmnnzp366quvVKlSJT3wwAO6ePGi3XmefvppjR07VgcPHtRDDz2kbt265Qvjli1bZgb1586dU/v27VWlShVt3rxZW7ZsUZUqVdS1a1dlZ2frwoUL6tWrl8LDw7Vnzx5t27ZNw4cP525lAK6PASM1NdWQZKSmppZ1UwDcBDIzM40DBw4YmZmZdtv3njxjhD79ubH35JlSacf1nm/79u2GJGPlypV22319fQ13d3fD3d3dmDhxomEYhvHVV18ZVatWNc6fP29XtlatWsbcuXMNwzCM559/3qhcubKRlpZm7v/nP/9ptGjRwjAMw0hPTzfc3NyMrVu32tUxdOhQ4+GHHzYMwzA2bNhgSDJWr159zfbfeeedRkxMjPk8NDTUmDlzpl2ZgQMHGsOHD7fb9s033xiVKlUyMjMzjUOHDhmSjO3bt5v7Dx48aEjKV9flnn/+eaNSpUrm62SxWIyqVasaX375pV05ScaqVasKrWfGjBlG06ZNzeetWrUyHnnkkULL5/XxmWeeMYKCgow9e/YUWtYw/u/1zGvn5Q+LxWL2cf369YaTk5Nx/Phx89j9+/cbkowdO3aYfb7rrrvM/SEhIcayZcvszvfiiy8arVq1MgzDMMaMGWPce++9xsWLF6/aRsMo/HfIMPibXZ5c7b0o6D3kM/Dm/Qxcv369Icn4/fffDcMwDH9/f2PHjh3G9u3bjeDgYMMwDOP48eOGJOOrr76ya+M//vEP8/nFixcNf39/Y86cOYZhGMbRo0cNScb3339v93qkpKSYxxTldSxIeHi48eSTTxa4r0WLFkZkZKRhGIaxZ88ew2KxGH/88Yfx119/GS4uLsZLL71kPPTQQ4ZhGMa0adPM97QgycnJhiRj7969dn2aNWuWXbmVK1caVapUMTIyMgzDuPT75ebmZqxdu9YwDMOYP3++UbduXbvP0KysLMNmsxnr1q0zTp8+bUgyNm7cWGhbLne1z1kAN5+ijp/KzwXMAIBy4cpvG3fs2KGLFy/qkUceUVZWliRp9+7dSk9Pl6+vr13ZzMxMu6n0NWrUkIeHh/k8KCjI/Cb6wIEDOn/+fL7ZLtnZ2WrcuLHdtitnzWRkZGjq1Kn6/PPP9fvvv+vChQvKzMw0vz0vzO7du3XkyBG7b2kNw9DFixd19OhR/fTTT3J2drY73x133FGkxVDr1q2rNWvWSJLOnj2rFStW6KGHHtKGDRsKnfXz8ccfa9asWTpy5IjS09N14cIFVa1a1dyfkJCgYcOGXfW8r7/+ujIyMrRr1y7ddttt12ynJH3zzTd274t06VvyPAcPHlRISIhCQkLMbXfeeae8vLx08OBBNW/e3O7YP/74QydOnNDQoUPt2nvhwgVz/aUhQ4aoU6dOqlu3rrp27aru3bsXaZFaoLTxGXj9n4Ft2rSRq6urNm7cqLvuukuZmZlq0qSJDMNQWlqaDh8+rG3btslqtap169Z2xzZs2ND8f4vFosDAwOuatXk9r2NRGYZh/hyEhYXJ19dXmzZtkouLi+666y717NlT//nPfyRduqzr8psR/Pzzz5oyZYq2b9+uP//805w5dPz4cYWFhZnlrnxPu3XrJmdnZ61Zs0b9+/fXJ598Ig8PD/NzMu+9u/Kz+/z58/r555/VuXNnDRkyRF26dFGnTp3UsWNH9e3bV0FBQTf0GgBwTAREAABJUu3atWWxWPJN7c8LHWy2/1vk9eLFiwoKCrKbdp/n8n9IXLnopsViMQfLef9du3atqlWrZlfOarXaPb/yco9//vOfWrdunf7973+rdu3astlsevDBB5WdffXbZ1+8eFEjRozQ2LFj8+279dZbdejQIbOd18vV1dVciFWSGjdurNWrV2vWrFlasmRJvvLbt29X//79NXXqVHXp0kWenp5avny5Xn/9dbPM5a95Ye655x6tXbtWH374oZ555pkitbVmzZr5/sF3+aK3l//j6HKFbc97L+fNm6cWLVrY7XNycpIkNWnSREePHtWXX36p+Ph49e3bVx07dizSOiFAaeAz8MY/AytXrqy7775bGzZs0F9//aW2bduav/utW7fWhg0btG3bNrVq1Upubm52x17tNSqK63kdiyI3N1eHDx82g3CLxaJ27dpp48aNcnV1VUREhMLCwpSbm6u9e/dq69atdneK69Gjh0JCQjRv3jwFBwfr4sWLCgsLy/feXPmeurq66sEHH9SyZcvUv39/LVu2TP369TM/my9evKimTZsWuCbULbfcIunSOnBjx45VbGysVqxYoX/961+Ki4tTy5Ytr/t1AOCYCIgAVCi/nclUSsbVB8A3ytvd9bpuhXyz8fX1VadOnTR79myNGTOm0DU4pEv/2E9KSpKzs/MNryNz5513ymq16vjx49d9K/hvvvlGQ4YM0QMPPCBJSk9Pt1uoU7o02M7Ntb+ddpMmTbR//367IOdy9erV04ULF7Rr1y7dfffdki6tdXHmzJnral8eJycnZWZmFrjvf//7n0JDQ/Xss8+a265cTLRhw4b66quv9OijjxZ6jrvvvltjxoxRly5d5OTkpH/+85831NbL3XnnnTp+/LhOnDhhziI6cOCAUlNTVa9evXzlAwICVK1aNf3yyy965JFHCq23atWq6tevn/r166cHH3xQXbt21V9//SUfH5+/3Wbg7+Iz8O99BrZv317Lly9XSkqK3YzE8PBwbdy4Udu2bbvqZ1lRuLq6SpJdv/7O61iQRYsWKSUlRX369DG3RURE6N1335Wrq6teeOEFWSwW3XPPPfr3v/+tzMxMc/2h06dP6+DBg5o7d67uueceSdKWLVuKfO5HHnlEnTt31v79+7Vhwwa9+OKL5r4mTZpoxYoV5qLohWncuLEaN26sSZMmqVWrVlq2bJlDBEQlOT68GkcfO+LmQ0AEoML47UymOr6+SZk5udcufANsLk6KHx/u0H/o3377bbVp00bNmjVTdHS0GjZsqEqVKmnnzp368ccf1bRpU0lSx44d1apVK/Xq1Uuvvvqq6tatq99//11ffPGFevXqVeglVZfz8PDQhAkT9NRTT+nixYtq27at0tLStHXrVlWpUkWDBw8u9NjatWtr5cqV6tGjhywWi6ZMmZLvG+caNWpo8+bN6t+/v6xWq/z8/PT000+rZcuWGjVqlIYNGyZ3d3cdPHhQcXFxiomJMS9/GjZsmN599105OzsrKiqqSDN5Lly4oKSkJEn/d4nZgQMH9PTTTxfah+PHj2v58uVq3ry51q5dq1WrVtmVef7559WhQwfVqlVL/fv314ULF/Tll19q4sSJduVatWqlL7/8Ul27dpWzs7Oeeuqpa7b3ajp27KiGDRvqkUce0axZs3ThwgWNHDlS4eHhhb630dHRGjt2rKpWrarIyEhlZWVp165dSklJ0bhx4zRz5kwFBQWpUaNGqlSpkj766CMFBgYW6fI9oLTwGXjjn4Ht27fXiy++qMTERE2YMMHcHh4erldeeUVnz57Nt0D19QoNDZXFYtHnn3+u++67Tzab7W+9jufOnVNSUpIuXLig3377TStXrtTMmTP1xBNP2LU1IiJCTz75pJydnc3gJyIiQuPHj1eTJk3MwCbv7mLvvvuugoKCdPz48SLP7JQuvVYBAQF65JFHVKNGDbtg55FHHtFrr72m+++/Xy+88IKqV6+u48ePa+XKlfrnP/+pnJwcvfvuu+rZs6eCg4N16NAh/fTTTxo0aND1vswVTkmPD6+GsSNuNgREACqMlIxsZebkala/Rjd0++KrOZKcrqgVCUrJyC7xP/JHktNLtP6/c55atWrp+++/17Rp0zRp0iSdPHlSVqtVd955pyZMmKCRI0dKujTl/osvvtCzzz6rxx57TH/88YcCAwPVrl07BQQEFPl8L774ovz9/TV9+nT98ssv8vLyUpMmTTR58uSrHjdz5kw99thjat26tfmPnrS0NLsyL7zwgkaMGKFatWopKytLhmGoYcOG2rRpk5599lndc889MgxDtWrVsrs714IFC/T444+bA/WXXnpJU6ZMuWZf9u/fb671ULlyZdWqVUtz5swpdHB+//3366mnntLo0aOVlZWlbt26acqUKYqOjjbLRERE6KOPPtKLL76oV155RVWrVlW7du0KrK9NmzZau3at7rvvPjk5ORV4CUlRWSwWrV69WmPGjFG7du1UqVIlde3aVTExMYUe8/jjj6ty5cp67bXXNHHiRLm7u6tBgwbmpRdVqlTRq6++qsOHD8vJyUnNmzfXF198oUqVuKGqo+Ez8P/cTJ+BrVq1Mi/pygvSJKl58+bKzc2VzWbLdwnq9apWrZqmTp2qZ555Ro8++qgGDRqkhQsX3vDrOG/ePM2bN0+urq7y9fVV06ZNtWLFCnNmVp6wsDD5+fkpNDTUDIPCw8OVm5trN2upUqVKWr58ucaOHauwsDDVrVtX//nPf+xmVF2NxWLRww8/rNdee03PPfec3b7KlStr8+bNevrpp9W7d2+dPXtW1apVU4cOHVS1alVlZmbqxx9/1KJFi3T69GkFBQVp9OjRGjFiRJHOXZGV5Pjwakpz7AiUFothGEZZN6KspaWlydPTU6mpqVedsgmgbO37LVXdY7bo8zFtFVbNs9zWff78eR09elQ1a9a0W2uhLL7h4pstVESF/Q5J/M0uT672XhT0HvIZCJQfV/ucrWhKcnxYHs8L3Iiijp+YQQQApaSal03x48NL9Rp5ro0HUF7wGQgAQPlGQAQApaial41/rABwWHwGAgBQfnHhPwAAAAAAgIMjIAIAAAAAAHBwBEQAUEK4BwBwY/jduXnwXgLlE7+bAApSpgHRnDlz1LBhQ1WtWlVVq1ZVq1at9OWXX5r7hwwZIovFYvdo2bKlXR1ZWVkaM2aM/Pz85O7urp49e+rkyZOl3RUAMLm4uEiSzp07V8YtASqmvN+dvN8lVDxOTk6SpOzs0luQGkDR8TkLoCBlukh19erV9corr6h27dqSpEWLFun+++/X999/r/r160uSunbtqgULFpjHuLq62tURFRWlzz77TMuXL5evr6/Gjx+v7t27a/fu3ebgBABKk5OTk7y8vJScnCxJqly5siwWSxm3Cij/DMPQuXPnlJycLC8vL/6OV2DOzs6qXLmy/vjjD7m4uKhSJSatA+UBn7MArqZMA6IePXrYPX/55Zc1Z84cbd++3QyIrFarAgMDCzw+NTVV8+fP1+LFi9WxY0dJ0pIlSxQSEqL4+Hh16dKlZDsAAIXI+9zKC4kAFJ2Xl1ehf/tRMVgsFgUFBeno0aP69ddfy7o5AK7A5yyAgpSb29zn5ubqo48+UkZGhlq1amVu37hxo/z9/eXl5aXw8HC9/PLL8vf3lyTt3r1bOTk56ty5s1k+ODhYYWFh2rp1a6EBUVZWlrKyssznaWlpJdQrAI4q7x9H/v7+ysnJKevmABWGi4sL32jfJFxdXVWnTh0uMwPKGT5nARSmzAOivXv3qlWrVjp//ryqVKmiVatW6c4775QkRUZG6qGHHlJoaKiOHj2qKVOm6N5779Xu3btltVqVlJQkV1dXeXt729UZEBCgpKSkQs85ffp0TZ06tUT7BQDSpcvNGIQBKIrp06dr5cqV+vHHH2Wz2dS6dWu9+uqrqlu3rlnGMAxNnTpV7777rlJSUtSiRQu99dZb5sxr6dIXYRMmTNAHH3ygzMxMdejQQW+//baqV69ulklJSdHYsWO1Zs0aSVLPnj0VExMjLy+vYu1TpUqV5ObmVqx1AgCAklHmF4TXrVtXCQkJ2r59u5544gkNHjxYBw4ckCT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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs and WDs\n", + "p_bh = np.where(k_out['phase'] == 103)[0]\n", + "p_ns = np.where(k_out['phase'] == 102)[0]\n", + "p_wd = np.where(k_out['phase'] == 101)[0]\n", + "\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(k_out[p_bh]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(k_out[p_wd]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8f6c414b-c1df-4059-b5ca-3f4c8aa7c655", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Smallest mass of a generated object: 0.07000011975637106\n" + ] + } + ], + "source": [ + "# Finding the minimum mass of a generated object\n", + "print(\"Smallest mass of a generated object: \" + str(np.min(k_out['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "186ded33-2af3-421a-9956-0a603998a50b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial mass of smallest generated black hole: 15.014691323167467\n", + "Initial mass of largest generated black hole: 119.74791889840289\n" + ] + } + ], + "source": [ + "# Finding the minimum/maximum inital mass of generated black holes\n", + "print(\"Initial mass of smallest generated black hole: \" + str(np.min(k_out[p_bh]['mass'])))\n", + "print(\"Initial mass of largest generated black hole: \" + str(np.max(k_out[p_bh]['mass'])))" + ] + }, + { + "cell_type": "markdown", + "id": "2243d818-83a7-4a41-80ed-80a5ca2c6760", + "metadata": {}, + "source": [ + "This cluster fits the expectation from imfr.py that black holes are between 15-120 solar masses and white dwarves are at least 0.5 solar masses, even with the addition of substellar primary objects. " + ] + }, + { + "cell_type": "markdown", + "id": "7e460fa5-a083-4f2c-bef8-eb7d8a972b99", + "metadata": {}, + "source": [ + "### Cluster 2: With Companions" + ] + }, + { + "cell_type": "markdown", + "id": "b0addabd-33a5-4e4f-82d2-d7b58c69fc0e", + "metadata": {}, + "source": [ + "For this cluster we are using the same isochrone as Cluster 1, just changing our IMF to allow for systems with companions." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "kc_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 687520 stars out of mass range\n", + "Found 134795 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "kc_cluster = synthetic.ResolvedCluster(my_iso, kc_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "kc_out = kc_cluster.star_systems\n", + "kc_comp = kc_cluster.companions" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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sSDJSU1NLPfbxxx83fHx8LllP1CyMRAIu0r59ezk5OZnbq6++Kun8ENoff/xRQ4cOlSSbUSd33nmn0tLSin0LMmDAAJvXN954oySZj+CsW7dO586d0/Dhw23O5+rqqrCwsBInP7z33nuLlWVkZOhvf/ubgoOD5ejoKCcnJzVq1EiStHfvXrttLms99u3bp99++01DhgyxGVrdqFEjuyOILtS0aVNt375d27dv15YtW7Rs2TK5ubmpR48edh8hK0tbz5w5o+TkZEVGRprDty9l8eLFuvPOO/Xwww/rww8/lKura5nbMmvWLLMtF26RkZE2cUVD1C8cOi5JN998s1q0aKEvv/yy1GusXbtWFotFDz74oM37Y7VaddNNN5nvT7NmzeTr66spU6bo7bffLvYtrj2enp7FfmaHDBmiwsJCffXVV2bMqFGjFB8fb36D9J///Ed79uzR448/flnXe+ihh7R69Wr98ccfWrRokbp3717q6jbZ2dmaMmWKmjVrJkdHRzk6Oqpu3bo6ffq0zc/4zTffrC+++EJPP/20Nm7cqJycHJvz+Pn5qWnTpnrllVc0Z84c7dy50+aROgC4mtEHurJ9oAvnRSr6b1hYmCSpRYsWCggIMPsLGzduVGBgoFq0aFHsPPbu7eU4e/asvvzyS91zzz1yd3cv9t6ePXtWW7duvezzXsi4aHR2165d5erqqqSkJEnnH0kMDw/XHXfcoc2bN+vMmTM6cuSIfv75Z5tH3M+dO6fY2Fi1bNlSzs7OcnR0lLOzs37++ecS3+uSfl6GDh0qFxcXm9FzH3zwgXJzczVq1KjLvic333yzvvvuO40bN07r1q277LmFSvt5uvDRxkcffVSSbOaLnD9/vlq3bq3bbrutzNcKCwtT06ZN9e677+qHH37Q9u3bS32UTTrfv+vZs6e8vb3l4OAgJycnPfvss/rjjz+UkZEh6fxjms7OzhozZowWL15cbJoF6fw9OnHihB544AF99tlndkffo/ojiYSrUr169eTm5lbiL9tly5Zp+/btNs9ESzIfGZo0aZJNB8vJyUnjxo2TpGIfiv7+/javXVxcJMn8w7bonB07dix2zhUrVhQ7n7u7e7GVqwoLC9W7d299+umnmjx5sr788ktt27bN/OV28R/RJSlrPYoeCbJarcXOUVJZaVxdXdWhQwd16NBBt9xyix544AF98cUXSktL07PPPlvqcWVta2ZmpgoKCnTttdeWqT7Lly+Xm5ubHn744WLzDthz3XXXmW25cLs4eVV070p6dCAoKMjmcauLHT9+XIZhKDAwsNj7s3XrVvP98fb2VnJystq0aaNnnnlGrVq1UlBQkJ577rkyrYoSGBhYrKzofb2wfuPHj9epU6f0/vvvSzrfkbn22mt111132b3Ghe677z65urpq7ty5WrNmjUaPHl1q7JAhQzR//nw9/PDDWrdunbZt26bt27frmmuusfkZf/311zVlyhStWrVK3bt3l5+fn+6++24zOWmxWPTll1+qT58+mj17ttq1a6drrrlGEyZMKNMjfwBQ09EHslWZfaDWrVurXr162rBhgzkfUlESSTq/0MjGjRuVm5urLVu2lLoqm717ezn++OMPnTt3TnFxccXaX/Ro11/9o7/oZ63osStXV1d17drVTCJ9+eWX6tWrl8LDw1VQUKCvv/7afKztwiTSxIkTNX36dN19991as2aNvvnmG23fvl033XRTiW0vqc/l5+enAQMG6F//+pc552N8fLxuvvlmtWrV6rLvydSpU/WPf/xDW7duVd++feXv768ePXoUW4WvNKX9PF3Y7woMDNSgQYO0YMECFRQU6Pvvv9fXX3992V/eWSwWjRo1SkuXLtXbb7+t66+/XrfeemuJsdu2bTMXulm4cKH++9//avv27Zo2bZqk//9Za9q0qZKSkhQQEKDHHntMTZs2VdOmTW3mhBo2bJjeffddHTp0SPfee68CAgLUqVMnm0cXUbNcndPT46rn4OCg22+/XevXr1daWprNL5mi1SwOHjxoc0y9evUknf9lMXDgwBLP27x588uqR9E5P/74Y/Nbs0spKcGxa9cufffdd4qPj9eIESPM8v3791d4PYo6Lenp6cX2lVR2OerXr6969erpu+++KzWmrG318/OTg4NDmSeTfv/99zV9+nSFhYVp/fr1atOmTbnacClF9y4tLa1Ycuu3334z34OS1KtXTxaLRV9//bXZUbzQhWWtW7fW8uXLZRiGvv/+e8XHx+v555+Xm5ubnn766UvWsagjfaGi9/XCDmuzZs3Ut29fvfHGG+rbt69Wr16tGTNmyMHB4ZLnv5i7u7sGDx6smTNnysvLq9R/V1lZWVq7dq2ee+45mzbk5ubqzz//tIn18PDQjBkzNGPGDB0/ftwcldS/f39z6eRGjRqZk2r+9NNP+vDDDxUTE6O8vDy9/fbbl9UGAKhp6AOVrx4V0QeyWCwKCwtTQkKCtm3bphMnTtgkkcLCwhQTE6MtW7bo7NmzpSaRKpKvr68cHBw0bNiwUuc1bNKkSbnPbxiG1qxZIw8PD3Xo0MEs79Gjh5599llt27ZNR48eVa9eveTp6amOHTsqMTFRv/32m66//noFBwebxyxdulTDhw9XbGyszTX+97//ycfHp9i1S/ticNSoUfroo4+UmJiohg0bavv27XrrrbfM/ZdzTxwdHTVx4kRNnDhRJ06cUFJSkp555hn16dNHR44csbuCYGk/TxcnCp944gktWbJEn332mRISEuTj42OODLwcI0eO1LPPPqu3337bZoXciy1fvlxOTk5au3atzQj9VatWFYu99dZbdeutt6qgoEA7duxQXFycoqKiFBgYqMGDB0s6f89HjRql06dP66uvvtJzzz2niIgI/fTTT2X694/qhSQSrlpTp07VF198ob/97W/6+OOP7a4K1rx5c4WEhOi7774r9survPr06SNHR0f98ssvJQ65LYuiX5AXJxcWLFhQLLa0b6rKWo/mzZurfv36+uCDDzRx4kTz2ocOHdLmzZvLNLFfaY4ePar//e9/l1yStqxtLVrd4qOPPtJLL710yQSNdD7plJSUpIiICHXv3l1ffPGFzTK1FeH222+XdL4D1LFjR7N8+/bt2rt3r/nNTkkiIiL08ssv69ixY8UekyuNxWLRTTfdpLlz5yo+Pl7ffvut3WNOnTql1atX2wyTX7ZsmTkZ6IWeeOIJ9e7dWyNGjJCDg4MeeeSRMtXrYo8++qiOHz+usLCwUh8jtFgsMgyj2Pv+z3/+0/wWsSSBgYEaOXKkvvvuO82bN6/E5aCvv/56/f3vf9cnn3xSpnsEALUBfaDLr0dF9YG6d++uTz75RK+88ooCAgJsHlcLCwvTH3/8obi4ODO2opTWfnd3d3Xv3l07d+7UjTfeWOpqfeU1Y8YM7dmzR88884zN7/mePXvqmWee0fTp03XttdfqhhtuMMtXr16t9PT0Yu+HxWIp9l5//vnnOnbsmJo1a1bmOvXu3VsNGjTQe++9p4YNG8rV1dVcvUwq/z3x8fHRfffdp2PHjikqKkoHDx68ZL9WUqk/T8OHD7eJa9++vbp06aJZs2Zp165dGjNmjDw8PMrc5iINGjTQU089pR9//NEm8Xoxi8UiR0dHmy8Ic3JytGTJklKPcXBwUKdOnXTDDTfo/fff17fffmsmkYp4eHiob9++ysvL0913363du3eTRKqBSCLhqtW1a1e98cYbGj9+vNq1a6cxY8aoVatWqlOnjtLS0vTJJ59Iks3Q6QULFqhv377q06ePRo4cqQYNGujPP//U3r179e233+qjjz66rDo0btxYzz//vKZNm6Zff/1Vd9xxh3x9fXX8+HFt27bNHFVxKTfccIOaNm2qp59+WoZhyM/PT2vWrClxiGjR6havvfaaRowYIScnJzVv3rzM9ahTp45eeOEFPfzww7rnnnv0yCOP6MSJE4qJibmsx9lycnLMoeYFBQU6cOCAZs+eLUmKioqqkLbOmTNH3bp1U6dOnfT000+rWbNmOn78uFavXq0FCxbI09PTJt7T01MJCQkaOHCguXpaRXbemjdvrjFjxiguLk516tRR3759dfDgQU2fPl3BwcF68sknSz22a9euGjNmjEaNGqUdO3botttuk4eHh9LS0rRp0ya1bt1ajz76qNauXas333xTd999t6677joZhqFPP/1UJ06cUK9evezW0d/fX48++qgOHz6s66+/Xv/+97+1cOFCPfroo2rYsKFNbK9evdSyZUtt2LBBDz74oAICAsp1X9q0aVPit1oX8vLy0m233aZXXnlF9erVU+PGjZWcnKxFixYV++axU6dOioiI0I033ihfX1/t3btXS5YsUefOneXu7q7vv/9ejz/+uO6//36FhITI2dlZ//nPf/T999/bHakFALUFfaCq6wMV9S1Wrlyp++67z2ZfaGio/P39tXLlSjVo0KBMq8SWlaenpxo1aqTPPvtMPXr0kJ+fn/k79bXXXlO3bt1066236tFHH1Xjxo116tQp7d+/X2vWrCnT6nMnTpww+3anT5/Wvn37tHz5cn399deKjIws9l62b99evr6+Wr9+vTkXkXQ+iVS0+teFj7JJ579Ui4+P1w033KAbb7xRKSkpeuWVV8o8fUERBwcHDR8+XHPmzDFHQnt7e9vElPWe9O/fX6GhoeZUBocOHdK8efPUqFGjMr1/GRkZ5s9TVlaWnnvuObm6umrq1KnFYp944gkNGjRIFovFfIy0PF5++WW7Mf369dOcOXM0ZMgQjRkzRn/88Yf+8Y9/FEvivf322/rPf/6jfv36qWHDhjp79qy5AlzR+/fII4/Izc1NXbt2Vf369ZWenq6ZM2fK29vb5otV1CBVNqU3UE2kpqYao0aNMpo0aWK4uLgYrq6uRrNmzYzhw4cbX375ZbH47777zoiMjDQCAgIMJycnw2q1Grfffrvx9ttvmzEXroRwoaJVES5edWrVqlVG9+7dDS8vL8PFxcVo1KiRcd999xlJSUlmzIgRIwwPD48S27Bnzx6jV69ehqenp+Hr62vcf//9xuHDh0tchWPq1KlGUFCQUadOnWJ1KUs9DMMw/vnPfxohISGGs7Ozcf311xvvvvtusRXFSnPx6mx16tQxgoKCjL59+xobN260iS1pdbbLaeuePXuM+++/3/D39zecnZ2Nhg0bGiNHjjTOnj1rc/4L36fc3Fzj3nvvNVxdXY3PP/+81HYUvZcfffRRifsfe+wx4+KP2IKCAmPWrFnG9ddfbzg5ORn16tUzHnzwQePIkSM2caXdy3fffdfo1KmT4eHhYbi5uRlNmzY1hg8fbuzYscMwDMP48ccfjQceeMBo2rSp4ebmZnh7exs333yzER8fX2o7ioSFhRmtWrUyNm7caHTo0MFwcXEx6tevbzzzzDPFVuIpEhMTY0gytm7davf8Reyt4GIYRomrsx09etS49957DV9fX8PT09O44447jF27dhmNGjUyRowYYcY9/fTTRocOHQxfX1/DxcXFuO6664wnn3zS+N///mcYhmEcP37cGDlypHHDDTcYHh4eRt26dY0bb7zRmDt3rnHu3LkytwMAagP6QP9fl8roAxWxWq2GJGP+/PnF9t19992GJGPo0KHF9l3Ovb14dTbDOL8qWtu2bQ0XFxdDks3vzwMHDhgPPfSQ0aBBA8PJycm45pprjC5duhgvvvii3fY0atTI7NdZLBajbt26RvPmzY1hw4YZ69atK/W4e+65x5BkvP/++2ZZXl6e4eHhYdSpU8fIzMy0ic/MzDRGjx5tBAQEGO7u7ka3bt2Mr7/+ulhb7fXRDMMwfvrpJ7POiYmJJcaU5Z68+uqrRpcuXYx69eqZfc3Ro0cbBw8evOQ9K6rjkiVLjAkTJhjXXHON4eLiYtx6661mv+5iubm5houLi3HHHXdc8twXKu1n5mIlrc727rvvGs2bNzf7UzNnzjQWLVpk0zffsmWLcc899xiNGjUyXFxcDH9/fyMsLMxYvXq1eZ7Fixcb3bt3NwIDAw1nZ2cjKCjIiIyMNL7//vsytwPVi8UwLpouHwCAMujQoYMsFou2b99e1VUBAACo1dasWaMBAwbo888/Nyf4BqoCj7MBAMrs5MmT2rVrl9auXauUlBStXLmyqqsEAABQa+3Zs0eHDh1SdHS02rRpo759+1Z1lXCVI4kEACizb7/9Vt27d5e/v7+ee+453X333VVdJQAAgFpr3Lhx+u9//6t27dpp8eLFpa46B1QWHmcDAAAAAACAXXWqugIAAAAAAACo/kgiAQAAAAAAwC6SSAAAAAAAALCLibXLqLCwUL/99ps8PT2ZzAwAgGrMMAydOnVKQUFBqlOH78uqEv0nAABqhjL3n4wqlJycbERERBj169c3JBkrV64sNXbMmDGGJGPu3Lk25WfPnjUef/xxw9/f33B3dzf69+9vHDlyxCbmzz//NB588EHDy8vL8PLyMh588EEjMzPzsup65MgRQxIbGxsbGxtbDdku7g+g8tF/YmNjY2Njq1mbvf5TlY5EOn36tG666SaNGjVK9957b6lxq1at0jfffKOgoKBi+6KiorRmzRotX75c/v7+io6OVkREhFJSUuTg4CBJGjJkiI4ePaqEhARJ0pgxYzRs2DCtWbOmzHX19PSUJB05ckReXl6X00wAAFCJTp48qeDgYPN3N6oO/ScAAGqGsvafqjSJ1LdvX/Xt2/eSMceOHdPjjz+udevWqV+/fjb7srKytGjRIi1ZskQ9e/aUJC1dulTBwcFKSkpSnz59tHfvXiUkJGjr1q3q1KmTJGnhwoXq3Lmz9u3bp+bNm5eprkVDsL28vOgEAQBQA/D4VNWj/wQAQM1ir/9UrScKKCws1LBhw/TUU0+pVatWxfanpKQoPz9fvXv3NsuCgoIUGhqqzZs3S5K2bNkib29vM4EkSbfccou8vb3NmJLk5ubq5MmTNhsAAAAAAMDVqlonkWbNmiVHR0dNmDChxP3p6elydnaWr6+vTXlgYKDS09PNmICAgGLHBgQEmDElmTlzpry9vc0tODj4L7QEAAAAAACgZqu2SaSUlBS99tprio+Pv+zh6IZh2BxT0vEXx1xs6tSpysrKMrcjR45cVh0AAAAAAABqkyqdE+lSvv76a2VkZKhhw4ZmWUFBgaKjozVv3jwdPHhQVqtVeXl5yszMtBmNlJGRoS5dukiSrFarjh8/Xuz8v//+uwIDA0u9vouLi1xcXCqwRQBQsoKCAuXn51d1NYAaw8nJyVw8AzVfYWGh8vLyqroaAC7A5yyA0lTbJNKwYcPMybKL9OnTR8OGDdOoUaMkSe3bt5eTk5MSExMVGRkpSUpLS9OuXbs0e/ZsSVLnzp2VlZWlbdu26eabb5YkffPNN8rKyjITTQBQFQzDUHp6uk6cOFHVVQFqHB8fH1mtVibPruHy8vJ04MABFRYWVnVVAFyEz1kAJanSJFJ2drb2799vvj5w4IBSU1Pl5+enhg0byt/f3ybeyclJVqvVXFHN29tbo0ePVnR0tPz9/eXn56dJkyapdevWZgKqRYsWuuOOO/TII49owYIFkqQxY8YoIiKizCuzAcCVUJRACggIkLu7O500oAwMw9CZM2eUkZEhSapfv34V1wjlZRiG0tLS5ODgoODgYNWpU21nWQCuKnzOAriUKk0i7dixQ927dzdfT5w4UZI0YsQIxcfHl+kcc+fOlaOjoyIjI5WTk6MePXooPj7eZvjl+++/rwkTJpiruA0YMEDz58+vuIYAwGUqKCgwE0gXJ8wBXJqbm5uk84+vBwQE8MhFDXXu3DmdOXNGQUFBcnd3r+rqALgAn7MASlOlSaTw8HAZhlHm+IMHDxYrc3V1VVxcnOLi4ko9zs/PT0uXLi1PFQHgiiiaA4k/nIDyKfq3k5+fzx83NVRBQYEkydnZuYprAqAkfM4CKAnjhgGgCvEIG1A+/NupPXgvgeqJf5sASkISCQAAAAAAAHZV29XZAOBqdexEjjJPV95y174ezmrg41Zp16utGjdurKioKEVFRf2l81gsFq1cuVJ33313tapXecTExGjVqlVKTU2t9Guj5uIzsGa6Up819s578OBBNWnSRDt37lSbNm0q9NoAgOJIIgFANXLsRI56vpqsnPyCSrumm5ODkqLDLuuPqPT0dM2cOVOff/65jh49Km9vb4WEhOjBBx/U8OHDa8xcT5WZYImJidGMGTPM115eXrrxxhv14osvKiws7Ipfv6w2btyo7t27KzMzUz4+Pjb7qjIhhasDn4GVq7L+TSckJKhv375KS0uT1Wo1y61Wq5ycnHTkyBGz7OjRowoODta6devMRXEuJTg4WGlpaapXr56kS3+GXa7w8HAlJydLOj93V7169dSuXTuNGjVKAwcO/EvnBoCaiiQSAFQjmafzlJNfoHmD2qhZQN0rfr39GdmKWpGqzNN5Zf4D6tdff1XXrl3l4+Oj2NhYtW7dWufOndNPP/2kd999V0FBQRowYMAVrnnpDMNQQUGBHB2r36+4Vq1aKSkpSZL0559/6h//+IciIiLMP0KBqx2fgX9ddfwM7NatmxwdHbVx40YNHjxYkrR3716dPXtWOTk52r9/v5o1ayZJ2rBhg5ycnNS1a9cyndvBwcEmMVXRHnnkET3//PPKz8/XsWPHtHLlSg0ePFgjR47UO++8c8Wue7H8/Hw5OTlV2vUAoDTMiQQA1VCzgLoKbeB9xbfy/JE2btw4OTo6aseOHYqMjFSLFi3UunVr3Xvvvfr888/Vv39/MzYrK0tjxoxRQECAvLy8dPvtt+u7774z98fExKhNmzZasmSJGjduLG9vbw0ePFinTp0yYwzD0OzZs3XdddfJzc1NN910kz7++GNz/8aNG2WxWLRu3Tp16NBBLi4u+vrrr/XLL7/orrvuUmBgoOrWrauOHTuaCRzp/DfMhw4d0pNPPimLxWIzgejmzZt12223yc3NTcHBwZowYYJOnz5t7s/IyFD//v3l5uamJk2a6P333y/TvXN0dJTVapXValXLli01Y8YMZWdn66effir1mClTpuj666+Xu7u7rrvuOk2fPt1c3a/I6tWr1aFDB7m6uqpevXqX/Ib8vffek7e3txITE8tU50s5fPiw7rrrLtWtW1deXl6KjIzU8ePHL3nMe++9pxYtWsjV1VU33HCD3nzzTXNfXl6eHn/8cdWvX1+urq5q3LixZs6c+ZfriZqHz8Da9RlYdP2NGzfa1Ltbt27q1q1bsfKbb75ZHh4eZtmZM2f00EMPydPTUw0bNrRJ3hw8eFAWi0Wpqak6ePCgunfvLkny9fWVxWLRyJEjy3QfS+Pu7i6r1arg4GDdcsstmjVrlhYsWKCFCxea9/Pee+/V+PHjzWOioqJksVi0e/duSdK5c+fk6empdevWSTo/Mqtbt27y8fGRv7+/IiIi9MsvvxRr04cffqjw8HC5urrqzTfflJubmxISEmzq9+mnn8rDw0PZ2dmSpGPHjmnQoEHy9fWVv7+/7rrrLpsVri+8vz4+PuratasOHTpk9z4AQBGSSACAMvvjjz+0fv16PfbYYzYd/AsV/SFiGIb69eun9PR0/fvf/1ZKSoratWunHj166M8//zTjf/nlF61atUpr167V2rVrlZycrJdfftnc//e//13vvfee3nrrLe3evVtPPvmkHnzwQfMRgyKTJ0/WzJkztXfvXt14443Kzs7WnXfeqaSkJO3cuVN9+vRR//79dfjwYUnnO97XXnutnn/+eaWlpSktLU2S9MMPP6hPnz4aOHCgvv/+e61YsUKbNm3S448/bl5r5MiROnjwoP7zn//o448/1ptvvqmMjIzLupe5ubmKj4+Xj4+PmjdvXmqcp6en4uPjtWfPHr322mtauHCh5s6da+7//PPPNXDgQPXr1087d+7Ul19+qQ4dOpR4rn/84x+aNGmS1q1bp169el1WfS9mGIbuvvtu/fnnn0pOTlZiYqJ++eUXDRo0qNRjFi5cqGnTpumll17S3r17FRsbq+nTp2vx4sWSpNdff12rV6/Whx9+qH379mnp0qVq3LjxX6onUJH4DDyvPJ+B3bt314YNG8zXGzZsUHh4uMLCwoqVFyWCirz66qvq0KGDdu7cqXHjxunRRx/Vjz/+WOwawcHB+uSTTyRJ+/btU1paml577bXLuo9lMWLECPn6+urTTz+VdD4hd2EiLDk5WfXq1TPPvX37dp09e9YcXXX69GlNnDhR27dv15dffqk6deronnvuUWFhoc11pkyZogkTJmjv3r26//771a9fv2IJu2XLlpnJ/DNnzqh79+6qW7euvvrqK23atEl169bVHXfcoby8PJ07d0533323wsLC9P3332vLli0aM2YMq7ABuDwGyiQrK8uQZGRlZVV1VQDUAjk5OcaePXuMnJwcm/Ifjp4wGk1Za/xw9ESl1ONyr7d161ZDkvHpp5/alPv7+xseHh6Gh4eHMXnyZMMwDOPLL780vLy8jLNnz9rENm3a1FiwYIFhGIbx3HPPGe7u7sbJkyfN/U899ZTRqVMnwzAMIzs723B1dTU2b95sc47Ro0cbDzzwgGEYhrFhwwZDkrFq1Sq79W/ZsqURFxdnvm7UqJExd+5cm5hhw4YZY8aMsSn7+uuvjTp16hg5OTnGvn37DEnG1q1bzf179+41JBU714Wee+45o06dOuZ9slgshpeXl/HFF1/YxEkyVq5cWep5Zs+ebbRv39583blzZ2Po0KGlxhe18emnnzbq169vfP/996XGGsb/38+iel64WSwWs43r1683HBwcjMOHD5vH7t6925BkbNu2zWzzTTfdZO4PDg42li1bZnO9F154wejcubNhGIYxfvx44/bbbzcKCwsvWUfDKP3fkGHwO7s6udR7UdJ7yGdg7f0MXL9+vSHJ+O233wzDMIyAgABj27ZtxtatW42goCDDMAzj8OHDhiTjyy+/tKnjgw8+aL4uLCw0AgICjLfeesswDMM4cOCAIcnYuXOnzf3IzMw0jynLfSxJWFiY8cQTT5S4r1OnTkbfvn0NwzCM77//3rBYLMbvv/9u/Pnnn4aTk5Px4osvGvfff79hGIYRGxtrvqclycjIMCQZP/zwg02b5s2bZxP36aefGnXr1jVOnz5tGMb5f1+urq7G559/bhiGYSxatMho3ry5zWdobm6u4ebmZqxbt874448/DEnGxo0bS63LhS71OQug9ilr/6n6PCwNAKgxLv7Wctu2bSosLNTQoUOVm5srSUpJSVF2drb8/f1tYnNycmyG7Tdu3Fienp7m6/r165vfaO/Zs0dnz54tNmomLy9Pbdu2tSm7ePTN6dOnNWPGDK1du1a//fabzp07p5ycHPNb+NKkpKRo//79Nt/2GoahwsJCHThwQD/99JMcHR1trnfDDTeUaQLX5s2ba/Xq1ZKkU6dOacWKFbr//vu1YcOGUkcPffzxx5o3b57279+v7OxsnTt3Tl5eXub+1NRUPfLII5e87quvvqrTp09rx44duu666+zWU5K+/vprm/dFOv9te5G9e/cqODhYwcHBZlnLli3l4+OjvXv3qmPHjjbH/v777zpy5IhGjx5tU99z586Z80GNHDlSvXr1UvPmzXXHHXcoIiKiTBPrApWNz8DL/wzs2rWrnJ2dtXHjRt10003KyclRu3btZBiGTp48qZ9//llbtmyRi4uLunTpYnPsjTfeaP6/xWKR1Wq9rNGfl3Mfy8owDPPnIDQ0VP7+/kpOTpaTk5NuuukmDRgwQK+//rqk84+QXbiAwi+//KLp06dr69at+t///meOQDp8+LBCQ0PNuIvf0379+snR0VGrV6/W4MGD9cknn8jT09P8nCx67y7+7D579qx++eUX9e7dWyNHjlSfPn3Uq1cv9ezZU5GRkapfv3657gGAqxNJJABAmTVr1kwWi6XYYwRFiQk3t/+fmLawsFD169e3GeJf5MI/Ni6eKNRisZgd6qL/fv7552rQoIFNnIuLi83rix8teeqpp7Ru3Tr94x//ULNmzeTm5qb77rtPeXmXXjq8sLBQY8eO1YQJE4rta9iwofbt22fW83I5Ozubk8dKUtu2bbVq1SrNmzdPS5cuLRa/detWDR48WDNmzFCfPn3k7e2t5cuX69VXXzVjLrznpbn11lv1+eef68MPP9TTTz9dpro2adKk2B+FF07Ue+EfUBcqrbzovVy4cKE6depks8/BwUGS1K5dOx04cEBffPGFkpKSFBkZqZ49e5Zp3hKgMvAZWP7PQHd3d918883asGGD/vzzT3Xr1s38t9+lSxdt2LBBW7ZsUefOneXq6mpz7KXuUVlczn0si4KCAv38889mstxisei2227Txo0b5ezsrPDwcIWGhqqgoEA//PCDNm/ebLMCXv/+/RUcHKyFCxcqKChIhYWFCg0NLfbeXPyeOjs767777tOyZcs0ePBgLVu2TIMGDTI/mwsLC9W+ffsS56i65pprJJ2fl27ChAlKSEjQihUr9Pe//12JiYm65ZZbLvs+ALg6kUSqBo6dyFHm6Uv/Qv8rfD2cL2vZWgAojb+/v3r16qX58+dr/Pjxpc4JIp1PCKSnp8vR0bHc89q0bNlSLi4uOnz4sM23uGXx9ddfa+TIkbrnnnskSdnZ2TaTi0rnO+QFBbZLibdr1067d++2SfZcqEWLFjp37px27Nihm2++WdL5uTdOnDhxWfUr4uDgoJycnBL3/fe//1WjRo00bdo0s+ziCVBvvPFGffnllxo1alSp17j55ps1fvx49enTRw4ODnrqqafKVdcLtWzZUocPH9aRI0fM0Uh79uxRVlaWWrRoUSw+MDBQDRo00K+//qqhQ4eWel4vLy8NGjRIgwYN0n333ac77rhDf/75p/z8/P5ynYG/is/Av/YZ2L17dy1fvlyZmZk2IxvDwsK0ceNGbdmy5ZKfZWXh7OwsSTbt+iv3sSSLFy9WZmam7r33XrMsPDxc77zzjpydnfX888/LYrHo1ltv1T/+8Q/l5OSY8yH98ccf2rt3rxYsWKBbb71VkrRp06YyX3vo0KHq3bu3du/erQ0bNuiFF14w97Vr104rVqwwJ3IvTdu2bdW2bVtNnTpVnTt31rJly66KJNKV/purNPwthtqGJFIVO3YiRz1fTVZOfoH94HJyc3JQUnQYH14AKsSbb76prl27qkOHDoqJidGNN96oOnXqaPv27frxxx/Vvn17SVLPnj3VuXNn3X333Zo1a5aaN2+u3377Tf/+97919913l/r41oU8PT01adIkPfnkkyosLFS3bt108uRJbd68WXXr1tWIESNKPbZZs2b69NNP1b9/f1ksFk2fPr3YN9eNGzfWV199pcGDB8vFxUX16tXTlClTdMstt+ixxx7TI488Ig8PD+3du1eJiYmKi4szH7V65JFH9M4778jR0VFRUVFlGhF07tw5paenS/r/x9n27NmjKVOmlNqGw4cPa/ny5erYsaM+//xzrVy50ibmueeeU48ePdS0aVMNHjxY586d0xdffKHJkyfbxHXu3FlffPGF7rjjDjk6OurJJ5+0W99L6dmzp2688UYNHTpU8+bN07lz5zRu3DiFhYWV+t7GxMRowoQJ8vLyUt++fZWbm6sdO3YoMzNTEydO1Ny5c1W/fn21adNGderU0UcffSSr1VqmRwWvJl999ZVeeeUVpaSkKC0tTStXrtTdd99tE7N3715NmTJFycnJKiwsVKtWrfThhx+qYcOGks5P7D5p0iR98MEHysnJUY8ePfTmm2/q2muvNc+RmZmpCRMmmI9gDhgwQHFxcVf9+8FnYPk/A7t3764XXnhBaWlpmjRpklkeFhaml19+WadOnSo2qfblatSokSwWi9auXas777xTbm5uf+k+njlzRunp6Tp37pyOHTumTz/9VHPnztWjjz5qU9fw8HA98cQTcnR0NJND4eHhio6OVrt27cykTtGqae+8847q16+vw4cPl3mEqHT+XgUGBmro0KFq3LixTfJn6NCheuWVV3TXXXfp+eef17XXXqvDhw/r008/1VNPPaX8/Hy98847GjBggIKCgrRv3z799NNPGj58+OXe5hqnMv7mKg1/i6G2IYlUxTJP5yknv0DzBrUp1zKz9uzPyFbUilRlns7jgwuoQfZnZFfb6zRt2lQ7d+5UbGyspk6dqqNHj8rFxUUtW7bUpEmTNG7cOEnnh/f/+9//1rRp0/TQQw/p999/l9Vq1W233abAwMAyX++FF15QQECAZs6cqV9//VU+Pj5q166dnnnmmUseN3fuXD300EPq0qWL+YfRyZMnbWKef/55jR07Vk2bNlVubq4Mw9CNN96o5ORkTZs2TbfeeqsMw1DTpk1tVh1777339PDDD5ud+RdffFHTp0+325bdu3ebc0+4u7uradOmeuutt0rtwN9111168skn9fjjjys3N1f9+vXT9OnTFRMTY8aEh4fro48+0gsvvKCXX35ZXl5euu2220o8X9euXfX555/rzjvvlIODQ4mPq5SVxWLRqlWrNH78eN12222qU6eO7rjjDsXFxZV6zMMPPyx3d3e98sormjx5sjw8PNS6dWvzMY+6detq1qxZ+vnnn+Xg4KCOHTvq3//+t+rUYTHZC50+fVo33XSTRo0aZTMSosgvv/yibt26afTo0ZoxY4a8vb21d+9em0eEoqKitGbNGi1fvlz+/v6Kjo5WRESEUlJSzEeMhgwZoqNHj5pLio8ZM0bDhg3TmjVrrngb+Qz8f7XpM7Bz587m42NFyTZJ6tixowoKCuTm5lbscdfL1aBBA82YMUNPP/20Ro0apeHDhys+Pr7c93HhwoVauHChnJ2d5e/vr/bt22vFihXmCK8ioaGhqlevnho1amQmjMLCwlRQUGAz+qlOnTpavny5JkyYoNDQUDVv3lyvv/66zcisS7FYLHrggQf0yiuv6Nlnn7XZ5+7urq+++kpTpkzRwIEDderUKTVo0EA9evS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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs and WDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "k_bh = np.concatenate([p2_bh, c_bh])\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "k_ns = np.concatenate([p2_ns, c_ns])\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "k_wd = np.concatenate([p2_wd, c_wd])\n", + "\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(kc_out[k_bh]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(kc_out[k_wd]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6a854062-8dd0-4411-a0d7-523dcc4aa77b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Smallest mass of a primary generated object: 0.07000003602252991\n", + "Smallest mass of a companion generated object: 0.010000123044113119\n" + ] + } + ], + "source": [ + "# Finding the minimum mass of generated objects\n", + "print(\"Smallest mass of a primary generated object: \" + str(np.min(kc_out['mass'])))\n", + "print(\"Smallest mass of a companion generated object: \" + str(np.min(kc_comp['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "398a4626-0e74-4f6d-a3a1-2c115fedb9c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial mass of smallest generated black hole: 0.0700493749164032\n", + "Initial mass of largest generated black hole: 119.95339215013136\n" + ] + } + ], + "source": [ + "# Finding the minimum/maximum inital masses of generated black holes\n", + "print(\"Initial mass of smallest generated black hole: \" + str(np.min(kc_out[k_bh]['mass'])))\n", + "print(\"Initial mass of largest generated black hole: \" + str(np.max(kc_out[k_bh]['mass'])))" + ] + }, + { + "cell_type": "markdown", + "id": "77b9203f-69ec-4a54-af84-581427feb4fd", + "metadata": {}, + "source": [ + "In this case, we are getting many instances of substellar mass blakc holes, which does not make sense. Why is it that adding companions introduces this issue?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "afe96713-78c9-4785-bdf9-6f3f36f2a4d5", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "imf_multi_resolved = multiplicity.MultiplicityResolvedDK()\n", + "c3_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi_resolved)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a6483a09-8712-48ed-9bc6-dbfbda7a32f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 694055 stars out of mass range\n", + "Found 136094 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "cluster3 = synthetic.ResolvedCluster(my_iso, c3_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "c3_out = cluster3.star_systems\n", + "c3_comp = cluster3.companions" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c598a266-297c-423d-b5f5-648f819c065e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs and WDs\n", + "p3_bh = np.where(c3_out['phase'] == 103)[0]\n", + "c3_bh = np.where(c3_comp['phase'] == 103)[0]\n", + "k3_bh = np.concatenate([p3_bh, c3_bh])\n", + "p3_ns = np.where(c3_out['phase'] == 102)[0]\n", + "c3_ns = np.where(c3_comp['phase'] == 102)[0]\n", + "k3_ns = np.concatenate([p3_ns, c3_ns])\n", + "p3_wd = np.where(c3_out['phase'] == 101)[0]\n", + "c3_wd = np.where(c3_comp['phase'] == 101)[0]\n", + "k3_wd = np.concatenate([p3_wd, c3_wd])\n", + "\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(c3_out[k3_bh]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(c3_out[k3_wd]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c1983a8f-67cf-41c1-bb0d-0869a543c4fa", + "metadata": {}, + "source": [ + "This is still the case when the companion objects are resolved." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1969480a-56b5-4109-993a-41267b1bcf24", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 261f72fd8f336a5f478c71e4c42b2b2b6acffd98 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 29 Jul 2024 09:19:13 -0700 Subject: [PATCH 009/191] New BD changes --- changes/BD_Evolution.ipynb | 172 +++++ changes/Compact_Multiplicity.ipynb | 66 +- changes/Test_BD_Cluster.ipynb | 1130 ++++++++++------------------ changes/bd_evo_csv/baraffe2003.csv | 1 + spisea/evolution.py | 7 +- spisea/imf/imf.py | 2 +- spisea/tests/test_synthetic.py | 45 +- 7 files changed, 676 insertions(+), 747 deletions(-) create mode 100644 changes/BD_Evolution.ipynb create mode 100644 changes/bd_evo_csv/baraffe2003.csv diff --git a/changes/BD_Evolution.ipynb b/changes/BD_Evolution.ipynb new file mode 100644 index 00000000..ef60330e --- /dev/null +++ b/changes/BD_Evolution.ipynb @@ -0,0 +1,172 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cf875d01-eefb-4ab5-8bc1-9d7ff0b68e6e", + "metadata": {}, + "source": [ + "## Attempting to Describe Brown Dwarf Evolution Using Known Relations" + ] + }, + { + "cell_type": "markdown", + "id": "edb00592-f126-4ae1-ab2b-13f7094e01ee", + "metadata": {}, + "source": [ + "We are trying to find a relation between age/mass and effective temperature of brown dwarf stars. To do this, we are looking at several evolutionary .csv files laid out in the [SPLAT evolutionary model for brown dwarves program](https://github.com/aburgasser/splat/tree/main/resources/EvolutionaryModels). Namely, we will be considering:\n", + "* [Baraffe 2003](https://github.com/aburgasser/splat/blob/main/resources/EvolutionaryModels/baraffe2003.csv)\n", + "* " + ] + }, + { + "cell_type": "markdown", + "id": "79e30b98-f4d3-44df-aa44-2cb7a223c747", + "metadata": {}, + "source": [ + "#### Importing Necessary Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "910e4a29-8083-4dca-b1ac-3d574999efae", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "205f6536-9dd1-444f-a194-c28610089e60", + "metadata": {}, + "source": [ + "#### Importing .csv Files for Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "620a1840-cf87-45c9-b65f-6b6b0e803192", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " age mass luminosity temperature gravity radius\n", + "0 0.001 0.0005 -5.370 628 2.645 0.176\n", + "1 0.001 0.0010 -4.715 942 2.996 0.166\n", + "2 0.001 0.0020 -4.137 1285 3.259 0.174\n", + "3 0.001 0.0030 -3.746 1553 3.372 0.187\n", + "4 0.001 0.0040 -3.482 1747 3.438 0.200\n", + ".. ... ... ... ... ... ...\n", + "230 10.000 0.0720 -4.472 1556 5.481 0.081\n", + "231 10.000 0.0750 -3.954 1997 5.415 0.089\n", + "232 10.000 0.0800 -3.602 2322 5.353 0.099\n", + "233 10.000 0.0900 -3.274 2624 5.289 0.113\n", + "234 10.000 0.1000 -3.082 2786 5.246 0.125\n", + "\n", + "[235 rows x 6 columns]\n" + ] + } + ], + "source": [ + "baraffe_2003 = pd.read_csv('/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/changes/bd_evo_csv/baraffe2003.csv')\n", + "print(baraffe_2003)" + ] + }, + { + "cell_type": "markdown", + "id": "fec3c4e4-52b4-46e9-8599-16170dc4eb3b", + "metadata": {}, + "source": [ + "#### Plotting Mass vs. Temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "634c1991-bc5f-4bf1-bd2b-7ba90acc7af0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/hf/8_ckhzzx55xc7f3s404_kn_4000131/T/ipykernel_33945/3087070154.py:12: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n", + " age_colors = plt.cm.get_cmap('viridis', len(b_ages)) # Adjust colormap range\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Filter .csv files to only include BD masses\n", + "filtered_baraffe_2003 = baraffe_2003[(baraffe_2003['mass'] >= 0.01) & (baraffe_2003['mass'] <= 0.08)]\n", + "\n", + "# Define variables\n", + "b_masses = filtered_baraffe_2003['mass']\n", + "b_teffs = filtered_baraffe_2003['temperature']\n", + "\n", + "# Look at age span\n", + "b_ages = np.unique(filtered_baraffe_2003['age'])\n", + "\n", + "# Define age-based color map\n", + "age_colors = plt.cm.get_cmap('viridis', len(b_ages)) # Adjust colormap range\n", + "\n", + "# Plot each age group separately\n", + "for i, age in enumerate(b_ages):\n", + " age_data = filtered_baraffe_2003[filtered_baraffe_2003['age'] == age]\n", + " plt.scatter(age_data['mass'], age_data['temperature'], label=f'Age {age:.2f} Myr',\n", + " c=[age_colors(i / (len(b_ages)-1))], alpha=0.7) # Normalize 'i' for colormap range\n", + "\n", + "# Add colorbar and legend\n", + "plt.colorbar(label='Age (Myr)')\n", + "plt.xlabel('Mass')\n", + "plt.ylabel('Temperature')\n", + "plt.title('Mass vs Temperature Colored by Age')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90980d47-aedc-484d-94b4-49f14ccc8b66", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index d223c0f1..983fcf1b 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", "metadata": {}, "outputs": [], @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", "metadata": {}, "outputs": [], @@ -223,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", "metadata": {}, "outputs": [ @@ -231,8 +231,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 687520 stars out of mass range\n", - "Found 134795 companions out of stellar mass range\n" + "Found 686912 stars out of mass range\n", + "Found 134402 companions out of stellar mass range\n" ] } ], @@ -248,13 +248,65 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, + "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass \n", + "--------------------\n", + " 0.5747159412207477\n", + " 0.6970586660858713\n", + " 0.601165124305441\n", + "0.056083697312291306\n", + " 0.02673063039645824\n", + " 0.1862915740843192\n", + " 0.07159040923670096\n", + " ...\n", + " 0.0830220943719863\n", + " 0.09156100383230333\n", + " 0.3808927106160393\n", + "0.016698205202363158\n", + " 0.4183953590905605\n", + " 0.0758085193857225\n", + " 0.3082346627668261\n", + "Length = 391314 rows Teff \n", + "------------------\n", + " 4068.981641477723\n", + " 4492.75426907755\n", + " 4126.083901944962\n", + " nan\n", + " nan\n", + "3234.0110881285427\n", + "2808.0618989378295\n", + " ...\n", + " 2898.061169580272\n", + " 2950.46139583484\n", + "3506.1448705814614\n", + " nan\n", + " 3566.917126729317\n", + "2845.0651718291647\n", + "3418.8421815545435\n", + "Length = 391314 rows\n" + ] + } + ], + "source": [ + "print(kc_comp['mass'], kc_comp['Teff'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] diff --git a/changes/Test_BD_Cluster.ipynb b/changes/Test_BD_Cluster.ipynb index 2dbd9d71..5625aa9a 100644 --- a/changes/Test_BD_Cluster.ipynb +++ b/changes/Test_BD_Cluster.ipynb @@ -43,536 +43,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "0cd4c0a3-39e8-4bbf-9eba-e1883b6dacec", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Changing to logg=4.00 for T= 32651 logg=3.99\n", - "Changing to logg=4.00 for T= 32840 logg=3.98\n", - "Changing to logg=4.00 for T= 33037 logg=3.97\n", - "Changing to logg=4.00 for T= 33144 logg=3.96\n", - "Changing to logg=4.00 for T= 33205 logg=3.95\n", - "Changing to logg=4.00 for T= 33358 logg=3.94\n", - "Changing to logg=4.00 for T= 33504 logg=3.93\n", - "Changing to logg=4.00 for T= 33651 logg=3.91\n", - "Changing to logg=4.00 for T= 33713 logg=3.91\n", - "Changing to logg=4.00 for T= 33775 logg=3.90\n", - "Changing to logg=4.00 for T= 33892 logg=3.89\n", - "Changing to logg=4.00 for T= 34002 logg=3.87\n", - "Changing to logg=4.00 for T= 34111 logg=3.86\n", - "Changing to logg=4.00 for T= 34182 logg=3.85\n", - "Changing to logg=4.00 for T= 34222 logg=3.85\n", - "Changing to logg=4.00 for T= 34332 logg=3.83\n", - "Changing to logg=4.00 for T= 34443 logg=3.82\n", - "Changing to logg=4.00 for T= 34546 logg=3.81\n", - "Changing to logg=4.00 for T= 34658 logg=3.80\n", - "Changing to logg=4.00 for T= 34674 logg=3.80\n", - "Changing to logg=4.00 for T= 34746 logg=3.79\n", - "Changing to logg=4.00 for T= 34842 logg=3.78\n", - "Changing to logg=4.00 for T= 34930 logg=3.77\n", - "Changing to logg=4.00 for T= 35019 logg=3.76\n", - "Changing to logg=4.00 for T= 35108 logg=3.75\n", - "Changing to logg=4.00 for T= 35124 logg=3.74\n", - "Changing to logg=4.00 for T= 35229 logg=3.74\n", - "Changing to logg=4.00 for T= 35351 logg=3.73\n", - "Changing to logg=4.00 for T= 35465 logg=3.72\n", - "Changing to logg=4.00 for T= 35588 logg=3.71\n", - "Changing to 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"Changing to logg=5.00 for T= 50699 logg=6.04\n", - "Changing to T= 50000 for T= 50839 logg=6.04\n", - "Changing to logg=5.00 for T= 50839 logg=6.04\n", - "Changing to T= 50000 for T= 50957 logg=6.04\n", - "Changing to logg=5.00 for T= 50957 logg=6.04\n", - "Changing to T= 50000 for T= 51086 logg=6.04\n", - "Changing to logg=5.00 for T= 51086 logg=6.04\n", - "Changing to T= 50000 for T= 51215 logg=6.04\n", - "Changing to logg=5.00 for T= 51215 logg=6.04\n", - "Changing to T= 50000 for T= 51345 logg=6.04\n", - "Changing to logg=5.00 for T= 51345 logg=6.04\n", - "Changing to T= 50000 for T= 51475 logg=6.04\n", - "Changing to logg=5.00 for T= 51475 logg=6.04\n", - "Changing to T= 50000 for T= 51606 logg=6.04\n", - "Changing to logg=5.00 for T= 51606 logg=6.04\n", - "Changing to T= 50000 for T= 51737 logg=6.05\n", - "Changing to logg=5.00 for T= 51737 logg=6.05\n", - "Changing to T= 50000 for T= 51868 logg=6.05\n", - "Changing to logg=5.00 for T= 51868 logg=6.05\n", - "Changing to T= 50000 for T= 52000 logg=6.05\n", - "Changing to logg=5.00 for T= 52000 logg=6.05\n", - "Changing to T= 50000 for T= 52119 logg=6.05\n", - "Changing to logg=5.00 for T= 52119 logg=6.05\n", - "Changing to T= 50000 for T= 52240 logg=6.05\n", - "Changing to logg=5.00 for T= 52240 logg=6.05\n", - "Changing to T= 50000 for T= 52384 logg=6.05\n", - "Changing to logg=5.00 for T= 52384 logg=6.05\n", - "Changing to T= 50000 for T= 52505 logg=6.05\n", - "Changing to logg=5.00 for T= 52505 logg=6.05\n", - "Changing to T= 50000 for T= 52626 logg=6.05\n", - "Changing to logg=5.00 for T= 52626 logg=6.05\n", - "Changing to T= 50000 for T= 52747 logg=6.05\n", - "Changing to logg=5.00 for T= 52747 logg=6.05\n", - "Changing to T= 50000 for T= 52759 logg=6.05\n", - "Changing to logg=5.00 for T= 52759 logg=6.05\n", - "Changing to T= 50000 for T= 52857 logg=6.05\n", - "Changing to logg=5.00 for T= 52857 logg=6.05\n", - "Changing to T= 50000 for T= 52991 logg=6.05\n", - "Changing to logg=5.00 for T= 52991 logg=6.05\n", - "Changing to T= 50000 for T= 53125 logg=6.06\n", - "Changing to logg=5.00 for T= 53125 logg=6.06\n", - "Changing to T= 50000 for T= 53235 logg=6.06\n", - "Changing to logg=5.00 for T= 53235 logg=6.06\n", - "Changing to T= 50000 for T= 53358 logg=6.06\n", - "Changing to logg=5.00 for T= 53358 logg=6.06\n", - "Changing to T= 50000 for T= 53481 logg=6.06\n", - "Changing to logg=5.00 for T= 53481 logg=6.06\n", - "Changing to T= 50000 for T= 53592 logg=6.06\n", - "Changing to logg=5.00 for T= 53592 logg=6.06\n", - "Changing to T= 50000 for T= 53728 logg=6.06\n", - "Changing to logg=5.00 for T= 53728 logg=6.06\n", - "Changing to T= 50000 for T= 53864 logg=6.06\n", - "Changing to logg=5.00 for T= 53864 logg=6.06\n", - "Changing to T= 50000 for T= 53976 logg=6.07\n", - "Changing to logg=5.00 for T= 53976 logg=6.07\n", - "Changing to T= 50000 for T= 54100 logg=6.07\n", - "Changing to logg=5.00 for T= 54100 logg=6.07\n", - "Changing to T= 50000 for T= 54225 logg=6.07\n", - "Changing to logg=5.00 for T= 54225 logg=6.07\n", - "Changing to T= 50000 for T= 54363 logg=6.07\n", - "Changing to logg=5.00 for T= 54363 logg=6.07\n", - "Changing to T= 50000 for T= 54475 logg=6.07\n", - "Changing to logg=5.00 for T= 54475 logg=6.07\n", - "Changing to T= 50000 for T= 54588 logg=6.07\n", - "Changing to logg=5.00 for T= 54588 logg=6.07\n", - "Changing to T= 50000 for T= 54714 logg=6.08\n", - "Changing to logg=5.00 for T= 54714 logg=6.08\n", - "Changing to T= 50000 for T= 54853 logg=6.08\n", - "Changing to logg=5.00 for T= 54853 logg=6.08\n", - "Changing to T= 50000 for T= 54967 logg=6.08\n", - "Changing to logg=5.00 for T= 54967 logg=6.08\n", - "Changing to T= 50000 for T= 55093 logg=6.08\n", - "Changing to logg=5.00 for T= 55093 logg=6.08\n", - "Changing to T= 50000 for T= 55208 logg=6.08\n", - "Changing to logg=5.00 for T= 55208 logg=6.08\n", - "Changing to T= 50000 for T= 55322 logg=6.09\n", - "Changing to logg=5.00 for T= 55322 logg=6.09\n", - "Changing to T= 50000 for T= 55450 logg=6.09\n", - "Changing to logg=5.00 for T= 55450 logg=6.09\n", - "Changing to T= 50000 for T= 55590 logg=6.09\n", - "Changing to logg=5.00 for T= 55590 logg=6.09\n", - "Changing to T= 50000 for T= 55719 logg=6.09\n", - "Changing to logg=5.00 for T= 55719 logg=6.09\n", - "Changing to T= 50000 for T= 55834 logg=6.09\n", - "Changing to logg=5.00 for T= 55834 logg=6.09\n", - "Changing to T= 50000 for T= 55873 logg=6.09\n", - "Changing to logg=5.00 for T= 55873 logg=6.09\n", - "Changing to T= 50000 for T= 55963 logg=6.09\n", - "Changing to logg=5.00 for T= 55963 logg=6.09\n", - "Changing to T= 50000 for T= 56092 logg=6.10\n", - "Changing to logg=5.00 for T= 56092 logg=6.10\n", - "Changing to T= 50000 for T= 56234 logg=6.10\n", - "Changing to logg=5.00 for T= 56234 logg=6.10\n", - "Changing to T= 50000 for T= 56364 logg=6.10\n", - "Changing to logg=5.00 for T= 56364 logg=6.10\n", - "Changing to T= 50000 for T= 56507 logg=6.10\n", - "Changing to logg=5.00 for T= 56507 logg=6.10\n", - "Changing to T= 50000 for T= 56624 logg=6.11\n", - "Changing to logg=5.00 for T= 56624 logg=6.11\n", - "Changing to T= 50000 for T= 56754 logg=6.11\n", - "Changing to logg=5.00 for T= 56754 logg=6.11\n", - "Changing to T= 50000 for T= 56885 logg=6.11\n", - "Changing to logg=5.00 for T= 56885 logg=6.11\n", - "Changing to T= 50000 for T= 57030 logg=6.11\n", - "Changing to logg=5.00 for T= 57030 logg=6.11\n", - "Changing to T= 50000 for T= 57161 logg=6.11\n", - "Changing to logg=5.00 for T= 57161 logg=6.11\n", - "Changing to T= 50000 for T= 57293 logg=6.11\n", - "Changing to logg=5.00 for T= 57293 logg=6.11\n", - "Changing to T= 50000 for T= 57425 logg=6.11\n", - "Changing to logg=5.00 for T= 57425 logg=6.11\n", - "Changing to T= 50000 for T= 57570 logg=6.11\n", - "Changing to logg=5.00 for T= 57570 logg=6.11\n", - "Changing to T= 50000 for T= 57703 logg=6.11\n", - "Changing to logg=5.00 for T= 57703 logg=6.11\n", - "Changing to T= 50000 for T= 57850 logg=6.12\n", - "Changing to logg=5.00 for T= 57850 logg=6.12\n", - "Changing to T= 50000 for T= 57983 logg=6.12\n", - "Changing to logg=5.00 for T= 57983 logg=6.12\n", - "Changing to T= 50000 for T= 58117 logg=6.12\n", - "Changing to logg=5.00 for T= 58117 logg=6.12\n", - "Changing to T= 50000 for T= 58251 logg=6.13\n", - "Changing to logg=5.00 for T= 58251 logg=6.13\n", - "Changing to T= 50000 for T= 58398 logg=6.13\n", - "Changing to logg=5.00 for T= 58398 logg=6.13\n", - "Changing to T= 50000 for T= 58546 logg=6.13\n", - "Changing to logg=5.00 for T= 58546 logg=6.13\n", - "Changing to T= 50000 for T= 58695 logg=6.13\n", - "Changing to logg=5.00 for T= 58695 logg=6.13\n", - "Changing to T= 50000 for T= 58844 logg=6.14\n", - "Changing to logg=5.00 for T= 58844 logg=6.14\n", - "Changing to T= 50000 for T= 58993 logg=6.14\n", - "Changing to logg=5.00 for T= 58993 logg=6.14\n", - "Changing to T= 50000 for T= 59143 logg=6.14\n", - "Changing to logg=5.00 for T= 59143 logg=6.14\n", - "Changing to T= 50000 for T= 59293 logg=6.15\n", - "Changing to logg=5.00 for T= 59293 logg=6.15\n", - "Changing to T= 50000 for T= 59334 logg=6.15\n", - "Changing to logg=5.00 for T= 59334 logg=6.15\n", - "Changing to T= 50000 for T= 59457 logg=6.15\n", - "Changing to logg=5.00 for T= 59457 logg=6.15\n", - "Changing to T= 50000 for T= 59621 logg=6.16\n", - "Changing to logg=5.00 for T= 59621 logg=6.16\n", - "Changing to T= 50000 for T= 59800 logg=6.16\n", - "Changing to logg=5.00 for T= 59800 logg=6.16\n", - "Changing to T= 50000 for T= 59965 logg=6.16\n", - "Changing to logg=5.00 for T= 59965 logg=6.16\n", - "Changing to T= 50000 for T= 60159 logg=6.17\n", - "Changing to logg=5.00 for T= 60159 logg=6.17\n", - "Changing to T= 50000 for T= 60353 logg=6.17\n", - "Changing to logg=5.00 for T= 60353 logg=6.17\n", - "Changing to T= 50000 for T= 60534 logg=6.18\n", - "Changing to logg=5.00 for T= 60534 logg=6.18\n", - "Changing to T= 50000 for T= 60758 logg=6.18\n", - "Changing to logg=5.00 for T= 60758 logg=6.18\n", - "Changing to T= 50000 for T= 60968 logg=6.18\n", - "Changing to logg=5.00 for T= 60968 logg=6.18\n", - "Changing to T= 50000 for T= 61235 logg=6.19\n", - "Changing to logg=5.00 for T= 61235 logg=6.19\n", - "Changing to T= 50000 for T= 61504 logg=6.20\n", - "Changing to logg=5.00 for T= 61504 logg=6.20\n", - "Changing to T= 50000 for T= 61844 logg=6.21\n", - "Changing to logg=5.00 for T= 61844 logg=6.21\n", - "Changing to T= 50000 for T= 62287 logg=6.23\n", - "Changing to logg=5.00 for T= 62287 logg=6.23\n", - "Changing to T= 50000 for T= 63038 logg=6.27\n", - "Changing to logg=5.00 for T= 63038 logg=6.27\n", - "Changing to T= 50000 for T= 64239 logg=6.32\n", - "Changing to logg=5.00 for T= 64239 logg=6.32\n", - "Changing to T= 50000 for T= 65464 logg=6.37\n", - "Changing to logg=5.00 for T= 65464 logg=6.37\n", - "Changing to T= 50000 for T= 66696 logg=6.42\n", - "Changing to logg=5.00 for T= 66696 logg=6.42\n", - "Changing to T= 50000 for T= 67967 logg=6.47\n", - "Changing to logg=5.00 for T= 67967 logg=6.47\n", - "Changing to T= 50000 for T= 69263 logg=6.53\n", - "Changing to logg=5.00 for T= 69263 logg=6.53\n", - "Changing to T= 50000 for T= 71367 logg=6.63\n", - "Changing to logg=5.00 for T= 71367 logg=6.63\n", - "Isochrone generation took 150.174449 s.\n", - "Making photometry for isochrone: log(t) = 6.70 AKs = 0.80 dist = 4000\n", - " Starting at: 2024-07-10 12:22:04.577984 Usually takes ~5 minutes\n", - "Starting filter: wfc3,ir,f127m Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2928 K m_hst_f127m = 22.41\n", - "M = 1.175 Msun T = 4611 K m_hst_f127m = 18.72\n", - "M = 1.650 Msun T = 5355 K m_hst_f127m = 17.71\n", - "M = 1.973 Msun T = 6311 K m_hst_f127m = 16.34\n", - "M = 2.292 Msun T = 9669 K m_hst_f127m = 16.68\n", - "M = 2.648 Msun T = 11771 K m_hst_f127m = 16.64\n", - "M = 2.819 Msun T = 12291 K m_hst_f127m = 16.51\n", - "M = 3.075 Msun T = 13014 K m_hst_f127m = 16.33\n", - "M = 3.332 Msun T = 13709 K m_hst_f127m = 16.16\n", - "M = 3.482 Msun T = 14099 K m_hst_f127m = 16.07\n", - "M = 3.615 Msun T = 14438 K m_hst_f127m = 15.99\n", - "M = 8.977 Msun T = 23502 K m_hst_f127m = 14.07\n", - "M = 49.000 Msun T = 32344 K m_hst_f127m = 9.23\n", - "M = 50.757 Msun T = 31046 K m_hst_f127m = 9.25\n", - "M = 52.540 Msun T = 41937 K m_hst_f127m = 10.13\n", - "M = 54.406 Msun T = 48753 K m_hst_f127m = 10.85\n", - "M = 56.318 Msun T = 65464 K m_hst_f127m = 12.01\n", - "Starting filter: wfc3,ir,f139m Elapsed time: 44.06 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2928 K m_hst_f139m = 22.11\n", - "M = 1.175 Msun T = 4611 K m_hst_f139m = 18.14\n", - "M = 1.650 Msun T = 5355 K m_hst_f139m = 17.18\n", - "M = 1.973 Msun T = 6311 K m_hst_f139m = 15.86\n", - "M = 2.292 Msun T = 9669 K m_hst_f139m = 16.28\n", - "M = 2.648 Msun T = 11771 K m_hst_f139m = 16.25\n", - "M = 2.819 Msun T = 12291 K m_hst_f139m = 16.12\n", - "M = 3.075 Msun T = 13014 K m_hst_f139m = 15.94\n", - "M = 3.332 Msun T = 13709 K m_hst_f139m = 15.77\n", - "M = 3.482 Msun T = 14099 K m_hst_f139m = 15.68\n", - "M = 3.615 Msun T = 14438 K m_hst_f139m = 15.60\n", - "M = 8.977 Msun T = 23502 K m_hst_f139m = 13.70\n", - "M = 49.000 Msun T = 32344 K m_hst_f139m = 8.87\n", - "M = 50.757 Msun T = 31046 K m_hst_f139m = 8.88\n", - "M = 52.540 Msun T = 41937 K m_hst_f139m = 9.77\n", - "M = 54.406 Msun T = 48753 K m_hst_f139m = 10.50\n", - "M = 56.318 Msun T = 65464 K m_hst_f139m = 11.66\n", - "Starting filter: wfc3,ir,f153m Elapsed time: 90.13 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2928 K m_hst_f153m = 21.45\n", - "M = 1.175 Msun T = 4611 K m_hst_f153m = 17.51\n", - "M = 1.650 Msun T = 5355 K m_hst_f153m = 16.63\n", - "M = 1.973 Msun T = 6311 K m_hst_f153m = 15.37\n", - "M = 2.292 Msun T = 9669 K m_hst_f153m = 15.89\n", - "M = 2.648 Msun T = 11771 K m_hst_f153m = 15.87\n", - "M = 2.819 Msun T = 12291 K m_hst_f153m = 15.75\n", - "M = 3.075 Msun T = 13014 K m_hst_f153m = 15.57\n", - "M = 3.332 Msun T = 13709 K m_hst_f153m = 15.40\n", - "M = 3.482 Msun T = 14099 K m_hst_f153m = 15.31\n", - "M = 3.615 Msun T = 14438 K m_hst_f153m = 15.23\n", - "M = 8.977 Msun T = 23502 K m_hst_f153m = 13.34\n", - "M = 49.000 Msun T = 32344 K m_hst_f153m = 8.52\n", - "M = 50.757 Msun T = 31046 K m_hst_f153m = 8.53\n", - "M = 52.540 Msun T = 41937 K m_hst_f153m = 9.42\n", - "M = 54.406 Msun T = 48753 K m_hst_f153m = 10.15\n", - "M = 56.318 Msun T = 65464 K m_hst_f153m = 11.31\n", - " Time taken: 137.05 seconds\n" - ] - } - ], + "outputs": [], "source": [ "# Define isochrone parameters\n", "logAge = np.log10(5*10**6.) # Age in log(years)\n", @@ -607,35 +81,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "5a6e081f-84d3-4efb-a6d5-59d5f60d845f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " L Teff ... m_hst_f153m \n", - " W K ... \n", - "---------------------- ------------------ ... ------------------\n", - " 6.591312905046435e+24 2928.195035574271 ... 21.454394826602254\n", - " 7.13135291244921e+24 2943.7437337117412 ... 21.3665241093368\n", - " 7.761969450327467e+24 2958.6936525140045 ... 21.272757288262653\n", - " 8.296060854703104e+24 2975.089258880875 ... 21.19905370697203\n", - " 8.830228238420424e+24 2992.264636608189 ... 21.13035092375595\n", - " 8.860779496066351e+24 3009.539168873201 ... 21.12685391099373\n", - " ... ... ... ...\n", - "2.1036424651269878e+32 63037.64788294872 ... 11.22232056398674\n", - "2.1760653272980036e+32 64239.1816824387 ... 11.267570563986745\n", - " 2.250981517613893e+32 65463.61740672747 ... 11.312820563986737\n", - "2.3279407848949313e+32 66696.03248243559 ... 11.357320563986734\n", - "2.4080856466771184e+32 67967.29719504561 ... 11.40257056398674\n", - "2.4909896846856737e+32 69262.79294373626 ... 11.447820563986737\n", - "2.6240489308607902e+32 71367.42051224748 ... 11.521320563986743\n", - "Length = 1605 rows\n" - ] - } - ], + "outputs": [], "source": [ "# The stars in the isochrone and associated properties \n", "# are stored in an astropy table called \"points\" \n", @@ -645,18 +94,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "1205c450-d7f2-4ad7-86b0-006762426d5c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 M_sun: F127M = 19.051 mag, F139M = 18.453 mag, F153M = 17.785 mag\n" - ] - } - ], + "outputs": [], "source": [ "# Example case:\n", "# Identify a 1 M_sun star, print F127M, F139M, and F153M mags\n", @@ -669,31 +110,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "a8169db9-486e-4d20-9a09-dfcd7ebbd3e1", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Make a color-magnitude diagram from the isochrone\n", "py.figure(1, figsize=(10,10))\n", @@ -718,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "493b8648-1c3b-4aab-858f-2f955195e634", "metadata": {}, "outputs": [], @@ -747,19 +167,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "0bf0fed8-2b70-41be-b950-83f1362af9f0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 60499 stars out of mass range\n", - "Found 11203 companions out of stellar mass range\n" - ] - } - ], + "outputs": [], "source": [ "# Define total cluster mass\n", "mass = 10**5.\n", @@ -770,35 +181,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "c8de1a27-0b07-404c-a13d-8434e204017d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass isMultiple ... m_hst_f153m N_companions\n", - "------------------- ---------- ... ------------------ ------------\n", - "0.31585425987928323 True ... 18.92999676208373 2\n", - "0.11445929647600789 False ... 20.934530625744358 0\n", - " 0.645145955409843 False ... 18.42659654946194 0\n", - " 0.2125430430785249 False ... 20.08717879087111 0\n", - " 0.4443648340365779 False ... 18.982532572754547 0\n", - " 0.9427790018124477 True ... 17.5522451444909 1\n", - "0.20316227789117086 False ... 20.155331870086147 0\n", - " ... ... ... ... ...\n", - "0.07822412513422393 False ... 21.306060916420513 0\n", - "0.17640075706320751 False ... 20.342815617558465 0\n", - "0.09652737394053626 False ... 21.126211979448616 0\n", - " 0.1622394758046402 False ... 20.45232101696704 0\n", - "0.39015806512343487 False ... 19.2083064020004 0\n", - "0.15023739165143188 False ... 20.558755046061993 0\n", - "0.24854036705122345 False ... 19.857602142038818 0\n", - "Length = 123017 rows\n" - ] - } - ], + "outputs": [], "source": [ "# Look at star systems table\n", "print(cluster.star_systems)" @@ -806,35 +192,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "6b10a958-a0f8-443c-be6b-e07c5de2d4dc", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "system_idx mass ... m_hst_f139m m_hst_f153m \n", - "---------- ------------------- ... ------------------ ------------------\n", - " 0 0.0460428924123163 ... nan nan\n", - " 0 0.24553283737800713 ... 20.536185197536078 19.87454892207238\n", - " 5 0.4308071906008011 ... 19.720217136851623 19.036277872397545\n", - " 8 0.05926443486987895 ... nan nan\n", - " 10 0.37939808636570665 ... 19.922230480262495 19.247518348082274\n", - " 15 0.12172477389722892 ... 21.49272357482301 20.84357767155626\n", - " 16 0.4299286896336996 ... 19.72390856044099 19.040131407955787\n", - " ... ... ... ... ...\n", - " 122997 0.24062971315528156 ... 20.56327953828116 19.90217696791337\n", - " 122998 0.44959030611032147 ... 19.653329536810013 18.96646512002086\n", - " 123000 0.3746449017452262 ... 19.94002433187758 19.265866413907457\n", - " 123000 0.3396256601362069 ... 20.078397682202386 19.40756594570426\n", - " 123006 0.08879348065800587 ... 21.800450628386503 21.146929171115758\n", - " 123006 0.06195810103955034 ... nan nan\n", - " 123006 0.04465994487393095 ... nan nan\n", - "Length = 34087 rows\n" - ] - } - ], + "outputs": [], "source": [ "# The companions table is accessed in a similar way\n", "print(cluster.companions)" @@ -842,31 +203,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "095a963e-c0de-4bbc-a03f-471f3cdb7b91", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", "# multiple systems on the photometry\n", @@ -887,21 +227,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "d694ed41-c732-4093-9f0a-ad021fe8397a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "minimum primary mass: 0.07000163959328269\n", - "minimum companion mass: 0.01000292189962548\n", - "maximum primary mass: 55.80384055631119\n", - "maximum companion mass: 44.92501458090958\n" - ] - } - ], + "outputs": [], "source": [ "print(\"minimum primary mass: \" + str(np.min(clust['mass'])))\n", "print(\"minimum companion mass: \" + str(np.min(cluster.companions['mass'])))\n", @@ -914,12 +243,12 @@ "id": "fa4c660d-ea4e-491a-95db-278617ee1757", "metadata": {}, "source": [ - "#### Creating a histogram to compare Weidner Kroupa and Muzic" + "### Creating a histogram to compare Weidner Kroupa and Muzic" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "c2540680-d69f-427f-b0d1-55130826ad8a", "metadata": {}, "outputs": [], @@ -931,66 +260,215 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "4d1c647e-8e1a-4a24-8b20-26a8b14f4660", "metadata": {}, + "outputs": [], + "source": [ + "# Define total cluster mass\n", + "mass = 10**5.\n", + "\n", + "# Make cluster objects for each\n", + "w_cluster = synthetic.ResolvedCluster(my_iso, w_imf, mass)\n", + "m_cluster = synthetic.ResolvedCluster(my_iso, m_imf, mass)" + ] + }, + { + "cell_type": "markdown", + "id": "f55b6d0a-ecc2-4bbb-aa5b-d8f23acfe441", + "metadata": {}, + "source": [ + "#### Comparing Weidner Kroupa and Muzic clusters data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb3847e7-c184-4d49-a7ec-69d0a1e50fea", + "metadata": {}, + "outputs": [], + "source": [ + "#show total numbers for each cluster\n", + "print(\"primary masses in Weidner Kroupa cluster: \" + str(len(w_cluster.star_systems)))\n", + "print(\"companion masses in Weidner Kroupa cluster: \" + str(len(w_cluster.companions)))\n", + "print(\"primary masses in Muzic cluster: \" + str(len(m_cluster.star_systems)))\n", + "print(\"companion masses in Muzic cluster: \" + str(len(m_cluster.companions)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b2656bf-6a7e-4ca5-ad09-6ae59e8c6d0d", + "metadata": {}, + "outputs": [], + "source": [ + "#Comparing max/min primary/companion masses\n", + "#Weidner Kroupa\n", + "print('\\033[1m' + \"Weidner Kroupa\" + '\\033[0m')\n", + "print(\"minimum primary mass: \" + str(np.min(w_cluster.star_systems['mass'])))\n", + "print(\"minimum companion mass: \" + str(np.min(w_cluster.companions['mass'])))\n", + "print(\"maximum primary mass: \" + str(np.max(w_cluster.star_systems['mass'])))\n", + "print(\"maximum companion mass: \" + str(np.max(w_cluster.companions['mass'])))\n", + "print('\\n')\n", + "\n", + "#Muzic\n", + "print('\\033[1m' + \"Muzic\" + '\\033[0m')\n", + "print(\"minimum primary mass: \" + str(np.min(m_cluster.star_systems['mass'])))\n", + "print(\"minimum companion mass: \" + str(np.min(m_cluster.companions['mass'])))\n", + "print(\"maximum primary mass: \" + str(np.max(m_cluster.star_systems['mass'])))\n", + "print(\"maximum companion mass: \" + str(np.max(m_cluster.companions['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17d1685d-0f63-4f6e-ad62-4ba3a03f0780", + "metadata": {}, + "outputs": [], + "source": [ + "#compare total cluster masses\n", + "#create combined arrays of primary/companion masses\n", + "all_m_masses = np.concatenate((m_cluster.star_systems['mass'], m_cluster.companions['mass']))\n", + "all_w_masses = np.concatenate((w_cluster.star_systems['mass'], w_cluster.companions['mass']))\n", + "\n", + "#find total of added masses\n", + "print(\"total mass of WD cluster: \" + str(np.sum(all_w_masses)))\n", + "print(\"total mass of M cluster: \" + str(np.sum(all_m_masses)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e2fe19b-9da8-4388-92bd-c1390444e8c4", + "metadata": {}, + "outputs": [], + "source": [ + "#define mass range of histogram\n", + "bin_edges = [0.01, 0.08, 0.5, 1, 2]\n", + "\n", + "#setting up subplots\n", + "fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(8,8))\n", + "plt.subplots_adjust(hspace=0.5, wspace=0.75)\n", + "\n", + "#create histograms to compare\n", + "counts_m, edges_m, _ = ax[0].hist(all_m_masses, bins=bin_edges, alpha=0.5, label='Muzic_2017', color='blue', edgecolor='black', histtype='bar')\n", + "counts_w, edges_w, _ = ax[1].hist(all_w_masses, bins=bin_edges, alpha=0.5, label='Weidner_Kroupa_2004', color='red', edgecolor='black', histtype='bar')\n", + "\n", + "#compute bin centers\n", + "bin_centers_m = (edges_m[:-1] + edges_m[1:]) / 2\n", + "bin_centers_w = (edges_w[:-1] + edges_w[1:]) / 2\n", + "\n", + "#generate x values for the broken power law curve\n", + "x_fit_m = np.linspace(bin_centers_m.min(), bin_centers_m.max(), 100)\n", + "x_fit_w = np.linspace(bin_centers_w.min(), bin_centers_w.max(), 100)\n", + "\n", + "\n", + "\n", + "#labels and legend\n", + "plt.xlabel('Stellar Mass (Solar Masses)')\n", + "plt.ylabel('Number of Stars')\n", + "plt.title('Comparison of Stellar Mass Distributions')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "04216fed-7f8b-4249-9c6c-29c82cc18bce", + "metadata": {}, + "source": [ + "## Generating a cluster with compound objects (using an IFMR)\n", + "This is before code is modified to allow for brown dwarves" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "25b1247a-0eb8-40bc-bf56-f89e788283cd", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Found 58485 stars out of mass range\n", - "Found 10731 companions out of stellar mass range\n", - "Found 256 stars out of mass range\n", - "Found 83 companions out of stellar mass range\n" + "Isochrone generation took 96.182577 s.\n", + "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", + " Starting at: 2024-07-15 14:29:26.015063 Usually takes ~5 minutes\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", + "M = 0.676 Msun T = 4395 K m_hst_f153m = 4.92\n", + "M = 0.786 Msun T = 4896 K m_hst_f153m = 4.41\n", + "M = 0.856 Msun T = 5198 K m_hst_f153m = 4.12\n", + "M = 0.956 Msun T = 5590 K m_hst_f153m = 3.74\n", + "M = 1.022 Msun T = 5808 K m_hst_f153m = 3.50\n", + "M = 1.079 Msun T = 5992 K m_hst_f153m = 3.30\n", + "M = 1.518 Msun T = 7482 K m_hst_f153m = 2.22\n", + "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", + "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", + "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", + " Time taken: 31.54 seconds\n" ] } ], "source": [ - "# Define total cluster mass\n", - "mass = 10**5.\n", - "\n", - "# Make cluster objects for each\n", - "w_cluster = synthetic.ResolvedCluster(my_iso, w_imf, mass)\n", - "m_cluster = synthetic.ResolvedCluster(my_iso, m_imf, mass)" + "# Create isochrone object \n", + "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", + " evo_model = evolution.MergedBaraffePisaEkstromParsec(),\n", + " filters=filt_list)" ] }, { "cell_type": "code", - "execution_count": 40, - "id": "eb3847e7-c184-4d49-a7ec-69d0a1e50fea", + "execution_count": 9, + "id": "01f6f94c-1b23-4ae1-bfc2-96e2d4cacd27", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "#imf_multi = multiplicity.MultiplicityUnresolved()\n", + "massLimits = np.array([0.01, 0.08, 0.5, 1, np.inf])\n", + "powers = np.array([-0.3, -1.3, -2.3, -2.35])\n", + "k_imf = imf.IMF_broken_powerlaw(massLimits, powers)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1a914c4c-ac7e-4c61-9e40-1b0d5dd5568f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "primary masses in Weidner Kroupa cluster: 119280\n", - "companion masses in Weidner Kroupa cluster: 32991\n", - "primary masses in Muzic cluster: 1113\n", - "companion masses in Muzic cluster: 682\n" + "Found 829408 stars out of mass range\n" ] } ], "source": [ - "#show total numbers for each cluster\n", - "print(\"primary masses in Weidner Kroupa cluster: \" + str(len(w_cluster.star_systems)))\n", - "print(\"companion masses in Weidner Kroupa cluster: \" + str(len(w_cluster.companions)))\n", - "print(\"primary masses in Muzic cluster: \" + str(len(m_cluster.star_systems)))\n", - "print(\"companion masses in Muzic cluster: \" + str(len(m_cluster.companions)))" + "# Make clusters\n", + "cluster_mass = 10**6\n", + "k_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "k_out = k_cluster.star_systems\n", + "#k_comp = k_cluster.companions" ] }, { "cell_type": "code", - "execution_count": 51, - "id": "4e2fe19b-9da8-4388-92bd-c1390444e8c4", + "execution_count": 11, + "id": "62293dea-989b-4870-a77b-ba677010da81", "metadata": {}, "outputs": [ { "data": { - "image/png": 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qpICAAN1333364Ycfzus8TrRt27ZqU7NOZLfbtWzZMt1yyy169tlnddNNNykkJETPP/+8Kisrz/k4NbnmwcHB1dqO/zxf6JSas7nYP5s2m02ff/65unXrpokTJ+rWW29Vw4YNNWLECHN6zrlgTjcAXKVcXFzUuXNnffrpp9qxY4caN258xvrjN6bCwsJqtb/++qvTfO6LpaioSL/73e/M50eOHNGePXvMvnzxxRf69ddftXTpUnN0W9Jp51me6c2FJ7v33nt17733qry8XKtWrVJqaqri4+PVrFkzRUVFKTAwUIWFhdVe9+uvv0rSRbsel+o40rFfNmJjY7Vo0SIVFxcrKCjotLXHj/vPf/5TTZs2veBjZ2Zmys3NTYsXL5anp6fZfrol7GrytTxZ//799cgjj+jo0aN6/fXXT1uXkZGhmJiYajWnClqPPfaYHnvsMR06dEj//ve/9fzzzysuLk7ff/+9mjZtKh8fH40fP17jx483R9qfeeYZ9ezZU//5z3/O+1zWrFmjoqKisy7t2apVK2VmZsowDK1bt07p6el64YUX5OXlpWeeeeacjlWTa378l7ITFRUVSfr//5Yc/zqXl5c71dX0F7aTWfEz07RpU82ePVuS9P333+v9999XcnKyKioq9MYbb5zTPhjpBoCr2JgxY2QYhgYOHKiKiopq2ysrK/Xxxx9Lku666y5Jx4LIiXJzc7Vp0yZzJZCL6eT1h99//30dOXLEfAPb8RBw8sorb7755kXrg4eHh6Kjo8033h1fmaNz587auHGjvvnmG6f6d955RzabrdpSfOerc+fO5i8XJx/H29vbaRm5c7Vz585T/nm8qqpKP/zwg7y9vXXNNddIOv2IfLdu3eTq6qoff/xRbdu2PeWjJmw2m1xdXZ3+zF9WVqZ58+bV8OzO7g9/+IP+8Ic/qH///me8fjabrdr31rp16844pcfHx0fdu3fX2LFjVVFRoQ0bNlSrCQ4OVr9+/fTQQw9p8+bNOnz48Hmdx969ezVkyBC5ubnp6aefPqfX2Gw23XzzzZo2bZquueYap+9fDw+Pizbt5cCBA/roo4+c2hYsWKB69erpzjvvlCRzxaATP4xJUrXXHe+bdPa/DEnW/2zecMMN+utf/6pWrVpVO8aZMNINAFexqKgovf766xo6dKgiIyP1xBNP6KabblJlZaW+/fZbvfXWW4qIiFDPnj3VvHlzDRo0SGlpaapXr566d+9url4SGhp6zjf9mvjwww/l6uqqrl27mquX3HzzzerTp48kqUOHDvL399eQIUP0/PPPy83NTfPnz9d33313Qcd97rnntGPHDnXu3FmNGzfWvn379Le//c1pvvjTTz+td955Rz169NALL7ygpk2b6pNPPtFrr72mJ5544rzmG5/K888/r8WLF6tTp0567rnnFBAQoPnz5+uTTz7RxIkTz7jyyenMmzdPb775puLj43XbbbfJbrdrx44d+vvf/64NGzboueeeM6f9HF/7+W9/+5v69u0rNzc3NW/eXM2aNdMLL7ygsWPH6qefftLdd98tf39/7dy5U2vWrDFHds9Vjx49NHXqVMXHx2vQoEHas2ePJk+efMqlLC+Up6en/vnPf561Li4uTi+++KKef/55RUdHa/PmzXrhhRcUFhbmtPLNwIED5eXlpY4dO6pRo0YqKipSamqq7Ha7+f6Adu3aKS4uTq1bt5a/v782bdqkefPmKSoq6rTTW070ww8/aNWqVTp69Kj27Nmj1atXa/bs2dq/f7/eeecd3XTTTad97eLFi/Xaa6/pvvvu07XXXivDMPThhx9q37596tq1q1nXqlUrLV26VB9//LEaNWokX19fNW/e/Kx9O5XAwEA98cQT2rZtm2644Qb961//0qxZs/TEE0+Yq9M4HA516dJFqamp8vf3V9OmTfX555+b08NOdPz78JVXXlH37t3l4uKi1q1bm9+nJ7rYP5vr1q3TsGHDdP/99ys8PFzu7u764osvtG7dunP+K4EkVi8BABxbQaRv375GkyZNDHd3d8PHx8do06aN8dxzzxnFxcVmXVVVlfHKK68YN9xwg+Hm5mY0aNDAeOSRR4zt27c77S86Otq46aabqh2nadOmRo8ePaq166RVN46vdpGXl2f07NnTqF+/vuHr62s89NBDxs6dO51eu2LFCiMqKsrw9vY2GjZsaDz++OPGN998U231gxNXsDjZyauXLF682Ojevbvxu9/9znB3dzeCgoKMe+65x/jqq6+cXrd161YjPj7eCAwMNNzc3IzmzZsbkyZNMqqqqsya4yskTJo06ZTnfabVMo5bv3690bNnT8Nutxvu7u7GzTff7HRuJ+7vXFYv2bhxo5GUlGS0bdvWaNiwoeHq6mr4+/sb0dHRxrx586rVjxkzxggJCTHq1atXbbWJRYsWGZ06dTL8/PwMDw8Po2nTpsaf/vQnY8mSJWbNua5e8vbbbxvNmzc3PDw8jGuvvdZITU01Zs+eXW11jNN9H53Omb72x51q9ZLy8nJj1KhRxu9+9zvD09PTuPXWW41FixZV+36ZO3eu0alTJyM4ONhwd3c3QkJCjD59+hjr1q0za5555hmjbdu2hr+/v3l+Tz/9tLF79+4z9uv4Ch/HH66urkZgYKARFRVlPPvss8bPP/9c7TUnryjyn//8x3jooYeM6667zvDy8jLsdrtx++23G+np6U6vy8/PNzp27Gh4e3sbksyvz/H95ebmnvVYhvH/f/6XLl1qtG3b1vDw8DAaNWpkPPvss0ZlZaXT6wsLC40//elPRkBAgGG3241HHnnEXG3lxO/x8vJy4/HHHzcaNmxo2Gw2p2OevHqJYVzcn82dO3ca/fr1M2688UbDx8fHqF+/vtG6dWtj2rRpTquenI3tfzsGAOCykZycrPHjx2vXrl2WzBUHgEuNOd0AAACAxQjdAAAAgMWYXgIAAABYjJFuAAAAwGKEbgAAAMBihG4AAADAYnXyw3GOHj2qX3/9Vb6+vhf0MbAAzp9hGDpw4IBCQkJUrx6/v+Pyw70CqF3cJ5zVydD966+/KjQ0tLa7AUDS9u3b1bhx49ruBlAN9wrg8sB94pgahe7jH1ZwouDgYBUVFUk69hvN+PHj9dZbb6mkpETt2rXTq6++6vTRpOXl5Ro1apTeffddlZWVqXPnznrttddq9MXw9fWVdOyL6OfnV5NTAHCR7N+/X6GhoebPI3C54V4B1C7uE85qPNJ90003acmSJeZzFxcX8/8nTpyoqVOnKj09XTfccINeeuklde3aVZs3bzYveGJioj7++GNlZmYqMDBQSUlJiouLU15entO+zuT4nwn9/Pz4hxSoZfzZHpcr7hXA5YH7xDE1Dt2urq5yOBzV2g3D0PTp0zV27Fj17t1bkjR37lwFBwdrwYIFGjx4sEpLSzV79mzNmzdPXbp0kSRlZGQoNDRUS5YsUbdu3S7wdAAAAIDLT41ntf/www8KCQlRWFiYHnzwQf3000+SpC1btqioqEixsbFmrYeHh6Kjo7VixQpJUl5eniorK51qQkJCFBERYdacSnl5ufbv3+/0AAAAAOqKGoXudu3a6Z133tFnn32mWbNmqaioSB06dNCePXvMed3BwcFOrzlxzndRUZHc3d3l7+9/2ppTSU1Nld1uNx+8MQYAAAB1SY2ml3Tv3t38/1atWikqKkrXXXed5s6dq/bt20uqPm/HMIyzzuU5W82YMWM0cuRI8/nxifkAAFxshmHoyJEjqqqqqu2uAJc9Nze3c35P3tXugpYM9PHxUatWrfTDDz/ovvvuk3RsNLtRo0ZmTXFxsTn67XA4VFFRoZKSEqfR7uLiYnXo0OG0x/Hw8JCHh8eFdBUAgLOqqKhQYWGhDh8+XNtdAeoEm82mxo0bq379+rXdlcveBYXu8vJybdq0SXfccYfCwsLkcDiUk5OjNm3aSDr2j9eyZcv0yiuvSJIiIyPl5uamnJwc9enTR5JUWFiogoICTZw48QJPBQCA83f06FFt2bJFLi4uCgkJkbu7O6suAGdgGIZ27dqlHTt2KDw8nBHvs6hR6B41apR69uypJk2aqLi4WC+99JL279+vvn37ymazKTExUSkpKQoPD1d4eLhSUlLk7e2t+Ph4SZLdbteAAQOUlJSkwMBABQQEaNSoUWrVqpW5mgkAALWhoqJCR48eVWhoqLy9vWu7O0Cd0LBhQ/3888+qrKwkdJ9FjUL3jh079NBDD2n37t1q2LCh2rdvr1WrVqlp06aSpNGjR6usrExDhw41PxwnOzvbaVH0adOmydXVVX369DE/HCc9PZ0vFADgssDHVQPnjr8GnTubYRhGbXeipvbv3y+73a7S0lI+8ACoJfwc4nJX0+/R3377TVu2bFFYWJg8PT0vQQ+Buu9MPzfcJ5zx6zwAAABgsQt6IyUAAFeD0tLSS7qiibe3t+x2+yU73snS09OVmJioffv2nbYmOTlZixYtUn5+/iXrF1CXEboBADiD0tJSzXzxRVXu3n3JjunWoIGGjRt3TsH7jTfe0J///GeVlJTI1fXYbf3gwYPy9/dX+/bt9dVXX5m1X331le68805t3rxZN9xww2n3+cADD+iee+658BO5BE4V/r/66iv17NlTCQkJmjFjRp2ed/zdd9/p5Zdf1vLly7V79241a9ZMQ4YM0VNPPeVUt379eg0bNkxr1qxRQECABg8erHHjxjmd+7JlyzRy5Eht2LBBISEhGj16tIYMGXLK42ZmZuqhhx7Svffeq0WLFll5ilcNQjcAAGdw+PBhVe7erd5eXmp4CVY12XX4sD7cvVuHDx8+p9DdqVMnHTx4UGvXrjU/qO6rr76Sw+FQbm6uDh8+bK7GsnTpUoWEhJwxcEuSl5eXvLy8LvxkLlBVVZVsNluN3tz6ySef6P7779ef//xnjR8//pQ1lZWVcnNzu1jdtFReXp4aNmyojIwMhYaGasWKFRo0aJBcXFw0bNgwScfmTnft2lWdOnVSbm6uvv/+e/Xr108+Pj5KSkqSJG3ZskX33HOPBg4cqIyMDH399dcaOnSoGjZsqD/+8Y9Ox9y6datGjRqlO+6445Kf75WMOd0AAJyDht7eauTra/mjpsG+efPmCgkJ0dKlS822pUuX6t5779V1112nFStWOLV36tRJFRUVGj16tH73u9/Jx8dH7dq1c3p9enq6rrnmGqfjvPzyywoODpavr68GDBig3377zWl7v379dN9992ny5Mlq1KiRAgMD9eSTT6qystKsOdfjLl68WC1btpSHh4e2bt16ztdiwYIF6t27t15++WWnwJ2cnKxbbrlFb7/9tq699lp5eHjIMAxt27ZN9957r+rXry8/Pz/16dNHO3furHZOJ0pMTFRMTIz5PCYmRsOGDdOwYcN0zTXXKDAwUH/961914joVGRkZatu2rXx9feVwOBQfH6/i4uJzOqf+/ftrxowZio6O1rXXXqtHHnlEjz32mD788EOzZv78+frtt9+Unp6uiIgI9e7dW88++6ymTp1q9uONN95QkyZNNH36dLVo0UKPP/64+vfvr8mTJzsdr6qqSg8//LDGjx+va6+99pz6iHND6AYAoI6LiYnRl19+aT7/8ssvFRMTo+joaLO9oqJCK1euVKdOnfTYY4/p66+/VmZmptatW6f7779fd999t3744YdT7v/999/X888/rwkTJmjt2rVq1KiRXnvttWp1X375pX788Ud9+eWXmjt3rtLT05Wenm5uP5fjHj58WKmpqfr73/+uDRs2KCgo6JyuwauvvqrHHntMs2fP1ogRI6pt/+9//6v3339fH3zwgTkV5b777tPevXu1bNky5eTk6Mcff9QDDzxwTsc70dy5c+Xq6qrVq1drxowZmjZtmv7+97+b2ysqKvTiiy/qu+++06JFi7Rlyxb169evxsc5rrS0VAEBAebzlStXKjo62unTu7t166Zff/1VP//8s1kTGxvrtJ9u3bpp7dq1Tr8YvfDCC2rYsKEGDBhw3v3DqTG9BACAOi4mJkZPP/20jhw5orKyMn377be68847VVVVpRkzZkiSVq1apbKyMsXExGjgwIHasWOHQkJCJB378LusrCzNmTNHKSkp1fY/ffp09e/fX48//rgk6aWXXtKSJUuqjXb7+/tr5syZcnFx0Y033qgePXro888/18CBA/Xjjz/q3XffPetxKysr9dprr+nmm28+5/PftGmThg0bptmzZ+uRRx45ZU1FRYXmzZunhg0bSpJycnK0bt06bdmyRaGhoZKkefPm6aabblJubq5uu+22cz5+aGiopk2bJpvNpubNm2v9+vWaNm2aBg4cKOnYaPVx1157rWbMmKHbb79dBw8erPHHp69cuVLvv/++PvnkE7OtqKhIzZo1c6oLDg42t4WFhamoqMhsO7HmyJEj2r17txo1aqSvv/5as2fP5s2xFmGkGwCAOq5Tp046dOiQcnNz9dVXX+mGG25QUFCQoqOjlZubq0OHDmnp0qVq0qSJvvnmGxmGoRtuuEH169c3H8uWLdOPP/54yv1v2rRJUVFRTm0nP5ekm266yenD7ho1amROozjX47q7u6t169Y1Ov/GjRvr1ltv1cSJE1VYWHjKmqZNm5qB+/g5hYaGmoFbklq2bKlrrrlGmzZtqtHx27dv7/SGxaioKP3www+qqqqSJH377be699571bRpU/n6+prTU7Zt21aj42zYsEH33nuvnnvuOXXt2tVp28lvFj0+reTE9jPVHDhwQI888ohmzZqlBg0a1KhfODeMdAMAUMddf/31aty4sb788kuVlJQoOjpakuRwOBQWFqavv/5aX375pe666y4dPXpULi4uysvLq/Zp0DUddT3ZyW9OtNlsOnr0qCSd83G9vLxqvNqIr6+vlixZotjYWHOqzfHR9ON8fHycnhuGccrjnNher149nfwZgidOxTgXhw4dUmxsrGJjY5WRkaGGDRtq27Zt6tatmyoqKs55Pxs3btRdd92lgQMH6q9//avTNofDoaKiIqe247/sHB/dPl2Nq6urAgMDtWHDBv3888/q2bOnuf34187V1VWbN2/Wddddd+4njmoY6QYA4ArQqVMnLV26VEuXLnV6o190dLQ+++wzrVq1Sp06dVKbNm1UVVWl4uJiXX/99U4Ph8Nxyn23aNFCq1atcmo7+fnZnM9xa8Lf319LliyRv7+/YmJi9Msvv5yxvmXLltq2bZu2b99utm3cuFGlpaVq0aKFJKlhw4bVRs5PNfXiVNcmPDxcLi4u+s9//qPdu3fr5Zdf1h133KEbb7zxnN9EedyGDRvUqVMn9e3bVxMmTKi2PSoqSv/+97+dQnx2drZCQkLMaSdRUVHKyclxel12drbatm0rNzc33XjjjVq/fr3y8/PNR69evdSpUyfl5+c7/UUA54eRbgAAzsGuS/ThOOd7nE6dOpmrhRwf6ZaOhe4nnnhCv/32mzp16qTQ0FA9/PDDevTRRzVlyhS1adNGu3fv1hdffKFWrVqdcn3up556Sn379lXbtm31+9//XvPnz9eGDRtqtLrFDTfcUOPj1pTdbld2drbuvvtuc8S7cePGp6zt0qWLWrdurYcffljTp0/XkSNHNHToUEVHR6tt27aSpLvuukuTJk3SO++8o6ioKGVkZKigoEBt2rRx2tf27ds1cuRIDR48WN98843S0tI0ZcoUSVKTJk3k7u6utLQ0DRkyRAUFBXrxxRfP+ZyOB+7Y2FiNHDnSHK12cXExp8vEx8dr/Pjx6tevn5599ln98MMPSklJ0XPPPWeO2g8ZMkQzZ87UyJEjNXDgQK1cuVKzZ8/Wu+++K0ny9PRURESE07GPr2BzcjvOD6EbAIAz8Pb2lluDBvpw926prOySHNOtQQNzbe1z1alTJ5WVlenGG290esNcdHS0Dhw4oOuuu84crZwzZ45eeuklJSUl6ZdfflFgYKCioqJOG3wfeOAB/fjjj/rLX/6i3377TX/84x/1xBNP6LPPPqtRH2t63PPh5+enzz77TN27d6+2qsuJbDabFi1apOHDh+vOO+9UvXr1dPfddystLc2s6datm8aNG6fRo0frt99+U//+/fXoo49q/fr1Tvt69NFHVVZWpttvv10uLi4aPny4Bg0aJOnYaHl6erqeffZZzZgxQ7feeqsmT56sXr16ndP5/OMf/9CuXbs0f/58zZ8/32xv2rSpuTKJ3W5XTk6OnnzySbVt21b+/v4aOXKkRo4cadaHhYXpX//6l55++mm9+uqrCgkJ0YwZM6qt0Q3r2IyTJyvVAfv375fdbldpaan8/PxquzvAVYmfQ1zuavo9+ttvv2nLli0KCwuTp6en07ar7WPgce5iYmJ0yy23aPr06bXdlVpxpp8b7hPOGOkGAOAs7HY7IRjABeGNlAAA4LJ14vKCJz+++uqr2u7eBRsyZMhpz2/IkCG13T1cRIx0AwCAy9aZPqjld7/73aXryGmc+DH25+OFF17QqFGjTrmNKRlXlqsudF/qeXlAXcR8UlxtSktLVVpaaq5LjMvH9ddfX9tdsFRQUNA5f9Q96rarKnSXlpbqxRdnavfumi1sD1xtGjRw07hxwwjeuCqUlpZq5osvyv3IEf3+/vtVWVlZ7Q1hAE6tDq7HUWuuqtB9+PBh7d5dKS+v3vL2bnj2FwBXocOHd2n37g91+PBhQjeuCocPH1bl7t2KcnNTVUWFDh8+LF9f39ruFlAnHP9AnpM/ZRTVXVWh+zhv74by9W1U290ALluXaCli4LJyTb162rRhg65p2FCurq7y9vau8ceRA1eTo0ePateuXfL29par61UZKWuEKwQAwP/8sHatbouNrfHHdANXq3r16qlJkyb8gnoOCN0AAJzA19dXQUFBqqzk/T/A2bi7u6tePVagPheEbgAATuLi4sIcVQAXFb+aAAAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDACRJR44c0V//+leFhYXJy8tL1157rV544QUdPXrUrDEMQ8nJyQoJCZGXl5diYmK0YcMGp/2Ul5dr+PDhatCggXx8fNSrVy/t2LHDqaakpEQJCQmy2+2y2+1KSEjQvn37nGq2bdumnj17ysfHRw0aNNCIESNUUVFh2fkDgJUI3QAASdIrr7yiN954QzNnztSmTZs0ceJETZo0SWlpaWbNxIkTNXXqVM2cOVO5ublyOBzq2rWrDhw4YNYkJiZq4cKFyszM1PLly3Xw4EHFxcWpqqrKrImPj1d+fr6ysrKUlZWl/Px8JSQkmNurqqrUo0cPHTp0SMuXL1dmZqY++OADJSUlXZqLAQAXmWttdwAAcHlYuXKl7r33XvXo0UOS1KxZM7377rtau3atpGOj3NOnT9fYsWPVu3dvSdLcuXMVHBysBQsWaPDgwSotLdXs2bM1b948denSRZKUkZGh0NBQLVmyRN26ddOmTZuUlZWlVatWqV27dpKkWbNmKSoqSps3b1bz5s2VnZ2tjRs3avv27QoJCZEkTZkyRf369dOECRPk5+d3qS8PAFwQRroBAJKk3//+9/r888/1/fffS5K+++47LV++XPfcc48kacuWLSoqKlJsbKz5Gg8PD0VHR2vFihWSpLy8PFVWVjrVhISEKCIiwqxZuXKl7Ha7GbglqX379rLb7U41ERERZuCWpG7duqm8vFx5eXmn7H95ebn279/v9ACAywUj3QAASdJf/vIXlZaW6sYbb5SLi4uqqqo0YcIEPfTQQ5KkoqIiSVJwcLDT64KDg7V161azxt3dXf7+/tVqjr++qKhIQUFB1Y4fFBTkVHPycfz9/eXu7m7WnCw1NVXjx4+v6WkDwCXBSDcAQJL03nvvKSMjQwsWLNA333yjuXPnavLkyZo7d65Tnc1mc3puGEa1tpOdXHOq+vOpOdGYMWNUWlpqPrZv337GPgHApcRINwBAkvTnP/9ZzzzzjB588EFJUqtWrbR161alpqaqb9++cjgcko6NQjdq1Mh8XXFxsTkq7XA4VFFRoZKSEqfR7uLiYnXo0MGs2blzZ7Xj79q1y2k/q1evdtpeUlKiysrKaiPgx3l4eMjDw+N8Tx8ALMVINwBAknT48GHVq+d8W3BxcTGXDAwLC5PD4VBOTo65vaKiQsuWLTMDdWRkpNzc3JxqCgsLVVBQYNZERUWptLRUa9asMWtWr16t0tJSp5qCggIVFhaaNdnZ2fLw8FBkZORFPnMAsB4j3QAASVLPnj01YcIENWnSRDfddJO+/fZbTZ06Vf3795d0bLpHYmKiUlJSFB4ervDwcKWkpMjb21vx8fGSJLvdrgEDBigpKUmBgYEKCAjQqFGj1KpVK3M1kxYtWujuu+/WwIED9eabb0qSBg0apLi4ODVv3lySFBsbq5YtWyohIUGTJk3S3r17NWrUKA0cOJCVSwDUSYRuAIAkKS0tTePGjdPQoUNVXFyskJAQDR48WM8995xZM3r0aJWVlWno0KEqKSlRu3btlJ2dLV9fX7Nm2rRpcnV1VZ8+fVRWVqbOnTsrPT1dLi4uZs38+fM1YsQIc5WTXr16aebMmeZ2FxcXffLJJxo6dKg6duwoLy8vxcfHa/LkyZfgSgDAxUfoBgBIknx9fTV9+nRNnz79tDU2m03JyclKTk4+bY2np6fS0tKcPlTnZAEBAcrIyDhjf5o0aaLFixefrdsAUCcwpxsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALAYoRsAAACwGKEbAAAAsBihGwAAALDYBYXu1NRU2Ww2JSYmmm2GYSg5OVkhISHy8vJSTEyMNmzY4PS68vJyDR8+XA0aNJCPj4969eqlHTt2XEhXAAAAgMvWeYfu3NxcvfXWW2rdurVT+8SJEzV16lTNnDlTubm5cjgc6tq1qw4cOGDWJCYmauHChcrMzNTy5ct18OBBxcXFqaqq6vzPBAAAALhMnVfoPnjwoB5++GHNmjVL/v7+ZrthGJo+fbrGjh2r3r17KyIiQnPnztXhw4e1YMECSVJpaalmz56tKVOmqEuXLmrTpo0yMjK0fv16LVmy5OKcFQAAAHAZOa/Q/eSTT6pHjx7q0qWLU/uWLVtUVFSk2NhYs83Dw0PR0dFasWKFJCkvL0+VlZVONSEhIYqIiDBrTlZeXq79+/c7PQAAAIC6wrWmL8jMzNQ333yj3NzcatuKiookScHBwU7twcHB2rp1q1nj7u7uNEJ+vOb460+Wmpqq8ePH17SrptLS0vN+LQAAAHChajTSvX37dj311FPKyMiQp6fnaetsNpvTc8MwqrWd7Ew1Y8aMUWlpqfnYvn37Ofe5tLRUL744Uy++OJMRcgAAANSKGoXuvLw8FRcXKzIyUq6urnJ1ddWyZcs0Y8YMubq6miPcJ49YFxcXm9scDocqKipUUlJy2pqTeXh4yM/Pz+lxrg4fPqzduyu1e3elysrKanK6AAAAwEVRo9DduXNnrV+/Xvn5+eajbdu2evjhh5Wfn69rr71WDodDOTk55msqKiq0bNkydejQQZIUGRkpNzc3p5rCwkIVFBSYNQCAS69Zs2ay2WzVHk8++aSki7ckbElJiRISEmS322W325WQkKB9+/Y51Wzbtk09e/aUj4+PGjRooBEjRqiiosLS8wcAK9VoTrevr68iIiKc2nx8fBQYGGi2JyYmKiUlReHh4QoPD1dKSoq8vb0VHx8vSbLb7RowYICSkpIUGBiogIAAjRo1Sq1atar2xkwAwKWTm5vrtHRrQUGBunbtqvvvv1/S/18SNj09XTfccINeeuklde3aVZs3b5avr6+kY/eAjz/+WJmZmQoMDFRSUpLi4uKUl5cnFxcXSVJ8fLx27NihrKwsSdKgQYOUkJCgjz/+WJJUVVWlHj16qGHDhlq+fLn27Nmjvn37yjAMpaWlXcpLAgAXTY3fSHk2o0ePVllZmYYOHaqSkhK1a9dO2dnZ5j/IkjRt2jS5urqqT58+KisrU+fOnZWenm7+gwwAuPQaNmzo9Pzll1/Wddddp+jo6GpLwkrS3LlzFRwcrAULFmjw4MHmkrDz5s0zB1EyMjIUGhqqJUuWqFu3btq0aZOysrK0atUqtWvXTpI0a9YsRUVFafPmzWrevLmys7O1ceNGbd++XSEhIZKkKVOmqF+/fpowYUKNphgCwOXigj8GfunSpZo+fbr53GazKTk5WYWFhfrtt9+0bNmyaqPjnp6eSktL0549e3T48GF9/PHHCg0NvdCuAAAukoqKCmVkZKh///6y2WwXbUnYlStXym63m4Fbktq3by+73e5UExERYQZuSerWrZvKy8uVl5d32j6zvCyAy9kFh24AwJVn0aJF2rdvn/r16yfpzEvCHt92LkvCFhUVKSgoqNrxgoKCnGpOPo6/v7/c3d1Pu7SsdGx52ePzxO12O4M5AC4rhG4AQDWzZ89W9+7dnUabpYuzJOyp6s+n5mQXsrwsAFiN0A0AcLJ161YtWbJEjz/+uNnmcDgkXfiSsA6HQzt37qx2zF27djnVnHyckpISVVZWnnZpWenClpcFAKsRugEATubMmaOgoCD16NHDbAsLC7soS8JGRUWptLRUa9asMWtWr16t0tJSp5qCggIVFhaaNdnZ2fLw8FBkZKQ1Jw0AFrvoq5cAAOquo0ePas6cOerbt69cXf//LcJms12UJWFbtGihu+++WwMHDtSbb74p6diSgXFxcWrevLkkKTY2Vi1btlRCQoImTZqkvXv3atSoURo4cCCj1wDqLEI3AMC0ZMkSbdu2Tf3796+27WItCTt//nyNGDHCXOWkV69emjlzprndxcVFn3zyiYYOHaqOHTvKy8tL8fHxmjx5soVnDgDWInQDAEyxsbEyDOOU244vCZucnHza1x9fEvZMH2ITEBCgjIyMM/ajSZMmWrx48Tn1GQDqAuZ0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMATL/88oseeeQRBQYGytvbW7fccovy8vLM7YZhKDk5WSEhIfLy8lJMTIw2bNjgtI/y8nINHz5cDRo0kI+Pj3r16qUdO3Y41ZSUlCghIUF2u112u10JCQnat2+fU822bdvUs2dP+fj4qEGDBhoxYoQqKiosO3cAsBKhGwAg6VgQ7tixo9zc3PTpp59q48aNmjJliq655hqzZuLEiZo6dapmzpyp3NxcORwOde3aVQcOHDBrEhMTtXDhQmVmZmr58uU6ePCg4uLiVFVVZdbEx8crPz9fWVlZysrKUn5+vhISEsztVVVV6tGjhw4dOqTly5crMzNTH3zwgZKSki7JtQCAi821tjsAALg8vPLKKwoNDdWcOXPMtmbNmpn/bxiGpk+frrFjx6p3796SpLlz5yo4OFgLFizQ4MGDVVpaqtmzZ2vevHnq0qWLJCkjI0OhoaFasmSJunXrpk2bNikrK0urVq1Su3btJEmzZs1SVFSUNm/erObNmys7O1sbN27U9u3bFRISIkmaMmWK+vXrpwkTJsjPz+8SXRUAuDgY6QYASJI++ugjtW3bVvfff7+CgoLUpk0bzZo1y9y+ZcsWFRUVKTY21mzz8PBQdHS0VqxYIUnKy8tTZWWlU01ISIgiIiLMmpUrV8put5uBW5Lat28vu93uVBMREWEGbknq1q2bysvLnaa7nKi8vFz79+93egDA5YLQDQCQJP300096/fXXFR4ers8++0xDhgzRiBEj9M4770iSioqKJEnBwcFOrwsODja3FRUVyd3dXf7+/mesCQoKqnb8oKAgp5qTj+Pv7y93d3ez5mSpqanmHHG73a7Q0NCaXgIAsAyhGwAgSTp69KhuvfVWpaSkqE2bNho8eLAGDhyo119/3anOZrM5PTcMo1rbyU6uOVX9+dScaMyYMSotLTUf27dvP2OfAOBSInQDACRJjRo1UsuWLZ3aWrRooW3btkmSHA6HJFUbaS4uLjZHpR0OhyoqKlRSUnLGmp07d1Y7/q5du5xqTj5OSUmJKisrq42AH+fh4SE/Pz+nBwBcLgjdAABJUseOHbV582antu+//15NmzaVJIWFhcnhcCgnJ8fcXlFRoWXLlqlDhw6SpMjISLm5uTnVFBYWqqCgwKyJiopSaWmp1qxZY9asXr1apaWlTjUFBQUqLCw0a7Kzs+Xh4aHIyMiLfOYAYD1WLwEASJKefvppdejQQSkpKerTp4/WrFmjt956S2+99ZakY9M9EhMTlZKSovDwcIWHhyslJUXe3t6Kj4+XJNntdg0YMEBJSUkKDAxUQECARo0apVatWpmrmbRo0UJ33323Bg4cqDfffFOSNGjQIMXFxal58+aSpNjYWLVs2VIJCQmaNGmS9u7dq1GjRmngwIGMYAOokwjdAABJ0m233aaFCxdqzJgxeuGFFxQWFqbp06fr4YcfNmtGjx6tsrIyDR06VCUlJWrXrp2ys7Pl6+tr1kybNk2urq7q06ePysrK1LlzZ6Wnp8vFxcWsmT9/vkaMGGGuctKrVy/NnDnT3O7i4qJPPvlEQ4cOVceOHeXl5aX4+HhNnjz5ElwJALj4CN0AAFNcXJzi4uJOu91msyk5OVnJycmnrfH09FRaWprS0tJOWxMQEKCMjIwz9qVJkyZavHjxWfsMAHUBc7oBAAAAixG6AQAAAIsRugEAAACLEboBAAAAixG6AQAAAIsRugEAAACLEboBAAAAixG6AQAAAIsRugEAAACLEboBAAAAixG6AQAAAIsRugEAAACLEboBAAAAixG6AQAAAIsRugEAAACLEboBAAAAixG6AQAAAIsRugEAAACLEboBAAAAixG6AQAAAIsRugEAAACLEboBAAAAixG6AQAAAIsRugEAAACLEboBAAAAixG6AQAAAIsRugEAAACLEboBAAAAixG6AQAAAIsRugEAAACLEboBAAAAixG6AQAAAIsRugEAAACLEboBAAAAixG6AQAAAIvVKHS//vrrat26tfz8/OTn56eoqCh9+umn5nbDMJScnKyQkBB5eXkpJiZGGzZscNpHeXm5hg8frgYNGsjHx0e9evXSjh07Ls7ZAAAAAJehGoXuxo0b6+WXX9batWu1du1a3XXXXbr33nvNYD1x4kRNnTpVM2fOVG5urhwOh7p27aoDBw6Y+0hMTNTChQuVmZmp5cuX6+DBg4qLi1NVVdXFPTMAAADgMlGj0N2zZ0/dc889uuGGG3TDDTdowoQJql+/vlatWiXDMDR9+nSNHTtWvXv3VkREhObOnavDhw9rwYIFkqTS0lLNnj1bU6ZMUZcuXdSmTRtlZGRo/fr1WrJkiSUnCAAAANS2857TXVVVpczMTB06dEhRUVHasmWLioqKFBsba9Z4eHgoOjpaK1askCTl5eWpsrLSqSYkJEQRERFmzamUl5dr//79Tg8AAACgrqhx6F6/fr3q168vDw8PDRkyRAsXLlTLli1VVFQkSQoODnaqDw4ONrcVFRXJ3d1d/v7+p605ldTUVNntdvMRGhpa024DAAAAtabGobt58+bKz8/XqlWr9MQTT6hv377auHGjud1msznVG4ZRre1kZ6sZM2aMSktLzcf27dtr2m0AAACg1tQ4dLu7u+v6669X27ZtlZqaqptvvll/+9vf5HA4JKnaiHVxcbE5+u1wOFRRUaGSkpLT1pyKh4eHuWLK8QcAAABQV1zwOt2GYai8vFxhYWFyOBzKyckxt1VUVGjZsmXq0KGDJCkyMlJubm5ONYWFhSooKDBrAAAXZt++fbXdBQDASWoUup999ll99dVX+vnnn7V+/XqNHTtWS5cu1cMPPyybzabExESlpKRo4cKFKigoUL9+/eTt7a34+HhJkt1u14ABA5SUlKTPP/9c3377rR555BG1atVKXbp0seQEAeBK9sorr+i9994zn/fp00eBgYH63e9+p++++64WewYAOJFrTYp37typhIQEFRYWym63q3Xr1srKylLXrl0lSaNHj1ZZWZmGDh2qkpIStWvXTtnZ2fL19TX3MW3aNLm6uqpPnz4qKytT586dlZ6eLhcXl4t7ZgBwFXjzzTeVkZEhScrJyVFOTo4+/fRTvf/++/rzn/+s7OzsWu4hAECqYeiePXv2GbfbbDYlJycrOTn5tDWenp5KS0tTWlpaTQ4NADiFwsJCc0WnxYsXq0+fPoqNjVWzZs3Url27Wu4dAOC4C57TDQCoPf7+/uaKTllZWeZUPcMw+KRfALiM1GikGwBweendu7fi4+MVHh6uPXv2qHv37pKk/Px8XX/99bXcOwDAcYRuAKjDpk2bprCwMG3btk0TJ05U/fr1JR2bdjJ06NBa7h0A4DhCNwDUUZWVlRo0aJDGjRuna6+91mlbYmJi7XQKAHBKzOkGgDrKzc1NCxcurO1uAADOAaEbAOqwP/zhD1q0aFFtdwMAcBZMLwGAOuz666/Xiy++qBUrVigyMlI+Pj5O20eMGFFLPQMAnIjQDQB12N///nddc801ysvLU15entM2m81G6AaAywShGwDqsC1bttR2FwAA54A53QAAAIDFGOkGgDpux44d+uijj7Rt2zZVVFQ4bZs6dWot9QoAcCJCNwDUYZ9//rl69eqlsLAwbd68WREREfr5559lGIZuvfXW2u4eAOB/mF4CAHXYmDFjlJSUpIKCAnl6euqDDz7Q9u3bFR0drfvvv79G+0pOTpbNZnN6OBwOc7thGEpOTlZISIi8vLwUExOjDRs2OO2jvLxcw4cPV4MGDeTj46NevXppx44dTjUlJSVKSEiQ3W6X3W5XQkKC9u3b51Szbds29ezZUz4+PmrQoIFGjBhRbRQfAOoSQjcA1GGbNm1S3759JUmurq4qKytT/fr19cILL+iVV16p8f5uuukmFRYWmo/169eb2yZOnKipU6dq5syZys3NlcPhUNeuXXXgwAGzJjExUQsXLlRmZqaWL1+ugwcPKi4uTlVVVWZNfHy88vPzlZWVpaysLOXn5yshIcHcXlVVpR49eujQoUNavny5MjMz9cEHHygpKel8LhEAXBaYXgIAdZiPj4/Ky8slSSEhIfrxxx910003SZJ2795d4/25uro6jW4fZxiGpk+frrFjx6p3796SpLlz5yo4OFgLFizQ4MGDVVpaqtmzZ2vevHnq0qWLJCkjI0OhoaFasmSJunXrpk2bNikrK0urVq1Su3btJEmzZs1SVFSUNm/erObNmys7O1sbN27U9u3bFRISIkmaMmWK+vXrpwkTJsjPz++UfS8vLzevhSTt37+/xucPAFZhpBsA6rD27dvr66+/liT16NFDSUlJmjBhgvr376/27dvXeH8//PCDQkJCFBYWpgcffFA//fSTpGNLExYVFSk2Ntas9fDwUHR0tFasWCFJysvLU2VlpVNNSEiIIiIizJqVK1fKbrebgfv4OdjtdqeaiIgIM3BLUrdu3VReXl5tLfITpaammlNW7Ha7QkNDa3z+AGAVQjcA1GFTp041A2xycrK6du2q9957T02bNtXs2bNrtK927drpnXfe0WeffaZZs2apqKhIHTp00J49e1RUVCRJCg4OdnpNcHCwua2oqEju7u7y9/c/Y01QUFC1YwcFBTnVnHwcf39/ubu7mzWnMmbMGJWWlpqP7du31+j8AcBKTC8BgDrs2muvNf/f29tbr7322nnvq3v37ub/t2rVSlFRUbruuus0d+5cc9TcZrM5vcYwjGptJzu55lT151NzMg8PD3l4eJyxLwBQWxjpBoA67Nprr9WePXuqte/bt88pkJ8PHx8ftWrVSj/88IM5z/vkkebi4mJzVNrhcKiiokIlJSVnrNm5c2e1Y+3atcup5uTjlJSUqLKystoIOADUFYRuAKjDfv75Z6eVQY4rLy/XL7/8ckH7Li8v16ZNm9SoUSOFhYXJ4XAoJyfH3F5RUaFly5apQ4cOkqTIyEi5ubk51RQWFqqgoMCsiYqKUmlpqdasWWPWrF69WqWlpU41BQUFKiwsNGuys7Pl4eGhyMjICzonAKgtTC8BgDroo48+Mv//s88+k91uN59XVVXp888/V7NmzWq0z1GjRqlnz55q0qSJiouL9dJLL2n//v3q27evbDabEhMTlZKSovDwcIWHhyslJUXe3t6Kj4+XJNntdg0YMEBJSUkKDAxUQECARo0apVatWpmrmbRo0UJ33323Bg4cqDfffFOSNGjQIMXFxal58+aSpNjYWLVs2VIJCQmaNGmS9u7dq1GjRmngwIGnXbkEAC53hG4AqIPuu+8+ScfmPh9fp/s4Nzc3NWvWTFOmTKnRPnfs2KGHHnpIu3fvVsOGDdW+fXutWrVKTZs2lSSNHj1aZWVlGjp0qEpKStSuXTtlZ2fL19fX3Me0adPk6uqqPn36qKysTJ07d1Z6erpcXFzMmvnz52vEiBHmKie9evXSzJkzze0uLi765JNPNHToUHXs2FFeXl6Kj4/X5MmTa3Q+AHA5IXQDQB109OhRSVJYWJhyc3PVoEGDC95nZmbmGbfbbDYlJycrOTn5tDWenp5KS0tTWlraaWsCAgKUkZFxxmM1adJEixcvPmMNANQlhG4AqMO2bNlS210AAJwD3kgJAHXQ6tWr9emnnzq1vfPOOwoLC1NQUJAGDRrk9OmMAIDaRegGgDooOTlZ69atM5+vX79eAwYMUJcuXfTMM8/o448/Vmpqai32EABwIkI3ANRB+fn56ty5s/k8MzNT7dq106xZszRy5EjNmDFD77//fi32EABwIkI3ANRBJSUlTh8Us2zZMt19993m89tuu42PQQeAywihGwDqoODgYPNNlBUVFfrmm28UFRVlbj9w4IDc3Nxqq3sAgJMQugGgDrr77rv1zDPP6KuvvtKYMWPk7e2tO+64w9y+bt06XXfddbXYQwDAiVgyEADqoJdeekm9e/dWdHS06tevr7lz58rd3d3c/vbbb5sfPgMAqH2EbgCogxo2bKivvvpKpaWlql+/vtMnPkrSP/7xD9WvX7+WegcAOBmhGwDqMLvdfsr2gICAS9wTAMCZMKcbAAAAsBihGwAAALAYoRsAAACwGKEbAOqYW2+9VSUlJZKkF154QYcPH67lHgEAzobQDQB1zKZNm3To0CFJ0vjx43Xw4MFa7hEA4GxYvQQA6phbbrlFjz32mH7/+9/LMAxNnjz5tMsDPvfcc5e4dwCAUyF0A0Adk56erueff16LFy+WzWbTp59+KlfX6v+c22w2QjcAXCYI3QBQxzRv3lyZmZmSpHr16unzzz9XUFBQLfcKAHAmhG4AqMOOHj1a210AAJwDQjcA1HE//vijpk+frk2bNslms6lFixZ66qmndN1119V21wAA/8PqJQBQh3322Wdq2bKl1qxZo9atWysiIkKrV6/WTTfdpJycnNruHgDgfxjpBoA67JlnntHTTz+tl19+uVr7X/7yF3Xt2rWWegYAOBEj3QBQh23atEkDBgyo1t6/f39t3LixFnoEADgVQjcA1GENGzZUfn5+tfb8/HxWNAGAywjTSwCgDhs4cKAGDRqkn376SR06dJDNZtPy5cv1yiuvKCkpqba7BwD4H0I3ANRh48aNk6+vr6ZMmaIxY8ZIkkJCQpScnKwRI0bUcu8AAMcRugGgDrPZbHr66af19NNP68CBA5IkX1/fWu4VAOBkhG4AuEIQtgHg8sUbKQEAAACLEboBAAAAixG6AQAAAIsRugGgjqqsrFSnTp30/fff13ZXAABnQegGgDrKzc1NBQUFstlstd0VAMBZELoBoA579NFHNXv27NruBgDgLFgyEADqsIqKCv39739XTk6O2rZtKx8fH6ftU6dOraWeAQBOROgGgDqsoKBAt956qyRVm9vNtBMAuHwQugGgDvvyyy9ruwsAgHPAnG4AuAL897//1WeffaaysjJJkmEYtdwjAMCJCN0AUIft2bNHnTt31g033KB77rlHhYWFkqTHH39cSUlJtdw7AMBxhG4AqMOefvppubm5adu2bfL29jbbH3jgAWVlZdVizwAAJ2JONwDUYdnZ2frss8/UuHFjp/bw8HBt3bq1lnoFADgZI90AUIcdOnTIaYT7uN27d8vDw6MWegQAOBVCNwDUYXfeeafeeecd87nNZtPRo0c1adIkderUqRZ7BgA4EdNLAKAOmzRpkmJiYrR27VpVVFRo9OjR2rBhg/bu3auvv/66trsHAPgfRroBoA5r2bKl1q1bp9tvv11du3bVoUOH1Lt3b3377be67rrrart7AID/YaQbAOo4h8Oh8ePH13Y3AABnQOgGgDqupKREs2fP1qZNm2Sz2dSiRQs99thjCggIqO2uAQD+h+klAFCHLVu2TGFhYZoxY4ZKSkq0d+9ezZgxQ2FhYVq2bFltdw8A8D+MdANAHfbkk0+qT58+ev311+Xi4iJJqqqq0tChQ/Xkk0+qoKCglnsIAJAY6QaAOu3HH39UUlKSGbglycXFRSNHjtSPP/5Yiz0DAJyI0A0Additt96qTZs2VWvftGmTbrnllkvfIQDAKTG9BADqmHXr1pn/P2LECD311FP673//q/bt20uSVq1apVdffVUvv/xybXURAHASQjcA1DG33HKLbDabDMMw20aPHl2tLj4+Xg888MCl7BoA4DQI3QBQx2zZsqW2uwAAqCFCNwDUMU2bNq3tLgAAaojQDQB13C+//KKvv/5axcXFOnr0qNO2ESNG1FKvAAAnInQDQB02Z84cDRkyRO7u7goMDJTNZjO32Ww2QjcAXCZYMhAA6rDnnntOzz33nEpLS/Xzzz9ry5Yt5uOnn3467/2mpqbKZrMpMTHRbDMMQ8nJyQoJCZGXl5diYmK0YcMGp9eVl5dr+PDhatCggXx8fNSrVy/t2LHDqaakpEQJCQmy2+2y2+1KSEjQvn37nGq2bdumnj17ysfHRw0aNNCIESNUUVFx3ucDALWN0A0Addjhw4f14IMPql69i/fPeW5urt566y21bt3aqX3ixImaOnWqZs6cqdzcXDkcDnXt2lUHDhwwaxITE7Vw4UJlZmZq+fLlOnjwoOLi4lRVVWXWxMfHKz8/X1lZWcrKylJ+fr4SEhLM7VVVVerRo4cOHTqk5cuXKzMzUx988IGSkpIu2jkCwKVG6AaAOmzAgAH6xz/+cdH2d/DgQT388MOaNWuW/P39zXbDMDR9+nSNHTtWvXv3VkREhObOnavDhw9rwYIFkqTS0lLNnj1bU6ZMUZcuXdSmTRtlZGRo/fr1WrJkiaRjH9qTlZWlv//974qKilJUVJRmzZqlxYsXa/PmzZKk7Oxsbdy4URkZGWrTpo26dOmiKVOmaNasWdq/f/9FO1cAuJSY0w0AdVhqaqri4uKUlZWlVq1ayc3NzWn71KlTa7S/J598Uj169FCXLl300ksvme1btmxRUVGRYmNjzTYPDw9FR0drxYoVGjx4sPLy8lRZWelUExISooiICK1YsULdunXTypUrZbfb1a5dO7Omffv2stvtWrFihZo3b66VK1cqIiJCISEhZk23bt1UXl6uvLw8derU6ZR9Ly8vV3l5ufmcgA7gckLoBoA6LCUlRZ999pmaN28uSdXeSFkTmZmZ+uabb5Sbm1ttW1FRkSQpODjYqT04OFhbt241a9zd3Z1GyI/XHH99UVGRgoKCqu0/KCjIqebk4/j7+8vd3d2sOZXU1FSNHz/+bKcJALWC0A0AddjUqVP19ttvq1+/fhe0n+3bt+upp55Sdna2PD09T1t3cpA3DOOs4f7kmlPVn0/NycaMGaORI0eaz/fv36/Q0NAz9g0ALhXmdANAHebh4aGOHTte8H7y8vJUXFysyMhIubq6ytXVVcuWLdOMGTPk6upqjjyfPNJcXFxsbnM4HKqoqFBJSckZa3bu3Fnt+Lt27XKqOfk4JSUlqqysrDYCfiIPDw/5+fk5PQDgckHoBoA67KmnnlJaWtoF76dz585av3698vPzzUfbtm318MMPKz8/X9dee60cDodycnLM11RUVGjZsmXq0KGDJCkyMlJubm5ONYWFhSooKDBroqKiVFpaqjVr1pg1q1evVmlpqVNNQUGBCgsLzZrs7Gx5eHgoMjLygs8VAGoD00sAoA5bs2aNvvjiCy1evFg33XRTtTdSfvjhh+e0H19fX0VERDi1+fj4KDAw0GxPTExUSkqKwsPDFR4erpSUFHl7eys+Pl6SZLfbNWDAACUlJSkwMFABAQEaNWqUWrVqpS5dukiSWrRoobvvvlsDBw7Um2++KUkaNGiQ4uLizHnpsbGxatmypRISEjRp0iTt3btXo0aN0sCBAxm9BlBnEboBoA675ppr1Lt370tyrNGjR6usrExDhw5VSUmJ2rVrp+zsbPn6+po106ZNk6urq/r06aOysjJ17txZ6enpcnFxMWvmz5+vESNGmKuc9OrVSzNnzjS3u7i46JNPPtHQoUPVsWNHeXl5KT4+XpMnT74k5wkAViB0A0AdNmfOHMv2vXTpUqfnNptNycnJSk5OPu1rPD09lZaWdsYpLwEBAcrIyDjjsZs0aaLFixfXpLsAcFljTjcAAABgMUa6AaAOCwsLO+Myej/99NMl7A0A4HQI3QBQhyUmJjo9r6ys1LfffqusrCz9+c9/rp1OAQCqIXQDQB321FNPnbL91Vdf1dq1ay9xbwAAp3PVzOmuqPhNu3btUkVFRW13BQAs1717d33wwQe13Q0AwP9cFSPdFRW/ad269ZoxY79++qlQAQG/6YQVrgDgivPPf/5TAQEBtd0NAMD/XBWh+8iRSv32m5sMo73Kyz9UZeWR2u4SAFwUbdq0cXojpWEYKioq0q5du/Taa6/VYs8AACe6KkL3cR4eDG8DuLLcd999Ts/r1aunhg0bKiYmRjfeeGPtdAoAUM1VFboB4Erz/PPP13YXAADn4Kp5IyUAAABQWxjpBoA6qF69emf8UBzp2Me2HznCe1gA4HJA6AaAOmjhwoWn3bZixQqlpaXJMIxL2CMAwJkQugGgDrr33nurtf3nP//RmDFj9PHHH+vhhx/Wiy++WAs9AwCcCnO6AaCO+/XXXzVw4EC1bt1aR44cUX5+vubOnasmTZrUdtcAAP9D6AaAOqq0tFR/+ctfdP3112vDhg36/PPP9fHHHysiIqK2uwYAOAnTSwCgDpo4caJeeeUVORwOvfvuu6ecbgIAuHwQugGgDnrmmWfk5eWl66+/XnPnztXcuXNPWffhhx9e4p4BAE6F0A0AddCjjz561iUDAQCXD0I3ANRB6enptd0FAEAN8EZKAAAAwGI1Ct2pqam67bbb5Ovrq6CgIN13333avHmzU41hGEpOTlZISIi8vLwUExOjDRs2ONWUl5dr+PDhatCggXx8fNSrVy/t2LHjws8GAAAAuAzVKHQvW7ZMTz75pFatWqWcnBwdOXJEsbGxOnTokFkzceJETZ06VTNnzlRubq4cDoe6du2qAwcOmDWJiYlauHChMjMztXz5ch08eFBxcXGqqqq6eGcGAAAAXCZqNKc7KyvL6fmcOXMUFBSkvLw83XnnnTIMQ9OnT9fYsWPVu3dvSdLcuXMVHBysBQsWaPDgwSotLdXs2bM1b948denSRZKUkZGh0NBQLVmyRN26dbtIpwYAAABcHi5oTndpaakkKSAgQJK0ZcsWFRUVKTY21qzx8PBQdHS0VqxYIUnKy8tTZWWlU01ISIgiIiLMmpOVl5dr//79Tg8AAACgrjjv0G0YhkaOHKnf//735qefFRUVSZKCg4OdaoODg81tRUVFcnd3l7+//2lrTpaamiq73W4+QkNDz7fbAAAAwCV33qF72LBhWrdund59991q205eO9YwjLOuJ3ummjFjxqi0tNR8bN++/Xy7DQAAAFxy5xW6hw8fro8++khffvmlGjdubLY7HA5JqjZiXVxcbI5+OxwOVVRUqKSk5LQ1J/Pw8JCfn5/TAwAAAKgrahS6DcPQsGHD9OGHH+qLL75QWFiY0/awsDA5HA7l5OSYbRUVFVq2bJk6dOggSYqMjJSbm5tTTWFhoQoKCswaAAAA4EpSo9VLnnzySS1YsED/93//J19fX3NE2263y8vLSzabTYmJiUpJSVF4eLjCw8OVkpIib29vxcfHm7UDBgxQUlKSAgMDFRAQoFGjRqlVq1bmaiYAAADAlaRGofv111+XJMXExDi1z5kzR/369ZMkjR49WmVlZRo6dKhKSkrUrl07ZWdny9fX16yfNm2aXF1d1adPH5WVlalz585KT0+Xi4vLhZ0NAAAAcBmqUeg2DOOsNTabTcnJyUpOTj5tjaenp9LS0pSWllaTwwMAAAB10gWt0w0AAADg7AjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAEnS66+/rtatW8vPz09+fn6KiorSp59+am43DEPJyckKCQmRl5eXYmJitGHDBqd9lJeXa/jw4WrQoIF8fHzUq1cv7dixw6mmpKRECQkJstvtstvtSkhI0L59+5xqtm3bpp49e8rHx0cNGjTQiBEjVFFRYdm5A4DVCN0AAElS48aN9fLLL2vt2rVau3at7rrrLt17771msJ44caKmTp2qmTNnKjc3Vw6HQ127dtWBAwfMfSQmJmrhwoXKzMzU8uXLdfDgQcXFxamqqsqsiY+PV35+vrKyspSVlaX8/HwlJCSY26uqqtSjRw8dOnRIy5cvV2Zmpj744AMlJSVduosBABeZa213AABweejZs6fT8wkTJuj111/XqlWr1LJlS02fPl1jx45V7969JUlz585VcHCwFixYoMGDB6u0tFSzZ8/WvHnz1KVLF0lSRkaGQkNDtWTJEnXr1k2bNm1SVlaWVq1apXbt2kmSZs2apaioKG3evFnNmzdXdna2Nm7cqO3btyskJESSNGXKFPXr108TJkyQn5/fJbwqAHBxMNINAKimqqpKmZmZOnTokKKiorRlyxYVFRUpNjbWrPHw8FB0dLRWrFghScrLy1NlZaVTTUhIiCIiIsyalStXym63m4Fbktq3by+73e5UExERYQZuSerWrZvKy8uVl5d32j6Xl5dr//79Tg8AuFwQugEApvXr16t+/fry8PDQkCFDtHDhQrVs2VJFRUWSpODgYKf64OBgc1tRUZHc3d3l7+9/xpqgoKBqxw0KCnKqOfk4/v7+cnd3N2tOJTU11ZwnbrfbFRoaWsOzBwDrELoBAKbmzZsrPz9fq1at0hNPPKG+fftq48aN5nabzeZUbxhGtbaTnVxzqvrzqTnZmDFjVFpaaj62b99+xn4BwKVE6AYAmNzd3XX99derbdu2Sk1N1c0336y//e1vcjgcklRtpLm4uNgclXY4HKqoqFBJSckZa3bu3FntuLt27XKqOfk4JSUlqqysrDYCfiIPDw9z5ZXjDwC4XBC6AQCnZRiGysvLFRYWJofDoZycHHNbRUWFli1bpg4dOkiSIiMj5ebm5lRTWFiogoICsyYqKkqlpaVas2aNWbN69WqVlpY61RQUFKiwsNCsyc7OloeHhyIjIy09XwCwCquXAAAkSc8++6y6d++u0NBQHThwQJmZmVq6dKmysrJks9mUmJiolJQUhYeHKzw8XCkpKfL29lZ8fLwkyW63a8CAAUpKSlJgYKACAgI0atQotWrVylzNpEWLFrr77rs1cOBAvfnmm5KkQYMGKS4uTs2bN5ckxcbGqmXLlkpISNCkSZO0d+9ejRo1SgMHDmT0GkCdRegGAEiSdu7cqYSEBBUWFsput6t169bKyspS165dJUmjR49WWVmZhg4dqpKSErVr107Z2dny9fU19zFt2jS5urqqT58+KisrU+fOnZWeni4XFxezZv78+RoxYoS5ykmvXr00c+ZMc7uLi4s++eQTDR06VB07dpSXl5fi4+M1efLkS3QlAODiI3QDACRJs2fPPuN2m82m5ORkJScnn7bG09NTaWlpSktLO21NQECAMjIyznisJk2aaPHixWesAYC6hDndAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxQjdAAAAgMUI3QAASVJqaqpuu+02+fr6KigoSPfdd582b97sVGMYhpKTkxUSEiIvLy/FxMRow4YNTjXl5eUaPny4GjRoIB8fH/Xq1Us7duxwqikpKVFCQoLsdrvsdrsSEhK0b98+p5pt27apZ8+e8vHxUYMGDTRixAhVVFRYcu4AYDVCNwBAkrRs2TI9+eSTWrVqlXJycnTkyBHFxsbq0KFDZs3EiRM1depUzZw5U7m5uXI4HOratasOHDhg1iQmJmrhwoXKzMzU8uXLdfDgQcXFxamqqsqsiY+PV35+vrKyspSVlaX8/HwlJCSY26uqqtSjRw8dOnRIy5cvV2Zmpj744AMlJSVdmosBABeZa213AABwecjKynJ6PmfOHAUFBSkvL0933nmnDMPQ9OnTNXbsWPXu3VuSNHfuXAUHB2vBggUaPHiwSktLNXv2bM2bN09dunSRJGVkZCg0NFRLlixRt27dtGnTJmVlZWnVqlVq166dJGnWrFmKiorS5s2b1bx5c2VnZ2vjxo3avn27QkJCJElTpkxRv379NGHCBPn5+V3CKwMAF46RbgDAKZWWlkqSAgICJElbtmxRUVGRYmNjzRoPDw9FR0drxYoVkqS8vDxVVlY61YSEhCgiIsKsWblypex2uxm4Jal9+/ay2+1ONREREWbglqRu3bqpvLxceXl5p+xveXm59u/f7/QAgMsFoRsAUI1hGBo5cqR+//vfKyIiQpJUVFQkSQoODnaqDQ4ONrcVFRXJ3d1d/v7+Z6wJCgqqdsygoCCnmpOP4+/vL3d3d7PmZKmpqeYccbvdrtDQ0JqeNgBYhtANAKhm2LBhWrdund59991q22w2m9NzwzCqtZ3s5JpT1Z9PzYnGjBmj0tJS87F9+/Yz9gkALiVCNwDAyfDhw/XRRx/pyy+/VOPGjc12h8MhSdVGmouLi81RaYfDoYqKCpWUlJyxZufOndWOu2vXLqeak49TUlKiysrKaiPgx3l4eMjPz8/pAQCXC0I3AEDSsVHkYcOG6cMPP9QXX3yhsLAwp+1hYWFyOBzKyckx2yoqKrRs2TJ16NBBkhQZGSk3NzenmsLCQhUUFJg1UVFRKi0t1Zo1a8ya1atXq7S01KmmoKBAhYWFZk12drY8PDwUGRl58U8eACzG6iUAAEnSk08+qQULFuj//u//5Ovra4402+12eXl5yWazKTExUSkpKQoPD1d4eLhSUlLk7e2t+Ph4s3bAgAFKSkpSYGCgAgICNGrUKLVq1cpczaRFixa6++67NXDgQL355puSpEGDBikuLk7NmzeXJMXGxqply5ZKSEjQpEmTtHfvXo0aNUoDBw5kBBtAnUToBgBIkl5//XVJUkxMjFP7nDlz1K9fP0nS6NGjVVZWpqFDh6qkpETt2rVTdna2fH19zfpp06bJ1dVVffr0UVlZmTp37qz09HS5uLiYNfPnz9eIESPMVU569eqlmTNnmttdXFz0ySefaOjQoerYsaO8vLwUHx+vyZMnW3T2AGAtQjcAQNKx6SVnY7PZlJycrOTk5NPWeHp6Ki0tTWlpaaetCQgIUEZGxhmP1aRJEy1evPisfQKAuoA53QAAAIDFCN0AAACAxQjdAAAAgMUI3QAAAIDFCN0AAACAxWocuv/973+rZ8+eCgkJkc1m06JFi5y2G4ah5ORkhYSEyMvLSzExMdqwYYNTTXl5uYYPH64GDRrIx8dHvXr10o4dOy7oRAAAAIDLVY1D96FDh3TzzTc7rad6ookTJ2rq1KmaOXOmcnNz5XA41LVrVx04cMCsSUxM1MKFC5WZmanly5fr4MGDiouLU1VV1fmfCQAAAHCZqvE63d27d1f37t1Puc0wDE2fPl1jx45V7969JUlz585VcHCwFixYoMGDB6u0tFSzZ8/WvHnzzE8ny8jIUGhoqJYsWaJu3bpdwOkAAAAAl5+LOqd7y5YtKioqMj9hTJI8PDwUHR2tFStWSJLy8vJUWVnpVBMSEqKIiAiz5mTl5eXav3+/0wMAAACoKy5q6C4qKpIkBQcHO7UHBweb24qKiuTu7i5/f//T1pwsNTVVdrvdfISGhl7MbgMAAACWsmT1EpvN5vTcMIxqbSc7U82YMWNUWlpqPrZv337R+goAAABY7aKGbofDIUnVRqyLi4vN0W+Hw6GKigqVlJSctuZkHh4e8vPzc3oAAAAAdcVFDd1hYWFyOBzKyckx2yoqKrRs2TJ16NBBkhQZGSk3NzenmsLCQhUUFJg1AAAAwJWkxquXHDx4UP/973/N51u2bFF+fr4CAgLUpEkTJSYmKiUlReHh4QoPD1dKSoq8vb0VHx8vSbLb7RowYICSkpIUGBiogIAAjRo1Sq1atTJXMwEAAACuJDUO3WvXrlWnTp3M5yNHjpQk9e3bV+np6Ro9erTKyso0dOhQlZSUqF27dsrOzpavr6/5mmnTpsnV1VV9+vRRWVmZOnfurPT0dLm4uFyEUwIAAAAuLzUO3TExMTIM47TbbTabkpOTlZycfNoaT09PpaWlKS0traaHBwAAAOocS1YvAQAAAPD/EboBAAAAixG6AQAAAIsRugEAAACLEboBAPif3yoqtHPnTpWWltZ2VwBcYWq8egkAAFeiAxUVWr9unY6mpMjepImGjRsnu91e290CcIVgpBsAAEm/HTkit99+052Gocrdu3X48OHa7hKAKwihGwCAE9g9PGq7CwCuQIRuAAAAwGKEbgAAAMBihG4AAADAYoRuAAAAwGKEbgAAAMBihG4AAADAYoRuAAAAwGKEbgAAAMBihG4AAADAYoRuAAAAwGKEbgAAAMBihG4AAADAYoRuAAAAwGKEbgAAAMBihG4AAADAYoRuAAAAwGKEbgAAAMBihG4AAADAYoRuAAAAwGKEbgAAAMBihG4AAADAYoRuAAAAwGKEbgAAAMBihG4AAADAYoRuAAAAwGKEbgAAAMBihG4AAADAYoRuAAAAwGKEbgAAAMBihG4AAADAYoRuAAAAwGKEbgAAAMBihG4AAADAYoRuAAAAwGKEbgCAJOnf//63evbsqZCQENlsNi1atMhpu2EYSk5OVkhIiLy8vBQTE6MNGzY41ZSXl2v48OFq0KCBfHx81KtXL+3YscOppqSkRAkJCbLb7bLb7UpISNC+ffucarZt26aePXvKx8dHDRo00IgRI1RRUWHFaQPAJeFa2x0AAFweDh06pJtvvlmPPfaY/vjHP1bbPnHiRE2dOlXp6em64YYb9NJLL6lr167avHmzfH19JUmJiYn6+OOPlZmZqcDAQCUlJSkuLk55eXlycXGRJMXHx2vHjh3KysqSJA0aNEgJCQn6+OOPJUlVVVXq0aOHGjZsqOXLl2vPnj3q27evDMNQWlraJbkWv1VUaOfOnZfkWEBd5O3tLbvdXtvdqFMI3QAASVL37t3VvXv3U24zDEPTp0/X2LFj1bt3b0nS3LlzFRwcrAULFmjw4MEqLS3V7NmzNW/ePHXp0kWSlJGRodDQUC1ZskTdunXTpk2blJWVpVWrVqldu3aSpFmzZikqKkqbN29W8+bNlZ2drY0bN2r79u0KCQmRJE2ZMkX9+vXThAkT5OfnZ+l1OFhRofUbN+poSoq8vb0tPRZQV7k1aKBh48YRvGuA0A0AOKstW7aoqKhIsbGxZpuHh4eio6O1YsUKDR48WHl5eaqsrHSqCQkJUUREhFasWKFu3bpp5cqVstvtZuCWpPbt28tut2vFihVq3ry5Vq5cqYiICDNwS1K3bt1UXl6uvLw8derU6ZR9LC8vV3l5ufl8//7953Wuv1VVye233/QHT081Cww8r30AV7Jdhw/rw927dfjwYUJ3DRC6AQBnVVRUJEkKDg52ag8ODtbWrVvNGnd3d/n7+1erOf76oqIiBQUFVdt/UFCQU83Jx/H395e7u7tZcyqpqakaP358Dc/s9Bp4eanR/6bNADhJWVlt96DO4Y2UAIBzZrPZnJ4bhlGt7WQn15yq/nxqTjZmzBiVlpaaj+3bt5+xXwBwKRG6AQBn5XA4JKnaSHNxcbE5Ku1wOFRRUaGSkpIz1pzqDYq7du1yqjn5OCUlJaqsrKw2An4iDw8P+fn5OT0A4HJB6AYAnFVYWJgcDodycnLMtoqKCi1btkwdOnSQJEVGRsrNzc2pprCwUAUFBWZNVFSUSktLtWbNGrNm9erVKi0tdaopKChQYWGhWZOdnS0PDw9FRkZaep4AYBXmdAMAJEkHDx7Uf//7X/P5li1blJ+fr4CAADVp0kSJiYlKSUlReHi4wsPDlfK/1T3i4+MlSXa7XQMGDFBSUpICAwMVEBCgUaNGqVWrVuZqJi1atNDdd9+tgQMH6s0335R0bMnAuLg4NW/eXJIUGxurli1bKiEhQZMmTdLevXs1atQoDRw4kNFrAHUWoRsAIElau3at08ogI0eOlCT17dtX6enpGj16tMrKyjR06FCVlJSoXbt2ys7ONtfolqRp06bJ1dVVffr0UVlZmTp37qz09HRzjW5Jmj9/vkaMGGGuctKrVy/NnDnT3O7i4qJPPvlEQ4cOVceOHeXl5aX4+HhNnjzZ6ksAAJYhdAMAJEkxMTEyDOO02202m5KTk5WcnHzaGk9PT6WlpZ3xQ2wCAgKUkZFxxr40adJEixcvPmufAaCuYE43AAAAYDFCNwAAAGAxQjcAAABgMUI3AAAAYDFCNwAAAGAxQjcAAABgMUI3AAAAYDFCNwAAAGAxQjcA4Kp28OBBFRUVqbKiora7AuAKRugGAFzVDh06dCx0HzlS210BcAUjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABYjdAMAAAAWI3QDAAAAFiN0AwAAABa74kP3wYMHtWtXsY4ePVrbXQEAAMBV6ooP3YcOHVJx8S5CNwAAAGrNFR+6AQAAgNpG6AYAAAAsRugGAAAALEboBgAAACxG6AYAAAAsRugGAAAALEboBgAAACxG6AYAAAAsRugGAAAALEboBgAAACxG6AYAAAAsRugGAAAALEboBgAAACxG6AYAAAAsRugGAAAALEboBgAAACxWq6H7tddeU1hYmDw9PRUZGamvvvqqNrsDALjMcJ8AcKWotdD93nvvKTExUWPHjtW3336rO+64Q927d9e2bdtqq0sAgMsI9wkAV5JaC91Tp07VgAED9Pjjj6tFixaaPn26QkND9frrr9dWlwAAlxHuEwCuJK61cdCKigrl5eXpmWeecWqPjY3VihUrqtWXl5ervLzcfF5aWipJ2r9//1mPdfDgQVVVHdHRo4YOHizW0aOVKi3dKjc34wLPArgylZXtVkVFuQ4cOCAfH5/T1h3/+TMMfpZw8dX0PiGd/73i4MGDqqyqUvHBg6o8etT879bSUhlubhd4JsCVZ3dZmcorKrhP1FCthO7du3erqqpKwcHBTu3BwcEqKiqqVp+amqrx48dXaw8NDa3Rcbdvz5Ykbdnyrxq9Drgavfvuy+dUd+DAAdntdot7g6tNTe8T0oXfK/5VUHDsv1u2OP0XwKm9/O6751THfeKYWgndx9lsNqfnhmFUa5OkMWPGaOTIkebzo0ePau/evQoMDDxl/Yn279+v0NBQbd++XX5+fhen43UY18MZ16O6c70mhmHowIEDCgkJuYS9w9XmXO8T0vnfK/h3wBnXozquiTPuE+enVkJ3gwYN5OLiUm20ori4uNqohiR5eHjIw8PDqe2aa66p0TH9/Pz4QTkB18MZ16O6c7kmjFzAKjW9T0gXfq/g3wFnXI/quCbOuE/UTK28kdLd3V2RkZHKyclxas/JyVGHDh1qo0sAgMsI9wkAV5pam14ycuRIJSQkqG3btoqKitJbb72lbdu2aciQIbXVJQDAZYT7BIArSa2F7gceeEB79uzRCy+8oMLCQkVEROhf//qXmjZtelGP4+Hhoeeff77anxyvVlwPZ1yP6rgmuFxwn6gdXI/quCbOuB7nx2awjgsAAABgqVr9GHgAAADgakDoBgAAACxG6AYAAAAsRugGAAAALHZFh+7XXntNYWFh8vT0VGRkpL766qva7tIlkZqaqttuu02+vr4KCgrSfffdp82bNzvVGIah5ORkhYSEyMvLSzExMdqwYUMt9fjSSk1Nlc1mU2Jiotl2NV6PX375RY888ogCAwPl7e2tW265RXl5eeb2q/Ga4OrEvYJ7xalwr+A+cbFdsaH7vffeU2JiosaOHatvv/1Wd9xxh7p3765t27bVdtcst2zZMj355JNatWqVcnJydOTIEcXGxurQoUNmzcSJEzV16lTNnDlTubm5cjgc6tq1qw4cOFCLPbdebm6u3nrrLbVu3dqp/Wq7HiUlJerYsaPc3Nz06aefauPGjZoyZYrTp/ddbdcEVyfuFdwrToV7BfcJSxhXqNtvv90YMmSIU9uNN95oPPPMM7XUo9pTXFxsSDKWLVtmGIZhHD161HA4HMbLL79s1vz222+G3W433njjjdrqpuUOHDhghIeHGzk5OUZ0dLTx1FNPGYZxdV6Pv/zlL8bvf//7026/Gq8Jrk7cK/4/7hXHcK84hvvExXdFjnRXVFQoLy9PsbGxTu2xsbFasWJFLfWq9pSWlkqSAgICJElbtmxRUVGR0/Xx8PBQdHT0FX19nnzySfXo0UNdunRxar8ar8dHH32ktm3b6v7771dQUJDatGmjWbNmmduvxmuCqw/3CmfcK47hXnEM94mL74oM3bt371ZVVZWCg4Od2oODg1VUVFRLvaodhmFo5MiR+v3vf6+IiAhJMq/B1XR9MjMz9c033yg1NbXatqvxevz00096/fXXFR4ers8++0xDhgzRiBEj9M4770i6Oq8Jrj7cK/4/7hXHcK/4/7hPXHy19jHwl4LNZnN6bhhGtbYr3bBhw7Ru3TotX7682rar5fps375dTz31lLKzs+Xp6XnauqvlekjS0aNH1bZtW6WkpEiS2rRpow0bNuj111/Xo48+atZdTdcEVy++z7lXSNwrTsZ94uK7Ike6GzRoIBcXl2q/aRUXF1f7jexKNnz4cH300Uf68ssv1bhxY7Pd4XBI0lVzffLy8lRcXKzIyEi5urrK1dVVy5Yt04wZM+Tq6mqe89VyPSSpUaNGatmypVNbixYtzDePXW3fI7g6ca84hnvFMdwrnHGfuPiuyNDt7u6uyMhI5eTkOLXn5OSoQ4cOtdSrS8cwDA0bNkwffvihvvjiC4WFhTltDwsLk8PhcLo+FRUVWrZs2RV5fTp37qz169crPz/ffLRt21YPP/yw8vPzde21115V10OSOnbsWG1psO+//15NmzaVdPV9j+DqxL2Ce8WJuFc44z5hgdp5/6b1MjMzDTc3N2P27NnGxo0bjcTERMPHx8f4+eefa7trlnviiScMu91uLF261CgsLDQfhw8fNmtefvllw263Gx9++KGxfv1646GHHjIaNWpk7N+/vxZ7fumc+I50w7j6rseaNWsMV1dXY8KECcYPP/xgzJ8/3/D29jYyMjLMmqvtmuDqxL2Ce8WZXM33Cu4TF98VG7oNwzBeffVVo2nTpoa7u7tx6623mssgXekknfIxZ84cs+bo0aPG888/bzgcDsPDw8O48847jfXr19depy+xk/8hvRqvx8cff2xEREQYHh4exo033mi89dZbTtuvxmuCqxP3Cu4Vp3O13yu4T1xcNsMwjNoZYwcAAACuDlfknG4AAADgckLoBgAAACxG6AYAAAAsRugGAAAALEboBgAAACxG6AYAAAAsRugGAAAALEboBgAAACxG6AYAoJbZbDYtWrRIkvTzzz/LZrMpPz+/VvtUW8aNG6dBgwZd1H0mJyfrlltuuaj7vNIsXrxYbdq00dGjR2u7K1csQjcAAOeguLhYgwcPVpMmTeTh4SGHw6Fu3bpp5cqVZs2J4flylJ6eLpvNphYtWlTb9v7778tms6lZs2aXvmP/s3PnTv3tb3/Ts88+a7ady3WvTTExMbLZbHr55Zerbbvnnntks9mUnJx86TtWQ3FxcbLZbFqwYEFtd+WKRegGAOAc/PGPf9R3332nuXPn6vvvv9dHH32kmJgY7d27t7a7Vk1FRcVpt/n4+Ki4uLhaaH377bfVpEkTq7t2RrNnz1ZUVJRT8L9crntlZeVpt4WGhmrOnDlObb/++qu++OILNWrUyOquXTSPPfaY0tLSarsbVyxCNwAAZ7Fv3z4tX75cr7zyijp16qSmTZvq9ttv15gxY9SjRw9JMoPiH/7wh2ojxh9//LEiIyPl6empa6+9VuPHj9eRI0fO6dhVVVUaMGCAwsLC5OXlpebNm+tvf/ubU02/fv103333KTU1VSEhIbrhhhtOuz9XV1fFx8fr7bffNtt27NihpUuXKj4+3qn2xx9/1L333qvg4GDVr19ft912m5YsWeJU89prryk8PFyenp4KDg7Wn/70J3PbP//5T7Vq1UpeXl4KDAxUly5ddOjQodP2LTMzU7169TKfn8t1l6Rt27bp3nvvVf369eXn56c+ffpo586dpz1Obm6uunbtqgYNGshutys6OlrffPONU43NZtMbb7yhe++9Vz4+PnrppZdOu7+4uDjt2bNHX3/9tdmWnp6u2NhYBQUFOdVmZGSobdu28vX1lcPhUHx8vIqLi83tJSUlevjhh9WwYUN5eXkpPDzcDPQVFRUaNmyYGjVqJE9PTzVr1kypqanma0tLSzVo0CAFBQXJz89Pd911l7777jtz+3fffadOnTrJ19dXfn5+ioyM1Nq1a83tvXr10po1a/TTTz+d9lxx/gjdAACcRf369VW/fn0tWrRI5eXlp6zJzc2VJM2ZM0eFhYXm888++0yPPPKIRowYoY0bN+rNN99Uenq6JkyYcE7HPnr0qBo3bqz3339fGzdu1HPPPadnn31W77//vlPd559/rk2bNiknJ0eLFy8+4z4HDBig9977f+3df0zV1f/A8Sc/BCx+RohsESEIdpFfN5yK89oEIzCCICRjKN5lI7YIW7jcAkRWQ5fBosxkUwoaqKBlDAvCSTSGAgGiIGsEUQOXAiWYP2aczx+O++1++VnG9/vdvq/Hdv/gvM+555zX3huve+55n3uUP/74A7iXID799NM4Ozsb1RsbGyMiIoJvvvmG1tZWwsLCiIyMpL+/H4Dm5mZSU1PZs2cP3d3dfPXVV+h0OgAGBwfZvHkzer2erq4uzp49S0xMDEqpKcc0MjLCxYsXCQoKMpTNJe5KKaKjoxkeHqauro6amhp6enqIj4+fdv6jo6Ns3bqV+vp6GhsbWbp0KREREYyOjhrVy8rKIioqio6ODvR6/bTvZ2FhQUJCgtFqd1FR0ZRt7ty5Q05ODu3t7Xz++ef09vaSlJRkuJ6RkUFnZyenT5+mq6uLjz76iIcffhiA999/n1OnTnHs2DG6u7spKSkxfLhTSrFx40auXLlCVVUVLS0taLVaQkJCDN8KJCQk8Mgjj9DU1ERLSwtvvvkmCxYsMPTt5ubGokWLqK+vn3au4j4oIYQQQsyqvLxcOTg4KCsrKxUcHKx27dql2tvbjeoA6uTJk0Zla9euVe+8845RWXFxsXJxcZmyXW9vrwJUa2vrtGNJSUlRsbGxhr+3bt2qnJ2d1e3bt2ecw5EjR5SdnZ1SSqmAgAD1ySefqPHxceXh4aG++OILlZeXp9zc3GZ8D41GowoKCpRSSlVUVChbW1t1/fr1SfVaWloUoPr6+mZ8vwmtra0KUP39/Ubls8W9urpamZmZGbW7dOmSAtT58+eVUkplZWUpf3//afu+e/eusrGxUV9++aWhDFBpaWmzjnvdunXqtddeU+3t7crGxkaNjY2puro6tWjRInXnzh3l7++vsrKypm1//vx5BajR0VGllFKRkZFq27ZtU9Z99dVX1fr169X4+Pika7W1tcrW1lbdunXLqNzDw0N9/PHHSimlbGxsVFFR0YzzCQwMVLt3756xjvhnZKVbCCGEmIPY2FgGBgY4deoUYWFhnD17Fq1WS1FR0YztWlpa2LNnj2HV1tramu3btzM4OGhYaZ7NwYMHCQoKwsnJCWtrawoLCw2rzRN8fX2xsLCY83z0ej1Hjhyhrq7OsKL93924cYOdO3ei0Wiwt7fH2tqay5cvG/resGEDbm5uLFmyhMTERD777DPDnPz9/QkJCcHX15e4uDgKCwsZGRmZdjw3b94EwMrKyqh8trh3dXXh6uqKq6uroc3EeLu6uqbs69dffyU5ORkvLy/s7Oyws7NjbGxsUkz/uuo+Gz8/P5YuXUp5eTmHDx8mMTHRaBV5QmtrK1FRUbi5uWFjY8OTTz4JYOj7lVdeoaysjICAAHbu3ElDQ4OhbVJSEm1tbXh7e5Oamkp1dbXhWktLC2NjYzg6Ohrda729vfT09ADw+uuv89JLLxEaGkpubq6h/K8WLlw45/tS/D2SdAshhBBzZGVlxYYNG8jMzKShoYGkpCSysrJmbDM+Pk52djZtbW2GV0dHBz/88MOkBHMqx44dY8eOHej1eqqrq2lra2Pbtm2THpZ88MEH/9ZcEhISaGxsZPfu3WzZsgVzc/NJddLT06moqODtt9+mvr6etrY2fH19DX3b2Njw/fffU1paiouLC5mZmfj7+/Pbb79hZmZGTU0Np0+fRqPRUFBQgLe3N729vVOOZ2ILxVSJ+UxxV0phYmIyqc105XAveW1paSE/P5+Ghgba2tpwdHS875jq9Xo+/PBDysvLp9xacuPGDZ566imsra0pKSmhqamJkydPAv/18Gt4eDg//fQTaWlpDAwMEBISwhtvvAGAVqult7eXnJwcbt68yaZNmwx76MfHx3FxcTG6z9ra2uju7iY9PR24d3TipUuX2LhxI2fOnEGj0Rj6nzA8PIyTk9PfmreYG0m6hRBCiH9Io9EYPRi4YMEC/vzzT6M6Wq2W7u5uPD09J71MTWf/N1xfX09wcDApKSkEBgbi6ek55Qrl3/XQQw/x7LPPUldXN+1+5fr6epKSknjuuefw9fVl8eLF9PX1GdUxNzcnNDSUffv2ceHCBfr6+jhz5gxw72HENWvWkJ2dTWtrKxYWFpOSvAkeHh7Y2trS2dk569j/GneNRkN/fz8///yz4XpnZye///77lEcjTswrNTWViIgIfHx8sLS05Nq1a7P2O5sXX3yRjo4Oli9fjkajmXT98uXLXLt2jdzcXNauXcuyZcuMHqKc4OTkRFJSEiUlJeTn53Po0CHDNVtbW+Lj4yksLOTo0aNUVFQwPDyMVqvlypUrmJubT7rPJj7QAHh5ebFjxw6qq6uJiYkx2od+69Ytenp6CAwMvO9YiMkmf6wVQgghhJGhoSHi4uLQ6/X4+flhY2NDc3Mz+/btIyoqylDvscceo7a2ljVr1mBpaYmDgwOZmZk888wzuLq6EhcXh6mpKRcuXKCjo2PGEzEmeHp68umnn/L111/j7u5OcXExTU1NuLu73/e8ioqKOHDgAI6OjtP2feLECSIjIzExMSEjI8Pox1MqKyv58ccf0el0ODg4UFVVxfj4ON7e3pw7d47a2lrDCR7nzp3j6tWr0ybCpqamhIaG8t133xEdHQ3MLe6hoaH4+fmRkJBAfn4+d+/eJSUlhXXr1k27PcTT05Pi4mKCgoK4fv066enpLFy48D4ieY+DgwODg4NTbisBePTRR7GwsKCgoIDk5GQuXrxITk6OUZ3MzEyeeOIJfHx8uH37NpWVlYaY5eXl4eLiQkBAAKamphw/fpzFixdjb29PaGgoq1evJjo6mr179+Lt7c3AwABVVVVER0fj4+NDeno6zz//PO7u7vzyyy80NTURGxtr6LuxsRFLS0tWr15937EQk8lKtxBCCDELa2trVq5cSV5eHjqdjuXLl5ORkcH27dv54IMPDPX2799PTU0Nrq6uhtXCsLAwKisrqampYcWKFaxatYr33nsPNze3OfWdnJxMTEwM8fHxrFy5kqGhIVJSUv6VeU0c5TedvLw8HBwcCA4OJjIykrCwMLRareG6vb09J06cYP369Tz++OMcPHiQ0tJSfHx8sLW15dtvvyUiIgIvLy/eeust9u/fT3h4+LT9vfzyy5SVlRkS+7nEfeIHiRwcHNDpdISGhrJkyRKOHj06bT+HDx9mZGSEwMBAEhMTSU1NnXS03z9lb28/7bYUJycnioqKOH78OBqNhtzcXN59912jOhYWFuzatQs/Pz90Oh1mZmaUlZUZ4rF3716CgoJYsWIFfX19VFVVYWpqiomJCVVVVeh0OvR6PV5eXrzwwgv09fXh7OyMmZkZQ0NDbNmyBS8vLzZt2kR4eDjZ2dmGvktLS0lISOCBBx74V2IhjJkoNc3ZPUIIIYQQ/4OUUqxatYq0tDQ2b978vz2c/1euXr3KsmXLaG5u/le+RRGTyUq3EEIIIf5PMDEx4dChQ3P+4SDx7+nt7eXAgQOScM8jWekWQgghhBBinslKtxBCCCGEEPNMkm4hhBBCCCHmmSTdQgghhBBCzDNJuoUQQgghhJhnknQLIYQQQggxzyTpFkIIIYQQYp5J0i2EEEIIIcQ8k6RbCCGEEEKIeSZJtxBCCCGEEPPsP4Zff7t92X3DAAAAAElFTkSuQmCC", + "image/png": 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vznff4cOHG97e3tdtJ+wLM4jgUBo0aCAXFxfr65133pH097TWX375Rb1795Ykm9kiDz/8sOLj43N9e9G5c2eb5dq1a0uS9bKY1atX6/Lly+rbt69NfW5uboqIiMjzRoLdunXLtS4xMVH/+Mc/FBoaKmdnZ7m4uCgsLEySdPDgwRv2uaDtOHTokM6cOaNevXrZTHcOCwu74cyfq1WuXFnbt2/X9u3btWXLFi1cuFAWi0WtWrW64WVdBenrxYsXtWHDBnXv3t06pfp65s6dq4cfflhPP/20Pv/8c7m5uRW4L2+99Za1L1e/unfvblMuZ9r41dO5Jen+++9XjRo1tHbt2nyP8c0338hkMunJJ5+0eX+CgoJUp04d6/tTpUoV+fj46IUXXtBHH32U69vXG/H09Mz1O9urVy9duXJFGzdutJYZMGCA5syZY/3m5/vvv9eBAwc0fPjwmzreU089peXLl+vcuXOaNWuWWrZsme9TYFJTU/XCCy+oSpUqcnZ2lrOzs8qWLau0tDSb3/H7779f3333nV588UWtX79e6enpNvX4+vqqcuXKevvtt/Xuu+9q165dNpe5AYAjYwxUtGOgq+9DlPPfiIgISVKNGjUUEBBgHS+sX79egYGBqlGjRq56bnRub8alS5e0du1aPfroo3J3d8/13l66dElbt2696XqvZlwzq7p58+Zyc3NTXFycpL8vE4yMjFT79u21efNmXbx4UadOndLhw4dtLju/fPmyJk6cqPvuu0+urq5ydnaWq6urDh8+nOd7ndfvS+/evWU2m21mvX322WfKyMjQgAEDbvqc3H///fr55581dOhQrV69+qbv5ZPf79PVlxs+88wzkmRzf8YPPvhAtWrVUosWLQp8rIiICFWuXFmffPKJ9u7dq+3bt+d7eZn09/iudevW8vLykpOTk1xcXPTKK6/o3LlzSkxMlPT3pZOurq4aPHiw5s6dm+vWB9Lf5+j8+fN64okn9PXXX99w1jxKPgIilDr+/v6yWCx5fpAuXLhQ27dvt7kGWZL1Mp4xY8bYDJ5cXFw0dOhQScr1D56fn5/NstlsliTr/7Tm1NmoUaNcdS5evDhXfe7u7rme8HTlyhW1bdtWS5Ys0dixY7V27Vpt27bN+sF17f8g56Wg7ci5TCcoKChXHXmty4+bm5saNmyohg0bqkmTJnriiSf03XffKT4+Xq+88kq++xW0r0lJScrOztbdd99doPYsWrRIFotFTz/99E0/mv6ee+6x9uXq17XBVM65y2s6f0hIiM0lUNc6e/asDMNQYGBgrvdn69at1vfHy8tLGzZsUN26dfXSSy+pZs2aCgkJ0auvvlqgp4cEBgbmWpfzvl7dvhEjRujChQtasGCBpL8HKXfffbceeeSRGx7jao899pjc3Nw0ZcoUrVixQgMHDsy3bK9evfTBBx/o6aef1urVq7Vt2zZt375dd911l83v+Pvvv68XXnhBy5YtU8uWLeXr66suXbpYg0eTyaS1a9eqXbt2mjx5surXr6+77rpLI0eOLNBleABg7xgD2bqTY6BatWrJ399f69ats95/KCcgkv5+aMf69euVkZGhLVu25Pv0shud25tx7tw5Xb58WdOmTcvV/5zLrW73f+hzftdyLoVyc3NT8+bNrQHR2rVr1aZNG0VGRio7O1s//PCD9VKzqwOiUaNGafz48erSpYtWrFihH3/8Udu3b1edOnXy7HteYy5fX1917txZn376qfUei3PmzNH999+vmjVr3vQ5GTdunP79739r69atioqKkp+fn1q1apXraXX5ye/36epxV2BgoHr06KGZM2cqOztbe/bs0Q8//HDTX8yZTCYNGDBA8+fP10cffaRq1arpwQcfzLPstm3brA+N+fjjj/W///1P27dv18svvyzp/37XKleurLi4OAUEBGjYsGGqXLmyKleubHMPpj59+uiTTz7RiRMn1K1bNwUEBKhx48Y2lxPCvjjmrd5Rqjk5Oemhhx7SmjVrFB8fb/MBkvPUh+PHj9vs4+/vL+nvD4KuXbvmWW/16tVvqh05dX755ZfWb7uuJ6/wYt++ffr55581Z84c9evXz7r+yJEjhd6OnAFJQkJCrm15rbsZwcHB8vf3188//5xvmYL21dfXV05OTgW+MfOCBQs0fvx4RUREaM2aNapbt+4t9eF6cs5dfHx8ruDqzJkz1vcgL/7+/jKZTPrhhx+sg8CrXb2uVq1aWrRokQzD0J49ezRnzhy99tprslgsevHFF6/bxpxB8tVy3terB6NVqlRRVFSUPvzwQ0VFRWn58uWaMGGCnJycrlv/tdzd3dWzZ09NmjRJ5cqVy/fvKjk5Wd98841effVVmz5kZGTor7/+sinr4eGhCRMmaMKECTp79qx1NlGnTp2sjw8OCwuz3qDy119/1eeff66YmBhlZmbqo48+uqk+AIC9YQx0a+0ojDGQyWRSRESEVq1apW3btun8+fM2AVFERIRiYmK0ZcsWXbp06ZYeb3+zfHx85OTkpD59+uR7H8FKlSrdcv2GYWjFihXy8PBQw4YNretbtWqlV155Rdu2bdPp06fVpk0beXp6qlGjRoqNjdWZM2dUrVo1hYaGWveZP3+++vbtq4kTJ9oc488//5S3t3euY+f3pd+AAQP0xRdfKDY2VhUqVND27ds1Y8YM6/abOSfOzs4aNWqURo0apfPnzysuLk4vvfSS2rVrp1OnTt3wSXv5/T5dGwI+++yzmjdvnr7++mutWrVK3t7e1hl9N6N///565ZVX9NFHH9k8SfZaixYtkouLi7755hubmfXLli3LVfbBBx/Ugw8+qOzsbO3YsUPTpk1TdHS0AgMD1bNnT0l/n/MBAwYoLS1NGzdu1KuvvqqOHTvq119/LdDfP0oWAiKUSuPGjdN3332nf/zjH/ryyy9v+PSs6tWrq2rVqvr5559zfTDdqnbt2snZ2VlHjx7NcxpsQeR8+F0bHMycOTNX2fy+YSpoO6pXr67g4GB99tlnGjVqlPXYJ06c0ObNmwt0k7z8nD59Wn/++ed1H8ta0L7mPAXiiy++0L/+9a/rhi/S34FSXFycOnbsqJYtW+q7776zeVRrYXjooYck/T24adSokXX99u3bdfDgQes3Mnnp2LGj3nzzTf3++++5Ll3Lj8lkUp06dTRlyhTNmTNHP/300w33uXDhgpYvX24zdX3hwoXWG2te7dlnn1Xbtm3Vr18/OTk5adCgQQVq17WeeeYZnT17VhEREfle2mcymWQYRq73/b///a/127+8BAYGqn///vr55581derUPB+JXK1aNf3zn//UV199VaBzBAClAWOgm29HYY2BWrZsqa+++kpvv/22AgICbC4hi4iI0Llz5zRt2jRr2cKSX//d3d3VsmVL7dq1S7Vr1873qXa3asKECTpw4IBeeuklm8/51q1b66WXXtL48eN19913695777WuX758uRISEnK9HyaTKdd7vXLlSv3++++qUqVKgdvUtm1blS9fXrNnz1aFChXk5uZmfcqXdOvnxNvbW4899ph+//13RUdH6/jx49cd10rK9/epb9++NuUaNGigZs2a6a233tK+ffs0ePBgeXh4FLjPOcqXL6/nn39ev/zyi02oei2TySRnZ2ebL//S09M1b968fPdxcnJS48aNde+992rBggX66aefrAFRDg8PD0VFRSkzM1NdunTR/v37CYjsEAERSqXmzZvrww8/1IgRI1S/fn0NHjxYNWvWVJkyZRQfH6+vvvpKkmymM8+cOVNRUVFq166d+vfvr/Lly+uvv/7SwYMH9dNPP+mLL764qTZUrFhRr732ml5++WX99ttvat++vXx8fHT27Flt27bNOhvieu69915VrlxZL774ogzDkK+vr1asWJHntM2cp0C899576tevn1xcXFS9evUCt6NMmTJ6/fXX9fTTT+vRRx/VoEGDdP78ecXExNzUJWbp6enW6d/Z2dk6duyYJk+eLEmKjo4ulL6+++67euCBB9S4cWO9+OKLqlKlis6ePavly5dr5syZ8vT0tCnv6empVatWqWvXrtanjBXmwKx69eoaPHiwpk2bpjJlyigqKkrHjx/X+PHjFRoaqueeey7ffZs3b67BgwdrwIAB2rFjh1q0aCEPDw/Fx8dr06ZNqlWrlp555hl98803mj59urp06aJ77rlHhmFoyZIlOn/+vNq0aXPDNvr5+emZZ57RyZMnVa1aNX377bf6+OOP9cwzz6hChQo2Zdu0aaP77rtP69at05NPPqmAgIBbOi9169bN89uoq5UrV04tWrTQ22+/LX9/f1WsWFEbNmzQrFmzcn1j2LhxY3Xs2FG1a9eWj4+PDh48qHnz5qlp06Zyd3fXnj17NHz4cD3++OOqWrWqXF1d9f3332vPnj03nGEFAKUFY6DiGwPljC2WLl2qxx57zGZbeHi4/Pz8tHTpUpUvX75AT1MtKE9PT4WFhenrr79Wq1at5Ovra/1Mfe+99/TAAw/owQcf1DPPPKOKFSvqwoULOnLkiFasWFGgp7SdP3/eOrZLS0vToUOHtGjRIv3www/q3r17rveyQYMG8vHx0Zo1a6z3/pH+DohynpJ19eVl0t9fmM2ZM0f33nuvateurZ07d+rtt98u8C0Fcjg5Oalv37569913rTOYvby8bMoU9Jx06tRJ4eHh1tsLnDhxQlOnTlVYWFiB3r/ExETr71NycrJeffVVubm5ady4cbnKPvvss+rRo4dMJpP10s5b8eabb96wTIcOHfTuu++qV69eGjx4sM6dO6d///vfuQK6jz76SN9//706dOigChUq6NKlS9YnpeW8f4MGDZLFYlHz5s0VHByshIQETZo0SV5eXjZfmsKOFNvtsYE7YPfu3caAAQOMSpUqGWaz2XBzczOqVKli9O3b11i7dm2u8j///LPRvXt3IyAgwHBxcTGCgoKMhx56yPjoo4+sZa5+YsDVcp4ecO3TmZYtW2a0bNnSKFeunGE2m42wsDDjscceM+Li4qxl+vXrZ3h4eOTZhwMHDhht2rQxPD09DR8fH+Pxxx83Tp48mefTKsaNG2eEhIQYZcqUydWWgrTDMAzjv//9r1G1alXD1dXVqFatmvHJJ5/kevJWfq59ilmZMmWMkJAQIyoqyli/fr1N2byeYnYzfT1w4IDx+OOPG35+foarq6tRoUIFo3///salS5ds6r/6fcrIyDC6detmuLm5GStXrsy3Hznv5RdffJHn9mHDhhnX/vOZnZ1tvPXWW0a1atUMFxcXw9/f33jyySeNU6dO2ZTL71x+8sknRuPGjQ0PDw/DYrEYlStXNvr27Wvs2LHDMAzD+OWXX4wnnnjCqFy5smGxWAwvLy/j/vvvN+bMmZNvP3JEREQYNWvWNNavX280bNjQMJvNRnBwsPHSSy/lemJNjpiYGEOSsXXr1hvWn+NGTzoxDCPPp5idPn3a6Natm+Hj42N4enoa7du3N/bt22eEhYUZ/fr1s5Z78cUXjYYNGxo+Pj6G2Ww27rnnHuO5554z/vzzT8MwDOPs2bNG//79jXvvvdfw8PAwypYta9SuXduYMmWKcfny5QL3AwBKA8ZA/9eWOzEGyhEUFGRIMj744INc27p06WJIMnr37p1r282c22ufYmYYfz89rF69eobZbDYk2Xx+Hjt2zHjqqaeM8uXLGy4uLsZdd91lNGvWzHjjjTdu2J+wsDDruM5kMhlly5Y1qlevbvTp08dYvXp1vvs9+uijhiRjwYIF1nWZmZmGh4eHUaZMGSMpKcmmfFJSkjFw4EAjICDAcHd3Nx544AHjhx9+yNXXG43RDMMwfv31V2ubY2Nj8yxTkHPyzjvvGM2aNTP8/f2tY82BAwcax48fv+45y2njvHnzjJEjRxp33XWXYTabjQcffNA6rrtWRkaGYTabjfbt21+37qvl9ztzrbyeYvbJJ58Y1atXt46nJk2aZMyaNctmbL5lyxbj0UcfNcLCwgyz2Wz4+fkZERERxvLly631zJ0712jZsqURGBhouLq6GiEhIUb37t2NPXv2FLgfKFlMhnHNrecBAA6vYcOGMplM2r59e3E3BQAAoFRbsWKFOnfurJUrV1pvlg0UBy4xAwBIklJSUrRv3z5988032rlzp5YuXVrcTQIAACi1Dhw4oBMnTmj06NGqW7euoqKiirtJcHAERAAASdJPP/2kli1bys/PT6+++qq6dOlS3E0CAAAotYYOHar//e9/ql+/vubOnZvv09mAO4VLzAAAAAAAABxcmeJuAAAAAAAAAIoXAREAAAAAAICDIyACAAAAAABwcMV6k+oZM2ZoxowZOn78uCSpZs2aeuWVV6x3bzcMQxMmTNB//vMfJSUlqXHjxvrwww9Vs2ZNax0ZGRkaM2aMPvvsM6Wnp6tVq1aaPn267r777gK348qVKzpz5ow8PT25MRgAACWYYRi6cOGCQkJCVKYM33MVJ8ZPAADYh4KOn4r1JtUrVqyQk5OTqlSpIkmaO3eu3n77be3atUs1a9bUW2+9pX/961+aM2eOqlWrpjfeeEMbN27UoUOH5OnpKUl65plntGLFCs2ZM0d+fn4aPXq0/vrrL+3cuVNOTk4Fasfp06cVGhpaZP0EAACF69SpUzf1ZRAKH+MnAADsy43GTyXuKWa+vr56++239dRTTykkJETR0dF64YUXJP09WygwMFBvvfWWhgwZouTkZN11112aN2+eevToIUk6c+aMQkND9e2336pdu3YFOmZycrK8vb116tQplStXrsj6BgAAbk9KSopCQ0N1/vx5eXl5FXdzHBrjJwAA7ENBx0/FeonZ1bKzs/XFF18oLS1NTZs21bFjx5SQkKC2bdtay5jNZkVERGjz5s0aMmSIdu7cqaysLJsyISEhCg8P1+bNmwscEOVMiy5XrhwDHAAA7ACXNBU/xk8AANiXG42fij0g2rt3r5o2bapLly6pbNmyWrp0qe677z5t3rxZkhQYGGhTPjAwUCdOnJAkJSQkyNXVVT4+PrnKJCQk5HvMjIwMZWRkWJdTUlIKqzsAAAAAAAB2p9jv7li9enXt3r1bW7du1TPPPKN+/frpwIED1u3XJlyGYdww9bpRmUmTJsnLy8v64vp5AAAAAADgyIo9IHJ1dVWVKlXUsGFDTZo0SXXq1NF7772noKAgSco1EygxMdE6qygoKEiZmZlKSkrKt0xexo0bp+TkZOvr1KlThdwrAAAAAAAA+1Hsl5hdyzAMZWRkqFKlSgoKClJsbKzq1asnScrMzNSGDRv01ltvSZIaNGggFxcXxcbGqnv37pKk+Ph47du3T5MnT873GGazWWaz+abblp2draysrFvoFeCYXFxcCvw0QQBA6XPlyhVlZmYWdzMAXIXxGYD8FGtA9NJLLykqKkqhoaG6cOGCFi1apPXr12vVqlUymUyKjo7WxIkTVbVqVVWtWlUTJ06Uu7u7evXqJUny8vLSwIEDNXr0aPn5+cnX11djxoxRrVq11Lp160Jrp2EYSkhI0Pnz5wutTsBReHt7KygoiBvKAoCDyczM1LFjx3TlypXibgqAazA+A5CXYg2Izp49qz59+ig+Pl5eXl6qXbu2Vq1apTZt2kiSxo4dq/T0dA0dOlRJSUlq3Lix1qxZI09PT2sdU6ZMkbOzs7p376709HS1atVKc+bMKdRUPCccCggIkLu7O/+QAgVgGIYuXryoxMRESVJwcHAxtwgAcKcYhqH4+Hg5OTkpNDRUZcoU+10NAIjxGYDrMxmGYRR3I4pbSkqKvLy8lJycnOsxrdnZ2fr1118VEBAgPz+/YmohYL/OnTunxMREVatWjenMAG7b9T6zcWdd773IysrSkSNHFBISIi8vr2JqIYD8MD4DHEtBx098nXMDOfcccnd3L+aWAPYp52+H+3cBgOPIzs6W9PfDSACUPIzPAOSFgKiAuKwMuDX87QCA4+IzACiZ+NsEkBcCIgAAAAAAAAdX4h5zby9+P5+upLQ799hWHw9Xlfe23LHjlVYVK1ZUdHS0oqOjb6sek8mkpUuXqkuXLiWqXbciJiZGy5Yt0+7du+/4sQEAjoXxk30qqnHKjeo9fvy4KlWqpF27dqlu3bqFemwAQG4ERLfg9/Ppav3OBqVnZd+xY1pcnBQ3OuKmBjkJCQmaNGmSVq5cqdOnT8vLy0tVq1bVk08+qb59+9rNfZXuZHgSExOjCRMmWJfLlSun2rVr64033lBERESRH7+g1q9fr5YtWyopKUne3t4224ozbAIAID+Mn+6sOzUeWLVqlaKiohQfH6+goCDr+qCgILm4uOjUqVPWdadPn1ZoaKhWr16ttm3b3rDu0NBQxcfHy9/fX9L1xz83KzIyUhs2bJD0972y/P39Vb9+fQ0YMEBdu3a9rboBwF4REN2CpLRMpWdla2qPuqoSULbIj3ckMVXRi3crKS2zwAOc3377Tc2bN5e3t7cmTpyoWrVq6fLly/r111/1ySefKCQkRJ07dy7ilufPMAxlZ2fL2bnk/QrWrFlTcXFxkqS//vpL//73v9WxY0frIBEAANw8xk+3rySOnx544AE5Oztr/fr16tmzpyTp4MGDunTpktLT03XkyBFVqVJFkrRu3Tq5uLioefPmBarbycnJJnQqbIMGDdJrr72mrKws/f7771q6dKl69uyp/v376z//+U+RHfdaWVlZcnFxuWPHA4D8cA+i21AloKzCy3sV+etWBlFDhw6Vs7OzduzYoe7du6tGjRqqVauWunXrppUrV6pTp07WssnJyRo8eLACAgJUrlw5PfTQQ/r555+t22NiYlS3bl3NmzdPFStWlJeXl3r27KkLFy5YyxiGocmTJ+uee+6RxWJRnTp19OWXX1q3r1+/XiaTSatXr1bDhg1lNpv1ww8/6OjRo3rkkUcUGBiosmXLqlGjRtZwRvr7250TJ07oueeek8lksrmh3ubNm9WiRQtZLBaFhoZq5MiRSktLs25PTExUp06dZLFYVKlSJS1YsKBA587Z2VlBQUEKCgrSfffdpwkTJig1NVW//vprvvu88MILqlatmtzd3XXPPfdo/PjxuZ4KsXz5cjVs2FBubm7y9/e/7rdTs2fPlpeXl2JjYwvU5us5efKkHnnkEZUtW1blypVT9+7ddfbs2evuM3v2bNWoUUNubm669957NX36dOu2zMxMDR8+XMHBwXJzc1PFihU1adKk224nAMAxMH4qXeOnnOOvX7/ept0PPPCAHnjggVzr77//fnl4eFjXXbx4UU899ZQ8PT1VoUIFm2Dm+PHjMplM2r17t44fP66WLVtKknx8fGQymdS/f/8Cncf8uLu7KygoSKGhoWrSpIneeustzZw5Ux9//LH1fHbr1k0jRoyw7hMdHS2TyaT9+/dLki5fvixPT0+tXr1a0t8zqh544AF5e3vLz89PHTt21NGjR3P16fPPP1dkZKTc3Nw0ffp0WSwWrVq1yqZ9S5YskYeHh1JTUyVJv//+u3r06CEfHx/5+fnpkUce0fHjx/M8v97e3mrevLlOnDhxw/MAADkIiEqhc+fOac2aNRo2bJjNB/DVcgYKhmGoQ4cOSkhI0LfffqudO3eqfv36atWqlf766y9r+aNHj2rZsmX65ptv9M0332jDhg168803rdv/+c9/avbs2ZoxY4b279+v5557Tk8++aR16m6OsWPHatKkSTp48KBq166t1NRUPfzww4qLi9OuXbvUrl07derUSSdPnpT09wfj3Xffrddee03x8fGKj4+XJO3du1ft2rVT165dtWfPHi1evFibNm3S8OHDrcfq37+/jh8/ru+//15ffvmlpk+frsTExJs6lxkZGZozZ468vb1VvXr1fMt5enpqzpw5OnDggN577z19/PHHmjJlinX7ypUr1bVrV3Xo0EG7du3S2rVr1bBhwzzr+ve//60xY8Zo9erVatOmzU2191qGYahLly7666+/tGHDBsXGxuro0aPq0aNHvvt8/PHHevnll/Wvf/1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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -998,35 +476,213 @@ } ], "source": [ - "#define masses\n", - "w_masses = w_cluster.star_systems['mass']\n", - "w_comps = w_cluster.companions['mass']\n", - "m_masses = m_cluster.star_systems['mass']\n", - "m_comps = m_cluster.companions['mass']\n", - "\n", - "#define mass range of histogram\n", - "bin_edges = [0.01, 0.08, 0.5, 1, 60]\n", + "# Locate BHs, NSs and WDs\n", + "p_bh = np.where(k_out['phase'] == 103)[0]\n", + "#c_bh = np.where(k_comp['phase'] == 103)[0]\n", + "#k_bh = np.concatenate([p_bh, c_bh])\n", + "p_ns = np.where(k_out['phase'] == 102)[0]\n", + "#c_ns = np.where(k_comp['phase'] == 102)[0]\n", + "#k_ns = np.concatenate([p_ns, c_ns])\n", + "p_wd = np.where(k_out['phase'] == 101)[0]\n", + "#c_wd = np.where(k_comp['phase'] == 101)[0]\n", + "#k_wd = np.concatenate([p_wd, c_wd])\n", "\n", - "#setting up subplots\n", - "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8,8))\n", - "plt.subplots_adjust(hspace=0.5, wspace=0.75)\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 1.4, 16)\n", "\n", - "#create histograms to compare\n", - "ax[0].hist(m_masses, bins=bin_edges, alpha=0.5, label='Muzic_2017', color='blue', edgecolor='black', histtype='bar')\n", - "ax[1].hist(w_masses, bins=bin_edges, alpha=0.5, label='Weidner_Kroupa_2004', color='red', edgecolor='black', histtype='bar')\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(k_out[p_bh]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", "\n", - "#labels and legend\n", - "plt.xlabel('Stellar Mass (Solar Masses)')\n", - "plt.ylabel('Number of Stars')\n", - "plt.title('Comparison of Stellar Mass Distributions')\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(k_out[p_wd]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", "plt.legend()\n", + "\n", "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d36bba0d-3037-46c0-91f3-321e47241204", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "973669.0207942065\n" + ] + } + ], + "source": [ + "all_masses = np.concatenate([k_out['mass'], k_comp['mass']])\n", + "tot_mass = np.sum(all_masses)\n", + "print(tot_mass)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1f999373-557e-42f1-9c0b-43d1cfab198e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.07000000391702135\n", + "0.010000022844115403\n" + ] + } + ], + "source": [ + "print(np.min(k_out['mass']))\n", + "print(np.min(k_comp['mass']))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7c2fa5e-49cd-4021-869a-6f9701677c68", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Current smallest mass of generated black holes: \" + str(np.min(k_out[k_bh]['mass_current'])))\n", + "print(\"Current largest mass of generated black holes: \" + str(np.max(k_out[k_bh]['mass_current'])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e056d1f-be87-43ee-9899-2a1859d5e2c5", + "metadata": {}, + "outputs": [], + "source": [ + "print('The cluster table contains these columns: {0}'.format(k_cluster.star_systems.keys()))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c02d7dbd-9690-49f4-931d-e7f66172ba12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass isMultiple systemMass ... metallicity m_hst_f153m\n", + "------------------ ---------- ------------------ ... ----------- -----------\n", + " 59.05436159479683 False 59.05436159479683 ... 0.0 nan\n", + " 39.55979231152168 False 39.55979231152168 ... 0.0 nan\n", + "18.241030351057457 False 18.241030351057457 ... 0.0 nan\n", + " 21.34571632826156 False 21.34571632826156 ... 0.0 nan\n", + " 25.73229556345863 False 25.73229556345863 ... 0.0 nan\n", + " 38.27638428342816 False 38.27638428342816 ... 0.0 nan\n", + " 29.70200219305832 False 29.70200219305832 ... 0.0 nan\n", + " ... ... ... ... ... ...\n", + "104.95164061880804 False 104.95164061880804 ... 0.0 nan\n", + "19.927108168048903 False 19.927108168048903 ... 0.0 nan\n", + "51.756416056664165 False 51.756416056664165 ... 0.0 nan\n", + " 30.9405328260462 False 30.9405328260462 ... 0.0 nan\n", + "16.452556081467357 False 16.452556081467357 ... 0.0 nan\n", + "15.253386148271723 False 15.253386148271723 ... 0.0 nan\n", + " 34.00333629883476 False 34.00333629883476 ... 0.0 nan\n", + "Length = 2327 rows\n" + ] + } + ], + "source": [ + "print(k_out[p_bh])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ddc351e5-e465-4f2e-9703-cd5c819a75f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass \n", + "------------------\n", + " 59.05436159479683\n", + " 39.55979231152168\n", + "18.241030351057457\n", + " 21.34571632826156\n", + " 25.73229556345863\n", + " 38.27638428342816\n", + " 29.70200219305832\n", + " ...\n", + "104.95164061880804\n", + "19.927108168048903\n", + "51.756416056664165\n", + " 30.9405328260462\n", + "16.452556081467357\n", + "15.253386148271723\n", + " 34.00333629883476\n", + "Length = 2327 rows mass_current \n", + "------------------\n", + " 7.69101697945427\n", + " 14.37645768969197\n", + " 6.490426115488822\n", + " 7.738537786899665\n", + " 9.302666353833182\n", + "13.811546507105783\n", + "10.635261248769924\n", + " ...\n", + "5.9065928872088955\n", + " 7.187073433524934\n", + " 9.426130388477304\n", + "11.054167247370941\n", + " 5.693090725172411\n", + " 5.119608907641256\n", + " 12.13042109233094\n", + "Length = 2327 rows\n" + ] + } + ], + "source": [ + "print(k_out[p_bh]['mass'], k_out[p_bh]['mass_current'])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f5ce39b9-2c6d-4e7c-a6f6-ffd7235ac9e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial mass of smallest generated black hole: 15.003458139141888\n", + "Initial mass of largest generated black hole: 119.60497046926774\n" + ] + } + ], + "source": [ + "print(\"Initial mass of smallest generated black hole: \" + str(np.min(k_out[p_bh]['mass'])))\n", + "print(\"Initial mass of largest generated black hole: \" + str(np.max(k_out[p_bh]['mass'])))" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "51924315-a841-4f2d-bddb-84a1641b8d82", + "id": "1c41b219-7a85-4519-af99-77145daaa418", "metadata": {}, "outputs": [], "source": [] diff --git a/changes/bd_evo_csv/baraffe2003.csv b/changes/bd_evo_csv/baraffe2003.csv new file mode 100644 index 00000000..94cb6209 --- /dev/null +++ b/changes/bd_evo_csv/baraffe2003.csv @@ -0,0 +1 @@ +age,mass,luminosity,temperature,gravity,radius 0.001,0.0005,-5.37,628,2.645,0.176 0.001,0.001,-4.715,942,2.996,0.166 0.001,0.002,-4.137,1285,3.259,0.174 0.001,0.003,-3.746,1553,3.372,0.187 0.001,0.004,-3.482,1747,3.438,0.2 0.001,0.005,-3.273,1901,3.472,0.215 0.001,0.006,-3.129,2004,3.499,0.228 0.001,0.007,-2.998,2098,3.515,0.242 0.001,0.008,-2.893,2159,3.517,0.258 0.001,0.009,-2.811,2207,3.525,0.271 0.001,0.01,-2.735,2251,3.529,0.285 0.001,0.012,-2.615,2321,3.541,0.307 0.001,0.015,-2.455,2400,3.537,0.345 0.001,0.02,-2.28,2484,3.546,0.395 0.001,0.03,-2.174,2598,3.694,0.408 0.001,0.04,-1.939,2746,3.681,0.478 0.001,0.05,-1.637,2768,3.489,0.666 0.001,0.06,-1.604,2824,3.57,0.665 0.001,0.07,-1.478,2853,3.529,0.753 0.001,0.072,-1.455,2858,3.52,0.771 0.001,0.075,-1.389,2833,3.457,0.847 0.001,0.08,-1.373,2869,3.492,0.84 0.001,0.09,-1.289,2867,3.457,0.927 0.001,0.1,-1.187,2856,3.394,1.051 0.005,0.0005,-6.079,455,2.794,0.148 0.005,0.001,-5.522,644,3.141,0.141 0.005,0.002,-4.92,901,3.425,0.143 0.005,0.003,-4.552,1098,3.576,0.148 0.005,0.004,-4.283,1263,3.675,0.152 0.005,0.005,-4.061,1413,3.745,0.157 0.005,0.006,-3.886,1543,3.802,0.161 0.005,0.007,-3.725,1668,3.843,0.166 0.005,0.008,-3.579,1786,3.874,0.171 0.005,0.009,-3.46,1885,3.9,0.176 0.005,0.01,-3.363,1965,3.921,0.181 0.005,0.012,-3.194,2102,3.948,0.192 0.005,0.015,-2.981,2264,3.96,0.212 0.005,0.02,-2.661,2452,3.905,0.261 0.005,0.03,-2.262,2616,3.795,0.363 0.005,0.04,-2.068,2754,3.814,0.41 0.005,0.05,-1.846,2806,3.722,0.51 0.005,0.06,-1.902,2875,3.899,0.455 0.005,0.07,-1.829,2916,3.917,0.481 0.005,0.072,-1.814,2921,3.918,0.488 0.005,0.075,-1.716,2912,3.832,0.55 0.005,0.08,-1.76,2946,3.925,0.51 0.005,0.09,-1.696,2974,3.928,0.54 0.005,0.1,-1.582,2983,3.865,0.611 0.01,0.0005,-6.38,394,2.847,0.14 0.01,0.001,-5.87,540,3.184,0.134 0.01,0.002,-5.298,746,3.474,0.136 0.01,0.003,-4.914,920,3.631,0.139 0.01,0.004,-4.645,1064,3.739,0.141 0.01,0.005,-4.429,1193,3.82,0.144 0.01,0.006,-4.244,1316,3.883,0.147 0.01,0.007,-4.091,1426,3.937,0.149 0.01,0.008,-3.945,1536,3.978,0.152 0.01,0.009,-3.82,1635,4.013,0.155 0.01,0.01,-3.704,1731,4.042,0.158 0.01,0.012,-3.488,1915,4.081,0.165 0.01,0.015,-3.125,2188,4.046,0.192 0.01,0.02,-2.709,2442,3.945,0.249 0.01,0.03,-2.435,2622,3.971,0.296 0.01,0.04,-2.411,2734,4.145,0.28 0.01,0.05,-2.233,2821,4.118,0.323 0.01,0.06,-2.188,2871,4.184,0.328 0.01,0.07,-2.099,2919,4.189,0.352 0.01,0.072,-2.083,2927,4.19,0.357 0.01,0.075,-2.022,2942,4.156,0.379 0.01,0.08,-2.016,2958,4.188,0.377 0.01,0.09,-1.943,2993,4.186,0.401 0.01,0.1,-1.854,3024,4.161,0.435 0.05,0.0005,-7.069,285,2.975,0.12 0.05,0.001,-6.587,375,3.267,0.122 0.05,0.002,-6.1,491,3.551,0.124 0.05,0.003,-5.789,585,3.719,0.125 0.05,0.004,-5.531,676,3.837,0.126 0.05,0.005,-5.336,756,3.933,0.126 0.05,0.006,-5.152,840,4.012,0.126 0.05,0.007,-4.978,928,4.078,0.127 0.05,0.008,-4.832,1010,4.136,0.127 0.05,0.009,-4.706,1085,4.187,0.127 0.05,0.01,-4.569,1171,4.228,0.127 0.05,0.012,-4.052,1520,4.243,0.137 0.05,0.015,-3.568,1903,4.246,0.153 0.05,0.02,-3.642,1909,4.45,0.139 0.05,0.03,-3.311,2246,4.578,0.147 0.05,0.04,-3.08,2480,4.644,0.158 0.05,0.05,-2.884,2651,4.661,0.173 0.05,0.06,-2.755,2759,4.68,0.185 0.05,0.07,-2.647,2837,4.688,0.198 0.05,0.072,-2.623,2852,4.685,0.202 0.05,0.075,-2.591,2872,4.683,0.206 0.05,0.08,-2.551,2901,4.689,0.212 0.05,0.09,-2.472,2948,4.689,0.225 0.05,0.1,-2.397,2989,4.683,0.238 0.1,0.0005,-7.418,240,3.02,0.114 0.1,0.001,-6.957,309,3.3,0.117 0.1,0.002,-6.383,425,3.58,0.12 0.1,0.003,-6.112,493,3.746,0.121 0.1,0.004,-5.88,563,3.869,0.122 0.1,0.005,-5.686,630,3.965,0.122 0.1,0.006,-5.534,688,4.048,0.121 0.1,0.007,-5.365,760,4.117,0.121 0.1,0.008,-5.246,816,4.18,0.12 0.1,0.009,-5.103,886,4.232,0.12 0.1,0.01,-4.978,953,4.279,0.12 0.1,0.012,-4.332,1335,4.297,0.129 0.1,0.015,-4.281,1399,4.424,0.124 0.1,0.02,-4.11,1561,4.569,0.122 0.1,0.03,-3.668,1979,4.715,0.126 0.1,0.04,-3.386,2270,4.797,0.132 0.1,0.05,-3.167,2493,4.837,0.141 0.1,0.06,-3.008,2648,4.863,0.15 0.1,0.07,-2.879,2762,4.874,0.16 0.1,0.072,-2.856,2782,4.875,0.162 0.1,0.075,-2.821,2809,4.875,0.166 0.1,0.08,-2.776,2846,4.88,0.17 0.1,0.09,-2.689,2910,4.884,0.18 0.1,0.1,-2.617,2960,4.887,0.189 0.12,0.0005,-7.526,227,3.032,0.113 0.12,0.001,-7.043,295,3.307,0.116 0.12,0.002,-6.481,403,3.588,0.119 0.12,0.003,-6.183,475,3.753,0.12 0.12,0.004,-5.97,537,3.876,0.121 0.12,0.005,-5.781,599,3.974,0.121 0.12,0.006,-5.602,665,4.055,0.12 0.12,0.007,-5.477,716,4.127,0.12 0.12,0.008,-5.326,783,4.188,0.119 0.12,0.009,-5.219,835,4.243,0.119 0.12,0.01,-5.088,900,4.29,0.119 0.12,0.012,-4.432,1273,4.314,0.126 0.12,0.015,-4.436,1298,4.45,0.121 0.12,0.02,-4.218,1484,4.59,0.119 0.12,0.03,-3.764,1905,4.745,0.122 0.12,0.04,-3.472,2204,4.831,0.127 0.12,0.05,-3.244,2441,4.878,0.135 0.12,0.06,-3.083,2606,4.909,0.142 0.12,0.07,-2.944,2733,4.921,0.152 0.12,0.072,-2.922,2753,4.923,0.153 0.12,0.075,-2.886,2783,4.923,0.157 0.12,0.08,-2.838,2824,4.929,0.161 0.12,0.09,-2.753,2894,4.937,0.169 0.12,0.1,-2.673,2949,4.936,0.178 0.5,0.0005,-8.415,141,3.097,0.105 0.5,0.001,-7.753,203,3.365,0.109 0.5,0.002,-7.218,272,3.639,0.112 0.5,0.003,-6.913,322,3.805,0.113 0.5,0.004,-6.67,370,3.928,0.114 0.5,0.005,-6.496,409,4.027,0.113 0.5,0.006,-6.34,449,4.112,0.113 0.5,0.007,-6.2,488,4.185,0.112 0.5,0.008,-6.08,525,4.249,0.111 0.5,0.009,-5.963,564,4.307,0.11 0.5,0.01,-5.864,599,4.358,0.11 0.5,0.012,-5.447,759,4.432,0.11 0.5,0.015,-5.404,791,4.557,0.107 0.5,0.02,-5.133,936,4.704,0.104 0.5,0.03,-4.636,1264,4.905,0.101 0.5,0.04,-4.255,1583,5.04,0.1 0.5,0.05,-3.955,1875,5.131,0.101 0.5,0.06,-3.729,2116,5.194,0.102 0.5,0.07,-3.534,2329,5.233,0.106 0.5,0.072,-3.498,2369,5.238,0.107 0.5,0.075,-3.445,2426,5.244,0.108 0.5,0.08,-3.356,2518,5.248,0.111 0.5,0.09,-3.189,2680,5.241,0.119 0.5,0.1,-3.047,2804,5.223,0.128 1,0.0005,-8.851,111,3.115,0.102 1,0.001,-8.185,160,3.386,0.106 1,0.002,-7.56,226,3.662,0.109 1,0.003,-7.244,270,3.827,0.111 1,0.004,-7.031,304,3.95,0.111 1,0.005,-6.831,342,4.051,0.11 1,0.006,-6.664,377,4.134,0.11 1,0.007,-6.556,403,4.208,0.109 1,0.008,-6.417,438,4.272,0.108 1,0.009,-6.325,464,4.331,0.107 1,0.01,-6.235,491,4.383,0.107 1,0.012,-5.955,578,4.467,0.106 1,0.015,-5.835,628,4.587,0.103 1,0.02,-5.514,766,4.736,0.1 1,0.03,-5.071,1009,4.948,0.096 1,0.04,-4.696,1271,5.099,0.093 1,0.05,-4.374,1543,5.211,0.092 1,0.06,-4.106,1801,5.291,0.092 1,0.07,-3.829,2082,5.333,0.094 1,0.072,-3.772,2140,5.336,0.095 1,0.075,-3.679,2234,5.334,0.098 1,0.08,-3.527,2383,5.323,0.102 1,0.09,-3.268,2627,5.285,0.113 1,0.1,-3.083,2784,5.246,0.125 5,0.002,-8.57,129,3.698,0.105 5,0.003,-8.166,162,3.868,0.105 5,0.004,-7.867,193,3.994,0.105 5,0.005,-7.644,220,4.095,0.105 5,0.006,-7.469,244,4.18,0.104 5,0.007,-7.328,265,4.254,0.103 5,0.008,-7.217,284,4.318,0.103 5,0.009,-7.124,301,4.376,0.102 5,0.01,-7.015,322,4.429,0.101 5,0.012,-6.823,361,4.519,0.1 5,0.015,-6.671,399,4.634,0.098 5,0.02,-6.401,473,4.786,0.095 5,0.03,-6.008,610,5.011,0.09 5,0.04,-5.67,760,5.179,0.085 5,0.05,-5.353,931,5.313,0.082 5,0.06,-5.058,1120,5.418,0.079 5,0.07,-4.504,1524,5.466,0.081 5,0.072,-4.278,1712,5.453,0.083 5,0.075,-3.942,2006,5.411,0.089 5,0.08,-3.603,2320,5.353,0.099 5,0.09,-3.275,2622,5.289,0.113 5,0.1,-3.083,2785,5.247,0.125 10,0.003,-8.629,125,3.879,0.104 10,0.004,-8.325,149,4.006,0.104 10,0.005,-8.087,172,4.109,0.103 10,0.006,-7.888,193,4.195,0.102 10,0.007,-7.724,213,4.27,0.102 10,0.008,-7.584,232,4.335,0.101 10,0.009,-7.469,249,4.393,0.1 10,0.01,-7.368,265,4.445,0.099 10,0.012,-7.204,293,4.536,0.098 10,0.015,-7.016,330,4.65,0.096 10,0.02,-6.759,389,4.802,0.093 10,0.03,-6.358,504,5.029,0.088 10,0.04,-6.004,634,5.2,0.083 10,0.05,-5.695,776,5.338,0.079 10,0.06,-5.393,941,5.45,0.076 10,0.07,-4.832,1289,5.503,0.078 10,0.072,-4.472,1556,5.481,0.081 10,0.075,-3.954,1997,5.415,0.089 10,0.08,-3.602,2322,5.353,0.099 10,0.09,-3.274,2624,5.289,0.113 10,0.1,-3.082,2786,5.246,0.125 \ No newline at end of file diff --git a/spisea/evolution.py b/spisea/evolution.py index 93c494cc..e27b0147 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1215,7 +1215,7 @@ class MergedBaraffePisaEkstromParsec(StellarEvolution): For logAge < 7.4: - + * 0.01 - 0.08 M_sun: assume mass does not change over time * Baraffe: 0.08 - 0.4 M_sun * Baraffe/Pisa transition: 0.4 - 0.5 M_sun * Pisa: 0.5 M_sun to the highest mass in Pisa isochrone (typically 5 - 7 Msun) @@ -1310,6 +1310,11 @@ def isochrone(self, age=1.e8, metallicity=0.0): bd_idx = iso['mass'] < 0.08 iso['mass_current'][bd_idx] = iso['mass'][bd_idx] + + # Handling NaN effectvie temperatures + nan_teff_idx = np.isnan(iso['logT']) + if np.any(nan_teff_idx): + iso['logT'][nan_teff_idx] = self.estimate_teff(iso['mass'][nan_teff_idx]) iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index c7e55056..a8019fb7 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -684,7 +684,7 @@ class Weidner_Kroupa_2004(IMF_broken_powerlaw): Mass range is 0.01 M_sun - inf M_sun. """ def __init__(self, multiplicity=None): - massLimits = np.array([0.01, 0.08, 0.5, 1, np.inf]) + massLimits = np.array([0.01, 0.08, 0.5, 1, 120]) powers = np.array([-0.3, -1.3, -2.3, -2.35]) IMF_broken_powerlaw.__init__(self, massLimits, powers, diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index bdd9064d..84d410cd 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -485,7 +485,50 @@ def test_ifmr_multiplicity(): idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 101) & (clust1['phase'] != 9) ) idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 101) & (comps2['phase'] != 9) ) assert len(idx[0]) == 0 - + + # Ensure no substellar mass compact objects are generated + # For cluster objects + MIN_MASS = 0.1 + BD_MIN_MASS = 0.01 + BD_MAX_MASS = 0.08 + + wd_idx = np.where(clust1['phase'] == 101) + ns_idx = np.where(clust1['phase'] == 102) + bh_idx = np.where(clust1['phase'] == 103) + bd_idx = np.where(clust1['phase'] == 99) + assert np.all(clust1['mass'][wd_idx] > MIN_MASS) + assert np.all(clust1['mass'][ns_idx] > MIN_MASS) + assert np.all(clust1['mass'][bh_idx] > MIN_MASS) + assert np.all(clust1['mass'][bd_idx] < MIN_MASS) + + # For companion objects + comp_wd_idx = np.where(comps2['phase'] == 101) + comp_ns_idx = np.where(comps2['phase'] == 102) + comp_bh_idx = np.where(comps2['phase'] == 103) + comp_bd_idx = np.where(comps2['phase'] == 103) + assert np.all(comps2['mass'][comp_wd_idx] > MIN_MASS) + assert np.all(comps2['mass'][comp_ns_idx] > MIN_MASS) + assert np.all(comps2['mass'][comp_bh_idx] > MIN_MASS) + assert np.all(comps2['mass'][comp_bd_idx] < MIN_MASS) + + # Ensure brown dwarfs are within 0.01 and 0.08 solar masses + assert np.all((clust1['mass'][bd_idx] >= BD_MIN_MASS) & + (clust1['mass'][bd_idx] <= BD_MAX_MASS)) + assert np.all((comps2['mass'][comp_bd_idx] >= BD_MIN_MASS) & + (comps2['mass'][comp_bd_idx] <= BD_MAX_MASS)) + + # Ensure no other objects are in the brown dwarf mass range + non_bd_idx = np.where((clust1['phase'] != 99) & + (clust1['mass'] >= BD_MIN_MASS) & + (clust1['mass'] <= BD_MAX_MASS)) + assert len(non_bd_idx[0]) == 0 # asserting no non-brown dwarf objects in BD mass range + + comp_non_bd_idx = np.where((comps2['phase'] != 99) & + (comps2['mass'] >= BD_MIN_MASS) & + (comps2['mass'] <= BD_MAX_MASS)) + assert len(comp_non_bd_idx[0]) == 0 # asserting no non-brown dwarf companions in BD mass range + +return return def test_metallicity(): From d0a1ab4256daa17bf5716ca8631515670cd7e4cf Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 29 Jul 2024 13:44:16 -0700 Subject: [PATCH 010/191] most current changes in testing documentation and IMF derivation --- changes/BD_IMF.ipynb | 2 +- spisea/tests/test_synthetic.py | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/changes/BD_IMF.ipynb b/changes/BD_IMF.ipynb index 41fc800b..895870be 100644 --- a/changes/BD_IMF.ipynb +++ b/changes/BD_IMF.ipynb @@ -952,7 +952,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 84d410cd..4b8b7b07 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -487,6 +487,9 @@ def test_ifmr_multiplicity(): assert len(idx[0]) == 0 # Ensure no substellar mass compact objects are generated + """ + 07/2024: Added more testing criteria for brown dwarf stars to ensure they are labeled appropriately for masses from 0.01 - 0.08 M_sun. + """ # For cluster objects MIN_MASS = 0.1 BD_MIN_MASS = 0.01 From 5379f36d7741ee14638d708515d2669726f4ca45 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 31 Jul 2024 13:22:01 -0700 Subject: [PATCH 011/191] Findings with companion objects being misclassified --- changes/Compact_Multiplicity.ipynb | 139 +++++++++++++++++------------ 1 file changed, 80 insertions(+), 59 deletions(-) diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index 983fcf1b..4de41355 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 20, "id": "c0a4a637-c694-4d3d-9b1f-3b1b66cc8a19", "metadata": {}, "outputs": [], @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", "metadata": {}, "outputs": [], @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "id": "ac6b221b-6bd5-41c5-8a5e-94a0daed1dba", "metadata": {}, "outputs": [], @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "id": "1a68a281-791a-4821-8799-c2df8e936286", "metadata": {}, "outputs": [ @@ -85,7 +85,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 907596 stars out of mass range\n" + "Found 908186 stars out of mass range\n" ] } ], @@ -100,13 +100,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 21, "id": "3bdd27c9-3820-4fff-9ed6-da26fb997e08", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -127,7 +127,7 @@ "\n", "plt.figure(figsize=(14,6))\n", "plt.subplot(1, 2, 1)\n", - "plt.hist(k_out[p_bh]['mass_current'], histtype = 'step',\n", + "plt.hist(k_out[p_bh]['mass'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -135,7 +135,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 2, 2)\n", - "plt.hist(k_out[p_wd]['mass_current'], histtype = 'step',\n", + "plt.hist(k_out[p_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", "metadata": {}, "outputs": [], @@ -231,8 +231,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 686912 stars out of mass range\n", - "Found 134402 companions out of stellar mass range\n" + "Found 690569 stars out of mass range\n", + "Found 135191 companions out of stellar mass range\n" ] } ], @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", "metadata": {}, "outputs": [ @@ -258,39 +258,39 @@ "text": [ " mass \n", "--------------------\n", - " 0.5747159412207477\n", - " 0.6970586660858713\n", - " 0.601165124305441\n", - "0.056083697312291306\n", - " 0.02673063039645824\n", - " 0.1862915740843192\n", - " 0.07159040923670096\n", + " 0.18808616996721866\n", + "0.016007058623676886\n", + " 0.18392056165672846\n", + "0.015105203133808686\n", + " 1.0282818358508474\n", + " 0.21083846100118134\n", + " 0.14199672005929817\n", " ...\n", - " 0.0830220943719863\n", - " 0.09156100383230333\n", - " 0.3808927106160393\n", - "0.016698205202363158\n", - " 0.4183953590905605\n", - " 0.0758085193857225\n", - " 0.3082346627668261\n", - "Length = 391314 rows Teff \n", + " 0.048264534414347\n", + " 0.09147624878262055\n", + " 1.1197916658217713\n", + " 0.1964383346903446\n", + " 0.1980378944762514\n", + " 0.3608681652858361\n", + "0.025696120026630187\n", + "Length = 393024 rows Teff \n", "------------------\n", - " 4068.981641477723\n", - " 4492.75426907755\n", - " 4126.083901944962\n", + "3238.9711755793223\n", " nan\n", + " 3227.457841740429\n", " nan\n", - "3234.0110881285427\n", - "2808.0618989378295\n", + " 5829.407699425202\n", + " 3286.658840436513\n", + " 3110.820131229178\n", " ...\n", - " 2898.061169580272\n", - " 2950.46139583484\n", - "3506.1448705814614\n", " nan\n", - " 3566.917126729317\n", - "2845.0651718291647\n", - "3418.8421815545435\n", - "Length = 391314 rows\n" + " 2950.023070827086\n", + " 6118.272466541432\n", + "3262.0557417620357\n", + "3266.4767685215697\n", + "3482.0151466756533\n", + " nan\n", + "Length = 393024 rows\n" ] } ], @@ -300,13 +300,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 23, "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -333,7 +333,7 @@ "\n", "plt.figure(figsize=(14,6))\n", "plt.subplot(1, 2, 1)\n", - "plt.hist(kc_out[k_bh]['mass_current'], histtype = 'step',\n", + "plt.hist(kc_out[p2_bh]['mass'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -341,7 +341,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 2, 2)\n", - "plt.hist(kc_out[k_wd]['mass_current'], histtype = 'step',\n", + "plt.hist(kc_out[k_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -353,7 +353,28 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 24, + "id": "eb20c163-8212-4885-98dd-eb10e0bb08f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Smallest mass of a primary generated black hole: 15.003127309066931\n", + "Smallest mass of a companion generated black hole: 0.07035468449081322\n" + ] + } + ], + "source": [ + "# Finding the minimum mass of generated black holes\n", + "print(\"Smallest mass of a primary generated black hole: \" + str(np.min(kc_out[p2_bh]['mass'])))\n", + "print(\"Smallest mass of a companion generated black hole: \" + str(np.min(kc_out[c_bh]['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "id": "6a854062-8dd0-4411-a0d7-523dcc4aa77b", "metadata": {}, "outputs": [ @@ -361,8 +382,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Smallest mass of a primary generated object: 0.07000003602252991\n", - "Smallest mass of a companion generated object: 0.010000123044113119\n" + "Smallest mass of a primary generated object: 0.07000003012576093\n", + "Smallest mass of a companion generated object: 0.010000040848729129\n" ] } ], @@ -374,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "id": "398a4626-0e74-4f6d-a3a1-2c115fedb9c1", "metadata": {}, "outputs": [ @@ -382,8 +403,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Initial mass of smallest generated black hole: 0.0700493749164032\n", - "Initial mass of largest generated black hole: 119.95339215013136\n" + "Initial mass of smallest generated black hole: 0.07035468449081322\n", + "Initial mass of largest generated black hole: 118.59389207250989\n" ] } ], @@ -398,12 +419,12 @@ "id": "77b9203f-69ec-4a54-af84-581427feb4fd", "metadata": {}, "source": [ - "In this case, we are getting many instances of substellar mass blakc holes, which does not make sense. Why is it that adding companions introduces this issue?" + "These statements show us that there is an issue with substellar-mass companions being erroneously identified as black holes and other compact remnants, but the issue does not extend to primary mass objects. Thus, we must introduce a bugfix to filter out this error." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 10, "id": "afe96713-78c9-4785-bdf9-6f3f36f2a4d5", "metadata": {}, "outputs": [], @@ -415,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 11, "id": "a6483a09-8712-48ed-9bc6-dbfbda7a32f9", "metadata": {}, "outputs": [ @@ -423,8 +444,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 694055 stars out of mass range\n", - "Found 136094 companions out of stellar mass range\n" + "Found 689034 stars out of mass range\n", + "Found 135376 companions out of stellar mass range\n" ] } ], @@ -440,13 +461,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 13, "id": "c598a266-297c-423d-b5f5-648f819c065e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -473,7 +494,7 @@ "\n", "plt.figure(figsize=(14,6))\n", "plt.subplot(1, 2, 1)\n", - "plt.hist(c3_out[k3_bh]['mass_current'], histtype = 'step',\n", + "plt.hist(c3_out[k3_bh]['mass'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -481,7 +502,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 2, 2)\n", - "plt.hist(c3_out[k3_wd]['mass_current'], histtype = 'step',\n", + "plt.hist(c3_out[k3_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", From aaa0ee23d4d5e4f0f1391c57c64d586f2604c401 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 9 Aug 2024 09:19:55 -0700 Subject: [PATCH 012/191] Added code for successful brown dwarf implementation, and new issues to address --- changes/Compact_Multiplicity.ipynb | 462 +++++++++++++++++++++++------ spisea/evolution.py | 3 +- spisea/ifmr.py | 46 ++- spisea/synthetic.py | 17 +- spisea/tests/test_synthetic.py | 21 +- 5 files changed, 431 insertions(+), 118 deletions(-) diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index 4de41355..8c27e7ab 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 1, "id": "c0a4a637-c694-4d3d-9b1f-3b1b66cc8a19", "metadata": {}, "outputs": [], @@ -51,10 +51,34 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 59, "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone generation took 80.686259 s.\n", + "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", + " Starting at: 2024-08-08 20:57:20.603079 Usually takes ~5 minutes\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", + "M = 0.676 Msun T = 4395 K m_hst_f153m = 4.92\n", + "M = 0.786 Msun T = 4896 K m_hst_f153m = 4.41\n", + "M = 0.856 Msun T = 5198 K m_hst_f153m = 4.12\n", + "M = 0.956 Msun T = 5590 K m_hst_f153m = 3.74\n", + "M = 1.022 Msun T = 5808 K m_hst_f153m = 3.50\n", + "M = 1.079 Msun T = 5992 K m_hst_f153m = 3.30\n", + "M = 1.518 Msun T = 7482 K m_hst_f153m = 2.22\n", + "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", + "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", + "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", + " Time taken: 21.66 seconds\n" + ] + } + ], "source": [ "# Create isochrone object \n", "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", @@ -66,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 60, "id": "ac6b221b-6bd5-41c5-8a5e-94a0daed1dba", "metadata": {}, "outputs": [], @@ -77,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 61, "id": "1a68a281-791a-4821-8799-c2df8e936286", "metadata": {}, "outputs": [ @@ -85,7 +109,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 908186 stars out of mass range\n" + "Brown dwarf indices: (array([ 0, 1, 2, ..., 1041613, 1041614, 1041615]),), Masses: mass \n", + "--------------------\n", + " 0.06752873088255393\n", + " 0.0262184880713122\n", + "0.011103322341764093\n", + " 0.07004122940473101\n", + "0.055860465009665516\n", + " 0.03849994739007916\n", + "0.036201844969911835\n", + " ...\n", + "0.026022885833328162\n", + " 0.06754269552994506\n", + "0.011731305085217554\n", + " 0.07086687808163461\n", + " 0.02602575168928598\n", + " 0.05665991329103694\n", + " 0.07735901867149753\n", + "Length = 1024861 rows\n" ] } ], @@ -100,15 +141,75 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 62, + "id": "45456043-0197-4896-bcb4-d2247a95386e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass isMultiple ... metallicity m_hst_f153m \n", + "-------------------- ---------- ... ----------- -----------------\n", + " 0.06752873088255393 False ... 0.0 nan\n", + " 0.21787124306310335 False ... 0.0 7.63530743580833\n", + " 0.0262184880713122 False ... 0.0 nan\n", + " 0.3087509497640693 False ... 0.0 7.086103270232351\n", + " 0.07004122940473101 False ... 0.0 nan\n", + "0.055860465009665516 False ... 0.0 nan\n", + " 0.2449826527701243 False ... 0.0 7.443908907848126\n", + " ... ... ... ... ...\n", + "0.047169363745193545 False ... 0.0 nan\n", + " 0.04388574160305706 False ... 0.0 nan\n", + " 0.08713365145144555 False ... 0.0 9.124319491534537\n", + " 0.5384991452265341 False ... 0.0 5.675504835409121\n", + "0.019767325458511932 False ... 0.0 nan\n", + " 0.16230926501233983 False ... 0.0 8.07930622339159\n", + " 0.12339530398894323 False ... 0.0 8.508529920543223\n", + "Length = 1026205 rows\n", + "119.2216436980943\n", + "0.010000064522612448\n" + ] + } + ], + "source": [ + "print(k_out[p_bd])\n", + "print(np.max(k_out[p_bd]['mass']))\n", + "print(np.min(k_out[p_bd]['mass']))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "4b4fffe4-e927-49f1-9d3d-b215ca2c98cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.311164368442878" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.min(k_out[p_wd]['mass'])" + ] + }, + { + "cell_type": "code", + "execution_count": 67, "id": "3bdd27c9-3820-4fff-9ed6-da26fb997e08", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -120,13 +221,16 @@ "p_bh = np.where(k_out['phase'] == 103)[0]\n", "p_ns = np.where(k_out['phase'] == 102)[0]\n", "p_wd = np.where(k_out['phase'] == 101)[0]\n", + "p_bd = np.where(k_out['phase'] == 99)[0]\n", "\n", "# Plot on histograms\n", "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(8, 30, 20)\n", "\n", "plt.figure(figsize=(14,6))\n", - "plt.subplot(1, 2, 1)\n", + "plt.subplot(2, 2, 1)\n", "plt.hist(k_out[p_bh]['mass'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", @@ -134,7 +238,7 @@ "plt.ylabel('Number')\n", "plt.legend()\n", "\n", - "plt.subplot(1, 2, 2)\n", + "plt.subplot(2, 2, 2)\n", "plt.hist(k_out[p_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", @@ -142,12 +246,126 @@ "plt.ylabel('Number')\n", "plt.legend()\n", "\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(k_out[p_bd]['mass'], histtype = 'step',\n", + " bins = bd_bins, label = 'Generated Brown Dwarves')\n", + "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(k_out[p_ns]['mass'], histtype = 'step',\n", + " bins = ns_bins, label = 'Generated Neutron Stars')\n", + "plt.title(\"Generated Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 68, + "id": "e8e3c545-0aba-43f8-ad46-ed9ba9720761", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BH max mass: 119.49401158306712\n", + "BH min mass: 15.015214381022997\n", + "\n", + "NS max mass: 118.51258738269632\n", + "NS min mass: 9.000988105453127\n", + "\n", + "WD max mass: 8.999748990796446\n", + "WD min mass: 5.311104032812196\n", + "\n", + "BD max mass: 0.07999996255862679\n", + "BH min mass: 0.010000049376939659\n", + "\n" + ] + } + ], + "source": [ + "# Checking that objects are in the correct mass ranges\n", + "print(\"BH max mass: \" + str(np.max(k_out[p_bh]['mass'])))\n", + "print(\"BH min mass: \" + str(np.min(k_out[p_bh]['mass'])) + '\\n')\n", + "\n", + "print(\"NS max mass: \" + str(np.max(k_out[p_ns]['mass'])))\n", + "print(\"NS min mass: \" + str(np.min(k_out[p_ns]['mass'])) + '\\n')\n", + "\n", + "print(\"WD max mass: \" + str(np.max(k_out[p_wd]['mass'])))\n", + "print(\"WD min mass: \" + str(np.min(k_out[p_wd]['mass'])) + '\\n')\n", + "\n", + "print(\"BD max mass: \" + str(np.max(k_out[p_bd]['mass'])))\n", + "print(\"BH min mass: \" + str(np.min(k_out[p_bd]['mass'])) + '\\n')" + ] + }, + { + "cell_type": "markdown", + "id": "11a7e249-23db-43e6-a071-838c676e61be", + "metadata": {}, + "source": [ + "This tells us that there are issues in accurately identifying white dwarf stars and neutron stars. White dwarves have an issue at lower masses, and neutron stars at the upper end." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "d1584ea1-6fdf-4dbc-be96-623c91bffb08", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mass_bins = np.linspace(0.01, 15, 1000)\n", + "plt.figure()\n", + "plt.hist(k_out['mass'], histtype = 'step',\n", + " bins = mass_bins, label = 'Generated Objects')\n", + "plt.title(\"Simulated Objects from 0.01 - 15 Solar Masses\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "95c79ef1-308b-4d46-b477-416643c7bc0e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5.311625251702762\n", + "8.999671191854413\n" + ] + } + ], + "source": [ + "print(np.min(k_out[p_wd]['mass']))\n", + "print(np.max(k_out[p_wd]['mass'])) #white dwarf ranges are messed up" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "id": "8f6c414b-c1df-4059-b5ca-3f4c8aa7c655", "metadata": {}, "outputs": [ @@ -155,7 +373,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Smallest mass of a generated object: 0.07000011975637106\n" + "Smallest mass of a generated object: 0.010000000568115546\n" ] } ], @@ -166,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "186ded33-2af3-421a-9956-0a603998a50b", "metadata": {}, "outputs": [ @@ -174,8 +392,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Initial mass of smallest generated black hole: 15.014691323167467\n", - "Initial mass of largest generated black hole: 119.74791889840289\n" + "Initial mass of smallest generated black hole: 15.002331855265554\n", + "Initial mass of largest generated black hole: 118.99598033857916\n" ] } ], @@ -211,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", "metadata": {}, "outputs": [], @@ -223,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", "metadata": {}, "outputs": [ @@ -231,8 +449,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 690569 stars out of mass range\n", - "Found 135191 companions out of stellar mass range\n" + "Brown dwarf indices: (array([ 0, 1, 2, ..., 782400, 782401, 782402]),), Masses: mass \n", + "--------------------\n", + " 0.02818070156567557\n", + "0.057477731545588766\n", + "0.050488840533678525\n", + " 0.04059754829381457\n", + " 0.0715824675309702\n", + "0.052250831887193906\n", + " 0.06839990496518711\n", + " ...\n", + " 0.0421694459314215\n", + "0.013298951799885059\n", + " 0.04585362159276258\n", + "0.017159730219879408\n", + " 0.04989123506548948\n", + "0.023690809192446777\n", + " 0.04091931301347838\n", + "Length = 769585 rows\n", + "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", + "----\n", + "Found 175492 companions out of stellar mass range\n" ] } ], @@ -248,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", "metadata": {}, "outputs": [ @@ -258,39 +495,39 @@ "text": [ " mass \n", "--------------------\n", - " 0.18808616996721866\n", - "0.016007058623676886\n", - " 0.18392056165672846\n", - "0.015105203133808686\n", - " 1.0282818358508474\n", - " 0.21083846100118134\n", - " 0.14199672005929817\n", + "0.010356220429645275\n", + " 1.027255369157919\n", + " 0.04728725326784277\n", + " 0.0458108242117786\n", + " 0.09466940653177572\n", + " 0.22804772872665638\n", + " 0.5530407053925155\n", " ...\n", - " 0.048264534414347\n", - " 0.09147624878262055\n", - " 1.1197916658217713\n", - " 0.1964383346903446\n", - " 0.1980378944762514\n", - " 0.3608681652858361\n", - "0.025696120026630187\n", - "Length = 393024 rows Teff \n", + " 0.3467687795811594\n", + " 0.02305569777333644\n", + " 0.1511085941801427\n", + " 0.24851815882478012\n", + " 0.06499991598727853\n", + "0.023044019171576186\n", + " 0.5446895523831808\n", + "Length = 429564 rows Teff \n", "------------------\n", - "3238.9711755793223\n", " nan\n", - " 3227.457841740429\n", + " 5825.765321681631\n", " nan\n", - " 5829.407699425202\n", - " 3286.658840436513\n", - " 3110.820131229178\n", + " nan\n", + " 2966.537023687665\n", + " 3310.184350302818\n", + "3987.9798523599243\n", " ...\n", + "3465.1148889898845\n", + " nan\n", + "3136.1270818647067\n", + " 3338.388828168428\n", " nan\n", - " 2950.023070827086\n", - " 6118.272466541432\n", - "3262.0557417620357\n", - "3266.4767685215697\n", - "3482.0151466756533\n", " nan\n", - "Length = 393024 rows\n" + " 3956.044246444265\n", + "Length = 429564 rows\n" ] } ], @@ -300,15 +537,15 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
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" ] }, "metadata": {}, @@ -316,7 +553,7 @@ } ], "source": [ - "# Locate BHs, NSs and WDs\n", + "# Locate BHs, NSs, WDs, and BDs\n", "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", "k_bh = np.concatenate([p2_bh, c_bh])\n", @@ -326,21 +563,25 @@ "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", "k_wd = np.concatenate([p2_wd, c_wd])\n", + "p2_bd = np.where(kc_out['phase'] == 99)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 99)[0]\n", + "k_bd = np.concatenate([p2_bd, c_bd])\n", "\n", "# Plot on histograms\n", "bh_bins = np.linspace(0.01, 16, 20)\n", "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "bd_bins = np.linspace(0.01, 2, 20)\n", "\n", "plt.figure(figsize=(14,6))\n", - "plt.subplot(1, 2, 1)\n", - "plt.hist(kc_out[p2_bh]['mass'], histtype = 'step',\n", + "plt.subplot(1, 3, 1)\n", + "plt.hist(kc_out[k_bh]['mass'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", "plt.ylabel('Number')\n", "plt.legend()\n", "\n", - "plt.subplot(1, 2, 2)\n", + "plt.subplot(1, 3, 2)\n", "plt.hist(kc_out[k_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", @@ -348,66 +589,111 @@ "plt.ylabel('Number')\n", "plt.legend()\n", "\n", + "plt.subplot(1, 3, 3)\n", + "plt.hist(kc_out[k_bd]['mass'], histtype = 'step',\n", + " bins = bd_bins, label = 'Generated Brown Dwarves')\n", + "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 24, - "id": "eb20c163-8212-4885-98dd-eb10e0bb08f7", + "execution_count": 37, + "id": "a64ebad4-b642-4e76-b12f-92402f241484", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Smallest mass of a primary generated black hole: 15.003127309066931\n", - "Smallest mass of a companion generated black hole: 0.07035468449081322\n" + "63.493905663432386\n", + "0.01001691235394781\n" ] } ], "source": [ - "# Finding the minimum mass of generated black holes\n", - "print(\"Smallest mass of a primary generated black hole: \" + str(np.min(kc_out[p2_bh]['mass'])))\n", - "print(\"Smallest mass of a companion generated black hole: \" + str(np.min(kc_out[c_bh]['mass'])))" + "print(np.max(kc_out[k_wd]['mass']))\n", + "print(np.min(kc_out[k_wd]['mass']))" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "6a854062-8dd0-4411-a0d7-523dcc4aa77b", + "execution_count": 24, + "id": "497b4d94-6722-4ad7-880a-2b53d5acc28d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Smallest mass of a primary generated object: 0.07000003012576093\n", - "Smallest mass of a companion generated object: 0.010000040848729129\n" + " mass isMultiple ... m_hst_f153m N_companions\n", + "-------------------- ---------- ... ------------------ ------------\n", + " 1.6412592122340768 True ... 1.891168727297026 2\n", + " 0.20991189263696136 False ... 7.70159095731871 0\n", + "0.017000902750311093 False ... nan 0\n", + "0.010753505226784877 False ... nan 0\n", + " 0.2688662867171623 True ... 6.5704973503446755 1\n", + " 0.1180619308611421 False ... 8.583856072073955 0\n", + " 0.08394927892076134 False ... 9.189440377342134 0\n", + " ... ... ... ... ...\n", + " 0.0206406101070209 False ... nan 0\n", + " 0.07136771909820422 False ... nan 0\n", + " 0.07067029373342434 True ... nan 2\n", + " 0.22895855805541523 False ... 7.553664542955746 0\n", + " 0.08333000096743302 True ... 9.202672780659682 1\n", + " 1.45216924939138 True ... 2.3052287375038536 1\n", + " 0.2641096309279692 False ... 7.327889430103641 0\n", + "Length = 3214 rows\n" ] } ], "source": [ - "# Finding the minimum mass of generated objects\n", - "print(\"Smallest mass of a primary generated object: \" + str(np.min(kc_out['mass'])))\n", - "print(\"Smallest mass of a companion generated object: \" + str(np.min(kc_comp['mass'])))" + "print(kc_out[c_wd])" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "398a4626-0e74-4f6d-a3a1-2c115fedb9c1", + "execution_count": 17, + "id": "eb20c163-8212-4885-98dd-eb10e0bb08f7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Initial mass of smallest generated black hole: 0.07035468449081322\n", - "Initial mass of largest generated black hole: 118.59389207250989\n" + "Smallest mass of a primary generated black hole: 15.001898748254684\n", + "Smallest mass of a companion generated black hole: 0.010087940766134134\n" ] } ], + "source": [ + "# Finding the minimum mass of generated black holes\n", + "print(\"Smallest mass of a primary generated black hole: \" + str(np.min(kc_out[p2_bh]['mass'])))\n", + "print(\"Smallest mass of a companion generated black hole: \" + str(np.min(kc_out[c_bh]['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a854062-8dd0-4411-a0d7-523dcc4aa77b", + "metadata": {}, + "outputs": [], + "source": [ + "# Finding the minimum mass of generated objects\n", + "print(\"Smallest mass of a primary generated object: \" + str(np.min(kc_out['mass'])))\n", + "print(\"Smallest mass of a companion generated object: \" + str(np.min(kc_comp['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "398a4626-0e74-4f6d-a3a1-2c115fedb9c1", + "metadata": {}, + "outputs": [], "source": [ "# Finding the minimum/maximum inital masses of generated black holes\n", "print(\"Initial mass of smallest generated black hole: \" + str(np.min(kc_out[k_bh]['mass'])))\n", @@ -424,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "afe96713-78c9-4785-bdf9-6f3f36f2a4d5", "metadata": {}, "outputs": [], @@ -436,19 +722,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "a6483a09-8712-48ed-9bc6-dbfbda7a32f9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 689034 stars out of mass range\n", - "Found 135376 companions out of stellar mass range\n" - ] - } - ], + "outputs": [], "source": [ "# Make cluster\n", "cluster_mass = 10**6\n", @@ -461,21 +738,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "c598a266-297c-423d-b5f5-648f819c065e", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Locate BHs, NSs and WDs\n", "p3_bh = np.where(c3_out['phase'] == 103)[0]\n", diff --git a/spisea/evolution.py b/spisea/evolution.py index e27b0147..d1dc1896 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1310,8 +1310,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): bd_idx = iso['mass'] < 0.08 iso['mass_current'][bd_idx] = iso['mass'][bd_idx] - - # Handling NaN effectvie temperatures + # Handling NaN effective temperatures nan_teff_idx = np.isnan(iso['logT']) if np.any(nan_teff_idx): iso['logT'][nan_teff_idx] = self.estimate_teff(iso['mass'][nan_teff_idx]) diff --git a/spisea/ifmr.py b/spisea/ifmr.py index f88afdbe..053b163d 100755 --- a/spisea/ifmr.py +++ b/spisea/ifmr.py @@ -36,24 +36,28 @@ def get_Z(self, Fe_H): """ return 10**(Fe_H - 1.85387) - def Kalirai_mass(self, MZAMS): + def Kalirai_mass(self, MZAMS): # for white dwarfs """ From Kalirai+08 https://ui.adsabs.harvard.edu/abs/2008ApJ...676..594K/abstract 1.16 < MZAMS < 6.5 But we use this function for anything between 0.5 and 9 depending on the IFMR. FIXME: need to extend these ranges... explain extension somewhere? Paper maybe? """ - + #debugging print statement + print(f"Input MZAMS: {MZAMS}") + result = 0.109*MZAMS + 0.394 final = np.zeros(len(MZAMS)) - bad_idx = np.where(MZAMS < 0.5) + bad_idx = np.where((MZAMS < 0.5) | (MZAMS >= 9)) final[bad_idx] = -99 - good_idx = np.where(MZAMS >= 0.5) + good_idx = np.where((MZAMS >= 0.5) & (MZAMS < 9)) final[good_idx] = result[good_idx] + print(f"Final masses: {final}") # Debugging print statement + return final @@ -552,11 +556,12 @@ def generate_death_mass(self, mass_array, metallicity_array): * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 + * BD: typecode = 99 A typecode of value -1 means you're outside the range of validity for applying the ifmr formula. A remnant mass of -99 means you're outside the range of validity for applying the ifmr formula. - Range of validity: MZAMS > 0.5 + Range of validity: MZAMS > 0.5 and 0.01 < MZAMS < 0.08 Returns ------- @@ -569,7 +574,7 @@ def generate_death_mass(self, mass_array, metallicity_array): #output_array[1] holds the remnant type output_array = np.zeros((2, len(mass_array))) - codes = {'WD': 101, 'NS': 102, 'BH': 103} + codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 99} #create array to store the remnant masses generated by Spera equations rem_mass_array = np.zeros(len(mass_array)) @@ -647,6 +652,12 @@ def generate_death_mass(self, mass_array, metallicity_array): #remnant masses of stars with Z > 4.0e-3 and MZAMS > 10.0 high_metal_high_mass_idx = np.where((Z_array > 4.0e-3) & (core_mass > 10.0)) rem_mass_array[high_metal_high_mass_idx] = self.M_rem_high_metal_high_mass(Z_array[high_metal_high_mass_idx], core_mass[high_metal_high_mass_idx]) + + #classify brown dwarfs before checking for bad indices (in case they are looped into them) + BD_idx = np.where((mass_array >= 0.01) & (mass_array < 0.08)) + rem_mass_array[BD_idx] = mass_array[BD_idx] + output_array[0][BD_idx] = rem_mass_array[BD_idx] + output_array[1][BD_idx] = codes['BD'] #assign object types based on remnant mass bad_idx = np.where(rem_mass_array < 0) #outside the range of validity for the ifmr @@ -751,6 +762,7 @@ def generate_death_mass(self, mass_array): * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 + * BD: typecode = 99 A typecode of value -1 means you're outside the range of validity for applying the ifmr formula. @@ -758,7 +770,7 @@ def generate_death_mass(self, mass_array): A remnant mass of -99 means you're outside the range of validity for applying the ifmr formula. - Range of validity: 0.5 < MZAMS < 120 + Range of validity: 0.01 < MZAMS < 0.08 and 0.5 < MZAMS < 120 Returns ------- @@ -774,23 +786,34 @@ def generate_death_mass(self, mass_array): #Random array to get probabilities for what type of object will form random_array = np.random.randint(1, 1001, size = len(mass_array)) - codes = {'WD': 101, 'NS': 102, 'BH': 103} + codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 99} """ The id_arrays are to separate all the different formation regimes """ - id_array0 = np.where((mass_array < 0.5) | (mass_array >= 120)) - output_array[0][id_array0] = -99 * np.ones(len(id_array0)) - output_array[1][id_array0] = -1 * np.ones(len(id_array0)) + #classifying brown dwarfs + id_array_BD = np.where((mass_array >= 0.01) & (mass_array < 0.08)) + output_array[0][id_array_BD] = mass_array[id_array_BD] + output_array[1][id_array_BD] = codes['BD'] + print(f'Brown dwarf indices: {id_array_BD}, Masses: {mass_array[id_array_BD]}') #for debugging + + #classifying invalid mass ranges + id_array0 = np.where((mass_array < 0.01) | ((mass_array >= 0.08) & (mass_array < 0.5)) | (mass_array >= 120)) + output_array[0][id_array0] = -99 + output_array[1][id_array0] = -1 + + #classifying white dwarfs id_array1 = np.where((mass_array >= 0.5) & (mass_array < 9)) output_array[0][id_array1] = self.Kalirai_mass(mass_array[id_array1]) output_array[1][id_array1]= codes['WD'] + #classifying neutron stars id_array2 = np.where((mass_array >= 9) & (mass_array < 15)) output_array[0][id_array2] = self.NS_mass(mass_array[id_array2]) output_array[1][id_array2] = codes['NS'] + #classifying black holes id_array3_BH = np.where((mass_array >= 15) & (mass_array < 17.8) & (random_array > 679)) output_array[0][id_array3_BH] = self.BH_mass_low(mass_array[id_array3_BH], 0.9) output_array[1][id_array3_BH] = codes['BH'] @@ -843,6 +866,7 @@ def generate_death_mass(self, mass_array): output_array[0][id_array10_NS] = self.NS_mass(mass_array[id_array10_NS]) output_array[1][id_array10_NS] = codes['NS'] + return(output_array) class IFMR_N20_Sukhbold(IFMR): diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 4e9cbcad..19ad14a1 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -232,7 +232,7 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # effect is so small # Convert nan_to_num to avoid errors on greater than, less than comparisons star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) - bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) + bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 99) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) # Print warning, if desired verbose=False if verbose: @@ -251,9 +251,10 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # Remnants have flux = 0 in all bands if they are generated here. ##### if self.ifmr != None: - # Identify compact objects as those with Teff = 0 or with phase > 100. + # Identify compact objects as those with Teff = 0 or with phase > 100 or BDs highest_mass_iso = self.iso.points['mass'].max() - idx_rem = np.where((np.isnan(star_systems['Teff'])) & (star_systems['mass'] > highest_mass_iso))[0] + idx_rem = np.where((np.isnan(star_systems['Teff'])) & (star_systems['mass'] > highest_mass_iso) | + (star_systems['mass'] < 0.08))[0] # Calculate remnant mass and ID for compact objects; update remnant_id and # remnant_mass arrays accordingly @@ -275,6 +276,11 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): for filt in self.filt_names: star_systems[filt][idx_rem_good] = np.full(len(idx_rem_good), np.nan) + # Handle brown dwarfs separately + idx_bd = np.where(star_systems['phase'] == 99)[0] + for filt in self.filt_names: + star_systems[filt][idx_bd] = np.full(len(idx_bd), np.nan) + return star_systems def _make_companions_table(self, star_systems, compMass): @@ -362,7 +368,7 @@ def _make_companions_table(self, star_systems, compMass): # Convert nan_to_num to avoid errors on greater than, less than comparisons companions_phase_non_nan = np.nan_to_num(companions['phase'], nan=-99) bad = np.where( (companions_phase_non_nan > 5) & - (companions_phase_non_nan < 101) & + (companions_phase_non_nan < 99) & (companions_phase_non_nan != 9) & (companions_phase_non_nan != -99)) # Print warning, if desired @@ -459,7 +465,8 @@ def _remove_bad_systems(self, star_systems, compMass): idx = np.where(star_systems_teff_non_nan > 0)[0] else: # Keep stars (with Teff) and any other compact objects (with phase info). - idx = np.where( (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) )[0] + idx = np.where( (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) | + (star_systems_phase_non_nan == 99) )[0] if len(idx) != N_systems and self.verbose: print( 'Found {0:d} stars out of mass range'.format(N_systems - len(idx))) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 4b8b7b07..0146b819 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -499,6 +499,8 @@ def test_ifmr_multiplicity(): ns_idx = np.where(clust1['phase'] == 102) bh_idx = np.where(clust1['phase'] == 103) bd_idx = np.where(clust1['phase'] == 99) + + assert len(clust1[bd_idx]) != 0 assert np.all(clust1['mass'][wd_idx] > MIN_MASS) assert np.all(clust1['mass'][ns_idx] > MIN_MASS) assert np.all(clust1['mass'][bh_idx] > MIN_MASS) @@ -530,8 +532,6 @@ def test_ifmr_multiplicity(): (comps2['mass'] >= BD_MIN_MASS) & (comps2['mass'] <= BD_MAX_MASS)) assert len(comp_non_bd_idx[0]) == 0 # asserting no non-brown dwarf companions in BD mass range - -return return def test_metallicity(): @@ -1011,3 +1011,20 @@ def test_Raithel18_IFMR_5(): assert len(WD_idx) == 2 , "There are not the right number of WDs for the Raithel18 IFMR" return + +# need to test Raithel18 phase designations, as there are anomalies with white dwarves and neutron stars +def test_Raithel18_phase_designation(): + """ + Check that the correct phases are returned for white dwarves and neutron stars when given several test MZAMS + """ + Raithel = ifmr.IFMR_Raithel18() + + #create an MZAMS array to cover all potential phase designations, and the expected phase designations + test_MZAMS = np.array([0.06, 0.3, 1.0, 3.0, 6.0, 10.0, 14.0, 15.0, 25.0, 100.0]) + expected_phases = np.array([99, 101, 101, 101, 101, 102, 102, 103, 103, 103]) + + #confirm that the expected phases match the ones assigned by the model + assert np.array_equal(actual_types, expected_types), \ + f"Phase designation mismatch. Expected: {expected_types}, Got: {actual_types}" + + print(f"Test passed. Actual types match expected types: {actual_types}") \ No newline at end of file From 0fa23fea004ce86057ea983caab6961aae48316b Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 9 Aug 2024 09:43:52 -0700 Subject: [PATCH 013/191] Testing improvements for Raithel phase designations --- spisea/tests/test_synthetic.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 0146b819..8322ca05 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -1020,11 +1020,19 @@ def test_Raithel18_phase_designation(): Raithel = ifmr.IFMR_Raithel18() #create an MZAMS array to cover all potential phase designations, and the expected phase designations - test_MZAMS = np.array([0.06, 0.3, 1.0, 3.0, 6.0, 10.0, 14.0, 15.0, 25.0, 100.0]) - expected_phases = np.array([99, 101, 101, 101, 101, 102, 102, 103, 103, 103]) + test_MZAMS = np.array([0.06, 0.3, 1.0, 3.0, 6.0, 10.0, 14.0, 25.0, 100.0]) + expected_phases = np.array([99, -1, 101, 101, 101, 102, 102, 103, 103]) + + #generate the output from Raithel IFMR + output_array = Raithel.generate_death_mass(test_MZAMS) + actual_phases = output_array[1] + #print the two arrays for direct comparison + print(f"Expected phases: {expected_phases}") + print(f"Actual phases: {actual_phases}") + #confirm that the expected phases match the ones assigned by the model - assert np.array_equal(actual_types, expected_types), \ - f"Phase designation mismatch. Expected: {expected_types}, Got: {actual_types}" + assert np.array_equal(actual_phases, expected_phases), \ + f"Phase designation mismatch. Expected: {expected_phases}, Got: {actual_phases}" - print(f"Test passed. Actual types match expected types: {actual_types}") \ No newline at end of file + print(f"Test passed. Actual types match expected types: {actual_phases}") \ No newline at end of file From 165ffa2ce6816817916bb52990de2ec3a7618374 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 9 Aug 2024 10:59:01 -0700 Subject: [PATCH 014/191] Outlined testing and issues with WDs and NSs --- changes/Test_BD_Cluster.ipynb | 824 ++++++++++++++++++++++++++++----- spisea/tests/test_synthetic.py | 4 +- 2 files changed, 721 insertions(+), 107 deletions(-) diff --git a/changes/Test_BD_Cluster.ipynb b/changes/Test_BD_Cluster.ipynb index 5625aa9a..d5a5da69 100644 --- a/changes/Test_BD_Cluster.ipynb +++ b/changes/Test_BD_Cluster.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 39, "id": "b7536665-00cf-49ad-9028-930f56cf3204", "metadata": {}, "outputs": [], @@ -43,10 +43,536 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "0cd4c0a3-39e8-4bbf-9eba-e1883b6dacec", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=4.00 for T= 32651 logg=3.99\n", + "Changing to logg=4.00 for T= 32840 logg=3.98\n", + "Changing to logg=4.00 for T= 33037 logg=3.97\n", + "Changing to logg=4.00 for T= 33144 logg=3.96\n", + "Changing to logg=4.00 for T= 33205 logg=3.95\n", + "Changing to logg=4.00 for T= 33358 logg=3.94\n", + "Changing to logg=4.00 for T= 33504 logg=3.93\n", + "Changing to logg=4.00 for T= 33651 logg=3.91\n", + "Changing to logg=4.00 for T= 33713 logg=3.91\n", + "Changing to logg=4.00 for T= 33775 logg=3.90\n", + "Changing to logg=4.00 for T= 33892 logg=3.89\n", + "Changing to logg=4.00 for T= 34002 logg=3.87\n", + "Changing to logg=4.00 for T= 34111 logg=3.86\n", + "Changing to logg=4.00 for T= 34182 logg=3.85\n", + "Changing to logg=4.00 for T= 34222 logg=3.85\n", + "Changing to logg=4.00 for T= 34332 logg=3.83\n", + "Changing to logg=4.00 for T= 34443 logg=3.82\n", + "Changing to logg=4.00 for T= 34546 logg=3.81\n", + "Changing to 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logg=5.00 for T= 42540 logg=5.21\n", + "Changing to logg=5.00 for T= 42472 logg=5.21\n", + "Changing to logg=5.00 for T= 42413 logg=5.21\n", + "Changing to logg=5.00 for T= 42345 logg=5.21\n", + "Changing to logg=5.00 for T= 42277 logg=5.21\n", + "Changing to logg=5.00 for T= 42209 logg=5.22\n", + "Changing to logg=5.00 for T= 42150 logg=5.22\n", + "Changing to logg=5.00 for T= 42082 logg=5.22\n", + "Changing to logg=5.00 for T= 42024 logg=5.22\n", + "Changing to logg=5.00 for T= 41976 logg=5.23\n", + "Changing to logg=5.00 for T= 41899 logg=5.23\n", + "Changing to logg=5.00 for T= 41850 logg=5.23\n", + "Changing to logg=5.00 for T= 41802 logg=5.23\n", + "Changing to logg=5.00 for T= 41754 logg=5.23\n", + "Changing to logg=5.00 for T= 41957 logg=5.26\n", + "Changing to logg=5.00 for T= 42024 logg=5.27\n", + "Changing to logg=5.00 for T= 42053 logg=5.28\n", + "Changing to logg=5.00 for T= 42053 logg=5.28\n", + "Changing to logg=5.00 for T= 42024 logg=5.29\n", + "Changing to logg=5.00 for T= 42034 logg=5.29\n", + "Changing to logg=5.00 for T= 42034 logg=5.30\n", + "Changing to logg=5.00 for T= 42015 logg=5.31\n", + "Changing to logg=5.00 for T= 41995 logg=5.31\n", + "Changing to logg=5.00 for T= 41966 logg=5.31\n", + "Changing to logg=5.00 for T= 41966 logg=5.32\n", + "Changing to logg=5.00 for T= 41947 logg=5.32\n", + "Changing to logg=5.00 for T= 41937 logg=5.33\n", + "Changing to logg=5.00 for T= 41889 logg=5.33\n", + "Changing to logg=5.00 for T= 41822 logg=5.33\n", + "Changing to logg=5.00 for T= 41783 logg=5.33\n", + "Changing to logg=5.00 for T= 41754 logg=5.33\n", + "Changing to logg=5.00 for T= 41200 logg=5.32\n", + "Changing to logg=5.00 for T= 40374 logg=5.30\n", + "Changing to logg=5.00 for T= 40337 logg=5.30\n", + "Changing to logg=5.00 for T= 40207 logg=5.30\n", + "Changing to logg=5.00 for T= 39455 logg=5.31\n", + "Changing to logg=5.00 for T= 35719 logg=5.31\n", + "Changing to logg=5.00 for T= 35645 logg=5.30\n", + "Changing to logg=5.00 for T= 35588 logg=5.30\n", + "Changing to logg=5.00 for T= 35571 logg=5.30\n", + "Changing to logg=5.00 for T= 34962 logg=5.25\n", + "Changing to logg=5.00 for T= 34906 logg=5.25\n", + "Changing to logg=5.00 for T= 34475 logg=5.21\n", + "Changing to logg=5.00 for T= 34277 logg=5.20\n", + "Changing to logg=5.00 for T= 34285 logg=5.20\n", + "Changing to logg=5.00 for T= 34658 logg=5.23\n", + "Changing to logg=5.00 for T= 34770 logg=5.24\n", + "Changing to logg=5.00 for T= 34253 logg=5.20\n", + "Changing to logg=5.00 for T= 33628 logg=5.15\n", + "Changing to logg=5.00 for T= 33312 logg=5.12\n", + "Changing to logg=5.00 for T= 33636 logg=5.15\n", + "Changing to logg=5.00 for T= 33643 logg=5.15\n", + "Changing to logg=5.00 for T= 33659 logg=5.15\n", + "Changing to logg=5.00 for T= 33651 logg=5.15\n", + "Changing to logg=5.00 for T= 33504 logg=5.13\n", + "Changing to logg=5.00 for T= 33466 logg=5.08\n", + "Changing to logg=5.00 for T= 33504 logg=5.08\n", + "Changing to logg=5.00 for T= 33558 logg=5.07\n", + "Changing to logg=5.00 for T= 33605 logg=5.08\n", + "Changing to logg=5.00 for T= 33651 logg=5.08\n", + "Changing to logg=5.00 for T= 33674 logg=5.07\n", + "Changing to logg=5.00 for T= 32870 logg=5.05\n", + "Changing to logg=5.00 for T= 30054 logg=5.01\n", + "Changing to logg=5.00 for T= 29819 logg=5.01\n", + "Changing to logg=5.00 for T= 35785 logg=5.39\n", + "Changing to logg=5.00 for T= 39683 logg=5.62\n", + "Changing to logg=5.00 for T= 39701 logg=5.62\n", + "Changing to logg=5.00 for T= 39655 logg=5.63\n", + "Changing to logg=5.00 for T= 39518 logg=5.63\n", + "Changing to logg=5.00 for T= 39373 logg=5.63\n", + "Changing to logg=5.00 for T= 39518 logg=5.65\n", + "Changing to logg=5.00 for T= 40031 logg=5.69\n", + "Changing to logg=5.00 for T= 40096 logg=5.70\n", + "Changing to logg=5.00 for T= 40495 logg=5.74\n", + "Changing to logg=5.00 for T= 41381 logg=5.82\n", + "Changing to logg=5.00 for T= 41976 logg=5.87\n", + "Changing to logg=5.00 for T= 42199 logg=5.90\n", + "Changing to logg=5.00 for T= 42228 logg=5.90\n", + "Changing to logg=5.00 for T= 42286 logg=5.90\n", + "Changing to logg=5.00 for T= 42345 logg=5.91\n", + "Changing to logg=5.00 for T= 42394 logg=5.92\n", + "Changing to logg=5.00 for T= 42462 logg=5.92\n", + "Changing to logg=5.00 for T= 42530 logg=5.92\n", + "Changing to logg=5.00 for T= 42619 logg=5.92\n", + "Changing to logg=5.00 for T= 42717 logg=5.93\n", + "Changing to logg=5.00 for T= 42825 logg=5.93\n", + "Changing to logg=5.00 for T= 42934 logg=5.94\n", + "Changing to logg=5.00 for T= 43053 logg=5.94\n", + "Changing to logg=5.00 for T= 43152 logg=5.94\n", + "Changing to logg=5.00 for T= 43271 logg=5.95\n", + "Changing to logg=5.00 for T= 43411 logg=5.95\n", + "Changing to logg=5.00 for T= 43561 logg=5.96\n", + "Changing to logg=5.00 for T= 43692 logg=5.96\n", + "Changing to logg=5.00 for T= 43833 logg=5.96\n", + "Changing to logg=5.00 for T= 43985 logg=5.97\n", + "Changing to logg=5.00 for T= 44147 logg=5.97\n", + "Changing to logg=5.00 for T= 44310 logg=5.97\n", + "Changing to logg=5.00 for T= 44484 logg=5.97\n", + "Changing to logg=5.00 for T= 44638 logg=5.97\n", + "Changing to logg=5.00 for T= 44802 logg=5.98\n", + "Changing to logg=5.00 for T= 44957 logg=5.98\n", + "Changing to logg=5.00 for T= 45144 logg=5.98\n", + "Changing to logg=5.00 for T= 45300 logg=5.99\n", + "Changing to logg=5.00 for T= 45478 logg=5.99\n", + "Changing to logg=5.00 for T= 45509 logg=5.99\n", + "Changing to logg=5.00 for T= 45646 logg=5.99\n", + "Changing to logg=5.00 for T= 45825 logg=5.99\n", + "Changing to logg=5.00 for T= 45983 logg=6.00\n", + "Changing to logg=5.00 for T= 46142 logg=6.00\n", + "Changing to logg=5.00 for T= 46313 logg=6.00\n", + "Changing to logg=5.00 for T= 46473 logg=6.00\n", + "Changing to logg=5.00 for T= 46623 logg=6.00\n", + "Changing to logg=5.00 for T= 46784 logg=6.00\n", + "Changing to logg=5.00 for T= 46946 logg=6.00\n", + "Changing to logg=5.00 for T= 47109 logg=6.00\n", + "Changing to logg=5.00 for T= 47261 logg=6.00\n", + "Changing to logg=5.00 for T= 47413 logg=6.01\n", + "Changing to logg=5.00 for T= 47566 logg=6.01\n", + "Changing to logg=5.00 for T= 47720 logg=6.01\n", + "Changing to logg=5.00 for T= 47863 logg=6.01\n", + "Changing to logg=5.00 for T= 48029 logg=6.01\n", + "Changing to logg=5.00 for T= 48173 logg=6.01\n", + "Changing to logg=5.00 for T= 48317 logg=6.02\n", + "Changing to logg=5.00 for T= 48473 logg=6.02\n", + "Changing to logg=5.00 for T= 48618 logg=6.02\n", + "Changing to logg=5.00 for T= 48753 logg=6.02\n", + "Changing to logg=5.00 for T= 48910 logg=6.02\n", + "Changing to logg=5.00 for T= 49057 logg=6.02\n", + "Changing to logg=5.00 for T= 49193 logg=6.02\n", + "Changing to logg=5.00 for T= 49329 logg=6.03\n", + "Changing to logg=5.00 for T= 49431 logg=6.03\n", + "Changing to logg=5.00 for T= 49465 logg=6.03\n", + "Changing to logg=5.00 for T= 49614 logg=6.03\n", + "Changing to logg=5.00 for T= 49751 logg=6.03\n", + "Changing to logg=5.00 for T= 49888 logg=6.03\n", + "Changing to T= 50000 for T= 50026 logg=6.03\n", + "Changing to logg=5.00 for T= 50026 logg=6.03\n", + "Changing to T= 50000 for T= 50176 logg=6.03\n", + "Changing to logg=5.00 for T= 50176 logg=6.03\n", + "Changing to T= 50000 for T= 50304 logg=6.03\n", + "Changing to logg=5.00 for T= 50304 logg=6.03\n", + "Changing to T= 50000 for T= 50431 logg=6.04\n", + "Changing to logg=5.00 for T= 50431 logg=6.04\n", + "Changing to T= 50000 for T= 50559 logg=6.04\n", + "Changing to logg=5.00 for T= 50559 logg=6.04\n", + "Changing to T= 50000 for T= 50699 logg=6.04\n", + "Changing to logg=5.00 for T= 50699 logg=6.04\n", + "Changing to T= 50000 for T= 50839 logg=6.04\n", + "Changing to logg=5.00 for T= 50839 logg=6.04\n", + "Changing to T= 50000 for T= 50957 logg=6.04\n", + "Changing to logg=5.00 for T= 50957 logg=6.04\n", + "Changing to T= 50000 for T= 51086 logg=6.04\n", + "Changing to logg=5.00 for T= 51086 logg=6.04\n", + "Changing to T= 50000 for T= 51215 logg=6.04\n", + "Changing to logg=5.00 for T= 51215 logg=6.04\n", + "Changing to T= 50000 for T= 51345 logg=6.04\n", + "Changing to logg=5.00 for T= 51345 logg=6.04\n", + "Changing to T= 50000 for T= 51475 logg=6.04\n", + "Changing to logg=5.00 for T= 51475 logg=6.04\n", + "Changing to T= 50000 for T= 51606 logg=6.04\n", + "Changing to logg=5.00 for T= 51606 logg=6.04\n", + "Changing to T= 50000 for T= 51737 logg=6.05\n", + "Changing to logg=5.00 for T= 51737 logg=6.05\n", + "Changing to T= 50000 for T= 51868 logg=6.05\n", + "Changing to logg=5.00 for T= 51868 logg=6.05\n", + "Changing to T= 50000 for T= 52000 logg=6.05\n", + "Changing to logg=5.00 for T= 52000 logg=6.05\n", + "Changing to T= 50000 for T= 52119 logg=6.05\n", + "Changing to logg=5.00 for T= 52119 logg=6.05\n", + "Changing to T= 50000 for T= 52240 logg=6.05\n", + "Changing to logg=5.00 for T= 52240 logg=6.05\n", + "Changing to T= 50000 for T= 52384 logg=6.05\n", + "Changing to logg=5.00 for T= 52384 logg=6.05\n", + "Changing to T= 50000 for T= 52505 logg=6.05\n", + "Changing to logg=5.00 for T= 52505 logg=6.05\n", + "Changing to T= 50000 for T= 52626 logg=6.05\n", + "Changing to logg=5.00 for T= 52626 logg=6.05\n", + "Changing to T= 50000 for T= 52747 logg=6.05\n", + "Changing to logg=5.00 for T= 52747 logg=6.05\n", + "Changing to T= 50000 for T= 52759 logg=6.05\n", + "Changing to logg=5.00 for T= 52759 logg=6.05\n", + "Changing to T= 50000 for T= 52857 logg=6.05\n", + "Changing to logg=5.00 for T= 52857 logg=6.05\n", + "Changing to T= 50000 for T= 52991 logg=6.05\n", + "Changing to logg=5.00 for T= 52991 logg=6.05\n", + "Changing to T= 50000 for T= 53125 logg=6.06\n", + "Changing to logg=5.00 for T= 53125 logg=6.06\n", + "Changing to T= 50000 for T= 53235 logg=6.06\n", + "Changing to logg=5.00 for T= 53235 logg=6.06\n", + "Changing to T= 50000 for T= 53358 logg=6.06\n", + "Changing to logg=5.00 for T= 53358 logg=6.06\n", + "Changing to T= 50000 for T= 53481 logg=6.06\n", + "Changing to logg=5.00 for T= 53481 logg=6.06\n", + "Changing to T= 50000 for T= 53592 logg=6.06\n", + "Changing to logg=5.00 for T= 53592 logg=6.06\n", + "Changing to T= 50000 for T= 53728 logg=6.06\n", + "Changing to logg=5.00 for T= 53728 logg=6.06\n", + "Changing to T= 50000 for T= 53864 logg=6.06\n", + "Changing to logg=5.00 for T= 53864 logg=6.06\n", + "Changing to T= 50000 for T= 53976 logg=6.07\n", + "Changing to logg=5.00 for T= 53976 logg=6.07\n", + "Changing to T= 50000 for T= 54100 logg=6.07\n", + "Changing to logg=5.00 for T= 54100 logg=6.07\n", + "Changing to T= 50000 for T= 54225 logg=6.07\n", + "Changing to logg=5.00 for T= 54225 logg=6.07\n", + "Changing to T= 50000 for T= 54363 logg=6.07\n", + "Changing to logg=5.00 for T= 54363 logg=6.07\n", + "Changing to T= 50000 for T= 54475 logg=6.07\n", + "Changing to logg=5.00 for T= 54475 logg=6.07\n", + "Changing to T= 50000 for T= 54588 logg=6.07\n", + "Changing to logg=5.00 for T= 54588 logg=6.07\n", + "Changing to T= 50000 for T= 54714 logg=6.08\n", + "Changing to logg=5.00 for T= 54714 logg=6.08\n", + "Changing to T= 50000 for T= 54853 logg=6.08\n", + "Changing to logg=5.00 for T= 54853 logg=6.08\n", + "Changing to T= 50000 for T= 54967 logg=6.08\n", + "Changing to logg=5.00 for T= 54967 logg=6.08\n", + "Changing to T= 50000 for T= 55093 logg=6.08\n", + "Changing to logg=5.00 for T= 55093 logg=6.08\n", + "Changing to T= 50000 for T= 55208 logg=6.08\n", + "Changing to logg=5.00 for T= 55208 logg=6.08\n", + "Changing to T= 50000 for T= 55322 logg=6.09\n", + "Changing to logg=5.00 for T= 55322 logg=6.09\n", + "Changing to T= 50000 for T= 55450 logg=6.09\n", + "Changing to logg=5.00 for T= 55450 logg=6.09\n", + "Changing to T= 50000 for T= 55590 logg=6.09\n", + "Changing to logg=5.00 for T= 55590 logg=6.09\n", + "Changing to T= 50000 for T= 55719 logg=6.09\n", + "Changing to logg=5.00 for T= 55719 logg=6.09\n", + "Changing to T= 50000 for T= 55834 logg=6.09\n", + "Changing to logg=5.00 for T= 55834 logg=6.09\n", + "Changing to T= 50000 for T= 55873 logg=6.09\n", + "Changing to logg=5.00 for T= 55873 logg=6.09\n", + "Changing to T= 50000 for T= 55963 logg=6.09\n", + "Changing to logg=5.00 for T= 55963 logg=6.09\n", + "Changing to T= 50000 for T= 56092 logg=6.10\n", + "Changing to logg=5.00 for T= 56092 logg=6.10\n", + "Changing to T= 50000 for T= 56234 logg=6.10\n", + "Changing to logg=5.00 for T= 56234 logg=6.10\n", + "Changing to T= 50000 for T= 56364 logg=6.10\n", + "Changing to logg=5.00 for T= 56364 logg=6.10\n", + "Changing to T= 50000 for T= 56507 logg=6.10\n", + "Changing to logg=5.00 for T= 56507 logg=6.10\n", + "Changing to T= 50000 for T= 56624 logg=6.11\n", + "Changing to logg=5.00 for T= 56624 logg=6.11\n", + "Changing to T= 50000 for T= 56754 logg=6.11\n", + "Changing to logg=5.00 for T= 56754 logg=6.11\n", + "Changing to T= 50000 for T= 56885 logg=6.11\n", + "Changing to logg=5.00 for T= 56885 logg=6.11\n", + "Changing to T= 50000 for T= 57030 logg=6.11\n", + "Changing to logg=5.00 for T= 57030 logg=6.11\n", + "Changing to T= 50000 for T= 57161 logg=6.11\n", + "Changing to logg=5.00 for T= 57161 logg=6.11\n", + "Changing to T= 50000 for T= 57293 logg=6.11\n", + "Changing to logg=5.00 for T= 57293 logg=6.11\n", + "Changing to T= 50000 for T= 57425 logg=6.11\n", + "Changing to logg=5.00 for T= 57425 logg=6.11\n", + "Changing to T= 50000 for T= 57570 logg=6.11\n", + "Changing to logg=5.00 for T= 57570 logg=6.11\n", + "Changing to T= 50000 for T= 57703 logg=6.11\n", + "Changing to logg=5.00 for T= 57703 logg=6.11\n", + "Changing to T= 50000 for T= 57850 logg=6.12\n", + "Changing to logg=5.00 for T= 57850 logg=6.12\n", + "Changing to T= 50000 for T= 57983 logg=6.12\n", + "Changing to logg=5.00 for T= 57983 logg=6.12\n", + "Changing to T= 50000 for T= 58117 logg=6.12\n", + "Changing to logg=5.00 for T= 58117 logg=6.12\n", + "Changing to T= 50000 for T= 58251 logg=6.13\n", + "Changing to logg=5.00 for T= 58251 logg=6.13\n", + "Changing to T= 50000 for T= 58398 logg=6.13\n", + "Changing to logg=5.00 for T= 58398 logg=6.13\n", + "Changing to T= 50000 for T= 58546 logg=6.13\n", + "Changing to logg=5.00 for T= 58546 logg=6.13\n", + "Changing to T= 50000 for T= 58695 logg=6.13\n", + "Changing to logg=5.00 for T= 58695 logg=6.13\n", + "Changing to T= 50000 for T= 58844 logg=6.14\n", + "Changing to logg=5.00 for T= 58844 logg=6.14\n", + "Changing to T= 50000 for T= 58993 logg=6.14\n", + "Changing to logg=5.00 for T= 58993 logg=6.14\n", + "Changing to T= 50000 for T= 59143 logg=6.14\n", + "Changing to logg=5.00 for T= 59143 logg=6.14\n", + "Changing to T= 50000 for T= 59293 logg=6.15\n", + "Changing to logg=5.00 for T= 59293 logg=6.15\n", + "Changing to T= 50000 for T= 59334 logg=6.15\n", + "Changing to logg=5.00 for T= 59334 logg=6.15\n", + "Changing to T= 50000 for T= 59457 logg=6.15\n", + "Changing to logg=5.00 for T= 59457 logg=6.15\n", + "Changing to T= 50000 for T= 59621 logg=6.16\n", + "Changing to logg=5.00 for T= 59621 logg=6.16\n", + "Changing to T= 50000 for T= 59800 logg=6.16\n", + "Changing to logg=5.00 for T= 59800 logg=6.16\n", + "Changing to T= 50000 for T= 59965 logg=6.16\n", + "Changing to logg=5.00 for T= 59965 logg=6.16\n", + "Changing to T= 50000 for T= 60159 logg=6.17\n", + "Changing to logg=5.00 for T= 60159 logg=6.17\n", + "Changing to T= 50000 for T= 60353 logg=6.17\n", + "Changing to logg=5.00 for T= 60353 logg=6.17\n", + "Changing to T= 50000 for T= 60534 logg=6.18\n", + "Changing to logg=5.00 for T= 60534 logg=6.18\n", + "Changing to T= 50000 for T= 60758 logg=6.18\n", + "Changing to logg=5.00 for T= 60758 logg=6.18\n", + "Changing to T= 50000 for T= 60968 logg=6.18\n", + "Changing to logg=5.00 for T= 60968 logg=6.18\n", + "Changing to T= 50000 for T= 61235 logg=6.19\n", + "Changing to logg=5.00 for T= 61235 logg=6.19\n", + "Changing to T= 50000 for T= 61504 logg=6.20\n", + "Changing to logg=5.00 for T= 61504 logg=6.20\n", + "Changing to T= 50000 for T= 61844 logg=6.21\n", + "Changing to logg=5.00 for T= 61844 logg=6.21\n", + "Changing to T= 50000 for T= 62287 logg=6.23\n", + "Changing to logg=5.00 for T= 62287 logg=6.23\n", + "Changing to T= 50000 for T= 63038 logg=6.27\n", + "Changing to logg=5.00 for T= 63038 logg=6.27\n", + "Changing to T= 50000 for T= 64239 logg=6.32\n", + "Changing to logg=5.00 for T= 64239 logg=6.32\n", + "Changing to T= 50000 for T= 65464 logg=6.37\n", + "Changing to logg=5.00 for T= 65464 logg=6.37\n", + "Changing to T= 50000 for T= 66696 logg=6.42\n", + "Changing to logg=5.00 for T= 66696 logg=6.42\n", + "Changing to T= 50000 for T= 67967 logg=6.47\n", + "Changing to logg=5.00 for T= 67967 logg=6.47\n", + "Changing to T= 50000 for T= 69263 logg=6.53\n", + "Changing to logg=5.00 for T= 69263 logg=6.53\n", + "Changing to T= 50000 for T= 71367 logg=6.63\n", + "Changing to logg=5.00 for T= 71367 logg=6.63\n", + "Isochrone generation took 112.722160 s.\n", + "Making photometry for isochrone: log(t) = 6.70 AKs = 0.80 dist = 4000\n", + " Starting at: 2024-08-02 09:54:23.120123 Usually takes ~5 minutes\n", + "Starting filter: wfc3,ir,f127m Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.070 Msun T = 2928 K m_hst_f127m = 22.41\n", + "M = 1.175 Msun T = 4611 K m_hst_f127m = 18.72\n", + "M = 1.650 Msun T = 5355 K m_hst_f127m = 17.71\n", + "M = 1.973 Msun T = 6311 K m_hst_f127m = 16.34\n", + "M = 2.292 Msun T = 9669 K m_hst_f127m = 16.68\n", + "M = 2.648 Msun T = 11771 K m_hst_f127m = 16.64\n", + "M = 2.819 Msun T = 12291 K m_hst_f127m = 16.51\n", + "M = 3.075 Msun T = 13014 K m_hst_f127m = 16.33\n", + "M = 3.332 Msun T = 13709 K m_hst_f127m = 16.16\n", + "M = 3.482 Msun T = 14099 K m_hst_f127m = 16.07\n", + "M = 3.615 Msun T = 14438 K m_hst_f127m = 15.99\n", + "M = 8.977 Msun T = 23502 K m_hst_f127m = 14.07\n", + "M = 49.000 Msun T = 32344 K m_hst_f127m = 9.23\n", + "M = 50.757 Msun T = 31046 K m_hst_f127m = 9.25\n", + "M = 52.540 Msun T = 41937 K m_hst_f127m = 10.13\n", + "M = 54.406 Msun T = 48753 K m_hst_f127m = 10.85\n", + "M = 56.318 Msun T = 65464 K m_hst_f127m = 12.01\n", + "Starting filter: wfc3,ir,f139m Elapsed time: 25.91 seconds\n", + "Starting synthetic photometry\n", + "M = 0.070 Msun T = 2928 K m_hst_f139m = 22.11\n", + "M = 1.175 Msun T = 4611 K m_hst_f139m = 18.14\n", + "M = 1.650 Msun T = 5355 K m_hst_f139m = 17.18\n", + "M = 1.973 Msun T = 6311 K m_hst_f139m = 15.86\n", + "M = 2.292 Msun T = 9669 K m_hst_f139m = 16.28\n", + "M = 2.648 Msun T = 11771 K m_hst_f139m = 16.25\n", + "M = 2.819 Msun T = 12291 K m_hst_f139m = 16.12\n", + "M = 3.075 Msun T = 13014 K m_hst_f139m = 15.94\n", + "M = 3.332 Msun T = 13709 K m_hst_f139m = 15.77\n", + "M = 3.482 Msun T = 14099 K m_hst_f139m = 15.68\n", + "M = 3.615 Msun T = 14438 K m_hst_f139m = 15.60\n", + "M = 8.977 Msun T = 23502 K m_hst_f139m = 13.70\n", + "M = 49.000 Msun T = 32344 K m_hst_f139m = 8.87\n", + "M = 50.757 Msun T = 31046 K m_hst_f139m = 8.88\n", + "M = 52.540 Msun T = 41937 K m_hst_f139m = 9.77\n", + "M = 54.406 Msun T = 48753 K m_hst_f139m = 10.50\n", + "M = 56.318 Msun T = 65464 K m_hst_f139m = 11.66\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 51.87 seconds\n", + "Starting synthetic photometry\n", + "M = 0.070 Msun T = 2928 K m_hst_f153m = 21.45\n", + "M = 1.175 Msun T = 4611 K m_hst_f153m = 17.51\n", + "M = 1.650 Msun T = 5355 K m_hst_f153m = 16.63\n", + "M = 1.973 Msun T = 6311 K m_hst_f153m = 15.37\n", + "M = 2.292 Msun T = 9669 K m_hst_f153m = 15.89\n", + "M = 2.648 Msun T = 11771 K m_hst_f153m = 15.87\n", + "M = 2.819 Msun T = 12291 K m_hst_f153m = 15.75\n", + "M = 3.075 Msun T = 13014 K m_hst_f153m = 15.57\n", + "M = 3.332 Msun T = 13709 K m_hst_f153m = 15.40\n", + "M = 3.482 Msun T = 14099 K m_hst_f153m = 15.31\n", + "M = 3.615 Msun T = 14438 K m_hst_f153m = 15.23\n", + "M = 8.977 Msun T = 23502 K m_hst_f153m = 13.34\n", + "M = 49.000 Msun T = 32344 K m_hst_f153m = 8.52\n", + "M = 50.757 Msun T = 31046 K m_hst_f153m = 8.53\n", + "M = 52.540 Msun T = 41937 K m_hst_f153m = 9.42\n", + "M = 54.406 Msun T = 48753 K m_hst_f153m = 10.15\n", + "M = 56.318 Msun T = 65464 K m_hst_f153m = 11.31\n", + " Time taken: 78.43 seconds\n" + ] + } + ], "source": [ "# Define isochrone parameters\n", "logAge = np.log10(5*10**6.) # Age in log(years)\n", @@ -383,34 +909,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "id": "25b1247a-0eb8-40bc-bf56-f89e788283cd", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Isochrone generation took 96.182577 s.\n", - "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", - " Starting at: 2024-07-15 14:29:26.015063 Usually takes ~5 minutes\n", - "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", - "M = 0.676 Msun T = 4395 K m_hst_f153m = 4.92\n", - "M = 0.786 Msun T = 4896 K m_hst_f153m = 4.41\n", - "M = 0.856 Msun T = 5198 K m_hst_f153m = 4.12\n", - "M = 0.956 Msun T = 5590 K m_hst_f153m = 3.74\n", - "M = 1.022 Msun T = 5808 K m_hst_f153m = 3.50\n", - "M = 1.079 Msun T = 5992 K m_hst_f153m = 3.30\n", - "M = 1.518 Msun T = 7482 K m_hst_f153m = 2.22\n", - "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", - "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", - "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", - " Time taken: 31.54 seconds\n" - ] - } - ], + "outputs": [], "source": [ "# Create isochrone object \n", "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", @@ -422,21 +924,21 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 31, "id": "01f6f94c-1b23-4ae1-bfc2-96e2d4cacd27", "metadata": {}, "outputs": [], "source": [ "# Create IMF objects \n", - "#imf_multi = multiplicity.MultiplicityUnresolved()\n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", "massLimits = np.array([0.01, 0.08, 0.5, 1, np.inf])\n", "powers = np.array([-0.3, -1.3, -2.3, -2.35])\n", - "k_imf = imf.IMF_broken_powerlaw(massLimits, powers)" + "k_imf = imf.IMF_broken_powerlaw(massLimits, powers, multiplicity=imf_multi)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 32, "id": "1a914c4c-ac7e-4c61-9e40-1b0d5dd5568f", "metadata": {}, "outputs": [ @@ -444,7 +946,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 829408 stars out of mass range\n" + "Found 580447 stars out of mass range\n", + "Found 113559 companions out of stellar mass range\n" ] } ], @@ -455,18 +958,18 @@ "\n", "# Get outputs\n", "k_out = k_cluster.star_systems\n", - "#k_comp = k_cluster.companions" + "k_comp = k_cluster.companions" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 33, "id": "62293dea-989b-4870-a77b-ba677010da81", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -478,14 +981,14 @@ "source": [ "# Locate BHs, NSs and WDs\n", "p_bh = np.where(k_out['phase'] == 103)[0]\n", - "#c_bh = np.where(k_comp['phase'] == 103)[0]\n", - "#k_bh = np.concatenate([p_bh, c_bh])\n", + "c_bh = np.where(k_comp['phase'] == 103)[0]\n", + "k_bh = np.concatenate([p_bh, c_bh])\n", "p_ns = np.where(k_out['phase'] == 102)[0]\n", - "#c_ns = np.where(k_comp['phase'] == 102)[0]\n", - "#k_ns = np.concatenate([p_ns, c_ns])\n", + "c_ns = np.where(k_comp['phase'] == 102)[0]\n", + "k_ns = np.concatenate([p_ns, c_ns])\n", "p_wd = np.where(k_out['phase'] == 101)[0]\n", - "#c_wd = np.where(k_comp['phase'] == 101)[0]\n", - "#k_wd = np.concatenate([p_wd, c_wd])\n", + "c_wd = np.where(k_comp['phase'] == 101)[0]\n", + "k_wd = np.concatenate([p_wd, c_wd])\n", "\n", "# Plot on histograms\n", "bh_bins = np.linspace(0.01, 16, 20)\n", @@ -513,7 +1016,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 40, "id": "d36bba0d-3037-46c0-91f3-321e47241204", "metadata": {}, "outputs": [ @@ -521,19 +1024,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "973669.0207942065\n" + "831012.4509070338\n", + "5.311329909891375\n" ] } ], "source": [ "all_masses = np.concatenate([k_out['mass'], k_comp['mass']])\n", "tot_mass = np.sum(all_masses)\n", - "print(tot_mass)" + "print(tot_mass)\n", + "print(np.min(k_out[p_wd]['mass']))" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 20, "id": "1f999373-557e-42f1-9c0b-43d1cfab198e", "metadata": {}, "outputs": [ @@ -541,8 +1046,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.07000000391702135\n", - "0.010000022844115403\n" + "0.0700003309744075\n", + "0.010000236016655941\n" ] } ], @@ -553,10 +1058,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "d7c2fa5e-49cd-4021-869a-6f9701677c68", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current smallest mass of generated black holes: 0.07006468311672817\n", + "Current largest mass of generated black holes: 15.65838834836072\n" + ] + } + ], "source": [ "print(\"Current smallest mass of generated black holes: \" + str(np.min(k_out[k_bh]['mass_current'])))\n", "print(\"Current largest mass of generated black holes: \" + str(np.max(k_out[k_bh]['mass_current'])))" @@ -574,7 +1088,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 22, "id": "c02d7dbd-9690-49f4-931d-e7f66172ba12", "metadata": {}, "outputs": [ @@ -582,24 +1096,24 @@ "name": "stdout", "output_type": "stream", "text": [ - " mass isMultiple systemMass ... metallicity m_hst_f153m\n", - "------------------ ---------- ------------------ ... ----------- -----------\n", - " 59.05436159479683 False 59.05436159479683 ... 0.0 nan\n", - " 39.55979231152168 False 39.55979231152168 ... 0.0 nan\n", - "18.241030351057457 False 18.241030351057457 ... 0.0 nan\n", - " 21.34571632826156 False 21.34571632826156 ... 0.0 nan\n", - " 25.73229556345863 False 25.73229556345863 ... 0.0 nan\n", - " 38.27638428342816 False 38.27638428342816 ... 0.0 nan\n", - " 29.70200219305832 False 29.70200219305832 ... 0.0 nan\n", - " ... ... ... ... ... ...\n", - "104.95164061880804 False 104.95164061880804 ... 0.0 nan\n", - "19.927108168048903 False 19.927108168048903 ... 0.0 nan\n", - "51.756416056664165 False 51.756416056664165 ... 0.0 nan\n", - " 30.9405328260462 False 30.9405328260462 ... 0.0 nan\n", - "16.452556081467357 False 16.452556081467357 ... 0.0 nan\n", - "15.253386148271723 False 15.253386148271723 ... 0.0 nan\n", - " 34.00333629883476 False 34.00333629883476 ... 0.0 nan\n", - "Length = 2327 rows\n" + " mass isMultiple ... m_hst_f153m N_companions\n", + "------------------ ---------- ... ------------------- ------------\n", + "34.858558890643366 True ... nan 1\n", + " 24.21988253820487 True ... 0.19920518920036126 1\n", + " 53.76008089560359 True ... -0.2181337523917536 4\n", + "15.882675891462688 True ... -1.810589141056601 2\n", + " 57.37043948819449 True ... nan 3\n", + "31.414432201808793 True ... nan 1\n", + "22.819575828477465 True ... 1.0957723191576627 5\n", + " ... ... ... ... ...\n", + "23.712544384283426 True ... 1.6681099957862053 1\n", + " 52.89729960707116 True ... 1.3625884002383177 3\n", + " 82.84997226207857 True ... nan 2\n", + " 32.27796062878089 True ... 0.43046734619105526 3\n", + " 26.92983956402475 True ... nan 2\n", + " 27.13236574281697 True ... nan 2\n", + " 36.6006664768621 True ... 2.5063372504019394 3\n", + "Length = 1671 rows\n" ] } ], @@ -609,7 +1123,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 23, "id": "ddc351e5-e465-4f2e-9703-cd5c819a75f2", "metadata": {}, "outputs": [ @@ -619,39 +1133,39 @@ "text": [ " mass \n", "------------------\n", - " 59.05436159479683\n", - " 39.55979231152168\n", - "18.241030351057457\n", - " 21.34571632826156\n", - " 25.73229556345863\n", - " 38.27638428342816\n", - " 29.70200219305832\n", + "34.858558890643366\n", + " 24.21988253820487\n", + " 53.76008089560359\n", + "15.882675891462688\n", + " 57.37043948819449\n", + "31.414432201808793\n", + "22.819575828477465\n", " ...\n", - "104.95164061880804\n", - "19.927108168048903\n", - "51.756416056664165\n", - " 30.9405328260462\n", - "16.452556081467357\n", - "15.253386148271723\n", - " 34.00333629883476\n", - "Length = 2327 rows mass_current \n", + "23.712544384283426\n", + " 52.89729960707116\n", + " 82.84997226207857\n", + " 32.27796062878089\n", + " 26.92983956402475\n", + " 27.13236574281697\n", + " 36.6006664768621\n", + "Length = 1671 rows mass_current \n", "------------------\n", - " 7.69101697945427\n", - " 14.37645768969197\n", - " 6.490426115488822\n", - " 7.738537786899665\n", - " 9.302666353833182\n", - "13.811546507105783\n", - "10.635261248769924\n", + "12.446576185012097\n", + " 8.78166110167993\n", + " 8.806507411947209\n", + " 5.424709536731119\n", + " 7.981409529281804\n", + "11.216283709433917\n", + " 8.283995574428168\n", " ...\n", - "5.9065928872088955\n", - " 7.187073433524934\n", - " 9.426130388477304\n", - "11.054167247370941\n", - " 5.693090725172411\n", - " 5.119608907641256\n", - " 12.13042109233094\n", - "Length = 2327 rows\n" + " 8.603336271797449\n", + " 9.056339719938071\n", + " 6.152701918097497\n", + "11.515219186451843\n", + " 9.707197419320156\n", + " 9.775168357448042\n", + "13.119673398892417\n", + "Length = 1671 rows\n" ] } ], @@ -661,7 +1175,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 24, "id": "f5ce39b9-2c6d-4e7c-a6f6-ffd7235ac9e8", "metadata": {}, "outputs": [ @@ -669,8 +1183,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Initial mass of smallest generated black hole: 15.003458139141888\n", - "Initial mass of largest generated black hole: 119.60497046926774\n" + "Initial mass of smallest generated black hole: 15.000468103060483\n", + "Initial mass of largest generated black hole: 117.48601718195576\n" ] } ], @@ -681,9 +1195,109 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "1c41b219-7a85-4519-af99-77145daaa418", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial mass of smallest generated comp black hole: 15.037154672016358\n", + "Initial mass of largest generated comp black hole: 111.30489194158751\n" + ] + } + ], + "source": [ + "print(\"Initial mass of smallest generated comp black hole: \" + str(np.min(k_comp[c_bh]['mass'])))\n", + "print(\"Initial mass of largest generated comp black hole: \" + str(np.max(k_comp[c_bh]['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3566ea45-31f6-4015-bf84-d34ff93ec18e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6et62TpQsnEEEu9KkSRM5OTlZH9OnT5d047TWX3/9Vf3795ckm7NFHnnkEcXExGT59qJHjx42z+vXry9J1sti1q9fr+vXr2vAgAE263NxcVFISEi2Awk+/vjjWabFxcXp73//uwIDA+Xo6CgnJydVqVJFknTkyJE77nNu6zh69KguXLigfv362ZzuXKVKlTue+XOz6tWra9euXdq1a5d27NihZcuWyWKxqEOHDne8rCs3+3r16lVFR0erd+/e1lOqb2fx4sV65JFH9Oyzz+rzzz+Xi4tLrvfl7bfftu7LzY/evXvbtMs8bfzm07kl6cEHH9QDDzygTZs25biNb7/9ViaTSU8//bTN6+Pn56cGDRpYX58aNWrIy8tL48eP1wcffJDl29c7cXd3z/Iz269fP2VkZGjr1q3WNoMHD9aiRYus3/z88MMPOnz4sEaOHHlX23vmmWe0Zs0aXbx4UfPnz1f79u1zvAtMYmKixo8frxo1asjR0VGOjo4qW7askpKSbH7GH3zwQX3//fd65ZVXtGXLFiUnJ9usp3z58qpevbreeecdzZgxQ3v37rW5zA0A7Bl9oILtA908DlHmvyEhIZKkBx54QD4+Ptb+wpYtW+Tr66sHHnggy3rudGzvxrVr17Rp0yY99thjcnV1zfLaXrt2TTt37rzr9d7MuOWs6tatW8vFxUUbN26UdOMywdDQUD388MPavn27rl69qrNnz+rYsWM2l51fv35dU6ZMUZ06deTs7CxHR0c5Ozvr2LFj2b7W2f289O/fX2az2east88++0wpKSkaPHjwXR+TBx98UL/88ouGDx+u9evX3/VYPjn9PN18ueHzzz8vSTbjM86ZM0f16tVTu3btcr2tkJAQVa9eXQsWLNCBAwe0a9euHC8vk2707zp27CgPDw85ODjIyclJr732mi5evKi4uDhJNy6ddHZ21nPPPafFixdnGfpAunGMLl26pKeeekpff/31Hc+aR/FHQIRSp0KFCrJYLNl+kC5btky7du2yuQZZkvUynrFjx9p0npycnDR8+HBJyvKG5+3tbfPcbDZLkvWP1sx1NmvWLMs6V6xYkWV9rq6uWe7wlJGRoc6dO2vlypUaN26cNm3apJ9++sn6wXXrH8jZyW0dmZfp+Pn5ZVlHdtNy4uLioqZNm6pp06Zq0aKFnnrqKX3//feKiYnRa6+9luNyud3X+Ph4paenq1KlSrmqZ/ny5bJYLHr22Wfv+tb0999/v3Vfbn7cGkxlHrvsTucPCAiwuQTqVn/88YcMw5Cvr2+W12fnzp3W18fDw0PR0dFq2LChXn31VdWtW1cBAQF6/fXXc3X3EF9f3yzTMl/Xm+sbNWqUrly5ok8//VTSjU5KpUqV9Oijj95xGzd74okn5OLiopkzZ+qbb77RkCFDcmzbr18/zZkzR88++6zWr1+vn376Sbt27dJ9991n8zP+3nvvafz48Vq9erXat2+v8uXLq2fPntbg0WQyadOmTerSpYumTZumxo0b67777tMLL7yQq8vwAKCkow9kqzD7QPXq1VOFChW0efNm6/hDmQGRdOOmHVu2bFFKSop27NiR493L7nRs78bFixd1/fp1zZ49O8v+Z15uda9/0Gf+rGVeCuXi4qLWrVtbA6JNmzapU6dOCg0NVXp6urZt22a91OzmgGj06NGaNGmSevbsqW+++Ub//e9/tWvXLjVo0CDbfc+uz1W+fHn16NFDn3zyiXWMxUWLFunBBx9U3bp17/qYTJgwQf/617+0c+dOhYWFydvbWx06dMhyt7qc5PTzdHO/y9fXV3369NGHH36o9PR07d+/X9u2bbvrL+ZMJpMGDx6spUuX6oMPPlDNmjXVtm3bbNv+9NNP1pvGfPzxx/rPf/6jXbt2aeLEiZL+72etevXq2rhxo3x8fDRixAhVr15d1atXtxmD6W9/+5sWLFig06dP6/HHH5ePj4+aN29uczkhShb7HOodpZqDg4MeeughbdiwQTExMTYfIJl3fTh16pTNMhUqVJB044OgV69e2a63Vq1ad1VH5jq//PJL67ddt5NdeHHw4EH98ssvWrRokQYOHGidfvz48XyvI7NDEhsbm2VedtPuhr+/vypUqKBffvklxza53dfy5cvLwcEh1wMzf/rpp5o0aZJCQkK0YcMGNWzYME/7cDuZxy4mJiZLcHXhwgXra5CdChUqyGQyadu2bdZO4M1unlavXj0tX75chmFo//79WrRokSZPniyLxaJXXnnltjVmdpJvlvm63twZrVGjhsLCwvT+++8rLCxMa9asUWRkpBwcHG67/lu5urqqb9++mjp1qsqVK5fj79Xly5f17bff6vXXX7fZh5SUFP311182bd3c3BQZGanIyEj98ccf1rOJunfvbr19cJUqVawDVP7222/6/PPPFRERodTUVH3wwQd3tQ8AUNLQB8pbHfnRBzKZTAoJCdG6dev0008/6dKlSzYBUUhIiCIiIrRjxw5du3YtT7e3v1teXl5ycHDQ3/72txzHEaxWrVqe128Yhr755hu5ubmpadOm1ukdOnTQa6+9pp9++knnzp1Tp06d5O7urmbNmikqKkoXLlxQzZo1FRgYaF1m6dKlGjBggKZMmWKzjT///FOenp5Ztp3Tl36DBw/WF198oaioKFWuXFm7du3SvHnzrPPv5pg4Ojpq9OjRGj16tC5duqSNGzfq1VdfVZcuXXT27Nk73mkvp5+nW0PAF198UUuWLNHXX3+tdevWydPT03pG390YNGiQXnvtNX3wwQc2d5K91fLly+Xk5KRvv/3W5sz61atXZ2nbtm1btW3bVunp6dq9e7dmz56t8PBw+fr6qm/fvpJuHPPBgwcrKSlJW7du1euvv65u3brpt99+y9XvP4oXAiKUShMmTND333+vv//97/ryyy/vePesWrVqKSgoSL/88kuWD6a86tKlixwdHXXixIlsT4PNjcwPv1uDgw8//DBL25y+YcptHbVq1ZK/v78+++wzjR492rrt06dPa/v27bkaJC8n586d059//nnb27Lmdl8z7wLxxRdf6J///OdtwxfpRqC0ceNGdevWTe3bt9f3339vc6vW/PDQQw9JutG5adasmXX6rl27dOTIEes3Mtnp1q2b3nrrLZ0/fz7LpWs5MZlMatCggWbOnKlFixbp559/vuMyV65c0Zo1a2xOXV+2bJl1YM2bvfjii+rcubMGDhwoBwcHDR06NFd13er555/XH3/8oZCQkBwv7TOZTDIMI8vr/u9//9v67V92fH19NWjQIP3yyy+aNWtWtrdErlmzpv7xj3/oq6++ytUxAoDSgD7Q3deRX32g9u3b66uvvtI777wjHx8fm0vIQkJCdPHiRc2ePdvaNr/ktP+urq5q37699u7dq/r16+d4V7u8ioyM1OHDh/Xqq6/afM537NhRr776qiZNmqRKlSqpdu3a1ulr1qxRbGxsltfDZDJlea3Xrl2r8+fPq0aNGrmuqXPnzqpYsaIWLlyoypUry8XFxXqXLynvx8TT01NPPPGEzp8/r/DwcJ06deq2/VpJOf48DRgwwKZdkyZN1KpVK7399ts6ePCgnnvuObm5ueV6nzNVrFhRL7/8sn799VebUPVWJpNJjo6ONl/+JScna8mSJTku4+DgoObNm6t27dr69NNP9fPPP1sDokxubm4KCwtTamqqevbsqUOHDhEQlUAERCiVWrdurffff1+jRo1S48aN9dxzz6lu3boqU6aMYmJi9NVXX0mSzenMH374ocLCwtSlSxcNGjRIFStW1F9//aUjR47o559/1hdffHFXNVStWlWTJ0/WxIkT9fvvv+vhhx+Wl5eX/vjjD/3000/WsyFup3bt2qpevbpeeeUVGYah8uXL65tvvsn2tM3Mu0C8++67GjhwoJycnFSrVq1c11GmTBm98cYbevbZZ/XYY49p6NChunTpkiIiIu7qErPk5GTr6d/p6ek6efKkpk2bJkkKDw/Pl32dMWOG2rRpo+bNm+uVV15RjRo19Mcff2jNmjX68MMP5e7ubtPe3d1d69atU69evax3GcvPjlmtWrX03HPPafbs2SpTpozCwsJ06tQpTZo0SYGBgXrppZdyXLZ169Z67rnnNHjwYO3evVvt2rWTm5ubYmJi9OOPP6pevXp6/vnn9e2332ru3Lnq2bOn7r//fhmGoZUrV+rSpUvq1KnTHWv09vbW888/rzNnzqhmzZr67rvv9PHHH+v5559X5cqVbdp26tRJderU0ebNm/X000/Lx8cnT8elYcOG2X4bdbNy5cqpXbt2euedd1ShQgVVrVpV0dHRmj9/fpZvDJs3b65u3bqpfv368vLy0pEjR7RkyRK1bNlSrq6u2r9/v0aOHKknn3xSQUFBcnZ21g8//KD9+/ff8QwrACgt6AMVXR8os2+xatUqPfHEEzbzgoOD5e3trVWrVqlixYq5uptqbrm7u6tKlSr6+uuv1aFDB5UvX976mfruu++qTZs2atu2rZ5//nlVrVpVV65c0fHjx/XNN9/k6i5tly5dsvbtkpKSdPToUS1fvlzbtm1T7969s7yWTZo0kZeXlzZs2GAd+0e6ERBl3iXr5svLpBtfmC1atEi1a9dW/fr1tWfPHr3zzju5HlIgk4ODgwYMGKAZM2ZYz2D28PCwaZPbY9K9e3cFBwdbhxc4ffq0Zs2apSpVquTq9YuLi7P+PF2+fFmvv/66XFxcNGHChCxtX3zxRfXp00cmk8l6aWdevPXWW3ds07VrV82YMUP9+vXTc889p4sXL+pf//pXloDugw8+0A8//KCuXbuqcuXKunbtmvVOaZmv39ChQ2WxWNS6dWv5+/srNjZWU6dOlYeHh82XpihBimx4bKAQ7Nu3zxg8eLBRrVo1w2w2Gy4uLkaNGjWMAQMGGJs2bcrS/pdffjF69+5t+Pj4GE5OToafn5/x0EMPGR988IG1zc13DLhZ5t0Dbr070+rVq4327dsb5cqVM8xms1GlShXjiSeeMDZu3GhtM3DgQMPNzS3bfTh8+LDRqVMnw93d3fDy8jKefPJJ48yZM9nerWLChAlGQECAUaZMmSy15KYOwzCMf//730ZQUJDh7Oxs1KxZ01iwYEGWO2/l5Na7mJUpU8YICAgwwsLCjC1btti0ze4uZnezr4cPHzaefPJJw9vb23B2djYqV65sDBo0yLh27ZrN+m9+nVJSUozHH3/ccHFxMdauXZvjfmS+ll988UW280eMGGHc+vaZnp5uvP3220bNmjUNJycno0KFCsbTTz9tnD171qZdTsdywYIFRvPmzQ03NzfDYrEY1atXNwYMGGDs3r3bMAzD+PXXX42nnnrKqF69umGxWAwPDw/jwQcfNBYtWpTjfmQKCQkx6tata2zZssVo2rSpYTabDX9/f+PVV1/NcseaTBEREYYkY+fOnXdcf6Y73enEMIxs72J27tw54/HHHze8vLwMd3d34+GHHzYOHjxoVKlSxRg4cKC13SuvvGI0bdrU8PLyMsxms3H//fcbL730kvHnn38ahmEYf/zxhzFo0CCjdu3ahpubm1G2bFmjfv36xsyZM43r16/nej8AoDSgD/R/tRRGHyiTn5+fIcmYM2dOlnk9e/Y0JBn9+/fPMu9uju2tdzEzjBt3D2vUqJFhNpsNSTafnydPnjSeeeYZo2LFioaTk5Nx3333Ga1atTLefPPNO+5PlSpVrP06k8lklC1b1qhVq5bxt7/9zVi/fn2Oyz322GOGJOPTTz+1TktNTTXc3NyMMmXKGPHx8Tbt4+PjjSFDhhg+Pj6Gq6ur0aZNG2Pbtm1Z9vVOfTTDMIzffvvNWnNUVFS2bXJzTKZPn260atXKqFChgrWvOWTIEOPUqVO3PWaZNS5ZssR44YUXjPvuu88wm81G27Ztrf26W6WkpBhms9l4+OGHb7vum+X0M3Or7O5itmDBAqNWrVrW/tTUqVON+fPn2/TNd+zYYTz22GNGlSpVDLPZbHh7exshISHGmjVrrOtZvHix0b59e8PX19dwdnY2AgICjN69exv79+/P9X6geDEZxi1DzwMA7F7Tpk1lMpm0a9euoi4FAACgVPvmm2/Uo0cPrV271jpYNlAUuMQMACBJSkhI0MGDB/Xtt99qz549WrVqVVGXBAAAUGodPnxYp0+f1pgxY9SwYUOFhYUVdUmwcwREAABJ0s8//6z27dvL29tbr7/+unr27FnUJQEAAJRaw4cP13/+8x81btxYixcvzvHubEBh4RIzAAAAAAAAO1emqAsAAAAAAABA0SIgAgAAAAAAsHMERAAAAAAAAHaOQaolZWRk6MKFC3J3d2dgMAAAijHDMHTlyhUFBASoTBm+5ypK9J8AACgZctt/IiCSdOHCBQUGBhZ1GQAAIJfOnj2rSpUqFXUZdo3+EwAAJcud+k8ERJLc3d0l3ThY5cqVK+JqAABAThISEhQYGGj97EbRof8EAEDJkNv+EwGRZD0tuly5cnRwAAAoAbikqejRfwIAoGS5U/+Ji/cBAAAAAADsHAERAAAAAACAnSMgAgAAAAAAsHOMQQQABSg9PV1paWlFXQZQYjg5OcnBwaGoy0A+ycjIUGpqalGXAeAmvM8CyAkBEQAUAMMwFBsbq0uXLhV1KUCJ4+npKT8/PwaiLuFSU1N18uRJZWRkFHUpAG7B+yyA7BAQAUAByAyHfHx85OrqSgcMyAXDMHT16lXFxcVJkvz9/Yu4IuSVYRiKiYmRg4ODAgMDVaYMoxoAxQHvswBuh4AIAPJZenq6NRzy9vYu6nKAEsVisUiS4uLi5OPjw2UQJdT169d19epVBQQEyNXVtajLAXAT3mcB5ISvcwAgn2WOOcQfRUDeZP7uMH5XyZWeni5JcnZ2LuJKAGSH91kA2SEgAoACwmVlQN7wu1N68FoCxRO/mwCyQ0AEAAAAAABg5xiDCAAK0flLyYpPKrxbPnu5Oauip6XQtldaVa1aVeHh4QoPD7+n9ZhMJq1atUo9e/YsVnXlRUREhFavXq19+/YV+rZRcvEeWDIV1HvNndZ76tQpVatWTXv37lXDhg3zddsAgKwIiACgkJy/lKyO06OVnJZeaNu0ODlo45iQu/oDKTY2VlOnTtXatWt17tw5eXh4KCgoSE8//bQGDBhQYsZWKszwJCIiQpGRkdbn5cqVU/369fXmm28qJCSkwLefW1u2bFH79u0VHx8vT09Pm3lFGTbBPvAeWLgK63d63bp1CgsLU0xMjPz8/KzT/fz85OTkpLNnz1qnnTt3ToGBgVq/fr06d+58x3UHBgYqJiZGFSpUkHT797C7FRoaqujoaEk3xsqqUKGCGjdurMGDB6tXr173tG4AKKkIiACgkMQnpSo5LV2z+jRUDZ+yBb6943GJCl+xT/FJqbn+4+j3339X69at5enpqSlTpqhevXq6fv26fvvtNy1YsEABAQHq0aNHAVeeM8MwlJ6eLkfH4vfxVbduXW3cuFGS9Ndff+lf//qXunXrZv0DE7B3vAfeu+L4HtimTRs5Ojpqy5Yt6tu3ryTpyJEjunbtmpKTk3X8+HHVqFFDkrR582Y5OTmpdevWuVq3g4ODTeiU34YOHarJkycrLS1N58+f16pVq9S3b18NGjRIH330UYFt91ZpaWlycnIqtO0BQE4YgwgAClkNn7IKruhR4I+8/AE2fPhwOTo6avfu3erdu7ceeOAB1atXT48//rjWrl2r7t27W9tevnxZzz33nHx8fFSuXDk99NBD+uWXX6zzIyIi1LBhQy1ZskRVq1aVh4eH+vbtqytXrljbGIahadOm6f7775fFYlGDBg305ZdfWudv2bJFJpNJ69evV9OmTWU2m7Vt2zadOHFCjz76qHx9fVW2bFk1a9bMGs5IN74ZPn36tF566SWZTCabwTi3b9+udu3ayWKxKDAwUC+88IKSkpKs8+Pi4tS9e3dZLBZVq1ZNn376aa6OnaOjo/z8/OTn56c6deooMjJSiYmJ+u2333JcZvz48apZs6ZcXV11//33a9KkSVnuKLNmzRo1bdpULi4uqlChwm2/2V64cKE8PDwUFRWVq5pv58yZM3r00UdVtmxZlStXTr1799Yff/xx22UWLlyoBx54QC4uLqpdu7bmzp1rnZeamqqRI0fK399fLi4uqlq1qqZOnXrPdaLk4T2wdL0HZm5/y5YtNnW3adNGbdq0yTL9wQcflJubm3Xa1atX9cwzz8jd3V2VK1e2CWZOnTolk8mkffv26dSpU2rfvr0kycvLSyaTSYMGDcrVccyJq6ur/Pz8FBgYqBYtWujtt9/Whx9+qI8//th6PB9//HGNGjXKukx4eLhMJpMOHTokSbp+/brc3d21fv16STfOqGrTpo08PT3l7e2tbt266cSJE1n26fPPP1doaKhcXFw0d+5cWSwWrVu3zqa+lStXys3NTYmJiZKk8+fPq0+fPvLy8pK3t7ceffRRnTp1Ktvj6+npqdatW+v06dN3PA4AkImACAAgSbp48aI2bNigESNG2HTeb5b5R4ZhGOratatiY2P13Xffac+ePWrcuLE6dOigv/76y9r+xIkTWr16tb799lt9++23io6O1ltvvWWd/49//EMLFy7UvHnzdOjQIb300kt6+umnraf9Zxo3bpymTp2qI0eOqH79+kpMTNQjjzyijRs3au/everSpYu6d++uM2fOSLrRqa5UqZImT56smJgYxcTESJIOHDigLl26qFevXtq/f79WrFihH3/8USNHjrRua9CgQTp16pR++OEHffnll5o7d67i4uLu6limpKRo0aJF8vT0VK1atXJs5+7urkWLFunw4cN699139fHHH2vmzJnW+WvXrlWvXr3UtWtX7d27V5s2bVLTpk2zXde//vUvjR07VuvXr1enTp3uqt5bGYahnj176q+//lJ0dLSioqJ04sQJ9enTJ8dlPv74Y02cOFH//Oc/deTIEU2ZMkWTJk3S4sWLJUnvvfee1qxZo88//1xHjx7V0qVLVbVq1XuqE8hPvAfekJf3wPbt22vz5s3W55s3b1ZoaKhCQkKyTM8MeTJNnz5dTZs21d69ezV8+HA9//zz+vXXX7NsIzAwUF999ZUk6ejRo4qJidG77757V8cxNwYOHCgvLy+tXLlS0o2w7eaQKzo6WhUqVLCue9euXbp27Zr1rKikpCSNHj1au3bt0qZNm1SmTBk99thjysjIsNnO+PHj9cILL+jIkSN68skn1bVr1yxh3LJly6xB/dWrV9W+fXuVLVtWW7du1Y8//qiyZcvq4YcfVmpqqq5fv66ePXsqJCRE+/fv144dO/Tcc89xtzIAd8eAcfnyZUOScfny5aIuBUApkJycbBw+fNhITk62mX7g3CWjyvhvjQPnLhVKHXe7vZ07dxqSjJUrV9pM9/b2Ntzc3Aw3Nzdj3LhxhmEYxqZNm4xy5coZ165ds2lbvXp148MPPzQMwzBef/11w9XV1UhISLDOf/nll43mzZsbhmEYiYmJhouLi7F9+3abdQwZMsR46qmnDMMwjM2bNxuSjNWrV9+x/jp16hizZ8+2Pq9SpYoxc+ZMmzZ/+9vfjOeee85m2rZt24wyZcoYycnJxtGjRw1Jxs6dO63zjxw5YkjKsq6bvf7660aZMmWsx8lkMhnlypUzvv/+e5t2koxVq1bluJ5p06YZTZo0sT5v2bKl0b9//xzbZ+7jK6+8Yvj7+xv79+/Psa1h/N/xzKzz5ofJZLLu44YNGwwHBwfjzJkz1mUPHTpkSDJ++ukn6z43aNDAOj8wMNBYtmyZzfbeeOMNo2XLloZhGMaoUaOMhx56yMjIyLhtjYaR8++QYfCZXZzc7rXI7jXkPbD0vgdu2LDBkGRcuHDBMAzD8PHxMX766Sdj586dRkBAgGEYhnHmzBlDkrFp0yabGp9++mnr84yMDMPHx8eYN2+eYRiGcfLkSUOSsXfvXpvjER8fb10mN8cxOyEhIcaLL76Y7bzmzZsbYWFhhmEYxv79+w2TyWT873//M/766y/DycnJePPNN40nn3zSMAzDmDJlivU1zU5cXJwhyThw4IDNPs2aNcum3cqVK42yZcsaSUlJhmHc+P1ycXEx1q5daxiGYcyfP9+oVauWzXtoSkqKYbFYjPXr1xsXL140JBlbtmzJsZab3e59FkDpk9v+U/G5gBkAUCzc+m3jTz/9pIyMDPXv318pKSmSpD179igxMVHe3t42bZOTk21Opa9atarc3d2tz/39/a3fRB8+fFjXrl3LcrZLamqqGjVqZDPt1rNmkpKSFBkZqW+//VYXLlzQ9evXlZycbP32PCd79uzR8ePHbb6lNQxDGRkZOnnypH777Tc5OjrabK927dq5Ggy1Vq1aWrNmjSTpypUrWrFihZ588klt3rw5x7N+vvzyS82aNUvHjx9XYmKirl+/rnLlylnn79u3T0OHDr3tdqdPn66kpCTt3r1b999//x3rlKRt27bZvC7SjW/JMx05ckSBgYEKDAy0TqtTp448PT115MgRNWvWzGbZ//3vfzp79qyGDBliU+/169et4y8NGjRInTp1Uq1atfTwww+rW7duuRqkFihsvAfe/Xtg69at5ezsrC1btqhBgwZKTk5W48aNZRiGEhISdOzYMe3YsUNms1mtWrWyWbZ+/frW/5tMJvn5+d3VWZt3cxxzyzAM689BcHCwvL29FR0dLScnJzVo0EA9evTQe++9J+nGZV0334zgxIkTmjRpknbu3Kk///zTeubQmTNnFBwcbG1362vatWtXOTo6as2aNerbt6+++uorubu7W98nM1+7W9+7r127phMnTqhz584aNGiQunTpok6dOqljx47q3bu3/P3983QMANinIg2Ipk6dqpUrV+rXX3+VxWJRq1at9Pbbb9ucjm8YhiIjI/XRRx8pPj5ezZs31/vvv6+6deta26SkpGjs2LH67LPPlJycrA4dOmju3LmqVKlSUewWAJRINWrUkMlkynJqf2boYLH83yCvGRkZ8vf3tzntPtPNf0jcOuimyWSydpYz/127dq0qVqxo085sNts8v/Vyj5dfflnr16/Xv/71L9WoUUMWi0VPPPGEUlNvf/vsjIwMDRs2TC+88EKWeZUrV9bRo0etdd4tZ2dn60CsktSoUSOtXr1as2bN0tKlS7O037lzp/r27avIyEh16dJFHh4eWr58uaZPn25tc/Mxz0nbtm21du1aff7553rllVdyVWu1atWy/MF386C3N/9xdLOcpme+lh9//LGaN29uM8/BwUGS1LhxY508eVLff/+9Nm7cqN69e6tjx465GicEKAy8B+b9PdDV1VUPPvigNm/erL/++ktt2rSx/u63atVKmzdv1o4dO9SyZUu5uLjYLHu7Y5Qbd3MccyM9PV3Hjh2zBuEmk0nt2rXTli1b5OzsrNDQUAUHBys9PV0HDhzQ9u3bbe4U1717dwUGBurjjz9WQECAMjIyFBwcnOW1ufU1dXZ21hNPPKFly5apb9++WrZsmfr06WN9b87IyFCTJk2yHRPqvvvuk3RjHLgXXnhB69at04oVK/SPf/xDUVFRatGixV0fBwD2qUgDoujoaI0YMULNmjXT9evXNXHiRHXu3FmHDx+2vmlOmzZNM2bM0KJFi1SzZk29+eab6tSpk44ePWpN0MPDw/XNN99o+fLl8vb21pgxY9StWzft2bPH+uEEoHQ4fylZ8Um37wDnlZeb813dCrm08fb2VqdOnTRnzhyNGjUqxzE4pBt/7MfGxsrR0THP48jUqVNHZrNZZ86cuetbwW/btk2DBg3SY489JklKTEy0GahTutHZTk+3vZ1248aNdejQIZsg52YPPPCArl+/rt27d+vBBx+UdGOsi0uXLt1VfZkcHByUnJyc7bz//Oc/qlKliiZOnGiddutgovXr19emTZs0ePDgHLfx4IMPatSoUerSpYscHBz08ssv56nWm9WpU0dnzpzR2bNnrWcRHT58WJcvX9YDDzyQpb2vr68qVqyo33//Xf37989xveXKlVOfPn3Up08fPfHEE3r44Yf1119/qXz58vdcM3CveA+8t/fA9u3ba/ny5YqPj7c5IzEkJERbtmzRjh07bvtelhvOzs6SZLNf93Ics7N48WLFx8fr8ccft04LDQ3VRx99JGdnZ02ePFkmk0lt27bVv/71LyUnJ1vHH7p48aKOHDmiDz/8UG3btpUk/fjjj7nedv/+/dW5c2cdOnRImzdv1htvvGGd17hxY61YscI6KHpOGjVqpEaNGmnChAlq2bKlli1bZhcBUUH2D2/H3vuOKH2KNCC6daT+hQsXysfHR3v27FG7du1kGIZmzZqliRMnWu/asnjxYvn6+mrZsmUaNmyYLl++rPnz52vJkiXq2LGjJGnp0qUKDAzUxo0b1aVLl0LfLwAF4/ylZHWcHq3ktPQ7N84Di5ODNo4JsesP+rlz56p169Zq2rSpIiIiVL9+fZUpU0a7du3Sr7/+qiZNmkiSOnbsqJYtW6pnz57WMz8vXLig7777Tj179szxkqqbubu7a+zYsXrppZeUkZGhNm3aKCEhQdu3b1fZsmU1cODAHJetUaOGVq5cqe7du8tkMmnSpElZvnGuWrWqtm7dqr59+8psNqtChQoaP368WrRooREjRmjo0KFyc3PTkSNHFBUVpdmzZ1svfxo6dKg++ugjOTo6Kjw8PFdn8ly/fl2xsbGS/u8Ss8OHD2v8+PE57sOZM2e0fPlyNWvWTGvXrtWqVats2rz++uvq0KGDqlevrr59++r69ev6/vvvNW7cOJt2LVu21Pfff6+HH35Yjo6Oeumll+5Y7+107NhR9evXV//+/TVr1ixdv35dw4cPV0hISI6vbUREhF544QWVK1dOYWFhSklJ0e7duxUfH6/Ro0dr5syZ8vf3V8OGDVWmTBl98cUX8vPzy9Xle0Bh4T0w7++B7du31xtvvKGYmBiNHTvWOj0kJERvvfWWrly5kmWA6rtVpUoVmUwmffvtt3rkkUdksVju6ThevXpVsbGxun79us6fP6+VK1dq5syZev75521qDQ0N1YsvvihHR0dr8BMaGqoxY8aocePG1sAm8+5iH330kfz9/XXmzJlcn9kp3ThWvr6+6t+/v6pWrWoT7PTv31/vvPOOHn30UU2ePFmVKlXSmTNntHLlSr388stKS0vTRx99pB49eiggIEBHjx7Vb7/9pgEDBtztYS5xCrp/eDv0HVHaFKsxiC5fvixJ1m8ST548qdjYWJsxCsxms0JCQrR9+3YNGzZMe/bsUVpamk2bgIAABQcHa/v27dkGRCkpKdZryCUpISGhoHYJQD6KT0pVclq6ZvVpmKfbF9/O8bhEha/Yp/ik1AL/kD8el1ig67+X7VSvXl179+7VlClTNGHCBJ07d05ms1l16tTR2LFjNXz4cEk3Trn/7rvvNHHiRD3zzDP63//+Jz8/P7Vr106+vr653t4bb7whHx8fTZ06Vb///rs8PT3VuHFjvfrqq7ddbubMmXrmmWfUqlUr6x89t76XT548WcOGDVP16tWVkpIiwzBUv359RUdHa+LEiWrbtq0Mw1D16tVt7s61cOFCPfvss9aO+ptvvqlJkybdcV8OHTpkHevB1dVV1atX17x583LsnD/66KN66aWXNHLkSKWkpKhr166aNGmSIiIirG1CQ0P1xRdf6I033tBbb72lcuXKqV27dtmur3Xr1lq7dq0eeeQROTg4ZHsJSW6ZTCatXr1ao0aNUrt27VSmTBk9/PDDmj17do7LPPvss3J1ddU777yjcePGyc3NTfXq1bNeelG2bFm9/fbbOnbsmBwcHNSsWTN99913KlOGG6raG94D/09peg9s2bKl9ZKuzCBNkpo1a6b09HRZLJYsl6DerYoVKyoyMlKvvPKKBg8erAEDBmjRokV5Po4ff/yxPv74Yzk7O8vb21tNmjTRihUrrGdmZQoODlaFChVUpUoVaxgUEhKi9PR0m7OWypQpo+XLl+uFF15QcHCwatWqpffee8/mjKrbMZlMeuqpp/TOO+/otddes5nn6uqqrVu3avz48erVq5euXLmiihUrqkOHDipXrpySk5P166+/avHixbp48aL8/f01cuRIDRs2LFfbLskKsn94O4XZdwQKi8kwDKOoi5BujGvw6KOPKj4+Xtu2bZMkbd++Xa1bt9b58+cVEBBgbfvcc8/p9OnTWr9+vZYtW6bBgwfbBD6S1LlzZ1WrVk0ffvhhlm1FREQoMjIyy/TLly/f9pRNAEXr4PnL6jb7R307qo2CK3oU23Vfu3ZNJ0+eVLVq1WzGWiiKb7j4ZgslUU6/Q9KNL3U8PDz4zC4GbvdaZPca8h4IFB+3e58taQqyf1gctwvkRW77T8XmDKKRI0dq//792V6ne+tAeTkNkpnbNhMmTNDo0aOtzxMSEmzu1AIABaGip0Ubx4QU6jXyXBsPoLjgPRAAgOKtWAREo0aN0po1a7R161abO4/5+flJkmJjY21u0RgXF2c9fdfPz0+pqamKj4+Xl5eXTZtbb6OZyWw25+muBgBwryp6WvhjBYDd4j0QAIDiq0gv/DcMQyNHjtTKlSv1ww8/qFq1ajbzq1WrJj8/P0VFRVmnpaamKjo62hr+NGnSRE5OTjZtYmJidPDgwRwDIgAAAAAAAPyfIj2DaMSIEVq2bJm+/vprubu7W+/+4uHhIYvFIpPJpPDwcE2ZMkVBQUEKCgrSlClT5Orqqn79+lnbDhkyRGPGjJG3t7fKly+vsWPHql69eta7mgEAAAAAACBnRRoQzZs3T5KyjOy/cOFCDRo0SJI0btw4JScna/jw4YqPj1fz5s21YcMGubu7W9vPnDlTjo6O6t27t5KTk9WhQwctWrRIDg4OhbUrAJBFMbkHAFDi8LtTevBaAsUTv5sAslPkl5hl98gMh6QbA1RHREQoJiZG165dU3R0tIKDg23W4+LiotmzZ+vixYu6evWqvvnmGwadBlBknJycJElXr14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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(k_comp[c_bh]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(k_comp[c_wd]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3b4167fc-fd67-4c11-9cb4-f46b1e8cb9a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9729527582037262\n" + ] + } + ], + "source": [ + "print(np.min(k_comp[c_wd]['mass_current']))" + ] + }, + { + "cell_type": "markdown", + "id": "c17cd2c5-164a-49be-b118-8023b6f3a3b4", + "metadata": {}, + "source": [ + "## New Cluster after addition of BD criterion" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "bd81a9c8-bc0c-46e3-a20d-bc2112a50f5c", + "metadata": {}, + "outputs": [], + "source": [ + "#locate brown dwarfs\n", + "p_bd = np.where(k_out['phase'] == 99)[0]\n", + "c_bd = np.where(k_comp['phase'] == 99)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f49be47c-dc94-467e-bf4d-41b6f926ace5", + "metadata": {}, "outputs": [], "source": [] } diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 8322ca05..57c46bd1 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -1020,8 +1020,8 @@ def test_Raithel18_phase_designation(): Raithel = ifmr.IFMR_Raithel18() #create an MZAMS array to cover all potential phase designations, and the expected phase designations - test_MZAMS = np.array([0.06, 0.3, 1.0, 3.0, 6.0, 10.0, 14.0, 25.0, 100.0]) - expected_phases = np.array([99, -1, 101, 101, 101, 102, 102, 103, 103]) + test_MZAMS = np.array([0.06, 0.3, 0.6, 1.0, 3.0, 6.0, 10.0, 14.0, 25.0, 100.0]) + expected_phases = np.array([99, -1, 101, 101, 101, 101, 102, 102, 103, 103]) #generate the output from Raithel IFMR output_array = Raithel.generate_death_mass(test_MZAMS) From 78d8712ec0688087c40358499131689a0ac819b1 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 9 Aug 2024 11:01:12 -0700 Subject: [PATCH 015/191] Added outline of issues in this notebook (not Test_BD_Cluster) --- changes/Compact_Multiplicity.ipynb | 159 +++++++++++++++++++++++++++++ 1 file changed, 159 insertions(+) diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index 8c27e7ab..347160e5 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -305,6 +305,165 @@ "print(\"BH min mass: \" + str(np.min(k_out[p_bd]['mass'])) + '\\n')" ] }, + { + "cell_type": "code", + "execution_count": 85, + "id": "ed4301d8-2eac-4f78-895b-c8c63b22fe5b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "float64\n", + "isWR\n", + "----\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " nan\n", + " nan\n", + " ...\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + "Length = 1363 rows\n" + ] + } + ], + "source": [ + "#check to see if large NS are Wolf-Rayet\n", + "print(k_out['isWR'].dtype)\n", + "large_NS = np.where(k_out[p_ns]['mass'] > 15)\n", + "print(k_out['isWR'][large_NS])" + ] + }, + { + "cell_type": "markdown", + "id": "11435009-9e98-4e31-a290-8c047b469ba5", + "metadata": {}, + "source": [ + "I am unsure on what the 0.0 and nan values correspond to." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "66c9450c-7758-402f-a3f6-5100ef05a01e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered Masses: mass \n", + "------------------\n", + " 0.76799007770147\n", + "1.1955031412949875\n", + "2.2249813543703456\n", + " 3.660007844283093\n", + "0.8470178943035628\n", + "0.8619046086355177\n", + "0.8153335900401504\n", + " ...\n", + "0.9029881664660251\n", + "0.5385601472294455\n", + "0.5055061897257748\n", + "0.6718807496769587\n", + "0.7515013567753969\n", + "0.7974747944530974\n", + " 1.030318707836774\n", + "Length = 391228 rows\n", + "phase\n", + "-----\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " ...\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + "Length = 391228 rows\n", + " mass_current \n", + "------------------\n", + " 0.76799007770147\n", + "1.1955031412949875\n", + "2.2249813543703456\n", + " 3.660007844283093\n", + "0.8470178943035628\n", + "0.8619046086355177\n", + "0.8153335900401504\n", + " ...\n", + "0.9029881664660251\n", + "0.5385601472294455\n", + "0.5055061897257748\n", + "0.6718807496769587\n", + "0.7515013567753969\n", + "0.7974747944530974\n", + " 1.030318707836774\n", + "Length = 391228 rows\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#check out the masses within the problem range for white dwarfs\n", + "wd_issue_masses = np.where((k_out['mass'] >=0.5) & (k_out['mass'] <5))\n", + "print(\"Filtered Masses: \", k_out['mass'][wd_issue_masses])\n", + "print(k_out['phase'][wd_issue_masses])\n", + "print(k_out['mass_current'][wd_issue_masses])\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(k_out['mass'][wd_issue_masses], histtype = 'step',\n", + " bins = bh_bins, label = 'Initial masses of Problem WDs')\n", + "plt.title(\"Generated Objects of Initial Mass from 0.5 - 5 $M_\\odot$\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(k_out['mass_current'][wd_issue_masses], histtype = 'step',\n", + " bins = wd_bins, label = 'Final masses of Problem WDs')\n", + "plt.title(\"Final Evolved Masses\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()" + ] + }, { "cell_type": "markdown", "id": "11a7e249-23db-43e6-a071-838c676e61be", From 3fb65af7d17a9c6eb425a27b3b8488f4213a713b Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 13 Aug 2024 18:34:55 -0700 Subject: [PATCH 016/191] Changes to multiplicity testing --- spisea/tests/test_synthetic.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 57c46bd1..cb84c72c 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -421,7 +421,7 @@ def test_ifmr_multiplicity(): startTime = time.time() - evo = evolution.MISTv1() + evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere ifmr_obj = ifmr.IFMR_Raithel18() @@ -435,8 +435,8 @@ def test_ifmr_multiplicity(): print('Constructed isochrone: %d seconds' % (time.time() - startTime)) # Now to create the cluster. - imf_mass_limits = np.array([0.07, 0.5, 1, np.inf]) - imf_powers = np.array([-1.3, -2.3, -2.3]) + imf_mass_limits = np.array([0.01, 0.07, 0.5, 1, np.inf]) + imf_powers = np.array([-0.3, -1.3, -2.3, -2.3]) ########## # Start without multiplicity and IFMR @@ -474,16 +474,19 @@ def test_ifmr_multiplicity(): assert len(np.where(clust2['phase'] == 102)) > 0 assert len(np.where(clust1['phase'] == 103)) > 0 # BH assert len(np.where(clust2['phase'] == 103)) > 0 + assert len(np.where(clust1['phase'] == 99)) > 0 # BD + assert len(np.where(clust2['phase'] == 99)) > 0 # Now check that we have companions that are WDs, NSs, and BHs assert len(np.where(comps2['phase'] == 101)) > 0 assert len(np.where(comps2['phase'] == 102)) > 0 assert len(np.where(comps2['phase'] == 103)) > 0 + assert len(np.where(comps2['phase'] == 99)) > 0 # Make sure no funky phase designations (due to interpolation effects) # slipped through - idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 101) & (clust1['phase'] != 9) ) - idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 101) & (comps2['phase'] != 9) ) + idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 99) & (clust1['phase'] != 9) ) + idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 99) & (comps2['phase'] != 9) ) assert len(idx[0]) == 0 # Ensure no substellar mass compact objects are generated @@ -510,7 +513,7 @@ def test_ifmr_multiplicity(): comp_wd_idx = np.where(comps2['phase'] == 101) comp_ns_idx = np.where(comps2['phase'] == 102) comp_bh_idx = np.where(comps2['phase'] == 103) - comp_bd_idx = np.where(comps2['phase'] == 103) + comp_bd_idx = np.where(comps2['phase'] == 99) assert np.all(comps2['mass'][comp_wd_idx] > MIN_MASS) assert np.all(comps2['mass'][comp_ns_idx] > MIN_MASS) assert np.all(comps2['mass'][comp_bh_idx] > MIN_MASS) From 2851408091f9b264a77fc3d2697b66874a252d8f Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 13 Aug 2024 18:38:53 -0700 Subject: [PATCH 017/191] Noncompanion exploration and companion decoding --- changes/Compact_Multiplicity.ipynb | 928 +++++++++++++++++++++++++---- 1 file changed, 796 insertions(+), 132 deletions(-) diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index 347160e5..0282c8bf 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -46,12 +46,12 @@ "id": "f8639685-3185-4e3e-88c6-98602bdaedc9", "metadata": {}, "source": [ - "### Cluster 1: Without Companions" + "## Cluster 1: Without Companions" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 219, "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", "metadata": {}, "outputs": [ @@ -59,9 +59,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Isochrone generation took 80.686259 s.\n", + "Isochrone generation took 71.359145 s.\n", "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", - " Starting at: 2024-08-08 20:57:20.603079 Usually takes ~5 minutes\n", + " Starting at: 2024-08-13 18:04:09.632289 Usually takes ~5 minutes\n", "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", "Starting synthetic photometry\n", "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", @@ -75,7 +75,7 @@ "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", - " Time taken: 21.66 seconds\n" + " Time taken: 17.31 seconds\n" ] } ], @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 90, "id": "1a68a281-791a-4821-8799-c2df8e936286", "metadata": {}, "outputs": [ @@ -109,24 +109,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 1041613, 1041614, 1041615]),), Masses: mass \n", + "Brown dwarf indices: (array([ 0, 1, 2, ..., 1037624, 1037625, 1037626]),), Masses: mass \n", "--------------------\n", - " 0.06752873088255393\n", - " 0.0262184880713122\n", - "0.011103322341764093\n", - " 0.07004122940473101\n", - "0.055860465009665516\n", - " 0.03849994739007916\n", - "0.036201844969911835\n", + "0.010581623004327498\n", + " 0.07746106089705293\n", + " 0.06953880412693926\n", + " 0.07376813803092074\n", + "0.018112506487775234\n", + " 0.05924633227110841\n", + " 0.076323411257296\n", " ...\n", - "0.026022885833328162\n", - " 0.06754269552994506\n", - "0.011731305085217554\n", - " 0.07086687808163461\n", - " 0.02602575168928598\n", - " 0.05665991329103694\n", - " 0.07735901867149753\n", - "Length = 1024861 rows\n" + "0.021666218437430496\n", + "0.026539284794723627\n", + " 0.07170977037349016\n", + "0.018694133568115678\n", + " 0.04969517873307298\n", + " 0.07788710880030814\n", + " 0.01568523000869492\n", + "Length = 1020837 rows\n", + "Found 925805 stars out of mass range\n" ] } ], @@ -134,9 +135,11 @@ "# Make cluster\n", "cluster_mass = 10**6\n", "k_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "t_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass)\n", "\n", "# Get outputs\n", - "k_out = k_cluster.star_systems" + "k_out = k_cluster.star_systems\n", + "t_out = t_cluster.star_systems" ] }, { @@ -199,15 +202,23 @@ "np.min(k_out[p_wd]['mass'])" ] }, + { + "cell_type": "markdown", + "id": "b2371c2d-4ea8-4bfb-bf67-59d86316132b", + "metadata": {}, + "source": [ + "### Exploring the properties of k_cluster" + ] + }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 109, "id": "3bdd27c9-3820-4fff-9ed6-da26fb997e08", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -267,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 110, "id": "e8e3c545-0aba-43f8-ad46-ed9ba9720761", "metadata": {}, "outputs": [ @@ -275,17 +286,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "BH max mass: 119.49401158306712\n", - "BH min mass: 15.015214381022997\n", + "BH max mass: 118.75715744712116\n", + "BH min mass: 15.035041823555424\n", "\n", - "NS max mass: 118.51258738269632\n", - "NS min mass: 9.000988105453127\n", + "NS max mass: 119.72357017983377\n", + "NS min mass: 9.000593434639391\n", "\n", - "WD max mass: 8.999748990796446\n", - "WD min mass: 5.311104032812196\n", + "WD max mass: 8.999786895675784\n", + "WD min mass: 5.311134878018857\n", "\n", - "BD max mass: 0.07999996255862679\n", - "BH min mass: 0.010000049376939659\n", + "BD max mass: 0.07999987827752757\n", + "BH min mass: 0.010000034092785876\n", "\n" ] } @@ -307,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 111, "id": "ed4301d8-2eac-4f78-895b-c8c63b22fe5b", "metadata": {}, "outputs": [ @@ -320,27 +331,27 @@ "----\n", " 0.0\n", " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " nan\n", " nan\n", - " ...\n", " 0.0\n", + " nan\n", " 0.0\n", " 0.0\n", + " ...\n", " 0.0\n", + " nan\n", + " nan\n", " 0.0\n", " 0.0\n", " 0.0\n", - "Length = 1363 rows\n" + " nan\n", + "Length = 1378 rows\n" ] } ], "source": [ "#check to see if large NS are Wolf-Rayet\n", "print(k_out['isWR'].dtype)\n", - "large_NS = np.where(k_out[p_ns]['mass'] > 15)\n", + "large_NS = np.where((k_out[p_ns]['mass']) > 15)\n", "print(k_out['isWR'][large_NS])" ] }, @@ -352,6 +363,163 @@ "I am unsure on what the 0.0 and nan values correspond to." ] }, + { + "cell_type": "code", + "execution_count": 115, + "id": "d0d021b1-d637-48c1-8641-990c3e947c81", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass isMultiple systemMass ... metallicity m_hst_f153m\n", + "------------------ ---------- ------------------ ... ----------- -----------\n", + " 87.01978581314896 False 87.01978581314896 ... 0.0 nan\n", + " 80.51538949145795 False 80.51538949145795 ... 0.0 nan\n", + " 21.33585631479835 False 21.33585631479835 ... 0.0 nan\n", + "17.635459786720876 False 17.635459786720876 ... 0.0 nan\n", + "20.277517524928232 False 20.277517524928232 ... 0.0 nan\n", + " 20.66032231704192 False 20.66032231704192 ... 0.0 nan\n", + "27.227632523655107 False 27.227632523655107 ... 0.0 nan\n", + " ... ... ... ... ... ...\n", + " 62.70378845410619 False 62.70378845410619 ... 0.0 nan\n", + " 20.88687294620209 False 20.88687294620209 ... 0.0 nan\n", + "20.379813469500725 False 20.379813469500725 ... 0.0 nan\n", + " 68.91988141238063 False 68.91988141238063 ... 0.0 nan\n", + "16.299795503051396 False 16.299795503051396 ... 0.0 nan\n", + "19.331661523732976 False 19.331661523732976 ... 0.0 nan\n", + " 17.17530617287834 False 17.17530617287834 ... 0.0 nan\n", + "Length = 1378 rows\n", + "isWR\n", + "----\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 1378 rows\n", + "isWR\n", + "----\n", + " 0.0\n", + " nan\n", + "isWR\n", + "----\n", + " nan\n", + " mass_current \n", + "------------------\n", + "1.3873021047437155\n", + "1.1867036763400425\n", + " 1.172988399792676\n", + "1.4760459379472195\n", + "1.3652646941863062\n", + " 1.34693018277759\n", + "1.3329667318702734\n", + " ...\n", + "1.3509102262959198\n", + "1.3492482970974988\n", + "1.3263769374090923\n", + "1.3292822711571697\n", + "1.3708253424214771\n", + "1.4880668288116823\n", + "1.3621508901941495\n", + "Length = 1378 rows\n" + ] + } + ], + "source": [ + "#check out the large NSs\n", + "print(k_out[p_ns][large_NS])\n", + "print(k_out[p_ns]['isWR'][large_NS])\n", + "print(np.unique(k_out['isWR']))\n", + "print(np.unique(k_out[p_ns]['isWR'][large_NS]))\n", + "print(k_out[p_ns]['mass_current'][large_NS])" + ] + }, + { + "cell_type": "markdown", + "id": "5f76dbc3-0fff-49a7-94f1-7513aa031ad6", + "metadata": {}, + "source": [ + "Assuming our assumption is correct, that the anomalous neutron stars are WR stars because of the huge disrepancy in initial and final masses." + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "fc18f900-4485-4d21-baeb-f0de55865f7d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.6520719390653884\n", + "1.058140703674423\n", + "119.72357017983377\n", + "15.00328698949453\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#inital masses of large NS and final masses\n", + "print(np.max(k_out[p_ns]['mass_current'][large_NS]))\n", + "print(np.min(k_out[p_ns]['mass_current'][large_NS]))\n", + "print(np.max(k_out[p_ns]['mass'][large_NS]))\n", + "print(np.min(k_out[p_ns]['mass'][large_NS]))\n", + "\n", + "#create bins\n", + "initial_bins = np.linspace(15, 120, 20)\n", + "final_bins = np.linspace(1.0, 1.7, 20)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.hist(k_out[p_ns]['mass'][large_NS], histtype = 'step',\n", + " bins = initial_bins, label = 'Neutron stars')\n", + "plt.title(\"Initial Masses of Large Generated Neutron Stars\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.hist(k_out[p_wd]['mass_current'][large_NS], histtype = 'step',\n", + " bins = final_bins, label = 'Neutron stars')\n", + "plt.title(\"Final Masses of Large Generated Neutron Stars\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()" + ] + }, { "cell_type": "code", "execution_count": 86, @@ -570,12 +738,364 @@ "This cluster fits the expectation from imfr.py that black holes are between 15-120 solar masses and white dwarves are at least 0.5 solar masses, even with the addition of substellar primary objects. " ] }, + { + "cell_type": "markdown", + "id": "a5da4347-311c-4fe4-b73f-6490b5d3c585", + "metadata": {}, + "source": [ + "### Exploring the t_cluster and comparing to troubleshoot" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "8ed736b8-1719-43c3-b629-4ce834a0d03e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs and WDs\n", + "t_bh = np.where(t_out['phase'] == 103)[0]\n", + "t_ns = np.where(t_out['phase'] == 102)[0]\n", + "t_wd = np.where(t_out['phase'] == 101)[0]\n", + "t_bd = np.where(t_out['phase'] == 99)[0]\n", + "\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(8, 30, 20)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(t_out[t_bh]['mass'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(t_out[t_wd]['mass'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(t_out[t_bd]['mass'], histtype = 'step',\n", + " bins = bd_bins, label = 'Generated Brown Dwarves')\n", + "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(t_out[t_ns]['mass'], histtype = 'step',\n", + " bins = ns_bins, label = 'Generated Neutron Stars')\n", + "plt.title(\"Generated Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "739eb6c6-eec8-4302-a2e4-00ca85353b3a", + "metadata": {}, + "source": [ + "### Exploring an Older Cluster" + ] + }, + { + "cell_type": "markdown", + "id": "331a8442-9686-43aa-9304-35ee4650e3a8", + "metadata": {}, + "source": [ + "Hopefully, in exploring an older cluster, we will see lower mass white dwarves, as they will have enough time to evolve into white dwarves at this point." + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "4528abef-8850-4972-af28-52648d87777a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=5.00 for T= 2306 logg=5.22\n", + "Isochrone generation took 17.886188 s.\n", + "Making photometry for isochrone: log(t) = 10.00 AKs = 0.00 dist = 10\n", + " Starting at: 2024-08-13 14:04:23.149590 Usually takes ~5 minutes\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.090 Msun T = 2306 K m_hst_f153m = 10.90\n", + "M = 1.034 Msun T = 3906 K m_hst_f153m = -3.64\n", + "M = 1.038 Msun T = 3301 K m_hst_f153m = -5.55\n", + " Time taken: 3.33 seconds\n" + ] + } + ], + "source": [ + "# Create isochrone object \n", + "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "o_iso = synthetic.IsochronePhot(10, 0, 10,\n", + " evo_model = evolution.MergedBaraffePisaEkstromParsec(),\n", + " filters=filt_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "338bab08-4432-4dd9-b29d-dd61851b8c3e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:698: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:698: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brown dwarf indices: (array([ 0, 1, 3, ..., 1183119, 1183120, 1183121]),), Masses: mass \n", + "--------------------\n", + " 0.06279592698333637\n", + " 0.07112795900232521\n", + " 0.07376225324790554\n", + " 0.03432324440866939\n", + "0.018997543688358758\n", + "0.052852687409104974\n", + " 0.03463925596802796\n", + " ...\n", + " 0.06347447823132187\n", + " 0.05466348135014649\n", + "0.061140370362200006\n", + " 0.01896811961118919\n", + " 0.06440976267517402\n", + " 0.04168622996255112\n", + "0.010201719779934896\n", + "Length = 1028674 rows\n", + "Found 108643 stars out of mass range\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "o_cluster = synthetic.ResolvedCluster(o_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "o_out = o_cluster.star_systems" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "4102bf35-8b91-44cb-a41a-081a1cb6bf00", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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yPl1dXTg7Oxc4yOTHH3+cb1pCQgI+//xzWFtbQ0tLC9ra2qhZsyYAICYm5rX7XNQ4rl27hvv378PDw0Oly3jNmjVf26PpRXXr1kVkZCQiIyMRERGB3bt3Q6FQwNXV9bWXxhVlX58+fYqwsDD069dP6pb+Ktu3b8eHH36I4cOH44cffoCurm6R92XRokXSvrz46Nevn0q7vK73L3aJB4D3338fDRs2xPHjxwvdRkBAAGQyGT799FOV10epVKJZs2bS61OvXj2YmJhgxowZWL9+fb5flV/HwMAg33vWw8MDubm5OHnypNRm6NCh2LZtm/SL1okTJ3D16lWMGzfujbb32Wef4dChQ3j06BE2b96Mjh07Fno3otTUVMyYMQP16tWDlpYWtLS0UKVKFaSlpam8x99//338+uuvmDlzJkJDQ5Genq6yHlNTU9StWxdLlizB8uXLceHCBZVLBYmIKjPmQCWbA704blTev87OzgCAhg0bwsLCQsoXQkNDYWlpiYYNG+Zbz+uO7Zt49uwZjh8/jo8++gh6enr5Xttnz57hzJkzb7zeF4mXeou3bdsWurq6OHbsGIDnl1q6uLigS5cuCA8Px9OnT3H37l389ddfKpfuZ2dnw8/PD40aNYKOjg60tLSgo6ODv/76q8DXuqD3y6BBgyCXy1V6833//ffIyMjA0KFD3/iYvP/++/jjjz8wZswYHD169I3HXirs/fTiJZujR48GAJXxNFevXo0mTZqgQ4cORd6Ws7Mz6tatiy1btuDSpUuIjIws9BI94Hl+5+bmBiMjI2hqakJbWxtz587Fo0ePkJCQAOD55ac6OjoYOXIktm/fnm/4COD5MXry5AkGDhyIn3/++bVXA1D5wGIU0VsyNzeHQqEo8Et79+7diIyMVLlmHIB0KdTUqVNVEjVtbW2MGTMGAPJ9uJqZmak8l8vlACCdIOets1WrVvnWuXfv3nzr09PTy3ensdzcXLi7u2P//v2YPn06jh8/jnPnzklfki+fjBekqHHkXeqkVCrzraOgaYXR1dWFg4MDHBwc0Lp1awwcOBC//vor4uLiMHfu3EKXK+q+JiYmIicnB9WrVy9SPHv27IFCocDw4cPzjcvwOnXq1JH25cXHy0WwvGNX0CURVlZWKpeRvezBgwcQQsDS0jLf63PmzBnp9TEyMkJYWBiaN2+OL774Ao0bN4aVlRXmzZtXpLvYWFpa5puW97q+GN/48eORkpKCXbt2AXieEFWvXh29evV67TZe1LdvX+jq6mLFihU4fPgwhg0bVmhbDw8PrF69GsOHD8fRo0dx7tw5REZG4r333lN5j3/zzTeYMWMGDh48iI4dO8LU1BS9e/eWipwymQzHjx9H586dsXjxYrRs2RLvvfceJkyYUKRLGYmIyjvmQKpKMwdq0qQJzM3NERISIo0XlVeMAp7f0CU0NBQZGRmIiIgo9C56rzu2b+LRo0fIzs7GqlWr8u1/3iVr71o8yHuv5V1Opquri7Zt20rFqOPHj6NTp05wcXFBTk4OTp06JV2u92IxavLkyZgzZw569+6Nw4cP4+zZs4iMjESzZs0K3PeCci5TU1P07NkT3333nTQm5rZt2/D++++jcePGb3xMZs2ahaVLl+LMmTPo2rUrzMzM4Orqmu+uiYUp7P30Yt5laWmJ/v37Y8OGDcjJycHFixdx6tSpN/4RUCaTYejQodi5cyfWr1+P+vXro3379gW2PXfunHRDoU2bNuG3335DZGQkZs+eDeD/32t169bFsWPHYGFhgbFjx6Ju3bqoW7euyphZnp6e2LJlC27fvo2PP/4YFhYWcHR0VLkkk8qfynkrAKJioKmpiQ8++ABBQUGIi4tT+bLKu/vIrVu3VJYxNzcH8PxLp0+fPgWu19bW9o3iyFvnTz/9JP2K9yoFFUouX76MP/74A9u2bcOQIUOk6Tdu3Cj2OPKSn/j4+HzzCpr2JqpWrQpzc3P88ccfhbYp6r6amppCU1OzyIN279q1C3PmzIGzszOCgoLQvHnzt9qHV8k7dnFxcfmKZPfv35deg4KYm5tDJpPh1KlTUsL5ohenNWnSBHv27IEQAhcvXsS2bdswf/58KBQKzJw585Ux5iXkL8p7XV9MfOvVq4euXbtizZo16Nq1Kw4dOgRfX19oamq+cv0v09PTw4ABA+Dv7w9DQ8NC/66SkpIQEBCAefPmqexDRkYGHj9+rNJWX18fvr6+8PX1xYMHD6ReUj169JBumV2zZk1p8NLr16/jhx9+gI+PDzIzM7F+/fo32gciovKGOdDbxVEcOZBMJoOzszMCAwNx7tw5PHnyRKUY5ezsDB8fH0RERODZs2eFFqOKk4mJCTQ1NeHp6VnouI+1a9d+6/ULIXD48GHo6+vDwcFBmu7q6oq5c+fi3LlzuHfvHjp16gQDAwO0atUKwcHBuH//PurXrw9ra2tpmZ07d2Lw4MHw8/NT2cbDhw9hbGycb9uF/cA4dOhQ/PjjjwgODkaNGjUQGRmJdevWSfPf5JhoaWlh8uTJmDx5Mp48eYJjx47hiy++QOfOnXH37t3X3vGxsPfTywXHiRMnYseOHfj5558RGBgIY2Njqafim/Dy8sLcuXOxfv16lTsav2zPnj3Q1tZGQECAyhUDBw8ezNe2ffv2aN++PXJycnD+/HmsWrUK3t7esLS0xIABAwA8P+ZDhw5FWloaTp48iXnz5qF79+64fv16kf7+qexhMYroHcyaNQu//vorPv/8c/z000+vvYubra0tbGxs8Mcff+T7EnxbnTt3hpaWFm7evFlgV+KiyPuifblIsWHDhnxtC/vlrKhx2NraomrVqvj+++8xefJkadu3b99GeHh4kQZQLMy9e/fw8OHDV96KuKj7mnc3kh9//BELFy58ZaEHeF68OnbsGLp3746OHTvi119/Vbk9cXH44IMPADxPpFq1aiVNj4yMRExMjPRLU0G6d++Or776Cv/880++y/8KI5PJ0KxZM6xYsQLbtm3D77///tplUlJScOjQIZXu/7t375YGXX3RxIkT4e7ujiFDhkBTUxMjRowoUlwvGz16NB48eABnZ+dCL4+UyWQQQuR73b/99lvpV82CWFpawsvLC3/88QdWrlxZ4G3A69evj//973/Yt29fkY4REVFFwBzozeMorhyoY8eO2LdvH5YsWQILCwuVy/CcnZ3x6NEjrFq1SmpbXArbfz09PXTs2BEXLlxA06ZNC7274tvy9fXF1atX8cUXX6h8z7u5ueGLL77AnDlzUL16dTRo0ECafujQIcTHx+d7PWQyWb7X+siRI/jnn39Qr169Isfk7u6OatWqYevWrahRowZ0dXWlu80Bb39MjI2N0bdvX/zzzz/w9vbGrVu3XpnXAij0/TR48GCVdvb29mjTpg0WLVqEy5cvY+TIkdDX1y/yPuepVq0apk2bhj///FOlgPsymUwGLS0tlR8a09PTsWPHjkKX0dTUhKOjIxo0aIBdu3bh999/l4pRefT19dG1a1dkZmaid+/euHLlCotR5RSLUUTvoG3btlizZg3Gjx+Pli1bYuTIkWjcuDE0NDQQFxeHffv2AYBKl/ANGzaga9eu6Ny5M7y8vFCtWjU8fvwYMTEx+P333/Hjjz++UQy1atXC/PnzMXv2bPz999/o0qULTExM8ODBA5w7d07q5fEqDRo0QN26dTFz5kwIIWBqaorDhw8X2PU1724kX3/9NYYMGQJtbW3Y2toWOQ4NDQ18+eWXGD58OD766COMGDECT548gY+Pzxtdppeeni51oc/JyUFsbCwWL14MAPD29i6WfV2+fDnatWsHR0dHzJw5E/Xq1cODBw9w6NAhbNiwAQYGBirtDQwMEBgYiD59+kh3uyvOJNDW1hYjR47EqlWroKGhga5du+LWrVuYM2cOrK2tMWnSpEKXbdu2LUaOHImhQ4fi/Pnz6NChA/T19REXF4fTp0+jSZMmGD16NAICArB27Vr07t0bderUgRAC+/fvx5MnT9CpU6fXxmhmZobRo0fjzp07qF+/Pn755Rds2rQJo0ePRo0aNVTadurUCY0aNUJISAg+/fRTWFhYvNVxad68eYG/sr3I0NAQHTp0wJIlS2Bubo5atWohLCwMmzdvzvdLqKOjI7p3746mTZvCxMQEMTEx2LFjB5ycnKCnp4eLFy9i3Lhx+OSTT2BjYwMdHR2cOHECFy9efG3PMSKiioI5kPpyoLzc4sCBA+jbt6/KPDs7O5iZmeHAgQOoVq1ake7qW1QGBgaoWbMmfv75Z7i6usLU1FT6Tv3666/Rrl07tG/fHqNHj0atWrWQkpKCGzdu4PDhw0W6W+CTJ0+k3C4tLQ3Xrl3Dnj17cOrUKfTr1y/fa2lvbw8TExMEBQVJYzUBz4tReXdre/ESPeD5j3Pbtm1DgwYN0LRpU0RFRWHJkiVFHpYhj6amJgYPHozly5dLPbONjIxU2hT1mPTo0QN2dnbSEA23b9/GypUrUbNmzSK9fgkJCdL7KSkpCfPmzYOuri5mzZqVr+3EiRPRv39/yGQy6fLYt/HVV1+9tk23bt2wfPlyeHh4YOTIkXj06BGWLl2arxi4fv16nDhxAt26dUONGjXw7Nkz6Y59ea/fiBEjoFAo0LZtW1StWhXx8fHw9/eHkZGRyg+0VM6obeh0ogokOjpaDB06VNSuXVvI5XKhq6sr6tWrJwYPHiyOHz+er/0ff/wh+vXrJywsLIS2trZQKpXigw8+EOvXr5favHjnihfl3cXi5buEHTx4UHTs2FEYGhoKuVwuatasKfr27SuOHTsmtRkyZIjQ19cvcB+uXr0qOnXqJAwMDISJiYn45JNPxJ07dwq8a8qsWbOElZWV0NDQyBdLUeIQQohvv/1W2NjYCB0dHVG/fn2xZcuWfHeAK8zLd9PT0NAQVlZWomvXriI0NFSlbUF303uTfb169ar45JNPhJmZmdDR0RE1atQQXl5e4tmzZyrrf/F1ysjIEB9//LHQ1dUVR44cKXQ/8l7LH3/8scD5Y8eOFS9/TOfk5IhFixaJ+vXrC21tbWFubi4+/fRTcffuXZV2hR3LLVu2CEdHR6Gvry8UCoWoW7euGDx4sDh//rwQQog///xTDBw4UNStW1coFAphZGQk3n//fbFt27ZC9yOPs7OzaNy4sQgNDRUODg5CLpeLqlWrii+++CLfnZPy+Pj4CADizJkzr11/ntfdcUcIUeDd9O7duyc+/vhjYWJiIgwMDESXLl3E5cuXRc2aNcWQIUOkdjNnzhQODg7CxMREyOVyUadOHTFp0iTx8OFDIYQQDx48EF5eXqJBgwZCX19fVKlSRTRt2lSsWLFCZGdnF3k/iIgqAuZA/x9LaeRAeZRKpQAgVq9enW9e7969BQAxaNCgfPPe5Ni+fDc9IZ7fxa5FixZCLpcLACrfn7GxseKzzz4T1apVE9ra2uK9994Tbdq0EQsWLHjt/tSsWVPK62QymahSpYqwtbUVnp6e4ujRo4Uu99FHHwkAYteuXdK0zMxMoa+vLzQ0NERiYqJK+8TERDFs2DBhYWEh9PT0RLt27cSpU6fy7evrcjQhhLh+/boUc3BwcIFtinJMli1bJtq0aSPMzc2lXHPYsGHi1q1brzxmeTHu2LFDTJgwQbz33ntCLpeL9u3bS3ndyzIyMoRcLhddunR55bpfVNh75mUF3U1vy5YtwtbWVsqn/P39xebNm1Vy84iICPHRRx+JmjVrCrlcLszMzISzs7M4dOiQtJ7t27eLjh07CktLS6GjoyOsrKxEv379xMWLF4u8H1T2yIR46dYEREREpcTBwQEymQyRkZHqDoWIiIioQjt8+DB69uyJI0eOSAOpE6kLL9MjIqJSlZycjMuXLyMgIABRUVE4cOCAukMiIiIiqrCuXr2K27dvY8qUKWjevDm6du2q7pCIWIwiIqLS9fvvv6Njx44wMzPDvHnz0Lt3b3WHRERERFRhjRkzBr/99htatmyJ7du3F3qXQKLSxMv0iIiIiIiIiIio1GioOwAiIiIiIiIiIqo8WIwiIiIiIiIiIqJSw2IUERERERERERGVGg5gXspyc3Nx//59GBgYcOA4IiKiMkwIgZSUFFhZWUFDg7/fqQtzJyIiovKjqPkTi1Gl7P79+7C2tlZ3GERERFREd+/eRfXq1dUdRqXF3ImIiKj8eV3+xGJUKTMwMADw/IUxNDRUczRERERUmOTkZFhbW0vf3aQezJ2IiIjKj6LmTyxGlbK87uWGhoZMqIiIiMoBXhqmXsydiIiIyp/X5U8cAIGIiIiIiIiIiEoNi1FERERERERERFRqWIwiIiIiIiIiIqJSwzGjyqCcnBxkZWWpOwyickNbWxuamprqDoOIiIiIihHPi4jKnuI692IxqgwRQiA+Ph5PnjxRdyhE5Y6xsTGUSiUHGiYiIiIq53heRFS2Fce5F4tRZUjeB66FhQX09PR4Uk1UBEIIPH36FAkJCQCAqlWrqjkiIiIiInoXPC8iKpuK89yLxagyIicnR/rANTMzU3c4ROWKQqEAACQkJMDCwoKX7BGVQf88SUdiWmaJrNtEXwfVjBUlsm6id1GS73uA732qmHheRFS2Fde5F4tRZUTetdB6enpqjoSofMr728nKymIxiqiM+edJOtyWhSE9K6dE1q/Q1sSxKc48KacypaTf9wDf+1Qx8byIqOwrjnMvFqPKGHZBJXo7/NshKrsS0zKRnpWDlf2bo55FlWJd942EVHjvjUZiWiZPyKlMKcn3PcD3PlV8zO2Iyq7i+PtkMYqIiIhKRT2LKrCrZqTuMIhKFd/3RERE+bEYVQ6U9HgDL+P4A8WjVq1a8Pb2hre39zutRyaT4cCBA+jdu3eZiutt+Pj44ODBg4iOji71bRMRERFR+cbzovKppM4/XrfeW7duoXbt2rhw4QKaN29erNumd8diVBlXGuMNvOxtxh+Ij4+Hv78/jhw5gnv37sHIyAg2Njb49NNPMXjw4HJzzXdpFmp8fHzg6+srPTc0NETTpk2xYMECODs7l/j2iyo0NBQdO3ZEYmIijI2NVeaps7BFRERERJUHz4tKV2nl+YGBgejatSvi4uKgVCql6UqlEtra2rh796407d69e7C2tsbRo0fh7u7+2nVbW1sjLi4O5ubmAF59XvOmXFxcEBYWBgDQ0dGBubk5WrZsiaFDh6JPnz7vtO7KgsWoMq6kxxt42duMP/D333+jbdu2MDY2hp+fH5o0aYLs7Gxcv34dW7ZsgZWVFXr27FnCkRdOCIGcnBxoaZW9t3vjxo1x7NgxAMDjx4+xdOlSdO/eXfriIiIielMv/9gBAJaWloiPjwfw/HvR19cXGzduRGJiIhwdHbFmzRo0btxYap+RkYGpU6fi+++/R3p6OlxdXbF27VpUr15dapOYmIgJEybg0KFDAICePXti1apVKgn+nTt3MHbsWJw4cQIKhQIeHh5YunQpdHR0SvAIEFFFxPOid1cWz4vatWsHLS0thIaGYsCAAQCAmJgYPHv2DOnp6bhx4wbq1asHAAgJCYG2tjbatm1bpHVramqqFLiK24gRIzB//nxkZWXhn3/+wYEDBzBgwAB4eXlh48aNJbbdl2VlZUFbW7vUtldcNNQdABVN3ngDJf14mw/2MWPGQEtLC+fPn0e/fv3QsGFDNGnSBB9//DGOHDmCHj16SG2TkpIwcuRIWFhYwNDQEB988AH++OMPab6Pjw+aN2+OHTt2oFatWjAyMsKAAQOQkpIitRFCYPHixahTpw4UCgWaNWuGn376SZofGhoKmUyGo0ePwsHBAXK5HKdOncLNmzfRq1cvWFpaokqVKmjVqpVUCAKeV7dv376NSZMmQSaTqQzKFh4ejg4dOkChUMDa2hoTJkxAWlqaND8hIQE9evSAQqFA7dq1sWvXriIdOy0tLSiVSiiVSjRq1Ai+vr5ITU3F9evXC11mxowZqF+/PvT09FCnTh3MmTNHuutInkOHDsHBwQG6urowNzd/ZXV+69atMDIyQnBwcJFifpU7d+6gV69eqFKlCgwNDdGvXz88ePDglcts3boVDRs2hK6uLho0aIC1a9dK8zIzMzFu3DhUrVoVurq6qFWrFvz9/d85TiKiiq5x48aIi4uTHpcuXZLmLV68GMuXL8fq1asRGRkJpVKJTp06qXzXent748CBA9izZw9Onz6N1NRUdO/eHTk5/98jwcPDA9HR0QgMDERgYCCio6Ph6ekpzc/JyUG3bt2QlpaG06dPY8+ePdi3bx+mTJlSOgeBiCoknhdVrPOivO2HhoaqxN2uXTu0a9cu3/T3338f+vr60rSnT5/is88+g4GBAWrUqKFSBLp16xZkMhmio6Nx69YtdOzYEQBgYmICmUwGLy+vIh3Hwujp6UGpVMLa2hqtW7fGokWLsGHDBmzatEk6nh9//DHGjx8vLePt7Q2ZTIYrV64AALKzs2FgYICjR48CeN5TrF27djA2NoaZmRm6d++Omzdv5tunH374AS4uLtDV1cXatWuhUCgQGBioEt/+/fuhr6+P1NRUAMA///yD/v37w8TEBGZmZujVqxdu3bpV4PE1NjZG27Ztcfv27dceh7fFYhS9k0ePHiEoKAhjx45V+VB4Ud6HlxAC3bp1Q3x8PH755RdERUWhZcuWcHV1xePHj6X2N2/exMGDBxEQEICAgACEhYXhq6++kub/73//w9atW7Fu3TpcuXIFkyZNwqeffip1k8wzffp0+Pv7IyYmBk2bNkVqaio+/PBDHDt2DBcuXEDnzp3Ro0cP3LlzB8DzP9bq1atj/vz5UvIOAJcuXULnzp3Rp08fXLx4EXv37sXp06cxbtw4aVteXl64desWTpw4gZ9++glr165FQkLCGx3LjIwMbNu2DcbGxrC1tS20nYGBAbZt24arV6/i66+/xqZNm7BixQpp/pEjR9CnTx9069YNFy5cwPHjx+Hg4FDgupYuXYqpU6fi6NGj6NSp0xvF+zIhBHr37o3Hjx8jLCwMwcHBuHnzJvr371/oMps2bcLs2bOxcOFCxMTEwM/PD3PmzMH27dsBAN988w0OHTqEH374AdeuXcPOnTtRq1atd4qTiKgyePHHDqVSiffeew/A88/qlStXYvbs2ejTpw/s7Oywfft2PH36FLt37wbw/ARp8+bNWLZsGdzc3NCiRQvs3LkTly5dkpLrmJgYBAYG4ttvv4WTkxOcnJywadMmBAQE4Nq1awCAoKAgXL16FTt37kSLFi3g5uaGZcuWYdOmTUhOTlbPgSEiKiE8L3rubc6LOnbsiJCQEOl5SEgIXFxc4OzsnG96XkEpz7Jly+Dg4IALFy5gzJgxGD16NP78889827C2tsa+ffsAANeuXUNcXBy+/vrrNzqORTFkyBCYmJhg//79AJ4X9l4sqIWFhcHc3Fxad2RkJJ49eyb19kpLS8PkyZMRGRmJ48ePQ0NDAx999BFyc3NVtjNjxgxMmDABMTEx+OSTT9CtW7d8hb/du3dLHQWePn2Kjh07okqVKjh58iROnz6NKlWqoEuXLsjMzER2djZ69+4NZ2dnXLx4ERERERg5cmTJ3tVSUKlKSkoSAERSUpLK9PT0dHH16lWRnp6uMv3SvSei5owAcenek1KJ7023d+bMGQFA7N+/X2W6mZmZ0NfXF/r6+mL69OlCCCGOHz8uDA0NxbNnz1Ta1q1bV2zYsEEIIcS8efOEnp6eSE5OluZPmzZNODo6CiGESE1NFbq6uiI8PFxlHcOGDRMDBw4UQggREhIiAIiDBw++Nv5GjRqJVatWSc9r1qwpVqxYodLG09NTjBw5UmXaqVOnhIaGhkhPTxfXrl0TAMSZM2ek+TExMQJAvnW9aN68eUJDQ0M6TjKZTBgaGopff/1VpR0AceDAgULXs3jxYmFvby89d3JyEoMGDSq0fd4+zpw5U1StWlVcvHix0LZC/P/xzIvzxYdMJpP2MSgoSGhqaoo7d+5Iy165ckUAEOfOnZP2uVmzZtJ8a2trsXv3bpXtffnll8LJyUkIIcT48ePFBx98IHJzc18ZoxCF/w0RkfqV5HdZSa67sO/ssi7vu7Rq1aqiVq1aon///uLmzZtCCCFu3rwpAIjff/9dZZmePXuKwYMHCyGef18DEI8fP1Zp07RpUzF37lwhhBCbN28WRkZG+bZtZGQktmzZIoQQYs6cOaJp06Yq8x8/fiwAiBMnThQa/7Nnz0RSUpL0uHv3brl8HUo6hyvtHJGotPC8qPKdFwUFBQkA4v79+0IIISwsLMS5c+fEmTNnhJWVlRBCiDt37ggA4vjx4yoxfvrpp9Lz3NxcYWFhIdatWyeEECI2NlYAEBcuXFA5HomJidIyRTmOBXF2dhYTJ04scJ6jo6Po2rWrEEKIixcvCplMJv7991/x+PFjoa2tLRYsWCA++eQTIYQQfn5+0mtakISEBAFAXLp0SWWfVq5cqdJu//79okqVKiItLU0I8TyH0dXVFUeOHBFCPP/etrW1VTmvysjIEAqFQhw9elQ8evRIABChoaGFxvKiV517FTV/KjsXi1K59nLF9Ny5c8jNzcWgQYOQkZEBAIiKikJqairMzMxU2qanp6t0PaxVqxYMDAyk51WrVpWq6VevXsWzZ8/y9eLJzMxEixYtVKa93BsoLS0Nvr6+CAgIwP3795GdnY309HTpF4DCREVF4caNGyqVZiEEcnNzERsbi+vXr0NLS0tlew0aNCjSoHi2trbSWBspKSnYu3cvPvnkE4SEhBTam+mnn37CypUrcePGDaSmpiI7OxuGhobS/OjoaIwYMeKV2122bBnS0tJw/vx51KlT57VxAsCpU6dUXhfgeaU/T0xMDKytrWFtbS1Na9SoEYyNjRETE4NWrVqpLPvvv//i7t27GDZsmEq82dnZ0nhZXl5e6NSpE2xtbdGlSxd07969SIMVEhFVZo6Ojvjuu+9Qv359PHjwAAsWLECbNm1w5coVadwoS0tLlWUsLS2lrvjx8fHQ0dGBiYlJvjZ5y8fHx8PCwiLfti0sLFTavLwdExMT6OjoSG0K4u/vn2/MKyKi8oLnRW9+XtS2bVvo6OggNDQUzZo1Q3p6Olq2bAkhBJKTk/HXX38hIiICcrkcbdq0UVm2adOm0v9lMhmUSuUbXaHyJsexqIQQ0vvAzs4OZmZmCAsLg7a2Npo1a4aePXvim2++AfD80rgXb1518+ZNzJkzB2fOnMHDhw+lHlF37tyBnZ2d1O7l17Rbt27Q0tLCoUOHMGDAAOzbtw8GBgbSuVPea/fy+dyzZ89w8+ZNuLu7w8vLC507d0anTp3g5uaGfv36oWrVqm91DIqCxSh6J/Xq1YNMJsvXFTKvwKFQ/P9gf7m5uahatapKN8U8L35AvTz4mkwmk/4I8/49cuQIqlWrptJOLperPH+5e+y0adNw9OhRLF26FPXq1YNCoUDfvn2Rmfnq28Pm5uZi1KhRmDBhQr55NWrUkC5HeJsujDo6OtKAfADQokULHDx4ECtXrsTOnTvztT9z5gwGDBgAX19fdO7cGUZGRtizZw+WLVsmtXnxmBemffv2OHLkCH744QfMnDmzSLHWrl073xfJi4Mfvvih+6LCpue9lps2bYKjo6PKPE1NTQBAy5YtERsbi19//RXHjh1Dv3794ObmVqRruImIKquuXbtK/2/SpAmcnJxQt25dbN++Ha1btwaQ/zursM/qV7Upymf+m3wv5Jk1axYmT54sPU9OTlb5oYOIqCziedHbnxfp6enh/fffR0hICB4/fox27dpJ5wNt2rRBSEgIIiIi4OTkBF1dXZVlX3WMiuJNjmNR5OTk4K+//pJ+iJfJZOjQoQNCQ0Oho6MDFxcX2NnZIScnB5cuXUJ4eLjKHQt79OgBa2trbNq0CVZWVsjNzYWdnV2+1+bl11RHRwd9+/bF7t27MWDAAOzevRv9+/eXztdyc3Nhb29f4BheeZfyb926FRMmTEBgYCD27t2L//3vfwgODpZyh+LGYhS9EzMzM3Tq1AmrV6/G+PHjC70+GnheWIiPj4eWltZbj/vTqFEjyOVy3LlzR6WCXBSnTp2Cl5cXPvroIwBAamqqyoBtwPM/4hcHZ82L+8qVKypFoxc1bNgQ2dnZOH/+PN5//30Az69DfvLkyRvFl0dTUxPp6ekFzvvtt99Qs2ZNzJ49W5r28qByTZs2xfHjxzF06NBCt/H+++9j/Pjx6Ny5MzQ1NTFt2rS3ivVFjRo1wp07d3D37l3ppOHq1atISkpCw4YN87W3tLREtWrV8Pfff2PQoEGFrtfQ0BD9+/dH//790bdvX3Tp0gWPHz+GqanpO8dMRFQZ6Ovro0mTJvjrr7/Qu3dvAM97Lb34a2dCQoLUi0mpVCIzMxOJiYkqvaMSEhKkX6SVSmWBN6j4999/VdZz9uxZlfmJiYnIysrK12PqRXK5/K1OAIiI1InnRe92XtSxY0fs2bMHiYmJKldfODs7IzQ0FBEREa88vymKvDu5vrhf73IcC7J9+3YkJibi448/lqa5uLhg48aN0NHRwfz58yGTydC+fXssXboU6enp0nhRjx49QkxMDDZs2ID27dsDAE6fPl3kbQ8aNAju7u64cuUKQkJC8OWXX0rzWrZsib1790oD5hemRYsWaNGiBWbNmgUnJyfs3r27xIpRHMCc3tnatWuRnZ0NBwcH7N27FzExMdJg03/++adU1XZzc4OTkxN69+6No0eP4tatWwgPD8f//vc/nD9/vkjbMjAwwNSpUzFp0iRs374dN2/exIULF7BmzRpp0OvC1KtXD/v370d0dDT++OMPeHh45Kua16pVCydPnsQ///yDhw8fAng+OFxERATGjh2L6Oho/PXXXzh06JB0V4S8S8hGjBiBs2fPIioqCsOHDy9SD6Xs7GzEx8cjPj4ef/31FxYsWICrV6+iV69ehe7DnTt3sGfPHty8eRPffPMNDhw4oNJm3rx5+P777zFv3jzExMTg0qVLWLx4cb51OTk54ddff8X8+fNVBkB/W25ubmjatCkGDRqE33//HefOncPgwYPh7Oxc6CWHPj4+8Pf3x9dff43r16/j0qVL2Lp1K5YvXw4AWLFiBfbs2YM///wT169fx48//gilUlmkSyCJiOi5jIwMxMTEoGrVqqhduzaUSqXKHVQzMzMRFhYmFZrs7e2hra2t0iYuLg6XL1+W2jg5OSEpKQnnzp2T2pw9exZJSUkqbS5fviwNfAs8H9RcLpfD3t6+RPeZiEgdeF709udFHTt2xF9//YXAwECVopCzszMCAgJU7ob3tmrWrAmZTIaAgAD8+++/SE1Nfafj+PTpU8THx+PevXs4e/YsZsyYgc8//xyjR49WidXFxQVXrlzBpUuXpCKTi4sLdu3ahZYtW0rFoby73G3cuBE3btzAiRMnVHoKv46zszMsLS0xaNAg1KpVS6WINGjQIJibm6NXr144deoUYmNjERYWhokTJ+LevXuIjY3FrFmzEBERgdu3byMoKAjXr18vsFNBcWHPqHLiRkJqmd1O3bp1ceHCBfj5+WHWrFm4d+8e5HI5GjVqhKlTp2LMmDEAnndR/OWXXzB79mx89tln+Pfff6FUKtGhQ4dX/kL6si+//BIWFhbw9/fH33//DWNjY7Rs2RJffPHFK5dbsWIFPvvsM7Rp0wbm5uaYMWNGvrv5zJ8/H6NGjULdunWRkZEBIQSaNm2KsLAwzJ49G+3bt4cQAnXr1lW5S9zWrVsxfPhw6QNgwYIFmDNnzmv35cqVK9Iv03p6eqhbty7WrVuHwYMHF9i+V69emDRpEsaNG4eMjAx069YNc+bMgY+Pj9TGxcUFP/74I7788kt89dVXMDQ0RIcOHQpcX9u2bXHkyBF8+OGH0NTULLDLbVHJZDIcPHgQ48ePR4cOHaChoYEuXbpg1apVhS4zfPhw6OnpYcmSJZg+fbr0631eV9UqVapg0aJF+Ouvv6CpqYlWrVrhl19+gYYG6+hERIWZOnUqevTogRo1aiAhIQELFixAcnIyhgwZAplMBm9vb/j5+cHGxgY2Njbw8/ODnp4ePDw8AABGRkYYNmwYpkyZAjMzM5iammLq1Klo0qQJ3NzcADz/9TvvhGPDhg0AgJEjR6J79+7SHWHd3d3RqFEjeHp6YsmSJXj8+DGmTp2KESNGvPJXWSKiV+F50f+rSOdFTk5OUq/YF3+waNWqFXJycqBQKPIN7fGmqlWrBl9fX8ycORNDhw7F4MGDsW3btrc+jps2bcKmTZugo6MDMzMz2NvbY+/evVKPszx2dnYwNzdHzZo1pe8/Z2dn5OTkqBTeNDQ0sGfPHkyYMAF2dnawtbXFN998o9JT7FVkMhkGDhyIJUuWYO7cuSrz9PT0cPLkScyYMQN9+vRBSkoKqlWrBldXVxgaGiI9PR1//vkntm/fjkePHqFq1aoYN24cRo0aVaRtvw2ZEEKU2Nopn+TkZBgZGSEpKUklEXv27BliY2NRu3Ztletg/3mSDrdlYUjPyilodSVCoa2JY1OcUc349RVsorKisL8hIlK/y/8kofuq0wgY3w521YzKzboL+84u6wYMGICTJ0/i4cOHeO+999C6dWt8+eWXaNSoEYDnYzb5+vpiw4YNSExMhKOjI9asWaMyMOqzZ88wbdo07N69G+np6XB1dcXatWtVxm56/PgxJkyYIN2Io2fPnli9erVK79U7d+5gzJgxOHHiBBQKBTw8PLB06dI3ugyvvL4OJfneLI31E6kLz4uIyr5XnXsV9XubPaPKuGrGChyb4ozEtFcPJlecTPR1+IFLRERUTu3Zs+eV82UyGXx8fFR61b5MV1cXq1atemXvVlNT0wJvtvGiGjVqICAg4JVtiIiKgudFRBULi1H/yc7Oho+PD3bt2iUN6unl5YX//e9/0iVBeb8kbty4UeWXxMaNG5dobNWMFfwQJCIiIiKiSo3nRUQVBwde+c+iRYuwfv16rF69GjExMVi8eDGWLFmi8ovg4sWLsXz5cqxevRqRkZFQKpXo1KkTUlJS1Bg5EREREREREVH5wWLUfyIiItCrVy9069YNtWrVQt++feHu7i7dzUAIgZUrV2L27Nno06cP7OzssH37djx9+hS7d+9Wc/REREREREREROUDi1H/adeuHY4fP47r168DAP744w+cPn0aH374IQAgNjYW8fHxcHd3l5aRy+VwdnZGeHh4oevNyMhAcnKyyuNVOJ480dvh3w4RERFRxcHcjqjsKo6/T44Z9Z8ZM2YgKSkJDRo0gKamJnJycrBw4UIMHDgQABAfHw8A+W61aWlpidu3bxe6Xn9/f/j6+r52+9ra2gCAp0+fQqHgddBEb+rp06cA/v9viYiIiIjKH54XEZV9xXHuxWLUf/bu3YudO3di9+7daNy4MaKjo+Ht7Q0rKysMGTJEaieTyVSWE0Lkm/aiWbNmYfLkydLz5ORkldsi59HU1ISxsTESEhIAAHp6eq9cLxE9J4TA06dPkZCQAGNjY2hqaqo7JCIiIiJ6SzwvIiq7ivPci8Wo/0ybNg0zZ87EgAEDAABNmjTB7du34e/vjyFDhkCpVAKAdKe9PAkJCfl6S71ILpdDLpcXKYa8beR98BJR0RkbG0t/Q0RERERUfvG8iKhsK45zLxaj/vP06VNoaKgOoaWpqYnc3FwAQO3ataFUKhEcHIwWLVoAADIzMxEWFoZFixYVSwwymQxVq1aFhYUFsrKyimWdRJWBtrY2e0QRERERVRA8LyIqu4rr3IvFqP/06NEDCxcuRI0aNdC4cWNcuHABy5cvx2effQbg+Qeit7c3/Pz8YGNjAxsbG/j5+UFPTw8eHh7FGoumpiZPrImIiIiIqFLjeRFRxcVi1H9WrVqFOXPmYMyYMUhISICVlRVGjRqFuXPnSm2mT5+O9PR0jBkzBomJiXB0dERQUBAMDAzUGDkRERERERERUfnBYtR/DAwMsHLlSqxcubLQNjKZDD4+PvDx8Sm1uIiIiIiIiIiIKhKN1zchIiIiIiIiIiIqHixGERERERERERFRqWExioiIiIiIiIiISg2LUUREREREREREVGpYjCIiIiIiIiIiolLDYhQREREREREREZUaFqOIiIiIiIiIiKjUsBhFRERERERERESlhsUoIiIiIiIiIiIqNSxGERERERERERFRqWExioiIiIiIiIiISg2LUUREREREREREVGpYjCIiIiIiIiIiolLDYhQREREREREREZUaFqOIiIiIiIiIiKjUsBhFRERERERERESlhsUoIiIiogrM398fMpkM3t7e0jQhBHx8fGBlZQWFQgEXFxdcuXJFZbmMjAyMHz8e5ubm0NfXR8+ePXHv3j2VNomJifD09ISRkRGMjIzg6emJJ0+eqLS5c+cOevToAX19fZibm2PChAnIzMwsqd0lIiKicoDFKCIiIqIKKjIyEhs3bkTTpk1Vpi9evBjLly/H6tWrERkZCaVSiU6dOiElJUVq4+3tjQMHDmDPnj04ffo0UlNT0b17d+Tk5EhtPDw8EB0djcDAQAQGBiI6Ohqenp7S/JycHHTr1g1paWk4ffo09uzZg3379mHKlCklv/NERERUZrEYRURERFQBpaamYtCgQdi0aRNMTEyk6UIIrFy5ErNnz0afPn1gZ2eH7du34+nTp9i9ezcAICkpCZs3b8ayZcvg5uaGFi1aYOfOnbh06RKOHTsGAIiJiUFgYCC+/fZbODk5wcnJCZs2bUJAQACuXbsGAAgKCsLVq1exc+dOtGjRAm5ubli2bBk2bdqE5OTk0j8oREREVCawGEVERERUAY0dOxbdunWDm5ubyvTY2FjEx8fD3d1dmiaXy+Hs7Izw8HAAQFRUFLKyslTaWFlZwc7OTmoTEREBIyMjODo6Sm1at24NIyMjlTZ2dnawsrKS2nTu3BkZGRmIiooqMO6MjAwkJyerPIiIiKhi0VJ3AERERERUvPbs2YPff/8dkZGR+ebFx8cDACwtLVWmW1pa4vbt21IbHR0dlR5VeW3ylo+Pj4eFhUW+9VtYWKi0eXk7JiYm0NHRkdq8zN/fH76+vkXZTSIiIiqn2DOKiIiIqAK5e/cuJk6ciJ07d0JXV7fQdjKZTOW5ECLftJe93Kag9m/T5kWzZs1CUlKS9Lh79+4rYyIiIqLyh8UoIiIiogokKioKCQkJsLe3h5aWFrS0tBAWFoZvvvkGWlpaUk+ll3smJSQkSPOUSiUyMzORmJj4yjYPHjzIt/1///1Xpc3L20lMTERWVla+HlN55HI5DA0NVR5ERERUsbAYRURERFSBuLq64tKlS4iOjpYeDg4OGDRoEKKjo1GnTh0olUoEBwdLy2RmZiIsLAxt2rQBANjb20NbW1ulTVxcHC5fviy1cXJyQlJSEs6dOye1OXv2LJKSklTaXL58GXFxcVKboKAgyOVy2Nvbl+hxICIiorKLY0YRERERVSAGBgaws7NTmaavrw8zMzNpure3N/z8/GBjYwMbGxv4+flBT08PHh4eAAAjIyMMGzYMU6ZMgZmZGUxNTTF16lQ0adJEGhC9YcOG6NKlC0aMGIENGzYAAEaOHInu3bvD1tYWAODu7o5GjRrB09MTS5YswePHjzF16lSMGDGCPZ6IiIgqMRajiIiIiCqZ6dOnIz09HWPGjEFiYiIcHR0RFBQEAwMDqc2KFSugpaWFfv36IT09Ha6urti2bRs0NTWlNrt27cKECROku+717NkTq1evluZramriyJEjGDNmDNq2bQuFQgEPDw8sXbq09HaWiIiIyhwWo4iIiIgquNDQUJXnMpkMPj4+8PHxKXQZXV1drFq1CqtWrSq0jampKXbu3PnKbdeoUQMBAQFvEi4RERFVcBwzioiIiIiIiIiISg2LUUREREREREREVGpYjCIiIiIiIiIiolLDYhQREREREREREZUaFqOIiIiIiIiIiKjUsBhFRERERERERESlhsUoIiIiIiIiIiIqNSxGveCff/7Bp59+CjMzM+jp6aF58+aIioqS5gsh4OPjAysrKygUCri4uODKlStqjJiIiIiIiIiIqHxhMeo/iYmJaNu2LbS1tfHrr7/i6tWrWLZsGYyNjaU2ixcvxvLly7F69WpERkZCqVSiU6dOSElJUV/gRERERERERETliJa6AygrFi1aBGtra2zdulWaVqtWLen/QgisXLkSs2fPRp8+fQAA27dvh6WlJXbv3o1Ro0aVdshEREREREREROUOe0b959ChQ3BwcMAnn3wCCwsLtGjRAps2bZLmx8bGIj4+Hu7u7tI0uVwOZ2dnhIeHF7rejIwMJCcnqzyIiIiIiIiIiCorFqP+8/fff2PdunWwsbHB0aNH8fnnn2PChAn47rvvAADx8fEAAEtLS5XlLC0tpXkF8ff3h5GRkfSwtrYuuZ0gIiIiIiIiIirjeJnef3Jzc+Hg4AA/Pz8AQIsWLXDlyhWsW7cOgwcPltrJZDKV5YQQ+aa9aNasWZg8ebL0PDk5mQUpIiIiIioWNxJSS2zdJvo6qGasKLH1ExFR5cVi1H+qVq2KRo0aqUxr2LAh9u3bBwBQKpUAnveQqlq1qtQmISEhX2+pF8nlcsjl8hKImIiIiIgqKxN9HSi0NeG9N7rEtqHQ1sSxKc4sSBERUbFjMeo/bdu2xbVr11SmXb9+HTVr1gQA1K5dG0qlEsHBwWjRogUAIDMzE2FhYVi0aFGpx0tERERElVc1YwWOTXFGYlpmiaz/RkIqvPdGIzEtk8UoIiIqdixG/WfSpElo06YN/Pz80K9fP5w7dw4bN27Exo0bATy/PM/b2xt+fn6wsbGBjY0N/Pz8oKenBw8PDzVHT0RERESVTTVjBQtFRERULpX7AcyzsrLQsWNHXL9+/Z3W06pVKxw4cADff/897Ozs8OWXX2LlypUYNGiQ1Gb69Onw9vbGmDFj4ODggH/++QdBQUEwMDB4190gIiKiSq64choiIiKisq7c94zS1tbG5cuXXzmIeFF1794d3bt3L3S+TCaDj48PfHx83nlbRERERC8qzpyGiIiIqCwr9z2jAGDw4MHYvHmzusMgIiIieifMaYiIiKgyKPc9o4DnA4l/++23CA4OhoODA/T19VXmL1++XE2RERERERUdcxoiIiKqDCpEMery5cto2bIlAOQbZ4Fd3YmIiKi8YE5DRERElUGFKEaFhISoOwQiIiKid8achoiIiCqDCjFmVJ4bN27g6NGjSE9PBwAIIdQcEREREdGbY05DREREFVmFKEY9evQIrq6uqF+/Pj788EPExcUBAIYPH44pU6aoOToiIiKiomFOQ0RERJVBhShGTZo0Cdra2rhz5w709PSk6f3790dgYKAaIyMiIiIqOuY0REREVBlUiDGjgoKCcPToUVSvXl1luo2NDW7fvq2mqIiIiIjeDHMaIiIiqgwqRM+otLQ0lV8P8zx8+BByuVwNERERERG9OeY0REREVBlUiGJUhw4d8N1330nPZTIZcnNzsWTJEnTs2FGNkREREREVXXHkNOvWrUPTpk1haGgIQ0NDODk54ddff5XmCyHg4+MDKysrKBQKuLi44MqVKyrryMjIwPjx42Fubg59fX307NkT9+7dU2mTmJgIT09PGBkZwcjICJ6ennjy5IlKmzt37qBHjx7Q19eHubk5JkyYgMzMzDc8KkRERFTRVIjL9JYsWQIXFxecP38emZmZmD59Oq5cuYLHjx/jt99+U3d4REREREVSHDlN9erV8dVXX6FevXoAgO3bt6NXr164cOECGjdujMWLF2P58uXYtm0b6tevjwULFqBTp064du0aDAwMAADe3t44fPgw9uzZAzMzM0yZMgXdu3dHVFQUNDU1AQAeHh64d++eNJbVyJEj4enpicOHDwMAcnJy0K1bN7z33ns4ffo0Hj16hCFDhkAIgVWrVhX3oSMiIqJypEIUoxo1aoSLFy9i3bp10NTURFpaGvr06YOxY8eiatWq6g6PiIiIqEiKI6fp0aOHyvOFCxdi3bp1OHPmDBo1aoSVK1di9uzZ6NOnD4DnxSpLS0vs3r0bo0aNQlJSEjZv3owdO3bAzc0NALBz505YW1vj2LFj6Ny5M2JiYhAYGIgzZ87A0dERALBp0yY4OTnh2rVrsLW1RVBQEK5evYq7d+/CysoKALBs2TJ4eXlh4cKFMDQ0LK7DRkREROVMhShGAYBSqYSvr6+6wyAiIiJ6J8WZ0+Tk5ODHH39EWloanJycEBsbi/j4eLi7u0tt5HI5nJ2dER4ejlGjRiEqKgpZWVkqbaysrGBnZ4fw8HB07twZERERMDIykgpRANC6dWsYGRkhPDwctra2iIiIgJ2dnVSIAoDOnTsjIyMDUVFRHEqBiIioEqswxajExERs3rwZMTExkMlkaNiwIYYOHQpTU1N1h0ZERERUZMWR01y6dAlOTk549uwZqlSpggMHDqBRo0YIDw8HAFhaWqq0t7S0lO7WFx8fDx0dHZiYmORrEx8fL7WxsLDIt10LCwuVNi9vx8TEBDo6OlKbgmRkZCAjI0N6npycXNTdJiIionKiQgxgHhYWhtq1a+Obb75BYmIiHj9+jG+++Qa1a9dGWFiYusMjIiIiKpLiymlsbW0RHR2NM2fOYPTo0RgyZAiuXr0qzZfJZCrthRD5pr3s5TYFtX+bNi/z9/eXBkU3MjKCtbX1K+MiIiKi8qdCFKPGjh2Lfv36ITY2Fvv378f+/fvx999/Y8CAARg7dqy6wyMiIiIqkuLKaXR0dFCvXj04ODjA398fzZo1w9dffw2lUgkA+XomJSQkSL2YlEolMjMzkZiY+Mo2Dx48yLfdf//9V6XNy9tJTExEVlZWvh5TL5o1axaSkpKkx927d4u830RERFQ+VIhi1M2bNzFlyhTp7i4AoKmpicmTJ+PmzZtqjIyIiIio6EoqpxFCICMjA7Vr14ZSqURwcLA0LzMzE2FhYWjTpg0AwN7eHtra2ipt4uLicPnyZamNk5MTkpKScO7cOanN2bNnkZSUpNLm8uXLiIuLk9oEBQVBLpfD3t6+0FjlcjkMDQ1VHkRERFSxVIgxo1q2bImYmBjY2tqqTI+JiUHz5s3VExQRERHRGyqOnOaLL75A165dYW1tjZSUFOzZswehoaEIDAyETCaDt7c3/Pz8YGNjAxsbG/j5+UFPTw8eHh4AACMjIwwbNgxTpkyBmZkZTE1NMXXqVDRp0kS6u17Dhg3RpUsXjBgxAhs2bAAAjBw5Et27d5did3d3R6NGjeDp6YklS5bg8ePHmDp1KkaMGMECExERUSVXbotRFy9elP4/YcIETJw4ETdu3EDr1q0BAGfOnMGaNWvw1VdfqStEIiIiotcq7pzmwYMH8PT0RFxcHIyMjNC0aVMEBgaiU6dOAIDp06cjPT0dY8aMQWJiIhwdHREUFAQDAwNpHStWrICWlhb69euH9PR0uLq6Ytu2bSo9tnbt2oUJEyZId93r2bMnVq9eLc3X1NTEkSNHMGbMGLRt2xYKhQIeHh5YunTp2x8sIiIiqhBkQgih7iDehoaGBmQyGV4XvkwmQ05OTilF9XrJyckwMjJCUlISfxUkIqJK4fI/Sei+6jQCxreDXTWjcrPu0vrOLq85TWkpr7lTSb43S0N5j5+IiNSjqN/b5bZnVGxsrLpDICIiInpnzGmIiIiosim3xaiaNWuqOwQiIiKid8achoiIiCqbcluMetk///yD3377DQkJCcjNzVWZN2HCBDVFRURERPRmmNMQERFRRVchilFbt27F559/Dh0dHZiZmUEmk0nzZDIZEzciIiIqF5jTEBERUWVQIYpRc+fOxdy5czFr1ixoaGioOxwiIiKit8KchoiIiCqDCpHlPH36FAMGDGDSRkREROUacxoiIiKqDCpEpjNs2DD8+OOP6g6DiIiI6J0wpyEiIqLKoEJcpufv74/u3bsjMDAQTZo0gba2tsr85cuXqykyIiIioqJjTkNERESVQYUoRvn5+eHo0aOwtbUFgHyDfRIRERGVB8xpiIiIqDKoEMWo5cuXY8uWLfDy8lJ3KERERERvjTkNERERVQYVYswouVyOtm3bqjsMIiIionfCnIaIiIgqgwpRjJo4cSJWrVql7jCIiIiI3glzGiIiIqoMKsRleufOncOJEycQEBCAxo0b5xvsc//+/WqKjIiIiKjomNMQERFRZVAhilHGxsbo06ePusMgIiIieifMaYiIiKgyqBDFqK1btxb7Ov39/fHFF19g4sSJWLlyJQBACAFfX19s3LgRiYmJcHR0xJo1a9C4ceNi3z4RERFVPiWR0xARERGVNRVizKjiFhkZiY0bN6Jp06Yq0xcvXozly5dj9erViIyMhFKpRKdOnZCSkqKmSImIiIiIiIiIypcK0TOqdu3akMlkhc7/+++/i7yu1NRUDBo0CJs2bcKCBQuk6UIIrFy5ErNnz5a6z2/fvh2WlpbYvXs3Ro0a9fY7QERERITizWmIiIiIyqoKUYzy9vZWeZ6VlYULFy4gMDAQ06ZNe6N1jR07Ft26dYObm5tKMSo2Nhbx8fFwd3eXpsnlcjg7OyM8PLzQYlRGRgYyMjKk58nJyW8UDxEREVUexZnTEBEREZVVFaIYNXHixAKnr1mzBufPny/yevbs2YPff/8dkZGR+ebFx8cDACwtLVWmW1pa4vbt24Wu09/fH76+vkWOgYiIiCqv4sppiIiIiMqyCj1mVNeuXbFv374itb179y4mTpyInTt3QldXt9B2L3edF0K8sjv9rFmzkJSUJD3u3r1btOCJiIiI/vMmOQ0RERFRWVchekYV5qeffoKpqWmR2kZFRSEhIQH29vbStJycHJw8eRKrV6/GtWvXADzvIVW1alWpTUJCQr7eUi+Sy+WQy+VvuQdEREREb5bTEBEREZV1FaIY1aJFC5XeSUIIxMfH499//8XatWuLtA5XV1dcunRJZdrQoUPRoEEDzJgxA3Xq1IFSqURwcDBatGgBAMjMzERYWBgWLVpUfDtDRERElVZx5DREREREZV2FKEb16tVLJXHT0NDAe++9BxcXFzRo0KBI6zAwMICdnZ3KNH19fZiZmUnTvb294efnBxsbG9jY2MDPzw96enrw8PAovp0hIiKiSqs4choiIiKisq5CFKN8fHxKZTvTp09Heno6xowZg8TERDg6OiIoKAgGBgalsn0iIiKq2EorpyEiIiJSp3JdjNLQ0Hjl4OHA8wHHs7Oz32r9oaGh+dbl4+PDRJGIiIiKVUnnNERERERlSbkuRh04cKDQeeHh4Vi1ahWEEKUYEREREdGbY05DRERElUm5Lkb16tUr37Q///wTs2bNwuHDhzFo0CB8+eWXaoiMiIiIqOiY0xAREVFloqHuAIrL/fv3MWLECDRt2hTZ2dmIjo7G9u3bUaNGDXWHRkRERFRkzGmIiIiooiv3xaikpCTMmDED9erVw5UrV3D8+HEcPnw4353xiIiIiMqy4spp/P390apVKxgYGMDCwgK9e/fGtWvXVNoIIeDj4wMrKysoFAq4uLjgypUrKm0yMjIwfvx4mJubQ19fHz179sS9e/dU2iQmJsLT0xNGRkYwMjKCp6cnnjx5otLmzp076NGjB/T19WFubo4JEyYgMzPzjfaJiIiIKpZyXYxavHgx6tSpg4CAAHz//fcIDw9H+/bt1R0WERER0RspzpwmLCwMY8eOxZkzZxAcHIzs7Gy4u7sjLS1NZXvLly/H6tWrERkZCaVSiU6dOiElJUVq4+3tjQMHDmDPnj04ffo0UlNT0b17d+Tk5EhtPDw8EB0djcDAQAQGBiI6Ohqenp7S/JycHHTr1g1paWk4ffo09uzZg3379mHKlClvtW9ERERUMZTrMaNmzpwJhUKBevXqYfv27di+fXuB7fbv31/KkREREREVXXHmNIGBgSrPt27dCgsLC0RFRaFDhw4QQmDlypWYPXs2+vTpAwDYvn07LC0tsXv3bowaNQpJSUnYvHkzduzYATc3NwDAzp07YW1tjWPHjqFz586IiYlBYGAgzpw5A0dHRwDApk2b4OTkhGvXrsHW1hZBQUG4evUq7t69CysrKwDAsmXL4OXlhYULF8LQ0PCtjxkRERGVX+W6GDV48ODX3gaZiIiIqKwryZwmKSkJAGBqagoAiI2NRXx8PNzd3aU2crkczs7OCA8Px6hRoxAVFYWsrCyVNlZWVrCzs0N4eDg6d+6MiIgIGBkZSYUoAGjdujWMjIwQHh4OW1tbREREwM7OTipEAUDnzp2RkZGBqKgodOzYMV+8GRkZyMjIkJ4nJycX38EgIiKiMqFcF6O2bdum7hCIiIiI3llJ5TRCCEyePBnt2rWTxp6Kj48HAFhaWqq0tbS0xO3bt6U2Ojo6MDExydcmb/n4+HhYWFjk26aFhYVKm5e3Y2JiAh0dHanNy/z9/eHr6/umu0pERETlSLkeM4qIiIiICjdu3DhcvHgR33//fb55L/fEEkK8tnfWy20Kav82bV40a9YsJCUlSY+7d+++MiYiIiIqf1iMIiIiIqqAxo8fj0OHDiEkJATVq1eXpiuVSgDI1zMpISFB6sWkVCqRmZmJxMTEV7Z58OBBvu3++++/Km1e3k5iYiKysrLy9ZjKI5fLYWhoqPIgIiKiioXFKCIiIqIKRAiBcePGYf/+/Thx4gRq166tMr927dpQKpUIDg6WpmVmZiIsLAxt2rQBANjb20NbW1ulTVxcHC5fviy1cXJyQlJSEs6dOye1OXv2LJKSklTaXL58GXFxcVKboKAgyOVy2NvbF//OExERUblQrseMIiIiIiJVY8eOxe7du/Hzzz/DwMBA6plkZGQEhUIBmUwGb29v+Pn5wcbGBjY2NvDz84Oenh48PDyktsOGDcOUKVNgZmYGU1NTTJ06FU2aNJHurtewYUN06dIFI0aMwIYNGwAAI0eORPfu3WFrawsAcHd3R6NGjeDp6YklS5bg8ePHmDp1KkaMGMEeT0RERJUYi1FEREREFci6desAAC4uLirTt27dCi8vLwDA9OnTkZ6ejjFjxiAxMRGOjo4ICgqCgYGB1H7FihXQ0tJCv379kJ6eDldXV2zbtg2amppSm127dmHChAnSXfd69uyJ1atXS/M1NTVx5MgRjBkzBm3btoVCoYCHhweWLl1aQntPRERE5QGLUUREREQViBDitW1kMhl8fHzg4+NTaBtdXV2sWrUKq1atKrSNqakpdu7c+cpt1ahRAwEBAa+NicqmGwmpJbJeE30dVDNWlMi6iYio7GMxioiIiIiIVJjo60ChrQnvvdElsn6FtiaOTXFmQYqIqJJiMYqIiIiIiFRUM1bg2BRnJKZlFvu6bySkwntvNBLTMlmMIiKqpFiMIiIiIiKifKoZK1gsIiKiEqGh7gCIiIiIiIiIiKjyYDGKiIiIiIiIiIhKDYtRRERERERERERUaliMIiIiIiIiIiKiUsNiFBERERERERERlRoWo4iIiIiIiIiIqNSwGEVERERERERERKWGxSgiIiIiIiIiIio1LEYREREREREREVGpYTGKiIiIiIiIiIhKDYtRRERERERERERUaliMIiIiIiIiIiKiUsNiFBERERERERERlRoWo4iIiIiIiIiIqNSwGEVERERERERERKWGxSgiIiIiIiIiIio1LEYREREREREREVGpYTHqP/7+/mjVqhUMDAxgYWGB3r1749q1aypthBDw8fGBlZUVFAoFXFxccOXKFTVFTERERERERERU/rAY9Z+wsDCMHTsWZ86cQXBwMLKzs+Hu7o60tDSpzeLFi7F8+XKsXr0akZGRUCqV6NSpE1JSUtQYORERERERERFR+aGl7gDKisDAQJXnW7duhYWFBaKiotChQwcIIbBy5UrMnj0bffr0AQBs374dlpaW2L17N0aNGqWOsImIiIiIiIiIyhX2jCpEUlISAMDU1BQAEBsbi/j4eLi7u0tt5HI5nJ2dER4eXuh6MjIykJycrPIgIiIiIiIiIqqsWIwqgBACkydPRrt27WBnZwcAiI+PBwBYWlqqtLW0tJTmFcTf3x9GRkbSw9rauuQCJyIiIiIiIiIq43iZXgHGjRuHixcv4vTp0/nmyWQyledCiHzTXjRr1ixMnjxZep6cnMyCFBERERFVejcSUkts3Sb6OqhmrCix9RMR0bthMeol48ePx6FDh3Dy5ElUr15dmq5UKgE87yFVtWpVaXpCQkK+3lIvksvlkMvlJRcwEREREVE5YqKvA4W2Jrz3RpfYNhTamjg2xZkFKSKiMorFqP8IITB+/HgcOHAAoaGhqF27tsr82rVrQ6lUIjg4GC1atAAAZGZmIiwsDIsWLVJHyEREREQFOnnyJJYsWYKoqCjExcXhwIED6N27tzRfCAFfX19s3LgRiYmJcHR0xJo1a9C4cWOpTUZGBqZOnYrvv/8e6enpcHV1xdq1a1V+rEtMTMSECRNw6NAhAEDPnj2xatUqGBsbS23u3LmDsWPH4sSJE1AoFPDw8MDSpUuho6NT4seByqZqxgocm+KMxLTMEln/jYRUeO+NRmJaJotRRERlFItR/xk7dix2796Nn3/+GQYGBtI4UEZGRlAoFJDJZPD29oafnx9sbGxgY2MDPz8/6OnpwcPDQ83RExEREf2/tLQ0NGvWDEOHDsXHH3+cb/7ixYuxfPlybNu2DfXr18eCBQvQqVMnXLt2DQYGBgAAb29vHD58GHv27IGZmRmmTJmC7t27IyoqCpqamgAADw8P3Lt3T7or8ciRI+Hp6YnDhw8DAHJyctCtWze89957OH36NB49eoQhQ4ZACIFVq1aV0tGgsqiasYKFIiKiSozFqP+sW7cOAODi4qIyfevWrfDy8gIATJ8+Henp6RgzZoz0K2JQUJCUtBERERGVBV27dkXXrl0LnCeEwMqVKzF79mz06dMHALB9+3ZYWlpi9+7dGDVqFJKSkrB582bs2LEDbm5uAICdO3fC2toax44dQ+fOnRETE4PAwECcOXMGjo6OAIBNmzbByckJ165dg62tLYKCgnD16lXcvXsXVlZWAIBly5bBy8sLCxcuhKGhYSkcDSIiIipreDe9/wghCnzkFaKA54OX+/j4IC4uDs+ePUNYWJh0tz0iIiKi8iA2Nhbx8fFwd3eXpsnlcjg7OyM8PBwAEBUVhaysLJU2VlZWsLOzk9pERETAyMhIKkQBQOvWrWFkZKTSxs7OTipEAUDnzp2RkZGBqKioAuPLyMhAcnKyyoOIiIgqFhajiIiIiCqRvKEIXr4Bi6WlpTQvPj4eOjo6MDExeWUbCwuLfOu3sLBQafPydkxMTKCjoyO1eZm/vz+MjIykB+9CTEREVPGwGEVERERUCclkMpXnQoh80172cpuC2r9NmxfNmjULSUlJ0uPu3buvjImIiIjKHxajiIiIiCoRpVIJAPl6JiUkJEi9mJRKJTIzM5GYmPjKNg8ePMi3/n///VelzcvbSUxMRFZWVr4eU3nkcjkMDQ1VHkRERFSxcABzIiIiwj9P0kv0NutUdtSuXRtKpRLBwcFo0aIFACAzMxNhYWFYtGgRAMDe3h7a2toIDg5Gv379AABxcXG4fPkyFi9eDABwcnJCUlISzp07h/fffx8AcPbsWSQlJaFNmzZSm4ULFyIuLg5Vq1YFAAQFBUEul8Pe3r5U95uIiIjKDhajiIiIKrl/nqTDbVkY0rNySmwbCm1NmOjrlNj6SVVqaipu3LghPY+NjUV0dDRMTU1Ro0YNeHt7w8/PDzY2NrCxsYGfnx/09PTg4eEBADAyMsKwYcMwZcoUmJmZwdTUFFOnTkWTJk2ku+s1bNgQXbp0wYgRI7BhwwYAwMiRI9G9e3fY2toCANzd3dGoUSN4enpiyZIlePz4MaZOnYoRI0awxxMREVElxmIUERFRJZeYlon0rBys7N8c9SyqlMg2TPR1UM1YUSLrpvzOnz+Pjh07Ss8nT54MABgyZAi2bduG6dOnIz09HWPGjEFiYiIcHR0RFBQEAwMDaZkVK1ZAS0sL/fr1Q3p6OlxdXbFt2zZoampKbXbt2oUJEyZId93r2bMnVq9eLc3X1NTEkSNHMGbMGLRt2xYKhQIeHh5YunRpSR8CIiIiKsNYjCIiIiIAQD2LKrCrZqTuMKgYuLi4QAhR6HyZTAYfHx/4+PgU2kZXVxerVq3CqlWrCm1jamqKnTt3vjKWGjVqICAg4LUxExERUeXBAcyJiIiIiIiIiKjUsBhFRERERERERESlhpfpERERERFRhVNSd/LkGHhERO+OxSgiIiIiIqowTPR1oNDWhPfe6BJZv0JbE8emOLMgRUT0DliMIiIiIiKiCqOasQLHpjgjMS2z2Nd9IyEV3nujkZiWyWIUEdE7YDGKiIiIiIgqlGrGChaLiIjKMA5gTkREREREREREpYbFKCIiIiIiIiIiKjUsRhERERERERERUalhMYqIiIiIiIiIiEoNBzAnIiIiIiJ6AzcSUkts3Sb6Ohx8nYgqPBajiIiIiIiIisBEXwcKbU14740usW0otDVxbIozC1JEVKGxGEVERERERFQE1YwVODbFGYlpmSWy/hsJqfDeG43EtEwWo4ioQmMxioiIiIiIqIiqGStYKCIiekccwJyIiIiIiIiIiEoNe0YRERERERGVIRwgnYgqOhajiIiIiIiIygAOkE5ElQWLUURERERERGUAB0gnosqCxSgiIiIiIqIyggOkE1FlwGIUERERERFRJVJSY1JxPCoiKioWo4iIiIiIiCqBkh6TiuNREVFRsRhFRERERERUCZTkmFR541FFxj5GokWVYl8/wJ5XRBUJi1FERERERESVREmNScU7ARLRm2AxioiIiIiIiN5Jad0JsKR6XrHXFVHpYjGKiIiIiIiI3llJ3gmwNMa7Wu9pDzN9nRJZP4tdRKpYjCIiIiIiIqIyrSR7Xj1Ky8TnO6IwZMu5Yl93Hha7iFSxGEVERERERERlXkn2vCrJSwzLe7GLhS4qCSxGvYW1a9diyZIliIuLQ+PGjbFy5Uq0b99e3WERERERlVnMn4ioLCvJQhdQvotdJd2rqzxjoe7tsRj1hvbu3Qtvb2+sXbsWbdu2xYYNG9C1a1dcvXoVNWrUUHd4RERERGUO8yciquzKa7GrNHp1lWe8w+PbYzHqDS1fvhzDhg3D8OHDAQArV67E0aNHsW7dOvj7+6s5OiIiIqKyh/kTEVHJKq+XMJZneXd4TEzLZDHqLbAY9QYyMzMRFRWFmTNnqkx3d3dHeHh4gctkZGQgIyNDep6UlAQASE5OLvb4/k1+hn9TM17fkIiI6AV//5uG3IynSE1JRnKyTN3hvJHUlOQSiz3vu1oIUazrrWzeNH8qzdwJKLn8qTz/XRERvchAAzAw4OfYy1JTcpGb8RQX/45DakrJfEeVpPeqyPGeoW6xr7eo+ROLUW/g4cOHyMnJgaWlpcp0S0tLxMfHF7iMv78/fH198023trYukRiJiIjeltNKdUfw9koy9pSUFBgZGZXcBiq4N82fKlruVJ7/roiI6PUGrVR3BGXT6/InFqPegkymWhUWQuSblmfWrFmYPHmy9Dw3NxePHz+GmZlZocu8jeTkZFhbW+Pu3bswNDQstvWWFdy/8o37V75x/8o37t/bE0IgJSUFVlZWxbreyqqo+dPrcqeK/p4ui3jM1YPHvfTxmKsHj3vpKwv5E4tRb8Dc3Byampr5fsVLSEjI92tfHrlcDrlcrjLN2Ni4pEKEoaFhhf4D5v6Vb9y/8o37V75x/94Oe0S9uzfNn4qaO1X093RZxGOuHjzupY/HXD143EufOvMnjWLfagWmo6MDe3t7BAcHq0wPDg5GmzZt1BQVERERUdnF/ImIiIhexp5Rb2jy5Mnw9PSEg4MDnJycsHHjRty5cweff/65ukMjIiIiKpOYPxEREdGLWIx6Q/3798ejR48wf/58xMXFwc7ODr/88gtq1qyp1rjkcjnmzZuXr1t7RcH9K9+4f+Ub96984/5RWVCc+RNf89LHY64ePO6lj8dcPXjcS19ZOOYywfsVExERERERERFRKeGYUUREREREREREVGpYjCIiIiIiIiIiolLDYhQREREREREREZUaFqOIiIiIiIiIiKjUsBhVAaxduxa1a9eGrq4u7O3tcerUKXWHVCz8/f3RqlUrGBgYwMLCAr1798a1a9fUHVaJ8ff3h0wmg7e3t7pDKTb//PMPPv30U5iZmUFPTw/NmzdHVFSUusMqNtnZ2fjf//6H2rVrQ6FQoE6dOpg/fz5yc3PVHdpbOXnyJHr06AErKyvIZDIcPHhQZb4QAj4+PrCysoJCoYCLiwuuXLminmDfwqv2LysrCzNmzECTJk2gr68PKysrDB48GPfv31dfwG/oda/fi0aNGgWZTIaVK1eWWnzvqij7FxMTg549e8LIyAgGBgZo3bo17ty5U/rBUomqqHlPWVTZcrGyqCLmh2VVRc9by5qKlkeXVWU5v2cxqpzbu3cvvL29MXv2bFy4cAHt27dH165dK0TyHRYWhrFjx+LMmTMIDg5GdnY23N3dkZaWpu7Qil1kZCQ2btyIpk2bqjuUYpOYmIi2bdtCW1sbv/76K65evYply5bB2NhY3aEVm0WLFmH9+vVYvXo1YmJisHjxYixZsgSrVq1Sd2hvJS0tDc2aNcPq1asLnL948WIsX74cq1evRmRkJJRKJTp16oSUlJRSjvTtvGr/nj59it9//x1z5szB77//jv379+P69evo2bOnGiJ9O697/fIcPHgQZ8+ehZWVVSlFVjxet383b95Eu3bt0KBBA4SGhuKPP/7AnDlzoKurW8qRUkmqyHlPWVSZcrGyqCLmh2VVZchby5qKlkeXVWU6vxdUrr3//vvi888/V5nWoEEDMXPmTDVFVHISEhIEABEWFqbuUIpVSkqKsLGxEcHBwcLZ2VlMnDhR3SEVixkzZoh27dqpO4wS1a1bN/HZZ5+pTOvTp4/49NNP1RRR8QEgDhw4ID3Pzc0VSqVSfPXVV9K0Z8+eCSMjI7F+/Xo1RPhuXt6/gpw7d04AELdv3y6doIpRYft37949Ua1aNXH58mVRs2ZNsWLFilKPrTgUtH/9+/evEH979GqVKe8piypqLlYWVdT8sKyqDHlrWVOR8+iyqqzl9+wZVY5lZmYiKioK7u7uKtPd3d0RHh6upqhKTlJSEgDA1NRUzZEUr7Fjx6Jbt25wc3NTdyjF6tChQ3BwcMAnn3wCCwsLtGjRAps2bVJ3WMWqXbt2OH78OK5fvw4A+OOPP3D69Gl8+OGHao6s+MXGxiI+Pl7l80Yul8PZ2blCft4Azz9zZDJZhflVNDc3F56enpg2bRoaN26s7nCKVW5uLo4cOYL69eujc+fOsLCwgKOj4ysvVaTyp7LlPWVRRc3FyqKKmh+WVZUhby1rKlMeXVapO7/XKvEtUIl5+PAhcnJyYGlpqTLd0tIS8fHxaoqqZAghMHnyZLRr1w52dnbqDqfY7NmzB7///jsiIyPVHUqx+/vvv7Fu3TpMnjwZX3zxBc6dO4cJEyZALpdj8ODB6g6vWMyYMQNJSUlo0KABNDU1kZOTg4ULF2LgwIHqDq3Y5X2mFPR5c/v2bXWEVKKePXuGmTNnwsPDA4aGhuoOp1gsWrQIWlpamDBhgrpDKXYJCQlITU3FV199hQULFmDRokUIDAxEnz59EBISAmdnZ3WHSMWgMuU9ZVFFzcXKooqcH5ZVlSFvLWsqUx5dVqk7v2cxqgKQyWQqz4UQ+aaVd+PGjcPFixdx+vRpdYdSbO7evYuJEyciKCioQo5pkpubCwcHB/j5+QEAWrRogStXrmDdunUV5kt979692LlzJ3bv3o3GjRsjOjoa3t7esLKywpAhQ9QdXomoDJ83WVlZGDBgAHJzc7F27Vp1h1MsoqKi8PXXX+P333+vcK8XAGmw0169emHSpEkAgObNmyM8PBzr169nMaqCqQyfQ2VRRczFyqKKnh+WVZUhby1rKmMeXVap63uVl+mVY+bm5tDU1Mz3a2BCQkK+6mZ5Nn78eBw6dAghISGoXr26usMpNlFRUUhISIC9vT20tLSgpaWFsLAwfPPNN9DS0kJOTo66Q3wnVatWRaNGjVSmNWzYsEINMjtt2jTMnDkTAwYMQJMmTeDp6YlJkybB399f3aEVO6VSCQAV/vMmKysL/fr1Q2xsLIKDgytMr6hTp04hISEBNWrUkD5vbt++jSlTpqBWrVrqDu+dmZubQ0tLq8J/5lR2lSXvKYsqai5WFlX0/LCsqgx5a1lTmfLoskrd+T2LUeWYjo4O7O3tERwcrDI9ODgYbdq0UVNUxUcIgXHjxmH//v04ceIEateure6QipWrqysuXbqE6Oho6eHg4IBBgwYhOjoampqa6g7xnbRt2zbf7Z+vX7+OmjVrqimi4vf06VNoaKh+jGpqalbIW9LWrl0bSqVS5fMmMzMTYWFhFeLzBvj/QtRff/2FY8eOwczMTN0hFRtPT09cvHhR5fPGysoK06ZNw9GjR9Ud3jvT0dFBq1atKvxnTmVX0fOesqii52JlUUXPD8uqypC3ljWVKY8uq9Sd3/MyvXJu8uTJ8PT0hIODA5ycnLBx40bcuXMHn3/+ubpDe2djx47F7t278fPPP8PAwECq2BoZGUGhUKg5undnYGCQb8wFfX19mJmZVYixGCZNmoQ2bdrAz88P/fr1w7lz57Bx40Zs3LhR3aEVmx49emDhwoWoUaMGGjdujAsXLmD58uX47LPP1B3aW0lNTcWNGzek57GxsYiOjoapqSlq1KgBb29v+Pn5wcbGBjY2NvDz84Oenh48PDzUGHXRvWr/rKys0LdvX/z+++8ICAhATk6O9JljamoKHR0ddYVdZK97/V4urmlra0OpVMLW1ra0Q30rr9u/adOmoX///ujQoQM6duyIwMBAHD58GKGhoeoLmopdRc57yqKKnouVRRU9PyyrKkPeWtZUtDy6rCrT+X2J36+PStyaNWtEzZo1hY6OjmjZsmWFud0ugAIfW7duVXdoJaai3br38OHDws7OTsjlctGgQQOxceNGdYdUrJKTk8XEiRNFjRo1hK6urqhTp46YPXu2yMjIUHdobyUkJKTAv7khQ4YIIZ7f/nXevHlCqVQKuVwuOnToIC5duqTeoN/Aq/YvNja20M+ckJAQdYdeJK97/V5Ws2ZNsWLFilKN8V0UZf82b94s6tWrJ3R1dUWzZs3EwYMH1RcwlZiKmveURZUxFyuLKlp+WFZV9Ly1rKloeXRZVZbze5kQQpRQnYuIiIiIiIiIiEgFx4wiAnDx4kUMGzYMdevWhUKhgEKhgI2NDUaNGoXz58+rO7xiFR4eDh8fHzx58qTY1+3l5VWkwZBdXFwgk8mkh7a2NmrVqoVhw4aVym1ES8O2bdtU9lFXVxdKpRIdO3aEv78/EhIS1B1imeTi4lIqlyHUqlULMpkMLi4uBc7/7rvvpNeOl3kREb055lbF401zqy5duuSbd+vWLchkMixdurTY43vRL7/8Ah8fnxLdxrs4evQo3N3dYWVlBblcDisrK7i4uOCrr75Saefn54eDBw+qJ8hChIaGQiaT4aeffirR7byYvxaU/wghUK9evVfmUERFxWIUVXobNmyAvb09zp49i4kTJyIgIABHjhyBt7c3rly5glatWuHmzZvqDrPYhIeHw9fXt0QSpjdRp04dREREICIiAsePH8f06dMREBCA9u3b4+nTp2qNrTht3boVERERCA4Oxpo1a9C8eXMsWrQIDRs2xLFjx9QdXqVmYGCAkydPFvj3vWXLlgpzJz0iotLG3Ep9jh49ihMnTqhl27/88gt8fX3Vsu3XWb9+Pbp06QJDQ0OsXr0aR48elfKxlws8ZbEYVdoMDAywefPmfNPDwsJw8+ZNGBgYqCEqqmg4gDlVar/99hvGjBmDbt264aefflIZpPiDDz7A2LFj8eOPP5bpQTqfPn0KPT09dYfxxhQKBVq3bi0979ChA3R1dTFs2DCcPn0a7u7uhS5bnvbZzs4ODg4O0vOPP/4YkyZNQrt27dCnTx/89ddfar0leXk6lsWtXbt2uHTpErZs2YKFCxdK02/evImTJ09i+PDh2LRpkxojJCIqf5hbqU/9+vWRnZ2N6dOnIzIyEjKZTN0hFUoIgWfPnpXa+8Df3x8dOnTIV3jy9PQslbu35eTkIDs7G3K5vMS3VRz69++PXbt2Yc2aNSo/zm3evBlOTk5ITk5WY3RUUbBnFFVqfn5+0NTUxIYNGwq9W9Ynn3wCKysrlWnnz59Hz549YWpqCl1dXbRo0QI//PCDSpu8bq4hISEYPXo0zM3NYWZmhj59+uD+/fv5trN37144OTlBX18fVapUQefOnXHhwgWVNl5eXqhSpQouXboEd3d3GBgYwNXVFcDzW1v36tUL1atXh66uLurVq4dRo0bh4cOH0vI+Pj6YNm0agOe38iyoG25R4sjbP1tbW8jlcjRs2BDffffdK4500RgZGQF4fpevF2OWyWT4/fff0bdvX5iYmKBu3boAgGfPnmHWrFmoXbs2dHR0UK1aNYwdO1bll8lp06bByMgIOTk50rTx48dDJpNhyZIl0rRHjx5BQ0MDq1atAvD/3aG///57zJ49G1ZWVjA0NISbm1u+W/++qRo1amDZsmVISUnBhg0bAABHjhyBTCZDZGSk1G7fvn2QyWTo1q2byvJNmzbFxx9/LD1fs2YNOnToAAsLC+jr66NJkyZYvHgxsrKyVJbLuwTu5MmTaNOmDfT09PDZZ5+hd+/eqFmzZoHJmKOjI1q2bCk9F0Jg7dq1aN68ORQKBUxMTNC3b1/8/fffKstduHAB3bt3h4WFhdQVvlu3brh3716RjtGpU6fQunVrKBQKVKtWDXPmzJFeQyEEbGxs0Llz53zLpaamwsjICGPHjn3tNjQ0NDB48GBs375dZd+3bNkCa2truLm55Vvm/PnzGDBgAGrVqgWFQoFatWph4MCB+S4vffr0KaZOnYratWtDV1cXpqamcHBwwPfffy+1+fvvvzFgwADpcgFLS0u4uroiOjq6SMeIiKgsYm6lvtxKW1sbCxcuRFRUFPbu3fva9vHx8Rg1ahSqV68OHR0d1K5dG76+vsjOzpba5OVDL1+ylXfp37Zt2wA8P45r1qwBAJVhCm7duiVNGzduHNavX4+GDRtCLpdj+/btAIDTp0/D1dUVBgYG0NPTQ5s2bXDkyJF8x+ZNXvuXPXr0CFWrVi1wnobG/58Sy2QypKWlYfv27dI+5F2O9u+//2LMmDFo1KgRqlSpAgsLC3zwwQc4depUgcdm8eLFWLBgAWrXrg25XI6QkBDk5uZiwYIFsLW1hUKhgLGxMZo2bYqvv/76tfsAPM99J0+eDKVSCYVCAWdnZ5X30o4dOyCTyRAREZFv2fnz50NbW7tIx2vgwIEAoJK3JCUlYd++fYXe7c7X1xeOjo4wNTWFoaEhWrZsic2bN+PlIapPnDgBFxcXmJmZQaFQoEaNGvj4449VroxYt24dmjVrhipVqsDAwAANGjTAF1988dq4qZwplWHSicqg7OxsoVAohJOT0xstd+LECaGjoyPat28v9u7dKwIDA4WXl1e+u8ts3bpVABB16tQR48ePF0ePHhXffvutMDExER07dlRZ58KFC4VMJhOfffaZCAgIEPv37xdOTk5CX19fXLlyRWo3ZMgQoa2tLWrVqiX8/f3F8ePHxdGjR4UQQqxbt074+/uLQ4cOibCwMLF9+3bRrFkzYWtrKzIzM4UQQty9e1eMHz9eABD79+8XERERIiIiQiQlJb1RHHn71qtXL3H48GGxc+dOUa9ePWFtbS1q1qz52mPo7OwsGjduLLKyskRWVpZIS0sTZ8+eFU2bNhV16tQRz549k9rOmzdPABA1a9YUM2bMEMHBweLgwYMiNzdXdO7cWWhpaYk5c+aIoKAgsXTpUqGvry9atGghrSMwMFAAEOHh4dI6GzRoIBQKhejUqZM0be/evQKAuHr1qhDi/+88UatWLTFo0CBx5MgR8f3334saNWoIGxsbkZ2d/cp9zDtGkZGRBc5PTU0VmpqawtXVVQghREpKitDW1hZ+fn5Sm88//1woFAqhr68vvYYPHjwQMplMrF27Vmo3adIksW7dOhEYGChOnDghVqxYIczNzcXQoUPzHXdTU1NhbW0tVq1aJUJCQkRYWJj4+eefBQARHBys0j4mJkYAEN988400bcSIEUJbW1tMmTJFBAYGit27d4sGDRoIS0tLER8fL+2bmZmZcHBwED/88IMICwsTe/fuFZ9//rl0fAvj7OwszMzMhJWVlfjmm2/E0aNHxYQJEwQAMXbsWKnd119/LWQymbh+/brK8mvWrBEAVN6vBalZs6bo1q2buHHjhpDJZOKXX34RQjz/XKhWrZqYO3eu+PHHH/PdTe/HH38Uc+fOFQcOHBBhYWFiz549wtnZWbz33nvi33//ldqNGjVK6OnpieXLl4uQkBAREBAgvvrqK7Fq1Sqpja2trahXr57YsWOHCAsLE/v27RNTpkwpN3fvIyJ6GXMr9edWubm5wt7eXtStW1eKMe+OsUuWLJHax8XFSevesGGDOHbsmPjyyy+FXC4XXl5eUru8fOjl76a8dea9Pjdu3BB9+/YVAKRjEBERIeVjAES1atVE06ZNxe7du8WJEyfE5cuXRWhoqNDW1hb29vZi79694uDBg8Ld3V3IZDKxZ8+et3rtC+Lm5ia0tLTEvHnzRHR0dKF5XEREhFAoFOLDDz+U9iHvdfrzzz/F6NGjxZ49e0RoaKgICAgQw4YNExoaGirHJ+/YVKtWTXTs2FH89NNPIigoSMTGxgp/f3+hqakp5s2bJ44fPy4CAwPFypUrhY+Pzyvjz3sdrK2t871HDA0Nxc2bN4UQQmRkZAilUikGDRqksnxWVpawsrISn3zyySu382L+6unpKd5//31p3rp164S+vr5ITk4WjRs3Fs7OzirLenl5ic2bN4vg4GARHBwsvvzyS6FQKISvr6/KsdHV1RWdOnUSBw8eFKGhoWLXrl3C09NTJCYmCiGE+P777wUAMX78eBEUFCSOHTsm1q9fLyZMmPDK2Kn8YTGKKq34+HgBQAwYMCDfvOzsbKlQkpWVJXJzc6V5DRo0EC1atBBZWVkqy3Tv3l1UrVpV5OTkCCH+/8N8zJgxKu0WL14sAIi4uDghhBB37twRWlpaYvz48SrtUlJShFKpFP369ZOmDRkyRAAQW7ZseeW+5ebmiqysLHH79m0BQPz888/SvCVLlggAIjY2VmWZosaRk5MjrKysRMuWLVWOy61bt4S2tnaREyYUcIvR+vXri5iYGJW2ecWouXPnqkzPKzItXrxYZXpeUSnvdrxpaWlCR0dHzJ8/XwghxL179wQAMWPGDKFQKKQkacSIEcLKykpaT96X/ocffqiy/h9++EFKtF7ldcUoIYSwtLQUDRs2lJ63a9dOfPDBB9LzevXqiWnTpgkNDQ3p1uW7du0SAPIVYfLk5OSIrKws8d133wlNTU3x+PFjaV7ecT9+/LjKMllZWcLS0lJ4eHioTJ8+fbrQ0dERDx8+FEI8T9AAiGXLlqm0u3v3rlAoFGL69OlCCCHOnz8vAIiDBw8Wuu+FyYvxxfesEM9fHw0NDXH79m0hxPPbARsYGOS71XWjRo2KlJTmFaPyttm3b18hhBBHjhwRMplMxMbGFliMell2drZITU0V+vr64uuvv5am29nZid69exe63MOHDwUAsXLlytfGSkRUXjC3ilVZprRzq8aNGwshhDh27JgAIP0AUlAxatSoUaJKlSrS92qepUuXqvyoU9RilBBCjB07VhTW1wGAMDIyUslLhBCidevWwsLCQqSkpEjTsrOzhZ2dnahevbp0PIr62hfmxo0bws7OTso5FQqFcHV1FatXr5aKdnn09fWl296/St572tXVVXz00UfS9Lxj82JBME/37t1F8+bNX7vul+W9DoW9R4YPHy5NmzdvntDR0REPHjyQpuXlx3n5ZGFezF/ztnn58mUhhBCtWrWSCpUFFaNelJePzp8/X5iZmUkx//TTTwKAiI6OLnTZcePGCWNj41fGSRUDL9MjKoC9vT20tbWlx7JlywAAN27cwJ9//olBgwYBALKzs6XHhx9+iLi4uHyXcPXs2VPledOmTQFAuqzn6NGjyM7OxuDBg1XWp6urC2dn5wLvZPHiJVp5EhIS8Pnnn8Pa2hpaWlrQ1tZGzZo1AQAxMTGv3eeixnHt2jXcv38fHh4eKmMR1KxZE23atHntdvLUrVsXkZGRiIyMREREBHbv3g2FQgFXV1f89ddfr93nvME5vby8VKZ/8skn0NfXx/HjxwEAenp6cHJykgYLDw4OhrGxMaZNm4bMzEycPn0aAHDs2LECL8t63ev3LsRL3ZZdXV3x22+/IT09Hbdv38aNGzcwYMAANG/eHMHBwVKcNWrUgI2NjbTchQsX0LNnT5iZmUFTUxPa2toYPHgwcnJycP36dZVtmJiY4IMPPlCZpqWlhU8//RT79+9HUlISgOdjG+zYsQO9evWCmZkZACAgIAAymQyffvqpyntEqVSiWbNm0nukXr16MDExwYwZM7B+/XpcvXr1jY6LgYFBvuPu4eGB3NxcnDx5UmozdOhQbNu2DWlpaQCevyeuXr2KcePGvdH2PvvsMxw6dAiPHj3C5s2b0bFjx0LvXJSamooZM2agXr160NLSgpaWFqpUqYK0tDSVv7P3338fv/76K2bOnInQ0FCkp6errMfU1BR169bFkiVLsHz5cly4cKFUxqwgIlIX5lYln1vlcXV1hbu7O+bPn4+UlJQC2wQEBKBjx46wsrJSia1r164Ang9UXdw++OADmJiYSM/T0tJw9uxZ9O3bF1WqVJGma2pqwtPTE/fu3Xvj174wdevWxR9//IGwsDD4+vrCzc0NkZGRGDduHJycnPDs2bMi7cP69evRsmVL6OrqSu+J48ePF/h+6Nmzp8rQE8Dz/OCPP/7AmDFjcPTo0Tcee6mw90hISIg0bfTo0QCgMu7l6tWr0aRJE3To0KHI23J2dkbdunWxZcsWXLp0CZGRkYVeogc8z8Pc3NxgZGQk5aNz587Fo0ePpLtIN2/eHDo6Ohg5ciS2b9+eb5gH4PkxevLkCQYOHIiff/5Z5bJYqlhYjKJKy9zcHAqFosAvr927dyMyMhKHDh1Smf7gwQMAwNSpU1USKm1tbYwZMwYA8n1g5p3I58kbuDDv5DRvna1atcq3zr179+Zbn56eXr67fOXm5sLd3R379+/H9OnTcfz4cZw7dw5nzpxR2darFDWOR48eAQCUSmW+dRQ0rTC6urpwcHCAg4MDWrdujYEDB+LXX39FXFwc5s6dm6/9y9f5P3r0CFpaWnjvvfdUpstkMiiVSilOAHBzc8OZM2eQlpaGY8eO4YMPPoCZmRns7e1x7NgxxMbGIjY2tsBi1Otev7eVlpaGR48eqYyZ4ebmhoyMDJw+fRrBwcEwNzdHixYt4ObmJhXTjh8/rhLnnTt30L59e/zzzz/4+uuvcerUKURGRkrjNrwcZ2HjJXz22Wd49uwZ9uzZA+B5Ah0XF4ehQ4dKbR48eAAhBCwtLfO9R86cOSO9R4yMjBAWFobmzZvjiy++QOPGjWFlZYV58+blG8eqIAUN6J733nrxdR0/fjxSUlKwa9cuAM8TrerVq6NXr16v3caL+vbtC11dXaxYsQKHDx/GsGHDCm3r4eGB1atXY/jw4Th69CjOnTuHyMhIvPfeeyrH+ptvvsGMGTNw8OBBdOzYEaampujdu7dUaJXJZDh+/Dg6d+6MxYsXo2XLlnjvvfcwYcKEQk8ciIjKOuZWqko7t3rRokWL8PDhQyxdurTQ2A4fPpwvrsaNGwPIf8yLw8s5SGJiIoQQBeYmefnRi9/7wLvlZRoaGujQoQPmzp2LQ4cO4f79++jfvz+ioqKwZcuW1y6/fPlyjB49Go6Ojti3bx/OnDmDyMhIdOnSpcDtF7Rfs2bNwtKlS3HmzBl07doVZmZmcHV1xfnz51+7faDw98iLx8nS0hL9+/fHhg0bkJOTg4sXL+LUqVNv/GOdTCbD0KFDsXPnTqxfvx7169dH+/btC2x77tw56eZDmzZtwm+//YbIyEjMnj0bwP+/PnXr1sWxY8dgYWGBsWPHom7duqhbt67KmFmenp7YsmULbt++jY8//vj/2rvzuKiq9w/gn2GZAQmGTTZFxA0XcMNCIUVDQRPNrExJAzO1XAnINC2xFNLcClPLTCwX/H5TS80MNEUNV5QUJVeUNBBTBEFkEM/vD3/cr8Muzgzb5/168cq595l7z5kFnp577jmwsbGBh4eHdGGW6g+upkcNlr6+Pl544QXExsYiPT1d7Q9G+/btAUCadLGYtbU1gEd/SIYOHVrmcV1cXJ6oHcXH/PHHH6WrbRUpa2WU5ORk/Pnnn4iOjkZgYKC0/eLFixpvR3ESkJGRUWpfWduehL29PaytrfHnn3+W2ley31ZWVnjw4AFu3rypVpASQiAjIwPPPvustM3HxwcfffQR9u/fjz179mD27NnS9tjYWDg7O0uPdeWXX35BUVGRNCkm8Giy8GeeeQa7d+/GlStX4OPjA5lMBh8fHyxatAjHjh1DWlqaWjHqp59+Ql5eHrZs2aL2vpU3CXZ5K+u0b98ezz33HNasWYPx48djzZo1cHBwUFvV0NraGjKZDAcOHChzNZjHt7m5uSEmJgZCCJw6dQrR0dH45JNPYGxsjOnTp1f42hQn748r/mw9noS2atUKAwYMwFdffYUBAwZg27ZtmDNnDvT19Ss8fkmNGjXC8OHDERkZCTMzs3K/29nZ2dixYwdmz56t1oeCggLcvn1bLdbExARz5szBnDlzcOPGDWmU1KBBg/DXX38BeHQ1s3jZ5PPnz+M///kPwsPDoVKpsHLlyifqAxFRbcDcqnrt0EZu1blzZ4wYMQKLFy/Giy++WGbbOnbsqLaa7OOKi0FGRkYAHv2te1x1ilUlX2cLCwvo6ekhPT29VGzxJNvFr6E2mJiYYMaMGdi0aROSk5MrjV+3bh169+6NFStWqG0v7yJSWZ8rAwMDhISEICQkBHfu3MHu3bvx4Ycfws/PD3///XelqziW9xkpWaSbOnUqfvjhB/z888/YtWsXzM3NpdGHTyIoKAgff/wxVq5cWe5nBQBiYmJgaGiIHTt2SJ8Z4FGeWlLPnj3Rs2dPFBUV4fjx44iKikJwcDBsbW0xfPhwAMDo0aMxevRo5OXlYf/+/Zg9ezb8/f1x/vz5Kn2nqW7gyChq0GbMmIGioiK88847VRqx4eLigtatW+PPP/+URvWU/DE1NX2iNvj5+cHAwACXLl0q95iVKf5jV7JAULxS2+PKu4JU1Xa4uLjA3t4eGzduVLvN7OrVq0hISHiivpd07do1/Pvvv7Cxsak0trhwtG7dOrXtmzdvRl5enlph6bnnnoOZmRmWLl2KjIwM9OvXD8CjkUgnT57Ef/7zH7Rv377Uyj5PIzc3FwDKXLEkLS0NYWFhUCqVGD9+vLTd0NAQvXr1QlxcHH7//XepnT179oSBgQFmzZolFaeKPf7ev/rqq1i8eDGEEGpDs6tq9OjROHLkCA4ePIjt27cjMDBQrbDj7+8PIQSuX79e5ufDzc2t1DFlMhk6deqEJUuWwNzcHCdOnKi0HXfv3i115XzDhg3SFc3HTZ06FadOnZLaOnbs2CfuN/BoSPugQYPw8ccfqyVRJfsihCj1Pfv222/VVmssydbWFkFBQRgxYgTOnTuntlpMsTZt2mDWrFlwc3Or0mtERFRbMbd68nZoK7eaO3cuVCoV5syZU2qfv78/kpOT0bJlyzLbVZwTFd+2furUKbXnl/w7XdHrUB4TExN4eHhgy5Yt0nNu3bqFxo0b47vvvkPTpk3Rpk2bKve3ImUVvID/3W6ZmpoqbVMoFGX2QSaTlfo8nDp1qsyV66rC3Nwcr776KiZOnIjbt2+XKtSWpbzPyOMXN4FHt8V6enpi/vz5WL9+PYKCgmBiYvLEbWzSpAnef/99DBo0SK0oW5JMJoOBgYFa3pifn48ffvih3Ofo6+vDw8NDGs1fVv5jYmKCAQMGYObMmVCpVDhz5swT94FqL46MogbNy8sLX331FSZPnoyuXbti3Lhx6NChg3SVZvPmzQCgNnT766+/xoABA+Dn54egoCA0adIEt2/fRkpKCk6cOIH//ve/T9SG5s2b45NPPsHMmTNx+fJl9O/fHxYWFrhx4waOHj0qjbCoSNu2bdGyZUtMnz4dQghYWlpi+/btZQ5nLS4YfPHFFwgMDIShoSFcXFyq3A49PT18+umnePvtt/Hyyy9j7NixuHPnDsLDw59oKHl+fr401L2oqAipqalYsGABACA4OLjS5xffmhUWFoacnBx4eXnh1KlTmD17NqytrTF27FgcPHgQ0dHR0NfXh7e3N7Zv3w5nZ2e0bNkSwKP3X6FQYM+ePZgyZUqV214VO3bsAADcvn0bhw8fxoMHDzBu3DikpKTAyMgIjRo1wtatW9G4cWMIIdC9e3ccPXoU/fr1k5boLR4BZWxsDE9PT8TGxqJjx45qxbp+/fpBLpdjxIgReO211/DBBx9gx44dyMrKeuI2jxgxAiEhIRgxYgQKCgpKzcfl5eWFcePGYfTo0Th+/Dh69eoFExMTpKen4+DBg3Bzc8O7776LHTt2YPny5RgyZAhatGgBIQS2bNmCO3fuSAW2ilhZWeHdd99FWloa2rRpg507d2LVqlV499130axZM7XYfv36oX379ti7dy9GjhxZpUJmWTp37lzm1bvHmZmZoVevXvj8889hbW2N5s2bIz4+HqtXr4a5ublarIeHB/z9/dGxY0dYWFggJSUFP/zwA3r06IFGjRrh1KlTmDRpEl577TW0bt0acrkcv//+O06dOlXpyDEiotqMuVXN5VYlOTs7491331W7BSooKAhr167FyJEjYWhoCE9PT0yZMgUuLi5YtGgRdu7ciaZNm+LQoUNo2rQp7Ozs0LdvX0RGRsLCwgJOTk7Ys2cPtmzZUu7rMH/+fAwYMAD6+vro2LEj5HJ5uW2MjIxEv3790KdPH4SFhWHdunVQKBQ4f/48Nm7cCJlMhl69euHAgQOlniuEkOZH+u6770oVZB7XoUMH+Pj4YMCAAWjZsiXu37+PI0eOYNGiRbCyssLhw4eRk5MDMzMzuLm5Yd++fdi+fTvs7e1hamoKFxcX+Pv749NPP8Xs2bPh7e2Nc+fO4ZNPPoGzszMePHhQpfdk0KBBcHV1Rbdu3dC4cWNcvXoVS5cuhZOTk9p8oOXJzMyUPiPZ2dmYPXs2jIyMMGPGjFKxU6dOxeuvvw6ZTCbd8lodn332WaUxAwcOxOLFixEQEIBx48bh1q1bWLhwYani3cqVK/H7779j4MCBaNasGe7fvy/dIlmc944dOxbGxsbw8vKCvb09MjIyEBkZCaVSqXbnA9UDup8znaj2SUpKEqNHjxbOzs5CoVAIIyMj0apVK/Hmm2+WWnlMCCH+/PNPMWzYMGFjYyMMDQ2FnZ2deOGFF8TKlSulmPJWUytvVZKffvpJ9OnTR5iZmQmFQiGcnJzEq6++Knbv3i3FBAYGChMTkzL7cPbsWdGvXz9hamoqLCwsxGuvvSbS0tIEADF79my12BkzZggHBwehp6dXqi1VaYcQQnz77beidevWQi6XizZt2ojvvvtOBAYGVms1PT09PeHg4CAGDBgg9u3bpxZbvJrezZs31bYHBgaKpk2bCoVCIZo1ayYMDQ2Fvb29GDdunDAzMxPNmjVTWwnliy++EADE2LFj1Y7Tr18/AUBs27ZNbXvx+/Tf//5XbXtZq8eUdO/ePWFsbKzWR7lcLmQymVAoFMLV1VVkZmZK8WvWrBFNmzYVAMSSJUsEANG6dWu1Y86bN08AECEhIaXOt337dtGpUydhZGQkDA0NRd++fcWvv/5a6r19fKWd8gQEBAgAwsvLq9yY7777Tnh4eAgTExNhbGwsWrZsKd58801x/PhxIcSjpY9HjBghWrZsKYyNjYVSqRTPPfeciI6OrvDcj7dx3759olu3bkKhUAh7e3vx4YcfllplqVh4eLgAIA4fPlzp8Ys9vppeecpaTe/atWvilVdeERYWFsLU1FT0799fJCcnCycnJ7XP2/Tp00W3bt2EhYWFUCgUokWLFuK9996TVia8ceOGCAoKEm3bthUmJibimWeeER07dhRLliwpd7lpIqK6hLnV/9qiq9yqrL/xN2/eFGZmZtJqeoGBgcLR0VEolUqRlpYmpkyZIpydnYWhoaGQyWRCLpcLNzc3kZubKx0jPT1dvPrqq8LS0lIolUoxcuRIaeXcx/OhgoIC8fbbb4vGjRsLmUymtsIgADFx4sQy237gwAHxwgsvCBMTEwFAdOjQQWzfvl0I8WgVQ1NTU2FlZVXqvV+zZo2wtrYWAMSnn35a4evz9ddfi6FDh4oWLVqIRo0aCblcLlq2bCneeecd8ffff4uuXbuK5cuXCyEefXa9vLxEo0aNBABp1biCggIRFhYmmjRpIoyMjETXrl3FTz/9VOo9Kmv1wmKLFi0Snp6ewtraWsjlctGsWTMxZswYceXKlQrbX/wZ/+GHH8SUKVNE48aNhUKhED179pTyr5IKCgqEQqEQ/fv3r/DYj6vKatBClL2a3nfffSdcXFykvCcyMlKsXr1a7XNw6NAh8fLLLwsnJyehUCiElZWV8Pb2VsvD165dK/r06SNsbW2FXC4XDg4OYtiwYeLUqVNV7gfVDSxGEVGdFBgYKF566SXh5uYm1q1bJ21fv369cHNzEy+99JJUHPj111+Fl5eXUCqVwtLSUgwcOFBcvHhR7Xj//e9/haurqzAyMhKWlpbCx8dHSsQq2leWzZs3C2tra7Vt586dk5LXtm3bSttzcnKEnZ2dmDVrlgAg0tLSnup1CQ8PFz179nyqY9Q17u7uolu3bjXdDCIiolqP+VPZ6mP+tG3bNgFA/PLLLzXdFKIycc4oIqrTRo8ejTVr1kiPv/vuu1LLzubl5SEkJATHjh3Dnj17oKenh5dffhkPHz4E8GgegREjRuCtt95CSkoK9u3bh6FDh0IIUeG+8uzfv7/UfBSJiYkwMjLCiBEjcOHCBWki0E8//RSdO3eWJm93dHR8qtfjueeew9GjR0tNNFrf5OTkICEhAR9++CESExOl1VqIiIiocsyf1NWn/Ons2bP49ddfERoais6dO2PAgAE13SSiMnHOKCKq00aNGoUZM2bgypUrkMlk+OOPPxATE4N9+/ZJMa+88orac1avXg0bGxucPXsWrq6uSE9Px4MHDzB06FBphY7ieQ/Onz9f7r7yXLlypdRk6CdOnEDHjh3Rpk0bmJiYICUlBSYmJli+fDmOHz+OhQsXwt3d/WlfDjRp0gQFBQXIyMio16uNnDhxAn369IGVlRVmz56NIUOG1HSTiIiI6gzmT+rqU/40YcIE/PHHH+jatSvWrl1b7krKRDWNxSgiqtOsra0xcOBArF27FkIIDBw4sNQywJcuXcJHH32Ew4cP499//5Wu6KWlpcHV1RWdOnWCj48P3Nzc4OfnB19fX7z66quwsLCocF958vPzS63IlpiYCHd3d8hkMnTs2BHJycmIiYnBuHHj0LZtWyQmJmrkypWxsTEAlLliW33Su3fvCq+uEhERUfmYP6mrT/nT4wVFotqMt+kRUZ331ltvITo6GmvXri01xBx4tHLJrVu3sGrVKhw5cgRHjhwBAKhUKgCPlpaNi4vDr7/+ivbt2yMqKgouLi5ITU2tcF95rK2tS61md/LkSXTt2hUA0KlTJ3zxxRc4evQoZs+eLS1VW7z/cffu3cP7778PT09PeHp6YuzYsbh161a55759+zYAoHHjxpW8akRERNSQMX/6H+ZPRLrHYhQR1Xn9+/eHSqWCSqWCn5+f2r5bt24hJSUFs2bNgo+PD9q1a1cq0QEAmUwGLy8vzJkzBydPnoRcLsfWrVsr3VeWLl264OzZs9Ljy5cv486dO9Iw8s6dO+P48eOYN28elEolTp8+jcLCwjKHmU+aNAmdOnVCQkICEhISMHz4cLz55pvljgpKTk5G06ZNS13dJCIiInoc86f/Yf5EpHu8TY+I6jx9fX2kpKRI/36chYUFrKys8M0338De3h5paWmYPn26WsyRI0ewZ88e+Pr6wsbGBkeOHMHNmzfRrl27CveVx8/PDzNmzEBWVhYsLCyQmJgIuVwOV1dXAEBgYCCGDBkCKysrAI/mQ7CwsICzs7PacfLz85GVlYWRI0ciPDwcABAeHo6ff/4ZFy9eROvWrUud+8CBA/D19X2yF5CIiIgaHOZP/8P8iUj3WIzSsYcPH+Kff/6BqakpJ5MjegoqlQqFhYXIyclR2178uLCwECqVCrm5uVi9ejWmTZuGDh06oHXr1liwYAEGDhyIe/fuIScnB3p6evj999+xePFi3L17F46Ojpg7dy68vLxw7ty5cveVPHcxJycndO7cWRr2fujQIbRr1w75+fnIz88HAMjlcty9excAcPjwYbi5uZU63r1796Q+BgYGSv0r7lfJ+Pv372PLli3YunVruW0joqoTQuDu3btwcHCAnh4Hk9cU5k5EmsP8ifkTkbZVNX+SCc4Aq1PXrl176qVHiYiISHf+/vtvNG3atKab0WAxdyIiIqp7KsufanRkVGRkJLZs2YK//voLxsbG8PT0xPz58+Hi4iLFCCEwZ84cfPPNN8jKyoKHhwe++uordOjQQYopKChAWFgYNm7ciPz8fPj4+GD58uVqHc/KysKUKVOwbds2AMDgwYMRFRUFc3NzKSYtLQ0TJ07E77//DmNjYwQEBGDhwoWQy+VSzOnTpzFp0iQcPXoUlpaWGD9+PD766KMqX6kzNTUF8OiNMTMzq9brRkRERNqXk5MDR0dH6W831QzmTkRERHVHVfOnGi1GxcfHY+LEiXj22Wfx4MEDzJw5E76+vjh79ixMTEwAAAsWLMDixYsRHR2NNm3aYO7cuejXrx/OnTsndS44OBjbt29HTEwMrKysEBoaCn9/fyQmJkr3PwcEBODatWvYtWsXAGDcuHEYNWoUtm/fDgAoKirCwIED0bhxYxw8eBC3bt1CYGAghBCIiooC8OhF7devH/r06YNjx47h/PnzCAoKgomJCUJDQ6vU5+KilZmZGRMqIiKiOqC+3hq2f/9+fP7550hMTER6ejq2bt2KIUOGAHh0q86sWbOwc+dOXL58GUqlEn379sVnn30GBwcH6RiauiBYEeZOREREdU+l+ZOoRTIzMwUAER8fL4QQ4uHDh8LOzk589tlnUsz9+/eFUqkUK1euFEIIcefOHWFoaChiYmKkmOvXrws9PT2xa9cuIYQQZ8+eFQDE4cOHpZhDhw4JAOKvv/4SQgixc+dOoaenJ65fvy7FbNy4USgUCpGdnS2EEGL58uVCqVSK+/fvSzGRkZHCwcFBPHz4sEp9zM7OFgCkYxIREVHtVN//Zu/cuVPMnDlTbN68WQAQW7dulfbduXNH9O3bV2zatEn89ddf4tChQ8LDw0O4u7urHeOdd94RTZo0EXFxceLEiROiT58+olOnTuLBgwdSTP/+/YWrq6tISEgQCQkJwtXVVfj7+1e5nfX9fSAiIqpPqvp3u1bNxpmdnQ0AsLS0BACkpqYiIyNDbWUDhUIBb29vJCQkAAASExNRWFioFuPg4ABXV1cp5tChQ1AqlfDw8JBiunfvDqVSqRbj6uqqdrXPz88PBQUFSExMlGK8vb2hUCjUYv755x9cuXJFky8FERERkVYNGDAAc+fOxdChQ0vtUyqViIuLw7Bhw+Di4oLu3bsjKioKiYmJSEtLA/Aob1u9ejUWLVqEvn37okuXLli3bh1Onz6N3bt3AwBSUlKwa9cufPvtt+jRowd69OiBVatWYceOHTh37pxO+0tERES1R60pRgkhEBISgueff15avjMjIwMAYGtrqxZra2sr7cvIyIBcLoeFhUWFMTY2NqXOaWNjoxZT8jwWFhaQy+UVxhQ/Lo4pqaCgADk5OWo/RERERHVNdnY2ZDKZdHudpi4IEhERUcNTo3NGPW7SpEk4deoUDh48WGpfyXsNhRCV3n9YMqaseE3EiP9fjLC89kRGRmLOnDkVtpWI6qeioiIUFhbWdDOIqByGhobS3JJUsfv372P69OkICAiQ5m3S1AXBkgoKClBQUCA95oU8IqoI8y0i3dJU/lQrilGTJ0/Gtm3bsH//frUJL+3s7AA8SmTs7e2l7ZmZmdKIJDs7O6hUKmRlZaklQ5mZmfD09JRibty4Ueq8N2/eVDvOkSNH1PZnZWWhsLBQLaZk4pSZmQmg9OitYjNmzEBISIj0uHhmeSKqv4QQyMjIwJ07d2q6KURUCXNzc9jZ2dXbSco1obCwEMOHD8fDhw+xfPnySuOrc7HvcbyQR0RVwXyLqOZoIn+q0WKUEAKTJ0/G1q1bsW/fPjg7O6vtd3Z2hp2dHeLi4tClSxcAgEqlQnx8PObPnw8AcHd3h6GhoTSvAQCkp6cjOTkZCxYsAAD06NED2dnZOHr0KJ577jkAwJEjR5CdnS0VrHr06IF58+YhPT1dKnzFxsZCoVDA3d1divnwww+hUqkgl8ulGAcHBzRv3rzMPioUCrU5poio/itOjGxsbNCoUSP+Ty5RLSSEwL1796SLSo9f9KL/KSwsxLBhw5Camorff/9dbTU7TV0QLIkX8oioKphvEemeJvOnGi1GTZw4ERs2bMDPP/8MU1NTadSRUqmEsbExZDIZgoODERERgdatW6N169aIiIhAo0aNEBAQIMWOGTMGoaGhsLKygqWlJcLCwuDm5oa+ffsCANq1a4f+/ftj7Nix+PrrrwEA48aNg7+/P1xcXAAAvr6+aN++PUaNGoXPP/8ct2/fRlhYGMaOHSslXgEBAZgzZw6CgoLw4Ycf4sKFC4iIiMDHH39cK375Xb+Tj6w8VU03o8ZYmMjRxNy4pptBDVxRUZGUGFlZWdV0c4ioAsbGj/5mZGZmwsbGhrfslVBciLpw4QL27t1b6neapi4IllSfLuRpOzdj7kMNFfMtopqjqfypRotRK1asAAD07t1bbfuaNWsQFBQEAJg2bRry8/MxYcIEZGVlwcPDA7GxsTA1NZXilyxZAgMDAwwbNgz5+fnw8fFBdHS02ouyfv16TJkyRZpkc/DgwVi2bJm0X19fH7/88gsmTJgALy8vGBsbIyAgAAsXLpRiileWmThxIrp16wYLCwuEhISoXb2rKdfv5KPvonjkFxbVdFNqjLGhPnaHejMpoxpVPGdBo0aNarglRFQVxd/VwsLCBleMys3NxcWLF6XHqampSEpKgqWlJRwcHPDqq6/ixIkT2LFjB4qKiqSLhpaWlpDL5Rq7IFhf6SI3Y+5DDRXzLaKapYn8SSaKZ+AmncjJyYFSqUR2drbaUPenlXw9G/5RB7H09c5oZfOMxo5bV1zMzEXwpiTsmPw8XJsoa7o51IDdv38fqampcHZ2hpGRUU03h4gqUdF3Vlt/s2uLffv2oU+fPqW2BwYGIjw8vNT0CcX27t0rXUi8f/8+3n//fWzYsEG6ILh8+XK12+pu376NKVOmYNu2bQD+d0GweFW+ytTV90HbuRlzH2rImG8R1SxN5E+1YgJz0pxWNs8wISEiIqJK9e7dGxVdk6zK9UojIyNERUUhKiqq3BhLS0usW7euWm2sD5ibERERlcZiFBGRjuhyXjfOI6I5zZs3R3BwMIKDg2u6KURERFQJXc+jy5xLM5hvNTwsRhER6YCu53Wr7jwiGRkZiIyMxC+//IJr165BqVSidevWGDlyJN588806MzeDLhOa8PBwtWXozczM0LFjR8ydOxfe3t5aP//Tio6OxujRowEAenp6MDMzQ5s2bTBw4EBMnToVSiVHdBARUd1QE/PoVifnYr715IrzrfHjx2PlypXS9qSkJHTp0gWpqanlrnBfHTKZDFu3bsWQIUM0dswn9fXXX2P58uW4ePEiDA0N4ezsjOHDh+ODDz4AAAQFBeHOnTv46aefaqyNT4PFKCIiHcjKUyG/sEgn87oVzyOSlad6osTo8uXL8PLygrm5OSIiIuDm5oYHDx7g/Pnz+O677+Dg4IDBgwdrseUVE0KgqKgIBga1709Xhw4dsHv3bgCP5sdZuHAh/P39pQSzLIWFhTA0NNRlM8tlZmaGc+fOQQiBO3fuICEhAZGRkVizZg3++OMPODg46KQdKpUKcrlcJ+ciIqL6R5f5FlC9nIv5VvUZGRlh9erVCAkJQZs2bWq6OVrN5Yr7+eWXX8Lb2xsFBQU4deoUzp49q/Fz1VhOKkinsrOzBQCRnZ2t0eOevnZHOH2wQ5y+dkejx60rGnr/qfbIz88XZ8+eFfn5+WrbdfkZre65/Pz8RNOmTUVubm6Z+x8+fCj9+86dO2Ls2LGicePGwtTUVPTp00ckJSVJ+2fPni06deokvv/+e+Hk5CTMzMzE66+/LnJyctSON3/+fOHs7CyMjIxEx44dxX//+19p/969ewUAsWvXLuHu7i4MDQ3F77//Li5evCgGDx4sbGxshImJiejWrZuIi4uTnuft7S0AqP0U++OPP0TPnj2FkZGRaNq0qZg8ebJaf2/cuCH8/f2FkZGRaN68uVi3bp1wcnISS5YsKfd1K+7r49LS0gQAcfToUWkbALFixQoxePBg0ahRI/Hxxx8LIYRYvny5aNGihTA0NBRt2rQR33//vfSckJAQ4e/vLz1esmSJACB27NghbWvTpo1YuXKlEEKIwMBA8dJLL4nPP/9c2NnZCUtLSzFhwgShUqnKbf+aNWuEUqkstf3GjRvC2tpavPHGG0IIIbZt2yaUSqUoKioSQghx8uRJAUCEhYVJzxk3bpwYPny4EEKIf//9VwwfPlw0adJEGBsbC1dXV7Fhwwa1c3h7e4uJEyeK9957T1hZWYlevXqJ4cOHi9dff10tTqVSCSsrK/Hdd98JISr/7Ny+fVsEBAQIa2trYWRkJFq1aiU9t6TyvrNCaO9vNj2Zuvo+aPv3PnMfashqQ75V3fMx33q6fKtfv37itddek7YX5yOpqanStjNnzogBAwYIExMTYWNjI0aOHClu3rwp7S/rXJ06dRKzZ8+W9j/eLycnJ7U2rF69Wjg7OwuZTCYePnworl69KgYPHixMTEyEqampeO2110RGRsYTvU8lvfTSSyIoKKjC16Pk6793714hhBDTpk0TrVu3FsbGxsLZ2VnMmjVLLRcsrx///e9/haurqzAyMhKWlpbCx8en3M+pJvInPV0VvYiIqPa6desWYmNjMXHiRJiYmJQZI5PJADy6YjZw4EBkZGRg586dSExMRNeuXeHj44Pbt29L8ZcuXcJPP/2EHTt2YMeOHYiPj8dnn30m7Z81axbWrFmDFStW4MyZM3jvvfcwcuRIxMfHq5132rRpiIyMREpKCjp27Ijc3Fy8+OKL2L17N06ePAk/Pz8MGjQIaWlpAIAtW7agadOm+OSTT5Ceno709HQAwOnTp+Hn54ehQ4fi1KlT2LRpEw4ePIhJkyZJ5woKCsKVK1fw+++/48cff8Ty5cuRmZn5RK9lQUEBoqOjYW5uXmrp+tmzZ+Oll17C6dOn8dZbb2Hr1q2YOnUqQkNDkZycjPHjx2P06NHYu3cvgEcTTB84cAAPHz4EAMTHx8Pa2lp6jTIyMnD+/Hm12wH37t2LS5cuYe/evVi7di2io6MRHR39RH0AABsbG7zxxhvYtm0bioqK0KtXL9y9excnT54ssy3Ao9XZitty//59uLu7Y8eOHUhOTsa4ceMwatQoHDlyRO08a9euhYGBAf744w98/fXX0jlzc3OlmN9++w15eXl45ZVXAFT+2fnoo49w9uxZ/Prrr0hJScGKFStgbW39xK8BERGRJjHfeuRp8q3PPvsMmzdvxrFjx8rcn56eDm9vb3Tu3BnHjx/Hrl27cOPGDQwbNqxKxwcgHXvNmjVIT09XO9fFixfxn//8B5s3b0ZSUhIAYMiQIbh9+zbi4+MRFxeHS5cu4fXXX1c7ZmXvU0l2dnY4fPgwrl69Wub+sLAwDBs2DP3795def09PTwCAqakpoqOjcfbsWXzxxRdYtWoVlixZovb8kv3IyMjAiBEj8NZbbyElJQX79u3D0KFDq7SYSbVVWKoijePIKO1o6P2n2qM2XKmrzrkOHz4sAIgtW7aobbeyshImJibCxMRETJs2TQghxJ49e4SZmZm4f/++WmzLli3F119/LYR4dMWlUaNGald83n//feHh4SGEECI3N1cYGRmJhIQEtWOMGTNGjBgxQgjxvyt1P/30U6Xtb9++vYiKipIel3XFa9SoUWLcuHFq2w4cOCD09PREfn6+OHfunAAgDh8+LO1PSUkRACq9Uqenpye9TjKZTJiZmYlff/1VLQ6ACA4OVtvm6ekpxo4dq7bttddeEy+++KIQ4tEVUT09PXH8+HHx8OFDYWVlJSIjI8Wzzz4rhBBiw4YNwtbWVnpuYGCgcHJyEg8ePFA7XsmRRo8rb2SUEEKsWLFCABA3btwQQgjRtWtXsXDhQiGEEEOGDBHz5s0Tcrlc5OTkiPT0dAFApKSklHuuF198UYSGhkqPvb29RefOndViVCqVsLa2VhshNmLECOkqaFU+O4MGDRKjR48utx2P48io2q+uvg8cGUWkPbUh36rO+ZhvPV2+VTwSffjw4eKFF14QQpQeGfXRRx8JX19ftef+/fffAoA4d+5cue1+fGSUEI/ytq1bt5Zqg6GhocjMzJS2xcbGCn19fZGWliZtO3PmjNoI+crep7L8888/onv37gKAaNOmjQgMDBSbNm2SRqgL8b8R8ZVZsGCBcHd3r7AfiYmJAoC4cuVKpccTQjP5U+27EZSIiGpM8dW4YkePHsXDhw/xxhtvoKCgAACQmJiI3NxcWFlZqcXm5+fj0qVL0uPmzZvD1NRUemxvby9d9Tp79izu37+Pfv36qR1DpVKhS5cuatu6deum9jgvLw9z5szBjh078M8//+DBgwfIz8+XrtSVJzExERcvXsT69eulbUIIPHz4EKmpqTh//jwMDAzUzte2bVuYm5tXeFwAcHFxwbZt2wAAd+/exaZNm/Daa69h7969ascr2ZeUlBSMGzdObZuXlxe++OILAIBSqUTnzp2xb98+GBoaQk9PD+PHj8fs2bNx9+5dtZFIxTp06AB9fX3psb29PU6fPl1pH8oi/v9qWPHnonfv3ti3bx9CQkJw4MABzJ07F5s3b8bBgwdx584d2Nraom3btgCAoqIifPbZZ9i0aROuX7+OgoICFBQUlLoSXPI1MTQ0xGuvvYb169dj1KhRyMvLw88//4wNGzYAqNpn591338Urr7yCEydOwNfXF0OGDJGuFhIREdU05lvVy7eKzZ07F+3atUNsbCxsbGxKnX/v3r145pnSc4ZdunTpqeeacnJyQuPGjaXHKSkpcHR0hKOjo7Stffv2MDc3R0pKCp599lkAFb9PZbG3t8ehQ4eQnJyM+Ph4JCQkIDAwEN9++y127doFPb3yb3L78ccfsXTpUly8eBG5ubl48OABzMzMKuxHp06d4OPjAzc3N/j5+cHX1xevvvoqLCwsqv7iPCEWo4iICK1atYJMJsNff/2ltr1FixYAAGPj/03K+fDhQ9jb22Pfvn2ljvN4IlFyIkSZTCbdblb8319++QVNmjRRi1MoFGqPSxYv3n//ffz2229YuHAhWrVqBWNjY7z66qtQqSpexvnhw4cYP348pkyZUmpfs2bNcO7cOamdT0oul6NVq1bS4y5duuCnn37C0qVLsW7dunL7Utb5hBBq24oLQHK5HN7e3rCwsECHDh3wxx9/YN++faVWsKnodX9SKSkpMDMzkxLh3r17Y/Xq1fjzzz+hp6eH9u3bw9vbG/Hx8cjKylIrjC1atAhLlizB0qVL4ebmBhMTEwQHB5d6n8p6Td544w14e3sjMzMTcXFxMDIywoABAwBU7bMzYMAAXL16Fb/88gt2794NHx8fTJw4EQsXLqzW60BERKQJzLeeLt8q1rJlS4wdOxbTp0/H6tWrS51/0KBBmD9/fqnn2dvbA3i0erAocftZYWFhlc5d8nUqmbeVt726+ZmrqytcXV0xceJEHDx4ED179kR8fDz69OlTZvzhw4cxfPhwzJkzB35+flAqlYiJicGiRYsq7Ie+vj7i4uKQkJCA2NhYREVFYebMmThy5AicnZ0rbWd1sBhFRESwsrJCv379sGzZMkyePLnceQwAoGvXrsjIyICBgUG1l9Bt3749FAoF0tLSSo3sqcyBAwcQFBSEl19+GQCQm5uLK1euqMXI5XIUFakv69y1a1ecOXNGrWj0uHbt2uHBgwc4fvw4nnvuOQDAuXPncOfOnSdqXzF9fX3k5+dXGNOuXTscPHgQb775prQtISEB7dq1kx4XF4AMDAzQt29fAIC3tzdiYmJKzRelSZmZmdiwYQOGDBkiXX0rnjdq6dKl8Pb2hkwmg7e3NyIjI5GVlYWpU6dKzz9w4ABeeukljBw5EsCj5PDChQtqfSuPp6cnHB0dsWnTJvz666947bXXpFX2qvrZady4MYKCghAUFISePXvi/fffZzGKiIhqFPMtzeVbH3/8MVq2bImYmJhS59+8eTOaN29e7oqAjRs3lua4AoCcnBykpqaqxRgaGpbqW1nat2+PtLQ0/P3339LoqLNnzyI7O7tKOc+TaN++PYBHo9aAsl//P/74A05OTpg5c6a0rbx5p0qSyWTw8vKCl5cXPv74Yzg5OWHr1q0ICQnRUA/UcQJzIiICACxfvhwPHjxAt27dsGnTJqSkpODcuXNYt24d/vrrL+nWr759+6JHjx4YMmQIfvvtN1y5cgUJCQmYNWsWjh8/XqVzmZqaIiwsDO+99x7Wrl2LS5cu4eTJk/jqq6+wdu3aCp/bqlUrbNmyBUlJSfjzzz8REBBQ6spS8+bNsX//fly/fh3//vsvAOCDDz7AoUOHMHHiRCQlJeHChQvYtm0bJk+eDODRrXb9+/fH2LFjceTIESQmJuLtt99Wu0pZngcPHiAjIwMZGRm4cOEC5s6di7Nnz+Kll16q8Hnvv/8+oqOjsXLlSly4cAGLFy/Gli1bEBYWJsUUF4C2b9+O3r17A3hUoFq3bh0aN24sJSZPQwiBjIwMpKenIyUlBd999x08PT2hVCrVJtcsvm1w3bp1Ult69eqFEydO4Pz589I24NH7VHyFLSUlBePHj0dGRkaV2iOTyRAQEICVK1ciLi5OKmgBVfvsfPzxx/j5559x8eJFnDlzBjt27NB4QkhERFQdzLeqn289ztbWFiEhIfjyyy/Vtk+cOBG3b9/GiBEjcPToUVy+fBmxsbF46623pMLNCy+8gB9++AEHDhxAcnIyAgMD1aY4KO7bnj17kJGRgaysrHLb0bdvX3Ts2BFvvPEGTpw4gaNHj+LNN9+Et7d3qVsfn8S7776LTz/9FH/88QeuXr2Kw4cP480330Tjxo3Ro0cPqY2nTp3CuXPn8O+//6KwsBCtWrVCWloaYmJicOnSJXz55ZfYunVrpec7cuQIIiIicPz4caSlpWHLli24efOmVvMnjowiItKhi5m5lQfV0DlatmyJkydPIiIiAjNmzMC1a9egUCjQvn17hIWFYcKECQAeFQp27tyJmTNn4q233sLNmzdhZ2eHXr16wdbWtsrn+/TTT2FjY4PIyEhcvnwZ5ubm6Nq1Kz788MMKn7dkyRK89dZb8PT0hLW1NT744APk5OSoxXzyyScYP348WrZsiYKCAggh0LFjR8THx2PmzJno2bMnhBBo2bKl2mona9aswdtvvw1vb2/Y2tpi7ty5+Oijjyrty5kzZ6Sh340aNULLli2xYsUKtRFPZRkyZAi++OILfP7555gyZQqcnZ2xZs0ataKOUqlEly5dkJaWJhWeevbsiYcPH2psVFROTg7s7e0hk8lgZmYGFxcXBAYGYurUqaXmGOjTpw9OnDghtdHCwgLt27fHP//8o5awfPTRR0hNTYWfnx8aNWqEcePGYciQIcjOzq5Sm9544w1ERETAyckJXl5eavsq++zI5XLMmDEDV65cgbGxMXr27FnqyikREdVfusi3qnse5lvVz7dKev/997FixQrcv39f2ubg4IA//vgDH3zwAfz8/FBQUAAnJyf0799fGuk9Y8YMXL58Gf7+/lAqlfj0009LjYxatGgRQkJCsGrVKjRp0qTUqLBiMpkMP/30EyZPnoxevXpBT08P/fv3R1RU1BP353F9+/bFd999hxUrVuDWrVuwtrZGjx49sGfPHmn6hLFjx2Lfvn3o1q0bcnNzsXfvXrz00kt47733MGnSJBQUFGDgwIH46KOPEB4eXuH5zMzMsH//fixduhQ5OTlwcnLCokWLpGkStEEmSt4sSVqVk5MDpVKJ7OzsUgn+00i+ng3/qIPYMfl5uDZRauy4dUVD7z/VHvfv30dqaiqcnZ1hZGQkbb9+Jx99F8Ujv7Dy4b6aYGyoj92h3mhi/mRXmYgamvK+s4D2/mbTk6mr74O2cxPmPtSQ1ZZ8C2DORQ2TJvInjowiItKBJubG2B3qjay8iid91BQLEzmTIiIiImpQdJ1vAcy5iKqLxSgiIh1pYm7MZIWIiIhIi5hvEdUNnMCciIiIiIiIiIh0hsUoIiIiIiIiIiLSGRajiIi0gGtDENUN/K4SEdVd/B1OVDM08d1jMYqISIMMDQ0BAPfu3avhlhBRVRR/V4u/u0REVPsx3yKqWZrInziBORGRBunr68Pc3ByZmZkAgEaNGkEmk9Vwq4ioJCEE7t27h8zMTJibm0NfX7+mm0RERFXEfIuoZmgyf2IxiohIw+zs7ABASpCIqPYyNzeXvrNERFR3MN8iqjmayJ9YjCIi0jCZTAZ7e3vY2NigsLCwpptDROUwNDTkiCgiojqK+RZRzdBU/sRiFBGRlujr6/N/dImIiIi0iPkWUd3ECcyJiIiIiIiIiEhnWIwiIiIiaoD279+PQYMGwcHBATKZDD/99JPafiEEwsPD4eDgAGNjY/Tu3RtnzpxRiykoKMDkyZNhbW0NExMTDB48GNeuXVOLycrKwqhRo6BUKqFUKjFq1CjcuXNHy70jIiKi2ozFKCIiIqIGKC8vD506dcKyZcvK3L9gwQIsXrwYy5Ytw7Fjx2BnZ4d+/frh7t27UkxwcDC2bt2KmJgYHDx4ELm5ufD390dRUZEUExAQgKSkJOzatQu7du1CUlISRo0apfX+ERERUe3FOaOIiIiIGqABAwZgwIABZe4TQmDp0qWYOXMmhg4dCgBYu3YtbG1tsWHDBowfPx7Z2dlYvXo1fvjhB/Tt2xcAsG7dOjg6OmL37t3w8/NDSkoKdu3ahcOHD8PDwwMAsGrVKvTo0QPnzp2Di4uLbjpLREREtQqLUVSvXMzMrekm1BgLEzmamBvXdDOIiKgeSE1NRUZGBnx9faVtCoUC3t7eSEhIwPjx45GYmIjCwkK1GAcHB7i6uiIhIQF+fn44dOgQlEqlVIgCgO7du0OpVCIhIYHFKCIiogaKxSiqFyxM5DA21EfwpqSabkqNMTbUx+5QbxakiIjoqWVkZAAAbG1t1bbb2tri6tWrUoxcLoeFhUWpmOLnZ2RkwMbGptTxbWxspJiSCgoKUFBQID3OycmpfkcaAG1eiOOFLiIi0hYWo6heaGJujN2h3sjKU9V0U2rExcxcBG9KQlaeikkjERFpjEwmU3sshCi1raSSMWXFV3ScyMhIzJkzpxqtbVh0cSGOF7qIiEhbWIyieqOJuTGTJSIiIg2ws7MD8Ghkk729vbQ9MzNTGi1lZ2cHlUqFrKwstdFRmZmZ8PT0lGJu3LhR6vg3b94sNeqq2IwZMxASEiI9zsnJgaOj49N3qp7R9oU4XugiIiJtYjGKiIiIiNQ4OzvDzs4OcXFx6NKlCwBApVIhPj4e8+fPBwC4u7vD0NAQcXFxGDZsGAAgPT0dycnJWLBgAQCgR48eyM7OxtGjR/Hcc88BAI4cOYLs7GypYFWSQqGAQqHQdhfrBV6IIyKiuorFKCIiIqIGKDc3FxcvXpQep6amIikpCZaWlmjWrBmCg4MRERGB1q1bo3Xr1oiIiECjRo0QEBAAAFAqlRgzZgxCQ0NhZWUFS0tLhIWFwc3NTVpdr127dujfvz/Gjh2Lr7/+GgAwbtw4+Pv7c/JyIiKiBozFKCIiIqIG6Pjx4+jTp4/0uPjWuMDAQERHR2PatGnIz8/HhAkTkJWVBQ8PD8TGxsLU1FR6zpIlS2BgYIBhw4YhPz8fPj4+iI6Ohr6+vhSzfv16TJkyRVp1b/DgwVi2bJmOeklERES1EYtRRERERA1Q7969IYQod79MJkN4eDjCw8PLjTEyMkJUVBSioqLKjbG0tMS6deuepqlERERUz+jVdAOIiIiIiIiIiKjhYDGKiIiIiIiIiIh0hsUoIiIiIiIiIiLSGRajiIiIiIiIiIhIZ1iMIiIiIiIiIiIinanRYtT+/fsxaNAgODg4QCaT4aefflLbHxQUBJlMpvbTvXt3tZiCggJMnjwZ1tbWMDExweDBg3Ht2jW1mKysLIwaNQpKpRJKpRKjRo3CnTt31GLS0tIwaNAgmJiYwNraGlOmTIFKpVKLOX36NLy9vWFsbIwmTZrgk08+qXAVGiIiIiIiIiIiUlejxai8vDx06tQJy5YtKzemf//+SE9Pl3527typtj84OBhbt25FTEwMDh48iNzcXPj7+6OoqEiKCQgIQFJSEnbt2oVdu3YhKSkJo0aNkvYXFRVh4MCByMvLw8GDBxETE4PNmzcjNDRUisnJyUG/fv3g4OCAY8eOISoqCgsXLsTixYs1+IoQEREREREREdVvBjV58gEDBmDAgAEVxigUCtjZ2ZW5Lzs7G6tXr8YPP/yAvn37AgDWrVsHR0dH7N69G35+fkhJScGuXbtw+PBheHh4AABWrVqFHj164Ny5c3BxcUFsbCzOnj2Lv//+Gw4ODgCARYsWISgoCPPmzYOZmRnWr1+P+/fvIzo6GgqFAq6urjh//jwWL16MkJAQyGQyDb4yRERERERERET1U40Wo6pi3759sLGxgbm5Oby9vTFv3jzY2NgAABITE1FYWAhfX18p3sHBAa6urkhISICfnx8OHToEpVIpFaIAoHv37lAqlUhISICLiwsOHToEV1dXqRAFAH5+figoKEBiYiL69OmDQ4cOwdvbGwqFQi1mxowZuHLlCpydnXXwahBV7GJmbk03ocZYmMjRxNy4pptBRERERERElajVxagBAwbgtddeg5OTE1JTU/HRRx/hhRdeQGJiIhQKBTIyMiCXy2FhYaH2PFtbW2RkZAAAMjIypOLV42xsbNRibG1t1fZbWFhALperxTRv3rzUeYr3lVeMKigoQEFBgfQ4JyfnCV4BoqqxMJHD2FAfwZuSaropNcbYUB+7Q71ZkCIiIiIiIqrlanUx6vXXX5f+7erqim7dusHJyQm//PILhg4dWu7zhBBqt82VdQudJmKKJy+v6Ba9yMhIzJkzp9z9RJrQxNwYu0O9kZWnqjy4HrqYmYvgTUnIylOxGEVERERERFTL1epiVEn29vZwcnLChQsXAAB2dnZQqVTIyspSGx2VmZkJT09PKebGjRuljnXz5k1pZJOdnR2OHDmitj8rKwuFhYVqMcWjpB4/D4BSo6oeN2PGDISEhEiPc3Jy4OjoWOU+E1VVE3NjFmKIiIiIiIio1qvR1fSe1K1bt/D333/D3t4eAODu7g5DQ0PExcVJMenp6UhOTpaKUT169EB2djaOHj0qxRw5cgTZ2dlqMcnJyUhPT5diYmNjoVAo4O7uLsXs378fKpVKLcbBwaHU7XuPUygUMDMzU/shIiIiIiIiImqoarQYlZubi6SkJCQlJQEAUlNTkZSUhLS0NOTm5iIsLAyHDh3ClStXsG/fPgwaNAjW1tZ4+eWXAQBKpRJjxoxBaGgo9uzZg5MnT2LkyJFwc3OTVtdr164d+vfvj7Fjx+Lw4cM4fPgwxo4dC39/f7i4uAAAfH190b59e4waNQonT57Enj17EBYWhrFjx0rFo4CAACgUCgQFBSE5ORlbt25FREQEV9IjIiIiIiIiInoCNXqb3vHjx9GnTx/pcfHtbIGBgVixYgVOnz6N77//Hnfu3IG9vT369OmDTZs2wdTUVHrOkiVLYGBggGHDhiE/Px8+Pj6Ijo6Gvr6+FLN+/XpMmTJFWnVv8ODBWLZsmbRfX18fv/zyCyZMmAAvLy8YGxsjICAACxculGKUSiXi4uIwceJEdOvWDRYWFggJCVG7BY+IiIiIiIiIiCpWo8Wo3r17S5OAl+W3336r9BhGRkaIiopCVFRUuTGWlpZYt25dhcdp1qwZduzYUWGMm5sb9u/fX2mbiIiIiIiIiIiobHVqzigiIiIiIiIiIqrbWIwiIiIiIiIiIiKdYTGKiIiIiIiIiIh0pkbnjCIi0qSLmbk13YQaY2EiRxNz45puBhERERERUaVYjCKiOs/CRA5jQ30Eb0qq6abUGGNDfewO9WZBioiIiIiIaj0Wo4iozmtibozdod7IylPVdFNqxMXMXARvSkJWnorFKCIiIiIiqvVYjCKieqGJuTELMURERERERHUAJzAnIiIiolIePHiAWbNmwdnZGcbGxmjRogU++eQTPHz4UIoRQiA8PBwODg4wNjZG7969cebMGbXjFBQUYPLkybC2toaJiQkGDx6Ma9eu6bo7REREVIuwGEVEREREpcyfPx8rV67EsmXLkJKSggULFuDzzz9HVFSUFLNgwQIsXrwYy5Ytw7Fjx2BnZ4d+/frh7t27UkxwcDC2bt2KmJgYHDx4ELm5ufD390dRUVFNdIuIiIhqAd6mR0RERESlHDp0CC+99BIGDhwIAGjevDk2btyI48ePA3g0Kmrp0qWYOXMmhg4dCgBYu3YtbG1tsWHDBowfPx7Z2dlYvXo1fvjhB/Tt2xcAsG7dOjg6OmL37t3w8/Ormc4RERFRjeLIKCIiIiIq5fnnn8eePXtw/vx5AMCff/6JgwcP4sUXXwQApKamIiMjA76+vtJzFAoFvL29kZCQAABITExEYWGhWoyDgwNcXV2lmJIKCgqQk5Oj9kNERET1C0dGEREREVEpH3zwAbKzs9G2bVvo6+ujqKgI8+bNw4gRIwAAGRkZAABbW1u159na2uLq1atSjFwuh4WFRamY4ueXFBkZiTlz5mi6O0RERFSLsBhFRFRPXMzMrekm1BgLEzlXUyTSsE2bNmHdunXYsGEDOnTogKSkJAQHB8PBwQGBgYFSnEwmU3ueEKLUtpIqipkxYwZCQkKkxzk5OXB0dHyKnhAREVFtw2IUEVEdZ2Eih7GhPoI3JdV0U2qMsaE+dod6syBFpEHvv/8+pk+fjuHDhwMA3NzccPXqVURGRiIwMBB2dnYAHo1+sre3l56XmZkpjZays7ODSqVCVlaW2uiozMxMeHp6lnlehUIBhUKhrW4RERFRLcBiFBFRHdfE3Bi7Q72Rlaeq6abUiIuZuQjelISsPBWLUUQadO/ePejpqU8vqq+vj4cPHwIAnJ2dYWdnh7i4OHTp0gUAoFKpEB8fj/nz5wMA3N3dYWhoiLi4OAwbNgwAkJ6ejuTkZCxYsECHvSEiIqLahMUoIqJ6oIm5MQsxRKRRgwYNwrx589CsWTN06NABJ0+exOLFi/HWW28BeHR7XnBwMCIiItC6dWu0bt0aERERaNSoEQICAgAASqUSY8aMQWhoKKysrGBpaYmwsDC4ublJq+sRERFRw8NiFBERERGVEhUVhY8++ggTJkxAZmYmHBwcMH78eHz88cdSzLRp05Cfn48JEyYgKysLHh4eiI2NhampqRSzZMkSGBgYYNiwYcjPz4ePjw+io6Ohr69fE90iIiKiWkCv8hB1hYWF6NOnj7TMLxERERHphi7zMFNTUyxduhRXr15Ffn4+Ll26hLlz50Iul0sxMpkM4eHhSE9Px/379xEfHw9XV1e14xgZGSEqKgq3bt3CvXv3sH37dk5ITkRE1MA98cgoQ0NDJCcnV7pKChERkS5xNUHeptkQMA8jIiKi+qBat+m9+eabWL16NT777DNNt4eIiOiJcDVBribY0DAPIyIiorquWsUolUqFb7/9FnFxcejWrRtMTEzU9i9evFgjjSMiIqoMVxPkaoINDfMwIiIiquuqVYxKTk5G165dAaDUnAUcNk5ERLrG1QSpIWEeRkRERHVdtYpRe/fu1XQ7iIiIiKgKmIcRERFRXVetYlSxixcv4tKlS+jVqxeMjY0hhOAVOSIiohrACdwb3sg45mGkC9r63dJQv7dERPRItYpRt27dwrBhw7B3717IZDJcuHABLVq0wNtvvw1zc3MsWrRI0+0kIiKiMnAC94Y3gTvzMNIFbf9uaWjfWyIiUletYtR7770HQ0NDpKWloV27dtL2119/He+99x6TICIiIh3hBO4NbwJ35mGkC9r83dIQv7dERKSuWsWo2NhY/Pbbb2jatKna9tatW+Pq1asaaRgRERFVDSdwb1iYh5Gu8HcLERFpi151npSXl4dGjRqV2v7vv/9CoVA8daOIiIiIqGzMw4iIiKiuq1YxqlevXvj++++lxzKZDA8fPsTnn3+OPn36aKxxRERERKSOeRgRERHVddW6Te/zzz9H7969cfz4cahUKkybNg1nzpzB7du38ccff2i6jURERET0/5iHERERUV1XrZFR7du3x6lTp/Dcc8+hX79+yMvLw9ChQ3Hy5Em0bNlS020kIiIiov/HPIyIiIjqumqNjAIAOzs7zJkzR5NtISIiIqIqYB5GREREdVm1i1FZWVlYvXo1UlJSIJPJ0K5dO4wePRqWlpaabB8RERERlcA8jIiIiOqyat2mFx8fD2dnZ3z55ZfIysrC7du38eWXX8LZ2Rnx8fGabiMRERER/T/mYURERFTXVWtk1MSJEzFs2DCsWLEC+vr6AICioiJMmDABEydORHJyskYbSURERESPMA8jIiKiuq5aI6MuXbqE0NBQKQECAH19fYSEhODSpUsaaxwRERERqWMeRkRERHVdtYpRXbt2RUpKSqntKSkp6Ny589O2iYiIiIjKwTyMiIiI6roq36Z36tQp6d9TpkzB1KlTcfHiRXTv3h0AcPjwYXz11Vf47LPPNN9KIiIiogaMeRgRERHVJ1UuRnXu3BkymQxCCGnbtGnTSsUFBATg9ddf10zriIiIiIh5GBEREdUrVS5GpaamarMdRERERFQO5mFERERUn1S5GOXk5KTNdhARERFROZiHERERUX1SrQnMAeD69ev4z3/+g2XLluHLL79U+6mq/fv3Y9CgQXBwcIBMJsNPP/2ktl8IgfDwcDg4OMDY2Bi9e/fGmTNn1GIKCgowefJkWFtbw8TEBIMHD8a1a9fUYrKysjBq1CgolUoolUqMGjUKd+7cUYtJS0vDoEGDYGJiAmtra0yZMgUqlUot5vTp0/D29oaxsTGaNGmCTz75RG24PBEREZEuaCIPq+p5Ro4cCSsrKzRq1AidO3dGYmKitF9TuRoRERE1LFUeGfW4NWvW4J133oFcLoeVlRVkMpm0TyaTYcqUKVU6Tl5eHjp16oTRo0fjlVdeKbV/wYIFWLx4MaKjo9GmTRvMnTsX/fr1w7lz52BqagoACA4Oxvbt2xETEwMrKyuEhobC398fiYmJ0pLHAQEBuHbtGnbt2gUAGDduHEaNGoXt27cDAIqKijBw4EA0btwYBw8exK1btxAYGAghBKKiogAAOTk56NevH/r06YNjx47h/PnzCAoKgomJCUJDQ6vzMhIRERE9MU3lYZXJysqCl5cX+vTpg19//RU2Nja4dOkSzM3NpRhN5WpERETUwIhqaNq0qZg7d64oKiqqztPLBEBs3bpVevzw4UNhZ2cnPvvsM2nb/fv3hVKpFCtXrhRCCHHnzh1haGgoYmJipJjr168LPT09sWvXLiGEEGfPnhUAxOHDh6WYQ4cOCQDir7/+EkIIsXPnTqGnpyeuX78uxWzcuFEoFAqRnZ0thBBi+fLlQqlUivv370sxkZGRwsHBQTx8+LDK/czOzhYApONqyulrd4TTBzvE6Wt3NHpcIiKi2kybf/+09Tf7aWkjDyvLBx98IJ5//vly92sqV6tMbX0fKsPcrHx8bYiI6q+q/t2u1m169+7dw/Dhw6GnV+27/CqVmpqKjIwM+Pr6StsUCgW8vb2RkJAAAEhMTERhYaFajIODA1xdXaWYQ4cOQalUwsPDQ4rp3r07lEqlWoyrqyscHBykGD8/PxQUFEhD0Q8dOgRvb28oFAq1mH/++QdXrlwptx8FBQXIyclR+yEiIiKqLl3kYQCwbds2dOvWDa+99hpsbGzQpUsXrFq1StqvqVytJOZORERE9V+1spgxY8bgv//9r6bboiYjIwMAYGtrq7bd1tZW2peRkQG5XA4LC4sKY2xsbEod38bGRi2m5HksLCwgl8srjCl+XBxTlsjISGmuKqVSCUdHx4o7TkRERFQBXeRhAHD58mWsWLECrVu3xm+//YZ33nkHU6ZMwffffw9Ac7laScydiIiI6r9qzRkVGRkJf39/7Nq1C25ubjA0NFTbv3jxYo00DoDaPAjAo4kyS24rqWRMWfGaiBH/P3l5Re2ZMWMGQkJCpMc5OTlMqoiIiKjadJWHPXz4EN26dUNERAQAoEuXLjhz5gxWrFiBN998U4rTRK72OOZODcfFzFytHdvCRI4m5sZaOz4RET2dahWjIiIi8Ntvv8HFxQUAKi3qVIednR2AR1fU7O3tpe2ZmZnSFTg7OzuoVCpkZWWpXXHLzMyEp6enFHPjxo1Sx79586bacY4cOaK2PysrC4WFhWoxJa/gZWZmAih9RfBxCoVC7dY+IiIioqehizwMAOzt7dG+fXu1be3atcPmzZsBaC5XK4m5U/1nYSKHsaE+gjclae0cxob62B3qzYIUEVEtVa1i1OLFi/Hdd98hKChIw835H2dnZ9jZ2SEuLg5dunQBAKhUKsTHx2P+/PkAAHd3dxgaGiIuLg7Dhg0DAKSnpyM5ORkLFiwAAPTo0QPZ2dk4evQonnvuOQDAkSNHkJ2dLSVBPXr0wLx585Ceni4lU7GxsVAoFHB3d5diPvzwQ6hUKsjlcinGwcEBzZs319rrQERERPQ4XeRhAODl5YVz586pbTt//jycnJwAaC5Xo4anibkxdod6IytPpZXjX8zMRfCmJGTlqViMIiKqpapVjFIoFPDy8nrqk+fm5uLixYvS49TUVCQlJcHS0hLNmjVDcHAwIiIi0Lp1a7Ru3RoRERFo1KgRAgICAABKpRJjxoxBaGgorKysYGlpibCwMLi5uaFv374AHl3B69+/P8aOHYuvv/4aADBu3Dj4+/tLVxR9fX3Rvn17jBo1Cp9//jlu376NsLAwjB07FmZmZgCAgIAAzJkzB0FBQfjwww9x4cIFRERE4OOPP9boVUgiIiKiimgqD6vMe++9B09PT0RERGDYsGE4evQovvnmG3zzzTcAHo3C0kSuRg1TE3NjFoqIiBqwahWjpk6diqioKHz55ZdPdfLjx4+jT58+0uPi+QECAwMRHR2NadOmIT8/HxMmTEBWVhY8PDwQGxsLU1NT6TlLliyBgYEBhg0bhvz8fPj4+CA6Ohr6+vpSzPr16zFlyhRpJZfBgwdj2bJl0n59fX388ssvmDBhAry8vGBsbIyAgAAsXLhQilEqlYiLi8PEiRPRrVs3WFhYICQkRG1OAyIiIiJt01QeVplnn30WW7duxYwZM/DJJ5/A2dkZS5cuxRtvvCHFaCpXIyIiooZFJopn4X4CL7/8Mn7//XdYWVmhQ4cOpSbO3LJli8YaWN/k5ORAqVQiOztbGnWlCcnXs+EfdRA7Jj8P1yZKjR2XiIioNtPm3z9t/c1+Wg0tD6ut70NlmJvVHL72REQ1p6p/t6s1Msrc3BxDhw6tduOIiIiIqHqYhxEREVFdV61i1Jo1azTdDiIiIiKqAuZhREREVNfp1XQDiIiIiIiIiIio4ajWyChnZ+cKV5C7fPlytRtEREREROVjHkZERER1XbWKUcHBwWqPCwsLcfLkSezatQvvv/++JtpFRERERGVgHkZERER1XbWKUVOnTi1z+1dffYXjx48/VYOIiIiIqHzMw4iIiKiu0+icUQMGDMDmzZs1eUgiIiIiqgLmYURERFRXaLQY9eOPP8LS0lKThyQiIiKiKmAeRkRERHVFtW7T69Kli9rEmUIIZGRk4ObNm1i+fLnGGkdERERE6piHERERUV1XrWLUSy+9pJYE6enpoXHjxujduzfatm2rscYRERERkTrmYURERFTXVasYFR4eruFmEBEREVFVMA8jIiKiuu6JilF6enpqV+LKIpPJ8ODBg6dqFBERERGpYx5GRERE9cUTFaO2bt1a7r6EhARERUVBCPHUjSIiIiIidczDiIiIqL54omLUSy+9VGrbX3/9hRkzZmD79u1444038Omnn2qscURERET0CPMwIiIiqi/0qvvEf/75B2PHjkXHjh3x4MEDJCUlYe3atWjWrJkm20dEREREJTAPIyIiorrsiYtR2dnZ+OCDD9CqVSucOXMGe/bswfbt2+Hq6qqN9hERERHR/2MeRkRERPXBE92mt2DBAsyfPx92dnbYuHFjmcPFiYiIiEjzmIcRERFRffFExajp06fD2NgYrVq1wtq1a7F27doy47Zs2aKRxhERERHRI8zDiIiIqL54omLUm2++WemSwkRERESkeczDiIiIqL54omJUdHS0lppBRERERBVhHkZERET1xRMVo4iIiIiIiOqCi5m5WjmuhYkcTcyNtXJsIqKGgsUoIiIiIiKqNyxM5DA21EfwpiStHN/YUB+7Q71ZkCIiegosRhERERERUb3RxNwYu0O9kZWn0vixL2bmInhTErLyVCxGERE9BRajiIiIiKhSkZGR+PDDDzF16lQsXboUACCEwJw5c/DNN98gKysLHh4e+Oqrr9ChQwfpeQUFBQgLC8PGjRuRn58PHx8fLF++HE2bNq2hnlBD0MTcmMUiIqJaTK+mG0BEREREtduxY8fwzTffoGPHjmrbFyxYgMWLF2PZsmU4duwY7Ozs0K9fP9y9e1eKCQ4OxtatWxETE4ODBw8iNzcX/v7+KCoq0nU3iIiIqJZgMYqIiIiIypWbm4s33ngDq1atgoWFhbRdCIGlS5di5syZGDp0KFxdXbF27Vrcu3cPGzZsAABkZ2dj9erVWLRoEfr27YsuXbpg3bp1OH36NHbv3l1TXSIiIqIaxmIUEREREZVr4sSJGDhwIPr27au2PTU1FRkZGfD19ZW2KRQKeHt7IyEhAQCQmJiIwsJCtRgHBwe4urpKMSUVFBQgJydH7YeIiIjqF84ZRURERERliomJwYkTJ3Ds2LFS+zIyMgAAtra2atttbW1x9epVKUYul6uNqCqOKX5+SZGRkZgzZ44mmk9ERES1FEdGEREREVEpf//9N6ZOnYp169bByMio3DiZTKb2WAhRaltJFcXMmDED2dnZ0s/ff//95I0nIiKiWo3FKCIiIiIqJTExEZmZmXB3d4eBgQEMDAwQHx+PL7/8EgYGBtKIqJIjnDIzM6V9dnZ2UKlUyMrKKjemJIVCATMzM7UfIiIiql9YjCIiIiKiUnx8fHD69GkkJSVJP926dcMbb7yBpKQktGjRAnZ2doiLi5Oeo1KpEB8fD09PTwCAu7s7DA0N1WLS09ORnJwsxRAREVHDwzmjiIiIiKgUU1NTuLq6qm0zMTGBlZWVtD04OBgRERFo3bo1WrdujYiICDRq1AgBAQEAAKVSiTFjxiA0NBRWVlawtLREWFgY3NzcSk2ITkRERA0Hi1FEREREVC3Tpk1Dfn4+JkyYgKysLHh4eCA2NhampqZSzJIlS2BgYIBhw4YhPz8fPj4+iI6Ohr6+fg22nIiIiGoSi1FEREREVCX79u1TeyyTyRAeHo7w8PByn2NkZISoqChERUVpt3FERERUZ3DOKCIiIiIiIiIi0hkWo4iIiIiIiIiISGdYjCIiIiIiIiIiIp1hMYqIiIiIiIiIiHSGxSgiIiIiIiIiItIZFqOIiIiIiIiIiEhnanUxKjw8HDKZTO3Hzs5O2i+EQHh4OBwcHGBsbIzevXvjzJkzascoKCjA5MmTYW1tDRMTEwwePBjXrl1Ti8nKysKoUaOgVCqhVCoxatQo3LlzRy0mLS0NgwYNgomJCaytrTFlyhSoVCqt9Z2IiIiIiIiIqD6q1cUoAOjQoQPS09Oln9OnT0v7FixYgMWLF2PZsmU4duwY7Ozs0K9fP9y9e1eKCQ4OxtatWxETE4ODBw8iNzcX/v7+KCoqkmICAgKQlJSEXbt2YdeuXUhKSsKoUaOk/UVFRRg4cCDy8vJw8OBBxMTEYPPmzQgNDdXNi0BEREREREREVE8Y1HQDKmNgYKA2GqqYEAJLly7FzJkzMXToUADA2rVrYWtriw0bNmD8+PHIzs7G6tWr8cMPP6Bv374AgHXr1sHR0RG7d++Gn58fUlJSsGvXLhw+fBgeHh4AgFWrVqFHjx44d+4cXFxcEBsbi7Nnz+Lvv/+Gg4MDAGDRokUICgrCvHnzYGZmpqNXg4iIiIiIiIiobqv1xagLFy7AwcEBCoUCHh4eiIiIQIsWLZCamoqMjAz4+vpKsQqFAt7e3khISMD48eORmJiIwsJCtRgHBwe4uroiISEBfn5+OHToEJRKpVSIAoDu3btDqVQiISEBLi4uOHToEFxdXaVCFAD4+fmhoKAAiYmJ6NOnT7ntLygoQEFBgfQ4JydHUy8NERERERHVgIuZuVo7toWJHE3MjbV2fCKi2qBWF6M8PDzw/fffo02bNrhx4wbmzp0LT09PnDlzBhkZGQAAW1tbtefY2tri6tWrAICMjAzI5XJYWFiUiil+fkZGBmxsbEqd28bGRi2m5HksLCwgl8ulmPJERkZizpw5T9BrIiIiIiKqjSxM5DA21EfwpiStncPYUB+7Q71ZkCKieq1WF6MGDBgg/dvNzQ09evRAy5YtsXbtWnTv3h0AIJPJ1J4jhCi1raSSMWXFVyemLDNmzEBISIj0OCcnB46OjhU+h4iIiIiIap8m5sbYHeqNrDztLGR0MTMXwZuSkJWnYjGKiOq1Wl2MKsnExARubm64cOEChgwZAuDRqCV7e3spJjMzUxrFZGdnB5VKhaysLLXRUZmZmfD09JRibty4UepcN2/eVDvOkSNH1PZnZWWhsLCw1IipkhQKBRQKxZN3loiIiIiIap0m5sYsFBERPaVav5re4woKCpCSkgJ7e3s4OzvDzs4OcXFx0n6VSoX4+Hip0OTu7g5DQ0O1mPT0dCQnJ0sxPXr0QHZ2No4ePSrFHDlyBNnZ2WoxycnJSE9Pl2JiY2OhUCjg7u6u1T4TEREREREREdUntXpkVFhYGAYNGoRmzZohMzMTc+fORU5ODgIDAyGTyRAcHIyIiAi0bt0arVu3RkREBBo1aoSAgAAAgFKpxJgxYxAaGgorKytYWloiLCwMbm5u0up67dq1Q//+/TF27Fh8/fXXAIBx48bB398fLi4uAABfX1+0b98eo0aNwueff47bt28jLCwMY8eO5Up6RERERERERERPoFYXo65du4YRI0bg33//RePGjdG9e3ccPnwYTk5OAIBp06YhPz8fEyZMQFZWFjw8PBAbGwtTU1PpGEuWLIGBgQGGDRuG/Px8+Pj4IDo6Gvr6+lLM+vXrMWXKFGnVvcGDB2PZsmXSfn19ffzyyy+YMGECvLy8YGxsjICAACxcuFBHrwQRERERERERUf1Qq4tRMTExFe6XyWQIDw9HeHh4uTFGRkaIiopCVFRUuTGWlpZYt25dhedq1qwZduzYUWEMERERERERERFVrE7NGUVERERERERERHUbi1FERERERERERKQzLEYREREREREREZHO1Oo5o4iIiIiIiBqai5m5Wju2hYkcTcyNtXZ8IqKqYDGKiIiIiIioFrAwkcPYUB/Bm5K0dg5jQ33sDvVmQYqIahSLUURERERERLVAE3Nj7A71RlaeSivHv5iZi+BNScjKU7EYRUQ1isUoIiIiIiolMjISW7ZswV9//QVjY2N4enpi/vz5cHFxkWKEEJgzZw6++eYbZGVlwcPDA1999RU6dOggxRQUFCAsLAwbN25Efn4+fHx8sHz5cjRt2rQmukVU6zUxN2ahiIjqPU5gTkRERESlxMfHY+LEiTh8+DDi4uLw4MED+Pr6Ii8vT4pZsGABFi9ejGXLluHYsWOws7NDv379cPfuXSkmODgYW7duRUxMDA4ePIjc3Fz4+/ujqKioJrpFREREtQBHRhERERFRKbt27VJ7vGbNGtjY2CAxMRG9evWCEAJLly7FzJkzMXToUADA2rVrYWtriw0bNmD8+PHIzs7G6tWr8cMPP6Bv374AgHXr1sHR0RG7d++Gn5+fzvtFRERENY/FKCIiIiKqVHZ2NgDA0tISAJCamoqMjAz4+vpKMQqFAt7e3khISMD48eORmJiIwsJCtRgHBwe4uroiISGhzGJUQUEBCgoKpMc5OTna6hIA4PqdfK3Mz6PN1dCInpa2Pp9cqY+IqorFKCIiIiKqkBACISEheP755+Hq6goAyMjIAADY2tqqxdra2uLq1atSjFwuh4WFRamY4ueXFBkZiTlz5mi6C2W6ficffRfFI79QO7cMGhvqw8JErpVjE1WHtlfr40p9RFRVLEYRERERUYUmTZqEU6dO4eDBg6X2yWQytcdCiFLbSqooZsaMGQgJCZEe5+TkwNHRsRqtrlxWngr5hUVY+npntLJ5RuPH5ygRqm20uVofV+ojoifBYhQRERERlWvy5MnYtm0b9u/fr7YCnp2dHYBHo5/s7e2l7ZmZmdJoKTs7O6hUKmRlZamNjsrMzISnp2eZ51MoFFAoFNroSrla2TwD1yZKnZ6TqKZwtT4iqg24mh4RERERlSKEwKRJk7Blyxb8/vvvcHZ2Vtvv7OwMOzs7xMXFSdtUKhXi4+OlQpO7uzsMDQ3VYtLT05GcnFxuMYqIiIjqP46MIiIiIqJSJk6ciA0bNuDnn3+GqampNMeTUqmEsbExZDIZgoODERERgdatW6N169aIiIhAo0aNEBAQIMWOGTMGoaGhsLKygqWlJcLCwuDm5iatrkdEREQND4tRRERERFTKihUrAAC9e/dW275mzRoEBQUBAKZNm4b8/HxMmDABWVlZ8PDwQGxsLExNTaX4JUuWwMDAAMOGDUN+fj58fHwQHR0NfX19XXWFiIiIahkWo4iIiIioFCFEpTEymQzh4eEIDw8vN8bIyAhRUVGIiorSYOuIiIioLuOcUUREREREREREpDMsRhERERERERERkc6wGEVERERERERERDrDYhQREREREREREekMi1FERERERERERKQzLEYREREREREREZHOGNR0A4iIiIiIiKh+uJiZq7VjW5jI0cTcWGvHJyLdYTGKiIiIiIiInoqFiRzGhvoI3pSktXMYG+pjd6g3C1JE9QCLUURERERERPRUmpgbY3eoN7LyVFo5/sXMXARvSkJWnorFKKJ6gMUoIiIiIiIiempNzI1ZKCKiKmExioiIiIiIiOoEbc1JxfmoiHSLxSgiIiIiIiKq1bQ9JxXnoyLSLRajiIiIiIiIqFbT5pxUnI+qZl2/k6+1ucY44q32YjGKiIiIiIiIaj3OSVX/XL+Tj76L4pFfWKSV43PEW+3FYhQRERERERER6VxWngr5hUVY+npntLJ5RqPH5oi32o3FKCIiIiIiImrwtDU5OsDbxSrTyuYZuDZR1nQzSIdYjCIiIiIiIqIGS9uTowO8XYyoJBajiIiIiIiIqMHS5uToAG8XIyoLi1FERERERETUoHFy9PqLt1/WTixGEREREREREVG9wtsvazcWo4iIiIiIiIioXuHtl7Ubi1FEREREREREVO/w9svaS6+mG0BERERERERERA0Hi1HVsHz5cjg7O8PIyAju7u44cOBATTeJiIiIqFZj/kRERETFeJveE9q0aROCg4OxfPlyeHl54euvv8aAAQNw9uxZNGvWrKabR0RERFTrMH8iIqq7rt/J1+q8S9QwsRj1hBYvXowxY8bg7bffBgAsXboUv/32G1asWIHIyMgabh0RERFR7cP8iYiobrp+Jx99F8Ujv7BIa+cwNtSHhYlca8en2onFqCegUqmQmJiI6dOnq2339fVFQkJCmc8pKChAQUGB9Dg7OxsAkJOTo9G25d7NwcOCe8i9m4OcHJlGj01ERFRbafPvX/HfaiGERo/b0Dxp/qSr3Alg/kREulH8u+bU5XTk3tX87zJtunwzD3m5d/HZUDe0aGyilXOYN5LDVK8QOTmFWjm+ttTl9xUAGj+jQGMzI40ft6r5E4tRT+Dff/9FUVERbG1t1bbb2toiIyOjzOdERkZizpw5pbY7OjpqpY09lmrlsERERLWaNv/+3b17F0qlUnsnqOeeNH/Sde4EMH8iIt14Y2lNt6D66nLbtY2vTdkqy59YjKoGmUz9ypkQotS2YjNmzEBISIj0+OHDh7h9+zasrKzKfU515OTkwNHREX///TfMzMw0dty6gv1n/9l/9p/9Z/813X8hBO7evQsHBweNHrehqmr+pKvcqa5o6N/x2ojvSe3E96V24vtSO9WG/InFqCdgbW0NfX39UlfxMjMzS13tK6ZQKKBQKNS2mZuba6uJMDMza9Bfcvaf/Wf/2f+Giv3XTv85IurpPWn+pOvcqa5o6N/x2ojvSe3E96V24vtSO9Vk/qSn8bPWY3K5HO7u7oiLi1PbHhcXB09PzxpqFREREVHtxfyJiIiISuLIqCcUEhKCUaNGoVu3bujRowe++eYbpKWl4Z133qnpphERERHVSsyfiIiI6HEsRj2h119/Hbdu3cInn3yC9PR0uLq6YufOnXBycqrRdikUCsyePbvUsPaGgv1n/9l/9p/9Z/+p9qqt+VNdwM947cP3pHbi+1I78X2pnWrD+yITXK+YiIiIiIiIiIh0hHNGERERERERERGRzrAYRUREREREREREOsNiFBERERERERER6QyLUUREREREREREpDMsRtVSy5cvh7OzM4yMjODu7o4DBw5UGB8fHw93d3cYGRmhRYsWWLlypdr+M2fO4JVXXkHz5s0hk8mwdOlSLbb+6Wm6/6tWrULPnj1hYWEBCwsL9O3bF0ePHtVmF56Kpvu/ZcsWdOvWDebm5jAxMUHnzp3xww8/aLMLT0XT/X9cTEwMZDIZhgwZouFWa5amX4Po6GjIZLJSP/fv39dmN6pNG5+BO3fuYOLEibC3t4eRkRHatWuHnTt3aqsLT0XT/e/du3eZ7//AgQO12Y1q08b7v3TpUri4uMDY2BiOjo547733au3nnxqe/fv3Y9CgQXBwcIBMJsNPP/2ktl8IgfDwcDg4OMDY2Bi9e/fGmTNnaqaxDUhl70tQUFCp36vdu3evmcY2EJGRkXj22WdhamoKGxsbDBkyBOfOnVOL4fdF96ryvvD7onsrVqxAx44dYWZmBjMzM/To0QO//vqrtL/GvyuCap2YmBhhaGgoVq1aJc6ePSumTp0qTExMxNWrV8uMv3z5smjUqJGYOnWqOHv2rFi1apUwNDQUP/74oxRz9OhRERYWJjZu3Cjs7OzEkiVLdNSbJ6eN/gcEBIivvvpKnDx5UqSkpIjRo0cLpVIprl27pqtuVZk2+r93716xZcsWcfbsWXHx4kWxdOlSoa+vL3bt2qWrblWZNvpf7MqVK6JJkyaiZ8+e4qWXXtJyT6pPG6/BmjVrhJmZmUhPT1f7qY200f+CggLRrVs38eKLL4qDBw+KK1euiAMHDoikpCRddavKtNH/W7duqb3vycnJQl9fX6xZs0ZHvao6bfR/3bp1QqFQiPXr14vU1FTx22+/CXt7exEcHKyrbhFVaOfOnWLmzJli8+bNAoDYunWr2v7PPvtMmJqais2bN4vTp0+L119/Xdjb24ucnJyaaXADUdn7EhgYKPr376/2+/XWrVs109gGws/PT6xZs0YkJyeLpKQkMXDgQNGsWTORm5srxfD7ontVeV/4fdG9bdu2iV9++UWcO3dOnDt3Tnz44YfC0NBQJCcnCyFq/rvCYlQt9Nxzz4l33nlHbVvbtm3F9OnTy4yfNm2aaNu2rdq28ePHi+7du5cZ7+TkVKuLUdruvxBCPHjwQJiamoq1a9c+fYM1TBf9F0KILl26iFmzZj1dY7VAW/1/8OCB8PLyEt9++60IDAys1cUobbwGa9asEUqlUuNt1QZt9H/FihWiRYsWQqVSab7BGqaL3wFLliwRpqamaklibaGN/k+cOFG88MILajEhISHi+eef11CriTSnZNHj4cOHws7OTnz22WfStvv37wulUilWrlxZAy1smMorRtXmfKIhyMzMFABEfHy8EILfl9qi5PsiBL8vtYWFhYX49ttva8V3hbfp1TIqlQqJiYnw9fVV2+7r64uEhIQyn3Po0KFS8X5+fjh+/DgKCwu11lZt0FX/7927h8LCQlhaWmqm4Rqii/4LIbBnzx6cO3cOvXr10lzjNUCb/f/kk0/QuHFjjBkzRvMN1yBtvga5ublwcnJC06ZN4e/vj5MnT2q+A09JW/3ftm0bevTogYkTJ8LW1haurq6IiIhAUVGRdjpSTbr6Hbh69WoMHz4cJiYmmmm4hmir/88//zwSExOl27MvX76MnTt31trbFIkel5qaioyMDLXPuUKhgLe3d7nfC9Kdffv2wcbGBm3atMHYsWORmZlZ001qULKzswFAyun5fakdSr4vxfh9qTlFRUWIiYlBXl4eevToUSu+KyxG1TL//vsvioqKYGtrq7bd1tYWGRkZZT4nIyOjzPgHDx7g33//1VpbtUFX/Z8+fTqaNGmCvn37aqbhGqLN/mdnZ+OZZ56BXC7HwIEDERUVhX79+mm+E09BW/3/448/sHr1aqxatUo7Ddcgbb0Gbdu2RXR0NLZt24aNGzfCyMgIXl5euHDhgnY6Uk3a6v/ly5fx448/oqioCDt37sSsWbOwaNEizJs3TzsdqSZd/A48evQokpOT8fbbb2uu4Rqirf4PHz4cn376KZ5//nkYGhqiZcuW6NOnD6ZPn66djhBpUPFn/0m+F6QbAwYMwPr16/H7779j0aJFOHbsGF544QUUFBTUdNMaBCEEQkJC8Pzzz8PV1RUAvy+1QVnvC8DvS005ffo0nnnmGSgUCrzzzjvYunUr2rdvXyu+KwY6OQs9MZlMpvZYCFFqW2XxZW2vK7TZ/wULFmDjxo3Yt28fjIyMNNBazdNG/01NTZGUlITc3Fzs2bMHISEhaNGiBXr37q25hmuIJvt/9+5djBw5EqtWrYK1tbXmG6slmv4MdO/eXW2SSC8vL3Tt2hVRUVH48ssvNdVsjdF0/x8+fAgbGxt888030NfXh7u7O/755x98/vnn+PjjjzXc+qenzd+Bq1evhqurK5577jkNtFQ7NN3/ffv2Yd68eVi+fDk8PDxw8eJFTJ06Ffb29vjoo4803Hoi7XjS7wVp3+uvvy7929XVFd26dYOTkxN++eUXDB06tAZb1jBMmjQJp06dwsGDB0vt4/el5pT3vvD7UjNcXFyQlJSEO3fuYPPmzQgMDER8fLy0vya/KyxG1TLW1tbQ19cvVY3MzMwsVbUsZmdnV2a8gYEBrKystNZWbdB2/xcuXIiIiAjs3r0bHTt21GzjNUCb/dfT00OrVq0AAJ07d0ZKSgoiIyNrVTFKG/0/c+YMrly5gkGDBkn7Hz58CAAwMDDAuXPn0LJlSw33pPp09TtAT08Pzz77bK0bGaWt/tvb28PQ0BD6+vpSTLt27ZCRkQGVSgW5XK7hnlSPtt//e/fuISYmBp988olmG64h2ur/Rx99hFGjRkmjwdzc3JCXl4dx48Zh5syZ0NPjQHGqvezs7AA8GvFhb28vba/oe0E1w97eHk5OTrXub2t9NHnyZGzbtg379+9H06ZNpe38vtSs8t6XsvD7ohtyuVz6f8Bu3brh2LFj+OKLL/DBBx8AqNnvCrOvWkYul8Pd3R1xcXFq2+Pi4uDp6Vnmc3r06FEqPjY2Ft26dYOhoaHW2qoN2uz/559/jk8//RS7du1Ct27dNN94DdDl+y+EqHXDYrXR/7Zt2+L06dNISkqSfgYPHow+ffogKSkJjo6OWutPdejqMyCEQFJSktofn9pAW/338vLCxYsXpUIkAJw/fx729va1phAFaP/9/89//oOCggKMHDlSsw3XEG31/969e6UKTvr6+hCPFnLRYA+INM/Z2Rl2dnZqn3OVSoX4+PhyvxdUM27duoW///671v1trU+EEJg0aRK2bNmC33//Hc7Ozmr7+X2pGZW9L2Xh96VmFP8/YK34ruhkmnR6IsXLWq9evVqcPXtWBAcHCxMTE3HlyhUhhBDTp08Xo0aNkuKLl7V+7733xNmzZ8Xq1avLXNb85MmT4uTJk8Le3l6EhYWJkydPigsXLui8f5XRRv/nz58v5HK5+PHHH9WWE717967O+1cZbfQ/IiJCxMbGikuXLomUlBSxaNEiYWBgIFatWqXz/lVGG/0vqbav5qGN1yA8PFzs2rVLXLp0SZw8eVKMHj1aGBgYiCNHjui8f5XRRv/T0tLEM888IyZNmiTOnTsnduzYIWxsbMTcuXN13r/KaPM78Pzzz4vXX39dZ32pDm30f/bs2cLU1FRs3LhRXL58WcTGxoqWLVuKYcOG6bx/RGW5e/eulKcBEIsXLxYnT54UV69eFUI8Wn5bqVSKLVu2iNOnT4sRI0ZwqXodqOh9uXv3rggNDRUJCQkiNTVV7N27V/To0UM0adKE74sWvfvuu0KpVIp9+/ap5fT37t2TYvh90b3K3hd+X2rGjBkzxP79+0Vqaqo4deqU+PDDD4Wenp6IjY0VQtT8d4XFqFrqq6++Ek5OTkIul4uuXbuWWhbT29tbLX7fvn2iS5cuQi6Xi+bNm4sVK1ao7U9NTRUASv2UPE5toen+Ozk5ldn/2bNn66A3T07T/Z85c6Zo1aqVMDIyEhYWFqJHjx4iJiZGF12pFk33v6TaXowSQvOvQXBwsGjWrJmQy+WicePGwtfXVyQkJOiiK9Wijc9AQkKC8PDwEAqFQrRo0ULMmzdPPHjwQNtdqRZt9P/cuXMCgJSA1Gaa7n9hYaEIDw8XLVu2FEZGRsLR0VFMmDBBZGVl6aA3RJXbu3dvmXlKYGCgEOLRcvWzZ88WdnZ2QqFQiF69eonTp0/XbKMbgIrel3v37glfX1/RuHFjYWhoKJo1ayYCAwNFWlpaTTe7Xivr/QAg1qxZI8Xw+6J7lb0v/L7UjLfeekvKpxo3bix8fHzU8sCa/q7IhOD4dCIiIiIiIiIi0g3OGUVERERERERERDrDYhQREREREREREekMi1FERERERERERKQzLEYREREREREREZHOsBhFREREREREREQ6w2IUERERERERERHpDItRRERERERERESkMyxGERERERERERGRzrAYRUREREREREREOsNiFBGRFty6dQs2Nja4cuWKzs756quvYvHixTo7HxEREZEmMX8iajhYjCKiOikoKAgymQzvvPNOqX0TJkyATCZDUFCQ7hv2/yIjIzFo0CA0b95c2tarVy/IZDJ8+umnarFCCHh4eEAmk+Hjjz+u9jk//vhjzJs3Dzk5OdU+BhEREdVfzJ9KY/5EVDNYjCKiOsvR0RExMTHIz8+Xtt2/fx8bN25Es2bNaqxd+fn5WL16Nd5++21pmxACSUlJcHJywunTp9Xi165di3/++QcA0LVr12qft2PHjmjevDnWr19f7WMQERFR/cb8SR3zJ6KawWIUEdVZXbt2RbNmzbBlyxZp25YtW+Do6IguXbpI23bt2oXnn38e5ubmsLKygr+/Py5duqR2rB9//BFubm4wNjaGlZUV+vbti7y8vEr3leXXX3+FgYEBevToIW27cOEC7t69i6CgILVk6u7du5gxY4Z0FdLd3f2pXpPBgwdj48aNT3UMIiIiqr+YP5XG/IlI91iMIqI6bfTo0VizZo30+LvvvsNbb72lFpOXl4eQkBAcO3YMe/bsgZ6eHl5++WU8fPgQAJCeno4RI0bgrbfeQkpKCvbt24ehQ4dCCFHhvvLs378f3bp1U9uWmJgIIyMjjBgxAhcuXEBBQQEA4NNPP0Xnzp1hb28Pa2trODo6PtXr8dxzz+Ho0aPS8YmIiIhKYv6kjvkTke4Z1HQDiIiexqhRozBjxgxcuXIFMpkMf/zxB2JiYrBv3z4p5pVXXlF7zurVq2FjY4OzZ8/C1dUV6enpePDgAYYOHQonJycAgJubGwDg/Pnz5e4rz5UrV+Dg4KC27cSJE+jYsSPatGkDExMTpKSkwMTEBMuXL8fx48excOHCp76qBwBNmjRBQUEBMjIypPYSERERPY75kzrmT0S6x5FRRFSnWVtbY+DAgVi7di3WrFmDgQMHwtraWi3m0qVLCAgIQIsWLWBmZgZnZ2cAQFpaGgCgU6dO8PHxgZubG1577TWsWrUKWVlZle4rT35+PoyMjNS2JSYmwt3dHTKZDB07dkRycjLee+89jBs3Dm3btkViYuJTzXdQzNjYGABw7969pz4WERER1U/Mn9QxfyLSPRajiKjOe+uttxAdHY21a9eWGmIOAIMGDcKtW7ewatUqHDlyBEeOHAEAqFQqAIC+vj7i4uLw66+/on379oiKioKLiwtSU1Mr3Fcea2vrUgnXyZMnpWSpU6dO+OKLL3D06FHMnj0bKpUKZ86cKTOZunfvHt5//314enrC09MTY8eOxa1bt8o99+3btwEAjRs3ruRVIyIiooaM+dP/MH8i0j0Wo4iozuvfvz9UKhVUKhX8/PzU9t26dQspKSmYNWsWfHx80K5duzKvzMlkMnh5eWHOnDk4efIk5HI5tm7dWum+snTp0gVnz56VHl++fBl37tyRhpF37twZx48fx7x586BUKnH69GkUFhaWOcx80qRJ6NSpExISEpCQkIDhw4fjzTffLHfOheTkZDRt2rTU1U0iIiKixzF/+h/mT0S6xzmjiKjO09fXR0pKivTvx1lYWMDKygrffPMN7O3tkZaWhunTp6vFHDlyBHv27IGvry9sbGxw5MgR3Lx5E+3atatwX3n8/PwwY8YMZGVlwcLCAomJiZDL5XB1dQUABAYGYsiQIbCysgLwaD4ECwsLafh7sfz8fGRlZWHkyJEIDw8HAISHh+Pnn3/GxYsX0bp161LnPnDgAHx9fZ/sBSQiIqIGh/nT/zB/ItI9FqOIqF4wMzMrc7uenh5iYmIwZcoUuLq6wsXFBV9++SV69+6t9tz9+/dj6dKlyMnJgZOTExYtWoQBAwYgJSWl3H3lcXNzQ7du3fCf//wH48ePx4kTJ+Dq6gpDQ0MAgKGhodqVtxMnTqgtpVzs8at3kyZNqvQ1uH//PrZu3Yrffvut0lgiIiIi5k/Mn4hqikxUtL4mERFVy86dOxEWFobk5GTo6VX/juigoCD069cPb7zxBgBgz549WLhwIXbu3AmZTKYW+9VXX+Hnn39GbGzsU7WdiIiIqCYwfyJqODgyiohIC1588UVcuHAB169fh6OjY7WPs3z5csyaNQtffvklZDIZ2rVrh3Xr1pVKpIBHVwyjoqKeptlERERENYb5E1HDwZFRRERERERERESkM1xNj4iIiIiIiIiIdIbFKCIiIiIiIiIi0hkWo4iIiIiIiIiISGdYjCIiIiIiIiIiIp1hMYqIiIiIiIiIiHSGxSgiIiIiIiIiItIZFqOIiIiIiIiIiEhnWIwiIiIiIiIiIiKdYTGKiIiIiIiIiIh0hsUoIiIiIiIiIiLSGRajiIiIiIiIiIhIZ/4P5KKEaHc4LsMAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs and WDs\n", + "o_bh = np.where(o_out['phase'] == 103)[0]\n", + "o_ns = np.where(o_out['phase'] == 102)[0]\n", + "o_wd = np.where(o_out['phase'] == 101)[0]\n", + "o_bd = np.where(o_out['phase'] == 99)[0]\n", + "\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(8, 30, 20)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(o_out[o_bh]['mass'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(o_out[o_wd]['mass'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(o_out[o_bd]['mass'], histtype = 'step',\n", + " bins = bd_bins, label = 'Generated Brown Dwarves')\n", + "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(o_out[o_ns]['mass'], histtype = 'step',\n", + " bins = ns_bins, label = 'Generated Neutron Stars')\n", + "plt.title(\"Generated Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "75ad5ea9-9668-47d5-9fb0-1d17ad9913b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0382949743021217\n", + "8.999575157847088\n" + ] + } + ], + "source": [ + "# exploring the mass limits to generated WDs\n", + "print(np.min(o_out[o_wd]['mass']))\n", + "print(np.max(o_out[o_wd]['mass']))" + ] + }, + { + "cell_type": "markdown", + "id": "12c64ec0-58db-48fb-bca4-ae022702c0ca", + "metadata": {}, + "source": [ + "This is more expected, suggesting our unexpected results for white dwarf masses were due to the age of the cluster itself and not designation issues." + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "37509686-120b-41e6-96e4-97c7ccaa7cf1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass isMultiple systemMass ... metallicity m_hst_f153m\n", + "------------------ ---------- ------------------ ... ----------- -----------\n", + "15.284314934206362 False 15.284314934206362 ... 0.0 nan\n", + " 17.1290304145272 False 17.1290304145272 ... 0.0 nan\n", + " 18.95921354685369 False 18.95921354685369 ... 0.0 nan\n", + "18.896393352954313 False 18.896393352954313 ... 0.0 nan\n", + "20.962240635300414 False 20.962240635300414 ... 0.0 nan\n", + "15.026617026908085 False 15.026617026908085 ... 0.0 nan\n", + " 76.13596178064363 False 76.13596178064363 ... 0.0 nan\n", + " ... ... ... ... ... ...\n", + " 26.37054586898205 False 26.37054586898205 ... 0.0 nan\n", + "15.233818106481106 False 15.233818106481106 ... 0.0 nan\n", + " 19.36143672592774 False 19.36143672592774 ... 0.0 nan\n", + " 15.57854891895509 False 15.57854891895509 ... 0.0 nan\n", + "16.464720574303342 False 16.464720574303342 ... 0.0 nan\n", + "18.964812829033175 False 18.964812829033175 ... 0.0 nan\n", + " 16.1815428883449 False 16.1815428883449 ... 0.0 nan\n", + "Length = 1274 rows\n", + "isWR\n", + "----\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 1274 rows\n", + "isWR\n", + "----\n", + " 0.0\n", + " nan\n", + "isWR\n", + "----\n", + " nan\n", + " mass_current \n", + "------------------\n", + "1.3582329780993132\n", + " 1.373628116212405\n", + "1.2664447140529644\n", + "1.4158069262026158\n", + " 1.355217219139285\n", + "1.3046710483080488\n", + "1.2388328883877409\n", + " ...\n", + "1.4451775646969733\n", + "1.4624420030217151\n", + "1.2629887776924635\n", + "1.2784084162413294\n", + " 1.39953679687133\n", + "1.2970430314419577\n", + "1.3580723274397928\n", + "Length = 1274 rows\n" + ] + } + ], + "source": [ + "#exploring high mass NSs\n", + "large_o_NS = np.where((o_out[o_ns]['mass']) > 15)\n", + "print(o_out[o_ns][large_o_NS])\n", + "print(o_out[o_ns]['isWR'][large_o_NS])\n", + "print(np.unique(o_out['isWR']))\n", + "print(np.unique(o_out[o_ns]['isWR'][large_o_NS]))\n", + "print(o_out[o_ns]['mass_current'][large_o_NS])" + ] + }, { "cell_type": "markdown", "id": "7e460fa5-a083-4f2c-bef8-eb7d8a972b99", "metadata": {}, "source": [ - "### Cluster 2: With Companions" + "## Cluster 2: With Companions" ] }, { @@ -588,7 +1108,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 220, "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", "metadata": {}, "outputs": [], @@ -600,7 +1120,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 222, "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", "metadata": {}, "outputs": [ @@ -608,27 +1128,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 782400, 782401, 782402]),), Masses: mass \n", + "Brown dwarf indices: (array([ 0, 1, 2, ..., 783572, 783573, 783574]),), Masses: mass \n", "--------------------\n", - " 0.02818070156567557\n", - "0.057477731545588766\n", - "0.050488840533678525\n", - " 0.04059754829381457\n", - " 0.0715824675309702\n", - "0.052250831887193906\n", - " 0.06839990496518711\n", + " 0.06693276196287956\n", + " 0.04699446349914049\n", + "0.021914789420151674\n", + " 0.03465494731829008\n", + " 0.06694609948025984\n", + "0.033097609438405166\n", + " 0.06379944380267108\n", " ...\n", - " 0.0421694459314215\n", - "0.013298951799885059\n", - " 0.04585362159276258\n", - "0.017159730219879408\n", - " 0.04989123506548948\n", - "0.023690809192446777\n", - " 0.04091931301347838\n", - "Length = 769585 rows\n", + "0.030416028803757145\n", + " 0.06506905305535454\n", + "0.045851451825413045\n", + " 0.05977579693942302\n", + "0.027669020976738186\n", + "0.016900448679756944\n", + "0.017397789924083088\n", + "Length = 770794 rows\n", "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", "----\n", - "Found 175492 companions out of stellar mass range\n" + "Found 176029 companions out of stellar mass range\n" ] } ], @@ -644,7 +1164,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 223, "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", "metadata": {}, "outputs": [ @@ -652,57 +1172,119 @@ "name": "stdout", "output_type": "stream", "text": [ - " mass \n", - "--------------------\n", - "0.010356220429645275\n", - " 1.027255369157919\n", - " 0.04728725326784277\n", - " 0.0458108242117786\n", - " 0.09466940653177572\n", - " 0.22804772872665638\n", - " 0.5530407053925155\n", - " ...\n", - " 0.3467687795811594\n", - " 0.02305569777333644\n", - " 0.1511085941801427\n", - " 0.24851815882478012\n", - " 0.06499991598727853\n", - "0.023044019171576186\n", - " 0.5446895523831808\n", - "Length = 429564 rows Teff \n", + " mass \n", + "-------------------\n", + " 0.1233262445951276\n", + " 0.2080613737453061\n", + "0.10384849953125234\n", + " 0.1736000003863225\n", + " 0.1458567566791392\n", + "0.14644751461127803\n", + " 0.6803574894852811\n", + " ...\n", + "0.08147468426694787\n", + " 0.1985936289536088\n", + " 0.6023752975149558\n", + " 1.4474686274512238\n", + " 0.2209924104780056\n", + "0.02286298804285546\n", + " 5.813934763247579\n", + "Length = 430889 rows Teff \n", "------------------\n", - " nan\n", - " 5825.765321681631\n", - " nan\n", - " nan\n", - " 2966.537023687665\n", - " 3310.184350302818\n", - "3987.9798523599243\n", + "3058.6083474502793\n", + "3282.8772110629448\n", + "3004.3525429335605\n", + "3198.7657683182606\n", + "3121.5408408103367\n", + "3123.1815879966903\n", + " 4415.385915529131\n", " ...\n", - "3465.1148889898845\n", - " nan\n", - "3136.1270818647067\n", - " 3338.388828168428\n", + " 2888.159816199592\n", + " 3268.012764248169\n", + " 4128.701802797316\n", + " 7171.279931264803\n", + "3300.4969767740236\n", " nan\n", " nan\n", - " 3956.044246444265\n", - "Length = 429564 rows\n" + "Length = 430889 rows\n", + "0\n" ] } ], "source": [ - "print(kc_comp['mass'], kc_comp['Teff'])" + "print(kc_comp['mass'], kc_comp['Teff'])\n", + "print(len(kc_comp[c_bd]))" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 216, + "id": "1a908d42-28fd-4baf-9ed1-fb659132d621", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "system_idx mass ... metallicity m_hst_f153m \n", + "---------- -------------------- ... ----------- ----------------\n", + " 12 0.06646180798489866 ... 0.0 nan\n", + " 46 0.023012369331536837 ... 0.0 nan\n", + " 46 0.018471178544959374 ... 0.0 nan\n", + " 65 0.032997747650321915 ... 0.0 nan\n", + " 97 0.025634827285352435 ... 0.0 nan\n", + " 121 0.06455445811667415 ... 0.0 nan\n", + " 126 0.045057151520924446 ... 0.0 nan\n", + " ... ... ... ... ...\n", + " 2095614 0.04347367364887792 ... 0.0 nan\n", + " 2095634 0.06977937644714907 ... 0.0 nan\n", + " 2095634 0.01169204031289436 ... 0.0 nan\n", + " 2095641 0.07757530998522764 ... 0.0 9.33068823729235\n", + " 2095663 0.024710695578689927 ... 0.0 nan\n", + " 2095670 0.05139467006684135 ... 0.0 nan\n", + " 2095674 0.04032752505521463 ... 0.0 nan\n", + "Length = 185218 rows\n", + " Teff \n", + "----------------\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + "2859.25294280096\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 185218 rows\n", + "phase\n", + "-----\n", + " 1.0\n", + " nan\n" + ] + } + ], + "source": [ + "need_bd = np.where(kc_comp['mass'] < 0.08)\n", + "print(kc_comp[need_bd])\n", + "print(kc_comp[need_bd]['Teff'])\n", + "print(np.unique(kc_comp[need_bd]['phase']))" + ] + }, + { + "cell_type": "code", + "execution_count": 230, "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -715,16 +1297,16 @@ "# Locate BHs, NSs, WDs, and BDs\n", "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", - "k_bh = np.concatenate([p2_bh, c_bh])\n", + "bh_masses = np.concatenate([kc_out['mass'][p2_bh], kc_comp['mass'][c_bh]])\n", "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", - "k_ns = np.concatenate([p2_ns, c_ns])\n", + "ns_masses = np.concatenate([kc_out['mass'][p2_ns], kc_comp['mass'][c_ns]])\n", "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", - "k_wd = np.concatenate([p2_wd, c_wd])\n", + "wd_masses = np.concatenate([kc_out['mass'][p2_wd], kc_comp['mass'][c_wd]])\n", "p2_bd = np.where(kc_out['phase'] == 99)[0]\n", "c_bd = np.where(kc_comp['phase'] == 99)[0]\n", - "k_bd = np.concatenate([p2_bd, c_bd])\n", + "bd_masses = np.concatenate([kc_out['mass'][p2_bh], kc_comp['mass'][c_bh]])\n", "\n", "# Plot on histograms\n", "bh_bins = np.linspace(0.01, 16, 20)\n", @@ -733,7 +1315,7 @@ "\n", "plt.figure(figsize=(14,6))\n", "plt.subplot(1, 3, 1)\n", - "plt.hist(kc_out[k_bh]['mass'], histtype = 'step',\n", + "plt.hist(bh_masses, histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -741,7 +1323,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 3, 2)\n", - "plt.hist(kc_out[k_wd]['mass'], histtype = 'step',\n", + "plt.hist(kc_out[p2_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -749,7 +1331,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 3, 3)\n", - "plt.hist(kc_out[k_bd]['mass'], histtype = 'step',\n", + "plt.hist(bd_masses, histtype = 'step',\n", " bins = bd_bins, label = 'Generated Brown Dwarves')\n", "plt.title(\"Generated Brown Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -761,7 +1343,75 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 228, + "id": "ea063a9a-6268-4ab6-911d-0c6f33eef098", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BH prim max mass: 119.89570218214551\n", + "BH prim min mass: 15.025999572935213\n", + "BH comp max mass: 119.37716775564101\n", + "BH comp min mass: 15.009358951094368\n", + "\n", + "NS prim max mass: 119.61968070913697\n", + "NS prim min mass: 9.002475201776234\n", + "NS comp max mass: 115.47061424546564\n", + "NS comp min mass: 9.00056928420967\n", + "\n", + "WD prim max mass: 8.999794986324147\n", + "WD prim min mass: 5.311067490186374\n", + "WD comp max mass: 8.99707592319193\n", + "WD comp min mass: 5.311666754267032\n", + "\n", + "BD prim max mass: 0.07999992186315226\n", + "BD prim min mass: 0.01000001336207612\n" + ] + }, + { + "ename": "ValueError", + "evalue": "zero-size array to reduction operation maximum which has no identity", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[228], line 19\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD prim max mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(np\u001b[38;5;241m.\u001b[39mmax(kc_out[p2_bd][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m])))\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD prim min mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(np\u001b[38;5;241m.\u001b[39mmin(kc_out[p2_bd][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m])))\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD comp max mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkc_comp\u001b[49m\u001b[43m[\u001b[49m\u001b[43mc_bd\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmass\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m))\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD comp min mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(np\u001b[38;5;241m.\u001b[39mmin(kc_comp[c_ns][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m])) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mamax\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "File \u001b[0;32m/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:2820\u001b[0m, in \u001b[0;36mamax\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 2703\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_amax_dispatcher)\n\u001b[1;32m 2704\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mamax\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue, initial\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue,\n\u001b[1;32m 2705\u001b[0m where\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue):\n\u001b[1;32m 2706\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2707\u001b[0m \u001b[38;5;124;03m Return the maximum of an array or maximum along an axis.\u001b[39;00m\n\u001b[1;32m 2708\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2818\u001b[0m \u001b[38;5;124;03m 5\u001b[39;00m\n\u001b[1;32m 2819\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2820\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaximum\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmax\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2821\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:84\u001b[0m, in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m reduction(axis\u001b[38;5;241m=\u001b[39maxis, dtype\u001b[38;5;241m=\u001b[39mdtype, out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpasskwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ufunc\u001b[38;5;241m.\u001b[39mreduce(obj, axis, dtype, out, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n", + "File \u001b[0;32m/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:41\u001b[0m, in \u001b[0;36m_amax\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amax\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 40\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m umr_maximum(a, axis, \u001b[38;5;28;01mNone\u001b[39;00m, out, keepdims, initial, where)\n", + "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation maximum which has no identity" + ] + } + ], + "source": [ + "# Checking that objects are in the correct mass ranges\n", + "print(\"BH prim max mass: \" + str(np.max(kc_out['mass'][p2_bh])))\n", + "print(\"BH prim min mass: \" + str(np.min(kc_out['mass'][p2_bh])))\n", + "print(\"BH comp max mass: \" + str(np.max(kc_comp['mass'][c_bh])))\n", + "print(\"BH comp min mass: \" + str(np.min(kc_comp['mass'][c_bh])) + '\\n')\n", + "\n", + "print(\"NS prim max mass: \" + str(np.max(kc_out[p2_ns]['mass'])))\n", + "print(\"NS prim min mass: \" + str(np.min(kc_out[p2_ns]['mass'])))\n", + "print(\"NS comp max mass: \" + str(np.max(kc_comp[c_ns]['mass'])))\n", + "print(\"NS comp min mass: \" + str(np.min(kc_comp[c_ns]['mass'])) + '\\n')\n", + "\n", + "print(\"WD prim max mass: \" + str(np.max(kc_out[p2_wd]['mass'])))\n", + "print(\"WD prim min mass: \" + str(np.min(kc_out[p2_wd]['mass'])))\n", + "print(\"WD comp max mass: \" + str(np.max(kc_comp[c_wd]['mass'])))\n", + "print(\"WD comp min mass: \" + str(np.min(kc_comp[c_wd]['mass'])) + '\\n')\n", + "\n", + "print(\"BD prim max mass: \" + str(np.max(kc_out[p2_bd]['mass'])))\n", + "print(\"BD prim min mass: \" + str(np.min(kc_out[p2_bd]['mass'])))\n", + "print(\"BD comp max mass: \" + str(np.max(kc_comp[c_bd]['mass'])))\n", + "print(\"BD comp min mass: \" + str(np.min(kc_comp[c_ns]['mass'])) + '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 188, "id": "a64ebad4-b642-4e76-b12f-92402f241484", "metadata": {}, "outputs": [ @@ -769,19 +1419,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "63.493905663432386\n", - "0.01001691235394781\n" + "88.54164657710947\n", + "0.010059908098432945\n", + "118.80539890808666\n", + "15.000513183956517\n" ] } ], "source": [ "print(np.max(kc_out[k_wd]['mass']))\n", - "print(np.min(kc_out[k_wd]['mass']))" + "print(np.min(kc_out[k_wd]['mass']))\n", + "\n", + "print(np.max(kc_out[p2_bh]['mass']))\n", + "print(np.min(kc_out[p2_bh]['mass']))" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 172, "id": "497b4d94-6722-4ad7-880a-2b53d5acc28d", "metadata": {}, "outputs": [ @@ -789,24 +1444,24 @@ "name": "stdout", "output_type": "stream", "text": [ - " mass isMultiple ... m_hst_f153m N_companions\n", - "-------------------- ---------- ... ------------------ ------------\n", - " 1.6412592122340768 True ... 1.891168727297026 2\n", - " 0.20991189263696136 False ... 7.70159095731871 0\n", - "0.017000902750311093 False ... nan 0\n", - "0.010753505226784877 False ... nan 0\n", - " 0.2688662867171623 True ... 6.5704973503446755 1\n", - " 0.1180619308611421 False ... 8.583856072073955 0\n", - " 0.08394927892076134 False ... 9.189440377342134 0\n", - " ... ... ... ... ...\n", - " 0.0206406101070209 False ... nan 0\n", - " 0.07136771909820422 False ... nan 0\n", - " 0.07067029373342434 True ... nan 2\n", - " 0.22895855805541523 False ... 7.553664542955746 0\n", - " 0.08333000096743302 True ... 9.202672780659682 1\n", - " 1.45216924939138 True ... 2.3052287375038536 1\n", - " 0.2641096309279692 False ... 7.327889430103641 0\n", - "Length = 3214 rows\n" + " mass isMultiple ... m_hst_f153m N_companions\n", + "-------------------- ---------- ... ------------------- ------------\n", + " 0.6586795096056381 False ... 5.025295725472892 0\n", + " 3.8427510670284337 True ... -0.6509998709738373 1\n", + " 0.2863865166483995 False ... 7.2088768596764785 0\n", + " 0.7742048335787876 False ... 4.460668851429343 0\n", + " 0.10766124147681298 True ... 8.760423625411967 1\n", + " 0.02589910470529386 False ... nan 0\n", + " 0.5326011325857933 False ... 5.718004847207575 0\n", + " ... ... ... ... ...\n", + " 0.07207704463160401 False ... nan 0\n", + " 0.2901218613714232 True ... 6.513996744341199 1\n", + " 0.8377075545324469 False ... 4.199858747334578 0\n", + "0.035545417415318484 False ... nan 0\n", + " 0.10799622753582329 False ... 8.754736726529003 0\n", + " 0.09016119379548757 False ... 9.064022685272212 0\n", + " 0.371310690849909 False ... 6.74904566406516 0\n", + "Length = 3285 rows\n" ] } ], @@ -816,7 +1471,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 174, "id": "eb20c163-8212-4885-98dd-eb10e0bb08f7", "metadata": {}, "outputs": [ @@ -824,8 +1479,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Smallest mass of a primary generated black hole: 15.001898748254684\n", - "Smallest mass of a companion generated black hole: 0.010087940766134134\n" + "Smallest mass of a primary generated black hole: 15.005003818897485\n", + "Smallest mass of a companion generated black hole: 0.010053181629822302\n" ] } ], @@ -837,10 +1492,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 173, "id": "6a854062-8dd0-4411-a0d7-523dcc4aa77b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Smallest mass of a primary generated object: 0.010000087950739485\n", + "Smallest mass of a companion generated object: 0.010000111957749505\n" + ] + } + ], "source": [ "# Finding the minimum mass of generated objects\n", "print(\"Smallest mass of a primary generated object: \" + str(np.min(kc_out['mass'])))\n", From 1d66451acd1775bc6b68202ad5900278716a1208 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 19 Aug 2024 09:33:32 -0700 Subject: [PATCH 018/191] BD updates --- changes/Compact_Multiplicity.ipynb | 430 ++++++++++++++++++++--------- spisea/synthetic.py | 33 ++- spisea/tests/test_synthetic.py | 6 +- 3 files changed, 333 insertions(+), 136 deletions(-) diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index 0282c8bf..abed9025 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 251, "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", "metadata": {}, "outputs": [ @@ -59,9 +59,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Isochrone generation took 71.359145 s.\n", + "Isochrone generation took 70.910534 s.\n", "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", - " Starting at: 2024-08-13 18:04:09.632289 Usually takes ~5 minutes\n", + " Starting at: 2024-08-16 12:58:00.472284 Usually takes ~5 minutes\n", "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", "Starting synthetic photometry\n", "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", @@ -75,7 +75,7 @@ "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", - " Time taken: 17.31 seconds\n" + " Time taken: 17.50 seconds\n" ] } ], @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 303, "id": "45456043-0197-4896-bcb4-d2247a95386e", "metadata": {}, "outputs": [ @@ -152,26 +152,26 @@ "name": "stdout", "output_type": "stream", "text": [ - " mass isMultiple ... metallicity m_hst_f153m \n", - "-------------------- ---------- ... ----------- -----------------\n", - " 0.06752873088255393 False ... 0.0 nan\n", - " 0.21787124306310335 False ... 0.0 7.63530743580833\n", - " 0.0262184880713122 False ... 0.0 nan\n", - " 0.3087509497640693 False ... 0.0 7.086103270232351\n", - " 0.07004122940473101 False ... 0.0 nan\n", - "0.055860465009665516 False ... 0.0 nan\n", - " 0.2449826527701243 False ... 0.0 7.443908907848126\n", - " ... ... ... ... ...\n", - "0.047169363745193545 False ... 0.0 nan\n", - " 0.04388574160305706 False ... 0.0 nan\n", - " 0.08713365145144555 False ... 0.0 9.124319491534537\n", - " 0.5384991452265341 False ... 0.0 5.675504835409121\n", - "0.019767325458511932 False ... 0.0 nan\n", - " 0.16230926501233983 False ... 0.0 8.07930622339159\n", - " 0.12339530398894323 False ... 0.0 8.508529920543223\n", - "Length = 1026205 rows\n", - "119.2216436980943\n", - "0.010000064522612448\n" + " mass isMultiple ... metallicity m_hst_f153m\n", + "-------------------- ---------- ... ----------- -----------\n", + "0.010581623004327498 False ... 0.0 nan\n", + " 0.07746106089705293 False ... 0.0 nan\n", + " 0.06953880412693926 False ... 0.0 nan\n", + " 0.07376813803092074 False ... 0.0 nan\n", + "0.018112506487775234 False ... 0.0 nan\n", + " 0.05924633227110841 False ... 0.0 nan\n", + " 0.076323411257296 False ... 0.0 nan\n", + " ... ... ... ... ...\n", + "0.021666218437430496 False ... 0.0 nan\n", + "0.026539284794723627 False ... 0.0 nan\n", + " 0.07170977037349016 False ... 0.0 nan\n", + "0.018694133568115678 False ... 0.0 nan\n", + " 0.04969517873307298 False ... 0.0 nan\n", + " 0.07788710880030814 False ... 0.0 nan\n", + " 0.01568523000869492 False ... 0.0 nan\n", + "Length = 1020837 rows\n", + "0.07999987827752757\n", + "0.010000034092785876\n" ] } ], @@ -446,6 +446,60 @@ "print(k_out[p_ns]['mass_current'][large_NS])" ] }, + { + "cell_type": "code", + "execution_count": 293, + "id": "028193f1-bcbc-483c-9641-df8c14cf983c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass_current \n", + "--------------------\n", + "0.010581623004327498\n", + " 0.07746106089705293\n", + " 0.06953880412693926\n", + " 0.07376813803092074\n", + "0.018112506487775234\n", + " 0.05924633227110841\n", + " 0.076323411257296\n", + " ...\n", + "0.021666218437430496\n", + "0.026539284794723627\n", + " 0.07170977037349016\n", + "0.018694133568115678\n", + " 0.04969517873307298\n", + " 0.07788710880030814\n", + " 0.01568523000869492\n", + "Length = 1020837 rows\n", + " mass \n", + "--------------------\n", + "0.010581623004327498\n", + " 0.07746106089705293\n", + " 0.06953880412693926\n", + " 0.07376813803092074\n", + "0.018112506487775234\n", + " 0.05924633227110841\n", + " 0.076323411257296\n", + " ...\n", + "0.021666218437430496\n", + "0.026539284794723627\n", + " 0.07170977037349016\n", + "0.018694133568115678\n", + " 0.04969517873307298\n", + " 0.07788710880030814\n", + " 0.01568523000869492\n", + "Length = 1020837 rows\n" + ] + } + ], + "source": [ + "print(k_out['mass_current'][p_bd])\n", + "print(k_out['mass'][p_bd])" + ] + }, { "cell_type": "markdown", "id": "5f76dbc3-0fff-49a7-94f1-7513aa031ad6", @@ -1108,7 +1162,7 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 286, "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", "metadata": {}, "outputs": [], @@ -1120,7 +1174,7 @@ }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 295, "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", "metadata": {}, "outputs": [ @@ -1128,27 +1182,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 783572, 783573, 783574]),), Masses: mass \n", + "Brown dwarf indices: (array([ 0, 1, 2, ..., 785354, 785355, 785356]),), Masses: mass \n", "--------------------\n", - " 0.06693276196287956\n", - " 0.04699446349914049\n", - "0.021914789420151674\n", - " 0.03465494731829008\n", - " 0.06694609948025984\n", - "0.033097609438405166\n", - " 0.06379944380267108\n", + " 0.07436657463591295\n", + " 0.06125115718404527\n", + " 0.07066751592079368\n", + " 0.07200706369071531\n", + "0.051506088266653524\n", + " 0.05929272238807183\n", + "0.049112273019809714\n", " ...\n", - "0.030416028803757145\n", - " 0.06506905305535454\n", - "0.045851451825413045\n", - " 0.05977579693942302\n", - "0.027669020976738186\n", - "0.016900448679756944\n", - "0.017397789924083088\n", - "Length = 770794 rows\n", + " 0.05799495176694396\n", + "0.031994021368442954\n", + " 0.06802099845333169\n", + " 0.06670578411126206\n", + "0.010433044273147809\n", + "0.030634450496813445\n", + "0.020885281173972783\n", + "Length = 772665 rows\n", "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", "----\n", - "Found 176029 companions out of stellar mass range\n" + "Found 175616 companions out of stellar mass range\n" ] } ], @@ -1164,7 +1218,7 @@ }, { "cell_type": "code", - "execution_count": 223, + "execution_count": 285, "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", "metadata": {}, "outputs": [ @@ -1172,41 +1226,41 @@ "name": "stdout", "output_type": "stream", "text": [ - " mass \n", - "-------------------\n", - " 0.1233262445951276\n", - " 0.2080613737453061\n", - "0.10384849953125234\n", - " 0.1736000003863225\n", - " 0.1458567566791392\n", - "0.14644751461127803\n", - " 0.6803574894852811\n", - " ...\n", - "0.08147468426694787\n", - " 0.1985936289536088\n", - " 0.6023752975149558\n", - " 1.4474686274512238\n", - " 0.2209924104780056\n", - "0.02286298804285546\n", - " 5.813934763247579\n", - "Length = 430889 rows Teff \n", + " mass \n", + "--------------------\n", + "0.022827194847619314\n", + " 0.10638031764197846\n", + " 0.03760679268091785\n", + " 0.22726350815590202\n", + "0.038753074343781856\n", + " 0.2504904685642538\n", + " 0.03951772983956194\n", + " ...\n", + "0.012231927508833364\n", + " 0.072612764979889\n", + " 0.1961780248469474\n", + " 0.08736265118080355\n", + " 0.07659346659266889\n", + " 1.19594339645191\n", + " 1.1853661916226508\n", + "Length = 435190 rows Teff \n", "------------------\n", - "3058.6083474502793\n", - "3282.8772110629448\n", - "3004.3525429335605\n", - "3198.7657683182606\n", - "3121.5408408103367\n", - "3123.1815879966903\n", - " 4415.385915529131\n", - " ...\n", - " 2888.159816199592\n", - " 3268.012764248169\n", - " 4128.701802797316\n", - " 7171.279931264803\n", - "3300.4969767740236\n", " nan\n", + " 3011.398083542016\n", + " nan\n", + "3309.1075685999776\n", + " nan\n", + "3341.1194688523947\n", " nan\n", - "Length = 430889 rows\n", + " ...\n", + " nan\n", + "2817.2104303556866\n", + " 3261.336270821613\n", + "2925.6827572130533\n", + "2851.3684944754505\n", + " 6338.378648763236\n", + " 6307.806682055694\n", + "Length = 435190 rows\n", "0\n" ] } @@ -1218,7 +1272,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 298, "id": "1a908d42-28fd-4baf-9ed1-fb659132d621", "metadata": {}, "outputs": [ @@ -1226,42 +1280,78 @@ "name": "stdout", "output_type": "stream", "text": [ - "system_idx mass ... metallicity m_hst_f153m \n", - "---------- -------------------- ... ----------- ----------------\n", - " 12 0.06646180798489866 ... 0.0 nan\n", - " 46 0.023012369331536837 ... 0.0 nan\n", - " 46 0.018471178544959374 ... 0.0 nan\n", - " 65 0.032997747650321915 ... 0.0 nan\n", - " 97 0.025634827285352435 ... 0.0 nan\n", - " 121 0.06455445811667415 ... 0.0 nan\n", - " 126 0.045057151520924446 ... 0.0 nan\n", - " ... ... ... ... ...\n", - " 2095614 0.04347367364887792 ... 0.0 nan\n", - " 2095634 0.06977937644714907 ... 0.0 nan\n", - " 2095634 0.01169204031289436 ... 0.0 nan\n", - " 2095641 0.07757530998522764 ... 0.0 9.33068823729235\n", - " 2095663 0.024710695578689927 ... 0.0 nan\n", - " 2095670 0.05139467006684135 ... 0.0 nan\n", - " 2095674 0.04032752505521463 ... 0.0 nan\n", - "Length = 185218 rows\n", - " Teff \n", - "----------------\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - "2859.25294280096\n", - " nan\n", - " nan\n", - " nan\n", - "Length = 185218 rows\n", + " mass_current \n", + "-------------------\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "0.07904209649092925\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 182938 rows\n", + "system_idx mass ... metallicity m_hst_f153m \n", + "---------- -------------------- ... ----------- -----------------\n", + " 2 0.04059931139737934 ... 0.0 nan\n", + " 2 0.04033587588071577 ... 0.0 nan\n", + " 34 0.059462609507120824 ... 0.0 nan\n", + " 48 0.014810980619283313 ... 0.0 nan\n", + " 48 0.07904209649092925 ... 0.0 9.296290412739976\n", + " 70 0.010050222257410254 ... 0.0 nan\n", + " 76 0.021551221548502034 ... 0.0 nan\n", + " ... ... ... ... ...\n", + " 2074705 0.0318924914104722 ... 0.0 nan\n", + " 2074719 0.024133829456656052 ... 0.0 nan\n", + " 2074721 0.013583472609030067 ... 0.0 nan\n", + " 2074726 0.03713310656117373 ... 0.0 nan\n", + " 2074742 0.04297247209243148 ... 0.0 nan\n", + " 2074752 0.031034988651296436 ... 0.0 nan\n", + " 2074757 0.01213677256547114 ... 0.0 nan\n", + "Length = 182938 rows\n", + "phase\n", + "-----\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " 1.0\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 182938 rows\n", + "Teff\n", + "----\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 1579 rows\n", "phase\n", "-----\n", " 1.0\n", @@ -1271,20 +1361,23 @@ ], "source": [ "need_bd = np.where(kc_comp['mass'] < 0.08)\n", + "print(kc_comp['mass_current'][need_bd])\n", + "bhs = np.where(kc_comp['phase'] == 103)\n", "print(kc_comp[need_bd])\n", - "print(kc_comp[need_bd]['Teff'])\n", - "print(np.unique(kc_comp[need_bd]['phase']))" + "print(kc_comp[need_bd]['phase'])\n", + "print(kc_comp['Teff'][bhs])\n", + "print(np.unique(kc_comp['phase'][need_bd]))" ] }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 294, "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -1297,16 +1390,16 @@ "# Locate BHs, NSs, WDs, and BDs\n", "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", - "bh_masses = np.concatenate([kc_out['mass'][p2_bh], kc_comp['mass'][c_bh]])\n", + "k_bh = np.concatenate([p2_bh, c_bh])\n", "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", - "ns_masses = np.concatenate([kc_out['mass'][p2_ns], kc_comp['mass'][c_ns]])\n", + "k_ns = np.concatenate([p2_ns, c_ns])\n", "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", - "wd_masses = np.concatenate([kc_out['mass'][p2_wd], kc_comp['mass'][c_wd]])\n", + "k_wd = np.concatenate([p2_wd, c_wd])\n", "p2_bd = np.where(kc_out['phase'] == 99)[0]\n", "c_bd = np.where(kc_comp['phase'] == 99)[0]\n", - "bd_masses = np.concatenate([kc_out['mass'][p2_bh], kc_comp['mass'][c_bh]])\n", + "k_bd = np.concatenate([p2_bd, c_bd])\n", "\n", "# Plot on histograms\n", "bh_bins = np.linspace(0.01, 16, 20)\n", @@ -1315,7 +1408,7 @@ "\n", "plt.figure(figsize=(14,6))\n", "plt.subplot(1, 3, 1)\n", - "plt.hist(bh_masses, histtype = 'step',\n", + "plt.hist(kc_comp[c_bh]['mass'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -1323,7 +1416,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 3, 2)\n", - "plt.hist(kc_out[p2_wd]['mass'], histtype = 'step',\n", + "plt.hist(kc_out[k_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -1331,7 +1424,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 3, 3)\n", - "plt.hist(bd_masses, histtype = 'step',\n", + "plt.hist(kc_comp['mass'][need_bd], histtype = 'step',\n", " bins = bd_bins, label = 'Generated Brown Dwarves')\n", "plt.title(\"Generated Brown Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -1611,9 +1704,94 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 276, "id": "1969480a-56b5-4109-993a-41267b1bcf24", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brown dwarf indices: (array([ 0, 1, 2, ..., 693572, 693573, 693574]),), Masses: mass \n", + "--------------------\n", + " 0.03330731848800373\n", + "0.045130822625746865\n", + " 0.04311839070080252\n", + "0.036366767190959895\n", + "0.034795730457169875\n", + " 0.07590327272894272\n", + " 0.04405898676312338\n", + " ...\n", + "0.022974850570129105\n", + " 0.07743240221765976\n", + " 0.06465930761239451\n", + "0.020632183162231716\n", + " 0.04965218683400625\n", + "0.021590467618339538\n", + " 0.06019784339988129\n", + "Length = 682107 rows\n", + "Found 179 stars out of mass range\n", + "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", + "----\n", + "Found 143800 companions out of stellar mass range\n" + ] + } + ], + "source": [ + " # Now to create the cluster.\n", + "imf_mass_limits = np.array([0.01, 0.07, 0.5, 1, np.inf])\n", + "imf_powers = np.array([-0.3, -1.3, -2.3, -2.3])\n", + "\n", + " ##########\n", + " # Start without multiplicity and IFMR\n", + " ##########\n", + "\"\"\" my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", + " multiplicity=None)\n", + " print('Constructed IMF: %d seconds' % (time.time() - startTime)) \n", + " \n", + " cluster1 = syn.ResolvedCluster(my_iso, my_imf1, cluster_mass, ifmr=ifmr_obj)\n", + " clust1 = cluster1.star_systems\n", + " print('Constructed cluster: %d seconds' % (time.time() - startTime))\"\"\"\n", + "\n", + " \n", + " ##########\n", + " # Test with multiplicity and IFMR\n", + " ##########\n", + "multi = multiplicity.MultiplicityUnresolved()\n", + "my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", + " multiplicity=multi)\n", + "\n", + " \n", + "cluster2 = synthetic.ResolvedCluster(my_iso, my_imf2, cluster_mass, ifmr=my_ifmr)\n", + "clust2 = cluster2.star_systems\n", + "comps2 = cluster2.companions" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "id": "a63128a1-23d2-48a2-a0b7-994d1d723bb7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "system_idx mass Teff L logg isWR mass_current phase metallicity m_hst_f153m\n", + "---------- ---- ---- --- ---- ---- ------------ ----- ----------- -----------\n" + ] + } + ], + "source": [ + "bd_idx = np.where(comps2['phase'] == 99)\n", + "print(comps2[bd_idx])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c90f58d-c82c-4350-82f0-3f85de4a4c65", + "metadata": {}, "outputs": [], "source": [] } diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 19ad14a1..c793bfcd 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -359,7 +359,7 @@ def _make_companions_table(self, star_systems, compMass): companions['isWR'][cdx] = np.round(self.iso_interps['isWR'](comp_mass)) companions['mass_current'] = self.iso_interps['mass_current'](companions['mass']) companions['phase'] = np.round(self.iso_interps['phase'](companions['mass'])) - companions['metallicity'] = np.ones(N_comp_tot)*self.iso.metallicity + companions['metallicity'] = np.ones(N_comp_tot)*self.iso.metallicity #**** # For a very small fraction of stars, the star phase falls on integers in-between # the ones we have definition for, as a result of the interpolation. For these @@ -401,14 +401,21 @@ def _make_companions_table(self, star_systems, compMass): star_systems[filt][idx[good]] = -2.5 * np.log10(f1[good] + f2[good]) star_systems[filt][idx[bad]] = np.nan + """# Assigning brown dwarf companions the correct phase/properties + bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] + companions['phase'][bd_idx] = 99 + companions['Teff'][bd_idx] = np.nan + for filt in self.filt_names: + companions[filt][bd_idx] = np.full(len(bd_idx), np.nan)""" + ##### # Make Remnants with flux = 0 in all bands. ##### if self.ifmr != None: - # Identify compact objects as those with Teff = 0 or with masses above the max iso mass + # Identify compact objects as those with Teff = 0 or with masses above the max iso mass, or BDs highest_mass_iso = self.iso.points['mass'].max() - cdx_rem = np.where(np.isnan(companions['Teff']) & - (companions['mass'] > highest_mass_iso))[0] + cdx_rem = np.where((np.isnan(companions['Teff']) & + (companions['mass'] > highest_mass_iso)) | (companions['mass'] < 0.08))[0] # Calculate remnant mass and ID for compact objects; update remnant_id and # remnant_mass arrays accordingly @@ -417,11 +424,11 @@ def _make_companions_table(self, star_systems, compMass): metallicity_array=companions['metallicity'][cdx_rem]) else: r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem]) - +# SEPERATE BDS IN OWN FUNCTION # Drop remnants where it is not relevant (e.g. not a compact object or # outside mass range IFMR is defined for) - good = np.where(r_id_tmp > 0) + good = np.where(r_id_tmp > 0) #**** cdx_rem_good = cdx_rem[good] companions['mass_current'][cdx_rem_good] = r_mass_tmp[good] @@ -431,7 +438,14 @@ def _make_companions_table(self, star_systems, compMass): for filt in self.filt_names: companions[filt][cdx_rem_good] = np.full(len(cdx_rem_good), np.nan) - + # Assigning brown dwarf companions the correct phase/properties + bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] + companions['phase'][bd_idx] = 99 + companions['mass_current'][bd_idx] = companions['mass'][bd_idx] + for filt in self.filt_names: + companions[filt][bd_idx] = np.full(len(bd_idx), np.nan) + + #BD INITIAL PHASE # Notify if we have a lot of bad ones. # Convert nan_to_num to avoid errors on greater than, less than comparisons companions_teff_non_nan = np.nan_to_num(companions['Teff'], nan=-99) @@ -439,6 +453,7 @@ def _make_companions_table(self, star_systems, compMass): if len(idx) != N_comp_tot and self.verbose: print( 'Found {0:d} companions out of stellar mass range'.format(N_comp_tot - len(idx))) + #BD FINAL PHASE # Double check that everything behaved properly. assert companions['mass'][idx].min() > 0 @@ -465,8 +480,8 @@ def _remove_bad_systems(self, star_systems, compMass): idx = np.where(star_systems_teff_non_nan > 0)[0] else: # Keep stars (with Teff) and any other compact objects (with phase info). - idx = np.where( (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) | - (star_systems_phase_non_nan == 99) )[0] + idx = np.where((star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) | + (star_systems_phase_non_nan == 99))[0] if len(idx) != N_systems and self.verbose: print( 'Found {0:d} stars out of mass range'.format(N_systems - len(idx))) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index cb84c72c..23f5a7a8 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -477,7 +477,7 @@ def test_ifmr_multiplicity(): assert len(np.where(clust1['phase'] == 99)) > 0 # BD assert len(np.where(clust2['phase'] == 99)) > 0 - # Now check that we have companions that are WDs, NSs, and BHs + # Now check that we have companions that are WDs, NSs, BHs, and BDs assert len(np.where(comps2['phase'] == 101)) > 0 assert len(np.where(comps2['phase'] == 102)) > 0 assert len(np.where(comps2['phase'] == 103)) > 0 @@ -535,6 +535,10 @@ def test_ifmr_multiplicity(): (comps2['mass'] >= BD_MIN_MASS) & (comps2['mass'] <= BD_MAX_MASS)) assert len(comp_non_bd_idx[0]) == 0 # asserting no non-brown dwarf companions in BD mass range + + #print statements for debugging: + print(comps2['mass'][comp_bd_idx]) + print(np.unique(comps2['phase'][comp_bd_idx])) return def test_metallicity(): From 0dcfdcf2caf0472c0af996f60b1775cc1da37d9e Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Thu, 22 Aug 2024 15:30:44 -0700 Subject: [PATCH 019/191] Curiosities in BD assignment --- changes/Compact_Multiplicity.ipynb | 366 +++++++++++++++++++++++------ spisea/ifmr.py | 2 +- spisea/synthetic.py | 29 ++- spisea/tests/test_synthetic.py | 8 +- 4 files changed, 331 insertions(+), 74 deletions(-) diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index abed9025..a2c1abec 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -1162,7 +1162,7 @@ }, { "cell_type": "code", - "execution_count": 286, + "execution_count": 304, "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", "metadata": {}, "outputs": [], @@ -1174,7 +1174,7 @@ }, { "cell_type": "code", - "execution_count": 295, + "execution_count": 305, "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", "metadata": {}, "outputs": [ @@ -1182,27 +1182,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 785354, 785355, 785356]),), Masses: mass \n", + "Brown dwarf indices: (array([ 0, 1, 2, ..., 785509, 785510, 785511]),), Masses: mass \n", "--------------------\n", - " 0.07436657463591295\n", - " 0.06125115718404527\n", - " 0.07066751592079368\n", - " 0.07200706369071531\n", - "0.051506088266653524\n", - " 0.05929272238807183\n", - "0.049112273019809714\n", + " 0.06814878672190731\n", + " 0.04773849561965738\n", + " 0.06827493028013426\n", + " 0.0359072610482999\n", + "0.023766688780906337\n", + " 0.05802498588206111\n", + " 0.07564077829371584\n", " ...\n", - " 0.05799495176694396\n", - "0.031994021368442954\n", - " 0.06802099845333169\n", - " 0.06670578411126206\n", - "0.010433044273147809\n", - "0.030634450496813445\n", - "0.020885281173972783\n", - "Length = 772665 rows\n", + " 0.05364431139484768\n", + "0.029481672087096702\n", + "0.012506309252072858\n", + "0.014174332139873898\n", + " 0.04149099895456249\n", + " 0.04355582847263964\n", + "0.036374123910035944\n", + "Length = 772782 rows\n", "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", "----\n", - "Found 175616 companions out of stellar mass range\n" + "Found 175850 companions out of stellar mass range\n" ] } ], @@ -1218,7 +1218,7 @@ }, { "cell_type": "code", - "execution_count": 285, + "execution_count": 306, "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", "metadata": {}, "outputs": [ @@ -1228,39 +1228,39 @@ "text": [ " mass \n", "--------------------\n", - "0.022827194847619314\n", - " 0.10638031764197846\n", - " 0.03760679268091785\n", - " 0.22726350815590202\n", - "0.038753074343781856\n", - " 0.2504904685642538\n", - " 0.03951772983956194\n", + " 0.05968994825198586\n", + " 0.5806518980715113\n", + "0.029660673068744366\n", + " 1.0561490103453701\n", + " 0.17057943576263854\n", + " 0.27545527781952783\n", + " 0.02648169160335085\n", " ...\n", - "0.012231927508833364\n", - " 0.072612764979889\n", - " 0.1961780248469474\n", - " 0.08736265118080355\n", - " 0.07659346659266889\n", - " 1.19594339645191\n", - " 1.1853661916226508\n", - "Length = 435190 rows Teff \n", + " 0.40403729711714403\n", + " 0.05200567264641382\n", + " 0.04220512783952523\n", + "0.035754850072584596\n", + " 0.12340265134211305\n", + " 0.07380553065721931\n", + "0.013647742599581188\n", + "Length = 431550 rows Teff \n", "------------------\n", " nan\n", - " 3011.398083542016\n", + " 4085.526723430826\n", " nan\n", - "3309.1075685999776\n", - " nan\n", - "3341.1194688523947\n", + "5920.0062733779405\n", + "3190.3383809176958\n", + "3375.2593307941474\n", " nan\n", " ...\n", + "3536.9941918044533\n", + " nan\n", + " nan\n", + " nan\n", + "3058.8221931306266\n", + "2827.8838719105515\n", " nan\n", - "2817.2104303556866\n", - " 3261.336270821613\n", - "2925.6827572130533\n", - "2851.3684944754505\n", - " 6338.378648763236\n", - " 6307.806682055694\n", - "Length = 435190 rows\n", + "Length = 431550 rows\n", "0\n" ] } @@ -1272,7 +1272,7 @@ }, { "cell_type": "code", - "execution_count": 298, + "execution_count": 307, "id": "1a908d42-28fd-4baf-9ed1-fb659132d621", "metadata": {}, "outputs": [ @@ -1286,54 +1286,54 @@ " nan\n", " nan\n", " nan\n", - "0.07904209649092925\n", " nan\n", " nan\n", - " ...\n", " nan\n", + " ...\n", " nan\n", " nan\n", " nan\n", " nan\n", " nan\n", + "0.07380553065721931\n", " nan\n", - "Length = 182938 rows\n", + "Length = 183555 rows\n", "system_idx mass ... metallicity m_hst_f153m \n", "---------- -------------------- ... ----------- -----------------\n", - " 2 0.04059931139737934 ... 0.0 nan\n", - " 2 0.04033587588071577 ... 0.0 nan\n", - " 34 0.059462609507120824 ... 0.0 nan\n", - " 48 0.014810980619283313 ... 0.0 nan\n", - " 48 0.07904209649092925 ... 0.0 9.296290412739976\n", - " 70 0.010050222257410254 ... 0.0 nan\n", - " 76 0.021551221548502034 ... 0.0 nan\n", + " 1 0.05968994825198586 ... 0.0 nan\n", + " 19 0.029660673068744366 ... 0.0 nan\n", + " 36 0.02648169160335085 ... 0.0 nan\n", + " 47 0.0676793749481354 ... 0.0 nan\n", + " 47 0.040357487164688594 ... 0.0 nan\n", + " 62 0.06369313059388905 ... 0.0 nan\n", + " 69 0.012985139409248137 ... 0.0 nan\n", " ... ... ... ... ...\n", - " 2074705 0.0318924914104722 ... 0.0 nan\n", - " 2074719 0.024133829456656052 ... 0.0 nan\n", - " 2074721 0.013583472609030067 ... 0.0 nan\n", - " 2074726 0.03713310656117373 ... 0.0 nan\n", - " 2074742 0.04297247209243148 ... 0.0 nan\n", - " 2074752 0.031034988651296436 ... 0.0 nan\n", - " 2074757 0.01213677256547114 ... 0.0 nan\n", - "Length = 182938 rows\n", + " 2075867 0.014832191284296728 ... 0.0 nan\n", + " 2075875 0.010915381583182204 ... 0.0 nan\n", + " 2075893 0.05200567264641382 ... 0.0 nan\n", + " 2075900 0.04220512783952523 ... 0.0 nan\n", + " 2075900 0.035754850072584596 ... 0.0 nan\n", + " 2075906 0.07380553065721931 ... 0.0 9.421319357047143\n", + " 2075908 0.013647742599581188 ... 0.0 nan\n", + "Length = 183555 rows\n", "phase\n", "-----\n", " nan\n", " nan\n", " nan\n", " nan\n", - " 1.0\n", " nan\n", " nan\n", - " ...\n", " nan\n", + " ...\n", " nan\n", " nan\n", " nan\n", " nan\n", " nan\n", + " 1.0\n", " nan\n", - "Length = 182938 rows\n", + "Length = 183555 rows\n", "Teff\n", "----\n", " nan\n", @@ -1351,7 +1351,7 @@ " nan\n", " nan\n", " nan\n", - "Length = 1579 rows\n", + "Length = 1497 rows\n", "phase\n", "-----\n", " 1.0\n", @@ -1789,9 +1789,235 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 311, "id": "0c90f58d-c82c-4350-82f0-3f85de4a4c65", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brown dwarf indices: (array([ 0, 1, 2, ..., 702295, 702297, 702298]),), Masses: mass \n", + "--------------------\n", + " 0.07112387932532963\n", + " 0.0764559041392305\n", + " 0.0625861330758897\n", + "0.010813223441380892\n", + "0.024579329319661714\n", + " 0.0153132496210663\n", + " 0.01502460044894114\n", + " ...\n", + " 0.06604816419573725\n", + "0.035960727819530976\n", + " 0.03392139750322173\n", + "0.049320264833810586\n", + " 0.05499753185245202\n", + "0.015053394497169099\n", + " 0.03366986670153467\n", + "Length = 634240 rows\n", + "Found 59455 stars out of mass range\n", + "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", + "----\n", + "Found 164173 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "# Define cluster parameters\n", + "logAge = 9.7\n", + "AKs = 0.0\n", + "distance = 1000\n", + "cluster_mass = 1e6\n", + "mass_sampling = 5\n", + "\n", + "evo = evolution.MergedBaraffePisaEkstromParsec()\n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "ifmr_obj = ifmr.IFMR_Raithel18()\n", + "\n", + "red_law = reddening.RedLawNishiyama09()\n", + " \n", + "iso = synthetic.IsochronePhot(logAge, AKs, distance,\n", + " evo_model=evo, atm_func=atm_func,\n", + " red_law=red_law, filters=filt_list,\n", + " mass_sampling=mass_sampling)\n", + "\n", + "multi = multiplicity.MultiplicityUnresolved()\n", + "my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", + " multiplicity=multi)\n", + " \n", + "cluster2 = synthetic.ResolvedCluster(iso, my_imf2, cluster_mass, ifmr=ifmr_obj)\n", + "clust2 = cluster2.star_systems\n", + "comps2 = cluster2.companions" + ] + }, + { + "cell_type": "markdown", + "id": "e6e12c06-fe63-4a9f-b7ea-26473dcdfb53", + "metadata": {}, + "source": [ + "# Creating the same conditions as test_ifmr_multiplicity" + ] + }, + { + "cell_type": "code", + "execution_count": 325, + "id": "005fa638-2a2d-4798-9637-8d16dce382a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=5.00 for T= 2305 logg=5.22\n", + "Isochrone generation took 2.873671 s.\n", + "Making photometry for isochrone: log(t) = 9.70 AKs = 0.00 dist = 1000\n", + " Starting at: 2024-08-21 14:26:02.531482 Usually takes ~5 minutes\n", + "Starting filter: nirc2,Kp Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.090 Msun T = 2305 K m_nirc2_Kp = 20.34\n", + "Starting filter: nirc2,H Elapsed time: 0.60 seconds\n", + "Starting synthetic photometry\n", + "M = 0.090 Msun T = 2305 K m_nirc2_H = 20.57\n", + "Starting filter: nirc2,J Elapsed time: 1.20 seconds\n", + "Starting synthetic photometry\n", + "M = 0.090 Msun T = 2305 K m_nirc2_J = 21.05\n", + " Time taken: 1.68 seconds\n", + "Brown dwarf indices: (array([ 0, 1, 2, ..., 764740, 764741, 764742]),), Masses: mass \n", + "--------------------\n", + "0.010443375377695562\n", + " 0.06511402241508014\n", + " 0.07176328265824769\n", + " 0.01532590021508947\n", + "0.032173564214380404\n", + "0.033550210563551695\n", + " 0.07626969082525512\n", + " ...\n", + "0.012806933245100089\n", + " 0.0747383470817761\n", + " 0.03550080854686576\n", + "0.050186096415334905\n", + "0.043859454865114604\n", + " 0.036543785344645\n", + "0.043050926420120574\n", + "Length = 690613 rows\n", + "Found 64893 stars out of mass range\n", + "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", + "----\n", + "Found 178337 companions out of stellar mass range\n", + "Primary star phases: phase\n", + "-----\n", + " 1.0\n", + " 99.0\n", + "101.0\n", + "102.0\n", + "103.0\n", + "Companion star phases: phase\n", + "-----\n", + " 1.0\n", + "101.0\n", + "102.0\n", + "103.0\n", + " nan\n" + ] + } + ], + "source": [ + "# Initialize the isochrone and cluster objects\n", + "logAge = 9.7\n", + "AKs = 0.0\n", + "distance = 1000\n", + "cluster_mass = 1e6\n", + "mass_sampling = 5\n", + "\n", + "filt_list = ['nirc2,Kp', 'nirc2,H', 'nirc2,J']\n", + "\n", + "evo = evolution.MergedBaraffePisaEkstromParsec()\n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "ifmr_obj = ifmr.IFMR_Raithel18()\n", + "red_law = reddening.RedLawNishiyama09()\n", + "\n", + "iso = synthetic.IsochronePhot(logAge, AKs, distance,\n", + " evo_model=evo, atm_func=atm_func,\n", + " red_law=red_law, filters=filt_list,\n", + " mass_sampling=mass_sampling)\n", + "\n", + "# Recreate the cluster with multiplicity and IFMR\n", + "multi = multiplicity.MultiplicityUnresolved()\n", + "imf_mass_limits = np.array([0.01, 0.07, 0.5, 1, np.inf])\n", + "imf_powers = np.array([-0.3, -1.3, -2.3, -2.3])\n", + "my_imf = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", + " multiplicity=multi)\n", + "\n", + "cluster = synthetic.ResolvedCluster(iso, my_imf, cluster_mass, ifmr=ifmr_obj)\n", + "clust = cluster.star_systems\n", + "comps = cluster.companions\n", + "\n", + "# Debugging prints\n", + "print('Primary star phases:', np.unique(clust['phase']))\n", + "print('Companion star phases:', np.unique(comps['phase']))" + ] + }, + { + "cell_type": "code", + "execution_count": 327, + "id": "c1937ef3-c11a-40e2-8c22-04ae352ff7a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "phase\n", + "-----\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 142106 rows\n" + ] + } + ], + "source": [ + "comps_bds = np.where((comps['mass'] >= 0.01) & (comps['mass'] < 0.08))\n", + "print(comps[comps_bds]['phase'])" + ] + }, + { + "cell_type": "code", + "execution_count": 323, + "id": "a3fcf655-bebb-4657-8c94-c0a4879b0c56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "system_idx mass Teff L logg isWR mass_current phase metallicity m_hst_f153m\n", + "---------- ---- ---- --- ---- ---- ------------ ----- ----------- -----------\n" + ] + } + ], + "source": [ + "comps_phase_nan = np.where(comps['phase'] == np.nan)\n", + "print(comps[comps_phase_nan])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f47a6c02-4ace-4b7d-b959-102c893afd7e", + "metadata": {}, "outputs": [], "source": [] } diff --git a/spisea/ifmr.py b/spisea/ifmr.py index 053b163d..1451ed11 100755 --- a/spisea/ifmr.py +++ b/spisea/ifmr.py @@ -732,7 +732,7 @@ def NS_mass(self, MZAMS): """ Drawing the NS mass from a Gaussian distrobuton based on observational data. - Gaussian fit by Emily Ramey and Sergiy Vasylyev of University of Caifornia, Berkeley using a + Gaussian fit by Emily Ramey and Sergiy Vasylyev of University of California, Berkeley using a sample of NSs from Ozel & Freire (2016) — J1811+2405 Ng et al. (2020), J2302+4442 Kirichenko et al. (2018), J2215+5135 Linares et al. (2018), J1913+1102 Ferdman & Collaboration (2017), J1411+2551 Martinez et al. (2017), diff --git a/spisea/synthetic.py b/spisea/synthetic.py index c793bfcd..bfc4f586 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -437,6 +437,11 @@ def _make_companions_table(self, star_systems, compMass): # Give remnants a magnitude of nan, so they can be filtered out later when calculating flux. for filt in self.filt_names: companions[filt][cdx_rem_good] = np.full(len(cdx_rem_good), np.nan) + + """# Debugging print before and after assigning brown dwarf phase + print("Before assigning BD phase:") + bd_idx_before = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] + print(f"Brown dwarfs before phase assignment (mass, phase): {list(zip(companions['mass'][bd_idx_before], companions['phase'][bd_idx_before]))}")""" # Assigning brown dwarf companions the correct phase/properties bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] @@ -445,7 +450,10 @@ def _make_companions_table(self, star_systems, compMass): for filt in self.filt_names: companions[filt][bd_idx] = np.full(len(bd_idx), np.nan) - #BD INITIAL PHASE + """print("After assigning BD phase:") + print(f"Brown dwarfs after phase assignment (mass, phase): {list(zip(companions['mass'][bd_idx], companions['phase'][bd_idx]))}")""" + + # Notify if we have a lot of bad ones. # Convert nan_to_num to avoid errors on greater than, less than comparisons companions_teff_non_nan = np.nan_to_num(companions['Teff'], nan=-99) @@ -453,7 +461,11 @@ def _make_companions_table(self, star_systems, compMass): if len(idx) != N_comp_tot and self.verbose: print( 'Found {0:d} companions out of stellar mass range'.format(N_comp_tot - len(idx))) - #BD FINAL PHASE + # Final check for brown dwarf phase + bd_idx_final = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] + print("Final BD phase check:") + """print(f"Brown dwarfs final phase check (mass, phase): {list(zip(companions['mass'][bd_idx_final], companions['phase'][bd_idx_final]))}")""" + # Double check that everything behaved properly. assert companions['mass'][idx].min() > 0 @@ -469,6 +481,13 @@ def _remove_bad_systems(self, star_systems, compMass): removed. If self.ifmr != None, then we will save the high mass systems since they will be plugged into an ifmr later. """ + """ # Print companion masses and phases before any filtering + bd_idx_initial = [np.where((np.array(cm) >= 0.01) & (np.array(cm) < 0.08))[0] for cm in compMass] + print("Before filtering - CompMass (brown dwarfs):") + for i, bdi in enumerate(bd_idx_initial): + if len(bdi) > 0: + print(f"System {i}: Masses: {np.array(compMass[i])[bdi]}, Phases: {np.full(len(bdi), 99)}")""" + N_systems = len(star_systems) # Get rid of the bad ones @@ -492,6 +511,12 @@ def _remove_bad_systems(self, star_systems, compMass): if self.imf.make_multiples: # Clean up companion stuff (which we haven't handled yet) compMass = [compMass[ii] for ii in idx] + + """bd_idx_after = [np.where((np.array(cm) >= 0.01) & (np.array(cm) < 0.08))[0] for cm in compMass] + print("After filtering - CompMass (brown dwarfs):") + for i, bdi in enumerate(bd_idx_after): + if len(bdi) > 0: + print(f"System {i}: Masses: {np.array(compMass[i])[bdi]}, Phases: {np.full(len(bdi), 99)}")""" return star_systems, compMass diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 23f5a7a8..b164ebe8 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -538,7 +538,13 @@ def test_ifmr_multiplicity(): #print statements for debugging: print(comps2['mass'][comp_bd_idx]) - print(np.unique(comps2['phase'][comp_bd_idx])) + print(np.unique(comps2['phase'])) + comp_phase_nan = np.where(comps2['phase'] == np.nan) + print(comps2[comp_phase_nan]) + comp_phase_1 = np.where(comps2['phase'] == 1.0) + print(comps2[comp_phase_1]) + comps_bds = np.where((comps2['mass'] >= 0.01) & (comps2['mass'] < 0.08)) + print(comps2[comps_bds]) return def test_metallicity(): From cc856a459c1994eebc2f32b132d60e3537a7221c Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 4 Sep 2024 13:52:51 -0700 Subject: [PATCH 020/191] Adding in BD temperatures --- changes/BD_Clusters.ipynb | 534 +++++++++++++++++++++++++++++++++ spisea/evolution.py | 54 +++- spisea/synthetic.py | 59 ++-- spisea/tests/test_synthetic.py | 10 + 4 files changed, 629 insertions(+), 28 deletions(-) create mode 100644 changes/BD_Clusters.ipynb diff --git a/changes/BD_Clusters.ipynb b/changes/BD_Clusters.ipynb new file mode 100644 index 00000000..df1bb2b7 --- /dev/null +++ b/changes/BD_Clusters.ipynb @@ -0,0 +1,534 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "38ea9963-fc45-4dbc-86b8-fa882b608583", + "metadata": {}, + "source": [ + "# Simulating Stellar Clusters with Brown Dwarfs" + ] + }, + { + "cell_type": "markdown", + "id": "4a8eebf3-dd6d-4331-943a-57f7b5f8833f", + "metadata": {}, + "source": [ + "#### Importing Necessary Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "81f8a8a2-18b9-4ec5-a3a5-16a728e24163", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary packages. \n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pylab as py\n", + "from scipy.interpolate import RegularGridInterpolator\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.table import vstack\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "8da89871-b687-44e2-a7e4-a6fc753f5222", + "metadata": {}, + "source": [ + "## Case 1: Cluster Without Companions" + ] + }, + { + "cell_type": "markdown", + "id": "db8bfa5e-1106-4cbf-969b-0816e91fc5aa", + "metadata": {}, + "source": [ + "For this stellar cluster, we will be using the MergedBaraffePisaEkstromParsec evolution model, IMFR_Raithel18 initial-final mass relation, and the Weidner_Kroupa_2004 initial mass function, all of which have been modified to describe substellar masses." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7f0e899f-6cb7-48f1-acfc-1fd9c4df4135", + "metadata": {}, + "outputs": [], + "source": [ + "# Create isochrone object \n", + "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only need 1 filter\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", + " evo_model = evolution.MergedBaraffePisaEkstromParsec(), #our new evolution model for BDs\n", + " filters=filt_list)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e79ee070-7f0d-4d46-b477-d22bd4fcb810", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "k_imf = imf.Weidner_Kroupa_2004()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "79704dca-9b9e-4572-b49f-760511e06b08", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "k_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "k_out = k_cluster.star_systems" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a192b379-25fb-43ac-a799-6ba4a98d5df9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs, WDs, and BDs\n", + "p_bh = np.where(k_out['phase'] == 103)[0]\n", + "p_ns = np.where(k_out['phase'] == 102)[0]\n", + "p_wd = np.where(k_out['phase'] == 101)[0]\n", + "p_bd = np.where(k_out['phase'] == 99)[0]\n", + "\n", + "# Determine individual histogram bins\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(8, 30, 20)\n", + "\n", + "# Plot BHs\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(k_out[p_bh]['mass'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(k_out[p_wd]['mass'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(k_out[p_bd]['mass'], histtype = 'step',\n", + " bins = bd_bins, label = 'Generated Brown Dwarves')\n", + "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(k_out[p_ns]['mass'], histtype = 'step',\n", + " bins = ns_bins, label = 'Generated Neutron Stars')\n", + "plt.title(\"Generated Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7c9bbd80-1243-485d-a944-9bfafc5049fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BH max mass: 119.13565618748322\n", + "BH min mass: 15.007725869985796\n", + "\n", + "NS max mass: 119.95876844996108\n", + "NS min mass: 9.001129278483436\n", + "\n", + "WD max mass: 8.999341744945873\n", + "WD min mass: 5.311091008003858\n", + "\n", + "BD max mass: 0.07999986295473586\n", + "BD min mass: 0.010000044934131811\n", + "\n" + ] + } + ], + "source": [ + "# Checking that objects are in the correct mass ranges\n", + "print(\"BH max mass: \" + str(np.max(k_out[p_bh]['mass'])))\n", + "print(\"BH min mass: \" + str(np.min(k_out[p_bh]['mass'])) + '\\n')\n", + "\n", + "print(\"NS max mass: \" + str(np.max(k_out[p_ns]['mass'])))\n", + "print(\"NS min mass: \" + str(np.min(k_out[p_ns]['mass'])) + '\\n')\n", + "\n", + "print(\"WD max mass: \" + str(np.max(k_out[p_wd]['mass'])))\n", + "print(\"WD min mass: \" + str(np.min(k_out[p_wd]['mass'])) + '\\n')\n", + "\n", + "print(\"BD max mass: \" + str(np.max(k_out[p_bd]['mass'])))\n", + "print(\"BD min mass: \" + str(np.min(k_out[p_bd]['mass'])) + '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a3c2d1e2-fcfa-429a-92e7-b2c31e034482", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Teff \n", + "------------------\n", + " 653.0891938350937\n", + " 651.8458273479349\n", + " 707.4325441828001\n", + "488.01700684829456\n", + " 871.6562359235475\n", + " 407.9503554901987\n", + " 930.6014364016501\n", + " ...\n", + "392.28042415955446\n", + " 908.5461410822766\n", + " 1393.091834142745\n", + " 998.2260271590488\n", + " 665.474820549251\n", + " 675.9825874832916\n", + "1122.0753000795612\n", + "Length = 1028848 rows\n" + ] + } + ], + "source": [ + "print(k_out[p_bd]['Teff'])" + ] + }, + { + "cell_type": "markdown", + "id": "c5cf0b4c-2b80-4b0c-9adf-e3d590dd39a3", + "metadata": {}, + "source": [ + "It is worth noting [explain about big NS's after confirmation from Matt] ..... \n", + "white dwarfs capped at low end due to age of cluster" + ] + }, + { + "cell_type": "markdown", + "id": "4e0ca5d6-c984-45be-982a-4e76e6083dde", + "metadata": {}, + "source": [ + "## Case 2: Cluster With Companions" + ] + }, + { + "cell_type": "markdown", + "id": "f64318cd-f631-4a24-9abe-93c96fa17251", + "metadata": {}, + "source": [ + "For this cluster we are using the same isochrone as the one generated in Case 1, just changing our IMF to allow for systems with companions." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "104a64d0-6a52-4b26-a418-0df265902a92", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "kc_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6042a1ae-86c2-44d3-b718-fd678185a6e7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 7301 companions out of stellar mass range\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "kc_cluster = synthetic.ResolvedCluster(my_iso, kc_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "kc_out = kc_cluster.star_systems\n", + "kc_comp = kc_cluster.companions" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c44480e6-fa97-44c3-9993-148316f746c0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs, WDs, and BDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "k_bh = vstack([kc_out[p2_bh], kc_comp[c_bh]])\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "k_ns = vstack([kc_out[p2_ns], kc_comp[c_ns]])\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", + "p2_bd = np.where(kc_out['phase'] == 99)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 99)[0]\n", + "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", + "\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(8, 30, 20)\n", + "\n", + "# Plot BHs\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(k_bh['mass'], histtype='step', bins=bh_bins, label='Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(k_wd['mass'], histtype = 'step', bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(k_bd['mass'], histtype = 'step', bins = bd_bins, label = 'Generated Brown Dwarves')\n", + "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(k_ns['mass'], histtype = 'step', bins = ns_bins, label = 'Generated Neutron Stars')\n", + "plt.title(\"Generated Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f8a2e33b-266e-4a6f-b3f6-f25a1cd8d86e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Teff \n", + "------------------\n", + " 589.5738241278044\n", + " 949.9191202547576\n", + " 535.3337220492646\n", + " 798.6701941060301\n", + " 883.180077123407\n", + "1009.6437674139902\n", + "444.11890279356027\n", + " ...\n", + "1625.8241118113378\n", + "1556.8961357273806\n", + " 1949.159944699517\n", + " 475.3197378938071\n", + "1445.6688176627986\n", + " 929.9466889780698\n", + " 905.5149940088668\n", + "Length = 964495 rows\n" + ] + } + ], + "source": [ + "print(k_bd['Teff'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "fedb95f5-8f7a-41e8-b586-20ce6e497126", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BH prim max mass: 119.64792937371239\n", + "BH prim min mass: 15.003687171639145\n", + "BH comp max mass: 108.78065103375774\n", + "BH comp min mass: 15.051696275245545\n", + "\n", + "NS prim max mass: 117.89439381247894\n", + "NS prim min mass: 9.001659607779285\n", + "NS comp max mass: 109.06172525179247\n", + "NS comp min mass: 9.000122949672017\n", + "\n", + "WD prim max mass: 8.99922965392098\n", + "WD prim min mass: 5.311110801935481\n", + "WD comp max mass: 8.997715563589054\n", + "WD comp min mass: 5.31119691635049\n", + "\n", + "BD prim max mass: 0.07999992301784681\n", + "BD prim min mass: 0.01000005942626443\n", + "BD comp max mass: 0.07999668766843566\n", + "BD comp min mass: 0.010000432726566368\n", + "\n" + ] + } + ], + "source": [ + "# Checking that objects are in the correct mass ranges\n", + "print(\"BH prim max mass: \" + str(np.max(kc_out['mass'][p2_bh])))\n", + "print(\"BH prim min mass: \" + str(np.min(kc_out['mass'][p2_bh])))\n", + "print(\"BH comp max mass: \" + str(np.max(kc_comp['mass'][c_bh])))\n", + "print(\"BH comp min mass: \" + str(np.min(kc_comp['mass'][c_bh])) + '\\n')\n", + "\n", + "print(\"NS prim max mass: \" + str(np.max(kc_out[p2_ns]['mass'])))\n", + "print(\"NS prim min mass: \" + str(np.min(kc_out[p2_ns]['mass'])))\n", + "print(\"NS comp max mass: \" + str(np.max(kc_comp[c_ns]['mass'])))\n", + "print(\"NS comp min mass: \" + str(np.min(kc_comp[c_ns]['mass'])) + '\\n')\n", + "\n", + "print(\"WD prim max mass: \" + str(np.max(kc_out[p2_wd]['mass'])))\n", + "print(\"WD prim min mass: \" + str(np.min(kc_out[p2_wd]['mass'])))\n", + "print(\"WD comp max mass: \" + str(np.max(kc_comp[c_wd]['mass'])))\n", + "print(\"WD comp min mass: \" + str(np.min(kc_comp[c_wd]['mass'])) + '\\n')\n", + "\n", + "print(\"BD prim max mass: \" + str(np.max(kc_out[p2_bd]['mass'])))\n", + "print(\"BD prim min mass: \" + str(np.min(kc_out[p2_bd]['mass'])))\n", + "print(\"BD comp max mass: \" + str(np.max(kc_comp[c_bd]['mass'])))\n", + "print(\"BD comp min mass: \" + str(np.min(kc_comp[c_bd]['mass'])) + '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5e9c3bc-ea61-4f1e-b13c-9bbcfc9f2830", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/spisea/evolution.py b/spisea/evolution.py index d1dc1896..f168acda 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -4,12 +4,14 @@ import numpy as np import os import glob +import pandas as pd import pdb import warnings from astropy.table import Table, vstack, Column from scipy import interpolate import pylab as py from spisea.utils import objects +from scipy.interpolate import RegularGridInterpolator from spisea import exceptions logger = logging.getLogger('evolution') @@ -1305,6 +1307,10 @@ def isochrone(self, age=1.e8, metallicity=0.0): idx_WR = np.where(iso['logT'] != iso['logT_WR']) isWR[idx_WR] = True iso.add_column(isWR) + + iso.meta['log_age'] = log_age + iso.meta['metallicity_in'] = metallicity + iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) # Assume mass of brown dwarfs does not change over their lifetime bd_idx = iso['mass'] < 0.08 @@ -1314,10 +1320,52 @@ def isochrone(self, age=1.e8, metallicity=0.0): nan_teff_idx = np.isnan(iso['logT']) if np.any(nan_teff_idx): iso['logT'][nan_teff_idx] = self.estimate_teff(iso['mass'][nan_teff_idx]) + + def get_temperature(self, masses, ages): + """ + Use interpolation to get temperatures for brown dwarfs based on mass and age. + + Parameters: + ----------- + masses : array-like + Array of brown dwarf masses. + ages : array-like + Array of ages corresponding to the masses. + + Returns: + -------- + temperatures : array + Array of interpolated temperatures. + """ + + # Load the CSV file + data = pd.read_csv('/u/caitlinbegbie/code/SPISEA/changes/bd_evo_csv/baraffe2003.csv') + + # Create columns of relevant data + mass_grid = np.unique(data['mass'].values) + age_grid = np.unique(data['age'].values) + temperatures = data['temperature'].values + + # Create a 2D grid for temperatures + temp_grid = np.zeros((len(age_grid), len(mass_grid))) + + # Fill the temperature grid with actual values + for i, age in enumerate(age_grid): + for j, mass in enumerate(mass_grid): + temp_grid[i, j] = np.mean(temperatures[(data['age'] == age) & (data['mass'] == mass)]) + + # Set up the interpolator + interpolator = RegularGridInterpolator((age_grid, mass_grid), temp_grid, bounds_error=False, fill_value=np.nan) + + # Get temperatures for the provided masses and ages + temperatures = interpolator(np.column_stack((ages, masses))) - iso.meta['log_age'] = log_age - iso.meta['metallicity_in'] = metallicity - iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) + # Make sure masses array is not empty + if len(masses) == 0: + print("No masses available for temperature calculation.") + return + + return temperatures return iso diff --git a/spisea/synthetic.py b/spisea/synthetic.py index bfc4f586..eaa713d5 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -192,6 +192,7 @@ def set_filter_names(self): filt_names.append(col_name) return filt_names + def _make_star_systems_table(self, mass, isMulti, sysMass): """ @@ -276,8 +277,24 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): for filt in self.filt_names: star_systems[filt][idx_rem_good] = np.full(len(idx_rem_good), np.nan) - # Handle brown dwarfs separately + + # Handle brown dwarfs separately and assign temperatures idx_bd = np.where(star_systems['phase'] == 99)[0] + + # Define the class for BDs + evo_model = evolution.MergedBaraffePisaEkstromParsec() + + # Use the instance to call get_temperature + masses = star_systems[idx_bd]['mass_current'] + ages = np.full(len(masses), self.iso.points.meta['LOGAGE']) + temperatures = evo_model.get_temperature(masses, ages) + + # Convert the numpy array to an astropy Column + temperatures_column = Column(temperatures) + + # Assign temperatures to the Table column + star_systems['Teff'][idx_bd] = temperatures_column + for filt in self.filt_names: star_systems[filt][idx_bd] = np.full(len(idx_bd), np.nan) @@ -401,12 +418,6 @@ def _make_companions_table(self, star_systems, compMass): star_systems[filt][idx[good]] = -2.5 * np.log10(f1[good] + f2[good]) star_systems[filt][idx[bad]] = np.nan - """# Assigning brown dwarf companions the correct phase/properties - bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] - companions['phase'][bd_idx] = 99 - companions['Teff'][bd_idx] = np.nan - for filt in self.filt_names: - companions[filt][bd_idx] = np.full(len(bd_idx), np.nan)""" ##### # Make Remnants with flux = 0 in all bands. @@ -423,8 +434,7 @@ def _make_companions_table(self, star_systems, compMass): r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem], metallicity_array=companions['metallicity'][cdx_rem]) else: - r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem]) -# SEPERATE BDS IN OWN FUNCTION + r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem]) # Drop remnants where it is not relevant (e.g. not a compact object or # outside mass range IFMR is defined for) @@ -437,11 +447,6 @@ def _make_companions_table(self, star_systems, compMass): # Give remnants a magnitude of nan, so they can be filtered out later when calculating flux. for filt in self.filt_names: companions[filt][cdx_rem_good] = np.full(len(cdx_rem_good), np.nan) - - """# Debugging print before and after assigning brown dwarf phase - print("Before assigning BD phase:") - bd_idx_before = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] - print(f"Brown dwarfs before phase assignment (mass, phase): {list(zip(companions['mass'][bd_idx_before], companions['phase'][bd_idx_before]))}")""" # Assigning brown dwarf companions the correct phase/properties bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] @@ -449,9 +454,20 @@ def _make_companions_table(self, star_systems, compMass): companions['mass_current'][bd_idx] = companions['mass'][bd_idx] for filt in self.filt_names: companions[filt][bd_idx] = np.full(len(bd_idx), np.nan) - - """print("After assigning BD phase:") - print(f"Brown dwarfs after phase assignment (mass, phase): {list(zip(companions['mass'][bd_idx], companions['phase'][bd_idx]))}")""" + + # Define the class for BDs + evo_model = evolution.MergedBaraffePisaEkstromParsec() + + # Use the instance to call get_temperature + c_masses = companions[bd_idx]['mass_current'] + c_ages = np.full(len(c_masses), self.iso.points.meta['LOGAGE']) + c_temperatures = evo_model.get_temperature(c_masses, c_ages) + + # Convert the numpy array to an astropy Column + c_temperatures_column = Column(c_temperatures) + + # Assign temperatures to the Table column + companions['Teff'][bd_idx] = c_temperatures_column # Notify if we have a lot of bad ones. @@ -463,8 +479,7 @@ def _make_companions_table(self, star_systems, compMass): # Final check for brown dwarf phase bd_idx_final = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] - print("Final BD phase check:") - """print(f"Brown dwarfs final phase check (mass, phase): {list(zip(companions['mass'][bd_idx_final], companions['phase'][bd_idx_final]))}")""" + # Double check that everything behaved properly. assert companions['mass'][idx].min() > 0 @@ -511,12 +526,6 @@ def _remove_bad_systems(self, star_systems, compMass): if self.imf.make_multiples: # Clean up companion stuff (which we haven't handled yet) compMass = [compMass[ii] for ii in idx] - - """bd_idx_after = [np.where((np.array(cm) >= 0.01) & (np.array(cm) < 0.08))[0] for cm in compMass] - print("After filtering - CompMass (brown dwarfs):") - for i, bdi in enumerate(bd_idx_after): - if len(bdi) > 0: - print(f"System {i}: Masses: {np.array(compMass[i])[bdi]}, Phases: {np.full(len(bdi), 99)}")""" return star_systems, compMass diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index b164ebe8..e146b273 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -488,6 +488,8 @@ def test_ifmr_multiplicity(): idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 99) & (clust1['phase'] != 9) ) idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 99) & (comps2['phase'] != 9) ) assert len(idx[0]) == 0 + + # Make sure BD temperatures are assigned correctly # Ensure no substellar mass compact objects are generated """ @@ -536,6 +538,10 @@ def test_ifmr_multiplicity(): (comps2['mass'] <= BD_MAX_MASS)) assert len(comp_non_bd_idx[0]) == 0 # asserting no non-brown dwarf companions in BD mass range + # Ensure BD temperature assignment is working correctly + assert np.all(clust1['Teff'][bd_idx] != np.nan) + assert np.all(comps2['Teff'][comp_bd_idx] != np.nan) + #print statements for debugging: print(comps2['mass'][comp_bd_idx]) print(np.unique(comps2['phase'])) @@ -545,6 +551,10 @@ def test_ifmr_multiplicity(): print(comps2[comp_phase_1]) comps_bds = np.where((comps2['mass'] >= 0.01) & (comps2['mass'] < 0.08)) print(comps2[comps_bds]) + + #make sure that bd temps are being assigned + print(clust1[bd_idx]['Teff']) + print(comps2[comp_bd_idx]['Teff']) return def test_metallicity(): From 53f2fd9a87657e8d7016a0c1aa33c78d57681825 Mon Sep 17 00:00:00 2001 From: caitlinbegbie <160803247+caitlinbegbie@users.noreply.github.com> Date: Wed, 4 Sep 2024 13:54:43 -0700 Subject: [PATCH 021/191] Delete changes/Compact_Multiplicity.ipynb --- changes/Compact_Multiplicity.ipynb | 2046 ---------------------------- 1 file changed, 2046 deletions(-) delete mode 100644 changes/Compact_Multiplicity.ipynb diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb deleted file mode 100644 index a2c1abec..00000000 --- a/changes/Compact_Multiplicity.ipynb +++ /dev/null @@ -1,2046 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c61a66d4-bc64-4080-ad07-38ae7f00670b", - "metadata": {}, - "source": [ - "## Comparing Clusters with Compact Objects: With and Without Companions" - ] - }, - { - "cell_type": "markdown", - "id": "77645bcf-b0f4-456b-89fd-eb9730b23c71", - "metadata": {}, - "source": [ - "In testing the addition of substellar objects into SPISEA, we have come across a curious problem. When generating a cluster with an IMFR and allowing for companions, we end up creating substellar mass compact objects, which does not make much sense. This is happening before modification of the code to specify brown dwarves as their own phase." - ] - }, - { - "cell_type": "markdown", - "id": "31969dcc-b1c4-4601-a44f-c9ba09a73188", - "metadata": {}, - "source": [ - "#### Importing the Necessary Packages:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c0a4a637-c694-4d3d-9b1f-3b1b66cc8a19", - "metadata": {}, - "outputs": [], - "source": [ - "# Import necessary packages. \n", - "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", - "from spisea.imf import imf, multiplicity\n", - "import numpy as np\n", - "import pylab as py\n", - "import pdb\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "f8639685-3185-4e3e-88c6-98602bdaedc9", - "metadata": {}, - "source": [ - "## Cluster 1: Without Companions" - ] - }, - { - "cell_type": "code", - "execution_count": 251, - "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Isochrone generation took 70.910534 s.\n", - "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", - " Starting at: 2024-08-16 12:58:00.472284 Usually takes ~5 minutes\n", - "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", - "M = 0.676 Msun T = 4395 K m_hst_f153m = 4.92\n", - "M = 0.786 Msun T = 4896 K m_hst_f153m = 4.41\n", - "M = 0.856 Msun T = 5198 K m_hst_f153m = 4.12\n", - "M = 0.956 Msun T = 5590 K m_hst_f153m = 3.74\n", - "M = 1.022 Msun T = 5808 K m_hst_f153m = 3.50\n", - "M = 1.079 Msun T = 5992 K m_hst_f153m = 3.30\n", - "M = 1.518 Msun T = 7482 K m_hst_f153m = 2.22\n", - "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", - "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", - "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", - " Time taken: 17.50 seconds\n" - ] - } - ], - "source": [ - "# Create isochrone object \n", - "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", - "my_ifmr = ifmr.IFMR_Raithel18()\n", - "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", - " evo_model = evolution.MergedBaraffePisaEkstromParsec(),\n", - " filters=filt_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "ac6b221b-6bd5-41c5-8a5e-94a0daed1dba", - "metadata": {}, - "outputs": [], - "source": [ - "# Create IMF objects \n", - "k_imf = imf.Weidner_Kroupa_2004()" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "1a68a281-791a-4821-8799-c2df8e936286", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 1037624, 1037625, 1037626]),), Masses: mass \n", - "--------------------\n", - "0.010581623004327498\n", - " 0.07746106089705293\n", - " 0.06953880412693926\n", - " 0.07376813803092074\n", - "0.018112506487775234\n", - " 0.05924633227110841\n", - " 0.076323411257296\n", - " ...\n", - "0.021666218437430496\n", - "0.026539284794723627\n", - " 0.07170977037349016\n", - "0.018694133568115678\n", - " 0.04969517873307298\n", - " 0.07788710880030814\n", - " 0.01568523000869492\n", - "Length = 1020837 rows\n", - "Found 925805 stars out of mass range\n" - ] - } - ], - "source": [ - "# Make cluster\n", - "cluster_mass = 10**6\n", - "k_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", - "t_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass)\n", - "\n", - "# Get outputs\n", - "k_out = k_cluster.star_systems\n", - "t_out = t_cluster.star_systems" - ] - }, - { - "cell_type": "code", - "execution_count": 303, - "id": "45456043-0197-4896-bcb4-d2247a95386e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass isMultiple ... metallicity m_hst_f153m\n", - "-------------------- ---------- ... ----------- -----------\n", - "0.010581623004327498 False ... 0.0 nan\n", - " 0.07746106089705293 False ... 0.0 nan\n", - " 0.06953880412693926 False ... 0.0 nan\n", - " 0.07376813803092074 False ... 0.0 nan\n", - "0.018112506487775234 False ... 0.0 nan\n", - " 0.05924633227110841 False ... 0.0 nan\n", - " 0.076323411257296 False ... 0.0 nan\n", - " ... ... ... ... ...\n", - "0.021666218437430496 False ... 0.0 nan\n", - "0.026539284794723627 False ... 0.0 nan\n", - " 0.07170977037349016 False ... 0.0 nan\n", - "0.018694133568115678 False ... 0.0 nan\n", - " 0.04969517873307298 False ... 0.0 nan\n", - " 0.07788710880030814 False ... 0.0 nan\n", - " 0.01568523000869492 False ... 0.0 nan\n", - "Length = 1020837 rows\n", - "0.07999987827752757\n", - "0.010000034092785876\n" - ] - } - ], - "source": [ - "print(k_out[p_bd])\n", - "print(np.max(k_out[p_bd]['mass']))\n", - "print(np.min(k_out[p_bd]['mass']))" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "4b4fffe4-e927-49f1-9d3d-b215ca2c98cc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5.311164368442878" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.min(k_out[p_wd]['mass'])" - ] - }, - { - "cell_type": "markdown", - "id": "b2371c2d-4ea8-4bfb-bf67-59d86316132b", - "metadata": {}, - "source": [ - "### Exploring the properties of k_cluster" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "3bdd27c9-3820-4fff-9ed6-da26fb997e08", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs and WDs\n", - "p_bh = np.where(k_out['phase'] == 103)[0]\n", - "p_ns = np.where(k_out['phase'] == 102)[0]\n", - "p_wd = np.where(k_out['phase'] == 101)[0]\n", - "p_bd = np.where(k_out['phase'] == 99)[0]\n", - "\n", - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 10, 20)\n", - "bd_bins = np.linspace(0.01, 0.08, 8)\n", - "ns_bins = np.linspace(8, 30, 20)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(2, 2, 1)\n", - "plt.hist(k_out[p_bh]['mass'], histtype = 'step',\n", - " bins = bh_bins, label = 'Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 2)\n", - "plt.hist(k_out[p_wd]['mass'], histtype = 'step',\n", - " bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 3)\n", - "plt.hist(k_out[p_bd]['mass'], histtype = 'step',\n", - " bins = bd_bins, label = 'Generated Brown Dwarves')\n", - "plt.title(\"Generated Brown Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 4)\n", - "plt.hist(k_out[p_ns]['mass'], histtype = 'step',\n", - " bins = ns_bins, label = 'Generated Neutron Stars')\n", - "plt.title(\"Generated Neutron Stars by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "id": "e8e3c545-0aba-43f8-ad46-ed9ba9720761", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BH max mass: 118.75715744712116\n", - "BH min mass: 15.035041823555424\n", - "\n", - "NS max mass: 119.72357017983377\n", - "NS min mass: 9.000593434639391\n", - "\n", - "WD max mass: 8.999786895675784\n", - "WD min mass: 5.311134878018857\n", - "\n", - "BD max mass: 0.07999987827752757\n", - "BH min mass: 0.010000034092785876\n", - "\n" - ] - } - ], - "source": [ - "# Checking that objects are in the correct mass ranges\n", - "print(\"BH max mass: \" + str(np.max(k_out[p_bh]['mass'])))\n", - "print(\"BH min mass: \" + str(np.min(k_out[p_bh]['mass'])) + '\\n')\n", - "\n", - "print(\"NS max mass: \" + str(np.max(k_out[p_ns]['mass'])))\n", - "print(\"NS min mass: \" + str(np.min(k_out[p_ns]['mass'])) + '\\n')\n", - "\n", - "print(\"WD max mass: \" + str(np.max(k_out[p_wd]['mass'])))\n", - "print(\"WD min mass: \" + str(np.min(k_out[p_wd]['mass'])) + '\\n')\n", - "\n", - "print(\"BD max mass: \" + str(np.max(k_out[p_bd]['mass'])))\n", - "print(\"BH min mass: \" + str(np.min(k_out[p_bd]['mass'])) + '\\n')" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "ed4301d8-2eac-4f78-895b-c8c63b22fe5b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "float64\n", - "isWR\n", - "----\n", - " 0.0\n", - " 0.0\n", - " nan\n", - " 0.0\n", - " nan\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0.0\n", - " nan\n", - " nan\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " nan\n", - "Length = 1378 rows\n" - ] - } - ], - "source": [ - "#check to see if large NS are Wolf-Rayet\n", - "print(k_out['isWR'].dtype)\n", - "large_NS = np.where((k_out[p_ns]['mass']) > 15)\n", - "print(k_out['isWR'][large_NS])" - ] - }, - { - "cell_type": "markdown", - "id": "11435009-9e98-4e31-a290-8c047b469ba5", - "metadata": {}, - "source": [ - "I am unsure on what the 0.0 and nan values correspond to." - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "d0d021b1-d637-48c1-8641-990c3e947c81", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass isMultiple systemMass ... metallicity m_hst_f153m\n", - "------------------ ---------- ------------------ ... ----------- -----------\n", - " 87.01978581314896 False 87.01978581314896 ... 0.0 nan\n", - " 80.51538949145795 False 80.51538949145795 ... 0.0 nan\n", - " 21.33585631479835 False 21.33585631479835 ... 0.0 nan\n", - "17.635459786720876 False 17.635459786720876 ... 0.0 nan\n", - "20.277517524928232 False 20.277517524928232 ... 0.0 nan\n", - " 20.66032231704192 False 20.66032231704192 ... 0.0 nan\n", - "27.227632523655107 False 27.227632523655107 ... 0.0 nan\n", - " ... ... ... ... ... ...\n", - " 62.70378845410619 False 62.70378845410619 ... 0.0 nan\n", - " 20.88687294620209 False 20.88687294620209 ... 0.0 nan\n", - "20.379813469500725 False 20.379813469500725 ... 0.0 nan\n", - " 68.91988141238063 False 68.91988141238063 ... 0.0 nan\n", - "16.299795503051396 False 16.299795503051396 ... 0.0 nan\n", - "19.331661523732976 False 19.331661523732976 ... 0.0 nan\n", - " 17.17530617287834 False 17.17530617287834 ... 0.0 nan\n", - "Length = 1378 rows\n", - "isWR\n", - "----\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - "Length = 1378 rows\n", - "isWR\n", - "----\n", - " 0.0\n", - " nan\n", - "isWR\n", - "----\n", - " nan\n", - " mass_current \n", - "------------------\n", - "1.3873021047437155\n", - "1.1867036763400425\n", - " 1.172988399792676\n", - "1.4760459379472195\n", - "1.3652646941863062\n", - " 1.34693018277759\n", - "1.3329667318702734\n", - " ...\n", - "1.3509102262959198\n", - "1.3492482970974988\n", - "1.3263769374090923\n", - "1.3292822711571697\n", - "1.3708253424214771\n", - "1.4880668288116823\n", - "1.3621508901941495\n", - "Length = 1378 rows\n" - ] - } - ], - "source": [ - "#check out the large NSs\n", - "print(k_out[p_ns][large_NS])\n", - "print(k_out[p_ns]['isWR'][large_NS])\n", - "print(np.unique(k_out['isWR']))\n", - "print(np.unique(k_out[p_ns]['isWR'][large_NS]))\n", - "print(k_out[p_ns]['mass_current'][large_NS])" - ] - }, - { - "cell_type": "code", - "execution_count": 293, - "id": "028193f1-bcbc-483c-9641-df8c14cf983c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass_current \n", - "--------------------\n", - "0.010581623004327498\n", - " 0.07746106089705293\n", - " 0.06953880412693926\n", - " 0.07376813803092074\n", - "0.018112506487775234\n", - " 0.05924633227110841\n", - " 0.076323411257296\n", - " ...\n", - "0.021666218437430496\n", - "0.026539284794723627\n", - " 0.07170977037349016\n", - "0.018694133568115678\n", - " 0.04969517873307298\n", - " 0.07788710880030814\n", - " 0.01568523000869492\n", - "Length = 1020837 rows\n", - " mass \n", - "--------------------\n", - "0.010581623004327498\n", - " 0.07746106089705293\n", - " 0.06953880412693926\n", - " 0.07376813803092074\n", - "0.018112506487775234\n", - " 0.05924633227110841\n", - " 0.076323411257296\n", - " ...\n", - "0.021666218437430496\n", - "0.026539284794723627\n", - " 0.07170977037349016\n", - "0.018694133568115678\n", - " 0.04969517873307298\n", - " 0.07788710880030814\n", - " 0.01568523000869492\n", - "Length = 1020837 rows\n" - ] - } - ], - "source": [ - "print(k_out['mass_current'][p_bd])\n", - "print(k_out['mass'][p_bd])" - ] - }, - { - "cell_type": "markdown", - "id": "5f76dbc3-0fff-49a7-94f1-7513aa031ad6", - "metadata": {}, - "source": [ - "Assuming our assumption is correct, that the anomalous neutron stars are WR stars because of the huge disrepancy in initial and final masses." - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "fc18f900-4485-4d21-baeb-f0de55865f7d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.6520719390653884\n", - "1.058140703674423\n", - "119.72357017983377\n", - "15.00328698949453\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#inital masses of large NS and final masses\n", - "print(np.max(k_out[p_ns]['mass_current'][large_NS]))\n", - "print(np.min(k_out[p_ns]['mass_current'][large_NS]))\n", - "print(np.max(k_out[p_ns]['mass'][large_NS]))\n", - "print(np.min(k_out[p_ns]['mass'][large_NS]))\n", - "\n", - "#create bins\n", - "initial_bins = np.linspace(15, 120, 20)\n", - "final_bins = np.linspace(1.0, 1.7, 20)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(2, 1, 1)\n", - "plt.hist(k_out[p_ns]['mass'][large_NS], histtype = 'step',\n", - " bins = initial_bins, label = 'Neutron stars')\n", - "plt.title(\"Initial Masses of Large Generated Neutron Stars\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 1, 2)\n", - "plt.hist(k_out[p_wd]['mass_current'][large_NS], histtype = 'step',\n", - " bins = final_bins, label = 'Neutron stars')\n", - "plt.title(\"Final Masses of Large Generated Neutron Stars\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "66c9450c-7758-402f-a3f6-5100ef05a01e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtered Masses: mass \n", - "------------------\n", - " 0.76799007770147\n", - "1.1955031412949875\n", - "2.2249813543703456\n", - " 3.660007844283093\n", - "0.8470178943035628\n", - "0.8619046086355177\n", - "0.8153335900401504\n", - " ...\n", - "0.9029881664660251\n", - "0.5385601472294455\n", - "0.5055061897257748\n", - "0.6718807496769587\n", - "0.7515013567753969\n", - "0.7974747944530974\n", - " 1.030318707836774\n", - "Length = 391228 rows\n", - "phase\n", - "-----\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " ...\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - "Length = 391228 rows\n", - " mass_current \n", - "------------------\n", - " 0.76799007770147\n", - "1.1955031412949875\n", - "2.2249813543703456\n", - " 3.660007844283093\n", - "0.8470178943035628\n", - "0.8619046086355177\n", - "0.8153335900401504\n", - " ...\n", - "0.9029881664660251\n", - "0.5385601472294455\n", - "0.5055061897257748\n", - "0.6718807496769587\n", - "0.7515013567753969\n", - "0.7974747944530974\n", - " 1.030318707836774\n", - "Length = 391228 rows\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#check out the masses within the problem range for white dwarfs\n", - "wd_issue_masses = np.where((k_out['mass'] >=0.5) & (k_out['mass'] <5))\n", - "print(\"Filtered Masses: \", k_out['mass'][wd_issue_masses])\n", - "print(k_out['phase'][wd_issue_masses])\n", - "print(k_out['mass_current'][wd_issue_masses])\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(1, 2, 1)\n", - "plt.hist(k_out['mass'][wd_issue_masses], histtype = 'step',\n", - " bins = bh_bins, label = 'Initial masses of Problem WDs')\n", - "plt.title(\"Generated Objects of Initial Mass from 0.5 - 5 $M_\\odot$\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.hist(k_out['mass_current'][wd_issue_masses], histtype = 'step',\n", - " bins = wd_bins, label = 'Final masses of Problem WDs')\n", - "plt.title(\"Final Evolved Masses\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "11a7e249-23db-43e6-a071-838c676e61be", - "metadata": {}, - "source": [ - "This tells us that there are issues in accurately identifying white dwarf stars and neutron stars. White dwarves have an issue at lower masses, and neutron stars at the upper end." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "d1584ea1-6fdf-4dbc-be96-623c91bffb08", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mass_bins = np.linspace(0.01, 15, 1000)\n", - "plt.figure()\n", - "plt.hist(k_out['mass'], histtype = 'step',\n", - " bins = mass_bins, label = 'Generated Objects')\n", - "plt.title(\"Simulated Objects from 0.01 - 15 Solar Masses\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "95c79ef1-308b-4d46-b477-416643c7bc0e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5.311625251702762\n", - "8.999671191854413\n" - ] - } - ], - "source": [ - "print(np.min(k_out[p_wd]['mass']))\n", - "print(np.max(k_out[p_wd]['mass'])) #white dwarf ranges are messed up" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8f6c414b-c1df-4059-b5ca-3f4c8aa7c655", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Smallest mass of a generated object: 0.010000000568115546\n" - ] - } - ], - "source": [ - "# Finding the minimum mass of a generated object\n", - "print(\"Smallest mass of a generated object: \" + str(np.min(k_out['mass'])))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "186ded33-2af3-421a-9956-0a603998a50b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial mass of smallest generated black hole: 15.002331855265554\n", - "Initial mass of largest generated black hole: 118.99598033857916\n" - ] - } - ], - "source": [ - "# Finding the minimum/maximum inital mass of generated black holes\n", - "print(\"Initial mass of smallest generated black hole: \" + str(np.min(k_out[p_bh]['mass'])))\n", - "print(\"Initial mass of largest generated black hole: \" + str(np.max(k_out[p_bh]['mass'])))" - ] - }, - { - "cell_type": "markdown", - "id": "2243d818-83a7-4a41-80ed-80a5ca2c6760", - "metadata": {}, - "source": [ - "This cluster fits the expectation from imfr.py that black holes are between 15-120 solar masses and white dwarves are at least 0.5 solar masses, even with the addition of substellar primary objects. " - ] - }, - { - "cell_type": "markdown", - "id": "a5da4347-311c-4fe4-b73f-6490b5d3c585", - "metadata": {}, - "source": [ - "### Exploring the t_cluster and comparing to troubleshoot" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "8ed736b8-1719-43c3-b629-4ce834a0d03e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs and WDs\n", - "t_bh = np.where(t_out['phase'] == 103)[0]\n", - "t_ns = np.where(t_out['phase'] == 102)[0]\n", - "t_wd = np.where(t_out['phase'] == 101)[0]\n", - "t_bd = np.where(t_out['phase'] == 99)[0]\n", - "\n", - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 10, 20)\n", - "bd_bins = np.linspace(0.01, 0.08, 8)\n", - "ns_bins = np.linspace(8, 30, 20)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(2, 2, 1)\n", - "plt.hist(t_out[t_bh]['mass'], histtype = 'step',\n", - " bins = bh_bins, label = 'Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 2)\n", - "plt.hist(t_out[t_wd]['mass'], histtype = 'step',\n", - " bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 3)\n", - "plt.hist(t_out[t_bd]['mass'], histtype = 'step',\n", - " bins = bd_bins, label = 'Generated Brown Dwarves')\n", - "plt.title(\"Generated Brown Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 4)\n", - "plt.hist(t_out[t_ns]['mass'], histtype = 'step',\n", - " bins = ns_bins, label = 'Generated Neutron Stars')\n", - "plt.title(\"Generated Neutron Stars by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "739eb6c6-eec8-4302-a2e4-00ca85353b3a", - "metadata": {}, - "source": [ - "### Exploring an Older Cluster" - ] - }, - { - "cell_type": "markdown", - "id": "331a8442-9686-43aa-9304-35ee4650e3a8", - "metadata": {}, - "source": [ - "Hopefully, in exploring an older cluster, we will see lower mass white dwarves, as they will have enough time to evolve into white dwarves at this point." - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "4528abef-8850-4972-af28-52648d87777a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Changing to logg=5.00 for T= 2306 logg=5.22\n", - "Isochrone generation took 17.886188 s.\n", - "Making photometry for isochrone: log(t) = 10.00 AKs = 0.00 dist = 10\n", - " Starting at: 2024-08-13 14:04:23.149590 Usually takes ~5 minutes\n", - "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.090 Msun T = 2306 K m_hst_f153m = 10.90\n", - "M = 1.034 Msun T = 3906 K m_hst_f153m = -3.64\n", - "M = 1.038 Msun T = 3301 K m_hst_f153m = -5.55\n", - " Time taken: 3.33 seconds\n" - ] - } - ], - "source": [ - "# Create isochrone object \n", - "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", - "my_ifmr = ifmr.IFMR_Raithel18()\n", - "o_iso = synthetic.IsochronePhot(10, 0, 10,\n", - " evo_model = evolution.MergedBaraffePisaEkstromParsec(),\n", - " filters=filt_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "338bab08-4432-4dd9-b29d-dd61851b8c3e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:698: RuntimeWarning: invalid value encountered in divide\n", - " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:698: RuntimeWarning: divide by zero encountered in divide\n", - " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brown dwarf indices: (array([ 0, 1, 3, ..., 1183119, 1183120, 1183121]),), Masses: mass \n", - "--------------------\n", - " 0.06279592698333637\n", - " 0.07112795900232521\n", - " 0.07376225324790554\n", - " 0.03432324440866939\n", - "0.018997543688358758\n", - "0.052852687409104974\n", - " 0.03463925596802796\n", - " ...\n", - " 0.06347447823132187\n", - " 0.05466348135014649\n", - "0.061140370362200006\n", - " 0.01896811961118919\n", - " 0.06440976267517402\n", - " 0.04168622996255112\n", - "0.010201719779934896\n", - "Length = 1028674 rows\n", - "Found 108643 stars out of mass range\n" - ] - } - ], - "source": [ - "# Make cluster\n", - "cluster_mass = 10**6\n", - "o_cluster = synthetic.ResolvedCluster(o_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", - "\n", - "# Get outputs\n", - "o_out = o_cluster.star_systems" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "id": "4102bf35-8b91-44cb-a41a-081a1cb6bf00", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs and WDs\n", - "o_bh = np.where(o_out['phase'] == 103)[0]\n", - "o_ns = np.where(o_out['phase'] == 102)[0]\n", - "o_wd = np.where(o_out['phase'] == 101)[0]\n", - "o_bd = np.where(o_out['phase'] == 99)[0]\n", - "\n", - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 10, 20)\n", - "bd_bins = np.linspace(0.01, 0.08, 8)\n", - "ns_bins = np.linspace(8, 30, 20)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(2, 2, 1)\n", - "plt.hist(o_out[o_bh]['mass'], histtype = 'step',\n", - " bins = bh_bins, label = 'Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 2)\n", - "plt.hist(o_out[o_wd]['mass'], histtype = 'step',\n", - " bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 3)\n", - "plt.hist(o_out[o_bd]['mass'], histtype = 'step',\n", - " bins = bd_bins, label = 'Generated Brown Dwarves')\n", - "plt.title(\"Generated Brown Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 4)\n", - "plt.hist(o_out[o_ns]['mass'], histtype = 'step',\n", - " bins = ns_bins, label = 'Generated Neutron Stars')\n", - "plt.title(\"Generated Neutron Stars by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "id": "75ad5ea9-9668-47d5-9fb0-1d17ad9913b3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0382949743021217\n", - "8.999575157847088\n" - ] - } - ], - "source": [ - "# exploring the mass limits to generated WDs\n", - "print(np.min(o_out[o_wd]['mass']))\n", - "print(np.max(o_out[o_wd]['mass']))" - ] - }, - { - "cell_type": "markdown", - "id": "12c64ec0-58db-48fb-bca4-ae022702c0ca", - "metadata": {}, - "source": [ - "This is more expected, suggesting our unexpected results for white dwarf masses were due to the age of the cluster itself and not designation issues." - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "id": "37509686-120b-41e6-96e4-97c7ccaa7cf1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass isMultiple systemMass ... metallicity m_hst_f153m\n", - "------------------ ---------- ------------------ ... ----------- -----------\n", - "15.284314934206362 False 15.284314934206362 ... 0.0 nan\n", - " 17.1290304145272 False 17.1290304145272 ... 0.0 nan\n", - " 18.95921354685369 False 18.95921354685369 ... 0.0 nan\n", - "18.896393352954313 False 18.896393352954313 ... 0.0 nan\n", - "20.962240635300414 False 20.962240635300414 ... 0.0 nan\n", - "15.026617026908085 False 15.026617026908085 ... 0.0 nan\n", - " 76.13596178064363 False 76.13596178064363 ... 0.0 nan\n", - " ... ... ... ... ... ...\n", - " 26.37054586898205 False 26.37054586898205 ... 0.0 nan\n", - "15.233818106481106 False 15.233818106481106 ... 0.0 nan\n", - " 19.36143672592774 False 19.36143672592774 ... 0.0 nan\n", - " 15.57854891895509 False 15.57854891895509 ... 0.0 nan\n", - "16.464720574303342 False 16.464720574303342 ... 0.0 nan\n", - "18.964812829033175 False 18.964812829033175 ... 0.0 nan\n", - " 16.1815428883449 False 16.1815428883449 ... 0.0 nan\n", - "Length = 1274 rows\n", - "isWR\n", - "----\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - "Length = 1274 rows\n", - "isWR\n", - "----\n", - " 0.0\n", - " nan\n", - "isWR\n", - "----\n", - " nan\n", - " mass_current \n", - "------------------\n", - "1.3582329780993132\n", - " 1.373628116212405\n", - "1.2664447140529644\n", - "1.4158069262026158\n", - " 1.355217219139285\n", - "1.3046710483080488\n", - "1.2388328883877409\n", - " ...\n", - "1.4451775646969733\n", - "1.4624420030217151\n", - "1.2629887776924635\n", - "1.2784084162413294\n", - " 1.39953679687133\n", - "1.2970430314419577\n", - "1.3580723274397928\n", - "Length = 1274 rows\n" - ] - } - ], - "source": [ - "#exploring high mass NSs\n", - "large_o_NS = np.where((o_out[o_ns]['mass']) > 15)\n", - "print(o_out[o_ns][large_o_NS])\n", - "print(o_out[o_ns]['isWR'][large_o_NS])\n", - "print(np.unique(o_out['isWR']))\n", - "print(np.unique(o_out[o_ns]['isWR'][large_o_NS]))\n", - "print(o_out[o_ns]['mass_current'][large_o_NS])" - ] - }, - { - "cell_type": "markdown", - "id": "7e460fa5-a083-4f2c-bef8-eb7d8a972b99", - "metadata": {}, - "source": [ - "## Cluster 2: With Companions" - ] - }, - { - "cell_type": "markdown", - "id": "b0addabd-33a5-4e4f-82d2-d7b58c69fc0e", - "metadata": {}, - "source": [ - "For this cluster we are using the same isochrone as Cluster 1, just changing our IMF to allow for systems with companions." - ] - }, - { - "cell_type": "code", - "execution_count": 304, - "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", - "metadata": {}, - "outputs": [], - "source": [ - "# Create IMF objects \n", - "imf_multi = multiplicity.MultiplicityUnresolved()\n", - "kc_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)" - ] - }, - { - "cell_type": "code", - "execution_count": 305, - "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 785509, 785510, 785511]),), Masses: mass \n", - "--------------------\n", - " 0.06814878672190731\n", - " 0.04773849561965738\n", - " 0.06827493028013426\n", - " 0.0359072610482999\n", - "0.023766688780906337\n", - " 0.05802498588206111\n", - " 0.07564077829371584\n", - " ...\n", - " 0.05364431139484768\n", - "0.029481672087096702\n", - "0.012506309252072858\n", - "0.014174332139873898\n", - " 0.04149099895456249\n", - " 0.04355582847263964\n", - "0.036374123910035944\n", - "Length = 772782 rows\n", - "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", - "----\n", - "Found 175850 companions out of stellar mass range\n" - ] - } - ], - "source": [ - "# Make cluster\n", - "cluster_mass = 10**6\n", - "kc_cluster = synthetic.ResolvedCluster(my_iso, kc_imf, cluster_mass, ifmr=my_ifmr)\n", - "\n", - "# Get outputs\n", - "kc_out = kc_cluster.star_systems\n", - "kc_comp = kc_cluster.companions" - ] - }, - { - "cell_type": "code", - "execution_count": 306, - "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass \n", - "--------------------\n", - " 0.05968994825198586\n", - " 0.5806518980715113\n", - "0.029660673068744366\n", - " 1.0561490103453701\n", - " 0.17057943576263854\n", - " 0.27545527781952783\n", - " 0.02648169160335085\n", - " ...\n", - " 0.40403729711714403\n", - " 0.05200567264641382\n", - " 0.04220512783952523\n", - "0.035754850072584596\n", - " 0.12340265134211305\n", - " 0.07380553065721931\n", - "0.013647742599581188\n", - "Length = 431550 rows Teff \n", - "------------------\n", - " nan\n", - " 4085.526723430826\n", - " nan\n", - "5920.0062733779405\n", - "3190.3383809176958\n", - "3375.2593307941474\n", - " nan\n", - " ...\n", - "3536.9941918044533\n", - " nan\n", - " nan\n", - " nan\n", - "3058.8221931306266\n", - "2827.8838719105515\n", - " nan\n", - "Length = 431550 rows\n", - "0\n" - ] - } - ], - "source": [ - "print(kc_comp['mass'], kc_comp['Teff'])\n", - "print(len(kc_comp[c_bd]))" - ] - }, - { - "cell_type": "code", - "execution_count": 307, - "id": "1a908d42-28fd-4baf-9ed1-fb659132d621", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass_current \n", - "-------------------\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - "0.07380553065721931\n", - " nan\n", - "Length = 183555 rows\n", - "system_idx mass ... metallicity m_hst_f153m \n", - "---------- -------------------- ... ----------- -----------------\n", - " 1 0.05968994825198586 ... 0.0 nan\n", - " 19 0.029660673068744366 ... 0.0 nan\n", - " 36 0.02648169160335085 ... 0.0 nan\n", - " 47 0.0676793749481354 ... 0.0 nan\n", - " 47 0.040357487164688594 ... 0.0 nan\n", - " 62 0.06369313059388905 ... 0.0 nan\n", - " 69 0.012985139409248137 ... 0.0 nan\n", - " ... ... ... ... ...\n", - " 2075867 0.014832191284296728 ... 0.0 nan\n", - " 2075875 0.010915381583182204 ... 0.0 nan\n", - " 2075893 0.05200567264641382 ... 0.0 nan\n", - " 2075900 0.04220512783952523 ... 0.0 nan\n", - " 2075900 0.035754850072584596 ... 0.0 nan\n", - " 2075906 0.07380553065721931 ... 0.0 9.421319357047143\n", - " 2075908 0.013647742599581188 ... 0.0 nan\n", - "Length = 183555 rows\n", - "phase\n", - "-----\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " 1.0\n", - " nan\n", - "Length = 183555 rows\n", - "Teff\n", - "----\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - "Length = 1497 rows\n", - "phase\n", - "-----\n", - " 1.0\n", - " nan\n" - ] - } - ], - "source": [ - "need_bd = np.where(kc_comp['mass'] < 0.08)\n", - "print(kc_comp['mass_current'][need_bd])\n", - "bhs = np.where(kc_comp['phase'] == 103)\n", - "print(kc_comp[need_bd])\n", - "print(kc_comp[need_bd]['phase'])\n", - "print(kc_comp['Teff'][bhs])\n", - "print(np.unique(kc_comp['phase'][need_bd]))" - ] - }, - { - "cell_type": "code", - "execution_count": 294, - "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs, WDs, and BDs\n", - "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", - "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", - "k_bh = np.concatenate([p2_bh, c_bh])\n", - "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", - "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", - "k_ns = np.concatenate([p2_ns, c_ns])\n", - "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", - "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", - "k_wd = np.concatenate([p2_wd, c_wd])\n", - "p2_bd = np.where(kc_out['phase'] == 99)[0]\n", - "c_bd = np.where(kc_comp['phase'] == 99)[0]\n", - "k_bd = np.concatenate([p2_bd, c_bd])\n", - "\n", - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 1.4, 16)\n", - "bd_bins = np.linspace(0.01, 2, 20)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(1, 3, 1)\n", - "plt.hist(kc_comp[c_bh]['mass'], histtype = 'step',\n", - " bins = bh_bins, label = 'Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 3, 2)\n", - "plt.hist(kc_out[k_wd]['mass'], histtype = 'step',\n", - " bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 3, 3)\n", - "plt.hist(kc_comp['mass'][need_bd], histtype = 'step',\n", - " bins = bd_bins, label = 'Generated Brown Dwarves')\n", - "plt.title(\"Generated Brown Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 228, - "id": "ea063a9a-6268-4ab6-911d-0c6f33eef098", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BH prim max mass: 119.89570218214551\n", - "BH prim min mass: 15.025999572935213\n", - "BH comp max mass: 119.37716775564101\n", - "BH comp min mass: 15.009358951094368\n", - "\n", - "NS prim max mass: 119.61968070913697\n", - "NS prim min mass: 9.002475201776234\n", - "NS comp max mass: 115.47061424546564\n", - "NS comp min mass: 9.00056928420967\n", - "\n", - "WD prim max mass: 8.999794986324147\n", - "WD prim min mass: 5.311067490186374\n", - "WD comp max mass: 8.99707592319193\n", - "WD comp min mass: 5.311666754267032\n", - "\n", - "BD prim max mass: 0.07999992186315226\n", - "BD prim min mass: 0.01000001336207612\n" - ] - }, - { - "ename": "ValueError", - "evalue": "zero-size array to reduction operation maximum which has no identity", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[228], line 19\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD prim max mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(np\u001b[38;5;241m.\u001b[39mmax(kc_out[p2_bd][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m])))\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD prim min mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(np\u001b[38;5;241m.\u001b[39mmin(kc_out[p2_bd][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m])))\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD comp max mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkc_comp\u001b[49m\u001b[43m[\u001b[49m\u001b[43mc_bd\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmass\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m))\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD comp min mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(np\u001b[38;5;241m.\u001b[39mmin(kc_comp[c_ns][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m])) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mamax\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "File \u001b[0;32m/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:2820\u001b[0m, in \u001b[0;36mamax\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 2703\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_amax_dispatcher)\n\u001b[1;32m 2704\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mamax\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue, initial\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue,\n\u001b[1;32m 2705\u001b[0m where\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue):\n\u001b[1;32m 2706\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2707\u001b[0m \u001b[38;5;124;03m Return the maximum of an array or maximum along an axis.\u001b[39;00m\n\u001b[1;32m 2708\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2818\u001b[0m \u001b[38;5;124;03m 5\u001b[39;00m\n\u001b[1;32m 2819\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2820\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaximum\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmax\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2821\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:84\u001b[0m, in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m reduction(axis\u001b[38;5;241m=\u001b[39maxis, dtype\u001b[38;5;241m=\u001b[39mdtype, out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpasskwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ufunc\u001b[38;5;241m.\u001b[39mreduce(obj, axis, dtype, out, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n", - "File \u001b[0;32m/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:41\u001b[0m, in \u001b[0;36m_amax\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amax\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 40\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m umr_maximum(a, axis, \u001b[38;5;28;01mNone\u001b[39;00m, out, keepdims, initial, where)\n", - "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation maximum which has no identity" - ] - } - ], - "source": [ - "# Checking that objects are in the correct mass ranges\n", - "print(\"BH prim max mass: \" + str(np.max(kc_out['mass'][p2_bh])))\n", - "print(\"BH prim min mass: \" + str(np.min(kc_out['mass'][p2_bh])))\n", - "print(\"BH comp max mass: \" + str(np.max(kc_comp['mass'][c_bh])))\n", - "print(\"BH comp min mass: \" + str(np.min(kc_comp['mass'][c_bh])) + '\\n')\n", - "\n", - "print(\"NS prim max mass: \" + str(np.max(kc_out[p2_ns]['mass'])))\n", - "print(\"NS prim min mass: \" + str(np.min(kc_out[p2_ns]['mass'])))\n", - "print(\"NS comp max mass: \" + str(np.max(kc_comp[c_ns]['mass'])))\n", - "print(\"NS comp min mass: \" + str(np.min(kc_comp[c_ns]['mass'])) + '\\n')\n", - "\n", - "print(\"WD prim max mass: \" + str(np.max(kc_out[p2_wd]['mass'])))\n", - "print(\"WD prim min mass: \" + str(np.min(kc_out[p2_wd]['mass'])))\n", - "print(\"WD comp max mass: \" + str(np.max(kc_comp[c_wd]['mass'])))\n", - "print(\"WD comp min mass: \" + str(np.min(kc_comp[c_wd]['mass'])) + '\\n')\n", - "\n", - "print(\"BD prim max mass: \" + str(np.max(kc_out[p2_bd]['mass'])))\n", - "print(\"BD prim min mass: \" + str(np.min(kc_out[p2_bd]['mass'])))\n", - "print(\"BD comp max mass: \" + str(np.max(kc_comp[c_bd]['mass'])))\n", - "print(\"BD comp min mass: \" + str(np.min(kc_comp[c_ns]['mass'])) + '\\n')" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "id": "a64ebad4-b642-4e76-b12f-92402f241484", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "88.54164657710947\n", - "0.010059908098432945\n", - "118.80539890808666\n", - "15.000513183956517\n" - ] - } - ], - "source": [ - "print(np.max(kc_out[k_wd]['mass']))\n", - "print(np.min(kc_out[k_wd]['mass']))\n", - "\n", - "print(np.max(kc_out[p2_bh]['mass']))\n", - "print(np.min(kc_out[p2_bh]['mass']))" - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "id": "497b4d94-6722-4ad7-880a-2b53d5acc28d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass isMultiple ... m_hst_f153m N_companions\n", - "-------------------- ---------- ... ------------------- ------------\n", - " 0.6586795096056381 False ... 5.025295725472892 0\n", - " 3.8427510670284337 True ... -0.6509998709738373 1\n", - " 0.2863865166483995 False ... 7.2088768596764785 0\n", - " 0.7742048335787876 False ... 4.460668851429343 0\n", - " 0.10766124147681298 True ... 8.760423625411967 1\n", - " 0.02589910470529386 False ... nan 0\n", - " 0.5326011325857933 False ... 5.718004847207575 0\n", - " ... ... ... ... ...\n", - " 0.07207704463160401 False ... nan 0\n", - " 0.2901218613714232 True ... 6.513996744341199 1\n", - " 0.8377075545324469 False ... 4.199858747334578 0\n", - "0.035545417415318484 False ... nan 0\n", - " 0.10799622753582329 False ... 8.754736726529003 0\n", - " 0.09016119379548757 False ... 9.064022685272212 0\n", - " 0.371310690849909 False ... 6.74904566406516 0\n", - "Length = 3285 rows\n" - ] - } - ], - "source": [ - "print(kc_out[c_wd])" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "id": "eb20c163-8212-4885-98dd-eb10e0bb08f7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Smallest mass of a primary generated black hole: 15.005003818897485\n", - "Smallest mass of a companion generated black hole: 0.010053181629822302\n" - ] - } - ], - "source": [ - "# Finding the minimum mass of generated black holes\n", - "print(\"Smallest mass of a primary generated black hole: \" + str(np.min(kc_out[p2_bh]['mass'])))\n", - "print(\"Smallest mass of a companion generated black hole: \" + str(np.min(kc_out[c_bh]['mass'])))" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "id": "6a854062-8dd0-4411-a0d7-523dcc4aa77b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Smallest mass of a primary generated object: 0.010000087950739485\n", - "Smallest mass of a companion generated object: 0.010000111957749505\n" - ] - } - ], - "source": [ - "# Finding the minimum mass of generated objects\n", - "print(\"Smallest mass of a primary generated object: \" + str(np.min(kc_out['mass'])))\n", - "print(\"Smallest mass of a companion generated object: \" + str(np.min(kc_comp['mass'])))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "398a4626-0e74-4f6d-a3a1-2c115fedb9c1", - "metadata": {}, - "outputs": [], - "source": [ - "# Finding the minimum/maximum inital masses of generated black holes\n", - "print(\"Initial mass of smallest generated black hole: \" + str(np.min(kc_out[k_bh]['mass'])))\n", - "print(\"Initial mass of largest generated black hole: \" + str(np.max(kc_out[k_bh]['mass'])))" - ] - }, - { - "cell_type": "markdown", - "id": "77b9203f-69ec-4a54-af84-581427feb4fd", - "metadata": {}, - "source": [ - "These statements show us that there is an issue with substellar-mass companions being erroneously identified as black holes and other compact remnants, but the issue does not extend to primary mass objects. Thus, we must introduce a bugfix to filter out this error." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "afe96713-78c9-4785-bdf9-6f3f36f2a4d5", - "metadata": {}, - "outputs": [], - "source": [ - "# Create IMF objects \n", - "imf_multi_resolved = multiplicity.MultiplicityResolvedDK()\n", - "c3_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi_resolved)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a6483a09-8712-48ed-9bc6-dbfbda7a32f9", - "metadata": {}, - "outputs": [], - "source": [ - "# Make cluster\n", - "cluster_mass = 10**6\n", - "cluster3 = synthetic.ResolvedCluster(my_iso, c3_imf, cluster_mass, ifmr=my_ifmr)\n", - "\n", - "# Get outputs\n", - "c3_out = cluster3.star_systems\n", - "c3_comp = cluster3.companions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c598a266-297c-423d-b5f5-648f819c065e", - "metadata": {}, - "outputs": [], - "source": [ - "# Locate BHs, NSs and WDs\n", - "p3_bh = np.where(c3_out['phase'] == 103)[0]\n", - "c3_bh = np.where(c3_comp['phase'] == 103)[0]\n", - "k3_bh = np.concatenate([p3_bh, c3_bh])\n", - "p3_ns = np.where(c3_out['phase'] == 102)[0]\n", - "c3_ns = np.where(c3_comp['phase'] == 102)[0]\n", - "k3_ns = np.concatenate([p3_ns, c3_ns])\n", - "p3_wd = np.where(c3_out['phase'] == 101)[0]\n", - "c3_wd = np.where(c3_comp['phase'] == 101)[0]\n", - "k3_wd = np.concatenate([p3_wd, c3_wd])\n", - "\n", - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 1.4, 16)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(1, 2, 1)\n", - "plt.hist(c3_out[k3_bh]['mass'], histtype = 'step',\n", - " bins = bh_bins, label = 'Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.hist(c3_out[k3_wd]['mass'], histtype = 'step',\n", - " bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c1983a8f-67cf-41c1-bb0d-0869a543c4fa", - "metadata": {}, - "source": [ - "This is still the case when the companion objects are resolved." - ] - }, - { - "cell_type": "code", - "execution_count": 276, - "id": "1969480a-56b5-4109-993a-41267b1bcf24", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 693572, 693573, 693574]),), Masses: mass \n", - "--------------------\n", - " 0.03330731848800373\n", - "0.045130822625746865\n", - " 0.04311839070080252\n", - "0.036366767190959895\n", - "0.034795730457169875\n", - " 0.07590327272894272\n", - " 0.04405898676312338\n", - " ...\n", - "0.022974850570129105\n", - " 0.07743240221765976\n", - " 0.06465930761239451\n", - "0.020632183162231716\n", - " 0.04965218683400625\n", - "0.021590467618339538\n", - " 0.06019784339988129\n", - "Length = 682107 rows\n", - "Found 179 stars out of mass range\n", - "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", - "----\n", - "Found 143800 companions out of stellar mass range\n" - ] - } - ], - "source": [ - " # Now to create the cluster.\n", - "imf_mass_limits = np.array([0.01, 0.07, 0.5, 1, np.inf])\n", - "imf_powers = np.array([-0.3, -1.3, -2.3, -2.3])\n", - "\n", - " ##########\n", - " # Start without multiplicity and IFMR\n", - " ##########\n", - "\"\"\" my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", - " multiplicity=None)\n", - " print('Constructed IMF: %d seconds' % (time.time() - startTime)) \n", - " \n", - " cluster1 = syn.ResolvedCluster(my_iso, my_imf1, cluster_mass, ifmr=ifmr_obj)\n", - " clust1 = cluster1.star_systems\n", - " print('Constructed cluster: %d seconds' % (time.time() - startTime))\"\"\"\n", - "\n", - " \n", - " ##########\n", - " # Test with multiplicity and IFMR\n", - " ##########\n", - "multi = multiplicity.MultiplicityUnresolved()\n", - "my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", - " multiplicity=multi)\n", - "\n", - " \n", - "cluster2 = synthetic.ResolvedCluster(my_iso, my_imf2, cluster_mass, ifmr=my_ifmr)\n", - "clust2 = cluster2.star_systems\n", - "comps2 = cluster2.companions" - ] - }, - { - "cell_type": "code", - "execution_count": 277, - "id": "a63128a1-23d2-48a2-a0b7-994d1d723bb7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "system_idx mass Teff L logg isWR mass_current phase metallicity m_hst_f153m\n", - "---------- ---- ---- --- ---- ---- ------------ ----- ----------- -----------\n" - ] - } - ], - "source": [ - "bd_idx = np.where(comps2['phase'] == 99)\n", - "print(comps2[bd_idx])" - ] - }, - { - "cell_type": "code", - "execution_count": 311, - "id": "0c90f58d-c82c-4350-82f0-3f85de4a4c65", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 702295, 702297, 702298]),), Masses: mass \n", - "--------------------\n", - " 0.07112387932532963\n", - " 0.0764559041392305\n", - " 0.0625861330758897\n", - "0.010813223441380892\n", - "0.024579329319661714\n", - " 0.0153132496210663\n", - " 0.01502460044894114\n", - " ...\n", - " 0.06604816419573725\n", - "0.035960727819530976\n", - " 0.03392139750322173\n", - "0.049320264833810586\n", - " 0.05499753185245202\n", - "0.015053394497169099\n", - " 0.03366986670153467\n", - "Length = 634240 rows\n", - "Found 59455 stars out of mass range\n", - "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", - "----\n", - "Found 164173 companions out of stellar mass range\n" - ] - } - ], - "source": [ - "# Define cluster parameters\n", - "logAge = 9.7\n", - "AKs = 0.0\n", - "distance = 1000\n", - "cluster_mass = 1e6\n", - "mass_sampling = 5\n", - "\n", - "evo = evolution.MergedBaraffePisaEkstromParsec()\n", - "atm_func = atmospheres.get_merged_atmosphere\n", - "ifmr_obj = ifmr.IFMR_Raithel18()\n", - "\n", - "red_law = reddening.RedLawNishiyama09()\n", - " \n", - "iso = synthetic.IsochronePhot(logAge, AKs, distance,\n", - " evo_model=evo, atm_func=atm_func,\n", - " red_law=red_law, filters=filt_list,\n", - " mass_sampling=mass_sampling)\n", - "\n", - "multi = multiplicity.MultiplicityUnresolved()\n", - "my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", - " multiplicity=multi)\n", - " \n", - "cluster2 = synthetic.ResolvedCluster(iso, my_imf2, cluster_mass, ifmr=ifmr_obj)\n", - "clust2 = cluster2.star_systems\n", - "comps2 = cluster2.companions" - ] - }, - { - "cell_type": "markdown", - "id": "e6e12c06-fe63-4a9f-b7ea-26473dcdfb53", - "metadata": {}, - "source": [ - "# Creating the same conditions as test_ifmr_multiplicity" - ] - }, - { - "cell_type": "code", - "execution_count": 325, - "id": "005fa638-2a2d-4798-9637-8d16dce382a8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Changing to logg=5.00 for T= 2305 logg=5.22\n", - "Isochrone generation took 2.873671 s.\n", - "Making photometry for isochrone: log(t) = 9.70 AKs = 0.00 dist = 1000\n", - " Starting at: 2024-08-21 14:26:02.531482 Usually takes ~5 minutes\n", - "Starting filter: nirc2,Kp Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.090 Msun T = 2305 K m_nirc2_Kp = 20.34\n", - "Starting filter: nirc2,H Elapsed time: 0.60 seconds\n", - "Starting synthetic photometry\n", - "M = 0.090 Msun T = 2305 K m_nirc2_H = 20.57\n", - "Starting filter: nirc2,J Elapsed time: 1.20 seconds\n", - "Starting synthetic photometry\n", - "M = 0.090 Msun T = 2305 K m_nirc2_J = 21.05\n", - " Time taken: 1.68 seconds\n", - "Brown dwarf indices: (array([ 0, 1, 2, ..., 764740, 764741, 764742]),), Masses: mass \n", - "--------------------\n", - "0.010443375377695562\n", - " 0.06511402241508014\n", - " 0.07176328265824769\n", - " 0.01532590021508947\n", - "0.032173564214380404\n", - "0.033550210563551695\n", - " 0.07626969082525512\n", - " ...\n", - "0.012806933245100089\n", - " 0.0747383470817761\n", - " 0.03550080854686576\n", - "0.050186096415334905\n", - "0.043859454865114604\n", - " 0.036543785344645\n", - "0.043050926420120574\n", - "Length = 690613 rows\n", - "Found 64893 stars out of mass range\n", - "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", - "----\n", - "Found 178337 companions out of stellar mass range\n", - "Primary star phases: phase\n", - "-----\n", - " 1.0\n", - " 99.0\n", - "101.0\n", - "102.0\n", - "103.0\n", - "Companion star phases: phase\n", - "-----\n", - " 1.0\n", - "101.0\n", - "102.0\n", - "103.0\n", - " nan\n" - ] - } - ], - "source": [ - "# Initialize the isochrone and cluster objects\n", - "logAge = 9.7\n", - "AKs = 0.0\n", - "distance = 1000\n", - "cluster_mass = 1e6\n", - "mass_sampling = 5\n", - "\n", - "filt_list = ['nirc2,Kp', 'nirc2,H', 'nirc2,J']\n", - "\n", - "evo = evolution.MergedBaraffePisaEkstromParsec()\n", - "atm_func = atmospheres.get_merged_atmosphere\n", - "ifmr_obj = ifmr.IFMR_Raithel18()\n", - "red_law = reddening.RedLawNishiyama09()\n", - "\n", - "iso = synthetic.IsochronePhot(logAge, AKs, distance,\n", - " evo_model=evo, atm_func=atm_func,\n", - " red_law=red_law, filters=filt_list,\n", - " mass_sampling=mass_sampling)\n", - "\n", - "# Recreate the cluster with multiplicity and IFMR\n", - "multi = multiplicity.MultiplicityUnresolved()\n", - "imf_mass_limits = np.array([0.01, 0.07, 0.5, 1, np.inf])\n", - "imf_powers = np.array([-0.3, -1.3, -2.3, -2.3])\n", - "my_imf = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", - " multiplicity=multi)\n", - "\n", - "cluster = synthetic.ResolvedCluster(iso, my_imf, cluster_mass, ifmr=ifmr_obj)\n", - "clust = cluster.star_systems\n", - "comps = cluster.companions\n", - "\n", - "# Debugging prints\n", - "print('Primary star phases:', np.unique(clust['phase']))\n", - "print('Companion star phases:', np.unique(comps['phase']))" - ] - }, - { - "cell_type": "code", - "execution_count": 327, - "id": "c1937ef3-c11a-40e2-8c22-04ae352ff7a8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "phase\n", - "-----\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - "Length = 142106 rows\n" - ] - } - ], - "source": [ - "comps_bds = np.where((comps['mass'] >= 0.01) & (comps['mass'] < 0.08))\n", - "print(comps[comps_bds]['phase'])" - ] - }, - { - "cell_type": "code", - "execution_count": 323, - "id": "a3fcf655-bebb-4657-8c94-c0a4879b0c56", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "system_idx mass Teff L logg isWR mass_current phase metallicity m_hst_f153m\n", - "---------- ---- ---- --- ---- ---- ------------ ----- ----------- -----------\n" - ] - } - ], - "source": [ - "comps_phase_nan = np.where(comps['phase'] == np.nan)\n", - "print(comps[comps_phase_nan])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f47a6c02-4ace-4b7d-b959-102c893afd7e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From c92297cfd5cf2fbb157920e709b74d3fe048b6c2 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 4 Sep 2024 16:37:27 -0700 Subject: [PATCH 022/191] put in general path to csv --- spisea/evolution.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index f168acda..3326fc6d 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1338,8 +1338,14 @@ def get_temperature(self, masses, ages): Array of interpolated temperatures. """ + # Set the project root two levels up from 'spisea/evolution.py' + project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) + + # Construct the full path to the CSV file in 'changes/bd_evo_csv' + csv_path = os.path.join(project_root, 'SPISEA', 'changes', 'bd_evo_csv', 'baraffe2003.csv') + # Load the CSV file - data = pd.read_csv('/u/caitlinbegbie/code/SPISEA/changes/bd_evo_csv/baraffe2003.csv') + data = pd.read_csv(csv_path) # Create columns of relevant data mass_grid = np.unique(data['mass'].values) From b93c57a216fbab9f938807c2cd236074ee9a1b23 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 4 Sep 2024 16:40:53 -0700 Subject: [PATCH 023/191] remove old debugging statements --- spisea/ifmr.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/spisea/ifmr.py b/spisea/ifmr.py index 1451ed11..672ad818 100755 --- a/spisea/ifmr.py +++ b/spisea/ifmr.py @@ -43,8 +43,6 @@ def Kalirai_mass(self, MZAMS): # for white dwarfs But we use this function for anything between 0.5 and 9 depending on the IFMR. FIXME: need to extend these ranges... explain extension somewhere? Paper maybe? """ - #debugging print statement - print(f"Input MZAMS: {MZAMS}") result = 0.109*MZAMS + 0.394 @@ -56,7 +54,6 @@ def Kalirai_mass(self, MZAMS): # for white dwarfs good_idx = np.where((MZAMS >= 0.5) & (MZAMS < 9)) final[good_idx] = result[good_idx] - print(f"Final masses: {final}") # Debugging print statement return final @@ -796,7 +793,6 @@ def generate_death_mass(self, mass_array): id_array_BD = np.where((mass_array >= 0.01) & (mass_array < 0.08)) output_array[0][id_array_BD] = mass_array[id_array_BD] output_array[1][id_array_BD] = codes['BD'] - print(f'Brown dwarf indices: {id_array_BD}, Masses: {mass_array[id_array_BD]}') #for debugging #classifying invalid mass ranges id_array0 = np.where((mass_array < 0.01) | ((mass_array >= 0.08) & (mass_array < 0.5)) | (mass_array >= 120)) From 7e048ea51cb1befba9139a68656cf1335ec09d91 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 18 Sep 2024 12:14:15 -0700 Subject: [PATCH 024/191] Changing BD phase to 90 --- changes/BD_Clusters.ipynb | 165 +++++++++++++++++++++------------ spisea/ifmr.py | 8 +- spisea/synthetic.py | 16 +--- spisea/tests/test_synthetic.py | 18 ++-- 4 files changed, 122 insertions(+), 85 deletions(-) diff --git a/changes/BD_Clusters.ipynb b/changes/BD_Clusters.ipynb index df1bb2b7..5ffd47ed 100644 --- a/changes/BD_Clusters.ipynb +++ b/changes/BD_Clusters.ipynb @@ -112,7 +112,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -126,7 +126,7 @@ "p_bh = np.where(k_out['phase'] == 103)[0]\n", "p_ns = np.where(k_out['phase'] == 102)[0]\n", "p_wd = np.where(k_out['phase'] == 101)[0]\n", - "p_bd = np.where(k_out['phase'] == 99)[0]\n", + "p_bd = np.where(k_out['phase'] == 90)[0]\n", "\n", "# Determine individual histogram bins\n", "bh_bins = np.linspace(0.01, 16, 20)\n", @@ -187,17 +187,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "BH max mass: 119.13565618748322\n", - "BH min mass: 15.007725869985796\n", + "BH max mass: 119.93239322786152\n", + "BH min mass: 15.001864046701314\n", "\n", - "NS max mass: 119.95876844996108\n", - "NS min mass: 9.001129278483436\n", + "NS max mass: 118.23513027048241\n", + "NS min mass: 9.00008078654941\n", "\n", - "WD max mass: 8.999341744945873\n", - "WD min mass: 5.311091008003858\n", + "WD max mass: 8.99872832681908\n", + "WD min mass: 5.311599156274761\n", "\n", - "BD max mass: 0.07999986295473586\n", - "BD min mass: 0.010000044934131811\n", + "BD max mass: 0.07999995308017924\n", + "BD min mass: 0.010000015332213386\n", "\n" ] } @@ -229,22 +229,22 @@ "text": [ " Teff \n", "------------------\n", - " 653.0891938350937\n", - " 651.8458273479349\n", - " 707.4325441828001\n", - "488.01700684829456\n", - " 871.6562359235475\n", - " 407.9503554901987\n", - " 930.6014364016501\n", + " 680.8331610608817\n", + " 570.8134778988632\n", + "1458.5749556855367\n", + " 530.4032032477386\n", + " 609.224312952465\n", + "494.41076310951894\n", + " 692.7514016813559\n", " ...\n", - "392.28042415955446\n", - " 908.5461410822766\n", - " 1393.091834142745\n", - " 998.2260271590488\n", - " 665.474820549251\n", - " 675.9825874832916\n", - "1122.0753000795612\n", - "Length = 1028848 rows\n" + " 921.297090918629\n", + " 733.558254330926\n", + " 668.3518603255377\n", + " 807.1505091097516\n", + " 588.2307845514122\n", + "353.02982279111575\n", + " 963.2369850122022\n", + "Length = 1024345 rows\n" ] } ], @@ -309,7 +309,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 7301 companions out of stellar mass range\n" + "Found 7406 companions out of stellar mass range\n" ] }, { @@ -341,7 +341,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -361,8 +361,8 @@ "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", - "p2_bd = np.where(kc_out['phase'] == 99)[0]\n", - "c_bd = np.where(kc_comp['phase'] == 99)[0]\n", + "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", "\n", "# Plot on histograms\n", @@ -422,22 +422,22 @@ "text": [ " Teff \n", "------------------\n", - " 589.5738241278044\n", - " 949.9191202547576\n", - " 535.3337220492646\n", - " 798.6701941060301\n", - " 883.180077123407\n", - "1009.6437674139902\n", - "444.11890279356027\n", + " 444.1173953888183\n", + " 693.3637752947118\n", + " 651.3987402554264\n", + " 714.1331032782153\n", + " 851.9772176003223\n", + " 783.8497395750999\n", + " 359.6196362456533\n", " ...\n", - "1625.8241118113378\n", - "1556.8961357273806\n", - " 1949.159944699517\n", - " 475.3197378938071\n", - "1445.6688176627986\n", - " 929.9466889780698\n", - " 905.5149940088668\n", - "Length = 964495 rows\n" + " 411.0731940270949\n", + " 449.6176329353576\n", + " 836.2301296016228\n", + "444.36050056433794\n", + "391.81361354085544\n", + "382.88818149261937\n", + "510.87160667571027\n", + "Length = 960373 rows\n" ] } ], @@ -455,25 +455,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "BH prim max mass: 119.64792937371239\n", - "BH prim min mass: 15.003687171639145\n", - "BH comp max mass: 108.78065103375774\n", - "BH comp min mass: 15.051696275245545\n", + "BH prim max mass: 119.898750058991\n", + "BH prim min mass: 15.01534492995759\n", + "BH comp max mass: 104.62467533647252\n", + "BH comp min mass: 15.00689100967727\n", "\n", - "NS prim max mass: 117.89439381247894\n", - "NS prim min mass: 9.001659607779285\n", - "NS comp max mass: 109.06172525179247\n", - "NS comp min mass: 9.000122949672017\n", + "NS prim max mass: 118.85881346572698\n", + "NS prim min mass: 9.000112173407095\n", + "NS comp max mass: 110.40621252485172\n", + "NS comp min mass: 9.001900741677009\n", "\n", - "WD prim max mass: 8.99922965392098\n", - "WD prim min mass: 5.311110801935481\n", - "WD comp max mass: 8.997715563589054\n", - "WD comp min mass: 5.31119691635049\n", + "WD prim max mass: 8.99910211758367\n", + "WD prim min mass: 5.311343510677102\n", + "WD comp max mass: 8.99933929534237\n", + "WD comp min mass: 5.31290506400592\n", "\n", - "BD prim max mass: 0.07999992301784681\n", - "BD prim min mass: 0.01000005942626443\n", - "BD comp max mass: 0.07999668766843566\n", - "BD comp min mass: 0.010000432726566368\n", + "BD prim max mass: 0.07999998562994273\n", + "BD prim min mass: 0.01000004628780762\n", + "BD comp max mass: 0.0799995996476194\n", + "BD comp min mass: 0.010000293534472888\n", "\n" ] } @@ -503,9 +503,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "a5e9c3bc-ea61-4f1e-b13c-9bbcfc9f2830", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(k_bd[\"mass_current\"], k_bd['Teff'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "65f1a0ad-61ac-4606-907b-fbf8bdbd2271", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "287.8007498624834" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.min(k_bd['Teff'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92975ed8-a4c1-4383-a7ef-11c88fb362a7", + "metadata": {}, "outputs": [], "source": [] } diff --git a/spisea/ifmr.py b/spisea/ifmr.py index 672ad818..a413f240 100755 --- a/spisea/ifmr.py +++ b/spisea/ifmr.py @@ -553,7 +553,7 @@ def generate_death_mass(self, mass_array, metallicity_array): * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 - * BD: typecode = 99 + * BD: typecode = 90 A typecode of value -1 means you're outside the range of validity for applying the ifmr formula. A remnant mass of -99 means you're outside the range of @@ -571,7 +571,7 @@ def generate_death_mass(self, mass_array, metallicity_array): #output_array[1] holds the remnant type output_array = np.zeros((2, len(mass_array))) - codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 99} + codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 90} #create array to store the remnant masses generated by Spera equations rem_mass_array = np.zeros(len(mass_array)) @@ -759,7 +759,7 @@ def generate_death_mass(self, mass_array): * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 - * BD: typecode = 99 + * BD: typecode = 90 A typecode of value -1 means you're outside the range of validity for applying the ifmr formula. @@ -783,7 +783,7 @@ def generate_death_mass(self, mass_array): #Random array to get probabilities for what type of object will form random_array = np.random.randint(1, 1001, size = len(mass_array)) - codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 99} + codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 90} """ The id_arrays are to separate all the different formation regimes diff --git a/spisea/synthetic.py b/spisea/synthetic.py index eaa713d5..afde2c63 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -233,7 +233,7 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # effect is so small # Convert nan_to_num to avoid errors on greater than, less than comparisons star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) - bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 99) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) + bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 90) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) # Print warning, if desired verbose=False if verbose: @@ -279,7 +279,7 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # Handle brown dwarfs separately and assign temperatures - idx_bd = np.where(star_systems['phase'] == 99)[0] + idx_bd = np.where(star_systems['phase'] == 90)[0] # Define the class for BDs evo_model = evolution.MergedBaraffePisaEkstromParsec() @@ -385,7 +385,7 @@ def _make_companions_table(self, star_systems, compMass): # Convert nan_to_num to avoid errors on greater than, less than comparisons companions_phase_non_nan = np.nan_to_num(companions['phase'], nan=-99) bad = np.where( (companions_phase_non_nan > 5) & - (companions_phase_non_nan < 99) & + (companions_phase_non_nan < 90) & (companions_phase_non_nan != 9) & (companions_phase_non_nan != -99)) # Print warning, if desired @@ -450,7 +450,7 @@ def _make_companions_table(self, star_systems, compMass): # Assigning brown dwarf companions the correct phase/properties bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] - companions['phase'][bd_idx] = 99 + companions['phase'][bd_idx] = 90 companions['mass_current'][bd_idx] = companions['mass'][bd_idx] for filt in self.filt_names: companions[filt][bd_idx] = np.full(len(bd_idx), np.nan) @@ -496,12 +496,6 @@ def _remove_bad_systems(self, star_systems, compMass): removed. If self.ifmr != None, then we will save the high mass systems since they will be plugged into an ifmr later. """ - """ # Print companion masses and phases before any filtering - bd_idx_initial = [np.where((np.array(cm) >= 0.01) & (np.array(cm) < 0.08))[0] for cm in compMass] - print("Before filtering - CompMass (brown dwarfs):") - for i, bdi in enumerate(bd_idx_initial): - if len(bdi) > 0: - print(f"System {i}: Masses: {np.array(compMass[i])[bdi]}, Phases: {np.full(len(bdi), 99)}")""" N_systems = len(star_systems) @@ -515,7 +509,7 @@ def _remove_bad_systems(self, star_systems, compMass): else: # Keep stars (with Teff) and any other compact objects (with phase info). idx = np.where((star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) | - (star_systems_phase_non_nan == 99))[0] + (star_systems_phase_non_nan == 90))[0] if len(idx) != N_systems and self.verbose: print( 'Found {0:d} stars out of mass range'.format(N_systems - len(idx))) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index e146b273..a94dee67 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -474,19 +474,19 @@ def test_ifmr_multiplicity(): assert len(np.where(clust2['phase'] == 102)) > 0 assert len(np.where(clust1['phase'] == 103)) > 0 # BH assert len(np.where(clust2['phase'] == 103)) > 0 - assert len(np.where(clust1['phase'] == 99)) > 0 # BD - assert len(np.where(clust2['phase'] == 99)) > 0 + assert len(np.where(clust1['phase'] == 90)) > 0 # BD + assert len(np.where(clust2['phase'] == 90)) > 0 # Now check that we have companions that are WDs, NSs, BHs, and BDs assert len(np.where(comps2['phase'] == 101)) > 0 assert len(np.where(comps2['phase'] == 102)) > 0 assert len(np.where(comps2['phase'] == 103)) > 0 - assert len(np.where(comps2['phase'] == 99)) > 0 + assert len(np.where(comps2['phase'] == 90)) > 0 # Make sure no funky phase designations (due to interpolation effects) # slipped through - idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 99) & (clust1['phase'] != 9) ) - idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 99) & (comps2['phase'] != 9) ) + idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 90) & (clust1['phase'] != 9) ) + idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 90) & (comps2['phase'] != 9) ) assert len(idx[0]) == 0 # Make sure BD temperatures are assigned correctly @@ -503,7 +503,7 @@ def test_ifmr_multiplicity(): wd_idx = np.where(clust1['phase'] == 101) ns_idx = np.where(clust1['phase'] == 102) bh_idx = np.where(clust1['phase'] == 103) - bd_idx = np.where(clust1['phase'] == 99) + bd_idx = np.where(clust1['phase'] == 90) assert len(clust1[bd_idx]) != 0 assert np.all(clust1['mass'][wd_idx] > MIN_MASS) @@ -515,7 +515,7 @@ def test_ifmr_multiplicity(): comp_wd_idx = np.where(comps2['phase'] == 101) comp_ns_idx = np.where(comps2['phase'] == 102) comp_bh_idx = np.where(comps2['phase'] == 103) - comp_bd_idx = np.where(comps2['phase'] == 99) + comp_bd_idx = np.where(comps2['phase'] == 90) assert np.all(comps2['mass'][comp_wd_idx] > MIN_MASS) assert np.all(comps2['mass'][comp_ns_idx] > MIN_MASS) assert np.all(comps2['mass'][comp_bh_idx] > MIN_MASS) @@ -528,12 +528,12 @@ def test_ifmr_multiplicity(): (comps2['mass'][comp_bd_idx] <= BD_MAX_MASS)) # Ensure no other objects are in the brown dwarf mass range - non_bd_idx = np.where((clust1['phase'] != 99) & + non_bd_idx = np.where((clust1['phase'] != 90) & (clust1['mass'] >= BD_MIN_MASS) & (clust1['mass'] <= BD_MAX_MASS)) assert len(non_bd_idx[0]) == 0 # asserting no non-brown dwarf objects in BD mass range - comp_non_bd_idx = np.where((comps2['phase'] != 99) & + comp_non_bd_idx = np.where((comps2['phase'] != 90) & (comps2['mass'] >= BD_MIN_MASS) & (comps2['mass'] <= BD_MAX_MASS)) assert len(comp_non_bd_idx[0]) == 0 # asserting no non-brown dwarf companions in BD mass range From b00bdb1eea3e3f25538166bd5ea740a04db272f5 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 4 Nov 2024 13:45:55 -0800 Subject: [PATCH 025/191] Addition of Meisner 2023 for BD atmospheres of metallicities -1 - 0.3 --- spisea/atmospheres.py | 312 +++++++++++++++++++++++++++++++++++- spisea/tests/test_models.py | 21 ++- 2 files changed, 328 insertions(+), 5 deletions(-) diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index 8df2fbe1..b6ecf375 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -624,17 +624,107 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) +def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): + """ + Return atmosphere from CIFIST2011 grid + (`Allard et al. 2012 `_) + + Grid originally downloaded `here `_ + + Notes + ------ + Grid Range: + + * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 + + Teff and gravity ranges depend on metallicity: + + [M/H] = -2.5 + + * Teff: 2600 - 4600 K + * gravity: 4.5 - 5.5 + + [M/H] = -2.0 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.5 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.0 + + * Teff: 2600 - 7000 + * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 + + [M/H] = -0.5 + + * Teff: 1000 -7000 + * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 + + [M/H] = 0 + + * Teff: 750 - 7000 + * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 + + [M/H] = 0.5 + + * Teff: 1000 - 5000 + * gravity: 3.5 - 5.0 + + + Alpha enhancement: + + * [M/H]= -0.0, +0.5 no anhancement + * [M/H]= -0.5 with [alpha/H]=+0.2 + * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + FILL IN W MEISNER + """ + if rebin == True: + atm_name = 'Meisner2023_rebin' + else: + atm_name = 'Meisner2023' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') + print( 'Could not find Meisner2023 atmosphere model for') print( ' temperature = %d' % temperature) print( ' metallicity = %.1f' % metallicity) print( ' log gravity = %.1f' % gravity) return sp - + def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): """ Return white dwarf atmospheres from @@ -723,6 +813,8 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): return sp + + #---------------------------------------------------------------------# def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, rebin=True): @@ -767,6 +859,8 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose * T < 3800, logg < 2.5: PHOENIX v16 * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 + * 1200 < T <= 1000, logg > 2.5: BTSettl_CIFITS2011_2015/Meisner2023 merge + * 1000 < T <= 250, logg > 2.5: Meisner2023 Otherwise, if T < 3800 and [M/H] != 0: @@ -777,6 +871,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) * PHOENIXv16 (`Husser et al. 2013 `_) * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) + * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) LTE WARNING: @@ -910,6 +1005,45 @@ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) return bbspec +def get_bd_atmosphere(metallicity=0, temperature=1500, gravity=4, verbose=False): + """ + Return the brown dwarf atmosphere from + `Meisner et al. 2020 `_. + If desired parameters are + outside of grid, return a blackbody spectrum instead + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + if verbose: + print('Meisner2023 atmosphere') + + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + except pysynphot.exceptions.ParameterOutOfBounds: + # Use a black-body atmosphere. + bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) + return bbspec + def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, verbose=False, rebin=None, wave_min=500, wave_max=50_000, wave_num=20_000): @@ -2020,6 +2154,180 @@ def rebin_BTSettl(make_unique=False): return +def organize_all_Meisner2023_atmospheres(): + """ + Construct cdbs-ready atmospheres for the Meisner2023 grid. + The code expects tp be run in cdbs/grid/Meisner2023, and expects that the + individual model files have been downloaded from online + and processed into python-readable ascii files. + """ + orig_dir = os.getcwd() + dirs = ['mm10', 'mm05', 'mp00', 'mp03'] + + # Go through each directory, turning each spectrum into a cdbs-ready file. + # Save as a fits file, for faster access later + for ii in dirs: + print('Starting {0}'.format(ii)) + os.chdir(ii) + + files = glob.glob('*.fits') + count=0 + for jj in files: + # Open each .fits file and read the data + with fits.open(jj) as hdul: + data = hdul[1].data + wavelength = data['Wavelength'] # Assuming these columns exist + flux = data['Flux'] + + # Create new columns with desired format + c0 = fits.Column(name='Wavelength', format='D', array=wavelength) + c1 = fits.Column(name='Flux', format='E', array=flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Add unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write the new fits table in the cdbs directory + output_filename = '{0}.fits'.format(jj[:-5]) # Removing the original .fits extension + hdu_new.writeto(output_filename, overwrite=True) + hdu_new.close() + count += 1 + print('Done {0} of {1}'.format(count, len(files))) + + # Go back to original directory, move to next metallicity directory + os.chdir(orig_dir) + + return + +def make_Meisner2023_catalog(): + """ + Create cdbs catalog.fits of Meisner2023 grid. + THIS IS STEP 2, after organize_Meisner2023_atmospheres has + been run. + + Code expects to be run in cdbs/grid/Meisner2023 + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + dirs = ['mm10', 'mm05', 'mp00', 'mp03'] + + # Construct the catalog.fits file input. The input consists of + # and index string that specifies the stellar paramters, and a + # name string that points to the file + # Loop over all the metallicity directories to construct these inputs + index_str = [] + name_str = [] + for ii in dirs: + os.chdir(ii) + files = glob.glob('spec_jwst_*.fits') + + for jj in files: + # Parse temperature, log(g), and metallicity from filename + temp_str = jj.split('_')[2] + logg_str = jj.split('_')[3] + metal_str = jj.split('_')[4] + + # Extract temperature, surface gravity, and metallicity + temp = float(temp_str[1:]) # Temperature in Kelvin + logg = float(logg_str[1:]) # Surface gravity log(g) + + # Build metallicity value + if metal_str.startswith('m'): + metallicity = -1 * float(metal_str[1:]) + else: + metallicity = float(metal_str[1:]) + + # Construct index and filename strings + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metallicity, logg)) + name_str.append('{0}/{1}[Flux]'.format(ii, jj)) + + print('Processed directory:', ii) + os.chdir(start_dir) + + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_Meisner2023(make_unique=False): + """ + Rebin Meisner2023 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory: Meisner2023_rebin + + Code expects to be run in cdbs/grid directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Create a directory for rebinned Meisner2023 models + rebin_path = 'Meisner2023_rebin/' + if not os.path.exists(rebin_path): + os.mkdir(rebin_path) + + # Load the catalog.fits file and extract all spectra file paths + cat = Table.read('Meisner2023/catalog.fits') + files_all = [cat[ii]['FILENAME'].split('[')[0] for ii in range(len(cat))] + + print('Rebinning Meisner2023 spectra') + if make_unique: + print('Making unique') + make_wavelength_unique(files_all, 'Meisner2023') + print('Done') + + for ff, file in enumerate(files_all): + vals = cat[ff]['INDEX'].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the Meisner2023 spectrum and rebin its flux + try: + sp = pysynphot.Icat('Meisner2023', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_meisner.wave) + + # Create the output FITS file + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + # Write the new rebinned file in the Meisner2023_rebin directory + outfile = os.path.join(rebin_path, os.path.basename(file)) + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + except Exception as e: + print(f"Error processing {file}: {e}") + orig_file = os.path.join('Meisner2023', file) + outfile = os.path.join(rebin_path, os.path.basename(file)) + os.system(f'cp {orig_file} {outfile}') + + print('Done {0} of {1}'.format(ff + 1, len(files_all))) + + return + + + def organize_WDKoester_atmospheres(path_to_dir): """ Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index e80684be..334ef86e 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -79,13 +79,16 @@ def test_atmosphere_models(): from spisea import atmospheres as atm # Array of atmospheres - atm_arr = [atm.get_merged_atmosphere, atm.get_castelli_atmosphere, atm.get_phoenixv16_atmosphere, atm.get_BTSettl_2015_atmosphere, - atm.get_BTSettl_atmosphere, atm.get_kurucz_atmosphere, atm.get_phoenix_atmosphere, atm.get_wdKoester_atmosphere] + atm_arr = [atm.get_merged_atmosphere, atm.get_castelli_atmosphere, atm.get_phoenixv16_atmosphere, + atm.get_BTSettl_2015_atmosphere, atm.get_BTSettl_atmosphere, atm.get_kurucz_atmosphere, + atm.get_phoenix_atmosphere, atm.get_wdKoester_atmosphere, atm.get_Meisner2023_atmosphere] # Array of metallicities metals_range = [-2.0, 0, 0.15] + bd_metals_range = [-1.0, -0.5, 0, 0.3] metals_solar = [0] - metals_arr = [metals_solar, metals_range, metals_range, metals_solar, metals_range, metals_range, metals_range, metals_solar] + metals_arr = [metals_solar, metals_range, metals_range, metals_solar, metals_range, metals_range, metals_range, + metals_solar, bd_metals_range] assert len(atm_arr) == len(metals_arr) @@ -126,6 +129,18 @@ def test_atmosphere_models(): raise Exception('ATM TEST FAILED: {0}, temp = {2}'.format(atm_func, jj)) print('get_bb_atmosphere: all temps passed') + + # Test get_bd_atmosphere at different temps + # This func only requests temp + temp_range = [250, 400, 500, 750, 950, 1200] + atm_func = atm.get_bd_atmosphere + for jj in temp_range: + try: + test = atm_func(temperature=jj, verbose=True) + except: + raise Exception('ATM TEST FAILED: {0}, temp = {1}'.format(atm_func, jj)) + + print('get_bd_atmosphere: all temps passed') return From c41774a0b04dd728dba82fd4e9a8b5a78cd12dfc Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 4 Nov 2024 13:55:59 -0800 Subject: [PATCH 026/191] BD function for atmospheres --- spisea/synthetic.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index afde2c63..8344b92c 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -33,6 +33,7 @@ default_red_law = reddening.RedLawNishiyama09() default_atm_func = atm.get_merged_atmosphere default_wd_atm_func = atm.get_wd_atmosphere +default_bd_atm_func = atm.get_bd_atmosphere def Vega(): # Use Vega as our zeropoint... assume V=0.03 mag and all colors = 0.0 @@ -765,7 +766,7 @@ class Isochrone(object): """ def __init__(self, logAge, AKs, distance, metallicity=0.0, evo_model=default_evo_model, atm_func=default_atm_func, - wd_atm_func = default_wd_atm_func, + wd_atm_func = default_wd_atm_func, bd_atm_func = default_bd_atm_func, red_law=default_red_law, mass_sampling=1, wave_range=[3000, 52000], min_mass=None, max_mass=None, rebin=True): @@ -838,6 +839,10 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, if phase == 101: star = wd_atm_func(temperature=T, gravity=gravity, metallicity=metallicity, verbose=False) + elif phase == 90: + print(f"Applying brown dwarf model to object {ii}") + star = bd_atm_func(temperature=T, gravity=gravity, metallicity=metallicity, + verbose=False) else: star = atm_func(temperature=T, gravity=gravity, metallicity=metallicity, rebin=rebin) @@ -954,9 +959,13 @@ class IsochronePhot(Isochrone): Default is atmospheres.get_merged_atmosphere. wd_atm_func: white dwarf model atmosphere function, optional - Set the stellar atmosphere models for the white dwafs. + Set the stellar atmosphere models for the white dwarfs. Default is atmospheres.get_wd_atmosphere + bd_atm_func: brown dwarf model atmosphere function, optimal + Set the stellar atmosphere models for the brown dwarfs. + Default is atmospheres.get_bd_atmosphere + red_law : reddening law object, optional Define the reddening law for the synthetic photometry. Default is reddening.RedLawNishiyama09(). @@ -1003,7 +1012,7 @@ class IsochronePhot(Isochrone): def __init__(self, logAge, AKs, distance, metallicity=0.0, evo_model=default_evo_model, atm_func=default_atm_func, - wd_atm_func = default_wd_atm_func, + wd_atm_func = default_wd_atm_func, bd_atm_func = default_bd_atm_func, wave_range=[3000, 52000], red_law=default_red_law, mass_sampling=1, iso_dir='./', min_mass=None, max_mass=None, rebin=True, recomp=False, From 220e4dad760c1dca753c451c67f938319b68a4f3 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 13 Nov 2024 14:29:18 -0800 Subject: [PATCH 027/191] Current progress in BD implementation --- spisea/atmospheres.py | 59 ++++++++++++++++++++++++++++++---- spisea/evolution.py | 1 + spisea/imf/imf.py | 29 ++++++----------- spisea/imf/tests/test_bd.py | 6 ++-- spisea/imf/tests/test_class.py | 13 ++++---- spisea/synthetic.py | 5 ++- spisea/tests/test_models.py | 9 +++--- 7 files changed, 81 insertions(+), 41 deletions(-) diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index b6ecf375..a65e495e 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -724,6 +724,45 @@ def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, reb print( ' log gravity = %.1f' % gravity) return sp + +def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): + """ + Return atmosphere from Phillips et al., 2020 using ATMO model + (`Phillips et al. 2020 `_) + + Grid originally downloaded `here `_ + + Grid Range: + + * Teff: 200 - 3000 K + * gravity: 2.5 - 5.5 cgs + * [M/H] = 0 + """ + if rebin == True: + atm_name = 'Phillips2020_rebin' + else: + atm_name = 'Phillips2020' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Phillips2020 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): """ @@ -859,8 +898,8 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose * T < 3800, logg < 2.5: PHOENIX v16 * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - * 1200 < T <= 1000, logg > 2.5: BTSettl_CIFITS2011_2015/Meisner2023 merge - * 1000 < T <= 250, logg > 2.5: Meisner2023 + * 1200 < T <= 1000, logg > 2.5: BTSettl_CIFITS2011_2015/Phillips2020 merge + * 1000 < T <= 250, logg > 2.5: Phillips2020 Otherwise, if T < 3800 and [M/H] != 0: @@ -894,6 +933,14 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is # currently available here, so if non-solar metallicity, just stick with # the Phoenix grid + if (temperature <= 1200) & (metallicity == 0): + if verbose: + print( 'Phillips2020 atmosphere') + return get_Phillips2020_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + if (temperature <= 3800) & (metallicity == 0): # High gravity are in BTSettl regime if (temperature <= 3200) & (gravity > 2.5): @@ -1005,7 +1052,7 @@ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) return bbspec -def get_bd_atmosphere(metallicity=0, temperature=1500, gravity=4, verbose=False): +def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): """ Return the brown dwarf atmosphere from `Meisner et al. 2020 `_. @@ -1033,9 +1080,9 @@ def get_bd_atmosphere(metallicity=0, temperature=1500, gravity=4, verbose=False) """ try: if verbose: - print('Meisner2023 atmosphere') + print('Phillips2020 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, + return get_Phillips2020_atmosphere(metallicity=metallicity, temperature=temperature, gravity=gravity) @@ -2176,7 +2223,7 @@ def organize_all_Meisner2023_atmospheres(): # Open each .fits file and read the data with fits.open(jj) as hdul: data = hdul[1].data - wavelength = data['Wavelength'] # Assuming these columns exist + wavelength = data['Wavelength'] flux = data['Flux'] # Create new columns with desired format diff --git a/spisea/evolution.py b/spisea/evolution.py index 3326fc6d..7de8e217 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1320,6 +1320,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): nan_teff_idx = np.isnan(iso['logT']) if np.any(nan_teff_idx): iso['logT'][nan_teff_idx] = self.estimate_teff(iso['mass'][nan_teff_idx]) + return iso def get_temperature(self, masses, ages): """ diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index a8019fb7..847ad5ab 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -689,30 +689,19 @@ def __init__(self, multiplicity=None): IMF_broken_powerlaw.__init__(self, massLimits, powers, multiplicity=multiplicity) - -#added for brown dwarfs -class Muzic_2017(IMF_broken_powerlaw): - """ - Define IMF from `Mužić (2017) -`_. - Mass range is 0.01 M_sun - inf M_sun. - """ - def __init__(self, multiplicity=None): - massLimits = np.array([0.01, 0.5, 1, np.inf]) - powers = np.array([-0.71, -0.81, -1.60]) - - IMF_broken_powerlaw.__init__(self, massLimits, powers, - multiplicity=multiplicity) -class CombinedBD_2024(IMF_broken_powerlaw): +class CombinedWeidnerKroupaKirkpatrick_2024(IMF_broken_powerlaw): """ - Define IMF from several papers considering brown dwarf mass ranges - Derivation given in SPISEA/changes/BD_IMF - Mass range: 0.1 M_sun - 0.8 M_sun + Define combined IMF from several papers considering brown dwarf mass ranges. + Mass range: + * 0.01 M_sun - 8 M_sun: Kirkpatrick 2024 + `_. + * 8 M_sun - 120 M_sun: Weidner & Kroupa 2004 + `_. """ def __init__(self, multiplicity=None): - massLimits = np.array([0.01, 0.08]) - powers = np.array([-0.56]) + massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) + powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) IMF_broken_powerlaw.__init__(self, massLimits, powers, multiplicity=multiplicity) diff --git a/spisea/imf/tests/test_bd.py b/spisea/imf/tests/test_bd.py index a29005c6..7dda4cfe 100644 --- a/spisea/imf/tests/test_bd.py +++ b/spisea/imf/tests/test_bd.py @@ -9,9 +9,9 @@ def test_generate_cluster(): # Make multiplicity object imf_multi = multiplicity.MultiplicityUnresolved() - # Make IMF object; we'll use a broken power law with the parameters from Muzic+17 - massLimits = np.array([0.01, 0.5, 1, 120]) # Define boundaries of each mass segement - powers = np.array([-0.71, -0.81, -1.6]) # Power law slope associated with each mass segment + # Make IMF object; we'll use a broken power law with the parameters from CombinedWeidnerKroupaKirkpatrick_2024 + massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) # Define boundaries of each mass segement + powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) # Power law slope associated with each mass segment my_imf = imf.IMF_broken_powerlaw(massLimits, powers, imf_multi) # Define total cluster mass diff --git a/spisea/imf/tests/test_class.py b/spisea/imf/tests/test_class.py index 7c15b3dc..9da404d2 100644 --- a/spisea/imf/tests/test_class.py +++ b/spisea/imf/tests/test_class.py @@ -29,16 +29,15 @@ def test_Muzic(): return -""" -def test_CombinedBD(): +def test_CombinedWeidnerKroupaKirkpatrick_2024(): from .. import imf from .. import multiplicity - + # Make multiplicity object imf_multi = multiplicity.MultiplicityUnresolved() - # Use the IMF class from imf.py - my_imf = imf.CombinedBD_2024(multiplicity=imf_multi) + # Make IMF object; we'll use a broken power law with the parameters from Muzic+17 + my_imf = imf.CombinedWeidnerKroupaKirkpatrick_2024(multiplicity=imf_multi) # Define total cluster mass M_cl = 10**5. @@ -54,5 +53,5 @@ def test_CombinedBD(): mdx = np.where(isMulti)[0] for mm in mdx: assert len(compMass[mm]) > 0 - - return""" \ No newline at end of file + + return \ No newline at end of file diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 8344b92c..9a6c890c 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -789,6 +789,7 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, # Takes about 0.1 seconds. evol = evo_model.isochrone(age=10**logAge, metallicity=metallicity) + # Eliminate cases where log g is less than 0 idx = np.where(evol['logg'] > 0) @@ -841,7 +842,7 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, verbose=False) elif phase == 90: print(f"Applying brown dwarf model to object {ii}") - star = bd_atm_func(temperature=T, gravity=gravity, metallicity=metallicity, + star = bd_atm_func(temperature=T, gravity=gravity, metallicity=0, verbose=False) else: star = atm_func(temperature=T, gravity=gravity, metallicity=metallicity, @@ -1059,6 +1060,8 @@ def __init__(self, logAge, AKs, distance, metallicity=metallicity, evo_model=evo_model, atm_func=atm_func, wd_atm_func=wd_atm_func, + bd_atm_func=bd_atm_func, + wave_range=wave_range, red_law=red_law, mass_sampling=mass_sampling, min_mass=min_mass, max_mass=max_mass, rebin=rebin) diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 334ef86e..7d21b3e6 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -81,14 +81,15 @@ def test_atmosphere_models(): # Array of atmospheres atm_arr = [atm.get_merged_atmosphere, atm.get_castelli_atmosphere, atm.get_phoenixv16_atmosphere, atm.get_BTSettl_2015_atmosphere, atm.get_BTSettl_atmosphere, atm.get_kurucz_atmosphere, - atm.get_phoenix_atmosphere, atm.get_wdKoester_atmosphere, atm.get_Meisner2023_atmosphere] + atm.get_phoenix_atmosphere, atm.get_wdKoester_atmosphere, atm.get_Phillips2020_atmosphere, + atm.get_Meisner2023_atmosphere] # Array of metallicities metals_range = [-2.0, 0, 0.15] bd_metals_range = [-1.0, -0.5, 0, 0.3] metals_solar = [0] metals_arr = [metals_solar, metals_range, metals_range, metals_solar, metals_range, metals_range, metals_range, - metals_solar, bd_metals_range] + metals_solar, metals_solar, bd_metals_range] assert len(atm_arr) == len(metals_arr) @@ -106,7 +107,7 @@ def test_atmosphere_models(): print('Done {0}'.format(atm_func)) # Test get_merged_atmospheres at different temps - temp_range = [2000, 3500, 4000, 5250, 6000, 12000] + temp_range = [200, 1000, 2000, 3500, 4000, 5250, 6000, 12000] atm_func = atm.get_merged_atmosphere for ii in metals_range: for jj in temp_range: @@ -120,7 +121,7 @@ def test_atmosphere_models(): # Test get_bb_atmosphere at different temps # This func only requests temp - temp_range = [2000, 3500, 4000, 5250, 6000, 12000] + temp_range = [1000, 2000, 3500, 4000, 5250, 6000, 12000] atm_func = atm.get_bb_atmosphere for jj in temp_range: try: From ef4f814fd8e3b69186a5d35914f76a654699e4a8 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 18 Nov 2024 16:08:26 -0800 Subject: [PATCH 028/191] Notebook w/ Meisner vs. Phillips Flux Issues --- changes/BDClusters_AtmIssues.ipynb | 735 +++++++++++++++++++++++++++++ 1 file changed, 735 insertions(+) create mode 100644 changes/BDClusters_AtmIssues.ipynb diff --git a/changes/BDClusters_AtmIssues.ipynb b/changes/BDClusters_AtmIssues.ipynb new file mode 100644 index 00000000..54257c32 --- /dev/null +++ b/changes/BDClusters_AtmIssues.ipynb @@ -0,0 +1,735 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e99bddf5-2499-461b-9102-d8d450c3890f", + "metadata": {}, + "source": [ + "# New Additions!" + ] + }, + { + "cell_type": "markdown", + "id": "11bed8fe-4a9a-4499-8721-a34b6a9297cf", + "metadata": {}, + "source": [ + "### First Import Necessary Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "01b7a3c9-8d7a-483f-a25a-161817f55e9f", + "metadata": {}, + "outputs": [], + "source": [ + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pylab as py\n", + "from scipy.interpolate import RegularGridInterpolator\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.table import vstack\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload" + ] + }, + { + "cell_type": "markdown", + "id": "230b1ba3-eeca-4207-9125-08115cf15341", + "metadata": {}, + "source": [ + "## 1: BD objects in isochron" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bc90dc34-0747-4b24-805f-6e8abf081565", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone generation took 54.561135 s.\n", + "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", + " Starting at: 2024-11-12 17:33:26.509247 Usually takes ~5 minutes\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", + "M = 0.676 Msun T = 4395 K m_hst_f153m = 4.92\n", + "M = 0.786 Msun T = 4896 K m_hst_f153m = 4.41\n", + "M = 0.856 Msun T = 5198 K m_hst_f153m = 4.12\n", + "M = 0.956 Msun T = 5590 K m_hst_f153m = 3.74\n", + "M = 1.022 Msun T = 5808 K m_hst_f153m = 3.50\n", + "M = 1.079 Msun T = 5992 K m_hst_f153m = 3.30\n", + "M = 1.518 Msun T = 7482 K m_hst_f153m = 2.22\n", + "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", + "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", + "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", + " Time taken: 14.44 seconds\n" + ] + } + ], + "source": [ + "# Create isochrone object \n", + "logAge = np.log10(5*10**9.)\n", + "filt_list = ['wfc3,ir,f153m'] # Only 1 filter for plotting purposes\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", + " evo_model = evolution.MergedBaraffePisaEkstromParsec(), #our new evolution model for BDs\n", + " filters=filt_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "3bfb0efe-bf1a-4666-945b-057c64e6cccd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " L Teff ... phase m_hst_f153m \n", + " W K ... \n", + "---------------------- ------------------ ... ----- -----------------\n", + "5.2116102444796556e+23 2793.830151362531 ... 1 9.517653682370574\n", + " 5.890768137510784e+23 2838.5725586841204 ... 1 9.39108220785009\n" + ] + } + ], + "source": [ + "bd_idx = np.where(my_iso.points['mass'] < 0.08)\n", + "print(my_iso.points[bd_idx])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "998eaa3e-103d-40bf-85ba-125ad4a5ed75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brown dwarf (mass < 0.08 M_sun): F153M = 9.518 mag\n" + ] + } + ], + "source": [ + "# Create sample BD case\n", + "# Identify a brown dwarf with mass < 0.08 M_sun\n", + "bd_idx = np.where(my_iso.points['mass'] < 0.08)[0]\n", + "if len(bd_idx) > 0:\n", + " f153m = np.round(my_iso.points[bd_idx[0]]['m_hst_f153m'], decimals=3)\n", + " mass = my_iso.points[bd_idx[0]]['mass']\n", + " print('Brown dwarf (mass < 0.08 M_sun): F153M = {0} mag'.format(f153m))\n", + "else:\n", + " print('No brown dwarf with mass < 0.08 M_sun found.')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "dca63ebb-6c40-4f76-abb6-b709ccb71466", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make a mass-magnitude diagram from the isochrone and plot a brown dwarf\n", + "py.figure(1, figsize=(6,6))\n", + "py.clf()\n", + "py.plot(my_iso.points['mass'], my_iso.points['m_hst_f153m'], 'r-', label='generated isochron')\n", + "py.plot(my_iso.points['mass'][bd_idx[0]], my_iso.points['m_hst_f153m'][bd_idx[0]], 'b*', ms=15, label='BD mass')\n", + "py.title('Generated Isochron Containing Brown Dwarf Masses')\n", + "py.xlabel('Mass')\n", + "py.ylabel('F153M')\n", + "py.gca().invert_yaxis()\n", + "py.legend()\n", + "py.show()" + ] + }, + { + "cell_type": "markdown", + "id": "44b18dc0-a450-497c-9ef8-a23c31263718", + "metadata": {}, + "source": [ + "## 2. BD Objects in Cluster (Primary and Companions)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6d32d779-8e93-44e1-9375-7ff6e4f5f246", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "kc_imf = imf.CombinedWeidnerKroupaKirkpatrick_2024(multiplicity=imf_multi)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "76787edf-ced9-478a-8572-c3b5e327febe", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 8520 companions out of stellar mass range\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "kc_cluster = synthetic.ResolvedCluster(my_iso, kc_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "kc_out = kc_cluster.star_systems\n", + "kc_comp = kc_cluster.companions" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "9ca75682-5e3a-4178-b9a1-0db1ecc9b4a6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs, WDs, and BDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", + "\n", + "# Define bins for histograms\n", + "bh_bins = np.linspace(14, 100, 20)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(0, 30, 20)\n", + "\n", + "# Create subplots\n", + "plt.figure(figsize=(14,6))\n", + "\n", + "# Plot BHs\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(kc_out[p2_bh]['mass'], histtype='step', bins=bh_bins, label='Primary Black Holes', color='blue')\n", + "plt.hist(kc_comp[c_bh]['mass'], histtype='step', bins=bh_bins, label='Companion Black Holes', color='orange')\n", + "plt.title(\"Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(kc_out[p2_wd]['mass'], histtype='step', bins=wd_bins, label='Primary White Dwarves', color='blue')\n", + "plt.hist(kc_comp[c_wd]['mass'], histtype='step', bins=wd_bins, label='Companion White Dwarves', color='orange')\n", + "plt.title(\"White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(kc_out[p2_bd]['mass'], histtype='step', bins=bd_bins, label='Primary Brown Dwarves', color='blue')\n", + "plt.hist(kc_comp[c_bd]['mass'], histtype='step', bins=bd_bins, label='Companion Brown Dwarves', color='orange')\n", + "plt.title(\"Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(kc_out[p2_ns]['mass'], histtype='step', bins=ns_bins, label='Primary Neutron Stars', color='blue')\n", + "plt.hist(kc_comp[c_ns]['mass'], histtype='step', bins=ns_bins, label='Companion Neutron Stars', color='orange')\n", + "plt.title(\"Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "# Show the plots\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8f03eedf-780a-47cc-bcb5-1bd767ff4135", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "287.80180827780606\n", + "287.80439454686973\n", + "2321.1956912624596\n", + "2321.1948222119263\n" + ] + } + ], + "source": [ + "print(np.min(kc_out[p2_bd]['Teff']))\n", + "print(np.min(kc_comp[c_bd]['Teff']))\n", + "print(np.max(kc_out[p2_bd]['Teff']))\n", + "print(np.max(kc_comp[c_bd]['Teff']))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "fbbd559a-4f0d-426e-9c6c-798debb699dc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from astropy.io import fits\n", + "from astropy.table import Table\n", + "\n", + "# plotting 1200K star from Meisner vs. BTSettl\n", + "bt_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/BTSettl_rebin/btm05/lte033-3.0-0.5a+0.2.BT-Settl.spec.fits'\n", + "m_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/Meisner2023/mp00/spec_jwst_t1200_g4.5_p0_kg_g1.25.fits'\n", + "p_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/Phillips2020/spec_T1200_lg4.5_CEQ.fits'\n", + "\n", + "b_hdu_list = fits.open(bt_file)\n", + "m_hdu_list = fits.open(m_file)\n", + "p_hdu_list = fits.open(p_file)\n", + "\n", + "b_data = Table(b_hdu_list[1].data)\n", + "m_data = Table(m_hdu_list[1].data)\n", + "p_data = Table(p_hdu_list[1].data)\n", + "\n", + "b_w = np.array(b_data['Wavelength'])\n", + "b_f = np.array(b_data['Flux'])\n", + "m_w = np.array(m_data['Wavelength'])\n", + "m_f = np.array(m_data['Flux'])\n", + "p_w = np.array(p_data['Flux'])\n", + "p_f = np.array(p_data['Wavelength'])\n", + "\n", + "plt.figure()\n", + "plt.plot(b_w, b_f, label = 'BTSettl')\n", + "plt.plot(m_w, m_f, label = 'Meisner')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "dcb9ce22-afd5-4f75-a033-3f205e2e8613", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "90.9000015258789\n", + "0.0\n", + "2000.3333534194462\n", + "1.524743e-33\n", + "\n", + "\n", + "1600000.0\n", + "557203.6\n", + "299949.9969872583\n", + "1.1996874e-15\n" + ] + } + ], + "source": [ + "print(np.min(b_w))\n", + "print(np.min(b_f))\n", + "print(np.min(m_w))\n", + "print(np.min(m_f))\n", + "print('\\n')\n", + "print(np.max(b_w))\n", + "print(np.max(b_f))\n", + "print(np.max(m_w))\n", + "print(np.max(m_f))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a3e80ffe-ef8d-4fc8-8c2a-463c12c5e42d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (8,6))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(b_w, b_f)\n", + "plt.title('BTSettl Flux vs. Wavelength for 3000K star')\n", + "plt.xlabel('Wavelength (A)')\n", + "plt.ylabel('Flux (erg/cm$^2$/A)')\n", + "plt.xlim(0, 1e5)\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(m_w, m_f)\n", + "plt.title('Meisner Flux vs. Wavelength for 1200K star')\n", + "plt.xlabel('Wavelength (A)')\n", + "plt.ylabel('Flux (erg/cm$^2$/A)')\n", + "plt.xlim(0, 1e5)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "443dd316-d205-44a1-87b8-3c2252d28f64", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Where did Phillips peak for 1200K star?\n", + "plt.figure(figsize = (8,6))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(p_f, p_w)\n", + "plt.title('Phillips Flux vs. Wavelength for 1200K star')\n", + "plt.xlabel('Wavelength (A)')\n", + "plt.ylabel('Flux (erg/cm$^2$/A)')\n", + "plt.xlim(0, 1e5)\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(m_w, m_f)\n", + "plt.title('Meisner Flux vs. Wavelength for 1200K star')\n", + "plt.xlabel('Wavelength (A)')\n", + "plt.ylabel('Flux (erg/cm$^2$/A)')\n", + "plt.xlim(0, 1e5)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "5419c340-4abc-44a9-9ec3-c77bb35d1b2f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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0bt0avXr1KtZ+/fr1g6OjI7Zt2wYAePLkCQ4dOoSxY8fKP6Ctra0RFBSE1q1b47PPPkPz5s3h5OSERYsWITc3t1Txent74/bt23j06BECAgLg7u4u//D28vJCeHg40tLSEBAQACMjI7z++usAgPPnz6N3794AgE2bNuHMmTMIDQ3FwoULAUChi3nixInIysrCnj17ALxIpuPi4hS6ChMSEiCEgL29vdK1//fffwv9HSzpvq/+HgIvrtvLMScnJ8Pe3l6pnqqyohT1e18ST548gRBCZeLu5OQE4EXsLytOkl9aQghMnjwZO3fuhI+PD9566y2F7QWv/dWYgBd3sr48kL1WrVoq62VmZiInJ0dp0LuBgQEOHTqEXr164aOPPsKvv/5arJglEgn++ecf9OnTB99++y3atm0LW1tbzJgxo1jd7n5+fhg2bBhq166NnTt3IiQkRP4lNisrS6m+Lt9/0i7eXViIfv36yb/xq5KTk4PPP/8cu3btQmpqKtzc3PDNN9+Ues6nos5XQCqVKo0xKCvjx4/Hl19+ifXr12P58uVq6xUMRFc3sLSoKR8mTJiACRMmIDMzE6dOncKiRYvwxhtv4Pbt2yW6jdrGxgbAizmcirNfYd96tcXU1BQAlAYLq/vQj4iIwJdffol27dohNDQUq1evxpw5c4o8T0FLxE8//YTU1FTs3r0b2dnZSoOAW7RogT179kAIgStXrsDHxwdLly6FmZkZ5s+fX+LX5+3tjdWrVyMwMBCBgYHo37+/fFtBQnXq1Cn5gPiCBGzPnj0wNjbG33//LX+PAKhs0W3WrBnat2+Pbdu24YMPPsC2bdvg5OQkT9KAF9e+YAxYQRLyMlVl2thXnVq1aqkcBxYfH1/iY2lTjRo1YGBgoHCzQoGCm0gK/o4K6OrvpCDB2rZtG7Zs2aI0vhL47w7Fq1evKvxuFZS9fAdjwe92fHy8wv/Mq1evKhzrZaampjh48CAGDx6MadOmQSaTYfr06UXG7uzsLJ/m5vbt2/j999+xePFi5OTkFNkqtnPnTtSvXx979+5VeG/V3VBQFv+nSDvYkqWBCRMm4MyZM9izZw+uXLmCoUOHom/fvmonz9OWwMBA2NnZwdXVFe+9957KCfh0pXbt2vjkk08wcOBAjBs3Tm29N954A8nJycjPz1dq5fHw8EDjxo2LdT4LCwv069cPCxcuRE5OjtJ8OUXp06cPjIyMcO/ePZVxlMfUCwV3FF25ckWh/NChQ0p1MzMzMXToULi4uCAgIADTpk3D/Pnzce7cuWKda8KECcjKyoKvry98fHzg6emJJk2aqKwrkUjQqlUr/PDDD6hevTouXrxYshf2/7p27QpDQ0Ps27cP165dU/jSYW1tjdatW+O3337DgwcPFLoKJRIJjIyMFLrBnj9/jh07dqh9befOncPp06fx119/Ydy4cQr7vvHGGxBCIDY2VuV1b9GihdrXoMm+6nh5eSEiIkKpS7agNe5lr7aC6ZKFhQU6dOgAPz8/hXPKZDLs3LkTderUgaurq87jEELgvffew7Zt27BhwwalLwMFateujfbt22Pnzp3Iz8+Xl//777+4desWhgwZIi976623IJFIlO6+8/HxgZmZGfr27avyHKampjhw4AD69euHGTNm4McffyzRa3F1dcXnn3+OFi1aKPwdqbuuEokEJiYmCslTfHy8yrsLqWJhS1Yp3bt3D76+voiJiZE3qc+dOxdHjx7Ftm3bijXRXWn069cPQ4cOhbOzMyIjI/HFF1+ge/fuCAsLK9W369JYuXJlkXXeffdd7Nq1C/3798fMmTPRvn17GBsbIyYmBgEBAXjrrbcwePBglfu+9957MDMzQ+fOneHo6Ij4+HisWLEC1tbWaNeuXYlidXFxwdKlS7Fw4ULcv38fffv2RY0aNZCQkIDz58/DwsKiWHcQalO7du3QuHFjzJ07F3l5eahRowb279+P06dPK9WdMmUKoqKi5LF+//33CAkJwbvvvovw8HBUr1690HM1adIEnp6eWLFiBaKjo7Fx40aF7X///TfWrl2LQYMGoUGDBhBCwM/PD6mpqQrdkj169EBQUFCx7pwsmHrhwIEDMDAwkI/HKuDl5SWfaPPlJGvAgAFYvXo1Ro4ciffffx/JyclYtWqV2t/rESNGYM6cORgxYgSys7OVZtXv3Lkz3n//fUyYMAEXLlxA165dYWFhgbi4OJw+fRotWrSQz6f0Kk32VWfWrFnYunUr+vXrh6VLl8Le3h67d+/GzZs3AShOddCiRQv4+flh3bp1cHd3h4GBgU6/EKxYsQK9evWCt7c35s6dCxMTE6xduxYRERHw9fXVqOXkwoUL8ulK0tPTIYTAvn37ALz4WyhoYZ4xYwa2bNmCiRMnokWLFgpTUkilUrRp00b+/JtvvkGvXr0wdOhQTJ06FYmJiZg/fz7c3NwUkrPmzZtj0qRJWLRoEQwNDdGuXTscP34cGzduxLJly9TOkVVwzv379+Ptt9/GrFmzIJPJMHv2bJV1r1y5gmnTpmHo0KFo1KgRTExMcPLkSVy5ckWhNbigZW3v3r1o0KABTE1N0aJFC7zxxhvw8/PD1KlT8c477yA6OhpfffUVHB0ddf6lnXSsfMbbVzwAxP79++XPf//9dwFAWFhYKDyMjIzkd6wV3MlT2OOjjz4q1vnUefTokTA2Nla6i09bXr67sDCq7prJzc0Vq1atEq1atRKmpqaiWrVqokmTJuKDDz4Qd+7ckdd79e7C3377TXh7ewt7e3thYmIinJycxLBhw8SVK1eKjEvdHXoHDhwQ3t7ewsrKSkilUuHs7CzeeecdceLECXmdcePGCQsLi2K+M8V/b169u1AIIW7fvi169+4trKyshK2trZg+fbo4fPiwQuybNm0SAMS2bdsU9r17966wsrISgwYNKlacGzduFACEmZmZSEtLU9h28+ZNMWLECNGwYUNhZmYmrK2tRfv27YWPj49CvYI76Ipr3rx5AoDw8PBQ2nbgwAEBQJiYmIjMzEyFbVu3bhWNGzcWUqlUNGjQQKxYsUJs2bJF5d2YQggxcuRIAUB07txZbSxbt24VHTp0EBYWFsLMzEw0bNhQjB07Vly4cEFe59W7C0uyr5eXl2jevLnSvqqOGRERIXr27ClMTU1FzZo1xaRJk8Rvv/0mAIjLly/L66WkpIh33nlHVK9eXUgkEvl7r+7uQCFe/M9YtGiR2vehqP2Dg4NF9+7d5a+1Y8eO8rtQCxT3d/5lL9+19+rj5d9tZ2dntfVUXZvjx4+Ljh07yt/LsWPHioSEBKV6OTk5YtGiRaJevXrCxMREuLq6ip9++kllnKr+/rOzs8XAgQMFALFq1SqVrzEhIUGMHz9eNGnSRFhYWIhq1aqJli1bih9++EHhLtAHDx6I3r17C0tLS6XXtXLlSuHi4iKkUqlo2rSp2LRpk1i0aJHS311hnxukfyRCvDTLH6klkUiwf/9+DBo0CMCLyS1HjRqFa9euKXRRAEC1atXg4OCA3Nxc3Lt3r9Dj1qhRQ+XA11fPV5hGjRph8uTJ+PTTT4v9eohIP7z//vvw9fVFcnIyTExMyjscItIidheWUps2bZCfn4/ExES1d9gZGxurHf+iLcnJyYiOjubdJkQVwNKlS+Hk5IQGDRrg6dOn+Pvvv7F582Z8/vnnTLCIKiEmWYV4+vSpwrIkkZGRuHTpEmrWrAlXV1eMGjUKY8eOxffff482bdogKSkJJ0+eRIsWLZTuetH0fPXq1cPTp0+xePFivP3223B0dMSDBw/w2WefwcbGRu34JiLSH8bGxvjuu+8QExODvLw8NGrUCKtXr8bMmTPLOzQi0gF2FxYiMDBQ5cK148aNg4+PD3Jzc7Fs2TJs374dsbGxqFWrFjw9PbFkyZJS3X1U1PmeP3+OQYMGITw8HKmpqXB0dIS3tze++uorlZP1ERERUflhkkVERESkA5wni4iIiEgHmGQRERER6QAHvr9CJpPh0aNHsLS05NIFREREFYQQAhkZGXByclKY3Lc8Mcl6xaNHjziInIiIqIKKjo5GnTp1yjsMAEyylBQsXBwdHQ0rK6tyjoaIiIiKIz09HXXr1pV/jusDJlmvKOgitLKyYpJFRERUwejTUB/96LQkIiIiqmSYZBERERHpAJMsIiIiIh1gkkVERESkA0yyiIiIiHSASRYRERGRDjDJIiIiItIBJllEREREOsAki4iIiEgHmGQRERER6YBeJ1mnTp3CwIED4eTkBIlEggMHDhRa38/PD7169YKtrS2srKzg6emJY8eOlU2wRERERC/R6yQrMzMTrVq1wi+//FKs+qdOnUKvXr1w5MgRhIWFwdvbGwMHDkR4eLiOIyUiIiJSJBFCiPIOojgkEgn279+PQYMGlWi/5s2bY/jw4fjyyy+LVT89PR3W1tZIS0urkgtEP8/Jh5mJYXmHQUREVCL6+Pmt1y1ZmpLJZMjIyEDNmjXV1snOzkZ6errCo6o6dfsxmn55FKv9b5d3KERERBVepU6yvv/+e2RmZmLYsGFq66xYsQLW1tbyR926dcswQv3y5cEIAMBP/9wp50iIiIgqvkqbZPn6+mLx4sXYu3cv7Ozs1NZbsGAB0tLS5I/o6OgyjJKIiIgqK6PyDkAX9u7di0mTJuGPP/5Az549C60rlUohlUrLKDIiIiKqKipdS5avry/Gjx+P3bt3Y8CAAeUdDhEREVVRet2S9fTpU9y9e1f+PDIyEpcuXULNmjVRr149LFiwALGxsdi+fTuAFwnW2LFj8eOPP6Jjx46Ij48HAJiZmcHa2rpcXkNFUiFuMyUiIqog9Lol68KFC2jTpg3atGkDAJgzZw7atGkjn44hLi4OUVFR8vobNmxAXl4ePvroIzg6OsofM2fOLJf4iYiIqOrS65asbt26obBpvHx8fBSeBwYG6jYgIiIiomLS65YsIiIiooqKSRbJVYy5/4mIiCoGJllEREREOsAki4iIiEgHmGQRERER6QCTLCIiIiIdYJJFcoLTkRIREWkNkywiIiIiHWCSRURERKQDTLKIiIiIdIBJFhEREZEOMMkiIiIi0gEmWUREREQ6wCSLiIiISAeYZJEcF4gmIiLSHiZZRERERDrAJIuIiIhIB5hkEREREekAkyySS32WW94hEBERVRpMskjuaXZeeYdARERUaTDJIiIiItIBJllEREREOsAki4iIiEgHmGQRERER6QCTLCIiIiIdYJJFREREpANMsoiIiIh0gEkWERERkQ4wySIiIiLSASZZRERERDrAJIuIiIhIB5hkEREREekAkywiIiIiHWCSRURERKQDTLKIiIiIdIBJFhEREZEOMMkiIiIi0gEmWUREREQ6wCSLiIiISAeYZBEA4HlOfnmHQEREVKkwySIAwOWY1PIOgYiIqFJhkkVERESkA0yyCAAgRHlHQEREVLkwySIiIiLSASZZBACQSMo7AiIiospFr5OsU6dOYeDAgXBycoJEIsGBAweK3CcoKAju7u4wNTVFgwYNsH79et0HWgmwu5CIiEi79DrJyszMRKtWrfDLL78Uq35kZCT69++PLl26IDw8HJ999hlmzJiBP//8U8eREhERESkyKu8ACtOvXz/069ev2PXXr1+PevXqYc2aNQCApk2b4sKFC1i1ahXefvttHUVJREREpEyvW7JKKiQkBL1791Yo69OnDy5cuIDc3FyV+2RnZyM9PV3hQURERKSpSpVkxcfHw97eXqHM3t4eeXl5SEpKUrnPihUrYG1tLX/UrVu3LEIlIiKiSq5SJVkAIHnlNjnx/yO6Xy0vsGDBAqSlpckf0dHROo9RHwlw5DsREZE26fWYrJJycHBAfHy8QlliYiKMjIxQq1YtlftIpVJIpdKyCE+vPcvm2oVERETaVKlasjw9PeHv769Qdvz4cXh4eMDY2LicoqoYvjwYUd4hEBERVSp6nWQ9ffoUly5dwqVLlwC8mKLh0qVLiIqKAvCiq2/s2LHy+lOmTMHDhw8xZ84c3LhxA1u3bsWWLVswd+7c8gi/QnmUllXeIRAREVUqet1deOHCBXh7e8ufz5kzBwAwbtw4+Pj4IC4uTp5wAUD9+vVx5MgRzJ49G7/++iucnJzw008/cfoGIiIiKnMSITjX98vS09NhbW2NtLQ0WFlZlXc4ZcZl/mGF5w9WDiinSIiIiEpOHz+/9bq7kIiIiKiiYpJFREREpANMsghn76qeqJWIiIhKj0kWYeTmc+UdAhERUaXDJIuIiIhIB5hkEREREekAkywiIiIiHWCSRURERKQDTLKIiIiIdIBJVhUX8+RZeYdARERUKTHJquJinjxXWX40Iq6MIyEiIqpcmGSRSt8evVXeIRAREVVoTLKIiIiIdIBJFhEREZEOMMmq4iRqykWZRkFERFT5MMkiIiIi0gEmWUREREQ6wCSripNI1HUYEhERkSaYZBERERHpAJMsIiIiIh1gkkUqRSZllncIREREFRqTLCIiIiIdYJJFREREpANMsoiIiIh0gElWFVfYDA6J6VllFwgREVElwySL1LoYlVreIRAREVVYTLJIrTUnbpd3CERERBUWkywiIiIiHWCSVcVxUR0iIiLdYJJFREREpANMsoiIiIh0gElWFVfYFA5ERERUekyySK2cfFl5h0BERFRhMcmq4oRQv+3+Yy4STUREVFpMsoiIiIh0gElWFXc1Nq28QyAiIqqUmGRVcbcTMso7BCIiokqJSRYRERGRDjDJIiIiItIBJllVnIyzNBAREekEkywiIiIiHWCSRURERKQDTLKqOIFCZiMlIiKiUmOSVcUlpGeXdwhERESVEpOsKi7o9uPyDoGIiKhS0vska+3atahfvz5MTU3h7u6O4ODgQuvv2rULrVq1grm5ORwdHTFhwgQkJyeXUbREREREL+h1krV3717MmjULCxcuRHh4OLp06YJ+/fohKipKZf3Tp09j7NixmDRpEq5du4Y//vgDoaGhmDx5chlHTkRERFWdXidZq1evxqRJkzB58mQ0bdoUa9asQd26dbFu3TqV9f/991+4uLhgxowZqF+/Pl5//XV88MEHuHDhQhlHTkRERFWd3iZZOTk5CAsLQ+/evRXKe/fujbNnz6rcp1OnToiJicGRI0cghEBCQgL27duHAQMGqD1PdnY20tPTFR5VhRC8s5CIiEhXtJJk5ebmIjo6Grdu3UJKSoo2DomkpCTk5+fD3t5eodze3h7x8fEq9+nUqRN27dqF4cOHw8TEBA4ODqhevTp+/vlntedZsWIFrK2t5Y+6detqJf6K4J8bieUdAhERUaVV6iTr6dOn2LBhA7p16wZra2u4uLigWbNmsLW1hbOzM9577z2EhoZqHKBEIlF4LoRQKitw/fp1zJgxA19++SXCwsJw9OhRREZGYsqUKWqPv2DBAqSlpckf0dHRGsdcUcQ8eVbeIRAREVVaRqXZ6YcffsDy5cvh4uKCN998E/Pnz0ft2rVhZmaGlJQUREREIDg4GL169ULHjh3x888/o1GjRiU6h42NDQwNDZVarRITE5VatwqsWLECnTt3xieffAIAaNmyJSwsLNClSxcsW7YMjo6OSvtIpVJIpdISxUZERERUlFIlWWfPnkVAQABatGihcnv79u0xceJErF+/Hlu2bEFQUFCJkywTExO4u7vD398fgwcPlpf7+/vjrbfeUrnPs2fPYGSk+JIMDQ0BcPwRERERla1SJVl//PFHserduHEDU6dOLc0pAABz5szBmDFj4OHhAU9PT2zcuBFRUVHy7r8FCxYgNjYW27dvBwAMHDgQ7733HtatW4c+ffogLi4Os2bNQvv27eHk5FTqOIiIiIhKqlRJVmHS0tKwa9cubN68GZcvX0Z+fn6pjzV8+HAkJydj6dKliIuLg5ubG44cOQJnZ2cAQFxcnMKcWePHj0dGRgZ++eUXfPzxx6hevTq6d++Ob775RuPXVRmxbY+IiEh3JEJL/WgnT57E1q1b4efnB2dnZ7z99tt4++230aZNG20cvsykp6fD2toaaWlpsLKyKu9wdGrbmUgs+et6oXUerFQ//QUREZG+0MfPb41asmJiYuDj44OtW7ciMzMTw4YNQ25uLv788080a9ZMWzGSjqi+R5OIiIi0odRTOPTv3x/NmjXD9evX8fPPP+PRo0eFzkdF+ofdhURERLpT6pas48ePY8aMGfjwww9LfOcgERERUWVX6pas4OBgZGRkwMPDAx06dMAvv/yCx48fazM2IiIiogqr1EmWp6cnNm3ahLi4OHzwwQfYs2cPateuDZlMBn9/f2RkZGgzTiIiIqIKReO1C83NzTFx4kScPn0aV69exccff4yVK1fCzs4Ob775pjZiJCIiIqpwtLJAdIHGjRvj22+/RUxMDHx9fbV5aCIiIqIKpdRJ1meffYbz58+r3GZoaIhBgwbh0KFDpQ6MiIiIqCIrdZIVFxeHN954A46Ojnj//fdx+PBhZGdnazM2IiIiogqr1EnWtm3bkJCQgN9//x3Vq1fHxx9/DBsbGwwZMgQ+Pj5ISkrSZpxEREREFYpGY7IkEgm6dOmCb7/9Fjdv3sT58+fRsWNHbNq0CbVr10bXrl2xatUqxMbGaiteIiIiogpBqwPfmzZtinnz5uHMmTOIiYnBuHHjEBwczEHwREREVOVotHZhYWxtbTFp0iRMmjRJV6cgIiIi0lslbsl6/vy5yu6/a9euaSUgIiIiosqgREnWvn374Orqiv79+6Nly5Y4d+6cfNuYMWO0HhwRERFRRVWiJGvZsmW4ePEiLl++jK1bt2LixInYvXs3AEAIoZMAiYiIiCqiEo3Jys3Nha2tLQDAw8MDp06dwpAhQ3D37l1IJBKdBEhERERUEZWoJcvOzg5XrlyRP69Vqxb8/f1x48YNhXKqGGRsfCQiItKZEiVZO3bsgJ2dnUKZiYkJfH19ERQUpNXASPfuJGSUdwhERESVVom6C+vUqaOyPCsrC8bGxvj7778hk8kUtr355pulj450Kp9NWURERDqj8TxZR48exZgxY5CcnKy0TSKRID8/X9NTEBEREVU4Gs/4Pm3aNAwbNgxxcXGQyWQKDyZY+o33KhAREemOxklWYmIi5syZA3t7e23EQ2WIs24QERHpjsZJ1jvvvIPAwEAthEJERERUeWg8JuuXX37B0KFDERwcjBYtWsDY2Fhh+4wZMzQ9BekIuwuJiIh0R+Mka/fu3Th27BjMzMwQGBioMCmpRCJhkqXH2F1IRESkOxonWZ9//jmWLl2K+fPnw8BA495HIiIiokpB46woJycHw4cPZ4JFRERE9BKNM6Nx48Zh79692oiFylg++wuJiIh0RuPuwvz8fHz77bc4duwYWrZsqTTwffXq1ZqegnSEM74TERHpjsZJ1tWrV9GmTRsAQEREhMI2CW9fIyIioipK4yQrICBAG3EQERERVSocrU5ERESkAxonWStWrMDWrVuVyrdu3YpvvvlG08MTERERVUgaJ1kbNmxAkyZNlMqbN2+O9evXa3p4KmNt6lUv7xCIiIgqBY2TrPj4eDg6OiqV29raIi4uTtPDk5aIYk7XMKh1bR1HQkREVDVonGTVrVsXZ86cUSo/c+YMnJycND08aUFiRhY8V5zE98dvFVm3poVJGURERERU+Wl8d+HkyZMxa9Ys5Obmonv37gCAf/75B/PmzcPHH3+scYCkufWB9xGfnoWfT97Fx70by8s5wQYREZHuaJxkzZs3DykpKZg6dSpycnIAAKampvj000+xYMECjQMkzcnUdBVyKlIiIiLd0TjJkkgk+Oabb/DFF1/gxo0bMDMzQ6NGjSCVSrURH+kQV9UhIiLSnVKPyfrss89w/vx5+fNq1aqhXbt2cHNzY4KlZ4o76J2IiIi0p9RJVlxcHN544w04Ojri/fffx+HDh5Gdna3N2EjHmHoRERHpTqmTrG3btiEhIQG///47qlevjo8//hg2NjYYMmQIfHx8kJSUpM04SQdSn+UolXG5SSIiIu3QaAoHiUSCLl264Ntvv8XNmzdx/vx5dOzYEZs2bULt2rXRtWtXrFq1CrGxsdqKl0pBXYtV8B0mwkRERLqi1bULmzZtinnz5uHMmTOIjo7GuHHjEBwcDF9fX22ehoiIiEjv6WyBaDs7O0yaNAkHDx7E3LlzS32ctWvXon79+jA1NYW7uzuCg4MLrZ+dnY2FCxfC2dkZUqkUDRs2VLm2IhEREZEuaTyFw5w5c1SWSyQSmJqaolGjRnjzzTdRs2bNEh977969mDVrFtauXYvOnTtjw4YN6NevH65fv4569eqp3GfYsGFISEjAli1b8NprryExMRF5eXklPndlkpMnk/8skwkYGKgfeCXhFKVERERaoXGSFR4ejosXLyI/Px+NGzeGEAJ37tyBoaEhmjRpgrVr12LOnDk4ffo0mjVrVqJjr169GpMmTcLkyZMBAGvWrMGxY8ewbt06rFixQqn+0aNHERQUhPv378uTOhcXF01fYoX3KC1L/nNOvgymBoblGA0REVHVoHF34VtvvYWePXvi0aNHCAsLw8WLFxEbG4tevXphxIgRiI2NRdeuXTF79uwSHTcnJwdhYWHo3bu3Qnnv3r1x9uxZlfscOnQIHh4e+Pbbb1G7dm24urpi7ty5eP78udrzZGdnIz09XeFR2XCeLCIiorKncUvWd999B39/f1hZWcnLrKyssHjxYvTu3RszZ87El19+qZQsFSUpKQn5+fmwt7dXKLe3t0d8fLzKfe7fv4/Tp0/D1NQU+/fvR1JSEqZOnYqUlBS147JWrFiBJUuWlCi2ykxw9iwiIiKt0LglKy0tDYmJiUrljx8/lrcKVa9eXb6uYUlJXpm4SQihVFZAJpNBIpFg165daN++Pfr374/Vq1fDx8dHbWvWggULkJaWJn9ER0eXKk599vL7xUYtIiKisqGV7sKJEydi//79iImJQWxsLPbv349JkyZh0KBBAIDz58/D1dW1RMe1sbGBoaGhUqtVYmKiUutWAUdHR9SuXRvW1tbysqZNm0IIgZiYGJX7SKVSWFlZKTyqMg58JyIi0g6Nk6wNGzagR48eePfdd+Hs7Ix69erh3XffRY8ePbB+/XoAQJMmTbB58+YSHdfExATu7u7w9/dXKPf390enTp1U7tO5c2c8evQIT58+lZfdvn0bBgYGqFOnTglfWeXx8pgszuhORERUNjRKsnJzczFw4EB88sknSE5Olt9pmJycjI0bN8LCwgIA0Lp1a7Ru3brEx58zZw42b96MrVu34saNG5g9ezaioqIwZcoUAC+6+saOHSuvP3LkSNSqVQsTJkzA9evXcerUKXzyySeYOHEizMzMNHmpVUZpx2Tl5cuw+NA1HLumerwcERFRVaPRwHdjY2NERERAIpGgWrVqaNmypbbiAgAMHz4cycnJWLp0KeLi4uDm5oYjR47A2dkZwItFqqOiouT1q1WrBn9/f0yfPh0eHh6oVasWhg0bhmXLlmk1ropMV2Oy9oXFwOfsA/icfYAHKwfo5iREREQViMZ3F44dOxZbtmzBypUrtRGPkqlTp2Lq1Kkqt/n4+CiVNWnSRKmLkZRdiUnV6vHiXpqLi4iIiLSQZOXk5GDz5s3w9/eHh4eHvIuwwOrVqzU9BenAgfBHKstLO/A9J/+/WeWf5+TDzIQTnhIRUdWmcZIVERGBtm3bAngxyPxl6qZaIP1V2jFZz7L/W7oo5H4SujdRfQcoERFRVaFxkhUQEKCNOKiMxTx5ptXjvbweYlSydo9NRERUEWk8hQPpv9hU5YlY7z5+qqJm6W0780D+c56MM54SERFpJckKDg7G6NGj4enpidjYWADAjh07cPr0aW0cnjR0/3Fmieq3rVddo/NlZOUVXYmIiKiS0zjJ+vPPP9GnTx+YmZkhPDwc2dnZAICMjAx8/fXXGgdIFc+P/9wp7xCIiIjKncZJ1rJly7B+/Xps2rQJxsbG8vJOnTrh4sWLmh6eygFvWCAiItKcxknWrVu30LVrV6VyKysrpKamanp40pHC0iimWERERJrTOMlydHTE3bt3lcpPnz6NBg0aaHp40rLiTNHAhiwiIiLNaZxkffDBB5g5cybOnTsHiUSCR48eYdeuXZg7d67amdqJiIiIKjuN58maN28e0tLS4O3tjaysLHTt2hVSqRRz587FtGnTtBEjaVFkUiaaO1kXOu6qtLO+ExER0X80TrIAYPny5Vi4cCGuX78OmUyGZs2aoVq1ato4NGnZufspaO5kjcxsTrNARESkS6XqLoyKilIqMzc3h4eHB9q3b6+UYBXMnUXlb+nf1wEAyU9z1FdiQxYREZHGSpVktWvXDu+99x7Onz+vtk5aWho2bdoENzc3+Pn5lTpAKnslzbGe5+TrJA4iIqKKrFTdhTdu3MDXX3+Nvn37wtjYGB4eHnBycoKpqSmePHmC69ev49q1a/Dw8MB3332Hfv36aTtu0lQhmVRJ7y7cHvJAo1CIiIgqo1K1ZNWsWROrVq3Co0ePsG7dOri6uiIpKQl37ryY6XvUqFEICwvDmTNnmGDpKy0uL/iAC0ITEREp0Wjgu6mpKYYMGYIhQ4ZoKx6qgK7GppZ3CERERHpHKwtEUwXEwe1EREQ6xSSrqlLTXSgEYMAp34mIiDTGJIuUMMciIiLSHJMsUlCaBCsiNl37gRAREVVwTLJICZfVISIi0pzGSdaJEyfUbtuwYYOmhycNCaF68JVQMyhLTfUSy8mTaedAREREFZTGSdaAAQPw8ccfIyfnv2VaHj9+jIEDB2LBggWaHp40cCMuHR2+/qdczv08Jx95+Uy0iIio6tI4yTp16hT++usvtGvXDteuXcPhw4fh5uaGp0+f4vLly9qIkUrpk32XkZiRXeL9tDHwvcu3J9Fu+QnkMtEiIqIqSuMkq0OHDggPD0fLli3h7u6OwYMH4+OPP8bJkydRt25dbcRIpZSUoXoR6Lx8GXLzVfcLauvOwvSsPDx5losHSZnaOSAREVEFo5WB77du3UJoaCjq1KkDIyMj3Lx5E8+ecamV8hafnqWyPLOIBZ0lWpzDQabF5XuIiIgqEo2TrJUrV8LT0xO9evVCREQEQkND5S1bISEh2oiRylBJB75fe5RW6PZ8ZllERFRFaZxk/fjjjzhw4AB+/vlnmJqaonnz5jh//jyGDBmCbt26aSFE0jZtTjYaeOtxodv/vvJIeycjIiKqQDROsq5evYp+/foplBkbG+O7777D8ePHNT086cDfl+MK3V6SHKyohG1t4L0SHI2IiKjy0DjJsrGxUbvNy8tL08OTDny2/6rabRIJFO4ILGoahj3no7UWFxERUWVipOkBli5dWuj2L7/8UtNTUBkSAkjJ/O+uxMycfFibqc/Fo1J4gwMREZEqGidZ+/fvV3iem5uLyMhIGBkZoWHDhkyyKiDZS6PfDQ24xA4REVFpaJxkhYeHK5Wlp6dj/PjxGDx4sKaHp3Lw8g2BhtocJU9ERFSF6GSBaCsrKyxduhRffPGFLg5POibT1gKGREREVZhOkiwASE1NRVpa4XMokX6ScW4rIiIijWncXfjTTz8pPBdCIC4uDjt27EDfvn01PTyVgyYOVniQ/GJAuwATLiIiotLQOMn64YcfFJ4bGBjA1tYW48aNw4IFCzQ9PJUDC6nGvxZERERVnsafppGRkdqIg8pRh/o1cS4yRWfHP34tHr2bO+js+ERERPpIZ2OyqHzl5BU+iejLzE0M1W7Txhj493eEaX4QIiKiCqZULVlz5swpdt3Vq1eX5hSkoT8vxhS7rgDwdts6CI9+gl7N7BF0u/D1CImIiKhopUqyVM2NpYqEcyyVm/TnucWuKwTw/bBWEELwmhEREWlJqZKsgIAA3L9/Hy4uLjAwYI+jPipJL19BXVUJFu8tJCIiKp1SZ0iNGjVCUlKS/Pnw4cORkJCglaCobIlXBl6xLYuIiEhzpU6yXv1gPnLkCDIzMzUOiLSjJAPWZ/dyVXg+pG0dLUcDzNl7SevHJCIi0md639e3du1a1K9fH6ampnB3d0dwcHCx9jtz5gyMjIzQunVr3QZYCdSpYabwvE296vKfX02mS8svPFYrxyEiIqooSp1kSSQSpTE82h40vXfvXsyaNQsLFy5EeHg4unTpgn79+iEqKqrQ/dLS0jB27Fj06NFDq/EQERERFVepJyMVQmD8+PGQSqUAgKysLEyZMgUWFhYK9fz8/Eod3OrVqzFp0iRMnjwZALBmzRocO3YM69atw4oVK9Tu98EHH2DkyJEwNDTEgQMHSn3+qkLCUVhERERaV+oka9y4cQrPR48erXEwL8vJyUFYWBjmz5+vUN67d2+cPXtW7X7btm3DvXv3sHPnTixbtqzI82RnZyM7O1v+PD09vfRBV1CFNUDy7kIiIqLSKXWStW3bNm3GoSQpKQn5+fmwt7dXKLe3t0d8fLzKfe7cuYP58+cjODgYRkbFe2krVqzAkiVLNI5X32iysLOupsriPFxERFSV6P3A91c/lNV9UOfn52PkyJFYsmQJXF1dlbars2DBAqSlpckf0dHRGsesD86XYC3C6mbGpTrHzn8flqj+puD7pToPERFRRaTxAtG6YmNjA0NDQ6VWq8TERKXWLQDIyMjAhQsXEB4ejmnTpgEAZDIZhBAwMjLC8ePH0b17d6X9pFKpfFxZZRJ4q/hL4xgaqG9dKuzmws8PRJQkJGw8dR/vd21Yon2IiIgqKr1tyTIxMYG7uzv8/f0Vyv39/dGpUyel+lZWVrh69SouXbokf0yZMgWNGzfGpUuX0KFDh7IKvcLT3UB4dhUSEVHVobctWcCLhajHjBkDDw8PeHp6YuPGjYiKisKUKVMAvOjqi42Nxfbt22FgYAA3NzeF/e3s7GBqaqpUTkRERKRrep1kDR8+HMnJyVi6dCni4uLg5uaGI0eOwNnZGQAQFxdX5JxZpCHeXkhERFQqettdWGDq1Kl48OABsrOzERYWhq5du8q3+fj4IDAwUO2+ixcvxqVLl3QfZCVT2hsAOzWspd1AiIiIKjC9T7Ko4jAzNix0O2dvICKiqoRJFikvj/TSzyWZb4tJFBER0X+YZBERERHpAJMs0iIJbKqpn3OMDV1ERFSVMMkiJS93HxY2GalqvB2RiIgIYJJFWiSRKI/velliRrbabURERJUNkyzSGgmAzpzGgYiICACTLFJB8e7CkjEy5K8UERERwCSLtKjzazawNjMu7zCIiIj0ApMs0poR7euVYqA8ERFR5cQki5S8PHZdlCBrMjTgJA1EREQFmGRRoeLSslSWP8nMUSqTAHi/awNUk+r1uuNERERlgkkWFSo86onKcpmKFi6JBHCwNsXlRb3h3dhW16ERERHpNSZZpERhMtJS7GdoIIGZSeGLRRMREVV2TLKoUKUdyM4B8EREVNUxyaoi6ttYlOn5VHUnEhERVSVMsqqIfm4OpdpP3d2FTKGIiIgKxySriihkScFClTaZYkMWERFVdUyyqggJSpdlqUuWbidkFL6fmvJ8GbMvIiKqGphkVRFCyx18V2LSCt3es6mdynLf81FajYOIiEhfMcmqhFJUTBRa1oa618XGMe5K5RfVzLtFRERU2TDJqoQOXorV2rHUtX8V1floYCCBdxPl1iy/i9qLjYiISJ8xyaqEXk2ArEzVL3NT1IB4dXcX3kl8qvB8SJvaSnWMuJYhERFVYUyyKqHFf11XeD7W00Vt3dKkQWnPc7EvLEbxOCqyNVVlREREVQWTrErm7istTADwZmsntfUNSpEIJaQrLxrNRisiIiJFTLIqGVWD3k0M1V/morsLi3dewxJkWUcj4otdl4iIqKJiklXJqBpDZSCRlH4NwmJO/VCSrsEpO8NKFwwREVEFwiSrklGVEhWW/5Rm3JSqhM3WUlri4xAREVVmTLIqGVULM9tbmaqtr62hVCPb11NZvn50Wy2dgYiIqGJhklXZqGhlMjEyUD/flZbGZBkbqj5QXzfH4h2AiIiokmGSVcnkl3DwVWnXNFQ6DqdrICIiUsAkq5LZcz5aZXkNc2OV5UW2ZGkaEBERURXFJKuSOXw1TmW5panqJOtZTn6hxyvtXYlERERVHZMsKlRxp3BQt/wOERFRVcUkiwqlKncqbuJVoFtjWy1FQ0REVHEwyaIS+3x/hFJZYWnXuE4uOouFiIhIXzHJohK78PBJieob8s5DIiKqgphkEREREekAk6xKrlcze43218aAdg6JJyKiqohJViW3YbR7mZyHNxcSEREpYpJVyRkYaDYeKvVZrpYi0Ux2Xj4ysvQjFiIiouIwKu8ASHvSnqtPQmqYm5TqmJtPR+LzN5oVWa+k0zrIZKLYCWBGVi5aLD4uf97EwRL7p3aGmYlhic5JRERUltiSVYnk5MnUbuut4dgsTaga1+UbGoVL0anIlxWdnH1xQHHKiJvxGWjz1XE1tYmIiPQDk6wqQtNuQ02oSqMW7o/AoF/P4Pvjt4rcP/D2Y6WyrFwZbsanayE6IiIi3WCSVYkkpGeVdwgltjbwXpF11A2q77smWMvREBERaY/eJ1lr165F/fr1YWpqCnd3dwQHq/9g9fPzQ69evWBrawsrKyt4enri2LFjZRht+dp9Pkrn59gbquYcOry7sLCxZkRERPpKr5OsvXv3YtasWVi4cCHCw8PRpUsX9OvXD1FRqj/oT506hV69euHIkSMICwuDt7c3Bg4ciPDw8DKOvPL69M+rKsuNDNX/KtlYSHUVDpKfZuvs2ERERJrQ6yRr9erVmDRpEiZPnoymTZtizZo1qFu3LtatW6ey/po1azBv3jy0a9cOjRo1wtdff41GjRrhr7/+KuPIy0d5zlVV00L93Yst6ljr7Lzz9l3R2bGJiIg0obdJVk5ODsLCwtC7d2+F8t69e+Ps2bPFOoZMJkNGRgZq1qyptk52djbS09MVHlQyDWwtdHbsomac/+dmos7OTUREpAm9TbKSkpKQn58Pe3vFqQfs7e0RHx9frGN8//33yMzMxLBhw9TWWbFiBaytreWPunXrahR3ebqTkFEu59XlfYvFmOFBK0v/EBERaZveJlkFJBLFj3AhhFKZKr6+vli8eDH27t0LOzs7tfUWLFiAtLQ0+SM6OlrjmMtLRlaewvN33OuUUyTaczfxaZF17j0uug4REVFZ09sZ321sbGBoaKjUapWYmKjUuvWqvXv3YtKkSfjjjz/Qs2fPQutKpVJIpbobmF2WZK+06NhZls3rKk7SWxoh95IxYtO/RdZ7mPwMr9lZ6iQGIiKi0tLbliwTExO4u7vD399fodzf3x+dOnVSu5+vry/Gjx+P3bt3Y8CAAboOU688eZaj8Lw4uU9J8qOyXjtw7h+XVZZ/5N1Q4fmk3y6URThEREQlorctWQAwZ84cjBkzBh4eHvD09MTGjRsRFRWFKVOmAHjR1RcbG4vt27cDeJFgjR07Fj/++CM6duwobwUzMzODtbXu7nDTF0lPX0mytDxaKvCW8szruqRurNXHvRrDzckaH+66WKbxEBERlYTetmQBwPDhw7FmzRosXboUrVu3xqlTp3DkyBE4OzsDAOLi4hTmzNqwYQPy8vLw0UcfwdHRUf6YOXNmeb2EclWcVqqSjBl/tTuygKGWugvvJj5Fj+8DcSA8ttB6BgYS9GvhqJVzEhER6Ypet2QBwNSpUzF16lSV23x8fBSeBwYG6j6gCqSsVis0LMa6iNO7v4afT94ttE7P1UEAgFl7L2FQm9oq6zRztCp5gEREROVAr1uySEM6GpD+KiPDos9T3Vz9ZKUAkF7M8V7qXlJmdp7qDUREROWESVYlpq1uvALquhatTI2LsW/h/ZJtl/orlanaQ91hDl56VGQMREREZYlJViVWjF48rbCpVngrFQB0fs1G7ba5f1xGnopZR+PSsgo95ucDmsp/Dr5TtoPyiYiIisIkqxLr4mqrleN4fReAoNuPkZmjuktucpcGRR6jaSFjqfaFxZQqrhHt68l//l9E8VYBICIiKit6P/CdiiciNk3hebfGtmhdt7pCWd2aZohOea5QVpwexYfJzzBu63mYmxiq3O5Wu+ymx6j1UquZsSG/IxARkf7ip1Ql8c3RmwrPezZVnhX/0Eeva3SOZzn5Gu1fEu2Xn1BZvmJIC/nPxsUYcE+l9yj1OdeFJCLSAJOsSiL4TpLC89rVzZTq1LAoeuyUvkjMyFZZXqeGufxnXS3nU5XdSchAVPIz7AuLQaeVJ/H5gYjyDomIqMJid2El1dypaswn9ZF3Q/wacK+8w6gU0p7lotcPpxTKdp2LwvLBLdTsQUREhWFLViVVkVqtNNG+fi0AhQ+sp+J5lPZcZTm7DImISoctWZVUZexIs7OUKpUVDMbPyi278WKVlYGa7tdWS45jqEddvGZXTeGOTiIiKhyTrErKqJh33hmV1WRaWjCnl6tSmZnxiyQrMimzrMOpdNQNcUvPysOW05EAwCSLiKgE2F1YxRW13I0+ece9jlLZy60vATcTyzKcSuXPsBj8UsTakgDw5cEITNkRhrRnxVsGiYioKmOSRWXG0dpUo/1Vtc6ZGP1XNsEnFCH3kjU6R1X01+VH+PiPyzh0ueilibaHPMTRa/H45tjNIusSEVV1TLKozOhi/LSlqWKP94hN/2r/JJXcdN/wEu9zOz5DB5EQEVUuHJNVCdyISy/vEIpFqFzyWTP2VsqtY0IIzqFVDA+TM/GklN1+Fx4+QVZuPkyNVa8CQERETLIqhfgiFlIujKqZ4UuiXk3zoiuVsTyZ4GzwRYhLew6v7wI1OkbQ7cf4PTQak16vj06FLABORFRVMcmqBJ48y1F4PqGzS5H7dKhfE4Pb1MabrZ00Ovd7XYteHLqAJt2Fvu91LHbd+LQs1NXD5E+fvLMuRONjzP3jMjKy8vDPzUQ8WDlAC1EREVUuHJNVCcz5/bLCczenohdstrWU4t329WBuolmerWruKl2wsyr+ebp8G1Csejfj0+Ey/zB2/vtQ5faAW4m4//hpsc9bUcSnZSE2VfXEowVcapnjwcoBuL2sH6RGqv9NZGTl6SI8IqJKg0lWJVSW83Orm8BSFX2aNzwxPQt91wQDgMr1+cIePsGEbaHo/n1QWYemc8uP3Ch0ewNbC/hMaA/gxd2bh6a9jmpSNnoTEZUUk6xK6DW7amV2rlrVij/PlibdhRYlbHG7k5ABl/mH4XMmUuX214to7boakyr/+cT1hBKdW5V8mcD4beex8n/lO/XBiesJ+EvNVA3rR7vj7vJ+OPlxN7jYWMjLGztY4syn3Qs9LpfeISJSxiSrEmpdt3qZnattvRrFrqvJ7PIOJZxjq2Ch48V/XVcoz8mTYee/D5GTJyv2sSZvv1Cic+fly3A0Ih5JT7PlZUcj4hF46zHWB5XvYtaFvZa+bg5qVwqwNjcu/Li/XUBufvHfUyKiqoBJFpWac62SDS53q12+izjLZAKun/9PZffgq1RNAZGSmYPfzj7Ak8wcpW3RKc/Qc3UQ9oZGYcvpSEzZGQaPZSewI+QBIpMy8dHui1p5DZoorLXpw24Ni9x/mvdrarf9czMRg349U6q4iIgqKw60qKK00blzdGbXEtUvyfitlw1uU7tU+72qwWdHNNr/gx0XEPrgCfyvJ2Dn5A4IfZCC5YdvYOlbzbHmxB3cTXyKT/+8qrDPFwevaXRObYlMyoT3qkC12z/t26TIYxTVEHntUTruPX6KhrZl111NRKTP2JJFpWZmUrKJKBcOaFqq82iaZJ28mYBZe4o3q/n1R+m4FZ+hcrHk0AdPAACn7yYBAIauD8Gl6FSM2Pgvnmbr9512Xx4suvWuKK3rVS+yTo/vg/T+vSAiKitMsiq4ijTg2LmWRdGVVGjsYFnodvsipneY6HMBBy4Vvi7f7L2X8OHOMPT/KRh91pxCbn7x39fMnHycj0wpdv3yWFw5+E6SxsfwbmyHX0e2LbLeiiLuXiQiqiqYZFVwCenZRVeq4FQtnfOyv6a/jp9GtNHoHPvDY/G/iHj587wiBnF/uu9Kqc/VaunxUu9bGulZhSd1xZ31XyKRYEBLR/hMaFdovXMlSDiJiCozjsmq4Ng1A9hZmuLNVk5Ie56LL4oxqL04vj12q9Dtey9Ea3T8slxfsfUS5aSuhrkxzn3WE0+e5RSZxL6qW2O7QrffTax8E7gSEZUGW7IquH/vJ5fLeVvVKXpWeVWsTHWX1/dt7qC1Y+XLdNsNG3ArUafHLyCEwKsvxd25BsK/7A0TI4MSJ1gFdk7qoIXoiIgqNyZZFdyr0xF4udqWyXnXvKtZ95wu2OpwiZ/HGdrtlv34laWQdOXXgLtKZX9+2Enj477eqPAFoSvSWEEiIl1hklXJTC3GfEfaULeGWZmcZ2SHemVynqK0W35Cq8d78ixX54lITp4Mq47fVigrznxYxdXARv2NDDfjM7R2HiKiiopJViVTw6LwZW5a1H7RzfeOe52yCEdjS99sXqL6b7R01FEk2newiDseNdXtO+Wlg4ozH1Zx7ZjcAVO7NcS5z3ogcG43hW2p5XAHJRGRvuHA90qmqKVr9n3oiZgnz8ttwsiSDPbe/V4Htcu8qLNmeGv8fSWupGGVi1l7L6GhbTW0KOX4tsIMXX8Wj9KytH7cl9WuboZ5apK2Cw9S4Nmwlk7PT0Sk79iSVYFl5eYrPB/Q0hH1C+nCAQCpkWG5zsi9eZxHseu2rFO9xMc3MjTA3vc7lni/8jLwl9OQaXmQ/dGIOPnEqQUsTAyxY1J7rZ7nVS93H37vf5vjsoioymOSVYHdiEuX/9zcyQq/jmxbZtMClPY87VxqFv8cpToD0KFBLWwaW/xkrrzt+PehUtnznHy4zD8Ml/mHEXzncZHHuBKTiuWHr2OiTyim7FReJ/Ha0r7o0ki3N0XkvDK3WP0FR5Cdl6+mNhFR5cckqwJ7edzLq61alYEm+WJpp5goD4sOKa5vmJcvQ9Mvj8qfj9lyXv5zZFImJvqEIuzhfy1VSU+z8eYvZ7ApOBInbypPDVFWLXtWpsZKZe9tD6swv5sxT56h34/B+F3DOdCIiAowyarALsekyn/W8bROWvWRd/HucNOkt8m0kHUVZ3R/rfQHLgPBd1UvgRNyLxneqwJx8mYi3l53FiH3knE+MgVD14cUejyPErQeauKH4a2VEuNTtx+jyRdH4XcxBgBwPjIFLvMP4wf/21gfdA/XH6WrOFLh/gyLQfdVgQqTnm4Ovo8tpyPlzyNi03D2pffxSWYO3t0YAr+LMYhOeQYhBFKf5eDLgxFwmX8YfdecwvLDN3AjLh3z9l3BAr8ruBmfjtjU5/JjCCFwMz69yNUAiIgKcOB7BXYpOlX+cxn1EmqFZwMb/BpwT6fnUNWqAgCjO9bDnN6NYWVmjGWHy36NvTm9XLHa/7ZS+Yr/3UA1EyMIQOX2fJnAiE3/KpS9+lwdwyJuhtCWxg6WCF3YEx7LlKe7mPP7ZQxpWwfDNrxICH/85w4AYOX/bgIAVgxpgSFta0NqVPii4xlZufj4jxdzjH3mdxW/T/HE3cSn8mu569xDdG1kC5+zDwAA2ya0g3djO7T5yh8A8O/9F0v+VDc3VmgJvhmfoTDthO/5aPie/69F6/MBTfFbyANEp7xIugLmdity/CMREZOsCszM+L8PpAqUYxWbpo1zFiaGyMxR7KoqWKdvrKdLuSRZM3o0UplEbQi6X+h+EbFppTqf/+yupdqvtAwKyfYPhMeq3bbA7yr+uZGIj7wbok29GgBezPP15cEIxKY+R5dGNjh8NR6XX/picf5BChYfuiZPqADg/uNM3H+cKX8+YVsomqhYYLykU0y8+rvivSoQ15b0wZ7QaPRuZo+6Nc3lMZ+++xjtXGrCUk2iT0RVB5OsCkoIgQsvjcspqwHv2lDcUE2NNOvN/mNKJ6z2v4UTN/4bp9S+/ouuMxMNj13W4tNLNx1DI3vlBEOXCntfZ+29VOi+J24k4MSNBJXbgu+o7kJ9OcFSR1cTozZfdAwAsObEbczq6Yq8fBlW/H/LXOfXamHX5IpzlysR6UbF+qQhueiU5wpLvZRlilXD3FijLqii5vJaN6otLn7Rq8RzZL2qmZMVNo9rB4eX1uczNyn77xVD3eugq6stPvBqUOpj/FaMZOJVqlpwdK2a1Ahbx1ecOzu1ISMrD1/9fV2eYAHAmbvJ+PrIDTxKfY7AW4nou+YU5uy9BJf5h3FaTcJIRJUPW7IqqAsPUxSel2VD1tn5PTTa38OlJtq71MT5B4qvwdHaFL2b2aNfC+3O2l7N1AhQMb56rKcztocoT5+gbRM610czJyv5838X9EDHFf+U6Bhn75VsIfAjM7rA1b585kPr3sQeVxf3xtGIeDxMfoZfVKyfWBVsPHUfG0/91w1c0KI2ess5AMDXg1tgcJvaSHuei+0hD3AjLh0Btx5jTi9XfODVAHcSnqK5kxUkEonCnGNpz3Ox5XQkRnVwhoP1iy8QV2JSYVNNiifPcpCbL9C6bnV5/XyZkD8epT1HTXMTmEsNixz/RkSakwjOGKggPT0d1tbWSEtLg5WVVdE7lJNJPqH456Xb9RvbW+KYFsffuMw/rHbbg5UDdHKeyBX9ddLteTM+HbP3XsacXq7o1cxe7fl1YU4vV8zo0UipXNfn1eY10tSrr1Xd4H/6j5GBBHkygX5uDriT+FR+J6WxoQS5+f/9yx7mUQeTXm+APmtOKey/Y1J7SI0M5TcaqDOyQz0Mda+DCw+eYIynM0yNmXhRxaWPn99syaqg/lExH1JFVMvCBMmZOQB0N66siYMV/jezi06OXRh35xp4r4vqLsJ5fRvj26O3tHo+eyspEtKz9e6ut38+9kKP74NgbyVFyPweMDCQwMXGAjN8w8s7NL2V9/9zsvwvIl6h/OUECwB+vxCD3y/EKO3/8txqhdl9Lgq7z0UBAFKe5Wh1bUsiYpJVId17/FSprHYNM62e48NuDbEuUHmaBVtLqVbPU7emuTzJKg/LB7th4f4IrR930cBmmNC5vtrtU7u9BjNjQyz567pWznfxi16oaWGC6JRnWr9GmmpoW02pZe3NVk6ITnmG745pN9Gk0lsXeE/+N//14BYY0b4ufvznDi5GpWLjGHdEpTzDyZuJ6N3MHt/734arnSWe5eRh578P4ft+R8zeewk5+TJsG98er9m96KpOfZYDazNjLD98A79fiMbCAU3h4VITtaubIfRBCtq51GTrGVVq7C58hT42N77q++O38PPJF+Ncapgbo0P9WljyVnPYvzTAW1Npz3PRaslxhbINY9zRrbGtVsdyDPr1jHy+r/Lq4tJm193lL3vjUkwqXn/Nplg3B2wOvq92KonNYz0w+/dLyMjKU7v/nvc7opmTldp5wfSd+1f+5ZZkz+zRCFtPR8KzYS0M86iLydsvAAAW9GuiMIi9Z1N7tXc9knZ85N0QwXeS8DgjGxM714eLjcWLO4EFsOPfB+jW2A5utRVXcZDJBCSS0reA33/8FDaW0gr7t1PehBB6d1e7Pn5+631L1tq1a/Hdd98hLi4OzZs3x5o1a9Cli/qun6CgIMyZMwfXrl2Dk5MT5s2bhylTppRhxP8JfZACAwng7qy9GbczsnLlzfsA8HojW/w8oo3Wjl/A2swYXw1ywxcH/mvl6dPcQevn0Ye/0bDPe+KLgxE4cjW+6MqF+GF4K1ibG8PLtfhrBE7u0gBSY0OF97mRXTX8/oEnaliYFJpgze3tio4NamkUc3kL+6IXNgTdU0hqysKI9vUwu5crZvdylZedmOOFiw+fYKhHHbSoY415+65g0cDm6N7EDj+euI0/L8YqzAD/kXdDhUl1rc2Mkfa8ZPNv0Qsvv4/Ljyh/6Vh1XP0Yvtdfs8H5BymQyQTyZAL1apqjW2NbuNpb4vMDyq3UbrWtEBH7350wOya1R+eGNjBQ8aUoJ0+G2NTnCLiZiFZ1rdHYwQpPMnPw2f6rMDSQIDM7D0+e5aJHEzucf5CCBjbV8OfFGPRoYofTd5PwjnsdPM7IRqu61WFpaoQvD15DO5camNylAfaGRuPkzUR0algLHs414NnQBsmZ2Vjgd1Xp797EyACO1qaQCYFn2fnyLyamxgbIyi18BYImDpYqpzEpzr6aqF3dTOHvpWdTe1x7lIa4NMXpaFrWscaVmBfzADZ3ssK1QlaB6OfmgHORKUjJzMGP77bGo9Qs/HLyDqqZGiEhPRuy7Ge6eTEa0OuWrL1792LMmDFYu3YtOnfujA0bNmDz5s24fv066tWrp1Q/MjISbm5ueO+99/DBBx/gzJkzmDp1Knx9ffH2228X65zayoRjnjzD698EAAD+nv660rew0vr26E2sfakbb1BrJ6x5V/tJVoFb8RmYuSccc3q5orcOkiz/6wl4b/sFvNHSEb+MbKv14xdXvkzg9wvRWOB3FcCLGb4zs/PxwwnFf+7dGtviakwanmbnITvvxT+oQ9M6o2Wd6hqdPyUzB79fiEbIvWSsGd4aNSxMACi2ss3u6YpRHeuhupkx7j5+isb2lnr3TbK00rNy0XLx8SLrrRzSAvP//xoV182v+iIjKw+2llLEPHmG2tXNSv2+3YxPh6FEgvSsXLStVwOJGdno8PU/GNDSEb+ObIvE9CzUsDCBgUSCD3ZckM/RVru6GerbWOC0miWTdOUd9zro1cwewXce44OuDVGnxovXvuhgBH4rgztricqSLPsZotcM06uWLL1Osjp06IC2bdti3bp18rKmTZti0KBBWLFihVL9Tz/9FIcOHcKNG/99E5oyZQouX76MkJDC77IpUJwkKy9fhoSMbOTnC+QLgXyZDHkygbx8AZkQyM0XeHvdWbXn2DDGHY3sqsHIwAB5MtmL26vFi/3z///b2POcfCRnZiP5aQ5kQqCRvSXWBtzFuUjFaQ+2T2yPriVoOdFHCelZsK0mVflNsrztDY3Cp3+++FA/McdLPtZEJhPIzMlDNamRThOdy9GpeOvXMwBeJAtVYfxK2MMUGBoYIOBmIuLTsrD3QjRmdH8N03s0grGhAfJlAgnpWQh7+AQ9m9pDamSAEzcSkJ0nQy0LE1iZGcPV3hIyIWBoIIGxhvOtaUKIF3/TBXO+hT1MwaJD17BoYHPk5stw9m4yZvVshLuPnyIvX8DR2hTuy06gbb3q2DW5I8ZuPYfIpGf4aURrXItNh7WZMcylhhjQwhHZeTJ5l/76Me6oW8McEbFpeKOlI24nPEUTB0u1f1PZefnYEHQfDtamaOZohTd+Pg0AGNDCEYevxpXNm0OkZUyySiAnJwfm5ub4448/MHjwYHn5zJkzcenSJQQFBSnt07VrV7Rp0wY//vijvGz//v0YNmwYnj17BmNj5b737OxsZGf/N6lneno66tatW+hFik55hi7fBmjy8jR286u+iHnyDK/Zlf2Ek1VNvkyU2fp/qmg69oSoNHLzZfIE9VJ0KpIysmFsZIA1J27jrVZOGOPpAuD/E0khIDUyRGzqcxgbSmBnaYqs3HxkZOXhyNU4DPWoI58IOCE9CzfjM9C1kQ0kEgkS0rOwLvAeJnR2gXMtC/kxd56LQkJaFrq62uLkzURM7lIfRgYSRMSmw8TIABIJ4HcxBimZL+6KfPIsFxZSQ5y7n4KHyc+w9Uwk1o9ui75ujniU+hwOVqZIfZ6L4DuPAQButa3x5s+nlZbeAoCeTe3Q0LYaDAwkSH2Wiz7N7fHX5TikZ+VicJvaeJ6Tj4Z21XA5OhXtXGpitf9tdG9ih/9FxGF0R2fcTXyKN1o64szdZJibGOJ2Qgba16+JB0mZeM3OEucik9GpoQ3audTArYQMSCBBQkYWIIDXG9kgMSMbBhLAztIU2Xn5OBeZguaOVrC1lCr8H7j2KA3PcvLRzqUm8vJlChM4P8/Jh5GhBPkyAamRgcJ+Wbn5yJMJWJgY4ml2HnLzBWpamCAn78WX/tjUZ2hgU00hSS+4ieHV/0Nxac9hW00qP7dMJpCT/6Kb1dzEEI9Ss9DcyQomhgby/2MxT54hL1/AxcYCKZk5yJPJUMtCivTnuTCXGv5/XQny8mUwkEggkbxY9SL9eR5qWpjgVnwGpvtexOA2dTDW0xl1a5ojXybwNCMdtWrWYJJVHI8ePULt2rVx5swZdOrUSV7+9ddf47fffsOtW8p3Jbm6umL8+PH47LPP5GVnz55F586d8ejRIzg6Kk9yuXjxYixZskSpvLCLFJf2HF7fBcLIQAJDiQSGhpIXPxtIYGRggKfZeUWOzagmNZJ/0y7Yt2B/QwMJTI0NUMtCiprVTCCEQNjDJ0hIf5EMarP7kYiIqDLgwPdSeDVrLuqOBlX1VZUXWLBgAebMmSN/XtCSVRhHazPcXtav0DpERERUteltkmVjYwNDQ0PExyve8ZWYmAh7e3uV+zg4OKisb2RkhFq1VN+FJZVKIZXq17xCREREVPHp7QLRJiYmcHd3h7+/v0K5v7+/Qvfhyzw9PZXqHz9+HB4eHirHYxERERHpit4mWQAwZ84cbN68GVu3bsWNGzcwe/ZsREVFyee9WrBgAcaOHSuvP2XKFDx8+BBz5szBjRs3sHXrVmzZsgVz584tr5dAREREVZTedhcCwPDhw5GcnIylS5ciLi4Obm5uOHLkCJydnQEAcXFxiIr6b2LO+vXr48iRI5g9ezZ+/fVXODk54aeffir2HFlERERE2qK3dxeWF328O4GIiIgKp4+f33rdXUhERERUUTHJIiIiItIBJllEREREOsAki4iIiEgHmGQRERER6QCTLCIiIiIdYJJFREREpANMsoiIiIh0gEkWERERkQ7o9bI65aFgAvz09PRyjoSIiIiKq+BzW58WsmGS9Yrk5GQAQN26dcs5EiIiIiqp5ORkWFtbl3cYAJhkKalZsyYAICoqSm8uUlWVnp6OunXrIjo6Wm/WoaqqeC30C6+H/uC10B9paWmoV6+e/HNcHzDJeoWBwYthatbW1vyD0RNWVla8FnqC10K/8HroD14L/VHwOa4P9CcSIiIiokqESRYRERGRDjDJeoVUKsWiRYsglUrLO5Qqj9dCf/Ba6BdeD/3Ba6E/9PFaSIQ+3etIREREVEmwJYuIiIhIB5hkEREREekAkywiIiIiHWCSRURERKQDTLJesnbtWtSvXx+mpqZwd3dHcHBweYdUoaxYsQLt2rWDpaUl7OzsMGjQINy6dUuhjhACixcvhpOTE8zMzNCtWzdcu3ZNoU52djamT58OGxsbWFhY4M0330RMTIxCnSdPnmDMmDGwtraGtbU1xowZg9TUVIU6UVFRGDhwICwsLGBjY4MZM2YgJydHJ69d361YsQISiQSzZs2Sl/FalJ3Y2FiMHj0atWrVgrm5OVq3bo2wsDD5dl6LspOXl4fPP/8c9evXh5mZGRo0aIClS5dCJpPJ6/B66MapU6cwcOBAODk5QSKR4MCBAwrb9e19v3r1Kry8vGBmZobatWtj6dKlJV8XUZAQQog9e/YIY2NjsWnTJnH9+nUxc+ZMYWFhIR4+fFjeoVUYffr0Edu2bRMRERHi0qVLYsCAAaJevXri6dOn8jorV64UlpaW4s8//xRXr14Vw4cPF46OjiI9PV1eZ8qUKaJ27drC399fXLx4UXh7e4tWrVqJvLw8eZ2+ffsKNzc3cfbsWXH27Fnh5uYm3njjDfn2vLw84ebmJry9vcXFixeFv7+/cHJyEtOmTSubN0OPnD9/Xri4uIiWLVuKmTNnyst5LcpGSkqKcHZ2FuPHjxfnzp0TkZGR4sSJE+Lu3bvyOrwWZWfZsmWiVq1a4u+//xaRkZHijz/+ENWqVRNr1qyR1+H10I0jR46IhQsXij///FMAEPv371fYrk/ve1pamrC3txfvvvuuuHr1qvjzzz+FpaWlWLVqVYleM5Os/9e+fXsxZcoUhbImTZqI+fPnl1NEFV9iYqIAIIKCgoQQQshkMuHg4CBWrlwpr5OVlSWsra3F+vXrhRBCpKamCmNjY7Fnzx55ndjYWGFgYCCOHj0qhBDi+vXrAoD4999/5XVCQkIEAHHz5k0hxIs/ZgMDAxEbGyuv4+vrK6RSqUhLS9Pdi9YzGRkZolGjRsLf3194eXnJkyxei7Lz6aefitdff13tdl6LsjVgwAAxceJEhbIhQ4aI0aNHCyF4PcrKq0mWvr3va9euFdbW1iIrK0teZ8WKFcLJyUnIZLJiv052FwLIyclBWFgYevfurVDeu3dvnD17tpyiqvjS0tIA/LfodmRkJOLj4xXeZ6lUCi8vL/n7HBYWhtzcXIU6Tk5OcHNzk9cJCQmBtbU1OnToIK/TsWNHWFtbK9Rxc3ODk5OTvE6fPn2QnZ2t0E1T2X300UcYMGAAevbsqVDOa1F2Dh06BA8PDwwdOhR2dnZo06YNNm3aJN/Oa1G2Xn/9dfzzzz+4ffs2AODy5cs4ffo0+vfvD4DXo7zo2/seEhICLy8vhYlN+/Tpg0ePHuHBgwfFfl1cIBpAUlIS8vPzYW9vr1Bub2+P+Pj4coqqYhNCYM6cOXj99dfh5uYGAPL3UtX7/PDhQ3kdExMT1KhRQ6lOwf7x8fGws7NTOqednZ1CnVfPU6NGDZiYmFSZa7pnzx5cvHgRoaGhStt4LcrO/fv3sW7dOsyZMwefffYZzp8/jxkzZkAqlWLs2LG8FmXs008/RVpaGpo0aQJDQ0Pk5+dj+fLlGDFiBAD+bZQXfXvf4+Pj4eLionSegm3169cv1utikvUSiUSi8FwIoVRGxTNt2jRcuXIFp0+fVtpWmvf51Tqq6pemTmUVHR2NmTNn4vjx4zA1NVVbj9dC92QyGTw8PPD1118DANq0aYNr165h3bp1GDt2rLwer0XZ2Lt3L3bu3Indu3ejefPmuHTpEmbNmgUnJyeMGzdOXo/Xo3zo0/uuKhZ1+6rD7kIANjY2MDQ0VPrmkJiYqJTtUtGmT5+OQ4cOISAgAHXq1JGXOzg4AECh77ODgwNycnLw5MmTQuskJCQonffx48cKdV49z5MnT5Cbm1slrmlYWBgSExPh7u4OIyMjGBkZISgoCD/99BOMjIwUvpG9jNdC+xwdHdGsWTOFsqZNmyIqKgoA/y7K2ieffIL58+fj3XffRYsWLTBmzBjMnj0bK1asAMDrUV707X1XVScxMRGAcmtbYZhkATAxMYG7uzv8/f0Vyv39/dGpU6dyiqriEUJg2rRp8PPzw8mTJ5WaU+vXrw8HBweF9zknJwdBQUHy99nd3R3GxsYKdeLi4hARESGv4+npibS0NJw/f15e59y5c0hLS1OoExERgbi4OHmd48ePQyqVwt3dXfsvXs/06NEDV69exaVLl+QPDw8PjBo1CpcuXUKDBg14LcpI586dlaYyuX37NpydnQHw76KsPXv2DAYGih99hoaG8ikceD3Kh769756enjh16pTCtA7Hjx+Hk5OTUjdioYo9RL6SK5jCYcuWLeL69eti1qxZwsLCQjx48KC8Q6swPvzwQ2FtbS0CAwNFXFyc/PHs2TN5nZUrVwpra2vh5+cnrl69KkaMGKHyFt06deqIEydOiIsXL4ru3burvEW3ZcuWIiQkRISEhIgWLVqovEW3R48e4uLFi+LEiROiTp06lfbW6OJ4+e5CIXgtysr58+eFkZGRWL58ubhz547YtWuXMDc3Fzt37pTX4bUoO+PGjRO1a9eWT+Hg5+cnbGxsxLx58+R1eD10IyMjQ4SHh4vw8HABQKxevVqEh4fLp0rSp/c9NTVV2NvbixEjRoirV68KPz8/YWVlxSkcNPHrr78KZ2dnYWJiItq2bSufeoCKB4DKx7Zt2+R1ZDKZWLRokXBwcBBSqVR07dpVXL16VeE4z58/F9OmTRM1a9YUZmZm4o033hBRUVEKdZKTk8WoUaOEpaWlsLS0FKNGjRJPnjxRqPPw4UMxYMAAYWZmJmrWrCmmTZumcDtuVfNqksVrUXb++usv4ebmJqRSqWjSpInYuHGjwnZei7KTnp4uZs6cKerVqydMTU1FgwYNxMKFC0V2dra8Dq+HbgQEBKj8jBg3bpwQQv/e9ytXroguXboIqVQqHBwcxOLFi0s0fYMQQkiEKOn0pURERERUFI7JIiIiItIBJllEREREOsAki4iIiEgHmGQRERER6QCTLCIiIiIdYJJFREREpANMsoiIiIh0gEkWEVU4ixcvRuvWrcs7DDmJRIIDBw6UeL9bt27BwcEBGRkZJdqvXbt28PPzK/H5iKhsMckiIpXWr18PS0tL5OXlycuePn0KY2NjdOnSRaFucHAwJBIJbt++XdZhliltJ3cLFy7ERx99BEtLS6VtjRs3homJCWJjY5W2ffHFF5g/f758vT0i0k9MsohIJW9vbzx9+hQXLlyQlwUHB8PBwQGhoaF49uyZvDwwMBBOTk5wdXUtj1ArpJiYGBw6dAgTJkxQ2nb69GlkZWVh6NCh8PHxUdo+YMAApKWl4dixY2UQKRGVFpMsIlKpcePGcHJyQmBgoLwsMDAQb731Fho2bIizZ88qlHt7ewMAdu7cCQ8PD1haWsLBwQEjR45EYmIiAEAmk6FOnTpYv369wrkuXrwIiUSC+/fvAwDS0tLw/vvvw87ODlZWVujevTsuX75caLzbtm1D06ZNYWpqiiZNmmDt2rXybQ8ePIBEIoGfnx+8vb1hbm6OVq1aISQkROEYmzZtQt26dWFubo7Bgwdj9erVqF69OgDAx8cHS5YsweXLlyGRSCCRSBQSoKSkJAwePBjm5uZo1KgRDh06VGi8v//+O1q1aoU6deoobduyZQtGjhyJMWPGYOvWrXh19TNDQ0P0798fvr6+hZ6DiMoXkywiUqtbt24ICAiQPw8ICEC3bt3g5eUlL8/JyUFISIg8ycrJycFXX32Fy5cv48CBA4iMjMT48eMBAAYGBnj33Xexa9cuhfPs3r0bnp6eaNCgAYQQGDBgAOLj43HkyBGEhYWhbdu26NGjB1JSUlTGuWnTJixcuBDLly/HjRs38PXXX+OLL77Ab7/9plBv4cKFmDt3Li5dugRXV1eMGDFC3h165swZTJkyBTNnzsSlS5fQq1cvLF++XL7v8OHD8fHHH6N58+aIi4tDXFwchg8fLt++ZMkSDBs2DFeuXEH//v0xatQotfECwKlTp+Dh4aFUnpGRgT/++AOjR49Gr169kJmZqZDoFmjfvj2Cg4PVHp+I9ECJlpMmoipl48aNwsLCQuTm5or09HRhZGQkEhISxJ49e0SnTp2EEEIEBQUJAOLevXsqj3H+/HkBQGRkZAghhLh48aKQSCTiwYMHQggh8vPzRe3atcWvv/4qhBDin3/+EVZWViIrK0vhOA0bNhQbNmwQQgixaNEi0apVK/m2unXrit27dyvU/+qrr4Snp6cQQojIyEgBQGzevFm+/dq1awKAuHHjhhBCiOHDh4sBAwYoHGPUqFHC2tpa/vzV8xYAID7//HP586dPnwqJRCL+97//qXxPhBCiVatWYunSpUrlGzduFK1bt5Y/nzlzphg1apRSvYMHDwoDAwORn5+v9hxEVL7YkkVEanl7eyMzMxOhoaEIDg6Gq6sr7Ozs4OXlhdDQUHkrS7169dCgQQMAQHh4ON566y04OzvD0tIS3bp1AwBERUUBANq0aYMmTZrIu7qCgoKQmJiIYcOGAQDCwsLw9OlT1KpVC9WqVZM/IiMjce/ePaUYHz9+jOjoaEyaNEmh/rJly5Tqt2zZUv6zo6MjAMi7Mm/duoX27dsr1H/1eWFePraFhQUsLS3lx1bl+fPnMDU1VSrfsmULRo8eLX8+evRo+Pn5ITU1VaGemZkZZDIZsrOzix0jEZUto/IOgIj012uvvYY6deogICAAT548gZeXFwDAwcEB9evXx5kzZxAQEIDu3bsDADIzM9G7d2/07t0bO3fuhK2tLaKiotCnTx/k5OTIjztq1Cjs3r0b8+fPx+7du9GnTx/Y2NgAeDFuy9HRUWUXWcH4qJcV3GG3adMmdOjQQWGboaGhwnNjY2P5zxKJRGF/IYS8rIB4ZSxUYV4+dsHxC7v7z8bGBk+ePFEou379Os6dO4fQ0FB8+umn8vL8/Hz4+vriww8/lJelpKTA3NwcZmZmxY6RiMoWkywiKpS3tzcCAwPx5MkTfPLJJ/JyLy8vHDt2DP/++6/8DrmbN28iKSkJK1euRN26dQFA4e7EAiNHjsTnn3+OsLAw7Nu3D+vWrZNva9u2LeLj42FkZAQXF5ci47O3t0ft2rVx//59jBo1qtSvs0mTJjh//rxC2auxm5iYID8/v9TneFmbNm1w/fp1hbItW7aga9eu+PXXXxXKd+zYgS1btigkWREREWjbtq1WYiEi3WB3IREVytvbG6dPn8alS5fkLVnAiyRr06ZNyMrKkg96r1evHkxMTPDzzz/j/v37OHToEL766iulY9avXx+dOnXCpEmTkJeXh7feeku+rWfPnvD09MSgQYNw7NgxPHjwAGfPnsXnn3+uMmEDXsxftWLFCvz444+4ffs2rl69im3btmH16tXFfp3Tp0/HkSNHsHr1aty5cwcbNmzA//73P4XWLRcXF0RGRuLSpUtISkrSqKuuT58+CAkJkSdtubm52LFjB0aMGAE3NzeFx+TJkxEWFqZwh2VwcDB69+5d6vMTke4xySKiQnl7e+P58+d47bXXYG9vLy/38vJCRkYGGjZsKG+1srW1hY+PD/744w80a9YMK1euxKpVq1Qed9SoUbh8+TKGDBmi0OUlkUhw5MgRdO3aFRMnToSrqyveffddPHjwQOH8L5s8eTI2b94MHx8ftGjRAl5eXvDx8UH9+vWL/To7d+6M9evXY/Xq1WjVqhWOHj2K2bNnK4ybevvtt9G3b194e3vD1tZWoykU+vfvD2NjY5w4cQIAcOjQISQnJ2Pw4MFKdRs1aoQWLVpgy5YtAIDY2FicPXtW5RxbRKQ/JKIkgw6IiKqQ9957Dzdv3tTZVAlr167FwYMHSzyp6CeffIK0tDRs3LhRJ3ERkXZwTBYR0f9btWoVevXqBQsLC/zvf//Db7/9pjCpqba9//77ePLkCTIyMlQuraOOnZ0d5s6dq7O4iEg72JJFRPT/hg0bhsDAQGRkZKBBgwaYPn06pkyZUt5hEVEFxSSLiIiISAc48J2IiIhIB5hkEREREekAkywiIiIiHWCSRURERKQDTLKIiIiIdIBJFhEREZEOMMkiIiIi0gEmWUREREQ6wCSLiIiISAf+D8eusxrBKYWMAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(m_w, m_f)\n", + "plt.title('Meisner Flux vs. Wavelength for 1200K star')\n", + "plt.xlabel('Wavelength (A)')\n", + "plt.ylabel('Flux (erg/cm$^2$/A)')\n", + "plt.xlim(0, 1e5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "51fcf941-7b85-4c8a-851d-8d73f315e365", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ea2hooEmTJlLXQd54Bg4cWG5NXe/evaGuro6AgIBSzZuf+vLLL0udGyD7e08eI0eOlKpGt7OzQ6dOnaTeY9nZ2fj+++/RqFEjcLlccLlcaGtrIycnBzExMaX22b9/f4jFYvj7+yM3N1eueAwMDMrtdJuWloYePXrg9u3b+PvvvyXNUJ8aMmQINDQ0JM91dHQwYMAAhIeHo7i4GMCH9/uqVavg7OwMPp8PLpcLPp+P58+fl3lOFfn0epXl7NmzAAB/f//PlpX1PGWVn5+PIUOG4NWrVzh+/Di0tbVLlSlvlFFFo48WL16MQ4cO4eeff5b6rgMAX19ftGzZEpMmTapS7OXF5OPjgwMHDqCoqAi//vorhg0bVuZ5ldDU1MSdO3ekHr/++qtMx/viiy9Kbevn51ep2GX53jl79izc3d3lqkmtiLzfH+bm5mjXrp3UMhcXlwqbwpWBEqFqYGxsDIFAgNjYWJnKl1S5ljXKxdLSslSVrKGhYalyfD6/1HI+nw/gw5fVp8zNzaWec7lcGBkZVVj9W8LIyEjqeUkfqLy8PADAmzdvAACzZ88Gj8eTepR8yEuG76elpZWKpaz4AKBFixYwNjbGlStXJP2DShIhAOjatSuuXr2KgoICRERESPUdefPmDf78889S8ZRU5Vc0nYA8MQKlXx/gw2tU8vpUJp6KRkD16tULISEheP78Odzd3ZGamlpu2U/3I+97Tx7lvWYf73PkyJHYsmULJk6ciPPnz+P27du4c+cOTExMpF6vEmPHjsWuXbtw9epV9OvXDzk5OTLHU9Fr+O+//+LWrVvw8vJC8+bN5T6nwsJCyZDngIAALF68GN7e3vjzzz9x69Yt3LlzBy1btizznMojEAigq6v72XJv376Fmppaue/Hj8l6nrIoKCjA4MGD8ffff+OPP/5A+/btpdaXfA7Keg+lp6eX+T0GAMuXL8eKFSuwcuXKUkPiT5w4gXPnzmHdunXIzMxERkYGMjIyAHxoSszIyCiz7548xo0bh7dv32LVqlW4d+8eJkyYUGF5DoeDNm3aSD0cHR1lOpaenl6pbT/txiArWb533r59q9ARsfJ+f8gSY3WgPkLVQE1NDT179sTZs2eRmJj42TdeyZsjOTm5VNnXr19Xuo9GRVJSUmBlZSV5XlRUhLS0tDLfqPIqiXf+/PkYMmRImWVKviiMjIyQkpJSZnyfYhgG3bp1w7lz53D79m1kZGRIJULdunXDsmXLEBERgfz8fKlEyNjYGC4uLuXOq1PRl488McpK3ng+N3eHl5cXTp8+DW9vb7i7u+Py5ctSNVjl7Uee956GhgYKCgpK7fPdu3dlvkfLe81KjpmZmYm//voLS5cuxbx58yRlCgoKKpxja8KECeBwOJg4cSL69u2L0NBQaGlplVu+REWvYceOHfHVV19JfvS2bdtWZj+N8s6Jz+dLag0OHjyIMWPGYNWqVVLl3r17B319/c/GKUu8HzMxMUFxcTFSUlI+O2WArOf5OQUFBfD29saVK1dw+vTpMmuWWrRoAQB49OgRnJ2dJcuLiorw9OlTjBgxotQ2y5cvx7Jly7Bs2TIsWLCg1PrHjx+jqKhIagqNErt27cKuXbsQEhICb29vuc+phI2NDXr16oXly5fD0dERnTp1qvS+qqKk5rGgoEBqwI0sc8CVx8TEpFQn5qpQxW+XIlCNUDWZP38+WJbFpEmTUFhYWGq9SCTCn3/+CQCS6vqDBw9Klblz5w5iYmKqXH1dlkOHDkk9/+2331BUVKSQCRQdHR3RuHFjPHjwoNRfOyWPknk63N3dcenSJUktEvChs/mxY8fK3Le7uztycnKwfv16mJqaSlXxduvWDWlpadi8ebOkbIn+/fvj8ePHaNiwYZnxVJQIyRujLKoST3l69+6N06dP4+XLl3B3d5cpUZPnvWdvb4+HDx9Klfv3338lzZyfOnLkCFiWlTx/9eoVbty4IXmPMQwDlmVLjaoMDg6WNDOVZ9y4cfj111/x999/w8vLSyET0I0dOxZHjx7Fnj17MGbMmDJjOHnypFQNa1ZWFv7880906dJF0lzMMEypczpz5gySkpKkln1ak1pZJc2p27Ztk6m8LOdZkZKaoMuXL+P3339H7969yyzXvn17WFhYYO/evVLLT5w4gezs7FJ/JP3www9YtmwZFi1aVO4oNh8fH1y5cqXUA4AkMatosICsZs2ahQEDBmDx4sVV3ldllYwM/PQzV/K7URleXl64cuVKuZ9ZQL73pSp+uxSBaoSqSceOHbFt2zb4+fnBzc0NU6dORbNmzSASiXD//n3s3LkTzZs3x4ABA+Do6IjJkydj8+bN4HA48PLykvS8t7GxkYySUaSTJ0+Cy+XCw8NDMmqsZcuWGDZsmEL2v2PHDnh5eaF3797w8fGBlZUV0tPTERMTg3v37uH48eMAgEWLFuGPP/5Ajx49sGTJEggEAgQFBZXb5FGS3ISEhGDo0KFS65o3bw4jIyOEhITAyspKamRVYGAgwsLC0KlTJ0ybNg2Ojo7Iz89HXFwcQkNDsX379nJr7uSNURZViacinp6e+OOPPzBo0CBJzVBFtQTyvPdGjx6Nb775Bn5+fvjyyy/x6tUrrFu3DiYmJmXuOzU1FYMHD8akSZOQmZmJpUuXQkNDA/PnzwcA6OrqomvXrli/fj2MjY1hb2+Pa9eu4ddff5Wp5sTHxwccDgfjxo2Dl5cXzp49W2FfDlkMHToUAoEAQ4cORV5eHo4cOSJpYgY+1PZ6eHggICAAYrEYa9euhVAoxPLlyyVl+vfvj71798LJyQkuLi6IjIzE+vXrS13Phg0bQlNTE4cOHULTpk2hra0NS0tLuZPgLl26YPTo0VixYgXevHmD/v37Q11dHffv34dAIMB3330n93l+7jU6e/YsFi5cCCMjI6mpM3R1dSW1P2pqali3bh1Gjx6NKVOmYMSIEXj+/Dnmzp0LDw8PqYn5fvzxRyxZsgR9+vRBv379Sk3HUVIDZG9vX+6s2lZWVgqbCd/T0xOenp4K2Vdl9e3bF4aGhpKRgFwuF3v37i13OgFZBAYG4uzZs+jatSsWLFiAFi1aICMjA+fOnUNAQACcnJzkel+q4rdLIaq1azZho6Ki2LFjx7K2trYsn89ntbS02NatW7NLlixhU1NTJeWKi4vZtWvXsk2aNGF5PB5rbGzMfvPNN2xCQoLU/rp168Y2a9as1HHKm5wLn4ymKOn1HxkZyQ4YMIDV1tZmdXR02BEjRpSa1Ky8UWMlE/h9epyPR1qxLMs+ePCAHTZsGGtqasryeDzW3Nyc7dGjB7t9+3apcv/88w/boUMHVl1dnTU3N2fnzJnD7ty5s9zJCs3NzVkA7JYtW0qt8/b2ZgGwo0aNKrXu7du37LRp01gHBweWx+OxhoaGrJubG7tw4UI2Ozu7wnORNcbyrsOnr6Ws8VT0mle07uLFi6ympibr6OjIJiUlSUaa3Llzp1RZWd97YrGYXbduHdugQQNWQ0ODbdOmDXv58uVyR40dOHCAnTZtGmtiYsKqq6uzXbp0KXME0JdffskaGBiwOjo6bJ8+fdjHjx+XGqH26YSKHztw4ACrpqbGdurUiRUKheWOGivrc8OyZV+zK1eusNra2myfPn3Y3NxcyWu9du1advny5ay1tTXL5/PZ1q1bs+fPn5fa9v379+yECRNYU1NTViAQsF988QV7/fr1Mt8DR44cYZ2cnFgejyf1vqtoor5PR42x7Idr+PPPP7PNmzdn+Xw+q6enx3bs2JH9888/5TpPWaCMkU4lj0/Pj2VZ9vDhw6yLiwvL5/NZc3Nzdtq0aVKjy1j2/0cUlfeQJSZFjBqrSHmjxpQ5oeLt27fZTp06sVpaWqyVlRW7dOlSNjg4uErfOwkJCez48eNZc3NzlsfjsZaWluywYcOkvv/Le1+WNZKtqr9dZb2flY1h2Y/qqgkhRMGuXr0Kd3d3HD9+vFStXW0VFxcHBwcHrF+/HrNnz1Z1OISQKqA+QoQQQgipt6iPECGEkFKKi4tRUYOBrLMlE1LTUdMYIYSQUrp3717hxKJ2dnbl3rKDkNqEEiFCCCGlPHv2DFlZWeWuV1dXl8wNREhtRokQIYQQQuot6iP0GWKxGK9fv4aOjo7MM7sSQgghRLVYlkVWVhYsLS0rnDGdEqHPeP36NWxsbFQdBiGEEEIqISEhocIJaSkR+oySWz8kJCTIdMNDWYlEIly4cAGenp7l3kWc1Ex07Wonum61F1272kuV104oFMLGxkbyO14eSoTKERQUhKCgIMl9d3R1dRWeCJXcTZo+2LULXbvaia5b7UXXrvaqCdfuc91aaELFcvj7+yM6Ohp37txRdSiEEEIIURJKhAghhBBSb1EiRAghhJB6i/oIEUIIUbni4mKIRKIy14lEInC5XOTn50v6bZLaQZnXjsfjKeQ2L5QIEUIIURmWZZGSkoKMjIwKy5ibmyMhIYHmc6tllH3t9PX1YW5uXqV9UyJECCFEZUqSIFNTUwgEgjJ/0MRiMbKzs6GtrV3hxHik5lHWtWNZFrm5uUhNTQUAWFhYVHpflAgRQghRieLiYkkSZGRkVG45sViMwsJCaGhoUCJUyyjz2mlqagIAUlNTYWpqWulmMnpHEUIIUYmSPkECgUDFkZDaquS9U17/MllQIkQIIUSlqN8PqSxFvHcoESpHUFAQnJ2d0bZtW1WHQgghhBAloUSoHDSzNCGEEFL3USJECCGEqAjDMDh16pSqw6jXKBFSkYIiMVhW1VEQQgipDB8fH3h7e6s6DJWJj4/HgAEDoKWlBWNjY0ybNg2FhYWf3S4iIgI9evSAlpYW9PX10b17d+Tl5VVDxOWj4fMq8vPF5zh1Xw33mafwcLZAOwdD8LmUlxJCCKnZiouL0a9fP5iYmODvv/9GWloaxo4dC5ZlsXnz5nK3i4iIQJ8+fTB//nxs3rwZfD4fDx48UPmUCPTLqyL/vEhDWgGDfRHx+ObXW3D7IQz+h+7h5L1EpOd8PqsmhJC6iGVZ5BYWlXrkFRaXuVyRD7YK1fTdu3fHtGnTMHfuXBgaGsLc3BzLli2TKvP8+XN07doVGhoacHZ2RlhYWKn9JCUlYfjw4TAwMICRkREGDRqEuLg4AMDTp08hEAhw+PBhSfmTJ09CQ0MDjx49qnTs8rpw4QKio6Nx8OBBtG7dGr169cKPP/6IXbt2QSgUlrvdzJkzMW3aNMybNw/NmjVD48aNMXToUKirq1db7GWhGiEVOTqpHTb9Fgahti2u/vsW77ILceZRMs48SgaHAVxtDdCzqRl6NjVFY1NtGl5KCKkX8kTFcF5yXiXHjg7sDQG/8j+L+/btQ0BAAG7duoWIiAj4+Pigc+fO8PDwgFgsxpAhQ2BsbIybN29CKBRixowZUtvn5ubC3d0dXbp0QXh4OLhcLlasWIE+ffrg4cOHcHJywoYNG+Dn54fOnTuDx+Nh0qRJWLNmDVq0aFFuXNra2hXG3aVLF5w9e1bm84yIiEDz5s1haWkpWda7d28UFBQgMjIS7u7upbZJTU3FrVu3MGrUKHTq1An//fcfnJycsHLlSnzxxRcyH1sZKBFSES11LloasejbtxnU1Lh4kJiBy09TcTEmFTHJQtx99R53X73H2nNPYWOoiZ5OZujV1Iya0AghpIZycXHB0qVLAQCNGzfGli1bcOnSJXh4eODixYuIiYlBXFwcrK2tAQCrVq2Cl5eXZPujR4+Cw+EgODhY8sfvnj17oK+vj6tXr8LT0xN+fn4IDQ3F6NGjwefz4ebmhunTp1cYV1RUVIXrS2ZollVKSgrMzMyklhkYGIDP5yMlJaXMbV6+fAkAWLZsGTZs2IBWrVph//796NmzJx4/fozGjRvLFYMiUSJUA3A4DFrbGqC1rQFmeToiKSMPl2Pe4NLTVNz4Lw0J6XnYeyMOe2/EQVudi65NjNHTyQzuTqYw1OKrOnxCCFEYTZ4aogN7Sy0Ti8XIEmZBR1dHqf1JNHlVu5O5i4uL1HMLCwvJvbBiYmJga2srSYIAoGPHjlLlIyMj8eLFC+jo6Egtz8/Px3///Sd5vnv3bjRp0gQcDgePHz/+bItBo0aNKnU+AODl5YXr168DAOzs7PDkyRMAZU9kyLJsubGIxWIAwJQpUzBu3DgAQOvWrXHp0iXs3r0bq1evrnSMVUWJUA1kpa+J0R3tMbqjPXIKivD3i3e4FPMGl5++xbvsAoQ+SkHooxQwkiY0U/RqakZNaISQWo9hmFLNU2KxGEV8NQj4XJV3rK0Ij8eTes4wjCQBKKv/0aff12KxGG5ubjh06FCpsiYmJpL/P3jwADk5OeBwOEhJSZFqoipLVZrGgoODJaO6Ss7P3Nwct27dkir3/v17iESiUjVFJUpuiurs7Cy1vGnTpoiPj68wPmWjRKiG01Lnonczc/RuZg6xmMXDpExcinmDSzGpiE4WIvLVe0S+eo91555JmtB6NjVFewcjakIjhJAawtnZGfHx8Xj9+rUkcYmIiJAq4+rqimPHjsHU1BS6urpl7ic9PR0+Pj5YuHAhUlJSMGrUKNy7d6/C5q2qNI1ZWVmVWtaxY0esXLkSycnJkgTnwoULUFdXh5ubW5n7sbe3h6WlJZ49eya1/N9//5VqHlSFepEIDR48GFevXkXPnj1x4sQJVYdTaRwOg1Y2+mhlo49Zno54nZGHS09TcSnmTZlNaF0aG6NnUzO4O5rASFu1vfIJIaQ+69WrFxwdHTFmzBj8+OOPEAqFWLhwoVSZUaNGYf369Rg0aBACAwNhbW2N+Ph4nDx5EnPmzIG1tTV8fX1hY2ODRYsWobCwEK6urpg9ezaCgoLKPXZVmsbK4unpCWdnZ4wePRrr169Heno6Zs+ejUmTJkkSuKSkJPTs2RN79+6Fk5MTGIbBnDlzsHTpUrRs2RKtWrXCvn378PTpU5X/LteLRGjatGkYP3489u3bp+pQFMpSXxOjO9hhdAc75BYW4e/n73ApJhWXnqbiXXYBzj5OwdnHH5rQWtvoo2fTDx2um5hRExohhFQnDoeDkJAQTJgwAe3atYO9vT02bdqEPn36SMoIBAKEh4fj+++/x5AhQ5CVlQUrKyv07NkTurq62L9/P0JDQ3H//n1wuVxwuVwcOnQInTp1Qr9+/dC3b99qORc1NTWcOXNGMnpNU1MTI0eOxIYNGyRlRCIRnj17htzcXMmyGTNmID8/HzNnzkR6ejpatmyJsLAwNGzYsFriLg/DVmXihFrk6tWr2LJli9yZp1AohJ6eHjIzM8utqqwMkUiE0NBQ9O3bt1S7clWJxSwe/a8J7eL/mtA+Zm2giZ5OpujZ1AztGxhCnVu1DoL1jTKvHVEeum41T35+PmJjY+Hg4AANDY1yy4nFYgiFQujq6tboPkKkNGVfu4reQ7L+ftf4d1R4eDgGDBgAS0vLcu/JsnXrVsmL4ObmJunhXl9xOAxa2ugjwNMRodO7IGJ+D6zwbg53RxPwuRwkvs/DvohXGLP7NlwDwzD1YCSO301AWnaBqkMnhBBCqlWNbxrLyclBy5YtMW7cOHz55Zel1h87dgwzZszA1q1b0blzZ+zYsQNeXl6Ijo6Gra2t3McrKChAQcH/JwQls2SKRCKIRKLKn8gnSvalyH2Wx1jAxXA3Swx3s0RuYREi/kvH5WdvceXZW7zNLpRqQmtlrQd3RxP0cDShJrRyVOe1I4pD163mEYlEYFkWYrFYMrqqLCUNFyVlSe2h7GsnFovBsixEIhHU1KRbN2T9rNeqpjGGYRASEiJ1o7v27dvD1dUV27Ztkyxr2rQpvL29peYlkLVpbNmyZVi+fHmp5YcPH4ZAIKj6SdQgYhZIyAGevOfgyXsGiTnSSY+hOotmBh8ejXVZ0CA0QogicblcmJubw8bGBnw+zYlG5FdYWIiEhASkpKSgqKhIal1ubi5Gjhz52aaxGl8jVJHCwkJERkZi3rx5Uss9PT1x48aNSu1z/vz5CAgIkDwXCoWwsbGBp6enwvsIhYWFwcPDo8b0V0jOzMeV/9UURbxMR3qBGNdTGFxPAbT4aujcyAg9HE3QvYlxvR6FVhOvHfk8um41T35+PhISEqCtrV1hHyGWZZGVlQUdHR2qpa5llH3t8vPzoampKbmH28cquu/Zx2p1IvTu3TsUFxeXmsDJzMxMaprv3r174969e8jJyYG1tTVCQkLQtm3bMveprq5e5g3geDyeUr48lbXfyrA15mGssQ7Gdm6AvMJi/P3iHS4//TBnUWpWAS5Ep+JCdOqHJjQbffRqaoYeTqZwMq+fX0416doR2dF1qzmKi4vBMAw4HE6FHWlLmlRKypLaQ9nXjsPhgGGYMj/Xsn7Oa3UiVOLTH+FPp/k+f17+G/gFBQUhKCgIxcXFVY6vNtLkq8HD2QwezmYQi1k8fp35v6H5b/A4SYj78Rm4H5+B9eefwUpfEz2bmqKHkyk6NjSiUWiEEEJqjVqdCBkbG0NNTa3UTd5SU1PLneZbVv7+/vD395cMv6vPOBwGLtb6cLHWx0yPJkjJzMelp29wOSYVf794h6SMPOyPeIX9Ea8g4Kt9NJGjKUx06m8TGiGEkJqvVidCJXfeDQsLw+DBgyXLw8LCMGjQIBVGVreZ62lgVHs7jGpvh7zCYvzz4h0uPU3F5adv8EZYgPNP3uD8kzdgGKCltT56NTVFDyczNLWon01ohBBCaq4anwhlZ2fjxYsXkuexsbGIioqCoaEhbG1tERAQgNGjR6NNmzbo2LEjdu7cifj4ePj6+lbpuPW9aUxWmnw19HI2Qy9nM7BsczxOEuLS//oVPUrKRFRCBqISMrDhwr+w0tdEDydT9Ghqio4NjKBRxTs9E0IIIVVV4xOhu3fvwt3dXfK8ZETX2LFjsXfvXgwfPhxpaWkIDAxEcnIymjdvjtDQUNjZ2VXpuNQ0Jj+GYdDCWg8trPUwo1cTvBHm4/L/7oVW0oR24OYrHLj5oQnti0bG6NXUDN2dTGCqU/6IESK719mvYaxpDL4aDUUmpDYoa1oYUr1qfPf77t27g2XZUo+9e/dKyvj5+SEuLg4FBQWIjIxE165dVRcwkTDT1cCIdrYIHtsW9xd74texbTCyvS3MdNWRW1iMC9FvMPf3h2i38hIGBf2DzZeeI/q1ELVoaqsaRVQswreXv8VXf36FqNQoVYdDSJ3m4+NTr5OX+Ph4DBgwAFpaWjA2Nsa0adNQWFhYbvn09HR89913cHR0hEAggK2tLaZNm4bMzEypcvb29mAYRurx6RQ5ilbja4RUhZrGFEuTr4aeTc3Qs6kZWO/mePJaiIsxb3D5aSoeJmbiQUIGHiRk4Mewf2Gpp4EeTT/cC42a0GT3SvgK6XnpSMtPw5izYzCq6Sh81/o7CHh1ayJQQohqFRcXo1+/fjAxMcHff/+NtLQ0jB07FizLYvPmzWVu8/r1a7x+/RobNmyAs7MzXr16BV9fX7x+/brURMeBgYGYNGmS5Lm2trZSz6fG1wipir+/P6Kjo3Hnzh1Vh1LnMAyD5lYfms/++PYL3FrQE6uHtECvpqbQ4HHwOjMfB2/GY9yeO2gdGIZFpx6pOuRaoZFBI5z2Po2BDQeCBYuDMQcx5I8hiHgdoerQCJEdywKFOaUfotyylyvyUYXa6O7du2PatGmYO3cuDA0NYW5ujmXLlkmVef78uWTiP2dnZ4SFhZXaT1JSEoYPHw4DAwMYGRlh0KBBiIuLAwA8ffoUAoEAhw8flpQ/efIkNDQ08OhR9X1PXrhwAdHR0Th48CBat26NXr164ccff8SuXbvKncSwefPm+P333zFgwAA0bNgQPXr0wMqVK/Hnn3+WmhFaR0cH5ubmkoeyEyGqESIqV9KENqKdLfJFxbjx3ztcjEnF5ZhUpAjzwYBGmslKT10PK79Yib4OfbE8YjmSspMwOWwyvBt5Y3ab2dBTp/5upIYT5QKrLKUWcQDoV8exF7wG+FqV3nzfvn0ICAjArVu3EBERAR8fH3Tu3BkeHh4Qi8UYMmQIjI2NcfPmTQiFQsyYMUNq+9zcXLi7u6NLly4IDw8Hl8vFihUr0KdPHzx8+BBOTk7YsGED/Pz80LlzZ/B4PEyaNAlr1qxBixYtyo3rc4lEly5dcPbsWZnPMyIiAs2bN4el5f9fp969e0u6p3zcr7ciJbe+4HKlU5G1a9fihx9+gI2NDb766ivMmTNHqbdgoUSoHNQ0phoaPDX0cDJDDyczsN4snrwWQsCnpjF5dbbqjJBBIdh0bxOOPD2CUy9O4e+kv7Go/SL0tOup6vAIqZNcXFywdOlSAEDjxo2xZcsWXLp0CR4eHrh48SJiYmIQFxcHa2trAMCqVavg5eUl2f7o0aPgcDgIDg6WTDWyZ88e6Ovr4+rVq/D09ISfnx9CQ0MxevRoyRQy06dPrzCuqKioCtdramrKdZ4pKSml5uozMDAAn88vNa9fedLS0vDDDz9gypQpUsunT58OV1dXGBgY4Pbt25g/fz5iY2MRHBwsV4zyoESoHDRqTPVKmtBI5WjxtDC//Xz0ceiDJf8sQZwwDjOuzoCHnQcWtF8AY01jVYdISGk8wYeamY+IxWIIs7Kgq6Oj3FtsVLE/nYuLi9RzCwsLpKamAgBiYmJga2srSYIAoGPHjlLlIyMj8eLFC+jo6Egtz8/Px3///Sd5vnv3bjRp0gQcDgePHz/+7PxsjRo1qtT5AICXlxeuX78OALCzs8OTJ08AlL6jA1D6rg7lEQqF6NevH5ydnSWJY4mZM2dK/u/i4gIDAwMMHToUa9euhZGRUaXPoyKUCBFSx7U2bY0TA09gx4Md2P14N8JeheF2ym3MbTsXAxoMoEkuSc3CMKWbp8RigFf8YXkNvtfYp/e2YhhGcq+tskbDfvrZE4vFcHNzw6FDh0qVNTExkfz/wYMHyMnJAYfDQUpKilQTVVmq0jQWHByMvLw8AP9/fubm5rh165ZUuffv30MkEn32rg5ZWVno06cPtLW1ERIS8tn7gXXo0AEA8OLFC0qECCGVp66mjmmu0+Bp74kl/yxBTHoMFv69EKGxoVjSYQkstSv+IiWEVI2zszPi4+Px+vVrSeISESE9kMHV1RXHjh2DqakpdHV1y9xPeno6fHx8sHDhQqSkpGDUqFG4d+9ehc1bVWkas7KyKrWsY8eOWLlyJZKTk2FhYQHgQwdqdXV1uLm5lbsvoVCI3r17Q11dHX/88Uepu8WX5f79+wAgOY4y1NzUmhCicE6GTjjU7xCmu04Hn8PHP0n/YPDpwTjy9AjErFjV4RFSZ/Xq1QuOjo4YM2YMHjx4gOvXr2PhwoVSZUaNGgVjY2MMGjQI169fR2xsLK5du4bp06cjMTERAODr6wsbGxssWrQIP/30E1iWxezZsys8dqNGjSp8lJXsVMTT0xPOzs4YPXo07t+/j0uXLmH27NmYNGmSJIFLSkqCk5MTbt++DeBDTZCnpydycnLw66+/QigUIiUlBSkpKZK+uBEREfj5558RFRWF2NhY/Pbbb5gyZQoGDhwIW1tbuWKUByVC5QgKCoKzszPatm2r6lAIUSgeh4eJLSbixMATcDV1RW5RLlbdWoVx58YhNjNW1eERUidxOByEhISgoKAA7dq1w8SJE7Fy5UqpMgKBAOHh4bC1tcWQIUPQtGlTjB8/Hnl5edDV1cX+/fsRGhqKAwcOgMvlQiAQ4NChQwgODkZoaGi1nYuamhrOnDkDDQ0NdO7cGcOGDYO3tzc2bNggKSMSifDs2TPk5uYC+ND/6datW3j06BEaNWoECwsLySMhIQEAoK6ujmPHjqF79+5wdnbGkiVLMGnSJBw5ckSp58OwNI1vhUo6S5cM81MUkUiE0NBQ9O3b97NtpKRmqUvXTsyKcezZMWyM3IjcolzwOXxMbTUVY5uNBY9Tu8/tU3XputUV+fn5iI2NhYODQ4XNJGKxGEKhELq6usrtLE0UTtnXrqL3kKy/3/SOIqQe4zAcjHAagVODTqGzVWcUigvxy71fMPLMSMSkxag6PEIIUTpKhAghsNC2wLae27Dyi5XQU9fD0/SnGHFmBDZGbkRBcYFSjkmV0YSQmoASIUIIgA9DeQc2HIhTg07B084TxWwxfn38K4b+MRT33txT6LGKs3OQONUP2eHhCt0vIYTIixKhclBnaVJfGWsa48fuP2Jj940w1jRGnDAOY8+NxcqbK5EjylHIMdL37kX21atImjET+THUBEcIUR1KhMpBN10l9V1Pu544NegUhjQeAgA4+uwoBp8ejL+T/q7yvo0nT4KgQweIc3ORMMUXouTkKu+TEEIqgxIhQki59NT1sLzTcuzy3AUrbSsk5yRj6sWpWPj3QmTkZ1R6vwyfD+tNv0C9cSMUpaYiYfIUFGdlKS5wQgiRESVChJDP6mDRAScHnsQ3Tb8BAwZ//PcHBp0ehPNx5yvd6VlNVxc227dDzcQYBc+fI2n6dLAikYIjJ4SQilEiRAiRiYAnwPftvseBvgfQUK8h0vPTMfvabMy4MgNvc99Wap88KyvYbN8ORiBAzo0IJC9dRqPJCCHVihIhQohcWpq0xG8DfoNvS19wGS4uJ1zGoFODEPI8pFJJjGazZrD++SeAw0HmyZN4t22bEqImhJCyUSJECJEbX40P/1b+ONr/KJoZNUOWKAtLbizBpLBJSMxKlHt/2t26wXzJYgDAu02bkXn6tKJDJqRGYhgGp06dUnUY9RolQuWg4fOEfJ6joSMO9j2IWW6zoK6mjlvJtzDkjyE4EH0AxeJiufZl8PXXMJo4AQDwetFi5Ny8qYyQCVEIHx8feHt7qzoMlZk+fTrc3Nygrq6OVq1aybTNlClT0LBhQ2hqasLExASDBg3C06dPlRuoDCgRKgcNnydENlwOFz7NfXBy4Em0MWuDvKI8rLuzDmPOjcF/Gf/JtS+TgADoePUBRCIkfjcNBc+fKylqQkhVsCyL8ePHY/jw4TJv4+bmhj179iAmJgbnz38YaOHp6Sm5+7yqUCJECFEIW11b/Nr7VyzusBhaPC08fPsQX/35FbY/2A5RsWyjwRgOB5Zr1kDT1RXirCzET5kCUWqqkiMnNQnLssgV5ZZ65BXllblckY+qdNTv3r07pk2bhrlz58LQ0BDm5uZYtmyZVJnnz5+ja9eu0NDQgLOzM8LCwkrtJykpCcOHD4eBgQGMjIwwaNAgxMXFAQCePn0KgUCAw4cPS8qfPHkSGhoaePToUaVjr4xNmzbB398fDRo0kHmbyZMno2vXrrC3t4erqytWrFiBhIQEyfmpClelRyeE1CkchoNhjsPQ1borVtxcgWuJ1xAUFYSwV2EI7BSIZsbNPr8PdXVYB23Bq69HoPDVKyT6ToXdgf3gaGlVwxkQVcsrykP7w+1VcuxbI29BwBNUevt9+/YhICAAt27dQkREBHx8fNC5c2d4eHhALBZjyJAhMDY2xs2bNyEUCjFjxgyp7XNzc+Hu7o4uXbogPDwcXC4XK1asQJ8+ffDw4UM4OTlhw4YN8PPzQ+fOncHj8TBp0iSsWbMGLVq0KDcubW3tCuPu0qULzp49W+nzroycnBzs2bMHDg4OsLGxqdZjf4oSIUKIwplrmWNzj804G3sWa26vwb/v/8XI0JEY6zwWfq38oMHVqHB7roEBbHbtRNzwr5EfHY2kgFmwDtoChktfWaTmcnFxwdKlSwEAjRs3xpYtW3Dp0iV4eHjg4sWLiImJQVxcHKytrQEAq1atgpeXl2T7o0ePgsPhIDg4GAzDAAD27NkDfX19XL16FZ6envDz80NoaChGjx4NPp8PNzc3TJ8+vcK4oqKiKlyvqalZhbOWz9atWzF37lzk5OTAyckJYWFh4PP51Xb8stC3CiFEKRiGQd8GfdHBsgPW3F6Ds7FnsefJHlyKv4RlnZahrXnFAxH4traw2bYVr8b6IPvaNbxZtQpmixdLfiBI3aTJ1cStkbeklonFYmRlZUFHRwccjvJ6dGhyq5YQuLi4SD23sLBA6v+admNiYmBraytJggCgY8eOUuUjIyPx4sUL6OjoSC3Pz8/Hf//9f3+73bt3o0mTJuBwOHj8+PFnPxONGjWq1PkAgJeXF65fvw4AsLOzw5MnTyq9LwAYNWoUPDw8kJycjA0bNmDYsGH4559/oKFR8R9HykSJECFEqQw1DLGu6zr0c+iHwJuBiM+Kx/jz4zGsyTDMbz8fXE75X0OarVrBcv06JE2fgfeHj4BnZQ2jCeOrMXpS3RiGKdU8JRaLUcQtgoAnUGoiVFU8Hk/qOcMwEIvFAFBm/6NPExixWAw3NzccOnSoVFkTExPJ/x88eICcnBxwOBykpKTA0tKywriq0jQWHByMvLw8AKXPrzL09PSgp6eHxo0bo0OHDjAwMEBISAhGjBhR5X1XFiVChJBq0c2mG06ZncJPkT/hxL8nkFWYVWESVELX0xOi7+cidc1apK5fD56VJXT79KmGiAlRHGdnZ8THx+P169eSxCUiIkKqjKurK44dOwZTU1Po6uqWuZ/09HT4+Phg4cKFSElJwahRo3Dv3r0Km7eq0jRmZWVV4bZVxbIsCgoKlHqMz6FEiNQNb54AYUsAfdv/f+j9719tU4CaU2oEHb4OlnZcir4OfdFAT/bRJoZjx0KUkIj3hw7h9dzvwTU1hcDVVYmREqJYvXr1gqOjI8aMGYMff/wRQqEQCxculCozatQorF+/HoMGDUJgYCCsra0RHx+PkydPYs6cObC2toavry9sbGywaNEiFBYWwtXVFbNnz0ZQUFC5x65K01h5Xrx4gezsbKSkpCAvL0+SbDk7O4PP5yMpKQk9e/bE3r174eTkhJcvX+L48ePw9PSEiYkJkpKSsHbtWmhqaqJv374Kj08elAiRuuHtM+DFxbLXcTUAPRvpJEnfFtC3o0RJRT7XP+hTDMPAbMF8iJKTkX35MhL9/GF/9Aj49vbKCZAQBeNwOAgJCcGECRPQrl072NvbY9OmTejzUe2mQCBAeHg4vv/+ewwZMgRZWVmwsrJCz549oauri/379yM0NBT3798Hl8sFl8vFoUOH0KlTJ/Tr169aE4qJEyfi2rVrkuetW7cGAMTGxsLe3h4ikQjPnj1Dbm4uAEBDQwPXr1/Hxo0b8f79e5iZmaFr1664ceMGTE1Nqy3usjAs3eGwTEFBQQgKCkJxcTH+/fdfZGZmlltVWRkikQihoaHo27evQtpd6733r4DYcCAjXvqR9RpgxRVvK2eiRNdOdcS5uXg1ZizyHz8Gz9YW9kePgGtoKNO2dN1qnvz8fMTGxsLBwaHCzrJisRhCoRC6uro1uo8QKU3Z166i95BQKISent5nf7+pRqgc/v7+8Pf3l7yQpIYzsAMMRpdeXlQICJM+JEWZCR8SpsyE/0+UhElAUT6Q9vzDoyyfJEocXWtYvU8Dk2QKGDWgGqVqxBEIYLN9G+KGfw1RfDwS/fxhu3cPOCoccUIIqd0oESJ1G5cPGDp8eJSlWPT/iVJGvEyJkhqANgCwd+uHfaipA/qf1ijZ/f//tUwB+itWYbjGxrDZuQNxI0YiLyoKr+d+D6uNP4Oh15gQUgmUCJH6TY0HGNh/eJSlJFH6KEESp8chPTYKRpwcMFmvgeICIO3Fh0eZx6BESdHUGzaE9ZbNSJgwEVkXLiB1/QaYfT9X1WERQmohSoQIqUgZiVKxSIR/SvqacCBdo/RprZIwqXKJkt5H/9c2o0SpDFrt2sFi1Sq8njMH6Xv2gGdtBcNRo1QdFiGklpE7ETp37hy0tbXxxRdfAPjQqXjXrl1wdnZGUFAQDAwMFB4kITWWrDVKGfFARkmT26v/T5ZkSpT4ZXTmtqNECYDegP4QJSXh7caNeLNyFXgWltDp4a7qsAghtYjcidCcOXOwdu1aAMCjR48wa9YsBAQE4PLlywgICMCePXsUHiQhtZZMidLrD8lRxkd9k0qeCxOB4kIg/b8PjzKP8b9EqdN3QJtxyjqTGstoymSIkhKRcfwEkmbNgt3+/dBs0VzVYRFCagm5E6HY2Fg4OzsDAH7//Xf0798fq1atwr1791Q+KRIhtY4a738j3uzKXi+pUUqQTpBK/i9M+v9ESVxUvbHXEAzDwHzJEoiSU5Dz999ImDoV9kePgm+t3BlxCSF1g9yJEJ/Pl0yQdPHiRYwZMwYAYGhoCKFQqNjoCKnvZK5Rii9/ZFw9wPB4sNr4M16N+gYFz54hYcoU2B8+BDWa+oIQ8hlydyzo3LkzAgIC8MMPP+D27dvo168fAODff/+VuqsuIaQalNQoOXQB9Or3509NWxs2O7aDa2aGwv/+Q+J30yAuLFR1WISQGk7uRCgoKAg8Hg8nTpzAtm3bJDdkO3v2rNRU4YQQUt145uaw2bkDHC0t5N6+jeRFi8q86zchNQXDMDh16pSqw6jX5EqEioqKcOXKFezcuRMPHjzAhAkTJOt+/vlnbNq0SeEBKsJff/0FR0dHNG7cGMHBwaoOhxCiRBqOjrD65RdATQ3CP/7E2xr6vURqNx8fH3h7e6s6DJV48OABRowYARsbG2hqaqJp06b45ZdfPrvdlClT0LBhQ2hqasLExASDBg3C06dPqyHiismVCHG5XEydOhWFtai6uaioSDKq7d69e1i7di3S09NVHRYhRIm0v+gMi+XLAABp27Yj4/ffVRsQIXVIZGQkTExMcPDgQTx58gQLFy7E/PnzsWXLlgq3c3Nzw549exATE4Pz58+DZVl4enqiuLi4miIvm9xNY+3bt8f9+/eVEYtS3L59G82aNYOVlRV0dHTQt29fnD9/XtVhEUKUTH/oUBj5TgEAJC9ZitwbN1QcEZEFy7IQ5+aWfuTllb1cgY+qNKN2794d06ZNw9y5c2FoaAhzc3MsW7ZMqszz58/RtWtXaGhowNnZGWFhYaX2k5SUhOHDh8PAwABGRkYYNGgQ4uLiAABPnz6FQCDA4cOHJeVPnjwJDQ0NPHr0qNKxy2v8+PHYtGkTunXrhgYNGuCbb77BuHHjcPLkyQq3mzx5Mrp27Qp7e3u4urpixYoVSEhIkJyfqsg9aszPzw+zZs1CYmIi3NzcoKWlJbXexcVFYcEBQHh4ONavX4/IyEgkJycjJCSkVHXk1q1bsX79eiQnJ6NZs2bYuHEjunTpAgB4/fq1pB8TAFhbWyMpKUmhMRJCaiaT6dMhSnoN4Z9/IjlgFvgTJ3x+I6JSbF4enrm6lbnujZKP7XgvEoxAUOnt9+3bh4CAANy6dQsRERHw8fFB586d4eHhAbFYjCFDhsDY2Bg3b96EUCjEjBkzpLbPzc2Fu7s7unTpgvDwcHC5XKxYsQJ9+vTBw4cP4eTkhA0bNsDPzw+dO3cGj8fDpEmTsGbNGrRo0aLcuLS1tSuMu0uXLjh79mylzxsAMjMzYWhoKHP5nJwc7NmzBw4ODrCxsanSsatK7kRo+PDhAIBp06ZJljEMA5ZlwTCMwqu4cnJy0LJlS4wbNw5ffvllqfXHjh3DjBkzsHXrVnTu3Bk7duyAl5cXoqOjYWtrW2aGz1Rwp/CCggIUFBRInpdMCSASiSASiRRwRpDs7+N/Se1B1652MVm2FKKUFOTduQOrPXuR5+UFWNEcQzWBSCT6UAMkFkMsFgOA5F9VEIvFgIzHZ1lWEnsJFxcXLF68GADQsGFDbNmyBRcvXkTPnj1x4cIFxMTE4OXLl5IR1itWrEC/fv0k53/48GFwOBzs3LlT8jv166+/wtDQEJcvX4anpyd8fX1x5swZjB49Gnw+H66urvjuu+8qfN3u3btX4bloampW6XWPiIjAb7/9hj///LPUfkp+g0teq23btuH7779HTk4OnJyccP78eXC53EofXywWg2VZiEQiqKmpSa2T9Tu6UhMqVicvLy94eXmVu/6nn37ChAkTMHHiRADAxo0bcf78eWzbtg2rV6+GlZWVVA1QYmIi2rdvX+7+Vq9ejeXLl5dafuHCBQiq8JdCecqqGiW1A1272oPT1ws2r15BPTUV/40bj0TfKRBraKg6rHqPy+XC3Nwc2dnZkr6nLMvC7MpllcSTJRKBkXE+PJFIhKKiIskfy0VFRXBycpKaT8/Y2BhJSUkQCoWIioqCtbU1dHV1JWWaNWsGAMjLy4NQKMTNmzfx4sUL6H0y/1V+fj6ePHmCDh06APjwO9emTRtwOBzcuHEDWVlZFcZqamr62fMpbx7AoUOH4ubNmwAAGxsbRERESK2PiYmBt7c35s6di/bt25e7n5IY+/fvjw4dOiAlJQVbtmzBV199hXPnzkGjkp/HwsJC5OXlITw8HEVF0pPKlsx5+DlyJ0J2duXMgKsChYWFiIyMxLx586SWe3p64sb/+gO0a9cOjx8/RlJSEnR1dREaGoolS5aUu8/58+cjICBA8lwoFMLGxgaenp7Q1dVVWOwikQhhYWHw8PAAj8dT2H6J8tG1q53y2rbFq69HQCM5GS4XLsBi82YwdP1UKj8/HwkJCdDW1pb+IfwkEWBZFllZWdDR0amwRr868Xg8cLlcye8Cl8uFlpaW1O8Ej8eDmpoadHV1oa6uDg6HU+bviKamJnR1dcHlcuHm5oYDBw6UKmNiYiLZ9tatW8jNzQWHw0FOTs5nf5s+t/6LL75AaGhomev27NmDvLw8yfl8vK/o6Gh4e3tj0qRJCAwMLHP7T6+drq6upCmsZ8+eMDIywqVLlzBixIgKYyxPfn4+NDU1JX2vPibrJM+Vvvt8dHQ04uPjS40gGzhwYGV3Kbd3796huLgYZmZmUsvNzMyQkpIC4MOb88cff4S7uzvEYjHmzp0LIyOjcveprq4OdXX1Ust5PJ5SfvSUtV+ifHTtahk7OySN84F9cDBy/7mBtNWrYR4YWGN+WOuj4uJiMAwDDocDTgU3Di5pNikpWxMwDFMqnrKelyxr1qwZ4uPjkZKSAktLSwAfEhoAkvN3c3PDb7/9BnNz83KTl/T0dIwfPx4LFy5ESkoKRo8ejXv37kFTU7PcWKOioio8F01NzXJf1/L67zx58gQ9e/bE2LFjsWrVqnL3XdG143A4kmatyl5XDocDhmHK/D6W9ftZ7kTo5cuXGDx4MB49eiTpGwT8f78bVQyD+/SLrKS/UomBAwfKnaAFBQUhKChI5cP6CCGKU2BtDfN165A8fQYyjp8Az8oaxv8bWUaIMvXq1QuOjo4YM2YMfvzxRwiFQixcuFCqzKhRo7B+/XoMGjQIgYGBsLa2Rnx8PE6ePIk5c+bA2toavr6+sLGxwaJFi1BYWAhXV1fMnj0bQUFB5R67UaNGCj2XJ0+ewN3dHZ6enggICJBUPKipqcHExATAh9FvPXv2xN69e+Hk5ISXL1/i+PHj8PT0hImJCZKSkrB27Vpoamqq/D6lcqdg06dPh4ODA968eQOBQIAnT54gPDwcbdq0wdWrV5UQYvmMjY2hpqYmuQglUlNTS9USycvf3x/R0dG4c+dOlfZDCKlZtLp3h9mCBQCAtxs3IvPPv1QcEakPOBwOQkJCUFBQgHbt2mHixIlYuXKlVBmBQIDw8HDY2tpiyJAhaNq0KcaPH4+8vDzo6upi//79CA0NxYEDB8DlciEQCHDo0CEEBweX27SlDMePH8fbt29x6NAhWFhYSB5t27aVlBGJRHj27Jmkn46GhgauX7+Ovn37olGjRhg2bBi0tLRw48YNmfowKRUrJyMjI/bBgwcsy7Ksrq4u+/TpU5ZlWfbSpUtsq1at5N2dXACwISEhUsvatWvHTp06VWpZ06ZN2Xnz5inkmJmZmSwANjMzUyH7K1FYWMieOnWKLSwsVOh+ifLRtaudPr1uKavXsNGOTmxM8xZs9q1bKo6ufsrLy2Ojo6PZvLy8CssVFxez79+/Z4uLi6spMqIoyr52Fb2HZP39lrtGqLi4WDIngbGxMV6/fg3gQyfqZ8+eKTBF+yA7OxtRUVGSNs7Y2FhERUUhPj4eABAQEIDg4GDs3r0bMTExmDlzJuLj4+Hr61ul4wYFBcHZ2VkqwyWE1B2mc+dAx9MTrEiExG+/Q8F//6k6JEKICsjdR6h58+Z4+PAhGjRogPbt22PdunXg8/nYuXMnGjRooPAA7969C3d3d8nzkhFdY8eOxd69ezF8+HCkpaUhMDAQycnJaN68OUJDQ6s8us3f3x/+/v4QCoWlhjISQmo/hsOB5bq1iE9NRV5UFBImT4H9saPgGhurOjRCSDWSOxFatGgRcnJyAHyYDKp///7o0qULjIyMcPToUYUH2L17989Oe+7n5wc/Pz+FH5sQUrdxNDRgvTUIcV+PgCg+HglT/WC3by84SpgzjBBSM8ndNNa7d28MGTIEANCgQQNER0fj3bt3SE1NRc+ePRUeoKpQ0xgh9QPX0BC2O3dATV8f+Y8eIWn2HLA0WpSQekPuRGj8+PGlZrE0NDREbm4uxo8fr7DAVI1GjRFSf/Dt7WG9dSsYPh/Zly/jzeo1VboBJyGk9pA7Edq3b59klsmP5eXlYf/+/QoJihBCqpvAtTUs160FALw/eBDp+/apOCJCSHWQORESCoXIzMyUTJctFAolj/fv3yM0NFT1cwEQQkgV6PbpA9M5swEAqWvXQXj+goojIoQom8ydpfX19SXThTdp0qTUeoZhyrxZaW1FM0sTUj8Zjh+PwsREZBw5itdz54JnZgrNVq1UHRYhRElkToSuXLkClmXRo0cP/P777zA0NJSs4/P5sLOzk9w/pS6g4fOE1E8Mw8B84UIUvU5G9rVrSJjqB/tjR8G3tVV1aIQQJZA5EerWrRuADxMa2tra0o0KCSF1FsPlwuqnH/Fq9BjkR0cjYdJk2B09Aq6BgapDI3UMwzAICQmBt7e3qkOpt2TuI5Seno7ExETY2dlJkqAnT55g3LhxGDZsGA4fPqy0IAkhpLpxtLRgvX0buJYWKHz1Con+30JcUKDqsEgN4ePjU2+TlwcPHmDEiBGwsbGBpqYmmjZtil9++eWz202ZMgUNGzaEpqYmTExMMGjQIDx9+lSqjL29vaQbTslj3rx5yjoVAHIkQv7+/vjpp58kz1NTU9GlSxfcuXMHBQUF8PHxwYEDB5QSpCrQPEKEEJ6pKWx37ABHRwd59+7h9bx5YMViVYdFiEpFRkbCxMQEBw8exJMnT7Bw4ULMnz8fW7ZsqXA7Nzc37NmzBzExMTh//jxYloWnp2epvrgld4ooeSxatEiZpyN7InTz5k0MHDhQ8nz//v0wNDREVFQUTp8+jVWrViEoKEgpQaoCzSNECAEA9caNYb15E8DjIevsObz96A9Congsy0JUUFzqUVRYepmiH1WZO6p79+6YNm0a5s6dC0NDQ5ibm2PZsmVSZZ4/f46uXbtCQ0MDzs7OCAsLK7WfpKQkDB8+HAYGBjAyMsKgQYMQFxcHAHj69CkEAoFUC8zJkyehoaGBR48eVTp2eY0fPx6bNm1Ct27d0KBBA3zzzTcYN24cTp48WeF2kydPRteuXWFvbw9XV1esWLECCQkJkvMroaOjA3Nzc8mj5P6myiJzH6GUlBQ4ODhInl++fBmDBw8Gl/thFwMHDsTq1asVHyEhhKiYVocOsPghEMnz5iMt+FfwrK1h8PXXqg6rTioqFGPn9GsqOfbkX7qBp65W6e337duHgIAA3Lp1CxEREfDx8UHnzp3h4eEBsViMIUOGwNjYGDdv3oRQKMSMGTOkts/NzYW7uzu6dOmC8PBwcLlcrFixAn369MHDhw/h5OSEDRs2wM/PD507dwaPx8OkSZOwZs0atGjRoty4PpdIdOnSBWfPnq30eQNAZmam1CCqz8nJycGePXvg4OAAGxsbqXVr167FDz/8ABsbG3z11VeYM2cO+Hx+leKriMyJkK6uLjIyMiQ3M719+zYmTJggWc8wDAqo/ZwQUkfpe3tDlJSEd5u3ICXwB/AsLKD9v0EkhACAi4sLli5dCgBo3LgxtmzZgkuXLsHDwwMXL15ETEwM4uLiYG1tDQBYtWoVvLy8JNsfPXoUHA4HwcHBkr64e/bsgb6+Pq5evQpPT0/4+fkhNDQUo0ePBp/Ph5ubG6ZPn15hXFFRURWu19TUrMJZAxEREfjtt99w5syZz5bdunUr5s6di5ycHDg5OSEsLEwqyZk+fTpcXV1hYGCA27dvY/78+YiNjUVwcHCVYqyIzIlQu3btsGnTJuzatQsnT55EVlYWevToIVn/77//lsrqCCGkLjH284MoMQmZISFInBkAuwP7odmsmarDqlO4fA4m/yKdYIrFYmRlCaGjowsOR+4bIsh17KpwcXGRem5hYYHU1FQAQExMDGxtbSVJEAB07NhRqnxkZCRevHgBHR0dqeX5+fn477//JM93796NJk2agMPh4PHjx58dxd2oUaNKnQ8AeHl54fr16wAAOzs7PHnyRGr9kydPMGjQICxZsgQeHh6f3d+oUaPg4eGB5ORkbNiwAcOGDcM///wDDQ0NAMDMmTMlZV1cXGBgYIChQ4di7dq1MDIyqvR5VETmROiHH35Ar169cPDgQRQVFWHBggUw+Ggo6dGjRyVD7OsCmlCREPIphmFgEbgcRW9SkHMjAgm+vnA4dgy8OjSHmqoxDFOqeUosZsAtUANPXU2piVBV8Xg8qecMw0D8v871ZfU/+jSBEYvFcHNzw6FDh0qVNTExkfz/wYMHyMnJAYfDQUpKymfn8KtK01hwcLDktlqfnl90dDR69OiBSZMmydyhWU9PD3p6emjcuDE6dOgAAwMDhISEYMSIEWWW79ChAwDgxYsXqk+EWrVqhZiYGNy4cQPm5uZo37691Pqvv/4azs7OCg9QVWhCRUJIWRgeD1a//IJXo75Bwb//ImHKFNgdOgQ1XV1Vh0ZqMGdnZ8THx+P169eSxCUiIkKqjKurK44dOwZTU1PolvN+Sk9Ph4+PDxYuXIiUlBSMGjUK9+7dq7B5qypNY1ZWVmUuf/LkCXr06IGxY8di5cqVFe6/IizLVtit5v79+wA+1K4pi1ypdcm4/0+TIADo16+fVGdqQgipq9R0dGCzYzu4pqYoeP4CidOmgy0sVHVYpAbr1asXHB0dMWbMGDx48ADXr1/HwoULpcqMGjUKxsbGGDRoEK5fv47Y2Fhcu3YN06dPR2JiIgDA19cXNjY2WLRoEX766SewLIvZs2dXeOxGjRpV+Cgv2SnPkydP4O7uDg8PDwQEBCAlJQUpKSl4+/atpExSUhKcnJxw+/ZtAMDLly+xevVqREZGIj4+HhERERg2bBg0NTXRt29fAB8Sw59//hlRUVGIjY3Fb7/9hilTpmDgwIGwVeLM7jW3jpEQQmownoUFbHZsB0cgQO7Nm0hevKRKw69J3cbhcBASEoKCggK0a9cOEydOLFWTIhAIEB4eDltbWwwZMgRNmzbF+PHjkZeXB11dXezfvx+hoaE4cOAAuFwuBAIBDh06hODgYISGhlbbuRw/fhxv377FoUOHYGFhIXl8PO+eSCTCs2fPkJubCwDQ0NDA9evX0bdvXzRq1AjDhg2DlpYWbty4Iblhu7q6Oo4dO4bu3bvD2dkZS5YswaRJk3DkyBGlng/D0ie3QiVNY5mZmeVWVVaGSCRCaGgo+vbtW6rdldRsdO1qJ2Vdt+zwcCRM9QOKi2Hs7w+T775V2L7ruvz8fMTGxsLBwUHSWbYsYrEYQqEQurrK7SxNFE/Z166i95Csv9/0jiKEkCrQ7toV5kuWAADeBQUhI+SUagMihMhFpkQoICAAOTk5AIDw8HAUFRUpNShCCKlNDIYPg9GkSQCA5MWLkfNJJ1hCSM0lUyK0efNmZGdnAwDc3d2Rnp6u1KBqArrXGCFEHiYzZ0C3b1+gqAiJ301D/r//qjokQogMZBo+b29vj02bNsHT0xMsyyIiIkJqDqGPde3aVaEBqgoNnyeEyIPhcGCxZjVEqW+QdzcSCVN8YX/0KHhmpqoOrcajrqqkshTx3pEpEVq/fj18fX2xevVqMAyDwYMHl1mOYRiagJAQUm9x+HzYbNmCuBEjURgbi4SpvrA/cAAcLS1Vh1YjlXRaz83NrfJtHkj9VDIqrSoDIGRKhLy9veHt7Y3s7Gzo6uri2bNnkuFuhBBC/p+avj5sdu5A3PCvURAdg8SAANgEBYHhyjx/bb2hpqYGfX19yW0oBAJBmbeLEIvFKCwsRH5+Po0aq2WUde1YlkVubi5SU1Ohr68PNbXK3yxXrk+mtrY2rly5AgcHB8ld5wkhhEjj29jAZvs2vBozFjnXwpHywwqYL1v62XtC1Ufm5uYAIEmGysKyLPLy8qCpqUmvYS2j7Gunr68veQ9VltzZTLdu3VBcXIzff/8dMTExYBgGTZs2xaBBg6qUkRFCSF2i6eICqw3rkfjdNGQcOwa+jTWMJk5UdVg1DsMwsLCwgKmpKUQiUZllRCIRwsPD0bVrV5q7q5ZR5rXj8XgKyTvkToRevHiBfv36ITExEY6OjmBZVnLn+TNnzqBhw4ZVDooQQuoCnV69YDZ/Ht6sWo3UDT+CZ2n5YWQZKUVNTa3cHzU1NTUUFRVBQ0ODEqFapjZcO7kb7KZNm4YGDRogISEB9+7dw/379xEfHw8HBwdMmzZNGTESQkitZThmDAzGjAYAvP5+HnIjI1UcESHkY3InQteuXcO6detgaGgoWWZkZIQ1a9bg2rVrCg2OEELqArPvv4eORy+wIhES/fxR8DJW1SERQv5H7kRIXV0dWVlZpZZnZ2eDz+crJChCCKlLGDU1WK5bB42WLijOzETClCkoSktTdViEEFQiEerfvz8mT56MW7dugWVZsCyLmzdvwtfXFwMHDlRGjCpBM0sTQhSJo6kJm61bwbOxgSghAQl+fhDn5ak6LELqPbkToU2bNqFhw4bo2LEjNDQ0oKGhgc6dO6NRo0b45ZdflBGjSvj7+yM6Ohp37txRdSiEkDqCa2QEmx07wNHTQ/6Dh3g9dy5YmoSWEJWSe9SYvr4+Tp8+jRcvXiAmJgYsy8LZ2RmNGjVSRnyEEFKnqDdwgE3QFsSPG4+ssItIXbceZvPnqTosQuqtSs+K2KhRI0p+CCGkEgRt2sBizWq8njUb6fv2gWdtDcPR36g6LELqJZqrnBBCVECvXz+YBAQAAN6sWoWsS5dUHBEh9RMlQoQQoiJGkyZCf9gwgGWRNGs28h4+VHVIhNQ7lAgRQoiKMAwD8yWLodW1C9j8fCRM9UNhYqKqwyKkXqFEiBBCVIjhcmH1089Qd26K4rQ0JEyajOKMDFWHRUi9IXciZG9vj8DAQMTHxysjHkIIqXfUtLVgs207uObmKIyNReK330FcWKjqsAipF+ROhGbNmoXTp0+jQYMG8PDwwNGjR1FQUKCM2AghpN7gmZl+mGNIWxu5d+8ief4CsGKxqsMipM6TOxH67rvvEBkZicjISDg7O2PatGmwsLDAt99+i3v37ikjRkIIqRc0HJvAetMvAJcL4ZkzePvLJlWHREidV+k+Qi1btsQvv/yCpKQkLF26FMHBwWjbti1atmyJ3bt3g2VZRcZZJYMHD4aBgQGGDh2q6lAIIaRCWp06wSIwEACQtmMH3v/2m4ojIqRuq3QiJBKJ8Ntvv2HgwIGYNWsW2rRpg+DgYAwbNgwLFy7EqFGjFBlnlUybNg379+9XdRiEECIT/SGDYeznBwBIWR6I7OvXVRwRIXWX3DNL37t3D3v27MGRI0egpqaG0aNH4+eff4aTk5OkjKenJ7p27arQQKvC3d0dV69eVXUYhBAiM+PvvoUoKQmZp08jafoM2B06CI2mTVUdFiF1jtw1Qm3btsXz58+xbds2JCYmYsOGDVJJEAA4Ozvj66+/lml/4eHhGDBgACwtLcEwDE6dOlWqzNatW+Hg4AANDQ24ubnhOv11RAip4xiGgcUPgRB06ABxbi4SpvhClJys6rAIqXPkToRevnyJc+fO4auvvgKPxyuzjJaWFvbs2SPT/nJyctCyZUts2bKlzPXHjh3DjBkzsHDhQty/fx9dunSBl5eX1PB9Nzc3NG/evNTj9evX8p4eIYTUGAyfD+tNv0C9cSMUpaYiYYovirOyVB0WIXWK3E1j7u7uuHPnDoyMjKSWZ2RkwNXVFS9fvpRrf15eXvDy8ip3/U8//YQJEyZg4sSJAICNGzfi/Pnz2LZtG1avXg0AiIyMlPMsyldQUCA1HYBQKATwoU+USCRS2HFK9qXIfZLqQdeudqq1101TE+ZbtiBx1Dco+PdfJEybDsugLWDK+UO0Lqq1146o9NrJeky5E6G4uDgUFxeXWl5QUICkpCR5d1ehwsJCREZGYt68eVLLPT09cePGDYUeq8Tq1auxfPnyUssvXLgAgUCg8OOFhYUpfJ+ketC1q51q63VTH/E1bLbvQF5EBO5NnoI3Q78EGEbVYVWr2nrtiGquXW5urkzlZE6E/vjjD8n/z58/Dz09Pcnz4uJiXLp0Cfb29rJHKIN3796huLgYZmZmUsvNzMyQkpIi83569+6Ne/fuIScnB9bW1ggJCUHbtm3LLDt//nwE/O+O0MCHGiEbGxt4enpCV1e3cidSBpFIhLCwMHh4eJTbxEhqJrp2tVNduG45jo5InjYdenfvwqFDBxhOmazqkKpFXbh29ZUqr11Ji87nyJwIeXt7A/jQgW/s2LFS63g8Huzt7fHjjz/KHqEcmE/+6mFZttSyipw/f17msurq6lBXV0dQUBCCgoIktV88Hk8pF1FZ+yXKR9eudqrN102/Vy+wixchZXkg0rdsgYatDfQGDlR1WNWmNl+7+k4V107W48ncWVosFkMsFsPW1hapqamS52KxGAUFBXj27Bn69+9f6YDLYmxsDDU1tVK1P6mpqaVqiRTN398f0dHRuHPnjlKPQwgh8jAYMQKGE8YDAF4vXIScm7dUHBEhtZvco8ZiY2NhbGysjFhK4fP5cHNzK9W2GBYWhk6dOlVLDIQQUtOYzpoFHa8+gEiExO++Q8GLF6oOiZBaS6amsU2bNmHy5MnQ0NDApk0V3/tm2rRpcgWQnZ2NFx99iGNjYxEVFQVDQ0PY2toiICAAo0ePRps2bdCxY0fs3LkT8fHx8PX1les48vq0aYwQQmoKhsOB5Zo1iH+Tirx795AweQrsjx0F18RE1aERUuvIlAj9/PPPGDVqFDQ0NPDzzz+XW45hGLkTobt378Ld3V3yvKSj8tixY7F3714MHz4caWlpCAwMRHJyMpo3b47Q0FDY2dnJdRx5+fv7w9/fH0KhUKpjOCGE1AQcdXVYB23Bq69HoPDVKyT4ToXdgf3gKGF0KyF1mUyJUGxsbJn/V4Tu3bt/9gatfn5+8PvffXcIIYR8wDUwgM3OHYj7egTynzxB0qzZsN6yGYyamqpDI6TWqPRNV0sUFxcjKioK79+/V0Q8NUZQUBCcnZ3LHWZPCCE1Ad/ODtZbg8CoqyP7yhW8Wbnqs39cEkL+n9yJ0IwZM/Drr78C+JAEde3aFa6urrCxsalTNzalUWOEkNpC0Lo1LNetAxgG7w8fRvqevaoOiZBaQ+5E6MSJE2jZsiUA4M8//0RcXByePn0quR8YIYSQ6qfb2xOmc+cCAFLXrYPwnOzzpxFSn8mdCL179w7m5uYAgNDQUHz11Vdo0qQJJkyYgEePHik8QEIIIbIx9BkLg1GjAACv585F7r37Ko6IkJpP7kTIzMwM0dHRKC4uxrlz59CrVy8AH+7poVaHOuhRHyFCSG3DMAzMFsyHdo8eYAsLkejnh8K4OFWHRUiNJnciNG7cOAwbNgzNmzcHwzDw8PAAANy6dQtOTk4KD1BVqI8QIaQ2YtTUYLVhPTSaN0dxRgbip0xBUR0bzEKIIsmdCC1btgzBwcGYPHky/vnnH6irqwMA1NTUSt0lnhBCSPXjCASw2bYVPEtLiF7FI9HPH+L8fFWHRUiNJPNNVz82dOjQUss+vRErIYQQ1eGamMBm107EjRiJvPv38XrefFj99CMYTpVnTSGkTqlUInTp0iVcunRJcvPVj+3evVshgaka3WKDEFLbqTdsCOstmxE/YSKyzp1DqqUlzObOUXVYhNQocv9psHz5cnh6euLSpUt49+4d3r9/L/WoK6iPECGkLtBq1w6Wq1YCANJ370b64cMqjoiQmkXuGqHt27dj7969GD16tDLiIYQQomB6AwZAlJSEtxt/wZsVK8Ezt4BOD/fPb0hIPSB3jVBhYSE6deqkjFgIIYQoidGUKdD/aiggFiNp1izkPXqs6pAIqRHkToQmTpyIw1S1SgghtQrDMDBfsgRaX3wBNi8PCVOnQpSUpOqwCFE5uZvG8vPzsXPnTly8eBEuLi7g8XhS63/66SeFBUcIIURxGB4PVht/xqtR36Dg2TPET5kC+8OHoaarq+rQCFEZuROhhw8folWrVgCAx4+lq1YZhlFIUDUBjRojhNRFatrasNmxHXHDv0bhi/+Q+N002O7aCYbPV3VohKiE3InQlStXlBFHjePv7w9/f38IhULo6empOhxCCFEYnrk5bHZsx6tR3yD31i0kL14MizVr6tQfs4TIqtIza7148QLnz59HXl4eAIBlWYUFRQghRLk0nJxgtXEjoKaGzNN/4N3mzaoOiRCVkDsRSktLQ8+ePdGkSRP07dsXycnJAD50op41a5bCAySEEKIc2l2+gMXyZQCAd1u3IeP331UbECEqIHciNHPmTPB4PMTHx0MgEEiWDx8+HOfOnVNocIQQQpRLf+hQGPlOAQAkL12G7H/+UXFEhFQvuROhCxcuYO3atbC2tpZa3rhxY7x69UphgRFCCKkeJtOnQ3fAAKCoCEnTpiP/2b+qDomQaiN3IpSTkyNVE1Ti3bt3kjvRE0IIqT0YhoHFyhUQtG0LcU4OEqZMgejNG1WHRUi1kDsR6tq1K/bv3y95zjAMxGIx1q9fD3f3ujNle1BQEJydndG2bVtVh0IIIUrH4fNhvWUz+A0aoCglBQlTfFGcnaPqsAhROrkTofXr12PHjh3w8vJCYWEh5s6di+bNmyM8PBxr165VRowqQTddJYTUN2p6erDZuQNqRkYoePoUSTNnghWJVB0WIUoldyLk7OyMhw8fom3btvDw8EBOTg6GDBmC+/fvo2HDhsqIkRBCSDXhW1vDZvs2MJqayLl+HSmBP9D0KKROk3tCRQAwNzdHYGCgomMhhBBSA2i2aAGrHzcg8dvvkHH8OHjW1jCeMlnVYRGiFHInQs+fP8fp06cRFxcHhmHQoEEDeHt7w8HBQRnxEUIIUQGdHj1gtmAB3qxYgbc//wyepSX0BvRXdViEKJxcidDq1auxZMkSiMVimJqagmVZvH37Ft9//z1WrVqF2bNnKytOQggh1czwm1EQJSYife9eJC9YAJ65GQQ0gITUMTL3Ebpy5QoWLVqEhQsX4t27d0hOTkZKSgrevn2LefPmYd68eQgPD1dmrIQQQqqZ6dw50PH0BCsSIeHb71Dw8qWqQyJEoWROhLZv346JEydi2bJlMDAwkCw3NDREYGAgxo8fj23btiklSEIIIarBcDiwXLcWmi1bQpyZiYTJU1D07p2qwyJEYWROhG7fvo3Ro0eXu3706NG4efOmQoIihBBSc3A0NGC9bSt4trYQJSYiwc8f4v/dcJuQ2k7mROjNmzewt7cvd72DgwNSUlIUEVONQBMqEkLI/+MaGsJmx3ao6ekh/+FDJM2eA7a4WNVhEVJlMidC+fn54PP55a7n8XgoLCxUSFA1AU2oSAgh0tQdHGC9bSsYPh/Zly7hzZq6M4kuqb/kGjUWHBwMbW3tMtdlZWUpJCBCCCE1l8DVFZbr1iJpxky8P3AAfGsrGI4dq+qwCKk0mRMhW1tb7Nq167NlCCGE1G26ffpANCcJqes34M2ateBaWkLXw0PVYRFSKTInQnFxcUoMgxBCSG1iOH48ChMTkXHkKF7PngPe/n3QbNlS1WERIje57zVGCCGEMAwD84ULod2tG9iCAiRM9UNhfLyqwyJEbpQIEUIIqRSGy4XVTz9Cw9kZxenpH+YYev9e1WERIhdKhAghhFQaR0sL1tu3gWtpgcK4OCR++x3EBQWqDosQmVEiRAghpEp4pqaw3bEDHB0d5EVGInn+fLBisarDIkQmciVCRUVF2LdvX52aOJEQQkjVqTduDOvNmwAeD8LQs3j788+qDokQmciVCHG5XEydOhUFVO1JCCHkE1odOsDih0AAQNquYLw/ekzFERHyeXI3jbVv3x5RUVFKCIUQQkhtp+/tDePvvgUApPzwA7LDw1UcESEVk2tmaQDw8/NDQEAAEhIS4ObmBi0tLan1Li4uCgtOERISEjB69GikpqaCy+Vi8eLF+Oqrr1QdFiGE1FnGfn4QJSYhMyQEiTNmwv7gAWg4O6s6LELKJHciNHz4cADAtGnTJMsYhgHLsmAYBsU17CZ8XC4XGzduRKtWrZCamgpXV1f07du3VAJHCCFEMRiGgcXyZRClJCM34iYSpvjC/thR8CwtVR0aIaXInQjFxsYqIw6lsbCwgIWFBQDA1NQUhoaGSE9Pp0SIEEKUiOHzYb1pE16NHIWC58+RMMUXdocPQU1HR9WhESJF7j5CdnZ2FT7kFR4ejgEDBsDS0hIMw+DUqVOlymzduhUODg7Q0NCAm5sbrl+/LvdxAODu3bsQi8WwsbGp1PaEEEJkp6ajA5udO8A1MUHB8+dInDYNbGGhqsMiRIrcNUIAcODAAWzfvh2xsbGIiIiAnZ0dNm7cCAcHBwwaNEiufeXk5KBly5YYN24cvvzyy1Lrjx07hhkzZmDr1q3o3LkzduzYAS8vL0RHR0tu8urm5lbmSLYLFy7A8n9VsWlpaRgzZgyCg4MrjKegoEBqX0KhEAAgEokgEonkOreKlOxLkfsk1YOuXe1E101FjI1hEbQFiT7jkBtxE0mLFsN0xQ9gGEbmXdC1q71Uee1kPSbDsiwrz463bduGJUuWYMaMGVi5ciUeP36MBg0aYO/evdi3bx+uXLlSqYCBD+3KISEh8Pb2lixr3749XF1dsW3bNsmypk2bwtvbG6tXr5ZpvwUFBfDw8MCkSZMwevToCssuW7YMy5cvL7X88OHDEAgEsp0IIYQQKYKnz2C1bx8YsRjvPHohvVcvVYdE6rjc3FyMHDkSmZmZ0NXVLbec3ImQs7MzVq1aBW9vb+jo6ODBgwdo0KABHj9+jO7du+Pdu3eVDvrTRKiwsBACgQDHjx/H4MGDJeWmT5+OqKgoXLt27bP7ZFkWI0eOhKOjI5YtW/bZ8mXVCNnY2ODdu3cVvpDyEolECAsLg4eHB3g8nsL2S5SPrl3tRNdN9TKPn8DbwA/zDJmuXAHdgQNl2o6uXe2lymsnFAphbGz82USoUp2lW7duXWq5uro6cnJy5N1dhd69e4fi4mKYmZlJLTczM5N5dut//vkHx44dg4uLi6T/0YEDB9CiRYsyy6urq0NdXR1BQUEICgqSjILj8XhKuYjK2i9RPrp2tRNdN9UxHjkC4uRkpO3ahdSly6BhZQWtDh1k3p6uXe2limsn6/HkToQcHBwQFRVVqmP02bNn4aykeSI+bUsuGaoviy+++ALiStzzxt/fH/7+/hAKhdDT05N7e0IIIaWZzJwBUVIShKGhSPxuGuwPH4J648aqDovUY3InQnPmzIG/vz/y8/PBsixu376NI0eOYPXq1Z/tiCwvY2NjqKmplar9SU1NLVVLRAghpOZjOBxYrF4F0Zs3yI+JgehNKiVCRKXkToTGjRuHoqIizJ07V9IRycrKCr/88gu+/vprhQbH5/Ph5uaGsLAwqT5CYWFhco9Ok9enTWOEEEIUg6OuDq0l6xF7+RUcO7VXdTiknqvU8PlJkyZh0qRJePfuHcRiMUxNTSsdQHZ2Nl68eCF5Hhsbi6ioKBgaGsLW1hYBAQEYPXo02rRpg44dO2Lnzp2Ij4+Hr69vpY8pC2oaI4QQ5SjML8KfwS+QlyWCxun/0HFwI1WHROqxSiVCwIfmqWfPnoFhGDAMAxMTk0rt5+7du3B3d5c8DwgIAACMHTsWe/fuxfDhw5GWlobAwEAkJyejefPmCA0NrdTkjYQQQlSPr8FF56GNcXFPNO6dj4eeqQDOnen2G0Q15E6EhEIh/P39ceTIEUknZDU1NQwfPhxBQUFy1550794dnxvB7+fnBz8/P3lDrRJqGiOEEOVxbG+OzNRc3DkTh2uHnkHXSAPWToaqDovUQ3LfYmPixIm4desWzpw5g4yMDGRmZuKvv/7C3bt3MWnSJGXEqBL+/v6Ijo7GnTt3VB0KIYTUSW37O6BxWzOIxSzO7XyM9ymKnYKFEFnInQidOXMGu3fvRu/evaGrqwsdHR307t0bu3btwpkzZ5QRIyGEkDqIYRj0GOME8wZ6KMgtwl9bHiAvi+5FRqqX3ImQkZFRmc1fenp6MDAwUEhQhBBC6gcuTw19p7aArrEGhO/ycXb7IxSJqEsCqT5yJ0KLFi1CQEAAkpOTJctSUlIwZ84cLF68WKHBqVJQUBCcnZ3Rtm1bVYdCCCF1mqYOH/38W4KvyUXyf5m4vP/pZ/uOEqIoMnWWbt26tdRMzs+fP4ednZ3k7u/x8fFQV1fH27dvMWXKFOVEWs1o+DwhhFQfQwst9JnSHH9teoDnd95A30yAdv0dVB0WqQdkSoQ+vhs8IYQQogw2ToboOqIJrh56hjt/xULPRBOO7c1VHRap42RKhJYuXarsOAghhBA062KFzNQ83A+Lx+UDMdA10oCxnZaqwyJ1mNx9hD6WnZ0NoVAo9agrqI8QIYSoRsfBDdGgtQnERSxCtz+C8G2eqkMidZjciVBsbCz69esHLS0tyUgxAwMD6Ovr16lRYzSPECGEqAbDYdBrnDNM7XSQny3CuR1PIBapOipSV8k9s/SoUaMAALt374aZmZlUJ2pCCCFEEXh8NfT1c8GJNXeR8SYPeUWaKO4nBo+n6shIXSN3IvTw4UNERkbC0dFRGfEQQgghAAAtPXX083fByfX3UJAG/P3bC/Qc40x/gBOFkrtprG3btkhISFBGLIQQQogUY2sd9PRxAsDiWcQb3L8Qr+qQSB0jd41QcHAwfH19kZSUhObNm4P3ST2li4uLwoIjhBBCbJsbQr9pATJiNBAR8h/0TDXRsLWpqsMidYTcidDbt2/x33//Ydy4cZJlDMOAZVkwDFNn7tZOd58nhJCaQ9teBCsTBzwJT8bF3dHQnqUBM3tdVYdF6gC5m8bGjx+P1q1bIyIiAi9fvkRsbKzUv3UFjRojhJCapeOQhrBtZoQikRihWx8iKz1f1SGROkDuGqFXr17hjz/+QKNGjZQRDyGEEFImjhqD3hOb4eSGSKQl5eBM0AMMme0GvqbcP2WESMhdI9SjRw88ePBAGbEQQgghFeJrctHPvyUEunykJeXgfPATiIvFqg6L1GJyp9EDBgzAzJkz8ejRI7Ro0aJUZ+mBAwcqLDhCCCHkUzqGGujr54JTP95D/JM0/H38Bbp+3UTVYZFaSu5EyNfXFwAQGBhYal1d6ixNCCGk5jKz10Wv8c44t+MxHl1NhL6ZJlzcbVQdFqmF5G4aE4vF5T4oCSKEEFJdGrY2RcfBDQEAf//2HHGP3qk4IlIbVemmq3UZ3XSVEEJqvtaetmja2QIsC1wIfoJ3iVmqDonUMnI3jZXVJPaxJUuWVDqYmsTf3x/+/v4QCoXQ09NTdTiEEELKwDAMuo1whPBdPpKevceZoIcYOq8NtPTUVR0aqSXkToRCQkKknotEIsTGxoLL5aJhw4Z1JhEihBBSO6hxOegzuTl+XxeJjDe5OBP0EINnuYKnrqbq0EgtIHcidP/+/VLLhEIhfHx8MHjwYIUERQghhMhDQ4uH/t+64MTaSLyNz8LFPdHoM7k5GA7doJVUTCF9hHR1dREYGIjFixcrYneEEEKI3PRMBOjr2wIcLoOXUW8Rceo/VYdEagGFdZbOyMhAZmamonZHCCGEyM2ikT56jmkKALh/IR7Rf79WcUSkppO7aWzTpk1Sz1mWRXJyMg4cOIA+ffooLDBCCCGkMpq0M0dGah7u/BWLa4efQcdYAzZOhqoOi9RQcidCP//8s9RzDocDExMTjB07FvPnz1dYYIQQQkhlte1nj4w3uXh+5w3O7XiML+e6wdBCS9VhkRpI7kQoNjZWGXEQQgghCsMwDHqMcUJ2ej6S/8vEmaAHGPp9G2jq8FUdGqlhaELFctCEioQQUrtxeWrw8m0BXWMNCN/lI3TbIxSJ6A4IRJrMNULjx4//bBmGYfDrr79WKaCagiZUJISQ2k9Th4/+37bEibWRSHmZicv7n8JjvDMYhobVkw9kToTev39f7rri4mJcvHgRBQUFdSYRIoQQUjcYmGvBa0pz/LnpAZ7feQN9U020G9BA1WGRGkLmROjTGaVLnD59GgsWLIC6ujrNKk0IIaRGsnYyRLdRjrhy4CnunImDnqkAju3NVR0WqQEq3Ufon3/+wRdffIGRI0eif//+ePnyJebNm6fI2AghhBCFce5sCdfetgCAywdi8PpFhmoDIjWC3InQkydPMGDAAHTv3h2Ojo549uwZ1q5dCwMDA2XERwghhChMh0EN0aC1CcRFLM5ue4SM1FxVh0RUTOZEKCEhAePGjUOrVq3A5XLx8OFD/Prrr7C2tlZmfIQQQojCMBwGvcY5w9ROB/k5IpwJeoj8HJGqwyIqJHMfIUdHRzAMg1mzZqFTp054/vw5nj9/XqrcwIEDFRogIYQQokg8vhr6+rngxJq7yHiTi3M7H2HAd62gxqUZZeojmROh/Px8AMC6devKLcMwDIqLaY4GQgghNZuWnjr6+bfEyfWRSHqWgWuHn8F9tBMNq6+HZE5/xWLxZx+UBBFCCKktjK214TmxGRgGiLmRjPsX4lUdElEBqgckhBBSb9m3MMYXw5oAACJC/sOLyFQVR0SqGyVChBBC6jUXd2u4uH8Y+HNxbzTexApVHBGpTnU+EcrKykLbtm3RqlUrtGjRArt27VJ1SIQQQmqYzl81hl0LIxSLxDiz7SGy0vNVHRKpJnU+ERIIBLh27RqioqJw69YtrF69GmlpaaoOixBCSA3C4TDwnNAMRlbayBMW4kzQAxTmFak6LFIN6nwipKamBoFAAODDyLfi4mKwLKviqAghhNQ0fA0u+vm7QKDLR1pSDs4HP4a4WKzqsIiSVSoRysjIQHBwMObPn4/09HQAwL1795CUlCT3vsLDwzFgwABYWlqCYRicOnWqVJmtW7fCwcEBGhoacHNzw/Xr1+WOt2XLlrC2tsbcuXNhbGwsd5yEEELqPh1DDfTzdwGXx0H8k3Rc/+05/fFcx8k8j1CJhw8folevXtDT00NcXBwmTZoEQ0NDhISE4NWrV9i/f79c+8vJyUHLli0xbtw4fPnll6XWHzt2DDNmzMDWrVvRuXNn7NixA15eXoiOjoat7Yd7xri5uaGgoKDUthcuXIClpSX09fXx4MEDvHnzBkOGDMHQoUNhZmZWZjwFBQVS+xIKP3SaE4lEEIkUN/toyb4UuU9SPeja1U503Wqv6r52BpaacB/riLBfY/D4WhJ0jdXRvLtVtRy7rlHl507WYzKsnKlur1694OrqinXr1kFHRwcPHjxAgwYNcOPGDYwcORJxcXGVifdDMAyDkJAQeHt7S5a1b98erq6u2LZtm2RZ06ZN4e3tjdWrV8t9jKlTp6JHjx746quvyly/bNkyLF++vNTyw4cPS5rYCCGE1H1ZL3nIfKYBgIWRWx40TWmuvNokNzcXI0eORGZmJnR1dcstJ3eN0J07d7Bjx45Sy62srJCSkiLv7ipUWFiIyMjIUne19/T0xI0bN2Tax5s3b6CpqQldXV0IhUKEh4dj6tSp5ZafP38+AgICJM+FQiFsbGzg6elZ4QspL5FIhLCwMHh4eIDH4ylsv0T56NrVTnTdai9VXTuWZXH9yAs8jUhB5iNtdJ/ZEkbW2tV2/LpAlZ+7khadz5E7EdLQ0Chz58+ePYOJiYm8u6vQu3fvUFxcXKoZy8zMTOakKzExERMmTADLsmBZFt9++y1cXFzKLa+urg51dfVSy3k8nlIuorL2S5SPrl3tRNet9lLFtev+jROy3xcg8el7nN8ZjaHft4GWfunfCFIxVVw7WY8nd2fpQYMGITAwUNL2xjAM4uPjMW/evDL7+CjCp/d+YVlW5vvBuLm5ISoqCg8ePMDDhw8rrA36WFBQEJydndG2bVu54yWEEFI3qKlx0GdycxiYC5D9vgBntj6EqICayOoSuROhDRs24O3btzA1NUVeXh66deuGRo0aQUdHBytXrlRocMbGxlBTUytV+5OamlpuZ2dF8ff3R3R0NO7cuaPU4xBCCKnZ1AU89PNvCQ1tHt7GZyFs9xOwYhpJVlfInQjp6uri77//xu+//441a9bg22+/RWhoKK5duwYtLS2FBsfn8+Hm5oawsDCp5WFhYejUqZNCj/UpqhEihBBSQs9EE32nukCNy0Hsg3eICPlP1SERBZG7j1BcXBzs7e3Ro0cP9OjRo8oBZGdn48WLF5LnsbGxiIqKgqGhIWxtbREQEIDRo0ejTZs26NixI3bu3In4+Hj4+vpW+dgV8ff3h7+/P4RCIfT09JR6LEIIITWfRUM99BjrhLBfo3E/LB56pppo1oWG1dd2cidCDRo0QKdOnTB69Gh89dVXMDQ0rFIAd+/ehbu7u+R5yYitsWPHYu/evRg+fDjS0tIQGBiI5ORkNG/eHKGhobCzs6vScQkhhBB5NWlrjszUPNz+MxbXjvwLXWNN2DSt2u8gUS25m8bu3r2Ljh07YsWKFbC0tMSgQYNw/PjxMic0lEX37t0lI7o+fuzdu1dSxs/PD3FxcSgoKEBkZCS6du1aqWMRQgghVdWmrz2atDcDK2ZxbudjpL/OUXVIpArkToRcXV2xfv16xMfH4+zZszA1NcWUKVNgamqK8ePHKyNGlaA+QoQQQsrCMAx6fNMUFo30UJhXhDNbHyBXWKjqsEglVfqmqwzDwN3dHbt27cLFixfRoEED7Nu3T5GxqRSNGiOEEFIeNR4HXr4toGuiCeG7fJzd/hBFIhpWXxtVOhFKSEjAunXr0KpVK7Rt2xZaWlrYsmWLImMjhBBCaixNbT76+7tAXcBFykshLu+LoRu01kJyJ0I7d+5Et27d4ODggH379mHYsGH477//8Pfff8s8WWFtQE1jhBBCPsfAXAt9prQAh8Pg+d1U3P4rVtUhETnJnQj98MMPaNeuHe7evYsnT55gwYIFsLe3V0JoqkVNY4QQQmRh7WiA7t84AgDunonDs1uKve8mUS65h8/Hx8fLfHsLQgghpD5o2skSGW/ycO/8K1w+EAMdQw1YNtZXdVhEBjIlQg8fPkTz5s3B4XDw6NGjCstWdENTQgghpK7qMKgBMlNz8d/9twjd/hBDv28DfVOBqsMinyFTItSqVSukpKTA1NQUrVq1AsMwUh3CSp4zDIPiYuo1TwghpP5hOAx6jnNGVvo9pL7Kwpmgh/hyrhs0tKr3rutEPjIlQrGxsTAxMZH8vz4ICgpCUFAQJXaEEEJkxuOroa+fC06svYuMN7k4t+MRBkxrBTVupQdpEyWT6crY2dlJ+gW9evUKVlZWsLOzk3pYWVnh1atXSg22OlFnaUIIIZWhpaeO/v4twdNQQ9K/Gbh6+BkNq6/B5E5R3d3dkZ6eXmp5Zmam1D3DCCGEkPrKyEobvSc1B8MAT28k4975ulNRUNfInQiV9AX6VFpaGrS0tBQSFCGEEFLb2TUzQpfhTQAAN0+9xIvIVBVHRMoi8/D5IUOGAPjQMdrHxwfq6uqSdcXFxXj48CE6deqk+AgJIYSQWqpFd2tkpObi4eVEXNwbDR1DDZg56Ko6LPIRmWuE9PT0oKenB5ZloaOjI3mup6cHc3NzTJ48GQcPHlRmrNWKZpYmhBCiCJ2HNoZdCyMUi8Q4s+0hhGl5qg6JfETmGqE9e/YAAOzt7TF79uw63wzm7+8Pf39/CIVC6OnpqTocQgghtRSHw8BzQjOc3HAPaYnZOBP0EEPmuEFdU+45jYkSyN1HaOnSpXU+CSKEEEIUia/BRT8/Fwj0+Eh/nYMLux5DXCxWdVgElbjFBgCcOHECv/32G+Lj41FYWCi17t69ewoJjBBCCKlLdAw10M/PBSE/3kN8dDquH3uOriOa0G2rVEzuGqFNmzZh3LhxMDU1xf3799GuXTsYGRnh5cuX8PLyUkaMhBBCSJ1gaqcLj/HNAAZ4HJ6Eh5cTVR1SvSd3IrR161bs3LkTW7ZsAZ/Px9y5cxEWFoZp06YhMzNTGTESQgghdUaDViboNKQRAODvE88R+/CdiiOq3+ROhOLj4yXD5DU1NZGVlQUAGD16NI4cOaLY6AghhJA6qFUvGzh3sQRY4MKvT/A2IUvVIdVbcidC5ubmSEtLA/Dh1hs3b94E8OEeZHVpCnEaPk8IIURZGIZB16+bwNrJAEUFxTgT9BA5GQWqDqtekjsR6tGjB/78808AwIQJEzBz5kx4eHhg+PDhGDx4sMIDVBW61xghhBBlUlPjoM/k5jAwFyAnowBntj6EqIBu9F3d5B41tnPnTojFH4b8+fr6wtDQEH///TcGDBgAX19fhQdICCGE1FXqAh76f9sSJ9bexdv4LITtfoI+U1qAw6GRZNVF7kSIw+GAw/n/iqRhw4Zh2LBhCg2KEEIIqS90jTXh5euC0z/fR+yDd4g4+QKdhzZWdVj1hkyJ0MOHD2XeoYuLS6WDIYQQQuoji4Z66DHWCWG/RiPqYgL0TAVo3tVK1WHVCzIlQq1atQLDMJ/tDM0wDIqLqX2TEEIIkVeTtubITM3D7T9jEX70X+gZa8LG2VDVYdV5MiVCsbGxyo6DEEIIqffa9LVHZmoent1Kwbmdj/Dl3DYwtKTbWimTTImQnZ2dsuMghBBC6j2GYeD+jROEaXlIfpGJv4IeYOj3bSDQ5as6tDpL7uHzAHDgwAF07twZlpaWePXqFQBg48aNOH36tEKDI4QQQuobNR4HfX1doGeiiay0fIRue4iiQup2oixyJ0Lbtm1DQEAA+vbti4yMDEmfIH19fWzcuFHR8RFCCCH1joY2D/38XaAu4OJNrBCX9seAFdedSYtrErkToc2bN2PXrl1YuHAh1NTUJMvbtGmDR48eKTQ4VaKZpQkhhKiSgbkWvP43p9CLu6m4/Rf111UGuROh2NhYtG7dutRydXV15OTkKCSomoBmliaEEKJqVo4G6P6NIwDgbmgcnt5MVnFEdY/ciZCDgwOioqJKLT979iycnZ0VERMhhBBC/qdpJ0u49vkwaOnKgad4/fy9iiOqW+SeWXrOnDnw9/dHfn4+WJbF7du3ceTIEaxevRrBwcHKiJEQQgip1zoMbIDM1Dz8dy8VodsfYejcNtA3E6g6rDpB7kRo3LhxKCoqwty5c5Gbm4uRI0fCysoKv/zyC77++mtlxEgIIYTUawyHQS+fpsh+n483sULJsHoNLZ6qQ6v1KjV8ftKkSXj16hVSU1ORkpKChIQETJgwAUlJSYqOjxBCCCEAuHw19J3qAh1DDWSm5uHs9kcoLhKrOqxar1KJUAljY2OYmpoiJSUF3333HRo1aqSouAghhBDyCYEuH/38XcDTUMPr5xm4evDpZ29/RSomcyKUkZGBUaNGwcTEBJaWlti0aRPEYjGWLFmCBg0a4ObNm9i9e7cyYyWEEELqPSMrbfSZ1BwMh8HTmym4d/6VqkOq1WTuI7RgwQKEh4dj7NixOHfuHGbOnIlz584hPz8fZ8+eRbdu3ZQZJyGEEEL+x7aZEboOb4xrR/7FzVMvoWciQCM3U1WHVSvJXCN05swZ7NmzBxs2bMAff/wBlmXRpEkTXL58mZIgQgghpJo172aNlj1sAAAX90YjJTZTxRHVTjInQq9fv5bME9SgQQNoaGhg4sSJSguMEEIIIRXrNLQR7FsYoVgkRujWhxC+y1N1SLWOzImQWCwGj/f/w/TU1NSgpaWllKCUITc3F3Z2dpg9e7aqQyGEEEIUgsNh4DGhGYxttJGXJcKZrQ9RkFek6rBqFZn7CLEsCx8fH6irqwMA8vPz4evrWyoZOnnypGIjVJCVK1eiffv2qg6DEEIIUSi+Bhf9/FxwYs1dpL/Owfldj9Hf3wUctSoNDK83ZH6Vxo4dC1NTU+jp6UFPTw/ffPMNLC0tJc9LHjXR8+fP8fTpU/Tt21fVoRBCCCEKp22ggX7+LcHlc5AQnY7wY89pWL2MZK4R2rNnj1ICCA8Px/r16xEZGYnk5GSEhITA29tbqszWrVuxfv16JCcno1mzZti4cSO6dOki8zFmz56N9evX48aNGwqOnhBCCKkZTGx14DG+Gc7ueIQn4UkwMBOgZU8bVYdV48l9iw1Fy8nJQcuWLTFu3Dh8+eWXpdYfO3YMM2bMwNatW9G5c2fs2LEDXl5eiI6Ohq2tLQDAzc0NBQUFpba9cOEC7ty5gyZNmqBJkyYyJUIFBQVS+xIKhQAAkUgEkUhU2dMspWRfitwnqR507Wonum61F1072dk000cHbwfcDInF3yeeQ8uAB7sWRiqLR5XXTtZjMmwNqjtjGKZUjVD79u3h6uqKbdu2SZY1bdoU3t7eWL169Wf3OX/+fBw8eBBqamrIzs6GSCTCrFmzsGTJkjLLL1u2DMuXLy+1/PDhwxAI6AZ3hBBCajaWBTKeqCMngQ9GjYVJ+1zw9erfrThK7oeamZkJXV3dcsvV6ESosLAQAoEAx48fx+DBgyXlpk+fjqioKFy7dk2u/e/duxePHz/Ghg0byi1TVo2QjY0N3r17V+ELKS+RSISwsDB4eHhIjcYjNR9du9qJrlvtRddOfuJiMc5uf4KkpxnQ0ufDe1YraOmrV3scqrx2QqEQxsbGn02EVN40VpF3796huLgYZmZmUsvNzMyQkpKilGOqq6tLRsZ9jMfjKeUiKmu/RPno2tVOdN1qL7p2cuABXpNb4Pf19/A+OQfnd0Zj8CxX8DVU87Ovimsn6/Fqxdg6hmGknrMsW2qZLHx8fCqsDfpYUFAQnJ2d0bZtW7mPQwghhKiauoCH/v4u0NTh4V1CNsJ2R0MsrjGNQDVGjU6EjI2NoaamVqr2JzU1tVQtkaL5+/sjOjoad+7cUepxCCGEEGXRNdZE36kuUONyEPfwHW6cfKHqkGqcGp0I8fl8uLm5ISwsTGp5WFgYOnXqpKKoCCGEkNrDvIEeevo0BQA8uJiAx+FJKo6oZlF5H6Hs7Gy8ePH/GWpsbCyioqJgaGgIW1tbBAQEYPTo0WjTpg06duyInTt3Ij4+Hr6+vkqNKygoCEFBQSguLlbqcQghhBBla9zGDJmpebj1x0uEH/0XusYasHVW3bD6mkTlidDdu3fh7u4ueR4QEADgw0zWe/fuxfDhw5GWlobAwEAkJyejefPmCA0NhZ2dnVLj8vf3h7+/P4RCYY2dMZsQQgiRlZuXHTJSc/HsZgrO73yMIXPdYGSpreqwVE7liVD37t0/Ow24n58f/Pz8qikiQgghpO5hGAbuo5yQlZaP1//X3t0HRVXvfwB/n11gAWXlSVbQBbUsnuRpUfPxShaEKNlPy9JLKr/ppxcap8wmG5vp6Y/mTuM0meAV06SyoEz5lVFJDqKhkjKRGGSgcCEVCDQeBORh9/eHub+77i67i7ucXfb9mmnGPed7zr63zxz343e/Z7fmT3y94xxWbImHp9xN7Giisus1QmLiXWNERDTaSF0lSF4/HePGe6DzWi8Kd57DQJ9zLwFhI2QE7xojIqLRyH2sK5Y8Gw2Zpwua6zpw9MNqaJz4tno2QkRERE7GW+GJ5A3TIZEKqD3bgrKvLokdSTRshIiIiJzQxPt8kPD3UABA+Tf/xq+nroqcSBxshIzgGiEiIhrtQmcHQpV86y7s4o9/xeXfroucaOSxETKCa4SIiMgZzFo6FfeqAqAe1OCbf1Xiz+ZusSONKDZCRERETkyQCFi0JgyKKXLc7B7A4ayf0dvVL3asEcNGiIiIyMm5uEmx+B9R8PJ1R3tLD77ZVYnBAbXYsUYEGyEjuEaIiIiciafcDSnPRsHNXYorNX/i2Me/mvzC49GAjZARXCNERETOxi9oLJKeiYQgEfDr6SaUf/tvsSPZHBshIiIi0gqO8MOCldMAAGX/ewk1Z5tFTmRbbISIiIhIR+TfJiF6kRIAcHRfNZoutYucyHbYCBEREZGeOcvvxeQofwwOqFG48xw6WnvEjmQTbISM4GJpIiJyZhKJgIfTw+GvHIuezn4czjqHmz0DYseyOjZCRnCxNBEROTs3dxekZERjzDg3XL96A9/lVGJwcHTdVs9GiIiIiIwa6yNDSmY0XNwkaKy+jhN5v42q2+rZCBEREdGQxgd7IfG/IwAB+OXEFfx8tFHsSFbDRoiIiIhMmhI9HnOX3wsAKP2iFnU//yFyIutgI0RERERmiV6kRMSCiYAGOLLnF/zR0Cl2pLvGRoiIiIjMIggC5q+cBmW4Lwb61Pg662d0Xe8VO9ZdYSNkBG+fJyIi0ieVSpD0TCR8g8bgRnsfvs4+h75ex72tno2QEbx9noiIyDCZhwtSMqLg4eWK1sYuFO2tglrtmHeSsREiIiIii8n9PbD4H1GQukpQf64VJ7+oFTvSsLARIiIiomGZMHUcFq0JAwD8fLQR50t+FzmR5dgIERER0bBNi1dg1qNTAQDH82vQ8EubyIksw0aIiIiI7orqkRCEPjABGrUG3+0+j7bLXWJHMhsbISIiIrorgiBg4d9DETTNG329g/g66xy6O/rEjmUWNkJERER016QuEiSvn45xAR7ovNaLwp3nMNA3KHYsk9gIERERkVW4j3XFksxoyMa4oLmuA8c+/g32/vusbISIiIjIarwVnli8YTokUgGXfmpFR42b2JGGxEbICH6zNBER0fAETfNBQlooAKDzogwXTjeLnMg4NkJG8JuliYiIhi/0gUDEJikBACfyanD5wnWRExnGRoiIiIhsIn5xCDwC+6Ee1OCbXZX4s7lb7Eh62AgRERGRTQgSAb7TexEw2Qs3uwdweMfP6O3qFzuWDjZCREREZDOCFEh8Jhxefu5o/6MHhf86h8F+tdixtNgIERERkU15yt2QkhkFN3cprta2o3j/r9DYyX31bISIiIjI5vyCxuKR/5kOQSLgwukmlH9TL3YkAGyEiIiIaIQow33xt6fuAwCUfVmHmjPi31bPRoiIiIhGTMT8iYh56NZt9Udzq9F0qV3UPGyEiIiIaETN/q97MSXaH4MDahTuPIeO1h7RsrARIiIiohElkQh4OD0C/sqx6OnsR0PVNdGyuIj2zCPIxcUFkZGRAID4+Hi8//77IiciIiJybq4yKVIyotF0qR33qgJEy+EUjZC3tzcqKirEjkFERET/YayPTNQmCOBHY0REROTERG+Ejh8/jqVLlyIoKAiCIKCgoEBvTHZ2NqZMmQJ3d3eoVCqcOHHCoufo6OiASqXCvHnzUFJSYqXkRERE5OhE/2jsxo0biI6Oxrp167B8+XK9/fn5+XjuueeQnZ2NuXPnYteuXUhOTkZVVRWCg4MBACqVCjdv3tQ79siRIwgKCkJ9fT2CgoJw/vx5pKSkoLKyEnK53GCemzdv6pyro6MDANDf34/+fuv9Psrtc1nznDQyWDvHxLo5LtbOcYlZO3OfU9DYy3dcAxAEAYcOHcKyZcu022bNmoW4uDjs3LlTuy0sLAzLli3DW2+9ZfFzJCcn480330R8fLzB/a+99hpef/11ve2ffPIJPD09LX4+IiIiGnnd3d1YtWoV2tvbjU5+AHYwIzSUvr4+lJeXY8uWLTrbExMTcfLkSbPOcf36dXh6ekImk+H3339HVVUVpk6danT8yy+/jE2bNmkfd3R0QKlUIjExccj/kZbq7+9HUVERHn74Ybi6ulrtvGR7rJ1jYt0cF2vnuMSs3e1PdEyx60aotbUVg4ODUCgUOtsVCgWamprMOkd1dTXWr18PiUQCQRDw7rvvwtfX1+h4mUwGmUymt93V1dUmRbTVecn2WDvHxLo5LtbOcYlRO3Ofz64bodsEQdB5rNFo9LYZM2fOHFRWVlr8nFlZWcjKysLg4KDFxxIREZFjEP2usaH4+/tDKpXqzf60tLTozRJZW2ZmJqqqqnDmzBmbPg8RERGJx64bITc3N6hUKhQVFelsLyoqwpw5c0RKRURERKOF6B+NdXV1oba2Vvu4rq4OFRUV8PX1RXBwMDZt2oS0tDTEx8dj9uzZyMnJQUNDAzZs2GDTXPxojIiIaPQTvRE6e/YsEhIStI9v37G1Zs0a7Nu3DytXrkRbWxveeOMNXL16FZGRkSgsLERISIhNc2VmZiIzMxMdHR0YN26cTZ+LiIiIxCF6I7Rw4UKY+iqjjIwMZGRkjFAiIiIichZ2vUZITFlZWQgPD8eMGTPEjkJEREQ2IvqMkL26/dFYe3s7vL29zf5iJnP19/eju7sbHR0d/F4MB8PaOSbWzXGxdo5LzNrdft829akTGyETOjs7AQBKpVLkJERERGSpzs7OIdf62tVvjdkjtVqNK1euwMvLS+dLHGfMmGH0O4YM7btz2+2f7mhsbLTqT3cMx1CvZaTOZclxpsYOd7+52+2ldtas23DPZ826mRrDa8665+I1Zzl7uOYsOU7saw4Qt3YajQadnZ0ICgqCRGJ8JRBnhEyQSCSYNGmS3napVGq0qIb2GRsvl8tF/0t5qNcyUuey5DhTY4e739LtYtfOmnUb7vmsWTdTY3jNWfdcvOYsZw/XnCXH2cs1B4hXO3Pu+uZi6WHKzMy0aN9Q48VmzWzDPZclx5kaO9z9lm4Xm7VzDed81qybqTG85qx7Ll5zlrOHa86S43jNmYcfjYnk9vcTtbe3i/6vU7IMa+eYWDfHxdo5LkeoHWeERCKTyfDqq68a/KV7sm+snWNi3RwXa+e4HKF2nBEiIiIip8UZISIiInJabISIiIjIabERIiIiIqfFRoiIiIicFhshIiIiclpshBxAY2MjFi5ciPDwcERFReHzzz8XOxKZ6bHHHoOPjw9WrFghdhQy4fDhw7j//vsxbdo0vP/++2LHITPxGnNM9vS+xtvnHcDVq1fR3NyMmJgYtLS0IC4uDhcuXMCYMWPEjkYmFBcXo6urC7m5uThw4IDYcciIgYEBhIeHo7i4GHK5HHFxcSgrK4Ovr6/Y0cgEXmOOyZ7e1zgj5AACAwMRExMDAAgICICvry+uXbsmbigyS0JCAry8vMSOQSb8+OOPiIiIwMSJE+Hl5YXFixfju+++EzsWmYHXmGOyp/c1NkJWcPz4cSxduhRBQUEQBAEFBQV6Y7KzszFlyhS4u7tDpVLhxIkTw3qus2fPQq1WQ6lU3mVqGsm6kW3dbS2vXLmCiRMnah9PmjQJly9fHonoTo3XoOOyZu3Efl9jI2QFN27cQHR0NHbs2GFwf35+Pp577jls3boVP/30E+bPn4/k5GQ0NDRox6hUKkRGRur9d+XKFe2YtrY2PP3008jJybH5a3IGI1U3sr27raWhFQKCINg0M1nnGiRxWKt2dvG+piGrAqA5dOiQzraZM2dqNmzYoLMtNDRUs2XLFrPP29vbq5k/f77mww8/tEZMuoOt6qbRaDTFxcWa5cuX321EMtNwallaWqpZtmyZdt/GjRs1+/fvt3lW+n93cw3yGhPXcGtnL+9rnBGysb6+PpSXlyMxMVFne2JiIk6ePGnWOTQaDdauXYsHH3wQaWlptohJd7BG3cg+mFPLmTNn4vz587h8+TI6OztRWFiIpKQkMeLSX3gNOi5zamdP72tshGystbUVg4ODUCgUOtsVCgWamprMOkdpaSny8/NRUFCAmJgYxMTEoLKy0hZx6S/WqBsAJCUl4fHHH0dhYSEmTZqEM2fOWDsqmWBOLV1cXLBt2zYkJCQgNjYWL774Ivz8/MSIS38x9xrkNWZ/zKmdPb2vuYjyrE7ozvUGGo3G7DUI8+bNg1qttkUsMuFu6gaAdx7ZEVO1TE1NRWpq6kjHIhNM1Y3XmP0aqnb29L7GGSEb8/f3h1Qq1ZtFaGlp0euWyX6wbqMHa+mYWDfH5Wi1YyNkY25ublCpVCgqKtLZXlRUhDlz5oiUikxh3UYP1tIxsW6Oy9Fqx4/GrKCrqwu1tbXax3V1daioqICvry+Cg4OxadMmpKWlIT4+HrNnz0ZOTg4aGhqwYcMGEVMT6zZ6sJaOiXVzXKOqdiLesTZqFBcXawDo/bdmzRrtmKysLE1ISIjGzc1NExcXpykpKREvMGk0GtZtNGEtHRPr5rhGU+34W2NERETktLhGiIiIiJwWGyEiIiJyWmyEiIiIyGmxESIiIiKnxUaIiIiInBYbISIiInJabISIiIjIabERIiIiIqfFRoiIiIicFhshInIaCxYswCeffCJ2jGE5fPgwYmNjoVarxY5CNKqwESIiq1i7di0EQTD4o4oZGRkQBAFr164d+WB/OXz4MJqamvDkk09qt02ePBmCICAvL09vfEREBARBwL59+0YwpXFLliyBIAgO28gR2Ss2QkRkNUqlEnl5eejp6dFu6+3txaefforg4GARkwHbt2/HunXrIJHo/rWnVCrxwQcf6Gw7ffo0mpqaMGbMmJGMaNK6devw3nvviR2DaFRhI0REVhMXF4fg4GAcPHhQu+3gwYNQKpWIjY3VGfvtt99i3rx58Pb2hp+fH5YsWYKLFy9q9/f19eHZZ59FYGAg3N3dMXnyZLz11lva/a+99hqCg4Mhk8kQFBSEjRs3Gs3V2tqK77//HqmpqXr7Vq9ejZKSEjQ2Nmq37d27F6tXr4aLi4vZr32oPIIgoKCgQGe8t7e3drapvr4egiDg4MGDSEhIgKenJ6Kjo3Hq1CmdY1JTU/Hjjz/i0qVLZucioqGxESIiq1q3bp3ODMvevXuRnp6uN+7GjRvYtGkTzpw5g6NHj0IikeCxxx7TroHZvn07vvzyS3z22We4cOECPv74Y0yePBkAcODAAbzzzjvYtWsXampqUFBQgOnTpxvN9MMPP8DT0xNhYWF6+xQKBZKSkpCbmwsA6O7uRn5+vsHMxliax5itW7di8+bNqKiowH333YennnoKAwMD2v0hISEICAjAiRMnLD43ERlm/j93iIjMkJaWhpdfflk7y1FaWoq8vDwcO3ZMZ9zy5ct1Hu/ZswcBAQGoqqpCZGQkGhoaMG3aNMybNw+CICAkJEQ7tqGhARMmTMBDDz0EV1dXBAcHY+bMmUYz1dfXQ6FQ6H0sdlt6ejpeeOEFbN26FQcOHMA999yDmJgYs1+zpXmM2bx5M1JSUgAAr7/+OiIiIlBbW4vQ0FDtmIkTJ6K+vt7icxORYZwRIiKr8vf3R0pKCnJzc/HBBx8gJSUF/v7+euMuXryIVatWYerUqZDL5ZgyZQqAW00FcGvxdUVFBe6//35s3LgRR44c0R77+OOPo6enB1OnTsUzzzyDQ4cO6cyc3Kmnpwfu7u5G96ekpKCrqwvHjx83OoM1FEvzGBMVFaX9c2BgIACgpaVFZ4yHhwe6u7stPjcRGcZGiIisLj09Hfv27UNubq7RpmLp0qVoa2vD7t27UVZWhrKyMgC31gYBt9Yb1dXV4c0330RPTw+eeOIJrFixAsCtBc4XLlxAVlYWPDw8kJGRgQULFqC/v9/gc/n7++P69etG87q4uCAtLQ2vvvoqysrKsHr1aoter6k8giBAo9HoHGMoq6urq/bPgiAAgN7t8teuXcP48eMtykdExrERIiKre+SRR9DX14e+vj4kJSXp7W9ra0N1dTVeeeUVLFq0CGFhYQYbFblcjpUrV2L37t3Iz8/HF198gWvXrgG4NTOSmpqK7du349ixYzh16hQqKysN5omNjUVTU9OQzVB6ejpKSkrw6KOPwsfHx+LXPFSe8ePH4+rVq9qxNTU1w5rV6e3txcWLF/UWnhPR8HGNEBFZnVQqRXV1tfbPd/Lx8YGfnx9ycnIQGBiIhoYGbNmyRWfMO++8g8DAQMTExEAikeDzzz/HhAkTtHdbDQ4OYtasWfD09MRHH30EDw8PnXVE/yk2Nhbjx49HaWkplixZYnBMWFgYWltb4enpafHrNZXnwQcfxI4dO/DAAw9ArVbjpZde0pn9Mdfp06chk8kwe/Zsi48lIsM4I0RENiGXyyGXyw3uk0gkyMvLQ3l5OSIjI/H888/j7bff1hkzduxY/POf/0R8fDxmzJiB+vp6FBYWQiKRwNvbG7t378bcuXMRFRWFo0eP4quvvoKfn5/B55NKpUhPT8f+/fuHzOzn5wcPDw+LX6upPNu2bYNSqcSCBQuwatUqbN68eVgN16efforVq1cP61giMkzQ3PnBNRHRKNTc3IyIiAiUl5cbnTmyZ3/88QdCQ0Nx9uxZ7cJyIrp7nBEiIqegUCiwZ88e7V1pjqaurg7Z2dlsgoisjDNCREQm7N+/H+vXrze4LyQkBL/88ssIJyIia2EjRERkQmdnJ5qbmw3uc3V1dciP2ojoFjZCRERE5LS4RoiIiIicFhshIiIiclpshIiIiMhpsREiIiIip8VGiIiIiJwWGyEiIiJyWmyEiIiIyGn9H5OAEBYtevFLAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting new IMF\n", + "\n", + "mass_range = np.linspace(0.01, 120, 10000)\n", + "\n", + "def power_law(masses, index):\n", + " return masses**index\n", + "\n", + "plt.figure()\n", + "\n", + "# Define each mass range and plot with the corresponding index\n", + "mass_1 = mass_range[(mass_range >= 0.01) & (mass_range < 0.05)]\n", + "plt.plot(mass_1, power_law(mass_1, -0.6), label='Index = -0.6')\n", + "\n", + "mass_2 = mass_range[(mass_range >= 0.05) & (mass_range < 0.22)]\n", + "plt.plot(mass_2, power_law(mass_2, -0.25), label='Index = -0.25')\n", + "\n", + "mass_3 = mass_range[(mass_range >= 0.22) & (mass_range < 0.55)]\n", + "plt.plot(mass_3, power_law(mass_3, -1.3), label='Index = -1.3')\n", + "\n", + "mass_4 = mass_range[(mass_range >= 0.55) & (mass_range < 8)]\n", + "plt.plot(mass_4, power_law(mass_4, -2.3), label='Index = -2.3')\n", + "\n", + "mass_5 = mass_range[(mass_range >= 8) & (mass_range <= 120)]\n", + "plt.plot(mass_5, power_law(mass_5, -2.35), label='Index = -2.35')\n", + "\n", + "# Add labels and legend\n", + "plt.xlabel('Mass (M_sun)')\n", + "plt.ylabel('Relative Number Density of Stars')\n", + "plt.legend()\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "plt.title('CombinedWeidnerKroupaKirkpatrick_2024 IMF Function')\n", + "plt.grid()\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "60c949a3-f40e-402f-8923-3e914d58cb3b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs, WDs, and BDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "k_bh = vstack([kc_out[p2_bh], kc_comp[c_bh]])\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "k_ns = vstack([kc_out[p2_ns], kc_comp[c_ns]])\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", + "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", + "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", + "\n", + "# Create subplots\n", + "plt.figure(figsize=(14,6))\n", + "\n", + "# Plot BHs\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(k_bh['mass'], histtype='step', bins=bh_bins, label='Progenitor Black Hole Masses', color='blue')\n", + "plt.hist(k_bh['mass_current'], histtype='step', bins=bh_bins, label='Current Black Hole Masses', color='orange')\n", + "plt.title(\"Black Holes by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(k_wd['mass'], histtype='step', bins=wd_bins, label='Progenitor White Dwarf Masses', color='blue')\n", + "plt.hist(k_wd['mass_current'], histtype='step', bins=wd_bins, label='Current White Dwarf Masses', color='orange')\n", + "plt.title(\"White Dwarves by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(k_bd['mass'], histtype='step', bins=bd_bins, label='Progenitor Brown Dwarf Masses', color='blue')\n", + "plt.hist(k_bd['mass_current'], histtype='step', bins=bd_bins, label='Current Brown Dwarf Masses', color='orange')\n", + "plt.title(\"Brown Dwarves by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(k_ns['mass'], histtype='step', bins=ns_bins, label='Progenitor Neutron Star Masses', color='blue')\n", + "plt.hist(k_ns['mass_current'], histtype='step', bins=ns_bins, label='Current Neutron Stars Masses', color='orange')\n", + "plt.title(\"Neutron Stars by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "# Show the plots\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "c397930b-0fa9-4592-b571-e3dfbf00b8f7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.scatter(kc_out[p2_bd]['mass'], kc_out[p2_bd]['Teff'])\n", + "plt.title('Brown Dwarf Assigned Teff vs. Mass')\n", + "plt.xlabel('Mass (M_$\\odot$)')\n", + "plt.ylabel('Effective Temperarture (K)')\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "4a03d8bd-18df-4d28-8106-19a9e14c12a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7870" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.min(k_ns['mass_current'])\n", + "np.max(k_ns['mass_current'])\n", + "len(k_ns['mass_current'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b514e4e-1ed0-457c-ad03-49bb64ec2236", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From a0e6c5aa9bbdf07b3fdf0aaa728b0c509ac36f60 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 19 Nov 2024 14:22:13 -0800 Subject: [PATCH 029/191] Explanation of Meisner vs. Phillips flux issue --- changes/BDClusters_AtmIssues.ipynb | 96 +++++++++++++----------------- 1 file changed, 40 insertions(+), 56 deletions(-) diff --git a/changes/BDClusters_AtmIssues.ipynb b/changes/BDClusters_AtmIssues.ipynb index 54257c32..516e7669 100644 --- a/changes/BDClusters_AtmIssues.ipynb +++ b/changes/BDClusters_AtmIssues.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "01b7a3c9-8d7a-483f-a25a-161817f55e9f", "metadata": {}, "outputs": [], @@ -47,34 +47,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "bc90dc34-0747-4b24-805f-6e8abf081565", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Isochrone generation took 54.561135 s.\n", - "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", - " Starting at: 2024-11-12 17:33:26.509247 Usually takes ~5 minutes\n", - "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", - "M = 0.676 Msun T = 4395 K m_hst_f153m = 4.92\n", - "M = 0.786 Msun T = 4896 K m_hst_f153m = 4.41\n", - "M = 0.856 Msun T = 5198 K m_hst_f153m = 4.12\n", - "M = 0.956 Msun T = 5590 K m_hst_f153m = 3.74\n", - "M = 1.022 Msun T = 5808 K m_hst_f153m = 3.50\n", - "M = 1.079 Msun T = 5992 K m_hst_f153m = 3.30\n", - "M = 1.518 Msun T = 7482 K m_hst_f153m = 2.22\n", - "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", - "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", - "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", - " Time taken: 14.44 seconds\n" - ] - } - ], + "outputs": [], "source": [ "# Create isochrone object \n", "logAge = np.log10(5*10**9.)\n", @@ -87,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 3, "id": "3bfb0efe-bf1a-4666-945b-057c64e6cccd", "metadata": {}, "outputs": [ @@ -110,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "998eaa3e-103d-40bf-85ba-125ad4a5ed75", "metadata": {}, "outputs": [ @@ -136,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 5, "id": "dca63ebb-6c40-4f76-abb6-b709ccb71466", "metadata": {}, "outputs": [ @@ -175,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "6d32d779-8e93-44e1-9375-7ff6e4f5f246", "metadata": {}, "outputs": [], @@ -187,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "76787edf-ced9-478a-8572-c3b5e327febe", "metadata": {}, "outputs": [ @@ -205,7 +181,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 8520 companions out of stellar mass range\n" + "Found 8211 companions out of stellar mass range\n" ] }, { @@ -231,13 +207,13 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 8, "id": "9ca75682-5e3a-4178-b9a1-0db1ecc9b4a6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -311,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "8f03eedf-780a-47cc-bcb5-1bd767ff4135", "metadata": {}, "outputs": [ @@ -319,10 +295,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "287.80180827780606\n", - "287.80439454686973\n", - "2321.1956912624596\n", - "2321.1948222119263\n" + "287.8044158821004\n", + "287.80301331492325\n", + "2321.1831860770926\n", + "2321.183556796764\n" ] } ], @@ -335,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 10, "id": "fbbd559a-4f0d-426e-9c6c-798debb699dc", "metadata": {}, "outputs": [ @@ -354,10 +330,10 @@ "from astropy.io import fits\n", "from astropy.table import Table\n", "\n", - "# plotting 1200K star from Meisner vs. BTSettl\n", + "# plotting 1200K star from Meisner vs. BTSettl 3000K\n", "bt_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/BTSettl_rebin/btm05/lte033-3.0-0.5a+0.2.BT-Settl.spec.fits'\n", "m_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/Meisner2023/mp00/spec_jwst_t1200_g4.5_p0_kg_g1.25.fits'\n", - "p_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/Phillips2020/spec_T1200_lg4.5_CEQ.fits'\n", + "p_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/Phillips2020/spec_T1200_lg4.5_CEQ.fits' # Phillips 1200K star for reference\n", "\n", "b_hdu_list = fits.open(bt_file)\n", "m_hdu_list = fits.open(m_file)\n", @@ -383,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 11, "id": "dcb9ce22-afd5-4f75-a033-3f205e2e8613", "metadata": {}, "outputs": [ @@ -418,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 12, "id": "a3e80ffe-ef8d-4fc8-8c2a-463c12c5e42d", "metadata": {}, "outputs": [ @@ -455,8 +431,8 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "443dd316-d205-44a1-87b8-3c2252d28f64", + "execution_count": 13, + "id": "f48f74a1-3b51-4cc1-98ef-fdbf0e4f175c", "metadata": {}, "outputs": [ { @@ -492,9 +468,17 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "d2aaf035-d3b8-4667-862c-6c430d78df63", + "metadata": {}, + "source": [ + "It becomes clear that the Meisner model peaks at fluxes much, much smaller than expected, and much, much smaller than those given by the Phillips model for a star under the same conditions (Teff = 1200K, metallicity = 0, log g = 4.5) and unit scalings. This implies that there might be some conversion factor that we are missing, due to conditions and the fact that both papers use the ATMO model for their model generation." + ] + }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 14, "id": "5419c340-4abc-44a9-9ec3-c77bb35d1b2f", "metadata": {}, "outputs": [ @@ -521,7 +505,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 15, "id": "51fcf941-7b85-4c8a-851d-8d73f315e365", "metadata": {}, "outputs": [ @@ -576,13 +560,13 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 16, "id": "60c949a3-f40e-402f-8923-3e914d58cb3b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -654,13 +638,13 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 17, "id": "c397930b-0fa9-4592-b571-e3dfbf00b8f7", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -681,17 +665,17 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 18, "id": "4a03d8bd-18df-4d28-8106-19a9e14c12a4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "7870" + "7747" ] }, - "execution_count": 65, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } From 39fb1c2bfc053916634a5d7938b72626544e3286 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 2 Dec 2024 14:32:18 -0800 Subject: [PATCH 030/191] implementation of new atmo model --- spisea/atmospheres.py | 17 ++++++++++------- spisea/tests/test_models.py | 2 +- 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index a65e495e..5f71f33c 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -933,10 +933,10 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is # currently available here, so if non-solar metallicity, just stick with # the Phoenix grid - if (temperature <= 1200) & (metallicity == 0): + if (temperature <= 1200): if verbose: - print( 'Phillips2020 atmosphere') - return get_Phillips2020_atmosphere(metallicity=metallicity, + print( 'Meisner2023 atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, temperature=temperature, gravity=gravity, rebin=rebin) @@ -1080,9 +1080,9 @@ def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False) """ try: if verbose: - print('Phillips2020 atmosphere') + print('Meisner2023 atmosphere') - return get_Phillips2020_atmosphere(metallicity=metallicity, + return get_Meisner2023_atmosphere(metallicity=metallicity, temperature=temperature, gravity=gravity) @@ -2226,9 +2226,12 @@ def organize_all_Meisner2023_atmospheres(): wavelength = data['Wavelength'] flux = data['Flux'] + # Make flux independent of R&D + flux_new = flux / 5e-20 + # Create new columns with desired format c0 = fits.Column(name='Wavelength', format='D', array=wavelength) - c1 = fits.Column(name='Flux', format='E', array=flux) + c1 = fits.Column(name='Flux', format='E', array=flux_new) cols = fits.ColDefs([c0, c1]) tbhdu = fits.BinTableHDU.from_columns(cols) @@ -2346,7 +2349,7 @@ def rebin_Meisner2023(make_unique=False): # Fetch the Meisner2023 spectrum and rebin its flux try: sp = pysynphot.Icat('Meisner2023', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_meisner.wave) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) # Create the output FITS file c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 7d21b3e6..f44f58bc 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -109,7 +109,7 @@ def test_atmosphere_models(): # Test get_merged_atmospheres at different temps temp_range = [200, 1000, 2000, 3500, 4000, 5250, 6000, 12000] atm_func = atm.get_merged_atmosphere - for ii in metals_range: + for ii in bd_metals_range: for jj in temp_range: try: test = atm_func(metallicity=ii, temperature=jj, verbose=True) From dd193152d00ffddfd6deeb15f335e021d55b3242 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 21 Jan 2025 08:46:38 -0800 Subject: [PATCH 031/191] Addition of Phillips2020 evolution class --- spisea/evolution.py | 294 ++++++++++++++++++++++++++++++++++++ spisea/tests/test_models.py | 6 +- 2 files changed, 297 insertions(+), 3 deletions(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index 7de8e217..9a4b2c16 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -528,6 +528,300 @@ def format_isochrones(input_iso_dir, metallicity_list): os.chdir(start_dir) return +class Phillips2020(StellarEvolution): + """ + Evolution models from + `Phillips et al. 2020 `_. + + Downloaded from `here _` + + Notes + ----- + Evolution model parameters used in download: + + * Assume chemical equilibrium + * Solar metallicity + * For young BDs + CHANGE!!! + """ + + def __init__(self): + r""" + Define intrinsic properties for the Phillips brown dwarf stellar models + """ + # specify location of model files + self.model_dir = models_dir + 'Phillips2020/' + + # specifying metallicity + self.z_list = [0.015] + self.z_file_map = {0.015: 'z00'} + self.z_solar = 0.015 + + # populate list of isochrone ages (log scale) + self.age_list = np.arange(6.0, 10.0, 0.001) + + # define required evo_grid number + self.evo_grid_min = 1.0 + + def isochrone(self, age= 1.e8, metallicity=0.0): + r""" + Extract an individual isochrone from the Phillips2020 collection. + """ + # Error check to see if installed evolution model + # grid is compatible with code version. Also return + # current grid num + self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) + + # convert metallicity to mass fraction + z_defined = self.z_solar*10.**metallicity + + log_age = math.log10(age) + + # check age and metallicity are within bounds + if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): + logger.error('Requested age {0} is out of bounds.'.format(log_age)) + + if ((z_defined < np.min(self.z_list)) or + (z_defined > np.max(self.z_list))): + logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) + + # find closest metallicity value + z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] + z_dir = self.z_file_map[self.z_list[z_idx]] + + # Specify subdirectory for metallicity + iso_path = os.path.join(self.model_dir, 'iso', z_dir) + + # Find nearest age in grid to input grid by parsing through available files + p_files = glob.glob(os.path.join(iso_path, 'iso_*.fits')) + p_ages = np.array([float(f.split('_')[1].replace('.fits', '')) for f in p_files]) + close_age = np.argmin(abs(p_ages - log_age)) + close_file = p_files[close_age] + print(f"Found nearest age file as {close_file} for requested age of {log_age}") + + # Make sure the closest file exists + if not os.path.exists(close_file): + raise FileNotFoundError(f"Isochrone file not found: {close_file}.") + + # return isochrone data + iso = Table.read(close_file, format='fits') + iso.rename_column('Z', 'Z') + iso.rename_column('Age', 'logAge') + iso.rename_column('Mass', 'mass') + iso.rename_column('Mass_current', 'mass_current') + iso.rename_column('Luminosity', 'logL') + iso.rename_column('Teff', 'logT') + iso.rename_column('Gravity', 'logg') + iso['logT_WR'] = iso['logT'] + + # Phillips doesn't identify WR stars, so identify all as "False" + isWR = Column([False] * len(iso), name='isWR') + iso.add_column(isWR) + + iso.meta['log_age'] = log_age + iso.meta['metallicity_in'] = metallicity + iso.meta['metallicity_act'] = metallicity + + return iso + + def format_isochrones(input_iso_dir, metallicity_list): + r""" + Change + """ + # Store current directory for later + start_dir = os.getcwd() + + # Move into isochrone directory + os.chdir(input_iso_dir) + + # Work on each metallicity isochrones individually + for metal in metallicity_list: + # More into metallicity directory, read isochrone file + os.chdir(metal) + + isoFile = glob.glob('output*') + print( 'Read Input: this is slow') + iso = Table.read(isoFile[0], format='fits') + print( 'Done') + + ages_all = iso['col2'] + + # Extract the unique ages + age_arr = np.unique(ages_all) + + # For each unique age, extract the proper rows and make corresponding + # table + print( 'Making individual isochrone files') + for age in age_arr: + good = np.where(ages_all == age) + tmp = iso[good] + + #Write table + tmp.write('iso_{0:4.2f}.fits'.format(age)) + + # Move back into iso directory + os.chdir('..') + + # Return to starting directory + os.chdir(start_dir) + return + + +class Marley(StellarEvolution): + """ + Evolution models from + `Marley et al. 2021 `_. + + Downloaded from `here _` + + Notes + ----- + Evolution model parameters used in download: + + * + CHANGE!!! + """ + def __init__(self): + r""" + Define intrinsic properties for the Marley brown dwarf stellar models. + """ + # populate list of model masses (in solar masses) + #mass_list = [(0.1 + i*0.005) for i in range(181)] + + # populate list of isochrone ages (log scale) + self.age_list = np.arange(6.0, 10.0+0.176, 0.01) + + # define metallicity parameters for Parsec models + self.z_list = [-0.5, 0.0, 0.5] + + # Specify location of model files + self.model_dir = models_dir+'Marley2021/' + + # Specifying metallicity + self.z_solar = 0.0 + self.z_file_map = { + -0.5: 'nc-0.5_co1.0_mass_age', + 0.0: 'nc+0.0_co1.0_mass_age', + 0.5: 'nc+0.5_co1.0_mass_age' + } + + # Define required evo_grid number + self.evo_grid_min = 1.0 + + def isochrone(self, age=1.e8, metallicity=0.0): + r""" + Extract an individual isochrone from the Marley collection. + """ + # Error check to see if installed evolution model + # grid is compatible with code version. Also return + # current grid num + self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) + + # convert metallicity to mass fraction + z_defined = self.z_solar*10.**metallicity + + log_age = math.log10(age) + + # check age and metallicity are within bounds + if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): + logger.error('Requested age {0} is out of bounds.'.format(log_age)) + + if ((z_defined < np.min(self.z_list)) or + (z_defined > np.max(self.z_list))): + logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) + + # find closest metallicity value + z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] + z_dir = self.z_file_map[self.z_list[z_idx]] + + # Find the corresponding file + file_name = self.model_dir + self.z_file_map[z_closest] + + # Load the metallicity file + iso_table = Table.read(metallicity_file, format='ascii') + iso_table = isotable[['Mass', 'log age', 'Teff', 'log g', 'Radius', 'log L']] + + # Filter for the specific age + good = np.where(np.isclose(iso_table["log age"], log_age, atol=0.01))[0] + if len(good) == 0: + raise ValueError(f"No data found for requested age {log_age:.2f} in file {metallicity_file}.") + + iso = iso_table[good] + + # put temperature into logT + iso['Teff'] = np.log10(iso['Teff']) + + # return isochrone data + iso.rename_column('Mass', 'mass_current') + iso.rename_column('log age', 'logAge') + iso.rename_column('Teff', '') + iso.rename_column('log g', 'logg') + iso.rename_column('Radius', 'logL') + iso.rename_column('col6', 'logT') + iso.rename_column('col7', 'logg') + iso.rename_column('col15', 'phase') + iso['logT_WR'] = iso['logT'] + + # Parsec doesn't identify WR stars, so identify all as "False" + isWR = Column([False] * len(iso), name='isWR') + iso.add_column(isWR) + + iso.meta['log_age'] = log_age + iso.meta['metallicity_in'] = metallicity + iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) + + return iso + + def format_isochrones(input_iso_dir, metallicity_list): + r""" + Parse isochrone file downloaded from Parsec version 1.2 for different + metallicities, create individual isochrone files for the different ages. + + input_iso_dir: points to ParsecV1.2s/iso directory. Assumes metallicity + subdirectories already exist with isochrone files downloaded in them + (isochrones files expected to start with "output*") + + metallicity_list format: absolute (vs. relative to solar), + z + : e.g. Z = 0.014 --> z014 + """ + # Store current directory for later + start_dir = os.getcwd() + + # Move into isochrone directory + os.chdir(input_iso_dir) + + # Work on each metallicity isochrones individually + for metal in metallicity_list: + # More into metallicity directory, read isochrone file + os.chdir(metal) + + isoFile = glob.glob('output*') + print( 'Read Input: this is slow') + iso = Table.read(isoFile[0], format='fits') + print( 'Done') + + ages_all = iso['col2'] + + # Extract the unique ages + age_arr = np.unique(ages_all) + + # For each unique age, extract the proper rows and make corresponding + # table + print( 'Making individual isochrone files') + for age in age_arr: + good = np.where(ages_all == age) + tmp = iso[good] + + #Write table + tmp.write('iso_{0:4.2f}.fits'.format(age)) + + # Move back into iso directory + os.chdir('..') + + # Return to starting directory + os.chdir(start_dir) + return + #---------------------------------------# # Now for the Pisa (Tognelli+11) models #---------------------------------------# diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index f44f58bc..44a7f183 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -34,14 +34,14 @@ def test_evolution_models(): # Array of evolution models to test evo_models = [evolution.MISTv1(version=1.2), evolution.MergedBaraffePisaEkstromParsec(), - evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa()] + evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa(), evolution.Phillips2020()] # Array of age_ranges for the specific evolution models to test - age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr] + age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr] # Array of metallicities for the specific evolution models to test - metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar] + metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar] assert len(evo_models) == len(age_vals) == len(metal_vals) From 43a3f5802944f1c5c68d93b651eeb973825e94b6 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 20 Feb 2025 16:24:24 -0800 Subject: [PATCH 032/191] Fix variable name and runtime error --- spisea/imf/imf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 758eda00..4380e012 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -280,10 +280,10 @@ def __init__(self, mass_limits, powers, multiplicity=None): if len(mass_limits) != len(powers) + 1: msg = 'Incorrect specification of multi-part powerlaw.\n' msg += ' len(massLimts) != len(powers)+1\n' - msg += ' len(massLimits) = \n' + len(massLimits) + msg += ' len(massLimits) = \n' + len(mass_limits) msg += ' len(powers) = \n' + len(powers) - raise RuntimeException(msg) + raise RuntimeError(msg) self._mass_limits = np.atleast_1d(mass_limits) self._m_limits_low = mass_limits[0:-1] From 0e2ac34cd50338a4d7cb69982cd116760a0635d6 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 25 Feb 2025 15:10:12 -0800 Subject: [PATCH 033/191] Added Marley evolution class --- spisea/evolution.py | 80 ++++++++++++++++++------------------- spisea/tests/test_models.py | 8 ++-- 2 files changed, 44 insertions(+), 44 deletions(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index 9a4b2c16..63caccc5 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -667,7 +667,7 @@ def format_isochrones(input_iso_dir, metallicity_list): return -class Marley(StellarEvolution): +class Marley2021(StellarEvolution): """ Evolution models from `Marley et al. 2021 `_. @@ -687,30 +687,34 @@ def __init__(self): """ # populate list of model masses (in solar masses) #mass_list = [(0.1 + i*0.005) for i in range(181)] - - # populate list of isochrone ages (log scale) - self.age_list = np.arange(6.0, 10.0+0.176, 0.01) # define metallicity parameters for Parsec models - self.z_list = [-0.5, 0.0, 0.5] + self.z_solar = 0.0142 + self.z_list = [self.z_solar * (10.**m) for m in [-0.5, 0.0, 0.5]] + + # populate list of isochrone ages (log scale) + self.age_list = [10.0, 7.0, 8.0, 9.0, 6.0, 7.176091259055681, 8.176091259055681, 9.176091259055681, 6.301029995663981, + 7.301029995663981, 8.301029995663981, 9.301029995663981, 6.477121254719663, 7.477121254719663, + 8.477121254719663, 9.477121254719663, 6.6020599913279625, 7.6020599913279625, 8.602059991327963, + 9.602059991327963, 6.778151250383644, 7.778151250383644, 8.778151250383644, 9.778151250383644, + 6.903089986991944, 7.903089986991944, 8.903089986991944, 9.903089986991944] # Specify location of model files self.model_dir = models_dir+'Marley2021/' # Specifying metallicity - self.z_solar = 0.0 self.z_file_map = { - -0.5: 'nc-0.5_co1.0_mass_age', - 0.0: 'nc+0.0_co1.0_mass_age', - 0.5: 'nc+0.5_co1.0_mass_age' + self.z_list[0]: 'zm05/', + self.z_list[1]: 'zp00/', + self.z_list[2]: 'zp05/' } # Define required evo_grid number - self.evo_grid_min = 1.0 + self.evo_grid_min = 1.0 def isochrone(self, age=1.e8, metallicity=0.0): r""" - Extract an individual isochrone from the Marley collection. + Extract an individual isochrone from the Marley2021 collection. """ # Error check to see if installed evolution model # grid is compatible with code version. Also return @@ -730,59 +734,53 @@ def isochrone(self, age=1.e8, metallicity=0.0): (z_defined > np.max(self.z_list))): logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - # Find the corresponding file - file_name = self.model_dir + self.z_file_map[z_closest] - - # Load the metallicity file - iso_table = Table.read(metallicity_file, format='ascii') - iso_table = isotable[['Mass', 'log age', 'Teff', 'log g', 'Radius', 'log L']] - - # Filter for the specific age - good = np.where(np.isclose(iso_table["log age"], log_age, atol=0.01))[0] - if len(good) == 0: - raise ValueError(f"No data found for requested age {log_age:.2f} in file {metallicity_file}.") + # Find closest age in grid + age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] + iso_file = f'iso_{self.age_list[age_idx]}.fits' - iso = iso_table[good] + # Create path to iso file + full_iso_file = os.path.join(self.model_dir, 'iso', z_dir, iso_file) - # put temperature into logT - iso['Teff'] = np.log10(iso['Teff']) + print(f"Found nearest age file as {full_iso_file} for requested age of {log_age}") + + # Make sure the closest file exists + #if not os.path.exists(close_file): + #raise FileNotFoundError(f"Isochrone file not found: {close_file}.") # return isochrone data - iso.rename_column('Mass', 'mass_current') - iso.rename_column('log age', 'logAge') - iso.rename_column('Teff', '') - iso.rename_column('log g', 'logg') - iso.rename_column('Radius', 'logL') - iso.rename_column('col6', 'logT') - iso.rename_column('col7', 'logg') - iso.rename_column('col15', 'phase') + iso = Table.read(full_iso_file, format='fits') + iso.rename_column('Z', 'Z') + iso.rename_column('Age', 'logAge') + iso.rename_column('Mass', 'mass') + iso.rename_column('Mass_current', 'mass_current') + iso.rename_column('log_L', 'logL') + iso.rename_column('Teff', 'logT') + iso.rename_column('logg', 'logg') + iso.rename_column('Radius', 'radius') iso['logT_WR'] = iso['logT'] - # Parsec doesn't identify WR stars, so identify all as "False" + # Marley doesn't identify WR stars, so identify all as "False" isWR = Column([False] * len(iso), name='isWR') iso.add_column(isWR) iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity - iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) + iso.meta['metallicity_act'] = np.log10(z_defined / self.z_solar) return iso def format_isochrones(input_iso_dir, metallicity_list): r""" - Parse isochrone file downloaded from Parsec version 1.2 for different + Parse isochrone files downloaded from Marley 2021 for different metallicities, create individual isochrone files for the different ages. - input_iso_dir: points to ParsecV1.2s/iso directory. Assumes metallicity + input_iso_dir: points to Marley2021/iso directory. Assumes metallicity subdirectories already exist with isochrone files downloaded in them (isochrones files expected to start with "output*") - metallicity_list format: absolute (vs. relative to solar), - z + : e.g. Z = 0.014 --> z014 """ # Store current directory for later start_dir = os.getcwd() @@ -800,7 +798,7 @@ def format_isochrones(input_iso_dir, metallicity_list): iso = Table.read(isoFile[0], format='fits') print( 'Done') - ages_all = iso['col2'] + ages_all = iso['Age'] # Extract the unique ages age_arr = np.unique(ages_all) diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 44a7f183..3103dcd1 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -31,17 +31,19 @@ def test_evolution_models(): # Metallicity ranges to test (if applicable) metal_range = [-2.5, -1.5, 0, 0.25, 0.4] metal_solar = [0] + metal_Marley = [-0.5, 0.0, 0.5] # Array of evolution models to test evo_models = [evolution.MISTv1(version=1.2), evolution.MergedBaraffePisaEkstromParsec(), - evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa(), evolution.Phillips2020()] + evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa(), + evolution.Phillips2020(), evolution.Marley2021()] # Array of age_ranges for the specific evolution models to test - age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr] + age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr, age_all_arr] # Array of metallicities for the specific evolution models to test - metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar] + metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_Marley] assert len(evo_models) == len(age_vals) == len(metal_vals) From cfa29511541bad5668bf4158b0534124b1e588b1 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 15 Apr 2025 14:51:15 -0700 Subject: [PATCH 034/191] Evolution model changes and exploring intermediary data generation --- changes/evolution_gaussian_fit.ipynb | 168 +++++++++++++++++++++++++++ spisea/evolution.py | 100 ++++++++++++++++ spisea/tests/test_models.py | 8 +- 3 files changed, 273 insertions(+), 3 deletions(-) create mode 100644 changes/evolution_gaussian_fit.ipynb diff --git a/changes/evolution_gaussian_fit.ipynb b/changes/evolution_gaussian_fit.ipynb new file mode 100644 index 00000000..b749ed58 --- /dev/null +++ b/changes/evolution_gaussian_fit.ipynb @@ -0,0 +1,168 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9aaeb072-9ae6-4362-978f-4d9fd5802cd7", + "metadata": {}, + "source": [ + "## Generating Intermediary Data for New Non-Overlapping Evolutionary Data" + ] + }, + { + "cell_type": "markdown", + "id": "5459271a-7e3f-4723-91eb-b8735a2a5ff1", + "metadata": {}, + "source": [ + "Oh no! Unfortunately our new evolutionary models for brown dwarf masses does not align with those previously implemented, leaving a mass gap. To address this, we have decided to use Gaussian processes to fit the two data regimes (Phillips and Pisa) and generate the associated data in this range. The parameters in question are mass, luminosity, effective temperature, and log gravity. This notebook shows an example case for solar metallicity and a log age of ~7.13." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1cad6635-a51f-426b-a207-6e2be5710586", + "metadata": {}, + "outputs": [], + "source": [ + "# importing necessary packages\n", + "import matplotlib.pyplot as plt\n", + "from astropy.table import Table\n", + "import numpy as np\n", + "from sklearn.gaussian_process import GaussianProcessRegressor\n", + "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C, RationalQuadratic as RQ, WhiteKernel, ExpSineSquared as Exp" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d22dab8a-4834-48ed-99b0-47295b685e38", + "metadata": {}, + "outputs": [], + "source": [ + "# importing compatible age fits files \n", + "phillips = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/iso/z00/iso_7.128205127364955.fits'\n", + "pisa = '/System/Volumes/Data/mnt/g/lu/models/evolution/Pisa2011/iso/z015/iso_7.13.fits'\n", + "\n", + "# loading tables and extracting data\n", + "phil_tbl = Table.read(phillips, format='fits')\n", + "pisa_tbl = Table.read(pisa, format='fits')\n", + "\n", + "phil_mass = phil_tbl['Mass']\n", + "pisa_mass = pisa_tbl['col3']\n", + "\n", + "phil_Teff = phil_tbl['Teff']\n", + "pisa_Teff = pisa_tbl['col2']\n", + "\n", + "phil_L = phil_tbl['Luminosity']\n", + "pisa_L = pisa_tbl['col1']\n", + "\n", + "phil_logg = phil_tbl['Gravity']\n", + "pisa_logg = pisa_tbl['col4']" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "fd004b17-6528-4633-a5a1-2ab841c41bb9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating graphs of mass vs. luminosity, Teff, and gravity\n", + "fig, axs = plt.subplots(3, 1, figsize=(10, 10))\n", + "\n", + "# create array for mass gap\n", + "mass_gap = np.linspace(np.max(phil_mass), np.min(pisa_mass), 1000)\n", + "\n", + "# mass vs. luminosity\n", + "axs[0].plot(phil_mass, phil_L, color='c', label='Phillips')\n", + "axs[0].plot(pisa_mass, pisa_L, color='pink', label='Pisa')\n", + "axs[0].set_title('Mass vs. Luminosity for logAge=7.13')\n", + "y_min0 = axs[0].get_ylim()[0]\n", + "y_max0 = axs[0].get_ylim()[1]\n", + "axs[0].fill_between(mass_gap, y_min0, y_max0, color='gray', label='mass gap', alpha=0.3)\n", + "axs[0].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[0].set_ylabel('Luminosity (L/$L_{\\odot}$)')\n", + "axs[0].set_xlim(0,1)\n", + "axs[0].set_ylim(-4,0)\n", + "axs[0].legend()\n", + "\n", + "# mass vs. Teff\n", + "axs[1].plot(phil_mass, phil_Teff, color='c', label='Phillips')\n", + "axs[1].plot(pisa_mass, pisa_Teff, color='pink', label='Pisa')\n", + "y_min1 = axs[1].get_ylim()[0]\n", + "y_max1 = axs[1].get_ylim()[1]\n", + "axs[1].fill_between(mass_gap, y_min1, y_max1, color='gray', label='mass gap', alpha=0.3)\n", + "axs[1].set_title('Mass vs. Effective Temperature for logAge=7.13')\n", + "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[1].set_ylabel('log(Teff) (log(K))')\n", + "axs[1].set_xlim(0,1)\n", + "axs[1].set_ylim(3,4)\n", + "axs[1].legend()\n", + "\n", + "# mass vs. logg (problem child)\n", + "axs[2].plot(phil_mass, phil_logg, color='c', label='Phillips')\n", + "axs[2].plot(pisa_mass, pisa_logg, color='pink', label='Pisa')\n", + "y_min2 = axs[2].get_ylim()[0]\n", + "y_max2 = axs[2].get_ylim()[1]\n", + "axs[2].fill_between(mass_gap, y_min2, y_max2, color='gray', label='mass gap', alpha=0.3)\n", + "axs[2].set_title('Mass vs. Gravity for logAge=7.13')\n", + "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[2].set_ylabel('logg')\n", + "axs[2].set_xlim(0,1)\n", + "axs[2].set_ylim(4,4.5)\n", + "axs[2].legend()\n", + "\n", + "fig.tight_layout()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "35e9b3a9-8efe-4668-a359-c8332e34fbba", + "metadata": {}, + "source": [ + "From these zoomed in graphs it becomes clear that though a mass gap is present, luminosty and effective temperature still seem to line up if a model connects them. In contrast, the graph for log gravity is very clearly disconnected, which is worth refining as new models continue to be generated. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "650ec2bd-9904-4ae2-9fa5-304a392ecb3e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/spisea/evolution.py b/spisea/evolution.py index 63caccc5..7c529e77 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1495,6 +1495,106 @@ def format_isochrones(self): #==============================# # Merged model classes #==============================# +class MergedPhillipsBaraffePisaEkstromParsec(StellarEvolution): + """ + All merge -- only young and solar metals right now. + + Parameters + ---------- + rot: boolean, optional + If true, then use rotating Ekstrom models. Default is true. + """ + def __init__(self, rot=True): + # populate list of model masses (in solar masses) + mass_list = [(0.01 + i*0.005) for i in range(181)] # generates masses from 0.01 - 1 M_sun + + # define metallicity parameters for Geneva models + z_list = [0.015] + + # populate list of isochrone ages (log scale) + age_list = np.arange(6.0, 7.4, 0.01).tolist() + + # specify location of model files + model_dir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/' + StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) + self.z_solar = 0.015 + + # Switch to specify rotating/non-rotating models + if rot: + self.z_file_map = {0.015: 'z015_rot/'} + else: + self.z_file_map = {0.015: 'z015_norot/'} + + # Define required evo_grid number + self.evo_grid_min = 1.0 + + + def isochrone(self, age=1.e8, metallicity=0.0): + r""" + Extract an individual isochrone from the Baraffe-Pisa-Ekstrom-Parsec + collection + """ + # Error check to see if installed evolution model + # grid is compatible with code version. Also return + # current grid num + self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) + + # convert metallicity to mass fraction + z_defined = self.z_solar*10.**metallicity + + log_age = math.log10(age) + + # check age and metallicity are within bounds + if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): + logger.error('Requested age {0} is out of bounds.'.format(log_age)) + + if ((z_defined < np.min(self.z_list)) or + (z_defined > np.max(self.z_list))): + logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) + + # Find nearest age in grid to input grid + age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] + iso_file = 'iso_{0:.2f}.dat'.format(self.age_list[age_idx]) + + # find closest metallicity value + z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] + z_dir = self.z_file_map[self.z_list[z_idx]] + + # generate isochrone file string + full_iso_file = self.model_dir + z_dir + iso_file + + # return isochrone data + iso = Table.read(full_iso_file, format='ascii') + iso.rename_column('col1', 'mass') + iso.rename_column('col2', 'logT') + iso.rename_column('col3', 'logL') + iso.rename_column('col4', 'logg') + iso.rename_column('col5', 'logT_WR') + iso.rename_column('col6', 'mass_current') + iso.rename_column('col7', 'phase') + iso.rename_column('col8', 'model_ref') + + # Define "isWR" column based on phase info + isWR = Column([False] * len(iso), name='isWR') + idx_WR = np.where(iso['logT'] != iso['logT_WR']) + isWR[idx_WR] = True + iso.add_column(isWR) + + iso.meta['log_age'] = log_age + iso.meta['metallicity_in'] = metallicity + iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) + + # Assume mass of brown dwarfs does not change over their lifetime + #bd_idx = iso['mass'] < 0.08 + #iso['mass_current'][bd_idx] = iso['mass'][bd_idx] + + # Handling NaN effective temperatures + #nan_teff_idx = np.isnan(iso['logT']) + #if np.any(nan_teff_idx): + # iso['logT'][nan_teff_idx] = self.estimate_teff(iso['mass'][nan_teff_idx]) + + return iso + class MergedBaraffePisaEkstromParsec(StellarEvolution): """ This is a combination of several different evolution models: diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 3103dcd1..4ae078aa 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -27,6 +27,7 @@ def test_evolution_models(): age_young_arr = [6.7, 7.9] age_all_arr = [6.7, 8.0, 9.7] age_all_MIST_arr = [5.2, 6.7, 9.7, 10.13] + bd_young_test = [6.0, 6.5, 7.4] # Metallicity ranges to test (if applicable) metal_range = [-2.5, -1.5, 0, 0.25, 0.4] @@ -36,14 +37,15 @@ def test_evolution_models(): # Array of evolution models to test evo_models = [evolution.MISTv1(version=1.2), evolution.MergedBaraffePisaEkstromParsec(), evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa(), - evolution.Phillips2020(), evolution.Marley2021()] + evolution.Phillips2020(), evolution.Marley2021(), + evolution.MergedPhillipsBaraffePisaEkstromParsec()] # Array of age_ranges for the specific evolution models to test - age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr, age_all_arr] + age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr, age_all_arr, bd_young_test] # Array of metallicities for the specific evolution models to test - metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_Marley] + metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_Marley, metal_solar] assert len(evo_models) == len(age_vals) == len(metal_vals) From fec691ccc57ede37f7087d4881bd35496880e9f8 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 16 Apr 2025 16:44:25 -0700 Subject: [PATCH 035/191] Fixes to chi-squared and nice results --- changes/evolution_gaussian_fit.ipynb | 457 ++++++++++++++++++++++++++- 1 file changed, 449 insertions(+), 8 deletions(-) diff --git a/changes/evolution_gaussian_fit.ipynb b/changes/evolution_gaussian_fit.ipynb index b749ed58..13fe6269 100644 --- a/changes/evolution_gaussian_fit.ipynb +++ b/changes/evolution_gaussian_fit.ipynb @@ -5,7 +5,7 @@ "id": "9aaeb072-9ae6-4362-978f-4d9fd5802cd7", "metadata": {}, "source": [ - "## Generating Intermediary Data for New Non-Overlapping Evolutionary Data" + "## Generating Intermediary Data for Non-Overlapping Evolutionary Models" ] }, { @@ -13,12 +13,12 @@ "id": "5459271a-7e3f-4723-91eb-b8735a2a5ff1", "metadata": {}, "source": [ - "Oh no! Unfortunately our new evolutionary models for brown dwarf masses does not align with those previously implemented, leaving a mass gap. To address this, we have decided to use Gaussian processes to fit the two data regimes (Phillips and Pisa) and generate the associated data in this range. The parameters in question are mass, luminosity, effective temperature, and log gravity. This notebook shows an example case for solar metallicity and a log age of ~7.13." + "Oh no! Unfortunately our new evolutionary models for brown dwarf masses do not align with those previously implemented, leaving a mass gap. To address this, we have decided to use Gaussian processes to fit the two data regimes (Phillips and Pisa) and generate the associated data in this range. The parameters in question are mass, luminosity, effective temperature, and log gravity. This notebook shows an example case for solar metallicity and a log age of ~7.13." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "1cad6635-a51f-426b-a207-6e2be5710586", "metadata": {}, "outputs": [], @@ -28,12 +28,13 @@ "from astropy.table import Table\n", "import numpy as np\n", "from sklearn.gaussian_process import GaussianProcessRegressor\n", - "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C, RationalQuadratic as RQ, WhiteKernel, ExpSineSquared as Exp" + "from sklearn.gaussian_process.kernels import ConstantKernel as C, Matern\n", + "from sklearn.preprocessing import StandardScaler" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "d22dab8a-4834-48ed-99b0-47295b685e38", "metadata": {}, "outputs": [], @@ -61,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 3, "id": "fd004b17-6528-4633-a5a1-2ab841c41bb9", "metadata": {}, "outputs": [ @@ -132,14 +133,454 @@ "id": "35e9b3a9-8efe-4668-a359-c8332e34fbba", "metadata": {}, "source": [ - "From these zoomed in graphs it becomes clear that though a mass gap is present, luminosty and effective temperature still seem to line up if a model connects them. In contrast, the graph for log gravity is very clearly disconnected, which is worth refining as new models continue to be generated. " + "From these zoomed in graphs it becomes clear that though a mass gap is present, luminosty and effective temperature still seem to line up if a model connects them. In contrast, the graph for log gravity is very clearly disconnected, which is worth refining as new models continue to be generated. To best address this problem, we decided to use GaussianProcessRegressor with the Matern kernel, as through trial and error this appeared to be the best fit. We then decided to generate a chi-squared heat map for luminosity to find the ideal parameters for length_scale and nu." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, + "id": "8df930e4-19df-4651-960b-91c7aa522482", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0005 6.8546\n" + ] + } + ], + "source": [ + "# creating arrays\n", + "## mass\n", + "phil_mass = phil_tbl['Mass']\n", + "pisa_mass = pisa_tbl['col3']\n", + "combined_masses = np.concatenate([phil_mass, pisa_mass])\n", + "\n", + "## Teff\n", + "phil_Teff = phil_tbl['Teff']\n", + "pisa_Teff = pisa_tbl['col2']\n", + "combined_Teff = np.concatenate([phil_Teff, pisa_Teff])\n", + "\n", + "## Luminosity\n", + "phil_L = phil_tbl['Luminosity']\n", + "pisa_L = pisa_tbl['col1']\n", + "combined_L = np.concatenate([phil_L, pisa_L])\n", + "\n", + "## logg\n", + "phil_logg = phil_tbl['Gravity']\n", + "pisa_logg = pisa_tbl['col4']\n", + "combined_logg = np.concatenate([phil_logg, pisa_logg])\n", + "print(np.min(phil_mass), np.max(pisa_mass))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, "id": "650ec2bd-9904-4ae2-9fa5-304a392ecb3e", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating input array (mass) and output array (luminosity)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# Scale X and y\n", + "#X_scaler = StandardScaler()\n", + "#y_scaler = StandardScaler()\n", + "\n", + "#X = X_scaler.fit_transform(combined_masses.reshape(-1, 1))\n", + "#y = y_scaler.fit_transform(combined_L.reshape(-1, 1)).ravel()\n", + "\n", + "# Define grid\n", + "length_scales = np.logspace(-2, 1, 20)\n", + "nus = [0.5, 1.5, 2.5] # available Matern values\n", + "\n", + "chi2_map = np.zeros((len(nus), len(length_scales)))\n", + "\n", + "# Grid search\n", + "for i, nu in enumerate(nus):\n", + " for j, ls in enumerate(length_scales):\n", + " kernel = Matern(length_scale=ls, nu=nu)\n", + " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + " \n", + " try:\n", + " gp.fit(X, y)\n", + " y_pred = gp.predict(X)\n", + " #for k, val in enumerate(y_pred):\n", + " # if (X[k] > 0.39) and (X[k] < 0.42):\n", + " # print(X[k], y[k], y_pred[k], (y - y_pred)[k])\n", + " avg_diff = (np.sum(y - y_pred)) / len(y_pred)\n", + " chi2 = np.sum(((y - y_pred)**2) / avg_diff)\n", + " chi2_map[i, j] = chi2\n", + " except Exception as e:\n", + " chi2_map[i, j] = np.nan\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(10, 5))\n", + "im = plt.imshow(chi2_map, aspect='auto', origin='lower',\n", + " extent=[np.log10(length_scales[0]), np.log10(length_scales[-1]), 0, len(nus)-1],\n", + " cmap='viridis')\n", + "plt.colorbar(im, label=r'$\\chi^2$')\n", + "plt.xticks(np.log10(length_scales), labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", + "plt.xlabel('Length Scale')\n", + "plt.ylabel(r'$\\nu$')\n", + "plt.title('Chi-Squared Heatmap for Luminosity Fit Parameters')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "7a4edb19-1411-44f6-a50d-d1f1fc5b48e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.5 0.5455594781168517\n" + ] + } + ], + "source": [ + "i_best, j_best = np.unravel_index(np.nanargmin(chi2_map), chi2_map.shape)\n", + "best_nu = nus[i_best]\n", + "best_length_scale = length_scales[j_best]\n", + "print(best_nu, best_length_scale)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "79f3a7d1-ecd9-4d77-8923-6f729f9f66e3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Looking at physical interpretation of heatmap parameters\n", + "\n", + "# creating input array (mass) and output array (everything else)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# trying different kernel types\n", + "kernel = Matern(length_scale=best_length_scale, nu=best_nu)\n", + "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + "gp.fit(X, y)\n", + "y_pred_1, sigma_1 = gp.predict(x, return_std=True)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_L, color='blue', label='actual values')\n", + "plt.plot(x, y_pred_1, color='orange', label='predicted values')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('Luminosity')\n", + "plt.title('Best Fit by Heat Map')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(-4,0)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "99653c01-83e7-44c1-8a70-a04c185b0c3e", + "metadata": {}, + "source": [ + "This produced a pretty good fit! We can them repeat the process to find the best parameters for effective temperature and log gravity." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "2b89c29a-9c7e-4f31-94a6-094df81e01c1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating input array (mass) and output array (luminosity)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y_Teff = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# Scale X and y\n", + "#X_scaler = StandardScaler()\n", + "#y_scaler = StandardScaler()\n", + "\n", + "#X = X_scaler.fit_transform(combined_masses.reshape(-1, 1))\n", + "#y = y_scaler.fit_transform(combined_L.reshape(-1, 1)).ravel()\n", + "\n", + "# Define grid\n", + "length_scales = np.logspace(-2, 1, 20)\n", + "nus = [0.5, 1.5, 2.5] # available Matern values\n", + "\n", + "chi2_map_Teff = np.zeros((len(nus), len(length_scales)))\n", + "\n", + "# Grid search\n", + "for i, nu in enumerate(nus):\n", + " for j, ls in enumerate(length_scales):\n", + " kernel = Matern(length_scale=ls, nu=nu)\n", + " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + " \n", + " try:\n", + " gp.fit(X, y_Teff)\n", + " y_pred_Teff = gp.predict(X)\n", + " avg_diff_Teff = (np.sum(y_Teff - y_pred_Teff)) / len(y_pred_Teff)\n", + " chi2_Teff = np.sum(((y_Teff - y_pred_Teff)**2) / avg_diff_Teff)\n", + " #chi2_Teff = np.sum(((y_Teff - y_pred_Teff)**2) / y_pred_Teff)\n", + " chi2_map_Teff[i, j] = chi2_Teff\n", + " except Exception as e:\n", + " chi2_map_Teff[i, j] = np.nan\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(10, 5))\n", + "im = plt.imshow(chi2_map_Teff, aspect='auto', origin='lower',\n", + " extent=[np.log10(length_scales[0]), np.log10(length_scales[-1]), 0, len(nus)-1],\n", + " cmap='viridis')\n", + "plt.colorbar(im, label=r'$\\chi^2$')\n", + "plt.xticks(np.log10(length_scales), labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", + "plt.xlabel('Length Scale')\n", + "plt.ylabel(r'$\\nu$')\n", + "plt.title('Chi-Squared Heatmap for Teff Fit Parameters')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "b29c1755-4b8e-404b-a8f5-024a759179af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.5 0.5455594781168517\n" + ] + } + ], + "source": [ + "i_best_Teff, j_best_Teff = np.unravel_index(np.nanargmin(chi2_map_Teff), chi2_map_Teff.shape)\n", + "best_nu_Teff = nus[i_best_Teff]\n", + "best_length_scale_Teff = length_scales[j_best_Teff]\n", + "print(best_nu_Teff, best_length_scale_Teff)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "926cbb27-cf76-4259-bff4-63cdad0fce02", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Looking at physical interpretation of heatmap parameters\n", + "\n", + "# creating input array (mass) and output array (everything else)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# trying different kernel types\n", + "kernel = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", + "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + "gp.fit(X, y_Teff)\n", + "y_pred_Teff, sigma_Teff = gp.predict(x, return_std=True)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_Teff, color='blue', label='actual values')\n", + "plt.plot(x, y_pred_Teff, color='orange', label='predicted values')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('Teff')\n", + "plt.title('Best Fit by Heat Map')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(3.25,3.75)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "fb250ffb-e8f5-4b66-a9d0-ae79190d1d0d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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P95q3d+9eHTlyRNmyZfO5j+SkO6Piz58/v+c4pZSyn25yXJI0ePBgDR48+IKxvfTSSxo0aJD69u2rZ555RpGRkcqSJYuefPLJS05a8+bN6/V3tmzZlD17doWGhqaaf+zYMc/fn3/+ue6++27dddddGjJkiKKiopQ1a1a98cYbqfpMS6mPY8p5Bw8evORBgw4ePOizT2ahQoU8j6eUnhGH07qvgwcPeiXmyc6dl7z8+ZZN63s8NDTU57mVkW644Qa9/PLLcrlcyp49u0qVKuX13rn33nv1448/6sknn1Tt2rUVHh4ul8ultm3b+rzG+HodHnvsMb3++usaNmyYmjRpojx58igoKEi9evXyuQ1f5+yF5p8+fVo5c+bU3r17ZWY+j7t0djTmi0nr9SOZr+fbuHFjffHFF3r11VfVtWtXxcfHq1KlShoxYkSqHzgAZI6TJ0+qatWq6tGjh88f0y5m8ODB6tu3r9e8Fi1aeH6UQvqRtAJXqCxZsqhFixb65ptvtHPnzgwZGfR8atasqaVLl3rNS/6C7ktwcLBGjRqll19+WWvWrJH0b7IcFxenwoULe5Y9c+ZMqoQiOUGKj4/36md37hfAefPmaffu3VqwYIGndVWSjhw54jMuX4M7RUZGKl++fOcdZTlXrlyXHH9GSO77N3z4cE+/0XMl9+2bMWOGmjZtqjfeeMPr8fT0Fb5UM2bMUIkSJTRr1iyv43y+PplxcXHnnXc5P2Lky5dPe/bsSTV/9+7dkpSqL2V6RqhN677y5cvnc6Cuc5978vNN/qHiQssGWkRExHkT46NHj+p///ufRo0apccff9wzP7m/sy++XocZM2aoa9euevbZZ73mHzhwQLlz57784M8RGRkpl8uln3/+2Wd/3rT08U3r9SPZ+c67Dh06qEOHDoqPj9dvv/2m8ePH695771Xx4sVVv379NDwbAOnRpk0br4qicyUkJGjkyJH64IMPdOTIEVWuXFnPP/+85/ZxOXPmVM6cOT3Lr1y5UuvWrdOUKVMyO/RrBuXBwBVs+PDhMjP17t1bCQkJqR5PTEzUV199le795MqVy9OSmjwltyz4+vIu/Vuam5zcJl/Yz72P5Mcff5xq8JjkVqxVq1Z5zT/3uSR/ATz3y+Wbb76Zlqcl6Wxp4cGDB5WUlJTqOdaqVcuTGF5K/BmhXLlyKlOmjFauXOkzrlq1anm+ELtcrlTHYNWqVVq8eLHXvORlLtSqfrlcLpeyZcvm9aU8Li7O5+jBkvTjjz96JWlJSUmaNWuWSpUqdVk/wLRo0ULr1q3Tn3/+6TX/vffek8vlSlUpkB5p3VeTJk10/PhxffPNN17LnTtKcrly5RQVFZVqVOjt27dr0aJFGRb3uS5WYXGpXC6XzCzVufj2228rKSnpkrZz7jbmzJmT4WWzN998s8xMu3bt8vn+qlKlimfZ8x2rtF4/0iokJERNmjTxdBdYvnx5+p4kgAzRo0cP/frrr/roo488o+y3bt1aGzdu9Ln822+/rbJly6pRo0Z+jvTqRUsrcAWrX7++3njjDfXr1081a9bUgw8+qEqVKikxMVHLly/XW2+9pcqVK6t9+/aZFsNNN92k6667Tu3bt1f58uXldru1YsUKvfjii8qZM6ceffRRSWf7k913332aNGmSgoODdeONN2rNmjWe0VtTatu2rfLmzauePXtqzJgxypo1q2JiYrRjxw6v5Ro0aKA8efKob9++GjVqlIKDg/XBBx9o5cqVaY6/U6dO+uCDD9S2bVs9+uijqlOnjoKDg7Vz507Nnz9fHTp00G233XZJ8WeUN998U23atNFNN92k7t27q3Dhwjp06JDWr1+vP//8U5988omks1+cn3nmGY0aNUpNmjRRbGysxowZoxIlSngl1Lly5VKxYsX03//+Vy1atFDevHkVGRmZIbdUSb6VR79+/XTnnXdqx44deuaZZxQdHe3zQz0yMlLNmzfXk08+6Rk9eMOGDRe97c35DBw4UO+9957atWunMWPGqFixYpozZ44mT56sBx98MFU/wvRI6766deuml19+Wffdd5/Gjh2r0qVL65tvvtF3330n6d9bQgUFBenpp59Wnz59dOedd+r+++/XkSNH9PTTTys6Otrn6L8ZoUqVKvroo480a9YslSxZUqGhoV6J2qUKDw9X48aN9cILL3jOq4ULF+qdd965pBbSm2++WTExMSpfvryuv/56/fHHH3rhhRcyvJqkYcOGeuCBB9SjRw8tW7ZMjRs3Vo4cObRnzx798ssvqlKliufes1WqVNHnn3+uN954QzVr1lRQUJBq1aqV5uvHhTz11FPauXOnWrRooeuuu05HjhzRK6+84tU/H0DgbN68WR9++KF27tzp+SF+8ODB+vbbbzV9+vRUVSHx8fH64IMPvCpOkAECOAgUgAyyYsUK69atmxUtWtSyZctmOXLksOrVq9tTTz1l+/bt8yzna9RVs7Mjj6YcRfRSRg+eNWuW3XvvvVamTBnLmTOnBQcHW9GiRa1Lly62bt06r2Xj4+Nt0KBBVqBAAQsNDbV69erZ4sWLU43Oama2ZMkSa9CggeXIkcMKFy5so0aNsrfffjvV6MGLFi2y+vXrW/bs2S1//vzWq1cv+/PPP71GZzU7O3pwjhw5fD6HxMREmzhxolWtWtVCQ0MtZ86cVr58eevTp49t3LjxsuL3RZL179/f52OffPKJz2O+cuVKu/vuu61AgQIWHBxsUVFR1rx5c5syZYpXXIMHD7bChQtbaGio1ahRw7744gvr1q1bqhFPf/jhB6tevbqFhIR4jXqcPLrs/v37vZY/33Fr0qSJVapUyWvec889Z8WLF7eQkBCrUKGCTZ061eeotcnHYfLkyVaqVCkLDg628uXL2wcffHChw+dxvvN427Ztdu+991q+fPksODjYypUrZy+88ILXKMvJowe/8MILadpX8v7OfX3Tsi8zs+3bt9vtt99uOXPmtFy5ctkdd9xhX3/9tUmy//73v17LvvXWW1a6dGnLli2blS1b1qZNm2YdOnRINfqyL75ej5R8vQ5bt261Vq1aWa5cuUzSRUcpPt9xT2nnzp12xx13WJ48eSxXrlzWunVrW7NmTapjmDx68NKlS1Nt4/Dhw9azZ08rUKCAZc+e3W644Qb7+eefz3ud+uSTT7zWP9+2z3eOT5s2zerWrWs5cuSwsLAwK1WqlHXt2tWWLVvmWebQoUN25513Wu7cuc3lcnkdy7ReP853/P73v/9ZmzZtrHDhwpYtWzYrUKCAtW3b1n7++ecLHmsAmUOSzZ492/P3xx9/bJIsR44cXlPWrFnt7rvvTrX+zJkzLWvWrLZnzx4/Rn31c5mZ+TdNBgBvxYsXV9OmTRUTExPoUOAHLpdL/fv312uvvRboUALi2Wef1ciRI7V9+/YLth4eOXJEZcuW1a233qq33nrLjxECwLXL5XJp9uzZuvXWWyVJs2bNUufOnbV27VplyZLFa9mcOXOmGliwRYsWCg8PT3VfZ6QP5cEAAGSS5MS8fPnySkxM1Lx58/Tqq6/qvvvu80pY4+LiNG7cODVr1kz58uXTtm3b9PLLL+v48eOeEnsAgP9Vr15dSUlJ2rdv30X7qG7ZskXz58/Xl19+6aforh0krQAAZJLs2bPr5Zdf1tatWxUfH6+iRYtq2LBhGjlypNdyISEh2rp1q/r166dDhw4pe/bsqlevnqZMmaJKlSoFKHoAuDacOHFCmzZt8vy9ZcsWrVixQnnz5lXZsmXVuXNnde3aVS+++KKqV6+uAwcOaN68eapSpYratm3rWW/atGmKjo6+4EjEuDyUBwMAAAC4Zi1YsMDnKPPdunVTTEyMEhMTNXbsWL333nvatWuX8uXLp/r16+vpp5/2DGDndrtVrFgxde3aVePGjfP3U7jqBTRpHT9+vD7//HNt2LBBYWFhatCggZ5//vkLDhF/vpNq/fr1Kl++fGaGCwAAAADws4Dep3XhwoXq37+/fvvtN82dO1dnzpxRq1atdPLkyYuuGxsbqz179nimMmXK+CFiAAAAAIA/Oao8eP/+/SpQoIAWLlyoxo0b+1wmuaX18OHDl3TfNwAAAADAlcdRAzEdPXpUkpQ3b96LLlu9enWdPn1aFStW1MiRI32WDEtnb/AbHx/v+dvtduvQoUPKly+fXC5XxgQOAAAAIBUz0/Hjx1WoUCEFBQW0yPOSnT59WgkJCRmyrWzZsik0NDRDtnUtckxLq5mpQ4cOOnz4sH7++efzLhcbG6uffvpJNWvWVHx8vN5//31NmTJFCxYs8Nk6O3r0aD399NOZGToAAACAC9ixY8cF703tNKdPn1aJYjkVty8pQ7YXFRWlLVu2kLheJsckrf3799ecOXP0yy+/XPIJ3b59e7lcLp/3RDq3pfXo0aMqWrSomn/aXVlzZEt33IDTuI0KgivR9sN5Ah1CKuFhpwIdgpcDh3MFOoRUCuY9FugQUsmf/USgQ0jl/RILAh2Cl97bbwh0CFcEJ36eHDidI9AheAmSI75GeymQ/XigQ/CSeDJBs2/5WEeOHFFERESgw0mzY8eOKSIiQtv+KK7wXOlrIT523K1iNbfq6NGjCg8Pz6AIry2OKA9++OGH9eWXX+qnn366rF9g6tWrpxkzZvh8LCQkRCEhIanmZ82RTcEkrbgKOfFLBi4uS3zq61SgZc3uDnQIXoLinffrdNYc8RdfyM+Cs2dMKVtGSu8XvozG53/aOPHzJGsWZ10rnZi0Bmd33nVJ0hXbLS9nLpdy5kpf7G5dmc/dSQKatJqZHn74Yc2ePVsLFixQiRIlLms7y5cvV3R0dAZHBwAAAOBalmRuJaXzt4kkc9aPwFeigCat/fv318yZM/Xf//5XuXLlUlxcnCQpIiJCYWFhkqThw4dr165deu+99yRJkyZNUvHixVWpUiUlJCRoxowZ+uyzz/TZZ58F7HkAAAAAADJHQJPWN954Q5LUtGlTr/nTp09X9+7dJUl79uzR9u3bPY8lJCRo8ODB2rVrl8LCwlSpUiXNmTNHbdu29VfYAAAAAK4Bbpnc6SwDT+/6cEB58MXExMR4/T106FANHTo0kyICAAAAgLPcciu9xb3p3wKcNTICAAAAAAApOGL0YAAAAABwmiQzJaXzDqHpXR8krQAAAADgE31anYHyYAAAAACAY9HSCgAAAAA+uGVKoqU14EhaAQAAAMAHyoOdgfJgAAAAAIBj0dIKAAAAAD4werAzkLQCAAAAgA/u/5/Suw2kD+XBAAAAAOBD0v8PxJTe6VL89NNPat++vQoVKiSXy6UvvvjioussXLhQNWvWVGhoqEqWLKkpU6Zc5jN2JpJWAAAAAHCIkydPqmrVqnrttdfStPyWLVvUtm1bNWrUSMuXL9cTTzyhRx55RJ999lkmR+o/lAcDAAAAgA9JdnZK7zYuRZs2bdSmTZs0Lz9lyhQVLVpUkyZNkiRVqFBBy5Yt08SJE3XHHXdc2s4dipZWAAAAAPDBnUFTZlq8eLFatWrlNe+mm27SsmXLlJiYmMl79w9aWgEAAAAgkx07dszr75CQEIWEhKR7u3FxcSpYsKDXvIIFC+rMmTM6cOCAoqOj072PQKOlFQAAAAB8cMulpHRObrkkSUWKFFFERIRnGj9+fIbF6XK5vP62/7/Nzrnzr1S0tAIAAACAD247O6V3G5K0Y8cOhYeHe+ZnRCurJEVFRSkuLs5r3r59+5Q1a1bly5cvQ/YRaCStAAAAAJDJwsPDvZLWjFK/fn199dVXXvO+//571apVS8HBwRm+v0CgPBgAAAAAfEhvaXDydClOnDihFStWaMWKFZLO3tJmxYoV2r59uyRp+PDh6tq1q2f5vn37atu2bXrssce0fv16TZs2Te+8844GDx6cYcch0GhpBQAAAAAfLifp9LWNS7Fs2TI1a9bM8/djjz0mSerWrZtiYmK0Z88eTwIrSSVKlNDXX3+tgQMH6vXXX1ehQoX06quvXjW3u5FIWgEAAADAMZo2beoZSMmXmJiYVPOaNGmiP//8MxOjCiySVgAAAADwwW0uuS19La3pXR8krQAAAADgUyDKg5EaAzEBAAAAAByLllYAAAAA8CFJQUpKZztfUgbFci0jaQUAAAAAHywD+rQafVrTjfJgAAAAAIBj0dIKAAAAAD4wEJMzkLQCAAAAgA9JFqQkS2ef1vPfchVpRHkwAAAAAMCxaGkFAAAAAB/ccsmdznY+t2hqTS+SVgAAAADwgT6tzkB5MAAAAADAsWhpBQAAAAAfMmYgJsqD04ukFQAAAAB8ONunNX3lveldHyStAAAAAOCTW0FKYiCmgKNPKwAAAADAsWhpBQAAAAAf6NPqDCStAAAAAOCDW0Hcp9UBKA8GAAAAADgWLa0AAAAA4EOSuZRk6Rv9N73rg6QVAAAAAHxKyoDRg5MoD043yoMBAAAAAI5FSysAAAAA+OC2ILnTOXqwm9GD042kFQAAAAB8oDzYGSgPBgAAAAA4Fi2tAAAAAOCDW+kf/dedMaFc00haAQAAAMAHt4LkTmdxanrXB+XBAAAAAAAHo6UVAAAAAHxIsiAlpXP04PSuD5JWAAAAAPDJLZfcSm+f1vStD8qDAQAAAAAORksrAAAAAPhAebAzcAQBAAAAwIckBWXIdDkmT56sEiVKKDQ0VDVr1tTPP/983mUXLFggl8uVatqwYcPlPnVHIWkFAAAAAAeZNWuWBgwYoBEjRmj58uVq1KiR2rRpo+3bt19wvdjYWO3Zs8czlSlTxk8RZy6SVgAAAADwwW2uDJku1UsvvaSePXuqV69eqlChgiZNmqQiRYrojTfeuOB6BQoUUFRUlGfKkiXL5T51RyFpBQAAAAAf3BlQGuy+xJQrISFBf/zxh1q1auU1v1WrVlq0aNEF161evbqio6PVokULzZ8//5Kfr1MxEBMAAAAA+OC2ILnTOZBS8vrHjh3zmh8SEqKQkJBUyx84cEBJSUkqWLCg1/yCBQsqLi7O5z6io6P11ltvqWbNmoqPj9f777+vFi1aaMGCBWrcuHG64ncCklYAAAAAyGRFihTx+nvUqFEaPXr0eZd3ubzLis0s1bxk5cqVU7ly5Tx/169fXzt27NDEiRNJWgEAAADgapUkl5J06X1Sz92GJO3YsUPh4eGe+b5aWSUpMjJSWbJkSdWqum/fvlStrxdSr149zZgx4zIidh76tAIAAACAD8nlwemdJCk8PNxrOl/Smi1bNtWsWVNz5871mj937lw1aNAgzbEvX75c0dHRl//kHYSWVgAAAABwkMcee0xdunRRrVq1VL9+fb311lvavn27+vbtK0kaPny4du3apffee0+SNGnSJBUvXlyVKlVSQkKCZsyYoc8++0yfffZZIJ9GhiFpBQAAAAAfkqQMKA++dB07dtTBgwc1ZswY7dmzR5UrV9bXX3+tYsWKSZL27Nnjdc/WhIQEDR48WLt27VJYWJgqVaqkOXPmqG3btumK3SlIWgEAAADAh4wcPfhS9evXT/369fP5WExMjNffQ4cO1dChQy9rP1cC+rQCAAAAAByLllYAAAAA8CHJgpSUzpbW9K4PklYAAAAA8MnkkjudfVotneuD8mAAAAAAgIPR0goAAAAAPlAe7AwkrQAAAADgg9tcclv6ynvTuz4oDwYAAAAAOBgtrQAAAADgQ5KClJTOdr70rg+SVgAAAADwifJgZyDtBwAAAAA4Fi2tAAAAAOCDW0Fyp7OdL73rg6QVAAAAAHxKMpeS0lnem971QXkwAAAAAMDBaGkFAAAAAB8YiMkZSFoBAAAAwAezILktfcWpls71QXkwAAAAAMDBaGkFAAAAAB+S5FKS0jkQUzrXB0krAAAAAPjktvT3SXVbBgVzDSNpBQAAAAAf3BnQpzW964M+rQAAAAAAB6OlFQAAAAB8cMsldzr7pKZ3fZC0AgAAAIBPSeZSUjr7tKZ3fVAeDAAAAABwMFpaAQAAAMAHBmJyBpJWAAAAAPDBLVf6b3lDn9Z0I+0HAAAAADgWLa0AAAAA4INlwOjBRktrupG0AgAAAIAPbsuA8mBGD043yoMBAAAAAI5FSysAAAAA+MDowc5A0goAAAAAPlAe7Ayk/QAAAAAAxyJpBQAAAAAf3P8/enB6p8sxefJklShRQqGhoapZs6Z+/vnnCy6/cOFC1axZU6GhoSpZsqSmTJlyWft1IpJWAAAAAPAhuTw4vdOlmjVrlgYMGKARI0Zo+fLlatSokdq0aaPt27f7XH7Lli1q27atGjVqpOXLl+uJJ57QI488os8++yy9h8ARSFoBAAAAwEFeeukl9ezZU7169VKFChU0adIkFSlSRG+88YbP5adMmaKiRYtq0qRJqlChgnr16qX7779fEydO9HPkmYOkFQAAAAB8CERLa0JCgv744w+1atXKa36rVq20aNEin+ssXrw41fI33XSTli1bpsTExEt70g7E6MEAAAAA4ENGjh587Ngxr/khISEKCQlJtfyBAweUlJSkggULes0vWLCg4uLifO4jLi7O5/JnzpzRgQMHFB0dnZ6nEHC0tAIAAABAJitSpIgiIiI80/jx4y+4vMvlnSybWap5F1ve1/wrES2tAAAAAOBDRra07tixQ+Hh4Z75vlpZJSkyMlJZsmRJ1aq6b9++VK2pyaKionwunzVrVuXLly894TsCLa0AAAAA4IMp/be9sf/fVnh4uNd0vqQ1W7ZsqlmzpubOnes1f+7cuWrQoIHPderXr59q+e+//161atVScHBweg9DwJG0AgAAAIAPgbrlzWOPPaa3335b06ZN0/r16zVw4EBt375dffv2lSQNHz5cXbt29Szft29fbdu2TY899pjWr1+vadOm6Z133tHgwYMz7FgEEuXBAAAAAOAgHTt21MGDBzVmzBjt2bNHlStX1tdff61ixYpJkvbs2eN1z9YSJUro66+/1sCBA/X666+rUKFCevXVV3XHHXcE6ilkKJJWAAAAAPAhI/u0Xqp+/fqpX79+Ph+LiYlJNa9Jkyb6888/L2tfTkfSCgAAAAA+BDJpxb/o0woAAAAAcCxaWgEAAADAB1panYGkFQAAAAB8MHPJ0pl0pnd9UB4MAAAAAHAwWloBAAAAwAe3XHIrneXB6VwfJK0AAAAA4BN9Wp2B8mAAAAAAgGPR0goAAAAAPjAQkzOQtAIAAACAD5QHOwPlwQAAAAAAx6KlFQAAAAB8oDzYGUhagatMkMsCHQIuQ8l8BwMdguPlK/xPoEO4IiS4nffRfuvGmwIdghduP5E2QXLe50lQix2BDsFLjp/yBzqEVI4lhAU6BC+JCVkCHUK6WAaUB5O0ph/lwQAAAAAAx3Lez7EAAAAA4AAmydJZdOC8moUrD0krAAAAAPjglkuudHYpoEtC+lEeDAAAAABwLFpaAQAAAMAHRg92BpJWAAAAAPDBbS650pl0pnf0YVAeDAAAAABwMFpaAQAAAMAHswwYPZjhg9ONpBUAAAAAfKBPqzOQtAIAAACADyStzkCfVgAAAACAY9HSCgAAAAA+MHqwM5C0AgAAAIAPDMTkDJQHAwAAAAAci5ZWAAAAAPDhbEtregdiyqBgrmEkrQAAAADgA6MHOwPlwQAAAAAAx6KlFQAAAAB8sP+f0rsNpA9JKwAAAAD4QHmwM1AeDAAAAABwLJJWAAAAAPDFMmjKJIcPH1aXLl0UERGhiIgIdenSRUeOHLngOt27d5fL5fKa6tWrl3lBZgDKgwEAAADAlwwoD1Ymlgffe++92rlzp7799ltJ0gMPPKAuXbroq6++uuB6rVu31vTp0z1/Z8uWLdNizAgkrQAAAABwhVm/fr2+/fZb/fbbb6pbt64kaerUqapfv75iY2NVrly5864bEhKiqKgof4WabpQHAwAAAIAPZhkzSdKxY8e8pvj4+HTFtnjxYkVERHgSVkmqV6+eIiIitGjRoguuu2DBAhUoUEBly5ZV7969tW/fvnTFktlIWgEAAADAh+TRg9M7SVKRIkU8fU8jIiI0fvz4dMUWFxenAgUKpJpfoEABxcXFnXe9Nm3a6IMPPtC8efP04osvaunSpWrevHm6k+jMRHkwAAAAAGSyHTt2KDw83PN3SEiIz+VGjx6tp59++oLbWrp0qSTJ5UrdX9bMfM5P1rFjR8//K1eurFq1aqlYsWKaM2eObr/99gvuN1BIWgEAAADAF3OlfyCl/18/PDzcK2k9n4ceekidOnW64DLFixfXqlWrtHfv3lSP7d+/XwULFkxzeNHR0SpWrJg2btyY5nX8jaQVAAAAAHxI2Sc1Pdu4FJGRkYqMjLzocvXr19fRo0e1ZMkS1alTR5L0+++/6+jRo2rQoEGa93fw4EHt2LFD0dHRlxaoH9GnFQAAAACuMBUqVFDr1q3Vu3dv/fbbb/rtt9/Uu3dv3XzzzV4jB5cvX16zZ8+WJJ04cUKDBw/W4sWLtXXrVi1YsEDt27dXZGSkbrvttkA9lYsiaQUAAAAAXyyDpkzywQcfqEqVKmrVqpVatWql66+/Xu+//77XMrGxsTp69KgkKUuWLFq9erU6dOigsmXLqlu3bipbtqwWL16sXLlyZV6g6UR5MAAAAAD4kHL03/RsI7PkzZtXM2bMuMj+/82aw8LC9N1332VaPJmFpBUAAAAAzicTW0qRNpQHAwAAAAAci5ZWAAAAAPDB6eXB1wqSVgAAAADwJSMGUqK8ON0oDwYAAAAAOBYtrQAAAADgk+v/p/RuA+lB0goAAAAAvlAe7AiUBwMAAAAAHIuWVgAAAADwhZZWRyBpBQAAAABfzHV2Su82kC6UBwMAAAAAHIuWVgAAAADwwezslN5tIH1IWgEAAADAF/q0OgLlwQAAAAAAx6KlFQAAAAB8YSAmRyBpBQAAAAAfXHZ2Su82kD6UBwMAAAAAHIuWVgAAAADwhYGYHIGkFQAAAAB8oU+rI1AeDAAAAABwLFpaAQAAAMAXyoMdgaQVAAAAAHwhaXUEyoMBAAAAAI5FSysAAAAA+EJLqyOQtAIAAACAL4we7AiUBwMAAACADy7LmOlqcurUKe3atSvV/LVr12baPklaAQAAAAAX9emnn6ps2bJq27atrr/+ev3++++ex7p06ZJp+w1o0vrTTz+pffv2KlSokFwul7744osLLr9gwQK5XK5U04YNG/wTMAAAAIBrh2XQdJUYO3as/vzzT61cuVLTpk3T/fffr5kzZ0qSzDLviV5yn1a3261ff/1V+/fvV4MGDRQVFXXZOz958qSqVq2qHj166I477kjzerGxsQoPD/f8nT9//suOAQAAAABwcYmJiZ7cq1atWvrpp590++23a9OmTXK5Mq/v7iUnrQUKFFB8fLxCQ0N15MgR3XffffrPf/6jnDlzXvLO27RpozZt2lzyegUKFFDu3LkveT0AAAAAwOUpUKCAVq1apeuvv16SlC9fPs2dO1fdunXTqlWrMm2/l1we/Mknn+j48ePav3+/li5dqm3btqlOnTqKi4vLjPh8ql69uqKjo9WiRQvNnz//gsvGx8fr2LFjXhMAAAAAXIxLGTAQU6CfRAZ6//33VaBAAa952bJl04cffqiFCxdm2n4vOWlt1qyZ5//VqlXTjz/+qHbt2umGG27Qnj17MjS4c0VHR+utt97SZ599ps8//1zlypVTixYt9NNPP513nfHjxysiIsIzFSlSJFNjBAAAAHCVSL7lTXqnq8R1112Xqnvot99+K0lq2LBhpu033QMxnTx5Uj179lTZsmXVqlWrjIjpvMqVK6fevXurRo0aql+/viZPnqx27dpp4sSJ511n+PDhOnr0qGfasWNHpsYIAAAAAP4wbtw4NWjQQNmzZ09z90kz0+jRo1WoUCGFhYWpadOm6bpdza233qpHH31U8fHxl72Ni7nkpLVHjx5q1aqVKlas6Gm9rFSpkr799ltt3rw5M2K8oHr16mnjxo3nfTwkJETh4eFeEwAAAABclMNHD05ISNBdd92lBx98MM3rTJgwQS+99JJee+01LV26VFFRUWrZsqWOHz9+WTH88ssv+u6771SzZs3z9mvdvXu3OnTocFnbly5jIKa9e/eqWLFiatCggQoXLuw1RUZGXnYgl2v58uWKjo72+34BAAAAXOUyIunMxKT16aefliTFxMSkLRQzTZo0SSNGjNDtt98uSXr33XdVsGBBzZw5U3369LnkGGrVqqXly5fr0UcfVd26dTVu3Dg99thjks7eeWbDhg166aWXtHjx4kvedrJLTlq//vrry97ZuU6cOKFNmzZ5/t6yZYtWrFihvHnzqmjRoho+fLh27dql9957T5I0adIkFS9eXJUqVVJCQoJmzJihzz77TJ999lmGxQQAAAAAGe3cAWFDQkIUEhLi1xi2bNmiuLg4r26dISEhatKkiRYtWnRZSaskhYWFady4ccqWLZuGDBmiDz/8UG63W+vWrVN8fLyKFSum8ePHX3bcl5y0ZqRly5Z5DeyUnJF369ZNMTEx2rNnj7Zv3+55PCEhQYMHD9auXbsUFhamSpUqac6cOWrbtq3fYwcAAABwdUseATi925CUakDYUaNGafTo0enb+CVKvuNLwYIFveYXLFhQ27Ztu6xtvvnmm3r66ae1d+9ehYWFqXbt2nK5XPr999/Vv39/jR07VhEREemKO6BJa9OmTWV2/rPg3GbuoUOHaujQoZkcFQAAAAAoQ8uDd+zY4TW+zvlaWUePHu0p+z2fpUuXqlatWpcdksvlPaKxmaWal1YjR47UnXfeqQEDBqhs2bKe7bz88st64okndPLkSb322mvKnj37Zccb0KQVAAAAAK4FaR0U9qGHHlKnTp0uuEzx4sUvK4bk29XExcV5jQu0b9++VK2vadW0aVONHj061foDBw7UjTfeqM6dO+v666/XBx98oLp1617WPkhaAQAAAMCXAAzEFBkZmWkD3JYoUUJRUVGaO3euqlevLulsF8yFCxfq+eefv6xtfvLJJ+d9rEqVKlq6dKmGDRumxo0bX/ZtcdJ9n1YAAAAAuBol92lN75RZtm/frhUrVmj79u1KSkrSihUrtGLFCp04ccKzTPny5TV79uyzz8fl0oABA/Tss89q9uzZWrNmjbp3767s2bPr3nvvzZQYQ0JCNGnSJP3vf/+77G3Q0goAAAAAV6CnnnpK7777rufv5NbT+fPnq2nTppKk2NhYHT161LPM0KFDderUKfXr10+HDx9W3bp19f333ytXrlyZGmvLli0ve12SVgAAAADwxVxnp/RuI5PExMRc9B6t5w5863K5NHr0aL+PXJweJK0AAAAA4EsA+rQiNfq0AgAAAAAci5ZWAAAAAPAhIwZSysyBmK4VJK0AAAAA4AvlwY5A0goAAAAAvmTELWtIWtONPq0AAAAAAMeipRUAAAAAfKE82BFIWgEAAADAF5JWR6A8GAAAAADgWLS0AgAAAIAP3PLGGWhpBQAAAAA4FkkrAAAAAMCxKA8GAAAAAF8YiMkRSFoBAAAAwAf6tDoD5cEAAAAAAMeipRUAAAAAzoeW0oAjaQUAAAAAX+jT6giUBwMAAAAAHIuWVgAAAADwgYGYnIGkFQAAAAB8oTzYESgPBgAAAAA4Fi2tAAAAAOAD5cHOQNIKAAAAAL5QHuwIlAcDAAAAAByLllYAAAAA8IWWVkcgaQUAAAAAH+jT6gyUBwMAAAAAHIuWVgAAAADwhfJgRyBpBQAAAABfSFodgaQVAAAAAHygT6sz0KcVAAAAAOBYtLQCAAAAgC+UBzsCLa0AAAAA4ENyeXB6p8wybtw4NWjQQNmzZ1fu3LnTtE737t3lcrm8pnr16mVekBmApBUAAAAArkAJCQm666679OCDD17Seq1bt9aePXs809dff51JEWYMyoMBAAAAwBeHlwc//fTTkqSYmJhLWi8kJERRUVGZEFHmoKUVAAAAAHyxDJokHTt2zGuKj4/361NJacGCBSpQoIDKli2r3r17a9++fQGLJS1IWgEAAAAgkxUpUkQRERGeafz48QGJo02bNvrggw80b948vfjii1q6dKmaN28e0CT6YigPBgAAAAAfXP8/pXcbkrRjxw6Fh4d75oeEhPhcfvTo0Z6y3/NZunSpatWqdVnxdOzY0fP/ypUrq1atWipWrJjmzJmj22+//bK2mdlIWgEAAADAlwzs0xoeHu6VtJ7PQw89pE6dOl1wmeLFi6czqH9FR0erWLFi2rhxY4ZtM6ORtAIAAACAQ0RGRioyMtJv+zt48KB27Nih6Ohov+3zUtGnFQAAAAB8cPp9Wrdv364VK1Zo+/btSkpK0ooVK7RixQqdOHHCs0z58uU1e/ZsSdKJEyc0ePBgLV68WFu3btWCBQvUvn17RUZG6rbbbsu8QNOJllYAAAAA8MXht7x56qmn9O6773r+rl69uiRp/vz5atq0qSQpNjZWR48elSRlyZJFq1ev1nvvvacjR44oOjpazZo106xZs5QrV67MCzSdSFoBAAAA4AoUExNz0Xu0mv2bNYeFhem7777L5KgyHkkrAAAAAJxPJraUIm1IWgEAAADAh4zok5qZfVqvFQzEBAAAAABwLFpaAQAAAMAXhw/EdK0gaQUAAAAAHygPdgbKgwEAAAAAjkVLKwAAAAD4QnmwI5C0AgAAAIAPlAc7A0krAADIVG65Ah0CLoMTX7eQhVGBDsHLGZKRi3LieYQrD0krAAAAAPhCebAjkLQCAAAAgC8krY5A0goAAAAAPtCn1Rm45Q0AAAAAwLFoaQUAAAAAXygPdgSSVgAAAADwwWUml6Uv60zv+qA8GAAAAADgYLS0AgAAAIAvlAc7AkkrAAAAAPjA6MHOQHkwAAAAAMCxaGkFAAAAAF8oD3YEklYAAAAA8IHyYGegPBgAAAAA4Fi0tAIAAACAL5QHOwJJKwAAAAD4QHmwM1AeDAAAAABwLFpaAQAAAMAXyoMdgaQVAAAAAM6D8t7AozwYAAAAAOBYtLQCAAAAgC9mZ6f0bgPpQtIKAAAAAD4werAzUB4MAAAAAHAsWloBAAAAwBdGD3YEWloBAAAAwAeXO2OmzLB161b17NlTJUqUUFhYmEqVKqVRo0YpISHhguuZmUaPHq1ChQopLCxMTZs21dq1azMnyAxC0goAAAAAvlgGTZlgw4YNcrvdevPNN7V27Vq9/PLLmjJlip544okLrjdhwgS99NJLeu2117R06VJFRUWpZcuWOn78eOYEmgEoDwYAAACAK0zr1q3VunVrz98lS5ZUbGys3njjDU2cONHnOmamSZMmacSIEbr99tslSe+++64KFiyomTNnqk+fPn6J/VLR0goAAAAAPiSPHpzeSZKOHTvmNcXHx2d4vEePHlXevHnP+/iWLVsUFxenVq1aeeaFhISoSZMmWrRoUYbHk1FIWgEAAADAl+T7tKZ3klSkSBFFRER4pvHjx2doqJs3b9Z//vMf9e3b97zLxMXFSZIKFizoNb9gwYKex5yIpBUAAAAAMtmOHTt09OhRzzR8+HCfy40ePVoul+uC07Jly7zW2b17t1q3bq277rpLvXr1umgsLpfL628zSzXPSejTCgAAAAA+pCzvTc82JCk8PFzh4eEXXf6hhx5Sp06dLrhM8eLFPf/fvXu3mjVrpvr16+utt9664HpRUVGSzra4RkdHe+bv27cvVeurk5C0AgAAAIAvAbhPa2RkpCIjI9O07K5du9SsWTPVrFlT06dPV1DQhQtpS5QooaioKM2dO1fVq1eXJCUkJGjhwoV6/vnnLy1QP6I8GAAAAACuMLt371bTpk1VpEgRTZw4Ufv371dcXFyqvqnly5fX7NmzJZ0tCx4wYICeffZZzZ49W2vWrFH37t2VPXt23XvvvYF4GmlCSysAAAAA+JCR5cEZ7fvvv9emTZu0adMmXXfddV6Pmf2709jYWB09etTz99ChQ3Xq1Cn169dPhw8fVt26dfX9998rV65cmRNoBiBpBQAAAABfUoz+m65tZILu3bure/fuadi99/5dLpdGjx6t0aNHZ0pcmYHyYAAAAACAY9HSCgAAAAA+OLk8+FpC0goAAAAAvgRg9GCkRnkwAAAAAMCxaGkFAAAAAB8oD3YGklYAAAAA8MVtZ6f0bgPpQnkwAAAAAMCxaGkFAAAAAF8YiMkRSFoBAAAAwAeXMqBPa4ZEcm2jPBgAAAAA4Fi0tAIAAACAL2Znp/RuA+lC0goAAAAAPnDLG2egPBgAAAAA4Fi0tAIAAACAL4we7AgkrQAAAADgg8tMrnT2SU3v+iBpBQAAAADf3P8/pXcbSBf6tAIAAAAAHIuWVgAAAADwgfJgZyBpBQAAAABfGIjJESgPBgAAAAA4Fi2tAAAAAOCL2dkpvdtAupC0AgAAAIAPLjs7pXcbSB/KgwEAAAAAjkVLKwAAAAD4QnmwI5C0AgAAAIAPLvfZKb3bQPpQHgwAAAAAcCxaWgEAAADAF8qDHYGkFQAAAAB8sf+f0rsNpAvlwQAAAAAAx6KlFQAAAAB8cJnJlc7y3vSuD5JWAAAAAPCNPq2OQHkwAAAAAMCxSFoBAAAAwBeT5E7nlEkNrVu3blXPnj1VokQJhYWFqVSpUho1apQSEhIuuF737t3lcrm8pnr16mVOkBmE8mAAAAAA8MHJfVo3bNggt9utN998U6VLl9aaNWvUu3dvnTx5UhMnTrzguq1bt9b06dM9f2fLli1TYswoJK0AAAAAcIVp3bq1Wrdu7fm7ZMmSio2N1RtvvHHRpDUkJERRUVGZHWKGoTwYAAAAAHwx/TsY02VP/gv36NGjyps370WXW7BggQoUKKCyZcuqd+/e2rdvnx+iu3y0tAIAAACALxk4evCxY8e8ZoeEhCgkJCR9205h8+bN+s9//qMXX3zxgsu1adNGd911l4oVK6YtW7boySefVPPmzfXHH39kaDwZiZZWAAAAAPAlvYMwJU+SihQpooiICM80fvx4n7scPXp0qoGSzp2WLVvmtc7u3bvVunVr3XXXXerVq9cFn1LHjh3Vrl07Va5cWe3bt9c333yjv/76S3PmzLmcI+QXtLQCAAAAQCbbsWOHwsPDPX+fr1XzoYceUqdOnS64reLFi3v+v3v3bjVr1kz169fXW2+9dclxRUdHq1ixYtq4ceMlr+svJK0AAAAA4ENGjh4cHh7ulbSeT2RkpCIjI9O07V27dqlZs2aqWbOmpk+frqCgSy+kPXjwoHbs2KHo6OhLXtdfKA8GAAAAAF/SPQhTBvSJPY/du3eradOmKlKkiCZOnKj9+/crLi5OcXFxXsuVL19es2fPliSdOHFCgwcP1uLFi7V161YtWLBA7du3V2RkpG677bZMiTMj0NIKAAAAAFeY77//Xps2bdKmTZt03XXXeT1mKRLl2NhYHT16VJKUJUsWrV69Wu+9956OHDmi6OhoNWvWTLNmzVKuXLn8Gv+lIGkFAAAAAF8ycPTgjNa9e3d17949Dbv/d/9hYWH67rvvMiWezETSCgAAAAC+ODhpvZbQpxUAAAAA4Fi0tAIAAACAL25JrgzYBtKFpBUAAAAAfMjIW97g8lEeDAAAAABwLFpaAQAAAMAXBmJyBJJWAAAAAPDFbZIrnUmnm6Q1vSgPBgAAAAA4Fi2tAAAAAOAL5cGOQNIKAAAAAD5lQNIqktb0ojwYAAAAAOBYtLQCAAAAgC+UBzsCSSsAAAAA+OI2pbu8l9GD043yYAAAAACAY9HSCgAAAAC+mPvslN5tIF1IWgEAAADAF/q0OgLlwQAAAAAAx6KlFQAAAAB8YSAmRyBpBQAAAABfKA92BJJWAAAAAPDFlAFJa4ZEck2jTysAAAAAwLFoaQUAAAAAXygPdgSSVgAAAADwxe2WlM77rLq5T2t6UR4MAAAAAHAsWloBAAAAwBfKgx2BpBUAAAAAfCFpdQTKgwEAAAAAjkVLKwAAAAD44jal+0arblpa04ukFQAAAAB8MHPLLH2j/6Z3fVAeDAAAAABwMFpaAQAAAMAXs/SX9zIQU7qRtAIAAACAL5YBfVpJWtON8mAAAAAAuALdcsstKlq0qEJDQxUdHa0uXbpo9+7dF1zHzDR69GgVKlRIYWFhatq0qdauXeuniC8PSSsAAAAA+OJ2Z8yUSZo1a6aPP/5YsbGx+uyzz7R582bdeeedF1xnwoQJeumll/Taa69p6dKlioqKUsuWLXX8+PFMizO9KA8GAAAAAF8cXh48cOBAz/+LFSumxx9/XLfeeqsSExMVHBzsIxTTpEmTNGLECN1+++2SpHfffVcFCxbUzJkz1adPn0yLNT1oaQUAAACAK9yhQ4f0wQcfqEGDBj4TVknasmWL4uLi1KpVK8+8kJAQNWnSRIsWLfJXqJeMpBUAAAAAfDC3O0MmSTp27JjXFB8fnyExDhs2TDly5FC+fPm0fft2/fe//z3vsnFxcZKkggULes0vWLCg5zEnImkFAAAAAF/MMmaSVKRIEUVERHim8ePH+9zl6NGj5XK5LjgtW7bMs/yQIUO0fPlyff/998qSJYu6du0qu0hJssvlOudpWqp5TkKfVgAAAADIZDt27FB4eLjn75CQEJ/LPfTQQ+rUqdMFt1W8eHHP/yMjIxUZGamyZcuqQoUKKlKkiH777TfVr18/1XpRUVGSzra4RkdHe+bv27cvVeurk5C0AgAAAIAvbpNcGTMQU3h4uFfSej7JSejl7ersvs5XelyiRAlFRUVp7ty5ql69uiQpISFBCxcu1PPPP39Z+/QHyoMBAAAAwBczydzpnDJn9OAlS5botdde04oVK7Rt2zbNnz9f9957r0qVKuXVylq+fHnNnj1b0tmy4AEDBujZZ5/V7NmztWbNGnXv3l3Zs2fXvffemylxZgRaWgEAAADAB3ObLJ0trRfrX3q5wsLC9Pnnn2vUqFE6efKkoqOj1bp1a3300UdepcexsbE6evSo5++hQ4fq1KlT6tevnw4fPqy6devq+++/V65cuTIlzoxA0goAAAAAV5gqVapo3rx5F13u3KTZ5XJp9OjRGj16dCZFlvECXh48efJklShRQqGhoapZs6Z+/vnn8y67YMECn6NnbdiwwY8RAwAAALgmpLs0+P8npEtAW1pnzZqlAQMGaPLkyWrYsKHefPNNtWnTRuvWrVPRokXPu15sbKxXJ+b8+fP7I1wAAAAA1xAnlwdfSwLa0vrSSy+pZ8+e6tWrlypUqKBJkyapSJEieuONNy64XoECBRQVFeWZsmTJ4qeIAQAAAAD+FLCW1oSEBP3xxx96/PHHvea3atVKixYtuuC61atX1+nTp1WxYkWNHDlSzZo1O++y8fHxXkM+J3dCPnMyIR3RAwAAALiY5O/cV2pr4xmLT3d57xklZlA0166AJa0HDhxQUlJSqpvYFixYUHFxcT7XiY6O1ltvvaWaNWsqPj5e77//vlq0aKEFCxaocePGPtcZP368nn766VTz590Zk+7nAAAAAODiDh48qIiIiECHkWbZsmVTVFSUfon7OkO2FxUVpWzZsmXItq5FLgvQzx67d+9W4cKFtWjRIq/7CI0bN07vv/9+mgdXat++vVwul7788kufj5/b0nrkyBEVK1ZM27dvd9wb59ixYypSpIh27NiRphsP+5NTY3NqXBKxXQ6nxiUR2+VyamxOjUsitsvh1LgkYrscTo1LIrbLcfToURUtWlSHDx9W7ty5Ax3OJTl9+rQSEjKmOjNbtmwKDQ3NkG1diwLW0hoZGaksWbKkalXdt29fqtbXC6lXr55mzJhx3sdDQkK87lOULCIiwlFv6JTCw8OJ7RI5NS6J2C6HU+OSiO1yOTU2p8YlEdvlcGpcErFdDqfGJRHb5QgKCvhNSy5ZaGgoiaZDBOzsyZYtm2rWrKm5c+d6zZ87d64aNGiQ5u0sX75c0dHRGR0eAAAAAMABAnrLm8cee0xdunRRrVq1VL9+fb311lvavn27+vbtK0kaPny4du3apffee0+SNGnSJBUvXlyVKlVSQkKCZsyYoc8++0yfffZZIJ8GAAAAACCTBDRp7dixow4ePKgxY8Zoz549qly5sr7++msVK1ZMkrRnzx5t377ds3xCQoIGDx6sXbt2KSwsTJUqVdKcOXPUtm3bNO8zJCREo0aN8lkyHGjEdumcGpdEbJfDqXFJxHa5nBqbU+OSiO1yODUuidguh1Pjkojtcjg1LlxZAjYQEwAAAAAAF3Pl9YgGAAAAAFwzSFoBAAAAAI5F0goAAAAAcCySVgAAAACAY5G0AgAAAAAci6T1Apw8sLLb7Q50CFccJ7+eTuXkY+bE2Pbs2aMtW7YEOowrkpOvaU471+Lj4x19vCTnHTNcnivhXMOl4/2JKxFJ6zmOHz+uAwcO6J9//pHL5Qp0OF7+/vtvzZo1S5IUFBTkmA8SJ1/8nPx6Hjt2TNu3b9f+/fsd81pKZ7+kxMfHy8wcd8xOnjypY8eO6fTp046Lbd26dWrcuLE+/fRTSc5Jwpx6nknOvaZJzj3X1q5dqz59+mjlypVKTEwMdDheli9frnvuuUeSHHXMzscpn10JCQmBDsEnJ59r53LKa+nk662TP9uBtCBpTWH16tVq1qyZmjVrpvLly2vIkCFavny5pMBfEA8fPqwbbrhB48aN09tvvy3p7Je8QMe1efNmvffeezpy5EhA4/DFya/n2rVr1aZNG7Vr105VqlTRO++8E9B4kq1fv16dOnVS06ZNVaNGDc2fP19S4I+XdPb1vPHGG9WyZUtVqlRJjz/+uFatWiUp8PGtXLlStWvX1v79+/Xuu+/K7XYrKCjwl1ennmeSc69pknPPtTVr1qhRo0YKCwtTZGSkgoODAxbLuVauXKkbbrhBhQsX9prvhNdTknbv3q0lS5boq6++0qFDhySdTawDnVhs2LBB/fv31++//x7QOM7l5HNt165d+umnn/Tf//5X+/fvl+SM19LJ11snf7antHHjRo0dO1Z9+/bVzJkztXHjRs9jTosVAWAwM7OtW7daZGSkPfTQQ/bDDz/Y008/bU2bNrVq1arZvHnzzMzM7XYHLL7t27dboUKFrGHDhtakSRN7++23LSEhwczMEhMTAxJTbGys5cqVy1wul7322mt2/PjxgMThi5NfzzVr1li+fPlswIABtnjxYuvfv7/lzp3bDh8+HJB4UsYVGRlp/fr1s9dee81uu+02i4yMtN27dwc0LjOzTZs2WYECBWzAgAG2aNEiGzdunJUtW9aqVq1qixcvNrPAvZ4rVqywsLAwGzlypG3atMmKFy9uU6ZMCUgsKTn1PEvmxGuamXPPtSNHjtgNN9xgDz/8sGfetm3bbMuWLXbgwIGAxWV29j2QI0cOGzx4cED2fzErV660woULW7NmzSwsLMwaNmxoI0eO9ByvpKSkgMQVHx9v7dq1s/DwcOvdu7ctWbLE81ggv2+k5VwLlJUrV1p0dLTVqFHDXC6X1alTx4YNGxbw19LJ11snf7antGbNGsudO7e1aNHCbrzxRouIiLA2bdrYjBkzPMsE8n2BwCNp/X/vv/++NWzY0OvL0sKFC61jx45WpkwZ+/nnnwMY3Vm9e/e2efPm2T333GP169e3d99918zOvtH97dixY9axY0e7//77bciQIRYUFGSvvPKKYxJXp76ecXFxVqdOHRs4cKBn3uHDh61169a2evVq27Ztmx08eNDvce3fv98aN25sjz76qNf8ypUr29ixY80ssB8WI0eOtLvvvttr3sMPP2wul8vKlStnv/76a0DiWr58uYWFhdkTTzxhZmffF40aNbIOHToEJJ5kTj3PzuWka1oyp55rhw8ftoYNG9rGjRstISHBOnToYLVr17YCBQpYixYtbO7cuWbm//fpzp07LXfu3NaxY0czM0tISLBhw4bZHXfcYfXq1bM333zT/v77b7/GlNKuXbusXLlyNnLkSDty5IjFxcVZ586dzeVyWY8ePTzHK1DXtx49eljdunWtbt261rlzZ/vtt9+8Hg9EEpbWc83fDh48aBUrVrRBgwbZ/v37bc+ePTZs2DCrUaOG3XnnnZ5j5e/X0snXW6d/tic7deqU3XbbbdavXz/PvN9++83uueceq1Wrlr399tsBjA5OEfj6NYeIj4/Xhg0btG/fPs+8xo0ba+DAgapatarGjh2r7du3ByQ2+/+SiM2bN2v37t36z3/+oyJFiigmJkbVq1dX8+bNlZCQ4NfSmJMnT6patWpq27atJkyYoDFjxmjgwIGaNm2aTpw44bc4zsepr+fOnTvVpk0bPfzww555kyZN0o8//qi77rpLzZs3V9++fbVhwwa/xhUbG6tTp06pW7dukqSkpCRJUqlSpTyl34HsA3Po0CElJibK7XZ7+n9Vr15d7du3V9myZfWf//wnICXqH3/8sQYMGKBx48bJ7XYrV65cGj16tL777jt98cUXfo8nmVPPs2ROvKYlc+q5tmfPHq1Zs0anT5/Www8/rFOnTumll17Siy++qOuuu07du3fX4sWL/f4+/fvvv1W1alXt3btXy5Yt0y233KLffvtNBQoUULFixfTKK6/oueee0549e/waV7KVK1cqV65cGjhwoMLDw1WwYEE98sgjioyM1M8//6wHHnhAkv+vb8nvgapVq6pPnz4aN26c1q9frzfeeENbtmzRhAkTdObMmYB0M0jrueZvcXFxOn36tO677z5FRkYqKipKI0aM0EMPPaS//vpL999/f0D6azr5evvXX385+rM9WUhIiHbu3KncuXN75tWtW1cjR45UpUqVFBMTo2+//TZwAcIZApszO8fChQutbNmy9vHHH6f6ZXP27NlWsmRJmz9/fkBiS47n2WeftZEjR5rZ2V+zixUrZjly5PC08pj59xezHTt2eO3vmWeesaCgIJs0aZKnxfXMmTO2d+9ev8WUHM/8+fMd+3pu3brV8/+pU6eay+Wy999/37Zv324ffvihVatWzd544w2/xJLy2Lz33nue/yeXaT744IOpfqE9ffq0X2Iz+/f1HD58uBUuXNjWr19vp06dsp07d1qBAgVs6tSp9v7771uBAgW8jmuguN1u27NnjzVr1sz69+9vZmffA4GwefNmz/8DfZ6ZeZ9rycfEKde0Y8eOef4/YsQIR51rbrfb3G63xcfHW9u2bW3YsGHWpk0b++WXXzzLrF+/3tq3b28jRozwrONPc+fOtfbt21twcLDddNNNXiWkr7/+ukVHR9vChQv9GlOymTNnWsmSJW3Xrl2eeb/++qs1aNDAHn/8catSpYr99NNPZhaYFqePPvrI2rdvb2Zmn376qdWrV89Kly5tLpfLE7O/4zp9+rQjz7WtW7da0aJFvT6rzMz++ecfe/3116169er2/vvv+y2elFJWEzjhepuSEz/bU0pKSrJ//vnHOnToYH369LGkpCSvz4vly5dbw4YNrXfv3mbmjJZhBMY1n7SmfGPcddddVrhwYfvjjz9SLVexYkW/99c59405Y8YMa926tZmdLSkqWLCg3XjjjdasWTN77bXX/BpbSilLcMeMGeMpFT5w4IANHTrU7r//fouPj8/UGBISEiwxMdHrmN15552OeD3j4uLOm7ivWLHCFi1a5DWvTp061q1bt0yPa+PGjTZ9+nTbvn271/yU74kHHnjA7rnnHs/fL730ksXExGR6ydrRo0ft4MGDXrE1atTI8uTJY7Vr17YcOXLYAw884Hksf/789vHHH2dqTBdy7nv1pZdestDQUL8mN76OWbJAnmdm/55r5/ahcsI1LTY21m644QavpKpJkyYBP9dSXjeSz6+RI0darly5LDQ01H7//Xev5e+991675ZZbMj0us3/PtW3btnnmffvtt/bII494ul6kvEYULFjQRo0a5ZfYzrVx40bLkSOH9e/f3xYuXGi///67hYeHe0ojS5Uq5fm/vyT/EGFm9vPPP1vt2rU9jzVp0sRCQkKsXbt2tnLlSr/Ek/z5mdKIESMcca79888/nkTr6NGj1rx5c+vQoYPt27cv1XLNmjWzLl26+CWu5GPm67Mw0NfbVatW2Z9//plqvhM+2y9k2rRpljVrVvviiy/M7Gy8ye+Tjz/+2IKDg72uObj2XJNJ6+7du23ZsmWev5MviGZmjRs3tpIlS9ovv/ziNSjIjTfeaK+++qrfY0v5Zfi3336zFi1a2H333WfR0dG2efNmO3TokLVq1cratGmTqR3+N23aZOPGjbMuXbrYu+++azt37vR6POWF7plnnrGQkBCrXbu2ZcmSxVasWJFpcZmd/eW3V69e1rBhQxs0aJDXh0WgX8+tW7da7ty57dZbb/UkE+f7UEhKSrITJ07Y7bffnulf2FeuXGl58+a1QYMGeRKrlH27kmPs27ev9ejRw8zMnnzySXO5XLZ69epMjW316tXWtGlTK1++vNWpU8drUKPJkyfblClT7JNPPvHMW7dunVWoUMHrfZNZLvY+SD6GR48etZo1a9qjjz7ql0GFzj1mF+r/48/zzMz3uZZ8fv36668Bu6aZnf0FP0eOHOZyuez111/3eiyQ55qv60ayXr16mcvlsvvvv9+r9bBv37722GOPZfqXznPPtTfffNPz2F9//eX1A2VSUpLt2bPH6tSpY59//nmmxpXs9OnTduLECa/Pznnz5lnBggWtWLFiFhkZ6fWDZdu2bT1VEf6I61wnTpywG2+80U6dOmX33XefFS5c2J5//nmrX7++dejQwZYvX56pcZ37+Zk82JhZ4M+1NWvWWI8ePeyXX37xnFcrVqywkJAQ69u3r1eFhNnZyo369etneovhuccs5QBa5wrE9dblctnTTz993njMAvPZntLOnTtt4cKF9sUXX1hcXJxnft++fS0sLCxVv+nffvvNKlWq5HUe4tpzzSWt69evtzx58libNm28fj1M/mJ5/Phxu/HGGy06OtoGDRpkU6ZMsUcffdRy585tsbGxAYkt+cP39OnTVrZsWStSpIjXr2j79u2zHTt2ZFpcq1evtujoaGvXrp3deOONljNnTnv88cfNzDsBS/n/atWqWb58+TL9l+JVq1ZZvnz5rEePHvboo49a5cqVbciQIZ5Yjh8/bs2bNw/I62l29stSeHi4tW/f3jp37pwqcT23he7JJ5+0EiVK2KZNmzItpl27dlnp0qVt6NChXvNPnTqV6v8PPfSQDR8+3F544QULDQ312WqdkdatW2d58uSxIUOG2Lvvvmt9+vSx1q1b29GjR30uf+bMGRsxYoRVrFjR9uzZk6mxpfV9kOyRRx6xkiVL2j///JOpcfk6Zm3atLGTJ096zq9zE2d/nGdmFz/Xzpw5YyVLlvT7Nc3s31Gfn3vuORs7dqxFRUVdcJ/+PNfOd91I1rNnT4uOjrYbb7zRnnrqKevVq5dFRETY2rVrMzWu851rFxqA76mnnrLy5cv7pYVk3bp1dsstt1itWrWsbt26Nnv2bM/7b/fu3bZmzRqvH1FPnz5tLVq0sEmTJplZ5pUdnhvXF1984fkR9dixY1atWjUrUaKEFSpUyJOkzpgxw5o1a5apX9LP9/mZskvD/fffb4UKFfL7ubZ69WrLnTu39evXL9WPg19++aWFhIRY9+7dbd26dZ753bt3tzvuuCNTfyg83zEz+/f8ObdLiL+ut8kDA557vU0pOaH392d7Sr5GgB46dKinAqFr164WEhJib775pv3111926tQpGzJkiFWoUMERAwgicK6ppHXv3r3WqFEja968uZUtW9buuOMOr+Qw5RfPkSNHWuvWra1cuXLWsmXLTP+1M62xLVq0yDZu3Ogz5sywfft2q1Chgg0bNswz76OPPrLs2bN7xZEsISHB+vfvby6Xy1atWpWpsW3dutVKlChhw4cP98x77rnnrHPnznbixAmvD67hw4f79fVMtmHDBqtWrZqNHTvWGjRoYPfee6+nrClli8R///tfGzhwoOXJk8dnWU9G+uGHH6xhw4Z25swZS0hIsEGDBlmrVq2sffv2NnHiRK9lBw4caC6Xy3LlymVLly7N1Lji4+Ota9eu9uCDD3rmff3119a2bVvbvXu3Vx9Nt9ttf/75pz344IMWHh6e6cfsUt4HyV9c/vrrLytRokSqL1wZ6WLH7NxRW/15npld+FybMGGCmZktWbLE1q9f71nHH+Vpya01ydeOn3/+2UqVKuXpD3ful15/nmtmF75uJJs6dardf//9VrduXbvnnnsy/QfCSz3XPvvsM3v44YctIiLCL9fbtWvXWmRkpPXt29emTp1q7dq1sxIlSpy3RP/AgQP2xBNPWMGCBTM1mThfXCmT+EmTJln9+vVTteCf25KYkS72+ZmytXLKlCl+PdeOHj1qTZs2tQEDBnjmbd682f766y/bv3+/mZ29tkRGRlr9+vWtUaNG1rFjR8uVK1emxnaxY3butcuf19uNGzdaUFCQjRs3zszOfhf78MMPbdSoUfbee++l+vz252d7SucbAbp69ep21113eXWFyJs3rxUuXNiqVatmBQoU8Mu1F852TSWty5Yts44dO9ry5ctt6dKlVqZMmVTJYcpS4ZMnT9qRI0fs5MmTjojN35KSkuytt96yO+64w3bu3OkpGz18+LBVqFAh1dD8ZmdbNseNG5fpv9olJSXZjBkz7OGHH/bqL/roo49azZo1rVy5ctaxY0f7z3/+43nsn3/+8dvraXb219bdu3db8+bN7fDhw/bOO+9Y48aNrWfPnnbTTTfZE0884flF9tVXX7Wbb77ZL7f6eP31161evXpmZtaiRQu76aabbPjw4da/f3/Lnj271735Hn/8cXO5XF6/ZmeWpKQka9iwodfgEE899ZRFR0dbyZIlrVSpUvbQQw95Hlu2bJk988wzmf6L/+W8D9xutyUmJp63hTgjY7uUY/bKK6/47Twzu/C5FhYW5nWLCH85duyY1apVyzOQTLLWrVtb3bp1fa7zxx9/+OVcM7v4dePcVpTTp0/7pQT9Us+1CRMmWPPmzf1Scrh//35r0qSJ17XLzKxMmTL21FNPpVp+zZo1NnToUCtYsGCmfhFOa1z79+/3JGNmmT/QTFo+Pzt16pSqnNVf59qRI0esbt26tmbNGouPj7fbbrvNqlWrZoULF/a6X3JsbKy98sor1r17dxs+fHimfk6l9Zil7M7ir+vtmTNn7D//+Y+5XC6bPn26mZ293latWtUqVqxo1113nTVo0MDrfqfDhw/322d7SmvXrrWSJUt6/ZB17NgxmzZtmlWpUsW6devmOf+XLl1qs2fPtk8++YS+rDCzayxpPX78uFdp0O+//26lS5e2O+64w+uLZ8rE1Wmx+Xsk0rlz59ozzzzjNe/MmTNWqlQp+/TTT32u468Yd+zY4fWF6Mknn7SwsDB7+eWX7Y033rAHH3zQrr/+eq8+OoHQtm1bW7BggZmZvfvuuxYdHW0hISH25Zdfei2Xmb+qp7RkyRIrXry4Pffcc9ayZUvPh0FiYqJ9+OGHlj9/fvvf//7nWT6zyzTNzn4hSExMtIEDB1qdOnVs9OjRNnDgQMuePbvNmjXLFixYYLNmzbKcOXN6fSnw13v1hx9+uOT3QWZLTowv9Zj56zwzu/C5NnPmTMufP7/NmTPHb/Ek27Bhg+f/yV/Cf/31V7vuuuvsgw8+8LmOvz8XLnbd8PeAKadOnUrTuZZyhNQjR474JbbFixdbq1atPC2VyVUs9957r6d0M6Xjx4/b3LlzM32gtEWLFl1SXP6U1s9PXz/KZSa32+1pnd68ebM9/PDDdtNNN9nPP/9sn3/+ud17770WFhaWqvXeHyPKbt++/ZKPmb+ut7t377bnnnvOwsPDrUCBAnb77bd7qoBWrlxpnTp1subNm3sNhuePz/ZzpWUE6OT7dQPnuqaSVrN/E6rkLyBLlizxJIe///67JSUl2ZgxY2zWrFnE9v/O7X955swZq1SpktfAGp9//rlfBsI5X2xmZkOGDLGvvvrK8/eqVassPDw8IK+l2dkvKG6329q2besZ5KVbt24WERFhNWrUsB49egTkNi1///233XLLLVanTh1PK1iyvXv3Wvny5b0GV/GnRYsW2eDBg61Lly5Wr149r8FxTpw4YQ0aNLhgf52M5isxuNj7wB8lTCm/oC1ZssRRxyylrVu3OvZcO/e13bt3r9WuXdvuv/9+v8YRFxfnlRAnj5jphOvGhg0bbNCgQZ6/nXyuJbcwmf37WTpgwADr16+f13KZ3cc85f4vJa5A3G7EaZ+fKa9rbdq0sV69elnLli1t3rx5nvl79uyxdu3aWd++fS0hISHTfyQ/d/sp/3bCMUtp9+7dNnbsWGvdunWqATC///57c7lctmTJkoDeMubIkSPWrFkzR4wAjStP1kDfJzYzbdmyRT/++KNOnDihChUq6KabblKWLFnkdrsVHBwst9ut2rVr68MPP9Q999yjF154QYmJifr++++1dOnSazK2lHGVL19erVu3VlBQkM6cOaOsWbPK7XbL5XIpV65cCg8PlyQNHz5cr7/+ulatWpVpcZ0bW/IxSxnbhAkTJElut1tBQUHKkyePKlasqKioqEyN69zYKlasqFatWilbtmySpA4dOujMmTPq2bOnvv/+e/30009aunSpJk2apGeffVavv/66smbNnLeir2NWokQJde7cWQ8++KAOHz6sOXPmqF27dpKkAgUK6LrrrlNISEimxHO+2JLPtfr166tmzZpyuVxq0KCBzMyzfI4cOZQrVy5FRERkemxHjhxRWFiYQkJClJSUpCxZsngec7lcAXsf+Iqrdu3aqlmzppKSkgJ6zHbv3q3169dr//79qlevnooXL65ixYqpS5cu6tu3rw4dOhTQc+1///ufjh49qkqVKum2225TUFCQ1zIFChTQkCFDdN9996lHjx664YYbMj2u5cuXq2bNmvrxxx/VrFkzSWfPL5fLpVtvvVWJiYkBuW5I0qpVq9S8eXOdOnVK3bp1U5UqVRxzrqWUmJio4OBgde/eXdLZ63/y+zU+Pl779+/3LPvKK69Ikh5++OFUr39G2bRpk+bOnau2bduqWLFijonrfO8BM5PL5Qro52dCQoKyZcumxMREz+dmw4YNFRMTo7i4OOXNm9cTW1RUlPLnz699+/YpODg4U+P666+/9MILL+j48ePKmzevJk+e7PmeFhQUFNBjtnXrVv366686cuSIypUrpxtvvFHR0dHq3r27WrVqpYoVK3rFlitXLlWoUEH58uWTy+XK9PiSHT9+XMePH1euXLkUHBysiIgIvfDCC2rYsKGeeuopTZgwQbly5ZIkhYWFqWXLlvrqq68UHx/vl88GXGECmzNnntWrV1vevHmtRYsWVqhQIatYsaI1a9bMM+R88i9Nyb80Ll682Fwul+XJkyfTB41wamwXiytZYmKiValSxb7++msbPXq0Zc+ePdM78qflmJ376+ETTzxh119/vV9Glb1QbNOmTTOXy2WFCxf2ao2eNm2abdmyxa9xNW7c2BPXxx9/bMWKFbOqVavalClTbMmSJTZkyBCLiorK1LjOF1uTJk08sSUlJVm3bt3soYceshUrVtiRI0fsiSeesOjoaJ8DgGWkdevWWe3atW3s2LGeVhlfv0z7+33gK67kX/2T4wvUMVu1apWVLFnS6tevb1mzZrVmzZp5lb9/+umnVrx48YCca6tWrbKoqChr166dlS5d2urXr++5D+C5tm/fbo0bN7Zhw4ZlevntihUrLFeuXPbYY4/5fPydd94JyHUjObbQ0FB78MEHrWjRol79f5OPS6DONbOzt8v49ddfPX+f+/5MjnHQoEF+va1H8u2dhgwZ4jkOKSuVAhXXxd4Dgfz8XL9+vXXv3t0aNWpkffv29dzn1+zfW+7ccccdXqPG9uvXzx566KFU92bPSKtXr7Z8+fLZfffdZ127drWKFSt6Ros3876HaDJ/HbNVq1ZZ/vz57ZZbbrFSpUpZ1apVrUWLFp7PT1+tz8OGDbN69er5dfTdVatWWc2aNa18+fJWpkwZ69mzp6d/7xdffBGwEaBx5boqk9aTJ09aw4YNPaMcHj582L755hurXLmyVapUyXNPqOQ39qlTpzyjHGb2QBtOjS2tcSX3PaxXr55VqFDBQkNDM70sOK2xJduwYYMNHTrUcufOnen3iL1QbBUrVvTENmHCBM/gVP7oi3ahuMqXL++Ja86cOdajRw/LkSOHVa5c2SpXrpzp5a1pfT1jYmKsQoUKFh0dbbVq1bJixYplemzbtm2zqlWrWoECBaxhw4Y2ceLE8yau/nwfpDWuGTNm+P2Ybdq0yYoUKWIjRoywAwcO2LZt26xBgwapSry+//57v59rsbGxVqhQIRsxYoS53W7bu3evValSxauPr5n3aMF9+vSxEiVKeN0CKqOtXr3asmfPbiNHjjSzs6/h+vXr7YcffvAayfaFF17w63XD7OxIyWFhYZ4v6OPHj7eSJUumGkwmEOea2dkkJ3/+/Na0aVOvstGU74Pkz8/HH3/cBg8ebOPHj8/09+ju3butTJkyXveANTOv2wEln2f+jCut74Fk/vz8XLVqleXJk8f69Oljjz76qDVt2tT69+/v9d7r06ePlSpVymrXrm2jRo2yrl27Zvr3oSNHjlidOnU8PyidOnXK+vXrZ6NHj/a5vD+P2YEDB6xq1aqekeyPHDli06dPN5fLZY0bN/YMEpX8fli/fr0NHjzYIiIiMn3U55S2bdtm+fPnt0ceecTmz59v48aNsxtvvNEKFSrkuaYFYgRoXNmuyqT10KFDVqVKFa++Zm632/766y+rUaOGVatWzWv+li1brHTp0n4ZcMCpsV0srqpVq3rmHz161KpUqWK5c+fO9NvaXGpssbGxnqH5M/vD41Jj86eLxVWlShXP/ISEBNuzZ4/t3LnTDh06FPDYrr/+es/8X375xd59912bOXNmqntVZjS3222TJ0+21q1b25IlS6x3795Wp04drwQxZavJ8ePH/fI+SEtcKZOuRYsW+e2YnT592gYPHmydO3e2kydPepKFL7/80goXLmwHDhzw+tU/KSnJb+fa6dOnbeDAgdajRw9LSEjwfInr3Lmz9evXzwYOHGgvvPCCZ/mU983MzD6jp0+ftptvvtmCgoI889q2bWs1a9Y0l8tlVatWtZ49e2ba/i9k586ddt1113n1S/3pp5+sUKFCFhMTY2beA1L581wzO9v/t2nTptakSRNr2LCh3Xrrrfbjjz96Hj/3h6UhQ4aYy+WyHDlyZHo10A8//GD16tWzM2fO2JkzZ+yRRx6xVq1aWaNGjezVV18NSFyX+h7YsGGD3z4/t2zZYiVLlvRqxR8/frx17tzZTp8+7TXy+gcffGA9evSwRo0a2b333pvp3zs2btxoFSpU8LoLQp8+faxBgwbWunVru+WWWzz3z12/fr1fv3OsXLnSKleu7PXj1s6dO61ChQpWsGBBq127tmf+hg0b7J577rFq1ar57TZ/yT799FNr2LChVx/y5cuX22233WYRERGeH7j++usvv40AjSvfVZm0Jg+Qcu4w82Zn3/Bly5a1/v37e833521QnBjbpcb1ySef+OX2D5cT27p161K1vgYqtjJlyqR6PZ0QV9myZT0tnf4elCEtsfXt29evMSXbuXOnffbZZ2Z2Ns5evXp5EsTk92HK4/Xhhx/65X2QlrgCUU516tQpGz58uL3zzjte8xctWmR58+b1GqnS386cOWOLFy/2+rI2duxYCwoKst69e9sdd9xh5cuXtzvvvNOvcSUlJdmiRYusXLly1qBBA2vZsqXdfPPNtmDBAlu7dq299NJLVrFixYCMLrtnz55Uo5qbmfXs2dNKlSrlVb4fCCtXrrRbb73Vfv/9d1u4cOFFE9fRo0dbzpw5/fJFePr06dawYUMzM2vatKm1adPGRo4caYMGDbKgoCCvAa38FdflvAf88fnpdrvt008/tb59+3rta9CgQVa1alWrVKmStWnTJtUgbf4YeMnMbN++fVaiRAnr2bOnHThwwJ566ikLCQmxMWPG2EsvvWR169a1ChUqeH7AWbt2rd++c6xcudKKFi1qs2fP9szbsGGDVa1a1T788EMrXry41w8RK1euDMh1+O2337YcOXJ43crJ7Gyst956q9WsWTPVbWwCOUAUrgxXXdKafNKPHj3a6tevn+p2Cm6320aNGmWNGjVK1VfzWo3tUuLy5+0yLjW2lGVYTovNXz+KXGpcvAcu7PTp056WzRdffNHzq3HKEUGdFFcgbhWQcgTI5GRm27ZtVr58eTt8+LDnsUDcczplIv/XX39ZoUKFvEb7fOutt6xUqVIWGxvr99iWLVtmFStWtJo1a3pabcz+LUVs2LChX69pvr4wJr+e8+fP97q9U6CS1qSkJK8fiebPn+9JXH/44QfP/JSvu7/u77ho0SLLnTu3TZw40dq2bWs7d+70PPb5559bUFCQ17nnj5Zps7S/B9avX++XeJLt3bvX69ZTY8aMsRw5ctirr75qU6ZM8fR5T9nH1V8SEhLsjTfesCJFithNN91kYWFh9tFHH3ke//vvvy137tz24Ycf+j22/fv3W7Nmzey2226zCRMm2Jw5cyx37tyee17feeedAavUSGnFihVWtWpVe/PNN1ONjP3dd99Z1apVPbfWC9T1BFeezBmmLoCSR0Xr0qWLzEyvv/66FixY4PV4xYoVtXv3bv3zzz/EdolxnT592m9xXWpsp06dcmxsJ0+edGRcvAfOLykpSSEhIXr11VdVtWpVzZo1S6+//rr69u2r3r17a9u2bY6Lq2fPnn6PK3/+/JIkM/OMfJqQkKAjR4543pMjR45Unz59dODAAb/GlnJ03TJlymjlypW6+eab5Xa7JUmRkZHKli2b30e8laQaNWrogw8+0Lhx41SgQAFJZ0f6DA0NVbFixXTs2LFMG0nWF18jiibvv0mTJoqMjFRMTIzXfH8LCgryjIqalJSkpk2baty4cdq3b59ee+01zZs3T5I0YsQIffLJJ5KkokWLZnpcZqYKFSrolltu0QcffKCdO3eqcOHCnsdatmypqlWrauvWrZ51ihQpkulxSWl/D+TJk8cv8SQrUKCAypUr5/n7wIED+uSTT/Twww+rT58+uv/+++VyuQJynQ0ODlavXr20cuVKvfTSSypZsqQaNGgg6ezrmZiYqOjoaL+MEpySmSkyMlKvvvqqJOntt9/WgAED1L9/f7300kuSzh7XXbt2+TUuX6pWraoKFSrolVde0aJFi5SUlOR5rFWrVoqPj/e8XwN1PcGV56o8U8xMJUuW1FtvvaXt27drwoQJng/b+Ph4LVmyRIUKFVJYWBixOTwuYru64nJ6bCllyZJFSUlJCg0N1WuvvaZq1app5MiR+vDDD7VkyRIVK1aMuFJImfScOnVKx44dU3BwsMaMGaMJEyZo6tSpioyMDEhs9v+3ZsmXL5+kf78kLVq0SCVLllSOHDn8HpPL5dL111+vli1behKL5Lg2btyoqlWrZvotPdIiKSlJLpdLo0eP1h9//KE5c+YEOiRJ8tw+pkmTJho/frz279+v119/XTfffLNefvlllS1b1m+xuFwu5c6dW+3bt9eJEye0evVqff/9957HcubMqTx58ig0NNRvMZ3Lie+BlHG98soratOmjSeZDg8PV7FixfyWGFqK2zdJZ5P9PHnyKDo6WtmyZdPChQslnX09P/zwQ7lcLr+eY8n7drvdqly5smJiYrRkyRLNnTtXY8eO9TyHuLg4XX/99X6N61zJr+GHH36onDlzqm/fvvruu++UmJjoWaZ06dK67rrrAhUirlT+a9TNeImJiV6DQpj9W2aQ/O/atWvttttus9KlS1vhwoWtSZMmfrmtjVNjc2pcxHZ1xXUlx3au5PkPPvig5cmTJ9UoqtdCXJca2+bNm6169erWu3dvCwkJyfQRxi8lNjOzgwcP2vDhwy1fvnyZOqjL5caVP3/+TO8rfamxJQ/S5I/bAaVVypLmH3/80cLCwvwyguuF4pg1a5ZVqlTJrrvuOps+fbotXLjQHn/8cYuOjra///7br7FciL/eA2nl6/YxVapUydT+mLt3777o++zo0aN21113Wb169axRo0bWqVMny5cvX6Z/Rp08eTLV+/NCNm7caE888YTlyZPH76XevqQsS2/WrJlVqlTJevToYW+++ab169fPwsPDvcrDgbS4YpPWtWvX2t1332033HCDde/e3WbOnOl5LLmjfvIH6/79+23p0qU2ZswYmzZtWqbfR86psTk1LmK7uuK6GmI719SpU83lcmXqLT2cGtflxLZhwwZzuVxeo0Q6Jbbvv//eHnjgAStRokSmfvG81Li++eYb69q1qxUuXNhxxyxZTExMpv84crEYfImPj7f+/ftb7ty5MzW+C8WVMpH/8ccfrU+fPhYaGmpVqlSx66+/PlNf0xMnTtixY8e8Rty9EH+9B8zOJsfr16+3v/76y+Lj4y+6fGxsrA0aNMjy5MmTqT8+7Ny50/Lly2e33XbbeUdwTk6kt27dai+88ILdfffdNmzYsExPtlavXm233HKL/fTTT6n6g/qyd+9eGz16tBUpUsQvt526kJTvg5SJ69ixY61t27ZWsWJFa9Wqld9/WMLVwWV2Tk3EFeCvv/5SnTp11L59e5UpU0Y//vijjh8/rqpVq2r69OmSzvapypYtG7E5PC5iu7riuppj27Jli0qUKHFNxXW5scXFxal///4aN26cypcv76jYdu/erXnz5umGG25Q8eLFHRPXzp079e2336p58+YqWbJkpsR1ubGdOXPGq29kZvrrr7/01Vdf6d5771V0dLTPZczMqxx9x44dqlGjhubMmaM6deoELK5zj9Pu3bsVHBzsKTXNDOvWrdPAgQO1f/9+7d27VxMmTFDnzp29jpHb7fbqN+iP94AkrVmzRl27dtWZM2f0119/aeTIkRo+fLintFvyfi3XrVunqVOn6vfff9cbb7yhqlWrZlps8+fPV6tWrdS4cWNdd911evTRR1WjRg1JZ49XUlKSgoODPfElv7bnHsuMtnbtWjVq1Ej33HOPnnjiCU/f6GR2trHJK4aEhATFxcUpa9asKlSoUKbFllJsbKzefPNN7d69W9WqVVOrVq28jl9QUJDcbrdcLpfXeXj8+HGFhIQEtFQeV7DA5cuXx+1224gRI7yGaD958qS99tprVqVKFbv77ru9lp82bZrfRulzamxOjYvYrq64iO3qiutyY9uyZYuZWZpaVfwdW/L9VzPz1grpiSuzy26dfK6ZnS1xzJs3r7lcLhs+fHiq22WYnf+1O3XqlOPiyuzXc+3atZYvXz4bOHCgzZw50x577DELDg4+b+upv94DKWMbPHiwrV271iZOnGgul8vrfPIVw8qVK23v3r2ZGpvZ2RbgW265xd58802rUaOGde7c2dNKn/J1mzZtmtcI1Jl53E6cOGGtWrXy3I7O7Ox9YFesWOHzfXhubP6ydu1ai4iIsJtvvtnuu+8+i4qKskaNGtmLL77oWSZlK2vyZwKQXldc0mpm1r17d7vhhhu85v3zzz/29ttvW/Xq1e3xxx83M7Nff/3VSpcubffdd59f7u3l5NicGhexXV1xEdvVFdflxNa5c2dLTEz0yz33nBqbU+O6nNj8da6dOHHC7r//fuvevbu99tpr5nK5bMiQIT4TRDOzCRMm2JgxYzx/Z9axS29cmeXgwYPWqlUre+SRR7zmN2vWzDMv5TH55ZdfrEyZMn451/bv32+NGze2Rx991DPP7XZb69atbdGiRbZ8+XLbsWOH57HnnnvORo8enWnxnOvMmTO2b98+K1u2rO3cudM+//xzq127tvXu3dsaNGhgd9xxh5mZ/fTTT1amTBm/vQdOnz5tN9xwg/3555925swZu+mmm6x27dqWK1cuq1evnr399tueZf0dW7KEhATr2rWr1211tm3bZn379rUaNWrY2LFjvZafOHGi3XjjjZk+rgGuDVdU0pp8kX311Vetfv36qTqbHz161IYOHWp169a1Q4cOmdnZe5D5c/ADp8Xm1LiI7eqKi9iurriI7eqKy+mxmZ1NnF9//XXP/TBnzZp13gTx4MGD1rFjR6tbt64dPHjwmowrLi7O6tSpYz/99JOZ/ds62LNnT+vcubPPdfz1eh44cMCeffZZ++uvvzzzxowZYy6Xy6pVq2bXXXed3XTTTfbzzz/biRMnrGPHjla/fn07cOBApsdm9u97oXPnzvbtt9+amdmcOXMsMjLScuXK5XUf7jfffNNv74G4uDjLnz+/ff/99zZw4EC76aabbMWKFfbNN9947lv7ySefeJafOnWq32JLqWXLlnb//feb2b/Hcvfu3TZgwACrV6+ezZgxw7NsTEyMNW7c2OtHCuByXVFJa7JNmzZZZGSk9ejRw44dO+b12O7duy0oKMhzE3Ric3ZcxHZ1xUVsV1dcxHZ1xeX02E6cOOH190cffWQul8sGDx7sSWjOnDljhw8ftoMHD2bqyLJXQlwpk8LkkWafeuop69Kli9dyR44c8Us8KaU8tz788ENzuVz20Ucf2cGDB23hwoVWp04dGzVqlJmZ/f333347Zil17drVU1nQs2dPy5Mnj1WsWNHuv/9++/XXX/0ej9vttk6dOtlDDz1kN998syehNjPbsWOH3Xfffda3b99M73pxPmfOnLGEhATr0aOH3XbbbXbq1Clzu92eH0y2bdtmbdq0sVtuucVrvbQOEAZczBWZtJqZzZs3z0JCQqx///5ev3YeOHDAatasafPnzye2KyQuYru64iK2qysuYru64nJ6bGZnvxwnt+AkJzxDhgyxXbt22YABA+zWW29N06iq10pcKftgjhgxwlq1auX5+9lnn7UXX3zRq4+hv23dutX++OMPr3nt27e3m2++2S8l8edK3mdMTIw99dRT9uCDD3puSfT5559bqVKlrG/fvp6kzJ+WLl1qOXLkMJfLZV9++aXXY4MGDbLGjRv7PaZzy48XLFhgWbJksVdeecUzL/kcXLJkiblcLlu+fHlAXltc3a7YpNXM7Msvv7SQkBC77bbbbObMmbZmzRobNmyYFSxY0K+DR1xJsTk1LmK7uuIitqsrLmK7uuJyemxm5tWC89FHH1lwcLCVK1fOsmbNGtDbejg5LjOzkSNHWps2bczM7MknnzSXy+Wo24u43W47ffq03XPPPTZu3LiAxrJw4UJzuVwWFRXl1edy9uzZASm7TfbTTz+Zy+Wym2++2esWTo888oj16tXrku7fml6xsbE2ceLEVC3hEydOtKCgIJs6darX/HXr1lmlSpUsNjbWbzHi2nFFJ61mZn/88Yc1adLEihYtaiVLlrRy5coF/D5VyZwam1PjMiO2qykuM2K7muIyI7arKS4zZ8dmdjbBSU7Gmjdvbnnz5rVVq1YFOCpnxpWcSI8aNcoeeOABe+GFFywkJCRVC6cTPPnkk1a0aFGv8uZASEhIsHfeecdWrlxpZpk/ovKlWLhwoRUqVMjq1KljPXv2tC5dulhERIStXr3abzFcaNTskydP2tNPP20ul8tGjBhhy5Yts/3799vjjz9uJUuWtLi4OL/FiWvHFXmf1nMdO3ZMhw4d0okTJxQVFaXIyMhAh+Th1NicGpdEbJfDqXFJxHY5nBqXRGyXw6lxSc6OTZKSkpI0ZMgQTZo0SStWrND1118f6JAkOTeucePG6cknn1R4eLh++OEH1apVK9AheXz66adasGCBPvroI82dO1fVq1cPdEiZft/V9IiNjdWMGTP022+/qUyZMurXr58qV67sl32fPHlSjzzyiNxut2rVqqWHH35YgwcP1pAhQ5Q/f35JZ4/dBx98oKFDhyooKEjh4eE6fvy4vvrqK0e8trj6XBVJKwAAuPokJSUpJiZGNWvWVLVq1QIdjodT41q2bJnq1KmjNWvWqGLFioEOx8vatWs1ZswYjRo1ynGxOZnb7ZYkvybXp06d0vTp05UvXz517NhRH3/8sTp16pQqcZWkrVu3avv27Tp16pQqV66swoUL+y1OXFtIWgEAgGOZmVwuV6DDSMWpcZ08eVI5cuQIdBg+JSYmKjg4ONBhIA3OPY9mzZqle+65R4MGDdKwYcMUGRmpM2fOaPfu3SpatGgAI8W1ImugAwAAADgfJyaGknPjcmrCKomE9QqSfB4lJSUpKChIHTt2lJnp3nvvlcvl0oABAzRx4kRt27ZN7733nrJnz+7Y9wSuDrS0AgAAAPDJzg7cqqCgIM2aNUtdunRRyZIltXnzZi1dutRRJfK4epG0AgAAADiv5HTB5XKpRYsWWrFihRYsWKAqVaoEODJcKygPBgAAAHBeLpfLM2r2/PnztWLFChJW+JUzx/kGAAAA4CiVKlXSn3/+6ZjbPOHaQXkwAAAAgIty6qjZuPrR0goAAADgokhYESgkrQAAAAAAxyJpBQAAAAA4FkkrAAAAAMCxSFoBAAAAAI5F0goAAAAAcCySVgAAAACAY5G0AgAcqXv37rr11lsDHUYqo0ePVrVq1QIdBgAA1wySVgC4hjkhMdy6datcLpdWrFiR7m0lJSVp/PjxKl++vMLCwpQ3b17Vq1dP06dPT3+gAAAgILIGOgAAADLK6NGj9dZbb+m1115TrVq1dOzYMS1btkyHDx8OdGgAAOAy0dIKADivdevWqW3btsqZM6cKFiyoLl266MCBA57HmzZtqkceeURDhw5V3rx5FRUVpdGjR3ttY8OGDbrhhhsUGhqqihUr6ocffpDL5dIXX3whSSpRooQkqXr16nK5XGratKnX+hMnTlR0dLTy5cun/v37KzEx8bzxfvXVV+rXr5/uuusulShRQlWrVlXPnj312GOPeZZxu916/vnnVbp0aYWEhKho0aIaN26c5/Fhw4apbNmyyp49u0qWLKknn3zygvuUpOnTp6tChQoKDQ1V+fLlNXny5AsuDwAA0o6kFQDg0549e9SkSRNVq1ZNy5Yt07fffqu9e/fq7rvv9lru3XffVY4cOfT7779rwoQJGjNmjObOnSvpbIJ46623Knv27Pr999/11ltvacSIEV7rL1myRJL0ww8/aM+ePfr88889j82fP1+bN2/W/Pnz9e677yomJkYxMTHnjTkqKkrz5s3T/v37z7vM8OHD9fzzz+vJJ5/UunXrNHPmTBUsWNDzeK5cuRQTE6N169bplVde0dSpU/Xyyy+fd3tTp07ViBEjNG7cOK1fv17PPvusnnzySb377rvnXQcAAKSdy8ws0EEAAAKje/fuOnLkiKfVM6WnnnpKv//+u7777jvPvJ07d6pIkSKKjY1V2bJl1bRpUyUlJennn3/2LFOnTh01b95czz33nL799lu1b99eO3bsUFRUlKSzyWnLli01e/Zs3Xrrrdq6datKlCih5cuXew1w1L17dy1YsECbN29WlixZJEl33323goKC9NFHH/l8PuvWrdOdd96p2NhYVapUSQ0aNFCHDh3Upk0bSdLx48eVP39+vfbaa+rVq1eajtELL7ygWbNmadmyZZLOliB/8cUXnj64RYsW1fPPP6977rnHs87YsWP19ddfa9GiRWnaBwAAOD/6tAIAfPrjjz80f/585cyZM9VjmzdvVtmyZSVJ119/vddj0dHR2rdvnyQpNjZWRYoU8SSs0tmkNq0qVarkSViTt7169erzLl+xYkWtWbNGf/zxh3755Rf99NNPat++vbp37663335b69evV3x8vFq0aHHebXz66aeaNGmSNm3apBMnTujMmTMKDw/3uez+/fu1Y8cO9ezZU7179/bMP3PmjCIiItL8PAEAwPmRtAIAfHK73Wrfvr2ef/75VI9FR0d7/h8cHOz1mMvlktvtliSZmVwu12XHcKFtn09QUJBq166t2rVra+DAgZoxY4a6dOmiESNGKCws7ILr/vbbb+rUqZOefvpp3XTTTYqIiNBHH32kF1980efyybFMnTpVdevW9XosZbINAAAuH0krAMCnGjVq6LPPPlPx4sWVNevlfVyUL19e27dv1969ez39RpcuXeq1TLZs2SSdvV1NZqhYsaIk6eTJkypTpozCwsL0448/+iwP/vXXX1WsWDGvfrfbtm0777YLFiyowoUL6++//1bnzp0zPngAAEDSCgDXuqNHj6a6R2revHnVv39/TZ06Vffcc4+GDBmiyMhIbdq0SR999JGmTp2appbEli1bqlSpUurWrZsmTJig48ePexLC5BbYAgUKKCwsTN9++62uu+46hYaGXnZp7Z133qmGDRuqQYMGioqK0pYtWzR8+HCVLVtW5cuXV9asWTVs2DANHTpU2bJlU8OGDbV//36tXbtWPXv2VOnSpbV9+3Z99NFHql27tubMmaPZs2dfcJ+jR4/WI488ovDwcLVp00bx8fGe2+ykHLUYAABcHkYPBoBr3IIFC1S9enWv6amnnlKhQoX066+/KikpSTfddJMqV66sRx99VBEREQoKStvHR5YsWfTFF1/oxIkTql27tnr16qWRI0dKkkJDQyVJWbNm1auvvqo333xThQoVUocOHS77udx000366quv1L59e5UtW1bdunVT+fLl9f3333tai5988kkNGjRITz31lCpUqKCOHTt6+uB26NBBAwcO1EMPPaRq1app0aJFevLJJy+4z169euntt99WTEyMqlSpoiZNmigmJsZzKx8AAJA+jB4MAPCrX3/9VTfccIM2bdqkUqVKBTocAADgcCStAIBMNXv2bOXMmVNlypTRpk2b9OijjypPnjz65ZdfAh0aAAC4AtCnFQCQqY4fP66hQ4dqx44dioyM1I033nje0XgBAADORUsrAAAAAMCxGIgJAAAAAOBYJK0AAAAAAMciaQUAAAAAOBZJKwAAAADAsUhaAQAAAACORdIKAAAAAHAsklYAAAAAgGORtAIAAAAAHIukFQAAAADgWP8Hoi8n77ZwhQ8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating input array (mass) and output array (logg)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y_logg = combined_logg #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# Scale X and y\n", + "#X_scaler = StandardScaler()\n", + "#y_scaler = StandardScaler()\n", + "\n", + "#X = X_scaler.fit_transform(combined_masses.reshape(-1, 1))\n", + "#y = y_scaler.fit_transform(combined_L.reshape(-1, 1)).ravel()\n", + "\n", + "# Define grid\n", + "length_scales = np.logspace(-2, 1, 20)\n", + "nus = [0.5, 1.5, 2.5] # available Matern values\n", + "\n", + "chi2_map_logg = np.zeros((len(nus), len(length_scales)))\n", + "\n", + "# Grid search\n", + "for i, nu in enumerate(nus):\n", + " for j, ls in enumerate(length_scales):\n", + " kernel = Matern(length_scale=ls, nu=nu)\n", + " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + " \n", + " try:\n", + " gp.fit(X, y_logg)\n", + " y_pred_logg = gp.predict(X)\n", + " avg_diff_logg = (np.sum(y_logg - y_pred_logg)) / len(y_pred_logg)\n", + " chi2_logg = np.sum(((y_logg - y_pred_logg)**2) / avg_diff_logg)\n", + " chi2_map_logg[i, j] = chi2_logg\n", + " except Exception as e:\n", + " chi2_map_logg[i, j] = np.nan\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(10, 5))\n", + "im = plt.imshow(chi2_map_logg, aspect='auto', origin='lower',\n", + " extent=[np.log10(length_scales[0]), np.log10(length_scales[-1]), 0, len(nus)-1],\n", + " cmap='viridis')\n", + "plt.colorbar(im, label=r'$\\chi^2$')\n", + "plt.xticks(np.log10(length_scales), labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", + "plt.xlabel('Length Scale')\n", + "plt.ylabel(r'$\\nu$')\n", + "plt.title('Chi-Squared Heatmap for logg Fit Parameters')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "850e9dc3-5120-46ff-a15b-ccecc379e33d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.5 4.832930238571752\n" + ] + } + ], + "source": [ + "i_best_logg, j_best_logg = np.unravel_index(np.nanargmin(chi2_map_logg), chi2_map_logg.shape)\n", + "best_nu_logg = nus[i_best_logg]\n", + "best_length_scale_logg = length_scales[j_best_logg]\n", + "print(best_nu_logg, best_length_scale_logg)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "ce7d8969-3bb4-4154-8d1a-baaa1975baa6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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vd7luERIRkZJlDUEhISFUqlRJC81KmcnKyuLYsWMkJCRQq1atUvtnUUFIRMRJmc1mWwgKCgoq6+qIEBwczLFjx8jMzMTd3b1UrlluNl0VEZHSZR0TVKlSpTKuiYiFtUussEvoFAcFIRERJ6fuMCkvyuKfRQUhERERcVoKQiIiIuK0FIRERESKybBhw+jXr1+JXmPSpEm0aNGiRK/hTBSERETkupjNsHo1zJ9v+VuK41yviYKEZKfp8yIics1iYmDMGDhy5Mq5sDB4+20YMKDs6iVSWGoREhGRaxITA3fdZR+CAI4etZyPiSmZ6y5ZsoSbb76ZypUrExQUxB133MGBAwfsyhw5coRBgwYRGBiIj48Pbdq0YePGjcyePZvJkyezY8cOTCYTJpOJ2bNnExcXh8lkYvv27bbXOH/+PCaTidWrVwOWKd0PPvggUVFReHt706BBA95+++1C1zsxMRFvb2+WLFlidz4mJgYfHx+Sk5MBePrpp6lfvz6VKlWidu3aPP/88wVuf9K5c2fGjh1rd65fv34MGzbMdpyens5TTz1FzZo18fHxoV27drb3BXDo0CH69OlDlSpV8PHxoXHjxixevLjQ760iU4uQiIgUmdlsaQnKa5MmwwCTCcaOhb59wdW1eK998eJFxo8fT9OmTbl48SIvvPAC/fv3Z/v27bi4uJCcnEynTp2oWbMm3333HdWrV2fr1q1kZWVx7733smvXLpYsWcKKFSsACAgI4MSJE1e9blZWFmFhYXz55ZdUrVqV9evX88gjjxAaGso999xz1ecHBATQu3dv5s6dy6233mo7P2/ePPr27Yuvry9g2ZR89uzZ1KhRg507d/Lwww/j5+fHU089dY2fGAwfPpy4uDgWLFhAjRo1+Prrr7n11lvZuXMn9erVY/To0aSnp7NmzRp8fHzYvXu3rT6OTkFIRESKbO3a3C1B2RkGxMdbynXuXLzXHjhwoN3xjBkzCAkJYffu3TRp0oR58+Zx6tQpNm3aRGBgIAB169a1lff19cXNzY3q1asX6bru7u5MnjzZdhwVFcX69ev58ssvCxWEAIYMGcLQoUO5dOkSlSpVIikpiR9//JFFixbZyjz33HO2+5GRkfzrX//iiy++uOYgdODAAebPn8+RI0eoUaMGAE8++SRLlixh1qxZ/Pe//+Xw4cMMHDiQpk2bAlC7du1rulZFpCAkIiJFlpBQvOWK4sCBAzz//PP89ttvnD59mqysLAAOHz5MkyZN2L59Oy1btrSFoOL00Ucf8emnn3Lo0CFSUlJIT08v0sDr3r174+bmxnfffcegQYNYtGgRfn5+9OzZ01Zm4cKFTJs2jb///pvk5GQyMzOva5PRrVu3YhgG9evXtzuflpZm21rliSee4J///CfLli2je/fuDBw4kGbNml3zNSsSjRESEZEiCw0t3nJF0adPH86cOcP06dPZuHEjGzduBCzjYAC8vb2L/JouLpafQyNbX1/OcTlffvkl48aNY8SIESxbtozt27czfPhw23ULw8PDg7vuuot58+YBlm6xe++9Fzc3S7vEb7/9xqBBg7jtttv44Ycf2LZtG88++2yB13BxcbGrd866Z2Vl4erqypYtW9i+fbvttmfPHtsYp4ceeoiDBw/yj3/8g507d9KmTRvefffdQr+vikxBSEREiqxjR8vssPx2RDCZIDzcUq44nTlzhj179vDcc8/RrVs3GjZsyLlz5+zKNGvWjO3bt3P27Nk8X8PDwyPXXlbBwcEAJGRrwso+cBpg7dq1dOjQgVGjRtGyZUvq1q2ba5B2YQwZMoQlS5bw559/smrVKoYMGWJ7bN26dURERPDss8/Spk0b6tWrx6FDhwp8veDgYLt6m81mdu3aZTtu2bIlZrOZkydPUrduXbtb9u7B8PBwRo4cSUxMDP/617+YPn16kd9bRaQgJCIiRebqapkiD7nDkPV42rTiHyhdpUoVgoKC+OSTT/j7779ZuXIl48ePtyszePBgqlevTr9+/Vi3bh0HDx5k0aJFbNiwAbCMu4mNjWX79u2cPn2atLQ0vL29ufHGG3n55ZfZvXs3a9assRurA5ZxRps3b2bp0qXs27eP559/nk2bNhX5PXTq1Ilq1aoxZMgQIiMjufHGG+2ucfjwYRYsWMCBAwd45513+Prrrwt8va5du/Ljjz/y448/8tdffzFq1CjOnz9ve7x+/fq2sUkxMTHExsayadMmXnnlFdvMsLFjx7J06VJiY2PZunUrK1eupGHDhkV+bxWRgpCIiFyTAQNg4UKoWdP+fFiY5XxJrCPk4uLCggUL2LJlC02aNGHcuHG89tprdmU8PDxYtmwZISEh3H777TRt2pSXX34Z18upbODAgdx666106dKF4OBg5s+fD8DMmTPJyMigTZs2jBkzhhdffNHudUeOHMmAAQO49957adeuHWfOnGHUqFFFfg8mk4nBgwezY8cOu9YggL59+zJu3Dgee+wxWrRowfr163n++ecLfL0RI0bwwAMPMHToUDp16kRUVBRdunSxKzNr1iyGDh3Kv/71Lxo0aMCdd97Jxo0bCQ8PByytSKNHj6Zhw4bceuutNGjQgA8++KDI760iMhk5OxYdXFJSEgEBASQmJl7X4DMRkYouNTWV2NhYoqKi8PLyuubXMZsts8MSEixjgjp2LP6WIHEOBf0zWVK/35o1JiIi18XVtfinyIuUFnWNiYiIiNNSEBIRERGnpSAkIiIiTktBSERERJyWgpCIiIg4LQUhERERcVoKQiIiIuK0FIRERETEaSkIiYiIFCAyMpJp06bZjk0mE998802p12PSpEm0aNGiRK8xe/ZsKleuXKLXKG8UhERERIogISGB2267rVBlSyO8yPXRFhsiIuLw0tPT8fDwKJbXql69erG8jpQPahESERELw4DMi2VzK8L+3507d+axxx7jscceo3LlygQFBfHcc8+RfQ/xyMhIXnzxRYYNG0ZAQAAPP/wwAOvXr+eWW27B29ub8PBwnnjiCS5evGh73smTJ+nTpw/e3t5ERUUxd+7cXNfP2TV25MgRBg0aRGBgID4+PrRp04aNGzcye/ZsJk+ezI4dOzCZTJhMJmbPng1AYmIijzzyCCEhIfj7+9O1a1d27Nhhd52XX36ZatWq4efnx4MPPkhqamq+n0lWVhZhYWF89NFHdue3bt2KyWTi4MGDALz55ps0bdoUHx8fwsPDGTVqFMnJyfm+7rBhw+jXr5/dubFjx9I52+ZyhmHw6quvUrt2bby9vWnevDkLFy60PX7u3DmGDBlCcHAw3t7e1KtXj1mzZuV7zdKmFiEREbEwX4Ivfcvm2vckg5tPoYt/9tlnPPjgg2zcuJHNmzfzyCOPEBERYQs8AK+99hrPP/88zz33HAA7d+6kV69eTJkyhRkzZnDq1ClboLL+MA8bNoz4+HhWrlyJh4cHTzzxBCdPnsy3HsnJyXTq1ImaNWvy3XffUb16dbZu3UpWVhb33nsvu3btYsmSJaxYsQKAgIAADMOgd+/eBAYGsnjxYgICAvj444/p1q0b+/btIzAwkC+//JKJEyfy/vvv07FjR/73v//xzjvvULt27Tzr4eLiwqBBg5g7dy4jR460nZ83bx7t27e3Pc/FxYV33nmHyMhIYmNjGTVqFE899RQffPBBoT/7nJ577jliYmL48MMPqVevHmvWrOH+++8nODiYTp068fzzz7N7925++uknqlatyt9//01KSso1X6+4KQiJiEiFEx4ezltvvYXJZKJBgwbs3LmTt956yy4Ide3alSeffNJ2PHToUO677z7Gjh0LQL169XjnnXfo1KkTH374IYcPH+ann37it99+o127dgDMmDGDhg0b5luPefPmcerUKTZt2kRgYCAAdevWtT3u6+uLm5ubXXfaypUr2blzJydPnsTT0xOA119/nW+++YaFCxfyyCOPMG3aNEaMGMFDDz0EwIsvvsiKFSsKbBUaMmQIb775JocOHSIiIoKsrCwWLFjA//3f/9nKWN87QFRUFFOmTOGf//znNQehixcv8uabb7Jy5Urat28PQO3atfn111/5+OOP6dSpE4cPH6Zly5a0adMGsLTWlScKQiIiYuFaydIyU1bXLoIbb7wRk8lkO27fvj1vvPEGZrMZV1dXANsPr9WWLVv4+++/7bq7DMMgKyuL2NhY9u3bh5ubm93zoqOjC5xFtX37dlq2bGkLQYWxZcsWkpOTCQoKsjufkpLCgQMHANizZ49dy471Pa5atSrf123ZsiXR0dHMnz+fZ555hl9++YWTJ09yzz332MqsWrWK//73v+zevZukpCQyMzNJTU3l4sWL+PgUvkXOavfu3aSmptKjRw+78+np6bRs2RKAf/7znwwcOJCtW7fSs2dP+vXrR4cOHYp8rZKiICQiIhYmU5G6p8q7nD/sWVlZPProozzxxBO5ytaqVYu9e/cC2AWsq/H29i5yvbKysggNDWX16tW5HrveqetDhgxh3rx5PPPMM8ybN49evXpRtWpVAA4dOsTtt9/OyJEjmTJlCoGBgfz66688+OCDZGRk5Pl6Li4udmOvALuyWVlZAPz444/UrFnTrpy1teu2227j0KFD/Pjjj6xYsYJu3boxevRoXn/99et6r8VFQUhERCqc3377LddxvXr1bK1BeWnVqhV//vmnXddVdg0bNiQzM5PNmzfTtm1bAPbu3cv58+fzfc1mzZrx6aefcvbs2TxbhTw8PDCbzbnqcfz4cdzc3PLtJmrYsCG//fYbQ4cOtXuPV3Pffffx3HPPsWXLFhYuXMiHH35oe2zz5s1kZmbyxhtv4OJimSv15ZdfFvh6wcHB7Nq1y+7c9u3bcXd3B6BRo0Z4enpy+PBhOnXqVODrDBs2jGHDhtGxY0f+/e9/l5sgpFljIiJS4cTHxzN+/Hj27t3L/PnzeffddxkzZkyBz3n66afZsGEDo0ePZvv27ezfv5/vvvuOxx9/HIAGDRpw66238vDDD7Nx40a2bNnCQw89VGCrz+DBg6levTr9+vVj3bp1HDx4kEWLFrFhwwYA26Dk7du3c/r0adLS0ujevTvt27enX79+LF26lLi4ONavX89zzz3H5s2bARgzZgwzZ85k5syZ7Nu3j4kTJ/Lnn39e9XOJioqiQ4cOPPjgg2RmZtK3b1/bY3Xq1CEzM5N3332XgwcP8r///S/XLLOcunbtyubNm5kzZw779+9n4sSJdsHIz8+PJ598knHjxvHZZ59x4MABtm3bxvvvv89nn30GwAsvvMC3337L33//zZ9//skPP/xQ4Lir0qYgJCIiFc7QoUNJSUmhbdu2jB49mscff5xHHnmkwOc0a9aMX375hf3799OxY0datmzJ888/T2hoqK3MrFmzCA8Pp1OnTgwYMMA2xT0/Hh4eLFu2jJCQEG6//XaaNm3Kyy+/bGuZGjhwILfeeitdunQhODiY+fPnYzKZWLx4MbfccgsjRoygfv36DBo0iLi4OKpVqwbAvffeywsvvMDTTz9N69atOXToEP/85z8L9dkMGTKEHTt2MGDAALsQ16JFC958801eeeUVmjRpwty5c5k6dWqBr9WrVy+ef/55nnrqKW644QYuXLhg10oFMGXKFF544QWmTp1Kw4YN6dWrF99//z1RUVG2z2jChAk0a9aMW265BVdXVxYsWFCo91IaTEbOzj8Hl5SUREBAAImJifj7+5d1dUREykxqaiqxsbFERUXh5eVV1tUptM6dO9OiRQu7bS/EMRT0z2RJ/X6rRUhEREScloKQiIiIOC3NGhMRkQolr2nnItdKLUIiIiLitBSEREScnHVRPJGyVhbzt9Q1JiLipDw8PHBxceHYsWMEBwfj4eFRpFWVRYqTYRicOnUKk8lkW7CxNCgIiYg4KRcXF6KiokhISODYsWNlXR0RTCYTYWFhBa4QXtwUhEREnJiHhwe1atUiMzMz11YQIqXN3d29VEMQKAiJiDg9a1dEaXZHiJQXGiwtIiIiTktBSERERJyWgpCIiIg4LQUhERERcVoKQiIiIuK0FIRERETEaSkIiYiIiNNSEBIRERGnpSAkIiIiTktBSERERJyWgpCIiIg4LQUhERERcVoKQiIiIuK0FIRERETEaSkIiYiIiNMqN0Fo6tSpmEwmxo4dW6jy69atw83NjRYtWpRovURERMRxlYsgtGnTJj755BOaNWtWqPKJiYkMHTqUbt26lXDNRERExJGVeRBKTk5myJAhTJ8+nSpVqhTqOY8++ij33Xcf7du3v2rZtLQ0kpKS7G4iIiIiUA6C0OjRo+nduzfdu3cvVPlZs2Zx4MABJk6cWKjyU6dOJSAgwHYLDw+/nuqKiIiIA3Ery4svWLCArVu3smnTpkKV379/P8888wxr167Fza1wVZ8wYQLjx4+3HSclJSkMiYiICFCGQSg+Pp4xY8awbNkyvLy8rlrebDZz3333MXnyZOrXr1/o63h6euLp6Xk9VRUREREHZTIMwyiLC3/zzTf0798fV1dX2zmz2YzJZMLFxYW0tDS7x86fP0+VKlXszmVlZWEYBq6urixbtoyuXbte9bpJSUkEBASQmJiIv79/8b4pERERKREl9ftdZi1C3bp1Y+fOnXbnhg8fTnR0NE8//bRd4AHw9/fPVf6DDz5g5cqVLFy4kKioqBKvs4iIiDiWMgtCfn5+NGnSxO6cj48PQUFBtvMTJkzg6NGjzJkzBxcXl1zlQ0JC8PLyynVeREREpDDKfNZYQRISEjh8+HBZV0NEREQcVJmNESorGiMkIiJS8ZTU73e5bhESERERKUkKQiIiIuK0FIRERETEaSkIiYiIiNNSEBIRERGnpSAkIiIiTktBSERERJyWgpCIiIg4rTLbYkNERKQgZjOsXQsJCRASYjl3/DicOgXBwVC9uv25oCA4c+bqf6/1uadOWe4DVK4M589b7gcGWuqX3+MFlXVxgc6dLbccW2xKKVEQEhGRUmENNkeP5h9mrOfWroV334WzZ8u2zqXhxRfBywt694b27S1BKa/wpRBVMrTFhoiIXBOzGVavttyysq78UOfVohIXB/PmWX7EpXj5+MDAgRAWZjnOq4UqMNASMGvWhI4dK2ZwKqnfb7UIiYhILlcLOatWwVdfQXJyWdc0LwYebulU8riEm2smri5mPN3T8HJPxdsjBS/3VNxcMjGZDMsNw1YGICvLhSwj2y3bsWGYLFcwTKRnepCS4U1quhepGV6kpHtzKb0S6ZkegKnU3u3FizBnTuHL+/lBz57Qrp19993Zs3DkCNSqBV27Ok9Lk1qEREScUM5uquwtOKUZcjzdU6nqd5og3zP4eycRUCnR8tc70Xbfz+sC3h4peLqn4elmCTSe7ml4e6RQyeMSlTwvWf56XLKc87yEq0tWyVc+H5lmV5JTfUlO8yU51ZeLaT6W46KcS/UlMSWAcxercP5iZbKM0k8k1u66nIHp/Pmy6ZYrqd9vBSEREQeXc9BxyY2/MfDxvEiw/ymq+p0m2O8Uwf6nbH9znqvqd5rKPonFXYk8pWRrtUnN8CLT7IZhmDAwYRgmsgwXUjO8MAwTLqYsXFyyLH9z3jdlYWAp4+GWbmth8vZIKdHwlZTix7mLVWy3s8mBnLoQzInEapxMCuFEYjW7+4mXAiiNVqnsYcnaohQWBlWrFn9XnIJQMVEQEhFHlNcMq5MnYf9+mD7d8gN1PdxcMwitnEBY4BFqBR0mMjiOqOBYwgKPUL3ycVvA8fZILfJrZ2S6cfZiIImXAkhK8SfxUgCJKVfuX0j1IyXdm7QMT1IzvEjP9CAt05NLaZW4lH75lmb/NyXdm5R0bzLM7hhG6awU4+aaQSWPS/h4XsTXK9l28/G8iK9njmOv5Kues7SIXbimuqSme3IiqRrHz1fnRGI1jidWt9y/fO54ouX8+YuVOXexSom2OPn5QffulrFM19PtpiBUTBSERKSiKsmw4+qSSWRwHA1C9xIVHEtkcByRwXHUCjpsCTsBx3FxKdzPRWq6J6cuBHMqKZhTF4I5faGq7X7OcycSq3H+UmVKc0xNReLumk5ln/MEeCdSxeccVXzOEeh7lkDfswT7naJawAlC/E9SLeCE7X5ApaQiXcOc5cLJxBASzofabsfO1eDY+RqWv5dvJ5NCMGcVz9Di7LPkCttypCBUTBSERKQ8K8mw4+aaQVjgEUvIqRpH3Wp/06DGXqJD/6Je9f14uqcX+Pz0THeOnq3JkbNhxJ2OJPZkFIdOR3AiqRonE0Ms4eZCMMmpvijYlB0v9xRbMKpe+TjV/C1/qwcct50LrZxAiP9JfL0uFvp1zVkunEisxrFzNTh0OoKDJ2sTeyrK9jfuVCTpmZ7XXG9fX8vstx498g5GCkLFREFIRMpaSYUdN9cMwgPjiQqJJaLqISKqHiKyqqVlJ6LqIcICj+Dmas73+SnpXuxNaMCBE3U4fKYWcaciOXQ6gvgz4Rw5G8apC8Gl1s0kpcPVJZNg/1NUD7CEoxpVjlGjyjFCKyfYHVcPOF7gPzsAWVkmjp2vYQlGJ6M4eKo2B0/W5s8jjfnzSOMihyQ/P0soatTI0pXWqlUSgYEKQtdNQUhESkNJtuz4eycSXeMvGtbcQ8Maeyz3a+yhdsjBq/5YpaZ7cuh0BHGnIzl4sjb7Euqz51hD/joWzeEztcpl0AkMhMcft7QQONLK0uvWweLFkFr0YVWlzsVkJiTgpG2cmHWMWO2Qg7a/BbUuZWS6sedYQ3Ycbs62uJZsjm3DltjWXErzKXQdvLySSE1VELpuCkIiUlyyh53QUOjQAdavh2+/hblzr3fxQIPqlY8TXeMvGtXcTaOau4kOtYSfGlUS8n1WSroXh05HWMLOqUjiTkfaWnZiT0VxIrFamYad4GAYPBiiogpeWRoswTE0tOIuAFgYV1uvKb+Vpdetg+XL4cK1jaUuAQZV/U7bBaOokFjqVdtP0/CdBPnlnqJoznJhV3wTNh5ox4b97fl17838faIu+XerJgEKQtdNQUhEiiqv1p0ffsgddlxdLWULy9M9lciqcUSFXPk/66jgWOpUO0CdkAP4eee/kM+xc6HsOdrQ1ppj/XvsXA1Ke3yOdYG+nNtD5GyFqcirGpdH2deCOnEi/604wFJm0aKyWgDTICzwCC0ittMiYjutIrdyQ51NhAUezVXyRGII6/bdxK97b2bNX7ew/VCLbAO0FYSKhYKQiOSnsIGn8CytOnWr/W0XdKzBJ68fArv6ZLkQezKK3UcbseeYJfTsOWoJPEkpAddSoSIrKOQo3FQsOVufcnbfnT1radH8/ffS6a6rUeUo7epspG2d37mp/jra1vk914D9Cym+rNt3Ez//2Y1lO9vxx+FOCkLXS0FIRIo78LiYzNQOOUiT8F00CdtF47A/ia7xF3Wr/X3VWTkXUnxts26stwMn6nDwpGWg6fXMwimMKlWgb1/L2i4KOQJXD0y//VYyY5s83VNpHbWFm+v/SsfotXRssNZuKYCkSxDwMApC10tBSMQ5lMRgZX/vROpW+5t61ffbpp03rLmHBqF7811I0JzlYjfVOPZklC3kHDxZmzPJQZR0V1ZYGDz8MNSrZ/9ZOPr4Gyk5eYUl68rSFy/CypWQeJ2LhruYzDQJ30WXRqvo3mQFLSJ+IfzxZAWh66UgJOJ4cu6bdT07nbu5ZlC32t80DvuTRjV3U7/6PupW/5u61f6mqt+ZfJ93Kc2b3UcbsetIE/480pjdRxux/3g94k5FkmH2uPY3V0gKO1Ke5By/dOrU9Xe7uZjOkmUEafd5EZHs/5H9+WfLLK2i7pvlYjITFRJLk7BdNAnfReOaf9IkfBcNQvfi4ZaR7/OOn6/G/uP12H+8Hn8lRNsGLMeejCrxjTEVdqSicHW1rP2TU/aWpN27YckSuHSpcK+ZZZRMZFEQEpFyrThae4L9T9Is/A+aR+ygWfgfNA3fScOae/LtzkpO9WH30Ub8eaQxexMa8PeJuuw/Xo+DJ2uTnOpXPG8sHwo74shcXaFbN8sNcgejn3++/i61olIQEpFy5fpaewxqVT1M0/CdtIzYRuuoLbSO2kJ4UN4DglLSvdhztCF/Hm3Mrvgmtr8lvbCgwo6IRV7ByPrv//LlEBNT8mslaYyQiJSZ623t8fNO4qb667i5/q/cWPc32tTenOeGk1lZJg6crMOOw83Zcag5f8Q3488jjYu9OyvnOkLBwTBkCNxxh+VYYUekaLL/N+LHH5OYP1/rCF03BSGRsmc2w0svwdtvF761xzqDpH29DdxQexNt6/xOo5q7cXXJsiuXnunOX8ei2X6oBdviWrIlrjXb4lqWWJeWNez07XtlZWnrStMKPCLFp6R+v9U1JiIlKuc09rVr4Y03rr7CrZtrBjfU3kSXRqu4ucGvtK+7gco+uQcPHDhRmzV/3cKG/e3ZeKAde442LJFZWoVt3clrgKiIlF8KQiJS7Kzhpyh7bplMWbSM2Eb3Jivo3Gg1HRuszbUY4YUUX377+0Y2HmjHpoM3sOnADSScr1GsdVd3lohzURASkWJxLeEnPOgwPZosp2ezZXRr/HOudXpOXwhi1e4urPnrFtbtu4k/DjfLtu/QtdNgZRGxUhCqCNLPwdktcPEwZGWAZ1Wo3BT86oGpdDdXFOd2vas1V650jo7Ra+nW+Gd6NVtKdI29do8npfixancXVv7ZlVW7u7DrSJPrmr2l1h0RuRoFofLKMODkL7DndUj4CYys3GUqhUPkfVBvFPjUKv06ilO4lpYeC4PI4Dhuqr/OdmsStgsXlyvzMzLNrvx+oC3LdvZk+a4e/H6gLZlm92uuq3XfrO7dtU+WiBSOglA5kP3/skNDoWObBFx/HwrHV1wp5Fsb/BqAizukHodzO+BSPOx+Bf56Cxr/HzR6BlxLdoNGcQ7XEn7cXdNpU3szN9VfR/t6G2hfdwOhVY7nKrf3WH1W7enCsj96snJ3VxIvVb6mOgYHw+DBEBWlDUJF5NopCJWxhQth1KgrPzTt6v7GN+MHUj3gGLh4Qu3hED0O/OvbPzEzxdJStPcdS8vRzklwaAG0/RhCbin19yEVX1HDj5trBm2iNtOl0Sq6Nl5Jh3rrqeSZYlcmI9ONrXGtWLfvJtvtRGL1a6qfWntEpCRoHaEy9O9/w+uvXzkecMMi5o2+D0/3dP480oijtb+m5131838BsHShHf4StoyB1BOWcw3GQctXwUU5V/KWsxXy9GkYN+5qY3wMGtXcTY+my+neZAW3RK/B39t+yddTSVVtgWfD/vZsiW1NaoZ3keun1h4Ryamkfr8VhMrIk09a1lKx6tVsCd/960483DJY9PsAhn08myrBfsTGFvI//unnYPsz8PcnluPqPeDmL8GjcklUXyqIXN2uHS0tPmPGXH1gM0CNKkfp3mSF5dZ4Ra6urrPJVVi1u4vttvtoI6DoA/h9fODuu9XaIyL5UxAqJuUhCH31Fdxzz5XjpuF/sGFSe3y8LrFgw70MeX+ubdn/VauKuEBbfAys/weYL1lmlnVZCt6hxVp/KX8KG3iCguDMmfxfp3Klc9zScA3dGv9M9yYraFRzj93jKelerPnrFpbv7MHK3V3Zcaj5dW1RERhoqeOzzyr4iEjBtLK0gzCbLdN5rfy9E1k0diA+XpdYvrM7Qz+cY/fDkpBQxAuED4CedWD1bXB+Jyy/Bbqvhko1i6X+Uv7ExBQ+8GQ/5+1xiea1dtCm9mbaRG3mhjqbiA79y25WV1aWic2xbVixqzvLd/Vgw/72pGV4FbmOmsYuIuWVglAp69gRMjKsRwYzHxlBvep/c+h0LQa9tyDX1gCh19KYU6U59FgHP3eF5L8tf7uvVsuQg8je+rN/P0yaZBkqll3OEOTpnkrT8J20idpsCz6Nw/7EzdVMTn8da8DKP7vy85/dWLW7C+cuBl5TPbPvwaXAIyLllYJQKRo/HjZsuHL8WM/3GNg2hrQMD+56eyFnk4Psyvv7W35ArolvFHRbBSs6wYV9sLIHdF8Dntf2oyalJ+eO7NbWneBgOHDg6gsXmkxZRNf4i/Z1N9Cu7kZuqL2JJmG7cHfLzFX2+PlqbDp4A5tj27D5oOV2MqnaNdU7+2rNau0RkYpCQaiUfPUVvPXWleNmtXbw+n1PAvDkvNfZfPCGXM8ZN+46f0h8Iy0tQctvhsQ/4Zc7oOvP4Fb0WTxScrIHn59/toztKeyO7GAZ19Ou7kba19vAjXV/o12djXluTnr6QpAl7MS2sYSfg204dq4GRR3crG4uEXEkGixdCtLTwdf3SpeYm2sGGye3o1XUNr7dcif93vyGnD9G3t5w4UIx/bCc/xNWdLTMLIu4Dzp8rq05ytD1BB8Xk5nGYX9yY93fbMGnYc2/cpW7mFqJTQdv4Le/b+T3A23ZEteaw6drcS0zukDdXCJS9jRYuoKKiYFhw7KPC4J/936NVlHbOHMhkEdnfExeP05z5hTjj03lxtAxxtI9dmieZQxRo6eK6cXlaq4n+Ph6XeCG2pu4se5v3NzgV26qv46ASkm5yu1LqMdvf9/Ihv3t+e3vG9kZ3/S6NydV+BERZ6AgVIJiYmDgQPtzDWvuZuKAyQCM+d/bea6yO3483HVXMVemWmdo/TZsHm1ZbyigCdS8vZgvIlZmM6xeDR99BEuXWlr3rs6gQeheW0vPjXV/o0n4Llxd7PeZu5Diy8YD7WzBZ+Pf7TiTXLVQ9TKZLAOrc84qCw+3rGsVHGw/BV/hR0QcnYJQCTGbLVOas3MxmZn5yAg83dP5YVtv5q4bkut57dvbL7RYrOr9E87vsCy6uH4w9PwNAhqW0MWck9kML70Er70GyclXK21Qr/p+boleQ6eGv9Ct8c/UqJJ7vYS4UxG20LN2b0f+ONzsqq09+QWesDCYNs3SypNz3SGFHhFxRgpCJWTt2pwzewxeH/IkN9bdSOIl/zy7xNzdLc8rMSYTtH4XEvfAqbWwpi/02ggeVUrwoo4tZ7fXV18VHIACfc/QrfHP9Gq2lF7NlhIWeNTu8ZR0L34/0NbWxbXxQDuOny/6sgeFCTxFWqhTRMRBKQiVkJwLIf5f3/8y7rZpAIye/T7HzuVe4HDevFL4v3JXD+i4EJbcABf2w7rB0OlHcFFzQGEVZXNSV5dM2tXdyK3NltCr2VLaRG22W7AwLcODjQfaseavW1j5Z1fW7+9Q5AULra0/kyfnPXVdgUdEJH8KQiVk794r9308k3myt2V31dGz32Puuvtzlb/nnhIYF5QfrxDo9C0s6wAJS2H709Dq9as/z4kVJfxU8TlLn1bf06fV93RvvCLXVPZd8Y1ZurMXS//oxdq/Ol7TpqTZWVt/Bgy4rpcREXFKCkIlwGyGl1++cjyi80yq+Jxn77H6fLjin7nKu7tbWoNKVZUWcONsWHcv/PUGBLaByEGlXInyzzrm5+23C57pVdXvFP3afMNdbRfStdFKu8ULzyZXYfmuHvy04zaW/dGThPM1rqtOWrhQRKT4KAiVgClTIC3Nct/VJZNxt1pWUnxryTgMwyVX+c8/L6Mfsoh74Nx22D0Vfn/IMq1eg6ftWn9mzoSk3LPVAQj2P8nAGxYxsO0iOjdcbbddxR+Hm/L15v78uK03W2JbX9PGpFWqWMb4dO16ZWVp7cwuIlK8FISKmdkM//3vleP+bb4mKiSOU0lVmbN2aK7yN91kvxN9qWv2HzjzG5xYBb/eBb1+BzefMqxQ2Shs15ePZzL92nzDfR3m0bPpMrvwszW2JV/9fjeLfh/I/uP1i1wHa/Dp3l2BR0SktCgIFbMpU+w3Vf33Ha8B8P7y0aSkV7Ir6+4Ov/xSuvXLxcUNOsyDJa0gcTf8PhLaz3GalacL0/Xl5ppBr2ZLua/DPPq2+hYfr0u2xzYfbM2XG+9h0e8DOXiyTpGureAjIlL2FISKUc7WoJsb/ErbOptISffigxWjcpX/v/8rJz983tXhpgWWXerjPoeQjlD3kbKuVYkpbNdXg9C/eKjLpzzQ8TOC/U/bzu8/Xpe564Ywf8Ng9iU0KPR1fXzg7rsVfEREyhMFoWJk3xoE/7rdsjLiZ2sf4FRSiF1ZDw94/vnSrN1VhNwCzf9rmUG2+QnL4OnAVmVdq2JVmNYfT/dUBt6wiEe6fkKnhmts54+fr8aCDYOYu34Imw+2obB7dnl5WTYnHTnSMo1dwUdEpHxREComZjO8+eaV4zrV/ubOVt8BMO2nsbnKT5hQDn8UGz4Jp36Fo9/Dr3fDrVvAo3JZ16pYxMTAI4/Yr7KcXf3QvTzS9ROGdZxNkJ8lJZmzXPhxW28+Xf0Qi7ffXqS9uwIDLSuLP/tsOfyeRUTERkGomKxda7+f1BO93sHFxWDx9tvYmxBtV7bctQZZmVwsU+qXtIbkg/DbMOj4dYUfL7RwoaVLKieTKYvbWyxmTK+36dF0he384dPhfLr6IWasfjDPhS9zCg6GwYMhKkozu0REKhoFoWLy9ddX7gdUOs+ITjMBeOuncbnKlsvWICvPQLj5K1h+Exz5Fva8Co2eLutaFYl1w9PVq2HPHvvvBqCS50WG3TKbMb3epn7ofgCyskz8uL03H/08kiU7br3qdHd/fxgxQjuzi4hUdApCxcBshg8/vHJ8/02f4+t1kV3xjVmxq7td2XLbGpRdUBto/Q5sGgk7/g+C2kK1LmVdq6u62oanft5JPNHrHcbd+pat++v8xQCmr3qY95eP5tDpyKteQ11eIiKORUGoGOQcJD204xwAPln1CDkH1f7znxXkB7TuI3B6A8R+Br/eYxkv5FOrrGuVp6sFoEDfMzzU+VOe7P26bfbX38fr8PbSMcz6ZTgX03wLfP3gYBgyRK0/IiKOSEHoOuUcJN0g9C/a1tlERqYbCzbk3rKiX7/Sq9t1MZnghg/h/B9wbhusHQDd14Lb9e2LVZyuFoCaR2zn8Z7vcl+HeXh7pAKw91h9JsVM4svf7imw+0tdXyIiziH3fg9lZOrUqZhMJsaOHZtvmV9//ZWbbrqJoKAgvL29iY6O5q233iq9SuYh5yDpf9z8PwCW/HFrrinz/v6WH9UKw80bOsaAZxCc3QK/P2zZ5ryMWMf+zJ1rCSmVK8PEifYhyM01g3tu/IK1L9zM9v+25MHOM/H2SGVrbEuGfTyLxk//yYINg/MNQYGBll3cz56Ft97SlHcREUdXLlqENm3axCeffEKzZs0KLOfj48Njjz1Gs2bN8PHx4ddff+XRRx/Fx8eHRx4pmwUAjx69ct9kyrIFoby20xg3rgL+qPpGwk1fwqqeEDcXAppA42dKvRoLF8KoUflvfVEt4DiPdP2Ekd0+okaVBAAyMt1Y+PtdvLvscTbsb09+a/+o9UdExHkVOQgNGzaMESNGcMsttxRLBZKTkxkyZAjTp0/nxRdfLLBsy5Ytadmype04MjKSmJgY1q5dm28QSktLI826AyqQlN8ywtdoxZVZ17SK3EqtqvFcSPHl+2197MpViEHS+aneFVq/DZsfgx0TwK8e1BpYKpc2my3jc774Iu/HW0dtZtxtb3F3u6/wcLMM1Eo4V52PVz7Kxysf5fj50Hxf22SytCg995zCj4iIsypy19iFCxfo2bMn9erV47///S9HszeJXIPRo0fTu3dvunfvfvXCOWzbto3169fTqVOnfMtMnTqVgIAA2y08PPx6qmvHbLZs02DVu+WPACzb2ZO0DC+7shVmkHR+6o+GeqMt9zfcD6fWl+jlzGb4z38gICCvEGRwa/OfWP1cJza/eANDbpqHh1sG6/e1Z/B784gYc4jJMZMKDEFged2JEyv49yIiItelyEFo0aJFHD16lMcee4yvvvqKyMhIbrvtNhYuXEhG9qlThbBgwQK2bt3K1KlTi/S8sLAwPD09adOmDaNHj+ahhx7Kt+yECRNITEy03eLj44t0rYKsXQvnzl05vr35YgAWb789V9kKM0i6IK2nQY07wJwKq3vD2a0lcpmFCy1jdSZOhIsX7R/r1HA16ybexE9P3U6nhmvIyHRjztp/0PrZzdw0eT0LNgwmw+xR4OuHh8OiRXkvsigiIs7lmsYIBQUFMWbMGMaMGcO2bduYOXMm//jHP/D19eX+++9n1KhR1KtXr8DXiI+PZ8yYMSxbtgwvL68Cy+a0du1akpOT+e2333jmmWeoW7cugwcPzrOsp6cnnp6eRXr9wsreGBbsf5Ibam8C4Kcdt9mVCwysYIOk8+PiBjcvgFW94NQ6WNkduq2EKi2K5eUL6gZrEbGNVwY/Tc+mywG4lObNhz//k7d+GsfRs2FXfW2NAxIRkbxc12DphIQEli1bxrJly3B1deX222/nzz//pFGjRrz66quMG5d7VWWrLVu2cPLkSVq3bm07ZzabWbNmDe+99x5paWm45vNrFRUVBUDTpk05ceIEkyZNyjcIlaTs44NuiV6Di4vBjkPNSDhfw65c374O9MPr5gOdf7KEodMbLoehVVC56TW/pHUa/Kuv5m4BCvQ9w0v3PMsjXT7BxcUgPdOdT1Y+wkvfPnvVri9teCoiIldT5CCUkZHBd999x6xZs1i2bBnNmjVj3LhxDBkyBD8/P8DS5fXPf/6zwCDUrVs3du7caXdu+PDhREdH8/TTT+cbgnIyDMNuMHRpyTk+qEM9y5iZX/fdnKtst26lVatS4u53OQz1hDO/w8/dLoehxkV+qfw2Q3UxmXm463ReuvtZ2yrQ89cP4v++/C9xp6IKfE1fX/j3v7X6s4iIXF2Rg1BoaChZWVkMHjyY33//nRYtWuQq06tXLypXrlzg6/j5+dGkSRO7cz4+PgQFBdnOT5gwgaNHjzJnjmWl5vfff59atWoRHW3ZxPTXX3/l9ddf5/HHHy/q27huOccHdahvCULr93XIVbbm1fftrHg8AqDLUkuL0Nkt8HMn6PgNhOQOgvmJiYGBeUw+a19vPe898BitorYBsONQMx777D1+3Vtw/6ICkIiIFFWRg9Bbb73F3XffXeC4nipVqhAbG3tdFQNL19vhw4dtx1lZWUyYMIHY2Fjc3NyoU6cOL7/8Mo8++uh1X6uoso8P8nJPoVWkZeDw+v32QchhxgflxaMydFlm6SY7uxlWdoU270Gdh6+6Y73ZbGkJyi7E/wSvDn6KB26xBN9zFyvz/FdT+OjnkZiz8v9HVft/iYjItSpyEFq1ahX9+vXLFYQuXrzI448/zsyZM6+5MqtXr7Y7nj17tt3x448/XiatP3nJPj6oddQWPNwySDhXnbhTkXblHGp8UF48A6H7atjwAMQvgt8fhWNLoO3H4BWc79Neeil7d5jBwLaL+GjESKr6nSEry8TMX0bwf1/+N9fq3FaVKsFDD0H//hr8LCIi167I0+c/++wzUlJScp1PSUmxdWE5upzjg9rX2wDAhr9zr17scOOD8uLmAzd/CS1eBRd3OPI1fFcH/pgE6Ym5ipvN8Pbblvu1Qw7ww5N3sHDM3VT1O8P2Q825ceJvPPzpp3mGIF9fyxYYSUmW19AgaBERuR6FbhFKSkrCMAwMw+DChQt2LUJms5nFixcTEpL3/707mpzjg2xBaH/7XGUdcnxQXkwu0OjfUL0bbHzIslHrrsmw7x2oPQJCe0HlZuAZyOZVcXSqvZPud65gRKeZeHmkkZ7pzivfP82Ur5/Pdx2ge++17DOm4CMiIsWl0EGocuXKmEwmTCYT9evXz/W4yWRi8uTJxVq58iohwf64RcR2ADYdvMHufFCQA48Pyk9gK7h1M8THwB8vQNIe+OsNy+2ydkBMtgmFy3b24PHP3mVfQoM8X9LfHz79VAsgiohI8St0EFq1ahWGYdC1a1cWLVpEYGCg7TEPDw8iIiKoUaNGAa/gOLI3fPl6XaB2iGVg+M54+7V0HnvMSVsvTC5Q6y4I6w9Hv7d0lZ1cCxfjAAMzXmyPbcS2uJbMW38fq3Z3Ia8NUb294ZlnNAhaRERKTqGDkHU/r9jYWGrVqoXpKrOCnEWTsF0AHDlbk7PJQXaPOV1rUE4urhDez3IDMKdD5gVwqUy/2q4cPQqGkfdT/f0tO817FLxbhoiIyHUpVBD6448/aNKkCS4uLiQmJuZaCDG7Zs2aFVvlyqsffrhyv1mtP4DcrUEAJ0+WVo0qCFcPcA3CFctA57vussyyzysMzZqlECQiIiWvUEGoRYsWHD9+nJCQEFq0aIHJZMLI49fLZDJhNpuLvZLlidkMn39+5dgahP44nDsAhha8A4RTGzDAsrnqmDFw5MiV8+HhMG2a5XEREZGSVqggFBsbS3BwsO2+M1u7Fk6fvnLcLDzvIBQcrK6xqxkwwLLO0tq1lgHooaFaE0hEREpXoYJQRESE7X5wcDCVKlUqsQqVd/Yzxgyahlu6CXN2jQ0Zoh/0wnB1tawFJCIiUhaKvKBiSEgI999/P0uXLiUrK6sk6lSuZZ8xVi3gBJV9EjFnubA3x9TvO+4o5YqJiIhIkRU5CM2ZM4e0tDT69+9PjRo1GDNmDJs2bSqJupV79UP3ARB3KpL0TM8yro2IiIgUVZGD0IABA/jqq684ceIEU6dOZc+ePXTo0IH69evzn//8pyTqWK5knzFWv7olCO07nnuBSc0YExERKf+KHISs/Pz8GD58OMuWLWPHjh34+Pg4/MrSOWeMWVuE9iXkDkKaMSYiIlL+XXMQSk1N5csvv6Rfv360atWKM2fO8OSTTxZn3cqdnDPG8msR0owxERGRiqHQK0tbLVu2jLlz5/LNN9/g6urKXXfdxdKlS20rTzuynHuM5dcipBljIiIiFUORg1C/fv3o3bs3n332Gb1798bd3b0k6lUuZe/ucjGZqVvtbyB3i1DfvqVZKxEREblWRQ5Cx48fx9/fvyTqUu516GBp6TGbIaLqITzcMkhN9yT+TLitjKurpZyIiIiUf0UeI5Q9BKWkpJCUlGR3c2Tr11tCEECDGnsB2H+iHoZx5WM0my3lREREpPwrchC6ePEijz32GCEhIfj6+lKlShW7myP79tsr92uHHATgwIk6ucrlHEskIiIi5VORg9BTTz3FypUr+eCDD/D09OTTTz9l8uTJ1KhRgzlz5pREHcuFnFPnI6vGARB7KipXWU2dFxERqRiKPEbo+++/Z86cOXTu3JkRI0bQsWNH6tatS0REBHPnzmXIkCElUc8yl3PqfGRwHGBZVTo7TZ0XERGpOIrcInT27FmioiytIP7+/pw9exaAm2++mTVr1hRv7cqRnN1dEVUPAXDodITdeU2dFxERqTiKHIRq165NXFwcAI0aNeLLL78ELC1FlStXLs66lSs5u7usXWNxpyPtzmvqvIiISMVR5CA0fPhwduzYAcCECRNsY4XGjRvHv//972KvYHlhnToPUMnzIiEBpwD7rjFNnRcREalYijxGaNy4cbb7Xbp04a+//mLz5s3UqVOH5s2bF2vlypPsU+drBR0G4PzFABIvVbaVsU6d79y59OsnIiIiRVekFqGMjAy6dOnCvn37bOdq1arFgAEDHDoEgf0YIetA6Zzjg3KWExERkfKtSEHI3d2dXbt2YTKZSqo+5Vb2MUL5DZTOWU5ERETKtyKPERo6dCgzZswoibqUa6dOXblfs8pRAOLPhtuVCQ/X1HkREZGKpMhjhNLT0/n0009Zvnw5bdq0wcfHx+7xN998s9gqV16YzTB+/JXjGlWOAXDsXA27cm++qanzIiIiFUmRg9CuXbto1aoVgN1YIcBhu8zWroUjR64cW4NQwnn7frCqVUuzViIiInK9ihyEVq1aVRL1KNdyDoAOrWw5kbNFSAOlRUREKpYijxFyRjkHQNeonHeLkAZKi4iIVCyFDkIJCQk8++yztuObb76ZVq1a2W433HADR48eLZFKlrWOHSEoyHLf3TXdtphi9hahoCANlBYREaloCh2EPvjgA86fP2873rFjBx07dqRv37707dsXV1dX3nrrrZKoY7lSvfJxANIz3TmTHFTGtREREZHrUegxQt9//z2vvfaa3bkxY8ZQu3ZtAG688UbGjx/P66+/Xrw1LAfWroUzZyz3reODjp+vjmFcyZFnzljKaVVpERGRiqPQLUJxcXHUqVPHdtyjRw+7qfMNGjQgNja2eGtXTmQfBG2bOn++RoHlREREpPwrdItQZmYmiYmJtuOYmBi7x8+dO4eLi2OOvc4+CDq/NYRylhMREZHyr9DJpUGDBqxfvz7fx9euXUv9+vWLpVLlTfZVpa1dYzlnjGlVaRERkYqn0EFo0KBBvPDCC/zxxx+5HtuxYweTJ09m8ODBxVq58iDnqtLBfpZUdDIpxK6cVpUWERGpeArdNTZ27Fh++OEHWrduTY8ePWjQoAEmk4m//vqL5cuX0759e8aOHVuCVS0bOVeVrup3GoDTF+yXkdaq0iIiIhVPoYOQu7s7y5cv580332TBggWsXr0agHr16jFlyhTGjRuHu7t7SdWzzOQcAJ1fENJAaRERkYqnSFtseHh48Mwzz/DMM8+UVH3KnZwDoPMLQhooLSIiUvE45jSvYtSxI4SFgXU/WWsQOpUUDFjOa6C0iIhIxaQgdBWurvD222AYYDJlEeRrWVnR2iJkGDBtmgZKi4iIVEQKQkVQudJ53FzNANpeQ0RExAEoCF2F2QxjxljuW7vFklL8SM/0BCxdY2PHWsqJiIhIxaIgdBXZp8/nNVDaMCA+3lJOREREKpYizRoDGJ99dcFsTCYTXl5e1K1bl759+xIYGHjdlSsPsk+LzzlQOr9yIiIiUjEUOQht27aNrVu3YjabadCgAYZhsH//flxdXYmOjuaDDz7gX//6F7/++iuNGjUqiTqXquzT4vObOp+znIiIiFQMRe4a69u3L927d+fYsWNs2bKFrVu3cvToUXr06MHgwYM5evQot9xyC+PGjSuJ+pa67NPn8wpCmj4vIiJScRU5CL322mtMmTIFf39/2zl/f38mTZrEq6++SqVKlXjhhRfYsmVLsVa0rLi6wuDBlrFA1iCUc8aYps+LiIhUTEUOQomJiZw8eTLX+VOnTpGUlARA5cqVSU9Pv/7alQMxMfD665b7lSudB+DsxSvjn558EgYMKIOKiYiIyHW7pq6xESNG8PXXX3PkyBGOHj3K119/zYMPPki/fv0A+P3336lfv35x17XUWafOG4bl2BqEEi8F2MosWKCp8yIiIhVVkQdLf/zxx4wbN45BgwaRmZlpeRE3Nx544AHeeustAKKjo/n000+Lt6ZlIOfO85V9zgNw/lJl2znr1PnOnUu1aiIiIlIMihyEfH19mT59Om+99RYHDx7EMAzq1KmDr6+vrUyLFi2Ks45lJueUeGuL0PmLlQssJyIiIhVDkYOQla+vL4GBgZhMJrsQ5EhyTom3BaFsLUJ5lRMREZGKochjhLKysvjPf/5DQEAAERER1KpVi8qVKzNlyhSysrJKoo5lJufO8wGVEoErQUhT50VERCq2IrcIPfvss8yYMYOXX36Zm266CcMwWLduHZMmTSI1NZWXXnqpJOpZJqw7z991F5hMhl3XmDUcaeq8iIhIxVXkIPTZZ5/x6aefcuedd9rONW/enJo1azJq1CiHCkJgmRr/5JPwwbupeLpblgRITAnAxQXGj9fUeRERkYqsyF1jZ8+eJTo6Otf56Ohozp49WyyVKk+s6wj5eZ0HwJzlQnKqL2az5XxMTNnWT0RERK5dkYNQ8+bNee+993Kdf++992jevHmxVKq8yL6OUPY1hAzjysc2dqzWERIREamoitw19uqrr9K7d29WrFhB+/btMZlMrF+/nvj4eBYvXlwSdSwz2dcRymvGmGFoHSEREZGKrMgtQp06dWLfvn3079+f8+fPc/bsWQYMGMDevXvp6GDTp7KvD2SbMZZjDaGc5URERKTiKHIQAqhRowYvvfQSixYtIiYmhhdffBGz2cyIESOuuSJTp07FZDIxduzYfMvExMTQo0cPgoOD8ff3p3379ixduvSar3k12dcHsnWNpQQUWE5EREQqjmsKQnk5e/Ysn3322TU9d9OmTXzyySc0a9aswHJr1qyhR48eLF68mC1bttClSxf69OnDtm3brum6V5N9HSHb9hrZWoS0jpCIiEjFVmxB6FolJyczZMgQpk+fTpUqVQosO23aNJ566iluuOEG6tWrx3//+1/q1avH999/n+9z0tLSSEpKsrsVlnUdIYAqOfYZ0zpCIiIiFV+ZB6HRo0fTu3dvunfvXuTnZmVlceHCBQIDA/MtM3XqVAICAmy38PDwIl1jwAD48kuoHmQZI2TdeT4sDBYu1DpCIiIiFVmZBqEFCxawdetWpk6dek3Pf+ONN7h48SL33HNPvmUmTJhAYmKi7RYfH1+ka8TEwLhx4GJOBuBCqh9Vq8IbbygEiYiIVHSFnj4/4Cq/+ufPny/ShePj4xkzZgzLli3Dy8urSM8FmD9/PpMmTeLbb78lJCQk33Kenp54enoW+fXBEoLuussyTd7P+wIAyam+nDkD995r6RJTGBIREam4Ch2EAgJyz5bK+fjQoUMLfeEtW7Zw8uRJWrdubTtnNptZs2YN7733HmlpabjmM/jmiy++4MEHH+Srr766pi61wsi+mCKAr5elRSg5zRfDsIwRGjsW+vbVGCEREZGKqtBBaNasWcV64W7durFz5067c8OHDyc6Opqnn3463xA0f/58RowYwfz58+ndu3ex1im77IspAvh6Xg5Cqb6AFlMUERFxBEVeWbq4+Pn50aRJE7tzPj4+BAUF2c5PmDCBo0ePMmfOHMASgoYOHcrbb7/NjTfeyPHjxwHw9va+aotVUeVcJNHaInQhxa/AciIiIlJxlPmssYIkJCRw+PBh2/HHH39MZmYmo0ePJjQ01HYbM2ZMsV875yKJ2bvGCionIiIiFUeZtQjlZfXq1XbHs2fPLvDxkmRdTPHoUUs3WM6uMZPJ8rgWUxQREam4ynWLUFnKvpiiyWQ/a0yLKYqIiDgGBaECDBhgWTSxZk37FiEtpigiIuIYFISuYsAAiDtoxsfrEgD/m+9LbKxCkIiIiCNQECoM86Urd138CigoIiIiFYmC0FXExEDbVpZuMXOWC526ehEZaTkvIiIiFZuCUAGsW2wknc0+Y8zE0aOW8wpDIiIiFZuCUD6yb7FhW0Mo26rSYNliw2wuowqKiIjIdVMQykf2LTayT523yr7FhoiIiFRMCkL5yL51hm3qfI5VpXOWExERkYpFQSgf2bfOyNk1ll85ERERqVgUhPJh3WLDZAJvjxQALqVXsj1uMkF4uLbYEBERqcgUhPKRfYuNSpeDUEq6N4C22BAREXEQCkIFsG6xUT3YEoRSM7wAtMWGiIiIgyhXu8+XRwMGQL96KbAT2nXwZtVdlu4wtQSJiIhUfApCheBipAJQp543dW4o48qIiIhIsVHX2FWYzXA41tI1dviYtxZQFBERcSAKQgWIiYHISPjua0sQ+uxz7TMmIiLiSBSE8mHdZ+zIkSvT51PSvbXPmIiIiANREMpD9n3GALzcLWOEUtK9tc+YiIiIA1EQykP2fcYgW4tQhmUdIe0zJiIi4hgUhPKQc/8waxCyriOUXzkRERGpWBSE8pBz/zBvd/uVpfMrJyIiIhWLglAesu8zBuDlcWWMEGifMREREUehIJSH7PuMmUz2LULaZ0xERMRxKAjlw7rPWM2a9tPntc+YiIiI41AQKsCAARAXB7VqWoLQ2+96ERurECQiIuIotNcYlvWA1q61zAILDbXfVNXVFVzdUyENWrf1BnWHiYiIOAynD0IxMZbFE7OvGxQWZhkjZGv5MVtahHDzzvV8ERERqbicumss+zYa2dlto2EYV4KQq4KQiIiII3HaIJRzG43s7LbRyMgAI8tywtUrd2ERERGpsJw2CK1fn7slKDvrNhrrf027ctJFQUhERMSROG0QOn68kOUS0m33V6/10EarIiIiDsRpg1D16oUrN3miJQhlml3p0tWVyMjLY4dERESkwnPaINShg/02GmBQxedsrnIXEi1BKD3TA8gxkFpEREQqNKcNQjm30Xhl8NOc/SSILo1W2pXzcLUPQnYDqdVNJiIiUqE5bRAC+200nrrjNQAmDphMQMCVMh5u9kEIrgykXru2VKsrIiIixczpF1QcMAAyM4FMy7GnexqJiVcetwUhs0eu5yYklEIFRUREpMQ4dYsQwFNPwb335v94Xi1CVqGhJVUrERERKQ1O3SL01Vfw2mvg6pKZb5m8gpDJZBlo3bFjiVdRRERESpDTtgiZzTBqlOW+j+dF23kT9ktNe7pZFlS0BiHrLLNp065szCoiIiIVk9MGofXr4fRpy31fr2TbeWsLUM5jaxAKC7MMsLZtyCoiIiIVltN2jWVfWTp7EMp+H64EoYjaHqxaZekOU0uQiIiIY3DaIJR9ZWlfzyvhx8s91a5c1UBLEAoO8aBz59KomYiIiJQWp+0as64sDfatQNYxQVaPPHi5q8wl96wxERERqdicNghZV5Y2mexbgbw8rtz/97+hbRsFIREREUfltEEIrqwsXaP6lQHSnm5pBAfDl1/Cq68CWQpCIiIijsppxwhZDRgAfVulw3rLsad7OgnHDFzdLs+TtwYhV8+yqaCIiIiUGKduEbJyxX7KvKsp27FahERERByWghBAVlr+xwpCIiIiDktBCK6EHStztiBkVhASERFxVApCcCXs2I6zrSWkFiERERGHpSAEuVuELneNmc0Qf8jy2OGjHpjNpV0xERERKUkKQpBn11hMDERGwtcxlsc++58HkZEQE1PqtRMREZESoiAEuYLQyhVp3HUXHDliv+nq0aNw110KQyIiIo5CQQhyBaG330rDMCz3PVyvBCHrubFjUTeZiIiIA1AQglxBKPHclVlj2VuEAAwD4uNh7drSq56IiIiUDAUhyBWEsm+86uluuZ9utp81lpBQ8tUSERGRkuX0QchshqPx9kHI2gqU/b61RcgqNLTk6yYiIiIly6mDkHVm2JLF9itLW1uBIHcQMpkgPBw6diy1aoqIiEgJcdpNV7/7DoYOtYz5yd4CBDlahLINljZd3od12jRwdS2tmoqIiEhJcdoWoaef5srMMLf8xwhlbxEKC4OFCy071ouIiEjF57QtQseOXblvbfWxHecxRmjSfzxo1EMtQSIiIo7EaYNQdrlahPIYI9S0uScoBImIiDiUctM1NnXqVEwmE2PHjs23TEJCAvfddx8NGjTAxcWlwLJFYQ07mWZL0sneQmQLSS7uxXItERERKT/KRRDatGkTn3zyCc2aNSuwXFpaGsHBwTz77LM0b978uq5Zo8aV+9awcyHVD7jSImQygbdnhqWQSUFIRETE0ZR5EEpOTmbIkCFMnz6dKlWqFFg2MjKSt99+m6FDhxIQEFCo109LSyMpKcnuBvDKK9hmgVmDUHKqr90xQLWQy0FILUIiIiIOp8yD0OjRo+nduzfdu3cvkdefOnUqAQEBtlt4eDgAd95pmQEWFpZHi5BbGuHhlsd9vNQ1JiIi4qjKNAgtWLCArVu3MnXq1BK7xoQJE0hMTLTd4uPjbY8NGABxcdCgniXsBIZYWoSGDE4nNvbyNPksa4uQByIiIuJYymzWWHx8PGPGjGHZsmV4eXmV2HU8PT3x9PTM93FXV/D1ToMLUD3MF05AeI20KzPEstQ1JiIi4qjKLAht2bKFkydP0rp1a9s5s9nMmjVreO+990hLS8O1tBbtycq0/HXzvXycbTq9oSAkIiLiqMosCHXr1o2dO3fanRs+fDjR0dE8/fTTpReC4ErYcfOx/DVn23ssS7PGREREHFWZBSE/Pz+aNGlid87Hx4egoCDb+QkTJnD06FHmzJljK7N9+3bAMtvs1KlTbN++HQ8PDxo1anTtlbG1CF0OQtYWIcNQ15iIiIgDK9crSyckJHD48GG7cy1btrTd37JlC/PmzSMiIoK4uLhrv5BxOQi5WoPQ5RYhwwxc3pBMQUhERMThlKsgtHr1arvj2bNn5ypjWHdKLU62FqFKl48vtwhZW4NAs8ZEREQcUJmvI1QuGDm6xqxjhIzsQUgtQiIiIo5GQQiutPy4FtAipMHSIiIiDkdBCK60CLlbp89fbhGyBSETuGjreREREUejIGQYlwdFk22wdI4WIXWLiYiIOCQFIWsIgiuDpS+PETJnWgJRRpYHq1eD2YyIiIg4EAUha7cY2K0jFBMD3bpYWoQuJLvTpQtERkJMTOlXUUREREqGglBWtiB0ebD0peQ07roLzpy2BKEMs6Vr7OhRuOsuhSERERFHoSCUfYr85RahixfSMQxwd7UPQtYljMaOVTeZiIiII3D6IGTOuNIitGGTpUXI3cUyRsgWhDKvDJY2DIiPh7VrS7GSIiIiUiKcOgjFxEDbGyxBKNPsyqD7vQDwcLMMks7ZIpRdQkIpVVJERERKjNMGoe++s4z3OXnCGoTcSM+0bKPh6W5pEbIGIuv57EJDS6miIiIiUmLK1V5jpenppy3dXG6ul4NQlhtpGZ4AuLpk4WIy59kiZDJBWBh07Fj6dRYREZHi5bQtQseOWf66ueRuEQJLq5C7m30QMpksj02bBq5aaFpERKTCc9ogZGUNO5lZbqRletrOe7ql5WoRCguDhQthwIDSr6eIiIgUP6ftGrPK3iKUab7ycXi4pdvNGnvrLXj8cbUEiYiIOBKnbRGqUcPS1WUdI2Rp9TGRmm5pFfJ0T7MNlnZ1d1cIEhERcUBOG4ReecXy1931SosQQLrZMk7Iwy0dj8vdZvUaeCgEiYiIOCCnDUJ33mkZ7xNa/cqsMcA2c8zTLY2QqpYgVL2Gdp8XERFxRE49RmjAAOjbPgNWQUh1N1asAL9THpAFn81Kp1V4BmwFTApCIiIijshpW4SsXE2WFqGAADe6dQMvb0uLUJuWabhweR8yFwUhERERR+T0Qci2+7w17LheXksoKx2yFIREREQcmYKQcTkImS73ErpcXkvInGYJQwAuubfYEBERkYpPQcgahFysQUgtQiIiIs5CQSgrR4uQ6+UWoaw0MC4HIQ2WFhERcUgKQrZWnxwtQma1CImIiDg6BaH8xghlpSkIiYiIODgFISPHrDGNERIREXEaCkL5jRHSrDERERGHpyCUbdaY2QwnTllCz9/708kyq0VIRETEkSkIXe7+OnLUjchI+OEnS4vQjOlpfBOjWWMiIiKOTEHocovQL2vdOHIE0jOv7D6fkW4JQtt3KgiJiIg4IqcPQllmSxDKMFvCTvbd5z1cLWOEPp/nQXp62dRPRERESo7TB6G4g5YglGm2DJZON19pEfJ0TwPg9DlPwsIgJqZs6igiIiIlw+mDUPKFy0EoyxKEsrcIebql2c6dOgV33aUwJCIi4kicPgj5++ZoEcrM3SJkDUcAY8eC2Vy6dRQREZGS4fRBqFa4ZUC02doilHm5Rcg9W4vQ5XOGAfHxsHZtGVRUREREip3TByEXitYiBJCQUIoVFBERkRLj9EHIurL0HXe6U7VqPmOEMu2DUGho6VZRRERESoaC0OV1hBpEu3H0KHh6598iZDJBeDh07Fg2VRUREZHipSCUbYsNDw8YMjTvMUImk6XYtGng6loG9RQREZFi59RByGyGY0ctQehgnGWvsRvaWlqE/CrZtwiFhcHChTBgQJlVV0RERIqZ0wah776DyEhY+pNl1tjHn1j2Glv3m6VFqG7tNPx8UgGYPceT2FiFIBEREUfjtEHoH/+AI0fAzfXKFhtHjsCLUy0tQofj0nExLC1CZxM91R0mIiLigJw2CFm5ueS9snQlj0u4umQBMHS4p1aUFhERcUAKQq55ryPk751kK5OW4akVpUVERByQgpCLfRCyrhmUPQilZnhqRWkREREH5PRByN3NMlja2jWWnOoLgJ93MgAZmW5kmt0BrSgtIiLiaJw+COVsEUq8FGD3eHKar+2+VpQWERFxLE4dhEymbGOELrcIJabkCEKXW4iCgrSitIiIiKNx2iD0v/9BzZrg6mIZAW3OssyPv5RWiUzzlbnyF9N8ADhzBr79tvTrKSIiIiXHaYPQnXdCXBy0bGEJQs8954qvL4CJpBR/WzlrixCgmWMiIiIOxmmDEFj2DKvsb0k2u/9yJdkyPtpunFD2IKSZYyIiIo7FqYMQYNt09dvv3Gynso8TupDqZ1dc3WMiIiKOQ0HIsLQInU+8Mi7o/MXKtvsnk0Lsis+dq+4xERERR6EgZNgPlgY4cjbMdj9nEDp1St1jIiIijkJBKI8gdOh0hO3+sXM1cj1FCyuKiIg4BgWhy0HI1+9KEDp4srbt/u6jjXI9RQsrioiIOAYFoSzLYOlu3a8EoR+23cHpC0EcOVuTDfvb2xXXwooiIiKOw+3qRRzc5Rahm26+EoROJlWj7vi/yTS7kZJeya74E09Ypt2LiIhIxacWoctBqHlLN8LCLNtuACReqszFbPuMgaU16NlnS7uCIiIiUlIUhC4HIVdXV95+23LKGoZy+uQTtQaJiIg4EgWhy0EIkysDBsDChZY9yLILD4dFi2DAgNKvnoiIiJScchOEpk6dislkYuzYsQWW++WXX2jdujVeXl7Url2bjz766PoufHllaUyWpp4BAyx7kK1aBfPmWf7GxioEiYiIOKJyMVh606ZNfPLJJzRr1qzAcrGxsdx+++08/PDDfP7556xbt45Ro0YRHBzMwIEDr+3i2VqErFxdoXPna3s5ERERqTjKvEUoOTmZIUOGMH36dKpUqVJg2Y8++ohatWoxbdo0GjZsyEMPPcSIESN4/fXXr70CWbmDkIiIiDiHMm8RGj16NL1796Z79+68+OKLBZbdsGEDPXv2tDvXq1cvZsyYQUZGBu7u7rmek5aWRlpamu04MTERgKSkJMuJi5mQCVxIAVPS9b0ZERERKRHW323DMIr1dcs0CC1YsICtW7eyadOmQpU/fvw41apVsztXrVo1MjMzOX36NKF5LPk8depUJk+enOt8eHh4jjOtCl1vERERKRtnzpwhICCg2F6vzIJQfHw8Y8aMYdmyZXh5eRX6eaYcc9utyTDneasJEyYwfvx42/H58+eJiIjg8OHDxfpByrVJSkoiPDyc+Ph4/P39y7o6Tk3fRfmh76L80HdRfiQmJlKrVi0CAwOL9XXLLAht2bKFkydP0rp1a9s5s9nMmjVreO+990hLS8M1x6I91atX5/jx43bnTp48iZubG0FBQXlex9PTE09Pz1znAwIC9A91OeLv76/vo5zQd1F+6LsoP/RdlB8uLsU7vLnMglC3bt3YuXOn3bnhw4cTHR3N008/nSsEAbRv357vv//e7tyyZcto06ZNnuODRERERApSZkHIz8+PJk2a2J3z8fEhKCjIdn7ChAkcPXqUOXPmADBy5Ejee+89xo8fz8MPP8yGDRuYMWMG8+fPL/X6i4iISMVX5tPnC5KQkMDhw4dtx1FRUSxevJjVq1fTokULpkyZwjvvvFOkNYQ8PT2ZOHFint1lUvr0fZQf+i7KD30X5Ye+i/KjpL4Lk1Hc89BEREREKohy3SIkIiIiUpIUhERERMRpKQiJiIiI01IQEhEREaelICQiIiJOyyGD0AcffEBUVBReXl60bt2atWvXFlj+l19+oXXr1nh5eVG7dm0++uijUqqp4yvKdxETE0OPHj0IDg7G39+f9u3bs3Tp0lKsreMr6r8bVuvWrcPNzY0WLVqUbAWdSFG/i7S0NJ599lkiIiLw9PSkTp06zJw5s5Rq69iK+l3MnTuX5s2bU6lSJUJDQxk+fDhnzpwppdo6rjVr1tCnTx9q1KiByWTim2++uepziuX323AwCxYsMNzd3Y3p06cbu3fvNsaMGWP4+PgYhw4dyrP8wYMHjUqVKhljxowxdu/ebUyfPt1wd3c3Fi5cWMo1dzxF/S7GjBljvPLKK8bvv/9u7Nu3z5gwYYLh7u5ubN26tZRr7piK+n1YnT9/3qhdu7bRs2dPo3nz5qVTWQd3Ld/FnXfeabRr185Yvny5ERsba2zcuNFYt25dKdbaMRX1u1i7dq3h4uJivP3228bBgweNtWvXGo0bNzb69etXyjV3PIsXLzaeffZZY9GiRQZgfP311wWWL67fb4cLQm3btjVGjhxpdy46Otp45pln8iz/1FNPGdHR0XbnHn30UePGG28ssTo6i6J+F3lp1KiRMXny5OKumlO61u/j3nvvNZ577jlj4sSJCkLFpKjfxU8//WQEBAQYZ86cKY3qOZWifhevvfaaUbt2bbtz77zzjhEWFlZidXRGhQlCxfX77VBdY+np6WzZsoWePXvane/Zsyfr16/P8zkbNmzIVb5Xr15s3ryZjIyMEquro7uW7yKnrKwsLly4UOw7DTuja/0+Zs2axYEDB5g4cWJJV9FpXMt38d1339GmTRteffVVatasSf369XnyySdJSUkpjSo7rGv5Ljp06MCRI0dYvHgxhmFw4sQJFi5cSO/evUujypJNcf1+l9leYyXh9OnTmM1mqlWrZne+WrVquXattzp+/Hie5TMzMzl9+jShoaElVl9Hdi3fRU5vvPEGFy9e5J577imJKjqVa/k+9u/fzzPPPMPatWtxc3Oo/1SUqWv5Lg4ePMivv/6Kl5cXX3/9NadPn2bUqFGcPXtW44Suw7V8Fx06dGDu3Lnce++9pKamkpmZyZ133sm7775bGlWWbIrr99uhWoSsTCaT3bFhGLnOXa18Xuel6Ir6XVjNnz+fSZMm8cUXXxASElJS1XM6hf0+zGYz9913H5MnT6Z+/fqlVT2nUpR/N7KysjCZTMydO5e2bdty++238+abbzJ79my1ChWDonwXu3fv5oknnuCFF15gy5YtLFmyhNjYWEaOHFkaVZUciuP326H+N69q1aq4urrmSvInT57MlRqtqlevnmd5Nzc3goKCSqyuju5avgurL774ggcffJCvvvqK7t27l2Q1nUZRv48LFy6wefNmtm3bxmOPPQZYfowNw8DNzY1ly5bRtWvXUqm7o7mWfzdCQ0OpWbMmAQEBtnMNGzbEMAyOHDlCvXr1SrTOjupavoupU6dy00038e9//xuAZs2a4ePjQ8eOHXnxxRfVi1CKiuv326FahDw8PGjdujXLly+3O798+XI6dOiQ53Pat2+fq/yyZcto06YN7u7uJVZXR3ct3wVYWoKGDRvGvHnz1OdejIr6ffj7+7Nz5062b99uu40cOZIGDRqwfft22rVrV1pVdzjX8u/GTTfdxLFjx0hOTrad27dvHy4uLoSFhZVofR3ZtXwXly5dwsXF/qfT1dUVuNIaIaWj2H6/izS0ugKwToWcMWOGsXv3bmPs2LGGj4+PERcXZxiGYTzzzDPGP/7xD1t56/S7cePGGbt37zZmzJih6fPFpKjfxbx58ww3Nzfj/fffNxISEmy38+fPl9VbcChF/T5y0qyx4lPU7+LChQtGWFiYcddddxl//vmn8csvvxj16tUzHnroobJ6Cw6jqN/FrFmzDDc3N+ODDz4wDhw4YPz6669GmzZtjLZt25bVW3AYFy5cMLZt22Zs27bNAIw333zT2LZtm20pg5L6/Xa4IGQYhvH+++8bERERhoeHh9GqVSvjl19+sT32wAMPGJ06dbIrv3r1aqNly5aGh4eHERkZaXz44YelXGPHVZTvolOnTgaQ6/bAAw+UfsUdVFH/3chOQah4FfW72LNnj9G9e3fD29vbCAsLM8aPH29cunSplGvtmIr6XbzzzjtGo0aNDG9vbyM0NNQYMmSIceTIkVKuteNZtWpVgb8BJfX7bTIMteWJiIiIc3KoMUIiIiIiRaEgJCIiIk5LQUhEREScloKQiIiIOC0FIREREXFaCkIiIiLitBSERERExGkpCImIiIjTUhASERERp6UgJCJO48yZM4SEhBAXF1di17jrrrt48803S+z1RaR4KQiJSLEZNmwYJpOJkSNH5nps1KhRmEwmhg0bVvoVu2zq1Kn06dOHyMhI27lbbrkFk8nElClT7MoahkG7du0wmUy88MILhb7GCy+8wEsvvURSUlJxVVtESpCCkIgUq/DwcBYsWEBKSortXGpqKvPnz6dWrVplVq+UlBRmzJjBQw89ZDtnGAbbt28nIiKCnTt32pX/7LPPOHbsGACtWrUq9HWaNWtGZGQkc+fOLZ6Ki0iJUhASkWLVqlUratWqRUxMjO1cTEwM4eHhtGzZ0nZuyZIl3HzzzVSuXJmgoCDuuOMODhw4YPdaCxcupGnTpnh7exMUFET37t25ePHiVR/Ly08//YSbmxvt27e3ndu/fz8XLlxg2LBhdkHowoULTJgwwdZ61bp16yJ9BnfeeSfz588v0nNEpGwoCIlIsRs+fDizZs2yHc+cOZMRI0bYlbl48SLjx49n06ZN/Pzzz7i4uNC/f3+ysrIASEhIYPDgwYwYMYI9e/awevVqBgwYgGEYBT6WnzVr1tCmTRu7c1u2bMHLy4vBgwezf/9+0tLSAJgyZQotWrQgNDSUqlWrEh4eXqT337ZtW37//Xfb64lI+eVW1hUQEcfzj3/8gwkTJhAXF4fJZGLdunUsWLCA1atX28oMHDjQ7jkzZswgJCSE3bt306RJExISEsjMzGTAgAFEREQA0LRpUwD27duX72P5iYuLo0aNGnbntm7dSrNmzahfvz4+Pj7s2bMHHx8fPvjgAzZv3szrr79e5NYggJo1a5KWlsbx48dt9ROR8kktQiJS7KpWrUrv3r357LPPmDVrFr1796Zq1ap2ZQ4cOMB9991H7dq18ff3JyoqCoDDhw8D0Lx5c7p160bTpk25++67mT59OufOnbvqY/lJSUnBy8vL7tyWLVto3bo1JpOJZs2asWvXLsaNG8cjjzxCdHQ0W7ZsKdL4ICtvb28ALl26VOTnikjpUhASkRIxYsQIZs+ezWeffZarWwygT58+nDlzhunTp7Nx40Y2btwIQHp6OgCurq4sX76cn376iUaNGvHuu+/SoEEDYmNjC3wsP1WrVs0VlrZt22YLOs2bN+ftt9/m999/Z+LEiaSnp/Pnn3/aBaFLly7x73//mw4dOtChQwcefvhhzpw5k+taZ8+eBSA4OLiIn5qIlDYFIREpEbfeeivp6emkp6fTq1cvu8fOnDnDnj17eO655+jWrRsNGzbMs0XHZDJx0003MXnyZLZt24aHhwdff/31VR/LS8uWLdm9e7ft+ODBg5w/f97W9dWiRQs2b97MSy+9REBAADt37iQjI8Oua+yxxx6jefPmrF+/nvXr1zNo0CCGDh2aa2zSrl27CAsLy9UKJiLlj8YIiUiJcHV1Zc+ePbb72VWpUoWgoCA++eQTQkNDOXz4MM8884xdmY0bN/Lzzz/Ts2dPQkJC2LhxI6dOnaJhw4YFPpafXr16MWHCBM6dO0eVKlXYsmULHh4eNGnSBIAHHniAfv36ERQUBFjGD1WpUsXWZZeSksK5c+e4//77mTRpEgCTJk3i22+/5e+//6ZevXq2a61du5aePXte3wcoIqVCQUhESoy/v3+e511cXFiwYAFPPPEETZo0oUGDBrzzzjt07tzZ7rlr1qxh2rRpJCUlERERwRtvvMFtt93Gnj178n0sP02bNqVNmzZ8+eWXPProo2zdupUmTZrg7u4OgLu7u10LztatW+2m+2dv9XnsscfyvU5qaipff/01S5cuvernIyJlz2QUNN9URMSBLF68mCeffJJdu3bh4lL0kQHDhg2jR48eDBkyBICff/6Z119/ncWLF2MymQB4//33+fbbb1m2bFmx1l1ESoZahETEadx+++3s37+fo0ePFnltIIAPPviA5557jnfeeQeTyUTDhg35/PPPbSEILC1L7777bnFWW0RKkFqERERExGlp1piIiIg4LQUhERERcVoKQiIiIuK0FIRERETEaSkIiYiIiNNSEBIRERGnpSAkIiIiTktBSERERJyWgpCIiIg4LQUhERERcVr/DwSeFgdVgmX3AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Looking at physical interpretation of heatmap parameters\n", + "\n", + "# creating input array (mass) and output array (everything else)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y = combined_logg #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# trying different kernel types\n", + "kernel = Matern(length_scale=best_length_scale_logg, nu=best_nu_logg)\n", + "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + "gp.fit(X, y_logg)\n", + "y_pred_logg, sigma_logg = gp.predict(x, return_std=True)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_logg, color='blue', label='actual values')\n", + "plt.plot(x, y_pred_logg, color='orange', label='predicted values')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('Log Gravity')\n", + "plt.title('Best Fit by Heat Map')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(4.0,4.5)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c130f0f1-9330-421a-9e8c-eedd4974e659", + "metadata": {}, "outputs": [], "source": [] } From e88571fddc07792b7986e81f79e70b95c8a7a2ac Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Sat, 26 Apr 2025 16:26:53 -0700 Subject: [PATCH 036/191] Add random generator support --- spisea/imf/imf.py | 197 ++++++++++++++++++++++------------------------ 1 file changed, 92 insertions(+), 105 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 4380e012..bc4ff564 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -47,19 +47,21 @@ class IMF(object): If None, no multiplicity is assumed. Otherwise, use multiplicity object to create multiple star systems. """ - def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None): + def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None, seed=None): self._multi_props = multiplicity - self._mass_limits = massLimits - - if multiplicity == None: - self.make_multiples = False - else: + self._mass_limits = np.atleast_1d(massLimits) + self.seed = seed + self.rng = np.random.default_rng(seed) + + if multiplicity: self.make_multiples = True + else: + self.make_multiples = False return - def generate_cluster(self, totalMass, seed=None): + def generate_cluster(self, totalMass): """ Generate a cluster of stellar systems with the specified IMF. @@ -113,22 +115,19 @@ def generate_cluster(self, totalMass, seed=None): # Generate output arrays. masses = np.array([], dtype=float) isMultiple = np.array([], dtype=bool) - compMasses = np.array([], dtype=object) + # compMasses = {} # Hashmap for index -> compMasses for faster lookup + compMasses = [] systemMasses = np.array([], dtype=float) # Loop through and add stars to the cluster until we get to # the desired total cluster mass. totalMassTally = 0 loopCnt = 0 - - # Set the random seed, if desired - if seed: - np.random.seed(seed=seed) + # start_while = time.time() while totalMassTally < totalMass: # Generate a random number array. - uniX = np.random.rand(int(newStarCount)) - + uniX = self.rng.random(int(newStarCount)) # Convert into the IMF from the inverted CDF newMasses = self.dice_star_cl(uniX) @@ -136,33 +135,31 @@ def generate_cluster(self, totalMass, seed=None): test = np.isnan(newMasses) if np.sum(test) > 0: raise ValueError('Nan detected in cluster mass') - + # Dealing with multiplicity - if self._multi_props != None: - newCompMasses = np.empty((len(newMasses),), dtype=object) - newCompMasses.fill([]) - + if self._multi_props: + # newCompMasses = np.empty((len(newMasses),), dtype=object) + # newCompMasses.fill([]) # Determine the multiplicity of every star MF = self._multi_props.multiplicity_fraction(newMasses) CSF = self._multi_props.companion_star_fraction(newMasses) - - newIsMultiple = np.random.rand(int(newStarCount)) < MF - # Copy over the primary masses. Eventually add the companions. - newSystemMasses = newMasses.copy() + newIsMultiple = self.rng.random(int(newStarCount)) < MF # Function to calculate multiple systems more efficiently - newCompMasses, newSystemMasses, newIsMultiple = self.calc_multi(newMasses, newCompMasses, - newSystemMasses, newIsMultiple, - CSF, MF) - + # start_calc = time.time() + newCompMasses, newSystemMasses, newIsMultiple = self.calc_multi(newMasses, newIsMultiple, CSF, MF) + # end_calc = time.time() + # print('Time taken for calc_multi: ', end_calc - start_calc) newTotalMassTally = newSystemMasses.sum() isMultiple = np.append(isMultiple, newIsMultiple) systemMasses = np.append(systemMasses, newSystemMasses) - compMasses = np.append(compMasses, newCompMasses) + compMasses.append(newCompMasses) + else: newTotalMassTally = newMasses.sum() - + # end_while = time.time() + # print('Time taken for while loop: ', end_while - start_while) # Append to our primary masses array masses = np.append(masses, newMasses) @@ -176,6 +173,25 @@ def generate_cluster(self, totalMass, seed=None): # Make a running sum of the system masses if self._multi_props: + # Concatenate the companion masses + if len(compMasses) > 1: + max_cols = max(compMass.shape[1] for compMass in compMasses) + + # Pad each array to have the same number of columns + padded_arrays = [ + np.ma.masked_all((compMass.shape[0], max_cols)) for compMass in compMasses + ] + + for i, compMass in enumerate(compMasses): + padded_arrays[i][:, :compMass.shape[1]] = compMass + + # Vertically stack the padded arrays + compMasses = np.ma.vstack(padded_arrays) + + else: + compMasses = compMasses[0] + + # Make a running sum of the system masses massCumSum = systemMasses.cumsum() else: massCumSum = masses.cumsum() @@ -196,60 +212,39 @@ def generate_cluster(self, totalMass, seed=None): return (masses, isMultiple, compMasses, systemMasses) - def calc_multi(self, newMasses, compMasses, newSystemMasses, newIsMultiple, CSF, MF): + def calc_multi(self, newMasses, newIsMultiple, CSF, MF): """ Helper function to calculate multiples more efficiently. We will use array operations as much as possible """ - # Identify multiple systems, calculate number of companions for - # each - idx = np.where(newIsMultiple == True)[0] - n_comp_arr = 1 + np.random.poisson((CSF[idx] / MF[idx]) - 1) - if self._multi_props.companion_max == True: - too_many = np.where(n_comp_arr > self._multi_props.CSF_max)[0] - n_comp_arr[too_many] = self._multi_props.CSF_max - primary = newMasses[idx] + # Copy over the primary masses. Eventually add the companions. + newSystemMasses = newMasses.copy() + + # Identify multiple systems, calculate number of companions for each + multiple_idx = np.where(newIsMultiple)[0] + comp_nums = 1 + self.rng.poisson((CSF[multiple_idx] / MF[multiple_idx]) - 1) + if self._multi_props.companion_max: + too_many = np.where(comp_nums > self._multi_props.CSF_max)[0] + comp_nums[too_many] = self._multi_props.CSF_max + primary = newMasses[multiple_idx] # We will deal with each number of multiple system independently. This is # so we can put in uniform arrays in _multi_props.random_q. - num = np.unique(n_comp_arr) - for ii in num: - tmp = np.where(n_comp_arr == ii)[0] - - if ii == 1: - # Single companion case - q_values = self._multi_props.random_q(np.random.rand(len(tmp))) - - # Calculate mass of companion - m_comp = q_values * primary[tmp] - - # Only keep companions that are more than the minimum mass. Update - # compMasses, newSystemMasses, and newIsMultiple appropriately - good = np.where(m_comp >= self._mass_limits[0])[0] - for jj in good: - compMasses[idx[tmp[jj]]] = np.transpose([m_comp[jj]]) - newSystemMasses[idx[tmp[jj]]] += compMasses[idx[tmp[jj]]] - - bad = np.where(m_comp < self._mass_limits[0])[0] - newIsMultiple[idx[tmp[bad]]] = False - else: - # Multple companion case - q_values = self._multi_props.random_q(np.random.rand(len(tmp), ii)) - - # Calculate masses of companions - m_comp = np.multiply(q_values, np.transpose([primary[tmp]])) - - # Update compMasses, newSystemMasses, and newIsMultiple appropriately - for jj in range(len(tmp)): - m_comp_tmp = m_comp[jj] - compMasses[idx[tmp[jj]]] = m_comp_tmp[m_comp_tmp >= self._mass_limits[0]] - newSystemMasses[idx[tmp[jj]]] += compMasses[idx[tmp[jj]]].sum() - - # Double check for the case when we drop all companions. - # This happens a lot near the minimum allowed mass. - if len(compMasses[idx[tmp[jj]]]) == 0: - newIsMultiple[idx[tmp[jj]]] = False - + comp_unique = np.unique(comp_nums) + comp_indices = [np.where(comp_nums == i)[0] for i in comp_unique] + compMasses = np.zeros((len(newMasses), max(comp_unique))) + + for comp_num, comp_index in zip(comp_unique, comp_indices): + # Calculate masses of companions + q_values = self._multi_props.random_q(self.rng.random((len(comp_index), comp_num))) + m_comp = np.multiply(q_values, np.transpose([primary[comp_index]])) + compMasses[multiple_idx[comp_index], :comp_num] = m_comp + + # Mask out companions that are less than the minimum mass + compMasses = np.ma.MaskedArray(compMasses, mask=compMasses < self._mass_limits[0]) + newSystemMasses[multiple_idx] += compMasses[multiple_idx].sum(axis=1) + newIsMultiple = np.any(~compMasses.mask, axis=1) + return compMasses, newSystemMasses, newIsMultiple @@ -258,7 +253,7 @@ class IMF_broken_powerlaw(IMF): Initialize a multi-part power-law with N parts. Each part of the power-law is described with a probability density function: - P(m) \propto m ** power[n] + P(m) ∝ m ** power[n] for mass_limits[n] < m <= mass_limits[n+1]. @@ -276,7 +271,9 @@ class IMF_broken_powerlaw(IMF): If None, no multiplicity is assumed. Otherwise, use multiplicity object to create multiple star systems. """ - def __init__(self, mass_limits, powers, multiplicity=None): + def __init__(self, mass_limits, powers, multiplicity=None, seed=None): + super().__init__(massLimits=mass_limits, multiplicity=multiplicity, seed=seed) + powers = np.atleast_1d(powers) if len(mass_limits) != len(powers) + 1: msg = 'Incorrect specification of multi-part powerlaw.\n' msg += ' len(massLimts) != len(powers)+1\n' @@ -284,17 +281,10 @@ def __init__(self, mass_limits, powers, multiplicity=None): msg += ' len(powers) = \n' + len(powers) raise RuntimeError(msg) - - self._mass_limits = np.atleast_1d(mass_limits) + mass_limits = np.atleast_1d(mass_limits) self._m_limits_low = mass_limits[0:-1] self._m_limits_high = mass_limits[1:] - self._powers = powers - self._multi_props = multiplicity - - if multiplicity == None: - self.make_multiples = False - else: - self.make_multiples = True + self._powers = np.atleast_1d(powers) # Calculate the coeffs to make the function continuous nterms = len(self._powers) @@ -595,23 +585,23 @@ def dice_star_cl(self, r): r = np.atleast_1d(r) # Make sure it is an array x = r * self.lamda[-1] - y = np.zeros(len(r), dtype=float) - z = np.ones(len(r), dtype=float) + y = np.zeros_like(r) + z = np.ones_like(r) # Loop through the different parts of the power law. for i in range(self.nterms): #-----For i = 0 --> n, where n is the number of intervals aux = x - self.lamda[i] #---Should this be i - 1? # Only continue for those entries that are in later segments - idx = np.where(aux >= 0)[0] + idx = aux >= 0 # Maybe we are all done? - if len(idx) == 0: + if sum(idx) == 0: break x_tmp = x[idx] aux_tmp = aux[idx] - + t1 = aux_tmp / (self.coeffs[i] * self.k) t1 += prim_power(self._m_limits_low[i], self._powers[i]) y_i = gamma_closed(x_tmp, self.lamda[i], self.lamda[i+1]) @@ -711,10 +701,10 @@ def prim_power(m, power): power = np.repeat(power, len(m)) z = 1.0 + power - val = (m**z) / z - - val[power == -1] = np.log(m[power == -1]) - + val = np.empty_like(m) + valid_idx = power != -1 + val[valid_idx] = (m[valid_idx]**z[valid_idx]) / z[valid_idx] + val[~valid_idx] = np.log(m[~valid_idx]) if returnFloat: return val[0] @@ -737,17 +727,14 @@ def inv_prim_power(x, power): power = np.repeat(power, len(x)) if x.shape != power.shape: - pdb.set_trace() + raise ValueError('spisea.imf.inv_prim_power: Dimension mismatch, x and power must have the same shape') z = 1.0 + power - val = (z * x)**(1.0 / z) - - #--------------BUG CHECK---------------------# - # This line doesn't make sense if x is an N-element array and - # power is just a 1-element array, which it appears to be for - # imf.generate_cluster - val[power == -1] = np.exp(x[power == -1]) - #-----------------------------------------------# + val = np.empty_like(x) + valid_idx = power != -1 + val[valid_idx] = (z[valid_idx] * x[valid_idx])**(1.0 / z[valid_idx]) + val[~valid_idx] = np.exp(x[~valid_idx]) + if returnFloat: return val[0] else: From fd875c553c6f530c5dae72ff99496866ed06eb8f Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Sat, 26 Apr 2025 16:28:54 -0700 Subject: [PATCH 037/191] Suppress divided by zero warnings --- spisea/ifmr.py | 101 ++++++++++++++++++++++++++----------------------- 1 file changed, 53 insertions(+), 48 deletions(-) diff --git a/spisea/ifmr.py b/spisea/ifmr.py index f88afdbe..2b67ca7c 100755 --- a/spisea/ifmr.py +++ b/spisea/ifmr.py @@ -25,8 +25,9 @@ import numpy as np class IFMR(object): - def __init__(self): - pass + def __init__(self, seed=None): + self.seed = seed + self.rng = np.random.default_rng(seed) def get_Z(self, Fe_H): """ @@ -48,11 +49,9 @@ def Kalirai_mass(self, MZAMS): final = np.zeros(len(MZAMS)) - bad_idx = np.where(MZAMS < 0.5) - final[bad_idx] = -99 - - good_idx = np.where(MZAMS >= 0.5) + good_idx = MZAMS >= 0.5 final[good_idx] = result[good_idx] + final[~good_idx] = -99 return final @@ -730,7 +729,7 @@ def NS_mass(self, MZAMS): Kiziltan et al. (2010). """ - return np.random.normal(loc=1.36, scale=0.09, size=len(MZAMS)) + return self.rng.normal(loc=1.36, scale=0.09, size=len(MZAMS)) def generate_death_mass(self, mass_array): """ @@ -772,7 +771,7 @@ def generate_death_mass(self, mass_array): output_array = np.zeros((2, len(mass_array))) #Random array to get probabilities for what type of object will form - random_array = np.random.randint(1, 1001, size = len(mass_array)) + random_array = self.rng.intergers(1, 1001, size = len(mass_array)) codes = {'WD': 101, 'NS': 102, 'BH': 103} @@ -893,9 +892,9 @@ def NS_mass(self, MZAMS): """ if isinstance(MZAMS, np.ndarray): - return np.random.normal(loc=1.36, scale=0.09, size=len(MZAMS)) + return self.rng.normal(loc=1.36, scale=0.09, size=len(MZAMS)) else: - return np.random.normal(loc=1.36, scale=0.09, size=1)[0] + return self.rng.normal(loc=1.36, scale=0.09, size=1)[0] def BH_mass_low(self, MZAMS): @@ -914,8 +913,7 @@ def BH_mass_high(self, MZAMS, Z): # Solar metallicity (what Sam is using) Zsun = 0.014 - zfrac = Z/Zsun - + zfrac = np.atleast_1d(Z/Zsun) # super-solar Z gives identical results as solar Z above_idx = np.where(zfrac > 1) if len(above_idx) > 1: @@ -938,15 +936,14 @@ def prob_BH_high(self, Z): # Solar metallicity (what Sam is using) Zsun = 0.014 - zfrac = Z/Zsun + Z = np.atleast_1d(Z) + # Convert from [Fe/H] to Z + zfrac = Z / Zsun # super-solar Z gives identical results as solar Z - if zfrac > 1: - zfrac = 1.0 - - if zfrac < 0: - raise ValueError('Z must be non-negative') - + zfrac[zfrac > 1] = 1.0 + zfrac[zfrac < 0] = np.nan + pBH = 1 - 0.8*zfrac return pBH @@ -985,75 +982,83 @@ def generate_death_mass(self, mass_array, metallicity_array): """ #output_array[0] holds the remnant mass #output_array[1] holds the remnant type + mass_array = np.atleast_1d(mass_array) + metallicity_array = np.atleast_1d(metallicity_array) + output_array = np.zeros((2, len(mass_array))) codes = {'WD': 101, 'NS': 102, 'BH': 103} + # Array to store the remnant masses - rem_mass_array = np.zeros(len(mass_array)) + # rem_mass_array = np.zeros(len(mass_array)) # Convert from [Fe/H] to Z # FIXME: if have Fe/H = nan that makes Z = 0. Is that the behavior we want? Z_array = np.zeros((len(metallicity_array))) - metal_idx = np.where(metallicity_array != np.nan) + metal_idx = ~np.isnan(metallicity_array) Z_array[metal_idx] = self.get_Z(metallicity_array[metal_idx]) # Random array to get probabilities for what type of object will form - random_array = np.random.randint(1, 101, size = len(mass_array)) + random_array = self.rng.integers(1, 101, size=len(mass_array)) - id_array0 = np.where((mass_array < 0.5) | (mass_array >= 120)) - output_array[0][id_array0] = -99 * np.ones(len(id_array0)) - output_array[1][id_array0] = -1 * np.ones(len(id_array0)) + id_array0 = (mass_array < 0.5) | (mass_array >= 120) + output_array[0][id_array0] = -99 + output_array[1][id_array0] = -1 - id_array1 = np.where((mass_array >= 0.5) & (mass_array < 9)) + id_array1 = (mass_array >= 0.5) & (mass_array < 9) output_array[0][id_array1] = self.Kalirai_mass(mass_array[id_array1]) output_array[1][id_array1]= codes['WD'] - id_array2 = np.where((mass_array >= 9) & (mass_array < 15)) + id_array2 = (mass_array >= 9) & (mass_array < 15) output_array[0][id_array2] = self.NS_mass(mass_array[id_array2]) output_array[1][id_array2] = codes['NS'] - id_array3_BH = np.where((mass_array >= 15) & (mass_array < 21.8) & (random_array > 75)) + id_array3_BH = (mass_array >= 15) & (mass_array < 21.8) & (random_array > 75) output_array[0][id_array3_BH] = self.BH_mass_low(mass_array[id_array3_BH]) output_array[1][id_array3_BH] = codes['BH'] - id_array3_NS = np.where((mass_array >= 15) & (mass_array < 21.8) & (random_array <= 75)) + id_array3_NS = (mass_array >= 15) & (mass_array < 21.8) & (random_array <= 75) output_array[0][id_array3_NS] = self.NS_mass(mass_array[id_array3_NS]) output_array[1][id_array3_NS] = codes['NS'] - id_array4 = np.where((mass_array >= 21.8) & (mass_array < 25.2)) + id_array4 = (mass_array >= 21.8) & (mass_array < 25.2) output_array[0][id_array4] = self.BH_mass_low(mass_array[id_array4]) output_array[1][id_array4] = codes['BH'] - id_array5 = np.where((mass_array >= 25.2) & (mass_array < 27.4)) + id_array5 = (mass_array >= 25.2) & (mass_array < 27.4) output_array[0][id_array5] = self.NS_mass(mass_array[id_array5]) output_array[1][id_array5] = codes['NS'] - id_array6 = np.where((mass_array >= 27.4) & (mass_array < 39.6)) + id_array6 = (mass_array >= 27.4) & (mass_array < 39.6) output_array[0][id_array6] = self.BH_mass_low(mass_array[id_array6]) output_array[1][id_array6] = codes['BH'] - id_array7 = np.where((mass_array >= 39.6) & (mass_array < 60)) + id_array7 = (mass_array >= 39.6) & (mass_array < 60) output_array[0][id_array7] = self.BH_mass_high(mass_array[id_array7], Z_array[id_array7]) output_array[1][id_array7] = codes['BH'] - id_array8 = np.where((mass_array >= 60) & (mass_array < 120)) - for i in range(0, len(id_array8[0])): - pBH = self.prob_BH_high(Z_array[id_array8][i]) - if random_array[id_array8][i] > 100*pBH: - output_array[0][id_array8[0][i]] = self.BH_mass_high(mass_array[id_array8][i], - Z_array[id_array8][i]) - output_array[1][id_array8[0][i]] = codes['BH'] - - else: - output_array[0][id_array8[0][i]] = self.NS_mass(mass_array[id_array8][i]) - output_array[1][id_array8[0][i]] = codes['NS'] + BH_or_NS = np.where((mass_array >= 60) & (mass_array < 120))[0] + pBH = self.prob_BH_high(Z_array[BH_or_NS]) + is_BH = random_array[BH_or_NS] > 100 * pBH + + id_array8 = BH_or_NS[is_BH] + id_array9 = BH_or_NS[~is_BH] + + # Assign BH masses and types for BH-forming indices + output_array[0][id_array8] = self.BH_mass_high(mass_array[id_array8], Z_array[id_array8]) + output_array[1][id_array8] = codes['BH'] + + # Assign NS masses and types for NS-forming indices + output_array[0][id_array9] = self.NS_mass(mass_array[id_array9]) + output_array[1][id_array9] = codes['NS'] + #this is where sam's janky fix for unphysical BH massses goes #any BH with mass less then 3 M_sun is reassigned as a NS #and given a mass from the NS mass dist instead - id_array9 = np.where((output_array[1] == codes['BH']) & (output_array[0] < 3.0)) - output_array[0][id_array9] = self.NS_mass(mass_array[id_array9]) - output_array[1][id_array9] = codes['NS'] + id_array10 = (output_array[1] == codes['BH']) & (output_array[0] < 3.0) + output_array[0][id_array10] = self.NS_mass(mass_array[id_array10]) + output_array[1][id_array10] = codes['NS'] - return(output_array) + return output_array From bb49170a4366edc775e9c2d900196e4d0a6b47d6 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Sat, 26 Apr 2025 16:29:46 -0700 Subject: [PATCH 038/191] Add verbose control --- spisea/atmospheres.py | 24 +++++++++++++----------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index 8df2fbe1..35caeffd 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -12,7 +12,7 @@ log = logging.getLogger('atmospheres') -def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4): +def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4, verbose=False): """ Given atmosphere model, get temperature and gravity bounds """ @@ -77,14 +77,15 @@ def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4 if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) - # Print out changes, if any - if temperature_new != temperature: - teff_msg = 'Changing to T={0:6.0f} for T={1:6.0f} logg={2:4.2f}' - print( teff_msg.format(temperature_new, temperature, gravity)) - - if gravity_new != gravity: - logg_msg = 'Changing to logg={0:4.2f} for T={1:6.0f} logg={2:4.2f}' - print( logg_msg.format(gravity_new, temperature, gravity)) + if verbose: + # Print out changes, if any + if temperature_new != temperature: + teff_msg = 'Changing to T={0:6.0f} for T={1:6.0f} logg={2:4.2f}' + print( teff_msg.format(temperature_new, temperature, gravity)) + + if gravity_new != gravity: + logg_msg = 'Changing to logg={0:4.2f} for T={1:6.0f} logg={2:4.2f}' + print( logg_msg.format(gravity_new, temperature, gravity)) return (temperature_new, gravity_new) @@ -274,7 +275,7 @@ def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, return sp -def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True): +def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False): """ metallicity = [M/H] (def = 0) temperature = Kelvin (def = 24000) @@ -285,7 +286,8 @@ def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebi # Take care of atmospheres outside the catalog boundaries logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' if gravity > 4.3: - print( logg_msg.format(4.3, temperature, gravity)) + if verbose: + print( logg_msg.format(4.3, temperature, gravity)) gravity = 4.3 if rebin: From ce64564d3c60d7e883669dc753b25f160e7cb745 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Sat, 26 Apr 2025 16:30:36 -0700 Subject: [PATCH 039/191] Major Update: Accelerate generating resolved cluster by vectorization --- spisea/synthetic.py | 547 +++++++++++++++++++++++++++----------------- 1 file changed, 334 insertions(+), 213 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index d60119bc..50593f12 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -37,7 +37,7 @@ def Vega(): # Use Vega as our zeropoint... assume V=0.03 mag and all colors = 0.0 # These parameters are defined in Girardi+02 - vega = atm.get_kurucz_atmosphere(temperature=9550, + vega = atm.get_kurucz_atmosphere(temperature=9550, gravity=3.95, metallicity=-0.5) @@ -47,22 +47,39 @@ def Vega(): # This is (R/d)**2 as reported by Girardi et al. 2002, page 198, col 1. # and is used to convert to flux observed at Earth. - vega *= 6.247e-17 - + vega *= 6.247e-17 + return vega vega = Vega() +class Interpolator(object): + def __init__(self, xp, yp): + """Wrapper for np.interp to allow for pickling in multiprocessing. + + Parameters + ---------- + xp: array-like + x data points for interpolation + yp: array-like + y data points for interpolation + """ + self.xp = xp + self.yp = yp + + def __call__(self, x): + return np.interp(x, self.xp, self.yp, left=np.nan, right=np.nan) + class Cluster(object): """ Base class to create a cluster with user-specified isochrone, - imf, ifmr, and total mass. + imf, ifmr, and total mass. Parameters ----------- iso: isochrone object SPISEA isochrone object - + imf: imf object SPISEA IMF object @@ -90,17 +107,18 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=False, self.ifmr = ifmr self.cluster_mass = cluster_mass self.seed = seed - + self.rng = np.random.default_rng(self.seed) + return - + class ResolvedCluster(Cluster): """ Cluster sub-class that produces a *resolved* stellar cluster. - A table is output with the synthetic photometry and intrinsic - properties of the individual stars (or stellar systems, if + A table is output with the synthetic photometry and intrinsic + properties of the individual stars (or stellar systems, if mutliplicity is used in the IMF object). - If multiplicity is used, than a second table is produced that + If multiplicity is used, than a second table is produced that contains the properties of the companion stars independent of their primary stars. @@ -108,7 +126,7 @@ class ResolvedCluster(Cluster): ----------- iso: isochrone object SPISEA isochrone object - + imf: imf object SPISEA IMF object @@ -136,12 +154,13 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, if seed is not None and verbose: print('WARNING: random seed set to %i' % seed) - t1 = time.time() - ##### + ##### # Sample the IMF to build up our cluster mass. ##### - mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass, - seed=seed) + # start0 = time.time() + mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass) + # end0 = time.time() + # print('IMF sampling took {0:f} s.'.format(end0 - start0)) # Figure out the filters we will make. self.filt_names = self.set_filter_names() @@ -153,31 +172,49 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, interp_keys = ['Teff', 'L', 'logg', 'isWR', 'mass_current', 'phase'] + self.filt_names self.iso_interps = {} for ikey in interp_keys: - self.iso_interps[ikey] = interpolate.interp1d(self.iso.points['mass'], self.iso.points[ikey], - kind='linear', bounds_error=False, fill_value=np.nan) - - ##### + # self.iso_interps[ikey] = interpolate.interp1d(self.iso.points['mass'], self.iso.points[ikey], + # kind='linear', bounds_error=False, fill_value=np.nan) + self.iso_interps[ikey] = Interpolator(self.iso.points['mass'], self.iso.points[ikey]) + + ##### # Make a table to contain all the information about each stellar system. ##### + # start1 = time.time() star_systems = self._make_star_systems_table(mass, isMulti, sysMass) - + # end1 = time.time() + # print('Star systems table took {0:f} s.'.format(end1 - start1)) + # Trim out bad systems; specifically, stars with masses outside those provided # by the model isochrone (except for compact objects). + # start2 = time.time() star_systems, compMass = self._remove_bad_systems(star_systems, compMass) + # end2 = time.time() + # print('Bad system removal took {0:f} s.'.format(end2-start2)) - ##### + ##### # Make a table to contain all the information about companions. ##### if self.imf.make_multiples: - companions = self._make_companions_table(star_systems, compMass) - + # start3 = time.time() + star_systems, companions = self._make_companions_table_new(star_systems, compMass) + # end3 = time.time() + # print('Companion table new took {0:f} s.'.format(end3 - start3)) + self.companions = companions + + # compMass = [ + # [value for value, mask in zip(row, row_mask) if not mask] + # for row, row_mask in zip(compMass.data, compMass.mask) + # ] + # start3 = time.time() + # star_systems, companions = self._make_companions_table(star_systems, compMass) + # end3 = time.time() + # print('Companion table took {0:f} s.'.format(end3-start3)) + # self.companions = companions + ##### # Save our arrays to the object ##### self.star_systems = star_systems - - if self.imf.make_multiples: - self.companions = companions return @@ -186,13 +223,13 @@ def set_filter_names(self): Set filter column names """ filt_names = [] - + for col_name in self.iso.points.colnames: if 'm_' in col_name: filt_names.append(col_name) return filt_names - + def _make_star_systems_table(self, mass, isMulti, sysMass): """ Make a star_systems table and get synthetic photometry for each primary star. @@ -201,28 +238,20 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): names=['mass', 'isMultiple', 'systemMass']) N_systems = len(star_systems) - # Add columns for the Teff, L, logg, isWR, mass_current, phase for the primary stars. - star_systems.add_column( Column(np.zeros(N_systems, dtype=float), name='Teff') ) - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='L') ) - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='logg') ) - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='isWR') ) - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='mass_current') ) - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='phase') ) - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name='metallicity') ) + # Use our pre-built interpolators to fetch values from the isochrone for each star. + for key in ['Teff', 'L', 'logg', 'mass_current']: + star_systems.add_column(Column(self.iso_interps[key](star_systems['mass']), name=key)) + + # Treat out-of-range mass as isWR=True + star_systems.add_column(Column(~(self.iso_interps['isWR'](star_systems['mass']) < 0.5), name='isWR')) + star_systems.add_column(Column(np.round(self.iso_interps['phase'](star_systems['mass'])), name='phase')) + + star_systems['metallicity'] = np.ones(N_systems) * self.iso.metallicity # Add the filter columns to the table. They are empty so far. # Keep track of the filter names in : filt_names for filt in self.filt_names: - star_systems.add_column( Column(np.empty(N_systems, dtype=float), name=filt) ) - - # Use our pre-built interpolators to fetch values from the isochrone for each star. - star_systems['Teff'] = self.iso_interps['Teff'](star_systems['mass']) - star_systems['L'] = self.iso_interps['L'](star_systems['mass']) - star_systems['logg'] = self.iso_interps['logg'](star_systems['mass']) - star_systems['isWR'] = np.round(self.iso_interps['isWR'](star_systems['mass'])) - star_systems['mass_current'] = self.iso_interps['mass_current'](star_systems['mass']) - star_systems['phase'] = np.round(self.iso_interps['phase'](star_systems['mass'])) - star_systems['metallicity'] = np.ones(N_systems)*self.iso.metallicity + star_systems.add_column(Column(self.iso_interps[filt](star_systems['mass']), name=filt)) # For a very small fraction of stars, the star phase falls on integers in-between # the ones we have definition for, as a result of the interpolation. For these @@ -232,29 +261,22 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # effect is so small # Convert nan_to_num to avoid errors on greater than, less than comparisons star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) - bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) - # Print warning, if desired - verbose=False - if verbose: - for ii in range(len(bad[0])): - print('WARNING: changing phase {0} to 5'.format(star_systems['phase'][bad[0][ii]])) + bad = (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99) star_systems['phase'][bad] = 5 - - for filt in self.filt_names: - star_systems[filt] = self.iso_interps[filt](star_systems['mass']) + ##### # Make Remnants # Note: Some models already have WDs in them. If they do, then they shouldn't # be handled by this code here (because their Teff > 0). - # + # # Remnants have flux = 0 in all bands if they are generated here. - ##### + ##### if self.ifmr != None: # Identify compact objects as those with Teff = 0 or with phase > 100. highest_mass_iso = self.iso.points['mass'].max() idx_rem = np.where((np.isnan(star_systems['Teff'])) & (star_systems['mass'] > highest_mass_iso))[0] - + # Calculate remnant mass and ID for compact objects; update remnant_id and # remnant_mass arrays accordingly if 'metallicity_array' in inspect.getfullargspec(self.ifmr.generate_death_mass).args: @@ -265,7 +287,7 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # Drop remnants where it is not relevant (e.g. not a compact object or # outside mass range IFMR is defined for) - good = np.where(r_id_tmp > 0) + good = r_id_tmp > 0 idx_rem_good = idx_rem[good] star_systems['mass_current'][idx_rem_good] = r_mass_tmp[good] @@ -276,13 +298,111 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): star_systems[filt][idx_rem_good] = np.full(len(idx_rem_good), np.nan) return star_systems - + + + def _make_companions_table_new(self, star_systems, compMass): + """Make companions table for resolved clusters with multiplicity. + + Parameters + ---------- + star_systems : astropy.table.Table + Table containing the properties of the primary stars. + compMass : numpy.ma.MaskedArray + Masked array containing the masses of the companions. + + Returns + ------- + companions : astropy.table.Table + """ + N_systems = len(star_systems) + N_companions = np.sum(~compMass.mask, axis=1) + N_comp_tot = np.sum(N_companions) + star_systems.add_column(Column(N_companions, name='N_companions')) + system_index = np.repeat(np.arange(N_systems), N_companions) + companions = Table([system_index], names=['system_idx']) + companions.add_column(np.zeros(N_comp_tot, dtype=float), name='mass') + + if isinstance(self.imf._multi_props, multiplicity.MultiplicityResolvedDK): + companions.add_column(Column(self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx']]), name='log_a')) + companions.add_column(Column(self.imf._multi_props.random_e(self.rng.random(N_comp_tot)), name='e')) + companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters( + self.rng.random(N_comp_tot), + self.rng.random(N_comp_tot), + self.rng.random(N_comp_tot) + ) + + companions['mass'] = compMass.compressed() + for key in ['Teff', 'L', 'logg', 'mass_current']: + companions[key] = self.iso_interps[key](companions['mass']) + + for key in ['isWR', 'phase']: + companions[key] = np.round(self.iso_interps[key](companions['mass'])) + + companions['metallicity'] = np.ones(N_comp_tot) * self.iso.metallicity + + # For a very small fraction of stars, the star phase falls on integers in-between + # the ones we have definition for, as a result of the interpolation. For these + # stars, round phase down to nearest defined phase (e.g., if phase is 71, + # then round it down to 5, rather than up to 101). + # Convert nan_to_num to avoid errors on greater than, less than comparisons + companions_phase_non_nan = np.nan_to_num(companions['phase'], nan=-99) + companions['phase'][ + (companions_phase_non_nan > 5) & + (companions_phase_non_nan < 101) & + (companions_phase_non_nan != 9) & + (companions_phase_non_nan != -99) + ] = 5 + + # Update primary fluxes to include the flux of companions. + for filt in self.filt_names: + companions[filt] = self.iso_interps[filt](companions['mass']) + primary_flux = 10**(-star_systems[filt] / 2.5) + # Sum the flux of all companions in each system + companions_flux = np.bincount(companions['system_idx'], weights=10**(-companions[filt] / 2.5), minlength=N_systems) + combined_flux = np.nansum(np.vstack((primary_flux, companions_flux)), axis=0) + combined_flux[combined_flux == 0] = np.nan + star_systems[filt] = -2.5 * np.log10(combined_flux) + + ##### + # Make Remnants with flux = 0 in all bands. + ##### + if self.ifmr: + # Identify compact objects as those with Teff = 0 or with masses above the max iso mass + highest_mass_iso = self.iso.points['mass'].max() + remnant_idx = np.where(np.isnan(companions['Teff']) & (companions['mass'] > highest_mass_iso))[0] + self.remnant_idx_new = remnant_idx + # Calculate remnant mass and ID for compact objects; update remnant_id and remnant_mass arrays accordingly + if 'metallicity_array' in inspect.getfullargspec(self.ifmr.generate_death_mass).args: + remnant_mass, remnant_code = self.ifmr.generate_death_mass(mass_array=companions['mass'][remnant_idx], metallicity_array=companions['metallicity'][remnant_idx]) + else: + remnant_mass, remnant_code = self.ifmr.generate_death_mass(mass_array=companions['mass'][remnant_idx]) + + # Drop remnants where it is not relevant (e.g. not a compact object or outside mass range IFMR is defined for) + remnant_valid = remnant_code > 0 + remnant_valid_idx = remnant_idx[remnant_valid] + self.remnant_mass_new = remnant_mass + self.remnant_valid_idx_new = remnant_valid_idx + companions['mass_current'][remnant_valid_idx] = remnant_mass[remnant_valid] + companions['phase'][remnant_valid_idx] = remnant_code[remnant_valid] + # Give remnants a magnitude of nan, so they can be filtered out later when calculating flux. + for filt in self.filt_names: + companions[filt][remnant_valid_idx] = np.full(len(remnant_idx[remnant_valid]), np.nan) + + companions_teff_non_nan = np.nan_to_num(companions['Teff'], nan=-99) + if self.verbose and sum(companions_teff_non_nan > 0) != N_comp_tot: + print(f'Found {N_comp_tot - sum(companions_teff_non_nan > 0):d} companions out of stellar mass range') + + assert companions['mass'][companions_teff_non_nan > 0].min() > 0, "Companion mass is not positive" + + return star_systems, companions + + def _make_companions_table(self, star_systems, compMass): N_systems = len(star_systems) - + ##### - # MULTIPLICITY + # MULTIPLICITY # Make a second table containing all the companion-star masses. # This table will be much longer... here are the arrays: # sysIndex - the index of the system this star belongs too @@ -306,25 +426,29 @@ def _make_companions_table(self, star_systems, compMass): companions.add_column( Column(np.empty(N_comp_tot, dtype=float), name='metallicity') ) for filt in self.filt_names: companions.add_column( Column(np.empty(N_comp_tot, dtype=float), name=filt) ) - + if isinstance(self.imf._multi_props, multiplicity.MultiplicityResolvedDK): companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='log_a') ) companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='e') ) companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='i', description = 'degrees') ) companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='Omega') ) companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='omega') ) - + for ii in range(len(companions)): companions['log_a'][ii] = self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx'][ii]]) - - companions['e'] = self.imf._multi_props.random_e(np.random.rand(N_comp_tot)) - companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters(np.random.rand(N_comp_tot),np.random.rand(N_comp_tot),np.random.rand(N_comp_tot)) + + companions['e'] = self.imf._multi_props.random_e(self.rng.random(N_comp_tot)) + companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters( + self.rng.random(N_comp_tot), + self.rng.random(N_comp_tot), + self.rng.random(N_comp_tot) + ) # Make an array that maps system index (ii), companion index (cc) to # the place in the 1D companions array. N_comp_max = N_companions.max() - + comp_index = np.zeros((N_systems, N_comp_max), dtype=int) kk = 0 for ii in range(N_systems): @@ -340,9 +464,10 @@ def _make_companions_table(self, star_systems, compMass): idx = np.where(N_companions >= cc)[0] # Get the location in the companions array for each system and - # the cc'th companion. + # the cc'th companion. cdx = comp_index[idx, cc-1] - + + # companions['mass'][cdx] = compMass[idx, cc-1] companions['mass'][cdx] = [compMass[ii][cc-1] for ii in idx] comp_mass = companions['mass'][cdx] @@ -391,13 +516,13 @@ def _make_companions_table(self, star_systems, compMass): # as np.nan. Otherwise, add fluxes together good = np.where( (f1 != 0) | (f2 != 0) ) bad = np.where( (f1 == 0) & (f2 == 0) ) - + star_systems[filt][idx[good]] = -2.5 * np.log10(f1[good] + f2[good]) star_systems[filt][idx[bad]] = np.nan ##### # Make Remnants with flux = 0 in all bands. - ##### + ##### if self.ifmr != None: # Identify compact objects as those with Teff = 0 or with masses above the max iso mass highest_mass_iso = self.iso.points['mass'].max() @@ -411,7 +536,7 @@ def _make_companions_table(self, star_systems, compMass): metallicity_array=companions['metallicity'][cdx_rem]) else: r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem]) - + # Drop remnants where it is not relevant (e.g. not a compact object or # outside mass range IFMR is defined for) @@ -420,7 +545,7 @@ def _make_companions_table(self, star_systems, compMass): companions['mass_current'][cdx_rem_good] = r_mass_tmp[good] companions['phase'][cdx_rem_good] = r_id_tmp[good] - + # Give remnants a magnitude of nan, so they can be filtered out later when calculating flux. for filt in self.filt_names: companions[filt][cdx_rem_good] = np.full(len(cdx_rem_good), np.nan) @@ -434,18 +559,18 @@ def _make_companions_table(self, star_systems, compMass): print( 'Found {0:d} companions out of stellar mass range'.format(N_comp_tot - len(idx))) # Double check that everything behaved properly. - assert companions['mass'][idx].min() > 0 + # assert companions['mass'][idx].min() > 0, "Companion mass must be positive" + + return star_systems, companions - return companions - def _remove_bad_systems(self, star_systems, compMass): """ Helper function to remove stars with masses outside the isochrone - mass range from the cluster. These stars are identified by having + mass range from the cluster. These stars are identified by having a Teff = 0, as set up by _make_star_systems_table_interp. - If self.ifmr == None, then both high and low-mass bad systems are - removed. If self.ifmr != None, then we will save the high mass systems + If self.ifmr == None, then both high and low-mass bad systems are + removed. If self.ifmr != None, then we will save the high mass systems since they will be plugged into an ifmr later. """ N_systems = len(star_systems) @@ -456,12 +581,12 @@ def _remove_bad_systems(self, star_systems, compMass): star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) if self.ifmr == None: # Keep only those stars with Teff assigned. - idx = np.where(star_systems_teff_non_nan > 0)[0] + idx = star_systems_teff_non_nan > 0 else: - # Keep stars (with Teff) and any other compact objects (with phase info). - idx = np.where( (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) )[0] + # Keep stars (with Teff) and any other compact objects (with phase info). + idx = (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) - if len(idx) != N_systems and self.verbose: + if self.verbose and sum(idx) != N_systems: print( 'Found {0:d} stars out of mass range'.format(N_systems - len(idx))) star_systems = star_systems[idx] @@ -469,8 +594,8 @@ def _remove_bad_systems(self, star_systems, compMass): if self.imf.make_multiples: # Clean up companion stuff (which we haven't handled yet) - compMass = [compMass[ii] for ii in idx] - + compMass = compMass[idx] + return star_systems, compMass @@ -483,7 +608,7 @@ class ResolvedClusterDiffRedden(ResolvedCluster): ----------- iso: isochrone object SPISEA isochrone object - + imf: imf object SPISEA IMF object @@ -493,7 +618,7 @@ class ResolvedClusterDiffRedden(ResolvedCluster): delta_AKs: float Amount of differential extinction to apply to synthetic photometry, in terms of magnitudes of extinction in the Ks filter. Specifically, - delta_AKs defines the standard deviation of a Gaussian distribution + delta_AKs defines the standard deviation of a Gaussian distribution from which the delta_AKs values will be drawn from for each individual system. @@ -516,14 +641,10 @@ def __init__(self, iso, imf, cluster_mass, deltaAKs, ResolvedCluster.__init__(self, iso, imf, cluster_mass, ifmr=ifmr, verbose=verbose, seed=seed) - # Set random seed, if desired - if seed is not None: - np.random.seed(seed=seed) - # Extract the extinction law from the isochrone object redlaw_str = iso.points.meta['REDLAW'] red_law = reddening.get_red_law(redlaw_str) - + # For a given delta_AKs (Gaussian sigma of reddening distribution at Ks), # figure out the equivalent delta_filt values for all other filters. #t1 = time.time() @@ -535,7 +656,7 @@ def __init__(self, iso, imf, cluster_mass, deltaAKs, for filt in self.filt_names: obs_str = get_obs_str(filt) filt_info = get_filter_info(obs_str) - + mag_lo = mag_in_filter(red_vega_lo, filt_info) mag_hi = mag_in_filter(red_vega_hi, filt_info) delta_red_filt[filt] = mag_hi - mag_lo @@ -543,7 +664,7 @@ def __init__(self, iso, imf, cluster_mass, deltaAKs, # Perturb all of star systems' photometry by a random amount corresponding to # differential de-reddening. The distribution is normal with a width of # Aks +/- deltaAKs in each filter - rand_red = np.random.randn(len(self.star_systems)) + rand_red = self.rng.standard_normal(len(self.star_systems)) for filt in self.filt_names: self.star_systems[filt] += rand_red * delta_red_filt[filt] @@ -563,18 +684,18 @@ def __init__(self, iso, imf, cluster_mass, deltaAKs, #t2 = time.time() #print 'Diff redden: {0}'.format(t2 - t1) return - + class UnresolvedCluster(Cluster): """ Cluster sub-class that produces an *unresolved* stellar cluster. - Output is a combined spectrum that is the sum of the individual + Output is a combined spectrum that is the sum of the individual spectra of the cluster stars. Parameters ----------- iso: isochrone object SPISEA isochrone object - + imf: imf object SPISEA IMF object @@ -592,10 +713,10 @@ def __init__(self, iso, imf, cluster_mass, wave_range=[3000, 52000], verbose=False): # Doesn't do much. Cluster.__init__(self, iso, imf, cluster_mass, verbose=verbose) - + # Sample a power-law IMF randomly self.mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass) - + temp = np.zeros(len(self.mass), dtype=float) self.mass_all = np.zeros(len(self.mass), dtype=float) self.spec_list = [None] * len(self.mass) @@ -629,7 +750,7 @@ def __init__(self, iso, imf, cluster_mass, tmpspectrim = spectrum.trimSpectrum(tmpspecresamp,wave_range[0],wave_range[1]) self.spec_list_trim[ii] = tmpspectrim spec_list_trim_np[:,ii] = np.asarray(tmpspectrim._fluxtable) - + t2 = time.time() print( 'Mass matching took {0:f} s.'.format(t2-t1)) @@ -651,7 +772,7 @@ def __init__(self, iso, imf, cluster_mass, self.spec_trim = np.sum(spec_list_trim_np,1) self.wave_trim = self.spec_list_trim[0].wave - + t4 = time.time() print( 'Spec trimming took {0:f}s'.format(t4-t3)) @@ -659,10 +780,10 @@ def __init__(self, iso, imf, cluster_mass, print( 'Total cluster mass is {0:f} M_sun'.format(self.mass_tot)) return - + class Isochrone(object): """ - Base Isochrone class. + Base Isochrone class. Parameters ---------- @@ -680,20 +801,20 @@ class Isochrone(object): Default is 0. evo_model: model evolution class, optional - Set the stellar evolution model class. + Set the stellar evolution model class. Default is evolution.MISTv1(). atm_func: model atmosphere function, optional - Set the stellar atmosphere models for the stars. + Set the stellar atmosphere models for the stars. Default is get_merged_atmosphere. wd_atm_func: white dwarf model atmosphere function, optional - Set the stellar atmosphere models for the white dwafs. - Default is get_wd_atmosphere + Set the stellar atmosphere models for the white dwafs. + Default is get_wd_atmosphere mass_sampling : int, optional Sample the raw isochrone every `mass_sampling` steps. The default - is mass_sampling = 0, which is the native isochrone mass sampling + is mass_sampling = 0, which is the native isochrone mass sampling of the evolution model. wave_range : list, optional @@ -722,7 +843,7 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, t1 = time.time() - + c = constants # Assert that the wavelength ranges are within the limits of the @@ -733,7 +854,7 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, except: print('Desired wavelength range invalid. Limit to 1000 - 10000 A') return - + # Get solar metallicity models for a population at a specific age. # Takes about 0.1 seconds. evol = evo_model.isochrone(age=10**logAge, @@ -749,7 +870,7 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, evol = evol[idx] if max_mass != None: idx = np.where(evol['mass'] <= max_mass) - evol = evol[idx] + evol = evol[idx] # Trim down the table by selecting every Nth point where # N = mass sampling factor. @@ -799,10 +920,10 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, star *= (R / distance)**2 # in erg s^-1 cm^-2 A^-1 # Redden the spectrum. This doesn't take much time at all. - red = red_law.reddening(AKs).resample(star.wave) + red = red_law.reddening(AKs).resample(star.wave) star *= red - - # Save the final spectrum to our spec_list for later use. + + # Save the final spectrum to our spec_list for later use. self.spec_list.append(star) # Append all the meta data to the summary table. @@ -830,7 +951,7 @@ def plot_HR_diagram(self, savefile=None): Parameters ----------- savefile: path or None, optional - Path to file plot too, if desired. + Path to file plot too, if desired. Default is None """ plt.clf() @@ -839,7 +960,7 @@ def plot_HR_diagram(self, savefile=None): plt.gca().invert_xaxis() plt.xlabel(r'T$_{\mathrm{eff}}$ (K)') plt.ylabel('Luminosity (erg / s)') - + fmt_title = 'logAge={0:.2f}, d={1:.2f} kpc, AKs={2:.2f}' plt.title(fmt_title.format(self.points.meta['LOGAGE'], self.points.meta['DISTANCE']/1e3, @@ -847,7 +968,7 @@ def plot_HR_diagram(self, savefile=None): if savefile != None: plt.savefig(savefile) - + return def plot_mass_luminosity(self, savefile=None): @@ -857,28 +978,28 @@ def plot_mass_luminosity(self, savefile=None): Parameters ----------- savefile: path or None, optional - Path to file plot too, if desired. + Path to file plot too, if desired. Default is None """ plt.clf() plt.loglog(self.points['mass'], self.points['L'], 'k.') plt.xlabel(r'Mass (M$_\odot$)') plt.ylabel('Luminosity (erg / s)') - + fmt_title = 'logAge={0:.2f}, d={1:.2f} kpc, AKs={2:.2f}' plt.title(fmt_title.format(self.points.meta['LOGAGE'], self.points.meta['DISTANCE']/1e3, self.points.meta['AKS'])) - + if savefile != None: plt.savefig(savefile) - + return class IsochronePhot(Isochrone): """ - Make an isochrone with synthetic photometry in various filters. - Load from file if possible. + Make an isochrone with synthetic photometry in various filters. + Load from file if possible. Parameters ---------- @@ -896,16 +1017,16 @@ class IsochronePhot(Isochrone): Default is 0. evo_model: model evolution class, optional - Set the stellar evolution model class. + Set the stellar evolution model class. Default is evolution.MISTv1(). atm_func: model atmosphere function, optional - Set the stellar atmosphere models for the stars. + Set the stellar atmosphere models for the stars. Default is atmospheres.get_merged_atmosphere. wd_atm_func: white dwarf model atmosphere function, optional - Set the stellar atmosphere models for the white dwafs. - Default is atmospheres.get_wd_atmosphere + Set the stellar atmosphere models for the white dwafs. + Default is atmospheres.get_wd_atmosphere red_law : reddening law object, optional Define the reddening law for the synthetic photometry. @@ -913,14 +1034,14 @@ class IsochronePhot(Isochrone): iso_dir : path, optional Path to isochrone directory. Code will check isochrone - directory to see if isochrone file already exists; if it - does, it will just read the isochrone. If the isochrone + directory to see if isochrone file already exists; if it + does, it will just read the isochrone. If the isochrone file doesn't exist, then save isochrone to the isochrone directory. mass_sampling : int, optional Sample the raw isochrone every `mass_sampling` steps. The default - is mass_sampling = 0, which is the native isochrone mass sampling + is mass_sampling = 0, which is the native isochrone mass sampling of the evolution model. wave_range : list, optional @@ -941,14 +1062,14 @@ class IsochronePhot(Isochrone): which is often sufficient synthetic photometry in most cases. recomp : boolean, optional - If true, recalculate the isochrone photometry even if + If true, recalculate the isochrone photometry even if the savefile exists. You should recompute anytime you change the filter set (see filters below). filters : array of strings, optional Define what filters the synthetic photometry - will be calculated for, via the filter string - identifier. + will be calculated for, via the filter string + identifier. """ def __init__(self, logAge, AKs, distance, metallicity=0.0, @@ -983,11 +1104,11 @@ def __init__(self, logAge, AKs, distance, else: metal_pre = 'p' metal_flag = int(abs(metallicity)*10) - + save_file_fmt = '{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}_{4}{5:2s}.fits' self.save_file = save_file_fmt.format(iso_dir, logAge, AKs, str(distance).zfill(5), metal_pre, str(metal_flag).zfill(2)) self.save_file_legacy = save_file_fmt.format(iso_dir, logAge, AKs, str(distance).zfill(5), metal_pre, str(metal_flag).zfill(2)) - + # Expected filters self.filters = filters @@ -1004,7 +1125,7 @@ def __init__(self, logAge, AKs, distance, red_law=red_law, mass_sampling=mass_sampling, min_mass=min_mass, max_mass=max_mass, rebin=rebin) self.verbose = True - + # Make photometry self.make_photometry(rebin=rebin, vega=vega) else: @@ -1018,11 +1139,11 @@ def __init__(self, logAge, AKs, distance, return def make_photometry(self, rebin=True, vega=vega): - """ + """ Make synthetic photometry for the specified filters. This function udpates the self.points table to include new columns with the photometry. - + """ startTime = time.time() @@ -1040,7 +1161,7 @@ def make_photometry(self, rebin=True, vega=vega): for ii in self.filters: prt_fmt = 'Starting filter: {0:s} Elapsed time: {1:.2f} seconds' print( prt_fmt.format(ii, time.time() - startTime)) - + filt = get_filter_info(ii, rebin=rebin, vega=vega) filt_name = get_filter_col_name(ii) @@ -1048,15 +1169,15 @@ def make_photometry(self, rebin=True, vega=vega): col_name = 'm_' + filt_name mag_col = Column(np.zeros(npoints, dtype=float), name=col_name) self.points.add_column(mag_col) - + # Loop through each star in the isochrone and do the filter integration print('Starting synthetic photometry') for ss in range(npoints): star = self.spec_list[ss] # These are already extincted, observed spectra. star_mag = mag_in_filter(star, filt) - + self.points[col_name][ss] = star_mag - + if (self.verbose and (ss % 100) == 0): print( verbose_fmt.format(self.points['mass'][ss], self.points['Teff'][ss], filt_name, star_mag)) @@ -1073,27 +1194,27 @@ def make_photometry(self, rebin=True, vega=vega): def check_save_file(self, evo_model, atm_func, red_law): """ - Check to see if save_file exists, as saved by the save_file - and save_file_legacy objects. If the filename exists, check the + Check to see if save_file exists, as saved by the save_file + and save_file_legacy objects. If the filename exists, check the meta-data as well. returns a boolean: True is file exists, false otherwise """ out_bool = False - + if os.path.exists(self.save_file) | os.path.exists(self.save_file_legacy): try: tmp = Table.read(self.save_file) except: tmp = Table.read(self.save_file_legacy) - - + + # See if the meta-data matches: evo model, atm_func, redlaw if ( (tmp.meta['EVOMODEL'] == type(evo_model).__name__) & (tmp.meta['ATMFUNC'] == atm_func.__name__) & (tmp.meta['REDLAW'] == red_law.name) ): out_bool = True - + return out_bool def plot_CMD(self, mag1, mag2, savefile=None): @@ -1107,7 +1228,7 @@ def plot_CMD(self, mag1, mag2, savefile=None): mag2 : string The name of the second magnitude column to be plotted. savefile : string (default None) - If a savefile is specified, then the plot will be saved to that file. + If a savefile is specified, then the plot will be saved to that file. """ plt.clf() plt.plot(self.points[mag1] - self.points[mag2], self.points[mag1], @@ -1115,7 +1236,7 @@ def plot_CMD(self, mag1, mag2, savefile=None): plt.gca().invert_yaxis() plt.xlabel(mag1 + ' - ' + mag2 + ' (mag)') plt.ylabel(mag1 + ' (mag)') - + fmt_title = 'logAge={0:.2f}, d={1:.2f} kpc, AKs={2:.2f}' plt.title(fmt_title.format(self.points.meta['LOGAGE'], self.points.meta['DISTANCE']/1e3, @@ -1123,34 +1244,34 @@ def plot_CMD(self, mag1, mag2, savefile=None): if savefile != None: plt.savefig(savefile) - + return def plot_mass_magnitude(self, mag, savefile=None): """ Make a standard mass-luminosity relation plot for this isochrone. - + Parameters ---------- mag : string The name of the magnitude column to be plotted. savefile : string (default None) - If a savefile is specified, then the plot will be saved to that file. + If a savefile is specified, then the plot will be saved to that file. """ plt.clf() plt.semilogx(self.points['mass'], self.points[mag], 'k.') plt.gca().invert_yaxis() plt.xlabel(r'Mass (M$_\odot$)') plt.ylabel(mag + ' (mag)') - + fmt_title = 'logAge={0:.2f}, d={1:.2f} kpc, AKs={2:.2f}' plt.title(fmt_title.format(self.points.meta['LOGAGE'], self.points.meta['DISTANCE']/1e3, self.points.meta['AKS'])) - + if savefile != None: plt.savefig(savefile) - + return #===================================================# @@ -1172,7 +1293,7 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, Functions on this object: apply_reddening (Apply reddening with defined redlaw and AKs) make_photometry (make synthetic photometry for spectra) - + Parameters ---------- logAge : float @@ -1210,13 +1331,13 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, spectrum. This is very useful to save computation time down the road. """ - t1 = time.time() + t1 = time.time() c = constants # Get solar metallicity models for a population at a specific age. # Takes about 0.1 seconds. - evol = evo_model.isochrone(age=10**logAge) # solar metallicity - + evol = evo_model.isochrone(age=10**logAge) # solar metallicity + # Eliminate cases where log g is less than 0 idx = np.where(evol['logg'] > 0) evol = evol[idx] @@ -1227,8 +1348,8 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, evol = evol[idx] if max_mass != None: idx = np.where(evol['mass'] <= max_mass) - evol = evol[idx] - + evol = evol[idx] + # Trim down the table by selecting every Nth point where # N = mass sampling factor. evol = evol[::mass_sampling] @@ -1263,18 +1384,18 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, # Get the atmosphere model now. Wavelength is in Angstroms # This is the time-intensive call... everything else is negligable. star = atm_func(temperature=T, gravity=gravity) - + # Trim wavelength range down to JHKL range (0.5 - 5.2 microns) star = spectrum.trimSpectrum(star, wave_range[0], wave_range[1]) # Convert into flux observed at Earth (unreddened) star *= (R / distance)**2 # in erg s^-1 cm^-2 A^-1 - - # Save the final spectrum to our spec_list for later use. + + # Save the final spectrum to our spec_list for later use. self.spec_list.append(star) # Append all the meta data to the summary table. - + tab.meta['ATMFUNC'] = atm_func.__name__ tab.meta['EVOMODEL'] = type(evo_model).__name__ tab.meta['LOGAGE'] = logAge @@ -1283,10 +1404,10 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, tab.meta['WAVEMAX'] = wave_range[1] self.points = tab - + t2 = time.time() print('Isochrone generation took {0:f} s.'.format(t2-t1)) - + return def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max=None): @@ -1298,7 +1419,7 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= ---------- AKs: float Total extinction in AKs - + extinction_law: SPISEA extinction object Extinction law to be used on the spectra @@ -1308,13 +1429,13 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= dAKs_max: float or None If not none, defines the maximum |dAKs| a star can - have in gaussian distribution case + have in gaussian distribution case dist: string, 'uniform' or 'gaussian' Distribution to draw differential reddening from. If uniform, dAKs will cut off at Aks +/- dAKs. Otherwise, will draw from Gaussian of width AKs +/- dAks - + """ self.AKs = np.ones(len(self.spec_list)) # Apply reddening to each object in the spec list @@ -1325,25 +1446,25 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= # extinction law if dAKs != 0: if dist == 'gaussian': - AKs_act = np.random.normal(loc=AKs, scale=dAKs) + AKs_act = self.rng.normal(loc=AKs, scale=dAKs) # Apply dAKs_max if desired. Redo if diff > dAKs_max if dAKs_max != None: diff = abs(AKs_act - AKs) while diff > dAKs_max: print('While loop active') - AKs_act = np.random.normal(loc=AKs, scale=dAKs) + AKs_act = self.rng.normal(loc=AKs, scale=dAKs) diff = abs(AKs_act - AKs) elif dist == 'uniform': low = AKs - dAKs high = AKs + dAKs - AKs_act = np.random.uniform(low=low, high=high) + AKs_act = self.rng.uniform(low=low, high=high) else: print('dist {0} undefined'.format(dist)) return else: AKs_act = AKs - red = extinction_law.reddening(AKs_act).resample(star.wave) + red = extinction_law.reddening(AKs_act).resample(star.wave) star *= red # Update the spectrum in spec list @@ -1356,7 +1477,7 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= return def make_photometry(self, filters, rebin=True): - """ + """ Make synthetic photometry for the specified filters. This function udpates the self.points table to include new columns with the photometry. @@ -1366,11 +1487,11 @@ def make_photometry(self, filters, rebin=True): filters : dictionary A dictionary containing the filter name (for the output columns) and the filter specification string that can be processed by pysynphot. - + rebin: boolean - True to rebin filter function (only used if non-zero transmission points are + True to rebin filter function (only used if non-zero transmission points are larger than 1500 points) - + """ npoints = len(self.points) @@ -1387,35 +1508,35 @@ def make_photometry(self, filters, rebin=True): col_name = 'mag_' + filt_name mag_col = Column(np.zeros(npoints, dtype=float), name=col_name) self.points.add_column(mag_col) - + # Loop through each star in the isochrone and do the filter integration for ss in range(npoints): star = self.spec_list[ss] # These are already extincted, observed spectra. star_mag = mag_in_filter(star, filt) - + self.points[col_name][ss] = star_mag - - + + endTime = time.time() print( ' Time taken: {0:.2f} seconds'.format(endTime - ts)) return def get_filter_info(name, vega=vega, rebin=True): - """ + """ Define filter functions, setting ZP according to Vega spectrum. Input name is the SPISEA obs_string """ tmp = name.split(',') filterName = tmp[-1] - + if name.startswith('nirc2'): filt = filters.get_nirc2_filt(filterName) elif name.startswith('2mass'): filt = filters.get_2mass_filt(filterName) - + elif name.startswith('vista'): filt = filters.get_vista_filt(filterName) @@ -1430,10 +1551,10 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('jg'): filt = filters.get_Johnson_Glass_filt(filterName) - + elif name.startswith('nirc1'): filt = filters.get_nirc1_filt(filterName) - + elif name.startswith('ctio_osiris'): filt = filters.get_ctio_osiris_filt(filterName) @@ -1445,7 +1566,7 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('ukirt'): filt = filters.get_ukirt_filt(filterName) - + elif name.startswith('keck_osiris'): filt = filters.get_keck_osiris_filt(filterName) @@ -1458,17 +1579,17 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('hawki'): filt = filters.get_hawki_filt(filterName) - + elif name.startswith('rubin'): filt = filters.get_rubin_filt(filterName) - + else: # Otherwise, look for the filter info in the cdbs/mtab and cdbs/comp files try: filt = ObsBandpass(name) except: raise Exception('Filter {0} not understood. Check spelling and make sure cdbs/mtab and cdbs/comp files are up to date'.format(name)) - + # Convert to ArraySpectralElement for resampling. filt = spectrum.ArraySpectralElement(filt.wave, filt.throughput, waveunits=filt.waveunits, @@ -1486,14 +1607,14 @@ def get_filter_info(name, vega=vega, rebin=True): # Otherwise, throw an error idx = np.where(filt.throughput > 0.001)[0] if (min(filt.wave[idx]) < min(vega.wave)) | (max(filt.wave[idx]) > max(vega.wave)): - raise ValueError('Vega spectrum doesnt cover filter wavelength range!') + raise ValueError('Vega spectrum doesnt cover filter wavelength range!') vega_obs = obs.Observation(vega, filt, binset=filt.wave, force='taper') #vega_flux = vega_obs.binflux.sum() diff = np.diff(vega_obs.binwave) diff = np.append(diff, diff[-1]) vega_flux = np.sum(vega_obs.binflux * diff) - + vega_mag = 0.03 filt.flux0 = vega_flux @@ -1504,7 +1625,7 @@ def get_filter_info(name, vega=vega, rebin=True): def get_filter_col_name(obs_str): """ - Get standard column name for synthetic photometry based on + Get standard column name for synthetic photometry based on the input string. The input string is expected to be an appropriate SPISEA obs_string """ @@ -1523,7 +1644,7 @@ def get_filter_col_name(obs_str): filt_name = 'hst_{0}'.format(tmp[-1]) else: filt_name = '{0}_{1}'.format(tmp[0], tmp[1]) - + return filt_name def get_obs_str(col): @@ -1533,7 +1654,7 @@ def get_obs_str(col): """ # Remove the trailing m_ name = col[2:] - + # Define dictionary for filters filt_list = {'hst_f127m': 'wfc3,ir,f127m', 'hst_f139m': 'wfc3,ir,f139m', 'hst_f153m': 'wfc3,ir,f153m', 'hst_f814w': 'acs,wfc1,f814w', 'hst_f125w': 'wfc3,ir,f125w', 'hst_f160w': 'wfc3,ir,f160w', @@ -1555,7 +1676,7 @@ def get_obs_str(col): 'jwst_F187N': 'jwst,F187N', 'jwst_F200W': 'jwst,F200W', 'jwst_F210M': 'jwst,F210M', - 'jwst_F250M': 'jwst,F250M', + 'jwst_F250M': 'jwst,F250M', 'jwst_F277W': 'jwst,F277W', 'jwst_F300M': 'jwst,F300M', 'jwst_F322W2': 'jwst,F322W2', @@ -1575,7 +1696,7 @@ def get_obs_str(col): 'nirc2_FeII': 'nirc2,FeII', 'nirc2_Brgamma': 'nirc2,Brgamma', '2mass_J': '2mass,J', '2mass_H': '2mass,H', '2mass_Ks': '2mass,Ks', 'ubv_U':'ubv,U', 'ubv_B':'ubv,B', 'ubv_V':'ubv,V', 'ubv_R':'ubv,R', - 'ubv_I':'ubv,I', + 'ubv_I':'ubv,I', 'jg_J': 'jg,J', 'jg_H': 'jg,H', 'jg_K': 'jg,K', 'nirc1_K':'nirc1,K', 'nirc1_H':'nirc1,H', 'naco_J':'naco,J', 'naco_H':'naco,H', 'naco_Ks':'naco,Ks', @@ -1603,7 +1724,7 @@ def get_obs_str(col): 'rubin_y':'rubin,y'} obs_str = filt_list[name] - + return obs_str def rebin_spec(wave, specin, wavnew): @@ -1616,7 +1737,7 @@ def rebin_spec(wave, specin, wavnew): f = np.ones(len(wave)) filt = spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') obs_f = obs.Observation(spec, filt, binset=wavnew, force='taper') - + return obs_f.binflux def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model, @@ -1627,7 +1748,7 @@ def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model, 'wfc3,ir,f153m']): """ Wrapper routine to generate a grid of isochrones of different ages, - extinctions, and distances. + extinctions, and distances. Parameters: ---------- @@ -1639,7 +1760,7 @@ def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model, dist_arr: array Array of distances to loop over (pc) - + evo_models: SPISEA evolution object Which evolution models to use @@ -1656,7 +1777,7 @@ def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model, Mass sampling of isochrone, relative to original mass sampling filters: dictionary - Which filters to do the synthetic photometry on + Which filters to do the synthetic photometry on """ print( '**************************************') print( 'Start generating isochrones') @@ -1701,7 +1822,7 @@ def mag_in_filter(star, filt): diff = np.diff(star_in_filter.binwave) diff = np.append(diff, diff[-1]) star_flux = np.sum(star_in_filter.binflux * diff) - + star_mag = -2.5 * math.log10(star_flux / filt.flux0) + filt.mag0 return star_mag @@ -1724,10 +1845,10 @@ def match_model_masses(isoMasses, starMasses): idx = np.where(dm_frac > 0.1)[0] indices[idx] = -1 - + return indices - + def get_evo_model_by_string(evo_model_string): return getattr(evolution, evo_model_string) @@ -1738,7 +1859,7 @@ def calc_ab_vega_filter_conversion(filt_str): AB and Vega magnitudes for a given filter: m_AB - m_vega - Note: this conversion is just the vega magnitude in + Note: this conversion is just the vega magnitude in AB system Parameters: @@ -1758,14 +1879,14 @@ def calc_ab_vega_filter_conversion(filt_str): filt_wave = filt.wave filt_mu = c / filt_wave s_filt = filt.throughput - + # Interpolate the filter function, determine what the # filter function is at the exact sampling of the # vega spectrum (in freq space) filt_interp = scipy.interpolate.interp1d(filt_mu, s_filt, kind='linear', bounds_error=False, fill_value=0) s_interp = filt_interp(vega_mu) - + # Now for the m_ab calculation mu_diff = np.diff(vega_mu) numerator = np.sum(vega_flux_mu[:-1] * s_interp[:-1] * mu_diff) @@ -1783,10 +1904,10 @@ def calc_ab_vega_filter_conversion(filt_str): # fill_value=0) #s_interp = filt_interp(vega.wave) - # Calculate the numerator + # Calculate the numerator #diff = np.diff(vega.wave) #numerator2 = np.sum((vega.wave[:-1]**2. / c) * vega.flux[:-1] * s_interp[:-1] * diff) - + # Now we need to intergrate the filter response for the denominator #denominator2 = np.sum(s_interp[:-1] * diff) From 52acd64be3b4baaad72e0a070f21b5872b15774e Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 29 Apr 2025 15:06:48 -0700 Subject: [PATCH 040/191] Improvements to evolution merge region --- changes/evolution_gaussian_fit.ipynb | 768 +++++++++++++++++++++++---- 1 file changed, 677 insertions(+), 91 deletions(-) diff --git a/changes/evolution_gaussian_fit.ipynb b/changes/evolution_gaussian_fit.ipynb index 13fe6269..957d09b3 100644 --- a/changes/evolution_gaussian_fit.ipynb +++ b/changes/evolution_gaussian_fit.ipynb @@ -29,7 +29,8 @@ "import numpy as np\n", "from sklearn.gaussian_process import GaussianProcessRegressor\n", "from sklearn.gaussian_process.kernels import ConstantKernel as C, Matern\n", - "from sklearn.preprocessing import StandardScaler" + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import KFold" ] }, { @@ -68,9 +69,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -79,7 +80,7 @@ ], "source": [ "# creating graphs of mass vs. luminosity, Teff, and gravity\n", - "fig, axs = plt.subplots(3, 1, figsize=(10, 10))\n", + "fig, axs = plt.subplots(3, 1, figsize=(6, 10))\n", "\n", "# create array for mass gap\n", "mass_gap = np.linspace(np.max(phil_mass), np.min(pisa_mass), 1000)\n", @@ -87,7 +88,7 @@ "# mass vs. luminosity\n", "axs[0].plot(phil_mass, phil_L, color='c', label='Phillips')\n", "axs[0].plot(pisa_mass, pisa_L, color='pink', label='Pisa')\n", - "axs[0].set_title('Mass vs. Luminosity for logAge=7.13')\n", + "axs[0].set_title('Mass vs. Luminosity') # for logAge=7.13')\n", "y_min0 = axs[0].get_ylim()[0]\n", "y_max0 = axs[0].get_ylim()[1]\n", "axs[0].fill_between(mass_gap, y_min0, y_max0, color='gray', label='mass gap', alpha=0.3)\n", @@ -103,7 +104,7 @@ "y_min1 = axs[1].get_ylim()[0]\n", "y_max1 = axs[1].get_ylim()[1]\n", "axs[1].fill_between(mass_gap, y_min1, y_max1, color='gray', label='mass gap', alpha=0.3)\n", - "axs[1].set_title('Mass vs. Effective Temperature for logAge=7.13')\n", + "axs[1].set_title('Mass vs. Effective Temperature') # for logAge=7.13')\n", "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", "axs[1].set_ylabel('log(Teff) (log(K))')\n", "axs[1].set_xlim(0,1)\n", @@ -116,7 +117,7 @@ "y_min2 = axs[2].get_ylim()[0]\n", "y_max2 = axs[2].get_ylim()[1]\n", "axs[2].fill_between(mass_gap, y_min2, y_max2, color='gray', label='mass gap', alpha=0.3)\n", - "axs[2].set_title('Mass vs. Gravity for logAge=7.13')\n", + "axs[2].set_title('Mass vs. Gravity') # for logAge=7.13'\n", "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", "axs[2].set_ylabel('logg')\n", "axs[2].set_xlim(0,1)\n", @@ -133,7 +134,7 @@ "id": "35e9b3a9-8efe-4668-a359-c8332e34fbba", "metadata": {}, "source": [ - "From these zoomed in graphs it becomes clear that though a mass gap is present, luminosty and effective temperature still seem to line up if a model connects them. In contrast, the graph for log gravity is very clearly disconnected, which is worth refining as new models continue to be generated. To best address this problem, we decided to use GaussianProcessRegressor with the Matern kernel, as through trial and error this appeared to be the best fit. We then decided to generate a chi-squared heat map for luminosity to find the ideal parameters for length_scale and nu." + "From these zoomed in graphs it becomes clear that though a mass gap is present, luminosty and effective temperature still seem to line up if a model connects them. In contrast, the graph for log gravity is very clearly disconnected, which is worth refining as new models continue to be generated. To best address this problem, we decided to use GaussianProcessRegressor with the Matern kernel, as through trial and error this appeared to be the best fit. We then decided to generate a chi-squared heat map for luminosity to find the ideal parameters for length_scale and nu. This was done with and without the use of the kFold class from sklearn, with a determination that using it produces a much better fit." ] }, { @@ -146,7 +147,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.0005 6.8546\n" + "0.075 0.2019\n" ] } ], @@ -171,18 +172,18 @@ "phil_logg = phil_tbl['Gravity']\n", "pisa_logg = pisa_tbl['col4']\n", "combined_logg = np.concatenate([phil_logg, pisa_logg])\n", - "print(np.min(phil_mass), np.max(pisa_mass))" + "print(np.max(phil_mass), np.min(pisa_mass))" ] }, { "cell_type": "code", - "execution_count": 30, - "id": "650ec2bd-9904-4ae2-9fa5-304a392ecb3e", + "execution_count": 25, + "id": "38a5d619-8cb2-40d6-962b-6f1491366962", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -192,52 +193,187 @@ } ], "source": [ + "### USING SKLEARN\n", + "\n", "# creating input array (mass) and output array (luminosity)\n", "X = combined_masses.reshape(-1, 1)\n", "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", "\n", - "# Scale X and y\n", - "#X_scaler = StandardScaler()\n", - "#y_scaler = StandardScaler()\n", + "# Define grid\n", + "length_scales = np.logspace(-1, 2, 20)\n", + "nus = [0.5, 1.5, 2.5] # available Matern values\n", "\n", - "#X = X_scaler.fit_transform(combined_masses.reshape(-1, 1))\n", - "#y = y_scaler.fit_transform(combined_L.reshape(-1, 1)).ravel()\n", + "chi2_map = np.zeros((len(nus), len(length_scales)))\n", + "N_data = len(y)\n", + "N_params = 2\n", + "\n", + "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n", + "chi2_map = np.zeros((len(nus), len(length_scales)))\n", + "\n", + "for i, nu in enumerate(nus):\n", + " for j, ls in enumerate(length_scales):\n", + " kernel = Matern(length_scale=ls, nu=nu)\n", + " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + "\n", + " residuals = []\n", + " try:\n", + " for train_idx, test_idx in kf.split(X):\n", + " gp.fit(X[train_idx], y[train_idx])\n", + " y_pred = gp.predict(X[test_idx])\n", + " residuals.append((y[test_idx] - y_pred)**2)\n", + "\n", + " residuals = np.concatenate(residuals)\n", + " chi2 = np.sum(residuals) # Assume unity residuals\n", + " chi2_red = chi2 / (len(y) - N_params) # Reduced chi^2\n", + " chi2_map[i, j] = np.log10(chi2_red)\n", + " except Exception as e:\n", + " chi2_map[i, j] = np.nan\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(10, 5))\n", + "im = plt.imshow(chi2_map, aspect='auto', origin='lower',\n", + " extent=[length_scales[0], length_scales[-1], 0, len(nus)-1],\n", + " cmap='viridis')\n", + "plt.colorbar(im, label=r'$\\chi^2$')\n", + "plt.xticks(length_scales, labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", + "plt.xlabel('Length Scale')\n", + "plt.ylabel(r'$\\nu$')\n", + "plt.xscale('log')\n", + "plt.title('Chi-Squared Heatmap for Luminosity Fit Parameters')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "7a4edb19-1411-44f6-a50d-d1f1fc5b48e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.5 1.2742749857031335\n" + ] + } + ], + "source": [ + "i_best, j_best = np.unravel_index(np.nanargmin(chi2_map), chi2_map.shape)\n", + "best_nu = nus[i_best]\n", + "best_length_scale = length_scales[j_best]\n", + "print(best_nu, best_length_scale)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "79f3a7d1-ecd9-4d77-8923-6f729f9f66e3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Looking at physical interpretation of heatmap parameters\n", + "\n", + "# creating input array (mass) and output array (luminosity)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# trying different kernel types\n", + "kernel = Matern(length_scale=best_length_scale, nu=best_nu)\n", + "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + "gp.fit(X, y)\n", + "y_pred_1, sigma_1 = gp.predict(x, return_std=True)\n", + "#chi2 = np.sum((y - y_pred_1)**2) #unit test for 2d function\n", + "#print(chi2)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_L, color='blue', label='actual values')\n", + "plt.plot(x, y_pred_1, color='orange', label='predicted values')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('Luminosity')\n", + "plt.title(' Reduced $\\chi^2$ Determination of Luminosity Best Fit Using kFold')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(-4,0)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "80feb1ea-eee1-489c-9fa1-05111ba6b86d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### WITHOUT KFOLD\n", + "X = combined_masses.reshape(-1, 1)\n", + "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", "\n", "# Define grid\n", "length_scales = np.logspace(-2, 1, 20)\n", "nus = [0.5, 1.5, 2.5] # available Matern values\n", "\n", "chi2_map = np.zeros((len(nus), len(length_scales)))\n", + "N_data = len(y)\n", + "N_params = 2\n", + "\n", + "chi2_map = np.zeros((len(nus), len(length_scales)))\n", "\n", - "# Grid search\n", "for i, nu in enumerate(nus):\n", " for j, ls in enumerate(length_scales):\n", " kernel = Matern(length_scale=ls, nu=nu)\n", " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", - " \n", + "\n", " try:\n", " gp.fit(X, y)\n", " y_pred = gp.predict(X)\n", - " #for k, val in enumerate(y_pred):\n", - " # if (X[k] > 0.39) and (X[k] < 0.42):\n", - " # print(X[k], y[k], y_pred[k], (y - y_pred)[k])\n", - " avg_diff = (np.sum(y - y_pred)) / len(y_pred)\n", - " chi2 = np.sum(((y - y_pred)**2) / avg_diff)\n", - " chi2_map[i, j] = chi2\n", + " residuals = (y - y_pred)**2\n", + "\n", + " chi2 = np.sum(residuals) # assume unity residuals\n", + " chi2_red = chi2 / (N_data - N_params) # reduced chi^2\n", + " chi2_map[i, j] = np.log10(chi2_red)\n", + "\n", " except Exception as e:\n", + " print(f\"Error at nu={nu}, length_scale={ls}: {e}\")\n", " chi2_map[i, j] = np.nan\n", "\n", "# Plotting\n", "plt.figure(figsize=(10, 5))\n", "im = plt.imshow(chi2_map, aspect='auto', origin='lower',\n", - " extent=[np.log10(length_scales[0]), np.log10(length_scales[-1]), 0, len(nus)-1],\n", + " extent=[length_scales[0], length_scales[-1], 0, len(nus)-1],\n", " cmap='viridis')\n", "plt.colorbar(im, label=r'$\\chi^2$')\n", - "plt.xticks(np.log10(length_scales), labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.xticks(length_scales, labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", "plt.xlabel('Length Scale')\n", "plt.ylabel(r'$\\nu$')\n", + "plt.xscale('log')\n", "plt.title('Chi-Squared Heatmap for Luminosity Fit Parameters')\n", "plt.tight_layout()\n", "plt.show()\n" @@ -245,15 +381,15 @@ }, { "cell_type": "code", - "execution_count": 31, - "id": "7a4edb19-1411-44f6-a50d-d1f1fc5b48e6", + "execution_count": 9, + "id": "aab48c6d-6d83-4ed6-9b70-6cf8d2842cd6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.5 0.5455594781168517\n" + "0.5 0.01\n" ] } ], @@ -266,13 +402,13 @@ }, { "cell_type": "code", - "execution_count": 32, - "id": "79f3a7d1-ecd9-4d77-8923-6f729f9f66e3", + "execution_count": 10, + "id": "3fec5172-ea8b-4f28-810f-c5577edfceb3", "metadata": {}, "outputs": [ { "data": { - "image/png": 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XZsqUKfTt2zfPazObzSQlJeX5+4rkhLOzM46OjoVdhohIiVWs1nEqDHFxcfj4+BAbG5vh5HDDMIiJieHKlSsFX5xIBsqWLYu/v7/WHRORUu1Of7/tVax6nIqi1NBUqVIlPDw89MdKCo1hGNy4cYNz584BZLjchoiI5I6CUy6YzWZraNJTelIUuLu7A5anRytVqqRhOxGRPFasJocXNalzmjw8PAq5EpFbUv951Jw7EZG8p+CUBzQ8J0WJ/nkUEck/Ck4iIiIi2aTgJCIiIpJNCk5SpAwdOpRevXrl6z3GjRtHs2bN8vUeIiJSMik4FQFmM6xeDT/8YPlfs7mwK8qagoeIiJRWWo6gkEVEwAsvwMmTt45VrQqffAJ9+hReXSIiIpKeepwKUUQE9OtnG5oATp2yHI+IyJ/7LlmyhHvvvZeyZctSoUIFHnjgAY4cOWLT5uTJkwwcOJDy5cvj6elJSEgImzdvZsaMGYwfP54///wTk8mEyWRixowZREdHYzKZiIyMtL7HlStXMJlMrF69GrCse/Xkk09SvXp13N3dqVu3Lp988km2646NjcXd3Z0lS5bYHI+IiMDT05Nr164B8Morr1CnTh08PDyoUaMGb7zxRpaP5oeHhzNy5EibY7169WLo0KHW14mJibz88stUqVIFT09PWrVqZf25AI4dO0aPHj0oV64cnp6eNGzYkMWLF2f7ZxMRkeJBPU6FxGy29DRltOGNYYDJBCNHQs+ekNdrGF6/fp3Ro0fTuHFjrl+/zptvvknv3r2JjIzEwcGBa9euERYWRpUqVfjll1/w9/dnx44dpKSkMGDAAHbv3s2SJUv4/fffAfDx8eHs2bN3vG9KSgpVq1blxx9/xNfXlw0bNvDMM88QEBDAQw89dMfrfXx86N69O7NmzaJr167W47Nnz6Znz56UKVMGAC8vL2bMmEHlypXZtWsXTz/9NF5eXrz88st2/sbg8ccfJzo6mjlz5lC5cmUWLFhA165d2bVrF7Vr1+a5554jMTGRtWvX4unpyd69e631iIhIyaHgVEjWrUvf05SWYcCJE5Z24eF5e+/bNzmeNm0alSpVYu/evTRq1IjZs2dz/vx5tm7dSvny5QGoVauWtX2ZMmVwcnLC398/R/d1dnZm/Pjx1tfVq1dnw4YN/Pjjj9kKTgCDBw/mscce48aNG3h4eBAXF8eiRYuYP3++tc3rr79u/T44OJgXX3yRuXPn2h2cjhw5wg8//MDJkyepXLkyAC+99BJLlixh+vTpvPPOOxw/fpy+ffvSuHFjAGrUqGHXvUREpGhTcCokZ87kbbucOHLkCG+88QabNm3iwoULpKSkAHD8+HEaNWpEZGQkzZs3t4amvPTll1/y3//+l2PHjhEfH09iYmKOJpp3794dJycnfvnlFwYOHMj8+fPx8vKic+fO1jbz5s1j8uTJHD58mGvXrpGcnJyrDR537NiBYRjUqVPH5nhCQoJ1q53nn3+ef/zjHyxbtoyOHTvSt29fmjRpYvc9RUSkaNIcp0KS3f1X82Of1h49enDx4kW+/vprNm/ezObNmwHLPB64td9ZTjg4WP5RMtKMPd4+r+jHH39k1KhRPPHEEyxbtozIyEgef/xx632zw8XFhX79+jF79mzAMkw3YMAAnJws/w2wadMmBg4cSLdu3fj111/ZuXMnr732Wpb3cHBwsKn79tpTUlJwdHRk+/btREZGWr/27dtnnaP11FNPcfToUR599FF27dpFSEgIU6dOzfbPJSIixYOCUyFp29by9Fxmu2OYTBAYaGmXly5evMi+fft4/fXX6dChA/Xr1+fy5cs2bZo0aUJkZCSXLl3K8D1cXFww37ZmQsWKFQE4k6aLLO1EcYB169bRpk0bhg8fTvPmzalVq1a6SenZMXjwYJYsWcKePXtYtWoVgwcPtp5bv349QUFBvPbaa4SEhFC7dm2OHTuW5ftVrFjRpm6z2czu3butr5s3b47ZbObcuXPUqlXL5ivtcGVgYCDDhg0jIiKCF198ka+//jrHP5uIiBRtCk6FxNHRsuQApA9Pqa8nT877ieHlypWjQoUKfPXVVxw+fJiVK1cyevRomzYPP/ww/v7+9OrVi/Xr13P06FHmz5/Pxo0bAcu8oaioKCIjI7lw4QIJCQm4u7vTunVr3n33Xfbu3cvatWtt5hqBZZ7Utm3bWLp0KQcPHuSNN95g69atOf4ZwsLC8PPzY/DgwQQHB9O6dWubexw/fpw5c+Zw5MgRpkyZwoIFC7J8v/bt27No0SIWLVrE/v37GT58OFeuXLGer1OnjnVuVUREBFFRUWzdupX33nvP+uTcyJEjWbp0KVFRUezYsYOVK1dSv379HP9sIiJStCk4FaI+fWDePKhSxfZ41aqW4/mxjpODgwNz5sxh+/btNGrUiFGjRvHBBx/YtHFxcWHZsmVUqlSJ+++/n8aNG/Puu+/i+HeK69u3L127dqVdu3ZUrFiRH374AYBvvvmGpKQkQkJCeOGFF3jrrbds3nfYsGH06dOHAQMG0KpVKy5evMjw4cNz/DOYTCYefvhh/vzzT5veJoCePXsyatQoRowYQbNmzdiwYQNvvPFGlu/3xBNPMGTIEB577DHCwsKoXr067dq1s2kzffp0HnvsMV588UXq1q3Lgw8+yObNmwkMDAQsvVTPPfcc9evXp2vXrtStW5fPP/88xz+biIjkTuqi0q+8kj/vbzJun9whNuLi4vDx8SE2NjbdBOObN28SFRVF9erVcXNzs/seZrPl6bkzZyxzmtq2zfueJik98uqfSxGR4sRshgkTYNIkuHoVIA7I+O93buipuiLA0THvlxwQEREp6VJ7l778En7+GbJY6zjPKDiJiIhIsZE2LP36K9y8WbD3V3ASERGRIi11SsvChfD113DjRuZtK3qfo9/d8+jR4nvufz/va1FwEhERkSInbViaMQNiYzNvW9bjMr3vWsDA0Dl0aLgCR4cU4rIIV7mh4CQiIiJFRvpJ3hnzdo+lR4v/8VCrH+nadAkuTrcmOG05chcz1/YCXsvz+hScREREpFCk9iqdOgVnz8L69fC//2U+ybusx2V6hvxMv7vn0bnxMpuw9OexJszdNIC5mwZw9FxNLE/VKTiJiIhIMZfdXiWACmUu0LPlz/RrNY+ODX/H2SnZem7PyQbM29KPuZsGsO9Ug3yu2kLBSURERPJdTpYOqOh9jl4tF9K/1U+0a7AKJ8db23z9dbwxP23uz/ytfQssLKWl4CQiIiL5IidLB/j5xNDnrgj63T2PsPprcHRIsZ7bEdWceVv6MX9rXw6eqZv/hWdBwUnyXXBwMCNHjmTkyJGAZcuUBQsW0KtXrwKtY9y4cSxcuDDd5sN5acaMGYwcOdJmrzsRkdIkJ2EpoOxp+t49n353z6Nt3XU4ONzazGTrkRDmbenHvC39/p6zlD1ubvDAA/Doo9CzZy5+kEwoOEmBO3PmDOXKlctW24IIOyIiYr/UoLR6NezdC0uWZL3OUtXyJ6xh6d66623ObTrcinmbLWHp2IXgbNfg7GwJScOGWXbicHSEuDh7fpo7U3CSbElMTMTFxSVP3svf3z9P3kdERApHTlfvDvKNtoal0NqbbM6tP9iGnzb3J2JrH05crJajOry8YPRoeOONgtvj1aFgblOKGAYkXy/4rxzs1RweHs6IESMYMWIEZcuWpUKFCrz++uuk3e85ODiYt956i6FDh+Lj48PTTz8NwIYNG7jvvvtwd3cnMDCQ559/nuvXr1uvO3fuHD169MDd3Z3q1asza9asdPc3mUwsXLjQ+vrkyZMMHDiQ8uXL4+npSUhICJs3b2bGjBmMHz+eP//8E5PJhMlkYsaMGQDExsbyzDPPUKlSJby9vWnfvj1//vmnzX3effdd/Pz88PLy4sknn+RmFv9mp6SkULVqVb788kub4zt27MBkMnH06FEAJk2aROPGjfH09CQwMJDhw4dz7dq1TN936NCh6YYkR44cSXiazQkNw+D999+nRo0auLu707RpU+bNm2c9f/nyZQYPHkzFihVxd3endu3aTJ8+PdN7iojkB7MZVqyA/v2hTBno2BHmzcssNBnUq7yPVx+cyJb/u4voT6rz0eCXCK29iZQUE2v23cc/Z06hyoiT3Dt+PZ8sGZnt0OTjA88/D6tWweXLMG5cwYUmUI9T3jPfgB/LFPx9H7oGTp7Zbj5z5kyefPJJNm/ezLZt23jmmWcICgqyBiSADz74gDfeeIPXX38dgF27dtGlSxcmTJjAtGnTOH/+vDWApf4hHzp0KCdOnGDlypW4uLjw/PPPc+7cuUzruHbtGmFhYVSpUoVffvkFf39/duzYQUpKCgMGDGD37t0sWbKE33//HQAfHx8Mw6B79+6UL1+exYsX4+Pjw3/+8x86dOjAwYMHKV++PD/++CNjx47ls88+o23btnz33XdMmTKFGjVqZFiHg4MDAwcOZNasWQwbNsx6fPbs2YSGhlqvc3BwYMqUKQQHBxMVFcXw4cN5+eWX+fzzz7P9u7/d66+/TkREBF988QW1a9dm7dq1PPLII1SsWJGwsDDeeOMN9u7dy2+//Yavry+HDx8mPj7e7vuJiGRXTnqWTKYUWtfaRK+QhfRs8TN1Kx+89T4pDqzZF8a8Lf1YsK03MVcCclSHhwc89RT07g1t2xZsULqdglMpFRgYyMcff4zJZKJu3brs2rWLjz/+2CY4tW/fnpdeesn6+rHHHmPQoEHWSd61a9dmypQphIWF8cUXX3D8+HF+++03Nm3aRKtWrQCYNm0a9evXz7SO2bNnc/78ebZu3Ur58uUBqFWrlvV8mTJlcHJyshneW7lyJbt27eLcuXO4uroC8OGHH7Jw4ULmzZvHM888w+TJk3niiSd46qmnAHjrrbf4/fffs+x1Gjx4MJMmTeLYsWMEBQWRkpLCnDlz+Pe//21tk/qzA1SvXp0JEybwj3/8w+7gdP36dSZNmsTKlSsJDQ0FoEaNGvzxxx/85z//ISwsjOPHj9O8eXNCQkIAS2+giEh+yUlYcnW+SfsGK+kVspAHW/yCf9mz1nMJSS6s2NOBn7f3ZOG2XpyL88tRHamTvNPOWyoKFJzymqOHpfenMO6bA61bt8ZkMllfh4aG8tFHH2E2m3H8+5/O1D/UqbZv387hw4dtht8MwyAlJYWoqCgOHjyIk5OTzXX16tWjbNmymdYRGRlJ8+bNraEpO7Zv3861a9eoUKGCzfH4+HiOHDkCwL59+2x6jlJ/xlWrVmX6vs2bN6devXr88MMPvPrqq6xZs4Zz587x0EMPWdusWrWKd955h7179xIXF0dycjI3b97k+vXreHpmv8cv1d69e7l58yadOnWyOZ6YmEjz5s0B+Mc//kHfvn3ZsWMHnTt3plevXrRp0ybH9xIRyUja1btXrICffoIsZiBQ1uMy9zdbTK+QhXRr+htl3G5N17hy3YdFkd1ZuK0XS/7qyrWbXtmuw90dunWDBg0sQakohaW0FJzymsmUoyGzouz2IJCSksKzzz7L888/n65ttWrVOHDgAIBNILsTd3f3HNeVkpJCQEAAq1evTncuq5CWHYMHD2b27Nm8+uqrzJ49my5duuDr6wvAsWPHuP/++xk2bBgTJkygfPny/PHHHzz55JMkZbKSm4ODg83cMcCmbUqKZZ2SRYsWUaVKFZt2qb1p3bp149ixYyxatIjff/+dDh068Nxzz/Hhhx/m6mcVkdIrJxvogkHdgAN0b76I7s0W0bbuOpvVu09eqmLtVVqzL4wkc/YfJCqqvUpZKTbB6fLlyzz//PP88ssvADz44INMnTo1yz+UQ4cOZebMmTbHWrVqxaZNmzK5ovS4/XewadMmateube1tykiLFi3Ys2ePzVBaWvXr1yc5OZlt27Zx9913A3DgwIEs1zRq0qQJ//3vf7l06VKGvU4uLi6YzWabYy1atCAmJgYnJ6dMh63q16/Ppk2beOyxx2x+xjsZNGgQr7/+Otu3b2fevHl88cUX1nPbtm0jOTmZjz76CAcHy3MVP/74Y5bvV7FiRXbv3m1zLDIyEmdnZwAaNGiAq6srx48fJywsLMv3GTp0KEOHDqVt27b861//UnASkWzJaD+45cuz3urE1fkmYfXWWMNSTb+jNud3n2jIwu29+Hl7T7ZHtcQwsv+sWXEMS2kVm+A0aNAgTp48yZIlSwB45plnePTRR/nf//6X5XVdu3a1eQIprx6pL+5OnDjB6NGjefbZZ9mxYwdTp07lo48+yvKaV155hdatW/Pcc8/x9NNP4+npyb59+1i+fDlTp06lbt26dO3alaeffpqvvvoKJycnRo4cmWWv0sMPP8w777xDr169mDhxIgEBAezcuZPKlSsTGhpqnYQdGRlJ1apV8fLyomPHjoSGhtKrVy/ee+896taty+nTp1m8eDG9evUiJCSEF154gSFDhhASEsK9997LrFmz2LNnT6aTw1NVr16dNm3a8OSTT5KcnEzPNKun1axZk+TkZKZOnUqPHj1Yv359uqfwbte+fXs++OADvv32W0JDQ/n+++/ZvXu3dRjOy8uLl156iVGjRpGSksK9995LXFwcGzZsoEyZMgwZMoQ333yTli1b0rBhQxISEvj111+znDcmIpJ2ntLSpXfeDw6gSvmT3N90Md2bL6Jjw9/xdLu1GFNCkgtr9oexaGd3FkV258jZjP8DOjMZrbNUbBnFwN69ew3A2LRpk/XYxo0bDcDYv39/ptcNGTLE6NmzZ67uHRsbawBGbGxsunPx8fHG3r17jfj4+Fzdo6CFhYUZw4cPN4YNG2Z4e3sb5cqVM1599VUjJSXF2iYoKMj4+OOP0127ZcsWo1OnTkaZMmUMT09Po0mTJsbbb79tPX/mzBmje/fuhqurq1GtWjXj22+/TfdegLFgwQLr6+joaKNv376Gt7e34eHhYYSEhBibN282DMMwbt68afTt29coW7asARjTp083DMMw4uLijH/+859G5cqVDWdnZyMwMNAYPHiwcfz4cev7vv3224avr69RpkwZY8iQIcbLL79sNG3a9I6/n88++8wAjMceeyzduUmTJhkBAQGGu7u70aVLF+Pbb781AOPy5cuGYRjG9OnTDR8fH5tr3nzzTcPPz8/w8fExRo0aZYwYMcIICwuznk9JSTE++eQTo27duoazs7NRsWJFo0uXLsaaNWsMwzCMCRMmGPXr1zfc3d2N8uXLGz179jSOHj2aaf3F9Z9LEbFfcrJhrFplGN9/bxiPP24YZcoYhmWdmsy/HEzJRmjt9cZb/f9t7HynqWHMwubr5NTKxldPPWX0bLnA8HS9esf3y+jLy8swxo611FfQsvr7nRsmw8jBAkCF5JtvvmH06NHphnzKli3Lxx9/zOOPP57hdUOHDmXhwoW4uLhQtmxZwsLCePvtt6lUqVKm90pISCAhIcH6Oi4ujsDAQGJjY/H29rZpe/PmTaKioqhevTpubm72/4AFLDw8nGbNmjF58uTCLkXyQXH951JEcsaeXqUKZS7QqfFyujdbRNemS/D1umg9l5JiYvORViyK7M6ind2JPNYMyP6cVTc36N4dQkPB3x+qVCncpQPi4uLw8fHJ8O93bhSLobqYmJgMw06lSpWIiYnJ9Lpu3brRv39/goKCiIqK4o033qB9+/Zs377dOvH2dhMnTmT8+PF5VruIiEheyOnTbwBOjkm0rrWJLk2W0qXxUlpW326zH9zl62VZ+lcXFkV2Z8mfXblwtWKOairu85XsUajBady4cXcMKVu3bgUyflLLMIwsn+AaMGCA9ftGjRoREhJCUFAQixYtok+fPhleM2bMGEaPHm19ndrjJCIiUpBu3wNuxYo7Pf0GYFDL7zAdGq2gS+OltG+4Eh8P203b/jzWhCV/dWVRZHc2HGyDOSVnUaA0hqW0CjU4jRgxgoEDB2bZJjg4mL/++ouzZ8+mO3f+/Hn8/LK/oFZAQABBQUEcOnQo0zaurq6Z9kaVFBk9xi8iIoXLnh4lgICyp+nQaAUdGq6gfYOVVPM9YXP+fJwvy3d3YulfXVi2q3OOV+0GhaW0CjU4+fr6WtfIyUpoaCixsbFs2bLF+pj75s2biY2NzdFCgBcvXuTEiRMEBOT8HxoREZG8ZF+PkmUByvAGq+nQ0BKW6lfZb3M+IcmFjYdCrWFpR3SLHC0XkMrLC7p0UVi6XbGY41S/fn3rY+7/+c9/AMtyBA888AB169a1tqtXrx4TJ06kd+/eXLt2jXHjxtG3b18CAgKIjo7m3//+N76+vvTu3TtP60tdxFCkKNA/jyJFV062M0nl7nKDe+v+Ye1RalF9B44Ot/49T0kxsT2qJSv2dGDFng6sP3gP8Yk5200ilY8PDBlSNPaEK6qKRXACmDVrFs8//zydO3cGLAtgfvrppzZtDhw4QOzfcd3R0ZFdu3bx7bffcuXKFQICAmjXrh1z587Fyyv7S8BnxcXFBQcHB06fPk3FihVxcXHJ0arZInnJMAwSExM5f/48Dg4OWrNMpJCl7VFKSbEMwc2fn70J3XfV2GrtUQqtvRFX50SbNvtO1bMGpdV7w7lyo5xdNXp6Qv/+0LFj4T8FV1wUi+UICtOdHmdMTEzkzJkz3LhxI4OrRQqeh4cHAQEBCk4ihcCeHiWTKYVGVXdbglKjFYTVW4OXu226OnGxqjUordzTntOXq2Tybpnz9IS+faFqVXBwKNr7weWFUr0cQVHm4uJCtWrVSE5OTrc1iEhBc3R0xMnJST2fIgXEvgndBjUqHbUGpXb1V1HJ57xNiwtXK7BqbztLWNrdgcNna5GTNZVSaZ5S3lNwygMmkwlnZ2fr/mMiIlLypA1J589DdDTMnm35/k78fGJo33CldfgtuOIxm/PXb3qw9sB9rNht6VX683hTuyZ0a+gt/yk4iYiIZOD23qSff4ZLl7J3bVmPy9xXf601KDWsutfmfGKyM5sOt7YOvW0+3Ioks33D6+pVKlgKTiIiIuQuKJXzvETbeusIr7+a8PqraVrtT5sVulNSTEQea2adp7TuQFtuJHjaXauefis8Ck4iIlLqpIakM2egUiXL91OnZj8o+Zc9Q9u662hbdx331VtL48BdNkEJ4MDpOqzc254Vezqwam87Ll2rYFetXl7QuXPR2QOutFNwEhGREi83vUmpk7nvq7fWEpbqraO2/+F0rfaeqs+afWGs3hfO2v332bVCdyr1KBVdCk4iIlJimc3w9tvwySfZD0qpywO0rbfOGpYqlztj0yYlxcSfx5uy7kBb1u6/j3X723IuLvtbgKXl5gbdu6tHqbhQcBIRkRIho6fepk+HuLisr3NyTKJF8A5rSLq37h+UL3PZpk1isjNbj95lDUkbDrUh9kZZu2vVhO7iS8FJRESKndwsDeDpeo27am61zk8KrbURTzfbRYyv3fRkw6E2rNtv6VHacuRubia521WrepRKFgUnEREp0nITksCgesUoQmtvJLT2RtrU3kCTan/h5Gi7YPHFq+VZd6Ct5Wt/W3Yea06y2f61+dSjVHIpOImISJGRu5AEbs7xtKy+3RqSQmtvxL/s2XTtjl8IZP3Be1i7/z7W7r+Pfafr27XgJEDFivDww1C9uuV79SiVbApOIiJSKHIbkgCqlj9hE5KaB+/ExSnJpk1isjM7oluw4WAbNh4KZePhUE5dqmp33eXKQc+eWp27tFJwEhGRApMalhYuhBkzIDY2+9c6OybSPHinNSSF1t5IYIWT6dqduezPhkO3QtL2qJYkJLnZXbOCkqSl4CQiIvnCbIbVqy1fKSmWnqX//S9ni0yG1tpoDUkh1bfh5pJg0ybZ7Mifx5taepMOh7LhYBuOXQjCng1xUykoSVYUnEREJE/cvsjkTz/BtWvZu9bJMYkmgX/ZTOKuXik6XbsLVyvYhKRtUSG52rqkfHn45z8t4ejcOQgIUFCSrCk4iYhIjuW2N6lCmQs2IemuGlvTLQmQkmJi14nG1pC08VAoh8/WQr1JUpgUnERE5I5y05vkYDLTsOoem0ncdQIOpWt35boPGw+HsvGQJShtOXo3V+O9c117xYoweLAlMCkoSW4pOImIiFXazW8DAqBNG3j33ZxtWVLW4zKtam22hqRWtTbj7X41Xbu9p+pbQ9LGw6HsP13P7iUBUmlpAMlvCk4iIqVcalj6+WeYNct2OQAHB8tQXGZMphTqBhywedKtYdW96dpdjS/D5iOtrCFp8+FWXL5ePld1KyRJYVBwEhEpZdL2Kh06BF9/DSfTP9UPpA9NZdyu0qrmZmtICq29kXKeV9JddyimlqU36e9lAXafaESKYX+iUUiSokLBSUSkFMiqVylzBjX9jlh7k9rU3kCjwN04OtimqRsJ7mw9epc1JG063JrzcZXsrlUhSYoyBScRkRIibU9Spb9zy7lzd+5VSuXucoO7amy1mcRd0ftCunbR54NuLTB5KJQ/jzfN1b5uAD4+MGQI9O6tkCRFm4KTiEgxlZMht/QMgnyP2YSkptX+xNkp2aZVQpIL26Na2gSlM1cq57p2LQsgxZWCk4hIMWLfkJtl89sW1XfYrMRdudyZdO1OXapsDUkbDrVhZ3RzEpNdc1Wzlxd07gyhoeDvr6AkxZuCk4hIEZPbITcwqOZ73CYkNQuKTLf5bVKyEzuPNbf2JG041IYTFwPJzQKToN4kKdkUnEREigB7e5IAXJ1v0rL69jv2JsVc8WPT4dbWkLQ9qiXxiR65qlu9SVLaKDiJiBQC++cnGVSvGEWrWptpXWsTrWttonnwznS9SclmRyKPNbP0Jv29Gnf0+WBy2pvk6GipNZV6k6S0U3ASEclHGQ27/fpr9nuVPF2vcVfNrdbepFY1N1PJJ/2FZ2MrWYfcNh4OZdvRELt7k9JuUdKmDWzYcGslcQUlKe0UnERE8lBun3SrE3CQ1rU2EVp7I61rbaJx4K506yYlJjuzM7o5mw63ZvORVnb3JqWqWhWefhpq1844HIWH2/W2IiWSgpOIiB1y25ME4OUex901tlhDUutam6jglX5DuOMXAq3DbZsOtybyWDMSktxyVb82vhWxj4KTiEg25WYCd3ZX4b6Z6Mq2qBDrJO5Nh1tz+nKVXNd+p14lEckeBScRkSzYG5acHJNoHrSTe+qs5966f3BPnfX4lz2brl30+SCbCdx/HmtKktnFrlrThqO0yxgoKInkHQUnERFyP/Tm7R5L61qbuLfuH9xb9w9a1dyMh2u8TZvbV+HecKgNMVcCclW3htxECpaCk4iUWrkZeqta/oS1J+neun/QJPAvHBwMmzaXrpVj/cF7+OPAvaw/eA/bokJyPTdJQ24ihUvBSURKhdt7lNatg6lT4VL6udjpOJjMNArcbQ1J99b5g2q+J9K1O3K2hjUk/XHwXvafrodhONhVb2pP0gMPWF5ryE2kaCg2wentt99m0aJFREZG4uLiwpUrV+54jWEYjB8/nq+++orLly/TqlUrPvvsMxo2bJj/BYtIoUoNSqdOwYoVll6l7IQkAHeXG9xdc4s1JIXW3oiPR5xNm2SzIzujm1tD0vqD9+Rq2E09SSLFQ7EJTomJifTv35/Q0FCmTZuWrWvef/99Jk2axIwZM6hTpw5vvfUWnTp14sCBA3h5eeVzxSJSUHLTmwRQyfuszSTuFsE7cHZKtmlzNb4MGw+HWnuUNh9uxfWEMrmqW/OTRIofk2EYxp2bFR0zZsxg5MiRd+xxMgyDypUrM3LkSF555RUAEhIS8PPz47333uPZZ5/N1v3i4uLw8fEhNjYWb2/v3JYvInkgN71JYFA34MCtYbe6f1Db/3C6VicvVeGPA/dag9KuE40xp+T8vzX1pJtI4civv9/Fpscpp6KiooiJiaFz587WY66uroSFhbFhw4ZMg1NCQgIJCQnW13FxcRm2E5GCZzbD22/DJ59kPyg5OiTTLCiS++qt5b56a7mnznoqel+waZOSYmL3yUY285OOX6iGvStxqydJpOQqscEpJiYGAD8/P5vjfn5+HDt2LNPrJk6cyPjx4/O1NhG5s4yG3z76CK5dy/o6Z8dEWlbfTlj9NYTVX8M9ddbj7X7Vpk18ohubD7eyhqSNh0KJvVE2xzVqArdI6VOowWncuHF3DClbt24lJCTE7nuYTLb/xWgYRrpjaY0ZM4bRo0dbX8fFxREYGGj3/UUkZ3Laq+TqfJNWNTdzX721hNVfQ2itjXi63bBpc/l6Wf44cC9r99/HHwfuZXtUS7sXmVRvkkjpVqjBacSIEQwcODDLNsHBwXa9t7+/P2DpeQoIuPWky7lz59L1QqXl6uqKq6urXfcUkezJaLHJc+csm+JOmQIXL2Z+ravzTUJrbyS8/mrC66+mVc3NuLkk2LQ5H+fL2v33sWZ/GGv338eu441JMexPOApLIpKqUIOTr68vvr6++fLe1atXx9/fn+XLl9O8eXPA8mTemjVreO+99/LlniKSOXsXm3R0SCakxjbaN1hJh4YruKfO+nRB6cxlf9bsD2PNvjDW7A9j36n62DM/ydsbnnhCQ28ikrliM8fp+PHjXLp0iePHj2M2m4mMjASgVq1alCljeSS4Xr16TJw4kd69e2MymRg5ciTvvPMOtWvXpnbt2rzzzjt4eHgwaNCgQvxJREoPe8KSyZRCw6p76NBwBe0brCSs/pp0ayidvhzAqr3tWL0vnDX7wjgUUxt7J3IDlC8PL7wAr72mgCQiWSs2wenNN99k5syZ1tepvUirVq0iPDwcgAMHDhAbG2tt8/LLLxMfH8/w4cOtC2AuW7ZMaziJ5KOchyWDGpWO0r7hSmtYquRje9Hl62VZtbcdK/Z0YMXuDhw4Uxd7g1L58vDPf1p6kdSjJCI5VezWcSpoWsdJJGtp5ysdOgRffw0nT2Z9jX/ZM9aht/YNVxJc0fZJ1+s3PVh3oC0r9nRg5Z72RB5rZvccpXLlLHOTOnaEKlUUkkRKC63jJCJFRk57lcp6XCa8wWpLWGq0ggZV9tmcT0x2ZtPh1qzc054Vezqw+XAru556U2+SiOQ3BScRyVLaHqWAALhwAUaNyrpXycP1OvfUWW8demtRfQeODinW8ykpJnYea27tUVp3oC03EjxzXJunJ/Tvr94kESk4Ck4ikqmICMuk6TsNvTk7JnJ3zS3WeUqhtTfi4pRk02bfqXqs3NueFbs7sHpfOJevl7e7Lk3mFpHCouAkIkDGPUsPPQQZz4I0qF9lH10aL6Vzk2W0rbuOMm7XbVocvxBomcy9pwOr9rbj9OUqdtWl4TcRKUoUnERKoewMvzk62oamMm5X6dRoOfc3W0yXJksJrGDbDXU+zpeVe9tb5ykdOVsTe558S7sprkKSiBQ1Ck4ipUx2h9/MZqjpd5gHmv9K92aLCKu/xmb47WaiK2v2h7FsV2d+392RXScaYxgO2a5D+7yJSHGk4CRSguVs+A2cHJO4t+4f1rBUr/IBm/MHTtdh8Z/3s+TPrqzdfx83k9xzVI+2LhGR4k7BSaQEudOaSrcPvwH4ep2nW9PfeKD5r3RpstRmle6kZCfW7A9j0c7uLIrszqGYOjmuSWFJREoSBSeREsBshrffhk8+gUuXsm4HBk2D/rT2KrWquRkHh1tp6lxsRRZFWoLS8l2diIv3yVEtmqMkIiWZgpNIMZTas3TqFKxYAT/9BNeuZd7e3eUGHRqusISl5ouoWv6UzfkdUc35decD/LrzAbZFheRorhKoV0lESg8FJ5FiwmyG1avhyy9h6VK4ejXr9tV8j9G92SIeaP4r7Rqswt3lpvXc9Zse/L6nI7/ufIDFkfdnuVSAo2NqT5VFYCB89JElLKXOnVJYEpHSQsFJpIhLHYb74IOse5VMphTurrmFni1/5oHmv9I4cLfN+ahzwSyK7M6vOx9g9b5wEpLcsryv6e+VBH74QSFJRCSVgpNIEZOTYTgnxyTC66+mz10R9Gz5M5XLnbn1PikOrD94D7/ufIBFO7uz91QDcrKuUtWqMHky9OmTu59HRKQkUXASKSKyO8HbzTmeTo2X0/eu+TzY8hfKeV6xnouL92Jx5P38suNBlvzZNdNtTTT8JiJiHwUnkUKSk54lF6cEujZdwoDWc3mwxS8225ucja3Ewm29WLCtN6v2tiMx2TXTe2r4TUQkdxScRApYdnuWnByT6Njodwa0nkuvlgsp6xlrPXfsQjUWbO3N/K192XCwDSlG9lKPht9ERHJHwUmkgGRnkreDyUx4g9UMaD2XvnfNp4LXrWR18lIVftz0EHM3DWDLkbvJznyl8uUt26toTSURkbyh4CSSz+4UmEymFO6ps54BrefSv9VP+Pmcs547G1uJnzb3Z+6mAaw/eE+211cqUwb+9S947TUFJRGRvKTgJJJH0m53UqmS5divv8I330Bc3O2tDe6uuYWBoXPo3+onmwUpL14tz/ytfZm7aQBr9oVhTsn+v6apPUwKTCIi+UPBSSSXsjtnCQyaB+9kQOu5PNTqR6pXiraeuXLdhwXbejN30wBW7OlAstk5W/f29IT+/aFjR6hSRUNxIiL5TcFJxA6pvUs//5xZj9ItQb7RPNb2Wx6553vqBByyHr9205Oft/dk7qYBLP2rS5ZPw6Xl5gYPPADDhkF4uIKSiEhBUnASyYHs9i55ul6jX6t5DGk7k3YNVluPxye68evOB5i7aQCLI+8nPtEj2/fWMJyISOFTcBK5g+z2LplMKYTVX8PQtjPoe/d861pLKSkmVuzpwMx1Q/h5e0+u3fTK1n01DCciUvQoOIlkIru9SzX9DvPYvd/yWNtvCa54zHr84JnazFg7lO/+eJSTlwKzfV/1LImIFF0KTiJpZLd3yds9lv6tfmJI25m0rfeH9fiV6z7M3TSAGWuHsulwa7Kz1pJ6lkREig8FJxGy17vkYDLTvuFKht43g94hC/Bwjbdcm+LAsl2dmbnWMhR3M8k9W/fUWksiIsWPgpOUatlZzbtOwAGGtJ3JY22/tVlvae+p+sxYO5Tv/3iEM1cqZ/ueGooTESm+FJykVMnuxrre7rE83OYHhrSdSWjtTdbjl66V44eNDzNj7VC2HQ0hO0NxYNlQd/Bg6NlTQ3EiIsWZgpOUGvPmwfDhcP585m1aVt/GsA5f8nDoD3i63QAg2ezIb392Y+a6IfxvR487rrdUvjz885+WgHTunPaIExEpSRScpMQzmy29PXPnZnzezTmegaFzGN7xc+6quc16fM/JBkxb/SSzNwzibKx/lvfw9oYnnlCPkohISafgJCVW6vyl99+H69fTnw+uGMU/On7Bk2HTqOBlmRGekOTCT5v78+WKYaw/eA93GorTfCURkdJFwUlKFLMZVq+GL7+0bLB786bteZMphY6NfmdEp095oPmvODgYAESdC+aLFf9g+prHuXC1Ypb3UO+SiEjppeAkJcKdno4r43aVoffNYESnT6lb+aD1+NK/OvPpshEsjryfFCPrBKTeJRERUXCSYu1Ogama7zGe7zKFp8L/i4+HZTXLuHgvpq95nM9/H87BM3WzfH/1LomISFoKTlLs3Gk4DqBJtT95+YH3GdB6Lk6OZgAOnK7DJ0tf4Ls/Hr3jfnFanFJERDJSbILT22+/zaJFi4iMjMTFxYUrV67c8ZqhQ4cyc+ZMm2OtWrVi06ZNmVwhRdmdF6s0CG+wmlceeI+uTZdajy7f1ZHJS0by25/dMAyHLO+hwCQiIlkpNsEpMTGR/v37ExoayrRp07J9XdeuXZk+fbr1tYuLS36UJ/ksIgKeeQYuXkx/zsFkplfIQl7p8R5319wKWLZB+XHTQ7z/68tEHmt+x/fX/CUREcmOYhOcxo8fD8CMGTNydJ2rqyv+/lmvwSNFW0QE9O2b/rir800evfc7/tX9A+oEHAIgPtGNb9Y8wUeLXiTqfI0s39fDA556Cnr31vwlERHJnmITnOy1evVqKlWqRNmyZQkLC+Ptt9+mUqVKmbZPSEggISHB+jouLq4gypRMmM2WnqC0vN1j+UfHL3ihyycElIsBLFuhfLb8OaYu+yfn4zL/fEHDcSIiYr8SHZy6detG//79CQoKIioqijfeeIP27duzfft2XF0z3jZj4sSJ1t4tKXzr1sHJk5bvA8qeZmS3yQzr8CXe7lcBOH4hkEm/jea/q57iekKZLN9LgUlERHKrUIPTuHHj7hhStm7dSkhIiF3vP2DAAOv3jRo1IiQkhKCgIBYtWkSfPn0yvGbMmDGMHj3a+jouLo7AwEC77i+5d+YM1PQ7zKs93uXRe7/D1TkRgN0nGvL+ry/zw8aHSTY7Z/keCkwiIpJXCjU4jRgxgoEDB2bZJjg4OM/uFxAQQFBQEIcOHcq0jaura6a9UVLArkUT7j6B/R/MtC4psHZ/W9773yssjryfrLZDcXODBx6AYcMgPFyBSURE8kahBidfX198fX0L7H4XL17kxIkTBAQEFNg9xQ43z8OucXDkawJSksARFkXez1sLXmfT4dAsL1XvkoiI5KesF7UpQo4fP05kZCTHjx/HbDYTGRlJZGQk19Is6FOvXj0WLFgAwLVr13jppZfYuHEj0dHRrF69mh49euDr60vv3r0L68eQrJgTYd8k+F9tOPQ5pCSBf0dWu2ygx4eL2Hwk89BUpgyMHw9XrsCbbyo0iYhI/ig2k8PffPNNm8Usmze3rM2zatUqwsPDAThw4ACxsbEAODo6smvXLr799luuXLlCQEAA7dq1Y+7cuXh5Zb1qtBQww4DTi2DHi3D1733kyjWHFpPAL5xwYN48y9N1qRPFAby8oEsXDceJiEjBMRmGYRR2EUVZXFwcPj4+xMbG4u3tXdjllDyx+2DHKDjz90rfbpWg6TtQfSg42CYhs9nylN2ZMxAQoLWXREQkc/n197vY9DhJCZNihv2T4K/XISURHJyh7kho9Do4Z/wPuKOjpWdJRESksCg4ScG7fgw2DoFzayyvK98PLSaDd+1CLUtEROROFJyk4BgGRM+Cbc9BUhw4eVoCU80nwZT50gIiIiJFhYKTFIzEy7DlH3B8ruV1hdbQ5jvwqlW4dYmIiOSAgpPkv5gVlqG5+FNgcoRGY6HhGHDQP34iIlK82PWX6/r163h6euZ1LVLSmG9C5Bg4MNny2qs2hH4PvncXalkiIiL2smsBTD8/P5544gn++OOPvK5HSorrJ2BZm1uhqdYw6LZToUlERIo1u4LTDz/8QGxsLB06dKBOnTq8++67nD59Oq9rk+LqwhZYejdc3gmuFSHsV7j7C8tkcBERkWLMruDUo0cP5s+fz+nTp/nHP/7BDz/8QFBQEA888AAREREkJyfndZ1SXBz7EVaEwc0Y8GkEXbdCle6FXZWIiEieyLOVw6dOncq//vUvEhMT8fX1ZdiwYbz66qt4eHjkxdsXGq0cnk2GAbsnwK6xlteVu8M9P4CztrcREZGCVyRXDo+JieHbb79l+vTpHD9+nH79+vHkk09y+vRp3n33XTZt2sSyZcvyqlYpqsw3YdMTcOwHy+u6o6D5B+m2TBERESnu7ApOERERTJ8+naVLl9KgQQOee+45HnnkEcqWLWtt06xZM+tGvFKCxcfA2l5wcTOYnOCuz6HW04VdlYiISL6wKzg9/vjjDBw4kPXr13PXXXdl2KZGjRq89tpruSpOirjYfbCqK9w4Ds5loe188G9f2FWJiIjkG7vmON24caPYz13KLs1xysTFrbC6GyRctKzPFPYreNcp7KpERESA/Pv7bddTdV5eXpw7dy7d8YsXL+LoqHktJd7ZVbCivSU0lb8LOm1QaBIRkVLBruCUWSdVQkICLi4uuSpIiriTP8OqbpB8DfzaQ4cV4OZb2FWJiIgUiBzNcZoyZQoAJpOJ//73v5QpU8Z6zmw2s3btWurVq5e3FUrRcexH2DAIDDNU7WVZbsDRrbCrEhERKTA5Ck4ff/wxYOlx+vLLL22G5VxcXAgODubLL7/M2wqlaDix4FZoqj4EWv1Xm/SKiEipk6O/fFFRUQC0a9eOiIgIypUrly9FSRFzajGsH2AJTcGPQutvwGTXKK+IiEixZleXwapVq/K6DimqYn6HdX0gJQmqDVBoEhGRUi3bwWn06NFMmDABT09PRo8enWXbSZMm5bowKQLOroE1D0JKgmVOU5vvNDwnIiKlWrb/Cu7cuZOkpCTr95kxmUy5r0oK3/kNsKY7mOOh8v1wzxxwcC7sqkRERApVnm3yW1KVygUwL26DlR0gKQ78O0LY//T0nIiIFCtFagHM28XFxbFw4UL279+fF28nhelyJKzqbAlNle6D+35WaBIREfmbXcHpoYce4tNPPwUgPj6ekJAQHnroIRo3bsz8+fPztEApQFf2wMpOkHgZfEMt26g4lY6tdURERLLDruC0du1a2rZtC8CCBQswDIMrV64wZcoU3nrrrTwtUApI3EHL8FzCBSgfAuG/gbNXYVclIiJSpNgVnGJjYylfvjwAS5YsoW/fvnh4eNC9e3cOHTqUpwVKAbh6xLL33M2zULYptFsKLj6FXZWIiEiRY1dwCgwMZOPGjVy/fp0lS5bQuXNnAC5fvoybm+bDFCvXj1lCU/wp8GkA7ZeDa/nCrkpERKRIsmtRnpEjRzJ48GDKlClDUFAQ4eHhgGUIr3HjxnlZn+SnG6dgRQe4cRy86kD7FeBWsbCrEhERKbLsCk7Dhw/n7rvv5sSJE3Tq1AkHB0vHVY0aNTTHqbi4eQFWdoRrR6BMDeiwAtz9C7sqERGRIi3X6zilXl5SF74skes4Jd+whKYLG8EjEDqtA8+gwq5KREQkzxS5dZy+/fZbGjdujLu7O+7u7jRp0oTvvvsuzwqTfJJihg2DLaHJuSy0W6LQJCIikk12DdVNmjSJN954gxEjRnDPPfdgGAbr169n2LBhXLhwgVGjRuV1nZIXDAO2Pw8nF4KDK4T9YpkQLiIiItli11Bd9erVGT9+PI899pjN8ZkzZzJu3DiioqLyrMDCVqKG6va8C3+OAUxw749QrV9hVyQiIpIvitRQ3ZkzZ2jTpk26423atOHMmTO5LkryQdT3f4cmoOVkhSYRERE72BWcatWqxY8//pju+Ny5c6ldu3aui5I8dnY1bH7C8n39l6Du84VajoiISHFl1xyn8ePHM2DAANauXcs999yDyWTijz/+YMWKFRkGqtyKjo5mwoQJrFy5kpiYGCpXrswjjzzCa6+9houLS6bXGYbB+PHj+eqrr7h8+TKtWrXis88+o2HDhnleY5EVdxDW9YGUJKj2EDR7r7ArEhERKbbs6nHq27cvmzdvxtfXl4ULFxIREYGvry9btmyhd+/eeV0j+/fvJyUlhf/85z/s2bOHjz/+mC+//JJ///vfWV73/vvvM2nSJD799FO2bt2Kv78/nTp14urVq3leY5GUeBnW9LD8b4XW0HoGmOx+kFJERKTUy/U6ToXlgw8+4IsvvuDo0aMZnjcMg8qVKzNy5EheeeUVABISEvDz8+O9997j2WefzfC6hIQEEhISrK/j4uIIDAwsfpPDU5JgVVc4uxI8qkGXLeDuV9hViYiIFIj8mhxu11AdQEpKCocPH+bcuXOkpKTYnLvvvvtyXdidpN1oOCNRUVHExMRY99EDcHV1JSwsjA0bNmQanCZOnMj48ePzvN4Ct/0FS2hyKgNh/1NoEhERyQN2BadNmzYxaNAgjh07xu0dViaTCbPZnCfFZebIkSNMnTqVjz76KNM2MTExAPj52QYGPz8/jh07lul1Y8aMYfTo0dbXqT1OxUrUd3DoC8AEbWZDuSaFXZGIiEiJYNeEl2HDhhESEsLu3bu5dOkSly9ftn5dunQp2+8zbtw4TCZTll/btm2zueb06dN07dqV/v3789RTT93xHrdvBWMYRpbbw7i6uuLt7W3zVaxc2QVb/u5Na/QmVO1RuPWIiIiUIHb1OB06dIh58+ZRq1atXN18xIgRDBw4MMs2wcHB1u9Pnz5Nu3btCA0N5auvvsryOn9/y4a1MTExBAQEWI+fO3cuXS9UiZF0Fdb1A3M8+HeGRm8UdkUiIiIlil3BqVWrVhw+fDjXwcnX1xdfX99stT116hTt2rWjZcuWTJ8+HQeHrDvLqlevjr+/P8uXL6d58+YAJCYmsmbNGt57rwQ+km8YsPlJuHoQPKpCm1ng4FjYVYmIiJQodgWnf/7zn7z44ovExMTQuHFjnJ2dbc43aZK3c2pOnz5NeHg41apV48MPP+T8+fPWc6k9SwD16tVj4sSJ9O7dG5PJxMiRI3nnnXeoXbs2tWvX5p133sHDw4NBgwblaX1FwsGpcPwnMDnBvT+BW/YCqYiIiGSfXcGpb9++ADzxxBPWYyaTyTp/KK8nhy9btozDhw9z+PBhqlatanMu7eT0AwcOEBsba3398ssvEx8fz/Dhw60LYC5btgwvL688ra/Qnd8IO160fN/iI/BtXbj1iIiIlFB2reOU1VNpAEFBQXYXVNQU+U1+b56HJS3gxknLyuD3zIEsJr+LiIiUBkVqHaeSFIyKG7MZ1q2DM2cgwN9MmPEIphsnwbsutPqvQpOIiEg+ynZw+uWXX+jWrRvOzs788ssvWbZ98MEHc12YpDdvHvzjH3DhguX12D4TCO+7jGQ8cLp3PjiXsCFIERGRIibbQ3UODg7ExMRQqVKlLJ9oK4gFMAtSURmqe/ll+OCDW687N17Kby93w8HB4JHPv6PP6Efo06fQyhMRESlSCn2oLu22KrdvsSL566efbENT1fInmPXcYBwcDL74fRiz1j/Ckv3Qsyc4agUCERGRfGPXyuFScMxmeOyxW69NphSmP/s4vl4X2Xa0JaO+/xiAixdh9erCqVFERKS0sHuT3y1btrB69eoMN/mdNGlSrgsTi0GD4ObNW6+f6/QZHRut4PpNDx7+9AcSktys51avhg4dCr5GERGR0sKu4PTOO+/w+uuvU7duXfz8/Gz2fstqHzjJmZ9+gh9/vPW6TsAB3n/4ZQBenvM+h8/WLqTKRERESie7gtMnn3zCN998w9ChQ/O4HEl1+xCdg8nMzGFDcHe5ybJdnfji93+kuyY8vODqExERKY3smuPk4ODAPffck9e1SBoTJtgO0T0ZPo3WtTYTe8ObJ7+ahmHYfnTe3gpOIiIi+c2u4DRq1Cg+++yzvK5F/mY2w1tv3XpdvsxFJg4YA8Cb8/6Pk5cC010zbZqeqBMREclvdg3VvfTSS3Tv3p2aNWvSoEGDdJv8RkRE5ElxpdV991nCU6oJ/d6ggtcldp1oxGfLn0vX/qGHoF+/AixQRESklLIrOP3zn/9k1apVtGvXjgoVKmhCeB6aOxc2bLj1unnwDoZ1+BKAETM+xZxi+5G5ucHs2QVZoYiISOllV3D69ttvmT9/Pt27d8/reko1sxkeffTWa5MphU+HjMDBwWD2hodZuz8s3TXffachOhERkYJi1xyn8uXLU7NmzbyupdSbMAGSkm69fvTe72hTZyPXbnryr9kfpGuvIToREZGCZVdwGjduHGPHjuXGjRt5XU+pZTbDO+/ceu3tHmtds+n/FrzJ6ctVbNo7O2uITkREpKDZNVQ3ZcoUjhw5gp+fH8HBwekmh+/YsSNPiitNbu9tGt93LH4+59h/ui6TfxuZrv3332uITkREpKDZFZx69eqVx2WUbrf3NjUK3MWIzp8C8Py3U0gyu9i0b9PGMkwnIiIiBcuu4DR27Ni8rqNUGzTItrfpw0Ev4eRoZv6WPizf1dmmraMjrF1bwAWKiIgIYOccJ8k7t+9H167BSro0WUZisnOGE8Jff11DdCIiIoXFrh4nBweHLNduMqddvVEydft+dGBYVwj/z4pniTpfw6a9iwu88UbB1SciIiK27ApOCxYssHmdlJTEzp07mTlzJuPHj8+TwkqD2/ej6xWykFa1tnDtpidvLXw9XfsxY9TbJCIiUphMhmEYefVms2fPZu7cufz888959ZaFLi4uDh8fH2JjY/H29s6z9zWbwcsL4uMtrx0dkvnr3SY0qLKPCQte5815E2zau7jAjRsKTiIiItmRX3+/83SOU6tWrfj999/z8i1LrNWrb4UmgEfu/Z4GVfZx8Wp5Plz0Urr26m0SEREpfHkWnOLj45k6dSpVqlS5c2Phyy9vfW8ypfBqj3cBePd/rxIX72PT1t1dc5tERESKArvmOJUrV85mcrhhGFy9ehUPDw++//77PCuupDKbIe1o5oMtfqFe5QNcue7DlyuGpWv/7bfqbRIRESkK7ApOkydPtnnt4OBAxYoVadiwIWPHjuXBBx/Mi9pKLNtVwg1e6fEeAJ//PpxrN71s2mo/OhERkaIjTyeH//nnn7Ro0aJELUeQ15PLzGbL0FtqcLqnzh/8MbYtNxNdCR4ZzdlYf2tbZ2fLPCj1NomIiORMsZgcLnd2+550/+wyFYDv1j9qE5oAevZUaBIRESlKFJwKkNkM779/63VA2dP0CYkA4NNlI9K1H5Z+upOIiIgUIgWnAnT7EgTPtP8KZ6dk1u2/l7+ON7Vp6+4O4eEFWp6IiIjcQY4mh/fp0yfL81euXMlNLSVe2iUInByTeKb9VwB8tvy5dG1fflnDdCIiIkVNjoKTj4/PHc8/Zrv5mvzt9iUIeocsoHK5M8Rc8SNiq20g1Z50IiIiRVOOgtP06dPzq44S7/ZJ4SM6fwrAf1Y+S5LZxaatVgkXEREpmvJ0OYKSKC8eZ7x9CYJGgbvY9W4Tks2OBL1wjNOXb622rj3pREREck/LERRjt/c2PdfpMwAWbOttE5oAHnxQoUlERKSoKhbBKTo6mieffJLq1avj7u5OzZo1GTt2LImJiVleN3ToUEwmk81X69atC6hqi9uXIPDxuMKj93wHaAkCERGR4sauLVcK2v79+0lJSeE///kPtWrVYvfu3Tz99NNcv36dDz/8MMtru3btajM3y8XFJYvWee/2JQiGtJ2Jp9sNdp9oyNr999m01RIEIiIiRVuxCE5du3ala9eu1tc1atTgwIEDfPHFF3cMTq6urvj7+2fZJq2EhAQSEhKsr+Pi4nJecBpplyAwmVIY3vFzIHUJApNNWy1BICIiUrQVi6G6jMTGxlK+fPk7tlu9ejWVKlWiTp06PP3005w7dy7L9hMnTsTHx8f6FRgYaHeNty9B0LHR79StfJDYG95898ejNm21BIGIiEjRVyyD05EjR5g6dSrD7jAhqFu3bsyaNYuVK1fy0UcfsXXrVtq3b2/To3S7MWPGEBsba/06ceKE3XXePin8yfBpAHy77jGuJ5S57b7qbRIRESnqCnU5gnHjxjF+/Pgs22zdupWQkBDr69OnTxMWFkZYWBj//e9/c3S/M2fOEBQUxJw5c+64Cnoqex9nNJuhXDm4etXy2ts9lrOf++HmkkCL17azM7qFta2WIBAREclb+bUcQaHOcRoxYgQDBw7Msk1wcLD1+9OnT9OuXTtCQ0P56quvcny/gIAAgoKCOHToUI6vzal1626FJoB+d8/DzSWBPScbsDO6uU1bLUEgIiJSPBRqcPL19cXX1zdbbU+dOkW7du1o2bIl06dPx8Eh56OMFy9e5MSJEwQEBOT42pxasMD29aP3WpYgsMxtsp0UriUIREREiodiMcfp9OnThIeHExgYyIcffsj58+eJiYkhJibGpl29evVY8HdiuXbtGi+99BIbN24kOjqa1atX06NHD3x9fendu3e+1ms2Q9pRxGq+xwhvsAaA2RsG2bT18NASBCIiIsVFsViOYNmyZRw+fJjDhw9TtWpVm3Npp2gdOHCA2NhYABwdHdm1axfffvstV65cISAggHbt2jF37ly8vLzytd7Vqy1zllINajMbgFV7wzlxsZpN26ef1jCdiIhIcVEsgtPQoUMZOnToHdulDVHu7u4sXbo0H6vKXNq1m8C4bZjOVq9eBVKSiIiI5IFiMVRXnJjN8Ouvt163CN5Bgyr7iE90Y/6WvjZtvb2hbdsCLlBERETspuCUx1avhps3b71O7W36ZceDxMX72LQdNUrDdCIiIsWJglMeSztM5+iQzMNtfgDSD9NppXAREZHip1jMcSrqzGZLT9PKlRARcet4p8bL8fM5x7nYiiz9q4vNNVq7SUREpPhRcMqliAh45hm4eDH9udRhujmbBpJsdrY5p7WbREREih8Fp1yIiIC+fTM+V8btKr1aLgTg+z8esTnn7q61m0RERIojzXGyk9kMzz+f+fneIQvwcI3nwOk6bD16l8257t01TCciIlIcKTjZad06OHXK9lhwxShCa28ADJ7r9BkA369/BG2xIiIiUjJoqM5OZ87Yvm4RvJ0N49rg6pzIgdN1qFv5IPGJbvxn5bM27TRMJyIiUnypx8lOt+8T/GL3j3B1TgSgbuWDAHz82yjOx1WyaffyyxqmExERKa7U42Sntm3BywuuXgUwaN9gJQAjv/uYxoG72H+6Hh//NsrmGq3dJCIiUrwpOOWC898rDARWOIF/2bMkJTvx5YphJCS5Zdh+zBj1NomIiBRnGqqz07p1cOmS5ftafocBOHquRqahqUwZ9TaJiIgUdwpOdvr551vf1/Q7AsCRczUzbT9zpnqbREREijsFJzuYzfD997dep/Y4HT5bK11bNzeYPx/69Cmo6kRERCS/aI6THdatgwsXbr2uVuE4ANHng23aeXvD+fOWSeEiIiJS/KnHyQ63r+FUyfscADGx/jbHn3hCoUlERKQkUXCyw+1rOFXysQSn83EVbY737FlQFYmIiEhBUHCyQ9u2UKHCrdcVvc4DcC7NYpcVKljaiYiISMmh4JRLJlMKFb3TBycREREpeRSc7LBuHVy8aPm+vOclHB1SALhw1dfa5uJFSzsREREpORSc7JB2cnhqb9Ola+VINjtn2k5ERESKPwUnO6SdHJ4anNL2NmXUTkRERIo/BSc7tG0LVauCyQTe7nEAXLlR1nreZILAQE0OFxERKWkUnOzg6AgPPwyGcSs4xcV727SZPFlbrIiIiJQ0Ck52iIiADz+0fO/jHgvYBqeXXtIWKyIiIiWRglMOmc3wwguW3ia41eMUe8PH2mbOHEs7ERERKVkUnHJo3To4efLW64yG6k6c0FIEIiIiJZGCUw7dvsSAj0f6obqM2omIiEjxp+CUQ7cvMZDRUF1G7URERKT4U3DKobRLEUD6oTotRSAiIlJyKTjlkKMjfPKJ5XuTyXaoLjVMaSkCERGRkknByQ59+liWHHBwsO1xcnDQUgQiIiIlmYKTHVLXcTKb08xxivfBbLYcj4go5AJFREQkXyg45dDt6zilLoB5Nd7L2mbkSK3jJCIiUhIVm+D04IMPUq1aNdzc3AgICODRRx/l9OnTWV5jGAbjxo2jcuXKuLu7Ex4ezp49e3JVx+3rOHm6Xgfg6k2vv++pdZxERERKqmITnNq1a8ePP/7IgQMHmD9/PkeOHKFfv35ZXvP+++8zadIkPv30U7Zu3Yq/vz+dOnXi6tWrdtdhuz6TgafbDQBuJHhk0U5ERERKAqfCLiC7Ro0aZf0+KCiIV199lV69epGUlISzs3O69oZhMHnyZF577TX6/D1be+bMmfj5+TF79myeffZZu+pIuz6Tm/NN6/c3Ej0ybSciIiIlQ7HpcUrr0qVLzJo1izZt2mQYmgCioqKIiYmhc+fO1mOurq6EhYWxYcOGTN87ISGBuLg4m6+00q7j5OF6w3o8PtEd0DpOIiIiJVmxCk6vvPIKnp6eVKhQgePHj/Pzzz9n2jYmJgYAPz8/m+N+fn7WcxmZOHEiPj4+1q/AwECb82nXcfL8OzglJLlgTnHSOk4iIiIlXKEGp3HjxmEymbL82rZtm7X9v/71L3bu3MmyZctwdHTksccew0h9vC0TptQ08zfDMNIdS2vMmDHExsZav06cOJGuTZ8+MG8e1KhmmRieOkxXtarluNZxEhERKZkKdY7TiBEjGDhwYJZtgoODrd/7+vri6+tLnTp1qF+/PoGBgWzatInQ0NB01/n7+wOWnqeANBOOzp07l64XKi1XV1dcXV3vWHufPtCz7Q1YDq6enqxaZRmeU0+TiIhIyVWowSk1CNkjtacpISEhw/PVq1fH39+f5cuX07x5cwASExNZs2YN7733nn0Fp2E2w187btAcwNFDoUlERKQUKBZznLZs2cKnn35KZGQkx44dY9WqVQwaNIiaNWva9DbVq1ePBQsWAJYhupEjR/LOO++wYMECdu/ezdChQ/Hw8GDQoEG5qiciAoKD4ZV/WeY4HTzqQXCwVgwXEREp6YrFcgTu7u5EREQwduxYrl+/TkBAAF27dmXOnDk2w2oHDhwgNjbW+vrll18mPj6e4cOHc/nyZVq1asWyZcvw8vLK6DbZEhEB/fpZFrps6XdrDadTpyzHNcdJRESk5DIZd5pdXcrFxcXh4+NDbGwsnp7eBAffWjn84Tazmf3cYH7f3YFOE3/HZLJMEI+K0rCdiIhIYUr799vb2zvP3rdYDNUVFZltt5K6ari2WxERESnZFJxy4PZtVDxcLEN11xM8s2wnIiIiJYOCUw7cvo1K6srh2m5FRESkdFBwyoG0263ArR6n1KE6bbciIiJSsik45UDa7VbS7lV3I9FD262IiIiUAgpOOZS63UqVKml6nBI9tN2KiIhIKaDgZIc+fSA6Gnr3sDxV99jjnkRFKTSJiIiUdApOdnJ0BH9fS49TzToeGp4TEREpBRScciPZEpxw8si6nYiIiJQICk65Yf47ODkqOImIiJQGCk65oR4nERGRUkXBKTfU4yQiIlKqKDjZyWyG+GuW4LTjL3fM5kIuSERERPKdgpMdIiIgOBiuXIgH4Iln3AkOthwXERGRkkvBKYciIqBfPzh5EtxcbgIQn+jOqVOW4wpPIiIiJZeCUw6YzfDCC2AYltfuzpYep5tJbtZjI0eiYTsREZESSsEpB9ats/Q0WRi4uSQAlh4nsASqEycs7URERKTkUXDKgTNnbn3v5nzT+n1qcMqonYiIiJQcCk45EBBw6/u0welmklum7URERKTkUHDKgbZtoWpVMJnA3cUyvynZ7Eiy2RmwHA8MtLQTERGRkkfBKQccHeGTTyzfu//9RF1qb5PJZDk+eTLa8FdERKSEUnDKoT59YN48CA609Dilzm+qWtVyvE+fwqxORERE8pNTYRdQHPXpAz3bxsNy8PB2Y9Uqy/CceppERERKNgUnOzkalqE6Ty93wsMLtxYREREpGBqqs5fZMlSHo3vW7URERKTEUHCyl/nv5Qgc3bJuJyIiIiWGgpO91OMkIiJS6ig42Us9TiIiIqWOgpO91OMkIiJS6ig42cFshsMHLMHp7EU3zOZCLkhEREQKhIJTDkVEQHAw/Pcry1Ddot/cCQ62HBcREZGSTcEpByIioF8/OHny1l51N5PcOHXKclzhSUREpGRTcMomsxleeAEMw/LazdnS4xSf5G49NnIkGrYTEREpwRScsmnDBktPU6rUHqfUveoMA06cgHXrCqM6ERERKQgKTtkUE2P7OrXH6WaS7XIEZ84UVEUiIiJS0IpNcHrwwQepVq0abm5uBAQE8Oijj3L69Oksrxk6dCgmk8nmq3Xr1nbd39/f9vXtPU6pAgLsensREREpBopNcGrXrh0//vgjBw4cYP78+Rw5coR+/frd8bquXbty5swZ69fixYvtun+bNlC1KphMltdpJ4eD5XhgILRta9fbi4iISDHgVNgFZNeoUaOs3wcFBfHqq6/Sq1cvkpKScHZ2zvQ6V1dX/G/vLrKDoyN88onl6TmTKc3k8ER3a5iaPNnSTkREREqmYtPjlNalS5eYNWsWbdq0yTI0AaxevZpKlSpRp04dnn76ac6dO5dl+4SEBOLi4my+UvXpA/PmQZUq4O58q8epalXL8T59cv+ziYiISNFVrILTK6+8gqenJxUqVOD48eP8/PPPWbbv1q0bs2bNYuXKlXz00Uds3bqV9u3bk5CQkOk1EydOxMfHx/oVGBhoc75PH4iOhpDmlh6nf7/uTlSUQpOIiEhpYDKM1FWICt64ceMYP358lm22bt1KSEgIABcuXODSpUscO3aM8ePH4+Pjw6+//oopdazsDs6cOUNQUBBz5syhTyZJJyEhwSZYxcXFERgYSGxsLN7e3rca/tYCLu+E8N+gctds3V9EREQKRlxcHD4+Pun/fudSoc5xGjFiBAMHDsyyTXBwsPV7X19ffH19qVOnDvXr1ycwMJBNmzYRGhqarfsFBAQQFBTEoUOHMm3j6uqKq6vrnd/MbOlxwtEt63YiIiJSYhRqcEoNQvZI7SjLatjtdhcvXuTEiRME5MWaAWbLHCcc3bNuJyIiIiVGsZjjtGXLFj799FMiIyM5duwYq1atYtCgQdSsWdOmt6levXosWLAAgGvXrvHSSy+xceNGoqOjWb16NT169MDX15fevXvnvihrcFKPk4iISGlRLJYjcHd3JyIigrFjx3L9+nUCAgLo2rUrc+bMsRlWO3DgALGxsQA4Ojqya9cuvv32W65cuUJAQADt2rVj7ty5eHl55b4o61CdepxERERKi2IRnBo3bszKlSvv2C7tPHd3d3eWLl2af0Wpx0lERKTUKRZDdUWOkQIpiZbv1eMkIiJSaig42cGceNP6/boN7pjNhViMiIiIFBgFpxyKiIBmjW8Fp3ad3AgOthwXERGRkk3BKQciIix71V26YJnflJTshDnFiVOnLMcVnkREREo2BadsMpvhhRfAMMDd5dY+dWA5BjByJBq2ExERKcEUnLJpwwY4edLyvZuzZaguPvHWxHDDgBMnYN26wqhORERECoKCUzbFxNz6/vYep7TOnCmoikRERKSgKThlk7//re8z6nFKlRe7uYiIiEjRpOCUTW3aQNWqYDLd6nGKT7oVnEwmCAyEtm0Lq0IRERHJbwpO2eToCJ98Yvne3cXS45Q6VGcyWY5PnmxpJyIiIiWTglMO9OkD8+ZBFb+/e5z+HqqrWtVyvE+fwqxORERE8lux2KuuKOnTB3o1uQlboG4DN1atsgzPqadJRESk5FNwsoODYelxqlzVncr3FXIxIiIiUmA0VGcPsyU44Zh+OQIREREpuRSc7GH+e686x/TLEYiIiEjJpeCUQ2YzRB+x9DidinHTFisiIiKliIJTDkREQHAwzJt7A4BZP3oSHKzNfUVEREoLBads+uUX6NfPsl+dh4slON1I8ODUKctxhScREZGST8Epm155xbKRL4CH69/BKdHDemzkSDRsJyIiUsIpOGXT6dO3vk/b4wSWQHXiBKxbVxiViYiISEFRcLKDp+t1AK4neNocP3OmMKoRERGRgqLgZIe0Q3VpBQQURjUiIiJSUBScsqly5Vub+d4+VGcyQWCgZesVERERKbkUnLLpvfcs/2sy2fY4pYapyZO1X52IiEhJp+CUTQ8+CPPmQZUqtj1OVatajvfpU8gFioiISL5TcMqBPn0gOhqqBliC05TPPYiKUmgSEREpLRSc7OBoWJ6qSzF53KGliIiIlCQKTjlg2XLFgGRLj1Pv/tpyRUREpDRRcMqm1C1Xzp5JwsnRskT4jURtuSIiIlKaKDhlU+qWK6lP1IFlcri2XBERESk9FJyyKXXLldQn6pLNjiSZnQFtuSIiIlJaKDjlkO2q4Sabc9pyRUREpGRTcMqh1H3qUlcNT0tbroiIiJRsCk7ZlLrlSupQXdoNfrXlioiISOmg4JRNqVuueLrZbvCrLVdERERKj2IXnBISEmjWrBkmk4nIyMgs2xqGwbhx46hcuTLu7u6Eh4ezZ88eu+6buuVKYIDtBr/ackVERKT0KHbB6eWXX6Zy5crZavv+++8zadIkPv30U7Zu3Yq/vz+dOnXi6tWrdt27Z08Y/YIlOFUM8OD339GWKyIiIqVIsQpOv/32G8uWLePDDz+8Y1vDMJg8eTKvvfYaffr0oVGjRsycOZMbN24we/bsHN/7l18gOBgmvW8JTvsOejB0KPz8c47fSkRERIopp8IuILvOnj3L008/zcKFC/HwuPMecVFRUcTExNC5c2frMVdXV8LCwtiwYQPPPvtshtclJCSQkJBgfR0bGwvAo4/GAeBU+yJxN+DCVWdOnoyjb1/47jvLUJ6IiIgUDXFxlr/bRupK1XmkWAQnwzAYOnQow4YNIyQkhOjo6DteExMTA4Cfn5/NcT8/P44dO5bpdRMnTmT8+PEZnAkE4L+rLF/wM+ADwKOPZuOHEBERkQJ38eJFfHx88uz9CjU4jRs3LpOQcsvWrVvZsGEDcXFxjBkzJsf3MJlsF6k0DCPdsbTGjBnD6NGjra+vXLlCUFAQx48fz9NfvORcXFwcgYGBnDhxAm9v78Iup1TTZ1G06PMoOvRZFB2xsbFUq1aN8uXL5+n7FmpwGjFiBAMHDsyyTXBwMG+99RabNm3C1dXV5lxISAiDBw9m5syZ6a7z9/cHLD1PAWlWpjx37ly6Xqi0XF1d090HwMfHR/8SFBHe3t76LIoIfRZFiz6PokOfRdHh4JC307kLNTj5+vri6+t7x3ZTpkzhrbfesr4+ffo0Xbp0Ye7cubRq1SrDa6pXr46/vz/Lly+nefPmACQmJrJmzRreS12USURERCQHisUcp2rVqtm8LlOmDAA1a9akatWq1uP16tVj4sSJ9O7dG5PJxMiRI3nnnXeoXbs2tWvX5p133sHDw4NBgwYVaP0iIiJSMhSL4JRdBw4csD4FB5Y1n+Lj4xk+fDiXL1+mVatWLFu2DC8vr2y/p6urK2PHjs1w+E4Klj6LokOfRdGiz6Po0GdRdOTXZ2Ey8vo5PREREZESqlgtgCkiIiJSmBScRERERLJJwUlEREQkmxScRERERLJJwUlEREQkmxScgM8//5zq1avj5uZGy5YtWbduXZbt16xZQ8uWLXFzc6NGjRp8+eWXBVRpyZeTzyIiIoJOnTpRsWJFvL29CQ0NZenSpQVYbcmW038vUq1fvx4nJyeaNWuWvwWWIjn9LBISEnjttdcICgrC1dWVmjVr8s033xRQtSVfTj+PWbNm0bRpUzw8PAgICODxxx/n4sWLBVRtybV27Vp69OhB5cqVMZlMLFy48I7X5Mnfb6OUmzNnjuHs7Gx8/fXXxt69e40XXnjB8PT0NI4dO5Zh+6NHjxoeHh7GCy+8YOzdu9f4+uuvDWdnZ2PevHkFXHnJk9PP4oUXXjDee+89Y8uWLcbBgweNMWPGGM7OzsaOHTsKuPKSJ6efRaorV64YNWrUMDp37mw0bdq0YIot4ez5LB588EGjVatWxvLly42oqChj8+bNxvr16wuw6pIrp5/HunXrDAcHB+OTTz4xjh49aqxbt85o2LCh0atXrwKuvORZvHix8dprrxnz5883AGPBggVZts+rv9+lPjjdfffdxrBhw2yO1atXz3j11VczbP/yyy8b9erVszn27LPPGq1bt863GkuLnH4WGWnQoIExfvz4vC6t1LH3sxgwYIDx+uuvG2PHjlVwyiM5/Sx+++03w8fHx7h48WJBlFfq5PTz+OCDD4waNWrYHJsyZYpRtWrVfKuxNMpOcMqrv9+leqguMTGR7du307lzZ5vjnTt3ZsOGDRles3HjxnTtu3TpwrZt20hKSsq3Wks6ez6L26WkpHD16tU83wm7tLH3s5g+fTpHjhxh7Nix+V1iqWHPZ/HLL78QEhLC+++/T5UqVahTpw4vvfQS8fHxBVFyiWbP59GmTRtOnjzJ4sWLMQyDs2fPMm/ePLp3714QJUsaefX3u0RtuZJTFy5cwGw24+fnZ3Pcz8+PmJiYDK+JiYnJsH1ycjIXLlwgICAg3+otyez5LG730Ucfcf36dR566KH8KLHUsOezOHToEK+++irr1q3DyalU/99KnrLnszh69Ch//PEHbm5uLFiwgAsXLjB8+HAuXbqkeU65ZM/n0aZNG2bNmsWAAQO4efMmycnJPPjgg0ydOrUgSpY08urvd6nucUplMplsXhuGke7YndpndFxyLqefRaoffviBcePGMXfuXCpVqpRf5ZUq2f0szGYzgwYNYvz48dSpU6egyitVcvLvRUpKCiaTiVmzZnH33Xdz//33M2nSJGbMmKFepzySk89j7969PP/887z55pts376dJUuWEBUVxbBhwwqiVLlNXvz9LtX/aejr64ujo2O6/1I4d+5culSayt/fP8P2Tk5OVKhQId9qLens+SxSzZ07lyeffJKffvqJjh075meZpUJOP4urV6+ybds2du7cyYgRIwDLH2/DMHBycmLZsmW0b9++QGovaez59yIgIIAqVarg4+NjPVa/fn0Mw+DkyZPUrl07X2suyez5PCZOnMg999zDv/71LwCaNGmCp6cnbdu25a233tIoRQHKq7/fpbrHycXFhZYtW7J8+XKb48uXL6dNmzYZXhMaGpqu/bJlywgJCcHZ2Tnfai3p7PkswNLTNHToUGbPnq05A3kkp5+Ft7c3u3btIjIy0vo1bNgw6tatS2RkJK1atSqo0ksce/69uOeeezh9+jTXrl2zHjt48CAODg5UrVo1X+st6ez5PG7cuIGDg+2fWkdHR+BWb4cUjDz7+52jqeQlUOqjpdOmTTP27t1rjBw50vD09DSio6MNwzCMV1991Xj00Uet7VMfZxw1apSxd+9eY9q0aVqOII/k9LOYPXu24eTkZHz22WfGmTNnrF9XrlwprB+hxMjpZ3E7PVWXd3L6WVy9etWoWrWq0a9fP2PPnj3GmjVrjNq1axtPPfVUYf0IJUpOP4/p06cbTk5Oxueff24cOXLE+OOPP4yQkBDj7rvvLqwfocS4evWqsXPnTmPnzp0GYEyaNMnYuXOndWmI/Pr7XeqDk2EYxmeffWYEBQUZLi4uRosWLYw1a9ZYzw0ZMsQICwuzab969WqjefPmhouLixEcHGx88cUXBVxxyZWTzyIsLMwA0n0NGTKk4AsvgXL670VaCk55K6efxb59+4yOHTsa7u7uRtWqVY3Ro0cbN27cKOCqS66cfh5TpkwxGjRoYLi7uxsBAQHG4MGDjZMnTxZw1SXPqlWrsvwbkF9/v02Gob5CERERkewo1XOcRERERHJCwUlEREQkmxScRERERLJJwUlEREQkmxScRERERLJJwUlEREQkmxScRERERLJJwUlEREQkmxScRERERLJJwUlEJAMXL16kUqVKREdH59s9+vXrx6RJk/Lt/UUk7yk4iUihGDp0KCaTiWHDhqU7N3z4cEwmE0OHDi34wv42ceJEevToQXBwsPXYfffdh8lkYsKECTZtDcOgVatWmEwm3nzzzWzf48033+Ttt98mLi4ur8oWkXym4CQihSYwMJA5c+YQHx9vPXbz5k1++OEHqlWrVmh1xcfHM23aNJ566inrMcMwiIyMJCgoiF27dtm0nzlzJqdPnwagRYsW2b5PkyZNCA4OZtasWXlTuIjkOwUnESk0LVq0oFq1akRERFiPRUREEBgYSPPmza3HlixZwr333kvZsmWpUKECDzzwAEeOHLF5r3nz5tG4cWPc3d2pUKECHTt25Pr163c8l5HffvsNJycnQkNDrccOHTrE1atXGTp0qE1wunr1KmPGjLH2jrVs2TJHv4MHH3yQH374IUfXiEjhUXASkUL1+OOPM336dOvrb775hieeeMKmzfXr1xk9ejRbt25lxYoVODg40Lt3b1JSUgA4c+YMDz/8ME888QT79u1j9erV9OnTB8MwsjyXmbVr1xISEmJzbPv27bi5ufHwww9z6NAhEhISAJgwYQLNmjUjICAAX19fAgMDc/Tz33333WzZssX6fiJStDkVdgEiUro9+uijjBkzhujoaEwmE+vXr2fOnDmsXr3a2qZv374210ybNo1KlSqxd+9eGjVqxJkzZ0hOTqZPnz4EBQUB0LhxYwAOHjyY6bnMREdHU7lyZZtjO3bsoEmTJtSpUwdPT0/27duHp6cnn3/+Odu2bePDDz/McW8TQJUqVUhISCAmJsZan4gUXepxEpFC5evrS/fu3Zk5cybTp0+ne/fu+Pr62rQ5cuQIgwYNokaNGnh7e1O9enUAjh8/DkDTpk3p0KEDjRs3pn///nz99ddcvnz5jucyEx8fj5ubm82x7du307JlS0wmE02aNGH37t2MGjWKZ555hnr16rF9+/YczW9K5e7uDsCNGzdyfK2IFDwFJxEpdE888QQzZsxg5syZ6YbpAHr06MHFixf5+uuv2bx5M5s3bwYgMTERAEdHR5YvX85vv/1GgwYNmDp1KnXr1iUqKirLc5nx9fVNF6527txpDUZNmzblk08+YcuWLYwdO5bExET27NljE5xu3LjBv/71L9q0aUObNm14+umnuXjxYrp7Xbp0CYCKFSvm8LcmIoVBwUlECl3Xrl1JTEwkMTGRLl262Jy7ePEi+/bt4/XXX6dDhw7Ur18/wx4jk8nEPffcw/jx49m5cycuLi4sWLDgjucy0rx5c/bu3Wt9ffToUa5cuWIdimvWrBnbtm3j7bffxsfHh127dpGUlGQzVDdixAiaNm3Khg0b2LBhAwMHDuSxxx5LN7dq9+7dVK1aNV0vm4gUTZrjJCKFztHRkX379lm/T6tcuXJUqFCBr776ioCAAI4fP86rr75q02bz5s2sWLGCzp07U6lSJTZv3sz58+epX79+lucy06VLF8aMGcPly5cpV64c27dvx8XFhUaNGgEwZMgQevXqRYUKFQDL/Kdy5cpZhxDj4+O5fPkyjzzyCOPGjQNg3Lhx/Pzzzxw+fJjatWtb77Vu3To6d+6cu1+giBQYBScRKRK8vb0zPO7g4MCcOXN4/vnnadSoEXXr1mXKlCmEh4fbXLt27VomT55MXFwcQUFBfPTRR3Tr1o19+/Zlei4zjRs3JiQkhB9//JFnn32WHTt20KhRI5ydnQFwdna26SHasWOHzfIJaXuVRowYkel9bt68yYIFC1i6dOkdfz8iUjSYjKyeyRURKaUWL17MSy+9xO7du3FwyPmshqFDh9KpUycGDx4MwIoVK/jwww9ZvHgxJpMJgM8++4yff/6ZZcuW5WntIpJ/1OMkIpKB+++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", 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" ] @@ -294,13 +430,15 @@ "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", "gp.fit(X, y)\n", "y_pred_1, sigma_1 = gp.predict(x, return_std=True)\n", + "#chi2 = np.sum((y - y_pred_1)**2) #unit test for 2d function\n", + "#print(chi2)\n", "\n", "plt.figure()\n", "plt.scatter(X, combined_L, color='blue', label='actual values')\n", "plt.plot(x, y_pred_1, color='orange', label='predicted values')\n", "plt.xlabel('Mass ($M_{\\odot}$)')\n", "plt.ylabel('Luminosity')\n", - "plt.title('Best Fit by Heat Map')\n", + "plt.title('Reduced $\\chi^2$ Determination of Luminosity Best Fit Without kFold')\n", "plt.xlim(0, 1)\n", "plt.ylim(-4,0)\n", "plt.legend()\n", @@ -312,18 +450,29 @@ "id": "99653c01-83e7-44c1-8a70-a04c185b0c3e", "metadata": {}, "source": [ - "This produced a pretty good fit! We can them repeat the process to find the best parameters for effective temperature and log gravity." + "Through the use of kFold we produced a pretty good fit! We can then repeat the process to find the best parameters for effective temperature and log gravity." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 11, + "id": "53552603-6dce-4c96-9d22-0facf0c28767", + "metadata": {}, + "outputs": [], + "source": [ + "#assume unity uncertainties and focus on gap between model and data -- gives small value but scale by relative \n", + "#number of data points -- should give value close to 1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "id": "2b89c29a-9c7e-4f31-94a6-094df81e01c1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -333,9 +482,9 @@ } ], "source": [ - "# creating input array (mass) and output array (luminosity)\n", + "# creating input array (mass) and output array (teff)\n", "X = combined_masses.reshape(-1, 1)\n", - "y_Teff = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", + "y_teff = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", "\n", "# Scale X and y\n", @@ -346,45 +495,53 @@ "#y = y_scaler.fit_transform(combined_L.reshape(-1, 1)).ravel()\n", "\n", "# Define grid\n", - "length_scales = np.logspace(-2, 1, 20)\n", + "length_scales = np.logspace(-1, 2, 20)\n", "nus = [0.5, 1.5, 2.5] # available Matern values\n", "\n", - "chi2_map_Teff = np.zeros((len(nus), len(length_scales)))\n", + "chi2_map_teff = np.zeros((len(nus), len(length_scales)))\n", + "N_data_teff = len(y_teff)\n", + "N_params = 2\n", + "\n", + "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n", + "chi2_map_teff = np.zeros((len(nus), len(length_scales)))\n", "\n", - "# Grid search\n", "for i, nu in enumerate(nus):\n", " for j, ls in enumerate(length_scales):\n", " kernel = Matern(length_scale=ls, nu=nu)\n", " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", " \n", + " residuals_teff = []\n", " try:\n", - " gp.fit(X, y_Teff)\n", - " y_pred_Teff = gp.predict(X)\n", - " avg_diff_Teff = (np.sum(y_Teff - y_pred_Teff)) / len(y_pred_Teff)\n", - " chi2_Teff = np.sum(((y_Teff - y_pred_Teff)**2) / avg_diff_Teff)\n", - " #chi2_Teff = np.sum(((y_Teff - y_pred_Teff)**2) / y_pred_Teff)\n", - " chi2_map_Teff[i, j] = chi2_Teff\n", - " except Exception as e:\n", - " chi2_map_Teff[i, j] = np.nan\n", + " for train_idx, test_idx in kf.split(X):\n", + " gp.fit(X[train_idx], y_teff[train_idx])\n", + " y_pred_teff = gp.predict(X[test_idx])\n", + " residuals_teff.append((y_teff[test_idx] - y_pred_teff)**2)\n", "\n", + " residuals_teff = np.concatenate(residuals_teff) \n", + " chi2_teff = np.sum(residuals_teff)\n", + " chi2_red_teff = chi2_teff / (N_data_teff - N_params)\n", + " chi2_map_teff[i, j] = np.log10(chi2_red_teff)\n", + " except Exception as e:\n", + " chi2_map_teff[i, j] = np.nan\n", "# Plotting\n", "plt.figure(figsize=(10, 5))\n", - "im = plt.imshow(chi2_map_Teff, aspect='auto', origin='lower',\n", - " extent=[np.log10(length_scales[0]), np.log10(length_scales[-1]), 0, len(nus)-1],\n", + "im = plt.imshow(chi2_map_teff, aspect='auto', origin='lower',\n", + " extent=[length_scales[0], length_scales[-1], 0, len(nus)-1],\n", " cmap='viridis')\n", "plt.colorbar(im, label=r'$\\chi^2$')\n", - "plt.xticks(np.log10(length_scales), labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.xticks(length_scales, labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", "plt.xlabel('Length Scale')\n", "plt.ylabel(r'$\\nu$')\n", - "plt.title('Chi-Squared Heatmap for Teff Fit Parameters')\n", + "plt.xscale('log')\n", + "plt.title('Chi-Squared Heatmap for Luminosity Fit Parameters')\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 13, "id": "b29c1755-4b8e-404b-a8f5-024a759179af", "metadata": {}, "outputs": [ @@ -392,12 +549,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.5 0.5455594781168517\n" + "1.5 1.2742749857031335\n" ] } ], "source": [ - "i_best_Teff, j_best_Teff = np.unravel_index(np.nanargmin(chi2_map_Teff), chi2_map_Teff.shape)\n", + "i_best_Teff, j_best_Teff = np.unravel_index(np.nanargmin(chi2_map_teff), chi2_map_teff.shape)\n", "best_nu_Teff = nus[i_best_Teff]\n", "best_length_scale_Teff = length_scales[j_best_Teff]\n", "print(best_nu_Teff, best_length_scale_Teff)" @@ -405,13 +562,13 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 14, "id": "926cbb27-cf76-4259-bff4-63cdad0fce02", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -425,21 +582,21 @@ "\n", "# creating input array (mass) and output array (everything else)\n", "X = combined_masses.reshape(-1, 1)\n", - "y = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", + "y_teff = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", "\n", "# trying different kernel types\n", "kernel = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", - "gp.fit(X, y_Teff)\n", - "y_pred_Teff, sigma_Teff = gp.predict(x, return_std=True)\n", + "gp.fit(X, y_teff)\n", + "y_pred_teff, sigma_teff = gp.predict(x, return_std=True)\n", "\n", "plt.figure()\n", "plt.scatter(X, combined_Teff, color='blue', label='actual values')\n", - "plt.plot(x, y_pred_Teff, color='orange', label='predicted values')\n", + "plt.plot(x, y_pred_teff, color='orange', label='predicted values')\n", "plt.xlabel('Mass ($M_{\\odot}$)')\n", "plt.ylabel('Teff')\n", - "plt.title('Best Fit by Heat Map')\n", + "plt.title('Reduced $\\chi^2$ Determination of Effective Temperature Best Fit')\n", "plt.xlim(0, 1)\n", "plt.ylim(3.25,3.75)\n", "plt.legend()\n", @@ -448,13 +605,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 15, "id": "fb250ffb-e8f5-4b66-a9d0-ae79190d1d0d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -464,57 +621,59 @@ } ], "source": [ - "# creating input array (mass) and output array (logg)\n", + "# creating input array (mass) and output array (luminosity)\n", "X = combined_masses.reshape(-1, 1)\n", "y_logg = combined_logg #np.array([combined_Teff, combined_L, combined_logg])\n", "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", "\n", - "# Scale X and y\n", - "#X_scaler = StandardScaler()\n", - "#y_scaler = StandardScaler()\n", - "\n", - "#X = X_scaler.fit_transform(combined_masses.reshape(-1, 1))\n", - "#y = y_scaler.fit_transform(combined_L.reshape(-1, 1)).ravel()\n", - "\n", "# Define grid\n", - "length_scales = np.logspace(-2, 1, 20)\n", + "length_scales = np.logspace(-1, 2, 20)\n", "nus = [0.5, 1.5, 2.5] # available Matern values\n", "\n", "chi2_map_logg = np.zeros((len(nus), len(length_scales)))\n", + "N_data_logg = len(y_logg)\n", + "N_params = 2\n", + "\n", + "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n", "\n", - "# Grid search\n", "for i, nu in enumerate(nus):\n", " for j, ls in enumerate(length_scales):\n", " kernel = Matern(length_scale=ls, nu=nu)\n", " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", " \n", + " residuals_logg = []\n", " try:\n", - " gp.fit(X, y_logg)\n", - " y_pred_logg = gp.predict(X)\n", - " avg_diff_logg = (np.sum(y_logg - y_pred_logg)) / len(y_pred_logg)\n", - " chi2_logg = np.sum(((y_logg - y_pred_logg)**2) / avg_diff_logg)\n", - " chi2_map_logg[i, j] = chi2_logg\n", + " for train_idx, test_idx in kf.split(X):\n", + " gp.fit(X[train_idx], y_logg[train_idx])\n", + " y_pred_logg = gp.predict(X[test_idx])\n", + " residuals_logg.append((y_logg[test_idx] - y_pred_logg)**2)\n", + "\n", + " residuals_logg = np.concatenate(residuals_logg) \n", + " chi2_logg = np.sum(residuals_logg)\n", + " chi2_red_logg = chi2_logg / (N_data_logg - N_params)\n", + " chi2_map_logg[i, j] = np.log10(chi2_red_logg)\n", " except Exception as e:\n", " chi2_map_logg[i, j] = np.nan\n", "\n", "# Plotting\n", "plt.figure(figsize=(10, 5))\n", "im = plt.imshow(chi2_map_logg, aspect='auto', origin='lower',\n", - " extent=[np.log10(length_scales[0]), np.log10(length_scales[-1]), 0, len(nus)-1],\n", + " extent=[length_scales[0], length_scales[-1], 0, len(nus)-1],\n", " cmap='viridis')\n", "plt.colorbar(im, label=r'$\\chi^2$')\n", - "plt.xticks(np.log10(length_scales), labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.xticks(length_scales, labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", "plt.xlabel('Length Scale')\n", "plt.ylabel(r'$\\nu$')\n", - "plt.title('Chi-Squared Heatmap for logg Fit Parameters')\n", + "plt.xscale('log')\n", + "plt.title('Chi-Squared Heatmap for log g Fit Parameters')\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 16, "id": "850e9dc3-5120-46ff-a15b-ccecc379e33d", "metadata": {}, "outputs": [ @@ -522,7 +681,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.5 4.832930238571752\n" + "2.5 0.20691380811147897\n" ] } ], @@ -535,13 +694,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 17, "id": "ce7d8969-3bb4-4154-8d1a-baaa1975baa6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -569,17 +728,444 @@ "plt.plot(x, y_pred_logg, color='orange', label='predicted values')\n", "plt.xlabel('Mass ($M_{\\odot}$)')\n", "plt.ylabel('Log Gravity')\n", - "plt.title('Best Fit by Heat Map')\n", + "plt.title('Reduced $\\chi^2$ Determination of Gravity Best Fit')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(4.0,4.5)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "147e614a-4e8f-485e-abc3-195630b144ae", + "metadata": {}, + "source": [ + "### Generating Data in the Interpolated Region" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d78bbc34-8c01-46a2-83da-43ffde446c5d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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9/Prrr3Bzc8Obb76JevXqoV69elixYkWpfVX/j0Xh/5lo2LAhwsPDER4ejgkTJmjti4mJQceOHXH//n2sWLECJ06cQHh4uCboLPz3Vdy/C+r3VJe/Y6p4nONE5Uoul+OFF17Azz//jHv37ul8d1dx/wfq6uqK5s2b4+OPPy72GC8vLwDA9u3bYWVlhZ9++glKpVKz39j1aUri4uKCvLw8PHr0SOuLVN8vLW9vb7Rp06bEawDSXJ/CYmNjteYDlZdmzZph+/btEELgr7/+wqZNm/Dhhx/CxsYG77//vl7nKvh6Cv+OFPd6jF3vR6lUIiUlpcj2ylSfp6zP3MLCAs7OzgCkv5Xly5dj+fLliImJwd69e/H+++8jISGhzDtYSzJy5EjMmDEDq1evRvv27REfH48333xT7/M899xzaNiwIT788EN07dq1xLmPR48eRWxsLMLCwjRZJgCaeUsF+fr6Yv369QCAGzdu4Pvvv0dISAhycnKwZs0aAEDHjh3RsWNHqFQqnDt3DqtWrUJwcDDc3d0xfPjwYvsQFBQES0tL7N27VyuzZmNjo/nb/Omnn7SO2bNnD9LT0xEaGgpfX1/N9oiIiGKv8c8//xTZpv63orj/uSHTY8aJyt3MmTMhhMCECROQk5NTZH9ubi727dtX5nn69OmDy5cvo169emjTpk2RhzpwkslksLS01Br2yMzMxP/+978i59Q1w6IP9T/yO3bs0Nq+fft2o12jQ4cOsLGxKXL79r1793D06FG88MILRrlO4axHcWQyGVq0aIHPP/8cNWrUwIULF/S+jnrYrfDrCQ8Px9WrV432ekri5+eHGzduIDs7W7MtMTERJ0+eLNfr6qNhw4aoXbs2tm7dCiGEZnt6ejp27dqFDh06FDvJuU6dOpg8eTK6du1a5mdT2t+DUqnUDHUtW7YMLVu2xHPPPWfQa5k9ezb69u2Ld955p8Q26uC48A0bX3/9dannDggIwOzZs9GsWbNiX69cLke7du00GaDS3hNPT0+MGzcO+/fv1/nvt7h+CyFKLCGSlpZW5OaBrVu3wsLCAv/97391uiZVLGacqNx16NABX331FSZNmoTWrVvjjTfeQJMmTZCbm4uLFy9i7dq1aNq0Kfr27VvqeT788EMcPnwYgYGBmDJlCho2bIisrCxER0fjwIEDWLNmDby9vdG7d28sW7YMI0eOxGuvvYbExER89tlnxd4xp86a7NixA3Xr1oVSqUSzZs2e6vX26NEDzz33HN555x2kpqaidevWOHXqFDZv3gwARrkDrEaNGpgzZw4++OADjBkzBiNGjEBiYiLmz58PpVKJefPmPfU1AKBp06YAgLVr18LBwQFKpRL+/v44deoUvvzySwwYMAB169aFEAKhoaFITk7WqtGjq4YNG+K1117DqlWrYGFhgZ49e2ruqvPx8cHUqVOf+rXs27cPDg4ORbYPGTIEo0ePxtdff42XXnoJEyZMQGJiIpYsWQJHR8envq6xWFhYYMmSJRg1ahT69OmD119/HdnZ2fj000+RnJyMTz75BACQkpKCzp07Y+TIkWjUqBEcHBwQHh6OgwcPYtCgQaVeo1mzZggLC8O+ffvg6ekJBwcHNGzYULN/0qRJWLJkCc6fP49vvvnG4Nfy0ksv4aWXXiq1TWBgIJydnTFx4kTMmzcPVlZW2LJlC/7880+tdn/99RcmT56MoUOHokGDBlAoFDh69Cj++usvTeZzzZo1OHr0KHr37o06deogKytLc3dbly5dSu3H8uXLERUVhVGjRmHv3r3o378/vLy8kJGRgWvXrmH79u1QKpWwsrICINWmUygUGDFiBKZPn46srCx89dVXmmHUwlxcXPDGG28gJiYGAQEBOHDgANatW4c33nhDaw4iVSImnZpO1UpERIR4+eWXRZ06dYRCoRB2dnaiVatWYu7cuVoVutU1horz4MEDMWXKFOHv7y+srKxEzZo1RevWrcWsWbO0arts2LBBNGzYUFhbW4u6deuKRYsWifXr1wsAIioqStMuOjpadOvWTTg4OOhcx6nw3VnqekcFz/vo0SPxyiuviBo1aghbW1vRtWtXcfr0aQFArFixotT3qaQ6TsX55ptvRPPmzYVCoRBOTk6if//+Re4uLK2OU2HF3V22fPly4e/vL+RyueY9uXbtmhgxYoSoV6+esLGxEU5OTqJt27Zi06ZNZfa5pPdRXccpICBAWFlZCVdXV/HSSy+VWMdJV+rrlfRQ+/bbb0Xjxo2FUqkUzzzzjNixY0eJd9UV/mzUd5398MMPWtvVvxsFq6OXVsepMBRzF+aePXs0NX/s7OzECy+8IP744w/N/qysLDFx4kTRvHlz4ejoKGxsbETDhg3FvHnztGoWFXdXXUREhHjuueeEra2tpo5TYUFBQaJmzZoiIyOjyL7i6Pr7XNxddSdPnhQdOnQQtra2olatWuLVV18VFy5c0Prb/Oeff8TYsWNFo0aNhJ2dnbC3txfNmzcXn3/+ueYuxVOnTomBAwcKX19fYW1tLVxcXESnTp3E3r17dXoNKpVKbN68WXTt2lW4uroKS0tLze/8nDlzxL1797Ta79u3T7Ro0UIolUpRu3Zt8d5774mff/65yB2q6t/lsLAw0aZNG2FtbS08PT3FBx98UOqdemRaMiEK5HyJqNyoa/D88ccfCAwMNHV3iPSWkJAAX19fvPXWW1iyZImpu2P2goKC8PDhQ001fTIPHKojKgfbtm3D/fv30axZM1hYWOD06dP49NNP8d///pdBE5mde/fu4fbt2/j0009hYWGBt99+29RdIjIZBk5E5cDBwQHbt2/HggULkJ6eDk9PT4wdOxYLFiwwddeI9PbNN9/gww8/hJ+fH7Zs2YLatWubuktEJsOhOiIiIiIdmV05gi+//BL+/v6aasonTpwotf3x48fRunVrKJVK1K1bV1PTg4iIiEhfZhU47dixA8HBwZg1axYuXryIjh07omfPniUuZxAVFYVevXqhY8eOuHjxIj744ANMmTKl2MUkiYiIiMpiVkN17dq1w7PPPouvvvpKs61x48YYMGAAFi1aVKT9jBkzsHfvXs2aRgAwceJE/Pnnnzh16lSx18jOztYqgpefn49Hjx7BxcXF6NWKiYiIqHwIIZCWlgYvLy+j1M9TM5vJ4Tk5OTh//nyRpRy6detWYnXfU6dOadYsU+vevTvWr1+P3NxcTcGyghYtWoT58+cbr+NERERkMnfv3tV5uS9dmE3g9PDhQ6hUqiILIrq7u5e4Blh8fHyx7fPy8vDw4cNiF8ucOXMmpk2bpnmekpKCOnXq4O7du5WqijARERGVLDU1FT4+PsWuGPA0zCZwUis8XCaEKHUIrbj2xW1Xs7a2LnZpDkdHRwZOREREZsbY02zMZnK4q6sr5HJ5kexSQkJCkaySmoeHR7HtLS0tueo0ERER6c1sAieFQoHWrVvj8OHDWtvVi74Wp0OHDkXaHzp0CG3atCl2fhMRERFRacwmcAKAadOm4ZtvvsGGDRtw9epVTJ06FTExMZg4cSIAaX7SmDFjNO0nTpyIO3fuYNq0abh69So2bNiA9evX49133zXVSyAiIiIzZlZznIYNG4bExER8+OGHiIuLQ9OmTXHgwAH4+voCAOLi4rRqOvn7++PAgQOYOnUqVq9eDS8vL6xcuRKDBw82et9UKhVyc3ONfl4iKl9WVlaQy+Wm7gYRmQmzquNkCqmpqXByckJKSkqxk8OFEIiPj0dycnLFd46IjKJGjRrw8PBgrTaiKqSs729DmVXGqTJSB01ubm6wtbXlP7xEZkQIgYyMDCQkJABAsSVKiIgKYuD0FFQqlSZo4l16RObJxsYGgHTHrZubG4ftiKhUZjU5vLJRz2mytbU1cU+I6Gmo/4Y5T5GIysLAyQg4PEdk3vg3TES6YuBEREREpCMGTkREREQ6YuBElcrYsWMxYMCAcr1GSEgIWrZsWa7XICKiqomBUyWgUgFhYcC2bdJ/VSpT96h0DDyIiKi6YjkCEwsNBd5+G7h3799t3t7AihXAoEGm6xcREREVxYyTCYWGAkOGaAdNAHD/vrQ9NLR8rnvw4EH85z//QY0aNeDi4oI+ffogMjJSq829e/cwfPhw1KxZE3Z2dmjTpg3OnDmDTZs2Yf78+fjzzz8hk8kgk8mwadMmREdHQyaTISIiQnOO5ORkyGQyhIWFAZDqXo0fPx7+/v6wsbFBw4YNsWLFCp37nZKSAhsbGxw8eFBre2hoKOzs7PD48WMAwIwZMxAQEABbW1vUrVsXc+bMKfU286CgIAQHB2ttGzBgAMaOHat5npOTg+nTp6N27dqws7NDu3btNK8LAO7cuYO+ffvC2dkZdnZ2aNKkCQ4cOKDzayMiIvPAjJOJqFRSpqm4BW+EAGQyIDgY6N8fMHY9vvT0dEybNg3NmjVDeno65s6di4EDByIiIgIWFhZ4/PgxOnXqhNq1a2Pv3r3w8PDAhQsXkJ+fj2HDhuHy5cs4ePAgfv31VwCAk5MT/vnnnzKvm5+fD29vb3z//fdwdXXFyZMn8dprr8HT0xMvvvhimcc7OTmhd+/e2LJlC3r06KHZvnXrVvTv3x/29vYAAAcHB2zatAleXl64dOkSJkyYAAcHB0yfPt3Adwx45ZVXEB0dje3bt8PLywu7d+9Gjx49cOnSJTRo0ABvvvkmcnJy8Ntvv8HOzg5XrlzR9IeIiKoOBk4mcuJE0UxTQUIAd+9K7YKCjHvtwoscr1+/Hm5ubrhy5QqaNm2KrVu34sGDBwgPD0fNmjUBAPXr19e0t7e3h6WlJTw8PPS6rpWVFebPn6957u/vj5MnT+L777/XKXACgFGjRmHMmDHIyMiAra0tUlNTsX//fuzatUvTZvbs2Zqf/fz88M4772DHjh0GB06RkZHYtm0b7t27By8vLwDAu+++i4MHD2Ljxo1YuHAhYmJiMHjwYDRr1gwAULduXYOuRURElRsDJxOJizNuO31ERkZizpw5OH36NB4+fIj8/HwAQExMDJo2bYqIiAi0atVKEzQZ05o1a/DNN9/gzp07yMzMRE5Ojl4TzXv37g1LS0vs3bsXw4cPx65du+Dg4IBu3bpp2uzcuRPLly/HrVu38PjxY+Tl5T3VAo8XLlyAEAIBAQFa27OzszVL7UyZMgVvvPEGDh06hC5dumDw4MFo3ry5wdckIqLKiXOcTETXtUTLY83Rvn37IjExEevWrcOZM2dw5swZANI8HuDftbv0YWEh/SqJAmOPhecVff/995g6dSrGjRuHQ4cOISIiAq+88ormurpQKBQYMmQItm7dCkAaphs2bBgsLaX/Bzh9+jSGDx+Onj174qeffsLFixcxa9asUq9hYWGh1e/Cfc/Pz4dcLsf58+cRERGheVy9elUzR+vVV1/F7du3MXr0aFy6dAlt2rTBqlWrdH5dRERkHhg4mUjHjtLdcyWt9CCTAT4+UjtjSkxMxNWrVzF79my88MILaNy4MZKSkrTaNG/eHBEREXj06FGx51AoFFAVqplQq1YtAEBcgRRZwYniAHDixAkEBgZi0qRJaNWqFerXr19kUrouRo0ahYMHD+Lvv//GsWPHMGrUKM2+P/74A76+vpg1axbatGmDBg0a4M6dO6Wer1atWlr9VqlUuHz5suZ5q1atoFKpkJCQgPr162s9Cg5X+vj4YOLEiQgNDcU777yDdevW6f3aiIiocmPgZCJyuVRyACgaPKmfL19u/Inhzs7OcHFxwdq1a3Hr1i0cPXoU06ZN02ozYsQIeHh4YMCAAfjjjz9w+/Zt7Nq1C6dOnQIgzRuKiopCREQEHj58iOzsbNjY2KB9+/b45JNPcOXKFfz2229ac40AaZ7UuXPn8Msvv+DGjRuYM2cOwsPD9X4NnTp1gru7O0aNGgU/Pz+0b99e6xoxMTHYvn07IiMjsXLlSuzevbvU8z3//PPYv38/9u/fj2vXrmHSpElITk7W7A8ICNDMrQoNDUVUVBTCw8OxePFizZ1zwcHB+OWXXxAVFYULFy7g6NGjaNy4sd6vjYiIKjcGTiY0aBCwcydQu7b2dm9vaXt51HGysLDA9u3bcf78eTRt2hRTp07Fp59+qtVGoVDg0KFDcHNzQ69evdCsWTN88sknkD+J4gYPHowePXqgc+fOqFWrFrZt2wYA2LBhA3Jzc9GmTRu8/fbbWLBggdZ5J06ciEGDBmHYsGFo164dEhMTMWnSJL1fg0wmw4gRI/Dnn39qZZsAoH///pg6dSomT56Mli1b4uTJk5gzZ06p5xs3bhxefvlljBkzBp06dYK/vz86d+6s1Wbjxo0YM2YM3nnnHTRs2BD9+vXDmTNn4OPjA0DKUr355pto3LgxevTogYYNG+LLL7/U+7UREdHTUReVnjGjfM4vE4Und5CW1NRUODk5ISUlpcgE46ysLERFRcHf3x9KpdLga6hU0t1zcXHSnKaOHY2faSKikhnrb5mITEelAj76CFi2DEhLA4BUAMV/fz8N3lVXCcjlxi85QEREVNWps0tr1gA//giUUuvYaBg4ERERkdkoGCz99BOQlVWx12fgRERERJWaekrLnj3AunVARobp+sLAiYiIiCqdgsHSpk1ASoqpeyRh4ERERESVRtFJ3pULAyciIiIyCXVW6f594J9/gD/+APbtq5hJ3oZi4EREREQVqrJnlUrDwImIiIjKnSlKB5QHBk5ERERULkxdOqA8cMkVqvaCgoIQHBxs6m5UW9HR0ZDJZEUWhSYi86RSAUeOAEOHAvb2QJcu0jJiFRU0KZXAkCFSVqs8MONUGQgBZOcAeSrAUg5YK4qu/EtagoKC0LJlSyxfvtzUXSEiqtbUWaWwMODKFeDgwYqvs2RlBfTvD0ycKK3EIZcDqanlcy0GTqaWngk8TAIysoD8fMDCArBVAq7OgJ2NqXtX4XJzc2FlZWXqbhARUSkqyxCcgwMwbRowZ07FrfHKoTpTSs8E7icAjzMAK0spYLKylJ7fT5D2l4O0tDSMGjUKdnZ28PT0xOeff15kuConJwfTp09H7dq1YWdnh3bt2iEsLEyzf9OmTahRowZ++eUXNG7cGPb29ujRowfi4uK0rrVx40Y0btwYSqUSjRo1wpdffqnZpx6i+f777xEUFASlUonvvvsOiYmJGDFiBLy9vWFra4tmzZph27ZtmuPGjh2L48ePY8WKFZDJZJDJZIiOjgYAXLlyBb169YK9vT3c3d0xevRoPHz4UHNseno6xowZA3t7e3h6emLp0qVlvl8hISFo2bIlNmzYgDp16sDe3h5vvPEGVCoVlixZAg8PD7i5ueHjjz/WOm7ZsmVo1qwZ7Ozs4OPjg0mTJuHx48ea/Xfu3EHfvn3h7OwMOzs7NGnSBAcOHAAAJCUlYdSoUahVqxZsbGzQoEEDbNy48ak+0++++w5t2rSBg4MDPDw8MHLkSCQkJGj2h4WFQSaTYf/+/WjRogWUSiXatWuHS5culXjdESNGYPjw4VrbcnNz4erqqunvwYMH8Z///Ac1atSAi4sL+vTpg8jIyBLPqf7dKmjPnj2QFcrC7tu3D61bt4ZSqUTdunUxf/585OXlafaHhISgTp06sLa2hpeXF6ZMmVLiNYmobKYeglNzcgKmTAGOHQOSkoCQkIoLmgAAgkqVkpIiAIiUlJQi+zIzM8WVK1dEZmam/ifOzxci+r4Ql28KcfuuEFH3/n3cvittj46V2hnZq6++Knx9fcWvv/4qLl26JAYOHCgcHBzE22+/rWkzcuRIERgYKH777Tdx69Yt8emnnwpra2tx48YNIYQQGzduFFZWVqJLly4iPDxcnD9/XjRu3FiMHDlSc461a9cKT09PsWvXLnH79m2xa9cuUbNmTbFp0yYhhBBRUVECgPDz89O0uX//vrh375749NNPxcWLF0VkZKRYuXKlkMvl4vTp00IIIZKTk0WHDh3EhAkTRFxcnIiLixN5eXkiNjZWuLq6ipkzZ4qrV6+KCxcuiK5du4rOnTtr+vTGG28Ib29vcejQIfHXX3+JPn36CHt7e63XXti8efOEvb29GDJkiPj777/F3r17hUKhEN27dxdvvfWWuHbtmtiwYYMAIE6dOqU57vPPPxdHjx4Vt2/fFkeOHBENGzYUb7zxhmZ/7969RdeuXcVff/0lIiMjxb59+8Tx48eFEEK8+eabomXLliI8PFxERUWJw4cPi7179z7VZ7p+/Xpx4MABERkZKU6dOiXat28vevbsqdl/7NgxAUA0btxY6/3x8/MTOTk5xV533759wsbGRqSlpWltUyqVmr+ZnTt3il27dokbN26Iixcvir59+4pmzZoJlUql9Xtw8eJFIYT0u+Xk5KR1nd27d4uC/1wdPHhQODo6ik2bNonIyEhx6NAh4efnJ0JCQoQQQvzwww/C0dFRHDhwQNy5c0ecOXNGrF27tsT376n+lomqsLw8IX79VYghQ4RQKoWQ5pZU/MPWVogpU4Q4dkzqky5K+/5+GgycylBugVNmlhBXIoW4eUc7aFI/bt6R9mdmGeFV/Cs1NVVYWVmJH374QbMtOTlZ2Nraar5kb926JWQymbh//77WsS+88IKYOXOmEEL6cgMgbt26pdm/evVq4e7urnnu4+Mjtm7dqnWOjz76SHTo0EEI8e8X5vLly8vsd69evcQ777yjed6pU6ciwc6cOXNEt27dtLbdvXtXABDXr18XaWlpQqFQiO3bt2v2JyYmChsbmzIDJ1tbW5GamqrZ1r17d+Hn56f58hdCiIYNG4pFixaVeJ7vv/9euLi4aJ43a9ZM80VfWN++fcUrr7xS4rkK0uUzLc7Zs2cFAE3Qow6cint/duzYUew5cnJyhKurq9i8ebNm24gRI8TQoUNLvG5CQoIAIC5duiSEMCxw6tixo1i4cKFWm//973/C09NTCCHE0qVLRUBAQIkBX2EMnIj+VVmCJaVS6sOvv+oeLBVUXoET5ziZSp5KmtMkL2G0VG4BZAupnRHdvn0bubm5aNu2rWabk5MTGjZsqHl+4cIFCCEQEBCgdWx2djZcXFw0z21tbVGvXj3Nc09PT83Qz4MHD3D37l2MHz8eEyZM0LTJy8uDk5OT1nnbtGmj9VylUuGTTz7Bjh07cP/+fWRnZyM7Oxt2dnalvrbz58/j2LFjsLe3L7IvMjISmZmZyMnJQYcOHTTba9asqfXaS+Ln5wcHBwfNc3d3d8jlclhYWGhtKzj0dezYMSxcuBBXrlxBamoq8vLykJWVhfT0dNjZ2WHKlCl44403cOjQIXTp0gWDBw9G8+bNAQBvvPEGBg8ejAsXLqBbt24YMGAAAgMDi+2bLp8pAFy8eBEhISGIiIjAo0ePkJ+fDwCIiYnBM888o2lX3Ptz9erVYq9tZWWFoUOHYsuWLRg9ejTS09Px448/YuvWrZo2kZGRmDNnDk6fPo2HDx9qXbdp06YlvOOlO3/+PMLDw7WGR1UqFbKyspCRkYGhQ4di+fLlqFu3Lnr06IFevXqhb9++sLTkP3lEhRWs3n3kCPDDD0CBWQUVxsYG6NkTeOYZaYK3epJ3ZcN/RUzFUi5NBFflSz8XpsoHLGTF73sKQggAKDJfRL0dAPLz8yGXy3H+/HnIC/3WFgxKCk/ilslkmvOovxzXrVuHdu3aabUrfM7CAdHSpUvx+eefY/ny5Zo5QsHBwcjJySn1teXn56Nv375YvHhxkX2enp64efNmqceXprjXWtw29eu+c+cOevXqhYkTJ+Kjjz5CzZo18fvvv2P8+PHIfVL17dVXX0X37t2xf/9+HDp0CIsWLcLSpUvx1ltvoWfPnrhz5w7279+PX3/9FS+88ALefPNNfPbZZ0X6pstnmp6ejm7duqFbt2747rvvUKtWLcTExKB79+5lvq/FnbugUaNGoVOnTkhISMDhw4ehVCrRs2dPzf6+ffvCx8cH69atg5eXF/Lz89G0adMSr2thYaHVdwCa90wtPz8f8+fPx6BBg4ocr1Qq4ePjg+vXr+Pw4cP49ddfMWnSJHz66ac4fvw4bz4gQuVZQFepBPr00b4brrIzm8nhSUlJGD16NJycnODk5ITRo0cjOTm51GPGjh2rmTysfrRv375iOlwWa4U0GTw7R8pKFqQuT2BrI7Uzonr16sHKygpnz57VbEtNTdUKKlq1agWVSoWEhATUr19f6+Hh4aHTddzd3VG7dm3cvn27yDn8/f1LPfbEiRPo378/XnrpJbRo0QJ169YtEvQoFAqoVNrZuGeffRZ///03/Pz8ilzTzs4O9evXh5WVFU6fPq05JikpCTdu3NDpNenj3LlzyMvLw9KlS9G+fXsEBAQgNja2SDsfHx9MnDgRoaGheOedd7Bu3TrNvlq1amHs2LH47rvvsHz5cqxdu7bYa+nymV67dg0PHz7EJ598go4dO6JRo0Za2bGCint/GjVqVOJrDQwMhI+PD3bs2IEtW7Zg6NChUCik39vExERcvXoVs2fPxgsvvIDGjRsjKSmpxHOpX3daWhrS09M12wrXeHr22Wdx/fr1Ip9z/fr1NVlAGxsb9OvXDytXrkRYWBhOnTpV6kR3oqpKfQfcli3SEieDBwPOzkDnzsCKFRUfNKnrLP36q5TZ+uEH4IUXzCNoAswo4zRy5Ejcu3cPBw8eBAC89tprGD16NPbt21fqcT169NC6G0n9D7rJyWRSyYHsXKkUgbVCGp5T5UtBk5UV4FrD6PWcHBwc8PLLL+O9995DzZo14ebmhnnz5sHCwkKTVQgICMCoUaMwZswYLF26FK1atcLDhw9x9OhRNGvWDL169dLpWiEhIZgyZQocHR3Rs2dPZGdn49y5c0hKSsK0adNKPK5+/frYtWsXTp48CWdnZyxbtgzx8fFo3Lixpo2fnx/OnDmD6Oho2Nvbo2bNmnjzzTexbt06jBgxAu+99x5cXV1x69YtbN++HevWrYO9vT3Gjx+P9957Dy4uLnB3d8esWbO0htuMpV69esjLy8OqVavQt29f/PHHH1izZo1Wm+DgYPTs2RMBAQFISkrC0aNHNa9x7ty5aN26NZo0aYLs7Gz89NNPWq+/IF0+0zp16kChUGDVqlWYOHEiLl++jI8++qjY83344Yda74+rqysGDBhQ4muVyWQYOXIk1qxZgxs3buDYsWOafc7OznBxccHatWvh6emJmJgYvP/++6W+d+3atYOtrS0++OADvPXWWzh79iw2bdqk1Wbu3Lno06cPfHx8MHToUFhYWOCvv/7CpUuXsGDBAmzatAkqlUpzrv/973+wsbGBr69vqdcmqioKlgv45RfTrwdXXJ0ls2XUGVPl5MqVKwKA5q4qIYQ4deqUACCuXbtW4nEvv/yy6N+//1Ndu9wmh6s9zpDurrsSKcTlW9J/o2Ol7eUkNTVVjBw5Utja2goPDw+xbNky0bZtW/H+++9r2uTk5Ii5c+cKPz8/YWVlJTw8PMTAgQPFX3/9JYTQbQKvEEJs2bJFtGzZUigUCuHs7Cz++9//itDQUCFE0UnBaomJiaJ///7C3t5euLm5idmzZ4sxY8ZofZbXr18X7du3FzY2NgKAiIqKEkIIcePGDTFw4EBRo0YNYWNjIxo1aiSCg4NF/pO7E9PS0sRLL70kbG1thbu7u1iyZEmxE80LmjdvnmjRooXWtuJ+twqfZ9myZcLT01PY2NiI7t27i82bNwsAIikpSQghxOTJk0W9evWEtbW1qFWrlhg9erR4+PChEEKaRN+4cWNhY2MjatasKfr37y9u375dYh91+Uy3bt0q/Pz8hLW1tejQoYPYu3ev1vuvnhy+b98+0aRJE6FQKMT//d//iYiIiBKvq/b3338LAMLX11fzXqsdPnxYNG7cWFhbW4vmzZuLsLAwAUDs3r1bCFH878Hu3btF/fr1hVKpFH369BFr164t8rt18OBBERgYKGxsbISjo6No27at5s653bt3i3bt2glHR0dhZ2cn2rdvL3799dcS+8/J4WTu8vKkO86++06IV14Rwt7edJO6Cz4cHISYN8+wyd1Pq7wmh8uEKDxOVPls2LAB06ZNKzI0V6NGDXz++ed45ZVXij1u7Nix2LNnDxQKBWrUqIFOnTrh448/hpubW4nXUk9EVktNTYWPjw9SUlLg6Oio1TYrKwtRUVHw9/eHUqk0/AWauHJ4eno6ateujaVLl2L8+PEVdl0qP4Z8pmFhYejcuTOSkpKK1FGq6oz2t0xUgSpbVkmpBHr3Bjp0ADw8gNq1gY4dTZddSk1NhZOTU7Hf30/DLIbq4uPjiw123NzcEB8fX+JxPXv2xNChQ+Hr64uoqCjMmTMHzz//PM6fPw9ra+tij1m0aBHmz59vtL7rRCYDlMX3pzxcvHgR165dQ9u2bZGSkoIPP/wQANC/f/8K6wMZFz9Toqqvstz9VpA5Tu5+WiYNnEJCQsoMUsLDwwEUf1ePEKLUu32GDRum+blp06Zo06YNfH19sX///mLvxgGAmTNnas2/UWecqprPPvsM169fh0KhQOvWrXHixAm4urqaulv0FPiZElUthdeAO3LEdHe/FVQdg6WCTBo4TZ48uchyDYX5+fnhr7/+wj///FNk34MHD+Du7q7z9Tw9PeHr61vqbenW1tYlZqOqilatWuH8+fOm7gYZkTE+06CgoCJlAIio4lTGjJJadQ+WCjJp4OTq6qrT/xF36NABKSkpOHv2rKbI35kzZ5CSklJiUcDiJCYm4u7du/D09DS4z0RERMZQWTNKag4OQPfuDJYKM4s5To0bN0aPHj0wYcIEfP311wCkcgR9+vTRqo7cqFEjLFq0CAMHDsTjx48REhKCwYMHw9PTE9HR0fjggw/g6uqKgQMHGrV/6qKHRGSe+DdMFaXghO6ffqr4BXLL4uQEvPwyMHCgaSd2V2ZmETgBwJYtWzBlyhR069YNANCvXz988cUXWm2uX7+OlCfhulwux6VLl7B582YkJyfD09MTnTt3xo4dO7SWzngaCoUCFhYWiI2NRa1ataBQKEqdc0VElYsQAjk5OXjw4AEsLCwqT503qhIKZpTy86UhuF27Ks/wGwDY2QFDhwJdupj+LjhzYRblCEyprNsZc3JyEBcXh4yMDBP0joiMwdbWFp6engyc6KlV5oySnZ1UNdzbW1rxqzKvB2cM1bocQWWmUChQp04d5OXlFVkChIgqP7lcDktLS2aLySCVeUI3wHlK5YGBkxGoF3zl4qFERFVXwSDpwQMgOhrYulX6ubLg0Fv5Y+BERERUjMLZpB9/BB49MnWvimJWqWIxcCIiIoL5BEoA734zJQZORERU7aiDpLg4wM1N+nnVqsoZKDk4AN26VZ414Ko7Bk5ERFTlmVM2CWBGqTJj4ERERFWWSgV8/DGwYkXlDZSUSqB3b2aUzAUDJyIiqhKKu+tt40YgNdXUPSuKE7rNFwMnIiIyO+ZQGkCNGaWqhYETERFVauYUJKkxo1R1MXAiIqJKwxyDpFq1gBEjAH9/6WdmlKo2Bk5ERGQS5hgkAYCzM9C/P6tzV1cMnIiIqMKog6U9e4BNm4CUFFP3qGwMlKggBk5ERFQuVCogLEx65OdLmaV9+ypvWQA1BkpUGgZORERkFIWLTP7wA/D4sal7VbqaNYG33pKCo4QEwNOTgRKVjoETERHpjdkkqq4YOBERUZnMMZukVqsWMGqUFDAxUKKnxcCJiIg0Ci5+6+kJBAYCn3xSuZcsKYilAai8MXAiIqrm1MHSjz8CW7ZolwOwsJCG4iojBklkCgyciIiqmYJZpZs3gXXrgHv3im9bWYImBklUWTBwIiKqBkrLKlU2DJKoMmPgRERURRTMJLm5SdsSEsrOKlUGTk7Ayy8DAwcySKLKjYETEZGZ0mfIrbJhWQAyVwyciIjMiDkNuak5OADdugEdOgAeHgyUyLwxcCIiqmTMecgNYDaJqjYGTkRElYA5ZpIAZpOo+mHgRERkAuYyP0kul/qqxmwSVXcMnIiIylFxw24//VS5s0oFlygJDAROnvy3kjgDJaruGDgRERmRuWSSCvL2BiZMABo0KD44CgoyWdeIKh0GTkREBjDHTFJBXPiWyDAMnIiIdGSuE7iBsrNKRKQbBk5ERKUwp2CpYHBUsIwBAyUi42HgREQE8x1645AbUcVi4ERE1ZY5ZZPUOORGZFoMnIioWiicUTpxAli1Cnj0yNQ9K546k9Snj/ScQ25ElYPZBE4ff/wx9u/fj4iICCgUCiQnJ5d5jBAC8+fPx9q1a5GUlIR27dph9erVaNKkSfl3mIhMSh0o3b8PHDkiZZUqa5AEMJNEZC7MJnDKycnB0KFD0aFDB6xfv16nY5YsWYJly5Zh06ZNCAgIwIIFC9C1a1dcv34dDg4O5dxjIqoo5pZNUuP8JCLzIxNCCFN3Qh+bNm1CcHBwmRknIQS8vLwQHByMGTNmAACys7Ph7u6OxYsX4/XXX9fpeqmpqXByckJKSgocHR2ftvtEZATmlE3inW5EplFe399mk3HSV1RUFOLj49GtWzfNNmtra3Tq1AknT54sMXDKzs5Gdna25nlqamq595WIdKNSAR9/DKxYUXkDJYCZJKKqrMoGTvHx8QAAd3d3re3u7u64c+dOicctWrQI8+fPL9e+EVHZiht+W7oUePzY1D37FydwE1U/Jg2cQkJCygxSwsPD0aZNG4OvIZPJtJ4LIYpsK2jmzJmYNm2a5nlqaip8fHwMvj4R6aeyZ5WYTSKq3kwaOE2ePBnDhw8vtY2fn59B5/bw8AAgZZ48PT012xMSEopkoQqytraGtbW1QdckIt0UV2wyIUFaFHflSiAx0bT9K4zBEhGpmTRwcnV1haura7mc29/fHx4eHjh8+DBatWoFQLoz7/jx41i8eHG5XJOISmYOxSYdHYFx4zj0RkQlM5s5TjExMXj06BFiYmKgUqkQEREBAKhfvz7s7e0BAI0aNcKiRYswcOBAyGQyBAcHY+HChWjQoAEaNGiAhQsXwtbWFiNHjjThKyGqPswhWAKAmjWBt98GZs1igEREpTObwGnu3Ln49ttvNc/VWaRjx44hKCgIAHD9+nWkpKRo2kyfPh2ZmZmYNGmSpgDmoUOHWMOJqBxV9mCpZk3grbekLBIzSkSkL7Or41TRWMeJqHQF5yvdvAmsWwfcu2fqXv3L2Vmam9SlC1C7NoMkouqCdZyIqNKorFklZpOIqLwxcCKiUhXMKHl6Ag8fAlOnVo6skp0dMHQos0lEVHEYOBFRiUJDpUnTlSFIKoiTuYnIVBg4ERGA4jNLL74ImHoWJIffiKgyYeBEVA3pMvwml5smaCq4KC6DJCKqbBg4EVUzug6/qVTl2w+u80ZE5oiBE1EVVtmG37h0CRGZOwZORFVIWTWVTDH8xmCJiKoSBk5EVYBKBXz8MbBiBfDoUentyhvnKBFRVcbAicgMqTNL9+8DR44AP/wAPH5suv4wq0RE1QUDJyIzoVIBYWHAmjXAL78AaWkVc125XDtT5eMDLF0qBUvquVMMloioumDgRFTJqYfhPv20YrNKMpn0323bGCQREakxcCKqZCrLMJy3N7B8OTBoUMVfm4iosmLgRFRJ6DrB2xg4/EZEZBgGTkQmYorMEoffiIieDgMnogpWkZmlwjj8RkT0dBg4EVUQU0zyrllTWl6FNZWIiIyDgRNROTNFwGRvD7z3HjBrFgMlIiJjYuBEZCQFlztxc5O2/fQTsGEDkJpaMX1QZ5gYMBERlQ8GTkRPyZRzluzsgKFDgS5dgNq1ORRHRFTeGDgRGUCdXfrxx4rNKAGAUgn06QNMnAgEBTFQIiKqSAyciPRgyuwSh+GIiEyPgRNRGUyVXeIwHBFR5cPAiagEpsouMbNERFR5MXAiKsAU2SVmloiIzAcDJyKYJrvEWktEROaHgRNVa6as5s2AiYjI/DBwomrFFAvrAtKCuqNGAf37cyiOiMicMXCiamPnTmDSJODBg/K9Ts2awFtvSQFSQgLXiCMiqkoYOFGVp1JJ2Z4dO8rvGo6OwLhxzCgREVV1DJyoylLPX1qyBEhPL59rcL4SEVH1wsCJqhSVCggLA9askRbYzcoy/jWYXSIiqr4YOFGVUBF3xzG7REREDJzIrJV3wMTsEhERFcTAicxORQzHsTglEREVx8LUHdDVxx9/jMDAQNja2qJGjRo6HTN27FjIZDKtR/v27cu3o1RuVHkCKxZnI7BFBsa/lI1du4TRgyZ7e2D+fCA5GZg7l0ETERFpM5uMU05ODoYOHYoOHTpg/fr1Oh/Xo0cPbNy4UfNcoVCUR/eonB3+MROn9yehjmsW3hmSj8wcC1y7o8Se351xLcbmqc/P+UtERKQLswmc5s+fDwDYtGmTXsdZW1vDw8OjHHpEFUIInNiXikcXHuLZuipcv6tEepYF7JT5eDYgA3Xcc7Fyl5tBwZOtLfDqq8DAgZy/REREujGboTpDhYWFwc3NDQEBAZgwYQISEhJKbZ+dnY3U1FStB5lIeibyo2NR49FdBDZ9jNq1stG8XgacHVRIzZDj72glXJ1y0f8/yZDJhM6nVQ/HpaZKi/oGBTFoIiIi3VTpwKlnz57YsmULjh49iqVLlyI8PBzPP/88srOzSzxm0aJFcHJy0jx8fHwqsMcEABACSEoBomORcicNFjKBew8tkZkth6dLLto1ToerUx4AGe4mKNDYNxN13HPKPC3nLxER0dMyaeAUEhJSZPJ24ce5c+cMPv+wYcPQu3dvNG3aFH379sXPP/+MGzduYP/+/SUeM3PmTKSkpGged+/eNfj6ZID0TOBOLBB5F0h9DIu8bLg65sFSBmTnWuBBsiVslSo0qpMFGYD0LAsoFQIONqoST8mAiYiIjMWkc5wmT56M4cOHl9rGz8/PaNfz9PSEr68vbt68WWIba2trWFtbG+2apAMhgOwcIC0DeJgE5OYC+QJQWEKWLWCtUMHbPQd3/7FGRrYFUtPlcHXKhZO9Cvn5QFaODGmZ2tGQUgn06QNMnMihOCIiMh6TBk6urq5wdXWtsOslJibi7t278PT0rLBrUhnSM6VgKT0TSM8AVPmApaUUOFlZwMERiHkgRw3bPLg65SImwRo5eTI42AIKSxVq1VDh/A07xPwj3S3J+ktERFSezGaOU0xMDCIiIhATEwOVSoWIiAhERETgcYFy0Y0aNcLu3bsBAI8fP8a7776LU6dOITo6GmFhYejbty9cXV0xcOBAU70MUiswjwkpjwGZBQAZYCkH8lSah8xCBgdnK2TmyFHTQQVH2zxYW+VDJhPwdc/FgxQr/Ph7DdjZyTgcR0RE5c5syhHMnTsX3377reZ5q1atAADHjh1DUFAQAOD69etISUkBAMjlcly6dAmbN29GcnIyPD090blzZ+zYsQMODg4V3n8qID0TePDoybCcCpBbSMNzqnzA2gqwsJDKg+fkAnI5arpaIAlWyE3Pga0yH7WU+UhItkL4DXskWzrjiw02HI4jIqIKIRNC6H4fdzWUmpoKJycnpKSkwNHR0dTdMW9CAMmpQOxDKSjKzZWCJpkMyMmTskzWVoCVJZCbJ7WxtASs5IAQELkqJGcpkJpphSQbVzT7jyPkljJTvyoiIqqEyuv722wyTmTmCmeZZJAyTBYKKXhSWGplmWApfzLf6cl/8/Igs7KCs78jnF2d4Wv39NXCiYiI9MXAicpPKXfLQQgpgMrOAWTWT4InqyeZqDxALgMsZICtUjrO1gbwdAWcHaUMFRERkQkwcKLyUcbdcgCkITj1kJzSukCWyUIaurN8MmnJyRFwrQEwy0RERCbGwImMLz0TuJ/wZA6THEXulpNbSPOYrKykQEqlAlR5UiZJJpMCLBsboJYz4GALWCuYZSIiokqBgRMZV34+EP8QyMyShtnyhTQsZ2lZ5G45zfBcdo6UacrPl4KpGg6AqzMzTEREVOkYFDilp6fDzs7O2H0hc5eeCcQ9AB4mS5O/c57cNScgBU8WheYxWcmldnI5oFBI+ziPiYiIKjGDCmC6u7tj3Lhx+P33343dHzJHBYtZpqVLwZCV5ZP6THlSJikn70nmSa5911xOrvRzTUfAzwuo6cSgiYiIKi2DAqdt27YhJSUFL7zwAgICAvDJJ58gNjbW2H0jc1BoUV5k5z6Zs5T/b4ZJhid30eVJ+9R3y1lbAY4OQF1voI4nh+aIiKjSMyhw6tu3L3bt2oXY2Fi88cYb2LZtG3x9fdGnTx+EhoYiLy/P2P2kykg9CTz18b9lBqzk0tBcdq40EVwmk4IniycTxHNV/2aUnJhlIiIi82K0yuGrVq3Ce++9h5ycHLi6umLixIl4//33YWtra4zTmwwrhxdDCCArG7j3D5CZLQVGqY+l4TmZTAqYsrKln5XWT+Y7qQCllTSXiXfLERFROauUlcPj4+OxefNmbNy4ETExMRgyZAjGjx+P2NhYfPLJJzh9+jQOHTpkrL5SZaCuz5SWLhW2tLAA8vL+nQAue5JZslY8WX9OJW0HAAc7wMOVQ3JERGS2DAqcQkNDsXHjRvzyyy945pln8Oabb+Kll15CjRo1NG1atmypWYiXqojC9ZnkFgUmgAsgR0jzltTBU76QgqQ8FWBnC/jXlgItIiIiM2VQ4PTKK69g+PDh+OOPP/B///d/xbapW7cuZs2a9VSdo0qkcH0m4N+ClepaTOoJ4Jbyf7NMqnzARimVGWDQREREZs6gOU4ZGRlmP3dJV5zjhKL1mSws/s0oqVTS3CZ10KSwelIhPE8qZlnLmcUsiYiowpXX97dBKQAHBwckJCQU2Z6YmAi5XP7UnaJKRD08l55ZfH0mmUz6WQgAMmmojmUGiIioijJoqK6kJFV2djYUCsVTdYgqkYLDc+qK34CUcbJ6EjCp5zrl5Ert88FFeYmIqMrSK3BauXIlAEAmk+Gbb76Bvb29Zp9KpcJvv/2GRo0aGbeHZBqFh+eyc54MzeVrTwBX5UulBSxk0sK83m5PShCwzAAREVU9egVOn3/+OQAp47RmzRqtYTmFQgE/Pz+sWbPGuD2kiqcensvM+nd4DpCySiohFbdUPNmmElItJxsl4FVL+i8REVEVpVfgFBUVBQDo3LkzQkND4ezsXC6dIhMSQqrTlJsL2FprD89ZK6TMEyBlmvLzpZ/tbVmfiYiIqgWD5jgdO3bM2P2gykAIqQJ46mNpTpPcUso25eRKc5rUpQfyVFKwlJPL+kxERFSt6Bw4TZs2DR999BHs7Owwbdq0UtsuW7bsqTtGFUxdETz1MfA4Q5r0rbCSSgqo8v+tzwRIE8BzclmfiYiIqh2dA6eLFy8iNzdX83NJZJwUbH4KVgS3sgIsLZ+sL5crBU1Ka2mfugQBwOE5IiKqlnQOnAoOz3GorgopXBFcLv93eM5SLg3L5eYBjvaAKg/IyObwHBERVVtG+eZLTU3Fnj17cO3aNWOcjipKeiZw+x7wIAnIygaS06ShOisrKYDKU0llBnJypEAqJ4/Dc0REVK0Z9O334osv4osvvgAAZGZmok2bNnjxxRfRrFkz7Nq1y6gdpHJSUkXwnFwpiFJaS3OcBJ7MccoF7O2A2m4cniMiomrLoMDpt99+Q8eOHQEAu3fvhhACycnJWLlyJRYsWGDUDlI5KFxyQJ09srCQAihVgeE5BzvA3kZaOqWOB4MmIiKq1gwKnFJSUlCzZk0AwMGDBzF48GDY2tqid+/euHnzplE7SOUgKxtIy5CG4SCTgqU8lRRQqSuC5+ZKAZRKJa0752jPauBERFTtGRQ4+fj44NSpU0hPT8fBgwfRrVs3AEBSUhKUSlaOrtTSM4F7/wBp6UBqujSvSVVgsV71XXMqAWRkSfOdXGswaCIiIoKBBTCDg4MxatQo2Nvbw9fXF0FBQQCkIbxmzZoZs39kTOp5TVlZ0nwmuYUUEOWppP/K5awITkREVAqDAqdJkyahbdu2uHv3Lrp27QqLJ3Nk6tatyzlOlVXBeU32ttKCvTm50jCdlaWUbbKwkBbszWTJASIiouLIhBDiaU6gPryqFr5MTU2Fk5MTUlJS4OjoaOruGEa9lEpMnHSnnEIB5OUCqRnSHCZLudQmLx+wsZbuqOPdc0REZMbK6/vb4HTC5s2b0axZM9jY2MDGxgbNmzfH//73P6N1jIwkPVMKmGLipKVUUh4DKWmAkAGOtlIgpcr/d4jORsmgiYiIqAQGDdUtW7YMc+bMweTJk/Hcc89BCIE//vgDEydOxMOHDzF16lRj95MMUdZSKg52gJP9k/IDT+6g83aTgiciIiIqwqChOn9/f8yfPx9jxozR2v7tt98iJCQEUVFRRuugqZntUJ0Q/2aZbJ8EQimPtZdSUSikwAlP7qCzt5NqNVXRYVciIqo+yuv726CMU1xcHAIDA4tsDwwMRFxc3FN3ioxAXatJLpOyS3I5YKeUfi64lEp2jpRpYtkBIiKiMhk0x6l+/fr4/vvvi2zfsWMHGjRo8NSdoqdUuFZTUmrReU1cSoWIiEhvBmWc5s+fj2HDhuG3337Dc889B5lMht9//x1HjhwpNqB6WtHR0fjoo49w9OhRxMfHw8vLCy+99BJmzZoFhUJR4nFCCMyfPx9r165FUlIS2rVrh9WrV6NJkyZG72OlUVKtpsLzmrJzpaCpjierghMREenIoIzT4MGDcebMGbi6umLPnj0IDQ2Fq6srzp49i4EDBxq7j7h27Rry8/Px9ddf4++//8bnn3+ONWvW4IMPPij1uCVLlmDZsmX44osvEB4eDg8PD3Tt2hVpaWlG72OlULhWk/qOOZns3zXoMrKeZJu4lAoREZG+nrqOk6l8+umn+Oqrr3D79u1i9wsh4OXlheDgYMyYMQMAkJ2dDXd3dyxevBivv/56scdlZ2cjOztb8zw1NRU+Pj7mMTk8MwuIvCfNa7KyksoLpLFWExERVT+VanI4AOTn5+PWrVtISEhAvnqJjif++9//PnXHylJwoeHiREVFIT4+XrOOHgBYW1ujU6dOOHnyZImB06JFizB//nyj97fcpWcCsQnSvCYLmVTx28pSCpByc6XK4EL8W6vJqxaDJiIiIj0ZFDidPn0aI0eOxJ07d1A4YSWTyaBSqYzSuZJERkZi1apVWLp0aYlt4uPjAQDu7u5a293d3XHnzp0Sj5s5cyamTZumea7OOFVqZc1rsrcF7GWs1URERPSUDJrjNHHiRLRp0waXL1/Go0ePkJSUpHk8evRI5/OEhIRAJpOV+jh37pzWMbGxsejRoweGDh2KV199tcxrFF4KRghR6vIw1tbWcHR01HpUarrMa8rMBizkUrbJwV7KQhEREZHeDMo43bx5Ezt37kT9+vWf6uKTJ0/G8OHDS23j5+en+Tk2NhadO3dGhw4dsHbt2lKP8/DwACBlnjw9PTXbExISimShzFrBek35Qip2qcqXhuYs5VL2KTsHeCyTAibWaiIiIjKYQYFTu3btcOvWracOnFxdXeHq6qpT2/v376Nz585o3bo1Nm7cCAuL0pNl/v7+8PDwwOHDh9GqVSsAQE5ODo4fP47Fixc/Vb8rDc5rIiIiqlAGBU5vvfUW3nnnHcTHx6NZs2awsrLS2t+8eXOjdE4tNjYWQUFBqFOnDj777DM8ePBAs0+dWQKARo0aYdGiRRg4cCBkMhmCg4OxcOFCNGjQAA0aNMDChQtha2uLkSNHGrV/JsF5TURERBXOoMBp8ODBAIBx48ZptslkMs38IWNPDj906BBu3bqFW7duwdvbW2tfwcnp169fR0pKiub59OnTkZmZiUmTJmkKYB46dAgODg5G7V+FKzyvKV9IAZOVpfTIzZPmNTnaAfm5nNdERERkJAbVcSrtrjQA8PX1NbhDlU2lXOQ3KxuIui8FSZZyKYBKZb0mIiIitUpVx6kqBUbmRpUncPl0Ftyyc6GylsOrDmBhZSWtQZeexXlNRERE5UjnwGnv3r3o2bMnrKyssHfv3lLb9uvX76k7RkX9sjsT4T8noZF3OpwaZiE9MwfRf1vBs74N6jW0ApyelB/gvCYiIqJyofNQnYWFBeLj4+Hm5lbqHW0VUQCzIlWWobrPPsyEXXICXJ1ycTfBCi3rZ8K7Vi5yVQIZWZao6WeHeg0tpWxTRhZgbwfU8WDpASIiqpZMPlRXcFmVwkusUPn64QeBxOtJ8A3Ixd/RSgAyXLmjhINtPmyVKjja5eH+zUz4+9vCIjdHWqeO9ZqIiIiMzqDK4VRxVCpg9ns5aOSbhbsJCgBSMPQwxQpnrtoiLtEKeSoZfGpl4+H9HCnTxMngRERE5cLgRX7Pnj2LsLCwYhf5XbZs2VN3jCQjRwIKCxVsFPlIz9KOcx+mWOGPS5ZwdshDgE8O/ol1xctBTsw0ERERlRODAqeFCxdi9uzZaNiwIdzd3bXWfittHTjSzw8/AN9/D/h6yJGZYwE7ZT5SM+RabQRkyFNZ4J8kSzy0s2HQREREVI4MCpxWrFiBDRs2YOzYsUbuDqmpVMCYMdLPMf8ocO2OEs8GZGjmOP1LwMctB+dv2KHrOIUpukpERFRtGDTHycLCAs8995yx+0IFfPSRtJoKAAghw57fnfEwxQpN/LLgaKuC3ELA0VaFJn5ZeJBihSMRNRAUxGwTERFReTIocJo6dSpWr15t7L7QEyoVsGCB9rZrMTZYucsNF27YwsUpDwE+2XBxysP5G3ZYtcsNM+bbQC4v/nxERERkHAYtuZKfn4/evXvjxo0beOaZZ4os8hsaGmq0DpqaKeo4PfcccPJk8ftkMoE67jlwsFEhLVOOmH8UGDpUhh07KqRrREREZsHkdZwKeuutt3Ds2DF07twZLi4unBBuRDt2lBw0AdKw3Z34fxfsVSqBrVsroGNERERkWOC0efNm7Nq1C7179zZ2f6o1lQoYPVq/Y/73P3CIjoiIqIIYNMepZs2aqFevnrH7Uu199BGQm6t7+xdfBIYMKb/+EBERkTaDAqeQkBDMmzcPGRkZxu5PtaVSAQsX6t7eyopDdERERBXNoKG6lStXIjIyEu7u7vDz8ysyOfzChQtG6Vx1om+26bvvOERHRERU0QwKnAYMGGDkblRv+mabAgOlYToiIiKqWAYFTvPmzTN2P6q1kSN1zzbJ5cBvv5Vvf4iIiKh4Bs1xIuNRr0enq9mzOURHRERkKgYVwLSwsCi1dpNKpXqqTlUm5VkAU6UC7O3/XVqlLAoFkJHBwImIiKgslaoA5u7du7We5+bm4uLFi/j2228xf/58o3SsOii4Hp0uZs5k0ERERGRKBmWcSrJ161bs2LEDP/74o7FOaXLlFbGqVICDA5CZqVt7ZpuIiIh0V17f30ad49SuXTv8+uuvxjxllRUWpnvQBDDbREREVBkYLXDKzMzEqlWrULt2bWOdskpbs0b3tjY2wJw55dcXIiIi0o1Bc5ycnZ21JocLIZCWlgZbW1t89913RutcVaVSAfqMZm7ezGwTERFRZWBQ4LR8+XKt5xYWFqhVqxaaNGmCefPmoV+/fsboW5WlT5VwrkdHRERUeRh1cviff/6JZ599luUISqFSSUNvugROVlbSPChmm4iIiPRjFpPDqWz6ZJv692fQREREVJkwcKpAKhWwZInu7SdOLL++EBERkf4YOFUgfUoQ2NgAQUHl2RsiIiLSl16TwwcNGlTq/uTk5KfpS5WnTwmC6dM5TEdERFTZ6BU4OTk5lbl/zJgxT9WhqkqfEgQKBes2ERERVUZ6BU4bN24sr35UefpMCmeVcCIiosrJqOUIqiJj3M6oTwkCrklHRET09FiOwIzpk23q149BExERUWVlFoFTdHQ0xo8fD39/f9jY2KBevXqYN28ecnJySj1u7NixkMlkWo/27dtXUK8lLEFARERUdRi05EpFu3btGvLz8/H111+jfv36uHz5MiZMmID09HR89tlnpR7bo0cPrblZCoWivLurhSUIiIiIqg6zCJx69OiBHj16aJ7XrVsX169fx1dffVVm4GRtbQ0PDw+dr5WdnY3s7GzN89TUVP07XABLEBAREVUdZjFUV5yUlBTUrFmzzHZhYWFwc3NDQEAAJkyYgISEhFLbL1q0CE5OTpqHj4+PwX1kCQIiIqKqxSwDp8jISKxatQoTy5gQ1LNnT2zZsgVHjx7F0qVLER4ejueff14ro1TYzJkzkZKSonncvXvX4H6yBAEREVHVYtJyBCEhIZg/f36pbcLDw9GmTRvN89jYWHTq1AmdOnXCN998o9f14uLi4Ovri+3bt5dZBV3N0NsZVSrA2RlISyu7LUsQEBERGVd5lSMw6RynyZMnY/jw4aW28fPz0/wcGxuLzp07o0OHDli7dq3e1/P09ISvry9u3ryp97H6OnFCt6AJYAkCIiIic2HSwMnV1RWurq46tb1//z46d+6M1q1bY+PGjbCw0H+UMTExEXfv3oWnp6fex+pr927d27IEARERkXkwizlOsbGxCAoKgo+PDz777DM8ePAA8fHxiI+P12rXqFEj7H4SsTx+/BjvvvsuTp06hejoaISFhaFv375wdXXFwIEDy7W/KhWg6yiirS1LEBAREZkLsyhHcOjQIdy6dQu3bt2Ct7e31r6CU7SuX7+OlJQUAIBcLselS5ewefNmJCcnw9PTE507d8aOHTvg4OBQrv0NC5PmLOliwgQO0xEREZkLrlVXBkMmlw0dCuzcqdv5jx1jxomIiMjYuFadmVCpgJ9+0q2toyPQsWP59oeIiIiMh4GTkYWFAVlZurWdOpXDdEREROaEgZOR6brECiuFExERmR+zmBxe2alUUqbp6FEgNFS3Y1i7iYiIyPwwcHpKoaHAa68BiYn6HcfaTUREROaHgdNTCA0FBg/W/zgbG95JR0REZI44x8lAKhUwZYphx/buzWE6IiIic8TAyUAnTgD37xt2LIfpiIiIzBMDJwPFxRl2HIfpiIiIzBcDJwMZuk7w9OkcpiMiIjJXDJwM1LEjoO+Sd6zdREREZN4YOD0FKyv92s+cyWwTERGROWPgZKATJ4BHj3Rvb2/PbBMREZG5Y+BkoB9/1K/9t98y20RERGTuGDgZQKUCvvtOt7ZKJbBrFzBoUPn2iYiIiMofK4cb4MQJ4OHDsts5OgIPHkiTwomIiMj8MeNkAF1rOI0bx6CJiIioKmHgZABdazj171++/SAiIqKKxcDJAB07Ai4upbdxcZHaERERUdXBwImIiIhIRwycDHDiBJCYWHqbxESpHREREVUdDJwMoOvkcEMXAiYiIqLKiYGTAXSdHG7oQsBERERUOTFwMkDHjoC3NyCTFb9fJgN8fDg5nIiIqKph4GQAuRwYMQIQouQ2y5dziRUiIqKqhoGTAUJDgc8+K3n/u+9yiRUiIqKqiIGTnlQq4O23S882bd8utSMiIqKqhYGTnk6cAO7dK73N3bssRUBERFQVMXDSE0sREBERVV8MnPTEUgRERETVFwMnPbEUARERUfXFwElPcjmwYoX0c+HgSf2cpQiIiIiqJgZOBhg0SCo5YFHo3bOwYCkCIiKiqoyBkwHUdZwKlxxQqaTtoaGm6RcRERGVLwZOetKljlNwMOs4ERERVUVmEzj169cPderUgVKphKenJ0aPHo3Y2NhSjxFCICQkBF5eXrCxsUFQUBD+/vvvp+pHWXWchGAdJyIioqrKbAKnzp074/vvv8f169exa9cuREZGYsiQIaUes2TJEixbtgxffPEFwsPD4eHhga5duyItLc3gfrCOExERUfUlE6K0QafKa+/evRgwYACys7NhZWVVZL8QAl5eXggODsaMGTMAANnZ2XB3d8fixYvx+uuv63Sd1NRUODk5ISUlBY6OjggLAzp3Lvu4Y8eAoCA9XhAREREZTeHvb2Mxm4xTQY8ePcKWLVsQGBhYbNAEAFFRUYiPj0e3bt0026ytrdGpUyecPHmyxHNnZ2cjNTVV61EQ6zgRERFVX2YVOM2YMQN2dnZwcXFBTEwMfvzxxxLbxsfHAwDc3d21tru7u2v2FWfRokVwcnLSPHx8fLT2s44TERFR9WXSwCkkJAQymazUx7lz5zTt33vvPVy8eBGHDh2CXC7HmDFjUNZIo6xQdCOEKLKtoJkzZyIlJUXzuHv3bpE2gwYBO3cCtWtrb/f2lrazjhMREVHVZGnKi0+ePBnDhw8vtY2fn5/mZ1dXV7i6uiIgIACNGzeGj48PTp8+jQ4dOhQ5zsPDA4CUefIssHBcQkJCkSxUQdbW1rC2ti6z74MGAf37S3fPxcVJa9N17MhMExERUVVm0sBJHQgZQp1pys7OLna/v78/PDw8cPjwYbRq1QoAkJOTg+PHj2Px4sWGdbgAlYpBExERUXVjFnOczp49iy+++AIRERG4c+cOjh07hpEjR6JevXpa2aZGjRph9+7dAKQhuuDgYCxcuBC7d+/G5cuXMXbsWNja2mLkyJFP1Z/QUMDPT7q7buRI6b9+fqwYTkREVNWZNOOkKxsbG4SGhmLevHlIT0+Hp6cnevToge3bt2sNq12/fh0pKSma59OnT0dmZiYmTZqEpKQktGvXDocOHYKDg4PBfQkNBYYMKVo5/P59aTvnOBEREVVdZlvHqaIUrANhZ+cIP7+SK4fLZNIE8agoDtsRERGZEus4VQJcboWIiKh6Y+CkBy63QkREVL0xcNJDgaoGRmlHRERE5oWBkx643AoREVH1xsBJD1xuhYiIqHpj4KQnLrdCRERUfZlFHafKhsutEBERVU8MnAwklwNBQabuBREREVUkDtURERER6YiBExEREZGOGDgRERER6YiBExEREZGOODncQCoV76ojIiKqbhg4GSA0FHj7be0Ff729peKYrONERERUdXGoTk+hocCQIdpBEwDcvy9tDw01Tb+IiIio/DFw0oNKJWWahCi6T70tOFhqR0RERFUPAyc9nDhRNNNUkBDA3btSOyIiIqp6GDjpIS7OuO2IiIjIvDBw0oOnp3HbERERkXlh4KSHjh2lu+dksuL3y2SAj4/UjoiIiKoeBk56kMulkgNA0eBJ/Xz5ctZzIiIiqqoYOOlp0CBg506gdm3t7d7e0nbWcSIiIqq6WADTAIMGAf37s3I4ERFRdcPAyUByORAUZOpeEBERUUXiUB0RERGRjhg4EREREemIgRMRERGRjhg4EREREemIgRMRERGRjnhXnQFUKpYiICIiqo4YOOkpNBR4+23g3r1/t3l7SxXFWfySiIioauNQnR5CQ4EhQ7SDJgC4f1/aHhpqmn4RERFRxWDgpCOVSso0CVF0n3pbcLDUjoiIiKomBk46OnmyaKapICGAu3eluU9ERERUNTFw0lF8vG7t4uLKtx9ERERkOmYTOPXr1w916tSBUqmEp6cnRo8ejdjY2FKPGTt2LGQymdajffv2Bl3fw0O3dp6eBp2eiIiIzIDZBE6dO3fG999/j+vXr2PXrl2IjIzEkCFDyjyuR48eiIuL0zwOHDhg0PUDA6W752Sy4vfLZICPj1SagIiIiKomsylHMHXqVM3Pvr6+eP/99zFgwADk5ubCysqqxOOsra3hoWu6qBRyuVRyYMgQKUgqOElcHUwtX856TkRERFWZ2WScCnr06BG2bNmCwMDAUoMmAAgLC4ObmxsCAgIwYcIEJCQklNo+OzsbqampWg+1QYOAnTuB2rW1j/H2lrazjhMREVHVJhOiuBvsK6cZM2bgiy++QEZGBtq3b4+ffvoJLi4uJbbfsWMH7O3t4evri6ioKMyZMwd5eXk4f/48rK2tiz0mJCQE8+fPL7I9JSUFjo6OAFg5nIiIqLJLTU2Fk5OT1ve3MZg0cCopSCkoPDwcbdq0AQA8fPgQjx49wp07dzB//nw4OTnhp59+gqykiUeFxMXFwdfXF9u3b8egEtJD2dnZyM7O1jxPTU2Fj4+P0d94IiIiKj/lFTiZdI7T5MmTMXz48FLb+Pn5aX52dXWFq6srAgIC0LhxY/j4+OD06dPo0KGDTtfz9PSEr68vbt68WWIba2vrErNRREREVL2ZNHBSB0KGUCfKCmaHypKYmIi7d+/CkzUDiIiIyABmMTn87Nmz+OKLLxAREYE7d+7g2LFjGDlyJOrVq6eVbWrUqBF2794NAHj8+DHeffddnDp1CtHR0QgLC0Pfvn3h6uqKgQMHmuqlEBERkRkzi3IENjY2CA0Nxbx585Ceng5PT0/06NED27dv1xpWu379OlJSUgAAcrkcly5dwubNm5GcnAxPT0907twZO3bsgIODg6leChEREZkxs7qrzhTKa3IZERERlZ/y+v42i6E6IiIiosrALIbqKhvWcSIiIqqeGDjpKTQUePtt4N69f7d5e0vLsbByOBERUdXGoTo9hIZKa9UVDJoA4P59aXtoqGn6RURERBWDgZOOVCop01TcVHr1tuBgqR0RERFVTQycdHTyZNFMU0FCAHfvSnOfiIiIqGpi4KSj+Hjd2sXFlW8/iIiIyHQYOOnIw0O3dlzNhYiIqOpi4KSjwEDp7jmZrPj9Mhng4yOVJiAiIqKqiYGTjuRyqeQAUDR4Uj9fvpz1nIiIiKoyBk56GDQI2LkTqF1be7u3t7SddZyIiIiqNhbA1NOgQUD//qwcTkREVB0xcDKAXA4EBZm6F0RERFTROFRHREREpCMGTkREREQ64lCdnlQqzm8iIiKqrhg46SE0VFqvruDSK97eUpkC3lFHRERU9XGoTkd79wJDhhRdr+7+fWl7aKhp+kVEREQVh4GTjmbMkBbyLUy9LThYGsYjIiKiqouBk45iY0veJwRw964094mIiIiqLgZORhQXZ+oeEBERUXli4GREnp6m7gERERGVJwZOOvLyKrq4r5pMBvj4SKUJiIiIqOpi4KSjxYul/xYOntTPly9nPSciIqKqjoGTjvr1A3buBGrX1t7u7S1tZx0nIiKiqo8FMPUwaBDQvz8rhxMREVVXzDgRERER6YgZJz1wyRUiIqLqjRknHXHJFSIiImLgpCMuuUJEREQMnHTEJVeIiIiIgZMRcckVIiKiqo2BkxFxyRUiIqKqjYGTjrjkChERETFw0hGXXCEiIiKzC5yys7PRsmVLyGQyRERElNpWCIGQkBB4eXnBxsYGQUFB+Pvvvw26LpdcISIiIrMrgDl9+nR4eXnhzz//LLPtkiVLsGzZMmzatAkBAQFYsGABunbtiuvXr8PBwUHva/fvDzg5AWFh0vOgIOnBTBMREVH1YFYZp59//hmHDh3CZ599VmZbIQSWL1+OWbNmYdCgQWjatCm+/fZbZGRkYOvWrXpfe+9ewM8P6NIFWLBAeowdC/z4o/6vg4iIiMyT2WSc/vnnH0yYMAF79uyBra1tme2joqIQHx+Pbt26abZZW1ujU6dOOHnyJF5//fVij8vOzkZ2drbmeUpKCgBg9OjUIm3v3QMGDwb+9z9pKI+IiIgqh9RU6XtbFFe9+imYReAkhMDYsWMxceJEtGnTBtHR0WUeEx8fDwBwd3fX2u7u7o47d+6UeNyiRYswf/78Yvb4lHjM6NFldoeIiIhMIDExEU5OTkY7n0kDp5CQkBKClH+Fh4fj5MmTSE1NxcyZM/W+hqzQbXBCiCLbCpo5cyamTZumeZ6cnAxfX1/ExMQY9Y0n/aWmpsLHxwd3796Fo6OjqbtTrfGzqFz4eVQe/Cwqj5SUFNSpUwc1a9Y06nlNGjhNnjwZw4cPL7WNn58fFixYgNOnT8Pa2lprX5s2bTBq1Ch8++23RY7z8PAAIGWePAtUpkxISCiShSrI2tq6yHUAwMnJiX8ElYSjoyM/i0qCn0Xlws+j8uBnUXlYWBh3OrdJAydXV1e4urqW2W7lypVYsGCB5nlsbCy6d++OHTt2oF27dsUe4+/vDw8PDxw+fBitWrUCAOTk5OD48eNYrC7KRERERKQHs5jjVKdOHa3n9vb2AIB69erB29tbs71Ro0ZYtGgRBg4cCJlMhuDgYCxcuBANGjRAgwYNsHDhQtja2mLkyJEV2n8iIiKqGswicNLV9evXNXfBAVLNp8zMTEyaNAlJSUlo164dDh06pFcNJ2tra8ybN6/Y4TuqWPwsKg9+FpULP4/Kg59F5VFen4VMGPs+PSIiIqIqyqwKYBIRERGZEgMnIiIiIh0xcCIiIiLSEQMnIiIiIh0xcCIiIiLSEQMnAF9++SX8/f2hVCrRunVrnDhxotT2x48fR+vWraFUKlG3bl2sWbOmgnpa9enzWYSGhqJr166oVasWHB0d0aFDB/zyyy8V2NuqTd+/C7U//vgDlpaWaNmyZfl2sBrR97PIzs7GrFmz4OvrC2tra9SrVw8bNmyooN5Wffp+Hlu2bEGLFi1ga2sLT09PvPLKK0hMTKyg3lZdv/32G/r27QsvLy/IZDLs2bOnzGOM8v0tqrnt27cLKysrsW7dOnHlyhXx9ttvCzs7O3Hnzp1i29++fVvY2tqKt99+W1y5ckWsW7dOWFlZiZ07d1Zwz6sefT+Lt99+WyxevFicPXtW3LhxQ8ycOVNYWVmJCxcuVHDPqx59Pwu15ORkUbduXdGtWzfRokWLiulsFWfIZ9GvXz/Rrl07cfjwYREVFSXOnDkj/vjjjwrsddWl7+dx4sQJYWFhIVasWCFu374tTpw4IZo0aSIGDBhQwT2veg4cOCBmzZoldu3aJQCI3bt3l9reWN/f1T5watu2rZg4caLWtkaNGon333+/2PbTp08XjRo10tr2+uuvi/bt25dbH6sLfT+L4jzzzDNi/vz5xu5atWPoZzFs2DAxe/ZsMW/ePAZORqLvZ/Hzzz8LJycnkZiYWBHdq3b0/Tw+/fRTUbduXa1tK1euFN7e3uXWx+pIl8DJWN/f1XqoLicnB+fPn0e3bt20tnfr1g0nT54s9phTp04Vad+9e3ecO3cOubm55dbXqs6Qz6Kw/Px8pKWlGX0l7OrG0M9i48aNiIyMxLx588q7i9WGIZ/F3r170aZNGyxZsgS1a9dGQEAA3n33XWRmZlZEl6s0Qz6PwMBA3Lt3DwcOHIAQAv/88w927tyJ3r17V0SXqQBjfX9XqSVX9PXw4UOoVCq4u7trbXd3d0d8fHyxx8THxxfbPi8vDw8fPoSnp2e59bcqM+SzKGzp0qVIT0/Hiy++WB5drDYM+Sxu3ryJ999/HydOnIClZbX+Z8WoDPksbt++jd9//x1KpRK7d+/Gw4cPMWnSJDx69IjznJ6SIZ9HYGAgtmzZgmHDhiErKwt5eXno168fVq1aVRFdpgKM9f1drTNOajKZTOu5EKLItrLaF7ed9KfvZ6G2bds2hISEYMeOHXBzcyuv7lUrun4WKpUKI0eOxPz58xEQEFBR3atW9Pm7yM/Ph0wmw5YtW9C2bVv06tULy5Ytw6ZNm5h1MhJ9Po8rV65gypQpmDt3Ls6fP4+DBw8iKioKEydOrIiuUiHG+P6u1v9r6OrqCrlcXuT/FBISEopEpWoeHh7Ftre0tISLi0u59bWqM+SzUNuxYwfGjx+PH374AV26dCnPblYL+n4WaWlpOHfuHC5evIjJkycDkL68hRCwtLTEoUOH8Pzzz1dI36saQ/4uPD09Ubt2bTg5OWm2NW7cGEII3Lt3Dw0aNCjXPldlhnweixYtwnPPPYf33nsPANC8eXPY2dmhY8eOWLBgAUcpKpCxvr+rdcZJoVCgdevWOHz4sNb2w4cPIzAwsNhjOnToUKT9oUOH0KZNG1hZWZVbX6s6Qz4LQMo0jR07Flu3buWcASPR97NwdHTEpUuXEBERoXlMnDgRDRs2REREBNq1a1dRXa9yDPm7eO655xAbG4vHjx9rtt24cQMWFhbw9vYu1/5WdYZ8HhkZGbCw0P6qlcvlAP7NdlDFMNr3t15Tyasg9a2l69evF1euXBHBwcHCzs5OREdHCyGEeP/998Xo0aM17dW3M06dOlVcuXJFrF+/nuUIjETfz2Lr1q3C0tJSrF69WsTFxWkeycnJpnoJVYa+n0VhvKvOePT9LNLS0oS3t7cYMmSI+Pvvv8Xx48dFgwYNxKuvvmqql1Cl6Pt5bNy4UVhaWoovv/xSREZGit9//120adNGtG3b1lQvocpIS0sTFy9eFBcvXhQAxLJly8TFixc1pSHK6/u72gdOQgixevVq4evrKxQKhXj22WfF8ePHNftefvll0alTJ632YWFholWrVkKhUAg/Pz/x1VdfVXCPqy59PotOnToJAEUeL7/8csV3vArS9++iIAZOxqXvZ3H16lXRpUsXYWNjI7y9vcW0adNERkZGBfe66tL381i5cqV45plnhI2NjfD09BSjRo0S9+7dq+BeVz3Hjh0r9TugvL6/ZUIwV0hERESki2o9x4mIiIhIHwyciIiIiHTEwImIiIhIRwyciIiIiHTEwImIiIhIRwyciIiIiHTEwImIiIhIRwyciIiIiHTEwImIiIhIRwyciIiKkZiYCDc3N0RHR5fbNYYMGYJly5aV2/mJyPgYOBGRSYwdOxYymQwTJ04ssm/SpEmQyWQYO3ZsxXfsiUWLFqFv377w8/PTbPvvf/8LmUyGjz76SKutEALt2rWDTCbD3Llzdb7G3Llz8fHHHyM1NdVY3SaicsbAiYhMxsfHB9u3b0dmZqZmW1ZWFrZt24Y6deqYrF+ZmZlYv349Xn31Vc02IQQiIiLg6+uLS5cuabX/9ttvERsbCwB49tlndb5O8+bN4efnhy1bthin40RU7hg4EZHJPPvss6hTpw5CQ0M120JDQ+Hj44NWrVppth08eBD/+c9/UKNGDbi4uKBPnz6IjIzUOtfOnTvRrFkz2NjYwMXFBV26dEF6enqZ+4rz888/w9LSEh06dNBsu3nzJtLS0jB27FitwCktLQ0zZ87UZMdat26t13vQr18/bNu2Ta9jiMh0GDgRkUm98sor2Lhxo+b5hg0bMG7cOK026enpmDZtGsLDw3HkyBFYWFhg4MCByM/PBwDExcVhxIgRGDduHK5evYqwsDAMGjQIQohS95Xkt99+Q5s2bbS2nT9/HkqlEiNGjMDNmzeRnZ0NAPjoo4/QsmVLeHp6wtXVFT4+Pnq9/rZt2+Ls2bOa8xFR5WZp6g4QUfU2evRozJw5E9HR0ZDJZPjjjz+wfft2hIWFadoMHjxY65j169fDzc0NV65cQdOmTREXF4e8vDwMGjQIvr6+AIBmzZoBAG7cuFHivpJER0fDy8tLa9uFCxfQvHlzBAQEwM7ODlevXoWdnR2+/PJLnDt3Dp999pne2SYAqF27NrKzsxEfH6/pHxFVXsw4EZFJubq6onfv3vj222+xceNG9O7dG66urlptIiMjMXLkSNStWxeOjo7w9/cHAMTExAAAWrRogRdeeAHNmjXD0KFDsW7dOiQlJZW5rySZmZlQKpVa286fP4/WrVtDJpOhefPmuHz5MqZOnYrXXnsNjRo1wvnz5/Wa36RmY2MDAMjIyND7WCKqeAyciMjkxo0bh02bNuHbb78tMkwHAH379kViYiLWrVuHM2fO4MyZMwCAnJwcAIBcLsfhw4fx888/45lnnsGqVavQsGFDREVFlbqvJK6urkWCq4sXL2oCoxYtWmDFihU4e/Ys5s2bh5ycHPz9999agVNGRgbee+89BAYGIjAwEBMmTEBiYmKRaz169AgAUKtWLT3fNSIyBQZORGRyPXr0QE5ODnJyctC9e3etfYmJibh69Spmz56NF154AY0bNy42YySTyfDcc89h/vz5uHjxIhQKBXbv3l3mvuK0atUKV65c0Ty/ffs2kpOTNUNxLVu2xLlz5/Dxxx/DyckJly5dQm5urtZQ3eTJk9GiRQucPHkSJ0+exPDhwzFmzJgic6suX74Mb2/vIlk2IqqcOMeJiExOLpfj6tWrmp8LcnZ2houLC9auXQtPT0/ExMTg/fff12pz5swZHDlyBN26dYObmxvOnDmDBw8eoHHjxqXuK0n37t0xc+ZMJCUlwdnZGefPn4dCoUDTpk0BAC+//DIGDBgAFxcXANL8J2dnZ80QYmZmJpKSkvDSSy8hJCQEABASEoIff/wRt27dQoMGDTTXOnHiBLp16/Z0byARVRgGTkRUKTg6Oha73cLCAtu3b8eUKVPQtGlTNGzYECtXrkRQUJDWsb/99huWL1+O1NRU+Pr6YunSpejZsyeuXr1a4r6SNGvWDG3atMH333+P119/HRcuXEDTpk1hZWUFALCystLKEF24cEGrfELBrNLkyZNLvE5WVhZ2796NX375pcz3h4gqB5ko7Z5cIqJq6sCBA3j33Xdx+fJlWFjoP6th7Nix6Nq1K0aNGgUAOHLkCD777DMcOHAAMpkMALB69Wr8+OOPOHTokFH7TkTlhxknIqJi9OrVCzdv3sT9+/f1rs0EAF9++SVmz56NlStXQiaToXHjxvjuu+80QRMgZa5WrVplzG4TUTljxomIiIhIR7yrjoiIiEhHDJyIiIiIdMTAiYiIiEhHDJyIiIiIdMTAiYiIiEhHDJyIiIiIdMTAiYiIiEhHDJyIiIiIdMTAiYiIiEhHDJyIiIiIdPT/8yf9CE1FE68AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# starting with luminosity\n", + "\n", + "best_kernel_L = Matern(length_scale=best_length_scale, nu=best_nu)\n", + "gp_best_L = GaussianProcessRegressor(kernel=best_kernel_L, optimizer=None, normalize_y=True)\n", + "gp_best_L.fit(X, y)\n", + "\n", + "mass_vals = np.linspace(np.max(phil_mass), np.min(pisa_mass), 20).reshape(-1, 1)\n", + "L_pred = gp_best_L.predict(mass_vals)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_L, color='blue', label='actual values')\n", + "plt.scatter(mass_vals, L_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", + "plt.title('Creating Points for Luminosity Mass Gap')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('Luminosity')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(-4,0)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5aee3424-7efa-4bed-8030-4e70eb1f47c2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# moving to effective temperature\n", + "\n", + "best_kernel_teff = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", + "gp_best_teff = GaussianProcessRegressor(kernel=best_kernel_teff, optimizer=None, normalize_y=True)\n", + "gp_best_teff.fit(X, y_teff)\n", + "\n", + "mass_vals = np.linspace(np.max(phil_mass), np.min(pisa_mass), 20).reshape(-1, 1)\n", + "teff_pred = gp_best_teff.predict(mass_vals)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_Teff, color='blue', label='actual values')\n", + "plt.scatter(mass_vals, teff_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", + "plt.title('Creating Points for Teff Mass Gap')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('Teff')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(3.25,3.75)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "53238900-30e4-4015-b033-d81dd763d773", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# moving to effective temperature\n", + "\n", + "best_kernel_logg = Matern(length_scale=best_length_scale_logg, nu=best_nu_logg)\n", + "gp_best_logg = GaussianProcessRegressor(kernel=best_kernel_logg, optimizer=None, normalize_y=True)\n", + "gp_best_logg.fit(X, y_logg)\n", + "\n", + "mass_vals = np.linspace(np.max(phil_mass), np.min(pisa_mass), 20).reshape(-1, 1)\n", + "logg_pred = gp_best_logg.predict(mass_vals)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_logg, color='blue', label='actual values')\n", + "plt.scatter(mass_vals, logg_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", + "plt.title('Creating Points for logg Mass Gap')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('logg')\n", "plt.xlim(0, 1)\n", "plt.ylim(4.0,4.5)\n", "plt.legend()\n", "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3da35d6f-d88c-4582-be4f-7b65f069e784", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating graphs of mass vs. luminosity, Teff, and gravity\n", + "fig, axs = plt.subplots(3, 1, figsize=(6, 10))\n", + "\n", + "# plotting generated data for luminosity\n", + "axs[0].scatter(combined_masses, combined_L, color='blue', label='actual values')\n", + "axs[0].scatter(mass_vals, L_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[0].set_title('Creating Points for Luminosity Mass Gap')\n", + "axs[0].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[0].set_ylabel('Luminosity')\n", + "axs[0].set_xlim(0, 1)\n", + "#axs[0].set_ylim(-4,0)\n", + "axs[0].legend()\n", + "\n", + "# mass vs. Teff\n", + "axs[1].scatter(combined_masses, combined_Teff, color='blue', label='actual values')\n", + "axs[1].scatter(mass_vals, teff_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[1].set_title('Creating Points for Teff Mass Gap')\n", + "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[1].set_ylabel('Teff')\n", + "axs[1].set_xlim(0, 1)\n", + "#axs[1].set_ylim(3.25,3.75)\n", + "axs[1].legend()\n", + "\n", + "# mass vs. logg (problem child)\n", + "axs[2].scatter(combined_masses, combined_logg, color='blue', label='actual values')\n", + "axs[2].scatter(mass_vals, logg_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[2].set_title('Creating Points for logg Mass Gap')\n", + "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[2].set_ylabel('logg')\n", + "axs[2].set_xlim(0, 1)\n", + "#axs[2].set_ylim(4.0,4.5)\n", + "axs[2].legend()\n", + "\n", + "fig.tight_layout()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d3d88985-6d1f-4aa2-bd88-5f69b73897ea", + "metadata": {}, + "source": [ + "### Testing Fit Parameters for Another Cluster Age" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c8631679-e803-4d74-a549-68f41ba1196c", + "metadata": {}, + "outputs": [], + "source": [ + "# importing compatible age fits files \n", + "phillips2 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/iso/z00/iso_8.051282050278353.fits'\n", + "pisa2 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Pisa2011/iso/z015/iso_8.00.fits'\n", + "\n", + "# loading tables and extracting data\n", + "phil2_tbl = Table.read(phillips2, format='fits')\n", + "pisa2_tbl = Table.read(pisa2, format='fits')\n", + "\n", + "phil2_mass = phil2_tbl['Mass']\n", + "pisa2_mass = pisa2_tbl['col3']\n", + "combined_masses2 = np.concatenate([phil2_mass, pisa2_mass])\n", + "\n", + "phil2_teff = phil2_tbl['Teff']\n", + "pisa2_teff = pisa2_tbl['col2']\n", + "combined_teff2 = np.concatenate([phil2_teff, pisa2_teff])\n", + "\n", + "phil2_L = phil2_tbl['Luminosity']\n", + "pisa2_L = pisa2_tbl['col1']\n", + "combined_L2 = np.concatenate([phil2_L, pisa2_L])\n", + "\n", + "phil2_logg = phil2_tbl['Gravity']\n", + "pisa2_logg = pisa2_tbl['col4']\n", + "combined_logg2 = np.concatenate([phil2_logg, pisa2_logg])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "a95f9710-bc1b-40ae-9b4b-73dec1e9e6a6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating graphs of mass vs. luminosity, Teff, and gravity\n", + "fig, axs = plt.subplots(3, 1, figsize=(6, 10))\n", + "\n", + "\n", + "# generated data for mass vs. luminosity\n", + "best_kernel_L2 = Matern(length_scale=best_length_scale, nu=best_nu)\n", + "gp_best_L2 = GaussianProcessRegressor(kernel=best_kernel_L2, optimizer=None, normalize_y=True)\n", + "gp_best_L2.fit(combined_masses2.reshape(-1, 1), combined_L2)\n", + "\n", + "mass_vals2 = np.linspace(np.max(phil2_mass), np.min(pisa2_mass), 20).reshape(-1, 1)\n", + "L_pred2 = gp_best_L2.predict(mass_vals2)\n", + "\n", + "# plotting generated data for luminosity\n", + "axs[0].scatter(combined_masses2, combined_L2, color='blue', label='actual values')\n", + "axs[0].scatter(mass_vals2, L_pred2, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[0].set_title('Creating Points for Luminosity Mass Gap')\n", + "axs[0].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[0].set_ylabel('Luminosity')\n", + "axs[0].set_xlim(0, 1)\n", + "#axs[0].set_ylim(-4,0)\n", + "axs[0].legend()\n", + "\n", + "# generated data for mass vs. teff\n", + "best_kernel_teff2 = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", + "gp_best_teff2 = GaussianProcessRegressor(kernel=best_kernel_teff2, optimizer=None, normalize_y=True)\n", + "gp_best_teff2.fit(combined_masses2.reshape(-1, 1), combined_teff2)\n", + "\n", + "teff_pred2 = gp_best_teff2.predict(mass_vals2)\n", + "\n", + "# mass vs. Teff\n", + "axs[1].scatter(combined_masses2, combined_teff2, color='blue', label='actual values')\n", + "axs[1].scatter(mass_vals2, teff_pred2, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[1].set_title('Creating Points for Teff Mass Gap')\n", + "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[1].set_ylabel('Teff')\n", + "axs[1].set_xlim(0, 1)\n", + "#axs[1].set_ylim(3.25,3.75)\n", + "axs[1].legend()\n", + "\n", + "# generated data for mass vs. logg\n", + "best_kernel_logg2 = Matern(length_scale=best_length_scale_logg, nu=best_nu_logg)\n", + "gp_best_logg2 = GaussianProcessRegressor(kernel=best_kernel_logg2, optimizer=None, normalize_y=True)\n", + "gp_best_logg2.fit(combined_masses2.reshape(-1, 1), combined_logg2)\n", + "\n", + "logg_pred2 = gp_best_logg2.predict(mass_vals2)\n", + "\n", + "# mass vs. logg (problem child)\n", + "axs[2].scatter(combined_masses2, combined_logg2, color='blue', label='actual values')\n", + "axs[2].scatter(mass_vals2, logg_pred2, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[2].set_title('Creating Points for logg Mass Gap')\n", + "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[2].set_ylabel('logg')\n", + "axs[2].set_xlim(0, 1)\n", + "#axs[2].set_ylim(4.0,4.5)\n", + "axs[2].legend()\n", + "\n", + "fig.tight_layout()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "95d0dfdc-6f4d-49be-89c7-cc8337b466c3", + "metadata": {}, + "source": [ + "### Now for logAge = 6.00!" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "2a136d24-1b35-4164-882f-a78d70c3583a", + "metadata": {}, + "outputs": [], + "source": [ + "# importing compatible age fits files \n", + "phillips3 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/iso/z00/iso_6.0.fits'\n", + "pisa3 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Pisa2011/iso/z015/iso_6.00.fits'\n", + "\n", + "# loading tables and extracting data\n", + "phil3_tbl = Table.read(phillips3, format='fits')\n", + "pisa3_tbl = Table.read(pisa3, format='fits')\n", + "\n", + "phil3_mass = phil3_tbl['Mass']\n", + "pisa3_mass = pisa3_tbl['col3']\n", + "combined_masses3 = np.concatenate([phil3_mass, pisa3_mass])\n", + "\n", + "phil3_teff = phil3_tbl['Teff']\n", + "pisa3_teff = pisa3_tbl['col2']\n", + "combined_teff3 = np.concatenate([phil3_teff, pisa3_teff])\n", + "\n", + "phil3_L = phil3_tbl['Luminosity']\n", + "pisa3_L = pisa3_tbl['col1']\n", + "combined_L3 = np.concatenate([phil3_L, pisa3_L])\n", + "\n", + "phil3_logg = phil3_tbl['Gravity']\n", + "pisa3_logg = pisa3_tbl['col4']\n", + "combined_logg3 = np.concatenate([phil3_logg, pisa3_logg])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "e6040f03-c6e6-4b09-90d5-8d89f593822d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating graphs of mass vs. luminosity, Teff, and gravity\n", + "fig, axs = plt.subplots(3, 1, figsize=(6, 10))\n", + "\n", + "\n", + "# generated data for mass vs. luminosity\n", + "best_kernel_L3 = Matern(length_scale=best_length_scale, nu=best_nu)\n", + "gp_best_L3 = GaussianProcessRegressor(kernel=best_kernel_L3, optimizer=None, normalize_y=True)\n", + "gp_best_L3.fit(combined_masses3.reshape(-1, 1), combined_L3)\n", + "\n", + "mass_vals3 = np.linspace(np.max(phil3_mass), np.min(pisa3_mass), 20).reshape(-1, 1)\n", + "L_pred3 = gp_best_L3.predict(mass_vals3)\n", + "\n", + "# plotting generated data for luminosity\n", + "axs[0].scatter(combined_masses3, combined_L3, color='blue', label='actual values')\n", + "axs[0].scatter(mass_vals3, L_pred3, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[0].set_title('Creating Points for Luminosity Mass Gap')\n", + "axs[0].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[0].set_ylabel('Luminosity')\n", + "axs[0].set_xlim(0, 1)\n", + "#axs[0].set_ylim(-4,0)\n", + "axs[0].legend()\n", + "\n", + "# generated data for mass vs. teff\n", + "best_kernel_teff3 = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", + "gp_best_teff3 = GaussianProcessRegressor(kernel=best_kernel_teff3, optimizer=None, normalize_y=True)\n", + "gp_best_teff3.fit(combined_masses3.reshape(-1, 1), combined_teff3)\n", + "\n", + "teff_pred3 = gp_best_teff3.predict(mass_vals3)\n", + "\n", + "# mass vs. Teff\n", + "axs[1].scatter(combined_masses3, combined_teff3, color='blue', label='actual values')\n", + "axs[1].scatter(mass_vals3, teff_pred3, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[1].set_title('Creating Points for Teff Mass Gap')\n", + "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[1].set_ylabel('Teff')\n", + "axs[1].set_xlim(0, 1)\n", + "#axs[1].set_ylim(3.25,3.75)\n", + "axs[1].legend()\n", + "\n", + "# generated data for mass vs. logg\n", + "best_kernel_logg3 = Matern(length_scale=best_length_scale_logg, nu=best_nu_logg)\n", + "gp_best_logg3 = GaussianProcessRegressor(kernel=best_kernel_logg3, optimizer=None, normalize_y=True)\n", + "gp_best_logg3.fit(combined_masses3.reshape(-1, 1), combined_logg3)\n", + "\n", + "logg_pred3 = gp_best_logg3.predict(mass_vals3)\n", + "\n", + "# mass vs. logg (problem child)\n", + "axs[2].scatter(combined_masses3, combined_logg3, color='blue', label='actual values')\n", + "axs[2].scatter(mass_vals3, logg_pred3, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[2].set_title('Creating Points for logg Mass Gap')\n", + "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[2].set_ylabel('logg')\n", + "axs[2].set_xlim(0, 1)\n", + "#axs[2].set_ylim(4.0,4.5)\n", + "axs[2].legend()\n", + "\n", + "fig.tight_layout()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "01fbb0b7-0d7c-4e04-b458-5635d4c0fcb5", + "metadata": {}, + "source": [ + "### Creating a Function to Apply Model to All Pisa/Phillips Files" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "c130f0f1-9330-421a-9e8c-eedd4974e659", + "id": "accb8141-1888-49cf-84be-161bc3dbc197", "metadata": {}, "outputs": [], "source": [] From 3a9abd443a2d14cd6933c2cc0fcc9f4e89b4dde5 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 15 May 2025 11:46:37 -0700 Subject: [PATCH 041/191] Added verbose control in IsochronePhot; Switched to super init --- spisea/synthetic.py | 98 ++++++++++++++++++++++++++++----------------- 1 file changed, 61 insertions(+), 37 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 883fae76..c1d71741 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -146,7 +146,7 @@ class ResolvedCluster(Cluster): vebose: boolean True for verbose output. """ - def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, + def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=False, seed=None): Cluster.__init__(self, iso, imf, cluster_mass, ifmr=ifmr, verbose=verbose, seed=seed) @@ -587,7 +587,7 @@ def _remove_bad_systems(self, star_systems, compMass): idx = (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) if self.verbose and sum(idx) != N_systems: - print( 'Found {0:d} stars out of mass range'.format(N_systems - len(idx))) + print( 'Found {0:d} stars out of mass range'.format(N_systems - sum(idx))) star_systems = star_systems[idx] N_systems = len(star_systems) @@ -839,10 +839,11 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, wd_atm_func = default_wd_atm_func, red_law=default_red_law, mass_sampling=1, wave_range=[3000, 52000], min_mass=None, max_mass=None, - rebin=True): + rebin=True, verbose=False): - - t1 = time.time() + self.verbose = verbose + if self.verbose: + t1 = time.time() c = constants @@ -939,9 +940,10 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, tab.meta['WAVEMAX'] = wave_range[1] self.points = tab - - t2 = time.time() - print( 'Isochrone generation took {0:f} s.'.format(t2-t1)) + + if self.verbose: + t2 = time.time() + print( 'Isochrone generation took {0:f} s.'.format(t2-t1)) return def plot_HR_diagram(self, savefile=None): @@ -1079,8 +1081,8 @@ def __init__(self, logAge, AKs, distance, red_law=default_red_law, mass_sampling=1, iso_dir='./', min_mass=None, max_mass=None, rebin=True, recomp=False, filters=['ubv,U', 'ubv,B', 'ubv,V', - 'ubv,R', 'ubv,I']): - + 'ubv,R', 'ubv,I'], + verbose=False): self.metallicity = metallicity # Make the iso_dir, if it doesn't already exist @@ -1093,10 +1095,10 @@ def __init__(self, logAge, AKs, distance, # properly if metallicity == 0.0: save_file_fmt = '{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}_p00.fits' - self.save_file = save_file_fmt.format(iso_dir, logAge, AKs, str(distance).zfill(5)) + self.save_file = save_file_fmt.format(iso_dir, logAge, AKs, str(round(distance)).zfill(5)) save_file_legacy = '{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}.fits' - self.save_file_legacy = save_file_legacy.format(iso_dir, logAge, AKs, str(distance).zfill(5)) + self.save_file_legacy = save_file_legacy.format(iso_dir, logAge, AKs, str(round(distance)).zfill(5)) else: # Set metallicity flag if metallicity < 0: @@ -1106,34 +1108,42 @@ def __init__(self, logAge, AKs, distance, metal_flag = int(abs(metallicity)*10) save_file_fmt = '{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}_{4}{5:2s}.fits' - self.save_file = save_file_fmt.format(iso_dir, logAge, AKs, str(distance).zfill(5), metal_pre, str(metal_flag).zfill(2)) - self.save_file_legacy = save_file_fmt.format(iso_dir, logAge, AKs, str(distance).zfill(5), metal_pre, str(metal_flag).zfill(2)) - + self.save_file = save_file_fmt.format(iso_dir, logAge, AKs, str(round(distance)).zfill(5), metal_pre, str(metal_flag).zfill(2)) + self.save_file_legacy = save_file_fmt.format(iso_dir, logAge, AKs, str(round(distance)).zfill(5), metal_pre, str(metal_flag).zfill(2)) + # Expected filters self.filters = filters # Recalculate isochrone if save_file doesn't exist or recomp == True - file_exists = self.check_save_file(evo_model, atm_func, red_law) + file_exists = self.check_save_file(evo_model, atm_func, red_law, verbose=verbose) if (not file_exists) | (recomp==True): self.recalc = True - Isochrone.__init__(self, logAge, AKs, distance, - metallicity=metallicity, - evo_model=evo_model, atm_func=atm_func, - wd_atm_func=wd_atm_func, - wave_range=wave_range, - red_law=red_law, mass_sampling=mass_sampling, - min_mass=min_mass, max_mass=max_mass, rebin=rebin) - self.verbose = True + if verbose: + print(f'Generating new isochrone of log(t)={logAge:.2f}, AKs={AKs:.2f}, d={distance} pc') + # user_input = input(f"Isochrone file {self.save_file} does not exist or needs to be regenerated. Do you want to proceed? (yes/no): ").strip().lower() + # if user_input != 'yes': + # print("Operation canceled by the user.") + # return + super().__init__(logAge, AKs, distance, + metallicity=metallicity, + evo_model=evo_model, atm_func=atm_func, + wd_atm_func=wd_atm_func, + wave_range=wave_range, + red_law=red_law, mass_sampling=mass_sampling, + min_mass=min_mass, max_mass=max_mass, rebin=rebin, verbose=verbose) # Make photometry self.make_photometry(rebin=rebin, vega=vega) + if self.verbose: + print(f'Isochrone saved to {self.save_file}') else: self.recalc = False try: self.points = Table.read(self.save_file) except: self.points = Table.read(self.save_file_legacy) + # print(f'Isochrone loaded from existing file: {self.save_file}') # Add some error checking. return @@ -1145,13 +1155,12 @@ def make_photometry(self, rebin=True, vega=vega): photometry. """ - startTime = time.time() - - meta = self.points.meta + if self.verbose: + startTime = time.time() - print( 'Making photometry for isochrone: log(t) = %.2f AKs = %.2f dist = %d' % \ - (meta['LOGAGE'], meta['AKS'], meta['DISTANCE'])) - print( ' Starting at: ', datetime.datetime.now(), ' Usually takes ~5 minutes') + # print('Making photometry for isochrone: log(t) = %.2f AKs = %.2f dist = %d' % \ + # (meta['LOGAGE'], meta['AKS'], meta['DISTANCE'])) + # print( 'Starting at: ', datetime.datetime.now(), ' Usually takes ~5 minutes') npoints = len(self.points) verbose_fmt = 'M = {0:7.3f} Msun T = {1:5.0f} K m_{2:s} = {3:4.2f}' @@ -1159,8 +1168,9 @@ def make_photometry(self, rebin=True, vega=vega): # Loop through the filters, get filter info, make photometry for # all stars in this filter. for ii in self.filters: - prt_fmt = 'Starting filter: {0:s} Elapsed time: {1:.2f} seconds' - print( prt_fmt.format(ii, time.time() - startTime)) + if self.verbose: + prt_fmt = 'Starting filter: {0:s} Elapsed time: {1:.2f} seconds' + print( prt_fmt.format(ii, time.time() - startTime)) filt = get_filter_info(ii, rebin=rebin, vega=vega) filt_name = get_filter_col_name(ii) @@ -1171,19 +1181,21 @@ def make_photometry(self, rebin=True, vega=vega): self.points.add_column(mag_col) # Loop through each star in the isochrone and do the filter integration - print('Starting synthetic photometry') + if self.verbose: + print('Starting synthetic photometry') for ss in range(npoints): star = self.spec_list[ss] # These are already extincted, observed spectra. star_mag = mag_in_filter(star, filt) self.points[col_name][ss] = star_mag - if (self.verbose and (ss % 100) == 0): + if self.verbose and (ss % 100) == 0: print( verbose_fmt.format(self.points['mass'][ss], self.points['Teff'][ss], filt_name, star_mag)) - endTime = time.time() - print( ' Time taken: {0:.2f} seconds'.format(endTime - startTime)) + if self.verbose: + endTime = time.time() + print( 'Time taken: {0:.2f} seconds'.format(endTime - startTime)) if self.save_file != None: with warnings.catch_warnings(): @@ -1192,7 +1204,7 @@ def make_photometry(self, rebin=True, vega=vega): return - def check_save_file(self, evo_model, atm_func, red_law): + def check_save_file(self, evo_model, atm_func, red_law, verbose=False): """ Check to see if save_file exists, as saved by the save_file and save_file_legacy objects. If the filename exists, check the @@ -1214,6 +1226,18 @@ def check_save_file(self, evo_model, atm_func, red_law): (tmp.meta['ATMFUNC'] == atm_func.__name__) & (tmp.meta['REDLAW'] == red_law.name) ): out_bool = True + else: + if verbose: + print(f'Isochrone file {self.save_file} exists, but meta-data does not match.') + if tmp.meta['EVOMODEL'] != type(evo_model).__name__: + print(f' EVOMODEL: {tmp.meta["EVOMODEL"]} != {type(evo_model).__name__}') + if tmp.meta['ATMFUNC'] != atm_func.__name__: + print(f' ATMFUNC: {tmp.meta["ATMFUNC"]} != {atm_func.__name__}') + if tmp.meta['REDLAW'] != red_law.name: + print(f' REDLAW: {tmp.meta["REDLAW"]} != {red_law.name}') + else: + if verbose: + print(f'Isochrone file {self.save_file} or {self.save_file_legacy} does not exist, generating new one.') return out_bool From 283387e11ba91e315199607b75b46fd3f41cd733 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Sat, 19 Jul 2025 10:25:03 -0700 Subject: [PATCH 042/191] Finishing touches on IMF --- spisea/imf/imf.py | 9 +++++---- spisea/imf/tests/test_imf.py | 6 +++--- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 847ad5ab..fa05ce17 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -690,14 +690,15 @@ def __init__(self, multiplicity=None): IMF_broken_powerlaw.__init__(self, massLimits, powers, multiplicity=multiplicity) -class CombinedWeidnerKroupaKirkpatrick_2024(IMF_broken_powerlaw): +class Salpeter_Kirkpatrick_2024(IMF_broken_powerlaw): """ - Define combined IMF from several papers considering brown dwarf mass ranges. + Define combined IMF from Kirkpatrick (2024) and Salpeter (1955) to allow + inclusion of the brown dwarf mass range. Mass range: * 0.01 M_sun - 8 M_sun: Kirkpatrick 2024 `_. - * 8 M_sun - 120 M_sun: Weidner & Kroupa 2004 - `_. + * 8 M_sun - 120 M_sun: Salpeter 1955 + `_. """ def __init__(self, multiplicity=None): massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) diff --git a/spisea/imf/tests/test_imf.py b/spisea/imf/tests/test_imf.py index 571f6edc..4ba59b23 100755 --- a/spisea/imf/tests/test_imf.py +++ b/spisea/imf/tests/test_imf.py @@ -9,9 +9,9 @@ def test_generate_cluster(): # Make multiplicity object imf_multi = multiplicity.MultiplicityUnresolved() - # Make IMF object; we'll use a broken power law with the parameters from Kroupa+01 - massLimits = np.array([0.08, 0.5, 1, 120]) # Define boundaries of each mass segement - powers = np.array([-1.3, -2.3, -2.3]) # Power law slope associated with each mass segment + # Make IMF object; we'll use a broken power law with the parameters from Salpeter_Kirkpatrick+24 (updated with brown dwarf addition + massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) # Define boundaries of each mass segement + powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) # Power law slope associated with each mass segment my_imf = imf.IMF_broken_powerlaw(massLimits, powers, imf_multi) # Define total cluster mass From 8991819878dd19ea143f7ad9b14fa39267eedad2 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 15 Sep 2025 14:56:41 -0700 Subject: [PATCH 043/191] Addition of brown dwarf specific multiplicity conditions. --- spisea/imf/imf.py | 53 ++++++++++++++++------- spisea/imf/multiplicity.py | 30 +++++++++++++- spisea/imf/tests/test_multiplicity.py | 60 ++++++++++++++++++++++++--- 3 files changed, 123 insertions(+), 20 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index fa05ce17..4bcd06c1 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -199,7 +199,10 @@ def generate_cluster(self, totalMass, seed=None): def calc_multi(self, newMasses, compMasses, newSystemMasses, newIsMultiple, CSF, MF): """ Helper function to calculate multiples more efficiently. - We will use array operations as much as possible + We will use array operations as much as possible. + Uses Fontanive+18 parameters for brown dwarf masses + (M <= 0.08 M_sun) while keeping default parameters for + all other stellar primaries. """ # Identify multiple systems, calculate number of companions for # each @@ -215,13 +218,27 @@ def calc_multi(self, newMasses, compMasses, newSystemMasses, newIsMultiple, CSF, num = np.unique(n_comp_arr) for ii in num: tmp = np.where(n_comp_arr == ii)[0] + prim_subset = primary[tmp] + + # define masks based on stellar or substellar range + bd_mask = prim_subset <= 0.08 + star_mask = ~bd_mask if ii == 1: # Single companion case - q_values = self._multi_props.random_q(np.random.rand(len(tmp))) + q_values = np.empty(len(tmp)) + + if np.any(star_mask): + rand_vals = np.random.rand(star_mask.sum()) + q_values[star_mask] = self._multi_props.random_q(rand_vals) + + if np.any(bd_mask): + rand_vals = np.random.rand(bd_mask.sum()) + b = 1.0 + 6.1 #gamma from Fontanive+18 + q_values[bd_mask] = (rand_vals * (1.0 - self._multi_props.q_min ** b) + self._multi_props.q_min ** b) ** (1.0 / b) # Calculate mass of companion - m_comp = q_values * primary[tmp] + m_comp = q_values * prim_subset # Only keep companions that are more than the minimum mass. Update # compMasses, newSystemMasses, and newIsMultiple appropriately @@ -233,22 +250,30 @@ def calc_multi(self, newMasses, compMasses, newSystemMasses, newIsMultiple, CSF, bad = np.where(m_comp < self._mass_limits[0])[0] newIsMultiple[idx[tmp[bad]]] = False else: - # Multple companion case - q_values = self._multi_props.random_q(np.random.rand(len(tmp), ii)) - - # Calculate masses of companions - m_comp = np.multiply(q_values, np.transpose([primary[tmp]])) - - # Update compMasses, newSystemMasses, and newIsMultiple appropriately + # Multi-companion case for jj in range(len(tmp)): - m_comp_tmp = m_comp[jj] + prim = prim_subset[jj] + + # Finding q values of stellar and substellar primaries + if prim <= 0.08: + # BD case (Fontanive+18) + b = 1.0 + 6.1 + rand_vals = np.random.rand(ii) + q_values = (rand_vals * (1.0 - self._multi_props.q_min ** b) + + self._multi_props.q_min ** b) ** (1.0 / b) + else: + # Stellar case (Duchene & Kraus) + q_values = self._multi_props.random_q(np.random.rand(ii)) + + # Calculate masses of companions & update compMasses, newSystemMasses, and newIsMultiple appropriately + m_comp_tmp = q_values * prim compMasses[idx[tmp[jj]]] = m_comp_tmp[m_comp_tmp >= self._mass_limits[0]] newSystemMasses[idx[tmp[jj]]] += compMasses[idx[tmp[jj]]].sum() - - # Double check for the case when we drop all companions. - # This happens a lot near the minimum allowed mass. + + # Drop system if no valid companions remain if len(compMasses[idx[tmp[jj]]]) == 0: newIsMultiple[idx[tmp[jj]]] = False + return compMasses, newSystemMasses, newIsMultiple diff --git a/spisea/imf/multiplicity.py b/spisea/imf/multiplicity.py index 0d28ced6..00c2b809 100755 --- a/spisea/imf/multiplicity.py +++ b/spisea/imf/multiplicity.py @@ -41,6 +41,11 @@ class MultiplicityUnresolved(object): MF(mass) = MF_amp * (mass ** MF_power) + However, in the brown dwarf mass regime, it is currently recognized + that only binaries are possible, and the MF decreases dissimilarly + to higher masses (> 0.08 solar masses). The values for this range + are given by Aberasturi et al. (2014) and Fontanive et al. (2023). + **Companion Star Fraction** -- the expected number of companions in a multiple system. The companion star fraction (CSF) also changes with mass and this dependency can be described as @@ -52,6 +57,9 @@ class MultiplicityUnresolved(object): value, CSF_max. The actual number of companions is drawn from a Poisson distribution with an expectation value of CSF. + In the brown dwarf regime we impose an assumption that only + binary systems are possible due to current literature trends. + **Mass Ratio (Q)** -- The ratio between the companion star mass and primary star mass, Q = (m_comp / m_prim ) has a probability density function described by a powerlaw:: @@ -116,6 +124,9 @@ def multiplicity_fraction(self, mass): Given a star's mass, determine the probability that the star is in a multiple system (multiplicity fraction = MF). + Modified to allow binary fraction to decrease in brown dwarf regime. + Supported by Aberasturi et al. (2014) and Fontanive et al. (2018). + Parameters ---------- mass : float or numpy array @@ -133,6 +144,13 @@ def multiplicity_fraction(self, mass): if np.isscalar(mf): if mf > 1: mf = 1 + # physically override mf for brown dwarfs + if (mass <= 0.08) & (mass > 0.06): + mf = 0.16 + if (mass <= 0.06) & (mass > 0.02): + mf = 0.08 + if (mass < 0.02): + mf = 0 else: mf[mf > 1] = 1 @@ -141,7 +159,8 @@ def multiplicity_fraction(self, mass): def companion_star_fraction(self, mass): """ Given a star's mass, determine the average number of - companion stars (companion star fraction = CSF). + companion stars (companion star fraction = CSF). For + brown dwarfs we impose a hard limit of one companion. Parameters ---------- @@ -160,6 +179,8 @@ def companion_star_fraction(self, mass): if np.isscalar(csf): if csf > self.CSF_max: csf = self.CSF_max + if (mass <= 0.08): + csf = self.multiplicity_fraction(mass) else: csf[csf > self.CSF_max] = self.CSF_max @@ -210,6 +231,8 @@ class MultiplicityResolvedDK(MultiplicityUnresolved): """ Sub-class of MultiplicityUnresolved that adds semimajor axis and eccentricity information for multiple objects from distributions described in Duchene and Kraus 2013 + + For brown dwarf regime, mean separation and std are given by Fontanive et al. (2018). Parameters -------------- @@ -246,6 +269,8 @@ def log_semimajoraxis(self, mass): Generate the semimajor axis for a given mass. The mean and standard deviation of a given mass are determined by fitting the data from fitting the semimajor axis data as a function of mass in table 1 of Duchene and Kraus 2013. Then a random semimajor axis is drawn from a log normal distribution with that mean and standard deviation. + + The brown dwarf range is described by mean seperation and std values given in Fontanive et al. (2018). Parameters ---------- @@ -265,6 +290,9 @@ def log_semimajoraxis(self, mass): log_a_std = log_a_std_func(np.log10(2.9)) #sigma_log(a) if log_a_std < 0.1: log_a_std = 0.1 + if mass <= 0.08: + log_a_mean = np.log10(2.9) + log_a_std = 0.21 log_semimajoraxis = np.random.normal(log_a_mean, log_a_std) while 10**log_semimajoraxis > 2000 or log_semimajoraxis < -2: #AU diff --git a/spisea/imf/tests/test_multiplicity.py b/spisea/imf/tests/test_multiplicity.py index 3a9dc9b7..13152477 100755 --- a/spisea/imf/tests/test_multiplicity.py +++ b/spisea/imf/tests/test_multiplicity.py @@ -33,6 +33,7 @@ def test_create_MultiplicityUnresolved(): assert mu2.q_pow == 0.4 assert mu2.q_min == 0.04 + def test_multiplicity_fraction(): """ Test creating a MultiplicityUnresolved object and getting @@ -66,6 +67,14 @@ def test_multiplicity_fraction(): mf2_3 = mu1.multiplicity_fraction(0.1) np.testing.assert_almost_equal(mf2_3, 0.136, decimal=2) + # Test brown dwarf mass fractions + mf_bd1 = mu1.multiplicity_fraction(0.07) # near upper BD limit + mf_bd2 = mu1.multiplicity_fraction(0.04) # mid BD + mf_bd3 = mu1.multiplicity_fraction(0.01) # lower BD limit + assert np.isclose(mf_bd1, 0.16, atol=0.01) + assert np.isclose(mf_bd2, 0.08, atol=0.01) + assert np.isclose(mf_bd3, 0.0, atol=1e-6) + def test_multiplicity_fraction_array(): """ @@ -77,12 +86,23 @@ def test_multiplicity_fraction_array(): # First set of multiplicity parameters mu1 = multiplicity.MultiplicityUnresolved() - mass_array = np.array([1.0, 10.0, 0.1]) + mass_array = np.array([1.0, 10.0, 0.1, 0.07, 0.04, 0.01]) mf_array = mu1.multiplicity_fraction(mass_array) + # Stellar regime checks np.testing.assert_almost_equal(mf_array[0], 0.44, decimal=2) np.testing.assert_almost_equal(mf_array[1], 1.0, decimal=2) np.testing.assert_almost_equal(mf_array[2], 0.136, decimal=2) + + # BD regime checks + # interpolation between values implies lower masses --> lower mf + assert mf_array[3] < mf_array[2] + assert mf_array[4] <= mf_array[3] + assert mf_array[5] <= mf_array[4] + + # Ensure mf stars within reasonable bound (upper limit is 0.2) + assert np.all(mf_array[3:] >= 0.0) + assert np.all(mf_array[3:] <= 0.2) def test_companion_star_fraction(): @@ -117,11 +137,20 @@ def test_companion_star_fraction(): # csf2_3 = mu1.companion_star_fraction(0.1) # np.testing.assert_almost_equal(csf2_3, 0.159, decimal=2) + # Test brown dwarf csf + csf_bd1 = mu1.companion_star_fraction(0.07) + csf_bd2 = mu1.companion_star_fraction(0.04) + csf_bd3 = mu1.companion_star_fraction(0.01) + assert np.isclose(csf_bd1, 0.16, atol=0.01) + assert np.isclose(csf_bd2, 0.08, atol=0.01) + assert np.isclose(csf_bd3, 0.0, atol=1e-6) + def test_resolvedmult(): """ Test creating a MultiplicityResolvedDK object and that the parameters it's populated with are correct. + Updated to test for specific brown dwarf characteristics. """ from spisea import synthetic, evolution, atmospheres, reddening, ifmr from spisea.imf import imf, multiplicity @@ -132,7 +161,7 @@ def test_resolvedmult(): dist = 4000 # distance in parsecs metallicity = 0 # metallicity in [M/H] atm_func = atmospheres.get_merged_atmosphere - evo_merged = evolution.MISTv1() + evo_merged = evolution.MergedPhillipsBaraffePisaEkstromParsec() redlaw = reddening.RedLawCardelli(3.1) # Rv = 3.1 filt_list = ['nirc2,J', 'nirc2,Kp'] @@ -149,7 +178,7 @@ def test_resolvedmult(): clust_multiplicity = multiplicity.MultiplicityResolvedDK() # Multiplicity is defined in the IMF object - clust_imf_Mult = imf.Kroupa_2001(multiplicity=clust_multiplicity) + clust_imf_Mult = imf.Salpeter_Kirkpatrick_2024(multiplicity=clust_multiplicity) # Make clusters clust_Mult = synthetic.ResolvedCluster(iso_merged, clust_imf_Mult, clust_mtot) @@ -184,7 +213,28 @@ def test_resolvedmult(): n, bins = np.histogram(clust_Mult.companions['i']) bin_centers = 0.5*(bins[1:] + bins[:-1]) assert all(np.abs(i) < 0.15 for i in n/max(n) - np.sin(np.pi*bin_centers/180)) - - return + #checks for brown dwarf specific features + bd_idx = np.where(clust_Mult.star_systems['mass'] < 0.08)[0] + + # Map primaries → companions + comp_rows = [] + start = 0 + for ii, N in enumerate(clust_Mult.star_systems['N_companions']): + if ii in bd_idx and N > 0: + comp_rows.extend(range(start, start+N)) + start += N + + bd_companions = clust_Mult.companions[comp_rows] + if len(bd_companions) > 30: # only test if enough BD binaries + mean_log_a = np.mean(bd_companions['log_a']) + std_log_a = np.std(bd_companions['log_a']) + + # Expect lognormal centered near log10(2.9 AU), width ~0.21 + assert abs(mean_log_a - np.log10(2.9)) < 0.25, \ + f"BD mean log(a) off: {mean_log_a:.2f}" + assert abs(std_log_a - 0.21) < 0.15, \ + f"BD sigma log(a) off: {std_log_a:.2f}" + + return From 01d3fa34d000887d6a9732f32c4b47ced089995a Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 15 Sep 2025 15:54:11 -0700 Subject: [PATCH 044/191] Enforcing brown dwarf binaries --- spisea/imf/imf.py | 4 ++++ spisea/imf/tests/test_imf.py | 1 - spisea/imf/tests/test_multiplicity.py | 7 +++++-- 3 files changed, 9 insertions(+), 3 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 4bcd06c1..bddbd657 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -213,6 +213,10 @@ def calc_multi(self, newMasses, compMasses, newSystemMasses, newIsMultiple, CSF, n_comp_arr[too_many] = self._multi_props.CSF_max primary = newMasses[idx] + # limiting BD companions to 1 (mass-based) + bd_mask = primary <= 0.08 + n_comp_arr[bd_mask & (n_comp_arr > 1)] = 1 + # We will deal with each number of multiple system independently. This is # so we can put in uniform arrays in _multi_props.random_q. num = np.unique(n_comp_arr) diff --git a/spisea/imf/tests/test_imf.py b/spisea/imf/tests/test_imf.py index 4ba59b23..373cf8f4 100755 --- a/spisea/imf/tests/test_imf.py +++ b/spisea/imf/tests/test_imf.py @@ -169,7 +169,6 @@ def test_xi2(): plt.clf() plt.loglog(masses[sdx], pdf_sorted, 'k.') plt.axis('equal') - # pdb.set_trace() return diff --git a/spisea/imf/tests/test_multiplicity.py b/spisea/imf/tests/test_multiplicity.py index 13152477..8412dc2b 100755 --- a/spisea/imf/tests/test_multiplicity.py +++ b/spisea/imf/tests/test_multiplicity.py @@ -216,8 +216,11 @@ def test_resolvedmult(): #checks for brown dwarf specific features bd_idx = np.where(clust_Mult.star_systems['mass'] < 0.08)[0] + + #check there is only one possible companion per BD + assert all(clust_Mult.star_systems['N_companions'][bd_idx] <= 1), \ + "Brown dwarf primaries have >1 companion." - # Map primaries → companions comp_rows = [] start = 0 for ii, N in enumerate(clust_Mult.star_systems['N_companions']): @@ -231,7 +234,7 @@ def test_resolvedmult(): mean_log_a = np.mean(bd_companions['log_a']) std_log_a = np.std(bd_companions['log_a']) - # Expect lognormal centered near log10(2.9 AU), width ~0.21 + #expect lognormal centered near log10(2.9 AU), width ~0.21 assert abs(mean_log_a - np.log10(2.9)) < 0.25, \ f"BD mean log(a) off: {mean_log_a:.2f}" assert abs(std_log_a - 0.21) < 0.15, \ From a452fa79892b540b894e361939a9bde78244c108 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 8 Oct 2025 22:01:09 -0700 Subject: [PATCH 045/191] Raise informative error message for age and metallicity ranges --- spisea/evolution.py | 68 ++++++++++++++++++++++----------------------- 1 file changed, 34 insertions(+), 34 deletions(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index de5de196..82a74df8 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -131,12 +131,12 @@ def isochrone(self, age=1.e8, metallicity=0.0): # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if ((z_defined < np.min(self.z_list)) or (z_defined > np.max(self.z_list))): - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + # convert age (in yrs) to log scale and find nearest value in grid log_age = np.log10(age) @@ -206,12 +206,12 @@ def isochrone(self, age=1.e8, metallicity=0.0): # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if ((z_defined < np.min(self.z_list)) or (z_defined > np.max(self.z_list))): - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) @@ -437,12 +437,12 @@ def isochrone(self, age=1.e8, metallicity=0.0): # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if ((z_defined < np.min(self.z_list)) or (z_defined > np.max(self.z_list))): - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) @@ -584,12 +584,12 @@ def isochrone(self, age=1.e8, metallicity=0.0): # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + return if ((z_defined < np.min(self.z_list)) or (z_defined > np.max(self.z_list))): - logger.error('Requested metallicity {0} is out of bounds for evolution model. Available z-vals: {1}.'.format(z_defined, self.z_list)) - + raise ValueError(f'Requested metallicity {z_defined} is out of bounds for evolution model. Available z-vals: {self.z_list}.') + # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) @@ -743,12 +743,12 @@ def isochrone(self, age=5.e7, metallicity=0.0): # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if ((z_defined < np.min(self.z_list)) or (z_defined > np.max(self.z_list))): - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) @@ -1080,11 +1080,11 @@ def isochrone(self, age=1.e8, metallicity=0.0): # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if ((z_defined < np.min(self.z_list)) or (z_defined > np.max(self.z_list))): - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] @@ -1273,11 +1273,11 @@ def isochrone(self, age=1.e8, metallicity=0.0): # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if ((z_defined < np.min(self.z_list)) or (z_defined > np.max(self.z_list))): - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] @@ -1365,11 +1365,11 @@ def isochrone(self, age=1.e8, metallicity=0.0): # check age and metallicity are within bounds if (log_age < self.age_list[0]) or (log_age > self.age_list[-1]): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if not z_defined in self.z_list: - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) @@ -1476,11 +1476,11 @@ def isochrone(self, age=1.e8, metallicity=0.0): # check age and metallicity are within bounds if (log_age < self.age_list[0]) or (log_age > self.age_list[-1]): - logger.error('Requested age {0} is out of bounds.'.format(log_age)) - + raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') + if not z_defined in self.z_list: - logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - + raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) From 8704f540b8f7ee81fd3540a18945e0b02280c00e Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 8 Oct 2025 22:03:54 -0700 Subject: [PATCH 046/191] Add confirmation before generating isochrone when verbose=True; Change metallicity flag to z*100 in file names --- spisea/synthetic.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index c1d71741..866bb6bd 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1105,11 +1105,11 @@ def __init__(self, logAge, AKs, distance, metal_pre = 'm' else: metal_pre = 'p' - metal_flag = int(abs(metallicity)*10) + metal_flag = int(abs(metallicity)*100) save_file_fmt = '{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}_{4}{5:2s}.fits' - self.save_file = save_file_fmt.format(iso_dir, logAge, AKs, str(round(distance)).zfill(5), metal_pre, str(metal_flag).zfill(2)) - self.save_file_legacy = save_file_fmt.format(iso_dir, logAge, AKs, str(round(distance)).zfill(5), metal_pre, str(metal_flag).zfill(2)) + self.save_file = save_file_fmt.format(iso_dir, logAge, AKs, str(round(distance)).zfill(5), metal_pre, str(metal_flag).zfill(3)) + self.save_file_legacy = save_file_fmt.format(iso_dir, logAge, AKs, str(round(distance)).zfill(5), metal_pre, str(metal_flag).zfill(3)) # Expected filters self.filters = filters @@ -1121,10 +1121,12 @@ def __init__(self, logAge, AKs, distance, self.recalc = True if verbose: print(f'Generating new isochrone of log(t)={logAge:.2f}, AKs={AKs:.2f}, d={distance} pc') - # user_input = input(f"Isochrone file {self.save_file} does not exist or needs to be regenerated. Do you want to proceed? (yes/no): ").strip().lower() - # if user_input != 'yes': - # print("Operation canceled by the user.") - # return + + user_input = input(f"Isochrone file {self.save_file} does not exist or needs to be regenerated. Do you want to proceed? (yes/no): ").strip().lower() + if user_input != 'yes': + print("Operation canceled by the user.") + return + super().__init__(logAge, AKs, distance, metallicity=metallicity, evo_model=evo_model, atm_func=atm_func, From f114b78f38f4cf042428300fc821abefa63c745c Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 24 Oct 2025 15:05:31 -0700 Subject: [PATCH 047/191] Updates to evo/atmo --- changes/bd_evo_csv/baraffe2003.csv | 1 - spisea/atmospheres.py | 135 ++++++++++------------ spisea/evolution.py | 74 +------------ spisea/imf/imf.py | 3 +- spisea/synthetic.py | 172 +++++++++++++++++++---------- spisea/tests/test_models.py | 4 +- spisea/tests/test_synthetic.py | 14 +-- 7 files changed, 192 insertions(+), 211 deletions(-) delete mode 100644 changes/bd_evo_csv/baraffe2003.csv diff --git a/changes/bd_evo_csv/baraffe2003.csv b/changes/bd_evo_csv/baraffe2003.csv deleted file mode 100644 index 94cb6209..00000000 --- a/changes/bd_evo_csv/baraffe2003.csv +++ /dev/null @@ -1 +0,0 @@ -age,mass,luminosity,temperature,gravity,radius 0.001,0.0005,-5.37,628,2.645,0.176 0.001,0.001,-4.715,942,2.996,0.166 0.001,0.002,-4.137,1285,3.259,0.174 0.001,0.003,-3.746,1553,3.372,0.187 0.001,0.004,-3.482,1747,3.438,0.2 0.001,0.005,-3.273,1901,3.472,0.215 0.001,0.006,-3.129,2004,3.499,0.228 0.001,0.007,-2.998,2098,3.515,0.242 0.001,0.008,-2.893,2159,3.517,0.258 0.001,0.009,-2.811,2207,3.525,0.271 0.001,0.01,-2.735,2251,3.529,0.285 0.001,0.012,-2.615,2321,3.541,0.307 0.001,0.015,-2.455,2400,3.537,0.345 0.001,0.02,-2.28,2484,3.546,0.395 0.001,0.03,-2.174,2598,3.694,0.408 0.001,0.04,-1.939,2746,3.681,0.478 0.001,0.05,-1.637,2768,3.489,0.666 0.001,0.06,-1.604,2824,3.57,0.665 0.001,0.07,-1.478,2853,3.529,0.753 0.001,0.072,-1.455,2858,3.52,0.771 0.001,0.075,-1.389,2833,3.457,0.847 0.001,0.08,-1.373,2869,3.492,0.84 0.001,0.09,-1.289,2867,3.457,0.927 0.001,0.1,-1.187,2856,3.394,1.051 0.005,0.0005,-6.079,455,2.794,0.148 0.005,0.001,-5.522,644,3.141,0.141 0.005,0.002,-4.92,901,3.425,0.143 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5f71f33c..0f4c90e5 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -523,6 +523,7 @@ def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebi gravity=gravity) sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + #print(dir(obj)) # Do some error checking @@ -607,6 +608,10 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, which is often sufficient synthetic photometry in most cases. + + **PRINT STATEMENTS TO DEBUG + **check get_atmosphere_bounds + **comment out try/except clause and check break """ if rebin == True: atm_name = 'BTSettl_rebin' @@ -626,78 +631,16 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): """ - Return atmosphere from CIFIST2011 grid - (`Allard et al. 2012 `_) - - Grid originally downloaded `here `_ - - Notes - ------ - Grid Range: - - * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - - Teff and gravity ranges depend on metallicity: + Return atmosphere from Meisner2023 grid + (`Meisner et al. 2023 `_) - [M/H] = -2.5 + Grid originally downloaded `here `_ - * Teff: 2600 - 4600 K - * gravity: 4.5 - 5.5 + Grid range: + * Teff = 250 - 1200 K + * gravity: 2.5 - 5.5 cgs (in steps of 0.5) + * [M/H] = -1.0, -0.5, 0, +0, +0.3 - [M/H] = -2.0 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.5 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.0 - - * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 - - [M/H] = -0.5 - - * Teff: 1000 -7000 - * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 - - [M/H] = 0 - - * Teff: 750 - 7000 - * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 - - [M/H] = 0.5 - - * Teff: 1000 - 5000 - * gravity: 3.5 - 5.0 - - - Alpha enhancement: - - * [M/H]= -0.0, +0.5 no anhancement - * [M/H]= -0.5 with [alpha/H]=+0.2 - * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - FILL IN W MEISNER """ if rebin == True: atm_name = 'Meisner2023_rebin' @@ -733,7 +676,6 @@ def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, re Grid originally downloaded `here `_ Grid Range: - * Teff: 200 - 3000 K * gravity: 2.5 - 5.5 cgs * [M/H] = 0 @@ -852,7 +794,33 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): return sp +def get_BTSettl_meisner_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model + and Meisner2023. + Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 + """ + try: + sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_meisner', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find BTSettl-Meisner merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp #---------------------------------------------------------------------# def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, @@ -898,8 +866,8 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose * T < 3800, logg < 2.5: PHOENIX v16 * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - * 1200 < T <= 1000, logg > 2.5: BTSettl_CIFITS2011_2015/Phillips2020 merge - * 1000 < T <= 250, logg > 2.5: Phillips2020 + * 1200 < T <= 1000, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge + * 1000 < T <= 250, logg > 2.5: Meisner2023 Otherwise, if T < 3800 and [M/H] != 0: @@ -933,13 +901,29 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is # currently available here, so if non-solar metallicity, just stick with # the Phoenix grid - if (temperature <= 1200): + if (temperature < 1000): if verbose: print( 'Meisner2023 atmosphere') return get_Meisner2023_atmosphere(metallicity=metallicity, temperature=temperature, gravity=gravity, rebin=rebin) + + if (temperature <= 1200) & (temperature >= 1000): + if (gravity >= 3.5): + if verbose: + print( 'BTSettl/Meisner2023 merged atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + if (gravity < 3.5) & (gravity >=2.5): + if verbose: + print( 'Meisner2023 atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) if (temperature <= 3800) & (metallicity == 0): # High gravity are in BTSettl regime @@ -1052,10 +1036,11 @@ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) return bbspec + def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): """ Return the brown dwarf atmosphere from - `Meisner et al. 2020 `_. + `Meisner et al. 2023 `_. If desired parameters are outside of grid, return a blackbody spectrum instead @@ -1087,7 +1072,7 @@ def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False) gravity=gravity) except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere. + # Use a black-body atmosphere bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) return bbspec diff --git a/spisea/evolution.py b/spisea/evolution.py index 7c529e77..d07bf2b0 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1497,7 +1497,7 @@ def format_isochrones(self): #==============================# class MergedPhillipsBaraffePisaEkstromParsec(StellarEvolution): """ - All merge -- only young and solar metals right now. + All merged! Parameters ---------- @@ -1512,7 +1512,7 @@ def __init__(self, rot=True): z_list = [0.015] # populate list of isochrone ages (log scale) - age_list = np.arange(6.0, 7.4, 0.01).tolist() + age_list = np.arange(6.0, 10.0, 0.01).tolist() # specify location of model files model_dir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/' @@ -1595,6 +1595,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): return iso + class MergedBaraffePisaEkstromParsec(StellarEvolution): """ This is a combination of several different evolution models: @@ -1609,7 +1610,7 @@ class MergedBaraffePisaEkstromParsec(StellarEvolution): For logAge < 7.4: - * 0.01 - 0.08 M_sun: assume mass does not change over time + * Baraffe: 0.08 - 0.4 M_sun * Baraffe/Pisa transition: 0.4 - 0.5 M_sun * Pisa: 0.5 M_sun to the highest mass in Pisa isochrone (typically 5 - 7 Msun) @@ -1626,7 +1627,7 @@ class MergedBaraffePisaEkstromParsec(StellarEvolution): """ def __init__(self, rot=True): # populate list of model masses (in solar masses) - mass_list = [(0.01 + i*0.005) for i in range(181)] # generates masses from 0.01 - 1 M_sun + mass_list = [(0.1 + i*0.005) for i in range(181)] # define metallicity parameters for Geneva models z_list = [0.015] @@ -1699,74 +1700,11 @@ def isochrone(self, age=1.e8, metallicity=0.0): idx_WR = np.where(iso['logT'] != iso['logT_WR']) isWR[idx_WR] = True iso.add_column(isWR) - + iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) - # Assume mass of brown dwarfs does not change over their lifetime - bd_idx = iso['mass'] < 0.08 - iso['mass_current'][bd_idx] = iso['mass'][bd_idx] - - # Handling NaN effective temperatures - nan_teff_idx = np.isnan(iso['logT']) - if np.any(nan_teff_idx): - iso['logT'][nan_teff_idx] = self.estimate_teff(iso['mass'][nan_teff_idx]) - return iso - - def get_temperature(self, masses, ages): - """ - Use interpolation to get temperatures for brown dwarfs based on mass and age. - - Parameters: - ----------- - masses : array-like - Array of brown dwarf masses. - ages : array-like - Array of ages corresponding to the masses. - - Returns: - -------- - temperatures : array - Array of interpolated temperatures. - """ - - # Set the project root two levels up from 'spisea/evolution.py' - project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) - - # Construct the full path to the CSV file in 'changes/bd_evo_csv' - csv_path = os.path.join(project_root, 'SPISEA', 'changes', 'bd_evo_csv', 'baraffe2003.csv') - - # Load the CSV file - data = pd.read_csv(csv_path) - - # Create columns of relevant data - mass_grid = np.unique(data['mass'].values) - age_grid = np.unique(data['age'].values) - temperatures = data['temperature'].values - - # Create a 2D grid for temperatures - temp_grid = np.zeros((len(age_grid), len(mass_grid))) - - # Fill the temperature grid with actual values - for i, age in enumerate(age_grid): - for j, mass in enumerate(mass_grid): - temp_grid[i, j] = np.mean(temperatures[(data['age'] == age) & (data['mass'] == mass)]) - - # Set up the interpolator - interpolator = RegularGridInterpolator((age_grid, mass_grid), temp_grid, bounds_error=False, fill_value=np.nan) - - # Get temperatures for the provided masses and ages - temperatures = interpolator(np.column_stack((ages, masses))) - - # Make sure masses array is not empty - if len(masses) == 0: - print("No masses available for temperature calculation.") - return - - return temperatures - - return iso diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index bddbd657..adf0c3b0 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -239,7 +239,8 @@ def calc_multi(self, newMasses, compMasses, newSystemMasses, newIsMultiple, CSF, if np.any(bd_mask): rand_vals = np.random.rand(bd_mask.sum()) b = 1.0 + 6.1 #gamma from Fontanive+18 - q_values[bd_mask] = (rand_vals * (1.0 - self._multi_props.q_min ** b) + self._multi_props.q_min ** b) ** (1.0 / b) + q_values[bd_mask] = (rand_vals * (1.0 - self._multi_props.q_min ** b) + + self._multi_props.q_min ** b) ** (1.0 / b) # Calculate mass of companion m_comp = q_values * prim_subset diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 9a6c890c..c794c171 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -13,7 +13,7 @@ from pysynphot import observation as obs import pysynphot from astropy import constants, units -from astropy.table import Table, Column, MaskedColumn +from astropy.table import Table, Column, MaskedColumn, vstack import pickle import time, datetime import math @@ -33,7 +33,6 @@ default_red_law = reddening.RedLawNishiyama09() default_atm_func = atm.get_merged_atmosphere default_wd_atm_func = atm.get_wd_atmosphere -default_bd_atm_func = atm.get_bd_atmosphere def Vega(): # Use Vega as our zeropoint... assume V=0.03 mag and all colors = 0.0 @@ -255,8 +254,9 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): if self.ifmr != None: # Identify compact objects as those with Teff = 0 or with phase > 100 or BDs highest_mass_iso = self.iso.points['mass'].max() - idx_rem = np.where((np.isnan(star_systems['Teff'])) & (star_systems['mass'] > highest_mass_iso) | - (star_systems['mass'] < 0.08))[0] + idx_rem = np.where((np.isnan(star_systems['Teff']) & (star_systems['mass'] > highest_mass_iso)))[0] + + # Calculate remnant mass and ID for compact objects; update remnant_id and # remnant_mass arrays accordingly @@ -278,26 +278,22 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): for filt in self.filt_names: star_systems[filt][idx_rem_good] = np.full(len(idx_rem_good), np.nan) - - # Handle brown dwarfs separately and assign temperatures - idx_bd = np.where(star_systems['phase'] == 90)[0] - - # Define the class for BDs - evo_model = evolution.MergedBaraffePisaEkstromParsec() - - # Use the instance to call get_temperature - masses = star_systems[idx_bd]['mass_current'] - ages = np.full(len(masses), self.iso.points.meta['LOGAGE']) - temperatures = evo_model.get_temperature(masses, ages) - - # Convert the numpy array to an astropy Column - temperatures_column = Column(temperatures) - - # Assign temperatures to the Table column - star_systems['Teff'][idx_bd] = temperatures_column - - for filt in self.filt_names: - star_systems[filt][idx_bd] = np.full(len(idx_bd), np.nan) + # override remnant conditions for BDs + idx_bd = np.where(star_systems['mass'] < 0.08)[0] + for i in idx_bd: + T = star_systems['Teff'][i] + g = star_systems['logg'][i] + print(T,g) + + try: + star_bd = atm.get_bd_atmosphere(temperature=T, gravity=g, metallicity=0, verbose=False) + for filt in self.filt_names: + star_systems[filt][i] = star_bd[filt] + except Exception as e: + print(f"[Isochrone] BD atmosphere model failed for T={T}, logg={g}, idx={i}: {e}") + # Keep filter magnitudes as NaN if model fails + for filt in self.filt_names: + star_systems[filt][i] = np.nan return star_systems @@ -424,10 +420,12 @@ def _make_companions_table(self, star_systems, compMass): # Make Remnants with flux = 0 in all bands. ##### if self.ifmr != None: - # Identify compact objects as those with Teff = 0 or with masses above the max iso mass, or BDs + # Identify compact objects as those with Teff = 0 or with masses above the max iso mass + # Exclude BDs from designation highest_mass_iso = self.iso.points['mass'].max() - cdx_rem = np.where((np.isnan(companions['Teff']) & - (companions['mass'] > highest_mass_iso)) | (companions['mass'] < 0.08))[0] + cdx_rem = np.where((np.isnan(companions['Teff'])) & + (companions['mass'] > highest_mass_iso) & + (companions['mass'] >= 0.08))[0] # Calculate remnant mass and ID for compact objects; update remnant_id and # remnant_mass arrays accordingly @@ -449,26 +447,37 @@ def _make_companions_table(self, star_systems, compMass): for filt in self.filt_names: companions[filt][cdx_rem_good] = np.full(len(cdx_rem_good), np.nan) - # Assigning brown dwarf companions the correct phase/properties - bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] - companions['phase'][bd_idx] = 90 - companions['mass_current'][bd_idx] = companions['mass'][bd_idx] - for filt in self.filt_names: - companions[filt][bd_idx] = np.full(len(bd_idx), np.nan) - - # Define the class for BDs - evo_model = evolution.MergedBaraffePisaEkstromParsec() - - # Use the instance to call get_temperature - c_masses = companions[bd_idx]['mass_current'] - c_ages = np.full(len(c_masses), self.iso.points.meta['LOGAGE']) - c_temperatures = evo_model.get_temperature(c_masses, c_ages) - - # Convert the numpy array to an astropy Column - c_temperatures_column = Column(c_temperatures) - - # Assign temperatures to the Table column - companions['Teff'][bd_idx] = c_temperatures_column + # For brown dwarfs, we have Teff and logg from the isochrone. + # We want to use the atmosphere model to get their magnitudes + idx_bd = np.where(companions['mass'] < 0.08)[0] + for i in idx_bd: + T = companions['Teff'][i] + g = companions['logg'][i] + + # Check if we have valid Teff and logg + if np.isnan(T) or np.isnan(g): + print('T/logg assigned nan for BD object!') + continue + + try: + # Use the default atmosphere function to get the spectrum. + star_bd_spec = default_atm_func(temperature=T, gravity=g, metallicity=self.iso.metallicity) + + # Redden the spectrum + red = default_red_law.reddening(self.iso.points.meta['AKS']).resample(star_bd_spec.wave) + star_bd_spec *= red + + # Calculate magnitude in each filter + for filt_name_long in self.filt_names: + obs_str = get_obs_str(filt_name_long) + filt_info = get_filter_info(obs_str) + mag = mag_in_filter(star_bd_spec, filt_info) + companions[filt_name_long][i] = mag + except Exception as e: + print(f"[ResolvedCluster] BD companion atmosphere model failed for T={T}, logg={g}, idx={i}: {e}") + # Keep filter magnitudes as NaN if model fails + for filt in self.filt_names: + companions[filt][i] = np.nan # Notify if we have a lot of bad ones. @@ -740,7 +749,11 @@ class Isochrone(object): wd_atm_func: white dwarf model atmosphere function, optional Set the stellar atmosphere models for the white dwafs. - Default is get_wd_atmosphere + Default is get_wd_atmosphere + + bd_atm_func: brown dwarf model atmosphere function, optional + Set the stellar atmosphere models for the brown dwafs. + Default is get_bd_atmosphere mass_sampling : int, optional Sample the raw isochrone every `mass_sampling` steps. The default @@ -766,7 +779,7 @@ class Isochrone(object): """ def __init__(self, logAge, AKs, distance, metallicity=0.0, evo_model=default_evo_model, atm_func=default_atm_func, - wd_atm_func = default_wd_atm_func, bd_atm_func = default_bd_atm_func, + wd_atm_func = default_wd_atm_func, #bd_atm_func = default_bd_atm_func, red_law=default_red_law, mass_sampling=1, wave_range=[3000, 52000], min_mass=None, max_mass=None, rebin=True): @@ -789,6 +802,52 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, # Takes about 0.1 seconds. evol = evo_model.isochrone(age=10**logAge, metallicity=metallicity) + + # Specify MergedPhillipsBaraffePisaEkstromParsec for brown dwarfs in all cases + try: + bd_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() + + # Clamp to valid BD grid domain + logAge_bd = np.clip(logAge, 6.0, 10.0) + metallicity_bd = 0.0 # Phillips2020 only supports solar [Fe/H] + + # Notify user if clamping occurred + if logAge != logAge_bd: + print(f"[Isochrone] Adjusted BD logAge from {logAge:.2f} → {logAge_bd:.2f} (model valid range 6–10)") + print(f"[Isochrone] Using solar metallicity for BD grid ([Fe/H]={metallicity_bd:.2f})") + + # Compute BD isochrone + evol_bd = bd_model.isochrone(age=10**logAge_bd, metallicity=metallicity_bd) + + # If no data at specified age, find the closest available age in the model grid. + if len(evol_bd) == 0: + print(f"[Isochrone] Warning: No BD data at logAge={logAge_bd:.2f}. Searching for closest age.") + # Find the closest age in the model's grid + available_ages = np.unique(bd_model.model['logAge']) + closest_logAge = available_ages[np.argmin(np.abs(available_ages - logAge_bd))] + + if closest_logAge != logAge_bd: + print(f"[Isochrone] Using closest available BD logAge: {closest_logAge:.2f}") + logAge_bd = closest_logAge + evol_bd = bd_model.isochrone(age=10**logAge_bd, metallicity=metallicity_bd) + + # Define BD threshold and merge + bd_thresh = 0.075 + evol_stars = evol[evol['mass'] >= bd_thresh] + evol_bd = evol_bd[evol_bd['mass'] < bd_thresh] + + if len(evol_bd) > 0: + evol = vstack([evol_bd, evol_stars]) + evol.sort('mass') + print(f"[Isochrone] Merged brown dwarf evolution below {bd_thresh:.3f} Msun " + f"from PhillipsBaraffePisaMerged model (logAge={logAge_bd:.2f}, [Fe/H]=0.0).") + else: + print("[Isochrone] Warning: Brown dwarf model returned no data below threshold.") + + except Exception as e: + import traceback + print(f"[Isochrone] Warning: Failed to merge brown dwarf model ({e})") + traceback.print_exc() # Eliminate cases where log g is less than 0 @@ -808,7 +867,7 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, evol = evol[::mass_sampling] # Give luminosity, temperature, mass, radius units (astropy units). - L_all = 10**evol['logL'] * c.L_sun # luminsoity in W + L_all = 10**evol['logL'] * c.L_sun # luminosity in W T_all = 10**evol['logT'] * units.K R_all = np.sqrt(L_all / (4.0 * math.pi * c.sigma_sb * T_all**4)) mass_all = evol['mass'] * units.Msun # masses in solar masses @@ -837,13 +896,14 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, # This is the time-intensive call... everything else is negligable. # If source is a star, pull from star atmospheres. If it is a WD, # pull from WD atmospheres + if phase == 101: star = wd_atm_func(temperature=T, gravity=gravity, metallicity=metallicity, verbose=False) - elif phase == 90: - print(f"Applying brown dwarf model to object {ii}") - star = bd_atm_func(temperature=T, gravity=gravity, metallicity=0, - verbose=False) + #elif phase == 90: + # print(f"Applying brown dwarf model to object {ii}") + # star = bd_atm_func(temperature=T, gravity=gravity, metallicity=0, + # verbose=False) else: star = atm_func(temperature=T, gravity=gravity, metallicity=metallicity, rebin=rebin) @@ -1013,7 +1073,7 @@ class IsochronePhot(Isochrone): def __init__(self, logAge, AKs, distance, metallicity=0.0, evo_model=default_evo_model, atm_func=default_atm_func, - wd_atm_func = default_wd_atm_func, bd_atm_func = default_bd_atm_func, + wd_atm_func = default_wd_atm_func, #bd_atm_func = default_bd_atm_func, wave_range=[3000, 52000], red_law=default_red_law, mass_sampling=1, iso_dir='./', min_mass=None, max_mass=None, rebin=True, recomp=False, @@ -1060,7 +1120,7 @@ def __init__(self, logAge, AKs, distance, metallicity=metallicity, evo_model=evo_model, atm_func=atm_func, wd_atm_func=wd_atm_func, - bd_atm_func=bd_atm_func, + #bd_atm_func=bd_atm_func, wave_range=wave_range, red_law=red_law, mass_sampling=mass_sampling, diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 4ae078aa..94bbbe12 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -27,7 +27,7 @@ def test_evolution_models(): age_young_arr = [6.7, 7.9] age_all_arr = [6.7, 8.0, 9.7] age_all_MIST_arr = [5.2, 6.7, 9.7, 10.13] - bd_young_test = [6.0, 6.5, 7.4] + bd_test = [6.0, 6.5, 7.4, 8.4, 10.0] # Metallicity ranges to test (if applicable) metal_range = [-2.5, -1.5, 0, 0.25, 0.4] @@ -42,7 +42,7 @@ def test_evolution_models(): # Array of age_ranges for the specific evolution models to test - age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr, age_all_arr, bd_young_test] + age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr, age_all_arr, bd_test] # Array of metallicities for the specific evolution models to test metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_Marley, metal_solar] diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index a94dee67..7ae1b0b1 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -211,8 +211,8 @@ def test_ResolvedCluster(): print('Constructed isochrone: %d seconds' % (time.time() - startTime)) # Now to create the cluster. - imf_mass_limits = np.array([0.07, 0.5, 1, np.inf]) - imf_powers = np.array([-1.3, -2.3, -2.3]) + imf_mass_limits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) + imf_powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) ########## # Start without multiplicity @@ -390,7 +390,7 @@ def test_UnresolvedCluster(): startTime = time.time() multi = multiplicity.MultiplicityUnresolved() - imf_in = imf.Kroupa_2001(multiplicity=multi) + imf_in = imf.Salpeter_Kirkpatrick_2024(multiplicity=multi) evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere iso = syn.Isochrone(log_age, AKs, distance, metallicity=metallicity, @@ -441,8 +441,7 @@ def test_ifmr_multiplicity(): ########## # Start without multiplicity and IFMR ########## - my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, - multiplicity=None) + my_imf1 = imf.Salpeter_Kirkpatrick_2024(multiplicity=None) print('Constructed IMF: %d seconds' % (time.time() - startTime)) cluster1 = syn.ResolvedCluster(iso, my_imf1, cluster_mass, ifmr=ifmr_obj) @@ -454,8 +453,7 @@ def test_ifmr_multiplicity(): # Test with multiplicity and IFMR ########## multi = multiplicity.MultiplicityUnresolved() - my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, - multiplicity=multi) + my_imf2 = imf.Salpeter_Kirkpatrick_2024(multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) cluster2 = syn.ResolvedCluster(iso, my_imf2, cluster_mass, ifmr=ifmr_obj) @@ -1044,7 +1042,7 @@ def test_Raithel18_phase_designation(): #create an MZAMS array to cover all potential phase designations, and the expected phase designations test_MZAMS = np.array([0.06, 0.3, 0.6, 1.0, 3.0, 6.0, 10.0, 14.0, 25.0, 100.0]) - expected_phases = np.array([99, -1, 101, 101, 101, 101, 102, 102, 103, 103]) + expected_phases = np.array([90., -1., 101., 101., 101., 101., 102., 102., 103., 103.]) #generate the output from Raithel IFMR output_array = Raithel.generate_death_mass(test_MZAMS) From 180860705e7b3ee8f8c7c47f538df33d6204dca8 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 17 Nov 2025 16:14:18 -0800 Subject: [PATCH 048/191] Updating phase designation in interpolated regions --- changes/mass_to_age.py | 2518 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2518 insertions(+) create mode 100644 changes/mass_to_age.py diff --git a/changes/mass_to_age.py b/changes/mass_to_age.py new file mode 100644 index 00000000..0ac86c9c --- /dev/null +++ b/changes/mass_to_age.py @@ -0,0 +1,2518 @@ +import math +import logging +from numpy import genfromtxt +import numpy as np +import os +import glob +import pandas as pd +import pdb +import warnings +from astropy.table import Table, vstack, Column +from scipy import interpolate +import pylab as py +from spisea.utils import objects +from scipy.interpolate import RegularGridInterpolator +from spisea import exceptions +import re +import matplotlib +import matplotlib.pyplot as plt +from spisea import atmospheres + +logger = logging.getLogger('evolution') + +# Fetch root directory of evolution models. +try: + models_dir = os.environ['SPISEA_MODELS'] + models_dir += '/evolution/' +except KeyError: + warnings.warn("SPISEA_MODELS is undefined; functionality " + "will be SEVERELY crippled.") + models_dir = '' + +# Code to deconstruct current Phillips2020 evolution files and reconstruct iso files based on age + +# Define input and output directories +input_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/z00_mass' +output_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/z00_age' +os.makedirs(output_dir, exist_ok=True) + +# Combine data from all files +combined_data = [] + +for filename in os.listdir(input_dir): + if filename.endswith('.txt'): + file_path = os.path.join(input_dir, filename) + table = Table.read(file_path, format='ascii') + combined_data.append(table) + +# Concatenate all data into a single table +all_data = combined_data[0] + +# Add the remaining tables to the main table +for table in combined_data[1:]: + all_data = vstack([all_data, table]) # Use vstack to stack tables + +# Group data by age +ages = np.unique(all_data['Age']) + +# Iterate over each unique age and filter data +for age in ages: + age_group = all_data[all_data['Age'] == age] + output_path = os.path.join(output_dir, f"ATMO_{age}.txt") + + # Save each age group as a .txt file + age_group.write(output_path, format='ascii', overwrite=True) + +print(f"New files created in: {output_dir}") + +# Code to eradicate duplicate lines seen in above code +"""new_input_dir = output_dir + +for filename2 in os.listdir(new_input_dir): + if filename2.endswith('.txt'): + file_path2 = os.path.join(new_input_dir, filename2) + + # Remove duplicates before loading as a table + with open(file_path2, 'r') as f: + lines = f.readlines() + + header = lines[0] + data_lines = [line for line in lines[1:] if not line.startswith(header.split()[0])] + + with open(file_path2, 'w') as f: + f.write(header) + f.writelines(data_lines) + + # Now process with Astropy Table + table2 = Table.read(file_path2, format='ascii') + unique_table2 = unique(table2) + unique_table2.write(file_path2, format='ascii', overwrite=True) + +print('files overwritten successfully!') + + +# Reformatting files to match Parsec +def reformat(): + for file in os.listdir(output_dir): + if file.endswith('.txt'): + new_fp = os.path.join(output_dir, file) + + # Add metallicity column of all 0.0 + r_table = Table.read(new_fp, format='ascii') + r_table.add_column(0.0, name='Z', index=0) + + # Duplicate mass to include current mass column + r_table.add_column(r_table['Mass'], name='Mass_current') + + # Update age column to make it log scale + r_table['Age'] = np.log10(r_table['Age'] * 1e9) #originally in Gyr + + # Update Teff column to make it log scale + r_table['Teff'] = np.log10(r_table['Teff']) + + # Reorder columns to match Parsec + new_order = ['Z', 'Age', 'Mass', 'Mass_current', 'Luminosity', 'Teff', 'Gravity', 'Gaia_Gbp', 'Gaia_G', 'Gaia_Grp'] + t_new = r_table[new_order] + t_new.write(new_fp, format='ascii', overwrite=True) + + print(f"{file} reformatted successfully!") + + return + +# Transform reformatted .txt files to iso fits files +def ATMO_to_iso(): + i_dir = output_dir + o_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/iso' #output directory SPISEA will pull from + + for file in os.listdir(i_dir): + if file.endswith('.txt'): + fp = os.path.join(i_dir, file) + + try: + age_str = file.split('_')[1].split('.txt')[0] + age = float(age_str) + log_age = np.log10(age * 1e9) + except (IndexError, ValueError): + print(f"{file} cannot be processed.") + continue + + try: + # load in .txt file as ascii table + table = Table.read(fp, format='ascii') + except Exception as e: + print(f"{fp} could not be read: {e}") + continue + + #create output file name + o_file = os.path.join(o_dir, f'iso_{log_age}.fits') + + try: + # write to output directory as fits file + table.write(o_file, format='fits', overwrite=True) + print(f"{o_file} created successfully!") + except Exception as e: + print(f"{o_file} could not be generated: {e}") + continue + + return + + +""" +### CODE TO UNPACK MARLEY FILES ### +def Marley_deconstruct(): + """ + Code to deconstruct big Marley files into individual age files + """ + in_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/Marley_age' + out_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/iso' + + # Set path to each file + for filename in os.listdir(in_dir): + file_path = os.path.join(in_dir, filename) + + # Extract metallicity from filename + match = re.search(r'nc([+-]\d+\.\d+)_co', filename) + metallicity = match.group(1) if match else "unknown" + + # Deconstruct initial format of single column table + with open(file_path, 'r') as f: + lines = f.readlines() + header = ['Age', 'Mass', 'log_L', 'Teff', 'logg', 'Radius'] + data = [line.strip().split() for line in lines[1:]] + data = [row for row in data if len(row) > 1] + table = Table(rows=data, names=header) + + # Find unique ages to create iso age_based files + ages = np.unique(table['Age']) + + for age in ages: + age_group = table[table['Age'] == age] + out_path = os.path.join(out_dir, f"Marley_{metallicity}_{age}.txt") + + # Save each age group as a .txt file + age_group.write(out_path, format='ascii', overwrite=True) + + print(f"New files created in: {out_dir}") + + return + +# Reformatting files to match Parsec +def reformat_Marley(): + age_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/age_txt' + for file in os.listdir(age_dir): + if file.endswith('.txt'): + new_fp = os.path.join(age_dir, file) + + # Add metallicity column + table = Table.read(new_fp, format='ascii') + + # Extract metallicity from filename + match = re.search(r'Marley_([+-]?\d+\.\d+)_\d+\.\d+.txt', file) + metallicity = float(match.group(1)) if match else np.nan + + # Replace or add metallicity column + if 'Z' in table.colnames: + table.replace_column('Z', [metallicity] * len(table)) + else: + table.add_column([metallicity] * len(table), name='Z', index=0) + + # Duplicate mass to include current mass column + #table.add_column(table['Mass'], name='Mass_current') + + # Update age column to make it log scale + #table['Age'] = np.log10(table['Age'] * 1e9) #originally in Gyr + + # Update Teff column to make it log scale + #table['Teff'] = np.log10(table['Teff']) + + # Reorder columns to match Parsec + new_order = ['Z', 'Age', 'Mass', 'Mass_current', 'log_L', 'Teff', 'logg', 'Radius'] + t_new = table[new_order] + t_new.write(new_fp, format='ascii', overwrite=True) + + print(f"{file} reformatted successfully!") + + return + +def Marley_to_iso(): + in_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/age_txt' + out_dir_1 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/iso/zm05' + out_dir_2 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/iso/zp00' + out_dir_3 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/iso/zp05' + + for file in os.listdir(in_dir): + if file.endswith('.txt'): + fp = os.path.join(in_dir, file) + + try: + parts = file.split('_') + metal_str = parts[1] # Second part is metallicity + age_str = parts[2].split('.txt')[0] + + age = float(age_str) + metallicity = float(metal_str) + log_age = np.log10(age * 1e9) + except (IndexError, ValueError): + print(f"{file} cannot be processed.") + continue + + try: + # load in .txt file as ascii table + table = Table.read(fp, format='ascii') + except Exception as e: + print(f"{fp} could not be read: {e}") + continue + + # Determine output directory based on metallicity + if metallicity == -0.5: + out_dir = out_dir_1 + elif metallicity == 0.0: + out_dir = out_dir_2 + elif metallicity == 0.5: + out_dir = out_dir_3 + else: + print(f"Skipping {file}: Unexpected metallicity value ({metallicity}).") + continue + + # Create output filename + out_file = os.path.join(out_dir, f'iso_{log_age}.fits') + + try: + # Write to output directory as FITS file + table.write(out_file, format='fits', overwrite=True) + print(f"{out_file} created successfully!") + except Exception as e: + print(f"Error writing {out_file}: {e}") + continue + + return + +### CREATING MERGED EVO MODEL WITH PHILLIPS ### +def get_phillips_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Phillips isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'Phillips2020/iso/' + metSuffix = 'z00/' + if metallicity != 'solar': + print( 'Non-solar Phillips 2020 metallicities not supported yet') + return + rootDir += metSuffix + + # List available isochrone files + available_ages = [] + for filename in os.listdir(rootDir): + if filename.startswith('iso_') and filename.endswith('.fits'): + age_str = filename.split('_')[1].split('.fits')[0] # Extract the age part from filename + available_ages.append(float(age_str)) + + # Find the closest available age from Phillips + closest_age = min(available_ages, key=lambda x: abs(x - logAge)) + print(closest_age) + + # Load the corresponding isochrone + isoFile = rootDir + f'iso_{closest_age}.fits' + print(f"Loading isochrone for logAge = {closest_age}") + + # Check to see if isochrone exists + if not os.path.exists(isoFile): + print( f'Phillips isochrone for logAge = {closest_age} does\'t exist') + print( 'Quitting') + return + + data = Table.read(isoFile, format='fits') + print("Available Columns:", data.keys()) + cols = data.keys() + mass = data[cols[2]] #Note: this is initial mass, in M_sun + logT = data[cols[5]] # K + logL = data[cols[4]] # L_sun + logg = data[cols[6]] + mass_current = data[cols[3]] # Matches initial mass -- BD masses assumed to not change w current models + phase = np.ones(len(mass), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = mass_current + obj.phase = phase + + return obj + +def get_Baraffe15_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Baraffe+15 isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + ALSO interpolates isochrone to a finer mass grid. + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'Baraffe15/iso/' + if metallicity != 'solar': + print( 'Non-solar Baraffe+15 metallicities not supported yet') + return + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.fits' % logAge + if not os.path.exists(isoFile): + print( 'Baraffe+15 isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='fits') + mass = data['Mass'] #Note: this is initial mass, in M_sun + logT = np.log10(data['Teff']) # K + logL = data['logL'] # L_sun + logg = data['logG'] + + # Interpolate isochrone to finer mass grid. Spacing + # is one model every 0.02 M_sun down to 0.2 M_sun, then + # one model every 0.005 M_sun down to 0.07 M_sun + new_masses0 = np.arange(min(mass), 0.1, 0.005) + new_masses2 = np.arange(0.1, max(mass), 0.02) + + #new_masses = np.concatenate((new_masses0, new_masses1, new_masses2)) + new_masses = np.concatenate((new_masses0, new_masses2)) + + # Build interpolators in linear space + f_logT = interpolate.interp1d(mass, 10**logT, kind='linear') + f_logL = interpolate.interp1d(mass, 10**logL, kind='linear') + f_logg = interpolate.interp1d(mass, 10**logg, kind='linear') + + # Do interpolation, convert back to logspace + logT_interp = np.log10(f_logT(new_masses)) + logL_interp = np.log10(f_logL(new_masses)) + logg_interp = np.log10(f_logg(new_masses)) + + # Hack, add new mass_current and phase columns. + mass_current = np.array(new_masses) + phase = np.ones(len(new_masses), dtype=int) + + # Test the interpolation, if desired + test = False + if test: + py.figure(1, figsize=(10,10)) + py.clf() + py.plot(mass, logT, 'k.', ms = 10, label='Orig') + py.plot(new_masses, logT_interp, 'r.', ms=7, label='Interp') + py.xlabel('Mass') + py.ylabel('logT') + py.legend() + + py.figure(2, figsize=(10,10)) + py.clf() + py.plot(mass, logL, 'k.', ms = 10, label='Orig') + py.plot(new_masses, logL_interp, 'r.', ms=7, label='Interp') + py.xlabel('Mass') + py.ylabel('logL') + py.legend() + + py.figure(3, figsize=(10,10)) + py.clf() + py.plot(mass, logg, 'k.', ms = 10, label='Orig') + py.plot(new_masses, logg_interp, 'r.', ms=7, label='Interp') + py.xlabel('Mass') + py.ylabel('logg') + py.legend() + + pdb.set_trace() + + # Make isochrone + obj = objects.DataHolder() + obj.mass = new_masses + obj.logT = logT_interp + obj.logg = logg_interp + obj.logL = logL_interp + obj.mass_current = mass_current + obj.phase = phase + + return obj + +def merge_isochrone_baraffe_phillips(logAge, metallicity='solar'): + """ + Function to merge Baraffe+15 and Phillips 2020 models. Will take + 100% Phillips2020 between 0.01 - 0.07 M_sun, transition between + 0.07 - 0.075 M_sun, and take 100% Baraffe from 0.075 M_sun and up. + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print( 'Non-solar metallicity not supported yet') + return + + # Get individual Baraffe and Phillips isochrones at desired age. Note + # that this will also give the Baraffe models a finer mass sampling + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPhillips = get_phillips_isochrone(logAge, metallicity=metallicity) + + # Identify M >= 0.075 M_sun in Baraffe and M <= 0.07 M_sun in Phillips + good_b = np.where(isoBaraffe.mass >= 0.075) + good_p = np.where(isoPhillips.mass <= 0.07) + + # Sample between 0.4 M_sun and 0.5 M_sun in steps of 0.02 M_sun. + # Will do linear combo of Baraffe and Phillips over this range + mid_mass = np.arange(0.07, 0.075, 0.002) + mid_logT = [] + mid_logL = [] + mid_logG = [] + mid_Mcurr = [] + mid_phase = [] + for mass in mid_mass: + # Find the appropriate masses in Baraffe + Phillips to build from. + # Baraffe has identical sampling over this range, and Phillips sampling + # is very close. As a result, we will just take the closest mass model + # to each mid_mass + idx_b = np.where( abs(isoBaraffe.mass - mass) == min(abs(isoBaraffe.mass - mass)) ) + idx_p = np.where( abs(isoPhillips.mass - mass) == min(abs(isoPhillips.mass - mass)) ) + + # Quality control check: we won't let the difference between model mass and + # chosen mass to be >= 0.01 M_sun + if ((isoPhillips.mass[idx_p] - mass) >= 0.002) | ((isoBaraffe.mass[idx_b] - mass) >= 0.002): + print( 'WARNING: Baraffe or Phillips model interpolation between 0.01 - 0.08 M_sun may \ + be inaccurate. Check this!') + pdb.set_trace() + + # Now, do the linear combo of models at this mass, weighted by distance from + # 0.07 or 0.075 (whichever is appropriate) + diff = 0.075 - 0.07 + weight_p = (0.075 - mass) / diff + weight_b = 1.0 - weight_p + print( 'Baraffe {0} and Phillips {1} at mass {2}'.format(weight_p, weight_b, mass)) + + # Now, do the merge IN LINEAR SPACE! + Teff = (10**isoPhillips.logT[idx_p] * weight_p) + \ + (10**isoBaraffe.logT[idx_b] * weight_b) + L = (10**isoPhillips.logL[idx_p] * weight_p) + \ + (10**isoBaraffe.logL[idx_b] * weight_b) + g = (10**isoPhillips.logg[idx_p] * weight_p) + \ + (10**isoBaraffe.logg[idx_b] * weight_b) + mcurr = (isoPhillips.mass_current[idx_p] * weight_p) + \ + (isoBaraffe.mass_current[idx_b] * weight_b) + phase = np.round((isoPhillips.mass_current[idx_p] * weight_p) + \ + (isoBaraffe.mass_current[idx_b] * weight_b)) + + mid_logT = np.concatenate((mid_logT, np.log10(Teff))) + mid_logL = np.concatenate((mid_logL, np.log10(L))) + mid_logG = np.concatenate((mid_logG, np.log10(g))) + mid_Mcurr = np.concatenate((mid_Mcurr, mcurr)) + mid_phase = np.concatenate((mid_phase, phase)) + + # Now, final isochrone will be combination of Baraffe at M>=0.075, + # Phillips at M<=0.075, and the combination inbetween + mass = np.concatenate((isoPhillips.mass[good_p], mid_mass, isoBaraffe.mass[good_b])) + logT = np.concatenate((isoPhillips.logT[good_p], mid_logT, isoBaraffe.logT[good_b])) + logL = np.concatenate((isoPhillips.logL[good_p], mid_logL, isoBaraffe.logL[good_b])) + logG = np.concatenate((isoPhillips.logg[good_p], mid_logG, isoBaraffe.logg[good_b])) + mcurr = np.concatenate((isoPhillips.mass_current[good_p], mid_Mcurr, isoBaraffe.mass_current[good_b])) + phase = np.concatenate((isoPhillips.phase[good_p], mid_phase, isoBaraffe.phase[good_b])) + + # Also add a source flag + source = np.concatenate( (['Phillips']*len(good_p[0]), + ['Baraffe+Phillips']*len(mid_mass), + ['Baraffe']*len(good_b[0])) ) + + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + +def merge_isochrone_baraffe_pisa(logAge, metallicity='solar'): + """ + Function to merge Baraffe+15 and Pisa 2011 models. Will take + 100% Baraffe+15 between 0.07 - 0.4 M_sun, transition between + 0.4 - 0.5 M_sun, and take 100% Pisa from 0.5 M_sun and up. + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print( 'Non-solar metallicity not supported yet') + return + + # Get individual Baraffe and Pisa isochrones at desired age. Note + # that this will also give the Baraffe models a finer mass sampling + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + + # Identify M <= 0.4 M_sun in Baraffe and M >= 0.5 M_sun in Pisa + good_b = np.where(isoBaraffe.mass <= 0.4) + good_p = np.where(isoPisa.mass >= 0.5) + + # Sample between 0.4 M_sun and 0.5 M_sun in steps of 0.02 M_sun. + # Will do linear combo of Baraffe and Pisa over this range + mid_mass = np.arange(0.4, 0.5+0.01, 0.02) + mid_logT = [] + mid_logL = [] + mid_logG = [] + mid_Mcurr = [] + mid_phase = [] + for mass in mid_mass: + # Find the appropriate masses in Baraffe + Pisa to build from. + # Baraffe has identical sampling over this range, and Pisa sampling + # is very close. As a result, we will just take the closest mass model + # to each mid_mass + idx_b = np.where( abs(isoBaraffe.mass - mass) == min(abs(isoBaraffe.mass - mass)) ) + idx_p = np.where( abs(isoPisa.mass - mass) == min(abs(isoPisa.mass - mass)) ) + + # Quality control check: we won't let the difference between model mass and + # chosen mass to be >= 0.01 M_sun + if ((isoPisa.mass[idx_p] - mass) >= 0.02) | ((isoBaraffe.mass[idx_b] - mass) >= 0.02): + print( 'WARNING: Baraffe or Pisa model interpolation between 0.4 - 0.5 M_sun may \ + be inaccurate. Check this!') + pdb.set_trace() + + # Now, do the linear combo of models at this mass, weighted by distance from + # 0.4 or 0.5 (whichever is appropriate) + diff = 0.5 - 0.4 + weight_b = (0.5 - mass) / diff + weight_p = 1.0 - weight_b + print( 'Baraffe {0} and Pisa {1} at mass {2}'.format(weight_b, weight_p, mass)) + + # Now, do the merge IN LINEAR SPACE! + Teff = (10**isoBaraffe.logT[idx_b] * weight_b) + \ + (10**isoPisa.logT[idx_p] * weight_p) + L = (10**isoBaraffe.logL[idx_b] * weight_b) + \ + (10**isoPisa.logL[idx_p] * weight_p) + g = (10**isoBaraffe.logg[idx_b] * weight_b) + \ + (10**isoPisa.logg[idx_p] * weight_p) + mcurr = (isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p) + phase = np.round((isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p)) + + mid_logT = np.concatenate((mid_logT, np.log10(Teff))) + mid_logL = np.concatenate((mid_logL, np.log10(L))) + mid_logG = np.concatenate((mid_logG, np.log10(g))) + mid_Mcurr = np.concatenate((mid_Mcurr, mcurr)) + mid_phase = np.concatenate((mid_phase, phase)) + + # Now, final isochrone will be combination of Baraffe at M<=0.4, + # Pisa at M>=0.5, and the combination inbetween + mass = np.concatenate((isoBaraffe.mass[good_b], mid_mass, isoPisa.mass[good_p])) + logT = np.concatenate((isoBaraffe.logT[good_b], mid_logT, isoPisa.logT[good_p])) + logL = np.concatenate((isoBaraffe.logL[good_b], mid_logL, isoPisa.logL[good_p])) + logG = np.concatenate((isoBaraffe.logg[good_b], mid_logG, isoPisa.logg[good_p])) + mcurr = np.concatenate((isoBaraffe.mass_current[good_b], mid_Mcurr, isoPisa.mass_current[good_p])) + phase = np.concatenate((isoBaraffe.phase[good_b], mid_phase, isoPisa.phase[good_p])) + + # Also add a source flag + source = np.concatenate( (['Baraffe']*len(good_b[0]), ['Baraffe+Pisa']*len(mid_mass), + ['Pisa']*len(good_p[0])) ) + + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + + +import matplotlib.pyplot as plt +import numpy as np + +"""def plot_merged_isochrone(iso): + "" + Function to plot the merged isochrone model to visualize the transition + between Phillips 2020 and Baraffe+15 models. + + Parameters: + ----------- + iso : DataHolder object + Merged isochrone returned by merge_isochrone_baraffe_phillips(). + "" + + # Define colors for different sources + color_map = {'Phillips': 'blue', 'Baraffe': 'red', 'Baraffe+Phillips': 'pink'} + + # Create figure and subplots + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + + # Mass vs. logT (Effective Temperature) + for source in np.unique(iso.source): + mask = iso.source == source + axes[0].scatter(iso.mass[mask], iso.logT[mask], label=source, color=color_map[source], alpha=0.7) + axes[0].plot(iso.mass[mask], iso.logT[mask], label='linear representation') + axes[0].set_xlabel("Mass ($M_{\odot}$)") + axes[0].set_ylabel("$\log T_{\mathrm{eff}}$ (K)") + axes[0].set_title("Mass vs. Effective Temperature") + axes[0].axvline(0.07, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].axvline(0.08, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].set_xlim(0.0, 0.1) + axes[0].legend() + + # Mass vs. logL (Luminosity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[1].scatter(iso.mass[mask], iso.logL[mask], label=source, color=color_map[source], alpha=0.7) + axes[1].plot(iso.mass[mask], iso.logL[mask], label='linear representation') + axes[1].set_xlabel("Mass ($M_{\odot}$)") + axes[1].set_ylabel("$\log L$ ($L_{\odot}$)") + axes[1].set_title("Mass vs. Luminosity") + axes[1].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[1].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[1].set_xlim(0.0, 0.1) + axes[1].legend() + + # Mass vs. logg (Surface Gravity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[2].scatter(iso.mass[mask], iso.logg[mask], label=source, color=color_map[source], alpha=0.7) + axes[2].plot(iso.mass[mask], iso.logg[mask], label='linear representation') + axes[2].set_xlabel("Mass ($M_{\odot}$)") + axes[2].set_ylabel("$\log g$ (cm/s²)") + axes[2].set_title("Mass vs. Surface Gravity") + axes[2].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[2].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[2].set_xlim(0.0, 0.1) + axes[2].legend() + + # Adjust layout and show plot + plt.tight_layout() + plt.show() +""" + +import numpy as np +import matplotlib.pyplot as plt + +def plot_merged_isochrone(iso): + """ + Function to plot the merged isochrone model to visualize the transition + between Phillips 2020 and Baraffe+15 models. + + Parameters: + ----------- + iso : DataHolder object + Merged isochrone returned by merge_isochrone_baraffe_phillips(). + """ + + # Define colors for different sources + color_map = {'Phillips': 'blue', 'Baraffe': 'red', 'Baraffe+Phillips': 'pink'} + + # Create figure and subplots + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + + # Mass vs. logT (Effective Temperature) + for source in np.unique(iso.source): + mask = iso.source == source + axes[0].scatter(iso.mass[mask], iso.logT[mask], label=source, color=color_map[source], alpha=0.7) + axes[0].plot(iso.mass[mask], iso.logT[mask], color=color_map[source]) # Fixed plot function + axes[0].set_xlabel("Mass ($M_{\odot}$)") + axes[0].set_ylabel("$\log T_{\mathrm{eff}}$ (K)") + axes[0].set_title("Mass vs. Effective Temperature") + axes[0].axvline(0.07, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].axvline(0.08, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].set_xlim(0.0, 0.1) + axes[0].legend() + + # Mass vs. logL (Luminosity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[1].scatter(iso.mass[mask], iso.logL[mask], label=source, color=color_map[source], alpha=0.7) + axes[1].plot(iso.mass[mask], iso.logL[mask], color=color_map[source]) # Fixed plot function + axes[1].set_xlabel("Mass ($M_{\odot}$)") + axes[1].set_ylabel("$\log L$ ($L_{\odot}$)") + axes[1].set_title("Mass vs. Luminosity") + axes[1].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[1].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[1].set_xlim(0.0, 0.1) + axes[1].legend() + + # Mass vs. logg (Surface Gravity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[2].scatter(iso.mass[mask], iso.logg[mask], label=source, color=color_map[source], alpha=0.7) + axes[2].plot(iso.mass[mask], iso.logg[mask], color=color_map[source]) # Fixed plot function + axes[2].set_xlabel("Mass ($M_{\odot}$)") + axes[2].set_ylabel("$\log g$ (cm/s²)") + axes[2].set_title("Mass vs. Surface Gravity") + axes[2].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[2].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[2].set_xlim(0.0, 0.1) + axes[2].legend() + + # Adjust layout and show plot + plt.tight_layout() + plt.show() + +def plot_merged_bpp_isochrone(iso): + """ + Function to plot the merged isochrone model to visualize the transition + between Phillips 2020, Baraffe+15, and Pisa models. + + Parameters: + ----------- + iso : DataHolder object + Merged isochrone returned by merge_isochrone_baraffe_phillips(). + """ + + # Define colors for different sources + color_map = {'Phillips': 'blue', + 'Baraffe': 'red', + 'Pisa':'green', + 'Baraffe+Phillips': 'pink', + 'Baraffe+Pisa': 'cyan' + } + + # Create figure and subplots + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + + # Mass vs. logT (Effective Temperature) + for source in np.unique(iso.source): + mask = iso.source == source + axes[0].scatter(iso.mass[mask], iso.logT[mask], label=source, color=color_map[source], alpha=0.7) + axes[0].plot(iso.mass[mask], iso.logT[mask], color=color_map[source]) # Fixed plot function + axes[0].set_xlabel("Mass ($M_{\odot}$)") + axes[0].set_ylabel("$\log T_{\mathrm{eff}}$ (K)") + axes[0].set_title("Mass vs. Effective Temperature") + axes[0].axvline(0.07, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].axvline(0.08, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].legend() + + # Mass vs. logL (Luminosity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[1].scatter(iso.mass[mask], iso.logL[mask], label=source, color=color_map[source], alpha=0.7) + axes[1].plot(iso.mass[mask], iso.logL[mask], color=color_map[source]) # Fixed plot function + axes[1].set_xlabel("Mass ($M_{\odot}$)") + axes[1].set_ylabel("$\log L$ ($L_{\odot}$)") + axes[1].set_title("Mass vs. Luminosity") + axes[1].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[1].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[1].legend() + + # Mass vs. logg (Surface Gravity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[2].scatter(iso.mass[mask], iso.logg[mask], label=source, color=color_map[source], alpha=0.7) + axes[2].plot(iso.mass[mask], iso.logg[mask], color=color_map[source]) # Fixed plot function + axes[2].set_xlabel("Mass ($M_{\odot}$)") + axes[2].set_ylabel("$\log g$ (cm/s²)") + axes[2].set_title("Mass vs. Surface Gravity") + axes[2].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[2].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[2].legend() + + # Adjust layout and show plot + plt.tight_layout() + plt.show() + +"""def merge_isochrone_pisa_baraffe_phillips(logAge, metallicity='solar', rotation=True, iso_in=None): #????? + "" + Function to merge Pisa 2011, Baraffe, and Phillips 2020 models. Solar metallicity is + Z = 0.015 for Pisa 2011 and Z = 0.015 for Phillips 2020. + + If iso_in = None, will take Pisa models to smallest available mass, + then switch to Baraffe/Phillips. If iso_in is defined, then will take this + isochrone to highest mass and switch to Ekstrom + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + "" + # Get individual Ekstrom and Pisa isochrones at desired age + isoBaraffePhillips = merge_Ekstrom_isochrone(logAge, metallicity=metallicity, + rotation=rotation) + + if iso_in == None: + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + # Create array specifying source for Pisa isochrone + isoPisa.source = np.array(['Pisa']*len(isoPisa.mass)) + else: + # iso_in isn't really a Pisa isochrone (it could be Baraffe-Pisa merge), + # but name it isoPisa for simplicity anyway. + isoPisa = iso_in + + # Take Pisa isochrone as high up in mass as it goes, then switch to Ekstrom. + # Will trim Ekstrom isochrone here + max_Pisa = max(isoPisa.mass) + good = np.where(isoEkstrom.mass > max_Pisa) + isoEkstrom.mass = isoEkstrom.mass[good] + isoEkstrom.logT = isoEkstrom.logT[good] + isoEkstrom.logg = isoEkstrom.logg[good] + isoEkstrom.logL = isoEkstrom.logL[good] + isoEkstrom.mass_current = isoEkstrom.mass_current[good] + isoEkstrom.phase = isoEkstrom.phase[good] + isoEkstrom.logT_WR = isoEkstrom.logT_WR[good] + + # Make array containing Ekstrom source ID + isoEkstrom.source = np.array(['Ekstrom']*len(isoEkstrom.mass)) + + # Combine the arrays + M = np.append(isoPisa.mass, isoEkstrom.mass) + logT = np.append(isoPisa.logT, isoEkstrom.logT) + logg = np.append(isoPisa.logg, isoEkstrom.logg) + logL = np.append(isoPisa.logL, isoEkstrom.logL) + mcurr = np.append(isoPisa.mass_current, isoEkstrom.mass_current) + phase = np.append(isoPisa.phase, isoEkstrom.phase) + logT_WR = np.append(isoPisa.logT, isoEkstrom.logT_WR) + source = np.append(isoPisa.source, isoEkstrom.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + return iso +""" + +def merge_isochrone_baraffe_pisa(logAge, metallicity='solar'): + """ + Function to merge Baraffe+15 and Pisa 2011 models. Will take + 100% Baraffe+15 between 0.07 - 0.4 M_sun, transition between + 0.4 - 0.5 M_sun, and take 100% Pisa from 0.5 M_sun and up. + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print( 'Non-solar metallicity not supported yet') + return + + # Get individual Baraffe and Pisa isochrones at desired age. Note + # that this will also give the Baraffe models a finer mass sampling + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + + # Identify M <= 0.4 M_sun in Baraffe and M >= 0.5 M_sun in Pisa + good_b = np.where(isoBaraffe.mass <= 0.4) + good_p = np.where(isoPisa.mass >= 0.5) + + # Sample between 0.4 M_sun and 0.5 M_sun in steps of 0.02 M_sun. + # Will do linear combo of Baraffe and Pisa over this range + mid_mass = np.arange(0.4, 0.5+0.01, 0.02) + mid_logT = [] + mid_logL = [] + mid_logG = [] + mid_Mcurr = [] + mid_phase = [] + for mass in mid_mass: + # Find the appropriate masses in Baraffe + Pisa to build from. + # Baraffe has identical sampling over this range, and Pisa sampling + # is very close. As a result, we will just take the closest mass model + # to each mid_mass + idx_b = np.where( abs(isoBaraffe.mass - mass) == min(abs(isoBaraffe.mass - mass)) ) + idx_p = np.where( abs(isoPisa.mass - mass) == min(abs(isoPisa.mass - mass)) ) + + # Quality control check: we won't let the difference between model mass and + # chosen mass to be >= 0.01 M_sun + if ((isoPisa.mass[idx_p] - mass) >= 0.02) | ((isoBaraffe.mass[idx_b] - mass) >= 0.02): + print( 'WARNING: Baraffe or Pisa model interpolation between 0.4 - 0.5 M_sun may \ + be inaccurate. Check this!') + pdb.set_trace() + + # Now, do the linear combo of models at this mass, weighted by distance from + # 0.4 or 0.5 (whichever is appropriate) + diff = 0.5 - 0.4 + weight_b = (0.5 - mass) / diff + weight_p = 1.0 - weight_b + print( 'Baraffe {0} and Pisa {1} at mass {2}'.format(weight_b, weight_p, mass)) + + # Now, do the merge IN LINEAR SPACE! + Teff = (10**isoBaraffe.logT[idx_b] * weight_b) + \ + (10**isoPisa.logT[idx_p] * weight_p) + L = (10**isoBaraffe.logL[idx_b] * weight_b) + \ + (10**isoPisa.logL[idx_p] * weight_p) + g = (10**isoBaraffe.logg[idx_b] * weight_b) + \ + (10**isoPisa.logg[idx_p] * weight_p) + mcurr = (isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p) + phase = np.round((isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p)) + + mid_logT = np.concatenate((mid_logT, np.log10(Teff))) + mid_logL = np.concatenate((mid_logL, np.log10(L))) + mid_logG = np.concatenate((mid_logG, np.log10(g))) + mid_Mcurr = np.concatenate((mid_Mcurr, mcurr)) + mid_phase = np.concatenate((mid_phase, phase)) + + # Now, final isochrone will be combination of Baraffe at M<=0.4, + # Pisa at M>=0.5, and the combination inbetween + mass = np.concatenate((isoBaraffe.mass[good_b], mid_mass, isoPisa.mass[good_p])) + logT = np.concatenate((isoBaraffe.logT[good_b], mid_logT, isoPisa.logT[good_p])) + logL = np.concatenate((isoBaraffe.logL[good_b], mid_logL, isoPisa.logL[good_p])) + logG = np.concatenate((isoBaraffe.logg[good_b], mid_logG, isoPisa.logg[good_p])) + mcurr = np.concatenate((isoBaraffe.mass_current[good_b], mid_Mcurr, isoPisa.mass_current[good_p])) + phase = np.concatenate((isoBaraffe.phase[good_b], mid_phase, isoPisa.phase[good_p])) + + # Also add a source flag + source = np.concatenate( (['Baraffe']*len(good_b[0]), ['Baraffe+Pisa']*len(mid_mass), + ['Pisa']*len(good_p[0])) ) + + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + +def merge_isochrone_baraffe_pisa_phillips(logAge, metallicity='solar', rotation=True, iso_in=None): + """ + Merges Phillips, Baraffe, and Pisa isochrones to create a complete evolutionary track. + + Inputs: + logAge - Logarithmic Age + metallicity - Metallicity (default is 'solar') + rotation - Boolean indicating whether to include rotation in the models (default is True) + + Outputs: + iso - An object containing the merged isochrone data. + """ + # Merge Phillips with Baraffe + print(f"Merging Phillips with Baraffe for logAge = {logAge}") + isoBaraffePhillips = merge_isochrone_baraffe_phillips(logAge, metallicity=metallicity) + + # Merge Baraffe+Phillips with Pisa (depending on the logAge) + if logAge <= 7.4: + print(f"Merging Baraffe+Phillips with Pisa for logAge = {logAge}") + iso = merge_isochrone_baraffe_pisa(logAge, metallicity=metallicity, iso_in=isoBaraffePhillips) + else: + # If logAge > 7.4, you may want to merge with different models (Parsec/Ekstrom) + print(f"Merging Baraffe+Phillips with higher mass models for logAge = {logAge}") + iso = merge_isochrone_baraffe_pisa(logAge, metallicity=metallicity, iso_in=isoBaraffePhillips) + + return iso + + +def merge_all_isochrones_phillips_baraffe_pisa_ekstrom_parsec(metallicity='solar', rotation=True, plot=False): + """ + Make evolutionary isochrones containing a continuous distribution of + masses from the PMS to the MS. The models used are the following: + + PMS + Phillips from 0.01 - 0.07 M_sun + Baraffe+15 from 0.07 - 0.4 M_sun + Pisa 2011 from 0.5 - top of grid (~7 M_sun) + + MS (M > Pisa 2011) + Ekstrom+12 for logAge < 7.4 + Parsec V1.2s for logAge > 7.4 + + BD stars + Phillips for 6.0 <= logAge <= 10.0 + + metallicity = 'solar' --> Ekstrom+12 z014, Pisa2011 z015, Parsec z015, Phillips z00 + + if plot = True, will make plots of merged isochrones in 'plots' directory, + which must already exist + + Code is expected to be run in merged model working directory. + """ + # Root data directory for Ekstrom+12 isochrones + rootDirE = models_dir + 'Ekstrom2012/iso/' + metalPart = 'z014/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rotPart = 'rot/' + if not rotation: + rotPart = 'norot/' + rootDirE += metalPart+rotPart + + # Root data directory for the Baraffe isochrones + rootDirBaraffe = models_dir + 'Baraffe15/iso/' + + # Root data directory for Pisa isochrones + rootDirPisa = models_dir + 'Pisa2011/iso/' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirPisa += metSuffix + + # Root data directory for Parsec isochrones + rootDirParsec = models_dir + 'ParsecV1.2s/iso/' + metalSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirParsec += metalSuffix + + # Root data directory for Phillips isochrones + rootDirPhillips = models_dir + 'Phillips2020/iso/' + mSuffix = 'z00/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirPhillips +=mSuffix + + # Search both directories for iso_*.dat files + isoFilesE = glob.glob(rootDirE + 'iso_*.dat') + isoFilesB = glob.glob(rootDirBaraffe + 'iso_*.fits') + isoFilesPi = glob.glob(rootDirPisa + 'iso_*.dat') + isoFilesPa = glob.glob(rootDirParsec + 'iso_*') + isoFilesPh = glob.glob(rootDirPhillips + 'iso_*.fits') + + # Output of merged isochrones + if rotation == True: + outDir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/{0}_rot_test/'.format(metSuffix[:-1]) + # Raise error if wanting Phillips data? + else: + outDir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/{0}_norot/'.format(metSuffix[:-1]) + if not os.path.exists(outDir): + os.mkdir(outDir) + + # Isolate the iso*.dat file names + for ii in range(len(isoFilesE)): + isoFilesE[ii] = isoFilesE[ii].split('/')[-1] + + for ii in range(len(isoFilesB)): + isoFilesB[ii] = isoFilesB[ii].split('/')[-1] + + for ii in range(len(isoFilesPi)): + isoFilesPi[ii] = isoFilesPi[ii].split('/')[-1] + + for ii in range(len(isoFilesPa)): + isoFilesPa[ii] = isoFilesPa[ii].split('/')[-1] + + for ii in range(len(isoFilesPh)): + isoFilesPh[ii] = isoFilesPh[ii].split('/')[-1] + + # Loop through the Pisa isochrones, adding the MS and Baraffe models + # as appropriate + for ii in range(len(isoFilesPi)): + isoFilePi = isoFilesPi[ii] + + logAgeStr = isoFilePi.replace('iso_', '').replace('.dat', '') + logAge = float(logAgeStr) + + #-----PRE-MAIN SEQUENCE----# + # Merge with the Baraffe+15 models from 0.07 - 0.4 M_sun. Includes + # transition region between 0.4 - 0.5 M_sun in which we shift + # from 100% Baraffe to 100% Pisa + + print( 'Merging isochrones Pisa + Baraffe + Phillips from ', isoFilePi) + isoPMS = merge_isochrone_phillips_baraffe_pisa(logAge, metallicity=metallicity) + + #--------MAIN SEQUENCE-------# + # Case where logAge <= 7.4, we merge with Ekstrom. Otherwise, merge + # which parsec + if logAge <= 7.4: + if isoFilePi not in isoFilesE: + print( 'Skipping isochrones from ', isoFilePi) + print( 'PROBLEM WITH PISA OR EKSTROM') + pdb.set_trace() + + print( 'Merging isochrones Phillips+Pisa+Ekstrom from ', isoFilePi) + iso = merge_isochrone_phillips_pisa_Ekstrom(logAge, metallicity=metallicity, + rotation=rotation, iso_in=isoPMS) + else: + if isoFilePi not in isoFilesPa: + print( 'Skipping isochrones from ', isoFilePi) + print( 'PROBLEM WITH PISA OR PARSEC') + pdb.set_trace() + + print( 'Merging isochrones Phillips+Pisa+Parsec from ', isoFilePi) + iso = merge_isochrone_phillips_pisa_parsec(logAge, metallicity=metallicity, + iso_in=isoPMS) + + # Make test plot, if desired. These are put in plots directory + if plot: + # Make different models different colors + phillips_ind = np.where(iso.source == 'Phillips') + merge_pb = np.where(iso.source == 'Phillips+Baraffe') + baraffe_ind = np.where(iso.source == 'Baraffe') + merge_ind = np.where(iso.source == 'Baraffe+Pisa') + pisa_ind = np.where(iso.source == 'Pisa') + MS_ind = np.where( (iso.source == 'Ekstrom') | (iso.source == 'Parsec')) + #Extract age + logAge = isoFilePi.split('_')[1][:-4] + + # Temporarily turn off interactive plot. Turn back on at end + py.ioff() + + py.figure(1) + py.clf() + py.plot(iso.logT[phillips_ind], iso.logL[phillips_ind], 'c-', label = 'Phillips', + linewidth=2) + py.plot(iso.logT[merge_pb], iso.logL[merge_pb], 'y-', label = 'Phillips+Baraffe', + linewidth=2) + py.plot(iso.logT[baraffe_ind], iso.logL[baraffe_ind], 'k-', label = 'Baraffe', + linewidth=2) + py.plot(iso.logT[merge_ind], iso.logL[merge_ind], 'r-', label = 'Baraffe+Pisa', + linewidth=2) + py.plot(iso.logT[pisa_ind], iso.logL[pisa_ind], 'g-', label = 'Pisa', + linewidth=2) + py.plot(iso.logT[MS_ind], iso.logL[MS_ind], 'b-', + label = 'Ekstrom/Parsec', linewidth=2) + py.xlabel('log Teff') + py.ylabel('log L') + py.title('Log Age = {0:s}'.format(logAge)) + py.legend(loc=3) + py.axis([4.9, 3.2, -3.0, 8]) + py.savefig(outDir+'plots/iso_'+logAge+'.png') + + py.ion() + + + _out = open(outDir + isoFilePi, 'w') + + hdr_fmt = '%12s %10s %10s %10s %10s %12s %5s %-10s\n' + _out.write(hdr_fmt % + ('# M_init', 'log T', 'log L', 'log g', 'logT_WR', 'M_curr', 'phase', 'Source')) + _out.write(hdr_fmt % + ('# (Msun)', '(Kelvin)', '(Lsun)', '(cgs)', '(Kelvin)', '(Msun)', '()', '()')) + + for kk in range(len(iso.mass)): + _out.write('%12.6f %10.4f %10.4f %10.4f %10.4f %12.6f %5d %-10s\n' % + (iso.mass[kk], iso.logT[kk], iso.logL[kk], + iso.logg[kk], iso.logT_WR[kk], + iso.mass_current[kk], iso.phase[kk], iso.source[kk])) + + _out.close() + return + +def get_pisa_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Pisa isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'Pisa2011/iso/' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar Pisa 2011 metallicities not supported yet') + return + rootDir += metSuffix + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.dat' % logAge + if not os.path.exists(isoFile): + print( 'Pisa isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='ascii') + cols = data.keys() + mass = data[cols[2]] #Note: this is initial mass, in M_sun + logT = data[cols[1]] # K + logL = data[cols[0]] # L_sun + logg = data[cols[3]] + + # Hack: Make mass_current and phase + mass_current = np.array(mass) + phase = np.ones(len(mass), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = mass_current + obj.phase = phase + + return obj + +def get_phillips_pisa_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Phillips/Pisa isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'merged/phillips_pisa' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar Phillips 2020 & Pisa 2011 metallicities not supported yet') + return + rootDir += metSuffix + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.fits' % logAge + if not os.path.exists(isoFile): + print( 'Phillips/Pisa isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='fits') + cols = data.keys() + mass = data[cols[0]] #Note: this is initial mass, in M_sun + logT = data[cols[2]] # K + logL = data[cols[1]] # L_sun + logg = data[cols[3]] + interpolated = data['interpolated'] + + # warning if data point is coming from interpolated region + if warn_interpolated and np.any(interpolated): + print( f"Warning: {np.sum(interpolated)} of {len(interpolated)} data points are interpolated.") + + # Hack: Make mass_current and phase + mass_current = np.array(mass) + phase = np.ones(len(mass), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = mass_current + obj.phase = phase + obj.interpolated = interpolated + + return obj + +def merge_isochrone_baraffe_pisa(logAge, metallicity='solar'): + """ + Function to merge Baraffe+15 and Pisa 2011 models. Will take + 100% Baraffe+15 between 0.07 - 0.4 M_sun, transition between + 0.4 - 0.5 M_sun, and take 100% Pisa from 0.5 M_sun and up. + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print( 'Non-solar metallicity not supported yet') + return + + # Get individual Baraffe and Pisa isochrones at desired age. Note + # that this will also give the Baraffe models a finer mass sampling + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + + # Identify M <= 0.4 M_sun in Baraffe and M >= 0.5 M_sun in Pisa + good_b = np.where(isoBaraffe.mass <= 0.4) + good_p = np.where(isoPisa.mass >= 0.5) + + # Sample between 0.4 M_sun and 0.5 M_sun in steps of 0.02 M_sun. + # Will do linear combo of Baraffe and Pisa over this range + mid_mass = np.arange(0.4, 0.5+0.01, 0.02) + mid_logT = [] + mid_logL = [] + mid_logG = [] + mid_Mcurr = [] + mid_phase = [] + for mass in mid_mass: + # Find the appropriate masses in Baraffe + Pisa to build from. + # Baraffe has identical sampling over this range, and Pisa sampling + # is very close. As a result, we will just take the closest mass model + # to each mid_mass + idx_b = np.where( abs(isoBaraffe.mass - mass) == min(abs(isoBaraffe.mass - mass)) ) + idx_p = np.where( abs(isoPisa.mass - mass) == min(abs(isoPisa.mass - mass)) ) + + # Quality control check: we won't let the difference between model mass and + # chosen mass to be >= 0.01 M_sun + if ((isoPisa.mass[idx_p] - mass) >= 0.02) | ((isoBaraffe.mass[idx_b] - mass) >= 0.02): + print( 'WARNING: Baraffe or Pisa model interpolation between 0.4 - 0.5 M_sun may \ + be inaccurate. Check this!') + pdb.set_trace() + + # Now, do the linear combo of models at this mass, weighted by distance from + # 0.4 or 0.5 (whichever is appropriate) + diff = 0.5 - 0.4 + weight_b = (0.5 - mass) / diff + weight_p = 1.0 - weight_b + print( 'Baraffe {0} and Pisa {1} at mass {2}'.format(weight_b, weight_p, mass)) + + # Now, do the merge IN LINEAR SPACE! + Teff = (10**isoBaraffe.logT[idx_b] * weight_b) + \ + (10**isoPisa.logT[idx_p] * weight_p) + L = (10**isoBaraffe.logL[idx_b] * weight_b) + \ + (10**isoPisa.logL[idx_p] * weight_p) + g = (10**isoBaraffe.logg[idx_b] * weight_b) + \ + (10**isoPisa.logg[idx_p] * weight_p) + mcurr = (isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p) + phase = np.round((isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p)) + + mid_logT = np.concatenate((mid_logT, np.log10(Teff))) + mid_logL = np.concatenate((mid_logL, np.log10(L))) + mid_logG = np.concatenate((mid_logG, np.log10(g))) + mid_Mcurr = np.concatenate((mid_Mcurr, mcurr)) + mid_phase = np.concatenate((mid_phase, phase)) + + # Now, final isochrone will be combination of Baraffe at M<=0.4, + # Pisa at M>=0.5, and the combination inbetween + mass = np.concatenate((isoBaraffe.mass[good_b], mid_mass, isoPisa.mass[good_p])) + logT = np.concatenate((isoBaraffe.logT[good_b], mid_logT, isoPisa.logT[good_p])) + logL = np.concatenate((isoBaraffe.logL[good_b], mid_logL, isoPisa.logL[good_p])) + logG = np.concatenate((isoBaraffe.logg[good_b], mid_logG, isoPisa.logg[good_p])) + mcurr = np.concatenate((isoBaraffe.mass_current[good_b], mid_Mcurr, isoPisa.mass_current[good_p])) + phase = np.concatenate((isoBaraffe.phase[good_b], mid_phase, isoPisa.phase[good_p])) + + # Also add a source flag + source = np.concatenate( (['Baraffe']*len(good_b[0]), ['Baraffe+Pisa']*len(mid_mass), + ['Pisa']*len(good_p[0])) ) + + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + +def merge_isochrone_phillips_baraffe_pisa(logAge, metallicity='solar'): + """ + Function to merge Phillips 2020, Baraffe+15, and Pisa 2011 models. + + - Uses Phillips for M <= 0.07 M_sun + - Transitions from Phillips to Baraffe between 0.07 - 0.075 M_sun + - Uses Baraffe for 0.075 - 0.4 M_sun + - Transitions from Baraffe to Pisa between 0.4 - 0.5 M_sun + - Uses Pisa for M >= 0.5 M_sun + + Can only handle ages where models exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print('Non-solar metallicity not supported yet') + return + + # Load individual isochrones + isoPhillips = get_phillips_isochrone(logAge, metallicity=metallicity) + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + + # Identify mass ranges + good_p = np.where(isoPhillips.mass <= 0.07) + good_b = np.where((isoBaraffe.mass >= 0.075) & (isoBaraffe.mass <= 0.4)) + good_pisa = np.where(isoPisa.mass >= 0.5) + + # Transition Phillips → Baraffe (0.07 - 0.075 M_sun) + mid_mass_phillips_baraffe = np.arange(0.07, 0.075, 0.002) + mid_logT_phillips_baraffe, mid_logL_phillips_baraffe, mid_logG_phillips_baraffe = [], [], [] + mid_Mcurr_phillips_baraffe, mid_phase_phillips_baraffe = [], [] + + for mass in mid_mass_phillips_baraffe: + idx_p = np.argmin(np.abs(isoPhillips.mass - mass)) + idx_b = np.argmin(np.abs(isoBaraffe.mass - mass)) + + weight_p = (0.075 - mass) / 0.005 + weight_b = 1.0 - weight_p + + Teff = (10**isoPhillips.logT[idx_p] * weight_p) + (10**isoBaraffe.logT[idx_b] * weight_b) + L = (10**isoPhillips.logL[idx_p] * weight_p) + (10**isoBaraffe.logL[idx_b] * weight_b) + g = (10**isoPhillips.logg[idx_p] * weight_p) + (10**isoBaraffe.logg[idx_b] * weight_b) + mcurr = (isoPhillips.mass_current[idx_p] * weight_p) + (isoBaraffe.mass_current[idx_b] * weight_b) + phase = np.round((isoPhillips.phase[idx_p] * weight_p) + (isoBaraffe.phase[idx_b] * weight_b)) + + mid_logT_phillips_baraffe.append(np.log10(Teff)) + mid_logL_phillips_baraffe.append(np.log10(L)) + mid_logG_phillips_baraffe.append(np.log10(g)) + mid_Mcurr_phillips_baraffe.append(mcurr) + mid_phase_phillips_baraffe.append(phase) + + # Transition Baraffe → Pisa (0.4 - 0.5 M_sun) + mid_mass_baraffe_pisa = np.arange(0.4, 0.5 + 0.01, 0.02) + mid_logT_baraffe_pisa, mid_logL_baraffe_pisa, mid_logG_baraffe_pisa = [], [], [] + mid_Mcurr_baraffe_pisa, mid_phase_baraffe_pisa = [], [] + + for mass in mid_mass_baraffe_pisa: + idx_b = np.argmin(np.abs(isoBaraffe.mass - mass)) + idx_p = np.argmin(np.abs(isoPisa.mass - mass)) + + weight_b = (0.5 - mass) / 0.1 + weight_p = 1.0 - weight_b + + Teff = (10**isoBaraffe.logT[idx_b] * weight_b) + (10**isoPisa.logT[idx_p] * weight_p) + L = (10**isoBaraffe.logL[idx_b] * weight_b) + (10**isoPisa.logL[idx_p] * weight_p) + g = (10**isoBaraffe.logg[idx_b] * weight_b) + (10**isoPisa.logg[idx_p] * weight_p) + mcurr = (isoBaraffe.mass_current[idx_b] * weight_b) + (isoPisa.mass_current[idx_p] * weight_p) + phase = np.round((isoBaraffe.phase[idx_b] * weight_b) + (isoPisa.phase[idx_p] * weight_p)) + + mid_logT_baraffe_pisa.append(np.log10(Teff)) + mid_logL_baraffe_pisa.append(np.log10(L)) + mid_logG_baraffe_pisa.append(np.log10(g)) + mid_Mcurr_baraffe_pisa.append(mcurr) + mid_phase_baraffe_pisa.append(phase) + + # Merge all mass regimes + mass = np.concatenate((isoPhillips.mass[good_p], mid_mass_phillips_baraffe, isoBaraffe.mass[good_b], + mid_mass_baraffe_pisa, isoPisa.mass[good_pisa])) + + logT = np.concatenate((isoPhillips.logT[good_p], mid_logT_phillips_baraffe, isoBaraffe.logT[good_b], + mid_logT_baraffe_pisa, isoPisa.logT[good_pisa])) + + logL = np.concatenate((isoPhillips.logL[good_p], mid_logL_phillips_baraffe, isoBaraffe.logL[good_b], + mid_logL_baraffe_pisa, isoPisa.logL[good_pisa])) + + logG = np.concatenate((isoPhillips.logg[good_p], mid_logG_phillips_baraffe, isoBaraffe.logg[good_b], + mid_logG_baraffe_pisa, isoPisa.logg[good_pisa])) + + mcurr = np.concatenate((isoPhillips.mass_current[good_p], + mid_Mcurr_phillips_baraffe, + isoBaraffe.mass_current[good_b], + mid_Mcurr_baraffe_pisa, + isoPisa.mass_current[good_pisa])) + + phase = np.concatenate((isoPhillips.phase[good_p], mid_phase_phillips_baraffe, isoBaraffe.phase[good_b], + mid_phase_baraffe_pisa, isoPisa.phase[good_pisa])) + + # Source flag + source = np.concatenate((['Phillips']*len(good_p[0]), ['Phillips+Baraffe']*len(mid_mass_phillips_baraffe), + ['Baraffe']*len(good_b[0]), ['Baraffe+Pisa']*len(mid_mass_baraffe_pisa), + ['Pisa']*len(good_pisa[0]))) + + # Create final merged isochrone object + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + +def merge_isochrone_pisa_Ekstrom(logAge, metallicity='solar', rotation=True, iso_in=None): + """ + Function to merge Pisa 2011 and Ekstrom 2012 models. Solar metallicity is + Z = 0.015 for Pisa 2011 and Z = 0.014 for Ekstrom+12. + + If iso_in = None, will take Pisa models to highest available mass, + then switch to Ekstrom. If iso_in is defined, then will take this + isochrone to highest mass and switch to Ekstrom + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + # Get individual Ekstrom and Pisa isochrones at desired age + isoEkstrom = get_Ekstrom_isochrone(logAge, metallicity=metallicity, + rotation=rotation) + + if iso_in == None: + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + # Create array specifying source for Pisa isochrone + isoPisa.source = np.array(['Pisa']*len(isoPisa.mass)) + else: + # iso_in isn't really a Pisa isochrone (it could be Baraffe-Pisa merge), + # but name it isoPisa for simplicity anyway. + isoPisa = iso_in + + # Take Pisa isochrone as high up in mass as it goes, then switch to Ekstrom. + # Will trim Ekstrom isochrone here + max_Pisa = max(isoPisa.mass) + good = np.where(isoEkstrom.mass > max_Pisa) + isoEkstrom.mass = isoEkstrom.mass[good] + isoEkstrom.logT = isoEkstrom.logT[good] + isoEkstrom.logg = isoEkstrom.logg[good] + isoEkstrom.logL = isoEkstrom.logL[good] + isoEkstrom.mass_current = isoEkstrom.mass_current[good] + isoEkstrom.phase = isoEkstrom.phase[good] + isoEkstrom.logT_WR = isoEkstrom.logT_WR[good] + + # Make array containing Ekstrom source ID + isoEkstrom.source = np.array(['Ekstrom']*len(isoEkstrom.mass)) + + # Combine the arrays + M = np.append(isoPisa.mass, isoEkstrom.mass) + logT = np.append(isoPisa.logT, isoEkstrom.logT) + logg = np.append(isoPisa.logg, isoEkstrom.logg) + logL = np.append(isoPisa.logL, isoEkstrom.logL) + mcurr = np.append(isoPisa.mass_current, isoEkstrom.mass_current) + phase = np.append(isoPisa.phase, isoEkstrom.phase) + logT_WR = np.append(isoPisa.logT, isoEkstrom.logT_WR) + source = np.append(isoPisa.source, isoEkstrom.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + return iso + +def merge_isochrone_phillips_pisa_Ekstrom(logAge, metallicity='solar', rotation=True, iso_in=None): + """ + Function to merge Phillips 2020, Pisa 2011, and Ekstrom 2012 models. Solar metallicity is + Z = 0.015 for Pisa 2011 and Phillips 2020 and Z = 0.014 for Ekstrom+12. + + If iso_in = None, will take Pisa/Phillips models to highest available mass, + then switch to Ekstrom. If iso_in is defined, then will take this + isochrone to highest mass and switch to Ekstrom + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + # Get individual Ekstrom and Pisa isochrones at desired age + isoEkstrom = get_Ekstrom_isochrone(logAge, metallicity=metallicity, + rotation=rotation) + + if iso_in == None: + isoPhPisa = get_phillips_pisa_isochrone(logAge, metallicity=metallicity) + # Create array specifying source for Pisa isochrone + isoPhPisa.source = np.array(['Phillips/Pisa']*len(isoPhPisa.mass)) + else: + # iso_in isn't really a Pisa isochrone (it could be Baraffe-Pisa merge), + # but name it isoPisa for simplicity anyway. + isoPhPisa = iso_in + + # Take Pisa isochrone as high up in mass as it goes, then switch to Ekstrom. + # Will trim Ekstrom isochrone here + max_PhPisa = max(isoPhPisa.mass) + good = np.where(isoEkstrom.mass > max_PhPisa) + isoEkstrom.mass = isoEkstrom.mass[good] + isoEkstrom.logT = isoEkstrom.logT[good] + isoEkstrom.logg = isoEkstrom.logg[good] + isoEkstrom.logL = isoEkstrom.logL[good] + isoEkstrom.mass_current = isoEkstrom.mass_current[good] + isoEkstrom.phase = isoEkstrom.phase[good] + isoEkstrom.logT_WR = isoEkstrom.logT_WR[good] + + # Make array containing Ekstrom source ID + isoEkstrom.source = np.array(['Ekstrom']*len(isoEkstrom.mass)) + + # Combine the arrays + M = np.append(isoPhPisa.mass, isoEkstrom.mass) + logT = np.append(isoPhPisa.logT, isoEkstrom.logT) + logg = np.append(isoPhPisa.logg, isoEkstrom.logg) + logL = np.append(isoPhPisa.logL, isoEkstrom.logL) + mcurr = np.append(isoPhPisa.mass_current, isoEkstrom.mass_current) + phase = np.append(isoPhPisa.phase, isoEkstrom.phase) + logT_WR = np.append(isoPhPisa.logT, isoEkstrom.logT_WR) + source = np.append(isoPhPisa.source, isoEkstrom.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + # add interpolation flag + if hasattr(isoPhPisa, 'interpolated'): + interpolated = np.append(isoPhPisa.interpolated, np.full(len(isoEkstrom.mass), False)) + iso.interpolated = interpolated + + # fix phase values in interpolated region (assign same as min-mass Parsec object) + min_ekstrom_mass_idx = np.argmin(isoEkstrom.mass) + min_ekstrom_phase = isoEkstrom.phase[min_ekstrom_mass_idx] + interp_mask = iso.interpolated == True + iso.phase[interp_mask] = min_ekstrom_phase + + return iso + +def get_Ekstrom_isochrone(logAge, metallicity='solar', rotation=True): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Ekstrom isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). Also interpolate model to finer mass grid + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'Ekstrom2012/iso/' + metSuffix = 'z014/' + if metallicity != 'solar': + print( 'Non-solar Ekstrom+12 metallicities not supported yet') + return + rotSuffix = 'rot/' + if not rotation: + rotSuffix = 'norot/' + + rootDir += metSuffix + rotSuffix + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.dat' % logAge + if not os.path.exists(isoFile): + print( 'Ekstrom isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='ascii') + cols = data.keys() + mass = data[cols[2]] #Note: this is initial mass, in M_sun + logT = data[cols[7]] # K + logL = data[cols[6]] # L_sun + logT_WR = data[cols[8]] # K; if this doesn't equal logT, we have a WR star + mass_curr = data[cols[3]] + phase = np.ones(len(data), dtype=int) + + # Need to calculate log g from mass and R + R_sun = 7.*10**10 #cm + M_sun = 2.*10**33 #g + G_const = 6.67*10**-8 #cgs + + radius = data[cols[19]] #R_sun + logg = np.log10( (G_const * np.array(mass).astype(float) * M_sun) / + (np.array(radius).astype(float) * R_sun)**2 ) + + # Interpolate isochrone to finer mass grid on main-ish sequence + # (1-60 M_sun, or the highest mass in the model); don't want to + # completely redo all sampling, just this region + if max(mass) > 60: + new_masses = np.arange(1, 60+0.1, 0.5) + else: + new_masses = np.arange(1, max(mass), 0.5) + mass_grid = np.append(new_masses, mass) + mass_grid.sort() # Make sure grid is in proper order + + # Build interpolators in linear space + f_logT = interpolate.interp1d(mass, 10**logT, kind='linear') + f_logL = interpolate.interp1d(mass, 10**logL, kind='linear') + f_logT_WR = interpolate.interp1d(mass, 10**logT_WR, kind='linear') + f_logg = interpolate.interp1d(mass, 10**logg, kind='linear') + f_Mcurr = interpolate.interp1d(mass, mass_curr, kind='linear') + f_phase = interpolate.interp1d(mass, phase, kind='linear') + + # Do interpolation, convert back to logspace + logT_interp = np.log10(f_logT(mass_grid)) + logL_interp = np.log10(f_logL(mass_grid)) + logT_WR_interp = np.log10(f_logT_WR(mass_grid)) + logg_interp = np.log10(f_logg(mass_grid)) + Mcurr_interp = f_Mcurr(mass_grid) + phase_interp = f_phase(mass_grid) + + # Make isochrone + obj = objects.DataHolder() + obj.mass = mass_grid + obj.logT = logT_interp + obj.logg = logg_interp + obj.logL = logL_interp + obj.logT_WR = logT_WR_interp + obj.mass_current = Mcurr_interp + obj.phase = phase_interp + + return obj + +def merge_isochrone_pisa_parsec(logAge, metallicity='solar', iso_in=None): + """ + Function to merge Pisa 2011 and ParsecV1.2s models. Solar metallicity is + Z = 0.015. + + If iso_in = None, will take Pisa models to highest available mass, + then switch to Parsec. If iso_in is defined, then will take this + isochrone to highest mass and switch to Parsec + + Can only handle ages at which both sets of models already exist: + logAge = 6.6 - 8.0, delta logAge = 0.01 + """ + isoParsec = get_parsec_isochrone(logAge, metallicity=metallicity) + # Make Parsec source array + isoParsec.source = np.array(['Parsec']*len(isoParsec.mass)) + + # Define isoPisa based on iso_in input + if iso_in != None: + isoPisa = iso_in + else: + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + isoPisa.source = np.array(['Pisa']*len(isoPisa.mass)) + + # Use Pisa model as high up as it goes, then switch to Parsec + max_Pisa = max(isoPisa.mass) + good = np.where(isoParsec.mass > max_Pisa) + isoParsec.mass = isoParsec.mass[good] + isoParsec.logT = isoParsec.logT[good] + isoParsec.logg = isoParsec.logg[good] + isoParsec.logL = isoParsec.logL[good] + isoParsec.mass_current = isoParsec.mass_current[good] + isoParsec.phase = isoParsec.phase[good] + isoParsec.source = isoParsec.source[good] + + # Combine the arrays + M = np.append(isoPisa.mass, isoParsec.mass) + logT = np.append(isoPisa.logT, isoParsec.logT) + logg = np.append(isoPisa.logg, isoParsec.logg) + logL = np.append(isoPisa.logL, isoParsec.logL) + mcurr = np.append(isoPisa.mass_current, isoParsec.mass_current) + phase = np.append(isoPisa.phase, isoParsec.phase) + logT_WR = np.append(isoPisa.logT, isoParsec.logT) + source = np.append(isoPisa.source, isoParsec.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + return iso + +def merge_isochrone_phillips_pisa_parsec(logAge, metallicity='solar', iso_in=None): + """ + Function to merge Phillips 2020, Pisa 2011, and ParsecV1.2s models. + Solar metallicity is Z = 0.015. + + If iso_in = None, will take Phillips/Pisa models to highest available mass, + then switch to Parsec. If iso_in is defined, then will take this + isochrone to highest mass and switch to Parsec + + Can only handle ages at which both sets of models already exist: + logAge = 6.6 - 8.0, delta logAge = 0.01 + """ + isoParsec = get_parsec_isochrone(logAge, metallicity=metallicity) + # Make Parsec source array + isoParsec.source = np.array(['Parsec']*len(isoParsec.mass)) + + # Define isoPhPisa based on iso_in input + if iso_in != None: + isoPhPisa = iso_in + else: + isoPhPisa = get_phillips_pisa_isochrone(logAge, metallicity=metallicity) + isoPhPisa.source = np.array(['Phillips/Pisa']*len(isoPhPisa.mass)) + + # Use Phillips/Pisa model as high up as it goes, then switch to Parsec + max_PhPisa = max(isoPhPisa.mass) + good = np.where(isoParsec.mass > max_PhPisa) + isoParsec.mass = isoParsec.mass[good] + isoParsec.logT = isoParsec.logT[good] + isoParsec.logg = isoParsec.logg[good] + isoParsec.logL = isoParsec.logL[good] + isoParsec.mass_current = isoParsec.mass_current[good] + isoParsec.phase = isoParsec.phase[good] + isoParsec.source = isoParsec.source[good] + + + # Combine the arrays + M = np.append(isoPhPisa.mass, isoParsec.mass) + logT = np.append(isoPhPisa.logT, isoParsec.logT) + logg = np.append(isoPhPisa.logg, isoParsec.logg) + logL = np.append(isoPhPisa.logL, isoParsec.logL) + mcurr = np.append(isoPhPisa.mass_current, isoParsec.mass_current) + phase = np.append(isoPhPisa.phase, isoParsec.phase) + logT_WR = np.append(isoPhPisa.logT, isoParsec.logT) + source = np.append(isoPhPisa.source, isoParsec.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + # add interpolation flag + if hasattr(isoPhPisa, 'interpolated'): + interpolated = np.append(isoPhPisa.interpolated, np.full(len(isoEkstrom.mass), False)) + iso.interpolated = interpolated + + # fix phase values in interpolated region (assign same as min-mass Parsec object) + min_parsec_mass_idx = np.argmin(isoParsec.mass) + min_parsec_phase = isoParsec.phase[min_parsec_mass_idx] + interp_mask = iso.interpolated == True + iso.phase[interp_mask] = min_parsec_phase + + return iso + +def make_parsec_iso(logAge, metallicity='solar'): + """ + Make parsec isochrone in Popstar iso object + """ + isoParsec = get_parsec_isochrone(logAge, metallicity=metallicity) + isoParsec.source = np.array(['Parsec']*len(isoParsec.mass)) + + iso = objects.DataHolder() + iso.mass = isoParsec.mass + iso.logL = isoParsec.logL + iso.logg = isoParsec.logg + iso.logT = isoParsec.logT + iso.mass_current = isoParsec.mass_current + iso.phase = isoParsec.phase + iso.logT_WR = isoParsec.logT + iso.source = isoParsec.source + + return iso +def get_phillips_parsec_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Phillips/Parsec isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'merged/phillips_parsec/' + print(rootDir) + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar Phillips 2020/Parsec 2011 metallicities not supported yet') + return + rootDir += metSuffix + print(rootDir) + + # Check to see if isochrone exists + #isoFile = rootDir + 'iso_%.2f.fits' % logAge + isoFile = os.path.join(rootDir, f'iso_{logAge:.2f}.fits') # not a valid path! + print(isoFile) + print(os.path.exists(isoFile)) + if not os.path.exists(isoFile): + print( 'Phillips/Parsec isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) # formatting in this is mismatched! + print( 'Quitting') #raise IO Error instead and merge with top print statement + return + + data = Table.read(isoFile, format='fits') + cols = data.keys() + mass = data[cols[0]] #Note: this is initial mass, in M_sun + logT = data[cols[2]] # K + logL = data[cols[1]] # L_sun + logg = data[cols[3]] + # current mass + Mcurr = data[cols[3]] #issue! + phase = np.ones(len(data), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = Mcurr + obj.phase = phase + + return obj + +def make_phillips_parsec_iso(logAge, metallicity='solar'): + """ + Make Phillips/Parsec isochrone in Popstar iso object + """ + isoPhParsec = get_phillips_parsec_isochrone(logAge, metallicity=metallicity) + isoPhParsec.source = np.array(['Phillips+Parsec']*len(isoPhParsec.mass)) + + iso = objects.DataHolder() + iso.mass = isoPhParsec.mass + iso.logL = isoPhParsec.logL + iso.logg = isoPhParsec.logg + iso.logT = isoPhParsec.logT + iso.mass_current = isoPhParsec.mass_current + iso.phase = isoPhParsec.phase + iso.logT_WR = isoPhParsec.logT + iso.source = isoPhParsec.source + + return iso + +def get_parsec_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Parsec isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'ParsecV1.2s/iso/' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar Parsec 2011 metallicities not supported yet') + return + rootDir += metSuffix + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.dat' % logAge + if not os.path.exists(isoFile): + print( 'Parsec isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='ascii') + cols = data.keys() + mass = data[cols[2]] #Note: this is initial mass, in M_sun + logT = data[cols[5]] # K + logL = data[cols[4]] # L_sun + logg = data[cols[6]] + Mcurr = data[cols[3]] + phase = np.ones(len(data), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = Mcurr + obj.phase = phase + + return obj + +#### CREATING FINAL ISOCHRONES #### + +def merge_all_isochrones_phillips_baraffe_pisa_ekstrom_parsec2(logAge_arr=np.arange(6.0, 10.1, 0.01), + metallicity='solar', + rotation=True, plot=False): + """ + Make evolutionary isochrones containing a continuous distribution of + masses from the PMS to the MS. The models used are the following: + + PMS (logAge < 8) + Phillips 2020 from 0.01 to 0.07 M_sun + Baraffe+15 from 0.075 - 0.4 M_sun + Pisa 2011 from 0.5 - top of grid (~7 M_sun) + + MS (M > Pisa 2011) + Ekstrom+12 for logAge < 7.4 + Parsec V1.2s for logAge > 7.4 + + metallicity = 'solar' --> Ekstrom+12 z014, Pisa2011 z015, Parsec z015 + + if plot = True, will make plots of merged isochrones in 'plots' directory, + which must already exist + + Code is expected to be run in merged model working directory. + """ + # Root data directory for Ekstrom+12 isochrones + rootDirE = models_dir + 'Ekstrom2012/iso/' + metalPart = 'z014/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rotPart = 'rot/' + if not rotation: + rotPart = 'norot/' + rootDirE += metalPart+rotPart + + # Root data directory for the Baraffe isochrones + rootDirBaraffe = models_dir + 'Baraffe15/iso/' + + # Root data directory for Pisa isochrones + rootDirPisa = models_dir + 'Pisa2011/iso/' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirPisa += metSuffix + + # Root data directory for Parsec isochrones + rootDirParsec = models_dir + 'ParsecV1.2s/iso/' + metalSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirParsec += metalSuffix + + # Root data directory for Phillips isochrones + rootDirPhillips = models_dir + 'Phillips2020/iso/' + metalSuffix = 'z00/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirPhillips += metalSuffix + + # Search both directories for iso_*.dat files + isoFilesE = glob.glob(rootDirE + 'iso_*.dat') + isoFilesB = glob.glob(rootDirBaraffe + 'iso_*.fits') + isoFilesPi = glob.glob(rootDirPisa + 'iso_*.dat') + isoFilesPa = glob.glob(rootDirParsec + 'iso_*') + isoFilesPh = glob.glob(rootDirPhillips + 'iso_*.fits') + + # Output of merged isochrones + if rotation == True: + outDir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/{0}_rot/'.format(metSuffix[:-1]) + #outDir = models_dir + 'merged/test/{0}_rot/'.format(metSuffix[:-1]) + else: + outDir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/{0}_norot/'.format(metSuffix[:-1]) + #outDir = models_dir + 'merged/test/{0}_norot/'.format(metSuffix[:-1]) + if not os.path.exists(outDir): + os.mkdir(outDir) + + # Isolate the iso*.dat file names + for ii in range(len(isoFilesE)): + isoFilesE[ii] = isoFilesE[ii].split('/')[-1] + + for ii in range(len(isoFilesB)): + isoFilesB[ii] = isoFilesB[ii].split('/')[-1] + + for ii in range(len(isoFilesPi)): + isoFilesPi[ii] = isoFilesPi[ii].split('/')[-1] + + for ii in range(len(isoFilesPa)): + isoFilesPa[ii] = isoFilesPa[ii].split('/')[-1] + + for ii in range(len(isoFilesPh)): + isoFilesPh[ii] = isoFilesPh[ii].split('/')[-1] + + # Loop through the Pisa isochrones, adding the MS and Baraffe/Phillips models + # as appropriate + for ii in range(len(logAge_arr)): + logAge = logAge_arr[ii] + iso_name = 'iso_{0:.2f}.dat'.format(logAge) + + #-----PRE-MAIN SEQUENCE (only for logAge < 8)----# + if logAge <= 8.0: + # Merge with the Phillips 2020 models from 0.01 - 0.07 M_sun and + # Baraffe+15 models from 0.075 - 0.4 M_sun. Includes + # transition region between 0.4 - 0.5 M_sun in which we shift + # from 100% Baraffe to 100% Pisa and 0.07 - 0.075 M_sun in which we + #shift from 100% Phillips to 100% Baraffe + + print( 'Merging isochrones Pisa + Phillips + Baraffe from ', iso_name) + isoPMS = merge_isochrone_phillips_baraffe_pisa(logAge, metallicity=metallicity) + + #--------MAIN SEQUENCE-------# + # Case where logAge <= 7.4, we merge with Ekstrom. Otherwise, merge + # which parsec + if logAge <= 7.4: + if iso_name not in isoFilesE: + print( 'Skipping isochrones from ', iso_name) + print( 'PROBLEM WITH PISA OR EKSTROM') + pdb.set_trace() + + print( 'Merging isochrones Phillips+Pisa+Ekstrom from ', iso_name) + iso = merge_isochrone_phillips_pisa_Ekstrom(logAge, metallicity=metallicity, + rotation=rotation, iso_in=isoPMS) + else: + if iso_name not in isoFilesPa: + print( 'Skipping isochrones from ', iso_name) + print( 'PROBLEM WITH PISA OR PARSEC') + pdb.set_trace() + + print( 'Merging isochrones Phillips+Pisa+Parsec from ', iso_name) + iso = merge_isochrone_phillips_pisa_parsec(logAge, metallicity=metallicity, + iso_in=isoPMS) + else: + # If logAge > 8.0, just take the Phillips/Parsec model for the entire isochrone + print( 'Making Phillips/Parsec from ', iso_name) + # change to fits format + iso = make_phillips_parsec_iso(logAge, metallicity=metallicity) + + # Make test plot, if desired. These are put in plots directory + if plot: + # Make different models different colors + phillips_ind = np.where(iso.source == 'Phillips') + merge_pb = np.where(iso.source == 'Phillips+Baraffe') + baraffe_ind = np.where(iso.source == 'Baraffe') + merge_ind = np.where(iso.source == 'Baraffe+Pisa') + pisa_ind = np.where(iso.source == 'Pisa') + MS_ind = np.where( (iso.source == 'Ekstrom') | (iso.source == 'Parsec')) + + # Temporarily turn off interactive plot. Turn back on at end + py.ioff() + + py.figure(1) + py.clf() + py.plot(iso.logT[phillips_ind], iso.logL[phillips_ind], 'c-', label = 'Phillips', + linewidth=2) + py.plot(iso.logT[merge_pb], iso.logL[merge_pb], 'y-', label = 'Phillips+Baraffe', + linewidth=2) + py.plot(iso.logT[baraffe_ind], iso.logL[baraffe_ind], 'k-', label = 'Baraffe', + linewidth=2) + py.plot(iso.logT[merge_ind], iso.logL[merge_ind], 'r-', label = 'Baraffe+Pisa', + linewidth=2) + py.plot(iso.logT[pisa_ind], iso.logL[pisa_ind], 'g-', label = 'Pisa', + linewidth=2) + py.plot(iso.logT[MS_ind], iso.logL[MS_ind], 'b-', + label = 'Ekstrom/Parsec', linewidth=2) + py.xlabel('log Teff') + py.ylabel('log L') + py.title('Log Age = {0}'.format(logAge)) + py.legend(loc=3) + py.axis([4.9, 3.2, -3.0, 8]) + py.savefig(outDir+'plots/iso_{0:.2f}.png'.format(logAge)) + + py.ion() + + + _out = open(outDir + iso_name, 'w') + + hdr_fmt = '%12s %10s %10s %10s %10s %12s %5s %-10s\n' + _out.write(hdr_fmt % + ('# M_init', 'log T', 'log L', 'log g', 'logT_WR', 'M_curr', 'phase', 'Source')) + _out.write(hdr_fmt % + ('# (Msun)', '(Kelvin)', '(Lsun)', '(cgs)', '(Kelvin)', '(Msun)', '()', '()')) + + for kk in range(len(iso.mass)): + _out.write('%12.6f %10.4f %10.4f %10.4f %10.4f %12.6f %5d %-10s\n' % + (iso.mass[kk], iso.logT[kk], iso.logL[kk], + iso.logg[kk], iso.logT_WR[kk], + iso.mass_current[kk], iso.phase[kk], iso.source[kk])) + + _out.close() + return + + +##### CREATING MERGED ATMOSPHERE MODEL ##### +def create_merged_models(cdbs_path, plot=False): + """ + for 1200 - 1000 K, merge Meisner and BTSettl! + + From 1000 K - 1200 K, merge the Meisner and BTSettl atmospheres. + More like BTSettl near 1200 K, more like Meisner near 1000 K + + cdbs_path is path to cdbs directory (including cdbs). Will make new directory + in cdbs/grid named "merged_meisner_BTsettl" with with merged models. If plot = True, will plot + the normalized merged model plus the original Meisner and BTSettl models + + Temp 1000 - 1200, steps of 20; logg 2.5 - 5.5, steps of 0.5, metallicity covering + Meisner range (2.5 -- 5.5, in steps of 0.5). (So, this is one model at 5250)? + + Note: metallicity directories created by hand + + Creates new directory "merged_meisner_BTSettl" in cdbs/grid with new spectrum + catalog file. + Also includes the atlas 5500K model and phoenix 5000K model, for interpolation purposes + """ + #Setting logg sampling + logg_arr = np.arange(2.5, 5.5+0.1, 0.5) + + # Setting metallicity sub-directories + atlas_dir = ['ckm25', 'ckm20', 'ckm15', 'ckm10', 'ckm05', 'ckp00', 'ckp02', 'ckp05'] + phoenix_dir = ['phoenixm30', 'phoenixm20', 'phoenixm15', 'phoenixm10', 'phoenixm05', 'phoenixm00', 'phoenixp05', 'phoenixp05'] + output_dir = ['mergedm25', 'mergedm20', 'mergedm15', 'mergedm10', 'mergedm05', 'mergedp00', 'mergedp02', 'mergedp05'] + metal_arr = [-2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.2, 0.5] + + assert len(atlas_dir) == len(phoenix_dir) == len(output_dir) + + # Make new cdbs merged directory, if it doesn't already exist + newPath = '{0}/grid/merged_atlas_phoenix'.format(cdbs_path) + if not os.path.exists(newPath): + os.mkdir(newPath) + + # For each metallicity, create merged model + for ii in range(len(output_dir)): + # Make metallicity dir for merged model + final_dir = '{0}/{1}'.format(newPath, output_dir[ii]) + if not os.path.exists(final_dir): + os.mkdir(final_dir) + + # Extract the relevant ATLAS and PHEONIX models near 5250 K + atlas_path = '{0}/grid/ck04models/{1}/'.format(cdbs_path, atlas_dir[ii]) + atlas_hdu = fits.open('{0}/{1}_5250.fits'.format(atlas_path, atlas_dir[ii])) + atlas_5250 = atlas_hdu[1].data + phoenix_path = '{0}/grid/phoenix_v16_rebin/{1}'.format(cdbs_path, phoenix_dir[ii]) + phoenix_hdu1 = fits.open('{0}/{1}_05200.fits'.format(phoenix_path, phoenix_dir[ii])) + phoenix_hdu2 = fits.open('{0}/{1}_05300.fits'.format(phoenix_path, phoenix_dir[ii])) + phoenix_5200 = phoenix_hdu1[1].data + phoenix_5300 = phoenix_hdu2[1].data + print('Done reading input models') + + # Trim both models to the wavelength region we want: VRIJHKL (0.25 - 4.2 mircons) + good = np.where( (atlas_5250['Wavelength'] > 2500) & + (atlas_5250['Wavelength'] < 52000) ) + atlas_5250 = atlas_5250[good] + good = np.where( (phoenix_5200['Wavelength'] > 2500) & + (phoenix_5200['Wavelength'] < 52000) ) + phoenix_5200 = phoenix_5200[good] + phoenix_5300 = phoenix_5300[good] + + #----------------------------# + # For each logg val, create phoenix spectrum for 5250 K, + # degrade resolution to match atlas, then average with + # 5250 K atlas model to get merged model at 5250 K + #----------------------------# + new_model = [] + phoenix_5250_f = [] + atlas_5250_f = [] + for logg in logg_arr: + # Create phoenix models at 5250 K + low = phoenix_5200['g{0:2.1f}'.format(logg)] + high = phoenix_5300['g{0:2.1f}'.format(logg)] + + arr = np.transpose([low, high]) + phoenix_5250 = np.mean(arr, axis=1) + + phoenix_5250_rebin = rebin_spec(phoenix_5200['Wavelength'], phoenix_5250, + atlas_5250['Wavelength']) + + # Store phoenix rebinned average spectrum + phoenix_5250_f.append(phoenix_5250_rebin) + + # Store atlas spectrum for later + grav = str(logg).split('.') + atlas_5250_f.append(atlas_5250['g'+grav[0]+grav[1]]) + + # Now, final 5250 K model will be average of atlas and phoenix + # models (since exactly inbetween 5000 K and 5500 K) + arr = np.transpose([phoenix_5250_rebin, atlas_5250['g'+grav[0]+grav[1]]]) + final = np.mean(arr, axis=1) + + new_model.append(final) + print('Done with logg {0:2.1f}'.format(logg)) + + # Create fits table with new model. First column is wavelength, followed by + # fluxes for different logg + wave = atlas_5250['Wavelength'] # Still same wavelength array as before + c0 = fits.Column(name='Wavelength', format='D', array=wave) + c1 = fits.Column(name='g0.0', format='E', array=new_model[0]) + c2 = fits.Column(name='g0.5', format='E', array=new_model[1]) + c3 = fits.Column(name='g1.0', format='E', array=new_model[2]) + c4 = fits.Column(name='g1.5', format='E', array=new_model[3]) + c5 = fits.Column(name='g2.0', format='E', array=new_model[4]) + c6 = fits.Column(name='g2.5', format='E', array=new_model[5]) + c7 = fits.Column(name='g3.0', format='E', array=new_model[6]) + c8 = fits.Column(name='g3.5', format='E', array=new_model[7]) + c9 = fits.Column(name='g4.0', format='E', array=new_model[8]) + c10 = fits.Column(name='g4.5', format='E', array=new_model[9]) + c11 = fits.Column(name='g5.0', format='E', array=new_model[10]) + + cols = fits.ColDefs([c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11]) + tbhdr = phoenix_hdu1[1].header + prihdr = phoenix_hdu1[0].header + prihdu = fits.PrimaryHDU(header=prihdr) + tbhdu = fits.BinTableHDU.from_columns(cols, header=tbhdr) + # Add TUNIT keysto tbhdu + tbhdu = make_merged_header(tbhdu) + + # Make final hdu table, save it + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto('{0}/{1}_05250.fits'.format(final_dir, output_dir[ii]), clobber=True) + + # Test plot, if desired + if plot == True: + py.figure(1, figsize=(10,10)) + py.clf() + # Plot merged model + py.semilogy(wave, wave * new_model[8], 'r-', label='Merged') + # Plot atlas 5250 K model + py.semilogy(wave, wave * atlas_5250_f[8], 'b-', label = 'Atlas') + # Plot phoenix 5250 K model + py.semilogy(wave, wave * phoenix_5250_f[8], 'g-', label = 'Phoenix') + py.legend() + py.xlabel('Wavelength (Angstrom)') + py.ylabel(r'log ($\lambda$ F$_{\lambda}$)') + py.axis([5000, 40000, 2*10**9, 4*10**10]) + py.title('Merged 5250 K Spectrum') + py.savefig('Merged_atlas_phoenix_{0}.png'.format(output_dir[ii])) + + pdb.set_trace() + py.close('all') + + # Copy the atlas 5500 K and phoenix 5000 K models into the + # merged directory to accompany the merged model. This is for + # interpolation purposes for pysynphot + cmd = 'cp {0}/{1}_5500.fits {2}'.format(atlas_path, atlas_dir[ii], final_dir) + cmd2 = 'cp {0}/{1}_05000.fits {2}'.format(phoenix_path, phoenix_dir[ii], final_dir) + os.system(cmd) + os.system(cmd2) + + cmd = 'mv {0}/{1}_5500.fits {0}/{2}_05500.fits'.format(final_dir, atlas_dir[ii], output_dir[ii]) + cmd2 = 'mv {0}/{1}_05000.fits {0}/{2}_05000.fits'.format(final_dir, phoenix_dir[ii], output_dir[ii]) + os.system(cmd) + os.system(cmd2) + + # Close all open hdu + atlas_hdu.close() + phoenix_hdu1.close() + phoenix_hdu2.close() + + print('Done {0}'.format(output_dir[ii])) + + # Make catalog.fits table for new merged model, plus the atlas and + # phoenix models at the high and low extremes of the temp range. Note temp + # of merged model goes first, for filename purposes + catalog, filenames = make_merged_catalog(output_dir, [5250, 5000, 5500], logg_arr, metal_arr) + catalog.write(cdbs_path+'/grid/merged_atlas_phoenix/catalog.fits', overwrite=True) + + return + + + +def create_merged_meisner_btsettl(cdbs_path, plot=False): + """ + From 1000 K - 1200 K, merge the BTSettl_CIFITS2011_2015 and Meisner 2023 atmospheres. + BTSettl at 1200 K, Meisner near 1000 K + + cdbs_path is path to cdbs directory (including cdbs). Will make new directory + in cdbs/grid named "merged_meisner_btsettl" with with merged models. If plot = True, will plot + the normalized merged model plus the original models + + + (Only 1 truely merged model at 3500 K, with log g from 2.5 - 5.5)? + + Creates new directory "merged_meisner_btsettl" in cdbs/grid with new spectrum + catalog file. + (Also includes the atlas 5500K model and phoenix 5000K model, for interpolation purposes)? + """ + # Set log g sampling + logg_solar_arr = np.arange(3.5, 5.5+0.1, 0.5) + logg_nonsolar_arr = np.array([3.5, 5.0]) + + # Setting metallicity sub-directories + meisner_dir = ['mm05', 'mp00'] + output_dir = ['mergedm05', 'mergedp00'] + metal_arr = [-0.5, 0] + + assert len(meisner_dir) == len(output_dir) + + # Make new cdbs merged directory, if it doesn't already exist + newPath = '{0}/grid/merged_meisner_BTSettl'.format(cdbs_path) + if not os.path.exists(newPath): + os.mkdir(newPath) + + # For each metallicity, create merged model + for ii in range(len(output_dir)): + # Make metallicity dir for merged model + final_dir = '{0}/{1}'.format(newPath, output_dir[ii]) + if not os.path.exists(final_dir): + os.mkdir(final_dir) + + # For each gravity, extract BTSettl and Meisner models at 1100 K and + # average them to get the merged model. Also write BTSettl models at + # 1200 K and Meisner models at 1000 K + if metal_arr[ii] == 0: + logg_tmp = logg_solar_arr + BT_func = atmospheres.get_BTSettl_atmosphere + else: + BT_func = atmospheres.get_BTSettl_atmosphere + logg_tmp = logg_nonsolar_arr + + for jj in logg_tmp: + BT_atmo = BT_func(temperature=1100, gravity=jj, rebin=True) + meisner_atmo = atmospheres.get_Meisner2023_atmosphere(temperature=1100, gravity=jj, rebin=True) + + # Check to make sure wavelengths are the same between atmospheres + print("BT_func assigned to:", BT_func) + print(f"BT_func({1100}, {jj}, rebin=True) -> {BT_atmo}") + diff = BT_atmo.wave - meisner_atmo.wave + if np.sum(diff > 0): + print('Wavelength mismatch problem!') + pdb.set_trace() + + merged_flux = np.mean(np.array([meisner_atmo.flux, BT_atmo.flux]), axis=0) + + # Create fits file with new atmosphere + c0 = fits.Column(name='Wavelength', format='D', array=BT_atmo.wave) + c1 = fits.Column(name='Flux', format='E', array=merged_flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Save fits file to merged Meisner-BTSettl direcotry + hdu_new.writeto('{0}/{1}_03500_{2}.fits'.format(final_dir, output_dir[ii], jj), overwrite=True) + + # Make test plot, if desired + if plot: + py.figure(1, figsize=(10,10)) + py.clf() + py.semilogy(BT_atmo.wave, BT_atmo.wave * merged_flux, 'r-', label='Merged') + py.semilogy(BT_atmo.wave, BT_atmo.wave * BT_atmo.flux, 'b-', label = 'BTSettl') + py.semilogy(meisner_atmo.wave, meisner_atmo.wave * meisner_atmo.flux, 'g-', label = 'Meisner') + py.legend() + py.xlabel('Wavelength (Angstrom)') + py.ylabel(r'log ($\lambda$ F$_{\lambda}$)') + py.xlim(5000, 40000) + py.ylim(10**8, 10**10) + py.title('Merged 1100 K Spectrum, Z = {1}, logg = {0}'.format(jj, metal_arr[ii])) + py.savefig('Merged_Meisner_BTSettl_{1}_{0}.png'.format(jj, metal_arr[ii])) + + # Now to bring over the 1000 K Meisner and 1200 K BTSettl models + BT_atmo = BT_func(temperature=1200, gravity=jj, rebin=True) + meisner_atmo = atmospheres.get_Meisner2023_atmosphere(temperature=1000, gravity=jj, rebin=True) + + # Create new fits files for these as well + c0_b = fits.Column(name='Wavelength', format='D', array=BT_atmo.wave) + c1_b = fits.Column(name='Flux', format='E', array=BT_atmo.flux) + c0_m = fits.Column(name='Wavelength', format='D', array=meisner_atmo.wave) + c1_m = fits.Column(name='Flux', format='E', array=meisner_atmo.flux) + + cols_b = fits.ColDefs([c0_b, c1_b]) + tbhdu_b = fits.BinTableHDU.from_columns(cols_b) + cols_m = fits.ColDefs([c0_m, c1_m]) + tbhdu_m = fits.BinTableHDU.from_columns(cols_m) + + prihdu = fits.PrimaryHDU() + tbhdu_b.header['TUNIT1'] = 'ANGSTROM' + tbhdu_b.header['TUNIT2'] = 'FLAM' + tbhdu_m.header['TUNIT1'] = 'ANGSTROM' + tbhdu_m.header['TUNIT2'] = 'FLAM' + + hdu_newb = fits.HDUList([prihdu, tbhdu_b]) + hdu_newm = fits.HDUList([prihdu, tbhdu_m]) + + # Save fits file to merged Meisner-BTSettl direcotry + hdu_newb.writeto('{0}/{1}_03200_{2}.fits'.format(final_dir, output_dir[ii], jj), clobber=True) + hdu_newp.writeto('{0}/{1}_03800_{2}.fits'.format(final_dir, output_dir[ii], jj), clobber=True) + hdu_new.close() + hdu_newb.close() + hdu_newp.close() + + print('Done {0}'.format(output_dir[ii])) + + return + +def make_catalog_merged_models2(path='/g/lu/models/cdbs/grid/merged_BTSettl_phoenix/'): + """ + Make cdbs catalog.fits file for merged BTSettl/phoenix direcotry. path should + point to this directory. + + Writes catalog.fits file in the cdbs directory + """ + output_dir = ['mergedm25', 'mergedm20', 'mergedm15', 'mergedm10', 'mergedm05', 'mergedp00', 'mergedp02', 'mergedp05'] + metal_arr = [-2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.2, 0.5] + + index_str = [] + name_str = [] + for ii in range(len(output_dir)): + files = glob.glob('{0}/{1}/*.fits'.format(path, output_dir[ii])) + + # Extract parameters for each atmosphere from the filename, + # construct columns for catalog file + for name in files: + final = name.split('/')[-1] + tmp = final.split('_') + temp = float(tmp[1]) # In kelvin + logg = float(tmp[2][:-5]) + + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_arr[ii], logg)) + name_str.append('{0}/{1}[Flux]'.format(output_dir[ii], final)) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write(path+'catalog.fits', format = 'fits', overwrite=True) + + return \ No newline at end of file From 119340ec73512983540c717bb5603d0c6b582678 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 17 Nov 2025 17:04:00 -0800 Subject: [PATCH 049/191] Fixed funny BD designations --- spisea/synthetic.py | 97 ++++++++++++++-------------------- spisea/tests/test_synthetic.py | 52 +++--------------- 2 files changed, 47 insertions(+), 102 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index c794c171..942a6b27 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -154,8 +154,9 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, self.iso_interps = {} for ikey in interp_keys: self.iso_interps[ikey] = interpolate.interp1d(self.iso.points['mass'], self.iso.points[ikey], - kind='linear', bounds_error=False, fill_value=np.nan) - + kind='linear', bounds_error=False, fill_value=np.nan, + assume_sorted=False) #BDs out of bounds + #____ ##### # Make a table to contain all the information about each stellar system. ##### @@ -232,14 +233,32 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # Note: this only becomes relevant when the cluster is > 10**6 M-sun, this # effect is so small # Convert nan_to_num to avoid errors on greater than, less than comparisons + + # Define brown dwarf mass range + bd_mask = (star_systems['mass'] >= 0.01) & (star_systems['mass'] <= 0.08) + #hard code BDs as 90 and invariant masses + star_systems['phase'][bd_mask] = 90 + star_systems['mass_current'][bd_mask] = star_systems['mass'][bd_mask] + + # Identify bad phases (non-brown-dwarfs only) star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) - bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 90) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) - # Print warning, if desired - verbose=False + bad = np.where( + (star_systems_phase_non_nan > 5) + & (star_systems_phase_non_nan < 90) + & (star_systems_phase_non_nan != 9) + & (star_systems_phase_non_nan != -99) + & (~bd_mask) + ) + + # Verbose warnings (optional) + verbose = False if verbose: for ii in range(len(bad[0])): - print('WARNING: changing phase {0} to 5'.format(star_systems['phase'][bad[0][ii]])) - star_systems['phase'][bad] = 5 + print(f"WARNING: changing phase {star_systems['phase'][bad[0][ii]]} to 5") + + # Only modify non-90, non-BD entries + mask = (np.floor(star_systems['phase'][bad]) != 90) + star_systems['phase'][bad][mask] = 5 for filt in self.filt_names: star_systems[filt] = self.iso_interps[filt](star_systems['mass']) @@ -278,23 +297,6 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): for filt in self.filt_names: star_systems[filt][idx_rem_good] = np.full(len(idx_rem_good), np.nan) - # override remnant conditions for BDs - idx_bd = np.where(star_systems['mass'] < 0.08)[0] - for i in idx_bd: - T = star_systems['Teff'][i] - g = star_systems['logg'][i] - print(T,g) - - try: - star_bd = atm.get_bd_atmosphere(temperature=T, gravity=g, metallicity=0, verbose=False) - for filt in self.filt_names: - star_systems[filt][i] = star_bd[filt] - except Exception as e: - print(f"[Isochrone] BD atmosphere model failed for T={T}, logg={g}, idx={i}: {e}") - # Keep filter magnitudes as NaN if model fails - for filt in self.filt_names: - star_systems[filt][i] = np.nan - return star_systems def _make_companions_table(self, star_systems, compMass): @@ -380,11 +382,20 @@ def _make_companions_table(self, star_systems, compMass): # stars, round phase down to nearest defined phase (e.g., if phase is 71, # then round it down to 5, rather than up to 101). # Convert nan_to_num to avoid errors on greater than, less than comparisons + + # reinforce BD phase of 90 and invariant masses + bd_mask = (companions['mass'] >= 0.01) & (companions['mass'] < 0.08) + companions['phase'][bd_mask] = 90 + companions['mass_current'][bd_mask] = companions['mass'][bd_mask] + companions_phase_non_nan = np.nan_to_num(companions['phase'], nan=-99) - bad = np.where( (companions_phase_non_nan > 5) & - (companions_phase_non_nan < 90) & - (companions_phase_non_nan != 9) & - (companions_phase_non_nan != -99)) + bad = np.where( + (companions_phase_non_nan > 5) & + (companions_phase_non_nan < 90) & + (companions_phase_non_nan != 9) & + (companions_phase_non_nan != -99) & + (~bd_mask)) + # Print warning, if desired verbose=False if verbose: @@ -458,26 +469,6 @@ def _make_companions_table(self, star_systems, compMass): if np.isnan(T) or np.isnan(g): print('T/logg assigned nan for BD object!') continue - - try: - # Use the default atmosphere function to get the spectrum. - star_bd_spec = default_atm_func(temperature=T, gravity=g, metallicity=self.iso.metallicity) - - # Redden the spectrum - red = default_red_law.reddening(self.iso.points.meta['AKS']).resample(star_bd_spec.wave) - star_bd_spec *= red - - # Calculate magnitude in each filter - for filt_name_long in self.filt_names: - obs_str = get_obs_str(filt_name_long) - filt_info = get_filter_info(obs_str) - mag = mag_in_filter(star_bd_spec, filt_info) - companions[filt_name_long][i] = mag - except Exception as e: - print(f"[ResolvedCluster] BD companion atmosphere model failed for T={T}, logg={g}, idx={i}: {e}") - # Keep filter magnitudes as NaN if model fails - for filt in self.filt_names: - companions[filt][i] = np.nan # Notify if we have a lot of bad ones. @@ -751,10 +742,6 @@ class Isochrone(object): Set the stellar atmosphere models for the white dwafs. Default is get_wd_atmosphere - bd_atm_func: brown dwarf model atmosphere function, optional - Set the stellar atmosphere models for the brown dwafs. - Default is get_bd_atmosphere - mass_sampling : int, optional Sample the raw isochrone every `mass_sampling` steps. The default is mass_sampling = 0, which is the native isochrone mass sampling @@ -779,7 +766,7 @@ class Isochrone(object): """ def __init__(self, logAge, AKs, distance, metallicity=0.0, evo_model=default_evo_model, atm_func=default_atm_func, - wd_atm_func = default_wd_atm_func, #bd_atm_func = default_bd_atm_func, + wd_atm_func = default_wd_atm_func, red_law=default_red_law, mass_sampling=1, wave_range=[3000, 52000], min_mass=None, max_mass=None, rebin=True): @@ -900,10 +887,6 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, if phase == 101: star = wd_atm_func(temperature=T, gravity=gravity, metallicity=metallicity, verbose=False) - #elif phase == 90: - # print(f"Applying brown dwarf model to object {ii}") - # star = bd_atm_func(temperature=T, gravity=gravity, metallicity=0, - # verbose=False) else: star = atm_func(temperature=T, gravity=gravity, metallicity=metallicity, rebin=rebin) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 7ae1b0b1..f319e337 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -430,7 +430,7 @@ def test_ifmr_multiplicity(): iso = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo, atm_func=atm_func, red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + mass_sampling=mass_sampling, recomp=True) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) @@ -483,13 +483,13 @@ def test_ifmr_multiplicity(): # Make sure no funky phase designations (due to interpolation effects) # slipped through + + bd_masses = np.where((clust1['mass'] >= 0.01) & (clust1['mass'] <= 0.08)) + bd_mask = (clust1['mass'] >= 0.01) & (clust1['mass'] <= 0.08) + print(clust1[['mass', 'phase']][bd_masses]) idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 90) & (clust1['phase'] != 9) ) - idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 90) & (comps2['phase'] != 9) ) assert len(idx[0]) == 0 - - # Make sure BD temperatures are assigned correctly - # Ensure no substellar mass compact objects are generated """ 07/2024: Added more testing criteria for brown dwarf stars to ensure they are labeled appropriately for masses from 0.01 - 0.08 M_sun. """ @@ -503,7 +503,7 @@ def test_ifmr_multiplicity(): bh_idx = np.where(clust1['phase'] == 103) bd_idx = np.where(clust1['phase'] == 90) - assert len(clust1[bd_idx]) != 0 + print(clust1[bd_idx]) assert np.all(clust1['mass'][wd_idx] > MIN_MASS) assert np.all(clust1['mass'][ns_idx] > MIN_MASS) assert np.all(clust1['mass'][bh_idx] > MIN_MASS) @@ -540,19 +540,6 @@ def test_ifmr_multiplicity(): assert np.all(clust1['Teff'][bd_idx] != np.nan) assert np.all(comps2['Teff'][comp_bd_idx] != np.nan) - #print statements for debugging: - print(comps2['mass'][comp_bd_idx]) - print(np.unique(comps2['phase'])) - comp_phase_nan = np.where(comps2['phase'] == np.nan) - print(comps2[comp_phase_nan]) - comp_phase_1 = np.where(comps2['phase'] == 1.0) - print(comps2[comp_phase_1]) - comps_bds = np.where((comps2['mass'] >= 0.01) & (comps2['mass'] < 0.08)) - print(comps2[comps_bds]) - - #make sure that bd temps are being assigned - print(clust1[bd_idx]['Teff']) - print(comps2[comp_bd_idx]['Teff']) return def test_metallicity(): @@ -1031,29 +1018,4 @@ def test_Raithel18_IFMR_5(): assert len(WD_idx) == 2 , "There are not the right number of WDs for the Raithel18 IFMR" - return - -# need to test Raithel18 phase designations, as there are anomalies with white dwarves and neutron stars -def test_Raithel18_phase_designation(): - """ - Check that the correct phases are returned for white dwarves and neutron stars when given several test MZAMS - """ - Raithel = ifmr.IFMR_Raithel18() - - #create an MZAMS array to cover all potential phase designations, and the expected phase designations - test_MZAMS = np.array([0.06, 0.3, 0.6, 1.0, 3.0, 6.0, 10.0, 14.0, 25.0, 100.0]) - expected_phases = np.array([90., -1., 101., 101., 101., 101., 102., 102., 103., 103.]) - - #generate the output from Raithel IFMR - output_array = Raithel.generate_death_mass(test_MZAMS) - actual_phases = output_array[1] - - #print the two arrays for direct comparison - print(f"Expected phases: {expected_phases}") - print(f"Actual phases: {actual_phases}") - - #confirm that the expected phases match the ones assigned by the model - assert np.array_equal(actual_phases, expected_phases), \ - f"Phase designation mismatch. Expected: {expected_phases}, Got: {actual_phases}" - - print(f"Test passed. Actual types match expected types: {actual_phases}") \ No newline at end of file + return \ No newline at end of file From 19d14da04f65e4a79fda9753fd12ec417416b475 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 17 Nov 2025 17:12:06 -0800 Subject: [PATCH 050/191] Cleaned up old IMF testing --- spisea/imf/tests/test_bd.py | 300 --------------------------------- spisea/imf/tests/test_class.py | 57 ------- 2 files changed, 357 deletions(-) delete mode 100644 spisea/imf/tests/test_bd.py delete mode 100644 spisea/imf/tests/test_class.py diff --git a/spisea/imf/tests/test_bd.py b/spisea/imf/tests/test_bd.py deleted file mode 100644 index 7dda4cfe..00000000 --- a/spisea/imf/tests/test_bd.py +++ /dev/null @@ -1,300 +0,0 @@ -import numpy as np -import time -import pdb - -def test_generate_cluster(): - from .. import imf - from .. import multiplicity - - # Make multiplicity object - imf_multi = multiplicity.MultiplicityUnresolved() - - # Make IMF object; we'll use a broken power law with the parameters from CombinedWeidnerKroupaKirkpatrick_2024 - massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) # Define boundaries of each mass segement - powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) # Power law slope associated with each mass segment - my_imf = imf.IMF_broken_powerlaw(massLimits, powers, imf_multi) - - # Define total cluster mass - M_cl = 10**5. - - mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) - - # Make sure that the total mass is always within the expected - # range of the requested mass. - assert np.abs(M_cl - sysMass.sum()) < 120.0 - - # Check that enough companions were generated. - # Should be greater than 25% of the stars with companions. - mdx = np.where(isMulti)[0] - for mm in mdx: - assert len(compMass[mm]) > 0 - - return - -def test_prim_power(): - from .. import imf - - #mass_limits = np.array([0.1, 1.0, 100.0]) - #powers = np.array([-2.0, -1.8]) - mass_limits = np.array([1.0, 100.0]) - powers = np.array([-2.0]) - - imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) - - Mcl = 1e5 - (mass, is_multi, c_mass, s_mass) = imf_tmp.generate_cluster(Mcl) - - print('mass shape = ', mass.shape) - print('mass array:') - print(mass) - print('is_multi array:') - print(is_multi) - print('c_mass array:') - print(c_mass) - print('s_mass array:') - print(s_mass) - print('np.isfinite(mass):') - print(np.isfinite(mass)) - print('Mass Sum: ', mass.sum(), ' (should be {0})'.format(Mcl)) - - return - -def test_xi(): - from .. import imf - - import cProfile, pstats, io - #from pstats import SortKey - pr = cProfile.Profile() - - mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) - powers = np.array([-0.3, -1.5, -2.3]) - imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) - - ########## - # - # Test validity of returned values. - # - ########## - N_size = 10 - m = np.linspace(0.2, 20, N_size) - val_good = np.array([1.6206566 , 0.26895718, 0.10135922, 0.05639448, 0.03703704, - 0.0243668 , 0.01613091, 0.01137139, 0.00839526, 0.00642142]) - - val_test = np.zeros(len(m), dtype=float) - for ii in range(len(m)): - val_test[ii] = imf_tmp.xi(m[ii]) - - # print(repr(val_test)) - np.testing.assert_almost_equal(val_test, val_good) - - ########## - # - # Performance testing - # - ########## - t1 = time.time() - # pr.enable() - - # Run a time test - N_size = int(1e4) - m = np.random.uniform(1.1, 99.0, size=N_size) - foo1 = imf_tmp.xi(m) - - # pr.disable() - t2 = time.time() - - print('test_xi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) - - # s = io.StringIO() - # sortby = SortKey.CUMULATIVE - # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) - # ps.print_stats() - # print(s.getvalue()) - - return - -def test_xi2(): - """ - Test that xi() produces the correct probability for - a given slope. - """ - from .. import imf - - import cProfile, pstats, io - #from pstats import SortKey - pr = cProfile.Profile() - - # Negative slope - mass_limits = np.array([1.0, 10.0]) - powers = np.array([-2.0]) - imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) - imf_tmp.normalize(1e4) - - tmp = np.random.rand(100) - masses = imf_tmp.dice_star_cl(tmp) - - pdf = imf_tmp.xi(masses) - - sdx = masses.argsort() - - pdf_sorted = pdf[sdx] - - print('Testing negative slope') - assert pdf_sorted[0] > pdf_sorted[-1] - assert pdf_sorted[0] > pdf_sorted[1] - assert pdf.max() == pdf_sorted[0] - - # Positive slope - mass_limits2 = np.array([1.0, 10.0]) - powers2 = np.array([2.0]) - imf_tmp2 = imf.IMF_broken_powerlaw(mass_limits2, powers2) - imf_tmp2.normalize(1e4) - - tmp2 = np.random.rand(100) - masses2 = imf_tmp2.dice_star_cl(tmp2) - - pdf2 = imf_tmp2.xi(masses2) - - sdx2 = masses2.argsort() - - pdf_sorted2 = pdf2[sdx2] - - print('Testing positive slope') - assert pdf_sorted2[0] < pdf_sorted2[-1] - assert pdf_sorted2[0] < pdf_sorted2[1] - assert pdf2.min() == pdf_sorted2[0] - - - import pylab as plt - plt.clf() - plt.loglog(masses[sdx], pdf_sorted, 'k.') - plt.axis('equal') - # pdb.set_trace() - - return - - - - -def test_mxi(): - from .. import imf - - import cProfile, pstats, io - #from pstats import SortKey - pr = cProfile.Profile() - - mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) - powers = np.array([-0.3, -1.5, -2.3]) - imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) - - ########## - # - # Test validity of returned values. - # - ########## - N_size = 10 - m = np.linspace(0.2, 20, N_size) - val_good = np.array([0.32413132, 0.64549722, 0.4662524 , 0.38348249, 0.33333333, - 0.27290819, 0.21615424, 0.17739363, 0.14943557, 0.12842838]) - val_test = np.zeros(len(m), dtype=float) - for ii in range(len(m)): - val_test[ii] = imf_tmp.m_xi(m[ii]) - - # print(repr(val_test)) - np.testing.assert_almost_equal(val_test, val_good) - assert val_test.shape == m.shape - - ########## - # - # Performance testing - # - ########## - t1 = time.time() - # pr.enable() - - # Run a time test - N_size = int(1e4) - m = np.random.uniform(1.1, 99.0, size=N_size) - foo1 = imf_tmp.m_xi(m) - assert foo1.shape == m.shape - - # pr.disable() - t2 = time.time() - - print('test_mxi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) - - # s = io.StringIO() - # sortby = SortKey.CUMULATIVE - # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) - # ps.print_stats() - # print(s.getvalue()) - - return - -def test_theta_closed(): - from .. import imf - - import cProfile, pstats, io - #from pstats import SortKey - - mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) - powers = np.array([-0.3, -1.5, -2.3]) - imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) - - ########## - # - # Test validity of returned values. - # - ########## - N_size = 10 - m = np.linspace(0.2, 20, N_size) - val_good = np.array([[1., 0., 0.], - [1., 1., 0.], - [1., 1., 0.], - [1., 1., 0.], - [1., 1., 0.], - [1., 1., 1.], - [1., 1., 1.], - [1., 1., 1.], - [1., 1., 1.], - [1., 1., 1.]]) - - val_test = np.zeros((len(m), len(powers)), dtype=float) - for ii in range(len(m)): - val_test[ii] = imf.theta_closed(m[ii] - imf_tmp._m_limits_low) - - np.testing.assert_equal(val_test, val_good) - - - - ########## - # - # Speed tests and performance profiling. - # - ########## - N_size = 10000 - m = np.linspace(1.1, 99, N_size) - - tmp = np.zeros((len(m), len(powers)), dtype=float) - - t1 = time.time() - # pr = cProfile.Profile() - # pr.enable() - - for ii in range(len(m)): - tmp[ii] = imf.theta_closed(m[ii] - imf_tmp._m_limits_low) - - # pr.disable() - t2 = time.time() - - print('Runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) - - # s = io.StringIO() - # sortby = SortKey.CUMULATIVE - # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) - # ps.print_stats() - # print(s.getvalue()) - - - return - diff --git a/spisea/imf/tests/test_class.py b/spisea/imf/tests/test_class.py deleted file mode 100644 index 9da404d2..00000000 --- a/spisea/imf/tests/test_class.py +++ /dev/null @@ -1,57 +0,0 @@ -import numpy as np -import time -import pdb - -def test_Muzic(): - from .. import imf - from .. import multiplicity - - # Make multiplicity object - imf_multi = multiplicity.MultiplicityUnresolved() - - # Make IMF object; we'll use a broken power law with the parameters from Muzic+17 - my_imf = imf.Muzic_2017(multiplicity=imf_multi) - - # Define total cluster mass - M_cl = 10**5. - - mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) - - # Make sure that the total mass is always within the expected - # range of the requested mass. - assert np.abs(M_cl - sysMass.sum()) < 120.0 - - # Check that enough companions were generated. - # Should be greater than 25% of the stars with companions. - mdx = np.where(isMulti)[0] - for mm in mdx: - assert len(compMass[mm]) > 0 - - return - -def test_CombinedWeidnerKroupaKirkpatrick_2024(): - from .. import imf - from .. import multiplicity - - # Make multiplicity object - imf_multi = multiplicity.MultiplicityUnresolved() - - # Make IMF object; we'll use a broken power law with the parameters from Muzic+17 - my_imf = imf.CombinedWeidnerKroupaKirkpatrick_2024(multiplicity=imf_multi) - - # Define total cluster mass - M_cl = 10**5. - - mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) - - # Make sure that the total mass is always within the expected - # range of the requested mass. - assert np.abs(M_cl - sysMass.sum()) < 120.0 - - # Check that enough companions were generated. - # Should be greater than 25% of the stars with companions. - mdx = np.where(isMulti)[0] - for mm in mdx: - assert len(compMass[mm]) > 0 - - return \ No newline at end of file From 057a873a5b9c40710f03930036360f90f794dbdd Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 17 Nov 2025 17:19:25 -0800 Subject: [PATCH 051/191] Updates to model testing --- spisea/tests/test_models.py | 1 - 1 file changed, 1 deletion(-) diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 94bbbe12..9046ea06 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -1,4 +1,3 @@ -# Test functions for the different stellar evolution and atmosphere models import numpy as np import pdb From 84ab57ee356a628109b61c1e5833e5f45cd27f23 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 21 Nov 2025 15:05:28 -0800 Subject: [PATCH 052/191] Updated cluster generation notebook with BDs --- changes/BD_Clusters.ipynb | 407 +++++++++++++++++++------------------- 1 file changed, 209 insertions(+), 198 deletions(-) diff --git a/changes/BD_Clusters.ipynb b/changes/BD_Clusters.ipynb index 5ffd47ed..637ffc12 100644 --- a/changes/BD_Clusters.ipynb +++ b/changes/BD_Clusters.ipynb @@ -33,23 +33,17 @@ "import pdb\n", "import matplotlib.pyplot as plt\n", "from astropy.table import vstack\n", - "%matplotlib inline" + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload" ] }, { "cell_type": "markdown", - "id": "8da89871-b687-44e2-a7e4-a6fc753f5222", - "metadata": {}, - "source": [ - "## Case 1: Cluster Without Companions" - ] - }, - { - "cell_type": "markdown", - "id": "db8bfa5e-1106-4cbf-969b-0816e91fc5aa", + "id": "7ab56664-88f3-4ff1-8afc-baea7af1ef94", "metadata": {}, "source": [ - "For this stellar cluster, we will be using the MergedBaraffePisaEkstromParsec evolution model, IMFR_Raithel18 initial-final mass relation, and the Weidner_Kroupa_2004 initial mass function, all of which have been modified to describe substellar masses." + "## Creating Isochrone Extending to Substellar Range" ] }, { @@ -62,39 +56,100 @@ "# Create isochrone object \n", "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only need 1 filter\n", "my_ifmr = ifmr.IFMR_Raithel18()\n", - "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", - " evo_model = evolution.MergedBaraffePisaEkstromParsec(), #our new evolution model for BDs\n", + "my_iso = synthetic.IsochronePhot(9, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(), #our new evolution model for BDs\n", " filters=filt_list)\n" ] }, { "cell_type": "code", "execution_count": 3, - "id": "e79ee070-7f0d-4d46-b477-d22bd4fcb810", + "id": "8a5bda53-583b-42ec-8448-399c8b98de64", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brown dwarf (mass < 0.08 M_sun): F153M = 19.201 mag\n" + ] + } + ], "source": [ - "# Create IMF objects \n", - "k_imf = imf.Weidner_Kroupa_2004()" + "# Creating a sample BD case for identification\n", + "bd_idx = np.where((my_iso.points['mass'] > 0.01) & (my_iso.points['mass'] < 0.08) )[0]\n", + "if len(bd_idx) > 0:\n", + " f153m = np.round(my_iso.points[bd_idx[0]]['m_hst_f153m'], decimals=3)\n", + " mass = my_iso.points[bd_idx[0]]['mass']\n", + " print('Brown dwarf (mass < 0.08 M_sun): F153M = {0} mag'.format(f153m))\n", + "else:\n", + " print('No brown dwarf with mass < 0.08 M_sun found.')" ] }, { "cell_type": "code", "execution_count": 4, - "id": "79704dca-9b9e-4572-b49f-760511e06b08", + "id": "3cf86905-d02a-4fd1-bb58-2f2432edcb4e", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], + "source": [ + "# Make a mass-magnitude diagram from the isochrone and plot brown dwarfs\n", + "py.figure(1, figsize=(6,6))\n", + "py.clf()\n", + "py.plot(my_iso.points['mass'], my_iso.points['m_hst_f153m'], 'r-', label='generated isochron')\n", + "py.plot(my_iso.points['mass'][bd_idx], my_iso.points['m_hst_f153m'][bd_idx], 'b*', ms=15, label='BD mass')\n", + "py.title('Generated Isochron Containing Brown Dwarf Masses')\n", + "py.xlabel('Mass')\n", + "py.ylabel('Magnitude in F153M filter')\n", + "py.gca().invert_yaxis()\n", + "py.legend()\n", + "py.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8da89871-b687-44e2-a7e4-a6fc753f5222", + "metadata": {}, + "source": [ + "## Case 1: Cluster Without Companions" + ] + }, + { + "cell_type": "markdown", + "id": "db8bfa5e-1106-4cbf-969b-0816e91fc5aa", + "metadata": {}, + "source": [ + "For this stellar cluster, we will be using the MergedBaraffePisaEkstromParsec evolution model, IMFR_Raithel18 initial-final mass relation, and the Salpeter_Kirkpatrick_2024 initial mass function, all of which have been modified to describe substellar masses." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e79ee070-7f0d-4d46-b477-d22bd4fcb810", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF object without companions \n", + "k_imf = imf.Salpeter_Kirkpatrick_2024()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "79704dca-9b9e-4572-b49f-760511e06b08", + "metadata": {}, + "outputs": [], "source": [ "# Make cluster\n", "cluster_mass = 10**6\n", @@ -106,13 +161,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "a192b379-25fb-43ac-a799-6ba4a98d5df9", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -137,7 +192,7 @@ "# Plot BHs\n", "plt.figure(figsize=(14,6))\n", "plt.subplot(2, 2, 1)\n", - "plt.hist(k_out[p_bh]['mass'], histtype = 'step',\n", + "plt.hist(k_out[p_bh]['mass_current'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -155,7 +210,7 @@ "\n", "# Plot BDs\n", "plt.subplot(2, 2, 3)\n", - "plt.hist(k_out[p_bd]['mass'], histtype = 'step',\n", + "plt.hist(k_out[p_bd]['mass_current'], histtype = 'step',\n", " bins = bd_bins, label = 'Generated Brown Dwarves')\n", "plt.title(\"Generated Brown Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -179,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "7c9bbd80-1243-485d-a944-9bfafc5049fd", "metadata": {}, "outputs": [ @@ -187,17 +242,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "BH max mass: 119.93239322786152\n", - "BH min mass: 15.001864046701314\n", + "BH max mass: 119.66334547857207\n", + "BH min mass: 15.0000788054688\n", "\n", - "NS max mass: 118.23513027048241\n", - "NS min mass: 9.00008078654941\n", + "NS max mass: 119.69186137462842\n", + "NS min mass: 9.00041007150772\n", "\n", - "WD max mass: 8.99872832681908\n", - "WD min mass: 5.311599156274761\n", + "WD max mass: 8.999600545649336\n", + "WD min mass: 2.3067911099815683\n", "\n", - "BD max mass: 0.07999995308017924\n", - "BD min mass: 0.010000015332213386\n", + "BD max mass: 0.07999963770010694\n", + "BD min mass: 0.010000021076120833\n", "\n" ] } @@ -217,41 +272,6 @@ "print(\"BD min mass: \" + str(np.min(k_out[p_bd]['mass'])) + '\\n')" ] }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a3c2d1e2-fcfa-429a-92e7-b2c31e034482", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Teff \n", - "------------------\n", - " 680.8331610608817\n", - " 570.8134778988632\n", - "1458.5749556855367\n", - " 530.4032032477386\n", - " 609.224312952465\n", - "494.41076310951894\n", - " 692.7514016813559\n", - " ...\n", - " 921.297090918629\n", - " 733.558254330926\n", - " 668.3518603255377\n", - " 807.1505091097516\n", - " 588.2307845514122\n", - "353.02982279111575\n", - " 963.2369850122022\n", - "Length = 1024345 rows\n" - ] - } - ], - "source": [ - "print(k_out[p_bd]['Teff'])" - ] - }, { "cell_type": "markdown", "id": "c5cf0b4c-2b80-4b0c-9adf-e3d590dd39a3", @@ -274,52 +294,32 @@ "id": "f64318cd-f631-4a24-9abe-93c96fa17251", "metadata": {}, "source": [ - "For this cluster we are using the same isochrone as the one generated in Case 1, just changing our IMF to allow for systems with companions." + "For this cluster we are using the same isochrone as the one generated in Case 1, just changing our IMF to allow for systems with companions. We then record the number of progenitors in each mass bin for black holes, neutron stars, white dwarfs, and brown dwarfs. We can then view how they all evolve over time." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "104a64d0-6a52-4b26-a418-0df265902a92", "metadata": {}, "outputs": [], "source": [ "# Create IMF objects \n", "imf_multi = multiplicity.MultiplicityUnresolved()\n", - "kc_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)" + "kc_imf = imf.Salpeter_Kirkpatrick_2024(multiplicity=imf_multi)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "6042a1ae-86c2-44d3-b718-fd678185a6e7", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Found 7406 companions out of stellar mass range\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n" + "Found 19241 companions out of stellar mass range\n" ] } ], @@ -335,13 +335,13 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "c44480e6-fa97-44c3-9993-148316f746c0", + "execution_count": 11, + "id": "f7f2111d-2cdb-4d3c-80fd-7b7025010841", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -354,52 +354,54 @@ "# Locate BHs, NSs, WDs, and BDs\n", "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", - "k_bh = vstack([kc_out[p2_bh], kc_comp[c_bh]])\n", "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", - "k_ns = vstack([kc_out[p2_ns], kc_comp[c_ns]])\n", "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", - "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", - "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", "\n", - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", + "# Define bins for histograms\n", + "bh_bins = np.linspace(14, 100, 20)\n", "wd_bins = np.linspace(0.4, 10, 20)\n", "bd_bins = np.linspace(0.01, 0.08, 8)\n", - "ns_bins = np.linspace(8, 30, 20)\n", + "ns_bins = np.linspace(0, 30, 20)\n", "\n", - "# Plot BHs\n", + "# Create subplots\n", "plt.figure(figsize=(14,6))\n", + "\n", + "# Plot BHs\n", "plt.subplot(2, 2, 1)\n", - "plt.hist(k_bh['mass'], histtype='step', bins=bh_bins, label='Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.hist(kc_out[p2_bh]['mass'], histtype='step', bins=bh_bins, label='Primary Black Holes', color='blue')\n", + "plt.hist(kc_comp[c_bh]['mass'], histtype='step', bins=bh_bins, label='Companion Black Holes', color='orange')\n", + "plt.title(\"Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", "plt.ylabel('Number')\n", "plt.legend()\n", "\n", "# Plot WDs\n", "plt.subplot(2, 2, 2)\n", - "plt.hist(k_wd['mass'], histtype = 'step', bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.hist(kc_out[p2_wd]['mass'], histtype='step', bins=wd_bins, label='Primary White Dwarves', color='blue')\n", + "plt.hist(kc_comp[c_wd]['mass'], histtype='step', bins=wd_bins, label='Companion White Dwarves', color='orange')\n", + "plt.title(\"White Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", "plt.ylabel('Number')\n", "plt.legend()\n", "\n", "# Plot BDs\n", "plt.subplot(2, 2, 3)\n", - "plt.hist(k_bd['mass'], histtype = 'step', bins = bd_bins, label = 'Generated Brown Dwarves')\n", - "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.hist(kc_out[p2_bd]['mass'], histtype='step', bins=bd_bins, label='Primary Brown Dwarves', color='blue')\n", + "plt.hist(kc_comp[c_bd]['mass'], histtype='step', bins=bd_bins, label='Companion Brown Dwarves', color='orange')\n", + "plt.title(\"Brown Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", "plt.ylabel('Number')\n", "plt.legend()\n", "\n", "# Plot NSs\n", "plt.subplot(2, 2, 4)\n", - "plt.hist(k_ns['mass'], histtype = 'step', bins = ns_bins, label = 'Generated Neutron Stars')\n", - "plt.title(\"Generated Neutron Stars by Mass\")\n", + "plt.hist(kc_out[p2_ns]['mass'], histtype='step', bins=ns_bins, label='Primary Neutron Stars', color='blue')\n", + "plt.hist(kc_comp[c_ns]['mass'], histtype='step', bins=ns_bins, label='Companion Neutron Stars', color='orange')\n", + "plt.title(\"Neutron Stars by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", "plt.ylabel('Number')\n", "plt.legend()\n", @@ -407,42 +409,8 @@ "# Adjust space between subplots\n", "plt.subplots_adjust(hspace=0.5)\n", "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f8a2e33b-266e-4a6f-b3f6-f25a1cd8d86e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Teff \n", - "------------------\n", - " 444.1173953888183\n", - " 693.3637752947118\n", - " 651.3987402554264\n", - " 714.1331032782153\n", - " 851.9772176003223\n", - " 783.8497395750999\n", - " 359.6196362456533\n", - " ...\n", - " 411.0731940270949\n", - " 449.6176329353576\n", - " 836.2301296016228\n", - "444.36050056433794\n", - "391.81361354085544\n", - "382.88818149261937\n", - "510.87160667571027\n", - "Length = 960373 rows\n" - ] - } - ], - "source": [ - "print(k_bd['Teff'])" + "# Show the plots\n", + "plt.show()\n" ] }, { @@ -455,25 +423,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "BH prim max mass: 119.898750058991\n", - "BH prim min mass: 15.01534492995759\n", - "BH comp max mass: 104.62467533647252\n", - "BH comp min mass: 15.00689100967727\n", + "BH prim max mass: 119.81313623903503\n", + "BH prim min mass: 15.00991526273759\n", + "BH comp max mass: 117.87922107922613\n", + "BH comp min mass: 15.025589362143613\n", "\n", - "NS prim max mass: 118.85881346572698\n", - "NS prim min mass: 9.000112173407095\n", - "NS comp max mass: 110.40621252485172\n", - "NS comp min mass: 9.001900741677009\n", + "NS prim max mass: 118.77258608698675\n", + "NS prim min mass: 9.000462873651996\n", + "NS comp max mass: 112.7083837457762\n", + "NS comp min mass: 9.001307704298794\n", "\n", - "WD prim max mass: 8.99910211758367\n", - "WD prim min mass: 5.311343510677102\n", - "WD comp max mass: 8.99933929534237\n", - "WD comp min mass: 5.31290506400592\n", + "WD prim max mass: 8.999282260049943\n", + "WD prim min mass: 2.3066340801114324\n", + "WD comp max mass: 8.995879334368274\n", + "WD comp min mass: 2.306670656743985\n", "\n", - "BD prim max mass: 0.07999998562994273\n", - "BD prim min mass: 0.01000004628780762\n", - "BD comp max mass: 0.0799995996476194\n", - "BD comp min mass: 0.010000293534472888\n", + "BD prim max mass: 0.07999996275188857\n", + "BD prim min mass: 0.010000063950493392\n", + "BD comp max mass: 0.07999976499332974\n", + "BD comp min mass: 0.010000270279172984\n", "\n" ] } @@ -502,55 +470,98 @@ ] }, { - "cell_type": "code", - "execution_count": 13, - "id": "a5e9c3bc-ea61-4f1e-b13c-9bbcfc9f2830", + "cell_type": "markdown", + "id": "3dd322a2-2d57-43f9-b9bd-5f0664dc571f", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "plt.scatter(k_bd[\"mass_current\"], k_bd['Teff'])\n", - "plt.show()" + "All of our brown dwarf primary and companion progenitor objects are in the correct mass range! Now let's see how everything evolves over time!" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "65f1a0ad-61ac-4606-907b-fbf8bdbd2271", + "execution_count": 13, + "id": "698c9fd3-02f4-49c1-bef8-16e102f30f72", "metadata": {}, "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "287.8007498624834" + "
" ] }, - "execution_count": 14, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "np.min(k_bd['Teff'])" + "# Locate BHs, NSs, WDs, and BDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "k_bh = vstack([kc_out[p2_bh], kc_comp[c_bh]])\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "k_ns = vstack([kc_out[p2_ns], kc_comp[c_ns]])\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", + "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", + "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", + "\n", + "# Create subplots\n", + "plt.figure(figsize=(14,6))\n", + "\n", + "# Plot BHs\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(k_bh['mass'], histtype='step', bins=bh_bins, label='Progenitor Black Hole Masses', color='blue')\n", + "plt.hist(k_bh['mass_current'], histtype='step', bins=bh_bins, label='Current Black Hole Masses', color='orange')\n", + "plt.title(\"Black Holes by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(k_wd['mass'], histtype='step', bins=wd_bins, label='Progenitor White Dwarf Masses', color='blue')\n", + "plt.hist(k_wd['mass_current'], histtype='step', bins=wd_bins, label='Current White Dwarf Masses', color='orange')\n", + "plt.title(\"White Dwarves by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(k_bd['mass'], histtype='step', bins=bd_bins, label='Progenitor Brown Dwarf Masses', color='blue')\n", + "plt.hist(k_bd['mass_current'], histtype='step', bins=bd_bins, label='Current Brown Dwarf Masses', color='orange')\n", + "plt.title(\"Brown Dwarves by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(k_ns['mass'], histtype='step', bins=ns_bins, label='Progenitor Neutron Star Masses', color='blue')\n", + "plt.hist(k_ns['mass_current'], histtype='step', bins=ns_bins, label='Current Neutron Stars Masses', color='orange')\n", + "plt.title(\"Neutron Stars by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "# Show the plots\n", + "plt.show()\n" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "92975ed8-a4c1-4383-a7ef-11c88fb362a7", + "cell_type": "markdown", + "id": "e63c4166-df0a-4314-9fa8-f2d5e505c1be", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "We expect brown dwarfs to not change over time, which is consistent with evolutionary models imposed for these objects." + ] } ], "metadata": { From e43229f53058ae7cf7259e4a299352a8c621a3a1 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 21 Nov 2025 15:07:32 -0800 Subject: [PATCH 053/191] Delete BD model override feature --- spisea/synthetic.py | 49 +-------------------------------------------- 1 file changed, 1 insertion(+), 48 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 942a6b27..94247205 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -788,54 +788,7 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, # Get solar metallicity models for a population at a specific age. # Takes about 0.1 seconds. evol = evo_model.isochrone(age=10**logAge, - metallicity=metallicity) - - # Specify MergedPhillipsBaraffePisaEkstromParsec for brown dwarfs in all cases - try: - bd_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() - - # Clamp to valid BD grid domain - logAge_bd = np.clip(logAge, 6.0, 10.0) - metallicity_bd = 0.0 # Phillips2020 only supports solar [Fe/H] - - # Notify user if clamping occurred - if logAge != logAge_bd: - print(f"[Isochrone] Adjusted BD logAge from {logAge:.2f} → {logAge_bd:.2f} (model valid range 6–10)") - print(f"[Isochrone] Using solar metallicity for BD grid ([Fe/H]={metallicity_bd:.2f})") - - # Compute BD isochrone - evol_bd = bd_model.isochrone(age=10**logAge_bd, metallicity=metallicity_bd) - - # If no data at specified age, find the closest available age in the model grid. - if len(evol_bd) == 0: - print(f"[Isochrone] Warning: No BD data at logAge={logAge_bd:.2f}. Searching for closest age.") - # Find the closest age in the model's grid - available_ages = np.unique(bd_model.model['logAge']) - closest_logAge = available_ages[np.argmin(np.abs(available_ages - logAge_bd))] - - if closest_logAge != logAge_bd: - print(f"[Isochrone] Using closest available BD logAge: {closest_logAge:.2f}") - logAge_bd = closest_logAge - evol_bd = bd_model.isochrone(age=10**logAge_bd, metallicity=metallicity_bd) - - # Define BD threshold and merge - bd_thresh = 0.075 - evol_stars = evol[evol['mass'] >= bd_thresh] - evol_bd = evol_bd[evol_bd['mass'] < bd_thresh] - - if len(evol_bd) > 0: - evol = vstack([evol_bd, evol_stars]) - evol.sort('mass') - print(f"[Isochrone] Merged brown dwarf evolution below {bd_thresh:.3f} Msun " - f"from PhillipsBaraffePisaMerged model (logAge={logAge_bd:.2f}, [Fe/H]=0.0).") - else: - print("[Isochrone] Warning: Brown dwarf model returned no data below threshold.") - - except Exception as e: - import traceback - print(f"[Isochrone] Warning: Failed to merge brown dwarf model ({e})") - traceback.print_exc() - + metallicity=metallicity) # Eliminate cases where log g is less than 0 idx = np.where(evol['logg'] > 0) From 7e50182a51b303865d49cec77587680b3e399e42 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 23 Jan 2026 14:04:37 -0800 Subject: [PATCH 054/191] Fixed docstrings for BD merged evolution model --- spisea/evolution.py | 27 ++++++++++++++++++++++++++- 1 file changed, 26 insertions(+), 1 deletion(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index d07bf2b0..a12014ad 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1497,8 +1497,33 @@ def format_isochrones(self): #==============================# class MergedPhillipsBaraffePisaEkstromParsec(StellarEvolution): """ - All merged! + This is a combination of several different evolution models: + + * Phillips (`Phillips et al. 2020 `_) + * Baraffe (`Baraffe et al. 2015 `_) + * Pisa (`Tognelli et al. 2011 `_) + * Geneva (`Ekstrom et al. 2012 `_) + * Parsec (version 1.2s, `Bressan+12 `_) + + The model used depends on the age of the population and what stellar masses + are being modeled: + + For logAge < 7.4: + + * Phillips: 0.01 - 0.07 M_sun + * Phillips/Baraffe transition: 0.070 - 0.075 M_sun + * Baraffe: 0.075 - 0.4 M_sun + * Baraffe/Pisa transition: 0.4 - 0.5 M_sun + * Pisa: 0.5 M_sun to the highest mass in Pisa isochrone (typically 5 - 7 Msun) + * Geneva: Highest mass of Pisa models to 120 M_sun + + For logAge > 7.4: + + * Phillips: 0.01 - 0.075 M_sun + * Phillips/Parsec v1.2s transition: 0.075 - 0.2 M_sun + * Parsec v1.2s: full mass range above 0.2 M_sun + Parameters ---------- rot: boolean, optional From ff7015ba4d57329e370a372ad73bf0bc0000ed80 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 26 Jan 2026 15:03:47 -0800 Subject: [PATCH 055/191] adding euclid VIS --- docs/add_filters.rst | 6 +- docs/filters.rst | 5 +- filt_func/euclid/VIS.dat | 551 ++++++++++++++++++++++++++++++++++++ spisea/synthetic.py | 1 + spisea/tests/test_models.py | 2 +- 5 files changed, 560 insertions(+), 5 deletions(-) create mode 100755 filt_func/euclid/VIS.dat diff --git a/docs/add_filters.rst b/docs/add_filters.rst index 1bbfef12..4fe10c88 100644 --- a/docs/add_filters.rst +++ b/docs/add_filters.rst @@ -12,8 +12,10 @@ If the user wants to add new photometric filters to SPISEA, there are 4 main ste call the new function in ``filters.py`` when the new filter string is called. 4) Edit the ``get_obs_str()`` function in ``synthetic.py`` to - convert between column name and filter string (e.g input string - for get_filter_info) for the new filters. + convert between column name and filter string (e.g input string + for get_filter_info) for the new filters. + 5) Add the filter to the ``filt_list`` in the ``test_filters()`` function in ``tests/test_models.py`` to + ensure the filter can be loaded properly. Additional documentation on this is coming soon. In the meantime, let us know on the Github `issue tracker `_ if you'd like to diff --git a/docs/filters.rst b/docs/filters.rst index ab2e5418..87bc99ab 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -83,9 +83,10 @@ Example: ``'decam,r'`` **Euclid** -`Euclid (NISP) space telescope filters `_ +Euclid space telescope `NISP filters `_ +and `VIS filter-free detector transmission curve `_ -Filters: Y, J, H +Filters: VIS, Y, J, H Example: ``'euclid,Y'`` diff --git a/filt_func/euclid/VIS.dat b/filt_func/euclid/VIS.dat new file mode 100755 index 00000000..8071c831 --- /dev/null +++ b/filt_func/euclid/VIS.dat @@ -0,0 +1,551 @@ +4369.190 0.00056679011507854 +4379.190 0.00095861003123264 +4389.180 0.0014222192668852 +4399.180 0.0017743627364614 +4409.170 0.0017759292855133 +4419.170 0.0015893083675879 +4429.160 0.0015197711551908 +4439.150 0.0016367665308994 +4449.150 0.0017004749033207 +4459.140 0.001630730073096 +4469.140 0.0021505801024037 +4479.130 0.0038159666691211 +4489.120 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get_obs_str(col): 'rubin_u':'rubin,u', 'rubin_z':'rubin,z', 'rubin_y':'rubin,y', + 'euclid_VIS':'euclid,VIS', 'euclid_Y':'euclid,Y', 'euclid_J':'euclid,J', 'euclid_H':'euclid,H'} diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 78d8e9e1..4c1af45e 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -186,7 +186,7 @@ def test_filters(): 'roman,wfi,f158', 'roman,wfi,f146', 'roman,wfi,f213', 'roman,wfi,f184', 'rubin,g', 'rubin,i', 'rubin,r', 'rubin,u', 'rubin,z', 'rubin,y', - 'euclid,Y', 'euclid,J', 'euclid,H'] + 'euclid,VIS', 'euclid,Y', 'euclid,J', 'euclid,H'] # Loop through filters to test that they work: get_filter_info for ii in filt_list: From d8391c96bbd70e4d99375215cccde2ef08119756 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 26 Jan 2026 15:13:44 -0800 Subject: [PATCH 056/191] Update filters.rst --- docs/filters.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/filters.rst b/docs/filters.rst index 87bc99ab..918f27f4 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -84,7 +84,7 @@ Example: ``'decam,r'`` **Euclid** Euclid space telescope `NISP filters `_ -and `VIS filter-free detector transmission curve `_ +and `VIS single filter `_ Filters: VIS, Y, J, H From d43fbf1138a784b1ac443ad57d77346e0eda500f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Sun, 1 Feb 2026 22:16:41 -0800 Subject: [PATCH 057/191] Passed all tests after merging with masked array companion mass update, except the model_young_cluster_object function in test_synthetic --- .gitignore | 5 +- spisea/ifmr.py | 2 +- spisea/imf/multiplicity.py | 32 +-- spisea/synthetic.py | 34 ++-- spisea/tests/test_exceptions.py | 8 +- spisea/tests/test_models.py | 24 +-- spisea/tests/test_reddening.py | 23 ++- spisea/tests/test_synthetic.py | 345 +++++++++++++++++++++++--------- 8 files changed, 311 insertions(+), 162 deletions(-) diff --git a/.gitignore b/.gitignore index 7909d367..b29e4f96 100755 --- a/.gitignore +++ b/.gitignore @@ -51,4 +51,7 @@ distribute-*.tar.gz # OS Generated Files .DS_Store -._* \ No newline at end of file +._* + +# Test files generated +spisea/tests/isochrones diff --git a/spisea/ifmr.py b/spisea/ifmr.py index 2b67ca7c..cafa66d6 100755 --- a/spisea/ifmr.py +++ b/spisea/ifmr.py @@ -771,7 +771,7 @@ def generate_death_mass(self, mass_array): output_array = np.zeros((2, len(mass_array))) #Random array to get probabilities for what type of object will form - random_array = self.rng.intergers(1, 1001, size = len(mass_array)) + random_array = self.rng.integers(1, 1001, size = len(mass_array)) codes = {'WD': 101, 'NS': 102, 'BH': 103} diff --git a/spisea/imf/multiplicity.py b/spisea/imf/multiplicity.py index 0d28ced6..685d5917 100755 --- a/spisea/imf/multiplicity.py +++ b/spisea/imf/multiplicity.py @@ -1,6 +1,7 @@ import numpy as np import astropy.modeling from random import choice +from scipy.stats import truncnorm defaultMF_amp = 0.44 defaultMF_power = 0.51 @@ -249,29 +250,34 @@ def log_semimajoraxis(self, mass): Parameters ---------- - mass : float - Mass of primary star + mass : array-like + Mass array of primary star Returns ------- - log_semimajoraxis : float + log_semimajoraxis : array-like Log of the semimajor axis/separation between the stars in units of AU """ a_mean_func = astropy.modeling.powerlaws.BrokenPowerLaw1D(amplitude=self.a_amp, x_break=self.a_break, alpha_1=self.a_slope1, alpha_2=self.a_slope2) log_a_mean = np.log10(a_mean_func(mass)) #mean log(a) log_a_std_func = astropy.modeling.models.Linear1D(slope=self.a_std_slope, intercept=self.a_std_intercept) log_a_std = log_a_std_func(np.log10(mass)) #sigma_log(a) - if mass >= 2.9: - log_a_std = log_a_std_func(np.log10(2.9)) #sigma_log(a) - if log_a_std < 0.1: - log_a_std = 0.1 - - log_semimajoraxis = np.random.normal(log_a_mean, log_a_std) - while 10**log_semimajoraxis > 2000 or log_semimajoraxis < -2: #AU - log_semimajoraxis = np.random.normal(log_a_mean, log_a_std) - + + large_mass_idx = mass >= 2.9 + log_a_std[large_mass_idx] = log_a_std_func(np.log10(2.9)) #sigma_log(a) + log_a_std = np.clip(log_a_std, 0.1, None) + + # Trunc normal distribution between -2 and 2000 AU + log_a_lower = np.log10(0.01) + log_a_upper = np.log10(2000) + + # Convert bounds to standard normal space + a_lower_std = (log_a_lower - log_a_mean) / log_a_std + a_upper_std = (log_a_upper - log_a_mean) / log_a_std + + log_semimajoraxis = truncnorm.rvs(a_lower_std, a_upper_std, loc=log_a_mean, scale=log_a_std) return log_semimajoraxis - + def random_e(self, x): """ Generate random eccentricity from the inverse of the CDF where the PDF is f(e) = 2e from Duchene and Kraus 2013 diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 73f222a8..697e36c6 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -677,10 +677,6 @@ def __init__(self, iso, imf, cluster_mass, deltaAKs, ResolvedCluster.__init__(self, iso, imf, cluster_mass, ifmr=ifmr, verbose=verbose, seed=seed) - # Set random seed, if desired - if seed is not None: - np.random.seed(seed=seed) - # Extract the extinction law from the isochrone object redlaw_str = iso.points.meta['REDLAW'] red_law = reddening.get_red_law(redlaw_str) @@ -1134,24 +1130,17 @@ def __init__(self, logAge, AKs, distance, # For solar metallicity case, allow for legacy isochrones (which didn't have # metallicity tag since they were all solar metallicity) to be read # properly - if metallicity == 0.0: - save_file_fmt = '{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}_p00.fits' - self.save_file = save_file_fmt.format(iso_dir, logAge, AKs, str(round(distance)).zfill(5)) + + # Set save file name + metal_value = round(abs(metallicity), 2) + metal_sign = 'm' if metallicity < 0 else 'p' + self.save_file = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}_{metal_sign}{metal_value:.2f}.fits' - save_file_legacy = '{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}.fits' - self.save_file_legacy = save_file_legacy.format(iso_dir, logAge, AKs, str(round(distance)).zfill(5)) + if metallicity == 0.0: + self.save_file_legacy = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}.fits' else: - # Set metallicity flag - if metallicity < 0: - metal_pre = 'm' - else: - metal_pre = 'p' - metal_flag = int(abs(metallicity)*100) + self.save_file_legacy = self.save_file - save_file_fmt = '{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}_{4}{5:2s}.fits' - self.save_file = save_file_fmt.format(iso_dir, logAge, AKs, str(round(distance)).zfill(5), metal_pre, str(metal_flag).zfill(3)) - self.save_file_legacy = save_file_fmt.format(iso_dir, logAge, AKs, str(round(distance)).zfill(5), metal_pre, str(metal_flag).zfill(3)) - # Expected filters self.filters = filters @@ -1259,9 +1248,10 @@ def make_photometry(self, rebin=True, vega=vega, comp_filters=None): # Loop through the filters, get filter info, make photometry for # all stars in this filter. for ii in comp_filters: - prt_fmt = 'Starting filter: {0:s} Elapsed time: {1:.2f} seconds' - print( prt_fmt.format(ii, time.time() - startTime)) - + if self.verbose: + prt_fmt = 'Starting filter: {0:s} Elapsed time: {1:.2f} seconds' + print( prt_fmt.format(ii, time.time() - startTime)) + filt = get_filter_info(ii, rebin=rebin, vega=vega) filt_name = get_filter_col_name(ii) diff --git a/spisea/tests/test_exceptions.py b/spisea/tests/test_exceptions.py index 4996bb3c..4fe73e5f 100644 --- a/spisea/tests/test_exceptions.py +++ b/spisea/tests/test_exceptions.py @@ -34,10 +34,4 @@ def test_grid_number_exception(): evolution.check_evo_grid_number(required_grid, models_dir) - return - - - - - - + return \ No newline at end of file diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 78d8e9e1..2fd66f22 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -35,7 +35,7 @@ def test_evolution_models(): evo_models = [evolution.MISTv1(version=1.2), evolution.MergedBaraffePisaEkstromParsec(), evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa()] - + # Array of age_ranges for the specific evolution models to test age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr] @@ -190,25 +190,19 @@ def test_filters(): # Loop through filters to test that they work: get_filter_info for ii in filt_list: - try: - filt = synthetic.get_filter_info(ii, rebin=True, vega=vega) - except: - raise Exception('get_filter_info TEST FAILED for {0}'.format(ii)) + filt = synthetic.get_filter_info(ii, rebin=True, vega=vega) print('get_filter_info pass') # Loop through filters to test that they work: get_obs_str for ii in filt_list: - try: - # Test going from col_name to obs_str - col_name = synthetic.get_filter_col_name(ii) - obs_str = synthetic.get_obs_str('m_{0}'.format(col_name)) - # Does the obs_str work? - filt_info = synthetic.get_filter_info(obs_str) - except: - raise Exception('get_obs_str TEST FAILED for {0}'.format(ii)) - + # Test going from col_name to obs_str + col_name = synthetic.get_filter_col_name(ii) + obs_str = synthetic.get_obs_str('m_{0}'.format(col_name)) + # Does the obs_str work? + filt_info = synthetic.get_filter_info(obs_str) + print('get_obs_str pass') print('Filters done') - return + return \ No newline at end of file diff --git a/spisea/tests/test_reddening.py b/spisea/tests/test_reddening.py index d4b99d88..2df86a9a 100644 --- a/spisea/tests/test_reddening.py +++ b/spisea/tests/test_reddening.py @@ -214,15 +214,22 @@ def test_red_law_IsochronePhot(): aks = aks_arr[ii] # Try to run isochrone phot - iso_test = synthetic.IsochronePhot(logAge, aks, dist, metallicity=0, - evo_model=evo_model, atm_func=atm_func, - red_law=redlaw, filters=filt_list, - min_mass=0.95, max_mass=1.05) - # Now remove the iso file to make sure we recalc each time - cmd = 'rm iso_6.70_*_08000_p00.fits' - os.system(cmd) + iso_test = synthetic.IsochronePhot( + logAge, + aks, + dist, + metallicity=0, + evo_model=evo_model, + atm_func=atm_func, + red_law=redlaw, + filters=filt_list, + min_mass=0.95, + max_mass=1.05, + iso_dir='isochrones/', + recomp=True + ) print('----EL {0} works OK!-----'.format(redlaw_arr[ii])) - + return def test_all_EL(): diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index ec2628a2..45dbbe6f 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -46,6 +46,7 @@ def test_iso_wave(): logAge = np.log10(5*10**6.) # Age in log(years) AKs = 0.8 # extinction in mags dist = 4000 # distance in parsec + iso_dir = 'isochrones/' # Define evolution/atmosphere models and extinction law (optional) evo_model = evolution.MergedBaraffePisaEkstromParsec() @@ -65,11 +66,17 @@ def test_iso_wave(): # Make Isochrone object. Will use wave_range = [3000,52000]. # Make sure range matches to resolution of atmosphere. wave_range1 = [3000, 52000] - my_iso = syn.IsochronePhot(logAge, AKs, dist, - evo_model=evo_model, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=10, wave_range=wave_range1, - recomp=True) + my_iso = syn.IsochronePhot( + logAge, AKs, dist, + evo_model=evo_model, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=10, + wave_range=wave_range1, + recomp=True, + iso_dir=iso_dir + ) test = my_iso.spec_list[0] @@ -79,11 +86,19 @@ def test_iso_wave(): # Now let's try changing the wave range. Is it carried through # properly? wave_range2 = [1200, 90000] - my_iso = syn.IsochronePhot(logAge, AKs, dist, - evo_model=evo_model, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=10, wave_range=wave_range2, - recomp=True) + my_iso = syn.IsochronePhot( + logAge, + AKs, + dist, + evo_model=evo_model, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=10, + wave_range=wave_range2, + recomp=True, + iso_dir=iso_dir + ) test2 = my_iso.spec_list[0] @@ -93,11 +108,19 @@ def test_iso_wave(): # Does the error exception catch the bad wave_range? wave_range3 = [1200, 1000000] try: - my_iso = syn.IsochronePhot(logAge, AKs, dist, - evo_model=evo_model, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=10, wave_range=wave_range3, - recomp=True) + my_iso = syn.IsochronePhot( + logAge, + AKs, + dist, + evo_model=evo_model, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=10, + wave_range=wave_range3, + recomp=True, + iso_dir=iso_dir + ) print('WAVE TEST FAILED!!! Should have crashed here, wavelength range out of bounds') raise ValueError() except: @@ -111,17 +134,24 @@ def test_IsochronePhot(plot=False): distance = 4000 filt_list = ['wfc3,ir,f127m', 'nirc2,J'] mass_sampling=1 - iso_dir = 'iso/' + iso_dir = 'isochrones/' evo_model = evolution.MISTv1() atm_func = atmospheres.get_merged_atmosphere redlaw = reddening.RedLawNishiyama09() startTime = time.time() - iso = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo_model, - atm_func=atm_func, red_law=redlaw, - filters=filt_list, - mass_sampling=mass_sampling, iso_dir=iso_dir) + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo_model, + atm_func=atm_func, + red_law=redlaw, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) endTime = time.time() print('IsochronePhot generated in: %d seconds' % (endTime - startTime)) # Typically takes 120 seconds if file is regenerated. @@ -142,35 +172,57 @@ def test_IsochronePhot(plot=False): iso.plot_mass_magnitude('mag160w') # Finally, let's test the isochronePhot file generation - assert os.path.exists('{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}_p00.fits'.format(iso_dir, logAge, - AKs, str(distance).zfill(5))) - + metal_value = 0. + metal_sign = 'm' if metal_value < 0 else 'p' + assert os.path.exists(f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(distance).zfill(5)}_{metal_sign}{metal_value:.2f}.fits') + # Check 1: If we try to remake the isochrone, does it read the file rather than # making a new one - iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo_model, - atm_func=atm_func, red_law=redlaw, - filters=filt_list, - mass_sampling=mass_sampling, iso_dir=iso_dir) + iso_new = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo_model, + atm_func=atm_func, + red_law=redlaw, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) assert iso_new.recalc == False # Check 2: Confirm that adding a new column to an existing isochrone works properly. # Does the new filter get added to the isochrone? And the old ones still there? # Does the computed data for the new filter match the same result if you fully regenerate the isochrone? - iso_new_addfilt = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo_model, - atm_func=atm_func, red_law=redlaw, - filters=filt_list+['2mass,Ks'], - mass_sampling=mass_sampling, iso_dir=iso_dir) + iso_new_addfilt = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo_model, + atm_func=atm_func, + red_law=redlaw, + filters=filt_list+['2mass,Ks'], + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) assert iso_new_addfilt.recalc == False assert 'm_2mass_Ks' in iso_new_addfilt.points.colnames assert 'm_nirc2_J' in iso_new_addfilt.points.colnames - iso_new_3filt = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo_model, - atm_func=atm_func, red_law=redlaw, - filters=filt_list+['2mass,Ks'], - mass_sampling=mass_sampling, iso_dir=iso_dir, - recomp=True) + iso_new_3filt = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo_model, + atm_func=atm_func, + red_law=redlaw, + filters=filt_list+['2mass,Ks'], + mass_sampling=mass_sampling, + recomp=True, + iso_dir=iso_dir + ) np.testing.assert_almost_equal(iso_new_addfilt.points['m_2mass_Ks'], iso_new_3filt.points['m_2mass_Ks']) assert iso_new_3filt.recalc==True @@ -179,26 +231,47 @@ def test_IsochronePhot(plot=False): evo2 = evolution.MergedBaraffePisaEkstromParsec() mass_sampling=20 - iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo2, - atm_func=atm_func, red_law=redlaw, - filters=filt_list, - mass_sampling=mass_sampling, iso_dir=iso_dir) + iso_new = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo2, + atm_func=atm_func, + red_law=redlaw, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) assert iso_new.recalc == True redlaw2 = reddening.RedLawHosek18b() - iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo2, - atm_func=atm_func, red_law=redlaw2, - filters=filt_list, - mass_sampling=mass_sampling, iso_dir=iso_dir) + iso_new = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo2, + atm_func=atm_func, + red_law=redlaw2, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) assert iso_new.recalc == True atm2 = atmospheres.get_castelli_atmosphere - iso_new = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo2, - atm_func=atm2, red_law=redlaw2, - filters=filt_list, - mass_sampling=mass_sampling, iso_dir=iso_dir) + iso_new = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo2, + atm_func=atm2, + red_law=redlaw2, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) assert iso_new.recalc == True @@ -211,6 +284,7 @@ def test_ResolvedCluster(): distance = 4000 cluster_mass = 10**5. mass_sampling=5 + iso_dir = 'isochrones/' # Test filters filt_list = ['nirc2,J', 'nirc2,Kp'] @@ -222,10 +296,17 @@ def test_ResolvedCluster(): red_law = reddening.RedLawNishiyama09() - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) @@ -287,7 +368,7 @@ def test_ResolvedCluster(): plt.plot(iso.points['m_nirc2_J'] - iso.points['m_nirc2_Kp'], iso.points['m_nirc2_J'], 'c-') plt.gca().invert_yaxis() plt.xlabel('J - Kp (mag)') - plt.ylabel('J (mag') + plt.ylabel('J (mag)') # Plot a mass-magnitude relationship. plt.figure(2) @@ -317,6 +398,7 @@ def test_ResolvedClusterDiffRedden(): cluster_mass = 10**5. deltaAKs = 0.05 mass_sampling=5 + iso_dir = 'isochrones/' # Test filters filt_list = ['nirc2,J', 'nirc2,Kp'] @@ -328,10 +410,17 @@ def test_ResolvedClusterDiffRedden(): red_law = reddening.RedLawNishiyama09() - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) @@ -434,6 +523,7 @@ def test_ifmr_multiplicity(): distance = 1000 cluster_mass = 1e6 mass_sampling = 5 + iso_dir = 'isochrones/' # Test all filters filt_list = ['nirc2,Kp', 'nirc2,H', 'nirc2,J'] @@ -446,10 +536,17 @@ def test_ifmr_multiplicity(): red_law = reddening.RedLawNishiyama09() - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) @@ -519,28 +616,46 @@ def test_metallicity(): atm_func = atmospheres.get_phoenixv16_atmosphere red_law = reddening.RedLawHosek18b() filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f139m', 'wfc3,ir,f153m'] + iso_dir = 'isochrones/' # Start with a solar metallicity isochrone - metallicity= 0.0 + metallicity= 0. + metal_sign = 'm' if metallicity < 0 else 'p' # Make Isochrone object, with high mass_sampling to decrease compute time - my_iso = syn.IsochronePhot(logAge, AKs, dist, metallicity=metallicity, - evo_model=evo_model, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=10) - + my_iso = syn.IsochronePhot( + logAge, + AKs, + dist, + metallicity=metallicity, + evo_model=evo_model, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=10, + iso_dir=iso_dir + ) + # Test isochrone properties assert my_iso.points.meta['METAL_IN'] == 0.0 - assert os.path.exists('iso_6.70_0.80_04000_p00.fits') + assert os.path.exists(f'{iso_dir}/iso_6.70_0.80_04000_p0.00.fits') # Now for non-solar metallicity metallicity= -1.5 # Make Isochrone object, with high mass_sampling to decrease compute time - my_iso = syn.IsochronePhot(logAge, AKs, dist, metallicity=metallicity, - evo_model=evo_model, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=10) + my_iso = syn.IsochronePhot( + logAge, + AKs, + dist, + metallicity=metallicity, + evo_model=evo_model, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=10, + iso_dir=iso_dir + ) # MIST model sub-directory names changed in SPISEA v2.1.4 update; # changing what "metal_act" value was. version 1 of MIST grid @@ -555,12 +670,12 @@ def test_metallicity(): metal_act = np.log10(0.00047 / 0.0142) # For Mist isochrones else: metal_act = np.log10(0.00045 / 0.0142) # For Mist isochrones - + # Test isochrone properties assert my_iso.points.meta['METAL_IN'] == -1.5 assert np.isclose(my_iso.points.meta['METAL_ACT'], metal_act) - assert os.path.exists('iso_6.70_0.80_04000_m15.fits') - + assert os.path.exists(f'{iso_dir}/iso_6.70_0.80_04000_m1.50.fits') + return def test_cluster_mass(): @@ -570,6 +685,7 @@ def test_cluster_mass(): distance = 4000 cluster_mass = 10**5. mass_sampling = 5 + iso_dir = 'isochrones/' # Test filters filt_list = ['nirc2,J', 'nirc2,Kp'] @@ -581,10 +697,17 @@ def test_cluster_mass(): atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawHosek18b() - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) @@ -651,6 +774,7 @@ def test_keep_low_mass_stars(): distance = 4000 cluster_mass = 10**5. mass_sampling = 5 + iso_dir = 'isochrones/' # Test filters filt_list = ['nirc2,J', 'nirc2,Kp'] @@ -660,10 +784,17 @@ def test_keep_low_mass_stars(): atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawHosek18b() - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) # Get the minimum mass in the isochrones. This should be the lowest # mass psosbile when keep_low_mass_stars == False. @@ -707,6 +838,7 @@ def test_compact_object_companions(): distance = 4000 cluster_mass = 10**4. mass_sampling=5 + iso_dir = 'isochrones/' # Test filters filt_list = ['nirc2,J', 'nirc2,Kp'] @@ -718,10 +850,17 @@ def test_compact_object_companions(): red_law = reddening.RedLawNishiyama09() - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=mass_sampling, + iso_dir=iso_dir + ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) @@ -748,6 +887,7 @@ def time_test_cluster(): AKs = 2.7 distance = 4000 cluster_mass = 10**4 + iso_dir = 'isochrones/' startTime = time.time() @@ -756,9 +896,16 @@ def time_test_cluster(): red_law = reddening.RedLawNishiyama09() filt_list = ['nirc2,J', 'nirc2,Kp'] - iso = syn.IsochronePhot(logAge, AKs, distance, - evo_model=evo, atm_func=atm_func, - red_law=red_law, filters=filt_list) + iso = syn.IsochronePhot( + logAge, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + iso_dir=iso_dir + ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) imf_limits = np.array([0.07, 0.5, 150]) @@ -782,7 +929,14 @@ def model_young_cluster_object(resolved=False): imf_in = imf.Kroupa_2001(multiplicity=multi) evo = evolution.MergedPisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere - iso = syn.Isochrone(log_age, AKs, distance, evo, mass_sampling=10) + iso = syn.Isochrone( + log_age, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + mass_sampling=10 + ) if resolved: cluster = syn.ResolvedCluster(iso, imf_in, cluster_mass) @@ -804,17 +958,18 @@ def model_young_cluster_object(resolved=False): plt.plot(wave, flux, 'k.') return - + def time_test_mass_match(): log_age = 6.7 AKs = 2.7 distance = 4000 cluster_mass = 5e3 - + iso_dir = 'isochrones/' + imf_in = imf.Kroupa_2001(multiplicity=None) start_time = time.time() - iso = syn.IsochronePhot(log_age, AKs, distance) + iso = syn.IsochronePhot(log_age, AKs, distance, iso_dir=iso_dir) iso_masses = iso.points['mass'] print('Generated iso masses in {0:.0f} s'.format(time.time() - start_time)) @@ -1051,4 +1206,4 @@ def test_Raithel18_IFMR_5(): assert len(WD_idx) == 2 , "There are not the right number of WDs for the Raithel18 IFMR" - return + return \ No newline at end of file From 1279e317966529dcb92dff13bcc82e5f82419f18 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 2 Feb 2026 13:48:07 -0800 Subject: [PATCH 058/191] Fixed evo model error in test, passed all tests --- spisea/tests/test_synthetic.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 45dbbe6f..ada9544f 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -924,10 +924,10 @@ def model_young_cluster_object(resolved=False): AKs = 0.1 distance = 8000.0 cluster_mass = 10000. - + multi = multiplicity.MultiplicityUnresolved() imf_in = imf.Kroupa_2001(multiplicity=multi) - evo = evolution.MergedPisaEkstromParsec() + evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere iso = syn.Isochrone( log_age, From 9d051cbabe09da0ecfd6c9024f209108e8dea87c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 5 Feb 2026 22:58:01 -0800 Subject: [PATCH 059/191] Nothing but remove trailing white spaces --- spisea/imf/imf.py | 142 +++++++++++++++++++++++----------------------- 1 file changed, 71 insertions(+), 71 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 5df02afa..2c493f54 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -4,7 +4,7 @@ # Original code was taken from libimf package written by Jan Pflamm-Altenburg # and has been modified only marginally. The libimf code was licensed under # a GNU General Public License. -# +# # When I use this code, I should cite Pflamm-Altenburg & Kroupa 2006 # # Unfortunately, the code was almost completely un-commented, so all @@ -22,29 +22,29 @@ class IMF(object): """ - The IMF base class. The mass sampling and multiplicity - implementation is here. + The IMF base class. The mass sampling and multiplicity + implementation is here. Notes ----- - Code author: J. Lu. + Code author: J. Lu. Original code was taken from libimf package written by Jan Pflamm-Altenburg (`Pflamm-Altenburg & Kroupa 2006 `_) and has been modified only marginally, though more convinient and general purpose functions have been added. The libimf code was licensed under - a GNU General Public License. + a GNU General Public License. Parameters ---------- massLimits : 2 element array; optional - Define the minimum and maximum stellar masses in the IMF, in - solar masses. First element is taken as the min, second element + Define the minimum and maximum stellar masses in the IMF, in + solar masses. First element is taken as the min, second element the max (e.g. `massLimits` = [min_mass, max_mass]). multiplicity : Multiplicity object or None - If None, no multiplicity is assumed. Otherwise, use + If None, no multiplicity is assumed. Otherwise, use multiplicity object to create multiple star systems. """ def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None, seed=None): @@ -52,19 +52,19 @@ def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None, seed=None) self._mass_limits = np.atleast_1d(massLimits) self.seed = seed self.rng = np.random.default_rng(seed) - + if multiplicity: self.make_multiples = True else: self.make_multiples = False - + return - + def generate_cluster(self, totalMass): """ Generate a cluster of stellar systems with the specified IMF. - + Randomly sample from an IMF with specified mass limits until the desired total mass is reached. The maximum stellar mass is not allowed to exceed the total cluster mass. @@ -96,8 +96,8 @@ def generate_cluster(self, totalMass): system and False for each single star. companionMasses : numpy float array - List of - + List of + """ initial_mass_limit = self._mass_limits[-1] @@ -124,19 +124,19 @@ def generate_cluster(self, totalMass): # the desired total cluster mass. totalMassTally = 0 loopCnt = 0 - + # start_while = time.time() while totalMassTally < totalMass: # Generate a random number array. uniX = self.rng.random(int(newStarCount)) # Convert into the IMF from the inverted CDF newMasses = self.dice_star_cl(uniX) - + # Testing for Nans produced in masses test = np.isnan(newMasses) if np.sum(test) > 0: raise ValueError('Nan detected in cluster mass') - + # Dealing with multiplicity if self._multi_props: # newCompMasses = np.empty((len(newMasses),), dtype=object) @@ -163,7 +163,7 @@ def generate_cluster(self, totalMass): # print('Time taken for while loop: ', end_while - start_while) # Append to our primary masses array masses = np.append(masses, newMasses) - + if (loopCnt >= 0): log.info('sample_imf: Loop %d added %.2e Msun to previous total of %.2e Msun' % (loopCnt, newTotalMassTally, totalMassTally)) @@ -171,27 +171,27 @@ def generate_cluster(self, totalMass): totalMassTally += newTotalMassTally newStarCount = mean_number * 0.1 # increase by 20% each pass loopCnt += 1 - + # Make a running sum of the system masses if self._multi_props: # Concatenate the companion masses if len(compMasses) > 1: max_cols = max(compMass.shape[1] for compMass in compMasses) - + # Pad each array to have the same number of columns padded_arrays = [ np.ma.masked_all((compMass.shape[0], max_cols)) for compMass in compMasses ] - + for i, compMass in enumerate(compMasses): padded_arrays[i][:, :compMass.shape[1]] = compMass - + # Vertically stack the padded arrays compMasses = np.ma.vstack(padded_arrays) - + else: compMasses = compMasses[0] - + # Make a running sum of the system masses massCumSum = systemMasses.cumsum() else: @@ -214,7 +214,7 @@ def generate_cluster(self, totalMass): self._mass_limits[-1] = initial_mass_limit return (masses, isMultiple, compMasses, systemMasses) - + def calc_multi(self, newMasses, newIsMultiple, CSF, MF): """ Helper function to calculate multiples more efficiently. @@ -222,8 +222,8 @@ def calc_multi(self, newMasses, newIsMultiple, CSF, MF): """ # Copy over the primary masses. Eventually add the companions. newSystemMasses = newMasses.copy() - - # Identify multiple systems, calculate number of companions for each + + # Identify multiple systems, calculate number of companions for each multiple_idx = np.where(newIsMultiple)[0] comp_nums = 1 + self.rng.poisson((CSF[multiple_idx] / MF[multiple_idx]) - 1) if self._multi_props.companion_max: @@ -236,21 +236,21 @@ def calc_multi(self, newMasses, newIsMultiple, CSF, MF): comp_unique = np.unique(comp_nums) comp_indices = [np.where(comp_nums == i)[0] for i in comp_unique] compMasses = np.zeros((len(newMasses), max(comp_unique))) - + for comp_num, comp_index in zip(comp_unique, comp_indices): # Calculate masses of companions q_values = self._multi_props.random_q(self.rng.random((len(comp_index), comp_num))) m_comp = np.multiply(q_values, np.transpose([primary[comp_index]])) compMasses[multiple_idx[comp_index], :comp_num] = m_comp - + # Mask out companions that are less than the minimum mass compMasses = np.ma.MaskedArray(compMasses, mask=compMasses < self._mass_limits[0]) newSystemMasses[multiple_idx] += compMasses[multiple_idx].sum(axis=1) newIsMultiple = np.any(~compMasses.mask, axis=1) - + return compMasses, newSystemMasses, newIsMultiple - - + + class IMF_broken_powerlaw(IMF): """ Initialize a multi-part power-law with N parts. Each part of the @@ -263,7 +263,7 @@ class IMF_broken_powerlaw(IMF): Parameters ---------- mass_limits : numpy array - Array of length (N + 1) with lower and upper limits of + Array of length (N + 1) with lower and upper limits of the power-law segments. powers : numpy array @@ -271,7 +271,7 @@ class IMF_broken_powerlaw(IMF): power-law segment. multiplicity : Multiplicity object or None - If None, no multiplicity is assumed. Otherwise, use + If None, no multiplicity is assumed. Otherwise, use multiplicity object to create multiple star systems. """ def __init__(self, mass_limits, powers, multiplicity=None, seed=None): @@ -316,7 +316,7 @@ def xi(self, m): xi - probability of measuring that mass. """ returnFloat = type(m) == float - + m = np.atleast_1d(m) # Temporary arrays @@ -333,7 +333,7 @@ def xi(self, m): # Maybe we are all done? if len(idx) == 0: break - + m_tmp = m[idx] aux_tmp = aux[idx] @@ -346,7 +346,7 @@ def xi(self, m): z *= delta(m - self._m_limits_high[i]) xi = self.k * z * y - + if returnFloat: return xi[0] else: @@ -374,7 +374,7 @@ def m_xi(self, m): # Maybe we are all done? if len(idx) == 0: break - + m_tmp = m[idx] aux_tmp = aux[idx] @@ -387,7 +387,7 @@ def m_xi(self, m): z *= delta(m - self._m_limits_high[i]) mxi = self.k * z * y - + if returnFloat: return mxi[0] else: @@ -395,23 +395,23 @@ def m_xi(self, m): def getProbabilityBetween(self, massLo, massHi): - """Return the integrated probability between some low and high + """Return the integrated probability between some low and high mass value. """ return self.int_xi(massLo, massHi) def int_xi(self, massLo, massHi): - """Return the integrated probability between some low and high + """Return the integrated probability between some low and high mass value. """ return self.prim_xi(massHi) - self.prim_xi(massLo) - + def getMassBetween(self, massLo, massHi): - """Return the integrated mass between some low and high + """Return the integrated mass between some low and high mass value. """ return self.int_mxi(massLo, massHi) - + def int_mxi(self, massLo, massHi): """Return the integrated total mass between some low and high stellar mass value. Be sure to normalize the IMF instance beforehand. @@ -433,7 +433,7 @@ def prim_xi(self, a): t3 = prim_power(self._m_limits_low, self._powers) y1 = (t1 * (t2 - t3)).sum() - t1 = gamma_closed(a[i], self._m_limits_low, self._m_limits_high) + t1 = gamma_closed(a[i], self._m_limits_low, self._m_limits_high) t1 *= self.coeffs t2 = prim_power(a[i], self._powers) t3 = prim_power(self._m_limits_low, self._powers) @@ -451,7 +451,7 @@ def prim_mxi(self, a): Helper function """ returnFloat = type(a) == float - + a = np.atleast_1d(a) val = np.zeros(len(a), dtype=float) @@ -460,8 +460,8 @@ def prim_mxi(self, a): t2 = prim_power(self._m_limits_high, self._powers+1) t3 = prim_power(self._m_limits_low, self._powers+1) y1 = (t1 * (t2 - t3)).sum() - - t1 = gamma_closed(a[i], self._m_limits_low, self._m_limits_high) + + t1 = gamma_closed(a[i], self._m_limits_low, self._m_limits_high) t1 *= self.coeffs t2 = prim_power(a[i], self._powers+1) t3 = prim_power(self._m_limits_low, self._powers+1) @@ -481,7 +481,7 @@ def normalize(self, Mcl, Mmin=None, Mmax=None): """ self.k = 1.0 self.Mcl = Mcl - + if Mmax == None: Mmax = self._m_limits_high[-1] @@ -491,7 +491,7 @@ def normalize(self, Mcl, Mmin=None, Mmax=None): if Mmax > Mcl: Mmax = Mcl - + if Mmax > self._m_limits_high[-1]: Mmax = self._m_limits_high[-1] @@ -500,7 +500,7 @@ def normalize(self, Mcl, Mmin=None, Mmax=None): self.norm_Mmin = Mmin self.norm_Mmax = Mmax - + self.k = Mcl / self.int_mxi(self.norm_Mmin, self.norm_Mmax) self.lamda = self.int_xi_cl(self._m_limits_low[0], self._mass_limits) @@ -519,7 +519,7 @@ def norm_cl_wk04(self, Mcl, Mmax=None, Mmin=None): if Mmax > Mcl: Mmax = Mcl - + if Mmax > self._m_limits_high[-1]: Mmax = self._m_limits_high[-1] @@ -587,24 +587,24 @@ def dice_star_cl(self, r): returnFloat = type(r) == float r = np.atleast_1d(r) # Make sure it is an array - x = r * self.lamda[-1] + x = r * self.lamda[-1] y = np.zeros_like(r) z = np.ones_like(r) # Loop through the different parts of the power law. for i in range(self.nterms): #-----For i = 0 --> n, where n is the number of intervals aux = x - self.lamda[i] #---Should this be i - 1? - + # Only continue for those entries that are in later segments idx = aux >= 0 # Maybe we are all done? if sum(idx) == 0: break - + x_tmp = x[idx] aux_tmp = aux[idx] - + t1 = aux_tmp / (self.coeffs[i] * self.k) t1 += prim_power(self._m_limits_low[i], self._powers[i]) y_i = gamma_closed(x_tmp, self.lamda[i], self.lamda[i+1]) @@ -684,7 +684,7 @@ def __init__(self, multiplicity=None): multiplicity=multiplicity) ################################################## -# +# # Generic functions -- see if we can move these up. # ################################################## @@ -708,7 +708,7 @@ def prim_power(m, power): valid_idx = power != -1 val[valid_idx] = (m[valid_idx]**z[valid_idx]) / z[valid_idx] val[~valid_idx] = np.log(m[~valid_idx]) - + if returnFloat: return val[0] else: @@ -716,7 +716,7 @@ def prim_power(m, power): def inv_prim_power(x, power): """ - returns ((1+power) * x)**(1.0 / (1 + power)) and handles the case + returns ((1+power) * x)**(1.0 / (1 + power)) and handles the case when power == -1. """ returnFloat = (type(x) == float) and (type(power) == float) @@ -731,7 +731,7 @@ def inv_prim_power(x, power): if x.shape != power.shape: raise ValueError('spisea.imf.inv_prim_power: Dimension mismatch, x and power must have the same shape') - + z = 1.0 + power val = np.empty_like(x) valid_idx = power != -1 @@ -742,7 +742,7 @@ def inv_prim_power(x, power): return val[0] else: return val - + def log_normal(m, mean_logm, sigma_logm): returnFloat = (type(m) == float) and (type(mean_logm) == float) and \ @@ -754,7 +754,7 @@ def log_normal(m, mean_logm, sigma_logm): z = np.log10(m) - mean_logm val = np.exp(-z**2 / (2.0 * sigma_logm**2)) / m - + if returnFloat: return val[0] else: @@ -770,7 +770,7 @@ def prim_log_normal(m, mean_logm, sigma_logm): mu = (np.log10(m) - mean_logm) / (1.4142135623731 * sigma_logm) val = 2.88586244942136 * sigma_logm * error(mu) - + if returnFloat: return val[0] else: @@ -783,10 +783,10 @@ def inv_prim_log_normal(x, mean_logm, sigma_logm): m = np.atleast_1d(m) mean_logm = np.atleat_1d(mean_logm) sigma_logm = np.atleat_1d(sigma_logm) - + mu = inv_error(0.346516861952484 * x / sigma_logm) val = 10.0**(1.4142135623731 * sigma_logm * mu + mean_logm) - + if returnFloat: return val[0] else: @@ -802,7 +802,7 @@ def mlog_normal(x, mean_logm, sigma_logm): z = np.log10(m) - mean_logm val = np.exp(-z**2 / (2.0 * sigma_logm**2)) - + if returnFloat: return val[0] else: @@ -823,12 +823,12 @@ def prim_mlog_normal(x, mean_logm, sigma_logm): val = error(eta) val *= 2.88586244942136 * sigma_logm * np.exp(2.30258509299405 * t1) - + if returnFloat: return val[0] else: return val - + def theta_closed(x): """ @@ -878,7 +878,7 @@ def delta(x): def gamma_closed(m, left, right): """ - + """ return theta_closed(m - left) * theta_closed(right - m) @@ -886,7 +886,7 @@ def gamma_closed(m, left, right): def error(x): x2 = x**2 ax2 = 0.140012288686666 * x2 - + val = np.sqrt(1.0 - np.exp(-x2*(1.27323954473516+ax2)/(1+ax2))) if x >=0: @@ -899,7 +899,7 @@ def inv_error(x): lnx2 = np.log(1.0 - x2) aux = 4.54688497944829 + (lnx2 / 2.0) y = -aux + np.sqrt(aux**2 - (lnx2 / 0.140012288686666)) - + val = np.sqrt(y) if x>=0: From 4cccd8c02d0e6b13741058af9aa3ce08c0bae697 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Fri, 6 Feb 2026 11:09:44 -0800 Subject: [PATCH 060/191] Complete missing docstring --- spisea/imf/imf.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 2c493f54..8dfc0e64 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -89,14 +89,17 @@ def generate_cluster(self, totalMass): Returns ------- masses : numpy float array - List of primary star masses. + Array of primary star masses. isMultiple : numpy boolean array - List of booleans with True for each primary star that is in a multiple + Array of booleans with True for each primary star that is in a multiple system and False for each single star. - companionMasses : numpy float array - List of + companionMasses : numpy masked array + Masked array of companion masses. Each row corresponds to a primary star, and each column corresponds to a companion. The mask is True for entries that are not valid companions (e.g. for single stars or for companions that are below the minimum mass limit). + + systemMasses : numpy float array + Array of total system masses (primary + companions) for each primary star. """ initial_mass_limit = self._mass_limits[-1] From 3237c57edc6e9226eab2233b936b0f438d373a92 Mon Sep 17 00:00:00 2001 From: Rocketpack23 Date: Wed, 11 Feb 2026 13:36:18 -0800 Subject: [PATCH 061/191] Added IRTF NSFCam L filter, updated filters.py and synthetic.py --- filt_func/nsfcam/L.dat | 390 +++++++++++++++++++++++++++++++++++ spisea/filters.py | 86 ++++---- spisea/synthetic.py | 446 ++++++++++++++++------------------------- 3 files changed, 606 insertions(+), 316 deletions(-) create mode 100755 filt_func/nsfcam/L.dat diff --git a/filt_func/nsfcam/L.dat b/filt_func/nsfcam/L.dat new file mode 100755 index 00000000..43589640 --- /dev/null +++ b/filt_func/nsfcam/L.dat @@ -0,0 +1,390 @@ +30156.8 0.000962257 +30175 0.00120592 +30193.2 0.00134921 +30211.5 0.00127125 +30229.7 0.00150251 +30248 0.00161243 +30266.3 0.00188732 +30284.7 0.00170279 +30303 0.00204206 +30321.4 0.00192571 +30339.8 0.00215411 +30358.2 0.0022676 +30376.7 0.00244951 +30395.1 0.00242066 +30413.6 0.0025301 +30432.1 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wavelength[idx+1] += 1.0e-8 - + diff = np.diff(wavelength) idx = np.where(diff <= 0)[0] #print( 'Duplicate entry loop' ) @@ -68,7 +68,7 @@ def get_2mass_filt(name): name='2MASS_{0}'.format(name)) return spectrum - + def get_vista_filt(name): """ @@ -79,21 +79,21 @@ def get_vista_filt(name): t = Table.read('{0}/vista/VISTA_Filters_at80K_forETC_{1}.dat'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find VISTA filter file {0}/vista/VISTA_Filters_at80K_forETC_{1}.dat'.format(filters_dir, name)) + raise ValueError('Could not find VISTA filter file {0}/vista/VISTA_Filters_at80K_forETC_{1}.dat'.format(filters_dir, name)) # Wavelength must be in angstroms, transmission in fraction wave = t['col1'] * 10 trans = t['col2'] * 0.01 - + # Change any negative numbers to 0, as well as anything shortward # of 0.4 microns or longward of 2.9 microns # (no VISTA filter transmissions beyond these boundaries) bad = np.where( (trans < 0) | (wave < 4000) | (wave > 29000) ) trans[bad] = 0 - + # Now we can define the VISTA filter bandpass objects spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='VISTA_{0}'.format(name)) - + return spectrum def get_decam_filt(name): @@ -104,18 +104,18 @@ def get_decam_filt(name): try: t = Table.read('{0}/decam/DECam_filters.txt'.format(filters_dir), format='ascii') t.rename_column('Y', 'y') - + cols = np.array(t.keys()) idx = np.where(cols == name)[0][0] trans = t[cols[idx]] except: - raise ValueError('Could not find DECAM filter {0} in {1}/decam/DECam_filters.txt'.format(name, filters_dir)) + raise ValueError('Could not find DECAM filter {0} in {1}/decam/DECam_filters.txt'.format(name, filters_dir)) # Limit to unmasked regions only mask = np.ma.getmask(trans) good = np.where(mask == False) - + # Convert wavelengths from nm to angstroms, while eliminating masked regions wave = t['wavelength'][good] * 10. trans = trans[good] @@ -126,7 +126,7 @@ def get_decam_filt(name): return spectrum -def get_PS1_filt(name): +def get_PS1_filt(name): """ Define PS1 filter as pysynphot object """ @@ -145,11 +145,11 @@ def get_PS1_filt(name): trans = t[cols[idx]] except: - raise ValueError('Could not find PS1 filter {0} in {1}/ps1'.format(name, filters_dir)) + raise ValueError('Could not find PS1 filter {0} in {1}/ps1'.format(name, filters_dir)) # Convert wavelengths from nm to angstroms wave = t['wave'] * 10. - + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ps1_{0}'.format(name)) return spectrum @@ -161,7 +161,7 @@ def get_jwst_filt(name): try: t = Table.read('{0}/jwst/{1}.txt'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find JWST filter {0} in {1}/jwst'.format(name, filters_dir)) + raise ValueError('Could not find JWST filter {0} in {1}/jwst'.format(name, filters_dir)) # Convert wavelengths to angstroms wave = t['microns'] * 10**4. @@ -170,10 +170,10 @@ def get_jwst_filt(name): # Change any negative numbers to 0 bad = np.where(trans < 0) trans[bad] = 0 - + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='jwst_{0}'.format(name)) - return spectrum + return spectrum def get_Johnson_Glass_filt(name): """ @@ -182,7 +182,7 @@ def get_Johnson_Glass_filt(name): try: t = Table.read('{0}/Johnson_Glass/{1}.txt'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find Johnson-Glass filter {0} in {1}/Johnson_Glass'.format(name, filters_dir)) + raise ValueError('Could not find Johnson-Glass filter {0} in {1}/Johnson_Glass'.format(name, filters_dir)) # Convert wavelengths to angstroms wave = t['col1'] * 10. @@ -191,10 +191,10 @@ def get_Johnson_Glass_filt(name): # Change any negative numbers to 0 bad = np.where(trans < 0) trans[bad] = 0 - + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='jg_{0}'.format(name)) - return spectrum + return spectrum def get_nirc1_filt(name): """ @@ -203,12 +203,12 @@ def get_nirc1_filt(name): try: t = Table.read('{0}/nirc1/{1}.txt'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find NIRC1 filter {0} in {1}/nirc1'.format(name, filters_dir)) + raise ValueError('Could not find NIRC1 filter {0} in {1}/nirc1'.format(name, filters_dir)) # Convert wavelengths to angstroms wave = t['col1'] * 10**4 trans = t['col2'] - + # Lets fix wavelength array for duplicate values or negative vals; # delete these entries diff = np.diff(wave) @@ -222,14 +222,14 @@ def get_nirc1_filt(name): diff = np.diff(wave) idx = np.where(diff <= 0)[0] - + # Change any negative transmission vals to 0 bad = np.where(trans < 0) trans[bad] = 0 spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='nirc1_{0}'.format(name)) - return spectrum + return spectrum def get_ctio_osiris_filt(name): """ @@ -238,7 +238,7 @@ def get_ctio_osiris_filt(name): try: t = Table.read('{0}/CTIO_OSIRIS/{1}.txt'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find CTIO/OSIRIS filter {0} in {1}/CTIO_OSIRIS'.format(name, filters_dir)) + raise ValueError('Could not find CTIO/OSIRIS filter {0} in {1}/CTIO_OSIRIS'.format(name, filters_dir)) # Convert wavelengths to angstroms wave = t['col1'] * 10**4 @@ -247,7 +247,7 @@ def get_ctio_osiris_filt(name): # Change any negative numbers to 0 bad = np.where(trans < 0) trans[bad] = 0 - + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ctio_osiris_{0}'.format(name)) return spectrum @@ -259,7 +259,7 @@ def get_naco_filt(name): try: t = Table.read('{0}/naco/{1}.dat'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find NACO filter {0} in {1}/naco'.format(name, filters_dir)) + raise ValueError('Could not find NACO filter {0} in {1}/naco'.format(name, filters_dir)) # Convert wavelengths to angstroms wave = t['col1'] * 10**4 @@ -268,7 +268,7 @@ def get_naco_filt(name): # Change any negative numbers to 0 bad = np.where(trans < 0) trans[bad] = 0 - + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='naco_{0}'.format(name)) return spectrum @@ -282,7 +282,7 @@ def get_ubv_filt(name): except: raise ValueError('Could not find ubv filter {0} in {1}/ubv'.format(name, filters_dir)) - # Convert wavelength from nm to angstroms + # Convert wavelength from nm to angstroms wave = t[t.keys()[0]] * 10. # Convert transmission to ratio (from percent) trans = t[t.keys()[1]] / 100. @@ -336,8 +336,8 @@ def get_keck_osiris_filt(name): def get_gaia_filt(version, name): """ Define Gaia filters as pysynphot object. - To avoid confusion, we will only support - the revised DR2 zeropoints from + To avoid confusion, we will only support + the revised DR2 zeropoints from Evans+18. version: specify dr1, dr2, or dr2_rev @@ -357,7 +357,7 @@ def get_gaia_filt(version, name): path = '{0}/gaia/dr2_rev/'.format(filters_dir) else: raise ValueError('GAIA filter version {0} not understood. Please use dr1, dr2, or dr2_rev'.format(version)) - + # Get the filter info try: t = Table.read('{0}/Gaia_passbands.txt'.format(path), format='ascii') @@ -380,7 +380,7 @@ def get_gaia_filt(version, name): bad = np.where(trans > 90) trans[bad] = 0 except: - raise ValueError('Could not find Gaia filter {0}'.format(name)) + raise ValueError('Could not find Gaia filter {0}'.format(name)) # Convert wavelengths to angstroms (from nm) wave = t['LAMBDA'] * 10 @@ -437,7 +437,7 @@ def get_rubin_filt(name): try: t = Table.read('{0}/rubin/{1}.dat'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find Rubin LSST filter file {0}/rubin/{1}.dat'.format(filters_dir, name)) + raise ValueError('Could not find Rubin LSST filter file {0}/total_/{1}.dat'.format(filters_dir, name)) wavelength = t[t.keys()[0]] transmission = t[t.keys()[1]] @@ -451,24 +451,20 @@ def get_rubin_filt(name): return spectrum -def get_euclid_filt(name): +def get_nsfcam_filt(name): + """ - Define the Euclid filters as a pysynphot spectrum object + Define irtf nsfcam filters as pysynphot object """ - # Read in filter info try: - t = Table.read('{0}/euclid/{1}.dat'.format(filters_dir, name), format='ascii') + t = Table.read('{0}/nsfcam/{1}.dat'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find Euclid filter file {0}/euclid/{1}.dat'.format(filters_dir, name)) - - wavelength = t[t.keys()[0]] - transmission = t[t.keys()[1]] + raise ValueError('Could not find nsfcam filter {0} in {1}/nsfcam'.format(name, filters_dir)) - # Convert wavelength to Angstroms - wavelength = wavelength * 10 + # Wavelength already in angstrom and and transmission in fraction + wave = t['col1'] + trans = t['col2'] - # Make spectrum object - spectrum = pysynphot.ArrayBandpass(wavelength, transmission, waveunits='angstrom', - name='euclid_{0}'.format(name)) + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='nsfcam_{0}'.format(name)) return spectrum diff --git a/spisea/synthetic.py b/spisea/synthetic.py index fa8220f6..882a18d3 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -37,7 +37,7 @@ def Vega(): # Use Vega as our zeropoint... assume V=0.03 mag and all colors = 0.0 # These parameters are defined in Girardi+02 - vega = atm.get_kurucz_atmosphere(temperature=9550, + vega = atm.get_kurucz_atmosphere(temperature=9550, gravity=3.95, metallicity=-0.5) @@ -47,8 +47,8 @@ def Vega(): # This is (R/d)**2 as reported by Girardi et al. 2002, page 198, col 1. # and is used to convert to flux observed at Earth. - vega *= 6.247e-17 - + vega *= 6.247e-17 + return vega vega = Vega() @@ -56,13 +56,13 @@ def Vega(): class Cluster(object): """ Base class to create a cluster with user-specified isochrone, - imf, ifmr, and total mass. + imf, ifmr, and total mass. Parameters ----------- iso: isochrone object SPISEA isochrone object - + imf: imf object SPISEA IMF object @@ -90,17 +90,17 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=False, self.ifmr = ifmr self.cluster_mass = cluster_mass self.seed = seed - + return - + class ResolvedCluster(Cluster): """ Cluster sub-class that produces a *resolved* stellar cluster. - A table is output with the synthetic photometry and intrinsic - properties of the individual stars (or stellar systems, if + A table is output with the synthetic photometry and intrinsic + properties of the individual stars (or stellar systems, if mutliplicity is used in the IMF object). - If multiplicity is used, than a second table is produced that + If multiplicity is used, than a second table is produced that contains the properties of the companion stars independent of their primary stars. @@ -108,7 +108,7 @@ class ResolvedCluster(Cluster): ----------- iso: isochrone object SPISEA isochrone object - + imf: imf object SPISEA IMF object @@ -120,12 +120,6 @@ class ResolvedCluster(Cluster): produced by the cluster at the given isochrone age. Otherwise, no compact remnants are produced. - keep_low_mass_stars: boolean (default False) - If True, the cluster will not cut out stars below the isochrone grid - on initial mass. They are assigned a current mass equal to their initial - mass, a phase of 98, and no other evolutionary properties or photometry. - If False, stars below the isochrone initial mass limit are cut out. - seed: int If set to non-None, all random sampling will be seeded with the specified seed, forcing identical output. @@ -135,7 +129,7 @@ class ResolvedCluster(Cluster): True for verbose output. """ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, - seed=None, keep_low_mass_stars=False): + seed=None): Cluster.__init__(self, iso, imf, cluster_mass, ifmr=ifmr, verbose=verbose, seed=seed) # Provide a user warning is random seed is set @@ -143,7 +137,7 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, print('WARNING: random seed set to %i' % seed) t1 = time.time() - ##### + ##### # Sample the IMF to build up our cluster mass. ##### mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass, @@ -161,27 +155,27 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, for ikey in interp_keys: self.iso_interps[ikey] = interpolate.interp1d(self.iso.points['mass'], self.iso.points[ikey], kind='linear', bounds_error=False, fill_value=np.nan) - - ##### + + ##### # Make a table to contain all the information about each stellar system. ##### star_systems = self._make_star_systems_table(mass, isMulti, sysMass) - + # Trim out bad systems; specifically, stars with masses outside those provided # by the model isochrone (except for compact objects). - star_systems, compMass = self._remove_bad_systems(star_systems, compMass, keep_low_mass_stars) + star_systems, compMass = self._remove_bad_systems(star_systems, compMass) - ##### + ##### # Make a table to contain all the information about companions. ##### if self.imf.make_multiples: companions = self._make_companions_table(star_systems, compMass) - + ##### # Save our arrays to the object ##### self.star_systems = star_systems - + if self.imf.make_multiples: self.companions = companions @@ -192,13 +186,13 @@ def set_filter_names(self): Set filter column names """ filt_names = [] - + for col_name in self.iso.points.colnames: if 'm_' in col_name: filt_names.append(col_name) return filt_names - + def _make_star_systems_table(self, mass, isMulti, sysMass): """ Make a star_systems table and get synthetic photometry for each primary star. @@ -245,7 +239,7 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): for ii in range(len(bad[0])): print('WARNING: changing phase {0} to 5'.format(star_systems['phase'][bad[0][ii]])) star_systems['phase'][bad] = 5 - + for filt in self.filt_names: star_systems[filt] = self.iso_interps[filt](star_systems['mass']) @@ -253,14 +247,14 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # Make Remnants # Note: Some models already have WDs in them. If they do, then they shouldn't # be handled by this code here (because their Teff > 0). - # + # # Remnants have flux = 0 in all bands if they are generated here. - ##### + ##### if self.ifmr != None: # Identify compact objects as those with Teff = 0 or with phase > 100. highest_mass_iso = self.iso.points['mass'].max() idx_rem = np.where((np.isnan(star_systems['Teff'])) & (star_systems['mass'] > highest_mass_iso))[0] - + # Calculate remnant mass and ID for compact objects; update remnant_id and # remnant_mass arrays accordingly if 'metallicity_array' in inspect.getfullargspec(self.ifmr.generate_death_mass).args: @@ -282,13 +276,13 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): star_systems[filt][idx_rem_good] = np.full(len(idx_rem_good), np.nan) return star_systems - + def _make_companions_table(self, star_systems, compMass): N_systems = len(star_systems) - + ##### - # MULTIPLICITY + # MULTIPLICITY # Make a second table containing all the companion-star masses. # This table will be much longer... here are the arrays: # sysIndex - the index of the system this star belongs too @@ -312,17 +306,17 @@ def _make_companions_table(self, star_systems, compMass): companions.add_column( Column(np.empty(N_comp_tot, dtype=float), name='metallicity') ) for filt in self.filt_names: companions.add_column( Column(np.empty(N_comp_tot, dtype=float), name=filt) ) - + if isinstance(self.imf._multi_props, multiplicity.MultiplicityResolvedDK): companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='log_a') ) companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='e') ) companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='i', description = 'degrees') ) companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='Omega') ) companions.add_column( Column(np.zeros(N_comp_tot, dtype=float), name='omega') ) - + for ii in range(len(companions)): companions['log_a'][ii] = self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx'][ii]]) - + companions['e'] = self.imf._multi_props.random_e(np.random.rand(N_comp_tot)) companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters(np.random.rand(N_comp_tot),np.random.rand(N_comp_tot),np.random.rand(N_comp_tot)) @@ -330,7 +324,7 @@ def _make_companions_table(self, star_systems, compMass): # Make an array that maps system index (ii), companion index (cc) to # the place in the 1D companions array. N_comp_max = N_companions.max() - + comp_index = np.zeros((N_systems, N_comp_max), dtype=int) kk = 0 for ii in range(N_systems): @@ -346,9 +340,9 @@ def _make_companions_table(self, star_systems, compMass): idx = np.where(N_companions >= cc)[0] # Get the location in the companions array for each system and - # the cc'th companion. + # the cc'th companion. cdx = comp_index[idx, cc-1] - + companions['mass'][cdx] = [compMass[ii][cc-1] for ii in idx] comp_mass = companions['mass'][cdx] @@ -397,13 +391,13 @@ def _make_companions_table(self, star_systems, compMass): # as np.nan. Otherwise, add fluxes together good = np.where( (f1 != 0) | (f2 != 0) ) bad = np.where( (f1 == 0) & (f2 == 0) ) - + star_systems[filt][idx[good]] = -2.5 * np.log10(f1[good] + f2[good]) star_systems[filt][idx[bad]] = np.nan ##### # Make Remnants with flux = 0 in all bands. - ##### + ##### if self.ifmr != None: # Identify compact objects as those with Teff = 0 or with masses above the max iso mass highest_mass_iso = self.iso.points['mass'].max() @@ -417,7 +411,7 @@ def _make_companions_table(self, star_systems, compMass): metallicity_array=companions['metallicity'][cdx_rem]) else: r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem]) - + # Drop remnants where it is not relevant (e.g. not a compact object or # outside mass range IFMR is defined for) @@ -426,7 +420,7 @@ def _make_companions_table(self, star_systems, compMass): companions['mass_current'][cdx_rem_good] = r_mass_tmp[good] companions['phase'][cdx_rem_good] = r_id_tmp[good] - + # Give remnants a magnitude of nan, so they can be filtered out later when calculating flux. for filt in self.filt_names: companions[filt][cdx_rem_good] = np.full(len(cdx_rem_good), np.nan) @@ -439,25 +433,19 @@ def _make_companions_table(self, star_systems, compMass): if len(idx) != N_comp_tot and self.verbose: print( 'Found {0:d} companions out of stellar mass range'.format(N_comp_tot - len(idx))) - # For low-mass stars and substellar objects below isochrone, assume no mass loss and set phase to 98 - low_mass_idxs = (companions['mass']0: - assert companions['mass'][idx].min() > 0 + assert companions['mass'][idx].min() > 0 return companions - - def _remove_bad_systems(self, star_systems, compMass, keep_low_mass_stars): + + def _remove_bad_systems(self, star_systems, compMass): """ Helper function to remove stars with masses outside the isochrone - mass range from the cluster. These stars are identified by having + mass range from the cluster. These stars are identified by having a Teff = 0, as set up by _make_star_systems_table_interp. - If self.ifmr == None, then both high and low-mass bad systems are - removed. If self.ifmr != None, then we will save the high mass systems + If self.ifmr == None, then both high and low-mass bad systems are + removed. If self.ifmr != None, then we will save the high mass systems since they will be plugged into an ifmr later. """ N_systems = len(star_systems) @@ -466,41 +454,23 @@ def _remove_bad_systems(self, star_systems, compMass, keep_low_mass_stars): # Convert nan_to_num to avoid errors on greater than, less than comparisons star_systems_teff_non_nan = np.nan_to_num(star_systems['Teff'], nan=-99) star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) - if (self.ifmr == None) and (not keep_low_mass_stars): - print('Remove low mass stars below grid and compact objects') + if self.ifmr == None: # Keep only those stars with Teff assigned. idx = np.where(star_systems_teff_non_nan > 0)[0] - elif not keep_low_mass_stars: - print('Remove low mass stars, keep compact objects') - # Keep stars (with Teff) and any other compact objects (with phase info). - idx = np.where( (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) )[0] - elif self.ifmr == None: - print('Remove compact objects, keep low mass stars below grid') - # Keep stars (with Teff) and objects below mass grid - idx = np.where( (star_systems_teff_non_nan > 0) | ((star_systems['mass'] 0) | (star_systems_phase_non_nan >= 0) | - ((star_systems['mass'] 0) | (star_systems_phase_non_nan >= 0) )[0] if len(idx) != N_systems and self.verbose: print( 'Found {0:d} stars out of mass range'.format(N_systems - len(idx))) - if keep_low_mass_stars: - lm_idx = np.where(star_systems['mass']0: - self.verbose = True - print('Missing photometry for',len(comp_filters),'filter - recomputing these columns:',comp_filters) - - print('Loading stellar spectra') - # Initialize output for stellar spectra - self.spec_list = [] - # For each isochrone point, extract the synthetic photometry. - for ii in range(len(self.points['Teff'])): - # Loop is currently taking about 0.11 s per iteration - gravity = float( self.points['logg'][ii] ) - L = float( (self.points['L'][ii]*units.W).cgs / (units.erg / units.s)) # in erg/s - T = float( self.points['Teff'][ii] ) # in Kelvin - R = float( (self.points['R'][ii]*units.m).to('pc') / units.pc) # in pc - phase = int(self.points['phase'][ii]) - # Get the atmosphere model now. Wavelength is in Angstroms - # This is the time-intensive call... everything else is negligable. - # If source is a star, pull from star atmospheres. If it is a WD, - # pull from WD atmospheres - if phase == 101: - star = wd_atm_func(temperature=T, gravity=gravity, metallicity=metallicity, - verbose=False) - else: - star = atm_func(temperature=T, gravity=gravity, metallicity=metallicity, - rebin=rebin) - # Trim wavelength range down to appropriate range - star = spectrum.trimSpectrum(star, wave_range[0], wave_range[1]) - # Convert into flux observed at Earth (unreddened) - star *= (R / self.points.meta["DISTANCE"])**2 # in erg s^-1 cm^-2 A^-1 - # Redden the spectrum. This doesn't take much time at all. - red = red_law.reddening(AKs).resample(star.wave) - star *= red - # Save the final spectrum to our spec_list for later use. - self.spec_list.append(star) - - self.make_photometry(rebin=rebin, vega=vega, comp_filters=comp_filters) - return - def make_photometry(self, rebin=True, vega=vega, comp_filters=None): - """ + def make_photometry(self, rebin=True, vega=vega): + """ Make synthetic photometry for the specified filters. This function udpates the self.points table to include new columns with the photometry. - + """ startTime = time.time() @@ -1110,16 +1035,12 @@ def make_photometry(self, rebin=True, vega=vega, comp_filters=None): npoints = len(self.points) verbose_fmt = 'M = {0:7.3f} Msun T = {1:5.0f} K m_{2:s} = {3:4.2f}' - #Calculate all filters, or select filters - if comp_filters is None: - comp_filters = self.filters - # Loop through the filters, get filter info, make photometry for # all stars in this filter. - for ii in comp_filters: + for ii in self.filters: prt_fmt = 'Starting filter: {0:s} Elapsed time: {1:.2f} seconds' print( prt_fmt.format(ii, time.time() - startTime)) - + filt = get_filter_info(ii, rebin=rebin, vega=vega) filt_name = get_filter_col_name(ii) @@ -1127,15 +1048,15 @@ def make_photometry(self, rebin=True, vega=vega, comp_filters=None): col_name = 'm_' + filt_name mag_col = Column(np.zeros(npoints, dtype=float), name=col_name) self.points.add_column(mag_col) - + # Loop through each star in the isochrone and do the filter integration print('Starting synthetic photometry') for ss in range(npoints): star = self.spec_list[ss] # These are already extincted, observed spectra. star_mag = mag_in_filter(star, filt) - + self.points[col_name][ss] = star_mag - + if (self.verbose and (ss % 100) == 0): print( verbose_fmt.format(self.points['mass'][ss], self.points['Teff'][ss], filt_name, star_mag)) @@ -1152,34 +1073,27 @@ def make_photometry(self, rebin=True, vega=vega, comp_filters=None): def check_save_file(self, evo_model, atm_func, red_law): """ - Check to see if save_file exists, as saved by the save_file - and save_file_legacy objects. If the filename exists, check the + Check to see if save_file exists, as saved by the save_file + and save_file_legacy objects. If the filename exists, check the meta-data as well. returns a boolean: True is file exists, false otherwise """ out_bool = False - + if os.path.exists(self.save_file) | os.path.exists(self.save_file_legacy): try: tmp = Table.read(self.save_file) except: tmp = Table.read(self.save_file_legacy) - - + + # See if the meta-data matches: evo model, atm_func, redlaw if ( (tmp.meta['EVOMODEL'] == type(evo_model).__name__) & (tmp.meta['ATMFUNC'] == atm_func.__name__) & (tmp.meta['REDLAW'] == red_law.name) ): out_bool = True - # Check model version if it was logged - if 'EVOMODELVERSION' in tmp.meta: - if tmp.meta['EVOMODELVERSION']!=evo_model.model_version_name: - out_bool=False - else: - print(f"No version information found for evolution model {type(evo_model).__name__}.") - return out_bool def plot_CMD(self, mag1, mag2, savefile=None): @@ -1193,7 +1107,7 @@ def plot_CMD(self, mag1, mag2, savefile=None): mag2 : string The name of the second magnitude column to be plotted. savefile : string (default None) - If a savefile is specified, then the plot will be saved to that file. + If a savefile is specified, then the plot will be saved to that file. """ plt.clf() plt.plot(self.points[mag1] - self.points[mag2], self.points[mag1], @@ -1201,7 +1115,7 @@ def plot_CMD(self, mag1, mag2, savefile=None): plt.gca().invert_yaxis() plt.xlabel(mag1 + ' - ' + mag2 + ' (mag)') plt.ylabel(mag1 + ' (mag)') - + fmt_title = 'logAge={0:.2f}, d={1:.2f} kpc, AKs={2:.2f}' plt.title(fmt_title.format(self.points.meta['LOGAGE'], self.points.meta['DISTANCE']/1e3, @@ -1209,34 +1123,34 @@ def plot_CMD(self, mag1, mag2, savefile=None): if savefile != None: plt.savefig(savefile) - + return def plot_mass_magnitude(self, mag, savefile=None): """ Make a standard mass-luminosity relation plot for this isochrone. - + Parameters ---------- mag : string The name of the magnitude column to be plotted. savefile : string (default None) - If a savefile is specified, then the plot will be saved to that file. + If a savefile is specified, then the plot will be saved to that file. """ plt.clf() plt.semilogx(self.points['mass'], self.points[mag], 'k.') plt.gca().invert_yaxis() plt.xlabel(r'Mass (M$_\odot$)') plt.ylabel(mag + ' (mag)') - + fmt_title = 'logAge={0:.2f}, d={1:.2f} kpc, AKs={2:.2f}' plt.title(fmt_title.format(self.points.meta['LOGAGE'], self.points.meta['DISTANCE']/1e3, self.points.meta['AKS'])) - + if savefile != None: plt.savefig(savefile) - + return #===================================================# @@ -1258,7 +1172,7 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, Functions on this object: apply_reddening (Apply reddening with defined redlaw and AKs) make_photometry (make synthetic photometry for spectra) - + Parameters ---------- logAge : float @@ -1296,13 +1210,13 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, spectrum. This is very useful to save computation time down the road. """ - t1 = time.time() + t1 = time.time() c = constants # Get solar metallicity models for a population at a specific age. # Takes about 0.1 seconds. - evol = evo_model.isochrone(age=10**logAge) # solar metallicity - + evol = evo_model.isochrone(age=10**logAge) # solar metallicity + # Eliminate cases where log g is less than 0 idx = np.where(evol['logg'] > 0) evol = evol[idx] @@ -1313,8 +1227,8 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, evol = evol[idx] if max_mass != None: idx = np.where(evol['mass'] <= max_mass) - evol = evol[idx] - + evol = evol[idx] + # Trim down the table by selecting every Nth point where # N = mass sampling factor. evol = evol[::mass_sampling] @@ -1349,31 +1263,30 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, # Get the atmosphere model now. Wavelength is in Angstroms # This is the time-intensive call... everything else is negligable. star = atm_func(temperature=T, gravity=gravity) - + # Trim wavelength range down to JHKL range (0.5 - 5.2 microns) star = spectrum.trimSpectrum(star, wave_range[0], wave_range[1]) # Convert into flux observed at Earth (unreddened) star *= (R / distance)**2 # in erg s^-1 cm^-2 A^-1 - - # Save the final spectrum to our spec_list for later use. + + # Save the final spectrum to our spec_list for later use. self.spec_list.append(star) # Append all the meta data to the summary table. - + tab.meta['ATMFUNC'] = atm_func.__name__ tab.meta['EVOMODEL'] = type(evo_model).__name__ - tab.meta['EVOMODELVERSION'] = evo_model.model_version_name tab.meta['LOGAGE'] = logAge tab.meta['DISTANCE'] = distance tab.meta['WAVEMIN'] = wave_range[0] tab.meta['WAVEMAX'] = wave_range[1] self.points = tab - + t2 = time.time() print('Isochrone generation took {0:f} s.'.format(t2-t1)) - + return def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max=None): @@ -1385,7 +1298,7 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= ---------- AKs: float Total extinction in AKs - + extinction_law: SPISEA extinction object Extinction law to be used on the spectra @@ -1395,13 +1308,13 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= dAKs_max: float or None If not none, defines the maximum |dAKs| a star can - have in gaussian distribution case + have in gaussian distribution case dist: string, 'uniform' or 'gaussian' Distribution to draw differential reddening from. If uniform, dAKs will cut off at Aks +/- dAKs. Otherwise, will draw from Gaussian of width AKs +/- dAks - + """ self.AKs = np.ones(len(self.spec_list)) # Apply reddening to each object in the spec list @@ -1430,7 +1343,7 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= else: AKs_act = AKs - red = extinction_law.reddening(AKs_act).resample(star.wave) + red = extinction_law.reddening(AKs_act).resample(star.wave) star *= red # Update the spectrum in spec list @@ -1443,7 +1356,7 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= return def make_photometry(self, filters, rebin=True): - """ + """ Make synthetic photometry for the specified filters. This function udpates the self.points table to include new columns with the photometry. @@ -1453,11 +1366,11 @@ def make_photometry(self, filters, rebin=True): filters : dictionary A dictionary containing the filter name (for the output columns) and the filter specification string that can be processed by pysynphot. - + rebin: boolean - True to rebin filter function (only used if non-zero transmission points are + True to rebin filter function (only used if non-zero transmission points are larger than 1500 points) - + """ npoints = len(self.points) @@ -1474,35 +1387,35 @@ def make_photometry(self, filters, rebin=True): col_name = 'mag_' + filt_name mag_col = Column(np.zeros(npoints, dtype=float), name=col_name) self.points.add_column(mag_col) - + # Loop through each star in the isochrone and do the filter integration for ss in range(npoints): star = self.spec_list[ss] # These are already extincted, observed spectra. star_mag = mag_in_filter(star, filt) - + self.points[col_name][ss] = star_mag - - + + endTime = time.time() print( ' Time taken: {0:.2f} seconds'.format(endTime - ts)) return def get_filter_info(name, vega=vega, rebin=True): - """ + """ Define filter functions, setting ZP according to Vega spectrum. Input name is the SPISEA obs_string """ tmp = name.split(',') filterName = tmp[-1] - + if name.startswith('nirc2'): filt = filters.get_nirc2_filt(filterName) elif name.startswith('2mass'): filt = filters.get_2mass_filt(filterName) - + elif name.startswith('vista'): filt = filters.get_vista_filt(filterName) @@ -1517,10 +1430,10 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('jg'): filt = filters.get_Johnson_Glass_filt(filterName) - + elif name.startswith('nirc1'): filt = filters.get_nirc1_filt(filterName) - + elif name.startswith('ctio_osiris'): filt = filters.get_ctio_osiris_filt(filterName) @@ -1532,7 +1445,7 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('ukirt'): filt = filters.get_ukirt_filt(filterName) - + elif name.startswith('keck_osiris'): filt = filters.get_keck_osiris_filt(filterName) @@ -1545,20 +1458,20 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('hawki'): filt = filters.get_hawki_filt(filterName) - + elif name.startswith('rubin'): filt = filters.get_rubin_filt(filterName) - elif name.startswith('euclid'): - filt = filters.get_euclid_filt(filterName) - + elif name.startswith('nsfcam'): + filt = filters.get_nsfcam_filt(filterName) + else: # Otherwise, look for the filter info in the cdbs/mtab and cdbs/comp files try: filt = ObsBandpass(name) except: raise Exception('Filter {0} not understood. Check spelling and make sure cdbs/mtab and cdbs/comp files are up to date'.format(name)) - + # Convert to ArraySpectralElement for resampling. filt = spectrum.ArraySpectralElement(filt.wave, filt.throughput, waveunits=filt.waveunits, @@ -1576,14 +1489,14 @@ def get_filter_info(name, vega=vega, rebin=True): # Otherwise, throw an error idx = np.where(filt.throughput > 0.001)[0] if (min(filt.wave[idx]) < min(vega.wave)) | (max(filt.wave[idx]) > max(vega.wave)): - raise ValueError('Vega spectrum doesnt cover filter wavelength range!') + raise ValueError('Vega spectrum doesnt cover filter wavelength range!') vega_obs = obs.Observation(vega, filt, binset=filt.wave, force='taper') #vega_flux = vega_obs.binflux.sum() diff = np.diff(vega_obs.binwave) diff = np.append(diff, diff[-1]) vega_flux = np.sum(vega_obs.binflux * diff) - + vega_mag = 0.03 filt.flux0 = vega_flux @@ -1594,7 +1507,7 @@ def get_filter_info(name, vega=vega, rebin=True): def get_filter_col_name(obs_str): """ - Get standard column name for synthetic photometry based on + Get standard column name for synthetic photometry based on the input string. The input string is expected to be an appropriate SPISEA obs_string """ @@ -1613,7 +1526,7 @@ def get_filter_col_name(obs_str): filt_name = 'hst_{0}'.format(tmp[-1]) else: filt_name = '{0}_{1}'.format(tmp[0], tmp[1]) - + return filt_name def get_obs_str(col): @@ -1623,7 +1536,7 @@ def get_obs_str(col): """ # Remove the trailing m_ name = col[2:] - + # Define dictionary for filters filt_list = {'hst_f127m': 'wfc3,ir,f127m', 'hst_f139m': 'wfc3,ir,f139m', 'hst_f153m': 'wfc3,ir,f153m', 'hst_f814w': 'acs,wfc1,f814w', 'hst_f125w': 'wfc3,ir,f125w', 'hst_f160w': 'wfc3,ir,f160w', @@ -1645,7 +1558,7 @@ def get_obs_str(col): 'jwst_F187N': 'jwst,F187N', 'jwst_F200W': 'jwst,F200W', 'jwst_F210M': 'jwst,F210M', - 'jwst_F250M': 'jwst,F250M', + 'jwst_F250M': 'jwst,F250M', 'jwst_F277W': 'jwst,F277W', 'jwst_F300M': 'jwst,F300M', 'jwst_F322W2': 'jwst,F322W2', @@ -1665,14 +1578,10 @@ def get_obs_str(col): 'nirc2_FeII': 'nirc2,FeII', 'nirc2_Brgamma': 'nirc2,Brgamma', '2mass_J': '2mass,J', '2mass_H': '2mass,H', '2mass_Ks': '2mass,Ks', 'ubv_U':'ubv,U', 'ubv_B':'ubv,B', 'ubv_V':'ubv,V', 'ubv_R':'ubv,R', - 'ubv_I':'ubv,I', + 'ubv_I':'ubv,I', 'jg_J': 'jg,J', 'jg_H': 'jg,H', 'jg_K': 'jg,K', 'nirc1_K':'nirc1,K', 'nirc1_H':'nirc1,H', 'naco_J':'naco,J', 'naco_H':'naco,H', 'naco_Ks':'naco,Ks', - 'naco_IB_2.00': 'naco,IB_2.00', 'naco_IB_2.03':'naco,IB_2.03', 'naco_IB_2.06':'naco,IB_2.06', - 'naco_IB_2.24':'naco,IB_2.24', 'naco_IB_2.27':'naco,IB_2.27', - 'naco_IB_2.30':'naco,IB_2.30', 'naco_IB_2.33':'naco,IB_2.33', - 'naco_IB_2.36':'naco,IB_2.36', 'ukirt_J':'ukirt,J', 'ukirt_H':'ukirt,H', 'ukirt_K':'ukirt,K', 'ctio_osiris_H': 'ctio_osiris,H', 'ctio_osiris_K': 'ctio_osiris,K', 'ztf_g':'ztf,g', 'ztf_r':'ztf,r', 'ztf_i':'ztf,i', @@ -1686,7 +1595,7 @@ def get_obs_str(col): 'roman_f106': 'roman,wfi,f106', 'roman_f129': 'roman,wfi,f129', 'roman_f158': 'roman,wfi,f158', - 'roman_f146': 'roman,wfi,f146', + 'roman_w146': 'roman,wfi,w146', 'roman_f213': 'roman,wfi,f213', 'roman_f184': 'roman,wfi,f184', 'rubin_g':'rubin,g', @@ -1694,14 +1603,10 @@ def get_obs_str(col): 'rubin_r':'rubin,r', 'rubin_u':'rubin,u', 'rubin_z':'rubin,z', - 'rubin_y':'rubin,y', - 'euclid_VIS':'euclid,VIS', - 'euclid_Y':'euclid,Y', - 'euclid_J':'euclid,J', - 'euclid_H':'euclid,H'} + 'rubin_y':'rubin,y'} obs_str = filt_list[name] - + return obs_str def rebin_spec(wave, specin, wavnew): @@ -1714,7 +1619,7 @@ def rebin_spec(wave, specin, wavnew): f = np.ones(len(wave)) filt = spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') obs_f = obs.Observation(spec, filt, binset=wavnew, force='taper') - + return obs_f.binflux def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model, @@ -1725,7 +1630,7 @@ def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model, 'wfc3,ir,f153m']): """ Wrapper routine to generate a grid of isochrones of different ages, - extinctions, and distances. + extinctions, and distances. Parameters: ---------- @@ -1737,7 +1642,7 @@ def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model, dist_arr: array Array of distances to loop over (pc) - + evo_models: SPISEA evolution object Which evolution models to use @@ -1754,7 +1659,7 @@ def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model, Mass sampling of isochrone, relative to original mass sampling filters: dictionary - Which filters to do the synthetic photometry on + Which filters to do the synthetic photometry on """ print( '**************************************') print( 'Start generating isochrones') @@ -1799,7 +1704,7 @@ def mag_in_filter(star, filt): diff = np.diff(star_in_filter.binwave) diff = np.append(diff, diff[-1]) star_flux = np.sum(star_in_filter.binflux * diff) - + star_mag = -2.5 * math.log10(star_flux / filt.flux0) + filt.mag0 return star_mag @@ -1822,10 +1727,10 @@ def match_model_masses(isoMasses, starMasses): idx = np.where(dm_frac > 0.1)[0] indices[idx] = -1 - + return indices - + def get_evo_model_by_string(evo_model_string): return getattr(evolution, evo_model_string) @@ -1836,7 +1741,7 @@ def calc_ab_vega_filter_conversion(filt_str): AB and Vega magnitudes for a given filter: m_AB - m_vega - Note: this conversion is just the vega magnitude in + Note: this conversion is just the vega magnitude in AB system Parameters: @@ -1856,14 +1761,14 @@ def calc_ab_vega_filter_conversion(filt_str): filt_wave = filt.wave filt_mu = c / filt_wave s_filt = filt.throughput - + # Interpolate the filter function, determine what the # filter function is at the exact sampling of the # vega spectrum (in freq space) filt_interp = scipy.interpolate.interp1d(filt_mu, s_filt, kind='linear', bounds_error=False, fill_value=0) s_interp = filt_interp(vega_mu) - + # Now for the m_ab calculation mu_diff = np.diff(vega_mu) numerator = np.sum(vega_flux_mu[:-1] * s_interp[:-1] * mu_diff) @@ -1881,10 +1786,10 @@ def calc_ab_vega_filter_conversion(filt_str): # fill_value=0) #s_interp = filt_interp(vega.wave) - # Calculate the numerator + # Calculate the numerator #diff = np.diff(vega.wave) #numerator2 = np.sum((vega.wave[:-1]**2. / c) * vega.flux[:-1] * s_interp[:-1] * diff) - + # Now we need to intergrate the filter response for the denominator #denominator2 = np.sum(s_interp[:-1] * diff) @@ -1892,4 +1797,3 @@ def calc_ab_vega_filter_conversion(filt_str): #vega_mag_ab2 = -2.5 * np.log10(numerator2 / denominator2) - 48.6 return vega_mag_ab - From c100459d619b000d6866ce77a37c1d3c10d3c059 Mon Sep 17 00:00:00 2001 From: Rocketpack23 Date: Wed, 11 Feb 2026 13:45:25 -0800 Subject: [PATCH 062/191] Edited filters.py to include irtf/nsfcam --- spisea/filters.py | 24 +++++++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/spisea/filters.py b/spisea/filters.py index 23221c6b..e2a2aee9 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -437,7 +437,7 @@ def get_rubin_filt(name): try: t = Table.read('{0}/rubin/{1}.dat'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find Rubin LSST filter file {0}/total_/{1}.dat'.format(filters_dir, name)) + raise ValueError('Could not find Rubin LSST filter file {0}/rubin/{1}.dat'.format(filters_dir, name)) wavelength = t[t.keys()[0]] transmission = t[t.keys()[1]] @@ -451,6 +451,28 @@ def get_rubin_filt(name): return spectrum +def get_euclid_filt(name): + """ + Define the Euclid filters as a pysynphot spectrum object + """ + # Read in filter info + try: + t = Table.read('{0}/euclid/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find Euclid filter file {0}/euclid/{1}.dat'.format(filters_dir, name)) + + wavelength = t[t.keys()[0]] + transmission = t[t.keys()[1]] + + # Convert wavelength to Angstroms + wavelength = wavelength * 10 + + # Make spectrum object + spectrum = pysynphot.ArrayBandpass(wavelength, transmission, waveunits='angstrom', + name='euclid_{0}'.format(name)) + + return spectrum + def get_nsfcam_filt(name): """ From e3f520c558f5a419c31ad62c3dc65fb38fac12ce Mon Sep 17 00:00:00 2001 From: Rocketpack23 Date: Wed, 11 Feb 2026 13:47:11 -0800 Subject: [PATCH 063/191] Edited synthetic.py to include irtf/nsfcam --- spisea/synthetic.py | 124 +++++++++++++++++++++++++++++++++++++++----- 1 file changed, 111 insertions(+), 13 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 882a18d3..4c98dc57 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -120,6 +120,12 @@ class ResolvedCluster(Cluster): produced by the cluster at the given isochrone age. Otherwise, no compact remnants are produced. + keep_low_mass_stars: boolean (default False) + If True, the cluster will not cut out stars below the isochrone grid + on initial mass. They are assigned a current mass equal to their initial + mass, a phase of 98, and no other evolutionary properties or photometry. + If False, stars below the isochrone initial mass limit are cut out. + seed: int If set to non-None, all random sampling will be seeded with the specified seed, forcing identical output. @@ -129,7 +135,7 @@ class ResolvedCluster(Cluster): True for verbose output. """ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, - seed=None): + seed=None, keep_low_mass_stars=False): Cluster.__init__(self, iso, imf, cluster_mass, ifmr=ifmr, verbose=verbose, seed=seed) # Provide a user warning is random seed is set @@ -163,7 +169,7 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, # Trim out bad systems; specifically, stars with masses outside those provided # by the model isochrone (except for compact objects). - star_systems, compMass = self._remove_bad_systems(star_systems, compMass) + star_systems, compMass = self._remove_bad_systems(star_systems, compMass, keep_low_mass_stars) ##### # Make a table to contain all the information about companions. @@ -433,13 +439,19 @@ def _make_companions_table(self, star_systems, compMass): if len(idx) != N_comp_tot and self.verbose: print( 'Found {0:d} companions out of stellar mass range'.format(N_comp_tot - len(idx))) + # For low-mass stars and substellar objects below isochrone, assume no mass loss and set phase to 98 + low_mass_idxs = (companions['mass'] 0 + if len(idx)>0: + assert companions['mass'][idx].min() > 0 return companions - def _remove_bad_systems(self, star_systems, compMass): + def _remove_bad_systems(self, star_systems, compMass, keep_low_mass_stars): """ Helper function to remove stars with masses outside the isochrone mass range from the cluster. These stars are identified by having @@ -454,16 +466,34 @@ def _remove_bad_systems(self, star_systems, compMass): # Convert nan_to_num to avoid errors on greater than, less than comparisons star_systems_teff_non_nan = np.nan_to_num(star_systems['Teff'], nan=-99) star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) - if self.ifmr == None: + if (self.ifmr == None) and (not keep_low_mass_stars): + print('Remove low mass stars below grid and compact objects') # Keep only those stars with Teff assigned. idx = np.where(star_systems_teff_non_nan > 0)[0] - else: + elif not keep_low_mass_stars: + print('Remove low mass stars, keep compact objects') # Keep stars (with Teff) and any other compact objects (with phase info). idx = np.where( (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) )[0] + elif self.ifmr == None: + print('Remove compact objects, keep low mass stars below grid') + # Keep stars (with Teff) and objects below mass grid + idx = np.where( (star_systems_teff_non_nan > 0) | ((star_systems['mass'] 0) | (star_systems_phase_non_nan >= 0) | + ((star_systems['mass']0: + self.verbose = True + print('Missing photometry for',len(comp_filters),'filter - recomputing these columns:',comp_filters) + + print('Loading stellar spectra') + # Initialize output for stellar spectra + self.spec_list = [] + # For each isochrone point, extract the synthetic photometry. + for ii in range(len(self.points['Teff'])): + # Loop is currently taking about 0.11 s per iteration + gravity = float( self.points['logg'][ii] ) + L = float( (self.points['L'][ii]*units.W).cgs / (units.erg / units.s)) # in erg/s + T = float( self.points['Teff'][ii] ) # in Kelvin + R = float( (self.points['R'][ii]*units.m).to('pc') / units.pc) # in pc + phase = int(self.points['phase'][ii]) + # Get the atmosphere model now. Wavelength is in Angstroms + # This is the time-intensive call... everything else is negligable. + # If source is a star, pull from star atmospheres. If it is a WD, + # pull from WD atmospheres + if phase == 101: + star = wd_atm_func(temperature=T, gravity=gravity, metallicity=metallicity, + verbose=False) + else: + star = atm_func(temperature=T, gravity=gravity, metallicity=metallicity, + rebin=rebin) + # Trim wavelength range down to appropriate range + star = spectrum.trimSpectrum(star, wave_range[0], wave_range[1]) + # Convert into flux observed at Earth (unreddened) + star *= (R / self.points.meta["DISTANCE"])**2 # in erg s^-1 cm^-2 A^-1 + # Redden the spectrum. This doesn't take much time at all. + red = red_law.reddening(AKs).resample(star.wave) + star *= red + # Save the final spectrum to our spec_list for later use. + self.spec_list.append(star) + + self.make_photometry(rebin=rebin, vega=vega, comp_filters=comp_filters) + return - def make_photometry(self, rebin=True, vega=vega): + def make_photometry(self, rebin=True, vega=vega, comp_filters=None): """ Make synthetic photometry for the specified filters. This function udpates the self.points table to include new columns with the @@ -1035,9 +1110,13 @@ def make_photometry(self, rebin=True, vega=vega): npoints = len(self.points) verbose_fmt = 'M = {0:7.3f} Msun T = {1:5.0f} K m_{2:s} = {3:4.2f}' + #Calculate all filters, or select filters + if comp_filters is None: + comp_filters = self.filters + # Loop through the filters, get filter info, make photometry for # all stars in this filter. - for ii in self.filters: + for ii in comp_filters: prt_fmt = 'Starting filter: {0:s} Elapsed time: {1:.2f} seconds' print( prt_fmt.format(ii, time.time() - startTime)) @@ -1094,6 +1173,13 @@ def check_save_file(self, evo_model, atm_func, red_law): (tmp.meta['REDLAW'] == red_law.name) ): out_bool = True + # Check model version if it was logged + if 'EVOMODELVERSION' in tmp.meta: + if tmp.meta['EVOMODELVERSION']!=evo_model.model_version_name: + out_bool=False + else: + print(f"No version information found for evolution model {type(evo_model).__name__}.") + return out_bool def plot_CMD(self, mag1, mag2, savefile=None): @@ -1277,6 +1363,7 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, tab.meta['ATMFUNC'] = atm_func.__name__ tab.meta['EVOMODEL'] = type(evo_model).__name__ + tab.meta['EVOMODELVERSION'] = evo_model.model_version_name tab.meta['LOGAGE'] = logAge tab.meta['DISTANCE'] = distance tab.meta['WAVEMIN'] = wave_range[0] @@ -1462,9 +1549,12 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('rubin'): filt = filters.get_rubin_filt(filterName) + elif name.startswith('euclid'): + filt = filters.get_euclid_filt(filterName) + elif name.startswith('nsfcam'): filt = filters.get_nsfcam_filt(filterName) - + else: # Otherwise, look for the filter info in the cdbs/mtab and cdbs/comp files try: @@ -1582,6 +1672,10 @@ def get_obs_str(col): 'jg_J': 'jg,J', 'jg_H': 'jg,H', 'jg_K': 'jg,K', 'nirc1_K':'nirc1,K', 'nirc1_H':'nirc1,H', 'naco_J':'naco,J', 'naco_H':'naco,H', 'naco_Ks':'naco,Ks', + 'naco_IB_2.00': 'naco,IB_2.00', 'naco_IB_2.03':'naco,IB_2.03', 'naco_IB_2.06':'naco,IB_2.06', + 'naco_IB_2.24':'naco,IB_2.24', 'naco_IB_2.27':'naco,IB_2.27', + 'naco_IB_2.30':'naco,IB_2.30', 'naco_IB_2.33':'naco,IB_2.33', + 'naco_IB_2.36':'naco,IB_2.36', 'ukirt_J':'ukirt,J', 'ukirt_H':'ukirt,H', 'ukirt_K':'ukirt,K', 'ctio_osiris_H': 'ctio_osiris,H', 'ctio_osiris_K': 'ctio_osiris,K', 'ztf_g':'ztf,g', 'ztf_r':'ztf,r', 'ztf_i':'ztf,i', @@ -1595,7 +1689,7 @@ def get_obs_str(col): 'roman_f106': 'roman,wfi,f106', 'roman_f129': 'roman,wfi,f129', 'roman_f158': 'roman,wfi,f158', - 'roman_w146': 'roman,wfi,w146', + 'roman_f146': 'roman,wfi,f146', 'roman_f213': 'roman,wfi,f213', 'roman_f184': 'roman,wfi,f184', 'rubin_g':'rubin,g', @@ -1603,7 +1697,11 @@ def get_obs_str(col): 'rubin_r':'rubin,r', 'rubin_u':'rubin,u', 'rubin_z':'rubin,z', - 'rubin_y':'rubin,y'} + 'rubin_y':'rubin,y', + 'euclid_VIS':'euclid,VIS', + 'euclid_Y':'euclid,Y', + 'euclid_J':'euclid,J', + 'euclid_H':'euclid,H'} obs_str = filt_list[name] From cde187d959e17912cce9c1ee488674b1102ae8ae Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 11 Feb 2026 17:15:23 -0800 Subject: [PATCH 064/191] Add random state testing --- spisea/synthetic.py | 3 ++- spisea/tests/test_synthetic.py | 37 ++++++++++++++++++++++++++++++++++ 2 files changed, 39 insertions(+), 1 deletion(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 697e36c6..49acbaa4 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -159,7 +159,8 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, # Provide a user warning is random seed is set if seed is not None and verbose: print('WARNING: random seed set to %i' % seed) - + imf.rng = self.rng + ##### # Sample the IMF to build up our cluster mass. ##### diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index ada9544f..1969ad21 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -1206,4 +1206,41 @@ def test_Raithel18_IFMR_5(): assert len(WD_idx) == 2 , "There are not the right number of WDs for the Raithel18 IFMR" + return + +def test_ResolvedCluster_random_state(): + """ + Test that the random state is properly set in ResolvedCluster, such that two clusters with the same seed have the same stars. + """ + log_age = 6.7 + AKs = 2.7 + distance = 4000 + cluster_mass = 10**4. + iso_dir = 'isochrones/' + + evo = evolution.MergedBaraffePisaEkstromParsec() + atm_func = atmospheres.get_merged_atmosphere + red_law = reddening.RedLawNishiyama09() + filt_list = ['nirc2,J', 'nirc2,Kp'] + + iso = syn.IsochronePhot( + log_age, + AKs, + distance, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + mass_sampling=10, + iso_dir=iso_dir + ) + + imf_limits = np.array([0.07, 0.5, 150]) + imf_powers = np.array([-1.3, -2.35]) + imf_multi = multiplicity.MultiplicityUnresolved() + imf_test = imf.IMF_broken_powerlaw(imf_limits, imf_powers, multiplicity=imf_multi) + + cluster1 = syn.ResolvedCluster(iso, imf_test, cluster_mass, seed=42) + cluster2 = syn.ResolvedCluster(iso, imf_test, cluster_mass, seed=42) + np.testing.assert_array_equal(cluster1.star_systems, cluster2.star_systems) return \ No newline at end of file From 1d845f3c25480372203f1a8936d148e0e27a6159 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 18 Feb 2026 22:45:08 -0800 Subject: [PATCH 065/191] Revert back to np.random.seed and check companion consistency --- .gitignore | 2 +- spisea/imf/imf.py | 15 +-- spisea/synthetic.py | 30 +++--- .../isochrones/iso_6.70_0.80_04000_m1.50.fits | 20 ++++ .../isochrones/iso_6.70_0.80_04000_p0.00.fits | 14 +++ .../isochrones/iso_6.70_1.67_08000_p0.00.fits | 2 + .../isochrones/iso_6.70_2.30_08000_p0.00.fits | 1 + .../isochrones/iso_6.70_2.40_04000_p0.00.fits | 93 ++++++++++++++++++ .../isochrones/iso_6.70_2.46_08000_p0.00.fits | 1 + .../isochrones/iso_6.70_2.62_08000_p0.00.fits | 1 + .../isochrones/iso_6.70_2.70_04000_p0.00.fits | 44 +++++++++ .../isochrones/iso_9.70_0.00_01000_p0.00.fits | 53 ++++++++++ spisea/tests/test_data/companions.pkl | Bin 0 -> 251721 bytes spisea/tests/test_synthetic.py | 35 ++++--- 14 files changed, 277 insertions(+), 34 deletions(-) create mode 100644 spisea/tests/isochrones/iso_6.70_0.80_04000_m1.50.fits create mode 100644 spisea/tests/isochrones/iso_6.70_0.80_04000_p0.00.fits create mode 100644 spisea/tests/isochrones/iso_6.70_1.67_08000_p0.00.fits create mode 100644 spisea/tests/isochrones/iso_6.70_2.30_08000_p0.00.fits create mode 100644 spisea/tests/isochrones/iso_6.70_2.40_04000_p0.00.fits create mode 100644 spisea/tests/isochrones/iso_6.70_2.46_08000_p0.00.fits create mode 100644 spisea/tests/isochrones/iso_6.70_2.62_08000_p0.00.fits create mode 100644 spisea/tests/isochrones/iso_6.70_2.70_04000_p0.00.fits create mode 100644 spisea/tests/isochrones/iso_9.70_0.00_01000_p0.00.fits create mode 100644 spisea/tests/test_data/companions.pkl diff --git a/.gitignore b/.gitignore index b29e4f96..ae231559 100755 --- a/.gitignore +++ b/.gitignore @@ -54,4 +54,4 @@ distribute-*.tar.gz ._* # Test files generated -spisea/tests/isochrones +# spisea/tests/isochrones diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 8dfc0e64..cb6866e6 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -51,7 +51,7 @@ def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None, seed=None) self._multi_props = multiplicity self._mass_limits = np.atleast_1d(massLimits) self.seed = seed - self.rng = np.random.default_rng(seed) + # self.rng = np.random.default_rng(seed) if multiplicity: self.make_multiples = True @@ -61,7 +61,7 @@ def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None, seed=None) return - def generate_cluster(self, totalMass): + def generate_cluster(self, totalMass, seed=None): """ Generate a cluster of stellar systems with the specified IMF. @@ -128,10 +128,13 @@ def generate_cluster(self, totalMass): totalMassTally = 0 loopCnt = 0 + if seed: + np.random.seed(seed=seed) + # start_while = time.time() while totalMassTally < totalMass: # Generate a random number array. - uniX = self.rng.random(int(newStarCount)) + uniX = np.random.rand(int(newStarCount)) # Convert into the IMF from the inverted CDF newMasses = self.dice_star_cl(uniX) @@ -148,7 +151,7 @@ def generate_cluster(self, totalMass): MF = self._multi_props.multiplicity_fraction(newMasses) CSF = self._multi_props.companion_star_fraction(newMasses) - newIsMultiple = self.rng.random(int(newStarCount)) < MF + newIsMultiple = np.random.rand(int(newStarCount)) < MF # Function to calculate multiple systems more efficiently # start_calc = time.time() @@ -228,7 +231,7 @@ def calc_multi(self, newMasses, newIsMultiple, CSF, MF): # Identify multiple systems, calculate number of companions for each multiple_idx = np.where(newIsMultiple)[0] - comp_nums = 1 + self.rng.poisson((CSF[multiple_idx] / MF[multiple_idx]) - 1) + comp_nums = 1 + np.random.poisson((CSF[multiple_idx] / MF[multiple_idx]) - 1) if self._multi_props.companion_max: too_many = np.where(comp_nums > self._multi_props.CSF_max)[0] comp_nums[too_many] = self._multi_props.CSF_max @@ -242,7 +245,7 @@ def calc_multi(self, newMasses, newIsMultiple, CSF, MF): for comp_num, comp_index in zip(comp_unique, comp_indices): # Calculate masses of companions - q_values = self._multi_props.random_q(self.rng.random((len(comp_index), comp_num))) + q_values = self._multi_props.random_q(np.random.rand(len(comp_index), comp_num)) m_comp = np.multiply(q_values, np.transpose([primary[comp_index]])) compMasses[multiple_idx[comp_index], :comp_num] = m_comp diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 49acbaa4..932198bf 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -107,7 +107,7 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=False, self.ifmr = ifmr self.cluster_mass = cluster_mass self.seed = seed - self.rng = np.random.default_rng(self.seed) + # self.rng = np.random.default_rng(self.seed) return @@ -159,13 +159,13 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, # Provide a user warning is random seed is set if seed is not None and verbose: print('WARNING: random seed set to %i' % seed) - imf.rng = self.rng + # imf.rng = self.rng ##### # Sample the IMF to build up our cluster mass. ##### # start0 = time.time() - mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass) + mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass, seed=seed) # end0 = time.time() # print('IMF sampling took {0:f} s.'.format(end0 - start0)) @@ -328,11 +328,11 @@ def _make_companions_table_new(self, star_systems, compMass): if isinstance(self.imf._multi_props, multiplicity.MultiplicityResolvedDK): companions.add_column(Column(self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx']]), name='log_a')) - companions.add_column(Column(self.imf._multi_props.random_e(self.rng.random(N_comp_tot)), name='e')) + companions.add_column(Column(self.imf._multi_props.random_e(np.random.rand(N_comp_tot)), name='e')) companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters( - self.rng.random(N_comp_tot), - self.rng.random(N_comp_tot), - self.rng.random(N_comp_tot) + np.random.rand(N_comp_tot), + np.random.rand(N_comp_tot), + np.random.rand(N_comp_tot) ) companions['mass'] = compMass.compressed() @@ -441,11 +441,11 @@ def _make_companions_table(self, star_systems, compMass): for ii in range(len(companions)): companions['log_a'][ii] = self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx'][ii]]) - companions['e'] = self.imf._multi_props.random_e(self.rng.random(N_comp_tot)) + companions['e'] = self.imf._multi_props.random_e(np.random.rand(N_comp_tot)) companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters( - self.rng.random(N_comp_tot), - self.rng.random(N_comp_tot), - self.rng.random(N_comp_tot) + np.random.rand(N_comp_tot), + np.random.rand(N_comp_tot), + np.random.rand(N_comp_tot) ) @@ -701,7 +701,7 @@ def __init__(self, iso, imf, cluster_mass, deltaAKs, # Perturb all of star systems' photometry by a random amount corresponding to # differential de-reddening. The distribution is normal with a width of # Aks +/- deltaAKs in each filter - rand_red = self.rng.standard_normal(len(self.star_systems)) + rand_red = np.random.randn(len(self.star_systems)) for filt in self.filt_names: self.star_systems[filt] += rand_red * delta_red_filt[filt] @@ -1561,18 +1561,18 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= # extinction law if dAKs != 0: if dist == 'gaussian': - AKs_act = self.rng.normal(loc=AKs, scale=dAKs) + AKs_act = np.random.normal(loc=AKs, scale=dAKs) # Apply dAKs_max if desired. Redo if diff > dAKs_max if dAKs_max != None: diff = abs(AKs_act - AKs) while diff > dAKs_max: print('While loop active') - AKs_act = self.rng.normal(loc=AKs, scale=dAKs) + AKs_act = np.random.normal(loc=AKs, scale=dAKs) diff = abs(AKs_act - AKs) elif dist == 'uniform': low = AKs - dAKs high = AKs + dAKs - AKs_act = self.rng.uniform(low=low, high=high) + AKs_act = np.random.uniform(low=low, high=high) else: print('dist {0} undefined'.format(dist)) return diff --git a/spisea/tests/isochrones/iso_6.70_0.80_04000_m1.50.fits b/spisea/tests/isochrones/iso_6.70_0.80_04000_m1.50.fits new file mode 100644 index 00000000..4f7413bb --- /dev/null +++ b/spisea/tests/isochrones/iso_6.70_0.80_04000_m1.50.fits @@ -0,0 +1,20 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 81 / length of dimension 1 NAXIS2 = 71 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 11 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'm_hst_f127m' TFORM9 = 'D ' TTYPE10 = 'm_hst_f139m' TFORM10 = 'D ' TTYPE11 = 'm_hst_f153m' TFORM11 = 'D ' REDLAW = 'H18b ' ATMFUNC = 'get_phoenixv16_atmosphere' EVOMODEL= 'MISTv1 ' HIERARCH EVOMODELVERSION = 'MISTv1.2' LOGAGE = 6.698970004336019 AKS = 0.8 DISTANCE= 4000 METAL_IN= -1.5 HIERARCH METAL_ACT = -1.4990758306077128 WAVEMIN = 3000 WAVEMAX = 52000 END E!L_@WO&A@%%o?@̱X2%F?$*@6YR*@5Aᚮ@5&SU6ZE(<i;@`Aр?4+Y[@XF?4*vb@56C@5m<@4ʂBmE306{@ 8.A$:?i`@]6h@F?iߠ4@5@4܋]@4P7E@*_@[Ag`?q@IF?ipX@4i V@4l%QB@3zn-EFgo6G@AxS%Y?֟aD @tnF?֟_AO@4\.@4@3oET{O.,@DֱZA~JC?{k +V@)(4F?{|@43T@3"g|@2u3E`ūi@sD[AFaY.O?}b9@%efF?{0(;Ŀ@3ph@3(L@2zWJEx@}[2Ao?UJ@,F?U:@2miS@2CJKc@1ZE F@)@Z'Aћ7!cn5?3T4@IwF?KGÿ@1^@11ؾ@0,rEGe9@%[Aʓ03?$C_@~CY(F? %8@1kC@1NB=JW@0v/F?'@1*@##@1t\zdd@1/EcG@ȱ3o(AE(_?Ml@A-F?W@1\?@1K H@08T~Eė3vW_@PfAffk@64q@}F@ƍ@1f":@1,Z@00 Eک7Qt`@Α +gBA09 d@ ۷@۸EF@A+}@1k :@1Q@0Պf=iEo! 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B4@Jm@QT?1&y@-@%YNE|KF>߲@2``BOq$-@J\(@͸T?1&y@.1@%uF'FU}ʉ@@\BK1ֻ@JƧ@wkPT?1&y@.2=,@%iF!`+@@"&ALl@J1&x@QT?1&y@.V7@%-8FԼU@I۬A@{@K I^5@>BT?1&y@.s-@%ņӹF|}p@5AT='6@KO;@T?1&y@.*@%'TF(?$Q@ɀA=$^v@K I^@$T?1&y@.!2@&dBXcF@stAfEp]@K'O;d@ ƧT?1&y@.ÿu@&T?1&y@/# +@&602bFpM@bD7A@KW+ I@%F +L0T?1&y@/Cγ@&V?EF' 5@&xWAَs@Kc +=p@-8YT?1&y@/bG\m/@&~FPU@c0\9A55@KpbM@.}VlT?1&y@/|Z@&# Fߵω@>AAJT$@K}V@6!.IT?1&y@/@'&F-Tp@#%A,m@K+ I@?|hsT?1&y@/MK@'lF+v;@;TwAg@K +=p@@4m9T?1&y@/z@'5sH kF$@RRQAێ{@K +=q@HT?1&y@/0k#@'PdqFc5Q@xA^E.|@KO;dZ@QuT?1&y@0T/m@'jPb)Fl4@$͟PmA+ޑE@K1'@ZQ_T?1&y@0BfS@'+3Fbn@c|,|3A38@KZ1@cS&T?1&y@0ot6@'ߖFɾ@N,AWG+@K&x@kC]T?1&y@0%,@'ÀmFbn@ +jA/2K@KE@tDT?1&y@02 ^@'̿g F]@`YC AvWyI@Kn@~ (T?1&y@0?yJ@'暊kF\,Q!9@vIA 3zD@KzH@͞T?1&y@0L/v@(;FU?3@ A[q@LR@@T?1&y@0WyJ@(kFc/ @x%A6O +4@L+@͞&T?1&y@0fs=@(4A5F%Lò@ +OvAN_@LƧ@AsT?1&y@0x &ִ@(Wt9gF2F@A՟@L(9Xb@yrGE8T?1&y@0{dk@(TS$ \ No newline at end of file diff --git a/spisea/tests/isochrones/iso_6.70_2.46_08000_p0.00.fits b/spisea/tests/isochrones/iso_6.70_2.46_08000_p0.00.fits new file mode 100644 index 00000000..a582318f --- /dev/null +++ b/spisea/tests/isochrones/iso_6.70_2.46_08000_p0.00.fits @@ -0,0 +1 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 89 / length of dimension 1 NAXIS2 = 3 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 12 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'm_hst_f127m' TFORM9 = 'D ' TTYPE10 = 'm_hst_f153m' TFORM10 = 'D ' TTYPE11 = 'm_nirc2_H' TFORM11 = 'D ' TTYPE12 = 'm_nirc2_Kp' TFORM12 = 'D ' REDLAW = 'S10 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MISTv1 ' HIERARCH EVOMODELVERSION = 'MISTv1.2' LOGAGE = 6.698970004336019 AKS = 2.46 DISTANCE= 8000 METAL_IN= 0 HIERARCH METAL_ACT = -0.00616030870481844 WAVEMIN = 3000 WAVEMAX = 52000 END Eg!溑@%^A$}Y(3,?L҄@F٠aF?L7@7@6n@62wjH|@4,"2Ei16@chAͦ1 ?Waq+@9F?W_IG@7(зU@6!9V@6!k~@4r}EkVHC@(AG-@?&ѓ.N@pF?%#,@7$fZf@6@6 l,}@4 q \ No newline at end of file diff --git a/spisea/tests/isochrones/iso_6.70_2.62_08000_p0.00.fits b/spisea/tests/isochrones/iso_6.70_2.62_08000_p0.00.fits new file mode 100644 index 00000000..dcd17c25 --- /dev/null +++ b/spisea/tests/isochrones/iso_6.70_2.62_08000_p0.00.fits @@ -0,0 +1 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 89 / length of dimension 1 NAXIS2 = 3 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 12 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'm_hst_f127m' TFORM9 = 'D ' TTYPE10 = 'm_hst_f153m' TFORM10 = 'D ' TTYPE11 = 'm_nirc2_H' TFORM11 = 'D ' TTYPE12 = 'm_nirc2_Kp' TFORM12 = 'D ' REDLAW = 'F11 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MISTv1 ' HIERARCH EVOMODELVERSION = 'MISTv1.2' LOGAGE = 6.698970004336019 AKS = 2.62 DISTANCE= 8000 METAL_IN= 0 HIERARCH METAL_ACT = -0.00616030870481844 WAVEMIN = 3000 WAVEMAX = 52000 END Eg!溑@%^A$}Y(3,?L҄@F٠aF?L7@:EZ @7-@6b]@4EgEi16@chAͦ1 ?Waq+@9F?W_IG@:/لT@7m@6Q@@46չEkVHC@(AG-@?&ѓ.N@pF?%#,@:#oe@7ۇ@6H[@4-{Pm \ No newline at end of file diff --git a/spisea/tests/isochrones/iso_6.70_2.70_04000_p0.00.fits b/spisea/tests/isochrones/iso_6.70_2.70_04000_p0.00.fits new file mode 100644 index 00000000..d8d7bbbe --- /dev/null +++ b/spisea/tests/isochrones/iso_6.70_2.70_04000_p0.00.fits @@ -0,0 +1,44 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 73 / length of dimension 1 NAXIS2 = 161 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 10 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'K ' TTYPE9 = 'm_nirc2_J' TFORM9 = 'D ' TTYPE10 = 'm_nirc2_Kp' TFORM10 = 'D ' REDLAW = 'N09 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MergedBaraffePisaEkstromParsec' HIERARCH EVOMODELVERSION = 'None ' LOGAGE = 6.7 AKS = 2.7 DISTANCE= 4000 METAL_IN= 0.0 HIERARCH METAL_ACT = 0.0 WAVEMIN = 3000 WAVEMAX = 52000 END E'| @c۳A$wv%4?Q@hr!F?Q@;_@6,2uZE/ޙC@ڈ@A=ˡX? +=p +@Q F? +=p +@:z@5_3EE0 @p]A|ʄ?QR@uF?QR@9$?@4ER/Y[a@^zAmnA?ݏ@@IQF?ݏ@9~~z~@3VEZyJ@aJAɅ ; ?dAג?O3@F?O3@7@2"X@4Ev3@܎0AOy?Yy@}!.HF?Yy@7T6{@2igE @[| @AЊqhi?a@qu!SF?a@7]<4@1GpEF\@^Y A]/xvH?'2@cwkF?'2@7jx5@1wMEIz@s4A+37?cs@SMjF?cs@7P6=@1|fjQE>~@mAԤE]?d8 @@NUF?d8 @74AkK@1 -tE 8I@4!Agm(?"$@)ᰉ'F?"$@7>4@1vEIA@|xMAMTЋ?8:6@TF?8:6@6+5+@11=.EBe@ǵ-AMAaJe?O@Q_F?O@6Ԓ?v|@1dq+E)Pج@$lP=pAؠF?ް@'/WF?ް@6:BE@1B}E?@} ˧A͸?*^o@?H˒:F?*^o@6"\@1EO8E@֙AۃreO;? 5@xF? 5@6^W}T@0WrEZ@4/vZA酘j?*Gfv@6CF?*Gfv@67~8>@0gEm2@ *mAe䌭Y? *@T*1F? 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.hist(n_comp[masses <= 0.08], bins=np.linspace(-0.5, 2.5, 4))\n", + "plt.xlabel('Number of Companions', fontsize=20)\n", + "plt.ylabel('Count', fontsize=20)\n", + "\n", + "ax = plt.gca() # get current axes\n", + "ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n", + "plt.xticks(fontsize=16)\n", + "plt.yticks(fontsize=16)\n", + "\n", + "plt.title('Companion Count Distribution (Brown Dwarfs)', fontsize=20)\n", + "plt.tight_layout()\n", + "#plt.savefig('bdbinarity.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 2.ipynb b/docs/paper_examples/Begbie+26/Figure 2.ipynb new file mode 100644 index 00000000..39920ecc --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 2.ipynb @@ -0,0 +1,115 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 2.\n", + "\n", + "Topic: Introducing and comparing initial mass functions extending to the brown dwarf mass regime. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "from spisea.imf import imf\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7e967d89-695f-4e5a-a5b4-0dc84b6cf04e", + "metadata": {}, + "outputs": [], + "source": [ + "# Define IMFs with substellar capabilities\n", + "simf = imf.Salpeter_Kirkpatrick_2024()\n", + "oimf = imf.Weidner_Kroupa_2004()\n", + "\n", + "# Normalize the IMFs\n", + "simf.normalize(Mcl=1e4) # 10^4 Msun cluster\n", + "oimf.normalize(Mcl=1e4)\n", + "\n", + "# Define a mass grid from 0.01 to 120 M_sun\n", + "m_min = 0.01\n", + "m_max = 120\n", + "\n", + "m_grid = np.logspace(np.log10(m_min), np.log10(m_max), 2000)\n", + "\n", + "# find the dN/dm values\n", + "s_xi_vals = simf.xi(m_grid) # dN/dm\n", + "o_xi_vals = oimf.xi(m_grid)\n", + "\n", + "s_xi_logm = m_grid * s_xi_vals\n", + "o_xi_logm = m_grid * o_xi_vals" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9570acfa-4cf2-42da-ab38-32b7ea02837a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the figure\n", + "plt.figure(figsize=(8,6))\n", + "\n", + "plt.loglog(m_grid, o_xi_vals, lw=2, color='black', label='Normalized Weidner_Kroupa_2004 IMF')\n", + "plt.loglog(m_grid, s_xi_vals, lw=2, color='red', label='Normalized Salpeter_Kirkpatrick_2024 IMF')\n", + "plt.axvspan(0.01, 0.08, alpha=0.3, color='tab:blue', label='Brown Dwarfs (M < 0.08 $M_\\\\odot$)')\n", + "\n", + "plt.xlabel(r'Mass $(M_\\odot)$', fontsize=16)\n", + "plt.ylabel(r'dN/dm', fontsize=16)\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.title('SPISEA Normalized Brown Dwarf IMFs', fontsize=20)\n", + "plt.legend(fontsize=14)\n", + "\n", + "plt.grid(alpha=0.3, which='both')\n", + "plt.tight_layout()\n", + "#plt.savefig('imf.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 3.ipynb b/docs/paper_examples/Begbie+26/Figure 3.ipynb new file mode 100644 index 00000000..383f5af0 --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 3.ipynb @@ -0,0 +1,172 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 3.\n", + "\n", + "Topic: Showing the atmospheric interpolation between BT-Settl (pre-existing) and Meisner (new) model grids to classify brown dwarf atmospheres." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "import matplotlib.pyplot as plt\n", + "from astropy.io import fits" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7e967d89-695f-4e5a-a5b4-0dc84b6cf04e", + "metadata": {}, + "outputs": [], + "source": [ + "# Define path to atmospheric model files\n", + "cdbs_path = '/System/Volumes/Data/mnt/g/lu/models/cdbs'\n", + "logg = 4.5\n", + "\n", + "# 1000 K, plot 1\n", + "merged_file_1000 = f'{cdbs_path}/grid/merged_BTSettl_meisner/merged_T1000_g{logg}_Z0.0.fits'\n", + "bt_file_1000 = f'{cdbs_path}/grid/BTSettl_rebin/btp00/lte010-{logg}-0.0a+0.0.BT-Settl.spec.fits'\n", + "meisner_file_1000 = f'{cdbs_path}/grid/Meisner2023_rebin/mp00/spec_jwst_t1000_g{logg}_p0_kg_g1.25.fits'\n", + "\n", + "# 1100 K, plot 2\n", + "merged_file_1100 = f'{cdbs_path}/grid/merged_BTSettl_meisner/merged_T1100_g{logg}_Z0.0.fits'\n", + "bt_file_1100 = f'{cdbs_path}/grid/BTSettl_rebin/btp00/lte011-{logg}-0.0a+0.0.BT-Settl.spec.fits'\n", + "meisner_file_1100 = f'{cdbs_path}/grid/Meisner2023_rebin/mp00/spec_jwst_t1100_g{logg}_p0_kg_g1.25.fits'\n", + "\n", + "# 1200 K, plot 3\n", + "merged_file_1200 = f'{cdbs_path}/grid/merged_BTSettl_meisner/merged_T1200_g{logg}_Z0.0.fits'\n", + "bt_file_1200 = f'{cdbs_path}/grid/BTSettl_rebin/btp00/lte012-{logg}-0.0a+0.0.BT-Settl.spec.fits'\n", + "meisner_file_1200 = f'{cdbs_path}/grid/Meisner2023_rebin/mp00/spec_jwst_t1200_g{logg}_p0_kg_g1.25.fits'\n", + "\n", + "with fits.open(merged_file_1000) as h:\n", + " wave = h[1].data['Wavelength']\n", + " merged_flux_1000 = h[1].data['Flux']\n", + "\n", + "with fits.open(bt_file_1000) as h:\n", + " bt_flux_1000 = h[1].data['Flux']\n", + "\n", + "with fits.open(meisner_file_1000) as h:\n", + " meis_flux_1000 = h[1].data['Flux']\n", + "\n", + "with fits.open(merged_file_1100) as h:\n", + " merged_flux_1100 = h[1].data['Flux']\n", + "\n", + "with fits.open(bt_file_1100) as h:\n", + " bt_flux_1100 = h[1].data['Flux']\n", + "\n", + "with fits.open(meisner_file_1100) as h:\n", + " meis_flux_1100 = h[1].data['Flux']\n", + "\n", + "with fits.open(merged_file_1200) as h:\n", + " merged_flux_1200 = h[1].data['Flux']\n", + "\n", + "with fits.open(bt_file_1200) as h:\n", + " bt_flux_1200 = h[1].data['Flux']\n", + "\n", + "with fits.open(meisner_file_1200) as h:\n", + " meis_flux_1200 = h[1].data['Flux']\n", + "\n", + "wave_scaled = wave/1e4" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9570acfa-4cf2-42da-ab38-32b7ea02837a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the figure\n", + "fig, axs = plt.subplots(1, 3, figsize=(15,6))\n", + "\n", + "axs[0].semilogy(wave_scaled, wave * merged_flux_1000, label='Merged', lw=4, color='gray')\n", + "axs[0].semilogy(wave_scaled, wave * bt_flux_1000, label='BT-Settl', lw=2, color='red', linestyle='--')\n", + "axs[0].semilogy(wave_scaled, wave * meis_flux_1000, label='Meisner', lw=2, color='blue', linestyle=':')\n", + "\n", + "axs[0].set_xlabel('Wavelength ($10^4$ Å)', fontsize=20)\n", + "axs[0].set_ylabel(r'$\\lambda F_\\lambda$', fontsize=20)\n", + "axs[0].set_xlim(1, 4)\n", + "axs[0].set_ylim(1e6, 2e8)\n", + "axs[0].tick_params(axis='both', labelsize=16)\n", + "axs[0].legend(loc='upper right', fontsize=18)\n", + "axs[0].set_title(r'T$_{eff}$=1000 K', fontsize=24)\n", + "axs[0].grid()\n", + "\n", + "\n", + "axs[1].semilogy(wave_scaled, wave * merged_flux_1100, label='Merged', lw=4, color='gray')\n", + "axs[1].semilogy(wave_scaled, wave * bt_flux_1100, label='BT-Settl', lw=2, color='red', linestyle='--')\n", + "axs[1].semilogy(wave_scaled, wave * meis_flux_1100, label='Meisner', lw=2, color='blue', linestyle=':')\n", + "\n", + "axs[1].set_xlabel('Wavelength ($10^4$ Å)', fontsize=20)\n", + "axs[1].set_ylabel(r'$\\lambda F_\\lambda$', fontsize=20)\n", + "axs[1].set_xlim(1, 4)\n", + "axs[1].set_ylim(3e6, 3e8)\n", + "axs[1].tick_params(axis='both', labelsize=16)\n", + "axs[1].legend(loc='upper right', fontsize=18)\n", + "axs[1].set_title(r'T$_{eff}$=1100 K', fontsize=24)\n", + "axs[1].grid()\n", + "\n", + "\n", + "axs[2].semilogy(wave_scaled, wave * merged_flux_1200, label='Merged', lw=4, color='gray')\n", + "axs[2].semilogy(wave_scaled, wave * bt_flux_1200, label='BT-Settl', lw=2, color='red', linestyle='--')\n", + "axs[2].semilogy(wave_scaled, wave * meis_flux_1200, label='Meisner', lw=2, color='blue', linestyle=':')\n", + "\n", + "axs[2].set_xlabel('Wavelength ($10^4$ Å)', fontsize=20)\n", + "axs[2].set_ylabel(r'$\\lambda F_\\lambda$', fontsize=20)\n", + "axs[2].set_xlim(1, 4)\n", + "axs[2].set_ylim(5e6, 3e8)\n", + "axs[2].legend(loc='upper right', fontsize=18)\n", + "axs[2].set_title(r'T$_{eff}$=1200 K', fontsize=24)\n", + "axs[2].grid()\n", + "\n", + "plt.suptitle('Merging BT-Settl and Meisner Atmospheric Grids from 1000 - 1200 K ($\\log g$=4.5)',\n", + " fontsize=24, fontweight='bold')\n", + "plt.tight_layout()\n", + "plt.savefig('atmo_merge.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 4.ipynb b/docs/paper_examples/Begbie+26/Figure 4.ipynb new file mode 100644 index 00000000..e3e5117a --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 4.ipynb @@ -0,0 +1,213 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 4.\n", + "\n", + "Topic: Showing the Gaussian Process-based interpolation between the Phillips and Pisa/Parsec Evolutionary Models" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from astropy.table import Table" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7e967d89-695f-4e5a-a5b4-0dc84b6cf04e", + "metadata": {}, + "outputs": [], + "source": [ + "# Unpacking the merged Phillips and Pisa/PARSEC files for logage 6 and 9\n", + "evo_folder = '/System/Volumes/Data/mnt/g/lu/models/evolution/merged'\n", + "\n", + "philpisafile = evo_folder + '/phillips_pisa/z015/iso_6.00.fits'\n", + "philparsfile = evo_folder + '/phillips_parsec/z015/iso_9.00.fits'\n", + "\n", + "philpisa = Table.read(philpisafile, format='fits')\n", + "philpars = Table.read(philparsfile, format='fits')\n", + "\n", + "philpisa_m = philpisa['Mass']\n", + "philpisa_l = philpisa['L']\n", + "philpisa_t = philpisa['Teff']\n", + "philpisa_g = philpisa['logg']\n", + "philpisa_interp = np.where(philpisa['interpolated'] == True)\n", + "phil1 = philpisa['Mass'] < 0.075\n", + "pisa = philpisa['Mass'] > 0.2\n", + "\n", + "philpars_m = philpars['Mass']\n", + "philpars_l = philpars['L']\n", + "philpars_t = philpars['Teff']\n", + "philpars_g = philpars['logg']\n", + "philpars_interp = np.where(philpars['interpolated'] == True)\n", + "phil2 = philpars['Mass'] <= 0.075\n", + "parsec = philpars['Mass'] >= 0.2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9570acfa-4cf2-42da-ab38-32b7ea02837a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the Phillips to Pisa interpolation\n", + "fig, axs = plt.subplots(1, 3, figsize=(15,5))\n", + "plt.suptitle('Interpolating from Phillips to Pisa for a 1 Myr Cluster', fontsize=30, fontweight='bold')\n", + "\n", + "axs[0].scatter(philpisa_m[phil1], philpisa_l[phil1], label='Phillips', color='red', marker='d', s=70)\n", + "axs[0].scatter(philpisa_m[pisa], philpisa_l[pisa], label='Pisa', color='blue', marker='s', s=70)\n", + "axs[0].scatter(philpisa_m[philpisa_interp], philpisa_l[philpisa_interp], label='Interpolated Values', color='black', s=100)\n", + "axs[0].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", + "axs[0].set_title('Mass vs. Luminosity', fontsize=24)\n", + "axs[0].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", + "axs[0].set_ylabel('Luminosity', fontsize=20)\n", + "axs[0].set_xlim(0,0.4)\n", + "axs[0].set_ylim(-4, 0.5)\n", + "axs[0].tick_params(axis='both', labelsize=18)\n", + "axs[0].grid()\n", + "axs[0].legend(loc='lower right', fontsize=16)\n", + "\n", + "axs[1].scatter(philpisa_m[phil1], philpisa_t[phil1], label='Phillips', color='red', marker='d', s=70)\n", + "axs[1].scatter(philpisa_m[pisa], philpisa_t[pisa], label='Pisa', color='blue', marker='s', s=70)\n", + "axs[1].scatter(philpisa_m[philpisa_interp], philpisa_t[philpisa_interp], label='Interpolated Values', color='black', s=100)\n", + "axs[1].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", + "axs[1].set_title('Mass vs. Effective Temperature', fontsize=24)\n", + "axs[1].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", + "axs[1].set_ylabel('Teff (K)', fontsize=20)\n", + "axs[1].set_xlim(0,0.4)\n", + "axs[1].set_ylim(2.7, 3.7)\n", + "axs[1].tick_params(axis='both', labelsize=18)\n", + "axs[1].grid()\n", + "axs[1].legend(loc='lower right', fontsize=16)\n", + "\n", + "axs[2].scatter(philpisa_m[phil1], philpisa_g[phil1], label='Phillips', color='red', marker='d', s=70)\n", + "axs[2].scatter(philpisa_m[pisa], philpisa_g[pisa], label='Pisa', color='blue', marker='s', s=70)\n", + "axs[2].scatter(philpisa_m[philpisa_interp], philpisa_g[philpisa_interp], label='Interpolated Values', color='black', s=100)\n", + "axs[2].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", + "axs[2].set_title('Mass vs. Surface Gravity', fontsize=24)\n", + "axs[2].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", + "axs[2].set_ylabel('log(gravity)', fontsize=20)\n", + "axs[2].set_xlim(0,0.4)\n", + "axs[2].set_ylim(2.5, 3.7)\n", + "axs[2].tick_params(axis='both', labelsize=18)\n", + "axs[2].grid()\n", + "axs[2].legend(loc='lower right', fontsize=16)\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('philtopisa.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "26eb9f68-be32-4910-b93f-2103f9b39790", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the Phillips to PARSEC interpolation\n", + "fig, axs = plt.subplots(1, 3, figsize=(15,5))\n", + "plt.suptitle('Interpolating from Phillips to PARSEC for a 1 Gyr Cluster', fontsize=30, fontweight='bold')\n", + "\n", + "axs[0].scatter(philpars_m[phil2], philpars_l[phil2], label='Phillips', color='red', marker='d', s=70)\n", + "axs[0].scatter(philpars_m[parsec], philpars_l[parsec], label='PARSEC', color='orange', marker='P', s=70)\n", + "axs[0].scatter(philpars_m[philpars_interp], philpars_l[philpars_interp], label='Interpolated Values', color='black', s=100)\n", + "axs[0].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", + "axs[0].set_title('Mass vs. Luminosity', fontsize=24)\n", + "axs[0].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", + "axs[0].set_ylabel('Luminosity', fontsize=20)\n", + "axs[0].set_xlim(0,0.4)\n", + "axs[0].set_ylim(-7, -1)\n", + "axs[0].tick_params(axis='both', labelsize=18)\n", + "axs[0].grid()\n", + "axs[0].legend(loc='lower right', fontsize=16)\n", + "\n", + "axs[1].scatter(philpars_m[phil2], philpars_t[phil2], label='Phillips', color='red', marker='d', s=70)\n", + "axs[1].scatter(philpars_m[parsec], philpars_t[parsec], label='PARSEC', color='orange', marker='P', s=70)\n", + "axs[1].scatter(philpars_m[philpars_interp], philpars_t[philpars_interp], label='Interpolated Values', color='black', s=100)\n", + "axs[1].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", + "axs[1].set_title('Mass vs. Effective Temperature', fontsize=24)\n", + "axs[1].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", + "axs[1].set_ylabel('Teff (K)', fontsize=20)\n", + "axs[1].set_xlim(0,0.4)\n", + "axs[1].set_ylim(2.4, 3.7)\n", + "axs[1].tick_params(axis='both', labelsize=18)\n", + "axs[1].grid()\n", + "axs[1].legend(loc='lower right', fontsize=16)\n", + "\n", + "axs[2].scatter(philpars_m[phil2], philpars_g[phil2], label='Phillips', color='red', marker='d', s=70)\n", + "axs[2].scatter(philpars_m[parsec], philpars_g[parsec], label='PARSEC', color='orange', marker='P', s=70)\n", + "axs[2].scatter(philpars_m[philpars_interp], philpars_g[philpars_interp], label='Interpolated Values', color='black', s=100)\n", + "axs[2].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", + "axs[2].set_title('Mass vs. Surface Gravity', fontsize=24)\n", + "axs[2].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", + "axs[2].set_ylabel('log(gravity)', fontsize=20)\n", + "axs[2].set_xlim(0,0.4)\n", + "axs[2].set_ylim(3.5, 5.5)\n", + "axs[2].tick_params(axis='both', labelsize=18)\n", + "axs[2].grid()\n", + "axs[2].legend(loc='lower right', fontsize=16)\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('philtoparsec.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 5.ipynb b/docs/paper_examples/Begbie+26/Figure 5.ipynb new file mode 100644 index 00000000..08e34412 --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 5.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 5.\n", + "\n", + "Topic: Comparing HR Diagrams with brown dwarfs at different ages to show new `MergedPhillipsBaraffePisaEkstromParsec` evolutionary model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining three different isochrones (logage = 6, 8, 10)\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "filt_list = ['wfc3,ir,f153m']\n", + "\n", + "young_iso = synthetic.IsochronePhot(6, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(), \n", + " filters=filt_list)\n", + "med_iso = synthetic.IsochronePhot(8, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(), \n", + " filters=filt_list)\n", + "old_iso = synthetic.IsochronePhot(10, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(), \n", + " filters=filt_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/caitlinbegbie/.local/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:501: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + } + ], + "source": [ + "# Defining our IMF with brown dwarfs\n", + "k_imf = imf.Salpeter_Kirkpatrick_2024()\n", + "\n", + "# Defining clusters for all isochrones\n", + "cluster_mass = 10**6\n", + "young_cluster = synthetic.ResolvedCluster(young_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "med_cluster = synthetic.ResolvedCluster(med_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "old_cluster = synthetic.ResolvedCluster(old_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "young_out = young_cluster.star_systems\n", + "med_out = med_cluster.star_systems\n", + "old_out = old_cluster.star_systems" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1c3f73bf-52ac-446a-897c-cdf5a91dde17", + "metadata": {}, + "outputs": [], + "source": [ + "young_teff = young_out['Teff']\n", + "young_lum = young_out['L']\n", + "med_teff = med_out['Teff']\n", + "med_lum = med_out['L']\n", + "old_teff = old_out['Teff']\n", + "old_lum = old_out['L']\n", + "\n", + "L_sun = 3.828 * 10**(26)\n", + "\n", + "bd_idx_young = young_out['phase'] == 90.0\n", + "bd_idx_med = med_out['phase'] == 90.0\n", + "bd_idx_old = old_out['phase'] == 90.0\n", + "\n", + "log_lum_young = np.log10(young_lum / L_sun)\n", + "log_teff_young = np.log10(young_teff)\n", + "\n", + "log_lum_med = np.log10(med_lum / L_sun)\n", + "log_teff_med = np.log10(med_teff)\n", + "\n", + "log_lum_old = np.log10(old_lum / L_sun)\n", + "log_teff_old = np.log10(old_teff)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cbbf0bea-0d3a-47fa-9bfb-c6ac0167bc0c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting CMDs\n", + "fig, axs = plt.subplots(1, 3, figsize=(15,6))\n", + "\n", + "plt.suptitle('Cluster Evolution over Time (with Brown Dwarfs)', fontsize=30, fontweight='bold')\n", + "\n", + "axs[0].scatter(log_teff_young[~bd_idx_young], log_lum_young[~bd_idx_young], s=50, color='black')\n", + "axs[0].scatter(log_teff_young[bd_idx_young], log_lum_young[bd_idx_young], marker='d', s=50, color='red', label='Brown Dwarfs')\n", + "axs[0].invert_xaxis()\n", + "axs[0].set_title('1 Myr Cluster', fontsize=25)\n", + "axs[0].set_xlabel('log(Teff) [K]', fontsize=20)\n", + "axs[0].set_ylabel('log(L/L$_\\odot$)', fontsize=20)\n", + "axs[0].tick_params(axis='both', labelsize=18)\n", + "axs[0].legend(loc='lower left', fontsize=18)\n", + "axs[0].grid()\n", + "\n", + "axs[1].scatter(log_teff_med[~bd_idx_med], log_lum_med[~bd_idx_med], s=50, color='black')\n", + "axs[1].scatter(log_teff_med[bd_idx_med], log_lum_med[bd_idx_med], marker='d', s=50, color='red', label='Brown Dwarfs')\n", + "axs[1].invert_xaxis()\n", + "axs[1].set_title('100 Myr Cluster', fontsize=25)\n", + "axs[1].set_xlabel('log(Teff) [K]', fontsize=20)\n", + "axs[1].set_ylabel('log(L/L$_\\odot$)', fontsize=20)\n", + "axs[1].tick_params(axis='both', labelsize=18)\n", + "axs[1].legend(loc='lower left', fontsize=18)\n", + "axs[1].grid()\n", + "\n", + "axs[2].scatter(log_teff_old[~bd_idx_old], log_lum_old[~bd_idx_old], s=50, color='black')\n", + "axs[2].scatter(log_teff_old[bd_idx_old], log_lum_old[bd_idx_old], marker='d', s=50, color='red', label='Brown Dwarfs')\n", + "axs[2].invert_xaxis()\n", + "axs[2].set_title('10 Gyr Cluster', fontsize=25)\n", + "axs[2].set_xlabel('log(Teff) [K]', fontsize=20)\n", + "axs[2].set_ylabel('log(L/L$_\\odot$)', fontsize=20)\n", + "axs[2].tick_params(axis='both', labelsize=18)\n", + "axs[2].legend(loc='lower left', fontsize=18)\n", + "axs[2].grid()\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('cmds.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 9.ipynb b/docs/paper_examples/Begbie+26/Figure 9.ipynb new file mode 100644 index 00000000..1c6edfec --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 9.ipynb @@ -0,0 +1,104 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 9.\n", + "\n", + "Topic: Comparing mass fraction as a function of primary mass. This shows the mass-dependent override imposed for brown dwarfs." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from spisea.imf import multiplicity" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# synthetic mass distribution (log-uniform)\n", + "N = 20000\n", + "masses = 10**np.random.uniform(np.log10(0.01), np.log10(10), N)\n", + "\n", + "mult = multiplicity.MultiplicityResolvedDK()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# multiplicity fraction vs. mass plot\n", + "mf = np.array([mult.multiplicity_fraction(m) for m in masses])\n", + "\n", + "bins = np.logspace(np.log10(0.01), np.log10(10), 25)\n", + "digitized = np.digitize(masses, bins)\n", + "\n", + "mf_mean = [mf[digitized == i].mean() for i in range(1, len(bins))]\n", + "\n", + "plt.figure()\n", + "plt.plot(bins[:-1], mf_mean, marker='o', markersize=8)\n", + "plt.xscale('log')\n", + "plt.xlabel('Primary Mass [M$_\\odot$]', fontsize=16)\n", + "plt.xticks(fontsize=12)\n", + "plt.ylabel('Multiplicity Fraction', fontsize=16)\n", + "plt.yticks(fontsize=12)\n", + "plt.title('Multiplicity Fraction vs Mass', fontsize=20)\n", + "plt.axvline(0.08, linestyle='--', label='BD boundary')\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "#plt.savefig('multfrac.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/README b/docs/paper_examples/Begbie+26/README new file mode 100644 index 00000000..b15fecba --- /dev/null +++ b/docs/paper_examples/Begbie+26/README @@ -0,0 +1 @@ +This directory contains jupyter notebooks to re-create the figures included in Begbie et al. (2026). The notebooks are labeled by figure number. \ No newline at end of file diff --git a/docs/paper_examples/Figure 2.ipynb b/docs/paper_examples/Hosek+20/Figure 2.ipynb similarity index 98% rename from docs/paper_examples/Figure 2.ipynb rename to docs/paper_examples/Hosek+20/Figure 2.ipynb index a6ba9fa9..52b2e00e 100644 --- a/docs/paper_examples/Figure 2.ipynb +++ b/docs/paper_examples/Hosek+20/Figure 2.ipynb @@ -181,7 +181,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -195,9 +195,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.11.0" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/paper_examples/Figure 3.ipynb b/docs/paper_examples/Hosek+20/Figure 3.ipynb similarity index 100% rename from docs/paper_examples/Figure 3.ipynb rename to docs/paper_examples/Hosek+20/Figure 3.ipynb diff --git a/docs/paper_examples/Figure 4.ipynb b/docs/paper_examples/Hosek+20/Figure 4.ipynb similarity index 100% rename from docs/paper_examples/Figure 4.ipynb rename to docs/paper_examples/Hosek+20/Figure 4.ipynb diff --git a/docs/paper_examples/Figure 5.ipynb b/docs/paper_examples/Hosek+20/Figure 5.ipynb similarity index 100% rename from docs/paper_examples/Figure 5.ipynb rename to docs/paper_examples/Hosek+20/Figure 5.ipynb diff --git a/docs/paper_examples/Figure 6.ipynb b/docs/paper_examples/Hosek+20/Figure 6.ipynb similarity index 100% rename from docs/paper_examples/Figure 6.ipynb rename to docs/paper_examples/Hosek+20/Figure 6.ipynb diff --git a/docs/paper_examples/Figure 7.ipynb b/docs/paper_examples/Hosek+20/Figure 7.ipynb similarity index 100% rename from docs/paper_examples/Figure 7.ipynb rename to docs/paper_examples/Hosek+20/Figure 7.ipynb diff --git a/docs/paper_examples/README b/docs/paper_examples/Hosek+20/README similarity index 100% rename from docs/paper_examples/README rename to docs/paper_examples/Hosek+20/README From ff6323174f8ef5ecd762e21b6fde4a51d36629d4 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 3 Apr 2026 17:12:07 -0700 Subject: [PATCH 108/191] Adding Begbie+26 Figures 6+7 --- docs/paper_examples/Begbie+26/Figure 6.ipynb | 333 +++++++++++++++++++ docs/paper_examples/Begbie+26/Figure 7.ipynb | 299 +++++++++++++++++ 2 files changed, 632 insertions(+) create mode 100644 docs/paper_examples/Begbie+26/Figure 6.ipynb create mode 100644 docs/paper_examples/Begbie+26/Figure 7.ipynb diff --git a/docs/paper_examples/Begbie+26/Figure 6.ipynb b/docs/paper_examples/Begbie+26/Figure 6.ipynb new file mode 100644 index 00000000..18dbc513 --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 6.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 6.\n", + "\n", + "Topic: Showing the simulated Pleiades cluster (via best-fit isochrone) against actual Pleiades data." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "# Importing necessary packages.\n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.io import fits\n", + "from astropy.table import Table, vstack\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# simulate Pleiades-like cluster (mass=800 M_sun, distance=440 ly -- http://www.pleiade.org/pleiades_03.html)\n", + " # age= 7.99, z = 0.015, per paper (Alfonso et al., 2023)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b91cb1a8-8fc6-4939-9f3b-420082b20340", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to T= 250 for T= 248 logg=3.06\n", + "Changing to logg=5.00 for T= 1542 logg=4.64\n", + "Changing to logg=5.00 for T= 1587 logg=4.66\n", + "Changing to logg=5.00 for T= 1632 logg=4.68\n", + "Changing to logg=5.00 for T= 1677 logg=4.70\n", + "Changing to logg=5.00 for T= 1725 logg=4.71\n", + "Changing to logg=5.00 for T= 1775 logg=4.73\n", + "Changing to logg=5.00 for T= 1820 logg=4.74\n", + "Changing to logg=5.00 for T= 1858 logg=4.75\n", + "Changing to logg=5.00 for T= 1895 logg=4.76\n", + "Changing to logg=5.00 for T= 1936 logg=4.78\n", + "Changing to logg=5.00 for T= 1977 logg=4.79\n", + "Isochrone generation took 13.853408 s.\n", + "Making photometry for isochrone: log(t) = 8.10 AKs = 0.00 dist = 134\n", + " Starting at: 2026-04-03 17:07:51.819141 Usually takes ~5 minutes\n", + "Starting filter: gaia,dr2_rev,G Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 248 K m_gaiaDR2_G = 37.41\n", + "M = 0.316 Msun T = 3192 K m_gaiaDR2_G = 16.60\n", + "M = 4.612 Msun T = 11585 K m_gaiaDR2_G = 3.29\n", + "M = 4.833 Msun T = 3785 K m_gaiaDR2_G = 2.11\n", + "Starting filter: gaia,dr2_rev,Gbp Elapsed time: 2.96 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 248 K m_gaiaDR2_Gbp = 41.58\n", + "M = 0.316 Msun T = 3192 K m_gaiaDR2_Gbp = 18.02\n", + "M = 4.612 Msun T = 11585 K m_gaiaDR2_Gbp = 3.24\n", + "M = 4.833 Msun T = 3785 K m_gaiaDR2_Gbp = 2.95\n", + "Starting filter: gaia,dr2_rev,Grp Elapsed time: 9.01 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 248 K m_gaiaDR2_Grp = 35.84\n", + "M = 0.316 Msun T = 3192 K m_gaiaDR2_Grp = 15.20\n", + "M = 4.612 Msun T = 11585 K m_gaiaDR2_Grp = 3.37\n", + "M = 4.833 Msun T = 3785 K m_gaiaDR2_Grp = 1.00\n", + " Time taken: 11.95 seconds\n" + ] + } + ], + "source": [ + "# Define isochrone parameters\n", + "logAge = 8.10 # Age in log(years)\n", + "AKs = 0.0 # extinction in mags (from https://academic.oup.com/mnras/article/343/4/1263/1067893)\n", + "dist = 134.905 # distance in parsec\n", + "metallicity = 0 # Metallicity in [M/H]\n", + "\n", + "# Define evolution/atmosphere models and extinction law\n", + "evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() \n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "red_law = reddening.RedLawHosek18b()\n", + "\n", + "# Specify Gaia filters\n", + "gaia_filts = ['gaia,dr2_rev,G', 'gaia,dr2_rev,Gbp', 'gaia,dr2_rev,Grp']\n", + "\n", + "# Specify the directory we want the output isochrone\n", + "# table saved in. If the directory does not already exist,\n", + "# SPISEA will create it.\n", + "iso_dir = 'isochrones/'\n", + "\n", + "# Make IsochronePhot object. Note that this will take a minute or two, \n", + "# unless the isochrone has been generated previously.\n", + "#\n", + "# Note that this is not show all of the user options \n", + "# for IsochronePhot. See docs for complete list of options.\n", + "my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n", + " evo_model=evo_model, atm_func=atm_func,\n", + " red_law=red_law, filters=gaia_filts,\n", + " iso_dir=iso_dir, recomp=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0eb22c21-cc15-4611-b9df-dcdf0473c365", + "metadata": {}, + "outputs": [], + "source": [ + "m = my_iso.points['mass']\n", + "\n", + "phillips = (m < 0.07) & (m >= 0.01)\n", + "phillips_baraffe = (m >= 0.07) & (m < 0.075)\n", + "\n", + "baraffe = (m >= 0.075) & (m < 0.4)\n", + "baraffe_pisa = (m >= 0.4) & (m < 0.5)\n", + "\n", + "pisa = (m >= 0.5) & (m < 7.0)\n", + "ms = m >= 7.0 # Ekstrom at this age\n", + "\n", + "color = my_iso.points['m_gaiaDR2_Gbp'] - my_iso.points['m_gaiaDR2_Grp']\n", + "mag = my_iso.points['m_gaiaDR2_G']" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c631060e-4f68-4534-9326-a5bf5cbef20a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 12 stars out of mass range\n" + ] + } + ], + "source": [ + "# plotting simulated Pleiades cluster\n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "my_imf = imf.Salpeter_Kirkpatrick_2024(multiplicity=imf_multi)\n", + "\n", + "mass = 800.\n", + "# Make cluster object\n", + "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)\n", + "\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points\n", + "\n", + "mask = (my_iso.points['mass'] >= 0.01)\n", + "\n", + "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", + "# multiple systems on the photometry\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points[mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1d4cf2ed-92fb-4a72-b64e-ecbb3ce47d11", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: UnitsWarning: 'log(cm.s**-2)' did not parse as fits unit: 'log' is not a recognized function If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n", + "WARNING: UnitsWarning: ''dex'' did not parse as fits unit: At col 0, Unit ''dex'' not supported by the FITS standard. If this is meant to be a custom unit, define it with 'u.def_unit'. To have it recognized inside a file reader or other code, enable it with 'u.add_enabled_units'. For details, see https://docs.astropy.org/en/latest/units/combining_and_defining.html [astropy.units.core]\n" + ] + }, + { + "data": { + "text/plain": [ + "2000" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pulling in real data\n", + "gaia_file = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/changes/13386c89-f30e-11f0-a3b5-bc97e148b76b-O-result.fits'\n", + "gaia_table = Table.read(gaia_file, format='fits')\n", + "len(gaia_table['designation'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1d18cfcf-df2b-4785-a2f3-9b8b9a74cd45", + "metadata": {}, + "outputs": [], + "source": [ + "# 3 degree viewing\n", + "# output limited to 2000 sources\n", + "with fits.open(gaia_file) as h:\n", + " real_g = h[1].data['phot_g_mean_mag']\n", + " real_bp_rp = h[1].data['bp_rp']\n", + " par = h[1].data['parallax']\n", + " pmra = h[1].data['pmra']\n", + " pmdec = h[1].data['pmdec']\n", + "\n", + "mask = (par > 6.5) & (par < 8.0) & \\\n", + " (pmra > 15) & (pmra < 25) & \\\n", + " (pmdec > -50) & (pmdec < -40)\n", + "\n", + "real_g = real_g[mask]\n", + "real_bp_rp = real_bp_rp[mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "514d3a68-227c-43a6-b2f8-e4355c4abe4f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting all together\n", + "fig, axs = plt.subplots(1, 3, figsize=(20,8))\n", + "\n", + "plt.suptitle('Pleiades Simulated Isochrone vs. Real/Simulated Data (Gaia Filters)', fontsize=30, fontweight='bold')\n", + "\n", + "axs[0].plot(color[phillips], mag[phillips], '-', color='purple', label='Phillips (BD)', linewidth=3)\n", + "axs[0].plot(color[phillips_baraffe], mag[phillips_baraffe], '-', color='violet', label='Phillips/Baraffe transition', linewidth=3)\n", + "axs[0].plot(color[baraffe], mag[baraffe], '-', color='orange', label='Baraffe', linewidth=3)\n", + "axs[0].plot(color[baraffe_pisa], mag[baraffe_pisa], '-', color='green', label='Baraffe/Pisa transition', linewidth=3)\n", + "axs[0].plot(color[pisa], mag[pisa], '-', color='blue', label='Pisa', linewidth=3)\n", + "#axs[0].plot(color[ms], mag[ms], '-', color='red', label='Ekstrom MS', linewidth=3)\n", + "axs[0].invert_yaxis()\n", + "axs[0].set_xlabel(r'$G_{BP}-G_{RP}$', fontsize=20)\n", + "axs[0].set_ylabel('G', fontsize=20)\n", + "axs[0].set_title('100 Myr Expected Pleiades Isochrone \\n (Colored by Evolutionary Model)', fontsize=24)\n", + "axs[0].legend(markerscale=4, fontsize=18, loc='upper right')\n", + "axs[0].tick_params(axis='both', labelsize=18)\n", + "axs[0].grid()\n", + "\n", + "axs[1].plot(clust['m_gaiaDR2_Gbp'] - clust['m_gaiaDR2_Grp'], clust['m_gaiaDR2_G'],\n", + " 'k.', ms=10, alpha=0.1, label='Simulated Pleiades Data')\n", + "axs[1].plot(iso['m_gaiaDR2_Gbp'] - iso['m_gaiaDR2_Grp'], iso['m_gaiaDR2_G'],\n", + " 'r-', linewidth=3, label='Theoretical Pleiades Isochrone')\n", + "axs[1].set_xlabel(r'$G_{BP}-G_{RP}$', fontsize=20)\n", + "axs[1].set_ylabel('G', fontsize=20)\n", + "axs[1].invert_yaxis()\n", + "axs[1].set_title('Simulated Pleiades Cluster', fontsize=24)\n", + "axs[1].legend(fontsize=18, loc='upper right')\n", + "axs[1].tick_params(axis='both', labelsize=18)\n", + "axs[1].grid()\n", + "\n", + "axs[2].plot(real_bp_rp, real_g,\n", + " 'm.', ms=10, alpha=0.3, label='real Gaia Pleiades Data')\n", + "axs[2].plot(iso['m_gaiaDR2_Gbp'] - iso['m_gaiaDR2_Grp'], iso['m_gaiaDR2_G'],\n", + " 'r-', linewidth=3, label='Theoretical Pleiades Isochrone')\n", + "axs[2].set_xlabel(r'$G_{BP}-G_{RP}$', fontsize=20)\n", + "axs[2].set_ylabel('G', fontsize=20)\n", + "axs[2].invert_yaxis()\n", + "axs[2].set_title('Real Pleiades Cluster vs. \\n Theoretical Isochrone', fontsize=24)\n", + "axs[2].legend(fontsize=18, loc='upper right')\n", + "axs[2].tick_params(axis='both', labelsize=18)\n", + "axs[2].grid()\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('pleiades_gaia.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 7.ipynb b/docs/paper_examples/Begbie+26/Figure 7.ipynb new file mode 100644 index 00000000..13f6bf4a --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 7.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 7.\n", + "\n", + "Topic: Showing the simulated Pleiades cluster (via best-fit isochrone) against actual Pleiades data from UKIDSS." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.io import fits\n", + "from astropy.table import Table, vstack\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# simulate Pleiades-like cluster (mass=800 M_sun, distance=440 ly -- http://www.pleiade.org/pleiades_03.html)\n", + " # age= 7.99, z = 0.015, per paper (Alfonso et al., 2023)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b91cb1a8-8fc6-4939-9f3b-420082b20340", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to T= 250 for T= 248 logg=3.06\n", + "Changing to logg=5.00 for T= 1542 logg=4.64\n", + "Changing to logg=5.00 for T= 1587 logg=4.66\n", + "Changing to logg=5.00 for T= 1632 logg=4.68\n", + "Changing to logg=5.00 for T= 1677 logg=4.70\n", + "Changing to logg=5.00 for T= 1725 logg=4.71\n", + "Changing to logg=5.00 for T= 1775 logg=4.73\n", + "Changing to logg=5.00 for T= 1820 logg=4.74\n", + "Changing to logg=5.00 for T= 1858 logg=4.75\n", + "Changing to logg=5.00 for T= 1895 logg=4.76\n", + "Changing to logg=5.00 for T= 1936 logg=4.78\n", + "Changing to logg=5.00 for T= 1977 logg=4.79\n", + "Isochrone generation took 13.891200 s.\n", + "Making photometry for isochrone: log(t) = 8.10 AKs = 0.00 dist = 134\n", + " Starting at: 2026-04-03 17:08:33.466620 Usually takes ~5 minutes\n", + "Starting filter: ukirt,J Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 248 K m_ukirt_J = 34.45\n", + "M = 0.316 Msun T = 3192 K m_ukirt_J = 13.27\n", + "M = 4.612 Msun T = 11585 K m_ukirt_J = 3.50\n", + "M = 4.833 Msun T = 3785 K m_ukirt_J = -0.44\n", + "Starting filter: ukirt,H Elapsed time: 1.63 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 248 K m_ukirt_H = 34.18\n", + "M = 0.316 Msun T = 3192 K m_ukirt_H = 12.72\n", + "M = 4.612 Msun T = 11585 K m_ukirt_H = 3.52\n", + "M = 4.833 Msun T = 3785 K m_ukirt_H = -1.24\n", + "Starting filter: ukirt,K Elapsed time: 3.91 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 248 K m_ukirt_K = 37.76\n", + "M = 0.316 Msun T = 3192 K m_ukirt_K = 12.45\n", + "M = 4.612 Msun T = 11585 K m_ukirt_K = 3.55\n", + "M = 4.833 Msun T = 3785 K m_ukirt_K = -1.45\n", + " Time taken: 6.20 seconds\n" + ] + } + ], + "source": [ + "# Define isochrone parameters\n", + "logAge = 8.10 # Age in log(years)\n", + "AKs = 0.0 # extinction in mags (from https://academic.oup.com/mnras/article/343/4/1263/1067893)\n", + "dist = 134.905 # distance in parsec\n", + "metallicity = 0 # Metallicity in [M/H]\n", + "\n", + "# Define evolution/atmosphere models and extinction law\n", + "evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() \n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "red_law = reddening.RedLawHosek18b()\n", + "\n", + "# Specify Gaia filters\n", + "ukidss_filts = ['ukirt,J', 'ukirt,H', 'ukirt,K']\n", + "\n", + "# Specify the directory we want the output isochrone\n", + "# table saved in. If the directory does not already exist,\n", + "# SPISEA will create it.\n", + "iso_dir = 'isochrones/'\n", + "\n", + "# Make IsochronePhot object. Note that this will take a minute or two, \n", + "# unless the isochrone has been generated previously.\n", + "#\n", + "# Note that this is not show all of the user options \n", + "# for IsochronePhot. See docs for complete list of options.\n", + "my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n", + " evo_model=evo_model, atm_func=atm_func,\n", + " red_law=red_law, filters=ukidss_filts,\n", + " iso_dir=iso_dir, recomp=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0eb22c21-cc15-4611-b9df-dcdf0473c365", + "metadata": {}, + "outputs": [], + "source": [ + "m = my_iso.points['mass']\n", + "\n", + "phillips = (m < 0.07) & (m >= 0.01)\n", + "phillips_baraffe = (m >= 0.07) & (m < 0.075)\n", + "\n", + "baraffe = (m >= 0.075) & (m < 0.4)\n", + "baraffe_pisa = (m >= 0.4) & (m < 0.5)\n", + "\n", + "pisa = (m >= 0.5) & (m < 7.0)\n", + "ms = m >= 7.0 # Ekstrom at this age\n", + "\n", + "color = my_iso.points['m_ukirt_J'] - my_iso.points['m_ukirt_K']\n", + "mag = my_iso.points['m_ukirt_J']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c631060e-4f68-4534-9326-a5bf5cbef20a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 12 stars out of mass range\n" + ] + } + ], + "source": [ + "# plotting simulated Pleiades cluster\n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "my_imf = imf.Salpeter_Kirkpatrick_2024(multiplicity=imf_multi)\n", + "\n", + "mass = 800.\n", + "# Make cluster object\n", + "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)\n", + "\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points\n", + "\n", + "mask = (my_iso.points['mass'] >= 0.01)\n", + "\n", + "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", + "# multiple systems on the photometry\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points[mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1d4cf2ed-92fb-4a72-b64e-ecbb3ce47d11", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: VerifyWarning: Invalid keyword for column 3: ASCII table null option (TNULLn) is longer than the column's character width and will be truncated (got '-32768'). [astropy.io.fits.column]\n" + ] + } + ], + "source": [ + "# read in ukidss data\n", + "other = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/changes/asu (2).fit'\n", + "tab = Table.read(other, format='fits', hdu=2)\n", + "\n", + "focus = np.where((tab['Jmag'] - tab['K2mag'] < 10))\n", + "minus = tab['Jmag'] - tab['K2mag']\n", + "J_K = minus[focus]\n", + "\n", + "j_err = tab['e_Jmag']\n", + "k_err = tab['e_K1mag']\n", + "jk_err = np.sqrt(j_err**2 + k_err**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "514d3a68-227c-43a6-b2f8-e4355c4abe4f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting all together\n", + "fig, axs = plt.subplots(1, 3, figsize=(20,8))\n", + "\n", + "plt.suptitle('Pleiades Simulated Isochrone vs. Real/Simulated Data (UKIDSS Filters)', fontsize=30, fontweight='bold')\n", + "\n", + "axs[0].plot(color[phillips], mag[phillips], '-', color='purple', label='Phillips (BD)', linewidth=3)\n", + "axs[0].plot(color[phillips_baraffe], mag[phillips_baraffe], '-', color='violet', label='Phillips/Baraffe transition', linewidth=3)\n", + "axs[0].plot(color[baraffe], mag[baraffe], '-', color='orange', label='Baraffe', linewidth=3)\n", + "axs[0].plot(color[baraffe_pisa], mag[baraffe_pisa], '-', color='green', label='Baraffe/Pisa transition', linewidth=3)\n", + "axs[0].plot(color[pisa], mag[pisa], '-', color='blue', label='Pisa', linewidth=3)\n", + "#axs[0].plot(color[ms], mag[ms], '-', color='red', label='Ekstrom MS', linewidth=3)\n", + "axs[0].invert_yaxis()\n", + "axs[0].set_xlabel('J - K1', fontsize=20)\n", + "axs[0].set_ylabel('J', fontsize=20)\n", + "axs[0].set_title('100 Myr Expected Pleiades Isochrone \\n (Colored by Evolutionary Model)', fontsize=24)\n", + "axs[0].legend(markerscale=4, fontsize=18, loc='upper right')\n", + "axs[0].tick_params(axis='both', labelsize=18)\n", + "axs[0].grid()\n", + "\n", + "axs[1].plot(clust['m_ukirt_J'] - clust['m_ukirt_K'], clust['m_ukirt_J'],\n", + " 'k.', ms=10, alpha=0.1, label='Simulated Pleiades Data')\n", + "axs[1].plot(my_iso.points['m_ukirt_J'][mask] - my_iso.points['m_ukirt_K'][mask], \n", + " my_iso.points['m_ukirt_J'][mask],\n", + " 'r-', linewidth=3, label='Theoretical Pleiades Isochrone')\n", + "axs[1].set_xlabel('J - K1', fontsize=20)\n", + "axs[1].set_ylabel('J', fontsize=20)\n", + "axs[1].invert_yaxis()\n", + "axs[1].set_title('Simulated Pleiades Cluster', fontsize=24)\n", + "axs[1].legend(fontsize=18, loc='upper right')\n", + "axs[1].tick_params(axis='both', labelsize=18)\n", + "axs[1].grid()\n", + "\n", + "axs[2].plot(J_K, tab['Jmag'][focus],\n", + " 'm.', ms=10, alpha=0.3, label='real Gaia Pleiades Data')\n", + "axs[2].errorbar(J_K, tab['Jmag'][focus], xerr=jk_err[focus], yerr=j_err[focus], color='m', fmt='none', ms=5, alpha=0.3, label='Data Errors')\n", + "axs[2].plot(my_iso.points['m_ukirt_J'][mask] - my_iso.points['m_ukirt_K'][mask], \n", + " my_iso.points['m_ukirt_J'][mask],\n", + " 'r-', linewidth=3, label='Theoretical Pleiades Isochrone')\n", + "axs[2].set_xlabel('J - K1', fontsize=20)\n", + "axs[2].set_ylabel('J', fontsize=20)\n", + "axs[2].invert_yaxis()\n", + "axs[2].set_title('Real Pleiades Cluster vs. \\n Theoretical Isochrone', fontsize=24)\n", + "axs[2].legend(fontsize=18, loc='upper right')\n", + "axs[2].tick_params(axis='both', labelsize=18)\n", + "axs[2].grid()\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('pleiades_ukidss.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From f0605b7da225058a0ae55a74fbd434726c76e51b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 6 Apr 2026 18:06:18 -0700 Subject: [PATCH 109/191] Replaced pickle files with fits files --- spisea/tests/test_data/companions.fits | Bin 0 -> 236160 bytes spisea/tests/test_data/companions.pkl | Bin 231086 -> 0 bytes spisea/tests/test_data/star_systems.fits | Bin 0 -> 1258560 bytes spisea/tests/test_data/star_systems.pkl | Bin 1253105 -> 0 bytes spisea/tests/test_synthetic.py | 28 ++++++++++------------- 5 files changed, 12 insertions(+), 16 deletions(-) create mode 100644 spisea/tests/test_data/companions.fits 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z#K5HUhw8n3!2@z19HdY;#%&1>Tt{rx!3)K-eM65It3T?%(DkLo4_z1fVs0Sv8Zr+t_vaX|D>Pqk9{n^v z_qF(%Pu6*!i(_y9P#y1&w-0#S&x)Z-8@X><{#p52^Pw-$;&VM#$3Dd!TkAj_$n$#h z8jt(}}s59GPEJoB=O(Vy3gJonY|;IR+#K&uX(7p-}` zKKbTZb90?z%&X;52Qm*a5IjbHyqJ%ek$t=|e8A@zc}6b}x==oJKugbcj=etMY3TLU zcO2SHq Date: Thu, 9 Apr 2026 16:23:09 -0700 Subject: [PATCH 110/191] add SODC extinction law from SynthPop --- spisea/reddening.py | 138 +++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 136 insertions(+), 2 deletions(-) diff --git a/spisea/reddening.py b/spisea/reddening.py index 0d25e5ae..80c028a3 100755 --- a/spisea/reddening.py +++ b/spisea/reddening.py @@ -63,7 +63,8 @@ def get_red_law(str): 'S16': RedLawSchlafly16, 'H18b': RedLawHosek18b, 'NL18': RedLawNoguerasLara18, - 'NL20': RedLawNoguerasLara20 + 'NL20': RedLawNoguerasLara20, + 'SODC': SODC } # Make reddening law object, including params if necessary. @@ -410,6 +411,139 @@ def Cardelli89(self, wavelength, AKs): return A_at_wave +class RedLawSODC(pysynphot.reddening.CustomRedLaw): + r""" + Defines the SODC extinction law from SynthPop, described by + `Klüter & Huston et al. (2025) `_. + The law is defined from 0.25 - 3.5 microns, and in terms + of :math:`A_{\lambda} / A_{Ks}`, where Ks is 2.174 microns. + + Parameters + ---------- + Rv : float + Ratio of absolute to selective extinction, :math:`A(V) / E(B-V)`. + The standard value for the diffuse ISM is 3.1. Toward the Galactic + bulge, 2.5 is more typical. + """ + def __init__(self, Rv): + # Fetch the extinction curve, pre-interpolate across 0.3-3 microns + wave = np.arange(0.25, 3.5, 0.001) + + # This will eventually be scaled by AKs when you + # call reddening(). Produces A_lambda for AKs = 1, which will be + # scaled later. Expects wavelength in microns + Alambda_scaled = RedLawSODC._derive_sodc(wave, Rv) + + # Convert wavelength to angstrom + wave *= 10 ** 4 + + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + waveunits='angstrom', + Avscaled=Alambda_scaled, + name='SODC', + litref='Klüter & Huston + 2025') + + # Set the upper/lower wavelength limits of law (in angstroms) + self.low_lim = min(wave) + self.high_lim = max(wave) + + # other info + self.scale_lambda = 0.549 + self.name = 'SODC,{0}'.format(Rv) + + @staticmethod + def _derive_sodc(wavelength, Rv): + """ + SODC extinction law. This produces extinction values expected + for AKs = 1 + """ + x = 1.0 / np.array(wavelength) + + # check for applicability + if (np.min(wavelength) < 0.25): + print( 'wavelength is shorter than applicable range for SODC law') + return None + + if (np.max(wavelength) > 3.5): + print( 'wavelength is longer than applicable range for SODC law') + return None + + # Set up some arrays for coefficients that we will need + a = np.zeros(len(x), dtype=float) + b = np.zeros(len(x), dtype=float) + + y = x - 1.82 + + # Calculate coefficients for long wavelengths (low wavenumber) + # Wavenumger <= 1.1 + idx = np.where(x <= 1.1)[0] + a[idx] = 0.53974 * x[idx] ** 2.255 + b[idx] = -0.495567 * x[idx] ** 2.255 + + # Calculate coefficients for short wavelengths + # 1.1 < wavenumber + idx = np.where((x > 1.1))[0] + yy = y[idx] + a[idx] = 1 + (0.104 * yy) - (0.609 * yy ** 2) + \ + (0.701 * yy ** 3) + (1.137* yy ** 4) - \ + (1.718 * yy ** 5) - (0.827 * yy ** 6) + \ + (1.647 * yy ** 7) - (0.505 * yy ** 8) + b[idx] = (1.952 * yy) + (2.908 * yy ** 2) - \ + (3.989 * yy ** 3) - (7.985 * yy ** 4) + \ + (11.102 * yy ** 5) + (5.491 * yy ** 6) - \ + (10.805 * yy ** 7) + (3.347 * yy ** 8) + + # A(lam) / A(V), from Eq. 1 + extinction = a + b/Rv + + # Now, want to produce A_lambda / AKs, to match other laws + k_ind = np.argmin(abs(x-0.46)) + Aks_Av = a[k_ind] + b[k_ind]/Rv # Aks / Av + Av_Aks = 1.0 / Aks_Av # Av / Aks + + output = extinction * Av_Aks # (A(lamb) / Av) * (Av / Aks) = (A(lamb) / Aks) + + return output + + def SODC(self, wavelength, AKs): + """ + Return the extinction at a given wavelength assuming the + extinction law and an overall `AKs` value. + + Parameters + ---------- + wavelength : float or array + Wavelength to return extinction for, in microns + AKs : float + Total extinction in AKs, in mags + """ + # If input entry is a single float, turn it into an array + try: + len(wavelength) + except: + wavelength = [wavelength] + + # Return error if any wavelength is beyond interpolation range of + # extinction law + if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): + return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) + + # Extract wave and A/AKs from law, turning wave into micron units + wave = self.wave * (10**-4) + law = self.obscuration + + # Find the value of the law at the closest points + # to wavelength + A_AKs_at_wave = [] + for ii in wavelength: + idx = np.where( abs(wave - ii) == min(abs(wave - ii)) ) + A_AKs_at_wave.append(law[idx][0]) + + # Now multiply by AKs (since law assumes AKs = 1) + A_at_wave = np.array(A_AKs_at_wave) * AKs + + return A_at_wave + class RedLawRomanZuniga07(pysynphot.reddening.CustomRedLaw): """ Defines extinction law from `Roman-Zuniga et al. 2007 @@ -2224,4 +2358,4 @@ def __init__(self, x, y, yp=None): y2 = solve_banded((1,1), mat, bb) self.x, self.y, self.y2 = (x, y, y2) def __call__(self, x): - return splint(self, x) + return splint(self, x) \ No newline at end of file From 4fc570aef7adc4161f960a5a14881322d0ba233a Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 17 Apr 2026 10:01:55 -0700 Subject: [PATCH 111/191] Corrected paper figures uploaded --- docs/paper_examples/Begbie+26/Figure 10.ipynb | 66 +- docs/paper_examples/Begbie+26/Figure 11.ipynb | 122 +++ docs/paper_examples/Begbie+26/Figure 12.ipynb | 100 +++ docs/paper_examples/Begbie+26/Figure 8.ipynb | 789 ++++++++++++++++++ docs/paper_examples/Begbie+26/Figure 9.ipynb | 104 --- 5 files changed, 1035 insertions(+), 146 deletions(-) create mode 100644 docs/paper_examples/Begbie+26/Figure 11.ipynb create mode 100644 docs/paper_examples/Begbie+26/Figure 12.ipynb create mode 100644 docs/paper_examples/Begbie+26/Figure 8.ipynb delete mode 100644 docs/paper_examples/Begbie+26/Figure 9.ipynb diff --git a/docs/paper_examples/Begbie+26/Figure 10.ipynb b/docs/paper_examples/Begbie+26/Figure 10.ipynb index 0f9432ee..1c6edfec 100644 --- a/docs/paper_examples/Begbie+26/Figure 10.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 10.ipynb @@ -5,9 +5,9 @@ "id": "51fdd39d-b433-4371-a487-fa5d855b79de", "metadata": {}, "source": [ - "## Below is the code to recreate Figure 10.\n", + "## Below is the code to recreate Figure 9.\n", "\n", - "Topic: Showing the brown dwarf companion count distribution to prove an enforced limit of one." + "Topic: Comparing mass fraction as a function of primary mass. This shows the mass-dependent override imposed for brown dwarfs." ] }, { @@ -20,8 +20,7 @@ "# Importing necessary packages.\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from spisea.imf import multiplicity\n", - "from matplotlib import ticker" + "from spisea.imf import multiplicity" ] }, { @@ -34,6 +33,7 @@ "# synthetic mass distribution (log-uniform)\n", "N = 20000\n", "masses = 10**np.random.uniform(np.log10(0.01), np.log10(10), N)\n", + "\n", "mult = multiplicity.MultiplicityResolvedDK()" ] }, @@ -42,38 +42,12 @@ "execution_count": 3, "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", "metadata": {}, - "outputs": [], - "source": [ - "# binarity (BD primaries have ≤ 1 companion)\n", - "mf = np.array([mult.multiplicity_fraction(m) for m in masses])\n", - "csf = np.array([mult.companion_star_fraction(m) for m in masses])\n", - "\n", - "rand = np.random.rand(len(masses))\n", - "is_mult = rand < mf\n", - "\n", - "n_comp = np.zeros(len(masses), dtype=int)\n", - "\n", - "# same process as in MultiplicityUnresolved\n", - "for i, m in enumerate(masses):\n", - " if not is_mult[i]:\n", - " n_comp[i] = 0\n", - " elif m <= 0.08:\n", - " n_comp[i] = 1 # hard BD limit\n", - " else:\n", - " n_comp[i] = mult.random_companion_count(1, csf[i], mf[i])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1bde3a03-d9c5-4762-a8be-7611f8668d1e", - "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
    " + "
    " ] }, "metadata": {}, @@ -81,19 +55,27 @@ } ], "source": [ - "plt.figure(figsize=(8,6))\n", - "plt.hist(n_comp[masses <= 0.08], bins=np.linspace(-0.5, 2.5, 4))\n", - "plt.xlabel('Number of Companions', fontsize=20)\n", - "plt.ylabel('Count', fontsize=20)\n", + "# multiplicity fraction vs. mass plot\n", + "mf = np.array([mult.multiplicity_fraction(m) for m in masses])\n", + "\n", + "bins = np.logspace(np.log10(0.01), np.log10(10), 25)\n", + "digitized = np.digitize(masses, bins)\n", "\n", - "ax = plt.gca() # get current axes\n", - "ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n", - "plt.xticks(fontsize=16)\n", - "plt.yticks(fontsize=16)\n", + "mf_mean = [mf[digitized == i].mean() for i in range(1, len(bins))]\n", "\n", - "plt.title('Companion Count Distribution (Brown Dwarfs)', fontsize=20)\n", + "plt.figure()\n", + "plt.plot(bins[:-1], mf_mean, marker='o', markersize=8)\n", + "plt.xscale('log')\n", + "plt.xlabel('Primary Mass [M$_\\odot$]', fontsize=16)\n", + "plt.xticks(fontsize=12)\n", + "plt.ylabel('Multiplicity Fraction', fontsize=16)\n", + "plt.yticks(fontsize=12)\n", + "plt.title('Multiplicity Fraction vs Mass', fontsize=20)\n", + "plt.axvline(0.08, linestyle='--', label='BD boundary')\n", + "plt.legend(fontsize=14)\n", + "plt.grid()\n", "plt.tight_layout()\n", - "#plt.savefig('bdbinarity.png')\n", + "#plt.savefig('multfrac.png')\n", "plt.show()" ] } diff --git a/docs/paper_examples/Begbie+26/Figure 11.ipynb b/docs/paper_examples/Begbie+26/Figure 11.ipynb new file mode 100644 index 00000000..0f9432ee --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 11.ipynb @@ -0,0 +1,122 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 10.\n", + "\n", + "Topic: Showing the brown dwarf companion count distribution to prove an enforced limit of one." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from spisea.imf import multiplicity\n", + "from matplotlib import ticker" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# synthetic mass distribution (log-uniform)\n", + "N = 20000\n", + "masses = 10**np.random.uniform(np.log10(0.01), np.log10(10), N)\n", + "mult = multiplicity.MultiplicityResolvedDK()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", + "metadata": {}, + "outputs": [], + "source": [ + "# binarity (BD primaries have ≤ 1 companion)\n", + "mf = np.array([mult.multiplicity_fraction(m) for m in masses])\n", + "csf = np.array([mult.companion_star_fraction(m) for m in masses])\n", + "\n", + "rand = np.random.rand(len(masses))\n", + "is_mult = rand < mf\n", + "\n", + "n_comp = np.zeros(len(masses), dtype=int)\n", + "\n", + "# same process as in MultiplicityUnresolved\n", + "for i, m in enumerate(masses):\n", + " if not is_mult[i]:\n", + " n_comp[i] = 0\n", + " elif m <= 0.08:\n", + " n_comp[i] = 1 # hard BD limit\n", + " else:\n", + " n_comp[i] = mult.random_companion_count(1, csf[i], mf[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1bde3a03-d9c5-4762-a8be-7611f8668d1e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "plt.hist(n_comp[masses <= 0.08], bins=np.linspace(-0.5, 2.5, 4))\n", + "plt.xlabel('Number of Companions', fontsize=20)\n", + "plt.ylabel('Count', fontsize=20)\n", + "\n", + "ax = plt.gca() # get current axes\n", + "ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n", + "plt.xticks(fontsize=16)\n", + "plt.yticks(fontsize=16)\n", + "\n", + "plt.title('Companion Count Distribution (Brown Dwarfs)', fontsize=20)\n", + "plt.tight_layout()\n", + "#plt.savefig('bdbinarity.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 12.ipynb b/docs/paper_examples/Begbie+26/Figure 12.ipynb new file mode 100644 index 00000000..aa7ca1d8 --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 12.ipynb @@ -0,0 +1,100 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 11.\n", + "\n", + "Topic: Showing the semimajor axis distribution as a function of initial mass (reflects changes induced for brown dwarfs)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from spisea.imf import multiplicity\n", + "from matplotlib import ticker" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# synthetic mass distribution (log-uniform)\n", + "N = 20000\n", + "masses = 10**np.random.uniform(np.log10(0.01), np.log10(10), N)\n", + "mult = multiplicity.MultiplicityResolvedDK()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# semimajor axis distribution\n", + "log_a = np.array([mult.log_semimajoraxis(m) for m in masses])\n", + "\n", + "plt.figure()\n", + "plt.scatter(masses, 10**log_a, s=1, alpha=0.3)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.xlabel('Primary Mass (M$_\\odot$)', fontsize=16)\n", + "plt.ylabel('Semimajor Axis (AU)', fontsize=16)\n", + "#plt.plot(bd_mass, bd_sep, 'r--', label='Fontanive+18 (approx)')\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.title('Semimajor Axis vs Mass', fontsize=20)\n", + "plt.axvline(0.08, linestyle='--', label='BD boundary')\n", + "plt.legend(fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig('semimajoraxis.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 8.ipynb b/docs/paper_examples/Begbie+26/Figure 8.ipynb new file mode 100644 index 00000000..14dea6db --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 8.ipynb @@ -0,0 +1,789 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure ?.\n", + "\n", + "Topic: Showing the simulated Upper Sco cluster (via best-fit isochrone) against actual data from UKIDSS." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.io import fits\n", + "from astropy.table import Table, vstack\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# simulate Upper Sco-like cluster (mass=1000-2000 M_sun, distance=145 pc, extinction=2)\n", + " # age= 5 myr, z = 0.015, per paper (Alfonso et al., 2023)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "b91cb1a8-8fc6-4939-9f3b-420082b20340", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=5.00 for T= 1614 logg=3.86\n", + "Changing to logg=5.00 for T= 1734 logg=3.90\n", + "Changing to logg=5.00 for T= 1844 logg=3.94\n", + "Changing to logg=5.00 for T= 1939 logg=3.96\n", + "Changing to logg=4.00 for T= 32651 logg=3.99\n", + "Changing to logg=4.00 for T= 32840 logg=3.98\n", + "Changing to logg=4.00 for T= 33037 logg=3.97\n", + "Changing to logg=4.00 for T= 33144 logg=3.96\n", + "Changing to logg=4.00 for T= 33205 logg=3.95\n", + "Changing to logg=4.00 for T= 33358 logg=3.94\n", + "Changing to logg=4.00 for T= 33504 logg=3.93\n", + "Changing to logg=4.00 for T= 33651 logg=3.91\n", + "Changing to logg=4.00 for T= 33713 logg=3.91\n", + "Changing to logg=4.00 for T= 33775 logg=3.90\n", + "Changing to logg=4.00 for T= 33892 logg=3.89\n", + "Changing to logg=4.00 for T= 34002 logg=3.87\n", + "Changing to logg=4.00 for T= 34111 logg=3.86\n", + "Changing to logg=4.00 for T= 34182 logg=3.85\n", + "Changing to logg=4.00 for T= 34222 logg=3.85\n", + "Changing to logg=4.00 for T= 34332 logg=3.83\n", + "Changing to logg=4.00 for T= 34443 logg=3.82\n", + "Changing to logg=4.00 for T= 34546 logg=3.81\n", + "Changing to logg=4.00 for T= 34658 logg=3.80\n", + "Changing to logg=4.00 for T= 34674 logg=3.80\n", + "Changing to logg=4.00 for T= 34746 logg=3.79\n", + "Changing to logg=4.00 for T= 34842 logg=3.78\n", + "Changing to logg=4.00 for T= 34930 logg=3.77\n", + "Changing to logg=4.00 for T= 35019 logg=3.76\n", + "Changing to logg=4.00 for T= 35108 logg=3.75\n", + "Changing to logg=4.00 for T= 35124 logg=3.74\n", + "Changing to logg=4.00 for T= 35229 logg=3.74\n", + "Changing to logg=4.00 for T= 35351 logg=3.73\n", + "Changing to logg=4.00 for T= 35465 logg=3.72\n", + "Changing to logg=4.00 for T= 35588 logg=3.71\n", + "Changing to logg=4.00 for T= 35711 logg=3.70\n", + "Changing to logg=4.00 for T= 35777 logg=3.70\n", + "Changing to logg=4.00 for T= 35851 logg=3.70\n", + "Changing to logg=4.00 for T= 36025 logg=3.69\n", + "Changing to logg=4.00 for T= 36199 logg=3.69\n", + "Changing to logg=4.00 for T= 36366 logg=3.69\n", + "Changing to logg=4.00 for T= 36543 logg=3.68\n", + "Changing to logg=4.00 for T= 36711 logg=3.68\n", + "Changing to logg=4.00 for T= 36796 logg=3.68\n", + "Changing to logg=4.00 for T= 36872 logg=3.68\n", + "Changing to logg=4.00 for T= 37008 logg=3.67\n", + "Changing to logg=4.00 for T= 37145 logg=3.67\n", + "Changing to logg=4.00 for T= 37291 logg=3.67\n", + "Changing to logg=4.00 for T= 37428 logg=3.66\n", + "Changing to logg=4.00 for T= 37566 logg=3.66\n", + "Changing to logg=4.00 for T= 37696 logg=3.65\n", + "Changing to logg=4.00 for T= 37679 logg=3.65\n", + "Changing to logg=4.00 for T= 37454 logg=3.65\n", + "Changing to logg=4.00 for T= 37239 logg=3.64\n", + "Changing to logg=4.00 for T= 37017 logg=3.64\n", + "Changing to logg=4.00 for T= 36796 logg=3.64\n", + "Changing to logg=4.00 for T= 36576 logg=3.63\n", + "Changing to logg=4.00 for T= 36350 logg=3.63\n", + "Changing to logg=4.00 for T= 36141 logg=3.62\n", + "Changing to logg=4.00 for T= 36124 logg=3.62\n", + "Changing to logg=4.00 for T= 35744 logg=3.62\n", + "Changing to logg=4.00 for T= 35367 logg=3.61\n", + "Changing to logg=4.00 for T= 34986 logg=3.61\n", + "Changing to logg=4.00 for T= 34602 logg=3.60\n", + "Changing to logg=4.00 for T= 34229 logg=3.60\n", + "Changing to logg=4.00 for T= 33845 logg=3.59\n", + "Changing to logg=4.00 for T= 33466 logg=3.59\n", + "Changing to logg=4.00 for T= 33235 logg=3.59\n", + "Changing to logg=4.00 for T= 33174 logg=3.59\n", + "Changing to logg=4.00 for T= 33007 logg=3.60\n", + "Changing to logg=4.00 for T= 32840 logg=3.60\n", + "Changing to logg=4.00 for T= 32674 logg=3.61\n", + "Changing to logg=4.00 for T= 32516 logg=3.62\n", + "Changing to logg=4.00 for T= 32344 logg=3.63\n", + "Changing to logg=4.00 for T= 32337 logg=3.63\n", + "Changing to logg=4.00 for T= 32359 logg=3.63\n", + "Changing to logg=4.00 for T= 32382 logg=3.63\n", + "Changing to logg=4.00 for T= 32412 logg=3.63\n", + "Changing to logg=4.00 for T= 32441 logg=3.64\n", + "Changing to logg=4.00 for T= 32471 logg=3.64\n", + "Changing to logg=4.00 for T= 32509 logg=3.64\n", + "Changing to logg=4.00 for T= 32554 logg=3.64\n", + "Changing to logg=4.00 for T= 32606 logg=3.65\n", + "Changing to logg=4.00 for T= 32659 logg=3.65\n", + "Changing to logg=4.00 for T= 32711 logg=3.65\n", + "Changing to logg=4.00 for T= 32772 logg=3.66\n", + "Changing to logg=4.00 for T= 32840 logg=3.66\n", + "Changing to logg=4.00 for T= 32915 logg=3.67\n", + "Changing to logg=4.00 for T= 32991 logg=3.67\n", + "Changing to logg=4.00 for T= 33090 logg=3.68\n", + "Changing to logg=4.00 for T= 33189 logg=3.68\n", + "Changing to logg=4.00 for T= 33320 logg=3.69\n", + "Changing to logg=4.00 for T= 33458 logg=3.70\n", + "Changing to logg=4.00 for T= 33620 logg=3.71\n", + "Changing to logg=4.00 for T= 33806 logg=3.72\n", + "Changing to logg=4.00 for T= 34033 logg=3.73\n", + "Changing to logg=4.00 for T= 34332 logg=3.74\n", + "Changing to logg=4.00 for T= 34626 logg=3.76\n", + "Changing to logg=4.00 for T= 35432 logg=3.79\n", + "Changing to logg=4.00 for T= 36526 logg=3.83\n", + "Changing to logg=4.00 for T= 37653 logg=3.88\n", + "Changing to logg=4.00 for T= 38036 logg=3.89\n", + "Changing to logg=4.00 for T= 38815 logg=3.92\n", + "Changing to logg=4.50 for T= 40542 logg=3.97\n", + "Changing to logg=4.00 for T= 31842 logg=3.57\n", + "Changing to logg=3.00 for T= 22151 logg=2.96\n", + "Changing to logg=2.50 for T= 13035 logg=2.11\n", + "Changing to logg=1.50 for T= 8576 logg=1.37\n", + "Changing to logg=2.00 for T= 9066 logg=1.48\n", + "Changing to logg=2.00 for T= 9583 logg=1.58\n", + "Changing to logg=2.00 for T= 10132 logg=1.69\n", + "Changing to logg=2.00 for T= 10713 logg=1.79\n", + "Changing to logg=2.00 for T= 11387 logg=1.89\n", + "Changing to logg=2.50 for T= 12123 logg=1.99\n", + "Changing to logg=2.50 for T= 12909 logg=2.09\n", + "Changing to logg=2.50 for T= 13750 logg=2.20\n", + "Changing to logg=2.50 for T= 14645 logg=2.30\n", + "Changing to logg=2.50 for T= 15603 logg=2.40\n", + "Changing to logg=2.50 for T= 16383 logg=2.49\n", + "Changing to logg=3.00 for T= 19094 logg=2.96\n", + "Changing to logg=5.00 for T= 41400 logg=5.02\n", + "Changing to logg=5.00 for T= 41947 logg=5.06\n", + "Changing to logg=5.00 for T= 42345 logg=5.10\n", + "Changing to logg=5.00 for T= 42452 logg=5.11\n", + "Changing to logg=5.00 for T= 42560 logg=5.12\n", + "Changing to logg=5.00 for T= 42668 logg=5.13\n", + "Changing to logg=5.00 for T= 42766 logg=5.14\n", + "Changing to logg=5.00 for T= 42875 logg=5.16\n", + "Changing to logg=5.00 for T= 42983 logg=5.17\n", + "Changing to logg=5.00 for T= 43033 logg=5.18\n", + "Changing to logg=5.00 for T= 42973 logg=5.18\n", + "Changing to logg=5.00 for T= 42914 logg=5.19\n", + "Changing to logg=5.00 for T= 42845 logg=5.19\n", + "Changing to logg=5.00 for T= 42786 logg=5.19\n", + "Changing to logg=5.00 for T= 42766 logg=5.19\n", + "Changing to logg=5.00 for T= 42727 logg=5.20\n", + "Changing to logg=5.00 for T= 42668 logg=5.20\n", + "Changing to logg=5.00 for T= 42599 logg=5.20\n", + "Changing to logg=5.00 for T= 42540 logg=5.21\n", + "Changing to logg=5.00 for T= 42472 logg=5.21\n", + "Changing to logg=5.00 for T= 42413 logg=5.21\n", + "Changing to logg=5.00 for T= 42345 logg=5.21\n", + "Changing to logg=5.00 for T= 42277 logg=5.21\n", + "Changing to logg=5.00 for T= 42209 logg=5.22\n", + "Changing to logg=5.00 for T= 42150 logg=5.22\n", + "Changing to logg=5.00 for T= 42082 logg=5.22\n", + "Changing to logg=5.00 for T= 42024 logg=5.22\n", + "Changing to logg=5.00 for T= 41976 logg=5.23\n", + "Changing to logg=5.00 for T= 41899 logg=5.23\n", + "Changing to logg=5.00 for T= 41850 logg=5.23\n", + "Changing to logg=5.00 for T= 41802 logg=5.23\n", + "Changing to logg=5.00 for T= 41754 logg=5.23\n", + "Changing to logg=5.00 for T= 41957 logg=5.26\n", + "Changing to logg=5.00 for T= 42024 logg=5.27\n", + "Changing to logg=5.00 for T= 42053 logg=5.28\n", + "Changing to logg=5.00 for T= 42053 logg=5.28\n", + "Changing to logg=5.00 for T= 42024 logg=5.29\n", + "Changing to logg=5.00 for T= 42034 logg=5.29\n", + "Changing to logg=5.00 for T= 42034 logg=5.30\n", + "Changing to logg=5.00 for T= 42015 logg=5.31\n", + "Changing to logg=5.00 for T= 41995 logg=5.31\n", + "Changing to logg=5.00 for T= 41966 logg=5.31\n", + "Changing to logg=5.00 for T= 41966 logg=5.32\n", + "Changing to logg=5.00 for T= 41947 logg=5.32\n", + "Changing to logg=5.00 for T= 41937 logg=5.33\n", + "Changing to logg=5.00 for T= 41889 logg=5.33\n", + "Changing to logg=5.00 for T= 41822 logg=5.33\n", + "Changing to logg=5.00 for T= 41783 logg=5.33\n", + "Changing to logg=5.00 for T= 41754 logg=5.33\n", + "Changing to logg=5.00 for T= 41200 logg=5.32\n", + "Changing to logg=5.00 for T= 40374 logg=5.30\n", + "Changing to logg=5.00 for T= 40337 logg=5.30\n", + "Changing to logg=5.00 for T= 40207 logg=5.30\n", + "Changing to logg=5.00 for T= 39455 logg=5.31\n", + "Changing to logg=5.00 for T= 35719 logg=5.31\n", + "Changing to logg=5.00 for T= 35645 logg=5.30\n", + "Changing to logg=5.00 for T= 35588 logg=5.30\n", + "Changing to logg=5.00 for T= 35571 logg=5.30\n", + "Changing to logg=5.00 for T= 34962 logg=5.25\n", + "Changing to logg=5.00 for T= 34906 logg=5.25\n", + "Changing to logg=5.00 for T= 34475 logg=5.21\n", + "Changing to logg=5.00 for T= 34277 logg=5.20\n", + "Changing to logg=5.00 for T= 34285 logg=5.20\n", + "Changing to logg=5.00 for T= 34658 logg=5.23\n", + "Changing to logg=5.00 for T= 34770 logg=5.24\n", + "Changing to logg=5.00 for T= 34253 logg=5.20\n", + "Changing to logg=5.00 for T= 33628 logg=5.15\n", + "Changing to logg=5.00 for T= 33312 logg=5.12\n", + "Changing to logg=5.00 for T= 33636 logg=5.15\n", + "Changing to logg=5.00 for T= 33643 logg=5.15\n", + "Changing to logg=5.00 for T= 33659 logg=5.15\n", + "Changing to logg=5.00 for T= 33651 logg=5.15\n", + "Changing to logg=5.00 for T= 33504 logg=5.13\n", + "Changing to logg=5.00 for T= 33466 logg=5.08\n", + "Changing to logg=5.00 for T= 33504 logg=5.08\n", + "Changing to logg=5.00 for T= 33558 logg=5.07\n", + "Changing to logg=5.00 for T= 33605 logg=5.08\n", + "Changing to logg=5.00 for T= 33651 logg=5.08\n", + "Changing to logg=5.00 for T= 33674 logg=5.07\n", + "Changing to logg=5.00 for T= 32870 logg=5.05\n", + "Changing to logg=5.00 for T= 30054 logg=5.01\n", + "Changing to logg=5.00 for T= 29819 logg=5.01\n", + "Changing to logg=5.00 for T= 35785 logg=5.39\n", + "Changing to logg=5.00 for T= 39683 logg=5.62\n", + "Changing to logg=5.00 for T= 39701 logg=5.62\n", + "Changing to logg=5.00 for T= 39655 logg=5.63\n", + "Changing to logg=5.00 for T= 39518 logg=5.63\n", + "Changing to logg=5.00 for T= 39373 logg=5.63\n", + "Changing to logg=5.00 for T= 39518 logg=5.65\n", + "Changing to logg=5.00 for T= 40031 logg=5.69\n", + "Changing to logg=5.00 for T= 40096 logg=5.70\n", + "Changing to logg=5.00 for T= 40495 logg=5.74\n", + "Changing to logg=5.00 for T= 41381 logg=5.82\n", + "Changing to logg=5.00 for T= 41976 logg=5.87\n", + "Changing to logg=5.00 for T= 42199 logg=5.90\n", + "Changing to logg=5.00 for T= 42228 logg=5.90\n", + "Changing to logg=5.00 for T= 42286 logg=5.90\n", + "Changing to logg=5.00 for T= 42345 logg=5.91\n", + "Changing to logg=5.00 for T= 42394 logg=5.92\n", + "Changing to logg=5.00 for T= 42462 logg=5.92\n", + "Changing to logg=5.00 for T= 42530 logg=5.92\n", + "Changing to logg=5.00 for T= 42619 logg=5.92\n", + "Changing to logg=5.00 for T= 42717 logg=5.93\n", + "Changing to logg=5.00 for T= 42825 logg=5.93\n", + "Changing to logg=5.00 for T= 42934 logg=5.94\n", + "Changing to logg=5.00 for T= 43053 logg=5.94\n", + "Changing to logg=5.00 for T= 43152 logg=5.94\n", + "Changing to logg=5.00 for T= 43271 logg=5.95\n", + "Changing to logg=5.00 for T= 43411 logg=5.95\n", + "Changing to logg=5.00 for T= 43561 logg=5.96\n", + "Changing to logg=5.00 for T= 43692 logg=5.96\n", + "Changing to logg=5.00 for T= 43833 logg=5.96\n", + "Changing to logg=5.00 for T= 43985 logg=5.97\n", + "Changing to logg=5.00 for T= 44147 logg=5.97\n", + "Changing to logg=5.00 for T= 44310 logg=5.97\n", + "Changing to logg=5.00 for T= 44484 logg=5.97\n", + "Changing to logg=5.00 for T= 44638 logg=5.97\n", + "Changing to logg=5.00 for T= 44802 logg=5.98\n", + "Changing to logg=5.00 for T= 44957 logg=5.98\n", + "Changing to logg=5.00 for T= 45144 logg=5.98\n", + "Changing to logg=5.00 for T= 45300 logg=5.99\n", + "Changing to logg=5.00 for T= 45478 logg=5.99\n", + "Changing to logg=5.00 for T= 45509 logg=5.99\n", + "Changing to logg=5.00 for T= 45646 logg=5.99\n", + "Changing to logg=5.00 for T= 45825 logg=5.99\n", + "Changing to logg=5.00 for T= 45983 logg=6.00\n", + "Changing to logg=5.00 for T= 46142 logg=6.00\n", + "Changing to logg=5.00 for T= 46313 logg=6.00\n", + "Changing to logg=5.00 for T= 46473 logg=6.00\n", + "Changing to logg=5.00 for T= 46623 logg=6.00\n", + "Changing to logg=5.00 for T= 46784 logg=6.00\n", + "Changing to logg=5.00 for T= 46946 logg=6.00\n", + "Changing to logg=5.00 for T= 47109 logg=6.00\n", + "Changing to logg=5.00 for T= 47261 logg=6.00\n", + "Changing to logg=5.00 for T= 47413 logg=6.01\n", + "Changing to logg=5.00 for T= 47566 logg=6.01\n", + "Changing to logg=5.00 for T= 47720 logg=6.01\n", + "Changing to logg=5.00 for T= 47863 logg=6.01\n", + "Changing to logg=5.00 for T= 48029 logg=6.01\n", + "Changing to logg=5.00 for T= 48173 logg=6.01\n", + "Changing to logg=5.00 for T= 48317 logg=6.02\n", + "Changing to logg=5.00 for T= 48473 logg=6.02\n", + "Changing to logg=5.00 for T= 48618 logg=6.02\n", + "Changing to logg=5.00 for T= 48753 logg=6.02\n", + "Changing to logg=5.00 for T= 48910 logg=6.02\n", + "Changing to logg=5.00 for T= 49057 logg=6.02\n", + "Changing to logg=5.00 for T= 49193 logg=6.02\n", + "Changing to logg=5.00 for T= 49329 logg=6.03\n", + "Changing to logg=5.00 for T= 49431 logg=6.03\n", + "Changing to logg=5.00 for T= 49465 logg=6.03\n", + "Changing to logg=5.00 for T= 49614 logg=6.03\n", + "Changing to logg=5.00 for T= 49751 logg=6.03\n", + "Changing to logg=5.00 for T= 49888 logg=6.03\n", + "Changing to T= 50000 for T= 50026 logg=6.03\n", + "Changing to logg=5.00 for T= 50026 logg=6.03\n", + "Changing to T= 50000 for T= 50176 logg=6.03\n", + "Changing to logg=5.00 for T= 50176 logg=6.03\n", + "Changing to T= 50000 for T= 50304 logg=6.03\n", + "Changing to logg=5.00 for T= 50304 logg=6.03\n", + "Changing to T= 50000 for T= 50431 logg=6.04\n", + "Changing to logg=5.00 for T= 50431 logg=6.04\n", + "Changing to T= 50000 for T= 50559 logg=6.04\n", + "Changing to logg=5.00 for T= 50559 logg=6.04\n", + "Changing to T= 50000 for T= 50699 logg=6.04\n", + "Changing to logg=5.00 for T= 50699 logg=6.04\n", + "Changing to T= 50000 for T= 50839 logg=6.04\n", + "Changing to logg=5.00 for T= 50839 logg=6.04\n", + "Changing to T= 50000 for T= 50957 logg=6.04\n", + "Changing to logg=5.00 for T= 50957 logg=6.04\n", + "Changing to T= 50000 for T= 51086 logg=6.04\n", + "Changing to logg=5.00 for T= 51086 logg=6.04\n", + "Changing to T= 50000 for T= 51215 logg=6.04\n", + "Changing to logg=5.00 for T= 51215 logg=6.04\n", + "Changing to T= 50000 for T= 51345 logg=6.04\n", + "Changing to logg=5.00 for T= 51345 logg=6.04\n", + "Changing to T= 50000 for T= 51475 logg=6.04\n", + "Changing to logg=5.00 for T= 51475 logg=6.04\n", + "Changing to T= 50000 for T= 51606 logg=6.04\n", + "Changing to logg=5.00 for T= 51606 logg=6.04\n", + "Changing to T= 50000 for T= 51737 logg=6.05\n", + "Changing to logg=5.00 for T= 51737 logg=6.05\n", + "Changing to T= 50000 for T= 51868 logg=6.05\n", + "Changing to logg=5.00 for T= 51868 logg=6.05\n", + "Changing to T= 50000 for T= 52000 logg=6.05\n", + "Changing to logg=5.00 for T= 52000 logg=6.05\n", + "Changing to T= 50000 for T= 52119 logg=6.05\n", + "Changing to logg=5.00 for T= 52119 logg=6.05\n", + "Changing to T= 50000 for T= 52240 logg=6.05\n", + "Changing to logg=5.00 for T= 52240 logg=6.05\n", + "Changing to T= 50000 for T= 52384 logg=6.05\n", + "Changing to logg=5.00 for T= 52384 logg=6.05\n", + "Changing to T= 50000 for T= 52505 logg=6.05\n", + "Changing to logg=5.00 for T= 52505 logg=6.05\n", + "Changing to T= 50000 for T= 52626 logg=6.05\n", + "Changing to logg=5.00 for T= 52626 logg=6.05\n", + "Changing to T= 50000 for T= 52747 logg=6.05\n", + "Changing to logg=5.00 for T= 52747 logg=6.05\n", + "Changing to T= 50000 for T= 52759 logg=6.05\n", + "Changing to logg=5.00 for T= 52759 logg=6.05\n", + "Changing to T= 50000 for T= 52857 logg=6.05\n", + "Changing to logg=5.00 for T= 52857 logg=6.05\n", + "Changing to T= 50000 for T= 52991 logg=6.05\n", + "Changing to logg=5.00 for T= 52991 logg=6.05\n", + "Changing to T= 50000 for T= 53125 logg=6.06\n", + "Changing to logg=5.00 for T= 53125 logg=6.06\n", + "Changing to T= 50000 for T= 53235 logg=6.06\n", + "Changing to logg=5.00 for T= 53235 logg=6.06\n", + "Changing to T= 50000 for T= 53358 logg=6.06\n", + "Changing to logg=5.00 for T= 53358 logg=6.06\n", + "Changing to T= 50000 for T= 53481 logg=6.06\n", + "Changing to logg=5.00 for T= 53481 logg=6.06\n", + "Changing to T= 50000 for T= 53592 logg=6.06\n", + "Changing to logg=5.00 for T= 53592 logg=6.06\n", + "Changing to T= 50000 for T= 53728 logg=6.06\n", + "Changing to logg=5.00 for T= 53728 logg=6.06\n", + "Changing to T= 50000 for T= 53864 logg=6.06\n", + "Changing to logg=5.00 for T= 53864 logg=6.06\n", + "Changing to T= 50000 for T= 53976 logg=6.07\n", + "Changing to logg=5.00 for T= 53976 logg=6.07\n", + "Changing to T= 50000 for T= 54100 logg=6.07\n", + "Changing to logg=5.00 for T= 54100 logg=6.07\n", + "Changing to T= 50000 for T= 54225 logg=6.07\n", + "Changing to logg=5.00 for T= 54225 logg=6.07\n", + "Changing to T= 50000 for T= 54363 logg=6.07\n", + "Changing to logg=5.00 for T= 54363 logg=6.07\n", + "Changing to T= 50000 for T= 54475 logg=6.07\n", + "Changing to logg=5.00 for T= 54475 logg=6.07\n", + "Changing to T= 50000 for T= 54588 logg=6.07\n", + "Changing to logg=5.00 for T= 54588 logg=6.07\n", + "Changing to T= 50000 for T= 54714 logg=6.08\n", + "Changing to logg=5.00 for T= 54714 logg=6.08\n", + "Changing to T= 50000 for T= 54853 logg=6.08\n", + "Changing to logg=5.00 for T= 54853 logg=6.08\n", + "Changing to T= 50000 for T= 54967 logg=6.08\n", + "Changing to logg=5.00 for T= 54967 logg=6.08\n", + "Changing to T= 50000 for T= 55093 logg=6.08\n", + "Changing to logg=5.00 for T= 55093 logg=6.08\n", + "Changing to T= 50000 for T= 55208 logg=6.08\n", + "Changing to logg=5.00 for T= 55208 logg=6.08\n", + "Changing to T= 50000 for T= 55322 logg=6.09\n", + "Changing to logg=5.00 for T= 55322 logg=6.09\n", + "Changing to T= 50000 for T= 55450 logg=6.09\n", + "Changing to logg=5.00 for T= 55450 logg=6.09\n", + "Changing to T= 50000 for T= 55590 logg=6.09\n", + "Changing to logg=5.00 for T= 55590 logg=6.09\n", + "Changing to T= 50000 for T= 55719 logg=6.09\n", + "Changing to logg=5.00 for T= 55719 logg=6.09\n", + "Changing to T= 50000 for T= 55834 logg=6.09\n", + "Changing to logg=5.00 for T= 55834 logg=6.09\n", + "Changing to T= 50000 for T= 55873 logg=6.09\n", + "Changing to logg=5.00 for T= 55873 logg=6.09\n", + "Changing to T= 50000 for T= 55963 logg=6.09\n", + "Changing to logg=5.00 for T= 55963 logg=6.09\n", + "Changing to T= 50000 for T= 56092 logg=6.10\n", + "Changing to logg=5.00 for T= 56092 logg=6.10\n", + "Changing to T= 50000 for T= 56234 logg=6.10\n", + "Changing to logg=5.00 for T= 56234 logg=6.10\n", + "Changing to T= 50000 for T= 56364 logg=6.10\n", + "Changing to logg=5.00 for T= 56364 logg=6.10\n", + "Changing to T= 50000 for T= 56507 logg=6.10\n", + "Changing to logg=5.00 for T= 56507 logg=6.10\n", + "Changing to T= 50000 for T= 56624 logg=6.11\n", + "Changing to logg=5.00 for T= 56624 logg=6.11\n", + "Changing to T= 50000 for T= 56754 logg=6.11\n", + "Changing to logg=5.00 for T= 56754 logg=6.11\n", + "Changing to T= 50000 for T= 56885 logg=6.11\n", + "Changing to logg=5.00 for T= 56885 logg=6.11\n", + "Changing to T= 50000 for T= 57030 logg=6.11\n", + "Changing to logg=5.00 for T= 57030 logg=6.11\n", + "Changing to T= 50000 for T= 57161 logg=6.11\n", + "Changing to logg=5.00 for T= 57161 logg=6.11\n", + "Changing to T= 50000 for T= 57293 logg=6.11\n", + "Changing to logg=5.00 for T= 57293 logg=6.11\n", + "Changing to T= 50000 for T= 57425 logg=6.11\n", + "Changing to logg=5.00 for T= 57425 logg=6.11\n", + "Changing to T= 50000 for T= 57570 logg=6.11\n", + "Changing to logg=5.00 for T= 57570 logg=6.11\n", + "Changing to T= 50000 for T= 57703 logg=6.11\n", + "Changing to logg=5.00 for T= 57703 logg=6.11\n", + "Changing to T= 50000 for T= 57850 logg=6.12\n", + "Changing to logg=5.00 for T= 57850 logg=6.12\n", + "Changing to T= 50000 for T= 57983 logg=6.12\n", + "Changing to logg=5.00 for T= 57983 logg=6.12\n", + "Changing to T= 50000 for T= 58117 logg=6.12\n", + "Changing to logg=5.00 for T= 58117 logg=6.12\n", + "Changing to T= 50000 for T= 58251 logg=6.13\n", + "Changing to logg=5.00 for T= 58251 logg=6.13\n", + "Changing to T= 50000 for T= 58398 logg=6.13\n", + "Changing to logg=5.00 for T= 58398 logg=6.13\n", + "Changing to T= 50000 for T= 58546 logg=6.13\n", + "Changing to logg=5.00 for T= 58546 logg=6.13\n", + "Changing to T= 50000 for T= 58695 logg=6.13\n", + "Changing to logg=5.00 for T= 58695 logg=6.13\n", + "Changing to T= 50000 for T= 58844 logg=6.14\n", + "Changing to logg=5.00 for T= 58844 logg=6.14\n", + "Changing to T= 50000 for T= 58993 logg=6.14\n", + "Changing to logg=5.00 for T= 58993 logg=6.14\n", + "Changing to T= 50000 for T= 59143 logg=6.14\n", + "Changing to logg=5.00 for T= 59143 logg=6.14\n", + "Changing to T= 50000 for T= 59293 logg=6.15\n", + "Changing to logg=5.00 for T= 59293 logg=6.15\n", + "Changing to T= 50000 for T= 59334 logg=6.15\n", + "Changing to logg=5.00 for T= 59334 logg=6.15\n", + "Changing to T= 50000 for T= 59457 logg=6.15\n", + "Changing to logg=5.00 for T= 59457 logg=6.15\n", + "Changing to T= 50000 for T= 59621 logg=6.16\n", + "Changing to logg=5.00 for T= 59621 logg=6.16\n", + "Changing to T= 50000 for T= 59800 logg=6.16\n", + "Changing to logg=5.00 for T= 59800 logg=6.16\n", + "Changing to T= 50000 for T= 59965 logg=6.16\n", + "Changing to logg=5.00 for T= 59965 logg=6.16\n", + "Changing to T= 50000 for T= 60159 logg=6.17\n", + "Changing to logg=5.00 for T= 60159 logg=6.17\n", + "Changing to T= 50000 for T= 60353 logg=6.17\n", + "Changing to logg=5.00 for T= 60353 logg=6.17\n", + "Changing to T= 50000 for T= 60534 logg=6.18\n", + "Changing to logg=5.00 for T= 60534 logg=6.18\n", + "Changing to T= 50000 for T= 60758 logg=6.18\n", + "Changing to logg=5.00 for T= 60758 logg=6.18\n", + "Changing to T= 50000 for T= 60968 logg=6.18\n", + "Changing to logg=5.00 for T= 60968 logg=6.18\n", + "Changing to T= 50000 for T= 61235 logg=6.19\n", + "Changing to logg=5.00 for T= 61235 logg=6.19\n", + "Changing to T= 50000 for T= 61504 logg=6.20\n", + "Changing to logg=5.00 for T= 61504 logg=6.20\n", + "Changing to T= 50000 for T= 61844 logg=6.21\n", + "Changing to logg=5.00 for T= 61844 logg=6.21\n", + "Changing to T= 50000 for T= 62287 logg=6.23\n", + "Changing to logg=5.00 for T= 62287 logg=6.23\n", + "Changing to T= 50000 for T= 63038 logg=6.27\n", + "Changing to logg=5.00 for T= 63038 logg=6.27\n", + "Changing to T= 50000 for T= 64239 logg=6.32\n", + "Changing to logg=5.00 for T= 64239 logg=6.32\n", + "Changing to T= 50000 for T= 65464 logg=6.37\n", + "Changing to logg=5.00 for T= 65464 logg=6.37\n", + "Changing to T= 50000 for T= 66696 logg=6.42\n", + "Changing to logg=5.00 for T= 66696 logg=6.42\n", + "Changing to T= 50000 for T= 67967 logg=6.47\n", + "Changing to logg=5.00 for T= 67967 logg=6.47\n", + "Changing to T= 50000 for T= 69263 logg=6.53\n", + "Changing to logg=5.00 for T= 69263 logg=6.53\n", + "Changing to T= 50000 for T= 71367 logg=6.63\n", + "Changing to logg=5.00 for T= 71367 logg=6.63\n", + "Isochrone generation took 90.083269 s.\n", + "Making photometry for isochrone: log(t) = 6.70 AKs = 0.05 dist = 145\n", + " Starting at: 2026-04-09 13:51:46.256195 Usually takes ~5 minutes\n", + "Starting filter: ukirt,J Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 432 K m_ukirt_J = 25.22\n", + "M = 0.500 Msun T = 3720 K m_ukirt_J = 10.68\n", + "M = 1.332 Msun T = 4834 K m_ukirt_J = 9.04\n", + "M = 1.747 Msun T = 5551 K m_ukirt_J = 7.97\n", + "M = 2.047 Msun T = 7076 K m_ukirt_J = 6.74\n", + "M = 2.392 Msun T = 10998 K m_ukirt_J = 7.19\n", + "M = 2.699 Msun T = 11926 K m_ukirt_J = 7.14\n", + "M = 2.877 Msun T = 12459 K m_ukirt_J = 7.02\n", + "M = 3.211 Msun T = 13384 K m_ukirt_J = 6.78\n", + "M = 3.375 Msun T = 13820 K m_ukirt_J = 6.68\n", + "M = 3.520 Msun T = 14194 K m_ukirt_J = 6.59\n", + "M = 3.646 Msun T = 14514 K m_ukirt_J = 6.52\n", + "M = 18.000 Msun T = 31681 K m_ukirt_J = 2.84\n", + "M = 49.494 Msun T = 37653 K m_ukirt_J = 0.12\n", + "M = 51.234 Msun T = 41812 K m_ukirt_J = 0.65\n", + "M = 53.034 Msun T = 33651 K m_ukirt_J = 0.13\n", + "M = 54.918 Msun T = 52240 K m_ukirt_J = 1.70\n", + "Starting filter: ukirt,H Elapsed time: 9.16 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 432 K m_ukirt_H = 25.71\n", + "M = 0.500 Msun T = 3720 K m_ukirt_H = 9.91\n", + "M = 1.332 Msun T = 4834 K m_ukirt_H = 8.46\n", + "M = 1.747 Msun T = 5551 K m_ukirt_H = 7.55\n", + "M = 2.047 Msun T = 7076 K m_ukirt_H = 6.52\n", + "M = 2.392 Msun T = 10998 K m_ukirt_H = 7.14\n", + "M = 2.699 Msun T = 11926 K m_ukirt_H = 7.10\n", + "M = 2.877 Msun T = 12459 K m_ukirt_H = 6.98\n", + "M = 3.211 Msun T = 13384 K m_ukirt_H = 6.75\n", + "M = 3.375 Msun T = 13820 K m_ukirt_H = 6.65\n", + "M = 3.520 Msun T = 14194 K m_ukirt_H = 6.56\n", + "M = 3.646 Msun T = 14514 K m_ukirt_H = 6.49\n", + "M = 18.000 Msun T = 31681 K m_ukirt_H = 2.88\n", + "M = 49.494 Msun T = 37653 K m_ukirt_H = 0.16\n", + "M = 51.234 Msun T = 41812 K m_ukirt_H = 0.69\n", + "M = 53.034 Msun T = 33651 K m_ukirt_H = 0.17\n", + "M = 54.918 Msun T = 52240 K m_ukirt_H = 1.74\n", + "Starting filter: ukirt,K Elapsed time: 22.05 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 432 K m_ukirt_K = 26.87\n", + "M = 0.500 Msun T = 3720 K m_ukirt_K = 9.66\n", + "M = 1.332 Msun T = 4834 K m_ukirt_K = 8.34\n", + "M = 1.747 Msun T = 5551 K m_ukirt_K = 7.46\n", + "M = 2.047 Msun T = 7076 K m_ukirt_K = 6.47\n", + "M = 2.392 Msun T = 10998 K m_ukirt_K = 7.12\n", + "M = 2.699 Msun T = 11926 K m_ukirt_K = 7.09\n", + "M = 2.877 Msun T = 12459 K m_ukirt_K = 6.97\n", + "M = 3.211 Msun T = 13384 K m_ukirt_K = 6.75\n", + "M = 3.375 Msun T = 13820 K m_ukirt_K = 6.65\n", + "M = 3.520 Msun T = 14194 K m_ukirt_K = 6.57\n", + "M = 3.646 Msun T = 14514 K m_ukirt_K = 6.50\n", + "M = 18.000 Msun T = 31681 K m_ukirt_K = 2.95\n", + "M = 49.494 Msun T = 37653 K m_ukirt_K = 0.23\n", + "M = 51.234 Msun T = 41812 K m_ukirt_K = 0.77\n", + "M = 53.034 Msun T = 33651 K m_ukirt_K = 0.24\n", + "M = 54.918 Msun T = 52240 K m_ukirt_K = 1.83\n", + " Time taken: 34.84 seconds\n" + ] + } + ], + "source": [ + "# Define isochrone parameters\n", + "logAge = np.log10(5*1e6) # Age in log(years)\n", + "AKs = 0.05 # extinction in mags\n", + "dist = 145 # distance in parsec\n", + "metallicity = 0 # Metallicity in [M/H]\n", + "\n", + "# Define evolution/atmosphere models and extinction law\n", + "evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() \n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "red_law = reddening.RedLawHosek18b()\n", + "\n", + "# Specify UKIDSS filters\n", + "ukidss_filts = ['ukirt,J', 'ukirt,H', 'ukirt,K']\n", + "\n", + "# Specify the directory we want the output isochrone\n", + "# table saved in. If the directory does not already exist,\n", + "# SPISEA will create it.\n", + "iso_dir = 'isochrones/'\n", + "\n", + "# Make IsochronePhot object. Note that this will take a minute or two, \n", + "# unless the isochrone has been generated previously.\n", + "#\n", + "# Note that this is not show all of the user options \n", + "# for IsochronePhot. See docs for complete list of options.\n", + "my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n", + " evo_model=evo_model, atm_func=atm_func,\n", + " red_law=red_law, filters=ukidss_filts,\n", + " iso_dir=iso_dir, recomp=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "0eb22c21-cc15-4611-b9df-dcdf0473c365", + "metadata": {}, + "outputs": [], + "source": [ + "m = my_iso.points['mass']\n", + "\n", + "phillips = (m < 0.07) & (m >= 0.01)\n", + "phillips_baraffe = (m >= 0.07) & (m < 0.075)\n", + "\n", + "baraffe = (m >= 0.075) & (m < 0.4)\n", + "baraffe_pisa = (m >= 0.4) & (m < 0.5)\n", + "\n", + "pisa = (m >= 0.5) & (m < 7.0)\n", + "ms = m >= 7.0 # Ekstrom at this age\n", + "\n", + "color = my_iso.points['m_ukirt_J'] - my_iso.points['m_ukirt_K']\n", + "mag = my_iso.points['m_ukirt_J']" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "c631060e-4f68-4534-9326-a5bf5cbef20a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 stars out of mass range\n" + ] + } + ], + "source": [ + "# plotting simulated Upper Sco cluster\n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "my_imf = imf.Salpeter_Kirkpatrick_2024(multiplicity=imf_multi)\n", + "\n", + "mass = 1500.\n", + "# Make cluster object\n", + "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)\n", + "\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points\n", + "\n", + "mask = (my_iso.points['mass'] >= 0.009)\n", + "\n", + "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", + "# multiple systems on the photometry\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points[mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "1d4cf2ed-92fb-4a72-b64e-ecbb3ce47d11", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: AstropyDeprecationWarning: Specified hdu=2 not found, reading in first available table (hdu=1) instead. This will result in an error in future versions! [astropy.io.fits.connect]\n" + ] + } + ], + "source": [ + "# read in ukidss data\n", + "other = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/changes/asu (3).fit'\n", + "tab = Table.read(other, format='fits', hdu=2)\n", + "tab\n", + "\n", + "focus = np.where((tab['Jmag'] - tab['Kmag'] < 10))\n", + "minus = tab['Jmag'] - tab['Kmag']\n", + "J_K = minus[focus]\n", + "\n", + "j_err = tab['e_Jmag']\n", + "k_err = tab['e_Kmag']\n", + "jk_err = np.sqrt(j_err**2 + k_err**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "514d3a68-227c-43a6-b2f8-e4355c4abe4f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting all together\n", + "fig, axs = plt.subplots(1, 3, figsize=(20,8))\n", + "\n", + "plt.suptitle('Upper Sco Simulated Isochrone vs. Real/Simulated Data (UKIDSS Filters)', fontsize=30, fontweight='bold')\n", + "\n", + "axs[0].plot(color[phillips], mag[phillips], '-', color='purple', label='Phillips (BD)', linewidth=3)\n", + "axs[0].plot(color[phillips_baraffe], mag[phillips_baraffe], '-', color='violet', label='Phillips/Baraffe transition', linewidth=3)\n", + "axs[0].plot(color[baraffe], mag[baraffe], '-', color='orange', label='Baraffe', linewidth=3)\n", + "axs[0].plot(color[baraffe_pisa], mag[baraffe_pisa], '-', color='green', label='Baraffe/Pisa transition', linewidth=3)\n", + "axs[0].plot(color[pisa], mag[pisa], '-', color='blue', label='Pisa', linewidth=3)\n", + "#axs[0].plot(color[ms], mag[ms], '-', color='red', label='Ekstrom MS', linewidth=3)\n", + "axs[0].invert_yaxis()\n", + "axs[0].set_xlabel('J - K', fontsize=20)\n", + "axs[0].set_ylabel('J', fontsize=20)\n", + "axs[0].set_title('5 Myr Expected Upper Sco Isochrone \\n (Colored by Evolutionary Model)', fontsize=24)\n", + "axs[0].legend(markerscale=4, fontsize=18, loc='upper right')\n", + "axs[0].tick_params(axis='both', labelsize=18)\n", + "axs[0].grid()\n", + "\n", + "axs[1].plot(clust['m_ukirt_J'] - clust['m_ukirt_K'], clust['m_ukirt_J'],\n", + " 'k.', ms=10, alpha=0.1, label='Simulated Upper Sco Data')\n", + "axs[1].plot(my_iso.points['m_ukirt_J'][mask] - my_iso.points['m_ukirt_K'][mask], \n", + " my_iso.points['m_ukirt_J'][mask],\n", + " 'r-', linewidth=3, label='Theoretical Upper Sco Isochrone')\n", + "axs[1].set_xlabel('J - K', fontsize=20)\n", + "axs[1].set_ylabel('J', fontsize=20)\n", + "axs[1].invert_yaxis()\n", + "axs[1].set_title('Simulated Upper Sco Cluster', fontsize=24)\n", + "axs[1].legend(fontsize=18, loc='upper right')\n", + "axs[1].tick_params(axis='both', labelsize=18)\n", + "axs[1].grid()\n", + "\n", + "axs[2].plot(J_K, tab['Jmag'][focus],\n", + " 'm.', ms=10, alpha=0.3, label='real Upper Sco Data (substellar)')\n", + "axs[2].errorbar(J_K, tab['Jmag'][focus], xerr=jk_err[focus], yerr=j_err[focus], color='m', fmt='none', ms=5, alpha=0.3, label='Data Errors')\n", + "axs[2].plot(my_iso.points['m_ukirt_J'][mask] - my_iso.points['m_ukirt_K'][mask], \n", + " my_iso.points['m_ukirt_J'][mask],\n", + " 'r-', linewidth=3, label='Theoretical Upper Sco Isochrone')\n", + "axs[2].set_xlabel('J - K', fontsize=20)\n", + "axs[2].set_ylabel('J', fontsize=20)\n", + "axs[2].invert_yaxis()\n", + "axs[2].set_title('Real Upper Sco Cluster vs. \\n Theoretical Isochrone', fontsize=24)\n", + "axs[2].legend(fontsize=18, loc='upper right')\n", + "axs[2].tick_params(axis='both', labelsize=18)\n", + "axs[2].grid()\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('uppersco_ukidss.png')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/paper_examples/Begbie+26/Figure 9.ipynb b/docs/paper_examples/Begbie+26/Figure 9.ipynb deleted file mode 100644 index 1c6edfec..00000000 --- a/docs/paper_examples/Begbie+26/Figure 9.ipynb +++ /dev/null @@ -1,104 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "51fdd39d-b433-4371-a487-fa5d855b79de", - "metadata": {}, - "source": [ - "## Below is the code to recreate Figure 9.\n", - "\n", - "Topic: Comparing mass fraction as a function of primary mass. This shows the mass-dependent override imposed for brown dwarfs." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3d80653b-a8ee-422d-b008-14dc490ad520", - "metadata": {}, - "outputs": [], - "source": [ - "# Importing necessary packages.\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from spisea.imf import multiplicity" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", - "metadata": {}, - "outputs": [], - "source": [ - "# synthetic mass distribution (log-uniform)\n", - "N = 20000\n", - "masses = 10**np.random.uniform(np.log10(0.01), np.log10(10), N)\n", - "\n", - "mult = multiplicity.MultiplicityResolvedDK()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# multiplicity fraction vs. mass plot\n", - "mf = np.array([mult.multiplicity_fraction(m) for m in masses])\n", - "\n", - "bins = np.logspace(np.log10(0.01), np.log10(10), 25)\n", - "digitized = np.digitize(masses, bins)\n", - "\n", - "mf_mean = [mf[digitized == i].mean() for i in range(1, len(bins))]\n", - "\n", - "plt.figure()\n", - "plt.plot(bins[:-1], mf_mean, marker='o', markersize=8)\n", - "plt.xscale('log')\n", - "plt.xlabel('Primary Mass [M$_\\odot$]', fontsize=16)\n", - "plt.xticks(fontsize=12)\n", - "plt.ylabel('Multiplicity Fraction', fontsize=16)\n", - "plt.yticks(fontsize=12)\n", - "plt.title('Multiplicity Fraction vs Mass', fontsize=20)\n", - "plt.axvline(0.08, linestyle='--', label='BD boundary')\n", - "plt.legend(fontsize=14)\n", - "plt.grid()\n", - "plt.tight_layout()\n", - "#plt.savefig('multfrac.png')\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 5966859596dbe7b7e3bffaf5a741e552db294f6a Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 17 Apr 2026 10:37:08 -0700 Subject: [PATCH 112/191] Final figure changes --- docs/paper_examples/Begbie+26/Figure 8.ipynb | 26 +- docs/paper_examples/Begbie+26/Figure 9.ipynb | 298 +++++++++++++++++++ 2 files changed, 311 insertions(+), 13 deletions(-) create mode 100644 docs/paper_examples/Begbie+26/Figure 9.ipynb diff --git a/docs/paper_examples/Begbie+26/Figure 8.ipynb b/docs/paper_examples/Begbie+26/Figure 8.ipynb index 14dea6db..0912e0de 100644 --- a/docs/paper_examples/Begbie+26/Figure 8.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 8.ipynb @@ -5,7 +5,7 @@ "id": "51fdd39d-b433-4371-a487-fa5d855b79de", "metadata": {}, "source": [ - "## Below is the code to recreate Figure ?.\n", + "## Below is the code to recreate Figure 8.\n", "\n", "Topic: Showing the simulated Upper Sco cluster (via best-fit isochrone) against actual data from UKIDSS." ] @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 3, "id": "b91cb1a8-8fc6-4939-9f3b-420082b20340", "metadata": {}, "outputs": [ @@ -515,9 +515,9 @@ "Changing to logg=5.00 for T= 69263 logg=6.53\n", "Changing to T= 50000 for T= 71367 logg=6.63\n", "Changing to logg=5.00 for T= 71367 logg=6.63\n", - "Isochrone generation took 90.083269 s.\n", + "Isochrone generation took 91.105072 s.\n", "Making photometry for isochrone: log(t) = 6.70 AKs = 0.05 dist = 145\n", - " Starting at: 2026-04-09 13:51:46.256195 Usually takes ~5 minutes\n", + " Starting at: 2026-04-17 10:05:57.727481 Usually takes ~5 minutes\n", "Starting filter: ukirt,J Elapsed time: 0.00 seconds\n", "Starting synthetic photometry\n", "M = 0.001 Msun T = 432 K m_ukirt_J = 25.22\n", @@ -537,7 +537,7 @@ "M = 51.234 Msun T = 41812 K m_ukirt_J = 0.65\n", "M = 53.034 Msun T = 33651 K m_ukirt_J = 0.13\n", "M = 54.918 Msun T = 52240 K m_ukirt_J = 1.70\n", - "Starting filter: ukirt,H Elapsed time: 9.16 seconds\n", + "Starting filter: ukirt,H Elapsed time: 9.43 seconds\n", "Starting synthetic photometry\n", "M = 0.001 Msun T = 432 K m_ukirt_H = 25.71\n", "M = 0.500 Msun T = 3720 K m_ukirt_H = 9.91\n", @@ -556,7 +556,7 @@ "M = 51.234 Msun T = 41812 K m_ukirt_H = 0.69\n", "M = 53.034 Msun T = 33651 K m_ukirt_H = 0.17\n", "M = 54.918 Msun T = 52240 K m_ukirt_H = 1.74\n", - "Starting filter: ukirt,K Elapsed time: 22.05 seconds\n", + "Starting filter: ukirt,K Elapsed time: 22.66 seconds\n", "Starting synthetic photometry\n", "M = 0.001 Msun T = 432 K m_ukirt_K = 26.87\n", "M = 0.500 Msun T = 3720 K m_ukirt_K = 9.66\n", @@ -575,7 +575,7 @@ "M = 51.234 Msun T = 41812 K m_ukirt_K = 0.77\n", "M = 53.034 Msun T = 33651 K m_ukirt_K = 0.24\n", "M = 54.918 Msun T = 52240 K m_ukirt_K = 1.83\n", - " Time taken: 34.84 seconds\n" + " Time taken: 35.83 seconds\n" ] } ], @@ -612,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 4, "id": "0eb22c21-cc15-4611-b9df-dcdf0473c365", "metadata": {}, "outputs": [], @@ -634,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 5, "id": "c631060e-4f68-4534-9326-a5bf5cbef20a", "metadata": {}, "outputs": [ @@ -668,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 6, "id": "1d4cf2ed-92fb-4a72-b64e-ecbb3ce47d11", "metadata": {}, "outputs": [ @@ -697,13 +697,13 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 7, "id": "514d3a68-227c-43a6-b2f8-e4355c4abe4f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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1dUXjxo1LXS6pTPf7hIQE3Lp1S+o9WVOnFxQU4M2bN5zxKPqsYWhoiAkTJmDChAkK7adOkZGRuHv3Lt6/f4/CwkI4ODjA29tb7m8uOTkZ//33H8LCwpCbmwtLS0u4urrCy8urVM84lQljDFFRUYiMjER0dDQ+fvyIrKws5OTkwMDAAMbGxqhWrRoaNGgAZ2fnCt3puqCgAE+fPsWrV6+QmJiI5ORk6Orqolq1arCzs4Onp6fKGgVjYmJw+/ZtxMTEIDc3FzY2NnB1dVVpHUhZEIlEMDMzE5cT+vTpg6VLl+LgwYP47rvveKdNzsnJwffff49r164JTu/Tp08IDw9HdHQ0Pnz4gMzMTGRlZUFHRwfGxsYwMTGBs7MzGjRoAAMDA1V8RJWq6Pn39/fnXFpP1v2hKlm3bh1++OEH3jDDhw/H7t27K+T13cjICEZGRrCzs0P79u0xe/Zs3L17F2PHjsWzZ894950zZw4GDBiAatWqldhWHmUAVdZv6ujoYPjw4Rg+fLhC+5HyU5b3Yj7BwcF4+PAh4uPjkZOTAxMTE0yZMgU6Ojqc+X716hVev36NmJgYZGRkICsrC3p6ejA2NoatrS1q164NZ2dnperLfHx8ODvt7N69G0uXLlU4TkKkMEJKKSIiggHgfHl7ewuOy9vbmzeuiIgIzn0XLVokKA8PHz5kPXr0YFpaWjLDWllZsSVLlrDs7GxBefbz8+NM18/PTxzu8uXLrG3btkwkEskM6+DgwDZu3MgKCgoEHy9JERER7IcffmAODg68xxAAs7GxYT/++CN7+/at4PiFHt8XL16wvn37Mg0NjRLh1q5dq9RnK87V1ZUzL1u2bFFJGkLk5+ezEydOsJ49ezJjY2O5x93AwIB16dKFbdy4UXAaz549Y+PHj2fW1tZy47eysmJjxoxhwcHBavzUn929e5czH1paWiwjI0NtafNdJxYtWsS5H985bG9vLw53/vx51qxZM5nhdHR02MSJE1lKSkqJ+MPDw9mAAQNknvtFv7tNmzYJ+o3v2LFDUF7L8hjJu5bv2LGDTZgwgTVp0oTp6enJPV+LXkZGRmz48OHswoULvPHLu9co8rp27RpvWvn5+ezQoUOsU6dOcj+Ljo4O8/b2ZkePHlX6+n3u3Dnm4eHBmYa7uzvbt2+fOLzQc1mVrl27xnscJO93XGJiYtjMmTOZo6Oj4O/K1taW9enTh61cuZI9fvxYcH6zs7PZtm3bWKtWrZi2tjZvGlpaWqxly5bsf//7H8vKylL6GH38+JGtWLGCNW7cmPM6UPyzjRkzht2+fVtmfPLO+aIyUUZGBps/fz6rWbMmZ9hOnTqxBw8eCPocZXG/DwwMZMOHD2cWFhZyj1OtWrXY1KlT2Zs3bwTlnzHh19B3796xkSNHMkNDQ5lhtbW12dixY1lMTIzgtCWpu2ymqIEDB3KmP27cOMHxrF27ljMeV1dXmfskJSWxJUuWMDc3N8G//+rVq7OuXbuyJUuWsJs3b6rqMKgU37kG8N9zGWOsb9++vPtzXR+4pKSksDVr1jB3d3e51yETExM2dOhQdufOHYU/szrv95KEXgeF2Lp1q6D94+LieNMMDQ0VnKaihD5XFifvPCxy79495u3tLfN5VFNTkw0ZMkTm9S4+Pp6NGTOG6ejoyIzf3Nyc/frrr4Ken+WVJ8rjGMkrO504cYJ9//33rGXLlszIyEjwOa+rq8v69evHjhw5IreMKDROea8dO3bIPYYXLlxgffr0kfv8qqmpyVq0aMG2bdsmuG6kuMDAQNa+fXvOOpB69eqxP//8U3x8hJ7LqqSq+qx3794Jut/z3c/u3bvHFixYwDp37syqV68u+HvX0NBgnp6ebOPGjSw5OZk3n/Lq3IS+ZP3eyiL/qjR69GiZ+dHW1mZpaWmC47G3t1fJectXBhfy+xaajxUrVgj6fhV9ti2r/PPJyspivXv3lvv5li5dKnP/8igD6Ovrc6Z36dIllaenCnz3YkB+fYuilK2nUrTMoYpro5BznTH134v5fk+S+duzZw+rV6+ezHBJSUkl4n348CEbOXIkq1atmqBjoaGhwZydnZmvry/bvHkze//+veDP0Lx5c5lxOjo6Co6DEC7UKE5KrTI1iv/yyy+cjeHFX87OziwsLExunuU9mOfl5bHJkycLvnl6enqyjx8/Cj5mGRkZbNKkSUxTU1PhG7WWlhabN28ey8vLk5uOkON79OhR3soxVTWK81Wo1qpVi0VGRqokHT5XrlxRqGGn+EuepKQkNnToUM4KBHmvgQMHsk+fPqnt89+7d483/W+++UbQeaUMdTWKFxYWsqlTpwo6vg0bNpQ6z06ePMnMzMwE7Tt48GCWm5vL+xkrY6O4sr8FyVePHj3Yu3fvZMZfVo3i165dU/q37ebmplDDbV5eHmdljKyXj48PS0tLq5SN4vv27eN94Bfy6ty5s6C8njx5kllZWSmVRvXq1dnRo0cVOja5ubls/vz5CjUOCTl2QhqDAgMDBZ+vOjo67NChQ3I/jzrv91FRUaxLly5KHSdNTU02adIklpmZKfczCLmG/vPPP4IbN2xtbVlISIjcdIuUVdlMUZcvX+ZM18TERNCxZYyxJk2acMazbt26EuGvXLkiuOKE61W3bl1VHw6VKG2juK+vL+/+QUFBgvOyYcMGZmJiotTx7devn+Dnj9J8j0Uvvvu9JFU2ivfq1UtqXw8PD5nh4uPj5eZdXZ0/1dkovnLlSkHPwXZ2dlJlmVu3bjFbW1tB32v79u1ldhyVVBkbxeU11Ah5tWzZkvc+oorfFcBfER8cHMzc3d2VitfOzo5dvXpVzrcjbd68eYKfZdu1a8diY2MrdaM4Y587tOvq6vLGx3eeymtoEvKytrZmp06d4kxDnY3iZZF/VSksLGQ1atSQmYdWrVopFFdlahSXlwYANmHCBFZYWKjQMSir/AuRlpbGO5AG4L7ul0cZwMDAgDO9pk2bsoSEBJWmpwrUKM7/qgj3YnmN4rm5uXKfQyQbxQsKCtj06dNLfWy4OqTIMmvWLM54nj17JjgeQmT5MuZxJQTA3Llz8fPPPyM/P19Q+NDQULRv3x5hYWFKp1lYWIjRo0fj77//FrzP3bt30aFDB3z8+FFu2PDwcDRv3hybN29GQUGBwvnLz8/H8uXL4e3tjbS0NIX3l3T58mUMGzYM2dnZpYpHiBo1anBue//+PerXr49Ro0bhyJEjnFNSlsacOXPQtWtXREREqDxuAHj69CmaNGmCQ4cO8a7vxOfYsWNo0qQJgoODVZy7z/i+AwDYvn07nJycsHDhQty8eRNZWVlqyYcqzZgxA3/++aegsCEhIejTpw/y8vJw7tw5DBgwAMnJyYL2PXLkCH7++edS5LTqunDhAjp06ID4+PhySX/ZsmXo3Lmz0r/t58+fw8vLC/v375cbtrCwEMOGDcPOnTsFx3/y5El89dVXZXKdVaWLFy/C19e3TK4DM2bMgI+PD+Li4pTa/+PHjxg0aBCmTZsmKHx0dDS8vLzw66+/lvn3cvXqVXTp0kXw+Zqbm4tRo0bhyZMnpUpX2fv9tWvX4O7ujitXriiVbkFBATZv3oyWLVvi3bt3SsVR5LfffsO4ceOQnp4uKHx0dDS++uorzmVDJFWksllxnTt35lxqJTU1FSdOnJAbx5MnTxAUFCRzm76+Pvz8/KTee/r0KXr16oXExESF8/sl4Cs7iEQiQdPqZ2dnw8fHB99//z1SU1OVysfp06fRuHFjPH36VKn9FVXW9/u0tDRcvXpV6j2uqXGrVavGO1XshQsXUKdOHcyYMQOXL19W+piXpT///BNz5swR9BwcFRWFHj16ICkpCY8ePULXrl0RHR0tKJ0bN25g4sSJpc1ulXTv3j14e3vj1atX5ZL+jh074OnpqfSzYVRUFLp27YrVq1cLCv/DDz9g+fLlgp9lb968ia5du1b6e4Wbm5vc38Dp06eVfsYXIjY2Fj4+Pjh8+LDa0lCnssp/0TTBsnh7e6s17fLy008/YcmSJbxhvvvuO2zevLlCL28mj5GREX799VfeMG/fvpVZni2PMgBf3drjx49Rt25dTJo0CWfOnMGnT59KnR4pP2V9L+Yzbtw47Nu3T3D4BQsWYN26daVOVxF869KfP3++7DJCqiRqFCdfhDt37uC3335TeL+YmBgMGzZMcEN6cYcPH1boJlPk2bNnGD9+PG+YhIQE9OjRAy9evFAqb5ICAgLQv39/5ObmKrV/QkICfH19lT5OivL09OTdnp2djb1792LIkCGwsbGBjY0N+vTpg0WLFuHSpUuCK8FlmTdvHlatWqW2B9no6Gj07NkTb9++LXVc79+/R69evTjXySoNe3t71KxZkzdMZGQkli1bhvbt28PY2BiNGjWCn58fNm7ciCdPnqi1MkBR7969w9q1axXaJyQkBJMnT8aIESNQWFio0L6rVq1CZGSkQvt8Kd68eYOvv/66zNNdt24dFi5cqPB3WVxOTg78/Pxw4cIF3nBr1qzBsWPHFI7f398f69evVzZ75UJoA3NpLV68GH/88YdK4vrzzz/ldl5JSkpC9+7d8ejRI5WkqagJEyYIaqSVlJubK3f9QD7K3u+fPn0KHx8flVTkhISEoHfv3kpXQr1//x5z585VeL/w8HC551dFKpvJIhKJeNc0FNJJZ9euXZzbhg0bBjMzM6n3Zs2apdLPUJUUFBTgwYMHnNubNm0KExMT3jgKCwvh6+uLU6dOlTo/sbGx6NmzZ6k7nQhVlvf7M2fOICcnR+o9rkZxTU1NNGvWjDe+uLg4/PHHH+jWrRvMzMzg4uKC4cOHY82aNbh3716ZPRMJNX36dIXCx8XFYcKECfDx8VG4Q9vBgwdx8+ZNhfb5Unz69AkDBgwodVlTUcePH8e4ceNK3XmvsLAQs2fPxrZt23jDHTlyRKmK85CQEMyfP1/J3FUc3377Le/2pKQkPH/+XK15YIzh66+/VkmdQnkoi/zfunWLc1tlW+teqJUrV/JunzlzJjZs2FCpG8SL9O/fH9bW1rxhbt++XeK98igDyKvfTElJwf/+9z/07dsXlpaWcHBwwMCBA7F8+XLcuHGjRPmmIujYsSNEIpHgF1/jZ1VR1vdiPrt27cLu3bsFh4+KisKqVauUTk9ZfNdiWb9fQhTB3f2JkCpEspCgpaWFVq1awc7ODklJSbh9+zbvSJyHDx9izZo1mDNnTqnS1dfXR+vWrVGjRg3ExcUhICCA92Z46tQpHDp0CEOHDpW53dfXl3cUu0gkQuPGjVG7dm1oa2vjzZs3CA4O5myIvHr1Kn7//XfMmzdP4Kf7P8+ePVN4n9IYPHiw3AK9pNjYWJw9exZnz54F8PkcaN++PXx9fTFkyBAYGRkJiufYsWNYsWKF3HCGhoZo0aIFrKysUFBQgLi4ODx+/FhQY/ywYcPkjsZwcnJCo0aNoKGhgadPn/KOOIiJicHQoUPVUmAYNGgQNm7cKChsQUEBQkJCEBISIi58VatWDQMGDMDIkSPLvTe25O/CyMhI3JB/9+5d3sbr4gXROnXqoFmzZsjKyoK/vz8yMzNl7ldYWIitW7fK7cFcmVlYWMDJyQmWlpYwMjKClpYW0tPTERUVhadPn/I+vF28eBGBgYFo1apVmeT19u3bmDFjBm8YS0tLuLm5wdraGvHx8QgJCeGc0SM/Px+jRo1CWFgYTE1NS2x/8+YNFi5cyJuehoYGWrRoAUdHR6SnpyMgIEA8eqYyjRQPCQnhvUZZWFjAw8MD1atXB/D5of/Dhw949eqVQh2YLl++LHfkg56eHlq3bg0rKyt8/PgRAQEBnL9RAFi6dCnatm2Lbt26ydz+9ddfC7r/1a5dG40aNYKpqSnS0tIQERGB58+fl7pSXHJ/R0dHeHh4oKCgANevX+cdferv74/Xr19zjhjmo8z9Pjc3Fz4+PnIbsRs1aoT69esjLy8Pjx8/5q0IDQkJwaRJkwTNylBc8ePu4eEBJycnfPr0Cf/99x/y8vI4992yZQsWLFjAWVlYkcpmXMaMGYMFCxbIvAZfvXoVUVFRsLOzk7lvfn4+b2fPSZMmSf2fnJzMOzOAsbExmjdvjpo1a0JDQwOpqamIj4/Hq1evBM++Uplt2LCBd1aL4sdTlpUrV+L48eO8YRwdHeHk5ARzc3O8ffsWT5484bz2RUdHY9y4cbh06ZLctItUhvt98WPk4uICV1dXzvCDBw/G3bt3BcXNGENoaChCQ0Nx8OBBAJ/Lkn379oWvry969uwJDY3yHYtQdI3R1dVF+/btYWlpieDgYN6GuaNHj0r9b2NjAy8vLzDGcP36dSQlJXHuu3nzZrRr1041ma+ATE1NUa9ePdSsWROGhobQ0dFBVlYWYmJi8OTJE94Oa8+fP8ehQ4cwfPjwMslrREQEfH19ecscJiYmaNSoEWxtbcUNtnzPo1OmTEGnTp1kzmSRmpqKyZMny82Xu7s7XFxckJubi7t374pnd6tMZVwuLi4usLW15T2GL1++RIMGDeTGpauri7p166J27dowNDSEgYEBcnJyEB8fj+fPn/POtpGTk4Ply5fjf//7n1KfQxUqcv75OqU1atRILWlWZPPnz8eyZcvKOxsq1alTJ95y68uXL2W+X9ZlgMGDB+PQoUOC0gM+j3J/+/atuGyjp6eHrl27wtfXF/3794eOjo7guEjZKOt7sTzXr19XKPzJkyd5Z0BzdHREw4YNYWpqipycHKSlpSEyMhLh4eG8z9by1KxZEzVq1JB5r+C7hhMiSHnM2U6qlsqwpnjRq3Pnzuz9+/dS+6Wnp7MJEybw7lezZk2Wk5MjM10h6yYNGzaMJSYmSu338eNH9tVXX/Hux7XW3cWLF3n38/HxYa9evSqx39OnT1n79u059zM2Nmbx8fFKH9+iV8OGDdmECRPY3Llz2cSJE1m3bt2Yrq6uytYUZ4zJPXZCX9WrV2fr16+Xu2ZSTk4Oq1u3Lm9clpaWbPv27TLPlfz8fHb58mU2YMAAxnXpPXXqFG/81apVY+fOnSux3+XLl5mlpSXvvidPnlTuQPN49+4dMzY2Vsn30KZNG3b37l1B6apjveyiV+/evaXWzcnNzRV0rmloaLBNmzZJpffmzRvOdcoAMC8vL868VsY1xXV1ddnQoUPZ3r17WUxMDG/YjIwM9ueffzIdHR3O9KZMmSK1T15eHouIiBC/pk2bxrmvp6enVNjir6ysLKm4W7duzRmXiYkJ27VrV4nfdXZ2Nlu/fj3vuoFz5syR+fn58g6ANWjQoMQaSTk5OWzevHlyz8WKtqb4iRMnOPf58ccfeddNDgsLY9u2bWO+vr7M1NSUc03xwsJC5uHhwZu/ESNGlLgPJycns6+//pp3Pw8PD5n3h6tXr8r9Ljp27Mju378vM8+JiYnsr7/+Ys7OzkqvKQ6A6enpsb1790rtl5KSUmLt3OKvzZs3cx53Vd/v169fzxuHo6Mju3PnTol87N+/n3cdepFIJLXurSR5a5IWpXvv3j2p/UJDQ1mdOnV493v58qXMNMujbKaskSNHcqa3fPlyzv3+/fdf3t9KcY8fP+YMP3ToUN41zN+/f8/27dvHxo0bx2rUqFFp1xSfNm2a1P3n2bNn7PTp07zfAfC5jJCfn8+b9sePH3nXEG/QoAHz9/cvsV9cXBybOHEib/rnz5/nTFfd93tJqlhTPCsrixkZGUntN3fuXN590tLSBK+jLeQ6eeHCBbn5ZEx9a4oDn9e0joqKEu9TWFjIpkyZIugzzJ8/X+p8TEhIYPXr1+cMX7NmTc68VsY1xevWrct69+7Ntm7dyt68ecMbNjc3l+3Zs4eZmZlxpte7d+8S+0leJ1avXs25r62tLW8ZNy0tTSre4cOHc8alra3N1q5dy9LT06X2KSgokPsZhg4dKvPzr127lvf7tbW1Zbdv3y6R3p9//sk0NDTknovqoMr6rCIdO3bkjXP9+vUy9xs9ejTz8PBgq1evZg8fPmQFBQW86dy4cYM1aNCAMx1TU9MS95LY2Fjx+XLz5k3efN68eZPzXPv48WO55F9VmjRpIjNNkUjEcnNzFYqrsqwpzvVasmSJQp+3vPKvqCVLlvDG179/f5n7lXUZoKCggDVt2lQl6Tk4OLB9+/YpfKwUJaQeXJGXvOtsWa0pXlXuxYwJvx7o6emx7t27s2nTprE5c+awUaNGscaNGzPg/9YU56q/0tDQYEeOHOHMQ15eHnv48CH7448/WI8ePZi2trZCa4ozxlirVq04856SkqJQXIRIokZxUmqVpVHc1dW1REOIJHkNX0ePHpW5n7zCQKdOnTgfBnJzc1nz5s1593/w4EGJ/bp06cIZvm/fvrwPDhkZGbw3R66KciGV5HZ2djIr3xhjLDU1tUSHhNL49OkTc3JyUrrQVfzVs2dPlpGRwZne/v37efevWbOmoIo5xhgLCAiQ+T7f+S8SidjNmzd54xSJRCr5HSri+PHjTEtLSyXfgba2Nvvf//4nN011NYo7OjrKrKC/ffu23Lz/9NNPMtNctmwZ5z56enqcnTEqY6P4p0+f5OaruKVLl3Km17BhQ959S5NXSbdu3eKMR0NDgwUGBvLu/9dff3Hub21tXeI7zsvLY6amppz7mJiYSFVaFyevE1dFaxQ/cOAA5z6KnDM5OTksKChIqbx1796d87dWWFjIevbsybv/tWvXSuzXrVs33n18fX0FVeLl5eXJvM8zJqxR/MCBAzL3TUhI4G2EmjhxImeeVH2/5ytz6Ovrs9evX3Pm5eDBg0qdd/IaiAwMDFhYWJjMffkafvmOeXmUzZTFd91zcXHh3G/QoEGc+23ZsqVE+MDAQM7wDx8+FJzf/Px8zt9JeRPSGKnoy8PDQ1BHCL4yhr29vdw4Bg8ezLn/sGHDOPcry/u9KhrFZXU65eqwJCkwMJAZGhqq5DsViURs4cKFctNUV6O4iYkJi4uLK7FfdHS03LxznQt79+7l3S82NlbmfpWxUVyZc37Pnj2c6ZmamvI2Fpb2OaDI+/fveZ8PDx8+zLv/2bNnOffV0dFhqampJfZxc3Pj3EdTU5OzMxtjjC1fvlzu+agO6mgUHzJkCG+cv/76q8z9lDnXIiMjeTsU8N0/VXGNLc/8lwbX85iVlZXCcVXmRnFjY2P2/PlzhT9zeeRfUZs2beKNr2vXrpz7lnUZIDw8nHdAhaKvMWPGyO2UUhpVtVFcUmW+FzMm7HowcOBAzvJaaGiouIMQV2faRo0aCT4OjH0eGMD37C8L3/NKcHCwQnERIonWFCdfjEWLFkFPT49zu7xpjBWdXqTI8uXLOafL0dbWxuLFixVKNyMjAzdu3OAMP3/+fGhqanJuNzAwgI+PD+f2c+fO8eaHi6mpKW7evImOHTvK3G5sbMw5HacyqlWrhsDAQHTv3l0l8Z0/fx5jxozh3F409TqXf/75Bw4ODoLSkjVFZFpaGgICAjj36devH9q2bcsb51dffcW5PSAggHeZAGX1798f58+fl7u+uBB5eXmYPHmy3GOtLj/++CP09fVLvN+0aVPe/bS1tTF79myZ2/imjszOzlbLd1JeqlWrpvA+Xl5enNu4pjNTNb5rXrdu3XjzCAAjRozg3BYbG4ugoCCp9x4/foyUlBTOfSZNmgRbW1vO7b/88gvvNb6i4bs27N27V3A8Ojo6cHd3l7lN3vrtv/32G+d01yKRSO6yGMXjz8jIwH///ccZ3s7ODv/884+g70lLS0vuunVcvLy8MGzYMJnbLCwseKfl5JuuUh5F7vcvX77knQZ9woQJqFevHuf2oUOHokmTJpzbL168KCzTxUydOhV169aVua19+/a8+8o6dhW1bMalTZs2nFODvnr1Cnfu3CnxflJSEv7991+Z+5iYmMi8FvL9/vfv3y94CQEh6ztWBYaGhvj5559x+/Zt8ZISfPjOi8mTJ8uNg+/+dfHiRc5pEivb/b741Om1a9dG8+bN5e7n5eWFW7du8V6jhGKMYenSpfj7779LHZcyxo8fjxo1apR438bGRub7krjWeJY3PXpp7jMVjarP+ZSUFMTExJQmS4KcP3+ec3kOV1dXDB48mHf/nj17wszMTOa23NzcEstjfPz4kXdK/oEDB/Le02fMmKHUsa6IlF0iR5nPb29vz7t28osXL5TKizIqS/4zMzM5n8fkXROrmrS0NHTo0AEhISHlnRWVK81SVWVdBqhTpw7u3r0rqHwixI4dOzB37lyVxEVKp6zvxUL5+PjgyJEjnM9rTk5O0NbWBsD9TPf69WvBSw0AgLm5ucK/KSsrK85tZVGWIlUXNYqTL4Kmpib69u3LG6ZBgwaoU6cO53ZZFYTyWFtbw9PTkzdMt27deBvri6cbEBCA3NxczvBeXl4QiUS8r/Xr13Pur+xDx4IFCwQ3CquKhYUFLly4gFOnTqF169alju/w4cPw9/eXue3atWuc+zk5OaF3796lSjsgIIB3rZX+/fvLjYMvTF5eHgIDA5XKmzxdunTB69evsXjxYt4HWiEKCwsxdepU5Ofnqyh3wvXq1Uvm+/r6+jAxMeHcz8vLC+bm5jK38RXgAMhdY7cySktLw8mTJzFt2jT07NkTzs7OqF69OgwNDaGhoSF1LeratStnPPn5+WVyfPg6PF24cEHu9ZTruy9S/Joq76GhX79+vNutrKzk3lcqkpYtW3Le46ZNmwYnJyd8/fXXWLJkCfbv348HDx4o/L3zNVA7ODjwVsACn9e15FuLq3hjZ0BAAO8auZMmTeK9r6vK0KFDebfb2NhwbivNb0uR+z3fdwOU/t724cMHhIaGCsqLJL5jZ2JiAkNDQ87tso5dRS2b8Zk4cSLntl27dpV478CBA5zn/ahRo2QeMwcHB9SuXVvmPmvWrEHt2rUxbNgwLFy4ELt370ZgYCA+ffok8BNULU5OTrh58yaWLFki6PqRlZXFez/56aef5J5zfL+tpKQk3vXOgcpxv8/Pzy/RmUPIdadIkyZN8OzZM6xbt46zI40ifvrpJyQnJ5c6HkVxlXEB/s4rtWrVQsOGDWVu+xLLuNnZ2bh48SJmzZqFvn37ws3NDVZWVjAyMoKmpqbUOe/k5MQbV2Jiotrzy1fGffHihdxrhIaGBu/5quoyro6ODnr06MEbprJISEjg3W5kZMS7vbCwEPfv38eyZcswZMgQeHh4wNraGqamptDS0irxXfGtO1sW51pxFT3/6enpnNsMDAxUnl5FFx8fj44dOyI4OLi8s6JSpf0dlnUZwMHBAXfv3sXOnTvRuHHjUqe3Zs0avH79utTxkNIp63uxENra2vjrr784Bw4U5+3tLfP97OxstGrVCi1btsSkSZOwatUqnDx5Es+ePeOtL1EE3zWZ71pOiDxa5Z0BUvkJvYiWZ1p169YVVLht2LAh3rx5I3NbbGyswulyjcKRpK2tDWdnZzx58kRQunwPDKogrwKMy9dff63inAjXr18/9OvXD69evcKZM2dw9epV3L59W6nKmL///hudOnWSeq+wsBAfPnzg3IergKAIeT3chBSK5Z1v6uxFZ2RkhEWLFmHBggW4cuUKLl++DH9/fzx58oRzpBGXN2/e4OLFi6XuaKAIPT093kYxQ0NDzvPJzc2Ncz9dXV3edMuj8V9dPn78iGXLlmHLli3Izs5WSZwpKSm8HRJUoayvqfJ+h1yVz8XD8M0sUZEYGBhg8uTJWLt2rcztYWFhCAsLK/G+nZ0dWrdujQ4dOmDw4MGwtLTkTIPvmAqtUGjUqBEiIiIExS/vnOnQoYOgNEtL3shZY2Njzm2lufYocr8vq3ubs7Oz4Dzp6OjI/Z0ZGxsjIyND5jZZx66ils34jBo1CnPmzJH5OQ8ePIh169ZJ3cNkNZQXmTRpEue2GTNmYNq0aTK3RUdH49ChQyXer169Olq1agVvb28MHjwYtWrV4vsoVcLr16/RrFkzzJw5E8uXL4eWFv9jelxcnMLlK0XFxcXJ7FxTme73169fL9GgMmDAAIXi0NHRwbRp0zB16lTcunULFy9ehL+/Px4+fMjbGUaW1NRU7Nu3D1OmTFFov9LiK6vydQKiMu5n6enpWL16NdavX887248iVBUPn8paxq0K5M2AwTWDHmMMu3btwrJlyxAeHq6SvJTFuVaksuSfr7FGR0dH4fjKqk5UnekkJCSgU6dOuHTpUpWZnUdeY6GQmSzLugygoaEBPz8/+Pn54dGjRzh37hz8/f1x9+5dZGZmKpReQUEBtmzZgtWrVyu0nypcu3atzJ6JK7qK+JzYpUsX3g70xXXo0AHNmjXDw4cPS2xjjOH+/fu4f/++1PsaGhpwcXFB27Zt0a1bN/Tp00epgQN85c2srCyF4yOkCI0UJ6Uma7phSYpMWSMvrLK9Ni0sLASF45vuSZkequpI9+PHjwrnQxHKNCQ7ODhUiGmmXFxcMGPGDJw7dw5JSUkIDg7G5s2b4evrK3h6b1mj2j59+sR7bipSmOAi73sVci7Jm65M3ecO8HlWhu7du+P333/Ho0ePkJycjMuXL2PJkiXo3Lmz4IdMeaMLVc3c3Jz3IZMv3/JGCn8Jnj59imbNmuHPP/9UWQU5ALVX+ANlf01NSkriDKuhoQFTU1O5cVa2qSV//fVXdOnSRaF9oqKicPjwYXz77bewsbHB+PHjOUeQ8n2HqrgPF49f3jmjinuCEPKmRpbXqKYMRe/3fMdKQ0ODcyo4Saq+t5mbm8s9NkVTxQlVEctm8piYmGD48OEytyUnJ+PUqVPi/1++fIl79+7JDNu2bVveRozvvvsOo0aNUihvHz9+xOnTpzFjxgw4ODhg0KBBePfunUJxVEaMMaxevRqDBw+W+0xUFmU6WeddZbvfF586vUaNGrzLEfERiURo164dli1bhoCAAKSkpODmzZtYuXIlevfuzdu4LKmsy7gA/3WUyrj83r9/j9atW+OXX35RaePcl1bGBYSdT5WtjCtLSEgIb4d6AKhfv36J93JzczF8+HCMGTNGZQ3KQNmca0Dlyj9fI4uiDZ2A6upE1VUfWmTIkCG82xMTE9GlSxfOMl9lwhjjne0RkP075FIeZQAPDw8sWLAA/v7+SElJwb1797B+/XoMHDhQ8P25PMocRFpFfE5UdOZBkUiEQ4cOKdRRubCwEC9evMDWrVsxePBgWFtbY/Xq1Qp3muR73iiL2flI1UWN4qTU5N2MFenNxjUqp4iQytOqjmstkvJUERrEi9PQ0EDjxo0xceJE7N27FzExMbhy5YrcNXo+fvxY4jyUd8zLcraEysbIyAhdunTBzz//jCtXruDjx49Yu3Yt7whGAJwjNtVFmR7hReSNlFE1IQ/VqqyolicjIwP9+/fH+/fvyyxNVVL3NbV4/BXxGq5u+vr6uHDhAjZu3Mi7TAmXvLw8/PPPP2jfvn25TAFZXEW5J8h7CFTH2vMV8X6vKCEPzxoaij0iVdbf9eTJkzm37dy5U/y3sqPEgc/Hcvfu3di/f7+gGZSKKywsxLFjx+Dl5VXmZQNVWLRoERhjKCwsRGxsLK5cuYKvvvqKd5+TJ09ixYoVvGHK4pwrnkZlu98zxqQ6dwDAV199pfDvm4uenh7atm2L2bNn48yZM0hISMCOHTvkTiteHuexsuXcsi7jAvLLuWVZxi0sLMSwYcPw9OnTMktTlSpaGfdLeWbeuHEj73Zzc3O4urqWeH/hwoUyZ0+pLCpT/vkaMBUdjQuork5UXn1oaTsq7d+/HyNGjOANk5ycjK5du1aaWcm4HDp0CPHx8bxh2rRpo3T8ZV0G0NLSQosWLTB16lQcPXoU8fHxOH78uNz1mStj2bmqKet7sRDKPNPXrVsXT58+xY8//qjUtSg5ORmzZ89WuLM032hwoZ1RCJGFGsVJqWlra/NeiOQVRCTxTfthbGys8MidIkLXJ+SrbFem17I60uUbGaapqYmsrCwwxkr1UpS8nrEVgUgkQufOnXHlyhXO9S2LFB8FYGlpyVuBporpcOSN+BNyLslrLJKXRlkwMTHB9OnTeSvYgbKd5q2y4Vt7vkhZjCAr8r///Y93NEDHjh1x6tQpREdHIzc3V+paI6/3dlng+13Mmzev1NfTxYsXS8XJdy8pLCwUdO5XhIZhRWlqamLKlCkICwvDnTt3sHz5cvj4+MDV1VVwpfvz58+xaNGiEu/zfYequA8Xj1/eQ6S6p0grT4re7/m+m8LCQkFr61aGe1tFLJsJ4eHhgRYtWsjcdunSJcTGxqKwsBB79+6VGcbS0hKDBg0SlNbw4cPx5MkTBAcH448//sCQIUPQuHFjwaOeYmNjMXXqVEFhKyKRSISaNWuic+fOOHnyJJYuXcob/pdffsGrV684t8s77y9dulTqc674tJeV7X5/586dEtM5Kzp1uiL09PQwevRonDlzhrdTEpVx+ckr55ZlGffkyZO8DUNNmzbFgQMH8PbtW2RnZ0ud8xWhIYLvOjFixIhSXyMkO08B8utLhJRfK2MZV1JQUBC2b9/OG6Zfv34lOgjEx8djzZo1nPtYWVlh3bp1eP78OTIyMkp8F/b29irJv7IqW/4NDQ05l+xQpP6yiLwGIqFxypsGubQzKWhqamLPnj3w8/PjDZeamoru3bvj5s2bpUqvvKSkpGDhwoW8Yezt7eHu7q6yNMu6DKClpYX+/fvjypUrvMvPUJmj/JX1vVgIZevwTU1NsWbNGsTExOD8+fOYM2cOunXrBgcHB8GdTg8ePIhjx44JTpNv5pWymqGPVE3UKE5UokGDBpzboqKiBE3nkZyczFuRzJeGPOHh4YJ6Z4aEhHBus7a2VjhdIb3K8/LyEBoaKjhdvot+QUFBlZjqSJ1MTU3Rr18/3jDFC5UaGhq806+rYkoieTdzrjXnFQmjzDmsLj4+PryjxdW9jnRFx/cglZaWxrtvcnJymVbE8RVoO3fujKtXr6Jfv36wsbEp0bEpISFB3dmTi++3FxgYWKbpAfz3IUXCVFQikQienp6YO3cuTpw4gefPnyMzMxNRUVG4efMmNmzYAC8vL8799+7dW2IaRb5jKuTaKS9c8Wunra0tb1w0Td3/+VLubZW5bMY10rugoAB79+7FlStXEBUVJTPMmDFjFB5J2rhxY/zwww84dOgQgoODkZGRgQ8fPuDOnTvYsmULunfvzrnv2bNnK8R9QxUWLFiAXr16cW7Pzc3F7NmzObdbWVnxVj6p4/5V2e73xadONzU1RadOndSebvPmzWWOAi1CZVz+WUz4yrmFhYUIDg5WdZY48Z3zbm5uCAwMxLBhw1C7du0S18KKcK3iuzfduXOnTNMDqn4ZNyIiAn369JHbsWPs2LEl3jt16hTnNOE6Ojq4desWpk2bBldX1xKdyRhjgjuBqktlzL+Dg4PM9+Pj4wV1Qpckr67y2bNnguLhC6elpQVnZ2eF8iWLhoYGtm/fjnHjxvGGS09PR8+ePStEJ3ZFZGZmYtCgQQgLC+MNJ+t3qAplXQawt7dH+/btyyw9oriyvheXBT09PfTo0QO//fYbLl68iIiICGRnZyMsLAwXL17E0qVLeQejyRskJYmvnYjrOk6IENQoTlSiXbt2nNsYYzh//rzcOM6dO6d0GvIUFBTg33//5Q0TEhKCN2/ecG7nq6TnEhsbi7t37/KGuXjxIu80cMXTbd26Ne+I+X/++UexTEq4ceOG0vuWpU2bNsk9X/jwFQx1dXVhZGRU4v2OHTty7vP69etS5Qf4/L3yrXFavGJPlhMnTnBu09LSQuvWrZXKG5fvvvsOkZGRSu0rEol4G8UtLS2VzFXVwHeOZmRk8K7Zd+jQoTKdyvf169ec2wYOHMg7VeKlS5eUTpevUjUnJ0dwPN7e3pzb/vvvP97Px+fDhw8yOzzJW7/p9OnTvNvj4uLk3lcqGw0NDdja2qJt27b47rvvcPPmTc5plpOTk0tUcvBVBERGRuLx48e86QcFBfFey4rH37p1a96GwM2bNyt0DlZlfN8NIOzedvLkSc5tVlZWKqkgLK3KXDYbNmwY5/JEu3bt4qy0EIlEmDhxokryYGVlBU9PT4wfPx4XLlxAz549ZYZjjOHhw4cyt+3cuRMikYjzVRH9+eefvNNanz59mrNx28DAgHOUPwDs3r1b4Ur9IkFBQTI7NJfX/V5ZxcvFffr0ETyN+E8//VSqxjm+chyVcfkr6Pmm5798+XKZNp7xnfN9+vThLQtU9DLumzdv4O/vr1C+iqSkpMjsnFDaMm5ubi4uXLigVJ7KU0FBAXbv3o2mTZvKnS3I29tbZr0W37nWpEkT3imSAwMDkZ6eLjzDEuR1UhF6vpVX/kuDq+GSMcZbLyiLvLpKIXVF0dHRvJ1+mjZtKrOeShkaGhrYsmUL7zI6wOd6h969e+Py5csqSVfdbt++jWbNmuHKlSu84czNzTFlyhTO7WVdBlixYkWppqunMod6VNZ7cXnR1tZG3bp10a1bNyxYsACBgYGcx/D+/fuC4+W6v9jY2NASu6RUqFGcqETXrl15t69YsYK3YiYnJwfLly8vVRryLFmyhLfxecGCBbz7F59CUKj58+dzro2Wn59fYmpdeekaGxujbdu2nOH37t0r94FTUkFBAU6fPo1mzZrh66+/FrxfeXr06BF69+6NZs2aYf/+/bxrjBSXm5vLWwnv4eEh8/3evXvzxjt+/Hi8e/dOUB5kFXiNjY15G63//fdf3Lp1i3N7YGAg7/feunVruet4K2rz5s1wcnLCmDFjFB4Fd+PGjRJTWkri+h6+FPKmRb169arM9xMTE/HLL7+oI0uc+KY/5huxfv/+faWmeirCdz6HhYUJbgzgG6lXWFiIb775RqH1K6OjozFjxgzUqVNH5m+9adOmMDU15dx/8+bNvBVqP//8M+dIjIooPz8fJ0+eVKhxRktLi3ft8eJTa/bo0YM3vrlz53J2FGGMYd68ebz7F4/f0NCQ9+H2/fv3mDBhgtx1UYHPx6cijyIuLVdXV95e4lu3buUdyXHo0CEEBQVxbu/evXuFaPCszGUzAwMDzrXdnj17xrk+aJcuXVC3bl258Z86dUqhchoA3o4OlX1qXUl169aVO1KJ7/mE7/4VHh6OuXPnKpSfR48eYeDAgfDw8JB5nMvrfq+MJ0+elJjqvX///oL3P3r0KBo3bowBAwbA399f0PW8SHh4OG9lH5VxlSvj5uTk4KefflJHljgpe85HRETg999/VzpdvjJuXFyc4OsgVwejIpMnT1aok8GnT5+wbNkyODo6yuyMXb16dbi5uXHuf+zYMd57+po1ayr8NZ4xhpSUFERGRuLMmTOYN28e6tSpAz8/P7nTFOvq6nKuN853rr19+5azHJuTk4Mff/xRcP6Lk1c/8Pz5c0HxlFf+S6N58+ac24TM+Cipbdu20NPT49y+a9cuuYMJFi9ezNuxvbT1ocWJRCJs2rQJ06ZN4w2XlZWFfv36CRroVJYyMjIQHR2NGzduYNWqVfD09ETbtm3x8uVLufuuWrWKdyr6si4DXL16FW3atEGnTp1w+vRphZ6bExMTeTthfelljtKorPdidYiIiFB4YEbNmjU5O2wIPXYxMTGcM+/wdQ4mRAhqFCcq0a1bN94HoODgYPTr109mz++3b9+ib9++vFMFNWzYEF26dClVHl+8eIE+ffqUaGzIyMjApEmTcOrUKc59a9asib59+yqV7tWrVzFy5MgSIzsTEhIwaNAgzhEvwOcCTLNmzUq8z/fgwBjD4MGDsWTJEs7RpNnZ2bh9+zZmz56NWrVq4auvvsKjR48EfqKK49GjR/D19YWVlRXGjBmDY8eOITY2ljP88+fP0atXL96Ccrdu3WS+P3DgQN5GmpiYGLRo0YJzZA5jDLdu3cKIESM4K85/+OEHzvgZY/Dx8ZHZe/7KlSv46quveB+ipk+fzrmtNPLz87Fz5054enrCxcUFv/zyC27evMnZiFhQUIBDhw5h4MCBvPFyfQ9fCnd3d96eqT/++GOJUcihoaHo1q0bb2cDdeDrffzXX3+VqNxkjOHIkSPo3r270qPYAPAuaZCQkIBvvvkGN2/eRHh4OCIjI8Wv4oXqdu3a8VaK3Lp1C23atOFdU+3t27fYvXs3unfvjtq1a+OPP/7gbATS0tLC6NGjOeMqWsOteCVUbm4u5s+fjy1btnDuWxHl5+ejf//+sLW1xcSJE3HkyBG56+UdPXqUt+KleI/gDh06oGnTppzhL168iFGjRpW4J6ampmLMmDG8aTVp0kRmpzi+aY0BiM8Hrntreno6tm/fjiZNmmDTpk28cVV2fPe2rKwsdOvWTeZD9sGDBzFmzBjOfUUikdyKvLJUmctmXFOoA+DshCNvdFGRiRMnombNmvj666+xZ88e3lGgwOcZOvim1KtqIwIWLFjAW5Hu7+/POYJk4sSJMDQ05Nx3zZo1GD58OGfHk8LCQjx9+hRr1qyBu7s7mjVrhuPHj3OWJ8vrfq+M4h1g9fX15VZKFscYw4kTJ9C5c2fY29tjzpw5uHLlCuf03owxXLp0CV27duX9vF96GdfGxgZWVlac25cuXVrinhAbG4t+/frxNqiqA985f+zYMezfv7/E+/7+/vD29uad1UkevjIuYwwjRozA1atXERYWJlXGLb7upr29Pe8zV2hoKDw9PXHmzBnORp8PHz7g6NGjGDhwIGxsbLBw4ULezzZ+/HjObQUFBejTp0+JTqOFhYXYuHGj3DWAy8t///0nnnVEQ0MDZmZmcHR0RN++fbFixQrBHeM3bNiAhg0bytzGd67FxcXhhx9+KDEyMSwsjLMMJZSJiQnv2rKzZ8/GsWPH8PLlS6lzrXgDb3nlvzTatGnDuU3R8peZmRnv811WVha6du0qc/aX9PR0zJw5k3dWIR0dHd6RzaWxbt06zJw5kzdMdnY2fHx85M6+qW6SMwAZGRnBzs4O3t7emDNnjuBOxiNGjJA7dTxQPmWAa9eu4auvvoKNjQ2+++47nDlzhrfx8O7du+jcuTPvch1fepmjNCrzvVjVXr16BS8vL7i6umLu3Lm4cuUK7wwfOTk5mDdvHmcehT7P8V2L+a7hhAjBPVcvIQoQiURYsGABRowYwRnmwoULcHR0RLNmzeDg4ADGGCIjI/Hw4UO5Pe8WLlyokpFAV69ehaOjI1q1agVbW1skJSXh9u3bctfonTZtmuDp9mQ5cOAATp48iTZt2qBGjRqIi4vD7du35Y4+nDVrlsz3+/Tpgw4dOuD69esyt+fm5mLx4sVYvnw5mjRpgtq1a0NbWxuJiYmIi4vDs2fPyrxySp3S0tKwc+dO8SgUe3t71KpVC5aWljAyMkJqaipevHghdxpkPT09zgKyjo4OfvvtNwwZMoRz//j4ePj5+WHKlClo0aIFrKysUFBQgI8fP+Lx48dye4/7+PjAy8uLc12ZT58+oWfPnnB2dkbjxo0hEokQEhKCFy9e8Mbr6emp0OgYZYWGhmLRokVYtGgRtLW14erqiho1asDS0hIikQhxcXF4/Pix3MJbnz59vvi1YQwNDdGyZUvOaVPfv3+PRo0aoV27dqhRowYiIyNx9+5dhXoxq4qnpyfn9MaZmZno0qULGjduDGdnZxQWFuLBgweCK4/48DVkA59HZu7du7fE+35+flIj1kQiEVavXs27RMKjR4/Qvn172NjYoEmTJjA3N0dmZiYSEhIQFhbG2xlHlqlTp/JOsf3s2TM0atQILVq0QJ06dZCWloaAgIAKP3qGz8ePH7FlyxZxo361atXg7OwMS0tLGBsbQ1NTE4mJiXjy5Ann+sUAYGRkBEdHR6n3RCIRVqxYwTtifN++fTh+/Lj4PpyQkIBbt24hMzOTN9/Lly+XWf7o3LkzevXqxTsd4pUrV8RlnkaNGsHExARpaWl49+4dnj59Km5slHcuV3aTJ0/GunXr8PbtW5nbIyIi4OXlBXd3d7i4uCA/Px8PHz7kDF9k8ODBFWr0Q2Uum7m5uaF9+/aCp2q3sbFRqLNoamoq9uzZgz179gD4PPLC2dkZVlZWMDY2ho6ODlJSUvDs2bMSo3sliUQiuet2VjY2NjaYNGkS1q1bxxlmwYIFMmcesbKywqxZs3hnnTp48CAOHTqE+vXrw8XFBYaGhkhJSUFCQgKePXsm9xlIUnnd75VRvFG8e/fuJdaxVURUVBRWrVqFVatWQVNTE87OzqhZsyaqV68OLS0tJCQkIDg4WG6nr8aNG8tdVuJL0KlTJxw4cEDmttTUVLRu3RqtW7dGrVq1EBsbi9u3b5fL9dHT05OzLF5YWAhfX18sXrwYDRs2hJaWFp48eYJXr16VOl13d3doaWkhPz9f5vaLFy/i4sWLJd739vYucQ9avnw5/v33X+Tm5sqMKzw8HH379oWlpSU8PDxgaWmJnJwcfPr0CREREXLvxcV98803WLZsGeeot+joaLRp0wbu7u6oX78+cnJycPfuXYXL0pXNsmXLeDsMyJt6fv369Thw4ACaNWsGMzMzvHnzBg8ePCj17FEaGhrw8PDA7du3ZW4PCwvDoEGDZG6T7EBVXvkvDU9PT1SrVk3m85UyS9fMmjULO3fu5KzjCwsLQ+vWreHi4gI3Nzfo6enhw4cPuHv3rtznkbFjx/KuS1xaq1evhra2NlasWMEZJjc3FwMHDsShQ4fKpG5JHXr37o0dO3YovF9ZlwESEhLw119/4a+//oJIJEK9evVgY2MDS0tL6OnpISkpCSEhIXLLWFZWVhg8eLBCn5X8n8p8L1aXly9f4rfffsNvv/0GALCzs0PdunVhZmYGIyMjMMbw4cMHPHz4kLf+m2upvOK4nqsB+SPwCZGLEaJCY8eOZQBU+po4caKgtBctWsQZh66urtLpN2vWjOXm5nKm6+fnp5Z0+/Xrx/t5Y2JimIODg0qPtb29vVLH19vbW9B3pCrqOM8AsIULF8pNe9asWSpJi8vbt2+ZtbW1yj5TzZo1WWRkpCoPv5impqbKvwM9PT327Nkz3nS9vb0591+0aBHnfnznMN+5zxhj9vb2SqUZERHB+3kjIiI49921a5fSx1EkEqn8GHH9zk+dOqV0Pt3c3JQ+Powx5u7urnCafn5+MuNasWKFys/nHTt2cOZ95cqVSsero6Oj9LmsrGvXril1XLOyslR6TMeNG8eZx/nz56s0rblz5/Iek4SEBFa/fv1Sp8N17Epz/WCMv3zCd99Wx/3+0aNHzMjISGXfjaurK0tKSuJMb8eOHaX6jSh7zS/rspkq7d+/X3Cefv75Z8HxWllZqexYdOnShTMdvu8cUN8jr7x0+c6XIh8+fGCGhoa88Zw5c0bmvnl5eax3794qPecA2deX8rjfK3MdDAsLKxFu9+7dcr8HSXXr1lX5MdXQ0GCXL1/mTZfvus11r2CsdOc/X7maL03GGG+a165d49zvv//+U/o48pVxlT1GXNfZx48f86ZXmnOe7/gwxli/fv0UTpPrHr1v3z6lPwfXi+/advjwYaXj5SvjyjuXlSXvOlOal66uLtu+fbvcPGRmZjIbGxul0rCysmLVqlVT6rtijLE//vhDqXQrSv5Lw9fXl/M8TE9PVzi+3bt3q/wcatq0KcvIyBCUPl/5Vcjvh+8ZoOilpaXFDh8+rNT+fM/GQvJfmtfkyZNZXl6eoONY1mWAzp07q+Uzb9u2TdDnVQZfeQWQf49TlLLPp/LqMOSpzPdivt+TvN9icefPn1dpvvfu3Sso3WbNmsnc38HBQaH8EyILTZ9OVGrTpk0YMGCAyuIbPHgwNmzYUOp4vLy8lJpuyMbGBgcPHoS2trZS6Q4ePBj9+vVTeD83Nzfe6ZMAwNraGpcuXeJdd5EIN2LECLnruwPAypUr8eOPP6ptDdPatWvj3LlzqFWrVqnjsrW1xdmzZ2Fvb6+CnKmflpYWjhw5wrsUw5fE19eXd515LmPHjuVd21bV+vXrp9TyEs2aNcPq1atLlfbSpUtLtb+kn376CYsWLYKGRtkUjWbOnCl3GQFZPDw88P3336shRxVfrVq1sHLlSs7ty5Ytw9SpU1WS1rfffotff/2VN4yFhQUuXboEd3d3laRZlTVt2hQnTpyAubl5qeNydXXFuXPnKuQ02pW5bDZw4EC5a/0CgKamJu9oN3UxMTHB33//XebplgUrKyt89913vGEWLFggc1pzLS0tHD58GL1791ZX9sTK836viGPHjkn9r62trfQyWKr0119/lXo5sKqiffv2GDp0qML79ezZs0xHvTVp0kTub1MWBwcHbN++vVRp//zzz6WaqU7SiBEjsHnzZujq6qokPnkGDx6s1NJd9vb2gp7HKwtvb28EBQXxLgVTRF9fH+vXr1e4jkFXVxeHDx+WuzY4n7Fjx5aYhUlR5Zn/0vD19ZX5fm5urswRoPKMGjUKK1eu5F0GTRGNGjXCqVOnSjXTiSIWL16MZcuW8YbJz8/H8OHDOWf7qGgcHR1x5swZbNq0CVpa5TdZblmXAX766Sd88803ZZZeVVWZ78UVVe/evTmvvZKio6M5p08Xsj8h8lCjOFEpHR0dHDt2DBs2bICJiYnS8ZiamuKvv/7C4cOHlW6QLm7Dhg346aefBBfUnZyccOPGDdSrV0/pNDU1NXHkyBH4+fkJ3qdly5a4fv26oIpJJycnPH78GN9++22pC961atXC2LFjSxVHWWnTpg3q1q2rkrgMDAywevVq7N69W1BjmEgkwpo1a3D+/Hm1NTY3adIEQUFBnFOVCfHVV18hKChIrVPLDhgwQGUPsA0aNMCNGzfQp08flcRXFWhqauLw4cOCp4rV0tLCggULsHXr1jJr2C1y4MABhR7yWrdujYsXL5b6Ab9v377YvHkz73qsili8eDH8/f1LfX3R0tJCjx490KxZM84wGhoaOHjwoEL3h06dOuHq1aswMjIqVf4qIy8vLwQEBKBatWq84davX4+jR48KuofKYmFhgYMHD4qnq5OnVq1auHfvHubMmaOyB+aqqkuXLggKCpK5RrsQGhoaGDduHB48eFChl9iorGUzHR0dQRX3ffr0gZ2dXRnk6P+4uLggICCgVGXyim7WrFm8ZaqgoKASjb1FDAwMcObMGfz111+lev4CPnc++Prrr2FhYSFze3nd7xVx4sQJqf87duyocCea3r17cx4DRdWuXRtnzpzBpEmTVBJfVbFlyxaFppIfN24cTp06xbv+sTqsXbsWo0aNEhy+fv368Pf35103XYhmzZrh0KFDKusANmHCBNy/f7/Uz4YikQjt2rXjXXYI+Hzc5s6dK7juxd3dHTdv3oS1tXWp8lfeDAwMMGTIEFy7dg3Xr19H/fr1Be87aNAgbNy4UXDDnbGxMU6dOlXqJRlMTExw9uxZuLq6liqe8sp/aXTr1o3znCt+LxFq9uzZ4mUblaWlpYXJkyfj3r17KhksoYj58+dj1apVvGEKCgowcuRI7N69u4xypRgNDQ106NABu3btQmhoqMIdB8u6DNClSxeVTY9vYWGBHTt28E6FT4Sr7Pfiiuabb77hfJ4p7uTJkzI7BANQqFxGCKfyHqpOqq60tDS2YcMG5unpKXcaLODzFEVeXl5s48aNLC0tTeH0hE6ncv36ddalSxfOqZ+trKzY4sWLWVZWlqB0hU5zd/z4cebl5cUZ1t7enm3YsIEVFBQo/NkZY+z9+/ds/vz5zMXFRdCULDo6OqxNmzZs/vz57ObNm6ywsJA3/oo0fXqR58+fs3Xr1rFhw4YxR0dHwVO1iEQi1rBhQ7Z8+XIWHR2tdPr5+fns6NGjrHv37oKmhTUwMGBdu3ZlGzZsEJzG06dP2dixYwVNP1q9enXm5+fHgoKClP5MisrJyWGXL19m8+bNY126dGGmpqaCvwd9fX3Wu3dvdvjwYd4lCor7UqZPL5KcnMxmzpzJObWqlpYW69Wrl9T3ro5jJO93XlBQwDZu3Mh7rJycnNjvv/8unrpM3nRWQo4PY4xFRUWxX3/9lXXr1o3Z2dnxTkMrbyrQos9y8uRJ1qtXL2ZsbCzofLa1tWW+vr5s27ZtLD4+XlC+i5w9e5Y1bdqUM243Nzf2999/i+8PpTmXlaXs9OmMMfb69Wu2efNmNmzYMFavXj3B04YZGBiwPn36sBMnTsi9RxWXlZXFtmzZwjw9PZmWlhZvOpqamqx58+Zs06ZNLDMzU+ljFBcXx5YtW8YaNmzINDQ05H4+Ozs7NmbMGHb79m2Z8VWl6dOLu337Nhs6dCgzNzcXdJy+++47FhYWJjj+8po+vTh1l81ULTw8XG4+z58/r1CcUVFRbOfOnczPz4+5ubkJXnpFR0eHde7cme3cuVPQdJeVefr0IgsXLuSNy9XVVe5zQlpaGtu0aRPz9PRk2traco+zSCRibm5ubMqUKezEiROCnn/K8n6v6HUwOjq6xDm8efNmQce/uPz8fHbz5k22ZMkS1qtXL1a9enVB5y4Apq2tzTp27Mi2b98ueNpbxr6c6dOLZGdns19//ZVz6mSRSMTat2/Prl+/rtZjJOS+sH//ft4p0W1tbdmCBQvEUy7LO3eFTi376dMntm7dOtanTx9mb2/PjIyMOK/TQu/R/v7+bMiQIbxTVku+LC0t2cCBA9nGjRvZu3fvBKVRJCAggLVv354zzw4ODmz58uUsOzubMVY+13Ih06eLRCKmra3NDAwMmLm5OXN0dGQeHh6sZ8+ebPLkyWzNmjXs5s2bguuQ+Ny9e5d169aNMy9GRkbMz89Paok0VZRbcnJy2MGDB9mIESOYq6srMzMz471nV7T8K2vJkiUy0zU2NlZqCvUi+fn57Pjx46xnz57MxMRE0DlWv359NnfuXPb+/Xul0izt9OmS1q5dKzfPGhoa7J9//hHvUxbTp2tpaTF9fX1mamrKatWqxRo3bsw6derExowZw5YuXcrOnDnDPn36pMzhk1IeZYCHDx+ylStXsv79+yu0HIGmpiZr2bIl+/PPP1liYmKpP7sQX8r06UUq471YldOnFxQUsHv37rEVK1awXr16sZo1awo+P2vUqMFGjx7NHj16pFCaXO0nfMtpEaIIEWMc3S4IUaGcnBw8evQIkZGRSE5ORkpKCoDPI8LNzMzg6OiIpk2blmoakcWLF2PJkiUyt3l7e+P69etS78XFxSEwMBDR0dFITU1FjRo14OLigtatWys0ynL06NHYtWuXzG1+fn7YuXOn1Htv377FvXv3EBsbi6ysLNSsWRMNGjRA8+bNBacpT2JiIh48eIDY2FgkJycjLS0Nenp6MDExgZWVFVxcXFC3bl2VjcKvKNLS0hAREYHIyEjExcUhIyMDGRkZ0NDQgLGxMUxMTFCnTh24u7urfIqugoIChISE4OXLl0hKSkJycjIKCwthZGQEGxsbODs7w83NrVRTNoWFhSE4OBifPn1CUlISGGMwNzeHhYUFGjVqBBcXFxV+IuUwxhAbG4vIyEi8e/cOSUlJyMjIQFZWlvgcrFatGho0aAAXFxeVTS32JcjJycHt27fx6tUrJCUlwdTUFHZ2dmjbtq3KejKrQmFhIYKDg/Ho0SMkJCRAS0tL/BvgGzldURUWFuLZs2d48eKF+LddUFAAY2NjmJqaok6dOqhfvz4sLS1LnVZYWBju3buHmJgYiEQiWFtbo3HjxmjYsKEKPknFkZaWhtevX+Pt27f48OED0tPTkZ2dDV1dXRgaGsLKygrOzs5wcXFRyfRimZmZuHfvHt6/f4+kpCSkpaXByMgI5ubmsLOzQ8uWLVU++j41NRUPHjxAdHQ0kpKSkJqaCh0dHZiZmcHe3h4NGjQo89G2FRFjDM+fP8ezZ8+QmJiIpKQkaGpqwtzcHJaWlvDw8Kg0S4DI86WWzYrLysoS//5jYmKQlpaGrKws6OjowMDAANWrV4eTkxPq168PQ0PD8s5upZadnY1Hjx4hIiJC/PylqakJY2NjWFhYwMnJCS4uLkof54p4v//rr7+kprvW0NBATExMqUftFomPj0dkZCTevn2LT58+icu4Ojo6MDY2hrm5OerXrw83NzeaPUQB+fn5uHv3Lp49e4ZPnz7BwMAAtra2aN26tcpGz6nKixcvcO/ePcTHxwP4vGxGnTp14OXlVeYzNalCaGgonj59isTERCQnJyM3NxdGRkYwNTWFvb096tevr5LR29HR0bh9+zZiYmKQm5sLa2tr1K9fHy1atFDBp6iaYmNjcfv2bbx//x7Z2dmoUaMG7Ozs0K5duzKdfUNZlSX/CQkJqFWrFrKzs0ts2759u6BZdOQpLCzE8+fPpeqKcnNzYWpqClNTU1hbW6N58+YVcmkg8n/KugyQlJSEiIgIvH37FvHx8cjMzERmZiY0NTVhYmICU1NTODk5oVGjRmU+iwpRrbK6F6tSXFwcwsPD8e7dO3z8+BEZGRnIy8uDgYEBDA0NUatWLfHzraJLa7x8+ZJz9pKzZ8+iV69eqvgI5AtHjeKkylC0UVxVFG0UJ4QQQgghhBCiWp07d4a/v7/4/7Zt2+LmzZvlmCNCCCEV3ffff4+NGzeWeN/T0xN37twphxwRQsiX68cff8TatWtLvO/u7o7Hjx8r3MhOiCyVrysrIYQQQgghhBBCyP+XmJiIGzduSL03YMCAcsoNIYSQymLevHkyR9revXsXAQEB5ZAjQgj5MqWmpmLbtm0yt/3yyy/UIE5URvl5fAkhhBBCCCGEEELKWUJCAubPny/13tChQ8spN4QQQioLa2trfP/991i1alWJbb///juOHz9eDrkihJAvzz///IPU1NQS73t5eaFfv37lkCNSVVGjOCGEEEIIIYQQQiotZ2dnLF68uLyzQQghpBJasGABdu/ejQ8fPki9f/LkSQQHB8Pd3b2cckYIIV+GrKwsrF69usT7IpEI69evL4cckaqMpk8nhBBCCCGEEEIIIYQQ8sUxNjbGb7/9VuJ9xhgWLlxYDjkihJAvy6ZNm0p0TAKA0aNHo2XLluWQI1KVUaM4IYQQQgghhBBCCCGEkC+Sn58fGGMlXqdPny7vrBFCSJU3Y8YMmdfg7du3l3fWSBVEjeKEEEIIIYQQQgghhBBCCCGEEEKqLGoUJ4QQQgghhBBCCCGEEEIIIYQQUmWJGGOsvDNBCCGEEEIIIYQQQgghhBBCCCGEqAONFCeEEEIIIYQQQgghhBBCCCGEEFJlUaM4IYQQQgghhBBCCCGEEEIIIYSQKosaxQkhhBBCCCGEEEIIIYQQQgghhFRZ1ChOCCGEEEIIIYQQQgghhBBCCCGkyqJGcUIIIYQQQgghhBBCCCGEEEIIIVUWNYoTQgghhBBCCCGEEEIIIYQQQgipsqhRnBBCCCGEEEIIIYQQQgghhBBCSJVFjeKEEEIIIYQQQgghhBBCCCGEEEKqLGoUJ4QQQgghhBBCCCGEEEIIIYQQUmVRozghhBBCCCGEEEIIIYQQQgghhJAqixrFCSGEEEIIIYQQQgghhBBCCCGEVFnUKE4IIYQQQgghhBBCCCGEEEIIIaTKokZxQgghhBBCCCGEEEIIIYQQQgghVRY1ihNCqpzRo0dDJBJBJBJh586d5Z0dQkgFtnjxYvH1YvHixeWdHUIIIYQQQgghRGWuX78ufubt0KFDeWeHkC8S1VUTUnF8UY3iO3fuFF98hL6WLVumsvQdHBxKxB8cHKxQHIMGDSoRR1W6kCr6/VTV40AqjsjISKnzLDIyUqH9O3ToQA1uFURubi4OHjyIkSNHwtXVFebm5tDS0oKhoSHs7OzQpk0bjB8/Hlu2bMHr16/LO7tqJ/lgLBKJyjs7hBBCCCGEEEKI2kjWz8h6GRsbo3bt2ujZsyeWL1+O6Ojo8s5yhSTZuDd69GiF9qV6iIolNDQUCxYsQKdOnWBjYwN9fX3o6OjA3NwcDRo0QN++fbFo0SJcuHABWVlZ5Z3dcpGSkoKDBw9i7NixaNKkCWxtbaGrqyu+XnTt2hXz5s1DYGBgeWeVECKQVnln4Eu3e/durFmzRlDYpKQknDlzRs05IuT/REZGwtHREQBgb2+vcIMwIRXF+fPnMWHCBERFRZXYlpmZiczMTERHRyMgIAD//PMPAMDPz4862xBCCCGEEEIIIV+A9PR0pKen4/3797hw4QIWL16MBQsWYOHChdSAS6qUlJQU/PDDD9ixY4fM7cnJyUhOTsbz58/FbRF6enp4/Pgx6tevX5ZZLTeZmZlYt24dfv/9dyQlJZXYnpubK75eXLlyBStWrICzszMWL16MYcOGVblrhoODA96+fQsAiIiIgIODQ/lmiJBS+GIbxevXr4/OnTvLDdeiRQu15mP//v1YtWoVNDU15YY9dOgQcnJy1JqfisTHxwe2traCw7u6uqoxN4SQymr37t0YM2YMCgsLxe/VrVsXTZo0gYWFBXJzcxEfH4/g4GCpnuDJycnlkFtCCCGEEEIIIYSoU4sWLdCyZUup91JSUhAcHIynT58CAPLy8rBo0SIkJyfjjz/+KI9sEqJyKSkp6Ny5Mx4+fCh+T19fHy1atICjoyP09PSQkpKCsLAwPH36VNwWkZ2djezs7PLKdpl69+4d+vbtiydPnki9X7t2bTRu3BjVq1dHQUEBPnz4gODgYMTFxQH4PPJ+xIgReP/+PWbPnl0eWSeECPDFNop7enpi48aN5Za+m5sbnj9/jg8fPuDSpUvo2bOn3H12794NANDR0UHt2rURFham7myWq2nTptFaN4SQUgkPD8eECRPEDeItWrTAX3/9xdnhKTw8HCdOnMD27dvLMpuEEEIIIYQQQggpI7169eJc4i4gIADDhg3D+/fvAQBr167FiBEj0Lx58zLMISHqMWPGDHGDuJaWFn755RdMnToVhoaGJcLm5OTg8uXLOHToEI4cOVLWWS0XkZGRaNWqFT58+ADg81Kvw4cPx7x589CgQYMS4RljePDgATZs2IB9+/ahsLAQmZmZZZ1tQogCvqg1xSuSUaNGif8uauzm8/r1a/HaFL169YKFhYXa8kYIIVXF2rVrxb1a69evj+vXr/POAFK3bl3MnDkTz58/F7y0BSGEEEIIIYQQQqqG1q1b49SpU1LTH2/ZsqUcc0SIasTHx0tNmb5lyxbMnTtXZoM4AOjq6qJPnz7Ys2cP3r9/jzp16pRVVstFbm4uBg8eLG4Q19PTw/Hjx7Fv3z6ZDeLA50bzFi1aYPfu3QgODkbDhg3LMsuEECVQo3g5adKkCRo3bgwAOHXqFFJTU3nDSzac+/n5qTVvhBBSVVy6dEn899SpU2FgYCB437p166ojS4QQQgghhBBCCKnAmjZtCm9vb/H/N27cKMfcEKIaV65cEc+kWLNmTYwePVrwvtWrV4eJiYmaclYxrFq1Cg8ePBD/v2vXLvj4+Ajev2HDhrhz5w66du2qhtwRQlSFGsXL0ddffw0AyMrK4p2ChDGGvXv3AgAsLCzQu3dv3ngfP34MkUgEkUgEc3NzZGVlCcpPSkoKDAwMxPuGhoZKbR89erR4286dOwF8XnN3/fr1aN++PWxtbaGlpQWRSFQh1uINDAyEtra2OM/Hjx/nDc8YQ69evcThW7Rogby8PKkwO3fuFG8vKjjk5+djz5496NatG+zs7KCrqwtbW1t89dVXctOU5f3791i6dCnatWsHGxsb6Orqolq1amjatClmzpxZ4nsR4vz585g4cSIaNmwICwsLaGtrw8zMDB4eHpg4cSJOnz6N/Pz8Ep/T0dFR/N7bt2/Fn734i8+LFy8wb948tGzZElZWVtDR0UH16tXh6emJn3/+GTExMQp9lsuXL2PIkCGoVasW9PT0YGtri86dO2Pnzp0lvq/KQtZvKzExEatWrULLli1RvXp16Ovro06dOhg3bpzUuj9cKsu5qu7rSlRUlPhvyfNZlVJTU7Fhwwb07dsXDg4OMDIygq6uLmxsbNC5c2csWbIEz549kxtPQkICfvvtN3h7e8Pa2hq6urqwtLRE06ZNMWvWLDx//lwt+RciPT0dmzdvRu/evVG7dm0YGBhAT08PdnZ2aNKkCfr3749NmzYJWtYjLy8PO3bsgI+PD+zt7aGvrw8TExO4uLhg7NixuHz5ssL5y87Oxvbt2zFkyBDUrVsXJiYm0NHRQY0aNdCuXTv89NNPuHv3ruD48vPzsXv3bnTp0gW2trbQ1dWFtbU1fHx8cObMGbn7y/r9FRQU4ODBg/jqq69Qp04d6OvrQyQS4eTJkyX2V9UxcnBwEOcjMjISwOffxMKFC+Hu7g4zMzMYGhqifv36+P777/H27Vuhh0iczz179mDIkCGoU6cOjI2NYWhoCEdHRwwfPhwnTpwAY0yhOAkhhBBCCCGkImjatKn4b6F1V6qqA0tJScGBAwcwceJEeHp6wtLSEjo6OjAxMUG9evUwYsQIHDlyRNzAWZVERkaKn2MdHBzE71+7dg0jRoxA3bp1oa+vD0tLS7Rt2xZ//vmnoLWuZT0fv3r1Cj/++CMaNGgAU1NTmJiYoFGjRpg3b57C9ZWMMZw4cQJ+fn5wdnaGqakp9PT0UKtWLfj4+GDXrl1Sda+KfPZbt25h3LhxqF+/PkxNTSESiTB9+nSF8gdI15HZ29vLrdNV1q1btzBt2jQ0bdoUNWrUgLa2tvjY+vn54cCBA4LaKy5evIhvvvkGzs7OMDExgb6+Puzt7dG/f3/s2LFDpfXAWVlZ+PPPP8X/DxgwAEOGDFE4HkNDQ7Rp00apPMiqI+Ujq+6Ji7+/P8aOHYtGjRrBzMwM2trasLS0hKurKzp06ID58+fjxo0bUsdU8nyUrC9ydHSU2T5w/fp1zvQ/ffqENWvWoGvXruI6fTMzM7i5uWHKlClSnRG4LF68WJxW0fIXWVlZ2LZtG7p164batWtDR0cHIpEIQUFBcuMrkpeXB0tLS3HcRbM1C9GpUyfxfhs2bJAZRpljT9SMfUF27NjBADAAzM/Pr8zTt7e3F6d//vx5FhsbyzQ1NRkA1r59e879rl+/Lt5vypQpjDHGPD09xe/t2LGjxD7NmjUTb9+7d6+g/P3999/ifdq1a1diu5+fn1Sat27dYrVq1RK/J/lKSkoSlGZxknFcu3ZNqTgk/fLLL+L4qlWrxt6/f88Zdt26deKwhoaGLDQ0tESY4udQbGwsa9u2rcxjUPTq2bMnS01NlZvXgoICtnDhQqanp8cbn5aWFps3bx4rLCyUG2dISAhr3rw5b3xFr6FDh8r8nEJesmRnZ7NJkyaJz3Gul76+PtuwYYPcz5Kbm8tGjRrFG5eXlxeLjo4uca6WRkREhFQaERERCu3v7e0t3nfRokUywxTPb2BgILO1teX8nBoaGmzu3Lm86VaWc1Xd1xUDAwPx/ps2bVJ4f3n+/vtvZm5uLuh3cv78ec54tm3bxkxNTXn319TUZNOnT2f5+fkqyfu1a9fk/o4ZYywgIID3fCz+ysvL44zrzp07rG7dunLj6Nq1K4uPjxf0OY4dOyY4f3///XeJ/RctWiT1G42KimKtW7fmjWfMmDGsoKCAM0/Ff3/R0dGsXbt2MuM6ceKE2o6RZLkjIiKCnThxgvc809fXZ2fOnBF03K9duyYon15eXiwqKkpQnIQQQgghhBCiLkLqZyTNmzdPHF5bW5s3rCrrwI4dO8Z0dXUFPeM2adJEbj2V5LO/t7e33M8tj2Q9jqL160LqISTr4ezt7Vlubi6bNGkS73FwdnZmz5494027+PPx1q1beY+zqakpO3LkiKDPFRwczJo0aSL3+3JxceHNZ/HPnpOTw/nZp02bJihvklatWiXev0aNGgrvL8/79+9Z165dBZ27np6enPHExcWxzp07y43DycmJ3b9/XyV53717t1Tct27dUkm8RYTUVStany2krSstLY3169dP0HcCgG3dulW8b/E6cXkvrracjRs3yq3zFIlE7JtvvmE5OTmcn7d4/d3z589ZgwYNZMb3+PFjucdPkuTvrKj9TZ6oqCimoaHBgM914HFxcVLbS3PsiXpp4QuVnJyMw4cP4/nz50hJSYGpqSlsbW3Rtm1buLq6lkkeatasia5du+LChQu4efMmIiMjpXqBFdm1a5f476LR5fJMmDABEydOBABs27YNvr6+cvfZtm2b+O+xY8fyhg0LC8P06dORkpICY2NjtG/fHjY2NkhKSqpQUwrNmzcPly5dwq1bt5CYmIivv/4aV65cgYaG9CQJT58+xZw5c8T/r1+/Hk5OTrxx5+XlwcfHB3fv3oWmpibatGkDJycnpKen48aNG4iNjQXweZR2z5494e/vDx0dHZlxFRQUYOjQoTh27Jj4PWtra3h6eqJGjRpIT0/H3bt3ER4ejvz8fCxfvhwfP37kXdPo+vXr6NevH9LS0sTv1a5dGy1btkS1atWQkZGBV69eITg4GHl5eVK9Kl1dXTFlyhSkpaWJp+43NjYWfP5lZGSge/fuuH37tvg9R0dHNG/eHObm5khKSkJAQACio6ORlZWF77//HqmpqZg3bx5nnL6+vlIzKlhYWKBjx44wMzPDmzdvcOPGDdy5cwf9+/dHvXr1BOWzInr79i1+/PFHJCUlwdDQEJ06dYKVlRU+fPiAa9euISMjA4WFhVixYgXy8/OxatUquXFW9HO1iDquK/Xq1cOTJ08AABs3bsTo0aOhr6+vVFzFTZ06VaoXoKamJlq0aAEnJyfo6enh48ePCAoKEvdA5uq5/Pvvv2PWrFni/3V1deHt7Y3atWsjKSkJ165dQ2JiIgoKCrBu3Tq8ffsWx44dU1uPXknv379H9+7dxdcRbW1ttGjRAvXq1YOBgQEyMjIQGRmJ4OBgucuA3LhxAz179kRmZqb4vZYtW6JBgwbIzc3FnTt3EB4eDuDzjBBt27bFrVu3UL16dc4416xZg1mzZolHI4tEIri7u8PNzQ1GRkZITEzE06dP8erVKwDc30GR9PR09OjRAyEhITAwMEC7du1Qq1YtpKWl4dq1a4iPjwcA7NixAy4uLlL3DS45OTno168fHj58CC0tLbRu3Rr16tVDdnY2Hj16pPZjVOTq1auYOHEiCgoKULt2bbRq1QomJiaIiIjA9evXkZ+fj6ysLAwZMgQhISG8MyscOXIEvr6+4l6senp68PLygoODAzQ1NREaGorAwEDk5+fjzp07aNWqFe7fvw8rKyu5+SSEEEIIIYSQikBypHCNGjU4w6m6Diw+Ph45OTkAADs7O7i5uaFmzZowMDBAeno6Xrx4gUePHoExhqCgILRr1w5BQUGwsLBQ0SevWObMmYPNmzcD+Dw9ddOmTSESifDo0SOEhIQAAEJDQ9GpUycEBAQIWvf69OnTmDZtGoDPdVrt27eHkZERXr9+jdu3b6OgoAApKSkYPnw4dHR00K9fP864bty4gb59+4rrRLS0tNC8eXO4uLhAW1sbkZGRuHXrFrKzs/Hq1Su0bt0agYGBgtoffvjhB/Fnb9SoEdzd3aGtrY3Q0NASddtCSNaXxsfHY/fu3YLreuV59uwZunbtKq5jBD7/blq3bo3q1asjOzsb4eHhePz4MbKysjjrZ+Li4tCmTRtx3QfweXlDT09P6Orq4vnz5+KZAF+/fo2OHTviwoULSo/OLuLv7y/+u3bt2qWOr6IYNWoUTp8+Lf6/Xr16aNq0KapVq4a8vDx8/PgRT58+FdddSjIxMcGUKVMAfF7at6hu8Ouvv4axsXGJ8La2tiXe++GHH7Bu3Trx/xYWFvDy8oKNjQ2ys7Px+PFjhISEgDGG7du3IyYmBmfPnpV7fn/69Ak9evTAu3fvoKenh3bt2sHe3h5paWm4c+eOkEMjZeTIkeLf2qFDh7Bu3TpoafE3ne7fv188W0e3bt1K3CdKc+yJmpVzo3yZEjr6tVGjRuzQoUMqT7/4SHHGGNu/f7/4vV9++aXEPpmZmczExIQBYPXr1xe/L2+keFpaGjMyMhL3tAkLC+PN25MnT8TxmZiYsIyMjBJhJHsraWlpiXvOpKWlSYXLzc3lHUHHR/J7UMVIccYYi4yMlOqN9Ntvv0ltz8rKkupVNGDAAM64JM8hHR0dBoA1bdqUvXr1SipcQUEBW7lyJROJROLwP//8M2e8CxculOqpd+jQIZnH8MiRI1Kfhes8fffuHbO0tBSHc3R0ZBcuXJAZNjExkW3evJnNnDmzxLbiPRSF+vrrr8X71a1bl12+fLlEmPz8fLZp0yZxr0xNTU0WEBAgM77iv90ff/yRZWdnS4UJCwsTz5BQ9N1w/T4UUdYjxYvyPmLECJacnCwVLjk5mQ0bNkwqP/7+/jLjrCznqrqvK5K9CAGwxo0bswMHDpSIX1GSM2sAYEOGDOGcieLp06ds6tSp7OLFiyW2BQQESPUk79GjB4uNjZUKk52dzWbNmiWV3po1a0qVf8aE9dCeNm2aeHu7du1YdHS0zHB5eXns+vXrzNfXV+ZI9sTERKnR3HXr1pXZm3fv3r1MX19fHK5v376c+T979qzUedupUyf24sULmWHfvHnDFi5cyHbu3Flim+Q5UnQ98vPzY58+fZIKl5GRwYYPHy4Oa2RkxNLT02WmJ/n7Kzqvvb29ZV4/iq5l6jhGkuUOXV1dZmhoyPbs2VNi9oaQkBCptMeMGcMZZ0hIiNQMDNOnTy9xrBhjLDw8XGp2ip49e3LGSQghhBBCCCHqpshI8by8PKlZ7AYOHMgZVtV1YKdPn2YrVqxgr1+/5kzzzZs3rHv37uJ0x44dyxm2Mo8U19bWZgCYhYWFzNn3zp07JzV7X4cOHThnK5R8PtbR0WEaGhps9erVJeqaXr58ydzd3cVhLS0tOWdpi42NZVZWVuKww4cPlzlT2ocPH1j//v3F4Ro1aiSz7kTysxfVFdWqVYvduHGjRNji9aJCpKSkiNsZio7v9OnTWUhIiMJxFY/XyclJ6pgdOHBA5neRnp7O9u3bx1nv0LNnT3E8BgYGbN++fSXC3L9/n9WpU0ccrlatWkrPWltEcia8wYMHlyouWcpjpPjjx4+l6rDOnTvHGVd4eDhbtmwZO336tMztxWdaEGLbtm1S6f/9998yR4L7+/tL1UmtXLlSZnyS9XdFdW2DBg1iHz9+lApXUFDAcnNzBeVRkuQ5JWQWxcaNG4vD79+/X2qbKo89UT1qFOd5jR49mncaWEXJahSXbPR2cnIqsc++ffvE+yxfvlz8vrxGccYYGzdunDjM/PnzefMm2fAxceJEmWEkL8wA2Lhx4wR+cuEk4/fx8WFTpkwR9OJrxGOMsQMHDkjd8B88eCDe9u2334q32drayqzcL1L8HLKzs2MJCQmc4ZctWyYOq6enJzPuiIgIcUHH2NiYs1GnyNWrV8Vxurq6yixg+Pr6isPY29uzDx8+8MbJRZlG8Rs3bkgdn+INfMVt375dHL5Hjx4ltufn5zM7OztB511CQkKJaZQrW6M4ANarVy/OBuCCggKpB58WLVrIDFdZzlV1X1eSkpKYg4NDieu7trY2a9GiBfv222/Z9u3b2cuXLwXHmZiYyIyNjcVxTZo0Sen8tW/fXhyPl5cX7zRBU6dOFYc1MTERNNU9HyEPo5JLcfA9kMvz888/i+MxMzNjb9++5Qx7/PhxqXz9999/JcLk5eVJfa99+vRR+n5dvOPE8OHDOcNmZWVJVYwcPHhQZrjiv79GjRqxzMxM3nyo+hgxJl3uEIlEvFP4nzlzRqrAznU8O3XqJA63bNky3s+Unp7OXF1dxeHv3LnDG54QQgghhBBC1EWRRvHFixdLPXPJ6uTOmOrrwBSRm5srbpTR09NjiYmJMsNV5kZx4PMSgrdv3+aM88aNG1Id5s+ePSsznOTzMVBy0JSk+Ph4Zm1tLQ47a9YsmeG++eYbwfVZ+fn5rGPHjrz1CcU/u4GBQYnBLaW1du3aEnVkAJi1tTXz8fFhy5YtY5cuXVJoMMn8+fPF8ZiamipUxybJ399fKk+nTp3iDBsRESE1GGfJkiVKpVmkqJEVAFu8eHGp4pKlPBrFN2zYIN4ur21IHkUbxVNTU5mZmRkDPnfw4Kq3KvL8+XPxMp0WFhYyB2wWr7/r1q2b0gMzZVmwYIGgukHGPg8YkaxDK55fVR57onpfXKO4o6Mj++mnn9iVK1dYTEwMy8nJYenp6ezZs2fsjz/+YLVr15b6cZWmsaM4WY3ijDE2duxY8fvFewkWNX5paGiwd+/eid8X0ih+7949cRhbW1vOdWhzcnKYhYWFOCzXWhySF2a+wlZpyLopC3kJabCV7Lnp7OzM0tPT2b///ivVYHD16lXeOIo3dGzfvp03fG5urlTDzbp160qEmT59unj70qVL5X4OxphUo+jDhw+ltkVFRUndyPkaQeRRplHcx8dHvM+ePXvkhi8sLGT169cXfwfFe3edPXtWqjAo77yT7IUmtBDBp6wbxYXM7PDq1SupAn9QUFCJMJXhXGWsbK4rr1+/Zo0aNZJ7HbG2tmbTp0+X2/j722+/Sf0ulOmdy9jnAp9k+rKOj6T09HSpGSA2b96sVLpFhDyMSvb0LT5zgVCFhYWsZs2a4niEjHKX7Bk8bNiwEtsPHjwo3m5oaFjiuqEIyUK1jo6O3EqM2bNni8PPmDFDZpjivz++HqGMqecYMSZd7uAbUS4rD0+fPi0RJigoSOo+KmR9e8lOad9//73c8IQQQgghhBCiDvLqZ1JSUth///1XYoa+6dOnc8ap6jowRa1cuVKcPtcow8reKP7111/LjVdycJCPj4/MMJLPx3Xq1JHbsX7r1q3i8JaWliXCx8fHi0f+m5qaCmpEDgwM5H1GL/7ZZ8+eLTdOZSxfvlxq1kJZLy0tLdapUye2Z88e3mf/7OxsceMnAPb3338rna+hQ4cKrsNgTPr8t7a25pwlQJ6UlBSpzy6rTrS0yqNR/Ndff1XZZ1K0UXzdunXi8HwzWUiaOHGieJ9jx46V2F68Ufz58+eKfgxer169EsdtYGDA+5ueM2cO7zVKlceeqJ7ii09UYj4+PggLC8OKFSvQuXNnWFtbQ0dHB4aGhnBzc8MPP/yAkJAQ9OnTR7zP5s2bcfPmTbXmS3LtjqL1mwEgNjYWV65cAQB06NABtWrVUijeFi1aoEmTJgCA6OhoXLx4UWa4U6dO4dOnTwCAxo0bo3nz5nLj7tatG8zNzRXKT3nbuHEj6tatC+DzWjPffPMNvvnmG/H2WbNmoVOnToLj09XVxZAhQ3jDaGtrY8SIEeL/r127ViLMuXPnxH8PGzZMUNqS+bx165bUtitXriA/Px8A4OTkhB49egiKUxXy8/Nx+fJlAJ/X0Bk4cKDcfUQiETp27AgAYIwhICBAarvkMevdu7fc827IkCHQ1dVVNOsVRps2bcTnKRdnZ2e0atVK/L+s80pSRT1Xi1PXdaVevXp4+PAhtmzZgqZNm3KGi42Nxbp16+Dm5ob58+eL14Up7sKFC+K/x48fr/T5JnmM3d3d4eHhwRve0NAQw4cPl7m/utSuXVv8919//aVUHC9evMCHDx8AABoaGvDz85O7z7hx48R/X79+vcR2ye9g+PDhsLS0VCpvxbVt2xY1a9bkDSN5DglZ98fc3Bzdu3fnDaOOY1Tc4MGDebcXrcdeRNZnk7wGDB48GJqamnLTVeQaQAghhBBCCCFlYcmSJRCJRFIvU1NTeHt74+DBgwA+r327cuVKrF27VmYc6qgDKy45ORkXLlzAmjVrMG/ePEydOhXfffed+HXp0iVx2KCgICEfvdIRsua15DP09evXwRjjDT9ixAi56wUPGzZMXN+TkJAgXr+8yJUrV8Rrv/fp0wdGRkZy8+np6QkDAwMAwp6Phda9KWru3Ll49uwZxowZI3NtaODz+e3v749Ro0ahcePGePr0qcxwd+7cQXJyMgDA2NhYUH0GF8l6Lsk6ey5jxowRrz0dGxuLV69eKZVu0VrZRYR8l5WBZJ3erl27kJGRUWZpq7sOuXHjxnB1dVUucxycnZ3F7WKZmZk4efKkzHCMMRw4cED8/8iRI0uEKc9jT+Tjv/pXMWZmZnLDGBsb4/Dhw2jSpAlCQ0MBACtXrkS7du3Ulq927drB0dEREREROHToENavXw8dHR3s3bsXBQUFAIQVAGQZP348pkyZAgDYtm0bevXqVSLMtm3bxH9LVrLzadasmVL5UcS1a9fQoUMHlcVnbGyMffv2oW3btsjPz8fhw4fF2zw8PLBs2TKF4mvUqBEMDQ3lhpNsvHz8+LHUtk+fPonPMwBYu3YtRCKR3DifP38u/vv9+/dS2+7cuSP+W5XHT4gnT56IL/J6enqYNWuWoP3u378v/rv455Es1Ht6esqNy8jICA0bNsTDhw8FpV3ReHl5CQrXqlUr8cNT8fOquIp6rhanzuuKtrY2xo8fj/HjxyMiIgLXr1/HnTt38OjRIzx58gS5ubnisHl5eVi+fDmioqKwa9euEnHdvXtX/HfRw6wyJI9xmzZtBO3Tpk0bbNiwAQDw6NEjpdMWaujQobh69SoAYP78+bh06RJ8fX3RtWtXODg4CIpD8nPWr18fFhYWcveRPB4fPnxATEwMbGxsxO9JXudK8x0U16hRI7lhJPOfkpIiN3yTJk3ED2lc1HGMilPFZwsMDJT6+7vvvpMbp2RlhLxrACGEEEIIIYRUBJqamli5ciXGjh3LGUYddWBFoqKi8NNPP+Ho0aPihld5EhISBIWrTEQikaC6QE9PT4hEIjDGkJycjMjISDg6OnKGF1L3Vrx+8fHjx+LBZ4D083FoaKig52MA4rq0pKQkZGRkcNbXaWtrC3qOV5aLiwu2b9+OTZs2ISAgALdu3cKDBw/w6NEjREdHS4V9/vw5Wrdujdu3b6Nx48ZS2yTrZ7y8vKCvr69UfqKjoxEfHy/+v3Xr1nL3qV69OpydnfHy5UsAn+vJ6tevr3DaxTsGpKenKxxHRdSrVy8YGRkhPT0djx8/houLC8aMGYPevXujWbNm0NbWVlvakr+P3bt3czYwS4qKihL/XV51yCNHjsSDBw8AAHv37pXZ4H3jxg28e/cOAGBtbS1zkGV5Hnsi3xfVKC6Uvr4+5syZIy74XLt2DTk5OWobfSoSiTBy5EgsXboUSUlJ+PfffzFw4EDs2bMHwOfRgUJ6G8oycuRIzJo1C5mZmfj333/x8eNHVK9eXbw9KipK3KtRV1cXvr6+guKVjKMy8fT0xOLFi7FgwQLxewYGBti/f7/CFyPJHj98JEf4f/z4UWpbbGys1P+bNm1SKA/A50KUpLi4OPHfderUUTi+0oiJiRH/nZ6ertTI0uKfR/KYKXLMK2ujuCrOK3XEqY5ztbiyuq44OjrC0dERY8aMAQBkZWXh+vXr2Lp1K06cOCEOt3v3bnz11VcYMGCA+L3U1FRkZWWJ/y/Nb0zyGNvb2wvaR7IhuiweeMeOHYtLly7h6NGjAID//vsP//33HwDAxsYG7dq1Q6dOndC/f3/O70+Zz2llZQU9PT1kZ2cD+PxZJRt81XWdMzU1lRtG8l6Rl5cnN7yQ81odx6g4VXw2yWu8v78//P39BeW1iLxrACGEEEIIIYSUhRYtWqBly5bi/9PT0/Hu3TsEBAQgJycHBQUFGDduHN68eYNff/1VZhzqqAMDPje+du7cWeHnp+KjXasCc3NzQaN2TUxMYGpqKh6x/PHjR95GcWXqF4vXk0l+//fv35fq7CBUUlISZ6O4ubm53NHsqqCnp4dOnTpJNey9efMGR48exbp168T1genp6Rg1ahSCgoKkBsmoqn5G8vjq6+ujRo0agvZzcHAQN4orW09mYmICLS0t8cyrRedRZVetWjXs2LEDvr6+yM3NRXR0NJYtW4Zly5ZBX18fLVu2hLe3N/r166fSRub09HSp61FRG5ciyqsOefjw4Zg5cyby8/Nx5coVxMXFwcrKSirMvn37pMLLmkWxvI49EeaLmj5dEZ07dxb/nZmZibdv36o1veJTqD9+/Fg8LcmAAQOUnrbDxMQEQ4cOBfC5grv4RWjHjh3iKYIHDBiAatWqCYpX2V5fFUHxC1mDBg3g5OSkcDxF093II1m4ycrKEo/+B4SNMpSn6IZdRPKmU9bTvajj80j2zlPmmJdW8QKo5IhiISR79ArpeKHMZ5T34FNRz9Xiyuu6oq+vj549e+L48eM4cuSIVGFm/fr1UmFVOaWS5Lkt9JxV5HtXBQ0NDRw+fBg7d+4s0Rs4JiYGhw4dwsSJE2FjY4Nx48YhMTGxRBzKfM7iYYt/VnVd54TMfqAoIee1Oo5Rcar4bKW9DkheUwghhBBCCCGkvPTq1QsbN24Uv3bu3Al/f39ERkZKLVu2fPlyHDp0SGYc6qgnycnJwcCBA8UNQlZWVli0aBGuX7+O9+/fIyMjA4WFhWCMgTGGHTt2iPflWgJO1STryUpTRyakwVdofRag/nqy4nGqu56sPOve69Spg9mzZ+PFixdo3769+P0nT56UWL5NVfUzZVEvwkdygILkrJeV3aBBg/DgwQMMHjwYOjo64vezsrLw33//4ZdffkHz5s3RvHlz3LhxQyVpVuY65Bo1aqBLly4APtdhFb/+5+bmigcOAbKnTi9SHseeCEON4hysra2l/i9ac1td6tWrJ54W5Pz581izZo14m7JTpxeZMGGC+G/JqdKLF574pgSqKl6/fo3p06dLvXf//n2sXr1a4bgyMzMFhZNcM0JfX1+qwU3yxm1mZiYu1Cry2rlzp1R6klO+lPV0L5Kfp0mTJkp9nsWLF0vFKVmgUuaYl1bx0ZWKHlPJ8EKWcFDmM3Kt/1OaOMviXK2IBg0aJLVuUWBgoFRBTJVTKkme20LPWUW+d1URiUTw8/NDcHAwwsLCsG3bNvj5+Un1AM7Pz8e2bdvQsmXLEr2nlfmcxcMW/6zleZ1TB3UcI3WQvA6cPHlSqesAIYQQQgghhFRUNWvWxN69e9G9e3fxe99++63MUYvqqAM7duwYIiIiAAB2dnYIDg7G4sWL4e3tDTs7OxgYGEh1eC6P0eGS9WQVpY4MUH89WfE4Jb//devWKfX9C12WrryYmppi7969UvWDN2/elAqjqvqZ8q4Xadu2rfhvyWUTKzKhHWEaNWqEw4cP4+PHjzhz5gxmz56NVq1aSQ3eevjwITp27IgjR46UOl/FOzUkJycr/Nso3vmiLEk2dEuOCgeAs2fPiu8Hbm5uaNq0KW9cZX3siTDUKM6h+MVXlSNPuRQ1fufl5Yl/cLa2tjLXJVCEl5eXeA2S58+fi9f6uHbtmrig5ejoWOp0Krq8vDyMGDFC/N1KrjGycOFChafbLlo7QpFwlpaWUtskR60nJyfLnQZbCMk4i77fsiKZ9ps3b1TSS1VyOhShx1yV69YaGxtDT09P/P+bN28E78sYk/oOhEztoorzSh1xquNcrah69uwp/jsvL0+qU5SJiYlUb8TS/MaUObclZy2R972rQ926dfHNN99g586dCA8PR2hoKGbOnCnu4R0eHo4lS5ZI7aPM54yPjxdPCw7wn49lfZ1TB3UcI3WQPO6vX79We3qEEEIIIYQQUtY0NDTwzz//iOuCExMTsXz58hLh1FEHdvXqVfHfP/zwQ4nZLotT98ymskg+vypSRwZ8rjOQFQ+XpKQkQQ3/qampUqNTy7qerCo/H9eqVQsNGjQQ/198eUVV1c9Ing9ZWVmCp0JXVT2ZZLvI27dvERAQoHRcypJsKJU3UhpQfES2iYkJevfujZUrVyIgIAAJCQnYuXOnuHNGYWEhvv32W6llI5VhZmYmtQxxZft99O/fX9xJ4969e1L537t3r/hvvlHixZXVsSfCUKM4h8ePH0v9X3zkuDoMHTq0xLrlI0eOhIZG6b+m8ePHi/8uGi0uOWr8m2++Ucu0sRXJzz//jAcPHgD43PPz5s2b4jXU8/Ly4Ovrq1APxKdPnwrquVbUCQEAPDw8pLZZW1tLrWNz6dIlwelz8fLyEv997dq1UsWl6DnRpEkT8TmcmpqKwMDAUqVfFGcRyWPJJT09HSEhIaVOV5Lk91Z0Dgnx8uVLqXNEyBohQo8Z33lVXEU9VysqyU4QAEpclz09PcV/K7qmsiTJ3oRCC9u3b98W/y3vey8LTk5OWL16NX755Rfxe6dPn5YKI/k5X758KXOK9eIkP2fNmjVLrJUteZ0rzXdQUajjGKmD5Ll/8eJFtadHCCGEEEIIIeXBzs5OaqbJjRs34sOHD1Jh1FEHJrlOtWRDJJfymHJXsm4rNDRUodHqkgOShNSRMcYEjdq9e/eueGYyMzMz3vXEAWF1b8XrF4vXwXxJz8eS9WTF68gk62cCAwOVbtSztbWVWkdcSD3Zp0+f8OrVK/H/paknGzx4sFSj+h9//KF0XMqSHOkuZNbioqV3lWViYgI/Pz/4+/uLv9eEhASZvw9F2whatmwp/ruy/T4MDAzg4+Mj/r9o8GpKSgrOnj0L4PPxGDFihNJpKHLsiepRozgHyWnFXV1dBfVeKy0zMzP07dtX6r3STp1eZNSoUeLRjYcOHUJ0dDROnDgBANDU1MTo0aNVkk5F9d9//2HVqlUAPl+0du7cCUtLS2zatEncI+fVq1clplbnk5OTg8OHD/OGycvLw/79+8X/d+zYsUSY3r17i/8umm6nNLp27Soetfn69etS3XgkCz15eXlyw+vr60v1rFu7dq3SaReRPGbnzp2T21h06NAhqTWKVEEyD0ePHhW8Lq7kd29nZ4e6devK3ScgIECq56wsoaGhUjdJWeeVpIp6rlZUQUFB4r+NjY1LTOklOZJ869atSp9vkr+Vx48fIzg4mDd8VlYWDh48KHP/8tanTx/x33FxcVLbXF1dUbNmTQCf1+OR7FXJRfIeLOtclPwODh48KLgHcUWljmOkDpLf89WrV0v9AEYIIYQQQgghFdWMGTPEjVTZ2dklll5URx2Y5MAoeQN3Hj58iPv375c6TUV5eXmJ6wvz8vJw/PhxQfulpqaKG5QAoEOHDoL227Nnj9wwksv1dejQQW4D3oEDB+SOxD1w4IB4zXRLS0s0bNhQanv37t3F9a9hYWE4c+aM3HxWRjk5OXj58qX4f8kBM8Dn88Hc3BzA5+n8d+/erXRaknUbQpZg3LVrl3iGBhsbG7i4uCidtr6+PqZOnSr+/9ixYzh27JjC8WRkZCg9ylyyM4dk3aQs2dnZ+Pfff5VKR1a6kp1witfrAYq3EUjWH23evFlqpsPKQNYU6keOHBHXAbdr105qHXplCTn2RPW+mEZxRda0OHHihNR6AYpMhVBaGzduxP3793H//n08evQIbm5uKonXzMwMgwYNAvD5BjV48GBxz63u3bvDzs5OJelURMnJyRg1apT4Jjl16lTx2kAmJiZSa6Ns3boVJ0+eFBz3woULeXturVq1CpGRkQA+96STdS7NmDFDnP6DBw9KTD3Mp3gvVeBzIWDo0KHi/ydOnKj0BdXMzExcII+Pjxd005szZ47472PHjim0jrSsz9O9e3fY2toC+PxAIBl/cZ8+fcKiRYsEpyfUhAkTxN9ReHg41q1bJ3ef8PBwqQeib7/9VlBajDFMnTqVc9qtwsJCTJ06Vdwg3bx5c7i7u8uNtyKeq2Xhhx9+kOo1Kk9cXBz+/PNP8f89evQoEWb8+PHiaXTevn2rUGcaSfXr10f79u3F/3///fe8v7GFCxciPj4ewOdrV2l6JAoltLFZckqx4p3IRCIRJkyYIP7/l19+QXR0NGdcZ8+elSrYT5o0qUSYAQMGiAuf6enpGDNmjKCppSoqdRwjdWjZsqW44oIxhpEjRyI1NVXQvrm5uTLX4SOEEEIIIYSQisjc3Bzff/+9+P/NmzeXeEZWdR1YnTp1xH+fOnWKc9/MzEypZ8iyZGxsLFVntHDhQkHPenPmzBHPYmhubo5hw4YJSm/v3r28jYw3b97EgQMHxP+PGzdObpxv3rzBmjVrOLcnJCRI1S+OHj1a3ABexNbWVuo4TJo0ifc5XlJhYWG5LEv477//4p9//hFUv1vkt99+k3ruL15PpqurK1XnOWfOHIXq4SRNnDhR/PeJEydw4cIFzrDv37/HsmXLpPYt7Sy4s2fPlhptPmrUKIUanp8+fQpPT0+lZ9eUHF195swZ3jq5n3/+WW6dndA6vfz8fKlp8WUNDrWwsBD/LeQ8nzhxoniQUVRUFL799lvBg6sSEhIED0hTly5duogHr4SFheHu3bsKtReq8tgTNWBfiB07drCWLVuyPXv2sJSUFJlhUlNT2S+//MK0tLQYAAaA1apVi6Wnp6skD/b29uJ4z58/X6q4PD09xXHt2LFD0D43b94U7yP5OnbsmKD9/fz8FE5TUZL5unbtmkriHDJkiDjOhg0bsqysrBJhFi5cKA5jYWHBYmJiZMa1Y8cOcTgdHR0GgHl4eLDQ0FCpcAUFBWzVqlVMJBKJwy9YsIAzj4sWLZL67H5+fuz9+/cyw+bn57PLly+zUaNGMWtra5lh3r17xywsLMTxOTo6sgsXLsgMm5SUxP73v/+xWbNmydxev359cTyHDh3i/AySJM8VkUjEZs6cyRISEmSGzc7OZidPnmQ+Pj6sRYsWMsNs27ZN6vjMnDmT5eTkSIUJDw9nzZo1k/puVHmuTps2TeozLViwgKWmpsoMe+bMGWZjYyMO7+DgwJKTkznjljxeRXn39fUtsU9ycjIbPny41LG4fPmyzDgry7mq7uuKlZUV09DQYL1792aHDx9maWlpMsMVFhayc+fOsbp164rzo6GhwR48eCAz/F9//SV1HIYMGcJ5HEJCQtjUqVPZxYsXS2wLCAhgmpqa4nj69u3L4uLipMLk5OSwefPmSaW3Zs0aBY9ESdeuXZOKUxZ9fX02fvx4du3aNZafny8zTGBgIHNwcBDHM378+BJhEhMTma2trTiMi4sLe/z4cYlwBw8eZIaGhlLHg8uZM2ekzttOnTqxFy9eyAwbERHBFi5cyHbt2lVim+Q5vWjRIs70ikgeN29vb5lhJH9/fn5+cuNkTD3HSLLcERERITcPQn6PT58+ZUZGRlL55LoOMcbY69ev2bJly5iNjQ37999/5eaBEEIIIYQQQtTB29tboWc/xhhLSEiQev6ZO3duiTCqrAO7cOGCVFy///57iWfx169fszZt2jAAUs+GXM+eQp5hFRUREcHMzMzE8TZo0IDdunVLZtj4+Hg2duxYqfqH9evX88ZdFE5bW1tcVyurXvP8+fPM3NxcHL59+/assLBQZrySz8c6OjpMQ0OD/f7776ygoEAq3KtXr1iTJk2k6omL19MUiYmJYdbW1uKw1tbW7MiRIyXiLBIVFcXWrVvHnJyc2IYNG3g/u729PecxUtbff//NADA7Ozs2f/589uzZM86wcXFxbOrUqVLfW79+/WSGTUlJkapPs7S0ZAcOHJD5XWRkZLD9+/ezMWPGyIyrZ8+e4niMjIzY4cOHS4R59OgRc3JyEoerVasWS0pKEnYQ5AgPD2c1atSQqhscNWoUe/78uczwhYWF7N69e+zrr79mGhoanNcXIfUthYWFUsexS5cuLDExUSpMRkYGmzVrFgPAdHV1eX//o0ePZm3btmU7d+4sEU+R+Ph4NmrUKHE8JiYmLDMzs0S4SZMmicNMnjxZZlzFSdaNAWC9e/fmrLcrLCxkAQEBbMqUKczAwEBm/a2i9XelNX36dHF6/fv3F3+/urq6cs83VR774seRlJ50F6cq7t69exg1ahS0tbXh6uoKFxcXmJmZIT8/H+/evUNgYKDU1DTm5uY4d+4cDA0NyzHXqtO2bVu4ubnh+fPn4vdq1KhRYsr2imL9+vU4evSo4PDu7u5Sa6cDn6daKZo2WldXF/v37y+xXjDwuXfV5cuXcefOHXz69Al+fn64ePEibw+zQYMGITw8HHfv3oWrqyvatWuHevXqIT09HTdu3JBaB6hVq1ZYsGABZ1yLFi1CZGQkdu3aBeDz9C979+5F06ZNUb9+fRgZGSE1NRVv375FcHCweOYDyV5akmrVqoVDhw7Bx8cH6enpiIiIQI8ePWBvb4+WLVuiWrVqSE9PR2hoKIKCgpCXl4evvvpKZlwDBw7Er7/+CuBzL6hdu3ahXr160NbWFof5/fffpfb53//+h9jYWFy6dAmMMfz+++/4888/0aJFC9StWxf6+vpISUlBeHg4nj59Kp5ChWs9oW+++QZnz54VT8n0+++/Y+fOnejYsSNMTU0RERGB//77D/n5+WjRogWcnJykpgJXhVWrVuHJkye4du0aGGNYtmwZ1qxZAy8vL9SuXRu6urpISEjA3bt3pXrMmZmZ4ciRIzA1NRWUzrx587B+/Xrs27cPp06dQqdOnWBlZYW4uDj4+/tLzXrx448/okuXLnLjrMjnalkoLCzE2bNncfbsWWhqaqJBgwaoV68eLCwswBjDhw8f8ODBgxK9tFetWsV5Tn777bcICQnB33//DQA4fPgwjh07hhYtWsDZ2Rl6enr4+PEjHj9+LB6BL2uK61atWuG3337DrFmzAHzutVu7dm107NgRtWrVQlJSEq5fvy7Vw7B///744YcfVHFo5MrKysLWrVuxdetWGBsbo0mTJqhduzYMDQ2RkJCAly9fSt1TqlevjsWLF5eIx9zcHPv370fPnj2RmZmJV69ewcPDA56ennBzc0Nubi7u3r2L169fi/dxcnLCtm3bOPPWu3dvrFixAj/99BOAz2uLu7m5wd3dHQ0aNICRkRESExPx5MkTcS9lVUxnpy7qOEbq0LBhQxw4cABDhw4V57Nr166oVasWWrRoAUtLS+Tm5uLjx48IDg5GVFRUmeaPEEIIIYQQQlTFwsICU6ZMwcqVKwF8nuFz1qxZ4umiAdXWgXXv3h3e3t7477//wBjDzJkz8ddff8HDwwOmpqZ4/fo1AgICUFBQAFtbW0ybNg2zZ88um4MhwcHBAXv27MGgQYOQk5ODZ8+eoW3btqhTpw6aNWuGatWqITMzE2/evMG9e/ekRiaPHDlSappqPjY2NhgwYADWrl2LHj16oHHjxmjatCkA4NGjR1JLetWoUQPbt28XNFp41apVmD59OmbOnIk//vgD7du3h5GREV6/fo1bt26JR6lqampi69atUmtdS7K2tsapU6fQq1cvJCQkIDY2FoMHD0aNGjXg6ekJKysrFBYW4tOnTwgJCcGbN28qxFKEUVFR+PXXX/Hrr7/CwsICHh4esLKygpGREdLS0hAaGopHjx5JjdZ1dnbG5s2bZcZnYmKC48ePo2vXroiPj0dCQgKGDx+O6dOno3Xr1qhevTqys7MRHh6OR48eISsri3PWyx07dqBNmzYIDw9Heno6hgwZAicnJ3h6ekJHRwcvXrzAnTt3xMfR0NAQBw4cKLH0obLq1KmDu3fvom/fvggJCUFhYSH27NmDPXv2wMHBAY0bN4alpSUKCgrw4cMHBAUFlZihVXJtcEWIRCKsWLECQ4YMAQBcuXIFjo6O6Ny5MywtLfHhwwfcuHEDycnJsLGxwZQpUzB//nzO+BhjuHXrFm7dugVNTU24uLjAzc0N5ubmyMrKQlRUFAICAsTLBACf69yLluCVNHDgQPH3//fff+PRo0fw8PCAgYGBOMzkyZOllg4dPXo03rx5g6VLlwL4POvhuXPn0LBhQzRs2BAmJibIyMhAdHQ0Hj9+jOTkZKWOm7qMHDlSPFts0TLEwOf6SHnnmyqPPVGDcmuOL2PFe1TIe3Xq1IlFRkaqNA/lPVKcMcbWrl0r9TlnzpwpeN+yHimu6Ourr76SiissLEyqN+fatWt50w4PD2fGxsbi8LJGYhYf/RcTE8NatWrFm69u3bpxzk5Q3IYNG6R6OPK9RCIRZw+9IkFBQczd3V1QfL6+vjLjSElJYW5ubrz7ypKfn88WLlzIDAwMBKWvra3NpkyZwvlZcnJy2IgRI3jjaNmyJYuKilLbuZqbm8t+/PFHqZHofC8vL68SI7NlKZ7fgIAAqZHmxV8aGhps1qxZnL1fGas856q6ryvffvut4HwWvezs7NjRo0cFxb9u3TpmYmIi6BjIGile5J9//pEbj6amJps2bRrniG1FCRkpLnkNlfdyd3fn7PFZJDAwkNWpU0duXF26dGHx8fGCPsfBgweZlZWVoDxu2bKlxP4VZaR4EVUeI3WMFC8SFBQknp1DyMvBwUHmyHdCCCGEEEIIKQvKjBRn7PNoPskR2bL2VWUd2IcPH5iHhwfv/m5ubv+PvfuOiuJ6+wD+XTpLF5EiRbGh2BXsitgx9kKIvcaosRtbYo8xib3HEruCvUaNioJGVFQsKHaU3nuH3Xn/4Lfz7sBW2GVBn885e9hl79y5Mzvlzq3My5cvFXr2VEdPcZEHDx7ILTMUvYyNjZk1a9bILMtimNK9pQsKCphJkybJjLtu3brMixcvZMZb8vn4r7/+klm+Z2pqqvCImZ8+fWK6deum8POxtbW1xJ7v6u4p/t9//zFubm6cUfcUeY0cOVKhMppPnz4xnTt3VijODh06SI0nLi6O8fT0lBtH3bp1mYcPH6pyF7EyMzOZlStXckZEkPdq1qwZc+bMGYnxKVPesmLFCpnradCgARMaGir3/J8+fbrCaTcxMZFYZiZu5MiRMuOQNuqvn5+fzHLuki93d3cmLy+vVDwV3VOcYRimYcOGpdIn7TcWp8p9Tz3FVe+r6Snu4+ODevXqISgoCEFBQfjw4QOSk5ORnJwMoVAIc3Nz1KlTB+3atcO3336L1q1bazrJajFkyBBOD8MJEyZoMDXqU1RUhBEjRrC9VHv27ImZM2fKXMbZ2Rlbt27F2LFjART32O3WrZvM+ZptbW0REBCAI0eO4OjRowgLC0NSUhKqVauG1q1bY+zYsRgyZIjC6Z4+fTrGjBmDw4cP4/r163j27BkSExORl5cHExMT2Nvbw9XVFR4eHvDy8oKDg4PM+Jo1a4aQkBCcO3cO586dQ1BQEOLj45GdnQ1TU1M4OzvD3d0d/fr1Y+dZL8nU1BQPHz7Ezp07cfHiRYSFhSEtLU3u/DPa2tpYuXIlfvzxRxw6dAg3btzAq1evkJSUhMLCQpiamsLJyQlNmjRB165d4eXlJXPeDD09PRw9ehSjR4/Gnj17EBQUhKSkJFhaWqJBgwb47rvvMGbMGOjp6cnf0WWkq6uL9evXY86cOTh8+DBu376NsLAwJCcno6CgABYWFrC3t0eHDh0wcOBAeHp6lmk97dq1w7Nnz7B7926cOXMGnz59QlZWFmxtbdG1a1dMnTpV6WtUZT9W1WX79u3YvHkzgoKCcOfOHTx69Ahv3rxBbGwsMjMzoaOjwx6LzZs3R9++fdGnTx+Fj6OZM2di5MiROHDgAK5du8Ye4wBQvXp1NGzYEF26dIG3tzfq1asnNZ4JEyZgwIAB2LNnD65cuYK3b98iJSUFJiYmcHBwQPfu3TF+/Hg0atRIJftFUcnJyQgMDERAQACCg4Px7t07xMfHIy8vD3w+H/b29mjVqhWGDBmC/v37Q0tLS2Z8bdu2RVhYGI4cOYJz587h6dOnSEhIgK6uLmxsbNCxY0f4+PigZ8+eCqfR29sb33zzDQ4dOoQrV66wx6JAIICFhQUaNGiAjh07YujQoWyL8spMHftIHZo1a4ZHjx7h33//xblz5/Dff/8hJiYGaWlp0NfXh5WVFerXr4+2bduiV69eaNeuXbnn9yKEEEIIIYSQimZlZYUffviBHSVx8+bNmDNnDkxNTdkwqiwDs7a2xr1797B37174+voiNDQUOTk5qFGjBho0aABvb2+MGDECfD4fDx8+rJB9II27uztCQ0Nx6dIlXLp0CUFBQYiNjUVaWhoMDQ1haWmJpk2bomvXrhg9ejSqVaum9Dp0dXWxe/duDBs2DPv27cPDhw8RGxsLPp8PFxcXDBs2DFOmTJE4MqgskydPRseOHbFr1y7cuHEDUVFRYBgGTk5O+Oabb/Djjz+iZs2aCsXl5OSEGzduICgoCCdPnkRgYCAiIyORmpoKHR0dWFpaol69emjdujV69uwJDw+PUnOUV4T27dvj4cOHiI6Oxq1bt/Dff//h5cuX+PjxI1JTU5Gfnw9jY2NYWlqiUaNGbD2J+Fz3sjg5OSEgIAA3b97EyZMncefOHcTGxiIjIwNGRkZwcnJCq1at0LdvX/Tv319qPNbW1rh58yauXbsGX19f3L17F3FxcSgsLESNGjXQokULDBw4ECNHjuSMZKpKxsbG+OWXXzBjxgxcvnwZ169fx+PHj5GYmIiUlBTo6emhWrVqcHFxQZs2bTBw4EDOfOTlsXTpUvTo0QNbt27FnTt3kJCQAFNTU9StWxfffvstJkyYAGNjYwQHB8uMZ+vWrZg6dSpu3LiB+/fv4+XLl4iIiGDLQy0tLeHq6oqePXti1KhRUkdEEDl06BD69u2Lo0eP4unTp0hKSmJHvpBl+PDhGDBgAHx9fXHt2jUEBwcjMTERWVlZMDIyQs2aNdmRTb28vFC/fn2l9pc6jRw5ktMb39zcHH379pW7nKr3PVEtHsNUgjE7SIU5cOAAxo0bBwDo0KED7t69q+EUVS3i+2/MmDE4cOCAZhNEvghjx45lhyPfv38/2zCjPOhYJYQQQgghhBBCCCFVyadPn1C7dm0AxZWsoinpyqtWrVr4/PkzACA8PBy1atVSSbyEEEKqFtldusgXZ+/evez7iRMnajAlhBBCCCGEEEIIIYQQQgghhBCiflQp/hV5/Pgx/vvvPwCAhYUFvL29NZwiQgghhBBCCCGEEEIIIYQQQghRL6oU/0rk5eVhxowZ7OcpU6bA0NBQgykihBBCCCGEEEIIIYQQQgghhBD109F0Aoj67Ny5Ex8+fEBaWhquX7+OiIgIAED16tUxb948DaeOEEIIIYQQQgghhBBCCCGEEELUjyrFv2B+fn4ICAjg/E9bWxv79u1DtWrVNJQqQgghhBBCCCGEEEIIIYQQQgipODR8+leAx+PBwsICXl5eCAgIQP/+/TWdJEIIIYQQQgghhBBCCCGEEEIIqRA8hmEYTSeCEEIIIYQQQgghhBBCCCGEEEIIUQfqKU4IIYQQQgghhBBCCCGEEEIIIeSLRZXihBBCCCGEEEIIIYQQQgghhBBCvlhUKU4IIYQQQgghhBBCCCGEEEIIIeSLRZXihBBCCCGEEEIIIYQQQgghhBBCvlhUKf4FyMzMhJ2dHXg8HgYPHqzp5KjE8uXLwePxwOPxsHz5ck0np1K7ffs2u688PDzKHd/YsWPZ+A4cOFDu+EjFq0rnz6dPn9i01qpVS9PJIURpmjrfatWqxa7306dPEsO0a9cOPB4Prq6uKCoqqrC0EVIRvub8iiLnPynm4eHB7qvbt29rOjmEfHVU/axKCCFEPahspuo5cOAA+5uNHTtW08lRWlVPf2X1NT8nE6IoqhT/AqxcuRKxsbHQ1tbGmjVrlFr2zZs32LBhA7y8vODi4gJLS0vo6urC0tISLi4u+Pbbb7FlyxbExsaqKfWEqJd4YaiyL8qUEaIc8cy36LVlyxal4ti2bRudiyryxx9/AABevXqFrVu3ajg1hBQLDAzE1KlT0bp1a1hZWUFPTw+GhoaoUaMGWrVqBR8fH6xbtw4PHjyAUCjUdHIJYYlX7vF4PKWXF2/MQAVUmpWVlYW9e/di+PDhqFu3LszMzKCjowMTExM4OTmhS5cumDZtGg4ePIioqChNJ1dj7ty5g19++QWdOnWCs7MzTE1Noa+vD2tra7Rs2RKTJk3C0aNHkZOTo+mkEkLIV0G8QbaqXoR8KcQr2alhByFEFqoUr+I+f/7MVjh4e3vDxcVFoeXCwsIwdOhQNGzYEHPnzsWVK1fw5s0bpKSkoKioCCkpKXjz5g38/Pwwc+ZMODg4YPDgwXj9+rU6N4cQUoVRy2IiycGDB5UKf+jQITWl5OvTqVMntlfW6tWrkZ6ertkEka/amzdv0KFDB3Tp0gU7d+7E48ePkZSUhMLCQuTl5SExMRFPnjyBr68v5s+fj7Zt28LZ2VnTySb/Q709yZfi0KFDcHR0xKRJk3Dy5El8+PABGRkZEAgEyMrKQkREBAIDA7Fjxw6MHTsWDg4OlX7kJVW7du0aWrdujc6dO2P16tW4e/cuwsPDkZmZiYKCAiQkJCAkJAR79+7FyJEjYW1tjTlz5iAlJUXTSVe5qjQCFyGEkKqLek0TQkjF0dF0Akj5rFq1CgUFBQCABQsWKLSMr68vxo0bh7y8PPZ/2traaNmyJZycnFCtWjWkpqYiOjoajx49QkFBAQQCAc6ePYuLFy/i8+fPsLOzU8v2EKJObm5ucHd3Vzh827Zt1ZgaQr4OT548wcuXL+Hq6io37Js3bxAcHFwBqfp6LFy4ELdv30ZKSgo2bNiAFStWaDpJ5Cv0/PlzdO3alVNhYmVlBTc3N9jY2EBLSwspKSl49eoV3r59y/YQT0tL01CKCSFfotWrV+OXX37h/M/V1RWNGzeGubk5cnNzERcXh5CQECQmJrJhvpZrkVAoxPz587FhwwbO//l8Ptq0aQMbGxsYGxsjISEB4eHhePHiBRiGQVZWFjZu3IirV6/i1atXGko9IYR8+dzd3TFt2jSZYQ4dOoTMzEwAQLdu3RTuPEUIIYR8LahSvAqLiYlhe9R17NgRTZs2lbvMzp07MW3aNDAMAwCwtLTEkiVLMHbsWFhYWJQKn5mZiX/++Qe//vorXrx4gaKiIrYSnpCqxsvLi1r4VzK1atVir0fky9KoUSO2YPTQoUP4/fff5S4j3qtcfHlSdj179kTdunXx/v17bNu2DQsWLACfz9d0sshXpLCwEN9++y1bIW5tbY1t27Zh0KBB0NbWLhU+JSUFFy5cwJEjR/D48WOp8R44cICGoCaEKOzu3bucCvFevXph8+bNaNCggcTwL168wJkzZ/D3339XVBI1bsSIEfD19WU/N2rUCKtWrULfvn2hr69fKnxMTAx8fX3x+++/IyEhgYZRJ4QQNfPy8oKXl5fMMJcuXWIrxUeOHEm9jr9gY8eOpd+XEELKgIZPr8J27NiBwsJCAMDEiRPlhn/w4AFmzpzJVkA1b94coaGhmD17tsQKcQAwMTGBt7c3nj17hr/++gsGBgaq2wBCCCFfrN69e8PKygoAcPToUblzAwuFQhw5cgRAcQ/SPn36qD2NXwMej4cJEyYAKK5sPHz4sIZTRL4258+fR1hYGADAwMAAt27dwtChQyVWiANAtWrVMHbsWNy4cQOPHj2qyKQSQr5ga9euZd937doVly9fllohDgBNmjTBsmXL8PHjR8yaNasCUqhZGzZs4FSIjxo1Cs+ePcPgwYMlVogDgJ2dHebMmYMPHz5g+vTpNDctIYQQQgghpNKjSvEqSigUsr1j9PT0MHDgQLnhR48ezVai16xZE/7+/rCxsVFofTweD5MnT8Z///0HExOT8iSdEELIV0BXVxc+Pj4AgOjoaNy8eVNm+Fu3biEyMhIA8N1330FHhwazUZXhw4ez77+mHm+kcvj333/Z9/3790fDhg0VXrZOnTrqSBIh5CsjFApx48YN9vP8+fOlNswpSVtbG7Vq1VJTyiqH8PBwLFy4kP3s5eWFgwcPKpwXMzY2xtatW/HXX3+pK4mEEEIIIYQQohJUKV5FBQQEIDo6GkBxS3czMzOZ4c+cOYO3b9+yn3fu3Cm1d7gsLVu2hKWlpcww165dw/jx41G/fn2YmprC0NAQTk5OGDRoEPbv389WzKtSYWEh9u/fj4EDB8LJyQmGhoYwNTVFgwYNMGHCBFy/fl2heGrVqgUejwcej4dPnz4BAD58+IAlS5agRYsWsLKygpaWFpo3by5x+ezsbOzcuRP9+vWDk5MT+Hw+TExMUK9ePYwfPx7+/v5KbVdUVBQWLFgAV1dXGBsbw8LCAk2bNsWSJUsQERGhVFzlkZKSgj/++APu7u6wsrKCoaEhnJ2dMXHiRJlDm4aEhLD708LCArm5uQqtLz09HXw+n11W/NjVpH79+rFp+u233xRebuXKlexyQ4YMkRm2os6f5cuXs2lSZEj527dvs+E9PDw43x04cAA8Hg+1a9dm//f582c2fMmXuE+fPrH/V7TA8f79+5g+fTpcXV1hYWEBAwMD2Nvbo3fv3ti2bRuys7PlxiFp+4uKinDo0CF0794dNWvWhL6+PmxtbTFw4EBcunRJobQJhULcuXMHS5cuRc+ePeHo6Ag+nw8DAwPY2dnB09MTa9asQVJSkkLxSdpvz549w8yZM9G4cWNUq1YNPB4PAwcORHx8PPT09MDj8aCjo8PeIxRJs6OjI7seRa+Xihg9ejT7XjTdhzTi34svpwxVnz8PHz7EuHHjULt2bRgaGsLGxgYdOnTA1q1bkZWVVaY0AsDNmzcxZcoUuLq6olq1atDX14ednR169eqFbdu2KXytVJSzszM7xcrDhw/x7t07lcZPiCxRUVHse/H7RHmNHTuWvW5JG0Zd0rU+JycHO3bsQKdOnWBjYwM9PT3UqlULkydPxufPn0vFkZSUhF9//RWtW7eGpaUljIyM0KRJE6xYsULu/UbWvVMaaffLskhPT8fx48fx/fffo02bNqhevTr09PRgamqKunXr4rvvvsPJkydljuQh2oddu3Zl/xcQECDx/i7vPh4cHIzZs2ejefPmsLKygp6eHmxsbNClSxf8/vvvSE1NVWr7Tp48ib59+8LOzg4GBgZwcnLCN998g1OnTlXZqVk8PDzY/Xn79m0AxefQ0qVL0bx5c1SrVg1GRkZwcXHBzJkzFcojyzsPbG1tYWBgAEdHR/j4+Cj9vAIAYWFhWLx4Mdzd3WFtbQ09PT1YWVmhTZs2WLp0KWJiYsq07bGxsVizZg3c3d1hY2MDbW1tmJubK52+pKQk5Ofns59VeS0SFx8fjz/++AM9evSAo6MjDA0NYWhoCEdHR/Tp0wd//PEH+4wpS0REBJYuXYq2bduy+9Pa2hpt27bFsmXL2EaEqrJ+/Xo2j2RkZIS9e/eW6RrUs2fPMq1fndfKyMhIrFixAp07d4a1tTX09fVhYmKC2rVrw93dHePGjcPx48dL5c1Fx+OKFSvY/61YsULitU/W0LUMw+Ds2bMYM2YM6tevDzMzMxgYGMDBwQEDBw7EwYMHUVRUJHMbpD0v3b17FxMnToSLiwvMzMzA4/G+ilENCCEV69GjR5g4cSLq168PPp8PCwsLuLu7Y82aNQqVvYhLTk7G+vXr0aNHDzg4OMDAwADm5uZo1KgRpk2bpvQoUVlZWdiyZQt69eoFe3t7GBgYwMLCAo0bN8b06dPx4MEDheJRptxF1dsleqYZN24c+7+DBw9KvN9IK4uTdy8S9/HjRyxfvhydO3dGzZo1YWBgAD6fD2dnZwwcOBBbt25FQkKC1OUTEhKwf/9+jBkzBi1atEC1atWgq6sLc3NzuLi4YNy4cbh27ZpCadGkpKQkrFu3Dt27d2efJfh8PpycnNCqVSv4+Phg//79CpWrqeo4FJeRkYGtW7eiX79+qFWrFoyNjdlyo27dumHFihV4+fKlwvFlZ2djx44d6NixI5sfcnBwgI+PD/777z+5y0t6nsjNzcW+ffvYsk9ReeTTp09LLa/Oc/XNmzeYNWsWGjZsCGNjY5iamqJZs2ZYtGiRwmWvIqqu1yGVGEOqpNmzZzMAGADM77//Lje8p6cnG75evXqMUChUeZri4+OZbt26seuR9qpXrx4THBwsM65ly5ax4ZctWyYz7P3795k6derIXW+PHj2YhIQEmXE5OTmx4cPDw5m//vqLMTAwKBVXs2bNSi174sQJxsbGRm46vvnmGyYtLU3e7mT8/PwYU1NTqfGYmpoyZ8+eZW7dusX+r0uXLnLjlWfMmDFsfPv372eCgoKYmjVrSk2HlpYWs2jRIqnxtWrVig175MgRhdKwc+dOdplOnTqVa3u6dOmi8LEkj6+vLxuXq6urwsvVr1+fXe7MmTMSw1T0+aPMOcYwjMzjbP/+/XLTLf4SFx4ezv7fyclJZhqysrIYb29vufHb2toy//zzj8y4Sm5/VFQU0759e5nxjhs3jhEIBFLjLCgokHmuiL+MjIyYw4cPy0wjwzCl9tuyZcsYbW3tUvENGDCAYRiGGTJkCPu/1atXy42fYRjmypUrnN9A1jbKI379WLBgAcMwDOPq6spuc2ZmpsTlsrKyGGNjY865tWDBAjauMWPGyFyvKs8fkZ9++onR0tKSGleDBg2YV69eKXUuRUREMB4eHnLTaWdnxwQGBsqMq+T9Sp6ZM2ey4Tds2KDQPiBEFby8vNhjb/jw4SqLt2R+RZKS5+e7d++Yxo0bSz33zMzMmMePH7PLX7hwgTEzM5N5HYiLi5OaxrLk0aTdL8Upcv6fPn2a0dfXV+ie1Lx5c6nxiO9DeS9p9/GUlBTO/Unay9zcnDl58qTcfZSens707NlTZlz9+vVj0tPTOfnAW7duyY1bFvHfU9bvI4347ybtmC2Z3gsXLjDm5uZSt1NfX5/ZunWrzPWWPA9ev37NNGrUSOb+Gzt2LFNQUCB3m/Ly8pgpU6ZIzJuIvwwNDeWms+S2nzt3jrGwsJB4niorISGBE4e8fKKyBAIBs2LFCobP58s9zrW0tJiXL19KjevXX3+V+Pwp/jIwMGB+++03laQ9NzeXk+6JEyeqJF4RRa6D6rpW7tq1izE0NFTo+tWhQwfOsuLHo7yXtHzqs2fPmObNm8tdvkGDBjKPiZLPS/n5+cyUKVMkxjVz5kyF9h8h5MulSH5DkpLXGqFQyCxdulTmM3Ht2rWZDx8+KBT/tm3bZOarATA8Ho8ZP348k5+fLze+ixcvKlQG+9133zHZ2dky4yp5T5FX7qLK7RJ/ppH3klUWJ6/MJC8vj5k2bRqjo6Mjdz26urpMRkZGqTg2b94sN88nenl6ejJJSUky06RM+hUhHp+s8kVpeUxJr5o1a8pcpyqPQ5GdO3cqnL4rV66UWr7kc/KrV6+Yhg0byoxn6dKlMtNU8nni1atXbFlfyVdISIja9lHJc3Xnzp0yn3ktLS0VLgNUdb0OqdxobNIqSrwnX8eOHWWGzc/Px71799jPPj4+Kp/vKz4+Hh06dMCHDx/Y/9WpUwdt2rSBvr4+Xr16xbb6effuHbp27YqrV6+iQ4cO5VpvYGAg+vTpg5ycHPZ/7u7ucHV1RUFBAe7fv8+m6fr16+jYsSPu3r3LznMry8mTJ/HTTz8BKB5uvn379jAzM0NMTAxSUlI4YTdu3Ii5c+eyvVJMTEzQrl07ODg4QCAQ4NWrVwgODgbDMLh06RK6dOmCe/fugc/nS1z3hQsX8N1330EgEAAoHravU6dOqFOnDtLT03H79m0kJSVh+PDhWLNmjfI7TkGfP3/GnDlzkJqaCiMjI3h6esLa2hpxcXG4desWsrOzIRQK8dtvv6GoqAh//PFHqTgmT56M77//HgCwb98+jBgxQu569+3bx74XzYVbGQwYMACmpqbIyMjAy5cv8ezZMzRr1kzmMsHBwWwvHgsLC/Tt27dUGE2dP6rSsGFDTJs2DZmZmWxvXxMTkzL39pUkJycHnp6eePjwIfs/Ozs7dOrUCcbGxnj//j3u3r0LgUCA2NhY9O/fH8ePH8fQoUPlxp2VlYXevXsjNDQUfD4fnTp1goODAzIzM3Hr1i22lez+/fvRoEEDLFiwQGI8AoGAbUVqbGwMV1dXODs7w9TUFIWFhYiKisL9+/eRkZGB7OxsjBo1Crq6uvD29lZoH/z5559sb5U6derA3d0dfD4fnz59gq6uLoDi8+306dMAiofJXrx4sdzrvfj5Nm7cOGhpqXYQmVGjRmHhwoXIzs7G6dOnMWbMmFJhTp8+zfa8Vva4Ucf5M3/+fKxbt479bGJigq5du6JGjRqIjo7GrVu38ObNG3h5ecmdvkQkLCwM3bp1Q2xsLIDiFq7NmzeHq6sr+Hw+oqOjERgYiMzMTMTExKBHjx64cuUKp3dmeXTq1AmbN28GUDyc9ezZs1USLyHy1K1bl31/6dIlhIWFKTWEuqpkZGTAy8sL7969g7m5OTw8PGBlZYXIyEj4+/ujoKAA6enp6NWrF96/f48nT55gyJAhKCwshJOTE9q1awdjY2O8fPkSQUFBAIpbpY8cOVKlI2yoSkJCAtsz1t7eHo0aNYKNjQ34fD6ysrIQFhaGJ0+egGEYPH36FJ06dcLTp09LjQjl7u6OadOmITo6GufOnQNQfP8dNGhQqXVKGk0qLi4Onp6e7LzyQHG+oXnz5jAxMUFCQgLu3r2LpKQkpKWlYfjw4Th8+LDU/GJBQQG8vLw4PRpq1qyJTp06gc/nIywsDPfv38fFixcV7jFTWT169AhLlixBQUEBqlWrBg8PD1SrVg0RERG4ffs2CgoKkJ+fjx9//BFaWlqYOnWq3DjT09Ph5eWFjx8/Qk9PDx4eHnB0dERKSgpu3brF9tY/cOAAcnJy4OfnJzWu7Oxs9OrVi/Nb1K5dG61bt4aFhQVSU1Nx7949REdHIzc3Fz/++CMyMjKwePFiuem8d+8eli9fjsLCQlhaWqJz586oXr06EhISEBISosDe47K0tIS5uTnS0tIAFPeM7t27t0qeiQUCAYYNG4azZ8+y/9PT00O7du1Qq1Yt6OjoIC4uDk+ePEFsbCyEQiEKCgokxjV9+nRs376d/Sx6/rKxsWGfv7KyspCXl4dFixYhPj4eGzduLFf6Hzx4wHmW/u6778oVX2Vx7tw5TJkyhf1samqKdu3awd7eHjo6OkhPT8fbt28RGhoq8fcYNGgQGjdujIcPHyI4OBgA4ObmBnd391Jh27ZtW+p/gYGB6NevHzIyMgAAOjo6aN26NRo0aABdXV18+vQJd+/eRV5eHt68eYP27dsjKChIofvj7NmzsWvXLgBAkyZN0KxZM+jq6uLt27cqz8cTQr5eK1aswMqVKwEAzZs3R5MmTaCrq4unT5/iyZMnAIqn3xg4cCAeP37MlklIMnv2bGzatIn9bGlpibZt28LOzg55eXkICQlBaGgoGIbB33//jZiYGFy+fFnqNc3Pzw8jRozglJl27NgRdevWRVZWFu7cucOOUnPs2DGEh4fD398fBgYGcrdbkXIXVW5X9+7dYWxsjNevX7NTzrm4uKBbt26l0lavXj256ZckKysLPXv2ZJ9hAIDP56Njx46wt7cHwzCIjo7G48ePkZycjMLCQnbfiouJiWH/7+zsjIYNG8LKygoGBgZIS0vDixcv2N7L/v7+6N69O+7fvw99ff0ypVsdHj16hKFDh7KjtBgaGqJt27aoVasW9PX1kZGRgQ8fPuDFixec/JEk6jgOZ8yYga1bt7KftbW14ebmhnr16sHAwACJiYl4+vQpO/JQXl6ezDTGxMSge/fuiImJgbm5OTtaWlJSEvz9/ZGeng6geITTRo0aKVQ+mZycjN69eyMiIgIGBgbo1KkTnJyckJmZifv376t9H4kcOHAAP/zwAwCgQYMGaN26NQwNDfH69Wv8999/YBgGycnJ6NevH8LCwmSONqXqeh1SBWisOp6UWWZmJttSj8fjSWy9Je7OnTucFi2XL19WeZr69OnDxs/n85mjR4+WChMcHMw4Ozuz4RwcHJjU1FSJ8SnS8y4lJYXTK7NOnToSW/8cOXKE00K8X79+UrdDvEWljo4Oo6enx+zevbtUz/q8vDz2/Y0bN9jfQ1dXl1m9ejWTlZVVKu6QkBBOr4wffvhBYhoSExOZ6tWrs+FatGjBvH37lhMmPz+f+emnnxgAjJ6entRWg2Uh3qJMFPd3331XqhVUWloa8+2333KOLX9//1LxZWZmsj1AeTwe8/79e5nrf/78ORufqampwi3ppFFlT3GGYZixY8ey8c2bN09u+BkzZrDhJ0+eLDGMJs4fVfYUF1Gm17eyy/zwww9sOG1tbWbDhg2lejS/ffuWMzKBqakp8/HjR4nxiW+/qFXhmDFjmOTkZE647OxsxsfHhw1rbGws8fxmmOLzcty4ccytW7ek9q7Ky8tj/vjjD7Z1rrm5udTe0wzDbQWpo6PDmJmZMWfPnpUYL8MwjFAoZGrXri3znBSXlJTEnudaWlrM58+fZYaXR1JP8aioKPYa6enpKXE5US9vLS0tJjo6mmEYxXuKq/r8uXXrFsPj8TitVdPT0zlh4uPj2V6K4tdgaedSdnY2p2Vu9+7dmTdv3pQKl56ezun5Y2trK7UFqrI9xd++fcuGt7CwkBueEFUp2bvWysqK2bBhAxMTE1OueJXtKS46V6dNm1YqbxEWFsbY2dmxYefPn8/Y29szenp6zN69e0vlA0+fPs3pZXH79m25217RPcUvXLjA/Pbbb8y7d++kxvPx40emV69ebFwTJkyQGrYs2yIQCJiuXbuyy7Vs2VJiXj03N5dZvnw5e+01MjJS6P7N4/GYdevWlcoPPH78mL3mi1+jq1pPcVHa58yZw3n2YBiGiY6O5owEpq+vz4SFhUmMU9J50L1791LnYG5uLjNr1izONv79999St2f06NFsuDp16jDXr18vFaaoqIjZsWMHm9fS1tZm7t27J3fbdXR0GB6Px6xatapUnqrkvlBUyZ5YnTp1Yi5evFjm+ETE8ysAmOnTp5fKT4o8ePCAGT16NBMaGlrqOz8/P048o0ePLpX/SE9PZ0aOHMkJd+rUqXKlf9WqVWxcWlpaMvOlZaGpnuLNmjXj/CbSnikzMzOZEydOsPnWkpR9ZmIYhomNjWWsra3Z5Xx8fJioqKhS4eLi4phBgwax4Zo0acIUFRWVCif+vCTqoefg4CBxVKHyHs+EkKpPFT3F9fT0GB6Px9SpU4d58OBBqbAnTpxgdHV12fAHDx6UGu++ffs45Sk7d+6U2GPa39+fU8YrbVTU9+/fs2WMAJjWrVuXKjMVCATM+vXrOb3cf/zxR6lpVLbcRR3bVZZe04ouIz7iora2NrNixQqJ5VoCgYDx9/dnBgwYILEcYt++fczWrVsl3tNEnj17xrRu3Zpd36pVq8qdfkUp0lN8wIABbJghQ4YwKSkpEsPl5eUxly9fZr7//nuJ36vjOBQfNRUoHmUtMjJSYtgXL14wM2bMYK5du1bqO/E8rygP/tNPP5XKCyUnJ3OeJ5ydnaWOLCyeHxI9Bw8dOpRJTEwstc2ivLu6z1V9fX3GyspKYm/5gIAAzui7K1askBqnqut1SNVAleJVUFBQEHsCyhvGg2EY5vDhw5yLxqdPn1SaHn9/f07858+flxo2PDycM6yMtIuSIg+fS5cuZcOYm5vLrMw5c+YMJ40BAQESw4lnHgH5w30LBAKmXr16CoePjY1latSowV5oJd3cFi1axMZnY2NT6gYjruSwaaquFAfAeHl5SR1OWSAQcApT3dzcJIabOHEiG2bJkiUy1y8+xK+0zIcyxAvY3NzcmGnTpin8klSgdePGDc75J2uo6aKiIk6BiKSCC02dP1WpUvz9+/ecDNKWLVukxpeSksLUqlWLDTtu3DiJ4UoOB+vj4yM1ztzcXMbBwYEN6+vrq9C2ybJ27Vo2vh07dkgNJ55GHo8n9dolbvXq1ewyI0aMkBl248aNbNhevXopvR0lSaoUZxiG6dGjBwMUF7ZGRERwlomMjGR/3549e7L/V6RSXB3nT7t27dgwPXv2lHqO5+bmlhoSU9q5tHLlSjZM7969mcLCQqnpZBhuRcPatWslhlG2UlwgEHAKLsrbAIIQZQwcOJBzroiuaQ0aNGBGjRrFbN68mXnw4IHcc0OcspXiQPGQ0NIcP368VBplTXMxefJkNtyUKVMkhtFkpbiiCgoKmKZNmzJA8ZDM0gqGyrIthw4dYpdp3ry53Mo28by9pH2amprKaegqa5qQ9+/fM0ZGRpz9WdUqxWUdWwzDMDk5OexvB4AZNmyYxHAlz4MWLVowubm5UuMVz7fb2dlJPC8DAwPZMPb29kxsbKzMbf/7778590FFtl3RaWAU9fHjR4lDURoaGjIdO3ZkZs+ezRw9elSpZ+U3b95w8qhlHdJcIBBwGjUOGTJEaqGkUCjkFOjWqVOnXFPfTJgwgY2rVq1aZY5HGk1UimdmZrLfOTg4lGvquLJUio8fP55dRt5w9EVFRZzGQ5KeM8Sfl4DiRqCSGlcSQgjDqKZSHCgedljUYF2SefPmyb23Z2RksNPAaGtryy3PePXqFTuFiKWlpcQGTeLPys7OzlIbuzMMw2zYsIENq6WlJbXRZclnFHnpVMd2qatS/Pr165ztO378uEJxl0daWho7DLWtra3EBl8Mo5lKcUtLSwYorlAtT0NAVR+HKSkpjImJiULPAPKULNeXNeVpXFwc55np/v37EsOVfJ6QVVYmou5zVV9fn3n27JnUOLdt28aGdXFxkRhGHfU6pGqgSvEq6NixY+zJ2qZNG7nhN2/ezLloqHreA/EWZ7J6YYv8/vvvbHhbW1uJD6nyHj6FQiFnnof169fLXa94b8Jvv/1WYhjxzKO7u7vcOM+dO8eG79atm9zwDMMwv/32m9R0CwQCznbt2rVLZlxpaWmcVleqrhRXpGf3mzdvOL0qnz59WirMw4cP2e9r1qwpNUOUn5/PZlAAKDzvhyzKzAdX8iWpoFkgEHBaecrqiVtyrmZJx7omzh9Fw4jTZKW4eOVo06ZN5RZsife00dfXl3jNK9lrSl5hrmhkBgDM3LlzFdo2WeLj49n4Bg8eLDWc+PGo6Fy8MTExbMtNAwMDmRnPJk2asPErMo+rPNIqxcUbZ61Zs4azjPg1UTwDqkiluKrPn5cvX3L2ubRedyI3b97khJd0LhUUFLCZZi0tLYUK26Ojo9nrapMmTSSGKUulmKOjI7uMtJ6thKhDVlYWM2zYMLn3XWNjY8bHx0eh47MsPcXj4+Olxpebm8uZj6xly5Yy1y/eKKdVq1YSw1SFSnGG4V4bL1y4IDFMWbZFvOHQnTt35IbPzc1lCxjNzMxKFbRs376djc/R0VHunNe//PILZ39WtUpxExOTUj2FSxIv6NTV1ZXYmLZkIZa8UWRSU1M5hWPnzp0rFUa8oYusxiMiQqGQcXFxYYDi5wtJ6RTf9po1ayrVSEZRwcHBnHuhtJezszPz888/yx3RQryBctu2bctc+Sr+zKCnpyd3vVFRUZyGblevXi3TehmG4fRUbt68eZnjkUYTleLR0dEq2yZln5kSEhLYe4mZmZlCBe7inR4k5WdLVlT99NNPZdkUQshXQlWV4vLKWV+9esWGtbS0lBhm06ZNbBhZIxKJ+/7779llTp8+zfkuNTWVk18v+X1JAoGAM+/xwoULJYYT325Fyl1UvV0Mo75K8d69e7NhvL29FYpXFcRHe3z+/LnEMJqoFBfln8ozep46jkPxjjNOTk7lGvlF/DnZyspKZmNYhmGY4cOHs+G3bt0qMUzJ54lXr17JjLMizlVZPcoZprjxiqhslMfjSXyuUnW9Dqk6aMKhKig+Pp59L2nuvpIyMzM5n42NjVWanlu3brHvx48fLze8+Jy1sbGxePPmjdLrDAsLQ1xcHABAS0tL4hy1JU2cOJF9f/v2bbnhv/32W7lh/vnnH6XCA4Cnpyf7/u7du5zvxLdLR0dHbpxmZmYYMGCAQustiw4dOqBOnToyw9SvXx/t2rVjP4sfDyJubm5o3rw5ACA6OhrXrl2TGNf58+eRnJwMAGjatClat25dxpSrj5aWFnx8fNjPR48elRpW/LsRI0ZInLdQE+dPVePv78++HzdunNz5HwcNGoRq1aoBAPLz8znzJknSsWNH2NjYyAzTokUL9r1o7h5ZhEIhgoODsWfPHixbtgyzZ8/G9OnT2Zdobi4AePr0qdz4AMWvMba2tvjmm28AFM8vdOzYMYnhgoOD8eLFCwBA9erV0b9/f4XiL4vBgwez957Dhw9zvhOfh17SHLWyqPr8EY/Pzc0NLi4uMuPr2rUr7O3tZYZ59OgROy99u3bt4OTkJDeddnZ27LpDQ0PZOVDLq3r16ux70b2GkIpgZGSEEydO4N9//0Xv3r2ho6MjMVxWVhaOHz8ODw8PDBgwgJ3fWBU6d+6MGjVqSP3ewMCAk+cZMmSIzPgaN27Mvg8PDy9/AtUoLS0NV69exfr167F48WLMmDGDc0/6999/2bCK3pPkiY2NZeOqWbMmOnbsKHcZAwMDNk+Znp6O0NBQzvfi12hvb2+Zc1cCwOjRo5VMdeUyYMAAmJqaygzTrVs39j5UWFhY6tmiJAcHB3h4eMgMY25uznm+KJm3LyoqwvXr1wEUP6/IO1cAgMfjoWvXrgAAhmFw7949meGHDBki9TpRHq1bt0ZYWBj+/PNP1K9fX2q4jx8/YvXq1ahXrx5nXseSrl69yr6fPn16mecoF8/r9unTB7a2tjLD16xZE71792Y/S3r+UpR4WYGqywk0xcrKCoaGhgCAFy9eIDAwsMLWfePGDeTn5wMAvvnmG4X2aZs2bdg5KeWdw4DizwSEEFIew4YNk/m9i4sLe61NTk5GVlZWqTCqLjO9d+8ee421tLSUW4ahpaXFKSdQ5H6pibJgdcnPz+eUf//4448qizshIQEXLlzA77//joULF+LHH3/kPF88evSIDauq5wtVcHR0BACkpqZKLSuTRx3HoXiectKkSSqbh71fv35y5+dWtryzadOmaNiwocwwFXGuyrtGmZiYsM/3DMMgIiKiVJiqci4T1VP9kyZRu+zsbPa9KAMii4mJCedzVlYWzMzMVJKW6OhotrAfANq3by93GSsrK9SvXx+vX78GADx58kRuxUNJISEh7HsXFxeFGgd06NCBfR8XF4eYmBjY2dlJDd+qVSu5cYpXtl2+fFmhG316ejr7PjIykvOd+PIuLi4K/U5t27aVWTFbHm3btlUoXLt27djCLfHfRtykSZMwbdo0AMC+ffvg5eVVKsy+ffvY9+KNGFRl2bJlWL58ebnjGTVqFNatWwcAOHXqFLZv314qw5KTk4Nz586xn0eOHFkqHk2dP1UJwzCc80L8PJZGV1cX7u7ubKbyyZMnnILDkpo0aSI3TvFrjPg5XFJRURG2bt2KDRs2ICoqSm68AJCUlKRQOEWuSSKTJk1ij799+/Zh6tSppcKIn2+jR4+Gnp6ewvEri8/nY8iQITh48CDCwsIQHBwMNzc3BAcHIywsDEBxAbioQFAR6jh/xI+1Nm3ayI2Px+OhTZs2Mn9r8ftEUlISpk+fLjdeAGxFOMMwiI6Ohrm5uULLySKeZxDPSxBSUXr06IEePXogOTkZAQEBuHfvHp48eYInT56UurZeuHABnTp1QlBQUKm8bFm4urrKDWNhYcG+b9SokcJhMzIyyp4wNYqKisLChQtx6tQptlBCHkXvSfKIX/sYhlH42vfhwwf2fWRkJJo2bcp+VvYaXbduXVhaWrINLqsaRfLhJe9DISEhGDhwoNTwbdq0Uajitl27dmxBYcm8/fPnz9l7iIGBAebPny83PqC4MZ5IyWegkpTJ8yiLz+dj3rx5mDdvHl69eoXAwEA8ePAAISEhCA0NhUAgYMNmZ2djxowZSEpKwooVKzjxxMfHcwoORZX+ZSG+jxXJ64rCXbx4EUBxfqasxK+vkio0qiJdXV0MGjQIx44dg0AgQPfu3TF06FAMHToUXbp0UajsoKzEr31v375V+NonOi9TU1ORnZ0NIyMjieF0dXUVenYhhJDyMDMzg4ODg8wwPB4PFhYWyM3NBVBcTlKyIZD4NfHQoUOcMjJpxJ+tS+YXxO+Xbdq0UagBnfh9NSQkBAzDyMwLKVsWrIrtUpenT58iLy8PQHH+R5H8szyvXr3CggULcOXKFU6eSRZVPV+ogre3N9asWQOguJzW19cX3t7e8PT0lNsoUUQdx+GDBw/Y9+XJU5akyvJOEUXOkYo4V1Wxbaqu1yFVB1WKV3GKFGqIekyKpKWlqaxSPDExkX1vaGgosweOuFq1arGVEmW5OYqvV5FedwBgbW0NAwMDNkOQlJQks1LcyspKbpwxMTHse0UyQSWV7AElvl2i1mvyyMuolkdZ0iC+DeJGjhyJ+fPnIycnBxcvXkRiYiJnH0dFRbG9TvT19TFixIhypFy9mjZtiiZNmuDFixdIT0/HpUuXSvWSOXfuHFuw1LJlS4mt6DR1/lQl6enpKCwsZD8rer7XqlWLfS9vHylyPRTvjSaeHnH5+fno378/p8edIkqO5iGNItckkd69e8PR0RERERF48uQJnj17hmbNmrHf5+bmwtfXl/08YcIExRNcRqNHj8bBgwcBFD84urm5sb3ERd8rQx3njzquweL3iTdv3pRpdAdV9ZZlGEYl8RBSXpaWlhg8eDAGDx4MoHh0jcePH+PIkSPYs2cPW7j28uVLLFmyBFu2bCn3OhW51os/rMsLLx62qKio7AlTk5CQEHTr1k3p64ei9yR5xK99MTEx2L59u9JxqCqfXFUrxVWZD1dlnOK/bVZWlkp+25KUyfOUR6NGjdCoUSNMmTIFQPHx/++//2L79u2cHiqrVq1Cv379OKNYiY/epq+vL/O5Up6yPNsqk9eVRbysQFUj01QGGzduxJMnT/D69WsUFhbi+PHjOH78OHg8HlxcXNCpUyf07NkTffv2lduDShni50dwcDCnMYiiUlNTpVaKW1hYqGUUBUIIEadombGscpKsrCxOvrLkiHGKkJUXLMv9sqCgAJmZmTJH4pGXB1HHdqmLeF7FwcGh3PePa9euYcCAAQo3thVR1fOFKixZsgQBAQH477//wDAMLl68yDYyrF27Njp16oTu3bvLHLFJ1cdhRkYG+/wLAM7OzspullSqKu8Up0g+vSLOVVVsm6rrdUjVQcOnV0HiD0g5OTlyw4tfVIDiVl2qIt6aXNqDmyTiYctyc6yI9SrSC1+RFlSylCxEFd8uRXtMKrP9yipLGqTtV1NTU3h7ewMovhGVzDju378fQqEQQPFQyyUbc1Q24pX2knrqHzlyhH0vqZc4oLnzpyop2WNF0f2kzD4q61CXJa1YsYKtENfS0sJ3332HkydPIiwsDOnp6SgoKADDMOxLRNGKSkWuSSJaWlqcim7xXuFA8QgHoutXu3bt5PaIVIWuXbuyhfG+vr7Izs5mK+YdHR3lDudakjrOH3Vcg8t7nwBUV+EmahQGqPfeQYiytLS04Obmhs2bN+Px48ecKS3EK8nLQ9lrvaruDZqQn5+PIUOGsA/p1tbWWLZsGW7fvo3IyEhkZ2dDKBSy96P9+/ezy4ryYuWljmufpvPJJQsSCwoKlFpevABR3tDvgGrz4aqMsyLua8rkeVTJxMQEQ4YMgb+/PzZs2MD+n2GYUsOoq3LY8bLkaVT1PCBeVhAREfHF9BavUaMGgoODsXz5ck6DBYZhEBYWht27d2Po0KGws7PD2rVrFe7tJo+6zw9NnRuEkK+LKvLB6s4LluV+CZS/LLgyPd/Lo8q8SmJiIry9vdn8bO3atfH777/jv//+Q0xMDHJycjjPF8uWLWOXVdXzhSrw+XzcunULGzduLDVdaHh4OA4dOoTRo0fD1tYW8+fPl/gcqurjUJ3T3qrjmVaRvEhFnKuV4TpVGRvHE8VQpXgVJF5QqEircDc3N87QzuJDcpSX+IVamaFYxcOWZUhMTa23JPEL9tOnTzkVXoq8Ss7VIb5dijR4ANQ7BG5Z0iBrv06ePJl9L15JV7JAtiJ6rZbXiBEj2LmJ//nnH07PisTERLbXu7a2NmcOcnGV5ThWhKYysSUzg4rup4reR/n5+ZzC0kOHDuHo0aMYOnQoXFxcYGpqyikAr4jGDBMmTIC2tjaA4oYb4oXx4udfRZ1vPB6PbSAiGkZcdA8bNWqU0hladZw/6rgGi98nZs2apfR9gmEYpRsMSCPeWlfRocEIqWgNGzbE+vXr2c95eXll6mlXFanqXnv69Gl2nnN7e3s8e/YMy5cvR5cuXWBvbw8+n8+55qrjniR+7Rs4cGCZrn1jx47lxKnpfHLJ3gjKVh6Kh1dkSgxV58NVFaf4b9u8efMy/baqmM5I3WbPno0ePXqwn+/cucP5XpXDjpclT6OqvG7Hjh3Z90KhkDMHaGWl6LXS2NgYy5YtQ2RkJIKDg7F+/XoMHDgQ1atXZ8OkpqZi0aJFGDJkiEpG1BE/PzZt2lSm86NkpwZCCKmKSlZwpaWlKX09FJ8PGyj//RIof/mQOrZLXVSZV9mzZw9bgdiiRQs8f/4cP/30E9q3bw9bW1sYGhqq/flCVXR1dTFr1iy8f/8eL168wPbt2/Htt9+iZs2abJicnBysW7cOXbt2LVUxrurjUNK0t1VdZThXFaHqeh1SdVCleBUk/pCkyJy1+vr6nLlWjx8/rrK0iA+ZkZubq/DQbZ8/f2bfiz+UlmW9ERERCi2TkJDA6SVXlvWWZG1tzb5/9+5dueMry3apc/4KRdMgHk7Wfm3bti0758erV69w//59AMCtW7fYwtvatWvD09OzrEmuMPb29ujcuTOA4grRkydPst/5+vqyrcW6devGacgiTlPnD8DtoaRIyzZVtIYtCzMzM05aFT0mVbGPlPHw4UM249qkSRO5w/+Lp09datasiT59+gAAUlJScPbsWQDF87UGBgYCKM6oikZwqAjiQ6QfOHBA4v8VpY7zRx3XYFXfJ8pKKBQiNjaW/UwFrqQyE127RMSP3apEU/famzdvsu9nz57NuQ5Joo57kjqufZrOJ5ccKvDjx48KL5uYmMgp4FJk2EFV58NVFaf4b/vx48dK1ftH1cSvRSWvQ+L7IT8/v1zXqbIc26rK67Zt25YzgoBoLvmKpO5rpZaWFlq3bo05c+bg7NmziI+Px927dzFw4EA2zPnz53H69Gml4pWksuT7CCFE08zNzTkdtDRVZip+v9TT0yt3RZs6tktdxO9JkZGR5erVKv588fPPP8vtzVwRZV6q0LhxY0ydOhXHjx9HVFQUQkJCOB1HHjx4UGqqIFUfh6amppze16Ly8aqsMpyriqB829eLKsWroCZNmrA9VGNjYxVqffXDDz+w79++fYtLly6pJC01a9bkzON67949ucskJydz5lRt2bKl0utt0aIF+/7169dISUmRu8x///3HvrexsSnXvG8ibdq0Yd9fu3at3PE1b96cff/69WuFHvhFFcvqEBQUpFA48TTI+z0nTZrEvhf1VhXvtTp+/PgqM2Sp+LDo4kOoi7+XNnQ6oLnzB+C2uFNkrs0XL17IDaOO343H43HOC0X2UVFRER4+fMh+Lus+Uob4PDSurq5yw4sqpdVN0ugMf//9N9sTxtvbW6VDM8nToEEDuLu7c/7Xpk0b1K9fX+m41HH+iB9rilxbGYaRO/qK+H0iICBA6fm3VOXDhw/sQ7CFhYXC88oSogkl53cVL3iqStRxr1WEOu5Jyt7j27Zty75/+fKlQg155VH2Gv3+/XuVziduY2PD6UGiTI9a8bC6urpsI1FZFMmHl7wPycvz3L9/X6HesLLy9s2bN2fPyYyMDIWfF6oi8WtRyeuQtbU1p4GZv79/mdcj/myrSH4G4D7blieva2BgwBmV4fjx45w5SCtCRV8rtbS00KFDB5w5cwa9evVi/3/hwoVSYZW99qm6fIAQQqoy8Wd/VVwTxe+XDx8+VGjqC/H7ZYsWLVRSbqXq7QLUU57WvHlzNi+Tk5NTrpFjlXm+EAgEnP1elTRv3hx79+7llKWVzB+o4zgUzz+UJ09ZWVSWc1Ueyrd9vahSvAoyMjJi539lGAbPnz+Xu8yQIUNQr1499vPUqVM5wz0r6smTJ6UeVLt27cq+F+/5J83BgwfZHgV2dnZo0KCB0ulo2LAh2/tWIBBw5m+WRnx4bvE0l8c333zDvj927BgSEhLKFV/Dhg3ZVkpFRUXsfLvSpKen4/z58+Vapyz37t3Dhw8fZIZ5+/YtpzBM3r4dNWoU2wLOz88P0dHRbA9WbW3tUkNlVmZDhw5lM5iBgYGIjIzE+/fv2Ywmn8/HoEGDZMahifMHKO6RL/L06VO54U+cOCE3jHjBYWFhYZnSJYn4yAEHDx6UW5h74cIF9jplYGCAdu3aqSwt0ogaKgHyhyYVCoXYvXu3upMEAPDy8mIL8G/evIkPHz7g4MGD7PeamKqgZK/wsvQSF1H1+SMe36NHj/D69WuZ8fn7+8ut6OnQoQM7TG5WVhb27NkjN53qIJ5XEM/4E1IZlbwvVdVGHLVq1WIf5t+/fy93KDxF7rWKUOae9PjxY4WGp1f2Hl+7dm00bNiQ/bxp0ya5y8gjfo328/OTmw7x+52qiE9n4efnp/By4r1v27Rpo9BcfBcuXJDbQPbGjRuIjo4GUFzZ3qFDB5nho6Ki5A7ZmZaWxnm+KJm3NzQ05OTNNm7cKDO+qkz8WiTpOiTek3z79u1lHn5bfH/+888/cp8p4+LicPXqVYnLl8XcuXOho6MDoDivIt6IWRn//vtvmZbT1LWSx+Ohb9++7GdJjQGUvfb16tWL3Zfv379XWWcEQgipisTLTHft2sUZvbMs2rdvzzZSS0xMxOXLl2WGZxiGkx9U1aiUqt4uQD3lafr6+px83LZt28oclzLPF+fOnUNcXFyZ11UZiP/GJfMH6jgOxfOUe/bs0VhnClWpLOeqPKqu1yFVB1WKV1Hi85vdvXtXbngtLS0cOHCAfUCLjIxEt27dFD7ZhUIh/vrrL3To0KFUz/Tvv/+efX/27FnOA3pJkZGRWL16NWfZsrT84fF4nFZbK1euZAuEJLl8+TIuXrzIfp4yZYrS65RkyJAhqFu3LoDiTMHIkSMVzrxkZWWVmi9DS0sL48aNYz+vWLFC5pDACxcuVOtcIwzDYMaMGVKHRRQKhZgxYwZbANS6dWs0a9ZMZpzm5uYYOnQogOI5ZoYNG8bOz9KrVy/Y29urcAvUy8zMjL2BMgyDY8eOcXqJDxw4UG4vXE2cPwDg5ubGLvvgwQOEhYVJDbtjxw68fPlSbpzm5uZsRjkhIUFlGflJkyax8T558kRmhXJ6ejp++ukn9rOPj0+pOUDVwdnZmX0fEBAgsxD7zz//xLNnz9SeJqC4ocn48eMBFB+jo0aNYq+VjRo1qpAGAyWNGzcOwcHB7Ks8DWFUff40atSI07tx1qxZUq9/eXl5mDdvntw06uvrY9asWeznxYsXK9XDSVW9tcTnQu3Zs6dK4iREERs2bMCNGzcUDl9UVISlS5eyn62trTk9hKsSU1NTtvFNUVERJ49QUkhIiMoazYjfk2Q1nszJyeHkp2WxtLRk38vKc4tbsGAB+37z5s1KHQeSCtK+++47tjI5MjISf/zxh9TlP3z4oJbKWvHRt27fvq3QcMv37t3jVIpPnTpVoXVlZmZi4cKFUr/Pzc3l3IcGDBig0LDsc+bMkVl4O3/+fPYZxdbWllNpKCL+254+fVqhhmkimigkLSgowPTp05UasSAsLAyHDx9mP/fu3btUmFmzZrF51KCgIPz+++9lSl/Pnj3ZBqv5+fmcfENJouezgoICAECdOnXQvXv3Mq1XxNnZGWvWrGE/X7x4EePGjVN4mNWsrCxMnz6dky9ThqqvlZmZmez+kUd8SE9J54+y176aNWtyRgqbMmWKwtdMoVCIxMREhcISQkhV8P3337MNxKOiojB16lSFG5AlJSWV6l1qbm7Omf5t/vz5MkdP3b59O1v2oqWlVeZGXyWperuAsuW1FTFnzhz2va+vr9zOV9Io+nyRmJiI2bNnl2kd6pafn69wGbqs/IE6jsNJkyax5cefP3+WmResCirLuSqPqut1SBXCkCrJ39+fAcAAYHr27Knwclu3bmWXA8BYWVkxmzZtYlJTUyWGz8zMZHx9fZkmTZqwy4SHh5cK16dPH/Z7Y2Nj5sSJE6XCPHnyhKlXrx4bzsHBQep6ly1bxoZbtmyZxDApKSlMzZo12XANGjRgQkJCSoXz9fVljIyM2HD9+vWTsncYxsnJSeZ2SnL9+nVGW1ubXa5t27bMw4cPpYZ/9uwZs3DhQsbCwoJ58eJFqe8TEhIYS0tLNr4WLVow796944TJz89nFixYwABg9PT02LBdunRRKM2yjBkzho1PFPeIESOYtLQ0Tri0tDTGx8eHczxdv35doXXcuXOHs5zodfr06XKnv6QuXbrIPZbK49y5c2z8TZo04Rzj//zzj0JxaOL8YRiG8fT0ZMM1a9aMiYyM5HxfWFjIrFu3jtHW1mb09fUVOs5cXFzYcH5+fnK3PTw8nA3v5OQkNdwPP/zAhtPR0WG2bdvGCAQCTpj3798z7u7ubDhTU1Pm48ePEuNTdB+J3Lp1S+b2CwQCzvWoR48eTHR0NCdMXl4e88svvzAAONckWbdiRcLI8/nzZ0ZLS6vU+bZ+/foyxymN+PVjwYIF5YpLdI0DwIwZM0ZqOFWfPzdv3uTspxEjRjDp6emcMPHx8UyvXr1KXYOlHUuZmZmMq6sr59jcvXs3k5+fLzF8UlISs2fPHqZly5bM3LlzJYZR9n7VtGlTNvzbt2/lhidEVby9vRkATMuWLZktW7YwMTExUsO+ePGC6d69O+cc3LBhg8Sw4teb/fv3Swyj7LVePM9w69YtueEVuUavXLmSDWNubs7cuXOnVJh//vmHqV69OudeKytOeef/1atX2e95PB6zbt06pqioiBPm3bt3TIcOHUrdk6RdbwUCASfcgwcPpKZPpKioiJPX0NPTY9auXctkZWVJDJ+ZmckcO3aM6dq1KzN06FCJYUT3UdG2rV+/vlR+4MmTJ4yzs3Opa7Qiv6kiBg4cyNmmTZs2SbyeCwQC5tChQ4ypqSkb3s3NjSksLJQat/gxKEr73Llzmby8PE64mJgYplu3bpywL1++lBin+HkgirNHjx6lzsW8vDxm9uzZnGNw7969UtMqfg7yeDxm3rx5TFJSksSweXl5zLlz55iBAwcybm5ucrddVb+VSG5uLrv93t7ezOXLl5nc3FyJYQsLC5ljx44xNWrUYNNjZGTERERESAw/f/58zj6bPn06k5ycLDHsw4cPmTFjxjChoaGlvvPz8+PEM3HiRCYzM5MTJiMjgxk3bhwn3KlTp5TcG5IJhUJm6NChnLgbN27MnD17Vmp+JTo6mlm/fj27ryTl5eXloUVUea28desWY2NjwyxdulTivmaY4uvTkSNHOHEdPXq0VLj79++z39vY2JR6JpYkJiaGsbW1ZZeztbVlTp48WepaJRIVFcVs2rSJqVevHrN169ZS3yv6vEQIIQzDzSdKyyNLUpZrjSLPpPv37+dcs/v27cuEhYVJDCsUCpl79+4x06ZNY/h8fqn7IMMwzIcPHxhjY2M2vnbt2jEfPnzghBEIBMyWLVs4ZbU//vij1O0oS7mLqrcrLi6OjcvAwID5/PmzUmmQVWYybNgwNpy2tjazYsUKJjs7u1Q4gUDA+Pv7MwMHDix1v9u1axcbh76+PnP48OFSyz9+/Jhp2LBhqecLac9hiqZfUeLxSTqGw8PDGXNzc2bOnDlSn2WEQiFz5coVplq1amxcv/76a6lw6jgOt2/fzjmmhg8fXqqcViQ0NJSZMWMGc+3atVLfKfKcLE6R30HZ52qGqRznqiLPF6qu1yFVQ3G3YVLldOnSBTVr1kR0dDRu376N9PR0hXpDTp8+HRYWFpgwYQLy8/ORmJiIWbNmYe7cuWjdujUcHR1hYWGBtLQ0REdHIzg4mNPKWk9PT+K8jvv370eHDh3w4cMHZGVlYfjw4ahXrx7atGkDPT09hIWFceawMzIywvHjx9mWdWVhYWGBY8eOoU+fPsjJycGbN2/QsmVLtGnTBo0aNUJBQQEePHiAd+/escvUq1ePM3+1KnTv3h07d+7EDz/8AIFAgPv378Pd3R316tVDixYtYGFhgdzcXMTFxeHp06dye+dbWVlhz549GDZsGAQCAUJCQuDi4oIuXbrA2dkZGRkZuHXrFhITE6Grq4vVq1dzesaq0uLFi7F582YcPXoU58+fh6enJ6ytrREfHw9/f39OC7s5c+Yo3EuhY8eOaNSoEV69esX+r0aNGujXr5/Kt0HcP//8I7PnfUl8Pl9mLySgeHhqS0tLJCcnc3p/1qhRgzOigyyaOH8AYM2aNWjfvj2EQiGePXuG+vXro1u3brCzs0NKSgoCAwORkJAAY2Nj/Pbbb/jxxx/lxjlkyBD8+uuvAIrnUz948CDq1q0LXV1dNsy6deuUTuu6devw6NEjBAcHo6ioCNOnT8fatWvRsWNHGBsb48OHDwgMDGRb3Oro6GDfvn2cYeLVSUtLC6tWrWJ7ZV+/fh3169dH+/bt4eTkhOTkZNy+fRupqakAgN27d2PEiBEVkjZHR0f06tULV65cYf+np6dXrmHLKxNVnz+enp6YPXs228Pw6NGjuHDhAjw9PVGjRg1ER0fD398feXl5qFWrFgYMGIDNmzfLTKOxsTEuXLiA7t27Izw8HBkZGZg8eTLmz5+Pdu3aoWbNmuDxeEhJSUFYWBjevHnD9lBXxXQf4eHh7PDpbm5unOlUCKkoT548wZMnTzBjxgzUqlULTZo0QfXq1aGrq4vU1FQ8f/4cb9684SwzaNAghe49ldmMGTOwa9cuxMTEIC0tDZ07d0aHDh3g4uKCvLw8zlQN+/fv54wYVFa9evVCly5dEBAQAIZhMG/ePGzfvh0tW7aEmZkZ3r17h3v37kEgEKBmzZqYOXOm3LyklpYWBg4cyPbg7Nq1K3r37g1HR0doa2sDAKpVq4bFixezy2hra+PEiRPo0aMHQkJCUFBQgIULF2LlypVo27YtHB0doaenh9TUVLx9+xavXr1iW+YPGTJEYjqWLFmC69evs9f1uXPnYuPGjejUqRP4fD5ev36Ne/fugWEYDBw4EKmpqQgICCj3PhW3f/9+vH//HqGhoSgoKMCsWbOwdOlStGvXDnZ2dtDW1kZ8fDyCgoI4+U57e3ucOHGCHblLnl9//RVLlizB+vXrsX//fnh6esLCwgIRERG4ffs2Z1jFP//8k51eS5apU6fi/PnzuH79OmrXrg0PDw84OjoiJSUFt27dQkpKCht2yJAhMqdZ+euvvxAbG4t///0XDMNg3bp12LJlC9zc3FCnTh0YGhoiPT0dHz58wIsXL9je6a1atVJo+9WhoKAAfn5+8PPzg66uLpo3b45atWrBwsICRUVFiI6OxsOHD9n8GlCcp/z777/h4OAgMc41a9bg9evX7Khk27Ztw+7du9G+fXvUqlULOjo6iIuLw+PHjxEbGwsAEnv/DB8+HIGBgdi+fTsAYO/evfDz80PXrl1hbW2NhIQE+Pv7c3rZzJo1S+q5oiwejwdfX1/MmTMHW7ZsAQCEhoZi0KBBMDIyQps2bWBrawsjIyMkJCSweQtR/grgzg2uLFVfK+Pi4rBy5UqsXLkS1tbWaNGiBWxsbCT+HgDQqVMnfPvtt6XicXNzg6OjIyIiIhAXFwcXFxf07NkT1atXZ0cccnNz4/SGsrW1xfnz5+Hl5YWkpCTExsZi2LBhqFGjBtq0aQNra2sIhUIkJycjNDQUHz9+LPPQ+4QQUtmNHTsWHz9+xKpVqwAUj+T5zz//oHHjxmjcuDFMTU2RnZ2N6OhohISEyJ3q09nZGXv37sWIESMgEAgQFBSEBg0aoFOnTqhTpw6ys7Nx584dzugwbdu2lVu2p+ntsra2RocOHfDff/8hLy8PzZo1Q+/evWFra8uOSlOnTh3OqEWK2rt3Lz5//szO7bxs2TL88ccf6NChAxwcHMAwDKKjo/Ho0SN2KsKS96WxY8diw4YNePv2LfLz8zFq1CisWbMGzZo1g4GBAUJDQ/Ho0SMAQLNmzdCrVy+V73NVSEtLw4YNG7BhwwZUq1YNLVq0QM2aNaGvr4+EhAQ8f/4c4eHhbPj69etj5syZpeJRx3E4depUhIaGYufOnQCKp4s5ffo03NzcUL9+fRgYGCAxMREhISH49OkTANVND6sOleVclUfV9TqkitBQZTxRgZ9//rlMrf8YprhF0aBBgzgtbGS9dHV1me+++65Uix5xcXFxnN4g0l5169aV2eKGYZRrgRQUFMT2BpH16t69O5OQkCAzrrL0FBfx9/fn9ESU93J1dS3Vk1TcsWPHOC2qSr5MTEyY06dPK9z6XlElW5Tdu3ePsbOzk5oOLS0tZv78+YxQKFRqPRs3buTEM2/evHKnXRLxVmHKvszMzBRax5QpU0otO2PGDKXSqanzZ9++fZwWcSVftra2TGBgoMLHWXp6OtOoUSOZ2yBOmdbImZmZzPDhw+XuI1tbW7m99FXdU1xk8eLFMtNmYGDA7Ny5k2EYxVo4KhJGEWfPnuXEJa0HXnlpoqc4w6j2/GGY4tbBc+fOZXg8ntS46tWrx7x8+VKpYyk5OZkZNmyYzHjFX+bm5syBAwckxqXM/Wrt2rVsWNHxR0hF2b17N1O7dm2l7r+GhobMypUrZfaorSo9xRmmuNdE9erVpW6vnp4es337doXjVOT8j4uLY1q2bClzPzdq1Ih5+fKlwr00IiIiZOYJpd3Hc3JymClTpjA6OjoK//5r1qyRmo60tDROL2lJLy8vLyYtLU1tvY8zMzOZUaNGSRyJRdKrT58+TFxcnNx4S6b3/PnzjJmZmcxjZ9OmTTLjLHkehIWFcUb2kfQaNWoUU1BQIDe9RUVFzC+//MLw+XyF9oOuri4zbdo0hbZdlQoLC5khQ4aUGqlH3qthw4YKpUUgEDCLFy8u1YNZ0ktbW1tqTzKGYZhVq1bJjcfAwEDmOVJely9fZlq0aKHwfrKwsGCWLFlSamQdhlE8D80wqrtW3r9/X+HrDVCcL87IyJC5P2T9JtKum58+fZJ7rRJ/WVtbM1evXi0VD/UUJ4Qoo7L1FBfx8/OTmY8s+XJ3dy81Uo64ixcvMtbW1nLj8fHxkdgrWpyse4o8qtyux48fc0YYKvkqeR9Vpqd1Tk4OM2nSJJnlf+L5DEn3xTdv3sgtf+/QoQMTFRWl0HNYRfcUj4qKUiivJnp5eHgwsbGxMtepyuNQZNOmTTKPA9GLx+NV6p7iIpo8V5V5vlB1vQ6p3KineBU2depU/PHHHygoKMDevXuVmpfV1dUVZ86cQVhYGC5fvoybN2/iw4cPSExMRFZWFkxNTdkW1Z06dcKwYcM485tIYm1tjZs3b+LatWvw9fXF3bt3ERcXh8LCQtSoUQMtWrTAwIEDMXLkSE6v0fJq27YtwsLCcOTIEZw7d45ttaOrqwsbGxt07NgRPj4+ap9DtWvXrnj9+jXOnj2Ly5cv4/79+4iLi0NGRgb4fD6sra3h4uKC9u3bo0+fPnLnx/Tx8UHHjh2xZcsWXL58GREREdDR0YGDgwO8vLwwZcoU1K5dG7dv31brdrVr1w7Pnj3D7t27cebMGXz69AlZWVmwtbVF165dMXXqVLRu3VrpeIcMGcKZZ0ZWT5TKbtSoUdi1axfnf+JzySlCU+fP+PHj0bZtW2zYsAH+/v6IjY2FgYEBateujSFDhuD7779H9erVFT7OTE1N8fDhQ+zcuRMXL15EWFgY0tLSVDK/uLGxMfz8/DBr1iwcPnwYt2/fRkxMDHJzc1G9enU0btwY33zzDcaPHw8jI6Nyr68sfv31V/Tp0wfbtm3D3bt3kZiYCBMTE9jb26N3796YMGGCRnrp9u3bF/r6+myvsokTJ1Z4GtRJ1ecPj8fDunXrMGzYMOzYsQO3b99GfHw8TE1NUadOHQwfPhwTJkyAqampUumsVq0aTpw4gdDQUBw/fhy3b99GeHg4kpOToaWlBXNzc9StWxctW7ZE9+7d0aNHDxgYGJR1twAAGIZhR0ixsLD4YkYIIFXHpEmTMGnSJISGhiIgIAD379/H69ev8fnzZ6Snp4NhGJiYmMDGxgZNmzaFp6cnhg4dCgsLC00nXWVatmyJ169fY8OGDbh48SLCw8MhFAphb2+PHj16YOrUqQr18lWGtbU17t27h71798LX1xehoaHIyclBjRo10KBBA3h7e2PEiBHg8/l4+PChQnE6ODjg2bNn2Lp1K/7991+8efMGmZmZcucdNjQ0xM6dO7FgwQIcOXIE/v7+ePv2LZKTkyEUCmFmZgZnZ2c0a9YM3bp1Q+/evWVeX83MzHDjxg34+fnh4MGDePLkCVJTU1GjRg00adIEY8aMwfDhw9lenOpgbGyMQ4cO4eeff8bRo0cRGBiId+/eISUlBUKhEBYWFqhVqxY6deqE4cOHlymvDAD9+/fH8+fPsWvXLly6dAmRkZHIz8+Hvb09evXqhenTp7NzMSvKxcUFwcHB+Pvvv3HixAm8f/+e3X/t27fH5MmT0a1bN4Xi0tbWxsqVK/Hjjz/i0KFDuHHjBl69eoWkpCQUFhbC1NQUTk5OaNKkCbp27QovLy+F5j1XNR0dHZw6dQq5ubm4e/cu7ty5g5CQELx79w5xcXHIysqCvr4+e59v0aIFBgwYAE9PT7Z3lixaWlr49ddfMWXKFBw4cADXr1/H+/fvkZSUBB0dHdSoUQOurq7o1q0bvL29UbNmTalx/fzzzxg1ahT27t2La9euITw8HGlpaTA3N4ezszN69eqFiRMnwtHRUZW7iMPLywt9+vTBnTt3cO3aNQQEBCA6OhqJiYkoLCyEubk5HBwc4Obmhm7duqFfv34SR5NTlqqulW3atEFCQgJu3LiBu3fvIiQkBB8+fEBycjIEAgH7O7dt2xYjR46Eu7u73P3x+PFjNn//+fNnZGVlye3d7eTkhBs3biAoKAgnT55EYGAgIiMjkZqaCh0dHVhaWqJevXpo3bo1evbsCQ8PD4VHkiCEkKpm+PDhGDBgAHx9fXHt2jUEBwezZdBGRkaoWbMmGjZsiE6dOsHLywv169eXGd8333yD9+/f4++//8alS5fw8uVLJCUlwdDQEHZ2dujatStGjx6NNm3aVJntatmyJZ4/f46tW7fC398fHz9+RFZWlsQ5yJVlaGiI3bt3Y86cOTh06BBu3ryJT58+ISUlBXp6erC1tUXTpk3Ro0cPeHt7Sxz9pX79+ggJCcH27dtx5swZvHnzBgUFBbCxsUGTJk3w3XffYdiwYZX2XlazZk0kJyfD398fd+7cwePHj/H+/XskJiaioKAAJiYmcHJyYkeAUWQ0VHUchzNnzsTIkSNx4MABXLt2jc1bA0D16tXRsGFDdOnSBd7e3lViFMDKcq7Ko+p6HVK58Rh5OXlSqU2aNAl79+4FAISEhNAJSaqMAwcOsMPedejQAXfv3tVwigj5ct2+fZsdVsnBwQGfPn1SqJCXVH3//vsvevXqBQD45ZdfsHLlSg2niBBCiDQeHh7scO+3bt2Ch4dHueNcvnw5VqxYAQBYtmwZli9fXu44CSGEEEIIIYSQqohKxKu4X375BXp6egBQKefqIEQaUWMO4MvrtUpIZSN+vo0fP54qxL8ia9euBVDcS3zu3LkaTg0hhBBCCCGEEEIIIYRoBpWKV3GOjo6YMWMGAODEiRN4/fq1hlNEiHyPHz/Gf//9B6C4osbb21vDKSLkyxUTE4NTp04BKB7mlBqhfD3u3r2LW7duAShuRGdmZqbhFBFCCCGEEEIIIYQQQohmUKX4F2Dp0qWwtbWFQCDAokWLNJ0cQmTKy8tjG3IAwJQpU2BoaKjBFBHy5RIIBJg1axY7l/jw4cNhb2+v4VSRivLTTz8BABo2bIgff/xRw6khhBBCCCGEEEIIIYQQzdHRdAJI+ZmYmCAmJkbTySBEqp07d+LDhw9IS0vD9evXERERAQCoXr065s2bp+HUEfJl8fPzQ3BwMLKyshAQEMCOIKKvr0/ziH5l7t27p+kkEEIIIYQQQgghhBBCSKVAleKEELXz8/NDQEAA53/a2trYt28fqlWrpqFUEfJlunLlCg4ePFjq/+vXr0f9+vU1kCJCCCGEEEIIIYQQQgghRLNo+HRCSIXh8XiwsLCAl5cXAgIC0L9/f00niZAvmomJCTw8PHDhwgVMmzZN08khhBBCCCGEEEIIIYQQQjSCxzAMo+lEEEIIIYQQQgghhBBCCCGEEEIIIepAw6ermFAoRExMDExMTMDj8TSdHEIIIYSQcmMYBpmZmbCzs4OWFg00VJEob0kIIYSQLxHlLzWH8peEEEII+dIomrekSnEVi4mJgYODg6aTQQghhBCicpGRkbC3t9d0Mr4qlLckhBBCyJeM8pcVj/KXhBBCCPlSyctbUqW4ipmYmAAo3vGmpqYaTo1khYWF+Pfff9GzZ0/o6upqOjlfNNrXFYf2dcWhfV1xaF9XLNrf0mVkZMDBwYHN55CKUxXylupE5yUpLzqGSHnRMUTKi44hySh/qTlfSv6Szi3Nov2vObTvNYv2v+bQvtesyr7/Fc1bUqW4iomGHTI1Na20GcvCwkLw+XyYmppWyoP3S0L7uuLQvq44tK8rDu3rikX7Wz4aXrHiVYW8pTrReUnKi44hUl50DJHyomNINspfVrwvJX9J55Zm0f7XHNr3mkX7X3No32tWVdn/8vKWNGkPIYQQQgghhBBCCCGEEEIIIYSQLxZVihNCCCGEEEIIIYQQQgghhBBCCPliUaU4IYQQQgghhBBCCCGEEEIIIYSQLxZVihNCCCGEEEIIIYQQQgghhBBCCPliUaU4IYQQQgghhBBCCCGEEEIIIYSQLxZVihNCCCGEEEIIIYQQQgghhBBCCPliUaU4IYQQQgghhBBCCCGEEEIIIYSQLxZVihNCCCGEEEIIIYQQQgghhBBCCPli6Wg6AYSQsmEYBoWFhRAKhZpOSqVQWFgIHR0d5OXlQSAQaDo5XzTa1xWH9nXF+tL3t5aWFnR1dcHj8TSdFEIUIsrrfMnnJVE/OoZIedExRMrrSz2GKG9JqiIqS1OdL/XaVhXQvtcs2v+aQ/tes9S9/ysqb0mV4oRUMQKBAElJScjMzERhYaGmk1NpMAwDGxsbREZG0kO5mtG+rji0ryvW17C/dXV1YWJigurVq0NbW1vTySFEIvG8TkFBwRd/XhL1+hqu7US96Bgi5fUlH0OUtyRVBZWlqd6XfG2r7Gjfaxbtf82hfa9ZFbH/KyJvSZXihFQhAoEAkZGRyM/Ph5mZGYyNjaGtrU03AQBCoRBZWVkwNjaGlhbNDKFOtK8rDu3rivUl72+GYSAQCJCVlYW0tDTk5ubCwcGBCi9JpVMyr8Pn85GXl/dFnpekYnzJ13ZSMegYIuX1JR5DlLckVQmVpanHl3htqypo32sW7X/NoX2vWerc/xWZt6RKcUKqkKSkJOTn58PR0RGGhoaaTk6lIhQKUVBQAAMDA7opqhnt64pD+7pifQ3729jYGGZmZoiIiEBSUhKsra01nSRCOErmdYRCIYRC4Rd9XhL1+hqu7US96Bgi5fUlH0OUtyRVAZWlqceXfG2r7Gjfaxbtf82hfa9ZFbH/KyJvSUcOIVUEwzDIzMyEmZkZZeIJIYSUmaGhIUxNTZGZmQmGYTSdHEJYlNchhBBCqh7KW5LKjPKXhBBCSNWi7rwlVYoTUkUUFhaisLAQxsbGmk4KIYSQKs7ExIS9rxBSWVBehxBCCKmaKG9JKivKXxJCCCFVjzrzllQpTkgVIRQKAYDm6CKEEFJuonuJ6N5CyufAgQPg8XhyXzdu3NB0Uis1yusQQgghVRPlLVWP8peqQflLQgghpOpRZ96S5hQnpIrh8XiaTgIhhJAqju4l6qGlpQUrKyup3+vr61dgaqouOj4JIYSQqoXu3epD+UvVoGOUEEIIqTrUed+mSnFCCCGEEEJUwMHBAZ8+fdJ0MgghhBBCyBeC8peEEEIIIapDw6cTQgghhBBCCCGEEEIIIYQQQgj5YlGlOCGEEEIIIYQQQgghhBBCCCGEkC8WVYoTQogSxo4dCx6Ph7Fjx6p8eXV8V9n9/PPP4PF4+OOPPzSdFLl8fX3B4/EwevRoTSeFEEIIUSsPDw/weDwsX75c00lR2vLly8Hj8eDh4aHppKgNj8cDj8fD7du3NZ0UQgghhJBKg/JIhFS8yla2+6VeBypz3UBERAT09fVRt25dFBQUaCQNyqBK8SqosBC4eBEICtJ0SgipmkSFpSVfBgYGsLe3R//+/XHixAkwDKPppH7RoqKisGHDBlhZWWHatGmlvhcVyJd8aWlpwdTUFM2bN8e8efPw+fNnqeuQ9FuLlre3t0f79u0xbdo0nDp1Su5Ne/jw4WjUqBGOHDmCJ0+elHv7CSFfnsTERLRq1QrGxsYwNDSEs7MzRo4c+cU9jJFKLD8fSEsDsrPBMAxOnjyJQYMGwcnJCYaGhjA2NkadOnXQsWNHzJkzB2fPnkVGRoamU10lPH36FMuXL8emTZs0nZQyO3DgAJsfUmR+2lq1alXZhpcV6dWrV5gxYwaaNWsGMzMz6Onpwc7ODi1atMCIESOwa9cuvH37VtPJVMjt27cl5r/5fD5sbGzQrFkzjB49Gjt37kRKSopa07Jp0yYsX74cT58+Vet6CKnsKH/5FRAKgcxMID0doHIojRC//ylybonKq77kRpiq8PnzZyxatAhubm6wsLCArq4urK2t0bRpUwwZMgSbNm3Cs2fPNJ1MhXz69Ik9RrS1tWFhYQFtbW0YGBigRo0aaNSoEb799lts2LAB0dHRak3LgQMHsHz58kpxH5BXtvu1WL58OZYvX67QM9aXyNHREePGjcOHDx+wfft2TSdHLh1NJ4Aoh2GAHj2AgIDizz//DKxapdk0EVKVWVtbs+/T09MRHR2N6OhoXLx4EQcOHMDZs2ehr6+vsvXZ2tqiQYMGsLW1rdRxVoQlS5YgNzcXk/mB9AABAABJREFUK1euhJGRkdRwurq6qFatGvu5sLAQKSkpePbsGZ49e4YdO3bg9OnT6NOnj8z1if/Wubm5iImJQXR0NIKCgrBjxw5YWlpi1apVmDJlCng8XqnltbS08Msvv8DHxwfz5s2Dv79/GbaaEPIly8nJwZMnT2BhYYHs7GyEh4cjPDwcR48exbhx47B7927o6MjOfufn5yM/P5/9LKqwLCwsRGFhoVrTr2mFhYVgGAZCoRBCoRAA2AZqov+T0nhZWUBMTPHf/0nLzMTAn39GwL177P90dHTA5/MRERGBjx8/4r///sPGjRuxb9++UpWeDg4OaNCgASwtLavcfhdv1CgUClV2DD158gQrVqyAk5MTZsyYUe50qoL4uaJo+LIs+7Wff7KOoXXr1mHJkiUoKipi/2dubo60tDTExsbi6dOnOHbsGLp06VIl8o7i22dhYQE9PT0AQFFREZKTkxEfH4/nz5/j8OHDmD17NiZNmoQ1a9bIzMuX1aZNm/D582c4OjqiadOmKo+/In0N9zLR9bawsBDa2toKLfOl52tUhfKX0onSLmsbJOUvK43CQvA+fwZPrIEiY24OxtlZg4lSXGW+tlVUHqnkshWlMu97kaNHj2LKlCnIyclh/2dqaoqcnBy8ePECL168wJkzZ+Dk5ISPHz9qMKWKEd/PpqamMDAwAI/Hg0AgQFpaGhITExEWFgY/Pz/89NNPGD58ODZt2oTq1aurPC0HDhxAQEAAGIZB586dVR6/MhYvXozc3FysWLEChoaGaj8elTn2K/K6v2LFCgBA586d4ejoqJZ1yNr2iromyFrPwoUL8ffff2P16tUYM2YMzM3Ny7UudeYtqVK8inn//v8rxAFg9WqgRQtg8GDNpYmQqiwuLo59LxQKERYWhtmzZ+P69eu4cuUKfv75Z/z5558qW99vv/2G3377TWXxqStOdYuOjsbRo0ehp6eH8ePHywzbvn37Uq0f8/LycP78eUybNg3JyckYOXIkIiIiZBbIif/WACAQCPDq1Stcv34d27ZtQ3h4OKZOnYq7d+/iyJEjEivGhw4dihkzZuDWrVt49OgRWrdurfhGE0K+WHZ2dli2bBkGDx6MBg0aQF9fHwKBAA8ePMCyZctw48YN7N+/H0ZGRti6davMuH777Tf2gUrcv//+Cz6fr65NqBR0dHRgY2ODrKysUqN3ZGZmaihVlQ9PIIBOTg4MUlOhLWWUk9HLliHg3j1oa2vjhx9+wNixY1G7dm1oaWmhqKgIr1+/xs2bN3Hq1Cnk5eWV6i0ufpxWtZ7kokL/oqIiTtrLewzl5eUBKM4vVpZ9kpOTo1RaRNsAAFlZWXKXFRV0FBYWVppt1qSSx9DFixexYMECAMX51Xnz5qFdu3YwMDAAAMTExCAoKAgXL15ERkZGldiH4gXXhw4dQseOHTnfR0REICgoCH///TcePnyIbdu24caNG7hy5Uq5C75KEh1/kq5RVdWXfC8rKChAbm4uAgMDOY1EZBE/3khplL9U3PXr16V+Jyt/qREMA+28PPDj46Et4VzhpaUhIyUFjJyGDpVJZby2KZtHEr8eKbKs6DpXMr9Z0SrjvgeKG5OOHTsWQqEQrq6u+Omnn9CtWze2zC4pKQkPHz7E5cuX8eTJkypxn88Sa4D822+/4bvvvuN8HxcXh+DgYBw6dAg3btzA8ePHcfv2bVy9elXllaSi4y8/P1+j+y4mJgbHjh2Dnp4ehg4dWqFpUeTYV/Y6oArqXKd4Q7CS65D1nTpI2v/m5ubo0aMH/vnnH2zfvh0//vhjudahzrxl1bnDEgCAlVXp/w0ZAiQlAZaWFZ8eQr4kWlpacHV1xYULF9CkSRO8f/8ef/31F3777Te5La+Jcvbs2QOBQIB+/fpxeoErysDAAN7e3igsLMSoUaOQkpKCwMBAub3FxWlra6NJkyZo0qQJpkyZggkTJsDX1xfHjh1D48aNsWjRolLL6OjoYPjw4di+fTv++usvqhQnpIo6cOAAxo0bV+blr1y5gt69e7Ofe/bsiZ49e3LCaGtro3379rh27RoGDx6M8+fPY8eOHZgxYwbq1asnNe5FixZhzpw57OeMjAw4ODigZ8+eMDU1LXOaq4K8vDxERkbC2NiYrVRiGAaZmZkwMTGR2Fjpq5GbC156OpCYCJ6c1s/vIiJw8c4dAMCqVavYSjtx7du3R/v27fHLL78gNzcXhoaGakm2JohG+NHR0YGpqanKjiHRMSmahqUy4PP5SqVFtA0AYGxsLHdZLa3i2dZ0dXUrzTZrgrRjaNeuXQCAxo0b49atW6WeF0xNTeHi4oJx48ZVmfNMvHJM0vHVuHFjNG7cGJMmTcKmTZswd+5cvH79GpMmTcK1a9dUmhbR8WdgYFDlj7+v4V6Wl5cHQ0NDdO7cmXOtkaUqVIIog/KXFa+wsBDXr19Hjx49oKurKzGMpPylRgiF4KWkgBcRITMYo6MDkzKUkWhCZb62KZtHknf/K0l0zxflNytaZd73ALB3714IhULUqFEDd+7cgZmZGed7U1NTODs749tvv60yeSRjY2P2vehaIr7/TU1NUb9+fYwYMQInTpzA6NGjERsbCx8fH4SEhKi0XFkUl76+vkav4Rs3boRAIMA333wDJyenClmnMse+stcBVVDnOkX3OUnPZrK+UyV5+3/MmDH4559/cPjwYSxevLhc1yd15i1pTvEqRlrj6ylTaNoZQlTFwMAAw4YNA1Dc8un169dSw546dQoeHh6oVq0a+Hw+mjdvjs2bN0sdqmTs2LEqn5tRVpyieY6WL1+OgoICrF27Fk2bNoWRkREsLCzQo0cPXLlyRWrcRUVF2L17Nzw8PFC9enXo6urCysoKbm5u+Pbbb/H3338rnV6GYbBv3z4AKNWyUlnNmzdn34u32lQWn8/HwYMH0aJFCwDA2rVrpc6TKErz8ePHy7VOQsjXQUtLC+vWrQNQ3Ovt4sWLMsOLHmzFX0Dxw83X8OLxeNDS0mJfooeokv//4l88HrRyc6EVHQ2t0FBohYWBFxMjt0IcAJ6KzV08cOBAuesyMjIq9T9PT09oa2tj5cqVpb5zdnaGtrY2Dh06hLy8PKxcuRKurq4wNjaGvb09xowZg8+fP7PhU1JSsGjRIri4uMDIyAh2dnaYPHkyEhMTJaZn/Pjx0NbWxvjx46Wm+dChQ9DW1oazs3Op78QfvEseQ/n5+bh06RK+//57tGzZEtbW1jA0NIS9vT0GDx6Ma9euSVyftrY2JkyYAKB4bkRtbW3OS9J+yszMxG+//YZ27drB0tIShoaGcHJywogRI/Dw4UOZv0l6ejoWLFiAevXqgc/no2bNmvD29kZISAhbUSjaPmVfyiwrIun8E217YGAgEhISMGPGDNSpUwd8Ph92dnYYNWoU3r59KzHeiIgIdvmIiAh8+PAB48ePh6OjIwwNDVGrVi1MnToVsbGxctN44cIFDB48GPb29jAwMIClpSU8PDywe/duCAQCicuIH98CgQAbN26Eu7s7qlWrxm6TpGOq5H4QzXXt5eUFPT09pc8z0Ss3NxebNm1C165dUaNGDRgaGsLR0RFdu3bFxo0bpZ4rgYGB8Pb2hoODAwwNDVGjRg306NEDBw8eBMMwZTo+lDlG5syZw84deePGDdy6datUmHfv3mH9+vXo2bMn6tWrByMjI5ibm6NVq1ZYunQpUlJSSi2zcuVKaGtr4/PnzwCACRMmlDrnyruOin59LfcyHo+n9H2flI2WFuUvxY8hZfOXFfpiGGjFx0MrNFRuhTgA8OrW1fi5LO0l6f7ZtWtXWFpaSrx/hoeHY+bMmXB1dYWpqSmMjY3h6uqKOXPmICoqSuI6ACAoKAiLFy9G+/bt4ejoCAMDA1hZWaFr164y7++i5UXniLIvZZYteT6Kv8TzytnZ2ViyZAkaNmwIIyMj1KhRA4MHD0ZwcLDUuBXJY71586b4eJFxbAcGBmLEiBGoVasW+Hw+LCws0LZtW6xbtw65ubkSlxHPh/N4PPz999/o3LkzrKys2G1SZP+I5gn38PCAhYWFzLCy8khFRUX4+++/4eXlBVtbWxgaGqJmzZro0KEDVq9ezXnmKLl+0UhZfD4flpaW6NixI7Zs2YLCwsIynwOlzlcp+//bb7/FmjVrAACvXr3C4cOHS4WJiorCzp070a9fP7i4uMDExASmpqZo3Lix1HNE9AwU8L9hhEV5JvFXREREudah6Et0fADAiBEjpIbLz8/Hhg0b0KFDB1haWkJfXx/W1tZo3Lgxxo0bh7Nnz8o8ByStFwC6desm9TlM/NxU9jlFS6v4WWz58uVo3bo1zM3NYWBgADs7OzRv3hxTp07l5HlF54yIKF2il6TnVS2tsj3DyMpPyvouMzMTJ06cwKhRo9CsWTNUr14dfD4ftWvXxsiRI2U+m4qOMU9PT2hpaeHMmTMYPHgwbG1toaOjU2r/DxgwACYmJnj37p3E368sx5k68pbU9bEKOnAAKFn39egRkJEBlGh4RQgpI3t7e/a9tFZG06dPx/bt26GlVdxjKDc3F8+ePcOsWbPw5MkTHDx4sKKSK1dBQQG6d++OO3fuQEdHB8bGxkhLS8ONGzdw48YNLFu2DMuXL+csIxAI4OXlxRmKzMzMDNnZ2UhJScH79+9x8uRJucOflxQaGoqoqCgAQKdOncq1XaKMNgA0aNCgXHHp6elh8eLFGDZsGDIyMnDu3DmJ2+bm5gYDAwNkZ2fjzp07SvVOJ4RUDj4+Pvjmm2/KvHzJlu7y1K1bF9WrV0dSUlKVmC/ta1dUVIScnBwIBAJoa2uDz+dXzIgxDAPk5gIpKUBqKiA296fCTE0BBwf2Y1RUFBo2bKjCRP6/9PR0tGvXDs+fP2fn1IuNjcWRI0dw8+ZN3Lt3D0KhEN27d0d4eDj4fD6EQiHi4+Oxb98+BAYG4tGjRxXaet/Pz4/Ti8/Q0BA6OjqIjY3F+fPncf78ecydO5etaBCxtrZGbm4uMjIyoKWlBasSw3eJ9xwBgAcPHmDAgAGIj48HAPY4ioqKgq+vL/z8/PDrr79KHJXm06dP8PDwYCsE9fT0kJOTg1OnTuHChQs4efKkSvaFqoSHh8PHxwdxcXEwNDSErq4u4uPjcezYMZw5cwZnz57l9Hws6cGDB5g0aRIyMzNhbGwMbW1tREZG4q+//sLJkydx/fp1tGzZstRyWVlZ8PHxwaVLl9j/mZqaIj09HXfu3MGdO3dw6NAhXL58GRYWFhLXnZeXBw8PD9y7dw86OjowMTEp0z4Q5WvL4smTJxg4cCAiIyMBFBfemZmZISYmBtHR0QgMDIS2tjZmzZrFWW7OnDnYuHEjgOLCLzMzM6SlpcHf3x/+/v44cuQIzp07V+ZtUtSSJUuwe/duFBYW4uDBg+jWrRvn+169erHHsiid6enpePr0KZ4+fYoDBw7g5s2bnHy8sbExrK2tkZiYCKFQCFNTU5k9yMqyDkLUgfKXhKOoCEhIAOLjAYEAglwGeXEMhPkMtPR5MLDhQdtQrAedpSXg6AgoOG+qJily/9yzZw+mTZvGDumrr68PLS0tvH79Gq9fv8b+/ftx6tQp9OjRg7NcREQEZ+oOHR0d8Pl8dnTAwMBAHDt2DNeuXav0vYtTU1Ph5uaGN2/eQE9PDwYGBkhOTsb58+dx8eJF7NmzR2Z5mrw81uHDhzFYwnyqRUVF+OGHH7B37172f8bGxsjOzkZwcDCCg4Px999/49q1a1J79zIMg+HDh+PUqVNs3kRSpbA85ckjhYeHo3///ggNDQXw//f4xMRExMXF4f79+0hJScGmTZs4y23atAlz5sxh5z4WlWMGBQUhKCgI+/fvx9WrV2Fra1vmtCli+vTp+P3335GUlISDBw+yDWxFRo8ezVZui9KZmZmJsLAwhIWF4cCBA7h06RLnfDA0NIS1tTVSUlJQWFgIIyOjUs8h4hW0ZVmHohQp283MzESnTp3YslvxPGtSUhLCwsIQEBCAIUOGKL1+RZTlOSUqKgodOnRAxP8aMYmO/6SkJMTHx+PFixd4/fo1m+c1MzODtbU1+9xnYWEBPT09Nr6Sz42qeIZR1saNGzlTqIiOmYiICERERMDX1xebNm3CjBkzZMYzd+5cbNiwATweD+bm5hKvCfr6+nB3d8fNmzdx9epVdO3aVSXboGrUU7wKGjSo9P/mzqUK8a8dI2SQnZj9Rb4YYcUPg/Dp0yf2vaThvS9cuIA9e/Zgw4YNSE1NRWpqKpKSkjBx4kQAxXPw+fv7V1Ry5dqxYwcePnyIXbt2ITMzE6mpqYiIiMDQoUMBACtWrMCFCxc4yxw/fhzXr1+HgYEB9u7di8zMTKSlpSE7Oxtv377FqVOnypRxCQwMBAA4ODjAxsamTNuTn5+P06dPY/bs2QCKh5Zr2rRpmeIS17t3bzYDKZ5xFKerq8sWzkoLQwip3PT19VG9evUyv6hn05crJyeHfTiMiYlh36t13tPcXCA6Gnj5Enj1CoiLU65CvFo1oFEjoFUroH59uHXowLYUnzt3Lt6K9RxXpeXLlyMjIwP//vsvsrOzkZmZiX///RfVq1dHbGwsFixYAB8fH5iZmSEoKAjZ2dnIysqCn58f+Hw+3r17hz/++EMtaZPG3NwckydPxq1bt5CUlIScnBxkZ2cjJiYGK1asgK6uLtavX18qTxQXF4fNmzcDKM6/xMXFcV7z5s1jw3769Am9e/dGfHw8hg4disePH7NzIsfHx+OXX36BtrY2Fi9ejHPnznHWIxAIMGzYMHz+/BkWFhY4ceIEsrOzkZ6ejpcvX6JNmzYYM2aM2veTMmbPng09PT3OcfDgwQM0adIEeXl58Pb2llkg+v3336N27dp48OABMjMzkZ2djWvXrsHR0REpKSkYNGiQxDnrRo0ahUuXLqFu3bo4duwYMjIykJ6ejpycHJw/fx7Ozs4ICgqSWdi8fft2PH/+HPv370dGRgZSUlKQlJSkcJ7S3d0dAHDixAkcO3ZM6khR0kRGRqJXr16IjIyEg4MDfH19kZmZiZSUFOTm5uLFixdYvnx5qcK0bdu2sRXikydPRkxMDFJTU5Geno6NGzdCR0cH/v7+mDRpklLpKQtbW1t2pCVJ+eK2bdti69ateP/+PfLy8pCamoq8vDzcuHED7u7uiI6OLjVy1Lx58xAXFweH/zXw2bx5c6lzrrzrIEQdKH9JAACFhcX5uufPgZgYQCBAXpwQif5FSL5bhNRHAiTfLUKifxHy4oSArS3QogVQu3aVqBAH/v/+uW/fPkRERCApKYlz/zx37hwmT54MAFi4cCE+ffqE3NxcZGdn4/Xr12xHhKFDh7IVTyI6OjoYMGAA/Pz8EB0djfz8fKSnpyMzMxP79++HnZ0d7ty5gyVLllT4ditrxYoVSEhI4OTnXr16hS5dukAoFOL777/HkydPpC4vL481fvx4iXmsefPmYe/evbC2tsaOHTuQnJyMzMxM5Obm4tatW2jRogXevHmDwYMHS827nDlzBufOncO6deuQmpqKlJQUpKeno1evXgptuyiPdO/ePaxfvx4FBQUKLSeSkZGBXr16ITQ0FBYWFti9ezdb9pqbm4s3b95g/fr1pSr1L126hNmzZ4NhGAwYMAAfP35EWloasrKycOjQIZiYmOD58+cYOnQoBAKBUmlSloGBATw9PQEUNwLNy8vjfN+4cWOsXbsWr169Qk5ODtLS0pCfn48HDx6gd+/eSE9Ph7e3N3Jzc9llvL29ERcXh/bt2wP4/zyT+MtBrIF0WdahKEXKdjdv3oxnz56hWrVqOH36NHJzc5Gamor8/HxER0fj0KFDpaYJUaWyPKcsX74cERERqFWrFm7cuIGCggKkpKQgPz8fnz59ws6dO9G2bVvONornTc+cOcP5PYKDgznxq+IZRlk2NjaYPXs27t+/j9TUVPZ68PHjR8ycORNAcYPbkJAQqXE8fvwYGzZswPz58/H27VskJSUhOztb4rQxbdq0AVC5y8ypp3gVZGoKDBsGiHcSOHkSmD5dc2kimpeTnIN1NdbJD1gFzUuYByMrowpbX0ZGBo4ePQqguEK8fv36pcKkpqZi//79nCHLLS0tsWfPHoSEhODx48c4fvw4mwHStPT0dOzbt49zU3VwcICfnx+6du2KwMBALFq0CP3792e/v3fvHoDiloXiLRp5PB6srKwwaNCgMlWKP3jwAADQrFkzhcLfu3ePk8EqLCxkhzZ3cHDATz/9VKqXe1kZGxvD2dkZ7969w4cPH6SGa9GiBe7du4egoCCVrJcQ8mX78OEDkpKSAAC1a9fWcGqINEVFRYiLi0N+fj4sLCzA4/HAMAwyMjIQFxcHR0dH1fUYLywEkpOLX2UohIClJVC9OmBsDJSYp6tWrVqYOHEi9uzZgxcvXsDFxQXNmzdHu3bt0KpVK7i7u8PV1bXc8w/m5+fj+vXrqFu3Lvu/Hj164Pfff8eECRNw4sQJWFtb4+XLl7C0tARQ3LBs+PDhCA0NxapVq+Dr64vVq1eXKx3KGDhwIAYOHFjq/7a2tli6dCn4fD7mz5+PLVu2cPJEypg/fz7S0tIwatQoHDp0iPNdjRo1sHLlSlhYWGDOnDlYvnw5Jz2nT5/Go0ePAAAnT57k9Lpt1KgRrl69iqZNmyItLa1MaVOH3NxcBAUFcUYkcHd3x40bN9CwYUOkpKTgt99+w/bt2yUur6Ojg+vXr6NGjRoAivOZPXv2xNWrV9G8eXNERERg165dmD9/PrvM5cuXce7cOdjY2OD27duoWbMm+52BgQH69++Pli1bwsXFBefOncPTp085U+6IZGVl4cKFC+jXrx/7P9Gxqojly5eje/fuKCoqwogRIzB37lx07twZrVu3RuvWreHu7g4jI+nPMIsXL0ZSUhIsLS3x33//cQox9fX12fm7xeXm5mLZsmUAinul/vXXX+x3RkZGmDVrFrS1tTFjxgz4+flh3rx5aN26tcLbVBbNmjXDw4cPERERgaKiIs510tfXt1R4PT09dOvWDTdv3kTdunXx5MkT3L17t0y9lCpqHYRURpS/rGSEwuJe4bGxxe//R5DLIO2xAIWZDAwdeOBp8cAIGeRlGSEtuTqszK05PTurAtH9s2/fvuzIiqL7Z0FBAab/r4B6165dpSp2GjRogBMnTmDAgAG4cOECNmzYwOnpa29vX6rRIFBcVjN27Fg0btwYbm5u2L17N9asWaPZeeLlSE9Px40bNzj5uYYNG+LKlSto1qwZ3r17h19++QWXL1+WuLwieay1a9dix44d7PehoaHYsmUL+Hw+rl+/jiZNmrDf6erqwsPDAwEBAWjUqBGePHmCCxcuSMwbZ2VlYcuWLfjxxx/Z/xkbG5fqlSzNwoULcfr0aWRmZmLevHn49ddf0aVLF7Ru3RqtWrVC27ZtYS5tjlgAf/75J969ewd9fX3cvHmTbYAn2o769etjzpw5pZZbsGABAKBjx444ffo0e27p6elh1KhRMDc3R//+/XHv3j2cPXuW7SikLs2aNcOJEydQUFCAqKgoznPTtm3bSoXX0dGBu7s7Ll26hJYtW+L58+c4ffo0Ro4cWab1q3MdipTtisqV582bxxnVQEtLix3GfNSoUUqtVxlleU4RpXnNmjWcc1dbWxtOTk6YMmVKmdOjqmcYZUlKM4/HQ+3atbFp0yYUFRVh+/bt2L59O2eECXFZWVmYM2cO1q5dy1739fX1JY42ITpfHz9+jIKCAk7P+cqCeopXUT4+3M937gD/G3GNEFJGaWlpuHnzJjw9PRETEwMAmDlzpsThQBwcHDB69GiJ8YgKUZ8/f66+xCrJwcFBYustLS0t/PzzzwCK57l58eIF+50og1qyN0Z5ifZtyV4v0hQWFiI+Pp59ic/1nZ6ezrZYVRXRyADS5hQHgOrVqwP4/20hhHy9RMOyyfpeVJmjpaVVrmE1iXrl5OQgJycHpqamnDm5RFOklLu3OMMAWVnAx4/FvYeiopSvEDcwAFxcinsTmZiUqhAX2bFjB3755RcYGRmBYRiEhIRgx44dmDBhApo0aQIbGxvMmTOHHeatLIYMGcIp2BER70EyefJkiZWMojAfPnxAdnZ2mdOgan379gVQPJdlWXqPpKSk4MyZMwCKCwOlEeUhnz17xvkNRJV7HTp0KDUMNQDw+Xz89NNPSqdLnYYNGyZxiP4aNWqwBTB+fn5Sl58yZQpbIS6uYcOGbEFlyUpPUWHNqFGjOIVJ4uzt7dnh+q5duyYxjKurK6dCXFldunTB1atX2WG54+LicOLECfz000/w9PSEhYUF+vbty/akEZednc3ul4ULF3IqxGW5fv06m0eV1ih06tSp7JCgx48fV3azlCY+qpas/HNJxsbG6NKlCwDg7t27Kk9XRa2DEHWg/GUVwjBAWlrxiD/R0ZwKcQDIi2NQkMLAwKa4QhwAeNUsYNDMBgWJRciLyJMQaeUm6/555coVREdHw9raWmIZlIgoLyTtHi1N69atUaNGDWRnZ+Pp06dKLVvRpOXnDA0N2fP36tWrUsuzZOWxvv/+ewDFo9WI27dvHxiGQd++fTkV4uJMTEzYinBp+9/CwoJdR1k0aNAAAQEBcHNzA1DcsejcuXP4+eef0adPH3b+ZEkNIACwc1VPnDiRUyEuy/Pnz/Hq1SsAYEdmKqlfv35sL/bKnEfS1tZmh/VWV/6lvOtQpGxXVK4cGxurfAJVoCzPKepMs6qeYVRN9Aws6zjQ0tJiG53IIyozLyoqQmJiYvkTqAZUKV5F9enDHS6dYQAZZQ2EECl4PB77srCwQPfu3fH48WMAwMiRI6UOyeTm5iZ1Ph07OzsAymV41M3Dw0Nqj7DOnTuzPTpEvZMAwMvLCzweDxcuXECfPn1w/PhxlVQCi26Ikoall6RLly5gGIbzErW4bdq0Kfbu3Qt3d3e8e/eu3GkD5BdAAP+f9sp6cyeEVJzPnz/D3d0df/31Fz5+/MheQ4RCIe7fv48+ffrg7NmzAIqHCaY5TSsvgUDA5gnEiT6XeYg90ZySr14Br18XzxmuwL2mFGtroGHD4t7hcujo6GDlypWIjo7G4cOHMXHiRDRr1oxtpZ2QkICNGzeicePGePjwofJpwf8Pi1g6mdbse1FBmKwwFd3rOT4+HsuWLUO7du1gaWkJHR0d9ndv1KgRgOIGEqmpqUrHHRQUxA5D6enpCRsbG4kvV1dXdhnRXMjA/+fDZI00VFlGIRJRJK3JyckIDw8v8/LPnz9n5yQF/r/AZvfu3VL3sY2NDW7cuAGAu4/FdejQQcaWKaZbt2549eoVbt++jUWLFsHT05PNJxYWFuKff/5Bly5dsHTpUs5yjx49YrdJmYp50THi4OAgcTQroLiQU7TvxPP26iIv73zp0iV4e3vD2dkZRkZGnOcvUWF+eeYcrah1EFKRKH9ZReTmAu/eAe/fS536RpjPADywFeKoZgFUtwRPRwvgAcJc5abeqAxk3T9F9+jU1FTY2tpKvUeLpviQdI8uKCjArl270LNnT9jZ2cHAwIBzXU9ISABQ+a/riuRxhEKh1CHUZS0vqjQrmccS7f8rV67IzCPt378fgPQ8kpubW7l7d7Zo0QIPHz5EcHAwVqxYgd69e7OjQAqFQgQEBGDQoEEYN24cJy/x+fNntuyxLHkkHR0dtkGcJKJ57CtDHunOnTsYO3YsXFxcYGxszDnORdNMlfc4V9c6FCnbFTXY2rZtG3x8fHDu3Dl2hJOKUJbnFFGaFy5ciMmTJ+Pq1atsz+jyUtUzTFl8/PgR8+bNQ6tWrWBubg5tbW32OPDy8gIg+zioW7euxIbMkogfE5W13JyGT6+iDAyAIUOA/zWcAgAcOwaITWdHCFGAeKGsaA6wFi1aYMSIEWwmUxITExOp34kqmMUL7zRNWgs0oHi7LS0tER8fzz5cAMXDDf3+++/4+eefcfXqVVy9ehVAcau1zp07Y/z48RJbvcojmkdHX19f6WVFTE1N0a1bN7i7u6NJkyb4/Pkzvv/+e5XM4y4qBJc1fKahoSEAlJoTiBDydQoODmbnitLX14eJiQkyMzORL1Y4Nm7cOGzZskVTSSQK0NbWZhtfiVeMiwozlB7aMje3eBjNlJRSvYaUoq8P1KpV3DNcSWZmZhg5ciQ7HF5eXh7u3r2LLVu24OLFi0hKSsKQIUPw7t07pYeflJYXEh86WZEwFZlfCgoKgpeXF6ci3tjYGHw+HzweDwKBgC2oyc7OZlu5K0q88aCivfDFRyAQ5cNk5dvs7e2VSpO6yUqr+HcJCQkSh/dVZPmioiKkpKTA2toahYWF7G+Unp6u0GhB0kZ5ULRgRx4tLS106dKFUwD7+vVrHD9+HOvXr0d2djZWrVoFd3d3tqBNfCQmScMOSqPIMQL8/3EinrdXF/EGJOKFYEKhECNHjuT0xNLR0YGFhQVb0J6eno68vLwyjxhREesgRFMof1mJMUzxMOmxsXIbOmrp8wAGYIQMeNUtiyvFeTwwAgZgAC3DqtdfTdb9U5QXKigoUCgvVHIu44SEBHTv3p0ziqGBgQGqV6/O5sUTExMhFAor/XVdmTxSeZcX5bFE+z8rKwtZWVly06juPBIAdloZkU+fPuH06dNYu3YtkpKScODAAbRq1Yoddr+8eaTq1avLLG/UVB6pZBnjggUL2EppoPhZUzz/kpWVhezs7HId5+pchyJlu9999x0ePnyIrVu3wtfXlx39qW7duujZsyfGjx+PVq1aKb1uRZXlHJo/fz6ePXuGEydOYM+ePdizZw94PB5cXV3Ru3dvTJo0SWrDVFlU+QyjrLNnz8LHx4eTfzA1NWUbHBUUFCA1NVXmcaDMNUFUZg5U3nJzqhSvwnx8uJXiISHFnU9cXDSXJqI5fEs+5iV8ma0i+JZ8tcWt6qHBK6uyzhs6f/58jBgxAidOnEBAQADu3buHqKgoHDt2DMeOHcPQoUNx7Ngx6OrqKhynKCNYlh5YJZmYmMDb2xt//PEHbt26hdjYWHa4yLLIysrCx48fAQB16tSRGk40CoAy804SQr5M1tbW2Lp1K4KCgvD06VMkJiYiNTUVBgYGqF27Ntq3b4/x48erpEciUS8+nw8+n4+MjAx2CHXRnOKGhobg8xXMj+TlATExxZXhsvB4xb2+c3IAab3QrawAe3tARXNNGhgYoHv37ujevTvGjh2LgwcPIioqClevXpU4n+CXpKioCD4+PkhLS0Pz5s2xZs0adOzYkVNx/+HDB3ZIeEVGjilJNJqAoaFhuQoxZOXbyjMXvHgBRclCaElE2yC+nCrTU5blxUds8PX1hbe3d5nXrc45XF1cXLBixQp06dIF3bt3B8Mw2Lt3r8qGOFZ0v5X391HEs2fPABQXXIs3eNm3bx+OHz8ObW1tLFmyBKNGjYKzszNntK1Ro0bhyJEjZTrfKmodhGgC5S8rsfx8IDy8eEocBRjY8KBXjYe8XDMYmFsU5y8FDPIi8qBnrQcDx8o7J7Y0su6fovt07969ceXKFaXjnj17Nl68eAFLS0v8+eef6NOnD9u7WMTBwQFRUVEqva5XdB5JkftzWe7hov2/du1ahYc6lkSdeaRatWph7ty56NOnD1q3bo3c3Fzs3buXrRQXV5Z9UBnzSPr6+pxK2OvXr7OV1VOnTsUPP/yAhg0bcvb7L7/8gtWrV5f5OFf3OhQt2920aROmT5+OkydP4s6dOwgKCsL79+/x/v177NixAzNnzsSmTZuUXr8iyvIb6+rqws/PD4sXL8aZM2dw9+5dPHjwAKGhoQgNDcXGjRvx+++/Y+7cuUrFq8pnGGUkJydj7NixyM/Ph6enJ5YuXQp3d3fOtevmzZvo3r27zHiUuSaIj5xbWcvNqVK8CuvatXgURfGGd8ePAytWaC5NRHN4WjwYWRlpOhmkkpI1BEp+fj6Sk5MBSG75ZWdnh1mzZmHWrFkAijN1GzZswKFDh3Dq1Cl06tQJM2bMUDgtovlmVDW8vHjL0U+fPpWrUvzq1atsRsXDw0NqOFHaFZ0XnRDy5TI0NMT06dMlPsSTqkVHRwc2NjaIi4vj9CQ2NDSEjY0Np7JHory84h5D/7unSqWvX1zZraVVXHkuqUJcTw9wcuLOl6RikydPxsGDBwEAb968Udt6lCXaz7JalSvSsr6koKAgfP78Gdra2rh06ZLEngPlbSwpKrTNzc3F+/fvJc65LkuNGjUQGRkpM99WniEUxXu+R0dHS5xjTyQvL0+h/I6s9ERHR7PvpfUuiIqKktrbQrS8jo4O2wPZwMAAZmZmSE9Px4sXLyqsQKmsPD09UbduXbx7945znonnVz9//qzw0Mei/RgZGSkznOh3UXdeNTY2FiEhIQBK551FvYEmTpyIFVIKKcp7zlXEOgjRBMpfVlKpqcCnT5Lzbrq6xQ0eCwo4/9Y25MH8G3ukvdJF7vtcgAeAAfSs9WDuYQ5tI/VVPmqCKC8k3tNbUYWFhThz5gyA4uGWv/3221JhxEf1UaWSeSR5RGHKmkcS/05WHkne+ksub2Njg/j4+DLt/4rWqFEjdOzYEdevX5eaR/r06ZPSeaTExETk5+dL7cFcUXmkvLw8djTLtm3bckblEuVfevXqhe3bt0tcXlV5JHWtQ5my3bp162LRokVYtGgRhEIhHj58iN9//x3nzp3D5s2b4enpif79+7PhtbW1IRAIyv08WJ7nlGbNmqFZs2YAihtXBwQEYOXKlQgMDMT8+fPRvXt39ntFaOoZ5p9//kFGRgYsLCxw8eJFiQ39VZ1XFj8mKmu5edUbo4WwtLWBkvmDY8fKNkUhIeTLFhAQILXl3507d1BUVAQAnCGNpGnSpAk2b97Mtkq/fv26UmkRzdcp6pFdXuKZHCOjsjcMKSgowJo1awAUDzcrq8ecaL4ZWYXJhBBCqh4+nw9HR0c4ODjAzs4ODg4OcHR0lN1LPD+/uIA0NFR2hbi5OVC/fvGwTtnZQERE8XzjJVlaAo0aqbVCHCgeNlykPFOaqJqFhQUA2ZV+Dx48UDpeUXxWVlZSh9ITzd8miajnqayeFO3bt2d7JIgKopQhyofdunVLapjyTBXTsmVLNn2iOe2kCQoKYhsKyhrWUFZaRd9Vq1ZN4tDpii7ftGlTzqhEojzoyZMn2TncKzPRuSZ+nrVu3ZoduvLixYsKxyU6RqKiovD27VuJYQQCAbvv3NzcypRmRf3666/sFAhjx47lfCc651q0aCFx2aysLJnnsiLnXHnXQQghChEKgc+fgQ8fJFeI29gU59tKVIgDABwdYdDKAVaDrWDZ1xIWXS1g2dcSVoOtYOBQ9XqJyyO6R0dHR8vNa5SUmJjIVoJJu67fvXtXLcPxOjs7s3lQeekODw9nhykvbx5JS0tL6rbKWv727dsASuexRPv/8uXLCg2frmmS8kiOjo7sEOdlySOJKjClEeX31Z1H2rZtG9uAQ9k8EsMwMvP8qsgjyVuHPGUt29XS0kLbtm1x6tQpODo6AihdrizveTAzMxNhYWFy11Xe5xQRHR0ddOvWDZcvX4a+vj4Yhin13Ch6xpL1m2jiGUa0Dxs0aCC1TEPWM3BZiMrMbW1tYW5urtK4VYUqxas4Hx/u5/fvgcePNZMWQkjlFRERwfYGEycUCtmK4IYNG6JJkybsd+JzjUgiauWo7LBKnTt3BlDc41zeOuTJz89nWxIbGxsr3IK0pNzcXIwdO5bt6bJo0SKZN25R4Zr43JGEEEK+DDo6OjA1NYWFhQVMTU2l9xAvKgIiI4srw6X1WNHSAmxtgaZNgbp1iwtSX74s7mlUkq5ucZjatQF5vdJlCA8Pl1pZJk48X9CyZcsyr0/VRC3ug4ODJRaEhIWFsfd+ZZj9r5FBfHy8xDkuo6KiZM7LampqCgCcUQRKqlGjBgYMGAAA+PPPP+X+DiV7Voh6DNy9e5ct7BSXm5uLP//8U2acslhYWKBHjx4AgL/++kvqcIcMw7DDLVarVk3mcHonT56UONJAUlIS/vrrLwCQ2RNi165dEnt8vXnzBqdOnZK4/OTJkwEAb9++lbs/srOzUSCpkkIF/v33X7nDTT579owdOlP8POPz+WwPuLVr18rt+S3So0cPdhjC5cuXSwzz119/sYX1PiULDFRo06ZNbM+j3r17l+opLjrnRNtf0qpVq5CZmSk1fkXOufKugxBC5CoqAt69AxITS3+np1fc2FEgkJwXdHIC/tcDUdtIG0YNjWDS0gRGDY2+uB7iIv369WN7+s6cOVPudDLieSHR9EWA5Ot6UVERlixZosLU/j8ej4dhw4YBKM7bfPjwQWrYtWvXAih+Zhg0aJDUcNLyc3l5eVi/fj2A4l680sqeZOWxdu/eDQAYPnw457tJkyaBx+MhLS0N8+fPl5o2oLhnvroqzv39/dlGc9JER0ezlXEln0XGjx8PANi7dy9bTidP06ZN2Yra1atXc4arFvnnn3/Y8jx15pFOnjyJxYsXAwAaN26MkSNHcr6Xl3/ZtWuXzMpmVeSR5K1DHkXKdmWV+Wpra7MNREuWK4ueB0+fPi1x2W3btilUnlyW5xRZ8err67NpLZlmRX4TTTzDiI6Dt2/fSmxQ9PTpUxw7dqzc6xEnOsdEx0hlRJXiVZy7O+DszP2fio9jQsgXwMzMDD/88AP27NnD3gQjIyPh4+PDto779ddfOcsMHDgQ48ePx5UrVzg39ZSUFKxbt45tUejl5aVUWjp06AAdHR0UFBTg6dOnZd6mV69ewdvbm83gTJs2TamebkKhEKGhodiwYQNcXV1x/PhxAMXzDv70009Sl4uPj0dERAQAqhQnhJCvEsMUF3yGhhbPYySpUky8MrxmzeLP4eHFPYwk9Q63sCjuHa6CltQvX75Ew4YN0bdvXxw6dAifPn1ivyssLERISAjGjRuHDRs2AADc3d3RsWPHcq9XVfr16wdjY2MUFhZi+PDh7H2+sLAQ58+fR/fu3cs0MkzHjh1hZGQEhmEwfPhwtsJaIBDg2rVr8PDwkDnvXOPGjQEAGRkZOHHixP+xd99hTV3/H8DfCQl7g2xE695arbVOVNx11a11a9XWun7VOivu+tU6Wmvde6Dgrlur1lXrrgsVxQEKiGwIISTn98c114QMAoSE8Xk9Tx7lnjs+Obm59+Sce87Rud4vv/wCNzc3pKSkoGnTpti0aZPa8H7x8fHYv38/vvrqK43KuB49evCVgj169MC+ffv4Cr1Hjx6hQ4cOiIuLy/N7V7Vw4UJYWloiJiYGzZs3x8mTJ/kKF8YY7ty5g+7du+PEiRMAgLlz5+rNb2tra7Rv3x5nzpzhG4ivX7+OoKAgxMfHw8HBAVOnTtW5vUwmQ5s2bXD9+nU+hjNnzqBdu3aQSqXw9/fH6NGj1bbp2rUrXwk9depUjBkzRu0BhKysLFy7dg0//vgjAgICCpxnuvTv3x9Vq1bFvHnzcP36dbWKq5iYGCxfvhxBQUFQKBQQiUQYP3682vYLFiyAu7s73r9/jyZNmmDv3r38PKZSqRT//fcfJk+ejO3bt/Pb2NjY8I3hu3fvxujRo/mHPDIyMvDbb7/xUx716dNHbw+2/Hj16hV27tyJpk2bYuLEiQC4UaSU5WhV7du3BwCsX78e69at4/MnJiYGEydOxP/+9z+98wwqv3NhYWE6H+Ao6DEIIUSvzEzg0SNA28M1yrJbfLz2BvNy5bjpckoZa2trrF69GgKBALdu3UKTJk3UyhoA9wDn2rVr0bBhQ6xevZpfbm9vz/eknDRpEv766y++N+X9+/fRsWNH3Lhxo0AjBOozY8YMuLi4ICMjAy1btkRYWJhao/6TJ08wcuRIvkF6woQJfE9XbZycnNCjRw+EhYXxozOGh4ejU6dOCA8Ph4WFBebOnatze0PKWDnnDa9bty5fDlizZg169eqFO3fu8NvL5XLcvXsX8+bNQ4UKFQpUJ6fPlClT8Mknn2Dq1Km4dOmS2jztCQkJ2LBhA5o2bco/uJZzfuYffvgBlSpVglQqRevWrbF+/XqkpKQA4MqOT548wdy5c7F06VK17RYvXgyAGxGzZ8+efI9VmUyGnTt38mXvxo0b6x0dMj9iYmKwf/9+dOrUCb1794ZMJoOvry/+/PNPjYesleWX48ePY968eUhPTwfANaguXLgQ33//vUFlpGPHjukc7r+gx8iNIXW7n3/+OcaNG4fz58/zxweAN2/e4Pvvv0dERAQAzXpl5ed08uRJzJ49m//s4+PjMWPGDCxdutSgHsj5+Z0SEBCAadOm4Z9//lFrII+IiMCAAQOQkZEBoVCIdu3aqW2n/Ex27typ82Egc/yGadu2LYRCIRISEjBgwAD+fMnKysLevXvRtm1bODg4FPg4qopFRzJGjCo5OZkBYMnJySY75owZjHG1cdzL25ux7Gzd62dlZbGDBw+yrKwsk8VYWhkzryUSCXv48CGTSCRGiKzkkcvlLDExkcnl8lzXnT17NgPA8nMJHDx4MAPABg8erHOdzZs3MwAsICAgT9sXRlqLFi0YADZt2jTWtGlTBoCJxWLm4uLC5wEANnPmTJ3bKl+Ojo7M0dFRbVnPnj0NyvOcunbtygCw6dOn61xHeXyxWMw8PT3VXra2tmpx9OnTR+v3TPWzVt3e2dmZCYVCtX24u7uzNWvW5Br72rVrGQBWt27dPL/vvMrLeU0KrjTld17vKeYo3xBOacp7bedlkftepqYy9vAhY9eva3/dvMnY69eMqd6TkpIYu3NH+/q3bzP2/r1RQzxx4oTa/Q0As7S0ZK6urkwgEKgt//TTT1l0dLTGPpT34NmzZ2ukBQQEMABs8+bNOmNQ7v/cuXNa0yMjI/l1IiMjNdI3bNigFqeDgwOztLRkAFijRo3YqlWrdJa1lPf+Fi1aMMbUz6E//vhDbb/29vbM2tqaLwccPnxYb1ytW7dWiykgIIAFBASw5cuXq61369YtVq5cOX5dgUDAXFxcmL29vdrxg4KCNI7x7Nkz5u/vz69jZWXFnJyc+M/x0KFDueZvbg4ePMgcHBz4/YhEIubm5sbnhTLmWbNm6dyHcr2NGzcyLy8vBoDZ2tqqvUcrKyv2559/amyr+vmHhITwsdjb26uV8Zydndn169e1Hj89PZ317dtXLT/t7OyYi4uLRhkvKipKbVt957c2uq5DyvetfAmFQubi4sKsrKw0zt/Q0FCt+7558ybz9fXl17WwsGAuLi5q39Wc5xdjjE2cOFHj/BKJRPyyli1bspSUFIPen6pz587x+3BxceHLzm5ubkwsFqu9LysrKzZ+/HiWnp6udV+JiYmsatWqavnj7OzMv7dRo0bp/R1z4cIFfl0LCwvm7e3Nf+eMdQxTKXL3skKQn/qK0lTGKWpKSt4bUueW77q05GTGbt3SXtaLi2NMoWDs+XPt5bv4+AK+s6Ip5/1T37Vtx44davd0ZVkj5z1y/vz5atvduHGD2dnZqd1rlOUEkUjEtm3bprcsWtAy0uXLl9Xu70KhkLm6umrUQQ0fPpzJZDKt+1DGt2zZMlalShWN8pzy3r1u3Tqt2xtaxgoJCdGa99nZ2WzChAlq8VpbWzM3Nze1sgIAdunSJbVtjXXPbNSokdpxBAIBc3Jy0shHS0tLtnLlSq37ePbsGatevbpGOcvCwoJfNn78eI3tli1bplaOcnZ25n9HAGC1atXS+vsnN6rlV0dHR+bh4cE8PT1ZmTJl1PavLLcMHDiQvdfxOy8rK4s1a9ZMoyynLMN26tSJzZw5U+03jaonT57w5XahUMg8PT35MtLr16+NcgxD5Fa3q/wuKI/v7Oys9v0GwCZOnKixXXZ2NmvZsqVG7AKBgAkEAjZ37ly95fmC/E7RVrbP+RtJW9l8+/bt/DpisZj5+vqygIAA1qRJE7X1CvIbJr9tAz/++KPafp2cnPhyffny5dnOnTv5tJz0/bbW5vHjx/x3Oy4uTus6hirMsiX1FC8B+vdX//vtW0DP1BmEkFLI0tISZ8+excKFC1GlShVIpVI4OTnxc6LMmzdPY5vffvsNixcvRseOHVGpUiUwxiCRSODj44MOHTogNDQUoaGh/Fw2eTFq1CgAwK5du3IddlImk/FDnSpfCoUC5cuXR9++fXH8+HGEhISozTWpjXLbuLg4ZGdnw8vLC40aNcKYMWMQFhaG6OhoPi59du7cqfYeCCGElALZ2dy84eHh3HzgOQkEgKcnUKsW4OfHDYUul3PbPH0KaBs+0NkZqFEDcHU1aqjt2rXD06dPsXLlSvTq1QvVqlWDlZUVkpKSYGtri0qVKqF3794ICQnB9evX4ePjY9TjG8Pw4cNx7NgxtGrVCo6OjsjOzkblypXx888/48KFC/nuITR69GgcPXoUgYGBsLe3R3Z2Nnx9ffH999/j7t27atPIaBMWFoaJEyeicuXKkMlkePnyJV6+fKkxTF69evXw8OFDrFq1CkFBQXB3d0dqaioUCgUqVaqE/v37IyQkROsw8J988gnu3LmDSZMmoXz58mCMwdraGj179sSVK1fQpUuXfL13VV27dsXTp08xe/ZsfPHFF3B0dERycjIsLS1Ro0YNjBkzBnfv3tXbe0k13tu3b+O7775DmTJlkJWVBQ8PD/Tr1w+3b99Gp06d9G7/+eef48aNGxg0aBCcnJz4z2TkyJG4d+8ePz9kTra2tti9ezfOnTuHgQMH4pNPPoFCoUBaWho8PDzQqlUr/O9//8PTp091ziFfUE+ePEFoaCi+/fZbNGrUCG5ubkhNTQVjDJ6enggMDMSCBQvw9OlT9OzZU+s+Pv30Uzx69Ag///wzGjVqBAcHB6Snp8PPzw+BgYFYtmwZ+uf8wQ9g2bJl+Ouvv9CjRw94enoiLS0NDg4OaNmyJTZt2oTTp08XuNdHYmIiX35OT0+Hi4sLateujYEDB+KPP/7A27dvsWLFCp3zEzo7O+PKlSuYMGECypUrBwsLC4hEIgQGBmL37t1Ys2aN3uM3b94cR48eRVBQEJycnBAbG8t/54x1DEII0SolhSu/5Rx+WSwGqlQB3N25EYDev1dPFwi44TxphAoMGDAAERERmDlzJho0aAB7e3skJSXB2toadevWxdixY3HmzBmNns7169fHv//+i969e8Pd3R0KhQIODg7o3bs3rly5goEDBxZq3I0bN0Z4eDiWLl2KwMBAfvQfoVCIypUrY8iQIbh48SI2bNige4qlD1xcXPDvv/9i6tSpKFu2LKRSKVxdXdG5c2dcvnwZI0eO1Lu9vjLWzZs3NXqqKllYWGD58uW4desWvvnmG1SpUgUWFhZITk6Gi4sLmjRpguDgYNy5c4fvmW9s586dw59//olJkyahWbNm8PT0hEQigUwmg7u7Oxo3bowZM2bg0aNHGDdunN73v3r1agQGBsLFxQVpaWnw9PTEF198gXnz5vGj1qiaOHEibty4ga+//hr+/v7IyMiAjY0NGjVqhGXLluHff/8t8O+flJQUxMXFITY2FsnJyXB0dES1atXQp08fLFu2DK9evcK2bdvgquN3nlgsxqlTpzB79mxUrlwZYrEYjDE0bNgQf/zxBw4fPqx3qspKlSrh3Llz6NKlC8qUKYP379/zZSTlqAQFPYYhcqvbDQkJwZw5c9C6dWuUL18eWVlZkMlkCAgIQJ8+fXD27Fl+9DJVFhYWOHr0KObMmYOqVavC0tISAoEAbdu2xcmTJ/H9998bFF9+fqecOnUK06ZNQ7NmzeDv78+PclCxYkUMHToU169f50djUPX1119j+/btaNq0KWxtbfH27Vu8fPkSUVFRauuZ4zfMzz//jG3btqFhw4awsbGBTCZDxYoVMX36dNy+fduo9QHKOvPu3bujTBEeLUXAcmuNIHmSkpICJycn/oJoKnXrAqpTRHzzDfBhagQNMpkMx44dQ8eOHXNtRCIFY8y8zszMRGRkJMqXL8/P5Uw+UigUSElJgaOjY74aaUuqwMBAXLhwAbNnz9Y572BeGSOvFQoFKleujGfPnuHChQtFep4RVS9evMAnn3wCBwcHREVFGX2ImZzovDat0pTfeb2nmKt8Q0pX3ms7L4vE9zItDXj+HNA1p5ezM9cQrvpdSknhGsS1bWNhAfj7cxWmeobrJsZRJM6hEkg51Py5c+c05pLOzYsXL1C+fHkA3BCq5cqVM3J0xkXnECmo0nAO5ae+ojSVcYqakpL3htS55fncTEsDnjwBPgzbzbO1BSpWBEQirkE857QOAgFQoYJRpsIpLkrDtS0/ypUrh5cvX2Lz5s0YMmRInrc3pIxFeW9elP8cc9TtUt6bl778Z4yhUqVKRjsfCrNsSWdOCdG3r/rfYWG66+0IIcTchEIh3zv9559/NnM0hlu8eDEYY5g2bVqhN4gTQggxM8aAmBjg8WPtBWtra6BSJa6CVPkjTS4HXr3iKlO1bePoyPUOd3enBnFCCCGEkKIkI4PrIZ6zQdzZmeshLhIBz55pbxCvWLFUNYgTQkhxrdslhWPv3r149uwZ2rVrV+Q7v1GjeAmRs1E8IQE4c8Y8sRBCiCH69u2Lhg0b4vjx47h27Zq5w8nV69evsWnTJpQtW1brUDmEEEJKEJkMiIgAoqK4xnFVQiHXM7x6dcDJ6ePytDTg4UMgLk5zf0IhEBDANaJbWhZu7IQQQgghJG+kUu6hxpxDpru4cD3ABQKubJicrJ4uFHLlO9UyISGElBLFrW6XFA6FQoG5c+dCKBRiyZIl5g4nV/onvyDFRrlyQKNGwD//fFwWEgJ07Gi2kAghRC+BQIC1a9fi4MGDiI+PN3c4uXr58iWmTZuGli1b0hQGhBBSkil7CWmbB9zenpsrUrVhW6EA3rzhepVr4+DAFdatrAolXEIIIYQQUgAKBTdVzoe5eHlOTkD58lx6RASQmqqermwQp1HkCCGlVHGr2yWF482bN+jVqxfKly+PWrVqmTucXFGjeAnSr596o/jBg4BEAtjYmC0kQgjRq27duqhbt665wzBI06ZN0bRpU3OHQQghpDClpnKVnjl7CQGAtzfg46M+7Hl6OjevZGam5voCAdej3MODhkonhBBCCCmqoqK4Mp0qBweuhzhj3MOSaWnq6UIhULky98AkIYSUYsWpbpcUDj8/PwQHB5s7DINRo3gJ0qsXMHHix6lvUlOBY8eAHj3MGxchxHzOnz9v7hAIIYSQ4iEpiZsnMudw6WIx10vI0fHjMoUCePuWe2ljZ8dtQyOLkBKK5fye5EG5cuUKtD0hhBBiNAkJmlPfWFlxDeIKBdcgnrPB3MKC6yFODeJEixcvXhRoeyojEUJI4aI5xUsQb28gMFB92c6dZgmFEEIIIYSQ4iM+nushnrMSyt6emztctUE8IwMID9feIC4QAL6+QNWq1CBOCCGEEFKUZWUBL1+qLxMIuAZxQHuDuEhEPcQJIYSQYowaxUuY/v3V//7zT+6hR0IIIYQQQogW8fGAth4dzs5cpadYzP3NGNcQ/ugR1zCek60t14Du7U3DpRNCCCGEFHWvX2tOmVO2LFf2e/JEd4O4nZ3pYiSEEEKIUVGjeAnTowc3yo+STAbs3Wu+eAghhBBCCCmykpK0N4i7u3O9hIQffi5lZnK9w6OjNXuTCwTcXONVqwI2NoUdMSGEEEIIKaikJCAxUX2Zqyv3UOSTJ5oPQIrFQJUq3EOQhBBCCCm2qFG8hHF2Brp0UV+2Y4dZQiGEEEIIIaToSkvj5hDPycsLCAjgGrsZA2JjgQcPNHsLAVwjeNWqXKO4kH5aEUIIIYQUeXI58OqV+jKRiCsDPn4MSCTqacoGcXr4kRBCCCn2qOamBBo4UP3vy5eB58/NEwshhBBCCCFFjkTCzROZs9e3pyfg58c1iEulXE+h16811wO4itNq1WgITUIIIYSQ4iQujptPXJWnJ/ewZGam+nJLS65B3NradPERQgghpNBQo3gJ1K4d4Oamvox6ixNCCCGEEAIgOxuIiNCcQ9LNjWsQZwx4947rHZ6aqrm9lRXXO9zPj3qHE0IIIYQUJzIZ8Pat+jIrK67sJ5VqLqcGcUIIIaREoVqcEsjSEujbV33Zjh3aO7gQQgghhBBSajDGzSGes9LTyYkbMl0m43qQv3wJKBSa23t4ANWrA/b2JgmXEEIIIYQY0du3mmU8qVSz57iyQdzKynSxEUIIIaTQUaM4gJSUFCxevBiNGzdGmTJlYGVlBT8/P7Rs2RLBwcFISkoyd4h59vXX6n8/fQr8+695YiGEEEIIIaRIiIkBcpbtbW2BTz4BEhK43uEpKZrbKYfOLFsWsLAwSaiEEEIIIcSIMjK4HuG5sbbmyn2WloUfEyGEEEJMSmTuAMzt3Llz6NevH2JjYwEAIpEI9vb2iI6ORnR0NM6fP49u3bqhbt265g00jz7/HKhUiWsMV9q+nVtOCCGEEEJIqZOcDERHqy8Tibge4pGRmo3lSmXKcEOlU2M4IYQQQkjxJJNx5b3chtG0sQEqVwbEYtPERQghhBCTKtU9xS9fvoxOnTohNjYWQUFBuHTpEqRSKRITE5GRkYEbN25gxowZcHJyMneoeSYQaPYWDwnhyoCEEEIIIYSUKu/fc/OI5+TszD1Fqq1BXCzmnjINCKAGcUIIIYSQ4ooxICpKc4j0nGxtqUGcEEIIKeFKbU/xjIwMDBo0CBKJBD169MDevXshFH58RsDGxgb169dH/fr1zRhlwQwYAMye/fHv9++BEyeA9u3NFxMhhBBCCCEmwxg3d+SbN9rT4+O1L3dzA/z9uZ7khBBCCCGk+EpMzH1ucDs77mFIKvsRQgghJVqp7Sm+fft2PH/+HDY2NlizZo1ag3hJUaEC0Lix+rIdO8wTCyGEEEIIISalUAAvXuhuENdGJAIqVgTKl6dKUUIIIYSQ4u7IESA1Vf869vbUIE4IIYSUEiWvJdhA27ZtAwB07doV7u7uZo6m8AwcqP73oUPcdIqEkPwZMmQIBAIBhgwZYvTtCyONmN+9e/fQu3dveHt7QyQSQSAQoG7dumrrXLx4EZ06dUKZMmVgYWEBgUCAbt26mSXe0iY4OBgCgQCBgYH53kdgYCAEAgGCg4ONFhchpICys7lh0d+/N3wbFxegRg1uSPVSbMuWLRAIBChXrpy5QynWjHF/MaaiFg8hRc358+chEAggEAjMHQohxFikUmDpUv3rUIO4SVD50jiU5blWrVqZOxQAVL4kJDdUviyaSmWjuFQqxY0bNwAALVq0wPPnzzF8+HD4+fnBysoKXl5e6Nq1K44fP27mSAuuVy/1qXCkUmD/fvoSktJNWWjL+bK2toafnx+6dOmCvXv3gjFm7lBLhLp160IgEODs2bMAgBcvXmjNf4FAAEtLS3h5eaFNmzZYu3YtZDKZmaMvmMjISDRp0gShoaGIiYmBk5MTPD091R7G+ueff9CqVSscO3YM79+/h6urKzw9PeHi4mKSGIODgxEcHIwXL16Y5HjFycGDBxEcHIyDBw+aOxRCSF4oFMCTJ7n3ClISiYBPPuGGWSrmc0jqur8a8tqyZYu5wy8W7ty5g+DgYKxYscLcoZiM8uEvQyo8VSt+zp8/X+ixFVeMMYSGhqJ79+4ICAiAjY0N7O3tUaFCBTRt2hSTJk3CgQMHkJKSYu5QDaJ8SJcaOwghRcrGjUBsrO50BweuQdzCwnQxFUO6yo4WFhZwcXHhH+yn8mX+UflSPypfGobKl4QYplQ+BvfixQtkZWUBAKKiolC7dm2kp6fD0tIStra2iI2NxeHDh3H48GGMHj0af/zxh5kjzj83N6BTJ0C1Pn/XLiEmTjRbSIQUKZ6envz/k5OTER0djejoaBw5cgRbtmzBgQMHYJXb3FN54O3tjSpVqsDb27tI79NYXr58ibt378LZ2RnNmzfXSHd0dISNjQ3/d0pKCmJjYxEbG4szZ85g3bp1OH36NFxdXU0ZttGsXbsWqampqFixIs6dOwc/Pz+NdVasWIHs7Gw0adIEhw8fVnuvCoWi0GOcM2cOAO4HSWksaLq7u6NKlSooW7asRtrBgwexdetWDB48WG/P/bJly6JKlSoleuQZQoqVqCggI8OwdZ2cgIAAwNKycGMyEdVyjaq0tDSkp6frXUf1fkx0u3PnDubMmYOAgABMmDBB53r67i+kdEtKSkK3bt1w4cIFfplIJIKtrS1evXqF58+f4/Lly1i+fDk2b95Mo0ERQkh+SKXAokW6G7wdHbkpc0rgdJrGRuXLwpfX8qW/v7/pgiPFApUvCTFcqWwUT0xM5P+/aNEiODo6Yvfu3ejRowfEYjFev36NKVOmICQkBGvWrEHVqlUxfvx4rfuSSqWQSqX838onbWQyWZHp4di3rwAHD378qC9cEOLrr22KTHwlmTKPjZHXMpkMjDEoFAqTNJQVN8pe3co8MmRdAHijMs+oQqHAo0ePMGnSJJw5cwbHjx/HjBkz8L///S9fx9FmwYIFWLBgAX88Q9+DvjR9+ywMeckDZQ/bDh06wMLCQuP8Xb58uUZBLDo6Gr/88gtWrlyJW7duYerUqVizZo1R34Op/PfffwCALl26wMfHR2t+3bt3DwDQp08fODs7q61T0PMtL0rrteXbb7/Ft99+C0Azvw3Nf9Wn34tLHirfn0wmg4UBPSOozECKjaQkIC4u9/UsLAB/f+4J0hI0lFlMTIzW5cHBwfxDULrWIcY1duxYjB071txhkCJo0KBBuHDhAiwsLDBhwgSMGjUKFSpUgFAoRHZ2Nh4+fIgTJ05g165d5g6VEEKKr02buAclAwI005ycuBGCqEHcILrKjrNnz8bcuXP1rkOMS1m+VCgUxaa3LzENKl8SYrhS2SiuWmGtUCiwZs0a9OnTh1/m7++PnTt34vHjx7h9+zbmz5+P7777DiIt88ssWrSIr2BSderUKdja2hbOG8gjCwsh7OzaIT39Yw+YCxf8UKbMaTNGVbqcPl3wvBaJRPDy8kJaWho/0gHRlGrAUKnaHmRR8vf3x/bt29GkSRM8f/4c69atw9SpU/nvv+qDDsYugOrbd2EeN78Myev9+/cDANq0acPHnZaWxqdnZmZqvB8HBwcEBwfjzp07uHDhAg4ePKj2YEJxoswjsVis83NT5oeFhYXOdQzJ64LKyMgoMueWuSnzuyh+74wlKysLEokEf//9N7Kzs3NdP8PQXreEmFNWFmDIVBCOjkC5ciWmdzghpPh4+vQpjhw5AgCYP38+pk6dqpYuEolQu3Zt1K5dG1OmTIFEIjFHmIQQUrxJpcCHB581uLgA5ctTgzghpMSg8iUhecRKof/++48BYACYv7+/zvV27NjBr/fPP/9oXSczM5MlJyfzr9evXzMALD4+nmVlZRWZ14gR2Qxg/MvPL4WlpaWbPa6S/kpPT2cHDx5k6ekFz+uUlBT24MEDlp6ezuRyOb1yvLKzs1liYiLLzs7Odd2ffvqJ/27rWmfq1Kn8Onfv3uWXDxo0iAFggwYNYnK5nO3Zs4e1aNGCubi4MBsbG1anTh22fPlyJpPJtO435/aFndaiRQsGgP30009MIpGwhQsXslq1ajFbW1vm7OzMWrduzf7880+d+SCVStkff/zBWrRowdzc3JhIJGKurq6sYsWKrFevXmz9+vU6t42Pj2cikYhZWlqyxMREfvmzZ8/4vN24caPO7SdNmsQAMFtbW63pDx8+ZIsXL2atW7dmn3zyCbO2tmYODg6sbt26bPr06Sw2NlbnvgMCAvjjJycns5kzZ7KaNWsye3t7BoA9e/aMyeVylpmZyU6cOMHGjh3L6tevz7y8vJhYLGZlypRhbdq0YTt27NB6zin3r+t19uxZvenKGFTP6/T0dLZixQrWvHlz5ubmxsRiMfP09GRdunRhR48ezfN3Rnne6HoFBATw66rGK5fL2Y0bN1i/fv2Yr68vE4lErEWLFvy6b9++ZevXr2fdunVjVatWZY6Ojsza2ppVqFCBDRs2jP3333+5xpTf75dcLme7d+9m7dq1Yx4eHkwkEjEnJydWsWJF9uWXX7LffvtN4xqqvB60aNGCz+8zZ87k+vmcPXtW6/dMV1yhoaGsY8eOzMPDg4nFYubh4cE6duzIwsLCCjU/dL3S09PZgwcPWEpKikH3oPj4eAaAJScnF1bxjOiQnJxcavJeIpGwhw8fMolEwi+Ty+X8PUQvhYKx8HDGrl/X/bp5k7HYWG7dUmb27Nn89Ss3mzdv5u8DjDF248YN1qtXL+bl5cUsLS1Z+fLl2cSJE1lCQoLe/UgkErZy5UqN+1bXrl3Z8ePH9W6bnZ3NNm7cyFq2bMnc3NyYpaUl8/HxYT179mTnzp3TuZ3yejx79myWlZXFli5dyurXr88cHR0ZAI1tIyIi2NixY1nVqlWZnZ0ds7GxYVWrVmXjx49nL1++1Nh/bveG2bNn8+sq87xFixY6442Pj2dz5sxhDRs2ZC4uLszKyooFBASwtm3bsj/++IMlJSWprR8bG8s2btzIunfvrnGPHT58OLt//77OYxkSjy7KfDVk23PnzvH5kTO/c55bp06dYu3bt2fu7u7M2tqaVa9enc2bN0/tGqDvPezZs4c1b96cubi4MFtbW/bpp5+y3377jWVnZ+uNMSkpic2fP581bNiQOTs7M0tLS+bn58f69u3Lrl69qrG+XC5nd+/e5d9XZGQki4iIYCNHjmTlypVjlpaW/HvKzd69e/n9PHz40KBtdHn48CH79ttvWbVq1Zi9vT2zs7NjlStXZn369OHLFzlJJBK2fPly9sUXXzBnZ2dmZWXFypYtywYOHMhu376d71gGDx6s9tnm9OjRIzZy5EhWqVIlZmNjw6ytrZmfnx/7/PPP2bRp09ijR4+0bpeUlMTmzJnD6tWrxxwcHJi1tTWrWLEiGz16NHv27Fmucf3zzz9syJAhrEKFCszW1pY5ODiwatWqsaFDh7KTJ0+qrat67jLG2NOnT9nQoUOZn58fs7S0ZL6+vmzEiBEsKipK67Fynt9//fUX69q1K/Py8mJCoZD169dP7TOJiIhgo0ePZhUrVuR/x9SrV4/NmTNH5z2/oDEqZWdns82bN7O2bdvyZVN3d3fWtm1btnv3bqbIx31S2z08N6WpjFPUlJS8z8rKYgcPHmRZWVnqCVOn8hWgkoAA9vD4cSa5fp2xZ89KZTmwsKjWreWmJJYvnZyczFq+1PUbicqXhV++1PYbNa/lS8YYi4yMpPKlHtrKl6p5T+VLIRs8eLDa+oVdvtRXP2Ps8mVhli1LZaP4+/fv+Q+6TZs2Ote7efMmv96ePXsM2ndRLVj+/ffHBnHl69q1rNw3JAWis4CeD/m5EJQmBleaM8Mqhn///Xd+ncuXL/PLlTfkwYMHs++++44BYEKhkDk7O6sVWgcNGqR1v6rbmyJNWcicNm0aa9asGQPARCKRRryqhWyl7Oxs1qZNG7X1nJycmJWVldoyXbZv384AsPbt26stVy30bd68Wef2QUFBDACrV6+e1nTVhmeBQMCcnZ2ZQCDgl/n6+rLw8HC92y5dupRVrlyZAWCWlpZ8vkRGRjLG1AsGAJiVlRXfcK589erVS+O8a9CgAfP09GRisZgBYHZ2dszT05N/Xb58mf+/UChkAJijo6PaOq9eveLP6/DwcFapUiW196v8AaZ8jRkzRmdeajNu3Djm6enJb+/i4qJ2/AYNGvDrquZDWFgY/76UP5ZUf8Qoz0fly9HRkYlEIrU8DAsL0xpTQb9fw4YNU1vP3t6e2draqi1TfrZKqj+AlPl98eJF5unpyaytrRkAZm1trZY3ys9QSfVHck5SqZT16dOHP75QKGQuLi785w6A9evXT+t9oqD5oU9e7ylFtXxTGpSmvC9Qo/ibN/obxB89YqwUl6Hy2yi+c+dO/prv5OSkdu2qUaMGS01N1bqPJ0+e5Pu+lZSUxAIDA/n1LCwsNO7xP/zwg9ZtldfjH3/8kTVu3Fij3KNaibZu3Tr+vSnvTzY2Nmr3r1OnTqnt39PTk29gFwqFGveGJUuWaOS5roq+kydPMhcXF/542spnBw4cUNumIPfYolZpuWrVKv4zdXZ2Vnsf9erV01oprvoepkyZwp9bOe+r7dq1Y5mZmVrj++eff9TKPxYWFszBwUHtXF24cKHaNjkbxXfu3MmXB21tbZmdnV2+Ki1znl958fPPP6u9Z2Wll+r5kZiYqLZNVFQUq1mzJp8uFovVvpdCoZD9+uuv+YpHX6P4qVOn1H4/iMVig36L3L9/n/n5+el8j/rO9+zsbDZu3Di1Y9jZ2amVC52cnNS2UT13//rrL/4zdnBwUDs/fXx8tFZcqp7fK1eu5M9vJycnJhaL1RrF9+zZo5YnDg4Oan/7+/trrdQuaIyMMRYTE8M+//xztbzJeX3u0qULk0qlWrfXhRrFi5eSkvda69wyM9UqP/lG8adPqUHcyPLbKF5SypfKcpy5ypfafiNR+dI05cucv1HzU75kTL1+lMqXmvQ1ip84cYLKl2KxWluAKcqXr1690nrtKYzyJTWKFwJfX18GgLVt21bnOjdu3OA/tL179xq036JasJTLGStXTr1RfNw4/U/Qk4IzZaO4QqFg8jR5iXwZ8iSRsRvFJ0+ezK+j+mSZ8obs4uLCLC0t2bJly/jve3x8PBsxYgS/3dmzZzX2a65GcWVj9po1a/hz6NWrV6xnz558vIcOHVLbVtmobW1tzTZs2MD/MMnOzmZPnjxhYWFhrEePHrqykN/3H3/8obY8t0bxN2/esP/7v//j19m1a5fW/ffp04f99ttvLCIigr+pSqVSdubMGdawYUMGgH366adat1U2itvb2zMvLy+2f/9+/nv6+vVrlp6ezhjjCrb9+/dnR48eZTExMfy5+P79e7Zy5Ur+h8vKlSu1HkdfY2nOWLTlhVwuZy9evGDlypVjAFirVq3Y33//zRfEk5KS2LJly/gCy4oVK3QeRxddPyxUqRaQ7O3tWceOHdW+F0+ePOH/HxwczGbOnMlu377N0tLS+Pdx//59NmDAAL7QGB0drXGcgny/Ll68yBe4Fy9ezN6/f8+nxcfHs5MnT7LBgwdrHFdbo7jyOqLvu6VK3+esPJcFAgGbNWsW/wMiISGBTZ8+nX8/P/74o1HzIzfUKF58lKa8z7VRXC5nLC5O8xUZydipU9pfp08zdv8+10Nc27ZF5WVA+aUg8tMobmtry6ysrNiIESPYq1evGGOMpaens1WrVvGVfbNmzdLYPjExsUD3rR49ejCAe1jt119/5e/Jb9++VXv4KWf5grGP12N7e3tmb2/PNm/ezNLS0lhiYiKLi4vj7w0HDhzgK0+mTp3KXrx4wRQKBVMoFCw8PJz16tWLAVylYM4ePTmf1s8tz7VV9N26dYt/8KpGjRrs2LFjfDkkPT2dXb9+nf3f//0fO3PmjNp2BbnHFqVKS1tbWyYWi1mvXr34c0sikbA1a9bwFTfdu3fX+R6UFSxjx45lcXFxjDHuWjlv3jy+omjixIka20dGRvKVZT179mQ3b95kMpmMMcb1kpo1axZf8aNaYZyzUdze3p59/vnn7Pr16/w6jx8/zjVvlDEoY6xVq5bB26lavXq1WuWSag+c9+/fs1OnTrE+ffqo3Teys7P5iionJye2Y8cOvvz87Nkz9uWXX/L7PHbsWJ5j0tcoXrFiRb7u5d69e/xyiUTC7t27x4KDg9mmTZvUtklJSWHly5dnAPegq3JUJMYYu3PnDmvUqBFfcXnnzh2NYyortQGwYcOGqeVzbGwsO3jwIOvTp4/aNqrnrouLC+vSpQtf3pVKpWzPnj18penAgQM1jqk8v62trZmFhQUbMmQIf35nZWWxW7duMblczm7evMlfQ5s0acLu3r3LGOPOs8OHDzNvb28GgFWoUEGjYaigMUqlUvbZZ5/xv5OOHj3KX2PT0tLY1q1bmYeHBwPAJkyYoLG9PtQoXryUlLzXWuc2aZJmo/ipU0ySkaF9J7rKlyXhVcjly/w0ipeU8mXGh/MpPj7ebOXLnHWgVL40XflS9TdqfsuXjKnXj1L5UpO+RnEqX3L5HxERwRhjJitffv311xrXnsIqX1KjeCEYOnQoA7gnJHQ1uCkbhACoXZD0KcoFyxkz1BvFPT0V7MM1mhQSUzaKy9PkLGFuQol8ydNyL8gbs1E8OTmZ+fj4MADM1dVVbZ+qT1Dq6uVcv359BoCNGDFCI81cjeIAN1R4TnK5nDVv3pwBYNWrV1dLGzNmDAPAvvnmG41tcsvrzMxMZm9vzwQCgUbBWbXQl7N3tOrTdc2aNWP79u3TeQx9UlNT+ac0L168qJGubIi2sLBgt27dytcxGGMsNDSUL1RoY4xG8bFjx/I//GQ6Ltr79+9nAJi7u7vOdXTR9cNClWoBqWHDhrkOS6pPp06dGAA2b948jbSCfL8WL17MF4jzojAbxaOiovgfP9OmTdO6rXKaALFYzN68eaOWVtDrjT7UKF58lKa8z7VRPC5OvTBbkl4fKl4KS34axfVd/5TXrooVK2qk/fDDD/m+b127do0/9tq1a7Vuq6zUdHd317iGqZZ7Dh8+zBjTLLdIpVL+AWltZSOlLl26MABs/PjxasuN0SjetGlTBoBVqlRJYwjLgtB3jy1KlZaq992cNmzYwK/z77//an0PuiqNGGNs5syZDOB6RuUsgyof2NS1LWOMLVu2jAFgderU4ZflbBQPCAjQ2YvNECNHjuT3JRAIWL169di3337LNm7cyO7du6f3geCEhAS+Uqpv374GD0MYEhLCH/PEiRMa6TKZjK/UrFmzZp7fk65G8djYWP64Ocs5+vz88898+Ui1olMpJSWFbxzp1KmTWtrjx4/5Xk5Tpkwx+Jiq527Lli21np+//vorA8BsbGw0rm+q5/dXX32llqZ6HWrfvj1//VRWGKq6desWX35U7R1ojBhXrVrFAK6xJCUlRWs+3LhxgwkEAmZpacliY2O1Z5YW1ChevJSUvNeoc5NKNcpYkoAA9vDePd3nJpUv8y0/jeIlpXyZkznKlznr5ah8abrypep9Pb/lS8bU60epfKlJV6P4kydPqHyZgynLl+/evVNbp7DKl4VZthSilBo6dCgA4PXr19izZ49GukKhwLJlywAAvr6++PTTT00aX2EYOFD979hYAc6cMU8shBRFSUlJOHv2LFq1aoU3b94AAMaPHw+hUPNS6e/vj0GDBmndT5cuXQAA//33X+EFm0f+/v78dU+VUCjEzJkzAQAPHz7EvXv3+DRnZ2cAQExMTJ6P99dffyEtLQ0NGjSAj4+PzvVSUlIQGxvLvzIyMvi0d+/eITo6GoyxPB/f3t4eLVq0AABcunRJ53rt27dHvXr18rx/pU6dOgEAnj17hrdv3+Z7P7owxrBz504AwP/93/9BJBJpXa9bt25wdHREfHw8bt68afQ4VE2ePBkWFhb53l6ZZ/o+l/x8v5Tn67t37yCXy/MdnzHt27cP2dnZsLa2xtSpU7WuM3PmTFhZWUEmkyEsLEzrOsXtekMIKTmUZYScunbtCgCIiIhQu3czxrBp0yYA+btvhYSEAAD8/PwwYsQIrdvOmzcPABAfH4/Tp09rXadGjRro3Lmz1rTjx48jOjoanp6eWstGSsrr7smTJ3Wukx9Pnz7l74ELFy6Ek5OT0fZtyD22qJg5c6bWMvbQoUPh5+cH4OP5oM1PP/2kdfnkyZNhY2OD7Oxs7Nu3j1+ekJCA/fv3A4DOezLw8XO/e/cuYmNjta4zduxY2Nvb69xHblavXo1Zs2bBzs4OjDHcvn0bq1evxvDhw1GrVi14eXlh0qRJWo8fFhaG1NRUiMViLFu2DAKBwKBjKus8vvjiC7Rr104jXSQSYfbs2QCA+/fvq/0mKAgHBwf+c85LWVkZb8+ePVGzZk2t+50yZQoA7judnJzMp23duhUKhQJubm6YM2dOvuKePn261vNTee2TSCR4+vSpzu2nTZumdXlSUhJ/TZk8eTJsbW011qlXrx6++uorAMDu3buNGuOGDRsAAN9++y0cHBy07rd+/fqoUaMGsrKycO7cOZ3HJ6RImjRJc1mdOkABfr8S46PyJZUvCwuVL0tH+dLe3p7KlypMXb589uyZWlpxLF+W2kbxZs2aoWfPngCAMWPGYM+ePZDJZAC4hvIBAwbg9u3bAIAFCxZoPRmKmypVgM8+U1+2fbt5YiGkqBAIBPzLxcUFQUFBfOH566+/xowZM7Ru99lnn+m8LigbgRMSEgon6HwIDAzUWahp3rw5/6Pixo0b/PKOHTtCIBDg8OHD6NChA3bv3s0/LJCbgwcPAvh409Rl8+bNYIzxr+zsbERFRWHNmjWIjY3FuHHj0L9/f50N43/++Sf69OmDTz75BHZ2dmqf5969ewEAUVFROo/fpEmTXN9LamoqlixZghYtWsDDwwOWlpb8MVQLGtHR0bnuK68ePnyIxMREAMCQIUPg5eWl9eXt7Y20tDQAwMuXL40ehypD8uzu3bv49ttvUbt2bTg6OkIoFPJ59u233wLQ/7nk5/sVFBQEa2tr3L59G82aNcPGjRsRGRlp6NsqFMrv02effQZHR0et67i4uKBBgwZq6+dU3K43hJCSwdXVFRUrVtSapvrAm/I+BXD3LeX1KD/3LeV1sGXLljqve9WqVYOvr6/a+jnpu1cpK/QSExPh7e2tM8aRI0dqxGcMV65cAQBYWFigQ4cOed6+oPfYokAkEqFZs2Za04RCIQIDAwHo/nz9/f11npuOjo6oX7++xvZXr16FQqEAALRq1Urn516jRg1+G12fvSFlIX1EIhHmzp2L6OhobN++HSNGjECdOnVgaWkJAIiLi8Py5ctRs2ZN/Pvvv2rbKs+f+vXrw9vb2+BjKvMiKChI5zotW7bkH3zUlfd5ZWNjg9atWwPgHkb96aefcO3aNWRlZencJisri3/YT1+8bdq0AcB1aLh16xa/XJlHbdq0gbW1db7i/vzzz7UuV7326Sp72djY6OxUcevWLf53jSHv7b///uPrqQoaY2pqKp+vs2bN0vkd8PLywuPHjwEU/u8KQowqKwv4/XfN5WvXmj4WohOVL6l8WViofFm6ypetWrUCQOVLwPTlS9Xrc3EtX2p/tKqU2LJlC+Li4vD333+jb9++sLKygq2trdoH+9NPP2Hw4MFmjNK4vv4auH79498HDgCpqYCOhzgIKfE8PT35/1tZWcHd3R316tXDgAED0LJlS53b6XryCQDfwKzrBmMOysK9NlZWVnBzc0NsbCzi4uL45U2bNsXixYsxc+ZMnDhxAidOnADAPV3bvHlzDBs2jK/kUsUYw5EjRwBwT+rmhYWFBXx9fTFq1Cj4+PigS5cuCAkJQYcOHdR6yioUCnz99ddqT7eJRCK4uLjwBb7k5GRkZmYiPT1d5/E8PDz0xvPkyRO0bt1arfBva2sLZ2dn/seU8ilLfcfJL9WHEN69e2fQNqpPVBeG3PJs1apVGD9+PP/DQCAQwMnJCVZWVgC4pwpTUlL05ld+vl+ffPIJNmzYgNGjR+Pq1au4evUqAKBMmTJo2bIl+vfvjy5duhj8xKsxKL9P+r5/APgnllW/f6qK2/WGEFIyGHLtAdSvPwW9b+XluhkdHa3zuqnvXqWMMSsrS2dPDVUSiSTXdfJCOQKPu7s77Ozs8rStMe6xRYG7uzsfszbKz1/X55vb+aFte9Vz05DPHdBdpsqtLGQoJycnfP311/j6668BAJmZmbh06RJ+/fVXHDlyBPHx8ejRoweePn3KV74pz5+AgIA8HcuQ75a1tTXc3d01fhMU1IYNG9ClSxfcvXsX8+bNw7x582BpaYnPPvsMXbt2xfDhw+Hq6sqvn5CQwI/6oy9eZfkJUP+s85tHqnRd/3Rd+1S5ubnpbHRRjdOQ95adnY2EhAS136z5jTEmJoa/dhj6MGVh/64gxKg+NNypadECcHYGVOp5iXlR+ZJD5Uvjo/Ilp7SUL9evX49u3bpR+RKmL19mZ2fz/y+u5ctS3ShuZ2eHc+fOYdOmTdi+fTvu37+P1NRU+Pr6olmzZvj+++/RuHFjc4dpVH37ApMmMcjlXKOARMI1jOsYlZUUIwJbAZwmGW94nKJEYFt4jVj5GRq8OMpvQ+DkyZMxYMAA7N27FxcuXMCVK1cQFRWFXbt2YdeuXejZsyd27doFsVjMb/Pvv//i7du3qFChgtrTkHnVuXNneHh4IC4uDrt27VJrFN+4cSN2794NCwsLzJgxAwMHDsQnn3yiVkAYOHAgduzYoXf49dyGAR86dCiioqJQrlw5LFmyBK1atVIrVMnlcr5gkJ9h3nOjOgx4TEyM1gKLqenLs0ePHmHChAlQKBTo1asXJk+erPZkKsB9diNGjCiU/BowYAA6dOiA0NBQnDt3DleuXMHr16+xd+9e7N27F82aNcOff/6ps9d2YTH0+2fKBntCiiU3NyAujpsl8e5d7evUrg0UxxGe3NzMHYFRGOu+VdDrpr57lTLG9u3b4/jx43kPzkjyes035z3WxsYGgGEVuKqVHMrtciro/S4/2ys/dxsbmwJXxBRkGhl9rK2tERQUhKCgIAwZMgRbt25FVFQUTpw4ofGgaX7z0BxlkrJly+LWrVs4ffo0jh07hsuXL+Pu3bu4fPkyLl++jEWLFiEsLIzv8WNoHKpp2tYzV7nK2OeHsd6H6vX5n3/+0dkTiJBiKSsL2LhRc/nhw7lvqyxflkRUvlRD5UtNVL4s2PZUvszbdlS+zD8qXxpXqW4UB7jhM0aMGKFzTpGSxsMDaNeO4dixjyf+9u3UKF4SCAQCCOyoQYVop2+YI6lUivfv3wPQ/mSij48PJkyYgAkTJgDghlVatmwZtm3bhrCwMDRr1gzjxo3j1z906BCA3IdON0RAQADi4uI0hsFWzgE0YsQInXO5FPSBh9evX/PD4+zevRuNGjUy+jFy4+Xlxf//3r17RaJRXJ+wsDDI5XJUq1YNISEhWp9iLOw8c3V1xahRozBq1CgA3HzvGzZswOLFi3Hx4kUEBwdj2bJlhRqDkvL79Pr1a73rKb+fZcqUKfSYCCnWhEKgTBng2TPAxUUzvVYtQE/vAFL4Cnrf8vDwwOPHjwv1uqmM0Vhz2uWVckjCd+/eIT093eDePOa8x7q7uwMwbKoY1XV0fT7v3r2DVCrV2ZtHuQ9dPWZyG75T2/bKz10ikSAiIkLn8JhFxTfffIOtW7cCAD/UIPDx/Hnx4kWe9ufh4YHXr1/r/W5lZmbyvwmMXSYRCoVo164dP99kamoqjhw5gmnTpuHVq1fo378/Xr16BUtLS7i6usLCwgJyuVxvvKppqvF6e3sjPDw8z3lkCqrnZFRUFCpUqKB1PeU5rhwJyxhUr8f37t0rNpWWhBjC4ptvNBe2bw84OgKZmfo3VpYvSZFF5cvcUfmSypeGoPIllS8BKl8CpXhO8dKsXz+F2t9nzwIGThNMCCmmLly4oPOpzosXL/JDnyjnNtanVq1aWLlyJT/fzenTp9XSDZ1P3BDKG3bOAr2ykFKvXj2t26WlpeHatWsFOrZqQUjXcc6cOVOgY+SmZs2a/NA1ygcBjE35dKAxnvpV5lmdOnV0DutT2HmWU4UKFbBo0SL0798fgOb5qo/yPeQ3b1TnCk9OTta6TlJSktrc44SQXKSkaB8Cs3x5ahAvAmrWrMmPxpGf+5byunnu3Dl+GLacwsPD+Uqp/Fw3leWX6Ohofv7HvCjovUE5EplcLs9TTyJz3mOVcyhGR0fnWhGkzFNXV1eUL19e6zrZ2dk6854xhr///huA7nLp69ev8ezZM61pqampuHnzpsb2jRs35ss8hVWmMiZ7e3v+/6qVu8rz58aNG3j79q3B+1PmxdmzZ3Wuc/78ef43QWGXSRwcHNC/f39s/NCzMzY2lm9IsLS0RO3atXONV3m+C4VCtTkWlXl0+vRpZObWGGZin376Kf/9NeS91alTR21EroJwcXFB9erVARSP7wAhhhJkZ0O4a5dmwr59pg+GFAoqX+aOypdUvjQElS+pfAlQ+RKgRvFSqXNnBhubj/MTMAZoKz8SQkqOV69e8U8DqlIoFFi4cCEAoFq1aqhVqxafJpVK9e5TOfeM6hAuERERePToEdzd3fkfBfl1/vx5vjCWs9Dq5MRNFXBXx/C58+bNQ2pqaoGOrzyGruOkpqZi/vz5BTpGbkQiET8P0NatW3P9cWXo/C2qlD8uk5KS8rxtTso8u3fvntYfc8ePH8f58+cLfBxtcjtflUNs5WXIoYLmTY8ePSASiZCZmYnFixdrXWfhwoWQSqUQi8Xo0aNHvo5DSKkhlwNPnmgut7MrMcNDFncikQjDhg0DkL/7Vt++fQFwlWMbNmzQus1PP/0EgOtdEhQUlOcYO3fuzPeGGD9+fK5DHeaMsaD3hooVK6J58+YAgOnTpyMlJcWg7cx5j/3qq6/46WIWLVqkc71nz54hLCwMANCzZ0+9w/ItWLBAa8X01q1b8erVKwBAnz59dG4/b948rct/+eUXSCQSiEQifPXVV/xyDw8P/oHNJUuW4Im2a4mK/JSpDBEZGZnrsQGoldtVK+R69eoFR0dHZGdnY+LEiQZXniu/W1evXsWpU6c00rOzszF37lwAXONDzZo1DdpvbrKysvSmqw6BqlpGU8YbFhaG+/fva2yXlpaG//3vfwCAjh07qpXbhwwZAgsLC7x//x6zZ88uUPzG5uzszPdmWrJkidbrz927d7HvQ2Nev379jHr8bz70pj179myuFZeF9R0gxNg+XbFCe4KtrUnjIIWHype5o/Ilh8qX+lH5ksqXAJUvAWoUL5VsbYEvvlB/6mf7djMFQwgxCScnJ4wZMwbr16/nn2h7/fo1+vXrh3PnzgHgCo+qunXrhmHDhuH48eNqBfOEhAQsXboUf/31FwCuoKCk7CX+5Zdf5nu+E6lUikOHDmHAgAEAuB9AqsOzA9w8TQCwfv16rFu3ji8QxcTEYOLEifjf//4HtwI2kFSvXh1ly5YFAAwbNox/KhTgCnyBgYFI1NZb0cgmT56MChUqIDs7G+3bt8eyZcvw7t07Pj05ORknTpzA4MGD0axZszzvX1ko3blzZ4HnQFJ+Lg8ePMB3333HF3bS09Oxdu1a9OzZs8Cfiy5jx45F7969sW/fPsSpzAmXlpaGNWvWYNu2bQDUz9fcKPPm4sWLCA8Pz3NMvr6+GD9+PADg559/xuzZs/nvUlJSEmbNmoUlS5YAACZNmsT/iCaE6HD7tvblVaqYNg6i16xZs/J932rYsCH/gND333+PVatW8femmJgYjBw5EqGhoQC4SivlA3p5YW1tjdWrV0MgEODWrVto0qQJTp48qVa5EhkZibVr16Jhw4ZYvXq12vbKe0NKSgr27t2b5+MDwMqVK2FtbY2nT5+iSZMmOHHiBGQy7qHljIwMXLt2DaNHj1brmWPOe2zZsmX5+9m6deswcuRItYq3jIwMhIWFoWXLlsjIyICrqytmzJihc3+2tra4dOkS+vfvz48KlJmZifXr12PMmDEAuBGHGjZsqHV7JycnbN26FePHj0d8fDwA7mHFhQsX8pWZ3333HXx9fdW2++WXX+Dm5oaUlBQ0bdoUmzZtUhvJJT4+Hvv378dXX31l9MoipQcPHqBatWro1KkTtm3bptYzSiaT4fbt2xg6dCg/1UvDhg3RtGlTtfeurKzbs2cPunfvjjt37vDpiYmJOHr0KLp27apWId6jRw9+OMPevXtj165d/DkXGRmJHj164OrVqwDA798Yrly5gtq1a2P58uV49OgRX1HNGMOVK1f4z9vPz0/t4dwxY8agfPnykMlk6NChA44fP85ve+/ePbRr1w6RkZGwtLTUeEi1YsWKmDx5Mv9eRowYgadPn/Lp79694/POHBYsWACxWIyIiAi0a9eO78GkUChw7NgxdOzYEdnZ2ahQoQI/HZCxjB49mj8PBg4ciJkzZ6qNjpWRkYHz589j7NixOofeJKRIkcngp62BVGW4bVIyUPkyd1S+pPJlaSlf/vvvv6hbty6VL1VQ+TKPGDGq5ORkBoAlJyebOxSdsrKy2Ny5lxjXR/zj69Ytc0dW8mRlZbGDBw+yrKysAu9LIpGwhw8fMolEYoTISh65XM4SExOZXC7Pdd3Zs2czACw/l8DBgwczAGzw4ME619m8eTMDwAICAvK0fWGktWjRggFg06ZNY02bNmUAmFgsZi4uLnweAGAzZ87Uua3y5ejoyBwdHdWW9ezZUy3Plcc4cOCAzvyJjIxU26enpyf/KlOmDBOJRHy6ra0tCwkJ0dhHYmIiq1q1Kr+eUChkzs7OTCAQMABs1KhRevMlICCAAWCbN2/WGSdjjB05ckQjHltbW/7/Z86c4dPOnTunMw9nz56t8xj6YlGe1xEREaxOnTpqee/s7KzxeVSsWFHv+9Fm+/bt/PZisZj5+vqygIAA1qRJE36dc+fOGfyd6du3r0acFhYWDACrX78+++233/L1/VDS9f1Sbqt82dvbM2dnZ7VlTZs2ZWlpaWrbKa8HLVq00LiOJCQksDJlyvDbu7u7s4CAABYQEMCuXr3K70Pf5yyVSlnv3r3VzlUXFxcmFAr5Zf369dN6nyjo9UafvN5TikP5pqQqTXmv7bxUfi8Vr18zdv265ovKRQbJS9nHkOuK6r08MjJSI/358+f5vm8lJSWplUFEIhFzcXHh7/EA2A8//KA1Lm3XY11lxB07dvD3dOVx3NzcmJWVlVqM8+fP1zhO69at+XQHBwf+3rB8+XJ+HdX7izYnT55kTk5OavfgnOWznOWpgtxjc4snNzKZjA0fPlzt+La2tszV1VXtnubl5cWuXLmidR+q59aqVav4z9TFxYWJxWJ+H3Xq1GHx8fF638OUKVP4+6qrqyufDwBYUFCQzvvbrVu3WLly5fh1BQIBc3FxYfb29mrvLSgoiN9GLpezu3fv6j3nDXXixAm14wBglpaWzNXVVe0cB8A+/fRTFh0drXU/CxcuVMt3Gxsb5uDgoLZ9YmKi2jZRUVGsRo0aasdVLSsJhUK2cuXKfL0vZZkl57mnWoZUnudubm5q5WtHR0f2999/a+zz3r17zNfXl1/P2tpa7RpiZWXFQkNDtcaTnZ3NvvvuO42yoep33snJSWes+ugq++u7dua8DoWEhDBLS0u1PLC2tub/9vf3Zw8fPtTYT0FjZIyxd+/esVatWqnljaOjo9pvKeU1MS/yU19Rmso4RU1JyXt5167qFZvKl8q5T3Vpheenn34qteVLXUxZvtRWvqXypWnKlznv6/kpXzKW+zlvqNJUvpTL5ezIkSNUvtTCVOXLI0eOaFx7CqN8WZhlS+opXkrVrBkPf3+mtmzdOjMFQwgpdJaWljh79iwWLlyIKlWqQCqVwsnJCa1bt8bRo0e1DhH022+/YfHixejYsSMqVaoExhgkEgl8fHzQoUMHhIaGIjQ0lJ+35N27d7hy5QpsbGzQtm1bg+JKSUlBbGws/4qPj4etrS3q16+PKVOm4MGDB1qHNnJ2dsaVK1cwYcIElCtXDhYWFhCJRAgMDMTu3buxZs2agmXYB19++SX+/vtvdOrUCc7OzsjOzoa7uzuGDh2KW7duoXXr1kY5Tm7Kly+PGzduYNu2bfjyyy/h7e2N9PR0ZGVloXz58ujevTs2bdrEP4GZF19//TW2b9+Opk2bwtbWFm/fvsXLly/5J2vzaufOnVixYgVq164NKysryOVy1KpVC4sWLcLly5fV5jAyplmzZuHXX39F9+7dUbVqVYhEIqSlpcHDwwNt2rTBpk2bcP78eY356fVxcXHB33//jb59+8LX1xfJycl4+fIlXr58afAcQpaWltizZw/27duHDh06wM3NDampqXBzc0OHDh2wf/9+7Nq1y2jz+RBSEgmzsiCIidGemI+eHKTwFeS+5eTkhLNnz2Ljxo0IDAyEg4MD0tLS4OXlhR49euDcuXP8KBsFMWDAAERERGDmzJlo0KAB7O3tkZSUBGtra9StWxdjx47FmTNn8OOPP2psGxYWhokTJ6Jy5cqQyWT8vSEvQ162bdsWT58+xYwZM1CvXj3Y2NhAIpGgXLlyaNeuHdauXYtWrVqpbWOueyzAjdyzYcMGXLp0CUOGDEHlypUhFAqRkpICNzc3BAYGYsmSJQgPD8cXX3yR6/6+++47nDx5Eu3bt4dQKIRQKETVqlUxd+5cXL16NddeSYsXL0ZISAiaNGkChUIBS0tL1K1bFytXrsSJEyd09vKqV68eHj58iFWrViEoKAju7u5ITU2FQqFApUqV0L9/f4SEhGD//v35yqfctGvXDk+fPsXKlSvRq1cvVKtWDVZWVkhKSoKtrS0qVaqE3r17IyQkBNevX4ePj4/W/UybNg13797FyJEjUbFiRQAAYwxVqlRBv379sH//fn4oViVfX1/cuHEDy5YtQ6NGjWBjY4OMjAz4+/tj4MCBuHnzpsboTAX12WefYe/evRgzZgzq168Pd3d3JCcn89+zKVOm4NGjR1pHO6pZsyYePHiA4OBg1K1bFyKRCFKpFBUqVMDo0aPx4MED9OzZU+txLSwssGrVKly6dAkDBgxA2bJlIZPJYGlpiRo1amD48OH8EJLm0KdPHzx48ACjRo1ChQoVIJVKIRKJULduXcyZMwf3799HtWrVCuXY7u7uOHPmDA4dOoSePXvC398fUqkUEokEvr6+6NChA1atWpXr/K6EmJ1MBuGhQ9rTAgNNGgoxDSpf5o7Kl1S+LA3ly3r16iEkJITKlzlQ+dJwgg+t/MRIUlJS4OTkhOTkZI2LRFEhk8lw7Ngx3LjxJebP/zi8sYMD8PYtNy0jMQ5lXnfs2LHADR6ZmZmIjIxE+fLl8zWUT0mnUCiQkpICR0dHvpGWAIGBgbhw4QJmz56N4OBgo+xTV15v2rQJw4cPR+fOnXH48GGjHKu0o/PatEpTfuf1nlIcyjclVWnKe23npUKhgPDWLe0biERA3bqmC5AUS6Xp2l7UbdmyBUOHDkVAQEC+KkSCg4MxZ84ctGjRotDmt9SGziFSUKXhHMpPfUVpKuMUNSUi77/8Ejh6VHP5mjWAytCwVJdWeErDta2oorz/yBzlS8p/86G8Ny9T5n9hli3pzCnFhgxRQCD4+HdqKrBnj/niIYQUb4c+PKXdrVs38wZCCCGkxBA8eKA7sXp10wVCCCGEEEKKBqlUe4M4AHzzjWljIYQQQkixQo3ipVjZskCHDurLaAh1Qkh+NWnSBLNnz0bXrl3NHQohhJCS4N07CKRS7WkeHoClpWnjIYQQQggh5te5s/bl48ZBrfcPIYQQQkgOInMHQMzrm2+AY8c+/n3tGvDff0Dt2uaLiRBSPE2ZMsXcIRBCCCkpsrKAly91p/v7my4WQgghhBBSNEgkwOnT2tMWLjRtLIQQQggpdqineCnXqRPg7a2+bP1688RCCCGEEEIIGOOe0tSlRg3qBUQIIYQQUhrlHPJSKSgIsLMzbSyEEEIIKXaoUbyUE4mA4cPVl23fDmRkmCceQohxnT9/HowxBAcHmzsUQgghxDAvXuhOc3UFbGxMFgohxHiGDBkCxhhe6PuO6xEcHAzGGM6fP2/UuAghhBQTaWnAhQva06iHDyGlEpUvCSF5RY3iBMOHq3e2SU4GQkPNFw8hhBBCCCmlJBIgPV13erlyJguFEEIIIYQUIW3aaF3MKlakMiIhhBBCDEKN4gTlygFt26ovW7fOLKEQQgghhJDSSiIB4uJ0p1epAgjp5wshhBBCSKmTnAz884/WJPmaNSYOhhBCCCHFFdUqEQDAN9+o/33lCvDggXliIYQQQgghpVC9ejqTmKMj4OBgwmAIIYQQQkiRERSkdbFcLAZr1szEwRBCCCGkuKJGcQIA6NwZ8PRUX0bT8RBCCCGEEJOYM0dvMqMhMQkhhBBCSqf374EbN7Qm/TdqlPqckIQQQgghelCjOAEAiMXA0KHqy7Zt40axJIQQQgghpNA8fgwEB3P/ZwwsR3KGhwcgEpk6KkIIIYQYiLGcd29CjKhDB51JUc2bmzAQQgghhBR31ChOeCNGqP+dmAiEhZknFkIIIYQQUgpkZQFVqwIAhOnpgFwOuUoys7NDFg2bTgghhBRpcjl39xYKqZqRGFlMDHD9utYk+Y8/QmFpaeKACCGEEFKcUWmV8CpU0JyiZ80a88RCCCGEEEJKgZYt+f+K4+Mhjo5GmkoyCwigITEJIYSQIi41NRVisRhisdjcoZCSpnt3nUmK0aNNGAghhBBCSgJqFCdqcpYnr1wBbt82TyyEEEIIIaQECwnhCpsfCAA4nD+P5LQ0SADA2xuwtjZXdIQQQggxgEQiQUpKChwcHCCgB9mIMb16Bfzzj/a0Pn0AX1/TxkMIIYSQYo8m5yNqunTh6h/fvv24bNUqYONG88VUGORyIDYWePMGSEgAkpOBlBQgLQ1IT//4r0wGKBQAY9y/qq+cyywsuHpbKyvuX2trQCQS4uXLioiIEMLOjltmYwM4OACOjh9fyr/poWpCCCGElAqvXgH9+mksdj94EJLq1fEqMBCOAgHs0tKQlZWFzMxMGpKV5ItCoaBziBQInUOkoEriOcQYg1wuR2pqKlJSUmBlZQV3d3dzh0VKmokTdaeNH2+6OAghhBBSYlCjOFEjFgNjxgA//fRx2c6dwOLFQHH7fZOQADx6BDx4ADx8CLx4AURHcw3hMTFcQ3bhswBQw+C1bWwAT0/Ay4t7qf7fz497ZWUBlpZACfktTQghhJDSRiYDAgK0JllkZsJ/4ULEt2iB1IwMJCYlQSKRwMbGhnqfkXxhjNE5RAqEziFSUCX5HBKLxXB2doa7uzssLCzMHQ4pSZ4+Bfbv157WoAHQqBGQnW3amAqAyRneH3sPAHDr6AaBRcm6FhBCCCHFBTWKEw0jRwLz5nH1lQAglQLr1gHTp5s3Ln0kEuDff4GLF4FLl4C7d7mG7+JGIuEa71+80EwLCODmeFeW+UWij73Slf/a2HD/pwZzQgghhBRZY8boTbbo1g2ezZvD40Mjwrlz59C8eXOap5Tki0wmw99//03nEMk3OodIQZXUc0goFEIsFpe4hn5SRAQH604bPx6g844QQggh+UCN4kSDlxc3Nc+OHR+XrV4NTJ5ctIb3Tk4G9u0DDh8GTp3iGpRLk+xs7pWerplmZQXY2nIvOzvuRQ9tl06bN2/GunXr8ODBA6SmpgIAli9fjgkTJgAA5HI5Vq5cie3bt+PJkyfIyMgAABw4cADdunUzU9SEEEJKrGPH9M/L4+gILFsGABAIBBCLxcjOzoa1tXWJakggpmNhYUHnECkQOodIQdE5REge3b8P7N6tPc3LC+jd27TxEEIIIaTEoEZxotX48eqN4tHRQEgIMHCg+WJSevOGqytdtw740MaXbyIRNyy8k9PHub2Vjch2dh+HKc/5Egg0/5bLuV71mZkf/5VIFHj9Og5OTh6QSoXIzAQyMri4U1K4F2PGyRdVUin3Skzk/hYIuPfj4AC4uHCN5aVZcHAw5syZozXNxsYGvr6+aNy4MUaNGoXGjRubODrj+eWXX/DDDz8AAEQiETw8PCAQCGBnZ8evM2HCBKxatQoAYGlpCU9PTwCAtbW16QMmhBBSskVFAZ07619n0SLA29s08RBCCCGEkKLnp590V5aNHs1V1hGT01aXJhAIYG9vD0dHR5QtWxb16tVDy5Yt0aVLF1gW0ueUlJSEFStWAODqtJydnQvlOLkpV64cXr58adC6gwcPxpYtWwo3IEIIIQahRnGiVYMGQJMmwOXLH5ctWQJ8/bX5RijKzgZWrgRmz9beO1obsRioXBmoXh2oUgXw9wd8fABfX+7fMmUKd6hxmUyOY8euoWPHjhCLNQ/EGPdelI3kCQlAbCw39HvOl4VF/vOeMSAtjXu9fcsNs+7mxr1K+4PqykZgAFAoFEhISEBERAQiIiKwbds2zJ49G8H6hu0qwpYuXQoAGDduHJYuXarRKyE1NRVr164FAPzvf//DDz/8QEPfEUIIKRzZ2UD//oBCoXudhg2BUaNMFxMhhBBCCClabtwADhzQniYWc43ixOyUdWmMMWRmZuLNmzeIjo7G1atXsXr1ari5uWHevHkYPXq00euZkpKS+Mb5IUOGmK1RXMna2hpOTk5618ktnRBCiOlQozjRafJk9Ubxe/e4YcrbtTN9LO/eAV99xc0XrotQCNSrBzRtCjRuDNSuDVSoULQbfQUCwN6ee+XWKSozE4iM5Br2LSw+9gZX9krPzASysgw7rkTCddaKjuYaxj09uYby0igmx+Tzcrkc//zzD8aPH4+bN29izpw5aNu2bbHrMf7u3Tv+vY0cOVLrMH3h4eGQyWQAgDFjxlCDOCGEkMITHAxcvKg73cICWLuW5nshhBBCCCnNZs7Unda3L1eBRcwuJiYGCoUCKSkpcHR0BGMMDx8+xOnTp7Fq1SpERkbi22+/xaVLl7Bjx44SXd/Up08f6gVOCCHFCDWKE506d+Z6Vz9+/HHZkiWmbxR/+RJo1Qp4/lwzTSgEvvwS6NkT6NiRa+At6UQiwNpaeyO2XK4ctp3rgZ6ezv1f16hTjAHx8dzLyYmbmsne3nyjARQFFhYWaNKkCQ4ePAh/f38AwKFDh4pdo7hybnAAsLe3z/c6hBBCSIGdPg0sXKh/nfHjgbp1TRIOIYQQQggpgi5dAk6e1J0+frzpYiF5YmFhgVq1aqFWrVoYPXo0hg8fjpCQEOzatQs1a9bEtGnTzB0iIYQQAgAoxIGjSXEnFAL/93/qy86eBW7dMl0MsbFAUJD2BvFhw4AnT4BDh7i5zktDg3huLCy4ucPd3YGAAG7Y+Lp1gUqVuIdp9U3nk5zMPQDx+HHhzXVenPj5+cHtw0mVlpamliaTyXD69GmMGzcODRo0gLe3NywtLeHh4YF27dph9+7dYDoy8Pz58xAIBPxTsrdv38aAAQPg5+cHsViMwMBAft24uDhs2rQJX331FapVqwYnJyfY2NigYsWKGDFiBB48eKBz/+XKleOXlS9fnj9muXLlsGXLFggEArVjKdNzLld69uwZvv/+e1SrVg2Ojo7w8fFBjRo1MGHCBLx69crAXCWEEFLqvH3Lzb+jr2Dh7w/kmJ+QEEIIIYSUMr/8ojutbVugfn3TxULyzdbWFlu3bkW9evUAAD///DMSEhLU1lEoFLh8+TKmTp2KRo0awc/PD5aWlnBzc0OLFi2wZs0afmRDVYGBgShfvjz/t2p9V876rPwewxQMrRtU1t8p6/jOnTuHbt26wdfXF25ubhg6dKjafp89e4YxY8agUqVKsLGxgaOjIz799FPMnTsXKSkpBYoFALKzs7Fu3ToEBgbC3d0dYrEYbm5uqFKlCvr06YNNmzYZN6MIIaQQUE9xotfAgcCsWVzjtNKSJcDu3YV/bMaAQYOAiAj15f7+3PGbNCn8GEoCCwuuF7iTE+Dnx81f/v49N3+5tvrptDTuYQMHB27edQcH08dcFERHR+P9+/cAgCpVqqilXb58GW3btuX/trKygpWVFd69e4dTp07h1KlTOHDgAEJCQiDUM2n9vn370K9fP8hkMjg6OkIkUr8kT5kyBVu3buX/dnR0RHZ2Np49e4Znz55hx44d2LlzJ3r06MGvY2lpCU9PT8jlcsTHxwMA3N3dYfFhONoyZcrAxsYGnp6eyMrKQmJiIgD1udVdXV3V4li/fj2+++47/seClZUVhEIhwsPDER4ejs2bNyMsLAxt2rTJJVcJIYSUKnI5MGAAEBenf73ffuOGqiGEEEIIIaXTq1fA4cO606dPN10spMAsLS0xffp09OrVCykpKTh48CCGDRvGp7969QpNmzbl/xaJRLC1tUVCQgL+/vtv/P3339i1axdOnjwJG5WhMl1dXeHu7q61vkuZXtBjmFpudYNKv/76KyZMmADGGJycnNTeNwDs3bsXgwYNglQqBQA4ODggKysLt2/fxu3bt7FhwwacPHkS1apVy1cscrkcHTt2xOnTp/llTk5OSE9PR0JCAp48eYK9e/eqfc6EEFIUUU9xope1NTBunPqyvXu5ua0L29at3BzmqmrWBK5dowZxrZgCyHyn9yWQvoOj5TuU936H2lXewdftHazYO4iyNV+SxHd49oB7pSfo32+hv5jCZNkol8tx9epVdO/eHQDg4eGBQYMGqa1jY2OD/v374+jRo4iJiYFEIkFqairev3+PlStXwtHREaGhoVi1apXeYw0ZMgRt2rTBo0ePkJycDIlEgvXr1/Pp5cuXx8yZM3H79m2kpaUhOTkZUqkU9+/fx4ABAyCVSjF48GC8efOG36Zx48aIiYnB9evX+WXXr19HTEwMv7xPnz6IiYnB/v37+XWU6TmXHzx4EN988w0AYOrUqXjx4gXS09MRHR2Nhw8f8j9wevbsST3GCSGEqJs/Hzh3Tv86XbtyL0IIIYQQUnqtXQsodNT9NG4MNG9u2nhIgbVv355vuL1w4YJamkgkQteuXbFnzx5ER0dDKpUiOTkZqamp2Lx5M3x8fHDx4kXMmDFDbbv9+/frrO/KWZ+V32OYWm51gwAQGxuLSZMmYfDgwXj16hUSEhLw9u1bzJw5EwBw69YtfP3115BKpWjSpAnu3r2LlJQUZGRk4PDhw/D29sbr16/RuXNnjdEwDY1l9+7dOH36NKytrbFhwwakpqYiKSkJEokEsbGx2L9/v1qnHUIIKaqopzjJ1ZgxwKJFXA9igCujLl4MrFlTeMdUKLhjqvLx4RrJvb0L77jFmvQ9sN/D4NXFALw/vHJ1N58xGctXcYB1mULZtZeXF/9/hUKBhIQEyOVyODo6YsCAAViwYAGcnZ3Vtvn888+xc+dOjX25urpi3Lhx8PHxQa9evfDrr79iXM6nSlRUr14dhw8fVnu6s1KlSvz/Z8+erbGNUChEjRo1sGPHDiQlJeHo0aPYtGkTXxA2pqysLIwdOxYAsGbNGv5pT4VCAYFAgCpVqmDv3r3o2rUrDh8+jGXLlmHFihVGj4MQQkgxdOFC7kOi29kBv/5qmngIIYQQQkjRJJUCORoB1UyfDnwY2rkoYXLD5x1kcsavn5ftBBZF730byt7eHp988gmePn2KZ8+eqaX5+fnh4MGDWrcZMmQIatasic8++wzr1q3DwoULYW1tnefjm+IYALBnzx6cOHFC7zr79+9H48aNtablVjcIAJmZmfjqq6+wefNmAFy9nIWFBSpUqAAAmDFjBmQyGSpWrIhTp07B1tYWAFeH2LlzZ/j5+aFhw4Z49uwZ1qxZgx9++CHPsVy5cgUAMGjQIAwfPpxPFwgE8PDwQPfu3fkORoQQUpRRozjJlYsLMHIksHz5x2WbNgEzZnBDmReGixe5IbxVrVlDDeLE+GJV5wZQkZGRgeTkZMTGxiIgICBP++zUqRMAbi6ft2/fwlvHiTt58mSN4Y7yepyjR4/i0qVL+d6HPsePH0d0dDQ8PT015ilSNWjQIBw+fBgnT54slDgIIYQUM1IpV3jUN484ACxdCpQta5qYCCGEEEJI0RQWBrx7pz2tTh2gY0fTxmOg98feG7wukzOk3kzl/za0sdu9s3ue4ypKlMOZ55xTPDcNGjSAh4cH4uLicOfOHTRq1MjosRnrGJmZmcjMzNS7TlZWls40Q+sGp02bpnV5UlISXx83efJkvkFcVb169fDVV19h79692L17t85GcX2xKDsMxcTE5BorIYQUZTR8OjHIDz8AVlYf/5bJcu/8UxB//qn+d40awJdfFt7xSOnFGFN7SSQS3L59G4MHD8aff/6J5s2ba32yNDU1FUuWLEGLFi3g4eEBS0tLCAQCCAQCtQJodHS0zmM3MWAegLt37+Lbb79F7dq14ejoCKFQyB/n22+/BQBERUXl/Y0bQNnYnpiYCG9vb3h5ecHLyws+Pj6oUqUKfHx84OXlhZEjRwIAXr58WShxEEIIKWZ++QV4+lT/Ou3aAaNGmSYeQgghhBBSdP3+u+60ItpLnBiG6XlINisrC2vWrEHbtm3h4+MDa2trvr5LIBAgLi4OQMHqvExxjMGDB2vULeZ8BQYG6tzekLpBGxsbfPrpp1rTbt26xedzUFCQzn20adMGAPDff/9BJpPlOZaOHTtCIBDg8OHD6NChA3bv3q02nSMhhBQX1FOcGMTHBxgxQr2cumULMG0a8GGkFqO6fVv97+7dqQxMTMPa2hp169bFhg0bkJCQgAMHDmDIkCF49eoVHB0dAQBPnjxB69at1QrNtra2cHZ2hlDIPWuk7IGenp6u81geHvqHu1+1ahXGjx8PxYd5tQQCAZycnGD14QkViUSClJQUvccoCGXhNisrS2ePelUSiaRQ4iCEEFKMvHzJzSWuj7MzsHEjFe4IIYQQQkq727eBq1e1p1WqBBThOYrdOroZvK7qkOluHd2K9bDoeZGYmAgAcHNTz6u4uDgEBQXh3r17/DJra2u4u7vzPZXfvXsHhUKR7zovUxzDGHKrGwS4/FPWN+akbNgHAF9fX5378PPzAwBkZ2cjISEBnp6eeYqladOmWLx4MWbOnIkTJ07wQ8b7+fkhKCgIgwYNQsuWLXN9L4QQYm7UKE4MNnMmN2y6st1LLgcWLOCWGVvOodPr1DH+MUocKzdu7m0jk2YB0VFASor2dBdXwN8P0FE2Mw4rw39oGNPIkSNx4MABJCcn49ixY+jbty8AYOjQoYiKikK5cuWwZMkStGrVih8SCgDkcjlEIu7yqu+pWH3DIz169AgTJkyAQqFAr169MHnyZNSpUweWlpb8Ohs3bsSIESP0HqMg5HI5AKB9+/Y4fvw4v1yhUCAlJYXvuU4IIYTwJk36WFjU5fffAT0VNoQQQgghpJTQ10t86lSgAFPOFba8Nmwr1xdYCEpFo3haWhqeP38OAPzc10oTJ07EvXv34ObmhiVLlqBDhw7w8vJSW8ff3x9RUVH5rvMyxTGMwZCh0wsy9aI2Ah0PJ+d2nMmTJ2PAgAHYu3cvLly4gCtXriAqKgpbtmzBli1b0LNnT+zatQtisdio8RJCiDFRozgxmJcX8N133PSPSlu3ckOrV69u3GPlHH2lfHnj7r9EEggB6zJG362VNVC+GpCUBLx+DeScBuddCpAWyY0YYG1t9MOblepc4pGRkQCA169f48qVKwCA3bt3a51zyBjz64SFhUEul6NatWoICQnR2vhc2PP4KH8sqD5VSwghhOh08iSwf7/+dXr1Avr1M008hBBCCCGk6EpMBHbt0p7m5wd8/bVp4yFGdeLECb6zherw4TKZDPs//GZYtWoV3wFFlVwuR3x8fL6PbYpjFBWqvbujoqI0HkBQTQMAkUgEFxeXfB/Px8cHEyZMwIQJEwBwdYa//vorNmzYgLCwMDRr1gzjxo3L9/4JIaSwURc/kieTJwM2Nh//ViiAiRMBYz5Ql53N9UJXZWdnvP2TvBMIABcX7uEHlQ7RPIkEePgQSEgwfWyFSXV4dLsPJ+Hr16/5ZfXq1dO63ZkzZwp8bOVx6tSpo7M3tjGOo49yLqHo6Gh+fnFCCCFEK6kU+P57/et4egKrV9Ow6YQQQgghhJuXUdcIQ5MnAyoj5ZHiJSsrCwsXLgQAODk5oVu3bnzau3fvkJmZCUB3vdqlS5f4dXJSrSPT1cO7oMcoTj799FM+T86ePatzPWUdYp06dYzak7tWrVpYv349X4d4+vRpo+2bEEIKAzWKkzzx8OBGxVR16hRw6JDxjiGTaS6jUVeKBpEI+OQT7pWznVahAJ4/53r5m3HUIaPapfLEcoMGDQBwhXmlu3fvamyTmpqK+bnNpWoA5XHu3buntZB//PhxnD9/vsDH0adz587w9vYGAIwfPx4ZGRl6108oaU9FEEIIMdwvvwBPn+pfZ8MGwN3dNPEQQgghhJCiS6HgHpbUxs0NGDHCtPEQo5FIJBgyZAhu374NAJg2bRqcnZ35dEdHR374bm31atnZ2ZgxY4bO/Ts6OvL/T0pK0rlOQY5RnDg7O6Ndu3YAgCVLlmitu7t79y727dsHAOiXz1G7pFKp3nSbD73ojD3UOyGEGBs1ipM8+/FHwMdHfdn33wNpaZrrMsYNuX3lCnD6NHDjBjcMtz7aGsC1NZQT83F1BapV0z5c+ps3wIsX3O+b4iomJgYzZ87E1q1bAQCNGjXCF198AQCoXr06ypYtCwAYNmwYbt68yW939epVBAYGIjExscAxtG/fHgDw4MEDfPfdd3yDc3p6OtauXYuePXvCza1w51q3trbG6tWrIRAIcOvWLTRp0gQnT55ElsoY+pGRkVi7di0aNmyI1bp+0BJCCCnZXr4EcnsgbPhw4MsvTRMPIYQQQggp2k6fBiIitKcNGwbY2po2HlIgCoUC9+/fx7Jly1CjRg3s3r0bADBw4EBMmTJFbV17e3u+V/GkSZPw119/QfGhEvH+/fvo2LEjbty4wY/YmJOzszN8fX0BAJs3b0Z2drbGOgU9RnGzYMECiMViREREoF27dvw0iAqFAseOHUPHjh2RnZ2NChUqYNSoUfk6Rrdu3TBs2DAcP35c7WGEhIQEzJ8/n++l3rFjxwK/H0IIKUw0pzjJMwcHYMkSYMCAj8uiooAFC4BFi7i/s7KA334Dfv8d+DAVM08gAGrUABo3Bjp2BNq3B6ysPqaLRFwvZNVG1VweRiNmYGPDNYy/egW8f6+e9v49dw5UqMB9nkWZct5spczMTCQnJ/N/16pVC/v27eOfMBUIBPj999/RvXt3PHjwAA0aNIDthx9rGRkZsLW1xeHDhxEUFFSguFq3bo2+ffsiJCQEf/zxB/744w84OzsjNTUVcrkc9evXx5AhQ/B9bkPVFlC3bt2wfft2fPPNN7hz5w7at28PkUgER0dHpKenqz0p2rVr10KNhRBCSBE1aZLuoS8BoFw5YNkyk4VDCCGEEEKKuN9/175cIADy2WhHTEdZl8YYQ1ZWFlJSUvhGZwBwd3fH/PnzdTbArlixAi1atEB0dDRat24NKysrWFpaIjU1FSKRCJs2bcKsWbOQnp6udfvRo0dj1qxZ+O2337Bu3Tp4eHhAKBSiUaNGCAkJMcoxDLVnzx6cOHFC7zr+/v64fv16gY6jT7169bB9+3YMGjQIly5dQu3ateHo6IisrCx+iHh/f38cOXIE9vb2+TqGRCLB5s2bsXnzZgAfe+ynpKTw6/Ts2RMjaJQHQkgRRz3FSb706we0bq2+bMkS4PZt4O1b4PPPgR9+0GwQB7je4/fvA+vWAd26Ad7ewDffAOfPf2wIV20kB6hRvKiysODquT90nFaTmgo8flz0P7vY2Fi1V0ZGBry8vNCuXTusX78eN27cgE+OoRG+/PJL/P333+jUqROcnZ2RnZ0Nd3d3DB06FLdu3ULrnF+OfNq5cydWrFiB2rVrw8rKCnK5HLVq1cKiRYtw+fLlfBdk82rAgAGIiIjAzJkz0aBBA9jb2yM5ORnW1taoW7cuxo4dizNnzuDHH380STyEEEKKkBMngP37dacLBNx8kSrDHBJCCCGEkFLsxQvgzz+1p7Vrx/WwIEWasg7t3bt3yM7OhpeXFxo1aoQxY8YgLCwM0dHRensk169fH//++y969+4Nd3d3KBQKODg4oHfv3rhy5QoGDhyo9/jTp0/HypUr0aBBA4jFYkRFReHly5eIiYkx2jEMlZmZqVG3mPP17t07oxxLnz59+uDBgwcYNWoUKlSoAKlUCpFIhLp162LOnDm4f/8+qlWrlu/9//bbb1i8eDE6duyISpUqgTEGiUQCHx8fdOnSBfv27UNoaKjanO+EEFIUCZi2yWpJvqWkpMDJyQnJyclqc5wUJTKZjB86RVyAybofPQJq1wZUR6mpVIlrKA0Pz98+fXyAHj24Xuaq/v4baNYs36GajbHyGuAKWZGRkShfvjystY1bbmZJSdyc4jmHTReLgYoVgcIekUihUCAlJQWOjo5UACtklNemQ3ltWqUpv/N6TykO5ZuSqkjnvVQK1Kype+hLgOtF/ssv+T6EMctSpHSic4gUFJ1DpKDoHNKuSJdxSjiz5/3UqcDixdrTDh8GOnc2aDeGfLeKSl0akzO8P8YNs+jW0Q0CC4HZYjGW0vT7uaihvDcvyn/zobw3L1Pmf37u34aWb+jMIflWrRowY4b6sqdPtTeIW1sDHh65D6X95o1mgzhQ9HsbE8DZGahSRXNOeJmM6zGe21zyhBBCCClmfvlFf4N45cq5zzVOCCGEEEJKj8xMYMMG7Wn+/tw8i4QQQgghhYQaxUmBTJsG1KunO71CBeDiRSAjA4iNBdLSgCtXuKHWmzY1/DgeHgWPlRQ+OzugalXuIQhVCgVXZ55z7nFCCCGEFFMvX+be4P3bb4CNjWniIYQQQgghRV9oqO7KoREjuOEnCSGEEEIKCTWKkwKxsgJ27tRe3ykQACEhXOO3QPBx/S++4OYbv3iRm0bo55+BWrV0H6NxY/3ppGixsuIaxh0cNNMiIwETTKNDCCGEkMI2cSIgkehO794daNvWdPEQQgghhJCib/p07cuFQmDoUNPGQgghhJBShxrFSYFVqwasXq25fMQIoEED/dsGBAA//gj89x/w4AEwdy7QvDnXyG5tDfTpA4SFfWxUJ8WDSMTNL+/mppn28iUQF2f6mAghhBBiJCdOAAcO6E63tgaWLTNdPIQQQgghpOi7eROIitKe1r49N3x6CSWwEMC9szvcO7uXiPnECSGEkOIqlxmeCTHMkCHc8OizZnFzSH/2GdcDPC+qV+des2YBjHHLqDG8+BIKgXLluAby2Fj1tFevuM+2TBmzhEYIIYSQ/JJKge+/17/OtGlcIYAQQgghhBCl//s/3WkjR5ouDkIIIYSUWtRTnBjNjz8Cr18D4eHc0Oiurvnfl0BADeIlgUAA+PkB3t6aaS9f0hzjhBBCSLHzyy9ARITu9HLlgMmTTRYOIYQQQggpBt6/By5c0J7m5QV06mTaeAghhBBSKlFPcWJUnp7cixAlgQDw9eX+ffNGPS0ykltekAcoCCGEEGIiL18C8+frX2fpUm4eHEIIIYQQQpTGj9edNmQIIBabLBRCCCGElF7UU5wQYhLe3tp7jEdGAklJJg+HEEIIIXk1cSIgkehOb9YM+Oor08VDCCGEEEKKPoUC2LlTd/qIEaaLhRBCCCGlGjWKE0JMQiAAfHw0RxJgDHj2DEhONk9chBBCCDHAiRPAgQP611m2jOa/IYQQQggh6n79VXdaq1ZAhQqmi4UQQgghpRo1ihNCTEY5x3iZMurLGeOmJ01NNU9chBBCCNFDKgU6dNC/zsCBQIMGpomHEEIIIYQUH4mJutNGjTJJCIwxkxyHEEIIIQVXmPdtahQnhJiUQACULQu4u6svVzaM6xuVlRBCCCFmMGeO/nQbG2DBAtPEQgghhBBCipdq1XSndetWqIcWCrmqb7lcXqjHIYQQQojxKO/byvu4MVGjOCHE5AQCICAAcHVVXy6XA0+fAllZ5omLEEIIITm8fAksWqR/nR9+APz9TRMPIYQQQkgpk5qaiuDgYNSqVQv29vZwcnLCZ599hl9++QVZxaECpU8fYOpUzeUdOwKWloV6aLFYDLFYjLS0tEI9DiGEEEKMJzU1lb+HG5vI6HskhBADCARA+fKAQgEkJX1cnpXFNYxXqQKI6ApFCCGEmFe5cvrTvbyAKVNMEgohhBBCSGnz8uVLBAYG4sWLFwAAW1tbSKVS3LhxAzdu3MDOnTtx9uxZuLi4mDdQfQQCIDxcc/m8eSY4tAAODg5ISkqCk5MTbGxsCv2YhBBCCMk/iUSClJQUODs7QyAQGH3/1ORECDEbZcP448dARsbH5RIJN5R65cpAIYyQQQghhBBDGFJRuWABYG9f+LEQQgghhJQycrkcnTt3xosXL+Dt7Y1t27YhKCgICoUCoaGhGDlyJG7fvo0BAwbg2LFj5g5Xt61bgYMHNZfXq2eSw7u7u0MikeDVq1dwdHSEg4MDLCwsCqWivTRRKBTIyspCZmZmoQxvS3SjvDcvyn/zobw3r8LMf8YY5HI5UlNTkZKSAisrK7jnnH/XSKhRnBBiVhYWQKVK3EPDUunH5WlpQGQk8MknXOM5IYQQQkxs50796XXrAoMHmyQUQgghhJDSZsuWLbh37x4AYN++ffjiiy8AcPNr9unTBwqFAv3798fx48dx9uxZtG7d2pzhaqdQAEOGaE97+BCoUaPQQ7CwsIC/vz/i4+ORmpqKJNXhCkm+McYgkUhgY2NDDxiYGOW9eVH+mw/lvXmZIv/FYjGcnZ3h7u4OCwuLQjkGNYoTQsxOLOZ6hYeHAzLZx+WJiUB0NODnZ77YCCGEkFJLdRgXbZYt455uI4QQQgghRrd161YAQMuWLfkGcVV9+/bFjBkzEBkZiW3bthXNRnFtw6YrTZkCHD1qkjAsLCzg6ekJDw8PyGQyKBQKkxy3JJPJZPj777/RvHnzQpnzlehGeW9elP/mQ3lvXoWd/0KhEGKxuNAfeKBGcUJIkWBlxfUYf/wYkMs/Lo+JASwtAQ8P88VGCCGElEr/93/AxIkAY5ppXboALVuaPiZCCCGEkFIgIyMDly9fBgB06NBB6zoCgQDt27fHH3/8gVOnTpkyPMM9eaI7LTnZdHF8IBAIYGlpafLjlkQWFhbIzs6GtbU1NU6ZGOW9eVH+mw/lvXmVlPyngfcJIUWGrS03XHpOr14BNLpV/m3evBlffPEFHB0dIRAIIBAIsGLFCj5dLpdj2bJlqFevHuzs7Ph1Dmqb86sIOX/+PB8rKfqCg4MhEAgQGBiY730EBgZCIBAgODjYaHERYmypqakIDg5GrVq1YG9vDycnJ3z22Wf45ZdfkJWVZe7w8mb8eODBA+1pS5eaNhZCCCGEkFLk0aNHfG/mmjVr6lxPmRYTE4OEhASTxJYnb9/qTlOdQ48QQgghxASoUZwQUqQ4OQEBAZrLnz/PfRRXQykb57S9bG1tUalSJQwePBhXrlwxzgHN6JdffsGwYcPwzz//QCKRwMPDA56enrCzs+PXmTBhAv7v//4Pd+7cQXZ2Njw9PeHp6Qlra2ujx7NixQoIBAIMHDiQX6Zs6Mz5sre3R+XKlTFkyBD8888/Ro/F1IKDgxEcHIwXL16YO5Qi5+DBgwgODi7yD2IQkpuXL1+idu3amDNnDu7fvw/GGKRSKW7cuIEffvgBjRo1QmJiornDNNzbt4C2h1AaNOCGdyGEEEIIIYXizZs3/P99fX11rqeaprpNkREfrzstNNR0cRBCCCGEgIZPJ4QUQWXKcHOLq/6eUyiAp0+BatW44dSNxdPTU+UYCiQkJCAiIgIRERHYtm0bZs+eXax7pS790JNv3LhxWLp0qcbQJqmpqVi7di0A4H//+x9++OGHQu15fejQIQBA165dNdLEYjFcXV35v+Pj4/H06VM8ffqU/yxmz57Np9va2qJKlSqFFquxzZkzBwD3EEC5cuXMG4wZuLu7o0qVKihbtqxG2sGDB7F161YMHjwY3bp107mPsmXLokqVKnB3dy/ESAnJH7lcjs6dO+PFixfw9vbGtm3bEBQUBIVCgdDQUIwcORK3b9/GgAEDcOzYMXOHm7vXr7nG77g4zbQ9e0wfDyGEEEJIKZKamsr/39bWVud6qmmq26iSSqWQqvTKTklJAcDNDSqTyQoaql4WZ8/q7JEl8/XlKn/ySRl7Yb8Hoh3lv/lQ3psX5b/5UN6bV1HPf0PjokZxQkiR5O3NjaT1/v3HZTIZNx1V1aqAyEhXr5iYGLW/5XI5/vnnH4wfPx43b97EnDlz0LZtWzRu3Ng4BzShd+/e8e9v5MiRWuf6CA8P528YY8aMKdQG8YSEBFy6dAlWVlZo166dRnrjxo1x/vx5/u+srCxcuHAB3377LSIiIhAcHIwGDRqgU6dOAICGDRsiPDy80OIlxjV27FiMHTu2QPvYtm2bkaIhxPi2bNmCe/fuAQD27duHL774AgAgFArRp08fKBQK9O/fH8ePH8fZs2fRunVrc4abu969tTeIA8BXXwGHDwNaHnIhhBBCCCFFy6JFi/iHtFWdOnVKb4O7MXS9cEHr8rujRuGFkR4UPX36tFH2Q/KH8t98KO/Ni/LffCjvzauo5n+GgcMMU6M4IaRIEgi4YdSlUiAt7ePyzEyuYbxKFcDCwvjHtbCwQJMmTXDw4EH4+/sD4Ho3F8dGcdUbgb29fb7XMZajR48iOzsbbdq0gYODQ67rW1paok2bNjh06BDq1auHrKwsrFq1im8UJ4SQomTr1q0AgJYtW/IN4qr69u2LGTNmIDIyEtu2bSvajeKMAfqmrbh7F/j1V5pXnBBCCCGkkKj+ZtZXyauaput39rRp0zBp0iT+75SUFPj7+6Nt27ZwdHQ0QrR5V33ePFR3di7QPmQyGU6fPo02bdpo7QRAChflv/lQ3psX5b/5UN6bV1HPf+VIOLmhRnFCSJElFAIVKwLh4VxjuFJGBjeUeqVKhdMwDgB+fn5wc3PD+/fvkabaKv+BTCbD+fPnceTIEVy5cgXR0dF4//49nJ2dUa9ePQwZMgR9+/bV2vP6/PnzaNmyJQCAMYbbt29j6dKluHDhAmJjY9GkSRO+x3RcXBz+/PNP/Pnnn3j06BHevHmDrKws+Pr6IjAwEBMnTkSNGjV07l+pfPny/P8DAgIQHByMoUOHqq2jGmuLFi3Uem0DwLNnz7BixQqcOXMGr1+/hkKhQEBAANq1a4dJkyZpHRZblXK+aG1Dp+tTvXp11K9fH1evXsX169f55TnzMafw8HAsW7YM58+fR1RUFBhjcHd3h6+vL1q1aoVBgwahatWqatvcunULf/75J86cOYOXL18iNjYW1tbWqFq1Krp3747vvvsuzw8PDBkyhG8sA6Dx2QQEBPDzjGs7N37++WdcvXrVKOdGzpgGDx6MLVu2ICwsDKtWrcJ///2HzMxMVK5cGUOHDsX3338PoVD7YHd79+7F5s2bcevWLSQkJMDOzg5lypRBtWrV0K5dOwwfPlxtXvrg4GDMmTNH7dzKea5u3bpVLa8A4Ny5cwgMDATADT1/4cIFvdMa7N+/H5s2bcL169eRmJgIFxcXfPbZZxg+fDi6d++uNz8GDRqElStXIiwsDKtXr85TfpDSLSMjA5cvXwYAdOjQQes6AoEA7du3xx9//IFTp06ZMry8M2Q+yiL4A4gQQgghpKTw8fHh/x8dHY3atWtrXS86OlrrNqqsrKxgZWWlsVwsFhdupXZ2tvblHTtCXKaM0Q5T6O+D6EX5bz6U9+ZF+W8+lPfmVVTz39CYqFGcEFKkiURc4/fjx0BW1sflaWlcj/FKlYw3lLoqZSM3AK3zVl++fBlt27bl/1b+yHz37h1OnTqFU6dO4cCBAwgJCdHbiLZv3z7069cPMpkMjo6OEOV4M1OmTFFrJHR0dER2djaePXuGZ8+eYceOHdi5cyd69OjBr2NpaQlPT0/I5XLEx8cD4OZztvjwBEGZMmVgY2MDT09PZGVlITExEYD6/Oqqc3sDwPr16/Hdd9/xQ61bWVlBKBQiPDwc4eHh2Lx5M8LCwtCmTRut7zMzMxMnT56EQCBAly5ddOaHLn5+fgAMf+Lr9OnT6Ny5Mz9vmlgshp2dHaKiohAVFYVr167B0tJSo2G1fv36/P+FQiEcHR2RlJSEa9eu4dq1a9i2bRvOnTsHDw8Pg2N3cnKCp6cnYmNjAQAuLi6wtLTk08voqAwojHNDm7Fjx+L333/n369EIsHdu3cxYcIE3Lp1S6ORGgCGDx+OTZs28X/b29tDJpMhIiICEREROHLkCDp16pTr3OnKczU5ORmZmZmwtraGk5OTxjqGyMrKwqBBg7Dnw1zHQqEQTk5OiI+Px9GjR3H06FH069cPW7du1VtImjx5MjZs2JCn/CDk0aNHUCgUAICaNWvqXE+ZFhMTg4SEBI1rbZEhkeS+Tp8+hR8HIYQQQkgpVa1aNQiFQigUCty/f1/ng5f3798HAHh5eRW9suXLl9qXjxpl2jgIIYQQQj6g7k6ElBAKpsC79Hcl8iW2VKByZc3G7/R0rmH8QzutUcjlcly9epXvUerh4YFBgwZprGdjY4P+/fvj6NGjiImJgUQiQWpqKt6/f4+VK1fC0dERoaGhWLVqld7jDRkyBG3atMGjR4+QnJwMiUSC9evX8+nly5fHzJkzcfv2baSlpSE5ORlSqRT379/HgAEDIJVKMXjwYLxR6dXXuHFjxMTEqPWqvn79OmJiYvjlffr0QUxMDPbv38+vo0zPufzgwYP45ptvAABTp07F8+fP8fbtW6SmpiI8PBy9evVCSkoKevbsiVevXml9n2fPnkV6ejoaNmwIb29vvXmijbIntaE/8r/99ltIpVK0bdsW9+7d4xv/JRIJ7t27h+DgYAQEBGhsFxQUhE2bNuHly5eQSqVITExERkYG9u/fjypVquDhw4cYPXp0nmJfuXKl2tz1+/fvV8tr1c9J1ZAhQxAUFIRr167xsRf03Mjp8OHDWL9+PZYtW4bExEQkJiYiPj4eI0aMAMDN4f3XX3+pbXPp0iVs2rQJQqEQixcvxvv375Gamor09HTEx8fj5MmTGDx4sEGN2cpztc+HxjXlean6MnTqgunTp2PPnj0QCASYNWsW3r9/j4SEBMTHx2P69OkAgN27d2PWrFk693HkyBFs27YNv/zyi8H5QQgAte+Zr6+vzvVU0/R9N80ut6fNqlcH6tY1SSiEEEIIIaWRra0tmjRpAgA4ceKE1nUYYzh58iQAqD20X2QcOqR9efv2po2DEEIIIeQD6ilOSAnxPuM9PJYa3nu1OIn7IQ5l7MqgcmWuEVx1BK6MDG5ZxYr527eXlxf/f4VCgYSEBMjlcjg6OmLAgAFYsGABnLXMc/X5559j586dGstdXV0xbtw4+Pj4oFevXvj1118xbtw4ncevXr06Dh8+zPfiBoBKlSrx/589e7bGNkKhEDVq1MCOHTuQlJSEo0ePYtOmTZg5c6ahb9tgWVlZGDt2LABgzZo1GDZsGBQKBVJSUiAQCFClShXs3bsXXbt2xeHDh7Fs2TKsWLFCYz+HPvwYzuvQ6QDw77//4ubNmwCARo0a5bp+XFwcIiIiAABbtmxRa4S3trZGzZo1dfbkPH36tMYyGxsbdO/eHQ0bNkSFChVw8OBBvHr1Ktfh4guqevXqOHToENLT0/llxj43EhMTsXnzZgwZMoRf5ubmhvXr1+P27du4efMmdu/ejVatWvHpV65cAcA9QDBlyhS1/bm5uaFt27Ymr5CJjo7GypUrAXAPbsydO5dPc3FxwYIFC5CZmYlly5Zh2bJlGD9+vNaHMxITE/H7779j9OjR/AgPueUHIQCQmprK/9/W1lbneqppqtuokkql/CgXwMcRMmQyGT9aR6GTSKBv0CnZ/v3GfSJN37E+HMdk752UOHQOkYKic4gUFJ1D2lF+5G7w4MG4ePEizp07h2vXruHzzz9XSw8NDcXz588BQOvD/Gb3f/+nfbmBo4ERQgghhBhbqW8UP336NNavX49r164hNjYWAoEA3t7e+OKLL/DNN9+gRYsW5g6REPKBrS1QpYpm73CJBHjyRAAvL835u3OjHNI6p4yMDCQnJyM2NlZrj+LcdOrUCQA3D/fbt2919o6ePHmyWoN4fo5z9OhRXLp0Kd/70Of48eOIjo6Gp6enxhzkqgYNGoTDhw/zT6mrYozhyJEjAPLWKP7mzRucPXsWU6ZMgUKhgEAgwIQJE3LdzsHBgR9mTl/e55Wvry/q1KmDf//9F1euXCn0RnFTnBv+/v46K0+6dOmCmzdv4r///lNbrnxI5N27d5DL5QWK0Vj27duH7OxsWFtbY+rUqVrXmTlzJn7//XdIpVKEhYXh+++/11jH398fffv21bq9rvwgxNgWLVqEOXPmaCw/deqU3gZ3Y/rk8GHU0pH2snVr3AkPB8LDTRKLkraHlgjJCzqHSEHROUQKis4hdRkZGeYOocgbPHgwVq5ciXv37qFHjx7YunUrWrduDYVCgX379mHkyJEAgA4dOqB169ZmjpYQQgghpOgrtY3ijDGMGTMGa9eu5ZdZW1tDIBAgMjISkZGR2LVrFyZOnIhly5aZMVJCiCobm48N46pzjEulAkRH28PenlvHUIwxtb8zMzMRHh6OVatWYePGjTh9+jRCQkLQrVs3jW1TU1OxZs0a/Pnnn3j06BGSkpK0Pu0eHR2ts2FWORyaPnfv3sXatWtx6dIlvHjxAmlpaRpxR0VF5bqf/FA2qCYmJqq9B8YYBIKPDyFkffgwXmqZM+yff/5BTEwMKlasiOrVq+s81oULF9T2qUosFmPZsmUIDAzMNWYbGxu0bt0ap0+fRvv27TF69Gh06tQJ9erVy3VIb4VCgZCQEISEhODOnTt49+4dMjMzNdYrrPxWZYpz47PPPtM5572Pjw8AICEhQW15UFAQrK2tcfv2bTRr1gzDhw9Hq1atUL58+VzjLSw3btwAwL0fR0dHreu4uLigQYMGuHz5Mr9+Tg0aNMhzfhACcA/jKOmr4FVNU91G1bRp0zBp0iT+75SUFPj7+6Nt27Y6z29jE/XqpTPNZ/16+Pj5mSQOgOtFdvr0abRp0wZisb7+64RoR+cQKSg6h0hB0TmknXI0HKKbSCTC4cOH0bJlS7x48QJBQUGwtbWFQqHgf6fWq1dP6yh2RZaVlbkjIIQQQkgpVmobxbds2cI3iPfs2RMLFy7kh6V9/PgxfvzxRxw6dAjLly9Hs2bN+PmFCSHmZ239sWFcZYRZZGdb4MkThsqV89Ywrr5va9StWxcbNmxAQkICDhw4gCFDhuDVq1dqjRFPnjxB69at1RocbW1t4ezszDeqKXuhqw5/nZOHh/4h71etWoXx48dDoVAAAAQCAZycnGD14YekRCJBSkqK3mMUhHLO26ysLJ296lVJJBKNZcqh07U9WKBKLBbzc4YLBALY2NjAx8cHjRs3xogRI1C5cmWD496wYQO6dOmCu3fvYt68eZg3bx4sLS3x2WefoWvXrhg+fLjG/OQZGRn48ssvce7cOX6ZpaUlXF1d+cqrhIQEyGSyQstvVaY4N3Q1ygFcBQygOazhJ598gg0bNmD06NG4evUqrl69CgAoU6YMWrZsif79+6NLly46H3AoDHFxcQD0z+UMAH4fGvKU6+eUn/wgBPj40ATAPQhVu3ZtretFR0dr3UaVlZUV/z1WJRaLTVORnpGhe2j0iRMhNtMDMCZ7/6TEonOIFBSdQ6Sg6BxSR3lhmHLlyuG///7D0qVLsX//fkRGRkIsFqNGjRro168fvv/++1wf/i5SisBIY4QQQggpvUpto/i2bdsAABUrVsTu3bv5ym4AqFKlCkJDQ1G1alU8f/4ce/fupUZxUuS52boh7gftDT3FnZutm8YyKyuuYfzpU274dCWZTIAnT1CghnGlkSNH4sCBA0hOTsaxY8fUhlUeOnQooqKiUK5cOSxZsgStWrVSa2SVy+X8dSVnz11V+oaefvToESZMmACFQoFevXph8uTJqFOnjtoP3o0bN2LEiBF6j1EQcrkcANC+fXscP34cAPg5xR0dHXX2qlVl6HzijRs3xvnz5wsW8Adly5bFrVu3cPr0aRw7dgyXL1/G3bt3cfnyZVy+fBmLFi1CWFiY2tzQCxYswLlz52BjY4OFCxfiq6++gr+/v1rjbrNmzXDp0qVCy29VFhYWfIN3TuY+NwYMGIAOHTogNDQU586dw5UrV/D69Wvs3bsXe/fuRbNmzfDnn3+arFerkqEN8aZssCelQ7Vq1fhpG+7fv48OHTpoXe/+/fsAAC8vL40Hc4qMf//VnaZjegJCCCGEEFJ4HBwcMGfOHK1T7BQ79FuMEEIIIWZUahvF3759CwCoU6eOWoO4klgsRt26dfH8+XOkpaWZOjxC8kwoEKKMXRlzh2FSlpYfe4yrjlYrk8EoDeOqc4lHRkby/3/9+jWuXLkCANi9ezcaNWqksW1MTEz+D/xBWFgY5HI5qlWrhpCQEK0N0MY4jj5eXl4AgHv37uVr+ydPniA8PBxlypRB48aNjRlaroRCIdq1a4d27doB4Ia7P3LkCKZNm4ZXr16hf//+ePXqFd+QHBISAgD46aefdM5dXtj5baiicG64urpi1KhRGDVqFADg2bNn2LBhAxYvXoyLFy8iODjYZNOPKHvVv379Wu96ypEdypQpXddKUvhsbW3RpEkTXLx4ESdOnMDkyZM11mGM4eTJkwCAtm3bmjpEwy1apH352LFALiNYEEIIIYQQopcBD9YTQgghhBSWUlsS+eSTTwBw87FmZ2drpMtkMty5cwcAN8coIaRoEom4xm87O/XesMqGcS2jeRtMdWh0Ozs7/v+qDW/16tXTuu2ZM2fyf+Acx6lTp47OHtnGOI4+ynmto6Oj+fnF8+LgwYMAgC+//NKgXuWFycHBAf3798fGjRsBcMPbqzb2K/Nb12f64sULRERE5Pv4yt7Jxui5XRTOjZwqVKiARYsWoX///gCA06dPG7yt8j3kN2+U9+kbN24gOTlZ6zpJSUlqc48TYmyDBw8GAJw7dw7Xrl3TSA8NDcXz588BAIMGDTJpbHly6pT25ToeFiKEEEIIIcRg1FOcEEIIIWZUahvFx4wZAwCIiIhAv3791Bo6Hj9+jN69e+P58+eoUKECJk6caK4wCSEGEImAihUZrK3VH3BRNoyrzjueF7t27eL/r/pwjJOTE///u3fvamyXmpqK+fPn5++gKpTHuXfvntbGwuPHjxttuHFdOnfuDG9vbwDA+PHjkaHaJV+LhIQEtb8NnU/cmLKysvSm26gMH6A6fL0yv7V9pgAwtYDDBiuHEk9KSirQfgDznhvSXL5QyvzVNzVATgXNmx49ekAkEiEzMxOLFy/Wus7ChQshlUohFovRo0ePfB2HEH0GDx6MWrVqgTGGHj164OzZswC4KSdCQ0MxcuRIAECHDh3QunVrc4aaPw4O5o6AEEIIIYQUd9QoTgghhBAzKrWN4p07d8by5cthaWmJsLAwVKpUCba2trC1tUXVqlVx/vx5jBkzBv/++6/J50QlhOSdhQXg7Z2mtcf406fcv4aKiYnBzJkzsXXrVgBAo0aN8MUXX/Dp1atXR9myZQEAw4YNw82bN/m0q1evIjAwEImJiQV4N5z27dsDAB48eIDvvvuOb3BOT0/H2rVr0bNnT7i5ac63bkzW1tZYvXo1BAIBbt26hSZNmuDkyZNqDc+RkZFYu3YtGjZsiNWrV/PL4+Li8M8//8DGxgZt2rQp1DhVXblyBbVr18by5cvx6NEjfl5uxhiuXLnCPxTl5+eHWrVq8dsp83v+/PnYv38/P4pIZGQk+vfvj71798LFxSXfcdWsWRMAsHPnzlwfLsiNOc+NsWPHonfv3ti3bx/i4uL45WlpaVizZg22bdsGAOjYsaPB+1TmzcWLFxEeHp7nmHx9fTF+/HgAwM8//4zZs2fzDexJSUmYNWsWlixZAgCYNGkS/6AHIcYkEolw+PBhlCtXDtHR0QgKCoKdnR3s7OzQu3dvpKSkoF69eti5c6e5Q80fudzcERBCCCGEkOJCVyUMDZ9OCCGEEDMqtXOKA8CECRNQqVIlDBs2DHFxcZCojLMslUqRmpqK5ORkuLq66tyHVCpV6zWXkpICgBt+XZaXVjgTUsZVVOMrSYyZ1zKZDIwxKBQKvpGPfMQYg4UFUKGCAs+eCZGe/vHp48xM4PFjhsqVGUQi9SGalXNmf1w3U2345Vq1aiE0NBSMMbXtfvvtN/To0QMPHjxAgwYNYGtrCwDIyMiAra0tDh48yM8Zm/Mz0/X/nFq2bIk+ffpgz549+OOPP/DHH3/A2dkZqampkMvlqF+/PgYPHoxx48Zp3VfO42g7liGxdOnSBVu3bsXo0aNx584ddOzYESKRCE5OTkhLS1O7Bnbp0oXfz6FDh6BQKNCmTRtYWVkZdN7m5dzWl6f37t3DpEmTMGnSJIjFYjg6OiI5OZlv6HZ0dMSOHTsgEAj4befOnYvTp08jNjaW73lsZ2fHnw8LFizAqVOncOHCBf67mBfffPMNLl++jH379uHw4cPw8PCASCSCn58f/v77b63vQ3nO5TxeQc8NXfs1JH+zsrIQGhqK0NBQAIC9vT1EIpFaL++mTZti2rRpatupfn9yHrN79+6YPn063r17h2rVqsHd3Z2fsmDXrl1o1KiRRvw59zF//ny8evUKoaGhmDt3LubPnw8nJyckJyfz6/bt2xdz5szRmR/69m/o97a4UJ5fMpnMoF79VGYwTLly5fDff/9h6dKl2L9/PyIjIyEWi1GjRg3069cP33//PSwtLc0dZv5Qrx5CCCGEEGKoR4+0L6cyJSGEEELMqNQ2imdkZGDo0KHYu3cvGjRogB07duDTTz8FYwy3b9/G9OnTsWPHDpw4cQJnz55F7dq1te5n0aJFmDNnjsbyU6dO8Y1kRVVe5nslBWOMvBaJRPDy8kJaWlquw0OXZhkZqfDwAKKjHZCV9bGhJzNTgPBwBXx81BtxY2Nj1bYXi8Xw9PREzZo10aVLF/Tt2xeWlpb8Ay9KzZs3x9GjR/HLL7/g2rVrkEgk8PDwQPfu3TF+/HhUqlRJJaYMte1Vewnn3G9Oq1evRt26dbFjxw5ERERALpejevXq6N69O7799lvs2/f/7N13fFv19f/x19Wwhm3Z8rYTO3uQhJkwS8oOs1BW2bTQ0vJlFEoLLdAWaCn8oJQORssOe7XsmbAJI0ASVgIJmU7syI4d25J9Zdkavz8UK5ZH4iS25fF+Ph73Qe7nXklHF0NknXvO+R8QT3B1fK7GxsakP3f1Wj2N5Qc/+AF77rkn9913H2+++SYrV66kvr6e9PR0Jk6cyD777MPRRx/N9773vcTzPPPMMwDMmjVri8/dlqgOh8NbvR49iX3SpEk88MADvP/++yxcuBCfz0dNTQ1Op5MxY8Zw0EEHcf7551NcXJz0OK/Xy5tvvslNN93EG2+8wYYNG3A4HBx++OH8/Oc/5+CDD+aVV14B4jdEbUusEL9h4D//+Q+zZ89myZIlrF+/PnGzQttzdfeeAoFAp+fbkZ+N9jfsdPc+mpubu3z8pZdeytSpU3n//fdZtmwZVVVVNDY2kp+fz7Rp0zjxxBM59dRTiUQiSY9r+++uq3/PVquVl156iZtuuomPP/6YDRs2UFNTA8Rb8red3/az0t31v/vuu/nBD37AQw89xOeff059fT05OTnstttu/PjHP+aYY44hGAwm3QTX8Xp0d727ux6DVUtLC8FgkPfeey9xXbdkR7sbDCeZmZlcd911XX4+HPC6GMcgIiIiIrLN1qzpel1JcREREUkhI9bVMNJh4MILL+TOO+9k4sSJfP7550kzZgGCwSC77bYby5YtY//99+f999/v8nm6qhQvLS2lpqZmwLZdb21tZe7cuRx22GHY7fZUhzOk9ea1bm5uZu3atYwePRqn09lLEQ4dsViMQCBAZmYmhmFsmiduEAol/8LldG6uGJft0/Fad8U0TQoKCgiFQlRWVpKfn9/PUQ4NPbnW0nuG0/Vubm5m9erVlJaW9ujvFL/fT15eHg0NDQP2881Q5ff7E10P+vzaL1sGkyZ1fczng8LCvn39LrS2tvLKK69w1FFH6XOrbBf9DMmO0s+Q7Cj9DHWtXz/jSJJ+ufZr18KmsXNJ8vOh3RiuHaH/tlJL1z91dO1TS9c/dXTtU2ugX/+efr4ZlmmhQCDA3XffDcRno3ZMiAO4XC4uuugifvnLXzJv3jyqq6spKCjodJ7D4cDhcHRat9vtA/IHo73BEONQ0RvXOhKJYBgGFosFi2YwddLWzrjtGjkcMHFi/Pv9dvet0NxssHSpwcSJ0MV/utIDHa91V+bOnUswGGTmzJkUpiCJMlT05FpL7xlO19tisWAYRo//ftLnhWHiz39OdQQiIiIiMhRs6rTVyRC/+VhEREQGtqH9jW83li1blmgVOm7cuG7Pa9/+eNWqVX0el4j0rrbEeMfkdygE334L6gbcd9LT07nmmmu45pprUh2KiIj01COPdH9MX2CKiIiISE91Vw2uz5QiIiKSQsOyUrx99dea7mbckDxrODMzs09jEpG+4XDEO8EuXZpcMd7aGl8bPx70n3fvmzVrFrNmzUp1GCIiIiIiIiLS37pLig/xjlwiIiIysA3LTyKTJ09OtEy/9957E1Xj7UUikUSLda/Xy6Tu5iuKyICXlgaTJ0PHSQmRSDwxXl0NsVhqYhMRERkUVNUjIiIiIj3VrtAoiT5TioiISAoNy6S4y+XiZz/7GQALFy7kBz/4AV999RXRaJRoNMqXX37JUUcdxYcffgjApZdeitVqTWXIIrKD7PZ4xXhGRudj5eXxTYlxEREREREREZEdpEpxERERGYCGZft0gJtuuonvvvuO1157LbE5Ng0eDrXrsXzaaadx9dVXpypMEelFNhtMmACrVkF9ffKxDRuguRnGjo0n0EVERKQdVfWIiIiISE9ppriIiIgMQMP29jyXy8Urr7zC008/zXHHHcfIkSOJbSoTLS0t5cQTT+Sll17iscceU5W4yBBitcK4cVBc3PlYIADffAONjf0fl4iIiIiIiIjIkKCkuIiIiAxAw7ZSHMAwDE466SROOumkVIciIv3IMGDECHA6YfXq5LbpLS3w7bfxpHlJiX5fExGRYcTtBtPs+pj+QhQRERGRnupuprjap4uIiEgK6ZOIiAxbubnxOeNdtUtfvz6eHG83TUFERGRoy8hIdQQiIiIiMhSoUlxEREQGICXFRWRYy8iAKVMgM7PzsaYmWLwYamv7Py4REZF+ZZrdf3kJ+gJTRERERHpOSXEREREZgJQUF5Fhz26HiRPjLdU7ikZh1SpYuRLC4f6PTUREpF+Ul6c6AhEREREZClpbYePGro+pfbqIiIikkD6JiIgQv1m5uBh22gkcjs7HN26EJUugsbH/YxMREelzq1dv+biqekRERESkJzZs6P6YPlOKiIhICikpLiLSTnp6vJ16bm7nYy0t8TnjlZUQi/V/bCIiIn1mzZpURyAiIiIiQ0FVVffHlBQXERGRFFJSXESkA6sVxoyBsWPjf+6oshKWLoVQqP9jExER6RNbS4rrC0wRERER6Ynu5omD2qeLiIhISumTiIhIN3Jy4lXjGRmdjzU2xtup19b2f1wiIiK9bivt08PhcP/EISIiIiKDmyrFRUREZIBSUlxEZAscDpg0CUaM6HwsEoFVq2DlSlCuQEREBrVVq7Z4eG1FBaZp9lMwIiIiIjJobalSXElxERERSSElxUVEtsIwoLgY9tzTYM89DRYseCfp+MaN8arxxsbUxCciIrLDVqzY4uGWUAifz6eKcRERERHZspaW7o+pfbqIiIikkD6JiMiwc+2112IYRqfN6XQycuRIjj32WJ566ilisViXj/d4Oq+1tMC338bnjXfzMBERkYEpEIANG7Z4SqbHQzAYVLW4iIiIiGw/VYqLiIhICtlSHYCISCoVFhYm/tzQ0EBFRQUVFRW8+OKLzJ49m2effRaHwwHApEmTABg3zs3YsbBmTbyFenuVleD3w5gx8dbrIiIiA97KlVs9xdj0BWak4198IiIiIiI9pUpxERERSSF9EhGRYc3n8yW2pqYmvv76aw477DAAXn31VX7/+98nzv3222/59ttv2WuvvcjJgSlTICOj83M2NsbbqdfW9te7EBER2QFbaZ0O0NYExWq19m0sIiIiIjJ0qVJcREREUkhJcRGRTSwWC1OnTuWFF15g/PjxANx1113dzk91OGDSJBgxovOxSARWrYpvKqoTEZEBrUOleGtpaadTAoEALpcLt9vdX1GJiIiIyFCjpLiIiIikkJLiIiIdOJ1OTj75ZCCeBPj2228BErPH33nnncS5hgHFxVBYWMe99/6RM8/cgwMP9LDvvmkcfngRs2btwmmnnc9LL73Z6XXKy8u54447OProo5k4cSLp6elkZGQwZcoULr30UsrLy/vl/YqIyDDXoVLcMm5cp1PSHA6Kioqw2TR9SURERES2k5LiIiIikkL6VktEpAsjR45M/Nnv92/x3HXr1rH//t9LJLEtFgsZGVk0NNSwcWMVy5d/xYoV37L77odQXLx5hNbZZ5/Nu+++m3ierKwsAoEA33zzDd988w2zZ8/mpZdeYv/99+/9NygiItKmQ1LcOmECtLsBDKC0tBSbqsRFREREZEfEYls/R0RERKSPKCkuMkREo0N3hnVu7uZEcn9ZvXp14s85OTlbPPfaa6+lvLyc0aNHc++997LzzgdSUWGlpSVCdfU6PvzwVSorV7N+PTQ0wOjR4HbDtGnTOPLIIzn22GMZPXo0LpeLcDjMwoULueaaa3jttdc45ZRTWL58OS6Xq2/fsIiIDF9r1ybvjx7d6RSb3d4/sYiIiIjI0KWkuIiIiKSQkuIiQ0RtLRQUpDqKvlFdDfn5/fd6fr+fRx99FIgnxCdOnLjF8z/88EMAbrjhBg455BAAPB5YudKK1TqKE088P3GuacI338TnkN922+2dOofZbDb22msvXnrpJfbYYw++/PJL/ve//3HmmWf24jsUERFpJxJJ3teNWCIiIiLSF5QUFxERkRTSTHERkU3q6+t58803Ofjgg6msrATgkksuwbKVMvXs7GwA1q9fn1hzOmHyZCgp6TwyKxaDdevg228hGOz6Oa1WK0cccQQA8+bN2743JCIi0ls0/1FERERERERERAYxVYqLyLBmbOFL/jPPPJOrr756q89xzDHH8NFHH/G73/2Ob7/9lhNOOIH99tsPj8dDSQlkZcHq1Z0T4E1N8Oij7zN37n188cXHrFu3jqampk7Pv27dum19WyIiIttPFTwiIiIi0hc6digSERER6UdKiovIsFZYWJj4s8PhIC8vj913350zzjiDgw46qEfPcfnll/PFF1/w1FNPcc8993DPPfdgGAZTp07liCOO4LzzzmOnnSZSWQk+3+bH3Xbbb3nooZsT+1arFa/XS1paGgCNjY00NTV1mSgXERHpV6oUFxEREZEdFQqlOgIREREZxpQUFxkicnPjs7eHotzcvntuX/ss9Xay2+08+eSTXHXVVTzzzDPMmzeP+fPn8/XXX/P111/z97//nZtuuolf//rXiarx996bm0iIn3TSBZx44v8xZsxO5OdbKS0Fux3+8Ic/cP311xNTxZ6IiPQn/b0jIiIiIn1BSXERERFJISXFRYYIiwXy81MdxfC26667suuuuwIQDod59913+dOf/sR7773H5ZdfzqGHHsquu+7KlCnwl788AcA++xzOb397R+I5Nm6EhgYYMQLWr9/xhL2IiEivUKW4iIiIiOyolpZURyAiIiLDmCXVAYiIDEU2m41DDjmEl19+GYfDQSwW44033gDAaoWNG9cCMG3a7p0eG4nAmjUx5sx5q19jFhERAVQpLiIiIiJ9Q5XiIiIikkJKiouI7KDQFn6pczgcWK1WgMQ/AbKysgCoqPiCsrJ4ory9//3vP6xduxKAYBCam3s5aBERkW2hSnERERER2VFKiouIiEgKKSkuIrKDRo0axZVXXsnHH3+clCBfvnw5Z5xxBqZpYrFYOPzwwxPHjjjiCABeffVV7rrrz4wZ00RODgQC9TzwwA3ccsvFZGXFh6mHw7B4MZSXx/8sIiLSpyxd/Iqgv4BEREREZEcpKS4iIiIppKS4iMgOqqqq4v/9v//Hvvvui9vtJicnB5fLxYQJE3j66acxDIO//e1v7LTTTonHnH322cycOROAP/7xj+TkZDJjRg6HHprLnXdezb77HsGJJ/5f4vxYDKqr4auvYP165SZERKQPOZ2d1/QFpoiIiIjsqEgkvomIiIikgJLiIiI7aM6cOVx55ZXMnDmT0tJSgsEgAOPHj+ecc87h008/5dJLL016jN1uZ86cOVxzzTVMnDgRu91OLBZjr7324o47/s1TT72AzWbt9FqRCFRUxJPjlZVKjouISB9wODqvKSkuIiIiIr1BnytFREQkRWypDkBEpL9de+21XHvttdv8uFgs1uX6YYcdxmGHHbbNz+d0OrcYyx13XMvvf38t1dXxSvH2IpF4Utzng8JCKCgAu32bQxAREemsq0rx5ub+j0NEREREhp7GRnC7Ux2FiIiIDEOqFBcRGaBsNigthWnTICen63Oi0Xg79a++grVrobW1f2MUEZEhSJXiIiIiItJXGhpSHYGIiIgMU0qKi4gMcA4HjB0LU6ZAdnbX50SjUFUFX34J5eXQ0tKvIYqIyFBis4Glw68JSoqLiIiISG9QUlxERERSRO3TRUQGCbcbxo+HYDBeHb5xY+dzYjGoroYNGyAvD4qKui74ExER2SKnE0xz877ap4uIiIhIb/D7Ux2BiIiIDFOqFBcRGWRcrnjl+NSpkJvb9TmxWDwx/tVXsGpVPJEuIiLSYx3vqFKluIiIiIj0BlWKi4iISIqoUlxEZJByuWDMGCguBp8PamvjyfCOamvjW3Z2/Nz09H4PVUREBhunM3lfleIiIiIi0huUFBcREZEUUVJcRGSQczph9OjNyfGamq6T4/X18c3jibdVz8wEw+jnYEVEZHDoeAdVY2Nq4hARERGRoUVJcREREUkRJcVFRIYIhwNGjYonx6uq4u3To9HO5/n98S09PX5uVpaS4yIi0kFmZvJ+IJCaOERERERkaFFSXERERFJESXERkSEmLQ1KS+PV4NXV8S0S6XxeUxMsXx5vw15UBDk5So6LiMgmHk/yvt+fmjhEREREZGjZsCHVEYiIiMgwpaS4iMgQZbfDiBHxhPeGDfHq8dbWzucFg7BqFVRUQGEh5OWB1dr/8YqIyACiSnERERER6Qs+X6ojEBERkWFKSXGRQSbW1bBokS2wWuOJ8YKC+Lxxnw9aWjqf19ICa9dCZSXk58c3h6P/4xWRvqe/S2SrOlaK19enJAwRERERGWROPRVmzNi8/+STcP/9m/eVFBcREZEUUVJcZJCwWCwARLrqgy3SAxZLPDGenw8bN8L69dDc3Pm8SCT+O6rPB15vvHo8I6P/4xWRvtP2d0nb3y0ineTnJ++rzaWIiIiI9MTYsfGtjd+vpLiIiIgMCEqKiwwSdrsdu91OY2MjGcpQyg4wDMjNjc8Qb2iIJ8ebmro+t64uvqWnx5Pj2dn9GqqI9JFAIJD4e0WkSx2T4tXVqYlDRERERAa3oqLk/fXrUxOHiIiIDHtKiosMEoZhkJmZSX19PVlZWbhcrlSHJIOcYcST3FlZ0NgYv1m7oaHrc5uaYOVKSEuD/HyDtDSjX2MVkd4TDAbx+/1kZ2djGPpvWbpRUJC8r0pxEREREdkeHZPiTU3xLyFU8CEiIiL9TElxkUEkLy+PYDBIeXk5Ho+HzMxMrFarkhpANBqlpaWF5uZmtQPeDnY7lJbGcyC1tfHq8K5GDre0QEUFGEYa9fUh8vIMzR3vQ/q57l9D+XrHYjEikQiBQAC/34/D4SAvLy/VYclA1jEprkpxEREREdkeHZPiEK8WnzCh/2MRERGRYU1JcZFBxGq1UlpaSk1NDYFAgPr6+lSHNGDEYjGCwSAul0s3CfQCpzN+47bfH58x3pUNG+C778DlAo8n/hjpXfq57l/D4Xrb7Xays7PJy8vDarWmOhwZyJQUFxEREZHekJEBmZkQCGxeKy9XUlxERET6nZLiIoOM1WqlsLCQgoICWltbiUajqQ5pQGhtbeW9997j+9//vmbk9qJwGN54Ax58EBYt2vK5EyfC2WfDMccoQd5b9HPdv4b69bZYLNjt9iGb8Jde1nGmeDAYb3WZng5AOBzGNE0ikQhWqxW3243Npl8tRERERKQLEybAwoWb95cuhUMOSV08IiIiMizpmyuRQcowDNLS0lIdxoBhtVoJh8M4nc4hmcxKpR/+ML7Nnw//+Ac8/XSMSKRzUm3NGpg7F/Ly4P/+L74VF/d3tEOLfq77l663SDuFhZ3XKithwgRM08Tn82GaJoZhEIvFcLvdFBUV4Xa7+z9WERERERnYJk1KTop//XXqYhEREZFha2gNzBQRkT6z997w+OPw3XdhTjxxGV5vF0PHgZoa+POfoawMfvQjeOutrueTi4jIAJaZCdnZyWurVhEOh/H5fIRCIbxeb2ILhUL4fD7C4XBKwhURERGRAWznnZP3v/oqNXGIiIjIsKakuIiIbJORI+Gss75h5cow//53/IbvroTD8PTT8Y5oU6bA7bfHZ5SLiMgAs8susNdemzevN74+ZkzyeatWYZompmni8XgSrfgNw8Dj8RAMBjFNs5+DFxEREZEBr6ukuO6eFxERkX6mpLiIiGyX9HQ4/3xYsgReeQVmzer+3G+/hYsvhhEj4MIL448REZEB4r//jc/IaNsOOCC+3jEpvno1kUgEwzA6zaZv249EIv0RsYiIiIgMJh2T4g0NsG5damIRERGRYUtJcRER2SEWCxx5JLz+enws2M9+Bi5X1+c2NsKdd8LUqXDggfDUU9DS0q/hiohIT3VRKW61WonFYsQ6VPa07Vut1v6KTkREREQGi7Iy8HiS19RCXURERPqZkuIiItJrpk6Fe+6Bykr4179g8uTuz333XTjlFBg1Cq69Nv4YEREZQDomxVeuxO1243a78fv9iUR4LBbD7/fjcrlwu90pCFREREREBjTDgGnTkteUFBcREZF+pqS4iIj0uuzseLv0JUvgjTfg+OPjFeVd8fnguuviyfFTT4V58zRaTERkQJgwIXl/8WJshkFRUREOh4P6+nrq6uqor6/H4XBQVFSEzWZLTawiIiIiMrB1NVdcREREpB8pKS4iIn3GMOCQQ+CZZ2DVKrjqKsjP7/rccBiefBJmzoTdd4e774ZAoH/jFRGRdnbbLXnfNGHpUtxuN2VlZZSWllJSUkJpaSllZWWqEhcRERGR7nVMin/+eUrCEBERkeFLSXEREekXZWXwl7/A2rXw2GOw//7dn/vFF/CLX0BxMfz0p/DRR6oeFxHpdwUFMHJk8tqCBQDYbDY8Hg9erxePx6MKcRERERHZst13T95fsgTq6lITi4iIiAxLSoqLiEi/cjjgtNPg/ffjN4afdx64XF2f29QE998P++0XHz92662wYUO/hisiMrxNn568v3BhauIQERERkcFt+vT4FwJtYrH4/DQRERGRfqKkuIiIpMyuu8bbpFdUwN/+BmPHdn/ukiXw61/DiBHwox/BnDkQjfZfrCIiw9IeeyTvb6oUFxERERHZJg4H7LNP8tp776UmFhERERmWlBQXEZGU83rhsstg2TJ46SU49liwWrs+t7UVnn4aDj8cxoyB666D8vL+jVdEZNjoWCm+aFHijqRwOIzf76eurg6/3084HE5BgCIiIiIyaHz/+8n7776bmjhERERkWFJSXEREBgyrFY4+Gp5/Pp7ovuEGGDeu+/PLy+Haa2H0aDjySPjf/6Clpb+iFREZBjpWijc2wnffYZom5eXllJeXU1lZmfizaZqpiVNEREREBr4DDkjeX7gQAoHUxCIiIiLDjpLiIiIyIJWUwJVXxqvH334bzjgjefxYe7EYvPYanHQSjBwJl18O337bv/GKiAxJxcXxrZ3I++/j8/kIhUJ4vd7EFgqFqKioYOPGjaoeFxEREZHO9t0XbLbN+5EIvPlm6uIRERGRYUVJcRERGdAsFjjwQHjkEVi/Hm67LT6LvDsbNsAtt8BOO8HMmfDoo6oeFxHZIfvtl7QbfeEFTNPE4/FgGAYAhmFgt9tZsWIFy5YtU/W4iIiIiHTmdsd/UW/vrbdSE4uIiIgMO0qKi4jIoOH1wkUXxUfafvYZnH8+eDzdnz9vHpx5Zry9+l/+Ek+Yi4jINjrmmKRd21tvYQmHEwlxiM8X37BhA62trWRmZiZVj/t8PlWMi4iIiEjcoYcm77/zTkrCEBERkeFHSXERERl0DAOmT4d//ztePf7gg51vNm9v/Xr4/e+htBR+9jP46qv+i1VEZNA7+uj4/3g3MZqacH7+ObFYLLEWDAYxTZOMjAxsm1piGoaBx+NJHBMRERER4cADk/e/+gpqalISioiIiAwvSoqLiMig5nbD2WfDe+/BN9/E54nn53d9bigE990Hu+wSvzn9pZcgGu3feEVEBp38fNh996Ql74IF+P3+RGI8HA5jmiZOpxOXy5U4r62aPBKJ9F+8IiIiIjJwzZgR/0W+vbffTk0sIiIiMqwoKS4iIkPG5Mlw882wbh08/TR8//vdn/vmm/CDH8CkSXD77RAI9F+cIiKDziGHJO16Pv4Yh8NBfX09dXV1NDU1Ybfbyc/PT1SKA4mkudVq7ddwRURERGSASkuD/fdPXnv11dTEIiIiIsOKkuIiIjLkpKXBSSfBu+/CggXxSnK7vetzly+Hiy+GkSPh17+GVav6N1YRkUGhw+xH62efUdbcTGlpKSUlJYwfP56xY8fS2tqaSITHYjH8fj8ulwt3x2ogERERERm+jj46ef+NN6DdaB4RERGRvqCkuIiIDGl77BGfOV5eDtdc031rdb8fbr0Vxo+HE0+E99/X7+QiIgkHHgh5eUlLtkcewePx4PV6ycnJoaioiNbWVsrLy1m7di21tbU4HA6KioqSqsdFREREZJjr0IWItWvjd6yLiIiI9CElxUVEZFgoKoJrr40nxx94AHbdtevzolF45pl46/UZM+Chh+KzyEVEhrW0NDjzzOS1hx6CTbPCTdOkpqaGaDSKxWIhHA4Ti8XIy8tTlbiIiIiIJJsyJf5LentvvJGaWERERGTYUFJcRESGFacTfvITWLQI3n4bjjsODKPrcxcuhB//GEaNgj/9Caqr+zVUEZGB5ZxzkvcrKmDuXMLhMD6fj1AoRH5+PqWlpYwePZq0tDRqamoIh8OpiVdEREREBibD6DSeh1deSU0sIiIiMmwoKS4iIsOSYcS7AT/3HHz3HVx6KWRmdn1uVVW89XppKZx7LnzxRT8GKiIyUOyyC0yfnrz2wAOYpolpmng8HoxNdxkZhoHH4yEYDGKaZgqCFREREZEB7fDDk/fnzoVAIDWxiIiIyLAwaJPipmny6quvcv3113PCCScwatQoDMPAMAyuvfbaHj1HVVUVv/71r5k0aRIul4ucnBxmzpzJvffeS0yDZEVEho1x4+Dvf4d16+Af/4CxY7s+r6Ul3np9t93goIPg+ecTnYNFRIaHjtXizz1HtKYm8Tm8vbb9iP5HKSIiIiIdHXMM2Gyb90MhePXV1MUjIiIiQ96gTYp/8sknHHXUUfzhD3/g2Wefpby8fJsev2DBAqZOncqtt97KsmXLsNlsBAIB5s2bx3nnnccRRxxBSENkRUSGFY8HLrkEli2LV5AfdFD3577zDvzwhzBxIvzzn+D391OQIiKpdNpp8fnibVpaiD32GPX19fj9/qRW6W03mVqt1v6OUkREREQGuuxsOPjg5LVnn01JKCIiIjI8DNqkOIDX6+WQQw7h8ssv5/HHH6eoqKhHj2toaOCYY46htraWyZMn8+mnnxIIBGhqauL222/HbrczZ84cfvWrX/XxOxARkYHIao3PGn/rLfj883hhZPscUHsrV8Zbr48cCTfeGL+5XURkyMrJgeOPT1pyPP44jY2NLFu2jHXr1hEMBonFYvj9flwuF263O0XBioiIiMiAdsIJyfsvv6xfqkVERKTPDNqk+MyZM9m4cSNvvPEGN998M6eeeioOh6NHj73lllvw+Xy4XC5eeeUVZsyYAUBaWhoXXngh1113HQB33303y5Yt67P3ICIiA9+uu8L990N5OVx3HRQWdn1eIABXXQXTpsErr/RvjCIi/apDC3X3kiVMdjjIy8ujtraW5cuXU1NTg8PhoKioCFv7tpgiIiIiIm2OOw7aj+AJBODNN1MXj4iIiAxpgzYpviNtGB966CEATj31VMaMGdPp+MUXX0xGRgaRSIRHH310u19HRESGjsJC+OMfYc0aePBB2H33rs9bvhyOPhqOPTZeRS4iMuQccgjRnJykJe+cOYwcOZIJEybg8XjIzc2lrKxMVeIiIiIi0r2iIthvv+S1Z55JTSwiIiIy5A3apPj2Wrp0aWL++JFHHtnlORkZGcycOROAOXPm9FtsIiIy8DkccPbZsGABvPdevNtb+xvb27z4IkyZEk+km2b/xyki0mdsNlqPPjppyf3ww9gMA4/Hg8fjIT09HZvNRnNzM+vWrWPVqlWsW7eO5ubmFAUtIiIiIgNSh9E8PP88RCKpiUVERESGtGGXFP/6668Tf542bVq357UdW7JkSZ/HJCIig49hwMyZ8L//waefwj77dD4nFII//xl22il+s3ss1v9xioj0hfBPfpK0b1u3DucbbxDb9D86q9VKTU0N8+fP59NPP+XLL7/k008/Zf78+dTU1KQgYhEREREZkDomxWtq4P33UxOLiIiIDGnDLileWVmZ+POIESO6Pa/tmN/vp7Gxsc/jEhGRwWv6dPjgA5g9GwoKOh8vL4cTT4RZs6DdvVkiIoOWY+ZMmnfeOWnN/fDD+P1+XC4XFouFxYsXEwgEKC0tTWyBQIDFixerYlxERERE4saOhV13TV57+OHUxCIiIiJDmi3VAfS3QCCQ+POWZhy2PxYIBMjIyOjyvFAoRCgUSuz7/X4AWltbaW1t3dFw+0RbXAM1vqFE17r/6Fr3H13r7p1+enye+J//bOGOOyxEIsl91d94A3bdNcYvfhHluuuiZGdv+fl0rfuXrnf3dE2kI5vdTuTCC+H88xNrjvffx2WaFIwaxcaNG9m4cSOlpaVYLPH7cC0WC0VFRVRUVFBTU8PIkSMTjw2Hw5imSSQSwWq14na7sdmG3a8qIiIiIgmzZ8/mnHPO2ep5c+fO5dBDD+2HiPrQqafCF19s3n/qKfjXvyA9PXUxiYiIyJCjb5p20I033sh1113XaX3OnDlbTLoPBHPnzk11CMOGrnX/0bXuP7rW3Tv4YBg3LpN7792Zr77KTzoWjRr8+99WnnmmmSuu+JRx4xq2+ny61v1L17sz0zRTHYIMQI7TTyd26aUYm6q+jXCY0jfewDp9OlVVVVgslkRCvE3bfvsbLUzTxOfzYZomhmEQi8Vwu90UFRVht9v77w2JiIiIDEAWi4X8/Pxujzscjn6Mpo+cdRZcfTVEo/H9xsb4rLKzz05tXCIiIjKkDLukeGZmZuLPpmni8Xi6PK/9l7/tH9PRlVdeyWWXXZbY9/v9lJaWMmvWrG6fO9VaW1uZO3cuhx12mL5o7GO61v1H17r/6Fr33Pnnw3//G+a3v7Wybl1y1XhVVTpXXXUA//hHhHPPjWEYnR+va92/dL2719YJRyRJZibGscfGK3k2sd5xB/z619jtdqLRKNFoNCkxHt30RWfbf2PhcBifz0coFMLr9SaS4n6/H5/PR3Fxcf++JxEREZEBprS0lNWrV6c6jL41YkR83thrr21emz1bSXERERHpVcMuKV5SUpL4c0VFRbeJ64qKCgA8Hk+3rdMhfjdmV3dk2u32Af+F+mCIcajQte4/utb9R9e6Z04/HY47Dm6+Ob61H6MbChn83//Z+OgjuPPO7jvD6Vr3L13vznQ9pFuXXZaUFGftWnjpJfKOOIKcnBx8Ph9FRUVYLBai0Sg+n4/s7Gyys7Px+/3U1dVRXV1NcXExxqa7gwzDwOPxUF9fry4FIiIiIsPFOeckJ8XffhtWrYIxY1IXk4iIiAwplq2fMrRMmzYt8eevv/662/Pajk2ZMqXPYxIRkaEtPR2uuw6++Qb23bfz8Ycegr33hqVL+z82EZEdsvfesOeeyWt33IHT6WTq1KlkZmZSUVHB2rVrqaioIDMzkzFjxlBdXU15eTmVlZX4fD4qKysJBoOJp2hLkEcikf58NyIiIiKSKsceC15v8tpDD6UmFhERERmShl1SfNKkSZSVlQHwWvu7D9tpamri/fffB2DWrFn9FpuIiAxto0fDO+/ApZd2PrZ4McyYAY8/3s9BiYjsqAsvTN5/4w345hvy8vLYe++9mT59OrvssgvTp09n+vTphMPhRLv0nJwcMjMzCYVCVFdXEw6HAYjFYgBYrdb+fjciIiIikgpOJ5x2WvLa7Nmb54yLiIiI7KBhlxQHOHvTPJonnniiy5k8d9xxB42NjVitVs4444x+jk5ERIaytDT4+9/h6achMzP5WGNjvN36j38MGuEsIoPGKadAbm7y2h//CIDT6WTkyJGMGTOGkSNHEo1GMU0Tj8eDYRi4XC7cbjcAzc3NBIPBxEzx9sdEREREhqsNGzYwffp0MjIycLlcjB07ljPPPJN33nkn1aH1vnPOSd5fvRrefTcloYiIiMjQM6hnitfV1SW1VIxuunPQNE1qamoS606nM2ku+G9+8xvuvfdefD4fRx99NA899BDTp0+npaWF++67jz/84Q8A/PznP2fixIn99G5ERGQ4Oekk2GUXOPlk+PLL5GMPPQTvvw8PPmikJjgRkW3hdMIvfgE33LB57b//hS++gF13BSAcDmOaJrW1tfj9fgzDwDAMrFYrOTk5bNy4kerqahwOB+FwGJfLRVFRETbboP51RURERGSHmabJwoUL8Xq9NDU1sWrVKlatWsWjjz7KOeecw913373Fz0yhUIhQKJTY92+6A7u1tZXW1tY+j3+b7LILtqlTMRYvTixF77+fyP77dzq1LfYB9x6GCV3/1NG1Ty1d/9TRtU+tgX79exrXoP6Waffdd2fNmjWd1v/617/y17/+NbH/4x//mNmzZyf2s7KyeOmllzj88MNZsmQJM2bMIDMzk+bm5sSFmzVrFn//+9/7/D2IiMjwNXEifPQRXHQRPPBA8rFVq+CQQ6xceWUBRx2VmvhERHrs8svhP/+BjRs3r91yCzz8MKZp4vP5Ejeufvvtt2RkZJCbm4vD4cDlcuH1erFarRQWFuL1enG73dhstgH7y5aIiIhIXyspKeGaa67hhBNOYNKkSTgcDiKRCPPnz+eaa67hjTfe4IEHHiA9PZ3bbrut2+e58cYbue666zqtz5kzZ0B25Rm3555Ma58Uf/pp5hx9NGGXq8vz586d21+hSRd0/VNH1z61dP1TR9c+tQbq9TdNs0fnDeqk+I6YPn06ixcv5qabbuKll15i7dq1pKenM23aNH784x9z7rnnYrEMy+7yIiLSj9xuuP9+OOQQuOCC5Lbpra0GN9+8J4ceCjNnpi5GEZGtys6GSy6Ba67ZvPbEE4T/9Cd8sRihUIjMzEzq6urIysqipaWFlpYWcnNzMU2T8vJyxowZw4gRI1QdLiIiIkK8YGfWrFlJa1arlf3224/XX3+dE044geeff54777yTX/7yl0yYMKHL57nyyiu57LLLEvt+v5/S0lJmzZqFx+Pp0/ewXaZPj88S38QWCnF4QwOxE09MOq21tZW5c+dy2GGHYbfb+zlI0fVPHV371NL1Tx1d+9Qa6Nff38NZpIP6G6eu5oFvi8LCQm699VZuvfXW3glIRERkO51xBnzve3DmmfDBB5vXm5ttHHNMjDffhBkzUhefiMhWXXgh3HQTtN2dGw4TveEGzEsuwev10tjYSGtrK2VlZdTU1FBXV4fVaiU9PZ1YLEZmZqYS4iIiIiI9YLFYuOWWW3j++eeJRqO8+OKLSYnv9hwOBw6Ho9O63W4fkF9qM3JkpyXb+efHx/V0YcC+j2FC1z91dO1TS9c/dXTtU2ugXv+exqRSaBERkQFi9Gh49104/fTkdb/fYNYs+PzzVEQlItJDublw7rlJS/bZs3GsXYthGEQikcSWkZGB2+3G6/VSUlJCUVFRl1/WioiIiEjXxo8fT15eHgArV65McTS9LDu789qGDf0ehoiIiAwtSoqLiIgMIFZrfL740Ucnr9fVwWGHQbvRaiIywMyePRvDMLa6vfHGG6kOte9ccQU4nYldIxym4J//JBaL0dLSQnV1NT6fj9raWpqamggGg1itVmw2G1arNYWBi4iIiMiA8atfdV5bs6b/4xAREZEhRUlxERGRASYtDf77XzjssGjSek1NPDG+g9NDRKSPWSwWCgsLu92GdEV0aSn88pdJS1mvv07wvfcIBAJYLBYMw8BisZCTk4NhGKxcuRK73Y7b7d6mlwqHw/j9furq6vD7/YTD4d58JyIiIiID2ooVK6ipqQFgzJgxKY6ml3WVFP/ww/6PQ0RERIYUJcVFREQGIKcTnn46wrRpyS3i1q+Hww9X5ziRgay0tBSfz9ftNnPmzFSH2Ld+9zvwepOWim69ldqaGrKzs2lpaaGlpYW0tDQMwyAajeLxeLZpnrhpmpSXl1NeXk5lZWXiz2bbPHMRERGRQSwWi231+OWXXw7Eb8g85phj+iOs/pOZ2Xmu2COPpCYWERERGTKUFBcRERmg3G64+ur57LtvcsX4smVw1FEQDKYoMBGRLfF64aqrkpYyPvuMyatXM2rUKHbbbTd23XVXRo0axYgRIygpKUmqnu9YAR6JRJKeKxwO4/P5CIVCeL3exBYKhfD5fKoYFxERkUFvzZo17LXXXtx1112sXLkykSSPRqN8/PHHHHnkkTz77LMA/OIXv2DSpEmpDLdvnHVW8v6nn8LSpamJRURERIYEJcVFREQGMJcrwvPPR9h55+T1zz6Dv/41NTGJiGzVRRdBWVnS0ug77yQrIyORxM7OziYjIwOr1ZqYJ96xAnzt2rVs2LAhqQLcNE1M08Tj8WAYBgCGYeDxeAgGg6oWFxERkSHh008/5fzzz2fcuHG4XC7y8/Nxu93su+++vP766wCcc845/Otf/0pxpH3k0EOhsDB57eGHUxOLiIiIDAlKiouIiAxw2dnw2mswalTy+t//Dn5/SkISEdkypxP+/OfkpeXLMR55JFHpFIvF8Pv9uFwu3G53lxXg2dnZhMNhqqqqEhXgkUgEwzASCfE2bfsdK8tFREREBpvCwkJuu+02Tj/9dKZMmYLH46G+vh673c7kyZM599xzmTdvHvfff/82jaAZVGy2rluoR6Ndny8iIiKyFUqKi4iIDAIlJfDcc8lr9fVwxx2piEZEtmTDhg1Mnz6djIwMXC4XY8eO5cwzz+Sdd95JdWj964wzYJddkpYKbruNBp+Puro66uvrsdlsuN1uAoEAPp+PQCDQqQLc5XIlVYBbrVZisVinWZtt+21V5yIiIiKDlcvl4qKLLuLRRx9l8eLFVFdX09raSiAQ4JtvvuG+++7je9/7XqrD7HsdW6ivWQPz5qUmFhERERn0huithCIiIkPPbrvBCSfAM89sXvvb3+DiiyEjI2VhiUgHpmmycOFCvF4vTU1NrFq1ilWrVvHoo49yzjnncPfdd3db0RMKhQiFQol9/6Z2EK2trbS2tvZL/L3J+MtfsP3gB4l9m8/HqBdfpOmCCwiFQolkuGEYNDQ0EAgEcDgcuFwuID4/3DAMWltbaW5uxuVyYbfbSUtLo7a2NpFAb6s6dzqd2O32QXmtpG+0/SzoZ0K2l36GZEfpZ6hruh7SI7vtBlOnwuLFm9cefhi+//2UhSQiIiKDl5LiIiIig8jvf5+cFK+thf/8B37zm9TFJCJxJSUlXHPNNZxwwglMmjQJh8NBJBJh/vz5XHPNNbzxxhs88MADpKenc9ttt3X5HDfeeCPXXXddp/U5c+bgdrv7+i30vliM/Xbemfyvvkos2W+5hfljxlDZ3Ew4HMblcmEYBqZpUllZyVdffUVeXl6i4jsWi/HRRx/x3XffJa5BKBSirq6OlpaWxPOmpaXh9Xr55ptv+vc9yqAwd+7cVIcgg5x+hmRH6WcoWVsHGJEtMgw480y48srNa08/DbfdBuoOJCIiIttISXEREZFBZPfd4Qc/gBdf3Lz217/CBRfAYMyXiQwls2bNYtasWUlrVquV/fbbj9dff50TTjiB559/njvvvJNf/vKXTJgwodNzXHnllVx22WWJfb/fT2lpKbNmzcLj8fT5e+gLRlER7LtvYj+tqYlDP/2UpT/7GdnZ2YlW6eFwmIqKCmpraxk/fjwej4fW1lZef/11Dj74YMaOHZtUYR8OhzFNk0gkgtVqxe12D92ZmrLdWltbmTt3Locddhh2uz3V4cggpJ8h2VH6GepaWzccka064wy46ipoG53T0BD/hfiHP0xpWCIiIjL46FsjERGRQeYPf0hOildXw913w6WXpiwkEdkKi8XCLbfcwvPPP080GuXFF19MSn63cTgcOByOTut2u33wfpG+zz5w4IHQbqa66447cB93HGkFBYk1u93OiBEjCAaDNDY2YrFYCIfD2Gw2Ro4cmWip3v78jmsi3RnU/w3JgKCfIdlR+hlKpmshPVZaGv8s+fbbm9ceflhJcREREdlmllQHICIiIttmzz3h8MOT1x58MDWxiEjPjR8/nry8PABWrlyZ4mj62SGHdFoq+stfiLVV/GzidDopKiqitLSUkpISRo4cSX5+/uBsHS8iIiIiveOss5L3X30VNmxITSwiIiIyaCkpLiIiMgideWbyvs+XmjhERHrkhBM6LXnefRe/359IjMdiMfx+P5mZmRQVFeH1evF4PInZ4iIiIiIyTJ14Ijidm/fDYSxPP526eERERGRQUlJcRERkEPruu+T9nXZKTRwi0nMrVqygpqYGgDFjxqQ4mn42ZUqXyy7TpL6+nrq6Ourr63E4HBQVFfVoNng4HMbv91NXV4ff7yccDvd21CIiIiIyEHg8ndqlGw8/nJpYREREZNDSTHEREZFBaMGC5P099khNHCISF4vFMAxji8cvv/xyID5f/Jhjjumv0AaOr76CnXdOWip7/HEa//hHIpEIVqsVt9vdo4S4aZr4fD5M08QwDGKxGG63m6KiIrVaFxERERmKzj4bnngisWtZsIDM1atTF4+IiIgMOqoUFxERGYQWLkzenz49NXGISNyaNWvYa6+9uOuuu1i5cmWiJXg0GuXjjz/myCOP5NlnnwXgF7/4BZMmTUpluKkxbRqccUbSkuWOO/A0NiZapfe0Qtzn8xEKhfB6vYktFArh8/lUMS4iIiIyFB12GJSUJC2VvfVWioIRERGRwUhJcRERkUFm/fr41p4qxUVS79NPP+X8889n3LhxuFwu8vPzcbvd7Lvvvrz++usAnHPOOfzrX/9KcaQpdO210D7x3dwMf/7zNj2FaZqYponH40lU5xuGgcfjIRgMYppmLwYsIiIiIgOCzRavFm+n9N13oaUlRQGJiIjIYKOkuIiIyCDTsUo8IwMmTEhNLCISV1hYyG233cbpp5/OlClT8Hg81NfXY7fbmTx5Mueeey7z5s3j/vvv71E19JA1fjz89KfJa/feCytX9vgpIpEIhmF0alffth+JRHY4TBEREREZgM45J2nX0dCA8corKQpGREREBpth/I2ciIjI4PT2hw1AVmJ/993BotvcRFLK5XJx0UUXcdFFF6U6lIHvD3+ABx+MV4kDhMNw3XXxtR6wWq3EYjFisRiRSIRgMEgkEsFisRAOh7FarX0YvIiIiIikzMSJ8L3vwQcfJJYsDz4IJ5+cwqBERERksNBX6CIiIoPMg699nbQ/fqo/RZGIiGyHESPggguS1x55BL75pkcPd7vduN1uqqqqWLduHZWVlfh8PpYtW0Z9fX3vxysiIiIiA8e55ybtGq+9Bj5fioIRERGRwURJcRERkUEk2BrEf8hZcOpxcMB1MOElCnb6NtVhiYhsm9/9Lj77oU00Cn/8Y7enh8Nh/H4/dXV1mKZJdnY2gUCA2tpaYrEYhmGQl5eHx+OhpqaGcDjcD29CRERERPrdySeD253YNSIRePjhFAYkIiIig4Xap4uIiAwi88rn0ZK+CiavgskvYDEsXPnzjakOS0Rk2+Tnw6WXwvXXb177739h0aL4TIh2QqEQa9eupaWlBcMwiMViRKNRHA4HEyZMIBqNYrVacblcWK1W6uvrMU0Tj8fTv+9JRERERPpeZib86Ecwe/bmtfvvh9/8BgwjZWGJiIjIwKdKcRERkUHkjZVvJO3vNWIvspxZ3ZwtIjKA/frXkJ2dvHb11Um74XCYuro6mpub8Xq9ic00TRoaGnC73WRnZ5OZmYnNZsPY9EVoJBLppzchIiIiIv3unHOS97/9FubPT00sIiIiMmgoKS4iIjKIzF05N2n/0DGHpigSEZEdlJ0NV1yRvPbqq/Dee4ld0zQJhUJ4PJ5EwtswDLKzswkGg5immfTwWCwGgNVq7dPQRURERCSFZs4kNn588tp996UmFhERERk0lBQXEREZJKoaq1jkW5S0duhYJcVFZBD75S+hsDB57corYVNyOxKJYBhGIiHexu1243K5qK+vTyTCY7EYfr8fl8uFu92cSREREREZYgyD6NlnJ689/jj4/amJR0RERAYFJcVFREQGiY5V4hlpGexbum+KohER6QXp6fDHPyavffghjU88gd/vJxaLJbb2rFYrubm5icR4XV0d9fX1OBwOioqKsNls/fgmtiwcDuP3+6mrq8Pv9xMOh1MdkoiIiMigFz3rLKKWdl9tNzXBY4+lLiAREREZ8JQUFxERGSReX/F60v4hYw4hzZqWomhERHrJz34GY8YkLdmuuYbyVauora3FMIxEghw2V4Tn5OQwadIkSktLKSkpobS0lLKysgFVJW6aJuXl5ZSXl1NZWZn4c8e27yIiIiKyjUaMoGrGjOS1u+5KdBwSERER6UhJcRERkUEgGovy+vLkpPjh4w5PUTQiIr0oLY3ItdcmLTm/+46Sd99NVFXbbLaUVITvSJV3OBzG5/MRCoXwer2JLRQK4fP5Oj2XKspFREREts3qwzv8Tvz55/DZZymJRURERAa+gdNXUERERLr1ue9zNpgbktYOH6+kuIgMDU3HHkvahAk4v/susZZx882w334Eg0GysrLIzMwkEolgtVpxu920tLQkqq4NwyAWi+F2uykqKkpUi4fDYUzTTHpcTxPppmni8/m2+Pxbe7xpmni9XgzDIBwOEwwGiUajbNiwgezsbHJycnrltURERESGo+rddiM2ahTGmjWbF//1L3j44dQFJSIiIgOWKsVFREQGgY5V4hNyJjDWOzZF0YiI9K5ILEbVJZckraVVVJB2//3U1dVRWVmJzWbD6/Xi8XgAElXYmZmZ2Gw2DMOgtraWioqKRDJ8e1uXb2uVd5fvKRLBMAwMwyAYDFJRUUFlZSXV1dWsX7+eNWvWYJpmr7yWiIiIyLBktRL96U+T1554AsrLUxOPiIiIDGhKiouIiAwCc1fOTdpX63QRGUqsViuB73+f5g5zIcc99hhei4XW1takBHFbFbbdbqeyspKKigqqq6vx+/2sWLECn8+3Q4nmtuf3eDwYhgGAYRh4PB6CwWCPEutWq5VYLEZrayvV1dW0tLTg8XgSW1sy3O/37/BriYiIiAxX0fPOg/addcJh+Mc/UhaPiIiIDFxqny4iIjLAma0mH6z9IGntsHGHpSgaEZHe53a7caens+rnP2endnMg7Q0NzPzwQ/LPOSeRIPZ4PEQikUQb8paWFrKyshJtxysrKzh7zJwAAQAASURBVFm1ahUej4e8vLxOieb6+vrE83SnfZV3e237kUikZ+/J7aa6ujrRAh6gsbERl8tFQUEBjY2NWK3WHX4tERERkWErNxd++lO47bbNa3ffDX/4A3i9/RNDNAp+P2zcCHV1EAjEk/NtW9vnOaczeXM4ul7r8LlQREREeoeS4iIiIgPcB2s/oCXSkti3GlYOGHVACiMSEeldNpuNoqIils6Ywfq996Z4/vzEsT0/+ADfhg2ENiXDIV6F3ZYkz87OTkooZ2Rk0NzcjMViwel0JuaJu1yuxDzxrSWa26q8Y7FY0nPHYrHE8Z6+p4aGBgKBQOJ5nE4nBQUF2O32pOfdkdcSERERGdYuuwzuvHNz8rmpCf79b7jqqt57jVgMVqyAjz+GpUth2bL4tnZtPBEejfbO69hs8UR/fj7k5cW3sjIYPTp5y8zsndcTEREZRpQUFxERGeDeXPpk0v5eI/Yiy5mVomhERPqG2+1m1KhRrL3sMopOPx1j05eattZWsm+5hYY//SmRIHa73aSlpSWS4hBPIjc2NuJ0OjFNk8rKSkKhUFIyOj8/H9h6ormtytvv9yfamsdiMfx+Py6XC3f7Fp09eE8tLS1kZGRgs9kSyfm2pHdmZiaRSGSHX0tERERk2Bo9Gn70I3j88c1r//xnPFnudO7Yc3/4ITzwALz8Mqxfv2PP1RPhMFRVxbctGTEC9tgjvn3ve3DAAZCW1vfxiYiIDGKaKS4iIjKQxWJ8tuKFpKVDxxySomBERPqWx+Mhc8YM6n70o6T19GefJWvVqkSC2GazUVxcjN1ux+/3J6qx09LS8Hq9NDc3Y7fbsVgsiRneoVCIlStXYrfbu0w0h8Nh/H4/dXV1mKZJXl4eDoeD+vp66urqqK+vx+FwUFRUlKg47+l7ys/Px2KxJBLj7ZPeHo+HoqKiXnktERERkWHr8suT96ur4aGHtv/5vvsOjjkmnnC+997+SYhvi4oKePFFuO46mDULCgrgZz+DL75IdWQiIiIDlr5hERERGcAKI5/yVkE9i7LgDTO+HeEOpzosEZE+0dZyvPrSS8l64QWsTU0AGLEYxbfeivXooxPn5uXlMXbsWBoaGnC73dhsNpxOJ9XV1VgsFiZPnkxdXR1+vx+IV5JHo1E8Hk+nRLNpmvh8PkzTTFRqu91u8vLyABIt2NteZ3vek8/no76+PrHucrkSSW+bzUZZWRmmae7Qa4mIiIgMW7vvDoceCm+8sXntllvi88a3dRzNW2/BCSdAQ8O2x2G3g8cT/6fNFt+s1nh79VAovjU3QzDYey3XIR7rfffFtwMOgEsugWOP3fb3LiIiMoT16FuWP/3pT732goZh4Ha78Xq9TJ48mRkzZpCm1i4iIiKdRVuY1vIAVgNmOOPb70aMgBm/T3VkIj2mz5GyrdxuNyP32IPWX/0K6/XXJ9atb78Nr78ORxwBxJPNI0eOxGazEQwGCYfDNDY2YrPZyM/Px+Px4Ha7CQaDiURzKBTC4XAkvV44HMbn8xEKhfB6vUnty2tqaigrK9vh5LTb7d5q0ttms+HxeHbodURERIY6fbaULbriiuSk+HffwfPPxxPcPfXll/EK8WCw6+OFhTBjBkyaBBMnwrhx8fnfOTng9UJ6Omwa37NV4fDmJHlbotzvhw0boKYm/s+qKlizBlavjm89qVh/9934NmYM3HADnHJKz2MSEREZwnr07c61116bmMXX2zweD+eddx7XXnut5uWJiIi0Y/nudjJiHX7h3e3/gS09NQGJbAd9jpTtYbPZsF15JbEHHsCoqNh84Ne/jlcAbUomd5VsDofDVFZWEovFsNlsZGZmAvFK8XA43GmeuGmamKZJZmYmjY2NSUnrxsZGTNPslWS1kt4iIiI7Tp8tZYsOPRR22w0+/3zz2k03wfHH9ywpHIvBWWd1TogXFsKll8aT6xMm9F6Cua2SPH0bfsdvboYVK+LvceFCmDcPPvmk63NXrYLTToPbb4/PWJ8+vVfCFhERGax6PFM8Fov1ydbQ0MDf/vY3Zs2aRWtra1++VxERkcGjYQmWxdclr+XuDaNPT008IjtAnyNlu7jdRK69NnltyRL497+TltqSzV6vNzE/3O124/f7icViAEkzvDt+yR2JRGhubqayspKKigqqqqqoqKigsrIyUWU+1LSfn+73+wmHNZZDREQGD322lG4ZRrxavL1PPoH33+/Z499+O14p3t7xx8crzn/3u3hleKorrp1OmDoVzjgD/vY3mD8f1q6NzxYvLOz6MR98AHvuGW8lX1XVv/GKiIgMID2qFH/77bd79UVbWlqoq6tj8eLFPP744yxfvpyPPvqI//znP1x88cW9+loiIiKDTqQZPjgVI9Lh7vTp/wSjx/eziQwI+hwpOyJ25pnU33gj2StXbl784x/jFS+b5n131JMZ3kmvEYuxYcMGXC4XWVlZifbpgUCAhoYGxo4d2xdvLWW6m59eVFSkqjgRERnw9NlSturkk+HKK+Mtx9v86U/JbdW78/rryfuTJsHjj0OH8TsDzsiR8c/Iv/0tPPVUvCp8wYLkc2IxuP/+eDv5J56IV9WLiIgMMz1Kih9wwAF9FsDVV1/NIYccwgcffMCTTz6pD5wiIiKLLof6r5LXJl4EeXunJh6RHaDPkbJDrFa++ulPmXn11ZvX6usJXXEFoX/8o9Nc7jY9meHdXndtWPuqPWuqbGl+us/n65X56SIiIn1Jny1lq2y2+MidX/5y89qbb8Jrr8ERR2z5scuWJe8ff/zAT4i353DE27+feSY8+yz85jfxFurt1dbC4YfD9dfHk+gW3XgvIiLDR8r/1ktLS+Pyyy8H4JtvvklxNCIiIilW8RIsuz1pKZY1DXb/a4oCEhm49DlyeNg4dSrRk09OWkubPZsNL7xAeXk5pml2+biObdW3lBDPy8vD4XAkqsMDgQAOh4Pc3NwhlRhvm5/u8XgS78swDDweD8FgsNtrKSIiMhzos+UQ8rOfQUlJ8trFF8dnha9YAXfeCVddBf/v/8Wrw0Oh+DmBQPJjCgr6J97eZhjx+edLlsANN3SeWR6Nxt//8cdDu85KIiIiQ92AKAMYPXo0AA0NDakNREREJJWCVfDxuUlLYdKI7fMIdqszRUGJDGz6HDk8tFx/PWkvvoiluRkAIxaj7PrrWf7kk/hghyqcrVYrTqeT/Px8mpubE5XlTqeTxsZGrFZrL76T1IpEIhiG0SnR37Y/FOeni4iIbAt9thwiXC7485/jM7TbLF8Obnc8YRyLJZ+fkRGvIn/zzeT1wd5Bx+mMt5L/yU/gF7+AF19MPv7CC/FZ4888AzvvnJIQRURE+tOA+Jt95MiR/P3vf091GCIiIqkTi8H8cyG0IWl5cdq5TPFMSVFQIgOfPkcOD025udRfcAFFt96aWLMvXUrJ44+z7qyzEtXP28PtduN2u5MqqNtairtcrpTN2Q6Hwz1u/95TVquVWCxGLBZLSozHNn0x3N83APTFexQREdkR+mw5hPzkJ3DvvfDRR8nrHRPiAI2N8N//dl4fKjcMFhfDc8/BTTfB738frxRvs3w57LNPvN36rFkpC1FERKQ/DIhvHHJycrjkkktSHYaIiEjqLL8LKl9JWoqWHMPq+sNRSlyke/ocOTxEIhFqzz6b3Ndfx754cWI98+9/Z+PEicRiMcaPH4/T2X1Xje4SsDabjaKiInw+H/Xt2ke6XC6Kior6NUnbFmMgEKCmpoZwOEw4HCYSiZCRkcHo0aO7TP73NLncdgOA3+/v0xsAehKPaZr4fD5M00zE4Xa7KSoqStmNCCIiIvpsOYRYLHD33bDHHtDaun3PMVSS4hC/HldeGa8MP+00qKnZfMw04dhj44nxI49MXYwiIiJ9bEAkxUVERIa1pnJYdHnymrOIyIy74M1PUxOTiMgAYrVaiVmt1N10E/nHHouxqbrF0tJC6V/+wouXXMLy5cvZZZddumylvrUErNvtpqysrE+rlreWKG6LMRAIJP4Zi8VIT0/H6XRSU1NDXV0du+++e1JifFuSy/1xA0BP4gmHw/h8PkKhEF6vNyk57/P5dqgdvoiIiEjCtGnxueG//nXnY8ceC1VV8MknXVePA0yd2rfxpcKhh8KCBXDSSfBpu+8bQiH44Q/jFfM/+EHKwhMREelL+qZBREQklWIx+OQXEG5MXt9nNjjyUxKSiMhA05a4rh07lrSzzyZ79uzEsdErVzL2gw9YUF/P+vXrOfDAAxk1atQ2J2BtNtsWW7D3uNV3LAa1tVBZGd8qKmgpL8esqCDa0IDFNKGpidbmZiyhEJbmZmLhMPZgkNJwGCMSYadQCKJRYgA2G4bVChYLkVgMa1oaMYcDw2IhZrVii0QosduxuFzEnE5iDgetVitRt5toTg4WtxvS0yE7G7KycGdnMyojg2ank0hmJkZuLq6iImy9UJ3d02ttmiamaSbOgfhcc4/HQ319/Q61wxcRERFJcuml8M47yfO0r7gi3koc4onxl16C55+Ht9+Ot1IHuPBCOPzw/o62f5SVwfvvx1vMP/HE5vWWFjjxRHjqqXiCXEREZIhRUlxERCSVVj8C619LXhv3Myg5fPtbvImIDDHtK5wXHn88ezz7LNkNDYnjh77yCjV77onP76eiogKHw9GrCdhuq5/tdtwLFsDnn8OSJbB4cXwuY0tL0uPTNm3dMQD7dlwXo5vn7b6JfJwVSO+46PVCYeHmragIxoyBsWNh3Lj4P10uAJqbm6mpqaG1tRW73U5eXh5Op7PH1zoSiWAYRtJc87ZzId4uX0RERKRXWCzxJO9118F778HBB8O1124+XlgIP/1pfAuHYcUKyMqKfxYayhwOeOQRsNvh4Yc3r7e2wsknx5PlJ56YuvhERET6gJLiIiIiqdJUDgs6zKtzFcPuf01NPCIiA1hbi/P169cz9/jjObldtbijsZH9/vtfZh9yCI2NjQQCgV5LwLZVPzc1NWG32zGqq/G+8AKZr7yC65tvev19pkxdXXz79tvuzykrI7TbbqwrLWXtqFH4x48nSnz+6tSpU7FarT261larlVgsRiwWSzo3tql1qdVq7d33JiIiIsOb0wk33rj182w2mDSp7+MZKKxWeOCB+Pt+4IHN6+EwnHIKPPpo/J8iIiJDhJLiIiIiqRBphvdPhJa65PU9/w1p2SkJSURkoLPZbGRlZfHZbrtRvu++lH30UeLYhPnzyc/JYV1xcaJ62ePxbHMCtmOb9HA4TG1tLdG6OooefJCRzzyDtbm5f97wQFNejqO8nPHAeKDV66Vhv/1YM3UqSwMBJuy1V4+udVs7fL/fj8fjSWqz7nK5Os1CFxEREZE+YrXCvfdu/mebSAROPz0+GujUU1MXn4iISC9SUlxERCQVFlwCGz9LXis7BUYel5p4REQGiYKCAjweD28deyynff45jmAwceyMefN44vvfZ8WKFWRnZ1NYWEhaWhrRaJS1a9eSnZ2N2+3GarV2mYDtqk16MBik6f332fuvf8Xl8/U8UMOAggIihYU0ZWdjKygglpFBs91OfSSCLTcX02LBU1xMmttNTUMDDU1NlJSVEYpE+GrxYjbW1JDucpGVmUmGy0Wu10tOdjYFeXlYgUhrKzVVVYSbmnBbLBgtLRjNzYQaGrCHw2Q5HFhaWiAQgIYGqK+Pb21/bmiIf9G5nex1deS9/DJ5L79M9G9/o3n//eGoo9jwve9hzc0lGo1isVhobW0lPT09ca3bt8Ovr69PPJ/L5aKoqKjTrPYez3MXERERkW1nscBdd8Urxv/zn83r0SicdRbk5MCsWamLT0REpJfomwQREZH+tvJBWH538lrmBNjrrtTEIyIyiOTk5LDzzjvzZSzGawcfzHEvv5w4lh8IMPOFF3jt2GNZunQpBQUFWK1WQqEQDQ0N+Hw+XC4Xubm55OTkJCVg29qkh0KhxEzsWCxG6N13+d7vfoe9tbXLeKIuF5Z99oFddoGpU2GnnWDUqPgcSrudWDhMTXk5oVAIj8eTiMNqtZKWlkb6iBG02mykBYM0LF9Oq9uNaZq0ZmaSFgphc7mIOJ1saGmh0eUiY+pUmrzeRGI4c1Miv77dzQFtyWXL1iquo1GorYWqqs5bRQWsXBmfq1lVtdV/L5ZIBPe77+J+913ybDaqd9uNNbNm4dtjD7Lz8iguLk5KZLe1w99asrvbee5FRaooFxEREektFgvceWc8MX777ZvXw2E44QR45x2YMSNl4YmIiPQGJcVFRET6U8MS+PT/ktesbpj5DKRlpSYmEZFBxGazMWXKFNLT05kPLF+4kPHr1yeO7/bRRwRnzuQ9w+D9999np512oqSkhMLCQkzTpL6+HrvdTklJCU6nM/E40zQxTTOREAcwWlrY+S9/6ZQQj1ksVB16KGsPOojRZ51F/ogRW4y3fVV0fX09Pp+PnJwcsrI2/3/f6XTGE9kWC8FgkNGjR+N0OrHZbDQ3N1NbW0sgEMDn8xEIBJISwz1JLnfJYoH8/Pg2bVr35zU1wfLlbJw7F//rr1P43Xe41qzp/mnDYYo++4yizz6jtaCAuuOOo+nss8neZ5+kuGw2Gx6Pp9vn6e5GBb/fj8/no6ysTBXjIiIiIr3FMOBf/4r/uX1ivKkJjjoK3n9/eM1cFxGRIUffIIiIiPSXsAnzfgSRYPL63vdC9haSESIiksTtdjNhwgQikQifnn8+o/7yF+wtLYnje9x2Gx+ecw7lwSD5+fnA5rbrmZmZ1NfX09LSkpQUj0QiGIaRNAvb/d//ktYu4Q7QMHUq315+OZEJE0hLS8ORmdlljB1bfpeUlFBfX49pmjgcDoLBIBs3biS4KcbW1lZsNlsiOW+32zFNE5vNRjgcxuFwxCvDMzPJysrqlBjeUnJ5h6Wnw6674p40ia/23JOvAwHKwmG8H32EZ948Mj79FGs3lfT26moK7rmH2AMP0HriiXDllYSnTu1REr/LGxUMA4/Hk7iWffq+RURERIYbw4B//hNqauCJJzavb9gAhx0G8+ZBWVnq4hMREdkBSoqLiIj0lwWXQMPi5LUJ/wejT0tNPCIig5jNZqOkpIRvdtqJT049le899FDimCMY5JRnnuH+c84hGAzS2NgIwIhNFd2NjY3U1tYCJBKyVquVWCxGLBZLJGCdc+YkvWZg551Z9u9/k56VRSwWS5qT3V5XLb8dDgctLS1kZGSw6667smHDBoLBIDU1Nfj9fkpLSxOPz8zMTCR72yrMx40bR3NzM1arNWWJYafTydSpU1m8eDGr6utZtd9+sN9+5KalscvatThfeAHb3LnxOeYdGOEwaU8+CU8+SfMRR1B1/vm0jB9PLBYjLS0Nj8eDw+FISpJ3daMCkNiPRCKdXqfjzQiGYWCaJnV1dTidTs0jFxEREdkaiwVmz44nxt94Y/P62rVwyCHw7rvxTkMiIiKDjL4NEBER6Q+rn4AV9yavZe8Ke9yamnhERIYAj8dDWVkZX+y3H6XLl1P24YeJYyN9PmY9/jj/bW4mr6CAcePG4XK5ME2T6upqAJqampLakLvdbvx+Px6PB8MwsC5dmvR6i/bdlzUrV+JyuSguLsbr9RIIBJISud21/K6srKS2tpaddtoJu93OiBEjCAaDhMPhRByNjY0UFBQQDodpbGwkIyMDt9tNVVUVGzduxOv14nK5gC0nhvtSXl4ee++9NzU1NbS2tmK328nLy8PpdOI/9VTWLVlC8bx5pD/2GGmLFnX5HBmvvcb4OXMwTz2V6osv5ju/n0gkwogRI7BYLIl/J13dqAAQi8UAsFqtSc/b8WYE0zTx+/1UVFSwfv36xL8nzSMXERER2QqHA555Jp4E//TTzevLl8N++8FLL6UuNhERke2kpLiIiEhfMys6zxG3pcP+T4HV2fVjRERkq2w2GzvttBONjY18et55ZK1YQVZVVeL4PqtX0/Lqq8zee2/WrFlD2O9n/KpVTF+/nuxQCAsQdLtpnDCBnFmzKBo7Ft+GDdTW1lJXV0dOXR32dq9X3tpKXV1dYsb3unXryM/Px+12k5OTw8iRIxOVyh1bfrvdbiorK2lubsZutydaobdpS/za7XYKCgqorq7G7/fT1NREY2MjFouFgoKCRJVzd4nh/uB0Ohk5cmSndbfbjbOgAN/RR+M57TTs33yD+6GHcD/9NJbm5qRzjWiU9Mceo/S554ieeSblxx+Pw+EgIyMj0Rq+pKSk040KbTPFXS5XUmK7480IkUiEhoYGAoEAkUiEjIyMeOJe88hFREREeiYzE159FQ44ABa363q3Zg22Aw/E+9vfxmeNi4iIDBL6FkBERKQvxWLw8bnQWp+8vud/wDMxJSGJiAwlHo+HPffck1V5eXxw2WUc/Pvf42w33/r7335LWloaHwHHzJ5NZofkbHrbH664ApvbTdnOO1M7fjy1OTnYm5qSzg0Hg4RCIWKxWKJFdyQSIT8/H7/fT2trKwUFBV22/LbZbMRiMcLhcNJ6W3LbbrcnqqJdLleikjwUCtHS0kJWVlZiBnp3ieFUs9lsFBUVJVq+U1wMv/0t6b/8JSUvvIDtttuw1NcnP8Y0GXf33RS/9BK1v/sdxgknJFrDt7S0JD/fJi6Xi6KioqSkdsf548FgkObmZoqKivj6668JBoO4XC7NIxcRERHZFrm5MHduPDH+3XeJZaO2lv3++EeYPh0OPjiFAYqIiPSckuIiIiJ96bt/gy95Ji2jToMxZ6YmHhGRIcjj8TB16lQikQhPffEFZzzxBNZoNHF8ny+/ZJ+ePJFpYps/n8L58yns4nCGxcKyujqCwSD5+fnk5eVhGAZpaWnEYjHWrVtHenp6ly2/nU4nTqcT0zTJzs7uVPWcl5dHS0tLoiraZrORkZFBNBpl0qRJ2O32rSaGBwK3201ZWVnSXG+3241txgz8551H81/+Qt7DD2MxzeTHVVbi/uUvCT3xBA3XXQfFxUQikUSL/E7P1+F9d5w/3rZvsVgAiG76eUhV23kRERGRQau4GD74AI4+OqmVuq25mdgxx8ALL8Chh6YwQBERkZ6xpDoAERGRIcv/HSz6TfKaqwRm3J6aeEREhjCbzUZBQQH1e+/NEz/8IRFL7/+qY7daMU2TxsZGnE5nUivzzMxMmpubiUQiiZbfbVXgbZXlpaWlZGVlUV9fT11dHfX19TgcDoqKinA6nRQVFeFwODodHzNmDGPHjqW0tJSSkhJKS0spKysbUFXi7dlsNjweD16vN5HgB3AXF9N4xRWseOUVmk4+ucvHOj78kPzDDyf/9tsTNzZ093zttZ8/3n6/LRnelhxvfzwcDuP3+6mrq8Pv93eq4hcRERGRTfLz4a234PDDk5aNYBCOOQZefjlFgYmIiPTcwCorEBERGSpiUZj/U4gEk9f3eQAcOamJSURkiMvLy6O4uJiFEycy97zzOOyee5IqxtsEdtsNf2kp7owMbFVVuL75Btv69Vt87qhhsKasjKjfT0tLC06nk7S0NILBYCLhCvEEbn5+frctv9PS0rqteu62ynrT8cHe7jvRXh1Yfc012I87jvwbbsC7ZEnSeUY0SsF//kPss8/gkUdg0qStPrfb7U6aP+5yuXA6nfh8Pux2Oy6XK6kyH6C8vBzTNBNV+263m6KiogF7s4GIiIhISmVkwPPPw6mnwnPPbV4PheCEE+LHjjgiZeGJiIhsjZLiIiIifWH53bDh/eS1CRdA8azUxCMiMgw4nU723XdfKioq+MBmo+GUUzjxySextUuMrz7nHFaffTYNDQ0UFBRQWFgYb0Pe2EjTvHk0vvkm6YsW4Vi0CPum+eOtdjv/239/VgQChMNh8vLycLvdNDc343A4cDgcBAIBHA4HmZmZO5TcbquKHqqSrk1JCaGDD2b9E0+Qe/PNpPl8Secan30Gu+8Of/sbnH8+dJjT3l6neeZAWloamZmZ2Gw2GhsbaW5uTrSqr6mpIRQKJWaQtyXMfT4fZWVlA64tvYiIiMiA4HDAU08RPfNMLE89tXm9pQV++EN4/HE4/viUhSciIrIl+k1fRESkt5nrYNEVyWvpo2H3m1MSjojIcDJy5EhOOukkPvroIypyc3nQ5eLQTz7B09RE84knYrnoIoqCQdLS0igtLd08l9vtxnHssVTtthvVTU3U+HzULlpES20ta2w2mgwDdyRCfn4++fn5NDc3EwqFSE9Pp6mpiXA4TGlpaSKhPdST2zui47UJX3wx5mmnEbn1Vpx//ztG+zbmwSBccAG8+CLcd198pmU3uroZwTAMqqurKS4uxul04na7MU0T0zQTCXGIzxr3eDzU1tbi8/lIT0/vdn65iIiIyLBmtxN58EHWbdhA2dtvb14PheCkk+Bf/4ILL0xdfCIiIt3Qb/ciIiK9KRaDTy+AcCB5fa+7wZaemphERIaZkSNH8oMf/ACfz8ea3Xdn5ZlnUlZWhsvlwrpp7nRRUdHmhPgm7auNQ14vwWnTaN64kRHRKDk5ORQUFFBSUpJItAaDQQzDwGq14vV6GTFihBKo28Fms+EpKoKbb4Yf/QjOPBOWLk0+6dVXYeed4e674+05t/Rc7RLura2tuN1uvF4vdrsdgEgkgmEYiYR4m+bmZtavX08gECA7O1st1UVERES6Y7Wy6KKLGFlcjOWxxzavR6Nw0UWwfDnccgtYramLUUREpAN9YyMiItKbyp+GiheT18b8GIoPS008IiLDlNPpZPTo0RQUFODz+QgGgzRvaofeNt+7qwR2W7VxXl4eo0aNIhQKJVqkt68aLiws7LY9uuyAGTNg4UK44gq4447kY7W1cOKJcO65cNttsJ2JaqvVSmzTzRFtifFwOExVVRUtLS3k5ubi8XjUUl1ERERkS6xWIvfei8XthnvvTT72j3/A4sXw0ENQVJSS8ERERDqypDoAERGRIaOlARZckrzmLIA9bk1NPCIikkhyl5aWUlJSQmlpKWVlZVus/G2rNs7Pz2fkyJHk5+fj8Xg6VZV7PB68Xm+nY7KD3G64/fZ4dXhX7dLvvx/23huWLNnOp3fjdrvx+/3EYjEATNOkpqaG3NzcxM9GW0v1YDCIaZrb/XZEREREhiybLd7J55prOh+bOxemTYNnnun/uERERLqgpLiIiEhv+fIP0OxLXpt+GzhyUhOPiIgASmAPWkccAV99Fa8O7+jrr2HPPaF9u84eamuT73A4qK+vp66ujrq6OhwOB4WFhUk/H22V5JFIZLvfhoiIiMiQZhhw7bXxqvBN42oS2jr9/OQn0NCQiuhEREQSBm1S3DRNXn31Va6//npOOOEERo0alZgLd+21127xsRUVFdx5552cfPLJjB8/HpfLhcvlYsyYMZx22mm89dZb/fMmRERk6Ni4EL7r0Oa15CgoOzk18YiIiAwFubnw9NPw4IOQkZF8zDThjDPgkkugtXWbnrarDgKFhYU4nc6k89oqya2ahykiIiKyZWedBe+803W79AcfhF12gXff7fewRERE2gzaEolPPvmEo446apsft3btWkaNGpX4cgPiX4jEYjFWr17N6tWreeKJJzj33HO5++679eWHiIhsXTQCn5wPsejmNasTZtwWv2NaREREtp9hwNlnw157wSmnwJdfJh//179g/vx41fjYsT1+2rYOAgCZmZm0tLTg9/vxeDwYhpGYKe5yubbYbl9ERERENtlvv3inn/PPh//9L/lYeTkcdBBcdhlcfz10uBlRRESkrw3aSnEAr9fLIYccwuWXX87jjz9OUVd3oXUQiUSIxWIccsghPPjgg1RUVNDU1ERjYyOLFy/muOOOA+D+++/fasW5iIgIACvuho2fJq9N/T1k9PyLeREREdmKyZPh44/h5z/vfGz+fNhtt+1qpw5dt1Svr6/H4XBQVFSklvsiIiIiPZWXF+/089BDsOkGxIRYDP72t/gYnM8/T0l4IiIyfA3a3+xnzpzJxo0bk9Z+97vfbfVxXq+XBQsWsMceeyStWywWpkyZwrPPPstRRx3Fa6+9xj/+8Q+uvvrqTi30REREEpo3wOdXJq95JsFOv0lNPCIiIkOZywV33QV77w0XXACh0OZjgQCccQbRl1+m6frrac7IoKmpiYaGBvx+P1lZWVtMbre1VDdNk0gkgtVqxe12b1NCPBwO4/f7CQQChMNhrFYr6enpOByObX4uERERkUHLMOLt1L///fg88XfeST7+9dfxLkB/+hNcfjmoW6uIiPSDQVspvr1tzbOysjolxNszDINzzz0XgMbGRr755pvteh0RERkmvvwDtDYkr+35b7A6UhOPiIjIcHDuufDBBzB+fKdDlscew7XbblTdeCPzP/6YJUuWsGDBAhYvXozf79/i07a1VPd6vXg8nm1KYpumydKlS/nkk0/4+OOPmTt3Lq+99hoffvghS5cupby8HNM0t/mtioiIiAxao0bBm2/Gq8MdHb4naW2FK6+EAw+ENWtSEp6IiAwvgzYp3pfaV4ZHIpEURiIiIgNa3Rew4p7ktVGnQ+FBqYlHRERkOJk+HRYuhB//uNMhm9/PtL//nQNvvBGnz4fP52PFihUsWrRoq4nx7REOh1m3bh0VFRXY7XacTid5eXnk5uZimibNzc00NTXh8/kIh8O9/voiIiIiA5bFEp8j/tlnsOuunY/Pmwc77wz33BNvry4iItJHlBTvwjub2rmkpaUxceLE1AYjIiIDUywGCy6FWHTzmtUNu9+UspBERESGncxMmD0bHnuMWMeZlUDhl19y5eOPM+Xdd8n1evH7/axevbrXE9OmabJx40ZsNhs2m42WlhYyMjLIzMzEarXi9/ux2+0Eg0FVi4uIiMjwNG0afPJJvDrc0iEtEQjAz38ORxwB5eWpiU9ERIY8JcU7WLVqFf/5z38AOOWUU/B08cWKiIgIa5+B6neS16b8DtwjUxKOiIjIsHbaaTR89BENRxzR6ZCjpYUZ99/PLr/+NbkNDTQ1NfV6YjoSiRCNRjEMg9imCifDMBJb2/G2c2Hz/PG6ujr8fr8qyEVERGToS0uDG26A996D0aM7H58zJ548v+020GcjERHpZT0fkDYMBINBTj75ZEzTJDc3lxtvvHGrjwmFQoRCocR+Wyu+1tZWWltb+yzWHdEW10CNbyjRte4/utb9R9caiEWwLfotRvsldxnhCZfEZ2L1El3r/qXr3T1dExEZDCwjR1L+17/iP/548q66CldtbdJx76JF7H3eeay96CIikyb16mtbrVYsFguxWAzLpsqntuR4LBZLHI9EIlitVkzTxOfzYZpmIpHudrspKirC7Xb3amwiIiIiA873vgeffx5vq37//cnHAgH45S/j6//+N+yzT0pCFBGRoUdJ8U3C4TCnn346CxYswG6389hjjzFixIitPu7GG2/kuuuu67Q+Z86cAf9lxty5c1MdwrCha91/dK37z3C+1tmRZRzQvCJp7bPIKVS+/nafvN5wvtapoOvdmVr9ishg4Ha7cbvd1O+7L9/dey8j/vEPJr37btI51uZmRt9yC61ffUX4/vsxMzISiWq3243Ntn2/IrvdbnJycvD7/cRiMdLS0mhsbCQWixGJRPB4PLS2tpKenk5aWhqVlZWEQiG8Xm8iKe73+/H5fJSVlW13HCIiIiKDRlYW3HcfnHginHceVFYmH//8c9h3X/jpT+HGGyE/PyVhiojI0KHftIm3rzvzzDN57rnnsNlsPPbYY8yaNatHj73yyiu57LLLEvt+v5/S0lJmzZo1YFuvt7a2MnfuXA477DDsdnuqwxnSdK37j651/9G1BsuSz2Hx5v1Y5kR2O/x6djOMbh+zPXSt+5eud/faOuGIiAxkNpuNoqIifD4fTUVFfPSTn7BkyhQOeOQRcgKBpHPtr79OZOpU/L/+NQ0//CEx6LJSOxwOY5rmVhPnNpuNkSNH0trayrp16wiFQtTV1QFQVFSEy+UiPT2doqIiWlpaME0zkRCHeKt1j8dDfX09pmkO2N8lRURERHrdUUfB4sXwm9/Ek+Qd3XcfPPUUXHEF/OpXkJ7e/zGKiMiQMOyT4m0J8SeffBKr1cojjzzCSSed1OPHOxwOHA5Hp3W73T7gv1AfDDEOFbrW/UfXuv8M62td/WbSrlFyJPa0tD57uWF9rVNA17szXQ8RGSzcbjdlZWXk5eVRXFzMmrFjeaCggNO/+ILi555LOtdaX8/IP/yB/Fdfpf7GG9lotSZVam9ri3O3282kSZMoLi4mEAgQDoexWq2kp6fjcDgSCfW6urrErPH22vbbZo6LiIiIDBvZ2XDvvXD22XDhhfD118nHAwH4wx/g9tvhj3+En/0sPp9cRERkG1hSHUAqRSIRzjjjDJ544olEQvyUU05JdVgiIjKQtfqh5qPktaKedRcRERGRvmez2fB4PIwaNYo999yTkkmTsN51F03/+x+xwsJO5zs+/JCCww5j5D//SWhTEjwcDuPz+RItztu2UCiEz+cjHA53+9o5OTmMGjWKcePGMXr0aPLz8/F4PIkKc6vVSiwWS8wcb9O2b7Vat/j+wuEwfr+furo6/H5/t7GIiIiIDDrf/z4sXAh/+xtkZHQ+XlUVT5qPHRs/p0M3IBERkS0ZtknxtoR4+wrxU089NdVhiYjIQFf1DsTafflssUPhASkLR0RERLpns9lwu914vV7STzgB48svaTn++E7nGS0tZN51FxOOPBLbbbdh1tYm2ph3bHEeDAYxTXO7Y2qbfd42fxxIzBR3uVxdVqG3MU2T8vJyysvLqaysTPx5R+IRERERGVDsdrjsMvj2WzjzTOhqVF1FRbzdemkpXHUV+Hz9H6eIiAw6wzIpHolEOP3003nyySex2Ww8+uijSoiLiEjPrJ+TvJ+/P9g0z0pERGRQKCigefZs1tx+O+ERIzodtvn9uH//ezJ22YW8Bx7A0tSUdLw3Wpy3zT53OBzU19dTV1dHfX09DoeDoqKiLmeWA9tdvS4iIiIyKI0YAQ8/DJ9/Hp873pWGBrjxRhg1Cn7+c1i2rF9DFBGRwWVQJ8Xr6uqoqalJbNFoFIjfPd9+vbGxMfGYSCTCWWedxVNPPYXNZuOxxx5Ty3QREem5qjeS99U6XUREZFBxu91EjjyS5c89R+AXvyBmt3c6x1JdTdGtt1K4115k3Hor0ZoaAoEANTU1VFVV7XDr8rbZ56WlpZSUlFBaWkpZWdlWq8T7qnpdREREZMDaZRd4+WV45x2YObPrc1pa4J57YPJkOPFEmD+/X0MUEZHBYVAnxXfffXfy8/MT29q1awH461//mrR+0UUXJR7zwQcf8PjjjwPxLxAuvvhiioqKut2efPLJlLw3EREZgMwK8C9NXis6NDWxiIiIyHZpq9ROy8mh/OKLWf788zTM6vomN0tDA56//Y3ifffFdvXVLHrtNT7//HM+/fRTFixYwMqVK7c5Gd02EzywaQZmZmZm0szx7kQiEQzDSCTE2/S0el2zyEVERGRQO+AAeO89+PBDOP74rtuqx2LwzDOwzz7x+eQvvgibCulERES2/Fv3EBRt95dga2srVVVVWzw/GAz2dUgiIjJY+N5M3rdng3f3lIQiIiIi26+tUts0TSIlJRhPP034iy+w/fnPMHdup/Otpsm4//2PUS+8wLKZM1lw8MEsraujtraWYDDI1KlTt5rUhni1t8/nwzRNDMMgFovhdrspKiraYpU4gNVqJRaLEYvFkhLjbXPJrVZrn7yuiIiIyICy777xxPfSpXDLLfDQQ/FK8Y7efz++TZ4MF18MZ50FmZn9H6+IiAwYg7pSfPXq1YkvBba0zZ49O/GYAw88sEePadt+8pOfpOz9iYjIAFPVISleeBBYuv8CWkRERAYum82Gx+PB6/XGK7VnzoQ5c+CTT+C447p+TGsrU956i9OvuYbvPfAAoa+/ZtGiRfh8vq2+3o7OBHe73bjdbvx+fyIRHovF8Pv9uFyubpPbmkUuIiIiQ9KkSfGW6atXw29/Cx5P1+d9+y1ceGF8RvnFF8M33/RrmCIiMnAM6qS4iIhIv4nFOleKFx2SmlhERESk7+y5Jzz3HHz5JS0nnkjM0vnXZmskws6ffMJpf/oT+91+O1VvvrnV5PKOzgRva/vucDior6+nrq6O+vp6bDYbbrebQCDQZVv0nrxuOBxm48aNrFmzhjVr1rBx40Yly0VERGRwKC6G//f/YO1auPnmePK7K4EA3H47TJkChx4an1Ou1uoiIsOKkuIiIiI9EVgGwYrktcKDUxOLiIiI9L2dd6b5/vv57oUXqP7BD4h00Z7cEosxccECpv/kJ8R++EPC8+d3O7d7R2eCw+a276WlpZSUlJCbm0ssFqOmpobKykrKy8spLy9PSrBv7XUDgQBLly5l/vz5LFq0iEWLFvHJJ5+wdOnSbZ6XLiIiIpIyHg9cfjmsXAkPPgg779z9uW++CcccE0+Q//OfUF/fb2GKiEjqKCkuIiLSE1VvJe+7isEzOTWxiIiISL9wu93YdtqJFVdeyeN/+hOL9t+fsN3e5bn2l1/Gts8+GMccQ+3zz7NkyRK++uqrRIvy9jPB22s/EzwcDnebVG/T1vY9MzMzPhM9EtliW/QtvW44HKaqqoqKigpcLhelpaWUlpbidDqpqKhg3bp1qhgXERGRwSUtDc4+G774Ij4a57jjoIvOP0B8Lvmll0JJCZx7LsyfH+8UKCIiQ5KS4iIiIj3h65AULzwEOlRciYiIyNDS1rI8NzcX65gxPH/IIdx95ZUsPPRQWp3OLh+T+f77jD/nHCafdx4tzz7LooULWbVqFcAWZ4IDiUrv7qq+2+tpO/YtzSIHCAaDWK1WMjMzExXlmZmZ2O126urqVC0uIiIig5NhwGGHxcfirFwJV14JeXldnxsMwgMPwD77wO67w7//DZs+K4mIyNChpLiIiMjWxGJQ/W7yWuFBqYlFRERE+pXb7Wbs2LHMnDmT3XbbDdfo0az+v//j7QceYPU55xD2eLp8XPbXX7P3n//MzAsuwHLffaz59lusVistLS3U1NQkZoI7HA7y8vKoqakhFAptseq7vZ62Y+9uFnnb68ZiMYLBIIFAgMbGxsTztj1HJBLpUQW7iIiIyIA1ahTccEN87vj998O0ad2f+8UXcMEF8erxn/8cPvtM1eMiIkOELdUBiIiIDHj+byC0IXmt8MCUhCIiIiL9z2azMXLkSDweD6tWraKpqQmr1UrDJZcQu/RScp54gvT//AdbXV2nx2asWUPGTTcRuvNOak44AePkk6nZVBnucDhwOp00NzdjmiZer7dT1Xd9fX2iIry99m3R2yfGO7Zjb2uxnp2dTXZ2NoZhYLVacbvd1NTUUFVVRV1dXeL5nU4nubm5iecIhUKJinHDMIjFYrjdboqKinC73YnXbf9abc9vs+krBxERERlAnE445xz4yU/gnXfiFeHPPgtd3fDX1AT33BPfdt453l79jDMgP7+/oxYRkV6i31BFRES2pmOVuHskpI9JTSwiIiKSMh6Ph6lTp3ZK/pq/+x1Lf/hDcp59Fu/99+Osru70WEcgwIgHH6T44YdZNnkyH06fTmCPPXBsSkIXFxeTk5OT9JiOVd/tE8+xWIy0tDT8fn+ihXpX7di7S2a3VX+73W6am5uxWCykp6fT1NTE6tWrycnJoaioCL/fn5hb3v41fD4fZWVl2Gw2TNPE5/NhmibRaJRgMEhaWhrFxcXkbWpTqoS5iIiIDBiGAQcdFN98vnj1+D33wOrVXZ//1Vfwq1/BFVfAMcfEE+RHHAH6PCMiMqjo/9oiIiJbU9UhKV5wgOaJi4iIDFM2m61T1bbb7caVl8e6E07g24MOYvRHH1H00EO4Vq7s9HhLNMrkJUuYvGQJDe+8w4pZs/hk3Diqqqpwu92kp6fjcrmw2WxJVd9dJZ4jkQgWi4WWlpZEktnlcnVqx95VMts0TVpaWpgwYQLr1q2joqKCxsZGotEora2tjB8/Hq/XS01NzRYr2N1uNz6fj1AohNPpZMOGDYl551VVVRQXFyfei2EYhMNhDMMgNzeXzMxMJchFREQktYqK4Kqr4Le/hblz4a674MUXYdNNiUlaW+OV5c8+G3/c2WfHK88nT+7/uEVEZJvpN08REZEticWg+p3ktYIDUxGJiIiIDFBtc7vD4TAbN27k6z32YNX3vkfW/PmUPfMM+QsXdvm4rLVr2eO++9jFZmPxuHGsX7aM1oMOImqxJOZ5Z2VlUVhYmEhyd0w8WyyWREV2W5K57diWktlts8PbZqbn5+fT1NQEQGtrK2PHjsVms211bnnba2VmZlJZWUlLS0uiVXtdXR2rVq3C7XYzadIkwuEwVVVV1NTUsH79egoLC/F4PJ1asYuIiIj0O6s1Xv19xBFQURGvHr/vPlizpuvzfT64+eb4tu++8eT4aadBRkb/xi0iIj2mpLiIiMiWBJZBc1XyWsEBqYlFREREBqy25LLH42H9+vW0trYSOuggPpg6lVGmycjnniP7hRewh0KdHmsLh9l16VJ2XbqUxtmzebesjPdGjCA0fjwFhYV89913FBcXM3HiRGpqaohGo4nEc1t781AoRGFhITabLZHw3lIyu/1McpvNhtfrxev1EovFqK+vx+FwEA6HCQQC8Rhtti4r2Nteq7m5mWAwSFZWVuJ1mpubE49tbGzE7/fT2tpKSUkJjY2NOJ1OQqFQUit2ERERkZQbMQL+8Ae4+ur47PH774f//Q82fbbp5KOP4ttvfgM//jH83//BTjv1a8giIrJ1+o1TRERkSzrOE3cVQ+b41MQiIiIiA1pbxXheXh6maRIKhUhPT8disVC7xx7MO+ooPM89x/SPPya7qqrL58gIBDh68WKOXryYjQsXsmzqVD4qLuaD4mKam5tpampi5MiRGIaB3W6nqakJh8PBhg0byM7OJicnh1gsRmNjYyKmrpLZbrcbt9u9xZnktbW11NXVsWHDhkRb9/z8fFpbW3G5XImq9FgslmiL3pYQb59wNwyDpqampKR5LBYjGo2SlZWVqF7v2Ja+TftZ6ppJLiIiIv3GYoGDD45vt98OTz4JDzwA8+d3fb7fD7fdFt8OPhguuACOO06zx0VEBgj931hERGRLOs0TP1DzxEVERGSL2s8dT09Px+fzEQgEsObkMH+vvVi4777sFwxS9vrrFH/6KbauZlYCOTU17PPuu+wDbExPZ+X8+SyZPJmVu+5KRlYWTU1NhEIhiouLiUajWCwWotEoGzdupK6ujurq6kTyu2Myuy2B7/P5qK+vT7xm+5nk4XCYCRMmsGHDBoLBIDU1Nfj9fsaMGUNRURE2my3x/LW1tUSj0UTivbGxEZfLlbTWljRvmy9utVqTqte70n6Wettj3W63Wq6LyIBnmibvvvsuCxYsYOHChSxYsIDy8nIArrnmGq699tqtPkdVVRU333wzL730EuXl5bhcLqZOncqPf/xjfvrTn3bqCCIifSg7G37xi/i2ZEk8Of7QQ1Bd3fX5b70V30aMgJ//HM47D4qL+zVkERFJpqS4iIjIltR2uPu3YGZq4hAREZFBye12U1ZWhmma5OXlkZWVxYoVK1jY1MQHxx9PZP/92X/dOqZ+9hk5y5d3+zw5TU3kfPghMz78EDMjg+XTpjG/pISanXYiPz+fUChEeXk5GzdupKSkhPHjx7Nhwwaam5upqamhoaGBUaNG4Xa74wn6TRXXbbG1r8LuOJN8xIgRBINBwuEwTU1N5OXlJRLSbcn1SCRCbW0tlZWVZGRk4HQ6KSwspLy8nHA4jNvtprGxkWg0mqhud7lcSdXrHYXDYXw+H6FQCK/Xm5hhXl1dTSAQYNKkSTidzr75FycisoM++eQTjjrqqO1+/IIFCzj88MOpra0FICMjg0AgwLx585g3bx5PP/00L7zwAg6Ho7dCFpGemjIF/vpXuOEGePVVuOceePll2PS5JklFBVxzDfz5z3DCCXDhhTBzpgouRERSQElxERGR7jTXQOOK5LXcfVITi4iIiAxabZXjHo+HwsJCdtppJ6qrq2ltbcXv94PFwuetrbR+8QVpjz3GHsuXk9Wuersjd2Mju3z8MbsAIaeTij32oGbmTNbvuitfffUV0WgUh8ORqOTOzc1l48aNieR3W8V1WloaHo8Hh8OR1Ja840xym81GZmZm4vU7Via63W7GjBlDZmYm69evp6WlBafTSSQSYcSIEUC8Ery1tZX169eTm5tLQUEBVqs10a69q6rv9sn55uZmqquraW5uJhaL4fP5MAyD8ePHq2JcRAYsr9fLHnvskdh+9atf4fP5tvq4hoYGjjnmGGpra5k8eTIPP/wwM2bMoKWlhXvuuYdf/epXzJkzh1/96lfceeed/fBORKRLdjsce2x8W70a7roL7r0Xamo6nxsOw1NPxbdp0+Kt1c88E9p9xhIRkb6lpLiIiEh3aj9J3re6IHvn1MQiIiIiQ4LNZqOgoICCggIgnvhdt24dq1ator6khC9nzeKVAw5ghmFQ8vHHTF26lJwNG7p9PkdzM2M//JCxH35Ia1oaX4wYwdJp06jYeWfC6el4PB6KiopoaGigpKSEMWPG4Ha7aWlpYeXKlYnEtcViSbQlt1qtiZng7RPgW6rqttls5OXlkZaWRiAQACAzMzPRRt40TXJzc6nZ9CVxc3Mzzc3NuFyuRCv2Nm0zxGtra2lqasLtdlNdXU1LS0ti/nnbc/p8PsrKyrp8vGaQi0gqzZw5k40bNyat/e53v+vRY2+55RZ8Ph8ul4tXXnmFMWPGAJCWlsaFF16I3+/nqquu4u677+bSSy9l4sSJvR6/iGyj0aPhxhvjVeH//S/ceSd89FHX5379dTwpfsUVcPbZ8T9Pndqv4YqIDEf6rVBERKQ7HZPiOdPBor86RUREpPe43W7Gjh2Lx+Nh/fr1OJ1Ovv32WxbV1fHB7rvz0r77MjkaZUZ5OeO++AL3smXdPpe9pYUZq1YxY9Uqoq+8wurRo/ly1Cg+HzmSSoeD6dOnE4lEsNvthEIhrFYrdrsdh8NBRkYGfr8fn89HSUkJaWlpVFZWJhLKTqcT0zS3WNXdcfZ3JBLB6XTidruTKuU7JqwB/H4/kUiEUCiE3++npaWFpqYmKisr8fv9hMNhCgoKkmaSZ2dnEwwGMU0zKfnu8/nw+/2EQiHC4TAZGRmMGTMmcY6ISH/o6gainnrooYcAOPXUUxMJ8fYuvvhibrjhBhobG3n00Ue57rrrtvu1RKSXOZ3xCvAzz4SFC+Hf/4ZHH4VgsPO5jY3x5Pmdd8KsWXDzzbDrrv0fs4jIMKFv9kVERLrTcZ547l6piUNERESGtLa53Hl5eYwbN4499tgDn89Hc3MzwWCQjIwMKgMBVre0UBIMUvDBBzhfeYX8Lcwgt0QijF2xgrErVvBDwJeTQ9Xq1ayZNo3lZWUEmpsZNWoUsViM3NzcRFV3fX099fX1tLa2UlNTQ0tLC7FYDJfLxciRIztVdUPn2d9tieu2JHv7Su62VvJt2ifTo9EoFRUVWK3WxI0CkUgkMZc8Ly8Pi8VCY2NjItkeCASIRCJAvPr8u+++Y8OGDTQ1NSVuAKiqqqKmpoZx48Ylqszb3q8qyEVkoFm6dCnl5eUAHHnkkV2ek5GRwcyZM3n11VeZM2eOkuIiA9Uee8Tnjd98M8yeHU9+d/f5bc4cmDsXfvaz+PzxwsJ+DVVEZDjQb38iIiJdicU6V4rn7p2aWERERGRYaD97vLS0FNjcCjwQCFBbWwtA3eTJrDr4YFpWrWLKsmUUzJtH7tdfY9nU3rwrRRs3UjR3LrvOnUuLy8XqiRNpmDmTJWPGsMwwaGxsTFR319bW4vV6mTJlCs3NzYkY0tLSSEtLS3retoS4z+cjJyeHSCSCzWbDMIxEkr19JXdXj21Lpjc2NpKWlobVamXDhg2MGDGCwsJCmpqa+Oabb/D5fGRkZOB0OhMzySFejWmaJsuXL+frr79m/fr11NfXk5mZSU5ODhkZGSxatIjPP/+c3NzcxCz1MWPGJNrJi4gMFF9//XXiz9OmTev2vGnTpvHqq6+yZMmS/ghLRHaE1wu/+hVccgm88UY8Of7iixCNJp8Xi8WT6E88AVdfHT/f6UxNzCIiQ5CS4iIiIl1pXAEtyfPfyFNSXERERPpX+0T5/2fvzsPkOMt773+rqqu7ep1Ns2iZ0WrZsuVVxsY2NjZ4AUwgGEhYTlgMWc4LnARCclgTOIGQACEnZCEJBBy2EHJiArExeMHyAniTLS+SbFnrjDT7TPf0Wr1U1ftHa1rT0kh4kWY00u9zXXOpn6eqq++qGY+n+677fma2Hu/q6mLytNPIXXIJg294A6NbtrDskUdY88QT9GzbhlWrHfGY4VKJtY8/Do8/zgbDYG9vL9vPOINn165lKJmks6uLdevW4Xke7e3thEIhWltbD0twT1d5Dw0NMT4+3lgjvL29HQDP88jn85TL5VnjKBaLFIvFRnW553mYpkkymSSXy1EqlUgmk6xevZpcLofjOCxevJhYLIZlWWSzWaLRaKPVezabbVS6x+NxXNdlcHCQfD7P4OAgnZ2dxGIxIpEIU1NT7Ny5E9u2WbVqlSrGReSEMTg42Hi8dOnSI+43vS2bzZLP50kkErPuVy6Xm34PZ7NZAKrVKtVq9ViEPC+mY1/I57CQ6fq/CFddVf/q78f86lcxv/51jLGx5n1yOfjIRwi+8hW8L36R4PWvb2zStZ9fuv7zR9d+fp3o1/+5xqV3fSIiIrMZP6R1utMFsb75iUVERESEw1uPL1q0iGKxSLlcZqCjg8E1a7jnNa+BXI6OTZvo3byZ5U89RXy2NSwPMIOAlf39rOzv57rbb2c8leKZlSvJv+xlPHP++SxZu5YlS5bg+z67du1iYmKCzs5OFi9ezOTkJOVymY6ODiqVCslkkomJCQYHB0mlUo3EteM4xOPxRkX2dOX5xMQEhUKh0cbcsiyCA9Xu01XrAJFIhL6+PsLhMJ7nkcvlAIhGo/T09FCpVCgWi4RCIUZHR7Esi2QyCcD+/fvZuXMn2WyW9vZ2isUijuMQDofJZrOMjY3R09Oj9cZF5IQx/TsOOGoni5nbcrncEZPin/vc52Ztr3777befFJ0y7rjjjvkO4ZSm6/8iXXIJ1gUXsPqHP+S0//xPQofcSGjs3UvozW9m96texVPvfjd+JNLYpms/v3T954+u/fw6Ua9/sVh8TvspKS4iIjJTNQsP/g70/3vzfMfFcGANShEREZETwcwkeTweJxKJsGPHDvZnMjyzeDFBTw+x17+es12XM3bupOXnP2fRgXVqj2RRNsuiA1XkXihE+qyzGD73XJ5YsoTNtRqJZBLLsgiHwyxfvpxzzjmHcDiM4zhks1lKpRK5XI5FixYB0NXVhWmajbXFK5VKYw3xQqHA4OAgnufR3d1NNBolGo02EkLTSfJsNksqlaK1tZXSgQT/zDXB0+l0Yx3z6X+nv6YT9/F4HMdxSKVS1A5U0U8n2MfHxxkfH6dYLBKNRmlra9N64yJy0vjoRz/Khz70ocY4m83S29vLtddeu6BvCKpWq9xxxx1cc8012LY93+GccnT9j7E3vIHgs5/F/+QnMb/97cM2r/zJT1ixbx+1b32L6tq1uvbzSD/780fXfn6d6Nd/uhPOr6J3eCIiIjPtuunwhDhoPXERERE5ocViMU4//XQWL15MJpNhcnKSXC6H7/u4rsvj559P9tpr6SiXWbdnDy333UfbQw8ROkqbOatWY9Hjj7Po8cdZD/xaKsXeM87gmeXL2WiabN26lWKxSCKRwDRN8vk8mUyGaDTK1NQUnZ2ddHV14TgO4+Pj7Nu3j3Q6Ta1Wo6uri1gsRjabZe/evRQKBVavXk1nZye7du0iCILGeuYAvu8zOjraSHp7nofjOE0V5qZp0tnZSaFQYGJigiAIKBQKxGIxfN8nEongOA6maTbasw8MDDA5OdlI6EO9Av+0007jzDPPJBqNzsW3T0SkYbrTBdC0ZMWhZlZEzXzOoSKRCJEZ1aXTbNs+IT/Ufr5OlvNYqHT9j6EVK+Bb34I/+AP4wz+Ee+5p2mw89RT2S1+K+dnPwqpVuvbzTNd//ujaz68T9fo/15iUFBcREZkp8+Ts8x0XzW0cIiIiIs9TKBSivb2d9vZ2Vq1a1WhTPr2erOd5TE1NUbzkEvqvuYatmzZx9sgISx55hGVPPEE0nT7q8ZPZLOsfeoj1Dz3EG4EdqRQ7Nm1i16pV7FqyBC8SwXVd2tvbSaVSxGIxDMNgbGyMZ599llAohO/7tLW1USgUCIKAarWK7/ts27aNXC5HX18fK1euJBqN4nleI+bpc5tOimez2Ub1eSwWIxaLkcvlaGlpIZlMks/nGRoaIpVKUSwWqVarjeS953mMjo6STqdZsmQJoVCIZDJJOBwmk8kwNDREPp+nUChw3nnnzcn3TkRk2pIlSxqP9+/ff8Sk+P79+wFIpVJHbJ0uIgvQhg1w991w003w/vfDzJbA5TLWhz/MSy+4AF7xCmhpmbcwRUQWIiXFRUREZrJmqQaK9ULX5XMfi4iIiMiLcOga5HBwPe/Ozk5KpRITq1eTftnLeCCbxd62jWVPPsmq7dvp2bUL88Ca3keyJptlzZNPwpNPUrMsnlm0iF/E4zyzbBm/zGbZvn07kUiESqWCYRh0dnYSBAGRSITh4WEcx+G0005j0aJFpFIpIpEItm3T0dFBJpNparHe3d1NLBYjGo1iGAapVKqxTyqVoqenh1qtxujoKKOjo9i2TVdXF4lEAtd1MQwDx3HYt28fmUyGsbGxRhJpurrdsiy6urrIZrN4nsfWrVspFAoMDQ019o/FYmqrLiLH1fr16xuPn3rqKdatWzfrfk899RQAZ5555pzEJSJzyDDg3e+Gl74UfvM34cnmAo7uRx/Ff9Ob4L//GxxnnoIUEVl49E5ORERkplDs8LlX3AWW3mSIiIjIwjedKE+lUlx88cVs2bKFkZER8oUCw+Ewuy69lLH3vIelySS9zz5L5Uc/IvmLX9D+K9ZoC3keZ42McBbArl3kH3iAxzs6eKy9ne2LFmGedhpjY2NMTk42qhqn26t3dHSQTCbp6emhXC6zb98+bNumra0N0zRpaWmhWq0yOjrK0qVLCYVCGIYB1NcFh3r7+FWrVpFKpRgYGCCXy2GaJpOTkxiGQTweJ5/Ps3v3bkzTZO3atY3K89HRUYrFIr29vVSrVYaHh8nlchiGwfbt25mcnMRxHJYtW0ZHRweLFy9m0aJFSo6LyHFx+umn09fXR39/Pz/5yU9485vffNg+hUKB++67D4Brr712rkMUkbmybh089BB8/OPwpS81bTLvvBPe+Ea4+WaYZYkEERE5nN7BiYiIzGQe8kai902QOm1+YhERERE5jhYtWsTFF1/M+Pg4pVKJfD6P67qYpknEcZhcvBjvssu49b77qDz1FC+ZmKD7iSfo272bcKVy1GMnKhUuGxrisqEhAEYefphNra083tnJA/E4LFrE4OAgxWKRxYsX09bW1mirDrB8+XIMw8CyLIBGUrtUKpFMJgmCAKCxfVosFmPFihWUy2UikQie5zXWC6/Vavi+31jPfHh4mEql0jiW67qk0+nG2uWe52FZFsVikYcffphnnnmG7u5u2tvbWbduHaeffvoR2xqLiLwY73jHO/jMZz7D9773PT75yU+yYsWKpu1///d/Tz6fx7Is3v72t89PkCIyNxwH/uqv4Prr60nwTObgth//GN7yFvj+9+EEXONXROREo6S4iIjITNYhSXH/6B/4ioiIiCxk0xXQ06bbq08nhGOxGIlEgh9Vq/z38DDlpUup5POsHBnh3LEx1g4MsGxwEOtAYvlIuotFXlMs8prBQQCeTSR4MJlk8+bNDF94IR3LlrFz505qtRpLliwhHo/T1tZGNBolGo1SKBTwfZ9KpUI2myWTyRAOh3FdF8/zKJfL5HI5yuVyY93xWCxGT08PixYtIpvNMjQ0RCQSIZVKMTU1xdTUFENDQ9RqNcLhMJZlsX//fiYnJ/F9nyAIaGlpoVAoMDIywv79+xkdHaWnp4d0Os3k5CSXXHKJEuMickTpdLrR0QLA930AisUi4+PjjXnHcZrWBf/whz/M1772NYaHh7n++uv55je/yYYNG6hUKvzLv/wLn/zkJwH4nd/5HdauXTtHZyMi8+oVr4Dbbye4+mqMmR18/uu/4AMfgH/8x3kLTURkoVBSXEREZCYz3Dz2y/MTh4iIiMg8mG0d8lWrVvG2t72NzZs3MzY2Ri6Xo1gs8kChwD3lMkxNsXZwkJU7d7Jm714Wz6xgOoLT8nlOy+f5H0NDVLZtY/uyZdzf0sLjS5awc/FiBgYGWLNmDUuWLKFSqTA8PEwmk2m0Xg+FQvi+z+joKK2trWQyGUzTZNWqVcRiMYIgIJvNMjw8TF9fH1BvNxyLxfB9n1AoRHt7O7lcjpGREcbHx9m/fz+hUIh4PE4oFMJ1XSYnJ5mYmCAIAizLIp/PUywWcRyHvXv30tnZyTnnnKNW6iIyq/PPP5+9e/ceNv+FL3yBL3zhC43xO9/5Tm666abGuKWlhVtuuYXrrruOrVu3cuGFF5JMJnFdl2q1CtTbpv/1X//1cT8HETmBvOQleLfeCtdcQ8h1D87/0z/B//f/wTnnzF9sIiILgN61iYiIzHRYUrw6P3GIiIiInEC6urp4xSteQbFYpFwuk8lkGBoaor+/nx07dvCLapV7WlsJX3IJqakpLpyaYum2bZw1PExLqXTUY4drNdbv2cN6gMcfZ7CtjSeWLWPz0qXct2wZVeoJ7XK5zIoVKzj//PPp6OggGo0CkM/nMU2TcDjM2NgY3d3djXbw2WwW0zQb1ZnxeJz9+/djGAahUIg1a9YQBAG9vb3s2bOHcDhMV1cXruvi+z75fJ7BwUFyuRyO45BOp0mn07S1tdHZ2cnExATFYlHV4iJyzG3YsIEtW7bwl3/5l9xyyy0MDAwQj8dZv34973znO7nxxhsxTXO+wxSRORZcfDEPfPKTvOwzn8GY+TfWn/4p/OAH8xeYiMgCoKS4iIjITIclxdU+XeRUUSwWueeee9i0aROPPvoomzZtor+/H4A//dM/5VOf+tSvPMbIyAif//znueWWW+jv7ycajXLWWWfxzne+k/e85z0YhnGcz0JE5PiZWUXe2dnJypUrWb9+PYODg+zYsYORkRGmpqbo7+/n7tZWYuvWcXMQ0DIwwKLNm3nJ1BRnT04SqdWO+jpL0mmWpNO86sknyVsWD7e18cv2dh5obWVLocDAwAA9PT2cf/75LFu2jHw+j23bdHR0MDo6ysTEBJlMhkqlQj6fJ5PJ0NraSq1Wo7W1lX379pHNZrFtm1wuRxAEtLa2smTJEmzbJhqNYhhGo017pVIhHA7T09NDrVajVquxb98+LMvC87ym1sgiIjPt2bPnRT2/u7ubL33pS3zpS186NgGJyElh8qyz8P/3/8aa+R71v/4LNm2CDRvmKywRkROekuIiIiIz+FWLmffaB24ZpbBETg0PPfQQr3nNa17w8zdt2sR1113HxMQEAIlEglwux/3338/999/Pf/zHf/CjH/2ISCRyrEIWEZlX0y3I29vbOeOMMxpJ5MHBQQYGBigWi+TzefqBrZbFI7EYizs66N69m+XPPssZ+/axZnIS8yjrkSc8j6vGx7nqwNq72+JxNsbjPNLdzR2jo3R2d5NMJuns7CSfzzfWJV+6dCk9PT0kk0lisRjZbLZR7d3a2ophGI31xzs6Okgmk40K8EgkQjabZWxsjEKhANBYpzyRSBCJRBqV6KZpYlnWXFxuERERkQb//e/H+vKXYXLy4ORXvgJf+9r8BSUicoJTUlxERGSG2oDJzFpxP1dBH3OKnDra2tq44IILGl8f/OAHGR4e/pXPm5qa4rWvfS0TExOcccYZfOtb3+LCCy+kUqnw1a9+lQ9+8IPcfvvtfPCDH+Qf/uEf5uBMRETm1swE+apVq3Bdl/Hx8UbyeHh4mJ07d9YryVes4Mm2Nu555StZkUzSsWkTa599ljP27iUxc33MWawrFFhXKMDoKJlnnuHRnh4eaGvj/s5OcpZFtVrFcRxaWlro6OhgxYoVnHnmmcTjcSqVCrVaDcMwmJqaolqtUq1WSSQSuK5LrVYjl8vh+z5tbW2Nm5ui0SjRaLRRFZ7P56lUKpTLZWKxGLFYbI6usoiIiMgBqRTceCN88YsH53btmr94REQWACXFRUREZgrZzeNA7dNFThWXX345kzPvsgc+8pGPPKfnfvGLX2R4eJhoNMqPf/xjVq5cCUA4HOZ973sf2WyWj33sY/zzP/8zf/AHf8DatWuPefwiIicSx3FYtmxZY1yr1diwYQOjo6MUCgWmpqbIZDKMjo7yi+XLua21lfarr+asQoHzh4bofPhhVhzyO/lQrZUKr+jv5xX9/dSAxxMJ7ksmuT+V4hHPwwqFWL58Obt27aK3t5elS5fS0dFBOBxmZGSEoaEhOjs76ezsbCS9n3nmGXzfZ8mSJSSTSUZHR4nH49i2TTqdZt++fXieRywWw3EcduzYwZo1a+jp6TnOV1REREQWIq/g4fa7+CUfM2ri9DlY8WNUfjE42DxeterYHFdE5CSlpLiIiMhMzgoquTcSYENgE0SXqVJc5BTxYtrffvOb3wTgLW95SyMhPtMHPvAB/vzP/5x8Ps93vvMdPv3pT7/g1xIRWYhCoRBdXV10dXUB9ST5dLv18847j2effZbx8XEGsll29/QwtWoVlT172DA2xgWDg5wzOkr0KGuRh4AN+Twb8nn+YGiI/aEQt0ej/He5zIPj4zz66KOsWLGCiy66iNWrV7N27VoqlQrt7e1YlkUQBIRCIaLRaKNqvFarYZomtVoN3/dxXZdSqdSoNp+cnOTJJ58E4HWvex3t7e1zcSlFRETkeTiuSelf8Xq1TI3SrhK1TA0MIIBQa4joqiih1tCLj+eRR5rHF174ouMXETmZKSkuIiIyQ5C8mOLoVxtjq9vCmcd4ROTE98wzz9Df3w/Aq1/96ln3SSQSXH755dx2223cfvvtSoqLyCnv0HbrF154IcPDw4yMjDA5OYnneTzyyCNs3LaNW7q6CPk+K/ft45J0mkvSafqKxaMef2mtxrtzOd6dy/H44CDfiUZ5pFbjl57Htm3baG9vp1ar0dHRgeM49bbu/f1UKhUsy6JUKhEKhXAch3Q6TT6fx/d94vE4iUSCJUuWEAqFGuuYP/nkk1x22WWEQvqYRURE5EThDrhkNmaojFQaSelwd5jWK1txeo/9pz0zXy+oBuSfyEMI2l7eRrg7jNvvMvajMYJyQPzMOHanjdPrvLB4Bgdh+/bmOSXFRUSOSu/WREREZjBso2kcVIN5ikREFoqnnnqq8Xj9+vVH3G/9+vXcdtttbN26dS7CEhFZUBzHYcWKFaxYsYJarUaxWGTNmjU89NBDbN++nZGREXZYFs8sW8ZPFy9mcanEVaUSix97jDUDA0SCI//Ndm65zLnlMoVHH+WO/n5+1NnJHeEwJdelt7eXVCpFsVhkdHQU27Yb8SQSCRYtWkQqlcI0TYIgIBaLEQ6HMQyDSqXC+Pg40WiUiYkJisUiqVRqri6ZiIiIHIVX8MhszFBNV4muiWJYBoEX4PbXE9edN3QetUL7+VaYewWPyZ9O4u5yCS0K4ZU8DNPAjJgUthbw8h4TP5mgNlkjCAJKO0tUx6u4e13K+8ssvnEx4a7wcz4/4847mydaWuC8857z80VETkVKiouIiMxwWFK8pqS4iBzd4Ix13JYuXXrE/aa3ZbNZ8vk8iUTiuMcmIrIQhUIhUqkUqVSKFStWMDw8zOTkJGNjYwwMDDA+Ps6+ffv4f7UasTe+kezQEGsGBli/dy8bRkZYXK3Oety47/Pro6P8+ugo20Ihvm6afGf3bnKhEL7vE41G6e7uxrZtSqUS+/bto7W1lVqtRktLC1NTU5TLZXzfJ5/PYxj1vxsTiQRBEOB53lxeJhERETkKt9+lMlIheloUw6z/P9uwDJw+h9KOEm6/S3xdfPbnHqXC3G63Z02WZx/Mkr4rjeEYGMMG1fEqtakaibMTVEerlHaV8NIekeURKkMVqpkq1ZEqRtjA3eviV3xaXtaC3Wo/pyS8efvtzROvfCWoY42IyFHpt6SIiMhMdvMwyAd4Yx5Wp1YWF5HZ5XK5xuNYLHbE/WZuy+VysybFy+Uy5XK5Mc5mswBUq1WqR0jynMymz/lUPHc5NvQztPBZlsXSpUsbNxa5rsvIyAh79+5l165dAIx2dPCEYfCzWIzR9naW5/O8KpfjhnSa7iOsQ76uVuMLwCeBvzVNvuD7TBSLTExMYNs2yWQSwzDI5XK4rotpmhSLRSqVColEgmKxSDQaJZ/PUyqVsCwL3/f1syaH0e+h2el6iMjx5pd8MGgkxKcZlgHGge2zOFqF+ch3R/ArPrWJGkbYINQawul1SF2UIn13mspoBWe5gxkxsRfZVEYquP0uGOClPcyECR7U0jVCrSHCfWFq6XrlePaBLMWtReLnxDHD5tHbvHsexl13Nc+96lXH6tI9L5XRCtmHsnhTHlaLReqi1POqeBcRmUtKiouIiMxgdVlgAtPvjXwo/KhA8t3Jw95IiYgca5/73OdmXW/89ttvP2rC/WR3xx13zHcIssDpZ+jk1NraSjabpaWlhTVr1pBMJqnVajxbrbI9HOZvUikuLxR4YzrN1eXyrB+ApICP+z7/H/BF4MtAvlplcnKSUChEJBLBNE1GRkao1WqEw2Fc12VqaqqxLno2m+XJJ5/E930sSzdSyuz0e6hZsVic7xBE5CRnRk0IIPCCeiL8gMALIDiwfRZHqjA3LIPxm8cxE/Uqbr/iUxmqUNpRYuyHY5T7y3gFj8ALMEMmpmMSSoYoj5QJRUMYEYPAD6gMV4B65blhGgS1AG/Cw1ntYDj1RHtkSaSpzft0XH7Jxw/5tG7biTEx0Rz4ddc1Hj7f1u8v1NSDUwx/fRh3v4thGARBwMR/T9BzYw8tF7cc89cTEXmxlBQXERGZwYyaRF4SofzgwUpNb9Cj/MsyzmWz3J0rIqe8ZDLZeHy09WRnfvg78zkzffSjH+VDH/pQY5zNZunt7eXaa689JdeprVar3HHHHVxzzTWNdX5Fng/9DJ06arUa2WyW/v5+HnvsMXbu3MnQ0BADo6P8n2yWL01N8RbX5dp9+1hWqRz2/Dbgs8CHgP8L/C0wVatRm1FpblkW4XAY27ZJJBLE43GWL1/OxRdfzK//+q9zxhlnzM3JyoKi30Ozm+6GIyJyvDh9DuHuMG6/i9PnNFV8h7vDOH2zf8YzW4W57/pkH8pSLVRJnZnCSlrkH8gTVAO8vEctXQMDQq0hqhNVIn0R/KpPYAQE5QA/5GPaJn7Zxy/7hNpCGHY9SV6brIEFoVT9ueX+MuHOcKPNe/bBLOX95UYrd6/msfiWJ5piDtaeQbHQgf9ojmqmirvLpZapHdb6fdaq8xeoMlph+OvDVMYrxNbFsByLoBZQ3F5k+OvDRFdGVTEuIiccJcVFREQOEb0qSvXZKv7kwVZahbsK+D0+sdWnbqWmiMxuyZIljcf79+8/YvJ6//79AKRSqSOuJx6JRIhEIofN27Z9Sn+Qfqqfv7x4+hk6+dm23VgT/Oyzz2Z4eJiRkRH27dvHwMAAv/zlL/nW6Cjf6Omhd8cO3prNcr3rcmiNWAfwZ8CHgb8HPg9MHdjmeR7hcJhly5aRSqXo7OzEcRy6u7tZunSpfsbkqPR7qJmuhYgcb1bcovXKVjIbM5R2lA5LEB+pcnq2CvPKWIXqZBUramFaJu5el6AcgA1BNYAQGGEDK2bhV33Ke8rYnTZe3iPcHWbRGxZRG61RGalQmaxXl1fGKgSFACNkgAnuXhcv62FYBn7FJ7E+QVANmPr5FFaLRXRNFKgnxdt2PNQUc3HFy5i4dQK/4lN4ooBhG7S+vJVwd7hxI8B01fmxqhjPPpTF3e+SOCdB5r4MbVe2YYQMYmtj5J/Mk30oy6LXLjomryUicqwoKS4iInIIwzZwz3QJ3RvCNOsflZqGycS/ThD902hT2y0RkfXr1zceP/XUU6xbt27W/Z566ikAzjzzzDmJS0TkVOU4DitWrGDFihVs2LCBbDbLhRdeyD333MPOnTt5rFjkd0yTxek0H61UeHMQHHaMFuBjwG8B7wTuPjA/Pj5OW1sbXV1dVKtVUqkUvb29p2Q3DxERkROd0+vQeUPn82olPluFuV/y8Ys+VtQiCAL8go/dY1N6ugSA4RtQq7ctd5Y7+EWfcEeYqlUldVGKxb+1mOpktZ6g31nCm/Lq+6wK45d8yoNlDMMgvCRMdHWU2lSN3BM5DAwM2yB+ThzDNMg+lCW5Ikfr+PammAtrXkF0TRR3n4vh1J9T2Fog1BLCdMxG1bnb7xJfFz8m19ab8jAM47DPyIyQgWEYeFPeMXkdEZFjafaFM0RERE5hQRBwy8du4dGfP9qYq1VrpF6Wqt9ZLCIyw+mnn05fXx8AP/nJT2bdp1AocN999wFw7bXXzllsIiKnuul1v1/2spfx+7//+3zwgx/kAx/4AC996UsZbG3lrabJecAPj/D8XuBn1Ncbn+7j8eyzz/Lss88SDodZtWoVa9euJRRSzYGIiMiJyIpbxNfFSV6QJL4u/isrpacrzO02m9KOEsXtRSrDFex2m+iaaL1VOhAUD7ROz9bqVeVGPXleHa/W1wmveIS7wrRd1YYVtxoJ+q7f7GLZ+5fR/qp2wl1h/KJP4AVU01XMhAkm2B027h6XwAvqrdZntHJ3fv6jpnh9J4XxipdjWAaFzQVM0yTSFaGWqVHeV6Y8UMbd41IZrdRbtR+r69pSv0EgqAW0vOzg+uFBLSAIAqyWY7+GuYjIi6WkuIiIyCGe+NYT7HtgHw/e/SCTo5MM9Q+xw9xBy7UtTW9ERESmveMd7wDge9/7Hnv27Dls+9///d+Tz+exLIu3v/3tcxydiIgAJBIJzj77bG688Ub+5m/+ht/93d9l+fLlPOM4/DpwDvA9wJ/luX8IPARM9/rYvXs3o6OjnHPOOfT29s7NCYiIiMicmE5gd1zfQdtVbXS9pYv269pxlterzKuTVdz9B5LWLSEiSyOEFoXq7dUdk9CiEM5ih7ZXtpG6+GA3mekEfcdrOlj2gWXEz4yDAeHOMGbUxN3ukv1lltLOElbUIr4+jhk2Cbx6V5vU6hLx//hSU6ylM67ECNfX7jZso56oJsAreOQeyZF9JEtuc47C1gKZ+zK4A+4xuUapi1I4Sx2K24sYBypIptcUd5Y6pC5SFx0ROfEoKS4iIjJDcaLI7R++Haiv0/SDf/0BP7vnZ7zkoy+Z58hEZC6k02nGx8cbX75fT40Ui8Wm+Xw+3/S8D3/4w/T09FAsFrn++uvZtGkTAJVKha985St88pOfBOB3fud3WLt27dyelIjIKaxWq5HNZkmn02SzWWq1WuPrLW95C3/7t3/La17zGpYsWcJWy+Kt1BPfd8xyrHOAh4H3HBjfe++9/PznP8dxnLk6HREREZkjMyvMUxtStF/XTnRVlOjqKFbSAg9C7SFCLSGsVgtqgA21bA3DMEhelKT9uvbDKtO9gkdhW4Hc5hzFZ4oYtkHi3AStl7WSuCCB3WFjRkyip0dJXphstHIPqh6pv3k/Zj7TdLz8S9/eSJq3XtlKqC1EeX+Z8mAZv+IT7g5jhAyiq6NgQWZjBq/w4lubh7vC9NzYQ3hRmPyTefKb8+SfzBNedGC+K/yiX0NE5FhTfy8REZEZNt+0meJYsTEu5Ar82nd+jVBE/8sUORWcf/757N2797D5L3zhC3zhC19ojN/5zndy0003NcYtLS3ccsstXHfddWzdupULL7yQZDKJ67pUq1Wg3jb9r//6r4/7OYiISF2xWGR4eJhisYhh1CunYrEYsViMYrFIMplk9erV/N7v/R7nnHMOjz76KPfeey/PZDJcB3wA+EtgZso7BnztwL9/C/zd3/0d5557Lm9729vm/gRFRERkzsxcnxwDxm4eI/ADarka1YkqVswisjiC6Zh0vK6D7rd2H5YQdwdcMhszVEYqVEYrFLcWCYKA7ANZWl/WSrgrjN1uU9hawHIs4mfECXeFyWzMYH/1rwhv3th0PP+Nv0Fw6RWN9c+tmEX8zDjjPxzHy3mYK0wqwxWqk1VaLm3BbreP6driLRe3EF0ZJftQFm/Kw2qxSF2UUkJcRE5Y+oRfRERkhv0P7G8aR1oinP5rp89TNCKykGzYsIEtW7bwl3/5l9xyyy0MDAwQj8dZv34973znO7nxxhsxTTVqEhGZC7VajeHhYcrlMm1tbY2keDabZWpqinK5TC6Xw3VdyuUyvb29nHPOOZxxxhl87WtfY3Jyki8DdwHfBs475PhfBH4BbCoW+eM//mPWrFnDRRddNMdnKSIiInPJils4fQ5mxCTSGyGyOILv+vhlH7/kE2oNEV0dpe3lbbNWiGc2Zqimq0TXRDFCBpXBCtXxKuX+Mun709gtNnaLjRW3SJyTwIpbWHGLzu6nMW//q6bjBUuWYH7l72h1k2Q2ZijtKIEBBJA4N0FkWYTY6TGMsEF5b5lw54FEtQF+abbFYl6YcFeYRa9ddMyOJyJyPCkpLiIiMsPW/7e1aVyeKs9TJCIyH2ZbD/z56O7u5ktf+hJf+tKXfvXOIiJy3BSLRYrFYiMhXqvVKJVK+L7P+Pg4k5OTdHd3k0qlSCQSeJ7HxMQEpVKJZcuWEQQB6XSaLcBLgS9QrxyfFqa+/vgFwP79+/nsZz/Lv/7rv9La2jr3JysiIiJzxu13CfyA6JoofrGeEI+dHiOoBRS2FcCE6kSV4e8MUx2vYi+yiZ8RJyCgMlIheloUwzSo5WsUtxep5Wr4FZ/qUBU/62PY9VbnzsoDvWrGxrBu/B/gH0xkB6aJ961vEersxIFGBbtf8jGjJl7RI3N3BmeFg2EZRJZE6s/zAgjAjOpmbRE5NSkpLiIiIiIiIiInFc/zMAwDwzAolUqMjo5SKpUwDIORkRGmpqZoaWkhmUxiWRaJRIJf/OIX/OxnP2NkZATTNAmHw1QqFcrA/wLywEdnvMYa4B+BtwMbN27k3nvv5XWve908nK2IiIjMFb/kY0ZMEuckKDxVwN3lUtlfwa/6eFMe+cfyZO7OUJ2o4ld9rIhFpC9C7LQYZszEMA1816e8r0wtV4OgvjZ5pDtCYAS4/S6h1hB2h11PhL/jHbC/uavhM69+KysuuLSR3Jle/3yaV/Aobi022qoblkHg1Y8d7g7j9DmIiJyKdEuQiIjIDBf//sXzHYKIiIiIvEiWZREEAdVqldHRUSqVCi0tLaRSKVKpFMlkkkKhwOTkJMPDw9xxxx3ceeedjIyM4Lpuo6o8FDpYS/AnwC8PeZ23AVcA2WyW2267bQ7PUEREROaDGTUhALvdpuWSFjp+vYNIbwS/5lMZrVDYVqC0t0TgBzgrHEKdIWpTNQpbCxSeLuAVPCpjFWqjNex2m1AyRG28hpfz8HIeQTXAy3m4Ay58/vPwk580vX5pzWXsbn8LEz+YqO8zCytu0XplK3ZbfQ3x4vYipR0l7Dab1itbD2vtLiJyqlBSXEREZIbTX9e8fni0IzpPkYiIiIjICxWLxYjFYoyOjlIsFkkkEgDk83lisRg9PT2kUikMw2Dbtm1s3LiR0dFRQqEQlmU1/jVNE9Osf3RSA94KVA95rem/Hh999NG5Oj0RERGZJ06fQ7g7jNvvYtgGke4I1akq1fEqRsTASlpYMYugFlDcWqQ6Vq13r7EMvCmP9M/SlJ4tUZ2qYoQMrJRFqCUEYTAMA9M0KQ+XKfzDTwg+8Ymm1/Zau8h/7B/xlxrU0jUyGzN4BW/2OHsdOm/opOP6DtquaqPj+g46b+jE6VWVuIicutQ+XUREZIZIS6RprDXFRURERBaeUChET08PU1NT5PN5TNPEMAwikQg9PT2Mjo6yfft2BgcHefbZZ0mn04TDYcLhMACFQgGAWq2G7/tEIhHK5TIhwD7ktTYd+HdsbGzOzk9ERETmx3QVdmZjhtKOEpXRSr0Ku9OmNl6DGnhZDyzq7dE9CCoBZtbEd30qIxWMkEFtsoYf+MRWxgj3hjFMAzNp4qU9wuEpOv/j9zG8gwnvwDDZf8nnIZcCDyJ9ESp7Krj9LvF1cbyCh9vvUp2sUkvXsNtsQu0hnD7nqJXh08+bXo/8V+0vs9N1FFkYlBQXERGZwY42f8zp13yCIMAwjHmKSEREREReiFgsxvLlyymXyySTSUKhELZtk81m2b17N3fffTdjY2NkMhnGxsYIh8NEo9FGlbht21QqFaC+RjnAGw95jWHgsQOP9feiiIjIqWG6Ctvtd8k/mgcgsjiCu8OlMlHBr/lYMYtQKoQRNigPlvFLPkbIwEgaWBEL0zYxqgbloTKhZIjwsjDlnWWslMnynX9KKDfc9Jrj572Pycq52JtyhNIhaqfVwKivce4OuGQ2Zig+U6S0s0QtVyOUChFdGSV2RozWK1tnrRCffl5lpAIGEEC4O3zE/WV2uo4iC4eS4iIiIjP4nj/fIYiIiIjIMZJKpejq6qJcLmNZFv39/TzyyCP8/Oc/Z3h4GNu2sW2bUChEEAQUi0XC4TCRSIRarUYoFMIwjEZy/L2HHP8WIDjwuLe3dy5PTURERObIkaqA4+viAJSHyoQXh3FWOfV26SEDL12vFvfTPn7RhxoEXkBQDQhCAYEfgAVGYFA2yrgDLmbEZFnkJyT23tv0+rlFL2Xqle8jGQ7VP7cagvwTeaI9UYIgILMxQ3m4TC1Xw4ybxPvi1NI1aoUaleEKmY0ZOm/obKpc9goemY0Zqukq0TUHlw50+91Z95fZHXodDcsg8AJdR5ETlJLiIiIiM2T3ZQ+bU9WPiIiIyMI03UZ9//79bNmyhc2bN3P//fczMDCAZVkUCgXC4TCJRIJCoUC5XKZWqxEE9VR3NFr/kLhSqXAFcNohx//6jMcvf/nL5+ScREREZO78qirg6TXGK6MVYqfFcHfXW5hXJ6v1BLbPwTvoAsCHoBw05oJQAAZ4RY9YZB/dj3yh6fVrsU5Gf/2vyD9RInlhEsM0CJIB5T1lokujGBhURipYUQsv6+EVPLycR2xNjPJQGXONSWXkYJv1xnn1u1RGKkRPi2KYBtmHsqQuSuH0OZR2lA7bX2Z36HUEMCyDcHeY3CM5zJhJ/My42qmLnCDM+Q5ARETkROKm3cPm+n/ePw+RiIiIiMixEIvFaGlpIZvNsnfvXorFIqlUitWrV7No0SKq1SrhcJhQKITv15fOCYKARCLRVP3924ccdyvwyxnj3/qt35qL0xEREZE5cmgVcHR1lOiaKNV0lczGDF7Ba6wxbrfZ1LI1zLBJuCeMc5pDZFkEs9WsJ9Oh/q/BwUR5UB+bLSam6bNm///B9Jo/lxr/jS8QvWQFpmNSGaxQGaxgZAzMuEninETjmEHtwNJ/0691IPMT1IJGm/WZ/JIPBo1E7jTDMmbdX2Y323WsjlXJPpil+HSRqfunmLh1grGbx3AHDv/MUUTmlpLiIiIiM6x51RrsWPO64nd/8u55ikZEREREjoWxsTGeeuophoaGAKhWq7iuSyqVIhwOUy6XMU2TaDRKW1sbPT099PT00NvbS6FQoBV40yHH/NqMx0uXLmXNmjVzdDYiIiIyF6argJ3lDoZlkNuUw7AMnD6nUX0NYLfb2B12vQLcP1ApvChcT1AXgdqBA3ozHk8LABeWFb5FytvWtCl36W9RXnMldrtN99u6Sb4kSWx9DG+VR+ysGM5KBzNqQlB/zSAIcFY59XboB3LaRsiAgPp+M0w/L/DqJevJDcl6OF4w6/4yu0Ovo+/65J/KU8vWCHWEcFY7h91IISLzR7/ZREREZoi2R3nFZ1/RNLfn7j2Mbhmdp4hERERE5MWYnJxk48aNPP7444yNjVEqlcjlcuzbt498Pk84HMZ1XXzfp7u7mxUrVtDa2kp7ezue5+F5Hm8HnBnHrADfnDF+17veNafnJCIiIsffc6mmdgdcRv5thOFvDVMeLmMvrhdauPtdqsNVfPdXVFxXIVrawfLaN5umS6Fe9ra9j+KuIvnH85gRs96ufYWDERg4y+qt26fbt3uuh5WyqE3UwIfKWAUrZeGXfMLdYZw+p+n4089z+10CL2haC3u2/WV2h17H6li9db6Bgd1uUxmozHojhYjMDyXFRUREDnHh/7yQeHfzukmP/+vj8xSNiIiIiLwQtVqNffv28cMf/pD777+fyclJ0ul0Y93wdDrN0NAQ2WwWgEgkQnt7O+VymXw+z8jICA8++CBweOv0HwATBx4bhsF73/veOTsvERERmRu/qpqaADIbM7i7XIyIQeKsRL1C26//fWC2mhDlYEvzWfmsrX0Jk4MVxAEWO7r+BI96JXhlsEJxS5Hi9iKlnSWCREDLy1uw4lajfXukJ0IoGcLLeeSfzOPlPULxEOGe+vrnh65nPbPte2lHqX7sHSXsNnvW/WV2h13HZ4tUJ6pYKYv4+jhG+OA642pLLzL/QvMdgIiIyIkmFAlx3rvO4+d/+fPG3FPfe4qr/+Lqw+4OFhEREZETT7FYZO/evTz00EPce++9DA0NEYlEsCyLyclJqtUqhmFQq9WIRCIkk0na29sJhUJEo1Esy2Lfvn1ks1k2AOcecvyZrdNf/epXs2LFirk7OREREZkTM6uAnT7nsGrqgIDKSIXQohDGsAFmPelp2AZW3MKwDfycj2/5h7dNP2AxP6aFLU1z+9veTt45C6fgE10ZxS/7xM+JE1kSwQ/5VLdVifRGDsbZ69B5Qyduv0t5f5nis0XMiEm4K0zqohThrvDs5zfjeX7Jx4yaOH2OEuLP08zrWNhawAgZJDckseIWdnu9c4Da0oucGJQUFxERmcXZbzu7KSmeHciy74F99F7aO49RiYiIiMihXNdlfHycarWKbdu0trayd+9etm/fzuDgIJOTk+RyOQzDwLIsKpUKpVIJwzBIJBIsXryY7u5ucrkcxWKRIAgoFovs27cPOLxKfDdw14zxpz/96bk6VREREZlD01XAmY0ZSjtK9YrvAMLd9err6lgVDLCcA0lkH8KLw5T7y40Euhk2IQV+2q9Xl89gG2lWBf/cNOfaSxle8V6oN7JpJFEjSyIkL0hSrVbrf4zMEquVsKgMVfALPn7RpzZZo5au0XplK07v7O3QrbhFfF181m3y3E1fR6fPwS/6VEYrs95Iobb0IvNLSXEREZFZdJ3dxaIzFjH+9Hhjbsv3tygpLiIiInICGR8fZ8uWLUxOTmKaJr7vY1kWxWKRWCyGZVl4nodlWY11w1OpFEEQEAQByWSS0047jWQyyZYtW6hWq0SjUdLpNJVKhW7gHYe85r9w8DPtq6++mgsvvHBuT1pERETmzNGqqb28BwGEOkKEWkNUxiqYIZMgCDBso95G3TawEzblUhnK1DMyBhghg9Wlr2AHuabX27viI/h+BHwPe4mN3W5T3lf+lRXGXsEjszFDNV0luibalIzNbMzQeUOnKsDnwK+6kULfA5H5pV4NIiIiszAMg7N+86ymuaf/62mCIDjCM0RERERkLrmuy5YtW8jlcvT29ja+xsfH2b59O0EQUCgUcF2XcrlMOp1mbGyMXC5X/7DaMAiFQqTTaUZGRjBNE9M0yWQyDAwMYAPfpb4U6LQK8I0Z44985CNzes4iIiIy96argJMXJImvizcSm9Pt1SsjFeJnxrESFrVCvTrby3tEV0exW238qn9wXXEf8CDlPk6Pf0fT64zar2Rk/Byq6SqRvgitl7ZSGa08pwpjt9+lMlLBWV6vTob6OtZOn0NlpILb7x7ryyJHMH0jRcf1HbRd1UbH9R103tB5xGp9EZk7qhQXERE5gnU3rOOeT9/TGE/tnWL4sWEWX7B4HqMSEREREahXiU9OTtLb24tp1u/5N02TVCrF8PAwiUSCdDpNJpNhaGiIQqEAQCgUIhQKNarH+/v7SSQShEIhxsfH2bVrF2HqCfFXHPKa/woMHnicSqW4+OKL5+hsRURE5EQzsyq4MlIh3BnGsAwiPREM28CMmPhFn/JomVAqhBfxCHIBeLAk+O+mY9WMBAPLPohVsbDiFonzE/gl/zlXGPuleuLdMI2mecMywDiwXeaM2tKLnJiUFBcRETmCrrO7aFvVRnpXujG39T+3KikuIiIicgKoVquN6u5p5XKZTCZDPp9nYGAA0zTxPI9qtUoQBEQiEWzbxjRNLMuio6MD27Zpa2tjeHiYXbt2EQduBq495PWGgD+ZMb788stJJBLH/0RFRETkhHWk9upQr97GhOIzxXq1eJX6GiymT5v3cNNx9kbeQdXqINxlYYQM7JRN61WtxM+IP6eW22bUhAACL2hUikN9TMCvbL8uInIqUFJcRETkCAzD4IwbzuCXX/xlY+6Z/3qGV372lfMYlYiIiIgA2LaN7/v4vo9pmo2q73379lEoFBgZGaFYLJLP54lEIvi+TyKRIBKJUKlU8H2fWq2GZVns2rWL7du30wbcClxyyGsVgNcCwzPmPvGJT8zVqYqIiMgJ7EhVwU6fg2EYRPoiuIMu5AADksEzhJlq2nesdjmV4QpmxgQLMhszRHojhBIhAj9oJNytxbMnyKdbubv9bv11Z6wp/lzar4uInAqUFBcRETmKdTesa0qKj20dY3LnJO2r2+cxKhERERFZtGgR7e3tDA8P09bWxsDAAPv27WP79u2Uy2WmpqZIp9MEQUA8HqdWq+F5HsViEcMwGtXi4+Pj7N69my7gTuDsQ14nB/wa8OiMuQ984AO89KUvnatTFRERkQXI7Xcpj5TxSh7UwEpaeEWP9lJzlXiBPtygB4OAIAgIKgHuiMvEjyeYvH2S2JkxQskQBGB1WBiecdhrzWzlXtpRqq9hHvCc26+LiJwKlBQXERE5iqUXLSXeFacwWmjMPfPDZ7jkQ4fWD4mIiIjIXHIch7POOotNmzbx4IMPsnfvXvbt28euXbsaleOmaVIulwmHw9i2jWEc/BA5HA4zOjrK/v376WD2hPg48Cpg04y5N7/5zXz5y18+7ucnIiIiC4NX8A5rn27FLdzdLu5Ol1q6hl/2CWoBgRvQ5m1qen7aeAmYYMbM+n6lgNpEjcK2AoZpYEQM2q5qw263Ke4qEuoP4RU87Fa76ThHauWuhLiISN2CXUiiWCxy22238ZnPfIYbbriB5cuXYxgGhmHwqU996gUd8/d+7/cax1ixYsUxjVdERBYm0zI57bWnNc09/V9Pz1M0IiIiIjJTa2srixYtwrZtyuUyg4ODZLNZstksQRDgOA5BEDA1NUUoFMJxHEzTxDAMBgcHGwnxOzg8Ib4PuILmhPirX/1qvv/978/V6YmIiMgJzh1wGbt5jIlbJ0jfnWbi1gnGbh6j+EyR/BN5fNcnvCRMuCdMUAkwqyVSwdamY6TZAAYYfj03wYF7+EzbJNwTxi/4FJ4qEFQDIn0RjIxBub88azzTrdyTFySJr3tu65G/EF7Bo7CtQO7RHIVtBbyCd1xeR0TkWFqwleIPPfQQr3nNa47Z8TZu3Mg///M/H7PjiYjIyWPdG9ax+eubG+P++/spjheJLYrNX1AiIiIiQjabZcuWLezYsYNHH32UwcHBxjrjoVCIcDhMrVajVqsRDocxDINoNMrw8DCu67IG+DFw2iHH3Qm8Etg7Y+6GG27gP//zP+fq1EREROQE5xU8MhszlIfLWI5F4AUYlkF5uMzYD8bwXI9IX4TCEwX8ig8+tJpPYvq1xjECTDKcixkzMSIGRmBg2PXkuBGpJ8jtTptaukZlrIK92AYDfNeft/N2B1wyGzNURiqHtWl3erV2uYicuBZspThAW1sbr3zlK/mjP/oj/u3f/o2enp4XdJxisch73/teQqEQF1544TGOUkREFrpVV6/Cjs1oSRXArrt2zV9AIiIiIqe4Wq1GNpvlkUce4ZZbbuHhhx9mcHCQXC6H67p4nkelUqFcLmMYBslkklQqRUdHR2O/S4AHODwhvhd4Bc0J8f/9v/+3EuIiIiLSxO13KT5TpLy/XK+YfqJeOV3eV6a4o4hf9ElekMT3fGqjNYJaQCuPNh0ja6zDs2LgQy1fwyt5GIaB3W1jYGDFLEKtIQIvwO13KT1bwkgbBH4wL+c8fSNANV0luiZKbG2M6Joo1XSVzMaMKsZF5IS2YCvFL7/8ciYnJ5vmPvKRj7ygY3384x9n586dfPzjH2ffvn088sgjxyJEERE5SYScECuuXMGzP362Mbfz9p2s/8318xiViIiIyKmpWCwyPDxMOp3mzjvv5Mknn6RarVKtVgEaleEAQRBg2zaWZZFIJNi6dSuFQoFrgB8A8UOOPUA9Id5/YGwYBu9+97v5sz/7s7k5OREREVkwqpNVSjtLWEmL8JJwY74yWKG0s4TX42HaJk6vQ3W8ClPQFhyynjgbwK9XfhuWATUwogaRnghYYCUsvLyHu8ellq0R+AFm2qSwuUBsaWzOK7PdfpfKSIXoaVEMs97n3bAMnD6H0o4Sbr9LfN2hf2GJiJwYFmyluGUdm7UwHnjgAb785S+zdu1aPvGJTxyTY4qIyMln1bWrmsZ7N+49wp4iIiIicrzUajWGh4cpl8tUq1WGh4epVCpUKhVCoRCxWAzDMKhWq9RqNaLRKNFolPb2dp555hny+Tw3ALdweEL8UeClwHQ/IMdx+Pa3v83rXve6uTxFERERWSBq6Rq1XA17kY1hGpR2lPByHuWhcr2qe0eJydsnKQ+VsRIWtj1FMtjRdIx0cAEYYCZNIksjRE+PEl0Txe6x6XhVB6G2ENlfZPHyHmbExGqx8BZ7uLtdJn86OeeV2X7Jr69/fiAhPs2w6q3e/dL8tXUXEflVFmxS/Fgol8vceOONBEHAP/3TP+E4Wu9CRERmt/KqlU3j9K402X3ZeYpGRERE5NRULBYpFoukUikmJiYYHR3F8+ofBpfLZTzPw/f9xr+1Wo1Fixaxd+9epqameBfwfSB8yHFvBa4ABg+MHcfh3//933nzm988V6cmIiIiC4zdZhNKhepV4D5Qg8LWAqUdJTAg1BbC930q+ypUp6q0hR5rer6HQ9Y8EyNmEEqEiK2L0XlDJ6mXpqila5QHy5i2SXhxmJaXtWA5FvhgTphUhiqk70wz9eDUnJ6zGTUhgMBrbt8eeAEEB7aLiJygTunfUP/n//wftm3bxnve8x6uvPLK+Q5HREROYF3ru3Bam2+e2nufqsVFRERE5pLn1dfZdF2X7du3Mzo6SqVSoVgsks/nKRaLRCIRQqEQLS0t9PT08MwzzzA2Nsb/Ar4BHNp37jvArwOFA2PbtvnWt76lCnERERE5qlB7iOjKKGbMpDxUxqt4lHaVMByD8JIw0TVRWl/Wit1tQxVagyebnj9lnkMQtgkvCmM5FkbIwG6zab2slfiZcZLnJmm5vIXE+Qm8KY9atoYRMQhaAsJLwniuR/b+7JxWizt9DuHuMG6/20iMT693Hu4O4/Sp8FBETlwLdk3xF+uxxx7j85//PN3d3Xz+859/wccpl8uUy+XGOJutVw3OXM/sRDMd14ka38lE13ru6FrPnVP5Wi+7dBk7fnywzdXe+/ZyxpvOOG6vdypf6/mg631kuiYiInKisCyLWq3G7t272b59O+VyGcOot+80TRPf96lWq1iWRTKZZNu2bbiuy/8E/maW430FeB8wXesUiUT49re/zZve9Ka5OSERERFZsJw+h9gZMSrDFcw1JrlHcrgxF7vLxi/4FJ4s0HpVK/Fz41QGKyTdp5qenwmdAyEwEyZ2m03gBZhhE8M2CHeFSVyQoDJUIf9YnvJQGTNiUn2qiuVYVBdVCbWGqOVqc7qOtxW3aL2ylczGTKMingDC3WFar2zFih+bZW9FRI6HUzIpXqvVuPHGG6nVanz5y1+mra3tBR/rc5/7HJ/+9KcPm7/99tuJxWIvJszj7o477pjvEE4ZutZzR9d67pyK17qwqNA03nLbFrzrjv/duKfitZ5Put6HKxaL8x2CiIgIALFYDNd1ueOOO3jiiScol8uUSiU8zyMUCmFZFr7vE4/H2blzJwBvB/5hlmP9OfDxGeO2tjZ+/OMf89KXvnQOzkREREQWupkJ4spIhSAIwIdQPISz3qnP1QL8nE84VSWR3tn0/Kx9FqFYiMAN8Aoe0RVRQh2hRtW13WEz9fMpvJJHLVMjlAwRVAPMgsnUvVO0vrQVu92e83W8nV6Hzhs6cftd/JKPGTVx+hwlxEXkhHdKJsX/4i/+gs2bN/Pa176W3/iN33hRx/roRz/Khz70ocY4m83S29vLtddeSyqVerGhHhfVapU77riDa665Btu25zuck5qu9dzRtZ47p/K1Hmgd4Fvf/FZj7O51ecVlr8BpOT6toU7laz0fdL2PbLoTjoiIyIlgeHiYhx9+mLGxMUzTxLZtarUaQRBgGAbhcJixsTEAXgfcNMsxPgL85Yzx+vXrueuuu+jq6jr+JyAiIiInjZkJ4sjiCNWxKqH2EKFkCCtpURuvURmu0FX5KQYHCysCTPLGOsy4iZf3MB2TcE+Y8kC5UXVdnahSGa1gt9sUtxXxEz5WyiKYCqACtWz975/5WMfbiltzVp0uInKsnHJJ8a1bt/Jnf/ZnJBIJ/uEfZrtX/PmJRCJEIpHD5m3bPuE/UF8IMZ4sdK3njq713DkVr3XfS/uwIhZe+cCbmACGHx7mtFefdlxf91S81vNJ1/twuh4iInKimJyc5PHHH6darRKPxwmHw5imSSKRYGpqCs/zGB8fB+AVwPc5/IOPP6E5IX7FFVdw1113EQqdch+RiIiIyDEwnSB2+hwqYxVym3K4+10M06AyVsGZ2sLKoeYlXAuh1Xi+QzDpYS2y6Pi1Drre2NVUdZ17NEctU8OKWUSWRfALPvYSmyAUYCdsapM1DNPQOt4iIs/R3N9CNM/e9773UalU+PjHP05bWxv5fL7pq1arARAEQWNO62iKiAhAKBJi6UuWNs3139c/T9GIiIiInHpGR0eZnJwkGo3i+z6jo6Pk83nK5TK+75PJZAC4GPghcOgt7H8F/NmM8RVXXME999yjhLiIiIi8IF7Bo7CtQO7RHG6/S/ur22l9eSvhzjChlhDhzjCLqvcd9rycsx4zatarvH0oPVtqJNen25CbUZOgEmCEDJIbkhi2gZf1oAB+zseMmSTOSahtuYjIc3TKvevbvXs3UG97/tGPfvSI+/X395NMJgH467/+a/7gD/5gLsITEZETXN/lffTffzARvvfevfMYjYiIiMippVgsks1m8X2fYrHI5OQknufh+z6lUgmAdcBtQOKQ534N+PCM8eWXX84999wzN4GLiIjISccdcBvriWMAAfXW55e30nJZC37JJwgCgi0e7G5+7qR1CVbcwggZhJeE8YoeYz8YY9kHljWS3E6fQ7gnTGFrASNsEGoL4bkeRsnAbDeJnR7DWakqcRGR5+qUqxQXERF5Mfou72saDz48SM2tzVM0IiIiIqeOYrHIyMgIU1NTDA0NMTExAUAsFsMwDAC6gB8DbYc89/vA784Yr1+/nnvvvXcOohYREZGTkVfwyGzMUE1Xia6JElsbI7omSjVdJXNfBq9YX3rPilmElx16qx5M+BfhlTx81yfcGcZeZFMZqeD2u419rLhF23VtBNUAd69LYAZYcYugOyCUCuEXfewOLXcmIvJcnXKV4nv27Dnq9ne9613867/+K8uXL/+V+4qIyKmn99JeDNMg8AMAvIrH/of2s/yK5fMcmYiIiMjJq1arsXv3biYnJxtt0l3XxbIsSqUSxWKRKPDfwIpDnnsb8D8A/8C4tbWVX/7yl3MYvYiIiJxs3H6XykiF6GlRDLN+c55hGVgxi/RdaYrPFAn3hCGAVMYgOuO5o/ZVmCkLy7EwIya+6xOKhzBsA7/kN72O5VikLkxRHilTfLqIvcTG932ia6PYLTbViSrhrvAcnrmIyMK1oJPi6XQaz/MaY9+v/w+jWCwyPj7emHcch0Ti8LuxREREni+nxaHnvB6GHh1qzO25Z4+S4iIiIiLHUTabZc+ePRQKBWKxGKlUilKphGVZTE5OAvAl4KJDnvcg8EagemBsGAZ33HGHPiMQERGRF8Uv+WDQSIgD+K5PYWsBr+QR7gkTXR3FL/tUxoOm55pGrX63ng/hZWGqk1XsDhu7xa6vMX7I69g9NsmLkqTjaSKrI+x7dh+pS1NUB6uHJdFFROTIFnT79PPPP5/Ozs7G18DAAABf+MIXmubf//73z3OkIiJyMum7ormF+t6NWldcRERE5HjK5XKMjo6STqepVqtYloVt24TDYXzfpwu48ZDn7AZeB5RmzH3nO9/hwgsvnLO4RUREZOHzCh6FbQVyj+YobCvgFbx68jqAwDuY8K6OValOVgm1hjCjJrlNOWoTNQK7uZLbsmuYYZOAAD/rY9j1CvNIXwSnr3mN8OnXMWyD9uvaia6OEiwKMGwDAg5LoouIyJHpN6aIiMjztOLKFU3jgV8MaF1xERERkePI8zxGRkYYHh6mUqlgmmZjDuoJ8ZkfN5eA1wCjM+a++tWv8ta3vnXughYREZEFzx1wGbt5jIlbJ0jfnWbi1gnGbh4DE8LdYdx+t5EY91yPWqaG3W4T7qz/ZeJXfAIr0nRMO15fR9wwDQzbIJQI4ax0aL2yFStuNfbzCh5+0ccv+WQfyuK7B6rCfSj3lwl3hw9LoouIyJEt6Pbpx2PN75tuuombbrrpmB9XREROHsuvWA4GcOBm4JpbY98D+w5LlouIiIjIseH7PqOjo+zZs4dMJkMoFGokxk3gdw7Z/9+Bp2eM//Ef/5H3vve9cxewiIiILHhewSOzMUM1XSW65uCq4G6/S+6hHKmLUmQfylLaUQIDKsMVLMcifmYc0zFJbkhSGawQGIekYdwSZtyELFTGK8RaYoSXhTEjB2sY3QGXzMYMlZEKfsXH3e3i7nIJrwpjDpmE1oYOS6KLiMjRLeikuIiIyHyItkVZfP7ipnXFd/9st5LiIiIiIsdJpVKhXC5jmiaVSoVarUYsFgPglcDKQ/b/yozHl156Kb/7u787V6GKiIjIScLtd6mMVIieFsUwDbIPZUldlMLpcyjtKBH4AZ03dOL2u/W1vQPIPZbDK3oEXoBhGYQ6QtSseNNx49mniOx9mGLpLAzToLSrxOi3R8nel6Xnxh4S6xONZLyz0sEMm8TXxyk8XiAIB9TOrtHxhg6cVlWJH2tewWt8P82oidPn6MYDkZOI2qeLiIi8ACtf2fzR6647d81TJCIiIiInv2w2i+M4LF68mGKxSD6fZ//+/QC8+5B9NwMPzRj/1V/91RxFKSIiIicTv+SDAYZpNM0blgFGfbsVt4ivi5O8IElyQ5L269qx22xKO0oUtxcpD5Thuqubn4/P8qG/IdQeIn5hnHBPmEhvhMp4heGvD5N7LEdlpIKz3CG/OQ+AFbNIXpSsryEeQYna4+BIrfLdAXe+QxORY0RJcRERkRfg0KT4/of2U86W5ykaERERkZNbJBLBNE0mJydxHIdoNEqtVqMVeMMh+359xuO+vj7OPPPMuQtUREREThpm1ISAxprhyQ1J4MA4OLD9EE6vQ+cNnXRc30HbVW10XN9Bzx+ed9h+0cou4mc6eGmP2ngNAoitjeHud8k9kjtqMp7KMT/VU97MVvnOSofY2hjRNVGq6SqZjRm8gjffIYrIMaCkuIiIyAvQ97I+rPDBu3IDL2DPxj3zF5CIiIjISSyZTFIsFhkZGcEwDCYnJwF4CzCzcWgF+O6M8e/+7u822qyLiIiIPB9On0O4O4zb7zbaoQdegNvvEu4O4/TN3r58ZvV4fF0ca9fWw/bJtlyKYYWgnm/HsA3w68nZ0q4SleEKXtFrJOLhYDKe8PE421PbdKv8mdX5hmXg9DlURiq4/aoWFzkZKCkuIiLyAoTjYXov7W2a23n7znmKRkREROTkNp3Ydl0X0zQZHh4GDm+d/kNg4sBjwzB417veRSgUmrM4RURE5ORhxS1ar2xtaode2lHCbrNpvbL1ubcw//GPD5vat+x/EdQCzJhJeHk9y53fkqcyXKGWqVEeKDNxywTV8SrAwWR8Vxi/0z9m5yh1z6VVvogsfHpnKCIi8gKtunZVU3X4zp8qKS4iIiJyPLiuSzweJxaLMTFRT3ufCVx0yH7fmPH4137t11iyZMlchSgiIiInoel26G6/i1/yMaMmTp/z/Nb0Ljcvt1defBZlZxnVJ/KEF4ex221Ku0tUhipEeiO0X9OOX/ZJ35MmfVeaxDkJDNsg3B0men4Uc6NJ/rE84WT4+ccis5rZKn+26vzZWuWLyMKj/5JFREReoDWvWtM0ntwxyeTOyXmKRkREROTkVSwWcRyH9vZ29u7dCxxeJT4I3D5j/OlPf3quwhMREZGT2GHt0J9vErq9vWlodraQOD+BFbWojdTIPZwj/0SeUGuISHeEWq6Gl/dInpfE7rKJro3ScX0HqYtS5B/OE3okRGZjholbJxi7eQx3QK29X6yZrfKnPZdW+SKysKhSXERE5AXqObeHeFecwmihMbfjth1c9P5Da5ZERERE5MUIh8MUi0XS6TTZbJYQ8FuH7PNNwJux/3nnnTenMYqIiIjMKplsHmfzxNfHabmsheKzRdxdLu4uF2eZQy1dT5IbhkEQBPiujxW1cPocxm4eozJSIbADMAADco/ncHe7tF/fTvyMF5CwF+Bgq/zMxgylHaX69Q0g3B1+fq3yReSEpqS4iIjIC2SYBquvW80T33qiMbf1P7YqKS4iIiJyjFmWRTqdZmBgAIBrge5D9pnZOv2yyy6bq9BEREREjq5WaxqaY/txljsYpoFhGLS+rJWMkaHUXyLcHcbusTFDJkEtoLCtQP6JPHa3TfHpIpVcBWuXxcTWCUzLJPADTMeklquROCdB65WtOL2qan4hjkmrfBE5oal9uoiIyIuw7o3rmsZ779tLbjA3T9GIiIiInJwKhQLDw8OMjY0BcMMh238JbJ8x/r3f+725Ck1ERETk6KrVpqFVmMBKDzfGdqeNETKopWuYUZPC5gLl/WVKO0pElkbAg9LTJUq7SwSFgCAVEFTqyXAzahJUA0LtIarpKpmNGbyCd2gE8hy96Fb5InJCU1JcRETkRVhz3RoiqcjBiQC2/r+t8xeQiIiIyElo//79DA8PMzU1hQW8/pDt35/xuKuriwsvvHAOoxMREZFTkVfwKGwrkHs0R2Fb4cjJ6F/7NXCaq7ed//4aAMkN9dbqZszEK3rkH81TfLpI9qEspR0lqiNVKhMVck/lcAdcfMPHyBl4rkeoNYRf9OtfJR+nz6EyUmlaF1tERA5SUlxERORFCDkhTn/96U1zW/59yzxFIyIiInJyGhoa4rHHHgPgcmDRIdt/MOPxa1/7WhKJxFyFJiIiIqcgd8Bl7OYxJm6dIH13molbJxi7eQx3YJaE9NKl8O53N02FN95MUPOpTlbJ3J+huLUIVfArPoZtYNgGoZYQhR0Fxn84TuGJAl7eo/hEEXPIhBr1NceL9TbfVtzCsAwwwC/5c3QVREQWFiXFRUREXqSzfuOspvHALwaYGpiap2hERERETj4//elPCYKA1wB3H7LtUWDvjPHFF19MKBSau+BERETklOIVPDIbM1TTVaJrokRXR4muiR69ffnv/m7TMDS5j+rtvyB9Rxp3r0tkSQS7y8aI1BPbXt7DTJn1RHnZx/d9oiui2J02FMAreVTHqoSXhQn3hLGSFoEXQABmVGkfEZHZ6LejiIjIi7T62tU4rc1tsLZ8X9XiIiIiIsfKT37yE84Hbp1l280zHnd0dLBmzRoMw5ijyERERORU4/a7VEYqOMsdDMsgtymHYRlHb19+zjmwdm3TVOvwHYR7wrRf3U7ivAQEYAQGZsSkOlGl3F8GwFnuUBur4U152B02/mofu9XGCBvY7TaRZRHsdhu33yXcHcbpcw5/fRERUVJcRETkxbLCFme84YymObVQFxERETl2stkslx9h28yk+MUXX4zjOFiWNRdhiYiIyCnIL/lggGE234R31PblhgG/8RtNU84vf0RkiY2z3CGUDGHYBnanTXhpGLvDrn912lhRC9M2sbtsQm31bjihjhCGYRCUA6y4RXlfGbvNpvXKVqy4/g4SEZmN+omJiIgcA+vfsp7N39jcGA8+PMjkzknaV7fPX1AiIiIiJ5HZVsf8ObBtxvjSSy/FcRxisdgcRSUiIiKnGjNqQgCBF2BYBskNSYBZ25d7BQ+338Uv+VgbXkuMzxw8zsg+7KGnCU7bgN1pE1sbo/hsEbvDJtIXwe6wcfe6WJ0WTrdDyyUtWB0WXtEjHo3TdkUbzkoHwzAwoyZOn6OEuIjIUahSXERE5BhY+YqVxBY1f/iqFuoiIiIix05hlrmZ9VbLly9n/fr1dHd3a01xEREROW6cPodwdxi3320kxgMvOKx9uTvgMnbzGBO3TpC+O834sz3UOvuajhUffgC338WwDVoubSHUEqK4vQiVeiV6qCUEHvUK8u4whm1gBAaJ8xN0vLqD1IYUyQuSxNfFlRAXEfkVlBQXERE5BsyQybo3rmuae/rmp+cpGhEREZGTi2maFA+Z2wIMzhj/j//xP1i2bBnJZHIOIxMREZFTjRW3aL2yFbvNprSjRHF7kdKOUlP7cq/gkdmYoZqu4qx0iK6OEj0thrvmsqZjxYcfaBzHK3okzk0QXxcnujZKpC9C4rwETp9DKB7C7Xcp7SwRJAJaXt6iJLiIyPOkW6dFRESOkTPfdCab/mlTYzz4yCBT/VO09LXMY1QiIiIiC18ymaQwNdU0d2iD9CVLllCtVtU6XURERI47p9eh84bORmv0Q9uXu/0ulZEK0dOi5B7JAZC6KIV/2Svgl//WOI714H10/mcL7rDfOI7dYVOdqM469kM+1W1VIr2ReTlvEZGFTElxERGRY2T5y5fjtDm4abcx9/R/Pc3F/+vieYxKREREZOFrbW2leEhSPD7jcSqVwrZtfH+2lcdFREREjj0rbhFfF591m1/ywai3QJ+pet7lzTsWi1hPPUL88ub5cFd41nG1WoXdLzJwEZFTlNqni4iIHCOWbXH6605vmtt287Z5ikZERETk5HHOOefgHjI3sz7qrLPOYsWKFYRCIYrFQxuti4iIiMwtM2pCAIEXkNyQJLmhvryLH2+nsuTM5p3vvPN5H98reOQ25Rj9z1FG/3OU7KYsXsE7FqGLiJy0lBQXERE5hs54wxlN4/77+smP5OcpGhEREZGTw+WXX35YUjwKTNdehcNhEokEjuPgefpAWEREROaX0+cQ7g7j9tf/gjEsg8ALcPtdque+vHnn55kUN8YMRv5lhP4v9jN80zBDNw0x8IUBBr86iDtw6F9MIiIyTUlxERGRY2j1taux43ZjHPgBW/9j6zxGJCIiIrLwLVu2jMSiRU1zYeBr1BPjDz74ID/72c+wLAvLsuYjRBEREZEGK27RemUrdptNaUeJ4vYipR0l7DYb+22vbt75wQchm31Ox/UKHqHHQuQ25TCiBvH1ceLr41gJi+ymLJM/nTysYtwreBS2Fcg9mqOwraCKchE5ZSkpLiIicgzZUfuwFupP/dtT8xSNiIiIyMmhpaWF8tKlZA6ZvxH4BOC6Lt/4xjcYHR0lFovNfYAiIiIih3B6HTpv6KTj+g7armqj4/oOOm/oJPyGV0B4xprhngf33POcjlnuL2PuNwnMgNL2Epn7M/Vke7eNYRkUnyk2qtMB3AGXsZvHmLh1gvTdaSZunWDs5jFVlIvIKUlJcRERkWPs7Led3TQe+MUAmT2Z+QlGRERE5CSwePFinh0Y4L2zbHvHgX/Hx8d54oknCIVCcxmaiIiIyBFZcYv4ujjJC5LE18Wx4hbE43Dppc07/vSnz+l4vuuDD2bIJLI0QmRJBMM0MEwD0zQJagF+yQfqFeKZjRmq6SrOSofo6ijRNVEqwxVGvjNC5ucZVY6LyClF7xRFRESOsdXXribaHqU0WWrMPfW9p3jZR142j1GJiIiILFz79+9ncnKS35pl2xMH/q3VapRKpVn2EBERETnBXHstbNx4cDxLUtwreLj9Ln7Jx4yaOH0OpmOCCYEXEF0VbZQ9Bn6A7/uEQiHMaH3S7XepjFSInhYl90gOgOjKKO5+l9KOEtXJKuGuMOHuMK1XtuL0Osf7rEVE5pUqxUVERI4xK2yx7k3rmuae/O6T8xSNiIiIyML3ta99jWuB1x8yvx/4/QOPQ6EQLS0tcxuYiIiIyAtx3XXN4x07YNeuxvBIbc8xwV/qgweVsQoGBgDVkSqBFxA7PYbTV09u+yUfDDDM+j5BJSD/VB6/6GN32IR7wkTXRKmmq2Q2ZlQxLiInPSXFRUREjoNDW6iPPjnK8ObheYpGREREZGHbsmULi2eZfw2w78Djjo4OrrnmmjmMSkREROQFOu886Oxsnrv9duDIbc+r6Sr5h/PUzqyR3JDEL/jkn8iTfyKPl/dIbUjRfl17vUU71CvGg3pVeXJDkkhvhFqmht1hU9lfofRsCcMycPocKiOVprXIRURORkqKi4iIHAfLL19OqjfVNPfYNx6bp2hEREREFrahoSFGDpkrc7B1OsCHPvQh1qxZM4dRiYiIiLxApllvoT7TT34CHGx77ix3yD2UY/Knk7h7XAzDwN3nYgQG3e/ppu/DffS8q4fF71pM7x/1suS3lzS1QHf6HMLd4UayO6gFBH5AdaKKGTcJpeqr6xqWAQaNtchFRE5WSoqLiIgcB4ZpcO47zm2ae/I7T+JV1IpKRERE5PkqFAqHJcUP/UDjfe97H7VajWw2SzqdJpvNUqvV5ipEERERkefn0BbqP/0pZLONtufViSqFpwsUny4yde8Uk3dMkvtFDmOfgRW3SG5I0vXGLrre2EVqQ6pRIT7Nilu0XtmK3WZT2lGiPFimOl7FjJp0/FoHqUvqxRyBF0BAYy1yEZGTlX7LiYiIHCfnveu8pnFposQz//3M/AQjIiIissBlDhnbQHzGuFgs0t/fT39/P4ODg43HxWJx7oIUERERea5e8xqw7YNj14Uf/AAzauKXffKP57EcC+JQnapSzVVx97jY99kUn3luf984vQ6dN3TScX0Hna/vpPVlrUSWRQgvCmNYBoEX4Pa7hLvDjbXIRUROVkqKi4iIHCfta9pZfsXyprnN39g8P8GIiIiILHCts8z1zHi8adMmyuUybW1tja9yuczw8LAqxkVEROTE09EBr3pV89x3v4vT52CYBqWdJaqFKu5Wl8r+CrV0DWeVg+EaTP5wEq/w3LoRWnGL+Lo4LZe10P32biI9EUo7ShS3FyntKGG32bRe2XpYpbmIyMlGSXEREZHj6Lx3n9c03vGTHeSH8/MTjIiIiMgC5s4y940Zj2+66SZSqRSGYQBgGAapVIpSqaRqcRERETkxvfWtzeO77sLKj5M4JwE+VEeqhJIh/LJPUAyIrowStAaUR8qNtcKfC6/gUdhWoDpWJX5mnNarWmm7qq1eQX5DZ9Na5CIiJyslxUVERI6jM990Jnb8YCuswAt44jtPzGNEIiIiIgtPKpViG3DzIfOXA9cceHzHHXcwMlJfebxWq5HL5ZiamiKfz1Mul+cwWhGR+pIOt912G5/5zGe44YYbWL58OYZhYBgGn/rUp4763E996lONfY/2tWPHjrk5GRE5fl73OojFDo49D/7jP3BWOjgrHSLLIiQ2JIieHiW+Po6ZqKd0TNusrz3+HLgDLmM3jzFx6wTpu9Ok705T3FrE7rSJr4urQlxEThmh+Q5ARETkZBZOhDnzTWfy+L8+3ph7/KbHueRDlzSqmERERETk6H7zN3+Tr371q7wLuABYMWPbJ4E7gHQ6zX333cdrX/taRkdHcV2XIAjIZrNEo1Hi8TixmR86i4gcRw899BCvec1rXtQxbNumvb39iNtDIX20K7LgxePw678O3/3uwbnvfhfn3f8Tp9ehvL9MuDtMeHGYwA+ojFYIogFWq0UQBBS2FfBLPmbUxOlzDktwewWPzMYM1XQVZ6WDYdU/i3L7XTIbM3Te0PmCkuJewcPtd4/62iIiJxr95SQiInKcnfvOc5uS4qNPjTL82DCLL1g8j1GJiIiILBx/8id/wne/+11yhQKfAb42Y9vlwMuBX5TL3H333Zx//vkEQUAymaRQKNDV1YVhGAwPD9PX16ckkojMmba2Ni644ILG1wc/+EGGh4ef8/MvvfRSNm7cePwCFJETw9ve1pwU/+UvsUb76XxDN+5ul8LWAnZ7vQuhlbAgAVbMIv9YnlqmBgYQQLg7TOuVrU2t0N1+l8pIhehpUXKP5ABIXZTC6XMo7Sjh9rs4fc7zSnC7A/WEemWkctTXFhE50ah9uoiIyHG24uUraFne0jS3+V83z08wIiIiIgvQsmXL+OY3vwnAN4G9h2z/5IF/n3jiCTZv3ozneeRyOcLhMN3d3bS3t2ttcRGZU5dffjmTk5PceeedfP7zn+ctb3kLkUhkvsMSkRPRNdfAoV0hvvc9YqfHWPahZbRc0kKoJYTdaRPqCuF3+BgYeEUPZ6VDdHWU6Joo1XSVzMYMXsFrHMYv+WCAYdYrxINKQHmgjLvHpTJaobit2NRafeLWCcZuHsMdmH298kMrz4/22iIiJxolxUVERI4zwzQ49x3nNs09+Z0n8Sp6oyAiIiLyXN1www38xV/8Bdg2f3nItlcClwQBmUyGJ554gkWLFrFkyRKWLl1KNBptLFvjefr7S0TmhmWpjbCIPEfhMLz5zc1zByrH7Tab6Joo4aVhIksjtLysBW+1h1/zcZY75B7KMfnTSdw9LoZpUO4v4/YfTGibURMCCLwAZ7mDV/bIPpIltzlH4ckCI98bobSr1EhwOyudoya4pyvPneUO+c15cptyGJaB0+dQGak0vbaIyIlGSXEREZE5cGhSvDRRYvut2+cpGhEREZGF6X3vex+vfOUr+bZtM3jIto8HAdVqlQcffJDR0VGSyWSjVXoQBICSVCIiInKCetvbmsdPPUXu2/ez+5O7GbppiKl7p5i8Y5Kx745h7jXBgNpEjcLTBUrPlsg/kSf3aI78ljzu7oOJaafPIdwdru/zVB4v72F32hghA3uRjVfwqGaq5B7KkduUI785f9QE96GV59MMywDjwHYROeV5BY/CtgK5R3MUthVOmC4SSoqLiIjMgfY17fRe1ts099i/PDZP0YiIiIgsTIlEgje84Q0kOzv5v7bdtO1az2NFtcrExAT/9V//RalUAuoJ8Ww2SzQaJRaLzUfYIiIvyJYtW1i/fj3RaJREIsHpp5/Ob//2b/PYY3ovKXLSednLYNmypqnaX/wjlfEKsXUxYutjxNbFqI5VCT0SojZRI/dEjlBHiMSGBOHFYcLdYbyCR/6JfCMBZcUtWq9sJfACSjtKlPeUyW/KUx2rEl0VJdwVxpvyqGVrjdc9WoJ7ZuV5ckOS5IYkUB8THNguIqc0d8B9XssyzCX9hhIREZkj5737vKbxjtt2kN2XnZ9gRERERBaoa665htNPP51vx2KMG81VSm/1PIrFIo8++ijbtm0jnU6TyWSIRCL09PQ0KsdFRBaC8fFxtm3bRiwWo1wus337dr72ta+xYcMGPvGJT8x3eCJyLJkmvPOdTVPJZ35EfJVB9oEsU/dPkX0gi7PWwSgaVAYruHtc/KyPu8el9GyJ6kQVZ4VD4AdNVd5Or0PL5S3Ez4zjrHCInhYlfkYcu7t+g2HgB0RPizaS3EdLcE9Xnk8f37AMAq/+euHuME6fcxwvkoic6LyCR2Zjhmq62liWIbometRlGeaS3g2KiIjMkfW/uZ6f/sFPqeQrQP1Nx2PfeIyXf/Ll8xyZiIiIyMLR29vLq171Kp5++mn+PZ/nfTPWCX+b5/FnpRI7duxgy5YtXHbZZViWRSwWU0JcRBaM0047jc9//vO8/vWvZ+XKldi2TaVSYePGjXzsYx9j06ZNfPazn6WtrY0//MM/POqxyuUy5XK5Mc5m6zdmV6tVqtXqcT2P42k69oV8DguZrv9x8o53EPrzP8c4sOxLqJYj/vRtpP1XNHYJzABCYHaY+FWf6mgVDCAAe7FNbH2MarpKJVchXA03nmckDawOi/iGeL0SHAiqAWbCxN3jEpgBfuATeAHl/jKhjhDWYuvw73EYEpclmLpnivwz+cZrh7vCJC5L4Id9/OrJ20JdP/vzR9d+fj3X61/cWaQ0WMJZ6TDyvREAOn+zE3uJTWlnifzOPLF1x75713P9udA7QhERkTkSToRZ/9b1PPrVRxtzj/3LY1zx8SsOW4tJRERERGYXCoV44xvfyI9//GO+l8nwvgNt0gFWBAGXAQ+WStx555285z3voa2tbf6CFRF5Ad7+9rcfNhcOh7n22mu54ooruOKKK3j44Yf51Kc+xXvf+15aWlqOeKzPfe5zfPrTnz5s/vbbbz8plpS444475juEU5qu/7H30vPOo3vGEgnhX97E2JVnN8Zjz45hBia73d31Ku2VAYZvEIQCgnAAu8AcMqnFavhDM5LTLthDNsazBn6nX+8h7IM1ZkEFBh8ZhMept0ZvDailagT3BEcONAZm3IQ8mHkT3/fhx9SP7dRfzxwzoQKEZ8yfJPSzP3907efXr7r+5k6T0NMh/KyPZVgA9D/WX982aFKza/i7j/2NM8Vi8Tntp6S4iIjIHLrgty9oSopP7Z1i1527WH3t6nmMSkRERGRhWb58Oddeey3/d8sWtpRKnDVj21trNe43DHbv3s2DDz7I9ddfP29xiogca47j8Od//udcc8015PN57rrrLm644YYj7v/Rj36UD33oQ41xNpult7eXa6+9llQqNRchHxfVapU77riDa665Btu25zucU46u//FjlMvwm7/ZGLdNbOWMlE+tZy1+zae4vchwxzAX/6+LcR90qaVrRPoijTbm5f4yobUhOt7QgRW3mo5dfkmZqXumqIxWDlZ4Xxcm8ZIE+OC7PqZjEumLHPbc2ZQHDhzPOHC8AoTjYaJLo5SeLVEpHJiv1ucTZ72w1zmR6Gd//ujaz6/nev2L24pMVieJro7C+fW56d9PpVSJ9qvbj0ul+HQnnF9FSXEREZE5tOTCJXSf083IEyONuUe/9qiS4iIiIiLPQygU4tWvfjXf/e53+c7kJH/uH6w2eKPv80HfZ3R0lAceeKCRFK/VahSLRTzPU0t1EVnQLrnkksbjXbt2HXXfSCRCJBI5bN627ZMiqXCynMdCpet/HLzhDdDdDSMHPzeKb/wuQ2f9MUEQEFkcoXpaldRpKeLJOJmNGSp7Dia5I90RWq9sxWk9vCzbXmUT7Y7i9rv4JR8zauL0OS8oMe0VPPI/z+PnfOJr4geTXrtKjD0wRnRNtGm+8FSB4QeGiSyPYEbMekK+O1yPtXfhlZDrZ3/+6NrPr191/ROrE5SWlKgOVnH6nMbvAHfQJbokSmJ1Ass+9jfDPNefCb37ExERmUOGYXDBb1/AbR+4rTH39H89TWG0QLwrPo+RiYiIiCws69atY+3atXz/2Wf5TKWCeWC+Fbje9/l/pRIPPPAA+Xwe0zQZHh6mWCxiGAZBEBCLxejp6Tkp2geLiIjIScK24d3vhr/4i8ZUx9it+Dd8FqszTvT8KNsf2Q6A0+vQeUPn80pyW3GL+LoX//mT2+9SGakQPS1K7pFcYz7cFcbd7xI/O05+cx6AoBLglT3Kw2ViZ8Zwljv1qvb9ZTIbM3Te0LngKsZFZHZW3KL1ylYyGzOUdpQOdqU4cBPMfP+3bv7qXURERORYOvvtZxNyDt6X5ld9Nv3zpnmMSERERGThcRyHK664gnQiwc8O2fZ2z6NWq7Fjxw4eeOABhoeHKZfLtLW1Nb7K5TLDw8PUarV5iV9E5IV64IEHGo9Xrlw5j5GIyHHx3vc2Dc18hu7w/Sx67SLCXeGmbdNJ7uQFSeLr4nOWcPJLPhhgmEbTfOAFGIaBX6m3ei9uL1LNVPGyHna7TeHJArlNOfKb8zh9DpWRCm6/Oycxi8jcmL5hp+P6DtquaqPj+g46b+g8IbpCKCkuIiIyx6JtUc76jbOa5h7++4fxKt48RSQiIiKyMF122WUsWrSIbx0y/2qgzzSZmprijjvuIJfLkUqlMIz6B7eGYZBKpSiVShSLxTmPW0TkSIIgOOr2crnMxz/+cQDi8TivfOUr5yIsEZlLq1fD1Vc3z/3zP89PLEdgRust0AMvILkh2fgyrHpHHjNs0v3Wbrrf2k3s9BiBHxD49erw4vYigR9gWAYYBxLsInJSma8bdn4VJcVFRETmwcW/f3HTOD+cZ8v3t8xTNCIiIiIL07p161i5ciU/CoXIzpi3gN+2LEqlEps3byafzzcS4tOmx56nGxNF5PhIp9OMj483vny/nvgpFotN8/l8vvGce++9l6uvvppvf/vb7Nu3rzFfrVa56667uPzyy3nwwQcB+JM/+RNaW1vn9JxEZI78zu80j++9FzZvnpdQZuP0OYS7w40qb8M68HeV6+EsdRqJbsMyMMMmtUyNUGuInnf10P3WblIvSRF4AQQHEuzU1ykvbCuQezRHYVsBr6C/0UTk2FJSXEREZB4svmAxy69Y3jT3wF8/8CurAkRERETkoEQiwUUXXYQXjfJvhyS931mrgecxNDTE6OjoYX9nTY8t68SoWhCRk8/5559PZ2dn42tgYACAL3zhC03z73//+xvPCYKAu+66i9/6rd+it7eXWCxGZ2cn8Xicq6++mocffhjTNPnYxz7GH//xH8/XqYnI8fb610NXV/PcX/3V/MQyi+l1g+02m9KOEsXtRUo7SkR6IvTc2EO4J9yY96Y8It0R7HYbM2I2Euhuv0u4O4zT5+AOuIzdPMbErROk704zcesEYzeP4Q6otbqIHDtKiouIiMyTi/+guVp86NEhBn4+ME/RiIiIiCxMV1xxBV1dXdwUbl5jszcIuMb3GR0d5bHHHiObzTYS4UEQkM1miUajxGKx+QhbRGRWZ599Nl/84hd54xvfyNq1a4lGo2QyGaLRKOeeey7vf//72bx5M5/97GfnO1QROZ7CYfjAB5rnvvc9GDhxPjc60rrBLRe3NM0vumERyz60jOiqaFMC3W6zab2yFYDMxgyViQrOSofo6ijOSodqukpmY0YV4yJyzITmOwAREZFT1emvO53Wla1kdmcacw/83wfoe1nf/AUlIiIissBceOGFnHvuudw9Ocmj1SoX+AfXpfztIOBn+Tx33nknV111VaN1MUA0GqWnp4dQSB+NiMjxsWfPnuf9nI6ODv7wD//w2AcjcgrwCh5uv4tf8jGjJk6fc8KsY/uC/M//CZ/7HBSL9XGthvnlL8NVVzV2qYxWyD6UxZvysFosUhelCHeFj3DAY2963eDnMu8sc2b9/hS2FaiMVKila+QnDi4nkdyQpLSjhNvvzvoaJ933W0SOO73zExERmSemZXLx/7qYn37wp425p3/wNJk9GVpXtM5fYCIiIiILSGtrK7/xG7/B5s2b+Zdcrikp/mrfZ1m1yrPPPsttt93Ghz/8YWzbxrIsYrGYEuIiIiInCXfArVcbj1TAAAIId4dpvbIVp9eZ7/BemI4OuPFG+Lu/a0yZ//iPxNatA2DqwSmGvz6Mu9/FMAyCIGDivyfoubGHlotb5ivqIzpSAt0v+fXv2SF9jQ3LAIPG+uQznZTfbxE57tQ+XUREZB6df+P5hJMH7+AN/IAH/u8D8xiRiIiIyMJz3XXXsWHDBm5NpcjNmA8BHwsCisUit912G8888wxtbW2kUiklxEVERE4SXsGbtf12ZaKy8Ntvf/CDYB2sfjbKZdZ/4xtURiv1hPioS2xdjNj6GLF1MSrj9fnKaOWYheAVPArbCuQezVHYVjjm19OMmhBA4rwEyQ3JxlfgBRAc2H5IPOmfpdVuXUSeNyXFRURE5lEkFeH8G89vmnvkK4+Q3pWep4hEREREFp7W1lZe/vKXU3UcbrLtpm1vq1ZZ5fsMDQ3xn//5n7iuO09RioiIyPHg9rv19ttjNfKb8+Q25chvzlMbq1EZqeD2L+D/969aBe9/f9PU4gcfpPLVW3D3u9RGa2QfyDJ1/xTZB7LE1sZw97tkH8oek5d3B1zGbh5j4tYJ0nenmbh1grGbx3AHjt01dfocwt1hyvvLwIEKcerf13B3GKevufLb7XfJ3JM57Pvt9DkL//stIseVkuIiIiLz7OLfvxjTPvi/ZK/icef/vnMeIxIRERFZeK688kpWrFjB30ajFGfMh4BPBAGWZfH444+zc+fO+QpRREREjoMjtd/G5IjttxeUT30KOjubphL//HFMv3J4y/GQgWEYeFMvvlr6SBX4x7oi24pbtF7Zit1mU9pRori9SGlHCbvNpvXK1sPWCZ/+fgYEFLcXKW4vEvjBUduti4iA1hQXERGZd20r27jo/RfxwF8fbJu+9f9tZcP7NsxjVCIiIiILy+rVq7nqqqv4p+3b+Ypl8YfewQ9q31Qu87VIhFypxJYtWzjrrLPmMVIRERE5lma2356uMgYIvAB3t3tY++0Fp7UVPvc5eO97G1P20E6W1z7I6Nv/EZyDldRBLSAIAqwWa5YDPT+NCvx0jfxEvjGf3JCktKOE2+/Oukb4C+H0OnTe0Inb7+KXfMyoidPnHJYQh/r3O3ZGjOjqKKmXpBrzR2q3LiIyTb8dRERETgBXfPIKou3Rprk7P3wngR/MU0QiIiIiC4vjOLz73e/m9NNP5+8ch4Jx8ENxC/hAJkOxWOTJJ59kfHx8/gIVERGRY+pI7bfL+8uztt9ekN79brjwwqap1NjP6f63/4lRq2CGTAwMituLOEsdUheljnCg5+5IFfjHqyLbilvE18VJXpAkvi4+a0Ic6t/vyOLIc263Pu14r40uIic+JcVFREROANG2KC//1Mub5oY2DZG+R2uLi8yVYrHIbbfdxmc+8xluuOEGli9fjmHUW8996lOfOupzP/WpTzX2PdrXjh075uZkREROUatXr+Y973kPkWXL+GZra9O263M5luZy9Pf3c++992ptcRERkZPE822/vSCZJnz96wQtLU3TyZH76fzm71HYNEn+yTzhRWF6buwh3BWe9TDPJzE8swI/uSHZ+JrviuwX8v2ei7XRReTEp/bpIiIiJ4gLf+9CHv77h5l4ZqIxN/TtIaqfrmK32PMYmcip4aGHHuI1r3nNizqGbdu0t7cfcXsopD+/RUSOtze84Q3s2LGDW3/2M37rwQdJBPXOO9PV4v+Sz3PnnXdy2mmncfbZZ89vsCIiInJMPJ/22wvW2Wfj3XorwbXXYheLjenU6H2sHf44uU9+g9Rli46YEHcH3Poa4SOVegV4AOHuMK1XtuL0Hl5dPbMC3+lzMCyj3pL+V1Rkz4Xn8/32Ch7pn6Wppqs4Kw+eR3l/mczGDJ03dJ5cPycickSqFBcRETlBWLbFtV+8tmmuOlHlgS89cIRniMix1tbWxitf+Ur+6I/+iH/7t3+jp6fneT3/0ksvZXh4+IhfK1asOD6Bi4hIQ2trK+94xzvoO/98vnPIjUqvymRw9uzh4Ycf5u6776ZWq81TlCIiInKsPdf22wtZcNFF/PJP/5QgmWyajzx2O4u+90HCnbMXVXgFr54Qn6jgrHSIro7irHSoTFTIbMw0VYxPV5MXnykSWRrBilknZAX+c/1+u/0umXsyVEeqjH5/lJF/GyH3aA6nz6EyUsHtV7W4yKlCpSoiIiInkNOuP41VV69i1527GnMPfPEBNrx3Ay29LUd5poi8WJdffjmTk5NNcx/5yEfmKRoREXkxTj/9dK6++mq+u20b/397dx4fVXn3//89W2bJvkECJCRsYbWAC1pBRBQFd6t1r0tdatW2itpqvSv+vlZvl9rerUvbW9tq1bpvRVFZhFvUqiwKsi+BhACBhIQsk0xmOb8/xgwJWQhkJjOTvJ6Px3k4OWfONde5vDi5Mp9zfa4rPvlEif7gF71mSZdu3qwbk5L07rvvavLkyZo4cWJ0KwsAAHAYqoqK5H/vPVlnzZLq6g4cePVVadYs6aqr2pzTWNKopvIm+ap8qqtscU5Aki94PHFUYruzya1pViVNTJItzRaXM/Cb1z43WU1yjXCF9pssJgWaAqpfW997swsAaIWZ4gAAxBCTyaQZv5shk9kU2ud1e/XizBflrnB3ciaA7rJY+MMXAHqTwYMHy9q/v17LzW21f1ZNjUYGAtq1a5fefvtt1bX8MhkAACAOGMcfL33wgZSU1PrAz38u7djR5v2BhkAwyH1wRMgsyRQ83pxm/ODZ5H63X01lTXIVueJqBn7zjPeG4gZZ061yjXK1Whu9qbxJ9avqVfOfGtYZB/oIguIAAMSY/kf114QfT2i1b++avXrh9BfUuJ+BOQAAQFfk5eWpf//++t/ERNWZDjxwaJZ0b02NfD6fvvjiC61YsSJ6lQQAADhSJ54ovfBC633790vXXy8ZRqvdZqdZMqSk8UmtAsNJ45MkI3i8Oc24b69PdV/XqXZ5req+rovLNOONpY3a++ZeVb5XqYaNDWra2aTKeZXyVniDM8QbA6paUiVZpKSJSZ2mkwfQexAUBwAgBk1/aLoyhrVeA3PXil16adZLaqpvilKtABzKmjVrNHbsWDmdTiUlJamoqEjXX3+9Vq5cGe2qAUCfk5GRoeOPP16B9HT9I7X1MjTT3G59v7ZWZWVl+vjjj9XYGD9f8gIAAISce6505ZWt933wgfTLX0ruAxkHHfkOJfRPkKfMIymYOlySPGUeJfRPkCPfEUozfnDUyGQxhWaTH47mmdq1K2pVv66+xwLNB6+f7hrlUtq0NCkgVS2oUt3qYMBfPsmWYZN7rTv0AIBvry/uHgAA0HWsKQ4AQAxyZbp06QeX6q8n/FXevd7Q/tLPSvXyuS/rsrmXyerg1zgQayoqKrRv3z6lpaWppqZGGzdu1MaNG/Xss8/qnnvu0QMPPNDp+R6PRx6PJ/RzTU2NJMnr9crr9XZ0Wq/VfM198doRHvQhHHvssSoqKtLfKir0w9pa9fMf+DL2V3v36nSzWfPnz9cxxxyjGTNmtDm/vT7k8/nkdrvl9/tlsVjkcrlktTIuQ/u4D7WP9gCAMPqf/5EWLJB27Tqw79FHpZdflh55RLr4YlkSLUo7OU3Vi6vVsLkhtF54Qv8EpZ2cJkuiRWanWa6RLjmHOkNBc0ky/EZoNnlXtbc2efNnOfIc4bv29j67g/XT7QPtkiG5ioLrijdsbJDffVCgvkU6eQC9D3+1AQAQo1LzUzXs/xumkvtLVL+7PrS/eGGxXrvoNf3wzR/KYouPdZyA3m748OF65JFHdO6556qwsFA2m01NTU1avHix7rnnHi1fvly//e1vlZ6ertmzZ3dYzkMPPaT777+/zf6PPvpILpcrkpcQ0+bPnx/tKiDO0Yf6Lr/fr4EDB+pLm00Pulz6Q21t6FiRz6fL6ur00o4d+sMf/qDy8nJlZma2W05zH/J4PKqqqpLH45HJZJJhGLLb7UpPT5fdbu+Ra0J84j7UmrvF7EUAQDelp0v/+7/SWWe13l9aKl16qfTEE9If/iDHMcco+4JsNZY0KtAQkNlpliPfEVoj3JHvkD3XLk+ZR458h0wWkwy/ocaSxtBs8q5oXpvcW+WVo7BFOaWNqlpUpX4X9ovouuQdrZ9utptlTbPKWegMposvblTS+KQ2DwA0Fjce1gMAAOIHQXEAAGKYPdeuy+ZdphdOfUENlQ2h/RvnbtRbV7ylC166QGYLA3Ug2i6//PI2+xISEjRjxgyddNJJOumkk/TVV19pzpw5uu6665R6UBrfZnfffbduv/320M81NTXKy8vTjBkzlJKSErH6xyqv16v58+frtNNOk81mi3Z1EIfoQ5CkqVOnyuVy6eUXX9TqzZs1rsUM1bvq6vTZ4MFqbGyUyWTSjBkzWs36btmHTCaTSktL1djYqJSUlFBQvKamRg6HQ3l5ecwYRxvch9rXnA0HABAmZ54p/fd/S/fcIwUOmuX86afSscdKV18ty4MPKnFUbrtFdGU2eVc0r01uz7W3mqmtgFS/ql6JYxNlcVnaDcyHQ8v10zsKeLdMJ9/yAYCW6eQB9D5x+9ea2+3WkiVLtHz5cq1YsULLly9XSUmJJOm+++7TnDlzulTO7t279eSTT+r9999XcXGxGhoa1K9fP40aNUonn3yyZs+ezR8tAICoyh6TrSs/ulLPTXtOnpoDaZXXvLpGNpdN5zx7jkxmUyclAIgmh8OhBx98UKeddprq6uq0cOFCXXDBBe2+1263tzvT0Gaz9ekxaV+/fnQffahvy8zM1MUXX6x169bpUbdbz2/fHjqW5vfr2tJSPTJggJYuXarJkydr+PDhbcqw2WxqaGhQU1OTMjMzZTKZWpVfXV0tr9crp9PZI9eE+MN9qDXaAgAi4Je/lGbOlG67TVq0qO3xf/xDeu21YOD89tslR9vAryPP0els8q7oaG1ymYOzyPe9t09mlzliadW7EvAO1wMAAOJL3AbFv/zyS82aNatbZbzyyiu64YYbQk+nJiQkyOl0qqSkRCUlJfrwww/1k5/8RGlpaWGoMQAARy53Yq4un3e5/nnaP+V1H5jd9PU/vpYtyaaZf5zZ6stZALHlhBNOCL3eunVrFGsCAH1TQUGBTjnlFD1bXKz5+/frtOrq0LHLqqr0akaGtmzZokWLFikvL0+Odr4k9vv9MplMbcZczT/7/f425wAAAPSoo44Kri/+zjvS7NnSwX9/1tdLv/619Mwz0iuvBGeQH8SSaFHiqMQjrkJHa5P73X7Vr6+X3+2Xa4yrVVr16sXVyr4gOyzB6K4GvMPxAEA4+Ov9Ua8D0FfEdb7V9PR0TZ8+XXfeeaf+9a9/KScnp8vnvvbaa7rssstUU1Ojiy++WCtXrpTH41F1dbVqa2v1ySef6LbbbuPJVQBAzMj7fp4uefcSWeytB8ZfPfGVFt69UIZhRKlmAAAAsc1qteqMM85QUVGRHsvOlqdFYNsq6Y5du7R//34tXLhQq1atarcMi8UiwzDajLmaf7ZY+PISAADEAJNJOu88ae3aYEr1pKS27ykulqZOld54I+wf33JtckmhwHj9t/XylntlBAzVfV2n2uW1qvu6Tr69PjWVN6mxpDF8dfgu4J15ZqbSp6Ur88xMZV+Q3WY2evMDAMkTk5U4KrHHg9GNpY3a++ZeVb5XqaqPq1T5XqX2vrlXjaXhawsAB8RtUHzKlCnat2+fFixYoEceeUSXXHJJu6km27Nr1y7deOONCgQCuu222/Tyyy9r/PjxoeNJSUmaPHmyHn/8cSUmHvkTUQAAhNuQ6UP0w9d/KLO19a/wTx/+VN88/02UagXgUP7zn/+EXhcWFkaxJgDQdw0YMEAXXXSRmgYO1N/T01sdO9nt1jEVFdqwYYPeeust7du3r835LpdLLpdLNTU1oUB485riTqdTLperR64DAACgS+z2YEr1TZuka68NBstbamiQLrxQevRRKYwTLZpnatvSbWrY3CD3RrcaNjfI4rQoITdBZvtBYSmzJFOLtOthrEc0A96H4qvxqWpRlZoqm+QodMg51ClHoUNNlU2qXlwtfz1ZiIBwi9ugeHeewP7jH/+oqqoqDRo0SP/93/8dxloBABB5I84aofNfOL/NOuIf/uJD1e2ui1KtgL7rUFkaPB6Pfv3rX0uSEhMTNX369J6oFgCgHZMmTdKMGTP09siR2mttvaLcr/buVaCxUZ999pkWLVokn8/X6rjValVOTo7sdruqq6tVVVWl6upq2e125eTkyGqN2xXqAABAb5aTIz37rLRsmdRiaa+Qu+6SfvITyette+wItTdTO3NWppKPTlbS+CQlH50c2pLGJ0lGMO16rPHX+1W/rl61K2pVv64+rIHq8hfKVb2kWt5yr/a8ukfl/ypX7YraiMycBxAUe3eZHvD8889Lkq644golJCREuTYAABy+sReP1dn/e3arfY3VjZp367wo1QjoHaqqqlRRURHaAoHgk+put7vV/rq6Aw+g/N///Z9OPfVUvfDCC9qxY0dov9fr1cKFCzVlyhR98cUXkqTf/OY3SktL69FrAgAckJKSohNPPFEpAwfqf/r1a3VsqMejn9fXq6GhQYsWLWp1T2/mcrmUn5+vvLw8DRgwQHl5ecrPz2eWOAAAiH0TJ0pLlkjXXdf22F//Kp15prR/f9g+7uCZ2q6RrnbTqnvKPEronyBHvqOz4npce6nNK9+qlGmv6dAnd0GgKfh9g8lqkmuES64RruAEmAjNnAfQB4PixcXF2rlzpyRp6tSpWrlypS6++OLQ0955eXm65JJL9Pnnn0e5pgAAdG7CtRM09pKxrfatfX2t1r21Lko1AuLfhAkTlJ2dHdpKS0slSY8++mir/bfcckvoHMMwtHDhQl155ZXKy8uTy+VSdna2EhMTdeqpp+qrr76S2WzWPffco7vuuitalwYAUHC29/jx4/W9731PczMz9a2j9ZevV+/cqeGNjSouLtby5cvbzBZvLiMlJUXp6elKSUlhhjgAAIgfNlswAP7ww22PzZ8vff/70p//LK1ZIwXCn868vbTqtnSb0k5Oi5n05obf6DC1ubfSK+sqa1hmjKefmi7XSFdczZwH4l2f+8tt48aNoddffvmlHnjgAXm9XjkcDjmdTu3YsUOvvPKKXn31Vf32t7/V3Xff3Wl5Ho9HHo8n9HNNTY2k4MwgbxjTjYRTc71itX69CW3dc2jrnkNb95yutPX0x6Zry/wtaqhsCO17/+b3NWjyIDnSYusJ21hH3+4YbdK5cePG6bHHHtPnn3+u1atXq6KiQtXV1XK5XBo9erSmTJmiG264QePGjYt2VQEACs4WnzVrllauXKkHvV79c/16NX8FazUM/WrjRv3YatXcuXM1cODAqNYVAAAg7EymYMr0IUOkK6+UGluk6V67VrrppuDrzExp8mRpypTgNmFCMKjeDc1p1RtLGhVoCMjsNMuR74iZgLgkVb5fKU+pRzXLamTPtauu8kCmOL/XL8saizwlnm5/7+YsdIZmzjvyHTJZTDL8RszOnAd6gz4XFK+qqgq9vv/++zVgwAA988wzOu2002Q2m7V+/XrdcsstWrhwoe655x6NGjVK5513XoflPfTQQ7r//vvb7P/oo49iPn3a/Pnzo12FPoO27jm0dc+hrXvOodo6+8pslfyhJPRz3a46/ePyfyj/5vxIV61Xom+35Xa7o12FHrNt27bDPiczM1OzZ88Of2UAABEzatQonXrqqXpj/369VF+vK7/LDCJJ4zwe3erxaJ7HowULFqiwsDCKNQUAAIiQCy+U8vKkc86R9uxpe7yyUnrnneAmSYmJwTXJm4PkkyZJRxADaU6rHi3+ev8hg/LNqc0PzrVsMpuCqc0buz+LvnnmfPXiajVsbpBMkgwpoX9CTM2cB3qTPhcUD7RI+REIBPTaa6/p+OOPD+0bOXKk3nnnHY0YMUI7d+7UnDlzOg2K33333br99ttDP9fU1CgvL08zZsxQSkpKRK6hu7xer+bPn6/TTjtNtm4+2YXO0dY9h7buObR1z+lqWxszDb26/lVt+WBLaN+++ft0xh1nqGBaQQ/UtHegb3esORMOAAC9hcPh0FlnnaWysjI919CgE8rLNaypKXT88nXr1DhihD7bskUNDQ1qbGxkfAAAAHqfSZOk//xHOuus4CzxztTXSwsWBDcpOGv8mGMOBMlPPFFKT498nbvBvcGtvW/tVVN5k0w2k2ypNtnz7Uo7OU2OvODM7MxZmapfV69AU0DOoc7Q2ueS5PP4FNgRkNkRntTm8TBzHuhN+lxQPDk5OfR68uTJrQLizRITE/XTn/5U9957r7755huVl5erf//+7ZZnt9tlt9vb7LfZbDH/B3M81LG3oK17Dm3dc2jrntOVtj77L2frqTFPqanuwJe5826ap5tW3ySbi/9Ph4O+3RbtAQDojQYNGqQf//jHqq6u1kNVVfrftWtDk4HMhqEr5s7VyuOP18a6On3++eeaOHGisrKyolpnAACAsCsslJYvl156SVq4UPq//5N27Dj0eV6v9Pnnwe2RR4Jp2ceOlU466UCgfMCAyNe/Cwy/ofoN9drx+x3y7PHImmqVyWySv9Evf5NfRsBQ9vnZsiRaZLKYOkxt3lTWJCPDkD2/bUzoSEV75jzQl4TncZY40nI9sFGjRnX4vpbHtm/fHtE6AQDQXan5qZr+39Nb7avaWqV/nfMvVW6qjFKtAAAAYltBQYHOPfdcNY4fr/knntjqmNPn02+WLZPKy/XRRx/ptddeU0VFRZRqCgAAEEEOh3TttdKLL0olJVJxsfT889L110sjR3atDMOQVq+WnnxSuuQSaeBAaehQ6ZprpL/9Tdq0KfieHmT4DRl+Q3te36OSh0pU+02tzDazjEZDgbqAGtY3qHZFrao+qtLu53er8v3gd2jNqc1t6TY1bG6Qe6NbDZsbZE23yjfWx0xuIE71uZnio0ePlsVikd/vl8lk6vB9Roubc2fvAwAgVhx707H69l/fqvTTA2tiFi8s1tNjn9YJd5ygKfdMUUJiQhRrCAAAEHuOOeYYffPNN1o0cKAG1tRo7OrVoWM5jY36zYoV+i+HQ19//bUqKip0+eWXa8iQIVGsMQAAQASZTFJBQXC78srgvj17pKVLpU8+CW4rV0qBLqyrvXVrcPvHP4I/5+RIp50WTNd++ulSamqELiKoOchdvbha7g1uySN5dnvk3+eXJFnSLTLKDflcvmDq8sGO0LntpTa35FpkLOnZwD6A8OlzM8UdDodOOukkSdLaTtbIWLdunaRgQLygoKAnqgYAQLeYzCad88w5sthbP63qb/Jr6YNL9eSoJ7X2jbWtHvwCAADo69LS0nTWWWcpd8AA/XH8eK056MvZcfX1euLLLzWsvl4rV67Uiy++qC1btsjn80WpxgAAAD2sXz/pgguk3/9eWrZMqqqSPvhA+vWvg+nS21litl27d0v//Kd08cVSVpZ0yinBMjdtimj1ncOdShiYIEumRSbDJGu6Vc7RTtkH2GXJtMiabVXS+CRlzspsdV5zavPkiclKHJXIDHEgzvW5oLgkXXPNNZKkpUuX6vPPP29z3O126+mnn5YkTZo0SdnZ2T1aPwAAjlTWyCxdNvcyJeUmtTlWU1qj1y58TS+c/oIqNpD6EwAAoFlRUZGuvvpqnTJrlv5+9tmqSGo9lhq0f7/+9MUXurC4WBvWrtW///1vrVq1SjU1NVGqcZDP51NNTY2qqqpUU1NDoB4AAPSMlJTgTO8HHpCWLJH27w/OJH/wQWnmzODxQ/H5pI8/lm6/XRoxQioqkmbPDu7zesNSzcxZmcqclamMGRlKPjpZiWMS5RrlUupJqUosSpRziFOWZItSj09Vzo9yZLKQNRjozeI6KF5VVaWKiorQFvguXYfb7W61v66urtV5l19+uY477jhJ0sUXX6wPP/wwdO769et1zjnnaOfOnTKbzfrtb3/bsxcFAEA3DTl1iG5Zf4tOmH2CzNa2v+q3zt+qp8c9rQW/WqCmuqYo1BAAACD2pKWl6ZRTTlHh8cfrz2eeqXqbrdVxm2Hosq+/1t0LFmjv8uX66KOP9Oabb2rnzp1Rqa/b7VZJSYlKSkq0c+fO0Gu32x2V+gAAgD7MbpdOPFG6+27p/felffuCKdb/+Efpwgul/v0PXcbGjdLjjwdnj2dnB9clf+EFqbLyiKtlsphkspjkLHTKOdiphKwEWRIt8u72qmlnk9zr3XLkONTvB/1kTelzqw0DfU5cB8UnTJig7Ozs0FZaGlxD9dFHH221/5Zbbml1ntls1jvvvKPRo0ertLRUZ5xxhpKSkpSWlqZRo0Zp4cKFstls+vOf/6xTTjklGpcGAEC32FPsmvHYDP3km5+oYFpBm+MBb0CfPvypnhz1pNa8uoaU6gAAAJIyMjI0duxY7e3fX7847jhtbWedyzEVFbr75ZeV9O67euedd/TYY49pdYt1yHuCz+fT7t275fF4lJ6eHto8Ho92797NjHEAABBdFos0frx0663Sa69Ju3YFg95PPCGdcYaUkND5+fv3S6+8ElzTvF8/afJk6eGHpTVrpCP4DsuSaFHayWlyDnEqoX+CbNk2WVOtSj0hVYNuHyRXkevIrhNAXOmzj77k5ORoxYoVeuKJJ/TKK69o48aNamhoUEFBgU455RTddtttGjt2bLSrCQBAt2SPztaPFv5Ia15do49mf6TastpWx2t21Oj1i19X4V8LNfNPM5U9iiVDAABA32W1WjVhwgRVV1fr7Zoa3ZGWpotWr9bFJSWtZhUk+Xy65fPPNTY3V/+9e7cerajQLbfcookTJ8pqjfxXLW63W263W+np6TKZgmk+TSaTUlJSVF1dLbfbrZSupC0FAADoCSaTNHx4cLv5ZqmuTlqwQJo7V3rvveBa4x0JBKRPPw1uv/qVVFAgnXWWdNZZ8h87RY3lhgINAZmdZjnyHR2u++3Icyj7gmw1ljR26f2d8df71bSrqdvlAOhZcR0U37ZtW7fOt9vtmj17tmbPnh2eCgEAEINMJpPGXjxWI84coSX/b4n+8/h/FPAFWr2neGGx/nzUnzXpF5M09TdTZU+2R6m2AAAA0ZWSkqJp06bJZrNp3rx5erqmRqvy8vSL5cvVv7Gx1XtP3rVLOXV1ur6+Xo8//riuu+46HXfccREPSPv9fplMplBAvFnzz36/P6KfDwAA0C1JSdJ55wW3QEBasSIYIP/3v4OvO7NtW3DG+RNPyGR3yTR8ippGT1dD0TRZhw9S2slpcuQ52j3VkmhR4qjEblXdtNekyrcq5a/0SyZJhpTQP6HTzwUQG+I6fToAAOi6hKQEnfbwabpp9U0acuqQNscDvoA+f+xzPTnySX378rekVAcAAH1WSkqKZsyYoZtvvlmTJk2Sc8YM/X8/+IEWDRzY5r0ja2v1+02btPnrr/WXv/xF7777rmpqaiJaP4vFIsMw2ozXmn+2WJipBAAA4oTZLB1zjDRnjrR8ubRjh/TXv0rnnCM5nZ2f6nHL9e2Hynz1Lg36f8cq/d4z5L311/K/MVdG2S4Z/vB8t2X4DRl+Q74an6yrrPJWeuUodMg51ClHoUNNlU2qWlQlX40vbJ8JIPzieqY4AAA4fFkjs3TFR1do3Rvr9OHtH6qmtPWXtrU7a/XGpW9o+V+Xa+afZqrfmH5RqikAAED0WK1WDR06VCeccIImTJigefPm6fHqan2Zna1b1qxRktcbeu8xjY16orRU96Sn6/XXX1daWprOOOOMiKVSd7lccrlcqqmpUUpKikwmkwzDUE1NjZxOp1wu1sUEAABxauBA6frrg1tDg/Txx9LcufK/9q4sFWWdnmov+Ub2km+kdx6XJPkz+stywjHShAnSxInB/w4eHEznfhgq36+UJLmL3bKsscib6FXd/roDbwhI9avqFagPyJ5nV9bZWYd3zQB6BDPFAQDog0wmk0ZfOFo3r7tZk++eLLOt7ZBg28fb9Jfxf9GXT34ZhRoCAADEBovFokGDBum8887TiBEj9GFamu6cPFkVBwW8j3e79eiWLaravVtvv/221q1bJ5/PF5E6Wa1W5eTkyG63q7q6WlVVVaqurpbdbldOTk6PrGsOAAAQcU6nNGuW9NRTqnp2par+Z7Hqr7hbnsETZejQgW3LvvLgmuUPPCBdcIFUWChlZkrTp0t33im99JK0fr3UxaVnAt5AKGW6e6Nb7o1uGQEjFGkLNAU6PR9AdPFXEgAAfVhCYoKmPzhd468er3k/m6ctH25pdTzgC2jeLfNUs6NG0x+c3mbdSgAAgL5iwIABuuaaa1RbW6vV69bpypwcvbhzpzICB778PHrvXj1fV6cPa2u1bNQo2e12DRo0KCIzt10ul/Lz8+V2u+X3+2WxWORyuQiIAwCAXinzzCxJJ0k6SfXr7tGeVzcoteoz2ZfPl+3rj2VuqDtUEUFVVdKiRcGtmcsljR/fekb5mDFSQkLws2dlSpKsg6wKfBNQ0oQkpR2fFjrd8BuyJFuUfmp6t9csBxA5/KUEAACUOSJTl8+7XOvfXq8Pf/Gh9pfsb3X80//+VHU763T2M2fLYmONSgAA0DeNGzdOt99+u5555hl9tH+/rjIMvbh7t1JazC4a3NCgG9aule/uu1W9cKGqrr5aCeefL5/fr4qKCnm9XtlsNmVlZcnhcHSrPlarVSkpKd29LAAAgCPir/ersaRRgYaAzE6zHPkOWRIj872RyXJgooaz0Km64QO1v+o8OaZfIlPAK+vqz2T5+APZd66Sbedamerru1642y199llwa2azSePGSRMmyPRdoNyRP0JGhqGmsiZZh1hlsphk+A15yjyy59rlLHS2qieA2EJQHAAASAqmVB91/igNO32YFt+/WJ898lmr4988/43qdtfpotcvkj3ZHqVaAgAARNeoUaN05513ymw2a+nSpbo7N1ePfv21XAelSrd6vcqaN0+aN09Nw4drx+mna/2xx8qfmqpAIKCMjAyNGTNGWVmsOQkAAOJPY2mjqhdXq6m8KZRSPKF/gtJOTpMjr3sP/h2KJdGitJPTVL24Wg2bG4Kfn3icEn48WbaT02QaYJM2bZJWrJBWrjzw36qqrn+I1xs8b8UK6dlnJUl2s1lT+g+QZfCx8mSNVtOgsWoaMEa2gmylnZwWsQcCAIQHQXEAANCKzWXTaQ+fpqyiLP37hn/L8BuhY1s+2qLnTn5Ol713mZJykqJYSwAAgOjJycnRDTfcoKamJq1Zs0Z3H3OMbl+1SoPd7nbfn7Bpk4Zt2qQhCQmqOvVU7T3vPK0NBLRw4UINGTJETqdT/fr1U0ZGBunPAQBATGj5fdDB/PV+VS2qkrfKK0ehIzRjurG0UVWLqpR9fnaHAeJwzaR25DmUfUF2xzPVR44Mbpdd9t0FGdL27a2D5CtWSLt2dfkzTYGAknftkHbtkEtvhfYbBYUyHT2xdfr1nJywXCeA8OEvLQAA0K4J105QUk6SXrvoNXnd3tD+XSt26dnvP6srPrhCmSMyo1hDAACA6Bk+fLhuvfVWvfHGG9q+fbv+3+jROtHr1eQ1azR01SqZD5o5LknmpiZlvv++Mt9/X/3699eCIUO0aMoUJeTkyG63a+TIkRo9erSysrIIjgMAgMMWznTmle9XdnjMU+pRzbIaJfRPUM3nNZIk5zCnTDKpflW9AvUB2fPazzKYdXb4suRYEi1dX8PbZJIKCoLb+ecf2L97d9tAeXHxYdXDtK1Y2lYsvfHGgZ25ua2D5BMnSoMHB+sBICr4CwsAAHRo+Kzhuurjq/TSmS/JXXFg5lN1cbX+duLfdOncSzVo0qAo1hAAACB6hg0bpuuvv17ffvuttm7dKsMwtPnSS1VtGCpcskQpL7+shJKSds/NKC/XD8vL5fvqK2057ji99r3v6bXVq5Wfn6+xY8dq0qRJ6tevXw9fEQAAiFc9mc480BQIvjAfdMB80PF4kJMjzZwZ3JpVVUlff906WL5+vRQ4jOvatSu4vf/+gX3p6W0D5cOHSxbSrgM9gaA4AADo1MDjBuraz67Vi2e8qKqtB9Zecle49dy053TRqxdpxFkjolhDAACA6MnIyND3v/99jRgxQrt27VJTU5MSHA6V/+hHqrnhBiV89pn0l79owBdftDt73OrzqeizzzT7yy/13ODBen/4cC1btkwLFy7UrFmz9P3vf19JSSxbAwBAX2f4jWBKc/93r80H0ptHIp155qyOswPWr6tXoCkg51CnUo5NaVVHS7JF6aemd30GdyxKT5emTQtuzdxu+Vas0Jp//lPjfD6Zv/5aWr06uPZ4V1VVSYsWBbdmLpc0fnzrYPmYMVJCQriuBsB3CIoDAIBDyhyeqWs/u1YvnfmSdi0/sNaSr8Gnl899WWf95SxNvG5iFGsIAAAQPVarVTk5OcrKypLb7Zbf75fFYpHL5ZJv4EB9kZ+vtTt2aNSXXyr99deVtHNnmzKcPp9+smWLZu7apT8OH67Pdu7U9u3bVVJSohkzZig/Pz8KVwYAAGJF5fuV8vv8si63qspUJYv1QJA7EunMO1v721nolD3XLk+ZR478A0F4T5lH9ly7nIXOsK0dHjNcLhmTJmlbZaVGz5ols80mNTVJa9e2Tr3+zTdSfX3Xy3W7pc8+C27NbDZp7NhgkLw5UH7UUVJiHD9oAMQAguIAAKBLkvon6erFV+vVC1/Vlg+3hPYbAUP/vv7fqimr0dTfTJWJtZEAAEAfZbValZKS0mbfmDFjtEbSiqQkVYwbJ8+HH2ry2rUas2GDLH5/q/cPdrv1u2++0f/l5OhZp1PffPONrFarLrzwQmaMAwCAdjWnKzdZTXKNcHV4PFwsiRalnZym6sXVatjc0CZd+5GuYx53EhKCs7zHjz+wz++XNm1qHShfuTI4S7yrvN7gOStXSs8+G9xnNksLFrSevQ7gsBAUBwAAXZaQlKBL/32p/n39v/XNc9+0OrZkzhLtXbNX4y4fp8FTBsuZ4YxSLQEAAGJLVlaWJk2apIqKCtXW1uqbpCQtnTZNy/fv1/B//EOTN25sc85Ju3fruL17tdgw9GVKitavX6+cnBx5vV7ZbDZlZWXJ4Qjv+qAAACB2Zc7KlNfrlc/wKX1mumw2W+hYy3TmLWdoRzKduSPPoewLstVY0qhAQ0Bmp1mOfEffCYh3xGKRRo4MbpddFtxnGNL27a2D5CtWBNcc76pAIFgmgCNGUBwAABwWi82ic/9+rpIHJmvpg0tbHVv72lqtfW2tZJJyvpejwVMHq+DkAg0+iSA5AADo2xwOhwYNGiRJys7O1vLly7VmzRr9afx4/dPh0OziYo2orW19jt+vMxYv1sSvv9YXFRUqmzZNcjoVCASUkZGhoqIiJSQkqPa785KTk5WSkiKrla97AADobUwWk0wBk2T57nWL4He00plbEi3xvXZ4mPjr/Z0/HGAySQUFwe388w/s3737wIzw5mD51q3tf0j//lJubiQvA+j1+CsJAAAcNpPJpOm/na7kAcmad+s8yTjoDYa0++vd2v31bn3xP19IJqn/uP4afPKBILkrs206LwAAgL4gKytLU6dO1bBhw9S/f399nJysO/PyNHndOt1UWqokr7fV+/tVV+vsv/5Vxv/+rzwDB6phyBDtyczUxqwsGaNGqWbAADV9FwjPzc1Vfn6+XC6X/H6/PB6P7Ha77Ha7XC4XAXMAAHoh0plHT2Npo6oXV6upvKlNuzvyDpHVJydHmjkzuDWrqpK+/rp1oHz9+uDa4gC6hb+EAADAETvu5uOUPCBZb172pnyNvo7faEjlq8pVvqpcX/7xS0lSv3H9ggHyqYM1+KTBSszmyWIAANB3OBwODR06VLm5uRo0aJDeffddvV1VpcVZWbp1927NKCmR+aBzTIYhx44dcuzYoXRJRd/tN0wmufv1U2VurvZmZ2tLfr78I0fKnZ8vr80ml8ul1NTU0H8tFossFov8fj8BcwAAegnSmYeP4T949keL/f7gfw2zIX+9X1WLqtRU2SRfpU8ms0mJ30uUZ6dHVYuqlH1+dpfav9Us/vT04LrhLdcOd7ulffu6e1lAn8dfOwAAoFtGnT9Kt2y8RV//42ttX7JdpZ+Wdh4g/86e1Xu0Z/Ueffmn74LkY/u1Sree2I8gOQAA6P1cLpdOPPFEFRYWavjw4Vq1apX+bTKpuKFB58yfr4E7dhyyDJNhKLG8XInl5cpvsd8wmeQZMEA1gwZpX06OqgYM0PZ+/VSfn68an09er1eJiYnKysrSkCFDVFBQIJeLbD4AAMQqf71f5lKz6lbWKSE5oU3Qm3Tm4VH5fmW7+/0+v6zLraoyVclitchT6lHNshol9E9Qw9YGSZIhQyaZVL+qXoH6gOx59kN+XtbZWZ2/weUKbgC6haA4AADottS8VE39r6nSf0k+j087v9qpbYu3afuS7Sr5tES+hi4Eyb/doz3f7tFXT34lScoenR1Kt14wtYAgOQAA6LWsVqvy8/P1ox/9SJs3b9b27dvV2NioFeeeq+olSzTo1VeVWlZ22OWaDEOOsjI5ysrUr8V+w2TSvtRU7cnM1L7cXO3LydHm0aPVeNppOur445kxDgBADGosbVTlgkpZl1lV7a6WxWzpeppuRESgKRB8cXB6H/NBxwHEBP7KAQAAYWW1W5U/OV/5k/OleyV/k19lX5Vp+5Lt2rZ4m0o/LZXX7T1kOXvX7tXetXu17KllkqSsUVmhdOsFUwuUlJMU6UsBAADoUS6XS6NHj1ZBQYH8fr8sFovM06dr+bnnyr13rwa73XJt2ybz+vXyfv21MsvLlXYEqTRNhqHM6mplVldLW7aE9hv/7/8pkJcnHXWUNHq0NGZMcBs5UkpMlM/nk9vtlt/vl2EE04qaTCZZLBbSrwMA0ImO0nF3VXOabm+lV4H+ATmHOGU2mdVY2nhYabrb0yp1NyRJmbMy293v9XrlM3xKn5kum82m+nX1CjQF5BzqVMqxKaH3GX5DlmSL0k9NZ+Z+C/56P+n9EVX8tQIAACLKkmBR/on5yj8xX1PumSJ/k187l383k3xxcCa5t/7QQfKKdRWqWFehZU9/FyQfmXUg3frUwUrOTY70pQAAAESc1WpVSkpKq31jxozRmjVrtKm6Wvre9+QfO1buWbNkt9uVbDbLsmmT+lVUyLJ+vRxbt6p/ZaWS9u6VyTi8L+BNhiFLSYlUUiLNndvigEmBwYPlKSyUp6BAdYMHa1d6uury8pSRlyebzSaTyaTMzEw5nc7vTiFYDgBAs47ScXdVc5puS7ZFtk9t2rt1r1wjXIedprs9h0zd3Qd19KCAKWCSLMHjJotJzkKn7Ll2eco8cuQ7ZLKYZPgNeco8sufa5Sx08tDBdxpLG1W9uFpN5U2SSZIhMh2gx/FXCQAA6FGWBIvyTshT3gl5mnL3FPm9fu1avkvbFm/TtsXbVLK0i0Hy9RWqWF+h5X9ZLknKHJHZKt168gCC5AAAoHfIysrSpEmTVFFRIa/XK5vNJpfLpV27dmn79u3anp6ujcnJSho3TjabTampqardvVtp5eXKq6mRf9UqZZSXK6W0VMkVFYcdLJdhyLxtmxK3bVOipGxJhQqmYXfn56t21Cjtyc/X5mHDVD9smMxOp9LS0iRJFotFaWlpSktLk91uJ0gOAMARaE7DbTIdFGAlTXdUWRItSjs5TdWLq9WwuaFNsLcvzILuShaEUKaDKq8chQceHghHpgPgcPBXCAAAiCqLzaJBxw/SoOMHafKvJgeD5Ct2HViT/JMSNdU1HbKcyo2VqtxYqRV/XSFJyhieEQyQfzeTPGVgyiFKAAAAiF0Oh0ODBg1qtS8lJUW5ubkaMmSI9uzZI6vVKovFoqqqKjmzsuTJytIGv181I0fK4XDIYrGoqbpa+z77TAP379fA/fvVr6JCmbt3K/kIZ5Ynbt+uxO3blfPdvoDVqtrCQlUUFKh25EhtTk/Xiv79NSAvT8nJyUpNTVVubq6ysrIIjgMA+oyO0nF3VXOablueTYHNATmGOpQ8MTgZgDTd0eXIcyj7guw+mxa8K1kQmjMdJPRPUM3nNZIk5zDnYWU6SD0jNSz1Rd/GXx8AACCmWGwWDZo0SIMmDdLkX05WwBcIBsmXBNOtb/9ku5pqDx0k37dpn/Zt2qcV//tdkHxYRnAm+dRgoDxlEEFyAAAQ36xWqzIyMpSRkaFhw4a1We/b7/fL4/HI7/erurpaDQ0N2utwaGN+vrbs369+/fopNTVVycnJ8tfWqkjSMS6XEjZtktaskdauDa45fhjBcrPPp9RNm5S6aZM0f77GS/ImJKhy8GBVDR2qulGjtHHsWNWceKIG5eXJ5XJFpG0AAIgl3U2hHUrTvcsj/3C/Uo5LkclkUmNJI2m6Y4Al0cJDCZ0IZTIwH3SATAfoYQTFAQBATDNbzRp43EANPG6gTrzzRAV8Ae3+eveBdOuflMhT4zlkOfs279O+zfu08pmVkqT0oemhNckLphbIlcsXsgAAIH61txZ5SwMHDpTb7ZbH49HgwYO1adMm7d+/X1arVQkJCeo3apRGHHWUErIOWle0oUF1y5ap6tNPlVZWJq1bJ8v69XLu3NnlmeW2piblbNqknE2bpA8+kCT5UlLUdNRRCkyeLPOkSdKxx0oDBx7x9QMA0Js1p+muXFAp8yqz3JvcspgtfSpNN2JTV7IgNGc6cBQ4ZFLw4Y2kiUmSup7pwBfwdb+y6PMIigMAgLhitpo14JgBGnDMAH3/ju8r4D8QJG+eSe7Zf+ggedWWKlVtqdLXf/takpQ1KkuWMRbtzN6p/OPzZTLzhDUAAOg9WgbNs7OzNWLEiFZrlGdlZcnhcLQ90emU44QT5B04UDs8HrlcLu3cuVPemhpZ1qxRwjffqGDvXjlXr1ZiWVmXA+XWmhpZly6Vli49sDM3Nxgcb7llZMjn86mmpka1tbXy+/0ym81KTExkjXIAQJ/iyHMo8/xM+ep9SjsuTQnJCX0qTTdiU1cyFDRnOmja3aTkY5MPrCl+OJkOmEyOMOCvBgAAENfMFrMGHD1AA44eoO/PDgbJy78pD61Jvv3/tquxuvGQ5VSsq5DWSf94/R9Kyk1S0blFGnnuSBVMK5DVzpAJAAD0Lu2tUd4Rq9WqnJwc7d69W3V1dbLZbKqR5B85Ut6hQ7XbbFZqaqpUU6OE1atlWblS/bdvV8bWrUqqqOh6pXbtkt59N7h9J1BYqPqiIpUPGqSdAwdqW0aGmmw2DRgwQAMGDFBqaqpSUlJkt9tlsVgIkgMAejVLokWBvICSJiTJZrNFuzpAlzRnOqheXK2GzQ2SSZIhMh2gx/FXAgAA6FXMFrNyJ+Yqd2KuTrj9hGCQfFWLIPmSQwfJ63bVafmfl2v5n5crITlBw84YppHnjdTwWcPlSGtnBhUAAEAv53K5lJ+fH1q3fMiQIZKkhoYGVXwX+Pa6XCo3mbQ3N1dbnU75fD5pzx71Ly1VblmZMrduVcbmzUqoru7y55qLi5VaXKxUSaMkGSaTagYNUuWQIfIcdZT2Dh2qzYMHK61fP0lSUlKSCgoKOk0lDwAAgJ7lyHMo+4JsNZY0KtAQkNlpJtMBehxBcQAA0KuZLWblTshV7oRcnXBbMEi+Z/UebVsSTLe+bck2NVZ1HCRvqm3S2tfWau1ra2W2mjV46mCNPG+kis4pUmp+ag9eCQAAQHR1tG55//79WwXL3W639u7dq9raWlUnJ2uLy6WtI0cq7+qrlZaaqlyfT9aVK5W0bp1SNm6UeflyqaamS3UwGYZSS0uVWloqLVkiSfJbraotLFTdqFHaW1CgzaNHa9jppytl8GDJxJI4AAAAscCSaDnk2uFAJBEUBwAAfYrZYlbO+BzljM/R8T8/XgF/QGVflGntm2u18uWV8pR1vB55wBdQ8cJiFS8s1rxb5ylnQk4wzfp5I9X/qP4y8aUrAADogw4OlmdkZCgnJ0dut1sej0fV1dWqrq5WIBCQw+FQvdUq59lnK+H662V2uaRAQNq0SfrqqwPbypVS46GXwJEki8+ntE2blLZpk1omhDdSU2UaOlQaMkQ6+L95eRJp1gEAAIA+g9E/AADo08wWs/K+n6ecY3PUOKVRxw05Tlve36IN72zQjv/skIyOz929crd2r9ytJXOWKHVwaihAPnjKYJmt5p67CAAAgBjTMlCenZ0tn88Xmk3eZu1vs1kqKgpuV1wR3Of1qu6LL7Tnvfdk/+YbpW7aJNfWrTIHAl2ug2n/fmnFiuDWtoLS4MHtB8yHDpWSk7vbBAAAAEC3+Ov9pJwPI4LiAAAALWSNzFLuuFxN/uVk1e2u04Z/b9CGdzZo64Kt8nv8HZ63f/t+ffnHL/XlH7+UI92hEWeOUNG5RRp2xjAlJCX04BUAAADEno5Sr3fIZpPj+OPl799fa4qLZTab1bhvnxI3bVLa5s1KXr9emVu3Kn3PniOrkM8nbdkS3ObPb3s8K6v9YPmQIdKAAcFAPgAAABAhjaWNql5crabyJskkyZAS+ico7eQ0OfIc0a5eXCIoDgA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    " ] @@ -760,7 +760,7 @@ "axs[2].grid()\n", "\n", "plt.tight_layout()\n", - "#plt.savefig('uppersco_ukidss.png')\n", + "plt.savefig('uppersco_ukidss.png')\n", "plt.show()" ] } diff --git a/docs/paper_examples/Begbie+26/Figure 9.ipynb b/docs/paper_examples/Begbie+26/Figure 9.ipynb new file mode 100644 index 00000000..96c17f08 --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 9.ipynb @@ -0,0 +1,298 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 9.\n", + "\n", + "Topic: Showing the simulated M44 (via best-fit isochrone) against actual 2mass data (shows continuity between papers)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.io import fits\n", + "from astropy.table import Table, vstack\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b91cb1a8-8fc6-4939-9f3b-420082b20340", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to T= 250 for T= 206 logg=3.41\n", + "Changing to logg=5.00 for T= 2017 logg=5.29\n", + "Changing to logg=5.00 for T= 2046 logg=5.29\n", + "Changing to logg=5.00 for T= 2075 logg=5.30\n", + "Changing to logg=5.00 for T= 2104 logg=5.30\n", + "Changing to logg=5.00 for T= 2131 logg=5.30\n", + "Changing to logg=5.00 for T= 2158 logg=5.31\n", + "Changing to logg=5.00 for T= 2185 logg=5.31\n", + "Changing to logg=5.00 for T= 2214 logg=5.31\n", + "Changing to logg=5.00 for T= 2242 logg=5.31\n", + "Changing to logg=5.00 for T= 2271 logg=5.32\n", + "Changing to logg=5.00 for T= 2300 logg=5.32\n", + "Changing to logg=5.00 for T= 2330 logg=5.32\n", + "Changing to logg=5.00 for T= 2330 logg=5.32\n", + "Isochrone generation took 12.527270 s.\n", + "Making photometry for isochrone: log(t) = 8.85 AKs = 0.01 dist = 187\n", + " Starting at: 2026-04-17 10:30:49.238432 Usually takes ~5 minutes\n", + "Starting filter: 2mass,J Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 206 K m_2mass_J = 36.27\n", + "M = 0.400 Msun T = 3344 K m_2mass_J = 13.76\n", + "M = 2.392 Msun T = 5047 K m_2mass_J = 5.61\n", + "Starting filter: 2mass,Ks Elapsed time: 0.48 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 206 K m_2mass_Ks = 38.77\n", + "M = 0.400 Msun T = 3344 K m_2mass_Ks = 12.89\n", + "M = 2.392 Msun T = 5047 K m_2mass_Ks = 5.04\n", + "Starting filter: ps1,i Elapsed time: 0.91 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 206 K m_ps1_i = 36.52\n", + "M = 0.400 Msun T = 3344 K m_ps1_i = 15.52\n", + "M = 2.392 Msun T = 5047 K m_ps1_i = 6.39\n", + "Starting filter: ps1,z Elapsed time: 2.37 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 206 K m_ps1_z = 35.85\n", + "M = 0.400 Msun T = 3344 K m_ps1_z = 14.78\n", + "M = 2.392 Msun T = 5047 K m_ps1_z = 6.13\n", + "Starting filter: ps1,y Elapsed time: 3.83 seconds\n", + "Starting synthetic photometry\n", + "M = 0.001 Msun T = 206 K m_ps1_y = 39.73\n", + "M = 0.400 Msun T = 3344 K m_ps1_y = 14.48\n", + "M = 2.392 Msun T = 5047 K m_ps1_y = 6.06\n", + " Time taken: 5.32 seconds\n" + ] + } + ], + "source": [ + "# Define isochrone parameters\n", + "logAge = 8.845 # Age in log(years)\n", + "AKs = 0.01 # extinction in mags\n", + "dist = 187 # distance in parsec\n", + "metallicity = 0 # Metallicity in [M/H]\n", + "\n", + "# Define evolution/atmosphere models and extinction law\n", + "evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() \n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "red_law = reddening.RedLawHosek18b()\n", + "\n", + "# Also specify filters for synthetic photometry (optional). Here we use \n", + "# the HST WFC3-IR F127M, F139M, and F153M filters\n", + "filt_list = ['2mass,J', '2mass,Ks', 'ps1,i', 'ps1,z', 'ps1,y']\n", + "\n", + "# Specify the directory we want the output isochrone\n", + "# table saved in. If the directory does not already exist,\n", + "# SPISEA will create it.\n", + "iso_dir = 'isochrones/'\n", + "\n", + "# Make IsochronePhot object. Note that this will take a minute or two, \n", + "# unless the isochrone has been generated previously.\n", + "#\n", + "# Note that this is not show all of the user options \n", + "# for IsochronePhot. See docs for complete list of options.\n", + "iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n", + " evo_model=evo_model, atm_func=atm_func,\n", + " red_law=red_law, filters=filt_list,\n", + " iso_dir=iso_dir, recomp=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c9645685-9f14-4251-9094-528e0d4d0b71", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    NumRAdegDEdegpmRAe_pmRApmDEe_pmDEgmage_gmagrmage_rmagimage_imagzmage_zmagymage_ymagJmage_JmagHmage_HmagKmage_KmagFlagName
    degdegmas / yrmas / yrmas / yrmas / yrmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmag
    int64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64int64str11
    1127.2026218.976639-35.65.6-15.05.620.390.0319.040.0117.170.016.40.015.930.0114.730.0214.080.0713.750.010--
    2127.2079419.988964-37.54.2-17.14.216.880.015.150.614.420.614.050.013.610.012.340.0211.730.0211.460.020--
    3127.2608419.636719-38.24.2-5.84.218.710.0117.390.016.420.099.099.099.099.014.630.0314.050.0413.80.040--
    4127.3061419.479976-36.81.3-15.41.311.60.611.20.611.030.610.960.610.930.610.020.029.730.029.650.020--
    5127.3285120.717992-41.94.1-17.34.117.650.016.470.015.270.014.720.014.470.013.280.0212.660.0212.40.020--
    6127.4277420.952028-33.34.1-15.54.199.099.099.099.099.099.099.099.099.099.013.340.0212.730.0312.50.020--
    7127.4348520.673132-38.71.3-12.61.312.440.015.720.012.160.011.430.011.350.010.350.029.910.039.810.021--
    8127.4617618.399097-28.24.2-14.94.221.560.0520.140.0318.240.0117.350.016.910.0115.570.0715.120.0914.590.080--
    9127.4876818.566668-39.24.1-16.54.116.180.018.880.0314.020.013.570.013.340.012.150.0211.480.0211.30.021--
    10127.4944519.483014-38.55.6-12.55.621.560.121.490.618.090.0117.270.016.870.0115.490.0514.990.0714.610.080--
    11127.5248818.279527-39.25.6-14.65.620.840.0618.810.617.650.0116.810.016.450.0115.190.0414.520.0614.30.060--
    12127.5586618.422182-39.24.1-21.54.118.450.0117.220.015.820.015.190.014.890.013.720.0213.090.0212.810.030--
    13127.6341521.170965-41.44.1-12.14.199.099.099.099.099.099.099.099.099.099.014.510.0213.80.0313.660.040--
    14127.6484818.222578-27.74.1-14.44.199.099.099.099.099.099.099.099.099.099.014.150.0313.570.0213.310.030--
    15127.6526618.377222-39.15.5-20.75.520.030.0218.720.0116.930.016.060.015.640.014.320.0313.720.0213.340.030--
    ...........................................................................
    1025132.4998118.364986-35.71.3-12.21.311.520.012.730.012.320.010.80.010.770.099.099.099.099.099.099.00BD+18 2049
    1026132.4999218.365058-35.71.3-12.21.311.40.611.050.610.930.610.90.610.890.610.020.029.730.039.640.020--
    1027132.522620.703691-29.44.1-11.74.199.099.099.099.099.099.099.099.099.099.015.290.0514.610.0714.360.071--
    1028132.5426720.148646-40.03.6-15.93.615.60.014.370.013.670.013.440.013.230.012.110.0211.480.0311.250.020--
    1029132.5451521.101212-34.94.0-9.64.099.099.099.099.099.099.099.099.099.099.013.90.0313.240.0413.050.031--
    1030132.5457119.551449-30.13.6-19.83.699.099.099.099.099.099.099.099.099.099.014.580.0313.940.0413.690.040--
    1031132.5568919.690012-30.45.0-14.95.021.540.0620.20.0418.30.0117.390.016.970.0115.750.0614.950.0714.510.070--
    1032132.5587620.567427-36.36.2-12.46.222.330.1720.860.0519.040.0218.280.0117.940.0216.450.1116.060.1715.80.210--
    1033132.5771919.428511-34.63.7-14.33.713.40.612.720.612.40.612.210.612.140.011.090.0210.660.0210.530.020--
    1034132.5947220.150953-32.43.7-6.03.799.099.099.099.099.099.099.099.099.099.014.690.0314.040.0413.770.040--
    1035132.6469920.710466-32.74.2-5.94.299.099.099.099.099.099.099.099.099.099.014.370.0313.660.0313.40.040--
    1036132.6504219.951848-36.85.0-16.15.020.780.0421.330.619.570.616.840.016.470.0115.080.0414.530.0514.330.060--
    1037132.7075919.810125-38.43.7-18.33.718.550.0117.230.015.740.015.080.014.780.013.530.0212.910.0212.650.020--
    1038132.7368819.616067-38.83.7-14.33.718.20.0116.950.015.680.015.020.014.780.013.570.0212.940.0212.690.030--
    1039132.8466719.855061-34.05.0-21.95.020.770.0519.410.0217.610.616.970.016.650.0115.230.0414.710.0514.450.061--
    1040132.8575819.315661-37.23.7-20.03.718.090.0116.830.015.430.014.790.014.490.013.30.0212.680.0212.40.020--
    " + ], + "text/plain": [ + "\n", + " Num RAdeg DEdeg pmRA e_pmRA pmDE e_pmDE gmag e_gmag rmag e_rmag ... ymag e_ymag Jmag e_Jmag Hmag e_Hmag Kmag e_Kmag Flag Name \n", + " deg deg mas / yr mas / yr mas / yr mas / yr mag mag mag mag ... mag mag mag mag mag mag mag mag \n", + "int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 ... float64 float64 float64 float64 float64 float64 float64 float64 int64 str11 \n", + "----- --------- --------- -------- -------- -------- -------- ------- ------- ------- ------- ... ------- ------- ------- ------- ------- ------- ------- ------- ----- ----------\n", + " 1 127.20262 18.976639 -35.6 5.6 -15.0 5.6 20.39 0.03 19.04 0.01 ... 15.93 0.01 14.73 0.02 14.08 0.07 13.75 0.01 0 --\n", + " 2 127.20794 19.988964 -37.5 4.2 -17.1 4.2 16.88 0.0 15.15 0.6 ... 13.61 0.0 12.34 0.02 11.73 0.02 11.46 0.02 0 --\n", + " 3 127.26084 19.636719 -38.2 4.2 -5.8 4.2 18.71 0.01 17.39 0.0 ... 99.0 99.0 14.63 0.03 14.05 0.04 13.8 0.04 0 --\n", + " 4 127.30614 19.479976 -36.8 1.3 -15.4 1.3 11.6 0.6 11.2 0.6 ... 10.93 0.6 10.02 0.02 9.73 0.02 9.65 0.02 0 --\n", + " 5 127.32851 20.717992 -41.9 4.1 -17.3 4.1 17.65 0.0 16.47 0.0 ... 14.47 0.0 13.28 0.02 12.66 0.02 12.4 0.02 0 --\n", + " 6 127.42774 20.952028 -33.3 4.1 -15.5 4.1 99.0 99.0 99.0 99.0 ... 99.0 99.0 13.34 0.02 12.73 0.03 12.5 0.02 0 --\n", + " 7 127.43485 20.673132 -38.7 1.3 -12.6 1.3 12.44 0.0 15.72 0.0 ... 11.35 0.0 10.35 0.02 9.91 0.03 9.81 0.02 1 --\n", + " 8 127.46176 18.399097 -28.2 4.2 -14.9 4.2 21.56 0.05 20.14 0.03 ... 16.91 0.01 15.57 0.07 15.12 0.09 14.59 0.08 0 --\n", + " 9 127.48768 18.566668 -39.2 4.1 -16.5 4.1 16.18 0.0 18.88 0.03 ... 13.34 0.0 12.15 0.02 11.48 0.02 11.3 0.02 1 --\n", + " 10 127.49445 19.483014 -38.5 5.6 -12.5 5.6 21.56 0.1 21.49 0.6 ... 16.87 0.01 15.49 0.05 14.99 0.07 14.61 0.08 0 --\n", + " 11 127.52488 18.279527 -39.2 5.6 -14.6 5.6 20.84 0.06 18.81 0.6 ... 16.45 0.01 15.19 0.04 14.52 0.06 14.3 0.06 0 --\n", + " 12 127.55866 18.422182 -39.2 4.1 -21.5 4.1 18.45 0.01 17.22 0.0 ... 14.89 0.0 13.72 0.02 13.09 0.02 12.81 0.03 0 --\n", + " 13 127.63415 21.170965 -41.4 4.1 -12.1 4.1 99.0 99.0 99.0 99.0 ... 99.0 99.0 14.51 0.02 13.8 0.03 13.66 0.04 0 --\n", + " 14 127.64848 18.222578 -27.7 4.1 -14.4 4.1 99.0 99.0 99.0 99.0 ... 99.0 99.0 14.15 0.03 13.57 0.02 13.31 0.03 0 --\n", + " 15 127.65266 18.377222 -39.1 5.5 -20.7 5.5 20.03 0.02 18.72 0.01 ... 15.64 0.0 14.32 0.03 13.72 0.02 13.34 0.03 0 --\n", + " ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + " 1025 132.49981 18.364986 -35.7 1.3 -12.2 1.3 11.52 0.0 12.73 0.0 ... 10.77 0.0 99.0 99.0 99.0 99.0 99.0 99.0 0 BD+18 2049\n", + " 1026 132.49992 18.365058 -35.7 1.3 -12.2 1.3 11.4 0.6 11.05 0.6 ... 10.89 0.6 10.02 0.02 9.73 0.03 9.64 0.02 0 --\n", + " 1027 132.5226 20.703691 -29.4 4.1 -11.7 4.1 99.0 99.0 99.0 99.0 ... 99.0 99.0 15.29 0.05 14.61 0.07 14.36 0.07 1 --\n", + " 1028 132.54267 20.148646 -40.0 3.6 -15.9 3.6 15.6 0.0 14.37 0.0 ... 13.23 0.0 12.11 0.02 11.48 0.03 11.25 0.02 0 --\n", + " 1029 132.54515 21.101212 -34.9 4.0 -9.6 4.0 99.0 99.0 99.0 99.0 ... 99.0 99.0 13.9 0.03 13.24 0.04 13.05 0.03 1 --\n", + " 1030 132.54571 19.551449 -30.1 3.6 -19.8 3.6 99.0 99.0 99.0 99.0 ... 99.0 99.0 14.58 0.03 13.94 0.04 13.69 0.04 0 --\n", + " 1031 132.55689 19.690012 -30.4 5.0 -14.9 5.0 21.54 0.06 20.2 0.04 ... 16.97 0.01 15.75 0.06 14.95 0.07 14.51 0.07 0 --\n", + " 1032 132.55876 20.567427 -36.3 6.2 -12.4 6.2 22.33 0.17 20.86 0.05 ... 17.94 0.02 16.45 0.11 16.06 0.17 15.8 0.21 0 --\n", + " 1033 132.57719 19.428511 -34.6 3.7 -14.3 3.7 13.4 0.6 12.72 0.6 ... 12.14 0.0 11.09 0.02 10.66 0.02 10.53 0.02 0 --\n", + " 1034 132.59472 20.150953 -32.4 3.7 -6.0 3.7 99.0 99.0 99.0 99.0 ... 99.0 99.0 14.69 0.03 14.04 0.04 13.77 0.04 0 --\n", + " 1035 132.64699 20.710466 -32.7 4.2 -5.9 4.2 99.0 99.0 99.0 99.0 ... 99.0 99.0 14.37 0.03 13.66 0.03 13.4 0.04 0 --\n", + " 1036 132.65042 19.951848 -36.8 5.0 -16.1 5.0 20.78 0.04 21.33 0.6 ... 16.47 0.01 15.08 0.04 14.53 0.05 14.33 0.06 0 --\n", + " 1037 132.70759 19.810125 -38.4 3.7 -18.3 3.7 18.55 0.01 17.23 0.0 ... 14.78 0.0 13.53 0.02 12.91 0.02 12.65 0.02 0 --\n", + " 1038 132.73688 19.616067 -38.8 3.7 -14.3 3.7 18.2 0.01 16.95 0.0 ... 14.78 0.0 13.57 0.02 12.94 0.02 12.69 0.03 0 --\n", + " 1039 132.84667 19.855061 -34.0 5.0 -21.9 5.0 20.77 0.05 19.41 0.02 ... 16.65 0.01 15.23 0.04 14.71 0.05 14.45 0.06 1 --\n", + " 1040 132.85758 19.315661 -37.2 3.7 -20.0 3.7 18.09 0.01 16.83 0.0 ... 14.49 0.0 13.3 0.02 12.68 0.02 12.4 0.02 0 --" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "other = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/changes/apj491325t1_mrt.txt'\n", + "tab = Table.read(other, format='ascii')\n", + "tab" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "399ff5ef-805e-4d97-845f-9269feaf0d03", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make a color-magnitude diagram from the isochrone\n", + "m = iso.points['mass']\n", + "J_iso = iso.points['m_2mass_J']\n", + "K_iso = iso.points['m_2mass_Ks']\n", + "\n", + "phillips = (m >= 0.01) & (m < 0.0755)\n", + "phillips_parsec = (m >= 0.075) & (m < 0.2)\n", + "\n", + "parsec = (m >= 0.2)\n", + "\n", + "#focus = np.where((tab['Jmag'] - tab['K2mag'] < 10))\n", + "#minus = tab['Jmag'] - tab['K2mag']\n", + "#J_K = minus[focus]\n", + "\n", + "py.figure(1, figsize=(8,8))\n", + "py.clf()\n", + "color = J_iso - K_iso\n", + "mag = J_iso\n", + "py.plot(tab['Jmag'] - tab['Kmag'], tab['Jmag'],\n", + " 'm.', ms=5, alpha=0.6, label='Observed')\n", + "py.plot(color[phillips], mag[phillips], '-', lw=2, color='purple', label='Phillips')\n", + "py.plot(color[phillips_parsec], mag[phillips_parsec], '-', lw=2, color='orange', label='Phillips/Parsec transition (interpolated)')\n", + "py.plot(color[parsec], mag[parsec], '-', lw=2, color='blue', label='Parsec')\n", + "\n", + "py.title('M44 Observed Cluster vs. Simulated \\n Isochrone (by Evolutionary Model)', fontsize=25)\n", + "py.xlabel('$J - K$', fontsize=20)\n", + "py.ylabel('$J$', fontsize=20)\n", + "py.ylim(0, 22)\n", + "py.gca().invert_yaxis()\n", + "py.grid()\n", + "py.legend(loc='upper right', fontsize=14)\n", + "py.tick_params(axis='both', labelsize=16)\n", + "#py.savefig('M44_comparison.png')\n", + "py.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From ba1c455df78b8d771cf41e66602e14f707d59429 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 17 Apr 2026 10:38:36 -0700 Subject: [PATCH 113/191] Changed log(a) distribution to interpolate stellar-->substellar --- spisea/imf/multiplicity.py | 53 ++++++++++++++++++++++++++++---------- 1 file changed, 39 insertions(+), 14 deletions(-) diff --git a/spisea/imf/multiplicity.py b/spisea/imf/multiplicity.py index 82a8c765..473fd519 100755 --- a/spisea/imf/multiplicity.py +++ b/spisea/imf/multiplicity.py @@ -276,7 +276,8 @@ def log_semimajoraxis(self, mass): by fitting the data from fitting the semimajor axis data as a function of mass in table 1 of Duchene and Kraus 2013. Then a random semimajor axis is drawn from a log normal distribution with that mean and standard deviation. - The brown dwarf range is described by mean seperation and std values given in Fontanive et al. (2018). + The brown dwarf range is covered by mass-dependent scaling of both the characteristic separation and dispersion + matching trends described in Fontanive et al. (2018). Parameters ---------- @@ -288,22 +289,46 @@ def log_semimajoraxis(self, mass): log_semimajoraxis : float Log of the semimajor axis/separation between the stars in units of AU """ - a_mean_func = astropy.modeling.powerlaws.BrokenPowerLaw1D(amplitude=self.a_amp, x_break=self.a_break, alpha_1=self.a_slope1, alpha_2=self.a_slope2) - log_a_mean = np.log10(a_mean_func(mass)) #mean log(a) - log_a_std_func = astropy.modeling.models.Linear1D(slope=self.a_std_slope, intercept=self.a_std_intercept) - log_a_std = log_a_std_func(np.log10(mass)) #sigma_log(a) + a_mean_func = astropy.modeling.powerlaws.BrokenPowerLaw1D( + amplitude=self.a_amp, + x_break=self.a_break, + alpha_1=self.a_slope1, + alpha_2=self.a_slope2 + ) + log_a_mean_star = np.log10(a_mean_func(mass)) #mean log(a) + log_a_std_func = astropy.modeling.models.Linear1D( + slope=self.a_std_slope, + intercept=self.a_std_intercept + ) + log_a_std_star = log_a_std_func(np.log10(mass)) #sigma_log(a) + if mass >= 2.9: - log_a_std = log_a_std_func(np.log10(2.9)) #sigma_log(a) - if log_a_std < 0.1: - log_a_std = 0.1 - if mass <= 0.08: - log_a_mean = np.log10(2.9) - log_a_std = 0.21 - + log_a_std_star = log_a_std_func(np.log10(2.9)) #sigma_log(a) + if log_a_std_star < 0.1: + log_a_std_star = 0.1 + + # additions for BD regime + logm = np.log10(mass) + log_a_mean_bd = np.interp( + logm, + [np.log10(0.01), np.log10(0.08)], + [np.log10(2.5), np.log10(8.0)] + ) + log_a_std_bd = np.interp( + logm, + [np.log10(0.01), np.log10(0.08)], + [0.25, 0.5] + ) + + # weighted transition for substellar --> stellar + w = 1 / (1 + np.exp(-(logm - np.log10(0.08)) / 0.15)) + log_a_mean = (1 - w) * log_a_mean_bd + w * log_a_mean_star + log_a_std = (1 - w) * log_a_std_bd + w * log_a_std_star log_semimajoraxis = np.random.normal(log_a_mean, log_a_std) - while 10**log_semimajoraxis > 2000 or log_semimajoraxis < -2: #AU + + while 10**log_semimajoraxis > 2000 or log_semimajoraxis < -2: log_semimajoraxis = np.random.normal(log_a_mean, log_a_std) - + return log_semimajoraxis def random_e(self, x): From cf9fc0d4cf3f80c9c9ad0358a50fd373b47b552e Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 20 Apr 2026 12:28:12 -0700 Subject: [PATCH 114/191] Adding cluster data files and reformatting figures with new filepaths --- docs/paper_examples/Begbie+26/Figure 6.ipynb | 2 +- docs/paper_examples/Begbie+26/Figure 7.ipynb | 2 +- docs/paper_examples/Begbie+26/Figure 8.ipynb | 2 +- docs/paper_examples/Begbie+26/Figure 9.ipynb | 2 +- ...-f30e-11f0-a3b5-bc97e148b76b-O-result.fits | 465 +++ ...-f8ae-11f0-9655-bc97e148b76b-O-result.fits | 503 +++ .../cluster_data/apj491325t1_mrt.txt | 1086 +++++++ .../Begbie+26/cluster_data/asu (2).fit | 2694 +++++++++++++++++ .../Begbie+26/cluster_data/asu (3).fit | 174 ++ .../Begbie+26/cluster_data/asu.fit | 2694 +++++++++++++++++ 10 files changed, 7620 insertions(+), 4 deletions(-) create mode 100644 docs/paper_examples/Begbie+26/cluster_data/13386c89-f30e-11f0-a3b5-bc97e148b76b-O-result.fits create mode 100644 docs/paper_examples/Begbie+26/cluster_data/9b2caaac-f8ae-11f0-9655-bc97e148b76b-O-result.fits create mode 100644 docs/paper_examples/Begbie+26/cluster_data/apj491325t1_mrt.txt create mode 100644 docs/paper_examples/Begbie+26/cluster_data/asu (2).fit create mode 100644 docs/paper_examples/Begbie+26/cluster_data/asu (3).fit create mode 100644 docs/paper_examples/Begbie+26/cluster_data/asu.fit diff --git a/docs/paper_examples/Begbie+26/Figure 6.ipynb b/docs/paper_examples/Begbie+26/Figure 6.ipynb index 18dbc513..3c0759d1 100644 --- a/docs/paper_examples/Begbie+26/Figure 6.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 6.ipynb @@ -213,7 +213,7 @@ ], "source": [ "# pulling in real data\n", - "gaia_file = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/changes/13386c89-f30e-11f0-a3b5-bc97e148b76b-O-result.fits'\n", + "gaia_file = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/docs/paper_examples/Begbie+26/cluster_data/13386c89-f30e-11f0-a3b5-bc97e148b76b-O-result.fits'\n", "gaia_table = Table.read(gaia_file, format='fits')\n", "len(gaia_table['designation'])" ] diff --git a/docs/paper_examples/Begbie+26/Figure 7.ipynb b/docs/paper_examples/Begbie+26/Figure 7.ipynb index 13f6bf4a..a63c322a 100644 --- a/docs/paper_examples/Begbie+26/Figure 7.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 7.ipynb @@ -193,7 +193,7 @@ ], "source": [ "# read in ukidss data\n", - "other = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/changes/asu (2).fit'\n", + "other = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/docs/paper_examples/Begbie+26/cluster_data/asu (2).fit'\n", "tab = Table.read(other, format='fits', hdu=2)\n", "\n", "focus = np.where((tab['Jmag'] - tab['K2mag'] < 10))\n", diff --git a/docs/paper_examples/Begbie+26/Figure 8.ipynb b/docs/paper_examples/Begbie+26/Figure 8.ipynb index 0912e0de..e3a77265 100644 --- a/docs/paper_examples/Begbie+26/Figure 8.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 8.ipynb @@ -682,7 +682,7 @@ ], "source": [ "# read in ukidss data\n", - "other = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/changes/asu (3).fit'\n", + "other = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/docs/paper_examples/Begbie+26/cluster_data/asu (3).fit'\n", "tab = Table.read(other, format='fits', hdu=2)\n", "tab\n", "\n", diff --git a/docs/paper_examples/Begbie+26/Figure 9.ipynb b/docs/paper_examples/Begbie+26/Figure 9.ipynb index 96c17f08..bccb1c46 100644 --- a/docs/paper_examples/Begbie+26/Figure 9.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 9.ipynb @@ -214,7 +214,7 @@ } ], "source": [ - "other = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/changes/apj491325t1_mrt.txt'\n", + "other = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/docs/paper_examples/Begbie+26/cluster_data/apj491325t1_mrt.txt'\n", "tab = Table.read(other, format='ascii')\n", "tab" ] diff --git a/docs/paper_examples/Begbie+26/cluster_data/13386c89-f30e-11f0-a3b5-bc97e148b76b-O-result.fits b/docs/paper_examples/Begbie+26/cluster_data/13386c89-f30e-11f0-a3b5-bc97e148b76b-O-result.fits new file mode 100644 index 00000000..f340b69d --- /dev/null +++ b/docs/paper_examples/Begbie+26/cluster_data/13386c89-f30e-11f0-a3b5-bc97e148b76b-O-result.fits @@ -0,0 +1,465 @@ +SIMPLE = T / Standard FITS format BITPIX = 8 / Character data NAXIS = 0 / No image, just extensions EXTEND = T / There are standard extensions COMMENT Dummy header; see following table extension END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / 8-bit bytes NAXIS = 2 / 2-dimensional table NAXIS1 = 176 / width of table in bytes NAXIS2 = 2000 / number of rows in table PCOUNT = 0 / heap size (no gap) GCOUNT = 1 / one data group TFIELDS = 32 / number of columns TTYPE1 = 'designation' / label for column 1 TFORM1 = '26A ' / format for column 1 TCOMM1 = 'Unique source designation (unique across all Data Releases)' TUCD1 = 'meta.id;meta.main' / VO Unified Content Descriptor for column 1 TTYPE2 = 'source_id' / label for column 2 TFORM2 = 'K ' / format for column 2 TCOMM2 = 'Unique source identifier (unique within a particular Data Release)' TUCD2 = 'meta.id ' / VO Unified Content Descriptor for column 2 TTYPE3 = 'ra ' / label for column 3 TFORM3 = 'D ' / format for column 3 TUNIT3 = 'deg ' / units for column 3 TCOMM3 = 'Right ascension' TUCD3 = 'pos.eq.ra;meta.main' / VO Unified Content Descriptor for column 3 TUTYP3 = 'stc:AstroCoords.Position3D.Value3.C1' / VO Utype for column 3 TTYPE4 = 'dec ' / label for column 4 TFORM4 = 'D ' / format for column 4 TUNIT4 = 'deg ' / units for column 4 TCOMM4 = 'Declination' TUCD4 = 'pos.eq.dec;meta.main' / VO Unified Content Descriptor for column 4 TUTYP4 = 'stc:AstroCoords.Position3D.Value3.C2' / VO Utype for column 4 TTYPE5 = 'parallax' / label for column 5 TFORM5 = 'D ' / format for column 5 TUNIT5 = 'mas ' / units for column 5 TCOMM5 = 'Parallax' TUCD5 = 'pos.parallax.trig' / VO Unified Content Descriptor for column 5 TTYPE6 = 'pmra ' / label for column 6 TFORM6 = 'D ' / format for column 6 TUNIT6 = 'mas.yr**-1' / units for column 6 TCOMM6 = 'Proper motion in right ascension direction' TUCD6 = 'pos.pm;pos.eq.ra' / VO Unified Content Descriptor for column 6 TUTYP6 = 'stc:AstroCoords.Velocity3D.Value3.C1' / VO Utype for column 6 TTYPE7 = 'pmdec ' / label for column 7 TFORM7 = 'D ' / format for column 7 TUNIT7 = 'mas.yr**-1' / units for column 7 TCOMM7 = 'Proper motion in declination direction' TUCD7 = 'pos.pm;pos.eq.dec' / VO Unified Content Descriptor for column 7 TUTYP7 = 'stc:AstroCoords.Velocity3D.Value3.C2' / VO Utype for column 7 TTYPE8 = 'ruwe ' / label for column 8 TFORM8 = 'E ' / format for column 8 TCOMM8 = 'Renormalised unit weight error' TUCD8 = 'stat.error' / VO Unified Content Descriptor for column 8 TTYPE9 = 'phot_g_mean_mag' / label for column 9 TFORM9 = 'E ' / format for column 9 TUNIT9 = 'mag ' / units for column 9 TCOMM9 = 'G-band mean magnitude' TUCD9 = 'phot.mag;em.opt' / VO Unified Content Descriptor for column 9 TTYPE10 = 'phot_bp_mean_mag' / label for column 10 TFORM10 = 'E ' / format for column 10 TUNIT10 = 'mag ' / units for column 10 TCOMM10 = 'Integrated BP mean magnitude' TUCD10 = 'phot.mag;em.opt.B' / VO Unified Content Descriptor for column 10 TTYPE11 = 'phot_rp_mean_mag' / label for column 11 TFORM11 = 'E ' / format for column 11 TUNIT11 = 'mag ' / units for column 11 TCOMM11 = 'Integrated RP mean magnitude' TUCD11 = 'phot.mag;em.opt.R' / VO Unified Content Descriptor for column 11 TTYPE12 = 'bp_rp ' / label for column 12 TFORM12 = 'E ' / format for column 12 TUNIT12 = 'mag ' / units for column 12 TCOMM12 = 'BP - RP colour' TUCD12 = 'phot.color;em.opt.B;em.opt.R' / VO Unified Content Descriptor for colTTYPE13 = 'phot_variable_flag' / label for column 13 TFORM13 = '13A ' / format for column 13 TCOMM13 = 'Photometric variability flag' TUCD13 = 'meta.code;src.var' / VO Unified Content Descriptor for column 13 TTYPE14 = 'non_single_star' / label for column 14 TFORM14 = 'I ' / format for column 14 TCOMM14 = 'Flag indicating the availability of additional information in the va'TUCD14 = 'meta.code.status' / VO Unified Content Descriptor for column 14 TTYPE15 = 'has_xp_continuous' / label for column 15 TFORM15 = 'L ' / format for column 15 TCOMM15 = 'Flag indicating the availability of mean BP/RP spectrum in continuou'TUCD15 = 'meta.code.status' / VO Unified Content Descriptor for column 15 TTYPE16 = 'has_xp_sampled' / label for column 16 TFORM16 = 'L ' / format for column 16 TCOMM16 = 'Flag indicating the availability of mean BP/RP spectrum in sampled f'TUCD16 = 'meta.code.status' / VO Unified Content Descriptor for column 16 TTYPE17 = 'has_rvs ' / label for column 17 TFORM17 = 'L ' / format for column 17 TCOMM17 = 'Flag indicating the availability of mean RVS spectrum for this sourc'TUCD17 = 'meta.code.status' / VO Unified Content Descriptor for column 17 TTYPE18 = 'has_epoch_photometry' / label for column 18 TFORM18 = 'L ' / format for column 18 TCOMM18 = 'Flag indicating the availability of epoch photometry for this source'TUCD18 = 'meta.code.status' / VO Unified Content Descriptor for column 18 TTYPE19 = 'has_epoch_rv' / label for column 19 TFORM19 = 'L ' / format for column 19 TCOMM19 = 'Flag indicating the availability of epoch radial velocity for this s'TUCD19 = 'meta.code.status' / VO Unified Content Descriptor for column 19 TTYPE20 = 'has_mcmc_gspphot' / label for column 20 TFORM20 = 'L ' / format for column 20 TCOMM20 = 'Flag indicating the availability of GSP-Phot MCMC samples for this s'TUCD20 = 'meta.code.status' / VO Unified Content Descriptor for column 20 TTYPE21 = 'has_mcmc_msc' / label for column 21 TFORM21 = 'L ' / format for column 21 TCOMM21 = 'Flag indicating the availability of MSC MCMC samples for this source'TUCD21 = 'meta.code.status' / VO Unified Content Descriptor for column 21 TTYPE22 = 'teff_gspphot' / label for column 22 TFORM22 = 'E ' / format for column 22 TUNIT22 = 'K ' / units for column 22 TCOMM22 = 'Effective temperature from GSP-Phot Aeneas best library using BP/RP 'TUCD22 = 'phys.temperature.effective' / VO Unified Content Descriptor for columTTYPE23 = 'logg_gspphot' / label for column 23 TFORM23 = 'E ' / format for column 23 TUNIT23 = 'log(cm.s**-2)' / units for column 23 TCOMM23 = 'Surface gravity from GSP-Phot Aeneas best library using BP/RP spectr'TUCD23 = 'phys.gravity' / VO Unified Content Descriptor for column 23 TTYPE24 = 'mh_gspphot' / label for column 24 TFORM24 = 'E ' / format for column 24 TUNIT24 = '''dex'' ' / units for column 24 TCOMM24 = 'Iron abundance from GSP-Phot Aeneas best library using BP/RP spectra'TUCD24 = 'phys.abund.Z' / VO Unified Content Descriptor for column 24 TTYPE25 = 'distance_gspphot' / label for column 25 TFORM25 = 'E ' / format for column 25 TUNIT25 = 'pc ' / units for column 25 TCOMM25 = 'Distance from GSP-Phot Aeneas best library using BP/RP spectra' TUCD25 = 'pos.distance;pos.eq' / VO Unified Content Descriptor for column 25 TTYPE26 = 'azero_gspphot' / label for column 26 TFORM26 = 'E ' / format for column 26 TUNIT26 = 'mag ' / units for column 26 TCOMM26 = 'Monochromatic extinction $A_0$ at 547.7nm from GSP-Phot Aeneas best 'TUCD26 = 'phys.absorption;em.opt' / VO Unified Content Descriptor for column 26TTYPE27 = 'ag_gspphot' / label for column 27 TFORM27 = 'E ' / format for column 27 TUNIT27 = 'mag ' / units for column 27 TCOMM27 = 'Extinction in G band from GSP-Phot Aeneas best library using BP/RP s'TUCD27 = 'phys.absorption;em.opt' / VO Unified Content Descriptor for column 27TTYPE28 = 'ebpminrp_gspphot' / label for column 28 TFORM28 = 'E ' / format for column 28 TUNIT28 = 'mag ' / units for column 28 TCOMM28 = 'Reddening $E(G_{\rm BP} - 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EXTEND = T / There are standard extensions COMMENT Dummy header; see following table extension END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / 8-bit bytes NAXIS = 2 / 2-dimensional table NAXIS1 = 172 / width of table in bytes NAXIS2 = 2000 / number of rows in table PCOUNT = 0 / heap size (no gap) GCOUNT = 1 / one data group TFIELDS = 32 / number of columns TTYPE1 = 'designation' / label for column 1 TFORM1 = '27A ' / format for column 1 TCOMM1 = 'Unique source designation (unique across all Data Releases)' TUCD1 = 'meta.id;meta.main' / VO Unified Content Descriptor for column 1 TTYPE2 = 'source_id' / label for column 2 TFORM2 = 'K ' / format for column 2 TCOMM2 = 'Unique source identifier (unique within a particular Data Release)' TUCD2 = 'meta.id ' / VO Unified Content Descriptor for column 2 TTYPE3 = 'ra ' / label for column 3 TFORM3 = 'D ' / format for column 3 TUNIT3 = 'deg ' / units for column 3 TCOMM3 = 'Right ascension' TUCD3 = 'pos.eq.ra;meta.main' / VO Unified Content Descriptor for column 3 TUTYP3 = 'stc:AstroCoords.Position3D.Value3.C1' / VO Utype for column 3 TTYPE4 = 'dec ' / label for column 4 TFORM4 = 'D ' / format for column 4 TUNIT4 = 'deg ' / units for column 4 TCOMM4 = 'Declination' TUCD4 = 'pos.eq.dec;meta.main' / VO Unified Content Descriptor for column 4 TUTYP4 = 'stc:AstroCoords.Position3D.Value3.C2' / VO Utype for column 4 TTYPE5 = 'parallax' / label for column 5 TFORM5 = 'D ' / format for column 5 TUNIT5 = 'mas ' / units for column 5 TCOMM5 = 'Parallax' TUCD5 = 'pos.parallax.trig' / VO Unified Content Descriptor for column 5 TTYPE6 = 'parallax_over_error' / label for column 6 TFORM6 = 'E ' / format for column 6 TCOMM6 = 'Parallax divided by its standard error' TUCD6 = 'stat.snr;pos.parallax.trig' / VO Unified Content Descriptor for columTTYPE7 = 'pmra ' / label for column 7 TFORM7 = 'D ' / format for column 7 TUNIT7 = 'mas.yr**-1' / units for column 7 TCOMM7 = 'Proper motion in right ascension direction' TUCD7 = 'pos.pm;pos.eq.ra' / VO Unified Content Descriptor for column 7 TUTYP7 = 'stc:AstroCoords.Velocity3D.Value3.C1' / VO Utype for column 7 TTYPE8 = 'pmdec ' / label for column 8 TFORM8 = 'D ' / format for column 8 TUNIT8 = 'mas.yr**-1' / units for column 8 TCOMM8 = 'Proper motion in declination direction' TUCD8 = 'pos.pm;pos.eq.dec' / VO Unified Content Descriptor for column 8 TUTYP8 = 'stc:AstroCoords.Velocity3D.Value3.C2' / VO Utype for column 8 TTYPE9 = 'ruwe ' / label for column 9 TFORM9 = 'E ' / format for column 9 TCOMM9 = 'Renormalised unit weight error' TUCD9 = 'stat.error' / VO Unified Content Descriptor for column 9 TTYPE10 = 'phot_g_mean_mag' / label for column 10 TFORM10 = 'E ' / format for column 10 TUNIT10 = 'mag ' / units for column 10 TCOMM10 = 'G-band mean magnitude' TUCD10 = 'phot.mag;em.opt' / VO Unified Content Descriptor for column 10 TTYPE11 = 'bp_rp ' / label for column 11 TFORM11 = 'E ' / format for column 11 TUNIT11 = 'mag ' / units for column 11 TCOMM11 = 'BP - RP colour' TUCD11 = 'phot.color;em.opt.B;em.opt.R' / VO Unified Content Descriptor for colTTYPE12 = 'radial_velocity' / label for column 12 TFORM12 = 'E ' / format for column 12 TUNIT12 = 'km.s**-1' / units for column 12 TCOMM12 = 'Radial velocity ' TUCD12 = 'spect.dopplerVeloc.opt;em.opt.I' / VO Unified Content Descriptor for TUTYP12 = 'stc:AstroCoords.Velocity3D.Value3.C3' / VO Utype for column 12 TTYPE13 = 'phot_variable_flag' / label for column 13 TFORM13 = '13A ' / format for column 13 TCOMM13 = 'Photometric variability flag' TUCD13 = 'meta.code;src.var' / VO Unified Content Descriptor for column 13 TTYPE14 = 'non_single_star' / label for column 14 TFORM14 = 'I ' / format for column 14 TCOMM14 = 'Flag indicating the availability of additional information in the va'TUCD14 = 'meta.code.status' / VO Unified Content Descriptor for column 14 TTYPE15 = 'has_xp_continuous' / label for column 15 TFORM15 = 'L ' / format for column 15 TCOMM15 = 'Flag indicating the availability of mean BP/RP spectrum in continuou'TUCD15 = 'meta.code.status' / VO Unified Content Descriptor for column 15 TTYPE16 = 'has_xp_sampled' / label for column 16 TFORM16 = 'L ' / format for column 16 TCOMM16 = 'Flag indicating the availability of mean BP/RP spectrum in sampled f'TUCD16 = 'meta.code.status' / VO Unified Content Descriptor for column 16 TTYPE17 = 'has_rvs ' / label for column 17 TFORM17 = 'L ' / format for column 17 TCOMM17 = 'Flag indicating the availability of mean RVS spectrum for this sourc'TUCD17 = 'meta.code.status' / VO Unified Content Descriptor for column 17 TTYPE18 = 'has_epoch_photometry' / label for column 18 TFORM18 = 'L ' / format for column 18 TCOMM18 = 'Flag indicating the availability of epoch photometry for this source'TUCD18 = 'meta.code.status' / VO Unified Content Descriptor for column 18 TTYPE19 = 'has_epoch_rv' / label for column 19 TFORM19 = 'L ' / format for column 19 TCOMM19 = 'Flag indicating the availability of epoch radial velocity for this s'TUCD19 = 'meta.code.status' / VO Unified Content Descriptor for column 19 TTYPE20 = 'has_mcmc_gspphot' / label for column 20 TFORM20 = 'L ' / format for column 20 TCOMM20 = 'Flag indicating the availability of GSP-Phot MCMC samples for this s'TUCD20 = 'meta.code.status' / VO Unified Content Descriptor for column 20 TTYPE21 = 'has_mcmc_msc' / label for column 21 TFORM21 = 'L ' / format for column 21 TCOMM21 = 'Flag indicating the availability of MSC MCMC samples for this source'TUCD21 = 'meta.code.status' / VO Unified Content Descriptor for column 21 TTYPE22 = 'teff_gspphot' / label for column 22 TFORM22 = 'E ' / format for column 22 TUNIT22 = 'K ' / units for column 22 TCOMM22 = 'Effective temperature from GSP-Phot Aeneas best library using BP/RP 'TUCD22 = 'phys.temperature.effective' / VO Unified Content Descriptor for columTTYPE23 = 'logg_gspphot' / label for column 23 TFORM23 = 'E ' / format for column 23 TUNIT23 = 'log(cm.s**-2)' / units for column 23 TCOMM23 = 'Surface gravity from GSP-Phot Aeneas best library using BP/RP spectr'TUCD23 = 'phys.gravity' / VO Unified Content Descriptor for column 23 TTYPE24 = 'mh_gspphot' / label for column 24 TFORM24 = 'E ' / format for column 24 TUNIT24 = '''dex'' ' / units for column 24 TCOMM24 = 'Iron abundance from GSP-Phot Aeneas best library using BP/RP spectra'TUCD24 = 'phys.abund.Z' / VO Unified Content Descriptor for column 24 TTYPE25 = 'distance_gspphot' / label for column 25 TFORM25 = 'E ' / format for column 25 TUNIT25 = 'pc ' / units for column 25 TCOMM25 = 'Distance from GSP-Phot Aeneas best library using BP/RP spectra' TUCD25 = 'pos.distance;pos.eq' / VO Unified Content Descriptor for column 25 TTYPE26 = 'azero_gspphot' / label for column 26 TFORM26 = 'E ' / format for column 26 TUNIT26 = 'mag ' / units for column 26 TCOMM26 = 'Monochromatic extinction $A_0$ at 547.7nm from GSP-Phot Aeneas best 'TUCD26 = 'phys.absorption;em.opt' / VO Unified Content Descriptor for column 26TTYPE27 = 'ag_gspphot' / label for column 27 TFORM27 = 'E ' / format for column 27 TUNIT27 = 'mag ' / units for column 27 TCOMM27 = 'Extinction in G band from GSP-Phot Aeneas best library using BP/RP s'TUCD27 = 'phys.absorption;em.opt' / VO Unified Content Descriptor for column 27TTYPE28 = 'ebpminrp_gspphot' / label for column 28 TFORM28 = 'E ' / format for column 28 TUNIT28 = 'mag ' / units for column 28 TCOMM28 = 'Reddening $E(G_{\rm BP} - G_{\rm RP})$ from GSP-Phot Aeneas best lib'TUCD28 = 'phot.color.excess' / VO Unified Content Descriptor for column 28 TTYPE29 = 'target_id' / label for column 29 TFORM29 = '3A ' / format for column 29 TTYPE30 = 'target_ra' / label for column 30 TFORM30 = 'D ' / format for column 30 TTYPE31 = 'target_dec' / label for column 31 TFORM31 = 'D ' / format for column 31 TTYPE32 = 'target_separation (deg)' / label for column 32 TFORM32 = 'D ' / format for column 32 DATE-HDU= '2026-01-23T22:55:27' / Date of HDU creation (UTC) STILVERS= '4.3-2 ' / Version of STIL software STILCLAS= 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661321746930753920 -| a@`A@3@i?̽F@pj$"4P?jAL?t NOT_AVAILABLEFFFFFTTEzu@j> D_< ';p;;XDm44@`?b@3Xv?;[?rGaia DR3 661302681574958208 -kII@`I'J&@39d\[?J`f@6$ j?%ҹ?~:A?NOT_AVAILABLEFFFFFFFm44@`?b@3Xv?<[FGaia DR3 661318830650337152 -y0@`BW@3A?mI]-Bb ##3?Ab?|Bp'VARIABLETTFTFTTE@rcC9=Ʌ=?=.}Vm44@`?b@3Xv?<: \ No newline at end of file diff --git a/docs/paper_examples/Begbie+26/cluster_data/apj491325t1_mrt.txt b/docs/paper_examples/Begbie+26/cluster_data/apj491325t1_mrt.txt new file mode 100644 index 00000000..e0919030 --- /dev/null +++ b/docs/paper_examples/Begbie+26/cluster_data/apj491325t1_mrt.txt @@ -0,0 +1,1086 @@ +Title: Characterization of the Praesepe Star Cluster by Photometry and Proper + Motions with 2MASS, PPPMXL and Pan-STARRS +Authors: Wang P.F., Chen W.P., Lin C.C., Pandey A.K., Huang C.K., Panwar N., + Lee C.H., Tsai M.F., Tang C.-H.., Goldman B., Burgett W.S., + Chambers K.C., Draper P.W., Flewelling H., Grav T., Heasley J.N., + Hodapp K.W., Huber M.E., Jedicke R., Kaiser N., Kudritzki R., + Luppino G.A., Lupton R.H., Magnier E.A., Metcalfe N., Monet D.G., + Morgan J.S., Onaka P.M., Price P.A., Stubbs C.W., Sweeney W., + Tonry J.L., Wainscoat R.J., Waters C. +Table: Member Candidates of Praesepe +================================================================================ +Byte-by-byte Description of file: apj491325t1_mrt.txt +-------------------------------------------------------------------------------- + Bytes Format Units Label Explanations +-------------------------------------------------------------------------------- + 1- 4 I4 --- Num Identification number + 6- 14 F9.5 deg RAdeg Right Ascension in decimal degrees (J2000) + 16- 24 F9.6 deg DEdeg Declination in decimal degrees (J2000) + 26- 30 F5.1 mas/yr pmRA Proper motion along RA times cos(DE) + 32- 35 F4.1 mas/yr e_pmRA Uncertainty in pmRA + 37- 41 F5.1 mas/yr pmDE Proper motion along DE + 43- 46 F4.1 mas/yr e_pmDE Uncertainty in pmDE + 48- 52 F5.2 mag gmag Pan-STARRS g band magnitude (1) + 54- 58 F5.2 mag e_gmag Uncertainty in gmag (1) + 60- 64 F5.2 mag rmag Pan-STARRS r band magnitude (1) + 66- 70 F5.2 mag e_rmag Uncertainty in rmag (1) + 72- 76 F5.2 mag imag Pan-STARRS i band magnitude (1) + 78- 82 F5.2 mag e_imag Uncertainty in imag (1) + 84- 88 F5.2 mag zmag Pan-STARRS z band magnitude (1) + 90- 94 F5.2 mag e_zmag Uncertainty in zmag (1) + 96-100 F5.2 mag ymag Pan-STARRS y band magnitude (1) + 102-106 F5.2 mag e_ymag Uncertainty in ymag (1) + 108-112 F5.2 mag Jmag 2MASS J band magnitude (1) + 114-118 F5.2 mag e_Jmag Uncertainty in Jmag (1) + 120-124 F5.2 mag Hmag 2MASS H band magnitude (1) + 126-130 F5.2 mag e_Hmag Uncertainty in Hmag (1) + 132-136 F5.2 mag Kmag 2MASS K_S_ band magnitude (1) + 138-142 F5.2 mag e_Kmag Uncertainty in Kmag (1) + 144 I1 --- Flag Binary flag (2) + 146-156 A11 --- Name Common source identifier +-------------------------------------------------------------------------------- +Note (1): A 99.00 indicates a null value. +Note (2): + 1 = possible binary system. + 0 = Not a binary system. +-------------------------------------------------------------------------------- + 1 127.20262 18.976639 -35.6 5.6 -15.0 5.6 20.39 0.03 19.04 0.01 17.17 0.00 16.40 0.00 15.93 0.01 14.73 0.02 14.08 0.07 13.75 0.01 0 + 2 127.20794 19.988964 -37.5 4.2 -17.1 4.2 16.88 0.00 15.15 0.60 14.42 0.60 14.05 0.00 13.61 0.00 12.34 0.02 11.73 0.02 11.46 0.02 0 + 3 127.26084 19.636719 -38.2 4.2 -5.8 4.2 18.71 0.01 17.39 0.00 16.42 0.00 99.00 99.00 99.00 99.00 14.63 0.03 14.05 0.04 13.80 0.04 0 + 4 127.30614 19.479976 -36.8 1.3 -15.4 1.3 11.60 0.60 11.20 0.60 11.03 0.60 10.96 0.60 10.93 0.60 10.02 0.02 9.73 0.02 9.65 0.02 0 + 5 127.32851 20.717992 -41.9 4.1 -17.3 4.1 17.65 0.00 16.47 0.00 15.27 0.00 14.72 0.00 14.47 0.00 13.28 0.02 12.66 0.02 12.40 0.02 0 + 6 127.42774 20.952028 -33.3 4.1 -15.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.34 0.02 12.73 0.03 12.50 0.02 0 + 7 127.43485 20.673132 -38.7 1.3 -12.6 1.3 12.44 0.00 15.72 0.00 12.16 0.00 11.43 0.00 11.35 0.00 10.35 0.02 9.91 0.03 9.81 0.02 1 + 8 127.46176 18.399097 -28.2 4.2 -14.9 4.2 21.56 0.05 20.14 0.03 18.24 0.01 17.35 0.00 16.91 0.01 15.57 0.07 15.12 0.09 14.59 0.08 0 + 9 127.48768 18.566668 -39.2 4.1 -16.5 4.1 16.18 0.00 18.88 0.03 14.02 0.00 13.57 0.00 13.34 0.00 12.15 0.02 11.48 0.02 11.30 0.02 1 + 10 127.49445 19.483014 -38.5 5.6 -12.5 5.6 21.56 0.10 21.49 0.60 18.09 0.01 17.27 0.00 16.87 0.01 15.49 0.05 14.99 0.07 14.61 0.08 0 + 11 127.52488 18.279527 -39.2 5.6 -14.6 5.6 20.84 0.06 18.81 0.60 17.65 0.01 16.81 0.00 16.45 0.01 15.19 0.04 14.52 0.06 14.30 0.06 0 + 12 127.55866 18.422182 -39.2 4.1 -21.5 4.1 18.45 0.01 17.22 0.00 15.82 0.00 15.19 0.00 14.89 0.00 13.72 0.02 13.09 0.02 12.81 0.03 0 + 13 127.63415 21.170965 -41.4 4.1 -12.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.51 0.02 13.80 0.03 13.66 0.04 0 + 14 127.64848 18.222578 -27.7 4.1 -14.4 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.15 0.03 13.57 0.02 13.31 0.03 0 + 15 127.65266 18.377222 -39.1 5.5 -20.7 5.5 20.03 0.02 18.72 0.01 16.93 0.00 16.06 0.00 15.64 0.00 14.32 0.03 13.72 0.02 13.34 0.03 0 + 16 127.67308 20.407421 -39.4 4.1 -13.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.72 0.03 14.08 0.04 13.88 0.05 0 + 17 127.71251 19.352421 -38.9 4.1 -13.9 4.1 15.38 0.00 14.17 0.00 13.69 0.60 13.28 0.00 13.12 0.00 12.02 0.02 11.36 0.02 11.19 0.02 0 + 18 127.71419 18.897652 -40.3 4.1 -17.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.26 0.03 13.62 0.03 13.39 0.03 0 + 19 127.73101 19.555473 -32.2 1.6 -17.6 1.6 11.19 0.60 10.83 0.60 10.69 0.60 10.63 0.60 10.61 0.60 9.73 0.02 9.46 0.02 9.37 0.02 0 + 20 127.73994 20.662451 -34.4 4.1 -11.7 4.1 16.03 0.00 14.82 0.00 14.15 0.00 13.83 0.00 13.67 0.00 12.60 0.02 11.90 0.02 11.72 0.02 0 + 21 127.74543 18.694725 -36.0 4.1 -17.4 4.1 16.06 0.00 14.83 0.00 14.04 0.00 13.71 0.00 13.53 0.00 12.46 0.02 11.76 0.02 11.59 0.02 0 + 22 127.75076 19.404428 -43.1 5.5 -18.9 5.5 20.50 0.02 19.08 0.01 17.35 0.00 16.60 0.00 16.22 0.00 14.94 0.05 14.26 0.05 13.99 0.05 0 + 23 127.77505 18.129977 -42.7 4.1 -12.0 4.1 20.27 0.04 18.31 0.60 17.29 0.00 16.47 0.00 16.10 0.01 14.84 0.04 14.17 0.04 13.90 0.05 0 + 24 127.77755 21.229530 -31.6 5.6 -10.6 5.6 18.97 0.01 99.00 99.00 99.00 99.00 15.54 0.00 15.15 0.01 13.94 0.03 13.33 0.02 13.02 0.03 1 + 25 127.80400 18.153683 -35.4 1.1 -11.9 0.8 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.02 0.03 8.77 0.01 8.74 0.02 1 BD+18 1959 + 26 127.80453 20.900096 -38.0 4.1 -11.7 4.1 18.52 0.01 17.29 0.00 15.87 0.00 15.20 0.00 14.90 0.00 13.69 0.02 13.05 0.03 12.80 0.03 0 + 27 127.80822 21.137447 -32.3 4.1 -19.6 4.1 20.24 0.02 18.93 0.01 17.29 0.00 16.53 0.00 16.18 0.01 14.91 0.04 14.37 0.05 14.09 0.05 0 + 28 127.86211 18.682350 -38.0 5.5 -17.3 5.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.25 0.03 13.59 0.03 13.32 0.03 0 + 29 127.87202 18.398043 -35.1 4.1 -5.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.63 0.02 13.05 0.03 12.75 0.02 0 + 30 127.87431 20.410384 -38.4 4.1 -16.0 4.1 15.33 0.00 14.13 0.00 13.39 0.60 12.99 0.00 12.78 0.00 11.67 0.02 10.95 0.02 10.77 0.02 1 + 31 127.88665 21.024420 -39.8 4.1 -14.3 4.1 15.27 0.00 14.00 0.00 13.39 0.60 13.15 0.00 13.00 0.00 11.89 0.02 11.25 0.01 11.12 0.02 0 + 32 127.91843 21.273458 -34.1 4.1 -21.4 4.1 19.67 0.01 18.41 0.01 16.83 0.00 16.07 0.00 15.74 0.00 14.42 0.03 13.80 0.03 13.59 0.03 0 + 33 127.91845 19.798370 -35.9 4.1 -14.1 4.1 15.69 0.00 18.45 0.02 13.74 0.60 13.31 0.00 13.11 0.00 11.92 0.02 11.22 0.02 11.03 0.02 0 + 34 127.92032 18.495262 -38.6 4.1 -18.8 4.1 18.67 0.01 17.47 0.01 16.08 0.00 15.44 0.00 15.15 0.00 13.97 0.02 13.37 0.04 13.11 0.03 1 + 35 127.92895 18.485112 -40.8 4.1 -19.4 4.1 19.34 0.01 18.11 0.01 16.55 0.00 15.83 0.00 15.50 0.00 14.25 0.03 13.67 0.03 13.37 0.03 0 + 36 127.94647 18.163225 -28.7 4.1 -17.0 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.55 0.03 13.94 0.04 13.70 0.04 1 + 37 127.96308 21.489645 -34.1 4.1 -16.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.83 0.04 14.12 0.03 13.89 0.05 0 + 38 128.00303 21.422300 -29.2 4.1 -16.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.50 0.03 13.84 0.03 13.62 0.04 0 + 39 128.00775 19.979740 -37.5 4.1 -17.7 4.1 19.18 0.01 17.91 0.01 16.36 0.00 15.65 0.00 15.33 0.00 14.10 0.03 13.43 0.02 13.21 0.03 0 + 40 128.01131 20.772398 -38.6 4.1 -11.9 4.1 19.36 0.02 17.07 0.60 16.31 0.60 15.71 0.00 15.37 0.00 14.15 0.03 13.48 0.02 13.25 0.03 0 + 41 128.02228 19.300140 -32.8 4.1 -8.8 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.61 0.02 11.89 0.02 11.79 0.01 0 + 42 128.03043 19.606693 -36.6 4.2 -9.9 4.2 21.11 0.05 19.63 0.02 17.82 0.01 17.02 0.00 16.63 0.01 15.25 0.04 14.70 0.06 14.44 0.07 0 + 43 128.03306 18.740739 -29.2 4.1 -15.6 4.1 17.96 0.01 16.75 0.00 15.49 0.00 14.95 0.00 14.68 0.00 13.51 0.02 12.83 0.02 12.57 0.02 1 + 44 128.03559 21.264164 -28.0 4.1 -14.0 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.10 0.03 14.43 0.05 14.20 0.05 0 + 45 128.04440 21.341547 -35.8 4.1 -9.8 4.1 14.85 0.00 13.70 0.60 13.29 0.60 12.96 0.00 12.84 0.00 11.77 0.02 11.10 0.02 10.96 0.01 1 + 46 128.06274 18.228382 -32.6 1.5 -17.3 1.6 11.06 0.60 10.81 0.60 10.76 0.60 10.78 0.60 10.80 0.60 9.98 0.02 9.73 0.02 9.66 0.02 0 + 47 128.07433 19.547048 -38.8 4.1 -15.6 4.1 18.85 0.01 17.47 0.00 16.12 0.00 15.44 0.00 15.14 0.00 13.88 0.02 13.27 0.04 13.02 0.03 1 + 48 128.07862 19.052386 -38.2 4.1 -16.3 4.1 18.38 0.01 16.54 0.60 15.85 0.00 15.21 0.00 14.93 0.00 13.67 0.02 13.06 0.03 12.83 0.03 1 + 49 128.09771 20.995792 -38.8 4.1 -11.9 4.1 13.24 0.60 12.58 0.60 12.30 0.60 12.14 0.60 12.06 0.00 11.04 0.02 10.59 0.01 10.47 0.02 0 + 50 128.13509 20.844718 -39.6 4.1 -16.0 4.1 18.31 0.01 20.99 0.18 15.78 0.00 15.10 0.00 14.83 0.00 13.60 0.02 12.95 0.02 12.72 0.02 1 + 51 128.13911 20.080077 -43.2 4.1 -14.1 4.1 13.58 0.60 12.85 0.60 12.57 0.60 12.42 0.60 12.35 0.00 11.28 0.02 10.73 0.02 10.63 0.02 0 + 52 128.16435 19.956201 -33.3 1.6 -16.0 1.6 12.49 0.60 12.02 0.60 11.85 0.60 11.79 0.60 11.75 0.60 10.82 0.02 10.43 0.01 10.37 0.02 0 + 53 128.18661 18.036126 -40.8 4.1 -11.5 4.1 17.27 0.00 15.98 0.00 14.82 0.00 14.11 0.00 13.84 0.00 12.64 0.02 12.00 0.03 11.75 0.02 1 + 54 128.19488 19.997695 -42.5 4.1 -17.9 4.1 18.64 0.01 17.40 0.00 15.98 0.00 15.34 0.00 15.04 0.00 13.80 0.02 13.25 0.02 12.95 0.03 1 + 55 128.20315 18.677969 -36.9 4.1 -15.8 4.1 18.32 0.01 17.11 0.00 15.63 0.00 14.94 0.00 14.59 0.00 13.40 0.02 12.76 0.03 12.47 0.02 1 + 56 128.20712 18.701740 -37.9 4.1 -13.0 4.1 12.90 0.60 12.19 0.60 11.94 0.60 11.83 0.60 11.74 0.60 10.70 0.02 10.12 0.02 10.06 0.02 0 + 57 128.21754 19.976632 -38.9 4.1 -15.9 4.1 13.43 0.60 12.74 0.60 12.44 0.60 12.26 0.60 12.18 0.00 11.15 0.02 10.68 0.01 10.56 0.02 0 + 58 128.22110 18.508148 -36.1 4.1 -16.0 4.1 17.36 0.00 15.71 0.60 15.04 0.01 14.51 0.00 14.27 0.00 13.12 0.02 12.48 0.02 12.27 0.02 0 + 59 128.23184 18.732811 -39.9 4.1 -15.3 4.1 13.35 0.60 12.68 0.60 12.44 0.60 12.32 0.60 12.18 0.00 11.23 0.02 10.69 0.02 10.60 0.02 0 + 60 128.23619 21.641569 -38.6 4.1 -19.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.95 0.04 14.36 0.05 14.09 0.05 0 + 61 128.23700 18.351124 -35.3 4.1 -15.4 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.96 0.02 13.24 0.03 13.03 0.03 1 + 62 128.24615 17.306547 -40.9 4.2 -11.9 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.02 0.02 12.42 0.02 12.19 0.02 0 + 63 128.25157 20.719512 -40.0 4.1 -12.6 4.1 17.52 0.00 16.30 0.00 14.99 0.00 14.33 0.00 14.05 0.00 12.84 0.02 12.20 0.02 11.98 0.02 0 + 64 128.25349 17.362900 -36.2 4.2 -18.6 4.2 13.13 0.60 12.64 0.60 12.41 0.60 12.29 0.60 12.14 0.00 11.29 0.02 10.96 0.02 10.84 0.02 0 + 65 128.26004 18.851074 -38.1 4.1 -19.9 4.1 19.17 0.01 18.04 0.02 16.52 0.00 15.85 0.00 15.55 0.00 14.33 0.03 13.75 0.03 13.51 0.03 1 + 66 128.26198 18.682622 -37.9 4.1 -16.7 4.1 13.81 0.00 13.16 0.60 12.78 0.60 12.54 0.60 12.38 0.00 11.33 0.02 10.84 0.02 10.68 0.02 0 + 67 128.27312 18.930171 -38.4 4.1 -17.0 4.1 17.63 0.00 16.40 0.00 15.12 0.00 14.53 0.00 14.26 0.00 13.07 0.02 12.42 0.02 12.19 0.02 0 + 68 128.27386 19.041114 -34.3 5.6 -6.8 5.6 21.56 0.09 22.00 0.60 20.17 0.60 17.23 0.01 16.84 0.01 15.58 0.06 14.97 0.06 14.63 0.08 1 + 69 128.28099 20.130049 -39.6 4.1 -13.1 4.1 14.62 0.00 13.60 0.60 13.20 0.60 12.93 0.00 12.75 0.00 11.68 0.02 11.06 0.02 10.93 0.02 0 + 70 128.28208 18.754352 -40.7 4.1 -13.4 4.1 19.42 0.01 17.37 0.60 16.60 0.60 15.97 0.00 15.63 0.01 14.40 0.03 13.83 0.04 13.57 0.04 0 + 71 128.28515 20.443656 -29.3 4.1 -14.0 4.1 17.66 0.00 16.43 0.00 15.03 0.00 14.38 0.00 14.09 0.00 12.82 0.02 12.22 0.01 11.94 0.02 0 + 72 128.29565 19.489466 -34.8 4.1 -13.7 4.1 19.00 0.01 17.77 0.01 16.19 0.00 15.42 0.00 15.07 0.00 13.80 0.03 13.20 0.03 12.91 0.03 0 + 73 128.30605 20.550304 -40.2 4.1 -12.1 4.1 18.21 0.01 17.02 0.00 15.55 0.00 14.89 0.00 14.58 0.00 13.34 0.02 12.68 0.02 12.48 0.02 0 + 74 128.30929 20.605881 -40.4 4.1 -11.5 4.1 19.92 0.01 18.65 0.01 17.01 0.00 16.24 0.00 15.92 0.00 14.58 0.04 13.95 0.03 13.79 0.04 0 + 75 128.30990 19.076763 -35.5 4.1 -16.0 4.1 17.04 0.00 20.16 0.09 14.76 0.00 14.31 0.00 14.11 0.00 12.97 0.02 12.30 0.02 12.09 0.02 0 + 76 128.31418 20.702433 -37.9 4.1 -16.8 4.1 13.02 0.60 12.40 0.60 12.18 0.60 12.07 0.60 12.01 0.60 11.02 0.02 10.55 0.02 10.44 0.02 0 + 77 128.31922 21.338971 -34.6 4.1 -11.8 4.1 18.06 0.01 20.90 0.11 15.44 0.60 14.72 0.00 14.43 0.00 13.18 0.02 12.57 0.02 12.27 0.03 0 + 78 128.32334 19.430706 -39.3 4.1 -11.3 4.1 14.31 0.00 13.39 0.60 13.02 0.60 12.81 0.60 12.62 0.00 11.59 0.02 10.99 0.02 10.86 0.02 0 + 79 128.32484 19.275762 -37.0 4.1 -15.3 4.1 18.85 0.01 17.61 0.01 16.05 0.00 15.31 0.00 14.96 0.00 13.73 0.02 13.06 0.03 12.81 0.03 0 + 80 128.34134 22.092892 -33.4 5.5 -20.1 5.5 20.80 0.03 19.43 0.02 17.77 0.00 17.01 0.00 16.67 0.01 15.23 0.05 14.74 0.05 14.40 0.06 1 + 81 128.35253 19.882715 -41.3 4.1 -13.2 4.1 18.79 0.01 17.52 0.00 15.97 0.00 15.25 0.00 14.92 0.00 13.68 0.02 13.05 0.03 12.78 0.03 0 + 82 128.36241 19.341318 -39.8 4.1 -9.0 4.1 16.21 0.00 19.07 0.04 14.03 0.00 13.59 0.00 13.38 0.00 12.23 0.02 11.53 0.02 11.35 0.02 0 + 83 128.39092 18.408327 -39.9 5.6 -11.5 5.6 20.59 0.02 19.25 0.02 17.53 0.00 16.76 0.00 16.39 0.01 15.06 0.05 14.51 0.05 14.22 0.05 0 + 84 128.39145 20.073792 -40.6 4.1 -11.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.39 0.02 12.74 0.03 12.55 0.02 0 + 85 128.39366 19.951620 -35.8 5.6 -21.4 5.6 20.95 0.07 21.35 0.60 19.62 0.60 16.88 0.00 16.50 0.01 15.16 0.04 14.63 0.05 14.32 0.06 1 + 86 128.39500 18.519157 -32.6 4.1 -8.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.64 0.02 11.99 0.02 11.84 0.02 0 + 87 128.40836 18.954860 -39.7 4.1 -16.0 4.1 17.73 0.00 16.54 0.00 15.33 0.00 14.79 0.00 14.55 0.00 13.35 0.02 12.73 0.02 12.52 0.02 0 + 88 128.40984 20.481240 -31.4 4.1 -14.6 4.1 19.11 0.01 18.03 0.01 16.55 0.00 15.87 0.00 15.53 0.00 14.32 0.02 13.65 0.03 13.43 0.03 0 + 89 128.41559 18.596889 -42.2 5.6 -13.0 5.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.97 0.04 14.35 0.05 14.11 0.05 0 + 90 128.43304 18.797438 -35.5 4.1 -15.2 4.1 17.87 0.00 16.63 0.00 15.41 0.00 14.90 0.00 14.61 0.00 13.44 0.02 12.79 0.03 12.64 0.02 0 + 91 128.43847 19.654393 -34.8 4.1 -10.8 4.1 18.81 0.01 17.53 0.00 15.94 0.00 15.21 0.00 14.87 0.00 13.61 0.02 12.98 0.02 12.67 0.03 0 + 92 128.44561 21.440963 -37.4 4.1 -9.6 4.1 15.27 0.00 18.22 0.01 13.54 0.60 13.24 0.00 13.07 0.00 11.97 0.02 11.31 0.02 11.15 0.02 0 + 93 128.46143 19.782934 -33.2 4.1 -15.2 4.1 16.30 0.00 19.04 0.04 14.22 0.00 13.86 0.00 13.68 0.00 12.57 0.02 11.93 0.02 11.73 0.02 0 + 94 128.46757 19.436574 -37.9 5.6 -18.5 5.6 20.63 0.04 19.30 0.02 17.62 0.01 16.83 0.00 16.42 0.01 15.13 0.04 14.53 0.05 14.45 0.07 1 + 95 128.49677 19.362609 -40.9 4.1 -13.8 4.1 15.59 0.00 14.36 0.00 13.60 0.60 13.18 0.00 12.98 0.00 11.86 0.02 11.19 0.02 11.00 0.02 0 + 96 128.50196 18.926677 -35.4 4.1 -15.5 4.1 18.16 0.01 16.95 0.00 15.58 0.00 14.93 0.00 14.62 0.00 13.43 0.02 12.76 0.02 12.51 0.02 1 + 97 128.50529 17.811222 -27.6 1.1 -12.5 1.1 9.23 0.60 9.07 0.60 9.01 0.60 8.94 0.60 8.94 0.60 8.18 0.02 7.99 0.01 7.92 0.01 1 BD+18 1976 + 98 128.50637 21.010821 -38.2 5.6 -16.1 5.6 20.12 0.03 18.84 0.02 17.14 0.00 16.35 0.00 16.00 0.00 14.77 0.03 14.09 0.03 13.89 0.04 0 + 99 128.50942 17.585442 -30.1 4.2 -8.9 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.56 0.03 13.86 0.04 13.68 0.05 0 + 100 128.51015 19.322755 -37.4 4.1 -13.6 4.1 16.92 0.00 15.53 0.60 14.78 0.60 14.09 0.00 13.84 0.00 12.66 0.02 12.05 0.02 11.76 0.02 1 + 101 128.51302 20.341817 -38.5 4.1 -17.8 4.1 18.53 0.01 16.86 0.60 16.08 0.60 15.37 0.00 15.08 0.00 13.88 0.02 13.28 0.03 12.97 0.03 0 + 102 128.51475 19.795274 -42.6 4.1 -16.0 4.1 13.77 0.00 13.08 0.60 12.73 0.60 12.52 0.60 12.39 0.00 11.32 0.02 10.82 0.02 10.68 0.02 1 + 103 128.51818 20.575100 -40.6 4.1 -15.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.04 0.02 10.57 0.02 10.44 0.02 0 + 104 128.52029 20.679791 -38.6 4.1 -11.4 4.1 17.98 0.01 16.76 0.00 15.40 0.00 14.76 0.00 14.48 0.00 13.25 0.02 12.66 0.02 12.39 0.02 0 + 105 128.52777 20.829654 -37.5 5.6 -15.0 5.6 19.97 0.02 18.64 0.01 17.08 0.00 16.33 0.00 15.98 0.00 14.65 0.03 14.06 0.02 13.86 0.04 0 + 106 128.54258 19.804923 -38.3 4.1 -14.6 4.1 14.54 0.00 13.38 0.60 13.08 0.60 12.93 0.60 12.80 0.00 11.74 0.02 11.08 0.02 10.99 0.02 0 + 107 128.54749 21.070778 -38.3 5.6 -10.1 5.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.00 0.03 14.28 0.04 14.09 0.05 0 + 108 128.55778 21.397819 -36.4 4.1 -16.7 4.1 19.27 0.01 17.99 0.01 16.48 0.00 15.77 0.00 15.46 0.00 14.27 0.02 13.57 0.03 13.30 0.04 0 + 109 128.57507 18.470961 -36.3 5.5 -19.8 5.5 19.87 0.02 18.54 0.01 16.89 0.00 16.11 0.00 15.73 0.00 14.50 0.03 13.79 0.04 13.72 0.04 0 + 110 128.58831 21.878825 -34.9 4.1 -14.2 4.1 13.19 0.60 12.58 0.60 12.35 0.60 12.25 0.60 12.19 0.00 11.19 0.02 10.69 0.02 10.63 0.02 0 + 111 128.60042 19.793386 -40.0 4.1 -15.3 4.1 13.63 0.00 12.97 0.60 12.64 0.60 12.44 0.60 12.26 0.00 11.28 0.02 10.78 0.02 10.63 0.02 0 + 112 128.61399 18.697243 -36.5 5.6 -17.9 5.6 21.07 0.04 19.75 0.02 17.93 0.01 17.09 0.00 16.69 0.01 15.45 0.04 14.73 0.03 14.47 0.06 0 + 113 128.62797 19.100083 -42.3 4.1 -10.5 4.1 18.18 0.01 16.98 0.00 15.67 0.00 15.06 0.00 14.79 0.00 13.60 0.02 12.96 0.03 12.73 0.02 1 + 114 128.64281 21.368677 -35.4 4.1 -14.0 4.1 18.63 0.01 17.08 0.60 15.83 0.00 15.10 0.00 14.77 0.00 13.51 0.02 12.88 0.02 12.64 0.02 0 + 115 128.64290 18.799022 -36.4 4.1 -16.9 4.1 17.83 0.00 16.64 0.00 15.34 0.00 14.74 0.00 14.47 0.00 13.25 0.02 12.60 0.02 12.35 0.02 0 + 116 128.64569 19.073014 -42.2 4.1 -18.4 4.1 19.50 0.01 18.18 0.01 16.60 0.00 15.85 0.00 15.48 0.00 14.26 0.02 13.62 0.02 13.35 0.03 0 + 117 128.65182 18.398058 -39.7 4.1 -10.1 4.1 15.78 0.00 19.03 0.03 13.85 0.00 13.57 0.00 13.39 0.00 12.29 0.02 11.60 0.02 11.43 0.02 0 + 118 128.66532 19.136829 -39.7 4.1 -18.7 4.1 18.41 0.01 17.39 0.00 15.92 0.00 15.23 0.00 14.92 0.00 13.69 0.02 13.05 0.02 12.79 0.02 0 + 119 128.69634 18.021154 -37.3 4.1 -8.4 4.1 14.20 0.00 13.32 0.60 12.95 0.60 12.74 0.60 12.56 0.00 11.53 0.02 10.96 0.02 10.83 0.02 0 + 120 128.70206 17.932964 -42.2 4.2 -15.5 4.2 20.31 0.04 19.04 0.01 17.28 0.01 16.48 0.00 16.13 0.01 14.76 0.04 14.16 0.04 13.92 0.05 0 + 121 128.70537 17.430019 -38.3 4.2 -14.0 4.2 17.82 0.01 20.95 0.11 15.25 0.00 14.63 0.00 14.37 0.00 13.16 0.02 12.50 0.02 12.30 0.02 0 + 122 128.70691 18.784455 -37.1 4.1 -14.0 4.1 16.96 0.00 20.20 0.11 14.70 0.00 14.25 0.00 14.00 0.00 12.82 0.02 12.18 0.02 11.96 0.02 0 + 123 128.72439 18.018211 -43.2 5.5 -11.2 5.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.67 0.03 14.06 0.04 13.76 0.04 1 + 124 128.72886 21.648463 -31.5 4.1 -15.9 4.1 19.26 0.01 18.02 0.01 16.49 0.00 15.80 0.00 15.48 0.00 14.25 0.02 13.60 0.03 13.39 0.03 0 + 125 128.72958 20.105474 -39.0 4.1 -16.3 4.1 16.66 0.00 15.43 0.00 14.49 0.00 14.08 0.00 13.90 0.00 12.77 0.02 12.11 0.02 11.91 0.02 0 + 126 128.73069 20.184406 -40.7 4.1 -20.8 4.1 19.32 0.01 17.98 0.01 16.33 0.00 15.55 0.00 15.22 0.00 13.86 0.03 13.28 0.03 13.01 0.03 0 + 127 128.73821 17.003277 -38.9 4.2 -12.3 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.27 0.03 13.69 0.03 13.41 0.03 0 + 128 128.73878 17.487793 -39.1 5.7 -15.1 5.7 20.97 0.05 19.63 0.02 17.86 0.01 17.08 0.00 16.73 0.01 15.47 0.05 14.66 0.06 14.45 0.07 0 + 129 128.74687 21.143692 -41.3 5.5 -6.3 5.5 19.86 0.02 17.97 0.60 16.80 0.00 15.99 0.00 15.61 0.00 14.35 0.03 13.74 0.03 13.45 0.03 0 + 130 128.74697 18.301517 -33.1 4.1 -16.4 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.73 0.03 14.04 0.03 13.84 0.04 0 + 131 128.74849 21.097000 -32.1 1.4 -16.2 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.01 0.02 9.76 0.03 9.68 0.02 1 + 132 128.75271 17.489502 -39.9 4.2 -20.2 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.18 0.02 13.57 0.02 13.38 0.03 0 + 133 128.76576 20.831211 -33.1 4.1 -9.4 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.18 0.02 12.54 0.02 12.36 0.02 0 + 134 128.77586 18.823513 -37.8 4.1 -18.1 4.1 18.81 0.01 17.59 0.01 16.05 0.00 15.39 0.00 15.04 0.00 13.84 0.02 13.23 0.03 12.95 0.02 0 + 135 128.77629 19.946653 -36.6 5.5 -15.6 5.5 20.82 0.04 19.59 0.02 17.64 0.00 16.70 0.00 16.24 0.00 14.95 0.05 14.26 0.04 13.99 0.05 0 + 136 128.78278 20.339761 -38.0 4.1 -14.0 4.1 18.15 0.01 16.89 0.00 15.64 0.00 15.05 0.00 14.79 0.00 13.60 0.02 12.98 0.03 12.75 0.02 0 + 137 128.78346 19.990328 -37.4 4.1 -11.9 4.1 13.83 0.00 16.84 0.01 13.64 0.00 12.44 0.00 12.19 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 138 128.79145 18.207809 -32.7 4.1 -16.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.69 0.03 13.14 0.02 12.89 0.02 0 + 139 128.81145 19.275456 -35.5 4.1 -16.8 4.1 17.54 0.00 15.90 0.60 15.04 0.00 14.45 0.00 14.18 0.00 12.94 0.02 12.36 0.02 12.07 0.02 0 + 140 128.81330 19.375414 -34.6 5.5 -21.2 5.5 20.92 0.03 19.56 0.02 17.78 0.01 16.97 0.00 16.59 0.01 15.24 0.05 14.58 0.05 14.32 0.06 1 + 141 128.81677 19.202154 -35.2 4.1 -19.4 4.1 19.45 0.01 17.49 0.60 16.64 0.00 15.92 0.00 15.60 0.00 14.35 0.02 13.76 0.03 13.46 0.03 0 + 142 128.82053 19.914836 -33.0 4.1 -11.2 4.1 17.48 0.00 16.23 0.00 15.08 0.00 14.57 0.00 14.32 0.00 13.14 0.03 12.53 0.02 12.29 0.02 0 + 143 128.82096 17.606751 -32.4 4.2 -7.6 4.2 19.38 0.01 18.09 0.01 16.45 0.00 15.70 0.00 15.32 0.00 14.06 0.03 13.46 0.02 13.23 0.03 1 + 144 128.82100 20.969681 -36.4 5.6 -6.9 5.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.22 0.05 14.64 0.06 14.29 0.05 0 + 145 128.82406 19.636136 -34.5 2.1 -12.3 2.2 12.50 0.60 11.93 0.60 11.68 0.60 11.54 0.60 11.48 0.60 10.50 0.02 10.12 0.02 10.01 0.02 0 + 146 128.82407 20.564629 -33.3 1.2 -12.4 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.88 0.01 7.81 0.01 7.76 0.01 0 HD 72757 + 147 128.83047 19.761436 -35.0 4.1 -13.9 4.1 19.37 0.01 18.11 0.01 16.56 0.00 15.82 0.00 15.49 0.00 14.25 0.03 13.63 0.03 13.32 0.03 1 + 148 128.83105 19.590069 -34.0 0.7 -12.2 0.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 5.37 0.02 5.10 0.03 5.00 0.01 0 35 Cnc + 149 128.84030 18.492831 -37.7 4.1 -17.1 4.1 17.49 0.01 16.26 0.00 15.25 0.01 14.59 0.00 14.34 0.00 13.18 0.02 12.56 0.03 12.27 0.02 0 + 150 128.86682 20.196461 -33.4 1.5 -15.2 1.5 10.61 0.60 10.28 0.60 10.16 0.60 10.12 0.60 10.11 0.60 9.19 0.03 8.97 0.01 8.92 0.02 0 BD+20 2119 + 151 128.89103 18.929839 -39.0 4.1 -14.4 4.1 15.86 0.00 14.63 0.00 14.05 0.00 13.57 0.00 13.39 0.00 12.27 0.02 11.61 0.02 11.42 0.01 0 + 152 128.89870 18.995710 -38.8 4.1 -13.0 4.1 16.06 0.00 14.84 0.00 14.07 0.00 13.73 0.00 13.56 0.00 12.47 0.02 11.79 0.02 11.59 0.02 0 + 153 128.90047 19.532837 -43.0 5.5 -14.8 5.5 20.88 0.04 18.76 0.60 17.78 0.01 16.96 0.00 16.56 0.01 15.26 0.04 14.65 0.06 14.38 0.06 1 + 154 128.91048 18.358934 -45.1 4.1 -15.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.86 0.03 13.37 0.02 13.06 0.03 0 + 155 128.91356 20.402708 -31.7 4.1 -13.3 4.1 19.28 0.01 17.91 0.01 16.34 0.00 15.57 0.00 15.21 0.00 13.96 0.02 13.33 0.03 13.05 0.02 0 + 156 128.91522 19.126752 -35.2 4.1 -12.5 4.1 17.79 0.00 16.60 0.00 15.29 0.00 14.70 0.00 14.42 0.00 13.23 0.02 12.50 0.02 12.29 0.02 0 + 157 128.91720 18.707836 -37.8 4.1 -14.0 4.1 18.14 0.01 16.92 0.00 15.47 0.00 14.80 0.00 14.50 0.00 13.28 0.02 12.64 0.02 12.36 0.02 0 + 158 128.93589 18.960629 -40.1 4.1 -13.8 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.40 0.03 13.84 0.03 13.55 0.03 0 + 159 128.93772 19.771095 -36.2 0.9 -13.3 0.7 7.23 0.60 7.41 0.60 7.58 0.60 7.70 0.60 7.77 0.60 7.14 0.01 7.12 0.01 7.12 0.01 1 HD 72846 + 160 128.93804 19.640580 -40.9 4.1 -10.0 4.1 13.30 0.00 16.85 0.01 13.31 0.00 12.65 0.00 12.11 0.00 11.12 0.02 10.68 0.02 10.54 0.01 0 + 161 128.94480 19.870931 -40.7 4.1 -11.1 4.1 15.24 0.00 14.02 0.00 13.51 0.00 13.13 0.00 12.99 0.00 11.89 0.02 11.28 0.02 11.06 0.01 0 + 162 128.94667 18.141630 -41.8 4.1 -14.4 4.1 18.47 0.01 16.71 0.60 15.89 0.60 15.14 0.00 14.82 0.00 13.62 0.02 12.96 0.02 12.69 0.02 0 + 163 128.94701 19.589605 -37.5 4.1 -17.9 4.1 19.46 0.01 17.98 0.60 16.65 0.00 15.91 0.00 15.60 0.00 14.37 0.02 13.72 0.03 13.47 0.03 1 + 164 128.95933 19.850018 -38.7 4.1 -15.8 4.1 19.61 0.01 18.33 0.01 16.71 0.00 16.01 0.00 15.66 0.00 14.38 0.04 13.88 0.04 13.56 0.04 0 + 165 128.95991 20.151270 -29.2 4.1 -9.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.82 0.03 13.17 0.03 12.94 0.02 1 + 166 128.96168 18.895420 -42.3 5.6 -16.7 5.6 17.78 0.01 16.55 0.00 15.15 0.00 14.48 0.00 14.16 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 167 128.96690 20.266310 -34.3 5.5 -10.9 5.5 20.81 0.05 19.30 0.02 17.50 0.00 16.63 0.00 16.21 0.00 14.83 0.03 14.24 0.04 13.84 0.03 0 + 168 128.97033 18.314147 -40.7 4.1 -10.9 4.1 17.73 0.01 16.31 0.00 15.09 0.00 14.56 0.00 14.29 0.00 13.14 0.02 12.52 0.02 12.26 0.03 0 + 169 128.97730 18.149378 -32.7 1.5 -9.7 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.76 0.02 9.52 0.02 9.44 0.03 1 + 170 128.98533 20.618590 -39.0 5.5 -14.2 5.5 20.55 0.03 19.31 0.01 17.48 0.01 16.71 0.00 16.30 0.00 15.03 0.04 14.36 0.05 14.08 0.05 0 + 171 128.98724 20.826298 -34.3 2.2 -19.4 2.2 12.30 0.60 11.71 0.60 11.51 0.60 11.42 0.60 11.36 0.60 10.38 0.02 9.91 0.02 9.83 0.02 0 + 172 128.98840 19.351791 -34.2 5.5 -14.2 5.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.05 0.04 14.58 0.05 14.15 0.04 0 + 173 128.99654 18.308223 -32.2 4.1 -14.1 4.1 17.41 0.01 16.16 0.00 15.24 0.60 14.53 0.00 14.30 0.00 13.14 0.02 12.49 0.02 12.28 0.02 0 + 174 128.99763 20.077918 -34.9 4.1 -14.2 4.1 18.99 0.01 17.71 0.01 16.18 0.00 15.47 0.00 15.17 0.00 13.88 0.03 13.28 0.03 13.00 0.02 0 + 175 128.99937 19.525518 -38.4 4.1 -10.4 4.1 13.64 0.00 12.90 0.60 12.57 0.60 12.39 0.60 12.31 0.00 11.21 0.02 10.67 0.02 10.57 0.02 0 + 176 129.00189 17.975905 -34.9 4.1 -16.3 4.1 17.23 0.00 15.98 0.00 14.84 0.00 14.30 0.00 14.07 0.00 12.87 0.02 12.22 0.02 12.00 0.02 1 + 177 129.00666 19.958706 -36.8 5.5 -8.0 5.5 21.08 0.04 19.84 0.02 17.85 0.00 16.96 0.00 16.53 0.01 15.19 0.04 14.69 0.05 14.32 0.05 1 + 178 129.01342 20.837688 -41.9 4.1 -16.1 4.1 20.01 0.03 18.68 0.01 17.04 0.00 16.30 0.01 15.92 0.00 14.68 0.02 14.03 0.03 13.78 0.03 0 + 179 129.01377 19.424652 -38.7 4.1 -10.0 4.1 19.64 0.02 17.76 0.60 16.85 0.60 16.12 0.00 15.77 0.01 14.51 0.03 13.94 0.03 13.66 0.03 0 + 180 129.01839 19.920279 -39.5 4.1 -16.7 4.1 15.29 0.00 14.11 0.00 13.65 0.60 13.22 0.00 13.08 0.00 11.99 0.02 11.30 0.02 11.16 0.02 1 + 181 129.02619 20.683304 -37.8 4.1 -13.6 4.1 16.06 0.00 14.83 0.00 99.00 99.00 13.67 0.00 13.44 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 182 129.02626 20.683307 -37.8 4.1 -13.6 4.1 16.05 0.00 14.77 0.60 14.18 0.60 13.79 0.60 13.44 0.00 12.32 0.02 11.66 0.02 11.45 0.02 1 + 183 129.03477 20.814672 -35.8 4.1 -15.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.62 0.03 14.02 0.03 13.78 0.04 0 + 184 129.03547 16.954896 -36.1 4.2 -6.2 4.2 17.23 0.00 16.00 0.00 14.95 0.00 14.48 0.00 14.25 0.00 13.13 0.02 12.55 0.03 12.28 0.02 0 + 185 129.03574 19.957039 -30.5 5.5 -17.3 5.5 19.50 0.01 18.16 0.01 16.53 0.00 15.75 0.00 15.40 0.00 14.16 0.02 13.50 0.03 13.20 0.03 0 + 186 129.03591 18.747452 -39.8 4.1 -17.8 4.1 15.54 0.00 14.30 0.00 13.92 0.00 13.37 0.00 13.25 0.00 12.11 0.02 11.43 0.02 11.29 0.02 0 + 187 129.03721 19.158567 -37.6 4.1 -16.8 4.1 17.89 0.01 16.65 0.00 15.36 0.00 14.78 0.00 14.49 0.00 13.27 0.02 12.61 0.03 12.38 0.02 0 + 188 129.03728 16.957916 -35.3 4.2 -7.3 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.34 0.02 11.71 0.02 11.55 0.02 0 + 189 129.03731 19.230000 -33.9 4.1 -13.1 4.1 17.05 0.00 15.85 0.00 14.78 0.00 14.30 0.00 14.07 0.00 12.94 0.02 12.27 0.02 12.04 0.02 0 + 190 129.04508 19.694791 -35.7 5.5 -5.0 5.5 20.50 0.03 18.44 0.60 17.50 0.60 16.71 0.01 16.35 0.01 15.07 0.03 14.51 0.03 14.22 0.05 0 + 191 129.04754 19.877847 -39.5 4.1 -11.6 4.1 16.18 0.00 14.93 0.00 14.19 0.00 13.81 0.00 13.63 0.00 12.53 0.02 11.85 0.02 11.66 0.02 0 + 192 129.05005 17.879600 -37.8 4.2 -14.8 4.2 19.65 0.02 18.44 0.01 16.83 0.00 16.08 0.00 15.77 0.00 14.50 0.03 13.84 0.03 13.55 0.04 0 + 193 129.05019 18.735466 -35.3 4.1 -20.5 4.1 18.99 0.01 17.67 0.01 16.16 0.00 15.43 0.00 15.07 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 194 129.05863 19.621498 -30.8 2.2 -9.7 2.2 13.60 0.60 12.82 0.60 12.41 0.60 12.15 0.60 11.89 0.00 10.93 0.02 10.47 0.02 10.36 0.02 0 + 195 129.05943 19.425065 -37.4 4.1 -15.5 4.1 17.46 0.00 16.26 0.00 14.89 0.00 14.28 0.00 13.98 0.00 12.76 0.02 12.15 0.02 11.91 0.02 0 + 196 129.06461 20.686041 -38.6 4.1 -16.5 4.1 16.24 0.00 14.99 0.00 14.17 0.00 13.85 0.00 13.63 0.00 12.51 0.02 11.87 0.02 11.63 0.02 0 + 197 129.06618 20.120241 -45.4 4.1 -13.9 4.1 16.35 0.00 15.11 0.00 14.26 0.00 13.90 0.00 13.74 0.00 12.58 0.02 11.90 0.02 11.75 0.02 0 + 198 129.06652 20.553092 -38.7 4.1 -15.1 4.1 15.33 0.00 14.11 0.00 13.63 0.00 13.29 0.00 13.14 0.00 12.04 0.02 11.42 0.02 11.23 0.02 0 + 199 129.06723 19.377951 -44.6 5.5 -11.8 5.5 20.02 0.02 18.70 0.02 17.03 0.00 16.29 0.00 15.95 0.01 14.63 0.03 13.98 0.03 13.70 0.04 0 + 200 129.06767 17.381580 -33.5 1.6 -8.1 1.7 11.69 0.60 11.31 0.60 11.19 0.60 11.16 0.60 11.15 0.60 10.26 0.02 9.93 0.02 9.86 0.01 1 + 201 129.06820 19.542041 -34.2 2.2 -17.5 2.2 11.88 0.60 11.34 0.60 11.13 0.60 11.04 0.60 10.99 0.60 10.03 0.02 9.64 0.02 9.52 0.02 0 + 202 129.07270 20.341539 -35.9 1.0 -13.8 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.21 0.01 7.20 0.01 7.17 0.03 0 HD 72942 + 203 129.07915 18.918962 -40.3 4.1 -19.5 4.1 17.57 0.00 16.38 0.00 15.17 0.00 14.65 0.00 14.41 0.00 13.21 0.02 12.60 0.02 12.35 0.03 0 + 204 129.07975 19.898557 -36.6 4.1 -15.1 4.1 18.08 0.01 16.82 0.00 15.52 0.00 14.93 0.00 14.64 0.00 13.48 0.02 12.84 0.02 12.57 0.02 0 + 205 129.08489 20.116744 -43.2 4.1 -11.5 4.1 18.51 0.01 17.29 0.00 15.90 0.00 15.26 0.00 14.99 0.00 13.77 0.02 13.11 0.02 12.90 0.02 0 + 206 129.08978 20.897382 -39.1 4.1 -14.0 4.1 17.71 0.01 16.52 0.00 15.30 0.00 14.74 0.00 14.48 0.00 13.28 0.02 12.68 0.02 12.43 0.03 0 + 207 129.09084 20.205452 -40.6 4.1 -17.2 4.1 17.38 0.00 16.16 0.00 14.99 0.00 14.47 0.00 14.22 0.00 13.06 0.02 12.43 0.02 12.17 0.02 0 + 208 129.09330 20.118583 -31.9 4.1 -12.7 4.1 19.45 0.01 18.13 0.01 16.61 0.00 15.92 0.00 15.62 0.00 14.36 0.03 13.75 0.03 13.45 0.03 0 + 209 129.09443 19.191459 -39.4 4.1 -11.4 4.1 13.13 0.00 15.17 0.00 13.12 0.00 12.38 0.00 11.76 0.00 10.76 0.02 10.35 0.02 10.26 0.02 0 + 210 129.09750 18.405818 -38.0 4.1 -14.4 4.1 16.66 0.00 15.42 0.00 14.49 0.00 14.07 0.00 13.86 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 211 129.10471 21.149002 -35.9 4.1 -13.5 4.1 16.90 0.00 15.69 0.00 14.94 0.60 14.22 0.00 14.03 0.00 12.88 0.02 12.23 0.03 11.98 0.02 0 + 212 129.11209 22.004673 -34.9 4.1 -16.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.60 0.03 12.95 0.03 12.75 0.02 0 + 213 129.11293 19.865147 -33.5 4.1 -13.9 4.1 16.10 0.00 14.86 0.00 13.92 0.00 13.47 0.00 13.26 0.00 12.10 0.03 11.46 0.01 11.23 0.02 0 + 214 129.11594 17.914867 -38.5 1.5 -14.1 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.72 0.02 9.48 0.02 9.40 0.02 0 + 215 129.11612 21.121153 -38.7 4.1 -12.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.72 0.02 11.11 0.01 10.96 0.02 0 + 216 129.11786 20.228555 -39.5 4.1 -10.0 4.1 12.99 0.60 12.38 0.60 12.15 0.60 12.03 0.60 11.97 0.60 10.98 0.02 10.52 0.02 10.44 0.02 0 + 217 129.12255 21.052867 -36.3 4.1 -11.2 4.1 18.97 0.02 17.69 0.01 16.43 0.60 15.58 0.00 15.27 0.00 14.08 0.02 13.41 0.02 13.19 0.02 0 + 218 129.12455 18.965845 -35.8 1.1 -13.4 1.2 9.48 0.60 9.32 0.60 9.30 0.60 9.31 0.60 9.31 0.60 8.51 0.02 8.36 0.01 8.30 0.01 0 BD+19 2045 + 219 129.12700 19.920524 -39.7 5.5 -20.6 5.5 20.30 0.02 19.01 0.01 17.28 0.00 16.49 0.00 16.09 0.00 14.80 0.03 14.19 0.02 13.87 0.04 0 + 220 129.13000 19.592605 -36.6 4.1 -15.5 4.1 18.75 0.01 17.53 0.01 16.08 0.00 15.42 0.00 15.13 0.00 13.90 0.02 13.28 0.02 13.01 0.02 1 + 221 129.13120 18.315246 -39.4 4.1 -17.3 4.1 19.18 0.01 17.90 0.00 16.30 0.00 15.54 0.00 15.19 0.00 13.88 0.03 13.34 0.03 13.01 0.03 0 + 222 129.13901 19.915091 -39.1 4.1 -15.7 4.1 16.98 0.00 15.77 0.00 14.71 0.00 14.27 0.00 14.07 0.00 12.89 0.03 12.28 0.02 12.01 0.02 0 + 223 129.14540 20.275177 -40.9 4.1 -14.5 4.1 17.46 0.00 16.24 0.00 15.03 0.00 14.51 0.00 14.25 0.00 13.02 0.02 12.38 0.02 12.14 0.02 1 + 224 129.15163 19.185179 -39.7 4.1 -10.0 4.1 15.28 0.00 14.11 0.00 13.51 0.00 13.38 0.00 13.12 0.00 12.02 0.02 11.42 0.02 11.20 0.02 1 + 225 129.15932 18.458836 -29.1 4.1 -9.0 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.52 0.03 13.88 0.02 13.65 0.03 0 + 226 129.16439 20.376067 -33.7 4.1 -15.5 4.1 18.54 0.01 17.29 0.00 15.85 0.00 15.20 0.00 14.94 0.00 13.61 0.02 13.01 0.02 12.75 0.02 0 + 227 129.16665 20.609791 -36.6 1.3 -14.7 1.3 12.17 0.00 11.87 0.00 11.94 0.00 11.59 0.00 11.39 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 228 129.17104 18.307276 -39.3 4.1 -19.6 4.1 17.01 0.00 15.80 0.00 14.75 0.00 14.28 0.00 14.07 0.00 12.92 0.02 12.26 0.02 12.03 0.02 0 + 229 129.17152 20.277744 -40.2 4.1 -16.0 4.1 18.98 0.01 17.67 0.01 16.23 0.00 15.58 0.00 15.27 0.00 14.05 0.02 13.49 0.03 13.16 0.03 1 + 230 129.17416 20.411080 -39.1 4.1 -13.6 4.1 13.48 0.60 12.74 0.60 12.46 0.60 12.32 0.60 12.11 0.00 11.18 0.02 10.62 0.02 10.52 0.02 0 + 231 129.17778 18.895201 -38.7 4.1 -10.8 4.1 14.52 0.00 13.51 0.60 13.11 0.60 12.87 0.60 12.65 0.00 11.59 0.02 10.97 0.02 10.84 0.02 0 + 232 129.18743 20.146029 -36.3 4.1 -10.8 4.1 19.84 0.02 18.59 0.01 16.97 0.00 16.24 0.00 15.89 0.00 14.57 0.03 14.00 0.04 13.78 0.03 0 + 233 129.19039 20.123951 -35.9 1.6 -10.7 1.6 12.01 0.60 11.57 0.60 11.39 0.60 11.30 0.60 11.27 0.60 10.34 0.02 10.03 0.02 9.94 0.02 0 + 234 129.19620 18.579675 -35.8 4.1 -13.5 4.1 13.23 0.60 12.60 0.60 12.34 0.60 12.19 0.60 12.22 0.00 11.11 0.02 10.68 0.02 10.59 0.02 0 + 235 129.20000 18.882805 -35.7 0.9 -11.4 0.8 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.04 0.01 7.95 0.01 7.94 0.03 0 HD 73045 + 236 129.20387 19.316466 -38.8 4.1 -15.5 4.1 17.16 0.00 15.94 0.00 14.78 0.00 14.26 0.00 14.02 0.00 12.84 0.02 12.16 0.02 11.95 0.02 0 + 237 129.20388 19.257387 -36.3 1.6 -12.9 1.7 11.61 0.60 11.20 0.60 11.06 0.60 11.01 0.60 10.99 0.60 10.08 0.02 9.75 0.03 9.69 0.02 0 + 238 129.21044 18.681675 -37.4 4.1 -16.5 4.1 19.26 0.02 18.05 0.01 16.52 0.00 15.79 0.00 15.44 0.00 14.24 0.02 13.59 0.03 13.34 0.04 1 + 239 129.21277 19.071823 -36.4 4.1 -12.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.11 0.02 13.53 0.02 13.24 0.02 0 + 240 129.21501 18.838670 -38.8 5.5 -17.7 5.5 19.56 0.01 18.24 0.01 16.70 0.00 15.96 0.00 15.61 0.00 14.31 0.02 13.80 0.03 13.48 0.03 0 + 241 129.21503 19.076404 -35.8 4.1 -14.5 4.1 14.23 0.00 13.44 0.60 13.06 0.60 12.84 0.60 12.62 0.00 11.58 0.02 10.98 0.02 10.85 0.02 0 + 242 129.22384 18.495829 -37.1 4.1 -9.0 4.1 14.50 0.00 13.53 0.60 13.15 0.60 12.93 0.60 12.72 0.00 11.67 0.02 11.05 0.02 10.95 0.02 0 + 243 129.22507 19.617138 -31.8 4.1 -14.9 4.1 19.96 0.02 18.67 0.01 17.06 0.00 16.32 0.00 16.00 0.00 14.65 0.03 14.10 0.04 13.81 0.03 0 + 244 129.22532 18.756870 -37.5 4.1 -7.6 4.1 13.01 0.60 12.41 0.60 12.17 0.60 12.06 0.60 11.99 0.60 11.00 0.02 10.54 0.03 10.47 0.02 0 + 245 129.23316 19.599150 -35.4 4.1 -10.2 4.1 17.78 0.00 16.55 0.00 15.31 0.00 14.73 0.00 14.52 0.00 13.30 0.02 12.65 0.02 12.43 0.02 0 + 246 129.23442 18.963341 -37.7 4.1 -12.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.78 0.02 11.10 0.02 10.91 0.02 0 + 247 129.23571 20.319447 -39.6 4.1 -12.9 4.1 16.36 0.00 15.09 0.00 14.01 0.00 13.81 0.00 13.29 0.00 12.07 0.02 11.44 0.02 11.23 0.02 0 + 248 129.23655 19.091115 -37.0 4.1 -15.5 4.1 15.28 0.00 14.05 0.00 13.51 0.60 13.12 0.00 12.96 0.00 11.80 0.02 11.12 0.02 10.97 0.02 0 + 249 129.24088 21.565474 -34.2 4.1 -17.2 4.1 15.03 0.00 13.89 0.00 13.66 0.60 13.13 0.00 13.01 0.00 11.95 0.02 11.27 0.02 11.12 0.02 1 + 250 129.24437 18.831177 -33.2 4.1 -16.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.11 0.03 13.44 0.03 13.26 0.02 0 + 251 129.24599 17.701742 -37.6 5.7 -14.7 5.7 21.38 0.07 19.95 0.03 17.79 0.60 17.17 0.00 16.75 0.01 15.54 0.05 14.73 0.05 14.72 0.09 1 + 252 129.24946 20.406500 -37.2 4.1 -14.4 4.1 15.50 0.00 14.30 0.00 13.64 0.00 13.39 0.00 13.22 0.00 12.13 0.02 11.50 0.02 11.32 0.02 0 + 253 129.25598 20.896657 -34.1 5.6 -13.6 5.6 20.56 0.04 19.29 0.02 17.50 0.01 16.73 0.00 16.34 0.01 14.93 0.04 14.42 0.04 14.18 0.05 0 + 254 129.25847 19.604757 -35.9 1.1 -13.9 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.31 0.01 8.15 0.03 8.06 0.01 0 HD 73081 + 255 129.26106 19.328458 -37.5 4.1 -12.9 4.1 17.51 0.00 16.31 0.00 15.11 0.00 14.55 0.00 14.26 0.00 13.05 0.02 12.43 0.02 12.19 0.02 0 + 256 129.26428 19.178087 -42.0 4.1 -9.8 4.1 14.02 0.00 13.24 0.60 12.88 0.60 12.67 0.60 12.49 0.00 11.44 0.02 10.84 0.02 10.73 0.02 0 + 257 129.26459 19.536000 -39.2 4.1 -14.0 4.1 17.44 0.01 16.20 0.01 14.95 0.00 14.31 0.00 14.09 0.00 12.93 0.02 12.29 0.02 12.10 0.02 0 + 258 129.26551 18.667342 -39.8 4.1 -15.0 4.1 14.64 0.00 13.56 0.00 13.32 0.60 12.93 0.00 12.76 0.00 11.69 0.02 11.05 0.02 10.89 0.02 0 + 259 129.27439 19.283030 -39.6 4.1 -9.0 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.21 0.03 13.63 0.03 13.31 0.04 1 + 260 129.27969 20.778861 -36.8 4.1 -12.6 4.1 18.53 0.01 17.29 0.00 15.86 0.00 15.19 0.00 14.88 0.00 13.66 0.02 13.04 0.03 12.76 0.02 0 + 261 129.28010 17.796881 -31.3 4.2 -18.1 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.65 0.02 12.97 0.02 12.77 0.02 0 + 262 129.29774 19.803656 -36.4 1.3 -13.3 1.3 11.60 0.60 11.20 0.60 11.05 0.60 10.99 0.60 10.98 0.60 10.07 0.02 9.78 0.02 9.69 0.02 0 + 263 129.29946 20.679817 -32.2 4.1 -17.3 4.1 17.55 0.00 16.28 0.00 15.14 0.00 14.65 0.00 14.40 0.00 13.23 0.02 12.58 0.03 12.29 0.02 1 + 264 129.30243 20.544807 -41.0 4.1 -13.0 4.1 15.38 0.00 14.16 0.00 13.56 0.00 13.32 0.00 13.14 0.00 12.13 0.02 11.48 0.03 11.37 0.02 0 + 265 129.30785 17.513550 -42.7 4.2 -9.7 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.18 0.03 10.66 0.03 10.51 0.03 0 + 266 129.31360 20.966413 -34.3 4.1 -18.6 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.43 0.02 12.83 0.02 12.57 0.03 0 + 267 129.31806 19.486197 -38.6 4.1 -11.2 4.1 14.48 0.00 13.35 0.60 12.91 0.60 12.64 0.60 12.37 0.00 11.29 0.02 10.61 0.02 10.47 0.02 0 + 268 129.32444 19.488034 -38.0 5.5 -9.1 5.5 21.14 0.04 19.66 0.03 17.87 0.01 17.02 0.01 16.60 0.01 15.24 0.05 14.72 0.04 14.35 0.06 0 + 269 129.32610 19.698930 -35.1 2.2 -15.2 2.2 12.02 0.00 15.67 0.00 11.77 0.00 11.19 0.00 11.17 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 270 129.33008 18.148255 -43.0 4.1 -8.7 4.1 15.07 0.00 13.93 0.00 13.64 0.60 13.19 0.00 12.99 0.00 11.94 0.02 11.33 0.02 11.14 0.02 1 + 271 129.33285 19.053303 -39.3 4.1 -15.9 4.1 17.12 0.00 15.92 0.00 14.74 0.00 14.19 0.00 13.95 0.00 12.75 0.02 12.09 0.02 11.83 0.02 0 + 272 129.33492 20.535525 -29.8 5.5 -9.8 5.5 20.73 0.04 19.39 0.01 17.52 0.01 16.66 0.00 16.24 0.01 15.02 0.05 14.41 0.05 14.02 0.04 0 + 273 129.34246 20.176991 -37.5 1.3 -16.4 1.3 11.92 0.00 11.42 0.00 11.13 0.00 11.49 0.00 11.23 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 274 129.34337 22.033413 -40.0 4.1 -18.2 4.1 18.10 0.01 16.91 0.00 15.42 0.00 14.70 0.00 14.39 0.00 13.11 0.02 12.52 0.02 12.25 0.03 1 + 275 129.34502 17.687540 -36.3 4.2 -12.0 4.2 18.94 0.02 17.73 0.01 16.30 0.60 15.52 0.00 15.20 0.00 13.92 0.03 13.33 0.03 13.10 0.03 1 + 276 129.34522 17.333260 -40.0 5.7 -6.1 5.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.49 0.03 13.83 0.02 13.54 0.03 0 + 277 129.35072 19.417019 -37.0 4.1 -14.5 4.1 16.54 0.00 20.10 0.09 14.31 0.00 13.78 0.00 13.58 0.00 12.38 0.02 11.74 0.03 11.54 0.02 0 + 278 129.35196 19.786631 -38.9 4.1 -12.7 4.1 19.57 0.01 18.29 0.01 16.72 0.00 16.02 0.00 15.68 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 279 129.35747 17.455038 -29.2 4.2 -13.9 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.85 0.03 14.23 0.03 13.93 0.04 1 + 280 129.35983 19.486872 -43.1 4.1 -12.2 4.1 14.33 0.00 13.71 0.60 13.21 0.60 12.88 0.60 12.63 0.00 11.56 0.02 11.02 0.02 10.83 0.02 1 + 281 129.35987 19.132104 -39.9 4.1 -11.4 4.1 15.38 0.00 19.18 0.04 13.79 0.00 13.28 0.00 13.27 0.00 12.02 0.02 11.39 0.02 11.19 0.02 1 + 282 129.36264 18.976650 -39.2 4.1 -17.0 4.1 17.83 0.01 16.61 0.00 15.34 0.00 14.78 0.00 14.53 0.00 13.31 0.02 12.74 0.02 12.47 0.02 0 + 283 129.36268 18.810943 -35.6 4.1 -17.7 4.1 19.43 0.01 18.15 0.01 16.67 0.00 15.92 0.00 15.59 0.00 14.31 0.02 13.73 0.03 13.47 0.03 0 + 284 129.36470 19.617519 -31.8 2.1 -12.2 2.1 11.83 0.60 11.38 0.60 11.23 0.60 11.19 0.60 11.16 0.60 10.25 0.03 9.89 0.03 9.81 0.02 0 + 285 129.36613 19.903502 -38.3 4.0 -13.5 4.0 18.12 0.01 16.86 0.01 15.56 0.00 14.95 0.00 14.63 0.00 13.41 0.03 12.85 0.03 12.53 0.02 1 + 286 129.36638 19.562600 -37.7 0.9 -14.0 0.9 9.89 0.60 9.70 0.60 9.63 0.60 9.57 0.60 9.53 0.60 8.72 0.01 8.58 0.03 8.46 0.02 0 BD+20 2130 + 287 129.36749 19.162333 -38.3 1.2 -13.6 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.66 0.03 8.49 0.04 8.40 0.01 1 BD+19 2050 + 288 129.36849 20.607912 -45.1 4.0 -12.5 4.0 16.20 0.00 14.94 0.00 14.10 0.00 13.72 0.00 13.50 0.00 12.36 0.02 11.63 0.03 11.49 0.02 0 + 289 129.37245 18.693190 -33.9 4.0 -18.9 4.0 18.65 0.01 17.37 0.00 16.05 0.00 15.47 0.00 15.17 0.00 14.01 0.03 13.37 0.04 13.15 0.03 1 + 290 129.37802 21.127830 -37.2 4.0 -13.7 4.0 18.74 0.01 17.51 0.00 16.15 0.00 15.52 0.00 15.23 0.00 14.08 0.03 13.38 0.03 13.14 0.02 0 + 291 129.37928 19.103922 -40.2 4.0 -15.3 4.0 14.33 0.00 13.47 0.60 13.04 0.60 12.78 0.60 12.60 0.00 11.49 0.02 10.90 0.03 10.77 0.02 0 + 292 129.38327 16.959908 -40.0 4.2 -11.6 4.2 18.85 0.01 17.59 0.00 16.20 0.00 15.52 0.00 15.22 0.00 13.91 0.02 13.37 0.03 13.09 0.03 0 + 293 129.38418 18.883966 -37.6 4.0 -15.6 4.0 17.03 0.00 15.81 0.00 14.79 0.00 14.29 0.00 14.07 0.00 12.94 0.02 12.27 0.03 12.04 0.02 0 + 294 129.38503 19.521658 -40.9 4.0 -17.2 4.0 16.77 0.00 15.51 0.00 14.55 0.00 14.08 0.00 13.88 0.00 12.72 0.02 11.94 0.03 11.79 0.02 0 + 295 129.38768 20.669431 -39.0 4.0 -11.8 4.0 19.51 0.01 18.24 0.01 16.75 0.00 16.09 0.00 15.76 0.00 14.57 0.03 13.93 0.03 13.68 0.04 0 + 296 129.38778 18.654289 -37.1 1.1 -14.6 1.1 10.97 0.60 10.64 0.60 10.53 0.60 10.50 0.60 10.49 0.60 9.62 0.03 9.35 0.03 9.28 0.02 1 + 297 129.39038 19.310986 -41.0 4.0 -16.7 4.0 17.28 0.00 16.10 0.00 14.90 0.00 14.35 0.00 14.11 0.00 12.91 0.02 12.25 0.03 12.02 0.02 0 + 298 129.39091 20.013681 -34.9 1.1 -14.2 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.10 0.02 8.00 0.04 7.95 0.03 0 HD 73161 + 299 129.39573 18.935492 -41.5 4.0 -19.0 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.65 0.03 13.07 0.03 12.85 0.02 0 + 300 129.39598 20.224018 -44.3 4.0 -12.6 4.0 16.15 0.00 14.91 0.00 13.97 0.00 13.69 0.00 13.35 0.00 12.23 0.02 11.54 0.03 11.36 0.02 1 + 301 129.39892 20.990957 -38.0 4.0 -14.5 4.0 13.42 0.60 12.64 0.60 12.28 0.60 12.07 0.60 11.97 0.60 10.89 0.02 10.37 0.03 10.25 0.01 0 + 302 129.40090 19.265081 -36.9 4.0 -14.0 4.0 13.91 0.00 13.05 0.60 12.75 0.60 12.59 0.60 12.44 0.00 11.43 0.02 10.86 0.03 10.76 0.02 0 + 303 129.40415 19.732918 -35.0 1.0 -14.3 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.37 0.02 7.31 0.02 7.29 0.01 0 HD 73174 + 304 129.40913 18.482478 -41.3 4.0 -9.1 4.0 13.55 0.60 12.80 0.60 12.43 0.60 12.20 0.60 12.31 0.00 11.02 0.02 10.60 0.04 10.43 0.02 0 + 305 129.41337 19.170061 -41.2 5.4 -7.8 5.4 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.06 0.03 14.45 0.06 14.16 0.05 0 + 306 129.41912 19.550865 -40.0 4.0 -15.4 4.0 18.41 0.01 16.73 0.60 15.81 0.00 15.18 0.00 14.90 0.00 13.72 0.03 13.05 0.03 12.80 0.02 0 + 307 129.41960 19.518433 -35.2 1.1 -12.4 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.78 0.04 7.73 0.05 7.66 0.01 0 BR Cnc + 308 129.42642 19.133681 -34.3 1.3 -12.5 1.2 10.12 0.60 9.82 0.60 9.74 0.60 9.75 0.60 9.75 0.60 8.91 0.04 8.65 0.05 8.58 0.02 1 BD+19 2052 + 309 129.43721 19.674712 -34.6 4.0 -13.8 4.0 17.30 0.00 16.08 0.00 14.85 0.00 14.30 0.00 14.03 0.00 12.82 0.02 12.26 0.03 11.99 0.02 0 + 310 129.44118 20.092247 -40.0 4.0 -6.9 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.66 0.04 14.03 0.04 13.76 0.04 0 + 311 129.44326 19.599280 -38.4 4.0 -8.6 4.0 12.73 0.60 12.16 0.60 11.91 0.60 11.77 0.60 11.71 0.60 10.73 0.02 10.34 0.03 10.24 0.02 0 + 312 129.44415 19.438365 -35.0 1.3 -13.0 1.3 10.86 0.60 10.56 0.60 10.47 0.60 10.45 0.60 10.46 0.60 9.59 0.03 9.33 0.03 9.28 0.02 0 + 313 129.44482 19.267237 -35.3 0.7 -12.9 0.6 6.76 0.60 6.79 0.60 6.87 0.60 6.90 0.60 6.97 0.60 6.27 0.02 6.19 0.03 6.16 0.01 1 HD 73210 + 314 129.44735 19.106832 -35.2 2.2 -14.7 2.2 12.66 0.00 13.76 0.00 12.59 0.00 12.12 0.00 11.63 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 315 129.44744 19.106879 -35.2 2.2 -14.7 2.2 12.52 0.60 11.98 0.60 11.78 0.60 11.68 0.60 11.64 0.60 10.67 0.02 10.25 0.03 10.20 0.02 0 + 316 129.45454 17.255005 -40.6 4.2 -13.5 4.2 15.84 0.00 14.64 0.00 13.88 0.00 13.54 0.00 13.39 0.00 12.27 0.02 11.59 0.02 11.39 0.02 0 + 317 129.45496 20.785213 -31.8 5.5 -9.7 5.5 21.41 0.06 20.06 0.02 18.11 0.01 17.22 0.00 16.80 0.01 15.38 0.04 14.90 0.06 14.58 0.07 0 + 318 129.45539 19.962874 -44.2 5.4 -16.9 5.4 21.11 0.04 19.69 0.02 17.85 0.01 16.99 0.00 16.53 0.01 15.25 0.05 14.56 0.05 14.22 0.05 0 + 319 129.45752 19.514097 -33.1 4.0 -9.4 4.0 16.53 0.00 20.07 0.10 14.38 0.00 13.96 0.00 13.79 0.00 12.60 0.02 11.99 0.03 11.78 0.02 0 + 320 129.45759 20.794638 -42.9 5.3 -15.8 5.3 20.16 0.02 18.82 0.01 17.12 0.00 16.36 0.00 16.00 0.00 14.76 0.04 14.09 0.04 13.78 0.03 0 + 321 129.45812 19.891312 -31.9 1.3 -19.6 1.3 11.68 0.60 11.14 0.60 10.94 0.60 10.86 0.60 10.81 0.60 9.86 0.03 9.46 0.03 9.33 0.02 0 + 322 129.46689 19.987194 -37.5 1.3 -15.1 1.3 11.65 0.60 11.25 0.60 11.08 0.60 11.01 0.60 10.98 0.60 10.08 0.02 9.79 0.03 9.69 0.02 1 + 323 129.47474 22.293125 -29.5 4.0 -10.7 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.61 0.03 12.96 0.03 12.69 0.02 0 + 324 129.47728 20.136709 -38.4 4.0 -16.0 4.0 16.64 0.00 15.39 0.00 14.43 0.00 14.05 0.00 13.82 0.00 12.68 0.03 12.00 0.03 11.82 0.02 0 + 325 129.47861 19.486011 -38.0 4.0 -14.5 4.0 18.69 0.01 17.41 0.01 15.97 0.00 15.32 0.00 15.01 0.00 13.76 0.03 13.12 0.04 12.90 0.03 0 + 326 129.48752 19.236229 -35.5 1.6 -14.0 1.6 12.08 0.60 11.63 0.60 11.47 0.60 11.41 0.60 11.38 0.60 10.47 0.03 10.12 0.03 10.04 0.02 0 + 327 129.48795 19.463843 -36.2 4.0 -11.6 4.0 18.48 0.01 17.23 0.00 15.84 0.00 15.20 0.00 14.89 0.00 13.66 0.03 13.02 0.03 12.77 0.03 1 + 328 129.48982 18.973124 -37.1 5.3 -8.7 5.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.80 0.03 14.12 0.05 13.85 0.04 1 + 329 129.50193 19.682268 -36.8 5.3 -11.5 5.3 21.08 0.04 19.76 0.02 17.81 0.01 16.89 0.00 16.43 0.00 14.99 0.04 14.46 0.05 14.12 0.05 1 + 330 129.50246 18.964673 -36.7 4.0 -12.9 4.0 18.14 0.01 16.91 0.00 15.44 0.00 14.76 0.00 14.43 0.00 13.22 0.03 12.59 0.03 12.35 0.02 0 + 331 129.50466 19.978590 -38.5 4.0 -14.5 4.0 18.99 0.01 17.73 0.01 16.26 0.60 15.37 0.00 15.03 0.00 13.81 0.03 13.10 0.03 12.86 0.02 0 + 332 129.50556 20.541504 -31.6 5.3 -18.4 5.3 20.57 0.03 19.19 0.02 17.34 0.60 16.57 0.00 16.15 0.01 14.80 0.03 14.26 0.04 13.88 0.04 0 + 333 129.51021 20.621231 -30.8 5.3 -15.6 5.3 20.55 0.03 19.19 0.03 17.37 0.60 16.67 0.00 16.27 0.01 14.94 0.04 14.28 0.04 14.01 0.04 0 + 334 129.51088 21.205471 -28.6 4.0 -15.2 4.0 14.79 0.00 13.72 0.00 13.41 0.60 13.09 0.00 12.95 0.00 11.88 0.02 11.27 0.03 11.15 0.02 1 + 335 129.51538 19.697548 -37.2 4.0 -8.8 4.0 17.90 0.01 16.66 0.00 15.46 0.00 14.83 0.00 14.60 0.00 13.40 0.02 12.73 0.03 12.50 0.02 0 + 336 129.51878 17.256494 -37.8 5.7 -5.1 5.7 21.34 0.05 20.10 0.03 18.16 0.01 17.32 0.01 16.89 0.01 15.60 0.05 14.92 0.06 14.52 0.07 0 + 337 129.51918 20.659772 -35.8 4.0 -15.8 4.0 18.87 0.01 17.71 0.01 16.20 0.00 15.55 0.00 15.23 0.00 14.01 0.02 13.38 0.04 13.14 0.03 1 + 338 129.52808 19.571599 -36.6 4.0 -18.5 4.0 19.36 0.02 18.12 0.01 16.59 0.00 15.88 0.00 15.56 0.00 14.34 0.03 13.71 0.04 13.39 0.03 0 + 339 129.52953 17.310587 -40.8 4.2 -11.3 4.2 16.56 0.00 15.35 0.00 14.47 0.60 14.00 0.00 13.82 0.00 12.68 0.02 12.00 0.02 11.81 0.02 1 + 340 129.52985 17.782413 -38.4 4.2 -19.5 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.00 0.02 12.36 0.02 12.12 0.02 0 + 341 129.53033 20.448792 -42.3 4.0 -14.1 4.0 17.10 0.00 15.87 0.00 15.08 0.60 14.32 0.00 14.08 0.00 12.94 0.02 12.31 0.03 12.04 0.02 0 + 342 129.53159 19.987877 -36.9 2.2 -14.5 2.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.46 0.03 10.01 0.03 9.90 0.02 0 + 343 129.53218 17.050684 -36.6 1.1 -13.1 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.94 0.04 8.79 0.03 8.70 0.02 1 BD+17 1894 + 344 129.53328 20.064033 -37.7 4.0 -14.2 4.0 19.39 0.01 18.17 0.01 16.63 0.00 15.95 0.00 15.62 0.00 14.40 0.03 13.71 0.03 13.48 0.03 1 + 345 129.53359 20.439466 -35.9 1.3 -15.1 1.3 12.10 0.60 11.62 0.60 11.43 0.60 11.34 0.60 11.30 0.60 10.36 0.02 10.01 0.03 9.93 0.02 0 + 346 129.53362 18.741635 -35.7 4.0 -22.4 4.0 19.99 0.01 18.79 0.01 17.16 0.00 16.37 0.00 16.01 0.00 14.73 0.04 14.17 0.04 13.81 0.04 0 + 347 129.53398 20.446129 -36.5 4.0 -11.2 4.0 17.05 0.00 15.84 0.00 15.09 0.60 14.29 0.00 14.06 0.00 12.94 0.02 12.26 0.03 12.02 0.02 0 + 348 129.55680 21.224794 -41.0 4.0 -10.9 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.37 0.03 13.72 0.04 13.48 0.02 0 + 349 129.55789 21.157247 -35.9 4.0 -18.1 4.0 17.40 0.00 16.18 0.00 15.05 0.00 14.53 0.00 14.30 0.00 13.09 0.02 12.47 0.03 12.21 0.02 0 + 350 129.55930 19.365353 -32.1 1.3 -9.2 1.2 11.32 0.60 10.84 0.60 10.67 0.60 10.61 0.60 10.58 0.60 9.65 0.03 9.28 0.03 9.19 0.02 0 + 351 129.56231 20.567775 -34.4 1.3 -14.1 1.3 11.45 0.60 11.09 0.60 10.99 0.60 10.98 0.60 10.97 0.60 10.11 0.02 9.80 0.03 9.72 0.02 1 + 352 129.56568 21.385649 -38.0 4.0 -16.4 4.0 16.19 0.00 14.87 0.00 14.14 0.00 13.79 0.00 13.61 0.00 12.46 0.02 11.82 0.03 11.63 0.02 0 + 353 129.57014 21.498238 -42.9 4.0 -8.3 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.52 0.03 12.94 0.04 12.72 0.03 0 + 354 129.57058 20.309780 -32.4 5.3 -15.2 5.3 19.46 0.02 18.36 0.01 16.63 0.60 15.80 0.00 15.44 0.00 14.10 0.03 13.53 0.04 13.24 0.03 1 + 355 129.58903 20.448088 -34.4 4.0 -12.8 4.0 15.96 0.00 14.78 0.00 99.00 99.00 13.66 0.00 13.48 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 356 129.58950 20.137364 -43.5 5.3 -18.4 5.3 20.21 0.02 18.86 0.01 17.13 0.01 16.21 0.00 15.82 0.01 14.52 0.03 13.80 0.03 13.54 0.03 0 + 357 129.59029 18.611097 -34.0 4.0 -8.4 4.0 13.83 0.00 12.93 0.60 12.50 0.60 12.25 0.60 12.08 0.00 10.96 0.02 10.32 0.03 10.18 0.02 0 + 358 129.59104 20.093251 -40.7 4.0 -9.7 4.0 20.48 0.02 19.17 0.02 17.41 0.01 16.62 0.00 16.21 0.01 14.88 0.03 14.30 0.04 13.89 0.04 0 + 359 129.59943 20.728031 -35.7 4.0 -11.4 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.08 0.02 11.43 0.03 11.30 0.02 0 + 360 129.60117 20.106041 -33.4 1.3 -14.4 1.3 10.84 0.60 10.53 0.60 10.41 0.60 10.36 0.60 10.35 0.60 9.46 0.02 9.24 0.03 9.18 0.02 1 + 361 129.60135 20.105960 -33.4 1.3 -14.4 1.3 13.09 0.00 99.00 99.00 99.00 99.00 15.39 0.01 11.70 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 362 129.60361 16.976683 -39.8 4.2 -10.7 4.2 17.57 0.01 16.36 0.00 15.18 0.00 14.64 0.00 14.36 0.00 13.22 0.02 12.60 0.02 12.35 0.02 0 + 363 129.60565 18.941670 -42.7 4.0 -15.2 4.0 17.20 0.00 15.96 0.00 14.71 0.00 14.11 0.00 13.82 0.00 12.64 0.03 11.96 0.03 11.73 0.02 0 + 364 129.61544 19.765459 -38.3 4.0 -11.4 4.0 16.77 0.00 15.55 0.00 14.50 0.00 14.07 0.00 13.82 0.00 12.63 0.02 11.97 0.03 11.77 0.02 0 + 365 129.61738 21.546100 -40.4 4.1 -15.8 4.1 13.99 0.00 13.09 0.60 12.63 0.60 12.34 0.60 12.05 0.00 11.00 0.02 10.34 0.02 10.20 0.01 0 + 366 129.62338 19.862507 -41.2 4.1 -7.7 4.1 14.55 0.00 13.60 0.60 13.19 0.60 12.86 0.00 12.74 0.00 11.67 0.02 11.06 0.02 10.93 0.02 0 + 367 129.62773 18.121733 -38.1 4.1 -8.6 4.1 16.70 0.00 15.42 0.00 14.25 0.60 13.70 0.00 13.45 0.00 12.15 0.02 11.50 0.02 11.28 0.02 1 + 368 129.62914 16.790207 -34.4 1.7 -8.6 1.7 12.01 0.60 11.59 0.60 11.44 0.60 11.38 0.60 11.36 0.60 10.45 0.02 10.10 0.02 10.00 0.02 0 + 369 129.63460 18.781326 -37.1 4.1 -13.1 4.1 17.81 0.00 16.62 0.00 15.40 0.00 14.84 0.00 14.58 0.00 13.43 0.02 12.83 0.02 12.53 0.02 0 + 370 129.63671 19.773754 -38.3 4.1 -12.0 4.1 16.88 0.00 15.69 0.00 14.70 0.00 14.30 0.00 14.04 0.00 12.88 0.02 12.25 0.02 12.01 0.02 0 + 371 129.63687 17.393894 -40.8 4.2 -14.9 4.2 20.07 0.02 18.77 0.01 17.15 0.01 16.43 0.00 16.09 0.01 14.79 0.03 14.13 0.04 14.02 0.05 0 + 372 129.64206 20.774774 -31.7 4.1 -11.4 4.1 19.35 0.02 18.10 0.01 16.56 0.00 15.88 0.00 15.56 0.00 14.28 0.02 13.69 0.03 13.36 0.03 0 + 373 129.64301 18.188072 -38.6 4.1 -8.5 4.1 16.26 0.00 15.01 0.00 14.16 0.00 14.00 0.00 13.63 0.00 12.49 0.02 11.82 0.02 11.63 0.02 0 + 374 129.64852 21.177267 -33.3 5.6 -19.5 5.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.16 0.04 14.65 0.06 14.28 0.07 1 + 375 129.65445 21.246874 -31.4 4.1 -13.5 4.1 19.51 0.02 18.34 0.01 16.83 0.00 16.03 0.00 15.69 0.00 14.45 0.03 13.82 0.03 13.54 0.04 0 + 376 129.65504 19.021113 -38.0 4.1 -11.8 4.1 14.28 0.00 13.39 0.60 12.94 0.60 12.66 0.60 12.49 0.00 11.35 0.02 10.76 0.02 10.61 0.01 0 + 377 129.65607 19.257972 -35.5 4.1 -19.1 4.1 18.12 0.01 16.81 0.00 15.43 0.00 14.80 0.00 14.51 0.00 13.32 0.02 12.59 0.02 12.40 0.02 0 + 378 129.65779 19.989754 -34.9 1.2 -13.2 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.70 0.01 7.66 0.03 7.61 0.01 0 CY Cnc + 379 129.66263 20.170768 -42.6 4.1 -14.7 4.1 17.90 0.01 16.67 0.00 15.31 0.00 14.69 0.00 14.38 0.00 13.16 0.02 12.48 0.02 12.28 0.02 0 + 380 129.66301 17.496805 -34.7 4.2 -17.8 4.2 20.35 0.02 19.01 0.01 17.35 0.01 16.59 0.00 16.20 0.00 14.94 0.03 14.32 0.04 13.96 0.05 0 + 381 129.66360 19.694452 -37.6 5.5 -12.1 5.5 20.46 0.04 19.10 0.01 17.28 0.01 16.46 0.00 16.03 0.00 14.70 0.03 14.10 0.04 13.87 0.05 0 + 382 129.66637 17.905825 -40.7 4.2 -15.5 4.2 20.00 0.02 18.07 0.60 17.38 0.02 16.39 0.00 16.04 0.00 14.81 0.04 14.27 0.04 13.94 0.05 0 + 383 129.67191 19.996413 -35.9 5.6 -21.7 5.6 20.98 0.04 19.75 0.02 17.80 0.01 16.96 0.00 16.53 0.01 15.23 0.04 14.61 0.06 14.27 0.06 0 + 384 129.67266 19.421699 -37.2 4.1 -16.4 4.1 15.20 0.00 14.00 0.00 13.67 0.60 13.29 0.00 13.07 0.00 12.00 0.02 11.34 0.02 11.19 0.02 0 + 385 129.67324 19.571678 -33.9 4.1 -21.1 4.1 20.16 0.02 18.86 0.01 17.22 0.00 16.48 0.00 16.11 0.01 14.83 0.03 14.23 0.04 13.96 0.05 0 + 386 129.67550 18.382074 -35.3 4.1 -19.2 4.1 19.52 0.02 18.19 0.01 16.52 0.00 15.74 0.00 15.35 0.01 14.05 0.03 13.48 0.03 13.27 0.04 0 + 387 129.68296 19.293956 -37.6 4.1 -16.5 4.1 17.47 0.00 16.27 0.00 14.93 0.00 14.33 0.00 14.19 0.00 12.83 0.02 12.19 0.02 11.96 0.02 0 + 388 129.68522 17.808142 -36.0 1.6 -15.8 1.6 12.18 0.00 11.90 0.00 11.34 0.00 11.92 0.00 11.36 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 389 129.69202 20.576737 -43.6 4.1 -17.3 4.1 12.97 0.60 12.34 0.60 12.06 0.60 11.91 0.60 11.84 0.60 10.82 0.02 10.40 0.02 10.31 0.02 0 + 390 129.69564 19.500927 -35.9 1.1 -13.3 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.35 0.02 8.30 0.04 8.22 0.02 0 HD 73397 + 391 129.69938 21.759250 -38.6 5.5 -11.2 5.5 19.90 0.02 18.63 0.01 16.95 0.00 16.25 0.00 15.91 0.01 14.41 0.03 13.86 0.04 13.53 0.04 0 + 392 129.69984 21.298427 -31.1 4.1 -17.8 4.1 18.51 0.01 17.34 0.00 15.96 0.00 15.18 0.00 14.89 0.00 13.63 0.02 13.04 0.02 12.75 0.02 1 + 393 129.70713 18.265840 -38.6 4.1 -8.5 4.1 13.04 0.60 12.41 0.60 12.15 0.60 12.00 0.60 11.94 0.60 10.92 0.01 10.49 0.02 10.40 0.02 0 + 394 129.70826 20.067606 -37.1 1.6 -13.2 1.6 10.82 0.60 10.55 0.60 10.50 0.60 10.52 0.60 10.53 0.60 9.70 0.02 9.43 0.02 9.37 0.02 0 + 395 129.71127 19.415017 -39.6 4.1 -15.9 4.1 17.65 0.00 16.41 0.00 15.18 0.00 14.63 0.00 14.39 0.00 13.19 0.02 12.56 0.02 12.33 0.02 1 + 396 129.71174 18.014558 -39.3 4.1 -10.8 4.1 18.59 0.01 17.35 0.00 15.96 0.00 15.35 0.00 15.07 0.00 13.93 0.02 13.28 0.02 12.98 0.03 1 + 397 129.71254 19.309278 -32.3 4.1 -18.5 4.1 19.53 0.02 18.02 0.01 16.36 0.00 15.62 0.00 15.41 0.01 13.91 0.02 13.37 0.03 13.04 0.03 0 + 398 129.71254 19.850549 -42.0 4.1 -12.5 4.1 18.06 0.01 16.79 0.00 15.39 0.00 14.77 0.00 14.45 0.00 13.22 0.02 12.63 0.03 12.36 0.03 0 + 399 129.72298 19.571379 -37.1 4.1 -15.9 4.1 14.73 0.00 13.71 0.60 13.29 0.60 13.07 0.00 12.82 0.00 11.73 0.02 11.10 0.02 10.97 0.02 0 + 400 129.72574 19.862364 -30.6 4.2 -18.1 4.2 20.74 0.03 19.42 0.02 17.64 0.01 16.84 0.00 16.45 0.01 15.18 0.04 14.59 0.05 14.35 0.06 1 + 401 129.72821 20.788027 -27.9 5.5 -13.9 5.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.97 0.04 14.31 0.05 14.06 0.06 0 + 402 129.72988 20.219160 -44.6 4.1 -16.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.45 0.02 12.84 0.03 12.55 0.02 0 + 403 129.73020 19.283794 -38.5 4.1 -16.3 4.1 16.55 0.00 15.32 0.00 14.38 0.00 13.96 0.00 13.76 0.00 12.63 0.02 11.93 0.02 11.74 0.01 0 + 404 129.73061 18.366547 -33.6 4.1 -13.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.68 0.02 12.07 0.02 11.94 0.02 0 + 405 129.73105 19.842572 -38.1 4.1 -10.8 4.1 19.63 0.02 18.23 0.01 16.70 0.00 15.99 0.00 15.64 0.00 14.40 0.03 13.76 0.04 13.58 0.04 0 + 406 129.73182 17.252653 -37.8 4.2 -14.4 4.2 18.18 0.01 16.95 0.00 15.98 0.60 15.09 0.00 14.70 0.00 13.65 0.02 13.08 0.03 12.71 0.02 0 + 407 129.73572 20.857691 -29.0 4.1 -16.6 4.1 19.51 0.01 18.23 0.01 16.51 0.00 15.71 0.00 15.34 0.00 14.02 0.02 13.45 0.03 13.12 0.03 1 + 408 129.73713 18.858109 -39.3 4.1 -17.2 4.1 17.70 0.00 16.49 0.00 15.13 0.00 14.53 0.00 14.22 0.00 13.03 0.02 12.43 0.02 12.16 0.02 0 + 409 129.73836 20.181554 -44.1 4.1 -16.2 4.1 14.47 0.00 13.46 0.60 13.04 0.60 12.78 0.60 12.57 0.00 11.48 0.02 10.86 0.02 10.72 0.02 0 + 410 129.74056 18.775224 -32.6 4.1 -12.5 4.1 18.50 0.01 17.24 0.00 15.81 0.00 15.10 0.00 14.78 0.00 13.54 0.02 12.92 0.03 12.71 0.02 0 + 411 129.75760 17.855749 -35.3 4.2 -15.9 4.2 16.55 0.00 15.35 0.00 14.38 0.00 13.96 0.00 13.76 0.00 12.66 0.02 11.98 0.02 11.77 0.02 0 + 412 129.75945 19.326242 -38.9 4.1 -15.4 4.1 12.84 0.60 12.26 0.60 11.97 0.60 11.80 0.60 11.74 0.60 10.73 0.02 10.37 0.02 10.26 0.02 1 + 413 129.76182 19.724714 -34.8 1.1 -13.6 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.37 0.02 8.13 0.01 8.12 0.01 0 BD+20 2140 + 414 129.76206 19.532544 -37.5 4.1 -16.9 4.1 17.62 0.00 16.35 0.00 15.10 0.60 14.36 0.00 14.06 0.00 12.83 0.02 12.18 0.02 11.96 0.02 1 + 415 129.76278 19.404308 -38.9 4.1 -16.2 4.1 19.37 0.02 18.12 0.01 16.55 0.00 15.84 0.00 15.49 0.00 14.26 0.02 13.64 0.03 13.41 0.03 0 + 416 129.76331 20.043778 -44.3 4.1 -13.7 4.1 14.97 0.00 13.83 0.00 13.49 0.60 13.07 0.00 12.93 0.00 11.87 0.02 11.21 0.02 11.05 0.02 0 + 417 129.76497 19.999762 -31.5 1.1 -12.6 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.86 0.01 7.82 0.01 7.77 0.01 0 HD 73430 + 418 129.76634 20.567277 -38.0 4.1 -11.3 4.1 17.69 0.01 16.50 0.00 15.34 0.60 14.62 0.00 14.34 0.00 13.11 0.02 12.46 0.02 12.28 0.02 0 + 419 129.76706 19.522671 -37.4 4.1 -12.3 4.1 14.27 0.00 13.48 0.60 13.08 0.60 12.83 0.60 12.60 0.00 11.56 0.02 10.99 0.02 10.86 0.01 0 + 420 129.77123 19.757346 -36.1 4.1 -15.6 4.1 19.06 0.02 17.81 0.01 16.40 0.60 15.62 0.00 15.29 0.00 14.01 0.02 13.42 0.03 13.16 0.03 0 + 421 129.77183 20.117176 -35.1 1.1 -14.3 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.60 0.03 8.45 0.02 8.41 0.02 1 HD 73429 + 422 129.77551 19.676794 -33.7 1.2 -13.9 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.86 0.02 6.77 0.02 6.71 0.01 0 HD 73449 + 423 129.77858 20.348396 -39.1 4.1 -18.2 4.1 16.79 0.00 15.56 0.00 14.57 0.00 14.13 0.00 13.93 0.00 12.80 0.02 12.14 0.02 11.91 0.02 0 + 424 129.78791 19.592422 -34.8 1.1 -14.1 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.01 0.01 7.95 0.02 7.88 0.02 0 BS Cnc + 425 129.79099 19.783030 -39.8 4.1 -13.9 4.1 16.65 0.00 15.42 0.00 14.18 0.00 13.70 0.00 13.44 0.00 12.18 0.02 11.54 0.02 11.35 0.02 0 + 426 129.79219 19.678467 -36.3 1.3 -12.4 1.3 9.62 0.60 9.48 0.60 9.45 0.60 9.44 0.60 9.43 0.60 8.66 0.02 8.50 0.02 8.41 0.01 1 BD+20 2143B + 427 129.79225 20.408369 -37.8 4.1 -13.6 4.1 14.90 0.00 13.74 0.00 13.48 0.60 13.16 0.00 12.86 0.00 11.78 0.02 11.17 0.02 11.00 0.02 0 + 428 129.79567 18.175972 -38.7 1.2 -13.2 1.1 10.52 0.60 10.25 0.60 10.16 0.60 10.13 0.60 10.12 0.60 9.31 0.03 9.07 0.02 8.99 0.02 1 + 429 129.80073 19.115601 -35.8 1.5 -15.1 1.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.56 0.01 9.31 0.02 9.26 0.02 0 + 430 129.80229 21.599173 -43.9 4.1 -9.9 4.1 20.12 0.02 18.88 0.01 17.25 0.00 16.50 0.00 16.15 0.01 14.82 0.03 14.29 0.04 14.00 0.05 0 + 431 129.81046 20.021976 -38.0 4.1 -14.3 4.1 16.54 0.00 15.31 0.00 14.39 0.00 13.97 0.00 13.75 0.00 12.60 0.02 11.94 0.02 11.72 0.02 0 + 432 129.81242 20.210755 -35.8 1.5 -14.1 1.6 11.24 0.60 10.94 0.60 10.87 0.60 10.87 0.60 10.88 0.60 10.04 0.02 9.77 0.02 9.65 0.02 0 + 433 129.81282 19.725436 -33.4 4.1 -12.8 4.1 17.63 0.01 16.45 0.00 15.04 0.60 14.45 0.00 14.18 0.00 12.88 0.02 12.26 0.02 12.02 0.02 1 + 434 129.81353 19.286503 -36.2 4.1 -16.3 4.1 17.23 0.00 15.70 0.60 15.11 0.01 14.42 0.00 14.18 0.00 13.04 0.02 12.39 0.02 12.21 0.02 0 + 435 129.81399 19.324551 -35.7 5.5 -18.2 5.5 20.29 0.03 18.98 0.02 17.30 0.01 16.54 0.00 16.15 0.00 14.86 0.03 14.18 0.04 13.99 0.05 0 + 436 129.81545 19.334006 -36.5 5.5 -14.8 5.5 20.68 0.04 19.16 0.02 17.52 0.01 16.73 0.00 16.34 0.01 15.00 0.04 14.46 0.05 14.18 0.06 0 + 437 129.81584 20.070585 -38.9 4.1 -13.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.74 0.02 11.05 0.02 10.87 0.02 1 + 438 129.81930 17.462899 -33.4 1.6 -13.3 1.6 12.23 0.60 11.72 0.60 11.51 0.60 11.40 0.60 11.36 0.60 10.40 0.02 10.03 0.02 9.97 0.02 0 + 439 129.81988 19.795154 -40.7 4.1 -12.4 4.1 17.11 0.00 15.87 0.00 14.74 0.00 14.35 0.00 14.14 0.00 12.97 0.02 12.34 0.02 12.08 0.02 0 + 440 129.82515 20.739257 -36.9 4.1 -8.3 4.1 16.98 0.00 15.77 0.00 14.74 0.00 14.28 0.00 14.07 0.00 12.91 0.02 12.24 0.02 12.06 0.02 1 + 441 129.82699 19.378917 -40.7 5.5 -12.0 5.5 20.64 0.03 19.27 0.02 17.54 0.01 16.73 0.00 16.32 0.01 15.01 0.04 14.44 0.05 14.01 0.05 1 + 442 129.82865 18.810121 -38.6 4.1 -18.2 4.1 18.47 0.01 17.24 0.00 15.85 0.00 15.19 0.00 14.88 0.00 13.69 0.02 13.03 0.02 12.79 0.03 0 + 443 129.83162 20.291820 -28.6 4.1 -13.4 4.1 19.21 0.02 17.92 0.01 16.37 0.00 15.68 0.00 15.35 0.00 14.07 0.02 13.49 0.03 13.16 0.03 0 + 444 129.83469 19.517481 -30.0 4.1 -17.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.22 0.02 12.58 0.02 12.37 0.02 1 + 445 129.83504 16.900545 -38.2 4.2 -6.6 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.78 0.04 14.11 0.04 13.86 0.05 0 + 446 129.83776 18.436688 -34.4 5.5 -17.5 5.5 20.33 0.02 18.99 0.01 17.29 0.00 16.43 0.00 16.03 0.00 14.68 0.03 14.13 0.03 13.85 0.04 0 + 447 129.83871 22.089042 -40.2 4.1 -11.2 4.1 18.72 0.01 17.51 0.00 16.08 0.00 15.45 0.00 15.16 0.00 13.94 0.02 13.33 0.03 13.06 0.03 0 + 448 129.83971 20.758124 -35.1 1.6 -14.8 1.6 12.06 0.60 11.61 0.60 11.46 0.60 11.41 0.60 11.39 0.60 10.48 0.02 10.10 0.02 10.03 0.02 1 + 449 129.84090 19.861178 -37.8 4.1 -12.9 4.1 12.81 0.60 12.24 0.60 12.02 0.60 11.90 0.60 11.85 0.60 10.87 0.02 10.45 0.02 10.37 0.02 0 + 450 129.84223 20.799537 -31.9 4.1 -15.6 4.1 19.58 0.02 18.28 0.01 16.74 0.60 15.86 0.00 15.49 0.01 14.22 0.03 13.64 0.02 13.36 0.04 0 + 451 129.84346 20.081881 -38.0 4.1 -10.8 4.1 17.70 0.00 16.44 0.00 15.45 0.60 14.81 0.00 14.56 0.00 13.36 0.02 12.75 0.02 12.51 0.02 1 + 452 129.84772 18.666450 -35.8 4.1 -9.3 4.1 16.74 0.00 15.48 0.00 14.53 0.00 14.10 0.00 13.91 0.00 12.72 0.03 12.08 0.03 11.86 0.02 0 + 453 129.85409 19.459361 -35.3 1.6 -13.9 1.6 10.92 0.60 10.53 0.60 10.37 0.60 10.29 0.60 10.27 0.60 9.36 0.02 9.09 0.02 9.00 0.01 0 BD+19 2061 + 454 129.86052 20.862381 -35.3 5.6 -10.1 5.6 20.51 0.05 19.19 0.01 17.38 0.01 16.70 0.01 16.31 0.01 15.06 0.04 14.54 0.05 14.14 0.06 1 + 455 129.86104 20.431169 -38.1 4.1 -10.1 4.1 18.28 0.01 17.06 0.00 15.63 0.00 15.02 0.00 14.68 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 456 129.86107 20.431175 -38.1 4.1 -10.1 4.1 18.31 0.01 16.73 0.60 15.82 0.60 14.98 0.00 14.67 0.00 13.46 0.02 12.91 0.02 12.66 0.02 0 + 457 129.86355 20.733051 -30.6 5.6 -10.6 5.6 21.53 0.08 20.07 0.03 18.10 0.01 17.20 0.00 16.73 0.01 15.32 0.04 14.79 0.05 14.51 0.08 1 + 458 129.86431 18.248767 -37.6 4.1 -6.7 4.1 19.28 0.02 18.05 0.01 16.48 0.00 15.76 0.00 15.43 0.00 14.18 0.03 13.59 0.04 13.25 0.03 1 + 459 129.86898 19.473595 -32.9 1.6 -11.5 1.6 12.09 0.60 11.49 0.60 11.23 0.60 11.09 0.60 11.03 0.60 10.03 0.02 9.63 0.02 9.53 0.02 0 + 460 129.87239 19.786582 -39.2 4.1 -11.1 4.1 13.18 0.60 12.41 0.60 12.09 0.60 11.91 0.60 11.80 0.60 10.74 0.02 10.22 0.02 10.06 0.01 0 + 461 129.87678 20.069092 -35.3 1.6 -12.9 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.14 0.04 8.84 0.03 8.81 0.01 1 BD+20 2146 + 462 129.87764 17.980894 -37.7 4.2 -13.5 4.2 19.54 0.02 18.20 0.01 16.69 0.00 15.99 0.00 15.66 0.00 14.43 0.03 13.74 0.03 13.51 0.04 0 + 463 129.87885 19.967164 -39.7 4.1 -16.5 4.1 18.84 0.01 17.53 0.00 15.96 0.00 15.24 0.00 14.85 0.00 13.55 0.02 12.92 0.02 12.69 0.02 1 + 464 129.88221 19.404843 -34.4 4.1 -10.2 4.1 18.41 0.01 17.14 0.00 15.77 0.00 15.12 0.00 14.82 0.00 13.57 0.02 12.99 0.02 12.73 0.03 0 + 465 129.88338 20.655642 -34.4 2.2 -12.1 2.2 12.64 0.60 11.95 0.60 11.65 0.60 11.47 0.60 11.39 0.60 10.36 0.02 9.90 0.02 9.77 0.01 0 + 466 129.88504 21.047928 -33.7 5.5 -14.0 5.5 19.71 0.01 18.51 0.01 16.89 0.00 16.15 0.00 15.81 0.01 14.61 0.03 13.91 0.04 13.63 0.04 0 + 467 129.89346 20.955739 -36.5 4.1 -10.7 4.1 14.00 0.00 13.37 0.60 12.95 0.60 12.70 0.60 12.49 0.00 11.44 0.02 10.89 0.02 10.75 0.02 0 + 468 129.89797 18.876825 -38.1 1.5 -12.6 1.6 10.92 0.60 10.61 0.60 10.52 0.60 10.51 0.60 10.52 0.60 9.66 0.02 9.38 0.02 9.33 0.02 1 + 469 129.89875 21.739249 -34.9 5.6 -14.1 5.6 21.35 0.06 20.01 0.03 17.79 0.60 17.21 0.01 16.81 0.01 15.53 0.05 14.83 0.06 14.66 0.10 0 + 470 129.90055 18.680263 -33.0 4.1 -11.7 4.1 20.57 0.02 19.18 0.01 17.46 0.00 16.61 0.00 16.18 0.00 14.89 0.04 14.37 0.05 13.89 0.05 0 + 471 129.90180 19.485484 -37.7 4.1 -12.4 4.1 15.73 0.00 14.54 0.00 13.82 0.00 13.66 0.00 13.37 0.00 12.25 0.02 11.60 0.02 11.39 0.02 1 + 472 129.90182 19.260517 -39.5 4.1 -12.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.81 0.02 11.18 0.02 11.01 0.02 0 + 473 129.90423 17.788836 -37.1 4.2 -10.6 4.2 16.13 0.00 14.91 0.00 14.12 0.00 13.75 0.00 13.58 0.00 12.48 0.02 11.76 0.02 11.60 0.02 1 + 474 129.90471 19.816105 -35.3 4.1 -10.6 4.1 15.85 0.00 14.62 0.00 13.86 0.00 13.56 0.00 13.40 0.00 12.29 0.02 11.62 0.02 11.39 0.01 0 + 475 129.90622 18.170371 -36.5 2.2 -15.1 2.2 12.30 0.60 11.84 0.60 11.70 0.60 11.67 0.60 11.64 0.60 10.76 0.02 10.32 0.02 10.24 0.02 0 + 476 129.90969 19.440906 -32.6 2.2 -10.3 2.2 13.04 0.60 12.38 0.60 12.10 0.60 11.94 0.60 11.86 0.60 10.84 0.02 10.39 0.02 10.29 0.02 0 + 477 129.91687 18.846989 -37.5 5.5 -15.2 5.5 20.44 0.03 19.12 0.02 17.44 0.01 16.63 0.00 16.27 0.01 14.97 0.04 14.34 0.04 14.02 0.06 0 + 478 129.91868 19.314940 -39.3 4.1 -9.5 4.1 19.10 0.01 17.88 0.01 16.35 0.00 15.65 0.00 15.31 0.00 14.08 0.03 13.47 0.02 13.23 0.03 0 + 479 129.91904 18.616286 -36.1 4.1 -12.4 4.1 19.19 0.01 17.96 0.00 16.33 0.00 15.55 0.00 15.20 0.00 13.95 0.02 13.32 0.03 13.00 0.03 0 + 480 129.92089 19.991330 -36.3 4.1 -10.9 4.1 15.95 0.00 14.75 0.00 13.98 0.00 13.55 0.00 13.33 0.00 12.18 0.02 11.50 0.02 11.32 0.02 0 + 481 129.92386 20.028191 -37.4 4.1 -12.1 4.1 16.37 0.00 15.14 0.00 14.30 0.00 13.93 0.00 13.74 0.00 12.60 0.02 11.94 0.02 11.76 0.02 0 + 482 129.92504 20.295849 -42.0 4.1 -14.5 4.1 19.19 0.01 17.89 0.00 16.29 0.00 15.59 0.00 15.24 0.00 14.03 0.02 13.46 0.03 13.14 0.03 0 + 483 129.92721 19.307965 -35.3 4.1 -19.6 4.1 18.68 0.01 17.53 0.00 15.91 0.00 15.20 0.00 14.86 0.00 13.60 0.02 13.01 0.02 12.70 0.02 0 + 484 129.92774 19.778486 -36.4 0.6 -12.4 0.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.11 0.02 5.99 0.01 6.00 0.01 0 BT Cnc + 485 129.92833 20.086218 -32.3 1.3 -12.7 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.25 0.01 7.18 0.01 7.16 0.01 0 HD 73574 + 486 129.93161 20.494304 -37.0 4.1 -12.6 4.1 18.40 0.01 17.13 0.00 15.77 0.00 15.15 0.00 14.86 0.00 13.62 0.02 12.98 0.02 12.75 0.02 1 + 487 129.93612 19.275230 -33.8 1.3 -14.0 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.23 0.01 7.22 0.01 7.09 10.00 0 BU Cnc + 488 129.93830 20.124289 -36.9 4.1 -13.5 4.1 18.19 0.01 16.96 0.00 15.43 0.00 14.75 0.00 14.41 0.00 13.14 0.02 12.50 0.02 12.30 0.02 1 + 489 129.94043 18.976122 -41.9 4.1 -12.9 4.1 18.06 0.01 16.85 0.00 15.71 0.60 14.91 0.00 14.64 0.00 13.46 0.02 12.80 0.02 12.56 0.02 0 + 490 129.94064 19.366995 -37.0 1.2 -14.0 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.56 0.02 9.36 0.02 9.26 0.02 0 + 491 129.94404 19.217806 -38.9 4.1 -11.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.35 0.02 11.68 0.02 11.51 0.02 0 + 492 129.94469 19.736814 -35.0 4.1 -9.3 4.1 19.77 0.02 18.43 0.01 16.89 0.60 16.12 0.00 15.78 0.00 14.52 0.03 13.94 0.04 13.64 0.05 1 + 493 129.94602 19.827628 -39.7 4.1 -10.2 4.1 13.20 0.60 12.54 0.60 12.26 0.60 12.10 0.60 12.06 0.00 11.01 0.02 10.56 0.02 10.44 0.02 0 + 494 129.94699 19.659530 -36.2 4.1 -14.5 4.1 18.93 0.01 17.64 0.01 16.14 0.60 15.38 0.00 15.05 0.00 13.78 0.02 13.20 0.03 12.90 0.03 0 + 495 129.95669 18.347410 -34.3 1.1 -14.0 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.44 0.02 8.29 0.02 8.28 0.02 0 HD 73620 + 496 129.96135 19.540823 -31.8 1.3 -12.9 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 5.18 0.24 4.63 0.19 4.39 0.04 0 HD 73598 + 497 129.96161 19.550557 -35.4 2.1 -14.2 2.1 12.75 0.00 99.00 99.00 99.00 99.00 12.85 0.00 11.52 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 498 129.96195 19.550633 -35.4 2.1 -14.2 2.1 12.42 0.60 11.89 0.60 11.63 0.60 11.47 0.60 11.41 0.60 10.42 0.02 10.09 0.02 10.00 0.02 0 + 499 129.96361 20.580526 -42.1 4.1 -12.1 4.1 16.65 0.00 15.43 0.00 14.44 0.00 13.97 0.00 13.72 0.00 12.49 0.02 11.78 0.02 11.59 0.02 0 DO Cnc + 500 129.96796 19.312554 -34.3 1.6 -15.2 1.6 10.60 0.60 10.33 0.60 10.23 0.60 10.17 0.60 10.13 0.60 9.28 0.02 9.09 0.02 9.01 0.02 0 + 501 129.96926 20.512866 -30.6 4.1 -12.0 4.1 18.06 0.01 16.82 0.00 15.69 0.60 14.88 0.00 14.59 0.00 13.37 0.02 12.73 0.02 12.52 0.02 1 + 502 129.96974 20.018088 -37.0 4.1 -15.4 4.1 18.27 0.01 17.00 0.00 15.65 0.00 15.06 0.00 14.77 0.00 13.57 0.02 12.90 0.03 12.66 0.03 0 + 503 129.97139 19.401014 -34.8 4.1 -5.6 4.1 18.69 0.01 17.41 0.00 16.04 0.00 15.39 0.00 15.11 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 504 129.97636 20.560240 -34.5 1.1 -13.0 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.54 0.01 8.40 0.01 8.38 0.02 1 HD 73597 + 505 129.97659 19.460309 -36.1 4.1 -18.1 4.1 19.31 0.01 18.04 0.01 16.51 0.00 15.83 0.00 15.50 0.00 14.24 0.02 13.61 0.04 13.36 0.04 0 + 506 129.97945 20.065042 -38.4 1.6 -12.7 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.20 0.02 8.99 0.02 8.96 0.02 0 BD+20 2151 + 507 129.98032 18.150421 -43.1 4.1 -17.8 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.83 0.03 14.14 0.04 13.96 0.05 0 + 508 129.98540 19.552986 -36.0 1.3 -16.4 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.91 0.01 6.86 0.02 6.79 0.01 0 HD 73618 + 509 129.99075 19.541483 -32.7 1.3 -15.1 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.07 0.01 7.03 0.00 7.01 0.02 0 HD 73619 + 510 129.99182 19.201538 -38.5 1.1 -13.6 1.2 9.47 0.60 9.34 0.60 9.36 0.60 9.43 0.60 9.47 0.60 8.69 0.01 8.51 0.01 8.48 0.02 0 HD 73641 + 511 129.99328 20.158283 -35.9 1.1 -13.9 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.27 0.01 8.22 0.01 8.10 0.01 0 HD 73616 + 512 129.99619 20.031467 -34.4 1.1 -16.9 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.45 0.03 8.25 0.01 8.21 0.02 1 + 513 129.99925 19.566718 -33.8 2.2 -12.1 2.2 12.29 0.60 11.61 0.60 11.34 0.60 11.21 0.60 11.12 0.60 10.11 0.02 9.63 0.02 9.48 0.02 0 + 514 129.99990 19.577891 -39.9 4.1 -8.2 4.1 13.14 0.60 12.52 0.60 12.30 0.60 12.19 0.60 12.10 0.00 11.13 0.01 10.65 0.02 10.55 0.02 0 + 515 130.00027 17.368334 -38.4 5.6 -14.9 5.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.01 0.03 14.44 0.04 14.06 0.04 1 + 516 130.00176 19.403193 -32.5 4.1 -13.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.99 0.04 14.23 0.04 14.03 0.05 0 + 517 130.00279 19.806551 -37.0 1.2 -12.8 1.2 10.41 0.60 10.20 0.60 10.15 0.60 10.15 0.60 10.15 0.60 9.33 0.02 9.15 0.02 9.08 0.02 1 + 518 130.00285 19.309625 -40.8 4.1 -14.8 4.1 15.00 0.00 13.83 0.00 13.41 0.60 12.99 0.00 12.81 0.00 11.74 0.02 11.10 0.02 10.92 0.02 0 + 519 130.00542 20.135612 -35.7 1.2 -14.3 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.85 0.03 8.69 0.02 8.62 0.01 1 HD 73640 + 520 130.00713 18.999870 -38.9 1.2 -12.0 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.05 0.01 8.77 0.02 8.70 0.02 0 TX Cnc + 521 130.00919 18.949157 -36.7 4.1 -11.9 4.1 16.25 0.00 15.01 0.00 13.99 0.00 13.44 0.00 13.19 0.00 12.00 0.01 11.38 0.01 11.16 0.02 0 + 522 130.01028 19.277691 -41.7 4.1 -17.5 4.1 18.86 0.01 17.66 0.01 16.21 0.00 15.57 0.00 15.28 0.00 14.02 0.03 13.40 0.03 13.23 0.04 0 + 523 130.01029 19.676472 -39.2 4.1 -16.1 4.1 18.44 0.01 17.22 0.00 15.94 0.60 15.20 0.00 14.91 0.00 13.71 0.02 13.12 0.02 12.85 0.02 0 + 524 130.01518 19.916535 -35.1 4.1 -16.2 4.1 18.24 0.01 17.04 0.00 15.54 0.00 14.83 0.00 14.50 0.00 13.23 0.02 12.60 0.02 12.35 0.02 0 + 525 130.01621 18.932585 -43.3 5.5 -13.4 5.5 20.72 0.03 19.39 0.02 17.64 0.01 16.82 0.01 16.43 0.01 15.09 0.04 14.49 0.05 14.17 0.06 1 + 526 130.01724 19.784408 -33.0 1.6 -14.1 1.6 12.50 0.60 11.94 0.60 11.68 0.60 11.54 0.60 11.49 0.60 10.51 0.02 10.12 0.02 10.00 0.02 0 + 527 130.01735 19.413961 -37.9 4.1 -14.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.84 0.02 13.21 0.02 12.97 0.03 0 + 528 130.02036 19.729239 -35.4 1.3 -10.9 1.3 10.04 0.60 9.80 0.60 9.73 0.60 9.73 0.60 9.75 0.60 8.91 0.01 8.69 0.02 8.65 0.02 1 BD+20 2157 + 529 130.02372 19.025199 -39.4 4.1 -11.2 4.1 13.10 0.00 12.44 0.00 12.58 0.00 12.99 0.00 11.74 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 530 130.02640 19.307332 -30.8 1.6 -14.0 1.6 11.48 0.60 10.98 0.60 10.78 0.60 10.69 0.60 10.65 0.60 9.70 0.02 9.32 0.02 9.23 0.02 0 + 531 130.02681 20.007801 -36.2 0.7 -11.5 0.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 4.69 0.03 4.47 0.19 4.20 0.02 0 39 CnC + 532 130.02704 21.264795 -34.3 4.1 -11.5 4.1 18.76 0.01 17.46 0.00 15.98 0.00 15.26 0.00 14.94 0.00 13.66 0.02 13.04 0.02 12.77 0.02 0 + 533 130.03209 21.062697 -37.9 0.9 -14.6 0.7 9.44 0.60 9.30 0.60 9.29 0.60 9.32 0.60 9.33 0.60 8.54 0.01 8.39 0.01 8.35 0.02 0 HD 73639 + 534 130.04025 19.621394 -34.0 2.2 -11.3 2.2 12.31 0.60 11.85 0.60 11.64 0.60 11.53 0.60 11.50 0.60 10.56 0.01 10.24 0.02 10.13 0.02 0 + 535 130.04091 18.097237 -36.3 4.1 -7.0 4.1 13.91 0.00 12.98 0.00 12.63 0.00 12.59 0.00 12.55 0.00 11.37 0.02 10.79 0.02 10.68 0.02 0 + 536 130.04169 20.418936 -36.3 4.1 -12.8 4.1 16.17 0.00 14.97 0.00 14.02 0.00 13.54 0.00 13.32 0.00 12.17 0.02 11.50 0.02 11.31 0.02 0 + 537 130.04511 18.982490 -37.9 4.1 -12.2 4.1 17.69 0.00 16.47 0.00 15.05 0.00 14.41 0.00 14.11 0.00 12.87 0.02 12.21 0.02 12.01 0.02 0 + 538 130.04773 19.971149 -31.2 1.5 -12.4 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.50 0.01 6.55 0.01 6.53 0.02 0 40 Cnc + 539 130.04820 19.653249 -38.4 4.1 -13.0 4.1 17.90 0.01 16.68 0.00 15.22 0.60 14.50 0.00 14.18 0.00 12.94 0.02 12.33 0.02 12.07 0.02 0 + 540 130.05124 19.639503 -34.8 1.3 -13.2 1.3 10.03 0.60 9.80 0.60 9.74 0.60 9.74 0.60 9.75 0.60 8.93 0.01 8.71 0.01 8.67 0.02 0 BD+20 2160 + 541 130.05449 20.057809 -40.3 4.1 -11.2 4.1 18.03 0.01 16.80 0.00 15.31 0.00 14.68 0.00 14.37 0.00 13.12 0.02 12.51 0.02 12.25 0.02 0 + 542 130.05597 19.778789 -38.0 4.1 -11.9 4.1 13.49 0.60 12.79 0.60 12.53 0.60 12.40 0.60 12.27 0.00 11.28 0.02 10.74 0.02 10.64 0.02 0 + 543 130.05736 19.748873 -38.7 4.1 -13.6 4.1 16.70 0.00 15.49 0.00 14.60 0.60 14.07 0.00 13.85 0.00 12.75 0.02 12.06 0.02 11.88 0.02 0 + 544 130.06325 20.087206 -38.6 4.1 -9.0 4.1 18.86 0.01 17.63 0.00 15.99 0.00 15.25 0.00 14.86 0.00 13.57 0.02 12.97 0.02 12.69 0.02 1 + 545 130.06396 19.994301 -34.3 1.1 -12.2 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.19 0.01 8.14 0.05 8.04 9.99 0 BD+20 2161 + 546 130.06451 19.458570 -40.7 4.1 -12.8 4.1 14.43 0.00 13.40 0.60 12.98 0.60 12.72 0.60 12.54 0.00 11.43 0.02 10.85 0.02 10.69 0.02 0 + 547 130.06536 19.915052 -39.6 4.1 -11.8 4.1 13.13 0.60 12.36 0.60 12.03 0.60 11.85 0.60 11.75 0.60 10.68 0.02 10.15 0.02 10.01 0.02 0 + 548 130.07033 20.711699 -41.1 5.5 -10.0 5.5 18.71 0.01 17.54 0.00 16.02 0.00 15.36 0.00 15.07 0.00 13.86 0.02 13.25 0.02 12.97 0.03 0 + 549 130.07053 20.700153 -37.3 5.6 -16.2 5.6 20.57 0.03 19.39 0.02 17.63 0.01 16.82 0.01 16.43 0.01 15.14 0.04 14.55 0.06 14.26 0.07 1 + 550 130.07101 18.608258 -36.6 4.1 -13.0 4.1 19.36 0.01 18.07 0.00 16.55 0.00 15.81 0.00 15.48 0.00 14.24 0.03 13.65 0.04 13.35 0.04 0 + 551 130.07344 19.787561 -35.3 1.3 -13.6 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.89 0.01 8.62 10.00 8.58 0.02 1 BD+20 2162 + 552 130.07544 19.532022 -34.0 1.3 -11.7 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.21 0.01 7.20 10.00 7.16 0.01 0 HD 73711 + 553 130.07821 17.550699 -28.4 4.2 -16.3 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.60 0.03 13.92 0.03 13.69 0.04 0 + 554 130.07875 20.191855 -43.4 4.1 -13.7 4.1 13.35 0.60 12.52 0.60 12.16 0.60 11.94 0.60 11.82 0.60 10.73 0.02 10.17 0.02 10.04 0.01 0 + 555 130.07947 18.361925 -34.0 4.1 -15.1 4.1 17.79 0.01 16.56 0.00 15.38 0.00 14.83 0.00 14.58 0.00 13.39 0.02 12.77 0.02 12.56 0.02 1 + 556 130.08003 18.211390 -38.7 4.2 -19.3 4.2 20.95 0.04 19.58 0.02 17.85 0.01 17.07 0.00 16.67 0.01 15.44 0.05 14.77 0.06 14.79 0.10 1 + 557 130.08251 18.927268 -38.9 4.1 -7.4 4.1 16.84 0.00 15.59 0.00 14.43 0.00 13.87 0.00 13.62 0.00 12.42 0.02 11.76 0.02 11.54 0.02 0 + 558 130.08391 19.349001 -35.8 1.4 -12.9 1.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.17 0.01 6.09 10.00 6.05 0.01 0 HD 73712 + 559 130.08647 19.686691 -36.4 1.2 -12.6 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.33 0.01 7.29 0.01 7.28 0.02 0 HD 73709 + 560 130.08883 19.181564 -39.2 4.1 -13.2 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.48 0.02 10.76 0.02 10.76 0.03 0 + 561 130.09206 19.669907 -34.7 1.2 -14.1 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 4.91 0.21 4.32 0.21 4.18 0.03 0 HR 3428 + 562 130.09229 18.123537 -36.9 4.1 -8.6 4.1 16.07 0.00 14.82 0.00 14.03 0.00 13.70 0.00 13.52 0.00 12.38 0.02 11.71 0.02 11.52 0.02 0 + 563 130.09295 20.106789 -36.6 1.4 -12.9 1.5 10.14 0.60 9.92 0.60 9.89 0.60 9.92 0.60 9.95 0.60 9.14 0.02 8.92 0.03 8.85 0.01 0 BD+20 2164 + 564 130.09328 20.640851 -43.2 4.1 -14.6 4.1 15.47 0.00 14.25 0.00 13.61 0.00 13.90 0.00 13.19 0.00 12.09 0.02 11.43 0.02 11.22 0.01 0 + 565 130.09459 19.464775 -33.7 1.6 -11.7 1.6 11.08 0.60 10.74 0.60 10.61 0.60 10.55 0.60 10.52 0.60 9.64 0.02 9.40 0.02 9.34 0.02 0 + 566 130.09696 19.673239 -36.2 1.5 -14.5 1.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.37 0.03 9.08 0.03 9.01 0.02 1 + 567 130.09780 19.834986 -33.8 1.2 -13.1 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.64 0.01 7.67 0.02 7.59 0.01 0 HD 73730 + 568 130.10145 21.231138 -37.1 4.1 -14.0 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.76 0.03 14.05 0.04 13.89 0.06 0 + 569 130.10163 18.453835 -34.3 2.1 -11.2 2.2 12.16 0.60 11.56 0.60 11.30 0.60 11.16 0.60 11.10 0.60 10.10 0.02 9.70 0.02 9.60 0.02 0 + 570 130.10664 19.475762 -33.2 1.6 -12.0 1.6 9.98 0.60 9.78 0.60 9.76 0.60 9.79 0.60 9.82 0.60 9.00 0.03 8.78 0.02 8.76 0.02 0 + 571 130.10895 19.686443 -35.8 1.0 -15.3 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.56 0.01 8.46 0.04 8.36 0.02 0 BD+20 2170 + 572 130.11061 20.253657 -40.1 4.1 -14.7 4.1 17.26 0.00 16.06 0.00 14.97 0.00 14.45 0.00 14.21 0.00 13.06 0.02 12.45 0.03 12.19 0.02 0 + 573 130.11112 17.350071 -33.3 4.2 -10.6 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.04 0.02 13.39 0.03 13.19 0.03 0 + 574 130.11148 20.182007 -32.2 1.2 -12.5 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.56 0.01 7.43 0.02 7.43 0.01 0 BQ Cnc + 575 130.11258 19.544865 -35.2 0.6 -12.8 0.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 5.94 0.01 5.92 0.02 5.88 0.01 0 eps Cnc + 576 130.11290 16.785095 -31.7 1.7 -13.5 1.7 12.48 0.00 13.72 0.00 99.00 99.00 11.57 0.00 11.38 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 577 130.11298 16.785190 -31.7 1.7 -13.5 1.7 11.97 0.60 11.58 0.60 11.43 0.60 11.37 0.60 11.35 0.60 10.51 0.02 10.12 0.02 10.03 0.02 1 + 578 130.11421 19.278004 -33.6 1.6 -11.4 1.6 11.54 0.60 11.15 0.60 11.00 0.60 10.94 0.60 10.92 0.60 10.01 0.02 9.70 0.02 9.65 0.02 1 + 579 130.11463 19.655497 -35.9 4.1 -10.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.30 0.02 10.81 0.02 10.69 0.02 0 + 580 130.11754 18.935793 -39.8 4.1 -13.2 4.1 16.43 0.00 15.19 0.00 14.33 0.00 13.92 0.00 13.74 0.00 12.58 0.02 11.90 0.02 11.71 0.02 0 + 581 130.12174 18.952671 -32.7 9.9 -14.4 9.9 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.75 0.02 11.06 0.02 10.86 0.02 0 + 582 130.12735 19.932989 -39.9 4.1 -10.3 4.1 18.21 0.01 16.59 0.00 15.30 0.00 14.76 0.00 14.47 0.00 13.27 0.02 12.61 0.02 12.39 0.03 0 + 583 130.12802 21.392551 -30.8 4.1 -12.4 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.09 0.02 13.43 0.03 13.26 0.03 0 + 584 130.12934 18.432256 -34.3 4.1 -11.9 4.1 17.62 0.00 16.39 0.00 15.22 0.00 14.69 0.00 14.44 0.00 13.25 0.02 12.63 0.02 12.37 0.02 0 + 585 130.13119 19.695279 -37.1 4.1 -10.1 4.1 15.82 0.00 14.60 0.60 13.99 0.60 13.43 0.00 13.21 0.00 12.10 0.02 11.46 0.02 11.22 0.02 0 + 586 130.13206 19.850292 -34.5 1.6 -14.0 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.29 0.02 9.98 0.02 9.91 0.02 1 + 587 130.13262 20.201716 -37.1 1.6 -14.4 1.6 11.64 0.60 11.26 0.60 11.15 0.60 11.13 0.60 11.12 0.60 10.26 0.02 9.90 0.02 9.83 0.01 0 + 588 130.13736 19.194330 -37.6 1.1 -14.1 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.08 0.03 7.99 0.02 7.96 0.00 0 BV Cnc + 589 130.13932 19.633547 -39.8 4.1 -10.0 4.1 12.62 0.60 12.05 0.60 11.83 0.60 11.73 0.60 11.68 0.60 10.71 0.02 10.26 0.02 10.17 0.02 0 + 590 130.13974 21.315163 -42.9 1.6 -10.5 1.6 11.95 0.60 11.56 0.60 11.43 0.60 11.39 0.60 11.37 0.60 10.47 0.02 10.15 0.02 10.10 0.02 0 + 591 130.13994 18.674534 -35.0 1.3 -12.2 1.3 12.01 0.60 11.54 0.60 11.34 0.60 11.23 0.60 11.19 0.60 10.25 0.02 9.94 0.02 9.83 0.01 1 + 592 130.14225 18.359168 -33.3 4.1 -13.0 4.1 19.59 0.01 18.29 0.01 16.78 0.00 16.04 0.00 15.72 0.00 14.42 0.03 13.74 0.03 13.62 0.05 0 + 593 130.14553 20.601029 -37.9 5.5 -19.1 5.5 20.26 0.02 18.98 0.01 17.27 0.00 16.49 0.00 16.11 0.01 14.88 0.03 14.24 0.05 13.96 0.05 0 + 594 130.15089 21.561703 -36.1 1.6 -12.5 1.6 12.00 0.60 11.56 0.60 11.40 0.60 11.33 0.60 11.31 0.60 10.39 0.02 10.02 0.02 9.97 0.02 0 + 595 130.15099 17.950097 -36.4 4.2 -15.0 4.2 18.27 0.01 17.01 0.00 15.63 0.00 14.95 0.00 14.67 0.00 13.47 0.02 12.84 0.02 12.58 0.02 0 + 596 130.15774 20.338282 -38.2 4.1 -17.7 4.1 15.68 0.00 14.47 0.00 13.81 0.00 13.72 0.00 13.32 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 597 130.16350 19.228298 -35.1 1.3 -13.5 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.33 0.01 7.24 0.01 7.23 0.01 0 BN Cnc + 598 130.16406 19.939966 -36.2 4.1 -13.0 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.56 0.03 12.99 0.03 12.69 0.03 0 + 599 130.16415 19.715386 -39.4 4.1 -15.1 4.1 18.34 0.01 17.12 0.00 15.70 0.00 15.09 0.00 14.86 0.00 13.46 0.02 12.87 0.02 12.59 0.02 0 + 600 130.16458 18.818244 -37.0 4.1 -10.9 4.1 14.93 0.00 13.63 0.00 13.39 0.60 13.15 0.00 12.82 0.00 11.76 0.01 11.16 0.02 11.00 0.01 0 + 601 130.16629 19.669215 -35.2 1.3 -13.8 1.3 11.43 0.60 10.95 0.60 10.73 0.60 10.60 0.60 10.56 0.60 9.61 0.02 9.31 0.02 9.19 0.02 0 + 602 130.17348 19.500204 -35.3 5.5 -13.3 5.5 20.08 0.03 18.74 0.01 16.96 0.00 16.12 0.00 15.71 0.00 14.36 0.03 13.87 0.03 13.54 0.04 0 + 603 130.17453 19.223679 -34.1 1.6 -9.4 1.6 11.07 0.60 10.65 0.60 10.48 0.60 10.40 0.60 10.38 0.60 9.46 0.02 9.15 0.02 9.06 0.02 0 + 604 130.17695 19.565966 -36.1 1.3 -15.4 1.3 11.61 0.60 11.22 0.60 11.07 0.60 11.01 0.60 10.99 0.60 10.09 0.02 9.78 0.02 9.71 0.02 0 + 605 130.18005 19.719328 -34.7 0.7 -11.6 0.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.40 0.01 6.37 0.02 6.33 0.01 0 42 Cnc + 606 130.18445 20.471863 -37.4 4.1 -14.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.24 0.02 10.58 0.02 10.40 0.02 0 + 607 130.18489 18.656539 -37.5 4.1 -7.4 4.1 13.32 0.00 12.90 0.60 12.54 0.60 12.31 0.60 12.10 0.00 11.13 0.02 10.65 0.02 10.50 0.01 1 + 608 130.19192 16.818708 -41.3 5.6 -8.7 5.6 20.55 0.05 19.27 0.01 17.50 0.01 16.62 0.00 16.25 0.01 14.93 0.04 14.35 0.04 14.06 0.05 0 + 609 130.19201 19.309612 -35.6 1.3 -13.3 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.73 0.02 8.56 0.02 8.53 0.02 0 BD+19 2074 + 610 130.19541 20.474765 -33.8 4.1 -18.8 4.1 16.69 0.00 15.48 0.00 14.45 0.00 14.04 0.00 13.74 0.00 12.58 0.02 11.97 0.02 11.68 0.02 0 + 611 130.19841 18.903316 -36.1 1.6 -14.9 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.13 0.02 9.78 0.02 9.69 0.02 0 + 612 130.19904 20.479955 -38.1 4.1 -13.4 4.1 17.74 0.01 16.56 0.00 15.25 0.00 14.64 0.00 14.38 0.00 13.13 0.02 12.51 0.02 12.25 0.02 0 + 613 130.19988 19.658928 -38.2 1.3 -14.7 1.3 11.43 0.60 10.95 0.60 10.76 0.60 10.67 0.60 10.63 0.60 9.69 0.02 9.34 0.02 9.25 0.02 0 + 614 130.20137 19.921923 -35.5 1.6 -15.2 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.86 0.02 9.59 0.02 9.51 0.02 0 + 615 130.20220 21.497087 -33.9 4.1 -18.2 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.03 0.02 12.39 0.02 12.22 0.02 0 + 616 130.21603 19.941717 -39.9 4.1 -15.7 4.1 17.68 0.00 16.42 0.00 15.49 0.60 14.70 0.00 14.46 0.01 13.30 0.02 12.68 0.03 12.40 0.02 0 + 617 130.21789 19.174544 -36.7 4.1 -11.9 4.1 17.91 0.01 16.68 0.00 15.22 0.00 14.57 0.00 14.26 0.00 12.99 0.02 12.38 0.02 12.10 0.02 0 + 618 130.21866 20.266527 -32.7 1.1 -12.1 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.91 0.01 7.85 0.04 7.80 0.01 0 BW Cnc + 619 130.21880 19.483208 -36.2 1.6 -13.8 1.6 10.65 0.60 10.42 0.60 10.31 0.60 10.21 0.60 10.17 0.60 9.34 0.02 9.15 0.02 9.05 0.02 0 + 620 130.22184 18.748313 -34.8 4.1 -16.7 4.1 19.93 0.02 18.62 0.01 17.01 0.00 16.21 0.01 15.87 0.01 14.59 0.03 13.97 0.04 13.67 0.04 1 + 621 130.22415 19.378876 -40.1 4.1 -13.9 4.1 16.37 0.00 15.11 0.00 99.00 99.00 13.86 0.00 13.67 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 622 130.22435 20.050632 -29.9 4.1 -17.3 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.73 0.03 14.08 0.04 13.76 0.05 1 + 623 130.22479 20.090093 -43.3 4.1 -16.7 4.1 18.36 0.01 17.15 0.00 15.81 0.00 15.24 0.00 14.95 0.00 13.80 0.02 13.19 0.03 12.88 0.03 0 + 624 130.22856 19.935194 -37.4 1.6 -15.9 1.6 12.45 0.60 11.91 0.60 11.71 0.60 11.61 0.60 11.56 0.60 10.60 0.02 10.19 0.02 10.13 0.02 0 + 625 130.23037 18.583094 -37.9 4.1 -10.6 4.1 15.09 0.00 13.93 0.00 13.55 0.60 13.22 0.00 13.01 0.00 11.92 0.02 11.26 0.02 11.13 0.02 0 + 626 130.23110 18.297845 -43.4 4.1 -10.0 4.1 19.72 0.01 18.51 0.01 16.99 0.00 16.28 0.00 15.94 0.00 14.71 0.03 14.05 0.04 13.86 0.05 0 + 627 130.23208 18.826146 -33.5 4.1 -15.2 4.1 19.31 0.01 18.06 0.01 16.60 0.00 15.90 0.00 15.58 0.00 14.34 0.02 13.75 0.03 13.45 0.04 0 + 628 130.23292 18.246181 -36.9 4.1 -13.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.68 0.02 13.01 0.03 12.83 0.03 0 + 629 130.23460 19.580351 -34.4 0.7 -10.7 0.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 6.37 0.01 6.33 0.01 6.28 0.01 0 EP Cnc + 630 130.23624 19.734787 -36.7 2.2 -11.9 2.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.72 0.02 10.31 0.03 10.21 0.02 0 + 631 130.23723 19.934862 -36.4 1.1 -11.9 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.13 0.01 8.08 0.03 8.05 0.02 0 HD 73818 + 632 130.23957 20.478164 -41.4 5.6 -7.7 5.6 20.64 0.05 19.40 0.02 17.63 0.01 16.85 0.00 16.45 0.01 15.09 0.03 14.56 0.06 14.31 0.08 1 + 633 130.24344 18.846168 -36.6 4.1 -19.9 4.1 19.02 0.01 17.77 0.01 16.24 0.00 15.58 0.00 15.24 0.00 13.99 0.03 13.43 0.03 13.13 0.03 0 + 634 130.24432 18.675075 -39.3 4.1 -11.5 4.1 14.19 0.00 13.41 0.60 12.91 0.60 12.60 0.60 12.35 0.00 11.25 0.02 10.69 0.02 10.49 0.01 0 + 635 130.24473 22.480510 -35.1 5.6 -14.6 5.6 21.69 0.10 20.39 0.02 18.44 0.01 17.54 0.01 17.08 0.01 15.69 0.06 15.23 0.10 14.89 0.12 0 + 636 130.24860 18.367892 -38.5 7.5 -5.8 7.0 12.17 0.60 11.58 0.60 11.38 0.60 11.29 0.60 11.23 0.60 10.26 0.02 9.78 0.02 9.68 0.02 1 + 637 130.25317 16.613337 -36.8 1.7 -5.4 1.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.83 0.02 9.40 0.03 9.33 0.01 0 + 638 130.25727 17.814317 -38.8 3.9 -20.2 3.9 19.63 0.01 18.38 0.01 16.80 0.00 16.06 0.00 15.69 0.01 14.51 0.04 13.80 0.04 13.51 0.03 0 + 639 130.26090 20.457693 -37.6 4.1 -10.7 4.1 12.83 0.60 12.25 0.60 11.98 0.60 11.83 0.60 11.77 0.60 10.78 0.02 10.41 0.02 10.29 0.01 0 + 640 130.26091 18.168523 -30.8 4.1 -10.0 4.1 20.39 0.02 19.04 0.01 17.31 0.01 16.55 0.00 16.14 0.01 14.87 0.03 14.27 0.04 13.89 0.05 0 + 641 130.26295 18.931933 -32.8 4.1 -9.1 4.1 18.81 0.01 17.55 0.00 16.13 0.00 15.50 0.00 15.19 0.00 13.96 0.02 13.28 0.03 13.10 0.03 0 + 642 130.26391 18.621075 -41.4 5.6 -11.6 5.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.32 0.05 14.88 0.07 14.36 0.08 0 + 643 130.27208 20.473473 -40.0 4.1 -13.7 4.1 15.72 0.00 14.50 0.00 13.82 0.00 13.52 0.00 13.35 0.00 12.27 0.02 11.60 0.02 11.41 0.02 0 + 644 130.27685 19.102931 -39.8 5.5 -12.8 5.5 20.72 0.04 19.44 0.02 17.63 0.01 16.80 0.01 16.39 0.01 15.11 0.04 14.52 0.04 14.17 0.06 0 + 645 130.27863 19.443587 -38.8 4.1 -16.7 4.1 18.56 0.01 17.30 0.00 15.93 0.60 15.25 0.00 14.95 0.00 13.75 0.02 13.09 0.02 12.85 0.03 0 + 646 130.28010 19.446905 -45.1 4.1 -12.4 4.1 12.85 0.60 12.24 0.60 12.01 0.60 11.89 0.60 11.83 0.60 10.84 0.02 10.40 0.02 10.29 0.02 0 + 647 130.28075 19.071274 -39.5 1.0 -12.3 1.1 10.35 0.60 9.99 0.60 9.89 0.60 9.88 0.60 9.87 0.60 8.99 0.01 8.67 0.02 8.64 0.02 0 BD+19 2076 + 648 130.28851 17.571369 -32.2 3.9 -13.5 3.9 20.27 0.02 18.91 0.01 17.22 0.00 16.44 0.00 16.07 0.01 14.74 0.04 14.22 0.04 13.86 0.04 0 + 649 130.28995 19.855122 -38.0 1.3 -14.5 1.3 11.28 0.60 10.76 0.60 10.53 0.60 10.41 0.60 10.36 0.60 9.41 0.02 9.06 0.02 8.94 0.02 0 BD+19 2077 + 650 130.29073 19.935349 -45.1 4.1 -11.9 4.1 14.10 0.00 13.14 0.60 12.83 0.60 12.68 0.60 12.48 0.00 11.50 0.02 10.89 0.02 10.76 0.02 0 + 651 130.29159 19.508765 -36.7 1.3 -10.4 1.3 12.67 0.00 99.00 99.00 99.00 99.00 11.41 0.00 11.86 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 BD+20 2176 + 652 130.29177 19.508927 -36.7 1.3 -10.4 1.3 10.33 0.60 10.07 0.60 10.01 0.60 10.01 0.60 10.02 0.60 9.18 0.01 8.93 0.02 8.91 0.02 1 BD+20 2176 + 653 130.29229 19.203668 -34.6 5.5 -19.8 5.5 20.65 0.03 19.24 0.02 19.44 0.60 16.60 0.00 16.22 0.00 14.89 0.04 14.29 0.05 13.90 0.05 0 + 654 130.29289 19.818599 -35.6 1.6 -11.9 1.6 11.80 0.60 11.35 0.60 11.19 0.60 11.13 0.60 11.10 0.60 10.18 0.02 9.82 0.02 9.75 0.02 1 + 655 130.29373 18.268613 -38.8 4.1 -14.8 4.1 17.12 0.00 15.88 0.00 14.78 0.00 14.26 0.00 14.04 0.00 12.87 0.02 12.20 0.02 11.96 0.02 0 + 656 130.29377 19.935187 -32.7 4.1 -10.1 4.1 17.51 0.00 16.27 0.00 15.08 0.60 14.35 0.00 14.06 0.00 12.86 0.02 12.26 0.02 11.97 0.02 1 + 657 130.29450 19.829590 -37.2 1.1 -11.1 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.36 0.02 8.24 0.01 8.19 0.01 0 HD 73854 + 658 130.29472 19.031613 -32.8 4.1 -8.6 4.1 19.01 0.01 17.74 0.01 16.07 0.60 15.34 0.00 14.98 0.00 13.70 0.02 13.08 0.03 12.84 0.03 0 + 659 130.29602 20.377347 -33.0 4.1 -19.4 4.1 16.64 0.00 15.45 0.00 14.48 0.00 14.04 0.00 13.83 0.00 12.69 0.02 12.00 0.02 11.82 0.01 0 + 660 130.29701 19.529628 -37.4 4.1 -11.0 4.1 17.16 0.00 15.95 0.00 14.76 0.00 14.21 0.00 13.94 0.00 12.77 0.02 12.14 0.02 11.94 0.02 1 + 661 130.29828 22.264299 -36.4 4.1 -18.6 4.1 17.26 0.01 16.03 0.00 14.89 0.00 14.37 0.00 14.13 0.00 12.99 0.02 12.40 0.02 12.10 0.02 1 + 662 130.30730 20.347727 -31.3 4.1 -12.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.67 0.02 13.02 0.03 12.86 0.02 0 + 663 130.30738 19.921982 -36.4 1.1 -11.6 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.88 0.01 7.82 0.01 7.77 0.01 0 HD 73872 + 664 130.30793 18.969147 -40.0 4.1 -10.1 4.1 16.98 0.00 15.75 0.00 14.59 0.00 14.06 0.00 13.81 0.00 12.64 0.02 11.99 0.02 11.76 0.01 0 + 665 130.30847 20.741569 -40.6 4.1 -14.3 4.1 17.98 0.01 16.64 0.60 15.38 0.60 14.58 0.00 14.27 0.00 13.01 0.02 12.38 0.02 12.15 0.02 1 + 666 130.31013 20.996198 -38.6 5.5 -16.2 5.5 19.81 0.02 18.60 0.01 16.96 0.00 16.23 0.00 15.88 0.00 14.62 0.03 14.02 0.04 13.66 0.04 0 + 667 130.31204 17.460153 -37.4 5.2 -6.9 5.2 20.14 0.02 18.84 0.01 17.22 0.00 16.46 0.00 16.09 0.01 14.84 0.04 14.23 0.05 13.95 0.04 0 + 668 130.31414 20.037763 -42.4 4.1 -10.9 4.1 14.83 0.00 13.74 0.00 13.46 0.60 13.01 0.00 12.87 0.00 11.83 0.02 11.18 0.02 11.02 0.02 0 + 669 130.31420 19.086234 -32.0 4.1 -10.7 4.1 17.46 0.00 16.24 0.00 15.06 0.00 14.53 0.00 14.30 0.00 13.11 0.02 12.49 0.02 12.25 0.02 0 + 670 130.31786 20.815221 -42.5 5.6 -17.7 5.6 21.37 0.06 19.91 0.02 17.96 0.01 17.09 0.00 16.68 0.01 15.36 0.05 14.63 0.05 14.48 0.09 1 + 671 130.32280 20.542493 -42.8 4.1 -11.5 4.1 19.07 0.01 17.83 0.00 16.29 0.00 15.59 0.00 15.29 0.00 14.02 0.02 13.42 0.03 13.11 0.03 1 + 672 130.32669 19.260960 -35.9 1.1 -11.4 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.43 0.01 7.35 0.04 7.29 0.02 0 HI Cnc + 673 130.32949 19.088511 -40.6 4.1 -16.9 4.1 19.11 0.01 17.76 0.01 16.10 0.60 15.36 0.00 15.04 0.00 13.70 0.02 13.13 0.03 12.87 0.02 1 + 674 130.33004 20.777552 -38.2 4.1 -15.5 4.1 13.86 0.00 13.31 0.60 12.90 0.60 12.64 0.60 12.41 0.00 11.38 0.02 10.84 0.02 10.70 0.02 0 + 675 130.33198 19.098448 -34.9 4.1 -16.2 4.1 20.42 0.03 18.43 0.60 17.20 0.60 16.60 0.00 16.20 0.01 14.93 0.04 14.34 0.05 14.00 0.06 0 + 676 130.33293 19.634624 -39.0 4.1 -8.3 4.1 14.01 0.00 13.25 0.60 12.89 0.60 12.67 0.60 12.47 0.00 11.45 0.02 10.90 0.02 10.76 0.02 1 + 677 130.33466 18.961917 -33.9 4.1 -12.9 4.1 19.13 0.01 17.87 0.01 16.36 0.00 15.64 0.00 15.31 0.01 14.09 0.03 13.47 0.03 13.16 0.03 0 + 678 130.33509 19.622886 -40.2 4.1 -12.5 4.1 16.50 0.00 15.28 0.00 14.38 0.00 13.97 0.00 13.78 0.00 12.68 0.02 11.96 0.02 11.79 0.02 1 + 679 130.33687 20.346544 -37.1 4.1 -18.7 4.1 17.35 0.00 16.14 0.00 15.03 0.00 14.54 0.00 14.32 0.00 13.16 0.02 12.52 0.02 12.27 0.02 0 + 680 130.33710 21.915005 -39.6 4.1 -8.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.29 0.02 12.60 0.02 12.43 0.02 0 + 681 130.34401 18.933893 -38.1 4.1 -10.1 4.1 13.36 0.00 12.94 0.60 12.57 0.60 12.35 0.60 12.17 0.00 11.16 0.02 10.68 0.02 10.54 0.02 0 + 682 130.34871 20.939491 -41.9 5.6 -19.9 5.6 20.91 0.04 19.66 0.02 17.84 0.01 17.01 0.01 16.62 0.01 15.33 0.05 14.71 0.06 14.46 0.08 0 + 683 130.34952 20.249232 -44.3 4.1 -11.8 4.1 15.28 0.00 14.06 0.00 13.53 0.60 13.11 0.00 12.75 0.00 11.63 0.02 10.98 0.02 10.78 0.02 0 + 684 130.35066 18.234055 -36.4 4.1 -16.8 4.1 16.35 0.00 15.11 0.00 14.21 0.00 13.77 0.00 13.55 0.00 12.28 0.02 11.62 0.02 11.45 0.02 0 + 685 130.35182 20.130431 -40.0 4.1 -19.3 4.1 17.17 0.00 15.96 0.00 14.64 0.00 14.03 0.00 13.74 0.00 12.53 0.02 11.90 0.02 11.64 0.02 0 + 686 130.35753 19.943461 -34.7 1.1 -10.5 1.1 11.04 0.00 12.54 0.00 12.46 0.00 11.30 0.00 10.60 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 BD+20 2181 + 687 130.35758 19.943593 -34.7 1.1 -10.5 1.1 11.04 0.60 10.71 0.60 10.59 0.60 10.54 0.60 10.53 0.60 9.66 0.02 9.40 0.02 9.33 0.02 1 BD+20 2181 + 688 130.35835 19.987520 -38.2 4.1 -14.3 4.1 18.86 0.01 17.50 0.01 16.11 0.00 15.43 0.00 15.10 0.00 13.89 0.02 13.26 0.03 13.08 0.03 1 + 689 130.35886 17.803518 -35.3 3.9 -11.1 3.9 18.44 0.01 17.18 0.00 15.80 0.00 15.14 0.00 14.82 0.00 13.65 0.03 12.97 0.03 12.77 0.02 0 + 690 130.36157 19.876831 -33.0 4.1 -7.0 4.1 20.10 0.02 18.82 0.01 17.08 0.00 16.28 0.00 15.89 0.00 14.66 0.03 13.95 0.04 13.77 0.05 0 + 691 130.36242 19.542488 -37.5 1.1 -12.0 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.94 0.01 8.78 0.02 8.72 0.02 0 BD+20 2180 + 692 130.36493 19.277601 -35.6 4.1 -8.9 4.1 16.62 0.00 15.41 0.00 14.43 0.00 13.96 0.00 13.74 0.00 12.59 0.02 11.92 0.02 11.72 0.02 0 + 693 130.36542 21.061336 -38.7 4.1 -14.2 4.1 18.54 0.01 17.35 0.00 15.89 0.00 15.24 0.00 14.92 0.00 13.66 0.02 13.06 0.03 12.76 0.03 0 + 694 130.36955 19.746706 -38.5 1.3 -13.2 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.81 0.02 9.55 0.02 9.47 0.02 0 + 695 130.36997 19.975588 -41.4 4.1 -14.4 4.1 17.46 0.00 16.26 0.00 15.04 0.00 14.43 0.00 14.15 0.00 12.99 0.02 12.36 0.02 12.10 0.02 0 + 696 130.37057 22.268163 -38.6 1.0 -16.0 1.0 10.49 0.60 10.15 0.60 10.01 0.60 9.95 0.60 9.93 0.60 9.04 0.01 8.78 0.01 8.70 0.01 0 BD+22 1975 + 697 130.37071 18.759704 -29.5 5.5 -9.5 5.5 20.67 0.03 19.32 0.02 17.52 0.60 16.81 0.00 16.42 0.01 15.12 0.04 14.51 0.06 14.19 0.06 0 + 698 130.37784 18.871887 -39.1 4.1 -13.6 4.1 13.34 0.60 12.57 0.60 12.19 0.60 11.96 0.60 11.85 0.60 10.77 0.02 10.31 0.03 10.15 0.02 0 + 699 130.38139 18.500551 -35.1 1.2 -13.3 1.3 10.40 0.60 10.18 0.60 10.14 0.60 10.16 0.60 10.18 0.60 9.36 0.02 9.14 0.02 9.10 0.02 0 + 700 130.38470 18.669642 -30.0 4.1 -10.1 4.1 18.44 0.01 17.21 0.00 15.91 0.00 15.27 0.00 14.98 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 701 130.38473 18.669647 -30.0 4.1 -10.1 4.1 18.47 0.01 16.90 0.60 16.08 0.60 15.28 0.00 14.99 0.00 13.80 0.02 13.18 0.03 12.92 0.03 1 + 702 130.38539 20.101895 -33.2 4.1 -11.2 4.1 19.65 0.02 18.38 0.01 16.86 0.00 16.16 0.00 15.84 0.01 14.58 0.03 13.94 0.04 13.72 0.04 0 + 703 130.38969 19.550054 -39.8 4.1 -13.2 4.1 19.75 0.01 18.50 0.01 16.91 0.01 16.19 0.00 15.87 0.00 14.57 0.03 13.95 0.04 13.68 0.04 0 + 704 130.39087 19.969095 -36.7 1.3 -16.4 1.3 12.23 0.00 99.00 99.00 99.00 99.00 11.74 0.00 11.23 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 705 130.39103 19.969041 -36.7 1.3 -16.4 1.3 12.35 0.60 11.83 0.60 11.57 0.60 11.42 0.60 11.35 0.60 10.39 0.02 10.08 0.02 9.93 0.01 0 + 706 130.39826 17.490854 -41.3 3.9 -11.1 3.9 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.86 0.02 13.27 0.04 13.05 0.03 0 + 707 130.39862 18.743064 -35.7 4.1 -14.7 4.1 17.66 0.01 16.47 0.00 15.25 0.60 14.65 0.00 14.39 0.00 13.15 0.02 12.53 0.02 12.32 0.02 1 + 708 130.39931 21.293613 -38.9 5.5 -16.3 5.5 19.41 0.01 18.15 0.00 16.60 0.00 15.88 0.00 15.53 0.00 14.29 0.03 13.64 0.04 13.45 0.04 0 + 709 130.39989 19.107092 -35.0 4.1 -13.1 4.1 14.70 0.00 13.72 0.60 13.30 0.60 12.93 0.00 12.81 0.00 11.75 0.02 11.12 0.02 10.98 0.02 0 + 710 130.40087 19.142651 -36.0 1.0 -14.3 1.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.52 0.01 8.43 0.01 8.35 0.02 0 HD 73937 + 711 130.40200 20.567871 -43.5 4.1 -18.1 4.1 19.22 0.01 17.93 0.01 16.40 0.00 15.68 0.00 15.36 0.00 14.14 0.03 13.48 0.03 13.18 0.03 1 + 712 130.40248 18.904285 -36.6 4.1 -18.3 4.1 18.06 0.01 17.14 0.02 15.46 0.00 14.80 0.00 14.52 0.00 13.31 0.02 12.67 0.02 12.44 0.03 0 + 713 130.40561 20.210232 -39.8 4.1 -13.8 4.1 19.18 0.01 17.95 0.01 16.40 0.00 15.71 0.00 15.41 0.01 14.19 0.03 13.48 0.03 13.33 0.03 0 + 714 130.40578 19.520531 -39.3 4.1 -11.0 4.1 14.04 0.00 13.35 0.60 12.94 0.60 12.68 0.60 12.47 0.00 11.43 0.02 10.88 0.02 10.73 0.01 1 + 715 130.41027 17.640000 -40.2 3.9 -11.7 3.9 15.85 0.00 14.62 0.00 13.90 0.00 13.56 0.00 13.41 0.00 12.28 0.02 11.60 0.03 11.43 0.01 0 + 716 130.41331 19.674503 -39.2 4.1 -13.8 4.1 17.76 0.01 16.59 0.00 15.25 0.00 14.65 0.00 14.39 0.00 13.19 0.02 12.56 0.02 12.30 0.02 0 + 717 130.41673 20.672199 -36.8 0.8 -13.9 0.9 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.79 0.02 8.64 0.01 8.59 0.01 0 BD+21 1891 + 718 130.42382 19.832625 -37.1 4.1 -20.0 4.1 19.50 0.01 18.21 0.01 16.72 0.00 16.04 0.00 15.74 0.01 14.52 0.03 13.83 0.03 13.60 0.04 1 + 719 130.42519 20.568891 -41.3 5.5 -13.9 5.5 20.02 0.03 18.79 0.01 17.14 0.00 16.36 0.00 16.01 0.00 14.80 0.03 14.07 0.04 13.83 0.04 0 + 720 130.42622 19.660547 -37.1 0.9 -12.9 0.9 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.69 0.03 8.57 0.02 8.48 0.02 1 BD+20 2183 + 721 130.42633 18.213284 -36.4 4.2 -19.6 4.2 20.97 0.03 19.66 0.02 17.83 0.01 17.00 0.00 16.60 0.01 15.27 0.04 14.71 0.06 14.27 0.06 0 + 722 130.43048 21.361687 -38.5 5.5 -15.9 5.5 20.24 0.02 18.90 0.01 17.28 0.00 16.48 0.00 16.14 0.00 14.90 0.03 14.20 0.04 13.91 0.05 0 + 723 130.43080 21.497311 -34.8 5.5 -16.5 5.5 20.71 0.04 19.29 0.01 17.43 0.01 16.49 0.00 16.05 0.00 14.71 0.03 14.06 0.04 13.63 0.04 0 + 724 130.43194 19.962133 -40.5 2.2 -13.8 2.2 12.82 0.60 12.23 0.60 11.97 0.60 11.83 0.60 11.77 0.60 10.77 0.02 10.37 0.02 10.26 0.02 0 + 725 130.43260 20.226891 -36.0 1.3 -16.9 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.46 0.02 9.23 0.02 9.14 0.01 0 BD+20 2184 + 726 130.43276 19.302276 -38.1 4.1 -9.4 4.1 16.13 0.00 14.89 0.00 14.11 0.00 13.74 0.00 13.57 0.00 12.48 0.02 11.80 0.02 11.64 0.02 1 + 727 130.43381 20.604438 -35.1 4.1 -14.1 4.1 18.78 0.01 17.60 0.00 16.11 0.00 15.44 0.00 15.12 0.00 13.87 0.02 13.20 0.03 12.99 0.03 0 + 728 130.43946 19.267325 -37.6 1.2 -10.9 1.3 10.46 0.60 10.20 0.60 10.11 0.60 10.08 0.60 10.06 0.60 9.22 0.02 9.03 0.02 8.93 0.02 1 BD+19 2081 + 729 130.44956 20.389607 -38.8 5.6 -14.8 5.6 20.68 0.03 19.39 0.02 18.16 0.04 16.79 0.00 16.40 0.01 15.08 0.04 14.54 0.05 14.23 0.06 0 + 730 130.45071 19.458669 -43.1 4.1 -8.3 4.1 14.13 0.00 13.35 0.60 12.93 0.60 12.68 0.60 12.50 0.00 11.41 0.02 10.88 0.02 10.73 0.01 0 + 731 130.45552 19.196401 -37.8 4.1 -12.2 4.1 15.09 0.00 13.95 0.00 13.39 0.60 12.96 0.00 12.76 0.00 11.65 0.02 11.01 0.02 10.83 0.01 0 + 732 130.45600 20.076716 -33.9 4.1 -15.8 4.1 17.59 0.00 16.35 0.00 14.99 0.00 14.37 0.00 14.08 0.00 12.84 0.02 12.27 0.02 11.97 0.02 1 + 733 130.45847 19.659624 -37.2 4.1 -8.3 4.1 16.79 0.00 15.57 0.00 14.57 0.00 14.13 0.00 13.92 0.00 12.74 0.02 12.13 0.02 11.89 0.02 0 + 734 130.45867 19.874176 -38.4 1.1 -13.7 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 5.24 0.01 5.03 0.18 4.68 0.00 0 HD 73974 + 735 130.46067 19.494303 -33.0 5.5 -11.4 5.5 20.50 0.02 19.17 0.01 17.45 0.01 16.69 0.00 16.32 0.01 15.03 0.04 14.39 0.04 14.12 0.06 0 + 736 130.46624 20.346612 -38.1 4.1 -18.2 4.1 17.23 0.00 16.02 0.00 14.92 0.00 14.44 0.00 14.25 0.00 13.05 0.02 12.39 0.03 12.19 0.02 0 + 737 130.46652 20.166984 -39.5 1.6 -15.9 1.6 12.53 0.00 14.85 0.00 99.00 99.00 12.17 0.00 11.66 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 738 130.46753 19.707833 -32.7 4.1 -8.0 4.1 19.05 0.01 17.79 0.01 16.23 0.00 15.49 0.00 15.16 0.00 13.89 0.02 13.24 0.02 13.01 0.02 1 + 739 130.46774 18.051851 -38.3 4.1 -15.0 4.1 15.63 0.00 14.43 0.00 13.75 0.00 13.47 0.00 13.38 0.00 12.17 0.02 11.51 0.02 11.34 0.02 0 + 740 130.47143 20.159456 -36.8 1.1 -12.6 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.93 0.02 7.81 0.01 7.79 0.01 0 HD 73993 + 741 130.47219 19.265475 -32.1 5.5 -12.1 5.5 20.36 0.03 18.79 0.01 17.19 0.01 16.45 0.00 16.07 0.00 14.76 0.03 14.24 0.04 13.89 0.05 0 + 742 130.47658 19.257416 -35.9 1.3 -12.6 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.03 0.02 9.73 0.02 9.64 0.02 0 + 743 130.47758 18.302799 -30.3 4.1 -17.3 4.1 19.79 0.02 18.50 0.01 16.85 0.00 16.06 0.00 15.71 0.00 14.39 0.02 13.79 0.03 13.49 0.04 0 + 744 130.47971 17.094664 -44.9 2.2 -13.1 2.2 12.69 0.60 11.96 0.60 11.65 0.60 11.48 0.60 11.39 0.60 10.33 0.01 9.84 0.03 9.69 0.02 1 + 745 130.48273 19.689641 -36.2 1.1 -15.0 1.1 11.39 0.60 11.03 0.60 10.88 0.60 10.80 0.60 10.77 0.60 9.87 0.02 9.63 0.02 9.54 0.01 0 + 746 130.49076 18.911690 -37.5 1.0 -10.4 1.0 9.51 0.60 9.39 0.60 9.39 0.60 9.40 0.60 9.40 0.60 8.64 0.04 8.49 0.02 8.43 0.02 0 HD 73994 + 747 130.49510 20.107527 -43.9 4.1 -10.6 4.1 13.65 0.00 12.93 0.60 12.60 0.60 12.42 0.60 12.24 0.00 11.24 0.02 10.72 0.02 10.60 0.01 0 + 748 130.49671 20.918618 -36.0 1.3 -16.4 1.3 11.60 0.60 11.20 0.60 11.06 0.60 10.99 0.60 10.98 0.60 10.07 0.02 9.76 0.02 9.70 0.02 1 + 749 130.49721 19.745859 -40.8 4.1 -8.7 4.1 16.72 0.00 15.52 0.00 14.68 0.60 14.10 0.00 13.86 0.00 12.74 0.02 12.07 0.03 11.89 0.02 1 + 750 130.51354 21.172635 -43.5 5.5 -13.2 5.5 20.36 0.03 19.12 0.02 17.39 0.00 16.57 0.00 16.18 0.01 14.87 0.03 14.20 0.04 13.94 0.05 0 + 751 130.52147 20.965686 -39.6 4.1 -18.7 4.1 16.61 0.00 15.38 0.00 14.26 0.00 13.77 0.00 13.55 0.00 12.38 0.02 11.70 0.02 11.51 0.02 0 + 752 130.52709 19.411251 -37.5 1.1 -12.0 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.55 0.01 7.46 0.01 7.43 0.02 0 BX Cnc + 753 130.53260 22.184732 -38.8 4.1 -16.9 4.1 18.75 0.01 17.51 0.00 16.08 0.00 15.42 0.00 15.15 0.00 13.88 0.02 13.25 0.02 13.02 0.03 1 + 754 130.54277 18.766757 -33.2 4.1 -17.8 4.1 19.93 0.02 18.60 0.01 17.02 0.00 16.25 0.00 15.91 0.00 14.64 0.03 14.03 0.04 13.75 0.05 1 + 755 130.54504 18.934366 -35.8 1.0 -12.8 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.44 0.01 7.39 0.01 7.35 0.01 0 BY Cnc + 756 130.54788 19.277044 -37.5 2.2 -9.8 2.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.66 0.02 10.24 0.02 10.17 0.01 0 + 757 130.54891 17.519143 -43.8 7.1 -9.8 17.1 19.68 0.01 18.43 0.01 16.93 0.00 16.24 0.00 15.93 0.00 14.72 0.04 14.06 0.04 13.94 0.05 0 + 758 130.55133 19.213525 -38.8 4.1 -10.7 4.1 14.18 0.00 13.35 0.60 12.98 0.60 12.78 0.60 12.54 0.00 11.54 0.02 10.94 0.02 10.83 0.01 0 + 759 130.55271 21.996883 -31.4 1.3 -8.8 1.3 12.15 0.60 11.58 0.60 11.35 0.60 11.25 0.60 11.19 0.60 10.21 0.02 9.79 0.02 9.72 0.02 1 + 760 130.55289 18.683618 -34.1 4.1 -13.8 4.1 19.53 0.02 18.14 0.01 16.68 0.00 15.96 0.00 15.67 0.00 14.39 0.03 13.84 0.03 13.56 0.04 0 + 761 130.55343 19.267747 -38.4 4.1 -9.5 4.1 13.93 0.00 14.87 0.00 14.72 0.00 12.43 0.00 12.23 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 762 130.55673 19.835726 -40.8 4.1 -16.5 4.1 17.06 0.00 15.86 0.00 15.33 0.02 14.31 0.00 14.09 0.00 12.95 0.02 12.29 0.03 12.05 0.02 0 + 763 130.56446 19.687675 -38.0 1.1 -13.4 1.1 10.20 0.60 9.96 0.60 9.89 0.60 9.87 0.60 9.87 0.60 9.03 0.01 8.85 0.02 8.77 0.01 1 + 764 130.56447 19.815992 -40.0 4.1 -11.0 4.1 17.96 0.01 16.76 0.00 15.48 0.00 14.92 0.00 14.65 0.00 13.48 0.02 12.87 0.03 12.62 0.02 0 + 765 130.56926 20.092344 -43.1 4.1 -15.2 4.1 14.78 0.00 13.68 0.00 13.33 0.60 12.96 0.00 12.84 0.00 11.75 0.02 11.14 0.02 10.99 0.01 0 + 766 130.57298 17.987436 -42.2 4.1 -16.7 4.1 18.55 0.01 17.35 0.00 15.90 0.00 15.25 0.00 14.95 0.00 13.71 0.02 13.07 0.03 12.83 0.03 1 + 767 130.57629 18.392199 -34.6 4.1 -13.9 4.1 18.47 0.01 17.33 0.01 16.06 0.60 15.29 0.00 15.01 0.00 13.79 0.02 13.19 0.03 12.92 0.02 1 + 768 130.57841 20.409718 -33.8 10.3 -9.5 10.7 12.58 0.60 12.02 0.60 11.84 0.60 11.76 0.60 11.71 0.60 10.76 0.02 10.29 0.02 10.19 0.02 0 + 769 130.58004 19.037437 -32.2 4.1 -9.4 4.1 17.84 0.01 16.60 0.00 15.39 0.00 14.86 0.00 14.61 0.00 13.44 0.02 12.84 0.02 12.53 0.02 0 + 770 130.58360 19.151583 -37.1 4.1 -12.0 4.1 13.96 0.00 13.32 0.60 12.90 0.60 12.65 0.60 12.42 0.00 11.39 0.02 10.85 0.02 10.70 0.01 0 + 771 130.58374 20.036445 -35.9 1.1 -15.2 1.1 9.89 0.60 9.65 0.60 9.56 0.60 9.52 0.60 9.50 0.60 8.68 0.02 8.45 0.05 8.41 0.02 0 BD+20 2189 + 772 130.58784 20.055770 -38.0 4.1 -20.1 4.1 19.19 0.01 17.93 0.01 16.53 0.01 15.74 0.00 15.45 0.01 14.22 0.02 13.53 0.03 13.27 0.03 0 + 773 130.59013 20.181425 -34.4 1.1 -14.3 1.1 12.20 0.00 99.00 99.00 99.00 99.00 99.00 99.00 11.14 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 HD 74058 + 774 130.59014 20.181807 -34.4 1.1 -14.3 1.1 9.32 0.60 9.22 0.60 9.19 0.60 9.16 0.60 9.22 0.60 8.44 0.01 8.28 0.03 8.28 0.01 0 HD 74058 + 775 130.60829 21.230832 -37.1 4.1 -9.9 4.1 17.81 0.00 16.40 0.00 15.04 0.00 14.67 0.00 14.08 0.00 12.75 0.02 12.11 0.02 11.90 0.02 0 + 776 130.61681 17.246758 -43.9 3.9 -8.7 3.9 18.68 0.01 17.48 0.00 16.10 0.00 15.45 0.00 15.17 0.00 13.97 0.03 13.37 0.03 13.11 0.02 0 + 777 130.62355 19.331247 -32.2 4.1 -13.1 4.1 20.42 0.02 19.08 0.01 17.41 0.01 16.64 0.00 16.24 0.00 15.02 0.04 14.35 0.05 14.05 0.05 0 + 778 130.62442 19.975457 -39.0 4.1 -14.8 4.1 20.02 0.03 18.62 0.01 17.19 0.60 16.29 0.01 15.97 0.01 14.74 0.03 14.14 0.04 13.73 0.05 0 + 779 130.62798 19.116032 -36.9 4.1 -17.6 4.1 19.83 0.01 18.50 0.01 16.91 0.00 16.16 0.00 15.81 0.01 14.51 0.03 13.99 0.04 13.59 0.04 0 + 780 130.62811 19.491935 -36.3 4.1 -11.5 4.1 18.90 0.01 17.66 0.00 16.47 0.00 15.61 0.00 15.30 0.00 14.10 0.03 13.53 0.03 13.23 0.03 1 + 781 130.63347 18.591117 -32.6 4.1 -9.7 4.1 16.61 0.00 15.41 0.00 14.50 0.00 14.02 0.00 13.81 0.00 12.64 0.02 12.04 0.03 11.79 0.02 1 + 782 130.63429 19.396172 -36.1 1.5 -10.4 1.6 11.36 0.60 10.97 0.60 10.82 0.60 10.76 0.60 10.74 0.60 9.84 0.02 9.54 0.02 9.46 0.01 0 + 783 130.64018 18.458064 -44.7 5.6 -10.6 5.6 21.45 0.06 20.25 0.03 18.33 0.01 17.41 0.01 16.97 0.01 15.62 0.07 14.93 0.08 14.63 0.10 0 + 784 130.64168 19.603415 -38.4 4.1 -14.5 4.1 17.64 0.00 16.45 0.00 15.09 0.00 14.45 0.00 14.16 0.00 12.93 0.02 12.38 0.02 12.11 0.02 0 + 785 130.64512 20.994658 -40.7 4.1 -15.5 4.1 16.52 0.00 15.28 0.00 14.35 0.00 13.94 0.00 13.74 0.00 12.63 0.02 11.93 0.02 11.75 0.02 0 + 786 130.64520 17.991003 -32.4 1.5 -6.6 1.6 11.05 0.60 10.66 0.60 10.50 0.60 10.42 0.60 10.40 0.60 9.48 0.02 9.22 0.02 9.13 0.01 0 + 787 130.65332 18.388878 -32.5 1.4 -14.3 1.5 10.40 0.60 10.14 0.60 10.05 0.60 10.02 0.60 10.01 0.60 9.16 0.02 8.97 0.02 8.88 0.02 1 BD+18 2020 + 788 130.65408 20.142149 -43.5 4.1 -11.8 4.1 14.54 0.00 13.55 0.60 13.15 0.60 12.91 0.60 12.71 0.00 11.63 0.02 11.04 0.02 10.87 0.02 0 + 789 130.65664 19.988580 -31.8 4.1 -12.4 4.1 19.43 0.01 18.18 0.01 16.58 0.00 15.86 0.00 15.50 0.00 14.26 0.03 13.67 0.04 13.39 0.04 0 + 790 130.65953 18.541069 -41.3 5.6 -10.8 5.6 21.86 0.09 20.32 0.03 20.16 0.60 17.56 0.01 17.11 0.01 15.72 0.08 15.21 0.11 14.78 0.10 0 + 791 130.66422 19.414409 -40.0 4.1 -8.3 4.1 19.46 0.01 18.14 0.01 16.62 0.00 15.94 0.00 15.60 0.00 14.35 0.03 13.77 0.04 13.50 0.04 1 + 792 130.66748 19.133071 -35.4 1.6 -10.8 1.6 12.65 0.60 12.08 0.60 11.84 0.60 11.72 0.60 11.66 0.60 10.69 0.01 10.29 0.02 10.19 0.01 0 + 793 130.66964 19.543164 -38.1 1.3 -10.9 1.3 9.97 0.60 9.77 0.60 9.73 0.60 9.75 0.60 9.77 0.60 8.95 0.04 8.76 0.02 8.72 0.02 0 BD+20 2192 + 794 130.67064 19.532940 -30.5 4.1 -16.9 4.1 20.41 0.03 18.96 0.01 17.34 0.01 16.63 0.00 16.27 0.01 15.00 0.04 14.55 0.05 14.09 0.06 1 + 795 130.67521 19.292300 -37.1 4.1 -10.2 4.1 15.30 0.00 14.14 0.00 13.63 0.60 13.16 0.00 12.98 0.00 11.86 0.02 11.23 0.02 11.05 0.02 1 + 796 130.67698 19.099691 -35.7 1.6 -13.3 1.6 11.86 0.60 11.44 0.60 11.29 0.60 11.23 0.60 11.21 0.60 10.30 0.02 9.98 0.02 9.88 0.02 0 + 797 130.68209 19.623185 -36.3 2.2 -14.5 2.2 12.66 0.60 11.97 0.60 11.67 0.60 11.49 0.60 11.41 0.60 10.37 0.02 9.91 0.02 9.80 0.02 0 + 798 130.68498 19.579865 -38.3 1.1 -12.0 1.1 9.79 0.60 9.66 0.60 9.62 0.60 9.58 0.60 9.61 0.60 8.86 0.04 8.68 0.02 8.63 0.02 0 BD+20 2193 + 799 130.68578 18.466664 -40.2 4.1 -12.7 4.1 19.87 0.02 18.63 0.01 17.04 0.00 16.21 0.00 15.86 0.01 14.52 0.04 13.94 0.04 13.64 0.05 0 + 800 130.68869 18.859935 -35.9 1.5 -9.0 1.6 11.12 0.60 10.71 0.60 10.55 0.60 10.47 0.60 10.45 0.60 9.53 0.02 9.23 0.02 9.17 0.01 0 + 801 130.68899 20.592618 -35.4 5.6 -10.4 5.6 20.40 0.03 19.17 0.01 17.43 0.01 16.65 0.00 16.25 0.01 99.00 99.00 99.00 99.00 99.00 99.00 0 + 802 130.68901 20.592627 -35.4 5.6 -10.4 5.6 20.42 0.04 21.17 0.60 19.40 0.60 16.64 0.01 16.24 0.01 14.91 0.04 14.35 0.06 14.13 0.07 0 + 803 130.69142 21.271197 -40.6 4.1 -12.5 4.1 14.87 0.00 13.83 0.00 13.47 0.60 13.15 0.00 12.84 0.00 11.73 0.02 11.09 0.03 10.92 0.01 0 + 804 130.69395 18.438601 -36.5 5.5 -6.6 5.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.99 0.05 14.43 0.06 14.09 0.07 0 + 805 130.70192 20.573432 -34.9 2.2 -12.7 2.2 13.02 0.60 12.37 0.60 12.07 0.60 11.90 0.60 11.82 0.60 10.79 0.02 10.36 0.02 10.26 0.02 0 + 806 130.70691 18.859742 -31.7 4.1 -18.0 4.1 16.65 0.00 15.45 0.00 14.47 0.00 13.99 0.00 13.76 0.00 12.61 0.02 11.92 0.02 11.74 0.02 0 + 807 130.71032 19.917688 -40.5 4.1 -9.5 4.1 18.14 0.00 16.94 0.00 15.60 0.00 15.03 0.00 14.73 0.00 13.53 0.02 12.90 0.03 12.66 0.02 0 + 808 130.71040 20.334397 -39.4 5.5 -15.4 5.5 20.39 0.03 19.14 0.01 17.42 0.01 16.65 0.00 16.28 0.01 14.96 0.04 14.40 0.05 14.07 0.05 1 + 809 130.71775 19.862756 -39.7 4.1 -8.4 4.1 17.59 0.00 16.41 0.00 15.10 0.00 14.52 0.00 14.24 0.00 13.00 0.02 12.37 0.02 12.21 0.02 0 + 810 130.72113 20.819223 -38.3 1.0 -15.0 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.21 0.02 8.14 0.05 8.06 0.02 0 HD 74135 + 811 130.72961 20.520607 -42.1 5.6 -10.8 5.6 21.60 0.05 20.27 0.03 18.31 0.01 17.42 0.01 16.96 0.01 15.55 0.06 14.94 0.09 14.57 0.08 0 + 812 130.73635 20.071677 -39.2 4.1 -12.2 4.1 18.73 0.01 17.47 0.00 15.99 0.00 15.31 0.00 14.99 0.00 13.77 0.03 13.17 0.02 12.79 0.03 1 + 813 130.75219 20.337814 -36.3 1.6 -14.7 1.6 11.69 0.00 12.14 0.00 12.15 0.00 11.85 0.00 11.03 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 814 130.75769 19.901260 -39.0 5.5 -19.7 5.5 20.39 0.02 19.13 0.01 17.38 0.01 16.59 0.00 16.20 0.00 14.98 0.04 14.27 0.04 14.05 0.06 1 + 815 130.75999 19.167558 -37.3 2.2 -11.3 2.2 12.44 0.60 11.94 0.60 11.72 0.60 11.61 0.60 11.57 0.60 10.61 0.01 10.25 0.02 10.16 0.02 0 + 816 130.76196 21.753765 -42.5 4.1 -18.1 4.1 19.63 0.03 18.32 0.01 16.61 0.00 15.75 0.00 15.37 0.00 13.98 0.02 13.43 0.04 13.07 0.03 1 + 817 130.77203 19.465176 -39.3 4.1 -12.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.36 0.02 11.65 0.02 11.55 0.02 0 + 818 130.77312 18.918331 -32.7 5.5 -12.5 5.5 20.75 0.05 19.51 0.02 17.75 0.01 16.91 0.00 16.51 0.01 15.08 0.05 14.62 0.06 14.31 0.08 0 + 819 130.77474 19.437574 -36.3 1.4 -15.5 1.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.79 0.02 8.52 0.01 8.46 0.02 0 BD+19 2087 + 820 130.77552 19.414488 -36.7 4.1 -16.5 4.1 18.54 0.01 17.26 0.00 15.85 0.00 15.21 0.00 14.90 0.00 13.69 0.02 13.04 0.02 12.79 0.02 1 + 821 130.77783 19.791586 -38.9 4.1 -14.6 4.1 14.16 0.00 13.32 0.60 12.94 0.60 12.71 0.60 12.52 0.00 11.47 0.02 10.89 0.02 10.77 0.02 0 + 822 130.77783 19.791580 -38.9 4.1 -14.6 4.1 14.15 0.00 13.26 0.00 12.77 0.00 12.98 0.00 12.55 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 823 130.77940 19.068342 -37.0 1.1 -10.1 1.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.59 0.02 8.39 0.02 8.36 0.03 0 HD 74186 + 824 130.78419 19.713199 -38.1 4.1 -12.5 4.1 13.91 0.00 12.92 0.60 12.62 0.60 12.45 0.60 12.35 0.00 11.30 0.02 10.77 0.02 10.67 0.01 0 + 825 130.78497 19.468365 -36.3 4.1 -12.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.25 0.02 12.65 0.02 12.39 0.02 1 + 826 130.79486 19.526299 -38.6 2.1 -17.1 2.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 10.44 0.02 10.07 0.02 10.01 0.02 0 + 827 130.80009 18.695849 -35.4 5.5 -14.4 5.5 20.33 0.03 18.95 0.01 17.26 0.60 16.51 0.00 16.15 0.01 14.88 0.04 14.27 0.04 14.03 0.06 0 + 828 130.80263 19.574717 -38.0 4.1 -21.6 4.1 19.52 0.02 18.21 0.01 16.63 0.60 15.89 0.00 15.56 0.01 14.31 0.03 13.65 0.02 13.46 0.03 0 + 829 130.80374 18.530784 -35.7 4.1 -11.3 4.1 17.62 0.01 16.29 0.00 15.29 0.60 14.59 0.00 14.34 0.00 13.17 0.02 12.51 0.02 12.28 0.02 1 + 830 130.81102 17.708384 -33.2 3.8 -15.0 3.8 19.76 0.01 18.50 0.01 16.92 0.00 16.16 0.00 15.77 0.01 14.55 0.04 13.91 0.04 13.61 0.03 0 + 831 130.81335 20.065564 -43.1 4.1 -12.8 4.1 13.98 0.00 13.16 0.60 12.79 0.60 12.58 0.60 12.41 0.00 11.36 0.02 10.80 0.02 10.68 0.02 0 + 832 130.81569 20.475746 -37.3 4.1 -7.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.39 0.03 13.70 0.04 13.45 0.04 0 + 833 130.81611 19.109200 -32.1 4.1 -15.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.21 0.03 13.63 0.03 13.34 0.04 0 + 834 130.82100 20.411288 -37.1 5.6 -10.8 5.6 20.72 0.03 19.45 0.02 17.73 0.01 16.96 0.00 16.61 0.01 15.29 0.06 14.72 0.07 14.54 0.09 0 + 835 130.82427 20.510359 -37.2 1.6 -13.8 1.6 12.33 0.60 11.73 0.60 11.49 0.60 11.38 0.60 11.31 0.60 10.32 0.02 9.89 0.02 9.78 0.02 0 + 836 130.82671 19.518834 -42.3 4.1 -9.3 4.1 18.08 0.01 16.92 0.00 15.61 0.00 15.03 0.00 14.76 0.00 13.57 0.02 12.89 0.02 12.68 0.03 0 + 837 130.83410 19.769019 -40.7 1.5 -14.5 1.5 10.96 0.60 10.66 0.60 10.56 0.60 10.53 0.60 10.53 0.60 9.66 0.02 9.42 0.02 9.36 0.02 0 + 838 130.83446 20.079312 -43.5 4.1 -10.6 4.1 19.59 0.01 18.35 0.01 16.77 0.00 16.05 0.00 15.72 0.01 14.46 0.03 13.81 0.03 13.56 0.04 0 + 839 130.84321 19.200204 -38.0 4.1 -16.1 4.1 18.25 0.01 17.00 0.00 15.66 0.00 15.05 0.00 14.75 0.00 13.57 0.03 12.94 0.03 12.65 0.03 0 + 840 130.84348 20.907018 -38.6 5.6 -10.9 5.6 20.92 0.05 19.62 0.02 17.83 0.01 17.01 0.00 16.63 0.01 15.29 0.05 14.64 0.06 14.34 0.08 0 + 841 130.84390 21.671656 -38.1 1.3 -16.0 1.3 10.77 0.60 10.39 0.60 10.27 0.60 10.24 0.60 10.23 0.60 9.33 0.02 8.98 0.02 8.97 0.02 1 BD+22 1980 + 842 130.84627 19.785973 -31.1 4.1 -6.6 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.09 0.04 14.47 0.06 14.20 0.07 1 + 843 130.84959 18.679167 -30.3 4.1 -13.9 4.1 17.17 0.00 15.97 0.00 14.95 0.60 14.40 0.00 14.16 0.00 13.02 0.02 12.34 0.02 12.16 0.02 0 + 844 130.85413 20.565339 -39.2 4.1 -12.0 4.1 15.82 0.00 14.52 0.00 13.82 0.00 13.49 0.00 13.35 0.00 12.27 0.02 11.57 0.02 11.40 0.01 0 + 845 130.86488 19.827923 -39.9 4.1 -11.6 4.1 18.71 0.01 17.54 0.00 16.03 0.00 15.34 0.00 15.04 0.00 13.78 0.03 13.18 0.03 12.92 0.03 1 + 846 130.87931 18.548516 -36.0 4.1 -12.4 4.1 14.48 0.00 13.67 0.60 13.19 0.60 12.88 0.60 12.59 0.00 11.52 0.02 10.91 0.02 10.74 0.01 0 + 847 130.88487 19.743830 -39.8 2.2 -15.8 2.2 12.87 0.60 12.24 0.60 11.98 0.60 11.83 0.60 11.77 0.60 10.76 0.02 10.32 0.02 10.22 0.02 1 + 848 130.88582 19.992489 -41.1 3.9 -18.9 3.9 18.71 0.01 17.47 0.01 15.99 0.00 15.35 0.00 15.02 0.00 13.77 0.02 13.13 0.03 12.92 0.03 1 + 849 130.89052 19.406867 -28.4 4.1 -13.3 4.1 19.52 0.02 18.21 0.01 16.54 0.00 15.77 0.00 15.37 0.01 14.13 0.03 13.58 0.04 13.24 0.03 0 + 850 130.89416 18.753821 -33.4 4.1 -18.0 4.1 19.34 0.02 18.07 0.01 16.52 0.00 15.81 0.00 15.41 0.00 14.17 0.03 13.56 0.04 13.30 0.03 0 + 851 130.89721 19.003923 -35.0 4.1 -13.1 4.1 17.30 0.00 16.07 0.00 14.78 0.00 14.14 0.00 13.86 0.00 12.65 0.02 12.00 0.02 11.79 0.02 1 + 852 130.89793 19.456524 -38.5 3.9 -20.7 3.9 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.53 0.03 13.93 0.03 13.60 0.04 0 + 853 130.89806 20.189678 -39.6 1.2 -13.4 1.2 10.31 0.60 10.06 0.60 10.01 0.60 10.03 0.60 10.05 0.60 9.23 0.02 9.02 0.04 8.92 0.02 1 BD+20 2196 + 854 130.90235 21.319335 -37.4 4.1 -20.6 4.1 18.74 0.01 17.56 0.01 16.09 0.00 15.42 0.00 15.12 0.00 13.88 0.03 13.26 0.03 13.04 0.02 0 + 855 130.90342 20.540090 -36.2 4.1 -14.9 4.1 18.25 0.01 17.00 0.00 15.61 0.00 14.98 0.00 14.68 0.00 13.50 0.03 12.83 0.03 12.59 0.02 0 + 856 130.90361 20.537198 -40.5 4.1 -16.4 4.1 16.87 0.00 15.65 0.00 14.65 0.00 14.21 0.00 14.00 0.00 12.84 0.02 12.19 0.02 11.94 0.01 0 + 857 130.91159 22.269268 -39.5 1.6 -13.2 1.6 12.48 0.60 11.97 0.60 11.79 0.60 11.73 0.60 11.68 0.60 10.74 0.02 10.30 0.02 10.25 0.02 1 + 858 130.91381 18.425573 -36.1 4.1 -11.1 4.1 18.02 0.01 16.74 0.00 15.64 0.60 14.93 0.00 14.66 0.00 13.41 0.02 12.85 0.03 12.54 0.02 0 + 859 130.92938 17.908298 -38.7 4.0 -16.0 4.0 15.76 0.00 14.57 0.00 13.79 0.00 13.47 0.00 13.29 0.00 12.21 0.02 11.50 0.03 11.31 0.02 0 + 860 130.93143 19.075875 -32.5 6.7 -11.9 6.7 14.05 0.00 12.99 0.00 13.55 0.00 12.54 0.00 12.45 0.00 11.44 0.02 10.88 0.02 10.79 0.02 1 + 861 130.93629 19.066310 -36.0 4.1 -12.1 4.1 16.69 0.00 15.47 0.00 14.53 0.00 14.09 0.00 13.90 0.00 12.78 0.02 12.08 0.02 11.87 0.02 0 + 862 130.93631 21.209506 -38.1 4.1 -16.9 4.1 15.90 0.00 14.71 0.00 14.07 0.60 13.59 0.00 13.35 0.00 12.24 0.02 11.56 0.02 11.41 0.02 1 + 863 130.94385 17.156750 -42.0 2.2 -7.2 2.2 13.18 0.60 12.48 0.60 12.22 0.60 12.09 0.60 12.00 0.60 10.96 0.03 10.42 0.04 10.35 0.02 0 + 864 130.94726 18.050003 -27.7 4.1 -11.8 4.1 19.28 0.02 18.12 0.01 16.53 0.00 15.74 0.00 15.37 0.00 14.09 0.03 13.49 0.03 13.22 0.03 0 + 865 130.95056 18.800779 -35.4 1.4 -10.6 1.4 10.32 0.60 10.08 0.60 10.02 0.60 10.02 0.60 10.03 0.60 9.19 0.02 8.97 10.00 8.93 0.02 1 BD+19 2089 + 866 130.96007 17.418831 -32.9 4.0 -17.2 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.83 0.04 14.22 0.04 13.92 0.04 0 + 867 130.96177 20.365741 -35.7 4.1 -7.9 4.1 16.22 0.00 15.00 0.00 14.12 0.00 13.79 0.00 13.54 0.00 12.40 0.02 11.76 0.02 11.51 0.02 1 + 868 130.96576 17.655603 -34.1 3.8 -6.1 3.8 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.09 0.03 13.41 0.03 13.20 0.02 0 + 869 130.96580 19.913577 -42.1 4.1 -16.3 4.1 14.80 0.00 13.73 0.60 13.33 0.60 13.01 0.00 12.87 0.00 11.82 0.02 11.09 0.02 10.97 0.02 0 + 870 130.97775 18.893580 -34.7 4.1 -16.0 4.1 13.28 0.60 12.60 0.60 12.28 0.60 12.09 0.60 12.01 0.60 10.96 0.02 10.52 0.02 10.41 0.02 0 + 871 130.98597 19.271685 -32.1 5.6 -15.8 5.6 21.37 0.05 19.84 0.03 18.07 0.01 17.24 0.00 16.83 0.01 15.43 0.06 14.94 0.08 14.61 0.08 1 + 872 130.98628 19.725630 -39.8 4.1 -14.6 4.1 13.67 0.00 13.03 0.60 12.66 0.60 12.45 0.60 12.24 0.00 11.24 0.02 10.69 0.02 10.53 0.02 0 + 873 130.99686 19.316131 -33.7 4.1 -13.8 4.1 17.42 0.00 16.22 0.00 15.04 0.00 14.51 0.00 14.26 0.00 13.08 0.02 12.49 0.03 12.16 0.02 1 + 874 131.02903 19.791695 -33.1 4.1 -11.8 4.1 18.58 0.01 17.30 0.00 15.76 0.00 15.08 0.00 14.76 0.00 13.51 0.02 12.92 0.03 12.65 0.03 0 + 875 131.04542 21.793947 -40.1 4.1 -15.6 4.1 18.27 0.01 17.11 0.00 15.71 0.00 15.08 0.00 14.80 0.00 13.60 0.03 12.96 0.02 12.72 0.03 0 + 876 131.04840 20.216674 -32.4 4.1 -19.1 4.1 19.00 0.01 17.76 0.00 16.15 0.00 15.41 0.00 15.08 0.00 13.79 0.03 13.19 0.02 12.86 0.03 0 + 877 131.04989 17.902222 -36.4 1.4 -9.2 1.5 10.11 0.60 9.88 0.60 9.83 0.60 9.85 0.60 9.87 0.60 9.05 0.02 8.84 0.04 8.78 0.02 1 BD+18 2026 + 878 131.05509 18.819835 -41.3 3.9 -11.6 3.9 14.69 0.00 13.64 0.00 13.44 0.60 12.93 0.00 12.79 0.00 11.75 0.02 11.13 0.03 10.94 0.02 1 + 879 131.07104 18.736632 -35.5 2.1 -11.6 2.1 12.70 0.60 12.13 0.60 11.91 0.60 11.80 0.60 11.74 0.60 10.77 0.02 10.35 0.02 10.26 0.02 1 + 880 131.07596 20.830101 -28.4 4.1 -14.5 4.1 19.83 0.01 18.49 0.01 16.81 0.60 16.03 0.00 15.68 0.00 14.37 0.03 13.72 0.03 13.46 0.04 0 + 881 131.07962 18.936085 -38.0 4.1 -14.9 4.1 17.70 0.00 16.49 0.00 15.13 0.00 14.47 0.00 14.20 0.00 12.97 0.02 12.39 0.03 12.06 0.02 0 + 882 131.08841 19.936566 -37.2 4.1 -21.2 4.1 19.36 0.01 18.06 0.01 16.46 0.00 15.73 0.00 15.40 0.00 14.11 0.02 13.55 0.02 13.26 0.03 0 + 883 131.09406 18.385898 -35.0 4.1 -13.0 4.1 18.77 0.01 17.05 0.60 16.19 0.60 15.36 0.00 15.09 0.01 13.85 0.02 13.21 0.03 12.94 0.03 1 + 884 131.09662 20.232116 -37.6 4.1 -20.8 4.1 19.36 0.01 18.10 0.01 16.57 0.00 15.89 0.00 15.56 0.00 14.32 0.03 13.74 0.04 13.35 0.03 0 + 885 131.10549 19.808472 -28.0 4.1 -10.8 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.71 0.04 14.06 0.03 13.88 0.05 0 + 886 131.11330 18.872423 -34.9 4.1 -21.9 4.1 19.91 0.02 18.55 0.01 16.95 0.00 16.20 0.00 15.83 0.01 14.58 0.04 13.91 0.04 13.64 0.04 0 + 887 131.11533 18.969349 -37.4 4.1 -13.3 4.1 18.65 0.01 17.46 0.00 16.00 0.00 15.33 0.00 15.00 0.00 13.78 0.03 13.18 0.03 12.87 0.03 0 + 888 131.13264 19.554795 -34.9 4.1 -17.5 4.1 18.83 0.01 17.64 0.01 16.06 0.00 15.44 0.00 15.13 0.00 13.84 0.03 13.24 0.03 12.96 0.03 0 + 889 131.13412 21.403794 -41.3 4.1 -6.0 4.1 16.51 0.00 15.27 0.00 14.29 0.60 13.62 0.00 13.37 0.00 12.02 0.02 11.48 0.02 11.22 0.02 0 + 890 131.13570 21.683062 -41.5 4.1 -12.5 4.1 18.01 0.00 16.78 0.00 15.45 0.00 14.82 0.00 14.53 0.00 13.33 0.02 12.71 0.02 12.45 0.03 0 + 891 131.13697 18.964016 -38.8 4.1 -14.9 4.1 17.97 0.01 16.77 0.00 15.42 0.00 14.80 0.00 14.51 0.00 13.31 0.02 12.66 0.03 12.39 0.02 1 + 892 131.14362 20.341500 -32.6 4.1 -15.5 4.1 18.94 0.01 17.73 0.00 16.30 0.00 15.63 0.00 15.33 0.00 14.10 0.03 13.51 0.03 13.26 0.03 0 + 893 131.15047 18.599185 -39.0 4.1 -12.7 4.1 15.13 0.00 13.89 0.00 13.30 0.60 12.81 0.00 12.63 0.00 11.52 0.02 10.82 0.02 10.65 0.01 0 + 894 131.15430 19.710846 -34.7 1.2 -14.3 1.3 10.68 0.60 10.35 0.60 10.27 0.60 10.27 0.60 10.27 0.60 9.42 0.02 9.10 0.02 9.05 0.01 1 BD+20 2201 + 895 131.16861 21.764907 -39.0 4.1 -19.5 4.1 17.79 0.01 16.59 0.00 15.30 0.00 14.70 0.00 14.44 0.00 13.23 0.02 12.61 0.02 12.34 0.02 1 + 896 131.16971 20.193614 -37.9 4.1 -15.8 4.1 14.96 0.00 13.82 0.00 13.51 0.60 13.07 0.00 12.93 0.00 11.88 0.02 11.22 0.02 11.07 0.02 1 + 897 131.17812 20.963019 -42.4 4.1 -20.0 4.1 18.92 0.01 17.67 0.00 16.08 0.00 15.33 0.00 14.99 0.00 13.72 0.02 13.12 0.03 12.84 0.03 1 + 898 131.18660 19.406149 -36.3 4.1 -13.6 4.1 18.05 0.01 16.77 0.00 15.37 0.00 14.73 0.00 14.44 0.00 13.25 0.02 12.63 0.03 12.34 0.02 0 + 899 131.19332 22.205464 -40.0 5.9 -16.8 5.9 21.62 0.10 19.46 0.60 18.47 0.01 17.73 0.02 17.31 0.01 15.95 0.10 15.23 0.10 15.32 0.17 0 + 900 131.20296 20.290552 -38.6 4.1 -14.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.08 0.02 10.58 0.02 10.51 0.02 0 + 901 131.22143 16.966932 -31.7 5.5 -16.9 5.5 19.73 0.02 18.44 0.01 16.83 0.00 16.10 0.00 15.77 0.00 14.53 0.03 13.89 0.04 13.68 0.04 1 + 902 131.22435 21.618460 -37.6 4.1 -16.2 4.1 17.09 0.00 15.86 0.00 14.79 0.00 14.29 0.00 14.07 0.00 12.88 0.02 12.26 0.02 12.01 0.02 0 + 903 131.23261 20.931026 -28.7 4.1 -10.2 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.71 0.02 13.08 0.03 12.84 0.03 0 + 904 131.23508 18.371397 -39.2 4.1 -5.5 4.1 16.51 0.00 15.25 0.00 14.51 0.60 13.87 0.00 13.63 0.00 12.47 0.02 11.82 0.02 11.60 0.02 1 + 905 131.26090 20.512134 -28.8 4.1 -9.3 4.1 15.90 0.00 14.66 0.00 13.72 0.00 13.28 0.00 13.10 0.00 11.95 0.02 11.33 0.02 11.10 0.02 1 + 906 131.26752 20.357704 -39.1 1.3 -14.8 1.3 10.56 0.60 10.31 0.60 10.24 0.60 10.22 0.60 10.22 0.60 9.39 0.02 9.17 0.02 9.13 0.02 0 BD+20 2204 + 907 131.27450 19.299304 -33.5 4.1 -16.0 4.1 19.51 0.01 18.27 0.01 16.73 0.00 16.02 0.00 15.68 0.00 14.40 0.03 13.86 0.04 13.55 0.04 0 + 908 131.27534 20.453661 -35.4 5.7 -12.4 5.7 21.59 0.06 20.30 0.02 18.42 0.01 17.49 0.00 17.06 0.01 15.61 0.07 15.31 0.09 14.68 0.09 0 + 909 131.28053 20.394959 -39.8 1.6 -17.3 1.6 12.25 0.00 11.98 0.00 99.00 99.00 12.25 0.00 11.43 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 910 131.28063 20.395050 -39.8 1.6 -17.3 1.6 12.33 0.60 11.82 0.60 11.61 0.60 11.50 0.60 11.46 0.60 10.50 0.02 10.14 0.02 10.05 0.02 0 + 911 131.28553 19.152267 -37.0 3.9 -14.2 3.9 19.81 0.02 18.43 0.01 17.06 0.60 16.15 0.00 15.79 0.01 14.52 0.03 13.91 0.03 13.58 0.04 1 + 912 131.30460 19.686876 -36.9 1.5 -14.0 1.5 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 9.56 0.02 9.27 0.02 9.16 0.02 1 + 913 131.31118 20.997580 -40.3 1.1 -13.5 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.72 0.02 8.54 0.01 8.48 0.02 0 HD 74547 + 914 131.31214 18.761064 -31.1 4.1 -12.7 4.1 19.41 0.01 18.39 0.04 16.62 0.60 15.86 0.00 15.51 0.00 14.24 0.03 13.67 0.03 13.41 0.04 0 + 915 131.31477 21.059967 -36.1 4.1 -11.6 4.1 18.10 0.01 16.88 0.00 15.86 0.60 15.02 0.00 14.75 0.00 13.55 0.02 12.93 0.03 12.65 0.02 0 + 916 131.32505 18.890390 -37.8 1.1 -13.8 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.80 0.01 8.64 0.02 8.60 0.02 0 BD+19 2093 + 917 131.32981 19.002992 -36.6 4.1 -15.6 4.1 14.41 0.00 13.57 0.60 13.17 0.60 12.93 0.60 12.67 0.00 11.65 0.02 11.05 0.02 10.90 0.01 1 + 918 131.33556 18.875380 -36.3 1.1 -10.0 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.91 0.01 7.86 0.01 7.84 0.01 0 HD 74589 + 919 131.33765 18.825746 -40.0 5.5 -11.5 5.5 20.74 0.05 19.37 0.01 17.53 0.60 16.88 0.00 16.51 0.01 15.25 0.05 14.49 0.06 14.22 0.06 0 + 920 131.33811 18.884270 -30.4 5.5 -18.3 5.5 19.47 0.02 18.36 0.01 16.61 0.60 16.02 0.00 15.75 0.00 14.50 0.03 13.80 0.03 13.63 0.04 0 + 921 131.34302 19.827790 -39.4 3.9 -15.0 3.9 18.52 0.01 17.25 0.00 15.68 0.00 14.98 0.00 14.62 0.00 13.35 0.02 12.77 0.02 12.50 0.02 0 + 922 131.34325 19.035620 -33.4 3.9 -16.6 3.9 18.81 0.01 17.57 0.01 16.15 0.60 15.29 0.00 14.94 0.00 13.70 0.02 13.09 0.02 12.83 0.02 0 + 923 131.35700 18.713344 -39.2 3.9 -13.3 3.9 13.29 0.00 12.54 0.00 99.00 99.00 12.24 0.00 12.09 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 + 924 131.35817 20.427359 -44.9 4.1 -15.8 4.1 14.78 0.00 13.64 0.00 13.31 0.60 12.96 0.00 12.76 0.00 11.68 0.02 11.04 0.02 10.88 0.02 1 + 925 131.35818 20.422915 -39.6 4.1 -15.4 4.1 15.38 0.00 14.19 0.00 13.56 0.00 13.32 0.00 13.14 0.00 12.05 0.02 11.38 0.02 11.21 0.02 0 + 926 131.35845 19.698447 -35.2 3.9 -15.6 3.9 17.22 0.00 16.05 0.00 14.94 0.00 14.44 0.00 14.20 0.00 13.05 0.02 12.39 0.02 12.18 0.02 1 + 927 131.35954 19.784405 -34.0 3.9 -12.8 3.9 17.11 0.00 15.88 0.00 14.63 0.00 14.06 0.00 13.79 0.00 12.62 0.02 12.02 0.02 11.72 0.02 1 + 928 131.36090 19.236867 -36.1 3.9 -16.8 3.9 19.29 0.01 17.67 0.60 16.37 0.60 15.57 0.00 15.20 0.00 13.92 0.02 13.28 0.03 13.04 0.03 0 + 929 131.36648 21.653565 -35.2 0.9 -16.3 0.9 10.83 0.60 10.54 0.60 10.44 0.60 10.41 0.60 10.41 0.60 9.55 0.02 9.32 0.02 9.26 0.02 0 BD+22 1985 + 930 131.36683 21.800808 -37.3 4.1 -22.4 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.13 0.02 13.52 0.03 13.24 0.03 0 + 931 131.36772 20.395433 -38.5 0.8 -13.7 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.01 0.01 7.91 0.01 7.86 0.01 0 HD 74587 + 932 131.36906 18.996694 -33.0 5.6 -14.5 5.6 20.88 0.05 19.54 0.01 99.00 99.00 16.94 0.00 16.55 0.01 99.00 99.00 99.00 99.00 99.00 99.00 0 + 933 131.37705 20.590157 -37.3 0.8 -14.1 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.98 0.01 8.82 0.04 8.77 0.02 1 BD+21 1904 + 934 131.38152 20.751987 -33.9 4.1 -19.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.14 0.03 13.43 0.02 13.30 0.03 1 + 935 131.38399 18.964479 -29.3 3.9 -15.2 3.9 18.53 0.01 17.17 0.60 16.22 0.60 15.31 0.00 15.02 0.00 13.79 0.02 13.21 0.03 12.92 0.03 0 + 936 131.40092 21.255838 -38.4 4.1 -15.7 4.1 17.14 0.00 15.97 0.00 15.03 0.60 14.34 0.00 14.10 0.00 12.90 0.02 12.27 0.02 12.04 0.02 0 + 937 131.40350 18.598671 -32.2 3.7 -17.7 3.7 17.83 0.01 16.54 0.00 15.30 0.60 14.51 0.00 14.21 0.00 12.99 0.02 12.32 0.02 12.06 0.02 0 + 938 131.40358 18.723635 -31.3 3.7 -12.4 3.7 19.86 0.02 18.49 0.01 17.07 0.60 16.15 0.00 15.83 0.01 14.54 0.03 13.98 0.04 13.68 0.04 0 + 939 131.41861 20.173766 -36.5 3.9 -21.5 3.9 17.95 0.01 16.74 0.00 15.50 0.00 14.93 0.00 14.65 0.00 13.48 0.02 12.82 0.03 12.59 0.02 0 + 940 131.43522 19.675665 -35.8 3.7 -19.9 3.7 17.94 0.01 16.76 0.00 15.42 0.00 14.78 0.00 14.48 0.00 13.27 0.02 12.66 0.02 12.42 0.02 0 + 941 131.44111 20.494709 -39.3 4.1 -16.0 4.1 17.23 0.00 16.00 0.00 14.90 0.00 14.38 0.00 14.15 0.00 12.93 0.02 12.33 0.03 12.10 0.02 0 + 942 131.44390 19.049486 -40.7 0.9 -13.5 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.58 0.01 7.56 0.01 7.53 0.02 0 HD 74656 + 943 131.45272 21.532346 -32.6 4.1 -19.6 4.1 18.23 0.01 17.06 0.00 99.00 99.00 14.97 0.00 14.70 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 944 131.46413 19.424228 -39.0 3.7 -20.9 3.7 17.38 0.00 16.20 0.00 15.02 0.00 14.48 0.00 14.23 0.00 13.10 0.02 12.40 0.02 12.18 0.02 0 + 945 131.46750 18.926319 -36.3 3.7 -9.0 3.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.35 0.03 13.68 0.02 13.49 0.04 0 + 946 131.46989 19.316835 -41.4 3.7 -16.4 3.7 17.16 0.00 15.95 0.00 14.87 0.00 14.39 0.00 14.13 0.00 12.95 0.02 12.36 0.02 12.10 0.02 1 + 947 131.49643 19.253454 -42.2 3.7 -10.9 3.7 18.68 0.01 17.42 0.01 16.23 0.60 15.32 0.00 14.99 0.00 13.77 0.02 13.15 0.03 12.88 0.03 1 + 948 131.50000 17.827418 -38.0 3.7 -12.5 3.7 19.19 0.01 17.88 0.01 16.36 0.00 15.65 0.00 15.26 0.00 14.06 0.03 13.41 0.03 13.16 0.03 1 + 949 131.50930 19.116543 -34.2 3.7 -15.1 3.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.27 0.02 12.65 0.02 12.37 0.02 1 + 950 131.51127 17.021543 -37.4 3.7 -14.2 3.7 18.82 0.01 17.54 0.00 16.18 0.00 15.54 0.00 15.25 0.00 14.01 0.02 13.41 0.03 13.23 0.03 1 + 951 131.51318 19.529757 -40.1 3.7 -15.0 3.7 18.04 0.01 16.81 0.00 15.50 0.00 14.90 0.00 14.62 0.00 13.41 0.02 12.76 0.03 12.54 0.02 0 + 952 131.53395 18.040904 -34.2 5.0 -16.0 5.0 20.14 0.02 18.86 0.02 17.15 0.00 16.37 0.00 16.00 0.01 14.72 0.03 14.12 0.04 13.85 0.04 1 + 953 131.53818 21.607974 -38.5 4.1 -19.1 4.1 19.33 0.01 18.06 0.00 16.65 0.60 15.75 0.00 15.39 0.00 14.15 0.02 13.45 0.03 13.18 0.03 0 + 954 131.54198 19.528841 -41.1 3.7 -16.0 3.7 15.70 0.00 14.50 0.00 13.79 0.00 13.50 0.00 13.33 0.00 12.23 0.02 11.54 0.02 11.37 0.02 1 + 955 131.54881 20.633520 -33.3 4.1 -15.5 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.54 0.02 13.02 0.03 12.73 0.02 0 + 956 131.54890 18.179728 -35.3 1.3 -8.6 1.3 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.77 0.01 7.69 0.01 7.66 0.01 0 HD 74720 + 957 131.55448 20.728682 -36.4 4.1 -12.9 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.12 0.02 10.63 0.02 10.49 0.01 0 + 958 131.55743 20.856876 -37.8 4.1 -18.0 4.1 14.69 0.00 13.58 0.00 13.36 0.60 12.98 0.00 12.74 0.00 11.66 0.02 11.03 0.02 10.86 0.02 0 + 959 131.56442 19.709164 -37.7 0.8 -12.8 0.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 7.89 0.00 7.82 0.01 7.79 0.01 0 HD 74718 + 960 131.56539 18.097196 -32.1 5.0 -15.1 5.0 20.67 0.05 19.45 0.02 17.68 0.01 16.89 0.00 16.52 0.01 15.32 0.05 14.69 0.05 14.34 0.07 0 + 961 131.57603 18.719216 -30.4 3.7 -12.6 3.7 19.25 0.01 17.84 0.01 16.47 0.60 15.67 0.00 15.35 0.00 14.10 0.03 13.46 0.03 13.26 0.04 0 + 962 131.58348 21.008887 -31.2 5.5 -13.3 5.5 20.21 0.02 18.90 0.01 17.20 0.01 16.37 0.00 16.01 0.00 14.73 0.04 14.03 0.05 13.85 0.05 1 + 963 131.59942 19.967846 -37.6 3.7 -11.9 3.7 18.22 0.00 17.02 0.00 15.89 0.02 15.04 0.00 14.72 0.00 13.46 0.02 12.85 0.02 12.61 0.02 0 + 964 131.60132 20.527418 -32.6 4.1 -12.1 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.03 0.04 14.36 0.04 14.07 0.06 0 + 965 131.60959 17.845683 -43.6 3.7 -14.2 3.7 19.78 0.02 18.45 0.01 16.82 0.00 16.08 0.00 15.71 0.00 14.46 0.03 13.82 0.04 13.51 0.04 0 + 966 131.61414 19.209026 -32.1 5.0 -17.2 5.0 20.56 0.04 18.67 0.60 17.34 0.60 16.52 0.00 16.13 0.01 14.87 0.04 14.22 0.05 13.92 0.05 0 + 967 131.61494 18.118523 -42.4 3.7 -11.7 3.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.90 0.03 13.35 0.03 13.09 0.03 0 + 968 131.63757 18.906698 -36.2 3.7 -11.0 3.7 13.68 0.00 13.24 0.60 12.82 0.60 12.55 0.60 12.30 0.00 11.30 0.02 10.77 0.02 10.62 0.01 0 + 969 131.63866 18.760958 -37.0 0.9 -11.1 0.8 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.44 0.02 8.31 0.02 8.25 0.01 0 HD 74780 + 970 131.63971 18.235997 -35.9 3.7 -9.0 3.7 13.65 0.00 13.06 0.60 12.69 0.60 12.46 0.60 12.32 0.00 11.25 0.02 10.72 0.03 10.60 0.02 0 + 971 131.64520 19.257207 -40.0 3.7 -21.3 3.7 18.61 0.01 17.38 0.60 16.11 0.60 15.29 0.00 15.00 0.00 13.75 0.02 13.12 0.02 12.87 0.02 0 + 972 131.65281 17.259150 -31.2 3.7 -8.8 3.7 19.42 0.01 18.27 0.01 16.61 0.00 15.86 0.00 15.49 0.00 14.20 0.02 13.62 0.03 13.32 0.03 0 + 973 131.65323 19.226017 -30.8 3.7 -14.6 3.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.76 0.02 13.11 0.02 12.90 0.03 0 + 974 131.65913 19.879103 -30.1 3.6 -14.8 3.6 16.80 0.00 15.54 0.00 14.37 0.00 13.84 0.00 13.57 0.00 12.40 0.03 11.73 0.03 11.51 0.02 0 + 975 131.66218 19.619091 -40.2 3.6 -15.8 3.6 18.60 0.01 17.41 0.00 15.96 0.00 15.31 0.00 14.99 0.00 13.77 0.03 13.14 0.03 12.93 0.03 0 + 976 131.66791 19.275080 -40.0 3.6 -21.1 3.6 20.00 0.03 18.69 0.01 17.23 0.60 16.27 0.00 15.90 0.01 14.65 0.04 14.03 0.04 13.70 0.03 0 + 977 131.67829 19.426206 -40.7 3.6 -16.0 3.6 19.03 0.01 17.74 0.01 16.20 0.60 15.58 0.00 15.26 0.00 13.94 0.02 13.34 0.04 13.16 0.03 1 + 978 131.69710 19.644806 -38.0 1.3 -15.1 1.3 10.96 0.60 10.67 0.60 10.56 0.60 10.51 0.60 10.49 0.60 9.62 0.02 9.42 0.03 9.34 0.02 0 + 979 131.70871 21.020205 -37.1 2.2 -15.8 2.2 13.48 0.00 12.95 0.60 12.57 0.60 12.34 0.60 12.15 0.00 11.14 0.02 10.67 0.03 10.54 0.02 0 + 980 131.72367 19.049141 -38.2 4.8 -15.6 4.8 19.91 0.02 18.57 0.01 17.02 0.60 16.15 0.00 15.79 0.01 14.55 0.03 13.85 0.04 13.62 0.04 0 + 981 131.74762 19.916491 -36.7 4.9 -17.3 4.9 21.09 0.03 19.79 0.01 17.99 0.01 17.15 0.00 16.73 0.01 15.48 0.05 14.78 0.07 14.47 0.07 0 + 982 131.77058 18.928523 -36.3 4.8 -9.9 4.8 19.57 0.02 18.39 0.01 16.92 0.60 15.98 0.00 15.62 0.01 14.40 0.03 13.73 0.04 13.48 0.03 0 + 983 131.77175 19.036114 -34.7 4.9 -10.5 4.9 20.88 0.05 19.52 0.02 17.98 0.01 16.93 0.00 16.52 0.01 15.33 0.05 14.51 0.05 14.27 0.06 0 + 984 131.78785 18.193691 -37.5 3.6 -13.0 3.6 17.76 0.00 16.50 0.00 15.29 0.00 14.73 0.00 14.47 0.00 13.25 0.02 12.62 0.03 12.42 0.02 0 + 985 131.82542 20.485814 -34.5 4.0 -18.5 4.0 17.16 0.00 15.96 0.00 14.88 0.00 14.46 0.00 14.22 0.00 13.09 0.03 12.44 0.03 12.19 0.02 1 + 986 131.82939 21.183901 -39.9 5.3 -16.5 5.3 19.79 0.02 18.54 0.01 16.95 0.00 16.18 0.00 15.84 0.01 14.55 0.03 13.92 0.04 13.64 0.04 0 + 987 131.83071 19.214452 -34.9 3.6 -21.4 3.6 19.07 0.01 17.80 0.60 16.49 0.60 15.63 0.00 15.29 0.01 14.07 0.03 13.40 0.04 13.15 0.03 0 + 988 131.84004 20.656104 -40.7 4.0 -19.2 4.0 16.69 0.00 15.42 0.00 14.44 0.00 14.02 0.00 13.80 0.00 12.68 0.02 12.03 0.03 11.76 0.02 1 + 989 131.85583 19.643591 -33.0 3.6 -8.3 3.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.79 0.02 13.18 0.03 12.90 0.03 0 + 990 131.89366 17.630684 -35.5 3.7 -18.9 3.7 18.16 0.01 16.92 0.00 15.64 0.00 15.08 0.00 14.80 0.00 99.00 99.00 99.00 99.00 99.00 99.00 1 + 991 131.89443 19.138278 -37.4 3.6 -11.1 3.6 16.39 0.00 15.16 0.00 14.17 0.00 13.70 0.00 13.47 0.00 12.33 0.03 11.67 0.03 11.41 0.02 0 + 992 131.89895 21.926775 -37.5 2.2 -11.2 2.2 12.34 0.60 11.88 0.60 11.75 0.60 11.71 0.60 11.68 0.60 10.77 0.02 10.37 0.03 10.32 0.02 0 + 993 131.92408 18.941286 -35.8 3.6 -13.5 3.6 18.17 0.01 16.97 0.00 15.60 0.00 14.97 0.00 14.65 0.00 13.41 0.02 12.79 0.03 12.55 0.03 0 + 994 131.93787 18.356636 -37.4 3.6 -13.9 3.6 17.14 0.00 15.91 0.00 14.58 0.00 14.08 0.00 13.80 0.00 12.62 0.02 12.01 0.03 11.74 0.02 1 + 995 131.95349 18.605421 -38.9 3.6 -15.4 3.6 18.79 0.01 17.33 0.60 16.05 0.60 15.25 0.00 14.89 0.00 13.63 0.03 13.00 0.03 12.77 0.03 0 + 996 131.96041 19.153042 -31.6 3.6 -18.7 3.6 19.47 0.02 18.15 0.01 16.65 0.00 15.92 0.00 15.59 0.00 14.36 0.03 13.81 0.05 13.57 0.05 0 + 997 131.97423 19.131467 -28.3 3.6 -14.4 3.6 19.54 0.02 18.25 0.01 16.73 0.00 15.96 0.00 15.62 0.00 14.38 0.03 13.81 0.05 13.54 0.04 0 + 998 131.98889 19.513580 -38.6 4.9 -16.2 4.9 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.01 0.02 13.43 0.04 13.18 0.03 0 + 999 132.00089 20.401886 -39.2 4.1 -18.9 4.1 19.91 0.03 18.65 0.01 17.14 0.01 16.21 0.00 15.84 0.01 14.54 0.03 13.90 0.04 13.69 0.04 0 +1000 132.00715 18.677109 -36.3 1.1 -12.2 1.0 10.93 0.60 10.47 0.60 10.28 0.60 10.19 0.60 10.16 0.60 9.23 0.02 8.91 0.03 8.81 0.02 1 FV Cnc +1001 132.02054 19.470488 -40.1 3.6 -21.4 3.6 17.85 0.01 16.62 0.00 15.38 0.00 14.82 0.00 14.56 0.00 13.37 0.02 12.73 0.03 12.54 0.03 0 +1002 132.03096 19.916423 -36.6 3.6 -21.9 3.6 19.33 0.01 18.22 0.03 16.61 0.00 15.89 0.00 15.57 0.01 14.38 0.03 13.73 0.04 13.40 0.04 0 +1003 132.04785 18.191128 -41.1 3.6 -17.8 3.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.30 0.03 12.66 0.04 12.40 0.02 0 +1004 132.04808 21.443084 -38.1 5.4 -6.0 5.4 20.74 0.03 19.40 0.02 17.67 0.01 16.90 0.00 16.57 0.01 15.28 0.05 14.79 0.07 14.48 0.06 1 +1005 132.07279 17.540796 -44.2 3.7 -11.0 3.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 11.58 0.02 10.99 0.02 10.84 0.02 0 +1006 132.09381 18.612435 -33.1 3.6 -7.1 3.6 17.45 0.00 16.26 0.00 15.10 0.00 14.59 0.00 14.32 0.00 13.17 0.02 12.58 0.03 12.32 0.02 0 +1007 132.09814 19.836573 -39.6 3.6 -15.6 3.6 18.47 0.01 17.19 0.00 15.84 0.00 15.22 0.00 14.92 0.00 13.72 0.02 13.13 0.03 12.84 0.02 0 +1008 132.11589 18.345526 -36.2 1.6 -9.7 1.6 11.58 0.60 11.19 0.60 11.08 0.60 11.07 0.60 11.05 0.60 10.16 0.02 9.82 0.03 9.77 0.02 0 +1009 132.12522 18.950947 -38.3 4.9 -9.5 4.9 21.06 0.05 19.75 0.02 17.89 0.01 17.07 0.00 16.63 0.01 15.32 0.04 14.62 0.06 14.36 0.06 0 +1010 132.14370 19.932611 -40.7 3.6 -13.8 3.6 17.09 0.00 15.87 0.00 14.73 0.00 14.22 0.00 13.98 0.00 12.76 0.02 12.16 0.03 11.89 0.02 1 +1011 132.17179 19.497593 -33.7 3.6 -10.9 3.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.82 0.04 14.16 0.05 13.90 0.04 1 +1012 132.20464 20.224201 -43.7 3.6 -16.1 3.6 16.63 0.00 15.53 0.01 14.75 0.01 14.06 0.00 13.86 0.00 12.71 0.02 12.06 0.03 11.83 0.02 1 +1013 132.20815 20.443312 -38.9 4.0 -17.6 4.0 16.69 0.00 15.44 0.00 14.48 0.00 14.06 0.00 13.86 0.00 12.76 0.02 12.05 0.03 11.86 0.02 0 +1014 132.21411 20.904259 -38.2 5.4 -10.9 5.4 20.80 0.04 19.38 0.02 17.50 0.60 16.81 0.00 16.42 0.01 15.11 0.04 14.52 0.06 14.30 0.06 0 +1015 132.21922 19.046391 -29.6 3.6 -8.4 3.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.68 0.03 13.04 0.03 12.82 0.02 1 +1016 132.24953 19.672588 -28.1 4.8 -13.5 4.8 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.79 0.03 14.18 0.05 13.79 0.05 0 +1017 132.26029 21.556666 -42.3 4.0 -19.5 4.0 18.40 0.01 17.16 0.00 15.65 0.00 14.95 0.00 14.61 0.00 13.34 0.02 12.69 0.03 12.45 0.02 0 +1018 132.27775 19.686445 -34.1 2.2 -12.8 2.2 12.33 0.00 12.57 0.00 99.00 99.00 11.53 0.00 11.45 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 +1019 132.31686 18.410137 -29.8 3.6 -13.3 3.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.40 0.03 13.85 0.04 13.56 0.04 0 +1020 132.31881 21.586718 -36.6 4.0 -8.6 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 12.41 0.02 11.83 0.03 11.53 0.02 1 +1021 132.36141 18.522101 -36.7 3.6 -14.9 3.6 16.02 0.00 14.75 0.00 13.94 0.00 13.58 0.00 13.39 0.00 12.24 0.02 11.59 0.03 11.39 0.02 0 +1022 132.39123 20.508083 -37.9 1.1 -12.9 1.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 8.53 0.01 8.42 0.04 8.37 0.01 0 HD 75248 +1023 132.41125 20.961109 -37.4 6.0 -17.9 6.0 21.82 0.08 20.48 0.03 18.64 0.01 17.90 0.01 17.44 0.02 16.11 0.09 15.50 0.15 15.32 0.17 0 +1024 132.49733 19.166941 -39.1 3.6 -19.3 3.6 18.77 0.01 17.57 0.00 16.10 0.00 15.39 0.00 15.06 0.00 13.87 0.03 13.19 0.04 13.01 0.03 0 +1025 132.49981 18.364986 -35.7 1.3 -12.2 1.3 11.52 0.00 12.73 0.00 12.32 0.00 10.80 0.00 10.77 0.00 99.00 99.00 99.00 99.00 99.00 99.00 0 BD+18 2049 +1026 132.49992 18.365058 -35.7 1.3 -12.2 1.3 11.40 0.60 11.05 0.60 10.93 0.60 10.90 0.60 10.89 0.60 10.02 0.02 9.73 0.03 9.64 0.02 0 +1027 132.52260 20.703691 -29.4 4.1 -11.7 4.1 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 15.29 0.05 14.61 0.07 14.36 0.07 1 +1028 132.54267 20.148646 -40.0 3.6 -15.9 3.6 15.60 0.00 14.37 0.00 13.67 0.00 13.44 0.00 13.23 0.00 12.11 0.02 11.48 0.03 11.25 0.02 0 +1029 132.54515 21.101212 -34.9 4.0 -9.6 4.0 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 13.90 0.03 13.24 0.04 13.05 0.03 1 +1030 132.54571 19.551449 -30.1 3.6 -19.8 3.6 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.58 0.03 13.94 0.04 13.69 0.04 0 +1031 132.55689 19.690012 -30.4 5.0 -14.9 5.0 21.54 0.06 20.20 0.04 18.30 0.01 17.39 0.00 16.97 0.01 15.75 0.06 14.95 0.07 14.51 0.07 0 +1032 132.55876 20.567427 -36.3 6.2 -12.4 6.2 22.33 0.17 20.86 0.05 19.04 0.02 18.28 0.01 17.94 0.02 16.45 0.11 16.06 0.17 15.80 0.21 0 +1033 132.57719 19.428511 -34.6 3.7 -14.3 3.7 13.40 0.60 12.72 0.60 12.40 0.60 12.21 0.60 12.14 0.00 11.09 0.02 10.66 0.02 10.53 0.02 0 +1034 132.59472 20.150953 -32.4 3.7 -6.0 3.7 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.69 0.03 14.04 0.04 13.77 0.04 0 +1035 132.64699 20.710466 -32.7 4.2 -5.9 4.2 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 99.00 14.37 0.03 13.66 0.03 13.40 0.04 0 +1036 132.65042 19.951848 -36.8 5.0 -16.1 5.0 20.78 0.04 21.33 0.60 19.57 0.60 16.84 0.00 16.47 0.01 15.08 0.04 14.53 0.05 14.33 0.06 0 +1037 132.70759 19.810125 -38.4 3.7 -18.3 3.7 18.55 0.01 17.23 0.00 15.74 0.00 15.08 0.00 14.78 0.00 13.53 0.02 12.91 0.02 12.65 0.02 0 +1038 132.73688 19.616067 -38.8 3.7 -14.3 3.7 18.20 0.01 16.95 0.00 15.68 0.00 15.02 0.00 14.78 0.00 13.57 0.02 12.94 0.02 12.69 0.03 0 +1039 132.84667 19.855061 -34.0 5.0 -21.9 5.0 20.77 0.05 19.41 0.02 17.61 0.60 16.97 0.00 16.65 0.01 15.23 0.04 14.71 0.05 14.45 0.06 1 +1040 132.85758 19.315661 -37.2 3.7 -20.0 3.7 18.09 0.01 16.83 0.00 15.43 0.00 14.79 0.00 14.49 0.00 13.30 0.02 12.68 0.02 12.40 0.02 0 diff --git a/docs/paper_examples/Begbie+26/cluster_data/asu (2).fit b/docs/paper_examples/Begbie+26/cluster_data/asu (2).fit new file mode 100644 index 00000000..77254ca3 --- /dev/null +++ b/docs/paper_examples/Begbie+26/cluster_data/asu (2).fit @@ -0,0 +1,2694 @@ +SIMPLE = T / Standard FITS Format BITPIX = 8 / Character data NAXIS = 0 / No Image --- just extension(s) EXTEND = T / There are standard extensions ORIGIN = 'xml2fits_v1.95' / Converted from XML-Astrores to FITS e-mail: question@simbad.u-strasbg.fr LONGSTRN= 'OGIP 1.0' / Long string convention (&/CONTINUE) may be usedDATE = '2026-01-30' / Written on 2026-01-30:20:36:47 (GMT) by: www-data@vizier.cds.unistra.fr ********************************************************** EXCERPT from catalogues stored in VizieR (CDS) with the following conditions: ********************************************************** VizieR Astronomical Server vizier.cds.unistra.fr Date: 2026-01-30T20:36:47 [V7.5.4] In case of problem, please report to: cds-question@unistra.fr INFO = 'service_protocol=ASU' / IVOID of the protocol through which the data was retrieved # INFO = 'request_date=2026-01-30T20:36:47' / Query execution date # INFO = 'request=https://vizier.cds.unistra.fr/viz-bin/asu-fits?-oc.form=dec&'CONTINUE '&-out.max=unlimited&#out.form=FITS (ascii) Table&-order=I&-out.src=&'CONTINUE 'J/MNRAS/422/1495/tablea1,J/MNRAS/422/1495/tablec1&-c.eq=J2000&-c.r=&'CONTINUE ' 2&-c.u=arcmin&-c.geom=r&-x.rs=10&-source=J/MNRAS/422/1495/tablea1&'CONTINUE ' J/MNRAS/422/1495/tablec1&-out=RAJ2000&-out=DEJ2000&-out=M&-out=Zma&'CONTINUE 'g&-out=Ymag&-out=Jmag&-out=Hmag&-out=K1mag&-out=K2mag&-out=pmRA&-ou&'CONTINUE 't=e_pmRA&-out=pmDE&-out=e_pmDE&-out=chi2&-out=Mmb&-out=n_Mmb&-out=O&'CONTINUE 'Name&-out=GCS9&-out=SimbadName&-out=e_Zmag&-out=e_Ymag&-out=e_Jmag&&'CONTINUE '-out=e_Hmag&-out=e_K1mag&-out=e_K2mag&-out=n_Mmb&' / Full request URL (POST) # INFO = 'contact=cds-question@unistra.fr' / Email or URL to contact publisher # INFO = 'server_software=VizieR/7.5.4' / Software version # INFO = 'publisher=CDS' / Data centre that produced the VOTable # INFO = 'CatalogsExamined=2' 2 catalogues with potential matches were examined. INFO = 'ivoid=ivo://cds.vizier/j/mnras/422/1495' / IVOID of underlying data collection # INFO = 'data_ivoid=ivo://cds.vizier/j/mnras/422/1495' / IVOID of underlying data collection # INFO = 'creator=Lodieu N.' / First author or institution # INFO = 'cites=bibcode:2012MNRAS.422.1495L' / Article or Data origin sources # INFO = 'journal=MNRA' / Journal name # INFO = 'original_date=2012' / Year of the article publication # INFO = 'reference_url=https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/422&'CONTINUE '/1495 ' / Dataset landing page # INFO = 'citation=doi:10.26093/cds/vizier.74221495' / Dataset identifier that can be used for citation # INFO = 'publication_date=2024-07-17' / Date of first publication in the data centre # INFO = 'rights_uri=https://cds.unistra.fr/vizier-org/licences_vizier.html' / Licence URI # END XTENSION= 'TABLE ' / Ascii Table Extension (TAB and NEWLINE sep) BITPIX = 8 / Character data NAXIS = 2 / Simple 2-D matrix NAXIS1 = 224 / Number of bytes per record NAXIS2 = 1379 / Number of records PCOUNT = 0 / Get rid of random parameters GCOUNT = 1 / Only one group (isn't it obvious?) TFIELDS = 19 / Number of data fields (columns) CDS-CAT = 'J/MNRAS/422/1495' / Catalogue designation in CDS nomenclature UKIDSS Galactic Clusters Survey Pleiades members (Lodieu+ 2012) EXTNAME = 'J_MNRAS_422_1495_tablea1' / Identification of the table CDS-NAME= 'J/MNRAS/422/1495/tablea1' / Table name in METAtab Sample of 1379 known Pleiades member candidates previously published in the literature and recovered in GCS DR9 TBCOL1 = 2 / UCD=pos.eq.ra;meta.main char:11 offset=1 TFORM1 = 'A11 ' / Fortran Format TTYPE1 = 'RAJ2000 ' / Right ascension (J2000) TBCOL2 = 14 / UCD=pos.eq.dec;meta.main char:11 offset=13 TFORM2 = 'A11 ' / Fortran Format TTYPE2 = 'DEJ2000 ' / Declination (J2000) TBCOL3 = 26 / UCD=meta.number short: ....... offset=25 TFORM3 = 'I3 ' / Fortran Format TTYPE3 = 'M ' / Multiplicity of this star (table D1) TNULL3 = -32768 / NULL definition TBCOL4 = 30 / UCD=phot.mag;em.opt.I float: . offset=29 TFORM4 = 'F6.3 ' / Fortran Format TTYPE4 = 'Zmag ' / ? UKIDSS Z magnitude TUNIT4 = 'mag ' / magnitude TBCOL5 = 37 / UCD=phot.mag;em.IR.J float: .. offset=36 TFORM5 = 'F6.3 ' / Fortran Format TTYPE5 = 'Ymag ' / ? UKIDSS Y magnitude TUNIT5 = 'mag ' / magnitude TBCOL6 = 44 / UCD=phot.mag;em.IR.J float: .. offset=43 TFORM6 = 'F6.3 ' / Fortran Format TTYPE6 = 'Jmag ' / UKIDSS J magnitude TUNIT6 = 'mag ' / magnitude TBCOL7 = 51 / UCD=phot.mag;em.IR.H float: .. offset=50 TFORM7 = 'F6.3 ' / Fortran Format TTYPE7 = 'Hmag ' / UKIDSS H magnitude TUNIT7 = 'mag ' / magnitude TBCOL8 = 58 / UCD=phot.mag;em.IR.K float: .. offset=57 TFORM8 = 'F6.3 ' / Fortran Format TTYPE8 = 'K1mag ' / UKIDSS K magnitude at first epoch TUNIT8 = 'mag ' / magnitude TBCOL9 = 65 / UCD=phot.mag;em.IR.K float: .. offset=64 TFORM9 = 'F6.3 ' / Fortran Format TTYPE9 = 'K2mag ' / ? UKIDSS K magnitude at second epoch TUNIT9 = 'mag ' / magnitude TBCOL10 = 72 / UCD=pos.pm;pos.eq.ra float: .. offset=71 TFORM10 = 'F7.2 ' / Fortran Format TTYPE10 = 'pmRA ' / ? Proper motion along RA, pmRA*cosDE TUNIT10 = 'mas/yr ' / milli-second of arc per year TBCOL11 = 80 / UCD=stat.error;pos.pm;pos.eq.ra fl offset=79 TFORM11 = 'F5.2 ' / Fortran Format TTYPE11 = 'e_pmRA ' / ? rms uncertainty on pmRA TUNIT11 = 'mas/yr ' / milli-second of arc per year TBCOL12 = 86 / UCD=pos.pm;pos.eq.dec float: . offset=85 TFORM12 = 'F7.2 ' / Fortran Format TTYPE12 = 'pmDE ' / ? Proper motion along DE TUNIT12 = 'mas/yr ' / milli-second of arc per year TBCOL13 = 94 / UCD=stat.error;pos.pm;pos.eq.dec f offset=93 TFORM13 = 'F5.2 ' / Fortran Format TTYPE13 = 'e_pmDE ' / ? rms uncertainty on pmDE TUNIT13 = 'mas/yr ' / milli-second of arc per year TBCOL14 = 100 / UCD=stat.fit.chi2;stat.fit.param;m offset=99 TFORM14 = 'F6.2 ' / Fortran Format TTYPE14 = 'chi2 ' / ? Reduced chi^2^ statistic of the astrometric fit (only in tablea1.dat) TBCOL15 = 107 / UCD=stat.probability float: .. offset=106 TFORM15 = 'F5.2 ' / Fortran Format TTYPE15 = 'Mmb ' / [0/1]? Membership probability (tablea1.dat) TBCOL16 = 113 / UCD=meta.note char:4 ......... offset=112 TFORM16 = 'A4 ' / Fortran Format TTYPE16 = 'n_Mmb ' / Note on Mmb (tablea1.dat) (1) (link) TBCOL17 = 118 / UCD=meta.id;meta.main char:77* offset=117 TFORM17 = 'A77 ' / Fortran Format TTYPE17 = 'OName ' / Other name(s) (2) (link) TBCOL18 = 196 / UCD=meta.ref.url char:4 ...... offset=195 TFORM18 = 'A4 ' / Fortran Format TTYPE18 = 'GCS9 ' / Display the UKIDSS GCS-DR9 data, Cat. II/319 (link) TBCOL19 = 201 / UCD=meta.id char:23 .......... offset=200 TFORM19 = 'A23 ' / Fortran Format TTYPE19 = 'SimbadName' / Simbad column added by the CDS END +03 27 54.26 +24 56 10.9 0 13.808 13.400 12.820 12.255 12.005 16.62 6.95 -43.60 6.95 0.47 0.93 DH003 GCS9 Cl* Melotte 22 DH 003 +03 29 58.76 +23 22 18.3 0 13.640 13.198 12.672 12.244 11.843 11.851 21.18 3.41 -38.83 3.41 0.47 0.81 DH009 GCS9 Cl* Melotte 22 DH 009 +03 30 08.28 +22 38 38.3 0 15.346 14.741 13.680 13.020 12.895 12.805 16.33 3.42 -72.94 3.42 74.29 0.00 DH010 GCS9 Cl* Melotte 22 DH 010 +03 30 35.39 +23 03 07.9 0 16.316 15.841 15.233 14.594 14.298 14.288 15.61 3.45 -45.70 3.45 0.44 0.63 DH012 GCS9 Cl* Melotte 22 DH 012 +03 31 14.64 +25 58 51.7 0 12.750 12.443 11.925 11.381 11.076 11.107 33.12 3.36 -39.84 3.36 2.35 0.00 DH015 GCS9 Cl* Melotte 22 DH 015 +03 31 29.60 +26 30 12.1 0 14.670 14.195 13.596 13.007 12.703 12.734 21.27 2.96 -35.29 2.96 0.26 0.42 DH016 GCS9 Cl* Melotte 22 DH 016 +03 32 07.87 +23 13 57.2 0 14.025 13.621 13.052 12.517 12.218 12.199 18.14 3.41 -38.13 3.41 0.46 0.84 DH017 GCS9 Cl* Melotte 22 DH 017 +03 32 28.30 +22 28 06.3 0 17.598 17.092 16.403 15.754 15.410 15.409 10.61 3.53 -31.53 3.53 0.64 0.00 DH019 GCS9 Cl* Melotte 22 DH 019 +03 32 32.97 +22 18 12.1 0 14.922 14.467 13.885 13.344 13.019 13.018 22.36 3.41 -35.37 3.41 0.54 0.36 DH020 GCS9 Cl* Melotte 22 DH 020 +03 32 57.57 +27 17 19.4 0 15.608 15.014 14.363 13.807 13.462 13.477 15.57 3.76 -46.28 3.76 0.87 0.80 DH024 GCS9 Cl* Melotte 22 DH 024 +03 33 10.51 +22 31 19.0 0 12.839 12.556 12.064 11.566 11.249 11.279 31.96 3.41 -37.15 3.41 2.39 0.00 DH027 GCS9 Cl* Melotte 22 DH 027 +03 33 39.01 +22 21 08.5 0 15.193 14.805 14.251 13.715 13.455 13.430 16.11 3.42 -44.42 3.42 0.58 0.87 DH029 GCS9 Cl* Melotte 22 DH 029 +03 33 46.65 +23 48 19.4 0 13.819 13.463 12.918 12.375 12.087 12.098 18.43 2.60 -43.31 2.60 5.20 0.94 DH030 GCS9 Cl* Melotte 22 DH 030 +03 33 49.81 +22 56 19.6 0 15.069 14.626 14.048 13.507 13.194 13.190 16.86 3.05 -39.29 3.05 0.39 0.83 DH031 GCS9 Cl* Melotte 22 DH 031 +03 34 19.15 +22 27 59.6 0 14.056 13.606 13.051 12.550 12.227 12.213 27.04 2.94 -46.83 2.94 0.19 0.23 DH033 GCS9 Cl* Melotte 22 DH 033 +03 34 19.37 +22 48 42.2 0 16.004 15.439 14.812 14.262 13.912 13.908 21.53 3.06 -34.97 3.06 0.22 0.12 DH034 GCS9 Cl* Melotte 22 DH 034 +03 34 41.55 +26 09 27.1 0 15.437 14.878 14.245 13.705 13.344 24.47 5.32 -33.86 5.32 1.01 0.04 DH036 GCS9 Cl* Melotte 22 DH 036 +03 34 47.54 +26 22 13.4 0 14.564 14.112 13.529 12.977 12.672 12.674 24.94 3.30 -39.43 3.30 0.12 0.60 DH039 GCS9 Cl* Melotte 22 DH 039 +03 34 54.95 +22 04 46.2 0 13.105 12.793 12.313 11.794 11.512 11.566 22.30 2.93 -37.92 2.93 0.10 0.66 DH040 GCS9 Cl* Melotte 22 DH 040 +03 35 04.72 +25 50 48.0 0 15.967 15.347 14.711 14.131 13.773 14.82 5.33 -43.91 5.33 0.30 0.85 DH041 GCS9 Cl* Melotte 22 DH 041 +03 35 09.41 +24 14 19.8 0 12.614 12.358 11.884 11.322 11.049 11.083 16.71 2.60 -38.59 2.60 1.67 0.83 DH042 GCS9 Cl* Melotte 22 DH 042 +03 35 10.44 +24 31 54.4 0 14.645 14.122 13.531 12.972 12.661 12.629 17.82 2.63 -32.24 2.63 0.87 0.06 DH043 GCS9 Cl* Melotte 22 DH 043 +03 35 16.28 +23 47 57.8 0 15.198 14.649 14.056 13.523 13.179 13.195 21.38 2.61 -33.42 2.61 1.99 0.09 DH044 GCS9 Cl* Melotte 22 DH 044 +03 35 39.80 +25 38 45.2 0 14.957 14.508 13.916 13.319 13.037 15.42 5.32 -37.37 5.32 0.31 0.68 DH046 GCS9 Cl* Melotte 22 DH 046 +03 35 44.28 +22 27 09.7 0 13.612 13.310 12.830 12.275 11.996 12.017 19.82 2.93 -40.16 2.93 0.15 0.90 DH047 GCS9 Cl* Melotte 22 DH 047 +03 35 49.68 +25 04 48.0 0 13.372 13.029 12.506 11.959 11.707 11.697 32.03 2.62 -31.99 2.62 9.53 0.00 DH048 GCS9 Cl* Melotte 22 DH 048 +03 35 50.29 +25 42 20.5 0 13.820 13.379 12.791 12.239 11.978 28.77 5.30 -37.34 5.30 0.36 0.02 DH049 GCS9 Cl* Melotte 22 DH 049 +03 35 56.06 +25 21 59.3 0 13.217 12.868 12.366 11.780 11.558 18.66 5.28 -39.03 5.28 0.21 0.87 DH050 GCS9 Cl* Melotte 22 DH 050 +03 36 05.33 +25 21 03.4 0 14.386 13.934 13.321 12.796 12.470 19.93 5.29 -41.98 5.29 0.66 0.94 DH051 GCS9 Cl* Melotte 22 DH 051 +03 36 11.07 +23 48 23.0 0 15.038 14.538 13.935 13.415 13.063 13.085 20.28 2.61 -36.96 2.61 1.57 0.61 DH053 GCS9 Cl* Melotte 22 DH 053 +03 36 16.32 +25 08 48.8 0 13.071 12.678 12.129 11.591 11.290 11.282 19.36 2.62 -45.92 2.62 0.50 0.91 DH054 GCS9 Cl* Melotte 22 DH 054 +03 36 22.33 +22 44 32.7 0 13.694 13.229 12.673 12.138 11.842 11.852 22.55 3.04 -41.33 3.04 1.33 0.85 DH055 GCS9 Cl* Melotte 22 DH 055 +03 36 24.18 +22 37 24.3 0 14.137 13.724 13.127 12.613 12.276 12.327 17.43 2.94 -44.41 2.94 1.67 0.93 DH056 GCS9 Cl* Melotte 22 DH 056 +03 36 26.17 +22 14 45.4 0 14.225 13.898 13.373 12.799 12.515 12.522 26.10 2.94 -40.80 2.94 0.12 0.51 DH057 GCS9 Cl* Melotte 22 DH 057 +03 36 27.37 +24 41 17.1 0 13.905 13.442 12.879 12.371 12.078 12.072 21.62 2.62 -35.55 2.62 0.63 0.35 DH058 GCS9 Cl* Melotte 22 DH 058 +03 36 42.67 +28 21 05.9 0 15.297 14.744 14.124 13.558 13.240 13.241 21.45 4.94 -39.70 4.94 0.46 0.78 DH062 GCS9 Cl* Melotte 22 DH 062 +03 36 44.12 +22 01 38.8 0 14.988 14.410 13.794 13.301 12.965 12.979 22.41 2.94 -39.90 2.94 0.74 0.85 DH063 GCS9 Cl* Melotte 22 DH 063 +03 36 46.48 +28 15 05.9 0 15.528 14.984 14.370 13.865 13.552 13.542 25.67 4.95 -35.56 4.95 0.80 0.06 DH064 GCS9 Cl* Melotte 22 DH 064 +03 37 03.48 +24 44 35.3 0 13.096 12.748 12.245 11.693 11.446 11.458 21.23 2.48 -35.99 2.48 1.44 0.46 HCG6_HHJ392_DH066 GCS9 Cl* Melotte 22 DH 066 +03 37 10.51 +25 17 34.6 0 14.920 14.443 13.853 13.316 13.012 13.025 23.82 2.96 -34.70 2.96 0.86 0.16 DH067 GCS9 Cl* Melotte 22 DH 067 +03 37 11.98 +26 46 28.7 0 14.171 13.688 13.064 12.558 12.231 12.243 22.64 3.30 -37.41 3.30 0.54 0.65 HHJ242_DH068 GCS9 Cl* Melotte 22 HHJ 242 +03 37 15.61 +26 29 29.8 0 15.984 15.323 14.670 14.169 13.787 13.794 19.16 3.31 -39.61 3.31 0.75 0.85 HHJ19 GCS9 Cl* Melotte 22 HHJ 19 +03 37 16.53 +23 11 04.2 0 14.370 13.944 13.393 12.855 12.551 12.556 23.40 2.97 -41.45 2.97 0.52 0.84 HHJ179_DH069 GCS9 Cl* Melotte 22 HHJ 179 +03 37 16.71 +25 44 11.1 0 14.671 14.223 13.651 13.108 12.785 12.788 11.44 2.96 -37.28 2.96 0.91 0.22 HHJ154_DH070 GCS9 Cl* Melotte 22 HHJ 154 +03 37 26.39 +24 34 01.2 0 14.315 13.924 13.363 12.833 12.539 12.563 20.85 2.48 -42.78 2.48 0.79 0.93 DH071 GCS9 Cl* Melotte 22 DH 071 +03 37 26.62 +26 41 27.5 0 12.870 12.622 12.135 11.531 11.320 11.360 40.30 3.30 -47.71 3.30 0.63 0.00 HHJ422 GCS9 Cl* Melotte 22 HHJ 422 +03 37 30.28 +28 32 26.4 0 15.565 15.067 14.516 13.880 13.587 13.594 6.71 4.97 -40.29 4.97 0.50 0.02 DH072 GCS9 Cl* Melotte 22 DH 072 +03 37 30.69 +24 50 54.3 0 14.798 14.416 13.808 13.323 13.004 12.978 19.62 2.49 -34.54 2.49 6.17 0.34 HHJ110 GCS9 Cl* Melotte 22 HHJ 110 +03 37 34.18 +24 42 15.1 0 15.478 14.973 14.389 13.876 13.543 13.551 21.50 2.49 -36.80 2.49 2.46 0.51 DH074 GCS9 Cl* Melotte 22 DH 074 +03 37 36.01 +26 32 48.4 0 12.457 12.147 11.662 11.396 10.873 10.930 24.17 3.30 -45.76 3.30 1.02 0.48 DH075 GCS9 Cl* Melotte 22 DH 075 +03 37 37.70 +26 21 04.0 0 14.598 14.134 13.546 13.006 12.705 12.701 23.72 3.30 -44.04 3.30 0.33 0.83 HHJ172_DH076 GCS9 Cl* Melotte 22 HHJ 172 +03 37 40.23 +24 42 58.0 0 13.786 13.370 12.837 12.338 12.034 12.043 27.94 2.48 -39.53 2.48 1.40 0.09 HHJ283 GCS9 Cl* Melotte 22 HHJ 283 +03 37 47.51 +24 53 46.1 0 15.183 14.722 14.132 13.612 13.311 13.287 16.11 2.49 -40.51 2.49 1.67 0.86 HHJ72_DH078 GCS9 Cl* Melotte 22 HHJ 72 +03 37 48.93 +26 51 45.1 0 14.380 13.919 13.351 12.818 12.520 12.525 21.09 3.30 -46.90 3.30 0.33 0.86 HHJ137_DH079 GCS9 Cl* Melotte 22 HHJ 137 +03 37 54.79 +25 26 31.3 0 12.896 12.558 12.073 11.558 11.231 11.316 19.21 2.95 -37.64 2.95 0.58 0.83 HHJ402 GCS9 Cl* Melotte 22 HHJ 402 +03 37 56.42 +23 22 56.6 0 13.782 13.405 12.891 12.353 12.067 12.063 23.93 2.97 -39.47 2.97 0.16 0.65 HHJ268_DH080 GCS9 Cl* Melotte 22 HHJ 268 +03 38 02.05 +24 20 15.1 0 14.002 13.647 13.091 12.524 12.251 12.230 19.63 2.50 -45.83 2.50 2.15 0.92 HHJ248_DH081 GCS9 Cl* Melotte 22 HHJ 248 +03 38 08.81 +21 14 49.0 0 12.497 12.220 11.788 11.506 10.905 11.259 29.15 3.41 -36.90 3.41 0.74 0.04 DH082 GCS9 Cl* Melotte 22 DH 082 +03 38 10.27 +25 11 32.9 0 15.589 15.124 14.544 14.014 13.684 13.670 20.06 2.50 -43.22 2.50 3.31 0.88 HHJ35 GCS9 Cl* Melotte 22 HHJ 35 +03 38 13.04 +23 37 20.7 0 15.694 15.167 14.427 13.836 13.460 13.494 16.75 2.50 -45.95 2.50 2.58 0.84 HHJ32 GCS9 Cl* Melotte 22 HHJ 32 +03 38 13.04 +24 38 16.8 0 14.671 14.227 13.631 13.103 12.809 12.801 23.94 2.49 -49.13 2.49 0.97 0.42 HHJ149_DH084 GCS9 Cl* Melotte 22 HHJ 149 +03 38 24.22 +25 19 49.2 0 14.870 14.364 13.774 13.233 12.906 12.914 18.02 2.96 -40.03 2.96 0.46 0.92 DH086 GCS9 Cl* Melotte 22 DH 086 +03 38 24.80 +26 15 23.5 0 14.041 13.583 13.024 12.485 12.215 12.231 27.72 3.30 -43.66 3.30 0.30 0.27 HHJ295_DH087 GCS9 Cl* Melotte 22 HHJ 295 +03 38 27.52 +25 30 18.1 0 16.591 15.938 15.284 14.747 14.355 14.371 18.25 3.00 -40.21 3.00 1.94 0.69 HHJ2 GCS9 Cl* Melotte 22 HHJ 2 +03 38 27.83 +26 51 25.4 0 14.777 14.274 13.663 13.167 12.844 12.866 21.04 3.30 -38.12 3.30 0.31 0.81 HHJ121_DH089 GCS9 Cl* Melotte 22 HHJ 121 +03 38 34.21 +23 43 07.3 0 14.985 14.547 13.940 13.424 13.106 13.105 8.23 2.50 -53.41 2.50 12.39 0.00 HHJ98_DH090 GCS9 Cl* Melotte 22 HHJ 98 +03 38 34.49 +23 40 22.3 0 14.946 14.514 13.918 13.368 13.041 13.062 11.57 2.50 -46.35 2.50 3.34 0.48 HHJ97_DH091 GCS9 Cl* Melotte 22 HHJ 97 +03 38 43.30 +25 22 26.9 0 13.103 12.661 12.090 11.591 11.286 11.293 23.70 2.95 -41.82 2.95 1.32 0.79 HCG11_HHJ378_DH092 GCS9 Cl* Melotte 22 HCG 378 +03 38 45.75 +24 28 03.7 0 15.071 14.563 13.928 13.455 13.074 13.027 21.00 2.49 -40.88 2.49 2.42 0.84 HHJ91_DH094 GCS9 Cl* Melotte 22 HHJ 91 +03 38 53.89 +24 25 07.7 0 13.542 13.293 12.817 12.269 11.999 11.985 12.60 2.48 -33.86 2.48 1.14 0.03 HCG15 GCS9 Cl* Melotte 22 HCG 15 +03 38 54.16 +24 42 15.6 0 15.113 14.509 13.889 13.387 13.029 13.012 24.49 2.49 -40.01 2.49 1.14 0.50 HHJ63 GCS9 Cl* Melotte 22 HHJ 63 +03 39 03.31 +26 29 30.9 0 13.943 13.592 13.088 12.528 12.249 12.283 35.52 3.00 -41.84 3.00 0.41 0.00 HHJ317 GCS9 Cl* Melotte 22 HHJ 317 +03 39 04.06 +27 00 04.7 0 13.938 13.572 13.032 12.454 12.171 12.197 17.40 3.00 -34.30 3.00 0.70 0.22 DH098 GCS9 Cl* Melotte 22 DH 098 +03 39 08.13 +24 46 14.4 0 13.036 12.618 12.087 11.550 11.270 11.264 15.90 2.48 -44.93 2.48 0.74 0.91 HCG16_HHJ398_DH099 GCS9 Cl* Melotte 22 HCG 16 +03 39 13.33 +25 43 49.5 0 13.488 13.088 12.530 11.963 11.657 11.694 19.34 2.95 -39.87 2.95 0.43 0.90 HHJ349_DH100 GCS9 Cl* Melotte 22 HHJ 349 +03 39 15.56 +26 48 03.6 0 15.315 14.815 14.230 13.688 13.348 13.372 21.02 3.00 -38.68 3.00 0.54 0.74 HHJ66_DH102 GCS9 Cl* Melotte 22 HHJ 66 +03 39 16.75 +24 57 38.5 0 15.120 14.611 13.972 13.440 13.071 13.063 15.63 2.49 -40.66 2.49 3.81 0.85 DH103 GCS9 Cl* Melotte 22 DH 103 +03 39 17.05 +22 27 10.9 0 17.162 16.347 15.569 15.023 14.582 14.576 16.67 2.58 -43.68 2.58 2.56 0.69 IPMBD43 GCS9 Cl* Melotte 22 IPMBD 43 +03 39 22.07 +24 02 39.1 0 15.165 14.761 14.179 13.633 13.370 13.330 9.68 2.60 -31.09 2.60 1.34 0.00 BPL1 GCS9 Cl* Melotte 22 BPL 1 +03 39 22.42 +26 11 36.0 0 14.521 14.093 13.516 12.964 12.650 12.682 18.92 3.00 -45.37 3.00 1.11 0.93 HHJ224_DH104_Moraux2003_51 GCS9 Cl* Melotte 22 HHJ 224 +03 39 28.99 +25 34 55.8 0 13.441 13.038 12.541 12.001 11.753 11.765 23.56 2.95 -49.12 2.95 1.61 0.41 HHJ359 GCS9 Cl* Melotte 22 HHJ 359 +03 39 31.49 +20 04 40.4 0 13.372 13.019 12.545 11.954 11.659 11.704 32.73 3.42 -49.02 3.42 0.24 0.00 DH106 GCS9 Cl* Melotte 22 DH 106 +03 39 32.32 +24 16 01.1 0 13.986 13.660 13.081 12.505 12.208 12.233 21.26 2.50 -43.91 2.50 0.97 0.91 BPL2_DH107 GCS9 Cl* Melotte 22 BPL 2 +03 39 35.46 +24 07 06.1 0 12.806 12.545 12.021 11.498 11.181 11.228 18.04 2.49 -42.86 2.49 2.91 0.87 HHJ407_DH108 GCS9 Cl* Melotte 22 HHJ 407 +03 39 41.12 +23 28 23.3 0 14.958 14.527 13.924 13.411 13.080 13.046 23.64 2.98 -38.71 2.98 0.78 0.70 HHJ83 GCS9 Cl* Melotte 22 HHJ 83 +03 39 42.72 +23 54 27.6 0 14.142 13.756 13.179 12.650 12.363 12.332 22.18 2.50 -45.44 2.50 1.07 0.88 HCG22_T2B_BPL3_HHJ230_DH109 GCS9 Cl* Melotte 22 HCG 22 +03 39 43.32 +23 12 25.3 0 15.134 14.683 14.096 13.549 13.216 13.200 19.81 2.98 -37.33 2.98 0.27 0.67 DH110 GCS9 Cl* Melotte 22 DH 110 +03 39 44.19 +22 07 45.5 0 13.646 13.239 12.691 12.131 11.860 11.873 19.48 2.50 -45.62 2.50 0.96 0.92 HHJ141_DH111 GCS9 Cl* Melotte 22 HHJ 141 +03 39 44.37 +26 18 18.8 0 15.243 14.790 14.188 13.648 13.355 13.364 16.91 3.00 -36.58 3.00 0.49 0.59 Moraux2003_82 GCS9 Cl* Melotte 22 MBSC 82 +03 39 44.79 +22 39 15.3 0 14.615 14.124 13.554 13.015 12.716 12.691 17.16 2.98 -42.48 2.98 0.93 0.94 DH2004_112 GCS9 Cl* Melotte 22 DH 112 +03 39 46.35 +23 58 52.9 0 13.490 13.174 12.625 12.077 11.772 11.792 22.63 2.50 -38.42 2.50 3.77 0.69 HHJ324_BPL4_DH113 GCS9 Cl* Melotte 22 HHJ 324 +03 39 47.02 +26 21 14.3 0 14.554 14.117 13.546 12.997 12.694 12.688 28.81 3.00 -44.81 3.00 0.48 0.10 HHJ178_DH114_Moraux2003_57 GCS9 Cl* Melotte 22 HHJ 178 +03 39 47.99 +23 50 56.6 0 13.557 13.155 12.591 12.078 11.777 11.792 20.24 2.50 -41.77 2.50 1.60 0.92 HHJ291_BPL5_DH115 GCS9 Cl* Melotte 22 HHJ 291 +03 39 48.51 +23 46 03.8 0 15.028 14.539 13.964 13.446 13.119 13.083 18.84 2.50 -46.34 2.50 1.27 0.83 HHJ74_BPL6 GCS9 Cl* Melotte 22 HHJ 74 +03 39 49.72 +23 03 26.2 0 14.613 14.161 13.592 13.065 12.757 12.755 22.48 2.98 -41.93 2.98 0.30 0.89 HHJ138_DH117 GCS9 Cl* Melotte 22 HHJ 138 +03 39 50.68 +23 45 52.3 0 15.504 15.018 14.384 13.856 13.525 13.514 16.85 2.51 -41.64 2.51 1.25 0.89 BPL7_DH118 GCS9 Cl* Melotte 22 BPL 7 +03 39 53.53 +25 46 46.2 0 14.991 14.642 14.122 13.564 13.298 13.295 19.66 2.96 -45.57 2.96 0.24 0.92 DH2004_119 GCS9 Cl* Melotte 22 DH 119 +03 39 55.21 +24 12 54.1 0 15.371 14.909 14.293 13.783 13.432 13.440 18.81 2.50 -45.72 2.50 1.49 0.86 BPL8 GCS9 Cl* Melotte 22 BPL 8 +03 39 57.15 +26 07 00.1 0 15.350 14.830 14.198 13.660 13.331 13.308 21.95 2.96 -43.52 2.96 0.32 0.82 Moraux2003_94 GCS9 Cl* Melotte 22 MBSC 94 +03 39 57.35 +23 19 42.2 0 14.866 14.438 13.872 13.338 13.029 13.030 20.25 2.98 -49.85 2.98 0.39 0.61 HHJ103 GCS9 Cl* Melotte 22 HHJ 103 +03 39 57.85 +25 55 29.7 0 15.347 14.843 14.201 13.640 13.304 13.307 25.63 2.96 -42.96 2.96 0.41 0.40 DH120_Moraux2003_92 GCS9 Cl* Melotte 22 DH 120 +03 40 00.19 +23 26 05.7 0 12.733 12.412 11.891 11.375 11.054 11.085 19.30 2.97 -38.03 2.97 1.85 0.85 HCG26_SK802_HHJ403_DH121 GCS9 Cl* Melotte 22 HCG 26 +03 40 01.82 +22 59 20.2 0 12.400 12.141 11.716 11.468 10.921 11.098 22.71 2.97 -65.69 2.97 2.24 0.00 HHJ426 GCS9 Cl* Melotte 22 HHJ 426 +03 40 01.84 +25 04 19.5 0 14.919 14.417 13.786 13.289 12.952 12.925 20.07 2.49 -41.41 2.49 3.13 0.93 HHJ102 GCS9 Cl* Melotte 22 HHJ 102 +03 40 01.87 +24 46 25.9 0 14.203 13.811 13.240 12.713 12.468 12.420 19.86 2.48 -42.54 2.48 0.70 0.94 HCG24_T35B_HHJ226_DH122 GCS9 Cl* Melotte 22 HCG 24 +03 40 03.61 +24 30 02.7 0 13.674 13.270 12.776 12.243 11.963 11.937 2.13 2.48 -31.06 2.48 34.28 0.00 BPL9 GCS9 Cl* Melotte 22 BPL 9 +03 40 05.05 +25 31 36.6 0 13.648 13.372 12.896 12.232 12.056 12.038 42.65 2.95 -35.50 2.95 0.66 0.00 HHJ356 GCS9 Cl* Melotte 22 HHJ 356 +03 40 05.98 +25 40 20.8 0 14.788 14.303 13.742 13.196 12.896 12.880 18.83 2.96 -39.12 2.96 0.51 0.89 DH124 GCS9 Cl* Melotte 22 DH 124 +03 40 06.21 +28 08 32.0 0 15.387 14.854 14.239 13.731 13.422 13.401 13.90 4.95 -38.21 4.95 0.84 0.62 DH125 GCS9 Cl* Melotte 22 DH 125 +03 40 07.01 +22 38 47.5 0 14.975 14.452 13.904 13.372 13.034 13.020 17.42 2.98 -39.40 2.98 0.63 0.89 HHJ114 GCS9 Cl* Melotte 22 HHJ 114 +03 40 07.11 +24 13 03.3 0 15.403 14.934 14.318 13.802 13.450 13.451 16.92 2.50 -42.32 2.50 0.93 0.90 HHJ38_BPL10_DH126 GCS9 Cl* Melotte 22 HHJ 38 +03 40 09.69 +23 10 32.4 0 14.362 13.967 13.408 12.881 12.576 12.579 25.33 2.98 -38.03 2.98 0.76 0.41 HCG31_DH127 GCS9 Cl* Melotte 22 HCG 31 +03 40 10.93 +26 06 40.8 0 14.589 14.173 13.576 13.025 12.731 12.734 19.44 2.96 -36.36 2.96 0.32 0.67 HCG28_HHJ177_DH128 GCS9 Cl* Melotte 22 HCG 28 +03 40 11.04 +25 23 26.6 0 14.790 14.309 13.733 13.227 12.896 12.905 19.42 2.96 -38.70 2.96 0.26 0.87 HHJ147_DH129 GCS9 Cl* Melotte 22 HHJ 147 +03 40 11.93 +25 52 32.3 0 15.702 15.231 14.589 14.058 13.700 13.704 16.97 2.97 -35.30 2.97 0.57 0.39 HHJ29 GCS9 Cl* Melotte 22 HHJ 29 +03 40 12.74 +23 09 35.6 0 13.830 13.457 12.923 12.343 12.056 12.069 23.11 2.97 -33.99 2.97 0.50 0.08 HCG32_SK794_HHJ280_DH130 GCS9 Cl* Melotte 22 HCG 32 +03 40 14.80 +25 50 05.5 0 13.923 13.506 12.914 12.433 12.114 12.129 8.07 2.95 -22.54 2.95 0.30 0.00 SK781 GCS9 Cl* Melotte 22 SK 781 +03 40 14.91 +25 19 18.7 0 13.114 12.762 12.285 11.701 11.448 11.477 21.11 2.95 -40.08 2.95 0.67 0.87 SK785_HHJ397_DH131 GCS9 Cl* Melotte 22 SK 785 +03 40 15.93 +24 22 31.3 0 15.827 15.296 14.640 14.123 13.772 13.768 20.95 2.51 -35.43 2.51 4.56 0.34 BPL11 GCS9 Cl* Melotte 22 BPL 11 +03 40 23.06 +25 29 47.6 0 13.430 13.000 12.472 11.938 11.623 11.665 18.22 2.95 -41.47 2.95 0.50 0.93 HCG33_HHJ343_DH134 GCS9 Cl* Melotte 22 HCG 33 +03 40 23.84 +23 04 09.0 0 14.483 14.064 13.509 12.971 12.665 12.677 22.29 2.98 -42.37 2.98 0.58 0.90 HHJ162_DH135 GCS9 Cl* Melotte 22 HHJ 162 +03 40 24.20 +24 35 04.0 0 13.263 12.920 12.433 11.873 11.618 11.628 17.38 2.48 -40.53 2.48 0.86 0.91 HCG34_SK777_BPL12_DH136 GCS9 Cl* Melotte 22 HCG 34 +03 40 25.62 +24 06 00.0 0 14.659 14.287 13.684 13.159 12.829 12.878 20.15 2.50 -43.40 2.50 1.37 0.94 HHJ135_BPL13_DH138 GCS9 Cl* Melotte 22 HHJ 135 +03 40 26.27 +23 21 29.1 0 14.499 14.088 13.492 12.935 12.626 12.650 23.17 2.98 -38.33 2.98 0.57 0.71 HHJ167 GCS9 Cl* Melotte 22 HHJ 167 +03 40 26.41 +24 05 23.5 0 13.451 13.197 12.595 11.998 11.701 11.736 16.88 2.50 -38.77 2.50 1.79 0.84 SK778_HHJ338_BPL14_DH139 GCS9 Cl* Melotte 22 SK 778 +03 40 26.95 +24 14 14.2 0 14.326 13.942 13.369 12.825 12.542 12.531 18.94 2.50 -42.53 2.50 1.17 0.94 HHJ169_BPL15_DH140 GCS9 Cl* Melotte 22 HHJ 169 +03 40 27.93 +24 12 09.3 0 19.730 18.501 17.352 16.700 16.088 0.100 15.91 3.22 -42.54 3.22 5.78 0.62 int-pl-IZ-84;IPLJ0340279+241209_Y GCS9 Cl* Melotte 22 IPL 84 +03 40 31.17 +25 08 52.8 0 13.489 13.083 12.509 11.964 11.673 11.681 20.23 2.48 -43.75 2.48 2.05 0.93 HCG36_T102_HHJ329_DH142 GCS9 Cl* Melotte 22 HCG 36 +03 40 31.50 +23 33 02.0 0 12.655 12.386 11.899 11.459 11.074 11.141 21.40 2.12 -35.26 2.12 8.58 0.58 SK773_DH143 GCS9 Cl* Melotte 22 SK 773 +03 40 32.59 +25 28 40.6 0 15.026 14.501 13.946 13.420 13.114 13.099 10.36 2.23 -47.38 2.23 1.93 0.21 HHJ101_DH144 GCS9 Cl* Melotte 22 HHJ 101 +03 40 35.33 +20 57 56.6 0 13.012 12.647 12.149 11.582 11.260 11.302 23.55 3.58 -31.81 3.58 0.48 0.01 DH146 GCS9 Cl* Melotte 22 DH 146 +03 40 35.50 +23 13 07.4 0 17.972 16.910 16.076 15.510 15.014 15.020 12.82 2.37 -43.97 2.37 5.06 0.44 L07_A1_1 GCS9 +03 40 39.46 +23 26 34.8 0 16.232 15.654 15.015 14.460 14.071 14.075 15.56 2.27 -41.13 2.27 2.27 0.69 DH147_L07_A1_2 GCS9 Cl* Melotte 22 DH 147 +03 40 40.32 +25 50 48.1 0 14.039 13.616 13.025 12.447 12.168 12.150 17.46 2.23 -43.05 2.23 1.34 0.94 DH148 GCS9 Cl* Melotte 22 DH 148 +03 40 43.20 +22 49 53.8 0 14.757 14.295 13.730 13.177 12.897 12.868 17.03 2.25 -46.07 2.25 2.33 0.90 DH151_L07_193 GCS9 Cl* Melotte 22 DH 151 +03 40 43.27 +25 11 55.4 0 12.877 12.623 12.124 11.589 11.329 41.44 2.97 -27.65 2.97 3.03 0.00 SK754 GCS9 Cl* Melotte 22 SK 754 +03 40 49.40 +21 12 55.3 0 14.418 13.938 13.357 12.766 12.478 12.460 21.51 3.58 -35.17 3.58 0.41 0.39 DH152 GCS9 Cl* Melotte 22 DH 152 +03 40 51.08 +20 41 17.2 0 15.855 15.298 14.617 14.061 13.681 13.705 20.56 3.44 -38.49 3.44 0.28 0.75 DH155 GCS9 Cl* Melotte 22 DH 155 +03 40 51.46 +19 32 45.8 0 14.044 13.716 13.240 12.620 12.349 12.346 27.56 5.01 -46.86 5.01 1.30 0.17 DH157 GCS9 Cl* Melotte 22 DH 157 +03 40 51.82 +23 13 50.5 0 14.145 13.723 13.201 12.627 12.326 12.331 16.57 2.25 -41.87 2.25 1.21 0.93 HCG43_HHJ234_DH158_L07_169 GCS9 Cl* Melotte 22 HCG 43 +03 40 54.48 +22 54 25.5 0 14.916 14.411 13.824 13.282 12.957 12.960 17.93 2.25 -41.18 2.25 1.48 0.93 HHJ123_DH159_L07_182 GCS9 Cl* Melotte 22 HHJ 123 +03 40 55.05 +22 20 58.7 0 13.211 12.869 12.333 11.734 11.446 11.470 21.33 2.50 -35.72 2.50 1.06 0.40 HCG44_SK758_HHJ355_DH160 GCS9 Cl* Melotte 22 HCG 44 +03 40 55.30 +25 34 57.4 0 17.423 16.544 15.803 15.195 14.732 14.753 21.01 2.31 -41.51 2.31 7.86 0.69 L07_A1_4 GCS9 +03 40 56.06 +28 43 39.0 0 12.760 12.479 11.985 11.538 11.155 11.283 11.44 3.68 -37.58 3.68 0.85 0.21 DH161 GCS9 Cl* Melotte 22 DH 161 +03 40 59.26 +25 11 55.2 0 15.635 15.115 14.479 13.913 13.551 14.98 2.98 -43.05 2.98 0.63 0.86 HHJ41_DH162 GCS9 Cl* Melotte 22 HHJ 41 +03 41 00.39 +24 13 35.0 0 14.841 14.363 13.768 13.194 12.895 12.871 16.27 2.13 -39.79 2.13 0.81 0.89 BPL17 GCS9 Cl* Melotte 22 BPL 17 +03 41 01.99 +24 55 21.2 0 14.847 14.451 13.855 13.287 13.009 14.19 2.97 -32.57 2.97 1.14 0.03 HHJ126 GCS9 Cl* Melotte 22 HHJ 126 +03 41 02.99 +23 43 21.4 0 13.772 13.397 12.857 12.313 12.027 12.044 14.58 2.12 -38.42 2.12 0.61 0.71 HCG45_HHJ290_BPL18_DH163 GCS9 Cl* Melotte 22 HCG 45 +03 41 05.23 +23 50 14.9 0 15.720 15.171 14.571 14.019 13.727 13.698 14.75 2.14 -43.68 2.14 1.49 0.85 BPL19 GCS9 Cl* Melotte 22 BPL 19 +03 41 10.27 +25 45 55.9 0 13.996 13.553 13.015 12.467 12.161 12.180 18.53 2.23 -43.40 2.23 1.32 0.94 SK733_DH164 GCS9 Cl* Melotte 22 SK 733 +03 41 13.21 +24 05 25.6 0 20.248 19.168 17.791 16.898 16.325 0.166 14.11 3.48 -46.41 3.48 1.51 0.61 int-pl-IZ-81;IPLJ0341131+240525_Y GCS9 Cl* Melotte 22 IPL 81 +03 41 19.35 +23 51 41.7 0 14.700 14.230 13.634 13.065 12.795 12.782 15.93 2.13 -45.36 2.13 1.15 0.90 HCG48_DH167 GCS9 Cl* Melotte 22 HCG 48 +03 41 19.86 +25 06 49.0 0 14.586 14.170 13.598 13.072 12.788 18.41 2.97 -47.43 2.97 0.45 0.87 HHJ191 GCS9 Cl* Melotte 22 HHJ 191 +03 41 20.00 +22 37 53.7 0 13.771 13.385 12.814 12.255 11.965 11.999 11.28 2.50 -47.82 2.50 6.59 0.30 SK739_DH168 GCS9 Cl* Melotte 22 SK 739 +03 41 22.45 +24 23 51.6 0 15.326 14.820 14.189 13.641 13.290 13.310 17.58 2.13 -43.59 2.13 1.05 0.90 DH169 GCS9 Cl* Melotte 22 DH 169 +03 41 22.88 +23 55 48.1 0 14.135 13.702 13.150 12.615 12.323 12.335 24.42 2.12 -38.30 2.12 3.58 0.57 HCG49_HHJ233_BPL20_DH170 GCS9 Cl* Melotte 22 HCG 49 +03 41 23.63 +27 24 16.9 0 15.242 14.719 14.108 13.543 13.215 13.226 18.53 3.29 -35.23 3.29 0.33 0.40 DH171 GCS9 Cl* Melotte 22 DH 171 +03 41 24.65 +24 15 13.9 0 16.242 15.768 15.135 14.504 14.192 14.176 32.40 2.16 -41.17 2.16 1.95 0.00 DH172 GCS9 Cl* Melotte 22 DH 172 +03 41 24.69 +25 23 05.8 0 15.013 14.526 13.936 13.414 13.117 13.120 16.34 2.23 -38.60 2.23 3.60 0.78 HHJ117_L07_48 GCS9 Cl* Melotte 22 HHJ 117 +03 41 25.33 +22 32 55.8 0 13.437 13.076 12.526 11.946 11.699 11.718 20.54 2.50 -40.20 2.50 0.81 0.89 HCG51_SK732_HHJ345_DH173 GCS9 Cl* Melotte 22 HCG 51 +03 41 25.97 +23 15 35.8 0 14.670 14.193 13.617 13.061 12.761 12.768 19.37 2.25 -43.60 2.25 2.57 0.94 L07_170 GCS9 +03 41 26.36 +23 08 02.7 0 14.574 14.102 13.532 12.927 12.614 12.612 15.96 2.25 -41.75 2.25 11.80 0.92 DH174_L07_168 GCS9 Cl* Melotte 22 DH 174 +03 41 26.90 +24 01 02.3 0 13.364 12.912 12.305 11.845 11.430 11.454 25.17 2.12 -47.48 2.12 4.23 0.38 SK729_HHJ340_BPL21_DH175 GCS9 Cl* Melotte 22 SK 729 +03 41 29.39 +24 53 40.1 0 14.735 14.330 13.741 13.182 12.863 33.87 2.97 -32.81 2.97 1.30 0.00 Moraux2003_63 GCS9 Cl* Melotte 22 MBSC 63 +03 41 29.70 +24 32 26.5 0 13.241 12.967 12.496 11.906 11.680 23.06 2.97 -37.00 2.97 0.84 0.46 BPL22 GCS9 Cl* Melotte 22 BPL 22 +03 41 30.35 +25 17 05.9 0 16.646 15.972 15.208 14.682 14.349 14.526 13.78 2.27 -42.02 2.27 104.59 0.60 L07_A1_5_L07_214 GCS9 +03 41 31.21 +24 03 24.7 0 14.985 14.509 13.903 13.353 13.033 20.71 2.31 -39.58 2.31 2.29 0.89 HHJ90_BPL23_DH177 GCS9 Cl* Melotte 22 HHJ 90 +03 41 33.58 +27 09 50.1 0 15.162 14.567 13.912 13.377 13.020 13.045 19.34 3.29 -43.65 3.29 0.47 0.89 DH178 GCS9 Cl* Melotte 22 DH 178 +03 41 33.90 +23 11 44.9 0 16.186 15.643 15.029 14.509 14.159 14.137 30.84 2.27 -37.70 2.27 1.56 0.00 HHJ12_L07_A1_6 GCS9 Cl* Melotte 22 HHJ 12 +03 41 34.80 +23 38 15.6 0 15.153 14.666 14.080 13.522 13.242 13.186 15.91 2.13 -49.58 2.13 0.83 0.48 DH180 GCS9 Cl* Melotte 22 DH 180 +03 41 36.55 +22 41 01.7 0 16.280 15.724 15.122 14.591 14.266 14.232 32.02 2.27 -36.94 2.27 19.82 0.00 L07_photNM_21 GCS9 +03 41 37.26 +25 08 32.6 0 14.630 14.170 13.596 13.084 12.736 28.66 2.97 -44.60 2.97 1.75 0.12 HHJ148_DH182 GCS9 Cl* Melotte 22 HHJ 148 +03 41 38.81 +24 23 09.2 0 15.413 14.924 14.300 13.761 13.401 20.74 2.31 -48.47 2.31 2.29 0.59 BPL25 GCS9 Cl* Melotte 22 BPL 25 +03 41 38.87 +22 16 40.3 0 14.382 13.955 13.443 12.871 12.594 0.14 7.97 -57.33 7.97 1.28 0.00 HHJ176_DH183 GCS9 Cl* Melotte 22 HHJ 176 +03 41 39.84 +24 52 30.0 0 15.292 14.819 14.192 13.649 13.304 18.60 2.97 -44.72 2.97 1.71 0.88 HHJ69_Moraux2003_84 GCS9 Cl* Melotte 22 HHJ 69 +03 41 40.91 +25 54 24.1 1 16.893 16.001 15.180 14.574 14.122 14.125 16.94 2.26 -42.13 2.26 4.70 0.74 L07_A1_8_L07_248 GCS9 +03 41 41.46 +22 58 10.0 0 15.253 14.805 14.250 13.646 13.328 13.331 2.08 2.26 -35.29 2.26 3.25 0.00 L07_184 GCS9 +03 41 42.41 +23 54 57.1 1 16.171 15.464 14.709 14.124 13.686 14.07 2.31 -47.37 2.31 0.56 0.41 HHJ6_BPL26 GCS9 Cl* Melotte 22 HHJ 6 +03 41 43.72 +23 07 59.7 0 16.390 15.741 15.081 14.540 14.130 14.144 19.01 2.28 -38.92 2.28 5.75 0.60 L07_A1_9 GCS9 +03 41 45.07 +22 28 01.8 0 15.511 14.939 14.315 13.811 13.435 13.430 24.25 2.51 -44.15 2.51 2.91 0.59 DH185 GCS9 Cl* Melotte 22 DH 185 +03 41 46.44 +24 55 33.1 0 15.937 15.504 14.923 14.371 14.019 10.89 2.99 -39.19 2.99 1.45 0.34 DH186 GCS9 Cl* Melotte 22 DH 186 +03 41 47.09 +25 00 21.9 0 14.895 14.426 13.824 13.261 12.933 21.19 2.97 -41.96 2.97 0.91 0.92 HHJ125 GCS9 Cl* Melotte 22 HHJ 125 +03 41 48.05 +26 47 20.7 0 13.597 13.262 12.716 12.154 11.869 11.873 24.59 3.00 -39.18 3.00 0.69 0.53 HHJ357_DH188 GCS9 Cl* Melotte 22 HHJ 357 +03 41 49.60 +22 29 36.7 0 15.602 14.992 14.388 13.890 13.505 13.492 29.63 2.51 -46.82 2.51 3.79 0.01 DH189 GCS9 Cl* Melotte 22 DH 189 +03 41 51.50 +24 19 27.3 0 16.617 15.976 15.244 14.667 14.283 0.009 16.53 2.33 -37.48 2.33 3.69 0.46 int-pl-IZ-88;IPLJ0341515+241927_BPL27_Y GCS9 Cl* Melotte 22 IPL 88 +03 41 52.32 +24 41 57.6 0 14.986 14.480 13.904 13.361 13.012 18.58 2.97 -50.63 2.97 0.69 0.50 HHJ120_BPL28_DH190 GCS9 Cl* Melotte 22 HHJ 120 +03 41 52.94 +24 07 24.7 0 15.099 14.637 14.067 13.518 13.183 14.06 2.31 -43.29 2.31 2.22 0.82 HHJ108_BPL29_DH191 GCS9 Cl* Melotte 22 HHJ 108 +03 41 53.05 +27 04 42.9 0 14.776 14.268 13.678 13.174 12.842 12.833 15.90 3.29 -42.59 3.29 0.25 0.92 DH192 GCS9 Cl* Melotte 22 DH 192 +03 41 54.16 +23 05 04.7 1 17.349 16.376 15.522 14.975 14.415 14.418 18.19 2.30 -44.74 2.30 4.74 0.69 L07_A1_10 GCS9 +03 41 54.21 +25 43 47.1 0 13.586 13.204 12.696 12.122 11.843 11.866 18.41 2.23 -46.69 2.23 1.80 0.89 HCG55_B346_HHJ342_DH194 GCS9 Cl* Melotte 22 HCG 55 +03 41 56.49 +27 02 57.9 0 14.816 14.343 13.746 13.209 12.923 12.902 18.25 3.29 -47.59 3.29 0.17 0.86 DH195 GCS9 Cl* Melotte 22 DH 195 +03 41 56.72 +23 58 43.2 0 15.234 14.770 14.109 13.584 13.195 14.88 2.31 -33.12 2.31 0.99 0.07 BPL30 GCS9 Cl* Melotte 22 BPL 30 +03 41 58.66 +22 57 01.7 0 13.636 13.270 12.744 12.173 11.877 11.904 21.15 2.25 -41.08 2.25 10.25 0.90 HCG66_HHJ312_DH196_L07_16 GCS9 Cl* Melotte 22 HCG 66 +03 41 58.86 +26 12 21.1 0 13.697 13.292 12.754 12.161 11.889 11.902 14.33 3.00 -33.91 3.00 0.27 0.08 HCG56_B117_HHJ351_DH197_Moraux2003_16 GCS9 Cl* Melotte 22 HCG 56 +03 41 59.67 +24 42 18.3 0 16.192 15.586 14.953 14.380 14.016 16.64 2.99 -39.53 2.99 1.19 0.65 BPL31_Moraux2003_102 GCS9 Cl* Melotte 22 BPL 31 +03 41 59.75 +26 27 39.6 0 14.578 14.100 13.496 12.962 12.621 12.641 25.40 3.00 -41.25 3.00 0.32 0.64 HHJ213_DH200 GCS9 Cl* Melotte 22 HHJ 213 +03 42 00.02 +25 01 47.1 0 13.914 13.524 12.950 12.390 12.128 18.86 2.97 -46.97 2.97 1.15 0.88 SK702_HHJ277_DH201 GCS9 Cl* Melotte 22 SK 702 +03 42 00.05 +26 01 12.1 0 14.722 14.226 13.612 13.052 12.712 12.728 13.09 2.23 -31.95 2.23 3.88 0.01 L07_18 GCS9 +03 42 00.37 +24 10 12.6 0 16.212 15.647 14.984 14.441 14.064 17.68 2.32 -44.24 2.32 1.87 0.73 BPL32 GCS9 Cl* Melotte 22 BPL 32 +03 42 01.68 +22 23 26.6 0 14.215 13.807 13.265 12.703 12.396 33.36 7.96 -38.44 7.96 0.89 0.00 HCG67_HHJ201_DH202 GCS9 Cl* Melotte 22 HCG 67 +03 42 02.87 +24 12 36.0 0 13.316 12.980 12.469 11.883 11.629 12.04 2.30 -45.58 2.30 0.93 0.61 HCG63_HHJ350_BPL33_DH203 GCS9 Cl* Melotte 22 HCG 63 +03 42 02.93 +23 55 53.7 0 12.951 12.573 12.010 11.456 11.155 17.94 2.30 -39.33 2.30 0.46 0.87 HCG64_HHJ406_BPL34_DH204 GCS9 Cl* Melotte 22 HCG 64 +03 42 03.30 +24 32 13.3 0 13.337 13.002 12.464 11.883 11.656 17.31 2.97 -48.35 2.97 1.39 0.79 SK701_HHJ354_BPL35_DH205_Moraux2003_11 GCS9 Cl* Melotte 22 SK 701 +03 42 03.42 +25 22 39.1 0 14.406 13.913 13.303 12.729 12.430 12.430 22.60 2.23 -37.61 2.23 5.58 0.68 HHJ212_DH206_L07_41 GCS9 Cl* Melotte 22 HHJ 212 +03 42 04.16 +22 16 51.0 0 14.270 13.988 13.501 12.878 12.617 40.26 7.97 -17.56 7.97 1.17 0.00 SK708 GCS9 Cl* Melotte 22 SK 708 +03 42 05.74 +23 07 14.4 0 16.728 16.017 15.363 14.805 14.378 14.398 16.95 2.29 -37.00 2.29 2.73 0.41 L07_A1_11 GCS9 +03 42 08.29 +25 36 59.9 0 14.094 13.600 13.009 12.441 12.120 12.153 16.61 2.23 -37.66 2.23 2.38 0.77 HHJ270_DH211_L07_36 GCS9 Cl* Melotte 22 HHJ 270 +03 42 08.84 +23 35 16.8 0 12.944 12.632 12.126 11.626 11.318 11.344 14.70 2.12 -43.32 2.12 1.62 0.72 SK699_HHJ384_DH212 GCS9 Cl* Melotte 22 SK 699 +03 42 09.86 +27 57 25.2 0 13.631 13.340 12.849 12.206 11.986 11.978 33.43 3.68 -33.78 3.68 0.13 0.00 DH213 GCS9 Cl* Melotte 22 DH 213 +03 42 10.60 +22 18 05.5 0 15.177 14.710 14.151 13.625 13.280 10.07 8.00 -65.72 8.00 0.41 0.00 HHJ55 GCS9 Cl* Melotte 22 HHJ 55 +03 42 10.93 +24 05 08.4 0 12.830 12.470 11.954 11.688 11.337 16.83 2.30 -39.37 2.30 1.14 0.85 HCG68_T36_HHJ396_DH214 GCS9 Cl* Melotte 22 HCG 68 +03 42 10.99 +25 44 35.0 0 15.394 14.772 14.087 13.528 13.157 13.165 14.33 2.24 -42.76 2.24 3.83 0.83 L07_25 GCS9 +03 42 12.62 +26 49 44.9 0 14.299 13.821 13.209 12.670 12.342 12.345 19.56 3.00 -39.85 3.00 0.84 0.91 HHJ236_DH215 GCS9 Cl* Melotte 22 HHJ 236 +03 42 13.49 +24 18 49.6 0 15.627 15.114 14.453 13.891 13.540 13.15 2.31 -37.63 2.31 0.83 0.48 HHJ34_BPL36 GCS9 Cl* Melotte 22 HHJ 34 +03 42 13.90 +20 21 43.6 0 14.695 14.209 13.633 13.098 12.788 12.772 24.49 3.42 -44.30 3.42 0.53 0.76 DH216 GCS9 Cl* Melotte 22 DH 216 +03 42 15.37 +23 11 30.9 0 14.906 14.419 13.845 13.300 12.963 12.972 22.08 2.25 -38.79 2.25 1.60 0.81 HHJ109_L07_167 GCS9 Cl* Melotte 22 HHJ 109 +03 42 17.90 +24 06 57.6 0 14.857 14.378 13.774 13.232 12.897 17.09 2.30 -42.38 2.30 0.77 0.94 HHJ129_BPL37_DH217 GCS9 Cl* Melotte 22 HHJ 129 +03 42 18.88 +23 59 22.0 0 12.658 12.428 11.944 11.452 11.259 25.66 2.30 -50.92 2.30 2.16 0.01 SK687_HHJ437 GCS9 Cl* Melotte 22 SK 687 +03 42 22.13 +25 03 56.3 0 15.564 15.206 14.659 14.050 13.781 24.49 2.98 -48.94 2.98 8.93 0.19 DH219 GCS9 Cl* Melotte 22 DH 219 +03 42 26.28 +23 51 38.5 0 15.456 14.895 14.283 13.744 13.347 13.357 10.50 2.13 -43.07 2.13 2.55 0.42 HHJ49_BPL38_DH221 GCS9 Cl* Melotte 22 HHJ 49 +03 42 26.29 +24 14 07.7 0 14.155 13.741 13.173 12.659 12.325 12.320 10.47 2.12 -47.50 2.12 2.98 0.21 HCG73_SK680_HHJ262_BPL39_DH223 GCS9 Cl* Melotte 22 HCG 73 +03 42 27.31 +22 34 24.6 0 13.652 13.223 12.660 12.145 11.830 11.851 14.98 2.51 -42.82 2.51 1.87 0.90 HCG76_HHJ294_DH224 GCS9 Cl* Melotte 22 HCG 76 +03 42 28.43 +27 12 58.1 0 16.484 16.050 15.500 14.978 14.679 14.670 9.87 3.37 -42.08 3.37 0.41 0.18 DH226 GCS9 Cl* Melotte 22 DH 226 +03 42 28.66 +25 01 00.2 0 13.341 13.007 12.475 11.969 11.689 19.64 2.97 -44.22 2.97 1.06 0.93 SK676_HHJ362_DH227 GCS9 Cl* Melotte 22 SK 676 +03 42 29.43 +22 47 25.9 0 12.560 12.251 11.750 11.449 10.885 11.017 19.42 2.25 -40.94 2.25 7.62 0.90 HCG77_A80_DH228 GCS9 Cl* Melotte 22 HCG 77 +03 42 29.51 +22 23 45.7 0 12.961 12.706 12.271 11.802 11.535 25.41 7.95 -63.14 7.95 0.89 0.00 SK682 GCS9 Cl* Melotte 22 SK 682 +03 42 29.71 +20 48 39.6 0 14.264 13.827 13.235 12.712 12.379 12.411 20.54 3.42 -38.07 3.42 0.39 0.82 DH229 GCS9 Cl* Melotte 22 DH 229 +03 42 31.21 +24 49 21.2 0 15.091 14.553 13.952 13.433 13.118 23.06 2.97 -38.50 2.97 8.89 0.56 HCG74_HHJ82_DH230_Moraux2003_76 GCS9 Cl* Melotte 22 HCG 74 +03 42 33.97 +24 11 00.5 0 15.453 14.926 14.319 13.774 13.408 13.422 11.59 2.13 -52.78 2.13 8.09 0.02 BPL40_DH231 GCS9 Cl* Melotte 22 BPL 40 +03 42 36.27 +23 22 04.7 0 14.057 13.676 13.118 12.571 12.298 12.267 16.16 2.25 -37.36 2.25 1.08 0.73 HCG79_HHJ241_HHJ241_DH232 GCS9 Cl* Melotte 22 HCG 79 +03 42 36.95 +25 13 57.5 0 15.243 14.744 14.157 13.605 13.246 14.72 2.97 -38.46 2.97 2.57 0.70 DH233 GCS9 Cl* Melotte 22 DH 233 +03 42 40.23 +23 59 21.5 0 12.859 12.500 11.988 11.460 11.096 11.160 17.01 2.12 -46.73 2.12 1.30 0.59 HCG80_T3_HHJ409_DH234 GCS9 Cl* Melotte 22 HCG 80 +03 42 41.18 +24 01 42.7 0 14.985 14.503 13.921 13.357 13.014 13.025 20.15 2.13 -41.62 2.13 0.54 0.93 HCG82_HHJ115_BPL41_DH235 GCS9 Cl* Melotte 22 HCG 82 +03 42 41.85 +24 00 15.5 0 14.496 14.076 13.515 12.954 12.636 12.650 18.92 2.12 -44.12 2.12 0.31 0.94 BPL42_DH236 GCS9 Cl* Melotte 22 BPL 42 +03 42 42.12 +25 11 48.7 0 15.492 14.954 14.328 13.773 13.367 19.99 2.97 -46.20 2.97 1.90 0.82 HHJ45_DH237 GCS9 Cl* Melotte 22 HHJ 45 +03 42 42.41 +23 20 21.5 0 13.380 12.965 12.389 11.853 11.534 11.549 20.26 2.25 -20.48 2.25 1.34 0.00 HCG86_DH238 GCS9 Cl* Melotte 22 HCG 86 +03 42 43.81 +25 32 06.2 0 13.613 13.213 12.673 12.087 11.829 11.818 18.81 2.23 -44.63 2.23 2.28 0.93 SK665_HHJ341_DH239 GCS9 Cl* Melotte 22 SK 665 +03 42 44.37 +23 06 16.1 0 14.900 14.423 13.813 13.265 12.950 12.934 14.75 2.25 -47.61 2.25 5.30 0.75 HHJ119_DH240_L07_176 GCS9 Cl* Melotte 22 HHJ 119 +03 42 44.46 +23 58 15.2 0 15.186 14.686 14.062 13.536 13.187 13.188 15.12 2.13 -48.66 2.13 4.84 0.57 BPL43_DH241 GCS9 Cl* Melotte 22 BPL 43 +03 42 45.65 +23 49 22.6 0 15.473 14.898 14.286 13.714 13.361 13.361 22.64 2.13 -48.82 2.13 2.57 0.38 HHJ52_BPL44 GCS9 Cl* Melotte 22 HHJ 52 +03 42 47.30 +23 00 40.3 0 17.056 16.290 15.554 14.991 14.575 14.576 14.60 2.31 -44.06 2.31 5.33 0.59 L07_A1_13 GCS9 +03 42 48.17 +24 04 01.2 0 17.739 16.847 16.038 15.466 14.936 0.020 19.17 2.24 -33.98 2.24 3.95 0.16 int-pl-IZ-29;IPLJ0342481+240401_BPL45_Y GCS9 Cl* Melotte 22 IPL 29 +03 42 48.91 +23 34 48.4 0 14.623 14.157 13.593 13.067 12.741 12.742 24.86 2.13 -47.62 2.13 1.71 0.49 HHJ153_DH242 GCS9 Cl* Melotte 22 HHJ 153 +03 42 49.11 +24 10 15.4 0 13.648 13.221 12.670 12.134 11.801 11.828 31.63 2.12 -28.60 2.12 1.47 0.00 SK663_BPL46_DH243 GCS9 Cl* Melotte 22 SK 663 +03 42 51.68 +23 08 43.7 0 14.593 14.085 13.451 12.866 12.527 12.518 20.56 2.25 -41.00 2.25 6.70 0.92 L07_177 GCS9 +03 42 54.00 +26 08 16.1 0 14.730 14.262 13.672 13.136 12.798 12.810 19.45 2.23 -43.40 2.23 4.13 0.94 HHJ146_DH244_L07_22 GCS9 Cl* Melotte 22 HHJ 146 +03 42 55.88 +22 38 00.4 0 16.581 16.069 15.450 14.845 14.501 0.009 25.97 2.55 -3.49 2.55 1.79 0.00 int-pl-IZ-78;2MASSJ0342558+223800_Y GCS9 Cl* Melotte 22 IPL 78 +03 42 56.55 +22 51 17.9 0 15.901 15.338 14.689 14.137 13.782 13.760 16.35 2.27 -38.51 2.27 4.45 0.78 L07_191 GCS9 +03 42 56.55 +24 04 57.8 0 12.916 12.529 12.006 11.543 11.156 11.214 16.43 2.12 -38.79 2.12 1.08 0.82 HCG93_SK658_HHJ401_BPL48_DH245 GCS9 Cl* Melotte 22 HCG 93 +03 42 56.57 +24 13 45.4 0 14.910 14.439 13.859 13.319 12.979 12.984 21.34 2.13 -42.10 2.13 0.76 0.92 HCG92_BPL47 GCS9 Cl* Melotte 22 HCG 92 +03 42 58.60 +20 12 45.0 0 14.947 14.424 13.773 13.194 12.857 12.840 18.85 3.42 -32.72 3.42 0.30 0.10 DH247 GCS9 Cl* Melotte 22 DH 247 +03 42 59.92 +22 42 51.5 0 16.085 15.349 14.650 14.118 13.712 13.703 25.82 2.27 -43.19 2.27 33.85 0.17 L07_A1_14_L07_245 GCS9 +03 43 00.17 +24 43 52.3 0 17.791 16.841 16.022 15.425 14.969 32.72 3.09 -51.43 3.09 12.65 0.00 BPL49_CFHT-Pl-17_M7.9 GCS9 Cl* Melotte 22 BPL 49 +03 43 01.27 +24 14 52.2 0 15.292 14.794 14.179 13.655 13.284 13.296 22.91 2.13 -39.81 2.13 2.12 0.68 BPL50 GCS9 Cl* Melotte 22 BPL 50 +03 43 01.40 +23 29 30.4 0 14.868 14.435 13.861 13.324 13.014 13.042 18.85 2.25 -41.62 2.25 1.47 0.94 L07_158 GCS9 +03 43 03.83 +23 54 19.6 0 18.852 17.703 16.751 16.052 15.562 0.046 18.37 2.48 -38.49 2.48 2.92 0.66 int-pl-IZ-25;2MASSJ0343038+235420_Y GCS9 Cl* Melotte 22 IPL 25 +03 43 04.20 +22 48 03.3 0 12.462 12.136 11.621 11.436 10.779 10.986 18.93 2.25 -40.66 2.25 6.22 0.90 HCG101_B173_HHJ428_DH251 GCS9 Cl* Melotte 22 HCG 101 +03 43 04.37 +25 26 12.0 0 14.931 14.396 13.757 13.216 12.892 12.874 16.25 2.23 -36.14 2.23 8.13 0.56 HHJ107 GCS9 Cl* Melotte 22 HHJ 107 +03 43 05.54 +24 49 28.3 0 12.492 12.121 11.627 11.517 11.107 21.32 2.97 -42.76 2.97 2.77 0.86 HCG97_HHJ432_DH252 GCS9 Cl* Melotte 22 HCG 97 +03 43 06.50 +22 17 49.2 0 15.872 15.301 14.649 14.103 13.726 13.720 23.39 2.52 -40.65 2.52 4.64 0.67 HHJ18 GCS9 Cl* Melotte 22 HHJ 18 +03 43 07.58 +25 34 29.0 0 14.063 13.611 13.036 12.459 12.172 12.204 19.71 2.23 -40.51 2.23 2.69 0.92 HCG96_DH253_L07_34 GCS9 Cl* Melotte 22 HCG 96 +03 43 09.75 +24 41 32.7 0 13.167 12.734 12.234 11.768 11.425 23.06 2.97 -47.52 2.97 2.88 0.67 HCG100_SK654_T40_HHJ383_BPL51_DH254_Moraux2003_2 GCS9 Cl* Melotte 22 HCG 100 +03 43 11.57 +25 25 22.9 0 13.868 13.412 12.861 12.355 12.020 12.040 6.10 2.23 -53.18 2.23 6.99 0.00 SK650 GCS9 Cl* Melotte 22 SK 650 +03 43 11.65 +24 06 52.4 0 15.210 14.680 14.044 13.501 13.160 13.172 16.13 2.13 -38.16 2.13 3.13 0.74 HHJ71_BPL52 GCS9 Cl* Melotte 22 HHJ 71 +03 43 11.77 +25 31 31.9 0 16.035 15.427 14.767 14.237 13.883 13.872 18.38 2.25 -44.54 2.25 12.60 0.72 L07_A1_15 GCS9 +03 43 12.12 +24 44 45.1 0 13.538 13.088 12.533 12.023 11.684 21.38 2.97 -49.22 2.97 1.92 0.61 HCG102_SK653_HHJ328_BPL53_DH255 GCS9 Cl* Melotte 22 HCG 102 +03 43 13.07 +24 39 19.3 0 13.055 12.669 12.151 11.608 11.286 20.98 2.97 -41.82 2.97 1.16 0.91 HCG103_SK652_HHJ395_BPL54_DH256 GCS9 Cl* Melotte 22 HCG 103 +03 43 15.74 +25 20 29.5 0 13.094 12.779 12.277 11.779 11.507 11.524 15.79 2.23 -24.92 2.23 0.89 0.00 SK646 GCS9 Cl* Melotte 22 SK 646 +03 43 16.61 +23 50 01.5 0 14.279 13.794 13.239 12.641 12.382 12.371 15.68 2.12 -43.30 2.12 0.89 0.92 HHJ206_BPL56_DH258 GCS9 Cl* Melotte 22 HHJ 206 +03 43 16.88 +23 59 56.4 0 18.673 17.896 17.104 16.502 16.126 0.042 31.62 2.69 -8.78 2.69 3.68 0.00 int-pl-IZ-24;IPLJ0343168+235956_N GCS9 Cl* Melotte 22 IPL 24 +03 43 18.47 +26 40 24.3 0 13.001 12.604 12.085 11.533 11.239 11.287 49.10 2.94 -45.26 2.94 0.27 0.00 HHJ413 GCS9 Cl* Melotte 22 HHJ 413 +03 43 18.56 +26 30 18.9 0 15.438 14.940 14.415 13.971 13.634 13.654 24.39 2.95 -50.80 2.95 0.64 0.07 HHJ89 GCS9 Cl* Melotte 22 HHJ 89 +03 43 19.01 +22 47 10.4 0 14.679 14.176 13.584 13.040 12.725 12.734 16.91 2.25 -46.41 2.25 2.58 0.89 HCG110_HHJ143_DH259 GCS9 Cl* Melotte 22 HCG 110 +03 43 19.06 +26 04 43.9 0 14.168 13.751 13.169 12.614 12.292 12.355 13.73 2.23 -40.04 2.23 0.67 0.77 SK642_HHJ279_DH260 GCS9 Cl* Melotte 22 SK 642 +03 43 20.58 +24 26 34.9 0 15.208 14.646 14.060 13.490 13.168 18.87 2.97 -40.83 2.97 0.91 0.88 HCG106_HHJ88_BPL57_DH261_Moraux2003_79 GCS9 Cl* Melotte 22 HCG 106 +03 43 21.12 +24 11 09.0 0 15.279 14.803 14.182 13.683 13.346 13.361 11.26 2.13 -42.71 2.13 2.07 0.55 HHJ65 GCS9 Cl* Melotte 22 HHJ 65 +03 43 22.55 +23 00 56.5 0 16.186 15.558 14.898 14.365 13.983 13.988 16.28 2.27 -44.63 2.27 2.24 0.70 L07_A1_16 GCS9 +03 43 24.99 +23 52 01.2 0 16.867 16.116 15.368 14.820 14.365 0.011 14.16 2.34 -46.02 2.34 1.59 0.53 int-pl-IZ-6;2MASSJ0343249+235201_BPL58_Y GCS9 Cl* Melotte 22 IPL 6 +03 43 25.16 +22 53 44.3 0 14.833 14.335 13.716 13.175 12.858 12.813 14.86 2.25 -45.95 2.25 1.39 0.85 HHJ134_DH263_L07_179 GCS9 Cl* Melotte 22 HHJ 134 +03 43 26.20 +26 02 30.6 0 13.718 13.336 12.777 12.204 11.899 11.946 16.58 2.23 -41.36 2.23 3.41 0.92 HCG105_SK633_HHJ327_DH264 GCS9 Cl* Melotte 22 HCG 105 +03 43 26.44 +22 42 42.5 0 14.138 13.675 13.111 12.543 12.249 12.216 13.70 2.25 -37.63 2.25 1.69 0.56 HCG114_SK644_HHJ261_DH265_L07_189 GCS9 Cl* Melotte 22 HCG 114 +03 43 26.98 +24 27 09.6 0 13.246 12.790 12.281 11.739 11.444 16.27 2.97 -42.45 2.97 0.43 0.92 SK638_HHJ368_BPL59_DH267_Moraux2003_6 GCS9 Cl* Melotte 22 SK 638 +03 43 27.44 +22 37 41.0 0 15.234 14.678 14.118 13.589 13.256 13.235 23.77 2.51 -43.48 2.51 1.02 0.67 HHJ70 GCS9 Cl* Melotte 22 HHJ 70 +03 43 28.21 +24 53 30.9 0 13.950 13.546 12.992 12.414 12.165 18.00 2.97 -39.09 2.97 0.59 0.87 HCG109_SK635_DH268 GCS9 Cl* Melotte 22 HCG 109 +03 43 28.74 +24 09 06.1 0 15.175 14.685 14.073 13.555 13.200 17.98 2.31 -42.91 2.31 0.61 0.90 HHJ76 GCS9 Cl* Melotte 22 HHJ 76 +03 43 29.90 +24 39 23.4 0 15.428 14.876 14.288 13.760 13.405 11.50 2.98 -40.55 2.98 4.29 0.52 HHJ50_DH269_Moraux2003_87 GCS9 Cl* Melotte 22 HHJ 50 +03 43 34.14 +25 35 25.8 0 13.777 13.360 12.813 12.234 11.958 11.947 16.72 2.23 -43.78 2.23 1.22 0.93 HCG112_DH270_Moraux2003_19 GCS9 Cl* Melotte 22 HCG 112 +03 43 34.49 +25 57 30.6 1 16.571 15.727 14.909 14.359 13.909 13.901 20.92 2.25 -47.72 2.25 16.35 0.40 L07_A1_17 GCS9 +03 43 35.22 +25 24 30.8 0 13.655 13.247 12.728 12.188 11.914 11.926 10.07 2.23 -45.01 2.23 1.09 0.30 SK622_HHJ337_DH272 GCS9 Cl* Melotte 22 SK 622 +03 43 36.57 +23 12 34.1 0 14.208 13.781 13.203 12.644 12.333 12.343 23.03 2.25 -39.25 2.25 6.15 0.79 HCG122_T6_HHJ205_DH274_L07_174 GCS9 Cl* Melotte 22 HCG 122 +03 43 36.68 +25 47 00.5 0 13.800 13.402 12.848 12.282 12.004 11.994 21.04 2.23 -46.84 2.23 1.31 0.85 SK619_DH276_Moraux2003_22 GCS9 Cl* Melotte 22 SK 619 +03 43 37.11 +23 38 31.9 0 13.785 13.328 12.738 12.198 11.870 17.19 2.30 -45.95 2.30 0.60 0.91 SK630_DH278 GCS9 Cl* Melotte 22 SK 630 +03 43 37.32 +25 24 32.0 0 13.423 13.034 12.513 11.985 11.664 11.701 12.47 2.23 -45.17 2.23 1.32 0.69 HCG115_SK620_HHJ377_DH279 GCS9 Cl* Melotte 22 HCG 115 +03 43 39.05 +23 44 05.2 0 14.252 13.733 13.145 12.592 12.212 19.59 2.30 -41.08 2.30 5.74 0.93 HCG124_SK624_BPL61_DH281 GCS9 Cl* Melotte 22 HCG 124 +03 43 39.72 +23 41 32.4 0 15.672 15.129 14.470 13.912 13.544 18.33 2.31 -45.77 2.31 0.60 0.86 HHJ40 GCS9 Cl* Melotte 22 HHJ 40 +03 43 40.31 +24 30 11.2 0 18.552 17.490 16.494 15.853 15.304 12.03 3.27 -44.27 3.27 2.96 0.36 BPL62_Roque7_CFHT-Pl-24_M8.3 GCS9 Cl* Melotte 22 BPL 62 +03 43 42.14 +24 34 23.1 0 12.680 12.347 11.834 11.451 11.133 21.23 2.97 -39.67 2.97 3.04 0.87 HCG123_SK616_MT41_DH282 GCS9 Cl* Melotte 22 HCG 123 +03 43 42.90 +25 51 37.0 0 15.191 14.695 14.098 13.525 13.202 13.195 21.00 2.23 -46.18 2.23 7.04 0.78 HHJ77_Moraux2003_81_L07_27 GCS9 Cl* Melotte 22 HHJ 77 +03 43 43.15 +24 32 56.0 0 15.104 14.614 14.005 13.427 13.151 12.39 2.97 -42.08 2.97 0.40 0.69 DH283_Moraux2003_77 GCS9 Cl* Melotte 22 DH 283 +03 43 43.53 +24 12 50.0 0 15.544 15.025 14.407 13.872 13.518 18.44 2.31 -44.45 2.31 1.15 0.89 HHJ43_DH284 GCS9 Cl* Melotte 22 HHJ 43 +03 43 43.71 +24 29 15.5 0 13.243 12.898 12.337 11.802 11.502 18.55 2.97 -50.23 2.97 2.25 0.55 HHJ374_BPL63_DH285_Moraux2003_5 GCS9 Cl* Melotte 22 HHJ 374 +03 43 44.09 +25 39 49.5 0 15.071 14.589 13.986 13.428 13.120 13.122 17.56 2.23 -44.02 2.23 2.38 0.90 Moraux2003_72_L07_32 GCS9 Cl* Melotte 22 MBSC 72 +03 43 44.97 +23 03 21.1 0 13.553 13.162 12.604 12.043 11.693 11.711 17.85 2.25 -47.74 2.25 2.21 0.84 SK618_HHJ321_DH286 GCS9 Cl* Melotte 22 SK 618 +03 43 46.29 +23 58 37.9 0 19.124 17.930 16.876 16.248 15.628 0.057 20.13 2.62 -45.01 2.62 6.23 0.69 int-pl-IZ-20;IPLJ0343462+235838_Y GCS9 Cl* Melotte 22 IPL 20 +03 43 47.08 +26 04 35.3 0 13.857 13.453 12.895 12.305 12.017 12.025 19.24 2.23 -41.90 2.23 9.19 0.93 SK607_HHJ311_DH287 GCS9 Cl* Melotte 22 SK 607 +03 43 47.71 +28 15 59.3 0 14.415 14.156 13.720 13.100 12.988 12.927 15.96 3.69 -40.79 3.69 0.74 0.90 DH2004_289 GCS9 Cl* Melotte 22 DH 289 +03 43 48.45 +25 02 36.7 0 13.856 13.401 12.797 12.260 11.888 20.40 2.97 -43.69 2.97 0.33 0.93 HCG125_HHJ298_DH292 GCS9 Cl* Melotte 22 HCG 125 +03 43 50.56 +23 05 47.7 0 16.501 15.963 15.297 14.681 14.285 0.009 24.32 2.28 -51.66 2.28 5.95 0.02 int-pl-IZ-53_2MASSJ0343505+230548_Y_L07_241 GCS9 Cl* Melotte 22 IPL 53 +03 43 51.38 +21 09 03.1 0 14.541 14.138 13.586 13.020 12.740 12.734 30.15 2.65 -36.85 2.65 1.50 0.01 DH295 GCS9 Cl* Melotte 22 DH 295 +03 43 51.76 +24 14 15.9 0 14.292 13.838 13.229 12.650 12.358 22.34 2.30 -41.81 2.30 1.45 0.90 HCG128_BPL65_DH296 GCS9 Cl* Melotte 22 HCG 128 +03 43 52.15 +24 50 29.5 0 12.141 11.914 11.457 11.366 10.988 18.96 2.97 -35.05 2.97 4.37 0.60 HII191_HCG127_SK606_DH297 GCS9 HII191 +03 43 52.79 +25 29 30.3 0 14.450 13.990 13.414 12.853 12.576 12.580 22.91 2.23 -45.90 2.23 4.71 0.83 HHJ218_DH298Moraux2003_37 GCS9 Cl* Melotte 22 HHJ 218 +03 43 53.55 +24 31 11.4 0 18.970 17.709 16.649 15.942 15.298 13.04 3.29 -53.30 3.29 6.40 0.02 BPL66_Roque4 GCS9 Cl* Melotte 22 BPL 66 +03 43 53.88 +25 28 30.0 0 13.156 12.789 12.268 11.797 11.424 11.455 23.08 2.23 -46.36 2.23 3.97 0.76 HCG126_SK600_T69_HHJ391_DH299 GCS9 Cl* Melotte 22 HCG 126 +03 43 56.00 +25 36 25.2 0 16.718 15.979 15.290 14.710 14.319 14.333 23.18 2.27 -46.03 2.27 11.61 0.36 PLZJ50_L07_A1_18_L07_249 GCS9 Cl* Melotte 22 PlZJ 50 +03 43 56.70 +25 15 43.9 0 12.995 12.666 12.168 11.616 11.330 21.03 2.97 -42.94 2.97 0.51 0.86 SK596_DH300 GCS9 Cl* Melotte 22 SK 596 +03 43 56.71 +24 59 36.5 0 13.167 12.845 12.319 11.756 11.484 27.73 2.97 -45.73 2.97 1.35 0.13 HCG129_SK598_HHJ382_DH301 GCS9 Cl* Melotte 22 HCG 129 +03 43 57.00 +23 57 05.7 0 14.157 13.723 13.145 12.582 12.265 21.91 2.30 -44.52 2.30 1.37 0.90 HCG135_HHJ229_DH302 GCS9 Cl* Melotte 22 HCG 135 +03 43 57.29 +24 13 20.3 0 14.206 13.775 13.190 12.639 12.337 21.78 2.30 -37.51 2.30 1.14 0.72 HCG133_SK601_BPL67_DH303 GCS9 Cl* Melotte 22 HCG 133 +03 44 02.29 +25 03 53.5 0 12.928 12.605 12.098 11.550 11.827 17.46 2.97 -49.03 2.97 1.22 0.26 HCG134_SK591_T150_DH306 GCS9 Cl* Melotte 22 HCG 134 +03 44 02.50 +21 13 15.8 0 14.767 14.282 13.665 13.123 12.781 12.814 14.96 2.65 -40.08 2.65 0.41 0.85 DH307 GCS9 Cl* Melotte 22 DH 307 +03 44 05.25 +22 50 13.5 0 20.066 18.839 17.666 16.895 16.134 0.147 19.70 3.13 -45.22 3.13 3.08 0.57 int-pl-IZ-57;IPLJ0344052+225014_N GCS9 Cl* Melotte 22 IPL 57 +03 44 05.63 +23 03 42.4 0 15.169 14.803 14.258 13.565 13.302 13.294 17.87 2.26 -43.49 2.26 1.94 0.90 L07_180 GCS9 +03 44 08.81 +23 04 47.5 0 12.721 12.442 11.944 11.503 11.108 11.160 24.16 2.25 -46.50 2.25 1.05 0.39 HCG141_SK590_T71_DH312 GCS9 Cl* Melotte 22 HCG 141 +03 44 09.32 +23 08 46.8 0 14.412 13.974 13.380 12.815 12.486 12.506 19.99 2.25 -40.13 2.25 4.90 0.91 HHJ186_DH313_L07_172 GCS9 Cl* Melotte 22 HHJ 186 +03 44 09.61 +24 35 22.1 0 13.826 13.427 12.892 12.314 12.044 21.90 2.97 -41.53 2.97 0.60 0.89 SK586_DH314 GCS9 Cl* Melotte 22 SK 586 +03 44 09.92 +24 16 03.9 0 13.292 12.928 12.379 11.781 11.527 20.69 2.30 -36.49 2.30 0.27 0.57 HCG140_DH315 GCS9 Cl* Melotte 22 HCG 140 +03 44 10.76 +25 37 38.2 0 14.362 13.886 13.296 12.733 12.437 12.445 11.81 2.23 -44.39 2.23 6.39 0.62 HHJ235_DH316_Moraux2003_36 GCS9 Cl* Melotte 22 HHJ 235 +03 44 11.28 +24 52 34.2 0 15.374 14.904 14.280 13.727 13.434 24.96 2.98 -35.52 2.98 3.05 0.09 HHJ59 GCS9 Cl* Melotte 22 HHJ 59 +03 44 11.90 +22 53 36.9 0 15.170 14.818 14.272 13.668 13.416 13.423 12.90 2.26 -60.48 2.26 6.74 0.00 L07_178 GCS9 +03 44 11.92 +19 18 19.1 0 12.681 12.379 11.886 11.382 10.992 11.107 22.77 3.96 -43.73 3.96 2.20 0.77 DH318 GCS9 Cl* Melotte 22 DH 318 +03 44 12.14 +23 52 37.3 0 14.442 13.972 13.408 12.855 12.547 18.41 2.30 -47.45 2.30 1.87 0.87 HHJ217_DH319 GCS9 Cl* Melotte 22 HHJ 217 +03 44 13.97 +25 32 15.3 0 13.648 13.147 12.531 11.991 11.643 11.691 -5.25 2.23 -36.15 2.23 5.79 0.00 HCG138_SK579_DH320_Moraux2003_25 GCS9 Cl* Melotte 22 HCG 138 +03 44 16.46 +23 37 04.0 0 13.729 13.270 12.706 12.148 11.847 18.08 2.30 -44.62 2.30 1.22 0.93 HCG144_SK580_HHJ301_DH322 GCS9 Cl* Melotte 22 HCG 144 +03 44 17.76 +24 26 46.7 0 13.476 13.079 12.533 11.925 11.668 19.37 2.97 -46.95 2.97 0.14 0.88 HCG145_SK576_HHJ352_DH323 GCS9 Cl* Melotte 22 HCG 145 +03 44 19.06 +24 35 18.2 0 14.319 13.882 13.291 12.722 12.476 17.45 2.97 -46.24 2.97 0.54 0.90 HCG146_T18B_HHJ228_DH324 GCS9 Cl* Melotte 22 HCG 146 +03 44 20.66 +24 15 10.7 0 15.211 14.648 13.992 13.469 13.087 12.94 2.31 -38.96 2.31 0.51 0.59 HHJ57 GCS9 Cl* Melotte 22 HHJ 57 +03 44 20.87 +23 33 39.8 0 13.825 13.404 12.863 12.282 11.993 3.71 2.30 -36.55 2.30 6.76 0.00 HCG155_SK575_HHJ300_DH327 GCS9 Cl* Melotte 22 HCG 155 +03 44 22.14 +23 10 54.8 0 15.807 15.195 14.479 13.913 13.468 13.518 16.09 2.26 -42.11 2.26 1.84 0.88 DH329_L07_173 GCS9 Cl* Melotte 22 DH 329 +03 44 22.45 +23 39 01.3 0 19.107 17.857 16.874 16.254 15.666 15.660 15.39 2.40 -42.54 2.40 3.20 0.61 Roque5_L07_A1_19_L07_261 GCS9 Cl* Melotte 22 Roque 5 +03 44 23.24 +25 38 44.9 0 16.319 15.457 14.698 14.154 13.731 13.721 18.31 2.25 -50.40 2.25 6.34 0.20 BRB4_PLZJ29_L07_A1_78 GCS9 Cl* Melotte 22 BRB 4 +03 44 23.40 +25 21 29.9 0 13.413 12.983 12.404 11.920 11.628 11.662 8.92 2.23 -42.15 2.23 9.13 0.15 HCG143_SK568_T19B_HHJ348_DH331 GCS9 Cl* Melotte 22 HCG 143 +03 44 24.00 +21 24 20.8 0 15.184 14.768 14.212 13.673 13.353 13.359 16.88 2.66 -43.67 2.66 3.40 0.89 DH332 GCS9 Cl* Melotte 22 DH 332 +03 44 24.68 +24 51 53.2 0 13.805 13.361 12.765 12.246 11.942 18.59 2.97 -47.54 2.97 1.21 0.86 HCG148_SK569_HHJ307_DH333 GCS9 Cl* Melotte 22 HCG 148 +03 44 24.82 +24 46 06.0 0 12.688 12.372 11.878 11.507 11.202 17.32 2.97 -50.88 2.97 2.49 0.07 HCG149_SK570_T42B_DH334 GCS9 Cl* Melotte 22 HCG 149 +03 44 25.08 +25 34 03.9 0 15.079 14.489 13.830 13.302 12.949 12.973 19.98 2.23 -41.60 2.23 8.95 0.88 HHJ100_Moraux2003_75_L07_31 GCS9 Cl* Melotte 22 HHJ 100 +03 44 25.51 +22 27 49.8 0 16.405 15.794 15.102 14.589 14.223 14.185 22.07 2.53 -37.99 2.53 1.49 0.35 DH335_int-pl-IZ-72;2MASSJ0344255+222749_Y GCS9 Cl* Melotte 22 DH 335 +03 44 25.58 +22 40 07.9 0 16.220 15.599 14.929 14.388 14.003 14.006 11.28 2.25 -40.25 2.25 34.57 0.28 L07_A1_21_L07_246 GCS9 +03 44 25.60 +24 40 52.6 0 13.443 13.016 12.496 11.928 11.633 13.39 2.97 -47.71 2.97 1.86 0.62 HCG150_SK567_B179_HHJ364_DH336 GCS9 Cl* Melotte 22 HCG 150 +03 44 26.54 +24 29 11.3 0 14.133 13.585 12.995 12.394 12.124 20.35 2.97 -40.21 2.97 1.10 0.91 HHJ243_DH338 GCS9 Cl* Melotte 22 HHJ 243 +03 44 26.89 +24 24 31.5 0 13.155 12.822 12.306 11.723 11.468 3.74 2.97 -30.56 2.97 12.86 0.00 HCG156_HHJ400_DH339 GCS9 Cl* Melotte 22 HCG 156 +03 44 27.50 +24 14 17.0 0 14.633 14.101 13.503 12.965 12.632 12.628 20.52 2.22 -45.35 2.22 0.91 0.92 HHJ190_DH341_L07_97 GCS9 Cl* Melotte 22 HHJ 190 +03 44 27.92 +23 59 59.6 0 15.633 15.107 14.446 13.888 13.520 13.499 17.02 2.23 -41.63 2.23 2.29 0.89 DH342_L07_113 GCS9 Cl* Melotte 22 DH 342 +03 44 30.08 +25 35 46.8 0 12.678 11.857 11.349 11.013 11.058 18.21 2.27 -37.32 2.27 7.54 0.81 HCG152 GCS9 Cl* Melotte 22 HCG 152 +03 44 31.04 +22 15 14.9 0 13.542 13.130 12.619 12.024 11.745 11.753 24.71 2.51 -36.97 2.51 2.16 0.26 SK571_DH344 GCS9 Cl* Melotte 22 SK 571 +03 44 31.72 +23 35 26.0 0 14.020 13.606 13.060 12.451 12.150 12.189 19.59 2.22 -44.22 2.22 1.12 0.94 SK564_HHJ265_DH345 GCS9 Cl* Melotte 22 SK 564 +03 44 32.17 +25 08 12.3 0 15.305 14.832 14.220 13.652 13.316 13.309 17.86 2.21 -43.12 2.21 7.50 0.90 HHJ68_Moraux2003_85_L07_51 GCS9 Cl* Melotte 22 HHJ 68 +03 44 32.33 +25 25 17.9 0 16.994 16.209 15.450 14.883 14.476 14.883 14.24 2.27 -43.51 2.27 5.78 0.64 CFHT-Pl-10_M6.5_PLZJ60_L07_A1_22_L07_250 GCS9 Cl* Melotte 22 CFHT 10 +03 44 33.08 +25 45 09.5 0 14.367 13.898 13.319 12.711 12.428 12.423 14.12 2.23 -38.60 2.23 0.96 0.71 HCG157_DH346_Moraux2003_40 GCS9 Cl* Melotte 22 HCG 157 +03 44 33.49 +26 14 53.1 0 13.610 13.172 12.622 12.070 11.769 11.768 18.22 2.94 -41.78 2.94 0.25 0.93 HHJ334_DH347 GCS9 Cl* Melotte 22 HHJ 334 +03 44 34.30 +23 51 24.6 0 16.914 16.150 15.428 14.866 14.472 14.439 16.92 2.24 -42.74 2.24 3.77 0.74 L07_A1_23 GCS9 +03 44 35.16 +25 13 42.8 1 17.656 16.584 15.662 14.985 14.448 14.985 19.33 2.26 -44.97 2.26 16.37 0.68 CFHT-Pl-16_M9.3_L07_A1_24_L07_251 GCS9 Cl* Melotte 22 CFHT 16 +03 44 35.90 +23 34 41.9 1 16.307 15.672 14.985 14.376 13.990 13.985 16.94 2.23 -44.39 2.23 1.71 0.72 HHJ5_L07_A1_25_L07_236 GCS9 Cl* Melotte 22 HHJ 5 +03 44 35.92 +22 50 42.9 0 14.238 13.940 13.439 12.803 12.544 12.552 34.09 2.24 -47.63 2.24 0.63 0.00 L07_197 GCS9 +03 44 36.28 +23 30 10.9 0 13.348 13.023 12.482 11.996 11.620 11.651 16.76 2.23 -43.37 2.23 7.84 0.93 HCG161_SK559_A23_HHJ344_DH348 GCS9 Cl* Melotte 22 HCG 161 +03 44 37.78 +22 55 15.5 0 12.736 12.396 11.884 11.404 11.020 11.104 20.58 2.23 -42.26 2.23 0.64 0.88 HCG164 GCS9 Cl* Melotte 22 HCG 164 +03 44 38.95 +23 02 25.3 0 14.434 13.951 13.374 12.833 12.489 12.506 17.33 2.24 -45.39 2.24 2.32 0.92 HHJ189_DH351 GCS9 Cl* Melotte 22 HHJ 189 +03 44 40.31 +25 19 24.6 0 15.831 15.228 14.599 14.042 13.700 13.703 -10.25 2.24 -47.03 2.24 7.09 0.00 L07_45 GCS9 +03 44 46.14 +24 23 02.8 0 15.833 15.303 14.664 14.161 13.762 13.777 16.63 2.23 -45.44 2.23 6.36 0.86 HHJ24_L07_99 GCS9 Cl* Melotte 22 HHJ 24 +03 44 47.31 +19 55 41.4 0 15.884 15.400 14.799 14.273 13.946 13.957 20.01 4.01 -40.31 4.01 0.63 0.85 DH354 GCS9 Cl* Melotte 22 DH 354 +03 44 47.33 +24 00 37.7 0 14.063 13.661 13.140 12.609 12.311 12.315 19.39 2.22 -43.53 2.22 4.41 0.94 HHJ276_HCG167_DH355_L07_9 GCS9 Cl* Melotte 22 HHJ 276 +03 44 47.84 +24 12 52.5 0 14.358 13.900 13.297 12.745 12.458 12.431 18.38 2.22 -42.73 2.22 2.19 0.94 HCG166_HHJ239_DH357_L07_96 GCS9 Cl* Melotte 22 HCG 166 +03 44 51.51 +25 05 16.5 0 14.798 14.315 13.715 13.134 12.802 12.819 15.35 2.21 -42.48 2.21 1.69 0.91 L07_49 GCS9 +03 44 53.12 +23 34 22.8 0 17.306 16.472 15.734 15.124 14.710 14.700 18.37 2.26 -39.68 2.26 3.81 0.67 L07_A1_26_L07_235 GCS9 +03 44 53.21 +24 01 06.5 0 15.005 14.557 13.980 13.433 13.135 13.125 16.45 2.22 -38.79 2.22 3.95 0.80 DH2004_359 GCS9 Cl* Melotte 22 DH 359 +03 44 53.53 +25 36 19.2 0 16.354 15.837 15.249 14.663 14.358 14.353 25.93 2.25 -40.81 2.25 4.48 0.14 PLZJ56_None GCS9 Cl* Melotte 22 PlZJ 56 +03 44 56.01 +23 55 53.4 0 13.771 13.290 12.715 12.244 11.888 11.912 19.65 2.22 -40.21 2.22 1.00 0.91 HCG171_HHJ304 GCS9 Cl* Melotte 22 HCG 171 +03 44 56.69 +23 36 23.5 0 14.114 13.697 13.146 12.622 12.313 12.314 22.12 2.22 -39.49 2.22 8.34 0.85 HHJ269_DH361 GCS9 Cl* Melotte 22 HHJ 269 +03 44 58.02 +23 24 31.0 0 14.147 13.740 13.184 12.621 12.362 12.351 16.74 2.24 -37.83 2.24 2.38 0.79 HHJ223_DH362 GCS9 Cl* Melotte 22 HHJ 223 +03 44 58.59 +23 55 40.9 0 14.017 13.555 12.960 12.420 12.106 12.139 18.25 2.22 -38.70 2.22 2.02 0.87 HHJ274_DH363 GCS9 Cl* Melotte 22 HHJ 274 +03 44 59.48 +23 21 18.1 0 15.290 14.814 14.227 13.710 13.376 13.382 20.27 2.24 -46.79 2.24 1.03 0.77 HHJ54_DH365_L07_156 GCS9 Cl* Melotte 22 HHJ 54 +03 45 01.14 +24 46 40.9 0 14.445 13.990 13.411 12.861 12.557 12.556 13.05 2.21 -45.96 2.21 3.65 0.71 HCG172_SK538_HHJ216_DH366_L07_74 GCS9 Cl* Melotte 22 HCG 172 +03 45 01.21 +25 21 05.6 0 15.041 14.555 13.949 13.361 13.061 13.070 18.83 2.23 -41.05 2.23 5.45 0.89 DH367_Moraux2003_74_L07_46 GCS9 Cl* Melotte 22 DH 367 +03 45 02.88 +25 05 19.6 0 14.789 14.276 13.751 13.216 12.845 12.858 14.43 2.21 -38.87 2.21 1.84 0.75 HHJ139_DH368_Moraux2003_65_L07_50 GCS9 Cl* Melotte 22 HHJ 139 +03 45 03.18 +23 06 58.4 0 16.937 15.492 14.946 14.495 0.012 20.81 2.32 -40.36 2.32 5.52 0.62 int-pl-IZ-60;2MASSJ0345031+230658_Y GCS9 Cl* Melotte 22 IPL 60 +03 45 04.41 +24 15 16.6 0 20.479 19.040 17.775 16.979 16.350 16.230 20.21 2.72 -38.78 2.72 0.80 0.45 L07_A1_27 GCS9 +03 45 04.99 +23 46 06.4 0 14.919 14.308 13.687 13.174 12.822 12.835 16.11 2.22 -45.06 2.22 2.75 0.91 HHJ113_DH372 GCS9 Cl* Melotte 22 HHJ 113 +03 45 05.32 +25 29 10.9 0 13.161 12.814 12.312 11.731 11.448 11.485 16.08 2.23 -44.62 2.23 4.32 0.92 SK534_HHJ381_DH373 GCS9 Cl* Melotte 22 SK 534 +03 45 06.25 +28 42 19.1 0 14.628 14.156 13.575 13.049 12.714 12.725 22.01 2.95 -39.53 2.95 0.12 0.85 DH374 GCS9 Cl* Melotte 22 DH 374 +03 45 06.56 +24 40 42.7 0 15.393 14.853 14.208 13.659 13.314 13.323 13.49 2.21 -39.94 2.21 6.44 0.71 HHJ48_L07_73 GCS9 Cl* Melotte 22 HHJ 48 +03 45 06.79 +23 36 51.4 0 14.751 14.200 13.573 12.993 12.645 12.675 16.78 2.22 -41.22 2.22 3.68 0.92 HCG176_HHJ136 GCS9 Cl* Melotte 22 HCG 176 +03 45 08.41 +23 25 00.9 0 15.510 15.010 14.406 13.859 13.534 13.540 13.99 2.24 -40.94 2.24 3.78 0.79 L07_157 GCS9 +03 45 08.69 +22 38 30.3 0 14.314 13.895 13.322 12.787 12.483 12.496 27.40 2.51 -36.47 2.51 19.46 0.06 HHJ204_BPL70_DH376_L07_194 GCS9 Cl* Melotte 22 HHJ 204 +03 45 08.69 +24 24 09.3 0 16.507 15.843 15.142 14.587 14.235 14.195 16.54 2.23 -42.48 2.23 1.73 0.74 L07_A1_28 GCS9 +03 45 09.04 +25 22 29.7 0 15.145 14.500 13.831 13.259 12.901 12.920 20.44 2.23 -40.81 2.23 3.96 0.86 HHJ81_L07_47 GCS9 Cl* Melotte 22 HHJ 81 +03 45 09.04 +25 32 49.0 0 14.661 14.176 13.591 13.021 12.723 12.734 18.67 2.23 -43.15 2.23 1.78 0.94 HHJ164_DH377_Moraux2003_61 GCS9 Cl* Melotte 22 HHJ 164 +03 45 09.46 +23 58 44.7 1 16.974 16.250 15.438 14.872 14.424 14.410 16.07 2.25 -42.23 2.25 13.16 0.72 L07_A1_29 GCS9 +03 45 10.82 +23 02 58.0 0 14.146 13.717 13.137 12.612 12.306 12.336 16.69 2.24 -45.16 2.24 4.56 0.92 HCG186_SK535 GCS9 Cl* Melotte 22 HCG 186 +03 45 12.16 +23 21 52.9 0 14.046 13.642 13.082 12.524 12.260 12.255 17.35 2.24 -44.74 2.24 5.28 0.93 HCG185_SK532_HHJ255_DH379 GCS9 Cl* Melotte 22 HCG 185 +03 45 12.44 +22 41 50.8 0 14.099 13.675 13.124 12.550 12.250 12.251 19.96 2.24 -47.27 2.24 1.91 0.87 BPL71_SK533_HHJ254_DH380_L07_196 GCS9 Cl* Melotte 22 BPL 71 +03 45 12.62 +23 53 45.0 0 16.093 15.445 14.776 14.220 13.845 13.855 16.81 2.23 -44.76 2.23 3.02 0.71 HHJ14_PPL7_L07_133 GCS9 Cl* Melotte 22 HHJ 14 +03 45 13.14 +24 15 23.6 0 15.046 14.554 13.965 13.457 13.149 13.144 21.10 2.22 -42.90 2.22 1.18 0.86 HCG180_HHJ118_DH381_L07_98 GCS9 Cl* Melotte 22 HCG 180 +03 45 14.21 +25 05 19.4 0 12.232 11.987 11.572 11.411 10.770 11.068 -49.24 2.21 -120.03 2.21 2.79 0.00 HII566_HCG174 GCS9 HII566 +03 45 15.82 +25 06 36.5 0 12.587 12.238 11.785 11.593 10.986 11.109 16.46 2.21 -62.54 2.21 1.44 0.00 SK526_HHJ425 GCS9 Cl* Melotte 22 SK 526 +03 45 16.13 +24 07 16.0 0 13.242 12.918 12.418 12.034 11.570 11.682 19.29 2.22 -40.28 2.22 1.79 0.91 HCG183_HHJ394_DH383 GCS9 Cl* Melotte 22 HCG 183 +03 45 16.42 +23 34 01.6 0 15.628 15.046 14.415 13.871 13.502 13.519 16.66 2.22 -43.95 2.22 1.55 0.89 DH384 GCS9 Cl* Melotte 22 DH 384 +03 45 16.99 +25 15 47.5 0 12.911 12.493 11.990 11.694 11.141 11.228 18.30 2.21 -40.49 2.21 2.50 0.89 HCG178_SK525_A28_HHJ410_DH386 GCS9 Cl* Melotte 22 HCG 178 +03 45 18.15 +25 05 58.1 0 12.117 11.866 11.421 11.325 10.625 10.829 16.05 2.21 -37.44 2.21 2.36 0.74 HII590_HCG179_SK524_T45_DH387 GCS9 HII590 +03 45 21.12 +21 46 17.6 0 16.257 15.741 15.178 14.532 14.239 14.299 8.25 2.54 -32.01 2.54 32.48 0.00 L07_PM_NM_49 GCS9 +03 45 21.91 +26 28 41.9 0 14.370 14.041 13.523 12.837 12.615 12.636 32.97 2.94 -40.02 2.94 0.85 0.00 DH389 GCS9 Cl* Melotte 22 DH 389 +03 45 22.14 +21 52 40.0 0 16.265 15.746 15.102 14.554 14.203 14.180 -1.65 2.29 -61.68 2.29 5.14 0.00 L07_PM_NM_50 GCS9 +03 45 24.70 +24 38 46.4 0 15.819 15.190 14.549 13.983 13.645 13.672 18.14 2.22 -44.52 2.22 1.24 0.89 L07_72 GCS9 +03 45 24.79 +24 20 45.3 0 14.133 13.677 13.118 12.524 12.226 12.250 14.67 2.22 -40.59 2.22 8.47 0.85 DH392 GCS9 Cl* Melotte 22 DH 392 +03 45 26.56 +22 31 32.0 0 14.087 13.649 13.091 12.536 12.210 12.196 19.13 2.26 -36.34 2.26 5.52 0.67 HHJ267_BPL73_DH393_L07_199 GCS9 Cl* Melotte 22 HHJ 267 +03 45 26.99 +24 13 26.4 0 15.192 14.757 14.205 13.605 13.284 13.278 23.59 2.22 -49.95 2.22 2.92 0.17 HHJ99 GCS9 Cl* Melotte 22 HHJ 99 +03 45 27.52 +23 37 56.9 0 15.142 14.678 14.099 13.543 13.236 13.231 19.00 2.22 -42.76 2.22 1.56 0.90 HHJ106_L07_147 GCS9 Cl* Melotte 22 HHJ 106 +03 45 28.90 +27 28 07.2 0 12.839 12.475 11.993 11.460 11.179 11.209 18.03 3.44 -39.52 3.44 0.75 0.88 DH394 GCS9 Cl* Melotte 22 DH 394 +03 45 30.23 +24 18 45.3 0 12.817 12.487 11.994 11.683 11.104 11.215 15.71 2.22 -43.80 2.22 1.47 0.77 HII673 GCS9 HII673 +03 45 31.25 +25 46 33.3 0 14.192 13.836 13.341 12.732 12.466 12.457 29.01 2.23 -50.04 2.23 1.37 0.01 HHJ293 GCS9 Cl* Melotte 22 HHJ 293 +03 45 31.37 +24 52 47.4 1 17.332 16.330 15.465 14.839 14.354 14.326 16.69 2.24 -40.30 2.24 7.98 0.66 IPMBD29_L07_A1_30 GCS9 Cl* Melotte 22 IPMBD 29 +03 45 35.69 +24 24 34.1 0 16.519 15.801 15.133 14.567 14.196 14.181 18.31 2.23 -42.12 2.23 51.10 0.74 L07_A1_31 GCS9 +03 45 36.72 +24 39 06.5 0 13.244 12.776 12.207 11.739 11.342 11.388 13.96 2.21 -44.03 2.21 19.04 0.85 HCG194_HHJ380_DH397 GCS9 Cl* Melotte 22 HCG 194 +03 45 37.76 +23 43 50.1 1 16.240 15.456 14.715 14.172 13.756 13.742 20.91 2.23 -45.45 2.23 2.10 0.59 L07_A1_32_L07_238 GCS9 +03 45 37.79 +24 20 08.1 0 13.646 13.271 11.841 12.729 10.456 12.026 19.56 2.22 -44.01 2.22 11.15 0.93 HII717_HD23387_Tr215 GCS9 HII717 +03 45 38.99 +23 57 00.9 0 15.229 14.703 14.101 13.549 13.238 13.223 20.80 2.22 -37.93 2.22 13.30 0.69 L07_130 GCS9 +03 45 39.04 +25 13 27.6 0 12.529 12.273 11.767 11.514 10.907 11.035 15.68 2.21 -41.64 2.21 5.94 0.82 HCG196_SK510 GCS9 Cl* Melotte 22 HCG 196 +03 45 39.12 +22 04 23.1 0 15.161 14.641 14.018 13.458 13.114 13.120 19.28 2.27 -37.19 2.27 3.36 0.67 BPL75_L07_210 GCS9 Cl* Melotte 22 BPL 75 +03 45 39.29 +24 08 20.4 0 14.992 14.507 13.911 13.379 13.015 13.020 16.79 2.22 -43.96 2.22 1.38 0.93 HHJ130_DH398_L07_117 GCS9 Cl* Melotte 22 HHJ 130 +03 45 40.77 +28 32 05.8 0 15.158 14.639 14.012 13.516 13.166 13.169 22.95 2.96 -42.47 2.96 0.16 0.75 DH399 GCS9 Cl* Melotte 22 DH 399 +03 45 41.27 +23 54 09.7 1 17.166 16.189 15.360 14.782 14.305 14.309 17.46 2.24 -44.47 2.24 3.49 0.69 Roque15_L07_A1_33_L07_234 GCS9 Cl* Melotte 22 Roque 15 +03 45 42.33 +24 04 11.1 0 16.542 15.882 15.201 14.643 14.262 14.229 17.79 2.24 -40.28 2.24 11.53 0.70 L07_A1_34_L07_227 GCS9 +03 45 43.18 +26 02 26.6 0 14.195 13.765 13.158 12.560 12.284 12.301 19.62 2.23 -41.47 2.23 6.76 0.94 HHJ264_DH401 GCS9 Cl* Melotte 22 HHJ 264 +03 45 44.08 +24 04 26.6 0 12.115 11.903 11.473 11.507 10.786 11.052 20.45 2.22 -36.83 2.22 10.09 0.78 HII762_Tr228a_HCG200 GCS9 HII762 +03 45 45.41 +22 33 21.5 0 15.557 15.172 14.632 14.051 13.736 13.741 -0.07 2.27 -31.02 2.27 8.08 0.00 L07_200 GCS9 +03 45 46.48 +23 47 43.1 0 12.854 12.422 11.837 11.334 10.920 10.991 19.57 2.22 -40.02 2.22 2.51 0.89 HHJ421 GCS9 Cl* Melotte 22 HHJ 421 +03 45 46.90 +23 53 00.3 0 15.802 15.201 14.549 13.988 13.652 13.661 18.78 2.23 -44.72 2.23 3.12 0.88 L07_129 GCS9 +03 45 46.94 +21 44 49.0 0 14.850 14.329 13.677 13.182 12.811 12.850 25.93 2.65 -40.73 2.65 1.97 0.54 HHJ128 GCS9 Cl* Melotte 22 HHJ 128 +03 45 49.26 +25 24 46.1 0 16.825 16.314 15.704 15.073 14.758 14.754 26.80 2.29 -31.90 2.29 3.20 0.00 DH403 GCS9 Cl* Melotte 22 DH 403 +03 45 49.37 +24 25 07.1 0 13.560 13.177 12.633 12.107 11.764 11.792 17.27 2.21 -44.41 2.21 5.99 0.93 BPL77_L07_7 GCS9 Cl* Melotte 22 BPL 77 +03 45 49.94 +23 19 44.7 0 15.790 15.217 14.577 14.016 13.634 13.665 13.92 2.24 -38.67 2.24 2.39 0.66 L07_155 GCS9 +03 45 50.42 +22 36 05.6 0 17.653 16.839 16.051 15.524 15.034 0.021 14.53 2.39 -32.89 2.39 8.07 0.05 int-pl-IZ-44;2MASSJ0345504+223606_BPL78_Y_L07_A1_35_L07_247 GCS9 Cl* Melotte 22 IPL 44 +03 45 50.66 +24 09 03.5 1 17.478 16.582 15.705 15.095 14.580 14.560 16.01 2.26 -40.58 2.26 1.10 0.65 BPL79_Roque13_L07_A1_36 GCS9 Cl* Melotte 22 BPL 79 +03 45 51.09 +24 26 10.9 0 15.873 15.293 14.658 14.103 13.746 13.760 17.00 2.22 -46.18 2.22 1.18 0.84 HHJ25_L07_86 GCS9 Cl* Melotte 22 HHJ 25 +03 45 51.33 +24 17 44.3 0 14.634 14.154 13.573 12.983 12.723 12.717 14.02 2.22 -39.30 2.22 8.20 0.75 L07_92 GCS9 +03 45 51.64 +24 02 19.7 0 12.518 12.529 11.248 11.845 10.016 30.83 2.22 -25.84 2.22 9.31 0.00 HII804_HD23409_Tr235 GCS9 HII804 +03 45 51.95 +25 10 01.7 0 14.702 14.245 13.604 13.032 12.743 12.742 16.31 2.21 -41.80 2.21 7.90 0.92 HHJ166_BPL80_DH404_Moraux2003_54_L07_54 GCS9 Cl* Melotte 22 HHJ 166 +03 45 52.60 +25 54 59.8 0 13.823 13.352 12.746 12.195 11.859 11.879 17.10 2.23 -41.34 2.23 1.59 0.92 SK499_DH405 GCS9 Cl* Melotte 22 SK 499 +03 45 52.76 +23 27 54.0 0 14.143 13.734 13.161 12.615 12.286 12.305 12.01 2.24 -39.18 2.24 4.63 0.49 HCG202_SK504_HHJ227_DH407 GCS9 Cl* Melotte 22 HCG 202 +03 45 54.96 +23 33 57.8 0 17.116 16.325 15.553 15.023 14.564 14.574 18.67 2.25 -41.38 2.25 5.98 0.72 L07_A1_38_L07_237 GCS9 +03 45 54.99 +24 13 26.0 0 13.704 13.217 12.670 12.164 11.819 11.840 18.24 2.22 -42.71 2.22 0.61 0.94 BPL82 GCS9 Cl* Melotte 22 BPL 82 +03 45 56.97 +23 01 29.0 0 14.372 13.939 13.379 12.847 12.515 12.570 16.42 2.24 -40.16 2.24 1.23 0.90 HCG205_HHJ199_DH410 GCS9 Cl* Melotte 22 HCG 205 +03 45 57.09 +24 21 00.9 0 15.058 14.437 13.779 13.215 12.854 12.842 8.36 2.22 -35.88 2.22 11.01 0.01 L07_94 GCS9 +03 45 57.28 +25 11 12.7 0 13.246 12.837 12.272 12.049 11.431 11.482 11.26 2.21 -43.67 2.21 7.13 0.55 SK497_HHJ379_BPL83_DH411_Moraux2003_7 GCS9 Cl* Melotte 22 SK 497 +03 45 57.71 +24 03 04.9 0 15.821 15.269 14.671 14.132 13.755 13.740 14.71 2.23 -37.35 2.23 4.80 0.59 HHJ27_L07_116 GCS9 Cl* Melotte 22 HHJ 27 +03 45 57.78 +26 18 44.4 0 13.541 13.217 12.726 12.101 11.875 11.872 36.22 2.94 -32.09 2.94 0.34 0.00 Moraux2003_14 GCS9 Cl* Melotte 22 MBSC 14 +03 45 57.91 +24 08 40.9 0 15.680 15.158 14.543 14.017 13.670 13.654 18.27 2.23 -43.51 2.23 2.70 0.90 DH412_L07_118 GCS9 Cl* Melotte 22 DH 412 +03 45 58.80 +26 20 01.7 0 15.269 14.666 14.045 13.498 13.144 13.138 18.44 2.95 -38.73 2.95 0.40 0.81 HHJ67_DH413_Moraux2003_86 GCS9 Cl* Melotte 22 HHJ 67 +03 45 59.20 +23 48 46.8 0 14.973 14.506 13.923 13.375 13.073 13.092 19.52 2.22 -40.23 2.22 4.75 0.92 HHJ127 GCS9 Cl* Melotte 22 HHJ 127 +03 46 00.93 +22 12 29.4 0 16.023 15.409 14.742 14.256 13.858 13.866 18.94 2.28 -45.37 2.28 9.09 0.68 HHJ11_BPL84 GCS9 Cl* Melotte 22 HHJ 11 +03 46 02.26 +25 36 40.4 0 12.980 12.632 12.114 11.635 11.296 11.366 31.82 2.23 -37.17 2.23 6.80 0.00 SK491_HHJ399 GCS9 Cl* Melotte 22 SK 491 +03 46 02.53 +22 28 27.5 0 16.837 16.315 15.700 15.098 14.791 0.012 7.00 2.33 -22.57 2.33 3.08 0.00 int-pl-IZ-64;2MASSJ0346025+222827_N GCS9 Cl* Melotte 22 IPL 64 +03 46 02.96 +24 40 55.6 0 15.363 14.742 14.071 13.491 13.114 13.134 14.57 2.21 -41.20 2.21 7.46 0.83 BPL85_DH414 GCS9 Cl* Melotte 22 BPL 85 +03 46 03.45 +24 20 57.0 0 14.476 14.023 13.444 12.842 12.558 12.571 11.97 2.22 -38.92 2.22 3.54 0.46 HCG206_BPL86_DH415_L07_93 GCS9 Cl* Melotte 22 HCG 206 +03 46 03.67 +25 52 28.8 0 14.534 14.076 13.506 12.954 12.639 12.643 17.24 2.23 -43.26 2.23 3.39 0.94 HHJ182_DH416_L07_26 GCS9 Cl* Melotte 22 HHJ 182 +03 46 03.96 +24 23 08.9 0 15.655 15.066 14.448 13.848 13.511 13.513 31.07 2.23 0.78 2.23 0.19 0.00 BPL87 GCS9 Cl* Melotte 22 BPL 87 +03 46 04.30 +23 55 40.6 0 13.726 13.283 12.706 12.145 11.810 11.840 14.43 2.22 -40.75 2.22 3.63 0.84 HHJ363 GCS9 Cl* Melotte 22 HHJ 363 +03 46 04.57 +24 09 55.8 0 15.099 14.602 14.020 13.460 13.162 13.175 15.43 2.22 -40.43 2.22 2.54 0.84 BPL88 GCS9 Cl* Melotte 22 BPL 88 +03 46 05.64 +24 36 44.3 0 13.116 12.770 12.264 11.710 11.428 11.468 18.12 2.21 -41.35 2.21 3.04 0.93 SK488 GCS9 Cl* Melotte 22 SK 488 +03 46 05.69 +24 36 49.7 0 15.023 14.505 13.894 13.358 13.037 13.052 19.22 2.21 -40.25 2.21 4.06 0.87 L07_88 GCS9 +03 46 06.05 +23 58 19.1 0 16.000 15.453 14.820 14.319 13.925 13.902 16.34 2.23 -42.63 2.23 21.77 0.73 L07_114 GCS9 +03 46 06.21 +25 06 45.8 0 14.228 14.006 13.550 12.936 12.838 12.837 12.78 2.21 -34.39 2.21 1.09 0.08 L07_53 GCS9 +03 46 06.46 +25 25 11.5 0 14.674 14.289 13.759 13.117 12.837 12.862 18.32 2.23 -26.35 2.23 1.79 0.00 HHJ194_Moraux2003_59 GCS9 Cl* Melotte 22 HHJ 194 +03 46 06.52 +23 50 20.2 0 12.820 12.427 11.856 11.337 10.926 11.042 19.77 2.22 -37.28 2.22 3.27 0.81 HHJ435 GCS9 Cl* Melotte 22 HHJ 435 +03 46 06.67 +22 23 33.7 0 12.888 12.512 12.011 11.499 11.168 11.310 42.71 2.26 -18.94 2.26 1.18 0.00 BPL89 GCS9 Cl* Melotte 22 BPL 89 +03 46 07.51 +24 22 27.6 0 12.379 12.139 11.688 11.532 10.916 11.074 18.75 2.22 -42.30 2.22 0.96 0.89 HII890_HCG210 GCS9 HII890 +03 46 07.56 +23 44 42.6 0 16.202 15.245 14.240 13.330 12.791 12.804 15.30 2.22 -43.15 2.22 1.77 0.70 L07_239 GCS9 +03 46 08.70 +24 40 33.1 0 14.615 14.120 13.525 13.000 12.673 12.686 16.53 2.21 -42.88 2.21 5.02 0.93 HCG209_BPL90_DH422 GCS9 Cl* Melotte 22 HCG 209 +03 46 09.88 +21 05 52.4 0 13.956 13.557 12.996 12.456 12.173 12.162 25.89 2.65 -44.94 2.65 5.12 0.46 DH425 GCS9 Cl* Melotte 22 DH 425 +03 46 10.10 +26 00 09.0 0 15.207 14.716 14.091 13.542 13.230 13.219 15.35 2.23 -39.91 2.23 0.60 0.82 HHJ80_L07_17 GCS9 Cl* Melotte 22 HHJ 80 +03 46 10.23 +21 52 55.8 0 16.174 15.346 14.578 13.994 13.570 13.564 43.56 2.27 -44.28 2.27 1.50 0.00 L07_A1_42 GCS9 +03 46 12.67 +23 35 13.6 0 14.942 14.442 13.836 13.296 12.972 12.997 17.61 2.22 -42.42 2.22 4.56 0.94 L07_146 GCS9 +03 46 12.88 +24 03 15.6 0 12.012 11.833 11.399 11.455 10.715 11.045 22.36 2.22 -38.68 2.22 6.72 0.81 HII930_DH429 GCS9 Cl* Melotte 22 DH 429 +03 46 13.00 +27 02 11.7 0 15.061 14.740 14.217 13.668 13.401 13.421 18.46 2.95 -46.68 2.95 1.60 0.82 DH430 GCS9 Cl* Melotte 22 DH 430 +03 46 14.06 +23 21 56.4 0 17.097 16.349 15.606 15.047 14.618 14.641 17.81 2.27 -40.20 2.27 7.96 0.68 L07_A1_43 GCS9 +03 46 15.84 +22 02 41.0 0 13.656 13.264 12.746 12.128 11.854 11.872 24.87 2.26 -14.34 2.26 2.45 0.00 BPL93 GCS9 Cl* Melotte 22 BPL 93 +03 46 16.85 +24 26 27.3 0 14.565 14.151 13.599 13.055 12.761 12.781 35.34 2.21 -3.40 2.21 3.16 0.00 BPL94 GCS9 Cl* Melotte 22 BPL 94 +03 46 17.94 +24 41 09.3 0 13.394 13.027 12.512 11.992 11.646 11.701 19.02 2.21 -41.15 2.21 7.95 0.93 HHJ367_BPL95_DH433 GCS9 Cl* Melotte 22 HHJ 367 +03 46 18.54 +23 59 02.5 0 15.870 15.329 14.698 14.161 13.807 13.797 16.09 2.23 -43.73 2.23 36.19 0.88 L07_115 GCS9 +03 46 19.41 +23 00 55.7 0 15.317 14.693 14.026 13.491 13.124 13.115 13.77 2.24 -42.63 2.24 3.01 0.80 HHJ47_BPL96_DH435 GCS9 Cl* Melotte 22 HHJ 47 +03 46 19.43 +26 02 35.5 0 12.463 12.206 11.749 11.371 10.935 11.061 24.01 2.22 -39.85 2.22 4.18 0.74 SK474_DH436 GCS9 Cl* Melotte 22 SK 474 +03 46 19.86 +24 59 01.3 0 13.787 13.340 12.776 12.279 11.946 11.942 14.60 2.21 -42.76 2.21 2.73 0.88 HHJ303_BPL97_DH437_Moraux2003_27 GCS9 Cl* Melotte 22 HHJ 303 +03 46 20.65 +23 53 31.7 0 15.607 15.199 14.655 13.992 13.738 13.729 10.57 2.23 -42.96 2.23 9.25 0.44 L07_125 GCS9 +03 46 21.36 +24 33 52.2 0 15.032 14.512 13.919 13.388 13.052 13.066 14.20 2.21 -42.68 2.21 3.50 0.83 HHJ105_BPL98_DH439 GCS9 Cl* Melotte 22 HHJ 105 +03 46 21.40 +23 05 08.9 0 15.684 15.157 14.548 14.032 13.690 13.664 11.08 2.24 -47.42 2.24 27.35 0.29 HHJ33 GCS9 Cl* Melotte 22 HHJ 33 +03 46 21.62 +23 04 00.9 0 14.695 14.171 13.539 13.025 12.701 12.683 18.17 2.24 -41.21 2.24 22.02 0.93 DH440 GCS9 Cl* Melotte 22 DH 440 +03 46 22.20 +23 52 41.0 0 14.183 13.612 12.973 12.388 12.055 12.045 16.03 2.22 -41.36 2.22 4.55 0.91 DH441 GCS9 Cl* Melotte 22 DH 441 +03 46 22.25 +23 52 26.6 1 17.120 16.323 15.518 14.917 14.474 14.472 17.65 2.25 -38.04 2.25 4.78 0.56 L07_A1_44_L07_232 GCS9 +03 46 23.03 +24 36 17.9 0 14.585 14.122 13.563 13.027 12.710 12.729 16.93 2.21 -44.40 2.21 19.52 0.93 BPL99_DH442 GCS9 Cl* Melotte 22 BPL 99 +03 46 23.12 +24 20 36.0 0 18.242 17.172 16.247 15.654 15.145 15.106 17.01 2.29 -43.77 2.29 3.71 0.66 BPL100_Roque9_L07_A1_45 GCS9 Cl* Melotte 22 BPL 100 +03 46 23.47 +24 01 51.2 0 14.714 14.254 13.668 13.113 12.808 12.793 18.12 2.22 -43.07 2.22 5.91 0.94 HCG218_HHJ195_DH443 GCS9 Cl* Melotte 22 HCG 218 +03 46 23.72 +22 50 16.4 0 17.310 15.710 15.189 14.722 0.015 20.26 2.35 -45.42 2.35 5.66 0.64 int-pl-IZ-43;2MASSJ0346237+225016_Y GCS9 Cl* Melotte 22 IPL 43 +03 46 23.74 +26 34 23.0 0 14.730 14.243 13.647 13.132 12.839 12.799 24.18 2.93 -45.83 2.93 0.60 0.73 HHJ175_DH444_Moraux2003_62 GCS9 Cl* Melotte 22 HHJ 175 +03 46 24.12 +24 30 12.7 0 15.893 15.305 14.689 14.145 13.797 13.818 18.76 2.22 -43.84 2.22 3.56 0.90 BPL101 GCS9 Cl* Melotte 22 BPL 101 +03 46 24.64 +24 28 46.2 0 14.399 13.930 13.364 12.822 12.527 12.552 16.75 2.21 -43.26 2.21 2.85 0.93 HHJ249_BPL102_DH445_L07_87 GCS9 Cl* Melotte 22 HHJ 249 +03 46 25.18 +21 26 17.4 0 13.441 12.930 12.360 11.842 11.508 11.529 3.09 2.65 -44.47 2.65 1.82 0.00 DH446 GCS9 Cl* Melotte 22 DH 446 +03 46 25.39 +24 09 36.2 0 12.838 12.485 11.952 11.461 11.112 11.150 13.98 2.22 -43.42 2.22 0.43 0.64 HCG219_A2_HHJ429 GCS9 Cl* Melotte 22 HCG 219 +03 46 26.09 +24 05 09.5 1 16.810 15.967 15.160 14.584 14.118 14.098 19.41 2.24 -38.71 2.24 3.37 0.57 IPMBD25_L07_A1_46_L07_230 GCS9 Cl* Melotte 22 IPMBD 25 +03 46 27.01 +24 27 13.9 0 13.850 13.415 12.890 12.336 12.039 12.035 16.46 2.21 -47.67 2.21 2.66 0.82 HHJ326_BPL103_DH447 GCS9 Cl* Melotte 22 HHJ 326 +03 46 27.10 +21 48 22.6 1 19.797 18.650 17.374 16.564 15.848 15.925 20.95 2.98 -48.67 2.98 1.27 0.65 L07_A1_47 GCS9 +03 46 27.69 +23 48 45.5 0 14.944 14.345 13.639 13.015 12.617 12.609 18.50 2.22 -41.39 2.22 1.69 0.94 HHJ161 GCS9 Cl* Melotte 22 HHJ 161 +03 46 28.63 +24 45 32.1 0 12.120 11.913 11.480 11.309 10.657 10.853 15.43 2.21 -40.74 2.21 8.09 0.81 HII1029_HCG222_SK472_B212_DH451 GCS9 HII1029 +03 46 29.73 +21 02 16.7 0 14.557 14.008 13.402 12.879 12.566 12.551 19.59 2.65 -40.35 2.65 0.73 0.92 DH452 GCS9 Cl* Melotte 22 DH 452 +03 46 30.96 +23 00 15.1 0 14.562 14.242 13.760 13.105 12.885 12.870 29.31 2.24 -51.76 2.24 0.62 0.00 L07_181 GCS9 +03 46 31.02 +23 01 34.6 0 15.742 15.178 14.589 14.043 13.704 13.689 15.36 2.24 -40.21 2.24 5.54 0.83 HHJ30_DH453 GCS9 Cl* Melotte 22 HHJ 30 +03 46 31.32 +22 18 19.6 0 14.447 13.942 13.329 12.796 12.449 12.455 21.56 2.26 -42.37 2.26 4.04 0.92 HHJ160_BPL105_L07_204 GCS9 Cl* Melotte 22 HHJ 160 +03 46 32.13 +24 23 14.6 0 19.257 18.083 17.049 16.373 15.849 15.766 17.62 2.43 -39.19 2.43 3.18 0.57 L07_A1_48 GCS9 +03 46 32.69 +22 20 17.7 0 14.509 14.126 13.585 12.902 12.626 12.625 30.13 2.26 -27.71 2.26 1.50 0.00 SK471 GCS9 Cl* Melotte 22 SK 471 +03 46 32.79 +19 17 30.2 0 14.370 13.858 13.285 12.792 12.433 12.437 22.35 5.04 -42.45 5.04 1.46 0.90 DH455 GCS9 Cl* Melotte 22 DH 455 +03 46 34.16 +23 25 12.5 0 16.303 15.778 15.088 14.398 14.055 14.041 11.49 2.25 -25.44 2.25 5.43 0.00 HHJ9_L07_PM_NM_20 GCS9 Cl* Melotte 22 HHJ 9 +03 46 34.25 +23 50 03.6 0 19.871 18.546 17.459 16.666 16.090 16.024 20.67 2.58 -41.54 2.58 3.43 0.66 L07_A1_49_L07_260 GCS9 +03 46 34.99 +23 31 14.4 0 18.424 17.345 16.411 15.846 15.308 15.295 20.52 2.37 -42.62 2.37 4.06 0.70 L07_A1_50 GCS9 +03 46 35.36 +23 57 07.4 0 16.879 16.089 15.355 14.800 14.416 14.373 17.57 2.24 -42.62 2.24 11.08 0.75 L07_A1_51_L07_233 GCS9 +03 46 35.54 +24 01 35.4 0 14.809 14.275 13.660 13.113 12.776 12.774 22.25 2.22 -44.34 2.22 0.77 0.89 HHJ140_DH458 GCS9 Cl* Melotte 22 HHJ 140 +03 46 36.07 +23 04 17.2 0 13.827 13.418 12.875 12.391 12.040 12.042 18.53 2.24 -43.10 2.24 5.66 0.94 SK465_HHJ282_DH459 GCS9 Cl* Melotte 22 SK 465 +03 46 39.33 +24 06 11.3 0 12.740 12.808 11.163 12.306 9.936 10.900 30.68 2.22 -50.62 2.22 33.59 0.00 HII1122_HD23511_Tr327 GCS9 HII1122 +03 46 40.27 +25 43 53.4 0 13.808 13.431 12.871 12.280 12.005 12.002 16.42 2.23 -45.47 2.23 10.61 0.91 SK461_HHJ316_DH464_L07_3 GCS9 Cl* Melotte 22 SK 461 +03 46 40.41 +22 50 39.6 0 15.138 14.065 13.514 13.194 13.182 21.74 2.28 -48.62 2.28 4.41 0.50 BPL107 GCS9 Cl* Melotte 22 BPL 107 +03 46 40.60 +22 22 03.5 0 15.518 14.956 14.324 13.752 13.392 13.412 18.76 2.27 -40.88 2.27 4.92 0.88 L07_206 GCS9 +03 46 43.19 +23 37 21.9 0 15.226 14.671 14.057 13.407 13.081 13.076 20.88 2.22 -42.38 2.22 6.11 0.86 HHJ104_DH466_L07_138 GCS9 Cl* Melotte 22 HHJ 104 +03 46 43.60 +23 59 42.3 0 13.258 12.936 12.441 11.891 11.575 11.602 21.29 2.22 -43.53 2.22 0.97 0.91 DH467 GCS9 Cl* Melotte 22 DH 467 +03 46 44.79 +24 44 58.2 0 15.343 14.790 14.196 13.626 13.308 13.288 16.98 2.21 -43.55 2.21 4.59 0.90 HHJ75_BPL109_L07_67 GCS9 Cl* Melotte 22 HHJ 75 +03 46 44.88 +23 37 07.3 0 15.323 14.679 13.948 13.237 12.874 12.854 20.16 2.22 -39.81 2.22 5.80 0.83 L07_137 GCS9 +03 46 45.78 +25 27 30.2 0 13.675 13.271 12.734 12.160 11.896 11.911 15.18 2.23 -45.47 2.23 2.85 0.88 HCG233_SK458_T25B_HHJ319_Moraux2003_20_DH2004_468 GCS9 Cl* Melotte 22 HCG 233 +03 46 46.48 +23 24 02.8 0 14.716 14.273 13.660 13.004 12.658 12.671 33.44 2.24 -34.98 2.24 7.11 0.00 HHJ159 GCS9 Cl* Melotte 22 HHJ 159 +03 46 47.19 +25 20 53.1 0 14.444 13.909 13.306 12.771 12.437 12.445 23.84 2.23 -47.30 2.23 8.06 0.66 HHJ208_DH469_Moraux2003_45_L07_39 GCS9 Cl* Melotte 22 HHJ 208 +03 46 48.79 +23 04 07.3 0 13.100 12.757 12.271 11.916 11.443 11.467 18.54 2.23 -39.65 2.23 3.00 0.90 HCG245_HHJ370_DH470 GCS9 Cl* Melotte 22 HCG 245 +03 46 50.03 +24 00 23.6 0 17.416 16.456 15.617 15.025 14.512 14.503 16.75 2.25 -34.16 2.25 3.06 0.16 L07_A1_53_L07_229 GCS9 +03 46 50.09 +23 31 56.1 0 14.162 13.728 13.200 12.627 12.337 12.390 18.31 2.22 -43.65 2.22 4.16 0.94 HCG240_HHJ253_DH472_L07_134 GCS9 Cl* Melotte 22 HCG 240 +03 46 50.19 +22 12 42.3 0 15.579 15.015 14.384 13.861 13.495 13.514 17.03 2.27 -40.35 2.27 3.93 0.87 BPL110_L07_203 GCS9 Cl* Melotte 22 BPL 110 +03 46 51.83 +23 23 09.4 0 19.654 18.417 17.182 16.429 15.651 15.690 19.04 2.55 -23.47 2.55 10.80 0.00 L07_A1_54 GCS9 +03 46 52.29 +21 47 43.0 0 16.720 16.080 15.402 14.797 14.405 14.418 24.38 2.30 -20.65 2.30 1.45 0.00 L07_PM_NM_24 GCS9 +03 46 52.61 +23 38 43.0 0 14.301 13.763 13.138 12.512 12.160 12.174 15.66 2.22 -42.82 2.22 5.33 0.92 HHJ247_DH473 GCS9 Cl* Melotte 22 HHJ 247 +03 46 52.97 +24 15 07.8 0 16.407 15.752 15.069 14.513 14.131 14.122 19.64 2.23 -36.47 2.23 5.64 0.33 BPL112_L07_A1_55 GCS9 Cl* Melotte 22 BPL 112 +03 46 53.61 +24 17 14.8 0 12.961 12.563 12.023 11.548 11.200 11.255 17.64 2.22 -38.70 2.22 6.95 0.85 HCG244_SK454_BPL113_DH474 GCS9 Cl* Melotte 22 HCG 244 +03 46 53.96 +24 07 57.1 0 15.123 14.651 14.045 13.480 13.143 13.155 16.12 2.22 -38.73 2.22 1.08 0.78 15.76 GCS9 Cl* Melotte 22 MHO 8 +03 46 54.03 +25 14 44.8 0 13.248 12.893 12.369 11.790 11.469 11.490 19.95 2.21 -41.29 2.21 1.73 0.92 HCG241_SK453_B267_BPL114_Moraux2003_4_DH475 GCS9 Cl* Melotte 22 HCG 241 +03 46 54.39 +22 45 11.9 0 18.716 16.652 16.025 15.444 0.043 17.39 2.54 -35.13 2.54 10.55 0.43 int-pl-IZ-48;IPLJ0346543+224512_Y GCS9 Cl* Melotte 22 IPL 48 +03 46 55.32 +23 22 49.3 0 15.196 14.729 14.159 13.630 13.284 13.299 18.52 2.24 -41.36 2.24 6.26 0.89 HHJ95_DH479_L07_163 GCS9 Cl* Melotte 22 HHJ 95 +03 46 55.32 +24 11 16.6 0 14.959 14.371 13.702 13.181 12.828 12.830 19.31 2.22 -40.67 2.22 3.10 0.93 BPL116_DH478_L07_103 GCS9 Cl* Melotte 22 BPL 116 +03 46 55.36 +25 51 29.4 0 15.808 15.294 14.643 14.056 13.716 13.713 9.44 2.24 -34.52 2.24 8.63 0.01 HHJ31_Moraux2003_97_L07_28 GCS9 Cl* Melotte 22 HHJ 31 +03 46 55.49 +23 11 16.0 0 20.373 19.064 17.892 17.144 16.423 16.403 18.24 3.27 -33.97 3.27 3.33 0.33 L07_A1_56 GCS9 +03 46 55.78 +23 56 24.1 0 14.367 13.803 13.166 12.622 12.272 12.283 18.41 2.22 -39.68 2.22 2.24 0.91 HHJ257_DH480 GCS9 Cl* Melotte 22 HHJ 257 +03 46 57.10 +23 15 02.2 0 12.759 12.476 11.986 11.443 11.115 11.167 22.62 2.23 -41.65 2.23 2.12 0.83 HCG247_SK452_HHJ415_DH481 GCS9 Cl* Melotte 22 HCG 247 +03 46 57.83 +23 12 46.2 0 14.826 14.503 14.022 13.469 13.198 13.219 28.31 2.24 -40.75 2.24 3.54 0.15 L07_175 GCS9 +03 46 58.17 +23 33 38.8 0 14.657 14.204 13.650 13.114 12.801 12.816 19.41 2.22 -43.08 2.22 0.71 0.94 HHJ174_DH482_L07_136 GCS9 Cl* Melotte 22 HHJ 174 +03 46 58.26 +24 01 41.3 0 14.976 14.481 13.884 13.337 12.997 13.022 19.27 2.22 -37.41 2.22 4.06 0.79 L07_121 GCS9 +03 46 59.32 +24 01 42.8 0 13.995 13.544 12.956 12.408 12.067 12.100 19.84 2.22 -38.88 2.22 2.81 0.85 HHJ299_DH484 GCS9 Cl* Melotte 22 HHJ 299 +03 47 01.85 +24 13 28.1 0 16.253 15.613 14.933 14.410 14.020 14.022 15.79 2.23 -38.31 2.23 3.78 0.52 BPL122_L07_A1_57 GCS9 Cl* Melotte 22 BPL 122 +03 47 02.35 +23 32 36.0 0 15.608 14.962 14.262 13.719 13.314 13.303 17.87 2.22 -44.38 2.22 10.63 0.89 DH487_L07_135 GCS9 Cl* Melotte 22 DH 487 +03 47 03.78 +23 36 58.6 0 12.388 12.010 11.486 11.369 10.665 11.002 22.69 2.22 -39.02 2.22 1.39 0.80 HII1286_HCG251_SK444_DH489 GCS9 HII1286 +03 47 04.41 +24 47 27.3 0 20.675 19.510 18.057 17.121 16.518 16.537 14.19 3.20 -39.02 3.20 2.70 0.49 L07_A1_58 GCS9 +03 47 04.75 +25 22 50.0 0 13.074 12.642 12.061 11.601 11.223 11.305 21.56 2.23 -40.41 2.23 2.99 0.87 HCG248_SK443_B180_HHJ390_DH491 GCS9 Cl* Melotte 22 HCG 248 +03 47 05.71 +24 40 03.6 0 16.900 16.127 15.416 14.847 14.428 14.447 10.79 2.25 -41.09 2.25 2.09 0.26 BPL124_L07_A1_59 GCS9 Cl* Melotte 22 BPL 124 +03 47 05.79 +23 45 34.7 0 16.227 15.555 14.840 14.315 13.961 13.964 11.90 2.23 -39.81 2.23 18.37 0.33 L07_A1_60_L07_231 GCS9 +03 47 07.88 +24 23 37.8 0 15.094 14.598 13.954 13.415 13.080 13.110 14.17 2.22 -44.83 2.22 35.17 0.80 BPL125_DH494_Festin98_003 GCS9 Cl* Melotte 22 BPL 125 +03 47 08.15 +24 18 24.5 0 13.675 13.278 12.730 12.176 11.884 11.936 17.08 2.22 -40.00 2.22 1.55 0.90 HCG253_SK440_BPL126_DH495 GCS9 Cl* Melotte 22 HCG 253 +03 47 09.42 +24 15 34.7 0 15.682 15.099 14.442 13.938 13.584 13.587 15.53 2.22 -37.71 2.22 3.08 0.68 HHJ37_BPL128_DH496_L07_105 GCS9 Cl* Melotte 22 HHJ 37 +03 47 09.52 +23 25 56.3 0 14.905 14.464 13.880 13.361 13.040 13.087 20.47 2.24 -42.25 2.24 0.90 0.93 DH498 GCS9 Cl* Melotte 22 DH 498 +03 47 10.21 +24 43 35.3 0 15.533 15.060 14.487 13.950 13.645 13.650 17.16 2.22 -43.48 2.22 12.34 0.90 L07_65 GCS9 +03 47 10.65 +23 58 16.4 0 16.136 15.557 14.877 14.360 13.969 14.000 19.15 2.23 -41.37 2.23 2.39 0.72 L07_A1_61_L07_228 GCS9 +03 47 11.03 +24 13 51.5 0 15.376 14.852 14.229 13.692 13.377 13.365 16.70 2.22 -43.11 2.22 1.30 0.90 HCG254_BPL129_L07_104 GCS9 Cl* Melotte 22 HCG 254 +03 47 11.79 +24 13 31.3 1 16.305 15.554 14.792 14.261 13.841 13.850 16.88 2.23 -41.27 2.23 0.62 0.73 BPL130_L07_A1_62 GCS9 Cl* Melotte 22 BPL 130 +03 47 11.86 +24 13 53.8 0 15.279 14.681 14.013 13.491 13.135 13.158 17.87 2.22 -38.56 2.22 1.06 0.80 HHJ92_BPL131_DH499 GCS9 Cl* Melotte 22 HHJ 92 +03 47 13.67 +23 49 53.2 0 12.791 12.364 11.867 11.575 11.044 11.809 19.47 2.22 -40.49 2.22 5.11 0.89 HCG258_SK437_B363_DH500 GCS9 Cl* Melotte 22 HCG 258 +03 47 15.20 +25 24 19.0 0 17.217 16.524 15.790 15.226 14.834 14.844 17.61 2.31 -19.56 2.31 3.87 0.00 IPMBD26 GCS9 Cl* Melotte 22 IPMBD 26 +03 47 15.29 +25 06 55.3 0 13.353 12.925 12.359 11.788 11.481 11.497 20.29 2.21 -40.01 2.21 2.31 0.89 SK432_HHJ376_BPL133_DH502_Moraux2003_9 GCS9 Cl* Melotte 22 SK 432 +03 47 15.38 +23 26 05.8 0 14.386 13.944 13.385 12.825 12.503 12.475 15.92 2.24 -43.02 2.24 2.38 0.92 HHJ203_DH503_L07_161 GCS9 Cl* Melotte 22 HHJ 203 +03 47 15.46 +24 23 31.0 0 14.844 14.290 13.663 13.121 12.783 12.815 12.21 2.22 -46.33 2.22 85.06 0.58 BPL134_Festin98_002 GCS9 Cl* Melotte 22 BPL 134 +03 47 15.75 +22 21 16.8 0 14.199 13.704 13.140 12.641 12.297 12.323 20.22 2.26 -54.43 2.26 2.70 0.03 HCG267_BPL135_HHJ211_L07_205 GCS9 Cl* Melotte 22 HCG 267 +03 47 16.45 +24 44 50.1 0 14.575 13.990 13.385 12.836 12.484 12.492 19.48 2.21 -40.86 2.21 12.55 0.93 BPL136_L07_66 GCS9 Cl* Melotte 22 BPL 136 +03 47 17.92 +24 22 31.6 0 18.174 17.067 16.215 15.591 15.096 15.080 21.40 2.30 -42.73 2.30 6.15 0.68 BPL137_Teide1_M8_L07_A1_63 GCS9 Cl* Melotte 22 BPL 137 +03 47 18.10 +24 45 14.6 0 19.068 18.067 17.095 16.314 15.678 15.676 8.16 2.57 -37.97 2.57 4.23 0.15 L07_A1_64 GCS9 +03 47 19.34 +24 08 20.7 0 13.736 13.765 11.395 12.325 9.950 10.831 21.11 2.22 -61.40 2.22 7.54 0.00 HII1362_HD23607_Tr390 GCS9 HII1362 +03 47 20.43 +22 21 56.3 0 14.832 14.342 13.737 13.208 12.868 12.835 15.02 2.26 -38.93 2.26 3.34 0.80 L07_207 GCS9 +03 47 20.84 +25 05 12.1 0 12.899 12.550 12.021 11.403 11.153 11.187 17.78 2.21 -45.20 2.21 2.92 0.77 SK428_HHJ417_DH506 GCS9 Cl* Melotte 22 SK 428 +03 47 20.97 +23 48 11.9 0 13.896 13.945 11.841 12.647 10.307 11.499 2.96 2.22 -46.62 2.22 25.98 0.00 HII1380_HD23632_Tr397 GCS9 HII1380 +03 47 21.94 +26 22 47.2 0 14.456 14.014 13.425 12.835 12.542 12.547 14.19 2.93 -47.46 2.93 1.49 0.71 HHJ219_DH508_Moraux2003_44 GCS9 Cl* Melotte 22 HHJ 219 +03 47 22.03 +23 21 36.3 0 14.515 14.064 13.489 12.961 12.637 12.619 20.70 2.24 -41.53 2.24 3.13 0.93 L07_159 GCS9 +03 47 22.38 +24 14 18.8 0 15.323 14.727 14.093 13.545 13.176 13.163 19.02 2.22 -46.64 2.22 3.72 0.81 BPL139 GCS9 Cl* Melotte 22 BPL 139 +03 47 22.46 +22 31 10.8 0 15.350 14.836 14.214 13.686 13.314 13.291 20.04 2.27 -39.70 2.27 3.97 0.83 BPL140_L07_201 GCS9 Cl* Melotte 22 BPL 140 +03 47 22.68 +23 44 06.7 0 14.271 13.786 13.223 12.688 12.391 12.374 16.09 2.22 -43.44 2.22 30.63 0.92 HCG266_DH509 GCS9 Cl* Melotte 22 HCG 266 +03 47 22.76 +23 01 58.0 0 15.929 15.403 14.815 14.282 13.961 13.934 36.81 2.25 -28.10 2.25 3.24 0.00 HHJ23 GCS9 Cl* Melotte 22 HHJ 23 +03 47 22.91 +22 55 19.4 0 12.476 12.365 11.050 11.776 9.922 10.773 30.63 2.23 -35.96 2.23 35.83 0.01 HII1407_HD23610_TrS108 GCS9 HII1407 +03 47 25.11 +24 15 17.2 0 14.913 14.441 13.875 13.326 13.021 13.018 18.69 2.22 -45.16 2.22 3.29 0.93 HHJ198_BPL143 GCS9 Cl* Melotte 22 HHJ 198 +03 47 25.35 +24 02 56.8 0 13.454 13.128 12.616 12.074 11.774 11.806 20.89 2.22 -39.96 2.22 3.92 0.88 HHJ427_DH512 GCS9 Cl* Melotte 22 HHJ 427 +03 47 25.80 +25 08 32.8 0 13.339 12.873 12.278 11.753 11.412 11.453 19.88 2.21 -43.41 2.21 4.52 0.93 HCG263_SK423_T6B_HHJ361_BPL144_DH513_Moraux2003_10 GCS9 Cl* Melotte 22 HCG 263 +03 47 25.90 +25 26 26.4 0 14.461 13.904 13.274 12.733 12.387 12.372 15.47 2.23 -44.18 2.23 3.55 0.91 Moraux2003_49_L07_38 GCS9 Cl* Melotte 22 MBSC 49 +03 47 26.77 +23 38 02.4 0 14.194 13.696 13.125 12.569 12.249 12.245 18.70 2.22 -40.80 2.22 4.38 0.93 HCG269_HHJ263_DH514 GCS9 Cl* Melotte 22 HCG 269 +03 47 26.84 +23 40 41.8 0 13.124 13.039 11.380 12.020 10.138 13.738 12.70 2.22 -30.72 2.22 12.76 0.00 HII1425_HD23643_Tr410 GCS9 HII1425 +03 47 27.28 +24 49 16.3 0 14.616 14.190 13.656 12.958 12.729 12.717 31.79 2.21 -32.38 2.21 2.21 0.00 15.07 GCS9 Cl* Melotte 22 MHO 13 +03 47 27.72 +22 09 38.5 0 17.382 16.547 15.760 15.224 14.763 14.783 17.79 2.34 -41.87 2.34 6.30 0.72 L07_A1_65 GCS9 +03 47 28.12 +23 26 53.4 0 13.694 13.309 12.779 12.225 11.885 11.879 19.40 2.24 -40.95 2.24 3.65 0.92 SK417_HHJ315_DH515 GCS9 Cl* Melotte 22 SK 417 +03 47 28.41 +26 32 05.5 0 14.243 13.779 13.199 12.652 12.361 12.363 20.47 2.93 -43.56 2.93 0.39 0.93 DH516_Moraux2003_33 GCS9 Cl* Melotte 22 DH 516 +03 47 28.43 +24 40 33.0 0 14.715 14.245 13.694 13.118 12.817 12.766 15.65 2.21 -46.11 2.21 4.76 0.87 BPL145_DH517_L07_69 GCS9 Cl* Melotte 22 BPL 145 +03 47 29.59 +23 52 49.3 0 16.385 15.692 15.016 14.462 14.078 14.077 16.73 2.24 -43.66 2.24 1.74 0.74 L07_A1_66 GCS9 +03 47 29.93 +23 33 15.0 0 16.033 15.408 14.750 14.157 13.818 13.794 17.39 2.23 -43.72 2.23 2.95 0.74 HHJ16 GCS9 Cl* Melotte 22 HHJ 16 +03 47 30.59 +26 16 44.5 0 13.801 13.394 12.848 12.305 12.027 12.001 19.30 2.93 -44.23 2.93 0.21 0.93 HCG260_HHJ310_DH518_Moraux2003_26 GCS9 Cl* Melotte 22 HCG 260 +03 47 30.60 +24 22 13.8 0 13.050 12.722 12.199 11.658 11.352 11.381 18.57 2.22 -44.22 2.22 2.33 0.94 HCG273_HHJ408 GCS9 Cl* Melotte 22 HCG 273 +03 47 31.13 +21 10 51.1 0 14.679 14.224 13.640 13.111 12.780 12.789 23.35 3.05 -35.53 3.05 0.64 0.31 DH519 GCS9 Cl* Melotte 22 DH 519 +03 47 31.37 +25 25 11.0 0 15.908 15.366 14.702 14.048 13.679 13.650 15.53 2.24 -17.06 2.24 1.02 0.00 Moraux2003_104 GCS9 Cl* Melotte 22 MBSC 104 +03 47 31.65 +23 52 19.1 0 14.743 14.247 13.686 13.121 12.811 12.800 13.78 2.22 -44.81 2.22 2.44 0.82 L07_124 GCS9 +03 47 32.00 +24 10 24.7 0 14.749 14.293 13.677 13.143 12.827 12.808 19.77 2.22 -41.79 2.22 1.69 0.94 BPL147 GCS9 Cl* Melotte 22 BPL 147 +03 47 33.06 +25 38 18.4 0 14.492 14.001 13.394 12.814 12.503 12.490 15.50 2.23 -44.70 2.23 6.41 0.90 L07_29 GCS9 +03 47 33.46 +23 41 32.8 0 12.580 12.224 11.716 11.700 10.957 11.178 17.61 2.22 -41.13 2.22 0.71 0.88 HCG277_SK413_T105_HHJ424_DH520 GCS9 Cl* Melotte 22 HCG 277 +03 47 34.17 +25 43 05.9 0 14.233 13.788 13.217 12.675 12.390 12.374 14.30 2.23 -44.19 2.23 17.36 0.86 HHJ238_DH522_L07_30 GCS9 Cl* Melotte 22 HHJ 238 +03 47 34.52 +24 02 23.0 0 14.645 14.216 13.648 13.087 12.797 12.793 19.46 2.22 -36.75 2.22 2.40 0.72 DH523 GCS9 Cl* Melotte 22 DH 523 +03 47 35.86 +24 52 26.7 0 14.700 14.223 13.634 13.112 12.786 12.772 16.91 2.21 -45.88 2.21 10.09 0.91 HCG279_HHJ157_BPL149 GCS9 Cl* Melotte 22 HCG 279 +03 47 36.04 +23 28 26.7 0 15.203 14.719 14.127 13.601 13.266 13.247 18.53 2.24 -46.44 2.24 3.74 0.83 HHJ73_DH526_L07_162 GCS9 Cl* Melotte 22 HHJ 73 +03 47 37.35 +25 20 02.3 0 14.391 13.912 13.308 12.736 12.427 12.407 16.90 2.23 -44.53 2.23 1.96 0.93 Moraux2003_43 GCS9 Cl* Melotte 22 MBSC 43 +03 47 37.66 +24 24 23.1 0 14.791 14.244 13.638 13.102 12.757 12.763 18.80 2.21 -47.96 2.21 2.31 0.84 BPL150_L07_76 GCS9 Cl* Melotte 22 BPL 150 +03 47 38.05 +24 49 10.8 0 13.439 13.061 12.546 12.133 11.670 11.690 15.44 2.21 -43.50 2.21 7.15 0.91 SK409_HHJ360_BPL151_DH528 GCS9 Cl* Melotte 22 SK 409 +03 47 38.37 +24 35 59.7 0 15.455 14.891 14.304 13.737 13.408 13.395 15.03 2.21 -41.64 2.21 8.01 0.85 L07_80 GCS9 +03 47 39.02 +24 36 22.2 0 17.112 16.271 15.548 14.992 14.570 14.554 15.20 2.24 -45.02 2.24 12.06 0.59 BPL152_Roque16_CFHT-Pl-11_M6.0_BRB12_CFHT-Pl-11_L07_A1_67_L07_219 GCS9 Cl* Melotte 22 BPL 152 +03 47 39.36 +24 27 31.9 0 14.048 13.600 13.019 12.442 12.145 12.139 19.38 2.21 -43.38 2.21 4.21 0.94 HCG282_HHJ272_BPL153_DH530 GCS9 Cl* Melotte 22 HCG 282 +03 47 39.80 +23 00 04.4 0 15.088 14.635 14.074 13.518 13.202 13.203 16.06 2.24 -42.09 2.24 13.32 0.88 HHJ94 GCS9 Cl* Melotte 22 HHJ 94 +03 47 40.96 +21 49 05.0 0 15.807 15.243 14.629 14.089 13.750 13.707 22.10 2.28 -44.16 2.28 5.36 0.80 L07_213 GCS9 +03 47 42.86 +28 18 59.1 0 15.298 14.760 14.147 13.616 13.263 13.252 15.67 2.96 -35.98 2.96 0.05 0.45 DH533 GCS9 Cl* Melotte 22 DH 533 +03 47 43.88 +26 13 26.8 0 14.714 14.225 13.655 13.113 12.807 12.804 23.14 2.93 -41.65 2.93 0.35 0.86 DH534_Moraux2003_66 GCS9 Cl* Melotte 22 DH 534 +03 47 44.05 +24 03 56.3 0 16.061 15.644 15.077 14.512 14.197 14.197 23.29 2.23 -45.96 2.23 10.22 0.35 HHJ26_DH535 GCS9 Cl* Melotte 22 HHJ 26 +03 47 44.66 +23 42 03.1 0 14.538 14.079 13.508 12.937 12.650 12.660 20.25 2.22 -45.79 2.22 1.38 0.91 HHJ152_DH536_L07_139 GCS9 Cl* Melotte 22 HHJ 152 +03 47 44.67 +22 12 43.9 0 15.872 15.338 14.695 14.187 13.825 13.810 22.07 2.28 -45.80 2.28 5.26 0.74 HHJ15_BPL154 GCS9 Cl* Melotte 22 HHJ 15 +03 47 44.68 +22 23 53.0 0 14.454 14.006 13.424 12.875 12.554 12.562 22.28 2.26 -44.26 2.26 0.33 0.90 HCG299_HHJ163_BPL155_L07_208 GCS9 Cl* Melotte 22 HCG 299 +03 47 45.92 +24 38 01.3 0 14.579 14.033 13.437 12.860 12.493 12.514 19.47 2.21 -49.88 2.21 0.62 0.62 HCG287_HHJ168_BPL156_L07_68 GCS9 Cl* Melotte 22 HCG 287 +03 47 46.40 +24 03 02.3 0 12.979 12.677 12.180 11.634 11.322 11.381 19.77 2.22 -37.79 2.22 7.93 0.84 HHJ438 GCS9 Cl* Melotte 22 HHJ 438 +03 47 46.78 +25 35 16.6 0 20.351 18.565 17.424 16.687 16.154 16.095 18.60 3.00 -37.57 3.00 1.25 0.44 L07_A1_68 GCS9 +03 47 47.87 +25 13 34.3 0 14.065 13.593 12.961 12.408 12.049 12.055 18.78 2.21 -41.93 2.21 2.41 0.94 SK398_HHJ266_BPL157_DH537_Moraux2003_34 GCS9 Cl* Melotte 22 SK 398 +03 47 48.91 +24 17 06.5 0 20.292 19.272 17.873 17.039 16.410 16.385 13.21 2.89 -36.93 2.89 3.93 0.43 L07_A1_69 GCS9 +03 47 49.45 +23 31 52.8 0 17.078 16.295 15.581 15.025 14.611 14.602 17.03 2.25 -39.82 2.25 7.37 0.65 L07_A1_70 GCS9 +03 47 49.79 +24 25 43.1 0 14.551 14.074 13.497 12.962 12.669 12.650 20.57 2.21 -46.82 2.21 2.91 0.87 HCG292_HHJ202_BPL158_DH538_L07_77 GCS9 Cl* Melotte 22 HCG 292 +03 47 50.41 +23 54 47.8 0 18.179 17.138 16.311 15.622 15.093 15.090 15.88 2.32 -39.60 2.32 5.41 0.63 Festin98_007_L07_A1_71 GCS9 Cl* Melotte 22 NPL 007 +03 47 50.95 +24 30 18.6 0 13.152 12.806 12.298 11.944 11.443 11.472 18.40 2.21 -45.72 2.21 7.73 0.92 HCG295_HHJ389_BPL159_DH539 GCS9 Cl* Melotte 22 HCG 295 +03 47 51.97 +23 39 48.0 0 14.936 14.433 13.888 13.320 13.031 13.034 17.54 2.22 -44.32 2.22 6.79 0.94 HHJ122_DH540 GCS9 Cl* Melotte 22 HHJ 122 +03 47 52.88 +22 59 33.8 0 14.017 13.564 12.998 12.456 12.134 12.160 15.85 2.24 -40.42 2.24 11.92 0.89 SK394_HHJ258_BPL160_DH541 GCS9 Cl* Melotte 22 SK 394 +03 47 55.28 +23 19 05.8 0 13.814 13.443 12.910 12.377 12.092 12.111 14.50 2.24 -35.68 2.24 2.43 0.30 HCG302_SK392_HHJ289_DH542 GCS9 Cl* Melotte 22 HCG 302 +03 47 56.64 +24 15 31.7 0 15.315 14.766 14.163 13.625 13.282 13.281 21.88 2.22 -36.70 2.22 3.78 0.47 BPL162_L07_100 GCS9 Cl* Melotte 22 BPL 162 +03 47 56.66 +26 31 50.9 0 12.905 12.555 12.064 11.504 11.210 11.250 23.91 2.93 -40.70 2.93 2.10 0.76 HHJ423_DH543 GCS9 Cl* Melotte 22 HHJ 423 +03 47 58.04 +22 06 50.8 0 16.708 15.993 15.283 14.742 14.331 14.331 18.97 2.30 -42.48 2.30 4.22 0.74 BPL163_L07_A1_72 GCS9 Cl* Melotte 22 BPL 163 +03 47 59.38 +24 35 37.0 0 15.631 15.079 14.486 13.940 13.592 13.581 15.52 2.22 -43.79 2.22 3.92 0.87 BPL164_DH544_L07_79 GCS9 Cl* Melotte 22 BPL 164 +03 47 59.74 +22 36 01.8 0 18.043 17.083 16.209 15.609 15.090 15.115 21.40 2.40 -42.45 2.40 6.65 0.69 int-pl-IZ-33_2MASSJ0347597+223601_Y_L07_A1_73 GCS9 Cl* Melotte 22 IPL 33 +03 47 59.77 +22 38 30.1 0 12.945 12.429 11.820 11.418 11.025 11.072 30.05 2.26 -48.45 2.26 26.02 0.00 HHJ365_BPL165 GCS9 Cl* Melotte 22 HHJ 365 +03 48 04.67 +23 39 30.1 1 17.014 16.054 15.283 14.704 14.256 14.238 16.07 2.24 -44.27 2.24 5.65 0.66 PPl15_IPMBD23_L07_A1_74 GCS9 PPl15 +03 48 04.98 +23 24 13.4 0 15.976 15.415 14.783 14.280 13.912 13.935 16.66 2.25 -43.18 2.25 1.67 0.89 HHJ21_DH546_L07_160 GCS9 Cl* Melotte 22 HHJ 21 +03 48 05.72 +22 38 09.3 0 14.248 13.814 13.195 12.717 12.425 12.432 15.27 2.26 -47.03 2.26 31.26 0.82 HCG314_HHJ210_DH547_L07_202 GCS9 Cl* Melotte 22 HCG 314 +03 48 05.83 +23 02 02.7 0 13.241 12.881 12.377 11.963 11.540 11.562 16.95 2.23 -43.77 2.23 2.60 0.93 HCG309_SK385_T154_HHJ375_DH548_BPL166 GCS9 Cl* Melotte 22 HCG 309 +03 48 06.41 +24 06 51.7 0 14.633 14.200 13.603 13.064 12.753 12.790 18.58 2.22 -43.78 2.22 0.58 0.94 L07_122 GCS9 +03 48 06.64 +24 00 06.7 0 14.398 13.957 13.367 12.823 12.520 12.531 17.15 2.22 -39.79 2.22 1.28 0.90 HHJ240_DH549 GCS9 Cl* Melotte 22 HHJ 240 +03 48 07.96 +23 44 23.5 0 13.941 13.522 12.964 12.433 12.148 12.163 17.89 2.22 -45.77 2.22 0.64 0.92 HCG307_HHJ288_DH551 GCS9 Cl* Melotte 22 HCG 307 +03 48 08.96 +23 42 23.3 0 14.620 14.148 13.566 13.045 12.761 12.747 17.90 2.22 -46.56 2.22 1.13 0.90 HHJ156_DH552 GCS9 Cl* Melotte 22 HHJ 156 +03 48 09.22 +23 58 40.5 0 14.445 14.024 13.448 12.921 12.615 12.648 15.27 2.22 -41.80 2.22 0.91 0.90 HHJ225_DH553_L07_107 GCS9 Cl* Melotte 22 HHJ 225 +03 48 10.17 +23 00 03.9 0 12.412 12.198 11.771 11.631 10.997 11.133 18.14 2.23 -35.64 2.23 5.94 0.66 DH554 GCS9 Cl* Melotte 22 DH 554 +03 48 10.18 +23 59 20.1 0 15.509 15.004 14.370 13.840 13.478 13.492 20.23 2.22 -43.71 2.22 0.99 0.88 DH555_PPL12_L07_109 GCS9 Cl* Melotte 22 DH 555 +03 48 13.31 +23 58 46.8 0 14.206 13.766 13.187 12.664 12.353 12.394 16.71 2.22 -42.37 2.22 4.05 0.93 HCG311_T155_DH560_Festin98_001_L07_108 GCS9 Cl* Melotte 22 HCG 311 +03 48 13.78 +23 37 59.3 0 13.951 13.521 12.974 12.422 12.130 12.041 19.35 2.22 -44.07 2.22 2.65 0.94 HCG315_SK374_HHJ281_DH561_L07_10 GCS9 Cl* Melotte 22 HCG 315 +03 48 14.30 +24 15 50.5 0 16.637 15.926 15.218 14.677 14.267 14.265 19.81 2.24 -40.94 2.24 4.06 0.69 BPL169_L07_A1_75 GCS9 Cl* Melotte 22 BPL 169 +03 48 15.25 +23 26 05.4 0 14.320 13.888 13.339 12.781 12.516 12.495 14.81 2.13 -42.58 2.13 1.54 0.89 HHJ232 GCS9 Cl* Melotte 22 HHJ 232 +03 48 15.27 +23 42 03.4 0 13.599 13.175 12.588 12.112 11.768 11.801 18.45 2.22 -45.66 2.22 1.36 0.92 HHJ336_DH562 GCS9 Cl* Melotte 22 HHJ 336 +03 48 15.49 +25 14 36.4 0 14.957 14.500 13.897 13.347 13.006 13.051 13.38 2.21 -40.07 2.21 3.89 0.74 BPL170_DH563_Moraux2003_78_L07_58 GCS9 Cl* Melotte 22 BPL 170 +03 48 16.09 +23 35 15.2 0 14.654 14.122 13.520 12.988 12.645 12.641 20.07 2.22 -42.31 2.22 0.55 0.94 HHJ151_DH564_L07_144 GCS9 Cl* Melotte 22 HHJ 151 +03 48 16.38 +26 20 05.8 0 14.225 13.741 13.161 12.587 12.309 12.297 17.16 2.93 -24.32 2.93 0.43 0.00 HCG305 GCS9 Cl* Melotte 22 HCG 305 +03 48 16.58 +21 57 44.4 0 15.162 14.808 14.229 13.602 13.361 13.347 19.66 2.27 -27.09 2.27 22.21 0.00 L07_212 GCS9 +03 48 16.64 +26 30 11.6 0 14.415 13.953 13.400 12.855 12.541 12.576 17.23 2.93 -51.67 2.93 0.24 0.29 HHJ215_DH565 GCS9 Cl* Melotte 22 HHJ 215 +03 48 17.30 +24 30 15.7 0 12.184 11.939 11.515 11.450 10.723 10.898 26.02 2.21 -46.97 2.21 2.32 0.15 HII1785_HCG312_DH566 GCS9 HII1785 +03 48 17.37 +23 48 23.5 0 14.645 14.190 13.615 13.061 12.760 12.745 18.31 2.22 -42.91 2.22 0.63 0.94 HHJ188_L07_132 GCS9 Cl* Melotte 22 HHJ 188 +03 48 17.61 +22 04 00.9 0 14.425 13.954 13.373 12.851 12.543 12.515 19.94 2.26 -43.28 2.26 2.96 0.94 HHJ187_BPL171_L07_209 GCS9 Cl* Melotte 22 HHJ 187 +03 48 19.02 +24 25 12.7 0 17.655 16.668 15.930 15.365 14.935 14.953 14.16 2.30 -39.48 2.30 13.03 0.49 BPL172_Festin98_005_Roque12_L07_photNM_22 GCS9 Cl* Melotte 22 BPL 172 +03 48 19.84 +23 36 11.8 0 13.175 12.840 12.327 11.827 11.486 11.536 20.97 2.22 -44.35 2.22 1.90 0.91 DH568 GCS9 Cl* Melotte 22 DH 568 +03 48 20.29 +24 54 54.9 0 13.479 13.035 12.450 11.909 11.604 11.641 19.17 2.21 -42.06 2.21 2.97 0.94 HCG313_SK371_T23_BPL173 GCS9 Cl* Melotte 22 HCG 313 +03 48 20.58 +23 31 01.3 0 15.523 14.983 14.358 13.817 13.510 13.517 24.03 2.14 -49.73 2.14 6.53 0.15 HCG321_DH570 GCS9 Cl* Melotte 22 HCG 321 +03 48 21.54 +24 34 43.4 0 14.868 14.334 13.789 13.233 12.918 12.937 13.23 2.21 -46.01 2.21 3.06 0.73 BPL174_DH571_L07_78 GCS9 Cl* Melotte 22 BPL 174 +03 48 22.66 +22 52 21.3 0 13.174 12.807 12.291 11.768 11.473 11.505 20.45 2.13 -41.09 2.13 4.73 0.91 HCG323_SK370_A91_BPL175_DH572 GCS9 Cl* Melotte 22 HCG 323 +03 48 22.71 +23 27 42.8 0 14.643 14.181 13.574 13.026 12.787 12.738 19.88 2.13 -42.50 2.13 0.89 0.94 HHJ171_DH573 GCS9 Cl* Melotte 22 HHJ 171 +03 48 22.81 +24 48 53.4 0 13.811 13.364 12.850 12.329 12.012 12.039 16.29 2.21 -48.18 2.21 2.53 0.78 SK368_HHJ309_BPL176_DH574 GCS9 Cl* Melotte 22 SK 368 +03 48 23.92 +23 08 08.1 0 15.111 14.655 14.032 13.490 13.123 13.129 14.82 2.13 -40.92 2.13 1.68 0.83 DH576 GCS9 Cl* Melotte 22 DH 576 +03 48 25.24 +24 14 25.8 0 13.827 13.379 12.792 12.285 11.968 11.989 17.16 2.22 -43.14 2.22 5.84 0.94 HCG322_SK363_HHJ314_BPL178 GCS9 Cl* Melotte 22 HCG 322 +03 48 26.05 +25 14 40.8 0 12.786 12.471 11.969 11.411 11.122 11.211 18.96 2.21 -54.45 2.21 33.24 0.00 HCG317_DH578 GCS9 Cl* Melotte 22 HCG 317 +03 48 26.60 +23 11 29.5 0 13.307 12.741 12.122 11.691 11.279 11.326 25.82 2.13 -37.29 2.13 0.76 0.17 HCG332_SK362_HHJ339 GCS9 Cl* Melotte 22 HCG 332 +03 48 27.36 +23 46 16.2 0 20.628 19.658 18.134 17.347 16.505 16.519 18.01 2.93 -43.64 2.93 1.05 0.57 L07_A1_76 GCS9 +03 48 29.76 +23 58 05.8 0 14.876 14.413 13.823 13.281 12.988 12.938 17.30 2.22 -45.29 2.22 3.01 0.92 HCG328_WILL3_HHJ132 GCS9 Cl* Melotte 22 HCG 328 +03 48 30.29 +24 18 00.2 0 20.225 19.201 17.793 16.971 16.382 16.374 21.38 2.76 -51.81 2.76 2.78 0.54 L07_A1_77 GCS9 +03 48 30.74 +22 44 50.2 0 20.389 18.936 17.714 16.861 16.251 16.150 11.34 3.40 -38.38 3.40 1.90 0.47 Roque25 GCS9 Cl* Melotte 22 Roque 25 +03 48 31.06 +24 16 53.1 0 13.037 12.703 12.170 11.891 11.376 11.477 23.77 2.22 -43.77 2.22 10.39 0.79 HCG324_VM46_HHJ404_BPL180_DH580 GCS9 Cl* Melotte 22 HCG 324 +03 48 31.53 +24 34 37.2 1 19.218 17.883 16.715 15.977 15.357 15.312 11.92 2.37 -46.68 2.37 3.41 0.49 BRB16_PIZ1_L07_A1_78_L07_258 GCS9 Cl* Melotte 22 BRB 16 +03 48 31.84 +24 01 58.6 0 14.648 14.214 13.619 13.074 12.798 12.789 16.37 2.22 -42.70 2.22 6.09 0.93 HCG327_HHJ197_DH581_L07_110 GCS9 Cl* Melotte 22 HCG 327 +03 48 33.78 +24 01 58.8 0 14.698 14.252 13.698 13.136 12.861 12.892 16.27 2.22 -39.37 2.22 4.68 0.87 HHJ184_DH582_L07_111 GCS9 Cl* Melotte 22 HHJ 184 +03 48 35.20 +22 53 42.1 1 16.176 15.452 14.745 14.185 13.770 13.772 15.22 2.15 -45.62 2.15 1.57 0.62 HHJ10_BPL181_PPL10 GCS9 Cl* Melotte 22 HHJ 10 +03 48 35.49 +24 12 03.0 0 15.216 14.662 14.042 13.491 13.221 13.201 18.84 2.22 -39.61 2.22 2.04 0.85 HCG335_HHJ96_BPL182_DH583_L07_95 GCS9 Cl* Melotte 22 HCG 335 +03 48 36.34 +25 15 41.2 0 16.029 15.446 14.801 14.259 13.889 13.895 14.47 2.21 -46.15 2.21 13.10 0.54 BPL183_Moraux2003_105_L07_56 GCS9 Cl* Melotte 22 BPL 183 +03 48 38.37 +22 33 51.8 0 17.345 16.523 15.711 15.168 14.718 14.692 17.51 2.33 -38.63 2.33 7.70 0.60 L07_A1_79 GCS9 +03 48 39.31 +24 50 19.9 0 15.432 14.862 14.269 13.711 13.405 13.387 14.82 2.21 -44.18 2.21 1.67 0.84 DH585_L07_75 GCS9 Cl* Melotte 22 DH 585 +03 48 39.91 +24 12 42.7 0 13.512 13.161 12.640 12.085 11.832 11.837 14.85 2.22 -41.13 2.22 1.22 0.87 HCG337_SK353_HHJ347_BPL184 GCS9 Cl* Melotte 22 HCG 337 +03 48 40.43 +24 36 34.0 0 14.182 13.705 13.144 12.632 12.321 12.314 17.15 2.20 -46.57 2.20 2.52 0.89 HCG333_SK351_BPL185_DH586 GCS9 Cl* Melotte 22 HCG 333 +03 48 40.60 +25 01 19.8 0 15.780 15.212 14.553 14.023 13.660 13.652 17.54 2.21 -42.42 2.21 1.98 0.90 BPL186_L07_63 GCS9 Cl* Melotte 22 BPL 186 +03 48 40.99 +23 14 17.2 0 13.874 13.419 12.818 12.291 11.956 11.984 20.87 2.13 -42.75 2.13 4.91 0.92 HCG343_SK352 GCS9 Cl* Melotte 22 HCG 343 +03 48 42.15 +25 00 28.2 0 13.453 13.101 12.555 12.084 11.703 11.724 16.19 2.20 -44.52 2.20 0.66 0.92 HCG339_SK349_B207_BPL187 GCS9 Cl* Melotte 22 HCG 339 +03 48 42.69 +24 27 19.4 0 15.480 14.961 14.356 13.822 13.479 13.475 18.31 2.21 -46.04 2.21 0.44 0.85 HHJ44_WILL6_BPL188_DH587_L07_89 GCS9 Cl* Melotte 22 HHJ 44 +03 48 43.14 +26 32 20.9 0 14.239 13.774 13.191 12.678 12.379 12.364 25.22 2.93 -37.76 2.93 0.52 0.39 HHJ246_DH588 GCS9 Cl* Melotte 22 HHJ 246 +03 48 44.05 +25 06 22.4 0 14.492 14.076 13.483 12.898 12.639 12.631 16.69 2.20 -44.81 2.20 3.48 0.92 BPL189_DH589_Moraux2003_48_L07_55 GCS9 Cl* Melotte 22 BPL 189 +03 48 44.69 +24 37 23.5 0 16.260 15.584 14.911 14.419 14.002 13.967 17.45 2.22 -43.06 2.22 1.14 0.75 DH590_CFHT-Pl-5_M5.5_BRB7_CFHT-Pl-5_L07_A1_80_L07_223 GCS9 Cl* Melotte 22 DH 590 +03 48 45.35 +24 37 26.3 0 15.118 14.581 13.971 13.507 13.157 13.123 15.61 2.20 -45.81 2.20 2.78 0.83 HCG346_HHJ111_BPL190 GCS9 Cl* Melotte 22 HCG 346 +03 48 46.02 +24 10 12.5 0 14.464 14.027 13.476 12.931 12.631 12.661 15.18 2.22 -41.35 2.22 1.14 0.89 HHJ207_DH59_L07_112 GCS9 Cl* Melotte 22 HHJ 207 +03 48 48.62 +24 30 15.4 0 15.488 15.114 14.569 13.991 13.692 13.689 21.84 2.21 -38.84 2.21 6.82 0.70 L07_90 GCS9 +03 48 48.79 +23 24 48.0 0 15.140 14.625 14.030 13.486 13.166 13.171 15.92 2.13 -37.19 2.13 1.51 0.64 HHJ86 GCS9 Cl* Melotte 22 HHJ 86 +03 48 50.45 +22 44 29.8 1 16.562 15.825 15.098 14.531 14.141 14.143 15.64 2.16 -42.06 2.16 2.49 0.71 HHJ3_BPL191 GCS9 Cl* Melotte 22 HHJ 3 +03 48 50.45 +25 17 54.7 0 16.516 15.802 15.106 14.575 14.177 14.174 18.72 2.25 -45.60 2.25 2.79 0.67 BPL192_Moraux2003_109 GCS9 Cl* Melotte 22 BPL 192 +03 48 50.50 +24 08 27.2 0 16.669 16.113 15.512 14.949 14.599 14.574 7.25 2.25 -27.47 2.25 3.93 0.00 DH593 GCS9 Cl* Melotte 22 DH 593 +03 48 50.67 +23 04 30.0 0 15.010 14.511 13.936 13.425 13.098 13.108 17.50 2.13 -43.02 2.13 1.74 0.90 HHJ116 GCS9 Cl* Melotte 22 HHJ 116 +03 48 51.56 +24 12 17.3 0 12.963 12.601 12.088 11.632 11.240 11.331 19.13 2.22 -24.55 2.22 13.96 0.00 BPL193_ALR728 GCS9 Cl* Melotte 22 BPL 193 +03 48 52.29 +25 22 32.3 0 14.846 14.364 13.770 13.224 12.890 12.904 16.02 2.23 -33.87 2.23 4.39 0.17 L07_43 GCS9 +03 48 55.65 +24 21 40.1 0 16.220 15.636 14.963 14.416 14.055 14.041 17.62 2.23 -40.24 2.23 2.50 0.70 HHJ8_L07_A1_81 GCS9 Cl* Melotte 22 HHJ 8 +03 48 57.36 +24 19 43.6 0 12.735 12.382 11.845 11.772 11.088 11.294 19.80 2.22 -41.83 2.22 3.88 0.89 HCG349_SK340 GCS9 Cl* Melotte 22 HCG 349 +03 48 58.29 +23 05 39.0 0 13.329 12.999 12.460 11.907 11.630 11.649 20.85 2.13 -49.85 2.13 3.36 0.54 HHJ353_DH595 GCS9 Cl* Melotte 22 HHJ 353 +03 49 00.99 +24 54 10.0 0 13.608 13.216 12.673 12.188 11.812 11.833 24.02 2.20 -54.18 2.20 4.15 0.01 HCG351_SK335_HHJ332_BPL195 GCS9 Cl* Melotte 22 HCG 351 +03 49 01.02 +22 58 49.2 0 12.708 12.420 11.923 11.443 11.161 11.220 17.88 2.12 -44.01 2.12 2.32 0.84 HCG353_SK336_A55_HHJ414_DH597 GCS9 Cl* Melotte 22 HCG 353 +03 49 01.16 +23 38 15.5 0 15.613 15.042 14.427 13.885 13.548 13.580 15.19 2.22 -43.96 2.22 6.87 0.86 DH598_L07_145 GCS9 Cl* Melotte 22 DH 598 +03 49 01.50 +24 11 38.3 0 14.330 13.900 13.320 12.814 12.506 12.488 14.90 2.22 -46.83 2.22 49.18 0.81 HHJ231_BPL196_DH599 GCS9 Cl* Melotte 22 HHJ 231 +03 49 02.35 +25 43 24.1 0 12.899 12.711 12.370 11.912 11.751 11.756 13.74 2.23 -39.88 2.23 7.61 0.66 DH2004_600 GCS9 Cl* Melotte 22 DH 600 +03 49 02.97 +21 54 46.9 0 15.347 14.790 14.176 13.654 13.307 13.306 19.02 2.27 -47.56 2.27 3.03 0.75 BPL197_L07_211 GCS9 Cl* Melotte 22 BPL 197 +03 49 04.86 +23 33 39.3 0 17.145 16.308 15.573 15.032 14.579 14.568 16.74 2.25 -42.98 2.25 23.53 0.70 Roque47_IPMBD20_L07_A1_82 GCS9 Cl* Melotte 22 Roque 47 +03 49 05.18 +22 04 52.7 0 16.648 15.958 15.257 14.742 14.327 14.355 16.87 2.31 -38.67 2.31 5.97 0.59 L07_A1_83 GCS9 +03 49 05.93 +23 06 21.9 0 13.996 13.617 13.008 12.510 12.202 12.212 21.86 2.13 -55.37 2.13 8.61 0.00 HCG357_SK332_DH602 GCS9 Cl* Melotte 22 HCG 357 +03 49 06.76 +25 24 21.5 0 15.881 15.480 14.942 14.330 14.052 14.083 10.08 2.25 -28.77 2.25 1.80 0.00 L07_44 GCS9 +03 49 08.84 +25 53 48.6 0 13.042 12.708 12.195 11.732 11.323 11.398 14.88 2.23 -43.29 2.23 2.17 0.89 SK327_HHJ387_DH603_Moraux2003_8 GCS9 Cl* Melotte 22 SK 327 +03 49 11.95 +24 39 26.5 0 19.975 18.858 18.018 17.618 17.140 17.153 33.87 3.66 -38.06 3.66 1.67 0.11 L07_256 GCS9 +03 49 12.18 +25 20 31.7 0 15.633 15.245 14.696 14.123 13.823 13.846 16.17 2.24 -42.24 2.24 1.32 0.89 L07_42 GCS9 +03 49 15.12 +24 36 22.5 0 16.847 16.059 15.371 14.890 14.433 14.443 15.04 2.23 -40.44 2.23 9.93 0.64 BPL202_CFHT-Pl-9_M6.5_BRB10_CFHT-Pl-9_L07_A1_85_L07_222 GCS9 Cl* Melotte 22 BPL 202 +03 49 15.63 +23 22 49.1 0 15.460 14.919 14.290 13.736 13.436 13.422 19.79 2.26 -41.51 2.26 3.40 0.88 HHJ36_L07_152_DH610 GCS9 Cl* Melotte 22 HHJ 36 +03 49 16.18 +26 49 02.9 0 16.931 16.211 15.474 14.912 14.482 14.531 21.70 2.99 -47.50 2.99 1.06 0.37 IPMBD21 GCS9 Cl* Melotte 22 IPMBD 21 +03 49 16.42 +24 03 49.0 0 12.458 12.108 11.574 11.608 10.800 11.068 24.30 2.22 -41.94 2.22 7.58 0.71 HCG362 GCS9 Cl* Melotte 22 HCG 362 +03 49 20.31 +25 25 42.4 0 14.155 13.745 13.198 12.642 12.335 12.324 12.27 2.23 -40.97 2.23 5.46 0.66 HCG366_HHJ251_DH611_Moraux2003_31 GCS9 Cl* Melotte 22 HCG 366 +03 49 20.70 +24 52 35.2 0 15.612 15.094 14.529 14.021 13.731 13.738 44.56 2.21 -41.45 2.21 12.56 0.00 BPL203 GCS9 Cl* Melotte 22 BPL 203 +03 49 20.96 +26 11 33.5 0 16.986 16.399 15.818 15.206 14.866 14.910 12.65 3.05 -35.63 3.05 1.07 0.10 DH614 GCS9 Cl* Melotte 22 DH 614 +03 49 21.17 +23 34 02.0 0 18.421 17.444 16.621 16.028 15.535 15.548 15.81 2.36 -29.43 2.36 3.20 0.03 Roque8_L07_photNM_11 GCS9 Cl* Melotte 22 Roque 8 +03 49 21.25 +24 41 40.8 0 15.549 14.957 14.352 13.821 13.470 13.478 16.52 2.21 -46.03 2.21 4.32 0.84 HHJ51_BPL204_DH615 GCS9 Cl* Melotte 22 HHJ 51 +03 49 21.50 +23 39 06.3 0 13.751 13.346 12.809 12.251 11.951 12.003 13.80 2.22 -45.12 2.22 5.34 0.82 HCG363_SK319_HHJ306_DH616 GCS9 Cl* Melotte 22 HCG 363 +03 49 21.93 +24 54 43.2 0 14.596 14.111 13.560 13.037 12.733 12.726 19.53 2.20 -43.15 2.20 4.87 0.94 SK316_BPL205_DH617_L07_62 GCS9 Cl* Melotte 22 SK 316 +03 49 22.15 +25 47 37.7 0 14.407 13.954 13.393 12.801 12.496 12.504 19.36 2.23 -37.53 2.23 2.58 0.80 HHJ196_DH618_Moraux2003_52 GCS9 Cl* Melotte 22 HHJ 196 +03 49 22.52 +21 37 56.0 0 14.541 14.086 13.492 12.975 12.636 12.654 21.90 3.05 -33.73 3.05 1.35 0.15 DH2004_619 GCS9 Cl* Melotte 22 DH 619 +03 49 25.55 +22 18 44.4 0 15.321 14.823 14.225 13.678 13.329 13.321 21.74 2.51 -40.44 2.51 2.43 0.79 BPL206 GCS9 Cl* Melotte 22 BPL 206 +03 49 25.61 +23 02 49.9 0 15.146 14.643 14.021 13.499 13.173 13.172 20.07 2.25 -44.25 2.25 3.06 0.87 HHJ79_BPL207_DH620_L07_187 GCS9 Cl* Melotte 22 HHJ 79 +03 49 26.64 +22 50 54.5 0 14.354 13.911 13.327 12.787 12.467 12.447 15.77 2.25 -44.36 2.25 1.72 0.91 SK315_BPL208_L07_198 GCS9 Cl* Melotte 22 SK 315 +03 49 26.65 +22 52 20.5 0 14.721 14.312 13.776 13.161 12.926 12.924 34.03 2.25 -25.62 2.25 11.59 0.00 L07_185 GCS9 +03 49 27.58 +24 24 13.7 0 14.547 14.043 13.524 12.968 12.679 12.681 11.85 2.20 -46.15 2.20 90.55 0.54 HHJ192_DH621 GCS9 Cl* Melotte 22 HHJ 192 +03 49 27.65 +24 31 54.1 0 12.946 12.583 12.067 11.575 11.226 11.273 11.93 2.20 -42.04 2.20 4.32 0.40 HCG370_DH622 GCS9 Cl* Melotte 22 HCG 370 +03 49 28.80 +26 50 51.4 0 15.626 15.129 14.513 13.997 13.663 13.655 18.24 2.94 -35.09 2.94 0.50 0.37 Moraux2003_95 GCS9 Cl* Melotte 22 MBSC 95 +03 49 29.81 +23 38 55.2 0 14.604 14.083 13.516 12.985 12.654 12.674 15.93 2.22 -46.60 2.22 7.60 0.86 HHJ158_DH624_L07_149 GCS9 Cl* Melotte 22 HHJ 158 +03 49 31.22 +23 41 19.6 0 14.646 14.158 13.575 13.029 12.724 12.749 20.25 2.22 -44.15 2.22 5.61 0.93 HHJ142_DH625 GCS9 Cl* Melotte 22 HHJ 142 +03 49 32.16 +23 16 17.9 0 15.381 14.888 14.287 13.753 13.418 13.424 20.17 2.25 -40.90 2.25 1.24 0.86 DH626 GCS9 Cl* Melotte 22 DH 626 +03 49 32.57 +23 24 41.0 0 14.251 13.753 13.159 12.602 12.293 12.309 20.08 2.25 -40.04 2.25 6.19 0.91 HHJ245_L07_153 GCS9 Cl* Melotte 22 HHJ 245 +03 49 32.81 +25 47 46.8 0 14.438 13.998 13.465 12.891 12.567 12.569 17.13 2.23 -41.13 2.23 4.11 0.93 HHJ209_DH628_Moraux2003_60_L07_23 GCS9 Cl* Melotte 22 HHJ 209 +03 49 33.04 +24 32 02.4 0 13.402 13.042 12.522 12.024 11.684 11.695 13.04 2.20 -43.72 2.20 3.25 0.79 HCG372_SK309_T90_HHJ346_BPL209 GCS9 Cl* Melotte 22 HCG 372 +03 49 35.29 +25 59 34.6 0 14.594 14.082 13.534 12.974 12.654 12.671 22.66 2.23 -45.19 2.23 3.27 0.86 HHJ155_DH630 GCS9 Cl* Melotte 22 HHJ 155 +03 49 35.46 +22 23 26.1 0 14.930 14.487 13.916 13.356 13.068 13.048 19.51 2.51 -41.27 2.51 0.89 0.93 HHJ112_BPL211 GCS9 Cl* Melotte 22 HHJ 112 +03 49 36.32 +26 02 16.3 0 15.517 14.960 14.333 13.755 13.409 13.401 24.70 2.24 -41.43 2.24 4.90 0.53 HHJ42_DH633 GCS9 Cl* Melotte 22 HHJ 42 +03 49 38.24 +26 51 05.6 0 14.052 13.637 13.073 12.512 12.234 12.239 19.29 2.93 -43.50 2.93 0.33 0.94 DH635_Moraux2003_29 GCS9 Cl* Melotte 22 DH 635 +03 49 39.32 +23 34 54.9 0 17.647 17.116 16.453 15.939 15.609 15.592 -2.29 2.32 -44.61 2.32 7.80 0.00 Roque42 GCS9 Cl* Melotte 22 Roque 42 +03 49 41.14 +23 14 59.3 0 15.232 14.627 13.986 13.443 13.056 13.075 17.11 2.25 -39.25 2.25 3.53 0.83 L07_171 GCS9 +03 49 41.21 +22 56 40.5 0 16.238 15.641 14.994 14.420 14.076 14.084 20.33 2.27 -43.06 2.27 6.90 0.69 BPL213_L07_A1_86_L07_242 GCS9 Cl* Melotte 22 BPL 213 +03 49 41.34 +26 08 53.2 0 13.701 13.323 12.813 12.205 11.934 11.957 15.62 2.23 -40.88 2.23 5.88 0.89 HHJ305_DH636 GCS9 Cl* Melotte 22 HHJ 305 +03 49 41.71 +21 56 19.2 0 15.963 15.359 14.685 14.130 13.770 13.750 22.57 2.52 -39.99 2.52 0.65 0.72 BPL214 GCS9 Cl* Melotte 22 BPL 214 +03 49 43.17 +24 39 46.4 0 16.348 15.708 15.061 14.505 14.115 14.126 17.72 2.22 -38.37 2.22 1.42 0.57 BPL215_L07_A1_87_L07_218 GCS9 Cl* Melotte 22 BPL 215 +03 49 47.04 +25 42 36.8 0 14.023 13.513 12.863 12.308 11.966 11.992 18.14 2.23 -42.94 2.23 2.88 0.94 DH638_L07_5 GCS9 Cl* Melotte 22 DH 638 +03 49 48.16 +26 37 47.5 0 15.926 15.369 14.707 14.187 13.827 13.837 17.38 2.95 -44.63 2.95 0.39 0.89 Moraux2003_98 GCS9 Cl* Melotte 22 MBSC 98 +03 49 48.44 +22 10 48.3 0 13.806 13.390 12.863 12.289 11.986 11.996 12.57 2.51 -41.31 2.51 1.17 0.71 BPL216_DH639 GCS9 Cl* Melotte 22 BPL 216 +03 49 48.77 +23 42 59.3 0 19.186 18.198 17.242 16.647 16.119 16.132 -24.98 2.50 -59.62 2.50 3.68 0.00 L07_photNM_12 GCS9 +03 49 51.43 +25 15 30.4 0 20.207 19.539 18.563 17.881 17.415 17.463 21.41 4.65 -39.77 4.65 0.80 0.46 Roque2 GCS9 Cl* Melotte 22 Roque 2 +03 49 51.80 +21 18 26.1 0 12.546 12.207 11.705 11.428 10.854 11.164 28.74 3.05 -39.12 3.05 1.63 0.10 HCG378_DH640 GCS9 Cl* Melotte 22 HCG 378 +03 49 52.44 +24 03 43.0 0 16.100 15.534 14.882 14.353 13.970 13.984 20.16 2.23 -43.40 2.23 3.73 0.70 L07_A1_88 GCS9 +03 49 55.49 +24 06 05.0 0 14.506 14.048 13.452 12.874 12.596 12.614 17.10 2.22 -43.07 2.22 6.13 0.94 DH641 GCS9 Cl* Melotte 22 DH 641 +03 49 55.79 +24 44 31.3 0 13.226 12.862 12.357 11.905 11.499 11.560 17.55 2.20 -41.05 2.20 7.36 0.92 HCG380_SK294_T92_BPL217_DH642 GCS9 Cl* Melotte 22 HCG 380 +03 49 56.77 +25 22 22.6 0 14.259 13.890 13.375 12.874 12.557 12.584 16.93 2.23 -43.91 2.23 6.29 0.93 DH643 GCS9 Cl* Melotte 22 DH 643 +03 49 56.81 +24 59 07.1 0 16.463 15.839 15.148 14.608 14.190 14.194 14.39 2.22 -40.78 2.22 7.78 0.61 BPL218_L07_A1_89_L07_217 GCS9 Cl* Melotte 22 BPL 218 +03 49 57.64 +23 43 28.2 0 15.488 14.887 14.245 13.710 13.395 13.386 20.76 2.22 -42.85 2.22 32.32 0.87 DH644_L07_150 GCS9 Cl* Melotte 22 DH 644 +03 49 57.85 +23 41 49.6 0 18.821 17.908 17.116 16.554 16.119 16.122 12.13 2.44 -65.77 2.44 10.58 0.00 Roque6 GCS9 Cl* Melotte 22 Roque 6 +03 49 58.33 +25 06 20.9 0 15.359 14.837 14.232 13.680 13.379 13.380 15.84 2.21 -43.01 2.21 2.86 0.88 BPL219_L07_52 GCS9 Cl* Melotte 22 BPL 219 +03 49 58.34 +23 42 33.7 0 13.279 12.903 12.399 12.070 11.567 11.705 16.44 2.22 -43.28 2.22 24.46 0.93 SK293 GCS9 Cl* Melotte 22 SK 293 +03 49 58.61 +25 53 46.2 0 15.011 14.544 13.948 13.385 13.048 13.044 17.26 2.23 -41.56 2.23 2.86 0.89 L07_24 GCS9 +03 49 59.54 +24 11 45.6 0 15.530 15.002 14.367 13.846 13.517 13.502 18.52 2.22 -41.66 2.22 41.43 0.90 BPL220 GCS9 Cl* Melotte 22 BPL 220 +03 50 01.57 +25 24 01.5 0 13.034 12.688 12.188 11.755 11.405 11.453 14.76 2.23 -46.39 2.23 3.60 0.83 SK291_DH646 GCS9 Cl* Melotte 22 SK 291 +03 50 01.87 +25 12 40.9 0 14.210 13.771 13.231 12.666 12.337 12.348 13.84 2.20 -42.81 2.20 5.30 0.85 HCG385_SK289_BPL222_DH647 GCS9 Cl* Melotte 22 HCG 385 +03 50 02.18 +23 51 44.6 0 13.770 13.342 12.802 12.268 11.965 11.957 13.75 2.22 -45.17 2.22 5.36 0.81 HCG382_DH648 GCS9 Cl* Melotte 22 HCG 382 +03 50 03.94 +24 56 02.8 0 16.234 15.728 15.114 14.515 14.147 14.145 1.53 2.22 -47.12 2.22 1.59 0.00 BPL223_L07_PM_NM_51 GCS9 Cl* Melotte 22 BPL 223 +03 50 04.25 +23 10 44.5 0 14.127 13.785 13.209 12.563 12.306 12.299 22.40 2.25 -39.72 2.25 5.17 0.84 SK287_DH649 GCS9 Cl* Melotte 22 SK 287 +03 50 05.00 +23 18 17.2 0 15.407 14.881 14.271 13.751 13.450 13.442 15.82 2.26 -47.05 2.26 48.45 0.77 DH650_L07_151 GCS9 Cl* Melotte 22 DH 650 +03 50 06.43 +26 58 18.7 0 14.792 14.338 13.747 13.210 12.884 12.905 21.70 2.94 -42.45 2.94 0.15 0.92 DH652 GCS9 Cl* Melotte 22 DH 652 +03 50 06.56 +24 59 46.3 0 13.866 13.405 12.811 12.332 11.962 11.976 15.86 2.20 -43.61 2.20 4.23 0.92 BPL224_DH653 GCS9 Cl* Melotte 22 BPL 224 +03 50 08.27 +25 30 51.7 0 19.614 18.280 17.223 16.594 16.008 15.983 13.87 2.83 -45.74 2.83 0.96 0.58 L07_A1_90 GCS9 +03 50 08.42 +25 32 55.7 0 14.171 13.715 13.151 12.599 12.306 12.338 16.57 2.23 -43.79 2.23 1.82 0.93 HCG386_DH654_L07_35 GCS9 Cl* Melotte 22 HCG 386 +03 50 08.62 +24 40 17.7 0 15.348 14.804 14.219 13.674 13.351 13.360 17.39 2.21 -43.94 2.21 9.31 0.90 DH655 GCS9 Cl* Melotte 22 DH 655 +03 50 10.77 +24 28 41.4 0 14.754 14.283 13.690 13.174 12.826 12.850 15.24 2.20 -41.67 2.20 8.77 0.90 BPL225_DH656_L07_84 GCS9 Cl* Melotte 22 BPL 225 +03 50 12.46 +23 55 35.9 0 14.068 13.568 12.961 12.484 12.119 12.116 16.49 2.22 -44.32 2.22 2.36 0.92 SK279_DH659 GCS9 Cl* Melotte 22 SK 279 +03 50 12.60 +23 48 48.9 0 15.564 15.046 14.439 13.876 13.556 13.530 18.23 2.22 -47.50 2.22 4.65 0.77 DH660 GCS9 Cl* Melotte 22 DH 660 +03 50 12.87 +24 21 06.2 0 12.697 12.419 11.916 11.378 11.086 11.149 15.86 2.22 -37.83 2.22 8.13 0.75 HII2601_HCG390_SK278_DH661 GCS9 HII2601 +03 50 13.40 +23 59 29.8 0 20.475 19.183 17.880 16.935 16.219 16.193 42.39 2.66 -31.73 2.66 1.78 0.02 L07_A1_91 GCS9 +03 50 14.76 +25 25 29.8 0 14.761 14.292 13.705 13.158 12.819 12.827 16.00 2.23 -45.17 2.23 4.67 0.91 DH662_L07_40 GCS9 Cl* Melotte 22 DH 662 +03 50 15.27 +24 13 36.0 0 14.103 13.701 13.153 12.628 12.339 12.347 18.72 2.22 -42.98 2.22 0.48 0.94 HCG394_SK276_B214_BPL226_DH663 GCS9 Cl* Melotte 22 HCG 394 +03 50 15.33 +24 35 40.3 0 16.164 15.638 15.090 14.514 14.208 14.215 15.42 2.22 -15.83 2.22 5.62 0.00 L07_photNM_23 GCS9 +03 50 16.09 +24 08 34.7 0 19.950 18.556 17.365 16.687 16.076 16.043 18.03 2.53 -46.97 2.53 2.35 0.67 Roque30_L07_A1_92_L07_259 GCS9 Cl* Melotte 22 Roque 30 +03 50 17.59 +22 55 58.8 0 15.396 14.844 14.246 13.695 13.353 13.360 19.17 2.13 -44.31 2.13 2.13 0.89 DH664 GCS9 Cl* Melotte 22 DH 664 +03 50 18.22 +24 35 15.3 0 14.697 14.220 13.692 13.137 12.844 12.861 14.19 2.20 -44.75 2.20 7.48 0.85 BPL227_DH665+L07_85 GCS9 Cl* Melotte 22 BPL 227 +03 50 19.15 +24 16 34.0 0 16.445 15.797 15.103 14.564 14.190 14.166 20.67 2.23 -41.47 2.23 5.08 0.67 BPL228_L07_A1_93_L07_226 GCS9 Cl* Melotte 22 BPL 228 +03 50 22.01 +23 55 30.3 0 17.259 16.399 15.694 15.108 14.699 14.699 20.94 2.26 -44.68 2.26 1.80 0.65 L07_A1_94 GCS9 +03 50 22.04 +22 37 32.3 0 14.088 13.717 13.128 12.578 12.281 12.280 13.50 2.51 -32.83 2.51 5.52 0.03 HCG399_DH666 GCS9 Cl* Melotte 22 HCG 399 +03 50 23.69 +24 25 43.6 0 17.469 16.705 16.019 15.399 15.045 14.969 9.85 2.30 -45.98 2.30 3.57 0.11 L07_221 GCS9 +03 50 24.96 +20 42 17.8 0 12.468 12.207 11.761 11.397 10.921 10.930 13.33 3.42 -33.72 3.42 3.00 0.11 DH668 GCS9 Cl* Melotte 22 DH 668 +03 50 25.16 +23 55 41.8 0 14.632 14.160 13.598 13.057 12.753 12.750 15.61 2.22 -41.36 2.22 1.44 0.90 HCG396_DH669 GCS9 Cl* Melotte 22 HCG 396 +03 50 27.35 +23 22 44.9 0 14.389 13.925 13.358 12.817 12.499 12.514 24.33 2.13 -40.44 2.13 0.74 0.74 SK273_DH670 GCS9 Cl* Melotte 22 SK 273 +03 50 27.83 +23 03 54.9 0 14.836 14.340 13.762 13.228 12.911 12.910 17.92 2.13 -41.80 2.13 0.78 0.94 DH671 GCS9 Cl* Melotte 22 DH 671 +03 50 29.30 +24 56 34.6 0 14.639 14.187 13.579 12.980 12.665 12.647 23.93 2.20 -30.32 2.20 2.43 0.00 BPL229_Moraux2003_58_L07_64 GCS9 Cl* Melotte 22 BPL 229 +03 50 29.96 +25 03 06.7 0 13.449 13.075 12.549 12.026 11.684 11.707 16.35 2.20 -39.64 2.20 0.61 0.87 HCG403_SK272_B213_BPL230_DH672 GCS9 Cl* Melotte 22 HCG 403 +03 50 31.95 +22 08 47.5 0 13.879 13.507 12.966 12.405 12.094 12.099 20.97 2.51 -37.91 2.51 1.62 0.75 HCG401_DH673 GCS9 Cl* Melotte 22 HCG 401 +03 50 32.58 +24 08 52.9 0 19.612 18.727 17.919 17.469 16.994 16.954 32.96 3.31 -13.95 3.31 1.26 0.00 Roque31 GCS9 Cl* Melotte 22 Roque 31 +03 50 33.08 +24 20 21.6 0 14.355 13.948 13.365 12.845 12.537 12.525 16.87 2.22 -41.49 2.22 4.47 0.93 BPL231_DH674 GCS9 Cl* Melotte 22 BPL 231 +03 50 37.42 +22 28 08.0 0 14.173 13.774 13.224 12.669 12.379 12.378 20.76 2.51 -39.13 2.51 1.02 0.87 HCG404_DH677 GCS9 Cl* Melotte 22 HCG 404 +03 50 38.92 +23 13 02.5 0 13.465 13.101 12.583 12.025 11.734 11.729 18.28 2.13 -37.84 2.13 1.49 0.79 SK263_DH678 GCS9 Cl* Melotte 22 SK 263 +03 50 39.33 +26 16 03.7 0 16.134 15.515 14.868 14.337 13.976 13.962 26.51 2.97 -45.26 2.97 0.34 0.09 DH680 GCS9 Cl* Melotte 22 DH 680 +03 50 40.83 +24 40 02.6 0 15.326 14.799 14.224 13.664 13.348 13.340 14.95 2.21 -46.17 2.21 2.17 0.78 DH681_Moraux2003_83_L07_70 GCS9 Cl* Melotte 22 DH 681 +03 50 42.44 +24 12 55.4 0 15.040 14.585 13.995 13.458 13.144 13.158 16.99 2.22 -39.63 2.22 2.68 0.85 DH683_Moraux2003_73_L07_101 GCS9 Cl* Melotte 22 DH 683 +03 50 44.35 +25 07 05.1 0 15.181 14.601 13.930 13.397 13.036 13.031 21.00 2.20 -44.44 2.20 1.71 0.84 L07_60 GCS9 +03 50 45.38 +25 06 10.5 0 14.663 14.213 13.627 13.059 12.768 12.744 12.87 2.20 -29.87 2.20 1.06 0.00 L07_59 GCS9 +03 50 48.97 +22 40 11.8 0 13.084 12.693 12.183 11.610 11.323 11.328 23.79 2.13 -30.12 2.13 1.45 0.00 HCG409_DH684 GCS9 Cl* Melotte 22 HCG 409 +03 50 54.65 +24 21 55.7 0 14.574 14.149 13.570 13.048 12.740 12.793 17.43 2.22 -47.29 2.22 26.89 0.87 DH687_Moraux2003_55_L07_102 GCS9 Cl* Melotte 22 DH 687 +03 50 54.99 +24 33 03.5 0 14.875 14.403 13.831 13.280 12.966 12.967 14.29 2.20 -41.34 2.20 8.04 0.85 Moraux2003_69 GCS9 Cl* Melotte 22 MBSC 69 +03 50 57.41 +24 24 44.4 0 15.215 14.612 13.988 13.487 13.097 13.117 15.13 2.21 -45.11 2.21 3.95 0.83 L07_81 GCS9 +03 50 57.42 +24 06 30.7 0 13.535 13.175 12.661 12.135 11.812 11.815 14.04 2.22 -40.35 2.22 7.91 0.80 HCG415_SK251_DH689_Moraux2003_15 GCS9 Cl* Melotte 22 HCG 415 +03 51 03.62 +24 32 35.1 0 14.283 13.819 13.257 12.749 12.420 12.445 15.93 2.20 -47.96 2.20 8.27 0.78 DH691 GCS9 Cl* Melotte 22 DH 691 +03 51 05.08 +26 36 51.2 0 15.454 14.963 14.358 13.825 13.479 13.495 12.35 2.96 -37.88 2.96 0.51 0.41 DH692_Moraux2003_93 GCS9 Cl* Melotte 22 DH 692 +03 51 05.97 +24 36 16.9 0 17.107 16.333 15.649 15.153 14.702 14.712 14.09 2.26 -37.47 2.26 5.62 0.34 L07_A1_95_L07_220 GCS9 +03 51 06.13 +22 38 00.6 0 14.992 14.482 13.846 13.298 12.962 12.927 17.14 2.51 -38.08 2.51 2.59 0.82 DH694 GCS9 Cl* Melotte 22 DH 694 +03 51 07.11 +23 20 57.6 0 14.729 14.274 13.683 13.132 12.833 12.817 15.82 2.25 -41.10 2.25 10.33 0.90 DH695 GCS9 Cl* Melotte 22 DH 695 +03 51 11.88 +23 44 43.3 0 15.371 14.840 14.253 13.708 13.396 13.374 16.86 2.22 -43.64 2.22 19.86 0.89 BPL232_Moraux2003_90 GCS9 Cl* Melotte 22 BPL 232 +03 51 17.96 +26 01 22.1 0 14.855 14.349 13.719 13.135 12.816 12.793 17.37 2.23 -39.97 2.23 7.12 0.91 DH701_L07_20 GCS9 Cl* Melotte 22 DH 701 +03 51 18.72 +26 03 15.4 0 14.748 14.262 13.654 13.395 12.768 12.736 15.24 2.23 -41.60 2.23 3.57 0.90 L07_21 GCS9 +03 51 18.86 +26 03 08.7 0 13.191 12.827 12.287 11.714 11.406 11.426 21.16 2.23 -42.78 2.23 6.61 0.92 SK237 GCS9 Cl* Melotte 22 SK 237 +03 51 19.07 +24 10 13.1 0 13.112 12.775 12.233 11.720 11.389 11.438 20.55 2.22 -42.37 2.22 1.13 0.92 HCG424_SK235_BPL233_DH702_Moraux2003_3 GCS9 Cl* Melotte 22 HCG 424 +03 51 19.48 +23 09 49.4 0 14.028 13.596 13.027 12.440 12.139 12.139 19.25 2.25 -39.56 2.25 1.60 0.90 DH703_L07_12 GCS9 Cl* Melotte 22 DH 703 +03 51 19.77 +23 04 04.1 0 15.411 14.858 14.210 13.638 13.312 13.329 16.38 2.26 -41.02 2.26 7.69 0.88 DH704 GCS9 Cl* Melotte 22 DH 704 +03 51 20.68 +19 38 37.8 0 13.526 13.101 12.519 12.042 11.710 11.730 21.01 3.97 -48.07 3.97 0.42 0.77 DH705 GCS9 Cl* Melotte 22 DH 705 +03 51 23.82 +22 50 29.1 0 12.115 11.877 11.498 11.311 10.620 10.885 17.91 2.25 -42.23 2.25 3.94 0.88 DH706 GCS9 Cl* Melotte 22 DH 706 +03 51 23.89 +25 05 52.6 0 13.484 13.107 12.577 12.065 11.730 11.738 14.49 2.20 -42.99 2.20 32.31 0.88 SK231_DH708 GCS9 Cl* Melotte 22 SK 231 +03 51 24.17 +26 03 11.3 0 13.441 13.064 12.529 11.943 11.651 11.655 16.49 2.23 -42.88 2.23 2.49 0.93 HCG427_SK232_T29_DH709 GCS9 Cl* Melotte 22 HCG 427 +03 51 25.88 +24 47 38.7 0 12.962 12.520 12.000 11.783 11.161 11.285 16.49 2.20 -47.46 2.20 11.49 0.46 HCG428_SK230_DH712 GCS9 Cl* Melotte 22 HCG 428 +03 51 30.60 +27 22 49.5 0 14.946 14.405 13.807 13.265 12.919 12.927 22.57 2.88 -41.78 2.88 0.73 0.89 DH714 GCS9 Cl* Melotte 22 DH 714 +03 51 34.01 +24 34 08.9 0 14.400 13.929 13.362 12.776 12.488 12.482 18.64 2.20 -46.47 2.20 0.67 0.90 DH715_Moraux2003_46 GCS9 Cl* Melotte 22 DH 715 +03 51 36.41 +25 13 40.6 0 14.020 13.545 12.926 12.379 12.066 12.042 16.43 2.20 -40.71 2.20 2.11 0.91 DH717 GCS9 Cl* Melotte 22 DH 717 +03 51 38.96 +24 30 44.8 1 18.711 17.407 16.400 15.718 15.168 15.122 23.01 2.34 -40.47 2.34 14.05 0.64 L07_A1_97_L07_253 GCS9 +03 51 39.08 +23 22 04.3 0 15.003 14.530 13.931 13.387 13.058 13.060 21.33 2.25 -42.78 2.25 10.84 0.85 BPL237_L07_166 GCS9 Cl* Melotte 22 BPL 237 +03 51 42.34 +25 57 25.6 0 16.214 15.645 14.965 14.400 14.005 13.990 13.59 2.25 -34.32 2.25 4.49 0.06 L07_A1_98 GCS9 +03 51 44.95 +23 26 39.3 0 17.791 16.894 16.036 15.417 14.953 14.967 16.26 2.37 -39.72 2.37 9.87 0.62 BPL240_L07_A1_99_L07_240 GCS9 Cl* Melotte 22 BPL 240 +03 51 46.56 +23 23 34.8 0 13.746 13.429 12.922 12.317 12.066 12.079 28.17 2.25 -4.26 2.25 10.21 0.00 BPL241 GCS9 Cl* Melotte 22 BPL 241 +03 51 47.66 +24 39 58.9 0 20.158 18.804 17.528 16.773 16.100 16.094 14.64 2.71 -40.22 2.71 4.12 0.52 PLZJ21_PLIZ31 GCS9 Cl* Melotte 22 PlZJ 21 +03 51 50.60 +22 53 44.3 0 13.892 13.435 12.910 12.329 12.055 12.069 13.26 2.25 -40.14 2.25 2.45 0.72 HCG437_SK212_DH724_L07_14 GCS9 Cl* Melotte 22 HCG 437 +03 51 51.15 +23 17 41.1 0 13.445 13.060 12.527 12.014 11.711 11.750 17.13 2.25 -44.72 2.25 21.53 0.93 L07_13 GCS9 +03 51 51.55 +23 34 49.1 0 15.431 14.857 14.260 13.768 13.385 13.408 17.89 2.22 -44.43 2.22 19.31 0.89 BPL242_CFHT-Pl-1_M4.9_Moraux2003_91_BRB1_CFHT-Pl-1_L07_140 GCS9 Cl* Melotte 22 BPL 242 +03 51 54.52 +23 33 31.3 0 13.694 13.230 12.636 12.103 11.768 11.791 15.96 2.24 -48.77 2.24 31.11 0.70 HCG439_SK210_BPL244_DH727_Moraux2003_23 GCS9 Cl* Melotte 22 HCG 439 +03 51 55.05 +23 57 42.0 0 14.510 14.067 13.483 12.983 12.641 12.651 15.21 2.22 -46.11 2.22 4.30 0.86 HCG440_BPL245_DH728_L07_119 GCS9 Cl* Melotte 22 HCG 440 +03 51 57.53 +25 48 31.2 0 14.094 13.689 13.121 12.578 12.276 12.281 17.62 2.23 -42.66 2.23 7.40 0.94 HCG446_SK207_DH731 GCS9 Cl* Melotte 22 HCG 446 +03 51 58.35 +23 58 19.3 0 14.595 14.132 13.556 12.990 12.694 12.669 11.97 2.24 -42.06 2.24 1.15 0.65 BPL246_DH732_L07_120 GCS9 Cl* Melotte 22 BPL 246 +03 51 59.32 +24 39 58.8 0 13.828 13.385 12.845 12.293 12.017 12.027 13.69 2.20 -41.98 2.20 0.80 0.83 Moraux2003_28 GCS9 Cl* Melotte 22 MBSC 28 +03 51 59.93 +23 24 25.6 0 19.672 18.405 17.376 16.709 16.210 16.167 44.15 3.19 6.95 3.19 5.04 0.00 L07_photNM_15 GCS9 +03 52 01.65 +25 01 29.1 0 14.402 13.991 13.422 12.897 12.576 12.602 14.95 2.20 -43.92 2.20 3.33 0.89 HCG447_BPL248_DH733 GCS9 Cl* Melotte 22 HCG 447 +03 52 02.10 +23 15 45.4 0 18.708 17.679 16.659 16.057 15.473 15.529 12.75 2.53 -39.96 2.53 16.77 0.44 BPL249_L07_A1_100_L07_255 GCS9 Cl* Melotte 22 BPL 249 +03 52 02.28 +24 21 47.9 0 12.898 12.582 12.168 11.631 11.353 11.429 22.99 2.24 -48.15 2.24 4.71 0.28 SK204_DH734 GCS9 Cl* Melotte 22 SK 204 +03 52 02.64 +25 06 14.9 0 14.751 14.296 13.684 13.158 12.836 12.836 16.37 2.20 -40.83 2.20 2.28 0.91 BPL250_DH736_L07_57 GCS9 Cl* Melotte 22 BPL 250 +03 52 03.58 +25 01 13.6 0 14.781 14.353 13.772 13.239 12.900 12.934 15.18 2.20 -43.42 2.20 3.16 0.90 BPL251_DH737 GCS9 Cl* Melotte 22 BPL 251 +03 52 03.62 +23 17 21.3 0 14.608 14.228 13.716 13.085 12.840 12.838 8.56 2.26 -19.96 2.26 73.82 0.00 SK202 GCS9 Cl* Melotte 22 SK 202 +03 52 04.48 +24 14 39.6 0 14.840 14.365 13.783 13.223 12.926 12.897 16.25 2.24 -41.96 2.24 2.51 0.92 BPL252_DH739_L07_91 GCS9 Cl* Melotte 22 BPL 252 +03 52 05.58 +22 34 55.1 0 13.116 12.817 12.304 11.756 11.447 11.468 18.18 2.51 -46.19 2.51 1.01 0.91 SK201_DH740 GCS9 Cl* Melotte 22 SK 201 +03 52 05.83 +24 17 31.0 0 16.512 15.860 15.176 14.618 14.251 14.263 15.66 2.27 -40.51 2.27 2.70 0.67 BPL253_CFHT-Pl-7_M5.6_Moraux2003_108_BRB8_CFHT-Pl-7_L07_A1_101_L07_225 GCS9 Cl* Melotte 22 BPL 253 +03 52 06.67 +22 01 14.3 0 14.997 14.501 13.944 13.407 13.089 13.080 19.27 2.51 -49.89 2.51 1.48 0.62 DH741 GCS9 Cl* Melotte 22 DH 741 +03 52 06.72 +24 16 00.4 0 17.079 16.255 15.515 14.971 14.507 14.538 18.87 2.29 -40.00 2.29 6.89 0.68 BPL254_CFHT-Pl-13_M6.0_BRB11_CFHT-Pl-13,Teide2,CFHT-PLIZ-3_L07_A1_102_L07_224 GCS9 Cl* Melotte 22 BPL 254 +03 52 07.43 +25 53 02.7 0 13.127 12.750 12.267 11.768 11.478 11.454 17.32 2.93 -39.44 2.93 1.02 0.88 L07_2 GCS9 +03 52 07.96 +25 27 54.6 0 15.246 14.722 14.111 13.567 13.243 13.214 15.55 2.94 -37.68 2.94 0.42 0.67 HCG449_BPL255_DH742 GCS9 Cl* Melotte 22 HCG 449 +03 52 11.01 +24 09 21.6 0 14.675 14.287 13.746 13.155 12.875 12.891 19.68 2.24 -25.40 2.24 3.06 0.00 BPL256 GCS9 Cl* Melotte 22 BPL 256 +03 52 11.23 +20 52 33.4 0 14.537 14.059 13.462 12.929 12.611 12.655 19.77 3.43 -45.98 3.43 0.93 0.91 DH743 GCS9 Cl* Melotte 22 DH 743 +03 52 12.19 +26 22 08.9 0 13.194 12.777 12.227 11.737 11.397 11.430 14.22 2.95 -44.48 2.95 1.31 0.86 HCG452_DH744 GCS9 Cl* Melotte 22 HCG 452 +03 52 13.20 +26 11 45.3 0 13.003 12.662 12.154 11.606 11.285 11.326 20.95 2.95 -46.17 2.95 1.13 0.88 DH745 GCS9 Cl* Melotte 22 DH 745 +03 52 13.32 +26 08 36.8 0 13.531 13.173 12.637 12.086 11.799 11.827 13.73 2.93 -41.65 2.93 0.47 0.82 HCG451_SK197_DH746_L07_1 GCS9 Cl* Melotte 22 HCG 451 +03 52 15.01 +24 20 23.7 0 12.845 12.510 11.997 11.537 11.142 11.317 25.52 2.24 -17.36 2.24 2.89 0.00 BPL257 GCS9 Cl* Melotte 22 BPL 257 +03 52 15.20 +27 56 37.5 0 15.781 15.372 14.821 14.296 14.065 14.036 35.34 3.01 -42.14 3.01 0.77 0.00 DH747 GCS9 Cl* Melotte 22 DH 747 +03 52 17.44 +25 06 11.8 0 15.000 14.563 13.990 13.419 13.132 13.135 -0.61 2.20 -35.73 2.20 4.16 0.00 BPL258 GCS9 Cl* Melotte 22 BPL 258 +03 52 17.54 +24 27 19.7 0 14.481 13.947 13.400 12.888 12.573 12.615 5.97 2.20 -33.38 2.20 95.51 0.00 BPL259_DH748_Moraux2003_42 GCS9 Cl* Melotte 22 BPL 259 +03 52 18.46 +22 00 53.3 0 13.024 12.707 12.225 11.616 11.369 11.393 15.81 2.51 -42.10 2.51 0.71 0.91 DH749 GCS9 Cl* Melotte 22 DH 749 +03 52 18.64 +24 04 28.1 0 18.327 17.280 16.395 15.738 15.202 15.738 15.37 2.40 -46.07 2.40 9.62 0.50 CFHT-Pl-23_XX GCS9 Cl* Melotte 22 CFHT 23 +03 52 18.72 +23 52 36.6 0 15.095 14.577 13.986 13.440 13.130 13.140 15.56 2.25 -45.85 2.25 6.65 0.82 BPL260_L07_127 GCS9 Cl* Melotte 22 BPL 260 +03 52 20.66 +24 33 55.5 0 12.822 12.483 11.971 11.439 11.148 11.166 14.85 2.23 -40.95 2.23 5.83 0.78 HCG454_SK188_T159_DH750 GCS9 Cl* Melotte 22 HCG 454 +03 52 25.93 +21 50 31.5 0 13.817 13.529 13.062 12.397 12.193 12.161 16.43 2.51 -38.84 2.51 1.83 0.83 DH752 GCS9 Cl* Melotte 22 DH 752 +03 52 30.54 +19 40 39.6 0 14.510 14.026 13.382 12.839 12.507 12.549 19.00 3.98 -35.62 3.98 0.89 0.55 DH753 GCS9 Cl* Melotte 22 DH 753 +03 52 30.91 +24 32 39.5 0 13.409 12.945 12.392 11.866 11.560 11.576 14.80 2.23 -43.14 2.23 0.86 0.89 HCG456_SK178_BPL261_DH754_Moraux2003_12 GCS9 Cl* Melotte 22 HCG 456 +03 52 31.38 +25 15 07.5 0 13.670 13.259 12.739 12.160 11.887 11.901 16.76 2.23 -45.34 2.23 7.95 0.92 SK179_BPL262_DH755 GCS9 Cl* Melotte 22 SK 179 +03 52 31.60 +25 19 49.5 0 16.266 15.764 15.163 14.645 14.313 14.280 33.73 2.98 -58.22 2.98 0.55 0.00 BPL263 GCS9 Cl* Melotte 22 BPL 263 +03 52 33.34 +23 51 06.7 0 14.404 13.944 13.367 12.802 12.521 12.543 17.77 2.24 -41.30 2.24 5.90 0.93 BPL264_DH756_L07_126 GCS9 Cl* Melotte 22 BPL 264 +03 52 34.48 +22 30 07.5 0 13.080 12.786 12.289 11.679 11.396 11.423 16.37 2.51 -36.85 2.51 1.65 0.63 HCG458_SK174_sk174_DH757 GCS9 Cl* Melotte 22 HCG 458 +03 52 35.32 +25 01 04.5 0 14.769 14.195 13.590 13.034 12.700 12.703 17.35 2.23 -42.19 2.23 2.24 0.94 BPL265_DH758_L07_61 GCS9 Cl* Melotte 22 BPL 265 +03 52 38.91 +25 50 25.3 0 14.054 13.642 13.075 12.528 12.203 12.193 19.53 2.93 -39.16 2.93 0.35 0.89 HCG461_HHJ244_DH760_Moraux2003_30 GCS9 Cl* Melotte 22 HCG 461 +03 52 41.82 +26 46 10.5 0 14.924 14.450 13.835 13.290 12.967 12.980 15.22 2.96 -48.00 2.96 0.18 0.74 DH761 GCS9 Cl* Melotte 22 DH 761 +03 52 42.21 +25 10 42.2 0 15.719 15.130 14.525 13.956 13.610 13.606 7.18 2.24 -43.36 2.24 13.94 0.05 DH762 GCS9 Cl* Melotte 22 DH 762 +03 52 43.24 +24 27 58.5 0 14.755 14.266 13.689 13.131 12.827 12.833 17.78 2.23 -41.41 2.23 1.73 0.93 BPL266_DH763_L07_82 GCS9 Cl* Melotte 22 BPL 266 +03 52 43.47 +27 28 20.7 0 15.621 15.237 14.702 14.167 13.881 13.877 19.52 3.32 -29.27 3.32 0.59 0.00 DH764 GCS9 Cl* Melotte 22 DH 764 +03 52 44.29 +23 54 14.9 0 15.738 15.184 14.554 14.000 13.654 13.683 15.71 2.25 -44.96 2.25 4.54 0.86 BPL267_DH765_CFHT-Pl-2_M4.9_BRB2_CFHT-Pl-2_L07_128 GCS9 Cl* Melotte 22 BPL 267 +03 52 44.48 +24 20 59.3 0 15.025 14.546 13.944 13.403 13.091 13.085 16.54 2.25 -40.61 2.25 2.44 0.87 BPL268_DH766_Moraux2003_70 GCS9 Cl* Melotte 22 BPL 268 +03 52 46.45 +24 33 41.0 0 14.544 14.050 13.491 12.957 12.649 12.654 16.70 2.23 -37.50 2.23 1.87 0.76 BPL270_Moraux2003_47_L07_83 GCS9 Cl* Melotte 22 BPL 270 +03 52 51.72 +22 31 32.7 0 13.700 13.329 12.777 12.199 11.894 11.930 19.14 2.51 -43.95 2.51 1.02 0.94 HCG462_SK150_HHJ302_DH770 GCS9 Cl* Melotte 22 HCG 462 +03 52 51.79 +23 33 47.9 0 15.894 15.316 14.662 14.124 13.742 20.21 2.31 -42.32 2.31 4.57 0.88 HHJ22_BPL272_Moraux2003_99_BRB5_CFHT-Pl-3_MBSC99_L07_148 GCS9 Cl* Melotte 22 HHJ 22 +03 52 54.25 +25 17 43.3 0 14.245 13.815 13.273 12.729 12.417 12.415 19.70 2.93 -46.66 2.93 0.44 0.89 HCG464_SK151_BPL274_DH771 GCS9 Cl* Melotte 22 HCG 464 +03 52 55.92 +24 57 41.8 0 16.326 15.757 15.109 14.535 14.152 14.141 12.56 2.25 -44.02 2.25 2.34 0.48 BPL275_L07_A1_103_L07_252 GCS9 Cl* Melotte 22 BPL 275 +03 52 56.97 +22 26 01.1 0 13.248 12.945 12.403 11.839 11.543 11.594 20.34 2.51 -44.90 2.51 0.86 0.92 HCG463_SK143_HHJ358_DH772 GCS9 Cl* Melotte 22 HCG 463 +03 52 58.78 +25 26 19.0 0 15.258 14.748 14.176 13.639 13.295 13.303 17.32 2.94 -42.46 2.94 0.26 0.90 BPL277 GCS9 Cl* Melotte 22 BPL 277 +03 52 59.48 +22 46 59.9 0 16.051 15.626 15.081 14.514 14.235 14.263 6.77 2.28 -42.20 2.28 15.58 0.02 L07_192 GCS9 +03 53 01.63 +22 58 48.2 0 14.463 13.982 13.388 12.835 12.536 12.506 18.23 2.25 -41.99 2.25 16.24 0.94 HHJ181_DH776_L07_183 GCS9 Cl* Melotte 22 HHJ 181 +03 53 05.13 +25 04 15.6 0 15.954 15.369 14.759 14.236 13.861 13.829 7.81 2.24 -28.58 2.24 19.42 0.00 HHJ13_BPL278 GCS9 Cl* Melotte 22 HHJ 13 +03 53 07.29 +25 47 21.4 0 15.338 14.821 14.219 13.639 13.302 13.309 16.97 2.94 -34.85 2.94 1.34 0.32 HHJ53_DH778_Moraux2003_89 GCS9 Cl* Melotte 22 HHJ 53 +03 53 07.48 +25 18 03.4 0 15.833 15.247 14.597 14.025 13.647 13.618 2.66 2.95 -37.03 2.95 0.59 0.00 BPL279 GCS9 Cl* Melotte 22 BPL 279 +03 53 09.63 +23 33 47.7 0 16.108 15.503 14.823 14.280 13.916 19.95 2.32 -45.65 2.32 0.56 0.63 BPL280_CFHT-Pl-4_XX_Moraux2003_101_BRB6_CFHT-Pl-4,MBSC101 GCS9 Cl* Melotte 22 BPL 280 +03 53 10.16 +23 03 10.6 0 13.483 13.099 12.546 11.958 11.672 11.670 16.27 2.25 -43.64 2.25 0.47 0.93 HHJ330_DH779 GCS9 Cl* Melotte 22 HHJ 330 +03 53 12.52 +25 48 17.1 0 16.111 15.665 15.069 14.499 14.163 14.185 20.43 2.98 -8.43 2.98 0.61 0.00 Moraux2003_106 GCS9 Cl* Melotte 22 MBSC 106 +03 53 14.00 +19 42 59.3 0 15.849 15.386 14.792 14.189 13.888 13.898 9.11 4.02 -47.37 4.02 0.59 0.10 DH780 GCS9 Cl* Melotte 22 DH 780 +03 53 15.71 +22 52 14.3 0 13.571 13.174 12.615 12.057 11.772 11.797 16.50 2.25 -42.99 2.25 20.38 0.93 HCG467_SK134_B369_HHJ323_DH781_L07_15 GCS9 Cl* Melotte 22 HCG 467 +03 53 16.44 +23 20 58.1 0 15.199 14.687 14.094 13.508 13.225 13.211 16.27 2.26 -38.96 2.26 2.72 0.80 BPL281_L07_154 GCS9 Cl* Melotte 22 BPL 281 +03 53 21.68 +24 03 26.6 0 15.326 14.768 14.128 13.552 13.218 7.55 2.31 -30.24 2.31 2.14 0.00 BPL282 GCS9 Cl* Melotte 22 BPL 282 +03 53 24.12 +23 47 58.4 0 14.439 13.967 13.383 12.825 12.527 12.529 15.64 2.24 -40.80 2.24 4.05 0.89 HHJ173_BPL285_DH784_Moraux2003_39_L07_131 GCS9 Cl* Melotte 22 HHJ 173 +03 53 24.24 +25 14 37.7 0 16.763 16.041 15.329 14.792 14.388 14.383 18.76 2.27 -40.84 2.27 18.74 0.71 L07_A1_105 GCS9 +03 53 26.54 +24 46 44.8 0 16.063 15.580 14.980 14.429 14.130 14.105 -15.85 2.25 -65.72 2.25 2.82 0.00 L07_photNM_24 GCS9 +03 53 30.76 +19 54 24.1 0 13.671 13.329 12.773 12.177 11.869 11.915 21.18 3.97 -41.70 3.97 0.52 0.91 DH787 GCS9 Cl* Melotte 22 DH 787 +03 53 35.52 +26 07 08.0 0 14.722 14.217 13.594 13.076 12.734 12.735 14.81 2.94 -41.03 2.94 0.36 0.87 HHJ124_DH790_Moraux2003_64 GCS9 Cl* Melotte 22 HHJ 124 +03 53 44.53 +24 00 42.4 0 16.464 15.928 15.311 14.743 14.396 21.83 2.33 -19.33 2.33 2.88 0.00 BPL289 GCS9 Cl* Melotte 22 BPL 289 +03 53 47.58 +23 44 30.9 0 14.156 13.695 13.118 12.583 12.291 25.83 2.30 -36.52 2.30 12.91 0.18 HCG474_SK115_HHJ237_BPL290_Moraux2003_88 GCS9 Cl* Melotte 22 HCG 474 +03 53 48.04 +23 49 09.6 0 15.701 15.021 14.330 13.772 13.399 13.416 19.15 2.25 -46.61 2.25 7.95 0.81 HHJ28_BPL291 GCS9 Cl* Melotte 22 HHJ 28 +03 53 48.14 +23 58 12.3 0 16.128 15.626 15.014 14.480 14.122 45.32 2.32 -23.11 2.32 16.02 0.00 BPL292 GCS9 Cl* Melotte 22 BPL 292 +03 53 48.49 +23 29 09.3 0 13.958 13.639 13.144 12.476 12.276 12.276 22.11 2.25 -50.69 2.25 1.26 0.30 L07_11 GCS9 +03 53 49.10 +23 32 49.5 0 16.498 15.857 15.188 14.669 14.312 14.255 20.11 2.27 -19.91 2.27 1.92 0.00 BPL293 GCS9 Cl* Melotte 22 BPL 293 +03 53 51.53 +23 37 32.4 0 15.072 14.583 13.986 13.438 13.123 13.114 10.35 2.25 -31.49 2.25 0.90 0.00 L07_142 GCS9 +03 53 52.46 +22 37 34.1 0 13.916 13.447 12.840 12.297 11.984 11.987 21.25 2.50 -22.78 2.50 0.32 0.00 SK109 GCS9 Cl* Melotte 22 SK 109 +03 53 55.13 +23 23 36.1 1 16.947 16.027 15.172 14.569 14.088 14.081 19.17 2.28 -44.72 2.28 8.66 0.70 BPL294_CFHT-Pl-12_M8.0_BRB9_CFHT-Pl-12,CFHT-PLIZ-6_PLZJ9_PLIZ6 GCS9 Cl* Melotte 22 BPL 294 +03 54 00.71 +23 58 59.8 0 13.647 13.247 12.690 12.150 11.841 11.858 18.82 2.24 -49.03 2.24 7.85 0.73 HHJ322_BPL298 GCS9 Cl* Melotte 22 HHJ 322 +03 54 01.12 +23 19 41.0 0 15.933 15.380 14.713 14.186 13.841 13.816 15.07 2.27 -45.30 2.27 12.64 0.83 HHJ20_BPL299 GCS9 Cl* Melotte 22 HHJ 20 +03 54 02.73 +23 35 00.9 0 15.007 14.465 13.863 13.314 12.994 12.988 15.75 2.25 -46.40 2.25 6.97 0.80 BPL301_Moraux2003_68_L07_141 GCS9 Cl* Melotte 22 BPL 301 +03 54 04.89 +25 05 06.2 0 14.698 14.348 13.849 13.358 13.082 13.085 33.90 2.23 -56.38 2.23 10.70 0.00 BPL302 GCS9 Cl* Melotte 22 BPL 302 +03 54 05.35 +23 33 59.2 0 18.651 17.573 16.647 15.964 15.434 15.462 15.06 2.46 -40.45 2.46 5.84 0.61 BPL303_CFHT-Pl-25_M9.0_BRB15_CFHT-PLIZ-20,PLZJ11_L07_A1_106_L07_254 GCS9 Cl* Melotte 22 BPL 303 +03 54 11.49 +25 18 42.7 0 14.598 14.131 13.552 13.030 12.710 12.697 22.79 2.94 -43.58 2.94 0.56 0.88 HCG484_BPL304_DH796 GCS9 Cl* Melotte 22 HCG 484 +03 54 13.02 +23 20 50.8 0 13.540 13.162 12.611 12.064 11.777 11.770 19.39 2.25 -43.85 2.25 1.02 0.94 HCG482_SK97_HHJ325_BPL305_DH797_Moraux2003_13 GCS9 Cl* Melotte 22 HCG 482 +03 54 14.07 +23 17 51.9 0 20.028 18.873 17.601 16.818 16.138 16.100 17.07 3.03 -38.21 3.03 5.30 0.47 BRB18_L0.0_CFHT-PLIZ-28 GCS9 Cl* Melotte 22 BRB 18 +03 54 14.18 +25 37 57.2 0 13.687 13.351 12.846 12.276 12.008 11.988 15.24 2.93 -23.51 2.93 0.25 0.00 SK99_HHJ318 GCS9 Cl* Melotte 22 SK 99 +03 54 15.29 +25 09 52.2 0 17.907 17.026 16.179 15.579 15.102 15.061 14.10 2.34 -32.82 2.34 5.56 0.04 BPL306_L07_A1_107 GCS9 Cl* Melotte 22 BPL 306 +03 54 15.60 +24 20 45.7 0 15.915 15.353 14.671 14.117 13.832 13.778 15.59 2.25 -35.25 2.25 4.55 0.33 BPL307_BPL308_Moraux2003_96 GCS9 Cl* Melotte 22 BPL 307 +03 54 16.80 +22 00 16.6 0 15.893 15.489 14.921 14.365 14.077 14.066 16.81 2.53 -37.22 2.53 1.97 0.67 DH799 GCS9 Cl* Melotte 22 DH 799 +03 54 19.69 +24 41 52.0 0 13.851 13.644 13.223 12.660 12.539 12.515 24.74 2.23 -26.36 2.23 1.62 0.00 L07_6 GCS9 +03 54 21.23 +23 23 48.9 0 13.899 13.505 12.946 12.350 12.071 12.051 17.29 2.25 -14.89 2.25 10.35 0.00 BPL310 GCS9 Cl* Melotte 22 BPL 310 +03 54 22.49 +23 38 11.9 0 14.441 13.982 13.383 12.800 12.511 12.520 12.94 2.24 -43.81 2.24 7.78 0.77 HHJ214_BPL311_DH801_Moraux2003_38_L07_143 GCS9 Cl* Melotte 22 HHJ 214 +03 54 24.00 +23 51 58.7 0 15.891 15.288 14.667 14.110 13.776 13.780 14.24 2.25 -48.04 2.25 12.19 0.58 L07_123 GCS9 +03 54 24.39 +22 41 20.5 0 14.947 14.441 13.840 13.254 12.936 12.943 20.19 2.26 -45.53 2.26 6.55 0.92 L07_190 GCS9 +03 54 25.04 +24 42 43.4 0 14.895 14.283 13.653 13.120 12.763 12.750 20.21 2.23 -42.84 2.23 3.32 0.94 HCG487_HHJ131_BPL312_DH802_Moraux2003_67_L07_71 GCS9 Cl* Melotte 22 HCG 487 +03 54 28.11 +23 56 36.0 0 15.731 15.152 14.518 13.983 13.642 13.637 15.38 2.25 -40.85 2.25 2.36 0.85 BPL313 GCS9 Cl* Melotte 22 BPL 313 +03 54 31.49 +22 39 01.5 0 16.554 15.843 15.155 14.573 14.190 14.206 13.09 2.29 -41.56 2.29 3.40 0.53 L07_A1_108_L07_244 GCS9 +03 54 33.14 +23 33 39.5 0 13.404 13.138 12.649 12.026 11.829 11.837 29.86 2.24 -16.03 2.24 2.59 0.00 BPL314 GCS9 Cl* Melotte 22 BPL 314 +03 54 34.80 +21 53 01.7 0 12.781 11.982 11.442 11.104 11.183 19.60 2.62 -38.65 2.62 5.77 0.87 HCG488_DH804 GCS9 Cl* Melotte 22 HCG 488 +03 54 37.36 +23 13 32.5 0 14.469 14.018 13.413 12.815 12.514 12.535 19.90 2.25 -37.77 2.25 7.31 0.81 DH805 GCS9 Cl* Melotte 22 DH 805 +03 54 39.13 +24 35 54.5 0 15.791 15.217 14.572 14.039 13.702 13.694 20.01 2.24 -43.73 2.24 3.51 0.88 BPL315_DH806_Moraux2003_103 GCS9 Cl* Melotte 22 BPL 315 +03 54 40.56 +23 42 23.7 0 16.133 15.654 15.075 14.441 14.147 14.128 21.00 2.26 -8.72 2.26 2.22 0.00 L07_PM_NM_45 GCS9 +03 54 46.39 +25 02 58.2 0 15.875 15.298 14.639 14.106 13.746 13.746 12.58 2.24 -33.12 2.24 3.87 0.03 BPL317 GCS9 Cl* Melotte 22 BPL 317 +03 54 46.53 +25 31 34.9 0 14.741 14.146 13.512 12.988 12.618 12.627 21.22 2.94 -45.56 2.94 0.23 0.90 DH808 GCS9 Cl* Melotte 22 DH 808 +03 54 48.00 +25 12 30.2 0 13.344 13.058 12.571 11.963 11.714 11.736 19.70 2.23 -39.13 2.23 9.47 0.87 SK82_HHJ371_BPL318_DH809 GCS9 Cl* Melotte 22 SK 82 +03 54 55.34 +22 59 40.1 0 15.419 14.857 14.201 13.635 13.303 13.270 13.51 2.26 -44.04 2.26 3.01 0.78 HHJ62 GCS9 Cl* Melotte 22 HHJ 62 +03 54 58.03 +25 14 29.0 0 13.800 13.401 12.825 12.253 11.966 11.990 13.55 2.23 -44.32 2.23 3.97 0.82 SK76_BPL321_DH810 GCS9 Cl* Melotte 22 SK 76 +03 54 59.43 +23 58 07.6 0 18.399 17.751 17.008 16.573 16.182 16.573 -15.56 2.74 -76.49 2.74 2.13 0.00 CFHT-Pl-22_XX GCS9 Cl* Melotte 22 CFHT 22 +03 55 03.69 +25 13 16.7 0 15.227 14.748 14.150 13.541 13.229 13.257 24.07 2.23 -25.82 2.23 2.86 0.00 BPL322 GCS9 Cl* Melotte 22 BPL 322 +03 55 05.75 +23 03 17.1 0 17.281 16.747 16.124 15.457 15.159 15.148 22.55 2.35 -23.42 2.35 7.78 0.00 DH811 GCS9 Cl* Melotte 22 DH 811 +03 55 07.09 +24 17 25.7 0 13.823 13.494 12.962 12.312 12.069 12.072 45.79 2.24 -23.35 2.24 9.80 0.00 Moraux2003_24 GCS9 Cl* Melotte 22 MBSC 24 +03 55 08.98 +24 05 02.5 0 13.699 13.306 12.734 12.148 11.890 16.37 2.30 -40.35 2.30 2.15 0.90 SK65_HHJ313_BPL324_DH812_Moraux2003_18 GCS9 Cl* Melotte 22 SK 65 +03 55 10.57 +23 40 30.8 0 16.148 15.629 15.011 14.450 14.138 14.42 2.32 -24.94 2.32 4.22 0.00 Moraux2003_107 GCS9 Cl* Melotte 22 MBSC 107 +03 55 11.85 +22 58 02.5 0 15.060 14.458 13.794 13.249 12.894 12.890 18.52 2.26 -46.52 2.26 8.86 0.83 HHJ61 GCS9 Cl* Melotte 22 HHJ 61 +03 55 17.17 +23 53 16.7 0 16.639 15.930 15.233 14.684 14.291 14.302 -3.54 2.27 -45.04 2.27 8.18 0.00 BPL325 GCS9 Cl* Melotte 22 BPL 325 +03 55 20.68 +22 32 08.6 0 14.849 14.456 13.903 13.290 13.023 13.017 25.35 2.51 -36.82 2.51 2.55 0.26 HHJ144_DH814 GCS9 Cl* Melotte 22 HHJ 144 +03 55 23.08 +24 49 04.9 0 17.087 16.311 15.528 15.013 14.595 14.571 19.50 2.28 -42.09 2.28 7.67 0.72 BPL327_IPMBD11_PLZJ78_PLIZ2 GCS9 Cl* Melotte 22 BPL 327 +03 55 24.90 +23 27 21.2 0 14.218 13.952 13.438 12.772 12.577 12.555 17.19 2.25 -18.72 2.25 12.58 0.00 L07_165 GCS9 +03 55 27.06 +25 14 45.8 1 16.118 15.402 14.649 14.071 13.671 13.650 15.24 2.24 -39.66 2.24 7.07 0.60 BPL328_L07_A1_111_L07_216 GCS9 Cl* Melotte 22 BPL 328 +03 55 30.90 +23 23 50.9 0 13.961 13.590 13.001 12.425 12.158 12.161 18.03 2.25 -35.87 2.25 14.93 0.52 SK54_HHJ284_BPL329_DH815_Moraux2003_32 GCS9 Cl* Melotte 22 SK 54 +03 55 32.84 +23 19 08.0 0 12.548 12.391 11.984 11.529 11.394 11.417 25.21 2.25 -40.35 2.25 7.73 0.61 DH2004_816 GCS9 Cl* Melotte 22 DH 816 +03 55 34.43 +23 58 28.3 0 15.140 14.664 14.047 13.501 13.196 11.66 2.31 -40.75 2.31 6.79 0.55 HHJ78_BPL330_DH818_Moraux2003_80 GCS9 Cl* Melotte 22 HHJ 78 +03 55 44.34 +23 23 26.3 0 14.936 14.679 14.164 13.488 13.317 13.333 24.36 2.26 -31.05 2.26 5.80 0.00 L07_164 GCS9 +03 55 47.14 +25 14 39.5 0 17.252 16.517 15.773 15.223 14.793 14.775 11.42 2.29 -27.04 2.29 4.43 0.00 L07_A1_112 GCS9 +03 55 47.45 +22 50 50.1 0 19.943 18.550 17.433 16.681 16.086 16.123 15.62 2.97 -46.57 2.97 3.85 0.63 L07_A1_113 GCS9 +03 55 49.92 +23 48 22.5 0 15.778 15.219 14.575 14.013 13.650 13.627 40.52 2.97 -15.60 2.97 0.28 0.00 BPL332 GCS9 Cl* Melotte 22 BPL 332 +03 55 50.11 +21 43 27.7 0 13.074 12.662 12.128 11.773 11.254 11.294 11.27 3.36 -39.08 3.36 0.38 0.35 DH819 GCS9 Cl* Melotte 22 DH 819 +03 55 56.43 +25 17 59.8 0 13.549 13.137 12.580 11.969 11.690 11.703 19.79 2.93 -43.08 2.93 0.63 0.94 HCG504_SK37_DH822 GCS9 Cl* Melotte 22 HCG 504 +03 55 57.55 +21 10 38.7 0 13.543 13.117 12.574 12.061 11.714 11.762 6.82 3.36 -22.12 3.36 1.12 0.00 DH823 GCS9 Cl* Melotte 22 DH 823 +03 56 09.81 +22 27 59.3 0 13.653 13.341 12.818 12.142 11.912 11.933 27.43 2.50 -42.46 2.50 0.16 0.22 HHJ385 GCS9 Cl* Melotte 22 HHJ 385 +03 56 18.60 +23 57 51.6 0 12.871 12.580 12.095 11.500 11.219 11.290 20.17 2.96 -40.40 2.96 0.46 0.89 SK22_HHJ386_DH830 GCS9 Cl* Melotte 22 SK 22 +03 56 22.31 +21 07 16.9 0 13.818 13.393 12.829 12.286 11.984 12.076 13.77 3.36 -36.78 3.36 0.54 0.40 DH831 GCS9 Cl* Melotte 22 DH 831 +03 56 22.39 +24 37 22.8 0 17.293 16.827 16.215 15.615 15.296 15.316 19.53 2.64 -22.35 2.64 1.45 0.00 DH832 GCS9 Cl* Melotte 22 DH 832 +03 56 24.99 +23 05 26.6 0 12.641 12.387 11.904 11.372 11.058 11.087 17.85 2.99 -45.85 2.99 1.45 0.72 SK17_HHJ419 GCS9 Cl* Melotte 22 SK 17 +03 56 25.93 +24 16 51.3 0 12.568 12.306 11.841 11.303 10.972 11.236 22.45 2.96 -39.95 2.96 0.24 0.83 SK18_HHJ416_DH834 GCS9 Cl* Melotte 22 SK 18 +03 56 28.91 +24 01 54.1 0 14.448 14.038 13.469 12.930 12.618 12.620 19.11 2.96 -40.90 2.96 0.55 0.93 HHJ165_DH837_Moraux2003_41 GCS9 Cl* Melotte 22 HHJ 165 +03 56 36.56 +25 15 52.3 0 12.102 12.000 11.678 11.535 11.250 11.370 20.72 2.47 -47.20 2.47 9.47 0.56 DH2004_840 GCS9 Cl* Melotte 22 DH 840 +03 56 38.76 +21 35 08.3 0 14.888 14.393 13.787 13.259 12.937 12.927 18.67 3.36 -40.82 3.36 0.16 0.93 DH841 GCS9 Cl* Melotte 22 DH 841 +03 56 39.34 +24 31 42.3 0 16.146 15.667 15.060 14.438 14.108 14.131 6.45 2.50 -37.23 2.50 0.69 0.00 BPL338 GCS9 Cl* Melotte 22 BPL 338 +03 56 39.67 +22 41 12.0 0 16.699 16.176 15.614 15.000 14.672 14.657 23.26 3.06 -34.21 3.06 0.25 0.04 DH842 GCS9 Cl* Melotte 22 DH 842 +03 56 46.60 +26 21 00.4 0 15.182 14.574 13.929 13.398 13.046 13.035 17.21 2.62 -47.43 2.62 2.18 0.77 HHJ64_DH843 GCS9 Cl* Melotte 22 HHJ 64 +03 56 52.31 +25 10 05.1 1 16.146 15.491 14.756 14.192 13.771 13.801 16.66 2.50 -38.22 2.50 2.72 0.54 HHJ17 GCS9 Cl* Melotte 22 HHJ 17 +03 56 52.91 +23 25 43.7 0 14.029 13.589 12.994 12.445 12.127 12.117 16.82 3.00 -39.65 3.00 0.16 0.89 HHJ271_DH846_Moraux2003_35 GCS9 Cl* Melotte 22 HHJ 271 +03 56 55.47 +22 08 24.3 0 15.110 14.685 14.152 13.553 13.236 13.258 18.72 2.85 -38.55 2.85 0.38 0.80 DH847 GCS9 Cl* Melotte 22 DH 847 +03 56 57.08 +24 48 34.3 0 12.982 12.615 12.063 11.527 11.181 11.276 19.30 2.47 -39.94 2.47 2.78 0.89 HCG511_HHJ393_DH848 GCS9 Cl* Melotte 22 HCG 511 +03 57 05.73 +22 49 14.9 0 15.172 14.200 13.601 13.300 13.290 41.88 3.06 -25.84 3.06 0.73 0.00 HHJ85 GCS9 Cl* Melotte 22 HHJ 85 +03 57 08.61 +20 07 42.7 0 15.711 15.183 14.537 14.017 13.686 13.687 18.35 3.98 -41.60 3.98 0.70 0.90 DH849 GCS9 Cl* Melotte 22 DH 849 +03 57 09.81 +21 36 27.2 0 14.554 14.125 13.494 12.940 12.659 12.639 17.59 3.36 -34.33 3.36 1.52 0.30 DH850 GCS9 Cl* Melotte 22 DH 850 +03 57 40.68 +25 16 04.1 0 13.577 13.194 12.640 12.008 11.723 11.790 14.25 2.47 -36.24 2.47 0.52 0.37 DH856 GCS9 Cl* Melotte 22 DH 856 +03 57 42.98 +25 23 06.7 0 14.445 13.984 13.389 12.823 12.494 12.537 21.68 2.48 -43.65 2.48 2.98 0.92 HCG516_HHJ180_DH857 GCS9 Cl* Melotte 22 HCG 516 +03 57 49.37 +22 08 30.9 0 16.149 15.498 14.831 14.286 13.884 13.897 16.04 2.89 -42.24 2.89 0.50 0.72 HHJ7_DH858 GCS9 Cl* Melotte 22 HHJ 7 +03 57 49.44 +23 28 40.9 0 16.455 16.008 15.423 14.867 14.538 14.486 21.43 3.05 -44.61 3.05 1.11 0.60 HHJ4 GCS9 Cl* Melotte 22 HHJ 4 +03 57 52.35 +21 02 15.8 0 14.703 14.171 13.595 13.036 12.705 12.710 16.38 3.36 -34.99 3.36 0.07 0.37 DH859 GCS9 Cl* Melotte 22 DH 859 +03 57 55.85 +21 16 10.8 0 15.354 14.971 14.436 13.805 13.526 13.536 16.87 3.37 -36.80 3.37 0.12 0.62 DH860 GCS9 Cl* Melotte 22 DH 860 +03 58 01.97 +23 53 54.5 0 15.065 14.518 13.919 13.370 13.028 13.030 15.82 2.97 -43.34 2.97 0.06 0.88 HHJ84_DH862 GCS9 Cl* Melotte 22 HHJ 84 +03 58 13.93 +25 06 27.3 0 13.268 12.878 12.303 11.694 11.359 11.407 -0.09 2.47 -50.14 2.47 7.14 0.00 HHJ369 GCS9 Cl* Melotte 22 HHJ 369 +03 58 25.14 +24 00 58.5 0 14.312 13.884 13.306 12.766 12.449 12.466 17.99 2.96 -40.14 2.96 0.84 0.92 HHJ220_DH865 GCS9 Cl* Melotte 22 HHJ 220 +03 58 30.99 +23 04 12.7 0 15.161 14.622 14.030 13.494 13.122 13.120 16.32 3.00 -42.51 3.00 0.55 0.89 HHJ60_DH866 GCS9 Cl* Melotte 22 HHJ 60 +03 58 34.18 +22 40 11.1 0 15.075 14.525 13.922 13.337 13.003 13.039 18.08 3.00 -38.66 3.00 1.36 0.81 HHJ93_DH867 GCS9 Cl* Melotte 22 HHJ 93 +03 58 56.15 +23 27 53.5 0 12.935 12.592 12.055 11.526 11.193 11.220 20.59 2.99 -37.49 2.99 0.72 0.81 HHJ388_DH868 GCS9 Cl* Melotte 22 HHJ 388 +03 58 57.04 +23 42 31.1 0 12.744 12.419 11.916 11.544 11.113 11.318 22.87 2.96 -40.80 2.96 0.94 0.82 HCG519_HHJ411_DH870 GCS9 Cl* Melotte 22 HCG 519 +03 59 05.73 +26 24 26.6 0 14.015 13.667 13.180 12.570 12.328 12.338 18.44 2.48 -40.89 2.48 0.87 0.93 DH871 GCS9 Cl* Melotte 22 DH 871 +03 59 06.31 +25 03 20.0 0 13.383 12.988 12.334 11.705 11.382 11.486 4.31 2.47 -61.05 2.47 21.47 0.00 HHJ366_DH872 GCS9 Cl* Melotte 22 HHJ 366 +03 59 12.92 +24 17 18.4 0 14.762 14.271 13.702 13.114 12.808 12.827 15.00 2.97 -34.59 2.97 0.41 0.22 DH873 GCS9 Cl* Melotte 22 DH 873 +03 59 15.87 +25 54 08.3 0 14.875 14.465 13.906 13.228 12.965 12.971 32.55 2.48 -38.84 2.48 0.41 0.00 HHJ133 GCS9 Cl* Melotte 22 HHJ 133 +03 59 18.99 +23 31 23.9 0 14.624 14.200 13.596 13.059 12.742 12.746 17.69 3.00 -45.41 3.00 0.84 0.92 HHJ150_DH874 GCS9 Cl* Melotte 22 HHJ 150 +03 59 26.36 +22 21 22.2 0 15.719 15.289 14.695 14.117 13.819 13.820 31.13 2.88 -39.73 2.88 0.46 0.00 HHJ39 GCS9 Cl* Melotte 22 HHJ 39 +03 59 26.93 +21 48 18.7 0 14.594 14.072 13.475 12.948 12.625 12.648 19.51 2.87 -42.67 2.87 1.42 0.94 DH876 GCS9 Cl* Melotte 22 DH 876 +03 59 29.33 +21 39 56.0 0 15.657 15.115 14.478 13.964 13.589 13.587 22.43 3.78 -42.75 3.78 0.34 0.79 DH878 GCS9 Cl* Melotte 22 DH 878 +03 59 31.47 +21 16 18.3 0 13.596 13.197 12.656 12.116 11.837 11.824 22.53 3.75 -44.15 3.75 0.74 0.87 DH879 GCS9 Cl* Melotte 22 DH 879 +03 59 52.43 +22 21 57.6 0 13.184 12.972 12.496 11.861 11.662 11.699 22.40 2.84 -35.16 2.84 0.08 0.23 DH880 GCS9 Cl* Melotte 22 DH 880 +03 59 56.49 +23 40 15.3 0 16.179 15.510 14.834 14.290 13.933 18.14 3.57 -40.03 3.57 0.37 0.69 DH882 GCS9 Cl* Melotte 22 DH 882 +03 59 59.15 +23 41 04.6 0 15.249 14.710 14.084 13.517 13.217 20.59 3.55 -40.08 3.55 0.30 0.83 DH883 GCS9 Cl* Melotte 22 DH 883 +03 59 59.86 +22 05 29.3 0 14.814 14.347 13.744 13.188 12.863 12.877 16.36 2.87 -36.19 2.87 0.93 0.58 DH884 GCS9 Cl* Melotte 22 DH 884 +04 00 00.40 +23 47 07.2 0 15.850 15.374 14.799 14.250 13.943 27.42 3.57 -41.36 3.57 0.20 0.14 DH885 GCS9 Cl* Melotte 22 DH 885 +04 00 02.53 +26 36 02.0 0 14.847 14.477 13.972 13.308 13.038 13.047 4.33 2.48 -47.48 2.48 6.50 0.00 DH886 GCS9 Cl* Melotte 22 DH 886 +04 00 10.30 +22 02 17.0 0 14.399 13.919 13.384 12.864 12.513 12.521 19.86 2.87 -43.85 2.87 0.29 0.94 DH888 GCS9 Cl* Melotte 22 DH 888 +04 00 14.11 +24 48 51.4 0 14.369 14.008 13.496 12.927 12.677 12.641 14.14 2.89 -46.60 2.89 0.44 0.77 DH889 GCS9 Cl* Melotte 22 DH 889 +04 00 26.15 +23 26 17.3 0 13.910 13.401 12.803 12.276 11.930 11.924 16.57 3.35 -40.04 3.35 0.11 0.89 DH891 GCS9 Cl* Melotte 22 DH 891 +04 00 28.19 +23 51 24.0 0 13.939 13.453 12.831 12.277 12.003 13.71 3.54 -40.49 3.54 0.64 0.78 DH892 GCS9 Cl* Melotte 22 DH 892 +04 00 28.35 +27 20 54.5 0 14.954 14.552 14.004 13.379 13.085 13.092 16.55 2.95 -38.17 2.95 0.16 0.81 DH893 GCS9 Cl* Melotte 22 DH 893 +04 00 39.24 +26 44 19.0 0 12.865 12.547 12.023 11.446 11.137 11.155 14.68 2.48 -47.91 2.48 3.18 0.26 DH894 GCS9 Cl* Melotte 22 DH 894 +04 00 49.33 +25 12 10.6 0 13.940 13.688 13.208 12.567 12.370 12.375 19.90 2.89 -39.87 2.89 0.15 0.89 DH895 GCS9 Cl* Melotte 22 DH 895 +04 01 08.61 +22 57 22.3 0 14.093 13.707 13.221 12.574 12.334 12.323 41.92 3.35 -24.05 3.35 38.80 0.00 DH897 GCS9 Cl* Melotte 22 DH 897 +04 01 12.08 +23 54 12.8 0 13.698 13.200 12.655 12.127 11.850 15.17 3.54 -39.47 3.54 0.46 0.82 DH898 GCS9 Cl* Melotte 22 DH 898 +04 01 13.99 +23 10 29.9 0 13.872 13.459 12.893 12.304 12.031 12.021 20.88 3.35 -38.17 3.35 0.17 0.77 DH899 GCS9 Cl* Melotte 22 DH 899 +04 01 26.06 +21 35 08.5 0 14.044 13.640 13.072 12.491 12.192 12.191 18.27 3.75 -43.61 3.75 0.12 0.94 DH900 GCS9 Cl* Melotte 22 DH 900 +04 01 49.17 +22 10 05.0 0 14.076 13.630 13.057 12.554 12.239 12.210 23.64 3.40 -44.42 3.40 0.13 0.83 DH901 GCS9 Cl* Melotte 22 DH 901 +04 01 59.58 +25 19 12.2 0 16.333 15.812 15.179 14.530 14.202 14.168 19.23 3.35 -37.71 3.35 0.41 0.48 DH902 GCS9 Cl* Melotte 22 DH 902 +04 02 22.62 +24 48 24.0 0 14.717 14.421 13.944 13.270 13.053 13.046 15.08 2.90 -44.52 2.90 0.66 0.89 DH903 GCS9 Cl* Melotte 22 DH 903 +04 02 49.97 +23 30 38.4 0 13.869 13.468 12.896 12.332 12.053 12.047 20.08 3.35 -38.25 3.35 0.12 0.81 DH904 GCS9 Cl* Melotte 22 DH 904 +04 03 01.22 +25 24 23.0 0 17.268 16.527 15.813 15.214 14.808 14.778 24.72 3.40 -37.90 3.40 0.24 0.27 DH905 GCS9 Cl* Melotte 22 DH 905 +04 03 16.56 +24 35 19.6 0 15.089 14.701 14.123 13.564 13.268 13.248 21.78 2.90 -42.71 2.90 0.10 0.83 DH906 GCS9 Cl* Melotte 22 DH 906 +04 03 24.94 +22 52 17.0 0 13.410 13.177 12.786 12.268 12.138 12.178 17.60 3.35 -40.68 3.35 0.56 0.92 DH2004_907 GCS9 Cl* Melotte 22 DH 907 +04 03 40.67 +25 21 34.5 0 13.110 12.756 12.216 11.671 11.303 11.336 16.57 3.31 -34.58 3.31 0.13 0.25 DH908 GCS9 Cl* Melotte 22 DH 908 +04 03 43.84 +23 53 47.0 0 14.247 13.709 13.083 12.543 12.195 12.194 20.68 3.37 -43.87 3.37 0.36 0.93 DH909 GCS9 Cl* Melotte 22 DH 909 +04 03 49.57 +23 43 13.7 0 14.866 14.377 13.796 13.269 12.931 12.931 22.02 3.38 -44.78 3.38 0.18 0.90 DH910 GCS9 Cl* Melotte 22 DH 910 +04 04 35.56 +25 07 14.7 0 13.458 13.161 12.678 12.087 11.856 11.863 18.96 2.94 -34.69 2.94 1.29 0.30 DH911 GCS9 Cl* Melotte 22 DH 911 +04 04 45.65 +24 41 19.1 0 14.008 13.433 12.771 12.193 11.842 11.858 15.03 2.94 -29.97 2.94 0.86 0.00 DH912 GCS9 Cl* Melotte 22 DH 912 +04 05 13.75 +24 08 42.7 0 14.983 14.471 13.846 13.261 12.933 12.917 19.77 3.38 -41.50 3.38 0.54 0.94 DH915 GCS9 Cl* Melotte 22 DH 915 +04 06 29.99 +22 33 43.6 0 14.376 13.856 13.201 12.623 12.273 12.269 14.83 5.07 -33.96 5.07 0.55 0.14 DH2004_916 GCS9 Cl* Melotte 22 DH 916 +03 27 35.60 +24 31 42.8 0 11.913 11.791 11.262 11.438 10.650 29.84 6.95 -51.81 6.95 5.58 NM DH001 GCS9 Cl* Melotte 22 DH 001 +03 27 37.78 +24 59 00.4 0 18.239 17.866 17.264 16.663 16.418 4.37 9.41 -21.38 9.41 0.68 NM DH2004_2 GCS9 Cl* Melotte 22 DH 2 +03 40 27.44 +24 20 42.0 0 19.876 19.366 18.295 17.674 17.142 0.114 19.62 5.81 -2.47 5.81 2.79 NM int-pl-IZ-83;IPLJ0340274+242042_N GCS9 Cl* Melotte 22 IPL 83 +03 40 40.02 +24 44 08.9 0 13.551 13.221 12.733 12.866 11.933 11.954 100.30 2.48 -40.25 2.48 710.82 NM HCG40_SK760_HHJ412 GCS9 Cl* Melotte 22 HCG 40 +03 40 42.48 +22 25 52.9 0 17.673 17.180 16.589 16.016 15.679 15.679 20.34 2.74 -38.19 2.74 3.77 NM DH149 GCS9 Cl* Melotte 22 DH 149 +03 40 47.27 +23 52 35.9 0 16.329 15.775 15.144 14.569 14.220 14.229 -14.15 2.16 -37.59 2.16 2.18 NM BPL16 GCS9 Cl* Melotte 22 BPL 16 +03 40 52.79 +25 24 43.5 0 19.211 18.106 17.097 16.437 15.871 15.971 11.63 2.75 -0.11 2.75 1.90 NM L07_A1_3 GCS9 +03 41 13.48 +26 01 18.1 0 14.297 14.031 13.545 13.781 12.723 12.729 -45.56 2.23 13.58 2.23 883.14 NM L07_19 GCS9 +03 41 20.82 +25 41 15.4 0 14.693 14.445 13.990 13.378 13.244 13.251 15.68 2.23 -33.00 2.23 4.42 NM L07_37 GCS9 +03 41 31.45 +23 05 12.5 0 17.388 17.172 16.798 16.334 16.205 16.153 -4.54 2.83 5.67 2.83 3.33 NM L07_photNM_1 GCS9 +03 41 33.33 +24 00 56.8 0 16.757 16.263 15.632 15.043 14.669 0.010 -0.93 2.35 28.95 2.35 0.35 NM int-pl-IZ-13;IPLJ0341333+240056_N GCS9 Cl* Melotte 22 IPL 13 +03 41 33.76 +24 11 18.7 0 16.689 16.102 15.481 14.947 14.519 0.009 -17.54 2.34 -3.76 2.34 0.87 NM int-pl-IZ-89;IPLJ0341337+241118_BPL24_Y GCS9 Cl* Melotte 22 IPL 89 +03 41 37.74 +23 04 32.7 0 17.983 17.097 16.312 15.674 15.230 15.224 55.66 2.38 -15.82 2.38 1.35 NM L07_A1_7 GCS9 +03 41 45.01 +24 02 00.5 0 20.471 21.487 18.728 18.097 17.603 0.192 -13.87 6.33 2.10 6.33 2.08 NM int-pl-IZ-12;IPLJ0341450+240200_N GCS9 Cl* Melotte 22 IPL 12 +03 41 45.82 +23 14 26.1 0 16.057 15.577 14.958 14.278 13.965 13.959 11.09 2.27 -13.29 2.27 1.09 NM L07_PM_NM_1 GCS9 +03 42 01.36 +24 07 42.8 0 20.269 19.509 18.937 18.788 18.036 0.158 44.55 7.28 -34.90 7.28 2.78 NM int-pl-IZ-79;IPLJ0342013+240742_N GCS9 Cl* Melotte 22 IPL 79 +03 42 04.72 +23 29 04.2 0 16.255 15.752 15.126 14.475 14.133 14.135 5.09 2.27 -8.96 2.27 1.00 NM L07_PM_NM_2 GCS9 +03 42 07.98 +22 39 33.4 0 18.739 17.474 16.501 15.831 15.206 15.225 -14.88 2.43 -2.56 2.43 5.87 NM int-pl-IZ-76_2MASSJ0342080+223933_Y_L07_A1_12 GCS9 Cl* Melotte 22 IPL 76 +03 42 10.17 +22 48 44.5 0 17.259 16.355 15.543 14.932 14.469 14.500 21.01 2.29 -90.42 2.29 1.52 NM L07_PM_NM_3 GCS9 +03 42 16.89 +23 23 26.2 0 14.399 14.006 13.488 12.830 12.556 12.534 4.44 2.25 -20.25 2.25 2.48 NM SK694 GCS9 Cl* Melotte 22 SK 694 +03 42 26.81 +24 50 20.5 0 16.617 15.878 15.170 14.570 14.175 14.570 5.87 3.00 7.70 3.00 11.87 NM SFHT-Pl-8_XX GCS9 Cl* Melotte 22 CFHT 8 +03 42 27.94 +25 25 20.0 0 18.768 18.348 17.661 17.160 16.956 16.830 -1.58 3.77 -14.43 3.77 1.80 NM L07_photNM_2 GCS9 +03 42 28.52 +24 03 40.9 0 17.374 16.756 16.090 15.493 15.055 0.015 14.53 2.24 4.35 2.24 2.29 NM int-pl-IZ-32;IPLJ0342285+240340_N GCS9 Cl* Melotte 22 IPL 32 +03 42 43.46 +22 38 31.3 0 19.335 18.795 18.306 17.850 17.254 17.390 6.96 6.04 -3.70 6.04 3.63 NM L07_faintJK_9 GCS9 +03 42 46.24 +23 54 50.4 0 18.364 17.566 16.865 16.291 15.869 0.031 -9.31 2.56 -4.06 2.56 2.52 NM int-pl-IZ-30;2MASSJ0342462+235450_N GCS9 Cl* Melotte 22 IPL 30 +03 42 49.78 +23 55 53.0 0 17.226 16.592 15.902 15.202 14.834 0.014 -20.00 2.22 -24.83 2.22 1.37 NM int-pl-IZ-28;2MASSJ0342497+235553_N GCS9 Cl* Melotte 22 IPL 28 +03 43 03.28 +23 41 30.7 0 18.320 17.643 16.965 16.316 15.973 0.032 0.97 2.58 -4.45 2.58 2.37 NM int-pl-IZ-10;IPLJ0343032+234130_N GCS9 Cl* Melotte 22 IPL 10 +03 43 12.21 +23 09 16.5 0 18.623 17.585 16.672 16.054 15.582 15.559 23.62 2.52 -78.70 2.52 5.17 NM L07_photNM_3 GCS9 +03 43 13.91 +24 08 20.2 0 14.902 14.430 13.830 13.297 12.985 13.008 7.02 2.13 -16.75 2.13 1.63 NM BPL55 GCS9 Cl* Melotte 22 BPL 55 +03 43 13.93 +23 47 46.0 0 16.658 16.080 15.498 15.003 14.645 0.010 80.42 2.19 -64.23 2.19 0.96 NM int-pl-IZ-8;2MASSJ0343138+234746_N GCS9 Cl* Melotte 22 IPL 8 +03 43 29.16 +24 02 06.4 0 16.723 16.206 15.569 14.907 14.555 0.010 3.90 2.33 -14.75 2.33 0.81 NM int-pl-IZ-22;IPLJ0343291+240206_N GCS9 Cl* Melotte 22 IPL 22 +03 43 29.87 +24 15 47.3 0 13.312 13.075 12.598 11.994 11.788 -2.96 2.30 -30.76 2.30 0.73 NM BPL60 GCS9 Cl* Melotte 22 BPL 60 +03 43 41.55 +23 38 56.9 0 13.507 13.136 11.471 13.159 10.941 68.56 2.30 127.64 2.30 9.62 NM HII157_HD23157_Tr050 GCS9 HII157 +03 43 47.80 +24 03 17.0 0 19.840 18.837 18.173 17.520 17.095 0.106 17.69 3.88 0.19 3.88 4.05 NM int-pl-IZ-18;IPLJ0343477+240316_N GCS9 Cl* Melotte 22 IPL 18 +03 43 48.10 +22 42 19.4 0 16.799 16.182 15.580 15.085 14.719 0.011 -12.71 2.31 -6.73 2.31 5.51 NM int-pl-IZ-77;2MASSJ0343481+224219_N GCS9 Cl* Melotte 22 IPL 77 +03 43 49.31 +24 07 42.2 0 13.277 13.106 12.742 12.360 12.239 10.92 2.30 -2.78 2.30 1.99 NM BPL64 GCS9 Cl* Melotte 22 BPL 64 +03 43 52.03 +22 55 24.5 0 18.877 17.799 16.954 16.281 15.790 15.790 8.59 2.63 -3.83 2.63 2.80 NM int-pl-IZ-55_IPLJ0343520+225524_Y_L07_photNM_4 GCS9 Cl* Melotte 22 IPL 55 +03 43 53.05 +23 21 50.2 0 16.148 15.662 15.035 14.372 14.027 14.014 -14.95 2.27 -18.64 2.27 6.97 NM L07_PM_NM_4 GCS9 +03 44 09.32 +23 02 18.5 0 18.527 17.911 17.141 16.542 16.135 0.040 -0.31 2.78 0.00 2.78 4.10 NM int-pl-IZ-54;IPLJ0344093+230218_N GCS9 Cl* Melotte 22 IPL 54 +03 44 09.39 +23 17 07.2 0 16.376 15.837 15.168 14.608 14.201 14.250 -32.76 2.28 2.92 2.28 11.23 NM L07_PM_NM_5 GCS9 +03 44 10.08 +22 43 57.8 0 16.296 15.777 15.173 14.582 14.243 14.252 4.33 2.28 -0.56 2.28 2.86 NM L07_PM_NM_6 GCS9 +03 44 10.90 +23 40 15.0 0 19.101 18.430 17.764 17.019 16.548 -2.43 3.36 -8.57 3.36 1.93 NM Roque3 GCS9 Cl* Melotte 22 Roque 3 +03 44 12.66 +23 43 17.6 0 20.244 19.037 18.123 17.534 17.170 15.97 4.26 -3.99 4.26 3.70 NM Roque18 GCS9 Cl* Melotte 22 Roque 18 +03 44 12.68 +25 24 35.1 0 18.091 17.245 16.455 15.959 15.528 15.959 -5.40 2.49 16.14 2.49 2.91 NM CFHT-Pl-20_L07_photNM_5 GCS9 Cl* Melotte 22 CFHT 20 +03 44 20.84 +24 39 02.8 0 19.245 18.126 17.127 16.400 15.949 3.92 3.63 7.91 3.63 6.22 NM Roque36 GCS9 Cl* Melotte 22 Roque 36 +03 44 26.36 +26 02 30.9 0 13.193 12.826 12.324 11.355 11.457 11.506 1.38 2.23 -17.15 2.23 540.35 NM HCG142_SK561_T10_DH337 GCS9 Cl* Melotte 22 HCG 142 +03 44 26.46 +22 40 00.5 0 15.389 15.105 14.594 13.943 13.721 13.732 23.92 2.25 -18.53 2.25 38.11 NM L07_195 GCS9 +03 44 33.80 +22 42 49.2 0 16.520 15.949 15.320 14.714 14.349 14.347 3.12 2.26 9.87 2.26 2.53 NM L07_PM_NM_7 GCS9 +03 44 44.62 +22 49 10.5 0 19.674 19.358 18.763 18.140 18.089 0.100 0.73 5.90 -8.60 5.90 2.55 NM int-pl-IZ-52;IPLJ0344446+224911_N GCS9 Cl* Melotte 22 IPL 52 +03 44 50.99 +25 21 43.5 0 19.424 18.545 17.899 17.322 16.856 16.853 4.78 3.88 -9.91 3.88 4.18 NM L07_262 GCS9 +03 44 58.97 +23 23 19.9 0 11.820 11.684 11.266 11.507 10.629 10.890 19.95 2.23 -34.31 2.23 2.42 NM HII513_DH364 GCS9 Cl* Melotte 22 HII 513 +03 45 12.28 +22 58 31.8 0 14.301 14.092 13.643 13.024 12.948 12.939 17.40 2.24 -40.22 2.24 3.39 NM L07_188 GCS9 +03 45 23.81 +21 52 38.7 0 15.066 14.597 14.011 13.441 13.158 13.108 12.19 2.27 -5.33 2.27 2.63 NM BPL72 GCS9 Cl* Melotte 22 BPL 72 +03 45 25.31 +22 22 07.8 0 19.833 18.841 18.030 17.345 16.867 0.125 0.66 4.90 -0.37 4.90 2.84 NM int-pl-IZ-68;IPLJ0345252+222208_N GCS9 Cl* Melotte 22 IPL 68 +03 45 28.34 +23 48 09.6 0 17.089 16.378 15.577 14.726 14.303 14.314 12.66 2.24 -4.80 2.24 4.43 NM L07_PM_NM_8 GCS9 +03 45 29.87 +22 24 14.5 0 16.385 15.797 15.183 14.585 14.229 14.242 60.29 2.30 -74.75 2.30 16.18 NM BPL74_L07_PM_NM_9 GCS9 Cl* Melotte 22 BPL 74 +03 45 33.16 +25 34 30.0 0 18.115 17.203 16.409 15.847 15.398 15.847 -6.60 2.43 -17.46 2.43 3.65 NM CFHT-Pl-19_L07_photNM_6 GCS9 Cl* Melotte 22 CFHT 19 +03 45 34.50 +23 41 43.5 0 17.069 16.371 15.611 14.865 14.455 14.451 -12.61 2.24 -23.80 2.24 3.02 NM L07_PM_NM_10 GCS9 +03 45 36.44 +24 18 15.4 0 16.225 15.689 15.049 14.432 14.104 14.091 9.33 2.24 -4.26 2.24 4.09 NM L07_PM_NM_11 GCS9 +03 45 36.50 +22 28 31.9 0 19.069 18.388 17.472 17.000 16.703 0.064 -24.55 3.48 -17.14 3.48 10.20 NM int-pl-IZ-66;IPLJ0345365+222832_N GCS9 Cl* Melotte 22 IPL 66 +03 45 36.85 +23 44 47.8 0 16.877 16.225 15.470 14.619 14.255 14.237 18.08 2.24 0.32 2.24 17.83 NM L07_PM_NM_12 GCS9 +03 45 37.25 +23 49 21.0 0 16.385 15.847 15.119 14.210 13.883 13.880 -6.65 2.23 -8.20 2.23 1.69 NM L07_PM_NM_13 GCS9 +03 45 43.11 +25 40 23.1 0 16.198 15.009 13.924 13.240 12.663 12.643 -96.92 2.23 -40.65 2.23 2.96 NM L07_PM_NM_14 GCS9 +03 45 45.25 +22 58 44.6 0 16.700 16.061 15.359 14.836 14.433 0.010 57.50 2.26 -53.09 2.26 10.93 NM int-pl-IZ-58;2MASSJ0345452+225845_BPL76_Y GCS9 Cl* Melotte 22 IPL 58 +03 45 49.90 +23 45 59.8 0 16.226 15.601 14.814 13.904 13.593 13.583 0.36 2.23 -2.57 2.23 0.62 NM L07_PM_NM_15 GCS9 +03 45 52.65 +25 51 42.0 0 12.679 12.343 11.821 11.385 10.925 11.124 122.72 2.23 -53.12 2.23 13.46 NM HCG199_SK500_MT61_DH406 GCS9 Cl* Melotte 22 HCG 199 +03 45 53.20 +25 12 55.8 0 17.551 16.776 16.002 15.368 14.932 14.952 -4.73 2.27 -12.06 2.27 5.27 NM BPL81_L07_A1_37 GCS9 Cl* Melotte 22 BPL 81 +03 46 02.52 +23 45 33.2 0 18.177 17.368 16.459 15.481 15.025 15.026 -1.56 2.31 1.54 2.31 6.68 NM L07_A1_39 GCS9 +03 46 03.75 +23 44 35.6 0 18.178 17.270 16.413 15.556 15.053 15.070 -10.12 2.30 -4.53 2.30 21.32 NM L07_A1_40 GCS9 +03 46 04.92 +22 15 40.2 0 17.472 16.679 15.925 15.296 15.057 15.166 0.19 2.39 -8.27 2.39 2.60 NM L07_photNM_7 GCS9 +03 46 08.02 +23 45 35.5 0 18.882 17.941 16.854 15.857 15.336 15.348 -12.84 2.37 2.06 2.37 2.22 NM L07_A1_41 GCS9 +03 46 08.22 +23 21 38.7 0 17.105 16.500 15.752 15.026 14.646 14.651 2.05 2.28 -10.76 2.28 3.97 NM L07_PM_NM_16 GCS9 +03 46 08.37 +23 20 50.8 0 11.765 11.589 11.200 11.417 10.482 10.755 22.70 2.23 -33.02 2.23 6.52 NM HII915_HCG215 GCS9 HII915 +03 46 14.34 +23 51 02.4 0 15.627 14.918 14.181 13.826 13.458 13.484 1.83 2.22 13.02 2.22 301.52 NM HHJ56_DH432 GCS9 Cl* Melotte 22 HHJ 56 +03 46 14.48 +22 20 51.0 0 16.717 16.105 15.473 15.002 14.615 0.011 66.01 2.32 -69.67 2.32 3.61 NM int-pl-IZ-63_2MASSJ0346144+222051_N_L07_PM_NM_17 GCS9 Cl* Melotte 22 IPL 63 +03 46 26.75 +24 49 18.1 0 16.284 15.730 15.129 14.552 14.213 14.224 -2.42 2.22 -18.50 2.22 17.98 NM L07_PM_NM_18 GCS9 +03 46 32.99 +23 38 00.9 0 16.598 16.068 15.371 14.454 14.143 14.105 4.67 2.24 2.45 2.24 3.95 NM L07_PM_NM_19 GCS9 +03 46 36.82 +23 33 01.8 0 16.694 16.058 15.333 14.498 14.128 14.125 -2.77 2.24 -15.45 2.24 1.91 NM L07_PM_NM_21 GCS9 +03 46 38.36 +25 33 18.6 0 15.621 15.312 14.788 14.129 13.931 13.905 19.87 2.24 -46.62 2.24 9.86 NM L07_33 GCS9 +03 46 40.95 +22 22 38.1 0 19.242 18.150 16.917 16.183 15.497 15.480 69.74 2.66 -50.74 2.66 3.71 NM L07_A1_52 GCS9 +03 46 44.04 +23 38 13.4 0 17.173 16.531 15.775 14.937 14.576 14.555 6.12 2.26 -11.45 2.26 9.02 NM L07_PM_NM_22 GCS9 +03 46 48.31 +24 18 06.2 0 13.266 13.009 12.529 11.953 11.752 11.731 -12.75 2.22 2.02 2.22 6.44 NM HCG236_J311 GCS9 Cl* Melotte 22 HCG 236 +03 46 50.99 +25 40 44.5 0 16.272 15.767 15.181 14.488 14.196 14.200 -5.63 2.26 -22.81 2.26 3.53 NM L07_PM_NM_23 GCS9 +03 46 52.58 +24 17 17.0 0 15.299 14.842 14.265 13.643 13.345 13.340 20.07 2.22 1.29 2.22 10.34 NM BPL111 GCS9 Cl* Melotte 22 BPL 111 +03 47 02.12 +25 54 36.6 0 13.774 13.591 13.184 12.619 12.512 12.525 10.77 2.23 -49.85 2.23 2.44 NM L07_4 GCS9 +03 47 02.54 +25 13 45.5 0 16.469 16.185 15.655 14.946 14.723 14.728 4.07 2.25 -9.76 2.25 1.92 NM BPL123 GCS9 Cl* Melotte 22 BPL 123 +03 47 03.56 +24 49 11.6 0 13.153 13.091 11.335 12.348 10.073 NM HII1266_HD23567_Tr359b GCS9 HII1266 +03 47 05.82 +23 24 52.5 0 19.481 19.105 18.389 17.632 17.262 17.393 -2.31 4.89 5.06 4.89 3.79 NM Roque32 GCS9 Cl* Melotte 22 Roque 32 +03 47 06.65 +24 45 47.4 0 16.069 15.513 14.959 14.428 14.100 14.105 59.57 2.23 27.45 2.23 12.46 NM L07_PM_NM_25 GCS9 +03 47 07.73 +24 21 40.5 0 15.473 15.016 14.402 13.829 13.534 13.532 -25.04 2.22 -2.14 2.22 11.43 NM JS9 GCS9 Cl* Melotte 22 JS 9 +03 47 08.31 +22 33 10.0 0 16.577 15.993 15.342 14.844 14.453 0.010 -15.97 2.31 -6.87 2.31 5.75 NM int-pl-IZ-38;2MASSJ0347082+223309_BPL127_Y_L07_PM_NM_26 GCS9 Cl* Melotte 22 IPL 38 +03 47 13.69 +23 46 28.4 0 16.410 15.813 15.184 14.658 14.309 14.311 -22.45 2.24 11.13 2.24 31.03 NM L07_PM_NM_27 GCS9 +03 47 24.41 +23 54 52.7 0 13.573 13.598 11.481 12.458 10.127 10.989 -61.87 2.22 -1.72 2.22 12.56 NM HII1397_HD23631_Tr402 GCS9 HII1397 +03 47 30.66 +25 13 30.6 0 16.074 15.584 14.998 14.473 14.158 14.126 48.31 2.22 -52.27 2.22 3.66 NM BPL146 GCS9 Cl* Melotte 22 BPL 146 +03 47 36.27 +24 28 50.1 0 16.052 15.566 14.943 14.294 13.957 13.957 -9.42 2.22 -2.90 2.22 3.25 NM L07_PM_NM_28 GCS9 +03 47 37.55 +24 28 59.0 0 19.855 19.463 18.773 18.225 18.131 17.930 -4.03 5.91 20.75 5.91 1.09 NM Roque34 GCS9 Cl* Melotte 22 Roque 34 +03 47 38.75 +22 38 40.3 0 17.442 16.864 16.220 15.689 15.285 15.282 -23.29 2.39 -31.80 2.39 4.85 NM Roque44 GCS9 Cl* Melotte 22 Roque 44 +03 47 41.19 +23 44 24.9 0 11.990 11.816 11.404 11.437 10.714 11.008 21.37 2.22 -39.05 2.22 12.59 NM HII1532_HCG286_SK405_B270_DH531 GCS9 HII1532 +03 47 46.16 +25 21 42.1 0 19.442 18.364 17.367 16.890 16.342 16.383 69.70 3.46 -102.93 3.46 5.96 NM L07_photNM_8 GCS9 +03 47 46.90 +24 03 40.5 0 20.021 20.270 19.206 18.506 18.007 18.106 -3.67 5.35 0.47 5.35 4.33 NM Roque27 GCS9 Cl* Melotte 22 Roque 27 +03 48 02.09 +24 00 02.5 0 18.921 18.465 17.869 17.284 16.982 17.006 1.27 3.24 -5.14 3.24 1.15 NM Roque10 GCS9 Cl* Melotte 22 Roque 10 +03 48 03.68 +23 44 10.4 0 15.989 15.099 14.329 13.709 13.277 13.278 32.62 2.22 -124.76 2.22 0.68 NM NOT1 GCS9 Cl* Melotte 22 NOT 1 +03 48 04.74 +23 51 01.8 0 20.362 19.516 19.084 18.521 17.969 18.210 11.09 6.08 5.08 6.08 1.36 NM Festin98_009 GCS9 Cl* Melotte 22 NPL 009 +03 48 13.80 +24 28 04.0 0 17.517 16.753 16.083 15.485 15.072 15.099 1.43 2.31 -3.33 2.31 2.70 NM Roque43 GCS9 Cl* Melotte 22 Roque 43 +03 48 13.93 +24 38 30.5 0 16.849 16.681 16.451 16.076 15.939 15.998 -2.88 2.65 1.72 2.65 5.01 NM BPL168 GCS9 Cl* Melotte 22 BPL 168 +03 48 23.62 +24 22 35.2 0 16.199 15.558 14.889 14.317 13.945 13.947 -10.86 2.23 -14.81 2.23 2.24 NM BPL177_Festin98_004_L07_PM_NM_29 GCS9 Cl* Melotte 22 BPL 177 +03 48 25.61 +22 52 13.0 0 15.518 15.386 15.395 14.708 14.646 14.641 11.27 2.17 -37.83 2.17 450.04 NM BPL179 GCS9 Cl* Melotte 22 BPL 179 +03 48 30.10 +24 20 43.9 0 14.608 14.578 12.604 14.861 11.050 12.051 -94.69 2.22 157.47 2.22 6.88 NM HII1876_HD23763_Tr518 GCS9 HII1876 +03 48 32.39 +24 13 18.4 0 20.066 19.095 18.425 17.638 17.303 17.351 2.98 3.77 -23.67 3.77 2.76 NM Festin98_010 GCS9 Cl* Melotte 22 NPL 010 +03 48 32.67 +23 52 40.6 0 14.722 14.278 13.711 13.109 12.832 12.842 0.96 2.22 -5.44 2.22 5.32 NM WILL1 GCS9 Cl* Melotte 22 WBM 1 +03 48 36.30 +23 33 25.3 0 16.077 15.589 15.028 14.489 14.152 14.167 -14.94 2.23 -33.20 2.23 1.87 NM L07_PM_NM_30 GCS9 +03 48 44.14 +24 21 18.7 0 17.816 17.195 16.443 15.823 15.448 15.431 2.52 2.33 -8.88 2.33 4.62 NM Roque37 GCS9 Cl* Melotte 22 Roque 37 +03 48 48.45 +21 59 00.3 0 16.559 15.982 15.348 14.816 14.470 14.458 -16.99 2.31 5.11 2.31 5.45 NM L07_PM_NM_31 GCS9 +03 48 49.03 +24 20 25.3 0 19.222 18.227 17.237 16.569 16.059 16.074 -33.18 2.59 -58.05 2.59 4.06 NM Roque33_Festin98_008_L07_photNM_9 GCS9 Cl* Melotte 22 Roque 33 +03 48 55.23 +22 50 42.2 0 14.915 14.474 13.893 13.361 13.070 13.047 4.68 2.13 -22.37 2.13 1.12 NM BPL194 GCS9 Cl* Melotte 22 BPL 194 +03 48 58.61 +23 37 03.9 0 19.604 18.306 17.390 16.771 16.233 16.221 6.50 2.60 -15.01 2.60 4.76 NM L07_photNM_10 GCS9 +03 48 58.96 +25 06 14.0 0 11.958 11.762 11.365 11.410 10.602 10.877 7.12 2.20 -45.83 2.20 1.85 NM HII2082 GCS9 HII2082 +03 49 11.59 +24 26 17.4 0 16.051 15.500 14.910 14.436 14.081 14.097 33.46 2.21 -90.64 2.21 2.53 NM L07_PM_NM_32 GCS9 +03 49 12.12 +23 12 55.9 0 16.875 16.165 15.429 14.824 14.414 14.401 53.85 2.28 -0.69 2.28 4.85 NM L07_PM_NM_33 GCS9 +03 49 33.05 +26 50 43.0 0 16.972 16.327 15.617 15.078 14.687 14.684 28.54 2.99 9.02 2.99 0.42 NM IPMBD22 GCS9 Cl* Melotte 22 IPMBD 22 +03 49 45.95 +22 53 43.7 0 15.830 15.510 15.011 14.332 14.144 14.153 13.11 2.27 -37.98 2.27 1.52 NM L07_186 GCS9 +03 49 51.23 +25 26 06.6 0 19.731 18.569 17.609 17.074 16.515 16.545 2.12 3.45 13.47 3.45 1.53 NM L07_photNM_13 GCS9 +03 49 53.76 +23 59 01.4 0 17.271 16.739 16.088 15.537 15.167 15.153 15.94 2.29 -9.47 2.29 31.41 NM Roque46 GCS9 Cl* Melotte 22 Roque 46 +03 50 00.31 +24 28 15.5 0 18.288 17.707 17.006 16.494 16.087 16.072 2.01 2.60 -7.24 2.60 4.36 NM Roque40 GCS9 Cl* Melotte 22 Roque 40 +03 50 01.67 +24 36 48.6 0 15.586 15.002 14.433 13.921 13.543 13.549 5.46 2.21 -6.13 2.21 12.12 NM BPL221 GCS9 Cl* Melotte 22 BPL 221 +03 50 03.90 +23 58 34.1 0 14.131 13.765 13.259 12.738 12.457 12.446 -8.17 2.22 -11.58 2.22 19.96 NM HHJ256 GCS9 Cl* Melotte 22 HHJ 256 +03 50 05.96 +23 42 14.1 0 19.805 19.357 18.931 18.260 17.904 17.940 -0.52 5.77 8.42 5.77 5.36 NM Roque29 GCS9 Cl* Melotte 22 Roque 29 +03 50 15.55 +26 06 30.0 0 16.909 16.185 15.415 14.832 14.403 14.396 40.58 2.28 -66.43 2.28 2.93 NM L07_PM_NM_34 GCS9 +03 50 20.77 +24 08 40.9 0 19.627 19.216 18.782 18.095 17.744 17.820 -0.38 5.11 4.55 5.11 1.62 NM Roque28 GCS9 Cl* Melotte 22 Roque 28 +03 50 41.10 +25 44 21.6 0 15.634 15.517 15.188 14.817 14.704 14.716 -8.19 2.27 -10.74 2.27 17.17 NM L07_faintJK_1 GCS9 +03 51 04.05 +24 32 57.8 0 16.030 15.535 14.962 14.396 14.049 14.060 76.40 2.22 -22.07 2.22 9.89 NM L07_PM_NM_35 GCS9 +03 51 05.58 +24 44 12.2 0 11.945 11.736 11.358 11.489 10.568 10.909 20.38 2.20 -47.92 2.20 5.38 NM HII2927_HCG418_DH693 GCS9 HII2927 +03 51 09.72 +25 18 52.4 0 16.714 16.037 15.393 14.875 14.493 14.534 -18.86 2.28 -30.02 2.28 14.71 NM L07_PM_NM_52 GCS9 +03 51 10.52 +22 48 14.4 0 16.768 16.141 15.471 14.799 14.434 14.413 -4.75 2.28 -21.83 2.28 1.24 NM L07_PM_NM_36 GCS9 +03 51 22.47 +23 42 19.2 0 13.909 13.760 13.410 12.975 12.869 12.863 2.24 2.22 -6.49 2.22 1.31 NM BPL234 GCS9 Cl* Melotte 22 BPL 234 +03 51 27.90 +22 48 12.9 0 16.113 15.431 14.734 14.118 13.705 13.722 11.13 2.26 -12.61 2.26 4.23 NM L07_PM_NM_37 GCS9 +03 51 35.03 +24 03 34.8 0 16.774 16.625 16.231 15.758 15.586 15.587 -6.66 2.32 -6.66 2.32 40.44 NM L07_faintJK_4 GCS9 +03 51 37.72 +25 42 46.5 0 19.137 17.784 16.352 15.461 14.660 14.657 55.25 2.37 -38.67 2.37 4.06 NM L07_photNM_14 GCS9 +03 51 38.18 +23 03 11.2 0 16.892 16.218 15.549 14.978 14.588 14.593 -5.14 2.31 40.41 2.31 5.15 NM L07_PM_NM_38 GCS9 +03 51 53.92 +24 02 51.4 0 13.571 13.114 13.213 12.035 11.680 11.721 -34.06 2.22 -22.59 2.22 165.02 NM HCG441_SK211_BPL243_DH726_Moraux2003_17 GCS9 Cl* Melotte 22 HCG 441 +03 51 57.12 +24 57 06.3 0 16.193 15.875 15.320 14.797 14.498 14.480 15.09 2.23 -31.04 2.23 6.23 NM DH729 GCS9 Cl* Melotte 22 DH 729 +03 52 01.58 +24 20 11.7 0 16.260 15.770 15.166 14.508 14.197 14.223 4.89 2.26 -3.61 2.26 3.40 NM BPL247 GCS9 Cl* Melotte 22 BPL 247 +03 52 07.88 +23 59 13.0 0 16.169 15.371 14.636 14.066 13.640 14.066 18.40 2.25 -115.67 2.25 1.33 NM CFHT-Pl-6_BRB3_L07_PM_NM_39 GCS9 Cl* Melotte 22 CFHT 6 +03 52 13.44 +24 28 52.3 0 19.996 18.601 17.754 17.227 16.636 16.681 7.13 3.47 -14.21 3.47 2.49 NM L07_photNM_16 GCS9 +03 52 17.49 +22 51 01.9 0 16.649 16.084 15.415 14.781 14.414 14.402 4.92 2.30 -6.97 2.30 1.46 NM L07_PM_NM_40 GCS9 +03 52 24.00 +23 55 15.7 0 14.588 14.438 14.048 13.571 13.477 13.478 14.07 2.25 -36.84 2.25 1.05 NM DH2004_751 GCS9 Cl* Melotte 22 DH 751 +03 52 44.31 +24 14 13.2 0 13.520 13.343 12.946 12.457 12.359 12.354 19.94 2.24 -11.03 2.24 7.05 NM L07_8 GCS9 +03 52 46.44 +24 24 17.1 0 19.772 18.591 17.569 16.949 16.281 16.359 -3.07 2.92 -13.02 2.92 18.30 NM L07_photNM_17 GCS9 +03 52 47.18 +22 38 44.1 0 17.260 16.818 16.156 15.566 15.252 15.282 -40.26 2.44 15.01 2.44 273.74 NM L07_faintJK_10 GCS9 +03 52 49.69 +25 00 50.7 0 13.101 12.909 12.566 12.095 11.969 11.985 -1.51 2.23 -0.95 2.23 2.51 NM BPL271 GCS9 Cl* Melotte 22 BPL 271 +03 52 52.11 +25 10 26.0 0 13.187 12.804 12.306 11.803 11.485 11.517 43.14 2.23 -67.75 2.23 5.68 NM BPL273_SK152 GCS9 Cl* Melotte 22 BPL 273 +03 52 58.25 +23 26 19.1 0 13.562 13.427 13.136 12.789 12.721 12.722 7.00 2.25 1.17 2.25 5.43 NM BPL276 GCS9 Cl* Melotte 22 BPL 276 +03 53 21.62 +23 58 53.9 0 13.493 13.135 12.644 12.051 11.822 47.53 2.30 -56.56 2.30 1.90 NM SK132_DH782 GCS9 Cl* Melotte 22 SK 132 +03 53 23.13 +23 19 20.4 0 17.823 16.897 15.966 15.341 14.850 14.834 21.87 2.36 -4.56 2.36 1.54 NM BPL283_CFHT-Pl-18_M8_L07_A1_104 GCS9 Cl* Melotte 22 BPL 283 +03 53 23.37 +25 05 50.0 0 16.629 16.085 15.495 15.014 14.656 14.648 43.26 2.28 -95.99 2.28 1.97 NM BPL284 GCS9 Cl* Melotte 22 BPL 284 +03 53 34.55 +24 04 38.4 0 16.740 16.177 15.552 14.979 14.606 14.22 2.35 9.30 2.35 1.46 NM BPL287 GCS9 Cl* Melotte 22 BPL 287 +03 53 48.72 +25 04 20.1 0 16.777 16.308 15.238 15.126 14.827 14.820 -6.07 2.28 16.38 2.28 119.28 NM L07_PM_NM_41 GCS9 +03 53 59.92 +23 41 00.2 0 16.200 15.677 15.073 14.485 14.141 14.149 -0.43 2.26 -8.06 2.26 1.04 NM BPL296_L07_PM_NM_42 GCS9 Cl* Melotte 22 BPL 296 +03 54 01.40 +24 44 46.7 0 19.768 18.534 17.775 17.194 16.809 17.194 3.91 3.31 -23.74 3.31 1.84 NM CFHT-Pl-26_XX GCS9 Cl* Melotte 22 CFHT 26 +03 54 15.64 +25 21 18.9 0 15.714 15.104 14.458 13.881 13.530 13.501 1.39 2.95 -13.24 2.95 0.52 NM BPL309 GCS9 Cl* Melotte 22 BPL 309 +03 54 17.46 +23 11 56.9 0 16.231 15.688 15.035 14.453 14.100 14.085 -12.92 2.28 -3.82 2.28 10.54 NM L07_PM_NM_43 GCS9 +03 54 39.33 +23 03 12.4 0 16.447 15.693 14.998 14.476 14.064 14.083 6.47 2.28 -4.39 2.28 6.56 NM L07_PM_NM_44 GCS9 +03 54 44.20 +25 15 10.9 0 17.428 16.693 15.954 15.421 15.043 15.031 -6.19 2.31 -33.42 2.31 4.31 NM BPL316 GCS9 Cl* Melotte 22 BPL 316 +03 54 45.87 +22 53 54.8 0 17.409 16.961 16.437 15.849 15.518 15.619 17.05 2.56 -18.44 2.56 4.02 NM DH807 GCS9 Cl* Melotte 22 DH 807 +03 54 46.11 +23 00 20.6 0 17.056 16.404 15.700 15.125 14.741 14.732 -17.41 2.33 -4.76 2.33 7.01 NM L07_PM_NM_46 GCS9 +03 54 49.71 +25 06 24.2 0 13.354 13.219 12.850 12.393 12.309 12.312 -1.04 2.23 -7.78 2.23 6.31 NM BPL319 GCS9 Cl* Melotte 22 BPL 319 +03 54 51.47 +23 45 12.2 0 19.670 18.540 17.687 17.122 16.691 16.585 56.85 3.56 -24.59 3.56 2.56 NM BRB19_None GCS9 Cl* Melotte 22 BRB 19 +03 54 54.51 +22 50 21.5 0 16.464 15.871 15.230 14.673 14.333 14.338 2.56 2.30 -7.22 2.30 4.32 NM L07_PM_NM_47 GCS9 +03 54 55.74 +25 09 04.2 0 14.981 14.562 14.004 13.425 13.130 13.141 17.22 2.23 -9.40 2.23 12.41 NM BPL320 GCS9 Cl* Melotte 22 BPL 320 +03 54 55.92 +24 37 42.9 0 19.397 19.014 18.467 17.790 17.603 17.582 6.11 5.79 -3.45 5.79 6.15 NM L07_photNM_18 GCS9 +03 55 03.43 +24 28 54.6 0 19.678 18.842 18.141 17.524 17.349 17.331 3.23 4.67 -16.09 4.67 6.30 NM L07_257 GCS9 +03 55 06.14 +25 11 06.2 0 15.549 15.000 14.339 13.790 13.431 13.470 0.47 2.24 -5.03 2.24 4.70 NM BPL323 GCS9 Cl* Melotte 22 BPL 323 +03 55 08.19 +23 58 08.6 0 19.348 18.984 18.417 17.822 17.551 -0.94 4.78 -14.24 4.78 2.60 NM L07_faintYJ_30 GCS9 +03 55 12.61 +23 17 37.2 0 17.842 16.877 15.977 15.348 14.842 15.348 54.40 2.33 -48.46 2.33 8.41 NM CFHT-Pl-15_M7.0_BRB13_L07_A1_109 GCS9 Cl* Melotte 22 CFHT 15 +03 55 14.72 +22 42 08.5 0 16.652 16.043 15.366 14.734 14.395 14.359 1.47 2.29 -7.33 2.29 9.73 NM L07_PM_NM_53 GCS9 +03 55 18.11 +24 17 05.7 0 16.325 15.757 15.111 14.541 14.192 14.208 15.89 2.26 -2.19 2.26 10.94 NM BPL326_L07_A1_110 GCS9 Cl* Melotte 22 BPL 326 +03 55 19.92 +24 12 13.9 0 15.750 15.298 14.699 14.128 13.819 13.821 -7.38 2.25 -20.24 2.25 5.61 NM L07_106 GCS9 +03 55 30.07 +23 54 53.5 0 16.503 15.930 15.308 14.808 14.429 14.420 -15.28 2.27 -4.94 2.27 2.49 NM L07_PM_NM_48 GCS9 +03 55 39.59 +24 12 50.7 0 20.511 19.251 17.844 17.303 16.618 16.648 28.07 3.37 -158.64 3.37 1.11 NM L07_photNM_19 GCS9 +03 55 42.02 +22 57 01.4 0 18.646 17.343 15.943 14.999 14.186 14.191 161.10 2.34 -44.49 2.34 2.38 NM L07_photNM_20 GCS9 +03 55 45.49 +23 51 25.4 0 20.033 19.742 18.723 18.580 18.088 17.959 -5.63 8.56 -13.56 8.56 1.63 NM L07_faintJK_5 GCS9 +03 55 46.37 +23 21 16.0 0 13.188 12.908 12.386 11.820 11.587 11.619 18.86 2.25 -3.24 2.25 4.59 NM SK40 GCS9 Cl* Melotte 22 SK 40 +03 56 21.72 +25 21 10.4 0 16.646 15.945 15.246 14.665 14.234 14.261 4.82 2.53 -4.34 2.53 1.00 NM BPL336 GCS9 Cl* Melotte 22 BPL 336 +03 56 34.22 +24 15 13.2 0 17.385 16.926 16.372 15.806 15.535 15.527 12.43 3.15 -41.24 3.15 0.86 NM DH838 GCS9 Cl* Melotte 22 DH 838 +03 56 36.20 +25 18 05.7 0 16.008 15.372 14.712 14.140 13.761 13.809 8.47 2.50 -13.23 2.50 0.59 NM BPL337 GCS9 Cl* Melotte 22 BPL 337 +03 56 51.33 +25 02 25.4 0 13.297 12.918 12.362 11.804 11.466 11.541 28.86 2.47 0.62 2.47 0.95 NM BPL339 GCS9 Cl* Melotte 22 BPL 339 +03 56 51.90 +23 31 36.6 0 18.325 17.973 17.367 16.753 16.502 16.419 -3.97 3.81 -6.61 3.81 1.07 NM DH2004_844 GCS9 Cl* Melotte 22 DH 844 +03 57 58.45 +24 20 08.9 0 17.341 16.910 16.301 15.694 15.391 15.376 -2.76 3.11 -15.77 3.11 0.84 NM DH861 GCS9 Cl* Melotte 22 DH 861 +03 58 56.42 +24 18 32.2 0 14.148 13.730 13.145 12.585 12.404 12.406 61.58 2.96 -48.24 2.96 45.45 NM HHJ292_DH869 GCS9 Cl* Melotte 22 HHJ 292 +04 00 19.40 +24 41 05.0 0 18.236 17.805 17.236 16.704 16.312 16.366 2.20 3.49 -13.79 3.49 1.46 NM DH2004_890 GCS9 Cl* Melotte 22 DH 890 +03 32 33.72 +24 29 34.7 0 13.066 12.465 12.179 13.40 6.88 -33.70 6.88 4.83 noZY DH021 GCS9 Cl* Melotte 22 DH 021 +03 32 42.31 +23 34 00.0 0 13.393 12.828 12.539 12.522 50.10 2.64 -42.29 2.64 98.04 noZY DH022 GCS9 Cl* Melotte 22 DH 022 +03 34 22.13 +27 23 45.3 0 12.594 12.061 11.758 11.766 19.10 4.62 -39.68 4.62 1.84 noZY DH035 GCS9 Cl* Melotte 22 DH 035 +03 34 46.37 +27 55 32.4 0 12.506 11.899 11.620 11.663 17.73 4.62 -33.95 4.62 0.60 noZY DH038 GCS9 Cl* Melotte 22 DH 038 +03 43 59.09 +22 54 29.0 0 19.298 18.365 18.018 22.78 9.41 4.51 9.41 3.41 noZY int-pl-IZ-56;IPLJ0343590+225429_N GCS9 Cl* Melotte 22 IPL 56 +03 44 19.50 +22 39 03.5 0 18.709 18.333 17.679 17.927 28.27 8.54 -3.53 8.54 9.76 noZY L07_faintJK_8 GCS9 +03 44 27.29 +25 44 41.7 0 18.818 17.787 16.965 16.902 8.52 5.25 -26.68 5.25 1.95 noZY BRB27_CFHT-PLIZ1262_PLZJ32 GCS9 Cl* Melotte 22 BRB 27 +03 45 35.26 +23 36 39.6 0 18.891 18.394 18.140 18.489 25.34 8.42 14.07 8.42 6.64 noZY L07_faintJK_7 GCS9 +03 47 05.68 +20 00 37.0 0 14.107 13.553 13.218 13.230 21.25 2.66 -50.47 2.66 2.67 noZY DH492 GCS9 Cl* Melotte 22 DH 492 +03 47 51.19 +25 26 57.9 0 18.212 17.780 17.630 9.00 noZY L07_faintJK_12 GCS9 +03 48 11.42 +20 46 42.4 0 13.091 12.550 12.217 12.230 18.39 2.65 -43.76 2.65 4.29 noZY DH558 GCS9 Cl* Melotte 22 DH 558 +03 48 32.69 +25 06 05.1 0 18.548 17.996 17.438 17.609 0.60 5.00 3.81 5.00 2.60 noZY L07_faintJK_2 GCS9 +03 48 55.41 +24 20 09.6 0 19.996 19.199 18.367 18.093 -21.91 11.56 22.88 11.56 0.29 noY Roque19 GCS9 Cl* Melotte 22 Roque 19 +03 49 09.15 +24 19 59.9 0 16.407 15.807 15.534 15.463 16.82 2.45 -9.31 2.45 0.38 noZY 18.37 GCS9 Cl* Melotte 22 MHOBD 2 +03 49 11.04 +24 20 51.1 0 12.979 12.444 12.146 12.154 16.62 2.33 -46.70 2.33 1.45 noZY HCG355_HHJ287_BPL199_DH604 GCS9 Cl* Melotte 22 HCG 355 +03 49 11.42 +24 14 24.4 0 13.693 13.150 12.839 12.818 20.72 2.34 -43.10 2.34 1.94 noZY BPL200_DH606 GCS9 Cl* Melotte 22 BPL 200 +03 49 12.20 +23 53 12.2 0 11.727 14.085 10.383 11.580 5.42 2.33 -47.80 2.33 40.02 noZY HII2195_HD23863_Tr607 GCS9 HII2195 +03 49 12.52 +24 11 12.8 0 16.410 15.750 15.245 15.233 14.85 2.44 -40.77 2.44 1.58 noZY BPL201_L07_A1_84 GCS9 Cl* Melotte 22 BPL 201 +03 49 16.79 +24 23 45.7 0 11.577 12.188 10.276 10.839 -151.15 2.33 -101.06 2.33 28.91 noZY HII2220_HD23872_Tr613 GCS9 HII2220 +03 49 18.66 +23 46 48.8 0 14.294 13.665 13.393 13.394 15.38 2.34 -44.61 2.34 0.64 noZY DH2004_610 GCS9 Cl* Melotte 22 DH 610 +03 49 21.76 +24 22 51.2 0 12.746 17.742 11.121 25.15 2.33 -49.52 2.33 24.37 noZY HII2263_HD23873_Tr622 GCS9 HII2263 +03 49 32.54 +23 55 42.4 0 13.238 12.702 12.389 12.412 14.17 2.33 -43.14 2.33 0.80 noZY HCG371_SK310_HHJ221_DH627 GCS9 Cl* Melotte 22 HCG 371 +03 49 33.12 +24 13 04.6 0 14.583 14.051 13.701 13.694 19.96 2.34 -41.58 2.34 2.40 noZY BPL210 GCS9 Cl* Melotte 22 BPL 210 +03 49 36.12 +23 56 23.1 0 13.112 12.569 12.282 12.268 16.38 2.33 -43.47 2.33 1.74 noZY HCG373_SK307_HHJ286_DH632 GCS9 Cl* Melotte 22 HCG 373 +03 49 36.53 +24 18 14.0 0 13.243 12.679 12.382 12.419 13.89 2.34 -45.09 2.34 6.76 noZY HCG375_HHJ250_BPL212_DH634 GCS9 Cl* Melotte 22 HCG 375 +03 49 56.60 +24 20 56.3 0 11.532 12.226 10.215 11.260 27.98 2.33 -35.54 2.33 12.96 noZY HII2488_HD23948_Tr688 GCS9 HII2488 +03 50 58.17 +23 55 42.5 0 13.574 13.042 12.728 12.708 21.42 2.34 -47.56 2.34 23.03 noZY HCG414_DH690 GCS9 Cl* Melotte 22 HCG 414 +03 51 11.54 +24 23 13.0 0 12.113 11.631 11.289 11.323 13.08 2.33 -44.19 2.33 0.66 noZY HCG422_SK245_DH697_Moraux2003_1 GCS9 Cl* Melotte 22 HCG 422 +03 51 12.08 +23 55 57.2 0 11.712 11.710 10.948 11.183 25.68 2.33 -44.30 2.33 3.63 noZY HII2966_SK244_DH698 GCS9 HII2966 +03 51 18.22 +24 20 26.0 0 13.809 13.281 13.036 13.013 48.24 2.34 -48.37 2.34 4.66 noZY SK236 GCS9 Cl* Melotte 22 SK 236 +03 51 20.13 +23 45 17.9 0 19.366 18.614 17.836 18.128 -20.62 7.01 -49.89 7.01 3.15 noZY BRB33_None GCS9 Cl* Melotte 22 BRB 33 +03 51 21.34 +23 52 08.5 0 14.111 13.774 13.735 13.701 -2.74 2.34 -4.49 2.34 5.70 noZY Calar7_XX.X GCS9 Cl* Melotte 22 CALAR 7 +03 51 25.35 +23 53 21.5 0 11.431 11.542 10.779 11.060 19.95 2.33 -43.33 2.33 6.34 noZY HCG430_SK229_DH711_BPL235_CFHT-Pl-21_M8_BRB14_CFHT-Pl-21 GCS9 Cl* Melotte 22 HCG 430 +03 51 25.98 +24 15 29.9 0 19.310 18.627 17.965 17.950 16.58 6.80 -16.12 6.80 2.25 noZY BRB31_None GCS9 Cl* Melotte 22 BRB 31 +03 51 29.95 +23 53 57.0 0 11.172 11.359 10.547 10.916 18.96 2.33 -38.73 2.33 7.81 noZY HII3063_Tr862_HCG431_DH713 GCS9 HII3063 +03 51 34.26 +23 47 49.8 0 13.874 13.299 13.025 12.997 16.21 2.34 -45.54 2.34 0.73 noZY BPL236_DH716_Moraux2003_71 GCS9 Cl* Melotte 22 BPL 236 +03 51 39.20 +23 51 18.1 0 13.736 13.171 12.836 12.848 13.98 2.34 -44.51 2.34 8.17 noZY BPL238_DH720 GCS9 Cl* Melotte 22 BPL 238 +03 51 42.32 +24 21 41.3 0 13.568 13.042 12.728 12.744 19.28 2.34 -41.03 2.34 3.74 noZY BPL239_DH723_Moraux2003_53 GCS9 Cl* Melotte 22 BPL 239 +03 52 54.91 +24 37 18.1 0 18.803 17.754 16.926 16.989 12.05 3.86 -46.85 3.86 2.29 noZY PLZJ37_BRB28_L07_faintJK_11 GCS9 Cl* Melotte 22 PlZJ 37 +03 54 01.44 +23 49 57.5 0 18.686 17.711 16.999 16.927 35.53 4.84 -33.95 4.84 3.15 noZY BRB29_L4.5 GCS9 Cl* Melotte 22 BRB 29 +03 54 13.41 +23 32 22.2 0 18.655 18.120 18.105 18.015 11.41 7.09 -15.53 7.09 2.97 noZY L07_faintJK_6 GCS9 +03 55 57.94 +24 41 41.6 0 18.859 18.183 18.214 17.900 -8.05 7.61 14.92 7.61 1.53 noZY L07_faintJK_3 GCS9 +03 35 18.76 +23 26 20.7 0 15.450 14.829 14.310 13.909 13.942 15.05 3.12 -38.99 3.12 1.24 noZ DH045 GCS9 Cl* Melotte 22 DH 045 +03 36 38.40 +23 08 44.2 0 14.764 14.165 13.620 13.288 13.320 25.49 3.11 -39.56 3.11 0.46 noZ DH060 GCS9 Cl* Melotte 22 DH 060 +03 42 01.68 +23 58 22.4 0 19.951 18.621 17.888 17.153 5.67 5.52 3.25 5.52 6.41 noZ int-pl-IZ-14;IPLJ0342016+235823_N GCS9 Cl* Melotte 22 IPL 14 +03 42 14.28 +22 43 02.5 0 20.062 18.517 18.045 17.495 17.568 -21.44 5.77 0.43 5.77 2.76 noZ L07_faintYJ_1 GCS9 +03 42 59.31 +25 37 38.9 0 19.814 18.238 17.388 16.646 16.572 10.48 3.76 -35.70 3.76 6.35 noZ L07_faintYJ_2 GCS9 +03 43 21.40 +24 34 42.0 0 20.092 18.782 18.309 17.574 -29.69 9.19 -11.53 9.19 0.28 noZ Roque24 GCS9 Cl* Melotte 22 Roque 24 +03 43 25.89 +24 00 51.8 0 20.716 19.423 18.592 18.203 -16.04 8.54 2.59 8.54 1.10 noZ int-pl-IZ-23;IPLJ0343258+240051_N GCS9 Cl* Melotte 22 IPL 23 +03 43 26.65 +23 41 38.8 0 20.084 19.258 18.586 17.889 0.50 8.68 -12.21 8.68 1.87 noZ int-pl-IZ-4;IPLJ0343266+234139_N GCS9 Cl* Melotte 22 IPL 4 +03 43 49.93 +24 03 37.6 0 20.056 18.915 18.060 17.609 10.66 6.29 -12.02 6.29 7.85 noZ int-pl-IZ-17;IPLJ0343498+240337_N GCS9 Cl* Melotte 22 IPL 17 +03 44 00.27 +24 33 25.1 0 12.731 11.579 13.069 11.261 -135.16 3.07 159.00 3.07 1.29 noZ HII232_HD23194_Tr074 GCS9 HII232 +03 44 30.52 +24 21 17.4 0 19.589 18.397 17.705 17.310 17.563 9.90 4.99 2.04 4.99 5.70 noZ L07_faintYJ_3 GCS9 +03 44 31.28 +25 35 14.7 0 19.426 18.269 17.398 16.675 16.604 10.61 3.73 -39.40 3.73 1.93 noZ BRB22_CFHT-PLIZ2141_PLZJ61_L07_faintYJ_4 GCS9 Cl* Melotte 22 BRB 22 +03 44 47.33 +24 21 35.7 0 19.688 18.409 17.506 16.906 16.979 35.23 3.66 -31.91 3.66 3.07 noZ L07_faintYJ_5 GCS9 +03 44 49.73 +22 51 10.6 0 19.950 19.044 18.679 18.196 57.62 9.24 -32.33 9.24 2.37 noZ int-pl-IZ-51;IPLJ0344496+225111_N GCS9 Cl* Melotte 22 IPL 51 +03 44 51.70 +23 02 30.4 0 19.970 19.021 18.836 18.867 -3.01 8.34 -2.50 8.34 2.83 noZ int-pl-IZ-61;IPLJ0344516+230230_N GCS9 Cl* Melotte 22 IPL 61 +03 45 01.81 +24 04 17.7 0 20.559 19.037 18.830 18.050 18.035 34.71 7.32 -15.30 7.32 2.57 noZ L07_faintYJ_6 GCS9 +03 45 04.22 +26 40 44.6 0 14.156 13.564 13.007 12.685 12.707 21.95 3.00 -39.21 3.00 0.12 noZ HHJ185_DH371_Moraux2003_56 GCS9 Cl* Melotte 22 HHJ 185 +03 45 14.52 +22 29 28.6 0 19.756 18.383 17.410 16.493 14.00 4.20 -53.10 4.20 1.66 noZ int-pl-IZ-69;IPLJ0345144+222929_Y GCS9 Cl* Melotte 22 IPL 69 +03 45 20.05 +22 33 25.5 0 19.320 18.432 17.776 17.052 35.01 5.33 -16.01 5.33 2.52 noZ int-pl-IZ-75;IPLJ0345200+223325_N GCS9 Cl* Melotte 22 IPL 75 +03 45 27.73 +23 10 13.1 0 13.121 12.596 11.997 11.686 11.703 10.78 2.28 -31.45 2.28 3.05 noZ SK520 GCS9 Cl* Melotte 22 SK 520 +03 45 33.30 +23 34 34.3 0 19.697 18.123 17.189 16.502 16.479 20.21 2.91 -35.76 2.91 1.70 noZ L07_faintYJ_7 GCS9 +03 45 33.99 +23 11 06.5 0 14.420 13.828 13.290 12.974 12.977 22.86 2.28 -40.74 2.28 3.12 noZ DH396 GCS9 Cl* Melotte 22 DH 396 +03 45 35.06 +21 56 22.2 0 19.470 18.169 17.595 17.136 17.002 -7.69 5.05 -13.84 5.05 1.35 noZ L07_faintYJ_8 GCS9 +03 45 42.26 +22 48 07.5 0 16.198 15.598 15.013 14.722 1.77 2.31 -16.21 2.31 5.74 noZ int-pl-IZ-50;2MASSJ0345422+224807_N GCS9 Cl* Melotte 22 IPL 50 +03 45 55.17 +22 51 31.0 0 16.109 15.358 14.804 14.433 14.420 14.67 2.30 -48.69 2.30 5.52 noZ int-pl-IZ-42_2MASSJ0345551+225131_Y_L07_243 GCS9 Cl* Melotte 22 IPL 42 +03 46 08.43 +22 37 57.3 0 20.002 19.462 18.771 17.791 22.24 9.40 21.27 9.40 3.53 noZ int-pl-IZ-73;IPLJ0346083+223757_N GCS9 Cl* Melotte 22 IPL 73 +03 46 09.59 +22 42 37.2 0 13.286 12.709 12.202 11.874 11.888 14.57 2.28 -37.86 2.28 3.40 noZ HCG217_SK487_HHJ297_BPL91_DH424 GCS9 Cl* Melotte 22 HCG 217 +03 46 13.85 +22 42 19.6 0 13.878 13.286 12.749 12.429 12.464 15.81 2.28 -42.30 2.28 3.16 noZ HHJ200_BPL92_DH431 GCS9 Cl* Melotte 22 HHJ 200 +03 46 27.94 +23 42 39.0 0 19.824 18.591 17.757 17.478 17.275 -7.35 4.50 -5.31 4.50 4.50 noZ L07_faintYJ_9 GCS9 +03 46 29.11 +22 59 47.6 0 19.089 17.740 16.805 15.955 15.935 14.85 2.74 -37.94 2.74 1.38 noZ L07_faintYJ_10 GCS9 +03 46 43.13 +22 35 19.9 0 19.439 18.710 18.058 17.757 4.51 7.62 -19.89 7.62 4.95 noZ int-pl-IZ-40;IPLJ0346430+223520_N GCS9 Cl* Melotte 22 IPL 40 +03 46 51.06 +22 34 28.6 0 19.887 18.580 18.106 17.309 17.424 10.21 5.90 -91.85 5.90 0.99 noZ L07_faintYJ_11 GCS9 +03 47 23.86 +23 08 56.9 0 13.440 12.896 12.358 12.060 12.050 16.54 2.28 -42.81 2.28 0.72 noZ HCG270_SK427_HHJ275_DH511 GCS9 Cl* Melotte 22 HCG 270 +03 47 23.97 +22 42 37.3 0 16.123 15.354 14.809 14.402 14.361 -1.91 2.31 -9.05 2.31 13.33 noZ BPL142_int-pl-IZ-37;RPLJ0347239+22423_Roque17_Y GCS9 Cl* Melotte 22 BPL 142 +03 47 25.58 +22 41 05.7 0 16.097 15.475 14.892 14.567 50.44 2.31 -53.06 2.31 7.71 noZ int-pl-IZ-36;2MASSJ0347255+224105_N GCS9 Cl* Melotte 22 IPL 36 +03 47 34.79 +22 48 04.5 0 13.538 13.004 12.450 12.140 12.143 14.52 2.28 -42.93 2.28 3.56 noZ HCG284_HHJ278_BPL148_DH524 GCS9 Cl* Melotte 22 HCG 284 +03 47 38.47 +23 56 27.7 0 19.964 18.359 17.478 16.688 16.588 13.93 3.28 -42.66 3.28 0.74 noZ L07_faintYJ_12 GCS9 +03 47 41.41 +22 44 32.9 0 16.111 15.521 14.865 14.511 14.527 13.41 2.31 0.90 2.31 15.45 noZ int-pl-IZ-47_RPLJ0347413+224433_Roque48_N GCS9 Cl* Melotte 22 IPL 47 +03 47 43.67 +26 48 12.7 0 14.029 13.455 12.910 12.602 12.617 20.73 2.99 -29.14 2.99 0.15 noZ HHJ193 GCS9 Cl* Melotte 22 HHJ 193 +03 47 44.16 +24 57 24.0 0 20.205 18.716 17.889 17.262 17.066 10.23 4.17 -30.52 4.17 2.74 noZ L07_faintYJ_13 GCS9 +03 47 46.52 +24 55 46.5 0 19.536 18.271 17.418 16.608 16.543 20.20 3.10 -45.92 3.10 3.57 noZ L07_faintYJ_14 GCS9 +03 47 54.17 +22 39 25.5 0 14.311 13.757 13.214 12.899 12.921 18.12 2.28 -43.32 2.28 6.76 noZ HHJ145_BPL161 GCS9 Cl* Melotte 22 HHJ 145 +03 48 04.95 +22 51 29.4 0 19.175 18.460 17.613 17.200 9.01 5.49 -14.56 5.49 4.62 noZ int-pl-IZ-46;IPLJ0348048+225129_N GCS9 Cl* Melotte 22 IPL 46 +03 48 05.47 +21 57 31.2 0 20.160 18.769 17.961 17.316 17.407 2.75 6.05 -28.88 6.05 3.01 noZ L07_faintYJ_15 GCS9 +03 48 15.65 +25 50 08.9 0 19.868 18.490 17.658 16.734 16.758 10.53 4.42 -48.92 4.42 1.86 noZ L07_faintYJ_16 GCS9 +03 48 19.48 +23 56 24.6 0 20.187 19.592 18.702 18.018 18.761 -0.62 6.47 -7.62 6.47 0.70 noZ Festin98_011 GCS9 Cl* Melotte 22 NPL 011 +03 48 39.57 +23 56 11.7 0 20.722 19.221 18.750 18.179 18.196 3.85 6.28 -27.06 6.28 1.63 noZ L07_faintYJ_17 GCS9 +03 48 45.69 +25 42 01.5 0 13.138 12.569 11.988 11.686 11.709 18.97 2.27 -44.69 2.27 0.64 noZ HHJ333_DH591 GCS9 Cl* Melotte 22 HHJ 333 +03 48 49.37 +22 45 50.0 0 19.901 19.049 18.457 18.022 18.177 2.45 9.50 2.39 9.50 2.37 noZ Roque26 GCS9 Cl* Melotte 22 Roque 26 +03 49 12.65 +25 42 07.2 0 13.114 12.530 12.019 11.673 11.710 12.39 2.27 -44.95 2.27 17.46 noZ HCG365_SK325_HHJ331_DH608 GCS9 Cl* Melotte 22 HCG 365 +03 50 15.96 +24 23 28.6 0 20.125 18.728 17.791 16.895 16.836 19.25 3.46 -31.64 3.46 1.54 noZ L07_faintYJ_18 GCS9 +03 50 39.55 +25 02 54.6 0 19.786 18.225 17.358 16.563 16.529 19.29 3.10 -44.42 3.10 2.83 noZ BRB23_L3.5_L07_faintYJ_19 GCS9 Cl* Melotte 22 BRB 23 +03 50 41.21 +25 39 30.4 0 14.791 14.167 13.614 13.272 13.262 17.33 2.28 -46.49 2.28 6.53 noZ DH682 GCS9 Cl* Melotte 22 DH 682 +03 50 56.62 +25 35 06.3 0 12.970 12.392 11.893 11.585 11.581 14.82 2.27 -42.39 2.27 7.19 noZ HCG412_SK253_DH688 GCS9 Cl* Melotte 22 HCG 412 +03 51 29.47 +24 00 37.4 0 19.701 18.424 17.490 16.696 16.697 12.50 2.96 -40.44 2.96 3.20 noZ L07_faintYJ_20 GCS9 +03 51 41.62 +25 55 45.4 0 20.584 19.121 18.467 18.048 18.073 -19.45 8.36 -21.24 8.36 0.66 noZ L07_faintYJ_21 GCS9 +03 52 05.33 +25 37 34.0 0 18.965 17.690 17.946 17.510 17.886 153.64 8.22 -46.42 8.22 14.51 noZ L07_faintYJ_22 GCS9 +03 52 27.19 +23 12 08.0 0 19.317 18.006 17.090 16.359 16.438 23.09 3.70 -40.27 3.70 0.70 noZ L07_faintYJ_23 GCS9 +03 52 34.75 +22 56 04.5 0 19.602 18.412 17.568 17.084 17.019 34.50 4.45 -2.97 4.45 0.86 noZ L07_faintYJ_24 GCS9 +03 52 39.15 +24 46 29.4 0 19.271 18.066 17.106 16.509 16.474 18.41 3.31 -44.34 3.31 1.97 noZ BRB20_L1.0_CFHT-PLIZ-35_L07_faintYJ_25 GCS9 Cl* Melotte 22 BRB 20 +03 52 59.62 +24 42 35.6 0 20.657 19.082 18.693 18.234 18.298 1.65 9.10 16.05 9.10 3.50 noZ L07_faintYJ_26 GCS9 +03 52 59.70 +23 51 55.7 0 19.756 18.587 18.105 17.405 17.261 2.86 5.19 4.78 5.19 1.78 noZ BRB25_None GCS9 Cl* Melotte 22 BRB 25 +03 53 18.93 +23 12 39.1 0 20.060 18.328 17.612 16.887 16.883 -24.95 4.25 2.99 4.25 3.50 noZ L07_faintYJ_27 GCS9 +03 53 19.24 +24 53 30.2 0 18.319 17.810 17.093 16.905 16.967 5.95 3.90 1.25 3.90 2.08 noZ PLZJ112_None GCS9 Cl* Melotte 22 PlZJ 112 +03 53 24.50 +25 02 07.0 0 13.975 13.398 12.853 12.542 12.550 14.26 2.28 -40.35 2.28 34.86 noZ HCG471_HHJ170_BPL286_DH785 GCS9 Cl* Melotte 22 HCG 471 +03 53 40.96 +24 25 09.6 0 12.946 12.445 11.889 11.573 11.626 17.52 2.28 -38.69 2.28 14.78 noZ HCG473_SK122_HHJ372_BPL288 GCS9 Cl* Melotte 22 HCG 473 +03 53 58.23 +24 25 08.8 0 16.052 15.397 14.825 14.435 14.461 14.64 2.31 -34.72 2.31 1.63 noZ BPL295 GCS9 Cl* Melotte 22 BPL 295 +03 54 00.38 +24 34 49.8 0 15.467 14.807 14.279 13.873 13.921 17.48 2.29 -38.45 2.29 1.22 noZ BPL297_Moraux2003_100 GCS9 Cl* Melotte 22 BPL 297 +03 54 10.28 +23 41 40.0 0 19.260 18.143 17.171 16.393 16.395 13.79 3.22 -50.49 3.22 4.20 noZ PLZJ4_BRB21_L3.0_L07_faintYJ_28 GCS9 Cl* Melotte 22 PlZJ 4 +03 54 30.49 +25 11 21.8 0 19.979 18.677 18.127 17.536 17.561 -8.62 5.35 -35.68 5.35 0.30 noZ L07_faintYJ_29 GCS9 +03 55 49.29 +24 53 47.2 0 16.057 15.302 14.707 14.299 14.299 -23.95 2.30 -51.07 2.30 9.97 noZ BPL331 GCS9 Cl* Melotte 22 BPL 331 +03 55 58.17 +24 32 59.6 0 13.269 12.731 12.207 11.901 11.914 12.73 2.28 -40.90 2.28 6.04 noZ DH2004_824 GCS9 Cl* Melotte 22 DH 824 +03 55 58.84 +24 57 39.9 0 13.575 12.975 12.458 12.124 12.142 14.04 2.28 -42.01 2.28 6.81 noZ HHJ260_BPL333_DH825 GCS9 Cl* Melotte 22 HHJ 260 +03 56 11.39 +25 03 36.5 0 15.908 15.193 14.655 14.257 14.290 16.67 2.30 -41.79 2.30 70.90 noZ BPL334_L07_A1_114_L07_215 GCS9 Cl* Melotte 22 BPL 334 +03 41 52.33 +24 15 12.7 0 20.667 19.205 18.358 18.067 0.214 -19.58 9.32 -9.18 9.32 0.78 noY int-pl-IZ-87;IPLJ0341523+241512_N GCS9 Cl* Melotte 22 IPL 87 +03 46 28.19 +22 48 57.7 0 15.194 14.082 13.518 13.192 13.168 45.73 2.28 -58.16 2.28 2.41 noY HHJ87_BPL104 GCS9 Cl* Melotte 22 HHJ 87 +03 46 39.31 +22 47 48.2 0 17.213 16.235 15.626 15.376 15.346 -14.43 2.42 -20.96 2.42 3.51 noY HHJ1 GCS9 Cl* Melotte 22 HHJ 1 +03 46 42.37 +22 41 01.5 0 16.964 15.709 15.192 14.813 0.012 50.06 2.35 -12.61 2.35 5.12 noY int-pl-IZ-41;2MASSJ0346423+224101_N GCS9 Cl* Melotte 22 IPL 41 +03 46 59.02 +22 41 40.3 0 20.165 18.548 18.140 17.540 0.150 48.91 7.04 -26.50 7.04 1.44 noY int-pl-IZ-39;IPLJ0346589+224140_N GCS9 Cl* Melotte 22 IPL 39 +03 51 26.60 +22 48 45.9 0 18.883 16.679 15.938 15.325 15.339 26.84 2.54 -77.34 2.54 6.64 noY L07_A1_96 GCS9 XTENSION= 'TABLE ' / Ascii Table Extension (TAB and NEWLINE sep) BITPIX = 8 / Character data NAXIS = 2 / Simple 2-D matrix NAXIS1 = 180 / Number of bytes per record NAXIS2 = 1314 / Number of records PCOUNT = 0 / Get rid of random parameters GCOUNT = 1 / Only one group (isn't it obvious?) TFIELDS = 23 / Number of data fields (columns) EXTNAME = 'J_MNRAS_422_1495_tablec1' / Identification of the table CDS-NAME= 'J/MNRAS/422/1495/tablec1' / Table name in METAtab Coordinates, near-infrared (ZYJHK1K2) photometry and proper motions all new Pleiades member candidates identified in the UKIDSS GCS DR9 with the probabilistic and standard selection methods TBCOL1 = 2 / UCD=pos.eq.ra;meta.main char:12 offset=1 TFORM1 = 'A12 ' / Fortran Format TTYPE1 = 'RAJ2000 ' / Right ascension (J2000) TBCOL2 = 15 / UCD=pos.eq.dec;meta.main char:12 offset=14 TFORM2 = 'A12 ' / Fortran Format TTYPE2 = 'DEJ2000 ' / Declination (J2000) TBCOL3 = 28 / UCD=meta.number short: ....... offset=27 TFORM3 = 'I3 ' / Fortran Format TTYPE3 = 'M ' / Multiplicity of this star (table D1) TNULL3 = -32768 / NULL definition TBCOL4 = 32 / UCD=phot.mag;em.opt.I float: . offset=31 TFORM4 = 'F6.3 ' / Fortran Format TTYPE4 = 'Zmag ' / ? UKIDSS Z magnitude TUNIT4 = 'mag ' / magnitude TBCOL5 = 39 / UCD=phot.mag;em.IR.J float: .. offset=38 TFORM5 = 'F6.3 ' / Fortran Format TTYPE5 = 'Ymag ' / ? UKIDSS Y magnitude TUNIT5 = 'mag ' / magnitude TBCOL6 = 46 / UCD=phot.mag;em.IR.J float: .. offset=45 TFORM6 = 'F6.3 ' / Fortran Format TTYPE6 = 'Jmag ' / UKIDSS J magnitude TUNIT6 = 'mag ' / magnitude TBCOL7 = 53 / UCD=phot.mag;em.IR.H float: .. offset=52 TFORM7 = 'F6.3 ' / Fortran Format TTYPE7 = 'Hmag ' / UKIDSS H magnitude TUNIT7 = 'mag ' / magnitude TBCOL8 = 60 / UCD=phot.mag;em.IR.K float: .. offset=59 TFORM8 = 'F6.3 ' / Fortran Format TTYPE8 = 'K1mag ' / UKIDSS K magnitude, first epoch TUNIT8 = 'mag ' / magnitude TBCOL9 = 67 / UCD=phot.mag;em.IR.K float: .. offset=66 TFORM9 = 'F6.3 ' / Fortran Format TTYPE9 = 'K2mag ' / ? UKIDSS K magnitude, second epoch TUNIT9 = 'mag ' / magnitude TBCOL10 = 74 / UCD=pos.pm;pos.eq.ra float: .. offset=73 TFORM10 = 'F6.2 ' / Fortran Format TTYPE10 = 'pmRA ' / ? Proper motion along RA, pmRA*cosDE TUNIT10 = 'mas/yr ' / milli-second of arc per year TBCOL11 = 81 / UCD=stat.error;pos.pm;pos.eq.ra fl offset=80 TFORM11 = 'F5.2 ' / Fortran Format TTYPE11 = 'e_pmRA ' / ? rms uncertainty on pmRA*cosDE TUNIT11 = 'mas/yr ' / milli-second of arc per year TBCOL12 = 87 / UCD=pos.pm;pos.eq.dec float: . offset=86 TFORM12 = 'F6.2 ' / Fortran Format TTYPE12 = 'pmDE ' / ? Proper motion along DE TUNIT12 = 'mas/yr ' / milli-second of arc per year TBCOL13 = 94 / UCD=stat.error;pos.pm;pos.eq.dec f offset=93 TFORM13 = 'F5.2 ' / Fortran Format TTYPE13 = 'e_pmDE ' / ? rms uncertainty on pmDE TUNIT13 = 'mas/yr ' / milli-second of arc per year TBCOL14 = 100 / UCD=stat.probability float: .. offset=99 TFORM14 = 'F5.2 ' / Fortran Format TTYPE14 = 'Mmb ' / [0/1]? Membership probability TBCOL15 = 106 / UCD=meta.note short: ......... offset=105 TFORM15 = 'I2 ' / Fortran Format TTYPE15 = 'n_Mmb ' / [1/12] Method used for identification (1) TBCOL16 = 109 / UCD=meta.ref.url char:4 ...... offset=108 TFORM16 = 'A4 ' / Fortran Format TTYPE16 = 'GCS9 ' / Display the UKIDSS GCS-DR9 data, Cat. II/319 (link) TBCOL17 = 114 / UCD=meta.id char:24* ......... offset=113 TFORM17 = 'A24 ' / Fortran Format TTYPE17 = 'SimbadName' / Simbad column added by the CDS TBCOL18 = 139 / UCD=stat.error;phot.mag;em.opt.I f offset=138 TFORM18 = 'F6.3 ' / Fortran Format TTYPE18 = 'e_Zmag ' / ? rms uncertainty on Zmag TUNIT18 = 'mag ' / magnitude TBCOL19 = 146 / UCD=stat.error;phot.mag;em.opt.B f offset=145 TFORM19 = 'F6.3 ' / Fortran Format TTYPE19 = 'e_Ymag ' / ? rms uncertainty on Ymag TUNIT19 = 'mag ' / magnitude TBCOL20 = 153 / UCD=stat.error;phot.mag;em.IR.J fl offset=152 TFORM20 = 'F6.3 ' / Fortran Format TTYPE20 = 'e_Jmag ' / rms uncertainty on Jmag TUNIT20 = 'mag ' / magnitude TBCOL21 = 160 / UCD=stat.error;phot.mag;em.IR.H fl offset=159 TFORM21 = 'F6.3 ' / Fortran Format TTYPE21 = 'e_Hmag ' / rms uncertainty on Hmag TUNIT21 = 'mag ' / magnitude TBCOL22 = 167 / UCD=stat.error;phot.mag;em.IR.K fl offset=166 TFORM22 = 'F6.3 ' / Fortran Format TTYPE22 = 'e_K1mag ' / rms uncertainty on K1mag TUNIT22 = 'mag ' / magnitude TBCOL23 = 174 / UCD=stat.error;phot.mag;em.IR.K fl offset=173 TFORM23 = 'F6.3 ' / Fortran Format TTYPE23 = 'e_K2mag ' / ? rms uncertainty on K2mag TUNIT23 = 'mag ' / magnitude END +03 36 01.950 +27 11 04.70 1 16.248 15.492 14.774 14.234 13.810 13.782 15.79 3.79 -42.70 3.79 0.72 12 GCS9 UGCS J033601.95+271104.7 0.006 0.004 0.004 0.004 0.004 0.003 +03 51 27.880 +27 58 55.70 0 15.548 14.979 14.360 13.821 13.468 13.470 22.99 2.95 -41.88 2.95 0.74 12 GCS9 UGCS J035127.88+275855.7 0.005 0.003 0.004 0.003 0.003 0.002 +03 45 06.250 +28 42 19.10 0 14.628 14.156 13.575 13.049 12.714 12.725 22.01 2.95 -39.53 2.95 0.85 12 GCS9 Cl* Melotte 22 DH 374 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 06.210 +28 08 32.00 0 15.387 14.854 14.239 13.731 13.422 13.401 13.90 4.95 -38.21 4.95 0.62 12 GCS9 Cl* Melotte 22 DH 125 0.004 0.004 0.004 0.004 0.003 0.002 +03 50 54.420 +27 26 13.00 0 15.915 15.313 14.686 14.132 13.756 13.726 19.89 2.89 -41.91 2.89 0.88 12 GCS9 UGCS J035054.41+272612.9 0.006 0.004 0.004 0.004 0.004 0.003 +03 51 30.600 +27 22 49.50 0 14.946 14.405 13.807 13.265 12.919 12.927 22.57 2.88 -41.78 2.88 0.89 12 GCS9 Cl* Melotte 22 DH 714 0.004 0.003 0.003 0.002 0.002 0.002 +03 41 33.580 +27 09 50.10 0 15.162 14.567 13.912 13.377 13.020 13.045 19.34 3.29 -43.65 3.29 0.89 12 GCS9 Cl* Melotte 22 DH 178 0.004 0.003 0.003 0.002 0.002 0.002 +03 48 57.510 +27 38 25.60 0 15.618 15.106 14.510 13.963 13.618 13.613 16.32 2.89 -40.24 2.89 0.86 12 GCS9 UGCS J034857.50+273825.6 0.005 0.004 0.004 0.003 0.004 0.003 +03 41 38.790 +27 41 07.10 0 12.878 12.514 12.023 11.588 11.219 11.285 24.87 3.28 -39.81 3.28 0.65 12 GCS9 UGCS J034138.79+274107.0 0.001 0.001 0.001 0.001 0.001 0.001 +03 45 40.770 +28 32 05.80 0 15.158 14.639 14.012 13.516 13.166 13.169 22.95 2.96 -42.47 2.96 0.75 12 GCS9 Cl* Melotte 22 DH 399 0.004 0.003 0.003 0.003 0.003 0.002 +03 46 23.740 +26 34 23.00 0 14.730 14.243 13.647 13.132 12.839 12.799 24.18 2.93 -45.83 2.93 0.73 12 GCS9 Cl* Melotte 22 DH 444 0.003 0.003 0.002 0.002 0.002 0.001 +03 47 56.660 +26 31 50.90 0 12.905 12.555 12.064 11.504 11.210 11.250 23.91 2.93 -40.70 2.93 0.76 12 GCS9 Cl* Melotte 22 DH 543 0.001 0.001 0.001 0.001 0.001 0.001 +03 47 28.410 +26 32 05.50 0 14.243 13.779 13.199 12.652 12.361 12.363 20.47 2.93 -43.56 2.93 0.93 12 GCS9 Cl* Melotte 22 DH 516 0.003 0.002 0.002 0.002 0.001 0.001 +03 50 39.700 +26 34 20.10 0 14.506 14.061 13.500 12.959 12.652 12.675 20.26 2.95 -42.79 2.95 0.94 12 GCS9 UGCS J035039.70+263420.0 0.003 0.003 0.002 0.002 0.002 0.001 +03 50 39.700 +26 34 17.70 0 14.817 14.334 13.753 13.228 12.895 12.916 19.47 2.95 -42.84 2.95 0.94 12 GCS9 UGCS J035039.69+263417.7 0.004 0.003 0.003 0.002 0.002 0.001 +03 41 56.490 +27 02 57.90 0 14.816 14.343 13.746 13.209 12.923 12.902 18.25 3.29 -47.59 3.29 0.86 12 GCS9 Cl* Melotte 22 DH 195 0.003 0.003 0.003 0.002 0.002 0.002 +03 41 53.050 +27 04 42.90 0 14.776 14.268 13.678 13.174 12.842 12.833 15.90 3.29 -42.59 3.29 0.92 12 GCS9 Cl* Melotte 22 DH 192 0.003 0.003 0.003 0.002 0.002 0.002 +04 05 52.090 +27 09 13.10 0 15.196 14.742 14.147 13.596 13.266 13.266 22.95 2.96 -45.92 2.96 0.66 12 GCS9 UGCS J040552.08+270913.1 0.004 0.003 0.003 0.002 0.002 0.002 +03 43 44.090 +25 39 49.50 0 15.071 14.589 13.986 13.428 13.120 13.122 17.56 2.23 -44.02 2.23 0.90 12 GCS9 Cl* Melotte 22 MBSC 72 0.004 0.003 0.003 0.002 0.002 0.002 +03 44 10.760 +25 37 38.20 0 14.362 13.886 13.296 12.733 12.437 12.445 11.81 2.23 -44.39 2.23 0.62 12 GCS9 Cl* Melotte 22 DH 316 0.003 0.002 0.002 0.002 0.002 0.001 +03 43 34.140 +25 35 25.80 0 13.777 13.360 12.813 12.234 11.958 11.947 16.72 2.23 -43.78 2.23 0.93 12 GCS9 V* V622 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 25.080 +25 34 03.90 0 15.079 14.489 13.830 13.302 12.949 12.973 19.98 2.23 -41.60 2.23 0.88 12 GCS9 Cl* Melotte 22 HHJ 100 0.004 0.003 0.003 0.002 0.002 0.002 +03 43 52.790 +25 29 30.30 0 14.450 13.990 13.414 12.853 12.576 12.580 22.91 2.23 -45.90 2.23 0.83 12 GCS9 Cl* Melotte 22 DH 298 0.003 0.003 0.003 0.002 0.002 0.001 +03 43 53.880 +25 28 30.00 0 13.156 12.789 12.268 11.797 11.424 11.455 23.08 2.23 -46.36 2.23 0.76 12 GCS9 V* MQ Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 43 37.320 +25 24 32.00 0 13.423 13.034 12.513 11.985 11.664 11.701 12.47 2.23 -45.17 2.23 0.69 12 GCS9 V* LY Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 33.490 +26 14 53.10 0 13.610 13.172 12.622 12.070 11.769 11.768 18.22 2.94 -41.78 2.94 0.93 12 GCS9 Cl* Melotte 22 DH 347 0.002 0.002 0.002 0.001 0.001 0.001 +03 54 46.530 +25 31 34.90 0 14.741 14.146 13.512 12.988 12.618 12.627 21.22 2.94 -45.56 2.94 0.90 12 GCS9 Cl* Melotte 22 DH 808 0.003 0.003 0.002 0.002 0.002 0.001 +03 43 07.580 +25 34 29.00 0 14.063 13.611 13.036 12.459 12.172 12.204 19.71 2.23 -40.51 2.23 0.92 12 GCS9 V* V845 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 42 43.810 +25 32 06.20 0 13.613 13.213 12.673 12.087 11.829 11.818 18.81 2.23 -44.63 2.23 0.93 12 GCS9 Cl* Melotte 22 HHJ 341 0.002 0.002 0.002 0.001 0.001 0.001 +03 56 46.600 +26 21 00.40 0 15.182 14.574 13.929 13.398 13.046 13.035 17.21 2.62 -47.43 2.62 0.77 12 GCS9 Cl* Melotte 22 DH 843 0.004 0.003 0.003 0.002 0.003 0.002 +03 43 11.770 +25 31 31.90 0 16.035 15.427 14.767 14.237 13.883 13.872 18.38 2.25 -44.54 2.25 0.72 12 GCS9 UGCS J034311.76+253131.9 0.007 0.005 0.005 0.004 0.005 0.003 +03 47 25.900 +25 26 26.40 0 14.461 13.904 13.274 12.733 12.387 12.372 15.47 2.23 -44.18 2.23 0.91 12 GCS9 Cl* Melotte 22 MBSC 49 0.003 0.002 0.002 0.002 0.001 0.001 +03 47 37.350 +25 20 02.30 0 14.391 13.912 13.308 12.736 12.427 12.407 16.90 2.23 -44.53 2.23 0.93 12 GCS9 Cl* Melotte 22 MBSC 43 0.003 0.002 0.002 0.002 0.002 0.001 +03 36 42.670 +28 21 05.90 0 15.297 14.744 14.124 13.558 13.240 13.241 21.45 4.94 -39.70 4.94 0.78 12 GCS9 Cl* Melotte 22 DH 62 0.004 0.003 0.004 0.003 0.002 0.002 +03 45 58.800 +26 20 01.70 0 15.269 14.666 14.045 13.498 13.144 13.138 18.44 2.95 -38.73 2.95 0.81 12 GCS9 Cl* Melotte 22 DH 413 0.005 0.004 0.003 0.003 0.003 0.002 +03 38 43.300 +25 22 26.90 0 13.103 12.661 12.090 11.591 11.286 11.293 23.70 2.95 -41.82 2.95 0.79 12 GCS9 V* V783 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 41 11.540 +26 16 18.80 0 15.386 14.841 14.239 13.677 13.324 13.349 21.71 3.00 -37.68 3.00 0.60 12 GCS9 UGCS J034111.53+261618.8 0.005 0.003 0.004 0.003 0.003 0.002 +03 51 13.850 +25 23 11.30 0 13.741 13.323 12.796 12.246 11.944 12.001 16.21 2.23 -43.01 2.23 0.93 12 GCS9 V* V801 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 35 39.800 +25 38 45.20 0 14.957 14.508 13.916 13.319 13.037 15.42 5.32 -37.37 5.32 0.68 12 GCS9 Cl* Melotte 22 DH 46 0.003 0.003 0.003 0.003 0.001 +04 03 25.000 +28 32 23.60 0 14.208 13.836 13.311 12.724 12.453 12.501 24.32 2.96 -39.19 2.96 0.66 12 GCS9 UGCS J040325.00+283223.6 0.003 0.002 0.002 0.001 0.002 0.001 +03 55 56.430 +25 17 59.80 0 13.549 13.137 12.580 11.969 11.690 11.703 19.79 2.93 -43.08 2.93 0.94 12 GCS9 V* V690 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 50 08.420 +25 32 55.70 0 14.171 13.715 13.151 12.599 12.306 12.338 16.57 2.23 -43.79 2.23 0.93 12 GCS9 V* V671 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 49 47.040 +25 42 36.80 0 14.023 13.513 12.863 12.308 11.966 11.992 18.14 2.23 -42.94 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 638 0.002 0.002 0.002 0.001 0.001 0.001 +03 54 11.490 +25 18 42.70 0 14.598 14.131 13.552 13.030 12.710 12.697 22.79 2.94 -43.58 2.94 0.88 12 GCS9 V* V887 Tau 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 23.060 +25 29 47.60 0 13.430 13.000 12.472 11.938 11.623 11.665 18.22 2.95 -41.47 2.95 0.93 12 GCS9 V* KO Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 41 59.750 +26 27 39.60 0 14.578 14.100 13.496 12.962 12.621 12.641 25.40 3.00 -41.25 3.00 0.64 12 GCS9 Cl* Melotte 22 DH 200 0.003 0.002 0.003 0.002 0.002 0.001 +03 37 24.150 +25 43 20.10 0 12.730 12.420 11.937 11.360 11.085 11.153 22.40 2.95 -44.05 2.95 0.78 12 GCS9 UGCS J033724.15+254320.1 0.001 0.001 0.001 0.001 0.001 0.001 +03 46 45.780 +25 27 30.20 0 13.675 13.271 12.734 12.160 11.896 11.911 15.18 2.23 -45.47 2.23 0.88 12 GCS9 V* V532 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 34 47.540 +26 22 13.40 0 14.564 14.112 13.529 12.977 12.672 12.674 24.94 3.30 -39.43 3.30 0.60 12 GCS9 Cl* Melotte 22 DH 39 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 04.750 +25 22 50.00 0 13.074 12.642 12.061 11.601 11.223 11.305 21.56 2.23 -40.41 2.23 0.87 12 GCS9 V* V452 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 15.560 +26 48 03.60 0 15.315 14.815 14.230 13.688 13.348 13.372 21.02 3.00 -38.68 3.00 0.74 12 GCS9 Cl* Melotte 22 DH 102 0.004 0.003 0.004 0.003 0.003 0.002 +03 47 43.880 +26 13 26.80 0 14.714 14.225 13.655 13.113 12.807 12.804 23.14 2.93 -41.65 2.93 0.86 12 GCS9 Cl* Melotte 22 DH 534 0.003 0.003 0.003 0.002 0.002 0.001 +03 47 30.590 +26 16 44.50 0 13.801 13.394 12.848 12.305 12.027 12.001 19.30 2.93 -44.23 2.93 0.93 12 GCS9 V* V456 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 21.940 +26 22 47.20 0 14.456 14.014 13.425 12.835 12.542 12.547 14.19 2.93 -47.46 2.93 0.71 12 GCS9 Cl* Melotte 22 DH 508 0.003 0.002 0.002 0.002 0.002 0.001 +03 40 11.040 +25 23 26.60 0 14.790 14.309 13.733 13.227 12.896 12.905 19.42 2.96 -38.70 2.96 0.87 12 GCS9 Cl* Melotte 22 DH 129 0.004 0.003 0.003 0.002 0.002 0.002 +03 40 14.910 +25 19 18.70 0 13.114 12.762 12.285 11.701 11.448 11.477 21.11 2.95 -40.08 2.95 0.87 12 GCS9 Cl* Melotte 22 DH 131 0.002 0.001 0.001 0.001 0.001 0.001 +03 44 04.910 +26 58 10.50 0 15.115 14.597 13.992 13.447 13.119 13.096 16.42 2.94 -41.32 2.94 0.88 12 GCS9 UGCS J034404.91+265810.5 0.004 0.003 0.003 0.003 0.003 0.002 +03 51 39.510 +26 52 55.60 0 15.480 14.951 14.332 13.794 13.454 13.448 20.14 2.96 -44.60 2.96 0.87 12 GCS9 UGCS J035139.51+265255.5 0.005 0.004 0.004 0.003 0.003 0.002 +03 51 55.240 +26 57 40.20 0 13.156 12.689 12.074 11.716 11.274 11.253 17.80 2.95 -40.13 2.95 0.91 12 GCS9 UGCS J035155.24+265740.1 0.002 0.001 0.001 0.001 0.001 0.001 +03 52 13.200 +26 11 45.30 0 13.003 12.662 12.154 11.606 11.285 11.326 20.95 2.95 -46.17 2.95 0.88 12 GCS9 Cl* Melotte 22 DH 745 0.002 0.001 0.001 0.001 0.001 0.001 +03 52 12.190 +26 22 08.90 0 13.194 12.777 12.227 11.737 11.397 11.430 14.22 2.95 -44.48 2.95 0.86 12 GCS9 V* V564 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 35 56.060 +25 21 59.30 0 13.217 12.868 12.366 11.780 11.558 18.66 5.28 -39.03 5.28 0.87 12 GCS9 Cl* Melotte 22 DH 50 0.002 0.001 0.001 0.001 0.001 +03 36 05.330 +25 21 03.40 0 14.386 13.934 13.321 12.796 12.470 19.93 5.29 -41.98 5.29 0.94 12 GCS9 Cl* Melotte 22 DH 51 0.003 0.002 0.002 0.002 0.001 +03 42 38.350 +25 28 44.30 0 15.625 15.076 14.453 13.924 13.588 13.556 21.08 2.24 -41.30 2.24 0.85 12 GCS9 UGCS J034238.35+252844.2 0.006 0.005 0.004 0.003 0.004 0.002 +03 50 14.760 +25 25 29.80 0 14.761 14.292 13.705 13.158 12.819 12.827 16.00 2.23 -45.17 2.23 0.91 12 GCS9 Cl* Melotte 22 DH 662 0.003 0.003 0.003 0.002 0.002 0.001 +03 50 01.570 +25 24 01.50 0 13.034 12.688 12.188 11.755 11.405 11.453 14.76 2.23 -46.39 2.23 0.83 12 GCS9 Cl* Melotte 22 DH 646 0.002 0.001 0.001 0.001 0.001 0.001 +03 49 20.310 +25 25 42.40 0 14.155 13.745 13.198 12.642 12.335 12.324 12.27 2.23 -40.97 2.23 0.66 12 GCS9 V* V798 Tau 0.003 0.002 0.002 0.002 0.001 0.001 +03 46 47.190 +25 20 53.10 0 14.444 13.909 13.306 12.771 12.437 12.445 23.84 2.23 -47.30 2.23 0.66 12 GCS9 Cl* Melotte 22 DH 469 0.003 0.002 0.002 0.002 0.002 0.001 +03 41 04.150 +25 30 25.60 0 15.724 15.167 14.547 14.003 13.636 13.636 12.89 2.24 -40.93 2.24 0.71 12 GCS9 UGCS J034104.14+253025.6 0.006 0.005 0.005 0.003 0.004 0.003 +03 42 08.290 +25 36 59.90 0 14.094 13.600 13.009 12.441 12.120 12.153 16.61 2.23 -37.66 2.23 0.77 12 GCS9 Cl* Melotte 22 DH 211 0.003 0.002 0.002 0.001 0.001 0.001 +03 42 03.420 +25 22 39.10 0 14.406 13.913 13.303 12.729 12.430 12.430 22.60 2.23 -37.61 2.23 0.68 12 GCS9 Cl* Melotte 22 DH 206 0.003 0.002 0.002 0.002 0.001 0.001 +03 41 30.350 +25 17 05.90 0 16.646 15.972 15.208 14.682 14.349 14.526 13.78 2.27 -42.02 2.27 0.60 12 GCS9 UGCS J034130.35+251705.9 0.010 0.008 0.007 0.006 0.006 0.005 +03 52 41.820 +26 46 10.50 0 14.924 14.450 13.835 13.290 12.967 12.980 15.22 2.96 -48.00 2.96 0.74 12 GCS9 Cl* Melotte 22 DH 761 0.004 0.003 0.003 0.002 0.002 0.001 +03 45 09.040 +25 32 49.00 0 14.661 14.176 13.591 13.021 12.723 12.734 18.67 2.23 -43.15 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 377 0.003 0.003 0.003 0.002 0.002 0.001 +04 06 54.480 +26 20 07.50 0 12.888 12.543 11.998 11.384 11.046 11.113 14.03 2.96 -40.84 2.96 0.70 12 GCS9 UGCS J040654.47+262007.5 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 22.420 +26 11 36.00 0 14.521 14.093 13.516 12.964 12.650 12.682 18.92 3.00 -45.37 3.00 0.93 12 GCS9 Cl* Melotte 22 DH 104 0.003 0.002 0.002 0.002 0.002 0.001 +03 42 12.620 +26 49 44.90 0 14.299 13.821 13.209 12.670 12.342 12.345 19.56 3.00 -39.85 3.00 0.91 12 GCS9 Cl* Melotte 22 DH 215 0.003 0.002 0.002 0.001 0.001 0.001 +03 37 48.930 +26 51 45.10 0 14.380 13.919 13.351 12.818 12.520 12.525 21.09 3.30 -46.90 3.30 0.86 12 GCS9 Cl* Melotte 22 DH 79 0.003 0.002 0.002 0.002 0.002 0.001 +03 52 58.780 +25 26 19.00 0 15.258 14.748 14.176 13.639 13.295 13.303 17.32 2.94 -42.46 2.94 0.90 12 GCS9 Cl* Melotte 22 BPL 277 0.004 0.004 0.003 0.003 0.003 0.002 +03 52 54.250 +25 17 43.30 0 14.245 13.815 13.273 12.729 12.417 12.415 19.70 2.93 -46.66 2.93 0.89 12 GCS9 V* V477 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 48.160 +26 37 47.50 0 15.926 15.369 14.707 14.187 13.827 13.837 17.38 2.95 -44.63 2.95 0.89 12 GCS9 Cl* Melotte 22 MBSC 98 0.006 0.005 0.004 0.004 0.004 0.003 +03 38 27.830 +26 51 25.40 0 14.777 14.274 13.663 13.167 12.844 12.866 21.04 3.30 -38.12 3.30 0.81 12 GCS9 Cl* Melotte 22 DH 89 0.003 0.003 0.003 0.002 0.002 0.001 +03 37 15.610 +26 29 29.80 0 15.984 15.323 14.670 14.169 13.787 13.794 19.16 3.31 -39.61 3.31 0.85 12 GCS9 Cl* Melotte 22 STAR 15 0.007 0.005 0.005 0.004 0.004 0.003 +03 48 50.450 +25 17 54.70 0 16.516 15.802 15.106 14.575 14.177 14.174 18.72 2.25 -45.60 2.25 0.67 12 GCS9 Cl* Melotte 22 BPL 192 0.009 0.007 0.006 0.005 0.005 0.004 +03 52 07.960 +25 27 54.60 0 15.246 14.722 14.111 13.567 13.243 13.214 15.55 2.94 -37.68 2.94 0.67 12 GCS9 V* V390 Tau 0.004 0.004 0.003 0.003 0.003 0.002 +03 37 11.980 +26 46 28.70 0 14.171 13.688 13.064 12.558 12.231 12.243 22.64 3.30 -37.41 3.30 0.65 12 GCS9 Cl* Melotte 22 DH 68 0.003 0.002 0.002 0.001 0.001 0.001 +03 46 44.790 +24 44 58.20 0 15.343 14.790 14.196 13.626 13.308 13.288 16.98 2.21 -43.55 2.21 0.90 12 GCS9 Cl* Melotte 22 BPL 109 0.005 0.003 0.003 0.002 0.003 0.002 +03 47 16.450 +24 44 50.10 0 14.575 13.990 13.385 12.836 12.484 12.492 19.48 2.21 -40.86 2.21 0.93 12 GCS9 Cl* Melotte 22 BPL 136 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 38.050 +24 49 10.80 0 13.439 13.061 12.546 12.133 11.670 11.690 15.44 2.21 -43.50 2.21 0.91 12 GCS9 Cl* Melotte 22 BPL 151 0.002 0.001 0.001 0.001 0.001 0.001 +03 48 22.810 +24 48 53.40 0 13.811 13.364 12.850 12.329 12.012 12.039 16.29 2.21 -48.18 2.21 0.78 12 GCS9 Cl* Melotte 22 BPL 176 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 34.170 +25 43 05.90 0 14.233 13.788 13.217 12.675 12.390 12.374 14.30 2.23 -44.19 2.23 0.86 12 GCS9 Cl* Melotte 22 DH 522 0.003 0.002 0.002 0.002 0.001 0.001 +03 47 33.060 +25 38 18.40 0 14.492 14.001 13.394 12.814 12.503 12.490 15.50 2.23 -44.70 2.23 0.90 12 GCS9 V* V791 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 28.430 +24 40 33.00 0 14.715 14.245 13.694 13.118 12.817 12.766 15.65 2.21 -46.11 2.21 0.87 12 GCS9 Cl* Melotte 22 DH 517 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 05.980 +25 40 20.80 0 14.788 14.303 13.742 13.196 12.896 12.880 18.83 2.96 -39.12 2.96 0.89 12 GCS9 Cl* Melotte 22 DH 124 0.004 0.003 0.003 0.002 0.002 0.001 +03 46 15.110 +26 46 48.80 1 16.624 15.838 15.032 14.495 14.037 14.088 20.73 2.97 -42.79 2.97 0.68 12 GCS9 UGCS J034615.11+264648.8 0.011 0.007 0.006 0.005 0.005 0.003 +03 39 08.130 +24 46 14.40 0 13.036 12.618 12.087 11.550 11.270 11.264 15.90 2.48 -44.93 2.48 0.91 12 GCS9 V* KM Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 49 38.240 +26 51 05.60 0 14.052 13.637 13.073 12.512 12.234 12.239 19.29 2.93 -43.50 2.93 0.94 12 GCS9 Cl* Melotte 22 MBSC 29 0.002 0.002 0.002 0.001 0.001 0.001 +03 36 10.560 +24 45 59.70 0 14.996 14.487 13.888 13.330 13.004 12.994 17.12 2.63 -43.88 2.63 0.94 12 GCS9 UGCS J033610.55+244559.7 0.004 0.003 0.003 0.002 0.003 0.002 +03 50 06.430 +26 58 18.70 0 14.792 14.338 13.747 13.210 12.884 12.905 21.70 2.94 -42.45 2.94 0.92 12 GCS9 Cl* Melotte 22 DH 652 0.004 0.003 0.003 0.002 0.002 0.001 +03 50 40.830 +24 40 02.60 0 15.326 14.799 14.224 13.664 13.348 13.340 14.95 2.21 -46.17 2.21 0.78 12 GCS9 Cl* Melotte 22 DH 681 0.005 0.004 0.003 0.003 0.003 0.002 +03 46 35.360 +23 57 07.40 0 16.879 16.089 15.355 14.800 14.416 14.373 17.57 2.24 -42.62 2.24 0.75 12 GCS9 Cl* Melotte 22 SHF 35 0.012 0.007 0.006 0.005 0.006 0.004 +03 46 55.780 +23 56 24.10 0 14.367 13.803 13.166 12.622 12.272 12.283 18.41 2.22 -39.68 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 480 0.003 0.002 0.002 0.001 0.001 0.001 +03 47 00.360 +23 52 48.20 0 15.230 14.607 13.981 13.439 13.093 13.073 14.89 2.22 -45.67 2.22 0.80 12 GCS9 UGCS J034700.35+235248.1 0.005 0.003 0.003 0.002 0.002 0.002 +03 46 22.200 +23 52 41.00 0 14.183 13.612 12.973 12.388 12.055 12.045 16.03 2.22 -41.36 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 441 0.003 0.002 0.002 0.001 0.001 0.001 +03 47 13.670 +23 49 53.20 0 12.791 12.364 11.867 11.575 11.044 11.809 19.47 2.22 -40.49 2.22 0.89 12 GCS9 V* V645 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 46 27.690 +23 48 45.50 0 14.944 14.345 13.639 13.015 12.617 12.609 18.50 2.22 -41.39 2.22 0.94 12 GCS9 Cl* Melotte 22 HHJ 161 0.004 0.003 0.003 0.002 0.002 0.001 +03 51 58.820 +24 40 04.30 0 13.675 13.254 12.728 12.172 11.871 11.897 14.65 2.20 -42.70 2.20 0.88 12 GCS9 UGCS J035158.82+244004.3 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 59.320 +24 39 58.80 0 13.828 13.385 12.845 12.293 12.017 12.027 13.69 2.20 -41.98 2.20 0.83 12 GCS9 V* V387 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 10.960 +24 48 45.80 0 14.889 14.418 13.865 13.318 13.020 21.80 2.97 -46.03 2.97 0.87 12 GCS9 UGCS J034410.95+244845.7 0.004 0.003 0.003 0.002 0.002 +03 44 25.600 +24 40 52.60 0 13.443 13.016 12.496 11.928 11.633 13.39 2.97 -47.71 2.97 0.62 12 GCS9 V* V438 Tau 0.002 0.002 0.002 0.001 0.001 +03 43 12.120 +24 44 45.10 0 13.538 13.088 12.533 12.023 11.684 21.38 2.97 -49.22 2.97 0.61 12 GCS9 V* V509 Tau 0.002 0.002 0.001 0.001 0.001 +04 00 28.190 +23 51 24.00 0 13.939 13.453 12.831 12.277 12.003 13.71 3.54 -40.49 3.54 0.78 12 GCS9 Cl* Melotte 22 DH 892 0.002 0.002 0.002 0.001 0.001 +03 49 22.150 +25 47 37.70 0 14.407 13.954 13.393 12.801 12.496 12.504 19.36 2.23 -37.53 2.23 0.80 12 GCS9 Cl* Melotte 22 DH 618 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 32.810 +25 47 46.80 0 14.438 13.998 13.465 12.891 12.567 12.569 17.13 2.23 -41.13 2.23 0.93 12 GCS9 Cl* Melotte 22 DH 628 0.003 0.002 0.002 0.002 0.002 0.001 +03 43 09.750 +24 41 32.70 0 13.167 12.734 12.234 11.768 11.425 23.06 2.97 -47.52 2.97 0.67 12 GCS9 V* LU Tau 0.002 0.001 0.001 0.001 0.001 +03 43 13.070 +24 39 19.30 0 13.055 12.669 12.151 11.608 11.286 20.98 2.97 -41.82 2.97 0.91 12 GCS9 V* LV Tau 0.002 0.001 0.001 0.001 0.001 +03 49 58.610 +25 53 46.20 0 15.011 14.544 13.948 13.385 13.048 13.044 17.26 2.23 -41.56 2.23 0.89 12 GCS9 UGCS J034958.61+255346.1 0.004 0.003 0.003 0.002 0.002 0.002 +03 41 54.210 +25 43 47.10 0 13.586 13.204 12.696 12.122 11.843 11.866 18.41 2.23 -46.69 2.23 0.89 12 GCS9 V* V608 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 42 10.990 +25 44 35.00 0 15.394 14.772 14.087 13.528 13.157 13.165 14.33 2.24 -42.76 2.24 0.83 12 GCS9 UGCS J034210.99+254435.0 0.005 0.004 0.004 0.003 0.003 0.002 +03 53 09.630 +23 33 47.70 0 16.108 15.503 14.823 14.280 13.916 19.95 2.32 -45.65 2.32 0.63 12 GCS9 2MASS J03530962+2333480 0.007 0.005 0.005 0.005 0.003 +03 44 51.510 +25 05 16.50 0 14.798 14.315 13.715 13.134 12.802 12.819 15.35 2.21 -42.48 2.21 0.91 12 GCS9 UGCS J034451.50+250516.4 0.003 0.003 0.003 0.002 0.002 0.001 +03 45 02.880 +25 05 19.60 0 14.789 14.276 13.751 13.216 12.845 12.858 14.43 2.21 -38.87 2.21 0.75 12 GCS9 Cl* Melotte 22 DH 368 0.003 0.003 0.003 0.002 0.002 0.001 +03 45 18.150 +25 05 58.10 0 12.117 11.866 11.421 11.325 10.625 10.829 16.05 2.21 -37.44 2.21 0.74 12 GCS9 V* OR Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 44 32.170 +25 08 12.30 0 15.305 14.832 14.220 13.652 13.316 13.309 17.86 2.21 -43.12 2.21 0.90 12 GCS9 Cl* Melotte 22 HHJ 68 0.004 0.004 0.003 0.003 0.003 0.002 +03 52 51.790 +23 33 47.90 0 15.894 15.316 14.662 14.124 13.742 20.21 2.31 -42.32 2.31 0.88 12 GCS9 2MASS J03525177+2333483 0.006 0.005 0.005 0.004 0.003 +03 41 40.910 +25 54 24.10 1 16.893 16.001 15.180 14.574 14.122 14.125 16.94 2.26 -42.13 2.26 0.74 12 GCS9 2MASS J03414089+2554242 0.012 0.008 0.007 0.005 0.006 0.004 +03 45 16.990 +25 15 47.50 0 12.911 12.493 11.990 11.694 11.141 11.228 18.30 2.21 -40.49 2.21 0.89 12 GCS9 V* V520 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 48 44.050 +25 06 22.40 0 14.492 14.076 13.483 12.898 12.639 12.631 16.69 2.20 -44.81 2.20 0.92 12 GCS9 Cl* Melotte 22 DH 589 0.003 0.002 0.002 0.002 0.002 0.001 +04 04 37.180 +23 23 51.00 0 12.985 12.568 12.054 11.620 11.305 11.370 15.43 3.77 -45.51 3.77 0.63 12 GCS9 UGCS J040437.18+232351.0 0.001 0.001 0.001 0.001 0.001 0.001 +03 41 19.350 +23 51 41.70 0 14.700 14.230 13.634 13.065 12.795 12.782 15.93 2.13 -45.36 2.13 0.90 12 GCS9 V* V734 Tau 0.003 0.003 0.003 0.002 0.002 0.001 +03 49 58.330 +25 06 20.90 0 15.359 14.837 14.232 13.680 13.379 13.380 15.84 2.21 -43.01 2.21 0.88 12 GCS9 Cl* Melotte 22 BPL 219 0.005 0.003 0.003 0.003 0.003 0.002 +03 41 05.230 +23 50 14.90 0 15.720 15.171 14.571 14.019 13.727 13.698 14.75 2.14 -43.68 2.14 0.85 12 GCS9 Cl* Melotte 22 BPL 19 0.006 0.004 0.004 0.004 0.005 0.002 +03 50 01.870 +25 12 40.90 0 14.210 13.771 13.231 12.666 12.337 12.348 13.84 2.20 -42.81 2.20 0.85 12 GCS9 V* V552 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 51 17.960 +26 01 22.10 0 14.855 14.349 13.719 13.135 12.816 12.793 17.37 2.23 -39.97 2.23 0.91 12 GCS9 Cl* Melotte 22 DH 701 0.003 0.003 0.003 0.002 0.002 0.001 +03 51 18.720 +26 03 15.40 0 14.748 14.262 13.654 13.395 12.768 12.736 15.24 2.23 -41.60 2.23 0.90 12 GCS9 UGCS J035118.71+260315.4 0.003 0.003 0.003 0.002 0.002 0.001 +03 51 18.860 +26 03 08.70 0 13.191 12.827 12.287 11.714 11.406 11.426 21.16 2.23 -42.78 2.23 0.92 12 GCS9 Cl* Melotte 22 SK 237 0.002 0.001 0.001 0.001 0.001 0.001 +03 52 44.290 +23 54 14.90 0 15.738 15.184 14.554 14.000 13.654 13.683 15.71 2.25 -44.96 2.25 0.86 12 GCS9 2MASS J03524427+2354151 0.005 0.004 0.004 0.004 0.004 0.002 +03 51 24.170 +26 03 11.30 0 13.441 13.064 12.529 11.943 11.651 11.655 16.49 2.23 -42.88 2.23 0.93 12 GCS9 V* V380 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 52 33.340 +23 51 06.70 0 14.404 13.944 13.367 12.802 12.521 12.543 17.77 2.24 -41.30 2.24 0.93 12 GCS9 Cl* Melotte 22 DH 756 0.003 0.002 0.002 0.002 0.002 0.001 +03 52 18.720 +23 52 36.60 0 15.095 14.577 13.986 13.440 13.130 13.140 15.56 2.25 -45.85 2.25 0.82 12 GCS9 Cl* Melotte 22 BPL 260 0.004 0.003 0.003 0.002 0.002 0.002 +03 45 51.950 +25 10 01.70 0 14.702 14.245 13.604 13.032 12.743 12.742 16.31 2.21 -41.80 2.21 0.92 12 GCS9 Cl* Melotte 22 DH 404 0.003 0.003 0.002 0.002 0.002 0.001 +03 45 39.040 +25 13 27.60 0 12.529 12.273 11.767 11.514 10.907 11.035 15.68 2.21 -41.64 2.21 0.82 12 GCS9 V* OQ Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 46 50.190 +22 12 42.30 0 15.579 15.015 14.384 13.861 13.495 13.514 17.03 2.27 -40.35 2.27 0.87 12 GCS9 Cl* Melotte 22 BPL 110 0.006 0.004 0.004 0.003 0.004 0.002 +03 46 03.670 +25 52 28.80 0 14.534 14.076 13.506 12.954 12.639 12.643 17.24 2.23 -43.26 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 416 0.003 0.003 0.002 0.002 0.002 0.001 +03 45 52.600 +25 54 59.80 0 13.823 13.352 12.746 12.195 11.859 11.879 17.10 2.23 -41.34 2.23 0.92 12 GCS9 Cl* Melotte 22 DH 405 0.002 0.002 0.002 0.001 0.001 0.001 +03 57 33.070 +23 56 47.10 0 14.486 14.043 13.479 12.922 12.627 12.632 17.44 2.96 -47.07 2.96 0.88 12 GCS9 UGCS J035733.06+235647.0 0.003 0.002 0.002 0.002 0.002 0.001 +03 53 24.240 +25 14 37.70 0 16.763 16.041 15.329 14.792 14.388 14.383 18.76 2.27 -40.84 2.27 0.71 12 GCS9 UGCS J035324.24+251437.6 0.011 0.008 0.007 0.006 0.007 0.005 +04 00 26.150 +23 26 17.30 0 13.910 13.401 12.803 12.276 11.930 11.924 16.57 3.35 -40.04 3.35 0.89 12 GCS9 Cl* Melotte 22 DH 891 0.002 0.002 0.002 0.001 0.001 0.001 +04 00 16.930 +23 26 22.60 0 14.173 13.780 13.204 12.602 12.318 12.347 15.97 3.35 -48.85 3.35 0.70 12 GCS9 UGCS J040016.93+232622.5 0.002 0.002 0.002 0.002 0.002 0.001 +03 37 31.070 +23 46 07.20 0 15.431 14.956 14.357 13.829 13.476 13.487 16.45 2.50 -38.98 2.50 0.81 12 GCS9 UGCS J033731.07+234607.1 0.005 0.004 0.004 0.003 0.003 0.002 +04 00 40.260 +23 26 53.80 0 14.831 14.274 13.616 13.072 12.736 12.745 18.94 3.35 -45.90 3.35 0.92 12 GCS9 UGCS J040040.25+232653.8 0.004 0.003 0.003 0.002 0.002 0.001 +04 01 12.080 +23 54 12.80 0 13.698 13.200 12.655 12.127 11.850 15.17 3.54 -39.47 3.54 0.82 12 GCS9 Cl* Melotte 22 DH 898 0.002 0.001 0.001 0.001 0.001 +03 46 31.320 +22 18 19.60 0 14.447 13.942 13.329 12.796 12.449 12.455 21.56 2.26 -42.37 2.26 0.92 12 GCS9 V* V859 Tau 0.003 0.003 0.002 0.002 0.002 0.001 +03 46 40.600 +22 22 03.50 0 15.518 14.956 14.324 13.752 13.392 13.412 18.76 2.27 -40.88 2.27 0.88 12 GCS9 UGCS J034640.59+222203.5 0.006 0.004 0.004 0.003 0.004 0.002 +03 49 08.840 +25 53 48.60 0 13.042 12.708 12.195 11.732 11.323 11.398 14.88 2.23 -43.29 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 603 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 57.150 +26 07 00.10 0 15.350 14.830 14.198 13.660 13.331 13.308 21.95 2.96 -43.52 2.96 0.82 12 GCS9 Cl* Melotte 22 MBSC 94 0.005 0.004 0.004 0.003 0.003 0.002 +03 50 05.000 +23 18 17.20 0 15.407 14.881 14.271 13.751 13.450 13.442 15.82 2.26 -47.05 2.26 0.77 12 GCS9 Cl* Melotte 22 DH 650 0.005 0.004 0.003 0.003 0.003 0.002 +03 46 00.930 +22 12 29.40 0 16.023 15.409 14.742 14.256 13.858 13.866 18.94 2.28 -45.37 2.28 0.68 12 GCS9 Cl* Melotte 22 STAR 9 0.007 0.006 0.005 0.004 0.005 0.003 +03 40 10.930 +26 06 40.80 0 14.589 14.173 13.576 13.025 12.731 12.734 19.44 2.96 -36.36 2.96 0.67 12 GCS9 V* KS Tau 0.003 0.003 0.003 0.002 0.002 0.001 +03 49 15.630 +23 22 49.10 0 15.460 14.919 14.290 13.736 13.436 13.422 19.79 2.26 -41.51 2.26 0.88 12 GCS9 Cl* Melotte 22 STAR 28 0.005 0.004 0.004 0.003 0.003 0.002 +03 49 32.570 +23 24 41.00 0 14.251 13.753 13.159 12.602 12.293 12.309 20.08 2.25 -40.04 2.25 0.91 12 GCS9 Cl* Melotte 22 HHJ 245 0.003 0.002 0.002 0.002 0.001 0.001 +03 41 19.860 +25 06 49.00 0 14.586 14.170 13.598 13.072 12.788 18.41 2.97 -47.43 2.97 0.87 12 GCS9 Cl* Melotte 22 HHJ 191 0.003 0.003 0.002 0.002 0.001 +03 41 30.710 +25 11 52.30 0 15.090 14.523 13.857 13.309 12.925 18.12 2.97 -40.52 2.97 0.88 12 GCS9 UGCS J034130.71+251152.2 0.004 0.003 0.003 0.002 0.001 +03 40 59.260 +25 11 55.20 0 15.635 15.115 14.479 13.913 13.551 14.98 2.98 -43.05 2.98 0.86 12 GCS9 Cl* Melotte 22 DH 162 0.005 0.004 0.004 0.003 0.002 +03 40 39.460 +23 26 34.80 0 16.232 15.654 15.015 14.460 14.071 14.075 15.56 2.27 -41.13 2.27 0.69 12 GCS9 Cl* Melotte 22 DH 147 0.008 0.006 0.006 0.005 0.006 0.004 +03 53 16.440 +23 20 58.10 0 15.199 14.687 14.094 13.508 13.225 13.211 16.27 2.26 -38.96 2.26 0.80 12 GCS9 Cl* Melotte 22 BPL 281 0.004 0.003 0.003 0.003 0.003 0.002 +03 46 07.560 +23 44 42.60 0 16.202 15.245 14.240 13.330 12.791 12.804 15.30 2.22 -43.15 2.22 0.70 12 GCS9 UGCS J034607.55+234442.5 0.008 0.004 0.003 0.002 0.002 0.001 +03 38 06.260 +25 05 39.90 0 13.122 12.831 12.361 11.819 11.524 11.570 14.79 2.48 -45.21 2.48 0.87 12 GCS9 UGCS J033806.26+250539.9 0.002 0.001 0.001 0.001 0.001 0.001 +03 46 06.520 +23 50 20.20 0 12.820 12.427 11.856 11.337 10.926 11.042 19.77 2.22 -37.28 2.22 0.81 12 GCS9 Cl* Melotte 22 HII 892 0.001 0.001 0.001 0.001 0.001 0.000 +03 46 04.300 +23 55 40.60 0 13.726 13.283 12.706 12.145 11.810 11.840 14.43 2.22 -40.75 2.22 0.84 12 GCS9 Cl* Melotte 22 HHJ 363 0.002 0.002 0.002 0.001 0.001 0.001 +03 53 55.130 +23 23 36.10 1 16.947 16.027 15.172 14.569 14.088 14.081 19.17 2.28 -44.72 2.28 0.70 12 GCS9 2MASS J03535511+2323363 0.012 0.007 0.006 0.005 0.005 0.004 +03 45 59.200 +23 48 46.80 0 14.973 14.506 13.923 13.375 13.073 13.092 19.52 2.22 -40.23 2.22 0.92 12 GCS9 Cl* Melotte 22 HHJ 127 0.004 0.003 0.003 0.002 0.002 0.002 +03 45 49.420 +23 57 05.70 0 15.929 15.340 14.688 14.182 13.809 13.790 15.31 2.23 -44.67 2.23 0.85 12 GCS9 Cl* Melotte 22 SHF 16 0.006 0.005 0.004 0.003 0.004 0.003 +03 43 06.500 +22 17 49.20 0 15.872 15.301 14.649 14.103 13.726 13.720 23.39 2.52 -40.65 2.52 0.67 12 GCS9 Cl* Melotte 22 HHJ 18 0.006 0.005 0.004 0.004 0.004 0.003 +03 37 50.990 +25 09 25.30 0 13.080 12.684 12.159 11.654 11.378 11.376 21.24 2.48 -46.66 2.48 0.85 12 GCS9 UGCS J033750.99+250925.2 0.001 0.001 0.001 0.001 0.001 0.001 +03 45 46.900 +23 53 00.30 0 15.802 15.201 14.549 13.988 13.652 13.661 18.78 2.23 -44.72 2.23 0.88 12 GCS9 UGCS J034546.90+235300.3 0.006 0.004 0.004 0.003 0.003 0.002 +03 45 46.480 +23 47 43.10 0 12.854 12.422 11.837 11.334 10.920 10.991 19.57 2.22 -40.02 2.22 0.89 12 GCS9 Cl* Melotte 22 HHJ 421 0.001 0.001 0.001 0.001 0.001 0.000 +03 45 38.990 +23 57 00.90 0 15.229 14.703 14.101 13.549 13.238 13.223 20.80 2.22 -37.93 2.22 0.69 12 GCS9 UGCS J034538.98+235700.8 0.004 0.003 0.003 0.002 0.003 0.002 +03 45 38.910 +23 48 55.90 0 15.708 14.967 14.161 13.398 12.966 12.956 20.47 2.22 -40.70 2.22 0.85 12 GCS9 UGCS J034538.90+234855.8 0.006 0.004 0.003 0.002 0.002 0.001 +03 42 56.240 +22 22 38.50 0 15.481 14.854 14.196 13.662 13.283 13.273 23.05 2.51 -45.22 2.51 0.69 12 GCS9 UGCS J034256.23+222238.4 0.005 0.004 0.003 0.003 0.003 0.002 +03 29 58.760 +23 22 18.30 0 13.640 13.198 12.672 12.244 11.843 11.851 21.18 3.41 -38.83 3.41 0.81 12 GCS9 Cl* Melotte 22 DH 9 0.002 0.001 0.002 0.001 0.001 0.001 +03 53 48.040 +23 49 09.60 0 15.701 15.021 14.330 13.772 13.399 13.416 19.15 2.25 -46.61 2.25 0.81 12 GCS9 Cl* Melotte 22 BPL 291 0.005 0.004 0.004 0.003 0.003 0.002 +03 53 24.120 +23 47 58.40 0 14.439 13.967 13.383 12.825 12.527 12.529 15.64 2.24 -40.80 2.24 0.89 12 GCS9 Cl* Melotte 22 DH 784 0.003 0.002 0.002 0.002 0.002 0.001 +04 03 43.840 +23 53 47.00 0 14.247 13.709 13.083 12.543 12.195 12.194 20.68 3.37 -43.87 3.37 0.93 12 GCS9 Cl* Melotte 22 DH 909 0.002 0.002 0.002 0.001 0.001 0.001 +03 39 44.190 +22 07 45.50 0 13.646 13.239 12.691 12.131 11.860 11.873 19.48 2.50 -45.62 2.50 0.92 12 GCS9 Cl* Melotte 22 DH 111 0.002 0.002 0.002 0.001 0.001 0.001 +03 49 29.700 +22 18 13.30 0 14.741 14.294 13.748 13.159 12.858 12.841 19.08 2.51 -39.34 2.51 0.90 12 GCS9 UGCS J034929.70+221813.2 0.003 0.003 0.003 0.002 0.002 0.001 +03 49 25.550 +22 18 44.40 0 15.321 14.823 14.225 13.678 13.329 13.321 21.74 2.51 -40.44 2.51 0.79 12 GCS9 Cl* Melotte 22 BPL 206 0.004 0.004 0.003 0.003 0.003 0.002 +03 49 35.460 +22 23 26.10 0 14.930 14.487 13.916 13.356 13.068 13.048 19.51 2.51 -41.27 2.51 0.93 12 GCS9 Cl* Melotte 22 BPL 211 0.003 0.003 0.003 0.002 0.003 0.002 +03 45 12.620 +23 53 45.00 0 16.093 15.445 14.776 14.220 13.845 13.855 16.81 2.23 -44.76 2.23 0.71 12 GCS9 Cl* Melotte 22 HHJ 14 0.007 0.005 0.004 0.003 0.004 0.003 +03 52 38.910 +25 50 25.30 0 14.054 13.642 13.075 12.528 12.203 12.193 19.53 2.93 -39.16 2.93 0.89 12 GCS9 V* V768 Tau 0.002 0.002 0.002 0.002 0.001 0.001 +03 52 07.430 +25 53 02.70 0 13.127 12.750 12.267 11.768 11.478 11.454 17.32 2.93 -39.44 2.93 0.88 12 GCS9 UGCS J035207.42+255302.6 0.002 0.001 0.001 0.001 0.001 0.001 +03 52 31.380 +25 15 07.50 0 13.670 13.259 12.739 12.160 11.887 11.901 16.76 2.23 -45.34 2.23 0.92 12 GCS9 Cl* Melotte 22 DH 755 0.002 0.002 0.002 0.001 0.001 0.001 +03 43 47.080 +26 04 35.30 0 13.857 13.453 12.895 12.305 12.017 12.025 19.24 2.23 -41.90 2.23 0.93 12 GCS9 Cl* Melotte 22 HHJ 311 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 17.370 +23 48 23.50 0 14.645 14.190 13.615 13.061 12.760 12.745 18.31 2.22 -42.91 2.22 0.94 12 GCS9 Cl* Melotte 22 HHJ 188 0.003 0.003 0.003 0.002 0.002 0.001 +03 35 46.410 +22 24 49.70 0 15.172 14.684 14.105 13.586 13.262 13.247 22.16 2.94 -42.14 2.94 0.81 12 GCS9 UGCS J033546.40+222449.6 0.004 0.003 0.003 0.003 0.003 0.002 +03 42 02.930 +23 55 53.70 0 12.951 12.573 12.010 11.456 11.155 17.94 2.30 -39.33 2.30 0.87 12 GCS9 V* V727 Tau 0.001 0.001 0.001 0.001 0.000 +03 41 10.270 +25 45 55.90 0 13.996 13.553 13.015 12.467 12.161 12.180 18.53 2.23 -43.40 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 164 0.002 0.002 0.002 0.001 0.001 0.001 +03 40 40.320 +25 50 48.10 0 14.039 13.616 13.025 12.447 12.168 12.150 17.46 2.23 -43.05 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 148 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 33.080 +25 45 09.50 0 14.367 13.898 13.319 12.711 12.428 12.423 14.12 2.23 -38.60 2.23 0.71 12 GCS9 V* V742 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 33 46.650 +23 48 19.40 0 13.819 13.463 12.918 12.375 12.087 12.098 18.43 2.60 -43.31 2.60 0.94 12 GCS9 Cl* Melotte 22 DH 30 0.002 0.002 0.002 0.001 0.001 0.001 +03 57 30.160 +25 16 46.70 0 13.312 12.967 12.456 11.833 11.542 11.620 21.77 2.47 -45.25 2.47 0.88 12 GCS9 UGCS J035730.15+251646.7 0.002 0.001 0.002 0.001 0.001 0.001 +03 42 54.000 +26 08 16.10 0 14.730 14.262 13.672 13.136 12.798 12.810 19.45 2.23 -43.40 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 244 0.003 0.003 0.003 0.002 0.002 0.001 +03 45 04.990 +23 46 06.40 0 14.919 14.308 13.687 13.174 12.822 12.835 16.11 2.22 -45.06 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 372 0.004 0.003 0.003 0.002 0.002 0.001 +03 43 19.060 +26 04 43.90 0 14.168 13.751 13.169 12.614 12.292 12.355 13.73 2.23 -40.04 2.23 0.77 12 GCS9 Cl* Melotte 22 DH 260 0.003 0.002 0.002 0.001 0.001 0.001 +03 44 58.590 +23 55 40.90 0 14.017 13.555 12.960 12.420 12.106 12.139 18.25 2.22 -38.70 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 363 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 56.010 +23 55 53.40 0 13.771 13.290 12.715 12.244 11.888 11.912 19.65 2.22 -40.21 2.22 0.91 12 GCS9 V* NX Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 43 26.200 +26 02 30.60 0 13.718 13.336 12.777 12.204 11.899 11.946 16.58 2.23 -41.36 2.23 0.92 12 GCS9 V* V619 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 34.300 +23 51 24.60 0 16.914 16.150 15.428 14.866 14.472 14.439 16.92 2.24 -42.74 2.24 0.74 12 GCS9 Cl* Melotte 22 PPL 14 0.011 0.007 0.007 0.005 0.006 0.004 +03 36 16.320 +25 08 48.80 0 13.071 12.678 12.129 11.591 11.290 11.282 19.36 2.62 -45.92 2.62 0.91 12 GCS9 Cl* Melotte 22 DH 54 0.001 0.001 0.001 0.001 0.001 0.001 +03 51 57.530 +25 48 31.20 0 14.094 13.689 13.121 12.578 12.276 12.281 17.62 2.23 -42.66 2.23 0.94 12 GCS9 V* V561 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 58.040 +22 06 50.80 0 16.708 15.993 15.283 14.742 14.331 14.331 18.97 2.30 -42.48 2.30 0.74 12 GCS9 2MASS J03475802+2206511 0.011 0.008 0.007 0.006 0.008 0.004 +03 46 54.030 +25 14 44.80 0 13.248 12.893 12.369 11.790 11.469 11.490 19.95 2.21 -41.29 2.21 0.92 12 GCS9 V* V860 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 47 15.290 +25 06 55.30 0 13.353 12.925 12.359 11.788 11.481 11.497 20.29 2.21 -40.01 2.21 0.89 12 GCS9 Cl* Melotte 22 SK 432 0.002 0.001 0.001 0.001 0.001 0.001 +03 47 20.840 +25 05 12.10 0 12.899 12.550 12.021 11.403 11.153 11.187 17.78 2.21 -45.20 2.21 0.77 12 GCS9 Cl* Melotte 22 HHJ 417 0.001 0.001 0.001 0.001 0.001 0.000 +03 47 25.800 +25 08 32.80 0 13.339 12.873 12.278 11.753 11.412 11.453 19.88 2.21 -43.41 2.21 0.93 12 GCS9 V* V539 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 41 23.470 +22 01 53.90 0 14.746 14.157 13.488 12.874 12.525 12.523 15.55 2.51 -42.58 2.51 0.91 12 GCS9 UGCS J034123.46+220153.9 0.004 0.003 0.003 0.002 0.002 0.001 +03 47 47.870 +25 13 34.30 0 14.065 13.593 12.961 12.408 12.049 12.055 18.78 2.21 -41.93 2.21 0.94 12 GCS9 Cl* Melotte 22 DH 537 0.002 0.002 0.002 0.001 0.001 0.001 +03 52 18.460 +22 00 53.30 0 13.024 12.707 12.225 11.616 11.369 11.393 15.81 2.51 -42.10 2.51 0.91 12 GCS9 Cl* Melotte 22 DH 749 0.002 0.001 0.001 0.001 0.001 0.001 +03 48 15.490 +25 14 36.40 0 14.957 14.500 13.897 13.347 13.006 13.051 13.38 2.21 -40.07 2.21 0.74 12 GCS9 Cl* Melotte 22 DH 563 0.004 0.003 0.003 0.002 0.002 0.002 +03 42 36.950 +25 13 57.50 0 15.243 14.744 14.157 13.605 13.246 14.72 2.97 -38.46 2.97 0.70 12 GCS9 Cl* Melotte 22 DH 233 0.004 0.003 0.003 0.003 0.002 +03 42 42.120 +25 11 48.70 0 15.492 14.954 14.328 13.773 13.367 19.99 2.97 -46.20 2.97 0.82 12 GCS9 Cl* Melotte 22 DH 237 0.005 0.004 0.004 0.003 0.002 +04 00 10.300 +22 02 17.00 0 14.399 13.919 13.384 12.864 12.513 12.521 19.86 2.87 -43.85 2.87 0.94 12 GCS9 Cl* Melotte 22 DH 888 0.003 0.002 0.002 0.002 0.001 0.001 +03 47 20.430 +22 21 56.30 0 14.832 14.342 13.737 13.208 12.868 12.835 15.02 2.26 -38.93 2.26 0.80 12 GCS9 UGCS J034720.42+222156.3 0.004 0.003 0.003 0.002 0.002 0.001 +03 47 44.670 +22 12 43.90 0 15.872 15.338 14.695 14.187 13.825 13.810 22.07 2.28 -45.80 2.28 0.74 12 GCS9 Cl* Melotte 22 BPL 154 0.006 0.005 0.005 0.004 0.005 0.003 +03 47 44.680 +22 23 53.00 0 14.454 14.006 13.424 12.875 12.554 12.562 22.28 2.26 -44.26 2.26 0.90 12 GCS9 V* V335 Tau 0.003 0.003 0.002 0.002 0.002 0.001 +03 43 36.680 +25 47 00.50 0 13.800 13.402 12.848 12.282 12.004 11.994 21.04 2.23 -46.84 2.23 0.85 12 GCS9 Cl* Melotte 22 DH 276 0.002 0.002 0.002 0.001 0.001 0.001 +03 35 59.910 +26 01 32.20 0 15.077 14.539 13.909 13.336 12.989 17.12 5.31 -37.68 5.31 0.73 12 GCS9 UGCS J033559.91+260132.2 0.004 0.003 0.003 0.003 0.001 +03 43 42.900 +25 51 37.00 0 15.191 14.695 14.098 13.525 13.202 13.195 21.00 2.23 -46.18 2.23 0.78 12 GCS9 Cl* Melotte 22 HHJ 77 0.004 0.004 0.003 0.002 0.003 0.002 +03 50 44.350 +25 07 05.10 0 15.181 14.601 13.930 13.397 13.036 13.031 21.00 2.20 -44.44 2.20 0.84 12 GCS9 UGCS J035044.34+250705.0 0.004 0.003 0.003 0.002 0.002 0.001 +03 51 23.890 +25 05 52.60 0 13.484 13.107 12.577 12.065 11.730 11.738 14.49 2.20 -42.99 2.20 0.88 12 GCS9 Cl* Melotte 22 DH 708 0.002 0.001 0.001 0.001 0.001 0.001 +03 44 58.380 +22 11 30.10 0 15.843 15.286 14.652 14.119 13.760 13.765 15.85 2.52 -42.05 2.52 0.88 12 GCS9 UGCS J034458.38+221130.0 0.006 0.005 0.004 0.004 0.004 0.003 +03 43 56.700 +25 15 43.90 0 12.995 12.666 12.168 11.616 11.330 21.03 2.97 -42.94 2.97 0.86 12 GCS9 Cl* Melotte 22 SK 596 0.001 0.001 0.001 0.001 0.001 +03 54 28.110 +23 56 36.00 0 15.731 15.152 14.518 13.983 13.642 13.637 15.38 2.25 -40.85 2.25 0.85 12 GCS9 Cl* Melotte 22 BPL 313 0.006 0.004 0.004 0.004 0.004 0.002 +03 39 13.330 +25 43 49.50 0 13.488 13.088 12.530 11.963 11.657 11.694 19.34 2.95 -39.87 2.95 0.90 12 GCS9 Cl* Melotte 22 DH 100 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 36.410 +25 13 40.60 0 14.020 13.545 12.926 12.379 12.066 12.042 16.43 2.20 -40.71 2.20 0.91 12 GCS9 Cl* Melotte 22 DH 717 0.002 0.002 0.002 0.001 0.001 0.001 +03 52 02.640 +25 06 14.90 0 14.751 14.296 13.684 13.158 12.836 12.836 16.37 2.20 -40.83 2.20 0.91 12 GCS9 Cl* Melotte 22 DH 736 0.003 0.003 0.002 0.002 0.002 0.001 +03 55 27.060 +25 14 45.80 1 16.118 15.402 14.649 14.071 13.671 13.650 15.24 2.24 -39.66 2.24 0.60 12 GCS9 Cl* Melotte 22 BPL 328 0.008 0.005 0.004 0.004 0.004 0.002 +03 36 44.120 +22 01 38.80 0 14.988 14.410 13.794 13.301 12.965 12.979 22.41 2.94 -39.90 2.94 0.85 12 GCS9 Cl* Melotte 22 DH 63 0.004 0.003 0.003 0.002 0.002 0.002 +04 01 11.370 +22 10 37.80 0 15.142 14.644 14.046 13.493 13.176 13.178 24.34 3.41 -43.11 3.41 0.60 12 GCS9 UGCS J040111.36+221037.8 0.004 0.003 0.003 0.002 0.002 0.002 +03 43 57.000 +23 57 05.70 0 14.157 13.723 13.145 12.582 12.265 21.91 2.30 -44.52 2.30 0.90 12 GCS9 V* V739 Tau 0.003 0.002 0.002 0.001 0.001 +03 44 19.690 +23 53 45.60 0 15.780 15.031 14.317 13.697 13.262 15.52 2.31 -45.09 2.31 0.85 12 GCS9 Cl* Melotte 22 PPL 5 0.006 0.004 0.004 0.003 0.002 +03 44 12.140 +23 52 37.30 0 14.442 13.972 13.408 12.855 12.547 18.41 2.30 -47.45 2.30 0.87 12 GCS9 Cl* Melotte 22 HHJ 217 0.003 0.003 0.002 0.002 0.001 +03 44 14.650 +23 49 40.00 1 15.892 15.245 14.547 13.957 13.546 14.46 2.31 -40.15 2.31 0.79 12 GCS9 Cl* Melotte 22 PPL 11 0.006 0.005 0.004 0.004 0.002 +03 37 41.350 +22 17 19.00 0 15.026 14.524 13.892 13.401 13.008 13.055 19.22 2.94 -40.01 2.94 0.86 12 GCS9 UGCS J033741.35+221718.9 0.004 0.003 0.003 0.002 0.003 0.002 +03 39 42.720 +23 54 27.60 0 14.142 13.756 13.179 12.650 12.363 12.332 22.18 2.50 -45.44 2.50 0.88 12 GCS9 V* V489 Tau 0.002 0.002 0.002 0.002 0.002 0.001 +03 40 18.850 +23 54 23.30 0 14.517 14.091 13.554 13.046 12.719 12.723 16.56 2.50 -42.50 2.50 0.93 12 GCS9 UGCS J034018.84+235423.3 0.003 0.003 0.002 0.002 0.002 0.001 +03 39 47.990 +23 50 56.60 0 13.557 13.155 12.591 12.078 11.777 11.792 20.24 2.50 -41.77 2.50 0.92 12 GCS9 Cl* Melotte 22 DH 115 0.002 0.002 0.002 0.001 0.001 0.001 +04 04 34.790 +22 02 46.20 0 14.508 14.023 13.423 12.876 12.548 12.543 20.21 5.07 -43.45 5.07 0.94 12 GCS9 UGCS J040434.79+220246.2 0.003 0.002 0.002 0.002 0.001 0.001 +03 39 48.510 +23 46 03.80 0 15.028 14.539 13.964 13.446 13.119 13.083 18.84 2.50 -46.34 2.50 0.83 12 GCS9 Cl* Melotte 22 BPL 6 0.004 0.003 0.003 0.003 0.003 0.002 +03 39 50.680 +23 45 52.30 0 15.504 15.018 14.384 13.856 13.525 13.514 16.85 2.51 -41.64 2.51 0.89 12 GCS9 Cl* Melotte 22 DH 118 0.005 0.004 0.004 0.004 0.004 0.002 +03 58 01.970 +23 53 54.50 0 15.065 14.518 13.919 13.370 13.028 13.030 15.82 2.97 -43.34 2.97 0.88 12 GCS9 Cl* Melotte 22 DH 862 0.004 0.003 0.003 0.002 0.002 0.002 +03 59 31.470 +21 16 18.30 0 13.596 13.197 12.656 12.116 11.837 11.824 22.53 3.75 -44.15 3.75 0.87 12 GCS9 Cl* Melotte 22 DH 879 0.002 0.002 0.002 0.001 0.001 0.001 +03 49 48.440 +22 10 48.30 0 13.806 13.390 12.863 12.289 11.986 11.996 12.57 2.51 -41.31 2.51 0.71 12 GCS9 V* V877 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 40 01.840 +25 04 19.50 0 14.919 14.417 13.786 13.289 12.952 12.925 20.07 2.49 -41.41 2.49 0.93 12 GCS9 Cl* Melotte 22 HHJ 102 0.004 0.003 0.003 0.002 0.002 0.001 +03 40 31.170 +25 08 52.80 0 13.489 13.083 12.509 11.964 11.673 11.681 20.23 2.48 -43.75 2.48 0.93 12 GCS9 V* KV Tau 0.002 0.002 0.001 0.001 0.001 0.001 +03 48 05.870 +23 53 00.90 0 15.251 14.707 14.138 13.612 13.253 13.280 17.10 2.22 -37.49 2.22 0.71 12 GCS9 UGCS J034805.86+235300.8 0.004 0.003 0.003 0.002 0.003 0.002 +03 47 29.590 +23 52 49.30 0 16.385 15.692 15.016 14.462 14.078 14.077 16.73 2.24 -43.66 2.24 0.74 12 GCS9 Cl* Melotte 22 PPL 8 0.008 0.006 0.005 0.004 0.005 0.003 +03 47 31.650 +23 52 19.10 0 14.743 14.247 13.686 13.121 12.811 12.800 13.78 2.22 -44.81 2.22 0.82 12 GCS9 UGCS J034731.64+235219.0 0.003 0.003 0.003 0.002 0.002 0.001 +04 01 49.170 +22 10 05.00 0 14.076 13.630 13.057 12.554 12.239 12.210 23.64 3.40 -44.42 3.40 0.83 12 GCS9 Cl* Melotte 22 DH 901 0.002 0.002 0.002 0.001 0.001 0.001 +03 38 27.520 +25 30 18.10 0 16.591 15.938 15.284 14.747 14.355 14.371 18.25 3.00 -40.21 3.00 0.69 12 GCS9 Cl* Melotte 22 HHJ 2 0.011 0.007 0.007 0.005 0.006 0.004 +03 38 24.220 +25 19 49.20 0 14.870 14.364 13.774 13.233 12.906 12.914 18.02 2.96 -40.03 2.96 0.92 12 GCS9 Cl* Melotte 22 DH 86 0.004 0.003 0.003 0.002 0.002 0.001 +03 45 39.120 +22 04 23.10 0 15.161 14.641 14.018 13.458 13.114 13.120 19.28 2.27 -37.19 2.27 0.67 12 GCS9 Cl* Melotte 22 BPL 75 0.005 0.004 0.003 0.003 0.003 0.002 +03 37 54.790 +25 26 31.30 0 12.896 12.558 12.073 11.558 11.231 11.316 19.21 2.95 -37.64 2.95 0.83 12 GCS9 Cl* Melotte 22 HHJ 402 0.002 0.001 0.001 0.001 0.001 0.001 +03 54 58.030 +25 14 29.00 0 13.800 13.401 12.825 12.253 11.966 11.990 13.55 2.23 -44.32 2.23 0.82 12 GCS9 Cl* Melotte 22 DH 810 0.002 0.002 0.002 0.001 0.001 0.001 +03 50 31.950 +22 08 47.50 0 13.879 13.507 12.966 12.405 12.094 12.099 20.97 2.51 -37.91 2.51 0.75 12 GCS9 V* V674 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 57 49.370 +22 08 30.90 0 16.149 15.498 14.831 14.286 13.884 13.897 16.04 2.89 -42.24 2.89 0.72 12 GCS9 Cl* Melotte 22 DH 858 0.007 0.005 0.005 0.005 0.003 0.004 +03 44 02.500 +21 13 15.80 0 14.767 14.282 13.665 13.123 12.781 12.814 14.96 2.65 -40.08 2.65 0.85 12 GCS9 Cl* Melotte 22 DH 307 0.003 0.002 0.002 0.002 0.002 0.001 +03 50 25.160 +23 55 41.80 0 14.632 14.160 13.598 13.057 12.753 12.750 15.61 2.22 -41.36 2.22 0.90 12 GCS9 V* V373 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 50 12.460 +23 55 35.90 0 14.068 13.568 12.961 12.484 12.119 12.116 16.49 2.22 -44.32 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 659 0.002 0.002 0.002 0.001 0.001 0.001 +03 50 02.180 +23 51 44.60 0 13.770 13.342 12.802 12.268 11.965 11.957 13.75 2.22 -45.17 2.22 0.81 12 GCS9 V* V368 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 50 12.600 +23 48 48.90 0 15.564 15.046 14.439 13.876 13.556 13.530 18.23 2.22 -47.50 2.22 0.77 12 GCS9 Cl* Melotte 22 DH 660 0.005 0.004 0.004 0.003 0.003 0.002 +03 57 42.980 +25 23 06.70 0 14.445 13.984 13.389 12.823 12.494 12.537 21.68 2.48 -43.65 2.48 0.92 12 GCS9 V* V583 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 43 16.610 +23 50 01.50 0 14.279 13.794 13.239 12.641 12.382 12.371 15.68 2.12 -43.30 2.12 0.92 12 GCS9 Cl* Melotte 22 DH 258 0.003 0.002 0.002 0.002 0.002 0.001 +03 52 04.480 +24 14 39.60 0 14.840 14.365 13.783 13.223 12.926 12.897 16.25 2.24 -41.96 2.24 0.92 12 GCS9 Cl* Melotte 22 DH 739 0.003 0.003 0.003 0.002 0.002 0.001 +03 36 11.070 +23 48 23.00 0 15.038 14.538 13.935 13.415 13.063 13.085 20.28 2.61 -36.96 2.61 0.61 12 GCS9 Cl* Melotte 22 DH 53 0.004 0.003 0.003 0.003 0.003 0.002 +03 34 54.950 +22 04 46.20 0 13.105 12.793 12.313 11.794 11.512 11.566 22.30 2.93 -37.92 2.93 0.66 12 GCS9 Cl* Melotte 22 DH 40 0.001 0.001 0.001 0.001 0.001 0.001 +03 44 59.480 +23 21 18.10 0 15.290 14.814 14.227 13.710 13.376 13.382 20.27 2.24 -46.79 2.24 0.77 12 GCS9 Cl* Melotte 22 DH 365 0.005 0.004 0.004 0.003 0.003 0.002 +03 45 12.160 +23 21 52.90 0 14.046 13.642 13.082 12.524 12.260 12.255 17.35 2.24 -44.74 2.24 0.93 12 GCS9 V* V442 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 58.020 +23 24 31.00 0 14.147 13.740 13.184 12.621 12.362 12.351 16.74 2.24 -37.83 2.24 0.79 12 GCS9 Cl* Melotte 22 DH 362 0.003 0.002 0.002 0.001 0.001 0.001 +03 45 08.410 +23 25 00.90 0 15.510 15.010 14.406 13.859 13.534 13.540 13.99 2.24 -40.94 2.24 0.79 12 GCS9 UGCS J034508.41+232500.9 0.005 0.004 0.004 0.003 0.003 0.002 +03 45 54.990 +24 13 26.00 0 13.704 13.217 12.670 12.164 11.819 11.840 18.24 2.22 -42.71 2.22 0.94 12 GCS9 Cl* Melotte 22 BPL 82 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 36.280 +23 30 10.90 0 13.348 13.023 12.482 11.996 11.620 11.651 16.76 2.23 -43.37 2.23 0.93 12 GCS9 V* NT Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 45 40.250 +24 17 20.60 0 14.722 14.287 13.746 13.164 12.893 12.881 22.35 2.22 -39.61 2.22 0.84 12 GCS9 UGCS J034540.24+241720.5 0.003 0.003 0.003 0.002 0.002 0.001 +03 45 51.330 +24 17 44.30 0 14.634 14.154 13.573 12.983 12.723 12.717 14.02 2.22 -39.30 2.22 0.75 12 GCS9 UGCS J034551.33+241744.2 0.003 0.003 0.002 0.002 0.002 0.001 +03 45 30.230 +24 18 45.30 0 12.817 12.487 11.994 11.683 11.104 11.215 15.71 2.22 -43.80 2.22 0.77 12 GCS9 Cl* Melotte 22 MT 59 0.001 0.001 0.001 0.001 0.001 0.001 +03 45 49.940 +23 19 44.70 0 15.790 15.217 14.577 14.016 13.634 13.665 13.92 2.24 -38.67 2.24 0.66 12 GCS9 UGCS J034549.94+231944.7 0.006 0.005 0.004 0.003 0.004 0.003 +03 45 42.870 +23 20 12.20 0 15.145 14.690 14.081 13.551 13.217 13.217 17.99 2.24 -40.55 2.24 0.88 12 GCS9 UGCS J034542.87+232012.1 0.004 0.003 0.003 0.002 0.003 0.002 +03 45 37.790 +24 20 08.10 0 13.646 13.271 11.841 12.729 10.456 12.026 19.56 2.22 -44.01 2.22 0.93 12 GCS9 UGCS J034537.78+242008.1 0.002 0.002 0.001 0.001 0.000 0.001 +03 45 37.950 +24 20 03.40 0 13.151 12.791 12.328 11.940 11.503 11.541 16.88 2.22 -43.71 2.22 0.93 12 GCS9 UGCS J034537.95+242003.4 0.002 0.001 0.001 0.001 0.001 0.001 +03 45 24.790 +24 20 45.30 0 14.133 13.677 13.118 12.524 12.226 12.250 14.67 2.22 -40.59 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 392 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 07.510 +24 22 27.60 0 12.379 12.139 11.688 11.532 10.916 11.074 18.75 2.22 -42.30 2.22 0.89 12 GCS9 V* V638 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 41 24.690 +25 23 05.80 0 15.013 14.526 13.936 13.414 13.117 13.120 16.34 2.23 -38.60 2.23 0.78 12 GCS9 Cl* Melotte 22 HHJ 117 0.004 0.003 0.003 0.002 0.003 0.002 +03 52 05.830 +24 17 31.00 0 16.512 15.860 15.176 14.618 14.251 14.263 15.66 2.27 -40.51 2.27 0.67 12 GCS9 2MASS J03520581+2417314 0.009 0.007 0.006 0.006 0.007 0.004 +03 52 44.480 +24 20 59.30 0 15.025 14.546 13.944 13.403 13.091 13.085 16.54 2.25 -40.61 2.25 0.87 12 GCS9 Cl* Melotte 22 DH 766 0.004 0.003 0.003 0.002 0.003 0.002 +03 45 09.040 +25 22 29.70 0 15.145 14.500 13.831 13.259 12.901 12.920 20.44 2.23 -40.81 2.23 0.86 12 GCS9 Cl* Melotte 22 HHJ 81 0.004 0.003 0.003 0.002 0.002 0.001 +03 45 05.320 +25 29 10.90 0 13.161 12.814 12.312 11.731 11.448 11.485 16.08 2.23 -44.62 2.23 0.92 12 GCS9 Cl* Melotte 22 DH 373 0.002 0.001 0.001 0.001 0.001 0.001 +03 45 01.210 +25 21 05.60 0 15.041 14.555 13.949 13.361 13.061 13.070 18.83 2.23 -41.05 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 367 0.004 0.003 0.003 0.002 0.002 0.002 +03 38 02.050 +24 20 15.10 0 14.002 13.647 13.091 12.524 12.251 12.230 19.63 2.50 -45.83 2.50 0.92 12 GCS9 Cl* Melotte 22 DH 81 0.002 0.002 0.002 0.002 0.001 0.001 +03 44 32.330 +25 25 17.90 0 16.994 16.209 15.450 14.883 14.476 14.459 14.24 2.27 -43.51 2.27 0.64 12 GCS9 2MASS J03443231+2525181 0.012 0.009 0.008 0.007 0.007 0.005 +03 48 48.790 +23 24 48.00 0 15.140 14.625 14.030 13.486 13.166 13.171 15.92 2.13 -37.19 2.13 0.64 12 GCS9 Cl* Melotte 22 HHJ 86 0.004 0.003 0.003 0.003 0.002 0.002 +03 48 15.250 +23 26 05.40 0 14.320 13.888 13.339 12.781 12.516 12.495 14.81 2.13 -42.58 2.13 0.89 12 GCS9 Cl* Melotte 22 HHJ 232 0.003 0.002 0.002 0.002 0.001 0.001 +03 49 01.500 +24 11 38.30 0 14.330 13.900 13.320 12.814 12.506 12.488 14.90 2.22 -46.83 2.22 0.81 12 GCS9 Cl* Melotte 22 DH 599 0.003 0.002 0.002 0.002 0.002 0.001 +03 48 35.490 +24 12 03.00 0 15.216 14.662 14.042 13.491 13.221 13.201 18.84 2.22 -39.61 2.22 0.85 12 GCS9 V* V352 Tau 0.004 0.003 0.003 0.002 0.003 0.002 +03 48 39.910 +24 12 42.70 0 13.512 13.161 12.640 12.085 11.832 11.837 14.85 2.22 -41.13 2.22 0.87 12 GCS9 V* V873 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 22.710 +23 27 42.80 0 14.643 14.181 13.574 13.026 12.787 12.738 19.88 2.13 -42.50 2.13 0.94 12 GCS9 Cl* Melotte 22 DH 573 0.003 0.003 0.003 0.002 0.002 0.001 +03 48 25.240 +24 14 25.80 0 13.827 13.379 12.792 12.285 11.968 11.989 17.16 2.22 -43.14 2.22 0.94 12 GCS9 V* V871 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 14.300 +24 15 50.50 0 16.637 15.926 15.218 14.677 14.267 14.265 19.81 2.24 -40.94 2.24 0.69 12 GCS9 Cl* Melotte 22 BPL 169 0.010 0.007 0.006 0.005 0.006 0.004 +03 48 31.060 +24 16 53.10 0 13.037 12.703 12.170 11.891 11.376 11.477 23.77 2.22 -43.77 2.22 0.79 12 GCS9 V* V349 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 48 57.360 +24 19 43.60 0 12.735 12.382 11.845 11.772 11.088 11.294 19.80 2.22 -41.83 2.22 0.89 12 GCS9 V* V759 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 45 26.560 +22 31 32.00 0 14.087 13.649 13.091 12.536 12.210 12.196 19.13 2.26 -36.34 2.26 0.67 12 GCS9 Cl* Melotte 22 DH 393 0.003 0.002 0.002 0.001 0.002 0.001 +03 48 55.650 +24 21 40.10 0 16.220 15.636 14.963 14.416 14.055 14.041 17.62 2.23 -40.24 2.23 0.70 12 GCS9 Cl* Melotte 22 HHJ 8 0.007 0.005 0.005 0.004 0.005 0.003 +03 37 56.420 +23 22 56.60 0 13.782 13.405 12.891 12.353 12.067 12.063 23.93 2.97 -39.47 2.97 0.65 12 GCS9 Cl* Melotte 22 DH 80 0.002 0.002 0.002 0.001 0.001 0.001 +03 41 00.390 +24 13 35.00 0 14.841 14.363 13.768 13.194 12.895 12.871 16.27 2.13 -39.79 2.13 0.89 12 GCS9 Cl* Melotte 22 BPL 17 0.003 0.003 0.003 0.002 0.003 0.001 +03 41 22.450 +24 23 51.60 0 15.326 14.820 14.189 13.641 13.290 13.310 17.58 2.13 -43.59 2.13 0.90 12 GCS9 Cl* Melotte 22 DH 169 0.004 0.004 0.003 0.003 0.003 0.002 +03 50 37.420 +22 28 08.00 0 14.173 13.774 13.224 12.669 12.379 12.378 20.76 2.51 -39.13 2.51 0.87 12 GCS9 V* V878 Tau 0.002 0.002 0.002 0.002 0.002 0.001 +03 56 52.910 +23 25 43.70 0 14.029 13.589 12.994 12.445 12.127 12.117 16.82 3.00 -39.65 3.00 0.89 12 GCS9 Cl* Melotte 22 DH 846 0.003 0.002 0.002 0.001 0.001 0.001 +03 46 52.610 +23 38 43.00 0 14.301 13.763 13.138 12.512 12.160 12.174 15.66 2.22 -42.82 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 473 0.003 0.002 0.002 0.001 0.001 0.001 +03 46 43.190 +23 37 21.90 0 15.226 14.671 14.057 13.407 13.081 13.076 20.88 2.22 -42.38 2.22 0.86 12 GCS9 Cl* Melotte 22 DH 466 0.004 0.003 0.003 0.002 0.002 0.002 +03 46 44.880 +23 37 07.30 0 15.323 14.679 13.948 13.237 12.874 12.854 20.16 2.22 -39.81 2.22 0.83 12 GCS9 UGCS J034644.87+233707.2 0.005 0.003 0.003 0.002 0.002 0.001 +03 47 03.780 +23 36 58.60 0 12.388 12.010 11.486 11.369 10.665 11.002 22.69 2.22 -39.02 2.22 0.80 12 GCS9 V* QQ Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 46 58.170 +23 33 38.80 0 14.657 14.204 13.650 13.114 12.801 12.816 19.41 2.22 -43.08 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 482 0.003 0.003 0.003 0.002 0.002 0.001 +03 47 02.350 +23 32 36.00 0 15.608 14.962 14.262 13.719 13.314 13.303 17.87 2.22 -44.38 2.22 0.89 12 GCS9 Cl* Melotte 22 DH 487 0.006 0.004 0.004 0.003 0.003 0.002 +03 46 50.090 +23 31 56.10 0 14.162 13.728 13.200 12.627 12.337 12.390 18.31 2.22 -43.65 2.22 0.94 12 GCS9 V* PU Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 56 25.930 +24 16 51.30 0 12.568 12.306 11.841 11.303 10.972 11.236 22.45 2.96 -39.95 2.96 0.83 12 GCS9 Cl* Melotte 22 SK 18 0.001 0.001 0.001 0.001 0.001 0.001 +03 58 57.040 +23 42 31.10 0 12.744 12.419 11.916 11.544 11.113 11.318 22.87 2.96 -40.80 2.96 0.82 12 GCS9 V* V405 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 54 01.120 +23 19 41.00 0 15.933 15.380 14.713 14.186 13.841 13.816 15.07 2.27 -45.30 2.27 0.83 12 GCS9 Cl* Melotte 22 BPL 299 0.006 0.005 0.005 0.004 0.004 0.003 +03 54 13.020 +23 20 50.80 0 13.540 13.162 12.611 12.064 11.777 11.770 19.39 2.25 -43.85 2.25 0.94 12 GCS9 V* V400 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 42 02.870 +24 12 36.00 0 13.316 12.980 12.469 11.883 11.629 12.04 2.30 -45.58 2.30 0.61 12 GCS9 V* V497 Tau 0.002 0.002 0.001 0.001 0.001 +03 48 07.960 +23 44 23.50 0 13.941 13.522 12.964 12.433 12.148 12.163 17.89 2.22 -45.77 2.22 0.92 12 GCS9 V* V658 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 22.680 +23 44 06.70 0 14.271 13.786 13.223 12.688 12.391 12.374 16.09 2.22 -43.44 2.22 0.92 12 GCS9 V* V537 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 47 44.660 +23 42 03.10 0 14.538 14.079 13.508 12.937 12.650 12.660 20.25 2.22 -45.79 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 536 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 33.460 +23 41 32.80 0 12.580 12.224 11.716 11.700 10.957 11.178 17.61 2.22 -41.13 2.22 0.88 12 GCS9 V* QV Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 47 51.970 +23 39 48.00 0 14.936 14.433 13.888 13.320 13.031 13.034 17.54 2.22 -44.32 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 540 0.004 0.003 0.003 0.002 0.002 0.002 +03 47 26.770 +23 38 02.40 0 14.194 13.696 13.125 12.569 12.249 12.245 18.70 2.22 -40.80 2.22 0.93 12 GCS9 V* V864 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 47 29.930 +23 33 15.00 0 16.033 15.408 14.750 14.157 13.818 13.794 17.39 2.23 -43.72 2.23 0.74 12 GCS9 Cl* Melotte 22 HHJ 16 0.007 0.005 0.005 0.003 0.004 0.003 +03 43 39.050 +23 44 05.20 0 14.252 13.733 13.145 12.592 12.212 19.59 2.30 -41.08 2.30 0.93 12 GCS9 V* MP Tau 0.003 0.002 0.002 0.001 0.001 +03 43 39.720 +23 41 32.40 0 15.672 15.129 14.470 13.912 13.544 18.33 2.31 -45.77 2.31 0.86 12 GCS9 Cl* Melotte 22 HHJ 40 0.005 0.004 0.004 0.003 0.002 +03 43 37.110 +23 38 31.90 0 13.785 13.328 12.738 12.198 11.870 17.19 2.30 -45.95 2.30 0.91 12 GCS9 Cl* Melotte 22 DH 278 0.002 0.002 0.002 0.001 0.001 +03 44 16.460 +23 37 04.00 0 13.729 13.270 12.706 12.148 11.847 18.08 2.30 -44.62 2.30 0.93 12 GCS9 V* MW Tau 0.002 0.002 0.002 0.001 0.001 +03 44 47.840 +24 12 52.50 0 14.358 13.900 13.297 12.745 12.458 12.431 18.38 2.22 -42.73 2.22 0.94 12 GCS9 V* NU Tau 0.003 0.002 0.002 0.001 0.002 0.001 +03 44 27.500 +24 14 17.00 0 14.633 14.101 13.503 12.965 12.632 12.628 20.52 2.22 -45.35 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 341 0.003 0.002 0.002 0.002 0.002 0.001 +03 45 13.140 +24 15 23.60 0 15.046 14.554 13.965 13.457 13.149 13.144 21.10 2.22 -42.90 2.22 0.86 12 GCS9 V* NZ Tau 0.004 0.003 0.003 0.002 0.002 0.002 +04 02 49.970 +23 30 38.40 0 13.869 13.468 12.896 12.332 12.053 12.047 20.08 3.35 -38.25 3.35 0.81 12 GCS9 Cl* Melotte 22 DH 904 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 46.140 +24 23 02.80 0 15.833 15.303 14.664 14.161 13.762 13.777 16.63 2.23 -45.44 2.23 0.86 12 GCS9 Cl* Melotte 22 HHJ 24 0.006 0.005 0.004 0.003 0.004 0.003 +03 42 27.310 +22 34 24.60 0 13.652 13.223 12.660 12.145 11.830 11.851 14.98 2.51 -42.82 2.51 0.90 12 GCS9 V* V612 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 45 08.690 +24 24 09.30 0 16.507 15.843 15.142 14.587 14.235 14.195 16.54 2.23 -42.48 2.23 0.74 12 GCS9 UGCS J034508.68+242409.3 0.009 0.006 0.006 0.005 0.006 0.004 +03 57 08.610 +20 07 42.70 0 15.711 15.183 14.537 14.017 13.686 13.687 18.35 3.98 -41.60 3.98 0.90 12 GCS9 Cl* Melotte 22 DH 849 0.005 0.005 0.004 0.003 0.004 0.003 +03 51 11.880 +23 44 43.30 0 15.371 14.840 14.253 13.708 13.396 13.374 16.86 2.22 -43.64 2.22 0.89 12 GCS9 Cl* Melotte 22 BPL 232 0.005 0.004 0.003 0.002 0.003 0.002 +03 51 03.280 +23 35 00.10 0 15.395 14.846 14.243 13.676 13.365 13.342 15.36 2.22 -44.88 2.22 0.85 12 GCS9 UGCS J035103.27+233500.0 0.005 0.004 0.003 0.002 0.003 0.002 +03 51 51.550 +23 34 49.10 0 15.431 14.857 14.260 13.768 13.385 13.408 17.89 2.22 -44.43 2.22 0.89 12 GCS9 2MASS J03515154+2334494 0.005 0.004 0.003 0.003 0.003 0.002 +03 40 32.380 +19 54 30.00 0 15.279 14.784 14.160 13.581 13.241 13.222 13.86 5.02 -39.74 5.02 0.73 12 GCS9 UGCS J034032.38+195429.9 0.004 0.003 0.003 0.003 0.002 0.002 +03 42 34.030 +23 24 51.80 0 15.870 15.322 14.668 14.108 13.758 13.737 14.90 2.26 -38.79 2.26 0.74 12 GCS9 UGCS J034234.02+232451.8 0.006 0.005 0.004 0.004 0.004 0.003 +03 42 36.270 +23 22 04.70 0 14.057 13.676 13.118 12.571 12.298 12.267 16.16 2.25 -37.36 2.25 0.73 12 GCS9 V* V433 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 56 09.600 +22 28 01.40 0 14.827 14.311 13.669 13.091 12.772 12.772 24.94 2.51 -41.87 2.51 0.72 12 GCS9 2MASS J03560958+2228017 0.004 0.003 0.003 0.002 0.002 0.001 +03 43 01.400 +23 29 30.40 0 14.868 14.435 13.861 13.324 13.014 13.042 18.85 2.25 -41.62 2.25 0.94 12 GCS9 UGCS J034301.40+232930.4 0.004 0.003 0.003 0.002 0.002 0.002 +03 39 38.690 +23 23 20.20 0 14.410 14.024 13.460 12.927 12.628 12.603 18.87 2.98 -38.42 2.98 0.86 12 GCS9 UGCS J033938.69+232320.2 0.003 0.002 0.002 0.002 0.002 0.001 +03 39 41.120 +23 28 23.30 0 14.958 14.527 13.924 13.411 13.080 13.046 23.64 2.98 -38.71 2.98 0.70 12 GCS9 Cl* Melotte 22 HHJ 83 0.004 0.003 0.003 0.002 0.002 0.002 +03 39 57.350 +23 19 42.20 0 14.866 14.438 13.872 13.338 13.029 13.030 20.25 2.98 -49.85 2.98 0.61 12 GCS9 Cl* Melotte 22 HHJ 103 0.004 0.003 0.003 0.002 0.002 0.002 +03 40 00.190 +23 26 05.70 0 12.733 12.412 11.891 11.375 11.054 11.085 19.30 2.97 -38.03 2.97 0.85 12 GCS9 V* KU Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 53 30.760 +19 54 24.10 0 13.671 13.329 12.773 12.177 11.869 11.915 21.18 3.97 -41.70 3.97 0.91 12 GCS9 Cl* Melotte 22 DH 787 0.002 0.002 0.002 0.001 0.001 0.001 +03 40 26.270 +23 21 29.10 0 14.499 14.088 13.492 12.935 12.626 12.650 23.17 2.98 -38.33 2.98 0.71 12 GCS9 Cl* Melotte 22 HHJ 167 0.003 0.003 0.002 0.002 0.002 0.001 +03 51 06.130 +22 38 00.60 0 14.992 14.482 13.846 13.298 12.962 12.927 17.14 2.51 -38.08 2.51 0.82 12 GCS9 Cl* Melotte 22 DH 694 0.004 0.003 0.003 0.002 0.002 0.002 +03 54 22.490 +23 38 11.90 0 14.441 13.982 13.383 12.800 12.511 12.520 12.94 2.24 -43.81 2.24 0.77 12 GCS9 Cl* Melotte 22 DH 801 0.003 0.002 0.002 0.002 0.002 0.001 +03 54 02.730 +23 35 00.90 0 15.007 14.465 13.863 13.314 12.994 12.988 15.75 2.25 -46.40 2.25 0.80 12 GCS9 Cl* Melotte 22 BPL 301 0.004 0.003 0.003 0.002 0.002 0.002 +03 51 07.110 +23 20 57.60 0 14.729 14.274 13.683 13.132 12.833 12.817 15.82 2.25 -41.10 2.25 0.90 12 GCS9 Cl* Melotte 22 DH 695 0.003 0.003 0.003 0.002 0.002 0.001 +03 47 22.280 +24 16 59.90 0 16.097 15.485 14.840 14.281 13.922 13.904 20.93 2.23 -44.36 2.23 0.64 12 GCS9 UGCS J034722.27+241659.8 0.007 0.005 0.004 0.003 0.004 0.003 +03 47 22.380 +24 14 18.80 0 15.323 14.727 14.093 13.545 13.176 13.163 19.02 2.22 -46.64 2.22 0.81 12 GCS9 Cl* Melotte 22 BPL 139 0.004 0.003 0.003 0.002 0.002 0.002 +03 47 25.110 +24 15 17.20 0 14.913 14.441 13.875 13.326 13.021 13.018 18.69 2.22 -45.16 2.22 0.93 12 GCS9 Cl* Melotte 22 BPL 143 0.004 0.003 0.003 0.002 0.002 0.002 +03 47 29.480 +24 12 19.70 0 15.420 14.860 14.256 13.725 13.372 13.384 17.13 2.22 -47.51 2.22 0.76 12 GCS9 UGCS J034729.48+241219.7 0.005 0.003 0.003 0.002 0.003 0.002 +03 47 30.600 +24 22 13.80 0 13.050 12.722 12.199 11.658 11.352 11.381 18.57 2.22 -44.22 2.22 0.94 12 GCS9 V* QT Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 51 39.080 +23 22 04.30 0 15.003 14.530 13.931 13.387 13.058 13.060 21.33 2.25 -42.78 2.25 0.85 12 GCS9 Cl* Melotte 22 BPL 237 0.004 0.003 0.003 0.002 0.002 0.002 +03 52 05.580 +22 34 55.10 0 13.116 12.817 12.304 11.756 11.447 11.468 18.18 2.51 -46.19 2.51 0.91 12 GCS9 Cl* Melotte 22 DH 740 0.002 0.001 0.001 0.001 0.001 0.001 +03 52 34.480 +22 30 07.50 0 13.080 12.786 12.289 11.679 11.396 11.423 16.37 2.51 -36.85 2.51 0.63 12 GCS9 V* V395 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 46 22.470 +23 29 08.00 0 14.962 14.417 13.743 13.146 12.784 12.747 19.14 2.24 -43.87 2.24 0.94 12 GCS9 UGCS J034622.46+232908.0 0.004 0.003 0.003 0.002 0.002 0.001 +03 52 51.720 +22 31 32.70 0 13.700 13.329 12.777 12.199 11.894 11.930 19.14 2.51 -43.95 2.51 0.94 12 GCS9 V* V568 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 15.380 +23 26 05.80 0 14.386 13.944 13.385 12.825 12.503 12.475 15.92 2.24 -43.02 2.24 0.92 12 GCS9 Cl* Melotte 22 HHJ 203 0.003 0.002 0.002 0.002 0.002 0.001 +03 52 56.970 +22 26 01.10 0 13.248 12.945 12.403 11.839 11.543 11.594 20.34 2.51 -44.90 2.51 0.92 12 GCS9 V* V883 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 43 43.530 +24 12 50.00 0 15.544 15.025 14.407 13.872 13.518 18.44 2.31 -44.45 2.31 0.89 12 GCS9 Cl* Melotte 22 DH 284 0.005 0.004 0.004 0.003 0.002 +03 47 22.030 +23 21 36.30 0 14.515 14.064 13.489 12.961 12.637 12.619 20.70 2.24 -41.53 2.24 0.93 12 GCS9 UGCS J034722.02+232136.3 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 28.120 +23 26 53.40 0 13.694 13.309 12.779 12.225 11.885 11.879 19.40 2.24 -40.95 2.24 0.92 12 GCS9 Cl* Melotte 22 MSK 140 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 32.190 +23 18 23.30 0 14.313 13.888 13.351 12.830 12.511 12.509 18.09 2.24 -46.67 2.24 0.90 12 GCS9 UGCS J034732.19+231823.2 0.003 0.002 0.002 0.002 0.002 0.001 +03 43 51.760 +24 14 15.90 0 14.292 13.838 13.229 12.650 12.358 22.34 2.30 -41.81 2.30 0.90 12 GCS9 V* V847 Tau 0.003 0.002 0.002 0.002 0.001 +03 43 52.770 +24 18 48.40 0 16.556 15.907 15.211 14.628 14.221 19.79 2.32 -39.76 2.32 0.64 12 GCS9 UGCS J034352.77+241848.4 0.009 0.007 0.006 0.005 0.004 +03 47 36.040 +23 28 26.70 0 15.203 14.719 14.127 13.601 13.266 13.247 18.53 2.24 -46.44 2.24 0.83 12 GCS9 Cl* Melotte 22 DH 526 0.004 0.003 0.003 0.002 0.003 0.002 +03 43 57.290 +24 13 20.30 0 14.206 13.775 13.190 12.639 12.337 21.78 2.30 -37.51 2.30 0.72 12 GCS9 V* V511 Tau 0.003 0.002 0.002 0.001 0.001 +04 00 08.160 +22 32 01.10 1 16.449 15.820 15.102 14.503 14.098 14.104 16.31 2.88 -39.63 2.88 0.64 12 GCS9 UGCS J040008.16+223201.0 0.008 0.006 0.005 0.006 0.004 0.004 +03 48 04.980 +23 24 13.40 0 15.976 15.415 14.783 14.280 13.912 13.935 16.66 2.25 -43.18 2.25 0.89 12 GCS9 Cl* Melotte 22 DH 546 0.007 0.005 0.005 0.004 0.004 0.003 +03 39 55.210 +24 12 54.10 0 15.371 14.909 14.293 13.783 13.432 13.440 18.81 2.50 -45.72 2.50 0.86 12 GCS9 Cl* Melotte 22 MHO 2 0.005 0.004 0.003 0.003 0.003 0.002 +03 58 56.150 +23 27 53.50 0 12.935 12.592 12.055 11.526 11.193 11.220 20.59 2.99 -37.49 2.99 0.81 12 GCS9 Cl* Melotte 22 DH 868 0.002 0.001 0.001 0.001 0.001 0.001 +03 40 07.110 +24 13 03.30 0 15.403 14.934 14.318 13.802 13.450 13.451 16.92 2.50 -42.32 2.50 0.90 12 GCS9 Cl* Melotte 22 DH 126 0.005 0.004 0.004 0.003 0.003 0.002 +03 59 07.060 +22 27 48.20 0 14.565 14.030 13.393 12.824 12.466 12.480 16.23 2.84 -40.81 2.84 0.91 12 GCS9 UGCS J035907.05+222748.1 0.003 0.002 0.002 0.002 0.001 0.001 +03 59 18.990 +23 31 23.90 0 14.624 14.200 13.596 13.059 12.742 12.746 17.69 3.00 -45.41 3.00 0.92 12 GCS9 Cl* Melotte 22 DH 874 0.004 0.003 0.003 0.002 0.002 0.001 +03 40 26.950 +24 14 14.20 0 14.326 13.942 13.369 12.825 12.542 12.531 18.94 2.50 -42.53 2.50 0.94 12 GCS9 Cl* Melotte 22 DH 140 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 59.540 +24 11 45.60 0 15.530 15.002 14.367 13.846 13.517 13.502 18.52 2.22 -41.66 2.22 0.90 12 GCS9 Cl* Melotte 22 BPL 220 0.005 0.004 0.004 0.003 0.003 0.002 +03 50 27.350 +23 22 44.90 0 14.389 13.925 13.358 12.817 12.499 12.514 24.33 2.13 -40.44 2.13 0.74 12 GCS9 Cl* Melotte 22 DH 670 0.003 0.002 0.002 0.002 0.001 0.001 +03 50 12.870 +24 21 06.20 0 12.697 12.419 11.916 11.378 11.086 11.149 15.86 2.22 -37.83 2.22 0.75 12 GCS9 UGCS J035012.86+242106.2 0.001 0.001 0.001 0.001 0.001 0.000 +03 50 13.000 +24 21 07.00 0 13.248 12.864 12.329 11.821 11.509 11.534 20.58 2.22 -44.46 2.22 0.92 12 GCS9 UGCS J035012.99+242106.9 0.002 0.001 0.001 0.001 0.001 0.001 +03 50 15.270 +24 13 36.00 0 14.103 13.701 13.153 12.628 12.339 12.347 18.72 2.22 -42.98 2.22 0.94 12 GCS9 V* V469 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 50 19.150 +24 16 34.00 0 16.445 15.797 15.103 14.564 14.190 14.166 20.67 2.23 -41.47 2.23 0.67 12 GCS9 Cl* Melotte 22 BPL 228 0.008 0.006 0.005 0.004 0.005 0.003 +03 50 42.150 +23 29 33.10 0 15.510 14.987 14.385 13.858 13.532 13.543 21.92 2.14 -45.96 2.14 0.74 12 GCS9 UGCS J035042.14+232933.0 0.005 0.004 0.004 0.003 0.003 0.002 +03 50 33.080 +24 20 21.60 0 14.355 13.948 13.365 12.845 12.537 12.525 16.87 2.22 -41.49 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 674 0.003 0.002 0.002 0.002 0.002 0.001 +03 50 42.440 +24 12 55.40 0 15.040 14.585 13.995 13.458 13.144 13.158 16.99 2.22 -39.63 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 683 0.004 0.003 0.003 0.002 0.002 0.002 +03 50 54.650 +24 21 55.70 0 14.574 14.149 13.570 13.048 12.740 12.793 17.43 2.22 -47.29 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 687 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 22.460 +22 31 10.80 0 15.350 14.836 14.214 13.686 13.314 13.291 20.04 2.27 -39.70 2.27 0.83 12 GCS9 Cl* Melotte 22 BPL 140 0.005 0.004 0.004 0.003 0.003 0.002 +03 46 55.320 +23 22 49.30 0 15.196 14.729 14.159 13.630 13.284 13.299 18.52 2.24 -41.36 2.24 0.89 12 GCS9 Cl* Melotte 22 DH 479 0.004 0.003 0.003 0.002 0.003 0.002 +03 40 01.870 +24 46 25.90 0 14.203 13.811 13.240 12.713 12.468 12.420 19.86 2.48 -42.54 2.48 0.94 12 GCS9 V* V598 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 09.520 +23 25 56.30 0 14.905 14.464 13.880 13.361 13.040 13.087 20.47 2.24 -42.25 2.24 0.93 12 GCS9 Cl* Melotte 22 DH 498 0.004 0.003 0.003 0.002 0.002 0.002 +03 48 05.720 +22 38 09.30 0 14.248 13.814 13.195 12.717 12.425 12.432 15.27 2.26 -47.03 2.26 0.82 12 GCS9 V* V341 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 42 08.840 +23 35 16.80 0 12.944 12.632 12.126 11.626 11.318 11.344 14.70 2.12 -43.32 2.12 0.72 12 GCS9 Cl* Melotte 22 DH 212 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 32.320 +24 16 01.10 0 13.986 13.660 13.081 12.505 12.208 12.233 21.26 2.50 -43.91 2.50 0.91 12 GCS9 V* V597 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 41 25.330 +22 32 55.80 0 13.437 13.076 12.526 11.946 11.699 11.718 20.54 2.50 -40.20 2.50 0.89 12 GCS9 V* V492 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 59 59.150 +23 41 04.60 0 15.249 14.710 14.084 13.517 13.217 20.59 3.55 -40.08 3.55 0.83 12 GCS9 Cl* Melotte 22 DH 883 0.004 0.003 0.003 0.003 0.002 +03 59 56.490 +23 40 15.30 0 16.179 15.510 14.834 14.290 13.933 18.14 3.57 -40.03 3.57 0.69 12 GCS9 Cl* Melotte 22 DH 882 0.006 0.005 0.004 0.006 0.003 +04 03 49.570 +23 43 13.70 0 14.866 14.377 13.796 13.269 12.931 12.931 22.02 3.38 -44.78 3.38 0.90 12 GCS9 Cl* Melotte 22 DH 910 0.003 0.003 0.003 0.002 0.002 0.001 +04 01 13.990 +23 10 29.90 0 13.872 13.459 12.893 12.304 12.031 12.021 20.88 3.35 -38.17 3.35 0.77 12 GCS9 Cl* Melotte 22 DH 899 0.002 0.002 0.002 0.001 0.001 0.001 +03 45 16.420 +23 34 01.60 0 15.628 15.046 14.415 13.871 13.502 13.519 16.66 2.22 -43.95 2.22 0.89 12 GCS9 Cl* Melotte 22 DH 384 0.006 0.004 0.004 0.003 0.003 0.002 +03 45 06.790 +23 36 51.40 0 14.751 14.200 13.573 12.993 12.645 12.675 16.78 2.22 -41.22 2.22 0.92 12 GCS9 V* V516 Tau 0.003 0.003 0.002 0.002 0.002 0.001 +03 44 56.690 +23 36 23.50 0 14.114 13.697 13.146 12.622 12.313 12.314 22.12 2.22 -39.49 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 361 0.003 0.002 0.002 0.001 0.001 0.001 +03 44 51.260 +23 34 20.60 0 16.524 15.857 15.186 14.615 14.212 14.214 20.98 2.24 -41.95 2.24 0.66 12 GCS9 UGCS J034451.26+233420.5 0.009 0.006 0.006 0.005 0.005 0.004 +03 44 35.900 +23 34 41.90 1 16.307 15.672 14.985 14.376 13.990 13.985 16.94 2.23 -44.39 2.23 0.72 12 GCS9 Cl* Melotte 22 HHJ 5 0.008 0.006 0.005 0.004 0.004 0.003 +03 44 31.720 +23 35 26.00 0 14.020 13.606 13.060 12.451 12.150 12.189 19.59 2.22 -44.22 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 345 0.002 0.002 0.002 0.001 0.001 0.001 +03 49 01.160 +23 38 15.50 0 15.613 15.042 14.427 13.885 13.548 13.580 15.19 2.22 -43.96 2.22 0.86 12 GCS9 Cl* Melotte 22 DH 598 0.005 0.004 0.004 0.003 0.003 0.002 +03 36 22.330 +22 44 32.70 0 13.694 13.229 12.673 12.138 11.842 11.852 22.55 3.04 -41.33 3.04 0.85 12 GCS9 Cl* Melotte 22 DH 55 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 19.840 +23 36 11.80 0 13.175 12.840 12.327 11.827 11.486 11.536 20.97 2.22 -44.35 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 568 0.002 0.001 0.001 0.001 0.001 0.001 +03 48 16.090 +23 35 15.20 0 14.654 14.122 13.520 12.988 12.645 12.641 20.07 2.22 -42.31 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 564 0.003 0.002 0.002 0.002 0.002 0.001 +03 48 15.270 +23 42 03.40 0 13.599 13.175 12.588 12.112 11.768 11.801 18.45 2.22 -45.66 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 562 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 13.780 +23 37 59.30 0 13.951 13.521 12.974 12.422 12.130 12.041 19.35 2.22 -44.07 2.22 0.94 12 GCS9 V* V458 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 08.960 +23 42 23.30 0 14.620 14.148 13.566 13.045 12.761 12.747 17.90 2.22 -46.56 2.22 0.90 12 GCS9 Cl* Melotte 22 DH 552 0.003 0.002 0.002 0.002 0.002 0.001 +03 41 02.990 +23 43 21.40 0 13.772 13.397 12.857 12.313 12.027 12.044 14.58 2.12 -38.42 2.12 0.71 12 GCS9 V* V603 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 38 13.040 +23 37 20.70 0 15.694 15.167 14.427 13.836 13.460 13.494 16.75 2.50 -45.95 2.50 0.84 12 GCS9 Cl* Melotte 22 HHJ 32 0.005 0.004 0.004 0.003 0.003 0.002 +03 41 43.720 +23 07 59.70 0 16.390 15.741 15.081 14.540 14.130 14.144 19.01 2.28 -38.92 2.28 0.60 12 GCS9 UGCS J034143.72+230759.7 0.009 0.006 0.006 0.005 0.006 0.004 +03 42 15.370 +23 11 30.90 0 14.906 14.419 13.845 13.300 12.963 12.972 22.08 2.25 -38.79 2.25 0.81 12 GCS9 Cl* Melotte 22 HHJ 109 0.004 0.003 0.003 0.002 0.002 0.002 +03 54 25.040 +24 42 43.40 0 14.895 14.283 13.653 13.120 12.763 12.750 20.21 2.23 -42.84 2.23 0.94 12 GCS9 V* V401 Tau 0.004 0.003 0.003 0.002 0.002 0.001 +03 54 24.390 +22 41 20.50 0 14.947 14.441 13.840 13.254 12.936 12.943 20.19 2.26 -45.53 2.26 0.92 12 GCS9 UGCS J035424.39+224120.5 0.004 0.003 0.003 0.002 0.002 0.002 +03 49 58.340 +23 42 33.70 0 13.279 12.903 12.399 12.070 11.567 11.705 16.44 2.22 -43.28 2.22 0.93 12 GCS9 Cl* Melotte 22 MSK 211 0.002 0.001 0.001 0.001 0.001 0.001 +03 49 57.640 +23 43 28.20 0 15.488 14.887 14.245 13.710 13.395 13.386 20.76 2.22 -42.85 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 644 0.005 0.004 0.003 0.002 0.003 0.002 +04 01 39.830 +22 47 53.70 0 16.669 15.899 15.153 14.575 14.169 14.157 21.16 3.39 -42.62 3.39 0.65 12 GCS9 UGCS J040139.83+224753.7 0.009 0.006 0.006 0.006 0.006 0.004 +03 49 31.220 +23 41 19.60 0 14.646 14.158 13.575 13.029 12.724 12.749 20.25 2.22 -44.15 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 625 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 29.810 +23 38 55.20 0 14.604 14.083 13.516 12.985 12.654 12.674 15.93 2.22 -46.60 2.22 0.86 12 GCS9 Cl* Melotte 22 DH 624 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 21.500 +23 39 06.30 0 13.751 13.346 12.809 12.251 11.951 12.003 13.80 2.22 -45.12 2.22 0.82 12 GCS9 V* V551 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 12.670 +23 35 13.60 0 14.942 14.442 13.836 13.296 12.972 12.997 17.61 2.22 -42.42 2.22 0.94 12 GCS9 UGCS J034612.66+233513.5 0.004 0.003 0.003 0.002 0.002 0.001 +03 45 27.520 +23 37 56.90 0 15.142 14.678 14.099 13.543 13.236 13.231 19.00 2.22 -42.76 2.22 0.90 12 GCS9 Cl* Melotte 22 HHJ 106 0.004 0.003 0.003 0.002 0.002 0.002 +03 31 33.200 +27 32 49.10 0 14.985 14.331 13.622 13.095 12.730 20.59 6.93 -48.45 6.93 0.77 12 GCS9 UGCS J033133.20+273249.0 0.003 0.002 0.002 0.002 0.001 +03 48 23.920 +23 08 08.10 0 15.111 14.655 14.032 13.490 13.123 13.129 14.82 2.13 -40.92 2.13 0.83 12 GCS9 Cl* Melotte 22 DH 576 0.004 0.003 0.003 0.003 0.002 0.002 +03 48 40.990 +23 14 17.20 0 13.874 13.419 12.818 12.291 11.956 11.984 20.87 2.13 -42.75 2.13 0.92 12 GCS9 V* V875 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 50 15.430 +22 46 19.90 0 16.872 16.103 15.397 14.842 14.414 14.387 15.23 2.19 -42.79 2.19 0.70 12 GCS9 UGCS J035015.43+224619.8 0.011 0.008 0.007 0.007 0.006 0.005 +03 42 56.550 +22 51 17.90 0 15.901 15.338 14.689 14.137 13.782 13.760 16.35 2.27 -38.51 2.27 0.78 12 GCS9 UGCS J034256.54+225117.8 0.006 0.005 0.005 0.004 0.004 0.003 +03 43 04.200 +22 48 03.30 0 12.462 12.136 11.621 11.436 10.779 10.986 18.93 2.25 -40.66 2.25 0.90 12 GCS9 V* V435 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 42 29.430 +22 47 25.90 0 12.560 12.251 11.750 11.449 10.885 11.017 19.42 2.25 -40.94 2.25 0.90 12 GCS9 V* V613 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 43 19.010 +22 47 10.40 0 14.679 14.176 13.584 13.040 12.725 12.734 16.91 2.25 -46.41 2.25 0.89 12 GCS9 V* V621 Tau 0.003 0.003 0.003 0.002 0.002 0.001 +03 41 26.360 +23 08 02.70 0 14.574 14.102 13.532 12.927 12.614 12.612 15.96 2.25 -41.75 2.25 0.92 12 GCS9 Cl* Melotte 22 DH 174 0.003 0.002 0.003 0.002 0.002 0.001 +03 40 51.820 +23 13 50.50 0 14.145 13.723 13.201 12.627 12.326 12.331 16.57 2.25 -41.87 2.25 0.93 12 GCS9 V* V601 Tau 0.002 0.002 0.002 0.002 0.002 0.001 +03 41 25.970 +23 15 35.80 0 14.670 14.193 13.617 13.061 12.761 12.768 19.37 2.25 -43.60 2.25 0.94 12 GCS9 UGCS J034125.96+231535.8 0.003 0.003 0.003 0.002 0.002 0.001 +03 43 27.440 +22 37 41.00 0 15.234 14.678 14.118 13.589 13.256 13.235 23.77 2.51 -43.48 2.51 0.67 12 GCS9 V* LX Tau 0.004 0.003 0.003 0.003 0.003 0.002 +03 39 44.790 +22 39 15.30 0 14.615 14.124 13.554 13.015 12.716 12.691 17.16 2.98 -42.48 2.98 0.94 12 GCS9 Cl* Melotte 22 DH 112 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 07.010 +22 38 47.50 0 14.975 14.452 13.904 13.372 13.034 13.020 17.42 2.98 -39.40 2.98 0.89 12 GCS9 Cl* Melotte 22 HHJ 114 0.004 0.003 0.003 0.002 0.002 0.002 +03 34 35.570 +23 38 43.30 0 15.561 15.064 14.437 13.919 13.538 13.570 17.78 2.61 -36.55 2.61 0.60 12 GCS9 UGCS J033435.57+233843.3 0.005 0.004 0.004 0.003 0.004 0.002 +03 50 25.190 +22 40 31.70 0 15.262 14.699 14.109 13.541 13.204 13.220 18.66 2.13 -45.07 2.13 0.88 12 GCS9 UGCS J035025.18+224031.7 0.005 0.003 0.003 0.003 0.002 0.002 +03 50 04.250 +23 10 44.50 0 14.127 13.785 13.209 12.563 12.306 12.299 22.40 2.25 -39.72 2.25 0.84 12 GCS9 Cl* Melotte 22 DH 649 0.002 0.002 0.002 0.001 0.001 0.001 +03 49 41.140 +23 14 59.30 0 15.232 14.627 13.986 13.443 13.056 13.075 17.11 2.25 -39.25 2.25 0.83 12 GCS9 UGCS J034941.13+231459.2 0.004 0.003 0.003 0.003 0.002 0.002 +03 49 32.160 +23 16 17.90 0 15.381 14.888 14.287 13.753 13.418 13.424 20.17 2.25 -40.90 2.25 0.86 12 GCS9 Cl* Melotte 22 DH 626 0.005 0.004 0.003 0.003 0.003 0.002 +03 56 24.990 +23 05 26.60 0 12.641 12.387 11.904 11.372 11.058 11.087 17.85 2.99 -45.85 2.99 0.72 12 GCS9 Cl* Melotte 22 HHJ 419 0.001 0.001 0.001 0.001 0.001 0.000 +03 45 28.900 +27 28 07.20 0 12.839 12.475 11.993 11.460 11.179 11.209 18.03 3.44 -39.52 3.44 0.88 12 GCS9 Cl* Melotte 22 DH 394 0.001 0.001 0.001 0.001 0.001 0.001 +03 36 24.180 +22 37 24.30 0 14.137 13.724 13.127 12.613 12.276 12.327 17.43 2.94 -44.41 2.94 0.93 12 GCS9 UGCS J033624.18+223724.3 0.002 0.002 0.002 0.001 0.001 0.001 +03 43 36.570 +23 12 34.10 0 14.208 13.781 13.203 12.644 12.333 12.343 23.03 2.25 -39.25 2.25 0.79 12 GCS9 V* MN Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 44 09.320 +23 08 46.80 0 14.412 13.974 13.380 12.815 12.486 12.506 19.99 2.25 -40.13 2.25 0.91 12 GCS9 Cl* Melotte 22 DH 313 0.003 0.002 0.002 0.002 0.002 0.001 +03 44 22.140 +23 10 54.80 0 15.807 15.195 14.479 13.913 13.468 13.518 16.09 2.26 -42.11 2.26 0.88 12 GCS9 Cl* Melotte 22 DH 329 0.006 0.004 0.004 0.003 0.003 0.002 +03 45 24.700 +24 38 46.40 0 15.819 15.190 14.549 13.983 13.645 13.672 18.14 2.22 -44.52 2.22 0.89 12 GCS9 UGCS J034524.69+243846.4 0.006 0.004 0.004 0.003 0.004 0.002 +03 45 06.560 +24 40 42.70 0 15.393 14.853 14.208 13.659 13.314 13.323 13.49 2.21 -39.94 2.21 0.71 12 GCS9 Cl* Melotte 22 HHJ 48 0.005 0.004 0.003 0.002 0.003 0.002 +03 45 08.760 +24 50 31.50 0 13.942 13.474 12.930 12.429 12.086 12.117 17.85 2.21 -41.29 2.21 0.93 12 GCS9 UGCS J034508.76+245031.4 0.002 0.002 0.002 0.001 0.001 0.001 +03 37 16.530 +23 11 04.20 0 14.370 13.944 13.393 12.855 12.551 12.556 23.40 2.97 -41.45 2.97 0.84 12 GCS9 Cl* Melotte 22 DH 69 0.003 0.002 0.002 0.002 0.002 0.001 +03 45 01.140 +24 46 40.90 0 14.445 13.990 13.411 12.861 12.557 12.556 13.05 2.21 -45.96 2.21 0.71 12 GCS9 V* V744 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +04 06 28.550 +22 36 59.60 0 14.067 13.659 13.076 12.477 12.156 12.133 17.57 5.07 -40.55 5.07 0.92 12 GCS9 UGCS J040628.54+223659.6 0.002 0.002 0.002 0.002 0.001 0.001 +03 58 34.180 +22 40 11.10 0 15.075 14.525 13.922 13.337 13.003 13.039 18.08 3.00 -38.66 3.00 0.81 12 GCS9 Cl* Melotte 22 DH 867 0.004 0.004 0.003 0.002 0.002 0.002 +03 50 08.620 +24 40 17.70 0 15.348 14.804 14.219 13.674 13.351 13.360 17.39 2.21 -43.94 2.21 0.90 12 GCS9 Cl* Melotte 22 DH 655 0.005 0.003 0.003 0.002 0.003 0.002 +03 49 55.790 +24 44 31.30 0 13.226 12.862 12.357 11.905 11.499 11.560 17.55 2.20 -41.05 2.20 0.92 12 GCS9 V* V364 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 31 49.860 +22 50 24.50 0 14.569 14.083 13.494 12.998 12.676 12.690 25.11 3.42 -44.59 3.42 0.68 12 GCS9 UGCS J033149.85+225024.4 0.003 0.002 0.002 0.002 0.002 0.001 +03 32 07.870 +23 13 57.20 0 14.025 13.621 13.052 12.517 12.218 12.199 18.14 3.41 -38.13 3.41 0.84 12 GCS9 Cl* Melotte 22 DH 17 0.002 0.002 0.002 0.001 0.001 0.001 +03 37 26.390 +24 34 01.20 0 14.315 13.924 13.363 12.833 12.539 12.563 20.85 2.48 -42.78 2.48 0.93 12 GCS9 Cl* Melotte 22 DH 71 0.003 0.002 0.002 0.002 0.002 0.001 +03 42 44.370 +23 06 16.10 0 14.900 14.423 13.813 13.265 12.950 12.934 14.75 2.25 -47.61 2.25 0.75 12 GCS9 Cl* Melotte 22 DH 240 0.003 0.003 0.003 0.002 0.002 0.001 +03 42 51.680 +23 08 43.70 0 14.593 14.085 13.451 12.866 12.527 12.518 20.56 2.25 -41.00 2.25 0.92 12 GCS9 UGCS J034251.68+230843.6 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 21.250 +24 41 40.80 0 15.549 14.957 14.352 13.821 13.470 13.478 16.52 2.21 -46.03 2.21 0.84 12 GCS9 Cl* Melotte 22 DH 615 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 39.310 +24 50 19.90 0 15.432 14.862 14.269 13.711 13.405 13.387 14.82 2.21 -44.18 2.21 0.84 12 GCS9 Cl* Melotte 22 DH 585 0.005 0.004 0.003 0.003 0.003 0.002 +03 54 37.360 +23 13 32.50 0 14.469 14.018 13.413 12.815 12.514 12.535 19.90 2.25 -37.77 2.25 0.81 12 GCS9 Cl* Melotte 22 DH 805 0.003 0.002 0.002 0.002 0.002 0.001 +03 51 23.820 +22 50 29.10 0 12.115 11.877 11.498 11.311 10.620 10.885 17.91 2.25 -42.23 2.25 0.88 12 GCS9 Cl* Melotte 22 DH 706 0.001 0.001 0.001 0.001 0.001 0.000 +03 39 43.320 +23 12 25.30 0 15.134 14.683 14.096 13.549 13.216 13.200 19.81 2.98 -37.33 2.98 0.67 12 GCS9 Cl* Melotte 22 DH 110 0.004 0.003 0.003 0.003 0.002 0.002 +03 46 28.630 +24 45 32.10 0 12.120 11.913 11.480 11.309 10.657 10.853 15.43 2.21 -40.74 2.21 0.81 12 GCS9 V* V446 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 46 17.940 +24 41 09.30 0 13.394 13.027 12.512 11.992 11.646 11.701 19.02 2.21 -41.15 2.21 0.93 12 GCS9 Cl* Melotte 22 DH 433 0.002 0.001 0.001 0.001 0.001 0.001 +03 46 08.700 +24 40 33.10 0 14.615 14.120 13.525 13.000 12.673 12.686 16.53 2.21 -42.88 2.21 0.93 12 GCS9 V* V857 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 02.960 +24 40 55.60 0 15.363 14.742 14.071 13.491 13.114 13.134 14.57 2.21 -41.20 2.21 0.83 12 GCS9 Cl* Melotte 22 DH 414 0.005 0.003 0.003 0.002 0.002 0.002 +03 45 36.720 +24 39 06.50 0 13.244 12.776 12.207 11.739 11.342 11.388 13.96 2.21 -44.03 2.21 0.85 12 GCS9 V* V443 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 50 18.220 +24 35 15.30 0 14.697 14.220 13.692 13.137 12.844 12.861 14.19 2.20 -44.75 2.20 0.85 12 GCS9 Cl* Melotte 22 DH 665 0.003 0.003 0.003 0.002 0.002 0.001 +03 50 10.770 +24 28 41.40 0 14.754 14.283 13.690 13.174 12.826 12.850 15.24 2.20 -41.67 2.20 0.90 12 GCS9 Cl* Melotte 22 DH 656 0.004 0.003 0.003 0.002 0.002 0.001 +03 49 33.040 +24 32 02.40 0 13.402 13.042 12.522 12.024 11.684 11.695 13.04 2.20 -43.72 2.20 0.79 12 GCS9 V* V361 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 46 27.010 +24 27 13.90 0 13.850 13.415 12.890 12.336 12.039 12.035 16.46 2.21 -47.67 2.21 0.82 12 GCS9 Cl* Melotte 22 DH 447 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 24.640 +24 28 46.20 0 14.399 13.930 13.364 12.822 12.527 12.552 16.75 2.21 -43.26 2.21 0.93 12 GCS9 V* V1275 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 24.120 +24 30 12.70 0 15.893 15.305 14.689 14.145 13.797 13.818 18.76 2.22 -43.84 2.22 0.90 12 GCS9 Cl* Melotte 22 BPL 101 0.006 0.005 0.004 0.003 0.004 0.003 +03 46 23.030 +24 36 17.90 0 14.585 14.122 13.563 13.027 12.710 12.729 16.93 2.21 -44.40 2.21 0.93 12 GCS9 Cl* Melotte 22 DH 442 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 21.360 +24 33 52.20 0 15.032 14.512 13.919 13.388 13.052 13.066 14.20 2.21 -42.68 2.21 0.83 12 GCS9 Cl* Melotte 22 DH 439 0.004 0.003 0.003 0.002 0.002 0.001 +03 56 57.080 +24 48 34.30 0 12.982 12.615 12.063 11.527 11.181 11.276 19.30 2.47 -39.94 2.47 0.89 12 GCS9 V* V693 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 46 05.640 +24 36 44.30 0 13.116 12.770 12.264 11.710 11.428 11.468 18.12 2.21 -41.35 2.21 0.93 12 GCS9 Cl* Melotte 22 SK 488 0.002 0.001 0.001 0.001 0.001 0.001 +03 46 05.690 +24 36 49.70 0 15.023 14.505 13.894 13.358 13.037 13.052 19.22 2.21 -40.25 2.21 0.87 12 GCS9 UGCS J034605.69+243649.7 0.004 0.003 0.003 0.002 0.002 0.001 +03 45 51.090 +24 26 10.90 0 15.873 15.293 14.658 14.103 13.746 13.760 17.00 2.22 -46.18 2.22 0.84 12 GCS9 Cl* Melotte 22 HHJ 25 0.006 0.005 0.004 0.003 0.004 0.002 +03 45 49.370 +24 25 07.10 0 13.560 13.177 12.633 12.107 11.764 11.792 17.27 2.21 -44.41 2.21 0.93 12 GCS9 Cl* Melotte 22 BPL 77 0.002 0.002 0.002 0.001 0.001 0.001 +03 45 35.690 +24 24 34.10 0 16.519 15.801 15.133 14.567 14.196 14.181 18.31 2.23 -42.12 2.23 0.74 12 GCS9 UGCS J034535.69+242434.1 0.009 0.006 0.005 0.004 0.005 0.003 +03 41 59.670 +24 42 18.30 0 16.192 15.586 14.953 14.380 14.016 16.64 2.99 -39.53 2.99 0.65 12 GCS9 Cl* Melotte 22 BPL 31 0.007 0.006 0.005 0.005 0.003 +03 52 46.450 +24 33 41.00 0 14.544 14.050 13.491 12.957 12.649 12.654 16.70 2.23 -37.50 2.23 0.76 12 GCS9 Cl* Melotte 22 BPL 270 0.003 0.003 0.003 0.002 0.002 0.001 +03 52 43.240 +24 27 58.50 0 14.755 14.266 13.689 13.131 12.827 12.833 17.78 2.23 -41.41 2.23 0.93 12 GCS9 Cl* Melotte 22 DH 763 0.004 0.003 0.003 0.002 0.002 0.001 +03 52 30.910 +24 32 39.50 0 13.409 12.945 12.392 11.866 11.560 11.576 14.80 2.23 -43.14 2.23 0.89 12 GCS9 V* V476 Tau 0.002 0.001 0.002 0.001 0.001 0.001 +03 52 20.660 +24 33 55.50 0 12.822 12.483 11.971 11.439 11.148 11.166 14.85 2.23 -40.95 2.23 0.78 12 GCS9 V* V392 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 42 03.300 +24 32 13.30 0 13.337 13.002 12.464 11.883 11.656 17.31 2.97 -48.35 2.97 0.79 12 GCS9 Cl* Melotte 22 DH 205 0.002 0.002 0.001 0.001 0.001 +03 50 38.920 +23 13 02.50 0 13.465 13.101 12.583 12.025 11.734 11.729 18.28 2.13 -37.84 2.13 0.79 12 GCS9 Cl* Melotte 22 DH 678 0.002 0.002 0.002 0.001 0.001 0.001 +03 48 50.450 +22 44 29.80 1 16.562 15.825 15.098 14.531 14.141 14.143 15.64 2.16 -42.06 2.16 0.71 12 GCS9 Cl* Melotte 22 HHJ 3 0.010 0.006 0.006 0.005 0.004 0.004 +03 46 57.100 +23 15 02.20 0 12.759 12.476 11.986 11.443 11.115 11.167 22.62 2.23 -41.65 2.23 0.83 12 GCS9 V* V453 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 49 15.120 +24 36 22.50 0 16.847 16.059 15.371 14.890 14.433 14.443 15.04 2.23 -40.44 2.23 0.64 12 GCS9 UGCS J034915.12+243622.5 0.011 0.007 0.006 0.005 0.007 0.004 +03 48 45.350 +24 37 26.30 0 15.118 14.581 13.971 13.507 13.157 13.123 15.61 2.20 -45.81 2.20 0.83 12 GCS9 V* V463 Tau 0.004 0.003 0.003 0.002 0.002 0.002 +03 48 44.690 +24 37 23.50 0 16.260 15.584 14.911 14.419 14.002 13.967 17.45 2.22 -43.06 2.22 0.75 12 GCS9 2MASS J03484468+2437237 0.008 0.005 0.005 0.004 0.005 0.003 +03 48 42.690 +24 27 19.40 0 15.480 14.961 14.356 13.822 13.479 13.475 18.31 2.21 -46.04 2.21 0.85 12 GCS9 Cl* Melotte 22 DH 587 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 40.430 +24 36 34.00 0 14.182 13.705 13.144 12.632 12.321 12.314 17.15 2.20 -46.57 2.20 0.89 12 GCS9 V* V662 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 51 19.480 +23 09 49.40 0 14.028 13.596 13.027 12.440 12.139 12.139 19.25 2.25 -39.56 2.25 0.90 12 GCS9 Cl* Melotte 22 DH 703 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 51.150 +23 17 41.10 0 13.445 13.060 12.527 12.014 11.711 11.750 17.13 2.25 -44.72 2.25 0.93 12 GCS9 V* V802 Tau 0.002 0.002 0.001 0.001 0.001 0.001 +03 58 30.990 +23 04 12.70 0 15.161 14.622 14.030 13.494 13.122 13.120 16.32 3.00 -42.51 3.00 0.89 12 GCS9 Cl* Melotte 22 DH 866 0.005 0.004 0.003 0.002 0.002 0.002 +03 42 29.710 +20 48 39.60 0 14.264 13.827 13.235 12.712 12.379 12.411 20.54 3.42 -38.07 3.42 0.82 12 GCS9 Cl* Melotte 22 DH 229 0.002 0.002 0.002 0.002 0.001 0.001 +03 43 22.550 +23 00 56.50 0 16.186 15.558 14.898 14.365 13.983 13.988 16.28 2.27 -44.63 2.27 0.70 12 GCS9 UGCS J034322.55+230056.5 0.007 0.006 0.005 0.005 0.005 0.003 +03 51 19.770 +23 04 04.10 0 15.411 14.858 14.210 13.638 13.312 13.329 16.38 2.26 -41.02 2.26 0.88 12 GCS9 Cl* Melotte 22 DH 704 0.005 0.004 0.003 0.003 0.003 0.002 +03 51 50.600 +22 53 44.30 0 13.892 13.435 12.910 12.329 12.055 12.069 13.26 2.25 -40.14 2.25 0.72 12 GCS9 V* V386 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 52 11.230 +20 52 33.40 0 14.537 14.059 13.462 12.929 12.611 12.655 19.77 3.43 -45.98 3.43 0.91 12 GCS9 Cl* Melotte 22 DH 743 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 36.070 +23 04 17.20 0 13.827 13.418 12.875 12.391 12.040 12.042 18.53 2.24 -43.10 2.24 0.94 12 GCS9 Cl* Melotte 22 MSK 100 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 48.790 +23 04 07.30 0 13.100 12.757 12.271 11.916 11.443 11.467 18.54 2.23 -39.65 2.23 0.90 12 GCS9 V* V533 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 46 21.620 +23 04 00.90 0 14.695 14.171 13.539 13.025 12.701 12.683 18.17 2.24 -41.21 2.24 0.93 12 GCS9 Cl* Melotte 22 DH 440 0.003 0.003 0.003 0.002 0.002 0.001 +03 46 31.020 +23 01 34.60 0 15.742 15.178 14.589 14.043 13.704 13.689 15.36 2.24 -40.21 2.24 0.83 12 GCS9 Cl* Melotte 22 DH 453 0.006 0.004 0.004 0.003 0.004 0.003 +03 46 19.410 +23 00 55.70 0 15.317 14.693 14.026 13.491 13.124 13.115 13.77 2.24 -42.63 2.24 0.80 12 GCS9 Cl* Melotte 22 DH 435 0.005 0.003 0.003 0.002 0.002 0.002 +03 46 34.800 +22 56 07.50 0 12.325 12.095 11.650 11.585 10.883 11.064 23.59 2.23 -45.09 2.23 0.61 12 GCS9 V* V750 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 48 05.830 +23 02 02.70 0 13.241 12.881 12.377 11.963 11.540 11.562 16.95 2.23 -43.77 2.23 0.93 12 GCS9 V* V340 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 47 39.800 +23 00 04.40 0 15.088 14.635 14.074 13.518 13.202 13.203 16.06 2.24 -42.09 2.24 0.88 12 GCS9 Cl* Melotte 22 HHJ 94 0.004 0.003 0.003 0.002 0.003 0.002 +03 47 52.880 +22 59 33.80 0 14.017 13.564 12.998 12.456 12.134 12.160 15.85 2.24 -40.42 2.24 0.89 12 GCS9 Cl* Melotte 22 BPL 160 0.002 0.002 0.002 0.001 0.001 0.001 +03 43 44.970 +23 03 21.10 0 13.553 13.162 12.604 12.043 11.693 11.711 17.85 2.25 -47.74 2.25 0.84 12 GCS9 Cl* Melotte 22 HHJ 321 0.002 0.002 0.002 0.001 0.001 0.001 +03 43 25.160 +22 53 44.30 0 14.833 14.335 13.716 13.175 12.858 12.813 14.86 2.25 -45.95 2.25 0.85 12 GCS9 Cl* Melotte 22 DH 263 0.004 0.003 0.003 0.002 0.002 0.001 +03 50 27.830 +23 03 54.90 0 14.836 14.340 13.762 13.228 12.911 12.910 17.92 2.13 -41.80 2.13 0.94 12 GCS9 Cl* Melotte 22 DH 671 0.004 0.003 0.003 0.002 0.002 0.002 +03 50 17.590 +22 55 58.80 0 15.396 14.844 14.246 13.695 13.353 13.360 19.17 2.13 -44.31 2.13 0.89 12 GCS9 Cl* Melotte 22 DH 664 0.005 0.004 0.004 0.003 0.002 0.002 +03 54 55.340 +22 59 40.10 0 15.419 14.857 14.201 13.635 13.303 13.270 13.51 2.26 -44.04 2.26 0.78 12 GCS9 Cl* Melotte 22 HHJ 62 0.005 0.004 0.004 0.003 0.003 0.002 +03 55 11.850 +22 58 02.50 0 15.060 14.458 13.794 13.249 12.894 12.890 18.52 2.26 -46.52 2.26 0.83 12 GCS9 Cl* Melotte 22 HHJ 61 0.004 0.003 0.003 0.002 0.002 0.001 +03 40 23.840 +23 04 09.00 0 14.483 14.064 13.509 12.971 12.665 12.677 22.29 2.98 -42.37 2.98 0.90 12 GCS9 Cl* Melotte 22 DH 135 0.003 0.002 0.002 0.002 0.002 0.001 +03 39 49.720 +23 03 26.20 0 14.613 14.161 13.592 13.065 12.757 12.755 22.48 2.98 -41.93 2.98 0.89 12 GCS9 Cl* Melotte 22 DH 117 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 51.080 +20 41 17.20 0 15.855 15.298 14.617 14.061 13.681 13.705 20.56 3.44 -38.49 3.44 0.75 12 GCS9 Cl* Melotte 22 DH 155 0.005 0.004 0.004 0.003 0.004 0.003 +03 42 23.660 +27 45 56.30 0 13.845 13.397 12.838 12.326 11.997 12.001 23.57 3.28 -40.53 3.28 0.76 12 GCS9 UGCS J034223.65+274556.2 0.002 0.002 0.002 0.001 0.001 0.001 +03 49 01.020 +22 58 49.20 0 12.708 12.420 11.923 11.443 11.161 11.220 17.88 2.12 -44.01 2.12 0.84 12 GCS9 V* V548 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 48 50.670 +23 04 30.00 0 15.010 14.511 13.936 13.425 13.098 13.108 17.50 2.13 -43.02 2.13 0.90 12 GCS9 Cl* Melotte 22 HHJ 116 0.004 0.003 0.003 0.002 0.002 0.002 +03 48 35.200 +22 53 42.10 1 16.176 15.452 14.745 14.185 13.770 13.772 15.22 2.15 -45.62 2.15 0.62 12 GCS9 V* V661 Tau 0.007 0.005 0.005 0.004 0.003 0.003 +03 48 22.660 +22 52 21.30 0 13.174 12.807 12.291 11.768 11.473 11.505 20.45 2.13 -41.09 2.13 0.91 12 GCS9 V* V870 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 41 58.660 +22 57 01.70 0 13.636 13.270 12.744 12.173 11.877 11.904 21.15 2.25 -41.08 2.25 0.90 12 GCS9 V* V432 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 41 46.660 +23 01 19.60 0 15.129 14.622 14.032 13.502 13.175 13.164 18.10 2.25 -44.32 2.25 0.89 12 GCS9 UGCS J034146.65+230119.6 0.004 0.003 0.003 0.002 0.003 0.002 +03 40 54.480 +22 54 25.50 0 14.916 14.411 13.824 13.282 12.957 12.960 17.93 2.25 -41.18 2.25 0.93 12 GCS9 Cl* Melotte 22 DH 159 0.004 0.003 0.003 0.002 0.002 0.001 +03 53 15.710 +22 52 14.30 0 13.571 13.174 12.615 12.057 11.772 11.797 16.50 2.25 -42.99 2.25 0.93 12 GCS9 V* V681 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 53 10.160 +23 03 10.60 0 13.483 13.099 12.546 11.958 11.672 11.670 16.27 2.25 -43.64 2.25 0.93 12 GCS9 Cl* Melotte 22 DH 779 0.002 0.002 0.002 0.001 0.001 0.001 +03 53 01.630 +22 58 48.20 0 14.463 13.982 13.388 12.835 12.536 12.506 18.23 2.25 -41.99 2.25 0.94 12 GCS9 Cl* Melotte 22 DH 776 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 41.210 +22 56 40.50 0 16.238 15.641 14.994 14.420 14.076 14.084 20.33 2.27 -43.06 2.27 0.69 12 GCS9 Cl* Melotte 22 BPL 213 0.009 0.006 0.005 0.005 0.005 0.004 +03 49 25.610 +23 02 49.90 0 15.146 14.643 14.021 13.499 13.173 13.172 20.07 2.25 -44.25 2.25 0.87 12 GCS9 Cl* Melotte 22 DH 620 0.005 0.003 0.003 0.003 0.002 0.002 +03 45 56.970 +23 01 29.00 0 14.372 13.939 13.379 12.847 12.515 12.570 16.42 2.24 -40.16 2.24 0.90 12 GCS9 V* V524 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 57 35.270 +22 59 08.00 0 13.457 13.075 12.532 11.943 11.634 11.688 20.34 2.99 -43.22 2.99 0.93 12 GCS9 UGCS J035735.26+225907.9 0.002 0.002 0.002 0.001 0.001 0.001 +03 59 29.420 +20 34 29.20 0 16.672 16.008 15.311 14.764 14.373 14.392 16.88 4.05 -38.85 4.05 0.60 12 GCS9 UGCS J035929.42+203429.1 0.008 0.007 0.006 0.006 0.007 0.005 +03 33 49.810 +22 56 19.60 0 15.069 14.626 14.048 13.507 13.194 13.190 16.86 3.05 -39.29 3.05 0.83 12 GCS9 Cl* Melotte 22 DH 31 0.004 0.003 0.003 0.002 0.002 0.002 +03 45 10.820 +23 02 58.00 0 14.146 13.717 13.137 12.612 12.306 12.336 16.69 2.24 -45.16 2.24 0.92 12 GCS9 V* V440 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 44 38.950 +23 02 25.30 0 14.434 13.951 13.374 12.833 12.489 12.506 17.33 2.24 -45.39 2.24 0.92 12 GCS9 Cl* Melotte 22 DH 351 0.003 0.002 0.002 0.002 0.002 0.001 +03 44 37.780 +22 55 15.50 0 12.736 12.396 11.884 11.404 11.020 11.104 20.58 2.23 -42.26 2.23 0.88 12 GCS9 V* NS Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 51 20.680 +19 38 37.80 0 13.526 13.101 12.519 12.042 11.710 11.730 21.01 3.97 -48.07 3.97 0.77 12 GCS9 Cl* Melotte 22 DH 705 0.002 0.001 0.002 0.001 0.001 0.001 +03 53 35.390 +21 47 05.70 0 16.517 15.849 15.160 14.619 14.236 14.231 17.16 2.55 -42.61 2.55 0.75 12 GCS9 UGCS J035335.39+214705.6 0.010 0.007 0.007 0.005 0.005 0.004 +03 42 35.650 +21 50 29.70 0 12.761 12.502 12.030 11.527 11.224 11.275 23.68 2.51 -42.67 2.51 0.75 12 GCS9 V* V614 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 45 08.890 +19 43 29.90 0 16.502 15.850 15.168 14.631 14.237 14.214 17.00 4.02 -40.72 4.02 0.71 12 GCS9 UGCS J034508.89+194329.8 0.007 0.006 0.006 0.005 0.006 0.004 +03 49 41.710 +21 56 19.20 0 15.963 15.359 14.685 14.130 13.770 13.750 22.57 2.52 -39.99 2.52 0.72 12 GCS9 Cl* Melotte 22 BPL 214 0.006 0.005 0.004 0.004 0.004 0.003 +03 59 29.330 +21 39 56.00 0 15.657 15.115 14.478 13.964 13.589 13.587 22.43 3.78 -42.75 3.78 0.79 12 GCS9 Cl* Melotte 22 DH 878 0.005 0.004 0.005 0.004 0.002 0.003 +03 59 28.420 +19 30 41.20 0 14.604 14.084 13.471 12.921 12.572 12.598 16.99 3.04 -38.71 3.04 0.86 12 GCS9 UGCS J035928.42+193041.1 0.003 0.002 0.002 0.002 0.002 0.001 +03 57 01.510 +19 23 22.10 0 15.564 15.023 14.454 13.871 13.553 13.566 19.45 6.06 -38.27 6.06 0.77 12 GCS9 UGCS J035701.50+192322.0 0.004 0.003 0.004 0.003 0.003 0.002 +03 56 38.760 +21 35 08.30 0 14.888 14.393 13.787 13.259 12.937 12.927 18.67 3.36 -40.82 3.36 0.93 12 GCS9 Cl* Melotte 22 DH 841 0.003 0.003 0.003 0.002 0.002 0.001 +04 01 26.060 +21 35 08.50 0 14.044 13.640 13.072 12.491 12.192 12.191 18.27 3.75 -43.61 3.75 0.94 12 GCS9 Cl* Melotte 22 DH 900 0.002 0.002 0.002 0.001 0.001 0.001 +04 01 45.820 +21 44 04.90 0 16.644 15.914 15.183 14.661 14.260 14.256 18.38 3.82 -39.06 3.82 0.62 12 GCS9 UGCS J040145.82+214404.8 0.009 0.007 0.007 0.006 0.004 0.004 +03 46 16.080 +21 41 09.90 0 15.678 15.135 14.516 13.979 13.651 13.622 18.91 2.66 -43.14 2.66 0.90 12 GCS9 UGCS J034616.08+214109.8 0.005 0.004 0.004 0.003 0.004 0.002 +03 42 33.110 +21 42 16.60 0 14.159 13.678 13.082 12.475 12.163 12.158 18.09 3.58 -43.09 3.58 0.94 12 GCS9 UGCS J034233.10+214216.6 0.002 0.002 0.002 0.002 0.001 0.001 +03 35 04.720 +25 50 48.00 0 15.967 15.347 14.711 14.131 13.773 14.82 5.33 -43.91 5.33 0.85 12 GCS9 Cl* Melotte 22 DH 41 0.006 0.004 0.004 0.004 0.002 +03 44 11.920 +19 18 19.10 0 12.681 12.379 11.886 11.382 10.992 11.107 22.77 3.96 -43.73 3.96 0.77 12 GCS9 Cl* Melotte 22 DH 318 0.001 0.001 0.001 0.001 0.001 0.001 +03 32 57.570 +27 17 19.40 0 15.608 15.014 14.363 13.807 13.462 13.477 15.57 3.76 -46.28 3.76 0.80 12 GCS9 Cl* Melotte 22 DH 24 0.005 0.003 0.004 0.003 0.003 0.002 +03 46 40.270 +25 43 53.40 0 13.808 13.431 12.871 12.280 12.005 12.002 16.42 2.23 -45.47 2.23 0.91 12 GCS9 Cl* Melotte 22 DH 464 0.002 0.002 0.002 0.001 0.001 0.001 +03 40 37.820 +21 24 22.50 0 14.983 14.452 13.790 13.151 12.787 12.764 22.77 3.58 -38.56 3.58 0.76 12 GCS9 UGCS J034037.82+212422.5 0.003 0.003 0.003 0.002 0.002 0.001 +03 55 03.610 +21 31 09.60 0 15.425 14.932 14.277 13.754 13.419 13.419 21.47 3.36 -41.78 3.36 0.84 12 GCS9 UGCS J035503.61+213109.6 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 48.230 +21 24 40.50 0 14.474 14.049 13.470 12.887 12.617 12.608 22.67 3.05 -42.13 3.05 0.89 12 GCS9 UGCS J034848.23+212440.4 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 32.790 +19 17 30.20 0 14.370 13.858 13.285 12.792 12.433 12.437 22.35 5.04 -42.45 5.04 0.90 12 GCS9 Cl* Melotte 22 DH 455 0.002 0.002 0.002 0.002 0.001 0.001 +03 27 54.260 +24 56 10.90 0 13.808 13.400 12.820 12.255 12.005 16.62 6.95 -43.60 6.95 0.93 12 GCS9 Cl* Melotte 22 DH 3 0.002 0.002 0.002 0.001 0.001 +03 43 28.210 +24 53 30.90 0 13.950 13.546 12.992 12.414 12.165 18.00 2.97 -39.09 2.97 0.87 12 GCS9 V* V436 Tau 0.002 0.002 0.002 0.001 0.001 +03 43 48.450 +25 02 36.70 0 13.856 13.401 12.797 12.260 11.888 20.40 2.97 -43.69 2.97 0.93 12 GCS9 V* V625 Tau 0.002 0.002 0.002 0.001 0.001 +03 44 24.680 +24 51 53.20 0 13.805 13.361 12.765 12.246 11.942 18.59 2.97 -47.54 2.97 0.86 12 GCS9 V* V630 Tau 0.002 0.002 0.002 0.001 0.001 +03 49 13.230 +21 22 00.80 0 15.580 15.063 14.449 13.905 13.547 13.560 21.58 3.05 -41.77 3.05 0.83 12 GCS9 UGCS J034913.22+212200.8 0.005 0.004 0.004 0.003 0.003 0.002 +04 00 33.590 +21 27 10.10 0 15.280 14.774 14.134 13.608 13.262 13.253 18.18 3.00 -39.40 3.00 0.84 12 GCS9 UGCS J040033.58+212710.0 0.004 0.003 0.004 0.003 0.003 0.002 +03 50 29.960 +25 03 06.70 0 13.449 13.075 12.549 12.026 11.684 11.707 16.35 2.20 -39.64 2.20 0.87 12 GCS9 V* V470 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 47 44.440 +24 36 55.70 0 15.085 14.555 13.994 13.447 13.132 13.121 14.49 2.21 -41.75 2.21 0.83 12 GCS9 UGCS J034744.44+243655.6 0.004 0.003 0.003 0.002 0.002 0.002 +03 47 38.370 +24 35 59.70 0 15.455 14.891 14.304 13.737 13.408 13.395 15.03 2.21 -41.64 2.21 0.85 12 GCS9 UGCS J034738.36+243559.6 0.005 0.004 0.003 0.003 0.003 0.002 +03 47 59.380 +24 35 37.00 0 15.631 15.079 14.486 13.940 13.592 13.581 15.52 2.22 -43.79 2.22 0.87 12 GCS9 V* V1281 Tau 0.005 0.004 0.004 0.003 0.004 0.002 +03 48 21.540 +24 34 43.40 0 14.868 14.334 13.789 13.233 12.918 12.937 13.23 2.21 -46.01 2.21 0.73 12 GCS9 Cl* Melotte 22 DH 571 0.004 0.003 0.003 0.002 0.002 0.001 +03 47 50.950 +24 30 18.60 0 13.152 12.806 12.298 11.944 11.443 11.472 18.40 2.21 -45.72 2.21 0.92 12 GCS9 V* V654 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 47 39.360 +24 27 31.90 0 14.048 13.600 13.019 12.442 12.145 12.139 19.38 2.21 -43.38 2.21 0.94 12 GCS9 V* V457 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 49.790 +24 25 43.10 0 14.551 14.074 13.497 12.962 12.669 12.650 20.57 2.21 -46.82 2.21 0.87 12 GCS9 V* V866 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 37.660 +24 24 23.10 0 14.791 14.244 13.638 13.102 12.757 12.763 18.80 2.21 -47.96 2.21 0.84 12 GCS9 V* V1280 Tau 0.004 0.003 0.003 0.002 0.002 0.001 +03 47 32.140 +24 24 18.40 0 14.949 14.466 13.873 13.317 13.042 13.004 16.18 2.21 -46.43 2.21 0.88 12 GCS9 UGCS J034732.14+242418.3 0.004 0.003 0.003 0.002 0.002 0.002 +03 38 45.750 +24 28 03.70 0 15.071 14.563 13.928 13.455 13.074 13.027 21.00 2.49 -40.88 2.49 0.84 12 GCS9 Cl* Melotte 22 DH 94 0.004 0.003 0.003 0.003 0.002 0.002 +03 40 24.200 +24 35 04.00 0 13.263 12.920 12.433 11.873 11.618 11.628 17.38 2.48 -40.53 2.48 0.91 12 GCS9 V* V491 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 40 26.090 +24 32 13.00 0 14.779 14.302 13.776 13.231 12.923 12.924 16.10 2.49 -36.49 2.49 0.61 12 GCS9 UGCS J034026.08+243212.9 0.003 0.003 0.003 0.002 0.002 0.001 +03 35 57.210 +24 36 08.70 0 13.467 13.062 12.538 12.051 11.754 11.753 14.41 2.62 -44.34 2.62 0.87 12 GCS9 UGCS J033557.21+243608.7 0.002 0.001 0.001 0.001 0.001 0.001 +03 51 34.010 +24 34 08.90 0 14.400 13.929 13.362 12.776 12.488 12.482 18.64 2.20 -46.47 2.20 0.90 12 GCS9 Cl* Melotte 22 DH 715 0.003 0.002 0.002 0.001 0.002 0.001 +03 43 26.980 +24 27 09.60 0 13.246 12.790 12.281 11.739 11.444 16.27 2.97 -42.45 2.97 0.92 12 GCS9 Cl* Melotte 22 DH 267 0.002 0.001 0.001 0.001 0.001 +03 43 20.580 +24 26 34.90 0 15.208 14.646 14.060 13.490 13.168 18.87 2.97 -40.83 2.97 0.88 12 GCS9 V* V620 Tau 0.004 0.003 0.003 0.003 0.002 +03 44 09.610 +24 35 22.10 0 13.826 13.427 12.892 12.314 12.044 21.90 2.97 -41.53 2.97 0.89 12 GCS9 Cl* Melotte 22 DH 314 0.002 0.002 0.002 0.001 0.001 +03 44 19.060 +24 35 18.20 0 14.319 13.882 13.291 12.722 12.476 17.45 2.97 -46.24 2.97 0.90 12 GCS9 V* V512 Tau 0.003 0.002 0.002 0.002 0.001 +03 43 43.150 +24 32 56.00 0 15.104 14.614 14.005 13.427 13.151 12.39 2.97 -42.08 2.97 0.69 12 GCS9 Cl* Melotte 22 DH 283 0.004 0.003 0.003 0.002 0.002 +03 44 26.540 +24 29 11.30 0 14.133 13.585 12.995 12.394 12.124 20.35 2.97 -40.21 2.97 0.91 12 GCS9 Cl* Melotte 22 DH 338 0.003 0.002 0.002 0.001 0.001 +03 44 17.760 +24 26 46.70 0 13.476 13.079 12.533 11.925 11.668 19.37 2.97 -46.95 2.97 0.88 12 GCS9 V* V849 Tau 0.002 0.002 0.002 0.001 0.001 +03 54 39.130 +24 35 54.50 0 15.791 15.217 14.572 14.039 13.702 13.694 20.01 2.24 -43.73 2.24 0.88 12 GCS9 Cl* Melotte 22 DH 806 0.006 0.005 0.005 0.004 0.004 0.002 +03 37 37.700 +26 21 04.00 0 14.598 14.134 13.546 13.006 12.705 12.701 23.72 3.30 -44.04 3.30 0.83 12 GCS9 Cl* Melotte 22 DH 76 0.003 0.002 0.002 0.002 0.002 0.001 +03 50 54.990 +24 33 03.50 0 14.875 14.403 13.831 13.280 12.966 12.967 14.29 2.20 -41.34 2.20 0.85 12 GCS9 Cl* Melotte 22 MBSC 69 0.004 0.003 0.003 0.002 0.002 0.001 +03 51 03.620 +24 32 35.10 0 14.283 13.819 13.257 12.749 12.420 12.445 15.93 2.20 -47.96 2.20 0.78 12 GCS9 Cl* Melotte 22 DH 691 0.003 0.002 0.002 0.001 0.002 0.001 +03 50 57.410 +24 24 44.40 0 15.215 14.612 13.988 13.487 13.097 13.117 15.13 2.21 -45.11 2.21 0.83 12 GCS9 2MASS J03505739+2424447 0.004 0.003 0.003 0.002 0.002 0.002 +03 50 15.960 +26 11 37.00 0 16.295 15.586 14.902 14.337 13.979 13.985 18.50 2.96 -41.40 2.96 0.73 12 GCS9 UGCS J035015.96+261137.0 0.008 0.006 0.005 0.005 0.005 0.003 +03 42 56.570 +24 13 45.40 0 14.910 14.439 13.859 13.319 12.979 12.984 21.34 2.13 -42.10 2.13 0.92 12 GCS9 Cl* Melotte 22 BPL 47 0.004 0.003 0.003 0.002 0.003 0.001 +03 43 01.270 +24 14 52.20 0 15.292 14.794 14.179 13.655 13.284 13.296 22.91 2.13 -39.81 2.13 0.68 12 GCS9 Cl* Melotte 22 BPL 50 0.004 0.004 0.003 0.003 0.003 0.002 +03 43 13.470 +24 11 00.90 0 15.692 15.163 14.527 14.001 13.642 13.649 16.42 2.13 -42.52 2.13 0.89 12 GCS9 UGCS J034313.46+241100.9 0.005 0.004 0.004 0.003 0.004 0.002 +03 49 35.290 +25 59 34.60 0 14.594 14.082 13.534 12.974 12.654 12.671 22.66 2.23 -45.19 2.23 0.86 12 GCS9 Cl* Melotte 22 DH 630 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 41.340 +26 08 53.20 0 13.701 13.323 12.813 12.205 11.934 11.957 15.62 2.23 -40.88 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 636 0.002 0.002 0.002 0.001 0.001 0.001 +03 53 35.520 +26 07 08.00 0 14.722 14.217 13.594 13.076 12.734 12.735 14.81 2.94 -41.03 2.94 0.87 12 GCS9 Cl* Melotte 22 DH 790 0.003 0.003 0.002 0.002 0.002 0.001 +03 45 43.180 +26 02 26.60 0 14.195 13.765 13.158 12.560 12.284 12.301 19.62 2.23 -41.47 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 401 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 53.610 +24 17 14.80 0 12.961 12.563 12.023 11.548 11.200 11.255 17.64 2.22 -38.70 2.22 0.85 12 GCS9 V* PV Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 46 55.320 +24 11 16.60 0 14.959 14.371 13.702 13.181 12.828 12.830 19.31 2.22 -40.67 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 478 0.004 0.003 0.003 0.002 0.002 0.001 +03 47 07.880 +24 23 37.80 0 15.094 14.598 13.954 13.415 13.080 13.110 14.17 2.22 -44.83 2.22 0.80 12 GCS9 V* V1278 Tau 0.004 0.003 0.003 0.002 0.002 0.002 +03 47 08.150 +24 18 24.50 0 13.675 13.278 12.730 12.176 11.884 11.936 17.08 2.22 -40.00 2.22 0.90 12 GCS9 V* V861 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 09.420 +24 15 34.70 0 15.682 15.099 14.442 13.938 13.584 13.587 15.53 2.22 -37.71 2.22 0.68 12 GCS9 Cl* Melotte 22 DH 496 0.005 0.004 0.004 0.003 0.003 0.002 +03 47 11.030 +24 13 51.50 0 15.376 14.852 14.229 13.692 13.377 13.365 16.70 2.22 -43.11 2.22 0.90 12 GCS9 V* QS Tau 0.005 0.004 0.003 0.002 0.003 0.002 +03 47 11.790 +24 13 31.30 1 16.305 15.554 14.792 14.261 13.841 13.850 16.88 2.23 -41.27 2.23 0.73 12 GCS9 Cl* Melotte 22 BPL 130 0.008 0.005 0.004 0.003 0.004 0.003 +03 47 11.860 +24 13 53.80 0 15.279 14.681 14.013 13.491 13.135 13.158 17.87 2.22 -38.56 2.22 0.80 12 GCS9 Cl* Melotte 22 DH 499 0.004 0.003 0.003 0.002 0.002 0.002 +03 35 09.410 +24 14 19.80 0 12.614 12.358 11.884 11.322 11.049 11.083 16.71 2.60 -38.59 2.60 0.83 12 GCS9 Cl* Melotte 22 DH 42 0.001 0.001 0.001 0.001 0.001 0.000 +03 40 43.200 +22 49 53.80 0 14.757 14.295 13.730 13.177 12.897 12.868 17.03 2.25 -46.07 2.25 0.90 12 GCS9 Cl* Melotte 22 DH 151 0.003 0.003 0.003 0.002 0.002 0.001 +03 49 56.810 +24 59 07.10 0 16.463 15.839 15.148 14.608 14.190 14.194 14.39 2.22 -40.78 2.22 0.61 12 GCS9 Cl* Melotte 22 KPNO 5 0.009 0.006 0.005 0.005 0.006 0.004 +03 50 06.560 +24 59 46.30 0 13.866 13.405 12.811 12.332 11.962 11.976 15.86 2.20 -43.61 2.20 0.92 12 GCS9 Cl* Melotte 22 DH 653 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 19.860 +24 59 01.30 0 13.787 13.340 12.776 12.279 11.946 11.942 14.60 2.21 -42.76 2.21 0.88 12 GCS9 Cl* Melotte 22 DH 437 0.002 0.002 0.002 0.001 0.001 0.001 +03 45 12.440 +22 41 50.80 0 14.099 13.675 13.124 12.550 12.250 12.251 19.96 2.24 -47.27 2.24 0.87 12 GCS9 Cl* Melotte 22 DH 380 0.003 0.002 0.002 0.001 0.001 0.001 +03 52 35.320 +25 01 04.50 0 14.769 14.195 13.590 13.034 12.700 12.703 17.35 2.23 -42.19 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 758 0.004 0.003 0.003 0.002 0.002 0.001 +03 49 21.930 +24 54 43.20 0 14.596 14.111 13.560 13.037 12.733 12.726 19.53 2.20 -43.15 2.20 0.94 12 GCS9 Cl* Melotte 22 DH 617 0.003 0.002 0.002 0.002 0.002 0.001 +03 37 47.510 +24 53 46.10 0 15.183 14.722 14.132 13.612 13.311 13.287 16.11 2.49 -40.51 2.49 0.86 12 GCS9 Cl* Melotte 22 DH 78 0.004 0.003 0.003 0.003 0.003 0.002 +03 49 26.640 +22 50 54.50 0 14.354 13.911 13.327 12.787 12.467 12.447 15.77 2.25 -44.36 2.25 0.91 12 GCS9 Cl* Melotte 22 SK 315 0.003 0.002 0.002 0.002 0.002 0.001 +03 48 17.610 +22 04 00.90 0 14.425 13.954 13.373 12.851 12.543 12.515 19.94 2.26 -43.28 2.26 0.94 12 GCS9 Cl* Melotte 22 BPL 171 0.003 0.002 0.002 0.002 0.002 0.001 +03 48 42.150 +25 00 28.20 0 13.453 13.101 12.555 12.084 11.703 11.724 16.19 2.20 -44.52 2.20 0.92 12 GCS9 V* V461 Tau 0.002 0.002 0.001 0.001 0.001 0.001 +03 48 40.600 +25 01 19.80 0 15.780 15.212 14.553 14.023 13.660 13.652 17.54 2.21 -42.42 2.21 0.90 12 GCS9 Cl* Melotte 22 BPL 186 0.006 0.004 0.004 0.003 0.004 0.003 +03 41 39.840 +24 52 30.00 0 15.292 14.819 14.192 13.649 13.304 18.60 2.97 -44.72 2.97 0.88 12 GCS9 Cl* Melotte 22 HHJ 69 0.004 0.004 0.003 0.003 0.002 +03 41 47.090 +25 00 21.90 0 14.895 14.426 13.824 13.261 12.933 21.19 2.97 -41.96 2.97 0.92 12 GCS9 Cl* Melotte 22 HHJ 125 0.004 0.003 0.003 0.002 0.002 +03 42 28.660 +25 01 00.20 0 13.341 13.007 12.475 11.969 11.689 19.64 2.97 -44.22 2.97 0.93 12 GCS9 Cl* Melotte 22 HHJ 362 0.002 0.001 0.001 0.001 0.001 +03 42 00.020 +25 01 47.10 0 13.914 13.524 12.950 12.390 12.128 18.86 2.97 -46.97 2.97 0.88 12 GCS9 Cl* Melotte 22 DH 201 0.002 0.002 0.002 0.001 0.001 +03 47 35.860 +24 52 26.70 0 14.700 14.223 13.634 13.112 12.786 12.772 16.91 2.21 -45.88 2.21 0.91 12 GCS9 V* V752 Tau 0.003 0.003 0.002 0.002 0.002 0.001 +03 48 20.290 +24 54 54.90 0 13.479 13.035 12.450 11.909 11.604 11.641 19.17 2.21 -42.06 2.21 0.94 12 GCS9 V* V344 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 49 02.970 +21 54 46.90 0 15.347 14.790 14.176 13.654 13.307 13.306 19.02 2.27 -47.56 2.27 0.75 12 GCS9 Cl* Melotte 22 BPL 197 0.005 0.004 0.004 0.003 0.003 0.002 +03 47 40.960 +21 49 05.00 0 15.807 15.243 14.629 14.089 13.750 13.707 22.10 2.28 -44.16 2.28 0.80 12 GCS9 UGCS J034740.95+214905.0 0.007 0.005 0.005 0.004 0.005 0.003 +03 59 26.930 +21 48 18.70 0 14.594 14.072 13.475 12.948 12.625 12.648 19.51 2.87 -42.67 2.87 0.94 12 GCS9 Cl* Melotte 22 DH 876 0.003 0.002 0.002 0.002 0.001 0.001 +03 52 01.650 +25 01 29.10 0 14.402 13.991 13.422 12.897 12.576 12.602 14.95 2.20 -43.92 2.20 0.89 12 GCS9 V* V562 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 52 03.580 +25 01 13.60 0 14.781 14.353 13.772 13.239 12.900 12.934 15.18 2.20 -43.42 2.20 0.90 12 GCS9 Cl* Melotte 22 DH 737 0.004 0.003 0.003 0.002 0.002 0.001 +03 51 51.560 +21 49 16.80 0 14.207 13.736 13.193 12.644 12.340 12.343 24.16 2.51 -42.78 2.51 0.81 12 GCS9 UGCS J035151.56+214916.8 0.003 0.002 0.002 0.002 0.002 0.001 +03 39 16.750 +24 57 38.50 0 15.120 14.611 13.972 13.440 13.071 13.063 15.63 2.49 -40.66 2.49 0.85 12 GCS9 Cl* Melotte 22 DH 103 0.004 0.003 0.003 0.003 0.002 0.002 +03 40 20.090 +21 51 33.60 0 15.554 14.955 14.342 13.783 13.442 13.436 12.33 2.51 -42.73 2.51 0.69 12 GCS9 UGCS J034020.08+215133.6 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 29.760 +23 58 05.80 0 14.876 14.413 13.823 13.281 12.988 12.938 17.30 2.22 -45.29 2.22 0.92 12 GCS9 V* V460 Tau 0.004 0.003 0.003 0.002 0.002 0.001 +03 48 10.180 +23 59 20.10 0 15.509 15.004 14.370 13.840 13.478 13.492 20.23 2.22 -43.71 2.22 0.88 12 GCS9 Cl* Melotte 22 DH 555 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 33.780 +24 01 58.80 0 14.698 14.252 13.698 13.136 12.861 12.892 16.27 2.22 -39.37 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 582 0.003 0.003 0.003 0.002 0.002 0.001 +03 48 31.840 +24 01 58.60 0 14.648 14.214 13.619 13.074 12.798 12.789 16.37 2.22 -42.70 2.22 0.93 12 GCS9 V* V872 Tau 0.003 0.003 0.002 0.002 0.002 0.001 +03 58 07.690 +20 23 38.10 0 14.755 14.283 13.696 13.187 12.883 12.864 17.14 3.97 -39.44 3.97 0.89 12 GCS9 UGCS J035807.69+202338.0 0.003 0.003 0.003 0.002 0.002 0.001 +03 57 58.500 +20 24 19.50 0 14.806 14.330 13.710 13.193 12.861 12.867 16.74 3.97 -41.74 3.97 0.93 12 GCS9 UGCS J035758.50+202419.4 0.003 0.003 0.003 0.002 0.002 0.001 +04 05 13.750 +24 08 42.70 0 14.983 14.471 13.846 13.261 12.933 12.917 19.77 3.38 -41.50 3.38 0.94 12 GCS9 Cl* Melotte 22 DH 915 0.004 0.003 0.003 0.002 0.002 0.001 +03 45 09.460 +23 58 44.70 1 16.974 16.250 15.438 14.872 14.424 14.410 16.07 2.25 -42.23 2.25 0.72 12 GCS9 Cl* Melotte 22 PPL 2 0.013 0.008 0.007 0.006 0.007 0.005 +03 44 57.340 +23 59 32.30 0 13.672 13.326 12.770 12.194 11.941 11.960 16.63 2.22 -39.69 2.22 0.88 12 GCS9 V* V712 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 27.920 +23 59 59.60 0 15.633 15.107 14.446 13.888 13.520 13.499 17.02 2.23 -41.63 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 342 0.005 0.004 0.004 0.003 0.003 0.002 +03 44 47.330 +24 00 37.70 0 14.063 13.661 13.140 12.609 12.311 12.315 19.39 2.22 -43.53 2.22 0.94 12 GCS9 V* NW Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 53.210 +24 01 06.50 0 15.005 14.557 13.980 13.433 13.135 13.125 16.45 2.22 -38.79 2.22 0.80 12 GCS9 Cl* Melotte 22 DH 359 0.004 0.003 0.003 0.002 0.002 0.002 +03 45 09.810 +24 04 32.70 0 15.935 15.388 14.737 14.174 13.793 13.744 17.63 2.23 -39.69 2.23 0.85 12 GCS9 Cl* Melotte 22 SHF 5 0.007 0.005 0.004 0.003 0.004 0.003 +03 45 16.130 +24 07 16.00 0 13.242 12.918 12.418 12.034 11.570 11.682 19.29 2.22 -40.28 2.22 0.91 12 GCS9 V* V519 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 48 46.020 +24 10 12.50 0 14.464 14.027 13.476 12.931 12.631 12.661 15.18 2.22 -41.35 2.22 0.89 12 GCS9 Cl* Melotte 22 DH 592 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 06.050 +23 58 19.10 0 16.000 15.453 14.820 14.319 13.925 13.902 16.34 2.23 -42.63 2.23 0.73 12 GCS9 Cl* Melotte 22 SHF 26 0.006 0.005 0.005 0.004 0.005 0.003 +03 45 42.330 +24 04 11.10 0 16.542 15.882 15.201 14.643 14.262 14.229 17.79 2.24 -40.28 2.24 0.70 12 GCS9 Cl* Melotte 22 SHF 12 0.009 0.006 0.006 0.005 0.006 0.004 +03 45 39.290 +24 08 20.40 0 14.992 14.507 13.911 13.379 13.015 13.020 16.79 2.22 -43.96 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 398 0.004 0.003 0.003 0.002 0.002 0.002 +03 45 57.910 +24 08 40.90 0 15.680 15.158 14.543 14.017 13.670 13.654 18.27 2.23 -43.51 2.23 0.90 12 GCS9 Cl* Melotte 22 DH 412 0.005 0.004 0.004 0.003 0.004 0.003 +03 46 04.570 +24 09 55.80 0 15.099 14.602 14.020 13.460 13.162 13.175 15.43 2.22 -40.43 2.22 0.84 12 GCS9 2MASS J03460455+2409561 0.004 0.003 0.003 0.002 0.003 0.002 +03 49 52.440 +24 03 43.00 0 16.100 15.534 14.882 14.353 13.970 13.984 20.16 2.23 -43.40 2.23 0.70 12 GCS9 UGCS J034952.43+240342.9 0.007 0.005 0.005 0.004 0.005 0.003 +03 49 16.420 +24 03 49.00 0 12.458 12.108 11.574 11.608 10.800 11.068 24.30 2.22 -41.94 2.22 0.71 12 GCS9 V* V467 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 49 55.490 +24 06 05.00 0 14.506 14.048 13.452 12.874 12.596 12.614 17.10 2.22 -43.07 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 641 0.003 0.002 0.002 0.002 0.002 0.001 +03 51 58.350 +23 58 19.30 0 14.595 14.132 13.556 12.990 12.694 12.669 11.97 2.24 -42.06 2.24 0.65 12 GCS9 Cl* Melotte 22 DH 732 0.003 0.003 0.003 0.002 0.002 0.001 +03 59 23.500 +20 20 16.90 0 15.988 15.360 14.723 14.163 13.823 13.837 20.00 4.01 -37.38 4.01 0.67 12 GCS9 UGCS J035923.50+202016.8 0.006 0.005 0.004 0.004 0.004 0.003 +03 41 31.210 +24 03 24.70 0 14.985 14.509 13.903 13.353 13.033 20.71 2.31 -39.58 2.31 0.89 12 GCS9 Cl* Melotte 22 DH 177 0.004 0.003 0.003 0.002 0.002 +03 56 18.600 +23 57 51.60 0 12.871 12.580 12.095 11.500 11.219 11.290 20.17 2.96 -40.40 2.96 0.89 12 GCS9 Cl* Melotte 22 HHJ 386 0.001 0.001 0.001 0.001 0.001 0.001 +03 56 28.910 +24 01 54.10 0 14.448 14.038 13.469 12.930 12.618 12.620 19.11 2.96 -40.90 2.96 0.93 12 GCS9 Cl* Melotte 22 DH 837 0.003 0.002 0.002 0.002 0.002 0.001 +03 42 17.900 +24 06 57.60 0 14.857 14.378 13.774 13.232 12.897 17.09 2.30 -42.38 2.30 0.94 12 GCS9 Cl* Melotte 22 DH 217 0.003 0.003 0.003 0.002 0.001 +03 41 52.940 +24 07 24.70 0 15.099 14.637 14.067 13.518 13.183 14.06 2.31 -43.29 2.31 0.82 12 GCS9 Cl* Melotte 22 DH 191 0.004 0.003 0.003 0.003 0.002 +03 42 00.370 +24 10 12.60 0 16.212 15.647 14.984 14.441 14.064 17.68 2.32 -44.24 2.32 0.73 12 GCS9 Cl* Melotte 22 BPL 32 0.007 0.006 0.005 0.005 0.004 +03 42 13.900 +20 21 43.60 0 14.695 14.209 13.633 13.098 12.788 12.772 24.49 3.42 -44.30 3.42 0.76 12 GCS9 Cl* Melotte 22 DH 216 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 18.540 +23 59 02.50 0 15.870 15.329 14.698 14.161 13.807 13.797 16.09 2.23 -43.73 2.23 0.88 12 GCS9 Cl* Melotte 22 SHF 27 0.007 0.005 0.004 0.003 0.004 0.003 +03 46 23.470 +24 01 51.20 0 14.714 14.254 13.668 13.113 12.808 12.793 18.12 2.22 -43.07 2.22 0.94 12 GCS9 V* V528 Tau 0.003 0.003 0.002 0.002 0.002 0.001 +03 46 25.390 +24 09 36.20 0 12.838 12.485 11.952 11.461 11.112 11.150 13.98 2.22 -43.42 2.22 0.64 12 GCS9 V* OZ Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 46 35.540 +24 01 35.40 0 14.809 14.275 13.660 13.113 12.776 12.774 22.25 2.22 -44.34 2.22 0.89 12 GCS9 2MASS J03463552+2401355 0.003 0.003 0.002 0.002 0.002 0.001 +03 46 43.600 +23 59 42.30 0 13.258 12.936 12.441 11.891 11.575 11.602 21.29 2.22 -43.53 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 467 0.002 0.001 0.001 0.001 0.001 0.001 +03 46 51.440 +24 06 16.10 0 15.287 14.777 14.196 13.624 13.272 13.288 17.34 2.22 -38.74 2.22 0.81 12 GCS9 UGCS J034651.43+240616.1 0.004 0.003 0.003 0.002 0.003 0.002 +03 46 53.960 +24 07 57.10 0 15.123 14.651 14.045 13.480 13.143 13.155 16.12 2.22 -38.73 2.22 0.78 12 GCS9 Cl* Melotte 22 MHO 8 0.004 0.003 0.003 0.002 0.002 0.002 +03 46 58.260 +24 01 41.30 0 14.976 14.481 13.884 13.337 12.997 13.022 19.27 2.22 -37.41 2.22 0.79 12 GCS9 UGCS J034658.26+240141.3 0.004 0.003 0.003 0.002 0.002 0.001 +03 46 59.320 +24 01 42.80 0 13.995 13.544 12.956 12.408 12.067 12.100 19.84 2.22 -38.88 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 484 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 10.650 +23 58 16.40 0 16.136 15.557 14.877 14.360 13.969 14.000 19.15 2.23 -41.37 2.23 0.72 12 GCS9 Cl* Melotte 22 SHF 41 0.007 0.005 0.005 0.004 0.004 0.003 +03 47 13.170 +24 00 45.20 0 16.022 15.428 14.759 14.221 13.828 13.866 20.02 2.23 -40.38 2.23 0.66 12 GCS9 2MASS J03471316+2400453 0.007 0.005 0.004 0.003 0.004 0.003 +03 47 09.180 +24 03 07.70 0 13.411 13.064 12.521 11.964 11.675 11.719 20.92 2.22 -38.72 2.22 0.82 12 GCS9 Cl* Melotte 22 SRS 60765 0.002 0.001 0.001 0.001 0.001 0.001 +03 43 01.640 +20 20 13.80 0 15.588 15.080 14.494 13.912 13.618 13.585 15.94 3.44 -42.59 3.44 0.88 12 GCS9 UGCS J034301.63+202013.7 0.005 0.004 0.004 0.003 0.003 0.003 +03 55 08.980 +24 05 02.50 0 13.699 13.306 12.734 12.148 11.890 16.37 2.30 -40.35 2.30 0.90 12 GCS9 Cl* Melotte 22 DH 812 0.002 0.002 0.002 0.001 0.001 +03 50 57.420 +24 06 30.70 0 13.535 13.175 12.661 12.135 11.812 11.815 14.04 2.22 -40.35 2.22 0.80 12 GCS9 V* V800 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 19.070 +24 10 13.10 0 13.112 12.775 12.233 11.720 11.389 11.438 20.55 2.22 -42.37 2.22 0.92 12 GCS9 V* V466 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 51 55.050 +23 57 42.00 0 14.510 14.067 13.483 12.983 12.641 12.651 15.21 2.22 -46.11 2.22 0.86 12 GCS9 V* V388 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 25.350 +24 02 56.80 0 13.454 13.128 12.616 12.074 11.774 11.806 20.89 2.22 -39.96 2.22 0.88 12 GCS9 Cl* Melotte 22 HHJ 427 0.002 0.002 0.002 0.001 0.001 0.001 +03 47 32.000 +24 10 24.70 0 14.749 14.293 13.677 13.143 12.827 12.808 19.77 2.22 -41.79 2.22 0.94 12 GCS9 Cl* Melotte 22 BPL 147 0.003 0.003 0.003 0.002 0.002 0.001 +03 47 34.520 +24 02 23.00 0 14.645 14.216 13.648 13.087 12.797 12.793 19.46 2.22 -36.75 2.22 0.72 12 GCS9 Cl* Melotte 22 DH 523 0.003 0.003 0.002 0.002 0.002 0.001 +03 47 46.400 +24 03 02.30 0 12.979 12.677 12.180 11.634 11.322 11.381 19.77 2.22 -37.79 2.22 0.84 12 GCS9 Cl* Melotte 22 HHJ 438 0.001 0.001 0.001 0.001 0.001 0.001 +03 48 06.410 +24 06 51.70 0 14.633 14.200 13.603 13.064 12.753 12.790 18.58 2.22 -43.78 2.22 0.94 12 GCS9 UGCS J034806.41+240651.6 0.003 0.003 0.002 0.002 0.002 0.001 +03 48 06.640 +24 00 06.70 0 14.398 13.957 13.367 12.823 12.520 12.531 17.15 2.22 -39.79 2.22 0.90 12 GCS9 Cl* Melotte 22 DH 549 0.003 0.002 0.002 0.002 0.002 0.001 +03 48 09.220 +23 58 40.50 0 14.445 14.024 13.448 12.921 12.615 12.648 15.27 2.22 -41.80 2.22 0.90 12 GCS9 Cl* Melotte 22 DH 553 0.003 0.002 0.002 0.002 0.002 0.001 +03 48 13.310 +23 58 46.80 0 14.206 13.766 13.187 12.664 12.353 12.394 16.71 2.22 -42.37 2.22 0.93 12 GCS9 V* V342 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 43 28.740 +24 09 06.10 0 15.175 14.685 14.073 13.555 13.200 17.98 2.31 -42.91 2.31 0.90 12 GCS9 Cl* Melotte 22 HHJ 76 0.004 0.003 0.003 0.003 0.002 +03 58 25.140 +24 00 58.50 0 14.312 13.884 13.306 12.766 12.449 12.466 17.99 2.96 -40.14 2.96 0.92 12 GCS9 Cl* Melotte 22 DH 865 0.003 0.002 0.002 0.002 0.002 0.001 +03 39 09.870 +23 58 52.40 0 16.201 15.649 14.939 14.373 14.000 14.020 15.05 2.62 -40.39 2.62 0.63 12 GCS9 UGCS J033909.86+235852.4 0.007 0.006 0.005 0.005 0.004 0.003 +03 39 35.460 +24 07 06.10 0 12.806 12.545 12.021 11.498 11.181 11.228 18.04 2.49 -42.86 2.49 0.87 12 GCS9 Cl* Melotte 22 DH 108 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 46.350 +23 58 52.90 0 13.490 13.174 12.625 12.077 11.772 11.792 22.63 2.50 -38.42 2.50 0.69 12 GCS9 Cl* Melotte 22 DH 113 0.002 0.002 0.002 0.001 0.001 0.001 +03 42 41.180 +24 01 42.70 0 14.985 14.503 13.921 13.357 13.014 13.025 20.15 2.13 -41.62 2.13 0.93 12 GCS9 V* LQ Tau 0.004 0.003 0.003 0.002 0.002 0.002 +03 42 41.850 +24 00 15.50 0 14.496 14.076 13.515 12.954 12.636 12.650 18.92 2.12 -44.12 2.12 0.94 12 GCS9 Cl* Melotte 22 DH 236 0.003 0.003 0.002 0.002 0.002 0.001 +03 42 56.550 +24 04 57.80 0 12.916 12.529 12.006 11.543 11.156 11.214 16.43 2.12 -38.79 2.12 0.82 12 GCS9 V* LT Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 43 11.650 +24 06 52.40 0 15.210 14.680 14.044 13.501 13.160 13.172 16.13 2.13 -38.16 2.13 0.74 12 GCS9 Cl* Melotte 22 BPL 52 0.004 0.003 0.003 0.003 0.002 0.002 +03 40 25.620 +24 06 00.00 0 14.659 14.287 13.684 13.159 12.829 12.878 20.15 2.50 -43.40 2.50 0.94 12 GCS9 Cl* Melotte 22 DH 138 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 26.410 +24 05 23.50 0 13.451 13.197 12.595 11.998 11.701 11.736 16.88 2.50 -38.77 2.50 0.84 12 GCS9 Cl* Melotte 22 SK 778 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 29.730 +21 02 16.70 0 14.557 14.008 13.402 12.879 12.566 12.551 19.59 2.65 -40.35 2.65 0.92 12 GCS9 Cl* Melotte 22 DH 452 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 50.380 +20 57 59.90 0 14.307 13.848 13.262 12.675 12.412 12.387 20.01 3.05 -39.37 3.05 0.89 12 GCS9 UGCS J034950.38+205759.8 0.003 0.002 0.002 0.001 0.001 0.001 +03 43 05.730 +21 01 49.10 0 15.042 14.523 13.929 13.374 13.044 13.028 19.59 3.59 -44.27 3.59 0.88 12 GCS9 UGCS J034305.73+210149.1 0.004 0.003 0.003 0.003 0.002 0.002 +03 57 37.100 +21 01 48.00 0 15.678 15.108 14.501 13.963 13.607 13.593 20.33 3.37 -45.03 3.37 0.85 12 GCS9 UGCS J035737.10+210147.9 0.005 0.004 0.004 0.003 0.003 0.002 +03 58 10.250 +20 56 55.60 0 16.121 15.494 14.840 14.289 13.924 13.940 19.36 3.38 -39.83 3.38 0.65 12 GCS9 UGCS J035810.24+205655.5 0.007 0.005 0.005 0.004 0.004 0.003 +03 46 10.100 +26 00 09.00 0 15.207 14.716 14.091 13.542 13.230 13.219 15.35 2.23 -39.91 2.23 0.82 12 GCS9 Cl* Melotte 22 HHJ 80 0.004 0.004 0.003 0.003 0.003 0.002 +03 46 19.430 +26 02 35.50 0 12.463 12.206 11.749 11.371 10.935 11.061 24.01 2.22 -39.85 2.22 0.74 12 GCS9 Cl* Melotte 22 DH 436 0.001 0.001 0.001 0.001 0.001 0.000 +03 45 21.350 +26 05 25.50 0 15.885 15.309 14.637 14.115 13.734 13.722 19.33 2.24 -43.28 2.24 0.89 12 GCS9 UGCS J034521.34+260525.4 0.006 0.005 0.004 0.004 0.004 0.003 +03 52 13.320 +26 08 36.80 0 13.531 13.173 12.637 12.086 11.799 11.827 13.73 2.93 -41.65 2.93 0.82 12 GCS9 V* V715 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 45 41.270 +23 54 09.70 1 17.166 16.189 15.360 14.782 14.305 14.309 17.46 2.24 -44.47 2.24 0.69 12 GCS9 Cl* Melotte 22 PPL 1 0.014 0.008 0.006 0.005 0.006 0.004 +03 50 22.010 +23 55 30.30 0 17.259 16.399 15.694 15.108 14.699 14.699 20.94 2.26 -44.68 2.26 0.65 12 GCS9 UGCS J035022.01+235530.3 0.015 0.009 0.008 0.006 0.008 0.006 +03 52 06.720 +24 16 00.40 0 17.079 16.255 15.515 14.971 14.507 14.538 18.87 2.29 -40.00 2.29 0.68 12 GCS9 2MASS J03520670+2416008 0.013 0.009 0.008 0.008 0.008 0.005 +03 46 14.060 +23 21 56.40 0 17.097 16.349 15.606 15.047 14.618 14.641 17.81 2.27 -40.20 2.27 0.68 12 GCS9 UGCS J034614.06+232156.4 0.013 0.009 0.008 0.007 0.009 0.006 +03 39 17.050 +22 27 10.90 0 17.162 16.347 15.569 15.023 14.582 14.576 16.67 2.58 -43.68 2.58 0.69 12 GCS9 2MASS J03391704+2227111 0.013 0.010 0.008 0.007 0.008 0.005 +03 48 04.670 +23 39 30.10 1 17.014 16.054 15.283 14.704 14.256 14.238 16.07 2.24 -44.27 2.24 0.66 12 GCS9 UGCS J034804.66+233930.1 0.012 0.007 0.006 0.005 0.006 0.004 +03 47 49.450 +23 31 52.80 0 17.078 16.295 15.581 15.025 14.611 14.602 17.03 2.25 -39.82 2.25 0.65 12 GCS9 UGCS J034749.44+233152.8 0.013 0.009 0.007 0.006 0.008 0.005 +03 41 54.160 +23 05 04.70 1 17.349 16.376 15.522 14.975 14.415 14.418 18.19 2.30 -44.74 2.30 0.69 12 GCS9 Cl* Melotte 22 MHOBD 3 0.016 0.010 0.008 0.007 0.007 0.005 +03 55 23.080 +24 49 04.90 0 17.087 16.311 15.528 15.013 14.595 14.571 19.50 2.28 -42.09 2.28 0.72 12 GCS9 Cl* Melotte 22 BPL 327 0.014 0.009 0.008 0.007 0.008 0.005 +03 44 53.120 +23 34 22.80 0 17.306 16.472 15.734 15.124 14.710 14.700 18.37 2.26 -39.68 2.26 0.67 12 GCS9 UGCS J034453.12+233422.8 0.016 0.009 0.008 0.006 0.008 0.006 +03 41 05.410 +23 32 56.70 0 17.223 16.421 15.660 15.137 14.704 14.689 20.80 2.22 -41.06 2.22 0.68 12 GCS9 UGCS J034105.40+233256.6 0.015 0.010 0.008 0.008 0.008 0.006 +03 49 04.860 +23 33 39.30 0 17.145 16.308 15.573 15.032 14.579 14.568 16.74 2.25 -42.98 2.25 0.70 12 GCS9 Cl* Melotte 22 IPMBD 20 0.014 0.008 0.007 0.006 0.007 0.005 +03 45 54.960 +23 33 57.80 0 17.116 16.325 15.553 15.023 14.564 14.574 18.67 2.25 -41.38 2.25 0.72 12 GCS9 Cl* Melotte 22 SHF 21 0.013 0.008 0.007 0.006 0.007 0.005 +03 45 31.370 +24 52 47.40 1 17.332 16.330 15.465 14.839 14.354 14.326 16.69 2.24 -40.30 2.24 0.66 12 GCS9 2MASS J03453136+2452476 0.015 0.009 0.007 0.006 0.007 0.004 +03 36 54.100 +21 58 02.70 0 17.346 16.486 15.758 15.194 14.752 14.754 19.11 3.00 -39.30 3.00 0.65 12 GCS9 UGCS J033654.10+215802.7 0.014 0.009 0.008 0.009 0.010 0.006 +04 01 24.410 +20 17 15.60 0 17.022 16.289 15.614 15.051 14.645 14.638 22.29 3.22 -42.39 3.22 0.63 12 GCS9 UGCS J040124.40+201715.5 0.012 0.008 0.007 0.006 0.007 0.006 +03 38 10.200 +26 07 33.00 0 17.160 16.444 15.695 15.142 14.702 14.718 18.46 3.04 -40.95 3.04 0.71 12 GCS9 UGCS J033810.20+260733.0 0.015 0.010 0.010 0.007 0.009 0.007 +03 40 55.300 +25 34 57.40 0 17.423 16.544 15.803 15.195 14.732 14.753 21.01 2.31 -41.51 2.31 0.69 12 GCS9 UGCS J034055.30+253457.3 0.017 0.011 0.010 0.008 0.009 0.006 +03 30 44.540 +25 39 07.70 0 17.609 16.705 15.900 15.296 14.886 14.862 16.76 3.43 -42.84 3.43 0.70 12 GCS9 UGCS J033044.53+253907.6 0.016 0.010 0.009 0.008 0.009 0.007 +03 37 46.600 +26 50 44.50 0 17.381 16.593 15.840 15.292 14.877 14.867 19.58 3.37 -40.72 3.37 0.70 12 GCS9 UGCS J033746.59+265044.5 0.014 0.010 0.009 0.008 0.009 0.006 +03 44 35.160 +25 13 42.80 1 17.656 16.584 15.662 14.985 14.448 14.460 19.33 2.26 -44.97 2.26 0.68 12 GCS9 UGCS J034435.16+251342.7 0.020 0.010 0.007 0.006 0.008 0.005 +03 49 33.960 +22 16 35.90 0 17.942 16.978 16.144 15.535 15.049 15.042 21.92 2.64 -43.77 2.64 0.64 12 GCS9 UGCS J034933.96+221635.8 0.021 0.015 0.011 0.013 0.013 0.008 +03 47 27.720 +22 09 38.50 0 17.382 16.547 15.760 15.224 14.763 14.783 17.79 2.34 -41.87 2.34 0.72 12 GCS9 Cl* Melotte 22 MHOBD 5 0.017 0.011 0.010 0.009 0.011 0.006 +03 51 44.950 +23 26 39.30 0 17.791 16.894 16.036 15.417 14.953 14.967 16.26 2.37 -39.72 2.37 0.62 12 GCS9 2MASS J03514491+2326395 0.024 0.012 0.010 0.010 0.011 0.008 +03 50 52.170 +23 27 11.20 1 17.690 16.638 15.700 15.060 14.505 14.555 19.89 2.21 -41.33 2.21 0.71 12 GCS9 UGCS J035052.17+232711.2 0.019 0.010 0.008 0.007 0.006 0.005 +03 26 33.430 +22 39 42.90 0 17.587 16.833 16.109 15.489 15.093 15.096 20.93 5.59 -43.56 5.59 0.69 12 GCS9 UGCS J032633.42+223942.8 0.015 0.011 0.010 0.015 0.010 0.008 +03 42 18.010 +24 55 09.90 0 17.486 16.658 15.876 15.298 14.854 17.65 3.06 -39.29 3.06 0.64 12 GCS9 UGCS J034218.01+245509.9 0.017 0.011 0.009 0.010 0.007 +03 45 50.660 +24 09 03.50 1 17.478 16.582 15.705 15.095 14.580 14.560 16.01 2.26 -40.58 2.26 0.65 12 GCS9 2MASS J03455065+2409037 0.017 0.010 0.008 0.006 0.008 0.005 +03 47 50.410 +23 54 47.80 0 18.179 17.138 16.311 15.622 15.093 15.090 15.88 2.32 -39.60 2.32 0.63 12 GCS9 2MASS J03475038+2354480 0.028 0.014 0.011 0.009 0.012 0.008 +03 55 34.280 +22 23 29.20 0 18.055 17.108 16.189 15.579 15.117 15.096 18.47 2.72 -40.79 2.72 0.71 12 GCS9 UGCS J035534.28+222329.2 0.027 0.015 0.012 0.009 0.012 0.008 +03 40 06.590 +21 08 59.90 0 18.210 17.152 16.272 15.651 15.113 15.123 21.31 3.80 -40.02 3.80 0.68 12 GCS9 UGCS J034006.59+210859.9 0.024 0.013 0.011 0.016 0.009 0.007 +03 47 17.920 +24 22 31.60 0 18.174 17.067 16.215 15.591 15.096 15.080 21.40 2.30 -42.73 2.30 0.68 12 GCS9 UGCS J034717.92+242231.6 0.029 0.013 0.010 0.009 0.011 0.008 +03 46 34.990 +23 31 14.40 0 18.424 17.345 16.411 15.846 15.308 15.295 20.52 2.37 -42.62 2.37 0.70 12 GCS9 UGCS J034634.98+233114.4 0.036 0.016 0.013 0.011 0.014 0.009 +03 47 59.740 +22 36 01.80 0 18.043 17.083 16.209 15.609 15.090 15.115 21.40 2.40 -42.45 2.40 0.69 12 GCS9 2MASS J03475972+2236019 0.027 0.015 0.012 0.011 0.014 0.008 +03 45 50.630 +23 44 36.90 0 18.291 17.301 16.379 15.794 15.257 15.244 20.44 2.31 -41.86 2.31 0.71 12 GCS9 Cl* Melotte 22 NPNPL 1 0.032 0.016 0.012 0.011 0.013 0.009 +03 57 18.480 +21 37 32.30 0 18.299 17.369 16.382 15.808 15.239 15.276 21.09 3.54 -41.34 3.54 0.70 12 GCS9 UGCS J035718.48+213732.3 0.029 0.017 0.012 0.013 0.013 0.009 +03 51 38.960 +24 30 44.80 1 18.711 17.407 16.400 15.718 15.168 15.122 23.01 2.34 -40.47 2.34 0.64 12 GCS9 UGCS J035138.95+243044.7 0.045 0.017 0.012 0.011 0.013 0.008 +03 46 23.120 +24 20 36.00 0 18.242 17.172 16.247 15.654 15.145 15.106 17.01 2.29 -43.77 2.29 0.66 12 GCS9 2MASS J03462312+2420363 0.028 0.014 0.010 0.009 0.011 0.008 +03 59 09.870 +21 46 02.00 0 18.325 17.223 16.331 15.747 15.203 15.210 20.58 3.13 -40.99 3.13 0.71 12 GCS9 UGCS J035909.86+214602.0 0.029 0.014 0.012 0.018 0.009 0.009 +03 40 38.100 +26 38 29.20 0 18.747 17.679 16.599 15.993 15.481 15.481 18.85 3.21 -40.47 3.21 0.71 12 GCS9 UGCS J034038.09+263829.2 0.039 0.020 0.015 0.012 0.015 0.010 +03 46 34.250 +23 50 03.60 0 19.871 18.546 17.459 16.666 16.090 16.024 20.67 2.58 -41.54 2.58 0.66 12 GCS9 Cl* Melotte 22 NPNPL 2 0.149 0.046 0.027 0.023 0.028 0.018 +03 34 20.190 +25 22 05.10 0 19.676 18.250 17.178 16.453 15.945 20.30 6.48 -41.54 6.48 0.66 12 GCS9 UGCS J033420.18+252205.0 0.083 0.031 0.021 0.033 0.014 +03 43 03.830 +23 54 19.60 0 18.852 17.703 16.751 16.052 15.562 15.535 18.37 2.48 -38.49 2.48 0.66 12 GCS9 2MASS J03430378+2354200 0.046 0.024 0.017 0.018 0.025 0.012 +04 05 51.620 +23 44 48.10 0 19.517 18.119 17.046 16.350 15.809 15.769 19.98 3.68 -42.86 3.68 0.68 12 GCS9 UGCS J040551.61+234448.0 0.065 0.029 0.021 0.019 0.021 0.013 +03 49 04.740 +23 26 43.10 0 18.943 17.724 16.792 16.171 15.598 15.597 17.56 2.58 -40.60 2.58 0.70 12 GCS9 UGCS J034904.73+232643.1 0.051 0.024 0.019 0.021 0.016 0.014 +03 57 13.460 +24 22 51.80 0 19.665 18.458 17.407 16.653 16.109 16.086 17.40 3.82 -42.66 3.82 0.66 12 GCS9 UGCS J035713.46+242251.7 0.085 0.049 0.031 0.031 0.030 0.021 +03 54 05.350 +23 33 59.20 0 18.651 17.573 16.647 15.964 15.434 15.462 15.06 2.46 -40.45 2.46 0.61 12 GCS9 2MASS J03540532+2333597 0.041 0.024 0.018 0.016 0.017 0.011 +03 40 27.930 +24 12 09.30 0 19.730 18.501 17.352 16.700 16.088 16.073 15.91 3.22 -42.54 3.22 0.62 12 GCS9 Cl* Melotte 22 IPL 84 0.100 0.045 0.027 0.031 0.031 0.019 +03 45 11.730 +23 41 43.60 0 20.311 18.985 17.617 16.886 16.120 16.204 13.04 2.65 -46.83 2.65 0.62 12 GCS9 Cl* Melotte 22 NPNPL 3 0.181 0.062 0.029 0.026 0.027 0.020 +03 44 22.450 +23 39 01.30 0 19.107 17.857 16.874 16.254 15.666 15.660 15.39 2.40 -42.54 2.40 0.61 12 GCS9 UGCS J034422.44+233901.2 0.064 0.025 0.017 0.016 0.018 0.013 +03 55 47.450 +22 50 50.10 0 19.943 18.550 17.433 16.681 16.086 16.123 15.62 2.97 -46.57 2.97 0.63 12 GCS9 UGCS J035547.44+225050.0 0.122 0.050 0.032 0.029 0.032 0.022 +03 36 05.920 +23 01 23.70 0 19.381 18.086 17.063 16.451 15.840 15.845 24.12 3.64 -46.32 3.64 0.64 12 GCS9 UGCS J033605.92+230123.6 0.073 0.035 0.023 0.020 0.022 0.016 +03 46 27.100 +21 48 22.60 1 19.797 18.650 17.374 16.564 15.848 15.925 20.95 2.98 -48.67 2.98 0.65 12 GCS9 UGCS J034627.10+214822.5 0.125 0.059 0.032 0.026 0.026 0.017 +04 00 58.260 +21 33 31.70 0 19.241 18.035 16.948 16.250 15.691 15.702 18.08 4.76 -43.38 4.76 0.67 12 GCS9 UGCS J040058.25+213331.7 0.070 0.031 0.023 0.021 0.012 0.013 +03 42 18.080 +19 07 33.00 0 19.797 18.358 17.264 16.589 16.057 16.053 17.58 5.18 -47.78 5.18 0.65 12 GCS9 UGCS J034218.08+190732.9 0.081 0.035 0.022 0.024 0.025 0.020 +04 07 10.570 +24 59 49.20 0 18.433 17.603 16.733 15.987 15.491 15.459 19.65 3.35 -39.70 3.35 0.70 12 GCS9 UGCS J040710.56+245949.1 0.027 0.017 0.014 0.016 0.017 0.010 +03 42 30.590 +25 02 39.30 0 19.269 18.013 17.004 16.367 15.746 22.01 3.54 -44.77 3.54 0.68 12 GCS9 UGCS J034230.58+250239.2 0.068 0.033 0.021 0.025 0.016 +03 56 59.900 +24 05 35.50 0 19.904 19.003 17.628 16.950 16.225 16.196 16.25 4.13 -41.75 4.13 0.62 12 GCS9 UGCS J035659.90+240535.4 0.102 0.094 0.038 0.038 0.035 0.023 +03 41 13.210 +24 05 25.60 0 20.248 19.168 17.791 16.898 16.325 16.266 14.11 3.48 -46.41 3.48 0.61 12 GCS9 Cl* Melotte 22 IPL 81 0.166 0.092 0.041 0.042 0.039 0.025 +03 50 16.090 +24 08 34.70 0 19.950 18.556 17.365 16.687 16.076 16.043 18.03 2.53 -46.97 2.53 0.67 12 GCS9 UGCS J035016.08+240834.7 0.125 0.040 0.022 0.021 0.027 0.018 +03 43 46.290 +23 58 37.90 0 19.124 17.930 16.876 16.248 15.628 20.13 2.62 -45.01 2.62 0.69 12 GCS9 2MASS J03434627+2358382 0.057 0.026 0.017 0.020 0.012 +03 55 30.190 +21 01 10.20 0 18.713 17.522 16.584 15.983 15.414 15.354 19.46 3.52 -41.16 3.52 0.72 12 GCS9 UGCS J035530.19+210110.1 0.038 0.019 0.015 0.013 0.014 0.010 +03 44 09.010 +27 56 42.00 0 14.980 14.512 13.981 13.444 13.140 13.140 27.76 3.70 -38.32 3.70 2 GCS9 UGCS J034409.01+275642.0 0.003 0.003 0.003 0.003 0.003 0.002 +03 48 31.180 +29 22 39.20 0 13.011 12.688 12.199 11.692 11.374 11.413 5.92 5.84 -27.47 5.84 2 GCS9 UGCS J034831.17+292239.1 0.001 0.001 0.001 0.001 0.001 0.001 +03 47 08.900 +29 02 38.80 0 13.793 13.432 12.890 12.285 12.076 12.094 10.64 5.84 -40.62 5.84 2 GCS9 UGCS J034708.90+290238.8 0.002 0.002 0.002 0.001 0.001 0.001 +03 58 33.710 +28 07 09.10 0 16.950 16.428 15.680 15.099 14.755 14.708 24.72 4.01 -50.41 4.01 2 GCS9 UGCS J035833.70+280709.0 0.009 0.008 0.007 0.007 0.010 0.006 +03 53 07.090 +28 07 33.00 0 13.630 13.247 12.738 12.198 11.946 11.966 28.63 3.74 -50.15 3.74 2 GCS9 UGCS J035307.09+280733.0 0.002 0.001 0.002 0.001 0.001 0.001 +03 44 59.810 +29 12 29.50 0 16.688 16.081 15.393 14.773 14.418 14.415 6.41 5.88 -48.49 5.88 2 GCS9 UGCS J034459.81+291229.4 0.008 0.007 0.006 0.005 0.006 0.004 +03 40 56.060 +28 43 39.00 0 12.760 12.479 11.985 11.538 11.155 11.283 11.44 3.68 -37.58 3.68 2 GCS9 Cl* Melotte 22 DH 161 0.001 0.001 0.001 0.001 0.001 0.001 +03 31 12.320 +27 05 59.20 0 15.984 15.464 14.836 14.275 13.919 -2.97 7.05 -25.10 7.05 2 GCS9 UGCS J033112.32+270559.2 0.005 0.004 0.005 0.006 0.003 +03 48 55.420 +28 41 20.70 0 14.701 14.119 13.468 12.911 12.565 12.571 19.52 2.93 -34.58 2.93 2 GCS9 UGCS J034855.41+284120.6 0.003 0.002 0.002 0.002 0.002 0.001 +03 37 17.780 +28 45 35.80 0 14.292 13.909 13.380 12.741 12.457 12.474 11.77 4.93 -40.45 4.93 2 GCS9 UGCS J033717.77+284535.8 0.002 0.002 0.003 0.002 0.002 0.001 +03 56 10.690 +27 11 17.90 0 13.820 13.467 12.963 12.391 12.128 12.123 8.42 3.30 -35.60 3.30 2 GCS9 UGCS J035610.69+271117.8 0.002 0.002 0.002 0.001 0.001 0.001 +03 29 11.270 +27 09 52.50 0 14.529 13.987 13.341 12.786 12.440 27.16 6.92 -25.14 6.92 2 GCS9 UGCS J032911.26+270952.4 0.003 0.002 0.002 0.002 0.001 +03 29 03.100 +27 04 59.90 0 15.372 14.837 14.194 13.600 13.273 21.10 6.95 -29.46 6.95 2 GCS9 UGCS J032903.10+270459.8 0.004 0.003 0.003 0.003 0.002 +03 30 11.390 +27 37 55.80 0 13.268 12.939 12.423 12.042 11.670 11.668 15.01 4.88 -33.94 4.88 2 GCS9 UGCS J033011.39+273755.7 0.001 0.001 0.001 0.001 0.001 0.001 +03 45 10.110 +27 40 09.10 0 15.074 14.582 14.010 13.477 13.154 13.152 20.96 3.45 -35.33 3.45 2 GCS9 UGCS J034510.11+274009.0 0.004 0.003 0.003 0.003 0.003 0.002 +03 42 24.950 +27 32 21.50 0 15.402 14.912 14.307 13.812 13.463 13.477 18.17 3.30 -32.77 3.30 2 GCS9 UGCS J034224.95+273221.5 0.004 0.004 0.004 0.004 0.004 0.003 +03 41 23.630 +27 24 16.90 0 15.242 14.719 14.108 13.543 13.215 13.226 18.53 3.29 -35.23 3.29 2 GCS9 Cl* Melotte 22 DH 171 0.004 0.003 0.003 0.003 0.003 0.002 +03 42 24.930 +27 42 15.40 0 15.788 15.273 14.656 14.111 13.783 13.811 18.89 3.32 -32.08 3.32 2 GCS9 UGCS J034224.92+274215.4 0.006 0.005 0.004 0.004 0.005 0.004 +04 01 21.510 +27 12 33.20 0 14.198 13.815 13.270 12.730 12.466 12.482 26.22 4.23 -38.49 4.23 2 GCS9 UGCS J040121.51+271233.1 0.002 0.002 0.002 0.002 0.002 0.001 +03 53 57.160 +28 10 13.30 0 15.617 15.088 14.479 13.890 13.567 13.573 26.79 3.76 -35.49 3.76 2 GCS9 UGCS J035357.16+281013.2 0.005 0.004 0.004 0.003 0.003 0.002 +03 47 42.860 +28 18 59.10 0 15.298 14.760 14.147 13.616 13.263 13.252 15.67 2.96 -35.98 2.96 2 GCS9 Cl* Melotte 22 DH 533 0.004 0.003 0.004 0.003 0.003 0.002 +03 43 37.560 +26 32 00.90 0 16.418 15.769 15.103 14.563 14.210 14.181 23.24 2.97 -37.05 2.97 2 GCS9 UGCS J034337.55+263200.8 0.009 0.007 0.006 0.005 0.006 0.003 +03 48 52.950 +27 11 09.20 0 12.550 12.284 11.843 11.345 11.027 11.066 26.03 2.88 -33.77 2.88 2 GCS9 UGCS J034852.95+271109.1 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 28.990 +25 34 55.80 0 13.441 13.038 12.541 12.001 11.753 11.765 23.56 2.95 -49.12 2.95 2 GCS9 Cl* Melotte 22 HHJ 359 0.002 0.002 0.002 0.001 0.001 0.001 +04 06 08.010 +25 42 34.60 0 16.097 15.463 14.792 14.184 13.818 13.799 11.14 3.39 -47.13 3.39 2 GCS9 UGCS J040608.00+254234.5 0.006 0.005 0.005 0.005 0.005 0.003 +03 43 56.000 +25 36 25.20 0 16.718 15.979 15.290 14.710 14.319 14.333 23.18 2.27 -46.03 2.27 2 GCS9 Cl* Melotte 22 PLZJ 50 0.010 0.008 0.007 0.006 0.006 0.005 +03 52 16.560 +27 35 51.00 0 15.081 14.622 14.039 13.529 13.200 13.192 11.21 2.89 -39.30 2.89 2 GCS9 UGCS J035216.56+273551.0 0.004 0.003 0.003 0.003 0.003 0.002 +03 53 26.650 +26 11 49.50 0 12.887 12.578 12.116 11.553 11.277 11.275 9.78 2.95 -50.85 2.95 2 GCS9 UGCS J035326.65+261149.5 0.001 0.001 0.001 0.001 0.001 0.001 +03 28 11.330 +26 55 37.00 0 16.205 15.582 14.915 14.351 13.989 25.62 7.01 -37.29 7.01 2 GCS9 UGCS J032811.33+265537.0 0.006 0.005 0.005 0.006 0.003 +03 36 46.480 +28 15 05.90 0 15.528 14.984 14.370 13.865 13.552 13.542 25.67 4.95 -35.56 4.95 2 GCS9 Cl* Melotte 22 DH 64 0.005 0.004 0.004 0.003 0.003 0.002 +03 51 05.080 +26 36 51.20 0 15.454 14.963 14.358 13.825 13.479 13.495 12.35 2.96 -37.88 2.96 2 GCS9 Cl* Melotte 22 MBSC 93 0.005 0.004 0.004 0.003 0.003 0.002 +03 26 13.400 +25 32 35.70 0 13.384 12.899 12.323 11.923 11.539 8.41 6.87 -33.03 6.87 2 GCS9 UGCS J032613.39+253235.7 0.002 0.001 0.001 0.001 0.001 +03 47 22.280 +25 43 15.00 0 16.268 15.616 14.929 14.374 14.017 13.977 15.45 2.25 -46.31 2.25 2 GCS9 UGCS J034722.28+254315.0 0.007 0.006 0.005 0.005 0.005 0.004 +03 27 47.670 +26 57 06.10 0 15.256 14.836 14.217 13.603 13.265 20.38 6.95 -28.78 6.95 2 GCS9 UGCS J032747.66+265706.0 0.004 0.003 0.003 0.003 0.002 +03 35 50.290 +25 42 20.50 0 13.820 13.379 12.791 12.239 11.978 28.77 5.30 -37.34 5.30 2 GCS9 Cl* Melotte 22 DH 49 0.002 0.002 0.002 0.001 0.001 +03 39 04.060 +27 00 04.70 0 13.938 13.572 13.032 12.454 12.171 12.197 17.40 3.00 -34.30 3.00 2 GCS9 Cl* Melotte 22 DH 98 0.002 0.002 0.002 0.001 0.001 0.001 +03 39 53.750 +28 34 56.40 0 14.232 13.794 13.189 12.675 12.384 12.373 25.26 4.93 -55.59 4.93 2 GCS9 UGCS J033953.75+283456.3 0.002 0.002 0.002 0.002 0.001 0.001 +03 50 52.180 +26 11 40.10 0 15.944 15.346 14.710 14.173 13.814 13.827 23.44 2.97 -34.78 2.97 2 GCS9 UGCS J035052.18+261140.0 0.007 0.005 0.005 0.003 0.004 0.003 +04 03 40.670 +25 21 34.50 0 13.110 12.756 12.216 11.671 11.303 11.336 16.57 3.31 -34.58 3.31 2 GCS9 Cl* Melotte 22 DH 908 0.001 0.001 0.001 0.001 0.001 0.001 +03 37 37.450 +25 41 49.80 0 14.994 14.527 13.953 13.399 13.101 13.070 12.66 2.96 -38.52 2.96 2 GCS9 UGCS J033737.45+254149.7 0.004 0.003 0.003 0.002 0.002 0.002 +03 50 39.330 +26 16 03.70 0 16.134 15.515 14.868 14.337 13.976 13.962 26.51 2.97 -45.26 2.97 2 GCS9 Cl* Melotte 22 DH 680 0.008 0.006 0.005 0.004 0.005 0.003 +04 00 39.240 +26 44 19.00 0 12.865 12.547 12.023 11.446 11.137 11.155 14.68 2.48 -47.91 2.48 2 GCS9 2MASS J04003922+2644194 0.001 0.001 0.001 0.001 0.001 0.001 +03 37 36.010 +26 32 48.40 0 12.457 12.147 11.662 11.396 10.873 10.930 24.17 3.30 -45.76 3.30 2 GCS9 Cl* Melotte 22 DH 75 0.001 0.001 0.001 0.001 0.001 0.000 +03 43 04.370 +25 26 12.00 0 14.931 14.396 13.757 13.216 12.892 12.874 16.25 2.23 -36.14 2.23 2 GCS9 Cl* Melotte 22 HHJ 107 0.004 0.003 0.003 0.002 0.002 0.001 +03 43 38.900 +27 01 01.20 0 15.573 15.021 14.385 13.839 13.499 13.516 25.77 2.95 -39.98 2.95 2 GCS9 UGCS J034338.89+270101.1 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 43.140 +26 32 20.90 0 14.239 13.774 13.191 12.678 12.379 12.364 25.22 2.93 -37.76 2.93 2 GCS9 Cl* Melotte 22 DH 588 0.003 0.002 0.002 0.002 0.002 0.001 +03 39 44.370 +26 18 18.80 0 15.243 14.790 14.188 13.648 13.355 13.364 16.91 3.00 -36.58 3.00 2 GCS9 Cl* Melotte 22 MBSC 82 0.004 0.003 0.004 0.002 0.003 0.002 +03 33 08.260 +26 31 13.00 0 16.896 16.129 15.414 14.825 14.421 14.415 19.68 3.01 -38.39 3.01 2 GCS9 UGCS J033308.26+263113.0 0.013 0.008 0.008 0.006 0.007 0.004 +03 37 10.510 +25 17 34.60 0 14.920 14.443 13.853 13.316 13.012 13.025 23.82 2.96 -34.70 2.96 2 GCS9 Cl* Melotte 22 DH 67 0.004 0.003 0.003 0.002 0.002 0.002 +03 36 42.460 +25 19 26.50 0 15.840 15.296 14.666 14.163 13.816 13.794 15.86 2.97 -36.53 2.97 2 GCS9 UGCS J033642.46+251926.4 0.007 0.005 0.005 0.004 0.004 0.003 +04 06 52.120 +26 21 12.20 0 15.224 14.702 14.074 13.477 13.127 13.144 10.04 2.97 -34.88 2.97 2 GCS9 UGCS J040652.11+262112.2 0.004 0.004 0.003 0.002 0.002 0.002 +03 29 07.680 +25 21 43.50 0 15.576 15.018 14.363 13.811 13.445 13.438 26.60 3.37 -35.82 3.37 2 GCS9 UGCS J032907.68+252143.5 0.005 0.004 0.004 0.003 0.003 0.002 +04 00 33.240 +25 30 20.50 0 15.605 15.106 14.432 13.755 13.426 13.420 24.06 3.32 -32.29 3.32 2 GCS9 UGCS J040033.23+253020.4 0.005 0.004 0.004 0.003 0.003 0.002 +03 32 36.290 +26 58 10.00 0 16.087 15.489 14.800 14.224 13.863 13.827 21.67 2.97 -39.87 2.97 2 GCS9 UGCS J033236.29+265810.0 0.007 0.005 0.005 0.004 0.004 0.003 +03 49 16.180 +26 49 02.90 0 16.931 16.211 15.474 14.912 14.482 14.531 21.70 2.99 -47.50 2.99 2 GCS9 2MASS J03491617+2649031 0.011 0.008 0.007 0.007 0.007 0.004 +03 41 48.050 +26 47 20.70 0 13.597 13.262 12.716 12.154 11.869 11.873 24.59 3.00 -39.18 3.00 2 GCS9 Cl* Melotte 22 DH 188 0.002 0.002 0.002 0.001 0.001 0.001 +03 53 29.350 +26 40 42.90 0 12.928 12.672 12.185 11.564 11.326 11.333 12.54 2.95 -40.06 2.95 2 GCS9 UGCS J035329.34+264042.9 0.001 0.001 0.001 0.001 0.001 0.001 +03 53 29.690 +26 40 49.60 0 12.976 12.631 12.090 11.488 11.193 11.211 12.01 2.95 -40.62 2.95 2 GCS9 UGCS J035329.69+264049.5 0.001 0.001 0.001 0.001 0.001 0.001 +03 24 59.740 +25 34 04.50 0 16.878 16.243 15.582 14.987 14.606 38.59 7.21 -49.57 7.21 2 GCS9 UGCS J032459.74+253404.4 0.009 0.007 0.007 0.010 0.005 +03 31 29.600 +26 30 12.10 0 14.670 14.195 13.596 13.007 12.703 12.734 21.27 2.96 -35.29 2.96 2 GCS9 Cl* Melotte 22 DH 16 0.003 0.002 0.003 0.002 0.002 0.001 +03 53 04.920 +27 01 42.30 0 14.277 13.874 13.309 12.686 12.394 12.404 9.69 2.95 -37.92 2.95 2 GCS9 UGCS J035304.92+270142.2 0.003 0.002 0.002 0.001 0.002 0.001 +03 38 54.160 +24 42 15.60 0 15.113 14.509 13.889 13.387 13.029 13.012 24.49 2.49 -40.01 2.49 2 GCS9 Cl* Melotte 22 HHJ 63 0.004 0.003 0.003 0.003 0.002 0.002 +03 49 28.800 +26 50 51.40 0 15.626 15.129 14.513 13.997 13.663 13.655 18.24 2.94 -35.09 2.94 2 GCS9 Cl* Melotte 22 MBSC 95 0.005 0.004 0.004 0.004 0.003 0.002 +03 36 27.370 +24 41 17.10 0 13.905 13.442 12.879 12.371 12.078 12.072 21.62 2.62 -35.55 2.62 2 GCS9 V* KK Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 36 38.910 +24 38 48.70 0 15.678 15.146 14.550 14.008 13.684 13.736 21.00 2.64 -36.23 2.64 2 GCS9 UGCS J033638.90+243848.7 0.005 0.004 0.004 0.004 0.005 0.003 +03 51 25.880 +24 47 38.70 0 12.962 12.520 12.000 11.783 11.161 11.285 16.49 2.20 -47.46 2.20 2 GCS9 V* V558 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 35 19.700 +26 33 10.60 0 14.411 13.963 13.402 12.862 12.572 12.563 15.89 3.30 -32.50 3.30 2 GCS9 UGCS J033519.70+263310.6 0.003 0.002 0.002 0.002 0.002 0.001 +03 47 05.790 +23 45 34.70 0 16.227 15.555 14.840 14.315 13.961 13.964 11.90 2.23 -39.81 2.23 2 GCS9 UGCS J034705.79+234534.7 0.008 0.005 0.005 0.004 0.005 0.003 +03 43 29.900 +24 39 23.40 0 15.428 14.876 14.288 13.760 13.405 11.50 2.98 -40.55 2.98 2 GCS9 Cl* Melotte 22 DH 269 0.005 0.004 0.004 0.003 0.002 +03 27 11.130 +26 00 29.20 0 14.463 14.069 13.521 12.933 12.651 9.47 6.88 -28.40 6.88 2 GCS9 UGCS J032711.12+260029.1 0.003 0.002 0.002 0.002 0.001 +03 32 51.370 +25 54 56.70 0 15.038 14.557 13.922 13.352 13.023 14.47 5.31 -36.68 5.31 2 GCS9 UGCS J033251.37+255456.7 0.003 0.003 0.003 0.003 0.002 +03 31 20.710 +25 57 33.60 1 16.847 16.068 15.309 14.745 14.321 14.328 21.73 3.39 -38.42 3.39 2 GCS9 UGCS J033120.70+255733.5 0.009 0.007 0.006 0.005 0.006 0.004 +03 37 16.710 +25 44 11.10 0 14.671 14.223 13.651 13.108 12.785 12.788 11.44 2.96 -37.28 2.96 2 GCS9 Cl* Melotte 22 DH 70 0.004 0.003 0.003 0.002 0.002 0.001 +03 41 38.870 +22 16 40.30 0 14.382 13.955 13.443 12.871 12.594 0.14 7.97 -57.33 7.97 2 GCS9 Cl* Melotte 22 DH 183 0.003 0.002 0.002 0.002 0.002 +03 42 10.600 +22 18 05.50 0 15.177 14.710 14.151 13.625 13.280 10.07 8.00 -65.72 8.00 2 GCS9 Cl* Melotte 22 HHJ 55 0.004 0.003 0.003 0.003 0.003 +03 48 36.340 +25 15 41.20 0 16.029 15.446 14.801 14.259 13.889 13.895 14.47 2.21 -46.15 2.21 2 GCS9 Cl* Melotte 22 BPL 183 0.007 0.005 0.005 0.004 0.005 0.003 +03 42 01.680 +22 23 26.60 0 14.215 13.807 13.265 12.703 12.396 33.36 7.96 -38.44 7.96 2 GCS9 V* V495 Tau 0.003 0.002 0.002 0.002 0.002 +04 01 28.430 +23 30 59.60 1 16.318 15.539 14.772 14.202 13.788 13.788 24.55 3.37 -39.20 3.37 2 GCS9 UGCS J040128.42+233059.6 0.008 0.005 0.005 0.004 0.004 0.003 +03 53 07.290 +25 47 21.40 0 15.338 14.821 14.219 13.639 13.302 13.309 16.97 2.94 -34.85 2.94 2 GCS9 Cl* Melotte 22 DH 778 0.005 0.004 0.003 0.003 0.003 0.002 +03 34 41.550 +26 09 27.10 0 15.437 14.878 14.245 13.705 13.344 24.47 5.32 -33.86 5.32 2 GCS9 Cl* Melotte 22 DH 36 0.004 0.003 0.003 0.003 0.002 +03 34 46.960 +26 05 38.40 0 14.123 13.758 13.233 12.636 12.369 9.36 5.31 -55.43 5.31 2 GCS9 UGCS J033446.95+260538.4 0.002 0.002 0.002 0.002 0.001 +03 43 34.490 +25 57 30.60 1 16.571 15.727 14.909 14.359 13.909 13.901 20.92 2.25 -47.72 2.25 2 GCS9 UGCS J034334.48+255730.5 0.009 0.006 0.005 0.004 0.004 0.003 +03 56 52.310 +25 10 05.10 1 16.146 15.491 14.756 14.192 13.771 13.801 16.66 2.50 -38.22 2.50 2 GCS9 Cl* Melotte 22 HHJ 17 0.007 0.006 0.005 0.003 0.004 0.003 +03 41 42.410 +23 54 57.10 1 16.171 15.464 14.709 14.124 13.686 14.07 2.31 -47.37 2.31 2 GCS9 Cl* Melotte 22 STAR 5 0.007 0.006 0.004 0.004 0.002 +03 40 55.050 +22 20 58.70 0 13.211 12.869 12.333 11.734 11.446 11.470 21.33 2.50 -35.72 2.50 2 GCS9 V* V430 Tau 0.002 0.001 0.001 0.001 0.001 0.001 +03 56 40.450 +22 08 46.50 0 15.445 14.885 14.241 13.710 13.373 13.371 21.46 2.51 -48.69 2.51 2 GCS9 UGCS J035640.44+220846.5 0.005 0.004 0.004 0.003 0.003 0.002 +03 28 26.620 +26 02 11.60 0 16.852 16.148 15.367 14.824 14.432 4.01 7.08 -51.89 7.08 2 GCS9 UGCS J032826.62+260211.5 0.009 0.007 0.006 0.008 0.004 +03 57 40.680 +25 16 04.10 0 13.577 13.194 12.640 12.008 11.723 11.790 14.25 2.47 -36.24 2.47 2 GCS9 Cl* Melotte 22 DH 856 0.002 0.002 0.002 0.001 0.001 0.001 +03 43 25.970 +25 58 42.10 0 15.160 14.687 14.062 13.537 13.179 13.218 11.69 2.23 -36.19 2.23 2 GCS9 UGCS J034325.96+255842.1 0.004 0.003 0.003 0.002 0.003 0.002 +03 44 31.040 +22 15 14.90 0 13.542 13.130 12.619 12.024 11.745 11.753 24.71 2.51 -36.97 2.51 2 GCS9 Cl* Melotte 22 DH 344 0.002 0.002 0.002 0.001 0.001 0.001 +03 59 59.860 +22 05 29.30 0 14.814 14.347 13.744 13.188 12.863 12.877 16.36 2.87 -36.19 2.87 2 GCS9 Cl* Melotte 22 DH 884 0.003 0.003 0.003 0.002 0.002 0.002 +03 27 16.170 +25 51 42.80 0 14.414 14.020 13.433 12.884 12.573 7.75 6.88 -24.82 6.88 2 GCS9 UGCS J032716.16+255142.7 0.002 0.002 0.002 0.002 0.001 +03 45 11.520 +21 14 29.30 0 16.421 15.764 15.109 14.536 14.148 14.145 25.46 2.68 -40.57 2.68 2 GCS9 UGCS J034511.51+211429.2 0.008 0.006 0.005 0.005 0.007 0.004 +03 54 24.000 +23 51 58.70 0 15.891 15.288 14.667 14.110 13.776 13.780 14.24 2.25 -48.04 2.25 2 GCS9 UGCS J035423.99+235158.7 0.006 0.005 0.005 0.004 0.004 0.003 +03 26 24.300 +24 25 42.40 0 13.990 13.633 13.081 12.464 12.209 1.09 6.95 -51.34 6.95 2 GCS9 UGCS J032624.30+242542.3 0.002 0.002 0.002 0.001 0.001 +04 00 03.210 +22 24 46.00 1 16.534 15.875 15.128 14.550 14.132 14.141 15.92 2.87 -33.98 2.87 2 GCS9 UGCS J040003.20+222445.9 0.008 0.006 0.005 0.006 0.004 0.004 +03 28 02.300 +25 55 26.60 0 14.028 13.631 13.111 12.505 12.216 32.02 6.88 -55.34 6.88 2 GCS9 UGCS J032802.30+255526.5 0.002 0.002 0.002 0.002 0.001 +03 43 24.990 +23 52 01.20 0 16.867 16.116 15.368 14.820 14.365 14.16 2.34 -46.02 2.34 2 GCS9 2MASS J03432497+2352017 0.011 0.008 0.007 0.007 0.004 +04 03 30.230 +25 16 04.50 0 13.760 13.396 12.828 12.208 11.890 11.912 26.34 2.89 -36.38 2.89 2 GCS9 UGCS J040330.22+251604.5 0.002 0.002 0.002 0.001 0.001 0.001 +03 59 42.660 +21 12 14.90 0 15.123 14.590 13.965 13.401 13.060 13.062 28.07 3.76 -38.75 3.76 2 GCS9 UGCS J035942.66+211214.8 0.004 0.003 0.003 0.003 0.001 0.002 +04 02 55.110 +25 53 53.50 0 16.259 15.667 15.021 14.464 14.087 14.062 19.80 3.33 -38.23 3.33 2 GCS9 UGCS J040255.10+255353.5 0.007 0.005 0.005 0.004 0.005 0.004 +03 58 17.430 +22 11 52.70 1 16.259 15.503 14.768 14.193 13.787 13.778 20.64 2.89 -34.95 2.89 2 GCS9 UGCS J035817.43+221152.6 0.007 0.005 0.004 0.005 0.003 0.003 +04 09 15.300 +25 05 36.60 0 13.026 12.757 12.256 11.647 11.441 11.430 24.83 2.98 -38.40 2.98 2 GCS9 2MASS J04091529+2505368 0.002 0.001 0.001 0.001 0.001 0.001 +03 34 13.470 +25 25 25.30 0 14.776 14.276 13.694 13.124 12.802 25.97 5.29 -32.89 5.29 2 GCS9 UGCS J033413.46+252525.3 0.003 0.002 0.003 0.002 0.001 +03 59 59.850 +25 08 53.60 1 16.368 15.695 15.004 14.433 14.029 14.075 14.87 2.51 -35.69 2.51 2 GCS9 UGCS J035959.84+250853.6 0.008 0.006 0.006 0.004 0.005 0.003 +03 32 32.970 +22 18 12.10 0 14.922 14.467 13.885 13.344 13.019 13.018 22.36 3.41 -35.37 3.41 2 GCS9 Cl* Melotte 22 DH 20 0.003 0.003 0.003 0.002 0.002 0.002 +03 56 22.310 +21 07 16.90 0 13.818 13.393 12.829 12.286 11.984 12.076 13.77 3.36 -36.78 3.36 2 GCS9 Cl* Melotte 22 DH 831 0.002 0.002 0.002 0.001 0.001 0.001 +03 45 13.410 +23 31 00.70 0 13.938 13.443 12.795 12.223 11.868 11.886 12.02 2.24 -39.32 2.24 2 GCS9 V* OO Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 03.450 +24 20 57.00 0 14.476 14.023 13.444 12.842 12.558 12.571 11.97 2.22 -38.92 2.22 2 GCS9 V* V856 Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 45 52.760 +23 27 54.00 0 14.143 13.734 13.161 12.615 12.286 12.305 12.01 2.24 -39.18 2.24 2 GCS9 V* V747 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 40 49.400 +21 12 55.30 0 14.418 13.938 13.357 12.766 12.478 12.460 21.51 3.58 -35.17 3.58 2 GCS9 Cl* Melotte 22 DH 152 0.003 0.002 0.002 0.002 0.001 0.001 +03 52 02.280 +24 21 47.90 0 12.898 12.582 12.168 11.631 11.353 11.429 22.99 2.24 -48.15 2.24 2 GCS9 Cl* Melotte 22 DH 734 0.001 0.001 0.001 0.001 0.001 0.001 +03 39 10.220 +19 55 30.60 0 14.068 13.656 13.061 12.514 12.192 12.201 18.29 5.01 -32.86 5.01 2 GCS9 UGCS J033910.21+195530.5 0.002 0.002 0.002 0.002 0.001 0.001 +03 39 37.760 +19 55 53.30 0 14.324 13.934 13.412 12.811 12.532 12.528 4.36 5.02 -36.62 5.02 2 GCS9 UGCS J033937.75+195553.3 0.002 0.002 0.002 0.002 0.002 0.001 +03 47 31.130 +21 10 51.10 0 14.679 14.224 13.640 13.111 12.780 12.789 23.35 3.05 -35.53 3.05 2 GCS9 Cl* Melotte 22 DH 519 0.003 0.003 0.003 0.002 0.002 0.001 +03 54 49.920 +19 50 44.90 0 14.727 14.266 13.691 13.126 12.808 12.842 8.65 6.05 -45.98 6.05 2 GCS9 UGCS J035449.91+195044.9 0.003 0.002 0.002 0.002 0.002 0.001 +04 05 50.080 +22 35 53.80 0 14.329 13.919 13.378 12.805 12.498 12.507 14.32 4.97 -32.31 4.97 2 GCS9 UGCS J040550.08+223553.8 0.002 0.002 0.002 0.002 0.001 0.001 +03 54 38.020 +19 54 22.50 0 14.169 13.723 13.136 12.596 12.291 12.305 14.93 6.05 -51.92 6.05 2 GCS9 UGCS J035438.01+195422.5 0.002 0.002 0.002 0.001 0.001 0.001 +03 37 10.870 +21 12 09.70 0 15.115 14.622 14.008 13.457 13.133 13.127 27.96 3.41 -35.33 3.41 2 GCS9 UGCS J033710.86+211209.7 0.004 0.003 0.003 0.002 0.002 0.002 +03 57 19.330 +23 27 37.60 0 15.039 14.541 13.948 13.395 13.065 13.076 17.31 3.00 -34.18 3.00 2 GCS9 2MASS J03571931+2327379 0.004 0.004 0.003 0.002 0.002 0.002 +03 42 57.780 +19 47 06.10 0 16.796 16.118 15.392 14.796 14.398 14.370 19.85 4.07 -37.58 4.07 2 GCS9 UGCS J034257.78+194706.1 0.009 0.007 0.006 0.006 0.007 0.005 +03 37 28.280 +24 24 09.10 0 12.968 12.703 12.189 11.596 11.337 11.397 15.93 2.50 -35.80 2.50 2 GCS9 UGCS J033728.27+242409.1 0.001 0.001 0.001 0.001 0.001 0.001 +03 42 13.490 +24 18 49.60 0 15.627 15.114 14.453 13.891 13.540 13.15 2.31 -37.63 2.31 2 GCS9 Cl* Melotte 22 BPL 36 0.005 0.005 0.004 0.003 0.002 +03 41 51.500 +24 19 27.30 0 16.617 15.976 15.244 14.667 14.283 16.53 2.33 -37.48 2.33 2 GCS9 Cl* Melotte 22 BPL 27 0.009 0.008 0.006 0.006 0.004 +03 41 38.810 +24 23 09.20 0 15.413 14.924 14.300 13.761 13.401 20.74 2.31 -48.47 2.31 2 GCS9 Cl* Melotte 22 BPL 25 0.005 0.004 0.004 0.003 0.002 +03 53 53.320 +22 30 37.50 0 15.687 15.153 14.514 13.995 13.648 13.634 25.44 2.52 -43.14 2.52 2 GCS9 UGCS J035353.32+223037.5 0.006 0.005 0.004 0.003 0.004 0.003 +03 32 37.820 +23 25 59.00 0 15.028 14.538 13.929 13.363 13.041 13.050 20.57 3.42 -34.09 3.42 2 GCS9 UGCS J033237.82+232559.0 0.003 0.003 0.003 0.002 0.002 0.002 +03 41 45.070 +22 28 01.80 0 15.511 14.939 14.315 13.811 13.435 13.430 24.25 2.51 -44.15 2.51 2 GCS9 Cl* Melotte 22 DH 185 0.005 0.004 0.004 0.003 0.003 0.002 +03 54 15.600 +24 20 45.70 0 15.915 15.353 14.671 14.117 13.832 13.778 15.59 2.25 -35.25 2.25 2 GCS9 Cl* Melotte 22 BPL 308 0.006 0.005 0.004 0.004 0.004 0.003 +04 02 41.900 +22 28 18.30 0 16.439 15.775 15.058 14.528 14.129 14.101 18.57 3.42 -47.04 3.42 2 GCS9 UGCS J040241.90+222818.2 0.007 0.005 0.005 0.004 0.005 0.003 +03 40 28.570 +23 33 42.20 0 16.820 16.083 15.334 14.796 14.375 14.420 24.86 2.54 -48.66 2.54 2 GCS9 UGCS J034028.56+233342.2 0.011 0.008 0.007 0.007 0.007 0.005 +03 42 06.940 +19 54 06.00 0 15.496 15.005 14.353 13.759 13.458 13.462 10.23 5.03 -28.73 5.03 2 GCS9 UGCS J034206.94+195405.9 0.004 0.004 0.003 0.003 0.003 0.002 +03 47 56.640 +24 15 31.70 0 15.315 14.766 14.163 13.625 13.282 13.281 21.88 2.22 -36.70 2.22 2 GCS9 Cl* Melotte 22 BPL 162 0.004 0.003 0.003 0.002 0.003 0.002 +03 44 09.920 +24 16 03.90 0 13.292 12.928 12.379 11.781 11.527 20.69 2.30 -36.49 2.30 2 GCS9 V* V629 Tau 0.002 0.002 0.001 0.001 0.001 +03 47 55.280 +23 19 05.80 0 13.814 13.443 12.910 12.377 12.092 12.111 14.50 2.24 -35.68 2.24 2 GCS9 V* PS Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 44 20.660 +24 15 10.70 0 15.211 14.648 13.992 13.469 13.087 12.94 2.31 -38.96 2.31 2 GCS9 Cl* Melotte 22 HHJ 57 0.004 0.004 0.003 0.002 0.002 +03 40 15.930 +24 22 31.30 0 15.827 15.296 14.640 14.123 13.772 13.768 20.95 2.51 -35.43 2.51 2 GCS9 Cl* Melotte 22 BPL 11 0.006 0.005 0.004 0.004 0.004 0.003 +04 01 05.810 +23 34 43.90 0 14.117 13.665 13.116 12.586 12.289 26.79 3.54 -44.13 3.54 2 GCS9 UGCS J040105.81+233443.8 0.002 0.002 0.002 0.002 0.001 +04 00 50.520 +23 43 52.90 1 16.184 15.380 14.647 14.089 13.660 24.52 3.56 -40.90 3.56 2 GCS9 UGCS J040050.52+234352.9 0.006 0.004 0.004 0.004 0.002 +03 59 12.920 +24 17 18.40 0 14.762 14.271 13.702 13.114 12.808 12.827 15.00 2.97 -34.59 2.97 2 GCS9 Cl* Melotte 22 DH 873 0.003 0.003 0.003 0.002 0.002 0.001 +03 43 18.750 +20 07 44.40 0 14.292 13.846 13.306 12.791 12.491 12.478 12.00 3.42 -35.92 3.42 2 GCS9 UGCS J034318.74+200744.3 0.002 0.002 0.002 0.002 0.002 0.001 +03 28 16.070 +22 27 32.10 0 16.039 15.477 14.850 14.262 13.924 13.920 26.29 5.04 -36.31 5.04 2 GCS9 UGCS J032816.07+222732.0 0.005 0.004 0.004 0.005 0.003 0.003 +03 42 58.600 +20 12 45.00 0 14.947 14.424 13.773 13.194 12.857 12.840 18.85 3.42 -32.72 3.42 2 GCS9 Cl* Melotte 22 DH 247 0.003 0.003 0.003 0.002 0.002 0.001 +03 50 43.070 +23 19 55.70 0 16.002 15.424 14.795 14.265 13.908 13.920 23.59 2.14 -41.03 2.14 2 GCS9 UGCS J035043.06+231955.6 0.006 0.005 0.005 0.004 0.003 0.003 +03 55 30.900 +23 23 50.90 0 13.961 13.590 13.001 12.425 12.158 12.161 18.03 2.25 -35.87 2.25 2 GCS9 Cl* Melotte 22 DH 815 0.002 0.002 0.002 0.001 0.001 0.001 +03 41 20.000 +22 37 53.70 0 13.771 13.385 12.814 12.255 11.965 11.999 11.28 2.50 -47.82 2.50 2 GCS9 Cl* Melotte 22 DH 168 0.002 0.002 0.002 0.001 0.001 0.001 +03 37 26.250 +22 34 30.90 0 16.766 16.113 15.394 14.869 14.450 14.467 17.90 2.97 -38.24 2.97 2 GCS9 UGCS J033726.25+223430.8 0.010 0.008 0.006 0.006 0.007 0.005 +04 03 49.740 +23 43 25.20 0 16.769 16.047 15.317 14.756 14.349 14.341 25.63 3.40 -44.29 3.40 2 GCS9 UGCS J040349.73+234325.1 0.009 0.006 0.006 0.005 0.006 0.004 +03 58 52.130 +24 43 48.30 0 14.398 14.003 13.466 12.869 12.581 12.617 18.41 2.48 -35.59 2.48 2 GCS9 UGCS J035852.13+244348.3 0.003 0.003 0.003 0.002 0.002 0.001 +03 38 34.490 +23 40 22.30 0 14.946 14.514 13.918 13.368 13.041 13.062 11.57 2.50 -46.35 2.50 2 GCS9 Cl* Melotte 22 DH 91 0.004 0.003 0.003 0.002 0.002 0.002 +03 42 05.740 +23 07 14.40 0 16.728 16.017 15.363 14.805 14.378 14.398 16.95 2.29 -37.00 2.29 2 GCS9 UGCS J034205.74+230714.3 0.010 0.008 0.007 0.006 0.007 0.005 +03 26 44.060 +24 39 39.40 0 14.123 13.658 13.081 12.585 12.255 18.85 6.95 -22.57 6.95 2 GCS9 UGCS J032644.06+243939.4 0.002 0.002 0.002 0.002 0.001 +03 43 26.440 +22 42 42.50 0 14.138 13.675 13.111 12.543 12.249 12.216 13.70 2.25 -37.63 2.25 2 GCS9 V* V846 Tau 0.003 0.002 0.002 0.001 0.001 0.001 +03 54 31.490 +22 39 01.50 0 16.554 15.843 15.155 14.573 14.190 14.206 13.09 2.29 -41.56 2.29 2 GCS9 UGCS J035431.48+223901.5 0.009 0.007 0.006 0.005 0.006 0.004 +03 45 37.760 +23 43 50.10 1 16.240 15.456 14.715 14.172 13.756 13.742 20.91 2.23 -45.45 2.23 2 GCS9 Cl* Melotte 22 SHF 10 0.008 0.005 0.004 0.003 0.004 0.003 +03 30 43.820 +27 27 42.80 0 16.033 15.515 14.892 14.273 13.976 13.989 4.89 4.92 -41.76 4.92 2 GCS9 UGCS J033043.82+272742.8 0.005 0.004 0.004 0.005 0.004 0.003 +03 48 57.410 +23 13 59.10 1 16.835 16.033 15.222 14.673 14.194 14.194 14.93 2.17 -36.62 2.17 2 GCS9 UGCS J034857.40+231359.0 0.011 0.007 0.006 0.006 0.005 0.004 +03 33 14.390 +24 50 08.10 0 14.746 14.237 13.646 13.119 12.824 12.808 25.31 2.63 -48.57 2.63 2 GCS9 UGCS J033314.38+245008.1 0.003 0.002 0.002 0.002 0.002 0.001 +03 44 25.510 +22 27 49.80 0 16.405 15.794 15.102 14.589 14.223 14.185 22.07 2.53 -37.99 2.53 2 GCS9 2MASS J03442549+2227501 0.008 0.006 0.005 0.006 0.006 0.004 +03 44 29.500 +22 25 24.70 0 15.948 15.406 14.761 14.222 13.858 13.860 23.26 2.52 -37.84 2.52 2 GCS9 UGCS J034429.49+222524.6 0.006 0.005 0.004 0.005 0.004 0.003 +03 28 19.340 +24 49 48.70 0 13.462 13.155 12.649 12.117 11.814 11.03 6.95 -38.57 6.95 2 GCS9 UGCS J032819.34+244948.6 0.002 0.001 0.001 0.001 0.001 +03 28 05.420 +24 46 50.80 0 14.243 13.810 13.304 12.740 12.453 16.36 6.95 -28.13 6.95 2 GCS9 UGCS J032805.42+244650.7 0.002 0.002 0.002 0.002 0.001 +03 28 56.800 +24 45 20.20 0 14.988 14.482 13.880 13.310 13.039 7.77 6.96 -34.47 6.96 2 GCS9 UGCS J032856.79+244520.2 0.003 0.003 0.003 0.003 0.001 +03 37 34.180 +24 42 15.10 0 15.478 14.973 14.389 13.876 13.543 13.551 21.50 2.49 -36.80 2.49 2 GCS9 Cl* Melotte 22 DH 74 0.005 0.004 0.004 0.003 0.003 0.002 +03 37 03.480 +24 44 35.30 0 13.096 12.748 12.245 11.693 11.446 11.458 21.23 2.48 -35.99 2.48 2 GCS9 V* KL Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 56 28.630 +23 09 03.40 0 13.707 13.314 12.787 12.207 11.924 11.944 17.30 2.99 -35.17 2.99 2 GCS9 UGCS J035628.63+230903.4 0.002 0.002 0.002 0.001 0.001 0.001 +04 01 55.860 +24 44 02.30 0 14.205 13.759 13.190 12.627 12.326 12.347 16.22 2.89 -35.93 2.89 2 GCS9 UGCS J040155.85+244402.3 0.002 0.002 0.002 0.002 0.001 0.001 +03 40 56.930 +29 01 09.10 0 14.565 14.058 13.458 12.891 12.549 12.569 11.58 2.96 -44.15 2.96 2 GCS9 UGCS J034056.93+290109.1 0.003 0.002 0.002 0.002 0.002 0.001 +03 37 37.090 +23 10 57.80 0 14.626 14.203 13.619 13.102 12.764 12.763 21.66 2.98 -35.09 2.98 2 GCS9 UGCS J033737.08+231057.8 0.003 0.003 0.003 0.002 0.002 0.001 +04 06 29.990 +22 33 43.60 0 14.376 13.856 13.201 12.623 12.273 12.269 14.83 5.07 -33.96 5.07 2 GCS9 Cl* Melotte 22 DH 916 0.002 0.002 0.002 0.002 0.001 0.001 +04 06 48.060 +22 26 38.90 0 15.098 14.674 14.081 13.428 13.126 13.129 5.85 5.08 -27.50 5.08 2 GCS9 UGCS J040648.05+222638.9 0.003 0.003 0.003 0.003 0.002 0.002 +04 10 20.200 +22 49 44.20 0 16.876 16.094 15.352 14.801 14.394 14.366 31.66 6.12 -35.95 6.12 2 GCS9 UGCS J041020.20+224944.2 0.009 0.007 0.006 0.007 0.008 0.004 +04 10 52.290 +22 40 50.70 0 16.595 16.009 15.382 14.707 14.334 14.306 28.77 6.10 -34.40 6.10 2 GCS9 UGCS J041052.28+224050.7 0.008 0.006 0.006 0.006 0.007 0.004 +03 38 25.430 +24 27 11.90 0 16.581 15.897 15.248 14.690 14.314 14.316 23.60 2.52 -36.23 2.52 2 GCS9 UGCS J033825.42+242711.8 0.009 0.007 0.006 0.006 0.006 0.004 +03 49 43.170 +24 39 46.40 0 16.348 15.708 15.061 14.505 14.115 14.126 17.72 2.22 -38.37 2.22 2 GCS9 Cl* Melotte 22 BPL 215 0.008 0.006 0.005 0.004 0.005 0.003 +03 40 09.690 +23 10 32.40 0 14.362 13.967 13.408 12.881 12.576 12.579 25.33 2.98 -38.03 2.98 2 GCS9 V* KW Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 40 12.740 +23 09 35.60 0 13.830 13.457 12.923 12.343 12.056 12.069 23.11 2.97 -33.99 2.97 2 GCS9 V* V429 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +04 02 34.770 +23 08 28.40 0 14.671 14.234 13.621 13.093 12.774 12.817 13.37 3.35 -33.49 3.35 2 GCS9 UGCS J040234.77+230828.4 0.003 0.003 0.002 0.002 0.002 0.001 +03 49 27.580 +24 24 13.70 0 14.547 14.043 13.524 12.968 12.679 12.681 11.85 2.20 -46.15 2.20 2 GCS9 Cl* Melotte 22 DH 621 0.003 0.002 0.002 0.002 0.002 0.001 +03 49 27.650 +24 31 54.10 0 12.946 12.583 12.067 11.575 11.226 11.273 11.93 2.20 -42.04 2.20 2 GCS9 V* V359 Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 42 31.210 +24 49 21.20 0 15.091 14.553 13.952 13.433 13.118 23.06 2.97 -38.50 2.97 2 GCS9 V* V611 Tau 0.004 0.003 0.003 0.002 0.002 +03 41 52.320 +24 41 57.60 0 14.986 14.480 13.904 13.361 13.012 18.58 2.97 -50.63 2.97 2 GCS9 Cl* Melotte 22 DH 190 0.004 0.003 0.003 0.002 0.001 +03 38 13.040 +24 38 16.80 0 14.671 14.227 13.631 13.103 12.809 12.801 23.94 2.49 -49.13 2.49 2 GCS9 Cl* Melotte 22 DH 84 0.003 0.003 0.003 0.002 0.002 0.001 +04 06 49.560 +23 09 03.60 0 13.930 13.558 12.993 12.404 12.134 12.138 15.11 3.74 -32.63 3.74 2 GCS9 UGCS J040649.55+230903.6 0.002 0.002 0.002 0.001 0.001 0.001 +03 34 19.370 +22 48 42.20 0 16.004 15.439 14.812 14.262 13.912 13.908 21.53 3.06 -34.97 3.06 2 GCS9 Cl* Melotte 22 DH 34 0.007 0.005 0.005 0.004 0.004 0.003 +03 57 05.170 +20 44 16.30 0 16.134 15.509 14.849 14.335 13.970 13.945 23.22 4.01 -41.34 4.01 2 GCS9 UGCS J035705.17+204416.3 0.006 0.005 0.005 0.004 0.005 0.004 +03 50 09.720 +20 41 54.00 0 16.759 16.075 15.362 14.808 14.395 14.384 17.58 3.49 -35.38 3.49 2 GCS9 UGCS J035009.71+204154.0 0.008 0.006 0.006 0.006 0.007 0.006 +03 25 45.370 +22 56 36.80 0 13.572 13.247 12.749 12.147 11.900 11.922 8.91 5.07 -37.88 5.07 2 GCS9 UGCS J032545.37+225636.8 0.002 0.001 0.002 0.001 0.001 0.001 +03 25 38.730 +22 57 39.80 1 15.890 15.269 14.601 13.974 13.618 13.628 24.45 5.11 -47.02 5.11 2 GCS9 UGCS J032538.72+225739.8 0.005 0.004 0.004 0.004 0.003 0.002 +03 50 24.960 +20 42 17.80 0 12.468 12.207 11.761 11.397 10.921 10.930 13.33 3.42 -33.72 3.42 2 GCS9 Cl* Melotte 22 DH 668 0.001 0.001 0.001 0.001 0.001 0.001 +04 01 50.950 +22 59 15.50 0 16.363 15.631 14.906 14.311 13.912 13.906 20.72 3.38 -38.98 3.38 2 GCS9 UGCS J040150.95+225915.5 0.008 0.005 0.005 0.004 0.005 0.003 +03 41 13.500 +20 51 17.20 0 13.069 12.653 12.096 11.582 11.204 11.227 22.59 3.42 -36.61 3.42 2 GCS9 UGCS J034113.49+205117.2 0.001 0.001 0.001 0.001 0.001 0.001 +03 44 08.810 +23 04 47.50 0 12.721 12.442 11.944 11.503 11.108 11.160 24.16 2.25 -46.50 2.25 2 GCS9 V* MV Tau 0.001 0.001 0.001 0.001 0.001 0.001 +03 53 43.800 +27 50 19.20 0 16.584 16.006 15.337 14.720 14.344 14.344 13.37 3.36 -34.48 3.36 2 GCS9 UGCS J035343.79+275019.1 0.007 0.006 0.006 0.005 0.007 0.007 +03 47 15.660 +19 42 10.50 0 14.049 13.614 13.070 12.494 12.226 12.227 8.84 5.04 -28.59 5.04 2 GCS9 UGCS J034715.66+194210.4 0.002 0.002 0.002 0.001 0.001 0.001 +03 52 30.540 +19 40 39.60 0 14.510 14.026 13.382 12.839 12.507 12.549 19.00 3.98 -35.62 3.98 2 GCS9 Cl* Melotte 22 DH 753 0.003 0.002 0.002 0.002 0.002 0.001 +03 59 16.640 +19 44 27.20 0 14.532 14.106 13.533 12.964 12.659 12.670 11.83 3.04 -34.49 3.04 2 GCS9 UGCS J035916.64+194427.1 0.003 0.002 0.002 0.002 0.002 0.001 +03 55 23.970 +19 36 48.70 0 12.665 12.398 11.904 11.358 11.013 11.061 27.55 6.05 -29.31 6.05 2 GCS9 UGCS J035523.96+193648.7 0.001 0.001 0.001 0.001 0.001 0.000 +03 40 35.340 +21 33 33.30 0 14.187 13.742 13.238 12.559 12.246 12.253 9.37 3.58 -32.98 3.58 2 GCS9 UGCS J034035.33+213333.3 0.002 0.002 0.002 0.002 0.001 0.001 +03 40 47.810 +21 43 21.70 0 15.850 15.249 14.624 14.026 13.626 13.638 20.22 3.60 -35.96 3.60 2 GCS9 UGCS J034047.80+214321.6 0.005 0.004 0.004 0.004 0.003 0.002 +03 55 50.110 +21 43 27.70 0 13.074 12.662 12.128 11.773 11.254 11.294 11.27 3.36 -39.08 3.36 2 GCS9 Cl* Melotte 22 DH 819 0.001 0.001 0.001 0.001 0.001 0.001 +03 58 50.060 +21 36 52.90 0 15.384 14.775 14.109 13.558 13.179 13.201 24.07 3.76 -38.06 3.76 2 GCS9 UGCS J035850.06+213652.8 0.005 0.004 0.004 0.003 0.002 0.002 +03 44 52.870 +21 34 16.10 0 14.371 13.850 13.252 12.733 12.411 12.398 16.20 2.65 -35.83 2.65 2 GCS9 UGCS J034452.86+213416.0 0.003 0.002 0.002 0.002 0.002 0.001 +03 40 10.500 +21 44 45.50 0 14.286 13.854 13.282 12.790 12.436 12.464 26.94 3.58 -45.88 3.58 2 GCS9 UGCS J034010.50+214445.4 0.002 0.002 0.002 0.002 0.001 0.001 +03 56 33.510 +19 33 25.20 0 16.665 15.941 15.228 14.681 14.293 14.284 22.45 6.10 -29.57 6.10 2 GCS9 UGCS J035633.50+193325.1 0.008 0.006 0.006 0.005 0.005 0.004 +03 56 49.390 +19 30 05.50 0 13.596 13.242 12.727 12.205 11.878 11.890 12.45 6.05 -26.16 6.05 2 GCS9 UGCS J035649.38+193005.4 0.002 0.001 0.002 0.001 0.001 0.001 +03 56 23.930 +19 23 53.40 0 14.848 14.335 13.702 13.158 12.840 12.830 12.00 6.05 -30.52 6.05 2 GCS9 UGCS J035623.92+192353.3 0.003 0.002 0.002 0.002 0.002 0.001 +03 45 46.940 +21 44 49.00 0 14.850 14.329 13.677 13.182 12.811 12.850 25.93 2.65 -40.73 2.65 2 GCS9 Cl* Melotte 22 HHJ 128 0.003 0.003 0.003 0.002 0.002 0.001 +03 41 14.460 +19 20 30.70 0 12.714 12.465 12.013 11.465 11.274 11.312 32.96 5.01 -35.20 5.01 2 GCS9 UGCS J034114.45+192030.7 0.001 0.001 0.001 0.001 0.001 0.001 +03 57 09.810 +21 36 27.20 0 14.554 14.125 13.494 12.940 12.659 12.639 17.59 3.36 -34.33 3.36 2 GCS9 Cl* Melotte 22 DH 850 0.003 0.002 0.002 0.002 0.002 0.001 +03 50 29.740 +19 21 01.50 0 16.507 15.848 15.150 14.568 14.214 14.238 13.83 4.07 -37.81 4.07 2 GCS9 UGCS J035029.73+192101.5 0.007 0.006 0.006 0.006 0.006 0.004 +03 30 57.410 +27 16 46.30 0 16.086 15.546 14.839 14.272 13.954 5.81 7.01 -21.54 7.01 2 GCS9 UGCS J033057.40+271646.2 0.006 0.005 0.005 0.005 0.003 +03 42 38.220 +19 28 57.40 0 15.981 15.445 14.822 14.201 13.860 13.854 7.36 4.00 -35.21 4.00 2 GCS9 UGCS J034238.21+192857.4 0.006 0.005 0.005 0.004 0.004 0.003 +03 49 22.520 +21 37 56.00 0 14.541 14.086 13.492 12.975 12.636 12.654 21.90 3.05 -33.73 3.05 2 GCS9 Cl* Melotte 22 DH 619 0.003 0.002 0.002 0.002 0.002 0.001 +03 39 57.850 +25 55 29.70 0 15.347 14.843 14.201 13.640 13.304 13.307 25.63 2.96 -42.96 2.96 2 GCS9 Cl* Melotte 22 DH 120 0.005 0.004 0.004 0.003 0.003 0.002 +03 40 11.930 +25 52 32.30 0 15.702 15.231 14.589 14.058 13.700 13.704 16.97 2.97 -35.30 2.97 2 GCS9 Cl* Melotte 22 HHJ 29 0.006 0.005 0.005 0.003 0.004 0.003 +03 32 11.550 +21 27 55.70 1 16.385 15.652 14.906 14.339 13.949 13.920 13.64 3.89 -40.00 3.89 2 GCS9 UGCS J033211.55+212755.7 0.007 0.005 0.005 0.005 0.005 0.003 +03 43 27.900 +25 01 40.90 0 15.717 15.051 14.370 13.818 13.439 10.73 2.98 -49.42 2.98 2 GCS9 UGCS J034327.89+250140.9 0.006 0.004 0.004 0.003 0.002 +03 44 11.280 +24 52 34.20 0 15.374 14.904 14.280 13.727 13.434 24.96 2.98 -35.52 2.98 2 GCS9 Cl* Melotte 22 HHJ 59 0.005 0.004 0.003 0.003 0.002 +03 39 03.500 +21 25 19.90 0 14.680 14.219 13.605 13.055 12.701 12.734 25.01 3.41 -38.17 3.41 2 GCS9 UGCS J033903.49+212519.9 0.003 0.002 0.002 0.002 0.002 0.001 +03 57 14.880 +21 31 01.30 0 13.657 13.248 12.707 12.190 11.892 11.934 25.20 3.36 -39.29 3.36 2 GCS9 UGCS J035714.87+213101.3 0.002 0.002 0.002 0.001 0.001 0.001 +04 09 48.530 +24 54 00.50 0 15.210 14.746 14.051 13.310 12.940 21.67 4.44 -35.52 4.44 2 GCS9 UGCS J040948.53+245400.4 0.005 0.004 0.003 0.002 0.001 +03 43 43.710 +24 29 15.50 0 13.243 12.898 12.337 11.802 11.502 18.55 2.97 -50.23 2.97 2 GCS9 Cl* Melotte 22 DH 285 0.002 0.001 0.001 0.001 0.001 +03 38 24.800 +26 15 23.50 0 14.041 13.583 13.024 12.485 12.215 12.231 27.72 3.30 -43.66 3.30 2 GCS9 Cl* Melotte 22 DH 87 0.002 0.002 0.002 0.001 0.001 0.001 +04 05 41.700 +24 31 35.10 0 16.378 15.833 15.175 14.498 14.178 14.167 24.40 2.97 -34.22 2.97 2 GCS9 UGCS J040541.70+243135.0 0.007 0.005 0.005 0.005 0.005 0.003 +03 41 58.860 +26 12 21.10 0 13.697 13.292 12.754 12.161 11.889 11.902 14.33 3.00 -33.91 3.00 2 GCS9 V* KQ Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 33 32.390 +26 08 10.20 0 15.097 14.615 13.992 13.411 13.059 15.67 5.32 -27.22 5.32 2 GCS9 UGCS J033332.39+260810.1 0.004 0.003 0.003 0.003 0.002 +03 43 16.110 +24 22 28.00 0 16.600 15.927 15.220 14.697 14.293 14.308 13.35 2.16 -47.16 2.16 2 GCS9 UGCS J034316.11+242227.9 0.009 0.007 0.006 0.006 0.008 0.004 +03 49 36.320 +26 02 16.30 0 15.517 14.960 14.333 13.755 13.409 13.401 24.70 2.24 -41.43 2.24 2 GCS9 Cl* Melotte 22 DH 633 0.005 0.004 0.004 0.003 0.003 0.002 +03 46 52.970 +24 15 07.80 0 16.407 15.752 15.069 14.513 14.131 14.122 19.64 2.23 -36.47 2.23 2 GCS9 Cl* Melotte 22 MHO 7 0.008 0.006 0.005 0.004 0.005 0.003 +03 47 01.850 +24 13 28.10 0 16.253 15.613 14.933 14.410 14.020 14.022 15.79 2.23 -38.31 2.23 2 GCS9 Cl* Melotte 22 MHO 10 0.007 0.005 0.005 0.004 0.005 0.003 +03 47 15.460 +24 23 31.00 0 14.844 14.290 13.663 13.121 12.783 12.815 12.21 2.22 -46.33 2.22 2 GCS9 Cl* Melotte 22 MHO 12 0.003 0.003 0.003 0.002 0.002 0.001 +03 52 55.920 +24 57 41.80 0 16.326 15.757 15.109 14.535 14.152 14.141 12.56 2.25 -44.02 2.25 2 GCS9 Cl* Melotte 22 BPL 275 0.008 0.007 0.006 0.005 0.006 0.004 +03 44 25.580 +22 40 07.90 0 16.220 15.599 14.929 14.388 14.003 14.006 11.28 2.25 -40.25 2.25 2 GCS9 UGCS J034425.58+224007.8 0.008 0.005 0.005 0.004 0.005 0.003 +03 37 30.690 +24 50 54.30 0 14.798 14.416 13.808 13.323 13.004 12.978 19.62 2.49 -34.54 2.49 2 GCS9 Cl* Melotte 22 HHJ 110 0.003 0.003 0.003 0.002 0.002 0.002 +04 00 15.860 +25 01 46.30 0 13.460 13.089 12.543 11.954 11.659 11.681 10.70 2.89 -39.78 2.89 2 GCS9 UGCS J040015.86+250146.2 0.002 0.001 0.001 0.001 0.001 0.001 +03 49 05.180 +22 04 52.70 0 16.648 15.958 15.257 14.742 14.327 14.355 16.87 2.31 -38.67 2.31 2 GCS9 UGCS J034905.17+220452.6 0.011 0.008 0.007 0.006 0.007 0.005 +04 04 38.550 +21 46 47.70 0 13.894 13.477 12.956 12.429 12.123 12.120 29.74 5.07 -28.67 5.07 2 GCS9 UGCS J040438.55+214647.7 0.002 0.002 0.002 0.002 0.001 0.001 +04 07 29.340 +21 46 09.80 0 14.170 13.699 13.175 12.658 12.334 12.339 12.98 4.97 -51.85 4.97 2 GCS9 UGCS J040729.34+214609.8 0.002 0.002 0.002 0.002 0.001 0.001 +03 59 39.840 +21 54 27.70 0 13.543 13.158 12.617 12.028 11.764 11.777 10.82 2.84 -38.32 2.84 2 GCS9 UGCS J035939.84+215427.7 0.002 0.001 0.002 0.001 0.001 0.001 +03 40 11.980 +21 48 31.80 1 16.578 15.884 15.169 14.580 14.187 14.178 24.16 2.54 -40.67 2.54 2 GCS9 UGCS J034011.98+214831.7 0.010 0.007 0.007 0.005 0.006 0.004 +03 45 57.710 +24 03 04.90 0 15.821 15.269 14.671 14.132 13.755 13.740 14.71 2.23 -37.35 2.23 2 GCS9 Cl* Melotte 22 HHJ 27 0.006 0.005 0.004 0.003 0.004 0.003 +03 37 53.260 +20 23 01.30 0 14.549 14.107 13.550 13.014 12.698 12.732 23.35 3.86 -53.40 3.86 2 GCS9 UGCS J033753.25+202301.2 0.003 0.002 0.002 0.002 0.002 0.001 +03 32 00.120 +20 23 45.90 0 16.268 15.657 15.038 14.458 14.131 14.118 27.13 6.12 -30.46 6.12 2 GCS9 UGCS J033200.11+202345.8 0.007 0.005 0.005 0.004 0.005 0.003 +03 49 56.070 +20 22 52.30 0 14.520 14.010 13.453 12.888 12.591 12.601 27.18 3.42 -44.80 3.42 2 GCS9 UGCS J034956.07+202252.2 0.003 0.002 0.002 0.002 0.002 0.001 +03 46 26.090 +24 05 09.50 1 16.810 15.967 15.160 14.584 14.118 14.098 19.41 2.24 -38.71 2.24 2 GCS9 2MASS J03462608+2405096 0.011 0.006 0.005 0.004 0.005 0.003 +03 51 04.740 +20 14 06.10 0 14.534 14.114 13.550 12.881 12.645 12.663 15.57 3.42 -33.25 3.42 2 GCS9 UGCS J035104.73+201406.1 0.003 0.002 0.002 0.002 0.002 0.001 +03 50 33.870 +20 19 38.30 0 16.290 15.620 14.949 14.435 14.012 14.021 24.49 3.44 -42.26 3.44 2 GCS9 UGCS J035033.86+201938.3 0.007 0.005 0.005 0.004 0.004 0.004 +03 55 34.430 +23 58 28.30 0 15.140 14.664 14.047 13.501 13.196 11.66 2.31 -40.75 2.31 2 GCS9 Cl* Melotte 22 DH 818 0.004 0.003 0.003 0.003 0.002 +03 36 19.260 +24 10 52.30 0 13.345 13.022 12.518 11.979 11.704 11.709 25.71 2.60 -41.47 2.60 2 GCS9 UGCS J033619.26+241052.3 0.002 0.002 0.001 0.001 0.001 0.001 +03 42 40.230 +23 59 21.50 0 12.859 12.500 11.988 11.460 11.096 11.160 17.01 2.12 -46.73 2.12 2 GCS9 V* LO Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 28 35.630 +24 00 43.70 0 16.995 16.267 15.527 14.966 14.552 14.537 19.57 3.89 -36.94 3.89 2 GCS9 UGCS J032835.63+240043.7 0.010 0.007 0.007 0.006 0.007 0.005 +03 46 09.880 +21 05 52.40 0 13.956 13.557 12.996 12.456 12.173 12.162 25.89 2.65 -44.94 2.65 2 GCS9 Cl* Melotte 22 DH 425 0.002 0.002 0.002 0.001 0.001 0.001 +03 46 22.310 +21 05 20.00 0 16.232 15.519 14.873 14.327 13.935 13.935 22.61 2.67 -45.95 2.67 2 GCS9 UGCS J034622.31+210519.9 0.007 0.005 0.005 0.005 0.006 0.003 +03 57 52.350 +21 02 15.80 0 14.703 14.171 13.595 13.036 12.705 12.710 16.38 3.36 -34.99 3.36 2 GCS9 Cl* Melotte 22 DH 859 0.003 0.003 0.003 0.002 0.002 0.001 +03 40 35.330 +20 57 56.60 0 13.012 12.647 12.149 11.582 11.260 11.302 23.55 3.58 -31.81 3.58 2 GCS9 Cl* Melotte 22 DH 146 0.001 0.001 0.001 0.001 0.001 0.001 +03 32 35.580 +27 03 36.70 0 17.100 16.282 15.495 14.865 14.489 14.499 23.23 5.01 -52.46 5.01 2 GCS9 UGCS J033235.57+270336.6 0.010 0.007 0.006 0.009 0.006 0.005 +03 43 28.050 +26 29 55.50 0 17.173 16.372 15.618 15.060 14.682 14.649 17.49 3.01 -38.50 3.01 2 GCS9 UGCS J034328.04+262955.5 0.015 0.010 0.009 0.008 0.008 0.005 +03 36 53.300 +26 34 27.90 1 17.046 16.171 15.379 14.804 14.355 14.353 15.54 3.33 -34.04 3.33 2 GCS9 UGCS J033653.30+263427.9 0.012 0.008 0.007 0.006 0.006 0.004 +03 46 22.250 +23 52 26.60 1 17.120 16.323 15.518 14.917 14.474 14.472 17.65 2.25 -38.04 2.25 2 GCS9 Cl* Melotte 22 SHF 31 0.015 0.008 0.007 0.006 0.007 0.005 +04 00 11.720 +23 23 08.90 0 17.275 16.419 15.671 15.086 14.642 14.623 21.83 3.45 -37.29 3.45 2 GCS9 UGCS J040011.71+232308.9 0.014 0.009 0.008 0.008 0.009 0.006 +03 46 05.110 +23 45 34.90 1 17.229 16.347 15.522 14.851 14.385 14.404 15.88 2.25 -39.21 2.25 2 GCS9 Cl* Melotte 22 SHF 25 0.015 0.008 0.007 0.005 0.006 0.004 +03 58 00.620 +21 18 20.80 1 17.399 16.380 15.545 14.931 14.445 14.423 24.81 3.43 -32.13 3.43 2 GCS9 UGCS J035800.61+211820.8 0.015 0.009 0.007 0.006 0.007 0.006 +04 05 23.120 +22 29 03.30 0 17.120 16.364 15.605 15.015 14.577 14.567 24.92 5.10 -41.55 5.10 2 GCS9 UGCS J040523.12+222903.3 0.011 0.008 0.007 0.010 0.006 0.006 +03 47 20.480 +19 54 25.50 1 17.011 16.054 15.210 14.615 14.152 14.140 28.11 5.10 -39.39 5.10 2 GCS9 UGCS J034720.47+195425.5 0.010 0.006 0.006 0.006 0.005 0.004 +04 02 43.660 +22 43 42.50 0 17.125 16.400 15.711 15.021 14.640 14.615 22.00 3.44 -31.92 3.44 2 GCS9 UGCS J040243.65+224342.5 0.013 0.009 0.008 0.007 0.009 0.006 +03 40 45.160 +27 50 40.40 1 17.074 16.218 15.404 14.816 14.321 14.333 12.37 3.34 -40.37 3.34 2 GCS9 UGCS J034045.16+275040.4 0.012 0.008 0.007 0.007 0.007 0.005 +03 42 47.300 +23 00 40.30 0 17.056 16.290 15.554 14.991 14.575 14.576 14.60 2.31 -44.06 2.31 2 GCS9 UGCS J034247.29+230040.3 0.013 0.009 0.008 0.007 0.008 0.005 +03 45 19.730 +19 15 47.40 0 17.061 16.401 15.697 15.151 14.745 14.731 8.46 4.11 -32.05 4.11 2 GCS9 UGCS J034519.72+191547.4 0.010 0.008 0.008 0.007 0.009 0.006 +03 47 39.020 +24 36 22.20 0 17.112 16.271 15.548 14.992 14.570 14.554 15.20 2.24 -45.02 2.24 2 GCS9 2MASS J03473900+2436226 0.013 0.008 0.007 0.006 0.008 0.005 +03 51 05.970 +24 36 16.90 0 17.107 16.333 15.649 15.153 14.702 14.712 14.09 2.26 -37.47 2.26 2 GCS9 2MASS J03510597+2436171 0.013 0.008 0.007 0.007 0.009 0.006 +03 34 38.610 +24 51 28.50 1 17.097 16.247 15.459 14.920 14.445 14.466 13.39 2.69 -37.21 2.69 2 GCS9 UGCS J033438.61+245128.4 0.011 0.008 0.007 0.007 0.008 0.005 +04 09 17.800 +26 03 31.20 1 17.120 16.480 15.759 15.003 14.603 6.56 4.49 -33.73 4.49 2 GCS9 UGCS J040917.79+260331.2 0.013 0.010 0.009 0.006 0.005 +03 43 33.120 +27 55 49.60 0 17.880 16.894 16.092 15.463 15.044 15.026 17.70 4.09 -38.59 4.09 2 GCS9 UGCS J034333.12+275549.5 0.018 0.011 0.009 0.013 0.013 0.008 +04 00 18.830 +27 09 46.80 0 17.560 16.743 16.002 15.448 15.005 15.036 10.06 3.09 -34.16 3.09 2 GCS9 UGCS J040018.82+270946.7 0.019 0.012 0.012 0.009 0.010 0.009 +03 33 30.460 +27 32 55.40 0 17.238 16.553 15.809 15.260 14.841 14.846 18.69 3.90 -36.97 3.90 2 GCS9 UGCS J033330.46+273255.4 0.011 0.008 0.008 0.009 0.010 0.007 +04 04 24.850 +28 39 03.60 0 17.526 16.894 16.132 15.388 14.992 15.035 18.79 3.11 -35.18 3.11 2 GCS9 UGCS J040424.84+283903.5 0.016 0.013 0.011 0.008 0.010 0.008 +03 34 50.820 +25 34 46.40 0 17.916 16.901 16.095 15.494 15.066 21.10 5.70 -37.46 5.70 2 GCS9 UGCS J033450.81+253446.3 0.020 0.012 0.011 0.015 0.007 +04 03 01.220 +25 24 23.00 0 17.268 16.527 15.813 15.214 14.808 14.778 24.72 3.40 -37.90 3.40 2 GCS9 Cl* Melotte 22 DH 905 0.012 0.009 0.008 0.007 0.009 0.007 +03 52 15.630 +25 31 09.10 0 17.722 16.782 15.959 15.395 14.936 14.897 13.63 3.10 -42.16 3.10 2 GCS9 UGCS J035215.63+253109.1 0.019 0.012 0.010 0.010 0.010 0.008 +03 53 41.460 +26 42 22.30 0 17.559 16.958 16.192 15.590 15.213 15.193 20.55 3.12 -38.29 3.12 2 GCS9 UGCS J035341.45+264222.3 0.020 0.013 0.013 0.009 0.012 0.008 +03 48 38.370 +22 33 51.80 0 17.345 16.523 15.711 15.168 14.718 14.692 17.51 2.33 -38.63 2.33 2 GCS9 UGCS J034838.37+223351.7 0.017 0.011 0.008 0.008 0.009 0.006 +03 28 20.710 +25 15 40.00 0 17.594 16.841 16.097 15.503 15.097 11.14 7.37 -20.79 7.37 2 GCS9 UGCS J032820.71+251540.0 0.014 0.010 0.009 0.013 0.007 +03 58 22.560 +22 28 36.30 0 17.356 16.631 15.918 15.234 14.804 9.62 6.01 -58.05 6.01 2 GCS9 UGCS J035822.55+222836.3 0.014 0.010 0.009 0.012 0.008 +03 38 43.690 +24 11 24.50 0 17.599 16.762 15.913 15.343 14.861 14.851 24.38 2.57 -36.40 2.57 2 GCS9 UGCS J033843.68+241124.5 0.018 0.011 0.009 0.009 0.010 0.006 +03 40 35.500 +23 13 07.40 0 17.972 16.910 16.076 15.510 15.014 15.020 12.82 2.37 -43.97 2.37 2 GCS9 UGCS J034035.49+231307.3 0.024 0.014 0.012 0.011 0.013 0.008 +03 56 21.530 +23 08 41.50 0 17.934 16.982 16.151 15.536 15.055 15.042 18.32 3.20 -36.21 3.20 2 GCS9 UGCS J035621.52+230841.4 0.027 0.018 0.014 0.009 0.012 0.009 +03 56 40.930 +22 59 40.60 0 17.830 16.872 16.002 15.424 14.921 14.950 13.47 3.14 -35.44 3.14 2 GCS9 UGCS J035640.92+225940.5 0.027 0.015 0.011 0.008 0.009 0.007 +03 51 25.970 +20 36 12.10 0 17.108 16.526 15.822 15.270 14.881 14.835 15.46 3.52 -35.21 3.52 2 GCS9 UGCS J035125.96+203612.0 0.012 0.008 0.008 0.008 0.009 0.007 +03 37 27.990 +23 02 19.60 0 17.750 16.811 15.973 15.390 14.893 14.904 19.88 3.09 -49.46 3.09 2 GCS9 UGCS J033727.98+230219.5 0.019 0.012 0.010 0.009 0.010 0.007 +04 07 12.860 +27 26 05.40 0 17.420 16.620 15.882 15.297 14.866 14.886 11.86 3.06 -46.68 3.06 2 GCS9 UGCS J040712.86+272605.4 0.015 0.011 0.009 0.007 0.009 0.008 +03 31 17.910 +27 27 46.10 0 17.576 16.828 16.102 15.467 15.101 2.80 7.41 -33.43 7.41 2 GCS9 UGCS J033117.91+272746.1 0.014 0.010 0.010 0.014 0.007 +03 51 35.200 +19 26 25.80 0 17.395 16.730 16.037 15.430 15.101 15.089 13.46 4.25 -36.40 4.25 2 GCS9 UGCS J035135.19+192625.8 0.013 0.009 0.010 0.010 0.013 0.007 +03 48 19.020 +24 25 12.70 0 17.655 16.668 15.930 15.365 14.935 14.953 14.16 2.30 -39.48 2.30 2 GCS9 2MASS J03481900+2425130 0.020 0.011 0.009 0.008 0.011 0.007 +03 40 53.660 +28 21 11.50 1 18.880 17.570 16.490 15.852 15.194 15.199 13.91 4.06 -31.57 4.06 2 GCS9 UGCS J034053.65+282111.5 0.037 0.016 0.012 0.016 0.013 0.008 +03 34 04.410 +25 31 33.60 0 18.393 17.293 16.390 15.781 15.254 31.63 5.79 -38.18 5.79 2 GCS9 UGCS J033404.41+253133.5 0.029 0.015 0.012 0.018 0.008 +03 33 49.220 +19 59 52.00 1 18.539 18.104 16.453 15.767 15.433 15.492 1.67 6.44 -40.37 6.44 2 GCS9 UGCS J033349.21+195952.0 0.032 0.033 0.014 0.012 0.015 0.010 +03 57 30.820 +23 41 22.60 0 18.532 17.359 16.440 15.839 15.265 15.297 20.84 3.11 -46.90 3.11 2 GCS9 UGCS J035730.82+234122.5 0.032 0.018 0.013 0.013 0.013 0.009 +04 07 10.040 +24 12 32.40 0 18.235 17.375 16.480 15.868 15.404 15.382 20.30 3.50 -47.43 3.50 2 GCS9 UGCS J040710.04+241232.3 0.025 0.015 0.013 0.012 0.014 0.009 +03 39 14.770 +23 23 31.70 0 18.455 17.428 16.499 15.853 15.312 15.326 17.63 3.27 -35.01 3.27 2 GCS9 UGCS J033914.77+232331.7 0.034 0.019 0.015 0.013 0.014 0.011 +03 50 37.960 +23 03 05.00 0 18.420 17.267 16.354 15.734 15.191 15.195 15.37 2.36 -44.53 2.36 2 GCS9 UGCS J035037.95+230305.0 0.033 0.017 0.013 0.014 0.011 0.009 +03 36 03.850 +22 52 03.50 1 18.072 16.873 15.913 15.240 14.679 14.679 22.46 3.15 -46.19 3.15 2 GCS9 UGCS J033603.84+225203.4 0.026 0.014 0.010 0.008 0.008 0.006 +03 43 40.310 +24 30 11.20 0 18.552 17.490 16.494 15.853 15.304 12.03 3.27 -44.27 3.27 2 GCS9 2MASS J03434028+2430113 0.038 0.021 0.014 0.015 0.010 +03 52 18.640 +24 04 28.10 0 18.327 17.280 16.395 15.738 15.202 15.236 15.37 2.40 -46.07 2.40 2 GCS9 2MASS J03521864+2404283 0.031 0.019 0.015 0.014 0.015 0.010 +03 45 45.360 +27 59 27.60 0 19.568 18.074 17.104 16.454 15.870 15.859 22.97 3.88 -34.58 3.88 2 GCS9 UGCS J034545.35+275927.5 0.100 0.036 0.028 0.025 0.023 0.015 +03 53 00.850 +28 39 04.10 0 20.062 19.110 17.754 17.030 16.313 16.293 15.53 6.33 -39.81 6.33 2 GCS9 UGCS J035300.85+283904.1 0.100 0.060 0.035 0.039 0.038 0.026 +03 45 24.330 +29 23 10.90 0 19.979 18.833 17.716 17.085 16.422 16.407 4.16 7.63 -22.76 7.63 2 GCS9 UGCS J034524.32+292310.8 0.103 0.053 0.032 0.029 0.033 0.025 +03 49 16.680 +27 40 21.80 0 18.545 17.513 16.598 15.957 15.425 15.434 17.98 3.18 -33.47 3.18 2 GCS9 UGCS J034916.67+274021.8 0.031 0.018 0.015 0.016 0.017 0.015 +03 54 06.750 +25 37 45.30 0 18.855 17.669 16.724 16.098 15.522 15.498 15.81 3.44 -37.94 3.44 2 GCS9 UGCS J035406.75+253745.3 0.045 0.026 0.018 0.019 0.017 0.013 +03 50 08.270 +25 30 51.70 0 19.614 18.280 17.223 16.594 16.008 15.983 13.87 2.83 -45.74 2.83 2 GCS9 UGCS J035008.27+253051.6 0.108 0.041 0.027 0.027 0.026 0.020 +03 47 04.410 +24 47 27.30 0 20.675 19.510 18.057 17.121 16.518 16.537 14.19 3.20 -39.02 3.20 2 GCS9 UGCS J034704.41+244727.3 0.249 0.101 0.041 0.034 0.045 0.027 +03 47 46.780 +25 35 16.60 0 20.351 18.565 17.424 16.687 16.154 16.095 18.60 3.00 -37.57 3.00 2 GCS9 UGCS J034746.77+253516.5 0.168 0.059 0.033 0.033 0.032 0.022 +03 51 47.660 +24 39 58.90 0 20.158 18.804 17.528 16.773 16.100 16.094 14.64 2.71 -40.22 2.71 2 GCS9 UGCS J035147.65+243958.9 0.157 0.053 0.028 0.026 0.029 0.019 +03 56 16.380 +23 54 51.30 0 18.685 17.753 16.776 16.182 15.634 15.596 9.87 3.22 -39.24 3.22 2 GCS9 UGCS J035616.37+235451.3 0.036 0.024 0.017 0.019 0.018 0.012 +03 58 05.150 +22 17 27.90 0 20.161 18.583 17.333 16.594 15.971 15.972 23.29 3.91 -48.55 3.91 2 GCS9 UGCS J035805.15+221727.8 0.124 0.043 0.026 0.039 0.020 0.019 +03 48 27.360 +23 46 16.20 0 20.628 19.658 18.134 17.347 16.505 16.519 18.01 2.93 -43.64 2.93 2 GCS9 UGCS J034827.36+234616.2 0.246 0.114 0.045 0.038 0.039 0.027 +03 40 06.820 +25 15 49.20 0 18.988 17.792 16.838 16.183 15.664 15.610 20.86 2.86 -34.63 2.86 2 GCS9 UGCS J034006.81+251549.2 0.053 0.025 0.018 0.021 0.021 0.012 +03 55 27.630 +25 49 40.60 0 18.871 17.904 16.953 16.336 15.855 15.810 21.98 3.53 -52.06 3.53 2 GCS9 UGCS J035527.62+254940.6 0.048 0.027 0.020 0.017 0.022 0.017 +03 56 44.750 +25 30 10.70 0 18.802 17.819 16.859 16.230 15.664 15.687 25.10 3.01 -34.70 3.01 2 GCS9 UGCS J035644.74+253010.6 0.049 0.029 0.021 0.015 0.018 0.015 +03 45 04.410 +24 15 16.60 0 20.479 19.040 17.775 16.979 16.350 16.230 20.21 2.72 -38.78 2.72 2 GCS9 UGCS J034504.41+241516.6 0.203 0.070 0.036 0.031 0.037 0.023 +03 33 00.000 +24 18 24.70 0 20.076 18.520 17.447 16.641 16.125 16.084 18.97 3.41 -44.11 3.41 2 GCS9 UGCS J033259.99+241824.6 0.128 0.050 0.033 0.033 0.040 0.022 +03 56 00.910 +22 29 08.70 0 18.397 17.477 16.589 15.928 15.448 15.432 21.10 2.75 -37.24 2.75 2 GCS9 UGCS J035600.91+222908.7 0.033 0.019 0.015 0.012 0.015 0.011 +03 36 18.940 +23 33 17.70 0 20.060 18.441 17.328 16.631 16.003 16.073 17.36 3.52 -38.71 3.52 2 GCS9 UGCS J033618.93+233317.7 0.135 0.050 0.030 0.032 0.034 0.020 +03 47 48.910 +24 17 06.50 0 20.292 19.272 17.873 17.039 16.410 16.385 13.21 2.89 -36.93 2.89 2 GCS9 UGCS J034748.90+241706.5 0.177 0.075 0.033 0.029 0.036 0.025 +03 43 50.160 +24 12 29.70 0 20.264 18.895 17.579 16.861 16.149 20.17 3.10 -36.90 3.10 2 GCS9 UGCS J034350.15+241229.7 0.147 0.065 0.029 0.034 0.020 +03 44 05.250 +22 50 13.50 0 20.066 18.839 17.666 16.895 16.134 16.158 19.70 3.13 -45.22 3.13 2 GCS9 Cl* Melotte 22 IPL 57 0.147 0.066 0.039 0.035 0.032 0.021 +03 48 11.270 +23 17 25.20 0 19.477 18.335 17.220 16.665 16.007 16.019 24.70 3.10 -48.41 3.10 2 GCS9 UGCS J034811.27+231725.2 0.085 0.041 0.028 0.032 0.025 0.022 +03 27 04.710 +24 40 13.70 0 20.770 19.508 18.154 17.395 16.720 -20.28 14.33 -21.25 14.33 2 GCS9 UGCS J032704.71+244013.7 0.190 0.097 0.051 0.072 0.031 +03 44 18.350 +23 15 22.30 0 18.547 17.519 16.552 15.957 15.400 15.438 14.83 2.46 -47.82 2.46 2 GCS9 UGCS J034418.34+231522.3 0.040 0.020 0.015 0.014 0.016 0.010 +03 54 14.070 +23 17 51.90 0 20.028 18.873 17.601 16.818 16.138 16.100 17.07 3.03 -38.21 3.03 2 GCS9 UGCS J035414.06+231751.9 0.136 0.062 0.035 0.031 0.030 0.021 +03 46 55.490 +23 11 16.00 0 20.373 19.064 17.892 17.144 16.423 16.403 18.24 3.27 -33.97 3.27 2 GCS9 UGCS J034655.48+231116.0 0.180 0.066 0.041 0.032 0.038 0.026 +03 48 30.740 +22 44 50.20 0 20.389 18.936 17.714 16.861 16.251 16.150 11.34 3.40 -38.38 3.40 2 GCS9 UGCS J034830.74+224450.2 0.193 0.065 0.039 0.035 0.027 0.022 +03 48 31.530 +24 34 37.20 1 19.218 17.883 16.715 15.977 15.357 15.312 11.92 2.37 -46.68 2.37 2 GCS9 UGCS J034831.52+243437.2 0.065 0.025 0.015 0.012 0.014 0.009 +03 51 05.220 +23 15 37.70 0 20.207 19.022 17.842 17.041 16.374 16.313 17.05 3.13 -40.03 3.13 2 GCS9 UGCS J035105.21+231537.7 0.152 0.065 0.035 0.040 0.037 0.024 +03 52 02.100 +23 15 45.40 0 18.708 17.679 16.659 16.057 15.473 15.529 12.75 2.53 -39.96 2.53 2 GCS9 2MASS J03520209+2315451 0.043 0.022 0.014 0.017 0.017 0.012 +03 43 07.150 +20 41 57.00 0 19.556 18.299 17.188 16.411 15.877 15.884 23.63 4.17 -37.64 4.17 2 GCS9 UGCS J034307.15+204156.9 0.071 0.033 0.023 0.022 0.024 0.021 +03 33 30.240 +21 20 50.50 0 19.630 18.597 17.546 16.802 16.378 16.403 3.11 5.57 -44.71 5.57 2 GCS9 UGCS J033330.23+212050.5 0.069 0.040 0.028 0.032 0.035 0.026 +03 54 12.540 +19 15 05.80 0 20.321 18.802 17.562 16.719 16.048 16.072 7.03 6.90 -37.98 6.90 2 GCS9 UGCS J035412.54+191505.7 0.124 0.044 0.028 0.021 0.022 0.016 +03 39 18.810 +24 27 37.80 0 20.092 18.529 17.384 16.652 15.944 16.010 21.70 3.21 -42.51 3.21 2 GCS9 UGCS J033918.80+242737.8 0.132 0.052 0.031 0.033 0.028 0.018 +03 58 31.810 +26 47 38.70 0 20.342 18.971 17.810 17.035 16.342 16.469 14.01 3.98 -35.22 3.98 2 GCS9 UGCS J035831.80+264738.7 0.124 0.056 0.034 0.048 0.044 0.027 +03 38 05.890 +26 15 03.70 0 20.001 18.573 17.436 16.664 16.051 16.064 19.43 3.91 -39.39 3.91 2 GCS9 UGCS J033805.88+261503.7 0.126 0.049 0.030 0.025 0.027 0.018 +03 48 40.630 +26 17 09.70 0 20.203 18.726 17.655 17.168 16.407 16.359 19.80 4.09 -32.05 4.09 2 GCS9 UGCS J034840.63+261709.7 0.148 0.066 0.039 0.046 0.036 0.022 +03 32 58.900 +26 08 38.40 0 20.923 19.834 18.160 17.335 16.613 2.05 10.05 -30.53 10.05 2 GCS9 UGCS J033258.89+260838.3 0.244 0.133 0.055 0.077 0.030 +03 46 32.130 +24 23 14.60 0 19.257 18.083 17.049 16.373 15.849 15.766 17.62 2.43 -39.19 2.43 2 GCS9 Cl* Melotte 22 SHF 34 0.066 0.027 0.018 0.017 0.021 0.014 +03 46 20.270 +23 58 18.90 1 19.259 18.174 17.034 16.269 15.650 15.585 12.97 2.40 -35.00 2.40 2 GCS9 UGCS J034620.27+235818.8 0.146 0.047 0.023 0.017 0.018 0.012 +03 44 18.230 +24 05 47.20 0 20.411 19.235 17.772 16.984 16.316 20.09 3.23 -35.92 3.23 2 GCS9 UGCS J034418.23+240547.1 0.182 0.080 0.035 0.038 0.023 +03 34 34.800 +27 40 36.50 0 12.822 12.654 12.246 11.697 11.560 11.604 19.82 3.74 -37.63 3.74 0.83 1 GCS9 UGCS J033434.79+274036.5 0.001 0.001 0.001 0.001 0.001 0.001 +03 52 14.060 +28 24 40.90 0 12.345 12.198 11.835 11.446 11.311 11.332 15.22 2.93 -38.30 2.93 0.73 1 GCS9 UGCS J035214.06+282440.8 0.001 0.001 0.001 0.001 0.001 0.001 +03 58 18.450 +25 33 56.00 0 12.415 12.278 11.950 11.653 11.462 11.478 14.63 2.47 -38.40 2.47 0.69 1 GCS9 UGCS J035818.45+253356.0 0.001 0.001 0.001 0.001 0.001 0.001 +03 26 13.750 +25 27 46.80 0 12.564 12.357 11.926 11.507 11.265 20.99 6.87 -37.32 6.87 0.79 1 GCS9 UGCS J032613.74+252746.7 0.001 0.001 0.001 0.001 0.001 +03 33 38.020 +25 20 16.90 0 12.908 12.498 11.930 11.512 11.225 24.95 5.30 -41.14 5.30 0.65 1 GCS9 UGCS J033338.02+252016.8 0.001 0.001 0.001 0.001 0.001 +03 44 30.080 +25 35 46.80 0 12.678 11.857 11.349 11.013 11.058 18.21 2.27 -37.32 2.27 0.81 1 GCS9 V* V513 Tau 0.001 0.001 0.001 0.001 0.000 +03 43 05.540 +24 49 28.30 0 12.492 12.121 11.627 11.517 11.107 21.32 2.97 -42.76 2.97 0.86 1 GCS9 V* LS Tau 0.001 0.001 0.001 0.001 0.000 +03 43 52.150 +24 50 29.50 0 12.141 11.914 11.457 11.366 10.988 18.96 2.97 -35.05 2.97 0.60 1 GCS9 V* MR Tau 0.001 0.001 0.001 0.001 0.000 +03 49 02.350 +25 43 24.10 0 12.899 12.711 12.370 11.912 11.751 11.756 13.74 2.23 -39.88 2.23 0.66 1 GCS9 Cl* Melotte 22 DH 600 0.001 0.001 0.001 0.001 0.001 0.001 +03 42 02.850 +22 22 42.90 0 12.613 12.430 12.021 11.494 11.211 18.81 7.95 -45.14 7.95 0.79 1 GCS9 Cl* Melotte 22 SK 711 0.001 0.001 0.001 0.001 0.001 +03 52 42.510 +25 07 02.70 0 12.809 12.594 12.168 11.644 11.471 11.491 22.89 2.23 -36.88 2.23 0.67 1 GCS9 Cl* Melotte 22 SRS 33701 0.001 0.001 0.001 0.001 0.001 0.001 +03 52 10.580 +29 01 12.10 0 12.489 12.328 11.917 11.584 11.201 11.269 21.59 3.07 -35.82 3.07 0.64 1 GCS9 UGCS J035210.58+290112.1 0.001 0.001 0.001 0.001 0.001 0.001 +03 41 57.740 +23 40 05.80 0 12.352 12.196 11.846 11.578 11.384 11.414 14.80 2.12 -38.46 2.12 0.71 1 GCS9 Cl* Melotte 22 SRS 83446 0.001 0.001 0.001 0.001 0.001 0.001 +03 55 32.840 +23 19 08.00 0 12.548 12.391 11.984 11.529 11.394 11.417 25.21 2.25 -40.35 2.25 0.61 1 GCS9 Cl* Melotte 22 DH 816 0.001 0.001 0.001 0.001 0.001 0.001 +03 48 10.170 +23 00 03.90 0 12.412 12.198 11.771 11.631 10.997 11.133 18.14 2.23 -35.64 2.23 0.66 1 GCS9 Cl* Melotte 22 DH 554 0.001 0.001 0.001 0.001 0.001 0.001 +03 56 08.590 +21 45 47.70 0 12.574 11.870 11.320 11.082 11.146 18.71 2.62 -38.21 2.62 0.85 1 GCS9 V* V581 Tau 0.001 0.001 0.001 0.001 0.001 +03 53 45.760 +21 48 52.50 0 12.830 12.662 12.296 11.818 11.689 11.729 19.80 2.50 -39.40 2.50 0.88 1 GCS9 UGCS J035345.75+214852.5 0.001 0.001 0.001 0.001 0.001 0.001 +03 54 34.800 +21 53 01.70 0 12.781 11.982 11.442 11.104 11.183 19.60 2.62 -38.65 2.62 0.87 1 GCS9 V* V577 Tau 0.001 0.001 0.001 0.001 0.001 +03 43 42.140 +24 34 23.10 0 12.680 12.347 11.834 11.451 11.133 21.23 2.97 -39.67 2.97 0.87 1 GCS9 V* MO Tau 0.001 0.001 0.001 0.001 0.001 +04 00 59.070 +24 08 32.20 0 12.785 12.566 12.116 11.564 11.384 13.85 3.54 -39.58 3.54 0.66 1 GCS9 UGCS J040059.07+240832.2 0.001 0.001 0.001 0.001 0.001 +03 46 12.880 +24 03 15.60 0 12.012 11.833 11.399 11.455 10.715 11.045 22.36 2.22 -38.68 2.22 0.81 1 GCS9 V* V1189 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 45 44.080 +24 04 26.60 0 12.115 11.903 11.473 11.507 10.786 11.052 20.45 2.22 -36.83 2.22 0.78 1 GCS9 V* V787 Tau 0.001 0.001 0.001 0.001 0.001 0.000 +03 42 10.930 +24 05 08.40 0 12.830 12.470 11.954 11.688 11.337 16.83 2.30 -39.37 2.30 0.85 1 GCS9 V* LM Tau 0.001 0.001 0.001 0.001 0.001 +04 00 21.250 +28 16 49.70 0 13.141 12.934 12.512 12.011 11.880 11.905 21.55 3.86 -37.30 3.86 0.64 1 GCS9 UGCS J040021.24+281649.7 0.001 0.001 0.001 0.001 0.001 0.001 +03 51 47.460 +28 26 24.60 0 13.148 12.951 12.542 11.990 11.840 11.865 13.41 2.93 -47.11 2.93 0.68 1 GCS9 UGCS J035147.45+282624.6 0.001 0.001 0.001 0.001 0.001 0.001 +04 00 02.390 +25 39 34.00 0 13.797 13.476 12.981 12.320 12.070 12.076 20.75 3.31 -46.98 3.31 0.85 1 GCS9 UGCS J040002.38+253933.9 0.002 0.002 0.002 0.001 0.001 0.001 +03 37 18.450 +25 03 44.90 0 13.853 13.520 13.052 12.497 12.198 12.229 21.92 2.48 -45.60 2.48 0.86 1 GCS9 UGCS J033718.45+250344.8 0.002 0.002 0.002 0.002 0.001 0.001 +03 36 47.500 +26 38 27.60 0 13.112 12.943 12.541 12.080 11.931 11.931 18.09 3.30 -37.78 3.30 0.79 1 GCS9 UGCS J033647.50+263827.5 0.002 0.001 0.001 0.001 0.001 0.001 +04 00 49.330 +25 12 10.60 0 13.940 13.688 13.208 12.567 12.370 12.375 19.90 2.89 -39.87 2.89 0.89 1 GCS9 Cl* Melotte 22 DH 895 0.002 0.002 0.002 0.001 0.002 0.001 +03 54 48.000 +25 12 30.20 0 13.344 13.058 12.571 11.963 11.714 11.736 19.70 2.23 -39.12 2.23 0.87 1 GCS9 Cl* Melotte 22 BPL 318 0.002 0.002 0.002 0.001 0.001 0.001 +03 35 44.280 +22 27 09.70 0 13.612 13.310 12.830 12.275 11.996 12.017 19.82 2.93 -40.16 2.93 0.90 1 GCS9 Cl* Melotte 22 DH 47 0.002 0.002 0.002 0.001 0.001 0.001 +03 33 40.840 +20 09 48.00 0 13.488 13.221 12.781 12.282 12.017 12.032 21.02 6.09 -45.62 6.09 0.89 1 GCS9 UGCS J033340.83+200948.0 0.002 0.001 0.002 0.001 0.001 0.001 +03 36 29.050 +20 11 19.60 0 13.927 13.706 13.216 12.603 12.461 12.464 16.96 3.85 -40.17 3.85 0.90 1 GCS9 UGCS J033629.05+201119.6 0.002 0.002 0.002 0.001 0.002 0.001 +03 59 25.170 +24 14 56.40 0 13.930 13.724 13.271 12.606 12.467 12.483 16.99 2.96 -44.63 2.96 0.93 1 GCS9 UGCS J035925.17+241456.3 0.002 0.002 0.002 0.002 0.002 0.001 +03 37 17.950 +22 28 18.00 0 13.830 13.597 13.115 12.467 12.257 12.268 20.09 2.94 -37.15 2.94 0.70 1 GCS9 UGCS J033717.94+222817.9 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 54.520 +23 33 31.30 0 13.694 13.230 12.636 12.103 11.768 11.791 15.96 2.24 -48.77 2.24 0.70 1 GCS9 V* V389 Tau 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 03.830 +22 50 37.90 0 13.298 13.029 12.569 11.989 11.797 11.789 15.03 2.13 -40.74 2.13 0.87 1 GCS9 UGCS J035103.83+225037.9 0.002 0.001 0.002 0.001 0.001 0.001 +03 43 24.400 +23 13 30.20 0 13.528 12.672 12.085 11.771 11.765 20.41 2.30 -41.63 2.30 0.92 1 GCS9 UGCS J034324.40+231330.1 0.002 0.002 0.001 0.001 0.001 +03 32 15.300 +20 47 41.70 0 13.718 13.478 13.020 12.433 12.215 12.228 17.60 6.09 -40.17 6.09 0.91 1 GCS9 UGCS J033215.29+204741.6 0.002 0.002 0.002 0.001 0.001 0.001 +04 03 24.940 +22 52 17.00 0 13.410 13.177 12.786 12.268 12.138 12.178 17.60 3.35 -40.68 3.35 0.92 1 GCS9 Cl* Melotte 22 DH 907 0.002 0.002 0.002 0.001 0.001 0.001 +03 56 10.400 +23 02 23.70 0 13.356 12.519 11.944 11.625 11.650 15.48 3.06 -38.16 3.06 0.74 1 GCS9 UGCS J035610.39+230223.7 0.002 0.002 0.001 0.001 0.001 +03 52 25.930 +21 50 31.50 0 13.817 13.529 13.062 12.397 12.193 12.161 16.43 2.51 -38.84 2.51 0.83 1 GCS9 Cl* Melotte 22 DH 752 0.002 0.002 0.002 0.001 0.001 0.001 +03 55 35.350 +27 46 29.00 0 13.589 13.393 12.952 12.392 12.253 12.273 20.70 3.30 -40.12 3.30 0.89 1 GCS9 UGCS J035535.35+274628.9 0.002 0.002 0.002 0.001 0.001 0.001 +03 35 38.230 +21 51 25.40 0 13.279 13.079 12.659 12.077 11.911 11.936 19.33 2.93 -40.04 2.93 0.90 1 GCS9 UGCS J033538.23+215125.4 0.002 0.001 0.002 0.001 0.001 0.001 +03 47 21.730 +19 18 28.70 0 13.854 13.603 13.121 12.524 12.312 12.319 17.93 5.04 -48.08 5.04 0.82 1 GCS9 UGCS J034721.73+191828.6 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 51.900 +21 49 21.70 0 13.872 13.425 12.851 12.281 12.493 12.027 24.20 2.51 -40.53 2.51 0.69 1 GCS9 UGCS J035151.89+214921.6 0.002 0.002 0.002 0.001 0.002 0.001 +03 54 00.710 +23 58 59.80 0 13.647 13.247 12.690 12.150 11.841 11.858 18.82 2.24 -49.02 2.24 0.73 1 GCS9 Cl* Melotte 22 BPL 298 0.002 0.002 0.002 0.001 0.001 0.001 +03 51 31.610 +29 15 13.10 0 14.394 14.080 13.598 12.909 12.733 12.731 19.57 3.07 -37.15 3.07 0.76 1 GCS9 UGCS J035131.60+291513.1 0.003 0.002 0.002 0.002 0.002 0.001 +03 29 19.780 +27 12 30.00 0 14.890 14.572 14.060 13.447 13.169 16.72 6.94 -44.58 6.94 0.93 1 GCS9 UGCS J032919.78+271229.9 0.003 0.003 0.003 0.003 0.002 +04 00 28.350 +27 20 54.50 0 14.954 14.552 14.004 13.379 13.085 13.092 16.55 2.95 -38.17 2.95 0.81 1 GCS9 Cl* Melotte 22 DH 893 0.004 0.003 0.003 0.002 0.002 0.002 +03 43 47.710 +28 15 59.30 0 14.415 14.156 13.720 13.100 12.988 12.927 15.96 3.69 -40.79 3.69 0.90 1 GCS9 Cl* Melotte 22 DH 289 0.003 0.002 0.002 0.002 0.002 0.001 +03 53 49.640 +27 30 40.10 0 14.731 14.415 13.869 13.241 12.976 12.976 24.57 3.30 -41.14 3.30 0.74 1 GCS9 UGCS J035349.63+273040.0 0.003 0.003 0.003 0.002 0.002 0.003 +03 59 05.730 +26 24 26.60 0 14.015 13.667 13.180 12.570 12.328 12.338 18.44 2.48 -40.89 2.48 0.93 1 GCS9 Cl* Melotte 22 DH 871 0.002 0.002 0.002 0.002 0.002 0.001 +03 39 43.060 +28 31 56.40 0 14.162 13.895 13.403 12.759 12.563 12.544 20.48 4.93 -36.37 4.93 0.64 1 GCS9 UGCS J033943.05+283156.4 0.002 0.002 0.003 0.002 0.002 0.001 +03 58 28.350 +26 12 55.90 0 14.361 14.017 13.521 12.968 12.697 12.687 20.39 2.48 -41.41 2.48 0.93 1 GCS9 UGCS J035828.34+261255.8 0.002 0.002 0.002 0.002 0.002 0.001 +03 49 56.770 +25 22 22.60 0 14.259 13.890 13.375 12.874 12.557 12.584 16.93 2.23 -43.91 2.23 0.93 1 GCS9 Cl* Melotte 22 DH 643 0.003 0.002 0.002 0.002 0.002 0.001 +03 30 22.480 +26 24 32.00 0 14.792 14.449 13.980 13.319 13.102 18.94 6.94 -40.18 6.94 0.92 1 GCS9 UGCS J033022.47+262432.0 0.003 0.003 0.003 0.003 0.002 +03 47 45.920 +24 38 01.30 0 14.579 14.033 13.437 12.860 12.493 12.514 19.47 2.21 -49.88 2.21 0.62 1 GCS9 V* QZ Tau 0.003 0.002 0.002 0.002 0.002 0.001 +03 54 03.200 +25 54 51.90 0 14.437 14.189 13.723 13.123 12.856 12.865 12.69 2.94 -39.94 2.94 0.66 1 GCS9 UGCS J035403.20+255451.9 0.003 0.003 0.003 0.002 0.002 0.002 +03 52 06.670 +22 01 14.30 0 14.997 14.501 13.944 13.407 13.089 13.080 19.27 2.51 -49.89 2.51 0.62 1 GCS9 Cl* Melotte 22 DH 741 0.004 0.003 0.003 0.003 0.003 0.002 +03 45 02.710 +21 18 39.70 0 14.934 14.640 14.136 13.464 13.315 13.317 22.56 2.65 -46.52 2.65 0.82 1 GCS9 UGCS J034502.71+211839.6 0.003 0.003 0.003 0.002 0.003 0.002 +03 51 33.720 +29 00 34.70 0 14.788 14.525 14.036 13.372 13.189 13.178 12.05 3.07 -41.43 3.07 0.65 1 GCS9 UGCS J035133.72+290034.7 0.003 0.003 0.003 0.002 0.002 0.002 +03 42 19.290 +23 31 54.20 0 14.725 14.361 13.832 13.208 12.974 12.935 25.07 2.13 -45.62 2.13 0.64 1 GCS9 UGCS J034219.28+233154.1 0.003 0.003 0.003 0.002 0.003 0.001 +04 02 22.620 +24 48 24.00 0 14.717 14.421 13.944 13.270 13.053 13.046 15.08 2.90 -44.52 2.90 0.89 1 GCS9 Cl* Melotte 22 DH 903 0.003 0.003 0.003 0.002 0.002 0.002 +04 02 26.020 +20 01 20.20 0 14.492 14.212 13.752 13.090 12.861 12.872 11.66 3.13 -43.18 3.13 0.62 1 GCS9 UGCS J040226.01+200120.2 0.003 0.003 0.003 0.002 0.002 0.002 +04 00 14.110 +24 48 51.40 0 14.369 14.008 13.496 12.927 12.677 12.641 14.14 2.89 -46.60 2.89 0.77 1 GCS9 Cl* Melotte 22 DH 889 0.003 0.002 0.002 0.002 0.002 0.001 +03 57 21.710 +19 08 03.30 0 14.153 13.952 13.488 12.848 12.624 12.659 13.07 3.04 -47.13 3.04 0.63 1 GCS9 UGCS J035721.71+190803.2 0.002 0.002 0.002 0.002 0.002 0.001 +03 42 27.850 +20 36 34.50 0 14.470 14.181 13.664 13.020 12.780 12.791 20.91 3.42 -38.69 3.42 0.85 1 GCS9 UGCS J034227.84+203634.5 0.003 0.002 0.002 0.002 0.002 0.001 +03 44 44.440 +22 55 51.50 0 14.304 13.961 13.438 12.776 12.510 12.514 17.50 2.24 -39.82 2.24 0.91 1 GCS9 UGCS J034444.43+225551.4 0.003 0.002 0.002 0.002 0.002 0.001 +03 38 58.120 +21 39 37.00 0 14.879 14.515 13.971 13.347 13.097 13.072 12.89 3.41 -45.84 3.41 0.70 1 GCS9 UGCS J033858.11+213936.9 0.003 0.003 0.003 0.002 0.002 0.002 +03 39 53.530 +25 46 46.20 0 14.991 14.642 14.122 13.564 13.298 13.295 19.66 2.96 -45.57 2.96 0.92 1 GCS9 Cl* Melotte 22 DH 119 0.004 0.003 0.004 0.002 0.003 0.002 +03 37 22.250 +24 54 16.00 0 14.602 14.248 13.722 13.181 12.852 12.843 21.94 2.49 -40.27 2.49 0.88 1 GCS9 UGCS J033722.24+245415.9 0.003 0.003 0.003 0.002 0.002 0.001 +04 04 06.690 +24 06 38.90 0 14.458 14.219 13.754 13.172 13.023 13.019 18.45 3.38 -41.12 3.38 0.93 1 GCS9 UGCS J040406.69+240638.9 0.003 0.002 0.003 0.002 0.002 0.002 +03 47 59.670 +29 33 38.30 0 15.313 14.132 13.593 13.251 13.270 19.41 5.91 -43.49 5.91 0.89 1 GCS9 UGCS J034759.67+293338.3 0.004 0.003 0.002 0.003 0.002 +03 37 39.560 +27 58 59.00 0 15.396 15.042 14.537 13.913 13.660 13.707 17.33 4.96 -37.63 4.96 0.72 1 GCS9 UGCS J033739.56+275858.9 0.004 0.004 0.005 0.004 0.003 0.002 +04 01 54.700 +28 42 17.70 0 15.596 15.171 14.595 13.980 13.674 13.692 12.35 2.97 -40.08 2.97 0.60 1 GCS9 UGCS J040154.70+284217.7 0.005 0.004 0.004 0.003 0.004 0.003 +03 57 41.460 +28 16 35.80 0 15.528 15.146 14.578 13.949 13.678 13.674 15.59 3.87 -48.50 3.87 0.62 1 GCS9 UGCS J035741.46+281635.8 0.004 0.004 0.004 0.003 0.003 0.002 +03 51 29.350 +28 30 49.30 0 15.859 15.469 14.923 14.340 14.053 14.035 20.59 2.97 -37.30 2.97 0.63 1 GCS9 UGCS J035129.34+283049.2 0.005 0.004 0.005 0.004 0.005 0.003 +03 50 32.900 +26 42 57.20 0 15.534 15.123 14.574 13.937 13.670 13.669 21.11 2.97 -41.24 2.97 0.84 1 GCS9 UGCS J035032.90+264257.2 0.005 0.005 0.005 0.003 0.004 0.003 +04 05 57.780 +26 14 28.00 0 15.023 14.686 14.199 13.587 13.362 13.356 14.79 3.39 -37.90 3.39 0.65 1 GCS9 UGCS J040557.77+261428.0 0.004 0.003 0.003 0.003 0.003 0.002 +03 49 12.180 +25 20 31.70 0 15.633 15.245 14.696 14.123 13.823 13.846 16.17 2.24 -42.24 2.24 0.89 1 GCS9 UGCS J034912.17+252031.7 0.006 0.005 0.005 0.004 0.004 0.003 +03 47 10.210 +24 43 35.30 0 15.533 15.060 14.487 13.950 13.645 13.650 17.16 2.22 -43.48 2.22 0.90 1 GCS9 UGCS J034710.20+244335.3 0.005 0.004 0.004 0.003 0.004 0.002 +03 42 52.360 +26 29 09.40 0 15.538 15.176 14.662 14.108 13.827 13.821 18.20 2.95 -44.40 2.95 0.89 1 GCS9 UGCS J034252.35+262909.3 0.006 0.005 0.005 0.004 0.004 0.003 +03 45 36.120 +25 16 30.30 0 15.683 15.221 14.656 14.111 13.784 13.795 15.41 2.22 -40.16 2.22 0.83 1 GCS9 UGCS J034536.12+251630.2 0.005 0.004 0.004 0.003 0.004 0.003 +03 46 13.000 +27 02 11.70 0 15.061 14.740 14.217 13.668 13.401 13.421 18.46 2.95 -46.68 2.95 0.82 1 GCS9 Cl* Melotte 22 DH 430 0.004 0.004 0.004 0.003 0.003 0.002 +04 01 12.640 +23 50 20.20 0 15.772 15.257 14.698 14.117 13.795 17.28 3.56 -46.77 3.56 0.81 1 GCS9 UGCS J040112.64+235020.2 0.005 0.004 0.004 0.005 0.003 +03 38 10.270 +25 11 32.90 0 15.589 15.124 14.544 14.014 13.684 13.670 20.06 2.50 -43.22 2.50 0.88 1 GCS9 Cl* Melotte 22 HHJ 35 0.005 0.004 0.004 0.004 0.004 0.003 +03 56 11.670 +22 05 52.60 0 15.610 15.181 14.631 14.105 13.807 13.799 13.87 2.52 -42.35 2.52 0.81 1 GCS9 UGCS J035611.66+220552.5 0.005 0.005 0.005 0.003 0.004 0.003 +04 10 08.800 +25 45 52.30 0 15.752 14.719 14.169 13.826 13.848 15.83 3.05 -42.20 3.05 0.88 1 GCS9 UGCS J041008.80+254552.3 0.006 0.005 0.003 0.004 0.003 +04 04 11.640 +22 15 20.70 0 15.315 14.979 14.419 13.706 13.470 13.485 22.99 3.41 -38.88 3.41 0.60 1 GCS9 UGCS J040411.64+221520.6 0.004 0.003 0.004 0.003 0.003 0.002 +03 33 39.010 +22 21 08.50 0 15.193 14.805 14.251 13.715 13.455 13.430 16.11 3.42 -44.42 3.42 0.87 1 GCS9 Cl* Melotte 22 DH 29 0.004 0.003 0.003 0.003 0.003 0.002 +03 57 55.850 +21 16 10.80 0 15.354 14.971 14.436 13.805 13.526 13.536 16.86 3.37 -36.80 3.37 0.62 1 GCS9 Cl* Melotte 22 DH 860 0.004 0.004 0.004 0.003 0.003 0.003 +03 56 55.470 +22 08 24.30 0 15.110 14.685 14.152 13.553 13.236 13.258 18.72 2.85 -38.55 2.85 0.80 1 GCS9 Cl* Melotte 22 DH 847 0.004 0.003 0.003 0.003 0.002 0.002 +03 45 15.270 +28 49 08.30 0 15.861 15.413 14.849 14.234 13.940 13.925 16.55 5.87 -46.40 5.87 0.82 1 GCS9 UGCS J034515.27+284908.2 0.005 0.005 0.005 0.004 0.004 0.003 +03 54 16.800 +22 00 16.60 0 15.893 15.489 14.921 14.365 14.077 14.066 16.81 2.53 -37.22 2.53 0.67 1 GCS9 Cl* Melotte 22 DH 799 0.007 0.005 0.005 0.004 0.005 0.003 +03 38 06.920 +24 14 55.50 0 15.747 15.267 14.652 14.133 13.761 13.766 22.57 2.51 -42.72 2.51 0.78 1 GCS9 UGCS J033806.92+241455.5 0.005 0.005 0.004 0.004 0.004 0.003 +03 44 47.310 +19 55 41.40 0 15.884 15.400 14.799 14.273 13.946 13.957 20.01 4.01 -40.31 4.01 0.85 1 GCS9 Cl* Melotte 22 DH 354 0.005 0.004 0.004 0.004 0.005 0.003 +03 39 05.600 +24 12 41.20 0 15.799 15.460 14.905 14.352 14.029 14.047 14.71 2.51 -42.46 2.51 0.85 1 GCS9 UGCS J033905.60+241241.2 0.006 0.005 0.005 0.004 0.005 0.003 +03 58 23.360 +24 41 57.20 0 15.291 14.922 14.431 13.755 13.554 13.562 17.18 2.49 -38.00 2.49 0.76 1 GCS9 UGCS J035823.36+244157.2 0.005 0.004 0.004 0.003 0.003 0.002 +03 57 06.210 +23 13 00.70 0 15.484 14.319 13.773 13.434 13.441 15.53 3.07 -41.00 3.07 0.86 1 GCS9 UGCS J035706.21+231300.7 0.006 0.004 0.003 0.003 0.002 +04 08 59.010 +24 26 26.40 0 15.392 15.053 14.539 13.891 13.594 13.605 17.10 2.99 -46.14 2.99 0.84 1 GCS9 UGCS J040859.01+242626.3 0.005 0.004 0.004 0.003 0.003 0.002 +03 48 48.620 +24 30 15.40 0 15.488 15.114 14.569 13.991 13.692 13.689 21.84 2.21 -38.84 2.21 0.70 1 GCS9 UGCS J034848.61+243015.3 0.005 0.004 0.004 0.003 0.004 0.002 +03 34 41.090 +22 42 27.00 0 15.091 14.772 14.252 13.508 13.294 13.320 12.65 3.05 -42.76 3.05 0.72 1 GCS9 UGCS J033441.08+224226.9 0.004 0.003 0.003 0.002 0.003 0.002 +03 44 05.630 +23 03 42.40 0 15.169 14.803 14.258 13.565 13.302 13.294 17.87 2.26 -43.49 2.26 0.90 1 GCS9 UGCS J034405.62+230342.4 0.004 0.004 0.004 0.003 0.003 0.002 +03 29 48.420 +22 57 51.60 0 15.744 15.377 14.828 14.156 13.922 13.928 17.79 3.43 -41.86 3.43 0.90 1 GCS9 UGCS J032948.41+225751.6 0.005 0.004 0.005 0.004 0.004 0.003 +03 44 24.000 +21 24 20.80 0 15.184 14.768 14.212 13.673 13.353 13.359 16.88 2.66 -43.67 2.66 0.89 1 GCS9 Cl* Melotte 22 DH 332 0.004 0.003 0.003 0.003 0.004 0.002 +03 38 25.720 +21 39 49.20 0 15.288 14.944 14.425 13.874 13.590 13.648 16.06 3.42 -43.01 3.42 0.89 1 GCS9 UGCS J033825.71+213949.1 0.004 0.003 0.004 0.003 0.004 0.002 +03 54 06.960 +19 19 14.30 0 15.335 14.930 14.318 13.814 13.497 13.503 18.13 6.06 -42.85 6.06 0.90 1 GCS9 UGCS J035406.96+191914.2 0.004 0.003 0.003 0.003 0.003 0.002 +04 03 16.560 +24 35 19.60 0 15.089 14.701 14.123 13.564 13.268 13.248 21.78 2.90 -42.71 2.90 0.83 1 GCS9 Cl* Melotte 22 DH 906 0.004 0.003 0.003 0.003 0.003 0.002 +04 10 46.420 +24 32 13.90 0 15.786 15.329 14.767 14.099 13.762 13.766 15.88 2.99 -46.34 2.99 0.81 1 GCS9 UGCS J041046.41+243213.8 0.006 0.005 0.005 0.003 0.004 0.003 +03 45 36.750 +25 58 55.50 0 15.934 15.514 14.978 14.442 14.152 14.150 14.21 2.25 -38.02 2.25 0.62 1 GCS9 UGCS J034536.74+255855.5 0.006 0.006 0.006 0.005 0.006 0.004 +03 57 23.790 +24 08 50.00 0 15.309 14.982 14.445 13.865 13.611 13.611 13.28 2.97 -45.28 2.97 0.72 1 GCS9 UGCS J035723.79+240850.0 0.004 0.004 0.004 0.003 0.004 0.002 +03 27 27.940 +24 04 58.90 0 15.259 14.831 14.285 13.791 13.497 13.490 19.47 3.85 -48.39 3.85 0.66 1 GCS9 UGCS J032727.94+240458.8 0.004 0.003 0.003 0.003 0.003 0.002 +03 56 28.160 +28 03 48.20 0 16.015 15.511 14.948 14.304 14.050 14.022 16.65 3.90 -45.48 3.90 0.68 1 GCS9 UGCS J035628.16+280348.2 0.005 0.005 0.005 0.004 0.005 0.003 +03 39 03.310 +25 48 18.80 0 16.452 16.052 15.461 14.883 14.588 14.571 16.29 3.03 -46.33 3.03 0.62 1 GCS9 UGCS J033903.30+254818.8 0.010 0.008 0.008 0.006 0.007 0.005 +04 00 38.150 +19 49 22.40 0 16.562 16.095 15.532 15.014 14.678 14.708 14.99 3.08 -42.23 3.08 0.68 1 GCS9 UGCS J040038.14+194922.3 0.009 0.007 0.007 0.006 0.008 0.005 +03 45 03.180 +23 06 58.40 0 16.937 15.492 14.946 14.495 14.527 20.81 2.32 -40.36 2.32 0.62 1 GCS9 2MASS J03450316+2306586 0.012 0.007 0.006 0.008 0.005 +03 52 34.560 +24 25 22.20 0 16.121 15.669 15.096 14.512 14.246 14.236 16.91 2.25 -44.16 2.25 0.73 1 GCS9 UGCS J035234.55+242522.2 0.007 0.006 0.006 0.005 0.006 0.004 +03 30 35.390 +23 03 07.90 0 16.316 15.841 15.233 14.594 14.298 14.288 15.61 3.45 -45.70 3.45 0.63 1 GCS9 Cl* Melotte 22 DH 12 0.007 0.006 0.006 0.005 0.005 0.004 +03 40 28.370 +21 33 06.70 0 16.564 16.082 15.471 14.901 14.509 14.509 15.69 3.66 -45.19 3.66 0.66 1 GCS9 UGCS J034028.36+213306.7 0.008 0.007 0.007 0.009 0.006 0.005 +03 42 30.350 +25 34 26.80 0 17.540 16.951 16.269 15.675 15.305 15.293 15.12 2.38 -41.28 2.38 0.62 1 GCS9 UGCS J034230.35+253426.7 0.018 0.015 0.015 0.012 0.015 0.011 +03 51 17.870 +25 29 25.40 0 17.777 18.391 15.245 14.674 14.519 14.519 20.51 2.28 -39.66 2.28 0.65 1 GCS9 UGCS J035117.86+252925.4 0.022 0.048 0.006 0.006 0.008 0.005 +03 53 00.280 +25 40 08.50 0 17.276 16.722 16.169 15.583 15.273 15.241 18.78 3.13 -41.94 3.13 0.73 1 GCS9 UGCS J035300.28+254008.4 0.013 0.011 0.011 0.012 0.013 0.010 +04 06 09.290 +26 15 33.50 0 17.463 16.942 16.310 15.716 15.358 15.337 18.62 3.15 -39.15 3.15 0.64 1 GCS9 UGCS J040609.29+261533.5 0.016 0.014 0.013 0.010 0.013 0.010 +04 03 43.310 +23 56 52.90 0 17.197 16.623 15.998 15.428 15.061 15.045 15.09 3.44 -41.73 3.44 0.63 1 GCS9 UGCS J040343.31+235652.9 0.012 0.009 0.009 0.009 0.010 0.007 +03 36 57.480 +24 19 47.70 0 17.070 16.465 15.833 15.324 14.922 14.937 21.72 2.60 -40.73 2.60 0.64 1 GCS9 UGCS J033657.47+241947.7 0.012 0.010 0.009 0.010 0.012 0.008 +03 44 02.270 +28 51 32.30 0 17.372 16.793 16.123 15.540 15.176 15.169 19.81 6.04 -41.64 6.04 0.72 1 GCS9 UGCS J034402.26+285132.2 0.013 0.011 0.010 0.008 0.011 0.008 +04 03 04.580 +23 33 10.70 0 17.246 16.654 16.054 15.455 15.146 19.09 3.78 -42.76 3.78 0.73 1 GCS9 UGCS J040304.58+233310.6 0.013 0.010 0.010 0.014 0.009 +03 46 23.720 +22 50 16.40 0 17.310 15.710 15.189 14.722 14.694 20.26 2.35 -45.42 2.35 0.64 1 GCS9 2MASS J03462371+2250167 0.015 0.008 0.008 0.008 0.006 +04 11 03.840 +23 15 48.90 0 17.766 17.254 16.599 15.986 15.599 15.623 18.36 6.81 -46.34 6.81 0.61 1 GCS9 UGCS J041103.83+231548.9 0.017 0.014 0.014 0.017 0.021 0.012 +03 50 07.500 +19 37 06.20 0 17.466 16.910 16.332 15.761 15.401 15.371 17.29 4.51 -41.20 4.51 0.70 1 GCS9 UGCS J035007.50+193706.2 0.013 0.011 0.013 0.014 0.016 0.010 +03 37 45.480 +21 49 49.20 0 17.574 16.949 16.292 15.711 15.375 15.341 15.76 2.70 -40.52 2.70 0.63 1 GCS9 UGCS J033745.47+214949.2 0.019 0.014 0.014 0.011 0.016 0.010 +03 49 01.600 +24 54 30.20 0 17.757 17.163 16.566 16.016 15.638 15.597 20.82 2.38 -42.11 2.38 0.70 1 GCS9 UGCS J034901.60+245430.2 0.020 0.015 0.014 0.014 0.021 0.013 +03 43 53.770 +21 58 28.20 0 17.955 17.227 16.574 15.985 15.582 15.564 17.05 2.89 -42.45 2.89 0.71 1 GCS9 UGCS J034353.76+215828.1 0.024 0.018 0.015 0.017 0.019 0.017 +03 43 53.960 +21 58 24.40 0 17.255 16.562 15.936 15.332 14.986 14.994 20.06 2.65 -43.75 2.65 0.70 1 GCS9 UGCS J034353.95+215824.3 0.014 0.011 0.010 0.010 0.012 0.010 +03 59 40.860 +27 04 35.50 0 19.361 18.308 17.418 16.792 16.276 16.196 23.02 3.86 -48.45 3.86 0.63 1 GCS9 UGCS J035940.86+270435.4 0.083 0.044 0.038 0.029 0.032 0.026 +04 00 48.820 +28 32 53.60 0 19.819 18.841 18.112 17.507 16.848 16.975 23.80 5.55 -42.37 5.55 0.64 1 GCS9 UGCS J040048.81+283253.6 0.096 0.064 0.054 0.049 0.058 0.048 +04 10 54.540 +26 01 42.40 0 19.642 17.748 17.002 16.417 17.38 5.99 -47.11 5.99 0.66 1 GCS9 UGCS J041054.54+260142.3 0.091 0.036 0.027 0.022 +03 46 17.010 +27 55 27.80 0 19.334 18.660 17.847 17.282 17.030 17.052 16.50 7.11 -47.80 7.11 0.63 1 GCS9 UGCS J034617.01+275527.7 0.060 0.046 0.038 0.074 0.082 0.054 +03 49 04.260 +21 30 48.40 0 19.535 18.513 17.649 16.945 16.333 16.414 16.76 4.01 -42.72 4.01 0.65 1 GCS9 UGCS J034904.25+213048.3 0.082 0.042 0.035 0.027 0.032 0.026 +03 40 44.530 +28 27 22.00 0 20.858 19.520 18.607 17.739 17.353 17.491 13.92 12.21 -49.16 12.21 0.62 1 GCS9 UGCS J034044.53+282721.9 0.209 0.090 0.071 0.092 0.111 0.074 +04 03 33.870 +23 19 28.20 0 20.446 19.748 18.418 18.040 17.402 17.405 12.29 11.98 -53.62 11.98 0.60 1 GCS9 UGCS J040333.86+231928.2 0.145 0.111 0.065 0.114 0.117 0.063 +03 43 03.110 +19 55 13.30 0 20.210 19.510 18.492 18.006 17.591 17.575 17.22 11.92 -47.33 11.92 0.60 1 GCS9 UGCS J034303.11+195513.2 0.123 0.101 0.067 0.090 0.119 0.087 +03 25 30.920 +24 51 39.90 0 20.316 19.478 18.600 18.210 18.170 10.72 31.33 -45.76 31.33 0.60 1 GCS9 UGCS J032530.91+245139.8 0.126 0.096 0.079 0.160 0.120 +04 08 03.700 +25 03 18.60 0 20.585 19.668 18.717 17.693 17.074 7.72 8.93 -49.54 8.93 0.60 1 GCS9 UGCS J040803.69+250318.5 0.223 0.132 0.089 0.057 0.042 +03 41 51.050 +24 10 06.60 0 20.465 19.418 18.729 18.205 17.578 12.60 6.35 -49.41 6.35 0.62 1 GCS9 UGCS J034151.04+241006.5 0.190 0.108 0.093 0.133 0.082 +03 32 30.600 +27 09 39.20 0 20.343 18.971 18.172 16.968 17.036 7.52 10.85 -21.23 10.85 3 GCS9 UGCS J033230.59+270939.2 0.163 0.090 0.168 0.053 0.044 +03 29 01.870 +27 16 23.30 0 20.590 19.126 18.101 17.366 96.07 32.77 -88.72 32.77 3 GCS9 UGCS J032901.87+271623.2 0.208 0.117 0.144 0.049 +03 42 59.310 +25 37 38.90 0 19.814 18.238 17.388 16.646 16.572 10.48 3.76 -35.70 3.76 3 GCS9 UGCS J034259.30+253738.9 0.160 0.074 0.054 0.050 0.033 +03 35 28.030 +25 17 23.50 0 19.445 18.046 17.289 16.458 41.24 10.18 -21.88 10.18 3 GCS9 UGCS J033528.02+251723.4 0.094 0.048 0.072 0.025 +03 40 03.140 +25 40 05.50 0 19.571 18.217 17.364 16.488 16.514 13.38 5.06 -31.42 5.06 3 GCS9 UGCS J034003.14+254005.5 0.136 0.072 0.043 0.042 0.031 +03 33 24.060 +25 50 16.00 0 20.543 18.806 17.811 16.808 11.92 13.52 -32.48 13.52 3 GCS9 UGCS J033324.06+255015.9 0.245 0.094 0.114 0.036 +03 35 13.620 +25 07 52.90 0 19.550 18.161 17.249 16.645 16.523 24.66 4.39 -42.83 4.39 3 GCS9 UGCS J033513.62+250752.9 0.092 0.045 0.049 0.057 0.027 +03 48 15.650 +25 50 08.90 0 19.868 18.490 17.658 16.734 16.758 10.53 4.42 -48.92 4.42 3 GCS9 UGCS J034815.64+255008.9 0.161 0.074 0.071 0.053 0.039 +03 47 38.470 +23 56 27.70 0 19.964 18.359 17.478 16.688 16.588 13.93 3.28 -42.66 3.28 3 GCS9 UGCS J034738.47+235627.6 0.152 0.054 0.045 0.049 0.031 +03 28 30.710 +23 54 01.20 0 19.736 18.362 17.562 16.807 16.867 9.65 6.31 -32.88 6.31 3 GCS9 UGCS J032830.71+235401.2 0.108 0.055 0.054 0.051 0.038 +03 50 15.960 +24 23 28.60 0 20.125 18.728 17.791 16.895 16.836 19.25 3.46 -31.64 3.46 3 GCS9 UGCS J035015.96+242328.6 0.153 0.068 0.053 0.056 0.036 +03 45 33.300 +23 34 34.30 0 19.697 18.123 17.189 16.502 16.479 20.21 2.91 -35.76 2.91 3 GCS9 UGCS J034533.30+233434.2 0.120 0.044 0.034 0.039 0.026 +03 52 27.190 +23 12 08.00 0 19.317 18.006 17.090 16.359 16.438 23.09 3.70 -40.27 3.70 3 GCS9 UGCS J035227.18+231208.0 0.106 0.057 0.044 0.041 0.031 +03 45 14.520 +22 29 28.60 0 19.756 18.383 17.410 16.493 16.629 14.00 4.20 -53.10 4.20 3 GCS9 Cl* Melotte 22 IPL 69 0.132 0.063 0.064 0.043 0.030 +03 52 39.150 +24 46 29.40 0 19.271 18.066 17.106 16.509 16.474 18.41 3.31 -44.34 3.31 3 GCS9 UGCS J035239.15+244629.4 0.100 0.055 0.042 0.042 0.025 +03 46 29.110 +22 59 47.60 0 19.089 17.740 16.805 15.955 15.935 14.85 2.74 -37.94 2.74 3 GCS9 UGCS J034629.11+225947.6 0.074 0.040 0.027 0.025 0.017 +03 50 08.100 +23 00 16.60 0 20.272 18.537 17.648 16.732 16.829 12.67 6.26 -29.23 6.26 3 GCS9 UGCS J035008.10+230016.5 0.214 0.075 0.073 0.043 0.040 +03 53 18.940 +19 24 24.20 0 20.069 18.680 17.810 16.861 16.872 27.44 8.91 -38.90 8.91 3 GCS9 UGCS J035318.93+192424.1 0.147 0.084 0.085 0.069 0.036 +03 50 39.550 +25 02 54.60 0 19.786 18.225 17.358 16.563 16.529 19.29 3.10 -44.42 3.10 3 GCS9 UGCS J035039.54+250254.6 0.112 0.044 0.038 0.043 0.027 +03 51 29.470 +24 00 37.40 0 19.701 18.424 17.490 16.696 16.697 12.50 2.96 -40.44 2.96 3 GCS9 Cl* Melotte 22 PLIZ 161 0.107 0.052 0.041 0.047 0.031 +03 45 58.480 +23 41 53.90 0 18.591 17.562 16.741 16.714 19.16 3.22 -40.46 3.22 3 GCS9 Cl* Melotte 22 NPNPL 4 0.065 0.047 0.048 0.032 \ No newline at end of file diff --git a/docs/paper_examples/Begbie+26/cluster_data/asu (3).fit b/docs/paper_examples/Begbie+26/cluster_data/asu (3).fit new file mode 100644 index 00000000..95854e38 --- /dev/null +++ b/docs/paper_examples/Begbie+26/cluster_data/asu (3).fit @@ -0,0 +1,174 @@ +SIMPLE = T / Standard FITS Format BITPIX = 8 / Character data NAXIS = 0 / No Image --- just extension(s) EXTEND = T / There are standard extensions ORIGIN = 'xml2fits_v1.95' / Converted from XML-Astrores to FITS e-mail: question@simbad.u-strasbg.fr LONGSTRN= 'OGIP 1.0' / Long string convention (&/CONTINUE) may be usedDATE = '2026-04-09' / Written on 2026-04-09:20:08:38 (GMT) by: www-data@vizier.cds.unistra.fr ********************************************************** EXCERPT from catalogues stored in VizieR (CDS) with the following conditions: ********************************************************** VizieR Astronomical Server vizier.cds.unistra.fr Date: 2026-04-09T20:08:38 [V7.5.6] In case of problem, please report to: cds-question@unistra.fr INFO = 'service_protocol=ASU' / IVOID of the protocol through which the data was retrieved # INFO = 'request_date=2026-04-09T20:08:38' / Query execution date # INFO = 'request=https://vizier.cds.unistra.fr/viz-bin/asu-fits?-oc.form=dec&'CONTINUE '&-out.max=unlimited&#out.form=FITS (ascii) Table&-order=I&-out.src=&'CONTINUE 'J/MNRAS/374/372/tables&-c.eq=J2000&-c.r= 2&-c.u=arcmin&-c.geom=r&-&'CONTINUE 'source=J/MNRAS/374/372/tables&-out=M&-out=RAJ2000&-out=DEJ2000&-out&'CONTINUE '=Zmag&-out=e_Zmag&-out=Ymag&-out=e_Ymag&-out=Jmag&-out=e_Jmag&-out=&'CONTINUE 'Hmag&-out=e_Hmag&-out=Kmag&-out=e_Kmag&-out=pmRA&-out=pmDE&' / Full request URL (POST) # INFO = 'contact=cds-question@unistra.fr' / Email or URL to contact publisher # INFO = 'server_software=VizieR/7.5.6' / Software version # INFO = 'publisher=CDS' / Data centre that produced the VOTable # INFO = 'ivoid=ivo://cds.vizier/j/mnras/374/372' / IVOID of underlying data collection # INFO = 'data_ivoid=ivo://cds.vizier/j/mnras/374/372' / IVOID of underlying data collection # INFO = 'creator=Lodieu N.' / First author or institution # INFO = 'cites=bibcode:2007MNRAS.374..372L' / Article or Data origin sources # INFO = 'journal=MNRA' / Journal name # INFO = 'original_date=2007' / Year of the article publication # INFO = 'reference_url=https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/374&'CONTINUE '/372 ' / Dataset landing page # INFO = 'citation=doi:10.26093/cds/vizier.73740372' / Dataset identifier that can be used for citation # INFO = 'publication_date=2024-07-04' / Date of first publication in the data centre # INFO = 'rights_uri=https://cds.unistra.fr/vizier-org/licences_vizier.html' / Licence URI # END XTENSION= 'TABLE ' / Ascii Table Extension (TAB and NEWLINE sep) BITPIX = 8 / Character data NAXIS = 2 / Simple 2-D matrix NAXIS1 = 112 / Number of bytes per record NAXIS2 = 173 / Number of records PCOUNT = 0 / Get rid of random parameters GCOUNT = 1 / Only one group (isn't it obvious?) TFIELDS = 15 / Number of data fields (columns) CDS-CAT = 'J/MNRAS/374/372' / Catalogue designation in CDS nomenclature ZYJHK photometry in Upper Sco (Lodieu+, 2007) EXTNAME = 'J_MNRAS_374_372_tables' / Identification of the table CDS-NAME= 'J/MNRAS/374/372/tables' / Table name in METAtab Near-infrared (ZY JHK) photometry for member candidates and non-members in Upper Sco TBCOL1 = 2 / UCD=meta.code.member char:2 .. offset=1 TFORM1 = 'A2 ' / Fortran Format TTYPE1 = 'M ' / Simbad column added by the CDS TBCOL2 = 5 / UCD=pos.eq.ra;meta.main char:11 offset=4 TFORM2 = 'A11 ' / Fortran Format TTYPE2 = 'RAJ2000 ' / Right ascension (J2000) TBCOL3 = 17 / UCD=pos.eq.dec;meta.main char:11 offset=16 TFORM3 = 'A11 ' / Fortran Format TTYPE3 = 'DEJ2000 ' / Declination (J2000) TBCOL4 = 29 / UCD=phot.mag;em.opt.I float: . offset=28 TFORM4 = 'F6.3 ' / Fortran Format TTYPE4 = 'Zmag ' / ? Z (0.88um) magnitude TUNIT4 = 'mag ' / magnitude TBCOL5 = 36 / UCD=stat.error;phot.mag;em.opt.I f offset=35 TFORM5 = 'F6.3 ' / Fortran Format TTYPE5 = 'e_Zmag ' / ? rms uncertainty on Zmag TUNIT5 = 'mag ' / magnitude TBCOL6 = 43 / UCD=phot.mag;em.opt.I float: . offset=42 TFORM6 = 'F6.3 ' / Fortran Format TTYPE6 = 'Ymag ' / Y (1.03um) magnitude TUNIT6 = 'mag ' / magnitude TBCOL7 = 50 / UCD=stat.error;phot.mag;em.opt.B f offset=49 TFORM7 = 'F6.3 ' / Fortran Format TTYPE7 = 'e_Ymag ' / rms uncertainty on Ymag TUNIT7 = 'mag ' / magnitude TBCOL8 = 57 / UCD=phot.mag;em.IR.J float: .. offset=56 TFORM8 = 'F6.3 ' / Fortran Format TTYPE8 = 'Jmag ' / J (1.25um) magnitude TUNIT8 = 'mag ' / magnitude TBCOL9 = 64 / UCD=stat.error;phot.mag;em.IR.J fl offset=63 TFORM9 = 'F6.3 ' / Fortran Format TTYPE9 = 'e_Jmag ' / rms uncertainty on Jmag TUNIT9 = 'mag ' / magnitude TBCOL10 = 71 / UCD=phot.mag;em.IR.H float: .. offset=70 TFORM10 = 'F6.3 ' / Fortran Format TTYPE10 = 'Hmag ' / H (1.65um) magnitude TUNIT10 = 'mag ' / magnitude TBCOL11 = 78 / UCD=stat.error;phot.mag;em.IR.H fl offset=77 TFORM11 = 'F6.3 ' / Fortran Format TTYPE11 = 'e_Hmag ' / rms uncertainty on Hmag TUNIT11 = 'mag ' / magnitude TBCOL12 = 85 / UCD=phot.mag;em.IR.K float: .. offset=84 TFORM12 = 'F6.3 ' / Fortran Format TTYPE12 = 'Kmag ' / K (2.20um) magnitude TUNIT12 = 'mag ' / magnitude TBCOL13 = 92 / UCD=stat.error;phot.mag;em.IR.K fl offset=91 TFORM13 = 'F6.3 ' / Fortran Format TTYPE13 = 'e_Kmag ' / rms uncertainty on Kmag TUNIT13 = 'mag ' / magnitude TBCOL14 = 99 / UCD=pos.pm;pos.eq.ra float: .. offset=98 TFORM14 = 'F6.1 ' / Fortran Format TTYPE14 = 'pmRA ' / ? Proper motion along RA, muRA*cosDE TUNIT14 = 'mas/yr ' / milli-second of arc per year TBCOL15 = 106 / UCD=pos.pm;pos.eq.dec float: . offset=105 TFORM15 = 'F6.1 ' / Fortran Format TTYPE15 = 'pmDE ' / ? Proper motion along DE, muDE TUNIT15 = 'mas/yr ' / milli-second of arc per year END +m 16 06 03.75 -22 19 30.0 18.169 0.036 16.825 0.014 15.853 0.009 15.096 0.009 14.438 0.009 13.7 -26.5 +m 16 06 06.29 -23 35 13.3 18.430 0.041 17.150 0.018 16.204 0.012 15.540 0.012 14.973 0.012 +m 16 06 15.95 -22 18 28.0 14.307 0.003 13.705 0.002 13.166 0.002 12.570 0.001 12.248 0.002 -5.0 -15.2 +m 16 06 26.37 -23 06 11.4 14.477 0.003 13.770 0.002 13.205 0.002 12.616 0.001 12.271 0.002 0.7 -22.0 +m 16 06 34.61 -22 55 04.4 13.049 0.002 12.366 0.001 11.835 0.001 11.243 0.001 10.873 0.001 -13.4 -8.8 +m 16 06 38.24 -23 43 03.8 11.867 0.001 11.413 0.001 10.915 0.001 10.438 0.000 10.041 0.000 -13.5 -25.6 +m 16 06 39.22 -22 48 34.2 13.520 0.002 12.874 0.001 12.322 0.001 11.742 0.001 11.422 0.001 -9.5 -19.6 +m 16 06 48.18 -22 30 40.1 16.818 0.013 15.697 0.007 14.926 0.005 14.353 0.005 13.839 0.006 -22.7 -14.8 +m 16 06 49.10 -22 16 38.4 15.094 0.004 14.339 0.003 13.735 0.002 13.199 0.002 12.881 0.003 -6.8 -26.5 +m 16 06 50.18 -23 09 54.0 12.971 0.001 12.388 0.001 11.816 0.001 11.241 0.001 10.902 0.001 -22.9 -10.9 +m 16 07 06.33 -22 48 28.2 12.644 0.001 12.129 0.001 11.567 0.001 10.967 0.001 10.686 0.001 -11.4 -21.6 +m 16 07 08.81 -23 39 59.9 12.653 0.001 12.127 0.001 11.624 0.001 11.044 0.000 10.714 0.001 -4.7 -14.5 +m 16 07 14.79 -23 21 01.2 19.085 0.076 17.649 0.029 16.556 0.017 15.829 0.017 15.072 0.015 +m 16 07 21.96 -23 58 45.3 14.241 0.003 13.543 0.002 12.985 0.001 12.422 0.001 12.077 0.001 -8.3 -18.7 +m 16 07 23.82 -22 11 02.0 17.239 0.016 16.015 0.009 15.202 0.006 14.564 0.004 14.009 0.005 -11.0 -30.7 +m 16 07 26.41 -21 44 17.1 16.171 0.008 15.289 0.006 14.601 0.004 14.059 0.003 13.615 0.004 -13.2 -31.4 +m 16 07 27.82 -22 39 04.0 19.358 0.100 18.236 0.048 16.810 0.022 16.091 0.023 15.388 0.023 +m 16 07 37.99 -22 42 47.0 19.240 0.090 17.713 0.029 16.757 0.019 16.001 0.020 15.327 0.020 +m 16 07 45.21 -22 22 57.6 13.589 0.002 12.935 0.001 12.348 0.001 11.811 0.001 11.485 0.001 -17.4 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0.001 11.804 0.001 11.422 0.001 -14.2 -5.0 +n2 16 14 46.84 -23 41 57.2 16.314 0.009 15.494 0.006 14.835 0.004 14.251 0.004 13.815 0.005 -34.6 -14.9 +n2 16 15 16.02 -23 45 10.4 13.587 0.002 12.844 0.001 12.230 0.001 11.656 0.001 11.272 0.001 -11.0 3.9 +n2 16 15 16.66 -23 40 46.3 17.968 0.030 16.552 0.013 15.621 0.008 14.911 0.007 14.269 0.008 -35.1 -11.1 +n2 16 15 38.43 -23 41 56.0 13.399 0.002 12.760 0.001 12.208 0.001 11.584 0.001 11.267 0.001 -32.1 -16.2 +n2 16 16 20.11 -23 44 14.3 12.083 0.001 11.504 0.001 10.976 0.001 10.533 0.000 10.144 0.000 -33.2 -11.3 +n2 16 16 26.20 -23 50 48.8 12.051 0.001 11.601 0.001 11.018 0.001 10.474 0.000 9.968 0.000 -10.0 1.1 \ No newline at end of file diff --git a/docs/paper_examples/Begbie+26/cluster_data/asu.fit b/docs/paper_examples/Begbie+26/cluster_data/asu.fit new file mode 100644 index 00000000..ec95b30a --- /dev/null +++ b/docs/paper_examples/Begbie+26/cluster_data/asu.fit @@ -0,0 +1,2694 @@ +SIMPLE = T / Standard FITS Format BITPIX = 8 / Character data NAXIS = 0 / No Image --- just extension(s) EXTEND = T / There are standard extensions ORIGIN = 'xml2fits_v1.95' / Converted from XML-Astrores to FITS e-mail: question@simbad.u-strasbg.fr LONGSTRN= 'OGIP 1.0' / Long string convention (&/CONTINUE) may be usedDATE = '2026-01-16' / Written on 2026-01-16:19:27:04 (GMT) by: www-data@vizier.cds.unistra.fr ********************************************************** EXCERPT from catalogues stored in VizieR (CDS) with the following conditions: ********************************************************** VizieR Astronomical Server vizier.cds.unistra.fr Date: 2026-01-16T19:27:04 [V7.5.3] In case of problem, please report to: cds-question@unistra.fr INFO = 'service_protocol=ASU' / IVOID of the protocol through which the data was retrieved # INFO = 'request_date=2026-01-16T19:27:04' / Query execution date # INFO = 'request=https://vizier.cds.unistra.fr/viz-bin/asu-fits?-oc.form=dec&'CONTINUE '&-out.max=unlimited&#out.form=FITS (ascii) Table&-c.eq=J2000&-c.r= &'CONTINUE ' 2&-c.u=arcmin&-c.geom=r&-x.rs=10&-source=J/MNRAS/422/1495/tablea1,&'CONTINUE 'J/MNRAS/422/1495/tablec1&-order=I&-out=RAJ2000&-out=DEJ2000&-out=M&&'CONTINUE '-out=Zmag&-out=Ymag&-out=Jmag&-out=Hmag&-out=K1mag&-out=K2mag&-out=&'CONTINUE 'pmRA&-out=e_pmRA&-out=pmDE&-out=e_pmDE&-out=chi2&-out=Mmb&-out=n_Mm&'CONTINUE 'b&-out=OName&-out=GCS9&GCS9=GCS9&-out=SimbadName&-out=n_Mmb&' / Full request URL (POST) # INFO = 'contact=cds-question@unistra.fr' / Email or URL to contact publisher # INFO = 'server_software=7.5.3' / Software version # INFO = 'publisher=CDS' / Data centre that produced the VOTable # INFO = 'CatalogsExamined=2' 2 catalogues with potential matches were examined. INFO = 'ivoid=ivo://cds.vizier/j/mnras/422/1495' / IVOID of underlying data collection # INFO = 'creator=Lodieu N.' / First author or institution # INFO = 'cites=bibcode:2012MNRAS.422.1495L' / Article or Data origin sources # INFO = 'original_date=2012' / Year of the article publication # INFO = 'reference_url=https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/422&'CONTINUE '/1495 ' / Dataset landing page # INFO = 'citation=doi:10.26093/cds/vizier.74221495' / Dataset identifier that can be used for citation # INFO = 'publication_date=2024-07-17' / Date of first publication in the data centre # INFO = 'rights_uri=https://cds.unistra.fr/vizier-org/licences_vizier.html' / Licence URI # END XTENSION= 'TABLE ' / Ascii Table Extension (TAB and NEWLINE sep) BITPIX = 8 / Character data NAXIS = 2 / Simple 2-D matrix NAXIS1 = 224 / Number of bytes per record NAXIS2 = 1379 / Number of records PCOUNT = 0 / Get rid of random parameters GCOUNT = 1 / Only one group (isn't it obvious?) TFIELDS = 19 / Number of data fields (columns) CDS-CAT = 'J/MNRAS/422/1495' / Catalogue designation in CDS nomenclature UKIDSS Galactic Clusters Survey Pleiades members (Lodieu+ 2012) EXTNAME = 'J_MNRAS_422_1495_tablea1' / Identification of the table CDS-NAME= 'J/MNRAS/422/1495/tablea1' / Table name in METAtab Sample of 1379 known Pleiades member candidates previously published in the literature and recovered in GCS DR9 TBCOL1 = 2 / UCD=pos.eq.ra;meta.main char:11 offset=1 TFORM1 = 'A11 ' / Fortran Format TTYPE1 = 'RAJ2000 ' / Right ascension (J2000) TBCOL2 = 14 / UCD=pos.eq.dec;meta.main char:11 offset=13 TFORM2 = 'A11 ' / Fortran Format TTYPE2 = 'DEJ2000 ' / Declination (J2000) TBCOL3 = 26 / UCD=meta.number short: ....... offset=25 TFORM3 = 'I3 ' / Fortran Format TTYPE3 = 'M ' / Multiplicity of this star (table D1) TNULL3 = -32768 / NULL definition TBCOL4 = 30 / UCD=phot.mag;em.opt.I float: . offset=29 TFORM4 = 'F6.3 ' / Fortran Format TTYPE4 = 'Zmag ' / ? UKIDSS Z magnitude TUNIT4 = 'mag ' / magnitude TBCOL5 = 37 / UCD=phot.mag;em.IR.J float: .. offset=36 TFORM5 = 'F6.3 ' / Fortran Format TTYPE5 = 'Ymag ' / ? UKIDSS Y magnitude TUNIT5 = 'mag ' / magnitude TBCOL6 = 44 / UCD=phot.mag;em.IR.J float: .. offset=43 TFORM6 = 'F6.3 ' / Fortran Format TTYPE6 = 'Jmag ' / UKIDSS J magnitude TUNIT6 = 'mag ' / magnitude TBCOL7 = 51 / UCD=phot.mag;em.IR.H float: .. offset=50 TFORM7 = 'F6.3 ' / Fortran Format TTYPE7 = 'Hmag ' / UKIDSS H magnitude TUNIT7 = 'mag ' / magnitude TBCOL8 = 58 / UCD=phot.mag;em.IR.K float: .. offset=57 TFORM8 = 'F6.3 ' / Fortran Format TTYPE8 = 'K1mag ' / UKIDSS K magnitude at first epoch TUNIT8 = 'mag ' / magnitude TBCOL9 = 65 / UCD=phot.mag;em.IR.K float: .. offset=64 TFORM9 = 'F6.3 ' / Fortran Format TTYPE9 = 'K2mag ' / ? UKIDSS K magnitude at second epoch TUNIT9 = 'mag ' / magnitude TBCOL10 = 72 / UCD=pos.pm;pos.eq.ra float: .. offset=71 TFORM10 = 'F7.2 ' / Fortran Format TTYPE10 = 'pmRA ' / ? Proper motion along RA, pmRA*cosDE TUNIT10 = 'mas/yr ' / milli-second of arc per year TBCOL11 = 80 / UCD=stat.error;pos.pm;pos.eq.ra fl offset=79 TFORM11 = 'F5.2 ' / Fortran Format TTYPE11 = 'e_pmRA ' / ? rms uncertainty on pmRA TUNIT11 = 'mas/yr ' / milli-second of arc per year TBCOL12 = 86 / UCD=pos.pm;pos.eq.dec float: . offset=85 TFORM12 = 'F7.2 ' / Fortran Format TTYPE12 = 'pmDE ' / ? Proper motion along DE TUNIT12 = 'mas/yr ' / milli-second of arc per year TBCOL13 = 94 / UCD=stat.error;pos.pm;pos.eq.dec f offset=93 TFORM13 = 'F5.2 ' / Fortran Format TTYPE13 = 'e_pmDE ' / ? rms uncertainty on pmDE TUNIT13 = 'mas/yr ' / milli-second of arc per year TBCOL14 = 100 / UCD=stat.fit.chi2;stat.fit.param;m offset=99 TFORM14 = 'F6.2 ' / Fortran Format TTYPE14 = 'chi2 ' / ? Reduced chi^2^ statistic of the astrometric fit (only in tablea1.dat) TBCOL15 = 107 / UCD=stat.probability float: .. offset=106 TFORM15 = 'F5.2 ' / Fortran Format TTYPE15 = 'Mmb ' / [0/1]? Membership probability (tablea1.dat) TBCOL16 = 113 / UCD=meta.note char:4 ......... offset=112 TFORM16 = 'A4 ' / Fortran Format TTYPE16 = 'n_Mmb ' / Note on Mmb (tablea1.dat) (1) (link) TBCOL17 = 118 / UCD=meta.id;meta.main char:77* offset=117 TFORM17 = 'A77 ' / Fortran Format TTYPE17 = 'OName ' / Other name(s) (2) (link) TBCOL18 = 196 / UCD=meta.ref.url char:4 ...... offset=195 TFORM18 = 'A4 ' / Fortran Format TTYPE18 = 'GCS9 ' / Display the UKIDSS GCS-DR9 data, Cat. II/319 (link) TBCOL19 = 201 / UCD=meta.id char:23 .......... offset=200 TFORM19 = 'A23 ' / Fortran Format TTYPE19 = 'SimbadName' / Simbad column added by the CDS END +03 27 54.26 +24 56 10.9 0 13.808 13.400 12.820 12.255 12.005 16.62 6.95 -43.60 6.95 0.47 0.93 DH003 GCS9 Cl* Melotte 22 DH 003 +03 29 58.76 +23 22 18.3 0 13.640 13.198 12.672 12.244 11.843 11.851 21.18 3.41 -38.83 3.41 0.47 0.81 DH009 GCS9 Cl* Melotte 22 DH 009 +03 30 08.28 +22 38 38.3 0 15.346 14.741 13.680 13.020 12.895 12.805 16.33 3.42 -72.94 3.42 74.29 0.00 DH010 GCS9 Cl* Melotte 22 DH 010 +03 30 35.39 +23 03 07.9 0 16.316 15.841 15.233 14.594 14.298 14.288 15.61 3.45 -45.70 3.45 0.44 0.63 DH012 GCS9 Cl* Melotte 22 DH 012 +03 31 14.64 +25 58 51.7 0 12.750 12.443 11.925 11.381 11.076 11.107 33.12 3.36 -39.84 3.36 2.35 0.00 DH015 GCS9 Cl* Melotte 22 DH 015 +03 31 29.60 +26 30 12.1 0 14.670 14.195 13.596 13.007 12.703 12.734 21.27 2.96 -35.29 2.96 0.26 0.42 DH016 GCS9 Cl* Melotte 22 DH 016 +03 32 07.87 +23 13 57.2 0 14.025 13.621 13.052 12.517 12.218 12.199 18.14 3.41 -38.13 3.41 0.46 0.84 DH017 GCS9 Cl* Melotte 22 DH 017 +03 32 28.30 +22 28 06.3 0 17.598 17.092 16.403 15.754 15.410 15.409 10.61 3.53 -31.53 3.53 0.64 0.00 DH019 GCS9 Cl* Melotte 22 DH 019 +03 32 32.97 +22 18 12.1 0 14.922 14.467 13.885 13.344 13.019 13.018 22.36 3.41 -35.37 3.41 0.54 0.36 DH020 GCS9 Cl* Melotte 22 DH 020 +03 32 57.57 +27 17 19.4 0 15.608 15.014 14.363 13.807 13.462 13.477 15.57 3.76 -46.28 3.76 0.87 0.80 DH024 GCS9 Cl* Melotte 22 DH 024 +03 33 10.51 +22 31 19.0 0 12.839 12.556 12.064 11.566 11.249 11.279 31.96 3.41 -37.15 3.41 2.39 0.00 DH027 GCS9 Cl* Melotte 22 DH 027 +03 33 39.01 +22 21 08.5 0 15.193 14.805 14.251 13.715 13.455 13.430 16.11 3.42 -44.42 3.42 0.58 0.87 DH029 GCS9 Cl* Melotte 22 DH 029 +03 33 46.65 +23 48 19.4 0 13.819 13.463 12.918 12.375 12.087 12.098 18.43 2.60 -43.31 2.60 5.20 0.94 DH030 GCS9 Cl* Melotte 22 DH 030 +03 33 49.81 +22 56 19.6 0 15.069 14.626 14.048 13.507 13.194 13.190 16.86 3.05 -39.29 3.05 0.39 0.83 DH031 GCS9 Cl* Melotte 22 DH 031 +03 34 19.15 +22 27 59.6 0 14.056 13.606 13.051 12.550 12.227 12.213 27.04 2.94 -46.83 2.94 0.19 0.23 DH033 GCS9 Cl* Melotte 22 DH 033 +03 34 19.37 +22 48 42.2 0 16.004 15.439 14.812 14.262 13.912 13.908 21.53 3.06 -34.97 3.06 0.22 0.12 DH034 GCS9 Cl* Melotte 22 DH 034 +03 34 41.55 +26 09 27.1 0 15.437 14.878 14.245 13.705 13.344 24.47 5.32 -33.86 5.32 1.01 0.04 DH036 GCS9 Cl* Melotte 22 DH 036 +03 34 47.54 +26 22 13.4 0 14.564 14.112 13.529 12.977 12.672 12.674 24.94 3.30 -39.43 3.30 0.12 0.60 DH039 GCS9 Cl* Melotte 22 DH 039 +03 34 54.95 +22 04 46.2 0 13.105 12.793 12.313 11.794 11.512 11.566 22.30 2.93 -37.92 2.93 0.10 0.66 DH040 GCS9 Cl* Melotte 22 DH 040 +03 35 04.72 +25 50 48.0 0 15.967 15.347 14.711 14.131 13.773 14.82 5.33 -43.91 5.33 0.30 0.85 DH041 GCS9 Cl* Melotte 22 DH 041 +03 35 09.41 +24 14 19.8 0 12.614 12.358 11.884 11.322 11.049 11.083 16.71 2.60 -38.59 2.60 1.67 0.83 DH042 GCS9 Cl* Melotte 22 DH 042 +03 35 10.44 +24 31 54.4 0 14.645 14.122 13.531 12.972 12.661 12.629 17.82 2.63 -32.24 2.63 0.87 0.06 DH043 GCS9 Cl* Melotte 22 DH 043 +03 35 16.28 +23 47 57.8 0 15.198 14.649 14.056 13.523 13.179 13.195 21.38 2.61 -33.42 2.61 1.99 0.09 DH044 GCS9 Cl* Melotte 22 DH 044 +03 35 39.80 +25 38 45.2 0 14.957 14.508 13.916 13.319 13.037 15.42 5.32 -37.37 5.32 0.31 0.68 DH046 GCS9 Cl* Melotte 22 DH 046 +03 35 44.28 +22 27 09.7 0 13.612 13.310 12.830 12.275 11.996 12.017 19.82 2.93 -40.16 2.93 0.15 0.90 DH047 GCS9 Cl* Melotte 22 DH 047 +03 35 49.68 +25 04 48.0 0 13.372 13.029 12.506 11.959 11.707 11.697 32.03 2.62 -31.99 2.62 9.53 0.00 DH048 GCS9 Cl* Melotte 22 DH 048 +03 35 50.29 +25 42 20.5 0 13.820 13.379 12.791 12.239 11.978 28.77 5.30 -37.34 5.30 0.36 0.02 DH049 GCS9 Cl* Melotte 22 DH 049 +03 35 56.06 +25 21 59.3 0 13.217 12.868 12.366 11.780 11.558 18.66 5.28 -39.03 5.28 0.21 0.87 DH050 GCS9 Cl* Melotte 22 DH 050 +03 36 05.33 +25 21 03.4 0 14.386 13.934 13.321 12.796 12.470 19.93 5.29 -41.98 5.29 0.66 0.94 DH051 GCS9 Cl* Melotte 22 DH 051 +03 36 11.07 +23 48 23.0 0 15.038 14.538 13.935 13.415 13.063 13.085 20.28 2.61 -36.96 2.61 1.57 0.61 DH053 GCS9 Cl* Melotte 22 DH 053 +03 36 16.32 +25 08 48.8 0 13.071 12.678 12.129 11.591 11.290 11.282 19.36 2.62 -45.92 2.62 0.50 0.91 DH054 GCS9 Cl* Melotte 22 DH 054 +03 36 22.33 +22 44 32.7 0 13.694 13.229 12.673 12.138 11.842 11.852 22.55 3.04 -41.33 3.04 1.33 0.85 DH055 GCS9 Cl* Melotte 22 DH 055 +03 36 24.18 +22 37 24.3 0 14.137 13.724 13.127 12.613 12.276 12.327 17.43 2.94 -44.41 2.94 1.67 0.93 DH056 GCS9 Cl* Melotte 22 DH 056 +03 36 26.17 +22 14 45.4 0 14.225 13.898 13.373 12.799 12.515 12.522 26.10 2.94 -40.80 2.94 0.12 0.51 DH057 GCS9 Cl* Melotte 22 DH 057 +03 36 27.37 +24 41 17.1 0 13.905 13.442 12.879 12.371 12.078 12.072 21.62 2.62 -35.55 2.62 0.63 0.35 DH058 GCS9 Cl* Melotte 22 DH 058 +03 36 42.67 +28 21 05.9 0 15.297 14.744 14.124 13.558 13.240 13.241 21.45 4.94 -39.70 4.94 0.46 0.78 DH062 GCS9 Cl* Melotte 22 DH 062 +03 36 44.12 +22 01 38.8 0 14.988 14.410 13.794 13.301 12.965 12.979 22.41 2.94 -39.90 2.94 0.74 0.85 DH063 GCS9 Cl* Melotte 22 DH 063 +03 36 46.48 +28 15 05.9 0 15.528 14.984 14.370 13.865 13.552 13.542 25.67 4.95 -35.56 4.95 0.80 0.06 DH064 GCS9 Cl* Melotte 22 DH 064 +03 37 03.48 +24 44 35.3 0 13.096 12.748 12.245 11.693 11.446 11.458 21.23 2.48 -35.99 2.48 1.44 0.46 HCG6_HHJ392_DH066 GCS9 Cl* Melotte 22 DH 066 +03 37 10.51 +25 17 34.6 0 14.920 14.443 13.853 13.316 13.012 13.025 23.82 2.96 -34.70 2.96 0.86 0.16 DH067 GCS9 Cl* Melotte 22 DH 067 +03 37 11.98 +26 46 28.7 0 14.171 13.688 13.064 12.558 12.231 12.243 22.64 3.30 -37.41 3.30 0.54 0.65 HHJ242_DH068 GCS9 Cl* Melotte 22 HHJ 242 +03 37 15.61 +26 29 29.8 0 15.984 15.323 14.670 14.169 13.787 13.794 19.16 3.31 -39.61 3.31 0.75 0.85 HHJ19 GCS9 Cl* Melotte 22 HHJ 19 +03 37 16.53 +23 11 04.2 0 14.370 13.944 13.393 12.855 12.551 12.556 23.40 2.97 -41.45 2.97 0.52 0.84 HHJ179_DH069 GCS9 Cl* Melotte 22 HHJ 179 +03 37 16.71 +25 44 11.1 0 14.671 14.223 13.651 13.108 12.785 12.788 11.44 2.96 -37.28 2.96 0.91 0.22 HHJ154_DH070 GCS9 Cl* Melotte 22 HHJ 154 +03 37 26.39 +24 34 01.2 0 14.315 13.924 13.363 12.833 12.539 12.563 20.85 2.48 -42.78 2.48 0.79 0.93 DH071 GCS9 Cl* Melotte 22 DH 071 +03 37 26.62 +26 41 27.5 0 12.870 12.622 12.135 11.531 11.320 11.360 40.30 3.30 -47.71 3.30 0.63 0.00 HHJ422 GCS9 Cl* Melotte 22 HHJ 422 +03 37 30.28 +28 32 26.4 0 15.565 15.067 14.516 13.880 13.587 13.594 6.71 4.97 -40.29 4.97 0.50 0.02 DH072 GCS9 Cl* Melotte 22 DH 072 +03 37 30.69 +24 50 54.3 0 14.798 14.416 13.808 13.323 13.004 12.978 19.62 2.49 -34.54 2.49 6.17 0.34 HHJ110 GCS9 Cl* Melotte 22 HHJ 110 +03 37 34.18 +24 42 15.1 0 15.478 14.973 14.389 13.876 13.543 13.551 21.50 2.49 -36.80 2.49 2.46 0.51 DH074 GCS9 Cl* Melotte 22 DH 074 +03 37 36.01 +26 32 48.4 0 12.457 12.147 11.662 11.396 10.873 10.930 24.17 3.30 -45.76 3.30 1.02 0.48 DH075 GCS9 Cl* Melotte 22 DH 075 +03 37 37.70 +26 21 04.0 0 14.598 14.134 13.546 13.006 12.705 12.701 23.72 3.30 -44.04 3.30 0.33 0.83 HHJ172_DH076 GCS9 Cl* Melotte 22 HHJ 172 +03 37 40.23 +24 42 58.0 0 13.786 13.370 12.837 12.338 12.034 12.043 27.94 2.48 -39.53 2.48 1.40 0.09 HHJ283 GCS9 Cl* Melotte 22 HHJ 283 +03 37 47.51 +24 53 46.1 0 15.183 14.722 14.132 13.612 13.311 13.287 16.11 2.49 -40.51 2.49 1.67 0.86 HHJ72_DH078 GCS9 Cl* Melotte 22 HHJ 72 +03 37 48.93 +26 51 45.1 0 14.380 13.919 13.351 12.818 12.520 12.525 21.09 3.30 -46.90 3.30 0.33 0.86 HHJ137_DH079 GCS9 Cl* Melotte 22 HHJ 137 +03 37 54.79 +25 26 31.3 0 12.896 12.558 12.073 11.558 11.231 11.316 19.21 2.95 -37.64 2.95 0.58 0.83 HHJ402 GCS9 Cl* Melotte 22 HHJ 402 +03 37 56.42 +23 22 56.6 0 13.782 13.405 12.891 12.353 12.067 12.063 23.93 2.97 -39.47 2.97 0.16 0.65 HHJ268_DH080 GCS9 Cl* Melotte 22 HHJ 268 +03 38 02.05 +24 20 15.1 0 14.002 13.647 13.091 12.524 12.251 12.230 19.63 2.50 -45.83 2.50 2.15 0.92 HHJ248_DH081 GCS9 Cl* Melotte 22 HHJ 248 +03 38 08.81 +21 14 49.0 0 12.497 12.220 11.788 11.506 10.905 11.259 29.15 3.41 -36.90 3.41 0.74 0.04 DH082 GCS9 Cl* Melotte 22 DH 082 +03 38 10.27 +25 11 32.9 0 15.589 15.124 14.544 14.014 13.684 13.670 20.06 2.50 -43.22 2.50 3.31 0.88 HHJ35 GCS9 Cl* Melotte 22 HHJ 35 +03 38 13.04 +23 37 20.7 0 15.694 15.167 14.427 13.836 13.460 13.494 16.75 2.50 -45.95 2.50 2.58 0.84 HHJ32 GCS9 Cl* Melotte 22 HHJ 32 +03 38 13.04 +24 38 16.8 0 14.671 14.227 13.631 13.103 12.809 12.801 23.94 2.49 -49.13 2.49 0.97 0.42 HHJ149_DH084 GCS9 Cl* Melotte 22 HHJ 149 +03 38 24.22 +25 19 49.2 0 14.870 14.364 13.774 13.233 12.906 12.914 18.02 2.96 -40.03 2.96 0.46 0.92 DH086 GCS9 Cl* Melotte 22 DH 086 +03 38 24.80 +26 15 23.5 0 14.041 13.583 13.024 12.485 12.215 12.231 27.72 3.30 -43.66 3.30 0.30 0.27 HHJ295_DH087 GCS9 Cl* Melotte 22 HHJ 295 +03 38 27.52 +25 30 18.1 0 16.591 15.938 15.284 14.747 14.355 14.371 18.25 3.00 -40.21 3.00 1.94 0.69 HHJ2 GCS9 Cl* Melotte 22 HHJ 2 +03 38 27.83 +26 51 25.4 0 14.777 14.274 13.663 13.167 12.844 12.866 21.04 3.30 -38.12 3.30 0.31 0.81 HHJ121_DH089 GCS9 Cl* Melotte 22 HHJ 121 +03 38 34.21 +23 43 07.3 0 14.985 14.547 13.940 13.424 13.106 13.105 8.23 2.50 -53.41 2.50 12.39 0.00 HHJ98_DH090 GCS9 Cl* Melotte 22 HHJ 98 +03 38 34.49 +23 40 22.3 0 14.946 14.514 13.918 13.368 13.041 13.062 11.57 2.50 -46.35 2.50 3.34 0.48 HHJ97_DH091 GCS9 Cl* Melotte 22 HHJ 97 +03 38 43.30 +25 22 26.9 0 13.103 12.661 12.090 11.591 11.286 11.293 23.70 2.95 -41.82 2.95 1.32 0.79 HCG11_HHJ378_DH092 GCS9 Cl* Melotte 22 HCG 378 +03 38 45.75 +24 28 03.7 0 15.071 14.563 13.928 13.455 13.074 13.027 21.00 2.49 -40.88 2.49 2.42 0.84 HHJ91_DH094 GCS9 Cl* Melotte 22 HHJ 91 +03 38 53.89 +24 25 07.7 0 13.542 13.293 12.817 12.269 11.999 11.985 12.60 2.48 -33.86 2.48 1.14 0.03 HCG15 GCS9 Cl* Melotte 22 HCG 15 +03 38 54.16 +24 42 15.6 0 15.113 14.509 13.889 13.387 13.029 13.012 24.49 2.49 -40.01 2.49 1.14 0.50 HHJ63 GCS9 Cl* Melotte 22 HHJ 63 +03 39 03.31 +26 29 30.9 0 13.943 13.592 13.088 12.528 12.249 12.283 35.52 3.00 -41.84 3.00 0.41 0.00 HHJ317 GCS9 Cl* Melotte 22 HHJ 317 +03 39 04.06 +27 00 04.7 0 13.938 13.572 13.032 12.454 12.171 12.197 17.40 3.00 -34.30 3.00 0.70 0.22 DH098 GCS9 Cl* Melotte 22 DH 098 +03 39 08.13 +24 46 14.4 0 13.036 12.618 12.087 11.550 11.270 11.264 15.90 2.48 -44.93 2.48 0.74 0.91 HCG16_HHJ398_DH099 GCS9 Cl* Melotte 22 HCG 16 +03 39 13.33 +25 43 49.5 0 13.488 13.088 12.530 11.963 11.657 11.694 19.34 2.95 -39.87 2.95 0.43 0.90 HHJ349_DH100 GCS9 Cl* Melotte 22 HHJ 349 +03 39 15.56 +26 48 03.6 0 15.315 14.815 14.230 13.688 13.348 13.372 21.02 3.00 -38.68 3.00 0.54 0.74 HHJ66_DH102 GCS9 Cl* Melotte 22 HHJ 66 +03 39 16.75 +24 57 38.5 0 15.120 14.611 13.972 13.440 13.071 13.063 15.63 2.49 -40.66 2.49 3.81 0.85 DH103 GCS9 Cl* Melotte 22 DH 103 +03 39 17.05 +22 27 10.9 0 17.162 16.347 15.569 15.023 14.582 14.576 16.67 2.58 -43.68 2.58 2.56 0.69 IPMBD43 GCS9 Cl* Melotte 22 IPMBD 43 +03 39 22.07 +24 02 39.1 0 15.165 14.761 14.179 13.633 13.370 13.330 9.68 2.60 -31.09 2.60 1.34 0.00 BPL1 GCS9 Cl* Melotte 22 BPL 1 +03 39 22.42 +26 11 36.0 0 14.521 14.093 13.516 12.964 12.650 12.682 18.92 3.00 -45.37 3.00 1.11 0.93 HHJ224_DH104_Moraux2003_51 GCS9 Cl* Melotte 22 HHJ 224 +03 39 28.99 +25 34 55.8 0 13.441 13.038 12.541 12.001 11.753 11.765 23.56 2.95 -49.12 2.95 1.61 0.41 HHJ359 GCS9 Cl* Melotte 22 HHJ 359 +03 39 31.49 +20 04 40.4 0 13.372 13.019 12.545 11.954 11.659 11.704 32.73 3.42 -49.02 3.42 0.24 0.00 DH106 GCS9 Cl* Melotte 22 DH 106 +03 39 32.32 +24 16 01.1 0 13.986 13.660 13.081 12.505 12.208 12.233 21.26 2.50 -43.91 2.50 0.97 0.91 BPL2_DH107 GCS9 Cl* Melotte 22 BPL 2 +03 39 35.46 +24 07 06.1 0 12.806 12.545 12.021 11.498 11.181 11.228 18.04 2.49 -42.86 2.49 2.91 0.87 HHJ407_DH108 GCS9 Cl* Melotte 22 HHJ 407 +03 39 41.12 +23 28 23.3 0 14.958 14.527 13.924 13.411 13.080 13.046 23.64 2.98 -38.71 2.98 0.78 0.70 HHJ83 GCS9 Cl* Melotte 22 HHJ 83 +03 39 42.72 +23 54 27.6 0 14.142 13.756 13.179 12.650 12.363 12.332 22.18 2.50 -45.44 2.50 1.07 0.88 HCG22_T2B_BPL3_HHJ230_DH109 GCS9 Cl* Melotte 22 HCG 22 +03 39 43.32 +23 12 25.3 0 15.134 14.683 14.096 13.549 13.216 13.200 19.81 2.98 -37.33 2.98 0.27 0.67 DH110 GCS9 Cl* Melotte 22 DH 110 +03 39 44.19 +22 07 45.5 0 13.646 13.239 12.691 12.131 11.860 11.873 19.48 2.50 -45.62 2.50 0.96 0.92 HHJ141_DH111 GCS9 Cl* Melotte 22 HHJ 141 +03 39 44.37 +26 18 18.8 0 15.243 14.790 14.188 13.648 13.355 13.364 16.91 3.00 -36.58 3.00 0.49 0.59 Moraux2003_82 GCS9 Cl* Melotte 22 MBSC 82 +03 39 44.79 +22 39 15.3 0 14.615 14.124 13.554 13.015 12.716 12.691 17.16 2.98 -42.48 2.98 0.93 0.94 DH2004_112 GCS9 Cl* Melotte 22 DH 112 +03 39 46.35 +23 58 52.9 0 13.490 13.174 12.625 12.077 11.772 11.792 22.63 2.50 -38.42 2.50 3.77 0.69 HHJ324_BPL4_DH113 GCS9 Cl* Melotte 22 HHJ 324 +03 39 47.02 +26 21 14.3 0 14.554 14.117 13.546 12.997 12.694 12.688 28.81 3.00 -44.81 3.00 0.48 0.10 HHJ178_DH114_Moraux2003_57 GCS9 Cl* Melotte 22 HHJ 178 +03 39 47.99 +23 50 56.6 0 13.557 13.155 12.591 12.078 11.777 11.792 20.24 2.50 -41.77 2.50 1.60 0.92 HHJ291_BPL5_DH115 GCS9 Cl* Melotte 22 HHJ 291 +03 39 48.51 +23 46 03.8 0 15.028 14.539 13.964 13.446 13.119 13.083 18.84 2.50 -46.34 2.50 1.27 0.83 HHJ74_BPL6 GCS9 Cl* Melotte 22 HHJ 74 +03 39 49.72 +23 03 26.2 0 14.613 14.161 13.592 13.065 12.757 12.755 22.48 2.98 -41.93 2.98 0.30 0.89 HHJ138_DH117 GCS9 Cl* Melotte 22 HHJ 138 +03 39 50.68 +23 45 52.3 0 15.504 15.018 14.384 13.856 13.525 13.514 16.85 2.51 -41.64 2.51 1.25 0.89 BPL7_DH118 GCS9 Cl* Melotte 22 BPL 7 +03 39 53.53 +25 46 46.2 0 14.991 14.642 14.122 13.564 13.298 13.295 19.66 2.96 -45.57 2.96 0.24 0.92 DH2004_119 GCS9 Cl* Melotte 22 DH 119 +03 39 55.21 +24 12 54.1 0 15.371 14.909 14.293 13.783 13.432 13.440 18.81 2.50 -45.72 2.50 1.49 0.86 BPL8 GCS9 Cl* Melotte 22 BPL 8 +03 39 57.15 +26 07 00.1 0 15.350 14.830 14.198 13.660 13.331 13.308 21.95 2.96 -43.52 2.96 0.32 0.82 Moraux2003_94 GCS9 Cl* Melotte 22 MBSC 94 +03 39 57.35 +23 19 42.2 0 14.866 14.438 13.872 13.338 13.029 13.030 20.25 2.98 -49.85 2.98 0.39 0.61 HHJ103 GCS9 Cl* Melotte 22 HHJ 103 +03 39 57.85 +25 55 29.7 0 15.347 14.843 14.201 13.640 13.304 13.307 25.63 2.96 -42.96 2.96 0.41 0.40 DH120_Moraux2003_92 GCS9 Cl* Melotte 22 DH 120 +03 40 00.19 +23 26 05.7 0 12.733 12.412 11.891 11.375 11.054 11.085 19.30 2.97 -38.03 2.97 1.85 0.85 HCG26_SK802_HHJ403_DH121 GCS9 Cl* Melotte 22 HCG 26 +03 40 01.82 +22 59 20.2 0 12.400 12.141 11.716 11.468 10.921 11.098 22.71 2.97 -65.69 2.97 2.24 0.00 HHJ426 GCS9 Cl* Melotte 22 HHJ 426 +03 40 01.84 +25 04 19.5 0 14.919 14.417 13.786 13.289 12.952 12.925 20.07 2.49 -41.41 2.49 3.13 0.93 HHJ102 GCS9 Cl* Melotte 22 HHJ 102 +03 40 01.87 +24 46 25.9 0 14.203 13.811 13.240 12.713 12.468 12.420 19.86 2.48 -42.54 2.48 0.70 0.94 HCG24_T35B_HHJ226_DH122 GCS9 Cl* Melotte 22 HCG 24 +03 40 03.61 +24 30 02.7 0 13.674 13.270 12.776 12.243 11.963 11.937 2.13 2.48 -31.06 2.48 34.28 0.00 BPL9 GCS9 Cl* Melotte 22 BPL 9 +03 40 05.05 +25 31 36.6 0 13.648 13.372 12.896 12.232 12.056 12.038 42.65 2.95 -35.50 2.95 0.66 0.00 HHJ356 GCS9 Cl* Melotte 22 HHJ 356 +03 40 05.98 +25 40 20.8 0 14.788 14.303 13.742 13.196 12.896 12.880 18.83 2.96 -39.12 2.96 0.51 0.89 DH124 GCS9 Cl* Melotte 22 DH 124 +03 40 06.21 +28 08 32.0 0 15.387 14.854 14.239 13.731 13.422 13.401 13.90 4.95 -38.21 4.95 0.84 0.62 DH125 GCS9 Cl* Melotte 22 DH 125 +03 40 07.01 +22 38 47.5 0 14.975 14.452 13.904 13.372 13.034 13.020 17.42 2.98 -39.40 2.98 0.63 0.89 HHJ114 GCS9 Cl* Melotte 22 HHJ 114 +03 40 07.11 +24 13 03.3 0 15.403 14.934 14.318 13.802 13.450 13.451 16.92 2.50 -42.32 2.50 0.93 0.90 HHJ38_BPL10_DH126 GCS9 Cl* Melotte 22 HHJ 38 +03 40 09.69 +23 10 32.4 0 14.362 13.967 13.408 12.881 12.576 12.579 25.33 2.98 -38.03 2.98 0.76 0.41 HCG31_DH127 GCS9 Cl* Melotte 22 HCG 31 +03 40 10.93 +26 06 40.8 0 14.589 14.173 13.576 13.025 12.731 12.734 19.44 2.96 -36.36 2.96 0.32 0.67 HCG28_HHJ177_DH128 GCS9 Cl* Melotte 22 HCG 28 +03 40 11.04 +25 23 26.6 0 14.790 14.309 13.733 13.227 12.896 12.905 19.42 2.96 -38.70 2.96 0.26 0.87 HHJ147_DH129 GCS9 Cl* Melotte 22 HHJ 147 +03 40 11.93 +25 52 32.3 0 15.702 15.231 14.589 14.058 13.700 13.704 16.97 2.97 -35.30 2.97 0.57 0.39 HHJ29 GCS9 Cl* Melotte 22 HHJ 29 +03 40 12.74 +23 09 35.6 0 13.830 13.457 12.923 12.343 12.056 12.069 23.11 2.97 -33.99 2.97 0.50 0.08 HCG32_SK794_HHJ280_DH130 GCS9 Cl* Melotte 22 HCG 32 +03 40 14.80 +25 50 05.5 0 13.923 13.506 12.914 12.433 12.114 12.129 8.07 2.95 -22.54 2.95 0.30 0.00 SK781 GCS9 Cl* Melotte 22 SK 781 +03 40 14.91 +25 19 18.7 0 13.114 12.762 12.285 11.701 11.448 11.477 21.11 2.95 -40.08 2.95 0.67 0.87 SK785_HHJ397_DH131 GCS9 Cl* Melotte 22 SK 785 +03 40 15.93 +24 22 31.3 0 15.827 15.296 14.640 14.123 13.772 13.768 20.95 2.51 -35.43 2.51 4.56 0.34 BPL11 GCS9 Cl* Melotte 22 BPL 11 +03 40 23.06 +25 29 47.6 0 13.430 13.000 12.472 11.938 11.623 11.665 18.22 2.95 -41.47 2.95 0.50 0.93 HCG33_HHJ343_DH134 GCS9 Cl* Melotte 22 HCG 33 +03 40 23.84 +23 04 09.0 0 14.483 14.064 13.509 12.971 12.665 12.677 22.29 2.98 -42.37 2.98 0.58 0.90 HHJ162_DH135 GCS9 Cl* Melotte 22 HHJ 162 +03 40 24.20 +24 35 04.0 0 13.263 12.920 12.433 11.873 11.618 11.628 17.38 2.48 -40.53 2.48 0.86 0.91 HCG34_SK777_BPL12_DH136 GCS9 Cl* Melotte 22 HCG 34 +03 40 25.62 +24 06 00.0 0 14.659 14.287 13.684 13.159 12.829 12.878 20.15 2.50 -43.40 2.50 1.37 0.94 HHJ135_BPL13_DH138 GCS9 Cl* Melotte 22 HHJ 135 +03 40 26.27 +23 21 29.1 0 14.499 14.088 13.492 12.935 12.626 12.650 23.17 2.98 -38.33 2.98 0.57 0.71 HHJ167 GCS9 Cl* Melotte 22 HHJ 167 +03 40 26.41 +24 05 23.5 0 13.451 13.197 12.595 11.998 11.701 11.736 16.88 2.50 -38.77 2.50 1.79 0.84 SK778_HHJ338_BPL14_DH139 GCS9 Cl* Melotte 22 SK 778 +03 40 26.95 +24 14 14.2 0 14.326 13.942 13.369 12.825 12.542 12.531 18.94 2.50 -42.53 2.50 1.17 0.94 HHJ169_BPL15_DH140 GCS9 Cl* Melotte 22 HHJ 169 +03 40 27.93 +24 12 09.3 0 19.730 18.501 17.352 16.700 16.088 0.100 15.91 3.22 -42.54 3.22 5.78 0.62 int-pl-IZ-84;IPLJ0340279+241209_Y GCS9 Cl* Melotte 22 IPL 84 +03 40 31.17 +25 08 52.8 0 13.489 13.083 12.509 11.964 11.673 11.681 20.23 2.48 -43.75 2.48 2.05 0.93 HCG36_T102_HHJ329_DH142 GCS9 Cl* Melotte 22 HCG 36 +03 40 31.50 +23 33 02.0 0 12.655 12.386 11.899 11.459 11.074 11.141 21.40 2.12 -35.26 2.12 8.58 0.58 SK773_DH143 GCS9 Cl* Melotte 22 SK 773 +03 40 32.59 +25 28 40.6 0 15.026 14.501 13.946 13.420 13.114 13.099 10.36 2.23 -47.38 2.23 1.93 0.21 HHJ101_DH144 GCS9 Cl* Melotte 22 HHJ 101 +03 40 35.33 +20 57 56.6 0 13.012 12.647 12.149 11.582 11.260 11.302 23.55 3.58 -31.81 3.58 0.48 0.01 DH146 GCS9 Cl* Melotte 22 DH 146 +03 40 35.50 +23 13 07.4 0 17.972 16.910 16.076 15.510 15.014 15.020 12.82 2.37 -43.97 2.37 5.06 0.44 L07_A1_1 GCS9 +03 40 39.46 +23 26 34.8 0 16.232 15.654 15.015 14.460 14.071 14.075 15.56 2.27 -41.13 2.27 2.27 0.69 DH147_L07_A1_2 GCS9 Cl* Melotte 22 DH 147 +03 40 40.32 +25 50 48.1 0 14.039 13.616 13.025 12.447 12.168 12.150 17.46 2.23 -43.05 2.23 1.34 0.94 DH148 GCS9 Cl* Melotte 22 DH 148 +03 40 43.20 +22 49 53.8 0 14.757 14.295 13.730 13.177 12.897 12.868 17.03 2.25 -46.07 2.25 2.33 0.90 DH151_L07_193 GCS9 Cl* Melotte 22 DH 151 +03 40 43.27 +25 11 55.4 0 12.877 12.623 12.124 11.589 11.329 41.44 2.97 -27.65 2.97 3.03 0.00 SK754 GCS9 Cl* Melotte 22 SK 754 +03 40 49.40 +21 12 55.3 0 14.418 13.938 13.357 12.766 12.478 12.460 21.51 3.58 -35.17 3.58 0.41 0.39 DH152 GCS9 Cl* Melotte 22 DH 152 +03 40 51.08 +20 41 17.2 0 15.855 15.298 14.617 14.061 13.681 13.705 20.56 3.44 -38.49 3.44 0.28 0.75 DH155 GCS9 Cl* Melotte 22 DH 155 +03 40 51.46 +19 32 45.8 0 14.044 13.716 13.240 12.620 12.349 12.346 27.56 5.01 -46.86 5.01 1.30 0.17 DH157 GCS9 Cl* Melotte 22 DH 157 +03 40 51.82 +23 13 50.5 0 14.145 13.723 13.201 12.627 12.326 12.331 16.57 2.25 -41.87 2.25 1.21 0.93 HCG43_HHJ234_DH158_L07_169 GCS9 Cl* Melotte 22 HCG 43 +03 40 54.48 +22 54 25.5 0 14.916 14.411 13.824 13.282 12.957 12.960 17.93 2.25 -41.18 2.25 1.48 0.93 HHJ123_DH159_L07_182 GCS9 Cl* Melotte 22 HHJ 123 +03 40 55.05 +22 20 58.7 0 13.211 12.869 12.333 11.734 11.446 11.470 21.33 2.50 -35.72 2.50 1.06 0.40 HCG44_SK758_HHJ355_DH160 GCS9 Cl* Melotte 22 HCG 44 +03 40 55.30 +25 34 57.4 0 17.423 16.544 15.803 15.195 14.732 14.753 21.01 2.31 -41.51 2.31 7.86 0.69 L07_A1_4 GCS9 +03 40 56.06 +28 43 39.0 0 12.760 12.479 11.985 11.538 11.155 11.283 11.44 3.68 -37.58 3.68 0.85 0.21 DH161 GCS9 Cl* Melotte 22 DH 161 +03 40 59.26 +25 11 55.2 0 15.635 15.115 14.479 13.913 13.551 14.98 2.98 -43.05 2.98 0.63 0.86 HHJ41_DH162 GCS9 Cl* Melotte 22 HHJ 41 +03 41 00.39 +24 13 35.0 0 14.841 14.363 13.768 13.194 12.895 12.871 16.27 2.13 -39.79 2.13 0.81 0.89 BPL17 GCS9 Cl* Melotte 22 BPL 17 +03 41 01.99 +24 55 21.2 0 14.847 14.451 13.855 13.287 13.009 14.19 2.97 -32.57 2.97 1.14 0.03 HHJ126 GCS9 Cl* Melotte 22 HHJ 126 +03 41 02.99 +23 43 21.4 0 13.772 13.397 12.857 12.313 12.027 12.044 14.58 2.12 -38.42 2.12 0.61 0.71 HCG45_HHJ290_BPL18_DH163 GCS9 Cl* Melotte 22 HCG 45 +03 41 05.23 +23 50 14.9 0 15.720 15.171 14.571 14.019 13.727 13.698 14.75 2.14 -43.68 2.14 1.49 0.85 BPL19 GCS9 Cl* Melotte 22 BPL 19 +03 41 10.27 +25 45 55.9 0 13.996 13.553 13.015 12.467 12.161 12.180 18.53 2.23 -43.40 2.23 1.32 0.94 SK733_DH164 GCS9 Cl* Melotte 22 SK 733 +03 41 13.21 +24 05 25.6 0 20.248 19.168 17.791 16.898 16.325 0.166 14.11 3.48 -46.41 3.48 1.51 0.61 int-pl-IZ-81;IPLJ0341131+240525_Y GCS9 Cl* Melotte 22 IPL 81 +03 41 19.35 +23 51 41.7 0 14.700 14.230 13.634 13.065 12.795 12.782 15.93 2.13 -45.36 2.13 1.15 0.90 HCG48_DH167 GCS9 Cl* Melotte 22 HCG 48 +03 41 19.86 +25 06 49.0 0 14.586 14.170 13.598 13.072 12.788 18.41 2.97 -47.43 2.97 0.45 0.87 HHJ191 GCS9 Cl* Melotte 22 HHJ 191 +03 41 20.00 +22 37 53.7 0 13.771 13.385 12.814 12.255 11.965 11.999 11.28 2.50 -47.82 2.50 6.59 0.30 SK739_DH168 GCS9 Cl* Melotte 22 SK 739 +03 41 22.45 +24 23 51.6 0 15.326 14.820 14.189 13.641 13.290 13.310 17.58 2.13 -43.59 2.13 1.05 0.90 DH169 GCS9 Cl* Melotte 22 DH 169 +03 41 22.88 +23 55 48.1 0 14.135 13.702 13.150 12.615 12.323 12.335 24.42 2.12 -38.30 2.12 3.58 0.57 HCG49_HHJ233_BPL20_DH170 GCS9 Cl* Melotte 22 HCG 49 +03 41 23.63 +27 24 16.9 0 15.242 14.719 14.108 13.543 13.215 13.226 18.53 3.29 -35.23 3.29 0.33 0.40 DH171 GCS9 Cl* Melotte 22 DH 171 +03 41 24.65 +24 15 13.9 0 16.242 15.768 15.135 14.504 14.192 14.176 32.40 2.16 -41.17 2.16 1.95 0.00 DH172 GCS9 Cl* Melotte 22 DH 172 +03 41 24.69 +25 23 05.8 0 15.013 14.526 13.936 13.414 13.117 13.120 16.34 2.23 -38.60 2.23 3.60 0.78 HHJ117_L07_48 GCS9 Cl* Melotte 22 HHJ 117 +03 41 25.33 +22 32 55.8 0 13.437 13.076 12.526 11.946 11.699 11.718 20.54 2.50 -40.20 2.50 0.81 0.89 HCG51_SK732_HHJ345_DH173 GCS9 Cl* Melotte 22 HCG 51 +03 41 25.97 +23 15 35.8 0 14.670 14.193 13.617 13.061 12.761 12.768 19.37 2.25 -43.60 2.25 2.57 0.94 L07_170 GCS9 +03 41 26.36 +23 08 02.7 0 14.574 14.102 13.532 12.927 12.614 12.612 15.96 2.25 -41.75 2.25 11.80 0.92 DH174_L07_168 GCS9 Cl* Melotte 22 DH 174 +03 41 26.90 +24 01 02.3 0 13.364 12.912 12.305 11.845 11.430 11.454 25.17 2.12 -47.48 2.12 4.23 0.38 SK729_HHJ340_BPL21_DH175 GCS9 Cl* Melotte 22 SK 729 +03 41 29.39 +24 53 40.1 0 14.735 14.330 13.741 13.182 12.863 33.87 2.97 -32.81 2.97 1.30 0.00 Moraux2003_63 GCS9 Cl* Melotte 22 MBSC 63 +03 41 29.70 +24 32 26.5 0 13.241 12.967 12.496 11.906 11.680 23.06 2.97 -37.00 2.97 0.84 0.46 BPL22 GCS9 Cl* Melotte 22 BPL 22 +03 41 30.35 +25 17 05.9 0 16.646 15.972 15.208 14.682 14.349 14.526 13.78 2.27 -42.02 2.27 104.59 0.60 L07_A1_5_L07_214 GCS9 +03 41 31.21 +24 03 24.7 0 14.985 14.509 13.903 13.353 13.033 20.71 2.31 -39.58 2.31 2.29 0.89 HHJ90_BPL23_DH177 GCS9 Cl* Melotte 22 HHJ 90 +03 41 33.58 +27 09 50.1 0 15.162 14.567 13.912 13.377 13.020 13.045 19.34 3.29 -43.65 3.29 0.47 0.89 DH178 GCS9 Cl* Melotte 22 DH 178 +03 41 33.90 +23 11 44.9 0 16.186 15.643 15.029 14.509 14.159 14.137 30.84 2.27 -37.70 2.27 1.56 0.00 HHJ12_L07_A1_6 GCS9 Cl* Melotte 22 HHJ 12 +03 41 34.80 +23 38 15.6 0 15.153 14.666 14.080 13.522 13.242 13.186 15.91 2.13 -49.58 2.13 0.83 0.48 DH180 GCS9 Cl* Melotte 22 DH 180 +03 41 36.55 +22 41 01.7 0 16.280 15.724 15.122 14.591 14.266 14.232 32.02 2.27 -36.94 2.27 19.82 0.00 L07_photNM_21 GCS9 +03 41 37.26 +25 08 32.6 0 14.630 14.170 13.596 13.084 12.736 28.66 2.97 -44.60 2.97 1.75 0.12 HHJ148_DH182 GCS9 Cl* Melotte 22 HHJ 148 +03 41 38.81 +24 23 09.2 0 15.413 14.924 14.300 13.761 13.401 20.74 2.31 -48.47 2.31 2.29 0.59 BPL25 GCS9 Cl* Melotte 22 BPL 25 +03 41 38.87 +22 16 40.3 0 14.382 13.955 13.443 12.871 12.594 0.14 7.97 -57.33 7.97 1.28 0.00 HHJ176_DH183 GCS9 Cl* Melotte 22 HHJ 176 +03 41 39.84 +24 52 30.0 0 15.292 14.819 14.192 13.649 13.304 18.60 2.97 -44.72 2.97 1.71 0.88 HHJ69_Moraux2003_84 GCS9 Cl* Melotte 22 HHJ 69 +03 41 40.91 +25 54 24.1 1 16.893 16.001 15.180 14.574 14.122 14.125 16.94 2.26 -42.13 2.26 4.70 0.74 L07_A1_8_L07_248 GCS9 +03 41 41.46 +22 58 10.0 0 15.253 14.805 14.250 13.646 13.328 13.331 2.08 2.26 -35.29 2.26 3.25 0.00 L07_184 GCS9 +03 41 42.41 +23 54 57.1 1 16.171 15.464 14.709 14.124 13.686 14.07 2.31 -47.37 2.31 0.56 0.41 HHJ6_BPL26 GCS9 Cl* Melotte 22 HHJ 6 +03 41 43.72 +23 07 59.7 0 16.390 15.741 15.081 14.540 14.130 14.144 19.01 2.28 -38.92 2.28 5.75 0.60 L07_A1_9 GCS9 +03 41 45.07 +22 28 01.8 0 15.511 14.939 14.315 13.811 13.435 13.430 24.25 2.51 -44.15 2.51 2.91 0.59 DH185 GCS9 Cl* Melotte 22 DH 185 +03 41 46.44 +24 55 33.1 0 15.937 15.504 14.923 14.371 14.019 10.89 2.99 -39.19 2.99 1.45 0.34 DH186 GCS9 Cl* Melotte 22 DH 186 +03 41 47.09 +25 00 21.9 0 14.895 14.426 13.824 13.261 12.933 21.19 2.97 -41.96 2.97 0.91 0.92 HHJ125 GCS9 Cl* Melotte 22 HHJ 125 +03 41 48.05 +26 47 20.7 0 13.597 13.262 12.716 12.154 11.869 11.873 24.59 3.00 -39.18 3.00 0.69 0.53 HHJ357_DH188 GCS9 Cl* Melotte 22 HHJ 357 +03 41 49.60 +22 29 36.7 0 15.602 14.992 14.388 13.890 13.505 13.492 29.63 2.51 -46.82 2.51 3.79 0.01 DH189 GCS9 Cl* Melotte 22 DH 189 +03 41 51.50 +24 19 27.3 0 16.617 15.976 15.244 14.667 14.283 0.009 16.53 2.33 -37.48 2.33 3.69 0.46 int-pl-IZ-88;IPLJ0341515+241927_BPL27_Y GCS9 Cl* Melotte 22 IPL 88 +03 41 52.32 +24 41 57.6 0 14.986 14.480 13.904 13.361 13.012 18.58 2.97 -50.63 2.97 0.69 0.50 HHJ120_BPL28_DH190 GCS9 Cl* Melotte 22 HHJ 120 +03 41 52.94 +24 07 24.7 0 15.099 14.637 14.067 13.518 13.183 14.06 2.31 -43.29 2.31 2.22 0.82 HHJ108_BPL29_DH191 GCS9 Cl* Melotte 22 HHJ 108 +03 41 53.05 +27 04 42.9 0 14.776 14.268 13.678 13.174 12.842 12.833 15.90 3.29 -42.59 3.29 0.25 0.92 DH192 GCS9 Cl* Melotte 22 DH 192 +03 41 54.16 +23 05 04.7 1 17.349 16.376 15.522 14.975 14.415 14.418 18.19 2.30 -44.74 2.30 4.74 0.69 L07_A1_10 GCS9 +03 41 54.21 +25 43 47.1 0 13.586 13.204 12.696 12.122 11.843 11.866 18.41 2.23 -46.69 2.23 1.80 0.89 HCG55_B346_HHJ342_DH194 GCS9 Cl* Melotte 22 HCG 55 +03 41 56.49 +27 02 57.9 0 14.816 14.343 13.746 13.209 12.923 12.902 18.25 3.29 -47.59 3.29 0.17 0.86 DH195 GCS9 Cl* Melotte 22 DH 195 +03 41 56.72 +23 58 43.2 0 15.234 14.770 14.109 13.584 13.195 14.88 2.31 -33.12 2.31 0.99 0.07 BPL30 GCS9 Cl* Melotte 22 BPL 30 +03 41 58.66 +22 57 01.7 0 13.636 13.270 12.744 12.173 11.877 11.904 21.15 2.25 -41.08 2.25 10.25 0.90 HCG66_HHJ312_DH196_L07_16 GCS9 Cl* Melotte 22 HCG 66 +03 41 58.86 +26 12 21.1 0 13.697 13.292 12.754 12.161 11.889 11.902 14.33 3.00 -33.91 3.00 0.27 0.08 HCG56_B117_HHJ351_DH197_Moraux2003_16 GCS9 Cl* Melotte 22 HCG 56 +03 41 59.67 +24 42 18.3 0 16.192 15.586 14.953 14.380 14.016 16.64 2.99 -39.53 2.99 1.19 0.65 BPL31_Moraux2003_102 GCS9 Cl* Melotte 22 BPL 31 +03 41 59.75 +26 27 39.6 0 14.578 14.100 13.496 12.962 12.621 12.641 25.40 3.00 -41.25 3.00 0.32 0.64 HHJ213_DH200 GCS9 Cl* Melotte 22 HHJ 213 +03 42 00.02 +25 01 47.1 0 13.914 13.524 12.950 12.390 12.128 18.86 2.97 -46.97 2.97 1.15 0.88 SK702_HHJ277_DH201 GCS9 Cl* Melotte 22 SK 702 +03 42 00.05 +26 01 12.1 0 14.722 14.226 13.612 13.052 12.712 12.728 13.09 2.23 -31.95 2.23 3.88 0.01 L07_18 GCS9 +03 42 00.37 +24 10 12.6 0 16.212 15.647 14.984 14.441 14.064 17.68 2.32 -44.24 2.32 1.87 0.73 BPL32 GCS9 Cl* Melotte 22 BPL 32 +03 42 01.68 +22 23 26.6 0 14.215 13.807 13.265 12.703 12.396 33.36 7.96 -38.44 7.96 0.89 0.00 HCG67_HHJ201_DH202 GCS9 Cl* Melotte 22 HCG 67 +03 42 02.87 +24 12 36.0 0 13.316 12.980 12.469 11.883 11.629 12.04 2.30 -45.58 2.30 0.93 0.61 HCG63_HHJ350_BPL33_DH203 GCS9 Cl* Melotte 22 HCG 63 +03 42 02.93 +23 55 53.7 0 12.951 12.573 12.010 11.456 11.155 17.94 2.30 -39.33 2.30 0.46 0.87 HCG64_HHJ406_BPL34_DH204 GCS9 Cl* Melotte 22 HCG 64 +03 42 03.30 +24 32 13.3 0 13.337 13.002 12.464 11.883 11.656 17.31 2.97 -48.35 2.97 1.39 0.79 SK701_HHJ354_BPL35_DH205_Moraux2003_11 GCS9 Cl* Melotte 22 SK 701 +03 42 03.42 +25 22 39.1 0 14.406 13.913 13.303 12.729 12.430 12.430 22.60 2.23 -37.61 2.23 5.58 0.68 HHJ212_DH206_L07_41 GCS9 Cl* Melotte 22 HHJ 212 +03 42 04.16 +22 16 51.0 0 14.270 13.988 13.501 12.878 12.617 40.26 7.97 -17.56 7.97 1.17 0.00 SK708 GCS9 Cl* Melotte 22 SK 708 +03 42 05.74 +23 07 14.4 0 16.728 16.017 15.363 14.805 14.378 14.398 16.95 2.29 -37.00 2.29 2.73 0.41 L07_A1_11 GCS9 +03 42 08.29 +25 36 59.9 0 14.094 13.600 13.009 12.441 12.120 12.153 16.61 2.23 -37.66 2.23 2.38 0.77 HHJ270_DH211_L07_36 GCS9 Cl* Melotte 22 HHJ 270 +03 42 08.84 +23 35 16.8 0 12.944 12.632 12.126 11.626 11.318 11.344 14.70 2.12 -43.32 2.12 1.62 0.72 SK699_HHJ384_DH212 GCS9 Cl* Melotte 22 SK 699 +03 42 09.86 +27 57 25.2 0 13.631 13.340 12.849 12.206 11.986 11.978 33.43 3.68 -33.78 3.68 0.13 0.00 DH213 GCS9 Cl* Melotte 22 DH 213 +03 42 10.60 +22 18 05.5 0 15.177 14.710 14.151 13.625 13.280 10.07 8.00 -65.72 8.00 0.41 0.00 HHJ55 GCS9 Cl* Melotte 22 HHJ 55 +03 42 10.93 +24 05 08.4 0 12.830 12.470 11.954 11.688 11.337 16.83 2.30 -39.37 2.30 1.14 0.85 HCG68_T36_HHJ396_DH214 GCS9 Cl* Melotte 22 HCG 68 +03 42 10.99 +25 44 35.0 0 15.394 14.772 14.087 13.528 13.157 13.165 14.33 2.24 -42.76 2.24 3.83 0.83 L07_25 GCS9 +03 42 12.62 +26 49 44.9 0 14.299 13.821 13.209 12.670 12.342 12.345 19.56 3.00 -39.85 3.00 0.84 0.91 HHJ236_DH215 GCS9 Cl* Melotte 22 HHJ 236 +03 42 13.49 +24 18 49.6 0 15.627 15.114 14.453 13.891 13.540 13.15 2.31 -37.63 2.31 0.83 0.48 HHJ34_BPL36 GCS9 Cl* Melotte 22 HHJ 34 +03 42 13.90 +20 21 43.6 0 14.695 14.209 13.633 13.098 12.788 12.772 24.49 3.42 -44.30 3.42 0.53 0.76 DH216 GCS9 Cl* Melotte 22 DH 216 +03 42 15.37 +23 11 30.9 0 14.906 14.419 13.845 13.300 12.963 12.972 22.08 2.25 -38.79 2.25 1.60 0.81 HHJ109_L07_167 GCS9 Cl* Melotte 22 HHJ 109 +03 42 17.90 +24 06 57.6 0 14.857 14.378 13.774 13.232 12.897 17.09 2.30 -42.38 2.30 0.77 0.94 HHJ129_BPL37_DH217 GCS9 Cl* Melotte 22 HHJ 129 +03 42 18.88 +23 59 22.0 0 12.658 12.428 11.944 11.452 11.259 25.66 2.30 -50.92 2.30 2.16 0.01 SK687_HHJ437 GCS9 Cl* Melotte 22 SK 687 +03 42 22.13 +25 03 56.3 0 15.564 15.206 14.659 14.050 13.781 24.49 2.98 -48.94 2.98 8.93 0.19 DH219 GCS9 Cl* Melotte 22 DH 219 +03 42 26.28 +23 51 38.5 0 15.456 14.895 14.283 13.744 13.347 13.357 10.50 2.13 -43.07 2.13 2.55 0.42 HHJ49_BPL38_DH221 GCS9 Cl* Melotte 22 HHJ 49 +03 42 26.29 +24 14 07.7 0 14.155 13.741 13.173 12.659 12.325 12.320 10.47 2.12 -47.50 2.12 2.98 0.21 HCG73_SK680_HHJ262_BPL39_DH223 GCS9 Cl* Melotte 22 HCG 73 +03 42 27.31 +22 34 24.6 0 13.652 13.223 12.660 12.145 11.830 11.851 14.98 2.51 -42.82 2.51 1.87 0.90 HCG76_HHJ294_DH224 GCS9 Cl* Melotte 22 HCG 76 +03 42 28.43 +27 12 58.1 0 16.484 16.050 15.500 14.978 14.679 14.670 9.87 3.37 -42.08 3.37 0.41 0.18 DH226 GCS9 Cl* Melotte 22 DH 226 +03 42 28.66 +25 01 00.2 0 13.341 13.007 12.475 11.969 11.689 19.64 2.97 -44.22 2.97 1.06 0.93 SK676_HHJ362_DH227 GCS9 Cl* Melotte 22 SK 676 +03 42 29.43 +22 47 25.9 0 12.560 12.251 11.750 11.449 10.885 11.017 19.42 2.25 -40.94 2.25 7.62 0.90 HCG77_A80_DH228 GCS9 Cl* Melotte 22 HCG 77 +03 42 29.51 +22 23 45.7 0 12.961 12.706 12.271 11.802 11.535 25.41 7.95 -63.14 7.95 0.89 0.00 SK682 GCS9 Cl* Melotte 22 SK 682 +03 42 29.71 +20 48 39.6 0 14.264 13.827 13.235 12.712 12.379 12.411 20.54 3.42 -38.07 3.42 0.39 0.82 DH229 GCS9 Cl* Melotte 22 DH 229 +03 42 31.21 +24 49 21.2 0 15.091 14.553 13.952 13.433 13.118 23.06 2.97 -38.50 2.97 8.89 0.56 HCG74_HHJ82_DH230_Moraux2003_76 GCS9 Cl* Melotte 22 HCG 74 +03 42 33.97 +24 11 00.5 0 15.453 14.926 14.319 13.774 13.408 13.422 11.59 2.13 -52.78 2.13 8.09 0.02 BPL40_DH231 GCS9 Cl* Melotte 22 BPL 40 +03 42 36.27 +23 22 04.7 0 14.057 13.676 13.118 12.571 12.298 12.267 16.16 2.25 -37.36 2.25 1.08 0.73 HCG79_HHJ241_HHJ241_DH232 GCS9 Cl* Melotte 22 HCG 79 +03 42 36.95 +25 13 57.5 0 15.243 14.744 14.157 13.605 13.246 14.72 2.97 -38.46 2.97 2.57 0.70 DH233 GCS9 Cl* Melotte 22 DH 233 +03 42 40.23 +23 59 21.5 0 12.859 12.500 11.988 11.460 11.096 11.160 17.01 2.12 -46.73 2.12 1.30 0.59 HCG80_T3_HHJ409_DH234 GCS9 Cl* Melotte 22 HCG 80 +03 42 41.18 +24 01 42.7 0 14.985 14.503 13.921 13.357 13.014 13.025 20.15 2.13 -41.62 2.13 0.54 0.93 HCG82_HHJ115_BPL41_DH235 GCS9 Cl* Melotte 22 HCG 82 +03 42 41.85 +24 00 15.5 0 14.496 14.076 13.515 12.954 12.636 12.650 18.92 2.12 -44.12 2.12 0.31 0.94 BPL42_DH236 GCS9 Cl* Melotte 22 BPL 42 +03 42 42.12 +25 11 48.7 0 15.492 14.954 14.328 13.773 13.367 19.99 2.97 -46.20 2.97 1.90 0.82 HHJ45_DH237 GCS9 Cl* Melotte 22 HHJ 45 +03 42 42.41 +23 20 21.5 0 13.380 12.965 12.389 11.853 11.534 11.549 20.26 2.25 -20.48 2.25 1.34 0.00 HCG86_DH238 GCS9 Cl* Melotte 22 HCG 86 +03 42 43.81 +25 32 06.2 0 13.613 13.213 12.673 12.087 11.829 11.818 18.81 2.23 -44.63 2.23 2.28 0.93 SK665_HHJ341_DH239 GCS9 Cl* Melotte 22 SK 665 +03 42 44.37 +23 06 16.1 0 14.900 14.423 13.813 13.265 12.950 12.934 14.75 2.25 -47.61 2.25 5.30 0.75 HHJ119_DH240_L07_176 GCS9 Cl* Melotte 22 HHJ 119 +03 42 44.46 +23 58 15.2 0 15.186 14.686 14.062 13.536 13.187 13.188 15.12 2.13 -48.66 2.13 4.84 0.57 BPL43_DH241 GCS9 Cl* Melotte 22 BPL 43 +03 42 45.65 +23 49 22.6 0 15.473 14.898 14.286 13.714 13.361 13.361 22.64 2.13 -48.82 2.13 2.57 0.38 HHJ52_BPL44 GCS9 Cl* Melotte 22 HHJ 52 +03 42 47.30 +23 00 40.3 0 17.056 16.290 15.554 14.991 14.575 14.576 14.60 2.31 -44.06 2.31 5.33 0.59 L07_A1_13 GCS9 +03 42 48.17 +24 04 01.2 0 17.739 16.847 16.038 15.466 14.936 0.020 19.17 2.24 -33.98 2.24 3.95 0.16 int-pl-IZ-29;IPLJ0342481+240401_BPL45_Y GCS9 Cl* Melotte 22 IPL 29 +03 42 48.91 +23 34 48.4 0 14.623 14.157 13.593 13.067 12.741 12.742 24.86 2.13 -47.62 2.13 1.71 0.49 HHJ153_DH242 GCS9 Cl* Melotte 22 HHJ 153 +03 42 49.11 +24 10 15.4 0 13.648 13.221 12.670 12.134 11.801 11.828 31.63 2.12 -28.60 2.12 1.47 0.00 SK663_BPL46_DH243 GCS9 Cl* Melotte 22 SK 663 +03 42 51.68 +23 08 43.7 0 14.593 14.085 13.451 12.866 12.527 12.518 20.56 2.25 -41.00 2.25 6.70 0.92 L07_177 GCS9 +03 42 54.00 +26 08 16.1 0 14.730 14.262 13.672 13.136 12.798 12.810 19.45 2.23 -43.40 2.23 4.13 0.94 HHJ146_DH244_L07_22 GCS9 Cl* Melotte 22 HHJ 146 +03 42 55.88 +22 38 00.4 0 16.581 16.069 15.450 14.845 14.501 0.009 25.97 2.55 -3.49 2.55 1.79 0.00 int-pl-IZ-78;2MASSJ0342558+223800_Y GCS9 Cl* Melotte 22 IPL 78 +03 42 56.55 +22 51 17.9 0 15.901 15.338 14.689 14.137 13.782 13.760 16.35 2.27 -38.51 2.27 4.45 0.78 L07_191 GCS9 +03 42 56.55 +24 04 57.8 0 12.916 12.529 12.006 11.543 11.156 11.214 16.43 2.12 -38.79 2.12 1.08 0.82 HCG93_SK658_HHJ401_BPL48_DH245 GCS9 Cl* Melotte 22 HCG 93 +03 42 56.57 +24 13 45.4 0 14.910 14.439 13.859 13.319 12.979 12.984 21.34 2.13 -42.10 2.13 0.76 0.92 HCG92_BPL47 GCS9 Cl* Melotte 22 HCG 92 +03 42 58.60 +20 12 45.0 0 14.947 14.424 13.773 13.194 12.857 12.840 18.85 3.42 -32.72 3.42 0.30 0.10 DH247 GCS9 Cl* Melotte 22 DH 247 +03 42 59.92 +22 42 51.5 0 16.085 15.349 14.650 14.118 13.712 13.703 25.82 2.27 -43.19 2.27 33.85 0.17 L07_A1_14_L07_245 GCS9 +03 43 00.17 +24 43 52.3 0 17.791 16.841 16.022 15.425 14.969 32.72 3.09 -51.43 3.09 12.65 0.00 BPL49_CFHT-Pl-17_M7.9 GCS9 Cl* Melotte 22 BPL 49 +03 43 01.27 +24 14 52.2 0 15.292 14.794 14.179 13.655 13.284 13.296 22.91 2.13 -39.81 2.13 2.12 0.68 BPL50 GCS9 Cl* Melotte 22 BPL 50 +03 43 01.40 +23 29 30.4 0 14.868 14.435 13.861 13.324 13.014 13.042 18.85 2.25 -41.62 2.25 1.47 0.94 L07_158 GCS9 +03 43 03.83 +23 54 19.6 0 18.852 17.703 16.751 16.052 15.562 0.046 18.37 2.48 -38.49 2.48 2.92 0.66 int-pl-IZ-25;2MASSJ0343038+235420_Y GCS9 Cl* Melotte 22 IPL 25 +03 43 04.20 +22 48 03.3 0 12.462 12.136 11.621 11.436 10.779 10.986 18.93 2.25 -40.66 2.25 6.22 0.90 HCG101_B173_HHJ428_DH251 GCS9 Cl* Melotte 22 HCG 101 +03 43 04.37 +25 26 12.0 0 14.931 14.396 13.757 13.216 12.892 12.874 16.25 2.23 -36.14 2.23 8.13 0.56 HHJ107 GCS9 Cl* Melotte 22 HHJ 107 +03 43 05.54 +24 49 28.3 0 12.492 12.121 11.627 11.517 11.107 21.32 2.97 -42.76 2.97 2.77 0.86 HCG97_HHJ432_DH252 GCS9 Cl* Melotte 22 HCG 97 +03 43 06.50 +22 17 49.2 0 15.872 15.301 14.649 14.103 13.726 13.720 23.39 2.52 -40.65 2.52 4.64 0.67 HHJ18 GCS9 Cl* Melotte 22 HHJ 18 +03 43 07.58 +25 34 29.0 0 14.063 13.611 13.036 12.459 12.172 12.204 19.71 2.23 -40.51 2.23 2.69 0.92 HCG96_DH253_L07_34 GCS9 Cl* Melotte 22 HCG 96 +03 43 09.75 +24 41 32.7 0 13.167 12.734 12.234 11.768 11.425 23.06 2.97 -47.52 2.97 2.88 0.67 HCG100_SK654_T40_HHJ383_BPL51_DH254_Moraux2003_2 GCS9 Cl* Melotte 22 HCG 100 +03 43 11.57 +25 25 22.9 0 13.868 13.412 12.861 12.355 12.020 12.040 6.10 2.23 -53.18 2.23 6.99 0.00 SK650 GCS9 Cl* Melotte 22 SK 650 +03 43 11.65 +24 06 52.4 0 15.210 14.680 14.044 13.501 13.160 13.172 16.13 2.13 -38.16 2.13 3.13 0.74 HHJ71_BPL52 GCS9 Cl* Melotte 22 HHJ 71 +03 43 11.77 +25 31 31.9 0 16.035 15.427 14.767 14.237 13.883 13.872 18.38 2.25 -44.54 2.25 12.60 0.72 L07_A1_15 GCS9 +03 43 12.12 +24 44 45.1 0 13.538 13.088 12.533 12.023 11.684 21.38 2.97 -49.22 2.97 1.92 0.61 HCG102_SK653_HHJ328_BPL53_DH255 GCS9 Cl* Melotte 22 HCG 102 +03 43 13.07 +24 39 19.3 0 13.055 12.669 12.151 11.608 11.286 20.98 2.97 -41.82 2.97 1.16 0.91 HCG103_SK652_HHJ395_BPL54_DH256 GCS9 Cl* Melotte 22 HCG 103 +03 43 15.74 +25 20 29.5 0 13.094 12.779 12.277 11.779 11.507 11.524 15.79 2.23 -24.92 2.23 0.89 0.00 SK646 GCS9 Cl* Melotte 22 SK 646 +03 43 16.61 +23 50 01.5 0 14.279 13.794 13.239 12.641 12.382 12.371 15.68 2.12 -43.30 2.12 0.89 0.92 HHJ206_BPL56_DH258 GCS9 Cl* Melotte 22 HHJ 206 +03 43 16.88 +23 59 56.4 0 18.673 17.896 17.104 16.502 16.126 0.042 31.62 2.69 -8.78 2.69 3.68 0.00 int-pl-IZ-24;IPLJ0343168+235956_N GCS9 Cl* Melotte 22 IPL 24 +03 43 18.47 +26 40 24.3 0 13.001 12.604 12.085 11.533 11.239 11.287 49.10 2.94 -45.26 2.94 0.27 0.00 HHJ413 GCS9 Cl* Melotte 22 HHJ 413 +03 43 18.56 +26 30 18.9 0 15.438 14.940 14.415 13.971 13.634 13.654 24.39 2.95 -50.80 2.95 0.64 0.07 HHJ89 GCS9 Cl* Melotte 22 HHJ 89 +03 43 19.01 +22 47 10.4 0 14.679 14.176 13.584 13.040 12.725 12.734 16.91 2.25 -46.41 2.25 2.58 0.89 HCG110_HHJ143_DH259 GCS9 Cl* Melotte 22 HCG 110 +03 43 19.06 +26 04 43.9 0 14.168 13.751 13.169 12.614 12.292 12.355 13.73 2.23 -40.04 2.23 0.67 0.77 SK642_HHJ279_DH260 GCS9 Cl* Melotte 22 SK 642 +03 43 20.58 +24 26 34.9 0 15.208 14.646 14.060 13.490 13.168 18.87 2.97 -40.83 2.97 0.91 0.88 HCG106_HHJ88_BPL57_DH261_Moraux2003_79 GCS9 Cl* Melotte 22 HCG 106 +03 43 21.12 +24 11 09.0 0 15.279 14.803 14.182 13.683 13.346 13.361 11.26 2.13 -42.71 2.13 2.07 0.55 HHJ65 GCS9 Cl* Melotte 22 HHJ 65 +03 43 22.55 +23 00 56.5 0 16.186 15.558 14.898 14.365 13.983 13.988 16.28 2.27 -44.63 2.27 2.24 0.70 L07_A1_16 GCS9 +03 43 24.99 +23 52 01.2 0 16.867 16.116 15.368 14.820 14.365 0.011 14.16 2.34 -46.02 2.34 1.59 0.53 int-pl-IZ-6;2MASSJ0343249+235201_BPL58_Y GCS9 Cl* Melotte 22 IPL 6 +03 43 25.16 +22 53 44.3 0 14.833 14.335 13.716 13.175 12.858 12.813 14.86 2.25 -45.95 2.25 1.39 0.85 HHJ134_DH263_L07_179 GCS9 Cl* Melotte 22 HHJ 134 +03 43 26.20 +26 02 30.6 0 13.718 13.336 12.777 12.204 11.899 11.946 16.58 2.23 -41.36 2.23 3.41 0.92 HCG105_SK633_HHJ327_DH264 GCS9 Cl* Melotte 22 HCG 105 +03 43 26.44 +22 42 42.5 0 14.138 13.675 13.111 12.543 12.249 12.216 13.70 2.25 -37.63 2.25 1.69 0.56 HCG114_SK644_HHJ261_DH265_L07_189 GCS9 Cl* Melotte 22 HCG 114 +03 43 26.98 +24 27 09.6 0 13.246 12.790 12.281 11.739 11.444 16.27 2.97 -42.45 2.97 0.43 0.92 SK638_HHJ368_BPL59_DH267_Moraux2003_6 GCS9 Cl* Melotte 22 SK 638 +03 43 27.44 +22 37 41.0 0 15.234 14.678 14.118 13.589 13.256 13.235 23.77 2.51 -43.48 2.51 1.02 0.67 HHJ70 GCS9 Cl* Melotte 22 HHJ 70 +03 43 28.21 +24 53 30.9 0 13.950 13.546 12.992 12.414 12.165 18.00 2.97 -39.09 2.97 0.59 0.87 HCG109_SK635_DH268 GCS9 Cl* Melotte 22 HCG 109 +03 43 28.74 +24 09 06.1 0 15.175 14.685 14.073 13.555 13.200 17.98 2.31 -42.91 2.31 0.61 0.90 HHJ76 GCS9 Cl* Melotte 22 HHJ 76 +03 43 29.90 +24 39 23.4 0 15.428 14.876 14.288 13.760 13.405 11.50 2.98 -40.55 2.98 4.29 0.52 HHJ50_DH269_Moraux2003_87 GCS9 Cl* Melotte 22 HHJ 50 +03 43 34.14 +25 35 25.8 0 13.777 13.360 12.813 12.234 11.958 11.947 16.72 2.23 -43.78 2.23 1.22 0.93 HCG112_DH270_Moraux2003_19 GCS9 Cl* Melotte 22 HCG 112 +03 43 34.49 +25 57 30.6 1 16.571 15.727 14.909 14.359 13.909 13.901 20.92 2.25 -47.72 2.25 16.35 0.40 L07_A1_17 GCS9 +03 43 35.22 +25 24 30.8 0 13.655 13.247 12.728 12.188 11.914 11.926 10.07 2.23 -45.01 2.23 1.09 0.30 SK622_HHJ337_DH272 GCS9 Cl* Melotte 22 SK 622 +03 43 36.57 +23 12 34.1 0 14.208 13.781 13.203 12.644 12.333 12.343 23.03 2.25 -39.25 2.25 6.15 0.79 HCG122_T6_HHJ205_DH274_L07_174 GCS9 Cl* Melotte 22 HCG 122 +03 43 36.68 +25 47 00.5 0 13.800 13.402 12.848 12.282 12.004 11.994 21.04 2.23 -46.84 2.23 1.31 0.85 SK619_DH276_Moraux2003_22 GCS9 Cl* Melotte 22 SK 619 +03 43 37.11 +23 38 31.9 0 13.785 13.328 12.738 12.198 11.870 17.19 2.30 -45.95 2.30 0.60 0.91 SK630_DH278 GCS9 Cl* Melotte 22 SK 630 +03 43 37.32 +25 24 32.0 0 13.423 13.034 12.513 11.985 11.664 11.701 12.47 2.23 -45.17 2.23 1.32 0.69 HCG115_SK620_HHJ377_DH279 GCS9 Cl* Melotte 22 HCG 115 +03 43 39.05 +23 44 05.2 0 14.252 13.733 13.145 12.592 12.212 19.59 2.30 -41.08 2.30 5.74 0.93 HCG124_SK624_BPL61_DH281 GCS9 Cl* Melotte 22 HCG 124 +03 43 39.72 +23 41 32.4 0 15.672 15.129 14.470 13.912 13.544 18.33 2.31 -45.77 2.31 0.60 0.86 HHJ40 GCS9 Cl* Melotte 22 HHJ 40 +03 43 40.31 +24 30 11.2 0 18.552 17.490 16.494 15.853 15.304 12.03 3.27 -44.27 3.27 2.96 0.36 BPL62_Roque7_CFHT-Pl-24_M8.3 GCS9 Cl* Melotte 22 BPL 62 +03 43 42.14 +24 34 23.1 0 12.680 12.347 11.834 11.451 11.133 21.23 2.97 -39.67 2.97 3.04 0.87 HCG123_SK616_MT41_DH282 GCS9 Cl* Melotte 22 HCG 123 +03 43 42.90 +25 51 37.0 0 15.191 14.695 14.098 13.525 13.202 13.195 21.00 2.23 -46.18 2.23 7.04 0.78 HHJ77_Moraux2003_81_L07_27 GCS9 Cl* Melotte 22 HHJ 77 +03 43 43.15 +24 32 56.0 0 15.104 14.614 14.005 13.427 13.151 12.39 2.97 -42.08 2.97 0.40 0.69 DH283_Moraux2003_77 GCS9 Cl* Melotte 22 DH 283 +03 43 43.53 +24 12 50.0 0 15.544 15.025 14.407 13.872 13.518 18.44 2.31 -44.45 2.31 1.15 0.89 HHJ43_DH284 GCS9 Cl* Melotte 22 HHJ 43 +03 43 43.71 +24 29 15.5 0 13.243 12.898 12.337 11.802 11.502 18.55 2.97 -50.23 2.97 2.25 0.55 HHJ374_BPL63_DH285_Moraux2003_5 GCS9 Cl* Melotte 22 HHJ 374 +03 43 44.09 +25 39 49.5 0 15.071 14.589 13.986 13.428 13.120 13.122 17.56 2.23 -44.02 2.23 2.38 0.90 Moraux2003_72_L07_32 GCS9 Cl* Melotte 22 MBSC 72 +03 43 44.97 +23 03 21.1 0 13.553 13.162 12.604 12.043 11.693 11.711 17.85 2.25 -47.74 2.25 2.21 0.84 SK618_HHJ321_DH286 GCS9 Cl* Melotte 22 SK 618 +03 43 46.29 +23 58 37.9 0 19.124 17.930 16.876 16.248 15.628 0.057 20.13 2.62 -45.01 2.62 6.23 0.69 int-pl-IZ-20;IPLJ0343462+235838_Y GCS9 Cl* Melotte 22 IPL 20 +03 43 47.08 +26 04 35.3 0 13.857 13.453 12.895 12.305 12.017 12.025 19.24 2.23 -41.90 2.23 9.19 0.93 SK607_HHJ311_DH287 GCS9 Cl* Melotte 22 SK 607 +03 43 47.71 +28 15 59.3 0 14.415 14.156 13.720 13.100 12.988 12.927 15.96 3.69 -40.79 3.69 0.74 0.90 DH2004_289 GCS9 Cl* Melotte 22 DH 289 +03 43 48.45 +25 02 36.7 0 13.856 13.401 12.797 12.260 11.888 20.40 2.97 -43.69 2.97 0.33 0.93 HCG125_HHJ298_DH292 GCS9 Cl* Melotte 22 HCG 125 +03 43 50.56 +23 05 47.7 0 16.501 15.963 15.297 14.681 14.285 0.009 24.32 2.28 -51.66 2.28 5.95 0.02 int-pl-IZ-53_2MASSJ0343505+230548_Y_L07_241 GCS9 Cl* Melotte 22 IPL 53 +03 43 51.38 +21 09 03.1 0 14.541 14.138 13.586 13.020 12.740 12.734 30.15 2.65 -36.85 2.65 1.50 0.01 DH295 GCS9 Cl* Melotte 22 DH 295 +03 43 51.76 +24 14 15.9 0 14.292 13.838 13.229 12.650 12.358 22.34 2.30 -41.81 2.30 1.45 0.90 HCG128_BPL65_DH296 GCS9 Cl* Melotte 22 HCG 128 +03 43 52.15 +24 50 29.5 0 12.141 11.914 11.457 11.366 10.988 18.96 2.97 -35.05 2.97 4.37 0.60 HII191_HCG127_SK606_DH297 GCS9 HII191 +03 43 52.79 +25 29 30.3 0 14.450 13.990 13.414 12.853 12.576 12.580 22.91 2.23 -45.90 2.23 4.71 0.83 HHJ218_DH298Moraux2003_37 GCS9 Cl* Melotte 22 HHJ 218 +03 43 53.55 +24 31 11.4 0 18.970 17.709 16.649 15.942 15.298 13.04 3.29 -53.30 3.29 6.40 0.02 BPL66_Roque4 GCS9 Cl* Melotte 22 BPL 66 +03 43 53.88 +25 28 30.0 0 13.156 12.789 12.268 11.797 11.424 11.455 23.08 2.23 -46.36 2.23 3.97 0.76 HCG126_SK600_T69_HHJ391_DH299 GCS9 Cl* Melotte 22 HCG 126 +03 43 56.00 +25 36 25.2 0 16.718 15.979 15.290 14.710 14.319 14.333 23.18 2.27 -46.03 2.27 11.61 0.36 PLZJ50_L07_A1_18_L07_249 GCS9 Cl* Melotte 22 PlZJ 50 +03 43 56.70 +25 15 43.9 0 12.995 12.666 12.168 11.616 11.330 21.03 2.97 -42.94 2.97 0.51 0.86 SK596_DH300 GCS9 Cl* Melotte 22 SK 596 +03 43 56.71 +24 59 36.5 0 13.167 12.845 12.319 11.756 11.484 27.73 2.97 -45.73 2.97 1.35 0.13 HCG129_SK598_HHJ382_DH301 GCS9 Cl* Melotte 22 HCG 129 +03 43 57.00 +23 57 05.7 0 14.157 13.723 13.145 12.582 12.265 21.91 2.30 -44.52 2.30 1.37 0.90 HCG135_HHJ229_DH302 GCS9 Cl* Melotte 22 HCG 135 +03 43 57.29 +24 13 20.3 0 14.206 13.775 13.190 12.639 12.337 21.78 2.30 -37.51 2.30 1.14 0.72 HCG133_SK601_BPL67_DH303 GCS9 Cl* Melotte 22 HCG 133 +03 44 02.29 +25 03 53.5 0 12.928 12.605 12.098 11.550 11.827 17.46 2.97 -49.03 2.97 1.22 0.26 HCG134_SK591_T150_DH306 GCS9 Cl* Melotte 22 HCG 134 +03 44 02.50 +21 13 15.8 0 14.767 14.282 13.665 13.123 12.781 12.814 14.96 2.65 -40.08 2.65 0.41 0.85 DH307 GCS9 Cl* Melotte 22 DH 307 +03 44 05.25 +22 50 13.5 0 20.066 18.839 17.666 16.895 16.134 0.147 19.70 3.13 -45.22 3.13 3.08 0.57 int-pl-IZ-57;IPLJ0344052+225014_N GCS9 Cl* Melotte 22 IPL 57 +03 44 05.63 +23 03 42.4 0 15.169 14.803 14.258 13.565 13.302 13.294 17.87 2.26 -43.49 2.26 1.94 0.90 L07_180 GCS9 +03 44 08.81 +23 04 47.5 0 12.721 12.442 11.944 11.503 11.108 11.160 24.16 2.25 -46.50 2.25 1.05 0.39 HCG141_SK590_T71_DH312 GCS9 Cl* Melotte 22 HCG 141 +03 44 09.32 +23 08 46.8 0 14.412 13.974 13.380 12.815 12.486 12.506 19.99 2.25 -40.13 2.25 4.90 0.91 HHJ186_DH313_L07_172 GCS9 Cl* Melotte 22 HHJ 186 +03 44 09.61 +24 35 22.1 0 13.826 13.427 12.892 12.314 12.044 21.90 2.97 -41.53 2.97 0.60 0.89 SK586_DH314 GCS9 Cl* Melotte 22 SK 586 +03 44 09.92 +24 16 03.9 0 13.292 12.928 12.379 11.781 11.527 20.69 2.30 -36.49 2.30 0.27 0.57 HCG140_DH315 GCS9 Cl* Melotte 22 HCG 140 +03 44 10.76 +25 37 38.2 0 14.362 13.886 13.296 12.733 12.437 12.445 11.81 2.23 -44.39 2.23 6.39 0.62 HHJ235_DH316_Moraux2003_36 GCS9 Cl* Melotte 22 HHJ 235 +03 44 11.28 +24 52 34.2 0 15.374 14.904 14.280 13.727 13.434 24.96 2.98 -35.52 2.98 3.05 0.09 HHJ59 GCS9 Cl* Melotte 22 HHJ 59 +03 44 11.90 +22 53 36.9 0 15.170 14.818 14.272 13.668 13.416 13.423 12.90 2.26 -60.48 2.26 6.74 0.00 L07_178 GCS9 +03 44 11.92 +19 18 19.1 0 12.681 12.379 11.886 11.382 10.992 11.107 22.77 3.96 -43.73 3.96 2.20 0.77 DH318 GCS9 Cl* Melotte 22 DH 318 +03 44 12.14 +23 52 37.3 0 14.442 13.972 13.408 12.855 12.547 18.41 2.30 -47.45 2.30 1.87 0.87 HHJ217_DH319 GCS9 Cl* Melotte 22 HHJ 217 +03 44 13.97 +25 32 15.3 0 13.648 13.147 12.531 11.991 11.643 11.691 -5.25 2.23 -36.15 2.23 5.79 0.00 HCG138_SK579_DH320_Moraux2003_25 GCS9 Cl* Melotte 22 HCG 138 +03 44 16.46 +23 37 04.0 0 13.729 13.270 12.706 12.148 11.847 18.08 2.30 -44.62 2.30 1.22 0.93 HCG144_SK580_HHJ301_DH322 GCS9 Cl* Melotte 22 HCG 144 +03 44 17.76 +24 26 46.7 0 13.476 13.079 12.533 11.925 11.668 19.37 2.97 -46.95 2.97 0.14 0.88 HCG145_SK576_HHJ352_DH323 GCS9 Cl* Melotte 22 HCG 145 +03 44 19.06 +24 35 18.2 0 14.319 13.882 13.291 12.722 12.476 17.45 2.97 -46.24 2.97 0.54 0.90 HCG146_T18B_HHJ228_DH324 GCS9 Cl* Melotte 22 HCG 146 +03 44 20.66 +24 15 10.7 0 15.211 14.648 13.992 13.469 13.087 12.94 2.31 -38.96 2.31 0.51 0.59 HHJ57 GCS9 Cl* Melotte 22 HHJ 57 +03 44 20.87 +23 33 39.8 0 13.825 13.404 12.863 12.282 11.993 3.71 2.30 -36.55 2.30 6.76 0.00 HCG155_SK575_HHJ300_DH327 GCS9 Cl* Melotte 22 HCG 155 +03 44 22.14 +23 10 54.8 0 15.807 15.195 14.479 13.913 13.468 13.518 16.09 2.26 -42.11 2.26 1.84 0.88 DH329_L07_173 GCS9 Cl* Melotte 22 DH 329 +03 44 22.45 +23 39 01.3 0 19.107 17.857 16.874 16.254 15.666 15.660 15.39 2.40 -42.54 2.40 3.20 0.61 Roque5_L07_A1_19_L07_261 GCS9 Cl* Melotte 22 Roque 5 +03 44 23.24 +25 38 44.9 0 16.319 15.457 14.698 14.154 13.731 13.721 18.31 2.25 -50.40 2.25 6.34 0.20 BRB4_PLZJ29_L07_A1_78 GCS9 Cl* Melotte 22 BRB 4 +03 44 23.40 +25 21 29.9 0 13.413 12.983 12.404 11.920 11.628 11.662 8.92 2.23 -42.15 2.23 9.13 0.15 HCG143_SK568_T19B_HHJ348_DH331 GCS9 Cl* Melotte 22 HCG 143 +03 44 24.00 +21 24 20.8 0 15.184 14.768 14.212 13.673 13.353 13.359 16.88 2.66 -43.67 2.66 3.40 0.89 DH332 GCS9 Cl* Melotte 22 DH 332 +03 44 24.68 +24 51 53.2 0 13.805 13.361 12.765 12.246 11.942 18.59 2.97 -47.54 2.97 1.21 0.86 HCG148_SK569_HHJ307_DH333 GCS9 Cl* Melotte 22 HCG 148 +03 44 24.82 +24 46 06.0 0 12.688 12.372 11.878 11.507 11.202 17.32 2.97 -50.88 2.97 2.49 0.07 HCG149_SK570_T42B_DH334 GCS9 Cl* Melotte 22 HCG 149 +03 44 25.08 +25 34 03.9 0 15.079 14.489 13.830 13.302 12.949 12.973 19.98 2.23 -41.60 2.23 8.95 0.88 HHJ100_Moraux2003_75_L07_31 GCS9 Cl* Melotte 22 HHJ 100 +03 44 25.51 +22 27 49.8 0 16.405 15.794 15.102 14.589 14.223 14.185 22.07 2.53 -37.99 2.53 1.49 0.35 DH335_int-pl-IZ-72;2MASSJ0344255+222749_Y GCS9 Cl* Melotte 22 DH 335 +03 44 25.58 +22 40 07.9 0 16.220 15.599 14.929 14.388 14.003 14.006 11.28 2.25 -40.25 2.25 34.57 0.28 L07_A1_21_L07_246 GCS9 +03 44 25.60 +24 40 52.6 0 13.443 13.016 12.496 11.928 11.633 13.39 2.97 -47.71 2.97 1.86 0.62 HCG150_SK567_B179_HHJ364_DH336 GCS9 Cl* Melotte 22 HCG 150 +03 44 26.54 +24 29 11.3 0 14.133 13.585 12.995 12.394 12.124 20.35 2.97 -40.21 2.97 1.10 0.91 HHJ243_DH338 GCS9 Cl* Melotte 22 HHJ 243 +03 44 26.89 +24 24 31.5 0 13.155 12.822 12.306 11.723 11.468 3.74 2.97 -30.56 2.97 12.86 0.00 HCG156_HHJ400_DH339 GCS9 Cl* Melotte 22 HCG 156 +03 44 27.50 +24 14 17.0 0 14.633 14.101 13.503 12.965 12.632 12.628 20.52 2.22 -45.35 2.22 0.91 0.92 HHJ190_DH341_L07_97 GCS9 Cl* Melotte 22 HHJ 190 +03 44 27.92 +23 59 59.6 0 15.633 15.107 14.446 13.888 13.520 13.499 17.02 2.23 -41.63 2.23 2.29 0.89 DH342_L07_113 GCS9 Cl* Melotte 22 DH 342 +03 44 30.08 +25 35 46.8 0 12.678 11.857 11.349 11.013 11.058 18.21 2.27 -37.32 2.27 7.54 0.81 HCG152 GCS9 Cl* Melotte 22 HCG 152 +03 44 31.04 +22 15 14.9 0 13.542 13.130 12.619 12.024 11.745 11.753 24.71 2.51 -36.97 2.51 2.16 0.26 SK571_DH344 GCS9 Cl* Melotte 22 SK 571 +03 44 31.72 +23 35 26.0 0 14.020 13.606 13.060 12.451 12.150 12.189 19.59 2.22 -44.22 2.22 1.12 0.94 SK564_HHJ265_DH345 GCS9 Cl* Melotte 22 SK 564 +03 44 32.17 +25 08 12.3 0 15.305 14.832 14.220 13.652 13.316 13.309 17.86 2.21 -43.12 2.21 7.50 0.90 HHJ68_Moraux2003_85_L07_51 GCS9 Cl* Melotte 22 HHJ 68 +03 44 32.33 +25 25 17.9 0 16.994 16.209 15.450 14.883 14.476 14.883 14.24 2.27 -43.51 2.27 5.78 0.64 CFHT-Pl-10_M6.5_PLZJ60_L07_A1_22_L07_250 GCS9 Cl* Melotte 22 CFHT 10 +03 44 33.08 +25 45 09.5 0 14.367 13.898 13.319 12.711 12.428 12.423 14.12 2.23 -38.60 2.23 0.96 0.71 HCG157_DH346_Moraux2003_40 GCS9 Cl* Melotte 22 HCG 157 +03 44 33.49 +26 14 53.1 0 13.610 13.172 12.622 12.070 11.769 11.768 18.22 2.94 -41.78 2.94 0.25 0.93 HHJ334_DH347 GCS9 Cl* Melotte 22 HHJ 334 +03 44 34.30 +23 51 24.6 0 16.914 16.150 15.428 14.866 14.472 14.439 16.92 2.24 -42.74 2.24 3.77 0.74 L07_A1_23 GCS9 +03 44 35.16 +25 13 42.8 1 17.656 16.584 15.662 14.985 14.448 14.985 19.33 2.26 -44.97 2.26 16.37 0.68 CFHT-Pl-16_M9.3_L07_A1_24_L07_251 GCS9 Cl* Melotte 22 CFHT 16 +03 44 35.90 +23 34 41.9 1 16.307 15.672 14.985 14.376 13.990 13.985 16.94 2.23 -44.39 2.23 1.71 0.72 HHJ5_L07_A1_25_L07_236 GCS9 Cl* Melotte 22 HHJ 5 +03 44 35.92 +22 50 42.9 0 14.238 13.940 13.439 12.803 12.544 12.552 34.09 2.24 -47.63 2.24 0.63 0.00 L07_197 GCS9 +03 44 36.28 +23 30 10.9 0 13.348 13.023 12.482 11.996 11.620 11.651 16.76 2.23 -43.37 2.23 7.84 0.93 HCG161_SK559_A23_HHJ344_DH348 GCS9 Cl* Melotte 22 HCG 161 +03 44 37.78 +22 55 15.5 0 12.736 12.396 11.884 11.404 11.020 11.104 20.58 2.23 -42.26 2.23 0.64 0.88 HCG164 GCS9 Cl* Melotte 22 HCG 164 +03 44 38.95 +23 02 25.3 0 14.434 13.951 13.374 12.833 12.489 12.506 17.33 2.24 -45.39 2.24 2.32 0.92 HHJ189_DH351 GCS9 Cl* Melotte 22 HHJ 189 +03 44 40.31 +25 19 24.6 0 15.831 15.228 14.599 14.042 13.700 13.703 -10.25 2.24 -47.03 2.24 7.09 0.00 L07_45 GCS9 +03 44 46.14 +24 23 02.8 0 15.833 15.303 14.664 14.161 13.762 13.777 16.63 2.23 -45.44 2.23 6.36 0.86 HHJ24_L07_99 GCS9 Cl* Melotte 22 HHJ 24 +03 44 47.31 +19 55 41.4 0 15.884 15.400 14.799 14.273 13.946 13.957 20.01 4.01 -40.31 4.01 0.63 0.85 DH354 GCS9 Cl* Melotte 22 DH 354 +03 44 47.33 +24 00 37.7 0 14.063 13.661 13.140 12.609 12.311 12.315 19.39 2.22 -43.53 2.22 4.41 0.94 HHJ276_HCG167_DH355_L07_9 GCS9 Cl* Melotte 22 HHJ 276 +03 44 47.84 +24 12 52.5 0 14.358 13.900 13.297 12.745 12.458 12.431 18.38 2.22 -42.73 2.22 2.19 0.94 HCG166_HHJ239_DH357_L07_96 GCS9 Cl* Melotte 22 HCG 166 +03 44 51.51 +25 05 16.5 0 14.798 14.315 13.715 13.134 12.802 12.819 15.35 2.21 -42.48 2.21 1.69 0.91 L07_49 GCS9 +03 44 53.12 +23 34 22.8 0 17.306 16.472 15.734 15.124 14.710 14.700 18.37 2.26 -39.68 2.26 3.81 0.67 L07_A1_26_L07_235 GCS9 +03 44 53.21 +24 01 06.5 0 15.005 14.557 13.980 13.433 13.135 13.125 16.45 2.22 -38.79 2.22 3.95 0.80 DH2004_359 GCS9 Cl* Melotte 22 DH 359 +03 44 53.53 +25 36 19.2 0 16.354 15.837 15.249 14.663 14.358 14.353 25.93 2.25 -40.81 2.25 4.48 0.14 PLZJ56_None GCS9 Cl* Melotte 22 PlZJ 56 +03 44 56.01 +23 55 53.4 0 13.771 13.290 12.715 12.244 11.888 11.912 19.65 2.22 -40.21 2.22 1.00 0.91 HCG171_HHJ304 GCS9 Cl* Melotte 22 HCG 171 +03 44 56.69 +23 36 23.5 0 14.114 13.697 13.146 12.622 12.313 12.314 22.12 2.22 -39.49 2.22 8.34 0.85 HHJ269_DH361 GCS9 Cl* Melotte 22 HHJ 269 +03 44 58.02 +23 24 31.0 0 14.147 13.740 13.184 12.621 12.362 12.351 16.74 2.24 -37.83 2.24 2.38 0.79 HHJ223_DH362 GCS9 Cl* Melotte 22 HHJ 223 +03 44 58.59 +23 55 40.9 0 14.017 13.555 12.960 12.420 12.106 12.139 18.25 2.22 -38.70 2.22 2.02 0.87 HHJ274_DH363 GCS9 Cl* Melotte 22 HHJ 274 +03 44 59.48 +23 21 18.1 0 15.290 14.814 14.227 13.710 13.376 13.382 20.27 2.24 -46.79 2.24 1.03 0.77 HHJ54_DH365_L07_156 GCS9 Cl* Melotte 22 HHJ 54 +03 45 01.14 +24 46 40.9 0 14.445 13.990 13.411 12.861 12.557 12.556 13.05 2.21 -45.96 2.21 3.65 0.71 HCG172_SK538_HHJ216_DH366_L07_74 GCS9 Cl* Melotte 22 HCG 172 +03 45 01.21 +25 21 05.6 0 15.041 14.555 13.949 13.361 13.061 13.070 18.83 2.23 -41.05 2.23 5.45 0.89 DH367_Moraux2003_74_L07_46 GCS9 Cl* Melotte 22 DH 367 +03 45 02.88 +25 05 19.6 0 14.789 14.276 13.751 13.216 12.845 12.858 14.43 2.21 -38.87 2.21 1.84 0.75 HHJ139_DH368_Moraux2003_65_L07_50 GCS9 Cl* Melotte 22 HHJ 139 +03 45 03.18 +23 06 58.4 0 16.937 15.492 14.946 14.495 0.012 20.81 2.32 -40.36 2.32 5.52 0.62 int-pl-IZ-60;2MASSJ0345031+230658_Y GCS9 Cl* Melotte 22 IPL 60 +03 45 04.41 +24 15 16.6 0 20.479 19.040 17.775 16.979 16.350 16.230 20.21 2.72 -38.78 2.72 0.80 0.45 L07_A1_27 GCS9 +03 45 04.99 +23 46 06.4 0 14.919 14.308 13.687 13.174 12.822 12.835 16.11 2.22 -45.06 2.22 2.75 0.91 HHJ113_DH372 GCS9 Cl* Melotte 22 HHJ 113 +03 45 05.32 +25 29 10.9 0 13.161 12.814 12.312 11.731 11.448 11.485 16.08 2.23 -44.62 2.23 4.32 0.92 SK534_HHJ381_DH373 GCS9 Cl* Melotte 22 SK 534 +03 45 06.25 +28 42 19.1 0 14.628 14.156 13.575 13.049 12.714 12.725 22.01 2.95 -39.53 2.95 0.12 0.85 DH374 GCS9 Cl* Melotte 22 DH 374 +03 45 06.56 +24 40 42.7 0 15.393 14.853 14.208 13.659 13.314 13.323 13.49 2.21 -39.94 2.21 6.44 0.71 HHJ48_L07_73 GCS9 Cl* Melotte 22 HHJ 48 +03 45 06.79 +23 36 51.4 0 14.751 14.200 13.573 12.993 12.645 12.675 16.78 2.22 -41.22 2.22 3.68 0.92 HCG176_HHJ136 GCS9 Cl* Melotte 22 HCG 176 +03 45 08.41 +23 25 00.9 0 15.510 15.010 14.406 13.859 13.534 13.540 13.99 2.24 -40.94 2.24 3.78 0.79 L07_157 GCS9 +03 45 08.69 +22 38 30.3 0 14.314 13.895 13.322 12.787 12.483 12.496 27.40 2.51 -36.47 2.51 19.46 0.06 HHJ204_BPL70_DH376_L07_194 GCS9 Cl* Melotte 22 HHJ 204 +03 45 08.69 +24 24 09.3 0 16.507 15.843 15.142 14.587 14.235 14.195 16.54 2.23 -42.48 2.23 1.73 0.74 L07_A1_28 GCS9 +03 45 09.04 +25 22 29.7 0 15.145 14.500 13.831 13.259 12.901 12.920 20.44 2.23 -40.81 2.23 3.96 0.86 HHJ81_L07_47 GCS9 Cl* Melotte 22 HHJ 81 +03 45 09.04 +25 32 49.0 0 14.661 14.176 13.591 13.021 12.723 12.734 18.67 2.23 -43.15 2.23 1.78 0.94 HHJ164_DH377_Moraux2003_61 GCS9 Cl* Melotte 22 HHJ 164 +03 45 09.46 +23 58 44.7 1 16.974 16.250 15.438 14.872 14.424 14.410 16.07 2.25 -42.23 2.25 13.16 0.72 L07_A1_29 GCS9 +03 45 10.82 +23 02 58.0 0 14.146 13.717 13.137 12.612 12.306 12.336 16.69 2.24 -45.16 2.24 4.56 0.92 HCG186_SK535 GCS9 Cl* Melotte 22 HCG 186 +03 45 12.16 +23 21 52.9 0 14.046 13.642 13.082 12.524 12.260 12.255 17.35 2.24 -44.74 2.24 5.28 0.93 HCG185_SK532_HHJ255_DH379 GCS9 Cl* Melotte 22 HCG 185 +03 45 12.44 +22 41 50.8 0 14.099 13.675 13.124 12.550 12.250 12.251 19.96 2.24 -47.27 2.24 1.91 0.87 BPL71_SK533_HHJ254_DH380_L07_196 GCS9 Cl* Melotte 22 BPL 71 +03 45 12.62 +23 53 45.0 0 16.093 15.445 14.776 14.220 13.845 13.855 16.81 2.23 -44.76 2.23 3.02 0.71 HHJ14_PPL7_L07_133 GCS9 Cl* Melotte 22 HHJ 14 +03 45 13.14 +24 15 23.6 0 15.046 14.554 13.965 13.457 13.149 13.144 21.10 2.22 -42.90 2.22 1.18 0.86 HCG180_HHJ118_DH381_L07_98 GCS9 Cl* Melotte 22 HCG 180 +03 45 14.21 +25 05 19.4 0 12.232 11.987 11.572 11.411 10.770 11.068 -49.24 2.21 -120.03 2.21 2.79 0.00 HII566_HCG174 GCS9 HII566 +03 45 15.82 +25 06 36.5 0 12.587 12.238 11.785 11.593 10.986 11.109 16.46 2.21 -62.54 2.21 1.44 0.00 SK526_HHJ425 GCS9 Cl* Melotte 22 SK 526 +03 45 16.13 +24 07 16.0 0 13.242 12.918 12.418 12.034 11.570 11.682 19.29 2.22 -40.28 2.22 1.79 0.91 HCG183_HHJ394_DH383 GCS9 Cl* Melotte 22 HCG 183 +03 45 16.42 +23 34 01.6 0 15.628 15.046 14.415 13.871 13.502 13.519 16.66 2.22 -43.95 2.22 1.55 0.89 DH384 GCS9 Cl* Melotte 22 DH 384 +03 45 16.99 +25 15 47.5 0 12.911 12.493 11.990 11.694 11.141 11.228 18.30 2.21 -40.49 2.21 2.50 0.89 HCG178_SK525_A28_HHJ410_DH386 GCS9 Cl* Melotte 22 HCG 178 +03 45 18.15 +25 05 58.1 0 12.117 11.866 11.421 11.325 10.625 10.829 16.05 2.21 -37.44 2.21 2.36 0.74 HII590_HCG179_SK524_T45_DH387 GCS9 HII590 +03 45 21.12 +21 46 17.6 0 16.257 15.741 15.178 14.532 14.239 14.299 8.25 2.54 -32.01 2.54 32.48 0.00 L07_PM_NM_49 GCS9 +03 45 21.91 +26 28 41.9 0 14.370 14.041 13.523 12.837 12.615 12.636 32.97 2.94 -40.02 2.94 0.85 0.00 DH389 GCS9 Cl* Melotte 22 DH 389 +03 45 22.14 +21 52 40.0 0 16.265 15.746 15.102 14.554 14.203 14.180 -1.65 2.29 -61.68 2.29 5.14 0.00 L07_PM_NM_50 GCS9 +03 45 24.70 +24 38 46.4 0 15.819 15.190 14.549 13.983 13.645 13.672 18.14 2.22 -44.52 2.22 1.24 0.89 L07_72 GCS9 +03 45 24.79 +24 20 45.3 0 14.133 13.677 13.118 12.524 12.226 12.250 14.67 2.22 -40.59 2.22 8.47 0.85 DH392 GCS9 Cl* Melotte 22 DH 392 +03 45 26.56 +22 31 32.0 0 14.087 13.649 13.091 12.536 12.210 12.196 19.13 2.26 -36.34 2.26 5.52 0.67 HHJ267_BPL73_DH393_L07_199 GCS9 Cl* Melotte 22 HHJ 267 +03 45 26.99 +24 13 26.4 0 15.192 14.757 14.205 13.605 13.284 13.278 23.59 2.22 -49.95 2.22 2.92 0.17 HHJ99 GCS9 Cl* Melotte 22 HHJ 99 +03 45 27.52 +23 37 56.9 0 15.142 14.678 14.099 13.543 13.236 13.231 19.00 2.22 -42.76 2.22 1.56 0.90 HHJ106_L07_147 GCS9 Cl* Melotte 22 HHJ 106 +03 45 28.90 +27 28 07.2 0 12.839 12.475 11.993 11.460 11.179 11.209 18.03 3.44 -39.52 3.44 0.75 0.88 DH394 GCS9 Cl* Melotte 22 DH 394 +03 45 30.23 +24 18 45.3 0 12.817 12.487 11.994 11.683 11.104 11.215 15.71 2.22 -43.80 2.22 1.47 0.77 HII673 GCS9 HII673 +03 45 31.25 +25 46 33.3 0 14.192 13.836 13.341 12.732 12.466 12.457 29.01 2.23 -50.04 2.23 1.37 0.01 HHJ293 GCS9 Cl* Melotte 22 HHJ 293 +03 45 31.37 +24 52 47.4 1 17.332 16.330 15.465 14.839 14.354 14.326 16.69 2.24 -40.30 2.24 7.98 0.66 IPMBD29_L07_A1_30 GCS9 Cl* Melotte 22 IPMBD 29 +03 45 35.69 +24 24 34.1 0 16.519 15.801 15.133 14.567 14.196 14.181 18.31 2.23 -42.12 2.23 51.10 0.74 L07_A1_31 GCS9 +03 45 36.72 +24 39 06.5 0 13.244 12.776 12.207 11.739 11.342 11.388 13.96 2.21 -44.03 2.21 19.04 0.85 HCG194_HHJ380_DH397 GCS9 Cl* Melotte 22 HCG 194 +03 45 37.76 +23 43 50.1 1 16.240 15.456 14.715 14.172 13.756 13.742 20.91 2.23 -45.45 2.23 2.10 0.59 L07_A1_32_L07_238 GCS9 +03 45 37.79 +24 20 08.1 0 13.646 13.271 11.841 12.729 10.456 12.026 19.56 2.22 -44.01 2.22 11.15 0.93 HII717_HD23387_Tr215 GCS9 HII717 +03 45 38.99 +23 57 00.9 0 15.229 14.703 14.101 13.549 13.238 13.223 20.80 2.22 -37.93 2.22 13.30 0.69 L07_130 GCS9 +03 45 39.04 +25 13 27.6 0 12.529 12.273 11.767 11.514 10.907 11.035 15.68 2.21 -41.64 2.21 5.94 0.82 HCG196_SK510 GCS9 Cl* Melotte 22 HCG 196 +03 45 39.12 +22 04 23.1 0 15.161 14.641 14.018 13.458 13.114 13.120 19.28 2.27 -37.19 2.27 3.36 0.67 BPL75_L07_210 GCS9 Cl* Melotte 22 BPL 75 +03 45 39.29 +24 08 20.4 0 14.992 14.507 13.911 13.379 13.015 13.020 16.79 2.22 -43.96 2.22 1.38 0.93 HHJ130_DH398_L07_117 GCS9 Cl* Melotte 22 HHJ 130 +03 45 40.77 +28 32 05.8 0 15.158 14.639 14.012 13.516 13.166 13.169 22.95 2.96 -42.47 2.96 0.16 0.75 DH399 GCS9 Cl* Melotte 22 DH 399 +03 45 41.27 +23 54 09.7 1 17.166 16.189 15.360 14.782 14.305 14.309 17.46 2.24 -44.47 2.24 3.49 0.69 Roque15_L07_A1_33_L07_234 GCS9 Cl* Melotte 22 Roque 15 +03 45 42.33 +24 04 11.1 0 16.542 15.882 15.201 14.643 14.262 14.229 17.79 2.24 -40.28 2.24 11.53 0.70 L07_A1_34_L07_227 GCS9 +03 45 43.18 +26 02 26.6 0 14.195 13.765 13.158 12.560 12.284 12.301 19.62 2.23 -41.47 2.23 6.76 0.94 HHJ264_DH401 GCS9 Cl* Melotte 22 HHJ 264 +03 45 44.08 +24 04 26.6 0 12.115 11.903 11.473 11.507 10.786 11.052 20.45 2.22 -36.83 2.22 10.09 0.78 HII762_Tr228a_HCG200 GCS9 HII762 +03 45 45.41 +22 33 21.5 0 15.557 15.172 14.632 14.051 13.736 13.741 -0.07 2.27 -31.02 2.27 8.08 0.00 L07_200 GCS9 +03 45 46.48 +23 47 43.1 0 12.854 12.422 11.837 11.334 10.920 10.991 19.57 2.22 -40.02 2.22 2.51 0.89 HHJ421 GCS9 Cl* Melotte 22 HHJ 421 +03 45 46.90 +23 53 00.3 0 15.802 15.201 14.549 13.988 13.652 13.661 18.78 2.23 -44.72 2.23 3.12 0.88 L07_129 GCS9 +03 45 46.94 +21 44 49.0 0 14.850 14.329 13.677 13.182 12.811 12.850 25.93 2.65 -40.73 2.65 1.97 0.54 HHJ128 GCS9 Cl* Melotte 22 HHJ 128 +03 45 49.26 +25 24 46.1 0 16.825 16.314 15.704 15.073 14.758 14.754 26.80 2.29 -31.90 2.29 3.20 0.00 DH403 GCS9 Cl* Melotte 22 DH 403 +03 45 49.37 +24 25 07.1 0 13.560 13.177 12.633 12.107 11.764 11.792 17.27 2.21 -44.41 2.21 5.99 0.93 BPL77_L07_7 GCS9 Cl* Melotte 22 BPL 77 +03 45 49.94 +23 19 44.7 0 15.790 15.217 14.577 14.016 13.634 13.665 13.92 2.24 -38.67 2.24 2.39 0.66 L07_155 GCS9 +03 45 50.42 +22 36 05.6 0 17.653 16.839 16.051 15.524 15.034 0.021 14.53 2.39 -32.89 2.39 8.07 0.05 int-pl-IZ-44;2MASSJ0345504+223606_BPL78_Y_L07_A1_35_L07_247 GCS9 Cl* Melotte 22 IPL 44 +03 45 50.66 +24 09 03.5 1 17.478 16.582 15.705 15.095 14.580 14.560 16.01 2.26 -40.58 2.26 1.10 0.65 BPL79_Roque13_L07_A1_36 GCS9 Cl* Melotte 22 BPL 79 +03 45 51.09 +24 26 10.9 0 15.873 15.293 14.658 14.103 13.746 13.760 17.00 2.22 -46.18 2.22 1.18 0.84 HHJ25_L07_86 GCS9 Cl* Melotte 22 HHJ 25 +03 45 51.33 +24 17 44.3 0 14.634 14.154 13.573 12.983 12.723 12.717 14.02 2.22 -39.30 2.22 8.20 0.75 L07_92 GCS9 +03 45 51.64 +24 02 19.7 0 12.518 12.529 11.248 11.845 10.016 30.83 2.22 -25.84 2.22 9.31 0.00 HII804_HD23409_Tr235 GCS9 HII804 +03 45 51.95 +25 10 01.7 0 14.702 14.245 13.604 13.032 12.743 12.742 16.31 2.21 -41.80 2.21 7.90 0.92 HHJ166_BPL80_DH404_Moraux2003_54_L07_54 GCS9 Cl* Melotte 22 HHJ 166 +03 45 52.60 +25 54 59.8 0 13.823 13.352 12.746 12.195 11.859 11.879 17.10 2.23 -41.34 2.23 1.59 0.92 SK499_DH405 GCS9 Cl* Melotte 22 SK 499 +03 45 52.76 +23 27 54.0 0 14.143 13.734 13.161 12.615 12.286 12.305 12.01 2.24 -39.18 2.24 4.63 0.49 HCG202_SK504_HHJ227_DH407 GCS9 Cl* Melotte 22 HCG 202 +03 45 54.96 +23 33 57.8 0 17.116 16.325 15.553 15.023 14.564 14.574 18.67 2.25 -41.38 2.25 5.98 0.72 L07_A1_38_L07_237 GCS9 +03 45 54.99 +24 13 26.0 0 13.704 13.217 12.670 12.164 11.819 11.840 18.24 2.22 -42.71 2.22 0.61 0.94 BPL82 GCS9 Cl* Melotte 22 BPL 82 +03 45 56.97 +23 01 29.0 0 14.372 13.939 13.379 12.847 12.515 12.570 16.42 2.24 -40.16 2.24 1.23 0.90 HCG205_HHJ199_DH410 GCS9 Cl* Melotte 22 HCG 205 +03 45 57.09 +24 21 00.9 0 15.058 14.437 13.779 13.215 12.854 12.842 8.36 2.22 -35.88 2.22 11.01 0.01 L07_94 GCS9 +03 45 57.28 +25 11 12.7 0 13.246 12.837 12.272 12.049 11.431 11.482 11.26 2.21 -43.67 2.21 7.13 0.55 SK497_HHJ379_BPL83_DH411_Moraux2003_7 GCS9 Cl* Melotte 22 SK 497 +03 45 57.71 +24 03 04.9 0 15.821 15.269 14.671 14.132 13.755 13.740 14.71 2.23 -37.35 2.23 4.80 0.59 HHJ27_L07_116 GCS9 Cl* Melotte 22 HHJ 27 +03 45 57.78 +26 18 44.4 0 13.541 13.217 12.726 12.101 11.875 11.872 36.22 2.94 -32.09 2.94 0.34 0.00 Moraux2003_14 GCS9 Cl* Melotte 22 MBSC 14 +03 45 57.91 +24 08 40.9 0 15.680 15.158 14.543 14.017 13.670 13.654 18.27 2.23 -43.51 2.23 2.70 0.90 DH412_L07_118 GCS9 Cl* Melotte 22 DH 412 +03 45 58.80 +26 20 01.7 0 15.269 14.666 14.045 13.498 13.144 13.138 18.44 2.95 -38.73 2.95 0.40 0.81 HHJ67_DH413_Moraux2003_86 GCS9 Cl* Melotte 22 HHJ 67 +03 45 59.20 +23 48 46.8 0 14.973 14.506 13.923 13.375 13.073 13.092 19.52 2.22 -40.23 2.22 4.75 0.92 HHJ127 GCS9 Cl* Melotte 22 HHJ 127 +03 46 00.93 +22 12 29.4 0 16.023 15.409 14.742 14.256 13.858 13.866 18.94 2.28 -45.37 2.28 9.09 0.68 HHJ11_BPL84 GCS9 Cl* Melotte 22 HHJ 11 +03 46 02.26 +25 36 40.4 0 12.980 12.632 12.114 11.635 11.296 11.366 31.82 2.23 -37.17 2.23 6.80 0.00 SK491_HHJ399 GCS9 Cl* Melotte 22 SK 491 +03 46 02.53 +22 28 27.5 0 16.837 16.315 15.700 15.098 14.791 0.012 7.00 2.33 -22.57 2.33 3.08 0.00 int-pl-IZ-64;2MASSJ0346025+222827_N GCS9 Cl* Melotte 22 IPL 64 +03 46 02.96 +24 40 55.6 0 15.363 14.742 14.071 13.491 13.114 13.134 14.57 2.21 -41.20 2.21 7.46 0.83 BPL85_DH414 GCS9 Cl* Melotte 22 BPL 85 +03 46 03.45 +24 20 57.0 0 14.476 14.023 13.444 12.842 12.558 12.571 11.97 2.22 -38.92 2.22 3.54 0.46 HCG206_BPL86_DH415_L07_93 GCS9 Cl* Melotte 22 HCG 206 +03 46 03.67 +25 52 28.8 0 14.534 14.076 13.506 12.954 12.639 12.643 17.24 2.23 -43.26 2.23 3.39 0.94 HHJ182_DH416_L07_26 GCS9 Cl* Melotte 22 HHJ 182 +03 46 03.96 +24 23 08.9 0 15.655 15.066 14.448 13.848 13.511 13.513 31.07 2.23 0.78 2.23 0.19 0.00 BPL87 GCS9 Cl* Melotte 22 BPL 87 +03 46 04.30 +23 55 40.6 0 13.726 13.283 12.706 12.145 11.810 11.840 14.43 2.22 -40.75 2.22 3.63 0.84 HHJ363 GCS9 Cl* Melotte 22 HHJ 363 +03 46 04.57 +24 09 55.8 0 15.099 14.602 14.020 13.460 13.162 13.175 15.43 2.22 -40.43 2.22 2.54 0.84 BPL88 GCS9 Cl* Melotte 22 BPL 88 +03 46 05.64 +24 36 44.3 0 13.116 12.770 12.264 11.710 11.428 11.468 18.12 2.21 -41.35 2.21 3.04 0.93 SK488 GCS9 Cl* Melotte 22 SK 488 +03 46 05.69 +24 36 49.7 0 15.023 14.505 13.894 13.358 13.037 13.052 19.22 2.21 -40.25 2.21 4.06 0.87 L07_88 GCS9 +03 46 06.05 +23 58 19.1 0 16.000 15.453 14.820 14.319 13.925 13.902 16.34 2.23 -42.63 2.23 21.77 0.73 L07_114 GCS9 +03 46 06.21 +25 06 45.8 0 14.228 14.006 13.550 12.936 12.838 12.837 12.78 2.21 -34.39 2.21 1.09 0.08 L07_53 GCS9 +03 46 06.46 +25 25 11.5 0 14.674 14.289 13.759 13.117 12.837 12.862 18.32 2.23 -26.35 2.23 1.79 0.00 HHJ194_Moraux2003_59 GCS9 Cl* Melotte 22 HHJ 194 +03 46 06.52 +23 50 20.2 0 12.820 12.427 11.856 11.337 10.926 11.042 19.77 2.22 -37.28 2.22 3.27 0.81 HHJ435 GCS9 Cl* Melotte 22 HHJ 435 +03 46 06.67 +22 23 33.7 0 12.888 12.512 12.011 11.499 11.168 11.310 42.71 2.26 -18.94 2.26 1.18 0.00 BPL89 GCS9 Cl* Melotte 22 BPL 89 +03 46 07.51 +24 22 27.6 0 12.379 12.139 11.688 11.532 10.916 11.074 18.75 2.22 -42.30 2.22 0.96 0.89 HII890_HCG210 GCS9 HII890 +03 46 07.56 +23 44 42.6 0 16.202 15.245 14.240 13.330 12.791 12.804 15.30 2.22 -43.15 2.22 1.77 0.70 L07_239 GCS9 +03 46 08.70 +24 40 33.1 0 14.615 14.120 13.525 13.000 12.673 12.686 16.53 2.21 -42.88 2.21 5.02 0.93 HCG209_BPL90_DH422 GCS9 Cl* Melotte 22 HCG 209 +03 46 09.88 +21 05 52.4 0 13.956 13.557 12.996 12.456 12.173 12.162 25.89 2.65 -44.94 2.65 5.12 0.46 DH425 GCS9 Cl* Melotte 22 DH 425 +03 46 10.10 +26 00 09.0 0 15.207 14.716 14.091 13.542 13.230 13.219 15.35 2.23 -39.91 2.23 0.60 0.82 HHJ80_L07_17 GCS9 Cl* Melotte 22 HHJ 80 +03 46 10.23 +21 52 55.8 0 16.174 15.346 14.578 13.994 13.570 13.564 43.56 2.27 -44.28 2.27 1.50 0.00 L07_A1_42 GCS9 +03 46 12.67 +23 35 13.6 0 14.942 14.442 13.836 13.296 12.972 12.997 17.61 2.22 -42.42 2.22 4.56 0.94 L07_146 GCS9 +03 46 12.88 +24 03 15.6 0 12.012 11.833 11.399 11.455 10.715 11.045 22.36 2.22 -38.68 2.22 6.72 0.81 HII930_DH429 GCS9 Cl* Melotte 22 DH 429 +03 46 13.00 +27 02 11.7 0 15.061 14.740 14.217 13.668 13.401 13.421 18.46 2.95 -46.68 2.95 1.60 0.82 DH430 GCS9 Cl* Melotte 22 DH 430 +03 46 14.06 +23 21 56.4 0 17.097 16.349 15.606 15.047 14.618 14.641 17.81 2.27 -40.20 2.27 7.96 0.68 L07_A1_43 GCS9 +03 46 15.84 +22 02 41.0 0 13.656 13.264 12.746 12.128 11.854 11.872 24.87 2.26 -14.34 2.26 2.45 0.00 BPL93 GCS9 Cl* Melotte 22 BPL 93 +03 46 16.85 +24 26 27.3 0 14.565 14.151 13.599 13.055 12.761 12.781 35.34 2.21 -3.40 2.21 3.16 0.00 BPL94 GCS9 Cl* Melotte 22 BPL 94 +03 46 17.94 +24 41 09.3 0 13.394 13.027 12.512 11.992 11.646 11.701 19.02 2.21 -41.15 2.21 7.95 0.93 HHJ367_BPL95_DH433 GCS9 Cl* Melotte 22 HHJ 367 +03 46 18.54 +23 59 02.5 0 15.870 15.329 14.698 14.161 13.807 13.797 16.09 2.23 -43.73 2.23 36.19 0.88 L07_115 GCS9 +03 46 19.41 +23 00 55.7 0 15.317 14.693 14.026 13.491 13.124 13.115 13.77 2.24 -42.63 2.24 3.01 0.80 HHJ47_BPL96_DH435 GCS9 Cl* Melotte 22 HHJ 47 +03 46 19.43 +26 02 35.5 0 12.463 12.206 11.749 11.371 10.935 11.061 24.01 2.22 -39.85 2.22 4.18 0.74 SK474_DH436 GCS9 Cl* Melotte 22 SK 474 +03 46 19.86 +24 59 01.3 0 13.787 13.340 12.776 12.279 11.946 11.942 14.60 2.21 -42.76 2.21 2.73 0.88 HHJ303_BPL97_DH437_Moraux2003_27 GCS9 Cl* Melotte 22 HHJ 303 +03 46 20.65 +23 53 31.7 0 15.607 15.199 14.655 13.992 13.738 13.729 10.57 2.23 -42.96 2.23 9.25 0.44 L07_125 GCS9 +03 46 21.36 +24 33 52.2 0 15.032 14.512 13.919 13.388 13.052 13.066 14.20 2.21 -42.68 2.21 3.50 0.83 HHJ105_BPL98_DH439 GCS9 Cl* Melotte 22 HHJ 105 +03 46 21.40 +23 05 08.9 0 15.684 15.157 14.548 14.032 13.690 13.664 11.08 2.24 -47.42 2.24 27.35 0.29 HHJ33 GCS9 Cl* Melotte 22 HHJ 33 +03 46 21.62 +23 04 00.9 0 14.695 14.171 13.539 13.025 12.701 12.683 18.17 2.24 -41.21 2.24 22.02 0.93 DH440 GCS9 Cl* Melotte 22 DH 440 +03 46 22.20 +23 52 41.0 0 14.183 13.612 12.973 12.388 12.055 12.045 16.03 2.22 -41.36 2.22 4.55 0.91 DH441 GCS9 Cl* Melotte 22 DH 441 +03 46 22.25 +23 52 26.6 1 17.120 16.323 15.518 14.917 14.474 14.472 17.65 2.25 -38.04 2.25 4.78 0.56 L07_A1_44_L07_232 GCS9 +03 46 23.03 +24 36 17.9 0 14.585 14.122 13.563 13.027 12.710 12.729 16.93 2.21 -44.40 2.21 19.52 0.93 BPL99_DH442 GCS9 Cl* Melotte 22 BPL 99 +03 46 23.12 +24 20 36.0 0 18.242 17.172 16.247 15.654 15.145 15.106 17.01 2.29 -43.77 2.29 3.71 0.66 BPL100_Roque9_L07_A1_45 GCS9 Cl* Melotte 22 BPL 100 +03 46 23.47 +24 01 51.2 0 14.714 14.254 13.668 13.113 12.808 12.793 18.12 2.22 -43.07 2.22 5.91 0.94 HCG218_HHJ195_DH443 GCS9 Cl* Melotte 22 HCG 218 +03 46 23.72 +22 50 16.4 0 17.310 15.710 15.189 14.722 0.015 20.26 2.35 -45.42 2.35 5.66 0.64 int-pl-IZ-43;2MASSJ0346237+225016_Y GCS9 Cl* Melotte 22 IPL 43 +03 46 23.74 +26 34 23.0 0 14.730 14.243 13.647 13.132 12.839 12.799 24.18 2.93 -45.83 2.93 0.60 0.73 HHJ175_DH444_Moraux2003_62 GCS9 Cl* Melotte 22 HHJ 175 +03 46 24.12 +24 30 12.7 0 15.893 15.305 14.689 14.145 13.797 13.818 18.76 2.22 -43.84 2.22 3.56 0.90 BPL101 GCS9 Cl* Melotte 22 BPL 101 +03 46 24.64 +24 28 46.2 0 14.399 13.930 13.364 12.822 12.527 12.552 16.75 2.21 -43.26 2.21 2.85 0.93 HHJ249_BPL102_DH445_L07_87 GCS9 Cl* Melotte 22 HHJ 249 +03 46 25.18 +21 26 17.4 0 13.441 12.930 12.360 11.842 11.508 11.529 3.09 2.65 -44.47 2.65 1.82 0.00 DH446 GCS9 Cl* Melotte 22 DH 446 +03 46 25.39 +24 09 36.2 0 12.838 12.485 11.952 11.461 11.112 11.150 13.98 2.22 -43.42 2.22 0.43 0.64 HCG219_A2_HHJ429 GCS9 Cl* Melotte 22 HCG 219 +03 46 26.09 +24 05 09.5 1 16.810 15.967 15.160 14.584 14.118 14.098 19.41 2.24 -38.71 2.24 3.37 0.57 IPMBD25_L07_A1_46_L07_230 GCS9 Cl* Melotte 22 IPMBD 25 +03 46 27.01 +24 27 13.9 0 13.850 13.415 12.890 12.336 12.039 12.035 16.46 2.21 -47.67 2.21 2.66 0.82 HHJ326_BPL103_DH447 GCS9 Cl* Melotte 22 HHJ 326 +03 46 27.10 +21 48 22.6 1 19.797 18.650 17.374 16.564 15.848 15.925 20.95 2.98 -48.67 2.98 1.27 0.65 L07_A1_47 GCS9 +03 46 27.69 +23 48 45.5 0 14.944 14.345 13.639 13.015 12.617 12.609 18.50 2.22 -41.39 2.22 1.69 0.94 HHJ161 GCS9 Cl* Melotte 22 HHJ 161 +03 46 28.63 +24 45 32.1 0 12.120 11.913 11.480 11.309 10.657 10.853 15.43 2.21 -40.74 2.21 8.09 0.81 HII1029_HCG222_SK472_B212_DH451 GCS9 HII1029 +03 46 29.73 +21 02 16.7 0 14.557 14.008 13.402 12.879 12.566 12.551 19.59 2.65 -40.35 2.65 0.73 0.92 DH452 GCS9 Cl* Melotte 22 DH 452 +03 46 30.96 +23 00 15.1 0 14.562 14.242 13.760 13.105 12.885 12.870 29.31 2.24 -51.76 2.24 0.62 0.00 L07_181 GCS9 +03 46 31.02 +23 01 34.6 0 15.742 15.178 14.589 14.043 13.704 13.689 15.36 2.24 -40.21 2.24 5.54 0.83 HHJ30_DH453 GCS9 Cl* Melotte 22 HHJ 30 +03 46 31.32 +22 18 19.6 0 14.447 13.942 13.329 12.796 12.449 12.455 21.56 2.26 -42.37 2.26 4.04 0.92 HHJ160_BPL105_L07_204 GCS9 Cl* Melotte 22 HHJ 160 +03 46 32.13 +24 23 14.6 0 19.257 18.083 17.049 16.373 15.849 15.766 17.62 2.43 -39.19 2.43 3.18 0.57 L07_A1_48 GCS9 +03 46 32.69 +22 20 17.7 0 14.509 14.126 13.585 12.902 12.626 12.625 30.13 2.26 -27.71 2.26 1.50 0.00 SK471 GCS9 Cl* Melotte 22 SK 471 +03 46 32.79 +19 17 30.2 0 14.370 13.858 13.285 12.792 12.433 12.437 22.35 5.04 -42.45 5.04 1.46 0.90 DH455 GCS9 Cl* Melotte 22 DH 455 +03 46 34.16 +23 25 12.5 0 16.303 15.778 15.088 14.398 14.055 14.041 11.49 2.25 -25.44 2.25 5.43 0.00 HHJ9_L07_PM_NM_20 GCS9 Cl* Melotte 22 HHJ 9 +03 46 34.25 +23 50 03.6 0 19.871 18.546 17.459 16.666 16.090 16.024 20.67 2.58 -41.54 2.58 3.43 0.66 L07_A1_49_L07_260 GCS9 +03 46 34.99 +23 31 14.4 0 18.424 17.345 16.411 15.846 15.308 15.295 20.52 2.37 -42.62 2.37 4.06 0.70 L07_A1_50 GCS9 +03 46 35.36 +23 57 07.4 0 16.879 16.089 15.355 14.800 14.416 14.373 17.57 2.24 -42.62 2.24 11.08 0.75 L07_A1_51_L07_233 GCS9 +03 46 35.54 +24 01 35.4 0 14.809 14.275 13.660 13.113 12.776 12.774 22.25 2.22 -44.34 2.22 0.77 0.89 HHJ140_DH458 GCS9 Cl* Melotte 22 HHJ 140 +03 46 36.07 +23 04 17.2 0 13.827 13.418 12.875 12.391 12.040 12.042 18.53 2.24 -43.10 2.24 5.66 0.94 SK465_HHJ282_DH459 GCS9 Cl* Melotte 22 SK 465 +03 46 39.33 +24 06 11.3 0 12.740 12.808 11.163 12.306 9.936 10.900 30.68 2.22 -50.62 2.22 33.59 0.00 HII1122_HD23511_Tr327 GCS9 HII1122 +03 46 40.27 +25 43 53.4 0 13.808 13.431 12.871 12.280 12.005 12.002 16.42 2.23 -45.47 2.23 10.61 0.91 SK461_HHJ316_DH464_L07_3 GCS9 Cl* Melotte 22 SK 461 +03 46 40.41 +22 50 39.6 0 15.138 14.065 13.514 13.194 13.182 21.74 2.28 -48.62 2.28 4.41 0.50 BPL107 GCS9 Cl* Melotte 22 BPL 107 +03 46 40.60 +22 22 03.5 0 15.518 14.956 14.324 13.752 13.392 13.412 18.76 2.27 -40.88 2.27 4.92 0.88 L07_206 GCS9 +03 46 43.19 +23 37 21.9 0 15.226 14.671 14.057 13.407 13.081 13.076 20.88 2.22 -42.38 2.22 6.11 0.86 HHJ104_DH466_L07_138 GCS9 Cl* Melotte 22 HHJ 104 +03 46 43.60 +23 59 42.3 0 13.258 12.936 12.441 11.891 11.575 11.602 21.29 2.22 -43.53 2.22 0.97 0.91 DH467 GCS9 Cl* Melotte 22 DH 467 +03 46 44.79 +24 44 58.2 0 15.343 14.790 14.196 13.626 13.308 13.288 16.98 2.21 -43.55 2.21 4.59 0.90 HHJ75_BPL109_L07_67 GCS9 Cl* Melotte 22 HHJ 75 +03 46 44.88 +23 37 07.3 0 15.323 14.679 13.948 13.237 12.874 12.854 20.16 2.22 -39.81 2.22 5.80 0.83 L07_137 GCS9 +03 46 45.78 +25 27 30.2 0 13.675 13.271 12.734 12.160 11.896 11.911 15.18 2.23 -45.47 2.23 2.85 0.88 HCG233_SK458_T25B_HHJ319_Moraux2003_20_DH2004_468 GCS9 Cl* Melotte 22 HCG 233 +03 46 46.48 +23 24 02.8 0 14.716 14.273 13.660 13.004 12.658 12.671 33.44 2.24 -34.98 2.24 7.11 0.00 HHJ159 GCS9 Cl* Melotte 22 HHJ 159 +03 46 47.19 +25 20 53.1 0 14.444 13.909 13.306 12.771 12.437 12.445 23.84 2.23 -47.30 2.23 8.06 0.66 HHJ208_DH469_Moraux2003_45_L07_39 GCS9 Cl* Melotte 22 HHJ 208 +03 46 48.79 +23 04 07.3 0 13.100 12.757 12.271 11.916 11.443 11.467 18.54 2.23 -39.65 2.23 3.00 0.90 HCG245_HHJ370_DH470 GCS9 Cl* Melotte 22 HCG 245 +03 46 50.03 +24 00 23.6 0 17.416 16.456 15.617 15.025 14.512 14.503 16.75 2.25 -34.16 2.25 3.06 0.16 L07_A1_53_L07_229 GCS9 +03 46 50.09 +23 31 56.1 0 14.162 13.728 13.200 12.627 12.337 12.390 18.31 2.22 -43.65 2.22 4.16 0.94 HCG240_HHJ253_DH472_L07_134 GCS9 Cl* Melotte 22 HCG 240 +03 46 50.19 +22 12 42.3 0 15.579 15.015 14.384 13.861 13.495 13.514 17.03 2.27 -40.35 2.27 3.93 0.87 BPL110_L07_203 GCS9 Cl* Melotte 22 BPL 110 +03 46 51.83 +23 23 09.4 0 19.654 18.417 17.182 16.429 15.651 15.690 19.04 2.55 -23.47 2.55 10.80 0.00 L07_A1_54 GCS9 +03 46 52.29 +21 47 43.0 0 16.720 16.080 15.402 14.797 14.405 14.418 24.38 2.30 -20.65 2.30 1.45 0.00 L07_PM_NM_24 GCS9 +03 46 52.61 +23 38 43.0 0 14.301 13.763 13.138 12.512 12.160 12.174 15.66 2.22 -42.82 2.22 5.33 0.92 HHJ247_DH473 GCS9 Cl* Melotte 22 HHJ 247 +03 46 52.97 +24 15 07.8 0 16.407 15.752 15.069 14.513 14.131 14.122 19.64 2.23 -36.47 2.23 5.64 0.33 BPL112_L07_A1_55 GCS9 Cl* Melotte 22 BPL 112 +03 46 53.61 +24 17 14.8 0 12.961 12.563 12.023 11.548 11.200 11.255 17.64 2.22 -38.70 2.22 6.95 0.85 HCG244_SK454_BPL113_DH474 GCS9 Cl* Melotte 22 HCG 244 +03 46 53.96 +24 07 57.1 0 15.123 14.651 14.045 13.480 13.143 13.155 16.12 2.22 -38.73 2.22 1.08 0.78 15.76 GCS9 Cl* Melotte 22 MHO 8 +03 46 54.03 +25 14 44.8 0 13.248 12.893 12.369 11.790 11.469 11.490 19.95 2.21 -41.29 2.21 1.73 0.92 HCG241_SK453_B267_BPL114_Moraux2003_4_DH475 GCS9 Cl* Melotte 22 HCG 241 +03 46 54.39 +22 45 11.9 0 18.716 16.652 16.025 15.444 0.043 17.39 2.54 -35.13 2.54 10.55 0.43 int-pl-IZ-48;IPLJ0346543+224512_Y GCS9 Cl* Melotte 22 IPL 48 +03 46 55.32 +23 22 49.3 0 15.196 14.729 14.159 13.630 13.284 13.299 18.52 2.24 -41.36 2.24 6.26 0.89 HHJ95_DH479_L07_163 GCS9 Cl* Melotte 22 HHJ 95 +03 46 55.32 +24 11 16.6 0 14.959 14.371 13.702 13.181 12.828 12.830 19.31 2.22 -40.67 2.22 3.10 0.93 BPL116_DH478_L07_103 GCS9 Cl* Melotte 22 BPL 116 +03 46 55.36 +25 51 29.4 0 15.808 15.294 14.643 14.056 13.716 13.713 9.44 2.24 -34.52 2.24 8.63 0.01 HHJ31_Moraux2003_97_L07_28 GCS9 Cl* Melotte 22 HHJ 31 +03 46 55.49 +23 11 16.0 0 20.373 19.064 17.892 17.144 16.423 16.403 18.24 3.27 -33.97 3.27 3.33 0.33 L07_A1_56 GCS9 +03 46 55.78 +23 56 24.1 0 14.367 13.803 13.166 12.622 12.272 12.283 18.41 2.22 -39.68 2.22 2.24 0.91 HHJ257_DH480 GCS9 Cl* Melotte 22 HHJ 257 +03 46 57.10 +23 15 02.2 0 12.759 12.476 11.986 11.443 11.115 11.167 22.62 2.23 -41.65 2.23 2.12 0.83 HCG247_SK452_HHJ415_DH481 GCS9 Cl* Melotte 22 HCG 247 +03 46 57.83 +23 12 46.2 0 14.826 14.503 14.022 13.469 13.198 13.219 28.31 2.24 -40.75 2.24 3.54 0.15 L07_175 GCS9 +03 46 58.17 +23 33 38.8 0 14.657 14.204 13.650 13.114 12.801 12.816 19.41 2.22 -43.08 2.22 0.71 0.94 HHJ174_DH482_L07_136 GCS9 Cl* Melotte 22 HHJ 174 +03 46 58.26 +24 01 41.3 0 14.976 14.481 13.884 13.337 12.997 13.022 19.27 2.22 -37.41 2.22 4.06 0.79 L07_121 GCS9 +03 46 59.32 +24 01 42.8 0 13.995 13.544 12.956 12.408 12.067 12.100 19.84 2.22 -38.88 2.22 2.81 0.85 HHJ299_DH484 GCS9 Cl* Melotte 22 HHJ 299 +03 47 01.85 +24 13 28.1 0 16.253 15.613 14.933 14.410 14.020 14.022 15.79 2.23 -38.31 2.23 3.78 0.52 BPL122_L07_A1_57 GCS9 Cl* Melotte 22 BPL 122 +03 47 02.35 +23 32 36.0 0 15.608 14.962 14.262 13.719 13.314 13.303 17.87 2.22 -44.38 2.22 10.63 0.89 DH487_L07_135 GCS9 Cl* Melotte 22 DH 487 +03 47 03.78 +23 36 58.6 0 12.388 12.010 11.486 11.369 10.665 11.002 22.69 2.22 -39.02 2.22 1.39 0.80 HII1286_HCG251_SK444_DH489 GCS9 HII1286 +03 47 04.41 +24 47 27.3 0 20.675 19.510 18.057 17.121 16.518 16.537 14.19 3.20 -39.02 3.20 2.70 0.49 L07_A1_58 GCS9 +03 47 04.75 +25 22 50.0 0 13.074 12.642 12.061 11.601 11.223 11.305 21.56 2.23 -40.41 2.23 2.99 0.87 HCG248_SK443_B180_HHJ390_DH491 GCS9 Cl* Melotte 22 HCG 248 +03 47 05.71 +24 40 03.6 0 16.900 16.127 15.416 14.847 14.428 14.447 10.79 2.25 -41.09 2.25 2.09 0.26 BPL124_L07_A1_59 GCS9 Cl* Melotte 22 BPL 124 +03 47 05.79 +23 45 34.7 0 16.227 15.555 14.840 14.315 13.961 13.964 11.90 2.23 -39.81 2.23 18.37 0.33 L07_A1_60_L07_231 GCS9 +03 47 07.88 +24 23 37.8 0 15.094 14.598 13.954 13.415 13.080 13.110 14.17 2.22 -44.83 2.22 35.17 0.80 BPL125_DH494_Festin98_003 GCS9 Cl* Melotte 22 BPL 125 +03 47 08.15 +24 18 24.5 0 13.675 13.278 12.730 12.176 11.884 11.936 17.08 2.22 -40.00 2.22 1.55 0.90 HCG253_SK440_BPL126_DH495 GCS9 Cl* Melotte 22 HCG 253 +03 47 09.42 +24 15 34.7 0 15.682 15.099 14.442 13.938 13.584 13.587 15.53 2.22 -37.71 2.22 3.08 0.68 HHJ37_BPL128_DH496_L07_105 GCS9 Cl* Melotte 22 HHJ 37 +03 47 09.52 +23 25 56.3 0 14.905 14.464 13.880 13.361 13.040 13.087 20.47 2.24 -42.25 2.24 0.90 0.93 DH498 GCS9 Cl* Melotte 22 DH 498 +03 47 10.21 +24 43 35.3 0 15.533 15.060 14.487 13.950 13.645 13.650 17.16 2.22 -43.48 2.22 12.34 0.90 L07_65 GCS9 +03 47 10.65 +23 58 16.4 0 16.136 15.557 14.877 14.360 13.969 14.000 19.15 2.23 -41.37 2.23 2.39 0.72 L07_A1_61_L07_228 GCS9 +03 47 11.03 +24 13 51.5 0 15.376 14.852 14.229 13.692 13.377 13.365 16.70 2.22 -43.11 2.22 1.30 0.90 HCG254_BPL129_L07_104 GCS9 Cl* Melotte 22 HCG 254 +03 47 11.79 +24 13 31.3 1 16.305 15.554 14.792 14.261 13.841 13.850 16.88 2.23 -41.27 2.23 0.62 0.73 BPL130_L07_A1_62 GCS9 Cl* Melotte 22 BPL 130 +03 47 11.86 +24 13 53.8 0 15.279 14.681 14.013 13.491 13.135 13.158 17.87 2.22 -38.56 2.22 1.06 0.80 HHJ92_BPL131_DH499 GCS9 Cl* Melotte 22 HHJ 92 +03 47 13.67 +23 49 53.2 0 12.791 12.364 11.867 11.575 11.044 11.809 19.47 2.22 -40.49 2.22 5.11 0.89 HCG258_SK437_B363_DH500 GCS9 Cl* Melotte 22 HCG 258 +03 47 15.20 +25 24 19.0 0 17.217 16.524 15.790 15.226 14.834 14.844 17.61 2.31 -19.56 2.31 3.87 0.00 IPMBD26 GCS9 Cl* Melotte 22 IPMBD 26 +03 47 15.29 +25 06 55.3 0 13.353 12.925 12.359 11.788 11.481 11.497 20.29 2.21 -40.01 2.21 2.31 0.89 SK432_HHJ376_BPL133_DH502_Moraux2003_9 GCS9 Cl* Melotte 22 SK 432 +03 47 15.38 +23 26 05.8 0 14.386 13.944 13.385 12.825 12.503 12.475 15.92 2.24 -43.02 2.24 2.38 0.92 HHJ203_DH503_L07_161 GCS9 Cl* Melotte 22 HHJ 203 +03 47 15.46 +24 23 31.0 0 14.844 14.290 13.663 13.121 12.783 12.815 12.21 2.22 -46.33 2.22 85.06 0.58 BPL134_Festin98_002 GCS9 Cl* Melotte 22 BPL 134 +03 47 15.75 +22 21 16.8 0 14.199 13.704 13.140 12.641 12.297 12.323 20.22 2.26 -54.43 2.26 2.70 0.03 HCG267_BPL135_HHJ211_L07_205 GCS9 Cl* Melotte 22 HCG 267 +03 47 16.45 +24 44 50.1 0 14.575 13.990 13.385 12.836 12.484 12.492 19.48 2.21 -40.86 2.21 12.55 0.93 BPL136_L07_66 GCS9 Cl* Melotte 22 BPL 136 +03 47 17.92 +24 22 31.6 0 18.174 17.067 16.215 15.591 15.096 15.080 21.40 2.30 -42.73 2.30 6.15 0.68 BPL137_Teide1_M8_L07_A1_63 GCS9 Cl* Melotte 22 BPL 137 +03 47 18.10 +24 45 14.6 0 19.068 18.067 17.095 16.314 15.678 15.676 8.16 2.57 -37.97 2.57 4.23 0.15 L07_A1_64 GCS9 +03 47 19.34 +24 08 20.7 0 13.736 13.765 11.395 12.325 9.950 10.831 21.11 2.22 -61.40 2.22 7.54 0.00 HII1362_HD23607_Tr390 GCS9 HII1362 +03 47 20.43 +22 21 56.3 0 14.832 14.342 13.737 13.208 12.868 12.835 15.02 2.26 -38.93 2.26 3.34 0.80 L07_207 GCS9 +03 47 20.84 +25 05 12.1 0 12.899 12.550 12.021 11.403 11.153 11.187 17.78 2.21 -45.20 2.21 2.92 0.77 SK428_HHJ417_DH506 GCS9 Cl* Melotte 22 SK 428 +03 47 20.97 +23 48 11.9 0 13.896 13.945 11.841 12.647 10.307 11.499 2.96 2.22 -46.62 2.22 25.98 0.00 HII1380_HD23632_Tr397 GCS9 HII1380 +03 47 21.94 +26 22 47.2 0 14.456 14.014 13.425 12.835 12.542 12.547 14.19 2.93 -47.46 2.93 1.49 0.71 HHJ219_DH508_Moraux2003_44 GCS9 Cl* Melotte 22 HHJ 219 +03 47 22.03 +23 21 36.3 0 14.515 14.064 13.489 12.961 12.637 12.619 20.70 2.24 -41.53 2.24 3.13 0.93 L07_159 GCS9 +03 47 22.38 +24 14 18.8 0 15.323 14.727 14.093 13.545 13.176 13.163 19.02 2.22 -46.64 2.22 3.72 0.81 BPL139 GCS9 Cl* Melotte 22 BPL 139 +03 47 22.46 +22 31 10.8 0 15.350 14.836 14.214 13.686 13.314 13.291 20.04 2.27 -39.70 2.27 3.97 0.83 BPL140_L07_201 GCS9 Cl* Melotte 22 BPL 140 +03 47 22.68 +23 44 06.7 0 14.271 13.786 13.223 12.688 12.391 12.374 16.09 2.22 -43.44 2.22 30.63 0.92 HCG266_DH509 GCS9 Cl* Melotte 22 HCG 266 +03 47 22.76 +23 01 58.0 0 15.929 15.403 14.815 14.282 13.961 13.934 36.81 2.25 -28.10 2.25 3.24 0.00 HHJ23 GCS9 Cl* Melotte 22 HHJ 23 +03 47 22.91 +22 55 19.4 0 12.476 12.365 11.050 11.776 9.922 10.773 30.63 2.23 -35.96 2.23 35.83 0.01 HII1407_HD23610_TrS108 GCS9 HII1407 +03 47 25.11 +24 15 17.2 0 14.913 14.441 13.875 13.326 13.021 13.018 18.69 2.22 -45.16 2.22 3.29 0.93 HHJ198_BPL143 GCS9 Cl* Melotte 22 HHJ 198 +03 47 25.35 +24 02 56.8 0 13.454 13.128 12.616 12.074 11.774 11.806 20.89 2.22 -39.96 2.22 3.92 0.88 HHJ427_DH512 GCS9 Cl* Melotte 22 HHJ 427 +03 47 25.80 +25 08 32.8 0 13.339 12.873 12.278 11.753 11.412 11.453 19.88 2.21 -43.41 2.21 4.52 0.93 HCG263_SK423_T6B_HHJ361_BPL144_DH513_Moraux2003_10 GCS9 Cl* Melotte 22 HCG 263 +03 47 25.90 +25 26 26.4 0 14.461 13.904 13.274 12.733 12.387 12.372 15.47 2.23 -44.18 2.23 3.55 0.91 Moraux2003_49_L07_38 GCS9 Cl* Melotte 22 MBSC 49 +03 47 26.77 +23 38 02.4 0 14.194 13.696 13.125 12.569 12.249 12.245 18.70 2.22 -40.80 2.22 4.38 0.93 HCG269_HHJ263_DH514 GCS9 Cl* Melotte 22 HCG 269 +03 47 26.84 +23 40 41.8 0 13.124 13.039 11.380 12.020 10.138 13.738 12.70 2.22 -30.72 2.22 12.76 0.00 HII1425_HD23643_Tr410 GCS9 HII1425 +03 47 27.28 +24 49 16.3 0 14.616 14.190 13.656 12.958 12.729 12.717 31.79 2.21 -32.38 2.21 2.21 0.00 15.07 GCS9 Cl* Melotte 22 MHO 13 +03 47 27.72 +22 09 38.5 0 17.382 16.547 15.760 15.224 14.763 14.783 17.79 2.34 -41.87 2.34 6.30 0.72 L07_A1_65 GCS9 +03 47 28.12 +23 26 53.4 0 13.694 13.309 12.779 12.225 11.885 11.879 19.40 2.24 -40.95 2.24 3.65 0.92 SK417_HHJ315_DH515 GCS9 Cl* Melotte 22 SK 417 +03 47 28.41 +26 32 05.5 0 14.243 13.779 13.199 12.652 12.361 12.363 20.47 2.93 -43.56 2.93 0.39 0.93 DH516_Moraux2003_33 GCS9 Cl* Melotte 22 DH 516 +03 47 28.43 +24 40 33.0 0 14.715 14.245 13.694 13.118 12.817 12.766 15.65 2.21 -46.11 2.21 4.76 0.87 BPL145_DH517_L07_69 GCS9 Cl* Melotte 22 BPL 145 +03 47 29.59 +23 52 49.3 0 16.385 15.692 15.016 14.462 14.078 14.077 16.73 2.24 -43.66 2.24 1.74 0.74 L07_A1_66 GCS9 +03 47 29.93 +23 33 15.0 0 16.033 15.408 14.750 14.157 13.818 13.794 17.39 2.23 -43.72 2.23 2.95 0.74 HHJ16 GCS9 Cl* Melotte 22 HHJ 16 +03 47 30.59 +26 16 44.5 0 13.801 13.394 12.848 12.305 12.027 12.001 19.30 2.93 -44.23 2.93 0.21 0.93 HCG260_HHJ310_DH518_Moraux2003_26 GCS9 Cl* Melotte 22 HCG 260 +03 47 30.60 +24 22 13.8 0 13.050 12.722 12.199 11.658 11.352 11.381 18.57 2.22 -44.22 2.22 2.33 0.94 HCG273_HHJ408 GCS9 Cl* Melotte 22 HCG 273 +03 47 31.13 +21 10 51.1 0 14.679 14.224 13.640 13.111 12.780 12.789 23.35 3.05 -35.53 3.05 0.64 0.31 DH519 GCS9 Cl* Melotte 22 DH 519 +03 47 31.37 +25 25 11.0 0 15.908 15.366 14.702 14.048 13.679 13.650 15.53 2.24 -17.06 2.24 1.02 0.00 Moraux2003_104 GCS9 Cl* Melotte 22 MBSC 104 +03 47 31.65 +23 52 19.1 0 14.743 14.247 13.686 13.121 12.811 12.800 13.78 2.22 -44.81 2.22 2.44 0.82 L07_124 GCS9 +03 47 32.00 +24 10 24.7 0 14.749 14.293 13.677 13.143 12.827 12.808 19.77 2.22 -41.79 2.22 1.69 0.94 BPL147 GCS9 Cl* Melotte 22 BPL 147 +03 47 33.06 +25 38 18.4 0 14.492 14.001 13.394 12.814 12.503 12.490 15.50 2.23 -44.70 2.23 6.41 0.90 L07_29 GCS9 +03 47 33.46 +23 41 32.8 0 12.580 12.224 11.716 11.700 10.957 11.178 17.61 2.22 -41.13 2.22 0.71 0.88 HCG277_SK413_T105_HHJ424_DH520 GCS9 Cl* Melotte 22 HCG 277 +03 47 34.17 +25 43 05.9 0 14.233 13.788 13.217 12.675 12.390 12.374 14.30 2.23 -44.19 2.23 17.36 0.86 HHJ238_DH522_L07_30 GCS9 Cl* Melotte 22 HHJ 238 +03 47 34.52 +24 02 23.0 0 14.645 14.216 13.648 13.087 12.797 12.793 19.46 2.22 -36.75 2.22 2.40 0.72 DH523 GCS9 Cl* Melotte 22 DH 523 +03 47 35.86 +24 52 26.7 0 14.700 14.223 13.634 13.112 12.786 12.772 16.91 2.21 -45.88 2.21 10.09 0.91 HCG279_HHJ157_BPL149 GCS9 Cl* Melotte 22 HCG 279 +03 47 36.04 +23 28 26.7 0 15.203 14.719 14.127 13.601 13.266 13.247 18.53 2.24 -46.44 2.24 3.74 0.83 HHJ73_DH526_L07_162 GCS9 Cl* Melotte 22 HHJ 73 +03 47 37.35 +25 20 02.3 0 14.391 13.912 13.308 12.736 12.427 12.407 16.90 2.23 -44.53 2.23 1.96 0.93 Moraux2003_43 GCS9 Cl* Melotte 22 MBSC 43 +03 47 37.66 +24 24 23.1 0 14.791 14.244 13.638 13.102 12.757 12.763 18.80 2.21 -47.96 2.21 2.31 0.84 BPL150_L07_76 GCS9 Cl* Melotte 22 BPL 150 +03 47 38.05 +24 49 10.8 0 13.439 13.061 12.546 12.133 11.670 11.690 15.44 2.21 -43.50 2.21 7.15 0.91 SK409_HHJ360_BPL151_DH528 GCS9 Cl* Melotte 22 SK 409 +03 47 38.37 +24 35 59.7 0 15.455 14.891 14.304 13.737 13.408 13.395 15.03 2.21 -41.64 2.21 8.01 0.85 L07_80 GCS9 +03 47 39.02 +24 36 22.2 0 17.112 16.271 15.548 14.992 14.570 14.554 15.20 2.24 -45.02 2.24 12.06 0.59 BPL152_Roque16_CFHT-Pl-11_M6.0_BRB12_CFHT-Pl-11_L07_A1_67_L07_219 GCS9 Cl* Melotte 22 BPL 152 +03 47 39.36 +24 27 31.9 0 14.048 13.600 13.019 12.442 12.145 12.139 19.38 2.21 -43.38 2.21 4.21 0.94 HCG282_HHJ272_BPL153_DH530 GCS9 Cl* Melotte 22 HCG 282 +03 47 39.80 +23 00 04.4 0 15.088 14.635 14.074 13.518 13.202 13.203 16.06 2.24 -42.09 2.24 13.32 0.88 HHJ94 GCS9 Cl* Melotte 22 HHJ 94 +03 47 40.96 +21 49 05.0 0 15.807 15.243 14.629 14.089 13.750 13.707 22.10 2.28 -44.16 2.28 5.36 0.80 L07_213 GCS9 +03 47 42.86 +28 18 59.1 0 15.298 14.760 14.147 13.616 13.263 13.252 15.67 2.96 -35.98 2.96 0.05 0.45 DH533 GCS9 Cl* Melotte 22 DH 533 +03 47 43.88 +26 13 26.8 0 14.714 14.225 13.655 13.113 12.807 12.804 23.14 2.93 -41.65 2.93 0.35 0.86 DH534_Moraux2003_66 GCS9 Cl* Melotte 22 DH 534 +03 47 44.05 +24 03 56.3 0 16.061 15.644 15.077 14.512 14.197 14.197 23.29 2.23 -45.96 2.23 10.22 0.35 HHJ26_DH535 GCS9 Cl* Melotte 22 HHJ 26 +03 47 44.66 +23 42 03.1 0 14.538 14.079 13.508 12.937 12.650 12.660 20.25 2.22 -45.79 2.22 1.38 0.91 HHJ152_DH536_L07_139 GCS9 Cl* Melotte 22 HHJ 152 +03 47 44.67 +22 12 43.9 0 15.872 15.338 14.695 14.187 13.825 13.810 22.07 2.28 -45.80 2.28 5.26 0.74 HHJ15_BPL154 GCS9 Cl* Melotte 22 HHJ 15 +03 47 44.68 +22 23 53.0 0 14.454 14.006 13.424 12.875 12.554 12.562 22.28 2.26 -44.26 2.26 0.33 0.90 HCG299_HHJ163_BPL155_L07_208 GCS9 Cl* Melotte 22 HCG 299 +03 47 45.92 +24 38 01.3 0 14.579 14.033 13.437 12.860 12.493 12.514 19.47 2.21 -49.88 2.21 0.62 0.62 HCG287_HHJ168_BPL156_L07_68 GCS9 Cl* Melotte 22 HCG 287 +03 47 46.40 +24 03 02.3 0 12.979 12.677 12.180 11.634 11.322 11.381 19.77 2.22 -37.79 2.22 7.93 0.84 HHJ438 GCS9 Cl* Melotte 22 HHJ 438 +03 47 46.78 +25 35 16.6 0 20.351 18.565 17.424 16.687 16.154 16.095 18.60 3.00 -37.57 3.00 1.25 0.44 L07_A1_68 GCS9 +03 47 47.87 +25 13 34.3 0 14.065 13.593 12.961 12.408 12.049 12.055 18.78 2.21 -41.93 2.21 2.41 0.94 SK398_HHJ266_BPL157_DH537_Moraux2003_34 GCS9 Cl* Melotte 22 SK 398 +03 47 48.91 +24 17 06.5 0 20.292 19.272 17.873 17.039 16.410 16.385 13.21 2.89 -36.93 2.89 3.93 0.43 L07_A1_69 GCS9 +03 47 49.45 +23 31 52.8 0 17.078 16.295 15.581 15.025 14.611 14.602 17.03 2.25 -39.82 2.25 7.37 0.65 L07_A1_70 GCS9 +03 47 49.79 +24 25 43.1 0 14.551 14.074 13.497 12.962 12.669 12.650 20.57 2.21 -46.82 2.21 2.91 0.87 HCG292_HHJ202_BPL158_DH538_L07_77 GCS9 Cl* Melotte 22 HCG 292 +03 47 50.41 +23 54 47.8 0 18.179 17.138 16.311 15.622 15.093 15.090 15.88 2.32 -39.60 2.32 5.41 0.63 Festin98_007_L07_A1_71 GCS9 Cl* Melotte 22 NPL 007 +03 47 50.95 +24 30 18.6 0 13.152 12.806 12.298 11.944 11.443 11.472 18.40 2.21 -45.72 2.21 7.73 0.92 HCG295_HHJ389_BPL159_DH539 GCS9 Cl* Melotte 22 HCG 295 +03 47 51.97 +23 39 48.0 0 14.936 14.433 13.888 13.320 13.031 13.034 17.54 2.22 -44.32 2.22 6.79 0.94 HHJ122_DH540 GCS9 Cl* Melotte 22 HHJ 122 +03 47 52.88 +22 59 33.8 0 14.017 13.564 12.998 12.456 12.134 12.160 15.85 2.24 -40.42 2.24 11.92 0.89 SK394_HHJ258_BPL160_DH541 GCS9 Cl* Melotte 22 SK 394 +03 47 55.28 +23 19 05.8 0 13.814 13.443 12.910 12.377 12.092 12.111 14.50 2.24 -35.68 2.24 2.43 0.30 HCG302_SK392_HHJ289_DH542 GCS9 Cl* Melotte 22 HCG 302 +03 47 56.64 +24 15 31.7 0 15.315 14.766 14.163 13.625 13.282 13.281 21.88 2.22 -36.70 2.22 3.78 0.47 BPL162_L07_100 GCS9 Cl* Melotte 22 BPL 162 +03 47 56.66 +26 31 50.9 0 12.905 12.555 12.064 11.504 11.210 11.250 23.91 2.93 -40.70 2.93 2.10 0.76 HHJ423_DH543 GCS9 Cl* Melotte 22 HHJ 423 +03 47 58.04 +22 06 50.8 0 16.708 15.993 15.283 14.742 14.331 14.331 18.97 2.30 -42.48 2.30 4.22 0.74 BPL163_L07_A1_72 GCS9 Cl* Melotte 22 BPL 163 +03 47 59.38 +24 35 37.0 0 15.631 15.079 14.486 13.940 13.592 13.581 15.52 2.22 -43.79 2.22 3.92 0.87 BPL164_DH544_L07_79 GCS9 Cl* Melotte 22 BPL 164 +03 47 59.74 +22 36 01.8 0 18.043 17.083 16.209 15.609 15.090 15.115 21.40 2.40 -42.45 2.40 6.65 0.69 int-pl-IZ-33_2MASSJ0347597+223601_Y_L07_A1_73 GCS9 Cl* Melotte 22 IPL 33 +03 47 59.77 +22 38 30.1 0 12.945 12.429 11.820 11.418 11.025 11.072 30.05 2.26 -48.45 2.26 26.02 0.00 HHJ365_BPL165 GCS9 Cl* Melotte 22 HHJ 365 +03 48 04.67 +23 39 30.1 1 17.014 16.054 15.283 14.704 14.256 14.238 16.07 2.24 -44.27 2.24 5.65 0.66 PPl15_IPMBD23_L07_A1_74 GCS9 PPl15 +03 48 04.98 +23 24 13.4 0 15.976 15.415 14.783 14.280 13.912 13.935 16.66 2.25 -43.18 2.25 1.67 0.89 HHJ21_DH546_L07_160 GCS9 Cl* Melotte 22 HHJ 21 +03 48 05.72 +22 38 09.3 0 14.248 13.814 13.195 12.717 12.425 12.432 15.27 2.26 -47.03 2.26 31.26 0.82 HCG314_HHJ210_DH547_L07_202 GCS9 Cl* Melotte 22 HCG 314 +03 48 05.83 +23 02 02.7 0 13.241 12.881 12.377 11.963 11.540 11.562 16.95 2.23 -43.77 2.23 2.60 0.93 HCG309_SK385_T154_HHJ375_DH548_BPL166 GCS9 Cl* Melotte 22 HCG 309 +03 48 06.41 +24 06 51.7 0 14.633 14.200 13.603 13.064 12.753 12.790 18.58 2.22 -43.78 2.22 0.58 0.94 L07_122 GCS9 +03 48 06.64 +24 00 06.7 0 14.398 13.957 13.367 12.823 12.520 12.531 17.15 2.22 -39.79 2.22 1.28 0.90 HHJ240_DH549 GCS9 Cl* Melotte 22 HHJ 240 +03 48 07.96 +23 44 23.5 0 13.941 13.522 12.964 12.433 12.148 12.163 17.89 2.22 -45.77 2.22 0.64 0.92 HCG307_HHJ288_DH551 GCS9 Cl* Melotte 22 HCG 307 +03 48 08.96 +23 42 23.3 0 14.620 14.148 13.566 13.045 12.761 12.747 17.90 2.22 -46.56 2.22 1.13 0.90 HHJ156_DH552 GCS9 Cl* Melotte 22 HHJ 156 +03 48 09.22 +23 58 40.5 0 14.445 14.024 13.448 12.921 12.615 12.648 15.27 2.22 -41.80 2.22 0.91 0.90 HHJ225_DH553_L07_107 GCS9 Cl* Melotte 22 HHJ 225 +03 48 10.17 +23 00 03.9 0 12.412 12.198 11.771 11.631 10.997 11.133 18.14 2.23 -35.64 2.23 5.94 0.66 DH554 GCS9 Cl* Melotte 22 DH 554 +03 48 10.18 +23 59 20.1 0 15.509 15.004 14.370 13.840 13.478 13.492 20.23 2.22 -43.71 2.22 0.99 0.88 DH555_PPL12_L07_109 GCS9 Cl* Melotte 22 DH 555 +03 48 13.31 +23 58 46.8 0 14.206 13.766 13.187 12.664 12.353 12.394 16.71 2.22 -42.37 2.22 4.05 0.93 HCG311_T155_DH560_Festin98_001_L07_108 GCS9 Cl* Melotte 22 HCG 311 +03 48 13.78 +23 37 59.3 0 13.951 13.521 12.974 12.422 12.130 12.041 19.35 2.22 -44.07 2.22 2.65 0.94 HCG315_SK374_HHJ281_DH561_L07_10 GCS9 Cl* Melotte 22 HCG 315 +03 48 14.30 +24 15 50.5 0 16.637 15.926 15.218 14.677 14.267 14.265 19.81 2.24 -40.94 2.24 4.06 0.69 BPL169_L07_A1_75 GCS9 Cl* Melotte 22 BPL 169 +03 48 15.25 +23 26 05.4 0 14.320 13.888 13.339 12.781 12.516 12.495 14.81 2.13 -42.58 2.13 1.54 0.89 HHJ232 GCS9 Cl* Melotte 22 HHJ 232 +03 48 15.27 +23 42 03.4 0 13.599 13.175 12.588 12.112 11.768 11.801 18.45 2.22 -45.66 2.22 1.36 0.92 HHJ336_DH562 GCS9 Cl* Melotte 22 HHJ 336 +03 48 15.49 +25 14 36.4 0 14.957 14.500 13.897 13.347 13.006 13.051 13.38 2.21 -40.07 2.21 3.89 0.74 BPL170_DH563_Moraux2003_78_L07_58 GCS9 Cl* Melotte 22 BPL 170 +03 48 16.09 +23 35 15.2 0 14.654 14.122 13.520 12.988 12.645 12.641 20.07 2.22 -42.31 2.22 0.55 0.94 HHJ151_DH564_L07_144 GCS9 Cl* Melotte 22 HHJ 151 +03 48 16.38 +26 20 05.8 0 14.225 13.741 13.161 12.587 12.309 12.297 17.16 2.93 -24.32 2.93 0.43 0.00 HCG305 GCS9 Cl* Melotte 22 HCG 305 +03 48 16.58 +21 57 44.4 0 15.162 14.808 14.229 13.602 13.361 13.347 19.66 2.27 -27.09 2.27 22.21 0.00 L07_212 GCS9 +03 48 16.64 +26 30 11.6 0 14.415 13.953 13.400 12.855 12.541 12.576 17.23 2.93 -51.67 2.93 0.24 0.29 HHJ215_DH565 GCS9 Cl* Melotte 22 HHJ 215 +03 48 17.30 +24 30 15.7 0 12.184 11.939 11.515 11.450 10.723 10.898 26.02 2.21 -46.97 2.21 2.32 0.15 HII1785_HCG312_DH566 GCS9 HII1785 +03 48 17.37 +23 48 23.5 0 14.645 14.190 13.615 13.061 12.760 12.745 18.31 2.22 -42.91 2.22 0.63 0.94 HHJ188_L07_132 GCS9 Cl* Melotte 22 HHJ 188 +03 48 17.61 +22 04 00.9 0 14.425 13.954 13.373 12.851 12.543 12.515 19.94 2.26 -43.28 2.26 2.96 0.94 HHJ187_BPL171_L07_209 GCS9 Cl* Melotte 22 HHJ 187 +03 48 19.02 +24 25 12.7 0 17.655 16.668 15.930 15.365 14.935 14.953 14.16 2.30 -39.48 2.30 13.03 0.49 BPL172_Festin98_005_Roque12_L07_photNM_22 GCS9 Cl* Melotte 22 BPL 172 +03 48 19.84 +23 36 11.8 0 13.175 12.840 12.327 11.827 11.486 11.536 20.97 2.22 -44.35 2.22 1.90 0.91 DH568 GCS9 Cl* Melotte 22 DH 568 +03 48 20.29 +24 54 54.9 0 13.479 13.035 12.450 11.909 11.604 11.641 19.17 2.21 -42.06 2.21 2.97 0.94 HCG313_SK371_T23_BPL173 GCS9 Cl* Melotte 22 HCG 313 +03 48 20.58 +23 31 01.3 0 15.523 14.983 14.358 13.817 13.510 13.517 24.03 2.14 -49.73 2.14 6.53 0.15 HCG321_DH570 GCS9 Cl* Melotte 22 HCG 321 +03 48 21.54 +24 34 43.4 0 14.868 14.334 13.789 13.233 12.918 12.937 13.23 2.21 -46.01 2.21 3.06 0.73 BPL174_DH571_L07_78 GCS9 Cl* Melotte 22 BPL 174 +03 48 22.66 +22 52 21.3 0 13.174 12.807 12.291 11.768 11.473 11.505 20.45 2.13 -41.09 2.13 4.73 0.91 HCG323_SK370_A91_BPL175_DH572 GCS9 Cl* Melotte 22 HCG 323 +03 48 22.71 +23 27 42.8 0 14.643 14.181 13.574 13.026 12.787 12.738 19.88 2.13 -42.50 2.13 0.89 0.94 HHJ171_DH573 GCS9 Cl* Melotte 22 HHJ 171 +03 48 22.81 +24 48 53.4 0 13.811 13.364 12.850 12.329 12.012 12.039 16.29 2.21 -48.18 2.21 2.53 0.78 SK368_HHJ309_BPL176_DH574 GCS9 Cl* Melotte 22 SK 368 +03 48 23.92 +23 08 08.1 0 15.111 14.655 14.032 13.490 13.123 13.129 14.82 2.13 -40.92 2.13 1.68 0.83 DH576 GCS9 Cl* Melotte 22 DH 576 +03 48 25.24 +24 14 25.8 0 13.827 13.379 12.792 12.285 11.968 11.989 17.16 2.22 -43.14 2.22 5.84 0.94 HCG322_SK363_HHJ314_BPL178 GCS9 Cl* Melotte 22 HCG 322 +03 48 26.05 +25 14 40.8 0 12.786 12.471 11.969 11.411 11.122 11.211 18.96 2.21 -54.45 2.21 33.24 0.00 HCG317_DH578 GCS9 Cl* Melotte 22 HCG 317 +03 48 26.60 +23 11 29.5 0 13.307 12.741 12.122 11.691 11.279 11.326 25.82 2.13 -37.29 2.13 0.76 0.17 HCG332_SK362_HHJ339 GCS9 Cl* Melotte 22 HCG 332 +03 48 27.36 +23 46 16.2 0 20.628 19.658 18.134 17.347 16.505 16.519 18.01 2.93 -43.64 2.93 1.05 0.57 L07_A1_76 GCS9 +03 48 29.76 +23 58 05.8 0 14.876 14.413 13.823 13.281 12.988 12.938 17.30 2.22 -45.29 2.22 3.01 0.92 HCG328_WILL3_HHJ132 GCS9 Cl* Melotte 22 HCG 328 +03 48 30.29 +24 18 00.2 0 20.225 19.201 17.793 16.971 16.382 16.374 21.38 2.76 -51.81 2.76 2.78 0.54 L07_A1_77 GCS9 +03 48 30.74 +22 44 50.2 0 20.389 18.936 17.714 16.861 16.251 16.150 11.34 3.40 -38.38 3.40 1.90 0.47 Roque25 GCS9 Cl* Melotte 22 Roque 25 +03 48 31.06 +24 16 53.1 0 13.037 12.703 12.170 11.891 11.376 11.477 23.77 2.22 -43.77 2.22 10.39 0.79 HCG324_VM46_HHJ404_BPL180_DH580 GCS9 Cl* Melotte 22 HCG 324 +03 48 31.53 +24 34 37.2 1 19.218 17.883 16.715 15.977 15.357 15.312 11.92 2.37 -46.68 2.37 3.41 0.49 BRB16_PIZ1_L07_A1_78_L07_258 GCS9 Cl* Melotte 22 BRB 16 +03 48 31.84 +24 01 58.6 0 14.648 14.214 13.619 13.074 12.798 12.789 16.37 2.22 -42.70 2.22 6.09 0.93 HCG327_HHJ197_DH581_L07_110 GCS9 Cl* Melotte 22 HCG 327 +03 48 33.78 +24 01 58.8 0 14.698 14.252 13.698 13.136 12.861 12.892 16.27 2.22 -39.37 2.22 4.68 0.87 HHJ184_DH582_L07_111 GCS9 Cl* Melotte 22 HHJ 184 +03 48 35.20 +22 53 42.1 1 16.176 15.452 14.745 14.185 13.770 13.772 15.22 2.15 -45.62 2.15 1.57 0.62 HHJ10_BPL181_PPL10 GCS9 Cl* Melotte 22 HHJ 10 +03 48 35.49 +24 12 03.0 0 15.216 14.662 14.042 13.491 13.221 13.201 18.84 2.22 -39.61 2.22 2.04 0.85 HCG335_HHJ96_BPL182_DH583_L07_95 GCS9 Cl* Melotte 22 HCG 335 +03 48 36.34 +25 15 41.2 0 16.029 15.446 14.801 14.259 13.889 13.895 14.47 2.21 -46.15 2.21 13.10 0.54 BPL183_Moraux2003_105_L07_56 GCS9 Cl* Melotte 22 BPL 183 +03 48 38.37 +22 33 51.8 0 17.345 16.523 15.711 15.168 14.718 14.692 17.51 2.33 -38.63 2.33 7.70 0.60 L07_A1_79 GCS9 +03 48 39.31 +24 50 19.9 0 15.432 14.862 14.269 13.711 13.405 13.387 14.82 2.21 -44.18 2.21 1.67 0.84 DH585_L07_75 GCS9 Cl* Melotte 22 DH 585 +03 48 39.91 +24 12 42.7 0 13.512 13.161 12.640 12.085 11.832 11.837 14.85 2.22 -41.13 2.22 1.22 0.87 HCG337_SK353_HHJ347_BPL184 GCS9 Cl* Melotte 22 HCG 337 +03 48 40.43 +24 36 34.0 0 14.182 13.705 13.144 12.632 12.321 12.314 17.15 2.20 -46.57 2.20 2.52 0.89 HCG333_SK351_BPL185_DH586 GCS9 Cl* Melotte 22 HCG 333 +03 48 40.60 +25 01 19.8 0 15.780 15.212 14.553 14.023 13.660 13.652 17.54 2.21 -42.42 2.21 1.98 0.90 BPL186_L07_63 GCS9 Cl* Melotte 22 BPL 186 +03 48 40.99 +23 14 17.2 0 13.874 13.419 12.818 12.291 11.956 11.984 20.87 2.13 -42.75 2.13 4.91 0.92 HCG343_SK352 GCS9 Cl* Melotte 22 HCG 343 +03 48 42.15 +25 00 28.2 0 13.453 13.101 12.555 12.084 11.703 11.724 16.19 2.20 -44.52 2.20 0.66 0.92 HCG339_SK349_B207_BPL187 GCS9 Cl* Melotte 22 HCG 339 +03 48 42.69 +24 27 19.4 0 15.480 14.961 14.356 13.822 13.479 13.475 18.31 2.21 -46.04 2.21 0.44 0.85 HHJ44_WILL6_BPL188_DH587_L07_89 GCS9 Cl* Melotte 22 HHJ 44 +03 48 43.14 +26 32 20.9 0 14.239 13.774 13.191 12.678 12.379 12.364 25.22 2.93 -37.76 2.93 0.52 0.39 HHJ246_DH588 GCS9 Cl* Melotte 22 HHJ 246 +03 48 44.05 +25 06 22.4 0 14.492 14.076 13.483 12.898 12.639 12.631 16.69 2.20 -44.81 2.20 3.48 0.92 BPL189_DH589_Moraux2003_48_L07_55 GCS9 Cl* Melotte 22 BPL 189 +03 48 44.69 +24 37 23.5 0 16.260 15.584 14.911 14.419 14.002 13.967 17.45 2.22 -43.06 2.22 1.14 0.75 DH590_CFHT-Pl-5_M5.5_BRB7_CFHT-Pl-5_L07_A1_80_L07_223 GCS9 Cl* Melotte 22 DH 590 +03 48 45.35 +24 37 26.3 0 15.118 14.581 13.971 13.507 13.157 13.123 15.61 2.20 -45.81 2.20 2.78 0.83 HCG346_HHJ111_BPL190 GCS9 Cl* Melotte 22 HCG 346 +03 48 46.02 +24 10 12.5 0 14.464 14.027 13.476 12.931 12.631 12.661 15.18 2.22 -41.35 2.22 1.14 0.89 HHJ207_DH59_L07_112 GCS9 Cl* Melotte 22 HHJ 207 +03 48 48.62 +24 30 15.4 0 15.488 15.114 14.569 13.991 13.692 13.689 21.84 2.21 -38.84 2.21 6.82 0.70 L07_90 GCS9 +03 48 48.79 +23 24 48.0 0 15.140 14.625 14.030 13.486 13.166 13.171 15.92 2.13 -37.19 2.13 1.51 0.64 HHJ86 GCS9 Cl* Melotte 22 HHJ 86 +03 48 50.45 +22 44 29.8 1 16.562 15.825 15.098 14.531 14.141 14.143 15.64 2.16 -42.06 2.16 2.49 0.71 HHJ3_BPL191 GCS9 Cl* Melotte 22 HHJ 3 +03 48 50.45 +25 17 54.7 0 16.516 15.802 15.106 14.575 14.177 14.174 18.72 2.25 -45.60 2.25 2.79 0.67 BPL192_Moraux2003_109 GCS9 Cl* Melotte 22 BPL 192 +03 48 50.50 +24 08 27.2 0 16.669 16.113 15.512 14.949 14.599 14.574 7.25 2.25 -27.47 2.25 3.93 0.00 DH593 GCS9 Cl* Melotte 22 DH 593 +03 48 50.67 +23 04 30.0 0 15.010 14.511 13.936 13.425 13.098 13.108 17.50 2.13 -43.02 2.13 1.74 0.90 HHJ116 GCS9 Cl* Melotte 22 HHJ 116 +03 48 51.56 +24 12 17.3 0 12.963 12.601 12.088 11.632 11.240 11.331 19.13 2.22 -24.55 2.22 13.96 0.00 BPL193_ALR728 GCS9 Cl* Melotte 22 BPL 193 +03 48 52.29 +25 22 32.3 0 14.846 14.364 13.770 13.224 12.890 12.904 16.02 2.23 -33.87 2.23 4.39 0.17 L07_43 GCS9 +03 48 55.65 +24 21 40.1 0 16.220 15.636 14.963 14.416 14.055 14.041 17.62 2.23 -40.24 2.23 2.50 0.70 HHJ8_L07_A1_81 GCS9 Cl* Melotte 22 HHJ 8 +03 48 57.36 +24 19 43.6 0 12.735 12.382 11.845 11.772 11.088 11.294 19.80 2.22 -41.83 2.22 3.88 0.89 HCG349_SK340 GCS9 Cl* Melotte 22 HCG 349 +03 48 58.29 +23 05 39.0 0 13.329 12.999 12.460 11.907 11.630 11.649 20.85 2.13 -49.85 2.13 3.36 0.54 HHJ353_DH595 GCS9 Cl* Melotte 22 HHJ 353 +03 49 00.99 +24 54 10.0 0 13.608 13.216 12.673 12.188 11.812 11.833 24.02 2.20 -54.18 2.20 4.15 0.01 HCG351_SK335_HHJ332_BPL195 GCS9 Cl* Melotte 22 HCG 351 +03 49 01.02 +22 58 49.2 0 12.708 12.420 11.923 11.443 11.161 11.220 17.88 2.12 -44.01 2.12 2.32 0.84 HCG353_SK336_A55_HHJ414_DH597 GCS9 Cl* Melotte 22 HCG 353 +03 49 01.16 +23 38 15.5 0 15.613 15.042 14.427 13.885 13.548 13.580 15.19 2.22 -43.96 2.22 6.87 0.86 DH598_L07_145 GCS9 Cl* Melotte 22 DH 598 +03 49 01.50 +24 11 38.3 0 14.330 13.900 13.320 12.814 12.506 12.488 14.90 2.22 -46.83 2.22 49.18 0.81 HHJ231_BPL196_DH599 GCS9 Cl* Melotte 22 HHJ 231 +03 49 02.35 +25 43 24.1 0 12.899 12.711 12.370 11.912 11.751 11.756 13.74 2.23 -39.88 2.23 7.61 0.66 DH2004_600 GCS9 Cl* Melotte 22 DH 600 +03 49 02.97 +21 54 46.9 0 15.347 14.790 14.176 13.654 13.307 13.306 19.02 2.27 -47.56 2.27 3.03 0.75 BPL197_L07_211 GCS9 Cl* Melotte 22 BPL 197 +03 49 04.86 +23 33 39.3 0 17.145 16.308 15.573 15.032 14.579 14.568 16.74 2.25 -42.98 2.25 23.53 0.70 Roque47_IPMBD20_L07_A1_82 GCS9 Cl* Melotte 22 Roque 47 +03 49 05.18 +22 04 52.7 0 16.648 15.958 15.257 14.742 14.327 14.355 16.87 2.31 -38.67 2.31 5.97 0.59 L07_A1_83 GCS9 +03 49 05.93 +23 06 21.9 0 13.996 13.617 13.008 12.510 12.202 12.212 21.86 2.13 -55.37 2.13 8.61 0.00 HCG357_SK332_DH602 GCS9 Cl* Melotte 22 HCG 357 +03 49 06.76 +25 24 21.5 0 15.881 15.480 14.942 14.330 14.052 14.083 10.08 2.25 -28.77 2.25 1.80 0.00 L07_44 GCS9 +03 49 08.84 +25 53 48.6 0 13.042 12.708 12.195 11.732 11.323 11.398 14.88 2.23 -43.29 2.23 2.17 0.89 SK327_HHJ387_DH603_Moraux2003_8 GCS9 Cl* Melotte 22 SK 327 +03 49 11.95 +24 39 26.5 0 19.975 18.858 18.018 17.618 17.140 17.153 33.87 3.66 -38.06 3.66 1.67 0.11 L07_256 GCS9 +03 49 12.18 +25 20 31.7 0 15.633 15.245 14.696 14.123 13.823 13.846 16.17 2.24 -42.24 2.24 1.32 0.89 L07_42 GCS9 +03 49 15.12 +24 36 22.5 0 16.847 16.059 15.371 14.890 14.433 14.443 15.04 2.23 -40.44 2.23 9.93 0.64 BPL202_CFHT-Pl-9_M6.5_BRB10_CFHT-Pl-9_L07_A1_85_L07_222 GCS9 Cl* Melotte 22 BPL 202 +03 49 15.63 +23 22 49.1 0 15.460 14.919 14.290 13.736 13.436 13.422 19.79 2.26 -41.51 2.26 3.40 0.88 HHJ36_L07_152_DH610 GCS9 Cl* Melotte 22 HHJ 36 +03 49 16.18 +26 49 02.9 0 16.931 16.211 15.474 14.912 14.482 14.531 21.70 2.99 -47.50 2.99 1.06 0.37 IPMBD21 GCS9 Cl* Melotte 22 IPMBD 21 +03 49 16.42 +24 03 49.0 0 12.458 12.108 11.574 11.608 10.800 11.068 24.30 2.22 -41.94 2.22 7.58 0.71 HCG362 GCS9 Cl* Melotte 22 HCG 362 +03 49 20.31 +25 25 42.4 0 14.155 13.745 13.198 12.642 12.335 12.324 12.27 2.23 -40.97 2.23 5.46 0.66 HCG366_HHJ251_DH611_Moraux2003_31 GCS9 Cl* Melotte 22 HCG 366 +03 49 20.70 +24 52 35.2 0 15.612 15.094 14.529 14.021 13.731 13.738 44.56 2.21 -41.45 2.21 12.56 0.00 BPL203 GCS9 Cl* Melotte 22 BPL 203 +03 49 20.96 +26 11 33.5 0 16.986 16.399 15.818 15.206 14.866 14.910 12.65 3.05 -35.63 3.05 1.07 0.10 DH614 GCS9 Cl* Melotte 22 DH 614 +03 49 21.17 +23 34 02.0 0 18.421 17.444 16.621 16.028 15.535 15.548 15.81 2.36 -29.43 2.36 3.20 0.03 Roque8_L07_photNM_11 GCS9 Cl* Melotte 22 Roque 8 +03 49 21.25 +24 41 40.8 0 15.549 14.957 14.352 13.821 13.470 13.478 16.52 2.21 -46.03 2.21 4.32 0.84 HHJ51_BPL204_DH615 GCS9 Cl* Melotte 22 HHJ 51 +03 49 21.50 +23 39 06.3 0 13.751 13.346 12.809 12.251 11.951 12.003 13.80 2.22 -45.12 2.22 5.34 0.82 HCG363_SK319_HHJ306_DH616 GCS9 Cl* Melotte 22 HCG 363 +03 49 21.93 +24 54 43.2 0 14.596 14.111 13.560 13.037 12.733 12.726 19.53 2.20 -43.15 2.20 4.87 0.94 SK316_BPL205_DH617_L07_62 GCS9 Cl* Melotte 22 SK 316 +03 49 22.15 +25 47 37.7 0 14.407 13.954 13.393 12.801 12.496 12.504 19.36 2.23 -37.53 2.23 2.58 0.80 HHJ196_DH618_Moraux2003_52 GCS9 Cl* Melotte 22 HHJ 196 +03 49 22.52 +21 37 56.0 0 14.541 14.086 13.492 12.975 12.636 12.654 21.90 3.05 -33.73 3.05 1.35 0.15 DH2004_619 GCS9 Cl* Melotte 22 DH 619 +03 49 25.55 +22 18 44.4 0 15.321 14.823 14.225 13.678 13.329 13.321 21.74 2.51 -40.44 2.51 2.43 0.79 BPL206 GCS9 Cl* Melotte 22 BPL 206 +03 49 25.61 +23 02 49.9 0 15.146 14.643 14.021 13.499 13.173 13.172 20.07 2.25 -44.25 2.25 3.06 0.87 HHJ79_BPL207_DH620_L07_187 GCS9 Cl* Melotte 22 HHJ 79 +03 49 26.64 +22 50 54.5 0 14.354 13.911 13.327 12.787 12.467 12.447 15.77 2.25 -44.36 2.25 1.72 0.91 SK315_BPL208_L07_198 GCS9 Cl* Melotte 22 SK 315 +03 49 26.65 +22 52 20.5 0 14.721 14.312 13.776 13.161 12.926 12.924 34.03 2.25 -25.62 2.25 11.59 0.00 L07_185 GCS9 +03 49 27.58 +24 24 13.7 0 14.547 14.043 13.524 12.968 12.679 12.681 11.85 2.20 -46.15 2.20 90.55 0.54 HHJ192_DH621 GCS9 Cl* Melotte 22 HHJ 192 +03 49 27.65 +24 31 54.1 0 12.946 12.583 12.067 11.575 11.226 11.273 11.93 2.20 -42.04 2.20 4.32 0.40 HCG370_DH622 GCS9 Cl* Melotte 22 HCG 370 +03 49 28.80 +26 50 51.4 0 15.626 15.129 14.513 13.997 13.663 13.655 18.24 2.94 -35.09 2.94 0.50 0.37 Moraux2003_95 GCS9 Cl* Melotte 22 MBSC 95 +03 49 29.81 +23 38 55.2 0 14.604 14.083 13.516 12.985 12.654 12.674 15.93 2.22 -46.60 2.22 7.60 0.86 HHJ158_DH624_L07_149 GCS9 Cl* Melotte 22 HHJ 158 +03 49 31.22 +23 41 19.6 0 14.646 14.158 13.575 13.029 12.724 12.749 20.25 2.22 -44.15 2.22 5.61 0.93 HHJ142_DH625 GCS9 Cl* Melotte 22 HHJ 142 +03 49 32.16 +23 16 17.9 0 15.381 14.888 14.287 13.753 13.418 13.424 20.17 2.25 -40.90 2.25 1.24 0.86 DH626 GCS9 Cl* Melotte 22 DH 626 +03 49 32.57 +23 24 41.0 0 14.251 13.753 13.159 12.602 12.293 12.309 20.08 2.25 -40.04 2.25 6.19 0.91 HHJ245_L07_153 GCS9 Cl* Melotte 22 HHJ 245 +03 49 32.81 +25 47 46.8 0 14.438 13.998 13.465 12.891 12.567 12.569 17.13 2.23 -41.13 2.23 4.11 0.93 HHJ209_DH628_Moraux2003_60_L07_23 GCS9 Cl* Melotte 22 HHJ 209 +03 49 33.04 +24 32 02.4 0 13.402 13.042 12.522 12.024 11.684 11.695 13.04 2.20 -43.72 2.20 3.25 0.79 HCG372_SK309_T90_HHJ346_BPL209 GCS9 Cl* Melotte 22 HCG 372 +03 49 35.29 +25 59 34.6 0 14.594 14.082 13.534 12.974 12.654 12.671 22.66 2.23 -45.19 2.23 3.27 0.86 HHJ155_DH630 GCS9 Cl* Melotte 22 HHJ 155 +03 49 35.46 +22 23 26.1 0 14.930 14.487 13.916 13.356 13.068 13.048 19.51 2.51 -41.27 2.51 0.89 0.93 HHJ112_BPL211 GCS9 Cl* Melotte 22 HHJ 112 +03 49 36.32 +26 02 16.3 0 15.517 14.960 14.333 13.755 13.409 13.401 24.70 2.24 -41.43 2.24 4.90 0.53 HHJ42_DH633 GCS9 Cl* Melotte 22 HHJ 42 +03 49 38.24 +26 51 05.6 0 14.052 13.637 13.073 12.512 12.234 12.239 19.29 2.93 -43.50 2.93 0.33 0.94 DH635_Moraux2003_29 GCS9 Cl* Melotte 22 DH 635 +03 49 39.32 +23 34 54.9 0 17.647 17.116 16.453 15.939 15.609 15.592 -2.29 2.32 -44.61 2.32 7.80 0.00 Roque42 GCS9 Cl* Melotte 22 Roque 42 +03 49 41.14 +23 14 59.3 0 15.232 14.627 13.986 13.443 13.056 13.075 17.11 2.25 -39.25 2.25 3.53 0.83 L07_171 GCS9 +03 49 41.21 +22 56 40.5 0 16.238 15.641 14.994 14.420 14.076 14.084 20.33 2.27 -43.06 2.27 6.90 0.69 BPL213_L07_A1_86_L07_242 GCS9 Cl* Melotte 22 BPL 213 +03 49 41.34 +26 08 53.2 0 13.701 13.323 12.813 12.205 11.934 11.957 15.62 2.23 -40.88 2.23 5.88 0.89 HHJ305_DH636 GCS9 Cl* Melotte 22 HHJ 305 +03 49 41.71 +21 56 19.2 0 15.963 15.359 14.685 14.130 13.770 13.750 22.57 2.52 -39.99 2.52 0.65 0.72 BPL214 GCS9 Cl* Melotte 22 BPL 214 +03 49 43.17 +24 39 46.4 0 16.348 15.708 15.061 14.505 14.115 14.126 17.72 2.22 -38.37 2.22 1.42 0.57 BPL215_L07_A1_87_L07_218 GCS9 Cl* Melotte 22 BPL 215 +03 49 47.04 +25 42 36.8 0 14.023 13.513 12.863 12.308 11.966 11.992 18.14 2.23 -42.94 2.23 2.88 0.94 DH638_L07_5 GCS9 Cl* Melotte 22 DH 638 +03 49 48.16 +26 37 47.5 0 15.926 15.369 14.707 14.187 13.827 13.837 17.38 2.95 -44.63 2.95 0.39 0.89 Moraux2003_98 GCS9 Cl* Melotte 22 MBSC 98 +03 49 48.44 +22 10 48.3 0 13.806 13.390 12.863 12.289 11.986 11.996 12.57 2.51 -41.31 2.51 1.17 0.71 BPL216_DH639 GCS9 Cl* Melotte 22 BPL 216 +03 49 48.77 +23 42 59.3 0 19.186 18.198 17.242 16.647 16.119 16.132 -24.98 2.50 -59.62 2.50 3.68 0.00 L07_photNM_12 GCS9 +03 49 51.43 +25 15 30.4 0 20.207 19.539 18.563 17.881 17.415 17.463 21.41 4.65 -39.77 4.65 0.80 0.46 Roque2 GCS9 Cl* Melotte 22 Roque 2 +03 49 51.80 +21 18 26.1 0 12.546 12.207 11.705 11.428 10.854 11.164 28.74 3.05 -39.12 3.05 1.63 0.10 HCG378_DH640 GCS9 Cl* Melotte 22 HCG 378 +03 49 52.44 +24 03 43.0 0 16.100 15.534 14.882 14.353 13.970 13.984 20.16 2.23 -43.40 2.23 3.73 0.70 L07_A1_88 GCS9 +03 49 55.49 +24 06 05.0 0 14.506 14.048 13.452 12.874 12.596 12.614 17.10 2.22 -43.07 2.22 6.13 0.94 DH641 GCS9 Cl* Melotte 22 DH 641 +03 49 55.79 +24 44 31.3 0 13.226 12.862 12.357 11.905 11.499 11.560 17.55 2.20 -41.05 2.20 7.36 0.92 HCG380_SK294_T92_BPL217_DH642 GCS9 Cl* Melotte 22 HCG 380 +03 49 56.77 +25 22 22.6 0 14.259 13.890 13.375 12.874 12.557 12.584 16.93 2.23 -43.91 2.23 6.29 0.93 DH643 GCS9 Cl* Melotte 22 DH 643 +03 49 56.81 +24 59 07.1 0 16.463 15.839 15.148 14.608 14.190 14.194 14.39 2.22 -40.78 2.22 7.78 0.61 BPL218_L07_A1_89_L07_217 GCS9 Cl* Melotte 22 BPL 218 +03 49 57.64 +23 43 28.2 0 15.488 14.887 14.245 13.710 13.395 13.386 20.76 2.22 -42.85 2.22 32.32 0.87 DH644_L07_150 GCS9 Cl* Melotte 22 DH 644 +03 49 57.85 +23 41 49.6 0 18.821 17.908 17.116 16.554 16.119 16.122 12.13 2.44 -65.77 2.44 10.58 0.00 Roque6 GCS9 Cl* Melotte 22 Roque 6 +03 49 58.33 +25 06 20.9 0 15.359 14.837 14.232 13.680 13.379 13.380 15.84 2.21 -43.01 2.21 2.86 0.88 BPL219_L07_52 GCS9 Cl* Melotte 22 BPL 219 +03 49 58.34 +23 42 33.7 0 13.279 12.903 12.399 12.070 11.567 11.705 16.44 2.22 -43.28 2.22 24.46 0.93 SK293 GCS9 Cl* Melotte 22 SK 293 +03 49 58.61 +25 53 46.2 0 15.011 14.544 13.948 13.385 13.048 13.044 17.26 2.23 -41.56 2.23 2.86 0.89 L07_24 GCS9 +03 49 59.54 +24 11 45.6 0 15.530 15.002 14.367 13.846 13.517 13.502 18.52 2.22 -41.66 2.22 41.43 0.90 BPL220 GCS9 Cl* Melotte 22 BPL 220 +03 50 01.57 +25 24 01.5 0 13.034 12.688 12.188 11.755 11.405 11.453 14.76 2.23 -46.39 2.23 3.60 0.83 SK291_DH646 GCS9 Cl* Melotte 22 SK 291 +03 50 01.87 +25 12 40.9 0 14.210 13.771 13.231 12.666 12.337 12.348 13.84 2.20 -42.81 2.20 5.30 0.85 HCG385_SK289_BPL222_DH647 GCS9 Cl* Melotte 22 HCG 385 +03 50 02.18 +23 51 44.6 0 13.770 13.342 12.802 12.268 11.965 11.957 13.75 2.22 -45.17 2.22 5.36 0.81 HCG382_DH648 GCS9 Cl* Melotte 22 HCG 382 +03 50 03.94 +24 56 02.8 0 16.234 15.728 15.114 14.515 14.147 14.145 1.53 2.22 -47.12 2.22 1.59 0.00 BPL223_L07_PM_NM_51 GCS9 Cl* Melotte 22 BPL 223 +03 50 04.25 +23 10 44.5 0 14.127 13.785 13.209 12.563 12.306 12.299 22.40 2.25 -39.72 2.25 5.17 0.84 SK287_DH649 GCS9 Cl* Melotte 22 SK 287 +03 50 05.00 +23 18 17.2 0 15.407 14.881 14.271 13.751 13.450 13.442 15.82 2.26 -47.05 2.26 48.45 0.77 DH650_L07_151 GCS9 Cl* Melotte 22 DH 650 +03 50 06.43 +26 58 18.7 0 14.792 14.338 13.747 13.210 12.884 12.905 21.70 2.94 -42.45 2.94 0.15 0.92 DH652 GCS9 Cl* Melotte 22 DH 652 +03 50 06.56 +24 59 46.3 0 13.866 13.405 12.811 12.332 11.962 11.976 15.86 2.20 -43.61 2.20 4.23 0.92 BPL224_DH653 GCS9 Cl* Melotte 22 BPL 224 +03 50 08.27 +25 30 51.7 0 19.614 18.280 17.223 16.594 16.008 15.983 13.87 2.83 -45.74 2.83 0.96 0.58 L07_A1_90 GCS9 +03 50 08.42 +25 32 55.7 0 14.171 13.715 13.151 12.599 12.306 12.338 16.57 2.23 -43.79 2.23 1.82 0.93 HCG386_DH654_L07_35 GCS9 Cl* Melotte 22 HCG 386 +03 50 08.62 +24 40 17.7 0 15.348 14.804 14.219 13.674 13.351 13.360 17.39 2.21 -43.94 2.21 9.31 0.90 DH655 GCS9 Cl* Melotte 22 DH 655 +03 50 10.77 +24 28 41.4 0 14.754 14.283 13.690 13.174 12.826 12.850 15.24 2.20 -41.67 2.20 8.77 0.90 BPL225_DH656_L07_84 GCS9 Cl* Melotte 22 BPL 225 +03 50 12.46 +23 55 35.9 0 14.068 13.568 12.961 12.484 12.119 12.116 16.49 2.22 -44.32 2.22 2.36 0.92 SK279_DH659 GCS9 Cl* Melotte 22 SK 279 +03 50 12.60 +23 48 48.9 0 15.564 15.046 14.439 13.876 13.556 13.530 18.23 2.22 -47.50 2.22 4.65 0.77 DH660 GCS9 Cl* Melotte 22 DH 660 +03 50 12.87 +24 21 06.2 0 12.697 12.419 11.916 11.378 11.086 11.149 15.86 2.22 -37.83 2.22 8.13 0.75 HII2601_HCG390_SK278_DH661 GCS9 HII2601 +03 50 13.40 +23 59 29.8 0 20.475 19.183 17.880 16.935 16.219 16.193 42.39 2.66 -31.73 2.66 1.78 0.02 L07_A1_91 GCS9 +03 50 14.76 +25 25 29.8 0 14.761 14.292 13.705 13.158 12.819 12.827 16.00 2.23 -45.17 2.23 4.67 0.91 DH662_L07_40 GCS9 Cl* Melotte 22 DH 662 +03 50 15.27 +24 13 36.0 0 14.103 13.701 13.153 12.628 12.339 12.347 18.72 2.22 -42.98 2.22 0.48 0.94 HCG394_SK276_B214_BPL226_DH663 GCS9 Cl* Melotte 22 HCG 394 +03 50 15.33 +24 35 40.3 0 16.164 15.638 15.090 14.514 14.208 14.215 15.42 2.22 -15.83 2.22 5.62 0.00 L07_photNM_23 GCS9 +03 50 16.09 +24 08 34.7 0 19.950 18.556 17.365 16.687 16.076 16.043 18.03 2.53 -46.97 2.53 2.35 0.67 Roque30_L07_A1_92_L07_259 GCS9 Cl* Melotte 22 Roque 30 +03 50 17.59 +22 55 58.8 0 15.396 14.844 14.246 13.695 13.353 13.360 19.17 2.13 -44.31 2.13 2.13 0.89 DH664 GCS9 Cl* Melotte 22 DH 664 +03 50 18.22 +24 35 15.3 0 14.697 14.220 13.692 13.137 12.844 12.861 14.19 2.20 -44.75 2.20 7.48 0.85 BPL227_DH665+L07_85 GCS9 Cl* Melotte 22 BPL 227 +03 50 19.15 +24 16 34.0 0 16.445 15.797 15.103 14.564 14.190 14.166 20.67 2.23 -41.47 2.23 5.08 0.67 BPL228_L07_A1_93_L07_226 GCS9 Cl* Melotte 22 BPL 228 +03 50 22.01 +23 55 30.3 0 17.259 16.399 15.694 15.108 14.699 14.699 20.94 2.26 -44.68 2.26 1.80 0.65 L07_A1_94 GCS9 +03 50 22.04 +22 37 32.3 0 14.088 13.717 13.128 12.578 12.281 12.280 13.50 2.51 -32.83 2.51 5.52 0.03 HCG399_DH666 GCS9 Cl* Melotte 22 HCG 399 +03 50 23.69 +24 25 43.6 0 17.469 16.705 16.019 15.399 15.045 14.969 9.85 2.30 -45.98 2.30 3.57 0.11 L07_221 GCS9 +03 50 24.96 +20 42 17.8 0 12.468 12.207 11.761 11.397 10.921 10.930 13.33 3.42 -33.72 3.42 3.00 0.11 DH668 GCS9 Cl* Melotte 22 DH 668 +03 50 25.16 +23 55 41.8 0 14.632 14.160 13.598 13.057 12.753 12.750 15.61 2.22 -41.36 2.22 1.44 0.90 HCG396_DH669 GCS9 Cl* Melotte 22 HCG 396 +03 50 27.35 +23 22 44.9 0 14.389 13.925 13.358 12.817 12.499 12.514 24.33 2.13 -40.44 2.13 0.74 0.74 SK273_DH670 GCS9 Cl* Melotte 22 SK 273 +03 50 27.83 +23 03 54.9 0 14.836 14.340 13.762 13.228 12.911 12.910 17.92 2.13 -41.80 2.13 0.78 0.94 DH671 GCS9 Cl* Melotte 22 DH 671 +03 50 29.30 +24 56 34.6 0 14.639 14.187 13.579 12.980 12.665 12.647 23.93 2.20 -30.32 2.20 2.43 0.00 BPL229_Moraux2003_58_L07_64 GCS9 Cl* Melotte 22 BPL 229 +03 50 29.96 +25 03 06.7 0 13.449 13.075 12.549 12.026 11.684 11.707 16.35 2.20 -39.64 2.20 0.61 0.87 HCG403_SK272_B213_BPL230_DH672 GCS9 Cl* Melotte 22 HCG 403 +03 50 31.95 +22 08 47.5 0 13.879 13.507 12.966 12.405 12.094 12.099 20.97 2.51 -37.91 2.51 1.62 0.75 HCG401_DH673 GCS9 Cl* Melotte 22 HCG 401 +03 50 32.58 +24 08 52.9 0 19.612 18.727 17.919 17.469 16.994 16.954 32.96 3.31 -13.95 3.31 1.26 0.00 Roque31 GCS9 Cl* Melotte 22 Roque 31 +03 50 33.08 +24 20 21.6 0 14.355 13.948 13.365 12.845 12.537 12.525 16.87 2.22 -41.49 2.22 4.47 0.93 BPL231_DH674 GCS9 Cl* Melotte 22 BPL 231 +03 50 37.42 +22 28 08.0 0 14.173 13.774 13.224 12.669 12.379 12.378 20.76 2.51 -39.13 2.51 1.02 0.87 HCG404_DH677 GCS9 Cl* Melotte 22 HCG 404 +03 50 38.92 +23 13 02.5 0 13.465 13.101 12.583 12.025 11.734 11.729 18.28 2.13 -37.84 2.13 1.49 0.79 SK263_DH678 GCS9 Cl* Melotte 22 SK 263 +03 50 39.33 +26 16 03.7 0 16.134 15.515 14.868 14.337 13.976 13.962 26.51 2.97 -45.26 2.97 0.34 0.09 DH680 GCS9 Cl* Melotte 22 DH 680 +03 50 40.83 +24 40 02.6 0 15.326 14.799 14.224 13.664 13.348 13.340 14.95 2.21 -46.17 2.21 2.17 0.78 DH681_Moraux2003_83_L07_70 GCS9 Cl* Melotte 22 DH 681 +03 50 42.44 +24 12 55.4 0 15.040 14.585 13.995 13.458 13.144 13.158 16.99 2.22 -39.63 2.22 2.68 0.85 DH683_Moraux2003_73_L07_101 GCS9 Cl* Melotte 22 DH 683 +03 50 44.35 +25 07 05.1 0 15.181 14.601 13.930 13.397 13.036 13.031 21.00 2.20 -44.44 2.20 1.71 0.84 L07_60 GCS9 +03 50 45.38 +25 06 10.5 0 14.663 14.213 13.627 13.059 12.768 12.744 12.87 2.20 -29.87 2.20 1.06 0.00 L07_59 GCS9 +03 50 48.97 +22 40 11.8 0 13.084 12.693 12.183 11.610 11.323 11.328 23.79 2.13 -30.12 2.13 1.45 0.00 HCG409_DH684 GCS9 Cl* Melotte 22 HCG 409 +03 50 54.65 +24 21 55.7 0 14.574 14.149 13.570 13.048 12.740 12.793 17.43 2.22 -47.29 2.22 26.89 0.87 DH687_Moraux2003_55_L07_102 GCS9 Cl* Melotte 22 DH 687 +03 50 54.99 +24 33 03.5 0 14.875 14.403 13.831 13.280 12.966 12.967 14.29 2.20 -41.34 2.20 8.04 0.85 Moraux2003_69 GCS9 Cl* Melotte 22 MBSC 69 +03 50 57.41 +24 24 44.4 0 15.215 14.612 13.988 13.487 13.097 13.117 15.13 2.21 -45.11 2.21 3.95 0.83 L07_81 GCS9 +03 50 57.42 +24 06 30.7 0 13.535 13.175 12.661 12.135 11.812 11.815 14.04 2.22 -40.35 2.22 7.91 0.80 HCG415_SK251_DH689_Moraux2003_15 GCS9 Cl* Melotte 22 HCG 415 +03 51 03.62 +24 32 35.1 0 14.283 13.819 13.257 12.749 12.420 12.445 15.93 2.20 -47.96 2.20 8.27 0.78 DH691 GCS9 Cl* Melotte 22 DH 691 +03 51 05.08 +26 36 51.2 0 15.454 14.963 14.358 13.825 13.479 13.495 12.35 2.96 -37.88 2.96 0.51 0.41 DH692_Moraux2003_93 GCS9 Cl* Melotte 22 DH 692 +03 51 05.97 +24 36 16.9 0 17.107 16.333 15.649 15.153 14.702 14.712 14.09 2.26 -37.47 2.26 5.62 0.34 L07_A1_95_L07_220 GCS9 +03 51 06.13 +22 38 00.6 0 14.992 14.482 13.846 13.298 12.962 12.927 17.14 2.51 -38.08 2.51 2.59 0.82 DH694 GCS9 Cl* Melotte 22 DH 694 +03 51 07.11 +23 20 57.6 0 14.729 14.274 13.683 13.132 12.833 12.817 15.82 2.25 -41.10 2.25 10.33 0.90 DH695 GCS9 Cl* Melotte 22 DH 695 +03 51 11.88 +23 44 43.3 0 15.371 14.840 14.253 13.708 13.396 13.374 16.86 2.22 -43.64 2.22 19.86 0.89 BPL232_Moraux2003_90 GCS9 Cl* Melotte 22 BPL 232 +03 51 17.96 +26 01 22.1 0 14.855 14.349 13.719 13.135 12.816 12.793 17.37 2.23 -39.97 2.23 7.12 0.91 DH701_L07_20 GCS9 Cl* Melotte 22 DH 701 +03 51 18.72 +26 03 15.4 0 14.748 14.262 13.654 13.395 12.768 12.736 15.24 2.23 -41.60 2.23 3.57 0.90 L07_21 GCS9 +03 51 18.86 +26 03 08.7 0 13.191 12.827 12.287 11.714 11.406 11.426 21.16 2.23 -42.78 2.23 6.61 0.92 SK237 GCS9 Cl* Melotte 22 SK 237 +03 51 19.07 +24 10 13.1 0 13.112 12.775 12.233 11.720 11.389 11.438 20.55 2.22 -42.37 2.22 1.13 0.92 HCG424_SK235_BPL233_DH702_Moraux2003_3 GCS9 Cl* Melotte 22 HCG 424 +03 51 19.48 +23 09 49.4 0 14.028 13.596 13.027 12.440 12.139 12.139 19.25 2.25 -39.56 2.25 1.60 0.90 DH703_L07_12 GCS9 Cl* Melotte 22 DH 703 +03 51 19.77 +23 04 04.1 0 15.411 14.858 14.210 13.638 13.312 13.329 16.38 2.26 -41.02 2.26 7.69 0.88 DH704 GCS9 Cl* Melotte 22 DH 704 +03 51 20.68 +19 38 37.8 0 13.526 13.101 12.519 12.042 11.710 11.730 21.01 3.97 -48.07 3.97 0.42 0.77 DH705 GCS9 Cl* Melotte 22 DH 705 +03 51 23.82 +22 50 29.1 0 12.115 11.877 11.498 11.311 10.620 10.885 17.91 2.25 -42.23 2.25 3.94 0.88 DH706 GCS9 Cl* Melotte 22 DH 706 +03 51 23.89 +25 05 52.6 0 13.484 13.107 12.577 12.065 11.730 11.738 14.49 2.20 -42.99 2.20 32.31 0.88 SK231_DH708 GCS9 Cl* Melotte 22 SK 231 +03 51 24.17 +26 03 11.3 0 13.441 13.064 12.529 11.943 11.651 11.655 16.49 2.23 -42.88 2.23 2.49 0.93 HCG427_SK232_T29_DH709 GCS9 Cl* Melotte 22 HCG 427 +03 51 25.88 +24 47 38.7 0 12.962 12.520 12.000 11.783 11.161 11.285 16.49 2.20 -47.46 2.20 11.49 0.46 HCG428_SK230_DH712 GCS9 Cl* Melotte 22 HCG 428 +03 51 30.60 +27 22 49.5 0 14.946 14.405 13.807 13.265 12.919 12.927 22.57 2.88 -41.78 2.88 0.73 0.89 DH714 GCS9 Cl* Melotte 22 DH 714 +03 51 34.01 +24 34 08.9 0 14.400 13.929 13.362 12.776 12.488 12.482 18.64 2.20 -46.47 2.20 0.67 0.90 DH715_Moraux2003_46 GCS9 Cl* Melotte 22 DH 715 +03 51 36.41 +25 13 40.6 0 14.020 13.545 12.926 12.379 12.066 12.042 16.43 2.20 -40.71 2.20 2.11 0.91 DH717 GCS9 Cl* Melotte 22 DH 717 +03 51 38.96 +24 30 44.8 1 18.711 17.407 16.400 15.718 15.168 15.122 23.01 2.34 -40.47 2.34 14.05 0.64 L07_A1_97_L07_253 GCS9 +03 51 39.08 +23 22 04.3 0 15.003 14.530 13.931 13.387 13.058 13.060 21.33 2.25 -42.78 2.25 10.84 0.85 BPL237_L07_166 GCS9 Cl* Melotte 22 BPL 237 +03 51 42.34 +25 57 25.6 0 16.214 15.645 14.965 14.400 14.005 13.990 13.59 2.25 -34.32 2.25 4.49 0.06 L07_A1_98 GCS9 +03 51 44.95 +23 26 39.3 0 17.791 16.894 16.036 15.417 14.953 14.967 16.26 2.37 -39.72 2.37 9.87 0.62 BPL240_L07_A1_99_L07_240 GCS9 Cl* Melotte 22 BPL 240 +03 51 46.56 +23 23 34.8 0 13.746 13.429 12.922 12.317 12.066 12.079 28.17 2.25 -4.26 2.25 10.21 0.00 BPL241 GCS9 Cl* Melotte 22 BPL 241 +03 51 47.66 +24 39 58.9 0 20.158 18.804 17.528 16.773 16.100 16.094 14.64 2.71 -40.22 2.71 4.12 0.52 PLZJ21_PLIZ31 GCS9 Cl* Melotte 22 PlZJ 21 +03 51 50.60 +22 53 44.3 0 13.892 13.435 12.910 12.329 12.055 12.069 13.26 2.25 -40.14 2.25 2.45 0.72 HCG437_SK212_DH724_L07_14 GCS9 Cl* Melotte 22 HCG 437 +03 51 51.15 +23 17 41.1 0 13.445 13.060 12.527 12.014 11.711 11.750 17.13 2.25 -44.72 2.25 21.53 0.93 L07_13 GCS9 +03 51 51.55 +23 34 49.1 0 15.431 14.857 14.260 13.768 13.385 13.408 17.89 2.22 -44.43 2.22 19.31 0.89 BPL242_CFHT-Pl-1_M4.9_Moraux2003_91_BRB1_CFHT-Pl-1_L07_140 GCS9 Cl* Melotte 22 BPL 242 +03 51 54.52 +23 33 31.3 0 13.694 13.230 12.636 12.103 11.768 11.791 15.96 2.24 -48.77 2.24 31.11 0.70 HCG439_SK210_BPL244_DH727_Moraux2003_23 GCS9 Cl* Melotte 22 HCG 439 +03 51 55.05 +23 57 42.0 0 14.510 14.067 13.483 12.983 12.641 12.651 15.21 2.22 -46.11 2.22 4.30 0.86 HCG440_BPL245_DH728_L07_119 GCS9 Cl* Melotte 22 HCG 440 +03 51 57.53 +25 48 31.2 0 14.094 13.689 13.121 12.578 12.276 12.281 17.62 2.23 -42.66 2.23 7.40 0.94 HCG446_SK207_DH731 GCS9 Cl* Melotte 22 HCG 446 +03 51 58.35 +23 58 19.3 0 14.595 14.132 13.556 12.990 12.694 12.669 11.97 2.24 -42.06 2.24 1.15 0.65 BPL246_DH732_L07_120 GCS9 Cl* Melotte 22 BPL 246 +03 51 59.32 +24 39 58.8 0 13.828 13.385 12.845 12.293 12.017 12.027 13.69 2.20 -41.98 2.20 0.80 0.83 Moraux2003_28 GCS9 Cl* Melotte 22 MBSC 28 +03 51 59.93 +23 24 25.6 0 19.672 18.405 17.376 16.709 16.210 16.167 44.15 3.19 6.95 3.19 5.04 0.00 L07_photNM_15 GCS9 +03 52 01.65 +25 01 29.1 0 14.402 13.991 13.422 12.897 12.576 12.602 14.95 2.20 -43.92 2.20 3.33 0.89 HCG447_BPL248_DH733 GCS9 Cl* Melotte 22 HCG 447 +03 52 02.10 +23 15 45.4 0 18.708 17.679 16.659 16.057 15.473 15.529 12.75 2.53 -39.96 2.53 16.77 0.44 BPL249_L07_A1_100_L07_255 GCS9 Cl* Melotte 22 BPL 249 +03 52 02.28 +24 21 47.9 0 12.898 12.582 12.168 11.631 11.353 11.429 22.99 2.24 -48.15 2.24 4.71 0.28 SK204_DH734 GCS9 Cl* Melotte 22 SK 204 +03 52 02.64 +25 06 14.9 0 14.751 14.296 13.684 13.158 12.836 12.836 16.37 2.20 -40.83 2.20 2.28 0.91 BPL250_DH736_L07_57 GCS9 Cl* Melotte 22 BPL 250 +03 52 03.58 +25 01 13.6 0 14.781 14.353 13.772 13.239 12.900 12.934 15.18 2.20 -43.42 2.20 3.16 0.90 BPL251_DH737 GCS9 Cl* Melotte 22 BPL 251 +03 52 03.62 +23 17 21.3 0 14.608 14.228 13.716 13.085 12.840 12.838 8.56 2.26 -19.96 2.26 73.82 0.00 SK202 GCS9 Cl* Melotte 22 SK 202 +03 52 04.48 +24 14 39.6 0 14.840 14.365 13.783 13.223 12.926 12.897 16.25 2.24 -41.96 2.24 2.51 0.92 BPL252_DH739_L07_91 GCS9 Cl* Melotte 22 BPL 252 +03 52 05.58 +22 34 55.1 0 13.116 12.817 12.304 11.756 11.447 11.468 18.18 2.51 -46.19 2.51 1.01 0.91 SK201_DH740 GCS9 Cl* Melotte 22 SK 201 +03 52 05.83 +24 17 31.0 0 16.512 15.860 15.176 14.618 14.251 14.263 15.66 2.27 -40.51 2.27 2.70 0.67 BPL253_CFHT-Pl-7_M5.6_Moraux2003_108_BRB8_CFHT-Pl-7_L07_A1_101_L07_225 GCS9 Cl* Melotte 22 BPL 253 +03 52 06.67 +22 01 14.3 0 14.997 14.501 13.944 13.407 13.089 13.080 19.27 2.51 -49.89 2.51 1.48 0.62 DH741 GCS9 Cl* Melotte 22 DH 741 +03 52 06.72 +24 16 00.4 0 17.079 16.255 15.515 14.971 14.507 14.538 18.87 2.29 -40.00 2.29 6.89 0.68 BPL254_CFHT-Pl-13_M6.0_BRB11_CFHT-Pl-13,Teide2,CFHT-PLIZ-3_L07_A1_102_L07_224 GCS9 Cl* Melotte 22 BPL 254 +03 52 07.43 +25 53 02.7 0 13.127 12.750 12.267 11.768 11.478 11.454 17.32 2.93 -39.44 2.93 1.02 0.88 L07_2 GCS9 +03 52 07.96 +25 27 54.6 0 15.246 14.722 14.111 13.567 13.243 13.214 15.55 2.94 -37.68 2.94 0.42 0.67 HCG449_BPL255_DH742 GCS9 Cl* Melotte 22 HCG 449 +03 52 11.01 +24 09 21.6 0 14.675 14.287 13.746 13.155 12.875 12.891 19.68 2.24 -25.40 2.24 3.06 0.00 BPL256 GCS9 Cl* Melotte 22 BPL 256 +03 52 11.23 +20 52 33.4 0 14.537 14.059 13.462 12.929 12.611 12.655 19.77 3.43 -45.98 3.43 0.93 0.91 DH743 GCS9 Cl* Melotte 22 DH 743 +03 52 12.19 +26 22 08.9 0 13.194 12.777 12.227 11.737 11.397 11.430 14.22 2.95 -44.48 2.95 1.31 0.86 HCG452_DH744 GCS9 Cl* Melotte 22 HCG 452 +03 52 13.20 +26 11 45.3 0 13.003 12.662 12.154 11.606 11.285 11.326 20.95 2.95 -46.17 2.95 1.13 0.88 DH745 GCS9 Cl* Melotte 22 DH 745 +03 52 13.32 +26 08 36.8 0 13.531 13.173 12.637 12.086 11.799 11.827 13.73 2.93 -41.65 2.93 0.47 0.82 HCG451_SK197_DH746_L07_1 GCS9 Cl* Melotte 22 HCG 451 +03 52 15.01 +24 20 23.7 0 12.845 12.510 11.997 11.537 11.142 11.317 25.52 2.24 -17.36 2.24 2.89 0.00 BPL257 GCS9 Cl* Melotte 22 BPL 257 +03 52 15.20 +27 56 37.5 0 15.781 15.372 14.821 14.296 14.065 14.036 35.34 3.01 -42.14 3.01 0.77 0.00 DH747 GCS9 Cl* Melotte 22 DH 747 +03 52 17.44 +25 06 11.8 0 15.000 14.563 13.990 13.419 13.132 13.135 -0.61 2.20 -35.73 2.20 4.16 0.00 BPL258 GCS9 Cl* Melotte 22 BPL 258 +03 52 17.54 +24 27 19.7 0 14.481 13.947 13.400 12.888 12.573 12.615 5.97 2.20 -33.38 2.20 95.51 0.00 BPL259_DH748_Moraux2003_42 GCS9 Cl* Melotte 22 BPL 259 +03 52 18.46 +22 00 53.3 0 13.024 12.707 12.225 11.616 11.369 11.393 15.81 2.51 -42.10 2.51 0.71 0.91 DH749 GCS9 Cl* Melotte 22 DH 749 +03 52 18.64 +24 04 28.1 0 18.327 17.280 16.395 15.738 15.202 15.738 15.37 2.40 -46.07 2.40 9.62 0.50 CFHT-Pl-23_XX GCS9 Cl* Melotte 22 CFHT 23 +03 52 18.72 +23 52 36.6 0 15.095 14.577 13.986 13.440 13.130 13.140 15.56 2.25 -45.85 2.25 6.65 0.82 BPL260_L07_127 GCS9 Cl* Melotte 22 BPL 260 +03 52 20.66 +24 33 55.5 0 12.822 12.483 11.971 11.439 11.148 11.166 14.85 2.23 -40.95 2.23 5.83 0.78 HCG454_SK188_T159_DH750 GCS9 Cl* Melotte 22 HCG 454 +03 52 25.93 +21 50 31.5 0 13.817 13.529 13.062 12.397 12.193 12.161 16.43 2.51 -38.84 2.51 1.83 0.83 DH752 GCS9 Cl* Melotte 22 DH 752 +03 52 30.54 +19 40 39.6 0 14.510 14.026 13.382 12.839 12.507 12.549 19.00 3.98 -35.62 3.98 0.89 0.55 DH753 GCS9 Cl* Melotte 22 DH 753 +03 52 30.91 +24 32 39.5 0 13.409 12.945 12.392 11.866 11.560 11.576 14.80 2.23 -43.14 2.23 0.86 0.89 HCG456_SK178_BPL261_DH754_Moraux2003_12 GCS9 Cl* Melotte 22 HCG 456 +03 52 31.38 +25 15 07.5 0 13.670 13.259 12.739 12.160 11.887 11.901 16.76 2.23 -45.34 2.23 7.95 0.92 SK179_BPL262_DH755 GCS9 Cl* Melotte 22 SK 179 +03 52 31.60 +25 19 49.5 0 16.266 15.764 15.163 14.645 14.313 14.280 33.73 2.98 -58.22 2.98 0.55 0.00 BPL263 GCS9 Cl* Melotte 22 BPL 263 +03 52 33.34 +23 51 06.7 0 14.404 13.944 13.367 12.802 12.521 12.543 17.77 2.24 -41.30 2.24 5.90 0.93 BPL264_DH756_L07_126 GCS9 Cl* Melotte 22 BPL 264 +03 52 34.48 +22 30 07.5 0 13.080 12.786 12.289 11.679 11.396 11.423 16.37 2.51 -36.85 2.51 1.65 0.63 HCG458_SK174_sk174_DH757 GCS9 Cl* Melotte 22 HCG 458 +03 52 35.32 +25 01 04.5 0 14.769 14.195 13.590 13.034 12.700 12.703 17.35 2.23 -42.19 2.23 2.24 0.94 BPL265_DH758_L07_61 GCS9 Cl* Melotte 22 BPL 265 +03 52 38.91 +25 50 25.3 0 14.054 13.642 13.075 12.528 12.203 12.193 19.53 2.93 -39.16 2.93 0.35 0.89 HCG461_HHJ244_DH760_Moraux2003_30 GCS9 Cl* Melotte 22 HCG 461 +03 52 41.82 +26 46 10.5 0 14.924 14.450 13.835 13.290 12.967 12.980 15.22 2.96 -48.00 2.96 0.18 0.74 DH761 GCS9 Cl* Melotte 22 DH 761 +03 52 42.21 +25 10 42.2 0 15.719 15.130 14.525 13.956 13.610 13.606 7.18 2.24 -43.36 2.24 13.94 0.05 DH762 GCS9 Cl* Melotte 22 DH 762 +03 52 43.24 +24 27 58.5 0 14.755 14.266 13.689 13.131 12.827 12.833 17.78 2.23 -41.41 2.23 1.73 0.93 BPL266_DH763_L07_82 GCS9 Cl* Melotte 22 BPL 266 +03 52 43.47 +27 28 20.7 0 15.621 15.237 14.702 14.167 13.881 13.877 19.52 3.32 -29.27 3.32 0.59 0.00 DH764 GCS9 Cl* Melotte 22 DH 764 +03 52 44.29 +23 54 14.9 0 15.738 15.184 14.554 14.000 13.654 13.683 15.71 2.25 -44.96 2.25 4.54 0.86 BPL267_DH765_CFHT-Pl-2_M4.9_BRB2_CFHT-Pl-2_L07_128 GCS9 Cl* Melotte 22 BPL 267 +03 52 44.48 +24 20 59.3 0 15.025 14.546 13.944 13.403 13.091 13.085 16.54 2.25 -40.61 2.25 2.44 0.87 BPL268_DH766_Moraux2003_70 GCS9 Cl* Melotte 22 BPL 268 +03 52 46.45 +24 33 41.0 0 14.544 14.050 13.491 12.957 12.649 12.654 16.70 2.23 -37.50 2.23 1.87 0.76 BPL270_Moraux2003_47_L07_83 GCS9 Cl* Melotte 22 BPL 270 +03 52 51.72 +22 31 32.7 0 13.700 13.329 12.777 12.199 11.894 11.930 19.14 2.51 -43.95 2.51 1.02 0.94 HCG462_SK150_HHJ302_DH770 GCS9 Cl* Melotte 22 HCG 462 +03 52 51.79 +23 33 47.9 0 15.894 15.316 14.662 14.124 13.742 20.21 2.31 -42.32 2.31 4.57 0.88 HHJ22_BPL272_Moraux2003_99_BRB5_CFHT-Pl-3_MBSC99_L07_148 GCS9 Cl* Melotte 22 HHJ 22 +03 52 54.25 +25 17 43.3 0 14.245 13.815 13.273 12.729 12.417 12.415 19.70 2.93 -46.66 2.93 0.44 0.89 HCG464_SK151_BPL274_DH771 GCS9 Cl* Melotte 22 HCG 464 +03 52 55.92 +24 57 41.8 0 16.326 15.757 15.109 14.535 14.152 14.141 12.56 2.25 -44.02 2.25 2.34 0.48 BPL275_L07_A1_103_L07_252 GCS9 Cl* Melotte 22 BPL 275 +03 52 56.97 +22 26 01.1 0 13.248 12.945 12.403 11.839 11.543 11.594 20.34 2.51 -44.90 2.51 0.86 0.92 HCG463_SK143_HHJ358_DH772 GCS9 Cl* Melotte 22 HCG 463 +03 52 58.78 +25 26 19.0 0 15.258 14.748 14.176 13.639 13.295 13.303 17.32 2.94 -42.46 2.94 0.26 0.90 BPL277 GCS9 Cl* Melotte 22 BPL 277 +03 52 59.48 +22 46 59.9 0 16.051 15.626 15.081 14.514 14.235 14.263 6.77 2.28 -42.20 2.28 15.58 0.02 L07_192 GCS9 +03 53 01.63 +22 58 48.2 0 14.463 13.982 13.388 12.835 12.536 12.506 18.23 2.25 -41.99 2.25 16.24 0.94 HHJ181_DH776_L07_183 GCS9 Cl* Melotte 22 HHJ 181 +03 53 05.13 +25 04 15.6 0 15.954 15.369 14.759 14.236 13.861 13.829 7.81 2.24 -28.58 2.24 19.42 0.00 HHJ13_BPL278 GCS9 Cl* Melotte 22 HHJ 13 +03 53 07.29 +25 47 21.4 0 15.338 14.821 14.219 13.639 13.302 13.309 16.97 2.94 -34.85 2.94 1.34 0.32 HHJ53_DH778_Moraux2003_89 GCS9 Cl* Melotte 22 HHJ 53 +03 53 07.48 +25 18 03.4 0 15.833 15.247 14.597 14.025 13.647 13.618 2.66 2.95 -37.03 2.95 0.59 0.00 BPL279 GCS9 Cl* Melotte 22 BPL 279 +03 53 09.63 +23 33 47.7 0 16.108 15.503 14.823 14.280 13.916 19.95 2.32 -45.65 2.32 0.56 0.63 BPL280_CFHT-Pl-4_XX_Moraux2003_101_BRB6_CFHT-Pl-4,MBSC101 GCS9 Cl* Melotte 22 BPL 280 +03 53 10.16 +23 03 10.6 0 13.483 13.099 12.546 11.958 11.672 11.670 16.27 2.25 -43.64 2.25 0.47 0.93 HHJ330_DH779 GCS9 Cl* Melotte 22 HHJ 330 +03 53 12.52 +25 48 17.1 0 16.111 15.665 15.069 14.499 14.163 14.185 20.43 2.98 -8.43 2.98 0.61 0.00 Moraux2003_106 GCS9 Cl* Melotte 22 MBSC 106 +03 53 14.00 +19 42 59.3 0 15.849 15.386 14.792 14.189 13.888 13.898 9.11 4.02 -47.37 4.02 0.59 0.10 DH780 GCS9 Cl* Melotte 22 DH 780 +03 53 15.71 +22 52 14.3 0 13.571 13.174 12.615 12.057 11.772 11.797 16.50 2.25 -42.99 2.25 20.38 0.93 HCG467_SK134_B369_HHJ323_DH781_L07_15 GCS9 Cl* Melotte 22 HCG 467 +03 53 16.44 +23 20 58.1 0 15.199 14.687 14.094 13.508 13.225 13.211 16.27 2.26 -38.96 2.26 2.72 0.80 BPL281_L07_154 GCS9 Cl* Melotte 22 BPL 281 +03 53 21.68 +24 03 26.6 0 15.326 14.768 14.128 13.552 13.218 7.55 2.31 -30.24 2.31 2.14 0.00 BPL282 GCS9 Cl* Melotte 22 BPL 282 +03 53 24.12 +23 47 58.4 0 14.439 13.967 13.383 12.825 12.527 12.529 15.64 2.24 -40.80 2.24 4.05 0.89 HHJ173_BPL285_DH784_Moraux2003_39_L07_131 GCS9 Cl* Melotte 22 HHJ 173 +03 53 24.24 +25 14 37.7 0 16.763 16.041 15.329 14.792 14.388 14.383 18.76 2.27 -40.84 2.27 18.74 0.71 L07_A1_105 GCS9 +03 53 26.54 +24 46 44.8 0 16.063 15.580 14.980 14.429 14.130 14.105 -15.85 2.25 -65.72 2.25 2.82 0.00 L07_photNM_24 GCS9 +03 53 30.76 +19 54 24.1 0 13.671 13.329 12.773 12.177 11.869 11.915 21.18 3.97 -41.70 3.97 0.52 0.91 DH787 GCS9 Cl* Melotte 22 DH 787 +03 53 35.52 +26 07 08.0 0 14.722 14.217 13.594 13.076 12.734 12.735 14.81 2.94 -41.03 2.94 0.36 0.87 HHJ124_DH790_Moraux2003_64 GCS9 Cl* Melotte 22 HHJ 124 +03 53 44.53 +24 00 42.4 0 16.464 15.928 15.311 14.743 14.396 21.83 2.33 -19.33 2.33 2.88 0.00 BPL289 GCS9 Cl* Melotte 22 BPL 289 +03 53 47.58 +23 44 30.9 0 14.156 13.695 13.118 12.583 12.291 25.83 2.30 -36.52 2.30 12.91 0.18 HCG474_SK115_HHJ237_BPL290_Moraux2003_88 GCS9 Cl* Melotte 22 HCG 474 +03 53 48.04 +23 49 09.6 0 15.701 15.021 14.330 13.772 13.399 13.416 19.15 2.25 -46.61 2.25 7.95 0.81 HHJ28_BPL291 GCS9 Cl* Melotte 22 HHJ 28 +03 53 48.14 +23 58 12.3 0 16.128 15.626 15.014 14.480 14.122 45.32 2.32 -23.11 2.32 16.02 0.00 BPL292 GCS9 Cl* Melotte 22 BPL 292 +03 53 48.49 +23 29 09.3 0 13.958 13.639 13.144 12.476 12.276 12.276 22.11 2.25 -50.69 2.25 1.26 0.30 L07_11 GCS9 +03 53 49.10 +23 32 49.5 0 16.498 15.857 15.188 14.669 14.312 14.255 20.11 2.27 -19.91 2.27 1.92 0.00 BPL293 GCS9 Cl* Melotte 22 BPL 293 +03 53 51.53 +23 37 32.4 0 15.072 14.583 13.986 13.438 13.123 13.114 10.35 2.25 -31.49 2.25 0.90 0.00 L07_142 GCS9 +03 53 52.46 +22 37 34.1 0 13.916 13.447 12.840 12.297 11.984 11.987 21.25 2.50 -22.78 2.50 0.32 0.00 SK109 GCS9 Cl* Melotte 22 SK 109 +03 53 55.13 +23 23 36.1 1 16.947 16.027 15.172 14.569 14.088 14.081 19.17 2.28 -44.72 2.28 8.66 0.70 BPL294_CFHT-Pl-12_M8.0_BRB9_CFHT-Pl-12,CFHT-PLIZ-6_PLZJ9_PLIZ6 GCS9 Cl* Melotte 22 BPL 294 +03 54 00.71 +23 58 59.8 0 13.647 13.247 12.690 12.150 11.841 11.858 18.82 2.24 -49.03 2.24 7.85 0.73 HHJ322_BPL298 GCS9 Cl* Melotte 22 HHJ 322 +03 54 01.12 +23 19 41.0 0 15.933 15.380 14.713 14.186 13.841 13.816 15.07 2.27 -45.30 2.27 12.64 0.83 HHJ20_BPL299 GCS9 Cl* Melotte 22 HHJ 20 +03 54 02.73 +23 35 00.9 0 15.007 14.465 13.863 13.314 12.994 12.988 15.75 2.25 -46.40 2.25 6.97 0.80 BPL301_Moraux2003_68_L07_141 GCS9 Cl* Melotte 22 BPL 301 +03 54 04.89 +25 05 06.2 0 14.698 14.348 13.849 13.358 13.082 13.085 33.90 2.23 -56.38 2.23 10.70 0.00 BPL302 GCS9 Cl* Melotte 22 BPL 302 +03 54 05.35 +23 33 59.2 0 18.651 17.573 16.647 15.964 15.434 15.462 15.06 2.46 -40.45 2.46 5.84 0.61 BPL303_CFHT-Pl-25_M9.0_BRB15_CFHT-PLIZ-20,PLZJ11_L07_A1_106_L07_254 GCS9 Cl* Melotte 22 BPL 303 +03 54 11.49 +25 18 42.7 0 14.598 14.131 13.552 13.030 12.710 12.697 22.79 2.94 -43.58 2.94 0.56 0.88 HCG484_BPL304_DH796 GCS9 Cl* Melotte 22 HCG 484 +03 54 13.02 +23 20 50.8 0 13.540 13.162 12.611 12.064 11.777 11.770 19.39 2.25 -43.85 2.25 1.02 0.94 HCG482_SK97_HHJ325_BPL305_DH797_Moraux2003_13 GCS9 Cl* Melotte 22 HCG 482 +03 54 14.07 +23 17 51.9 0 20.028 18.873 17.601 16.818 16.138 16.100 17.07 3.03 -38.21 3.03 5.30 0.47 BRB18_L0.0_CFHT-PLIZ-28 GCS9 Cl* Melotte 22 BRB 18 +03 54 14.18 +25 37 57.2 0 13.687 13.351 12.846 12.276 12.008 11.988 15.24 2.93 -23.51 2.93 0.25 0.00 SK99_HHJ318 GCS9 Cl* Melotte 22 SK 99 +03 54 15.29 +25 09 52.2 0 17.907 17.026 16.179 15.579 15.102 15.061 14.10 2.34 -32.82 2.34 5.56 0.04 BPL306_L07_A1_107 GCS9 Cl* Melotte 22 BPL 306 +03 54 15.60 +24 20 45.7 0 15.915 15.353 14.671 14.117 13.832 13.778 15.59 2.25 -35.25 2.25 4.55 0.33 BPL307_BPL308_Moraux2003_96 GCS9 Cl* Melotte 22 BPL 307 +03 54 16.80 +22 00 16.6 0 15.893 15.489 14.921 14.365 14.077 14.066 16.81 2.53 -37.22 2.53 1.97 0.67 DH799 GCS9 Cl* Melotte 22 DH 799 +03 54 19.69 +24 41 52.0 0 13.851 13.644 13.223 12.660 12.539 12.515 24.74 2.23 -26.36 2.23 1.62 0.00 L07_6 GCS9 +03 54 21.23 +23 23 48.9 0 13.899 13.505 12.946 12.350 12.071 12.051 17.29 2.25 -14.89 2.25 10.35 0.00 BPL310 GCS9 Cl* Melotte 22 BPL 310 +03 54 22.49 +23 38 11.9 0 14.441 13.982 13.383 12.800 12.511 12.520 12.94 2.24 -43.81 2.24 7.78 0.77 HHJ214_BPL311_DH801_Moraux2003_38_L07_143 GCS9 Cl* Melotte 22 HHJ 214 +03 54 24.00 +23 51 58.7 0 15.891 15.288 14.667 14.110 13.776 13.780 14.24 2.25 -48.04 2.25 12.19 0.58 L07_123 GCS9 +03 54 24.39 +22 41 20.5 0 14.947 14.441 13.840 13.254 12.936 12.943 20.19 2.26 -45.53 2.26 6.55 0.92 L07_190 GCS9 +03 54 25.04 +24 42 43.4 0 14.895 14.283 13.653 13.120 12.763 12.750 20.21 2.23 -42.84 2.23 3.32 0.94 HCG487_HHJ131_BPL312_DH802_Moraux2003_67_L07_71 GCS9 Cl* Melotte 22 HCG 487 +03 54 28.11 +23 56 36.0 0 15.731 15.152 14.518 13.983 13.642 13.637 15.38 2.25 -40.85 2.25 2.36 0.85 BPL313 GCS9 Cl* Melotte 22 BPL 313 +03 54 31.49 +22 39 01.5 0 16.554 15.843 15.155 14.573 14.190 14.206 13.09 2.29 -41.56 2.29 3.40 0.53 L07_A1_108_L07_244 GCS9 +03 54 33.14 +23 33 39.5 0 13.404 13.138 12.649 12.026 11.829 11.837 29.86 2.24 -16.03 2.24 2.59 0.00 BPL314 GCS9 Cl* Melotte 22 BPL 314 +03 54 34.80 +21 53 01.7 0 12.781 11.982 11.442 11.104 11.183 19.60 2.62 -38.65 2.62 5.77 0.87 HCG488_DH804 GCS9 Cl* Melotte 22 HCG 488 +03 54 37.36 +23 13 32.5 0 14.469 14.018 13.413 12.815 12.514 12.535 19.90 2.25 -37.77 2.25 7.31 0.81 DH805 GCS9 Cl* Melotte 22 DH 805 +03 54 39.13 +24 35 54.5 0 15.791 15.217 14.572 14.039 13.702 13.694 20.01 2.24 -43.73 2.24 3.51 0.88 BPL315_DH806_Moraux2003_103 GCS9 Cl* Melotte 22 BPL 315 +03 54 40.56 +23 42 23.7 0 16.133 15.654 15.075 14.441 14.147 14.128 21.00 2.26 -8.72 2.26 2.22 0.00 L07_PM_NM_45 GCS9 +03 54 46.39 +25 02 58.2 0 15.875 15.298 14.639 14.106 13.746 13.746 12.58 2.24 -33.12 2.24 3.87 0.03 BPL317 GCS9 Cl* Melotte 22 BPL 317 +03 54 46.53 +25 31 34.9 0 14.741 14.146 13.512 12.988 12.618 12.627 21.22 2.94 -45.56 2.94 0.23 0.90 DH808 GCS9 Cl* Melotte 22 DH 808 +03 54 48.00 +25 12 30.2 0 13.344 13.058 12.571 11.963 11.714 11.736 19.70 2.23 -39.13 2.23 9.47 0.87 SK82_HHJ371_BPL318_DH809 GCS9 Cl* Melotte 22 SK 82 +03 54 55.34 +22 59 40.1 0 15.419 14.857 14.201 13.635 13.303 13.270 13.51 2.26 -44.04 2.26 3.01 0.78 HHJ62 GCS9 Cl* Melotte 22 HHJ 62 +03 54 58.03 +25 14 29.0 0 13.800 13.401 12.825 12.253 11.966 11.990 13.55 2.23 -44.32 2.23 3.97 0.82 SK76_BPL321_DH810 GCS9 Cl* Melotte 22 SK 76 +03 54 59.43 +23 58 07.6 0 18.399 17.751 17.008 16.573 16.182 16.573 -15.56 2.74 -76.49 2.74 2.13 0.00 CFHT-Pl-22_XX GCS9 Cl* Melotte 22 CFHT 22 +03 55 03.69 +25 13 16.7 0 15.227 14.748 14.150 13.541 13.229 13.257 24.07 2.23 -25.82 2.23 2.86 0.00 BPL322 GCS9 Cl* Melotte 22 BPL 322 +03 55 05.75 +23 03 17.1 0 17.281 16.747 16.124 15.457 15.159 15.148 22.55 2.35 -23.42 2.35 7.78 0.00 DH811 GCS9 Cl* Melotte 22 DH 811 +03 55 07.09 +24 17 25.7 0 13.823 13.494 12.962 12.312 12.069 12.072 45.79 2.24 -23.35 2.24 9.80 0.00 Moraux2003_24 GCS9 Cl* Melotte 22 MBSC 24 +03 55 08.98 +24 05 02.5 0 13.699 13.306 12.734 12.148 11.890 16.37 2.30 -40.35 2.30 2.15 0.90 SK65_HHJ313_BPL324_DH812_Moraux2003_18 GCS9 Cl* Melotte 22 SK 65 +03 55 10.57 +23 40 30.8 0 16.148 15.629 15.011 14.450 14.138 14.42 2.32 -24.94 2.32 4.22 0.00 Moraux2003_107 GCS9 Cl* Melotte 22 MBSC 107 +03 55 11.85 +22 58 02.5 0 15.060 14.458 13.794 13.249 12.894 12.890 18.52 2.26 -46.52 2.26 8.86 0.83 HHJ61 GCS9 Cl* Melotte 22 HHJ 61 +03 55 17.17 +23 53 16.7 0 16.639 15.930 15.233 14.684 14.291 14.302 -3.54 2.27 -45.04 2.27 8.18 0.00 BPL325 GCS9 Cl* Melotte 22 BPL 325 +03 55 20.68 +22 32 08.6 0 14.849 14.456 13.903 13.290 13.023 13.017 25.35 2.51 -36.82 2.51 2.55 0.26 HHJ144_DH814 GCS9 Cl* Melotte 22 HHJ 144 +03 55 23.08 +24 49 04.9 0 17.087 16.311 15.528 15.013 14.595 14.571 19.50 2.28 -42.09 2.28 7.67 0.72 BPL327_IPMBD11_PLZJ78_PLIZ2 GCS9 Cl* Melotte 22 BPL 327 +03 55 24.90 +23 27 21.2 0 14.218 13.952 13.438 12.772 12.577 12.555 17.19 2.25 -18.72 2.25 12.58 0.00 L07_165 GCS9 +03 55 27.06 +25 14 45.8 1 16.118 15.402 14.649 14.071 13.671 13.650 15.24 2.24 -39.66 2.24 7.07 0.60 BPL328_L07_A1_111_L07_216 GCS9 Cl* Melotte 22 BPL 328 +03 55 30.90 +23 23 50.9 0 13.961 13.590 13.001 12.425 12.158 12.161 18.03 2.25 -35.87 2.25 14.93 0.52 SK54_HHJ284_BPL329_DH815_Moraux2003_32 GCS9 Cl* Melotte 22 SK 54 +03 55 32.84 +23 19 08.0 0 12.548 12.391 11.984 11.529 11.394 11.417 25.21 2.25 -40.35 2.25 7.73 0.61 DH2004_816 GCS9 Cl* Melotte 22 DH 816 +03 55 34.43 +23 58 28.3 0 15.140 14.664 14.047 13.501 13.196 11.66 2.31 -40.75 2.31 6.79 0.55 HHJ78_BPL330_DH818_Moraux2003_80 GCS9 Cl* Melotte 22 HHJ 78 +03 55 44.34 +23 23 26.3 0 14.936 14.679 14.164 13.488 13.317 13.333 24.36 2.26 -31.05 2.26 5.80 0.00 L07_164 GCS9 +03 55 47.14 +25 14 39.5 0 17.252 16.517 15.773 15.223 14.793 14.775 11.42 2.29 -27.04 2.29 4.43 0.00 L07_A1_112 GCS9 +03 55 47.45 +22 50 50.1 0 19.943 18.550 17.433 16.681 16.086 16.123 15.62 2.97 -46.57 2.97 3.85 0.63 L07_A1_113 GCS9 +03 55 49.92 +23 48 22.5 0 15.778 15.219 14.575 14.013 13.650 13.627 40.52 2.97 -15.60 2.97 0.28 0.00 BPL332 GCS9 Cl* Melotte 22 BPL 332 +03 55 50.11 +21 43 27.7 0 13.074 12.662 12.128 11.773 11.254 11.294 11.27 3.36 -39.08 3.36 0.38 0.35 DH819 GCS9 Cl* Melotte 22 DH 819 +03 55 56.43 +25 17 59.8 0 13.549 13.137 12.580 11.969 11.690 11.703 19.79 2.93 -43.08 2.93 0.63 0.94 HCG504_SK37_DH822 GCS9 Cl* Melotte 22 HCG 504 +03 55 57.55 +21 10 38.7 0 13.543 13.117 12.574 12.061 11.714 11.762 6.82 3.36 -22.12 3.36 1.12 0.00 DH823 GCS9 Cl* Melotte 22 DH 823 +03 56 09.81 +22 27 59.3 0 13.653 13.341 12.818 12.142 11.912 11.933 27.43 2.50 -42.46 2.50 0.16 0.22 HHJ385 GCS9 Cl* Melotte 22 HHJ 385 +03 56 18.60 +23 57 51.6 0 12.871 12.580 12.095 11.500 11.219 11.290 20.17 2.96 -40.40 2.96 0.46 0.89 SK22_HHJ386_DH830 GCS9 Cl* Melotte 22 SK 22 +03 56 22.31 +21 07 16.9 0 13.818 13.393 12.829 12.286 11.984 12.076 13.77 3.36 -36.78 3.36 0.54 0.40 DH831 GCS9 Cl* Melotte 22 DH 831 +03 56 22.39 +24 37 22.8 0 17.293 16.827 16.215 15.615 15.296 15.316 19.53 2.64 -22.35 2.64 1.45 0.00 DH832 GCS9 Cl* Melotte 22 DH 832 +03 56 24.99 +23 05 26.6 0 12.641 12.387 11.904 11.372 11.058 11.087 17.85 2.99 -45.85 2.99 1.45 0.72 SK17_HHJ419 GCS9 Cl* Melotte 22 SK 17 +03 56 25.93 +24 16 51.3 0 12.568 12.306 11.841 11.303 10.972 11.236 22.45 2.96 -39.95 2.96 0.24 0.83 SK18_HHJ416_DH834 GCS9 Cl* Melotte 22 SK 18 +03 56 28.91 +24 01 54.1 0 14.448 14.038 13.469 12.930 12.618 12.620 19.11 2.96 -40.90 2.96 0.55 0.93 HHJ165_DH837_Moraux2003_41 GCS9 Cl* Melotte 22 HHJ 165 +03 56 36.56 +25 15 52.3 0 12.102 12.000 11.678 11.535 11.250 11.370 20.72 2.47 -47.20 2.47 9.47 0.56 DH2004_840 GCS9 Cl* Melotte 22 DH 840 +03 56 38.76 +21 35 08.3 0 14.888 14.393 13.787 13.259 12.937 12.927 18.67 3.36 -40.82 3.36 0.16 0.93 DH841 GCS9 Cl* Melotte 22 DH 841 +03 56 39.34 +24 31 42.3 0 16.146 15.667 15.060 14.438 14.108 14.131 6.45 2.50 -37.23 2.50 0.69 0.00 BPL338 GCS9 Cl* Melotte 22 BPL 338 +03 56 39.67 +22 41 12.0 0 16.699 16.176 15.614 15.000 14.672 14.657 23.26 3.06 -34.21 3.06 0.25 0.04 DH842 GCS9 Cl* Melotte 22 DH 842 +03 56 46.60 +26 21 00.4 0 15.182 14.574 13.929 13.398 13.046 13.035 17.21 2.62 -47.43 2.62 2.18 0.77 HHJ64_DH843 GCS9 Cl* Melotte 22 HHJ 64 +03 56 52.31 +25 10 05.1 1 16.146 15.491 14.756 14.192 13.771 13.801 16.66 2.50 -38.22 2.50 2.72 0.54 HHJ17 GCS9 Cl* Melotte 22 HHJ 17 +03 56 52.91 +23 25 43.7 0 14.029 13.589 12.994 12.445 12.127 12.117 16.82 3.00 -39.65 3.00 0.16 0.89 HHJ271_DH846_Moraux2003_35 GCS9 Cl* Melotte 22 HHJ 271 +03 56 55.47 +22 08 24.3 0 15.110 14.685 14.152 13.553 13.236 13.258 18.72 2.85 -38.55 2.85 0.38 0.80 DH847 GCS9 Cl* Melotte 22 DH 847 +03 56 57.08 +24 48 34.3 0 12.982 12.615 12.063 11.527 11.181 11.276 19.30 2.47 -39.94 2.47 2.78 0.89 HCG511_HHJ393_DH848 GCS9 Cl* Melotte 22 HCG 511 +03 57 05.73 +22 49 14.9 0 15.172 14.200 13.601 13.300 13.290 41.88 3.06 -25.84 3.06 0.73 0.00 HHJ85 GCS9 Cl* Melotte 22 HHJ 85 +03 57 08.61 +20 07 42.7 0 15.711 15.183 14.537 14.017 13.686 13.687 18.35 3.98 -41.60 3.98 0.70 0.90 DH849 GCS9 Cl* Melotte 22 DH 849 +03 57 09.81 +21 36 27.2 0 14.554 14.125 13.494 12.940 12.659 12.639 17.59 3.36 -34.33 3.36 1.52 0.30 DH850 GCS9 Cl* Melotte 22 DH 850 +03 57 40.68 +25 16 04.1 0 13.577 13.194 12.640 12.008 11.723 11.790 14.25 2.47 -36.24 2.47 0.52 0.37 DH856 GCS9 Cl* Melotte 22 DH 856 +03 57 42.98 +25 23 06.7 0 14.445 13.984 13.389 12.823 12.494 12.537 21.68 2.48 -43.65 2.48 2.98 0.92 HCG516_HHJ180_DH857 GCS9 Cl* Melotte 22 HCG 516 +03 57 49.37 +22 08 30.9 0 16.149 15.498 14.831 14.286 13.884 13.897 16.04 2.89 -42.24 2.89 0.50 0.72 HHJ7_DH858 GCS9 Cl* Melotte 22 HHJ 7 +03 57 49.44 +23 28 40.9 0 16.455 16.008 15.423 14.867 14.538 14.486 21.43 3.05 -44.61 3.05 1.11 0.60 HHJ4 GCS9 Cl* Melotte 22 HHJ 4 +03 57 52.35 +21 02 15.8 0 14.703 14.171 13.595 13.036 12.705 12.710 16.38 3.36 -34.99 3.36 0.07 0.37 DH859 GCS9 Cl* Melotte 22 DH 859 +03 57 55.85 +21 16 10.8 0 15.354 14.971 14.436 13.805 13.526 13.536 16.87 3.37 -36.80 3.37 0.12 0.62 DH860 GCS9 Cl* Melotte 22 DH 860 +03 58 01.97 +23 53 54.5 0 15.065 14.518 13.919 13.370 13.028 13.030 15.82 2.97 -43.34 2.97 0.06 0.88 HHJ84_DH862 GCS9 Cl* Melotte 22 HHJ 84 +03 58 13.93 +25 06 27.3 0 13.268 12.878 12.303 11.694 11.359 11.407 -0.09 2.47 -50.14 2.47 7.14 0.00 HHJ369 GCS9 Cl* Melotte 22 HHJ 369 +03 58 25.14 +24 00 58.5 0 14.312 13.884 13.306 12.766 12.449 12.466 17.99 2.96 -40.14 2.96 0.84 0.92 HHJ220_DH865 GCS9 Cl* Melotte 22 HHJ 220 +03 58 30.99 +23 04 12.7 0 15.161 14.622 14.030 13.494 13.122 13.120 16.32 3.00 -42.51 3.00 0.55 0.89 HHJ60_DH866 GCS9 Cl* Melotte 22 HHJ 60 +03 58 34.18 +22 40 11.1 0 15.075 14.525 13.922 13.337 13.003 13.039 18.08 3.00 -38.66 3.00 1.36 0.81 HHJ93_DH867 GCS9 Cl* Melotte 22 HHJ 93 +03 58 56.15 +23 27 53.5 0 12.935 12.592 12.055 11.526 11.193 11.220 20.59 2.99 -37.49 2.99 0.72 0.81 HHJ388_DH868 GCS9 Cl* Melotte 22 HHJ 388 +03 58 57.04 +23 42 31.1 0 12.744 12.419 11.916 11.544 11.113 11.318 22.87 2.96 -40.80 2.96 0.94 0.82 HCG519_HHJ411_DH870 GCS9 Cl* Melotte 22 HCG 519 +03 59 05.73 +26 24 26.6 0 14.015 13.667 13.180 12.570 12.328 12.338 18.44 2.48 -40.89 2.48 0.87 0.93 DH871 GCS9 Cl* Melotte 22 DH 871 +03 59 06.31 +25 03 20.0 0 13.383 12.988 12.334 11.705 11.382 11.486 4.31 2.47 -61.05 2.47 21.47 0.00 HHJ366_DH872 GCS9 Cl* Melotte 22 HHJ 366 +03 59 12.92 +24 17 18.4 0 14.762 14.271 13.702 13.114 12.808 12.827 15.00 2.97 -34.59 2.97 0.41 0.22 DH873 GCS9 Cl* Melotte 22 DH 873 +03 59 15.87 +25 54 08.3 0 14.875 14.465 13.906 13.228 12.965 12.971 32.55 2.48 -38.84 2.48 0.41 0.00 HHJ133 GCS9 Cl* Melotte 22 HHJ 133 +03 59 18.99 +23 31 23.9 0 14.624 14.200 13.596 13.059 12.742 12.746 17.69 3.00 -45.41 3.00 0.84 0.92 HHJ150_DH874 GCS9 Cl* Melotte 22 HHJ 150 +03 59 26.36 +22 21 22.2 0 15.719 15.289 14.695 14.117 13.819 13.820 31.13 2.88 -39.73 2.88 0.46 0.00 HHJ39 GCS9 Cl* Melotte 22 HHJ 39 +03 59 26.93 +21 48 18.7 0 14.594 14.072 13.475 12.948 12.625 12.648 19.51 2.87 -42.67 2.87 1.42 0.94 DH876 GCS9 Cl* Melotte 22 DH 876 +03 59 29.33 +21 39 56.0 0 15.657 15.115 14.478 13.964 13.589 13.587 22.43 3.78 -42.75 3.78 0.34 0.79 DH878 GCS9 Cl* Melotte 22 DH 878 +03 59 31.47 +21 16 18.3 0 13.596 13.197 12.656 12.116 11.837 11.824 22.53 3.75 -44.15 3.75 0.74 0.87 DH879 GCS9 Cl* Melotte 22 DH 879 +03 59 52.43 +22 21 57.6 0 13.184 12.972 12.496 11.861 11.662 11.699 22.40 2.84 -35.16 2.84 0.08 0.23 DH880 GCS9 Cl* Melotte 22 DH 880 +03 59 56.49 +23 40 15.3 0 16.179 15.510 14.834 14.290 13.933 18.14 3.57 -40.03 3.57 0.37 0.69 DH882 GCS9 Cl* Melotte 22 DH 882 +03 59 59.15 +23 41 04.6 0 15.249 14.710 14.084 13.517 13.217 20.59 3.55 -40.08 3.55 0.30 0.83 DH883 GCS9 Cl* Melotte 22 DH 883 +03 59 59.86 +22 05 29.3 0 14.814 14.347 13.744 13.188 12.863 12.877 16.36 2.87 -36.19 2.87 0.93 0.58 DH884 GCS9 Cl* Melotte 22 DH 884 +04 00 00.40 +23 47 07.2 0 15.850 15.374 14.799 14.250 13.943 27.42 3.57 -41.36 3.57 0.20 0.14 DH885 GCS9 Cl* Melotte 22 DH 885 +04 00 02.53 +26 36 02.0 0 14.847 14.477 13.972 13.308 13.038 13.047 4.33 2.48 -47.48 2.48 6.50 0.00 DH886 GCS9 Cl* Melotte 22 DH 886 +04 00 10.30 +22 02 17.0 0 14.399 13.919 13.384 12.864 12.513 12.521 19.86 2.87 -43.85 2.87 0.29 0.94 DH888 GCS9 Cl* Melotte 22 DH 888 +04 00 14.11 +24 48 51.4 0 14.369 14.008 13.496 12.927 12.677 12.641 14.14 2.89 -46.60 2.89 0.44 0.77 DH889 GCS9 Cl* Melotte 22 DH 889 +04 00 26.15 +23 26 17.3 0 13.910 13.401 12.803 12.276 11.930 11.924 16.57 3.35 -40.04 3.35 0.11 0.89 DH891 GCS9 Cl* Melotte 22 DH 891 +04 00 28.19 +23 51 24.0 0 13.939 13.453 12.831 12.277 12.003 13.71 3.54 -40.49 3.54 0.64 0.78 DH892 GCS9 Cl* Melotte 22 DH 892 +04 00 28.35 +27 20 54.5 0 14.954 14.552 14.004 13.379 13.085 13.092 16.55 2.95 -38.17 2.95 0.16 0.81 DH893 GCS9 Cl* Melotte 22 DH 893 +04 00 39.24 +26 44 19.0 0 12.865 12.547 12.023 11.446 11.137 11.155 14.68 2.48 -47.91 2.48 3.18 0.26 DH894 GCS9 Cl* Melotte 22 DH 894 +04 00 49.33 +25 12 10.6 0 13.940 13.688 13.208 12.567 12.370 12.375 19.90 2.89 -39.87 2.89 0.15 0.89 DH895 GCS9 Cl* Melotte 22 DH 895 +04 01 08.61 +22 57 22.3 0 14.093 13.707 13.221 12.574 12.334 12.323 41.92 3.35 -24.05 3.35 38.80 0.00 DH897 GCS9 Cl* Melotte 22 DH 897 +04 01 12.08 +23 54 12.8 0 13.698 13.200 12.655 12.127 11.850 15.17 3.54 -39.47 3.54 0.46 0.82 DH898 GCS9 Cl* Melotte 22 DH 898 +04 01 13.99 +23 10 29.9 0 13.872 13.459 12.893 12.304 12.031 12.021 20.88 3.35 -38.17 3.35 0.17 0.77 DH899 GCS9 Cl* Melotte 22 DH 899 +04 01 26.06 +21 35 08.5 0 14.044 13.640 13.072 12.491 12.192 12.191 18.27 3.75 -43.61 3.75 0.12 0.94 DH900 GCS9 Cl* Melotte 22 DH 900 +04 01 49.17 +22 10 05.0 0 14.076 13.630 13.057 12.554 12.239 12.210 23.64 3.40 -44.42 3.40 0.13 0.83 DH901 GCS9 Cl* Melotte 22 DH 901 +04 01 59.58 +25 19 12.2 0 16.333 15.812 15.179 14.530 14.202 14.168 19.23 3.35 -37.71 3.35 0.41 0.48 DH902 GCS9 Cl* Melotte 22 DH 902 +04 02 22.62 +24 48 24.0 0 14.717 14.421 13.944 13.270 13.053 13.046 15.08 2.90 -44.52 2.90 0.66 0.89 DH903 GCS9 Cl* Melotte 22 DH 903 +04 02 49.97 +23 30 38.4 0 13.869 13.468 12.896 12.332 12.053 12.047 20.08 3.35 -38.25 3.35 0.12 0.81 DH904 GCS9 Cl* Melotte 22 DH 904 +04 03 01.22 +25 24 23.0 0 17.268 16.527 15.813 15.214 14.808 14.778 24.72 3.40 -37.90 3.40 0.24 0.27 DH905 GCS9 Cl* Melotte 22 DH 905 +04 03 16.56 +24 35 19.6 0 15.089 14.701 14.123 13.564 13.268 13.248 21.78 2.90 -42.71 2.90 0.10 0.83 DH906 GCS9 Cl* Melotte 22 DH 906 +04 03 24.94 +22 52 17.0 0 13.410 13.177 12.786 12.268 12.138 12.178 17.60 3.35 -40.68 3.35 0.56 0.92 DH2004_907 GCS9 Cl* Melotte 22 DH 907 +04 03 40.67 +25 21 34.5 0 13.110 12.756 12.216 11.671 11.303 11.336 16.57 3.31 -34.58 3.31 0.13 0.25 DH908 GCS9 Cl* Melotte 22 DH 908 +04 03 43.84 +23 53 47.0 0 14.247 13.709 13.083 12.543 12.195 12.194 20.68 3.37 -43.87 3.37 0.36 0.93 DH909 GCS9 Cl* Melotte 22 DH 909 +04 03 49.57 +23 43 13.7 0 14.866 14.377 13.796 13.269 12.931 12.931 22.02 3.38 -44.78 3.38 0.18 0.90 DH910 GCS9 Cl* Melotte 22 DH 910 +04 04 35.56 +25 07 14.7 0 13.458 13.161 12.678 12.087 11.856 11.863 18.96 2.94 -34.69 2.94 1.29 0.30 DH911 GCS9 Cl* Melotte 22 DH 911 +04 04 45.65 +24 41 19.1 0 14.008 13.433 12.771 12.193 11.842 11.858 15.03 2.94 -29.97 2.94 0.86 0.00 DH912 GCS9 Cl* Melotte 22 DH 912 +04 05 13.75 +24 08 42.7 0 14.983 14.471 13.846 13.261 12.933 12.917 19.77 3.38 -41.50 3.38 0.54 0.94 DH915 GCS9 Cl* Melotte 22 DH 915 +04 06 29.99 +22 33 43.6 0 14.376 13.856 13.201 12.623 12.273 12.269 14.83 5.07 -33.96 5.07 0.55 0.14 DH2004_916 GCS9 Cl* Melotte 22 DH 916 +03 27 35.60 +24 31 42.8 0 11.913 11.791 11.262 11.438 10.650 29.84 6.95 -51.81 6.95 5.58 NM DH001 GCS9 Cl* Melotte 22 DH 001 +03 27 37.78 +24 59 00.4 0 18.239 17.866 17.264 16.663 16.418 4.37 9.41 -21.38 9.41 0.68 NM DH2004_2 GCS9 Cl* Melotte 22 DH 2 +03 40 27.44 +24 20 42.0 0 19.876 19.366 18.295 17.674 17.142 0.114 19.62 5.81 -2.47 5.81 2.79 NM int-pl-IZ-83;IPLJ0340274+242042_N GCS9 Cl* Melotte 22 IPL 83 +03 40 40.02 +24 44 08.9 0 13.551 13.221 12.733 12.866 11.933 11.954 100.30 2.48 -40.25 2.48 710.82 NM HCG40_SK760_HHJ412 GCS9 Cl* Melotte 22 HCG 40 +03 40 42.48 +22 25 52.9 0 17.673 17.180 16.589 16.016 15.679 15.679 20.34 2.74 -38.19 2.74 3.77 NM DH149 GCS9 Cl* Melotte 22 DH 149 +03 40 47.27 +23 52 35.9 0 16.329 15.775 15.144 14.569 14.220 14.229 -14.15 2.16 -37.59 2.16 2.18 NM BPL16 GCS9 Cl* Melotte 22 BPL 16 +03 40 52.79 +25 24 43.5 0 19.211 18.106 17.097 16.437 15.871 15.971 11.63 2.75 -0.11 2.75 1.90 NM L07_A1_3 GCS9 +03 41 13.48 +26 01 18.1 0 14.297 14.031 13.545 13.781 12.723 12.729 -45.56 2.23 13.58 2.23 883.14 NM L07_19 GCS9 +03 41 20.82 +25 41 15.4 0 14.693 14.445 13.990 13.378 13.244 13.251 15.68 2.23 -33.00 2.23 4.42 NM L07_37 GCS9 +03 41 31.45 +23 05 12.5 0 17.388 17.172 16.798 16.334 16.205 16.153 -4.54 2.83 5.67 2.83 3.33 NM L07_photNM_1 GCS9 +03 41 33.33 +24 00 56.8 0 16.757 16.263 15.632 15.043 14.669 0.010 -0.93 2.35 28.95 2.35 0.35 NM int-pl-IZ-13;IPLJ0341333+240056_N GCS9 Cl* Melotte 22 IPL 13 +03 41 33.76 +24 11 18.7 0 16.689 16.102 15.481 14.947 14.519 0.009 -17.54 2.34 -3.76 2.34 0.87 NM int-pl-IZ-89;IPLJ0341337+241118_BPL24_Y GCS9 Cl* Melotte 22 IPL 89 +03 41 37.74 +23 04 32.7 0 17.983 17.097 16.312 15.674 15.230 15.224 55.66 2.38 -15.82 2.38 1.35 NM L07_A1_7 GCS9 +03 41 45.01 +24 02 00.5 0 20.471 21.487 18.728 18.097 17.603 0.192 -13.87 6.33 2.10 6.33 2.08 NM int-pl-IZ-12;IPLJ0341450+240200_N GCS9 Cl* Melotte 22 IPL 12 +03 41 45.82 +23 14 26.1 0 16.057 15.577 14.958 14.278 13.965 13.959 11.09 2.27 -13.29 2.27 1.09 NM L07_PM_NM_1 GCS9 +03 42 01.36 +24 07 42.8 0 20.269 19.509 18.937 18.788 18.036 0.158 44.55 7.28 -34.90 7.28 2.78 NM int-pl-IZ-79;IPLJ0342013+240742_N GCS9 Cl* Melotte 22 IPL 79 +03 42 04.72 +23 29 04.2 0 16.255 15.752 15.126 14.475 14.133 14.135 5.09 2.27 -8.96 2.27 1.00 NM L07_PM_NM_2 GCS9 +03 42 07.98 +22 39 33.4 0 18.739 17.474 16.501 15.831 15.206 15.225 -14.88 2.43 -2.56 2.43 5.87 NM int-pl-IZ-76_2MASSJ0342080+223933_Y_L07_A1_12 GCS9 Cl* Melotte 22 IPL 76 +03 42 10.17 +22 48 44.5 0 17.259 16.355 15.543 14.932 14.469 14.500 21.01 2.29 -90.42 2.29 1.52 NM L07_PM_NM_3 GCS9 +03 42 16.89 +23 23 26.2 0 14.399 14.006 13.488 12.830 12.556 12.534 4.44 2.25 -20.25 2.25 2.48 NM SK694 GCS9 Cl* Melotte 22 SK 694 +03 42 26.81 +24 50 20.5 0 16.617 15.878 15.170 14.570 14.175 14.570 5.87 3.00 7.70 3.00 11.87 NM SFHT-Pl-8_XX GCS9 Cl* Melotte 22 CFHT 8 +03 42 27.94 +25 25 20.0 0 18.768 18.348 17.661 17.160 16.956 16.830 -1.58 3.77 -14.43 3.77 1.80 NM L07_photNM_2 GCS9 +03 42 28.52 +24 03 40.9 0 17.374 16.756 16.090 15.493 15.055 0.015 14.53 2.24 4.35 2.24 2.29 NM int-pl-IZ-32;IPLJ0342285+240340_N GCS9 Cl* Melotte 22 IPL 32 +03 42 43.46 +22 38 31.3 0 19.335 18.795 18.306 17.850 17.254 17.390 6.96 6.04 -3.70 6.04 3.63 NM L07_faintJK_9 GCS9 +03 42 46.24 +23 54 50.4 0 18.364 17.566 16.865 16.291 15.869 0.031 -9.31 2.56 -4.06 2.56 2.52 NM int-pl-IZ-30;2MASSJ0342462+235450_N GCS9 Cl* Melotte 22 IPL 30 +03 42 49.78 +23 55 53.0 0 17.226 16.592 15.902 15.202 14.834 0.014 -20.00 2.22 -24.83 2.22 1.37 NM int-pl-IZ-28;2MASSJ0342497+235553_N GCS9 Cl* Melotte 22 IPL 28 +03 43 03.28 +23 41 30.7 0 18.320 17.643 16.965 16.316 15.973 0.032 0.97 2.58 -4.45 2.58 2.37 NM int-pl-IZ-10;IPLJ0343032+234130_N GCS9 Cl* Melotte 22 IPL 10 +03 43 12.21 +23 09 16.5 0 18.623 17.585 16.672 16.054 15.582 15.559 23.62 2.52 -78.70 2.52 5.17 NM L07_photNM_3 GCS9 +03 43 13.91 +24 08 20.2 0 14.902 14.430 13.830 13.297 12.985 13.008 7.02 2.13 -16.75 2.13 1.63 NM BPL55 GCS9 Cl* Melotte 22 BPL 55 +03 43 13.93 +23 47 46.0 0 16.658 16.080 15.498 15.003 14.645 0.010 80.42 2.19 -64.23 2.19 0.96 NM int-pl-IZ-8;2MASSJ0343138+234746_N GCS9 Cl* Melotte 22 IPL 8 +03 43 29.16 +24 02 06.4 0 16.723 16.206 15.569 14.907 14.555 0.010 3.90 2.33 -14.75 2.33 0.81 NM int-pl-IZ-22;IPLJ0343291+240206_N GCS9 Cl* Melotte 22 IPL 22 +03 43 29.87 +24 15 47.3 0 13.312 13.075 12.598 11.994 11.788 -2.96 2.30 -30.76 2.30 0.73 NM BPL60 GCS9 Cl* Melotte 22 BPL 60 +03 43 41.55 +23 38 56.9 0 13.507 13.136 11.471 13.159 10.941 68.56 2.30 127.64 2.30 9.62 NM HII157_HD23157_Tr050 GCS9 HII157 +03 43 47.80 +24 03 17.0 0 19.840 18.837 18.173 17.520 17.095 0.106 17.69 3.88 0.19 3.88 4.05 NM int-pl-IZ-18;IPLJ0343477+240316_N GCS9 Cl* Melotte 22 IPL 18 +03 43 48.10 +22 42 19.4 0 16.799 16.182 15.580 15.085 14.719 0.011 -12.71 2.31 -6.73 2.31 5.51 NM int-pl-IZ-77;2MASSJ0343481+224219_N GCS9 Cl* Melotte 22 IPL 77 +03 43 49.31 +24 07 42.2 0 13.277 13.106 12.742 12.360 12.239 10.92 2.30 -2.78 2.30 1.99 NM BPL64 GCS9 Cl* Melotte 22 BPL 64 +03 43 52.03 +22 55 24.5 0 18.877 17.799 16.954 16.281 15.790 15.790 8.59 2.63 -3.83 2.63 2.80 NM int-pl-IZ-55_IPLJ0343520+225524_Y_L07_photNM_4 GCS9 Cl* Melotte 22 IPL 55 +03 43 53.05 +23 21 50.2 0 16.148 15.662 15.035 14.372 14.027 14.014 -14.95 2.27 -18.64 2.27 6.97 NM L07_PM_NM_4 GCS9 +03 44 09.32 +23 02 18.5 0 18.527 17.911 17.141 16.542 16.135 0.040 -0.31 2.78 0.00 2.78 4.10 NM int-pl-IZ-54;IPLJ0344093+230218_N GCS9 Cl* Melotte 22 IPL 54 +03 44 09.39 +23 17 07.2 0 16.376 15.837 15.168 14.608 14.201 14.250 -32.76 2.28 2.92 2.28 11.23 NM L07_PM_NM_5 GCS9 +03 44 10.08 +22 43 57.8 0 16.296 15.777 15.173 14.582 14.243 14.252 4.33 2.28 -0.56 2.28 2.86 NM L07_PM_NM_6 GCS9 +03 44 10.90 +23 40 15.0 0 19.101 18.430 17.764 17.019 16.548 -2.43 3.36 -8.57 3.36 1.93 NM Roque3 GCS9 Cl* Melotte 22 Roque 3 +03 44 12.66 +23 43 17.6 0 20.244 19.037 18.123 17.534 17.170 15.97 4.26 -3.99 4.26 3.70 NM Roque18 GCS9 Cl* Melotte 22 Roque 18 +03 44 12.68 +25 24 35.1 0 18.091 17.245 16.455 15.959 15.528 15.959 -5.40 2.49 16.14 2.49 2.91 NM CFHT-Pl-20_L07_photNM_5 GCS9 Cl* Melotte 22 CFHT 20 +03 44 20.84 +24 39 02.8 0 19.245 18.126 17.127 16.400 15.949 3.92 3.63 7.91 3.63 6.22 NM Roque36 GCS9 Cl* Melotte 22 Roque 36 +03 44 26.36 +26 02 30.9 0 13.193 12.826 12.324 11.355 11.457 11.506 1.38 2.23 -17.15 2.23 540.35 NM HCG142_SK561_T10_DH337 GCS9 Cl* Melotte 22 HCG 142 +03 44 26.46 +22 40 00.5 0 15.389 15.105 14.594 13.943 13.721 13.732 23.92 2.25 -18.53 2.25 38.11 NM L07_195 GCS9 +03 44 33.80 +22 42 49.2 0 16.520 15.949 15.320 14.714 14.349 14.347 3.12 2.26 9.87 2.26 2.53 NM L07_PM_NM_7 GCS9 +03 44 44.62 +22 49 10.5 0 19.674 19.358 18.763 18.140 18.089 0.100 0.73 5.90 -8.60 5.90 2.55 NM int-pl-IZ-52;IPLJ0344446+224911_N GCS9 Cl* Melotte 22 IPL 52 +03 44 50.99 +25 21 43.5 0 19.424 18.545 17.899 17.322 16.856 16.853 4.78 3.88 -9.91 3.88 4.18 NM L07_262 GCS9 +03 44 58.97 +23 23 19.9 0 11.820 11.684 11.266 11.507 10.629 10.890 19.95 2.23 -34.31 2.23 2.42 NM HII513_DH364 GCS9 Cl* Melotte 22 HII 513 +03 45 12.28 +22 58 31.8 0 14.301 14.092 13.643 13.024 12.948 12.939 17.40 2.24 -40.22 2.24 3.39 NM L07_188 GCS9 +03 45 23.81 +21 52 38.7 0 15.066 14.597 14.011 13.441 13.158 13.108 12.19 2.27 -5.33 2.27 2.63 NM BPL72 GCS9 Cl* Melotte 22 BPL 72 +03 45 25.31 +22 22 07.8 0 19.833 18.841 18.030 17.345 16.867 0.125 0.66 4.90 -0.37 4.90 2.84 NM int-pl-IZ-68;IPLJ0345252+222208_N GCS9 Cl* Melotte 22 IPL 68 +03 45 28.34 +23 48 09.6 0 17.089 16.378 15.577 14.726 14.303 14.314 12.66 2.24 -4.80 2.24 4.43 NM L07_PM_NM_8 GCS9 +03 45 29.87 +22 24 14.5 0 16.385 15.797 15.183 14.585 14.229 14.242 60.29 2.30 -74.75 2.30 16.18 NM BPL74_L07_PM_NM_9 GCS9 Cl* Melotte 22 BPL 74 +03 45 33.16 +25 34 30.0 0 18.115 17.203 16.409 15.847 15.398 15.847 -6.60 2.43 -17.46 2.43 3.65 NM CFHT-Pl-19_L07_photNM_6 GCS9 Cl* Melotte 22 CFHT 19 +03 45 34.50 +23 41 43.5 0 17.069 16.371 15.611 14.865 14.455 14.451 -12.61 2.24 -23.80 2.24 3.02 NM L07_PM_NM_10 GCS9 +03 45 36.44 +24 18 15.4 0 16.225 15.689 15.049 14.432 14.104 14.091 9.33 2.24 -4.26 2.24 4.09 NM L07_PM_NM_11 GCS9 +03 45 36.50 +22 28 31.9 0 19.069 18.388 17.472 17.000 16.703 0.064 -24.55 3.48 -17.14 3.48 10.20 NM int-pl-IZ-66;IPLJ0345365+222832_N GCS9 Cl* Melotte 22 IPL 66 +03 45 36.85 +23 44 47.8 0 16.877 16.225 15.470 14.619 14.255 14.237 18.08 2.24 0.32 2.24 17.83 NM L07_PM_NM_12 GCS9 +03 45 37.25 +23 49 21.0 0 16.385 15.847 15.119 14.210 13.883 13.880 -6.65 2.23 -8.20 2.23 1.69 NM L07_PM_NM_13 GCS9 +03 45 43.11 +25 40 23.1 0 16.198 15.009 13.924 13.240 12.663 12.643 -96.92 2.23 -40.65 2.23 2.96 NM L07_PM_NM_14 GCS9 +03 45 45.25 +22 58 44.6 0 16.700 16.061 15.359 14.836 14.433 0.010 57.50 2.26 -53.09 2.26 10.93 NM int-pl-IZ-58;2MASSJ0345452+225845_BPL76_Y GCS9 Cl* Melotte 22 IPL 58 +03 45 49.90 +23 45 59.8 0 16.226 15.601 14.814 13.904 13.593 13.583 0.36 2.23 -2.57 2.23 0.62 NM L07_PM_NM_15 GCS9 +03 45 52.65 +25 51 42.0 0 12.679 12.343 11.821 11.385 10.925 11.124 122.72 2.23 -53.12 2.23 13.46 NM HCG199_SK500_MT61_DH406 GCS9 Cl* Melotte 22 HCG 199 +03 45 53.20 +25 12 55.8 0 17.551 16.776 16.002 15.368 14.932 14.952 -4.73 2.27 -12.06 2.27 5.27 NM BPL81_L07_A1_37 GCS9 Cl* Melotte 22 BPL 81 +03 46 02.52 +23 45 33.2 0 18.177 17.368 16.459 15.481 15.025 15.026 -1.56 2.31 1.54 2.31 6.68 NM L07_A1_39 GCS9 +03 46 03.75 +23 44 35.6 0 18.178 17.270 16.413 15.556 15.053 15.070 -10.12 2.30 -4.53 2.30 21.32 NM L07_A1_40 GCS9 +03 46 04.92 +22 15 40.2 0 17.472 16.679 15.925 15.296 15.057 15.166 0.19 2.39 -8.27 2.39 2.60 NM L07_photNM_7 GCS9 +03 46 08.02 +23 45 35.5 0 18.882 17.941 16.854 15.857 15.336 15.348 -12.84 2.37 2.06 2.37 2.22 NM L07_A1_41 GCS9 +03 46 08.22 +23 21 38.7 0 17.105 16.500 15.752 15.026 14.646 14.651 2.05 2.28 -10.76 2.28 3.97 NM L07_PM_NM_16 GCS9 +03 46 08.37 +23 20 50.8 0 11.765 11.589 11.200 11.417 10.482 10.755 22.70 2.23 -33.02 2.23 6.52 NM HII915_HCG215 GCS9 HII915 +03 46 14.34 +23 51 02.4 0 15.627 14.918 14.181 13.826 13.458 13.484 1.83 2.22 13.02 2.22 301.52 NM HHJ56_DH432 GCS9 Cl* Melotte 22 HHJ 56 +03 46 14.48 +22 20 51.0 0 16.717 16.105 15.473 15.002 14.615 0.011 66.01 2.32 -69.67 2.32 3.61 NM int-pl-IZ-63_2MASSJ0346144+222051_N_L07_PM_NM_17 GCS9 Cl* Melotte 22 IPL 63 +03 46 26.75 +24 49 18.1 0 16.284 15.730 15.129 14.552 14.213 14.224 -2.42 2.22 -18.50 2.22 17.98 NM L07_PM_NM_18 GCS9 +03 46 32.99 +23 38 00.9 0 16.598 16.068 15.371 14.454 14.143 14.105 4.67 2.24 2.45 2.24 3.95 NM L07_PM_NM_19 GCS9 +03 46 36.82 +23 33 01.8 0 16.694 16.058 15.333 14.498 14.128 14.125 -2.77 2.24 -15.45 2.24 1.91 NM L07_PM_NM_21 GCS9 +03 46 38.36 +25 33 18.6 0 15.621 15.312 14.788 14.129 13.931 13.905 19.87 2.24 -46.62 2.24 9.86 NM L07_33 GCS9 +03 46 40.95 +22 22 38.1 0 19.242 18.150 16.917 16.183 15.497 15.480 69.74 2.66 -50.74 2.66 3.71 NM L07_A1_52 GCS9 +03 46 44.04 +23 38 13.4 0 17.173 16.531 15.775 14.937 14.576 14.555 6.12 2.26 -11.45 2.26 9.02 NM L07_PM_NM_22 GCS9 +03 46 48.31 +24 18 06.2 0 13.266 13.009 12.529 11.953 11.752 11.731 -12.75 2.22 2.02 2.22 6.44 NM HCG236_J311 GCS9 Cl* Melotte 22 HCG 236 +03 46 50.99 +25 40 44.5 0 16.272 15.767 15.181 14.488 14.196 14.200 -5.63 2.26 -22.81 2.26 3.53 NM L07_PM_NM_23 GCS9 +03 46 52.58 +24 17 17.0 0 15.299 14.842 14.265 13.643 13.345 13.340 20.07 2.22 1.29 2.22 10.34 NM BPL111 GCS9 Cl* Melotte 22 BPL 111 +03 47 02.12 +25 54 36.6 0 13.774 13.591 13.184 12.619 12.512 12.525 10.77 2.23 -49.85 2.23 2.44 NM L07_4 GCS9 +03 47 02.54 +25 13 45.5 0 16.469 16.185 15.655 14.946 14.723 14.728 4.07 2.25 -9.76 2.25 1.92 NM BPL123 GCS9 Cl* Melotte 22 BPL 123 +03 47 03.56 +24 49 11.6 0 13.153 13.091 11.335 12.348 10.073 NM HII1266_HD23567_Tr359b GCS9 HII1266 +03 47 05.82 +23 24 52.5 0 19.481 19.105 18.389 17.632 17.262 17.393 -2.31 4.89 5.06 4.89 3.79 NM Roque32 GCS9 Cl* Melotte 22 Roque 32 +03 47 06.65 +24 45 47.4 0 16.069 15.513 14.959 14.428 14.100 14.105 59.57 2.23 27.45 2.23 12.46 NM L07_PM_NM_25 GCS9 +03 47 07.73 +24 21 40.5 0 15.473 15.016 14.402 13.829 13.534 13.532 -25.04 2.22 -2.14 2.22 11.43 NM JS9 GCS9 Cl* Melotte 22 JS 9 +03 47 08.31 +22 33 10.0 0 16.577 15.993 15.342 14.844 14.453 0.010 -15.97 2.31 -6.87 2.31 5.75 NM int-pl-IZ-38;2MASSJ0347082+223309_BPL127_Y_L07_PM_NM_26 GCS9 Cl* Melotte 22 IPL 38 +03 47 13.69 +23 46 28.4 0 16.410 15.813 15.184 14.658 14.309 14.311 -22.45 2.24 11.13 2.24 31.03 NM L07_PM_NM_27 GCS9 +03 47 24.41 +23 54 52.7 0 13.573 13.598 11.481 12.458 10.127 10.989 -61.87 2.22 -1.72 2.22 12.56 NM HII1397_HD23631_Tr402 GCS9 HII1397 +03 47 30.66 +25 13 30.6 0 16.074 15.584 14.998 14.473 14.158 14.126 48.31 2.22 -52.27 2.22 3.66 NM BPL146 GCS9 Cl* Melotte 22 BPL 146 +03 47 36.27 +24 28 50.1 0 16.052 15.566 14.943 14.294 13.957 13.957 -9.42 2.22 -2.90 2.22 3.25 NM L07_PM_NM_28 GCS9 +03 47 37.55 +24 28 59.0 0 19.855 19.463 18.773 18.225 18.131 17.930 -4.03 5.91 20.75 5.91 1.09 NM Roque34 GCS9 Cl* Melotte 22 Roque 34 +03 47 38.75 +22 38 40.3 0 17.442 16.864 16.220 15.689 15.285 15.282 -23.29 2.39 -31.80 2.39 4.85 NM Roque44 GCS9 Cl* Melotte 22 Roque 44 +03 47 41.19 +23 44 24.9 0 11.990 11.816 11.404 11.437 10.714 11.008 21.37 2.22 -39.05 2.22 12.59 NM HII1532_HCG286_SK405_B270_DH531 GCS9 HII1532 +03 47 46.16 +25 21 42.1 0 19.442 18.364 17.367 16.890 16.342 16.383 69.70 3.46 -102.93 3.46 5.96 NM L07_photNM_8 GCS9 +03 47 46.90 +24 03 40.5 0 20.021 20.270 19.206 18.506 18.007 18.106 -3.67 5.35 0.47 5.35 4.33 NM Roque27 GCS9 Cl* Melotte 22 Roque 27 +03 48 02.09 +24 00 02.5 0 18.921 18.465 17.869 17.284 16.982 17.006 1.27 3.24 -5.14 3.24 1.15 NM Roque10 GCS9 Cl* Melotte 22 Roque 10 +03 48 03.68 +23 44 10.4 0 15.989 15.099 14.329 13.709 13.277 13.278 32.62 2.22 -124.76 2.22 0.68 NM NOT1 GCS9 Cl* Melotte 22 NOT 1 +03 48 04.74 +23 51 01.8 0 20.362 19.516 19.084 18.521 17.969 18.210 11.09 6.08 5.08 6.08 1.36 NM Festin98_009 GCS9 Cl* Melotte 22 NPL 009 +03 48 13.80 +24 28 04.0 0 17.517 16.753 16.083 15.485 15.072 15.099 1.43 2.31 -3.33 2.31 2.70 NM Roque43 GCS9 Cl* Melotte 22 Roque 43 +03 48 13.93 +24 38 30.5 0 16.849 16.681 16.451 16.076 15.939 15.998 -2.88 2.65 1.72 2.65 5.01 NM BPL168 GCS9 Cl* Melotte 22 BPL 168 +03 48 23.62 +24 22 35.2 0 16.199 15.558 14.889 14.317 13.945 13.947 -10.86 2.23 -14.81 2.23 2.24 NM BPL177_Festin98_004_L07_PM_NM_29 GCS9 Cl* Melotte 22 BPL 177 +03 48 25.61 +22 52 13.0 0 15.518 15.386 15.395 14.708 14.646 14.641 11.27 2.17 -37.83 2.17 450.04 NM BPL179 GCS9 Cl* Melotte 22 BPL 179 +03 48 30.10 +24 20 43.9 0 14.608 14.578 12.604 14.861 11.050 12.051 -94.69 2.22 157.47 2.22 6.88 NM HII1876_HD23763_Tr518 GCS9 HII1876 +03 48 32.39 +24 13 18.4 0 20.066 19.095 18.425 17.638 17.303 17.351 2.98 3.77 -23.67 3.77 2.76 NM Festin98_010 GCS9 Cl* Melotte 22 NPL 010 +03 48 32.67 +23 52 40.6 0 14.722 14.278 13.711 13.109 12.832 12.842 0.96 2.22 -5.44 2.22 5.32 NM WILL1 GCS9 Cl* Melotte 22 WBM 1 +03 48 36.30 +23 33 25.3 0 16.077 15.589 15.028 14.489 14.152 14.167 -14.94 2.23 -33.20 2.23 1.87 NM L07_PM_NM_30 GCS9 +03 48 44.14 +24 21 18.7 0 17.816 17.195 16.443 15.823 15.448 15.431 2.52 2.33 -8.88 2.33 4.62 NM Roque37 GCS9 Cl* Melotte 22 Roque 37 +03 48 48.45 +21 59 00.3 0 16.559 15.982 15.348 14.816 14.470 14.458 -16.99 2.31 5.11 2.31 5.45 NM L07_PM_NM_31 GCS9 +03 48 49.03 +24 20 25.3 0 19.222 18.227 17.237 16.569 16.059 16.074 -33.18 2.59 -58.05 2.59 4.06 NM Roque33_Festin98_008_L07_photNM_9 GCS9 Cl* Melotte 22 Roque 33 +03 48 55.23 +22 50 42.2 0 14.915 14.474 13.893 13.361 13.070 13.047 4.68 2.13 -22.37 2.13 1.12 NM BPL194 GCS9 Cl* Melotte 22 BPL 194 +03 48 58.61 +23 37 03.9 0 19.604 18.306 17.390 16.771 16.233 16.221 6.50 2.60 -15.01 2.60 4.76 NM L07_photNM_10 GCS9 +03 48 58.96 +25 06 14.0 0 11.958 11.762 11.365 11.410 10.602 10.877 7.12 2.20 -45.83 2.20 1.85 NM HII2082 GCS9 HII2082 +03 49 11.59 +24 26 17.4 0 16.051 15.500 14.910 14.436 14.081 14.097 33.46 2.21 -90.64 2.21 2.53 NM L07_PM_NM_32 GCS9 +03 49 12.12 +23 12 55.9 0 16.875 16.165 15.429 14.824 14.414 14.401 53.85 2.28 -0.69 2.28 4.85 NM L07_PM_NM_33 GCS9 +03 49 33.05 +26 50 43.0 0 16.972 16.327 15.617 15.078 14.687 14.684 28.54 2.99 9.02 2.99 0.42 NM IPMBD22 GCS9 Cl* Melotte 22 IPMBD 22 +03 49 45.95 +22 53 43.7 0 15.830 15.510 15.011 14.332 14.144 14.153 13.11 2.27 -37.98 2.27 1.52 NM L07_186 GCS9 +03 49 51.23 +25 26 06.6 0 19.731 18.569 17.609 17.074 16.515 16.545 2.12 3.45 13.47 3.45 1.53 NM L07_photNM_13 GCS9 +03 49 53.76 +23 59 01.4 0 17.271 16.739 16.088 15.537 15.167 15.153 15.94 2.29 -9.47 2.29 31.41 NM Roque46 GCS9 Cl* Melotte 22 Roque 46 +03 50 00.31 +24 28 15.5 0 18.288 17.707 17.006 16.494 16.087 16.072 2.01 2.60 -7.24 2.60 4.36 NM Roque40 GCS9 Cl* Melotte 22 Roque 40 +03 50 01.67 +24 36 48.6 0 15.586 15.002 14.433 13.921 13.543 13.549 5.46 2.21 -6.13 2.21 12.12 NM BPL221 GCS9 Cl* Melotte 22 BPL 221 +03 50 03.90 +23 58 34.1 0 14.131 13.765 13.259 12.738 12.457 12.446 -8.17 2.22 -11.58 2.22 19.96 NM HHJ256 GCS9 Cl* Melotte 22 HHJ 256 +03 50 05.96 +23 42 14.1 0 19.805 19.357 18.931 18.260 17.904 17.940 -0.52 5.77 8.42 5.77 5.36 NM Roque29 GCS9 Cl* Melotte 22 Roque 29 +03 50 15.55 +26 06 30.0 0 16.909 16.185 15.415 14.832 14.403 14.396 40.58 2.28 -66.43 2.28 2.93 NM L07_PM_NM_34 GCS9 +03 50 20.77 +24 08 40.9 0 19.627 19.216 18.782 18.095 17.744 17.820 -0.38 5.11 4.55 5.11 1.62 NM Roque28 GCS9 Cl* Melotte 22 Roque 28 +03 50 41.10 +25 44 21.6 0 15.634 15.517 15.188 14.817 14.704 14.716 -8.19 2.27 -10.74 2.27 17.17 NM L07_faintJK_1 GCS9 +03 51 04.05 +24 32 57.8 0 16.030 15.535 14.962 14.396 14.049 14.060 76.40 2.22 -22.07 2.22 9.89 NM L07_PM_NM_35 GCS9 +03 51 05.58 +24 44 12.2 0 11.945 11.736 11.358 11.489 10.568 10.909 20.38 2.20 -47.92 2.20 5.38 NM HII2927_HCG418_DH693 GCS9 HII2927 +03 51 09.72 +25 18 52.4 0 16.714 16.037 15.393 14.875 14.493 14.534 -18.86 2.28 -30.02 2.28 14.71 NM L07_PM_NM_52 GCS9 +03 51 10.52 +22 48 14.4 0 16.768 16.141 15.471 14.799 14.434 14.413 -4.75 2.28 -21.83 2.28 1.24 NM L07_PM_NM_36 GCS9 +03 51 22.47 +23 42 19.2 0 13.909 13.760 13.410 12.975 12.869 12.863 2.24 2.22 -6.49 2.22 1.31 NM BPL234 GCS9 Cl* Melotte 22 BPL 234 +03 51 27.90 +22 48 12.9 0 16.113 15.431 14.734 14.118 13.705 13.722 11.13 2.26 -12.61 2.26 4.23 NM L07_PM_NM_37 GCS9 +03 51 35.03 +24 03 34.8 0 16.774 16.625 16.231 15.758 15.586 15.587 -6.66 2.32 -6.66 2.32 40.44 NM L07_faintJK_4 GCS9 +03 51 37.72 +25 42 46.5 0 19.137 17.784 16.352 15.461 14.660 14.657 55.25 2.37 -38.67 2.37 4.06 NM L07_photNM_14 GCS9 +03 51 38.18 +23 03 11.2 0 16.892 16.218 15.549 14.978 14.588 14.593 -5.14 2.31 40.41 2.31 5.15 NM L07_PM_NM_38 GCS9 +03 51 53.92 +24 02 51.4 0 13.571 13.114 13.213 12.035 11.680 11.721 -34.06 2.22 -22.59 2.22 165.02 NM HCG441_SK211_BPL243_DH726_Moraux2003_17 GCS9 Cl* Melotte 22 HCG 441 +03 51 57.12 +24 57 06.3 0 16.193 15.875 15.320 14.797 14.498 14.480 15.09 2.23 -31.04 2.23 6.23 NM DH729 GCS9 Cl* Melotte 22 DH 729 +03 52 01.58 +24 20 11.7 0 16.260 15.770 15.166 14.508 14.197 14.223 4.89 2.26 -3.61 2.26 3.40 NM BPL247 GCS9 Cl* Melotte 22 BPL 247 +03 52 07.88 +23 59 13.0 0 16.169 15.371 14.636 14.066 13.640 14.066 18.40 2.25 -115.67 2.25 1.33 NM CFHT-Pl-6_BRB3_L07_PM_NM_39 GCS9 Cl* Melotte 22 CFHT 6 +03 52 13.44 +24 28 52.3 0 19.996 18.601 17.754 17.227 16.636 16.681 7.13 3.47 -14.21 3.47 2.49 NM L07_photNM_16 GCS9 +03 52 17.49 +22 51 01.9 0 16.649 16.084 15.415 14.781 14.414 14.402 4.92 2.30 -6.97 2.30 1.46 NM L07_PM_NM_40 GCS9 +03 52 24.00 +23 55 15.7 0 14.588 14.438 14.048 13.571 13.477 13.478 14.07 2.25 -36.84 2.25 1.05 NM DH2004_751 GCS9 Cl* Melotte 22 DH 751 +03 52 44.31 +24 14 13.2 0 13.520 13.343 12.946 12.457 12.359 12.354 19.94 2.24 -11.03 2.24 7.05 NM L07_8 GCS9 +03 52 46.44 +24 24 17.1 0 19.772 18.591 17.569 16.949 16.281 16.359 -3.07 2.92 -13.02 2.92 18.30 NM L07_photNM_17 GCS9 +03 52 47.18 +22 38 44.1 0 17.260 16.818 16.156 15.566 15.252 15.282 -40.26 2.44 15.01 2.44 273.74 NM L07_faintJK_10 GCS9 +03 52 49.69 +25 00 50.7 0 13.101 12.909 12.566 12.095 11.969 11.985 -1.51 2.23 -0.95 2.23 2.51 NM BPL271 GCS9 Cl* Melotte 22 BPL 271 +03 52 52.11 +25 10 26.0 0 13.187 12.804 12.306 11.803 11.485 11.517 43.14 2.23 -67.75 2.23 5.68 NM BPL273_SK152 GCS9 Cl* Melotte 22 BPL 273 +03 52 58.25 +23 26 19.1 0 13.562 13.427 13.136 12.789 12.721 12.722 7.00 2.25 1.17 2.25 5.43 NM BPL276 GCS9 Cl* Melotte 22 BPL 276 +03 53 21.62 +23 58 53.9 0 13.493 13.135 12.644 12.051 11.822 47.53 2.30 -56.56 2.30 1.90 NM SK132_DH782 GCS9 Cl* Melotte 22 SK 132 +03 53 23.13 +23 19 20.4 0 17.823 16.897 15.966 15.341 14.850 14.834 21.87 2.36 -4.56 2.36 1.54 NM BPL283_CFHT-Pl-18_M8_L07_A1_104 GCS9 Cl* Melotte 22 BPL 283 +03 53 23.37 +25 05 50.0 0 16.629 16.085 15.495 15.014 14.656 14.648 43.26 2.28 -95.99 2.28 1.97 NM BPL284 GCS9 Cl* Melotte 22 BPL 284 +03 53 34.55 +24 04 38.4 0 16.740 16.177 15.552 14.979 14.606 14.22 2.35 9.30 2.35 1.46 NM BPL287 GCS9 Cl* Melotte 22 BPL 287 +03 53 48.72 +25 04 20.1 0 16.777 16.308 15.238 15.126 14.827 14.820 -6.07 2.28 16.38 2.28 119.28 NM L07_PM_NM_41 GCS9 +03 53 59.92 +23 41 00.2 0 16.200 15.677 15.073 14.485 14.141 14.149 -0.43 2.26 -8.06 2.26 1.04 NM BPL296_L07_PM_NM_42 GCS9 Cl* Melotte 22 BPL 296 +03 54 01.40 +24 44 46.7 0 19.768 18.534 17.775 17.194 16.809 17.194 3.91 3.31 -23.74 3.31 1.84 NM CFHT-Pl-26_XX GCS9 Cl* Melotte 22 CFHT 26 +03 54 15.64 +25 21 18.9 0 15.714 15.104 14.458 13.881 13.530 13.501 1.39 2.95 -13.24 2.95 0.52 NM BPL309 GCS9 Cl* Melotte 22 BPL 309 +03 54 17.46 +23 11 56.9 0 16.231 15.688 15.035 14.453 14.100 14.085 -12.92 2.28 -3.82 2.28 10.54 NM L07_PM_NM_43 GCS9 +03 54 39.33 +23 03 12.4 0 16.447 15.693 14.998 14.476 14.064 14.083 6.47 2.28 -4.39 2.28 6.56 NM L07_PM_NM_44 GCS9 +03 54 44.20 +25 15 10.9 0 17.428 16.693 15.954 15.421 15.043 15.031 -6.19 2.31 -33.42 2.31 4.31 NM BPL316 GCS9 Cl* Melotte 22 BPL 316 +03 54 45.87 +22 53 54.8 0 17.409 16.961 16.437 15.849 15.518 15.619 17.05 2.56 -18.44 2.56 4.02 NM DH807 GCS9 Cl* Melotte 22 DH 807 +03 54 46.11 +23 00 20.6 0 17.056 16.404 15.700 15.125 14.741 14.732 -17.41 2.33 -4.76 2.33 7.01 NM L07_PM_NM_46 GCS9 +03 54 49.71 +25 06 24.2 0 13.354 13.219 12.850 12.393 12.309 12.312 -1.04 2.23 -7.78 2.23 6.31 NM BPL319 GCS9 Cl* Melotte 22 BPL 319 +03 54 51.47 +23 45 12.2 0 19.670 18.540 17.687 17.122 16.691 16.585 56.85 3.56 -24.59 3.56 2.56 NM BRB19_None GCS9 Cl* Melotte 22 BRB 19 +03 54 54.51 +22 50 21.5 0 16.464 15.871 15.230 14.673 14.333 14.338 2.56 2.30 -7.22 2.30 4.32 NM L07_PM_NM_47 GCS9 +03 54 55.74 +25 09 04.2 0 14.981 14.562 14.004 13.425 13.130 13.141 17.22 2.23 -9.40 2.23 12.41 NM BPL320 GCS9 Cl* Melotte 22 BPL 320 +03 54 55.92 +24 37 42.9 0 19.397 19.014 18.467 17.790 17.603 17.582 6.11 5.79 -3.45 5.79 6.15 NM L07_photNM_18 GCS9 +03 55 03.43 +24 28 54.6 0 19.678 18.842 18.141 17.524 17.349 17.331 3.23 4.67 -16.09 4.67 6.30 NM L07_257 GCS9 +03 55 06.14 +25 11 06.2 0 15.549 15.000 14.339 13.790 13.431 13.470 0.47 2.24 -5.03 2.24 4.70 NM BPL323 GCS9 Cl* Melotte 22 BPL 323 +03 55 08.19 +23 58 08.6 0 19.348 18.984 18.417 17.822 17.551 -0.94 4.78 -14.24 4.78 2.60 NM L07_faintYJ_30 GCS9 +03 55 12.61 +23 17 37.2 0 17.842 16.877 15.977 15.348 14.842 15.348 54.40 2.33 -48.46 2.33 8.41 NM CFHT-Pl-15_M7.0_BRB13_L07_A1_109 GCS9 Cl* Melotte 22 CFHT 15 +03 55 14.72 +22 42 08.5 0 16.652 16.043 15.366 14.734 14.395 14.359 1.47 2.29 -7.33 2.29 9.73 NM L07_PM_NM_53 GCS9 +03 55 18.11 +24 17 05.7 0 16.325 15.757 15.111 14.541 14.192 14.208 15.89 2.26 -2.19 2.26 10.94 NM BPL326_L07_A1_110 GCS9 Cl* Melotte 22 BPL 326 +03 55 19.92 +24 12 13.9 0 15.750 15.298 14.699 14.128 13.819 13.821 -7.38 2.25 -20.24 2.25 5.61 NM L07_106 GCS9 +03 55 30.07 +23 54 53.5 0 16.503 15.930 15.308 14.808 14.429 14.420 -15.28 2.27 -4.94 2.27 2.49 NM L07_PM_NM_48 GCS9 +03 55 39.59 +24 12 50.7 0 20.511 19.251 17.844 17.303 16.618 16.648 28.07 3.37 -158.64 3.37 1.11 NM L07_photNM_19 GCS9 +03 55 42.02 +22 57 01.4 0 18.646 17.343 15.943 14.999 14.186 14.191 161.10 2.34 -44.49 2.34 2.38 NM L07_photNM_20 GCS9 +03 55 45.49 +23 51 25.4 0 20.033 19.742 18.723 18.580 18.088 17.959 -5.63 8.56 -13.56 8.56 1.63 NM L07_faintJK_5 GCS9 +03 55 46.37 +23 21 16.0 0 13.188 12.908 12.386 11.820 11.587 11.619 18.86 2.25 -3.24 2.25 4.59 NM SK40 GCS9 Cl* Melotte 22 SK 40 +03 56 21.72 +25 21 10.4 0 16.646 15.945 15.246 14.665 14.234 14.261 4.82 2.53 -4.34 2.53 1.00 NM BPL336 GCS9 Cl* Melotte 22 BPL 336 +03 56 34.22 +24 15 13.2 0 17.385 16.926 16.372 15.806 15.535 15.527 12.43 3.15 -41.24 3.15 0.86 NM DH838 GCS9 Cl* Melotte 22 DH 838 +03 56 36.20 +25 18 05.7 0 16.008 15.372 14.712 14.140 13.761 13.809 8.47 2.50 -13.23 2.50 0.59 NM BPL337 GCS9 Cl* Melotte 22 BPL 337 +03 56 51.33 +25 02 25.4 0 13.297 12.918 12.362 11.804 11.466 11.541 28.86 2.47 0.62 2.47 0.95 NM BPL339 GCS9 Cl* Melotte 22 BPL 339 +03 56 51.90 +23 31 36.6 0 18.325 17.973 17.367 16.753 16.502 16.419 -3.97 3.81 -6.61 3.81 1.07 NM DH2004_844 GCS9 Cl* Melotte 22 DH 844 +03 57 58.45 +24 20 08.9 0 17.341 16.910 16.301 15.694 15.391 15.376 -2.76 3.11 -15.77 3.11 0.84 NM DH861 GCS9 Cl* Melotte 22 DH 861 +03 58 56.42 +24 18 32.2 0 14.148 13.730 13.145 12.585 12.404 12.406 61.58 2.96 -48.24 2.96 45.45 NM HHJ292_DH869 GCS9 Cl* Melotte 22 HHJ 292 +04 00 19.40 +24 41 05.0 0 18.236 17.805 17.236 16.704 16.312 16.366 2.20 3.49 -13.79 3.49 1.46 NM DH2004_890 GCS9 Cl* Melotte 22 DH 890 +03 32 33.72 +24 29 34.7 0 13.066 12.465 12.179 13.40 6.88 -33.70 6.88 4.83 noZY DH021 GCS9 Cl* Melotte 22 DH 021 +03 32 42.31 +23 34 00.0 0 13.393 12.828 12.539 12.522 50.10 2.64 -42.29 2.64 98.04 noZY DH022 GCS9 Cl* Melotte 22 DH 022 +03 34 22.13 +27 23 45.3 0 12.594 12.061 11.758 11.766 19.10 4.62 -39.68 4.62 1.84 noZY DH035 GCS9 Cl* Melotte 22 DH 035 +03 34 46.37 +27 55 32.4 0 12.506 11.899 11.620 11.663 17.73 4.62 -33.95 4.62 0.60 noZY DH038 GCS9 Cl* Melotte 22 DH 038 +03 43 59.09 +22 54 29.0 0 19.298 18.365 18.018 22.78 9.41 4.51 9.41 3.41 noZY int-pl-IZ-56;IPLJ0343590+225429_N GCS9 Cl* Melotte 22 IPL 56 +03 44 19.50 +22 39 03.5 0 18.709 18.333 17.679 17.927 28.27 8.54 -3.53 8.54 9.76 noZY L07_faintJK_8 GCS9 +03 44 27.29 +25 44 41.7 0 18.818 17.787 16.965 16.902 8.52 5.25 -26.68 5.25 1.95 noZY BRB27_CFHT-PLIZ1262_PLZJ32 GCS9 Cl* Melotte 22 BRB 27 +03 45 35.26 +23 36 39.6 0 18.891 18.394 18.140 18.489 25.34 8.42 14.07 8.42 6.64 noZY L07_faintJK_7 GCS9 +03 47 05.68 +20 00 37.0 0 14.107 13.553 13.218 13.230 21.25 2.66 -50.47 2.66 2.67 noZY DH492 GCS9 Cl* Melotte 22 DH 492 +03 47 51.19 +25 26 57.9 0 18.212 17.780 17.630 9.00 noZY L07_faintJK_12 GCS9 +03 48 11.42 +20 46 42.4 0 13.091 12.550 12.217 12.230 18.39 2.65 -43.76 2.65 4.29 noZY DH558 GCS9 Cl* Melotte 22 DH 558 +03 48 32.69 +25 06 05.1 0 18.548 17.996 17.438 17.609 0.60 5.00 3.81 5.00 2.60 noZY L07_faintJK_2 GCS9 +03 48 55.41 +24 20 09.6 0 19.996 19.199 18.367 18.093 -21.91 11.56 22.88 11.56 0.29 noY Roque19 GCS9 Cl* Melotte 22 Roque 19 +03 49 09.15 +24 19 59.9 0 16.407 15.807 15.534 15.463 16.82 2.45 -9.31 2.45 0.38 noZY 18.37 GCS9 Cl* Melotte 22 MHOBD 2 +03 49 11.04 +24 20 51.1 0 12.979 12.444 12.146 12.154 16.62 2.33 -46.70 2.33 1.45 noZY HCG355_HHJ287_BPL199_DH604 GCS9 Cl* Melotte 22 HCG 355 +03 49 11.42 +24 14 24.4 0 13.693 13.150 12.839 12.818 20.72 2.34 -43.10 2.34 1.94 noZY BPL200_DH606 GCS9 Cl* Melotte 22 BPL 200 +03 49 12.20 +23 53 12.2 0 11.727 14.085 10.383 11.580 5.42 2.33 -47.80 2.33 40.02 noZY HII2195_HD23863_Tr607 GCS9 HII2195 +03 49 12.52 +24 11 12.8 0 16.410 15.750 15.245 15.233 14.85 2.44 -40.77 2.44 1.58 noZY BPL201_L07_A1_84 GCS9 Cl* Melotte 22 BPL 201 +03 49 16.79 +24 23 45.7 0 11.577 12.188 10.276 10.839 -151.15 2.33 -101.06 2.33 28.91 noZY HII2220_HD23872_Tr613 GCS9 HII2220 +03 49 18.66 +23 46 48.8 0 14.294 13.665 13.393 13.394 15.38 2.34 -44.61 2.34 0.64 noZY DH2004_610 GCS9 Cl* Melotte 22 DH 610 +03 49 21.76 +24 22 51.2 0 12.746 17.742 11.121 25.15 2.33 -49.52 2.33 24.37 noZY HII2263_HD23873_Tr622 GCS9 HII2263 +03 49 32.54 +23 55 42.4 0 13.238 12.702 12.389 12.412 14.17 2.33 -43.14 2.33 0.80 noZY HCG371_SK310_HHJ221_DH627 GCS9 Cl* Melotte 22 HCG 371 +03 49 33.12 +24 13 04.6 0 14.583 14.051 13.701 13.694 19.96 2.34 -41.58 2.34 2.40 noZY BPL210 GCS9 Cl* Melotte 22 BPL 210 +03 49 36.12 +23 56 23.1 0 13.112 12.569 12.282 12.268 16.38 2.33 -43.47 2.33 1.74 noZY HCG373_SK307_HHJ286_DH632 GCS9 Cl* Melotte 22 HCG 373 +03 49 36.53 +24 18 14.0 0 13.243 12.679 12.382 12.419 13.89 2.34 -45.09 2.34 6.76 noZY HCG375_HHJ250_BPL212_DH634 GCS9 Cl* Melotte 22 HCG 375 +03 49 56.60 +24 20 56.3 0 11.532 12.226 10.215 11.260 27.98 2.33 -35.54 2.33 12.96 noZY HII2488_HD23948_Tr688 GCS9 HII2488 +03 50 58.17 +23 55 42.5 0 13.574 13.042 12.728 12.708 21.42 2.34 -47.56 2.34 23.03 noZY HCG414_DH690 GCS9 Cl* Melotte 22 HCG 414 +03 51 11.54 +24 23 13.0 0 12.113 11.631 11.289 11.323 13.08 2.33 -44.19 2.33 0.66 noZY HCG422_SK245_DH697_Moraux2003_1 GCS9 Cl* Melotte 22 HCG 422 +03 51 12.08 +23 55 57.2 0 11.712 11.710 10.948 11.183 25.68 2.33 -44.30 2.33 3.63 noZY HII2966_SK244_DH698 GCS9 HII2966 +03 51 18.22 +24 20 26.0 0 13.809 13.281 13.036 13.013 48.24 2.34 -48.37 2.34 4.66 noZY SK236 GCS9 Cl* Melotte 22 SK 236 +03 51 20.13 +23 45 17.9 0 19.366 18.614 17.836 18.128 -20.62 7.01 -49.89 7.01 3.15 noZY BRB33_None GCS9 Cl* Melotte 22 BRB 33 +03 51 21.34 +23 52 08.5 0 14.111 13.774 13.735 13.701 -2.74 2.34 -4.49 2.34 5.70 noZY Calar7_XX.X GCS9 Cl* Melotte 22 CALAR 7 +03 51 25.35 +23 53 21.5 0 11.431 11.542 10.779 11.060 19.95 2.33 -43.33 2.33 6.34 noZY HCG430_SK229_DH711_BPL235_CFHT-Pl-21_M8_BRB14_CFHT-Pl-21 GCS9 Cl* Melotte 22 HCG 430 +03 51 25.98 +24 15 29.9 0 19.310 18.627 17.965 17.950 16.58 6.80 -16.12 6.80 2.25 noZY BRB31_None GCS9 Cl* Melotte 22 BRB 31 +03 51 29.95 +23 53 57.0 0 11.172 11.359 10.547 10.916 18.96 2.33 -38.73 2.33 7.81 noZY HII3063_Tr862_HCG431_DH713 GCS9 HII3063 +03 51 34.26 +23 47 49.8 0 13.874 13.299 13.025 12.997 16.21 2.34 -45.54 2.34 0.73 noZY BPL236_DH716_Moraux2003_71 GCS9 Cl* Melotte 22 BPL 236 +03 51 39.20 +23 51 18.1 0 13.736 13.171 12.836 12.848 13.98 2.34 -44.51 2.34 8.17 noZY BPL238_DH720 GCS9 Cl* Melotte 22 BPL 238 +03 51 42.32 +24 21 41.3 0 13.568 13.042 12.728 12.744 19.28 2.34 -41.03 2.34 3.74 noZY BPL239_DH723_Moraux2003_53 GCS9 Cl* Melotte 22 BPL 239 +03 52 54.91 +24 37 18.1 0 18.803 17.754 16.926 16.989 12.05 3.86 -46.85 3.86 2.29 noZY PLZJ37_BRB28_L07_faintJK_11 GCS9 Cl* Melotte 22 PlZJ 37 +03 54 01.44 +23 49 57.5 0 18.686 17.711 16.999 16.927 35.53 4.84 -33.95 4.84 3.15 noZY BRB29_L4.5 GCS9 Cl* Melotte 22 BRB 29 +03 54 13.41 +23 32 22.2 0 18.655 18.120 18.105 18.015 11.41 7.09 -15.53 7.09 2.97 noZY L07_faintJK_6 GCS9 +03 55 57.94 +24 41 41.6 0 18.859 18.183 18.214 17.900 -8.05 7.61 14.92 7.61 1.53 noZY L07_faintJK_3 GCS9 +03 35 18.76 +23 26 20.7 0 15.450 14.829 14.310 13.909 13.942 15.05 3.12 -38.99 3.12 1.24 noZ DH045 GCS9 Cl* Melotte 22 DH 045 +03 36 38.40 +23 08 44.2 0 14.764 14.165 13.620 13.288 13.320 25.49 3.11 -39.56 3.11 0.46 noZ DH060 GCS9 Cl* Melotte 22 DH 060 +03 42 01.68 +23 58 22.4 0 19.951 18.621 17.888 17.153 5.67 5.52 3.25 5.52 6.41 noZ int-pl-IZ-14;IPLJ0342016+235823_N GCS9 Cl* Melotte 22 IPL 14 +03 42 14.28 +22 43 02.5 0 20.062 18.517 18.045 17.495 17.568 -21.44 5.77 0.43 5.77 2.76 noZ L07_faintYJ_1 GCS9 +03 42 59.31 +25 37 38.9 0 19.814 18.238 17.388 16.646 16.572 10.48 3.76 -35.70 3.76 6.35 noZ L07_faintYJ_2 GCS9 +03 43 21.40 +24 34 42.0 0 20.092 18.782 18.309 17.574 -29.69 9.19 -11.53 9.19 0.28 noZ Roque24 GCS9 Cl* Melotte 22 Roque 24 +03 43 25.89 +24 00 51.8 0 20.716 19.423 18.592 18.203 -16.04 8.54 2.59 8.54 1.10 noZ int-pl-IZ-23;IPLJ0343258+240051_N GCS9 Cl* Melotte 22 IPL 23 +03 43 26.65 +23 41 38.8 0 20.084 19.258 18.586 17.889 0.50 8.68 -12.21 8.68 1.87 noZ int-pl-IZ-4;IPLJ0343266+234139_N GCS9 Cl* Melotte 22 IPL 4 +03 43 49.93 +24 03 37.6 0 20.056 18.915 18.060 17.609 10.66 6.29 -12.02 6.29 7.85 noZ int-pl-IZ-17;IPLJ0343498+240337_N GCS9 Cl* Melotte 22 IPL 17 +03 44 00.27 +24 33 25.1 0 12.731 11.579 13.069 11.261 -135.16 3.07 159.00 3.07 1.29 noZ HII232_HD23194_Tr074 GCS9 HII232 +03 44 30.52 +24 21 17.4 0 19.589 18.397 17.705 17.310 17.563 9.90 4.99 2.04 4.99 5.70 noZ L07_faintYJ_3 GCS9 +03 44 31.28 +25 35 14.7 0 19.426 18.269 17.398 16.675 16.604 10.61 3.73 -39.40 3.73 1.93 noZ BRB22_CFHT-PLIZ2141_PLZJ61_L07_faintYJ_4 GCS9 Cl* Melotte 22 BRB 22 +03 44 47.33 +24 21 35.7 0 19.688 18.409 17.506 16.906 16.979 35.23 3.66 -31.91 3.66 3.07 noZ L07_faintYJ_5 GCS9 +03 44 49.73 +22 51 10.6 0 19.950 19.044 18.679 18.196 57.62 9.24 -32.33 9.24 2.37 noZ int-pl-IZ-51;IPLJ0344496+225111_N GCS9 Cl* Melotte 22 IPL 51 +03 44 51.70 +23 02 30.4 0 19.970 19.021 18.836 18.867 -3.01 8.34 -2.50 8.34 2.83 noZ int-pl-IZ-61;IPLJ0344516+230230_N GCS9 Cl* Melotte 22 IPL 61 +03 45 01.81 +24 04 17.7 0 20.559 19.037 18.830 18.050 18.035 34.71 7.32 -15.30 7.32 2.57 noZ L07_faintYJ_6 GCS9 +03 45 04.22 +26 40 44.6 0 14.156 13.564 13.007 12.685 12.707 21.95 3.00 -39.21 3.00 0.12 noZ HHJ185_DH371_Moraux2003_56 GCS9 Cl* Melotte 22 HHJ 185 +03 45 14.52 +22 29 28.6 0 19.756 18.383 17.410 16.493 14.00 4.20 -53.10 4.20 1.66 noZ int-pl-IZ-69;IPLJ0345144+222929_Y GCS9 Cl* Melotte 22 IPL 69 +03 45 20.05 +22 33 25.5 0 19.320 18.432 17.776 17.052 35.01 5.33 -16.01 5.33 2.52 noZ int-pl-IZ-75;IPLJ0345200+223325_N GCS9 Cl* Melotte 22 IPL 75 +03 45 27.73 +23 10 13.1 0 13.121 12.596 11.997 11.686 11.703 10.78 2.28 -31.45 2.28 3.05 noZ SK520 GCS9 Cl* Melotte 22 SK 520 +03 45 33.30 +23 34 34.3 0 19.697 18.123 17.189 16.502 16.479 20.21 2.91 -35.76 2.91 1.70 noZ L07_faintYJ_7 GCS9 +03 45 33.99 +23 11 06.5 0 14.420 13.828 13.290 12.974 12.977 22.86 2.28 -40.74 2.28 3.12 noZ DH396 GCS9 Cl* Melotte 22 DH 396 +03 45 35.06 +21 56 22.2 0 19.470 18.169 17.595 17.136 17.002 -7.69 5.05 -13.84 5.05 1.35 noZ L07_faintYJ_8 GCS9 +03 45 42.26 +22 48 07.5 0 16.198 15.598 15.013 14.722 1.77 2.31 -16.21 2.31 5.74 noZ int-pl-IZ-50;2MASSJ0345422+224807_N GCS9 Cl* Melotte 22 IPL 50 +03 45 55.17 +22 51 31.0 0 16.109 15.358 14.804 14.433 14.420 14.67 2.30 -48.69 2.30 5.52 noZ int-pl-IZ-42_2MASSJ0345551+225131_Y_L07_243 GCS9 Cl* Melotte 22 IPL 42 +03 46 08.43 +22 37 57.3 0 20.002 19.462 18.771 17.791 22.24 9.40 21.27 9.40 3.53 noZ int-pl-IZ-73;IPLJ0346083+223757_N GCS9 Cl* Melotte 22 IPL 73 +03 46 09.59 +22 42 37.2 0 13.286 12.709 12.202 11.874 11.888 14.57 2.28 -37.86 2.28 3.40 noZ HCG217_SK487_HHJ297_BPL91_DH424 GCS9 Cl* Melotte 22 HCG 217 +03 46 13.85 +22 42 19.6 0 13.878 13.286 12.749 12.429 12.464 15.81 2.28 -42.30 2.28 3.16 noZ HHJ200_BPL92_DH431 GCS9 Cl* Melotte 22 HHJ 200 +03 46 27.94 +23 42 39.0 0 19.824 18.591 17.757 17.478 17.275 -7.35 4.50 -5.31 4.50 4.50 noZ L07_faintYJ_9 GCS9 +03 46 29.11 +22 59 47.6 0 19.089 17.740 16.805 15.955 15.935 14.85 2.74 -37.94 2.74 1.38 noZ L07_faintYJ_10 GCS9 +03 46 43.13 +22 35 19.9 0 19.439 18.710 18.058 17.757 4.51 7.62 -19.89 7.62 4.95 noZ int-pl-IZ-40;IPLJ0346430+223520_N GCS9 Cl* Melotte 22 IPL 40 +03 46 51.06 +22 34 28.6 0 19.887 18.580 18.106 17.309 17.424 10.21 5.90 -91.85 5.90 0.99 noZ L07_faintYJ_11 GCS9 +03 47 23.86 +23 08 56.9 0 13.440 12.896 12.358 12.060 12.050 16.54 2.28 -42.81 2.28 0.72 noZ HCG270_SK427_HHJ275_DH511 GCS9 Cl* Melotte 22 HCG 270 +03 47 23.97 +22 42 37.3 0 16.123 15.354 14.809 14.402 14.361 -1.91 2.31 -9.05 2.31 13.33 noZ BPL142_int-pl-IZ-37;RPLJ0347239+22423_Roque17_Y GCS9 Cl* Melotte 22 BPL 142 +03 47 25.58 +22 41 05.7 0 16.097 15.475 14.892 14.567 50.44 2.31 -53.06 2.31 7.71 noZ int-pl-IZ-36;2MASSJ0347255+224105_N GCS9 Cl* Melotte 22 IPL 36 +03 47 34.79 +22 48 04.5 0 13.538 13.004 12.450 12.140 12.143 14.52 2.28 -42.93 2.28 3.56 noZ HCG284_HHJ278_BPL148_DH524 GCS9 Cl* Melotte 22 HCG 284 +03 47 38.47 +23 56 27.7 0 19.964 18.359 17.478 16.688 16.588 13.93 3.28 -42.66 3.28 0.74 noZ L07_faintYJ_12 GCS9 +03 47 41.41 +22 44 32.9 0 16.111 15.521 14.865 14.511 14.527 13.41 2.31 0.90 2.31 15.45 noZ int-pl-IZ-47_RPLJ0347413+224433_Roque48_N GCS9 Cl* Melotte 22 IPL 47 +03 47 43.67 +26 48 12.7 0 14.029 13.455 12.910 12.602 12.617 20.73 2.99 -29.14 2.99 0.15 noZ HHJ193 GCS9 Cl* Melotte 22 HHJ 193 +03 47 44.16 +24 57 24.0 0 20.205 18.716 17.889 17.262 17.066 10.23 4.17 -30.52 4.17 2.74 noZ L07_faintYJ_13 GCS9 +03 47 46.52 +24 55 46.5 0 19.536 18.271 17.418 16.608 16.543 20.20 3.10 -45.92 3.10 3.57 noZ L07_faintYJ_14 GCS9 +03 47 54.17 +22 39 25.5 0 14.311 13.757 13.214 12.899 12.921 18.12 2.28 -43.32 2.28 6.76 noZ HHJ145_BPL161 GCS9 Cl* Melotte 22 HHJ 145 +03 48 04.95 +22 51 29.4 0 19.175 18.460 17.613 17.200 9.01 5.49 -14.56 5.49 4.62 noZ int-pl-IZ-46;IPLJ0348048+225129_N GCS9 Cl* Melotte 22 IPL 46 +03 48 05.47 +21 57 31.2 0 20.160 18.769 17.961 17.316 17.407 2.75 6.05 -28.88 6.05 3.01 noZ L07_faintYJ_15 GCS9 +03 48 15.65 +25 50 08.9 0 19.868 18.490 17.658 16.734 16.758 10.53 4.42 -48.92 4.42 1.86 noZ L07_faintYJ_16 GCS9 +03 48 19.48 +23 56 24.6 0 20.187 19.592 18.702 18.018 18.761 -0.62 6.47 -7.62 6.47 0.70 noZ Festin98_011 GCS9 Cl* Melotte 22 NPL 011 +03 48 39.57 +23 56 11.7 0 20.722 19.221 18.750 18.179 18.196 3.85 6.28 -27.06 6.28 1.63 noZ L07_faintYJ_17 GCS9 +03 48 45.69 +25 42 01.5 0 13.138 12.569 11.988 11.686 11.709 18.97 2.27 -44.69 2.27 0.64 noZ HHJ333_DH591 GCS9 Cl* Melotte 22 HHJ 333 +03 48 49.37 +22 45 50.0 0 19.901 19.049 18.457 18.022 18.177 2.45 9.50 2.39 9.50 2.37 noZ Roque26 GCS9 Cl* Melotte 22 Roque 26 +03 49 12.65 +25 42 07.2 0 13.114 12.530 12.019 11.673 11.710 12.39 2.27 -44.95 2.27 17.46 noZ HCG365_SK325_HHJ331_DH608 GCS9 Cl* Melotte 22 HCG 365 +03 50 15.96 +24 23 28.6 0 20.125 18.728 17.791 16.895 16.836 19.25 3.46 -31.64 3.46 1.54 noZ L07_faintYJ_18 GCS9 +03 50 39.55 +25 02 54.6 0 19.786 18.225 17.358 16.563 16.529 19.29 3.10 -44.42 3.10 2.83 noZ BRB23_L3.5_L07_faintYJ_19 GCS9 Cl* Melotte 22 BRB 23 +03 50 41.21 +25 39 30.4 0 14.791 14.167 13.614 13.272 13.262 17.33 2.28 -46.49 2.28 6.53 noZ DH682 GCS9 Cl* Melotte 22 DH 682 +03 50 56.62 +25 35 06.3 0 12.970 12.392 11.893 11.585 11.581 14.82 2.27 -42.39 2.27 7.19 noZ HCG412_SK253_DH688 GCS9 Cl* Melotte 22 HCG 412 +03 51 29.47 +24 00 37.4 0 19.701 18.424 17.490 16.696 16.697 12.50 2.96 -40.44 2.96 3.20 noZ L07_faintYJ_20 GCS9 +03 51 41.62 +25 55 45.4 0 20.584 19.121 18.467 18.048 18.073 -19.45 8.36 -21.24 8.36 0.66 noZ L07_faintYJ_21 GCS9 +03 52 05.33 +25 37 34.0 0 18.965 17.690 17.946 17.510 17.886 153.64 8.22 -46.42 8.22 14.51 noZ L07_faintYJ_22 GCS9 +03 52 27.19 +23 12 08.0 0 19.317 18.006 17.090 16.359 16.438 23.09 3.70 -40.27 3.70 0.70 noZ L07_faintYJ_23 GCS9 +03 52 34.75 +22 56 04.5 0 19.602 18.412 17.568 17.084 17.019 34.50 4.45 -2.97 4.45 0.86 noZ L07_faintYJ_24 GCS9 +03 52 39.15 +24 46 29.4 0 19.271 18.066 17.106 16.509 16.474 18.41 3.31 -44.34 3.31 1.97 noZ BRB20_L1.0_CFHT-PLIZ-35_L07_faintYJ_25 GCS9 Cl* Melotte 22 BRB 20 +03 52 59.62 +24 42 35.6 0 20.657 19.082 18.693 18.234 18.298 1.65 9.10 16.05 9.10 3.50 noZ L07_faintYJ_26 GCS9 +03 52 59.70 +23 51 55.7 0 19.756 18.587 18.105 17.405 17.261 2.86 5.19 4.78 5.19 1.78 noZ BRB25_None GCS9 Cl* Melotte 22 BRB 25 +03 53 18.93 +23 12 39.1 0 20.060 18.328 17.612 16.887 16.883 -24.95 4.25 2.99 4.25 3.50 noZ L07_faintYJ_27 GCS9 +03 53 19.24 +24 53 30.2 0 18.319 17.810 17.093 16.905 16.967 5.95 3.90 1.25 3.90 2.08 noZ PLZJ112_None GCS9 Cl* Melotte 22 PlZJ 112 +03 53 24.50 +25 02 07.0 0 13.975 13.398 12.853 12.542 12.550 14.26 2.28 -40.35 2.28 34.86 noZ HCG471_HHJ170_BPL286_DH785 GCS9 Cl* Melotte 22 HCG 471 +03 53 40.96 +24 25 09.6 0 12.946 12.445 11.889 11.573 11.626 17.52 2.28 -38.69 2.28 14.78 noZ HCG473_SK122_HHJ372_BPL288 GCS9 Cl* Melotte 22 HCG 473 +03 53 58.23 +24 25 08.8 0 16.052 15.397 14.825 14.435 14.461 14.64 2.31 -34.72 2.31 1.63 noZ BPL295 GCS9 Cl* Melotte 22 BPL 295 +03 54 00.38 +24 34 49.8 0 15.467 14.807 14.279 13.873 13.921 17.48 2.29 -38.45 2.29 1.22 noZ BPL297_Moraux2003_100 GCS9 Cl* Melotte 22 BPL 297 +03 54 10.28 +23 41 40.0 0 19.260 18.143 17.171 16.393 16.395 13.79 3.22 -50.49 3.22 4.20 noZ PLZJ4_BRB21_L3.0_L07_faintYJ_28 GCS9 Cl* Melotte 22 PlZJ 4 +03 54 30.49 +25 11 21.8 0 19.979 18.677 18.127 17.536 17.561 -8.62 5.35 -35.68 5.35 0.30 noZ L07_faintYJ_29 GCS9 +03 55 49.29 +24 53 47.2 0 16.057 15.302 14.707 14.299 14.299 -23.95 2.30 -51.07 2.30 9.97 noZ BPL331 GCS9 Cl* Melotte 22 BPL 331 +03 55 58.17 +24 32 59.6 0 13.269 12.731 12.207 11.901 11.914 12.73 2.28 -40.90 2.28 6.04 noZ DH2004_824 GCS9 Cl* Melotte 22 DH 824 +03 55 58.84 +24 57 39.9 0 13.575 12.975 12.458 12.124 12.142 14.04 2.28 -42.01 2.28 6.81 noZ HHJ260_BPL333_DH825 GCS9 Cl* Melotte 22 HHJ 260 +03 56 11.39 +25 03 36.5 0 15.908 15.193 14.655 14.257 14.290 16.67 2.30 -41.79 2.30 70.90 noZ BPL334_L07_A1_114_L07_215 GCS9 Cl* Melotte 22 BPL 334 +03 41 52.33 +24 15 12.7 0 20.667 19.205 18.358 18.067 0.214 -19.58 9.32 -9.18 9.32 0.78 noY int-pl-IZ-87;IPLJ0341523+241512_N GCS9 Cl* Melotte 22 IPL 87 +03 46 28.19 +22 48 57.7 0 15.194 14.082 13.518 13.192 13.168 45.73 2.28 -58.16 2.28 2.41 noY HHJ87_BPL104 GCS9 Cl* Melotte 22 HHJ 87 +03 46 39.31 +22 47 48.2 0 17.213 16.235 15.626 15.376 15.346 -14.43 2.42 -20.96 2.42 3.51 noY HHJ1 GCS9 Cl* Melotte 22 HHJ 1 +03 46 42.37 +22 41 01.5 0 16.964 15.709 15.192 14.813 0.012 50.06 2.35 -12.61 2.35 5.12 noY int-pl-IZ-41;2MASSJ0346423+224101_N GCS9 Cl* Melotte 22 IPL 41 +03 46 59.02 +22 41 40.3 0 20.165 18.548 18.140 17.540 0.150 48.91 7.04 -26.50 7.04 1.44 noY int-pl-IZ-39;IPLJ0346589+224140_N GCS9 Cl* Melotte 22 IPL 39 +03 51 26.60 +22 48 45.9 0 18.883 16.679 15.938 15.325 15.339 26.84 2.54 -77.34 2.54 6.64 noY L07_A1_96 GCS9 XTENSION= 'TABLE ' / Ascii Table Extension (TAB and NEWLINE sep) BITPIX = 8 / Character data NAXIS = 2 / Simple 2-D matrix NAXIS1 = 138 / Number of bytes per record NAXIS2 = 1314 / Number of records PCOUNT = 0 / Get rid of random parameters GCOUNT = 1 / Only one group (isn't it obvious?) TFIELDS = 17 / Number of data fields (columns) EXTNAME = 'J_MNRAS_422_1495_tablec1' / Identification of the table CDS-NAME= 'J/MNRAS/422/1495/tablec1' / Table name in METAtab Coordinates, near-infrared (ZYJHK1K2) photometry and proper motions all new Pleiades member candidates identified in the UKIDSS GCS DR9 with the probabilistic and standard selection methods TBCOL1 = 2 / UCD=pos.eq.ra;meta.main char:12 offset=1 TFORM1 = 'A12 ' / Fortran Format TTYPE1 = 'RAJ2000 ' / Right ascension (J2000) TBCOL2 = 15 / UCD=pos.eq.dec;meta.main char:12 offset=14 TFORM2 = 'A12 ' / Fortran Format TTYPE2 = 'DEJ2000 ' / Declination (J2000) TBCOL3 = 28 / UCD=meta.number short: ....... offset=27 TFORM3 = 'I3 ' / Fortran Format TTYPE3 = 'M ' / Multiplicity of this star (table D1) TNULL3 = -32768 / NULL definition TBCOL4 = 32 / UCD=phot.mag;em.opt.I float: . offset=31 TFORM4 = 'F6.3 ' / Fortran Format TTYPE4 = 'Zmag ' / ? UKIDSS Z magnitude TUNIT4 = 'mag ' / magnitude TBCOL5 = 39 / UCD=phot.mag;em.IR.J float: .. offset=38 TFORM5 = 'F6.3 ' / Fortran Format TTYPE5 = 'Ymag ' / ? UKIDSS Y magnitude TUNIT5 = 'mag ' / magnitude TBCOL6 = 46 / UCD=phot.mag;em.IR.J float: .. offset=45 TFORM6 = 'F6.3 ' / Fortran Format TTYPE6 = 'Jmag ' / UKIDSS J magnitude TUNIT6 = 'mag ' / magnitude TBCOL7 = 53 / UCD=phot.mag;em.IR.H float: .. offset=52 TFORM7 = 'F6.3 ' / Fortran Format TTYPE7 = 'Hmag ' / UKIDSS H magnitude TUNIT7 = 'mag ' / magnitude TBCOL8 = 60 / UCD=phot.mag;em.IR.K float: .. offset=59 TFORM8 = 'F6.3 ' / Fortran Format TTYPE8 = 'K1mag ' / UKIDSS K magnitude, first epoch TUNIT8 = 'mag ' / magnitude TBCOL9 = 67 / UCD=phot.mag;em.IR.K float: .. offset=66 TFORM9 = 'F6.3 ' / Fortran Format TTYPE9 = 'K2mag ' / ? UKIDSS K magnitude, second epoch TUNIT9 = 'mag ' / magnitude TBCOL10 = 74 / UCD=pos.pm;pos.eq.ra float: .. offset=73 TFORM10 = 'F6.2 ' / Fortran Format TTYPE10 = 'pmRA ' / ? Proper motion along RA, pmRA*cosDE TUNIT10 = 'mas/yr ' / milli-second of arc per year TBCOL11 = 81 / UCD=stat.error;pos.pm;pos.eq.ra fl offset=80 TFORM11 = 'F5.2 ' / Fortran Format TTYPE11 = 'e_pmRA ' / ? rms uncertainty on pmRA*cosDE TUNIT11 = 'mas/yr ' / milli-second of arc per year TBCOL12 = 87 / UCD=pos.pm;pos.eq.dec float: . offset=86 TFORM12 = 'F6.2 ' / Fortran Format TTYPE12 = 'pmDE ' / ? Proper motion along DE TUNIT12 = 'mas/yr ' / milli-second of arc per year TBCOL13 = 94 / UCD=stat.error;pos.pm;pos.eq.dec f offset=93 TFORM13 = 'F5.2 ' / Fortran Format TTYPE13 = 'e_pmDE ' / ? rms uncertainty on pmDE TUNIT13 = 'mas/yr ' / milli-second of arc per year TBCOL14 = 100 / UCD=stat.probability float: .. offset=99 TFORM14 = 'F5.2 ' / Fortran Format TTYPE14 = 'Mmb ' / [0/1]? Membership probability TBCOL15 = 106 / UCD=meta.note short: ......... offset=105 TFORM15 = 'I2 ' / Fortran Format TTYPE15 = 'n_Mmb ' / [1/12] Method used for identification (1) (link) TBCOL16 = 109 / UCD=meta.ref.url char:4 ...... offset=108 TFORM16 = 'A4 ' / Fortran Format TTYPE16 = 'GCS9 ' / Display the UKIDSS GCS-DR9 data, Cat. II/319 (link) TBCOL17 = 114 / UCD=meta.id char:24* ......... offset=113 TFORM17 = 'A24 ' / Fortran Format TTYPE17 = 'SimbadName' / Simbad column added by the CDS END +03 36 01.950 +27 11 04.70 1 16.248 15.492 14.774 14.234 13.810 13.782 15.79 3.79 -42.70 3.79 0.72 12 GCS9 UGCS J033601.95+271104.7 +03 51 27.880 +27 58 55.70 0 15.548 14.979 14.360 13.821 13.468 13.470 22.99 2.95 -41.88 2.95 0.74 12 GCS9 UGCS J035127.88+275855.7 +03 45 06.250 +28 42 19.10 0 14.628 14.156 13.575 13.049 12.714 12.725 22.01 2.95 -39.53 2.95 0.85 12 GCS9 Cl* Melotte 22 DH 374 +03 40 06.210 +28 08 32.00 0 15.387 14.854 14.239 13.731 13.422 13.401 13.90 4.95 -38.21 4.95 0.62 12 GCS9 Cl* Melotte 22 DH 125 +03 50 54.420 +27 26 13.00 0 15.915 15.313 14.686 14.132 13.756 13.726 19.89 2.89 -41.91 2.89 0.88 12 GCS9 UGCS J035054.41+272612.9 +03 51 30.600 +27 22 49.50 0 14.946 14.405 13.807 13.265 12.919 12.927 22.57 2.88 -41.78 2.88 0.89 12 GCS9 Cl* Melotte 22 DH 714 +03 41 33.580 +27 09 50.10 0 15.162 14.567 13.912 13.377 13.020 13.045 19.34 3.29 -43.65 3.29 0.89 12 GCS9 Cl* Melotte 22 DH 178 +03 48 57.510 +27 38 25.60 0 15.618 15.106 14.510 13.963 13.618 13.613 16.32 2.89 -40.24 2.89 0.86 12 GCS9 UGCS J034857.50+273825.6 +03 41 38.790 +27 41 07.10 0 12.878 12.514 12.023 11.588 11.219 11.285 24.87 3.28 -39.81 3.28 0.65 12 GCS9 UGCS J034138.79+274107.0 +03 45 40.770 +28 32 05.80 0 15.158 14.639 14.012 13.516 13.166 13.169 22.95 2.96 -42.47 2.96 0.75 12 GCS9 Cl* Melotte 22 DH 399 +03 46 23.740 +26 34 23.00 0 14.730 14.243 13.647 13.132 12.839 12.799 24.18 2.93 -45.83 2.93 0.73 12 GCS9 Cl* Melotte 22 DH 444 +03 47 56.660 +26 31 50.90 0 12.905 12.555 12.064 11.504 11.210 11.250 23.91 2.93 -40.70 2.93 0.76 12 GCS9 Cl* Melotte 22 DH 543 +03 47 28.410 +26 32 05.50 0 14.243 13.779 13.199 12.652 12.361 12.363 20.47 2.93 -43.56 2.93 0.93 12 GCS9 Cl* Melotte 22 DH 516 +03 50 39.700 +26 34 20.10 0 14.506 14.061 13.500 12.959 12.652 12.675 20.26 2.95 -42.79 2.95 0.94 12 GCS9 UGCS J035039.70+263420.0 +03 50 39.700 +26 34 17.70 0 14.817 14.334 13.753 13.228 12.895 12.916 19.47 2.95 -42.84 2.95 0.94 12 GCS9 UGCS J035039.69+263417.7 +03 41 56.490 +27 02 57.90 0 14.816 14.343 13.746 13.209 12.923 12.902 18.25 3.29 -47.59 3.29 0.86 12 GCS9 Cl* Melotte 22 DH 195 +03 41 53.050 +27 04 42.90 0 14.776 14.268 13.678 13.174 12.842 12.833 15.90 3.29 -42.59 3.29 0.92 12 GCS9 Cl* Melotte 22 DH 192 +04 05 52.090 +27 09 13.10 0 15.196 14.742 14.147 13.596 13.266 13.266 22.95 2.96 -45.92 2.96 0.66 12 GCS9 UGCS J040552.08+270913.1 +03 43 44.090 +25 39 49.50 0 15.071 14.589 13.986 13.428 13.120 13.122 17.56 2.23 -44.02 2.23 0.90 12 GCS9 Cl* Melotte 22 MBSC 72 +03 44 10.760 +25 37 38.20 0 14.362 13.886 13.296 12.733 12.437 12.445 11.81 2.23 -44.39 2.23 0.62 12 GCS9 Cl* Melotte 22 DH 316 +03 43 34.140 +25 35 25.80 0 13.777 13.360 12.813 12.234 11.958 11.947 16.72 2.23 -43.78 2.23 0.93 12 GCS9 V* V622 Tau +03 44 25.080 +25 34 03.90 0 15.079 14.489 13.830 13.302 12.949 12.973 19.98 2.23 -41.60 2.23 0.88 12 GCS9 Cl* Melotte 22 HHJ 100 +03 43 52.790 +25 29 30.30 0 14.450 13.990 13.414 12.853 12.576 12.580 22.91 2.23 -45.90 2.23 0.83 12 GCS9 Cl* Melotte 22 DH 298 +03 43 53.880 +25 28 30.00 0 13.156 12.789 12.268 11.797 11.424 11.455 23.08 2.23 -46.36 2.23 0.76 12 GCS9 V* MQ Tau +03 43 37.320 +25 24 32.00 0 13.423 13.034 12.513 11.985 11.664 11.701 12.47 2.23 -45.17 2.23 0.69 12 GCS9 V* LY Tau +03 44 33.490 +26 14 53.10 0 13.610 13.172 12.622 12.070 11.769 11.768 18.22 2.94 -41.78 2.94 0.93 12 GCS9 Cl* Melotte 22 DH 347 +03 54 46.530 +25 31 34.90 0 14.741 14.146 13.512 12.988 12.618 12.627 21.22 2.94 -45.56 2.94 0.90 12 GCS9 Cl* Melotte 22 DH 808 +03 43 07.580 +25 34 29.00 0 14.063 13.611 13.036 12.459 12.172 12.204 19.71 2.23 -40.51 2.23 0.92 12 GCS9 V* V845 Tau +03 42 43.810 +25 32 06.20 0 13.613 13.213 12.673 12.087 11.829 11.818 18.81 2.23 -44.63 2.23 0.93 12 GCS9 Cl* Melotte 22 HHJ 341 +03 56 46.600 +26 21 00.40 0 15.182 14.574 13.929 13.398 13.046 13.035 17.21 2.62 -47.43 2.62 0.77 12 GCS9 Cl* Melotte 22 DH 843 +03 43 11.770 +25 31 31.90 0 16.035 15.427 14.767 14.237 13.883 13.872 18.38 2.25 -44.54 2.25 0.72 12 GCS9 UGCS J034311.76+253131.9 +03 47 25.900 +25 26 26.40 0 14.461 13.904 13.274 12.733 12.387 12.372 15.47 2.23 -44.18 2.23 0.91 12 GCS9 Cl* Melotte 22 MBSC 49 +03 47 37.350 +25 20 02.30 0 14.391 13.912 13.308 12.736 12.427 12.407 16.90 2.23 -44.53 2.23 0.93 12 GCS9 Cl* Melotte 22 MBSC 43 +03 36 42.670 +28 21 05.90 0 15.297 14.744 14.124 13.558 13.240 13.241 21.45 4.94 -39.70 4.94 0.78 12 GCS9 Cl* Melotte 22 DH 62 +03 45 58.800 +26 20 01.70 0 15.269 14.666 14.045 13.498 13.144 13.138 18.44 2.95 -38.73 2.95 0.81 12 GCS9 Cl* Melotte 22 DH 413 +03 38 43.300 +25 22 26.90 0 13.103 12.661 12.090 11.591 11.286 11.293 23.70 2.95 -41.82 2.95 0.79 12 GCS9 V* V783 Tau +03 41 11.540 +26 16 18.80 0 15.386 14.841 14.239 13.677 13.324 13.349 21.71 3.00 -37.68 3.00 0.60 12 GCS9 UGCS J034111.53+261618.8 +03 51 13.850 +25 23 11.30 0 13.741 13.323 12.796 12.246 11.944 12.001 16.21 2.23 -43.01 2.23 0.93 12 GCS9 V* V801 Tau +03 35 39.800 +25 38 45.20 0 14.957 14.508 13.916 13.319 13.037 15.42 5.32 -37.37 5.32 0.68 12 GCS9 Cl* Melotte 22 DH 46 +04 03 25.000 +28 32 23.60 0 14.208 13.836 13.311 12.724 12.453 12.501 24.32 2.96 -39.19 2.96 0.66 12 GCS9 UGCS J040325.00+283223.6 +03 55 56.430 +25 17 59.80 0 13.549 13.137 12.580 11.969 11.690 11.703 19.79 2.93 -43.08 2.93 0.94 12 GCS9 V* V690 Tau +03 50 08.420 +25 32 55.70 0 14.171 13.715 13.151 12.599 12.306 12.338 16.57 2.23 -43.79 2.23 0.93 12 GCS9 V* V671 Tau +03 49 47.040 +25 42 36.80 0 14.023 13.513 12.863 12.308 11.966 11.992 18.14 2.23 -42.94 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 638 +03 54 11.490 +25 18 42.70 0 14.598 14.131 13.552 13.030 12.710 12.697 22.79 2.94 -43.58 2.94 0.88 12 GCS9 V* V887 Tau +03 40 23.060 +25 29 47.60 0 13.430 13.000 12.472 11.938 11.623 11.665 18.22 2.95 -41.47 2.95 0.93 12 GCS9 V* KO Tau +03 41 59.750 +26 27 39.60 0 14.578 14.100 13.496 12.962 12.621 12.641 25.40 3.00 -41.25 3.00 0.64 12 GCS9 Cl* Melotte 22 DH 200 +03 37 24.150 +25 43 20.10 0 12.730 12.420 11.937 11.360 11.085 11.153 22.40 2.95 -44.05 2.95 0.78 12 GCS9 UGCS J033724.15+254320.1 +03 46 45.780 +25 27 30.20 0 13.675 13.271 12.734 12.160 11.896 11.911 15.18 2.23 -45.47 2.23 0.88 12 GCS9 V* V532 Tau +03 34 47.540 +26 22 13.40 0 14.564 14.112 13.529 12.977 12.672 12.674 24.94 3.30 -39.43 3.30 0.60 12 GCS9 Cl* Melotte 22 DH 39 +03 47 04.750 +25 22 50.00 0 13.074 12.642 12.061 11.601 11.223 11.305 21.56 2.23 -40.41 2.23 0.87 12 GCS9 V* V452 Tau +03 39 15.560 +26 48 03.60 0 15.315 14.815 14.230 13.688 13.348 13.372 21.02 3.00 -38.68 3.00 0.74 12 GCS9 Cl* Melotte 22 DH 102 +03 47 43.880 +26 13 26.80 0 14.714 14.225 13.655 13.113 12.807 12.804 23.14 2.93 -41.65 2.93 0.86 12 GCS9 Cl* Melotte 22 DH 534 +03 47 30.590 +26 16 44.50 0 13.801 13.394 12.848 12.305 12.027 12.001 19.30 2.93 -44.23 2.93 0.93 12 GCS9 V* V456 Tau +03 47 21.940 +26 22 47.20 0 14.456 14.014 13.425 12.835 12.542 12.547 14.19 2.93 -47.46 2.93 0.71 12 GCS9 Cl* Melotte 22 DH 508 +03 40 11.040 +25 23 26.60 0 14.790 14.309 13.733 13.227 12.896 12.905 19.42 2.96 -38.70 2.96 0.87 12 GCS9 Cl* Melotte 22 DH 129 +03 40 14.910 +25 19 18.70 0 13.114 12.762 12.285 11.701 11.448 11.477 21.11 2.95 -40.08 2.95 0.87 12 GCS9 Cl* Melotte 22 DH 131 +03 44 04.910 +26 58 10.50 0 15.115 14.597 13.992 13.447 13.119 13.096 16.42 2.94 -41.32 2.94 0.88 12 GCS9 UGCS J034404.91+265810.5 +03 51 39.510 +26 52 55.60 0 15.480 14.951 14.332 13.794 13.454 13.448 20.14 2.96 -44.60 2.96 0.87 12 GCS9 UGCS J035139.51+265255.5 +03 51 55.240 +26 57 40.20 0 13.156 12.689 12.074 11.716 11.274 11.253 17.80 2.95 -40.13 2.95 0.91 12 GCS9 UGCS J035155.24+265740.1 +03 52 13.200 +26 11 45.30 0 13.003 12.662 12.154 11.606 11.285 11.326 20.95 2.95 -46.17 2.95 0.88 12 GCS9 Cl* Melotte 22 DH 745 +03 52 12.190 +26 22 08.90 0 13.194 12.777 12.227 11.737 11.397 11.430 14.22 2.95 -44.48 2.95 0.86 12 GCS9 V* V564 Tau +03 35 56.060 +25 21 59.30 0 13.217 12.868 12.366 11.780 11.558 18.66 5.28 -39.03 5.28 0.87 12 GCS9 Cl* Melotte 22 DH 50 +03 36 05.330 +25 21 03.40 0 14.386 13.934 13.321 12.796 12.470 19.93 5.29 -41.98 5.29 0.94 12 GCS9 Cl* Melotte 22 DH 51 +03 42 38.350 +25 28 44.30 0 15.625 15.076 14.453 13.924 13.588 13.556 21.08 2.24 -41.30 2.24 0.85 12 GCS9 UGCS J034238.35+252844.2 +03 50 14.760 +25 25 29.80 0 14.761 14.292 13.705 13.158 12.819 12.827 16.00 2.23 -45.17 2.23 0.91 12 GCS9 Cl* Melotte 22 DH 662 +03 50 01.570 +25 24 01.50 0 13.034 12.688 12.188 11.755 11.405 11.453 14.76 2.23 -46.39 2.23 0.83 12 GCS9 Cl* Melotte 22 DH 646 +03 49 20.310 +25 25 42.40 0 14.155 13.745 13.198 12.642 12.335 12.324 12.27 2.23 -40.97 2.23 0.66 12 GCS9 V* V798 Tau +03 46 47.190 +25 20 53.10 0 14.444 13.909 13.306 12.771 12.437 12.445 23.84 2.23 -47.30 2.23 0.66 12 GCS9 Cl* Melotte 22 DH 469 +03 41 04.150 +25 30 25.60 0 15.724 15.167 14.547 14.003 13.636 13.636 12.89 2.24 -40.93 2.24 0.71 12 GCS9 UGCS J034104.14+253025.6 +03 42 08.290 +25 36 59.90 0 14.094 13.600 13.009 12.441 12.120 12.153 16.61 2.23 -37.66 2.23 0.77 12 GCS9 Cl* Melotte 22 DH 211 +03 42 03.420 +25 22 39.10 0 14.406 13.913 13.303 12.729 12.430 12.430 22.60 2.23 -37.61 2.23 0.68 12 GCS9 Cl* Melotte 22 DH 206 +03 41 30.350 +25 17 05.90 0 16.646 15.972 15.208 14.682 14.349 14.526 13.78 2.27 -42.02 2.27 0.60 12 GCS9 UGCS J034130.35+251705.9 +03 52 41.820 +26 46 10.50 0 14.924 14.450 13.835 13.290 12.967 12.980 15.22 2.96 -48.00 2.96 0.74 12 GCS9 Cl* Melotte 22 DH 761 +03 45 09.040 +25 32 49.00 0 14.661 14.176 13.591 13.021 12.723 12.734 18.67 2.23 -43.15 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 377 +04 06 54.480 +26 20 07.50 0 12.888 12.543 11.998 11.384 11.046 11.113 14.03 2.96 -40.84 2.96 0.70 12 GCS9 UGCS J040654.47+262007.5 +03 39 22.420 +26 11 36.00 0 14.521 14.093 13.516 12.964 12.650 12.682 18.92 3.00 -45.37 3.00 0.93 12 GCS9 Cl* Melotte 22 DH 104 +03 42 12.620 +26 49 44.90 0 14.299 13.821 13.209 12.670 12.342 12.345 19.56 3.00 -39.85 3.00 0.91 12 GCS9 Cl* Melotte 22 DH 215 +03 37 48.930 +26 51 45.10 0 14.380 13.919 13.351 12.818 12.520 12.525 21.09 3.30 -46.90 3.30 0.86 12 GCS9 Cl* Melotte 22 DH 79 +03 52 58.780 +25 26 19.00 0 15.258 14.748 14.176 13.639 13.295 13.303 17.32 2.94 -42.46 2.94 0.90 12 GCS9 Cl* Melotte 22 BPL 277 +03 52 54.250 +25 17 43.30 0 14.245 13.815 13.273 12.729 12.417 12.415 19.70 2.93 -46.66 2.93 0.89 12 GCS9 V* V477 Tau +03 49 48.160 +26 37 47.50 0 15.926 15.369 14.707 14.187 13.827 13.837 17.38 2.95 -44.63 2.95 0.89 12 GCS9 Cl* Melotte 22 MBSC 98 +03 38 27.830 +26 51 25.40 0 14.777 14.274 13.663 13.167 12.844 12.866 21.04 3.30 -38.12 3.30 0.81 12 GCS9 Cl* Melotte 22 DH 89 +03 37 15.610 +26 29 29.80 0 15.984 15.323 14.670 14.169 13.787 13.794 19.16 3.31 -39.61 3.31 0.85 12 GCS9 Cl* Melotte 22 STAR 15 +03 48 50.450 +25 17 54.70 0 16.516 15.802 15.106 14.575 14.177 14.174 18.72 2.25 -45.60 2.25 0.67 12 GCS9 Cl* Melotte 22 BPL 192 +03 52 07.960 +25 27 54.60 0 15.246 14.722 14.111 13.567 13.243 13.214 15.55 2.94 -37.68 2.94 0.67 12 GCS9 V* V390 Tau +03 37 11.980 +26 46 28.70 0 14.171 13.688 13.064 12.558 12.231 12.243 22.64 3.30 -37.41 3.30 0.65 12 GCS9 Cl* Melotte 22 DH 68 +03 46 44.790 +24 44 58.20 0 15.343 14.790 14.196 13.626 13.308 13.288 16.98 2.21 -43.55 2.21 0.90 12 GCS9 Cl* Melotte 22 BPL 109 +03 47 16.450 +24 44 50.10 0 14.575 13.990 13.385 12.836 12.484 12.492 19.48 2.21 -40.86 2.21 0.93 12 GCS9 Cl* Melotte 22 BPL 136 +03 47 38.050 +24 49 10.80 0 13.439 13.061 12.546 12.133 11.670 11.690 15.44 2.21 -43.50 2.21 0.91 12 GCS9 Cl* Melotte 22 BPL 151 +03 48 22.810 +24 48 53.40 0 13.811 13.364 12.850 12.329 12.012 12.039 16.29 2.21 -48.18 2.21 0.78 12 GCS9 Cl* Melotte 22 BPL 176 +03 47 34.170 +25 43 05.90 0 14.233 13.788 13.217 12.675 12.390 12.374 14.30 2.23 -44.19 2.23 0.86 12 GCS9 Cl* Melotte 22 DH 522 +03 47 33.060 +25 38 18.40 0 14.492 14.001 13.394 12.814 12.503 12.490 15.50 2.23 -44.70 2.23 0.90 12 GCS9 V* V791 Tau +03 47 28.430 +24 40 33.00 0 14.715 14.245 13.694 13.118 12.817 12.766 15.65 2.21 -46.11 2.21 0.87 12 GCS9 Cl* Melotte 22 DH 517 +03 40 05.980 +25 40 20.80 0 14.788 14.303 13.742 13.196 12.896 12.880 18.83 2.96 -39.12 2.96 0.89 12 GCS9 Cl* Melotte 22 DH 124 +03 46 15.110 +26 46 48.80 1 16.624 15.838 15.032 14.495 14.037 14.088 20.73 2.97 -42.79 2.97 0.68 12 GCS9 UGCS J034615.11+264648.8 +03 39 08.130 +24 46 14.40 0 13.036 12.618 12.087 11.550 11.270 11.264 15.90 2.48 -44.93 2.48 0.91 12 GCS9 V* KM Tau +03 49 38.240 +26 51 05.60 0 14.052 13.637 13.073 12.512 12.234 12.239 19.29 2.93 -43.50 2.93 0.94 12 GCS9 Cl* Melotte 22 MBSC 29 +03 36 10.560 +24 45 59.70 0 14.996 14.487 13.888 13.330 13.004 12.994 17.12 2.63 -43.88 2.63 0.94 12 GCS9 UGCS J033610.55+244559.7 +03 50 06.430 +26 58 18.70 0 14.792 14.338 13.747 13.210 12.884 12.905 21.70 2.94 -42.45 2.94 0.92 12 GCS9 Cl* Melotte 22 DH 652 +03 50 40.830 +24 40 02.60 0 15.326 14.799 14.224 13.664 13.348 13.340 14.95 2.21 -46.17 2.21 0.78 12 GCS9 Cl* Melotte 22 DH 681 +03 46 35.360 +23 57 07.40 0 16.879 16.089 15.355 14.800 14.416 14.373 17.57 2.24 -42.62 2.24 0.75 12 GCS9 Cl* Melotte 22 SHF 35 +03 46 55.780 +23 56 24.10 0 14.367 13.803 13.166 12.622 12.272 12.283 18.41 2.22 -39.68 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 480 +03 47 00.360 +23 52 48.20 0 15.230 14.607 13.981 13.439 13.093 13.073 14.89 2.22 -45.67 2.22 0.80 12 GCS9 UGCS J034700.35+235248.1 +03 46 22.200 +23 52 41.00 0 14.183 13.612 12.973 12.388 12.055 12.045 16.03 2.22 -41.36 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 441 +03 47 13.670 +23 49 53.20 0 12.791 12.364 11.867 11.575 11.044 11.809 19.47 2.22 -40.49 2.22 0.89 12 GCS9 V* V645 Tau +03 46 27.690 +23 48 45.50 0 14.944 14.345 13.639 13.015 12.617 12.609 18.50 2.22 -41.39 2.22 0.94 12 GCS9 Cl* Melotte 22 HHJ 161 +03 51 58.820 +24 40 04.30 0 13.675 13.254 12.728 12.172 11.871 11.897 14.65 2.20 -42.70 2.20 0.88 12 GCS9 UGCS J035158.82+244004.3 +03 51 59.320 +24 39 58.80 0 13.828 13.385 12.845 12.293 12.017 12.027 13.69 2.20 -41.98 2.20 0.83 12 GCS9 V* V387 Tau +03 44 10.960 +24 48 45.80 0 14.889 14.418 13.865 13.318 13.020 21.80 2.97 -46.03 2.97 0.87 12 GCS9 UGCS J034410.95+244845.7 +03 44 25.600 +24 40 52.60 0 13.443 13.016 12.496 11.928 11.633 13.39 2.97 -47.71 2.97 0.62 12 GCS9 V* V438 Tau +03 43 12.120 +24 44 45.10 0 13.538 13.088 12.533 12.023 11.684 21.38 2.97 -49.22 2.97 0.61 12 GCS9 V* V509 Tau +04 00 28.190 +23 51 24.00 0 13.939 13.453 12.831 12.277 12.003 13.71 3.54 -40.49 3.54 0.78 12 GCS9 Cl* Melotte 22 DH 892 +03 49 22.150 +25 47 37.70 0 14.407 13.954 13.393 12.801 12.496 12.504 19.36 2.23 -37.53 2.23 0.80 12 GCS9 Cl* Melotte 22 DH 618 +03 49 32.810 +25 47 46.80 0 14.438 13.998 13.465 12.891 12.567 12.569 17.13 2.23 -41.13 2.23 0.93 12 GCS9 Cl* Melotte 22 DH 628 +03 43 09.750 +24 41 32.70 0 13.167 12.734 12.234 11.768 11.425 23.06 2.97 -47.52 2.97 0.67 12 GCS9 V* LU Tau +03 43 13.070 +24 39 19.30 0 13.055 12.669 12.151 11.608 11.286 20.98 2.97 -41.82 2.97 0.91 12 GCS9 V* LV Tau +03 49 58.610 +25 53 46.20 0 15.011 14.544 13.948 13.385 13.048 13.044 17.26 2.23 -41.56 2.23 0.89 12 GCS9 UGCS J034958.61+255346.1 +03 41 54.210 +25 43 47.10 0 13.586 13.204 12.696 12.122 11.843 11.866 18.41 2.23 -46.69 2.23 0.89 12 GCS9 V* V608 Tau +03 42 10.990 +25 44 35.00 0 15.394 14.772 14.087 13.528 13.157 13.165 14.33 2.24 -42.76 2.24 0.83 12 GCS9 UGCS J034210.99+254435.0 +03 53 09.630 +23 33 47.70 0 16.108 15.503 14.823 14.280 13.916 19.95 2.32 -45.65 2.32 0.63 12 GCS9 2MASS J03530962+2333480 +03 44 51.510 +25 05 16.50 0 14.798 14.315 13.715 13.134 12.802 12.819 15.35 2.21 -42.48 2.21 0.91 12 GCS9 UGCS J034451.50+250516.4 +03 45 02.880 +25 05 19.60 0 14.789 14.276 13.751 13.216 12.845 12.858 14.43 2.21 -38.87 2.21 0.75 12 GCS9 Cl* Melotte 22 DH 368 +03 45 18.150 +25 05 58.10 0 12.117 11.866 11.421 11.325 10.625 10.829 16.05 2.21 -37.44 2.21 0.74 12 GCS9 V* OR Tau +03 44 32.170 +25 08 12.30 0 15.305 14.832 14.220 13.652 13.316 13.309 17.86 2.21 -43.12 2.21 0.90 12 GCS9 Cl* Melotte 22 HHJ 68 +03 52 51.790 +23 33 47.90 0 15.894 15.316 14.662 14.124 13.742 20.21 2.31 -42.32 2.31 0.88 12 GCS9 2MASS J03525177+2333483 +03 41 40.910 +25 54 24.10 1 16.893 16.001 15.180 14.574 14.122 14.125 16.94 2.26 -42.13 2.26 0.74 12 GCS9 2MASS J03414089+2554242 +03 45 16.990 +25 15 47.50 0 12.911 12.493 11.990 11.694 11.141 11.228 18.30 2.21 -40.49 2.21 0.89 12 GCS9 V* V520 Tau +03 48 44.050 +25 06 22.40 0 14.492 14.076 13.483 12.898 12.639 12.631 16.69 2.20 -44.81 2.20 0.92 12 GCS9 Cl* Melotte 22 DH 589 +04 04 37.180 +23 23 51.00 0 12.985 12.568 12.054 11.620 11.305 11.370 15.43 3.77 -45.51 3.77 0.63 12 GCS9 UGCS J040437.18+232351.0 +03 41 19.350 +23 51 41.70 0 14.700 14.230 13.634 13.065 12.795 12.782 15.93 2.13 -45.36 2.13 0.90 12 GCS9 V* V734 Tau +03 49 58.330 +25 06 20.90 0 15.359 14.837 14.232 13.680 13.379 13.380 15.84 2.21 -43.01 2.21 0.88 12 GCS9 Cl* Melotte 22 BPL 219 +03 41 05.230 +23 50 14.90 0 15.720 15.171 14.571 14.019 13.727 13.698 14.75 2.14 -43.68 2.14 0.85 12 GCS9 Cl* Melotte 22 BPL 19 +03 50 01.870 +25 12 40.90 0 14.210 13.771 13.231 12.666 12.337 12.348 13.84 2.20 -42.81 2.20 0.85 12 GCS9 V* V552 Tau +03 51 17.960 +26 01 22.10 0 14.855 14.349 13.719 13.135 12.816 12.793 17.37 2.23 -39.97 2.23 0.91 12 GCS9 Cl* Melotte 22 DH 701 +03 51 18.720 +26 03 15.40 0 14.748 14.262 13.654 13.395 12.768 12.736 15.24 2.23 -41.60 2.23 0.90 12 GCS9 UGCS J035118.71+260315.4 +03 51 18.860 +26 03 08.70 0 13.191 12.827 12.287 11.714 11.406 11.426 21.16 2.23 -42.78 2.23 0.92 12 GCS9 Cl* Melotte 22 SK 237 +03 52 44.290 +23 54 14.90 0 15.738 15.184 14.554 14.000 13.654 13.683 15.71 2.25 -44.96 2.25 0.86 12 GCS9 2MASS J03524427+2354151 +03 51 24.170 +26 03 11.30 0 13.441 13.064 12.529 11.943 11.651 11.655 16.49 2.23 -42.88 2.23 0.93 12 GCS9 V* V380 Tau +03 52 33.340 +23 51 06.70 0 14.404 13.944 13.367 12.802 12.521 12.543 17.77 2.24 -41.30 2.24 0.93 12 GCS9 Cl* Melotte 22 DH 756 +03 52 18.720 +23 52 36.60 0 15.095 14.577 13.986 13.440 13.130 13.140 15.56 2.25 -45.85 2.25 0.82 12 GCS9 Cl* Melotte 22 BPL 260 +03 45 51.950 +25 10 01.70 0 14.702 14.245 13.604 13.032 12.743 12.742 16.31 2.21 -41.80 2.21 0.92 12 GCS9 Cl* Melotte 22 DH 404 +03 45 39.040 +25 13 27.60 0 12.529 12.273 11.767 11.514 10.907 11.035 15.68 2.21 -41.64 2.21 0.82 12 GCS9 V* OQ Tau +03 46 50.190 +22 12 42.30 0 15.579 15.015 14.384 13.861 13.495 13.514 17.03 2.27 -40.35 2.27 0.87 12 GCS9 Cl* Melotte 22 BPL 110 +03 46 03.670 +25 52 28.80 0 14.534 14.076 13.506 12.954 12.639 12.643 17.24 2.23 -43.26 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 416 +03 45 52.600 +25 54 59.80 0 13.823 13.352 12.746 12.195 11.859 11.879 17.10 2.23 -41.34 2.23 0.92 12 GCS9 Cl* Melotte 22 DH 405 +03 57 33.070 +23 56 47.10 0 14.486 14.043 13.479 12.922 12.627 12.632 17.44 2.96 -47.07 2.96 0.88 12 GCS9 UGCS J035733.06+235647.0 +03 53 24.240 +25 14 37.70 0 16.763 16.041 15.329 14.792 14.388 14.383 18.76 2.27 -40.84 2.27 0.71 12 GCS9 UGCS J035324.24+251437.6 +04 00 26.150 +23 26 17.30 0 13.910 13.401 12.803 12.276 11.930 11.924 16.57 3.35 -40.04 3.35 0.89 12 GCS9 Cl* Melotte 22 DH 891 +04 00 16.930 +23 26 22.60 0 14.173 13.780 13.204 12.602 12.318 12.347 15.97 3.35 -48.85 3.35 0.70 12 GCS9 UGCS J040016.93+232622.5 +03 37 31.070 +23 46 07.20 0 15.431 14.956 14.357 13.829 13.476 13.487 16.45 2.50 -38.98 2.50 0.81 12 GCS9 UGCS J033731.07+234607.1 +04 00 40.260 +23 26 53.80 0 14.831 14.274 13.616 13.072 12.736 12.745 18.94 3.35 -45.90 3.35 0.92 12 GCS9 UGCS J040040.25+232653.8 +04 01 12.080 +23 54 12.80 0 13.698 13.200 12.655 12.127 11.850 15.17 3.54 -39.47 3.54 0.82 12 GCS9 Cl* Melotte 22 DH 898 +03 46 31.320 +22 18 19.60 0 14.447 13.942 13.329 12.796 12.449 12.455 21.56 2.26 -42.37 2.26 0.92 12 GCS9 V* V859 Tau +03 46 40.600 +22 22 03.50 0 15.518 14.956 14.324 13.752 13.392 13.412 18.76 2.27 -40.88 2.27 0.88 12 GCS9 UGCS J034640.59+222203.5 +03 49 08.840 +25 53 48.60 0 13.042 12.708 12.195 11.732 11.323 11.398 14.88 2.23 -43.29 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 603 +03 39 57.150 +26 07 00.10 0 15.350 14.830 14.198 13.660 13.331 13.308 21.95 2.96 -43.52 2.96 0.82 12 GCS9 Cl* Melotte 22 MBSC 94 +03 50 05.000 +23 18 17.20 0 15.407 14.881 14.271 13.751 13.450 13.442 15.82 2.26 -47.05 2.26 0.77 12 GCS9 Cl* Melotte 22 DH 650 +03 46 00.930 +22 12 29.40 0 16.023 15.409 14.742 14.256 13.858 13.866 18.94 2.28 -45.37 2.28 0.68 12 GCS9 Cl* Melotte 22 STAR 9 +03 40 10.930 +26 06 40.80 0 14.589 14.173 13.576 13.025 12.731 12.734 19.44 2.96 -36.36 2.96 0.67 12 GCS9 V* KS Tau +03 49 15.630 +23 22 49.10 0 15.460 14.919 14.290 13.736 13.436 13.422 19.79 2.26 -41.51 2.26 0.88 12 GCS9 Cl* Melotte 22 STAR 28 +03 49 32.570 +23 24 41.00 0 14.251 13.753 13.159 12.602 12.293 12.309 20.08 2.25 -40.04 2.25 0.91 12 GCS9 Cl* Melotte 22 HHJ 245 +03 41 19.860 +25 06 49.00 0 14.586 14.170 13.598 13.072 12.788 18.41 2.97 -47.43 2.97 0.87 12 GCS9 Cl* Melotte 22 HHJ 191 +03 41 30.710 +25 11 52.30 0 15.090 14.523 13.857 13.309 12.925 18.12 2.97 -40.52 2.97 0.88 12 GCS9 UGCS J034130.71+251152.2 +03 40 59.260 +25 11 55.20 0 15.635 15.115 14.479 13.913 13.551 14.98 2.98 -43.05 2.98 0.86 12 GCS9 Cl* Melotte 22 DH 162 +03 40 39.460 +23 26 34.80 0 16.232 15.654 15.015 14.460 14.071 14.075 15.56 2.27 -41.13 2.27 0.69 12 GCS9 Cl* Melotte 22 DH 147 +03 53 16.440 +23 20 58.10 0 15.199 14.687 14.094 13.508 13.225 13.211 16.27 2.26 -38.96 2.26 0.80 12 GCS9 Cl* Melotte 22 BPL 281 +03 46 07.560 +23 44 42.60 0 16.202 15.245 14.240 13.330 12.791 12.804 15.30 2.22 -43.15 2.22 0.70 12 GCS9 UGCS J034607.55+234442.5 +03 38 06.260 +25 05 39.90 0 13.122 12.831 12.361 11.819 11.524 11.570 14.79 2.48 -45.21 2.48 0.87 12 GCS9 UGCS J033806.26+250539.9 +03 46 06.520 +23 50 20.20 0 12.820 12.427 11.856 11.337 10.926 11.042 19.77 2.22 -37.28 2.22 0.81 12 GCS9 Cl* Melotte 22 HII 892 +03 46 04.300 +23 55 40.60 0 13.726 13.283 12.706 12.145 11.810 11.840 14.43 2.22 -40.75 2.22 0.84 12 GCS9 Cl* Melotte 22 HHJ 363 +03 53 55.130 +23 23 36.10 1 16.947 16.027 15.172 14.569 14.088 14.081 19.17 2.28 -44.72 2.28 0.70 12 GCS9 2MASS J03535511+2323363 +03 45 59.200 +23 48 46.80 0 14.973 14.506 13.923 13.375 13.073 13.092 19.52 2.22 -40.23 2.22 0.92 12 GCS9 Cl* Melotte 22 HHJ 127 +03 45 49.420 +23 57 05.70 0 15.929 15.340 14.688 14.182 13.809 13.790 15.31 2.23 -44.67 2.23 0.85 12 GCS9 Cl* Melotte 22 SHF 16 +03 43 06.500 +22 17 49.20 0 15.872 15.301 14.649 14.103 13.726 13.720 23.39 2.52 -40.65 2.52 0.67 12 GCS9 Cl* Melotte 22 HHJ 18 +03 37 50.990 +25 09 25.30 0 13.080 12.684 12.159 11.654 11.378 11.376 21.24 2.48 -46.66 2.48 0.85 12 GCS9 UGCS J033750.99+250925.2 +03 45 46.900 +23 53 00.30 0 15.802 15.201 14.549 13.988 13.652 13.661 18.78 2.23 -44.72 2.23 0.88 12 GCS9 UGCS J034546.90+235300.3 +03 45 46.480 +23 47 43.10 0 12.854 12.422 11.837 11.334 10.920 10.991 19.57 2.22 -40.02 2.22 0.89 12 GCS9 Cl* Melotte 22 HHJ 421 +03 45 38.990 +23 57 00.90 0 15.229 14.703 14.101 13.549 13.238 13.223 20.80 2.22 -37.93 2.22 0.69 12 GCS9 UGCS J034538.98+235700.8 +03 45 38.910 +23 48 55.90 0 15.708 14.967 14.161 13.398 12.966 12.956 20.47 2.22 -40.70 2.22 0.85 12 GCS9 UGCS J034538.90+234855.8 +03 42 56.240 +22 22 38.50 0 15.481 14.854 14.196 13.662 13.283 13.273 23.05 2.51 -45.22 2.51 0.69 12 GCS9 UGCS J034256.23+222238.4 +03 29 58.760 +23 22 18.30 0 13.640 13.198 12.672 12.244 11.843 11.851 21.18 3.41 -38.83 3.41 0.81 12 GCS9 Cl* Melotte 22 DH 9 +03 53 48.040 +23 49 09.60 0 15.701 15.021 14.330 13.772 13.399 13.416 19.15 2.25 -46.61 2.25 0.81 12 GCS9 Cl* Melotte 22 BPL 291 +03 53 24.120 +23 47 58.40 0 14.439 13.967 13.383 12.825 12.527 12.529 15.64 2.24 -40.80 2.24 0.89 12 GCS9 Cl* Melotte 22 DH 784 +04 03 43.840 +23 53 47.00 0 14.247 13.709 13.083 12.543 12.195 12.194 20.68 3.37 -43.87 3.37 0.93 12 GCS9 Cl* Melotte 22 DH 909 +03 39 44.190 +22 07 45.50 0 13.646 13.239 12.691 12.131 11.860 11.873 19.48 2.50 -45.62 2.50 0.92 12 GCS9 Cl* Melotte 22 DH 111 +03 49 29.700 +22 18 13.30 0 14.741 14.294 13.748 13.159 12.858 12.841 19.08 2.51 -39.34 2.51 0.90 12 GCS9 UGCS J034929.70+221813.2 +03 49 25.550 +22 18 44.40 0 15.321 14.823 14.225 13.678 13.329 13.321 21.74 2.51 -40.44 2.51 0.79 12 GCS9 Cl* Melotte 22 BPL 206 +03 49 35.460 +22 23 26.10 0 14.930 14.487 13.916 13.356 13.068 13.048 19.51 2.51 -41.27 2.51 0.93 12 GCS9 Cl* Melotte 22 BPL 211 +03 45 12.620 +23 53 45.00 0 16.093 15.445 14.776 14.220 13.845 13.855 16.81 2.23 -44.76 2.23 0.71 12 GCS9 Cl* Melotte 22 HHJ 14 +03 52 38.910 +25 50 25.30 0 14.054 13.642 13.075 12.528 12.203 12.193 19.53 2.93 -39.16 2.93 0.89 12 GCS9 V* V768 Tau +03 52 07.430 +25 53 02.70 0 13.127 12.750 12.267 11.768 11.478 11.454 17.32 2.93 -39.44 2.93 0.88 12 GCS9 UGCS J035207.42+255302.6 +03 52 31.380 +25 15 07.50 0 13.670 13.259 12.739 12.160 11.887 11.901 16.76 2.23 -45.34 2.23 0.92 12 GCS9 Cl* Melotte 22 DH 755 +03 43 47.080 +26 04 35.30 0 13.857 13.453 12.895 12.305 12.017 12.025 19.24 2.23 -41.90 2.23 0.93 12 GCS9 Cl* Melotte 22 HHJ 311 +03 48 17.370 +23 48 23.50 0 14.645 14.190 13.615 13.061 12.760 12.745 18.31 2.22 -42.91 2.22 0.94 12 GCS9 Cl* Melotte 22 HHJ 188 +03 35 46.410 +22 24 49.70 0 15.172 14.684 14.105 13.586 13.262 13.247 22.16 2.94 -42.14 2.94 0.81 12 GCS9 UGCS J033546.40+222449.6 +03 42 02.930 +23 55 53.70 0 12.951 12.573 12.010 11.456 11.155 17.94 2.30 -39.33 2.30 0.87 12 GCS9 V* V727 Tau +03 41 10.270 +25 45 55.90 0 13.996 13.553 13.015 12.467 12.161 12.180 18.53 2.23 -43.40 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 164 +03 40 40.320 +25 50 48.10 0 14.039 13.616 13.025 12.447 12.168 12.150 17.46 2.23 -43.05 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 148 +03 44 33.080 +25 45 09.50 0 14.367 13.898 13.319 12.711 12.428 12.423 14.12 2.23 -38.60 2.23 0.71 12 GCS9 V* V742 Tau +03 33 46.650 +23 48 19.40 0 13.819 13.463 12.918 12.375 12.087 12.098 18.43 2.60 -43.31 2.60 0.94 12 GCS9 Cl* Melotte 22 DH 30 +03 57 30.160 +25 16 46.70 0 13.312 12.967 12.456 11.833 11.542 11.620 21.77 2.47 -45.25 2.47 0.88 12 GCS9 UGCS J035730.15+251646.7 +03 42 54.000 +26 08 16.10 0 14.730 14.262 13.672 13.136 12.798 12.810 19.45 2.23 -43.40 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 244 +03 45 04.990 +23 46 06.40 0 14.919 14.308 13.687 13.174 12.822 12.835 16.11 2.22 -45.06 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 372 +03 43 19.060 +26 04 43.90 0 14.168 13.751 13.169 12.614 12.292 12.355 13.73 2.23 -40.04 2.23 0.77 12 GCS9 Cl* Melotte 22 DH 260 +03 44 58.590 +23 55 40.90 0 14.017 13.555 12.960 12.420 12.106 12.139 18.25 2.22 -38.70 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 363 +03 44 56.010 +23 55 53.40 0 13.771 13.290 12.715 12.244 11.888 11.912 19.65 2.22 -40.21 2.22 0.91 12 GCS9 V* NX Tau +03 43 26.200 +26 02 30.60 0 13.718 13.336 12.777 12.204 11.899 11.946 16.58 2.23 -41.36 2.23 0.92 12 GCS9 V* V619 Tau +03 44 34.300 +23 51 24.60 0 16.914 16.150 15.428 14.866 14.472 14.439 16.92 2.24 -42.74 2.24 0.74 12 GCS9 Cl* Melotte 22 PPL 14 +03 36 16.320 +25 08 48.80 0 13.071 12.678 12.129 11.591 11.290 11.282 19.36 2.62 -45.92 2.62 0.91 12 GCS9 Cl* Melotte 22 DH 54 +03 51 57.530 +25 48 31.20 0 14.094 13.689 13.121 12.578 12.276 12.281 17.62 2.23 -42.66 2.23 0.94 12 GCS9 V* V561 Tau +03 47 58.040 +22 06 50.80 0 16.708 15.993 15.283 14.742 14.331 14.331 18.97 2.30 -42.48 2.30 0.74 12 GCS9 2MASS J03475802+2206511 +03 46 54.030 +25 14 44.80 0 13.248 12.893 12.369 11.790 11.469 11.490 19.95 2.21 -41.29 2.21 0.92 12 GCS9 V* V860 Tau +03 47 15.290 +25 06 55.30 0 13.353 12.925 12.359 11.788 11.481 11.497 20.29 2.21 -40.01 2.21 0.89 12 GCS9 Cl* Melotte 22 SK 432 +03 47 20.840 +25 05 12.10 0 12.899 12.550 12.021 11.403 11.153 11.187 17.78 2.21 -45.20 2.21 0.77 12 GCS9 Cl* Melotte 22 HHJ 417 +03 47 25.800 +25 08 32.80 0 13.339 12.873 12.278 11.753 11.412 11.453 19.88 2.21 -43.41 2.21 0.93 12 GCS9 V* V539 Tau +03 41 23.470 +22 01 53.90 0 14.746 14.157 13.488 12.874 12.525 12.523 15.55 2.51 -42.58 2.51 0.91 12 GCS9 UGCS J034123.46+220153.9 +03 47 47.870 +25 13 34.30 0 14.065 13.593 12.961 12.408 12.049 12.055 18.78 2.21 -41.93 2.21 0.94 12 GCS9 Cl* Melotte 22 DH 537 +03 52 18.460 +22 00 53.30 0 13.024 12.707 12.225 11.616 11.369 11.393 15.81 2.51 -42.10 2.51 0.91 12 GCS9 Cl* Melotte 22 DH 749 +03 48 15.490 +25 14 36.40 0 14.957 14.500 13.897 13.347 13.006 13.051 13.38 2.21 -40.07 2.21 0.74 12 GCS9 Cl* Melotte 22 DH 563 +03 42 36.950 +25 13 57.50 0 15.243 14.744 14.157 13.605 13.246 14.72 2.97 -38.46 2.97 0.70 12 GCS9 Cl* Melotte 22 DH 233 +03 42 42.120 +25 11 48.70 0 15.492 14.954 14.328 13.773 13.367 19.99 2.97 -46.20 2.97 0.82 12 GCS9 Cl* Melotte 22 DH 237 +04 00 10.300 +22 02 17.00 0 14.399 13.919 13.384 12.864 12.513 12.521 19.86 2.87 -43.85 2.87 0.94 12 GCS9 Cl* Melotte 22 DH 888 +03 47 20.430 +22 21 56.30 0 14.832 14.342 13.737 13.208 12.868 12.835 15.02 2.26 -38.93 2.26 0.80 12 GCS9 UGCS J034720.42+222156.3 +03 47 44.670 +22 12 43.90 0 15.872 15.338 14.695 14.187 13.825 13.810 22.07 2.28 -45.80 2.28 0.74 12 GCS9 Cl* Melotte 22 BPL 154 +03 47 44.680 +22 23 53.00 0 14.454 14.006 13.424 12.875 12.554 12.562 22.28 2.26 -44.26 2.26 0.90 12 GCS9 V* V335 Tau +03 43 36.680 +25 47 00.50 0 13.800 13.402 12.848 12.282 12.004 11.994 21.04 2.23 -46.84 2.23 0.85 12 GCS9 Cl* Melotte 22 DH 276 +03 35 59.910 +26 01 32.20 0 15.077 14.539 13.909 13.336 12.989 17.12 5.31 -37.68 5.31 0.73 12 GCS9 UGCS J033559.91+260132.2 +03 43 42.900 +25 51 37.00 0 15.191 14.695 14.098 13.525 13.202 13.195 21.00 2.23 -46.18 2.23 0.78 12 GCS9 Cl* Melotte 22 HHJ 77 +03 50 44.350 +25 07 05.10 0 15.181 14.601 13.930 13.397 13.036 13.031 21.00 2.20 -44.44 2.20 0.84 12 GCS9 UGCS J035044.34+250705.0 +03 51 23.890 +25 05 52.60 0 13.484 13.107 12.577 12.065 11.730 11.738 14.49 2.20 -42.99 2.20 0.88 12 GCS9 Cl* Melotte 22 DH 708 +03 44 58.380 +22 11 30.10 0 15.843 15.286 14.652 14.119 13.760 13.765 15.85 2.52 -42.05 2.52 0.88 12 GCS9 UGCS J034458.38+221130.0 +03 43 56.700 +25 15 43.90 0 12.995 12.666 12.168 11.616 11.330 21.03 2.97 -42.94 2.97 0.86 12 GCS9 Cl* Melotte 22 SK 596 +03 54 28.110 +23 56 36.00 0 15.731 15.152 14.518 13.983 13.642 13.637 15.38 2.25 -40.85 2.25 0.85 12 GCS9 Cl* Melotte 22 BPL 313 +03 39 13.330 +25 43 49.50 0 13.488 13.088 12.530 11.963 11.657 11.694 19.34 2.95 -39.87 2.95 0.90 12 GCS9 Cl* Melotte 22 DH 100 +03 51 36.410 +25 13 40.60 0 14.020 13.545 12.926 12.379 12.066 12.042 16.43 2.20 -40.71 2.20 0.91 12 GCS9 Cl* Melotte 22 DH 717 +03 52 02.640 +25 06 14.90 0 14.751 14.296 13.684 13.158 12.836 12.836 16.37 2.20 -40.83 2.20 0.91 12 GCS9 Cl* Melotte 22 DH 736 +03 55 27.060 +25 14 45.80 1 16.118 15.402 14.649 14.071 13.671 13.650 15.24 2.24 -39.66 2.24 0.60 12 GCS9 Cl* Melotte 22 BPL 328 +03 36 44.120 +22 01 38.80 0 14.988 14.410 13.794 13.301 12.965 12.979 22.41 2.94 -39.90 2.94 0.85 12 GCS9 Cl* Melotte 22 DH 63 +04 01 11.370 +22 10 37.80 0 15.142 14.644 14.046 13.493 13.176 13.178 24.34 3.41 -43.11 3.41 0.60 12 GCS9 UGCS J040111.36+221037.8 +03 43 57.000 +23 57 05.70 0 14.157 13.723 13.145 12.582 12.265 21.91 2.30 -44.52 2.30 0.90 12 GCS9 V* V739 Tau +03 44 19.690 +23 53 45.60 0 15.780 15.031 14.317 13.697 13.262 15.52 2.31 -45.09 2.31 0.85 12 GCS9 Cl* Melotte 22 PPL 5 +03 44 12.140 +23 52 37.30 0 14.442 13.972 13.408 12.855 12.547 18.41 2.30 -47.45 2.30 0.87 12 GCS9 Cl* Melotte 22 HHJ 217 +03 44 14.650 +23 49 40.00 1 15.892 15.245 14.547 13.957 13.546 14.46 2.31 -40.15 2.31 0.79 12 GCS9 Cl* Melotte 22 PPL 11 +03 37 41.350 +22 17 19.00 0 15.026 14.524 13.892 13.401 13.008 13.055 19.22 2.94 -40.01 2.94 0.86 12 GCS9 UGCS J033741.35+221718.9 +03 39 42.720 +23 54 27.60 0 14.142 13.756 13.179 12.650 12.363 12.332 22.18 2.50 -45.44 2.50 0.88 12 GCS9 V* V489 Tau +03 40 18.850 +23 54 23.30 0 14.517 14.091 13.554 13.046 12.719 12.723 16.56 2.50 -42.50 2.50 0.93 12 GCS9 UGCS J034018.84+235423.3 +03 39 47.990 +23 50 56.60 0 13.557 13.155 12.591 12.078 11.777 11.792 20.24 2.50 -41.77 2.50 0.92 12 GCS9 Cl* Melotte 22 DH 115 +04 04 34.790 +22 02 46.20 0 14.508 14.023 13.423 12.876 12.548 12.543 20.21 5.07 -43.45 5.07 0.94 12 GCS9 UGCS J040434.79+220246.2 +03 39 48.510 +23 46 03.80 0 15.028 14.539 13.964 13.446 13.119 13.083 18.84 2.50 -46.34 2.50 0.83 12 GCS9 Cl* Melotte 22 BPL 6 +03 39 50.680 +23 45 52.30 0 15.504 15.018 14.384 13.856 13.525 13.514 16.85 2.51 -41.64 2.51 0.89 12 GCS9 Cl* Melotte 22 DH 118 +03 58 01.970 +23 53 54.50 0 15.065 14.518 13.919 13.370 13.028 13.030 15.82 2.97 -43.34 2.97 0.88 12 GCS9 Cl* Melotte 22 DH 862 +03 59 31.470 +21 16 18.30 0 13.596 13.197 12.656 12.116 11.837 11.824 22.53 3.75 -44.15 3.75 0.87 12 GCS9 Cl* Melotte 22 DH 879 +03 49 48.440 +22 10 48.30 0 13.806 13.390 12.863 12.289 11.986 11.996 12.57 2.51 -41.31 2.51 0.71 12 GCS9 V* V877 Tau +03 40 01.840 +25 04 19.50 0 14.919 14.417 13.786 13.289 12.952 12.925 20.07 2.49 -41.41 2.49 0.93 12 GCS9 Cl* Melotte 22 HHJ 102 +03 40 31.170 +25 08 52.80 0 13.489 13.083 12.509 11.964 11.673 11.681 20.23 2.48 -43.75 2.48 0.93 12 GCS9 V* KV Tau +03 48 05.870 +23 53 00.90 0 15.251 14.707 14.138 13.612 13.253 13.280 17.10 2.22 -37.49 2.22 0.71 12 GCS9 UGCS J034805.86+235300.8 +03 47 29.590 +23 52 49.30 0 16.385 15.692 15.016 14.462 14.078 14.077 16.73 2.24 -43.66 2.24 0.74 12 GCS9 Cl* Melotte 22 PPL 8 +03 47 31.650 +23 52 19.10 0 14.743 14.247 13.686 13.121 12.811 12.800 13.78 2.22 -44.81 2.22 0.82 12 GCS9 UGCS J034731.64+235219.0 +04 01 49.170 +22 10 05.00 0 14.076 13.630 13.057 12.554 12.239 12.210 23.64 3.40 -44.42 3.40 0.83 12 GCS9 Cl* Melotte 22 DH 901 +03 38 27.520 +25 30 18.10 0 16.591 15.938 15.284 14.747 14.355 14.371 18.25 3.00 -40.21 3.00 0.69 12 GCS9 Cl* Melotte 22 HHJ 2 +03 38 24.220 +25 19 49.20 0 14.870 14.364 13.774 13.233 12.906 12.914 18.02 2.96 -40.03 2.96 0.92 12 GCS9 Cl* Melotte 22 DH 86 +03 45 39.120 +22 04 23.10 0 15.161 14.641 14.018 13.458 13.114 13.120 19.28 2.27 -37.19 2.27 0.67 12 GCS9 Cl* Melotte 22 BPL 75 +03 37 54.790 +25 26 31.30 0 12.896 12.558 12.073 11.558 11.231 11.316 19.21 2.95 -37.64 2.95 0.83 12 GCS9 Cl* Melotte 22 HHJ 402 +03 54 58.030 +25 14 29.00 0 13.800 13.401 12.825 12.253 11.966 11.990 13.55 2.23 -44.32 2.23 0.82 12 GCS9 Cl* Melotte 22 DH 810 +03 50 31.950 +22 08 47.50 0 13.879 13.507 12.966 12.405 12.094 12.099 20.97 2.51 -37.91 2.51 0.75 12 GCS9 V* V674 Tau +03 57 49.370 +22 08 30.90 0 16.149 15.498 14.831 14.286 13.884 13.897 16.04 2.89 -42.24 2.89 0.72 12 GCS9 Cl* Melotte 22 DH 858 +03 44 02.500 +21 13 15.80 0 14.767 14.282 13.665 13.123 12.781 12.814 14.96 2.65 -40.08 2.65 0.85 12 GCS9 Cl* Melotte 22 DH 307 +03 50 25.160 +23 55 41.80 0 14.632 14.160 13.598 13.057 12.753 12.750 15.61 2.22 -41.36 2.22 0.90 12 GCS9 V* V373 Tau +03 50 12.460 +23 55 35.90 0 14.068 13.568 12.961 12.484 12.119 12.116 16.49 2.22 -44.32 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 659 +03 50 02.180 +23 51 44.60 0 13.770 13.342 12.802 12.268 11.965 11.957 13.75 2.22 -45.17 2.22 0.81 12 GCS9 V* V368 Tau +03 50 12.600 +23 48 48.90 0 15.564 15.046 14.439 13.876 13.556 13.530 18.23 2.22 -47.50 2.22 0.77 12 GCS9 Cl* Melotte 22 DH 660 +03 57 42.980 +25 23 06.70 0 14.445 13.984 13.389 12.823 12.494 12.537 21.68 2.48 -43.65 2.48 0.92 12 GCS9 V* V583 Tau +03 43 16.610 +23 50 01.50 0 14.279 13.794 13.239 12.641 12.382 12.371 15.68 2.12 -43.30 2.12 0.92 12 GCS9 Cl* Melotte 22 DH 258 +03 52 04.480 +24 14 39.60 0 14.840 14.365 13.783 13.223 12.926 12.897 16.25 2.24 -41.96 2.24 0.92 12 GCS9 Cl* Melotte 22 DH 739 +03 36 11.070 +23 48 23.00 0 15.038 14.538 13.935 13.415 13.063 13.085 20.28 2.61 -36.96 2.61 0.61 12 GCS9 Cl* Melotte 22 DH 53 +03 34 54.950 +22 04 46.20 0 13.105 12.793 12.313 11.794 11.512 11.566 22.30 2.93 -37.92 2.93 0.66 12 GCS9 Cl* Melotte 22 DH 40 +03 44 59.480 +23 21 18.10 0 15.290 14.814 14.227 13.710 13.376 13.382 20.27 2.24 -46.79 2.24 0.77 12 GCS9 Cl* Melotte 22 DH 365 +03 45 12.160 +23 21 52.90 0 14.046 13.642 13.082 12.524 12.260 12.255 17.35 2.24 -44.74 2.24 0.93 12 GCS9 V* V442 Tau +03 44 58.020 +23 24 31.00 0 14.147 13.740 13.184 12.621 12.362 12.351 16.74 2.24 -37.83 2.24 0.79 12 GCS9 Cl* Melotte 22 DH 362 +03 45 08.410 +23 25 00.90 0 15.510 15.010 14.406 13.859 13.534 13.540 13.99 2.24 -40.94 2.24 0.79 12 GCS9 UGCS J034508.41+232500.9 +03 45 54.990 +24 13 26.00 0 13.704 13.217 12.670 12.164 11.819 11.840 18.24 2.22 -42.71 2.22 0.94 12 GCS9 Cl* Melotte 22 BPL 82 +03 44 36.280 +23 30 10.90 0 13.348 13.023 12.482 11.996 11.620 11.651 16.76 2.23 -43.37 2.23 0.93 12 GCS9 V* NT Tau +03 45 40.250 +24 17 20.60 0 14.722 14.287 13.746 13.164 12.893 12.881 22.35 2.22 -39.61 2.22 0.84 12 GCS9 UGCS J034540.24+241720.5 +03 45 51.330 +24 17 44.30 0 14.634 14.154 13.573 12.983 12.723 12.717 14.02 2.22 -39.30 2.22 0.75 12 GCS9 UGCS J034551.33+241744.2 +03 45 30.230 +24 18 45.30 0 12.817 12.487 11.994 11.683 11.104 11.215 15.71 2.22 -43.80 2.22 0.77 12 GCS9 Cl* Melotte 22 MT 59 +03 45 49.940 +23 19 44.70 0 15.790 15.217 14.577 14.016 13.634 13.665 13.92 2.24 -38.67 2.24 0.66 12 GCS9 UGCS J034549.94+231944.7 +03 45 42.870 +23 20 12.20 0 15.145 14.690 14.081 13.551 13.217 13.217 17.99 2.24 -40.55 2.24 0.88 12 GCS9 UGCS J034542.87+232012.1 +03 45 37.790 +24 20 08.10 0 13.646 13.271 11.841 12.729 10.456 12.026 19.56 2.22 -44.01 2.22 0.93 12 GCS9 UGCS J034537.78+242008.1 +03 45 37.950 +24 20 03.40 0 13.151 12.791 12.328 11.940 11.503 11.541 16.88 2.22 -43.71 2.22 0.93 12 GCS9 UGCS J034537.95+242003.4 +03 45 24.790 +24 20 45.30 0 14.133 13.677 13.118 12.524 12.226 12.250 14.67 2.22 -40.59 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 392 +03 46 07.510 +24 22 27.60 0 12.379 12.139 11.688 11.532 10.916 11.074 18.75 2.22 -42.30 2.22 0.89 12 GCS9 V* V638 Tau +03 41 24.690 +25 23 05.80 0 15.013 14.526 13.936 13.414 13.117 13.120 16.34 2.23 -38.60 2.23 0.78 12 GCS9 Cl* Melotte 22 HHJ 117 +03 52 05.830 +24 17 31.00 0 16.512 15.860 15.176 14.618 14.251 14.263 15.66 2.27 -40.51 2.27 0.67 12 GCS9 2MASS J03520581+2417314 +03 52 44.480 +24 20 59.30 0 15.025 14.546 13.944 13.403 13.091 13.085 16.54 2.25 -40.61 2.25 0.87 12 GCS9 Cl* Melotte 22 DH 766 +03 45 09.040 +25 22 29.70 0 15.145 14.500 13.831 13.259 12.901 12.920 20.44 2.23 -40.81 2.23 0.86 12 GCS9 Cl* Melotte 22 HHJ 81 +03 45 05.320 +25 29 10.90 0 13.161 12.814 12.312 11.731 11.448 11.485 16.08 2.23 -44.62 2.23 0.92 12 GCS9 Cl* Melotte 22 DH 373 +03 45 01.210 +25 21 05.60 0 15.041 14.555 13.949 13.361 13.061 13.070 18.83 2.23 -41.05 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 367 +03 38 02.050 +24 20 15.10 0 14.002 13.647 13.091 12.524 12.251 12.230 19.63 2.50 -45.83 2.50 0.92 12 GCS9 Cl* Melotte 22 DH 81 +03 44 32.330 +25 25 17.90 0 16.994 16.209 15.450 14.883 14.476 14.459 14.24 2.27 -43.51 2.27 0.64 12 GCS9 2MASS J03443231+2525181 +03 48 48.790 +23 24 48.00 0 15.140 14.625 14.030 13.486 13.166 13.171 15.92 2.13 -37.19 2.13 0.64 12 GCS9 Cl* Melotte 22 HHJ 86 +03 48 15.250 +23 26 05.40 0 14.320 13.888 13.339 12.781 12.516 12.495 14.81 2.13 -42.58 2.13 0.89 12 GCS9 Cl* Melotte 22 HHJ 232 +03 49 01.500 +24 11 38.30 0 14.330 13.900 13.320 12.814 12.506 12.488 14.90 2.22 -46.83 2.22 0.81 12 GCS9 Cl* Melotte 22 DH 599 +03 48 35.490 +24 12 03.00 0 15.216 14.662 14.042 13.491 13.221 13.201 18.84 2.22 -39.61 2.22 0.85 12 GCS9 V* V352 Tau +03 48 39.910 +24 12 42.70 0 13.512 13.161 12.640 12.085 11.832 11.837 14.85 2.22 -41.13 2.22 0.87 12 GCS9 V* V873 Tau +03 48 22.710 +23 27 42.80 0 14.643 14.181 13.574 13.026 12.787 12.738 19.88 2.13 -42.50 2.13 0.94 12 GCS9 Cl* Melotte 22 DH 573 +03 48 25.240 +24 14 25.80 0 13.827 13.379 12.792 12.285 11.968 11.989 17.16 2.22 -43.14 2.22 0.94 12 GCS9 V* V871 Tau +03 48 14.300 +24 15 50.50 0 16.637 15.926 15.218 14.677 14.267 14.265 19.81 2.24 -40.94 2.24 0.69 12 GCS9 Cl* Melotte 22 BPL 169 +03 48 31.060 +24 16 53.10 0 13.037 12.703 12.170 11.891 11.376 11.477 23.77 2.22 -43.77 2.22 0.79 12 GCS9 V* V349 Tau +03 48 57.360 +24 19 43.60 0 12.735 12.382 11.845 11.772 11.088 11.294 19.80 2.22 -41.83 2.22 0.89 12 GCS9 V* V759 Tau +03 45 26.560 +22 31 32.00 0 14.087 13.649 13.091 12.536 12.210 12.196 19.13 2.26 -36.34 2.26 0.67 12 GCS9 Cl* Melotte 22 DH 393 +03 48 55.650 +24 21 40.10 0 16.220 15.636 14.963 14.416 14.055 14.041 17.62 2.23 -40.24 2.23 0.70 12 GCS9 Cl* Melotte 22 HHJ 8 +03 37 56.420 +23 22 56.60 0 13.782 13.405 12.891 12.353 12.067 12.063 23.93 2.97 -39.47 2.97 0.65 12 GCS9 Cl* Melotte 22 DH 80 +03 41 00.390 +24 13 35.00 0 14.841 14.363 13.768 13.194 12.895 12.871 16.27 2.13 -39.79 2.13 0.89 12 GCS9 Cl* Melotte 22 BPL 17 +03 41 22.450 +24 23 51.60 0 15.326 14.820 14.189 13.641 13.290 13.310 17.58 2.13 -43.59 2.13 0.90 12 GCS9 Cl* Melotte 22 DH 169 +03 50 37.420 +22 28 08.00 0 14.173 13.774 13.224 12.669 12.379 12.378 20.76 2.51 -39.13 2.51 0.87 12 GCS9 V* V878 Tau +03 56 52.910 +23 25 43.70 0 14.029 13.589 12.994 12.445 12.127 12.117 16.82 3.00 -39.65 3.00 0.89 12 GCS9 Cl* Melotte 22 DH 846 +03 46 52.610 +23 38 43.00 0 14.301 13.763 13.138 12.512 12.160 12.174 15.66 2.22 -42.82 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 473 +03 46 43.190 +23 37 21.90 0 15.226 14.671 14.057 13.407 13.081 13.076 20.88 2.22 -42.38 2.22 0.86 12 GCS9 Cl* Melotte 22 DH 466 +03 46 44.880 +23 37 07.30 0 15.323 14.679 13.948 13.237 12.874 12.854 20.16 2.22 -39.81 2.22 0.83 12 GCS9 UGCS J034644.87+233707.2 +03 47 03.780 +23 36 58.60 0 12.388 12.010 11.486 11.369 10.665 11.002 22.69 2.22 -39.02 2.22 0.80 12 GCS9 V* QQ Tau +03 46 58.170 +23 33 38.80 0 14.657 14.204 13.650 13.114 12.801 12.816 19.41 2.22 -43.08 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 482 +03 47 02.350 +23 32 36.00 0 15.608 14.962 14.262 13.719 13.314 13.303 17.87 2.22 -44.38 2.22 0.89 12 GCS9 Cl* Melotte 22 DH 487 +03 46 50.090 +23 31 56.10 0 14.162 13.728 13.200 12.627 12.337 12.390 18.31 2.22 -43.65 2.22 0.94 12 GCS9 V* PU Tau +03 56 25.930 +24 16 51.30 0 12.568 12.306 11.841 11.303 10.972 11.236 22.45 2.96 -39.95 2.96 0.83 12 GCS9 Cl* Melotte 22 SK 18 +03 58 57.040 +23 42 31.10 0 12.744 12.419 11.916 11.544 11.113 11.318 22.87 2.96 -40.80 2.96 0.82 12 GCS9 V* V405 Tau +03 54 01.120 +23 19 41.00 0 15.933 15.380 14.713 14.186 13.841 13.816 15.07 2.27 -45.30 2.27 0.83 12 GCS9 Cl* Melotte 22 BPL 299 +03 54 13.020 +23 20 50.80 0 13.540 13.162 12.611 12.064 11.777 11.770 19.39 2.25 -43.85 2.25 0.94 12 GCS9 V* V400 Tau +03 42 02.870 +24 12 36.00 0 13.316 12.980 12.469 11.883 11.629 12.04 2.30 -45.58 2.30 0.61 12 GCS9 V* V497 Tau +03 48 07.960 +23 44 23.50 0 13.941 13.522 12.964 12.433 12.148 12.163 17.89 2.22 -45.77 2.22 0.92 12 GCS9 V* V658 Tau +03 47 22.680 +23 44 06.70 0 14.271 13.786 13.223 12.688 12.391 12.374 16.09 2.22 -43.44 2.22 0.92 12 GCS9 V* V537 Tau +03 47 44.660 +23 42 03.10 0 14.538 14.079 13.508 12.937 12.650 12.660 20.25 2.22 -45.79 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 536 +03 47 33.460 +23 41 32.80 0 12.580 12.224 11.716 11.700 10.957 11.178 17.61 2.22 -41.13 2.22 0.88 12 GCS9 V* QV Tau +03 47 51.970 +23 39 48.00 0 14.936 14.433 13.888 13.320 13.031 13.034 17.54 2.22 -44.32 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 540 +03 47 26.770 +23 38 02.40 0 14.194 13.696 13.125 12.569 12.249 12.245 18.70 2.22 -40.80 2.22 0.93 12 GCS9 V* V864 Tau +03 47 29.930 +23 33 15.00 0 16.033 15.408 14.750 14.157 13.818 13.794 17.39 2.23 -43.72 2.23 0.74 12 GCS9 Cl* Melotte 22 HHJ 16 +03 43 39.050 +23 44 05.20 0 14.252 13.733 13.145 12.592 12.212 19.59 2.30 -41.08 2.30 0.93 12 GCS9 V* MP Tau +03 43 39.720 +23 41 32.40 0 15.672 15.129 14.470 13.912 13.544 18.33 2.31 -45.77 2.31 0.86 12 GCS9 Cl* Melotte 22 HHJ 40 +03 43 37.110 +23 38 31.90 0 13.785 13.328 12.738 12.198 11.870 17.19 2.30 -45.95 2.30 0.91 12 GCS9 Cl* Melotte 22 DH 278 +03 44 16.460 +23 37 04.00 0 13.729 13.270 12.706 12.148 11.847 18.08 2.30 -44.62 2.30 0.93 12 GCS9 V* MW Tau +03 44 47.840 +24 12 52.50 0 14.358 13.900 13.297 12.745 12.458 12.431 18.38 2.22 -42.73 2.22 0.94 12 GCS9 V* NU Tau +03 44 27.500 +24 14 17.00 0 14.633 14.101 13.503 12.965 12.632 12.628 20.52 2.22 -45.35 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 341 +03 45 13.140 +24 15 23.60 0 15.046 14.554 13.965 13.457 13.149 13.144 21.10 2.22 -42.90 2.22 0.86 12 GCS9 V* NZ Tau +04 02 49.970 +23 30 38.40 0 13.869 13.468 12.896 12.332 12.053 12.047 20.08 3.35 -38.25 3.35 0.81 12 GCS9 Cl* Melotte 22 DH 904 +03 44 46.140 +24 23 02.80 0 15.833 15.303 14.664 14.161 13.762 13.777 16.63 2.23 -45.44 2.23 0.86 12 GCS9 Cl* Melotte 22 HHJ 24 +03 42 27.310 +22 34 24.60 0 13.652 13.223 12.660 12.145 11.830 11.851 14.98 2.51 -42.82 2.51 0.90 12 GCS9 V* V612 Tau +03 45 08.690 +24 24 09.30 0 16.507 15.843 15.142 14.587 14.235 14.195 16.54 2.23 -42.48 2.23 0.74 12 GCS9 UGCS J034508.68+242409.3 +03 57 08.610 +20 07 42.70 0 15.711 15.183 14.537 14.017 13.686 13.687 18.35 3.98 -41.60 3.98 0.90 12 GCS9 Cl* Melotte 22 DH 849 +03 51 11.880 +23 44 43.30 0 15.371 14.840 14.253 13.708 13.396 13.374 16.86 2.22 -43.64 2.22 0.89 12 GCS9 Cl* Melotte 22 BPL 232 +03 51 03.280 +23 35 00.10 0 15.395 14.846 14.243 13.676 13.365 13.342 15.36 2.22 -44.88 2.22 0.85 12 GCS9 UGCS J035103.27+233500.0 +03 51 51.550 +23 34 49.10 0 15.431 14.857 14.260 13.768 13.385 13.408 17.89 2.22 -44.43 2.22 0.89 12 GCS9 2MASS J03515154+2334494 +03 40 32.380 +19 54 30.00 0 15.279 14.784 14.160 13.581 13.241 13.222 13.86 5.02 -39.74 5.02 0.73 12 GCS9 UGCS J034032.38+195429.9 +03 42 34.030 +23 24 51.80 0 15.870 15.322 14.668 14.108 13.758 13.737 14.90 2.26 -38.79 2.26 0.74 12 GCS9 UGCS J034234.02+232451.8 +03 42 36.270 +23 22 04.70 0 14.057 13.676 13.118 12.571 12.298 12.267 16.16 2.25 -37.36 2.25 0.73 12 GCS9 V* V433 Tau +03 56 09.600 +22 28 01.40 0 14.827 14.311 13.669 13.091 12.772 12.772 24.94 2.51 -41.87 2.51 0.72 12 GCS9 2MASS J03560958+2228017 +03 43 01.400 +23 29 30.40 0 14.868 14.435 13.861 13.324 13.014 13.042 18.85 2.25 -41.62 2.25 0.94 12 GCS9 UGCS J034301.40+232930.4 +03 39 38.690 +23 23 20.20 0 14.410 14.024 13.460 12.927 12.628 12.603 18.87 2.98 -38.42 2.98 0.86 12 GCS9 UGCS J033938.69+232320.2 +03 39 41.120 +23 28 23.30 0 14.958 14.527 13.924 13.411 13.080 13.046 23.64 2.98 -38.71 2.98 0.70 12 GCS9 Cl* Melotte 22 HHJ 83 +03 39 57.350 +23 19 42.20 0 14.866 14.438 13.872 13.338 13.029 13.030 20.25 2.98 -49.85 2.98 0.61 12 GCS9 Cl* Melotte 22 HHJ 103 +03 40 00.190 +23 26 05.70 0 12.733 12.412 11.891 11.375 11.054 11.085 19.30 2.97 -38.03 2.97 0.85 12 GCS9 V* KU Tau +03 53 30.760 +19 54 24.10 0 13.671 13.329 12.773 12.177 11.869 11.915 21.18 3.97 -41.70 3.97 0.91 12 GCS9 Cl* Melotte 22 DH 787 +03 40 26.270 +23 21 29.10 0 14.499 14.088 13.492 12.935 12.626 12.650 23.17 2.98 -38.33 2.98 0.71 12 GCS9 Cl* Melotte 22 HHJ 167 +03 51 06.130 +22 38 00.60 0 14.992 14.482 13.846 13.298 12.962 12.927 17.14 2.51 -38.08 2.51 0.82 12 GCS9 Cl* Melotte 22 DH 694 +03 54 22.490 +23 38 11.90 0 14.441 13.982 13.383 12.800 12.511 12.520 12.94 2.24 -43.81 2.24 0.77 12 GCS9 Cl* Melotte 22 DH 801 +03 54 02.730 +23 35 00.90 0 15.007 14.465 13.863 13.314 12.994 12.988 15.75 2.25 -46.40 2.25 0.80 12 GCS9 Cl* Melotte 22 BPL 301 +03 51 07.110 +23 20 57.60 0 14.729 14.274 13.683 13.132 12.833 12.817 15.82 2.25 -41.10 2.25 0.90 12 GCS9 Cl* Melotte 22 DH 695 +03 47 22.280 +24 16 59.90 0 16.097 15.485 14.840 14.281 13.922 13.904 20.93 2.23 -44.36 2.23 0.64 12 GCS9 UGCS J034722.27+241659.8 +03 47 22.380 +24 14 18.80 0 15.323 14.727 14.093 13.545 13.176 13.163 19.02 2.22 -46.64 2.22 0.81 12 GCS9 Cl* Melotte 22 BPL 139 +03 47 25.110 +24 15 17.20 0 14.913 14.441 13.875 13.326 13.021 13.018 18.69 2.22 -45.16 2.22 0.93 12 GCS9 Cl* Melotte 22 BPL 143 +03 47 29.480 +24 12 19.70 0 15.420 14.860 14.256 13.725 13.372 13.384 17.13 2.22 -47.51 2.22 0.76 12 GCS9 UGCS J034729.48+241219.7 +03 47 30.600 +24 22 13.80 0 13.050 12.722 12.199 11.658 11.352 11.381 18.57 2.22 -44.22 2.22 0.94 12 GCS9 V* QT Tau +03 51 39.080 +23 22 04.30 0 15.003 14.530 13.931 13.387 13.058 13.060 21.33 2.25 -42.78 2.25 0.85 12 GCS9 Cl* Melotte 22 BPL 237 +03 52 05.580 +22 34 55.10 0 13.116 12.817 12.304 11.756 11.447 11.468 18.18 2.51 -46.19 2.51 0.91 12 GCS9 Cl* Melotte 22 DH 740 +03 52 34.480 +22 30 07.50 0 13.080 12.786 12.289 11.679 11.396 11.423 16.37 2.51 -36.85 2.51 0.63 12 GCS9 V* V395 Tau +03 46 22.470 +23 29 08.00 0 14.962 14.417 13.743 13.146 12.784 12.747 19.14 2.24 -43.87 2.24 0.94 12 GCS9 UGCS J034622.46+232908.0 +03 52 51.720 +22 31 32.70 0 13.700 13.329 12.777 12.199 11.894 11.930 19.14 2.51 -43.95 2.51 0.94 12 GCS9 V* V568 Tau +03 47 15.380 +23 26 05.80 0 14.386 13.944 13.385 12.825 12.503 12.475 15.92 2.24 -43.02 2.24 0.92 12 GCS9 Cl* Melotte 22 HHJ 203 +03 52 56.970 +22 26 01.10 0 13.248 12.945 12.403 11.839 11.543 11.594 20.34 2.51 -44.90 2.51 0.92 12 GCS9 V* V883 Tau +03 43 43.530 +24 12 50.00 0 15.544 15.025 14.407 13.872 13.518 18.44 2.31 -44.45 2.31 0.89 12 GCS9 Cl* Melotte 22 DH 284 +03 47 22.030 +23 21 36.30 0 14.515 14.064 13.489 12.961 12.637 12.619 20.70 2.24 -41.53 2.24 0.93 12 GCS9 UGCS J034722.02+232136.3 +03 47 28.120 +23 26 53.40 0 13.694 13.309 12.779 12.225 11.885 11.879 19.40 2.24 -40.95 2.24 0.92 12 GCS9 Cl* Melotte 22 MSK 140 +03 47 32.190 +23 18 23.30 0 14.313 13.888 13.351 12.830 12.511 12.509 18.09 2.24 -46.67 2.24 0.90 12 GCS9 UGCS J034732.19+231823.2 +03 43 51.760 +24 14 15.90 0 14.292 13.838 13.229 12.650 12.358 22.34 2.30 -41.81 2.30 0.90 12 GCS9 V* V847 Tau +03 43 52.770 +24 18 48.40 0 16.556 15.907 15.211 14.628 14.221 19.79 2.32 -39.76 2.32 0.64 12 GCS9 UGCS J034352.77+241848.4 +03 47 36.040 +23 28 26.70 0 15.203 14.719 14.127 13.601 13.266 13.247 18.53 2.24 -46.44 2.24 0.83 12 GCS9 Cl* Melotte 22 DH 526 +03 43 57.290 +24 13 20.30 0 14.206 13.775 13.190 12.639 12.337 21.78 2.30 -37.51 2.30 0.72 12 GCS9 V* V511 Tau +04 00 08.160 +22 32 01.10 1 16.449 15.820 15.102 14.503 14.098 14.104 16.31 2.88 -39.63 2.88 0.64 12 GCS9 UGCS J040008.16+223201.0 +03 48 04.980 +23 24 13.40 0 15.976 15.415 14.783 14.280 13.912 13.935 16.66 2.25 -43.18 2.25 0.89 12 GCS9 Cl* Melotte 22 DH 546 +03 39 55.210 +24 12 54.10 0 15.371 14.909 14.293 13.783 13.432 13.440 18.81 2.50 -45.72 2.50 0.86 12 GCS9 Cl* Melotte 22 MHO 2 +03 58 56.150 +23 27 53.50 0 12.935 12.592 12.055 11.526 11.193 11.220 20.59 2.99 -37.49 2.99 0.81 12 GCS9 Cl* Melotte 22 DH 868 +03 40 07.110 +24 13 03.30 0 15.403 14.934 14.318 13.802 13.450 13.451 16.92 2.50 -42.32 2.50 0.90 12 GCS9 Cl* Melotte 22 DH 126 +03 59 07.060 +22 27 48.20 0 14.565 14.030 13.393 12.824 12.466 12.480 16.23 2.84 -40.81 2.84 0.91 12 GCS9 UGCS J035907.05+222748.1 +03 59 18.990 +23 31 23.90 0 14.624 14.200 13.596 13.059 12.742 12.746 17.69 3.00 -45.41 3.00 0.92 12 GCS9 Cl* Melotte 22 DH 874 +03 40 26.950 +24 14 14.20 0 14.326 13.942 13.369 12.825 12.542 12.531 18.94 2.50 -42.53 2.50 0.94 12 GCS9 Cl* Melotte 22 DH 140 +03 49 59.540 +24 11 45.60 0 15.530 15.002 14.367 13.846 13.517 13.502 18.52 2.22 -41.66 2.22 0.90 12 GCS9 Cl* Melotte 22 BPL 220 +03 50 27.350 +23 22 44.90 0 14.389 13.925 13.358 12.817 12.499 12.514 24.33 2.13 -40.44 2.13 0.74 12 GCS9 Cl* Melotte 22 DH 670 +03 50 12.870 +24 21 06.20 0 12.697 12.419 11.916 11.378 11.086 11.149 15.86 2.22 -37.83 2.22 0.75 12 GCS9 UGCS J035012.86+242106.2 +03 50 13.000 +24 21 07.00 0 13.248 12.864 12.329 11.821 11.509 11.534 20.58 2.22 -44.46 2.22 0.92 12 GCS9 UGCS J035012.99+242106.9 +03 50 15.270 +24 13 36.00 0 14.103 13.701 13.153 12.628 12.339 12.347 18.72 2.22 -42.98 2.22 0.94 12 GCS9 V* V469 Tau +03 50 19.150 +24 16 34.00 0 16.445 15.797 15.103 14.564 14.190 14.166 20.67 2.23 -41.47 2.23 0.67 12 GCS9 Cl* Melotte 22 BPL 228 +03 50 42.150 +23 29 33.10 0 15.510 14.987 14.385 13.858 13.532 13.543 21.92 2.14 -45.96 2.14 0.74 12 GCS9 UGCS J035042.14+232933.0 +03 50 33.080 +24 20 21.60 0 14.355 13.948 13.365 12.845 12.537 12.525 16.87 2.22 -41.49 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 674 +03 50 42.440 +24 12 55.40 0 15.040 14.585 13.995 13.458 13.144 13.158 16.99 2.22 -39.63 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 683 +03 50 54.650 +24 21 55.70 0 14.574 14.149 13.570 13.048 12.740 12.793 17.43 2.22 -47.29 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 687 +03 47 22.460 +22 31 10.80 0 15.350 14.836 14.214 13.686 13.314 13.291 20.04 2.27 -39.70 2.27 0.83 12 GCS9 Cl* Melotte 22 BPL 140 +03 46 55.320 +23 22 49.30 0 15.196 14.729 14.159 13.630 13.284 13.299 18.52 2.24 -41.36 2.24 0.89 12 GCS9 Cl* Melotte 22 DH 479 +03 40 01.870 +24 46 25.90 0 14.203 13.811 13.240 12.713 12.468 12.420 19.86 2.48 -42.54 2.48 0.94 12 GCS9 V* V598 Tau +03 47 09.520 +23 25 56.30 0 14.905 14.464 13.880 13.361 13.040 13.087 20.47 2.24 -42.25 2.24 0.93 12 GCS9 Cl* Melotte 22 DH 498 +03 48 05.720 +22 38 09.30 0 14.248 13.814 13.195 12.717 12.425 12.432 15.27 2.26 -47.03 2.26 0.82 12 GCS9 V* V341 Tau +03 42 08.840 +23 35 16.80 0 12.944 12.632 12.126 11.626 11.318 11.344 14.70 2.12 -43.32 2.12 0.72 12 GCS9 Cl* Melotte 22 DH 212 +03 39 32.320 +24 16 01.10 0 13.986 13.660 13.081 12.505 12.208 12.233 21.26 2.50 -43.91 2.50 0.91 12 GCS9 V* V597 Tau +03 41 25.330 +22 32 55.80 0 13.437 13.076 12.526 11.946 11.699 11.718 20.54 2.50 -40.20 2.50 0.89 12 GCS9 V* V492 Tau +03 59 59.150 +23 41 04.60 0 15.249 14.710 14.084 13.517 13.217 20.59 3.55 -40.08 3.55 0.83 12 GCS9 Cl* Melotte 22 DH 883 +03 59 56.490 +23 40 15.30 0 16.179 15.510 14.834 14.290 13.933 18.14 3.57 -40.03 3.57 0.69 12 GCS9 Cl* Melotte 22 DH 882 +04 03 49.570 +23 43 13.70 0 14.866 14.377 13.796 13.269 12.931 12.931 22.02 3.38 -44.78 3.38 0.90 12 GCS9 Cl* Melotte 22 DH 910 +04 01 13.990 +23 10 29.90 0 13.872 13.459 12.893 12.304 12.031 12.021 20.88 3.35 -38.17 3.35 0.77 12 GCS9 Cl* Melotte 22 DH 899 +03 45 16.420 +23 34 01.60 0 15.628 15.046 14.415 13.871 13.502 13.519 16.66 2.22 -43.95 2.22 0.89 12 GCS9 Cl* Melotte 22 DH 384 +03 45 06.790 +23 36 51.40 0 14.751 14.200 13.573 12.993 12.645 12.675 16.78 2.22 -41.22 2.22 0.92 12 GCS9 V* V516 Tau +03 44 56.690 +23 36 23.50 0 14.114 13.697 13.146 12.622 12.313 12.314 22.12 2.22 -39.49 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 361 +03 44 51.260 +23 34 20.60 0 16.524 15.857 15.186 14.615 14.212 14.214 20.98 2.24 -41.95 2.24 0.66 12 GCS9 UGCS J034451.26+233420.5 +03 44 35.900 +23 34 41.90 1 16.307 15.672 14.985 14.376 13.990 13.985 16.94 2.23 -44.39 2.23 0.72 12 GCS9 Cl* Melotte 22 HHJ 5 +03 44 31.720 +23 35 26.00 0 14.020 13.606 13.060 12.451 12.150 12.189 19.59 2.22 -44.22 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 345 +03 49 01.160 +23 38 15.50 0 15.613 15.042 14.427 13.885 13.548 13.580 15.19 2.22 -43.96 2.22 0.86 12 GCS9 Cl* Melotte 22 DH 598 +03 36 22.330 +22 44 32.70 0 13.694 13.229 12.673 12.138 11.842 11.852 22.55 3.04 -41.33 3.04 0.85 12 GCS9 Cl* Melotte 22 DH 55 +03 48 19.840 +23 36 11.80 0 13.175 12.840 12.327 11.827 11.486 11.536 20.97 2.22 -44.35 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 568 +03 48 16.090 +23 35 15.20 0 14.654 14.122 13.520 12.988 12.645 12.641 20.07 2.22 -42.31 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 564 +03 48 15.270 +23 42 03.40 0 13.599 13.175 12.588 12.112 11.768 11.801 18.45 2.22 -45.66 2.22 0.92 12 GCS9 Cl* Melotte 22 DH 562 +03 48 13.780 +23 37 59.30 0 13.951 13.521 12.974 12.422 12.130 12.041 19.35 2.22 -44.07 2.22 0.94 12 GCS9 V* V458 Tau +03 48 08.960 +23 42 23.30 0 14.620 14.148 13.566 13.045 12.761 12.747 17.90 2.22 -46.56 2.22 0.90 12 GCS9 Cl* Melotte 22 DH 552 +03 41 02.990 +23 43 21.40 0 13.772 13.397 12.857 12.313 12.027 12.044 14.58 2.12 -38.42 2.12 0.71 12 GCS9 V* V603 Tau +03 38 13.040 +23 37 20.70 0 15.694 15.167 14.427 13.836 13.460 13.494 16.75 2.50 -45.95 2.50 0.84 12 GCS9 Cl* Melotte 22 HHJ 32 +03 41 43.720 +23 07 59.70 0 16.390 15.741 15.081 14.540 14.130 14.144 19.01 2.28 -38.92 2.28 0.60 12 GCS9 UGCS J034143.72+230759.7 +03 42 15.370 +23 11 30.90 0 14.906 14.419 13.845 13.300 12.963 12.972 22.08 2.25 -38.79 2.25 0.81 12 GCS9 Cl* Melotte 22 HHJ 109 +03 54 25.040 +24 42 43.40 0 14.895 14.283 13.653 13.120 12.763 12.750 20.21 2.23 -42.84 2.23 0.94 12 GCS9 V* V401 Tau +03 54 24.390 +22 41 20.50 0 14.947 14.441 13.840 13.254 12.936 12.943 20.19 2.26 -45.53 2.26 0.92 12 GCS9 UGCS J035424.39+224120.5 +03 49 58.340 +23 42 33.70 0 13.279 12.903 12.399 12.070 11.567 11.705 16.44 2.22 -43.28 2.22 0.93 12 GCS9 Cl* Melotte 22 MSK 211 +03 49 57.640 +23 43 28.20 0 15.488 14.887 14.245 13.710 13.395 13.386 20.76 2.22 -42.85 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 644 +04 01 39.830 +22 47 53.70 0 16.669 15.899 15.153 14.575 14.169 14.157 21.16 3.39 -42.62 3.39 0.65 12 GCS9 UGCS J040139.83+224753.7 +03 49 31.220 +23 41 19.60 0 14.646 14.158 13.575 13.029 12.724 12.749 20.25 2.22 -44.15 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 625 +03 49 29.810 +23 38 55.20 0 14.604 14.083 13.516 12.985 12.654 12.674 15.93 2.22 -46.60 2.22 0.86 12 GCS9 Cl* Melotte 22 DH 624 +03 49 21.500 +23 39 06.30 0 13.751 13.346 12.809 12.251 11.951 12.003 13.80 2.22 -45.12 2.22 0.82 12 GCS9 V* V551 Tau +03 46 12.670 +23 35 13.60 0 14.942 14.442 13.836 13.296 12.972 12.997 17.61 2.22 -42.42 2.22 0.94 12 GCS9 UGCS J034612.66+233513.5 +03 45 27.520 +23 37 56.90 0 15.142 14.678 14.099 13.543 13.236 13.231 19.00 2.22 -42.76 2.22 0.90 12 GCS9 Cl* Melotte 22 HHJ 106 +03 31 33.200 +27 32 49.10 0 14.985 14.331 13.622 13.095 12.730 20.59 6.93 -48.45 6.93 0.77 12 GCS9 UGCS J033133.20+273249.0 +03 48 23.920 +23 08 08.10 0 15.111 14.655 14.032 13.490 13.123 13.129 14.82 2.13 -40.92 2.13 0.83 12 GCS9 Cl* Melotte 22 DH 576 +03 48 40.990 +23 14 17.20 0 13.874 13.419 12.818 12.291 11.956 11.984 20.87 2.13 -42.75 2.13 0.92 12 GCS9 V* V875 Tau +03 50 15.430 +22 46 19.90 0 16.872 16.103 15.397 14.842 14.414 14.387 15.23 2.19 -42.79 2.19 0.70 12 GCS9 UGCS J035015.43+224619.8 +03 42 56.550 +22 51 17.90 0 15.901 15.338 14.689 14.137 13.782 13.760 16.35 2.27 -38.51 2.27 0.78 12 GCS9 UGCS J034256.54+225117.8 +03 43 04.200 +22 48 03.30 0 12.462 12.136 11.621 11.436 10.779 10.986 18.93 2.25 -40.66 2.25 0.90 12 GCS9 V* V435 Tau +03 42 29.430 +22 47 25.90 0 12.560 12.251 11.750 11.449 10.885 11.017 19.42 2.25 -40.94 2.25 0.90 12 GCS9 V* V613 Tau +03 43 19.010 +22 47 10.40 0 14.679 14.176 13.584 13.040 12.725 12.734 16.91 2.25 -46.41 2.25 0.89 12 GCS9 V* V621 Tau +03 41 26.360 +23 08 02.70 0 14.574 14.102 13.532 12.927 12.614 12.612 15.96 2.25 -41.75 2.25 0.92 12 GCS9 Cl* Melotte 22 DH 174 +03 40 51.820 +23 13 50.50 0 14.145 13.723 13.201 12.627 12.326 12.331 16.57 2.25 -41.87 2.25 0.93 12 GCS9 V* V601 Tau +03 41 25.970 +23 15 35.80 0 14.670 14.193 13.617 13.061 12.761 12.768 19.37 2.25 -43.60 2.25 0.94 12 GCS9 UGCS J034125.96+231535.8 +03 43 27.440 +22 37 41.00 0 15.234 14.678 14.118 13.589 13.256 13.235 23.77 2.51 -43.48 2.51 0.67 12 GCS9 V* LX Tau +03 39 44.790 +22 39 15.30 0 14.615 14.124 13.554 13.015 12.716 12.691 17.16 2.98 -42.48 2.98 0.94 12 GCS9 Cl* Melotte 22 DH 112 +03 40 07.010 +22 38 47.50 0 14.975 14.452 13.904 13.372 13.034 13.020 17.42 2.98 -39.40 2.98 0.89 12 GCS9 Cl* Melotte 22 HHJ 114 +03 34 35.570 +23 38 43.30 0 15.561 15.064 14.437 13.919 13.538 13.570 17.78 2.61 -36.55 2.61 0.60 12 GCS9 UGCS J033435.57+233843.3 +03 50 25.190 +22 40 31.70 0 15.262 14.699 14.109 13.541 13.204 13.220 18.66 2.13 -45.07 2.13 0.88 12 GCS9 UGCS J035025.18+224031.7 +03 50 04.250 +23 10 44.50 0 14.127 13.785 13.209 12.563 12.306 12.299 22.40 2.25 -39.72 2.25 0.84 12 GCS9 Cl* Melotte 22 DH 649 +03 49 41.140 +23 14 59.30 0 15.232 14.627 13.986 13.443 13.056 13.075 17.11 2.25 -39.25 2.25 0.83 12 GCS9 UGCS J034941.13+231459.2 +03 49 32.160 +23 16 17.90 0 15.381 14.888 14.287 13.753 13.418 13.424 20.17 2.25 -40.90 2.25 0.86 12 GCS9 Cl* Melotte 22 DH 626 +03 56 24.990 +23 05 26.60 0 12.641 12.387 11.904 11.372 11.058 11.087 17.85 2.99 -45.85 2.99 0.72 12 GCS9 Cl* Melotte 22 HHJ 419 +03 45 28.900 +27 28 07.20 0 12.839 12.475 11.993 11.460 11.179 11.209 18.03 3.44 -39.52 3.44 0.88 12 GCS9 Cl* Melotte 22 DH 394 +03 36 24.180 +22 37 24.30 0 14.137 13.724 13.127 12.613 12.276 12.327 17.43 2.94 -44.41 2.94 0.93 12 GCS9 UGCS J033624.18+223724.3 +03 43 36.570 +23 12 34.10 0 14.208 13.781 13.203 12.644 12.333 12.343 23.03 2.25 -39.25 2.25 0.79 12 GCS9 V* MN Tau +03 44 09.320 +23 08 46.80 0 14.412 13.974 13.380 12.815 12.486 12.506 19.99 2.25 -40.13 2.25 0.91 12 GCS9 Cl* Melotte 22 DH 313 +03 44 22.140 +23 10 54.80 0 15.807 15.195 14.479 13.913 13.468 13.518 16.09 2.26 -42.11 2.26 0.88 12 GCS9 Cl* Melotte 22 DH 329 +03 45 24.700 +24 38 46.40 0 15.819 15.190 14.549 13.983 13.645 13.672 18.14 2.22 -44.52 2.22 0.89 12 GCS9 UGCS J034524.69+243846.4 +03 45 06.560 +24 40 42.70 0 15.393 14.853 14.208 13.659 13.314 13.323 13.49 2.21 -39.94 2.21 0.71 12 GCS9 Cl* Melotte 22 HHJ 48 +03 45 08.760 +24 50 31.50 0 13.942 13.474 12.930 12.429 12.086 12.117 17.85 2.21 -41.29 2.21 0.93 12 GCS9 UGCS J034508.76+245031.4 +03 37 16.530 +23 11 04.20 0 14.370 13.944 13.393 12.855 12.551 12.556 23.40 2.97 -41.45 2.97 0.84 12 GCS9 Cl* Melotte 22 DH 69 +03 45 01.140 +24 46 40.90 0 14.445 13.990 13.411 12.861 12.557 12.556 13.05 2.21 -45.96 2.21 0.71 12 GCS9 V* V744 Tau +04 06 28.550 +22 36 59.60 0 14.067 13.659 13.076 12.477 12.156 12.133 17.57 5.07 -40.55 5.07 0.92 12 GCS9 UGCS J040628.54+223659.6 +03 58 34.180 +22 40 11.10 0 15.075 14.525 13.922 13.337 13.003 13.039 18.08 3.00 -38.66 3.00 0.81 12 GCS9 Cl* Melotte 22 DH 867 +03 50 08.620 +24 40 17.70 0 15.348 14.804 14.219 13.674 13.351 13.360 17.39 2.21 -43.94 2.21 0.90 12 GCS9 Cl* Melotte 22 DH 655 +03 49 55.790 +24 44 31.30 0 13.226 12.862 12.357 11.905 11.499 11.560 17.55 2.20 -41.05 2.20 0.92 12 GCS9 V* V364 Tau +03 31 49.860 +22 50 24.50 0 14.569 14.083 13.494 12.998 12.676 12.690 25.11 3.42 -44.59 3.42 0.68 12 GCS9 UGCS J033149.85+225024.4 +03 32 07.870 +23 13 57.20 0 14.025 13.621 13.052 12.517 12.218 12.199 18.14 3.41 -38.13 3.41 0.84 12 GCS9 Cl* Melotte 22 DH 17 +03 37 26.390 +24 34 01.20 0 14.315 13.924 13.363 12.833 12.539 12.563 20.85 2.48 -42.78 2.48 0.93 12 GCS9 Cl* Melotte 22 DH 71 +03 42 44.370 +23 06 16.10 0 14.900 14.423 13.813 13.265 12.950 12.934 14.75 2.25 -47.61 2.25 0.75 12 GCS9 Cl* Melotte 22 DH 240 +03 42 51.680 +23 08 43.70 0 14.593 14.085 13.451 12.866 12.527 12.518 20.56 2.25 -41.00 2.25 0.92 12 GCS9 UGCS J034251.68+230843.6 +03 49 21.250 +24 41 40.80 0 15.549 14.957 14.352 13.821 13.470 13.478 16.52 2.21 -46.03 2.21 0.84 12 GCS9 Cl* Melotte 22 DH 615 +03 48 39.310 +24 50 19.90 0 15.432 14.862 14.269 13.711 13.405 13.387 14.82 2.21 -44.18 2.21 0.84 12 GCS9 Cl* Melotte 22 DH 585 +03 54 37.360 +23 13 32.50 0 14.469 14.018 13.413 12.815 12.514 12.535 19.90 2.25 -37.77 2.25 0.81 12 GCS9 Cl* Melotte 22 DH 805 +03 51 23.820 +22 50 29.10 0 12.115 11.877 11.498 11.311 10.620 10.885 17.91 2.25 -42.23 2.25 0.88 12 GCS9 Cl* Melotte 22 DH 706 +03 39 43.320 +23 12 25.30 0 15.134 14.683 14.096 13.549 13.216 13.200 19.81 2.98 -37.33 2.98 0.67 12 GCS9 Cl* Melotte 22 DH 110 +03 46 28.630 +24 45 32.10 0 12.120 11.913 11.480 11.309 10.657 10.853 15.43 2.21 -40.74 2.21 0.81 12 GCS9 V* V446 Tau +03 46 17.940 +24 41 09.30 0 13.394 13.027 12.512 11.992 11.646 11.701 19.02 2.21 -41.15 2.21 0.93 12 GCS9 Cl* Melotte 22 DH 433 +03 46 08.700 +24 40 33.10 0 14.615 14.120 13.525 13.000 12.673 12.686 16.53 2.21 -42.88 2.21 0.93 12 GCS9 V* V857 Tau +03 46 02.960 +24 40 55.60 0 15.363 14.742 14.071 13.491 13.114 13.134 14.57 2.21 -41.20 2.21 0.83 12 GCS9 Cl* Melotte 22 DH 414 +03 45 36.720 +24 39 06.50 0 13.244 12.776 12.207 11.739 11.342 11.388 13.96 2.21 -44.03 2.21 0.85 12 GCS9 V* V443 Tau +03 50 18.220 +24 35 15.30 0 14.697 14.220 13.692 13.137 12.844 12.861 14.19 2.20 -44.75 2.20 0.85 12 GCS9 Cl* Melotte 22 DH 665 +03 50 10.770 +24 28 41.40 0 14.754 14.283 13.690 13.174 12.826 12.850 15.24 2.20 -41.67 2.20 0.90 12 GCS9 Cl* Melotte 22 DH 656 +03 49 33.040 +24 32 02.40 0 13.402 13.042 12.522 12.024 11.684 11.695 13.04 2.20 -43.72 2.20 0.79 12 GCS9 V* V361 Tau +03 46 27.010 +24 27 13.90 0 13.850 13.415 12.890 12.336 12.039 12.035 16.46 2.21 -47.67 2.21 0.82 12 GCS9 Cl* Melotte 22 DH 447 +03 46 24.640 +24 28 46.20 0 14.399 13.930 13.364 12.822 12.527 12.552 16.75 2.21 -43.26 2.21 0.93 12 GCS9 V* V1275 Tau +03 46 24.120 +24 30 12.70 0 15.893 15.305 14.689 14.145 13.797 13.818 18.76 2.22 -43.84 2.22 0.90 12 GCS9 Cl* Melotte 22 BPL 101 +03 46 23.030 +24 36 17.90 0 14.585 14.122 13.563 13.027 12.710 12.729 16.93 2.21 -44.40 2.21 0.93 12 GCS9 Cl* Melotte 22 DH 442 +03 46 21.360 +24 33 52.20 0 15.032 14.512 13.919 13.388 13.052 13.066 14.20 2.21 -42.68 2.21 0.83 12 GCS9 Cl* Melotte 22 DH 439 +03 56 57.080 +24 48 34.30 0 12.982 12.615 12.063 11.527 11.181 11.276 19.30 2.47 -39.94 2.47 0.89 12 GCS9 V* V693 Tau +03 46 05.640 +24 36 44.30 0 13.116 12.770 12.264 11.710 11.428 11.468 18.12 2.21 -41.35 2.21 0.93 12 GCS9 Cl* Melotte 22 SK 488 +03 46 05.690 +24 36 49.70 0 15.023 14.505 13.894 13.358 13.037 13.052 19.22 2.21 -40.25 2.21 0.87 12 GCS9 UGCS J034605.69+243649.7 +03 45 51.090 +24 26 10.90 0 15.873 15.293 14.658 14.103 13.746 13.760 17.00 2.22 -46.18 2.22 0.84 12 GCS9 Cl* Melotte 22 HHJ 25 +03 45 49.370 +24 25 07.10 0 13.560 13.177 12.633 12.107 11.764 11.792 17.27 2.21 -44.41 2.21 0.93 12 GCS9 Cl* Melotte 22 BPL 77 +03 45 35.690 +24 24 34.10 0 16.519 15.801 15.133 14.567 14.196 14.181 18.31 2.23 -42.12 2.23 0.74 12 GCS9 UGCS J034535.69+242434.1 +03 41 59.670 +24 42 18.30 0 16.192 15.586 14.953 14.380 14.016 16.64 2.99 -39.53 2.99 0.65 12 GCS9 Cl* Melotte 22 BPL 31 +03 52 46.450 +24 33 41.00 0 14.544 14.050 13.491 12.957 12.649 12.654 16.70 2.23 -37.50 2.23 0.76 12 GCS9 Cl* Melotte 22 BPL 270 +03 52 43.240 +24 27 58.50 0 14.755 14.266 13.689 13.131 12.827 12.833 17.78 2.23 -41.41 2.23 0.93 12 GCS9 Cl* Melotte 22 DH 763 +03 52 30.910 +24 32 39.50 0 13.409 12.945 12.392 11.866 11.560 11.576 14.80 2.23 -43.14 2.23 0.89 12 GCS9 V* V476 Tau +03 52 20.660 +24 33 55.50 0 12.822 12.483 11.971 11.439 11.148 11.166 14.85 2.23 -40.95 2.23 0.78 12 GCS9 V* V392 Tau +03 42 03.300 +24 32 13.30 0 13.337 13.002 12.464 11.883 11.656 17.31 2.97 -48.35 2.97 0.79 12 GCS9 Cl* Melotte 22 DH 205 +03 50 38.920 +23 13 02.50 0 13.465 13.101 12.583 12.025 11.734 11.729 18.28 2.13 -37.84 2.13 0.79 12 GCS9 Cl* Melotte 22 DH 678 +03 48 50.450 +22 44 29.80 1 16.562 15.825 15.098 14.531 14.141 14.143 15.64 2.16 -42.06 2.16 0.71 12 GCS9 Cl* Melotte 22 HHJ 3 +03 46 57.100 +23 15 02.20 0 12.759 12.476 11.986 11.443 11.115 11.167 22.62 2.23 -41.65 2.23 0.83 12 GCS9 V* V453 Tau +03 49 15.120 +24 36 22.50 0 16.847 16.059 15.371 14.890 14.433 14.443 15.04 2.23 -40.44 2.23 0.64 12 GCS9 UGCS J034915.12+243622.5 +03 48 45.350 +24 37 26.30 0 15.118 14.581 13.971 13.507 13.157 13.123 15.61 2.20 -45.81 2.20 0.83 12 GCS9 V* V463 Tau +03 48 44.690 +24 37 23.50 0 16.260 15.584 14.911 14.419 14.002 13.967 17.45 2.22 -43.06 2.22 0.75 12 GCS9 2MASS J03484468+2437237 +03 48 42.690 +24 27 19.40 0 15.480 14.961 14.356 13.822 13.479 13.475 18.31 2.21 -46.04 2.21 0.85 12 GCS9 Cl* Melotte 22 DH 587 +03 48 40.430 +24 36 34.00 0 14.182 13.705 13.144 12.632 12.321 12.314 17.15 2.20 -46.57 2.20 0.89 12 GCS9 V* V662 Tau +03 51 19.480 +23 09 49.40 0 14.028 13.596 13.027 12.440 12.139 12.139 19.25 2.25 -39.56 2.25 0.90 12 GCS9 Cl* Melotte 22 DH 703 +03 51 51.150 +23 17 41.10 0 13.445 13.060 12.527 12.014 11.711 11.750 17.13 2.25 -44.72 2.25 0.93 12 GCS9 V* V802 Tau +03 58 30.990 +23 04 12.70 0 15.161 14.622 14.030 13.494 13.122 13.120 16.32 3.00 -42.51 3.00 0.89 12 GCS9 Cl* Melotte 22 DH 866 +03 42 29.710 +20 48 39.60 0 14.264 13.827 13.235 12.712 12.379 12.411 20.54 3.42 -38.07 3.42 0.82 12 GCS9 Cl* Melotte 22 DH 229 +03 43 22.550 +23 00 56.50 0 16.186 15.558 14.898 14.365 13.983 13.988 16.28 2.27 -44.63 2.27 0.70 12 GCS9 UGCS J034322.55+230056.5 +03 51 19.770 +23 04 04.10 0 15.411 14.858 14.210 13.638 13.312 13.329 16.38 2.26 -41.02 2.26 0.88 12 GCS9 Cl* Melotte 22 DH 704 +03 51 50.600 +22 53 44.30 0 13.892 13.435 12.910 12.329 12.055 12.069 13.26 2.25 -40.14 2.25 0.72 12 GCS9 V* V386 Tau +03 52 11.230 +20 52 33.40 0 14.537 14.059 13.462 12.929 12.611 12.655 19.77 3.43 -45.98 3.43 0.91 12 GCS9 Cl* Melotte 22 DH 743 +03 46 36.070 +23 04 17.20 0 13.827 13.418 12.875 12.391 12.040 12.042 18.53 2.24 -43.10 2.24 0.94 12 GCS9 Cl* Melotte 22 MSK 100 +03 46 48.790 +23 04 07.30 0 13.100 12.757 12.271 11.916 11.443 11.467 18.54 2.23 -39.65 2.23 0.90 12 GCS9 V* V533 Tau +03 46 21.620 +23 04 00.90 0 14.695 14.171 13.539 13.025 12.701 12.683 18.17 2.24 -41.21 2.24 0.93 12 GCS9 Cl* Melotte 22 DH 440 +03 46 31.020 +23 01 34.60 0 15.742 15.178 14.589 14.043 13.704 13.689 15.36 2.24 -40.21 2.24 0.83 12 GCS9 Cl* Melotte 22 DH 453 +03 46 19.410 +23 00 55.70 0 15.317 14.693 14.026 13.491 13.124 13.115 13.77 2.24 -42.63 2.24 0.80 12 GCS9 Cl* Melotte 22 DH 435 +03 46 34.800 +22 56 07.50 0 12.325 12.095 11.650 11.585 10.883 11.064 23.59 2.23 -45.09 2.23 0.61 12 GCS9 V* V750 Tau +03 48 05.830 +23 02 02.70 0 13.241 12.881 12.377 11.963 11.540 11.562 16.95 2.23 -43.77 2.23 0.93 12 GCS9 V* V340 Tau +03 47 39.800 +23 00 04.40 0 15.088 14.635 14.074 13.518 13.202 13.203 16.06 2.24 -42.09 2.24 0.88 12 GCS9 Cl* Melotte 22 HHJ 94 +03 47 52.880 +22 59 33.80 0 14.017 13.564 12.998 12.456 12.134 12.160 15.85 2.24 -40.42 2.24 0.89 12 GCS9 Cl* Melotte 22 BPL 160 +03 43 44.970 +23 03 21.10 0 13.553 13.162 12.604 12.043 11.693 11.711 17.85 2.25 -47.74 2.25 0.84 12 GCS9 Cl* Melotte 22 HHJ 321 +03 43 25.160 +22 53 44.30 0 14.833 14.335 13.716 13.175 12.858 12.813 14.86 2.25 -45.95 2.25 0.85 12 GCS9 Cl* Melotte 22 DH 263 +03 50 27.830 +23 03 54.90 0 14.836 14.340 13.762 13.228 12.911 12.910 17.92 2.13 -41.80 2.13 0.94 12 GCS9 Cl* Melotte 22 DH 671 +03 50 17.590 +22 55 58.80 0 15.396 14.844 14.246 13.695 13.353 13.360 19.17 2.13 -44.31 2.13 0.89 12 GCS9 Cl* Melotte 22 DH 664 +03 54 55.340 +22 59 40.10 0 15.419 14.857 14.201 13.635 13.303 13.270 13.51 2.26 -44.04 2.26 0.78 12 GCS9 Cl* Melotte 22 HHJ 62 +03 55 11.850 +22 58 02.50 0 15.060 14.458 13.794 13.249 12.894 12.890 18.52 2.26 -46.52 2.26 0.83 12 GCS9 Cl* Melotte 22 HHJ 61 +03 40 23.840 +23 04 09.00 0 14.483 14.064 13.509 12.971 12.665 12.677 22.29 2.98 -42.37 2.98 0.90 12 GCS9 Cl* Melotte 22 DH 135 +03 39 49.720 +23 03 26.20 0 14.613 14.161 13.592 13.065 12.757 12.755 22.48 2.98 -41.93 2.98 0.89 12 GCS9 Cl* Melotte 22 DH 117 +03 40 51.080 +20 41 17.20 0 15.855 15.298 14.617 14.061 13.681 13.705 20.56 3.44 -38.49 3.44 0.75 12 GCS9 Cl* Melotte 22 DH 155 +03 42 23.660 +27 45 56.30 0 13.845 13.397 12.838 12.326 11.997 12.001 23.57 3.28 -40.53 3.28 0.76 12 GCS9 UGCS J034223.65+274556.2 +03 49 01.020 +22 58 49.20 0 12.708 12.420 11.923 11.443 11.161 11.220 17.88 2.12 -44.01 2.12 0.84 12 GCS9 V* V548 Tau +03 48 50.670 +23 04 30.00 0 15.010 14.511 13.936 13.425 13.098 13.108 17.50 2.13 -43.02 2.13 0.90 12 GCS9 Cl* Melotte 22 HHJ 116 +03 48 35.200 +22 53 42.10 1 16.176 15.452 14.745 14.185 13.770 13.772 15.22 2.15 -45.62 2.15 0.62 12 GCS9 V* V661 Tau +03 48 22.660 +22 52 21.30 0 13.174 12.807 12.291 11.768 11.473 11.505 20.45 2.13 -41.09 2.13 0.91 12 GCS9 V* V870 Tau +03 41 58.660 +22 57 01.70 0 13.636 13.270 12.744 12.173 11.877 11.904 21.15 2.25 -41.08 2.25 0.90 12 GCS9 V* V432 Tau +03 41 46.660 +23 01 19.60 0 15.129 14.622 14.032 13.502 13.175 13.164 18.10 2.25 -44.32 2.25 0.89 12 GCS9 UGCS J034146.65+230119.6 +03 40 54.480 +22 54 25.50 0 14.916 14.411 13.824 13.282 12.957 12.960 17.93 2.25 -41.18 2.25 0.93 12 GCS9 Cl* Melotte 22 DH 159 +03 53 15.710 +22 52 14.30 0 13.571 13.174 12.615 12.057 11.772 11.797 16.50 2.25 -42.99 2.25 0.93 12 GCS9 V* V681 Tau +03 53 10.160 +23 03 10.60 0 13.483 13.099 12.546 11.958 11.672 11.670 16.27 2.25 -43.64 2.25 0.93 12 GCS9 Cl* Melotte 22 DH 779 +03 53 01.630 +22 58 48.20 0 14.463 13.982 13.388 12.835 12.536 12.506 18.23 2.25 -41.99 2.25 0.94 12 GCS9 Cl* Melotte 22 DH 776 +03 49 41.210 +22 56 40.50 0 16.238 15.641 14.994 14.420 14.076 14.084 20.33 2.27 -43.06 2.27 0.69 12 GCS9 Cl* Melotte 22 BPL 213 +03 49 25.610 +23 02 49.90 0 15.146 14.643 14.021 13.499 13.173 13.172 20.07 2.25 -44.25 2.25 0.87 12 GCS9 Cl* Melotte 22 DH 620 +03 45 56.970 +23 01 29.00 0 14.372 13.939 13.379 12.847 12.515 12.570 16.42 2.24 -40.16 2.24 0.90 12 GCS9 V* V524 Tau +03 57 35.270 +22 59 08.00 0 13.457 13.075 12.532 11.943 11.634 11.688 20.34 2.99 -43.22 2.99 0.93 12 GCS9 UGCS J035735.26+225907.9 +03 59 29.420 +20 34 29.20 0 16.672 16.008 15.311 14.764 14.373 14.392 16.88 4.05 -38.85 4.05 0.60 12 GCS9 UGCS J035929.42+203429.1 +03 33 49.810 +22 56 19.60 0 15.069 14.626 14.048 13.507 13.194 13.190 16.86 3.05 -39.29 3.05 0.83 12 GCS9 Cl* Melotte 22 DH 31 +03 45 10.820 +23 02 58.00 0 14.146 13.717 13.137 12.612 12.306 12.336 16.69 2.24 -45.16 2.24 0.92 12 GCS9 V* V440 Tau +03 44 38.950 +23 02 25.30 0 14.434 13.951 13.374 12.833 12.489 12.506 17.33 2.24 -45.39 2.24 0.92 12 GCS9 Cl* Melotte 22 DH 351 +03 44 37.780 +22 55 15.50 0 12.736 12.396 11.884 11.404 11.020 11.104 20.58 2.23 -42.26 2.23 0.88 12 GCS9 V* NS Tau +03 51 20.680 +19 38 37.80 0 13.526 13.101 12.519 12.042 11.710 11.730 21.01 3.97 -48.07 3.97 0.77 12 GCS9 Cl* Melotte 22 DH 705 +03 53 35.390 +21 47 05.70 0 16.517 15.849 15.160 14.619 14.236 14.231 17.16 2.55 -42.61 2.55 0.75 12 GCS9 UGCS J035335.39+214705.6 +03 42 35.650 +21 50 29.70 0 12.761 12.502 12.030 11.527 11.224 11.275 23.68 2.51 -42.67 2.51 0.75 12 GCS9 V* V614 Tau +03 45 08.890 +19 43 29.90 0 16.502 15.850 15.168 14.631 14.237 14.214 17.00 4.02 -40.72 4.02 0.71 12 GCS9 UGCS J034508.89+194329.8 +03 49 41.710 +21 56 19.20 0 15.963 15.359 14.685 14.130 13.770 13.750 22.57 2.52 -39.99 2.52 0.72 12 GCS9 Cl* Melotte 22 BPL 214 +03 59 29.330 +21 39 56.00 0 15.657 15.115 14.478 13.964 13.589 13.587 22.43 3.78 -42.75 3.78 0.79 12 GCS9 Cl* Melotte 22 DH 878 +03 59 28.420 +19 30 41.20 0 14.604 14.084 13.471 12.921 12.572 12.598 16.99 3.04 -38.71 3.04 0.86 12 GCS9 UGCS J035928.42+193041.1 +03 57 01.510 +19 23 22.10 0 15.564 15.023 14.454 13.871 13.553 13.566 19.45 6.06 -38.27 6.06 0.77 12 GCS9 UGCS J035701.50+192322.0 +03 56 38.760 +21 35 08.30 0 14.888 14.393 13.787 13.259 12.937 12.927 18.67 3.36 -40.82 3.36 0.93 12 GCS9 Cl* Melotte 22 DH 841 +04 01 26.060 +21 35 08.50 0 14.044 13.640 13.072 12.491 12.192 12.191 18.27 3.75 -43.61 3.75 0.94 12 GCS9 Cl* Melotte 22 DH 900 +04 01 45.820 +21 44 04.90 0 16.644 15.914 15.183 14.661 14.260 14.256 18.38 3.82 -39.06 3.82 0.62 12 GCS9 UGCS J040145.82+214404.8 +03 46 16.080 +21 41 09.90 0 15.678 15.135 14.516 13.979 13.651 13.622 18.91 2.66 -43.14 2.66 0.90 12 GCS9 UGCS J034616.08+214109.8 +03 42 33.110 +21 42 16.60 0 14.159 13.678 13.082 12.475 12.163 12.158 18.09 3.58 -43.09 3.58 0.94 12 GCS9 UGCS J034233.10+214216.6 +03 35 04.720 +25 50 48.00 0 15.967 15.347 14.711 14.131 13.773 14.82 5.33 -43.91 5.33 0.85 12 GCS9 Cl* Melotte 22 DH 41 +03 44 11.920 +19 18 19.10 0 12.681 12.379 11.886 11.382 10.992 11.107 22.77 3.96 -43.73 3.96 0.77 12 GCS9 Cl* Melotte 22 DH 318 +03 32 57.570 +27 17 19.40 0 15.608 15.014 14.363 13.807 13.462 13.477 15.57 3.76 -46.28 3.76 0.80 12 GCS9 Cl* Melotte 22 DH 24 +03 46 40.270 +25 43 53.40 0 13.808 13.431 12.871 12.280 12.005 12.002 16.42 2.23 -45.47 2.23 0.91 12 GCS9 Cl* Melotte 22 DH 464 +03 40 37.820 +21 24 22.50 0 14.983 14.452 13.790 13.151 12.787 12.764 22.77 3.58 -38.56 3.58 0.76 12 GCS9 UGCS J034037.82+212422.5 +03 55 03.610 +21 31 09.60 0 15.425 14.932 14.277 13.754 13.419 13.419 21.47 3.36 -41.78 3.36 0.84 12 GCS9 UGCS J035503.61+213109.6 +03 48 48.230 +21 24 40.50 0 14.474 14.049 13.470 12.887 12.617 12.608 22.67 3.05 -42.13 3.05 0.89 12 GCS9 UGCS J034848.23+212440.4 +03 46 32.790 +19 17 30.20 0 14.370 13.858 13.285 12.792 12.433 12.437 22.35 5.04 -42.45 5.04 0.90 12 GCS9 Cl* Melotte 22 DH 455 +03 27 54.260 +24 56 10.90 0 13.808 13.400 12.820 12.255 12.005 16.62 6.95 -43.60 6.95 0.93 12 GCS9 Cl* Melotte 22 DH 3 +03 43 28.210 +24 53 30.90 0 13.950 13.546 12.992 12.414 12.165 18.00 2.97 -39.09 2.97 0.87 12 GCS9 V* V436 Tau +03 43 48.450 +25 02 36.70 0 13.856 13.401 12.797 12.260 11.888 20.40 2.97 -43.69 2.97 0.93 12 GCS9 V* V625 Tau +03 44 24.680 +24 51 53.20 0 13.805 13.361 12.765 12.246 11.942 18.59 2.97 -47.54 2.97 0.86 12 GCS9 V* V630 Tau +03 49 13.230 +21 22 00.80 0 15.580 15.063 14.449 13.905 13.547 13.560 21.58 3.05 -41.77 3.05 0.83 12 GCS9 UGCS J034913.22+212200.8 +04 00 33.590 +21 27 10.10 0 15.280 14.774 14.134 13.608 13.262 13.253 18.18 3.00 -39.40 3.00 0.84 12 GCS9 UGCS J040033.58+212710.0 +03 50 29.960 +25 03 06.70 0 13.449 13.075 12.549 12.026 11.684 11.707 16.35 2.20 -39.64 2.20 0.87 12 GCS9 V* V470 Tau +03 47 44.440 +24 36 55.70 0 15.085 14.555 13.994 13.447 13.132 13.121 14.49 2.21 -41.75 2.21 0.83 12 GCS9 UGCS J034744.44+243655.6 +03 47 38.370 +24 35 59.70 0 15.455 14.891 14.304 13.737 13.408 13.395 15.03 2.21 -41.64 2.21 0.85 12 GCS9 UGCS J034738.36+243559.6 +03 47 59.380 +24 35 37.00 0 15.631 15.079 14.486 13.940 13.592 13.581 15.52 2.22 -43.79 2.22 0.87 12 GCS9 V* V1281 Tau +03 48 21.540 +24 34 43.40 0 14.868 14.334 13.789 13.233 12.918 12.937 13.23 2.21 -46.01 2.21 0.73 12 GCS9 Cl* Melotte 22 DH 571 +03 47 50.950 +24 30 18.60 0 13.152 12.806 12.298 11.944 11.443 11.472 18.40 2.21 -45.72 2.21 0.92 12 GCS9 V* V654 Tau +03 47 39.360 +24 27 31.90 0 14.048 13.600 13.019 12.442 12.145 12.139 19.38 2.21 -43.38 2.21 0.94 12 GCS9 V* V457 Tau +03 47 49.790 +24 25 43.10 0 14.551 14.074 13.497 12.962 12.669 12.650 20.57 2.21 -46.82 2.21 0.87 12 GCS9 V* V866 Tau +03 47 37.660 +24 24 23.10 0 14.791 14.244 13.638 13.102 12.757 12.763 18.80 2.21 -47.96 2.21 0.84 12 GCS9 V* V1280 Tau +03 47 32.140 +24 24 18.40 0 14.949 14.466 13.873 13.317 13.042 13.004 16.18 2.21 -46.43 2.21 0.88 12 GCS9 UGCS J034732.14+242418.3 +03 38 45.750 +24 28 03.70 0 15.071 14.563 13.928 13.455 13.074 13.027 21.00 2.49 -40.88 2.49 0.84 12 GCS9 Cl* Melotte 22 DH 94 +03 40 24.200 +24 35 04.00 0 13.263 12.920 12.433 11.873 11.618 11.628 17.38 2.48 -40.53 2.48 0.91 12 GCS9 V* V491 Tau +03 40 26.090 +24 32 13.00 0 14.779 14.302 13.776 13.231 12.923 12.924 16.10 2.49 -36.49 2.49 0.61 12 GCS9 UGCS J034026.08+243212.9 +03 35 57.210 +24 36 08.70 0 13.467 13.062 12.538 12.051 11.754 11.753 14.41 2.62 -44.34 2.62 0.87 12 GCS9 UGCS J033557.21+243608.7 +03 51 34.010 +24 34 08.90 0 14.400 13.929 13.362 12.776 12.488 12.482 18.64 2.20 -46.47 2.20 0.90 12 GCS9 Cl* Melotte 22 DH 715 +03 43 26.980 +24 27 09.60 0 13.246 12.790 12.281 11.739 11.444 16.27 2.97 -42.45 2.97 0.92 12 GCS9 Cl* Melotte 22 DH 267 +03 43 20.580 +24 26 34.90 0 15.208 14.646 14.060 13.490 13.168 18.87 2.97 -40.83 2.97 0.88 12 GCS9 V* V620 Tau +03 44 09.610 +24 35 22.10 0 13.826 13.427 12.892 12.314 12.044 21.90 2.97 -41.53 2.97 0.89 12 GCS9 Cl* Melotte 22 DH 314 +03 44 19.060 +24 35 18.20 0 14.319 13.882 13.291 12.722 12.476 17.45 2.97 -46.24 2.97 0.90 12 GCS9 V* V512 Tau +03 43 43.150 +24 32 56.00 0 15.104 14.614 14.005 13.427 13.151 12.39 2.97 -42.08 2.97 0.69 12 GCS9 Cl* Melotte 22 DH 283 +03 44 26.540 +24 29 11.30 0 14.133 13.585 12.995 12.394 12.124 20.35 2.97 -40.21 2.97 0.91 12 GCS9 Cl* Melotte 22 DH 338 +03 44 17.760 +24 26 46.70 0 13.476 13.079 12.533 11.925 11.668 19.37 2.97 -46.95 2.97 0.88 12 GCS9 V* V849 Tau +03 54 39.130 +24 35 54.50 0 15.791 15.217 14.572 14.039 13.702 13.694 20.01 2.24 -43.73 2.24 0.88 12 GCS9 Cl* Melotte 22 DH 806 +03 37 37.700 +26 21 04.00 0 14.598 14.134 13.546 13.006 12.705 12.701 23.72 3.30 -44.04 3.30 0.83 12 GCS9 Cl* Melotte 22 DH 76 +03 50 54.990 +24 33 03.50 0 14.875 14.403 13.831 13.280 12.966 12.967 14.29 2.20 -41.34 2.20 0.85 12 GCS9 Cl* Melotte 22 MBSC 69 +03 51 03.620 +24 32 35.10 0 14.283 13.819 13.257 12.749 12.420 12.445 15.93 2.20 -47.96 2.20 0.78 12 GCS9 Cl* Melotte 22 DH 691 +03 50 57.410 +24 24 44.40 0 15.215 14.612 13.988 13.487 13.097 13.117 15.13 2.21 -45.11 2.21 0.83 12 GCS9 2MASS J03505739+2424447 +03 50 15.960 +26 11 37.00 0 16.295 15.586 14.902 14.337 13.979 13.985 18.50 2.96 -41.40 2.96 0.73 12 GCS9 UGCS J035015.96+261137.0 +03 42 56.570 +24 13 45.40 0 14.910 14.439 13.859 13.319 12.979 12.984 21.34 2.13 -42.10 2.13 0.92 12 GCS9 Cl* Melotte 22 BPL 47 +03 43 01.270 +24 14 52.20 0 15.292 14.794 14.179 13.655 13.284 13.296 22.91 2.13 -39.81 2.13 0.68 12 GCS9 Cl* Melotte 22 BPL 50 +03 43 13.470 +24 11 00.90 0 15.692 15.163 14.527 14.001 13.642 13.649 16.42 2.13 -42.52 2.13 0.89 12 GCS9 UGCS J034313.46+241100.9 +03 49 35.290 +25 59 34.60 0 14.594 14.082 13.534 12.974 12.654 12.671 22.66 2.23 -45.19 2.23 0.86 12 GCS9 Cl* Melotte 22 DH 630 +03 49 41.340 +26 08 53.20 0 13.701 13.323 12.813 12.205 11.934 11.957 15.62 2.23 -40.88 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 636 +03 53 35.520 +26 07 08.00 0 14.722 14.217 13.594 13.076 12.734 12.735 14.81 2.94 -41.03 2.94 0.87 12 GCS9 Cl* Melotte 22 DH 790 +03 45 43.180 +26 02 26.60 0 14.195 13.765 13.158 12.560 12.284 12.301 19.62 2.23 -41.47 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 401 +03 46 53.610 +24 17 14.80 0 12.961 12.563 12.023 11.548 11.200 11.255 17.64 2.22 -38.70 2.22 0.85 12 GCS9 V* PV Tau +03 46 55.320 +24 11 16.60 0 14.959 14.371 13.702 13.181 12.828 12.830 19.31 2.22 -40.67 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 478 +03 47 07.880 +24 23 37.80 0 15.094 14.598 13.954 13.415 13.080 13.110 14.17 2.22 -44.83 2.22 0.80 12 GCS9 V* V1278 Tau +03 47 08.150 +24 18 24.50 0 13.675 13.278 12.730 12.176 11.884 11.936 17.08 2.22 -40.00 2.22 0.90 12 GCS9 V* V861 Tau +03 47 09.420 +24 15 34.70 0 15.682 15.099 14.442 13.938 13.584 13.587 15.53 2.22 -37.71 2.22 0.68 12 GCS9 Cl* Melotte 22 DH 496 +03 47 11.030 +24 13 51.50 0 15.376 14.852 14.229 13.692 13.377 13.365 16.70 2.22 -43.11 2.22 0.90 12 GCS9 V* QS Tau +03 47 11.790 +24 13 31.30 1 16.305 15.554 14.792 14.261 13.841 13.850 16.88 2.23 -41.27 2.23 0.73 12 GCS9 Cl* Melotte 22 BPL 130 +03 47 11.860 +24 13 53.80 0 15.279 14.681 14.013 13.491 13.135 13.158 17.87 2.22 -38.56 2.22 0.80 12 GCS9 Cl* Melotte 22 DH 499 +03 35 09.410 +24 14 19.80 0 12.614 12.358 11.884 11.322 11.049 11.083 16.71 2.60 -38.59 2.60 0.83 12 GCS9 Cl* Melotte 22 DH 42 +03 40 43.200 +22 49 53.80 0 14.757 14.295 13.730 13.177 12.897 12.868 17.03 2.25 -46.07 2.25 0.90 12 GCS9 Cl* Melotte 22 DH 151 +03 49 56.810 +24 59 07.10 0 16.463 15.839 15.148 14.608 14.190 14.194 14.39 2.22 -40.78 2.22 0.61 12 GCS9 Cl* Melotte 22 KPNO 5 +03 50 06.560 +24 59 46.30 0 13.866 13.405 12.811 12.332 11.962 11.976 15.86 2.20 -43.61 2.20 0.92 12 GCS9 Cl* Melotte 22 DH 653 +03 46 19.860 +24 59 01.30 0 13.787 13.340 12.776 12.279 11.946 11.942 14.60 2.21 -42.76 2.21 0.88 12 GCS9 Cl* Melotte 22 DH 437 +03 45 12.440 +22 41 50.80 0 14.099 13.675 13.124 12.550 12.250 12.251 19.96 2.24 -47.27 2.24 0.87 12 GCS9 Cl* Melotte 22 DH 380 +03 52 35.320 +25 01 04.50 0 14.769 14.195 13.590 13.034 12.700 12.703 17.35 2.23 -42.19 2.23 0.94 12 GCS9 Cl* Melotte 22 DH 758 +03 49 21.930 +24 54 43.20 0 14.596 14.111 13.560 13.037 12.733 12.726 19.53 2.20 -43.15 2.20 0.94 12 GCS9 Cl* Melotte 22 DH 617 +03 37 47.510 +24 53 46.10 0 15.183 14.722 14.132 13.612 13.311 13.287 16.11 2.49 -40.51 2.49 0.86 12 GCS9 Cl* Melotte 22 DH 78 +03 49 26.640 +22 50 54.50 0 14.354 13.911 13.327 12.787 12.467 12.447 15.77 2.25 -44.36 2.25 0.91 12 GCS9 Cl* Melotte 22 SK 315 +03 48 17.610 +22 04 00.90 0 14.425 13.954 13.373 12.851 12.543 12.515 19.94 2.26 -43.28 2.26 0.94 12 GCS9 Cl* Melotte 22 BPL 171 +03 48 42.150 +25 00 28.20 0 13.453 13.101 12.555 12.084 11.703 11.724 16.19 2.20 -44.52 2.20 0.92 12 GCS9 V* V461 Tau +03 48 40.600 +25 01 19.80 0 15.780 15.212 14.553 14.023 13.660 13.652 17.54 2.21 -42.42 2.21 0.90 12 GCS9 Cl* Melotte 22 BPL 186 +03 41 39.840 +24 52 30.00 0 15.292 14.819 14.192 13.649 13.304 18.60 2.97 -44.72 2.97 0.88 12 GCS9 Cl* Melotte 22 HHJ 69 +03 41 47.090 +25 00 21.90 0 14.895 14.426 13.824 13.261 12.933 21.19 2.97 -41.96 2.97 0.92 12 GCS9 Cl* Melotte 22 HHJ 125 +03 42 28.660 +25 01 00.20 0 13.341 13.007 12.475 11.969 11.689 19.64 2.97 -44.22 2.97 0.93 12 GCS9 Cl* Melotte 22 HHJ 362 +03 42 00.020 +25 01 47.10 0 13.914 13.524 12.950 12.390 12.128 18.86 2.97 -46.97 2.97 0.88 12 GCS9 Cl* Melotte 22 DH 201 +03 47 35.860 +24 52 26.70 0 14.700 14.223 13.634 13.112 12.786 12.772 16.91 2.21 -45.88 2.21 0.91 12 GCS9 V* V752 Tau +03 48 20.290 +24 54 54.90 0 13.479 13.035 12.450 11.909 11.604 11.641 19.17 2.21 -42.06 2.21 0.94 12 GCS9 V* V344 Tau +03 49 02.970 +21 54 46.90 0 15.347 14.790 14.176 13.654 13.307 13.306 19.02 2.27 -47.56 2.27 0.75 12 GCS9 Cl* Melotte 22 BPL 197 +03 47 40.960 +21 49 05.00 0 15.807 15.243 14.629 14.089 13.750 13.707 22.10 2.28 -44.16 2.28 0.80 12 GCS9 UGCS J034740.95+214905.0 +03 59 26.930 +21 48 18.70 0 14.594 14.072 13.475 12.948 12.625 12.648 19.51 2.87 -42.67 2.87 0.94 12 GCS9 Cl* Melotte 22 DH 876 +03 52 01.650 +25 01 29.10 0 14.402 13.991 13.422 12.897 12.576 12.602 14.95 2.20 -43.92 2.20 0.89 12 GCS9 V* V562 Tau +03 52 03.580 +25 01 13.60 0 14.781 14.353 13.772 13.239 12.900 12.934 15.18 2.20 -43.42 2.20 0.90 12 GCS9 Cl* Melotte 22 DH 737 +03 51 51.560 +21 49 16.80 0 14.207 13.736 13.193 12.644 12.340 12.343 24.16 2.51 -42.78 2.51 0.81 12 GCS9 UGCS J035151.56+214916.8 +03 39 16.750 +24 57 38.50 0 15.120 14.611 13.972 13.440 13.071 13.063 15.63 2.49 -40.66 2.49 0.85 12 GCS9 Cl* Melotte 22 DH 103 +03 40 20.090 +21 51 33.60 0 15.554 14.955 14.342 13.783 13.442 13.436 12.33 2.51 -42.73 2.51 0.69 12 GCS9 UGCS J034020.08+215133.6 +03 48 29.760 +23 58 05.80 0 14.876 14.413 13.823 13.281 12.988 12.938 17.30 2.22 -45.29 2.22 0.92 12 GCS9 V* V460 Tau +03 48 10.180 +23 59 20.10 0 15.509 15.004 14.370 13.840 13.478 13.492 20.23 2.22 -43.71 2.22 0.88 12 GCS9 Cl* Melotte 22 DH 555 +03 48 33.780 +24 01 58.80 0 14.698 14.252 13.698 13.136 12.861 12.892 16.27 2.22 -39.37 2.22 0.87 12 GCS9 Cl* Melotte 22 DH 582 +03 48 31.840 +24 01 58.60 0 14.648 14.214 13.619 13.074 12.798 12.789 16.37 2.22 -42.70 2.22 0.93 12 GCS9 V* V872 Tau +03 58 07.690 +20 23 38.10 0 14.755 14.283 13.696 13.187 12.883 12.864 17.14 3.97 -39.44 3.97 0.89 12 GCS9 UGCS J035807.69+202338.0 +03 57 58.500 +20 24 19.50 0 14.806 14.330 13.710 13.193 12.861 12.867 16.74 3.97 -41.74 3.97 0.93 12 GCS9 UGCS J035758.50+202419.4 +04 05 13.750 +24 08 42.70 0 14.983 14.471 13.846 13.261 12.933 12.917 19.77 3.38 -41.50 3.38 0.94 12 GCS9 Cl* Melotte 22 DH 915 +03 45 09.460 +23 58 44.70 1 16.974 16.250 15.438 14.872 14.424 14.410 16.07 2.25 -42.23 2.25 0.72 12 GCS9 Cl* Melotte 22 PPL 2 +03 44 57.340 +23 59 32.30 0 13.672 13.326 12.770 12.194 11.941 11.960 16.63 2.22 -39.69 2.22 0.88 12 GCS9 V* V712 Tau +03 44 27.920 +23 59 59.60 0 15.633 15.107 14.446 13.888 13.520 13.499 17.02 2.23 -41.63 2.23 0.89 12 GCS9 Cl* Melotte 22 DH 342 +03 44 47.330 +24 00 37.70 0 14.063 13.661 13.140 12.609 12.311 12.315 19.39 2.22 -43.53 2.22 0.94 12 GCS9 V* NW Tau +03 44 53.210 +24 01 06.50 0 15.005 14.557 13.980 13.433 13.135 13.125 16.45 2.22 -38.79 2.22 0.80 12 GCS9 Cl* Melotte 22 DH 359 +03 45 09.810 +24 04 32.70 0 15.935 15.388 14.737 14.174 13.793 13.744 17.63 2.23 -39.69 2.23 0.85 12 GCS9 Cl* Melotte 22 SHF 5 +03 45 16.130 +24 07 16.00 0 13.242 12.918 12.418 12.034 11.570 11.682 19.29 2.22 -40.28 2.22 0.91 12 GCS9 V* V519 Tau +03 48 46.020 +24 10 12.50 0 14.464 14.027 13.476 12.931 12.631 12.661 15.18 2.22 -41.35 2.22 0.89 12 GCS9 Cl* Melotte 22 DH 592 +03 46 06.050 +23 58 19.10 0 16.000 15.453 14.820 14.319 13.925 13.902 16.34 2.23 -42.63 2.23 0.73 12 GCS9 Cl* Melotte 22 SHF 26 +03 45 42.330 +24 04 11.10 0 16.542 15.882 15.201 14.643 14.262 14.229 17.79 2.24 -40.28 2.24 0.70 12 GCS9 Cl* Melotte 22 SHF 12 +03 45 39.290 +24 08 20.40 0 14.992 14.507 13.911 13.379 13.015 13.020 16.79 2.22 -43.96 2.22 0.93 12 GCS9 Cl* Melotte 22 DH 398 +03 45 57.910 +24 08 40.90 0 15.680 15.158 14.543 14.017 13.670 13.654 18.27 2.23 -43.51 2.23 0.90 12 GCS9 Cl* Melotte 22 DH 412 +03 46 04.570 +24 09 55.80 0 15.099 14.602 14.020 13.460 13.162 13.175 15.43 2.22 -40.43 2.22 0.84 12 GCS9 2MASS J03460455+2409561 +03 49 52.440 +24 03 43.00 0 16.100 15.534 14.882 14.353 13.970 13.984 20.16 2.23 -43.40 2.23 0.70 12 GCS9 UGCS J034952.43+240342.9 +03 49 16.420 +24 03 49.00 0 12.458 12.108 11.574 11.608 10.800 11.068 24.30 2.22 -41.94 2.22 0.71 12 GCS9 V* V467 Tau +03 49 55.490 +24 06 05.00 0 14.506 14.048 13.452 12.874 12.596 12.614 17.10 2.22 -43.07 2.22 0.94 12 GCS9 Cl* Melotte 22 DH 641 +03 51 58.350 +23 58 19.30 0 14.595 14.132 13.556 12.990 12.694 12.669 11.97 2.24 -42.06 2.24 0.65 12 GCS9 Cl* Melotte 22 DH 732 +03 59 23.500 +20 20 16.90 0 15.988 15.360 14.723 14.163 13.823 13.837 20.00 4.01 -37.38 4.01 0.67 12 GCS9 UGCS J035923.50+202016.8 +03 41 31.210 +24 03 24.70 0 14.985 14.509 13.903 13.353 13.033 20.71 2.31 -39.58 2.31 0.89 12 GCS9 Cl* Melotte 22 DH 177 +03 56 18.600 +23 57 51.60 0 12.871 12.580 12.095 11.500 11.219 11.290 20.17 2.96 -40.40 2.96 0.89 12 GCS9 Cl* Melotte 22 HHJ 386 +03 56 28.910 +24 01 54.10 0 14.448 14.038 13.469 12.930 12.618 12.620 19.11 2.96 -40.90 2.96 0.93 12 GCS9 Cl* Melotte 22 DH 837 +03 42 17.900 +24 06 57.60 0 14.857 14.378 13.774 13.232 12.897 17.09 2.30 -42.38 2.30 0.94 12 GCS9 Cl* Melotte 22 DH 217 +03 41 52.940 +24 07 24.70 0 15.099 14.637 14.067 13.518 13.183 14.06 2.31 -43.29 2.31 0.82 12 GCS9 Cl* Melotte 22 DH 191 +03 42 00.370 +24 10 12.60 0 16.212 15.647 14.984 14.441 14.064 17.68 2.32 -44.24 2.32 0.73 12 GCS9 Cl* Melotte 22 BPL 32 +03 42 13.900 +20 21 43.60 0 14.695 14.209 13.633 13.098 12.788 12.772 24.49 3.42 -44.30 3.42 0.76 12 GCS9 Cl* Melotte 22 DH 216 +03 46 18.540 +23 59 02.50 0 15.870 15.329 14.698 14.161 13.807 13.797 16.09 2.23 -43.73 2.23 0.88 12 GCS9 Cl* Melotte 22 SHF 27 +03 46 23.470 +24 01 51.20 0 14.714 14.254 13.668 13.113 12.808 12.793 18.12 2.22 -43.07 2.22 0.94 12 GCS9 V* V528 Tau +03 46 25.390 +24 09 36.20 0 12.838 12.485 11.952 11.461 11.112 11.150 13.98 2.22 -43.42 2.22 0.64 12 GCS9 V* OZ Tau +03 46 35.540 +24 01 35.40 0 14.809 14.275 13.660 13.113 12.776 12.774 22.25 2.22 -44.34 2.22 0.89 12 GCS9 2MASS J03463552+2401355 +03 46 43.600 +23 59 42.30 0 13.258 12.936 12.441 11.891 11.575 11.602 21.29 2.22 -43.53 2.22 0.91 12 GCS9 Cl* Melotte 22 DH 467 +03 46 51.440 +24 06 16.10 0 15.287 14.777 14.196 13.624 13.272 13.288 17.34 2.22 -38.74 2.22 0.81 12 GCS9 UGCS J034651.43+240616.1 +03 46 53.960 +24 07 57.10 0 15.123 14.651 14.045 13.480 13.143 13.155 16.12 2.22 -38.73 2.22 0.78 12 GCS9 Cl* Melotte 22 MHO 8 +03 46 58.260 +24 01 41.30 0 14.976 14.481 13.884 13.337 12.997 13.022 19.27 2.22 -37.41 2.22 0.79 12 GCS9 UGCS J034658.26+240141.3 +03 46 59.320 +24 01 42.80 0 13.995 13.544 12.956 12.408 12.067 12.100 19.84 2.22 -38.88 2.22 0.85 12 GCS9 Cl* Melotte 22 DH 484 +03 47 10.650 +23 58 16.40 0 16.136 15.557 14.877 14.360 13.969 14.000 19.15 2.23 -41.37 2.23 0.72 12 GCS9 Cl* Melotte 22 SHF 41 +03 47 13.170 +24 00 45.20 0 16.022 15.428 14.759 14.221 13.828 13.866 20.02 2.23 -40.38 2.23 0.66 12 GCS9 2MASS J03471316+2400453 +03 47 09.180 +24 03 07.70 0 13.411 13.064 12.521 11.964 11.675 11.719 20.92 2.22 -38.72 2.22 0.82 12 GCS9 Cl* Melotte 22 SRS 60765 +03 43 01.640 +20 20 13.80 0 15.588 15.080 14.494 13.912 13.618 13.585 15.94 3.44 -42.59 3.44 0.88 12 GCS9 UGCS J034301.63+202013.7 +03 55 08.980 +24 05 02.50 0 13.699 13.306 12.734 12.148 11.890 16.37 2.30 -40.35 2.30 0.90 12 GCS9 Cl* Melotte 22 DH 812 +03 50 57.420 +24 06 30.70 0 13.535 13.175 12.661 12.135 11.812 11.815 14.04 2.22 -40.35 2.22 0.80 12 GCS9 V* V800 Tau +03 51 19.070 +24 10 13.10 0 13.112 12.775 12.233 11.720 11.389 11.438 20.55 2.22 -42.37 2.22 0.92 12 GCS9 V* V466 Tau +03 51 55.050 +23 57 42.00 0 14.510 14.067 13.483 12.983 12.641 12.651 15.21 2.22 -46.11 2.22 0.86 12 GCS9 V* V388 Tau +03 47 25.350 +24 02 56.80 0 13.454 13.128 12.616 12.074 11.774 11.806 20.89 2.22 -39.96 2.22 0.88 12 GCS9 Cl* Melotte 22 HHJ 427 +03 47 32.000 +24 10 24.70 0 14.749 14.293 13.677 13.143 12.827 12.808 19.77 2.22 -41.79 2.22 0.94 12 GCS9 Cl* Melotte 22 BPL 147 +03 47 34.520 +24 02 23.00 0 14.645 14.216 13.648 13.087 12.797 12.793 19.46 2.22 -36.75 2.22 0.72 12 GCS9 Cl* Melotte 22 DH 523 +03 47 46.400 +24 03 02.30 0 12.979 12.677 12.180 11.634 11.322 11.381 19.77 2.22 -37.79 2.22 0.84 12 GCS9 Cl* Melotte 22 HHJ 438 +03 48 06.410 +24 06 51.70 0 14.633 14.200 13.603 13.064 12.753 12.790 18.58 2.22 -43.78 2.22 0.94 12 GCS9 UGCS J034806.41+240651.6 +03 48 06.640 +24 00 06.70 0 14.398 13.957 13.367 12.823 12.520 12.531 17.15 2.22 -39.79 2.22 0.90 12 GCS9 Cl* Melotte 22 DH 549 +03 48 09.220 +23 58 40.50 0 14.445 14.024 13.448 12.921 12.615 12.648 15.27 2.22 -41.80 2.22 0.90 12 GCS9 Cl* Melotte 22 DH 553 +03 48 13.310 +23 58 46.80 0 14.206 13.766 13.187 12.664 12.353 12.394 16.71 2.22 -42.37 2.22 0.93 12 GCS9 V* V342 Tau +03 43 28.740 +24 09 06.10 0 15.175 14.685 14.073 13.555 13.200 17.98 2.31 -42.91 2.31 0.90 12 GCS9 Cl* Melotte 22 HHJ 76 +03 58 25.140 +24 00 58.50 0 14.312 13.884 13.306 12.766 12.449 12.466 17.99 2.96 -40.14 2.96 0.92 12 GCS9 Cl* Melotte 22 DH 865 +03 39 09.870 +23 58 52.40 0 16.201 15.649 14.939 14.373 14.000 14.020 15.05 2.62 -40.39 2.62 0.63 12 GCS9 UGCS J033909.86+235852.4 +03 39 35.460 +24 07 06.10 0 12.806 12.545 12.021 11.498 11.181 11.228 18.04 2.49 -42.86 2.49 0.87 12 GCS9 Cl* Melotte 22 DH 108 +03 39 46.350 +23 58 52.90 0 13.490 13.174 12.625 12.077 11.772 11.792 22.63 2.50 -38.42 2.50 0.69 12 GCS9 Cl* Melotte 22 DH 113 +03 42 41.180 +24 01 42.70 0 14.985 14.503 13.921 13.357 13.014 13.025 20.15 2.13 -41.62 2.13 0.93 12 GCS9 V* LQ Tau +03 42 41.850 +24 00 15.50 0 14.496 14.076 13.515 12.954 12.636 12.650 18.92 2.12 -44.12 2.12 0.94 12 GCS9 Cl* Melotte 22 DH 236 +03 42 56.550 +24 04 57.80 0 12.916 12.529 12.006 11.543 11.156 11.214 16.43 2.12 -38.79 2.12 0.82 12 GCS9 V* LT Tau +03 43 11.650 +24 06 52.40 0 15.210 14.680 14.044 13.501 13.160 13.172 16.13 2.13 -38.16 2.13 0.74 12 GCS9 Cl* Melotte 22 BPL 52 +03 40 25.620 +24 06 00.00 0 14.659 14.287 13.684 13.159 12.829 12.878 20.15 2.50 -43.40 2.50 0.94 12 GCS9 Cl* Melotte 22 DH 138 +03 40 26.410 +24 05 23.50 0 13.451 13.197 12.595 11.998 11.701 11.736 16.88 2.50 -38.77 2.50 0.84 12 GCS9 Cl* Melotte 22 SK 778 +03 46 29.730 +21 02 16.70 0 14.557 14.008 13.402 12.879 12.566 12.551 19.59 2.65 -40.35 2.65 0.92 12 GCS9 Cl* Melotte 22 DH 452 +03 49 50.380 +20 57 59.90 0 14.307 13.848 13.262 12.675 12.412 12.387 20.01 3.05 -39.37 3.05 0.89 12 GCS9 UGCS J034950.38+205759.8 +03 43 05.730 +21 01 49.10 0 15.042 14.523 13.929 13.374 13.044 13.028 19.59 3.59 -44.27 3.59 0.88 12 GCS9 UGCS J034305.73+210149.1 +03 57 37.100 +21 01 48.00 0 15.678 15.108 14.501 13.963 13.607 13.593 20.33 3.37 -45.03 3.37 0.85 12 GCS9 UGCS J035737.10+210147.9 +03 58 10.250 +20 56 55.60 0 16.121 15.494 14.840 14.289 13.924 13.940 19.36 3.38 -39.83 3.38 0.65 12 GCS9 UGCS J035810.24+205655.5 +03 46 10.100 +26 00 09.00 0 15.207 14.716 14.091 13.542 13.230 13.219 15.35 2.23 -39.91 2.23 0.82 12 GCS9 Cl* Melotte 22 HHJ 80 +03 46 19.430 +26 02 35.50 0 12.463 12.206 11.749 11.371 10.935 11.061 24.01 2.22 -39.85 2.22 0.74 12 GCS9 Cl* Melotte 22 DH 436 +03 45 21.350 +26 05 25.50 0 15.885 15.309 14.637 14.115 13.734 13.722 19.33 2.24 -43.28 2.24 0.89 12 GCS9 UGCS J034521.34+260525.4 +03 52 13.320 +26 08 36.80 0 13.531 13.173 12.637 12.086 11.799 11.827 13.73 2.93 -41.65 2.93 0.82 12 GCS9 V* V715 Tau +03 45 41.270 +23 54 09.70 1 17.166 16.189 15.360 14.782 14.305 14.309 17.46 2.24 -44.47 2.24 0.69 12 GCS9 Cl* Melotte 22 PPL 1 +03 50 22.010 +23 55 30.30 0 17.259 16.399 15.694 15.108 14.699 14.699 20.94 2.26 -44.68 2.26 0.65 12 GCS9 UGCS J035022.01+235530.3 +03 52 06.720 +24 16 00.40 0 17.079 16.255 15.515 14.971 14.507 14.538 18.87 2.29 -40.00 2.29 0.68 12 GCS9 2MASS J03520670+2416008 +03 46 14.060 +23 21 56.40 0 17.097 16.349 15.606 15.047 14.618 14.641 17.81 2.27 -40.20 2.27 0.68 12 GCS9 UGCS J034614.06+232156.4 +03 39 17.050 +22 27 10.90 0 17.162 16.347 15.569 15.023 14.582 14.576 16.67 2.58 -43.68 2.58 0.69 12 GCS9 2MASS J03391704+2227111 +03 48 04.670 +23 39 30.10 1 17.014 16.054 15.283 14.704 14.256 14.238 16.07 2.24 -44.27 2.24 0.66 12 GCS9 UGCS J034804.66+233930.1 +03 47 49.450 +23 31 52.80 0 17.078 16.295 15.581 15.025 14.611 14.602 17.03 2.25 -39.82 2.25 0.65 12 GCS9 UGCS J034749.44+233152.8 +03 41 54.160 +23 05 04.70 1 17.349 16.376 15.522 14.975 14.415 14.418 18.19 2.30 -44.74 2.30 0.69 12 GCS9 Cl* Melotte 22 MHOBD 3 +03 55 23.080 +24 49 04.90 0 17.087 16.311 15.528 15.013 14.595 14.571 19.50 2.28 -42.09 2.28 0.72 12 GCS9 Cl* Melotte 22 BPL 327 +03 44 53.120 +23 34 22.80 0 17.306 16.472 15.734 15.124 14.710 14.700 18.37 2.26 -39.68 2.26 0.67 12 GCS9 UGCS J034453.12+233422.8 +03 41 05.410 +23 32 56.70 0 17.223 16.421 15.660 15.137 14.704 14.689 20.80 2.22 -41.06 2.22 0.68 12 GCS9 UGCS J034105.40+233256.6 +03 49 04.860 +23 33 39.30 0 17.145 16.308 15.573 15.032 14.579 14.568 16.74 2.25 -42.98 2.25 0.70 12 GCS9 Cl* Melotte 22 IPMBD 20 +03 45 54.960 +23 33 57.80 0 17.116 16.325 15.553 15.023 14.564 14.574 18.67 2.25 -41.38 2.25 0.72 12 GCS9 Cl* Melotte 22 SHF 21 +03 45 31.370 +24 52 47.40 1 17.332 16.330 15.465 14.839 14.354 14.326 16.69 2.24 -40.30 2.24 0.66 12 GCS9 2MASS J03453136+2452476 +03 36 54.100 +21 58 02.70 0 17.346 16.486 15.758 15.194 14.752 14.754 19.11 3.00 -39.30 3.00 0.65 12 GCS9 UGCS J033654.10+215802.7 +04 01 24.410 +20 17 15.60 0 17.022 16.289 15.614 15.051 14.645 14.638 22.29 3.22 -42.39 3.22 0.63 12 GCS9 UGCS J040124.40+201715.5 +03 38 10.200 +26 07 33.00 0 17.160 16.444 15.695 15.142 14.702 14.718 18.46 3.04 -40.95 3.04 0.71 12 GCS9 UGCS J033810.20+260733.0 +03 40 55.300 +25 34 57.40 0 17.423 16.544 15.803 15.195 14.732 14.753 21.01 2.31 -41.51 2.31 0.69 12 GCS9 UGCS J034055.30+253457.3 +03 30 44.540 +25 39 07.70 0 17.609 16.705 15.900 15.296 14.886 14.862 16.76 3.43 -42.84 3.43 0.70 12 GCS9 UGCS J033044.53+253907.6 +03 37 46.600 +26 50 44.50 0 17.381 16.593 15.840 15.292 14.877 14.867 19.58 3.37 -40.72 3.37 0.70 12 GCS9 UGCS J033746.59+265044.5 +03 44 35.160 +25 13 42.80 1 17.656 16.584 15.662 14.985 14.448 14.460 19.33 2.26 -44.97 2.26 0.68 12 GCS9 UGCS J034435.16+251342.7 +03 49 33.960 +22 16 35.90 0 17.942 16.978 16.144 15.535 15.049 15.042 21.92 2.64 -43.77 2.64 0.64 12 GCS9 UGCS J034933.96+221635.8 +03 47 27.720 +22 09 38.50 0 17.382 16.547 15.760 15.224 14.763 14.783 17.79 2.34 -41.87 2.34 0.72 12 GCS9 Cl* Melotte 22 MHOBD 5 +03 51 44.950 +23 26 39.30 0 17.791 16.894 16.036 15.417 14.953 14.967 16.26 2.37 -39.72 2.37 0.62 12 GCS9 2MASS J03514491+2326395 +03 50 52.170 +23 27 11.20 1 17.690 16.638 15.700 15.060 14.505 14.555 19.89 2.21 -41.33 2.21 0.71 12 GCS9 UGCS J035052.17+232711.2 +03 26 33.430 +22 39 42.90 0 17.587 16.833 16.109 15.489 15.093 15.096 20.93 5.59 -43.56 5.59 0.69 12 GCS9 UGCS J032633.42+223942.8 +03 42 18.010 +24 55 09.90 0 17.486 16.658 15.876 15.298 14.854 17.65 3.06 -39.29 3.06 0.64 12 GCS9 UGCS J034218.01+245509.9 +03 45 50.660 +24 09 03.50 1 17.478 16.582 15.705 15.095 14.580 14.560 16.01 2.26 -40.58 2.26 0.65 12 GCS9 2MASS J03455065+2409037 +03 47 50.410 +23 54 47.80 0 18.179 17.138 16.311 15.622 15.093 15.090 15.88 2.32 -39.60 2.32 0.63 12 GCS9 2MASS J03475038+2354480 +03 55 34.280 +22 23 29.20 0 18.055 17.108 16.189 15.579 15.117 15.096 18.47 2.72 -40.79 2.72 0.71 12 GCS9 UGCS J035534.28+222329.2 +03 40 06.590 +21 08 59.90 0 18.210 17.152 16.272 15.651 15.113 15.123 21.31 3.80 -40.02 3.80 0.68 12 GCS9 UGCS J034006.59+210859.9 +03 47 17.920 +24 22 31.60 0 18.174 17.067 16.215 15.591 15.096 15.080 21.40 2.30 -42.73 2.30 0.68 12 GCS9 UGCS J034717.92+242231.6 +03 46 34.990 +23 31 14.40 0 18.424 17.345 16.411 15.846 15.308 15.295 20.52 2.37 -42.62 2.37 0.70 12 GCS9 UGCS J034634.98+233114.4 +03 47 59.740 +22 36 01.80 0 18.043 17.083 16.209 15.609 15.090 15.115 21.40 2.40 -42.45 2.40 0.69 12 GCS9 2MASS J03475972+2236019 +03 45 50.630 +23 44 36.90 0 18.291 17.301 16.379 15.794 15.257 15.244 20.44 2.31 -41.86 2.31 0.71 12 GCS9 Cl* Melotte 22 NPNPL 1 +03 57 18.480 +21 37 32.30 0 18.299 17.369 16.382 15.808 15.239 15.276 21.09 3.54 -41.34 3.54 0.70 12 GCS9 UGCS J035718.48+213732.3 +03 51 38.960 +24 30 44.80 1 18.711 17.407 16.400 15.718 15.168 15.122 23.01 2.34 -40.47 2.34 0.64 12 GCS9 UGCS J035138.95+243044.7 +03 46 23.120 +24 20 36.00 0 18.242 17.172 16.247 15.654 15.145 15.106 17.01 2.29 -43.77 2.29 0.66 12 GCS9 2MASS J03462312+2420363 +03 59 09.870 +21 46 02.00 0 18.325 17.223 16.331 15.747 15.203 15.210 20.58 3.13 -40.99 3.13 0.71 12 GCS9 UGCS J035909.86+214602.0 +03 40 38.100 +26 38 29.20 0 18.747 17.679 16.599 15.993 15.481 15.481 18.85 3.21 -40.47 3.21 0.71 12 GCS9 UGCS J034038.09+263829.2 +03 46 34.250 +23 50 03.60 0 19.871 18.546 17.459 16.666 16.090 16.024 20.67 2.58 -41.54 2.58 0.66 12 GCS9 Cl* Melotte 22 NPNPL 2 +03 34 20.190 +25 22 05.10 0 19.676 18.250 17.178 16.453 15.945 20.30 6.48 -41.54 6.48 0.66 12 GCS9 UGCS J033420.18+252205.0 +03 43 03.830 +23 54 19.60 0 18.852 17.703 16.751 16.052 15.562 15.535 18.37 2.48 -38.49 2.48 0.66 12 GCS9 2MASS J03430378+2354200 +04 05 51.620 +23 44 48.10 0 19.517 18.119 17.046 16.350 15.809 15.769 19.98 3.68 -42.86 3.68 0.68 12 GCS9 UGCS J040551.61+234448.0 +03 49 04.740 +23 26 43.10 0 18.943 17.724 16.792 16.171 15.598 15.597 17.56 2.58 -40.60 2.58 0.70 12 GCS9 UGCS J034904.73+232643.1 +03 57 13.460 +24 22 51.80 0 19.665 18.458 17.407 16.653 16.109 16.086 17.40 3.82 -42.66 3.82 0.66 12 GCS9 UGCS J035713.46+242251.7 +03 54 05.350 +23 33 59.20 0 18.651 17.573 16.647 15.964 15.434 15.462 15.06 2.46 -40.45 2.46 0.61 12 GCS9 2MASS J03540532+2333597 +03 40 27.930 +24 12 09.30 0 19.730 18.501 17.352 16.700 16.088 16.073 15.91 3.22 -42.54 3.22 0.62 12 GCS9 Cl* Melotte 22 IPL 84 +03 45 11.730 +23 41 43.60 0 20.311 18.985 17.617 16.886 16.120 16.204 13.04 2.65 -46.83 2.65 0.62 12 GCS9 Cl* Melotte 22 NPNPL 3 +03 44 22.450 +23 39 01.30 0 19.107 17.857 16.874 16.254 15.666 15.660 15.39 2.40 -42.54 2.40 0.61 12 GCS9 UGCS J034422.44+233901.2 +03 55 47.450 +22 50 50.10 0 19.943 18.550 17.433 16.681 16.086 16.123 15.62 2.97 -46.57 2.97 0.63 12 GCS9 UGCS J035547.44+225050.0 +03 36 05.920 +23 01 23.70 0 19.381 18.086 17.063 16.451 15.840 15.845 24.12 3.64 -46.32 3.64 0.64 12 GCS9 UGCS J033605.92+230123.6 +03 46 27.100 +21 48 22.60 1 19.797 18.650 17.374 16.564 15.848 15.925 20.95 2.98 -48.67 2.98 0.65 12 GCS9 UGCS J034627.10+214822.5 +04 00 58.260 +21 33 31.70 0 19.241 18.035 16.948 16.250 15.691 15.702 18.08 4.76 -43.38 4.76 0.67 12 GCS9 UGCS J040058.25+213331.7 +03 42 18.080 +19 07 33.00 0 19.797 18.358 17.264 16.589 16.057 16.053 17.58 5.18 -47.78 5.18 0.65 12 GCS9 UGCS J034218.08+190732.9 +04 07 10.570 +24 59 49.20 0 18.433 17.603 16.733 15.987 15.491 15.459 19.65 3.35 -39.70 3.35 0.70 12 GCS9 UGCS J040710.56+245949.1 +03 42 30.590 +25 02 39.30 0 19.269 18.013 17.004 16.367 15.746 22.01 3.54 -44.77 3.54 0.68 12 GCS9 UGCS J034230.58+250239.2 +03 56 59.900 +24 05 35.50 0 19.904 19.003 17.628 16.950 16.225 16.196 16.25 4.13 -41.75 4.13 0.62 12 GCS9 UGCS J035659.90+240535.4 +03 41 13.210 +24 05 25.60 0 20.248 19.168 17.791 16.898 16.325 16.266 14.11 3.48 -46.41 3.48 0.61 12 GCS9 Cl* Melotte 22 IPL 81 +03 50 16.090 +24 08 34.70 0 19.950 18.556 17.365 16.687 16.076 16.043 18.03 2.53 -46.97 2.53 0.67 12 GCS9 UGCS J035016.08+240834.7 +03 43 46.290 +23 58 37.90 0 19.124 17.930 16.876 16.248 15.628 20.13 2.62 -45.01 2.62 0.69 12 GCS9 2MASS J03434627+2358382 +03 55 30.190 +21 01 10.20 0 18.713 17.522 16.584 15.983 15.414 15.354 19.46 3.52 -41.16 3.52 0.72 12 GCS9 UGCS J035530.19+210110.1 +03 44 09.010 +27 56 42.00 0 14.980 14.512 13.981 13.444 13.140 13.140 27.76 3.70 -38.32 3.70 2 GCS9 UGCS J034409.01+275642.0 +03 48 31.180 +29 22 39.20 0 13.011 12.688 12.199 11.692 11.374 11.413 5.92 5.84 -27.47 5.84 2 GCS9 UGCS J034831.17+292239.1 +03 47 08.900 +29 02 38.80 0 13.793 13.432 12.890 12.285 12.076 12.094 10.64 5.84 -40.62 5.84 2 GCS9 UGCS J034708.90+290238.8 +03 58 33.710 +28 07 09.10 0 16.950 16.428 15.680 15.099 14.755 14.708 24.72 4.01 -50.41 4.01 2 GCS9 UGCS J035833.70+280709.0 +03 53 07.090 +28 07 33.00 0 13.630 13.247 12.738 12.198 11.946 11.966 28.63 3.74 -50.15 3.74 2 GCS9 UGCS J035307.09+280733.0 +03 44 59.810 +29 12 29.50 0 16.688 16.081 15.393 14.773 14.418 14.415 6.41 5.88 -48.49 5.88 2 GCS9 UGCS J034459.81+291229.4 +03 40 56.060 +28 43 39.00 0 12.760 12.479 11.985 11.538 11.155 11.283 11.44 3.68 -37.58 3.68 2 GCS9 Cl* Melotte 22 DH 161 +03 31 12.320 +27 05 59.20 0 15.984 15.464 14.836 14.275 13.919 -2.97 7.05 -25.10 7.05 2 GCS9 UGCS J033112.32+270559.2 +03 48 55.420 +28 41 20.70 0 14.701 14.119 13.468 12.911 12.565 12.571 19.52 2.93 -34.58 2.93 2 GCS9 UGCS J034855.41+284120.6 +03 37 17.780 +28 45 35.80 0 14.292 13.909 13.380 12.741 12.457 12.474 11.77 4.93 -40.45 4.93 2 GCS9 UGCS J033717.77+284535.8 +03 56 10.690 +27 11 17.90 0 13.820 13.467 12.963 12.391 12.128 12.123 8.42 3.30 -35.60 3.30 2 GCS9 UGCS J035610.69+271117.8 +03 29 11.270 +27 09 52.50 0 14.529 13.987 13.341 12.786 12.440 27.16 6.92 -25.14 6.92 2 GCS9 UGCS J032911.26+270952.4 +03 29 03.100 +27 04 59.90 0 15.372 14.837 14.194 13.600 13.273 21.10 6.95 -29.46 6.95 2 GCS9 UGCS J032903.10+270459.8 +03 30 11.390 +27 37 55.80 0 13.268 12.939 12.423 12.042 11.670 11.668 15.01 4.88 -33.94 4.88 2 GCS9 UGCS J033011.39+273755.7 +03 45 10.110 +27 40 09.10 0 15.074 14.582 14.010 13.477 13.154 13.152 20.96 3.45 -35.33 3.45 2 GCS9 UGCS J034510.11+274009.0 +03 42 24.950 +27 32 21.50 0 15.402 14.912 14.307 13.812 13.463 13.477 18.17 3.30 -32.77 3.30 2 GCS9 UGCS J034224.95+273221.5 +03 41 23.630 +27 24 16.90 0 15.242 14.719 14.108 13.543 13.215 13.226 18.53 3.29 -35.23 3.29 2 GCS9 Cl* Melotte 22 DH 171 +03 42 24.930 +27 42 15.40 0 15.788 15.273 14.656 14.111 13.783 13.811 18.89 3.32 -32.08 3.32 2 GCS9 UGCS J034224.92+274215.4 +04 01 21.510 +27 12 33.20 0 14.198 13.815 13.270 12.730 12.466 12.482 26.22 4.23 -38.49 4.23 2 GCS9 UGCS J040121.51+271233.1 +03 53 57.160 +28 10 13.30 0 15.617 15.088 14.479 13.890 13.567 13.573 26.79 3.76 -35.49 3.76 2 GCS9 UGCS J035357.16+281013.2 +03 47 42.860 +28 18 59.10 0 15.298 14.760 14.147 13.616 13.263 13.252 15.67 2.96 -35.98 2.96 2 GCS9 Cl* Melotte 22 DH 533 +03 43 37.560 +26 32 00.90 0 16.418 15.769 15.103 14.563 14.210 14.181 23.24 2.97 -37.05 2.97 2 GCS9 UGCS J034337.55+263200.8 +03 48 52.950 +27 11 09.20 0 12.550 12.284 11.843 11.345 11.027 11.066 26.03 2.88 -33.77 2.88 2 GCS9 UGCS J034852.95+271109.1 +03 39 28.990 +25 34 55.80 0 13.441 13.038 12.541 12.001 11.753 11.765 23.56 2.95 -49.12 2.95 2 GCS9 Cl* Melotte 22 HHJ 359 +04 06 08.010 +25 42 34.60 0 16.097 15.463 14.792 14.184 13.818 13.799 11.14 3.39 -47.13 3.39 2 GCS9 UGCS J040608.00+254234.5 +03 43 56.000 +25 36 25.20 0 16.718 15.979 15.290 14.710 14.319 14.333 23.18 2.27 -46.03 2.27 2 GCS9 Cl* Melotte 22 PLZJ 50 +03 52 16.560 +27 35 51.00 0 15.081 14.622 14.039 13.529 13.200 13.192 11.21 2.89 -39.30 2.89 2 GCS9 UGCS J035216.56+273551.0 +03 53 26.650 +26 11 49.50 0 12.887 12.578 12.116 11.553 11.277 11.275 9.78 2.95 -50.85 2.95 2 GCS9 UGCS J035326.65+261149.5 +03 28 11.330 +26 55 37.00 0 16.205 15.582 14.915 14.351 13.989 25.62 7.01 -37.29 7.01 2 GCS9 UGCS J032811.33+265537.0 +03 36 46.480 +28 15 05.90 0 15.528 14.984 14.370 13.865 13.552 13.542 25.67 4.95 -35.56 4.95 2 GCS9 Cl* Melotte 22 DH 64 +03 51 05.080 +26 36 51.20 0 15.454 14.963 14.358 13.825 13.479 13.495 12.35 2.96 -37.88 2.96 2 GCS9 Cl* Melotte 22 MBSC 93 +03 26 13.400 +25 32 35.70 0 13.384 12.899 12.323 11.923 11.539 8.41 6.87 -33.03 6.87 2 GCS9 UGCS J032613.39+253235.7 +03 47 22.280 +25 43 15.00 0 16.268 15.616 14.929 14.374 14.017 13.977 15.45 2.25 -46.31 2.25 2 GCS9 UGCS J034722.28+254315.0 +03 27 47.670 +26 57 06.10 0 15.256 14.836 14.217 13.603 13.265 20.38 6.95 -28.78 6.95 2 GCS9 UGCS J032747.66+265706.0 +03 35 50.290 +25 42 20.50 0 13.820 13.379 12.791 12.239 11.978 28.77 5.30 -37.34 5.30 2 GCS9 Cl* Melotte 22 DH 49 +03 39 04.060 +27 00 04.70 0 13.938 13.572 13.032 12.454 12.171 12.197 17.40 3.00 -34.30 3.00 2 GCS9 Cl* Melotte 22 DH 98 +03 39 53.750 +28 34 56.40 0 14.232 13.794 13.189 12.675 12.384 12.373 25.26 4.93 -55.59 4.93 2 GCS9 UGCS J033953.75+283456.3 +03 50 52.180 +26 11 40.10 0 15.944 15.346 14.710 14.173 13.814 13.827 23.44 2.97 -34.78 2.97 2 GCS9 UGCS J035052.18+261140.0 +04 03 40.670 +25 21 34.50 0 13.110 12.756 12.216 11.671 11.303 11.336 16.57 3.31 -34.58 3.31 2 GCS9 Cl* Melotte 22 DH 908 +03 37 37.450 +25 41 49.80 0 14.994 14.527 13.953 13.399 13.101 13.070 12.66 2.96 -38.52 2.96 2 GCS9 UGCS J033737.45+254149.7 +03 50 39.330 +26 16 03.70 0 16.134 15.515 14.868 14.337 13.976 13.962 26.51 2.97 -45.26 2.97 2 GCS9 Cl* Melotte 22 DH 680 +04 00 39.240 +26 44 19.00 0 12.865 12.547 12.023 11.446 11.137 11.155 14.68 2.48 -47.91 2.48 2 GCS9 2MASS J04003922+2644194 +03 37 36.010 +26 32 48.40 0 12.457 12.147 11.662 11.396 10.873 10.930 24.17 3.30 -45.76 3.30 2 GCS9 Cl* Melotte 22 DH 75 +03 43 04.370 +25 26 12.00 0 14.931 14.396 13.757 13.216 12.892 12.874 16.25 2.23 -36.14 2.23 2 GCS9 Cl* Melotte 22 HHJ 107 +03 43 38.900 +27 01 01.20 0 15.573 15.021 14.385 13.839 13.499 13.516 25.77 2.95 -39.98 2.95 2 GCS9 UGCS J034338.89+270101.1 +03 48 43.140 +26 32 20.90 0 14.239 13.774 13.191 12.678 12.379 12.364 25.22 2.93 -37.76 2.93 2 GCS9 Cl* Melotte 22 DH 588 +03 39 44.370 +26 18 18.80 0 15.243 14.790 14.188 13.648 13.355 13.364 16.91 3.00 -36.58 3.00 2 GCS9 Cl* Melotte 22 MBSC 82 +03 33 08.260 +26 31 13.00 0 16.896 16.129 15.414 14.825 14.421 14.415 19.68 3.01 -38.39 3.01 2 GCS9 UGCS J033308.26+263113.0 +03 37 10.510 +25 17 34.60 0 14.920 14.443 13.853 13.316 13.012 13.025 23.82 2.96 -34.70 2.96 2 GCS9 Cl* Melotte 22 DH 67 +03 36 42.460 +25 19 26.50 0 15.840 15.296 14.666 14.163 13.816 13.794 15.86 2.97 -36.53 2.97 2 GCS9 UGCS J033642.46+251926.4 +04 06 52.120 +26 21 12.20 0 15.224 14.702 14.074 13.477 13.127 13.144 10.04 2.97 -34.88 2.97 2 GCS9 UGCS J040652.11+262112.2 +03 29 07.680 +25 21 43.50 0 15.576 15.018 14.363 13.811 13.445 13.438 26.60 3.37 -35.82 3.37 2 GCS9 UGCS J032907.68+252143.5 +04 00 33.240 +25 30 20.50 0 15.605 15.106 14.432 13.755 13.426 13.420 24.06 3.32 -32.29 3.32 2 GCS9 UGCS J040033.23+253020.4 +03 32 36.290 +26 58 10.00 0 16.087 15.489 14.800 14.224 13.863 13.827 21.67 2.97 -39.87 2.97 2 GCS9 UGCS J033236.29+265810.0 +03 49 16.180 +26 49 02.90 0 16.931 16.211 15.474 14.912 14.482 14.531 21.70 2.99 -47.50 2.99 2 GCS9 2MASS J03491617+2649031 +03 41 48.050 +26 47 20.70 0 13.597 13.262 12.716 12.154 11.869 11.873 24.59 3.00 -39.18 3.00 2 GCS9 Cl* Melotte 22 DH 188 +03 53 29.350 +26 40 42.90 0 12.928 12.672 12.185 11.564 11.326 11.333 12.54 2.95 -40.06 2.95 2 GCS9 UGCS J035329.34+264042.9 +03 53 29.690 +26 40 49.60 0 12.976 12.631 12.090 11.488 11.193 11.211 12.01 2.95 -40.62 2.95 2 GCS9 UGCS J035329.69+264049.5 +03 24 59.740 +25 34 04.50 0 16.878 16.243 15.582 14.987 14.606 38.59 7.21 -49.57 7.21 2 GCS9 UGCS J032459.74+253404.4 +03 31 29.600 +26 30 12.10 0 14.670 14.195 13.596 13.007 12.703 12.734 21.27 2.96 -35.29 2.96 2 GCS9 Cl* Melotte 22 DH 16 +03 53 04.920 +27 01 42.30 0 14.277 13.874 13.309 12.686 12.394 12.404 9.69 2.95 -37.92 2.95 2 GCS9 UGCS J035304.92+270142.2 +03 38 54.160 +24 42 15.60 0 15.113 14.509 13.889 13.387 13.029 13.012 24.49 2.49 -40.01 2.49 2 GCS9 Cl* Melotte 22 HHJ 63 +03 49 28.800 +26 50 51.40 0 15.626 15.129 14.513 13.997 13.663 13.655 18.24 2.94 -35.09 2.94 2 GCS9 Cl* Melotte 22 MBSC 95 +03 36 27.370 +24 41 17.10 0 13.905 13.442 12.879 12.371 12.078 12.072 21.62 2.62 -35.55 2.62 2 GCS9 V* KK Tau +03 36 38.910 +24 38 48.70 0 15.678 15.146 14.550 14.008 13.684 13.736 21.00 2.64 -36.23 2.64 2 GCS9 UGCS J033638.90+243848.7 +03 51 25.880 +24 47 38.70 0 12.962 12.520 12.000 11.783 11.161 11.285 16.49 2.20 -47.46 2.20 2 GCS9 V* V558 Tau +03 35 19.700 +26 33 10.60 0 14.411 13.963 13.402 12.862 12.572 12.563 15.89 3.30 -32.50 3.30 2 GCS9 UGCS J033519.70+263310.6 +03 47 05.790 +23 45 34.70 0 16.227 15.555 14.840 14.315 13.961 13.964 11.90 2.23 -39.81 2.23 2 GCS9 UGCS J034705.79+234534.7 +03 43 29.900 +24 39 23.40 0 15.428 14.876 14.288 13.760 13.405 11.50 2.98 -40.55 2.98 2 GCS9 Cl* Melotte 22 DH 269 +03 27 11.130 +26 00 29.20 0 14.463 14.069 13.521 12.933 12.651 9.47 6.88 -28.40 6.88 2 GCS9 UGCS J032711.12+260029.1 +03 32 51.370 +25 54 56.70 0 15.038 14.557 13.922 13.352 13.023 14.47 5.31 -36.68 5.31 2 GCS9 UGCS J033251.37+255456.7 +03 31 20.710 +25 57 33.60 1 16.847 16.068 15.309 14.745 14.321 14.328 21.73 3.39 -38.42 3.39 2 GCS9 UGCS J033120.70+255733.5 +03 37 16.710 +25 44 11.10 0 14.671 14.223 13.651 13.108 12.785 12.788 11.44 2.96 -37.28 2.96 2 GCS9 Cl* Melotte 22 DH 70 +03 41 38.870 +22 16 40.30 0 14.382 13.955 13.443 12.871 12.594 0.14 7.97 -57.33 7.97 2 GCS9 Cl* Melotte 22 DH 183 +03 42 10.600 +22 18 05.50 0 15.177 14.710 14.151 13.625 13.280 10.07 8.00 -65.72 8.00 2 GCS9 Cl* Melotte 22 HHJ 55 +03 48 36.340 +25 15 41.20 0 16.029 15.446 14.801 14.259 13.889 13.895 14.47 2.21 -46.15 2.21 2 GCS9 Cl* Melotte 22 BPL 183 +03 42 01.680 +22 23 26.60 0 14.215 13.807 13.265 12.703 12.396 33.36 7.96 -38.44 7.96 2 GCS9 V* V495 Tau +04 01 28.430 +23 30 59.60 1 16.318 15.539 14.772 14.202 13.788 13.788 24.55 3.37 -39.20 3.37 2 GCS9 UGCS J040128.42+233059.6 +03 53 07.290 +25 47 21.40 0 15.338 14.821 14.219 13.639 13.302 13.309 16.97 2.94 -34.85 2.94 2 GCS9 Cl* Melotte 22 DH 778 +03 34 41.550 +26 09 27.10 0 15.437 14.878 14.245 13.705 13.344 24.47 5.32 -33.86 5.32 2 GCS9 Cl* Melotte 22 DH 36 +03 34 46.960 +26 05 38.40 0 14.123 13.758 13.233 12.636 12.369 9.36 5.31 -55.43 5.31 2 GCS9 UGCS J033446.95+260538.4 +03 43 34.490 +25 57 30.60 1 16.571 15.727 14.909 14.359 13.909 13.901 20.92 2.25 -47.72 2.25 2 GCS9 UGCS J034334.48+255730.5 +03 56 52.310 +25 10 05.10 1 16.146 15.491 14.756 14.192 13.771 13.801 16.66 2.50 -38.22 2.50 2 GCS9 Cl* Melotte 22 HHJ 17 +03 41 42.410 +23 54 57.10 1 16.171 15.464 14.709 14.124 13.686 14.07 2.31 -47.37 2.31 2 GCS9 Cl* Melotte 22 STAR 5 +03 40 55.050 +22 20 58.70 0 13.211 12.869 12.333 11.734 11.446 11.470 21.33 2.50 -35.72 2.50 2 GCS9 V* V430 Tau +03 56 40.450 +22 08 46.50 0 15.445 14.885 14.241 13.710 13.373 13.371 21.46 2.51 -48.69 2.51 2 GCS9 UGCS J035640.44+220846.5 +03 28 26.620 +26 02 11.60 0 16.852 16.148 15.367 14.824 14.432 4.01 7.08 -51.89 7.08 2 GCS9 UGCS J032826.62+260211.5 +03 57 40.680 +25 16 04.10 0 13.577 13.194 12.640 12.008 11.723 11.790 14.25 2.47 -36.24 2.47 2 GCS9 Cl* Melotte 22 DH 856 +03 43 25.970 +25 58 42.10 0 15.160 14.687 14.062 13.537 13.179 13.218 11.69 2.23 -36.19 2.23 2 GCS9 UGCS J034325.96+255842.1 +03 44 31.040 +22 15 14.90 0 13.542 13.130 12.619 12.024 11.745 11.753 24.71 2.51 -36.97 2.51 2 GCS9 Cl* Melotte 22 DH 344 +03 59 59.860 +22 05 29.30 0 14.814 14.347 13.744 13.188 12.863 12.877 16.36 2.87 -36.19 2.87 2 GCS9 Cl* Melotte 22 DH 884 +03 27 16.170 +25 51 42.80 0 14.414 14.020 13.433 12.884 12.573 7.75 6.88 -24.82 6.88 2 GCS9 UGCS J032716.16+255142.7 +03 45 11.520 +21 14 29.30 0 16.421 15.764 15.109 14.536 14.148 14.145 25.46 2.68 -40.57 2.68 2 GCS9 UGCS J034511.51+211429.2 +03 54 24.000 +23 51 58.70 0 15.891 15.288 14.667 14.110 13.776 13.780 14.24 2.25 -48.04 2.25 2 GCS9 UGCS J035423.99+235158.7 +03 26 24.300 +24 25 42.40 0 13.990 13.633 13.081 12.464 12.209 1.09 6.95 -51.34 6.95 2 GCS9 UGCS J032624.30+242542.3 +04 00 03.210 +22 24 46.00 1 16.534 15.875 15.128 14.550 14.132 14.141 15.92 2.87 -33.98 2.87 2 GCS9 UGCS J040003.20+222445.9 +03 28 02.300 +25 55 26.60 0 14.028 13.631 13.111 12.505 12.216 32.02 6.88 -55.34 6.88 2 GCS9 UGCS J032802.30+255526.5 +03 43 24.990 +23 52 01.20 0 16.867 16.116 15.368 14.820 14.365 14.16 2.34 -46.02 2.34 2 GCS9 2MASS J03432497+2352017 +04 03 30.230 +25 16 04.50 0 13.760 13.396 12.828 12.208 11.890 11.912 26.34 2.89 -36.38 2.89 2 GCS9 UGCS J040330.22+251604.5 +03 59 42.660 +21 12 14.90 0 15.123 14.590 13.965 13.401 13.060 13.062 28.07 3.76 -38.75 3.76 2 GCS9 UGCS J035942.66+211214.8 +04 02 55.110 +25 53 53.50 0 16.259 15.667 15.021 14.464 14.087 14.062 19.80 3.33 -38.23 3.33 2 GCS9 UGCS J040255.10+255353.5 +03 58 17.430 +22 11 52.70 1 16.259 15.503 14.768 14.193 13.787 13.778 20.64 2.89 -34.95 2.89 2 GCS9 UGCS J035817.43+221152.6 +04 09 15.300 +25 05 36.60 0 13.026 12.757 12.256 11.647 11.441 11.430 24.83 2.98 -38.40 2.98 2 GCS9 2MASS J04091529+2505368 +03 34 13.470 +25 25 25.30 0 14.776 14.276 13.694 13.124 12.802 25.97 5.29 -32.89 5.29 2 GCS9 UGCS J033413.46+252525.3 +03 59 59.850 +25 08 53.60 1 16.368 15.695 15.004 14.433 14.029 14.075 14.87 2.51 -35.69 2.51 2 GCS9 UGCS J035959.84+250853.6 +03 32 32.970 +22 18 12.10 0 14.922 14.467 13.885 13.344 13.019 13.018 22.36 3.41 -35.37 3.41 2 GCS9 Cl* Melotte 22 DH 20 +03 56 22.310 +21 07 16.90 0 13.818 13.393 12.829 12.286 11.984 12.076 13.77 3.36 -36.78 3.36 2 GCS9 Cl* Melotte 22 DH 831 +03 45 13.410 +23 31 00.70 0 13.938 13.443 12.795 12.223 11.868 11.886 12.02 2.24 -39.32 2.24 2 GCS9 V* OO Tau +03 46 03.450 +24 20 57.00 0 14.476 14.023 13.444 12.842 12.558 12.571 11.97 2.22 -38.92 2.22 2 GCS9 V* V856 Tau +03 45 52.760 +23 27 54.00 0 14.143 13.734 13.161 12.615 12.286 12.305 12.01 2.24 -39.18 2.24 2 GCS9 V* V747 Tau +03 40 49.400 +21 12 55.30 0 14.418 13.938 13.357 12.766 12.478 12.460 21.51 3.58 -35.17 3.58 2 GCS9 Cl* Melotte 22 DH 152 +03 52 02.280 +24 21 47.90 0 12.898 12.582 12.168 11.631 11.353 11.429 22.99 2.24 -48.15 2.24 2 GCS9 Cl* Melotte 22 DH 734 +03 39 10.220 +19 55 30.60 0 14.068 13.656 13.061 12.514 12.192 12.201 18.29 5.01 -32.86 5.01 2 GCS9 UGCS J033910.21+195530.5 +03 39 37.760 +19 55 53.30 0 14.324 13.934 13.412 12.811 12.532 12.528 4.36 5.02 -36.62 5.02 2 GCS9 UGCS J033937.75+195553.3 +03 47 31.130 +21 10 51.10 0 14.679 14.224 13.640 13.111 12.780 12.789 23.35 3.05 -35.53 3.05 2 GCS9 Cl* Melotte 22 DH 519 +03 54 49.920 +19 50 44.90 0 14.727 14.266 13.691 13.126 12.808 12.842 8.65 6.05 -45.98 6.05 2 GCS9 UGCS J035449.91+195044.9 +04 05 50.080 +22 35 53.80 0 14.329 13.919 13.378 12.805 12.498 12.507 14.32 4.97 -32.31 4.97 2 GCS9 UGCS J040550.08+223553.8 +03 54 38.020 +19 54 22.50 0 14.169 13.723 13.136 12.596 12.291 12.305 14.93 6.05 -51.92 6.05 2 GCS9 UGCS J035438.01+195422.5 +03 37 10.870 +21 12 09.70 0 15.115 14.622 14.008 13.457 13.133 13.127 27.96 3.41 -35.33 3.41 2 GCS9 UGCS J033710.86+211209.7 +03 57 19.330 +23 27 37.60 0 15.039 14.541 13.948 13.395 13.065 13.076 17.31 3.00 -34.18 3.00 2 GCS9 2MASS J03571931+2327379 +03 42 57.780 +19 47 06.10 0 16.796 16.118 15.392 14.796 14.398 14.370 19.85 4.07 -37.58 4.07 2 GCS9 UGCS J034257.78+194706.1 +03 37 28.280 +24 24 09.10 0 12.968 12.703 12.189 11.596 11.337 11.397 15.93 2.50 -35.80 2.50 2 GCS9 UGCS J033728.27+242409.1 +03 42 13.490 +24 18 49.60 0 15.627 15.114 14.453 13.891 13.540 13.15 2.31 -37.63 2.31 2 GCS9 Cl* Melotte 22 BPL 36 +03 41 51.500 +24 19 27.30 0 16.617 15.976 15.244 14.667 14.283 16.53 2.33 -37.48 2.33 2 GCS9 Cl* Melotte 22 BPL 27 +03 41 38.810 +24 23 09.20 0 15.413 14.924 14.300 13.761 13.401 20.74 2.31 -48.47 2.31 2 GCS9 Cl* Melotte 22 BPL 25 +03 53 53.320 +22 30 37.50 0 15.687 15.153 14.514 13.995 13.648 13.634 25.44 2.52 -43.14 2.52 2 GCS9 UGCS J035353.32+223037.5 +03 32 37.820 +23 25 59.00 0 15.028 14.538 13.929 13.363 13.041 13.050 20.57 3.42 -34.09 3.42 2 GCS9 UGCS J033237.82+232559.0 +03 41 45.070 +22 28 01.80 0 15.511 14.939 14.315 13.811 13.435 13.430 24.25 2.51 -44.15 2.51 2 GCS9 Cl* Melotte 22 DH 185 +03 54 15.600 +24 20 45.70 0 15.915 15.353 14.671 14.117 13.832 13.778 15.59 2.25 -35.25 2.25 2 GCS9 Cl* Melotte 22 BPL 308 +04 02 41.900 +22 28 18.30 0 16.439 15.775 15.058 14.528 14.129 14.101 18.57 3.42 -47.04 3.42 2 GCS9 UGCS J040241.90+222818.2 +03 40 28.570 +23 33 42.20 0 16.820 16.083 15.334 14.796 14.375 14.420 24.86 2.54 -48.66 2.54 2 GCS9 UGCS J034028.56+233342.2 +03 42 06.940 +19 54 06.00 0 15.496 15.005 14.353 13.759 13.458 13.462 10.23 5.03 -28.73 5.03 2 GCS9 UGCS J034206.94+195405.9 +03 47 56.640 +24 15 31.70 0 15.315 14.766 14.163 13.625 13.282 13.281 21.88 2.22 -36.70 2.22 2 GCS9 Cl* Melotte 22 BPL 162 +03 44 09.920 +24 16 03.90 0 13.292 12.928 12.379 11.781 11.527 20.69 2.30 -36.49 2.30 2 GCS9 V* V629 Tau +03 47 55.280 +23 19 05.80 0 13.814 13.443 12.910 12.377 12.092 12.111 14.50 2.24 -35.68 2.24 2 GCS9 V* PS Tau +03 44 20.660 +24 15 10.70 0 15.211 14.648 13.992 13.469 13.087 12.94 2.31 -38.96 2.31 2 GCS9 Cl* Melotte 22 HHJ 57 +03 40 15.930 +24 22 31.30 0 15.827 15.296 14.640 14.123 13.772 13.768 20.95 2.51 -35.43 2.51 2 GCS9 Cl* Melotte 22 BPL 11 +04 01 05.810 +23 34 43.90 0 14.117 13.665 13.116 12.586 12.289 26.79 3.54 -44.13 3.54 2 GCS9 UGCS J040105.81+233443.8 +04 00 50.520 +23 43 52.90 1 16.184 15.380 14.647 14.089 13.660 24.52 3.56 -40.90 3.56 2 GCS9 UGCS J040050.52+234352.9 +03 59 12.920 +24 17 18.40 0 14.762 14.271 13.702 13.114 12.808 12.827 15.00 2.97 -34.59 2.97 2 GCS9 Cl* Melotte 22 DH 873 +03 43 18.750 +20 07 44.40 0 14.292 13.846 13.306 12.791 12.491 12.478 12.00 3.42 -35.92 3.42 2 GCS9 UGCS J034318.74+200744.3 +03 28 16.070 +22 27 32.10 0 16.039 15.477 14.850 14.262 13.924 13.920 26.29 5.04 -36.31 5.04 2 GCS9 UGCS J032816.07+222732.0 +03 42 58.600 +20 12 45.00 0 14.947 14.424 13.773 13.194 12.857 12.840 18.85 3.42 -32.72 3.42 2 GCS9 Cl* Melotte 22 DH 247 +03 50 43.070 +23 19 55.70 0 16.002 15.424 14.795 14.265 13.908 13.920 23.59 2.14 -41.03 2.14 2 GCS9 UGCS J035043.06+231955.6 +03 55 30.900 +23 23 50.90 0 13.961 13.590 13.001 12.425 12.158 12.161 18.03 2.25 -35.87 2.25 2 GCS9 Cl* Melotte 22 DH 815 +03 41 20.000 +22 37 53.70 0 13.771 13.385 12.814 12.255 11.965 11.999 11.28 2.50 -47.82 2.50 2 GCS9 Cl* Melotte 22 DH 168 +03 37 26.250 +22 34 30.90 0 16.766 16.113 15.394 14.869 14.450 14.467 17.90 2.97 -38.24 2.97 2 GCS9 UGCS J033726.25+223430.8 +04 03 49.740 +23 43 25.20 0 16.769 16.047 15.317 14.756 14.349 14.341 25.63 3.40 -44.29 3.40 2 GCS9 UGCS J040349.73+234325.1 +03 58 52.130 +24 43 48.30 0 14.398 14.003 13.466 12.869 12.581 12.617 18.41 2.48 -35.59 2.48 2 GCS9 UGCS J035852.13+244348.3 +03 38 34.490 +23 40 22.30 0 14.946 14.514 13.918 13.368 13.041 13.062 11.57 2.50 -46.35 2.50 2 GCS9 Cl* Melotte 22 DH 91 +03 42 05.740 +23 07 14.40 0 16.728 16.017 15.363 14.805 14.378 14.398 16.95 2.29 -37.00 2.29 2 GCS9 UGCS J034205.74+230714.3 +03 26 44.060 +24 39 39.40 0 14.123 13.658 13.081 12.585 12.255 18.85 6.95 -22.57 6.95 2 GCS9 UGCS J032644.06+243939.4 +03 43 26.440 +22 42 42.50 0 14.138 13.675 13.111 12.543 12.249 12.216 13.70 2.25 -37.63 2.25 2 GCS9 V* V846 Tau +03 54 31.490 +22 39 01.50 0 16.554 15.843 15.155 14.573 14.190 14.206 13.09 2.29 -41.56 2.29 2 GCS9 UGCS J035431.48+223901.5 +03 45 37.760 +23 43 50.10 1 16.240 15.456 14.715 14.172 13.756 13.742 20.91 2.23 -45.45 2.23 2 GCS9 Cl* Melotte 22 SHF 10 +03 30 43.820 +27 27 42.80 0 16.033 15.515 14.892 14.273 13.976 13.989 4.89 4.92 -41.76 4.92 2 GCS9 UGCS J033043.82+272742.8 +03 48 57.410 +23 13 59.10 1 16.835 16.033 15.222 14.673 14.194 14.194 14.93 2.17 -36.62 2.17 2 GCS9 UGCS J034857.40+231359.0 +03 33 14.390 +24 50 08.10 0 14.746 14.237 13.646 13.119 12.824 12.808 25.31 2.63 -48.57 2.63 2 GCS9 UGCS J033314.38+245008.1 +03 44 25.510 +22 27 49.80 0 16.405 15.794 15.102 14.589 14.223 14.185 22.07 2.53 -37.99 2.53 2 GCS9 2MASS J03442549+2227501 +03 44 29.500 +22 25 24.70 0 15.948 15.406 14.761 14.222 13.858 13.860 23.26 2.52 -37.84 2.52 2 GCS9 UGCS J034429.49+222524.6 +03 28 19.340 +24 49 48.70 0 13.462 13.155 12.649 12.117 11.814 11.03 6.95 -38.57 6.95 2 GCS9 UGCS J032819.34+244948.6 +03 28 05.420 +24 46 50.80 0 14.243 13.810 13.304 12.740 12.453 16.36 6.95 -28.13 6.95 2 GCS9 UGCS J032805.42+244650.7 +03 28 56.800 +24 45 20.20 0 14.988 14.482 13.880 13.310 13.039 7.77 6.96 -34.47 6.96 2 GCS9 UGCS J032856.79+244520.2 +03 37 34.180 +24 42 15.10 0 15.478 14.973 14.389 13.876 13.543 13.551 21.50 2.49 -36.80 2.49 2 GCS9 Cl* Melotte 22 DH 74 +03 37 03.480 +24 44 35.30 0 13.096 12.748 12.245 11.693 11.446 11.458 21.23 2.48 -35.99 2.48 2 GCS9 V* KL Tau +03 56 28.630 +23 09 03.40 0 13.707 13.314 12.787 12.207 11.924 11.944 17.30 2.99 -35.17 2.99 2 GCS9 UGCS J035628.63+230903.4 +04 01 55.860 +24 44 02.30 0 14.205 13.759 13.190 12.627 12.326 12.347 16.22 2.89 -35.93 2.89 2 GCS9 UGCS J040155.85+244402.3 +03 40 56.930 +29 01 09.10 0 14.565 14.058 13.458 12.891 12.549 12.569 11.58 2.96 -44.15 2.96 2 GCS9 UGCS J034056.93+290109.1 +03 37 37.090 +23 10 57.80 0 14.626 14.203 13.619 13.102 12.764 12.763 21.66 2.98 -35.09 2.98 2 GCS9 UGCS J033737.08+231057.8 +04 06 29.990 +22 33 43.60 0 14.376 13.856 13.201 12.623 12.273 12.269 14.83 5.07 -33.96 5.07 2 GCS9 Cl* Melotte 22 DH 916 +04 06 48.060 +22 26 38.90 0 15.098 14.674 14.081 13.428 13.126 13.129 5.85 5.08 -27.50 5.08 2 GCS9 UGCS J040648.05+222638.9 +04 10 20.200 +22 49 44.20 0 16.876 16.094 15.352 14.801 14.394 14.366 31.66 6.12 -35.95 6.12 2 GCS9 UGCS J041020.20+224944.2 +04 10 52.290 +22 40 50.70 0 16.595 16.009 15.382 14.707 14.334 14.306 28.77 6.10 -34.40 6.10 2 GCS9 UGCS J041052.28+224050.7 +03 38 25.430 +24 27 11.90 0 16.581 15.897 15.248 14.690 14.314 14.316 23.60 2.52 -36.23 2.52 2 GCS9 UGCS J033825.42+242711.8 +03 49 43.170 +24 39 46.40 0 16.348 15.708 15.061 14.505 14.115 14.126 17.72 2.22 -38.37 2.22 2 GCS9 Cl* Melotte 22 BPL 215 +03 40 09.690 +23 10 32.40 0 14.362 13.967 13.408 12.881 12.576 12.579 25.33 2.98 -38.03 2.98 2 GCS9 V* KW Tau +03 40 12.740 +23 09 35.60 0 13.830 13.457 12.923 12.343 12.056 12.069 23.11 2.97 -33.99 2.97 2 GCS9 V* V429 Tau +04 02 34.770 +23 08 28.40 0 14.671 14.234 13.621 13.093 12.774 12.817 13.37 3.35 -33.49 3.35 2 GCS9 UGCS J040234.77+230828.4 +03 49 27.580 +24 24 13.70 0 14.547 14.043 13.524 12.968 12.679 12.681 11.85 2.20 -46.15 2.20 2 GCS9 Cl* Melotte 22 DH 621 +03 49 27.650 +24 31 54.10 0 12.946 12.583 12.067 11.575 11.226 11.273 11.93 2.20 -42.04 2.20 2 GCS9 V* V359 Tau +03 42 31.210 +24 49 21.20 0 15.091 14.553 13.952 13.433 13.118 23.06 2.97 -38.50 2.97 2 GCS9 V* V611 Tau +03 41 52.320 +24 41 57.60 0 14.986 14.480 13.904 13.361 13.012 18.58 2.97 -50.63 2.97 2 GCS9 Cl* Melotte 22 DH 190 +03 38 13.040 +24 38 16.80 0 14.671 14.227 13.631 13.103 12.809 12.801 23.94 2.49 -49.13 2.49 2 GCS9 Cl* Melotte 22 DH 84 +04 06 49.560 +23 09 03.60 0 13.930 13.558 12.993 12.404 12.134 12.138 15.11 3.74 -32.63 3.74 2 GCS9 UGCS J040649.55+230903.6 +03 34 19.370 +22 48 42.20 0 16.004 15.439 14.812 14.262 13.912 13.908 21.53 3.06 -34.97 3.06 2 GCS9 Cl* Melotte 22 DH 34 +03 57 05.170 +20 44 16.30 0 16.134 15.509 14.849 14.335 13.970 13.945 23.22 4.01 -41.34 4.01 2 GCS9 UGCS J035705.17+204416.3 +03 50 09.720 +20 41 54.00 0 16.759 16.075 15.362 14.808 14.395 14.384 17.58 3.49 -35.38 3.49 2 GCS9 UGCS J035009.71+204154.0 +03 25 45.370 +22 56 36.80 0 13.572 13.247 12.749 12.147 11.900 11.922 8.91 5.07 -37.88 5.07 2 GCS9 UGCS J032545.37+225636.8 +03 25 38.730 +22 57 39.80 1 15.890 15.269 14.601 13.974 13.618 13.628 24.45 5.11 -47.02 5.11 2 GCS9 UGCS J032538.72+225739.8 +03 50 24.960 +20 42 17.80 0 12.468 12.207 11.761 11.397 10.921 10.930 13.33 3.42 -33.72 3.42 2 GCS9 Cl* Melotte 22 DH 668 +04 01 50.950 +22 59 15.50 0 16.363 15.631 14.906 14.311 13.912 13.906 20.72 3.38 -38.98 3.38 2 GCS9 UGCS J040150.95+225915.5 +03 41 13.500 +20 51 17.20 0 13.069 12.653 12.096 11.582 11.204 11.227 22.59 3.42 -36.61 3.42 2 GCS9 UGCS J034113.49+205117.2 +03 44 08.810 +23 04 47.50 0 12.721 12.442 11.944 11.503 11.108 11.160 24.16 2.25 -46.50 2.25 2 GCS9 V* MV Tau +03 53 43.800 +27 50 19.20 0 16.584 16.006 15.337 14.720 14.344 14.344 13.37 3.36 -34.48 3.36 2 GCS9 UGCS J035343.79+275019.1 +03 47 15.660 +19 42 10.50 0 14.049 13.614 13.070 12.494 12.226 12.227 8.84 5.04 -28.59 5.04 2 GCS9 UGCS J034715.66+194210.4 +03 52 30.540 +19 40 39.60 0 14.510 14.026 13.382 12.839 12.507 12.549 19.00 3.98 -35.62 3.98 2 GCS9 Cl* Melotte 22 DH 753 +03 59 16.640 +19 44 27.20 0 14.532 14.106 13.533 12.964 12.659 12.670 11.83 3.04 -34.49 3.04 2 GCS9 UGCS J035916.64+194427.1 +03 55 23.970 +19 36 48.70 0 12.665 12.398 11.904 11.358 11.013 11.061 27.55 6.05 -29.31 6.05 2 GCS9 UGCS J035523.96+193648.7 +03 40 35.340 +21 33 33.30 0 14.187 13.742 13.238 12.559 12.246 12.253 9.37 3.58 -32.98 3.58 2 GCS9 UGCS J034035.33+213333.3 +03 40 47.810 +21 43 21.70 0 15.850 15.249 14.624 14.026 13.626 13.638 20.22 3.60 -35.96 3.60 2 GCS9 UGCS J034047.80+214321.6 +03 55 50.110 +21 43 27.70 0 13.074 12.662 12.128 11.773 11.254 11.294 11.27 3.36 -39.08 3.36 2 GCS9 Cl* Melotte 22 DH 819 +03 58 50.060 +21 36 52.90 0 15.384 14.775 14.109 13.558 13.179 13.201 24.07 3.76 -38.06 3.76 2 GCS9 UGCS J035850.06+213652.8 +03 44 52.870 +21 34 16.10 0 14.371 13.850 13.252 12.733 12.411 12.398 16.20 2.65 -35.83 2.65 2 GCS9 UGCS J034452.86+213416.0 +03 40 10.500 +21 44 45.50 0 14.286 13.854 13.282 12.790 12.436 12.464 26.94 3.58 -45.88 3.58 2 GCS9 UGCS J034010.50+214445.4 +03 56 33.510 +19 33 25.20 0 16.665 15.941 15.228 14.681 14.293 14.284 22.45 6.10 -29.57 6.10 2 GCS9 UGCS J035633.50+193325.1 +03 56 49.390 +19 30 05.50 0 13.596 13.242 12.727 12.205 11.878 11.890 12.45 6.05 -26.16 6.05 2 GCS9 UGCS J035649.38+193005.4 +03 56 23.930 +19 23 53.40 0 14.848 14.335 13.702 13.158 12.840 12.830 12.00 6.05 -30.52 6.05 2 GCS9 UGCS J035623.92+192353.3 +03 45 46.940 +21 44 49.00 0 14.850 14.329 13.677 13.182 12.811 12.850 25.93 2.65 -40.73 2.65 2 GCS9 Cl* Melotte 22 HHJ 128 +03 41 14.460 +19 20 30.70 0 12.714 12.465 12.013 11.465 11.274 11.312 32.96 5.01 -35.20 5.01 2 GCS9 UGCS J034114.45+192030.7 +03 57 09.810 +21 36 27.20 0 14.554 14.125 13.494 12.940 12.659 12.639 17.59 3.36 -34.33 3.36 2 GCS9 Cl* Melotte 22 DH 850 +03 50 29.740 +19 21 01.50 0 16.507 15.848 15.150 14.568 14.214 14.238 13.83 4.07 -37.81 4.07 2 GCS9 UGCS J035029.73+192101.5 +03 30 57.410 +27 16 46.30 0 16.086 15.546 14.839 14.272 13.954 5.81 7.01 -21.54 7.01 2 GCS9 UGCS J033057.40+271646.2 +03 42 38.220 +19 28 57.40 0 15.981 15.445 14.822 14.201 13.860 13.854 7.36 4.00 -35.21 4.00 2 GCS9 UGCS J034238.21+192857.4 +03 49 22.520 +21 37 56.00 0 14.541 14.086 13.492 12.975 12.636 12.654 21.90 3.05 -33.73 3.05 2 GCS9 Cl* Melotte 22 DH 619 +03 39 57.850 +25 55 29.70 0 15.347 14.843 14.201 13.640 13.304 13.307 25.63 2.96 -42.96 2.96 2 GCS9 Cl* Melotte 22 DH 120 +03 40 11.930 +25 52 32.30 0 15.702 15.231 14.589 14.058 13.700 13.704 16.97 2.97 -35.30 2.97 2 GCS9 Cl* Melotte 22 HHJ 29 +03 32 11.550 +21 27 55.70 1 16.385 15.652 14.906 14.339 13.949 13.920 13.64 3.89 -40.00 3.89 2 GCS9 UGCS J033211.55+212755.7 +03 43 27.900 +25 01 40.90 0 15.717 15.051 14.370 13.818 13.439 10.73 2.98 -49.42 2.98 2 GCS9 UGCS J034327.89+250140.9 +03 44 11.280 +24 52 34.20 0 15.374 14.904 14.280 13.727 13.434 24.96 2.98 -35.52 2.98 2 GCS9 Cl* Melotte 22 HHJ 59 +03 39 03.500 +21 25 19.90 0 14.680 14.219 13.605 13.055 12.701 12.734 25.01 3.41 -38.17 3.41 2 GCS9 UGCS J033903.49+212519.9 +03 57 14.880 +21 31 01.30 0 13.657 13.248 12.707 12.190 11.892 11.934 25.20 3.36 -39.29 3.36 2 GCS9 UGCS J035714.87+213101.3 +04 09 48.530 +24 54 00.50 0 15.210 14.746 14.051 13.310 12.940 21.67 4.44 -35.52 4.44 2 GCS9 UGCS J040948.53+245400.4 +03 43 43.710 +24 29 15.50 0 13.243 12.898 12.337 11.802 11.502 18.55 2.97 -50.23 2.97 2 GCS9 Cl* Melotte 22 DH 285 +03 38 24.800 +26 15 23.50 0 14.041 13.583 13.024 12.485 12.215 12.231 27.72 3.30 -43.66 3.30 2 GCS9 Cl* Melotte 22 DH 87 +04 05 41.700 +24 31 35.10 0 16.378 15.833 15.175 14.498 14.178 14.167 24.40 2.97 -34.22 2.97 2 GCS9 UGCS J040541.70+243135.0 +03 41 58.860 +26 12 21.10 0 13.697 13.292 12.754 12.161 11.889 11.902 14.33 3.00 -33.91 3.00 2 GCS9 V* KQ Tau +03 33 32.390 +26 08 10.20 0 15.097 14.615 13.992 13.411 13.059 15.67 5.32 -27.22 5.32 2 GCS9 UGCS J033332.39+260810.1 +03 43 16.110 +24 22 28.00 0 16.600 15.927 15.220 14.697 14.293 14.308 13.35 2.16 -47.16 2.16 2 GCS9 UGCS J034316.11+242227.9 +03 49 36.320 +26 02 16.30 0 15.517 14.960 14.333 13.755 13.409 13.401 24.70 2.24 -41.43 2.24 2 GCS9 Cl* Melotte 22 DH 633 +03 46 52.970 +24 15 07.80 0 16.407 15.752 15.069 14.513 14.131 14.122 19.64 2.23 -36.47 2.23 2 GCS9 Cl* Melotte 22 MHO 7 +03 47 01.850 +24 13 28.10 0 16.253 15.613 14.933 14.410 14.020 14.022 15.79 2.23 -38.31 2.23 2 GCS9 Cl* Melotte 22 MHO 10 +03 47 15.460 +24 23 31.00 0 14.844 14.290 13.663 13.121 12.783 12.815 12.21 2.22 -46.33 2.22 2 GCS9 Cl* Melotte 22 MHO 12 +03 52 55.920 +24 57 41.80 0 16.326 15.757 15.109 14.535 14.152 14.141 12.56 2.25 -44.02 2.25 2 GCS9 Cl* Melotte 22 BPL 275 +03 44 25.580 +22 40 07.90 0 16.220 15.599 14.929 14.388 14.003 14.006 11.28 2.25 -40.25 2.25 2 GCS9 UGCS J034425.58+224007.8 +03 37 30.690 +24 50 54.30 0 14.798 14.416 13.808 13.323 13.004 12.978 19.62 2.49 -34.54 2.49 2 GCS9 Cl* Melotte 22 HHJ 110 +04 00 15.860 +25 01 46.30 0 13.460 13.089 12.543 11.954 11.659 11.681 10.70 2.89 -39.78 2.89 2 GCS9 UGCS J040015.86+250146.2 +03 49 05.180 +22 04 52.70 0 16.648 15.958 15.257 14.742 14.327 14.355 16.87 2.31 -38.67 2.31 2 GCS9 UGCS J034905.17+220452.6 +04 04 38.550 +21 46 47.70 0 13.894 13.477 12.956 12.429 12.123 12.120 29.74 5.07 -28.67 5.07 2 GCS9 UGCS J040438.55+214647.7 +04 07 29.340 +21 46 09.80 0 14.170 13.699 13.175 12.658 12.334 12.339 12.98 4.97 -51.85 4.97 2 GCS9 UGCS J040729.34+214609.8 +03 59 39.840 +21 54 27.70 0 13.543 13.158 12.617 12.028 11.764 11.777 10.82 2.84 -38.32 2.84 2 GCS9 UGCS J035939.84+215427.7 +03 40 11.980 +21 48 31.80 1 16.578 15.884 15.169 14.580 14.187 14.178 24.16 2.54 -40.67 2.54 2 GCS9 UGCS J034011.98+214831.7 +03 45 57.710 +24 03 04.90 0 15.821 15.269 14.671 14.132 13.755 13.740 14.71 2.23 -37.35 2.23 2 GCS9 Cl* Melotte 22 HHJ 27 +03 37 53.260 +20 23 01.30 0 14.549 14.107 13.550 13.014 12.698 12.732 23.35 3.86 -53.40 3.86 2 GCS9 UGCS J033753.25+202301.2 +03 32 00.120 +20 23 45.90 0 16.268 15.657 15.038 14.458 14.131 14.118 27.13 6.12 -30.46 6.12 2 GCS9 UGCS J033200.11+202345.8 +03 49 56.070 +20 22 52.30 0 14.520 14.010 13.453 12.888 12.591 12.601 27.18 3.42 -44.80 3.42 2 GCS9 UGCS J034956.07+202252.2 +03 46 26.090 +24 05 09.50 1 16.810 15.967 15.160 14.584 14.118 14.098 19.41 2.24 -38.71 2.24 2 GCS9 2MASS J03462608+2405096 +03 51 04.740 +20 14 06.10 0 14.534 14.114 13.550 12.881 12.645 12.663 15.57 3.42 -33.25 3.42 2 GCS9 UGCS J035104.73+201406.1 +03 50 33.870 +20 19 38.30 0 16.290 15.620 14.949 14.435 14.012 14.021 24.49 3.44 -42.26 3.44 2 GCS9 UGCS J035033.86+201938.3 +03 55 34.430 +23 58 28.30 0 15.140 14.664 14.047 13.501 13.196 11.66 2.31 -40.75 2.31 2 GCS9 Cl* Melotte 22 DH 818 +03 36 19.260 +24 10 52.30 0 13.345 13.022 12.518 11.979 11.704 11.709 25.71 2.60 -41.47 2.60 2 GCS9 UGCS J033619.26+241052.3 +03 42 40.230 +23 59 21.50 0 12.859 12.500 11.988 11.460 11.096 11.160 17.01 2.12 -46.73 2.12 2 GCS9 V* LO Tau +03 28 35.630 +24 00 43.70 0 16.995 16.267 15.527 14.966 14.552 14.537 19.57 3.89 -36.94 3.89 2 GCS9 UGCS J032835.63+240043.7 +03 46 09.880 +21 05 52.40 0 13.956 13.557 12.996 12.456 12.173 12.162 25.89 2.65 -44.94 2.65 2 GCS9 Cl* Melotte 22 DH 425 +03 46 22.310 +21 05 20.00 0 16.232 15.519 14.873 14.327 13.935 13.935 22.61 2.67 -45.95 2.67 2 GCS9 UGCS J034622.31+210519.9 +03 57 52.350 +21 02 15.80 0 14.703 14.171 13.595 13.036 12.705 12.710 16.38 3.36 -34.99 3.36 2 GCS9 Cl* Melotte 22 DH 859 +03 40 35.330 +20 57 56.60 0 13.012 12.647 12.149 11.582 11.260 11.302 23.55 3.58 -31.81 3.58 2 GCS9 Cl* Melotte 22 DH 146 +03 32 35.580 +27 03 36.70 0 17.100 16.282 15.495 14.865 14.489 14.499 23.23 5.01 -52.46 5.01 2 GCS9 UGCS J033235.57+270336.6 +03 43 28.050 +26 29 55.50 0 17.173 16.372 15.618 15.060 14.682 14.649 17.49 3.01 -38.50 3.01 2 GCS9 UGCS J034328.04+262955.5 +03 36 53.300 +26 34 27.90 1 17.046 16.171 15.379 14.804 14.355 14.353 15.54 3.33 -34.04 3.33 2 GCS9 UGCS J033653.30+263427.9 +03 46 22.250 +23 52 26.60 1 17.120 16.323 15.518 14.917 14.474 14.472 17.65 2.25 -38.04 2.25 2 GCS9 Cl* Melotte 22 SHF 31 +04 00 11.720 +23 23 08.90 0 17.275 16.419 15.671 15.086 14.642 14.623 21.83 3.45 -37.29 3.45 2 GCS9 UGCS J040011.71+232308.9 +03 46 05.110 +23 45 34.90 1 17.229 16.347 15.522 14.851 14.385 14.404 15.88 2.25 -39.21 2.25 2 GCS9 Cl* Melotte 22 SHF 25 +03 58 00.620 +21 18 20.80 1 17.399 16.380 15.545 14.931 14.445 14.423 24.81 3.43 -32.13 3.43 2 GCS9 UGCS J035800.61+211820.8 +04 05 23.120 +22 29 03.30 0 17.120 16.364 15.605 15.015 14.577 14.567 24.92 5.10 -41.55 5.10 2 GCS9 UGCS J040523.12+222903.3 +03 47 20.480 +19 54 25.50 1 17.011 16.054 15.210 14.615 14.152 14.140 28.11 5.10 -39.39 5.10 2 GCS9 UGCS J034720.47+195425.5 +04 02 43.660 +22 43 42.50 0 17.125 16.400 15.711 15.021 14.640 14.615 22.00 3.44 -31.92 3.44 2 GCS9 UGCS J040243.65+224342.5 +03 40 45.160 +27 50 40.40 1 17.074 16.218 15.404 14.816 14.321 14.333 12.37 3.34 -40.37 3.34 2 GCS9 UGCS J034045.16+275040.4 +03 42 47.300 +23 00 40.30 0 17.056 16.290 15.554 14.991 14.575 14.576 14.60 2.31 -44.06 2.31 2 GCS9 UGCS J034247.29+230040.3 +03 45 19.730 +19 15 47.40 0 17.061 16.401 15.697 15.151 14.745 14.731 8.46 4.11 -32.05 4.11 2 GCS9 UGCS J034519.72+191547.4 +03 47 39.020 +24 36 22.20 0 17.112 16.271 15.548 14.992 14.570 14.554 15.20 2.24 -45.02 2.24 2 GCS9 2MASS J03473900+2436226 +03 51 05.970 +24 36 16.90 0 17.107 16.333 15.649 15.153 14.702 14.712 14.09 2.26 -37.47 2.26 2 GCS9 2MASS J03510597+2436171 +03 34 38.610 +24 51 28.50 1 17.097 16.247 15.459 14.920 14.445 14.466 13.39 2.69 -37.21 2.69 2 GCS9 UGCS J033438.61+245128.4 +04 09 17.800 +26 03 31.20 1 17.120 16.480 15.759 15.003 14.603 6.56 4.49 -33.73 4.49 2 GCS9 UGCS J040917.79+260331.2 +03 43 33.120 +27 55 49.60 0 17.880 16.894 16.092 15.463 15.044 15.026 17.70 4.09 -38.59 4.09 2 GCS9 UGCS J034333.12+275549.5 +04 00 18.830 +27 09 46.80 0 17.560 16.743 16.002 15.448 15.005 15.036 10.06 3.09 -34.16 3.09 2 GCS9 UGCS J040018.82+270946.7 +03 33 30.460 +27 32 55.40 0 17.238 16.553 15.809 15.260 14.841 14.846 18.69 3.90 -36.97 3.90 2 GCS9 UGCS J033330.46+273255.4 +04 04 24.850 +28 39 03.60 0 17.526 16.894 16.132 15.388 14.992 15.035 18.79 3.11 -35.18 3.11 2 GCS9 UGCS J040424.84+283903.5 +03 34 50.820 +25 34 46.40 0 17.916 16.901 16.095 15.494 15.066 21.10 5.70 -37.46 5.70 2 GCS9 UGCS J033450.81+253446.3 +04 03 01.220 +25 24 23.00 0 17.268 16.527 15.813 15.214 14.808 14.778 24.72 3.40 -37.90 3.40 2 GCS9 Cl* Melotte 22 DH 905 +03 52 15.630 +25 31 09.10 0 17.722 16.782 15.959 15.395 14.936 14.897 13.63 3.10 -42.16 3.10 2 GCS9 UGCS J035215.63+253109.1 +03 53 41.460 +26 42 22.30 0 17.559 16.958 16.192 15.590 15.213 15.193 20.55 3.12 -38.29 3.12 2 GCS9 UGCS J035341.45+264222.3 +03 48 38.370 +22 33 51.80 0 17.345 16.523 15.711 15.168 14.718 14.692 17.51 2.33 -38.63 2.33 2 GCS9 UGCS J034838.37+223351.7 +03 28 20.710 +25 15 40.00 0 17.594 16.841 16.097 15.503 15.097 11.14 7.37 -20.79 7.37 2 GCS9 UGCS J032820.71+251540.0 +03 58 22.560 +22 28 36.30 0 17.356 16.631 15.918 15.234 14.804 9.62 6.01 -58.05 6.01 2 GCS9 UGCS J035822.55+222836.3 +03 38 43.690 +24 11 24.50 0 17.599 16.762 15.913 15.343 14.861 14.851 24.38 2.57 -36.40 2.57 2 GCS9 UGCS J033843.68+241124.5 +03 40 35.500 +23 13 07.40 0 17.972 16.910 16.076 15.510 15.014 15.020 12.82 2.37 -43.97 2.37 2 GCS9 UGCS J034035.49+231307.3 +03 56 21.530 +23 08 41.50 0 17.934 16.982 16.151 15.536 15.055 15.042 18.32 3.20 -36.21 3.20 2 GCS9 UGCS J035621.52+230841.4 +03 56 40.930 +22 59 40.60 0 17.830 16.872 16.002 15.424 14.921 14.950 13.47 3.14 -35.44 3.14 2 GCS9 UGCS J035640.92+225940.5 +03 51 25.970 +20 36 12.10 0 17.108 16.526 15.822 15.270 14.881 14.835 15.46 3.52 -35.21 3.52 2 GCS9 UGCS J035125.96+203612.0 +03 37 27.990 +23 02 19.60 0 17.750 16.811 15.973 15.390 14.893 14.904 19.88 3.09 -49.46 3.09 2 GCS9 UGCS J033727.98+230219.5 +04 07 12.860 +27 26 05.40 0 17.420 16.620 15.882 15.297 14.866 14.886 11.86 3.06 -46.68 3.06 2 GCS9 UGCS J040712.86+272605.4 +03 31 17.910 +27 27 46.10 0 17.576 16.828 16.102 15.467 15.101 2.80 7.41 -33.43 7.41 2 GCS9 UGCS J033117.91+272746.1 +03 51 35.200 +19 26 25.80 0 17.395 16.730 16.037 15.430 15.101 15.089 13.46 4.25 -36.40 4.25 2 GCS9 UGCS J035135.19+192625.8 +03 48 19.020 +24 25 12.70 0 17.655 16.668 15.930 15.365 14.935 14.953 14.16 2.30 -39.48 2.30 2 GCS9 2MASS J03481900+2425130 +03 40 53.660 +28 21 11.50 1 18.880 17.570 16.490 15.852 15.194 15.199 13.91 4.06 -31.57 4.06 2 GCS9 UGCS J034053.65+282111.5 +03 34 04.410 +25 31 33.60 0 18.393 17.293 16.390 15.781 15.254 31.63 5.79 -38.18 5.79 2 GCS9 UGCS J033404.41+253133.5 +03 33 49.220 +19 59 52.00 1 18.539 18.104 16.453 15.767 15.433 15.492 1.67 6.44 -40.37 6.44 2 GCS9 UGCS J033349.21+195952.0 +03 57 30.820 +23 41 22.60 0 18.532 17.359 16.440 15.839 15.265 15.297 20.84 3.11 -46.90 3.11 2 GCS9 UGCS J035730.82+234122.5 +04 07 10.040 +24 12 32.40 0 18.235 17.375 16.480 15.868 15.404 15.382 20.30 3.50 -47.43 3.50 2 GCS9 UGCS J040710.04+241232.3 +03 39 14.770 +23 23 31.70 0 18.455 17.428 16.499 15.853 15.312 15.326 17.63 3.27 -35.01 3.27 2 GCS9 UGCS J033914.77+232331.7 +03 50 37.960 +23 03 05.00 0 18.420 17.267 16.354 15.734 15.191 15.195 15.37 2.36 -44.53 2.36 2 GCS9 UGCS J035037.95+230305.0 +03 36 03.850 +22 52 03.50 1 18.072 16.873 15.913 15.240 14.679 14.679 22.46 3.15 -46.19 3.15 2 GCS9 UGCS J033603.84+225203.4 +03 43 40.310 +24 30 11.20 0 18.552 17.490 16.494 15.853 15.304 12.03 3.27 -44.27 3.27 2 GCS9 2MASS J03434028+2430113 +03 52 18.640 +24 04 28.10 0 18.327 17.280 16.395 15.738 15.202 15.236 15.37 2.40 -46.07 2.40 2 GCS9 2MASS J03521864+2404283 +03 45 45.360 +27 59 27.60 0 19.568 18.074 17.104 16.454 15.870 15.859 22.97 3.88 -34.58 3.88 2 GCS9 UGCS J034545.35+275927.5 +03 53 00.850 +28 39 04.10 0 20.062 19.110 17.754 17.030 16.313 16.293 15.53 6.33 -39.81 6.33 2 GCS9 UGCS J035300.85+283904.1 +03 45 24.330 +29 23 10.90 0 19.979 18.833 17.716 17.085 16.422 16.407 4.16 7.63 -22.76 7.63 2 GCS9 UGCS J034524.32+292310.8 +03 49 16.680 +27 40 21.80 0 18.545 17.513 16.598 15.957 15.425 15.434 17.98 3.18 -33.47 3.18 2 GCS9 UGCS J034916.67+274021.8 +03 54 06.750 +25 37 45.30 0 18.855 17.669 16.724 16.098 15.522 15.498 15.81 3.44 -37.94 3.44 2 GCS9 UGCS J035406.75+253745.3 +03 50 08.270 +25 30 51.70 0 19.614 18.280 17.223 16.594 16.008 15.983 13.87 2.83 -45.74 2.83 2 GCS9 UGCS J035008.27+253051.6 +03 47 04.410 +24 47 27.30 0 20.675 19.510 18.057 17.121 16.518 16.537 14.19 3.20 -39.02 3.20 2 GCS9 UGCS J034704.41+244727.3 +03 47 46.780 +25 35 16.60 0 20.351 18.565 17.424 16.687 16.154 16.095 18.60 3.00 -37.57 3.00 2 GCS9 UGCS J034746.77+253516.5 +03 51 47.660 +24 39 58.90 0 20.158 18.804 17.528 16.773 16.100 16.094 14.64 2.71 -40.22 2.71 2 GCS9 UGCS J035147.65+243958.9 +03 56 16.380 +23 54 51.30 0 18.685 17.753 16.776 16.182 15.634 15.596 9.87 3.22 -39.24 3.22 2 GCS9 UGCS J035616.37+235451.3 +03 58 05.150 +22 17 27.90 0 20.161 18.583 17.333 16.594 15.971 15.972 23.29 3.91 -48.55 3.91 2 GCS9 UGCS J035805.15+221727.8 +03 48 27.360 +23 46 16.20 0 20.628 19.658 18.134 17.347 16.505 16.519 18.01 2.93 -43.64 2.93 2 GCS9 UGCS J034827.36+234616.2 +03 40 06.820 +25 15 49.20 0 18.988 17.792 16.838 16.183 15.664 15.610 20.86 2.86 -34.63 2.86 2 GCS9 UGCS J034006.81+251549.2 +03 55 27.630 +25 49 40.60 0 18.871 17.904 16.953 16.336 15.855 15.810 21.98 3.53 -52.06 3.53 2 GCS9 UGCS J035527.62+254940.6 +03 56 44.750 +25 30 10.70 0 18.802 17.819 16.859 16.230 15.664 15.687 25.10 3.01 -34.70 3.01 2 GCS9 UGCS J035644.74+253010.6 +03 45 04.410 +24 15 16.60 0 20.479 19.040 17.775 16.979 16.350 16.230 20.21 2.72 -38.78 2.72 2 GCS9 UGCS J034504.41+241516.6 +03 33 00.000 +24 18 24.70 0 20.076 18.520 17.447 16.641 16.125 16.084 18.97 3.41 -44.11 3.41 2 GCS9 UGCS J033259.99+241824.6 +03 56 00.910 +22 29 08.70 0 18.397 17.477 16.589 15.928 15.448 15.432 21.10 2.75 -37.24 2.75 2 GCS9 UGCS J035600.91+222908.7 +03 36 18.940 +23 33 17.70 0 20.060 18.441 17.328 16.631 16.003 16.073 17.36 3.52 -38.71 3.52 2 GCS9 UGCS J033618.93+233317.7 +03 47 48.910 +24 17 06.50 0 20.292 19.272 17.873 17.039 16.410 16.385 13.21 2.89 -36.93 2.89 2 GCS9 UGCS J034748.90+241706.5 +03 43 50.160 +24 12 29.70 0 20.264 18.895 17.579 16.861 16.149 20.17 3.10 -36.90 3.10 2 GCS9 UGCS J034350.15+241229.7 +03 44 05.250 +22 50 13.50 0 20.066 18.839 17.666 16.895 16.134 16.158 19.70 3.13 -45.22 3.13 2 GCS9 Cl* Melotte 22 IPL 57 +03 48 11.270 +23 17 25.20 0 19.477 18.335 17.220 16.665 16.007 16.019 24.70 3.10 -48.41 3.10 2 GCS9 UGCS J034811.27+231725.2 +03 27 04.710 +24 40 13.70 0 20.770 19.508 18.154 17.395 16.720 -20.28 14.33 -21.25 14.33 2 GCS9 UGCS J032704.71+244013.7 +03 44 18.350 +23 15 22.30 0 18.547 17.519 16.552 15.957 15.400 15.438 14.83 2.46 -47.82 2.46 2 GCS9 UGCS J034418.34+231522.3 +03 54 14.070 +23 17 51.90 0 20.028 18.873 17.601 16.818 16.138 16.100 17.07 3.03 -38.21 3.03 2 GCS9 UGCS J035414.06+231751.9 +03 46 55.490 +23 11 16.00 0 20.373 19.064 17.892 17.144 16.423 16.403 18.24 3.27 -33.97 3.27 2 GCS9 UGCS J034655.48+231116.0 +03 48 30.740 +22 44 50.20 0 20.389 18.936 17.714 16.861 16.251 16.150 11.34 3.40 -38.38 3.40 2 GCS9 UGCS J034830.74+224450.2 +03 48 31.530 +24 34 37.20 1 19.218 17.883 16.715 15.977 15.357 15.312 11.92 2.37 -46.68 2.37 2 GCS9 UGCS J034831.52+243437.2 +03 51 05.220 +23 15 37.70 0 20.207 19.022 17.842 17.041 16.374 16.313 17.05 3.13 -40.03 3.13 2 GCS9 UGCS J035105.21+231537.7 +03 52 02.100 +23 15 45.40 0 18.708 17.679 16.659 16.057 15.473 15.529 12.75 2.53 -39.96 2.53 2 GCS9 2MASS J03520209+2315451 +03 43 07.150 +20 41 57.00 0 19.556 18.299 17.188 16.411 15.877 15.884 23.63 4.17 -37.64 4.17 2 GCS9 UGCS J034307.15+204156.9 +03 33 30.240 +21 20 50.50 0 19.630 18.597 17.546 16.802 16.378 16.403 3.11 5.57 -44.71 5.57 2 GCS9 UGCS J033330.23+212050.5 +03 54 12.540 +19 15 05.80 0 20.321 18.802 17.562 16.719 16.048 16.072 7.03 6.90 -37.98 6.90 2 GCS9 UGCS J035412.54+191505.7 +03 39 18.810 +24 27 37.80 0 20.092 18.529 17.384 16.652 15.944 16.010 21.70 3.21 -42.51 3.21 2 GCS9 UGCS J033918.80+242737.8 +03 58 31.810 +26 47 38.70 0 20.342 18.971 17.810 17.035 16.342 16.469 14.01 3.98 -35.22 3.98 2 GCS9 UGCS J035831.80+264738.7 +03 38 05.890 +26 15 03.70 0 20.001 18.573 17.436 16.664 16.051 16.064 19.43 3.91 -39.39 3.91 2 GCS9 UGCS J033805.88+261503.7 +03 48 40.630 +26 17 09.70 0 20.203 18.726 17.655 17.168 16.407 16.359 19.80 4.09 -32.05 4.09 2 GCS9 UGCS J034840.63+261709.7 +03 32 58.900 +26 08 38.40 0 20.923 19.834 18.160 17.335 16.613 2.05 10.05 -30.53 10.05 2 GCS9 UGCS J033258.89+260838.3 +03 46 32.130 +24 23 14.60 0 19.257 18.083 17.049 16.373 15.849 15.766 17.62 2.43 -39.19 2.43 2 GCS9 Cl* Melotte 22 SHF 34 +03 46 20.270 +23 58 18.90 1 19.259 18.174 17.034 16.269 15.650 15.585 12.97 2.40 -35.00 2.40 2 GCS9 UGCS J034620.27+235818.8 +03 44 18.230 +24 05 47.20 0 20.411 19.235 17.772 16.984 16.316 20.09 3.23 -35.92 3.23 2 GCS9 UGCS J034418.23+240547.1 +03 34 34.800 +27 40 36.50 0 12.822 12.654 12.246 11.697 11.560 11.604 19.82 3.74 -37.63 3.74 0.83 1 GCS9 UGCS J033434.79+274036.5 +03 52 14.060 +28 24 40.90 0 12.345 12.198 11.835 11.446 11.311 11.332 15.22 2.93 -38.30 2.93 0.73 1 GCS9 UGCS J035214.06+282440.8 +03 58 18.450 +25 33 56.00 0 12.415 12.278 11.950 11.653 11.462 11.478 14.63 2.47 -38.40 2.47 0.69 1 GCS9 UGCS J035818.45+253356.0 +03 26 13.750 +25 27 46.80 0 12.564 12.357 11.926 11.507 11.265 20.99 6.87 -37.32 6.87 0.79 1 GCS9 UGCS J032613.74+252746.7 +03 33 38.020 +25 20 16.90 0 12.908 12.498 11.930 11.512 11.225 24.95 5.30 -41.14 5.30 0.65 1 GCS9 UGCS J033338.02+252016.8 +03 44 30.080 +25 35 46.80 0 12.678 11.857 11.349 11.013 11.058 18.21 2.27 -37.32 2.27 0.81 1 GCS9 V* V513 Tau +03 43 05.540 +24 49 28.30 0 12.492 12.121 11.627 11.517 11.107 21.32 2.97 -42.76 2.97 0.86 1 GCS9 V* LS Tau +03 43 52.150 +24 50 29.50 0 12.141 11.914 11.457 11.366 10.988 18.96 2.97 -35.05 2.97 0.60 1 GCS9 V* MR Tau +03 49 02.350 +25 43 24.10 0 12.899 12.711 12.370 11.912 11.751 11.756 13.74 2.23 -39.88 2.23 0.66 1 GCS9 Cl* Melotte 22 DH 600 +03 42 02.850 +22 22 42.90 0 12.613 12.430 12.021 11.494 11.211 18.81 7.95 -45.14 7.95 0.79 1 GCS9 Cl* Melotte 22 SK 711 +03 52 42.510 +25 07 02.70 0 12.809 12.594 12.168 11.644 11.471 11.491 22.89 2.23 -36.88 2.23 0.67 1 GCS9 Cl* Melotte 22 SRS 33701 +03 52 10.580 +29 01 12.10 0 12.489 12.328 11.917 11.584 11.201 11.269 21.59 3.07 -35.82 3.07 0.64 1 GCS9 UGCS J035210.58+290112.1 +03 41 57.740 +23 40 05.80 0 12.352 12.196 11.846 11.578 11.384 11.414 14.80 2.12 -38.46 2.12 0.71 1 GCS9 Cl* Melotte 22 SRS 83446 +03 55 32.840 +23 19 08.00 0 12.548 12.391 11.984 11.529 11.394 11.417 25.21 2.25 -40.35 2.25 0.61 1 GCS9 Cl* Melotte 22 DH 816 +03 48 10.170 +23 00 03.90 0 12.412 12.198 11.771 11.631 10.997 11.133 18.14 2.23 -35.64 2.23 0.66 1 GCS9 Cl* Melotte 22 DH 554 +03 56 08.590 +21 45 47.70 0 12.574 11.870 11.320 11.082 11.146 18.71 2.62 -38.21 2.62 0.85 1 GCS9 V* V581 Tau +03 53 45.760 +21 48 52.50 0 12.830 12.662 12.296 11.818 11.689 11.729 19.80 2.50 -39.40 2.50 0.88 1 GCS9 UGCS J035345.75+214852.5 +03 54 34.800 +21 53 01.70 0 12.781 11.982 11.442 11.104 11.183 19.60 2.62 -38.65 2.62 0.87 1 GCS9 V* V577 Tau +03 43 42.140 +24 34 23.10 0 12.680 12.347 11.834 11.451 11.133 21.23 2.97 -39.67 2.97 0.87 1 GCS9 V* MO Tau +04 00 59.070 +24 08 32.20 0 12.785 12.566 12.116 11.564 11.384 13.85 3.54 -39.58 3.54 0.66 1 GCS9 UGCS J040059.07+240832.2 +03 46 12.880 +24 03 15.60 0 12.012 11.833 11.399 11.455 10.715 11.045 22.36 2.22 -38.68 2.22 0.81 1 GCS9 V* V1189 Tau +03 45 44.080 +24 04 26.60 0 12.115 11.903 11.473 11.507 10.786 11.052 20.45 2.22 -36.83 2.22 0.78 1 GCS9 V* V787 Tau +03 42 10.930 +24 05 08.40 0 12.830 12.470 11.954 11.688 11.337 16.83 2.30 -39.37 2.30 0.85 1 GCS9 V* LM Tau +04 00 21.250 +28 16 49.70 0 13.141 12.934 12.512 12.011 11.880 11.905 21.55 3.86 -37.30 3.86 0.64 1 GCS9 UGCS J040021.24+281649.7 +03 51 47.460 +28 26 24.60 0 13.148 12.951 12.542 11.990 11.840 11.865 13.41 2.93 -47.11 2.93 0.68 1 GCS9 UGCS J035147.45+282624.6 +04 00 02.390 +25 39 34.00 0 13.797 13.476 12.981 12.320 12.070 12.076 20.75 3.31 -46.98 3.31 0.85 1 GCS9 UGCS J040002.38+253933.9 +03 37 18.450 +25 03 44.90 0 13.853 13.520 13.052 12.497 12.198 12.229 21.92 2.48 -45.60 2.48 0.86 1 GCS9 UGCS J033718.45+250344.8 +03 36 47.500 +26 38 27.60 0 13.112 12.943 12.541 12.080 11.931 11.931 18.09 3.30 -37.78 3.30 0.79 1 GCS9 UGCS J033647.50+263827.5 +04 00 49.330 +25 12 10.60 0 13.940 13.688 13.208 12.567 12.370 12.375 19.90 2.89 -39.87 2.89 0.89 1 GCS9 Cl* Melotte 22 DH 895 +03 54 48.000 +25 12 30.20 0 13.344 13.058 12.571 11.963 11.714 11.736 19.70 2.23 -39.12 2.23 0.87 1 GCS9 Cl* Melotte 22 BPL 318 +03 35 44.280 +22 27 09.70 0 13.612 13.310 12.830 12.275 11.996 12.017 19.82 2.93 -40.16 2.93 0.90 1 GCS9 Cl* Melotte 22 DH 47 +03 33 40.840 +20 09 48.00 0 13.488 13.221 12.781 12.282 12.017 12.032 21.02 6.09 -45.62 6.09 0.89 1 GCS9 UGCS J033340.83+200948.0 +03 36 29.050 +20 11 19.60 0 13.927 13.706 13.216 12.603 12.461 12.464 16.96 3.85 -40.17 3.85 0.90 1 GCS9 UGCS J033629.05+201119.6 +03 59 25.170 +24 14 56.40 0 13.930 13.724 13.271 12.606 12.467 12.483 16.99 2.96 -44.63 2.96 0.93 1 GCS9 UGCS J035925.17+241456.3 +03 37 17.950 +22 28 18.00 0 13.830 13.597 13.115 12.467 12.257 12.268 20.09 2.94 -37.15 2.94 0.70 1 GCS9 UGCS J033717.94+222817.9 +03 51 54.520 +23 33 31.30 0 13.694 13.230 12.636 12.103 11.768 11.791 15.96 2.24 -48.77 2.24 0.70 1 GCS9 V* V389 Tau +03 51 03.830 +22 50 37.90 0 13.298 13.029 12.569 11.989 11.797 11.789 15.03 2.13 -40.74 2.13 0.87 1 GCS9 UGCS J035103.83+225037.9 +03 43 24.400 +23 13 30.20 0 13.528 12.672 12.085 11.771 11.765 20.41 2.30 -41.63 2.30 0.92 1 GCS9 UGCS J034324.40+231330.1 +03 32 15.300 +20 47 41.70 0 13.718 13.478 13.020 12.433 12.215 12.228 17.60 6.09 -40.17 6.09 0.91 1 GCS9 UGCS J033215.29+204741.6 +04 03 24.940 +22 52 17.00 0 13.410 13.177 12.786 12.268 12.138 12.178 17.60 3.35 -40.68 3.35 0.92 1 GCS9 Cl* Melotte 22 DH 907 +03 56 10.400 +23 02 23.70 0 13.356 12.519 11.944 11.625 11.650 15.48 3.06 -38.16 3.06 0.74 1 GCS9 UGCS J035610.39+230223.7 +03 52 25.930 +21 50 31.50 0 13.817 13.529 13.062 12.397 12.193 12.161 16.43 2.51 -38.84 2.51 0.83 1 GCS9 Cl* Melotte 22 DH 752 +03 55 35.350 +27 46 29.00 0 13.589 13.393 12.952 12.392 12.253 12.273 20.70 3.30 -40.12 3.30 0.89 1 GCS9 UGCS J035535.35+274628.9 +03 35 38.230 +21 51 25.40 0 13.279 13.079 12.659 12.077 11.911 11.936 19.33 2.93 -40.04 2.93 0.90 1 GCS9 UGCS J033538.23+215125.4 +03 47 21.730 +19 18 28.70 0 13.854 13.603 13.121 12.524 12.312 12.319 17.93 5.04 -48.08 5.04 0.82 1 GCS9 UGCS J034721.73+191828.6 +03 51 51.900 +21 49 21.70 0 13.872 13.425 12.851 12.281 12.493 12.027 24.20 2.51 -40.53 2.51 0.69 1 GCS9 UGCS J035151.89+214921.6 +03 54 00.710 +23 58 59.80 0 13.647 13.247 12.690 12.150 11.841 11.858 18.82 2.24 -49.02 2.24 0.73 1 GCS9 Cl* Melotte 22 BPL 298 +03 51 31.610 +29 15 13.10 0 14.394 14.080 13.598 12.909 12.733 12.731 19.57 3.07 -37.15 3.07 0.76 1 GCS9 UGCS J035131.60+291513.1 +03 29 19.780 +27 12 30.00 0 14.890 14.572 14.060 13.447 13.169 16.72 6.94 -44.58 6.94 0.93 1 GCS9 UGCS J032919.78+271229.9 +04 00 28.350 +27 20 54.50 0 14.954 14.552 14.004 13.379 13.085 13.092 16.55 2.95 -38.17 2.95 0.81 1 GCS9 Cl* Melotte 22 DH 893 +03 43 47.710 +28 15 59.30 0 14.415 14.156 13.720 13.100 12.988 12.927 15.96 3.69 -40.79 3.69 0.90 1 GCS9 Cl* Melotte 22 DH 289 +03 53 49.640 +27 30 40.10 0 14.731 14.415 13.869 13.241 12.976 12.976 24.57 3.30 -41.14 3.30 0.74 1 GCS9 UGCS J035349.63+273040.0 +03 59 05.730 +26 24 26.60 0 14.015 13.667 13.180 12.570 12.328 12.338 18.44 2.48 -40.89 2.48 0.93 1 GCS9 Cl* Melotte 22 DH 871 +03 39 43.060 +28 31 56.40 0 14.162 13.895 13.403 12.759 12.563 12.544 20.48 4.93 -36.37 4.93 0.64 1 GCS9 UGCS J033943.05+283156.4 +03 58 28.350 +26 12 55.90 0 14.361 14.017 13.521 12.968 12.697 12.687 20.39 2.48 -41.41 2.48 0.93 1 GCS9 UGCS J035828.34+261255.8 +03 49 56.770 +25 22 22.60 0 14.259 13.890 13.375 12.874 12.557 12.584 16.93 2.23 -43.91 2.23 0.93 1 GCS9 Cl* Melotte 22 DH 643 +03 30 22.480 +26 24 32.00 0 14.792 14.449 13.980 13.319 13.102 18.94 6.94 -40.18 6.94 0.92 1 GCS9 UGCS J033022.47+262432.0 +03 47 45.920 +24 38 01.30 0 14.579 14.033 13.437 12.860 12.493 12.514 19.47 2.21 -49.88 2.21 0.62 1 GCS9 V* QZ Tau +03 54 03.200 +25 54 51.90 0 14.437 14.189 13.723 13.123 12.856 12.865 12.69 2.94 -39.94 2.94 0.66 1 GCS9 UGCS J035403.20+255451.9 +03 52 06.670 +22 01 14.30 0 14.997 14.501 13.944 13.407 13.089 13.080 19.27 2.51 -49.89 2.51 0.62 1 GCS9 Cl* Melotte 22 DH 741 +03 45 02.710 +21 18 39.70 0 14.934 14.640 14.136 13.464 13.315 13.317 22.56 2.65 -46.52 2.65 0.82 1 GCS9 UGCS J034502.71+211839.6 +03 51 33.720 +29 00 34.70 0 14.788 14.525 14.036 13.372 13.189 13.178 12.05 3.07 -41.43 3.07 0.65 1 GCS9 UGCS J035133.72+290034.7 +03 42 19.290 +23 31 54.20 0 14.725 14.361 13.832 13.208 12.974 12.935 25.07 2.13 -45.62 2.13 0.64 1 GCS9 UGCS J034219.28+233154.1 +04 02 22.620 +24 48 24.00 0 14.717 14.421 13.944 13.270 13.053 13.046 15.08 2.90 -44.52 2.90 0.89 1 GCS9 Cl* Melotte 22 DH 903 +04 02 26.020 +20 01 20.20 0 14.492 14.212 13.752 13.090 12.861 12.872 11.66 3.13 -43.18 3.13 0.62 1 GCS9 UGCS J040226.01+200120.2 +04 00 14.110 +24 48 51.40 0 14.369 14.008 13.496 12.927 12.677 12.641 14.14 2.89 -46.60 2.89 0.77 1 GCS9 Cl* Melotte 22 DH 889 +03 57 21.710 +19 08 03.30 0 14.153 13.952 13.488 12.848 12.624 12.659 13.07 3.04 -47.13 3.04 0.63 1 GCS9 UGCS J035721.71+190803.2 +03 42 27.850 +20 36 34.50 0 14.470 14.181 13.664 13.020 12.780 12.791 20.91 3.42 -38.69 3.42 0.85 1 GCS9 UGCS J034227.84+203634.5 +03 44 44.440 +22 55 51.50 0 14.304 13.961 13.438 12.776 12.510 12.514 17.50 2.24 -39.82 2.24 0.91 1 GCS9 UGCS J034444.43+225551.4 +03 38 58.120 +21 39 37.00 0 14.879 14.515 13.971 13.347 13.097 13.072 12.89 3.41 -45.84 3.41 0.70 1 GCS9 UGCS J033858.11+213936.9 +03 39 53.530 +25 46 46.20 0 14.991 14.642 14.122 13.564 13.298 13.295 19.66 2.96 -45.57 2.96 0.92 1 GCS9 Cl* Melotte 22 DH 119 +03 37 22.250 +24 54 16.00 0 14.602 14.248 13.722 13.181 12.852 12.843 21.94 2.49 -40.27 2.49 0.88 1 GCS9 UGCS J033722.24+245415.9 +04 04 06.690 +24 06 38.90 0 14.458 14.219 13.754 13.172 13.023 13.019 18.45 3.38 -41.12 3.38 0.93 1 GCS9 UGCS J040406.69+240638.9 +03 47 59.670 +29 33 38.30 0 15.313 14.132 13.593 13.251 13.270 19.41 5.91 -43.49 5.91 0.89 1 GCS9 UGCS J034759.67+293338.3 +03 37 39.560 +27 58 59.00 0 15.396 15.042 14.537 13.913 13.660 13.707 17.33 4.96 -37.63 4.96 0.72 1 GCS9 UGCS J033739.56+275858.9 +04 01 54.700 +28 42 17.70 0 15.596 15.171 14.595 13.980 13.674 13.692 12.35 2.97 -40.08 2.97 0.60 1 GCS9 UGCS J040154.70+284217.7 +03 57 41.460 +28 16 35.80 0 15.528 15.146 14.578 13.949 13.678 13.674 15.59 3.87 -48.50 3.87 0.62 1 GCS9 UGCS J035741.46+281635.8 +03 51 29.350 +28 30 49.30 0 15.859 15.469 14.923 14.340 14.053 14.035 20.59 2.97 -37.30 2.97 0.63 1 GCS9 UGCS J035129.34+283049.2 +03 50 32.900 +26 42 57.20 0 15.534 15.123 14.574 13.937 13.670 13.669 21.11 2.97 -41.24 2.97 0.84 1 GCS9 UGCS J035032.90+264257.2 +04 05 57.780 +26 14 28.00 0 15.023 14.686 14.199 13.587 13.362 13.356 14.79 3.39 -37.90 3.39 0.65 1 GCS9 UGCS J040557.77+261428.0 +03 49 12.180 +25 20 31.70 0 15.633 15.245 14.696 14.123 13.823 13.846 16.17 2.24 -42.24 2.24 0.89 1 GCS9 UGCS J034912.17+252031.7 +03 47 10.210 +24 43 35.30 0 15.533 15.060 14.487 13.950 13.645 13.650 17.16 2.22 -43.48 2.22 0.90 1 GCS9 UGCS J034710.20+244335.3 +03 42 52.360 +26 29 09.40 0 15.538 15.176 14.662 14.108 13.827 13.821 18.20 2.95 -44.40 2.95 0.89 1 GCS9 UGCS J034252.35+262909.3 +03 45 36.120 +25 16 30.30 0 15.683 15.221 14.656 14.111 13.784 13.795 15.41 2.22 -40.16 2.22 0.83 1 GCS9 UGCS J034536.12+251630.2 +03 46 13.000 +27 02 11.70 0 15.061 14.740 14.217 13.668 13.401 13.421 18.46 2.95 -46.68 2.95 0.82 1 GCS9 Cl* Melotte 22 DH 430 +04 01 12.640 +23 50 20.20 0 15.772 15.257 14.698 14.117 13.795 17.28 3.56 -46.77 3.56 0.81 1 GCS9 UGCS J040112.64+235020.2 +03 38 10.270 +25 11 32.90 0 15.589 15.124 14.544 14.014 13.684 13.670 20.06 2.50 -43.22 2.50 0.88 1 GCS9 Cl* Melotte 22 HHJ 35 +03 56 11.670 +22 05 52.60 0 15.610 15.181 14.631 14.105 13.807 13.799 13.87 2.52 -42.35 2.52 0.81 1 GCS9 UGCS J035611.66+220552.5 +04 10 08.800 +25 45 52.30 0 15.752 14.719 14.169 13.826 13.848 15.83 3.05 -42.20 3.05 0.88 1 GCS9 UGCS J041008.80+254552.3 +04 04 11.640 +22 15 20.70 0 15.315 14.979 14.419 13.706 13.470 13.485 22.99 3.41 -38.88 3.41 0.60 1 GCS9 UGCS J040411.64+221520.6 +03 33 39.010 +22 21 08.50 0 15.193 14.805 14.251 13.715 13.455 13.430 16.11 3.42 -44.42 3.42 0.87 1 GCS9 Cl* Melotte 22 DH 29 +03 57 55.850 +21 16 10.80 0 15.354 14.971 14.436 13.805 13.526 13.536 16.86 3.37 -36.80 3.37 0.62 1 GCS9 Cl* Melotte 22 DH 860 +03 56 55.470 +22 08 24.30 0 15.110 14.685 14.152 13.553 13.236 13.258 18.72 2.85 -38.55 2.85 0.80 1 GCS9 Cl* Melotte 22 DH 847 +03 45 15.270 +28 49 08.30 0 15.861 15.413 14.849 14.234 13.940 13.925 16.55 5.87 -46.40 5.87 0.82 1 GCS9 UGCS J034515.27+284908.2 +03 54 16.800 +22 00 16.60 0 15.893 15.489 14.921 14.365 14.077 14.066 16.81 2.53 -37.22 2.53 0.67 1 GCS9 Cl* Melotte 22 DH 799 +03 38 06.920 +24 14 55.50 0 15.747 15.267 14.652 14.133 13.761 13.766 22.57 2.51 -42.72 2.51 0.78 1 GCS9 UGCS J033806.92+241455.5 +03 44 47.310 +19 55 41.40 0 15.884 15.400 14.799 14.273 13.946 13.957 20.01 4.01 -40.31 4.01 0.85 1 GCS9 Cl* Melotte 22 DH 354 +03 39 05.600 +24 12 41.20 0 15.799 15.460 14.905 14.352 14.029 14.047 14.71 2.51 -42.46 2.51 0.85 1 GCS9 UGCS J033905.60+241241.2 +03 58 23.360 +24 41 57.20 0 15.291 14.922 14.431 13.755 13.554 13.562 17.18 2.49 -38.00 2.49 0.76 1 GCS9 UGCS J035823.36+244157.2 +03 57 06.210 +23 13 00.70 0 15.484 14.319 13.773 13.434 13.441 15.53 3.07 -41.00 3.07 0.86 1 GCS9 UGCS J035706.21+231300.7 +04 08 59.010 +24 26 26.40 0 15.392 15.053 14.539 13.891 13.594 13.605 17.10 2.99 -46.14 2.99 0.84 1 GCS9 UGCS J040859.01+242626.3 +03 48 48.620 +24 30 15.40 0 15.488 15.114 14.569 13.991 13.692 13.689 21.84 2.21 -38.84 2.21 0.70 1 GCS9 UGCS J034848.61+243015.3 +03 34 41.090 +22 42 27.00 0 15.091 14.772 14.252 13.508 13.294 13.320 12.65 3.05 -42.76 3.05 0.72 1 GCS9 UGCS J033441.08+224226.9 +03 44 05.630 +23 03 42.40 0 15.169 14.803 14.258 13.565 13.302 13.294 17.87 2.26 -43.49 2.26 0.90 1 GCS9 UGCS J034405.62+230342.4 +03 29 48.420 +22 57 51.60 0 15.744 15.377 14.828 14.156 13.922 13.928 17.79 3.43 -41.86 3.43 0.90 1 GCS9 UGCS J032948.41+225751.6 +03 44 24.000 +21 24 20.80 0 15.184 14.768 14.212 13.673 13.353 13.359 16.88 2.66 -43.67 2.66 0.89 1 GCS9 Cl* Melotte 22 DH 332 +03 38 25.720 +21 39 49.20 0 15.288 14.944 14.425 13.874 13.590 13.648 16.06 3.42 -43.01 3.42 0.89 1 GCS9 UGCS J033825.71+213949.1 +03 54 06.960 +19 19 14.30 0 15.335 14.930 14.318 13.814 13.497 13.503 18.13 6.06 -42.85 6.06 0.90 1 GCS9 UGCS J035406.96+191914.2 +04 03 16.560 +24 35 19.60 0 15.089 14.701 14.123 13.564 13.268 13.248 21.78 2.90 -42.71 2.90 0.83 1 GCS9 Cl* Melotte 22 DH 906 +04 10 46.420 +24 32 13.90 0 15.786 15.329 14.767 14.099 13.762 13.766 15.88 2.99 -46.34 2.99 0.81 1 GCS9 UGCS J041046.41+243213.8 +03 45 36.750 +25 58 55.50 0 15.934 15.514 14.978 14.442 14.152 14.150 14.21 2.25 -38.02 2.25 0.62 1 GCS9 UGCS J034536.74+255855.5 +03 57 23.790 +24 08 50.00 0 15.309 14.982 14.445 13.865 13.611 13.611 13.28 2.97 -45.28 2.97 0.72 1 GCS9 UGCS J035723.79+240850.0 +03 27 27.940 +24 04 58.90 0 15.259 14.831 14.285 13.791 13.497 13.490 19.47 3.85 -48.39 3.85 0.66 1 GCS9 UGCS J032727.94+240458.8 +03 56 28.160 +28 03 48.20 0 16.015 15.511 14.948 14.304 14.050 14.022 16.65 3.90 -45.48 3.90 0.68 1 GCS9 UGCS J035628.16+280348.2 +03 39 03.310 +25 48 18.80 0 16.452 16.052 15.461 14.883 14.588 14.571 16.29 3.03 -46.33 3.03 0.62 1 GCS9 UGCS J033903.30+254818.8 +04 00 38.150 +19 49 22.40 0 16.562 16.095 15.532 15.014 14.678 14.708 14.99 3.08 -42.23 3.08 0.68 1 GCS9 UGCS J040038.14+194922.3 +03 45 03.180 +23 06 58.40 0 16.937 15.492 14.946 14.495 14.527 20.81 2.32 -40.36 2.32 0.62 1 GCS9 2MASS J03450316+2306586 +03 52 34.560 +24 25 22.20 0 16.121 15.669 15.096 14.512 14.246 14.236 16.91 2.25 -44.16 2.25 0.73 1 GCS9 UGCS J035234.55+242522.2 +03 30 35.390 +23 03 07.90 0 16.316 15.841 15.233 14.594 14.298 14.288 15.61 3.45 -45.70 3.45 0.63 1 GCS9 Cl* Melotte 22 DH 12 +03 40 28.370 +21 33 06.70 0 16.564 16.082 15.471 14.901 14.509 14.509 15.69 3.66 -45.19 3.66 0.66 1 GCS9 UGCS J034028.36+213306.7 +03 42 30.350 +25 34 26.80 0 17.540 16.951 16.269 15.675 15.305 15.293 15.12 2.38 -41.28 2.38 0.62 1 GCS9 UGCS J034230.35+253426.7 +03 51 17.870 +25 29 25.40 0 17.777 18.391 15.245 14.674 14.519 14.519 20.51 2.28 -39.66 2.28 0.65 1 GCS9 UGCS J035117.86+252925.4 +03 53 00.280 +25 40 08.50 0 17.276 16.722 16.169 15.583 15.273 15.241 18.78 3.13 -41.94 3.13 0.73 1 GCS9 UGCS J035300.28+254008.4 +04 06 09.290 +26 15 33.50 0 17.463 16.942 16.310 15.716 15.358 15.337 18.62 3.15 -39.15 3.15 0.64 1 GCS9 UGCS J040609.29+261533.5 +04 03 43.310 +23 56 52.90 0 17.197 16.623 15.998 15.428 15.061 15.045 15.09 3.44 -41.73 3.44 0.63 1 GCS9 UGCS J040343.31+235652.9 +03 36 57.480 +24 19 47.70 0 17.070 16.465 15.833 15.324 14.922 14.937 21.72 2.60 -40.73 2.60 0.64 1 GCS9 UGCS J033657.47+241947.7 +03 44 02.270 +28 51 32.30 0 17.372 16.793 16.123 15.540 15.176 15.169 19.81 6.04 -41.64 6.04 0.72 1 GCS9 UGCS J034402.26+285132.2 +04 03 04.580 +23 33 10.70 0 17.246 16.654 16.054 15.455 15.146 19.09 3.78 -42.76 3.78 0.73 1 GCS9 UGCS J040304.58+233310.6 +03 46 23.720 +22 50 16.40 0 17.310 15.710 15.189 14.722 14.694 20.26 2.35 -45.42 2.35 0.64 1 GCS9 2MASS J03462371+2250167 +04 11 03.840 +23 15 48.90 0 17.766 17.254 16.599 15.986 15.599 15.623 18.36 6.81 -46.34 6.81 0.61 1 GCS9 UGCS J041103.83+231548.9 +03 50 07.500 +19 37 06.20 0 17.466 16.910 16.332 15.761 15.401 15.371 17.29 4.51 -41.20 4.51 0.70 1 GCS9 UGCS J035007.50+193706.2 +03 37 45.480 +21 49 49.20 0 17.574 16.949 16.292 15.711 15.375 15.341 15.76 2.70 -40.52 2.70 0.63 1 GCS9 UGCS J033745.47+214949.2 +03 49 01.600 +24 54 30.20 0 17.757 17.163 16.566 16.016 15.638 15.597 20.82 2.38 -42.11 2.38 0.70 1 GCS9 UGCS J034901.60+245430.2 +03 43 53.770 +21 58 28.20 0 17.955 17.227 16.574 15.985 15.582 15.564 17.05 2.89 -42.45 2.89 0.71 1 GCS9 UGCS J034353.76+215828.1 +03 43 53.960 +21 58 24.40 0 17.255 16.562 15.936 15.332 14.986 14.994 20.06 2.65 -43.75 2.65 0.70 1 GCS9 UGCS J034353.95+215824.3 +03 59 40.860 +27 04 35.50 0 19.361 18.308 17.418 16.792 16.276 16.196 23.02 3.86 -48.45 3.86 0.63 1 GCS9 UGCS J035940.86+270435.4 +04 00 48.820 +28 32 53.60 0 19.819 18.841 18.112 17.507 16.848 16.975 23.80 5.55 -42.37 5.55 0.64 1 GCS9 UGCS J040048.81+283253.6 +04 10 54.540 +26 01 42.40 0 19.642 17.748 17.002 16.417 17.38 5.99 -47.11 5.99 0.66 1 GCS9 UGCS J041054.54+260142.3 +03 46 17.010 +27 55 27.80 0 19.334 18.660 17.847 17.282 17.030 17.052 16.50 7.11 -47.80 7.11 0.63 1 GCS9 UGCS J034617.01+275527.7 +03 49 04.260 +21 30 48.40 0 19.535 18.513 17.649 16.945 16.333 16.414 16.76 4.01 -42.72 4.01 0.65 1 GCS9 UGCS J034904.25+213048.3 +03 40 44.530 +28 27 22.00 0 20.858 19.520 18.607 17.739 17.353 17.491 13.92 12.21 -49.16 12.21 0.62 1 GCS9 UGCS J034044.53+282721.9 +04 03 33.870 +23 19 28.20 0 20.446 19.748 18.418 18.040 17.402 17.405 12.29 11.98 -53.62 11.98 0.60 1 GCS9 UGCS J040333.86+231928.2 +03 43 03.110 +19 55 13.30 0 20.210 19.510 18.492 18.006 17.591 17.575 17.22 11.92 -47.33 11.92 0.60 1 GCS9 UGCS J034303.11+195513.2 +03 25 30.920 +24 51 39.90 0 20.316 19.478 18.600 18.210 18.170 10.72 31.33 -45.76 31.33 0.60 1 GCS9 UGCS J032530.91+245139.8 +04 08 03.700 +25 03 18.60 0 20.585 19.668 18.717 17.693 17.074 7.72 8.93 -49.54 8.93 0.60 1 GCS9 UGCS J040803.69+250318.5 +03 41 51.050 +24 10 06.60 0 20.465 19.418 18.729 18.205 17.578 12.60 6.35 -49.41 6.35 0.62 1 GCS9 UGCS J034151.04+241006.5 +03 32 30.600 +27 09 39.20 0 20.343 18.971 18.172 16.968 17.036 7.52 10.85 -21.23 10.85 3 GCS9 UGCS J033230.59+270939.2 +03 29 01.870 +27 16 23.30 0 20.590 19.126 18.101 17.366 96.07 32.77 -88.72 32.77 3 GCS9 UGCS J032901.87+271623.2 +03 42 59.310 +25 37 38.90 0 19.814 18.238 17.388 16.646 16.572 10.48 3.76 -35.70 3.76 3 GCS9 UGCS J034259.30+253738.9 +03 35 28.030 +25 17 23.50 0 19.445 18.046 17.289 16.458 41.24 10.18 -21.88 10.18 3 GCS9 UGCS J033528.02+251723.4 +03 40 03.140 +25 40 05.50 0 19.571 18.217 17.364 16.488 16.514 13.38 5.06 -31.42 5.06 3 GCS9 UGCS J034003.14+254005.5 +03 33 24.060 +25 50 16.00 0 20.543 18.806 17.811 16.808 11.92 13.52 -32.48 13.52 3 GCS9 UGCS J033324.06+255015.9 +03 35 13.620 +25 07 52.90 0 19.550 18.161 17.249 16.645 16.523 24.66 4.39 -42.83 4.39 3 GCS9 UGCS J033513.62+250752.9 +03 48 15.650 +25 50 08.90 0 19.868 18.490 17.658 16.734 16.758 10.53 4.42 -48.92 4.42 3 GCS9 UGCS J034815.64+255008.9 +03 47 38.470 +23 56 27.70 0 19.964 18.359 17.478 16.688 16.588 13.93 3.28 -42.66 3.28 3 GCS9 UGCS J034738.47+235627.6 +03 28 30.710 +23 54 01.20 0 19.736 18.362 17.562 16.807 16.867 9.65 6.31 -32.88 6.31 3 GCS9 UGCS J032830.71+235401.2 +03 50 15.960 +24 23 28.60 0 20.125 18.728 17.791 16.895 16.836 19.25 3.46 -31.64 3.46 3 GCS9 UGCS J035015.96+242328.6 +03 45 33.300 +23 34 34.30 0 19.697 18.123 17.189 16.502 16.479 20.21 2.91 -35.76 2.91 3 GCS9 UGCS J034533.30+233434.2 +03 52 27.190 +23 12 08.00 0 19.317 18.006 17.090 16.359 16.438 23.09 3.70 -40.27 3.70 3 GCS9 UGCS J035227.18+231208.0 +03 45 14.520 +22 29 28.60 0 19.756 18.383 17.410 16.493 16.629 14.00 4.20 -53.10 4.20 3 GCS9 Cl* Melotte 22 IPL 69 +03 52 39.150 +24 46 29.40 0 19.271 18.066 17.106 16.509 16.474 18.41 3.31 -44.34 3.31 3 GCS9 UGCS J035239.15+244629.4 +03 46 29.110 +22 59 47.60 0 19.089 17.740 16.805 15.955 15.935 14.85 2.74 -37.94 2.74 3 GCS9 UGCS J034629.11+225947.6 +03 50 08.100 +23 00 16.60 0 20.272 18.537 17.648 16.732 16.829 12.67 6.26 -29.23 6.26 3 GCS9 UGCS J035008.10+230016.5 +03 53 18.940 +19 24 24.20 0 20.069 18.680 17.810 16.861 16.872 27.44 8.91 -38.90 8.91 3 GCS9 UGCS J035318.93+192424.1 +03 50 39.550 +25 02 54.60 0 19.786 18.225 17.358 16.563 16.529 19.29 3.10 -44.42 3.10 3 GCS9 UGCS J035039.54+250254.6 +03 51 29.470 +24 00 37.40 0 19.701 18.424 17.490 16.696 16.697 12.50 2.96 -40.44 2.96 3 GCS9 Cl* Melotte 22 PLIZ 161 +03 45 58.480 +23 41 53.90 0 18.591 17.562 16.741 16.714 19.16 3.22 -40.46 3.22 3 GCS9 Cl* Melotte 22 NPNPL 4 \ No newline at end of file From 4507acf434ee87552862b8108107b2e3fa7edf8d Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 20 Apr 2026 14:13:41 -0700 Subject: [PATCH 115/191] messy code clean-up --- changes/BDClusters_AtmIssues.ipynb | 719 --------- changes/BD_Clusters.ipynb | 588 -------- changes/BD_Evolution.ipynb | 172 --- changes/BD_IMF.ipynb | 960 ------------ changes/Exoplanet_IMF.ipynb | 608 -------- changes/Test_BD_Cluster.ipynb | 1326 ----------------- changes/evolution_gaussian_fit.ipynb | 1195 --------------- docs/Quick_Start_Make_Cluster_w_BDs.ipynb | 997 +++++++++++++ .../bd_merging_code.py | 10 +- 9 files changed, 1000 insertions(+), 5575 deletions(-) delete mode 100644 changes/BDClusters_AtmIssues.ipynb delete mode 100644 changes/BD_Clusters.ipynb delete mode 100644 changes/BD_Evolution.ipynb delete mode 100644 changes/BD_IMF.ipynb delete mode 100644 changes/Exoplanet_IMF.ipynb delete mode 100644 changes/Test_BD_Cluster.ipynb delete mode 100644 changes/evolution_gaussian_fit.ipynb create mode 100644 docs/Quick_Start_Make_Cluster_w_BDs.ipynb rename changes/mass_to_age.py => spisea/bd_merging_code.py (99%) diff --git a/changes/BDClusters_AtmIssues.ipynb b/changes/BDClusters_AtmIssues.ipynb deleted file mode 100644 index 516e7669..00000000 --- a/changes/BDClusters_AtmIssues.ipynb +++ /dev/null @@ -1,719 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e99bddf5-2499-461b-9102-d8d450c3890f", - "metadata": {}, - "source": [ - "# New Additions!" - ] - }, - { - "cell_type": "markdown", - "id": "11bed8fe-4a9a-4499-8721-a34b6a9297cf", - "metadata": {}, - "source": [ - "### First Import Necessary Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "01b7a3c9-8d7a-483f-a25a-161817f55e9f", - "metadata": {}, - "outputs": [], - "source": [ - "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", - "from spisea.imf import imf, multiplicity\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pylab as py\n", - "from scipy.interpolate import RegularGridInterpolator\n", - "import pdb\n", - "import matplotlib.pyplot as plt\n", - "from astropy.table import vstack\n", - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload" - ] - }, - { - "cell_type": "markdown", - "id": "230b1ba3-eeca-4207-9125-08115cf15341", - "metadata": {}, - "source": [ - "## 1: BD objects in isochron" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "bc90dc34-0747-4b24-805f-6e8abf081565", - "metadata": {}, - "outputs": [], - "source": [ - "# Create isochrone object \n", - "logAge = np.log10(5*10**9.)\n", - "filt_list = ['wfc3,ir,f153m'] # Only 1 filter for plotting purposes\n", - "my_ifmr = ifmr.IFMR_Raithel18()\n", - "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", - " evo_model = evolution.MergedBaraffePisaEkstromParsec(), #our new evolution model for BDs\n", - " filters=filt_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3bfb0efe-bf1a-4666-945b-057c64e6cccd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " L Teff ... phase m_hst_f153m \n", - " W K ... \n", - "---------------------- ------------------ ... ----- -----------------\n", - "5.2116102444796556e+23 2793.830151362531 ... 1 9.517653682370574\n", - " 5.890768137510784e+23 2838.5725586841204 ... 1 9.39108220785009\n" - ] - } - ], - "source": [ - "bd_idx = np.where(my_iso.points['mass'] < 0.08)\n", - "print(my_iso.points[bd_idx])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "998eaa3e-103d-40bf-85ba-125ad4a5ed75", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brown dwarf (mass < 0.08 M_sun): F153M = 9.518 mag\n" - ] - } - ], - "source": [ - "# Create sample BD case\n", - "# Identify a brown dwarf with mass < 0.08 M_sun\n", - "bd_idx = np.where(my_iso.points['mass'] < 0.08)[0]\n", - "if len(bd_idx) > 0:\n", - " f153m = np.round(my_iso.points[bd_idx[0]]['m_hst_f153m'], decimals=3)\n", - " mass = my_iso.points[bd_idx[0]]['mass']\n", - " print('Brown dwarf (mass < 0.08 M_sun): F153M = {0} mag'.format(f153m))\n", - "else:\n", - " print('No brown dwarf with mass < 0.08 M_sun found.')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "dca63ebb-6c40-4f76-abb6-b709ccb71466", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Make a mass-magnitude diagram from the isochrone and plot a brown dwarf\n", - "py.figure(1, figsize=(6,6))\n", - "py.clf()\n", - "py.plot(my_iso.points['mass'], my_iso.points['m_hst_f153m'], 'r-', label='generated isochron')\n", - "py.plot(my_iso.points['mass'][bd_idx[0]], my_iso.points['m_hst_f153m'][bd_idx[0]], 'b*', ms=15, label='BD mass')\n", - "py.title('Generated Isochron Containing Brown Dwarf Masses')\n", - "py.xlabel('Mass')\n", - "py.ylabel('F153M')\n", - "py.gca().invert_yaxis()\n", - "py.legend()\n", - "py.show()" - ] - }, - { - "cell_type": "markdown", - "id": "44b18dc0-a450-497c-9ef8-a23c31263718", - "metadata": {}, - "source": [ - "## 2. BD Objects in Cluster (Primary and Companions)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "6d32d779-8e93-44e1-9375-7ff6e4f5f246", - "metadata": {}, - "outputs": [], - "source": [ - "# Create IMF objects \n", - "imf_multi = multiplicity.MultiplicityUnresolved()\n", - "kc_imf = imf.CombinedWeidnerKroupaKirkpatrick_2024(multiplicity=imf_multi)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "76787edf-ced9-478a-8572-c3b5e327febe", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 8211 companions out of stellar mass range\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n" - ] - } - ], - "source": [ - "# Make cluster\n", - "cluster_mass = 10**6\n", - "kc_cluster = synthetic.ResolvedCluster(my_iso, kc_imf, cluster_mass, ifmr=my_ifmr)\n", - "\n", - "# Get outputs\n", - "kc_out = kc_cluster.star_systems\n", - "kc_comp = kc_cluster.companions" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9ca75682-5e3a-4178-b9a1-0db1ecc9b4a6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs, WDs, and BDs\n", - "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", - "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", - "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", - "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", - "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", - "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", - "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", - "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", - "\n", - "# Define bins for histograms\n", - "bh_bins = np.linspace(14, 100, 20)\n", - "wd_bins = np.linspace(0.4, 10, 20)\n", - "bd_bins = np.linspace(0.01, 0.08, 8)\n", - "ns_bins = np.linspace(0, 30, 20)\n", - "\n", - "# Create subplots\n", - "plt.figure(figsize=(14,6))\n", - "\n", - "# Plot BHs\n", - "plt.subplot(2, 2, 1)\n", - "plt.hist(kc_out[p2_bh]['mass'], histtype='step', bins=bh_bins, label='Primary Black Holes', color='blue')\n", - "plt.hist(kc_comp[c_bh]['mass'], histtype='step', bins=bh_bins, label='Companion Black Holes', color='orange')\n", - "plt.title(\"Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot WDs\n", - "plt.subplot(2, 2, 2)\n", - "plt.hist(kc_out[p2_wd]['mass'], histtype='step', bins=wd_bins, label='Primary White Dwarves', color='blue')\n", - "plt.hist(kc_comp[c_wd]['mass'], histtype='step', bins=wd_bins, label='Companion White Dwarves', color='orange')\n", - "plt.title(\"White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot BDs\n", - "plt.subplot(2, 2, 3)\n", - "plt.hist(kc_out[p2_bd]['mass'], histtype='step', bins=bd_bins, label='Primary Brown Dwarves', color='blue')\n", - "plt.hist(kc_comp[c_bd]['mass'], histtype='step', bins=bd_bins, label='Companion Brown Dwarves', color='orange')\n", - "plt.title(\"Brown Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot NSs\n", - "plt.subplot(2, 2, 4)\n", - "plt.hist(kc_out[p2_ns]['mass'], histtype='step', bins=ns_bins, label='Primary Neutron Stars', color='blue')\n", - "plt.hist(kc_comp[c_ns]['mass'], histtype='step', bins=ns_bins, label='Companion Neutron Stars', color='orange')\n", - "plt.title(\"Neutron Stars by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Adjust space between subplots\n", - "plt.subplots_adjust(hspace=0.5)\n", - "\n", - "# Show the plots\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "8f03eedf-780a-47cc-bcb5-1bd767ff4135", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "287.8044158821004\n", - "287.80301331492325\n", - "2321.1831860770926\n", - "2321.183556796764\n" - ] - } - ], - "source": [ - "print(np.min(kc_out[p2_bd]['Teff']))\n", - "print(np.min(kc_comp[c_bd]['Teff']))\n", - "print(np.max(kc_out[p2_bd]['Teff']))\n", - "print(np.max(kc_comp[c_bd]['Teff']))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fbbd559a-4f0d-426e-9c6c-798debb699dc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from astropy.io import fits\n", - "from astropy.table import Table\n", - "\n", - "# plotting 1200K star from Meisner vs. BTSettl 3000K\n", - "bt_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/BTSettl_rebin/btm05/lte033-3.0-0.5a+0.2.BT-Settl.spec.fits'\n", - "m_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/Meisner2023/mp00/spec_jwst_t1200_g4.5_p0_kg_g1.25.fits'\n", - "p_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/Phillips2020/spec_T1200_lg4.5_CEQ.fits' # Phillips 1200K star for reference\n", - "\n", - "b_hdu_list = fits.open(bt_file)\n", - "m_hdu_list = fits.open(m_file)\n", - "p_hdu_list = fits.open(p_file)\n", - "\n", - "b_data = Table(b_hdu_list[1].data)\n", - "m_data = Table(m_hdu_list[1].data)\n", - "p_data = Table(p_hdu_list[1].data)\n", - "\n", - "b_w = np.array(b_data['Wavelength'])\n", - "b_f = np.array(b_data['Flux'])\n", - "m_w = np.array(m_data['Wavelength'])\n", - "m_f = np.array(m_data['Flux'])\n", - "p_w = np.array(p_data['Flux'])\n", - "p_f = np.array(p_data['Wavelength'])\n", - "\n", - "plt.figure()\n", - "plt.plot(b_w, b_f, label = 'BTSettl')\n", - "plt.plot(m_w, m_f, label = 'Meisner')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "dcb9ce22-afd5-4f75-a033-3f205e2e8613", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "90.9000015258789\n", - "0.0\n", - "2000.3333534194462\n", - "1.524743e-33\n", - "\n", - "\n", - "1600000.0\n", - "557203.6\n", - "299949.9969872583\n", - "1.1996874e-15\n" - ] - } - ], - "source": [ - "print(np.min(b_w))\n", - "print(np.min(b_f))\n", - "print(np.min(m_w))\n", - "print(np.min(m_f))\n", - "print('\\n')\n", - "print(np.max(b_w))\n", - "print(np.max(b_f))\n", - "print(np.max(m_w))\n", - "print(np.max(m_f))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a3e80ffe-ef8d-4fc8-8c2a-463c12c5e42d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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lOq5///6oV68e1q5dCwBITk7G77//jpEjR0p/1CoUChw6dAjt27fHRx99hGeffRbu7u749NNPkZOTU6F4e/bsicuXL+Pu3bs4ePAgvL29pT94/fz8EBMTg5SUFBw8eBBWVlZ47rnnAAAnT55EQEAAAGDVqlU4duwYoqOjMWvWLABQG/43ZswYZGVlYfPmzQAKEtD4+Hi1YVz37t2DEAKurq4aP/vjx4+X+B4s77FF34dAwc+tcMwPHjyAq6urRjtt20pT2vu+PJKTkyGE0Jrsuru7AyiIvbCyJMYVJYTAW2+9hfXr1yMsLAxDhgxR26967kVjAgoqwBWezF6nTh2t7TIyMpCdna0x8d3CwgK///47+vTpg4kTJ+K7774rU8wymQz79+9H3759sWjRInTs2BF169bFlClTyjQkctu2bRg+fDjq16+P9evXIyoqSvrgJysrS6N9RV5/VuUiIiKzExwcjNmzZ2PlypX47LPPim2nmoxe3OTS0soLjx49GqNHj0ZGRgYOHz6MTz/9FAMHDsTly5fLVbLT2dkZQMEaG2U5rqRPV/XF1tYWADQmDBf3h3JcXBxmz56NTp06ITo6GqGhoQgJCSn1OqpPvL/99ls8evQIGzduhFKp1JgI3KZNG2zevBlCCJw5cwZhYWGYN28e7Ozs8OGHH5b7+fXs2ROhoaGIjIxEZGQkXnzxRWmfKgk5fPiwNClelbRs3rwZ1tbW2Llzp/QaAdC6zkbr1q3RuXNnrF27FuPHj8fatWvh7u4uJTZAwc9eNadF9Yd7Ydq26ePY4tSpU0frvJaEhIRyn0ufnJycYGFhoVawQEVVSEL1/0jFUP9PVEnJ2rVrsXr1ao35YsDTyl5nz55Ve2+pthWu/KV6byckJKjNMzl79qzauQqztbXFjh078NJLL2HSpEnIz8/H5MmTS429UaNGUkn1y5cv45dffsGcOXOQnZ1dau/L+vXr0bhxY2zZskXttS2uqEBFXn/2mBARkdmpX78+PvjgAwwaNAijRo0qtt3AgQPx4MED5OXlafQm+Pj4oEWLFmW6noODA/r3749Zs2YhOztbYz2D0vTt2xdWVlb4559/tMZhjDK/qko8Z86cUdv++++/a7TNyMjAq6++Ck9PTxw8eBCTJk3Chx9+iBMnTpTpWqNHj0ZWVhY2bdqEsLAw+Pr6omXLllrbymQytGvXDl9//TVq1aqFv//+u3xP7Innn38elpaW+O2333Du3Dm1imsKhQLt27fHTz/9hBs3bqgN45LJZLCyslIbopSZmYl169YV+9xOnDiBo0eP4o8//sCoUaPUjh04cCCEELhz547Wn3ubNm2KfQ66HFscPz8/xMXFaQyXU/X6FFa0t8WQHBwc0KVLF2zbtk3tmvn5+Vi/fj0aNGiA5s2bGzwOIQTGjRuHtWvX4vvvv9dIoFXq16+Pzp07Y/369cjLy5O2Hz9+HJcuXcKwYcOkbUOGDIFMJtOoWhUWFgY7Ozv069dP6zVsbW2xfft29O/fH1OmTME333xTrufSvHlzfPzxx2jTpo3a/6Pifq4ymQw2NjZqCUdCQoLWqlwVxR4TIiIyS59//nmpbV5//XVs2LABL774It577z107twZ1tbWuH37Ng4ePIghQ4bgpZde0nrsuHHjYGdnh+7du6NevXpISEjAwoULoVAo0KlTp3LF6unpiXnz5mHWrFm4du0a+vXrBycnJ9y7dw8nT56Eg4NDmSpv6VOnTp3QokULTJ8+Hbm5uXByckJ4eDiOHj2q0fadd97BzZs3pVi/+uorREVF4fXXX0dMTAxq1apV4rVatmwJX19fLFy4ELdu3cIPP/ygtn/nzp1Yvnw5hg4diiZNmkAIgW3btuHRo0dqQ8Z69+6NQ4cOlanimKrM7/bt22FhYSHNL1Hx8/OTFg8snJgMGDAAoaGhCAwMxNtvv40HDx5g8eLFxfZOjBgxAiEhIRgxYgSUSiWCg4PV9nfv3h1vv/02Ro8ejVOnTuH555+Hg4MD4uPjcfToUbRp00Za76IoXY4tztSpU7FmzRr0798f8+bNg6urKzZu3IiLFy8CUC+r26ZNG2zbtg0rVqyAt7c3LCwsDJpEL1y4EH369EHPnj0xffp02NjYYPny5YiLi8OmTZt06iE5deqUVBo7NTUVQgj89ttvAAr+L6h6MqdMmYLVq1djzJgxaNOmjVr5Y7lcjg4dOkjff/HFF+jTpw9effVVTJgwAYmJifjwww/h5eWlltA8++yzGDt2LD799FNYWlqiU6dO2LdvH3744QfMnz+/2DVMVNcMDw/Hyy+/jKlTpyI/Px/vv/++1rZnzpzBpEmT8Oqrr6JZs2awsbHBgQMHcObMGbVeR1UPzpYtW9CkSRPY2tqiTZs2GDhwILZt24YJEybglVdewa1bt/Cf//wH9erVw5UrV8r/omtTrqnyREREJqhwVa6SaKs2k5OTIxYvXizatWsnbG1tRY0aNUTLli3F+PHjxZUrV6R2Raty/fTTT6Jnz57C1dVV2NjYCHd3dzF8+HBx5syZUuMqrrLV9u3bRc+ePYWjo6OQy+WiUaNG4pVXXhF//vmn1GbUqFHCwcGhjK9M2V+bolW5hBDi8uXLIiAgQDg6Ooq6deuKyZMni127dqnFvmrVKgFArF27Vu3Yq1evCkdHRzF06NAyxfnDDz8IAMLOzk6kpKSo7bt48aIYMWKEaNq0qbCzsxMKhUJ07txZhIWFqbVTVZ4qqxkzZggAwsfHR2Pf9u3bBQBhY2MjMjIy1PatWbNGtGjRQsjlctGkSROxcOFCsXr1aq1VzIQQIjAwUAAQ3bt3LzaWNWvWiC5duggHBwdhZ2cnmjZtKkaOHClOnToltSlalas8x/r5+Ylnn31W41ht54yLixMvvPCCsLW1FbVr1xZjx44VP/30kwAg/ve//0ntHj58KF555RVRq1YtIZPJpNe+uKpaQhRUa/r000+LfR1KO/7IkSOiV69e0nPt2rWrVL1Npazv+cIKV7sq+ij83m7UqFGx7bT9bPbt2ye6du0qvZYjR44U9+7d02iXnZ0tPv30U9GwYUNhY2MjmjdvLr799lutcWr7/69UKsWgQYMEALF48WKtz/HevXsiODhYtGzZUjg4OIgaNWqItm3biq+//lqtetqNGzdEQECAqFmzpsbz+vzzz4Wnp6eQy+WiVatWYtWqVeLTTz/V+H8HQEycOFFrHCWRPTmYiIiIiEirt99+G5s2bcKDBw9gY2Nj7HDITHEoFxERERFJ5s2bB3d3dzRp0gTp6enYuXMnfvzxR3z88cdMSsigmJgQERERkcTa2hpffvklbt++jdzcXDRr1gyhoaF47733jB0amblqV5UrLCwMMplM7VG3bl34+/tj586dUrvg4GCNdtoeqklsOTk5+P7779GpUyfUrl0b9vb2aNSoEYYMGYLw8PAKxbpgwQKt5QfPnz+POXPmSJO0CgsODpYqqZSkpOeneh1u3LgBmUyGsLCwCsVflUyaNAkymUyjHOLDhw9hYWEBa2trpKenq+27ffs2ZDJZmcphGpKnp6fGZEpjKu59q/q/d+rUqQqfe+nSpXjmmWekqiCPHj2qeKCliI2NxYABA9CwYUPY2dmhdu3a8PX1xfr167W2//vvv/HCCy+gRo0aqFWrFoYNG4Zr164V+zxatmwJuVyOxo0bY+7cuVrXYkhMTERwcDCcnZ1hb28PX19f7N+/X6Odp6cnBg4cqLH9xx9/hKWlJQYPHqy1xnxZPX78GHPmzClxoTciMh8zZ87EpUuXkJGRAaVSibi4OEydOrVSylRT9VbtEhOVtWvXIioqCn/99Rd++OEHWFpaYtCgQfjjjz8AAJ988gmioqKkh2rxmgULFqht/+STTwAULLQ1efJk9OzZE+vXr8cff/yBjz/+GFZWVti7d2+FYiwpMZk7d67WxKQ87Ozs1J6L6qGq316dqCquFP3D69ChQ7CysoJMJtOoRKNtNWAq/n2rq9jYWEyZMgU9e/bEgQMHEBUVVeoaE7p49OgRPDw8sGDBAuzevRs///wzPD09ERQUhPnz56u1vXjxIvz9/ZGdnY1ffvkFa9asweXLl9GjRw/cv39fre1nn32G9957D8OGDcPevXsxYcIELFiwABMnTlRrp1Qq0bt3b+zfvx/ffPMNduzYAVdXV/Tr1w+HDh0qNf4vv/wS48aNwxtvvIFt27aprbdQXo8fP8bcuXOZmBARkUFV26FcXl5eaiXtVKUZN23ahEGDBqFp06Zo2rSptF/1aWOzZs3QtWtXtXNdv34dW7ZswezZs9XKOfbu3Rvjxo1Dfn6+gZ9NxVhYWGg8l+rK398fMpkMkZGReP3116XtqoW1hBA4ePCgWi3xyMhIWFhY4PnnnzdGyNWOal2IcePGoXPnzno55+PHj2Fvb691n7+/v9q6BkDBmgHXr1/HDz/8gI8//ljaPnv2bMjlcuzcuVNajdvb2xvNmjXD4sWL8cUXXwAoWAF4/vz5GDduHBYsWCBdJycnBx9//DGmTp2K1q1bAwBWr16NuLg4/PXXX/D19QVQkAS3a9cOM2bMKHF9iI8++ggLFy7E5MmT8c0335jsp5w5OTnSmhBERETVtsekKFtbW9jY2MDa2rrcxz548AAAUK9ePa37C9f8BgrqY0+fPh2NGzeGjY0N6tevj6lTpyIjI0NqI5PJkJGRgZ9++kkaYuXv74+wsDC8+uqrAAr+SFHtq6zhVsUNFZszZ47aHz+bN2+GTCbDsmXL1NqpanRHREQUe42hQ4eiUaNGWhO6Ll26oGPHjtL3v/76K7p06QKFQgF7e3s0adIEY8aMKffzqlOnDtq0aaPxiXBkZCT8/f3h5+cn9ZAU3texY0coFArcv38fEyZMQOvWrVGjRg24uLigV69eOHLkiNQ+JycHLi4uCAoK0rj+o0ePYGdnpzYsrCzvk+KU9ViZTIZJkyZh3bp1aNWqFezt7dGuXTu1YY0qO3bsQNu2bSGXy9GkSRN88803Gj/34t63haWlpeHdd9+VVtweNmyYtGpvcfz9/aWVdbt06aI2jBIA1qxZg3bt2sHW1ha1a9fGSy+9hAsXLqidIzg4GDVq1MDZs2cREBCAmjVronfv3qW+lkU5Ozur/SGdm5uLnTt34uWXX5aSEqBgdd2ePXuqDeXcs2cPsrKyNBbkGj16NIQQaj1N4eHhaNGihZSUAICVlRXefPNNnDx5Enfu3NGILT8/H++++y4WLlyI2bNn49tvvy1TUnLgwAH4+/ujTp06sLOzQ8OGDfHyyy/j8ePHuHHjBurWrQsAmDt3rsYw1qtXr2L06NFo1qwZ7O3tUb9+fQwaNEhasVglMjISMpkM69atw7Rp01C/fn3I5XJcvXq11PiIiKiaKHeB4SpOVdv6+PHjIicnR2RnZ4tbt26JKVOmCAsLC7Fnzx6tx6lqzv/6668a+9LT00WtWrWEm5ub+P7777XWMFfJyMgQ7du3F87OziI0NFT8+eef4ptvvhEKhUL06tVL5OfnCyGEiIqKEnZ2duLFF18UUVFRIioqSpw7d04kJiaKBQsWCADiu+++k/YlJiYKIYqvcV6Uqg52Tk6O2qNwHWtVHfHC9buLO7+2GtbvvPOOsLGxkeqI79+/X1hYWIiPP/64xNh27NghAIiIiAi17RcuXBAApLref/31l5DJZOL1118Xu3fvFgcOHBBr164VQUFBpT5/bd577z0BQNy9e1cIIURSUpKQyWRi79694r///a+wtLSUauvfvHlTABAffPCBEKKgxv67774rNm/eLCIjI8XOnTvF2LFjhYWFhdo6Be+//77WGv3Lly8XAKT1D8r6PhFCc+2B8hwLQHh6eorOnTuLX375RezevVv4+/sLKysr8c8//0jt/vvf/woLCwvh7+8vwsPDxa+//iq6dOkiPD091X7uxb1vhXj6f69JkyZi8uTJYu/eveLHH38UTk5OomfPniX+bM6dOyc+/vhj6f0YFRUlrl69KoQQ0v+HESNGiF27domff/5ZNGnSRCgUCnH58mXpHKNGjRLW1tbC09NTLFy4UOzfv1/s3bu3xOsKIUReXp7IyckRiYmJ4rvvvhNWVlZi5cqV0v6LFy9K/x+Lmj59upDJZCIzM1MIIcSHH34oAIj09HSNts7OzmLEiBHS925ubuLVV1/VaLdz504BQC32Ro0aiYCAAPH6668LmUwmvvnmm1Kfl8r169eFra2t6NOnj9i+fbuIjIwUGzZsEEFBQSI5OVlkZWWJPXv2CABi7Nix0s9V9fofOnRITJs2Tfz222/i0KFDIjw8XAwdOlTY2dmJixcvStdR/Q6tX7++eOWVV8Tvv/8udu7cKR48eFDmWImIyLxV28Sk6EMul4vly5cXe1xJiYkQQuzatUs4OztL56tTp4549dVXxe+//67WbuHChcLCwkJj0Z/ffvtNABC7d++Wtjk4OGgsdiWEEL/++qvWhbmEKF9iou11KLz4k66JSVZWlujQoYNo3LixOH/+vHB1dRV+fn5qyY82OTk5wtXVVQQGBqptnzFjhrCxsRFJSUlCCCEWL14sAIhHjx6V+nzLQrWY1saNG4UQQmzdulVYWVmJtLQ0kZqaKiwtLcXOnTuFEEJaaKrwz6uw3NxckZOTI3r37i1eeuklafuZM2cEAPHDDz+ote/cubPw9vaWvi/P+6RoYlKeYwEIV1dXkZqaKm1LSEgQFhYWYuHChdK2Tp06CQ8PD6FUKqVtaWlpok6dOho/9+Let6r/exMmTFDbvmjRIgFAxMfHaxyj7fjCzys5OVlKhAq7efOmkMvlau8h1Xt+zZo1JV6nqPHjx0v/P2xsbDR+Txw7dkwAEJs2bdI4VpU0qZLdcePGCblcrvU6zZs3FwEBAdL31tbWYvz48Rrt/vrrL7X3qRDqC3599NFH5Xp+qvdFbGxssW3u379fpkXRhCh472dnZ4tmzZqJ999/X9qu+h36/PPPlys+IiKqPqrtUK6ff/4Z0dHRiI6Oxn//+1+MGjUKEydO1Bh6VFYvvvgibt68ifDwcEyfPh3PPvsstm/fjsGDB2PSpElSu507d8LLywvt27dHbm6u9Ojbt680x6Gy2NnZSa+B6rF69Wq9nV8ul+OXX37BgwcP0LFjRwghsGnTJlhaWpZ4nGq4yrZt25CSkgIAyMvLw7p16zBkyBDUqVMHANCpUycAwPDhw/HLL79oHdpSHn5+frCwsJB+BpGRkfDx8UGNGjVQs2ZNdOzYURrOFRkZCSsrK7VCAStXrkTHjh1ha2sLKysrWFtbY//+/WpDitq0aQNvb2+sXbtW2nbhwgWcPHlSbQiaLu+T8h7bs2dPtUnkrq6ucHFxwb///gsAyMjIwKlTpzB06FC1+vU1atTAoEGDyv4CPzF48GC179u2bQsA0vXKIyoqCpmZmRpVyTw8PNCrVy+tFaxefvnlcl3jo48+QnR0NHbt2oUxY8Zg0qRJWLx4sUa7koZMFR3uVpZ25W3bvn17NGzYEMuWLcPx48eLPa6o9u3bw8bGBm+//TZ++umnYiuJFSc3NxcLFixA69atYWNjAysrK9jY2ODKlSsaw+mA8r/+5urw4cMYNGgQ3N3dIZPJDFIworzX01atkfMQiagyVdvEpFWrVvDx8YGPjw/69euH77//HgEBAZgxY0aFS5Da2dlh6NCh+PLLL3Ho0CFcvXoVrVu3xnfffSdN3L137x7OnDkDa2trtUfNmjUhhEBSUpIen2XJLCwspNdA9WjRooVer/HMM8+gR48eyMrKwhtvvFHsPJyixowZg6ysLGzevBkAsHfvXsTHx6uNzX/++eexfft25ObmYuTIkWjQoAG8vLywadOmCsVaq1YttG/fXko+Dh48CD8/P2m/n5+f9Ef9wYMH4ePjI/1BHxoainfffRddunTB1q1bcfz4cURHR6Nfv37IzMzUeG5RUVG4ePEigIIKcXK5HCNGjJDa6PI+Ke+xqkSvMLlcLsWdnJwMIQRcXV012mnbVpqi15PL5QCg8TqVRUnzu9zd3aX9Kvb29mrzQMqiYcOG8PHxwYsvvogVK1bg7bffxsyZM6VqW6rnU/RaQEG5aZlMhlq1aklts7Ky8PjxY61ta9euLX1fp06dYs8JQK0tANSvXx+RkZFwcnJC3759ERUVVabn17RpU/z5559wcXHBxIkTpcIf33zzTZmODwkJwSeffIKhQ4fijz/+wIkTJxAdHY127dpp/ZmW9XeAucvIyEC7du0q/GGYoa7Xr18/xMfHS4/du3dXSnxEREA1rsqlTdu2bbF3715cvnxZL1V/GjZsiLfffhtTp07FuXPn8Oyzz8LZ2Rl2dnZYs2aN1mOcnZ11vq4h2draQqlUamwv7g/lH3/8Ebt27ULnzp2xbNkyvPbaa+jSpUup12ndujU6d+6MtWvXYvz48Vi7di3c3d0REBCg1m7IkCEYMmQIlEoljh8/joULFyIwMBCenp5qk4bLqmfPnvjqq69w5swZnDt3DosWLZL2+fn5ITQ0FGfOnMGNGzfUEon169fD398fK1asUDtfWlqaxjVGjBiBkJAQhIWF4bPPPsO6deswdOhQODk5SW10eZ/o+z3m5OQEmUyGe/fuaewruu5LZVMlBfHx8Rr77t69q/Fc9VGdqnPnzli5ciWuXbuGunXromnTprCzs9OY7A0AZ8+exTPPPCOV6m3Tpo20vfD/g4SEBCQlJcHLy0va1qZNm2LPCUCtrUrjxo0RGRmJnj17om/fvtizZw+6detW6nPq0aMHevTogby8PJw6dQpLly7F1KlT4erqqlalTpv169dj5MiRUpUxlaSkJCkhK8xUK4RVtv79+6N///7F7s/OzsbHH3+MDRs24NGjR/Dy8sIXX3yhUUxCX9dTkcvlcHNzq9A1iIh0VW17TLSJjY0FAKkCTVmlpaVpLL6nohrK4O7uDqCg3Og///yDOnXqaPRW+Pj4qFW8KvypdWG6fMKsK09PTyQmJqr9kZqdna11rZazZ89iypQpGDlyJI4cOYK2bdvitddeQ3JycpmuNXr0aJw4cQJHjx7FH3/8gVGjRhU7DEwul8PPz08qyxoTE1OBZ/d0TZK5c+fCwsJCbaiW6mtVSejC65fIZDLp56Jy5swZrZ9aOzk5YejQofj555+xc+dOJCQkaFQSK8/7pChdjtXGwcEBPj4+2L59O7Kzs6Xt6enpWqt3Ffe+NQRfX1/Y2dlpLHp4+/ZtHDhwoEJVt0pz8OBBWFhYoEmTJgAKhh4OGjQI27ZtU0tEb968iYMHD2LYsGHStn79+sHW1lajip5q8cmhQ4dK21566SVcvHhRrSxwbm4u1q9fjy5duki/U4ry9PREZGQknJ2d0a9fPxw7dqzMz83S0hJdunSR1m36+++/AZT8O0fbe3/Xrl06D62s7kaPHo1jx45h8+bNOHPmDF599VX069cPV65cMeh1IyMj4eLigubNm2PcuHFITEw06PWIiNQYdYaLEagm0Koq+0RFRYmdO3eKMWPGCABqE5ULK2nye3R0tKhdu7aYMGGC2LJlizh8+LDYsWOHePvttwUA4e/vL/Ly8oQQBRW8OnToIBo0aCC++uorERERIfbu3StWrVolXn31VXH8+HHpvH5+fsLFxUX8/vvvIjo6Wqpwc+3aNQFADB06VBw5ckRER0dLE8LLW5WrJNomv1+7dk1YW1sLf39/sWvXLrF161bh5+cnGjdurDYJOj09XbRs2VK0bt1aqkD0zz//CIVCIYYMGVJqfEII8ejRI2FnZycaNGggAIhLly6p7f/kk0/E6NGjxfr160VkZKTYvn276Nmzp7C2thZxcXFSO0tLS9GrV68yXVM1yV0mk4lOnTpp7O/QoYOQyWTC2tpaZGRkSNtnz54tZDKZmD17tti/f79Yvny5cHNzE02bNtX689i7d68AIBo0aCAaNGggvT9UyvM+KTr5vTzHAhATJ07UiK/oOYtW5frtt99Ely5dRKNGjYRMJlM7trj3rbbJ60I8/b+lrZhDYcUdr5pgHhQUJHbv3i3WrVsnnnnmGa1VuUp7zxc2btw4MW3aNLFlyxYRGRkpfvvtN/Haa6+pVWNTuXDhgqhRo4Z4/vnnxe7du8W2bduEl5eXcHd3lyrmqcyfP1/IZDLx0UcficjISPHll18KuVwuxo0bp9YuKytLPPvss8LDw0Ns2LBBREREiJdeeklYWVmJyMhItbaNGjUSAwYMUNv277//iiZNmogaNWqIw4cPF/s8V6xYIV599VURFhYmDhw4IHbv3i1eeeUVrZW/WrRoIfbu3Suio6Ol6oMjR44UcrlcfP3112L//v1i0aJFom7duqJBgwbCz89POr60AiLVGQARHh4ufX/16lUhk8nEnTt31Nr17t1bzJw5U+/XU9m8ebPYuXOnOHv2rPj9999Fu3btxLPPPiuysrJ0viYRUVlU28Sk8EOhUIj27duL0NDQYn8Bl3RTTU5OFvPnzxe9evUS9evXFzY2NsLBwUG0b99ezJ8/Xzx+/FitfXp6uvj4449FixYthI2NjVAoFKJNmzbi/fffFwkJCVK72NhY0b17d2Fvby8AqN3klyxZIho3biwsLS3VkgdDJyZCCLF7927Rvn17YWdnJ5o0aSKWLVumUZXrzTffFPb29lKpWBVVRbGvv/661BiFECIwMFCjWpjKzp07Rf/+/aXX3MXFRbz44oviyJEjau2Kvnal6dy5swAgpk+frrFv6tSpWuNRKpVi+vTpon79+sLW1lZ07NhRbN++vdifR15envDw8BAAxKxZs7TGUdb3SdEkojzHljUxEUKI8PBw0aZNG2FjYyMaNmwoPv/8czFlyhTh5OSk1q64962hEhMhhPjxxx9F27Ztpec6ZMgQjfdeeROTNWvWiB49eghnZ2dhZWUlatWqJfz8/MS6deu0tj916pTo3bu3sLe3F46OjmLo0KFSSd2ivvnmG9G8eXPptfz0009Fdna2RruEhAQxcuRIUbt2bWFrayu6du2qUUZbCO2JiRAF1cmaNm0qHBwcxKFDh7TGEhUVJV566SXRqFEjIZfLRZ06dYSfn59GRcE///xTdOjQQcjlcgFAen8kJyeLsWPHChcXF2Fvby+ee+45ceTIEeHn58fEpIyKJgq//PKLACAcHBzUHlZWVmL48OFCiKe/n0t6aPu/re16xbl7966wtrYWW7du1cfTJCIqlUwIIfTbB0NE1UFOTg7at2+P+vXrY9++fcYOh6jKkslkCA8Pl4bybdmyBW+88QbOnTunMXy1Ro0acHNzQ05ODv75558Sz+vk5KS1QEXR65WkWbNmeOutt/B///d/ZX4+REQVxcnvRFQmY8eORZ8+fVCvXj0kJCRg5cqVuHDhQpmrNxFR2XTo0AF5eXlITExEjx49tLaxtrZGy5YtDRrHgwcPcOvWLVZSI6JKw8SEiMokLS0N06dPx/3792FtbY2OHTti9+7deOGFF4wdGlGVk56ejqtXr0rfX79+HbGxsahduzaaN2+ON954AyNHjsRXX32FDh06ICkpCQcOHECbNm3w4osv6vV6DRs2RHp6OubMmYOXX34Z9erVw40bN/DRRx/B2dkZL730kl6eMxFRaTiUi4iIqJKpyjoXNWrUKISFhSEnJwfz58/Hzz//jDt37qBOnTrw9fXF3LlzpbLT+rxeZmYmhg4dipiYGDx69Aj16tVDz5498Z///AceHh4Veo5EROXFxISIiIiIiIyO65gQEREREZHRMTEhIiIiIiKj4+R3EyKEUFs5mohIVzVr1oRMJjN2GAT+jici/TO33/FMTExIamoqatWqZewwiMiMPHr0CAqFwthhEPg7noj0z9x+xzMxMUG3bt2Co6OjscMgoiosNTWV1ZRMFH/HE5GuzPV3PBMTE6LqinN0dORNi4j0wpy6+Ks6/o4nIn0zt9/xnPxORERERERGx8SEiIiIiIiMjokJEREREREZHRMTIiIiIiIyOiYmRERERERkdExMiIiIiIjI6JiYEBERERGR0TExISIiIiIio2NiQkRERERERsfEhIiIiIiIjI6JCRERERERGR0TEyIiIiIiMjomJkREREREZHRMTIiIiIiIyOiYmBARERERkdExMSEiomrn8OHDGDRoENzd3SGTybB9+/YS22/btg19+vRB3bp14ejoCF9fX+zdu7dygiUiqiaYmBARUbWTkZGBdu3aYdmyZWVqf/jwYfTp0we7d+/G6dOn0bNnTwwaNAgxMTEGjpSIqPqwMnYAREREla1///7o379/mdsvWbJE7fsFCxZgx44d+OOPP9ChQwc9R0dEVD2xx6SaEULg7qNMCCGMHQoRUZWVn5+PtLQ01K5d29ihVAjvBURkipiYVDM/HL6Gbp8fwNd/XjF2KEREVdZXX32FjIwMDB8+vNg2SqUSqampag9TsezAVXT7/ACWHrhq7FCIiCRMTKqZhf+9CAD4dj8TEyKiiti0aRPmzJmDLVu2wMXFpdh2CxcuhEKhkB4eHh6VGGXJvoq4DAAIffIvEZEpYGJCRERURlu2bMHYsWPxyy+/4IUXXiix7cyZM5GSkiI9bt26VUlREhFVTZz8TkREVAabNm3CmDFjsGnTJgwYMKDU9nK5HHK5vBIiIyIyD0xMiIio2klPT8fVq0/nV1y/fh2xsbGoXbs2GjZsiJkzZ+LOnTv4+eefARQkJSNHjsQ333yDrl27IiEhAQBgZ2cHhUJhlOdARGRuOJSLiIiqnVOnTqFDhw5Sqd+QkBB06NABs2fPBgDEx8fj5s2bUvvvv/8eubm5mDhxIurVqyc93nvvPaPET0RkjthjQkRE1Y6/v3+JpXLDwsLUvo+MjDRsQERExB4TIiIiIiIyPiYm1djecwnGDoGIiIiICAATk2ojOzdfY9v4daeNEAkRERERkSbOMakGLiakot+SIxj7XGNjh0JEREREpBV7TKqB0H0FK/uuPnrdyJEQEREREWnHxKQaKL7uDBERERGRaWBiUg2UUBGTiIiIiMgkMDGpFpiZEBEREZFpY2JSDbDHhIiIiIhMHROTaoB5CRERERGZOiYm1YBglwkRERERmTgmJtVASWnJn+fvVVocRERERETFYWJSzX0UftbYIRARERERMTGpDkoayZWTl195gRARERERFYOJSTVQ0lAuC5ms0uIgIiIiIioOE5NqoKTJ7w8fZ1diJERERERE2jExqeZYsIuIiIiITAETEyIiIiIiMjomJtUAe0WIiIiIyNQxMakGRClrv3t+uAtnbj+qnGCIiIiIiLRgYkIAgMHLjhk7BCIiIiKqxpiYVANlGcpVy97a8IEQERERERWDiUk1UJbEpEezuoYPhIiIiIioGExMCADwx//uGjsEIiIiIqrGmJhUA6VNficiIiIiMjYmJtVAWcsF5+blGzYQIiIiIqJiMDGpBu6nK8vU7uHjbANHQkRERESkHROTaiAtK7dM7aKvJxs4EiIiIiIi7ZiYVAOlDdHq2aKgIle6MqcywiEiIiIi0sDEpBrIzSt5koncyhIAkF1KOyIiIiIiQ2FiUg3k5JfcY2JjVfA2yM7l5HciIiIiMg4mJtVAaT0mTEyIiIiIyNiYmFQDvk3rlLifiQkRERERGRsTk2qgsbNDifttLJ8kJnl5lREOEREREZEGJibVgLYFFl/xbgAAGNejMXtMiIiIiMjoTDYxmTNnDmQymdrDzc1N2i+EwJw5c+Du7g47Ozv4+/vj3LlzaudQKpWYPHkynJ2d4eDggMGDB+P27dtqbZKTkxEUFASFQgGFQoGgoCA8evRIrc3NmzcxaNAgODg4wNnZGVOmTEF2tvpihGfPnoWfnx/s7OxQv359zJs3D6KsS64bwQd9W+DPkOcxs38rqcckh1W5iIiIiMhITDYxAYBnn30W8fHx0uPs2bPSvkWLFiE0NBTLli1DdHQ03Nzc0KdPH6SlpUltpk6divDwcGzevBlHjx5Feno6Bg4ciLxCQ5YCAwMRGxuLPXv2YM+ePYiNjUVQUJC0Py8vDwMGDEBGRgaOHj2KzZs3Y+vWrZg2bZrUJjU1FX369IG7uzuio6OxdOlSLF68GKGhoQZ+hcpGQDPhsJDJ8IxLTVhYyKQeEyV7TIiIiIjISKyMHUBJrKys1HpJVIQQWLJkCWbNmoVhw4YBAH766Se4urpi48aNGD9+PFJSUrB69WqsW7cOL7zwAgBg/fr18PDwwJ9//om+ffviwoUL2LNnD44fP44uXboAAFatWgVfX19cunQJLVq0wL59+3D+/HncunUL7u7uAICvvvoKwcHB+Oyzz+Do6IgNGzYgKysLYWFhkMvl8PLywuXLlxEaGoqQkBDIZLJKesW009ZxY1EoJA7lIiIiIiJjM+kekytXrsDd3R2NGzfG66+/jmvXrgEArl+/joSEBAQEBEht5XI5/Pz88NdffwEATp8+jZycHLU27u7u8PLyktpERUVBoVBISQkAdO3aFQqFQq2Nl5eXlJQAQN++faFUKnH69GmpjZ+fH+RyuVqbu3fv4saNG3p+VcpP2wAti0LJktWTLCW3lPVOiIiIiIgMxWQTky5duuDnn3/G3r17sWrVKiQkJKBbt2548OABEhISAACurq5qx7i6ukr7EhISYGNjAycnpxLbuLi4aFzbxcVFrU3R6zg5OcHGxqbENqrvVW20USqVSE1NVXtUFu2JCeeYEBEREZFxmOxQrv79+0tft2nTBr6+vmjatCl++ukndO3aFQA0hkgJIUodNlW0jbb2+mijmvheUjwLFy7E3LlzS4xXH7TOwS8UltWTye95nPxOREREREZisj0mRTk4OKBNmza4cuWKNO+kaG9EYmKi1FPh5uaG7OxsJCcnl9jm3r17Gte6f/++Wpui10lOTkZOTk6JbRITEwFo9uoUNnPmTKSkpEiPW7dulfwiVMChy/exJy5eY3vhOSbsMSEiIiIiY6syiYlSqcSFCxdQr149NG7cGG5uboiIiJD2Z2dn49ChQ+jWrRsAwNvbG9bW1mpt4uPjERcXJ7Xx9fVFSkoKTp48KbU5ceIEUlJS1NrExcUhPv7pH/f79u2DXC6Ht7e31Obw4cNqJYT37dsHd3d3eHp6Fvuc5HI5HB0d1R76JITAqDUnkfw4R2Nf4aFclk8SkzzOMSEiIiIiIzHZxGT69Ok4dOgQrl+/jhMnTuCVV15BamoqRo0aBZlMhqlTp2LBggUIDw9HXFwcgoODYW9vj8DAQACAQqHA2LFjMW3aNOzfvx8xMTF488030aZNG6lKV6tWrdCvXz+MGzcOx48fx/HjxzFu3DgMHDgQLVq0AAAEBASgdevWCAoKQkxMDPbv34/p06dj3LhxUiIRGBgIuVyO4OBgxMXFITw8HAsWLDB6Ra6SOkAKJyb5T8Z6Hbx039AhERERERFpZbJzTG7fvo0RI0YgKSkJdevWRdeuXXH8+HE0atQIADBjxgxkZmZiwoQJSE5ORpcuXbBv3z7UrFlTOsfXX38NKysrDB8+HJmZmejduzfCwsJgaWkptdmwYQOmTJkiVe8aPHgwli1bJu23tLTErl27MGHCBHTv3h12dnYIDAzE4sWLpTYKhQIRERGYOHEifHx84OTkhJCQEISEhBj6ZSpRXgmZSeF86cDFROnrDGUuHOQm+7YgIiIiIjMlE6a8PHk1k5qaCoVCgZSUFL0M68rKyUPLT/Zo3Xd5fn9p/ZLgtScR+aS35NsRHTC4nbvWY4io6tD37xPSnSn9TDw/3CV9fePzAUaMhIgqwpR+n+iTyQ7lIt2V1GNSePL7cB8P6etHj7O1tCYiIiIiMiwmJmYsr4TOsMJzTPo+6yZ9PXvHOYPGRERERESkDRMTM5ZfxjkmlhbqE/Rz81idi4iIiIgqFxMTM1by5Pfiq4VlKPMMEQ4RERERUbGYmJixkoZyFdX9mTrS12lKzXVPiIiIiIgMiYmJGSvPeonWlk/fCo+z2WNCRERERJWLiYkZK0+Pyfjnm0pfbz55yxDhEBEREREVi4mJGStp8ntRvk2fDuVac+y6IcIhIiIiIioWExMzVtLkdyIiIiIiU8LExIyVZygXEREREZExMTExY+UZygUAXvUdAQD9Ci24SERERERUGZiYmLHyjuR6qUMDAIDcmm8LIiIiIqpc/AvUjAmULzOxerICfG4eh4ARERERUeViYmLGyrOOCQCoFoPfdTaeE+eJiIiIqFIxMTFj5e0xuZ2cKX39v9uP9BwNEREREVHxmJiYsfIW5XruGWfpa9WwLiIiIiKiysDExIyVNzGpaWslfW1lwbcGEREREVUe/vVpxso7lMuyUC9J3J0UfYdDRERERFQsJiZmrLzz1591V0hfz9h6Rs/REBGZjsOHD2PQoEFwd3eHTCbD9u3bSz3m0KFD8Pb2hq2tLZo0aYKVK1caPlAiomqEiYkZE+Ucy2XJeSVEVE1kZGSgXbt2WLZsWZnaX79+HS+++CJ69OiBmJgYfPTRR5gyZQq2bt1q4EiJiKoPq9KbUFWla8VfIQRkMiYrRGR++vfvj/79+5e5/cqVK9GwYUMsWbIEANCqVSucOnUKixcvxssvv2ygKImIqhf2mJg13TKTlMwcPcVBRFS1RUVFISAgQG1b3759cerUKeTkaP9dqVQqkZqaqvYgIqLiMTExY7r2mNxPU+onECKiKi4hIQGurq5q21xdXZGbm4ukpCStxyxcuBAKhUJ6eHh4VEaoRERVFhMTM1becsFFJaVn6ycQIiIzUHRoq2oeX3FDXmfOnImUlBTpcevWLYPHSERUlTExMWPlnfwOAMHdPKWvk9LZY0JEBABubm5ISEhQ25aYmAgrKyvUqVNH6zFyuRyOjo5qDyIiKh4TEzNWkaFccwY/i+eb1wUAZObk6TkiIqKqydfXFxEREWrb9u3bBx8fH1hbWxspKiIi88LExIyVd4FFFVurgrdFTl6+PsMhIjIZ6enpiI2NRWxsLICCcsCxsbG4efMmgIJhWCNHjpTav/POO/j3338REhKCCxcuYM2aNVi9ejWmT59ujPB1kqfrBEQiIgNhuWAzVtE5JtZPEpPcPN68iMg8nTp1Cj179pS+DwkJAQCMGjUKYWFhiI+Pl5IUAGjcuDF2796N999/H9999x3c3d3x7bffVslSwaduPDR2CEREWjExMWMVTUxsLNljQkTmzd/fv8R5eGFhYRrb/Pz88PfffxswqsqRp2tlFCIiA+FQLjNW0aFcqhXgH2dzjgkRERERVQ4mJmasosOIrZ4kJluiWdqSiIiIiCoHExMzVpFywQCQ/WQIV5O6DvoMh4iIiIioWExMzFhFhxEPbucOgAssEhGZJU4xISITxcTEjFV0jkndmnIAwP20LH2GQ0RERERULCYmZiy/gkW1XGraAgAeZGQjl5W5iIiIiKgSMDExY8X1l9R2sCnxuDoONrC0kEEIDuciIjI3kzfFGDsEIiKtmJiYseImv++e0qPE4ywsZKghL1ji5s6jTL3HRURExvMggx84EZFpYmJixoorF+ymsC312JTMHADAjtg7+gyJiIiIiEgrJiZmreKlV1Srv9taW+orGCIiIiKiYjExMWMVXWARAEZ09gAA2FrxLUJEREREhse/Os1YRdcxAQCrJz0m91KVeoqGiIiIiKh4TEzMWEXXMQGA/56NBwBsOXVLX+EQERERERWLiYkZ02Uo192Up4srpmXl6CEaIiIiIqLiMTExY8WVCy6v+2kczkVEREREhsXExIzpkpdseKuL9DUTEyIi85DKHnAiMmFMTMyYLnNMuj/jDCd7awBP1zQhIqKqTZmTb+wQiIiKxcTEjOXreP9p5lITAJCry2QVIiIiIqIyYGJixnRNJ6ytZACAnDx+wkZEZA5kMmNHQERUPCYmZkzXye/WT9YyycljjwkRERERGRYTEzOma1Eu1aR31ZomRERUtbHDhIhMGRMTM6bL5HcAOHc3FQCw/2KiPsIhIiIiIioWExMzpuuc9ToONvoJhIiITIKMk0yIyIQxMTFjug7lmjfECwDg08hJD9EQERERERWPiYkZ03Uol621avI7q3IREZkD9pcQkSljYmLGdB3KparKlc2qXERERERkYExMzJmOY7lsrJ4kJrl5+oiGiIiIiKhYTEzMmK49JlYWBZ3+eVz5nYjILHDuOxGZMiYmZkzXBRYtVImJrrPoiYiIiIhKwcTEjOna0WH55KO1fM59JyIyCzJOfyciE8bExIzp2s9h+aTHJJeZCREREREZGBMTM6brUC5LaY6JPqIhIiKjY4cJEZkwJiZmTNepIarEJJ9zTIiIiIjIwJiYmDFdF1i0kLEqFxGROWFVLiIyZUxMzJjOk99ZLpiIyOzdS80ydghERACYmJg1XUdgcR0TIiLzoq3D5Is9Fys9DiIibZiYmDGdh3JxHRMiIrN3+PJ9Y4dARASAiYlZ03nyu7SOCRMTIiJzINMyySQpPdsIkRARaWJiYsZ0X/m94N9cJiZEREREZGBMTMyYrvmElcXTtwd7TYiIqj4W5SIiU1ZlEpOFCxdCJpNh6tSp0jYhBObMmQN3d3fY2dnB398f586dUztOqVRi8uTJcHZ2hoODAwYPHozbt2+rtUlOTkZQUBAUCgUUCgWCgoLw6NEjtTY3b97EoEGD4ODgAGdnZ0yZMgXZ2erd32fPnoWfnx/s7OxQv359zJs3T+deC13oaygXwHkmRERERGRYVSIxiY6Oxg8//IC2bduqbV+0aBFCQ0OxbNkyREdHw83NDX369EFaWprUZurUqQgPD8fmzZtx9OhRpKenY+DAgcjLy5PaBAYGIjY2Fnv27MGePXsQGxuLoKAgaX9eXh4GDBiAjIwMHD16FJs3b8bWrVsxbdo0qU1qair69OkDd3d3REdHY+nSpVi8eDFCQ0MN+MqUTNeFEQt1mLAyFxEREREZlJWxAyhNeno63njjDaxatQrz58+XtgshsGTJEsyaNQvDhg0DAPz0009wdXXFxo0bMX78eKSkpGD16tVYt24dXnjhBQDA+vXr4eHhgT///BN9+/bFhQsXsGfPHhw/fhxdunQBAKxatQq+vr64dOkSWrRogX379uH8+fO4desW3N3dAQBfffUVgoOD8dlnn8HR0REbNmxAVlYWwsLCIJfL4eXlhcuXLyM0NBQhISFaJxwamq6phGodE4CJCREREREZlsn3mEycOBEDBgyQEguV69evIyEhAQEBAdI2uVwOPz8//PXXXwCA06dPIycnR62Nu7s7vLy8pDZRUVFQKBRSUgIAXbt2hUKhUGvj5eUlJSUA0LdvXyiVSpw+fVpq4+fnB7lcrtbm7t27uHHjhp5ejXLStceEQ7mIiIiIqJKYdI/J5s2b8ffffyM6OlpjX0JCAgDA1dVVbburqyv+/fdfqY2NjQ2cnJw02qiOT0hIgIuLi8b5XVxc1NoUvY6TkxNsbGzU2nh6empcR7WvcePGGtdQKpVQKpXS96mpqRptdKH75PeniQknvxMRERGRIZlsj8mtW7fw3nvvYf369bC1tS22XdEhUkKIUodNFW2jrb0+2qgmvhcXz8KFC6UJ9wqFAh4eHiXGXV66LrDIoVxEREREVFlMNjE5ffo0EhMT4e3tDSsrK1hZWeHQoUP49ttvYWVlpdYbUVhiYqK0z83NDdnZ2UhOTi6xzb179zSuf//+fbU2Ra+TnJyMnJycEtskJiYC0OzVUZk5cyZSUlKkx61bt0p/YcpBWy4xyrdRmY+XyWRQ5VRMTIiIqj7+JiciU2ayiUnv3r1x9uxZxMbGSg8fHx+88cYbiI2NRZMmTeDm5oaIiAjpmOzsbBw6dAjdunUDAHh7e8Pa2lqtTXx8POLi4qQ2vr6+SElJwcmTJ6U2J06cQEpKilqbuLg4xMfHS2327dsHuVwOb29vqc3hw4fVSgjv27cP7u7uGkO8VORyORwdHdUe+lR0Wsgfk57Dp4OeLdc5VCWDOceEiIiIiAzJZOeY1KxZE15eXmrbHBwcUKdOHWn71KlTsWDBAjRr1gzNmjXDggULYG9vj8DAQACAQqHA2LFjMW3aNNSpUwe1a9fG9OnT0aZNG2kyfatWrdCvXz+MGzcO33//PQDg7bffxsCBA9GiRQsAQEBAAFq3bo2goCB8+eWXePjwIaZPn45x48ZJyURgYCDmzp2L4OBgfPTRR7hy5QoWLFiA2bNnG6UiF6A5lKuZaw1YWJQvFksLGXLzBXtMiIiIiMigTDYxKYsZM2YgMzMTEyZMQHJyMrp06YJ9+/ahZs2aUpuvv/4aVlZWGD58ODIzM9G7d2+EhYXB0tJSarNhwwZMmTJFqt41ePBgLFu2TNpvaWmJXbt2YcKECejevTvs7OwQGBiIxYsXS20UCgUiIiIwceJE+Pj4wMnJCSEhIQgJCamEV0K7op0cFcmPVPNM8vP1EBARERlVbh5/mROR6ZIJYy5NTmpSU1OhUCiQkpKil2Fdn+06j1VHrkvfX57fHzZW5Ru91+bTvUhT5uLAND80qVtD55iIqHLo+/cJ6c4UfiZnbj/C4GXHNLbf+HyAEaIhoooyhd8nhmCyc0xId0VHX5VzFFfBMaoeE+avRERERGRATEzMmOZQrvJnJimZOQCA+2nZpbQkIiJTx8+YiMiUMTExY0V7OXSZgv/p73G6BUNEREREVAImJtWILsXBLt9L118gRERkFOwwISJTxsTEjBWta2CsssVERERERKVhYmLGuPQIEREVxkKcRGTKmJiYsaILLBIRERERmSomJmZM3z0mR68k6feERERUqfhxFRGZMiYmZkzfPfYnrj/Q7wmJiIiIiJ5gYmLW9JuZuNSU6/V8RERUuTjFhIhMGRMTM5afr/s5ejRzfvoNq3oREVVpf/+bbOwQiIiKxcTEjOlj8nsL15rS1zm5esh0iIjIaFYduWbsEIiIisXExIzpY/J74U6SnDwmJkRkPpYvX47GjRvD1tYW3t7eOHLkSIntN2zYgHbt2sHe3h716tXD6NGj8eAB594REekLExMzpo+xxD6etaWvs9ljQkRmYsuWLZg6dSpmzZqFmJgY9OjRA/3798fNmze1tj969ChGjhyJsWPH4ty5c/j1118RHR2Nt956q5Ij1w2nmBCRKWNiYsb0sZBWQGtX1HGwAcAeEyIyH6GhoRg7dizeeusttGrVCkuWLIGHhwdWrFihtf3x48fh6emJKVOmoHHjxnjuuecwfvx4nDp1qpIj1w0nvxORKWNiYsb0cf+RyWQY2Lae3s5HRGRs2dnZOH36NAICAtS2BwQE4K+//tJ6TLdu3XD79m3s3r0bQgjcu3cPv/32GwYMGFAZIRMRVQtMTMyYPnpMgILkpOB8ejkdEZFRJSUlIS8vD66urmrbXV1dkZCQoPWYbt26YcOGDXjttddgY2MDNzc31KpVC0uXLi32OkqlEqmpqWoPY0tKVxo7BCKiYjExMWP6WvldNQE+n5kJEZkRWZES6EIIjW0q58+fx5QpUzB79mycPn0ae/bswfXr1/HOO+8Ue/6FCxdCoVBIDw8PD73GT0RkbpiYmDF9pREyyPR6PiIiY3J2doalpaVG70hiYqJGL4rKwoUL0b17d3zwwQdo27Yt+vbti+XLl2PNmjWIj4/XeszMmTORkpIiPW7duqX350JEZE6YmJgxffVwWDz5AJEdJkRkDmxsbODt7Y2IiAi17REREejWrZvWYx4/fgwLC/VbpqWlJYDih83K5XI4OjqqPYiIqHhMTMyZnody6WvOChGRsYWEhODHH3/EmjVrcOHCBbz//vu4efOmNDRr5syZGDlypNR+0KBB2LZtG1asWIFr167h2LFjmDJlCjp37gx3d3djPQ0iIrNiZewAyHD0sfI7UGjyu17ORkRkfK+99hoePHiAefPmIT4+Hl5eXti9ezcaNWoEAIiPj1db0yQ4OBhpaWlYtmwZpk2bhlq1aqFXr1744osvjPUUiIjMDhMTKpVqKih7TIjInEyYMAETJkzQui8sLExj2+TJkzF58mQDR0VEVH1xKJcZ01cewXLBRERERGRoTEzMmP4Sk4J/9VV+mIiIiIioKCYmVCppKBdnmRARERGRgTAxMWP6m/z+5HzMS4iIiIjIQJiYmDF9JRIW0hwTZiZEREREZBhMTKhUT4dyaZedm8+khYiIiIh0wsTEjOktVXjSY/Jz1L8au1Iyc+C7cD/Grzutr6sRERERUTXExMSM6asTI+VxtvR1cka22r5dZ+LxICMb+87f08/FiIiIiKha4gKLVKr76Urp6/xC2c7C3Rfw/eFrxgiJiIiIiMwMe0zMmn66TO6nPU1McgstZlI0KcnOzdfL9YiIiIio+mFiYsb0NZQrK+dpwpFbwiqLj7Nz9XNBIiIiIqp2mJhQqQoP38rNK75XJCM7rzLCISIiIiIzpJc5Jjk5OUhISMDjx49Rt25d1K5dWx+nJR3pqypXXqFekpy84s+aoWSPCRHpF+8vRETVR4V7TNLT0/H999/D398fCoUCnp6eaN26NerWrYtGjRph3LhxiI6O1mesVE76WlukcI9J3J2UYtsxMSEifeD9hYioeqpQYvL111/D09MTq1atQq9evbBt2zbExsbi0qVLiIqKwqefforc3Fz06dMH/fr1w5UrV/QdN1WiwtNKpm6JLbbdYw7lIiId8f5CRFR9VWgo119//YWDBw+iTZs2Wvd37twZY8aMwcqVK7F69WocOnQIzZo10ylQKj99DeXK19LzcvdRpsa2dPaYEJGOeH8hIqq+KpSY/Prrr2Vqd+HCBUyYMKEilyA90FdVrvwilbhSs3Jw4voDjXasykVEuuL9hYio+tJ7Va6UlBQsX74cHTt2hLe3t75PT0aQVyTDOXs7BZtO3tJol67kUC4iMhzeX4iIzJveEpMDBw7gzTffRL169bB06VK8+OKLOHXqlL5OTxWgr6Fc9tbqHWtv/HgCJ68/lL4f0LYeACDm32Q9XZGI6CneX4iIqgedygXfvn0bYWFhWLNmDTIyMjB8+HDk5ORg69ataN26tb5ipArSV1Wub0a0R78lR4rd39/LDbvOxONaUoZerkdExPsLEVH1U+EekxdffBGtW7fG+fPnsXTpUty9exdLly7VZ2xkIlq6OZa436WmLQDg0ePsygiHiMwc7y9ERNVThXtM9u3bhylTpuDdd99lRZRqYMvbXfHaD8e17nOytwYAPMrMqcyQiMhM8f5CRFQ9VbjH5MiRI0hLS4OPjw+6dOmCZcuW4f79+/qMjXSkr6pcANClSR0M61hf6z7Fk8QkJTNHo4IXEVF58f5CRFQ9VTgx8fX1xapVqxAfH4/x48dj8+bNqF+/PvLz8xEREYG0tDR9xkkmwFIm07rdztoSQEEilJ2XX5khEZEZ4v2FiKh60rkql729PcaMGYOjR4/i7NmzmDZtGj7//HO4uLhg8ODB+oiRKkjorS5XAYtiEhPbJ4kJAChzmJgQkX7w/kJEVL3odR2TFi1aYNGiRbh9+zY2bdqkz1NTBehzKBcAWGh5t4z3awIrCxksnuQsylyuZUJE+sf7CxGR+atwYvLRRx/h5MmTWvdZWlpi6NCh+P333yscGJkemZYek1c6NoBMJoPcqqDXRJnLHhMi0g3vL0RE1VOFE5P4+HgMHDgQ9erVw9tvv41du3ZBqVTqMzbSkd57TLSM5KplbwMAkFsXvJWycthjQkS64f2FiKh6qnBisnbtWty7dw+//PILatWqhWnTpsHZ2RnDhg1DWFgYkpKS9BknVYC+55ikZeVqbKtbUw4AkFsVvJXYY0JEuuL9hYioetJpjolMJkOPHj2waNEiXLx4ESdPnkTXrl2xatUq1K9fH88//zwWL16MO3fu6CteMqIdsXeL3aeaAM85JkSkD7y/EBFVP3qd/N6qVSvMmDEDx44dw+3btzFq1CgcOXKEExWNRN9DuWo9Wa9EG6nHhFW5iMgAeH8hIjJ/FV75vTR169bF2LFjMXbsWENdgkqh76UO7a0t8QjaV3e34VAuIqokvL8QEZmncveYZGZmau06P3funF4CItNVUtJh+aSWcC5XfieiCuL9hYioeitXYvLbb7+hefPmePHFF9G2bVucOHFC2hcUFKT34EhHes4RSkpMrJ+U7MrLZ48JEZUf7y9ERFSuxGT+/Pn4+++/8b///Q9r1qzBmDFjsHHjRgCA0PeEBtKZvqtyZRdJTDa81UX62vJJYsIeEyKqCN5fiIioXHNMcnJyULduXQCAj48PDh8+jGHDhuHq1ataF98j85Kd9zQxiZ71glQqGACsLJ8kJnn8A4KIyo/3FyIiKlePiYuLC86cOSN9X6dOHURERODChQtq28k06PtDxnYetQAA9RS2akkJAFhxjgkR6YD3FyIiKldism7dOri4uKhts7GxwaZNm3Do0CG9Bka603eKsOKNjhjTvTE2v91VY5+VaihXHueYEFH58f5CRETlGsrVoEEDrduzsrJgbW2NnTt3Ir/I5OfBgwdXPDoyKe617DB7UGut+6ShXOwxIaIK4P2FiIh0Xsdkz549CAoKwoMHDzT2yWQy5OVxJXBjKTxh1NVRXkJL3amGcuUxMSEiPeH9hYioetF55fdJkyZh+PDhiI+PR35+vtqDNw3jKpwiGDpfUFXlyuFQLiLSE95fiIiqF50Tk8TERISEhMDV1VUf8ZCBGLrapmooF3tMiEhfeH8hIqpedE5MXnnlFURGRuohFNI39WTEsAmDFdcxISI94/2FiKh60XmOybJly/Dqq6/iyJEjaNOmDaytrdX2T5kyRddLUAUVThEM32PypFww1zEhIj3h/YWIqHrROTHZuHEj9u7dCzs7O0RGRqothCWTyXjjMBH5Bs5MVD0mefmcY0JE+sH7CxFR9aJzYvLxxx9j3rx5+PDDD2FhofPIMNKnQsmIofsxuMAiEekb7y9ERNWLzr/ps7Oz8dprr+n9prFixQq0bdsWjo6OcHR0hK+vL/773/9K+4UQmDNnDtzd3WFnZwd/f3+cO3dO7RxKpRKTJ0+Gs7MzHBwcMHjwYNy+fVutTXJyMoKCgqBQKKBQKBAUFIRHjx6ptbl58yYGDRoEBwcHODs7Y8qUKcjOzlZrc/bsWfj5+cHOzg7169fHvHnz1Mr1GoNaVS4DJwxcx4SI9M1Q9xciIjJNOv+2HzVqFLZs2aKPWNQ0aNAAn3/+OU6dOoVTp06hV69eGDJkiJR8LFq0CKGhoVi2bBmio6Ph5uaGPn36IC0tTTrH1KlTER4ejs2bN+Po0aNIT0/HwIED1cpMBgYGIjY2Fnv27MGePXsQGxuLoKAgaX9eXh4GDBiAjIwMHD16FJs3b8bWrVsxbdo0qU1qair69OkDd3d3REdHY+nSpVi8eDFCQ0P1/rpUlKHTBXsbSwBAamaOga9ERNWFoe4vRERkmnQeypWXl4dFixZh7969aNu2rcbkxIr+cT5o0CC17z/77DOsWLECx48fR+vWrbFkyRLMmjULw4YNAwD89NNPcHV1xcaNGzF+/HikpKRg9erVWLduHV544QUAwPr16+Hh4YE///wTffv2xYULF7Bnzx4cP34cXbp0AQCsWrUKvr6+uHTpElq0aIF9+/bh/PnzuHXrFtzd3QEAX331FYKDg/HZZ5/B0dERGzZsQFZWFsLCwiCXy+Hl5YXLly8jNDQUISEhauOiK0tSuhJnbqdI33do6GTQ63k42QMA/n3w2KDXIaLqw1D3FyIiMk0695icPXsWHTp0gIWFBeLi4hATEyM9YmNj9RBiwc1p8+bNyMjIgK+vL65fv46EhAQEBARIbeRyOfz8/PDXX38BAE6fPo2cnBy1Nu7u7vDy8pLaREVFQaFQSEkJAHTt2hUKhUKtjZeXl5SUAEDfvn2hVCpx+vRpqY2fnx/kcrlam7t37+LGjRt6eQ3K68OtZ6SvW7jWxFevtjPo9erVsgUAJKZlGfQ6RFR9VMb9hYiITIfOPSYHDx7URxxanT17Fr6+vsjKykKNGjUQHh6O1q1bS0lD0UW3XF1d8e+//wIAEhISYGNjAycnJ402CQkJUhsXFxeN67q4uKi1KXodJycn2NjYqLXx9PTUuI5qX+PGjbU+P6VSCaVSKX2fmppa/ItRThfinw5pm/liS9StKS+hte7srAuGcilzWZWLiPTDkPcXIiIyPSY9o7BFixaIjY3F8ePH8e6772LUqFE4f/68tL/oECkhRKnDpoq20dZeH21UE99LimfhwoXSpHuFQgEPD48SYzdlcismJkREpmh55FV8u/+KscMgIiqVzonJwoULsWbNGo3ta9aswRdffKHTuW1sbPDMM8/Ax8cHCxcuRLt27fDNN9/Azc0NAKQeC5XExESpp8LNzQ3Z2dlITk4usc29e/c0rnv//n21NkWvk5ycjJycnBLbJCYmAtDs1Sls5syZSElJkR63bt0q+QWpoMqY4yK3LngrKXPySmlJRFQ2hry/VBePs3OxaM8lhEZcxoN0ZekHEBEZkc6Jyffff4+WLVtqbH/22WexcuVKXU+vRggBpVKJxo0bw83NDREREdK+7OxsHDp0CN26dQMAeHt7w9raWq1NfHw84uLipDa+vr5ISUnByZMnpTYnTpxASkqKWpu4uDjEx8dLbfbt2we5XA5vb2+pzeHDh9VKCO/btw/u7u4aQ7wKk8vlUjlk1cMQKmPqvdzqSWLCHhMi0pPKvL+Yq8Il3HPyWM6diEybzolJQkIC6tWrp7G9bt26an/Ml9dHH32EI0eO4MaNGzh79ixmzZqFyMhIvPHGG5DJZJg6dSoWLFiA8PBwxMXFITg4GPb29ggMDAQAKBQKjB07FtOmTcP+/fsRExODN998E23atJGqdLVq1Qr9+vXDuHHjcPz4cRw/fhzjxo3DwIED0aJFCwBAQEAAWrdujaCgIMTExGD//v2YPn06xo0bJyUSgYGBkMvlCA4ORlxcHMLDw7FgwQKjVeQyBpsniUl2Xr7R128hIvNgqPsLERGZJp0nv3t4eODYsWMaE7yPHTumVsmqvO7du4egoCDEx8dDoVCgbdu22LNnD/r06QMAmDFjBjIzMzFhwgQkJyejS5cu2LdvH2rWrCmd4+uvv4aVlRWGDx+OzMxM9O7dG2FhYbC0tJTabNiwAVOmTJGqdw0ePBjLli2T9ltaWmLXrl2YMGECunfvDjs7OwQGBmLx4sVSG4VCgYiICEycOBE+Pj5wcnJCSEgIQkJCKvz8dVU4OaiM3Eg1x0SIgk/lbKyqR0JGRIZjqPsLERGZJp0Tk7feegtTp05FTk4OevXqBQDYv38/ZsyYobYIYXmtXr26xP0ymQxz5szBnDlzim1ja2uLpUuXYunSpcW2qV27NtavX1/itRo2bIidO3eW2KZNmzY4fPhwiW2MRVYJg7lUQ7kAQJmbJ/WgEBFVlKHuL0REZJp0TkxmzJiBhw8fYsKECdIcC1tbW/zf//0fZs6cqXOAVDGVPZhKPTHJR80S2hIRlQXvL0RE1YvOiYlMJsMXX3yBTz75BBcuXICdnR2aNWumttggGVdlDOWSyWSws7ZEZk4eMpS5cK7Bnz8R6Yb3F/0Slf6RFRFR+VR4vM1HH32kVs2qRo0a6NSpE7y8vHjTMAGF559X1myPOjVsAAAPMrJLaUlEVDzeX/SHs/2IqCqpcGISHx+PgQMHol69enj77bexa9cutVXMqfqp86SX5EE6ExMiqjjeX4iIqqcKJyZr167FvXv38Msvv6BWrVqYNm0anJ2dMWzYMISFhSEpKUmfcVI5qXXZV9JHZrXtrQEAyewxISId8P5iGDm5HMpFRKZNp9JJMpkMPXr0wKJFi3Dx4kWcPHkSXbt2xapVq1C/fn08//zzWLx4Me7cuaOveKkCKqMqF/C0ZHB2HhdZJCLd8P6if/lcY4qITJxea7q2atUKM2bMwLFjx3Dr1i2MGjUKR44cwaZNm/R5GTJR1k8qc+UwMSEiPeP9RXdMS4jI1BlssQkXFxeMHTsWO3bswPTp0w11GSqG2uT3ShrKZW1ZcCEmJkRkSPq6vyxfvhyNGzeGra0tvL29ceTIkRLbK5VKzJo1C40aNYJcLkfTpk2xZs2aCl+/sgn2mBCRidO5XHBxq5vLZDLY2tqiWbNmGDx4MGrXrq3rpaiCKqsqi42lqseENz8i0p0h7y9btmzB1KlTsXz5cnTv3h3ff/89+vfvj/Pnz6Nhw4Zajxk+fDju3buH1atX45lnnkFiYiJyc3PLfW1jOXaVc3OIyLTpnJjExMTg77//Rl5eHlq0aAEhBK5cuQJLS0u0bNkSy5cvR0hICI4ePYrWrVvrI2YqA2OkBtZPEhNlLntMiEh3hry/hIaGYuzYsXjrrbcAAEuWLMHevXuxYsUKLFy4UKP9nj17cOjQIVy7dk1KhDw9PXV+jpUpiRUTicjE6TyUa8iQIXjhhRdw9+5dnD59Gn///Tfu3LmDPn36YMSIEbhz5w6ef/55vP/++/qIlypAVkljuWw4x4SI9MhQ95fs7GycPn0aAQEBatsDAgLw119/aT3m999/h4+PDxYtWoT69eujefPmmD59OjIzM4u9jlKpRGpqqtrDmNiXTUSmTuceky+//BIRERFwdHSUtjk6OmLOnDkICAjAe++9h9mzZ2vcAMiwjDPH5Eliwh4TItIDQ91fkpKSkJeXB1dXV7Xtrq6uSEhI0HrMtWvXcPToUdja2iI8PBxJSUmYMGECHj58WOw8k4ULF2Lu3Lnlis2QOMeEiEydzj0mKSkpSExM1Nh+//596dOhWrVqITubXcjmzoaT34lIjwx9fynamyyEKLaHOT8/HzKZDBs2bEDnzp3x4osvIjQ0FGFhYcX2msycORMpKSnS49atWxWKU1+YlxCRqdPLUK4xY8YgPDwct2/fxp07dxAeHo6xY8di6NChAICTJ0+iefPmul6KyuXpHaiyJr+rekyyOfmdiPTAUPcXZ2dnWFpaavSOJCYmavSiqNSrVw/169eHQqGQtrVq1QpCCNy+fVvrMXK5HI6OjmqPylY40cpjZkJEJk7nxOT7779H79698frrr6NRo0Zo2LAhXn/9dfTu3RsrV64EALRs2RI//vijzsFSxVTaUC7OMSEiPTLU/cXGxgbe3t6IiIhQ2x4REYFu3bppPaZ79+64e/cu0tPTpW2XL1+GhYUFGjRoUM5nZhxcYJGITJ1Oc0xycnIwaNAgfP/99/j6669x7do1CCHQtGlT1KhRQ2rXvn17XeOkKkCaY8LEhIh0ZOj7S0hICIKCguDj4wNfX1/88MMPuHnzJt555x0ABcOw7ty5g59//hkAEBgYiP/85z8YPXo05s6di6SkJHzwwQcYM2YM7OzsdH6+lYJ5CRGZOJ0SE2tra8TFxUEmk6FGjRpo27atvuIivaqcLhP5kx6TzOy8SrkeEZkvQ99fXnvtNTx48ADz5s1DfHw8vLy8sHv3bjRq1AgAEB8fj5s3b0rta9SogYiICEyePBk+Pj6oU6cOhg8fjvnz5+s1LkNiXkJEpk7nqlwjR47E6tWr8fnnn+sjHtITY1TlcpBbAgAeMzEhIj0w9P1lwoQJmDBhgtZ9YWFhGttatmypMfyrKilvVa7QiMtwtLXCWz2aGCgiIiJ1Oicm2dnZ+PHHHxEREQEfHx84ODio7Q8NDdX1ElQBxvhkzMGm4O2UkV11VkImItPF+4t+lWeU7a2Hj/Ht/isAwMSEiCqNzolJXFwcOnbsCKBgImBhlbWwH5Wssn4KDvIniYmSiQkR6Y73F/1S5pa9N5s930RkDDonJgcPHtRHHKRnhbvsK+sG/jQx4Q2NiHTH+4t+/XKq7OuosIIXERmDzuWCiVQcbArmmKSzx4SIyOTklGONqcJ5yT/304tvSESkR3pJTI4cOYI333wTvr6+uHPnDgBg3bp1OHr0qD5OTxVQ+PZTWQMebK0LEpOsHPaYEJF+8P5iHKLQXWT/hXtGjISIqhOdE5OtW7eib9++sLOzQ0xMDJRKJQAgLS0NCxYs0DlA0l1lDcVWJSbK3PxyV38hIiqK9xfjKfwr/N8Hj40XCBFVKzonJvPnz8fKlSuxatUqWFtbS9u7deuGv//+W9fTUxVia/307aTM5SKLRKQb3l90p4/PpfL5ORMRVRKdE5NLly7h+eef19ju6OiIR48e6Xp60gNZpS2waCl9rcxhYkJEuuH9RXcVzSkizj8dvsUecCKqLDonJvXq1cPVq1c1th89ehRNmrD2ubEYY4FFa0sZLJ5cqzxlKYmItOH9RXfHriZV6LhvnqxhArBCFxFVHp0Tk/Hjx+O9997DiRMnIJPJcPfuXWzYsAHTp08vdkVdMjxjfMIlk8kKTYBnjwkR6Yb3F90lpil1Pkcux3IRUSXReR2TGTNmICUlBT179kRWVhaef/55yOVyTJ8+HZMmTdJHjFSFyK0s8Dg7D1nsMSEiHfH+orskPSQm4TF3EDq8ve7BEBGVQufEBAA+++wzzJo1C+fPn0d+fj5at26NGjVq6OPUVEFq5YIrcYHkgh6THM4xISK94P3F+DiSi4gqS4USk5s3b6Jhw4Zq2+zt7eHj46O1/Z07d1C/fv2KXIqqGGkoF3tMiKgCeH/Rr8r8YIqISFcVmmPSqVMnjBs3DidPniy2TUpKClatWgUvLy9s27atwgFSBRWe/F5pSywWDOUCuMgiEVUM7y/65WhrXXojIiITUaEekwsXLmDBggXo168frK2t4ePjA3d3d9ja2iI5ORnnz5/HuXPn4OPjgy+//BL9+/fXd9xUDpX5iZlctcgih3IRUQXw/qJfVpbsMiGiqqNCPSa1a9fG4sWLcffuXaxYsQLNmzdHUlISrlwpKC/4xhtv4PTp0zh27BhvGtWMrarHhEO5iKgCeH/RL84PIaKqRKfJ77a2thg2bBiGDRumr3jIACp/8jvLBRORbnh/ISKqfnRex4RMk1pVLiPMMeECi0RExsdV24moKmFiYqaMdTNijwkRERERVQQTk2qgcodysSoXEZGpYH8JEVUlTEzMlPpQrspTQ15QmjItK7cSr0pERNpwJBcRVSVMTEiv6tSwAQA8SFcaORIiIiIiqkp0Tkz+/PPPYvd9//33up6e9KAyh3I5P0lMkpiYEJGOeH/RHTtMiKgq0TkxGTBgAKZNm4bs7Gxp2/379zFo0CDMnDlT19OTXlReZqKwK0hMUjJzKu2aRGSeeH/RHatyEVFVonNicvjwYfzxxx/o1KkTzp07h127dsHLywvp6en43//+p48YqQKMdS+SP5n8np3HqlxEpBveX4iIqhedE5MuXbogJiYGbdu2hbe3N1566SVMmzYNBw4cgIeHhz5iJB1V5lAuueWTxCSXiQkR6Yb3FyKi6kUvk98vXbqE6OhoNGjQAFZWVrh48SIeP36sj1NTBQkjjSy2sWJiQkT6w/uLbjiSi4iqEp0Tk88//xy+vr7o06cP4uLiEB0dLX3CFRUVpY8YqQIK34wqs1wwExMi0hfeX4iIqhedE5NvvvkG27dvx9KlS2Fra4tnn30WJ0+exLBhw+Dv76+HEElXskocy6VKTJRMTIhIR7y/6M5YvedERBVhpesJzp49C2dnZ7Vt1tbW+PLLLzFw4EBdT08VZKxbkQ3nmBCRnvD+ort85iVEVIXo3GNS9KZRmJ+fn66nJz0wxlAuJatyEZGOeH/R3S+nbhk7BCKiMtO5x2TevHkl7p89e7aulyAdVWpVLitLAAU9JkKISh1GRkTmhfcX3V27n2HsEIiIykznxCQ8PFzt+5ycHFy/fh1WVlZo2rQpbxzVjKrHBABy8gRsrJiYEFHF8P5CRFS96JyYxMTEaGxLTU1FcHAwXnrpJV1PTxWlVpWr8pIDextLWFvKkJMncC81Cx617Svt2kRkXnh/0Q1XfSeiqkYv65gU5ejoiHnz5uGTTz4xxOmpnCpzNJW1pQVauNUEAFyIT628CxNRtcD7S9lFXrqvt3MlpSv1di4iouIYJDEBgEePHiElJcVQp6dSGLNEZG0HOQAgNSvXaDEQkfni/aVsrifpb35J1wX79XYuIqLi6DyU69tvv1X7XgiB+Ph4rFu3Dv369dP19FQF1ZAXTIDPUDIxIaKK4/1FNzl6rI6Yy7rDRFQJdE5Mvv76a7XvLSwsULduXYwaNQozZ87U9fRUQWorv1fy/HMHm4K3VToTEyLSAe8vurnx4HGFjuM6VERkLDonJtevX9dHHKRnxvxsy0Fe8LZijwkR6YL3F+Pgh0pEZCwGm2NCpqOy1xKpIWePCRFRVXXkiv4mzRMRlUeFekxCQkLK3DY0NLQilyAdFS4TWdkridStWTD5PSElq5KvTERVHe8v+lSxvvO7j/i7m4iMo0KJibba8tpw1W/jMeZQrkZ1CtYu+beC45uJqPri/UV/KrqMiTGrOhJR9VahxOTgwYO4du0aPD09YWHB0WCmyJiT351rFPSYpGTmVO6FiajK4/3F+DKz87Ruv5+mlHrEiYgMocK/9Zs1a4akpCTp+9deew337t3TS1CkX5W58jsA2FoXvK2ycrXf3IiISsL7i35UtMdk3fF/tW7nvEEiMrQKJyaiyG+83bt3IyNDf4s5UdUltypYxyQrh4kJEZUf7y/6UdEhWcX1mFhZcPgcERkW+8mrgcoeymVrrUpM8jX+wCAiItOmLGYdEytLJiZEZFgVTkxkMpnG5ENORjRNlf1TkVs/fVtl63HlYSKqHnh/0Q99D+O15M+AiAyswgssCiEQHBwMubxgIlxWVhbeeecdODg4qLXbtm2bbhFSlWP7ZCgXUNBrIi/0PRFRaXh/0Q99V9dickhEhlbhxGTUqFFq37/55ps6B0MGUsn3EmtLGSxkQL4AlDl5gJ115QZARFUa7y9ERNVThROTtWvX6jMOMqDKrsolk8lga22Jx9l5yMrhUC4iKh/eX/QjLYtVtIioauHkdzIIuRVLBhMRGdN/4xL0ej4uvEhEhsbEpBowxrBgVWUuJXtMiIiIiKgMTDYxWbhwITp16oSaNWvCxcUFQ4cOxaVLl9TaCCEwZ84cuLu7w87ODv7+/jh37pxaG6VSicmTJ8PZ2RkODg4YPHgwbt++rdYmOTkZQUFBUCgUUCgUCAoKwqNHj9Ta3Lx5E4MGDYKDgwOcnZ0xZcoUZGdnq7U5e/Ys/Pz8YGdnh/r162PevHkmUS7XGNMVpZLB7DEhIjIPxr+dEZGZM9nE5NChQ5g4cSKOHz+OiIgI5ObmIiAgQG2RrUWLFiE0NBTLli1DdHQ03Nzc0KdPH6SlpUltpk6divDwcGzevBlHjx5Feno6Bg4ciLy8p38wBwYGIjY2Fnv27MGePXsQGxuLoKAgaX9eXh4GDBiAjIwMHD16FJs3b8bWrVsxbdo0qU1qair69OkDd3d3REdHY+nSpVi8eDFCQ0MN/EqZJmkoFxdZJCIyC4lpSmOHQERmrsKT3w1tz549at+vXbsWLi4uOH36NJ5//nkIIbBkyRLMmjULw4YNAwD89NNPcHV1xcaNGzF+/HikpKRg9erVWLduHV544QUAwPr16+Hh4YE///wTffv2xYULF7Bnzx4cP34cXbp0AQCsWrUKvr6+uHTpElq0aIF9+/bh/PnzuHXrFtzd3QEAX331FYKDg/HZZ5/B0dERGzZsQFZWFsLCwiCXy+Hl5YXLly8jNDQUISEhRi2zaIxrywstskhERFXfwKVHcePzAcYOg4jMmMn2mBSVkpICAKhduzYA4Pr160hISEBAQIDURi6Xw8/PD3/99RcA4PTp08jJyVFr4+7uDi8vL6lNVFQUFAqFlJQAQNeuXaFQKNTaeHl5SUkJAPTt2xdKpRKnT5+W2vj5+Ul191Vt7t69ixs3bmh9TkqlEqmpqWoPQzDKUK4nPSZKDuUiIiIiojKoEomJEAIhISF47rnn4OXlBQBISCioNuLq6qrW1tXVVdqXkJAAGxsbODk5ldjGxcVF45ouLi5qbYpex8nJCTY2NiW2UX2valPUwoULpXktCoUCHh4epbwSVYdqjsmRy0lGjoSIiIpq7lrD2CEQEWmoEonJpEmTcObMGWzatEljX9FhSkKIUocuFW2jrb0+2qgmvhcXz8yZM5GSkiI9bt26VWLcFWWcqlwFb60tp27h5PWHlR8AEREVa94QLyi4+C0RmRiTT0wmT56M33//HQcPHkSDBg2k7W5ubgA0eyMSExOlngo3NzdkZ2cjOTm5xDb37t3TuO79+/fV2hS9TnJyMnJyckpsk5iYCECzV0dFLpfD0dFR7WEIlb3AIgDY2zydvnT0yv1Kvz4RERERVS0mm5gIITBp0iRs27YNBw4cQOPGjdX2N27cGG5uboiIiJC2ZWdn49ChQ+jWrRsAwNvbG9bW1mpt4uPjERcXJ7Xx9fVFSkoKTp48KbU5ceIEUlJS1NrExcUhPj5earNv3z7I5XJ4e3tLbQ4fPqxWQnjfvn1wd3eHp6ennl6VqqO/l9vTb4w48Z+IiIiIqgaTTUwmTpyI9evXY+PGjahZsyYSEhKQkJCAzMxMAAXDo6ZOnYoFCxYgPDwccXFxCA4Ohr29PQIDAwEACoUCY8eOxbRp07B//37ExMTgzTffRJs2baQqXa1atUK/fv0wbtw4HD9+HMePH8e4ceMwcOBAtGjRAgAQEBCA1q1bIygoCDExMdi/fz+mT5+OcePGSb0cgYGBkMvlCA4ORlxcHMLDw7FgwQKjV+QCYJTZ74WHCNxOflz5ARARUbFMYIktIiINJpuYrFixAikpKfD390e9evWkx5YtW6Q2M2bMwNSpUzFhwgT4+Pjgzp072LdvH2rWrCm1+frrrzF06FAMHz4c3bt3h729Pf744w9YWlpKbTZs2IA2bdogICAAAQEBaNu2LdatWyftt7S0xK5du2Bra4vu3btj+PDhGDp0KBYvXiy1USgUiIiIwO3bt+Hj44MJEyYgJCQEISEhBn6lSmeMvMjK8ulFt/19p/IDICKiYvl4OuFV7walNywi5XGOAaIhIipgsuuYlGXFdJlMhjlz5mDOnDnFtrG1tcXSpUuxdOnSYtvUrl0b69evL/FaDRs2xM6dO0ts06ZNGxw+fLjENtWFh5O92vdlKUpARESVw9rSAv283PDj0etwc7Qt83GL9l7EZy+1MWBkRFSdmWyPCemPMdIBF0dbfPHy05vXxYQ0I0RBRFS85cuXo3HjxrC1tYW3tzeOHDlSpuOOHTsGKysrtG/f3rABGpjqsyJVFcWyuPMo00DREBExMakWjNVT8VqnhujSuGBBzLN3UowSAxGRNlu2bMHUqVMxa9YsxMTEoEePHujfvz9u3rxZ4nEpKSkYOXIkevfuXUmRmhbOTSEiQ2JiQgblXEMOAMjM5grwRGQ6QkNDMXbsWLz11lto1aoVlixZAg8PD6xYsaLE48aPH4/AwED4+vpWUqSGx1yDiEwFE5NqwJgzO+xtCooMZGTnGjEKIqKnsrOzcfr0aQQEBKhtDwgIwF9//VXscWvXrsU///yDTz/9tEzXUSqVSE1NVXuYFs77IyLTwsSkGjDmnHMHeUF9hcdK9pgQkWlISkpCXl6exuK3rq6uGgvlqly5cgUffvghNmzYACurstWNWbhwIRQKhfTw8PDQOXZjY+8KERkSExMyKPaYEJGpKjr/rrjqgXl5eQgMDMTcuXPRvHnzMp9/5syZSElJkR63bt3SOWYiInNmsuWCSX9kRuyuVyUm7DEhIlPh7OwMS0tLjd6RxMREjV4UAEhLS8OpU6cQExODSZMmAQDy8/MhhICVlRX27duHXr16aRwnl8shl8sN8ySM5PDl+8YOgYjMGHtMqgFjDuWq7VBwU95y6haycpicEJHx2djYwNvbGxEREWrbIyIi0K1bN432jo6OOHv2LGJjY6XHO++8gxYtWiA2NhZdunSprNANonClrfgUlgMmIuNhj4kZKsvilJWlmWsN6evIS/fRz8vNiNEQERUICQlBUFAQfHx84Ovrix9++AE3b97EO++8A6BgGNadO3fw888/w8LCAl5eXmrHu7i4wNbWVmN7VaLtQ6vr9zMqPxAioieYmJgZIQRGrjlp7DAkHRs6SV//cz/diJEQET312muv4cGDB5g3bx7i4+Ph5eWF3bt3o1GjRgCA+Pj4Utc0ISIi/eJQLjPz6HEOjlxJUttmzKFclhYyBHfzBMC1TIjItEyYMAE3btyAUqnE6dOn8fzzz0v7wsLCEBkZWeyxc+bMQWxsrOGDrGSm099ORNURExMzk29Cw7hUbK0LJsBnco4JERERERWDiYmZ0ZaWGLMqFwDYPUlMHrPHhIjI5Aj2kxCRiWBiYma0dZgYcygX8LRkMKtyERGZDm23BhPsdCeiaoSJiZkxxU++bFVrmXCRRSIik2aK9xAiqj6YmJgbbT0mlR+FGg7lIiIiIqLSMDGpBmRGHstV28EaAPAgPduocRARUck4lIuIjImJiZkxxXuKh5M9AODWw8cmtfgjERExGSEi08HExMxoKxds7KFcDZ4kJmnKXKRk5hg5GiIiAozfm05EVBQTk2rA2PceuyeT3wFgeeQ/RoyEiIhKws4TIjImJiZmxtS75P9JTDd2CEREVAwOtyUiY2JiYma0LrBo7C4TADP7twRgGrEQERERkelhYmJm8vNN89OuRnUK5pk8zFAaORIiIiqscCeJad5BiKi6YGJClcK5hhwAcC+ViQkRkaElZ5Renp3910RkapiYmBlTHR7cpG4NAMCdR5lITMsycjREROYtLSvX2CEQEZUbExMzo61csCmo7WAjDefae+6ekaMhIjJvoqKDskzzFkJE1QQTEzNjqokJAPRs4QIAuJ382MiREBGRNhVOaIiI9ICJiZkx5VtKAyc7AMA/iRlGjoSIyLyZ8GdURETFYmJiZtJNeFyxj2dtAEDUP0lGjoSIiFTV2+88ypS2MaEhImNiYmJmhnx3zNghFKuxswMAICM7D1k5eUaOhojIfJUlv7ieVHrv9Tevt9c5FiKismJiQpXG0dZK+nrb33eMGAkREf37oPT5fkPa16+ESIiICjAxMRPpylyTXVxRpfCq7x+FnzViJERE5k1UcEwWh3IRkTFZld6ETN2th4/RY9FBdG1S29ihEBGRCahofsG8hIiMiT0mZmB7TMGwqOPXHho5ktItHdFB+jruTooRIyEiMl9l6fngyu9EZGqYmJgBWRW6u/Rp7Sp9PXDpUYxcc9KI0RARUWEVHQJGRKQPTEyoUtlaW2LHxO7S94cv30eG0nRLHBMRmauq9KEWEVUPTEzMgKyEu8ueqT0qMZKyaedRCx8PaCV9fyE+1YjREBGZI/Z8EFHVw8TEzNWvZWfsELR6q0cTNKpjDwB4ZWUU1zUhIjIBTGeIyJiYmFRxqVk5+HLvpWL3l9SbYmwTez4jfb366HUjRkJEZF7KNPldy/3h5HXTL6JCROaLiUkVtyLynxL3W5huXoJ+Xm7S13/9k2TESIiIzEtFez72xCWU2iblcU4Fz05EVDImJlVcWlbJNwiZCReEdLS1lr4+dvUBUjJ5syMi0gd9FtcqOiT46FV+kEREhsHExMyZ8EguAMDOyc9JX+87V/ondUREVLpr99P1dq5hHevr7VxERCVhYmLmLEw8M/Gqr8Do7p4AgKt6vJESEVVnBy4m6u1cRe8i/z7M0Nu5iYgKY2Ji5kw8LwEAuDnaAgDWHrth3ECIiKqRst4f2nnUUvt+0Z7iC64QEemCiUkVV9ocElPvMQEAN0VBYpKdm4/UUubMEBFR6Sr6q//Oo0yNbfWdTLPsPBGZHyYmVVxpNx/TT0uAF1q5Sl9fjE8zYiRERObhl1O3S21jysVRiKh6YmJi5qpAhwkc5FbwaeQEAHiYoTRyNERERERkDExMqrjS8g5TXmCxMBurgreiMjffyJEQERERkTEwMSGToEpMspmYEBFViiryuRURVSNMTMgk2Fg+SUzymJgQERERVUdMTKo4PS7ua1TSUK4cJiZERKbE3trK2CEQUTXBxIRMgtzKEgB7TIiITE3DOvbGDoGIqgkmJlWcMJcukydWHb5m7BCIiKiIoossEhEZAhOTKk6YyWCuQ5fvAwAeZGQjP988nhMRkSkr19x3c/sUjIhMEhMTMgl5+U+HcGlbeZiIiPSrPFW5mJYQUWVgYkImQTX5HQAyc/KMGAkRERERGQMTkyrOXHrXl47oKH3NylxERERE1Q8TEzIJnRvXRmNnBwBAVi57TIiITIm5fAhGRKaNiQmZDPmT4VxZHMpFRGRwsnJMf89nZkJElYCJCZkMuXXBWiYcykVEZHjlmfzu+aRHm4jIkJiYVHHm9BlW7pPFFef8cc7IkRARUWGvejcwdghEVA0wMSGTce5uKgDgdjLLBRMRVbaElKxi91lalGvVEyKiCmFiUsVtPHHT2CEYRB4XWSQiqlQ3Hz42dghEVM0xMSGTsTa4k/R1di7nmRARmYryTJQnIqooJiZkMno0c5a+VrJkMBGRQcnKM/udiKgSMDEhk2Fl+fTteOzqAyNGQkRUdQmW9iWiKoqJCZmkiRv/NnYIRERV0rWkDGOHQERUIUxMzBirqBARVT8VLR5SUk8LR30RUWWwMnYAZDjH/q+XsUMgIqJKdju5bNW1Cuca+XqqhPjXP0mAAHw8a8PGip99ElH5mPRvjcOHD2PQoEFwd3eHTCbD9u3b1fYLITBnzhy4u7vDzs4O/v7+OHdOfXE+pVKJyZMnw9nZGQ4ODhg8eDBu376t1iY5ORlBQUFQKBRQKBQICgrCo0eP1NrcvHkTgwYNgoODA5ydnTFlyhRkZ2ertTl79iz8/PxgZ2eH+vXrY968eUYb6+tcwwZuClujXJuIiIwnPOZumdoV7gWJuZVcrmtou7ddu5+OwFUnEPjjCTT/+L96S3aIqPow6cQkIyMD7dq1w7Jly7TuX7RoEUJDQ7Fs2TJER0fDzc0Nffr0QVpamtRm6tSpCA8Px+bNm3H06FGkp6dj4MCByMt7WvUpMDAQsbGx2LNnD/bs2YPY2FgEBQVJ+/Py8jBgwABkZGTg6NGj2Lx5M7Zu3Ypp06ZJbVJTU9GnTx+4u7sjOjoaS5cuxeLFixEaGmqAV6ZAZnZJlauqZr/7p4NaAwAcbdmZR0SkD509a5faRoiSq3QV3bP3XIJGm/d/+Z/a99ti7pQpPiIiFZP+669///7o37+/1n1CCCxZsgSzZs3CsGHDAAA//fQTXF1dsXHjRowfPx4pKSlYvXo11q1bhxdeeAEAsH79enh4eODPP/9E3759ceHCBezZswfHjx9Hly5dAACrVq2Cr68vLl26hBYtWmDfvn04f/48bt26BXd3dwDAV199heDgYHz22WdwdHTEhg0bkJWVhbCwMMjlcnh5eeHy5csIDQ1FSEiIQcoyrj56rdh9VXU88AutXDH3j/PIyeMnbURE+iC31v4ZZOFOD0sLGfLL8Xv3nfV/47d3fOFTKOk5e/uRWpvpv/4Pr3g3KFesRFS9mXSPSUmuX7+OhIQEBAQESNvkcjn8/Pzw119/AQBOnz6NnJwctTbu7u7w8vKS2kRFRUGhUEhJCQB07doVCoVCrY2Xl5eUlABA3759oVQqcfr0aamNn58f5HK5Wpu7d+/ixo0bWp+DUqlEamqq2qM87qUqi91XRfMS6QaamZOH1KwcI0dDRFT1lHUIcX6hdlYW5f9zIGj1yXIfQ0RUkiqbmCQkFHQju7q6qm13dXWV9iUkJMDGxgZOTk4ltnFxcdE4v4uLi1qbotdxcnKCjY1NiW1U36vaFLVw4UJpXotCoYCHh0fpT7wQAfOroCK3spS+XhH5jxEjISKqmnaeiS9Tu8L5i4VF+dc/4UK4RKRvVTYxUSk6REoIUeqwqaJttLXXRxvVL/ni4pk5cyZSUlKkx61bt0qMuzwsqmhmYltoyMGth2WrLENEVBHLly9H48aNYWtrC29vbxw5cqTYttu2bUOfPn1Qt25dODo6wtfXF3v37q3EaPUvr0iPScT5ezqdT9tc92v303U6JxFVL1U2MXFzcwOg2RuRmJgo9VS4ubkhOzsbycnJJba5d0/zl/H9+/fV2hS9TnJyMnJyckpsk5iYCECzV0dFLpfD0dFR7VEeJX24VTXTEvUeE+ca8hJaEhFV3JYtWzB16lTMmjULMTEx6NGjB/r374+bN29qbX/48GH06dMHu3fvxunTp9GzZ08MGjQIMTExlRy5/hRe78TSAvjx6PVi21pZav65UJb+lbSs3IqERkTVVJVNTBo3bgw3NzdERERI27Kzs3Ho0CF069YNAODt7Q1ra2u1NvHx8YiLi5Pa+Pr6IiUlBSdPPh0re+LECaSkpKi1iYuLQ3z80+7xffv2QS6Xw9vbW2pz+PBhtRLC+/btg7u7Ozw9PfX/AqDkm4IhJttXlo8HtAIAPMzILqUlEVHFhIaGYuzYsXjrrbfQqlUrLFmyBB4eHlixYoXW9kuWLMGMGTPQqVMnNGvWDAsWLECzZs3wxx9/VHLk+lO4nK+2XvbFr7aTvvZu5KSxvyxOXH9QoeOIqHoy6cQkPT0dsbGxiI2NBVAw4T02NhY3b96ETCbD1KlTsWDBAoSHhyMuLg7BwcGwt7dHYGAgAEChUGDs2LGYNm0a9u/fj5iYGLz55pto06aNVKWrVatW6NevH8aNG4fjx4/j+PHjGDduHAYOHIgWLVoAAAICAtC6dWsEBQUhJiYG+/fvx/Tp0zFu3DiplyMwMBByuRzBwcGIi4tDeHg4FixYYLCKXEDJPSZVmaOdNQAgjZPficgAsrOzcfr0abXCKEDB73pV0ZPS5OfnIy0tDbVrl16Kt6p67hln6WtLi4rdxxbsvqivcIioGjDpcsGnTp1Cz549pe9DQkIAAKNGjUJYWBhmzJiBzMxMTJgwAcnJyejSpQv27duHmjVrSsd8/fXXsLKywvDhw5GZmYnevXsjLCwMlpZPhwxt2LABU6ZMkW5SgwcPVls7xdLSErt27cKECRPQvXt32NnZITAwEIsXL5baKBQKREREYOLEifDx8YGTkxNCQkKkmA3D/Ca/A4CjbUFiksohAERkAElJScjLyyuxeEppvvrqK2RkZGD48OHFtlEqlVAqn1ZPLG/lRUMr7T5R2n5z/XCMiIzHpBMTf3//EquEyGQyzJkzB3PmzCm2ja2tLZYuXYqlS5cW26Z27dpYv359ibE0bNgQO3fuLLFNmzZtcPjw4RLb6FOJc0yqcmJiV/C2vPso08iREJE5q0jxFADYtGkT5syZgx07dmit6qiycOFCzJ07V+c4DaUqD/klIvNk0kO5qGSbo4uv4iWrstPfgToOBZPe41OymJwQkd45OzvD0tKyxOIpxdmyZQvGjh2LX375RRoSXBxDVl4sD/ZsEFFVwcTETFXlD8KaudSQKnKdKbKSMBGRrmxsbODt7a1WGAUAIiIipKIn2mzatAnBwcHYuHEjBgwYUOp1dK28qC8lrXlVkip8GyGiKoqJiZmqyjcUCwsZejQrmHR54wHXMiEi/QsJCcGPP/6INWvW4MKFC3j//fdx8+ZNvPPOOwAKejtGjhwptd+0aRNGjhyJr776Cl27dkVCQgISEhKQkpJirKdQZt4NS6+opWunyqaT2sssExGVh0nPMaGKq+pjh2s72AAAkh+zZDAR6d9rr72GBw8eYN68eYiPj4eXlxd2796NRo0aASgoLV94TZPvv/8eubm5mDhxIiZOnChtVxVjMWXNXGuW3kibctxGZm47W7FrEBEVwsTETFXttASo9aRkcMpjlgwmIsOYMGECJkyYoHVf0WQjMjLS8AEZSHG9IZX1+VVWTh5srS1Lb0hE1R6HcpmrKp6Z1LIvSEzYY0JEpJuSqltWhiv30o16fSKqOpiYmKkqnpdIk98T05SltCQiIkMoS3XH3Wfj8cqKkhel/PW0caqREVHVw6FcVdRf/ySVuN+iis8x8ahtDwC49ZDlgomIdFFch4k+yspP2PB3qW0uJqTpfB0iqh7YY1JFBa46UeL+Kp6XoFEde1jIgKR0Jf59kGHscIiIqqz8YjITQ94nRvo2kr5+/kmVRSKi0jAxMVNVeYFFAKhpa422DWoBAM7dTTVuMEREVZgxppg0fNLrDQDbY+9WfgBEVCUxMTFTVb3HBADqO9kBKFgBnoiIKqaieYku9xGPQonJ1UROfieismFiQibLzdEWAHAvlYkJEVFFFVeVq/Dm1EzN0uy6fL5VQ84prERUfkxMzFRVX2AReHpje5yda+RIiIiqpvq17ODy5EOeoprUdZC+/u30bY39XHuEiCobExMzVfXTEkBuXfD2VObkGzkSIqKq6acxnWBlof2O8IxLDenrDSduaux3YK8HEVUy/tYxU2bQYQJbq4JP67JymZgQEVXEMy41UcdBXunXZW8LEVUEe0zMlFkkJk9ubMqcPCNHQkRUdTk52FT6NTs2rFXp1ySiqo+JiZmq6uWCAUBuVfD2ZI8JEVHVYg7zHO8+ysQ3f15BUrrS2KEQVRscymWmzOCeIPWYZLHHhIiIKtHpfx/i5RVRAICoa0nY/LavkSMiqh7YY2KmzCAvkXpMlOwxISLSO0PfJz4e0AoA0KOKrfz+ODtXSkoA4Pi1h0aMhqh6YWJCJotzTIiIqi5VyXe5VdWaCJ+exRL1RMbCxIRMllQumD0mRERVjuWTMsV5+fwdTkRlw8TEXJnBJBOpXPCTHpN/H2TgamK6MUMiIqIysrIsuA/l5mtfed5kabl9en64C7cePq78WIiqGSYmZqrqpyVPe0yycvJw6+Fj9P7qEPotOYzzd1ONHBkRUfXzineDcrW3tCj4HZ5X1RKTYsz947yxQyAye0xMzJQZdJhIPSbK3Hwcu5qE3HyB3HyBqGsPjBwZEVHVV96Svi93LFti8p+hXgAgrThf5XpMinHi+gNkZnPOI5EhMTExU2aQl8C2UI/JP/efDuF6rOTERCIiU6WwswbwdI7JyesPq8wwqKycPHT+bL/WfWlZuRjy3dFKjoioemFiQiZLVcklXwC//++utD2Dn1gREWllCus+qT4YsyzUI9Nj0UHjBFNOo9dGl7j/8j3OcyQyJCYmZmTt6E7S1xZmMJZLNccEAO6lPl1593E2e0yIiLQ5V445eEIYdoiVpWXVuw9xqDCRcTExMSO+TepIX7vXsjNiJPqhWmCxqJ+j/oXnh7twIymjkiMiIjJt3+y/YuwQpDmOqjkm5sbQCR1RdcbExMysH9sFA9rUw+xBrY0dis5kMhmecakhfd+zRV21/f6LI3EhnhW6iIhUDl++X+a25Z38Xlb2NgXDcC2rWGJSeMhwSWJuPTJsIETVmJWxAyD9kcmA55o547lmzsYORW9+n9QdOXkCjrZW+PtmMg5eUr/phvzyP/z3vR5Gio6IqOpZFtjBoOf3a+4CALCyqFqffU7ZFFOmdsOW/4VvR3TA4HbuBo6IqPqpWr81qEQys6jFpc7exgoKO2vIZDJ4N6qN/dP8sHFcF7T3qAUAuBCfirg7KcYNkoioiqjjYIOBbQ37B7Wqp6Qq9ZgkpmWVq31ZkxgiKh8mJmakCt0DKqxp3Rro1tQZ2yd2x/P/396dx0VZrv8D/wzDMGzDyL6LuGKiuK8okaWpLXpaLE3r1KmfnTSXjmlp3zZLz2n5Wueopal1MrVFK+trbim4UZiIGwouKKggouw7zPX7gxwZhp2RYYbP+/Xi1cz93M/z3LfmNVzz3EvXyqFdRy9lm7dRRERWxq6GOX6Cxs2tqD7HJL6GIVDf/pHa4CFUt1Ntyxk/Ex7cwi0hatuYmFgRW2Xb+uvs5l05/yTuYrZ5G0JEZGW6+7o0+xrVn5jsPp1h8P5CZgHmfncML244gtJyHXQ6wZNrYjH326PNvndjLdtzrsbyiQMC4fTnnBkiuv3a1m+yVszVUWXuJrS4kd29AQCb4i4hp6jMzK0hIrIsdT1kN8UDeNtqywV/XG3FsMmf/a5/XaETnE7PQ3TSNXx7+JIJ7t441ZMmANj3ciS6emvw/iNhLd4eoraKiQlZrH5BrvrX38e1/AcZEVFrFt7ZeCGUhg7GGl7DIiqNncdY03LBr2w+rn99ObuoSrsEuirL8F7LK0F93t+eiCfXxKK8QteodlVX2/mBbo4AgHtDfZp1fSJqOCYmZLFUShuE+GgAAG/8lIC0nKJ6ziAiajvG9Gz6L9TT7+psVNbYOSY1PXfZEJuCkvL6d6d/am1svXX+s+csopOuYe+Zhi+RXJO53x0zKrv7zyfywO1bVpmIjDExIYvWp/2tpyZDFu/Gmv3JZmwNEZF1UNuaYl5FzYmMCPDaDyeMyqqqvoN9hU5wNDUb/7szCff9e5/B05ayiuZtePj9kctGZbPv6VLveYcv3mjWfYnIGBMTK/HciE7mboJZzLrb8MPjrZ8TcPBspplaQ0REN9X2pKGkXIcvf7toUFZTajFr4xH9Jrof7kzEg8sO4KNfz+DE5VwMW7L71n1M1uJbevhp663z0IqY23BnoraNiYkV+HlGOKZFdDR3M8zC28UeyYvH4r5evvqySZ/9Dp2ued+gERFZukHB7nUeb+wIJWUjT6itdtibOxp0/g/xVzDmo3149JOYWlfNApo31Cq/pLzJ5xKR6TExsQKh/to2PQZWoVDgP5P6GpRt4mR4ImrjOns5G5VJ9TFTjdC/g1u9dUb3uDU3w6YRn0t1tSv2Qt1Dppqzh9e3f6Q2/WTUvv8JETUNExOyGovGh+pfz/3uGHKLuYQwEZGpNGQnd1ubW79W1LRJY21WRJ3D+GUHmtSuxiRA1eUXN+yJSfTcO2ssH/Henibfm4iMMTEhq/H4wPYG73u9sQOJ6Xlmag0RUdvTv8OtBUn82jngsQGBDTpvedQ5lDdxCO7JKzlNOg8APtiZ1KB6Qe5ONZY34wEUEdWAiQlZDaWNAh9U2whr9NK9uFFQaqYWERG1Xv7tHEx+zYHBhsO9ljzUy+T3qO79HQ1LLhrqs6n9ayx/sLefSe9DRMaYmJBVeahfAA7Mv8ugrO/bO3HxeoGZWkRE1DIaMn+kag1bJX8FqC558VjcfYd3jcf8bkMiR0SGGJXI6vi3czCYbwIAEe9FIaHauvhERNbkOp8ON0r1RG7myC51LiTz/J1tc1l+opbExMQCXc0tNncTWr0nBgdh15wIg7KxH+/D7K/jUVjK5SGJyPo0djnf28GS5lxkFxoukPJCpPFu91W52KtuZ3OICExMLFJ8ara5m2AROns5I3HRvQZl3x+5jPB/7kFmfomZWkVEdHuU6XQmuc5TQzs0+dyGrNx1O1Q0YeL83O+OGbxXKc2f2BG1dUxMLND/+/KwuZtgMdS2Spx9Zwy6VFnP/0ZBKcL/uRv/8+MJPn0iIqtx0kTDVV0cmv5kIMRHY5I2NNavp642+pxd1c5p6n5gOUVcmp7IVJiYkNWzVdpg55wI7J8XiaeGdkBHTycUl+nw35iLGPGvPVj0cwLOZuRxiBcRWbSkOpZHd3WsTDYG17MbPFD7ju0NUdMv93NHd2vGFRumepLRWIFuDZvY/vOMcPylr79BWVmFaZ5UERETE2pDAlwd8cYDPbBrdgTmjwlBFy9nlJTr8Nn+ZNz94V7c8T/b8cGOxCYNCSAiMreqe4hUt2V6OF66pyuWPNSz3uuYeqrKs8M7mvaCNfjmj0uNql9UWmHwvqKiYXE/1F+LDx/tjU6et/Y10VnSxBqiVo6JCbU5NjYKTIvohB2zR+CLpwciLECrP/bv3Wcx6n+jseXolQYtvUlE1Fqs3p9c67FAN0fMGNkF7Rztbtv9590bUmN5Y3aAb47GxOy7PogyeP90eHCj7tUroJ3+dVkDkxoiqh8TEwsX4Mp11ZtKoVAgoqsnfpwejs1/HwpfrT0A4Ny1Ary44QjGfLQPn0afg45PUIjIAhw4e13/eu1fB9yWe6jrSDKqb65Y1f1ht39zwve2Jza4blqO4fzCZxqZmFR9sv7YyphGnUtEtWNiYuEWjutu7iZYhb7tXRHzykjEvXYPnhraAbY2CpxOz8PiX07j7g+j8fmBZM5BIaJWraDkVoyK7ObV5Oso6phl8mj/wDrOrP1LnA8eCWtyexpqedQ5/ev0nGJk1LK4SXKm4Ya7m54f2uiJ71WHb6XeKGrUuURUOyYmFo6jjUzLzckObzzQA1Fz78S0iE5wslPifGYB3vgpAT1e3467P4xGbPINczeTiMhIeQs83a0rManr86ilhnMBwJGULAxe/CsGvvsrUm8UGhz757bTiHw/yqCsX1Dtc3NqU72r1ZMdImoaJiZENQhwdcT8MSH4fcHdePvBHgh0c4AIcDYjH4+v+g2v/XACxy5lm7uZREQtSmNvW+ux1jLqdcLyg/rXz315GB/uSMRHu86grEKHFVWeqjSHr4u9wfsdJ9NNct2aRCVmYNC7u7A36dptuwdRa8HExMLwW5mW5ay2xZQhHbB3biSWTuwNdyc7VOgEX/52EQ/85wDmfBOPpKu1L9FJRGRp6hrVFOTuWOux1rhgyKm0XHy8+yz+d1cSPttX++IAjTXz7i4G7xf/chqn0nKx6OcEHK1nE+R1v13ER7vOoEInOHA2E7nFZfo/u2t5JVgedRYZecXILS7Daz+cwFNrD+Fqbgmmrok1Wk2MyNrU/tUHtUo3Cgx3LG8t31BZO4VCgfF9/PFgbz/EnLuOL2IuYPvJq9gcdxnfH7mMvw4NxkujusJJzX9SRGS96pqL0Ro+jupKjv657bTJ7qOxN96EcsxH+wAAn/25OlrSojE1DmFb+MMJAEBmfgm+/O0iOns542xGPiK7eeLA2esordDhX9tqnsjf/X+2Yf6YEJzNyEeIjwZ/a4GlmIlaEp+YWJjq+zj18HMxT0PaKIVCgaGdPfDplP7Y/PehuOcOb4gAaw4kY+iS3dhy9Iq5m0hEZBaN3c+jKXM76hP8ytZG1e/o4VR/pSbquvAXZOYbfpl4OfvWRPkvf7sIoHKIMADsSbyG0gZs1rjkl9P47vAlLPq/Uzh8kXMeybowMbEw1QO/m/PtW5Oe6ta3vStWTe2Pt8eHQuugQk5RGV7ccATP/vcP7D59lcsME5FFavKT33pCnpOd0uB9Y5fovR3mjal57xVTee6/fwAA3t16Cp1e3YphS3ab9PoPrYjBz8cqvxC7kFmAa3kl9ZxB1Lpx3ImFqZ6YmHiDXmqCKYOD8NiAQLy/IxGfRp/HzoSr2JlwFaH+LnhySAeM7enLIV5EZDGqJxAN1cNPW+fxrTOHI+K9KP371vD5pVI2vRUhPhqcTq97jmFcSjY6zP+/Jt+jIaavP4L9ZzKx8VAqAODCknG39X5EtxOfmFgYXf1PeckMVEobvDKmO36ZORzPhAfDWW2LE5dzMfe7Yxiy+FesiDqH8gY8oiciaooyE8aXB3o3fjPE9c8OgtbReN5FVUHuTnCpY1Uvcwj2cG7yuV8+M8iELWmem0kJUDnP5vilHCRdzUN+CfffIsvSuiIE1auxY3ipZXX3dcFr992Bp8ODse63i9gYm4KswjL8c9tpfHc4FbPu7toiOyATUdtSfS5DczjaNf5Xg6GdPBpUr+onWE0TyBvj22lDUFRagalrYpt0/jPhwQhuxhwTT40aq5/sj2e++KPJ17gdOr661WBPmZtPUCp0gnKdDmrbpj0RI2oJfGJiYSqqJSZOTfgAodvPv50D5t0bgphXRuKN+++Ao50S564VYMaGI5iw/AB+OnrFpN9wElHbdjW35eYWbJ81wuD9+r81/MlBZ69bTyiGdnJvVjv6B7liRFfPJp8/+56uzbo/AIzs7o177vBu9nVMqfr3l9O+PIzDF7PQ6dWt6LZwG46mZus/f0QEy6POYt+ZaygqrUByZgFOXM4xmCOZU1TWKpeCJuvE32otTPUJ1TY2rWGULtXGXqXEU8OCMbK7N5ZHncWmw5dxJCUbM1KOwFdrj/vD/BDe2QP9glw5D4WImiwjt7jF7tXNR2Pwfmjnhj0tAYB/P94H729PxN+Gd2z251ddSxe3pEXjQ7Ez4aq5m1GrbSfTsa3KBpAPLjtQ7zkdPZ1w/prhvmkOKiX2vhwJrYOqxmWQiUyBvwlZmF2nMszdBGqCQDdHLP5LL8y5pxu++v0i1v12EWk5xVi59zxW7j0PWxsF+ga5oruPBn2DXNHNR4MO7k6wV/GROxHV78DZzBa9Xw8/F5y8ktvo8wJcHbH0sT7Nvv+ySX2bfY3mTHyvyrvKLvC/vhSBkR9Em+S65lQ9KQGAorIKDHhnFzp7OWPXnAgztIraAiYmFmZDbIq5m0DN4KlRY9bdXTEtohN2nbqKPaevIeZcJq7kFCM2+QZik2/gi5jKte3tlDYI9XdBNx8NunlrEOqvRRcvTb0TTImo7bkZN1qKq6N5l6of18tX//q5ER2xcu/5Bp/73sO94OVib9K5Fm1pJayzGfnIzC+Bu5Od0VMrEUG5TqBUKKBQtJ6nWmQ5mJhYsAc4idpi2auUuK+XH+7rVfl3mJieh7iULCRcycXxyzk4dy0fecXliEvJRlxKtsG5Hdwd0dlLg67ezuji7YwuXhp08nSGQxOX+CQiumnz34e26P2eHR6MVfuSG3XOf58eaPD+1bHdG5yYjO/th0f6Bzbqfo0V5O6Ii9cLb+s9zK3/ol3o4eeCwtIKPDYgENcLSvHNH6nILizT1xnexQNfPjMIqTcK8eTaWEzo7Y+JAwJRUq5DoJujGVtPrRkTEwu2+C89zd0EMpFuPhqDcdsigoS0XMSnZiPlRiES0/OQlJ6HKznFuHC9EBeuF2LXqVtjmhUKwNfFHr7tHOCjtUfAn//1drGHl0YNbxd7eGrUHBpG1Ab8ND28Sefdc4c3+rY3/W7sdZnQJ6BRicn5d8c2a26KKYaR1WfXnAh0WfCLSa71j1Fd8f6OJJNcy9RuDuVb/MvpGo/vO5NpsIfLBzuT8MHOyr642Nti1dT+6OKtgZuTHYpKK6C2teG8WWJiYkmqrorx9oM9OFnaiikUCvTw0xptWHY9vwSn0/Nw5moezmTk48zVfCRl5CG7sAxXcopxJafuCbBaB5U+UfHSqOH1539dHFRwtFOinaMK7Rzs4OJgCxcHFTRqWz6KJ2rlSssNV/jrGVD3RodV/TQ9HPf/Zz8AQGOGPUbu8HOBh7MdMvNLG1S/tnB09H9GIeytHfr3Y3v6YOvxdIM6d4V4NbmdjaFS2qB/kCv+uJjVrOu8MyEUkwcF4d5QXxy+eAM/xl9Byo1ChAW2w/8dSzNRa80jt7gcE1f+ZlS+/tlBOJ2WBwc7JSb08Yed0gYKReVSx1eyi6FQAHa2NpjzTTwOnL2OxEX3NmtI3s3fq6p+zpVX6JBVWAZPjdqg7o2CUtirbJBwJRfeLvbw1drjekEplDYKqGxsoHVU4WpuMRSoXCWvm48G5TodzlzNx74z17DtZDq+emYwnNRK7DqVgWGd3ZGcWYA7fF1QIYI3f0pAezdHTOwfCFenyqGSxWUVOHwxC54aNbp6a1ChE9j8OUTOWldK42+2JrZ8+XK89957SEtLQ48ePbB06VIMHz7cJNc+n3lrMtrD/W7vo2hqndyd1RjWWY1hVVbBERFk5pciNasQ6TnFuJJdhCvZxbiaW/mTkVeCq7nFKCnXIaeoDDlFZTiTkd+g+9naKKB1UEHroILLn/9t56jSlxmUO6igrXLMQaVkUkOtWmPjdXR0NObMmYOTJ0/Cz88PL7/8MqZNm2bydl3NLcaMDUfw4l1dEN6l/hWvjl7KbvK9egZo4WSnREFpBSK7NfwXd1+tff2VGmj7rBHot2hXvfXOvjOm1piidVTh7fGheO2HE3B1VBkkJR3cHfHogEBMHhhksjbXZ+Nzg9G5iU9N3nu4Fzp6OqNfUOXTq85ezujs5YyJA9pDpxPY2ChwX880/Gt7Isb29MGyPecAAB891hszN8abqgt6/3qoF17edMzk163JpFW/61+/svl4vfW7LdyG5MVjoVAokJlfgnuX7m1wkmsOVZPn2iyp5QlUdboS6xwuyMTEhL7++mvMmjULy5cvx7Bhw/Dpp59izJgxSEhIQPv27Zt9/YQqK6DYq7hUH1VSKBTw1KiNvt2pSkSQW1SOjLxiXM0tMfhvRm4JsotKkVtUjrziMuQWlyO/pByl5TqU6wTXC0pxvaDxgV6lvJXUaOxVcFbbwkmthJPaFg4qJexVStirbKq8rvzp6a81Wo6UyNQaG6+Tk5MxduxYPPvss1i3bh0OHDiAv//97/D09MRDDz1k0rYNevdXAMATq39v0KTqg2evN+t+0S9H4nRaHoZ1bvi+Iq+O7Y7ich0e6RfQrHsDlV+4nHxzNHafzkBucRkWfH9Cf2zqkCBsPZ6GHbMjYKus+3NvyuAgjA31gYuDCn//Kk6/hO/G54bAx4SJVEPYKm1w9PVRGPjOLpRUe6J1MxGsyaEFd9cZy28OdRrT0xdjelYuAPBCZGfYKW1gq7TB0E4eePTTGDzcLwDvbU9sVh++eHoghnVyh63SBr+cSMOexGvNut7tEvzKVnM3gUxIIdb6LMgMBg0ahL59+2LFihX6su7du2P8+PFYvHhxvefn5uZCq9UiJycHLi4uRsc3x13CnG+Owt3JDodfu8ekbSeqrqi0AtlFpcgpKkN2YZn+aUtu0a3XN3+yCw3Ly3VNDytqWxv8MnM4Ono611+ZalVfPGnrGhuv582bhy1btuDUqVP6smnTpuHo0aOIiYlp0D0b+ndSdVw+AChtFDj37lijepezi+CgUmL4P3frf9F9fGB7i55/WFquQ9eFlU8amjOfpKxCh+6vbcOIrp5Y89QAUzaxSXIKy1BYVg5frQOAyi+LFAoFSst1UCkVt+Xp8uGLN7Al/gpGh/qgk6czvF3sceBsJkSAH+MvY2CwG0Z09YSjnRInr+Ri9+kMfZJTfTGVkvIKnM3Ix9bjafonNGReupJCpC591OpiPBMTEyktLYWjoyO+/fZbTJgwQV8+c+ZMxMfHIzraeF3zkpISlJTc2q03NzcXgYGBeGZlFJT2TqjQAToRVOgqf/b/uU79w/0C8P4jYbe/U0RNICIoLK0wSFryS8qRX1KG/JIKFJSUo7isAsVluj//W6F/n3g1D8mZBejo6YRQv4aPkydjpYX5+PRvI6zuQ8sUmhKvR4wYgT59+uCjjz7Sl33//fd49NFHUVhYCJXKeBnv2mJ81b+TX09dxY/xV1BarkNphQ5FpRWIOW/8BOQOXxcEuTuipFyHtJxinEoz3kNk7V8H4M6unhxCSS1GpxPsSczAwXPXcSW7CP+Z1BdKGwXKKnTILSqDq6Mdtp1MR+/AdmjnqMKxSzno294Vn0Sfw5BO7ugf5Ip/7z6LQxduICygHR4bGIijqTl46+eTiOzmhScGB2F51FlsPZ6On2eEo7uvC2KTb+DXU1fx2X7DRRPauzmiq7cGzmol/No5YEinyjkcZzPycSmrCL0D28HVyQ438ivnirRzrHyaf1eIF/JLynEhswAhvi6wtVHg3LV8eGrU8NLYo6xCh+jEawjv4lHnAjJlFTroRGCntEFhaUWN84BvDsUDKuePZBeWQWNvCye1LbILS5FdWAZXJzs42SmNnhCWlFdgx8mrePvnBGx8bjA81Dqr/PKJQ7lMJDMzExUVFfD29jYo9/b2Rnp6eo3nLF68GG+++aZR+Y6EDNioa19K774q67cTtTYKhQJO6spA69fOoVHnpt4oxOile3H+WkGNG3xRw1nr+GNTaEq8Tk9Pr7F+eXk5MjMz4etrHJdri/FVnb9WgC1Hr9Tb5oS0XCTUkIxU1S/IlUkJtSgbGwVGdvfGyO6G/zZUShu4O1cOSRvb89a/jcEdK4cLvjiyi76s6mugchPOqvvULJ/cz+D4kE7uGNLJHQvvu6Pe9g3v4tmgftirlPBwvjWErurCMyqlDe6+w7um0wyoqiQStS1OVPUJoL1KCR/trUSnnaMd2tWxP5DaVon7w/xw/59bReTmNn6DU0vAxMTEatpsqLYPildeeQVz5szRv7/5bdqrY0PgpKnM2pUKBWxsFLC1USCvpByhfi7o08LLORK1lEA3R3z93BDEXrhh7qZYvKKCPMxYau5WtG6Nide11a+p/KbaYnxVgzu6Y+G47lDb2sDuz5+Tl3MR5O6I+NQcPNTPH+k5xcgpKoOtjQJqWyVslQpcyS7CxAHtkV9Sjtlfx2P2PV3hYs/NV4nIsjExMREPDw8olUqjb9syMjKMvmW7Sa1WQ602nuQ2aVCQVT2WI2qMngHaRi13SjXLzc3FDHM3opVqSrz28fGpsb6trS3c3WueNF5bjK+qpv/fJ/y51caUIXWeCgDw1KjxwwvD6q9IRGQBuLSTidjZ2aFfv37YuXOnQfnOnTsxdGjL7qRLRES1a0q8HjJkiFH9HTt2oH///jXOLyEiosZjYmJCc+bMwWeffYY1a9bg1KlTmD17NlJSUm7LOvdERNR09cXrV155BVOnTtXXnzZtGi5evIg5c+bg1KlTWLNmDVavXo1//OMf5uoCEZHV4VAuE5o4cSKuX7+Ot956C2lpaQgNDcXWrVsRFNRymzoREVH96ovXaWlpSElJ0dcPDg7G1q1bMXv2bCxbtgx+fn74+OOPTb6HCRFRW8blglsR7jtARKbCeNL68O+EiEzFWuMJh3IREREREZHZMTEhIiIiIiKzY2JCRERERERmx8SEiIiIiIjMjokJERERERGZHRMTIiIiIiIyOyYmRERERERkdkxMiIiIiIjI7JiYEBERERGR2TExISIiIiIis2NiQkREREREZsfEhIiIiIiIzI6JCRERERERmR0TEyIiIiIiMjsmJkREREREZHa25m4A3SIiAIDc3Fwzt4SILN3NOHIzrpD5McYTkalYa4xnYtKKXL9+HQAQGBho5pYQkbW4fv06tFqtuZtBYIwnItOzthjPxKQVcXNzAwCkpKRY1f9kQGVmHxgYiNTUVLi4uJi7OSZnzf1j3yxTTk4O2rdvr48rZH6M8ZbLmvvHvlkma43xTExaERubyik/Wq3W6v4B3eTi4mK1fQOsu3/sm2W6GVfI/BjjLZ819499s0zWFuOtqzdERERERGSRmJgQEREREZHZMTFpRdRqNV5//XWo1WpzN8XkrLlvgHX3j32zTNbcN0tlzX8n1tw3wLr7x75ZJmvtm0KsbZ0xIiIiIiKyOHxiQkREREREZsfEhIiIiIiIzI6JCRERERERmR0Tk1Zk+fLlCA4Ohr29Pfr164d9+/aZrS2LFy/GgAEDoNFo4OXlhfHjxyMxMdGgjojgjTfegJ+fHxwcHHDnnXfi5MmTBnVKSkowY8YMeHh4wMnJCQ888AAuXbpkUCcrKwtTpkyBVquFVqvFlClTkJ2dbVAnJSUF999/P5ycnODh4YEXX3wRpaWlJuurQqHArFmzrKZvly9fxhNPPAF3d3c4Ojqid+/eOHz4sMX3r7y8HAsXLkRwcDAcHBzQsWNHvPXWW9DpdBbXt7179+L++++Hn58fFAoFfvjhB4Pjra0fx48fR0REBBwcHODv74+33noLnKLYOIzxjPGM8XVjjGeMh1CrsHHjRlGpVLJq1SpJSEiQmTNnipOTk1y8eNEs7Rk9erSsXbtWTpw4IfHx8TJu3Dhp37695Ofn6+ssWbJENBqNbNq0SY4fPy4TJ04UX19fyc3N1deZNm2a+Pv7y86dOyUuLk4iIyMlLCxMysvL9XXuvfdeCQ0NlYMHD8rBgwclNDRU7rvvPv3x8vJyCQ0NlcjISImLi5OdO3eKn5+fTJ8+vdn9jI2NlQ4dOkivXr1k5syZVtG3GzduSFBQkDz11FPy+++/S3JysuzatUvOnj1r8f1btGiRuLu7y88//yzJycny7bffirOzsyxdutTi+rZ161ZZsGCBbNq0SQDI999/b3C8NfUjJydHvL295bHHHpPjx4/Lpk2bRKPRyPvvv9+gvhJjPGM8Y3xDMMYzxjMxaSUGDhwo06ZNMygLCQmR+fPnm6lFhjIyMgSAREdHi4iITqcTHx8fWbJkib5OcXGxaLVa+eSTT0REJDs7W1QqlWzcuFFf5/Lly2JjYyPbtm0TEZGEhAQBIL/99pu+TkxMjACQ06dPi0jlP24bGxu5fPmyvs6GDRtErVZLTk5Ok/uUl5cnXbp0kZ07d0pERIT+Q8vS+zZv3jwJDw+v9bgl92/cuHHy9NNPG5T95S9/kSeeeMKi+1b9Q6u19WP58uWi1WqluLhYX2fx4sXi5+cnOp2uUX1tqxjjGeNN1TfGeMvrG2N8w3EoVytQWlqKw4cPY9SoUQblo0aNwsGDB83UKkM5OTkAADc3NwBAcnIy0tPTDdqsVqsRERGhb/Phw4dRVlZmUMfPzw+hoaH6OjExMdBqtRg0aJC+zuDBg6HVag3qhIaGws/PT19n9OjRKCkpMXh03VgvvPACxo0bh7vvvtug3NL7tmXLFvTv3x+PPPIIvLy80KdPH6xatcoq+hceHo5ff/0VSUlJAICjR49i//79GDt2rMX3rarW1o+YmBhEREQYrJc/evRoXLlyBRcuXGhWX9sCxnjGeMb4hmGMZ4y3bdG7UY0yMzNRUVEBb29vg3Jvb2+kp6ebqVW3iAjmzJmD8PBwhIaGAoC+XTW1+eLFi/o6dnZ2cHV1Napz8/z09HR4eXkZ3dPLy8ugTvX7uLq6ws7Orsl/Phs3bkRcXBwOHTpkdMzS+3b+/HmsWLECc+bMwauvvorY2Fi8+OKLUKvVmDp1qkX3b968ecjJyUFISAiUSiUqKirwzjvv4PHHH9ffz1L7VlVr60d6ejo6dOhgdJ+bx4KDg5vSzTaDMZ4x3pR9Y4y3zL5V1dr60ZpiPBOTVkShUBi8FxGjMnOYPn06jh07hv379xsda0qbq9epqX5T6jRUamoqZs6ciR07dsDe3r7WepbYNwDQ6XTo378/3n33XQBAnz59cPLkSaxYsQJTp06t9b6W0L+vv/4a69atw/r169GjRw/Ex8dj1qxZ8PPzw5NPPlnrPS2hbzVpTf2oqS21nUs1Y4xvXp2GYoyv+b6W0D/GeMZ4DuVqBTw8PKBUKo0y8IyMDKMst6XNmDEDW7ZswZ49exAQEKAv9/HxAYA62+zj44PS0lJkZWXVWefq1atG97127ZpBner3ycrKQllZWZP+fA4fPoyMjAz069cPtra2sLW1RXR0ND7++GPY2toafEtgaX0DAF9fX9xxxx0GZd27d0dKSor+npbav7lz52L+/Pl47LHH0LNnT0yZMgWzZ8/G4sWLLb5vVbW2ftRUJyMjA4DxN35kjDGeMd5UfQMY4y21b1W1tn60phjPxKQVsLOzQ79+/bBz506D8p07d2Lo0KFmaZOIYPr06di8eTN2795t9BgvODgYPj4+Bm0uLS1FdHS0vs39+vWDSqUyqJOWloYTJ07o6wwZMgQ5OTmIjY3V1/n999+Rk5NjUOfEiRNIS0vT19mxYwfUajX69evX6L6NHDkSx48fR3x8vP6nf//+mDx5MuLj49GxY0eL7RsADBs2zGjZz6SkJAQFBQGw7L+7wsJC2NgYhi2lUqlfStKS+1ZVa+vHkCFDsHfvXoPlJXfs2AE/Pz+jx/9kjDGeMZ4xvmEY4xnjuSpXK3FzKcnVq1dLQkKCzJo1S5ycnOTChQtmac/zzz8vWq1WoqKiJC0tTf9TWFior7NkyRLRarWyefNmOX78uDz++OM1LnUXEBAgu3btkri4OLnrrrtqXOquV69eEhMTIzExMdKzZ88al7obOXKkxMXFya5duyQgIMAkS0neVHXFFkvvW2xsrNja2so777wjZ86cka+++kocHR1l3bp1Ft+/J598Uvz9/fVLSW7evFk8PDzk5Zdftri+5eXlyZEjR+TIkSMCQD788EM5cuSIfvnY1tSP7Oxs8fb2lscff1yOHz8umzdvFhcXFy4X3AiM8YzxjPH1Y4xnjGdi0oosW7ZMgoKCxM7OTvr27atfttEcANT4s3btWn0dnU4nr7/+uvj4+IharZYRI0bI8ePHDa5TVFQk06dPFzc3N3FwcJD77rtPUlJSDOpcv35dJk+eLBqNRjQajUyePFmysrIM6ly8eFHGjRsnDg4O4ubmJtOnTzdY1q65qn9oWXrffvrpJwkNDRW1Wi0hISGycuVKg+OW2r/c3FyZOXOmtG/fXuzt7aVjx46yYMECKSkpsbi+7dmzp8Z/Y08++WSr7MexY8dk+PDholarxcfHR9544w0uFdxIjPGM8YzxdWOMZ4xXiHDrXiIiIiIiMi/OMSEiIiIiIrNjYkJERERERGbHxISIiIiIiMyOiQkREREREZkdExMiIiIiIjI7JiZERERERGR2TEyIiIiIiMjsmJgQEREREZHZMTEhMqM33ngDvXv3Nncz9BQKBX744YdGn5eYmAgfHx/k5eU16rwBAwZg8+bNjb4fEZElYIxnjKfGYWJCVu+TTz6BRqNBeXm5viw/Px8qlQrDhw83qLtv3z4oFAokJSW1dDNblKk/LBcsWIAXXngBGo3G6Fi3bt1gZ2eHy5cvGx177bXXMH/+fOh0OpO1hYjaFsZ4Y4zxZKmYmJDVi4yMRH5+Pv744w992b59++Dj44NDhw6hsLBQXx4VFQU/Pz907drVHE21SJcuXcKWLVvw17/+1ejY/v37UVxcjEceeQSff/650fFx48YhJycH27dvb4GWEpE1Yoy/vRjjqSUxMSGr161bN/j5+SEqKkpfFhUVhQcffBCdOnXCwYMHDcojIyMBAOvWrUP//v2h0Wjg4+ODSZMmISMjAwCg0+kQEBCATz75xOBecXFxUCgUOH/+PAAgJycHzz33HLy8vODi4oK77roLR48erbO9a9euRffu3WFvb4+QkBAsX75cf+zChQtQKBTYvHkzIiMj4ejoiLCwMMTExBhcY9WqVQgMDISjoyMmTJiADz/8EO3atQMAfP7553jzzTdx9OhRKBQKKBQKgw+UzMxMTJgwAY6OjujSpQu2bNlSZ3u/+eYbhIWFISAgwOjY6tWrMWnSJEyZMgVr1qyBiBgcVyqVGDt2LDZs2FDnPYiIasMYzxhPVkSI2oBJkybJqFGj9O8HDBgg3377rTz//PPy6quviohISUmJODg4yGeffSYiIqtXr5atW7fKuXPnJCYmRgYPHixjxozRX+Oll16S8PBwg/u89NJLMmTIEBER0el0MmzYMLn//vvl0KFDkpSUJC+99JK4u7vL9evXRUTk9ddfl7CwMP35K1euFF9fX9m0aZOcP39eNm3aJG5ubvL555+LiEhycrIAkJCQEPn5558lMTFRHn74YQkKCpKysjIREdm/f7/Y2NjIe++9J4mJibJs2TJxc3MTrVYrIiKFhYXy0ksvSY8ePSQtLU3S0tKksLBQREQASEBAgKxfv17OnDkjL774ojg7O+vbW5MHH3xQpk2bZlSem5srTk5OcuLECSkvLxdvb2/ZvXu3Ub3ly5dLhw4dar0+EVF9GOMZ48k6MDGhNmHlypXi5OQkZWVlkpubK7a2tnL16lXZuHGjDB06VEREoqOjBYCcO3euxmvExsYKAMnLyxMRkbi4OFEoFHLhwgUREamoqBB/f39ZtmyZiIj8+uuv4uLiIsXFxQbX6dSpk3z66aciYvyhFRgYKOvXrzeo//bbb+s/CG9+aN38YBUROXnypACQU6dOiYjIxIkTZdy4cQbXmDx5sv5Dq6b73gRAFi5cqH+fn58vCoVCfvnllxr/TEREwsLC5K233jIqX7lypfTu3Vv/fubMmTJ58mSjej/++KPY2NhIRUVFrfcgIqoLYzxjPFkHDuWiNiEyMhIFBQU4dOgQ9u3bh65du8LLywsRERE4dOgQCgoKEBUVhfbt26Njx44AgCNHjuDBBx9EUFAQNBoN7rzzTgBASkoKAKBPnz4ICQnRP6KOjo5GRkYGHn30UQDA4cOHkZ+fD3d3dzg7O+t/kpOTce7cOaM2Xrt2DampqXjmmWcM6i9atMiofq9evfSvfX19AUA/BCExMREDBw40qF/9fV2qXtvJyQkajUZ/7ZoUFRXB3t7eqHz16tV44okn9O+feOIJbN68GdnZ2Qb1HBwcoNPpUFJS0uA2EhFVxRjPGE/WwdbcDSBqCZ07d0ZAQAD27NmDrKwsREREAAB8fHwQHByMAwcOYM+ePbjrrrsAAAUFBRg1ahRGjRqFdevWwdPTEykpKRg9ejRKS0v11508eTLWr1+P+fPnY/369Rg9ejQ8PDwAVI5R9vX1NRj3fNPNscBV3Vy1ZNWqVRg0aJDBMaVSafBepVLpXysUCoPzRURfdpNUG/dbl6rXvnn9ulZU8fDwQFZWlkFZQkICfv/9dxw6dAjz5s3Tl1dUVGDDhg14/vnn9WU3btyAo6MjHBwcGtxGIqKqGOMZ48k6MDGhNiMyMhJRUVHIysrC3Llz9eURERHYvn07fvvtN/2qI6dPn0ZmZiaWLFmCwMBAADBY8eWmSZMmYeHChTh8+DC+++47rFixQn+sb9++SE9Ph62tLTp06FBv+7y9veHv74/z589j8uTJTe5nSEgIYmNjDcqqt93Ozg4VFRVNvkdVffr0QUJCgkHZ6tWrMWLECCxbtsyg/Msvv8Tq1asNPrROnDiBvn37mqQtRNR2McbfwhhPFsu8I8mIWs6aNWvEwcFBbG1tJT09XV++bt060Wg0AkBSUlJERCQjI0Ps7Oxk7ty5cu7cOfnxxx+la9euAkCOHDlicN2hQ4dKWFiYODs76ycYilROjAwPD5ewsDDZtm2bJCcny4EDB2TBggVy6NAhETEeB7xq1SpxcHCQpUuXSmJiohw7dkzWrFkjH3zwgYjcGn9ctQ1ZWVkCQPbs2SMityZGfvDBB5KUlCSffPKJuLu7S7t27fTnfPXVV+Lk5CRHjhyRa9eu6cdIA5Dvv//eoH9arVbWrl1b65/rli1bxMvLS8rLy0VEpLS0VDw9PWXFihVGdZOSkgSAxMfH68siIiJqHL9MRNQYjPGM8WT5mJhQm1F1tZOqUlNTBYB06tTJoHz9+vXSoUMHUavVMmTIENmyZUuNH1rLli0TADJ16lSje+bm5sqMGTPEz89PVCqVBAYGyuTJk/UfjjVNUPzqq6+kd+/eYmdnJ66urjJixAjZvHmzQR/q+tASqZyU6O/vLw4ODjJ+/HhZtGiR+Pj46I8XFxfLQw89JO3atRMA+g+lpnxolZeXi7+/v2zbtk1ERL777juxsbEx+MWgqp49e8qMGTNEROTSpUuiUqkkNTW11usTETUEYzxjPFk+hUgjBiYSkUV69tlncfr0aezbt++2XH/58uX48ccfG72J1ty5c5GTk4OVK1felnYREbUFjPFkLTjHhMgKvf/++7jnnnvg5OSEX375BV988YXBJl6m9txzzyErKwt5eXnQaDQNPs/Lywv/+Mc/blu7iIisEWM8WSs+MSGyQo8++iiioqKQl5eHjh07YsaMGZg2bZq5m0VERCbAGE/WiokJERERERGZHTdYJCIiIiIis2NiQkREREREZsfEhIiIiIiIzI6JCRERERERmR0TEyIiIiIiMjsmJkREREREZHZMTIiIiIiIyOyYmBARERERkdkxMSEiIiIiIrP7/4HB83gcgQtfAAAAAElFTkSuQmCC", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize = (8,6))\n", - "\n", - "plt.subplot(1, 2, 1)\n", - "plt.plot(b_w, b_f)\n", - "plt.title('BTSettl Flux vs. Wavelength for 3000K star')\n", - "plt.xlabel('Wavelength (A)')\n", - "plt.ylabel('Flux (erg/cm$^2$/A)')\n", - "plt.xlim(0, 1e5)\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.plot(m_w, m_f)\n", - "plt.title('Meisner Flux vs. Wavelength for 1200K star')\n", - "plt.xlabel('Wavelength (A)')\n", - "plt.ylabel('Flux (erg/cm$^2$/A)')\n", - "plt.xlim(0, 1e5)\n", - "\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "f48f74a1-3b51-4cc1-98ef-fdbf0e4f175c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Where did Phillips peak for 1200K star?\n", - "plt.figure(figsize = (8,6))\n", - "\n", - "plt.subplot(1, 2, 1)\n", - "plt.plot(p_f, p_w)\n", - "plt.title('Phillips Flux vs. Wavelength for 1200K star')\n", - "plt.xlabel('Wavelength (A)')\n", - "plt.ylabel('Flux (erg/cm$^2$/A)')\n", - "plt.xlim(0, 1e5)\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.plot(m_w, m_f)\n", - "plt.title('Meisner Flux vs. Wavelength for 1200K star')\n", - "plt.xlabel('Wavelength (A)')\n", - "plt.ylabel('Flux (erg/cm$^2$/A)')\n", - "plt.xlim(0, 1e5)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d2aaf035-d3b8-4667-862c-6c430d78df63", - "metadata": {}, - "source": [ - "It becomes clear that the Meisner model peaks at fluxes much, much smaller than expected, and much, much smaller than those given by the Phillips model for a star under the same conditions (Teff = 1200K, metallicity = 0, log g = 4.5) and unit scalings. This implies that there might be some conversion factor that we are missing, due to conditions and the fact that both papers use the ATMO model for their model generation." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "5419c340-4abc-44a9-9ec3-c77bb35d1b2f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(m_w, m_f)\n", - "plt.title('Meisner Flux vs. Wavelength for 1200K star')\n", - "plt.xlabel('Wavelength (A)')\n", - "plt.ylabel('Flux (erg/cm$^2$/A)')\n", - "plt.xlim(0, 1e5)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "51fcf941-7b85-4c8a-851d-8d73f315e365", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plotting new IMF\n", - "\n", - "mass_range = np.linspace(0.01, 120, 10000)\n", - "\n", - "def power_law(masses, index):\n", - " return masses**index\n", - "\n", - "plt.figure()\n", - "\n", - "# Define each mass range and plot with the corresponding index\n", - "mass_1 = mass_range[(mass_range >= 0.01) & (mass_range < 0.05)]\n", - "plt.plot(mass_1, power_law(mass_1, -0.6), label='Index = -0.6')\n", - "\n", - "mass_2 = mass_range[(mass_range >= 0.05) & (mass_range < 0.22)]\n", - "plt.plot(mass_2, power_law(mass_2, -0.25), label='Index = -0.25')\n", - "\n", - "mass_3 = mass_range[(mass_range >= 0.22) & (mass_range < 0.55)]\n", - "plt.plot(mass_3, power_law(mass_3, -1.3), label='Index = -1.3')\n", - "\n", - "mass_4 = mass_range[(mass_range >= 0.55) & (mass_range < 8)]\n", - "plt.plot(mass_4, power_law(mass_4, -2.3), label='Index = -2.3')\n", - "\n", - "mass_5 = mass_range[(mass_range >= 8) & (mass_range <= 120)]\n", - "plt.plot(mass_5, power_law(mass_5, -2.35), label='Index = -2.35')\n", - "\n", - "# Add labels and legend\n", - "plt.xlabel('Mass (M_sun)')\n", - "plt.ylabel('Relative Number Density of Stars')\n", - "plt.legend()\n", - "plt.yscale('log')\n", - "plt.xscale('log')\n", - "plt.title('CombinedWeidnerKroupaKirkpatrick_2024 IMF Function')\n", - "plt.grid()\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "60c949a3-f40e-402f-8923-3e914d58cb3b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs, WDs, and BDs\n", - "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", - "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", - "k_bh = vstack([kc_out[p2_bh], kc_comp[c_bh]])\n", - "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", - "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", - "k_ns = vstack([kc_out[p2_ns], kc_comp[c_ns]])\n", - "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", - "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", - "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", - "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", - "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", - "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", - "\n", - "# Create subplots\n", - "plt.figure(figsize=(14,6))\n", - "\n", - "# Plot BHs\n", - "plt.subplot(2, 2, 1)\n", - "plt.hist(k_bh['mass'], histtype='step', bins=bh_bins, label='Progenitor Black Hole Masses', color='blue')\n", - "plt.hist(k_bh['mass_current'], histtype='step', bins=bh_bins, label='Current Black Hole Masses', color='orange')\n", - "plt.title(\"Black Holes by Mass Over Time\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot WDs\n", - "plt.subplot(2, 2, 2)\n", - "plt.hist(k_wd['mass'], histtype='step', bins=wd_bins, label='Progenitor White Dwarf Masses', color='blue')\n", - "plt.hist(k_wd['mass_current'], histtype='step', bins=wd_bins, label='Current White Dwarf Masses', color='orange')\n", - "plt.title(\"White Dwarves by Mass Over Time\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot BDs\n", - "plt.subplot(2, 2, 3)\n", - "plt.hist(k_bd['mass'], histtype='step', bins=bd_bins, label='Progenitor Brown Dwarf Masses', color='blue')\n", - "plt.hist(k_bd['mass_current'], histtype='step', bins=bd_bins, label='Current Brown Dwarf Masses', color='orange')\n", - "plt.title(\"Brown Dwarves by Mass Over Time\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot NSs\n", - "plt.subplot(2, 2, 4)\n", - "plt.hist(k_ns['mass'], histtype='step', bins=ns_bins, label='Progenitor Neutron Star Masses', color='blue')\n", - "plt.hist(k_ns['mass_current'], histtype='step', bins=ns_bins, label='Current Neutron Stars Masses', color='orange')\n", - "plt.title(\"Neutron Stars by Mass Over Time\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Adjust space between subplots\n", - "plt.subplots_adjust(hspace=0.5)\n", - "\n", - "# Show the plots\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c397930b-0fa9-4592-b571-e3dfbf00b8f7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.scatter(kc_out[p2_bd]['mass'], kc_out[p2_bd]['Teff'])\n", - "plt.title('Brown Dwarf Assigned Teff vs. Mass')\n", - "plt.xlabel('Mass (M_$\\odot$)')\n", - "plt.ylabel('Effective Temperarture (K)')\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "4a03d8bd-18df-4d28-8106-19a9e14c12a4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7747" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.min(k_ns['mass_current'])\n", - "np.max(k_ns['mass_current'])\n", - "len(k_ns['mass_current'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6b514e4e-1ed0-457c-ad03-49bb64ec2236", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/changes/BD_Clusters.ipynb b/changes/BD_Clusters.ipynb deleted file mode 100644 index 637ffc12..00000000 --- a/changes/BD_Clusters.ipynb +++ /dev/null @@ -1,588 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "38ea9963-fc45-4dbc-86b8-fa882b608583", - "metadata": {}, - "source": [ - "# Simulating Stellar Clusters with Brown Dwarfs" - ] - }, - { - "cell_type": "markdown", - "id": "4a8eebf3-dd6d-4331-943a-57f7b5f8833f", - "metadata": {}, - "source": [ - "#### Importing Necessary Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "81f8a8a2-18b9-4ec5-a3a5-16a728e24163", - "metadata": {}, - "outputs": [], - "source": [ - "# Import necessary packages. \n", - "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", - "from spisea.imf import imf, multiplicity\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pylab as py\n", - "from scipy.interpolate import RegularGridInterpolator\n", - "import pdb\n", - "import matplotlib.pyplot as plt\n", - "from astropy.table import vstack\n", - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload" - ] - }, - { - "cell_type": "markdown", - "id": "7ab56664-88f3-4ff1-8afc-baea7af1ef94", - "metadata": {}, - "source": [ - "## Creating Isochrone Extending to Substellar Range" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7f0e899f-6cb7-48f1-acfc-1fd9c4df4135", - "metadata": {}, - "outputs": [], - "source": [ - "# Create isochrone object \n", - "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only need 1 filter\n", - "my_ifmr = ifmr.IFMR_Raithel18()\n", - "my_iso = synthetic.IsochronePhot(9, 0, 10,\n", - " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(), #our new evolution model for BDs\n", - " filters=filt_list)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8a5bda53-583b-42ec-8448-399c8b98de64", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brown dwarf (mass < 0.08 M_sun): F153M = 19.201 mag\n" - ] - } - ], - "source": [ - "# Creating a sample BD case for identification\n", - "bd_idx = np.where((my_iso.points['mass'] > 0.01) & (my_iso.points['mass'] < 0.08) )[0]\n", - "if len(bd_idx) > 0:\n", - " f153m = np.round(my_iso.points[bd_idx[0]]['m_hst_f153m'], decimals=3)\n", - " mass = my_iso.points[bd_idx[0]]['mass']\n", - " print('Brown dwarf (mass < 0.08 M_sun): F153M = {0} mag'.format(f153m))\n", - "else:\n", - " print('No brown dwarf with mass < 0.08 M_sun found.')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3cf86905-d02a-4fd1-bb58-2f2432edcb4e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Make a mass-magnitude diagram from the isochrone and plot brown dwarfs\n", - "py.figure(1, figsize=(6,6))\n", - "py.clf()\n", - "py.plot(my_iso.points['mass'], my_iso.points['m_hst_f153m'], 'r-', label='generated isochron')\n", - "py.plot(my_iso.points['mass'][bd_idx], my_iso.points['m_hst_f153m'][bd_idx], 'b*', ms=15, label='BD mass')\n", - "py.title('Generated Isochron Containing Brown Dwarf Masses')\n", - "py.xlabel('Mass')\n", - "py.ylabel('Magnitude in F153M filter')\n", - "py.gca().invert_yaxis()\n", - "py.legend()\n", - "py.show()" - ] - }, - { - "cell_type": "markdown", - "id": "8da89871-b687-44e2-a7e4-a6fc753f5222", - "metadata": {}, - "source": [ - "## Case 1: Cluster Without Companions" - ] - }, - { - "cell_type": "markdown", - "id": "db8bfa5e-1106-4cbf-969b-0816e91fc5aa", - "metadata": {}, - "source": [ - "For this stellar cluster, we will be using the MergedBaraffePisaEkstromParsec evolution model, IMFR_Raithel18 initial-final mass relation, and the Salpeter_Kirkpatrick_2024 initial mass function, all of which have been modified to describe substellar masses." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e79ee070-7f0d-4d46-b477-d22bd4fcb810", - "metadata": {}, - "outputs": [], - "source": [ - "# Create IMF object without companions \n", - "k_imf = imf.Salpeter_Kirkpatrick_2024()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "79704dca-9b9e-4572-b49f-760511e06b08", - "metadata": {}, - "outputs": [], - "source": [ - "# Make cluster\n", - "cluster_mass = 10**6\n", - "k_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", - "\n", - "# Get outputs\n", - "k_out = k_cluster.star_systems" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a192b379-25fb-43ac-a799-6ba4a98d5df9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs, WDs, and BDs\n", - "p_bh = np.where(k_out['phase'] == 103)[0]\n", - "p_ns = np.where(k_out['phase'] == 102)[0]\n", - "p_wd = np.where(k_out['phase'] == 101)[0]\n", - "p_bd = np.where(k_out['phase'] == 90)[0]\n", - "\n", - "# Determine individual histogram bins\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 10, 20)\n", - "bd_bins = np.linspace(0.01, 0.08, 8)\n", - "ns_bins = np.linspace(8, 30, 20)\n", - "\n", - "# Plot BHs\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(2, 2, 1)\n", - "plt.hist(k_out[p_bh]['mass_current'], histtype = 'step',\n", - " bins = bh_bins, label = 'Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot WDs\n", - "plt.subplot(2, 2, 2)\n", - "plt.hist(k_out[p_wd]['mass'], histtype = 'step',\n", - " bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot BDs\n", - "plt.subplot(2, 2, 3)\n", - "plt.hist(k_out[p_bd]['mass_current'], histtype = 'step',\n", - " bins = bd_bins, label = 'Generated Brown Dwarves')\n", - "plt.title(\"Generated Brown Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot NSs\n", - "plt.subplot(2, 2, 4)\n", - "plt.hist(k_out[p_ns]['mass'], histtype = 'step',\n", - " bins = ns_bins, label = 'Generated Neutron Stars')\n", - "plt.title(\"Generated Neutron Stars by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Adjust space between subplots\n", - "plt.subplots_adjust(hspace=0.5)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7c9bbd80-1243-485d-a944-9bfafc5049fd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BH max mass: 119.66334547857207\n", - "BH min mass: 15.0000788054688\n", - "\n", - "NS max mass: 119.69186137462842\n", - "NS min mass: 9.00041007150772\n", - "\n", - "WD max mass: 8.999600545649336\n", - "WD min mass: 2.3067911099815683\n", - "\n", - "BD max mass: 0.07999963770010694\n", - "BD min mass: 0.010000021076120833\n", - "\n" - ] - } - ], - "source": [ - "# Checking that objects are in the correct mass ranges\n", - "print(\"BH max mass: \" + str(np.max(k_out[p_bh]['mass'])))\n", - "print(\"BH min mass: \" + str(np.min(k_out[p_bh]['mass'])) + '\\n')\n", - "\n", - "print(\"NS max mass: \" + str(np.max(k_out[p_ns]['mass'])))\n", - "print(\"NS min mass: \" + str(np.min(k_out[p_ns]['mass'])) + '\\n')\n", - "\n", - "print(\"WD max mass: \" + str(np.max(k_out[p_wd]['mass'])))\n", - "print(\"WD min mass: \" + str(np.min(k_out[p_wd]['mass'])) + '\\n')\n", - "\n", - "print(\"BD max mass: \" + str(np.max(k_out[p_bd]['mass'])))\n", - "print(\"BD min mass: \" + str(np.min(k_out[p_bd]['mass'])) + '\\n')" - ] - }, - { - "cell_type": "markdown", - "id": "c5cf0b4c-2b80-4b0c-9adf-e3d590dd39a3", - "metadata": {}, - "source": [ - "It is worth noting [explain about big NS's after confirmation from Matt] ..... \n", - "white dwarfs capped at low end due to age of cluster" - ] - }, - { - "cell_type": "markdown", - "id": "4e0ca5d6-c984-45be-982a-4e76e6083dde", - "metadata": {}, - "source": [ - "## Case 2: Cluster With Companions" - ] - }, - { - "cell_type": "markdown", - "id": "f64318cd-f631-4a24-9abe-93c96fa17251", - "metadata": {}, - "source": [ - "For this cluster we are using the same isochrone as the one generated in Case 1, just changing our IMF to allow for systems with companions. We then record the number of progenitors in each mass bin for black holes, neutron stars, white dwarfs, and brown dwarfs. We can then view how they all evolve over time." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "104a64d0-6a52-4b26-a418-0df265902a92", - "metadata": {}, - "outputs": [], - "source": [ - "# Create IMF objects \n", - "imf_multi = multiplicity.MultiplicityUnresolved()\n", - "kc_imf = imf.Salpeter_Kirkpatrick_2024(multiplicity=imf_multi)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6042a1ae-86c2-44d3-b718-fd678185a6e7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 19241 companions out of stellar mass range\n" - ] - } - ], - "source": [ - "# Make cluster\n", - "cluster_mass = 10**6\n", - "kc_cluster = synthetic.ResolvedCluster(my_iso, kc_imf, cluster_mass, ifmr=my_ifmr)\n", - "\n", - "# Get outputs\n", - "kc_out = kc_cluster.star_systems\n", - "kc_comp = kc_cluster.companions" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f7f2111d-2cdb-4d3c-80fd-7b7025010841", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs, WDs, and BDs\n", - "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", - "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", - "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", - "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", - "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", - "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", - "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", - "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", - "\n", - "# Define bins for histograms\n", - "bh_bins = np.linspace(14, 100, 20)\n", - "wd_bins = np.linspace(0.4, 10, 20)\n", - "bd_bins = np.linspace(0.01, 0.08, 8)\n", - "ns_bins = np.linspace(0, 30, 20)\n", - "\n", - "# Create subplots\n", - "plt.figure(figsize=(14,6))\n", - "\n", - "# Plot BHs\n", - "plt.subplot(2, 2, 1)\n", - "plt.hist(kc_out[p2_bh]['mass'], histtype='step', bins=bh_bins, label='Primary Black Holes', color='blue')\n", - "plt.hist(kc_comp[c_bh]['mass'], histtype='step', bins=bh_bins, label='Companion Black Holes', color='orange')\n", - "plt.title(\"Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot WDs\n", - "plt.subplot(2, 2, 2)\n", - "plt.hist(kc_out[p2_wd]['mass'], histtype='step', bins=wd_bins, label='Primary White Dwarves', color='blue')\n", - "plt.hist(kc_comp[c_wd]['mass'], histtype='step', bins=wd_bins, label='Companion White Dwarves', color='orange')\n", - "plt.title(\"White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot BDs\n", - "plt.subplot(2, 2, 3)\n", - "plt.hist(kc_out[p2_bd]['mass'], histtype='step', bins=bd_bins, label='Primary Brown Dwarves', color='blue')\n", - "plt.hist(kc_comp[c_bd]['mass'], histtype='step', bins=bd_bins, label='Companion Brown Dwarves', color='orange')\n", - "plt.title(\"Brown Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot NSs\n", - "plt.subplot(2, 2, 4)\n", - "plt.hist(kc_out[p2_ns]['mass'], histtype='step', bins=ns_bins, label='Primary Neutron Stars', color='blue')\n", - "plt.hist(kc_comp[c_ns]['mass'], histtype='step', bins=ns_bins, label='Companion Neutron Stars', color='orange')\n", - "plt.title(\"Neutron Stars by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Adjust space between subplots\n", - "plt.subplots_adjust(hspace=0.5)\n", - "\n", - "# Show the plots\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "fedb95f5-8f7a-41e8-b586-20ce6e497126", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BH prim max mass: 119.81313623903503\n", - "BH prim min mass: 15.00991526273759\n", - "BH comp max mass: 117.87922107922613\n", - "BH comp min mass: 15.025589362143613\n", - "\n", - "NS prim max mass: 118.77258608698675\n", - "NS prim min mass: 9.000462873651996\n", - "NS comp max mass: 112.7083837457762\n", - "NS comp min mass: 9.001307704298794\n", - "\n", - "WD prim max mass: 8.999282260049943\n", - "WD prim min mass: 2.3066340801114324\n", - "WD comp max mass: 8.995879334368274\n", - "WD comp min mass: 2.306670656743985\n", - "\n", - "BD prim max mass: 0.07999996275188857\n", - "BD prim min mass: 0.010000063950493392\n", - "BD comp max mass: 0.07999976499332974\n", - "BD comp min mass: 0.010000270279172984\n", - "\n" - ] - } - ], - "source": [ - "# Checking that objects are in the correct mass ranges\n", - "print(\"BH prim max mass: \" + str(np.max(kc_out['mass'][p2_bh])))\n", - "print(\"BH prim min mass: \" + str(np.min(kc_out['mass'][p2_bh])))\n", - "print(\"BH comp max mass: \" + str(np.max(kc_comp['mass'][c_bh])))\n", - "print(\"BH comp min mass: \" + str(np.min(kc_comp['mass'][c_bh])) + '\\n')\n", - "\n", - "print(\"NS prim max mass: \" + str(np.max(kc_out[p2_ns]['mass'])))\n", - "print(\"NS prim min mass: \" + str(np.min(kc_out[p2_ns]['mass'])))\n", - "print(\"NS comp max mass: \" + str(np.max(kc_comp[c_ns]['mass'])))\n", - "print(\"NS comp min mass: \" + str(np.min(kc_comp[c_ns]['mass'])) + '\\n')\n", - "\n", - "print(\"WD prim max mass: \" + str(np.max(kc_out[p2_wd]['mass'])))\n", - "print(\"WD prim min mass: \" + str(np.min(kc_out[p2_wd]['mass'])))\n", - "print(\"WD comp max mass: \" + str(np.max(kc_comp[c_wd]['mass'])))\n", - "print(\"WD comp min mass: \" + str(np.min(kc_comp[c_wd]['mass'])) + '\\n')\n", - "\n", - "print(\"BD prim max mass: \" + str(np.max(kc_out[p2_bd]['mass'])))\n", - "print(\"BD prim min mass: \" + str(np.min(kc_out[p2_bd]['mass'])))\n", - "print(\"BD comp max mass: \" + str(np.max(kc_comp[c_bd]['mass'])))\n", - "print(\"BD comp min mass: \" + str(np.min(kc_comp[c_bd]['mass'])) + '\\n')" - ] - }, - { - "cell_type": "markdown", - "id": "3dd322a2-2d57-43f9-b9bd-5f0664dc571f", - "metadata": {}, - "source": [ - "All of our brown dwarf primary and companion progenitor objects are in the correct mass range! Now let's see how everything evolves over time!" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "698c9fd3-02f4-49c1-bef8-16e102f30f72", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs, WDs, and BDs\n", - "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", - "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", - "k_bh = vstack([kc_out[p2_bh], kc_comp[c_bh]])\n", - "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", - "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", - "k_ns = vstack([kc_out[p2_ns], kc_comp[c_ns]])\n", - "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", - "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", - "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", - "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", - "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", - "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", - "\n", - "# Create subplots\n", - "plt.figure(figsize=(14,6))\n", - "\n", - "# Plot BHs\n", - "plt.subplot(2, 2, 1)\n", - "plt.hist(k_bh['mass'], histtype='step', bins=bh_bins, label='Progenitor Black Hole Masses', color='blue')\n", - "plt.hist(k_bh['mass_current'], histtype='step', bins=bh_bins, label='Current Black Hole Masses', color='orange')\n", - "plt.title(\"Black Holes by Mass Over Time\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot WDs\n", - "plt.subplot(2, 2, 2)\n", - "plt.hist(k_wd['mass'], histtype='step', bins=wd_bins, label='Progenitor White Dwarf Masses', color='blue')\n", - "plt.hist(k_wd['mass_current'], histtype='step', bins=wd_bins, label='Current White Dwarf Masses', color='orange')\n", - "plt.title(\"White Dwarves by Mass Over Time\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot BDs\n", - "plt.subplot(2, 2, 3)\n", - "plt.hist(k_bd['mass'], histtype='step', bins=bd_bins, label='Progenitor Brown Dwarf Masses', color='blue')\n", - "plt.hist(k_bd['mass_current'], histtype='step', bins=bd_bins, label='Current Brown Dwarf Masses', color='orange')\n", - "plt.title(\"Brown Dwarves by Mass Over Time\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Plot NSs\n", - "plt.subplot(2, 2, 4)\n", - "plt.hist(k_ns['mass'], histtype='step', bins=ns_bins, label='Progenitor Neutron Star Masses', color='blue')\n", - "plt.hist(k_ns['mass_current'], histtype='step', bins=ns_bins, label='Current Neutron Stars Masses', color='orange')\n", - "plt.title(\"Neutron Stars by Mass Over Time\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "# Adjust space between subplots\n", - "plt.subplots_adjust(hspace=0.5)\n", - "\n", - "# Show the plots\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "e63c4166-df0a-4314-9fa8-f2d5e505c1be", - "metadata": {}, - "source": [ - "We expect brown dwarfs to not change over time, which is consistent with evolutionary models imposed for these objects." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/changes/BD_Evolution.ipynb b/changes/BD_Evolution.ipynb deleted file mode 100644 index ef60330e..00000000 --- a/changes/BD_Evolution.ipynb +++ /dev/null @@ -1,172 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cf875d01-eefb-4ab5-8bc1-9d7ff0b68e6e", - "metadata": {}, - "source": [ - "## Attempting to Describe Brown Dwarf Evolution Using Known Relations" - ] - }, - { - "cell_type": "markdown", - "id": "edb00592-f126-4ae1-ab2b-13f7094e01ee", - "metadata": {}, - "source": [ - "We are trying to find a relation between age/mass and effective temperature of brown dwarf stars. To do this, we are looking at several evolutionary .csv files laid out in the [SPLAT evolutionary model for brown dwarves program](https://github.com/aburgasser/splat/tree/main/resources/EvolutionaryModels). Namely, we will be considering:\n", - "* [Baraffe 2003](https://github.com/aburgasser/splat/blob/main/resources/EvolutionaryModels/baraffe2003.csv)\n", - "* " - ] - }, - { - "cell_type": "markdown", - "id": "79e30b98-f4d3-44df-aa44-2cb7a223c747", - "metadata": {}, - "source": [ - "#### Importing Necessary Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "910e4a29-8083-4dca-b1ac-3d574999efae", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "205f6536-9dd1-444f-a194-c28610089e60", - "metadata": {}, - "source": [ - "#### Importing .csv Files for Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "620a1840-cf87-45c9-b65f-6b6b0e803192", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " age mass luminosity temperature gravity radius\n", - "0 0.001 0.0005 -5.370 628 2.645 0.176\n", - "1 0.001 0.0010 -4.715 942 2.996 0.166\n", - "2 0.001 0.0020 -4.137 1285 3.259 0.174\n", - "3 0.001 0.0030 -3.746 1553 3.372 0.187\n", - "4 0.001 0.0040 -3.482 1747 3.438 0.200\n", - ".. ... ... ... ... ... ...\n", - "230 10.000 0.0720 -4.472 1556 5.481 0.081\n", - "231 10.000 0.0750 -3.954 1997 5.415 0.089\n", - "232 10.000 0.0800 -3.602 2322 5.353 0.099\n", - "233 10.000 0.0900 -3.274 2624 5.289 0.113\n", - "234 10.000 0.1000 -3.082 2786 5.246 0.125\n", - "\n", - "[235 rows x 6 columns]\n" - ] - } - ], - "source": [ - "baraffe_2003 = pd.read_csv('/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/changes/bd_evo_csv/baraffe2003.csv')\n", - "print(baraffe_2003)" - ] - }, - { - "cell_type": "markdown", - "id": "fec3c4e4-52b4-46e9-8599-16170dc4eb3b", - "metadata": {}, - "source": [ - "#### Plotting Mass vs. Temperature" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "634c1991-bc5f-4bf1-bd2b-7ba90acc7af0", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/hf/8_ckhzzx55xc7f3s404_kn_4000131/T/ipykernel_33945/3087070154.py:12: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n", - " age_colors = plt.cm.get_cmap('viridis', len(b_ages)) # Adjust colormap range\n" - ] - }, - { - "data": { - "image/png": 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Filter .csv files to only include BD masses\n", - "filtered_baraffe_2003 = baraffe_2003[(baraffe_2003['mass'] >= 0.01) & (baraffe_2003['mass'] <= 0.08)]\n", - "\n", - "# Define variables\n", - "b_masses = filtered_baraffe_2003['mass']\n", - "b_teffs = filtered_baraffe_2003['temperature']\n", - "\n", - "# Look at age span\n", - "b_ages = np.unique(filtered_baraffe_2003['age'])\n", - "\n", - "# Define age-based color map\n", - "age_colors = plt.cm.get_cmap('viridis', len(b_ages)) # Adjust colormap range\n", - "\n", - "# Plot each age group separately\n", - "for i, age in enumerate(b_ages):\n", - " age_data = filtered_baraffe_2003[filtered_baraffe_2003['age'] == age]\n", - " plt.scatter(age_data['mass'], age_data['temperature'], label=f'Age {age:.2f} Myr',\n", - " c=[age_colors(i / (len(b_ages)-1))], alpha=0.7) # Normalize 'i' for colormap range\n", - "\n", - "# Add colorbar and legend\n", - "plt.colorbar(label='Age (Myr)')\n", - "plt.xlabel('Mass')\n", - "plt.ylabel('Temperature')\n", - "plt.title('Mass vs Temperature Colored by Age')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90980d47-aedc-484d-94b4-49f14ccc8b66", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/changes/BD_IMF.ipynb b/changes/BD_IMF.ipynb deleted file mode 100644 index 895870be..00000000 --- a/changes/BD_IMF.ipynb +++ /dev/null @@ -1,960 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "11fc535c-f15b-4386-a98d-5d8f4c1c1ebf", - "metadata": {}, - "source": [ - "## Plotting Studied Power-Law Functions for Brown Dwarf Stars (BDs)" - ] - }, - { - "cell_type": "markdown", - "id": "b35147ec-8425-4834-8d36-2d3802823277", - "metadata": {}, - "source": [ - "#### Importing Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "de897cdf-e478-4d49-b16a-ea7a1e0d547d", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/caitlinbegbie/opt/anaconda3/lib/python3.9/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.1' currently installed).\n", - " from pandas.core.computation.check import NUMEXPR_INSTALLED\n", - "/Users/caitlinbegbie/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.4' currently installed).\n", - " from pandas.core import (\n", - "/Users/caitlinbegbie/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.4\n", - " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" - ] - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from scipy.optimize import curve_fit\n", - "import math\n", - "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score" - ] - }, - { - "cell_type": "markdown", - "id": "24c6f353-fbf2-412d-864d-6df254492717", - "metadata": {}, - "source": [ - "#### Defining a general power law function and mass range" - ] - }, - { - "cell_type": "markdown", - "id": "dc79ec2b-79ad-4e17-a010-701923320bf4", - "metadata": {}, - "source": [ - "All of the papers that we consider here are in agreement that the initial mass function (IMF) for brown dwarfs is best represented by a power law function, much like full-fledged stars and planets. The general power law equation is as follows:\n", - "\n", - "$ f(\\alpha) = M^{-\\alpha} $,\n", - "\n", - "Where M represents the mass of an object and $\\alpha$ represents the index of the function. \n", - "\n", - "We know that we can find a general power law to represent brown dwarfs in most star forming regions because, in a [2021 paper by M. J. Huston and K. L. Luhman](https://arxiv.org/pdf/2101.11497), a conclusion is made that there are not significant variations in the IMF of low mass stars and brown dwarfs based on star-forming conditions." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "2f67bd3c-cc80-4294-88e5-f257100893f6", - "metadata": {}, - "outputs": [], - "source": [ - "def power_law(M, a):\n", - " return M ** (-1 * a)\n", - "# M represents a mass value, and a is the associated alpha value of a specified paper\n", - "\n", - "# By NASA JPL, BDs have a mass range of 13-80 Jupiter masses (0.0124 - 0.0764 solar masses)\n", - "BD_masses = np.linspace(0.0124, 0.0764, 1000)" - ] - }, - { - "cell_type": "markdown", - "id": "1a4b6339-52d4-41a8-b890-5993559be0de", - "metadata": {}, - "source": [ - "We chose this mass range for brown dwarf stars because 80 Jupiter masses is approximately the lower mass limit for hydrogen burning, which brown dwarfs characteristically do not do. In addition, 13 Jupiter masses is the lower mass limit for deuterium burning, which is what defines a brown dwarf star. This mass range is supported by [NASA JPL](https://www.jpl.nasa.gov/images/pia23685-what-is-a-brown-dwarf)." - ] - }, - { - "cell_type": "markdown", - "id": "cbbf169d-5f2d-4fa2-83e9-20b977e299a1", - "metadata": {}, - "source": [ - "#### Defining different $\\alpha$ values and errors based on specified papers" - ] - }, - { - "cell_type": "markdown", - "id": "f5bcb285-3f28-406d-8377-8ea9376fe53e", - "metadata": {}, - "source": [ - "To create a general power law function for brown dwarfs we wanted to consider a plethora of functions already found. To do this, we found seven papers over a span of 25 years surveying different star-forming regions. The papers considered were as follows:\n", - "\n", - "1. \"L Dwarfs and the Substellar Mass Function\" ([I. Neill Reid, et al. 1999](https://iopscience.iop.org/article/10.1086/307589/pdf))\n", - "2. \"The Substellar Mass Function: A Baysian Approach\" ([P. R. Allen, et al. 2005](https://iopscience.iop.org/article/10.1086/429548/pdf))\n", - "3. \"The low-mass content of the massive young star cluster RCW 38\" ([K. Mužić, et al. 2017](https://academic.oup.com/mnras/article/471/3/3699/4044705))\n", - "4. \"Looking Deep into the Rosette Nebula’s Heart: The (Sub)stellar Content of the Massive Young Cluster NGC 2244\" ([K. Mužić, et al. 2019](https://ui.adsabs.harvard.edu/abs/2019ApJ...881...79M/abstract))\n", - "5. \"The Field Substellar Mass Function Based on the Full-sky 20 pc Census of 525 L, T, and Y Dwarfs\" ([J. D. Kirkpatrick, et al. 2021](https://iopscience.iop.org/article/10.3847/1538-4365/abd107/pdf))\n", - "6. \"A Self-consistent Model for Brown Dwarf Populations\" ([R. E. Ryan, Jr., et al. 2022](https://iopscience.iop.org/article/10.3847/1538-4357/ac6de5/pdf))\n", - "7. \"A Volume-limited Sample of Ultracool Dwarfs. II. The Substellar Age and Mass Functions in the Solar Neighborhood\" ([W. Best, et al. 2024](https://iopscience.iop.org/article/10.3847/1538-4357/ad39ef/pdf))\n", - "\n", - "Below we list the indexes of the power laws found in the specified papers and their respective errors." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "384973ef-5396-4d20-af19-5899db1a5673", - "metadata": {}, - "outputs": [], - "source": [ - "#Neill Reid 1999\n", - "NR_a = 1.3\n", - "NR_aerr = 0.3 #made up because actual paper does not include errors or an exact numerical conclusion\n", - "\n", - "#Allen 2005\n", - "A_a = 0.3\n", - "A_aerr = 0.6 #error > actual value --> unconstrained\n", - "\n", - "#Mužić 2017 (RCW 38)\n", - "M1_a = 0.29\n", - "M1_aerr = 0.11\n", - "\n", - "#Mužić 2019 (NGC 2244)\n", - "M2_a = 1.03\n", - "M2_aerr = 0.02\n", - "\n", - "#Kirkpatrick 2021\n", - "K_a = 0.6\n", - "K_aerr = 0.1\n", - "\n", - "#Ryan 2022\n", - "R_a = 0.71\n", - "R_aerr = 0.11\n", - "\n", - "#Best 2024\n", - "B_a = 0.58\n", - "B_paerr = 0.16\n", - "B_naerr = 0.20\n", - "B_avgerr = 0.18" - ] - }, - { - "cell_type": "markdown", - "id": "38e02440-ed2d-4e91-8b1f-a8c54408da42", - "metadata": {}, - "source": [ - "#### Plotting curves together (w/o errors)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "4e520010-9702-45f2-877c-1fb5104fa7a1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plotting different IMFs based on literature\n", - "plt.plot(BD_masses, power_law(BD_masses, NR_a), color = 'r', label = \"Neill Reid 1999\")\n", - "plt.plot(BD_masses, power_law(BD_masses, A_a), color = 'orange', label = \"Allen 2005\")\n", - "plt.plot(BD_masses, power_law(BD_masses, M1_a), color = 'y', label = \"Mužić 2017\")\n", - "plt.plot(BD_masses, power_law(BD_masses, M2_a), color = 'g', label = \"Mužić 2019\")\n", - "plt.plot(BD_masses, power_law(BD_masses, K_a), color = 'c', label = \"Kirkpatrick 2021\")\n", - "plt.plot(BD_masses, power_law(BD_masses, R_a), color = 'b', label = \"Ryan 2022\")\n", - "plt.plot(BD_masses, power_law(BD_masses, B_a), color = 'm', label = \"Best 2024\")\n", - "\n", - "#organizing the graph\n", - "plt.title(\"Brown Dwarf IMFs Based on Literature\")\n", - "plt.xlabel(\"Brown Dwarf Masses (solar masses)\")\n", - "plt.ylabel(\"M^-a\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e10c5f12-486a-44c8-ab70-bee7cd9b7998", - "metadata": {}, - "source": [ - "Looking at the curves, it is clear that the Neill Reid conclusion of the $\\alpha$ value is outdated in comparison to the other papers. This is most likely due to outdated theory and information, as this paper was created in 1999 using simulations, versus actual data of brown dwarf stars. With this in mind, we will subtract it from our future considerations as the rest of the papers considered since then agree that $\\alpha$ should be less than 1. In addition, it is explained in the Mužić 2019 paper that NGC 2244 is unusually dense, which is leading to a larger index value than typical. For this reason, I omitted this paper as well from consideration. Lastly, in Allen 2005, the error for the function is larger than the actual $\\alpha$ value, hinting that the function is unconstrained and should not be considered. Thus, we omitted this paper as well.\n", - "\n", - "Below is the graph subtracting the Neill Reid, Allen, and Mužić 2019 conclusions." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "11fc4c9d-0be7-4d75-a35c-4b82fae451f0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plotting different IMFs based on literature\n", - "plt.plot(BD_masses, power_law(BD_masses, M1_a), color = 'y', label = \"Mužić 2017\")\n", - "plt.plot(BD_masses, power_law(BD_masses, K_a), color = 'c', label = \"Kirkpatrick 2021\")\n", - "plt.plot(BD_masses, power_law(BD_masses, R_a), color = 'b', label = \"Ryan 2022\")\n", - "plt.plot(BD_masses, power_law(BD_masses, B_a), color = 'm', label = \"Best 2024\")\n", - "\n", - "#organizing the graph\n", - "plt.title(\"Brown Dwarf IMFs Based on Literature\")\n", - "plt.xlabel(\"Brown Dwarf Masses (solar masses)\")\n", - "plt.ylabel(\"M^-a\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "20d30430-4eb1-4c19-9179-3138a2814c6e", - "metadata": {}, - "source": [ - "#### Accounting for error in $\\alpha$" - ] - }, - { - "cell_type": "markdown", - "id": "64da7c68-85ef-40f6-a538-331d7f799572", - "metadata": {}, - "source": [ - "At this point we wanted to explore the individual functions and their errors to make sure that they seemed reasonable. Thus, we used the specified errors to find upper and lower bounds for each function and plotted the information." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "9d984c93-387a-4586-ba16-ac579562ca24", - "metadata": {}, - "outputs": [], - "source": [ - "#Mužić 2017 (RCW 38)\n", - "M1_aup = M1_a + M1_aerr\n", - "M1_alow = M1_a - M1_aerr\n", - "M1_outup = power_law(BD_masses, M1_aup)\n", - "M1_outlow = power_law(BD_masses, M1_alow)\n", - "\n", - "#Kirkpatrick 2021\n", - "K_aup = K_a + K_aerr\n", - "K_alow = K_a - K_aerr\n", - "K_outup = power_law(BD_masses, K_aup)\n", - "K_outlow = power_law(BD_masses, K_alow)\n", - "\n", - "#Ryan 2022\n", - "R_aup = R_a + R_aerr\n", - "R_alow = R_a - R_aerr\n", - "R_outup = power_law(BD_masses, R_aup)\n", - "R_outlow = power_law(BD_masses, R_alow)\n", - "\n", - "#Best 2024\n", - "B_aup = B_a + B_paerr\n", - "B_alow = B_a - B_naerr\n", - "B_outup = power_law(BD_masses, B_aup)\n", - "B_outlow = power_law(BD_masses, B_alow)" - ] - }, - { - "cell_type": "markdown", - "id": "7ac4acb4-f29b-4735-b58d-be7cd78d9f76", - "metadata": {}, - "source": [ - "#### Graphing Individual Power-Law Functions (Accounting for Error in $\\alpha$)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "ac20c536-15c8-4fc5-885e-190d3fd888d1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Setting up subplots\n", - "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8,8))\n", - "plt.subplots_adjust(hspace=0.5, wspace=0.75)\n", - "\n", - "#Mužić 2017\n", - "ax[0,0].plot(BD_masses, power_law(BD_masses, M1_a), color='y')\n", - "ax[0,0].fill_between(BD_masses, M1_outup, M1_outlow, color='y', alpha=0.2, label=\"error in $\\\\alpha$\")\n", - "ax[0,0].set_title(\"Mužić Mass Function for BDs in RCW 38\")\n", - "ax[0,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[0,0].set_ylabel(\"$M^{-0.29 ± 0.11}$\")\n", - "ax[0,0].legend()\n", - "\n", - "#Kirkpatrick 2021\n", - "ax[0,1].plot(BD_masses, power_law(BD_masses, K_a), color='c')\n", - "ax[0,1].fill_between(BD_masses, K_outup, K_outlow, color='c', alpha=0.2, label=\"error in $\\\\alpha$\")\n", - "ax[0,1].set_title(\"Kirkpatrick Mass Function for BDs within 20 pc\")\n", - "ax[0,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[0,1].set_ylabel(\"$M^{-0.6 ± 0.1}$\")\n", - "ax[0,1].legend()\n", - "\n", - "#Ryan 2022\n", - "ax[1,0].plot(BD_masses, power_law(BD_masses, R_a), color='b')\n", - "ax[1,0].fill_between(BD_masses, R_outup, R_outlow, color='b', alpha=0.2, label=\"error in $\\\\alpha$\")\n", - "ax[1,0].set_title(\"Ryan Mass Function for BDs in Milky Way Disk\")\n", - "ax[1,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[1,0].set_ylabel(\"$M^{-0.71 ± 0.11}$\")\n", - "ax[1,0].legend()\n", - "\n", - "#Best 2024\n", - "ax[1,1].plot(BD_masses, power_law(BD_masses, B_a), color='m')\n", - "ax[1,1].fill_between(BD_masses, B_outup, B_outlow, color='m', alpha=0.2, label=\"error in $\\\\alpha$\")\n", - "ax[1,1].set_title(\"Best Mass Function for BDs within 25 pc\")\n", - "ax[1,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[1,1].set_ylabel(\"$M^{-0.58^{+ 0.16}_{-0.20~}}$\")\n", - "ax[1,1].legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cbc89037-9574-487a-9f75-e17fee2667db", - "metadata": {}, - "source": [ - "### Creating a General Power-Law Curve Fit" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c594d201-d695-414d-9d23-05d6e1f2171b", - "metadata": {}, - "outputs": [], - "source": [ - "#Combine all of the power_law output data for all 5 papers\n", - "all_power = np.concatenate([power_law(BD_masses, M1_a), power_law(BD_masses, K_a), \n", - " power_law(BD_masses, R_a), power_law(BD_masses, B_a)])\n", - "all_err = np.concatenate([np.tile(M1_aerr, 1000), np.tile(K_aerr, 1000), \n", - " np.tile(R_aerr, 1000), np.tile(B_avgerr, 1000)])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5b054e0c-c0b6-4556-b649-a959fba74da0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4000 4000\n" - ] - } - ], - "source": [ - "#make sure all entries are appended\n", - "print(len(all_power), len(all_err))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a1aaa018-16da-44e0-9473-2611ec50ab2c", - "metadata": {}, - "outputs": [], - "source": [ - "#create a mass array of the same size\n", - "all_masses = np.tile(BD_masses, 4)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "baafa6a8-0c11-4380-899a-a61834474854", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4000" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#make sure it is the right size\n", - "len(all_masses)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "56aa35b6-aa24-4695-9094-fe5857846bd5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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X7OxshISE4OjRo1o79GZmZj70GqueV10blWeffVZj35EjR8LLy0t97cqzZcsWODg44LHHHtP4/erQoQNsbW3Vv1/79u1DZmYm3n333TJ91ErXwpXH0dERt27dwj///PPQbeszNkvVAytWrECTJk1gZmYGDw8PBAcHq//oJCQkQCqVlhmZ4unpCUdHRyQkJKgf69Wrl/qX+9ixY+jYsSM6duwIZ2dnHDt2DB4eHjh//rxG1Wh0dDSuXLlSYVOEqgOpSkBAgEFes8qDBw8wb948bNq0qcy50tPT1f/v1asXioqKcOrUKfj4+ODevXvo1asXLl26pBFumjdvDmdn5wrPZ29vD0D7h1tVmJmZoWHDhmUeHzNmDL7++mv8+eefGDduHLKysrBnzx68+OKL6j+YqgCqCpYVlf1hWrVqhX79+lXyFehP9R4MDg7WeNzCwgKNGjXSeI8CQIMGDWBhYaHXOXr37q3RoXjkyJFo3LgxXnvtNZw7d07n46jCpbYP1ZLvEV06Tuv6HlYp3Rzq5OQEAEhNTYW9vb36dz4wMFBju9LXtzyqa924cWONx93c3NTnUVGNelu5ciXi4uI0+n2pmsS1Ub1fJ06cWOE26enpZc6rYmdnV6b5tDTV72npn1fp1yeRSBAUFKR1vp/o6Gikp6fD3d293OdVPztVt4DKTmPxzjvvYP/+/ejcuTOCgoLQv39/jBs3Dj169KjU8UwVw0090LlzZ/VoqYro8o2hZ8+e+P7773H9+nUcO3YMvXr1UtdkHDt2DN7e3lAqlep2c6D4D1yrVq3w1VdflXtMHx8fjfva2rMrY/To0Th58iRmzZqFtm3bqof7Dhw4UONbX8eOHWFpaYmjR4/C19cX7u7uaNKkCXr16oWVK1ciPz8fx44dwxNPPKH1fEFBQTAzM8PFixd1Kl9F172ieXBKfhsuqWvXrvD398dvv/2GcePGYefOncjNzdUYBaJ6vT///HO5NS26dp6t7QzxHrK1tUWXLl2wY8cOvUakRUZGwt3dXWtQbNq0KQDg4sWLGr8rFdH1PaxS0egmUWIwQHX49NNP8eGHH+K5557D/Pnz1f2y3njjDZ2GUKu2Wbx4cYVDxFU1ZeVp1qwZIiIicOPGjQr7v124cAEADFJbrFQq4e7ujg0bNpT7fEVf8PTVrFkzREVFYdeuXdi7dy+2bt2KlStX4qOPPsK8efMMcg5TYBp/zajS/Pz8oFQqER0djWbNmqkfv3v3LtLS0uDn56d+TPWHeN++ffjnn3/w7rvvAij+1rtq1Sp4e3vDxsYGHTp0UO8TGBiI8+fPIyQkRKcAZUipqak4cOAA5s2bh48++kj9uOobYUkWFhbo3Lkzjh07Bl9fX/Vr7dWrF/Lz87FhwwbcvXtX3VG6ItbW1nj00Udx8OBB3Lx5s0x4K031rbP0qJ/StRG6GD16NJYuXYqMjAxs3rwZ/v7+6uYLAOpv6u7u7tVW82KIn7nqPRgVFYVGjRqpHy8oKEBcXJzRXktRURGA4toYXcLNqVOnEBsbW2aYeGnDhg3DwoUL8csvvzw03OjzHtaV6nc+NjZWo7YmKipKp31V5y/5s0hOTi4zIuj333/HI488gjVr1mg8npaWplFLVtF7RPV+tbe3r9TPeOjQofj111+xfv16fPDBB2Wez8jIwI4dO9C0adMyNdelr68QAjExMeoO7hWVd//+/ejRo4fWgK16XZGRkVrnctL2u2NjY4MxY8ZgzJgxKCgowJNPPokFCxZg9uzZdW7qDGNhn5t6bvDgwQBQZiZMVU1LyREUAQEBaNCgAZYsWYLCwkJ1NWivXr0QGxuL33//HV27dtWoARg9ejRu376N77//vsy5c3Nz1X1cjEH1Dbb0N9aKZv3s1asXQkNDcejQIfWHjqurK5o1a6YeaaLLN+05c+ZACIEJEyZo9IFROXfuHH766ScAxR8WMplM3ZdJZeXKlQ89T2ljxoxBfn4+fvrpJ+zdu7fMSJ8BAwbA3t4en376KQoLC8vsn5ycrPc5H0YVCnSdobg8/fr1g4WFBZYtW6bxs1yzZg3S09N1HuWjjwcPHuDkyZPw9PSssJmhpISEBEyaNAkWFhbq4e8V6datGwYOHIgffvihzGhBoDi0zZw5E4D+72FdDBo0CAA0pnDQ9Zj9+vWDubk5li9frlGm8vaVyWRlyr1lyxbcvn1b47GK3iMdOnRAYGAgvvjii3J/jx72fh05ciSaN2+Ozz77DGfPntV4TqlU4uWXX0Zqaqp6+oGS1q9fr9G0/PvvvyMpKUl97cozevRoKBQKzJ8/v8xzRUVF6tfXv39/2NnZYeHChcjLy9PYruT1srGxKbfZsfQUABYWFmjevDmEEOX+XtdXrLmp59q0aYOJEydi9erVSEtLQ58+fXDmzBn89NNPePzxx/HII49obN+rVy9s2rQJrVq1Utc6tG/fHjY2Nrh27VqZoYgTJkzAb7/9hpdeegmHDh1Cjx49oFAocPXqVfz222/43//+99AmM22Sk5PxySeflHk8ICAA48ePR+/evfH555+jsLAQDRo0wN9//62e36e0Xr16YcGCBbh586ZGiOnduze+++47+Pv7l9vfpbTu3btjxYoVeOWVV9C0aVONGYoPHz6MP//8U11mBwcHjBo1CsuXL4dEIkFgYCB27dpVpm+FLtq3b4+goCC8//77yM/PLzMxmb29PVatWoUJEyagffv2ePrpp+Hm5oYbN25g9+7d6NGjB7755hu9z6uNqhbv/fffx9NPPw1zc3MMGzZMr4kH3dzcMHv2bMybNw8DBw7E8OHDERUVhZUrV6JTp04PrSnRxe+//w5bW1sIIZCYmIg1a9YgNTUV3377bZlv0GFhYfjll1+gVCqRlpaGf/75B1u3boVEIsHPP/+s9du9yvr169G/f388+eSTGDZsGEJCQmBjY4Po6Ghs2rQJSUlJ+OKLL2Bvb6/Xe1gXbdu2xdixY7Fy5Uqkp6eje/fuOHDgAGJiYh66r5ubG2bOnImFCxdi6NChGDx4MMLDw/HXX39p1MYAxTUnH3/8MSZPnozu3bvj4sWL2LBhg0aND1Bck+Ho6Ihvv/0WdnZ2sLGxQZcuXRAQEIAffvgBgwYNQosWLTB58mQ0aNAAt2/fxqFDh2Bvb68e7l0eCwsL/P777wgJCUHPnj01ZijeuHEjwsLCMGPGjHJnlHZ2dlbvc/fuXXz99dcICgrC1KlTKzxfnz598OKLL2LhwoWIiIhA//79YW5ujujoaGzZsgVLly7FyJEjYW9vjyVLluD5559Hp06dMG7cODg5OeH8+fPIyclRf/Hp0KEDNm/ejLfeegudOnWCra0thg0bhv79+8PT0xM9evSAh4cHrly5gm+++QZDhgwx6kCGOqeGRmlRNVAN4/znn3+0bldYWCjmzZsnAgIChLm5ufDx8RGzZ88WeXl5ZbZdsWKFACBefvlljcf79esnAIgDBw6U2aegoEAsWrRItGjRQsjlcuHk5CQ6dOgg5s2bpzFkFoB49dVXdX59qmG/5d1CQkKEEELcunVLPPHEE8LR0VE4ODiIUaNGicTExDJDXoUQIiMjQ8hkMmFnZyeKiorUj//yyy8CgJgwYYLOZRNCiHPnzolx48YJb29vYW5uLpycnERISIj46aefNIbIJicni6eeekpYW1sLJycn8eKLL4rIyMhyh4Lb2NhoPef7778vAIigoKAKtzl06JAYMGCAcHBwEJaWliIwMFBMmjRJnD17VuuxVcNkt2zZUuE25V3X+fPniwYNGgipVPrQYeHlDQVX+eabb0TTpk2Fubm58PDwEC+//HKZ4dS6TA9Q3vlK3mxsbES3bt3Eb7/9prGtaii46mZmZiacnZ1Fly5dxOzZs0VCQoLO5xWieEjzF198ITp16iRsbW2FhYWFaNy4sXjttddETEyMejtd38MVXbvSw7mFECI3N1dMnz5duLi4CBsbGzFs2DBx8+bNhw4FF0IIhUIh5s2bJ7y8vISVlZXo27eviIyMFH5+fmWGgs+YMUO9XY8ePcSpU6dEnz59RJ8+fTTKuGPHDtG8eXNhZmZW5n0fHh4unnzySeHi4iLkcrnw8/MTo0ePLvdvTXnu3bsn3nrrLREUFCTkcrlwdHQU/fr1Uw//Lkn1Hv/111/F7Nmzhbu7u7CyshJDhgwp8/MtPRRcZfXq1aJDhw7CyspK2NnZiVatWom3335bJCYmamz3559/iu7duwsrKythb28vOnfuLH799Vf181lZWWLcuHHC0dFRAFCf67vvvhO9e/dWX4/AwEAxa9YsrdMP1EcSIaq5lxkREVEdN2HCBJw6dUqnGi+qfuxzQ0REpKekpKQyTXFUezDcEBER6ejChQv4+OOPcfToUYSEhNR0cagC7FBMRESko23btmH58uV4+umnMXv27JouDlWAfW6IiIjIpLBZioiIiEwKww0RERGZlHrZ50apVCIxMRF2dnbVviQAERERVY4QApmZmfD29i53nT2VehluEhMTH7rmDxEREdVON2/e1DpjfL0MN6opqm/evKl19V4iIiKqPTIyMuDj4/PQpSbqZbhRNUXZ29sz3BAREdUxD+tSwg7FREREZFIYboiIiMikMNwQERGRSamXfW6IiKh+UigUKCwsrOliUAXMzc0hk8mqfByGGyIiMnlCCNy5cwdpaWk1XRR6CEdHR3h6elZpHjqGGyIiMnmqYOPu7g5ra2tO4FoLCSGQk5ODe/fuAQC8vLwqfSyGGyIiMmkKhUIdbFxcXGq6OKSFlZUVAODevXtwd3evdBMVOxQTEZFJU/Wxsba2ruGSkC5UP6eq9I1iuCEionqBTVF1gyF+TmyWMhCFUuBM3APcy8yDu50lOgc4QyblLxIREVF1Y7gxgL2RSZj75yXcychXP+ZpL8fc4S0wsGXlO0QRERGV1rdvX7Rt2xZff/11TRel1mKzVBXtjUzCS7+EaQQbALiTkY+XfgnD3sikGioZERHVd4cPH4ZEIql3Q+AZbqpAoRR4d9tFrdu8u+0iFEpRTSUiIiJjUSgFTsWmYEfEbZyKTeHf9lqM4aYKTsemIC1He2/utJxCnI5NqaYSERGRMeyNTELPRQcx9vvTeH1TBMZ+fxo9Fx00eu18dnY2nn32Wdja2sLLywtffvmlxvM///wzOnbsCDs7O3h6emLcuHHqeWLi4+PxyCOPAACcnJwgkUgwadKk4tezdy969uwJR0dHuLi4YOjQoYiNjTXqa6lODDdVcOr6fZ22+yU03rgFISIio9kbmYSXfwlDUnqexuN30vPwspG7H8yaNQtHjhzBjh078Pfff+Pw4cMICwtTP19YWIj58+fj/Pnz+OOPPxAfH68OMD4+Pti6dSsAICoqCklJSVi6dCmA4tD01ltv4ezZszhw4ACkUimeeOIJKJVKo72W6sQOxVWi22io/ZfvQaEUHD1FRFTHKJQC83ZeRnkNUALFnwLzdl7GY809Df43PisrC2vWrMEvv/yCkJAQAMBPP/2Ehg0bqrd57rnn1P9v1KgRli1bhk6dOiErKwu2trZwdnYGALi7u8PR0VG97VNPPaVxrh9//BFubm64fPkyWrZsadDXURNYc1MF3QJ1m+myUCmw/EC0kUtDRESGdibuQZkam5IEgKT0PJyJe2Dwc8fGxqKgoABdunRRP+bs7Izg4GD1/XPnzmHYsGHw9fWFnZ0d+vTpAwC4ceOG1mNHR0dj7NixaNSoEezt7eHv76/TfnUFw00VdG3kArmZbpfwh+PX2fmMiKiOuZdZcbCpzHaGlJ2djQEDBsDe3h4bNmzAP//8g+3btwMACgoKtO47bNgwPHjwAN9//z1CQ0MRGhqq0351BcNNFcikEjza1F2nbbPyFUZJ9kREZDzudpYG3U4fgYGBMDc3VwcPAEhNTcW1a9cAAFevXkVKSgo+++wz9OrVC02bNlV3JlaxsLAAULy+lkpKSgqioqLwwQcfICQkBM2aNUNqaqrBy1+TGG6q6Jmufjpv+/clznlDRFSXdA5whpeDZYU9LCUAvByKZ6U3NFtbW0yZMgWzZs3CwYMHERkZiUmTJkEqLf7o9vX1hYWFBZYvX47r16/jzz//xPz58zWO4efnB4lEgl27diE5ORlZWVlwcnKCi4sLVq9ejZiYGBw8eBBvvfWWwctfkxhuqqhrIxdYmut2GTeE3mDTFBFRHSKTSjBnWHMAZYeQqO7PGdbcaANGFi9ejF69emHYsGHo168fevbsiQ4dOgAA3NzcsG7dOmzZsgXNmzfHZ599hi+++EJj/wYNGmDevHl499134eHhgWnTpkEqlWLTpk04d+4cWrZsiTfffBOLFy82SvlrikQIUe8+bTMyMuDg4ID09HTY29tX+Xjz/ozE2pMJOm37RkhjvPFYkyqfk4iIdJOXl4e4uDgEBATA0rJyzUd7I5Mwb+dljc7FXg6WmDOsOZfZMTBtPy9dP785FNwA+rfw0jnc/HD8Ol4Lacxh4UREdcjAll54rLknF0iuIxhuDKBzgDNs5DJk5yseuq2qY7Guw8iJiKh2kEkl/NtdR7DPjQHIpBJM7Rmg8/bsWExERGQ8DDcG8lpIE5jrWD256Z+b7FhMRERkJAw3BiKTSvBMV1+dts0tVHIxTSIiIiNhuDGg/i107zF/IjbZiCUhIiKqvxhuDKhzgLPOc96cjTet2SCJiIhqC4YbA5JJJejbxE2nbcNvpLHfDRERkREw3BjYhG7+Om3HlcKJiIiMg+HGwPRZKXzFoRjW3hARkUlYvXo1fHx8IJVK8fXXX2Pu3Llo27ZtjZSF4cbA9FkpnLU3RERkCjIyMjBt2jS88847uH37Nl544QXMnDkTBw4cUG8zadIkPP7449VSHoYbI9BnpfAfjl9n7Q0RERmdQqGAUqk0yrFv3LiBwsJCDBkyBF5eXrC2toatrS1cXGpmRmeGGyMobprSbUI/1XIMREREJSmVSnz++ecICgqCXC6Hr68vFixYAAA4fPgwJBIJ0tLS1NtHRERAIpEgPj4eALBu3To4Ojrizz//RPPmzSGXy/HDDz/A0tJSYz8AeP311/Hoo4+q7x8/fhy9evWClZUVfHx8MH36dGRnZ5dbznXr1qFVq1YAgEaNGqnLULJZau7cufjpp5+wY8cOSCQSSCQSHD582CDXqTwMN0Ygk0rwcp9AnbfncgxERNVICCA7u2ZuQvea+tmzZ+Ozzz7Dhx9+iMuXL2Pjxo3w8PDQ66Xm5ORg0aJF+OGHH3Dp0iWMHz8ejo6O2Lp1q3obhUKBzZs3Y/z48QCA2NhYDBw4EE899RQuXLiAzZs34/jx45g2bVq55xgzZgz2798PADhz5gySkpLg4+Ojsc3MmTMxevRoDBw4EElJSUhKSkL37t31ei364MKZRvJaSBOsOBSLQh2anDaE3sAHQ1twdVkiouqQkwPY2tbMubOyABubh26WmZmJpUuX4ptvvsHEiRMBAIGBgejZs6depyssLMTKlSvRpk0b9WNPP/00Nm7ciClTpgAADhw4gLS0NDz11FMAgIULF2L8+PF44403AACNGzfGsmXL0KdPH6xatQqWlpYa57CyslI3P7m5ucHT07NMOWxtbWFlZYX8/Pxynzc01twYiT7LMRQo2LGYiIj+c+XKFeTn5yMkJKRKx7GwsEDr1q01Hhs/fjwOHz6MxMREAMCGDRswZMgQODo6AgDOnz+PdevWwdbWVn0bMGAAlEol4uLiqlSe6sKaGyPq38ILa08m6LTtt0di8VpIY9beEBEZm7V1cQ1KTZ1bB1ZWVlqfl0qL6yZEiWauwsLCco8jkWh+rnTq1AmBgYHYtGkTXn75ZWzfvh3r1q1TP5+VlYUXX3wR06dPL3M8X1/dvrTXNIYbI+oc4AwbuQzZ+YqHbptXVLyYZo/GrtVQMiKiekwi0alpqCY1btwYVlZWOHDgAJ5//vkyz7u5Fc+Gn5SUBCcnJwDFHYp1NX78eGzYsAENGzaEVCrFkCFD1M+1b98ely9fRlBQUNVeRCkWFhZQKB7+eWgIbJYyIplUgqk9A3Tefv3peOMVhoiI6gxLS0u88847ePvtt7F+/XrExsbi9OnTWLNmDQAgKCgIPj4+mDt3LqKjo7F79258+eWXOh9//PjxCAsLw4IFCzBy5EjI5XL1c++88w5OnjyJadOmISIiAtHR0dixY0eFHYp15e/vjwsXLiAqKgr3798vt6bJUBhujOy1kCaQ6djSdPRaMue8ISIiAMCHH36IGTNm4KOPPkKzZs0wZswY3Lt3DwBgbm6OX3/9FVevXkXr1q2xaNEifPLJJzofOygoCJ07d8aFCxfUo6RUWrdujSNHjuDatWvo1asX2rVrh48++gje3t5Vej1Tp05FcHAwOnbsCDc3N5w4caJKx9NGIoQe49JMREZGBhwcHJCeng57e3ujn++ln89i76W7Om3769Su6BZYM5MeERGZory8PMTFxSEgIKDMSB+qfbT9vHT9/GbNTTXQdTFNgHPeEBERVRXDTTXo2sgFlua6XeoNoTfYNEVERFQFDDfVQCaVYGwnn4dvCM55Q0REVFUMN9WkfwsvnbddcSiGtTdERESVxHBTTVRz3uiiUMnaGyIiQ6uH42fqJEP8nBhuqom+c958eySWtTdERAZgbm4OoHgRSar9VD8n1c+tMjhDcTXSZzFNzlhMRGQYMpkMjo6O6jlirK2tyyxJQDVPCIGcnBzcu3cPjo6OkMl0a+0oD8NNNZJJJXj1kUB8fSBGp+3Xn45nuCEiMgDVStSqgEO1l6OjY5VXDme4qWavhTTB8oMxUOjQ4nTgyl0olIKLaRIRVZFEIoGXlxfc3d2NOu0/VY25uXmVamxUalW4WbhwIbZt24arV6/CysoK3bt3x6JFixAcHKzeJi8vDzNmzMCmTZuQn5+PAQMGYOXKlfDw8KjBkutOJpXgseYeOs1YXKQElh+IxhuPNamGkhERmT6ZTGaQD0+q3WpVh+IjR47g1VdfxenTp7Fv3z4UFhaif//+yM7OVm/z5ptvYufOndiyZQuOHDmCxMREPPnkkzVYav3pM2Mxh4UTERHpp1avLZWcnAx3d3ccOXIEvXv3Rnp6Otzc3LBx40aMHDkSAHD16lU0a9YMp06dQteuXXU6bnWvLVWaQinQ/KO/kF+k26V/I6Qxa2+IiKjeM4m1pdLT0wEAzs7OAIBz586hsLAQ/fr1U2/TtGlT+Pr64tSpUxUeJz8/HxkZGRq3miSTSvByn0Cdt2ftDRERke5qbbhRKpV444030KNHD7Rs2RIAcOfOHVhYWMDR0VFjWw8PD9y5c6fCYy1cuBAODg7qm4+PbkshGNNrIU1grmNHYU7qR0REpLtaG25effVVREZGYtOmTVU+1uzZs5Genq6+3bx50wAlrBrVsHBdsfaGiIhIN7Uy3EybNg27du3CoUOH0LBhQ/Xjnp6eKCgoQFpamsb2d+/e1TomXi6Xw97eXuNWG7D2hoiIyPBqVbgRQmDatGnYvn07Dh48iIAAzeUKOnToAHNzcxw4cED9WFRUFG7cuIFu3bpVd3GrTN/aGy7JQERE9HC1Kty8+uqr+OWXX7Bx40bY2dnhzp07uHPnDnJzcwEADg4OmDJlCt566y0cOnQI586dw+TJk9GtWzedR0rVNvrU3qiWZCAiIqKK1apws2rVKqSnp6Nv377w8vJS3zZv3qzeZsmSJRg6dCieeuop9O7dG56enti2bVsNlrpq9K29WX863niFISIiMgG1ep4bY6npeW5KUygFmry/R6clGcykQNQng7kkAxER1TsmMc9NfaFakkEXqiUZiIiIqHwMN7UEl2QgIiIyDIabWqJrIxfIzTgsnIiIqKoYbmoJLslARERkGAw3tQgn9SMiIqo6hptaRN9h4csPRrP2hoiIqBSGm1pGn9obhQBe/zXcyCUiIiKqWxhuahl9a292XUxCQZHSiCUiIiKqWxhuaiF9am8AYPa2C0YsDRERUd3CcFML6Vt7sz38NvveEBER/YvhppZ6LaQJZDpW3igFZy0mIiJSYbippWRSCV7tq3vtzTccOUVERASA4aZWe/2xYJ1rb4o4coqIiAgAw02tJpNK8NqjQTpvv+tiEvZcSDJiiYiIiGo/hptaTt+RU29vvcDmKSIiqtcYbmo5fUdOZeUX4XRsihFLREREVLsx3NQB+tberD8db7zCEBER1XIMN3WAvrU3f1+6y6YpIiKqtxhu6ojXQprATMfKGwFg9LcnjVoeIiKi2orhpo6QSSWYpsfIqXM30rDzfKIRS0RERFQ7MdzUIfr2vXlrczibp4iIqN5huKlD9O17U6jkxH5ERFT/MNzUMfrW3nBiPyIiqm8YbuoYmVSCJaPb6LUPJ/YjIqL6hOGmDhratgHa+zrovD0n9iMiovqE4aaO2vJSD50X1QSAxX9fNV5hiIiIahGGmzpK30U1I26ms+8NERHVCww3dZg+E/sBwJscGk5ERPUAw00dpu/EfvkKwaHhRERk8hhu6jgODSciItLEcFPH6TuxHwC89VsEm6eIiMhkMdyYgNdCmkCux9CpvCIllh+INmKJiIiIag7DjQmQSSVYMqatXvssPxjN2hsiIjJJDDcmYnBrbwxp5aHz9goBLN13zYglIiIiqhkMNyZk2dgOejVPLT8Uw9obIiIyOQw3JkTf5ikBYNSqE0YrDxERUU1guDExg1t7o2uAk87bh91Mx/xdl41YIiIiourFcGOC1k/pqtf2a47Hce4bIiIyGQw3JsjCTKpX52KAc98QEZHpYLgxUcvGdtBr1XDOfUNERKaC4cZEyaQSLNVz7ptlBzj3DRER1X0MNyZsaNsGaO/roPP2SnD0FBER1X0MNyZuy0s9oMe6mhw9RUREdR7DjYmTSSWY/miQXvtw9BQREdVlDDf1gL4LawLA65vC2f+GiIjqJIabeqAyC2sWKgVe/zXcOAUiIiIyIoabemJwa29M6emn1z67LiahoEhppBIREREZB8NNPfLh0JZo76P76CkAGLL0qJFKQ0REZBwMN/XMlpd7wEyPn3p0cjZHTxERUZ3CcFPPyKQSLHu6nV77cPQUERHVJQw39dDg1t4Y3FK/tade2xjG0VNERFQnMNzUU8vH6bf2lALAY18dNlZxiIiIDIbhpp6qzNpT1+/nsP8NERHVegw39Zi+a08Bxf1vODyciIhqM4abem7LS/qNngKA3osOGqcwREREBsBwU89VZvTUncx8PLf2jJFKREREVDUMN1Sp2YsPRiWz/w0REdVKDDcEoHj24nY+9nrtw/lviIioNmK4IbXfX+6p9xtiGue/ISKiWobhhtSK+9+01WsfJYCRK08YpTxERESVwXBDGoa2bYCQpq567RN+Kx3zdl4yUomIiIj0w3BDZayZ1AUBLlZ67bP2RDzm72LAISKimsdwQ+XaP+MRSPVYngEA1hyPx4LdHEFFREQ1i+GGyiWTSrBMz+UZAOD7YxxBRURENYvhhipUmf43APDm5nCOoCIiohrDcENaVab/Tb5CYPrGMCOViIiISDuGG3qo/TMe0Xv9qd2Rd9jBmIiIagTDDT1UZdafAtjBmIiIagbDDelkcGtvTO3lr/d+7GBMRETVjeGGdPb+kBYY3NJD7/1e5RINRERUjRhuSC/Lx3WAXKbfBDgCwKOLDxqnQERERKXUunBz9OhRDBs2DN7e3pBIJPjjjz80np80aRIkEonGbeDAgTVT2HpIJpVgSSXmv0lIzcOQpUcMXyAiIqJSal24yc7ORps2bbBixYoKtxk4cCCSkpLUt19//bUaS0iV7X9zKSmLAYeIiIzOrKYLUNqgQYMwaNAgrdvI5XJ4enpWU4moPO8PaQGlKB4RpY9LSVkYuuwodk3vbZyCERFRvVfram50cfjwYbi7uyM4OBgvv/wyUlJStG6fn5+PjIwMjRtV3YdDW2ByDz+994tMzMRza88YoURERER1MNwMHDgQ69evx4EDB7Bo0SIcOXIEgwYNgkKhqHCfhQsXwsHBQX3z8fGpxhKbtjnDWuLRYP2XaDgYlYx5OznJHxERGZ5ECFFrx+hKJBJs374djz/+eIXbXL9+HYGBgdi/fz9CQkLK3SY/Px/5+fnq+xkZGfDx8UF6ejrs7e0NXex6aejSI4hMytJ7vyk9/fHh0BZGKBEREZmajIwMODg4PPTzu87V3JTWqFEjuLq6IiYmpsJt5HI57O3tNW5kWLte74MWXrZ677fmeDyXaSAiIoPSO9zk5ubi9u3bZR6/dKlmPqBu3bqFlJQUeHl51cj56T+7qxBwuEwDEREZil7h5vfff0fjxo0xZMgQtG7dGqGhoernJkyYYJACZWVlISIiAhEREQCAuLg4RERE4MaNG8jKysKsWbNw+vRpxMfH48CBAxgxYgSCgoIwYMAAg5yfqmb3633g72yp935cpoGIiAxFr3DzySef4Ny5c4iIiMDatWsxZcoUbNy4EQBgqK47Z8+eRbt27dCuXfFCjW+99RbatWuHjz76CDKZDBcuXMDw4cPRpEkTTJkyBR06dMCxY8cgl8sNcn6qugMzH61UeyeXaSAiIkPQq0NxixYtNJqfHjx4gCeeeAIhISH4448/EBYWZpRCGpquHZKo8vZcSMQrG8P13s/D1hwn33sMMql+SzwQEZHpM0qHYnd3d1y4cEF939nZGfv27cOVK1c0Hieq7CzGd7MK0eS9PdgbySYqIiKqHL3Czc8//wx3d3eNxywsLPDrr7/iyBFOq0+a3h/SAlN6+uu9nwLAS7+EMeAQEVGl6D3PTWpqKv7++2/1iClvb28MGDAATk5ORimgMbBZqnrN33VJ72UagOLkHf3pYDZRERERACM1S61ZswbdunVDaGgolEollEolQkND0b17d6xZs6bKhSbTVNllGpQAun+6j52MiYhIL3rV3AQHByMsLAw2NjYaj2dlZaF9+/a4du2awQtoDKy5qRnPrQ3Fwaj7eu8nBfDNuHYY3Nrb8IUiIqI6wyg1NxKJBJmZmWUez8zMhETCpgPS7sfJXdCyEpP8KQG8sjEcC/dwoj8iIno4M302/uKLL9CnTx+0bNkSDRo0AFA8Q/ClS5fw5ZdfGqWAdYVCKXAm7gHuZebB3c4SnQOc2VekHLte74MhS4/gUiXWofruaBxaeTtiaFvW4BARUcX07lCsUChw5swZJCYmAijuUNy5c2fIZDKjFNAYDN0stTcyCXP/vIQ7Gf8tzulkbY4Fj7dkU0oFKhtwAOCbp9sx4BAR1UNGWzgzIyMDN27cQEJCgvqWkZFRpcLWZXsjk/DSL2EawQYAUnMK2ZSiRWXXoQKAaZvCsWA3F9skIqLycbRUFSiUAu9uu6h1m++Ocs2kilQl4Hx/LB7zdkYauERERGQKOFqqCs1SJ6LvY/ya0IduZyYBohZwvpaKVKWJKqSpG9ZM6mzgEhERUW3E0VLV4NR13YY1Fwlg1KoTRi5N3bX79T5o6W1XqX0PXE3Gc2sfHjCJiKj+4GipKtE90IXdTMf8XZfx4dDmRixP3bVrem9M/jEUh67pPw/Owaj7mPxjKNY+18UIJSMiorqGo6Wq0iwVcx/jf9Cv1mDluPYY3Nqr0uc0dZUNOADQwtMGu9/oa9gCERFRraHr57fe4cYUGCrcKJQCrefuRXaBUud9zKUSXP1kEPvfaDFs2VFcTCzb/KmLho5yHH+3n4FLREREtYHRhoJXpH379oY6VJ0hk0qweGQbvfYpVAq8/mu4kUpkGnZO741Hg90qte+ttHx0nP8316MiIqrHDBZuwsLCDHWoOmVwa29M6anfopC7LiahoEj32p766MfJnTG5h3+l9r2fXYig9/ZgV8RtwxaKiIjqBIOFm/rsw6Et0d7HQa99hiw9aqTSmI45w1pgSk//Su0rAEzbFIEp6ziSioiovtG7z01qair+/vtv3L5d/K3Y29sbAwYMgJOTk1EKaAzGWBVcoRQI/mAP9KmQmdzDH3OGtTDI+U3Z/F2XsOZ4fKX3b+lli12v9zFcgYiIqEYYpc8NZyiumEwqwbKn2+m1z9oT8Viwm8szPMyHQ1tgaq+ASu8fmZSFvp8fYD8cIqJ6gjMUG6jmRuWVX85iT+Rdvfbh8HDd7LmQhFc2Vr5vlxTAN+PacTFTIqI6ijMU15Dl4zpApuelmP5rGGsVdDC4tRdiPx0MV2u95p5UUwJ4ZWM45u/imlRERKZMr5qbXbt2YcaMGRXOUDx06FCjFdSQjFlzAwC7Im5j2qYIvfbp4OuIra/0MHhZTFXPz/bjVlr+wzeswKPBrvhxMmc0JiKqS4w2iR9nKNbNkyuPI+xGul77LB/bDsPasMlEV0OWHcWlSk72BwD+zpY4MPNRTqhIRFRHGH2G4qKiIpiZmVV4vzarjnBTmdFTUgDRn3L1cH1MWfcPDly9V+n9JQCWP90WQ9s2MFyhiIjIKIw+Q3Hnzp213q9vFEqBi9v2IXzOFzgVmwIAeo+eUgII+eKgEUpnutZM6oSlY9pWen/Oh0NEZHoqHW5KV/jUwyWq1PZGJuHZN35A01GD0PKTd/HZvJ/Q8ZN9AICpvfz1Olb8gzwMXcYJ/vQxol0DrBxXteU/Dly9j6FLjxioREREVJP0Djfr16/HTz/9hNTUVKxfvx7r168HgHo7WmpvZBJe+iUMJ6y88L/G3WCuVGDZzsUoTE3HKxvDIZVIMLClh17HjEzMxJR1/xipxKZpcGsvfPtMe71HqpXE+XCIiEyD3uFGVUMjhNCoramPNTcKpcC72y4W35FI8N7Aabhl7w6/tDtY8L8VgBD47mgchrb0hpmeV/rA1XvYeT7R8IU2YQNbeuHagsFo11C/pTBKin+Qx3WpiIjquEp3KG7fvr3GYpml79dmhupQfCL6Psav0eyr0f72Ffy24R2YCSVmDn4Dv7fqBxmApU+3w7RN+q0Gzg7GlTdv5yWsPRFfpWO097HHlpd78voTEdUSRu9QzD43wKnr98s8FtagGb7q9QwAYN6+b9Eo5RYUAL7cd1Xv1cPZwbjy5gyr2pINABB2M4O1OEREdVClw82ZM2e03q8fyv9G/22Xp3DStzVsCvOw/M/PIS8qQFxKLuKSc/BosKteZ4h/kIch7OhaKe8PaY6V49pX/k2O/0ZTPbf2tKGKRURERlbpv/vm5uYAgE8++UTjfn3SLdCl3MeVUhneGDoDKVb2aHHvOubu+xYAcDAqGQFudvB3ttTrPJeSsjiCqpIGt/ZC9KdV64cDAAejUtBp/t/sbExEVAfo1efm7bff1rgvhMAPP/yAqVOnAgA+//xzw5bOSAzV50ahFGg9dy+yC8qfqa9XXBh++m0OpBB4Z+Br2NxmAADgm6fbYfqmcOgxvx8A4NFgN/w4uX7PJ1QV83ddxprjcVU+zjec9I+IqEYYpc/Nb7/9hps3b6Jly5Zo0aIFWrZsCTMzM7Ro0QItWrSocqHrGplUgsUj21T4/LGA9vii9wQAwMf7vkWrpGgAwPRN4XpP8AcU1/zM23mpcoUlfDi0uJmqqt2Dp22KwJMrjrEWh4ioltKr5iY3NxcLFixAVFQUFi5ciKCgIDRq1AjXr183ZhkNztDLL8zfFYk1xxPKfU4ilFi9bQEeiwnFLXs3DJv4NVKtHdCuoQPa+ztWuJ82U3r648Oh9S9MGopCKRDyxSHEP8it0nG4dAMRUfUy6tpSMTExmDlzJoKDg7Fp0yYkJOj/AV2TjLG21HNrQ3EwquzoKQCwy8/Gnz+9gYDUJBz1b4dJo+ZCKZVhcg9/JNzPqnA/bRhwqu65tWdwMCq5ysfhkHEiouph1KHgQUFB+OOPP9CjRw+MHz++0oU0JT9O7oIAF6tyn8uU2+ClJ95HjrkcvePD8d6hHwEAa0/EI8DNFi29bPU+35rj8Viw+3KVylzf/Ti5M6b0rNpwcYBDxomIahu9w01oaCji4oo7ZTo7O8PZ2Rk7d+40eMHqov0zHkFFX96j3Pwxc/CbAIDnz+7A0xF7ARSHlG5BbnqPoAKA74/FYc+FpEqXl/7rh2NexVoX1ZDxJ9gXh4ioxunVLPXGG2/g7NmzKCoqQr9+/XDixAkMGTIEhw8fRuPGjbFkyRJjltVgjNEspbIr4jambYqo8PnXTvyKGcc3oFAqw7Oj5+OUX2sAlR9BJQEQw1mMq0yhFFi67xqWHYoxyPE4ooqIyPCM0uemZcuWuHjxIvLz89GwYUPcvn0bcrkcCoUCbdu2xcWLFw1SeGMzZrgBgCnrQnHgagX9aITAsp2LMfzKUaRZ2uLxCV8i3rnBv51T9V+iAQD8nCxx5J2QqhWaABSHnK4L9iE5u7DKx2rsZo3dr/eBhb4LixERUbmM1uemqKgI+fn5KCwsRF5eHgBAoVBAoVBUvrQmZs2kivvfQCLBrEGvI9wrGI55WVizdT7s87IgACz+3xVM7eWv9/kSUvMw+OvDVSky/UsmleCfD/vj0aZuVT5WdHIOmnzwF+bvijRAyYiISFd6hZspU6agWbNmaNu2LRYsWIAxY8bgtddeQ7du3fDUU08Zq4x10v4Zj1S4Eni+uRwvPPkBbtu5IfDBLfyw9WPIC/ORkJqHkzH3MaWnv97nu3wnGz0/21+1QpPaj5M6Y/lY/eciKs+a4wno8unfKCjSt9GRiIgqQ++h4PfvFze3uLq6Ii0tDfv374ePjw+6dOlilAIag7GbpVT2XEjEKxsrbmYKTo7Hlg3vwD4/G3ubdMMrI96FUipDS287dGnkgjXH4/U+Z0NHOY6/268KpaaSFEqBfl8eRlxKjkGON7ilB5aP68A+UkRElWC0ZilXV1e4uhYv/njr1i3k5OTAzMys8iU1YYNbe2ttZopy88cLT76PfJkZBl47hbn7VwNCIDIxE3HJ2ZjcQ79VxAHgVlo+BnOhTYORSSU4NOsRgwwZB4A9kXc5bJyIyMj0CjchIf91Wt24cSOefvppREZG4sUXX8Q333xj8MKZgveHtNAaUk77tsabQ2dCCQmeDd+NV0/9BgD/Ti4n0XsVcQC4nJSFvp8f4JBkA/pwaHNc+2QQGrvbVPlYqmHjj315iE1VRERGoFezVLt27RAeXtzM0qVLF+zYsQOenp7IyspC9+7dceHCBaMV1JCqq1mqJG0zGAPAxHM7MW//dwCAWYOmY0vr/gCKZyIOjb2PyKQsvc8pA7DimfYY2NKrUmWm8u08n4jXftV/VFtF2FRFRKQbozRLCSGQm5uL7OxsKJVKeHp6AgBsbW0hk8mqVmIT9+PkLlpnIv6pwzCs6jISAPDZ3m8w9MpRAMWT/HUJdEWLSsxirADw0i9h2HMhsVJlpvINa+ON2E8HI8DF2iDHY1MVEZFh6RVu0tLS1KuBp6SkICmpeHbcrKwsVGKJqnpn1+t9tIaURX0mYmObAZAJJZbs+hL9okMBFAec7kFuaO5ZuSaRVzaGY1cEA44hqfriLH26rUGOx6YqIiLDqdTCmaXl5OTg7t27CAgwTKdLY6uJZqmS+n5+APEP8sp9TqpU4Is9X+PJS4eQLzPD8099hGMB7QEUz2L82d7LuJWWX6nzTu3lj/eHcLFNQ1MoBUatOomwm2kGO+bAlu5YMa4jm6qIiEow6qrgdV1NhxuFUqDxe3sqXGpBplRg+Y5FGHztJHLN5Jg4eh7O+LQEUPWAM7mHH+YMa1nJkpM2O88n4vVN4TBkP+7pjwTi9ceCGXKIiGDkVcHL0759e0MdyuTJpBJ8M67iCeIUUhleHz4LBxt1hFVRPn78fR463CpeAXzapnAMauWF5pXogwMAa08kYMq6M5Xal7Qb1sYb0QsGY3BLT4Mdc9mhWAS9twdf/e8qR78REemINTc1UHOjsmD3JXx/LL7C5+VFBVjz+zz0TDiPbHNLTBn5EU77Fi+0OaWnP07H3selSoyiAoBHg13x4+S6M/FiXVNQpESfzw8iKaNyNWzlkQJYxgU5iageM1qzVGpqKv7++2/cvl08ssPb2xsDBgyAk5NT1UpcjWpLuAGA+bsuaZ2J2LIwD6u3LUDv+HDkmsnx/FMf4oR/WwBVDzhtG9pj6ys92eRhRPN3Xcaa43EGPSYX5CSi+soozVJr1qxBt27dEBoaCqVSCaVSidDQUHTv3h1r1qypcqHrow+HttC6llSeuSWmPvWhRhNV39izAIpHUXWt5DBxAIi4lYEm7+3B3sikSu1PD6ea/K9LgOHCv2pBzjHfnuTIKiKicuhVcxMcHIywsDDY2GgOSc7KykL79u1x7do1gxfQGGpTzY3KvJ2RWHsiocLnLYoK8c2fi9A/+jTyZWaYNuJd7GvcFUBxJ+F/4h4gMjGz0udfOa4dBrf2rvT+9HDGaKoCOAkgEdUfRqm5kUgkyMws+wGamZkJiYR/WKtizrCWWpdaKDAzxysj3sXu4B6QK4qwavunGHmxeBXwtScS4G4nx6PBbpU+/ysbw/FnGCeRMyYLMylOvdfPYOtUqeyJvIvA9/bgrU3hrMkhIoKeNTe7du3CjBkz0LJlSzRoUNyp8datW7h06RK+/PJLDB061GgFNaTaWHOjMmzZUVzUUgMjUyqw6K9lGBl5AADwad/JWN3lKQBASy9bdGrkirUn4it9/pCmrlgziR2Nja2gSIkJa04jNC7V4MdmTQ4RmSqjdShWKBQ4c+YMEhOLZ7z19vZG586d69TyC7U53ADAc2vP/LtwZgWEwOzDa/HimW0AgO86P4mFfScDEglaeNmia6Cr1k7KD9PC0wa73+hb6f1Jd8ZqqgI4Rw4RmR5O4qdFbQ83ADBv56WH1sC8ELoV7x1eCwD4vWUI3hk0HQqpDP7OlnikmWeVanBcbcwR+v5j/GCsJjsibuPNzREGnQBQ5bW+gXijP0MOEdV91RJu7ty5o148sy6pC+EGeHgnYwAYeXE/PvtrGcyEEocadcC04e8gW24NKYBHmrrhwFUtNUA6+IbzqlQbhVJg6b5rWH4oBsb4xsGaHCKq66ol3LRu3RoXLlyo7O41pq6EGwCYsu7MQwNKSEwoVuxYBMuiAlxx88dzI+cgyb64c3FIUzccvJpcpQ/L9j722PIy58OpLgqlwGsbw7An8o5Rjv9kW298NrIN58khojqnWpZfqIctWtVuzaTOCGmqfRTUgaAuGDN2IZJtHNEsOR5//DwDLe/EFD93NRnPdveFn5NlpcsQdjMDjd/bgz0XuLJ4dZBJJVj5TAeDz4+jsi0iEU0++Auv/HKWSzoQkUlizU0tr7lR0WWm2wbp97Dm93loej8BOeZyvDF0Jv5u0g1A8XIL9zLzqzQXDgBM6emHD4dy4c3qVFCkxJBlRxF9L9sox2dNDhHVFWyW0qIuhhsA2BWRiGmbwrVuY5ufg292LELfuHNQQoJFfSfiu85PARIJ/J0t0cjdDger2A+nnY89fmczVbXbeT4Rb2wOh8JIU9l08XfCz893ZcgholqL4UaLuhpuAGDPhSS8sjFM6zYypQJz93+HCeF7AAB/NuuNdwZOR66FJaQAJnf3w5qT2jsqP4wUwDec1bjaqTodf3M4xigjqwAg0NUGc4e3QPcgVwZYIqpVqiXctGvXDuHh2msSaqO6HG4AYG9kEl75JQxav8ALgWfC92DOgdUwVypwxc0fU5/8ALcci0e3Te7hi53hibifU1SlsrCZqmZUR8iRApjGEVZEVItwnhst6nq4AYo/3EK+OIT4B7lat+t46xJW/bEQbtlpSLW0w2vD38bxgHYAipuXkrMKcCs1r0plCXKzxh6uUl0jqiPkAOyXQ0S1g9HDzSeffIIOHTqgffv28PDwqHRBa4IphBuVyT+G4tC1+1q38cy4j2//+BRtk65BIZHi8z7PqvvhSAE097JHZFJG1cvSwxdzhrWq8nFIf8aeI0eF/XKIqCYZPdxIpVL1Ypmenp5o3769Oux06NBBvfZUbWRK4QbQYbkGAPKiAnz89yqMubgPALAvqDNmDn4T6VZ2AIC+wS44HJVS5bK42Zrj9Huc2bimKJQCX/8dhW8Oxxo15LBfDhHVBKOHmy5duiApKQmTJ0+Gq6srwsLCcO7cOVy9ehUKhQJubm5o37499uzZU+kXYSymFm4A4Pmf/sH+K/e0byQExkf8hY8OfA+5ohC37N0wffjbCGvQDADQtqEd0nKKHtrUpQvOhluzqqu5iv1yiKg6GX0Sv9DQUHz88cf4/vvvsX//fnzwwQe4cOECMjMzcfLkScyZMwcNGzbU+7hHjx7FsGHD4O3tDYlEgj/++EPjeSEEPvroI3h5ecHKygr9+vVDdHR0ZV+GyfhhYicsH9tO+0YSCTa0G4wnJnyJ607eaJiRjM0b38ULoVshEUpE3MpEwoNctPSqeuBbdigWTd7nxH81RSaV4K0BwYheMBjTHwmCsXKHEsU/68D39uCtTeEoKDLSOHUiIj1UqeF80qRJuHbtGoKDg9G+fXvMnj0bCoUCXbp0wcsvv4zVq1frfczs7Gy0adMGK1asKPf5zz//HMuWLcO3336L0NBQ2NjYYMCAAcjLq1qnWFMwrI03Yj8d/NDZiC97NMLwiV/jz2a9Ya5U4L3Da/HD1vlwykmHABCZlIGWXrZVLo9CAK9sDOdMuDWoukIO8N/Mx49+cQjHriXzZ05ENcZgo6Wio6Px1ltv4dy5c/jss8/w7LPPVvmYEokE27dvx+OPPw6guNbG29sbM2bMwMyZMwEA6enp8PDwwLp16/D000/rdFxTbJYqbeiyow+fjVgIjD3/P8zd/x3kikLcs3HC24Nex+HAjgAAPyc5JBKpQZqppBJg2RguwlnTVM1VK47EGG0yQBUJgCc4yoqIDKha1pZSKSoqQn5+PsaOHYuGDRti8uTJePDggSEOrSEuLg537txBv3791I85ODigS5cuOHXqVIX75efnIyMjQ+Nm6nZN742Qpu7aN5JI8GvbgRjx7Fe45uIL9+xUrPt9Lj7+exUsC/OQkJqP+Ae58HexqnJ5lAKYtikCT6w4xm/0NUhVk3Ptk8H4eXJnBLpZG+1cAv/V5oR8cZi1OURUbSodbj777DOMHz8erVu3hrW1Nbp3746VK1eic+fOWL16NRwcHAxZTgDAnTvFqySXHnru4eGhfq48CxcuhIODg/rm4+Nj8LLVRmsm6dAPB8BV9wAMm7gEP3YYDgB4Nnw3dq97A62SivsyxacU19wYokUj/GYGAt/bg10Rtw1wNKosmVSCXsFuODDjEaMt0FlS7P1sTPjxDILYN4eIqkGVhoL7+/tj4sSJGDt2LJo0aWLospVpljp58iR69OiBxMREeHl5qbcbPXo0JBIJNm/eXO5x8vPzkZ+fr76fkZEBHx8fk26WKknXCf8AoGdcOL7YswSeWQ9QKJVhaY+xWNV1FBRSGQDAw84CdzMLDFKuxm7W2M3J/2qNgiIl3t16HtvDE406jFyFw8mJSF9GHwrep08fREREIDMzEzY2NmjdujXat2+vvrVs2RIymazSLwAoG26uX7+OwMBAhIeHo23bthpladu2LZYuXarTcetDn5vyTFn3Dw5cfchwcQAOuZlY8PdKDL16DABwwTMIswa/gSg3f/U2VuYS5BYa5iNwYEt3rBjXkR9wtUR19stR6RXogtUTO8HKomp/M4jItFXb8gvR0dE4d+4cwsLC1Le0tDTI5XK0atUKZ86cqfSxK+pQPHPmTMyYMQNA8Qt1d3dnh2Id7TyfiOm/hj/8m7kQePzyYczb9y0c8rNRIDXDym6jsKLbaBTKzAEALtZmSKni2lQlcW6c2kWhFDgZfR9zd0UiNjmnWs7pYCnDq480xqQeAazRI6IyanRtqbi4OJw9exbh4eH49NNP9do3KysLMTExAIoX5vzqq6/wyCOPwNnZGb6+vli0aBE+++wz/PTTTwgICMCHH36ICxcu4PLly7C01D4EWqU+hxug+ENr5MoTCL+V/tBt3bIe4JO/V2JA9GkAwBU3f7w96HVc9GoMALCXS5GZrzRYM4YEwNLRbTC8vf5zJJHxFBQpMWHNaYTGpVbbOTv5O2LD890YcohIrc4unHn48GE88sgjZR6fOHEi1q1bByEE5syZg9WrVyMtLQ09e/bEypUr9erzU9/Djcr8XZex5njcwzcUAkOuHse8/d/CNScdCokU33d6HF/3HIc88+JAaWMhRXaB4dowPOzNceztfvxgq2VU/XL+iEg06szHJTlamaNPEzeM7NCQ/XOI6rk6G26qA8PNf/ZcSMK0jWHQJZY45aRjzoHVePzyEQDATQcPzOn3Ig4GdVZvIwEM2hmVCzXWTqomq5lbI3A3wzAdzHXBuXOI6jeGGy0YbjQplAKjVp1E2M00nbYPiQnFx39/iwaZxYt1/q9xV8zr9wIS7Yvn1bE2B3IKDVtGdjquvXILFJi6/h8cj6n6wqv6cLeT4/meAeyfQ1SPMNxowXBTPp07GwOwKsjD6yd/xZR//oC5UoEcczmW9hiLNR0fR5HMDABgbS5FTqFhh9uw03HtVROjrFQ4rJyofmC40YLhpmL61uI0SY7H/L9XocutSwCAay6+mPPYSzjl19qIpQSeZNNEraVqslp68BrOJqRV+/k7+jri9X5NGHSITBDDjRYMNw+383wiXt8UrlunUSHwVORBvHdoDVxyi5e22NukGz7t+xxuOHk9ZOeqGdzSA8vHdeCHWC1Vk7U5ANDJzxHTQxh0iEwFw40WDDe6USgFXtsYhj2RFS9tUZJDbibeOv4Lxof/BTOhRL7MDGs7jsA33cYgS268NYwANlfVdiVrc84lpFXLDMilMegQ1X0MN1ow3Ohnz4UkvPZrGBQ6vlMaJyfgw4M/oHd8OAAg2doRX/Z6Br+1fgxKqfFmoJUAeI0hp9ZTTw64MxKx96tncsDS2HRFVDcx3GjBcKM/fWtxIAQeuX4WHxz8AYEPihfJvOwegIV9J+OYfztAYtwPFPbJqRsKipRYe+I6VhyKRUae4Wa71kcjV2s83cmXo66I6gCGGy0YbiqvoEiJIcuOIvpetk7bmysKMSFsD14/sREO+cX7nPBrjUV9JuGCl+EXWy2NQ8jrjtwCBZ5YeRxX72TVWBk4vJyodmO40YLhpur06nCM4gkAp536Dc+E74ZcUfwNfXdwD3zZawKuuxh/qQXW5NQdqtqcNcficC+r+iYILM3Rygwv9QnEcz0b8X1DVEsw3GjBcGMYejdVAWiQfg9vHt+AJyMPQgqBIokUv7V+DEt7jMVdO1cjlrZYMw9bbHu1J1efriNqS9CxtzTDsNZe+GBoC753iGoQw40WDDeGVVCkRJ/PDyIpI1/nfZokx2PW0fV4LKZ41fg8MwtsbDMQq7qORLKts7GKquZpZ4HFo9qyQ2kdUhPrWpXHxlyKfs09udYVUQ1guNGC4cY4dkTcxpubI/T64Ol46xLeOfwTOt2+DKD6Q44EwLS+gXijP0dY1RU1PUlgaQEu1hjbmR2SiaoDw40WDDfGo5q0bfmhGN3nMhECPeMj8ObxDeiQeBVA9YccgP1y6iKFUuB4VDIW/HUZ13Ts5G5MbL4iMi6GGy0YboyvMv1xtIWc1Z2fxB174/fJAQBvezkWPtUaPRu7sTanDlHPn7MrErHJNTN/TkmWMglaNHDAgBaerNUhMhCGGy0YbqpPQZESE9acRmhcqu47lRNyCqRm+KNFX3zX+SnEuvoYqbRlsTanblJ1RN505ibiUmo+6ACAo5U5+jRxY18doipguNGC4ab66Ts/DgBACPSKD8erp35D15uR6of/17grvu0yEuENmhqhpOXr5O+IDc93Y8ipg2pb05UK++oQ6Y/hRguGm5qz83wi3votAoW6ruXwr3a3r+Kl0N8xIPq0+rHTPi2xqusoHAlob/QZj1U4yVvdVhvWuCqPtbkU7Xwd8ULvQDaHEmnBcKMFw03NUn3AzNwagbsZ+s1dEnj/Jl48sxWPXzoMC2XxZIBX3PzxY8cR+LN5H+SbWRijyOVq5GqNecNbsomhjlK9D7ecu4Gj0feRllszyz+Ux93OAo8182DHZKJSGG60YLipPSpbk+OZcR9Tzv6BcRF7YVOYBwC4b+2AjW0G4uf2Q6pthBVQPJz8CfbNqfMKipRYczwW3x25XquCjlwK+LnaoJmXA/vrUL3HcKMFw03tUpVRLvZ5WXj6/P8w8dwuNMhMBlDc+Xh30574seMIXPRqbIwiV4i1OaahtsyMXBEu9kn1FcONFgw3tVelOh4DkCkV6H/tFJ47+6d6QkAAONugGdZ2GI6/m3RFoczc0MXVqlegC1ZP7MRmhTquNo68KsnSTILmXg4Y2JJDzsn0MdxowXBT+1VlhehWSdGYfO5PDL1yTN0vJ9nGEZtb98emNgNwy8HD0MXVitP1mw7VyKtvj8Yg4lY6cguVNV2kMhh2yJQx3GjBcFN3VGqenH+5ZT3AM+F7MO78XrhlpwEAlJDgcKMO+KXdYBxu1AFKafXXqjzRxhuLRrF/jimo7c1XACCXSeDnYo0n2zfkCudU5zHcaMFwU/cUFCnxzA+ncCY+Te99zRRFeCz6NMZH/IWeCefVj9+2c8OmNv2xuXV/3LNzMWBpdcNh5aaltjdfqTDsUF3GcKMFw03dpVoZelt4YqX2D3hwG2Mj9mLUxf1wyssEABRJpDgY1BlbWvXDoUYdUSQzM2SRdeJuZ4HnezZi0DERJYeZn4l/gDt6TnlQneQyCZxsLBDoZsN5dqjWY7jRguGm7lMoBb7+OworjsTqtQq5iryoAIOiTmB8+F8aHZCTrR3xR4u+2NKqH665+RuuwHpwtDLDS30C+a3ahNSFvjolOVubI8DVhutiUa3DcKMFw43pMMRiiY2TEzDq4n48cekQ3HLS1I+f92yMLa364c/mfZBhaWugEuuHTVem6b8mrBuIS8mt6eI8lKWZFF4Oluge6MKJBalGMdxowXBjmgqKlHj79wjsiEiq1LT6Zooi9L1+DqMu7sOjsf/AXKkAAOTLzPF3467Y2vJRHPdvVyPNVgCbrkxVySasI9fuIz2v9kwgWBELCeDuYAkPe0vW7lC1YrjRguHGtBmiNsc5Jx2PXzqMURf3oVlyvPrxFCt77G7aCzua90FYg6YQkpr5g25vaYZhrb34LdoEqWp1/hd5B1F3M5FdULubsFSszaVo6GTFmZTJqBhutGC4qT+qMsoKACAEWtyNxcjIAxh65ZhGs9Ute3f82bw3/mjet8b65wCApUyCFg0c+A3aRNXVsAMAdnIZXG3lbM4ig2G40YLhpv5RfUCsOBSLjEpW+8uUCnRPOI8Rl49gwLWTsCv4r6/EFTd//Nm8D3Y2613tkwSWxn46pk21BtbWc7dw40EuCvRcl60msTmLqorhRguGm/qtKrMfq8gL8xES+w9GXD6MvtfPQq74LzBFeDXBnuAe+Cu4B246ehqiyJVmYyFFSFMPjOrow2YCE1WXww4AWJlJ4GIrZ+AhnTDcaMFwQ4BhanOA4sU7B0WdwOOXD6PLjUhIS3RnvugRiL+Ce2BPcA/EOzcwRLGrxN3OAo8182ATgQlTva/3XkzClTuZyCuqe3/iOfcOVYThRguGGyott0CBqev/wYmYlEqNtFJxy0pF/+hTGBR1At1uXIRM/Nc/4oqbP/YE98Ce4J6IdfWpeqGriH116gdTCDsAYGshha2lOQNPPcdwowXDDVVEPdJqZyRi71dtCn3nnHQ8Fn0ag6NOoHvCefXQcgCIdvHBvsZdsD+oC8K9g2ts1FVJjlbm6NPEjSNdTFzJDsrX72chLVfx8J1qKSszwEZuDl9nGy4UWk8w3GjBcEO6MOSiiA65mXgsJhSDok6gV1y4erVyoHhW5ANBnbE/qAuO+7dBnrllVYtuEAEu1hjb2ZcfGCauLi0VoQtzCeBgzcBjqhhutGC4IX2pOm1+d+Q60nKrNsmafV4W+l4/h34xoegbexb2Bf/VEOWayXHcvy32BXXBwaBOuG/jVNWiG4SlmQTNvRz4YVEPlAw7l5MykJieh5w6NPy8POYSwN7KDM42cjT35jw8dRnDjRYMN1QVhuqIDADmikJ0unkJj8WE4rHoUDTMuKd+TgkJIryb4FCjjjjcqCMiPQNrRfMVUDxhWztfR/Z9qCdKNmXFpWQjNaf2z6KsCwdLGWzlZrCRm3HywTqC4UYLhhsylNwCBT7eFYndF5KQkVfFvgtCoGlyPPr9G3Ta3InWeDrZ2hFHA9rhSKOOOBrQDmlWtee9y4UW65fStTtJ6Xl1anLBh3G0lMHW0pzD02shhhstGG7IGKq6tlVpHpn38WjsWfS9fhY9Es7DtsSkgQqJFOe9GuNIQAccbtQBF7wa15paHQCwsZChqacdPxjqkZK1O3cycpGWU4ScWr76uT4spIC13Ay2cjO093Xi3FE1hOFGC4YbMqaS32r3X71nkP4K5opCdLx1BX2un0Xf6+fQ9H6CxvMpVvY4GtAex/3b4YRfG9yxd63yOQ2J6w7VT6banFWSlRlgbcH+PNWF4UYLhhuqTgZtuvqXV0YyeseFoe/1c+gZH66xFAQAxDo3xHH/Njjh1xanfVshw9LWIOc1JC97OToHuPDDoB4p3ZyVkl1gkoEHAGzMJXCwsoCtJfvzGBLDjRYMN1RTDDm8XMVMUYQOt6+gd1wYeiScR6s7MRqTByokUlzwbIzj/m1xwr8NwrybocDM3CDnNiQna3M0Yr+deqd04LmflV+n5955GAe5FGYyKczNZJyQsBIYbrRguKHaQBV0Np25ibiUqk0YWJJ9Xha63biAHvHn0SPhPAIf3NJ4PtdMjn8aNscJ/zYI9WmFSI9AFMnMDHZ+Q7E2l6ChkzW/9dZD9S3wAMU1PeZmMvbpeQiGGy0Ybqi2KfnH/Gj0/SrPpVOSV0YyeiScR4/4CPRIOA/37FSN57PNLXGuQTOc9m2FUJ+WuODVGIWy2lezA7B2pz4r+TtyKTEdqTkFyMpX1rmFQvVlYy6BvaU5pFIJh6yD4UYrhhuq7VSTBv50Mt6wM8YKgcb3b6BnQgS63biIzjcj4ZinuTp6rpkcYQ2CEepTHHYivIORb2ZhuDIYkIUUcLOzhKcDh+zWVyXXzkp4kF0vAo9KfaztYbjRguGG6pKS31iPXLuP9CpOHFiSRCgRnJyALjcj0eXGRXS+dQmuOeka2+TLzBDhFYxQn5Y417A5who0RabcxmBlMDQrM8DF1pJzlNRjpQNPXqEwqWHpD2NlBliZy2BpbmZyHZoZbrRguKG6zFh9dQAAQiAo5Sa6/ht2utyMLNOMpYQE11x9ca5hM5xr0AxnGzTHDUdPQFJ7/2gy8JBCKXA8KhnfHo1BbHIWihRK5BXVr9ADAPZyKcykEgiJFG62FniyfUM817NRnfl9YLjRguGGTIXRFz0UAgGpiehy4yI63bqEDrevwj8tqcxmyTaO/wadZghr0AyRHkG1ckRWSVZmErjYyhl46rnSkw/mFiiRasA+b3WFhRSwspBBJkGtrvFhuNGC4YZMVclvp5eSMgw2r05JblmpaJ94BR1uXUGH21fQ6k6MxirnAJAvM8cFz8Y416ApIryDEeEVjDt2LrW6dgcALGWAraU57CzN0T3QBR8MbQErC1lNF4uqWenRWtn5hcjMUyDLhJaY0FdtqfFhuNGC4YbqC9Uf6d/OJuBgVLJR1v+RFxWg5Z0YdLx9GR1uX0WHW5fhkptRZru7ts4479UEEf/eLno1rtV9d1RkAOysOO0+lT9iq7715ymtZI2P3EwGmUxq1NpQhhstGG6ovirZ0fLKnUzkFRnh118I+KcmouPtK2h/+yraJF1DcHI8zETZD4AY54Y4790E4V7BOO/VBFfd/WvtMPTSrMwAG7k5fJ1tMLAlm7Xqs5JfIs7dSEV2vgL5RUrj/H7VMVN6+uHDoS0NdjyGGy0YboiKlexvcPVupkHWwSqPZWEeWt6NRZvEa2ibdA1tkq7BN/1ume3yZWa47B6ICO8miPQIQqRnIGJcfKCQ1o2mIXMJYGcpg5WFOYenU5n+PBBAToECaUZoLq7NWje0x5/TehnkWAw3WjDcEJWvusIOALhkp6H1nWi0TSwOO22SrsEpL7PMdnlmFrjiFoBIz0Bc9AjCJc9AXHP1rTM1PEBxLY+TtQWkUuNW2VPdUB/79EzpGYAPhzav8nEYbrRguCHSTbWu6iwE/NKS0CbpGtomXkOLe9fR/G5smUVBAaBAaoYoNz9c9AzCJY9ARHoE4qqbP/LN5cYrnxFYygAbuRnXGSIA5TdvySQwiSHrEgBRnwyqcqBnuNGC4Yaockp/40xKzzNKJ2UViVDCPzUJLe/EoMXdWLS8G4tWd2LgkJ9dZtsiiRTRrr645BGIK+4BuOwegKtu/ki1djBa+YxFNQkbm7dIpXQTl1AKZObXrdqeD4c0w5Rejap0DIYbLRhuiAynWmt3AEAINEy/qw46Le/GouWdmHJHaAHAHVtnXHULwBX3AFxx98cVtwDEOTeolYuFPoyFFHCxseA6Q6RW3gguhbL48doWfJ7t5oePR1StczHDjRYMN0TGU921OwAAIeCZmVIcdO7GoGlyPJreiy93wkGguONytKsfrrr5F4eef/+ti7U8KvVxnSHSrrxZmS1kUqTlFdXISC7W3BgZww1R9SpdpZ6eW2T8wAPAJj8HwfcT0OxeHJomx6v/tS2nHw9QPBfPVTd/XHP1xTVXX0S7+iHaxQfZcmujl9VYTHmdIaq8kr+TSek5yC9UQiGMV+MjlQBX57PPjVEx3BDVvNKBJy2nqFo6TUqEEg3T76HZvTiN0FNRLQ8A3LJ3Q7SrL6JdfBGtDj6+yLGwMnp5jclBLoWsFsw6S7WLMWp8XuwdgNmDOVrKqBhuiGqnmgo8AGBdkIumyfFokpyAJvdvoPH9G2iScgMeWQ8q3OeWvbtGLc81V1/EuPgg18KyWspsLHVlnSGqfhXV+FQ0qksqAab2MkywARhutGK4Iao7ajLwAIB9Xhaa3C8ReP79v1t2WrnbKyHBbQd3XHdugFjnhoh1aYjrzg0R49IQyTZOtX59LV2oanyUkLCPD2koKFLi51PxSHiQAz9na0zo5m/QmkCGGy0YbojqttLfHmtiAjTH3Ax14CkOPcX/uuWkVbhPhoU1rruoQo+POgAlOHnX+lXUdaXq42Mhk6JIgIuQkkEx3GjBcENkesobEpuVr0SBonr/xDnlpCPwwS0EptxCowe3EZhyE4EPbsE37S5k5ayvBQAKiRQ3HTwQ69IQsc4Ni0OPS0PEOTfAfWtHk6jtAQBzAHZWMnUzBufxIX0x3GjBcENUfxQUKbHmeCy2nruF5Mx8yCRAVkH1hx6LokL4piUhMOVWcfhRBaCUW7AvyKlwv0wLKyQ4eSPeyRtxTt5IcPJC3L/3U6wdTCb4AMV9fZytzVGgKO7HYS6TcmFS0sBwowXDDRHlFijw8a5InIy5j6y8QuQW1tAU90LALTsNgQ9ulqjtKQ4/DdLvQYqK/0RnWFgjwckLCf8Gn3gnb8Q7eZlk8AH+W5jUQiZFgULJUV71EMONFgw3RFSe8qa4T82tmQnPgOLaHp+0O/BPS4T/g0QEpCbCLzUJAamJ8M5I1in4xP8behKcvHDTwQM3HD1x19YZyjqy0ro+So7ykpvJIJGAi5WaGIYbLRhuiEgf5Q1/renFDOVFBWiYdvffwFMcfPxTE+GfmvTQ4JMvM8MtBw/c+jfs3HDwxA1HT9xyLL6fKbepxldSvRiA6jaGGy0YbojIEMpbxbk2rOkjLyoorvFJTYJ/6m0EpCbCJ+0ufNLvoGH6PZgrFVr3T7W0ww1HT9x08MBNx+Lgo7qfaO9WJ9fl0oelDLC2+K/js9xMBpmMAag2YLjRguGGiIyp9PpaOQVFNd7EpSJVKuCVmQLftDtomH4Hvml34Zt259/7d7UOZQeKR3Yl2rvhloM7btl74LaDG27bu+OWgztu27sjyd4VhTLTGNauDQNQzWC40YLhhohqSkUzvOYUKFCDrVxq1gW58EkvDjyq2h5V+PFJvwvLogKt+yshwT1bJ9y2d8ftfwPPbQd33LJ3wy0HD9y2d6/zMzjrqrwAxGawqjHZcDN37lzMmzdP47Hg4GBcvXpV52Mw3BBRbVR6BJeFTIbcQgXS8rQ3I1UXiVDCNTsNfmlJaJB+Dw3T76FBhurfZDTIuPfQ8AMAD6zscdve7b/wY++O2w5uuGXvjkR7N6Ra2ZvcSC9tSocgC5kUhUoBczMZAt1s8ELvQPRs7MbZn6H753edbDht0aIF9u/fr75vZlYnXwYRkQYrCxkWPtmmzOOlm7my8wtrpMZHSKRItnVGsq0zzjZsUc4GAi456WiQcQ8NNILPfwHIPj8bzrkZcM7NQKu7seWeJ8/MAkl2Lkiycyv+194Nd+xckWjniiR7VyTZuSLN0s5kAlCeAsjLLRlgVf8vwp2MfJyILV7fzMZcAjOZVB2AOB9QxepkKjAzM4Onp2dNF4OIqFrIpBL0CnZDr2C3cp8vr8YHqIE+PhIJUmwckWLjiAteTcrdxC4/Wx18GqhqfNLvoeG/991y0mBZVICA1CQEpFa8Unuumfzf4OP6bwj6L/gk2bki0d4NGXIbkwlAAJBdKIBCVfDRDEP3s9MQdjMNn/51VT0fkNys+H2QX1QcgsykEjjbyNHc2/QXQa2TzVKLFy+Gg4MDLC0t0a1bNyxcuBC+vr4V7pOfn4/8/Hz1/YyMDPj4+LBZiohMnqqPz96LSUh4kA2FsrjpQykkSMsrqunilWFRVAiPrBR4ZyTDM/M+vDPvF/+bUfyvV+Z9uOak63SsHHP5f7U/dm64Y+eCu3YuuGvrrL7dt3GCwgTn/NGVg6UM1uZSdQBS9Q2SSiWwkde+1eBNts/NX3/9haysLAQHByMpKQnz5s3D7du3ERkZCTs7u3L3Ka+fDgCGGyKq1yoa1ZVfVPPz+GgjLyqAR2YKvDOT4ZmpCkIp8MpMhldmCrwykuGSm6HTsRQSKe7bOP4bdkoGH80gVN/6AZWnZLNY6Vqh6moaM9lwU1paWhr8/Pzw1VdfYcqUKeVuw5obIiL9KZQCx6OS8e3RGMQmZ6FIoVR/qNWGYe3ayAvz4ZmVolHj45GVAs/MFHhkPYD7vzezChYzLS1fZoZ7ZcJPyUBU/G+23NrIr6xucLeT4/meAQYPOvUm3ABAp06d0K9fPyxcuFCn7Tlaioio6ioa1l7c7IVaM8qrIlKlAi45GXDPSoFnVnHo8ch8oHHfPeuBzs1gAJBtbolkG6d/b45ItnUqcb/4ds/WCSnWjiY/GSIASAC80DsAswc3N8jxTHq0VElZWVmIjY3FhAkTarooRET1ioWZFC/2CcKLfYLKfV7bKC+ZBDXe9KWUyorDh60TLqH81wAU9wNyy06FR1YK3LMelKj9+TcEZRaHIPuCHNgU5sEmLQn+aRV3hlZJsbL/L/TYlghENk64Z+usfi7d0rbONokJAN8djQMAgwUcXdS5mpuZM2di2LBh8PPzQ2JiIubMmYOIiAhcvnwZbm7ljyQojTU3RES1Q3mLlZbs3JpVoESBom58TFkX5MItO7X4lvXvv9lp/z327+OuOWkPXQKjpAKp2b8ByFGjBijF2gEp1o5IsXFAsrUTUmwckG5pCyGpfUPBpRLg6vxBVW6iMtmam1u3bmHs2LFISUmBm5sbevbsidOnT+scbIiIqPZ4WO0PUHaou7n0vzlealMAyrGwQoKFFRKcvLVuJxFKOOVmlgpBpW5ZxaHIKS8TFsoiNMhMRoPM5IeWoUgixQNrB6RYO+D+v8EnxdpR437xv464b+2APPPqmS1aKYCfT8VjSq9G1XK+OldzYwisuSEiMi0VzfWjqgVSjQCrayyKCuGaowpBmrVAzjnpcM1Og2tOOlxy0uCYl6X38bPNLdU1QPdLBKGS9+/bFD+WamVfpWHzz3bzw8cjWlZ6f8CEa26IiIhKq2h255JKN4FBAEJoNoPVdD+g0grMzJFo745Ee/eHbmuuKIRTToY67LjkpMOlRPhxzf73sZw0uGWnQa4oLO4jlJ4H3/S7OpUnzdIWKdYOeGDlgAfW9nhgZY8H1g7q2qL/7tsjyc5Vo4nMz7n6RpKx5oY1N0REVMLD+gHJzWRIzyuqVSFIb0LApiAXLjnpcC0RhDTulwhEzjkZkEK/uBA8YxvyzSzU9yPnDoCtZdXqVFhzQ0REVAm69AMCHh6CLGRSZBUokFVQC0OQRIJsuTWy5da44eT10M2lSgUc87LgnJNevDZYTvq/oUd1/9/HcosfkxcVagQbANj8z41q63PDcENERFQJuoagkkPiLyWmIzWnQL0MRsmZfmtbk1hJSqlM3fykk3IahRIe5Bi4VBVjuCEiIjKihy18WpK2WaFL1grV+kkSy5mXpzr73DDcEBER1RIyqQR9mrmjT7OHdyB+2CSJJUNRTQ+Xl0qACd38q+18DDdERER1kD41QkBxH6E1x2Ox9dwtJGfml7sAprE6TE/tZbzFNMvD0VIcLUVERFSGtrXDSocihVKU23FaIgFe6FX9a0sx3DDcEBERVZmqmWxr+C3kFCjQyd8ZE7v718iq4GyWIiIioirTt5nMmGrf6lpEREREVcBwQ0RERCaFzVIGUnqmSqlECg97Swxo4YlJPaq3lzgREVF9xg7FBuhQvHDPZXx3NE7rNhZSwNVWDltLMzTzcsDIDg3RPcgVMmnZiY6IiIioLHYoria6BBsAKFACiRn5QEY+rt3Lxo7ziQAAe7kUFmYy2Fmao3ugCz4Y2gJWFpVfUp6IiKi+Y7ipgoIipU7BRpuMfCWQr8T97ELEpeRgw5mbMAdgZyWDQgDmMil8nW0wsCWbt4iIiHTBcFMFP5+KN8pxCwE8yFWtGaLA/ew0hN1Mw6d/XYW5BLCzlMFCJkWRAGt8iIiISmG4qYLqXOFUpVCogk9x+KmoxkcmAawszOHpwE7NRERUvzDcVEF1rnCqC80aHwC5CtxOz8O5G8W1PhZSwNnaHAUKJZu8iIjIZHG0VBVGSxUUKdHkg78MWLKapWryKrlmiJBI4WZrgSfbN8RzPRsxABERUY3h2lJaGHIouK6jpUxFeQFIIQAzqQTONnI09+YwdyIiMg4OBa8mqpVO60vAKd3np6SUnCJEJ/83zN3BUgZrc6k6AKlWkpVKJbCRc74fIiIyDtbcGGhV8JIzFEfdzUR2OUu/U8VszCUwk0nVAahkrRD7BhEREcBmKa2MEW5KKyhSYs3xWGw9dwvJmflQKAWyGHgMouRweFXn6NKhiH2FiIhMD8ONFtURbsqjUAocj0rGt0djEJuchSKFEnlFAjmFDD3GZiEFrCxkkEmgNRQpIYGt3AztfZ0wqqMPm8yIiGoRhhstaircVKT0optCWfxBm1WgRIGi3v14ah0bcwnsLc1RsqmsvOYzmQSwNDfj+mFEREbCcKNFbQs32qiCz96LSUh4kA2FsvhDlDU+dYeDXAqZVKI1FJWuTeIEjEREZTHcaFGXwo02FdX4KASQX1Tc5EWmwUIKuNhY4GGhiLVJRGTKGG60MJVw8zClOzWX/DBkk1f9Zi+XwkzP2iT2TSKimsZwo0V9CTcPU1GTl+pDLDW3iLU/pDMrM8DKXPsoNl3DFCeFJKLyMNxowXCju4cFIFXzF/v/kLFV1LFblzDFaQOITAPDjRYMN4ZX3jD38j5kGISottN12oDKhil2GieqPIYbLRhuapa2jtClPwjYN4jqCwsp4GxtbrQwVd42MpkUHvYMWFR3MNxowXBTt1TUNFbRH3T2FSKqHCszwNHKXO/gVNnAxdF9pC+GGy0YbkxfydqhpPQc5Bc+/I9uZr6CS2QQ1UKl54qq7sBV3n4SCSCVsuarujHcaMFwQxVRKAVORt/Hb2cTcO5GKrLzFTr/EeTcQkT1m6UMsLaQVUsIq+qx6+p0Dgw3WjDckLGU7k8EAQih3x8zhiQiqgmq6RyqEqaMXaPFcKMFww3VduVNwFiZPzhcjZ6IapIEwAu9AzB7cHODHI/hRguGG6pPyhumX5Vqb/ZNIiJ9vWiggMNwowXDDVHVqPombTl3A5cS05GaU/DQUWy6Ns1xLiQi0yOVAFfnD6pyE5Wun99mVToLEdVLMqkEvYLd0CvYzSjH16djd1VqoThtAFH1UArg51PxmNKrUbWcj+GGiGodY4enkiozbUBVR7uw0zjVRwkPcqrtXAw3RFSvWZhJ8WKfILzYJ6haz1tep/HqHDrM2b+puvk5W1fbudjnhn1uiKieyi1Q4ONdkTgZcx9ZeYWwkBl34jvOFVV/sc8NERFVCysLGRY+2aami6FBl7mianqGYqWQIC2vqGYvVB0ztVf1zuDMmhvW3BARkZ5Kjhi8nJSBnIKiMosA19YZirMKqm86h5qa54Y1N0RERHqqzk7vxqBtOofaPEOxrhhuiIiI6pm6Hs4ehkuYEhERkUlhuCEiIiKTwnBDREREJoXhhoiIiEwKww0RERGZFIYbIiIiMikMN0RERGRSGG6IiIjIpDDcEBERkUmplzMUq5bTysjIqOGSEBERka5Un9sPWxazXoabzMxMAICPj08Nl4SIiIj0lZmZCQcHhwqfr5ergiuVSiQmJsLOzg4SiQRAcRr08fHBzZs3uVJ4Kbw25eN1qRivTfl4XcrH61IxXhtNQghkZmbC29sbUmnFPWvqZc2NVCpFw4YNy33O3t6eb6AK8NqUj9elYrw25eN1KR+vS8V4bf6jrcZGhR2KiYiIyKQw3BAREZFJYbj5l1wux5w5cyCXy2u6KLUOr035eF0qxmtTPl6X8vG6VIzXpnLqZYdiIiIiMl2suSEiIiKTwnBDREREJoXhhoiIiEwKww0RERGZFJMONytWrIC/vz8sLS3RpUsXnDlzRuv2W7ZsQdOmTWFpaYlWrVphz549Gs9v27YN/fv3h4uLCyQSCSIiIoxYeuMx5HUpLCzEO++8g1atWsHGxgbe3t549tlnkZiYaOyXYRSGfs/MnTsXTZs2hY2NDZycnNCvXz+EhoYa8yUYhaGvS0kvvfQSJBIJvv76awOXunoY+tpMmjQJEolE4zZw4EBjvgSjMMZ75sqVKxg+fDgcHBxgY2ODTp064caNG8Z6CUZh6OtS+r2iui1evNiYL6P2EyZq06ZNwsLCQvz444/i0qVLYurUqcLR0VHcvXu33O1PnDghZDKZ+Pzzz8Xly5fFBx98IMzNzcXFixfV26xfv17MmzdPfP/99wKACA8Pr6ZXYziGvi5paWmiX79+YvPmzeLq1avi1KlTonPnzqJDhw7V+bIMwhjvmQ0bNoh9+/aJ2NhYERkZKaZMmSLs7e3FvXv3qutlVZkxrovKtm3bRJs2bYS3t7dYsmSJkV+J4Rnj2kycOFEMHDhQJCUlqW8PHjyorpdkEMa4LjExMcLZ2VnMmjVLhIWFiZiYGLFjx44Kj1kbGeO6lHyfJCUliR9//FFIJBIRGxtbXS+rVjLZcNO5c2fx6quvqu8rFArh7e0tFi5cWO72o0ePFkOGDNF4rEuXLuLFF18ss21cXFydDTfGvC4qZ86cEQBEQkKCYQpdTarj2qSnpwsAYv/+/YYpdDUw1nW5deuWaNCggYiMjBR+fn51MtwY49pMnDhRjBgxwijlrS7GuC5jxowRzzzzjHEKXE2q42/MiBEjxKOPPmqYAtdhJtksVVBQgHPnzqFfv37qx6RSKfr164dTp06Vu8+pU6c0tgeAAQMGVLh9XVRd1yU9PR0SiQSOjo4GKXd1qI5rU1BQgNWrV8PBwQFt2rQxXOGNyFjXRalUYsKECZg1axZatGhhnMIbmTHfM4cPH4a7uzuCg4Px8ssvIyUlxfAvwEiMcV2USiV2796NJk2aYMCAAXB3d0eXLl3wxx9/GO11GFp1/I25e/cudu/ejSlTphiu4HWUSYab+/fvQ6FQwMPDQ+NxDw8P3Llzp9x97ty5o9f2dVF1XJe8vDy88847GDt2bJ1a5M2Y12bXrl2wtbWFpaUllixZgn379sHV1dWwL8BIjHVdFi1aBDMzM0yfPt3wha4mxro2AwcOxPr163HgwAEsWrQIR44cwaBBg6BQKAz/IozAGNfl3r17yMrKwmeffYaBAwfi77//xhNPPIEnn3wSR44cMc4LMbDq+Pv7008/wc7ODk8++aRhCl2H1ctVwck4CgsLMXr0aAghsGrVqpouTq3xyCOPICIiAvfv38f333+P0aNHIzQ0FO7u7jVdtBpx7tw5LF26FGFhYZBIJDVdnFrn6aefVv+/VatWaN26NQIDA3H48GGEhITUYMlqjlKpBACMGDECb775JgCgbdu2OHnyJL799lv06dOnJotXa/z4448YP348LC0ta7ooNc4ka25cXV0hk8lw9+5djcfv3r0LT0/Pcvfx9PTUa/u6yJjXRRVsEhISsG/fvjpVawMY99rY2NggKCgIXbt2xZo1a2BmZoY1a9YY9gUYiTGuy7Fjx3Dv3j34+vrCzMwMZmZmSEhIwIwZM+Dv72+U12EM1fV3plGjRnB1dUVMTEzVC10NjHFdXF1dYWZmhubNm2ts06xZszozWsrY75djx44hKioKzz//vOEKXYeZZLixsLBAhw4dcODAAfVjSqUSBw4cQLdu3crdp1u3bhrbA8C+ffsq3L4uMtZ1UQWb6Oho7N+/Hy4uLsZ5AUZUne8ZpVKJ/Pz8qhe6GhjjukyYMAEXLlxARESE+ubt7Y1Zs2bhf//7n/FejIFV13vm1q1bSElJgZeXl2EKbmTGuC4WFhbo1KkToqKiNLa5du0a/Pz8DPwKjMPY75c1a9agQ4cOdaY/n9HVdI9mY9m0aZOQy+Vi3bp14vLly+KFF14Qjo6O4s6dO0IIISZMmCDeffdd9fYnTpwQZmZm4osvvhBXrlwRc+bMKTPkLiUlRYSHh4vdu3cLAGLTpk0iPDxcJCUlVfvrqyxDX5eCggIxfPhw0bBhQxEREaExJDE/P79GXmNlGfraZGVlidmzZ4tTp06J+Ph4cfbsWTF58mQhl8tFZGRkjbzGyjDG71JpdXW0lKGvTWZmppg5c6Y4deqUiIuLE/v37xft27cXjRs3Fnl5eTXyGivDGO+Zbdu2CXNzc7F69WoRHR0tli9fLmQymTh27Fi1v77KMtbvUnp6urC2tharVq2q1tdTm5lsuBFCiOXLlwtfX19hYWEhOnfuLE6fPq1+rk+fPmLixIka2//222+iSZMmwsLCQrRo0ULs3r1b4/m1a9cKAGVuc+bMqYZXYziGvC6qYfHl3Q4dOlRNr8hwDHltcnNzxRNPPCG8vb2FhYWF8PLyEsOHDxdnzpyprpdjMIb+XSqtroYbIQx7bXJyckT//v2Fm5ubMDc3F35+fmLq1KnqD7+6xBjvmTVr1oigoCBhaWkp2rRpI/744w9jvwyDM8Z1+e6774SVlZVIS0szdvHrDIkQQtRMnRERERGR4ZlknxsiIiKqvxhuiIiIyKQw3BAREZFJYbghIiIik8JwQ0RERCaF4YaIiIhMCsMNERERmRSGG6J64s6dO3jsscdgY2MDR0fHKh+vb9++eOONN6q8TU1ISUmBu7s74uPjDXpciUSCP/74w6DHNDVdu3bF1q1ba7oYZOIYboh0NGnSJEgkErz00ktlnnv11VchkUgwadKk6i+YjpYsWYKkpCRERETg2rVrFW734MEDvPHGG/Dz84OFhQW8vb3x3HPPVWqBwm3btmH+/PlVKbYGQ4WlBQsWYMSIERoLdW7fvh1du3aFg4MD7Ozs0KJFi1oTzA4fPgyJRAInJyfk5eVpPPfPP/9AIpHUmRXWP/jgA7z77rvqlb6JjIHhhkgPPj4+2LRpE3Jzc9WP5eXlYePGjfD19a3Bkj1cbGwsOnTogMaNG8Pd3b3cbR48eICuXbti//79+PbbbxETE4NNmzYhJiYGnTp1wvXr1/U6p7OzM+zs7AxRfIPJycnBmjVrMGXKFPVjBw4cwJgxY/DUU0/hzJkzOHfuHBYsWIDCwsJqLVtBQYHW5+3s7LB9+3aNx9asWVPr33slDRo0CJmZmfjrr79quihkwhhuiPTQvn17+Pj4YNu2berHtm3bBl9fX7Rr105j271796Jnz55wdHSEi4sLhg4ditjYWPXzBQUFmDZtGry8vGBpaQk/Pz8sXLgQACCEwNy5c+Hr6wu5XA5vb29Mnz5da9lWrVqFwMBAWFhYIDg4GD///LP6OX9/f2zduhXr16/XWsP0/vvvIzExEfv378egQYPg6+uL3r1743//+x/Mzc3x6quvamxfVFSEadOmwcHBAa6urvjwww9RckWX0jUt+fn5mDlzJho0aAAbGxt06dIFhw8f1jjmiRMn0LdvX1hbW8PJyQkDBgxAamoqJk2ahCNHjmDp0qXqmor4+HikpqZi/PjxcHNzg5WVFRo3boy1a9dWeJ327NkDuVyOrl27qh/buXMnevTogVmzZiE4OBhNmjTB448/jhUrVuh8jcvzzjvvoEmTJrC2tkajRo3w4YcfagSmuXPnom3btvjhhx8QEBAAS0tLrcebOHEifvzxR/X93NxcbNq0CRMnTtTYLiUlBWPHjkWDBg1gbW2NVq1a4ddff9XY5vfff0erVq1gZWUFFxcX9OvXD9nZ2QCKa4o6d+6sbsLs0aMHEhIS1Pvu2LED7du3h6WlJRo1aoR58+ahqKgIwMPfuzKZDIMHD8amTZu0vlaiKqnRla2I6pCJEyeKESNGiK+++kqEhISoHw8JCRFLliwRI0aM0Fj07vfffxdbt24V0dHRIjw8XAwbNky0atVKKBQKIYQQixcvFj4+PuLo0aMiPj5eHDt2TGzcuFEIIcSWLVuEvb292LNnj0hISBChoaFi9erVFZZNtWLyihUrRFRUlPjyyy+FTCYTBw8eFEIIce/ePTFw4EAxevRokZSUVO4CewqFQjg6OooXXnih3HMsWLBASCQSkZKSIoQoXuTP1tZWvP766+Lq1avil19+EdbW1hrl7NOnj3j99dfV959//nnRvXt3cfToURETEyMWL14s5HK5uHbtmhBCiPDwcCGXy8XLL78sIiIiRGRkpFi+fLlITk4WaWlpolu3bmLq1KnqleeLiorEq6++Ktq2bSv++ecfERcXJ/bt2yf+/PPPCq/V9OnTxcCBAzUeW7hwoXBzc9O6cvnDrrEQQgAQ27dvV9+fP3++OHHihIiLixN//vmn8PDwEIsWLVI/P2fOHGFjYyMGDhwowsLCxPnz58s996FDhwQAERUVJeRyuUhISBBCCPHzzz+LNm3aiO3bt4uSf85v3bolFi9eLMLDw0VsbKxYtmyZkMlkIjQ0VAghRGJiojAzMxNfffWViIuLExcuXBArVqwQmZmZorCwUDg4OIiZM2eKmJgYcfnyZbFu3Tr1OY8ePSrs7e3FunXrRGxsrPj777+Fv7+/mDt3rhBCt/fuqlWrhJ+fX4XXmqiqGG6IdKQKN/fu3RNyuVzEx8eL+Ph4YWlpKZKTk8uEm9KSk5MFAPUH6GuvvSYeffRRoVQqy2z75ZdfiiZNmoiCggKdyta9e3cxdepUjcdGjRolBg8erL7/sPLduXNHAKhwde5t27YJAOoPyD59+ohmzZpplP+dd94RzZo1U98vGW4SEhKETCYTt2/f1jhuSEiImD17thBCiLFjx4oePXpUWMbSYUkIIYYNGyYmT55c4T6ljRgxQjz33HMaj2VlZYnBgwcLAMLPz0+MGTNGrFmzRuTl5am30eUalw43pS1evFh06NBBfX/OnDnC3Nxc3Lt3T2uZVeEmNTVVPP7442LevHlCCCEeeeQRsXTp0jLhpjxDhgwRM2bMEEIIce7cOQFAxMfHl9kuJSVFABCHDx8u9zghISHi008/1Xjs559/Fl5eXkII3d67O3bsEFKpVB30iQyNzVJEenJzc8OQIUOwbt06rF27FkOGDIGrq2uZ7aKjozF27Fg0atQI9vb26s6rqo65kyZNQkREBIKDgzF9+nT8/fff6n1HjRqF3NxcNGrUCFOnTsX27dvV1f7luXLlCnr06KHxWI8ePXDlyhW9X58o0az0MF27dtXoyNqtWzdER0dDoVCU2fbixYtQKBRo0qQJbG1t1bcjR46om+siIiIQEhKiV3lffvllbNq0CW3btsXbb7+NkydPat0+Nze3TPOPjY0Ndu/ejZiYGHzwwQewtbXFjBkz0LlzZ+Tk5ACo3DXevHkzevToAU9PT9ja2uKDDz4o0zHbz88Pbm5uOr/e5557DuvWrcP169dx6tQpjB8/vsw2CoUC8+fPR6tWreDs7AxbW1v873//U5+7TZs2CAkJQatWrTBq1Ch8//33SE1NBVDcT2rSpEkYMGAAhg0bhqVLlyIpKUl97PPnz+Pjjz/W+BlOnToVSUlJyMnJ0em9a2VlBaVSifz8fJ1fN5E+GG6IKkH1AfPTTz/hueeeK3ebYcOG4cGDB/j+++8RGhqK0NBQAP91Gm3fvj3i4uIwf/585ObmYvTo0Rg5ciSA4o7LUVFRWLlyJaysrPDKK6+gd+/eRu3g6ubmBkdHxwo/rK9cuQKJRIKgoKBKHT8rKwsymQznzp1DRESE+nblyhUsXboUQPGHnr4GDRqEhIQEvPnmm0hMTERISAhmzpxZ4faurq7qD/LSAgMD8fzzz+OHH35AWFgYLl++jM2bN+tdJgDq4DF48GDs2rUL4eHheP/998t0GraxsdHruIMGDUJubi6mTJmCYcOGwcXFpcw2ixcvxtKlS/HOO+/g0KFDiIiIwIABA9Tnlslk2LdvH/766y80b94cy5cvR3BwMOLi4gAAa9euxalTp9C9e3ds3rwZTZo0wenTpwEU/xznzZun8TO8ePEioqOjYWlpqdN798GDB7CxsanUz5tIFww3RJUwcOBAFBQUoLCwEAMGDCjzfEpKCqKiovDBBx8gJCQEzZo1K/cD1d7eHmPGjMH333+PzZs3Y+vWrXjw4AGA4g/6YcOGYdmyZTh8+DBOnTqFixcvllueZs2a4cSJExqPnThxAs2bN9f5NUmlUowePRobN27EnTt3NJ7Lzc3FypUrMWDAADg7O6sfVwU2ldOnT6Nx48aQyWRljt+uXTsoFArcu3cPQUFBGjdPT08AQOvWrXHgwIEKy2hhYVFurZCbmxsmTpyIX375BV9//TVWr15d4THatWuHy5cvV/i8ir+/P6ytrdWdbPW9xidPnoSfnx/ef/99dOzYEY0bN9bolFtZZmZmePbZZ3H48OEKg/WJEycwYsQIPPPMM2jTpg0aNWpUZvi/RCJBjx49MG/ePISHh8PCwkJjJFa7du0we/ZsnDx5Ei1btsTGjRsBFIfyqKioMj/DoKAgSKXFHykPe+9GRkaW6YBPZEhmNV0AorpIJpOpazjK+yB3cnKCi4sLVq9eDS8vL9y4cQPvvvuuxjZfffUVvLy80K5dO0ilUmzZsgWenp5wdHTEunXroFAo0KVLF1hbW+OXX36BlZUV/Pz8yi3PrFmzMHr0aLRr1w79+vXDzp07sW3bNuzfv1+v1/Xpp5/iwIEDeOyxx/D555+jZcuWiIuLwwcffIDCwsIyo4du3LiBt956Cy+++CLCwsKwfPlyfPnll+Ueu0mTJhg/fjyeffZZfPnll2jXrh2Sk5Nx4MABtG7dGkOGDMHs2bPRqlUrvPLKK3jppZdgYWGBQ4cOYdSoUXB1dYW/vz9CQ0MRHx8PW1tbODs7Y+7cuejQoQNatGiB/Px87Nq1C82aNavwNQ4YMACzZ89GamoqnJycABSPWsrJycHgwYPh5+eHtLQ0LFu2DIWFhXjssccqdY0bN26MGzduYNOmTejUqRN2795dZhh3Zc2fPx+zZs0qt9ZGde7ff/8dJ0+ehJOTE7766ivcvXtXHcRCQ0Nx4MAB9O/fH+7u7ggNDUVycjKaNWuGuLg4rF69GsOHD4e3tzeioqIQHR2NZ599FgDw0UcfYejQofD19cXIkSMhlUpx/vx5REZG4pNPPtHpvXvs2DH079/fINeCqFw13emHqK5QdSiuSOkOu/v27RPNmjUTcrlctG7dWhw+fFijw+nq1atF27ZthY2NjbC3txchISEiLCxMCCHE9u3bRZcuXYS9vb2wsbERXbt2Ffv379davpUrV4pGjRoJc3Nz0aRJE7F+/Xqt5atIcnKyeO2114SPj48wNzcXHh4eYtKkSerRMip9+vQRr7zyinjppZeEvb29cHJyEu+9955GB+PSHYALCgrERx99JPz9/YW5ubnw8vISTzzxhLhw4YJ6m8OHD4vu3bsLuVwuHB0dxYABA0RqaqoQQoioqCjRtWtXYWVlJQCIuLg4MX/+fNGsWTNhZWUlnJ2dxYgRI8T169e1vsbOnTuLb7/9Vn3/4MGD4qmnnhI+Pj7CwsJCeHh4iIEDB4pjx47pdY1RqkPxrFmzhIuLi7C1tRVjxowRS5YsEQ4ODurn58yZI9q0aaO1rEJodiguT+kOxSkpKWLEiBHC1tZWuLu7iw8++EA8++yz6vfv5cuXxYABA4Sbm5uQy+WiSZMmYvny5UKI4o7ljz/+uPDy8hIWFhbCz89PfPTRRxqdf/fu3Su6d+8urKyshL29vejcubN6RNTD3ru3bt0S5ubm4ubNmw993USVJRFCj96DRER66NatG0JCQvDJJ5/UdFE07N69G7NmzUJkZKS6KYWqxzvvvIPU1FStTYdEVcXfaiIyuPz8fJw9exaXLl1CixYtaro4ZQwZMgQvvPACbt++XdNFqXfc3d0NuiQHUXlYc0NEBvfHH3/g2WefxfDhw7F27VqYm5vXdJGIqB5huCEiIiKTwmYpIiIiMikMN0RERGRSGG6IiIjIpDDcEBERkUlhuCEiIiKTwnBDREREJoXhhoiIiEwKww0RERGZFIYbIiIiMin/B67/PoK8anRTAAAAAElFTkSuQmCC", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#use scipy curve fit to create a general power law\n", - "popt, pcov = curve_fit(power_law, sorted_masses, sorted_powers, sigma = sorted_errors)#, absolute_sigma = True) #popt gives fit value for a\n", - "avg_a = popt\n", - "avg_aerr = np.sqrt(np.diag(pcov)) #gives the error in a\n", - "\n", - "#create an array with mass values using fitted a\n", - "avg_power = power_law(sorted_masses, avg_a)\n", - "\n", - "#plot combined mass and power law datasets\n", - "plt.figure()\n", - "plt.scatter(sorted_masses, sorted_powers, label = 'data')\n", - "plt.plot(sorted_masses, avg_power, color = 'r', label = 'curve fit')\n", - "\n", - "#label graph\n", - "plt.title(\"Power Law Curve Fit for BD Candidate Objects\")\n", - "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", - "plt.ylabel(\"$M^{-0.584 ± 0.002}$\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "011eac9f-7e9d-4e5d-8bfc-f0380920f575", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.5836496] [0.00202593]\n" - ] - } - ], - "source": [ - "#determine the index of the power law based on the curve fit\n", - "print(avg_a, avg_aerr)" - ] - }, - { - "cell_type": "markdown", - "id": "0c050ca1-499e-426a-b4ef-0e4e64fc80a2", - "metadata": {}, - "source": [ - "Based on the curve fit, our a value for BDs is 0.5836496 ± 0.00202593. The error is smaller than expected, which may take more looking into. " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "de5f6d54-b675-447c-bef7-a979715eb5f0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#account for error in plot\n", - "avg_aup = avg_a + avg_aerr\n", - "avg_alow = avg_a - avg_aerr\n", - "power_outup = power_law(sorted_masses, avg_aup)\n", - "power_outlow = power_law(sorted_masses, avg_alow)\n", - "\n", - "plt.scatter(sorted_masses, sorted_powers, label = 'data')\n", - "plt.plot(sorted_masses, power_law(sorted_masses, avg_a), color= 'r', label = \"curve fit for power law\")\n", - "plt.fill_between(sorted_masses, power_outup, power_outlow, color = 'r', label = \"error in a\")\n", - "plt.title(\"Power Law Curve Fit for BD Candidate Objects\")\n", - "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", - "plt.ylabel(\"$M^{0.584 ± 0.002}$\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "83430453-9da9-49e6-a1b1-4f12db8b63a7", - "metadata": {}, - "source": [ - "#### Cleaning Up the Curve Fit" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "d1ef4b5a-06b7-4295-9c5a-7e9d231020ec", - "metadata": {}, - "outputs": [], - "source": [ - "#combining all mass and output data sets to fit\n", - "all_masses = np.array(all_masses)\n", - "all_power = np.array(all_power)\n", - "\n", - "#put combined mass data set into increasing order and reorder output array to match new indices \n", - "sorted_indices = np.argsort(all_masses)\n", - "sorted_masses = all_masses[sorted_indices]\n", - "sorted_powers = all_power[sorted_indices]\n", - "sorted_errors = all_err[sorted_indices]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "d3af8ed8-a459-486c-9f44-c6a9f1008d59", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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wAAAHDx6Ej48PmjVrJvUXfK9evQAA586dk3rewMBANG/eXGXXbmhoKPl/QUEB3rx5g86dOwOA1MiUgIAA3L59G7m5uQCAy5cvo3///vDz85PUMl26dAkcDgf+/v5VPp+49sbU1FRl11DR9OnTpb7ncDgYOXIkTp48KTXa5MCBA3B2dpaU9/Tp08jIyMDYsWOlfhY8Hg+dOnWq9LOoyuLFi3H69Gmpr4odHVXpzJkzKCoqwocffihVczJ16lSYmZlVqokSCASYNGmSXOfOz8+HQCCotN3AwEDyeHXHip+vpuPlfR7xzy81NRVHjx7F9OnT8fbbbyM0NBTW1tb46quv5LouedRU/uquvXxZHRwccOLECYwaNQrz5s3D1q1bER0djb1791Z7fJ8+fXD16lUMHjwYd+/exapVqxAcHAxnZ2f89ddfkv3kfY+ShcfjSTpci0QipKWlSWqhZI1MGz58eJU1htU9x6hRoyQj0Pbs2QMXFxcEBATI3F/e9yQLCwuEh4cjPj5e5nnMzc0BAP/88w/y8vLkLm92djaAmt+jTE1NJe9nYnp6evjggw8k3/P5fHzwwQdITk7GrVu3ZJ4nLS0NZ8+exahRo6Rqc1NTUxEcHIynT5/i9evXAIA///wTrVu3ltTMlif+TKmKsvejLlCQUYENGzbg9OnTOHfuHKKiovD8+XMEBwcDAF68eAEul1tphIiDgwMsLCzw4sULybaAgACpD/X27dujffv2sLKywqVLl5CVlYW7d+9K/QI/ffoUDx48gK2trdSXt7c3AFQaheHu7q7Sa09LS8PcuXNhb28PQ0ND2NraSp4jMzNT6tpKSkpw9epVPH78GMnJyQgICED37t2lrrl58+bVVjeLq2rFbxaqpqenh0aNGlXaPnr0aOTn50s+AHJycnDy5EmMHDlS8gYgDpu9evWq9PP4999/5R4R07JlS/Tu3VvqS/yBrA7i12DFERR8Ph8eHh5Sr1EAcHZ2lhotVB1DQ0MUFhZW2i4eaVL+Q0fWsQDkOl7e5xH/6+7ujk6dOkn2MzExwaBBg3D9+nWUlJRUWaaUlBQkJiZKvqobRl1T+au79vLHjxo1Sipgjhw5Enp6eggLC6v2eADo0KEDDh06hPT0dFy/fh0LFixAdnY2RowYgaioKACKvUfJsmvXLrRq1QoGBgawtraGra0tTpw4IfX7L6bs+8/bb7+NqKgo3L17F3v37sWYMWOq/OCV9z1p1apViIyMhIuLCzp27IilS5fi+fPnUmX9+OOP8csvv8DGxgbBwcHYsGGDzOsqTxxganqPys7OrhR2nJycYGxsLLVN/F5eVXPis2fPwBjDokWLKr3vLFmyBMB/nwPR0dFKT6mh7P2oCzT8WgU6duwosx2/vJrSLgD4+/tj69ateP78OS5duoSAgABJDcWlS5fg5OQEkUgkFWREIhFatmyJ77//XuY5XVxcpL6v6c1TUaNGjUJYWBjmz58PPz8/mJiYQCQSISQkRKpfR/v27WFgYICLFy+icePGsLOzg7e3NwICArBx40YUFhbi0qVLMv9SKM/Lywt6enq4f/++XOWr6r5XNc+MQCCo1J8DADp37gw3Nzf8/vvvePvtt3Hs2DHk5+dL9UMSX+/u3btl1qCUHzatyxR5DTk6OiIhIaHSdvE2Jyenao8tv2/F462srCS1HY6Ojjh37hwYY1I/84rPI/7X3t6+0jnt7OxQXFyM3NxcyV+fFXXo0EHqg33JkiVVTlBYU/mru/bqysrj8WBtbY309PRqjy+Pz+ejQ4cO6NChA7y9vTFp0iQcPHhQ8kEHyPceVdFvv/2GiRMn4q233sL8+fNhZ2cHHo+HFStWSPVpEVP2/adTp07w9PTEhx9+iJiYGLz99ttV7ivve9KoUaMQEBCAw4cP499//8Xq1auxcuVKHDp0CP369QMArFmzBhMnTsTRo0fx77//Ys6cOVixYgWuXbsm8w8eAJJ+Wvfu3auyjC9evEBWVpZKasfF1zRv3jzJH9AVqWqqBWXuR12oH++sWszV1RUikQhPnz6V6oiYlJSEjIwMuLq6SraJA8rp06dx48YNfPbZZwBKO/Zu2rRJktbbtWsnOcbT0xN3795FUFCQUm9EtZGeno7Q0FAsW7YMixcvlmwX10yUx+fz0bFjR1y6dAmNGzeWXGtAQAAKCwuxZ88eJCUlVdvRFwCMjIzQq1cvnD17Fq9evaoU1CqytLQEgEqd0Wr6K1OWUaNGYe3atcjKysKBAwfg5uYmqbIGSn8WQOkHYlWTeKmaKn7m4tfg48eP4eHhIdleVFSEmJiYWl2LuOlQJBJJBcTw8HAYGRlJ/tqUxdnZGba2tjInO7t+/brUHBd+fn745Zdf8PDhQ6kPh/DwcMnjQGk4cHBwkFS1lxcfHw8DA4NqmwT27Nkj1SRU/n5V5OvrCz09Pdy8eROjRo2SbC8qKkJERITUNlnEv+cVy1pUVIQ3b94o3EQjJv6jSxywFHmPquiPP/6Ah4cHDh06JPVaLB+QVGXs2LH46quv4OPjU+X8Joq8JwGlYXPGjBmYMWMGkpOT0bZtW3z99deSIAOU1pC2bNkSCxcuRFhYGLp164bNmzdX2Qzp7e0Nb29vHDlyBGvXrpX5evr1118BAAMHDpTaHh8fj9zcXKlaGXGH+KoGZohfg/r6+jX+rnp6etbY+bum9xRF70ddoKYlNevfvz+A0hEj5YlrUAYMGCDZ5u7uDmdnZ/zwww8oLi6WTIQVEBCA6Oho/PHHH+jcubPUX/ajRo3C69evsXXr1krPnZ+fL+mTog7ikTqs3EgAoPK1igUEBCA8PBznzp2TBBkbGxv4+Phg5cqVkn1qsmTJEjDGMH78eJlV+7du3cKuXbsAlL5J83g8qRFTALBx48Yan6ei0aNHo7CwELt27cKpU6cqfRAFBwfDzMwM33zzTaWRPgBkzkZbW+I3vNqMGujduzf4fD7WrVsn9bPctm0bMjMzpV6jihoxYgSSkpJw6NAhybY3b97g4MGDGDRokFT/kejo6Ep/xQ8fPhzHjx/Hq1evJNtCQ0Px5MkTjBw5UrJtyJAh0NfXl/q5MsawefNmODs7o2vXrpLto0ePxqtXr3D69GmpMh09ehS9evWSWSMn1q1bN6kmv+qCjLm5OXr37o3ffvtNqplh9+7dyMnJkSp/Xl4eHj16hDdv3ki29ejRA3Z2dtizZ4/UpG87d+6EUChEnz59qnxuAJIaqorEfWLETYmKvEdVJOs9IDw8HFevXq22bMp47733sGTJEqxZs0ah8gCVr00oFFZqErGzs4OTk5OkKTArK6tSM2PLli3B5XJlNheWt3jxYqSnp2PatGmVan9v3bqFlStXwtfXF8OHD5d6rKSkBD///LPk+6KiIvz888+wtbWV+gO2Yrl79OiBn3/+WWbtX/n3neHDh+Pu3btSI5nExPesqveU2twPdaMaGTVr3bo1JkyYgC1btiAjIwOBgYG4fv06du3ahbfeeqvS0NWAgADs378fLVu2lNQmtG3bFsbGxnjy5EmlKtXx48fj999/x7Rp03Du3Dl069YNQqEQjx49wu+//45//vmnxmav6qSkpMhM2u7u7hg3bhy6d++OVatWobi4GM7Ozvj3338RExMj81wBAQH4+uuv8erVK6nA0r17d/z8889wc3OTq3qya9eu2LBhA2bMmIFmzZpJzex7/vx5/PXXX5Iym5ubY+TIkVi/fj04HA48PT1x/PhxpWZwbdu2Lby8vPDFF1+gsLCw0vB2MzMzbNq0CePHj0fbtm0xZswY2Nra4uXLlzhx4gS6deuGn376SeHnrY74ze2LL77AmDFjoK+vj0GDBlVqZ6+Ora0tFixYgGXLliEkJASDBw/G48ePsXHjRnTo0AHvvPOO0uUbMWIEOnfujEmTJiEqKkoys69QKMSyZcuk9g0KCgIg3Rfg888/x8GDB9GzZ0/MnTsXOTk5WL16NVq2bCnV4bhRo0b48MMPsXr1ahQXF6NDhw44cuQILl26hD179kg+4ABgwYIF+P333zF8+HB8/PHHMDc3x+bNm1FcXIxvvvlG6WuV5euvv0bXrl0RGBiI999/H3FxcVizZg369u2LkJAQyX7Xr19Hz549pZqqBAIBVq9ejQkTJqB79+4YP348Xr58ibVr1yIgIEAydL4qs2fPRl5eHoYOHYpmzZqhqKgIYWFhktpE8f1T9D2qvIEDB+LQoUMYOnQoBgwYgJiYGGzevBnNmzdXaBkGebi6uta4zpiZmZlc70nZ2dlo1KgRRowYgdatW8PExARnzpzBjRs3JEHp7NmzmDVrFkaOHAlvb2+UlJRg9+7d4PF4lQJIRePGjcONGzewdu1aREVFYdy4cbC0tMTt27exfft2WFtb448//qg0fNzJyQkrV65EbGwsvL29ceDAAURERGDLli3VzuS7YcMG+Pv7o2XLlpg6dSo8PDyQlJSEq1evIi4uTrL0xvz58/HHH39g5MiRmDx5Mtq1a4e0tDT89ddf2Lx5M1q3bg1PT09YWFhg8+bNMDU1hbGxMTp16oS7d+8qfT/UTlPDpeqDqibEq6i4uJgtW7aMubu7M319febi4iJzsinG/pvYa/r06VLbe/fuzQCw0NDQSscUFRWxlStXshYtWjCBQMAsLS1Zu3bt2LJly6QmZELZhHjyEg8HlPUlnowsLi6ODR06lFlYWDBzc3M2cuRIFh8fX2mYMGOlE1DxeDxmamoqNYHcb7/9xgCw8ePHy102xhi7desWe/vtt5mTkxPT19dnlpaWLCgoiO3atUtqGHFKSgobPnw4MzIyYpaWluyDDz5gkZGRVU6IV50vvviCAWBeXl5V7nPu3DkWHBzMzM3NmYGBAfP09GQTJ05kN2/erPbcik6IJ7Z8+XLm7OzMuFxujUOxZQ2/Fvvpp59Ys2bNmL6+PrO3t2fTp0+vckI8RaSlpbEpU6Ywa2trZmRkxAIDA2X+zri6ujJXV9dK2yMjI1nfvn2ZkZERs7CwYOPGjWOJiYmV9hMKheybb76RTPTVokUL9ttvv8ksU3R0NBs6dCgzMzNjhoaGrFevXuz69esKXZe8Ll26xLp27coMDAyYra0tmzlzZqUZqcU/+4o/W8ZKhw63bt1aMrnbrFmzZM5oXdHff//NJk+ezJo1a8ZMTEwYn89nXl5ebPbs2ZVm9pX3Pari8GuRSCS55wKBgLVp04YdP36cTZgwQepnWX5CPHmJh19XR9Z7sDzvSYWFhWz+/PmsdevWzNTUlBkbG7PWrVtLTTT6/PlzNnnyZObp6ckMDAyYlZUV69mzJztz5ozc13DkyBHWp08fySR1Xl5e7JNPPpH5+ydrQjxXV1f2008/Se1X1YR40dHR7N1332UODg5MX1+fOTs7s4EDB7I//vhDar/U1FQ2a9Ys5uzsLJlAdcKECVITBh49elQyaar4uVRxP9SFw5iMukdCCCGEaJ3o6Gh4eXlh9+7dtaotrU+ojwwhhBCiI8T9YGxsbDRcEu1BfWQIIYQQHbB9+3Zs374dRkZGUiMmGzqqkSGEEEJ0wPvvv4+0tDQcPHgQFhYWmi6O1qA+MoQQQgjRWVQjQwghhBCdRUGGEEIIITqr3nf2FYlEiI+Ph6mpaZ1P4U8IIYQQ5TDGkJ2dDScnp2pn3K73QSY+Pr7G9XgIIYQQop1evXpV7azv9T7IiBfsevXqFczMzDRcGkIIIYTIIysrCy4uLtUu5Ao0gCAjbk4yMzOjIEMIIYTomJq6hVBnX0IIIYToLAoyhBBCCNFZFGQIIYQQorPqfR8ZQgghDYNQKERxcbGmi0HkpK+vDx6PV+vzUJAhhBCi0xhjSExMREZGhqaLQhRkYWEBBweHWs3zRkGGEEKIThOHGDs7OxgZGdHkpzqAMYa8vDwkJycDABwdHZU+FwUZQgghOksoFEpCjLW1taaLQxRgaGgIAEhOToadnZ3SzUzU2ZcQQojOEveJMTIy0nBJiDLEP7fa9G2iIEMIIUTnUXOSblLFz42alpQgFDFcj0lDcnYB7EwN0NHdCjwu/RIRQgghdY2CjIJORSZg2bEoJGQWSLZZGevjqyG+6N/KSYMlI4QQQpQzceJEZGRk4MiRI5ouisKoaUkBpyITMP2321IhBgDScosxY+8drDgZpaGSEUIIIXUnNjYWHA4HERERmi4KBRl5CUUMy45FgVWzz88XY3DyXkKdlYkQQojqCEUMV6NTcTTiNa5Gp0Ioqu4dX/WKiorq9PnqCwoycroek1apJkaWRUcj6/zFTwghpHZORSbAf+VZjN16DXP3R2Ds1mvwX3kWpyLV98dpjx49MGvWLHz44YewsbFBcHAwAOD7779Hy5YtYWxsDBcXF8yYMQM5OTkASudfsbW1xR9//CE5j5+fn9Q8LJcvX4ZAIEBeXp7M5xUKhfj4449hYWEBa2tr/O9//wNj0p9bp06dgr+/v2SfgQMHIjo6WvK4u7s7AKBNmzbgcDjo0aMHAODGjRvo06cPbGxsYG5ujsDAQNy+fbv2N6saFGTklJxdc4gBgNTcIlyPSVNzaQghhKhKVd0GEjMLMP2322oNM7t27QKfz8eVK1ewefNmAACXy8W6devw4MED7Nq1C2fPnsX//vc/AKWjfLp3747z588DANLT0/Hw4UPk5+fj0aNHAIALFy6gQ4cOVQ5JX7NmDXbu3Int27fj8uXLSEtLw+HDh6X2yc3Nxccff4ybN28iNDQUXC4XQ4cOhUgkAgBcv34dAHDmzBkkJCTg0KFDAIDs7GxMmDABly9fxrVr19CkSRP0798f2dnZqr1x5VBnXznZmRrIve+/DxLQxZMmZiKEEG1XXbcBBoADYNmxKPRp7qCW0alNmjTBqlWrpLZ9+OGHkv+7ubnhq6++wrRp07Bx40YApTU5P//8MwDg4sWLaNOmDRwcHHD+/Hk0a9YM58+fR2BgYJXP+eOPP2LBggUYNmwYAGDz5s34559/pPYZPny41Pfbt2+Hra0toqKi4OvrC1tbWwCAtbU1HBwcJPv16tVL6rgtW7bAwsICFy5cwMCBA+W5JQqjGhk5dXS3gqmBfLMO7r/xipqXCCFEB9TUbYABSMgsUFtNe7t27SptO3PmDIKCguDs7AxTU1OMHz8eqampkqaiwMBAREVFISUlBRcuXECPHj3Qo0cPnD9/HsXFxQgLC5M09VSUmZmJhIQEdOrUSbJNT08P7du3l9rv6dOnGDt2LDw8PGBmZgY3NzcAwMuXL6u9nqSkJEydOhVNmjSBubk5zMzMkJOTU+NxtUFBRk48Lgcj2jaSa9/8YhGuRaequUSEEEJqS95uA/LupyhjY2Op72NjYzFw4EC0atUKf/75J27duoUNGzYA+K8zcMuWLWFlZYULFy5IBZkLFy7gxo0bKC4uRteuXWtVrkGDBiEtLQ1bt25FeHg4wsPDpcpQlQkTJiAiIgJr165FWFgYIiIiYG1trdaOzBRkFNC3hfyLWv0WHqu+ghBCCFEJebsNKNK9oDZu3boFkUiENWvWoHPnzvD29kZ8fLzUPhwOBwEBATh69CgePHgAf39/tGrVCoWFhfj555/Rvn37SgFJzNzcHI6OjpJgAgAlJSW4deuW5PvU1FQ8fvwYCxcuRFBQEHx8fJCeni51Hj6fD6C043B5V65cwZw5c9C/f3+0aNECAoEAb968qdU9qQkFGQV0dLeCsUC+5qUzUcnUvEQIIVquo7sVHM0NUFXvFw4AR/PSGdzrgpeXF4qLi7F+/Xo8f/4cu3fvlnQCLq9Hjx7Yt28f/Pz8YGJiAi6Xi+7du2PPnj3V9o8BgLlz5+Lbb7/FkSNH8OjRI8yYMQMZGRmSxy0tLWFtbY0tW7bg2bNnOHv2LD7++GOpc9jZ2cHQ0BCnTp1CUlISMjMzAZT2+dm9ezcePnyI8PBwjBs3TrI4pLpQkFEAj8vBVH93ufYtFjGsD32q5hIRQgipDR6XgyWDmgNApTAj/n7JoOZ1tgxN69at8f3332PlypXw9fXFnj17sGLFikr7BQYGQigUSvWF6dGjR6VtsnzyyScYP348JkyYgC5dusDU1BRDhw6VPM7lcrF//37cunULvr6++Oijj7B69Wqpc+jp6WHdunX4+eef4eTkhCFDhgAAtm3bhvT0dLRt2xbjx4/HnDlzYGdnp/wNkQOHVRw8Xs9kZWXB3NwcmZmZMDMzq/X5hCKGZgtPolhU874Gelw8+DKE1mEihBA1KSgoQExMDNzd3WFgoHzzj6zlZxzNDbBkUHOE+MrfrYAoprqfn7yf3zT8WkE8Lge9mzvg78jEGvctKCnt9NutiU0dlIwQQoiyQnwd0ae5Ay0IrIMoyCjhnc6ucgUZoLTTLwUZQgjRfjwuh+YA00HUR0YJnT2sIdCTL6Wfe5RCnX4JIYQQNaEgowQel4PpgZ5y7StuXiKEEEKI6lGQUdLsIG/IWSmDX6/FqrUshBBCSENFQUZJPC4HbV0t5dr34hNqXiKEEELUgYJMLXSQc4Kk/GIRrYhNCCGEqAEFmVro6in/aKR/H6hvGXhCCCGkoaIgUwudPaxhoC/fLdwT/pKalwghhBAVoyBTCzwuB2M7uMi1b5GQliwghBCiu7Zs2QIXFxdwuVz8+OOPWLp0Kfz8/DRdLAoytaXIitibL0RTrQwhhBCdk5WVhVmzZuHTTz/F69ev8f7772PevHkIDQ2V7DNx4kS89dZbdV42CjK1pMiK2DSnDCGEEHURCoUQieRYCFAJL1++RHFxMQYMGABHR0cYGRnBxMQE1taanwmZgkwtKbIiNlC6ZAEhhBAiEomwatUqeHl5QSAQoHHjxvj6668BAOfPnweHw0FGRoZk/4iICHA4HMTGxgIAdu7cCQsLC/z1119o3rw5BAIBfvnlFxgYGEgdBwBz585Fr169JN9fvnwZAQEBMDQ0hIuLC+bMmYPc3FyZ5dy5cydatmwJAPDw8JCUoXzT0tKlS7Fr1y4cPXoUHA4HHA4H58+fV8l9qgkFGRWYHeQNfTkXFqMlCwghRM0YA3JzNfPF5H9/X7BgAb799lssWrQIUVFR2Lt3L+zt7RW61Ly8PKxcuRK//PILHjx4gHHjxsHCwgJ//vmnZB+hUIgDBw5g3LhxAIDo6GiEhIRg+PDhuHfvHg4cOIDLly9j1qxZMp9j9OjROHPmDADg+vXrSEhIgIuLdP/QefPmYdSoUQgJCUFCQgISEhLQtWtXha5FWbRopArwuBzM7OmJH0Of1bgvrYhNCCFqlpcHmJho5rlzcgBj4xp3y87Oxtq1a/HTTz9hwoQJAABPT0/4+/sr9HTFxcXYuHEjWrduLdk2ZswY7N27F1OmTAEAhIaGIiMjA8OHDwcArFixAuPGjcOHH34IAGjSpAnWrVuHwMBAbNq0CQYGBlLPYWhoKGlCsrW1hYODQ6VymJiYwNDQEIWFhTIfVyeqkVERWrKAEEKIvB4+fIjCwkIEBQXV6jx8Ph+tWrWS2jZu3DicP38e8fHxAIA9e/ZgwIABsLCwAADcvXsXO3fuhImJieQrODgYIpEIMTExtSqPJlCNjIqIlyy4Hpte476hD5MgFDHw5GyOIoQQogAjo9KaEU09txwMDQ2rfZzLLa1nYOWaqoqLi2Weh8OR/izp0KEDPD09sX//fkyfPh2HDx/Gzp07JY/n5OTggw8+wJw5cyqdr3HjxnKVX5tQkFGhDu5WcgWZEhGwPvQpPuzjXQelIoSQBobDkat5R5OaNGkCQ0NDhIaG4r333qv0uK2tLQAgISEBlpal6/pFRETIff5x48Zhz549aNSoEbhcLgYMGCB5rG3btoiKioKXl1ftLqICPp8PoVCo0nPKg5qWVEiRJQtoThlCCGm4DAwM8Omnn+J///sffv31V0RHR+PatWvYtm0bAMDLywsuLi5YunQpnj59ihMnTmDNmjVyn3/cuHG4ffs2vv76a4wYMQICgUDy2KeffoqwsDDMmjULERERePr0KY4ePVplZ195ubm54d69e3j8+DHevHkjswZJHSjIqFBnD2sI5OwoQ3PKEEJIw7Zo0SJ88sknWLx4MXx8fDB69GgkJycDAPT19bFv3z48evQIrVq1wsqVK/HVV1/JfW4vLy907NgR9+7dk4xWEmvVqhUuXLiAJ0+eICAgAG3atMHixYvh5ORUq+uZOnUqmjZtivbt28PW1hZXrlyp1fnkxWFMgbFiOigrKwvm5ubIzMyEmZmZ2p/vx9OP5Rq9BAD9fO2x6Z32ai4RIYTUXwUFBYiJiYG7u3ul0TZE+1X385P385tqZFRMkTllzkQlU/MSIYQQUgsUZFRMPKeMPIpFtJAkIYQQUhsUZNSgtFZGvn03nHtGtTKEEEKIkijIqAGPy0Hv5vLNbEi1MoQQQojyKMioyTudXeXel4ZiE0JI7dTzcSv1lip+bhRk1ISGYhNCiPrp6+sDKF08kege8c9N/HNUBs3sqyY8LgfTA+VbSBIoXX+JFpIkhBDF8Hg8WFhYSOZfMTIyqjRlP9E+jDHk5eUhOTkZFhYW4PF4Sp+LgowazQ7yxvqzzyCUo+aM1l8ihBDliFdbFocZojssLCxqvVo2BRk14nE56NPcHqceJNW4L62/RAghyuFwOHB0dISdnV2dTYtPak9fX79WNTFiGg0yK1aswKFDh/Do0SMYGhqia9euWLlyJZo2bSrZp6CgAJ988gn279+PwsJCBAcHY+PGjbC3t9dgyeU3voubXEEGKB2KPTuoCdXKEEKIEng8nko+GIlu0Whn3wsXLmDmzJm4du0aTp8+jeLiYvTt2xe5ubmSfT766CMcO3YMBw8exIULFxAfH49hw4ZpsNSKUaTTLw3FJoQQQhSjVWstpaSkwM7ODhcuXED37t2RmZkJW1tb7N27FyNGjAAAPHr0CD4+Prh69So6d+5c4znreq0lWRRZf0mfy8Gjr/pRrQwhhJAGTSfXWsrMzAQAWFlZAQBu3bqF4uJi9O7dW7JPs2bN0LhxY1y9elXmOQoLC5GVlSX1pWmKrL9EtTKEEEKI/LQmyIhEInz44Yfo1q0bfH19AQCJiYng8/mwsLCQ2tfe3h6JiYkyz7NixQqYm5tLvlxcXNRd9Bopsv4SQMsWEEIIIfLSmiAzc+ZMREZGYv/+/bU6z4IFC5CZmSn5evXqlYpKWDtUK0MIIYSonlYEmVmzZuH48eM4d+4cGjVqJNnu4OCAoqIiZGRkSO2flJRU5bhzgUAAMzMzqS9toGitDC1bQAghhNRMo0GGMYZZs2bh8OHDOHv2LNzd3aUeb9euHfT19REaGirZ9vjxY7x8+RJdunSp6+LWmiK1MrRsASGEEFIzjQaZmTNn4rfffsPevXthamqKxMREJCYmIj8/HwBgbm6OKVOm4OOPP8a5c+dw69YtTJo0CV26dJFrxJK2UbRW5tdrseorDCGEEFIPaHT4dVXrYezYsQMTJ04E8N+EePv27ZOaEE/eKY21Yfh1eUIRg/cXJ+VatkCPCzz+qj8NxSaEENLgyPv5rVXzyKiDtgUZAJi2+6bcs/1+GNSEli0ghBDS4OjkPDINxfgubnLvS0OxCSGEkKpRkNEAWraAEEIIUQ0KMhrA43IwPZAmyCOEEEJqi4KMhtAEeYQQQkjtUZDREJogjxBCCKk9CjIaRBPkEUIIIbVDQUaDFK2V+eLIfTWWhhBCCNE9FGQ0bHaQN3hyzncXm5qHY3fj1VsgQgghRIdQkNEwHpeDPs3t5d7/k98jqK8MIYQQUoaCjBZQZIK8IiGNYCKEEELEKMhogc4e1jDQl/9HQfPKEEIIIaUoyGgBHpeD74a3knt/mleGEEIIKUVBRksM9HNG28bmcu+//uxTqpUhhBDS4FGQ0SIHp3WTewSTkAFz991Rb4EIIYQQLUdBRovwuBzM7uUl9/7H7yegqESkxhIRQggh2o2CjJZRZLZfAFhw6J4aS0MIIYRoNwoyWkbR2X4P3X5NfWUIIYQ0WBRktJAis/0yUF8ZQgghDRcFGS3E43Iws4f8tTLH7yfg5L0ENZaIEEII0U4UZLTU3D5N5a6VAYCPaekCQgghDRAFGS2l6AimghIRTZJHCCGkwaEgo8UUHcFESxcQQghpaCjIaDFFRzDR0gWEEEIaGgoyWm52kDcECnSWoVoZQgghDQkFGS3H43Lww2g/ufenWhlCCCENCQUZHdC/lRP8XMzk3n9dKC0oSQghpGGgIKMj5gf7yL2vCMCozWHqKwwhhBCiJSjI6IjOHtYw5sv/47r1MgPH7sarsUSEEEKI5lGQ0RE8LgerR7RW6JiPDtyhJiZCCCH1GgUZHdK/lRMGtLSXe/8SEa3DRAghpH6jIKNj1o1tp9AkecfvJ6CoRKTGEhFCCCGaQ0FGx/C4HPwwSrEmpne3haupNIQQQohmUZDRQQP9nNG2sbnc+1+LSaPVsQkhhNRLFGR01MFp3aDA4tjU8ZcQQki9REFGR/G4HMxWYB2mQiGjjr+EEELqHQoyOmxun6ZQYBkmHL+fQE1MhBBC6hUKMjqMx+Vgdi8vhY6Zu5+amAghhNQfFGR0nKKrYxeLqImJEEJI/UFBRscpujo2QHPLEEIIqT8oyNQDis74CwAD1l5UU2kIIYSQukNBpp5YN7adQk1MT1Nysfx4lBpLRAghhKgfBZl6Qpkmpm2XY2gUEyGEEJ1GQaYe6d/KCf19FWtiolFMhBBCdBkFmXpm/dvtFJpbhkYxEUII0WUUZOoZHpeDtTSKiRBCSANBQaYeUnRRSYBGMRFCCNFNFGTqqYPTukFPgZ8ujWIihBCiiyjI1FM8LgfrxrRR6BgaxUQIIUTXUJCpx5QZxTRn320axUQIIURnUJCp5xQdxVTCgFGbw9RXIEIIIUSFKMjUc8qMYrr1MgPH7sarp0CEEEKIClGQaQCUGcU0Zx9NlEcIIUT7UZBpIBQdxcQAjNh4RW3lIYQQQlSBgkwDocwopjtxmVh27IGaSkQIIYTUHgWZBqR/KydM8XdV6JgdV2Lx9QmaX4YQQoh2oiDTwCwa6IsmtsYKHbP1Es0vQwghRDtRkGmATsztrvAxNL8MIYQQbURBpgHi63EVbmIqYcDITdT5lxBCiHahINNALRroC3cbQ4WOuf0qk9ZjIoQQolUoyDRgZz7uqdCQbIDWYyKEEKJdKMg0YMoMyQaA2XupvwwhhBDtQEGmgevfyglTA9wUOkYIoPeac2opDyGEEKIICjIEXwxogRAFV8mOSc3HlJ031FQiQgghRD4UZAgAYMPb7RTuLxP6KJkWlySEEKJRFGQIAOX7y8ylxSUJIYRoEAUZIqFMfxkRgKDvzqqlPIQQQkhNKMgQKV8MaIFJ3RSbLC82rQAD1l5QU4kIIYSQqlGQIZUsGeSLNi5mCh3zICEHA9ddVFOJCCGEENkoyBCZ/pjur/CLIzI+G5N3XFdLeQghhBBZKMgQmXhcDn56W/HOv2cfp2DZsQdqKBEhhBBSGQUZUqX+rZwUXlwSAHZcicXy4xRmCCGEqJ9Gg8zFixcxaNAgODk5gcPh4MiRI1KPT5w4ERwOR+orJCREM4VtoBYN9EWvpjYKH7ftMoUZQggh6qfRIJObm4vWrVtjw4YNVe4TEhKChIQEyde+ffvqsIQEALZP6gRfRxOFj9t2ORZfn6DVsgkhhKiPniafvF+/fujXr1+1+wgEAjg4ONRRiUhVjs8NRI9VoYhNK1DouK2XYtDGxRL9WzmqqWSEEEIaMq3vI3P+/HnY2dmhadOmmD59OlJTU6vdv7CwEFlZWVJfRDVC5/VS6gUzk1bLJoQQoiZaHWRCQkLw66+/IjQ0FCtXrsSFCxfQr18/CIXCKo9ZsWIFzM3NJV8uLi51WOL6TdmRTAxAr9U0+y8hhBDV4zDGtOJPZQ6Hg8OHD+Ott96qcp/nz5/D09MTZ86cQVBQkMx9CgsLUVhYKPk+KysLLi4uyMzMhJmZYpO8Edm+PvEAWy/FKnxcC0cTnJgbqPoCEUIIqXeysrJgbm5e4+e3VtfIVOTh4QEbGxs8e/asyn0EAgHMzMykvohqfTGgBab4uyl8HM3+SwghRNV0KsjExcUhNTUVjo7UcVTTFg1ULszQ7L+EEEJUSaNBJicnBxEREYiIiAAAxMTEICIiAi9fvkROTg7mz5+Pa9euITY2FqGhoRgyZAi8vLwQHBysyWKTMosGKr7AJECz/xJCCFEdjQaZmzdvok2bNmjTprQD6ccff4w2bdpg8eLF4PF4uHfvHgYPHgxvb29MmTIF7dq1w6VLlyAQCDRZbFLOkkHKTZhHs/8SQghRBa3p7Ksu8nYWIrUzcO0FRCbkKHzcpG6uWDLIVw0lIoQQosvqZWdfor2Ozw1ECyVm/91x5QWm7KQ+M4QQQpRDQYaozIm5gXCzMlD4uNBHKRRmCCGEKIWCDFEpZWf/DX1EHYAJIYQojoIMUSllZ/8FSjsALzsWqeISEUIIqc8oyBCV69/KCVMD3JQ6lvrMEEIIUQQFGaIWys7+C1CfGUIIIfKjIKMEoYjhanQqjka8xtXoVFrZuQrKzv4LlIaZJX9RMxMhhJDq6Wm6ALrmVGQClh2LQkJmgWSbqQEPK95qiYF+zhosmXZaNLAFAGDb5ViFj90V9gKvUnOxfVInFZeKEEJIfUE1Mgo4FZmA6b/dlgoxAJBdIMSs/RF4bxc1h8hSm5qZs4/fYODaC6otECGEkHqDgoychCKGZceiUF0j0pmH1LejKrUJM5EJORhAYYYQQogMFGTkdD0mrVJNjCyhj1Kw/HhUHZRI99QmzDygMEMIIUQGCjJySs6uOcSIbbscg5P3EtRYGt1V2zDTY1Uoda4mhBAiQUFGTnamik29P2ffbfrArUJtwkxsWgGafH4SJ+/Fq7ZQhBBCdBIFGTl1dLeCqQFP7v1LGDBqc5gaS6TbahNmRABm7L2Dr0/QkgaEENLQUZCRE4/LwYq3Wip0zK2XGTh2l2oOqrJoYAtMDXBX+vitl2Kx/DiFGUIIacgoyChgoJ8z2jY2V+iYD/ffoSamanwxoDk2vt1W6eO3Xab1mQghpCGjIKOgg9O6QU+BuyZkwOy9t9VXoHqgfytHPPmqHzhKHr/jygtM2n5NpWUihBCiG5QKMhkZGdixYwcmT56MoKAgdOnSBYMHD8aSJUsQFla/+4XwuBysG6PY6s4nIxNpFFMN+HpcbHpH+ZqZc09SEfDtGRWWiBBCiC5QKMjEx8fjvffeg6OjI7766ivk5+fDz88PQUFBaNSoEc6dO4c+ffqgefPmOHDggLrKrHH9Wzlhir+rQsfM3kujmGoS4uuIze+0Vbqa8FVGIdov/5fuMyGENCAcxpjc7/r29vaYMGECJk6ciObNm8vcJz8/H0eOHMG6deswfPhwzJs3T2WFVUZWVhbMzc2RmZkJMzMzlZ67z5rzeJqSK/f+HjZGODuvp0rLUB8JRQxB351DbFq+UsdzAKwf40drXxFCiA6T9/NboSCTmpoKa2truQuh6P7qoM4gU1QigvfCvxU6Zoq/OxYNlB0CibRJ28Nx7skbpY8PamaDbRNpwUlCCNFF8n5+K1SLr2go0XSIUTe+HhcDWtordMy2yzEoKhGpqUT1y47JndCrqa3Sx4c+ogUnCSGkvlOoRkaWqKgovHz5EkVFRVLbBw8eXKuCqYo6a2SA0maQpgtPQpFs4mAqwLUvequ8LPXVlJ3XEfooRenj3awMEDqvF3hcZcdFEUIIqWtqaVoq7/nz5xg6dCju378PDocD8Wk4nNIPC6FQqMxpVU7dQQYATt6Lx4y9dxQ6pldTW2yf1FEt5amPlh+PwrbLMUofT/1mCCFEt6ilaam8uXPnwt3dHcnJyTAyMsKDBw9w8eJFtG/fHufPn1f2tDpJmVFMZx+nYNkxmpVWXosG1m7iPAZg1v4ITN5B880QQkh9onSQuXr1Kr788kvY2NiAy+WCy+XC398fK1aswJw5c1RZRq0jFDGE33uBx1PnIvxBHIQihkUDfdHGRbEanx1XYvH1iSg1lbL+6d/KEdHf9IeNkZ7S5zj7OBUdaIg2IYTUG0oHGaFQCFNTUwCAjY0N4uNL1xRydXXF48ePVVM6LXQqMgH+34YCAwei6S/r8GT8B/D78h8cj3iNP6b7K3xDt16KocnyFMDjcnBzcTBaOJkqfY6U3GJ40QrahBBSLygdZHx9fXH37l0AQKdOnbBq1SpcuXIFX375JTw8PFRWQG1yKjIB03+7jYSsQvzUZRQAYPydk+h69xJm7Y/AB7tvYN0YP4XPO4smy1PYiTndEdTMTunjGUpX0F5+nNZpIoQQXaZ0kFm4cCFEotKhOl9++SViYmIQEBCAkydPYt26dSoroLYQihiWHYuCOG5ccm+LzZ2GAwBW/b0WTlnJOPMwBYcjXiOomY1C5xYBGLHximoL3ABsm9gBa0f71e4cl19g8o5w1RSIEEJInav18Ovy0tLSYGlpKRm5pA1UNWrpanQqxm6V7iiqLyzGwT3/g1/CU1xv1Bxjx66AkMvDpG5uOP8oCTGpis1MO6mbG5YMaqF0GRuqk/cSMKOWC3Pam+rj0qe9wVdkRVBCCCFqo/ZRS+Xt27cPubm5sLKy0qoQo0rJ2QWVthXz9DF78KfI5huiY1wUZoftB1DaibeXj71Cq2SLj1t+nEYyKap/q9I1mni1eOklZRfDe+HfmPHbTWrmI4QQHaKSIPPBBx8gKSlJFafSWnamBjK3v7JwwBfBswAAs8MOoPPLewCAbZdjMaGrm8LPs+0yjWRSRoivI5583R9tXSxqdZ6TkUloQh2BCSFEZ6gkyKiwdUprdXS3gqkBT+ZjfzUPxEHf3uAxEdb/tQp22akAgF1XYjGpm2LzywA0kklZPC4Hh2Z2w/qxbWp1HhGoIzAhhOgK6hAgJx6XgxVvtazy8cV9puGhrRtsczOw8ei30BcWo4QBd19moFdTxTr/AsBMGsmktEGtnRD9TX/YGuvX6jzbLr/A0A2X6OdACCFaTCVB5u+//4azc/2f+n2gnzPaNjaX+Vg+3wDThn6OLIEx2r9+iM/PbQcA3H6VCXdbU7hbGyr0XAxAr9Vna1vkBovH5eDGor7oqUSILO/Oqyx4fX4SxyNeq6hkhBBCVEklQcbf3x8CgUAVp9J6B6d1q7IT7wtLJ3w08GMAwKRbxzDkwTkApStef9KnmcKdf1+kF2AArd5cKzsmdcKkbm61Ood4eYNhVDtDCCFaR6GP1pCQEFy7VvNaNdnZ2Vi5ciU2bNigdMG0FY/LwboxVffBCPXqhHVdRgMAvj31E5olly50OHf/Hfw4SvG+Gw8ScjBw3UXlCksAAEsGtcDUAPdan+c21c4QQojWUWgemW3btmHx4sUwNzfHoEGD0L59ezg5OcHAwADp6emIiorC5cuXcfLkSQwYMACrV69G48aN1Vn+Gqlr9euvTzzA1kuxMh/jioTYeXApusfewStze7w1fg1SjS3gbm2I3s3tqzyuOrRadu2dvJeA2ftuQ6iCSpVeTa2xfVLn2p+IEEKITPJ+fis8IV5hYSEOHjyIAwcO4PLly8jMzCw9EYeD5s2bIzg4GFOmTIGPj0/trkBF1BVkAGDabzdxKlL2sHPz/Gwc2f0x3NMTcMO5OcaN+RpFevro1dQWrjZG2HHlhcLPRxPm1Z5QxDB7722cjEys9bnMDXi4sbAvTaJHCCFqoLYgU1FmZiby8/NhbW0Nff3ajRJRB3UGGaGIoenCkygRyX7cM/UVDu+eB7PCXPzp2wuf9P8I4HAwqZsbXrzJwdnHbxR+zin+blg0kMJMbRWViNB9ZSgSs4tqfa7+vvZY/3Y78Lj1czJIQgjRhDqb2dfc3BwODg5aGWLUrab+MtHWLpgx5DOUcLgYHnkW08L/BFA6g6+7rQl8HU0Ufs5tl2n2X1Xg63Fx7Ys+tVp4UuxkZBI8Pz+Jv27HqaBkhBBCFKF0kGkIk+DJo38rJ0wNcKvy8cvubbCs9/sAgP9d2IXgJ2EASgNJJ08btKAwo1HbJnbA+rFtoIq6lDm/30WP1aE0sokQQuqQUkGmqKgII0eOVHVZdNYXA1pUO4Pv7rYDsavtAHDBsPbYd2gXV7oEwbbLsejqZQs3K9nLH1SHwozqDGrthGff9EebRrLnCFJEbGoBPD8/ie//eUSBhhBC6oDCQSYnJwf9+vVDSUmJOsqjs5YM8kUbl6rb8L4Meh9nPDvAoKQI2/78Ep5vXgEoXY5gXl8fpRIlhRnV4XE5ODzLH1P8az9MGwDWnYtGky9oqDYhhKibQp+fb968QWBgIHg8Hg4ePKiuMumsP6b7V3lDhVweZg/+FHccm8KiIAe//r4Y9tmlnX1n779TbV+b6lCYUa1FA5tj49ttoa+CjrsiRhPpEUKIuikUZPz9/WFsbIwjR440yM69NeFxOfjp7aoDST7fAJNHLEa0lTOcs1Ow8+BSmBXkgAFY/c/DavvaVIfCjGr1b+WIR1/1w5yeXio53+1XWdTcRAghaqJQkImOjkZISAiMjIzUVR6dV1Pn33Qjc0wY9SWSjS3hkxKLLYe+gqC4EC/SCxD27A2m+Fd9bHUozKgWj8vBx8FNEf1Nf9iZ8FVyznXnouH9xUmcvBevkvMRQghRMMj8/vvv+Oqrr7B161Z1lade+GJAi2oDSZy5PSaOXIZsviE6v4rEpiMroC8sxoOEHFyLrl2YWXYsUrlCE5l4XA6uL+yDybVcr0lMyIAZe+9gxm83qXaGEEJUQOEJ8c6dO4dhw4Zhw4YNePvtt9VVLpVR54R4NVl+/AG2XY6t8vFOL+9j58GlMCwpxEnvrpg95FMIuTz4Opmig7uVUrP/AkCvpjbYPqmTkqUmVSkqEWHAuot4mpyrkvNxAMzu6Ym5fZrSZHqEEFKBWmf2vXXrFgYPHozXr7V/RIYmgwwALDsWWW0g6f78FrYeWg6BsASHWvTEJwM+AuNw0dPbBlwuB6GPUpR6Xl9HExyfG6hssUk1jt2Nx+x9d1R2Pi6AdWP8MNDPWWXnJIQQXaf2JQqePHkCb29vpQtYVzQdZABg8o7wapcj6PP0GjYd/gZ6TIS9rYPxefAsgMOpdZhp4WiCExRm1EIoYhix6QruvMpU2Tmb2BrhxNxAWruJEEJQB0sU6EKI0RbbJ3WqdjmC000648NB8yDkcPH23X+w7MxmgDGce/IGSZn5mKRk/4wHCTkYsPaCkqUm1eFxOTg80x/rx7aBqlqFnqbkwXvh35j22w3qP0MIIXJSuEZm8uTJcu23fft2pQqkatpQIyM2YO0FPEjIqfLxEffPYNXJteCCYY9fCBb2nQHG4da6z4yblQFC5/WifhhqosoVtcubQ/1nCCENmNqalrhcLlxdXdGmTZtq11s6fPiwIqdVG20KMoBiYeb3lr3xWchsiLi8WjczcQCsp34YanXyXgLm7r+DYhXWplCHYEJIQ6W2IDNz5kzs27cPrq6umDRpEt555x1YWVnVusDqom1BBgB6rApFbFpBlY8PeXAO35/4ATwmwuHmPTBvwEcQqiDMAEBvH1v8MqGj0seT6glFDGtPP8G6c89Uel7qEEwIaWjU2tm3sLAQhw4dwvbt2xEWFoYBAwZgypQp6Nu3Lzgc7fqrURuDjFDE0OTzkxBVs0//R5ex7q9V0GMiHG8WgI8Gfoxinj7crAzQy8cB26/EKv38NDxb/YQihpGbwnD7VYZKz0sdggkhDYXaRy2JvXjxAjt37sSvv/6KkpISPHjwACYmVXdsrWvaGGQA4OS9eMzYW/0Q3r5PruKnoyvBF5XgvHs7TH9rAfL5BuABmBzgjq2XYpR+fr9GZvhzhj81V6jZsbvx+PDAHQirS61K8LE3waGZ/jDk81R7YkII0RJqH7UkOQGXCw6HA8YYhEJhbU/XYPRv5YQPule/0vK/3l3w/rCFyNcToEfMLezd/wUs8rMgROmq2etGtYayMSQiLgven5/EqcgEJc9A5DGotROefNUfc3p6Kf2zkuVhUg58Fp9CnzXnUFSi4pRECCE6RKkgU1hYiH379qFPnz7w9vbG/fv38dNPP+Hly5daVRuj7Rb0L11puTrnPdtj3JivkGFggjYJj3Fwz6dwzCrtIzPn97uY0q36MFQdIYBpv92mtX/UTLxu07Nv+qO/r4NKzy0esj16cxgFGkJIg6Rw09KMGTOwf/9+uLi4YPLkyRg3bhxsbGzUVb5a09ampfKKSkRouvBvVPeDaJLyAr/+vhiOOamIN7XB+FHLEW3jAgAIamaLc49Squ1zU5N1o/wwuC11JK0LRSUijN92DeEx6So/dyc3S+x+rzP1oSGE6Dy1Dr9u3Lgx2rRpU23H3kOHDilyWrXRhSADyNdnxikrGbsPLIZnWhzSDUzx/rAvcMPFF0BpB97nKbmITctXugxtXcxwcDr1m6krRSUiBK46i4SsQpWfmwINIUTXqS3ITJw4Ua6RSTt27FDktGqjK0EGAFacjMLPF6vvwGuZl4kdfyyDX8ITFPL08L9+c3G0RU8ApWEG4ODsY+WHZ3MB/PR2G/Rv5aT0OYhijka8xkcHIqCOyXw7uFlgz3tdKNAQQnROnY1a0na6FGSA0knVZu69XW0zk0FxAX44/j36PQkDAPzYbSx+7PY2wOHAr5EZ/BpbYmeYcrMAi03xd8Wigb61OgeRn3j+mfXnnlX7s1cW1dAQQnQNBZkyuhZkgNIPtV6rz+JFetWT5nGYCJ9e2IVp4X8CAA4374FP+81FkZ4+uAB6NrOt1cR5ANDGxQx/UFNTnVLXcgdiNGybEKIrKMiU0cUgI1bTcgYAMCbiFJaf3gR9kRA3nJtj+tAFeGNsCaC0E/DZRym1+gufljbQDHV2CAYAB1M+Vo/0Q1cvGwqqhBCtREGmjC4HGQAYuO4iIuOzq92nW2wENh1ZAbPCXMSb2mDa0M9xz7F0dfIJXRvj/MPkamt35BHUzAbbJtJswHVN3YGGA2BWD0982JfWciKEaBcKMmV0PcgAwOQd12vswOuRGocth76CV1ocCnn6WNh3Bg626gOgtImoSMjwoIZAVBNaRVtzikpEeOeXq7gem6G25xjm54RvR7SmfjSEEK1AQaZMfQgyALDs2APsqGF9JZPCPPxwfA36PAsHAOxqOwDLe01FCU8PHAAtHM0QmZBV67KsG9Uag9s2qvV5iOKKSkQYsO4inibnqu05qB8NIUQbUJApU1+CDAAsP/4A2y7HVrsPh4kwO+wAPr68BwAQ3qgFZg35DCkmpf1mfB1NEFlDvxt5uFkbIPQTqp3RlGN34/Hx7xEoFqrv15f60RBCNImCTJn6FGQA4OsTUXItFtnr2XX8eOw7mBXlIcXYAnMHzkOYmx8AwNVSAA6HW6vJ88R+oo7AGiMUMYQ9fYOlxyMRnZKntufhABhKzU6EkDpGQaZMfQsyQOlcM7P23q5xSQKP1DhsPLICzd68gAgcrO86Bmu7jYGIy1NpU1MTWyOcmBtIH3IaVBdNTgDNR0MIqTt1tvp1bVy8eBGDBg2Ck5MTOBwOjhw5IvU4YwyLFy+Go6MjDA0N0bt3bzx9+lQzhdUi/Vs54uk3/eHnXH0we27dCG+9uwb7WvUFFwxzw/bhtwOLYJuTBgYgMiELvo4mtV6VWbxw4fLjkbU8E1EWX4+L0x/3wMMvQ9DMQX0Lt4bHpsN74d/o98MF5BfRaveEEM3TaJDJzc1F69atsWHDBpmPr1q1CuvWrcPmzZsRHh4OY2NjBAcHo6CgdkOJ6wMel4MjswPQq6lttfsV6BtgQb85mDNoHnL4huj68h5O7pgD/5jSdZ0iE3LQ2FIAv0a1r63advkFOn3zL63CrEGGfB5OfRiIJ1/1Qyd3S7U9z8OkHPgsPoXWS09hy4Vo+pkTQjRGa5qWOBwODh8+jLfeegtAaW2Mk5MTPvnkE8ybNw8AkJmZCXt7e+zcuRNjxoyR67z1sWmpovd23cCZh8k17ueRGocNR7+FT0osAGBb+yFY1f1dFOoLwAHQw9sW557UbjZgsf6+9lj/djvqJKphdTFsW4xGOxFCVEknmpaqExMTg8TERPTu3VuyzdzcHJ06dcLVq1erPK6wsBBZWVlSX/XdLxM6YP3YNjU2ET23boS3xq/Br20GAACm3DyKY7s+QvOk52AAzj1JgZu1Qa2bmgDgZGQSvD4/ieMRr1VwNqIsvh4Xv0/rhidf9cOwNupdCJRqaQghmqC1QSYxsXStGXt7e6nt9vb2ksdkWbFiBczNzSVfLi4uai2nthjU2gnPvumPNo3Mq92vUF+AxX2nY+KIJUgxtoB36ksc+fVjTLv2B7giIWJTC8AAuFkb1rpMDMCs/REYtuEShOpY2pnIja/Hxfej2yD6m/6Y3cMT6qwoyywQ4pu/H8F74d8YufkKBRpCiFppbZBR1oIFC5CZmSn5evXqlaaLVGd4XA4Oz/LHFH/3Gvc979kBwZM34J8mncEXleCzCzuxb9/naJSZBACITc2HvYm+Ssp1+1UWPD8/ib9ux6nkfER5PC4Hn4Q0w9Ov+2P3pI7wtDVS6/PdiM2A98K/0fHrM1RLQwhRC60NMg4ODgCApKQkqe1JSUmSx2QRCAQwMzOT+mpoFg1sjo1vt63xh5tmZI4Phn6B+f3mIIdviE5xD3Bq+yy8c/sEOEyEpJxiAICtigLNnN/vUmdgLcHjchDQ1Bahn/TEk6/6oaObhVqfLzm7UFJL0+u7c7j0JIVq6QghKqG1Qcbd3R0ODg4IDQ2VbMvKykJ4eDi6dOmiwZLpBvEQ7ZqamsDh4GCrvug3aT2uN2oOk6J8fHV6E/bv+xxuaaX9W1JyimHC56qk70xSVjG8F/6N0ZvDKNBoiYr9aNTdPfv5mzyM334dnp+fxMhNVyjUEEJqRaOjlnJycvDs2TMAQJs2bfD999+jZ8+esLKyQuPGjbFy5Up8++232LVrF9zd3bFo0SLcu3cPUVFRMDAwkOs5GsKopZosPx6FbZdrng2Yw0QYf/sEPr2wC8bFBSjQ4+O7gHewvf0QiLilI1EczPhIzCpSWdlCfO2w4e32NLpJiwhFDGtPP8GGC88grKOsyQUwq6cn5vahVbgJIaV0Ymbf8+fPo2fPnpW2T5gwATt37gRjDEuWLMGWLVuQkZEBf39/bNy4Ed7e3nI/BwWZUvLOBgwAjTKTsOLv9Qh4EQEAiHD0xqf95uCxrZtkHw5KO/Oqyhz6ENM6dbUEQkVN7Uzw+QAf+DexpdcDIQ2YTgSZukBB5j9CEcPITWG4/Sqj5p0Zw6h7p7Hw3DaYFeaihMPFtg5vYW23scjjl45oMjfgIbNAdbO7cgCspZW1tVJdzkdTXgdXC8wJ8qaFKwlpgCjIlKEgU9mxu/GYve+OXPvaZadi2Zmf0e9JGADgtaktvuw9Ff806QJwSj9YTPhc5BSprg3C3kwfl/7Xm9bz0UJFJSLsuPIc2y7FIDlHdU2M8hja2gkrR9LClYQ0FBRkylCQkU0oYui95jxiUuVrMugRfQNfnt6MxmXDs0M9O2Bp7w/wyqLqEWS1RQsUajdN1dLYmQrwnr87JnZzp9cGIfUYBZkyFGSqdzTiNT7cHyFXfxeD4gLMvHoQH4T/Cb6oBAV6fGzoPBJbOg5Dob4AAKDPBYpV3EG0g5sF9rzXhT60tJS4lmbDuWhkFZTU6XPbmfLxnr8HhRpC6iEKMmUoyNRMKGKYvfc2TkZWPWNyeZ6pr7D8303o+vIeACDOzA4rekzCiWb+kuYmdaAaGu2XXyTE0I2X8Sgxp86f28lMgBXDW1EnYULqCQoyZSjIyK+oRIQOX/0rXwdexjD44UV8dn4HnLLfAACuN2qOL4PeR6SDl1rLSYFG+2mylgYAPGyMMKZDY6qpIUSHUZApQ0FGcZN3XsfZR/Ktgm1QXIAPwg9hWvifMCwphAgc/OkbhNXdxyPZ1Fqt5aRAoxvyi4SY+usNXHmWqtIh+/Ki5idCdBMFmTIUZJRz7G485u6/A3knXHXIeoP/XdyFYQ/OAQBy9Q2wudNwbOvwlmS4trr42Jvg0Ex/GPJ5an0eUjuampemPDMDPQxq5YiFA1vQ64UQLUdBpgwFGeUp2ncGANq8foTFoVvRJuExACDF2ALruo7B/tbBKOapZs2mqjSxNcKJuYH0V7cOKCoR4bM/7+JIRLzcYVnVjPW56N3cASPaNaJ5agjRQhRkylCQqb2iEhEGrLuIp8m5cu3PYSIMfHgJn1z6DW4ZCQCAFxYOWBMwHsd8AsA46g0anjbGWDq4BX046QBxLc3a0Me4+TJTo2WhyfcI0S4UZMpQkFEdRZub9IXFGH33X8wN2wfb3AwAwAM7D6wKnIAL7m3VOsIJoPV7dI0k1Jx9gpsvMjRaFlomgRDNoyBThoKMainT3GRUlI9JN//CB+F/wqyotG/E9UbN8WO3txHm2lrtgQYAhvk54dsRNCusrhCKGC4/TsFnh+8hIatQo2WhEVCEaAYFmTIUZNSjqESE8duuITwmXe5jLPMyMePaQbx7+wQEwmIAdR9oaHI93aPpodzlWRjqIdDbjvrVEFIHKMiUoSCjXor2nwEA++w3mBb+J96OOKWxQEP9aHRTfpEQXx6PxIl7CchS4YKlymrf2AJze1O/GkLUgYJMGQoydePY3Xh8eOAOhAosT1BVoFnfdQwuubWpk0DDATCUmp10UlGJCP/7IwJHIxI0Mj9NRXamfPTxsaeh3YSoCAWZMhRk6o5QxLD29BOsP/dMoQ8WWYEm0t4TmzqNwN9Nu0LErZsPBaql0U3iTsIHb73EmUfJyFPhSuzKMuBx0MLZHMEtHKhvDSFKoiBThoJM3VOmQzDwX6AZfe9fGBWXdvCMtXDElk7D8KdvEAr1+OoobiU02km3aVvzEwBYGOoj0NuW+tYQogAKMmUoyGiOMh2CgdJOwRNuH8eEW8dhWZANoHRivW3t38KeNv2QLTBWR3FlorlFdJu4o/C2SzFIzinSdHEk3K2NMLYjjYQipDoUZMpQkNE88Syuh+7EK3ScYVEBxtz7B+9dPwLn7NK1n7L4Rvi9VR/sbDcIcRYO6iiuTNSXRvdpa6gx0ueiTWMLvN/dk+atIaQcCjJlKMhoD6GIYdaeW/j7QZJCx+kJSzD44QV8EP4nmr55WXouDhdnvDpie/shCHfxrZOOwWK0tpPuE4ea/ddfISZVM+s+VcXCUA8tnMwo2JAGj4JMGQoy2kfZGhoOEyHw+W1MuvUXAmNuS7ZH2bljR7vB+Kt5YJ31owEAcwMeZvZsQs0DOk6blkmQxdFMgI7u1tS/hjQ4FGTKUJDRXsrW0ACA55tXmHTrLwyPPAvDktKOwW+MzLHHrx/2+oUgydRG1cWtlpOZACuGt6K/oHWcNo6AqoiCDWkoKMiUoSCj/ZStoQEA8/xsjL37D969fRxO2W8AACUcLs406YQ9fv1w2c1P7YtUVkRT2tcf4hFQZ6KSkJJTrOniyORgJkAnCjakHqIgU4aCjO5Qdtg2UNqPJvjJVUy4fQwd46Ik219YOGCvXwgOtuyDNCNzVRZXLh42Rlg22Jc+YOoB8fpPX/8dhScKzGRd1yjYkPqCgkwZCjK6R1xDcyQiXu6VtstrkvICb989heH3QyWLVBby9HDKuxv2tOmH641a1GnnYLEAT2tsmdCBOgnXA+WboC48eYNMDa8BVR0rI334e9lgZHsXCjZEp1CQKUNBRneJPyzm/RmBpCzFh8saFhVg4KOLGBfxN/wSnkq2P7NqhIOteuNQi15IMbFSZZHlYqzPRe/mDvQXcz2izaOgKjLmc+FsYYhhbRthsr8HNX8SrUVBpgwFmfohv0iIoRsv41FijlLH+yY+w9sRf2NI1AUYFxcAKO1Lc8GjHQ627I1Qr44o5umrsshyo4UH65fytTXXY9OQqEQIr0sCHgeu1kYUbIjWoSBThoJM/aLsbMFiJoV5GPjwIkbeP4N28Y8k29MMzXC0eSAOtuyDKHsPVRVXYdT8VP+I+9ZsvvgMEXGZyC/WvpFQ5Ql4HFga8+Fpa0xz2RCNoiBThoJM/aSKWVo9U19hxP1QDHtwFvY5aZLtD+w88EfLIBzz6Y43xpaqKrJCqPmp/vqvGeolYlLzNV0cuVBzFNEECjJlKMjUf7WtpeGJhAiIuYMR98+gz7NrEAhLO24KOVxccW2No8174B/vLsgRGKmy2Aqh5qf6qXwzVHhMGpKytbsZSoxqbUhdoCBThoJMw1Hb0U4AYJGfhcFRFzD0wXm0SXgs2V6gx0eoZwccbd4D5z3ao0hPM/1pAMDOlI8+PvZYOLAFNUHVM+WDzcWnb5CRr72joSoy4XNhYqBP4YaoDAWZMhRkGh7xh8HS45GITlF+BIlrejwGR13AW1EX4JkWJ9meJTDGyabdcLR5IMJdfCHiai5MUBNU/SZuhvonMhExqblIz9OdYANQkxSpHQoyZSjINGyqqKUBY2iR/ByDoy5gcNQFOOakSh5KMbLAP95dcLJpN4Q3bgmhBkMNQLMK13flOw4/SMhCVoFQ00VSCJ8LGBvow9aET+GG1IiCTBkKMgQoV0tzLBLRb5SvpeEwETq+eoAhURfQ//FlWBT8Nxw81dAM/3h3wd9Nu+Fq41Yo4empouhKM+ZzEdTMniZCq8d0PdgAFG5I1SjIlKEgQypSxYgnoHRZhC4v76Hf4ysIfnIV1vlZkscyDEzwb5POONm0G8Jc/TTap0aM+tbUf+LA/vvNF7gcnapzTVFi+hzA3Egfja2MEeLrQDWMDRQFmTIUZEh1ikpE+N8fETgakYDa/CLwREJ0fBWJ/o+vIORJGGxzMySPZQmMcc6jPc54dcR5z/bIFhjXuty1ZcDjoIWzOYJb0IdEfVa+8/CD+Ey8zihAQYluvuXrcwAzQz1YGQvQ3Mmc+oU1ABRkylCQIfIQv+GvPfsEN19k1OpcXJEQHeKi0O/xFfR7EiY1R00xl4drLi1xpkknhHp1RJy5fS1LrhoWhvoI9LalD4cGQFwjeep+Ah4mZutssBEz4XPhaG5I4aYeoiBThoIMUZQqVznmMBHavH6M3tHh6PM0HE1SX0k9/tDWDae9OuFMk0647+AFxtGOmhFaQbnhKD8y6vmbHGTk614/m4rMDXgwNdCHvZkB1TrqMAoyZSjIkNpQVSdhMbe01wh6dh19n4WjfVwUeOy/6eoTTaxw1rMDLni0wxVXP41OwFeRo5kAE7q6UUfMBqA+NUeVZ6gHWBrxweVyKeDoCAoyZSjIEFVR9QrHFvlZ6Bl9E72fhSMw5jZMiv6brr6Yy8PNRs1xwb0dznu0wyNbN4CjHbUitMhgw1Mfa23EaNSU9qIgU4aCDFEHcfPTZ4fvISGrsNbn45cUo8vLe+jx/CZ6PL8J9/QEqccTTKxxwaM01Fxx89OKDsNiBnpcOJoboKunNY2IaiDK19pEJWThTU5hvQs3hnwe9HlcGjmlQRRkylCQIeqmquHc5bmmxyPw+S30eH4LXV7eh2HJf2GpmMvDbWcfXHBviyuurXHfwUujswtXpMcBbEwFNFV9A1Nfm6TK43MBa2M+eDxqnqoLFGTKUJAhdUnVzU8AICguRKdXkejx/BYCY27BM+211ONZAmNcbdwSl938cMXVD8+tnLWmGUrMykgf7jbG9MbfwJRvkkrIzEN6Xkm9CzcA9b9RFwoyZSjIEE1R5ein8lwyEhH4/Bb8X0Sg64t7MCuUPne8qQ2uuPrhsltrhLn6IcXEUmXPrSpG+lw0sjSEjyMNmW1oGkq4AaiJqrYoyJShIEO0Qflq9wtP3iCzQDUzrnJFQrRMfIZuL+6i24sItI+LgkAofe7HNo1xxdUP1xq3xHWXFsgw1M7fA6q1abjKz23zIi0XOYUiFAnr70eTPgcwNeDBkK8PB3OqwakKBZkyFGSINlJHvxoAMCguQPu4h5Jg45sYDW6FOYsf2roh3MUX4S6+uO7ii1RjC5U9vypRrU3DVjHcFBQz5BWLaj5Qh/G5pYG+WMSgr8dr8P3MKMiUoSBDtF35qvbHSdnILVLdm7VFfhY6v7yPbi/uotPLSHinvqy0z1NrF0mouebii2RTa5U9v6pZGurB1lRA4aaBKr+W1K2X6cjIK6n34UbMWJ8DPR63QTVTUZApQ0GG6JqiEhG2XY7GrrBYJGaprrYGAKxzM9Ah7gE6vYpE55f34ZMSW2mfGEtHhLu0xI1GLXCzkQ9eWDhqXefh8kwFPNiYCGj4dwNVfgXw6JQclAhFyCmq301T5YmbqQR6vHo3moqCTBkKMkSXqatvjZhFfhY6vioNNp1eRaJ50vNKTVEpRha47dwMt5x9cNO5OR44eKJQj6/ScqiSPgBzY32YGuhTuGnA8ouE+PJ4JMKevUFOQTGEIg4yVPz7o+0MeIARnwcRODAR6KFtY0uMbO+iMzWZFGTKUJAh9Yk6hneXZ1aQg3avH6LTy/to//ohWiY+rdR5uJCnh/sOTXDL2Ufypa39bMQo3BCg8kR+uYXF9XrUVHWM9TkwM9AHhwOtHTZOQaYMBRlSX1V8U36Vno9CFb8h80uK4Zv4DO1eP0T711Fo9/ohbPIyK+0XY+mI284+uO3UDBGO3nhs64YSnp5Ky6JqPACmhno6+ZcqUa2KQ8ILixtW81RF2lKTQ0GmDAUZ0pCIq9PPRCUhJadY9U/AGFwzEkqDTdxDtH39EN5vXlZqjirQ4+OBnQcinJrirqM3Ihy98dLCQav72ogZ6gHGAv0G06GSVE3cX+3PW3FIyS4Ej4MGHXCA/2pyAIZiEdRay0lBpgwFGdJQlR/hcTk6Fel56ukfYFaQgzbxj9EuLgp+CU/QOuEJzAsrTwCYZmiGu45NJMHmnqM30ozM1VImVdPnAGaGerAyFqC5E42Yauio/41sQc1ssG1iJ5Wdj4JMGQoyhJSqq2DDYSK4pSegdVmoaRP/GD7Jzyv1tQGAFxYOuOvojXsOXnhg74UH9h7IMjBRS7nUwdyABxOBHowFejQkvIGT1f+mITZRuVob4sL8Xio5FwWZMhRkCJGt/BtveEwakrJVO9S7PH5JMZqlxKB1whP4xT+GX8JTeKbFydw31sIRkfaeeODgiftl4SZdR2puxIz1OeDr68HWhI9hbRthsr8HNU81YBUn9xOKgMISUb3tZDy5mxsWD2pR6/NQkClDQYYQ+VT8izI+swB5KpycryKzghy0THwGv/jH8E2Khm9SNFwyk2TuG2dmiwf2nrjv4FUacuy9tHINqerQujukIll9cEQMyCgQarpotcIB8PirfrV+bVOQKUNBhhDllR/N8fxNDjLy1fsGa5GfhRZJz+Gb9Ay+idHwTXoG9/QEmfsmmVjhvr0nouw88MjOHQ/t3PHCwgEirm4NqxZPaGagrwcTA2qiIvWjmWrRAB9MCfCo1TkoyJShIEOI6tR1rQ0AmBbmonnS89Jam8Rn8E2KhmdqXKWRUgCQpy/AExtXPLR1w0M7dzyyc8cjWzed6ndTnrmAWzotPa27Q8rIaqYSihhy1Px7qKh3u7jiyyG+tToHBZkyFGQIUa+6rrUBAKOifDRLjoVv0jP4JMfAJyUWTVNewLCkUOb+cWa2pcHG1r3sXzfEWjrqXO2NWENcd4dUr+IfGXlFJWAihuxCoUZCDtXIqBAFGULqVsU31Dc5hXUSbrgiIVwzEuGTHINmyTHwSYmBT3IsGmUly9w/X0+Ax7aN8dTaFU9sGuOpTWM8sW2MeFNbnZjvRhY+F7A25mv1bK2k7lVcbDO3UFjWH0c9w8a5HODRcuojozIUZAjRPE2FG6C0U3HTlNjSgJMSi+bJMdXW3mTzDfHMujGe2DT+L+DYuCLR1FpnAw6gPbO1Eu2ijpqcD7q7Y0H/5rUuGwWZMhRkCNFO5d9AH8RnIimrsM6qwMW1N82SY+D95iWavHkJ7zcv4Z7+Gvoi2QErS2CMp9YuZeHGVRJ0kk2sdDrgALqx7g6pe1XV5Aj0eMgsKEFesfTvK5cDTA1QTYgBKMhIUJAhRHdoMtwAgL6wGG5p8fAuCzZN3ryA95uXcEuPhx6TXY5MgTGeWzVCtHXp13MrZzyzcsFLSwcU8/TrrOzqIh42zuMAhnx9OJhTyCGlikpE2H01Fi/S8uBqZYTxXdxU+pqgIFOGggwhuq1iuEnPK0JOYd0OQ9UXFsM97bVU7Y33m5dwrSbglHC4eGHpiOdWzoi2Eoec0n8zDOvHe5G4Tw7AUCRkNLqKqBQFmTIUZAipnypOJqaJIaj8kmK4p7+GR2ocPNPi4JH2Gp5l/zcpyq/yuFRDs/9qcawa4bm1M55bNcIrc3utXzVcEYZ6gKE+DwI9HrhcDi3lQBRCQaYMBRlCGg6hiOHy4xRsvvgM0Sk5KBGWTgNfsS1f7RiDXU4aPNPiJMHGMzUOHmlxaJSVUuVhJRwu4szt8cLSETGWToi1dEKspSNiLZ0QV89CDlA6Tw6Py4GQAXpcDi3KSaRQkClDQYYQUn6um8SsfOQVCjU2DbxhUQHc0+PhmfpKqhbHPf01jIplj6QC/gs5sZZOiLFywgsLR0nQqY8hByhdlNNIn4vCEhGNtmqAKMiUoSBDCJFF1jTw2QWamTwMgKQWxz09Hq7pCXBPj4db2ZdrRkKNIeeVhT1eWPwXcl5aOOClhQPizO1QoG9QhxdSd8RNV3weF0VCERiHSwt11iMUZMpQkCGEKEJW52KhCMgrEqKuW6gkGIN9Tirc0hPKwk2CJOS4pSdUOSeOWLKxpSTYvDK3x6uy/780d0CSqRUYp35+4JcfcSXQ4wFgKBYBpgb66OppjYUDW8CQr5uzOzcEFGTKUJAhhKhKfpEQXx6PRNizN8gpKAafJ3s+jTpVFnLK1+S4pifAJTMJjTMSYVaYW+3hhTw9xJnb45X5f0HnpYUDXlk44JWFPbIFxnV0IZrBA2BiQGFHG1GQKUNBhhCibuX74CRk5qGwWEOdjGUwK8hB44xEyZdLZiJcMkpDjnNWcpUTAIqlG5jipYUDXpvZ4rW5HV6b2Un+jTO309kFORVBYUczKMiUoSBDCNGUqmZG1ZaQwxMJ4ZCdWhpwMhLRuFzIcclMhE1eZo3nyOIblQWbckGnXNhJMbbQ+ZmP5aEPwNSQByEDeBxQvx0VqBdBZunSpVi2bJnUtqZNm+LRo0dyn4OCDCFEG1UcScVEDIUlIuQU1e1kf9UxKsqXNFE5ZybDOSu57N8UNMpMgnV+Vo3nKOTpl4acsnATV65WJ97MFokm1vVyxFVVZPXbKSyhwCNLvQkyf/zxB86cOSPZpqenBxsbG7nPQUGGEKJrxCHn1P0EvEjLhVAErarJETMsKoBTVgoaZZUPOclwzkyBc1YyHLJTwUX1HzEicJBiYokEU2skmNoi0dQa8aa2SDCzQYKpDRLMbJBsbNWgwg4A6HMAUwOeVNhpaPPt1Jsgc+TIEURERCh9DgoyhJD6pKqaHG0LOUDp0g4O2alolFkh6JSFHcfsFAiEJTWeR8jhIsXYAgmmtqWBx8xWEnzEgSfZxApCbsPsp2Ksz4GpQA9FQpGkaUugxwOPp9sLgNabILN69WqYm5vDwMAAXbp0wYoVK9C4ceMqjyksLERh4X9DEbOysuDi4kJBhhBS7ym6WrGmcZgIVnlZcMx+A6esFDhkv4FT9hs4ZL+BY1bp/+2zU8EXyRd2ko0tkWBmg/iymp0kE2skmVqV/mtS+m8+v37OqSMPAx5gxOdJhR1tXierXgSZv//+Gzk5OWjatCkSEhKwbNkyvH79GpGRkTA1NZV5jKx+NQAoyBBCGjxZo6vEH2ra1DenPA4TwSY3syzkpMAhOxWOWSmSwOOU9Qb2Oak1jr4Sy+IbIclUHGysyv5f+n2yiRUSTa2RYmxZL1Yurw1jfQ70eFypjsvi14qBvh5MDNS/bla9CDIVZWRkwNXVFd9//z2mTJkicx+qkSGEEOVUnCdHn1v6AaaNzVbllYadDDiW1eQ4Zpd+2eWkwSEntfTf7FQYFxfIfc43RublanKskGxiXVbL89/3b4zN6+1kgooa2toJK0e2VmnzVb0MMgDQoUMH9O7dGytWrJBrf+ojQwghtVddsxXAkJ5fgoIS7f44MS7MkwQb+7JwY18u6NjnpMEuJ02upiygdGmIVGMLpBhbIkXyryWSTawqbcvlGzaIYegfdHfHgv7NVXIueT+/daobeE5ODqKjozF+/HhNF4UQQhoUHpeDgKa2CGhqW+U+VY22EujxkF+suYU6xXIFRogWGCHa2qXKfThMBIv8bDjkpMI+O01Sq2NfIQDZ5GZAj4lgX7atJnn6gv+CjrElUkwsJd+X/0o1NtfpZq2fL8YAgMrCjDy0ukZm3rx5GDRoEFxdXREfH48lS5YgIiICUVFRsLWt+pepPKqRIYQQ7SBroc7y/XS0vQmrPJ5ICOvcDNjmppf7Kv3eLidNaptJUb5C504zNCsNO+UCT6qROd4YWyDVyAJvjMyRamSBNCNzFOlpX+jhcoBHy/vVupmpXtTIxMXFYezYsUhNTYWtrS38/f1x7do1uUMMIYQQ7aFIrU7F4eXaFnaEXB6STa2RbGpd475GRfmwqRh6ctJhV+F7m7wM6IuEsMrPglV+Fpq9eVHjubMExpJgk2psXhp4jCwkoUf8faqxOTINTOqkT4+IAbuvxmJKgIfanwvQ8hoZVaAaGUIIqV9qCjsixkFGgXz9XLSJuFmrfO1OaeBJg3VeJmxyM2CdlwnrvNJ/5R2pJVbC4SLNyFwq3KSWhR5JGDIyR2rZ9wX6yg9Vf7eLK74c4qv08UA9qZEhhBBCKuLrcfFBoBc+CPSqcp+KzVh5RSUyAg803m+nPMbhIt3IHOlG5nhSU8MDYzArzC0LN6XBxiYvE9a5GbDJy4B1brlteRmwKMiBHhPBLre0Jkge+XoCpBqZIc3IHOmGZkg1MkeaYen34n9TjcyRZmSOxApz9LhaGdXiTiiGamQIIYQ0WDX12+HzuMgpEiKnSPPNWbWhLyyGZV6WJNhYS0JPplQYss7LgG1uBgTCYoXOvyxoKna0HyL5PnJpMEwMaldXQjUyhBBCSA3k6bcDSAeeB/GZSM8rkhqVJV4PSVsnFizm6cvdpweMwbgoH5b5WbDOy4RV2b+WeVmwzs+EVV7ZV34WrPKyYJWXiVQjc6lTHLjxss76yFCQIYQQQmogb+ABSvvwbLscjT9vxSElu7BS2NH22ZTB4SBXYIRcgRHiLBzkO6ZC486LtDw1FEw2CjKEEEKICvH1uJjeowmm92hS475VzaZccT0krZ9wsMJkf3XZR4aCDCGEEKIhhnweVgxrLde+1a2VVX49JKGIabRPD5cDjO/iVmfPR0GGEEII0QHyjNYSE4oYLj9OweaLzxCdkoMSoajS4o/i2p7sQtV2Zp4a4K7SNZdqQkGGEEIIqWd4XA4CfewQ6GMn1/7VdWauGICqmqeHwwHeD1DdWkvyoiBDCCGENHCKdGYG/gs+f96JQ16REB3crDChq1ud1sSIUZAhhBBCiEIUDT7qVPfRiRBCCCFERSjIEEIIIURnUdOSEipOaZ1fLIQxXw8+juYY0a4RunrZgMfl1HwiQgghhNQKBRkFnYpMwMe/30VeUcWFxgrxJDkXR+/GAwDMDXgwNdCHvZkBgls4YGK3uh2ORgghhDQEtGikAk5FJmDab7eVPt6ABxgL9KCvx4OnrTHe7+4J/ya2VHtDCCGEVECLRqqYUMSw5Ghkrc5RIAQK8koAlCAxqxBXotMAAMb6HOjxuNDjcmBlLEBzJ2qiIoQQQuRBQUZO12PSkJRdpJZz5xYzoLi0qSo1rwRPU6SbqIz0uSgSMqrJIYQQQiqgICOn5OwCjTxvZoEQmQXi/jjSNTmGeoChPg8CPR64XA6MBdThmBBCSMNCQUZOdqYGmi5CJfklQH6JEIA46FTocCzggsflQMhAzVaEEELqJQoycurobgV7U77ampfUIbNQehGwqpqtCktEFHYIIYToJBq1pIDajlrSRcb6HJgK9CqtmMrjcWloOSGEELWR9/ObgoyCqp5HpmEz1AMsDPUltTviFVOLRdRJmRBCiOIoyJRRdZABpGf2vR6bhsQs3Wlu0gbi4eayloc30NeDiQF1WiaEkIaOgkwZdQSZioQihsuPU7D54jNEp+Qgr0iInCJRzQcSuVTsy1MxAFHfHkIIqX8oyJSpiyAjS8X1mPKKSpBXKERGATVJ1QVjfQ7MDPQBsCoDEI8DGPL14WBOfX0IIUTbUJApo6kgU5WKASe3sBiFxSIUlDDkFVMtjqbxuYC1MR81BSCBHg8cDsDlUqdnQghRBwoyZbQtyFSnYhNViVAEgR4P+cVUk6MrDHiAEZ9XbQCqGJKoXxAhhFRGQaaMLgWZ6pSvyXkQn4n0vCIIRaUfhFSbU/+Un8ywqgBEtUSEkPqMgkyZ+hJkalJUIsKOK8/xT2QiErPyAQYwVvrBR0GnYVOmlqjiNn0eF42tjBHiS+GIEFI3KMiUaShBpiZVNVuJP6wo7BBFKNKXqKaQRJ2uCSGyUJApQ0FGfuLmq99vvsCtl+nILRTK/HDKKRKhSFivXzZEw2RNsChv81p1wUkEDkwEemjb2BIj27tQfyRCtBgFmTIUZNQjv0iIL49HIuzZG+QUFIPPq/wBImKgTspE61U1VF/Z4CRrH1qdnhDFUZApQ0FGs6oabi7rTV8oYjSRIGkwKnboVrZZTt7jKEwRXUNBpgwFGd1SU1+eim/e1LeHEOWVXy5EFU13ih5Ho+xIdSjIlKEgU/9V1benpjdYcSdnQoj24HMBQz5PLcGpNk2FjMOFrQkfw9o2wmR/DwpcdYCCTBkKMqQ6RSUibLscjT9vxSElu1DuNzwR4yCjoETTxSeEaEhNgUvdtVmKHqeLUyhQkClDQYaoi6z1tJhIsTcXqhUihGhCVVMoKBqS1FlTRUGmDAUZou2qm8xQkTcXqiUihGjaB93dsaB/c5Wci4JMGQoypCFRRS1RxW00bxAhRBGqCjMUZMpQkCGk9pTtSyRP+35ekRA08IyQ+oPLAR4t71frZiYKMmUoyBCi/eSZYLG2wSm7UEjzFBFSRxYN8MGUAI9anUPez2+9Wj0LIYSogCGfhxXDWqv9eRQZqq+qmX1priPSEL1Iy6uz56IgQwhpMHhcDgKa2iKgqW2dPq88HbrVOVyXwhSpa65WRnX2XBRkCCFEzfh6XHwQ6IUPAr00VoaKYapiJ/C6nguFRtnVX1wOML6LW509HwUZQghpALQhTFVU01psmp5ETrxPRkEJzfekgKkBdTvhHgUZQgghGqGppj5llK/RSsjMqzFwadPMvnU5hYIq55GRF41aIoQQQuo5eadQoJl9tRAFGUIIIUT3yPv5rf2rRhFCCCGEVIGCDCGEEEJ0FgUZQgghhOgsCjKEEEII0VkUZAghhBCisyjIEEIIIURnUZAhhBBCiM6iIEMIIYQQnUVBhhBCCCE6q96vtSSeuDgrK0vDJSGEEEKIvMSf2zUtQFDvg0x2djYAwMXFRcMlIYQQQoiisrOzYW5uXuXj9X6tJZFIhPj4eJiamoLD4Ui2Z2VlwcXFBa9evaI1mMqh+yIb3Zeq0b2Rje5L1ejeyEb3RRpjDNnZ2XBycgKXW3VPmHpfI8PlctGoUaMqHzczM6MXjAx0X2Sj+1I1ujey0X2pGt0b2ei+/Ke6mhgx6uxLCCGEEJ1FQYYQQgghOqvBBhmBQIAlS5ZAIBBouihahe6LbHRfqkb3Rja6L1WjeyMb3Rfl1PvOvoQQQgipvxpsjQwhhBBCdB8FGUIIIYToLAoyhBBCCNFZFGQIIYQQorPqTZDZsGED3NzcYGBggE6dOuH69evV7n/w4EE0a9YMBgYGaNmyJU6ePCn1+KFDh9C3b19YW1uDw+EgIiJCjaVXL1Xem+LiYnz66ado2bIljI2N4eTkhHfffRfx8fHqvgyVU/VrZunSpWjWrBmMjY1haWmJ3r17Izw8XJ2XoBaqvi/lTZs2DRwOBz/++KOKS103VH1vJk6cCA6HI/UVEhKizktQC3W8Zh4+fIjBgwfD3NwcxsbG6NChA16+fKmuS1AbVd+biq8X8dfq1avVeRnajdUD+/fvZ3w+n23fvp09ePCATZ06lVlYWLCkpCSZ+1+5coXxeDy2atUqFhUVxRYuXMj09fXZ/fv3Jfv8+uuvbNmyZWzr1q0MALtz504dXY1qqfreZGRksN69e7MDBw6wR48esatXr7KOHTuydu3a1eVl1Zo6XjN79uxhp0+fZtHR0SwyMpJNmTKFmZmZseTk5Lq6rFpTx30RO3ToEGvdujVzcnJiP/zwg5qvRPXUcW8mTJjAQkJCWEJCguQrLS2tri5JJdRxX549e8asrKzY/Pnz2e3bt9mzZ8/Y0aNHqzyntlLHvSn/WklISGDbt29nHA6HRUdH19VlaZ16EWQ6duzIZs6cKfleKBQyJycntmLFCpn7jxo1ig0YMEBqW6dOndgHH3xQad+YmBidDjLqvDdi169fZwDYixcvVFPoOlAX9yUzM5MBYGfOnFFNoeuAuu5LXFwcc3Z2ZpGRkczV1VUng4w67s2ECRPYkCFD1FLeuqKO+zJ69Gj2zjvvqKfAdagu3meGDBnCevXqpZoC6yidb1oqKirCrVu30Lt3b8k2LpeL3r174+rVqzKPuXr1qtT+ABAcHFzl/rqqru5NZmYmOBwOLCwsVFJudauL+1JUVIQtW7bA3NwcrVu3Vl3h1Uhd90UkEmH8+PGYP38+WrRooZ7Cq5k6XzPnz5+HnZ0dmjZtiunTpyM1NVX1F6Am6rgvIpEIJ06cgLe3N4KDg2FnZ4dOnTrhyJEjarsOdaiL95mkpCScOHECU6ZMUV3BdZDOB5k3b95AKBTC3t5earu9vT0SExNlHpOYmKjQ/rqqLu5NQUEBPv30U4wdO1ZnFjlT5305fvw4TExMYGBggB9++AGnT5+GjY2Nai9ATdR1X1auXAk9PT3MmTNH9YWuI+q6NyEhIfj1118RGhqKlStX4sKFC+jXrx+EQqHqL0IN1HFfkpOTkZOTg2+//RYhISH4999/MXToUAwbNgwXLlxQz4WoQV28/+7atQumpqYYNmyYagqto+r96tdEfYqLizFq1CgwxrBp0yZNF0cr9OzZExEREXjz5g22bt2KUaNGITw8HHZ2dpoumkbcunULa9euxe3bt8HhcDRdHK0zZswYyf9btmyJVq1awdPTE+fPn0dQUJAGS6Y5IpEIADBkyBB89NFHAAA/Pz+EhYVh8+bNCAwM1GTxtMr27dsxbtw4GBgYaLooGqXzNTI2Njbg8XhISkqS2p6UlAQHBweZxzg4OCi0v65S570Rh5gXL17g9OnTOlMbA6j3vhgbG8PLywudO3fGtm3boKenh23btqn2AtREHffl0qVLSE5ORuPGjaGnpwc9PT28ePECn3zyCdzc3NRyHepQV+8zHh4esLGxwbNnz2pf6DqgjvtiY2MDPT09NG/eXGofHx8fnRq1pO7XzKVLl/D48WO89957qiu0jtL5IMPn89GuXTuEhoZKtolEIoSGhqJLly4yj+nSpYvU/gBw+vTpKvfXVeq6N+IQ8/TpU5w5cwbW1tbquQA1qcvXjEgkQmFhYe0LXQfUcV/Gjx+Pe/fuISIiQvLl5OSE+fPn459//lHfxahYXb1m4uLikJqaCkdHR9UUXM3UcV/4fD46dOiAx48fS+3z5MkTuLq6qvgK1Efdr5lt27ahXbt2OtMHT6003dtYFfbv388EAgHbuXMni4qKYu+//z6zsLBgiYmJjDHGxo8fzz777DPJ/leuXGF6enrsu+++Yw8fPmRLliypNMQtNTWV3blzh504cYIBYPv372d37txhCQkJdX59taHqe1NUVMQGDx7MGjVqxCIiIqSGARYWFmrkGpWh6vuSk5PDFixYwK5evcpiY2PZzZs32aRJk5hAIGCRkZEauUZlqON3qSJdHbWk6nuTnZ3N5s2bx65evcpiYmLYmTNnWNu2bVmTJk1YQUGBRq5RGep4zRw6dIjp6+uzLVu2sKdPn7L169czHo/HLl26VOfXVxvq+n3KzMxkRkZGbNOmTXV6PdqqXgQZxhhbv349a9y4MePz+axjx47s2rVrkscCAwPZhAkTpPb//fffmbe3N+Pz+axFixbsxIkTUo/v2LGDAaj0tWTJkjq4GtVS5b0RD0eX9XXu3Lk6uiLVUOV9yc/PZ0OHDmVOTk6Mz+czR0dHNnjwYHb9+vW6uhyVUfXvUkW6GmQYU+29ycvLY3379mW2trZMX1+fubq6sqlTp0o+5HSJOl4z27ZtY15eXszAwIC1bt2aHTlyRN2XoRbquDc///wzMzQ0ZBkZGeouvk7gMMaYZuqCCCGEEEJqR+f7yBBCCCGk4aIgQwghhBCdRUGGEEIIITqLggwhhBBCdBYFGUIIIYToLAoyhBBCCNFZFGQIIYQQorMoyBBSDyUmJqJPnz4wNjaGhYVFrc/Xo0cPfPjhh7XeRxNSU1NhZ2eH2NhYlZ6Xw+HgyJEjKj1nfdO5c2f8+eefmi4GqecoyBAiw8SJE8HhcDBt2rRKj82cORMcDgcTJ06s+4LJ6YcffkBCQgIiIiLw5MmTKvdLS0vDhx9+CFdXV/D5fDg5OWHy5MlKLc536NAhLF++vDbFlqKqYPT1119jyJAhUotUHj58GJ07d4a5uTlMTU3RokULrQlh58+fB4fDgaWlJQoKCqQeu3HjBjgcjs6sJL5w4UJ89tlnkhWtCVEHCjKEVMHFxQX79+9Hfn6+ZFtBQQH27t2Lxo0ba7BkNYuOjka7du3QpEkT2NnZydwnLS0NnTt3xpkzZ7B582Y8e/YM+/fvx7Nnz9ChQwc8f/5coee0srKCqampKoqvMnl5edi2bRumTJki2RYaGorRo0dj+PDhuH79Om7duoWvv/4axcXFdVq2oqKiah83NTXF4cOHpbZt27ZN61975fXr1w/Z2dn4+++/NV0UUo9RkCGkCm3btoWLiwsOHTok2Xbo0CE0btwYbdq0kdr31KlT8Pf3h4WFBaytrTFw4EBER0dLHi8qKsKsWbPg6OgIAwMDuLq6YsWKFQAAxhiWLl2Kxo0bQyAQwMnJCXPmzKm2bJs2bYKnpyf4fD6aNm2K3bt3Sx5zc3PDn3/+iV9//bXamqMvvvgC8fHxOHPmDPr164fGjRuje/fu+Oeff6Cvr4+ZM2dK7V9SUoJZs2bB3NwcNjY2WLRoEcqvcFKxBqWwsBDz5s2Ds7MzjI2N0alTJ5w/f17qnFeuXEGPHj1gZGQES0tLBAcHIz09HRMnTsSFCxewdu1aSQ1EbGws0tPTMW7cONja2sLQ0BBNmjTBjh07qrxPJ0+ehEAgQOfOnSXbjh07hm7dumH+/Plo2rQpvL298dZbb2HDhg1y32NZPv30U3h7e8PIyAgeHh5YtGiRVDhaunQp/Pz88Msvv8Dd3R0GBgbVnm/ChAnYvn275Pv8/Hzs378fEyZMkNovNTUVY8eOhbOzM4yMjNCyZUvs27dPap8//vgDLVu2hKGhIaytrdG7d2/k5uYCKK0B6tixo6QZslu3bnjx4oXk2KNHj6Jt27YwMDCAh4cHli1bhpKSEgA1v3Z5PB769++P/fv3V3uthNSKRld6IkRLTZgwgQ0ZMoR9//33LCgoSLI9KCiI/fDDD2zIkCFSi7398ccf7M8//2RPnz5ld+7cYYMGDWItW7ZkQqGQMcbY6tWrmYuLC7t48SKLjY1lly5dYnv37mWMMXbw4EFmZmbGTp48yV68eMHCw8PZli1bqiybeGXgDRs2sMePH7M1a9YwHo/Hzp49yxhjLDk5mYWEhLBRo0axhIQEmQvLCYVCZmFhwd5//32Zz/H1118zDofDUlNTGWOli9uZmJiwuXPnskePHrHffvuNGRkZSZUzMDCQzZ07V/L9e++9x7p27couXrzInj17xlavXs0EAgF78uQJY4yxO3fuMIFAwKZPn84iIiJYZGQkW79+PUtJSWEZGRmsS5cubOrUqZLV1UtKStjMmTOZn58fu3HjBouJiWGnT59mf/31V5X3as6cOSwkJERq24oVK5itrW21K3TXdI8ZYwwAO3z4sOT75cuXsytXrrCYmBj2119/MXt7e7Zy5UrJ40uWLGHGxsYsJCSE3b59m929e1fmc587d44BYI8fP2YCgYC9ePGCMcbY7t27WevWrdnhw4dZ+bfuuLg4tnr1anbnzh0WHR3N1q1bx3g8HgsPD2eMMRYfH8/09PTY999/z2JiYti9e/fYhg0bWHZ2NisuLmbm5uZs3rx57NmzZywqKort3LlT8pwXL15kZmZmbOfOnSw6Opr9+++/zM3NjS1dupQxJt9rd9OmTczV1bXKe01IbVGQIUQGcZBJTk5mAoGAxcbGstjYWGZgYMBSUlIqBZmKUlJSGADJh+Xs2bNZr169mEgkqrTvmjVrmLe3NysqKpKrbF27dmVTp06V2jZy5EjWv39/yfc1lS8xMZEBqHIV6kOHDjEAkg/DwMBA5uPjI1X+Tz/9lPn4+Ei+Lx9kXrx4wXg8Hnv9+rXUeYOCgtiCBQsYY4yNHTuWdevWrcoyVgxGjDE2aNAgNmnSpCqPqWjIkCFs8uTJUttycnJY//79GQDm6urKRo8ezbZt28YKCgok+8hzjysGmYpWr17N2rVrJ/l+yZIlTF9fnyUnJ1dbZnGQSU9PZ2+99RZbtmwZY4yxnj17srVr11YKMrIMGDCAffLJJ4wxxm7dusUAsNjY2Er7paamMgDs/PnzMs8TFBTEvvnmG6ltu3fvZo6Ojowx+V67R48eZVwuVxLqCVE1aloipBq2trYYMGAAdu7ciR07dmDAgAGwsbGptN/Tp08xduxYeHh4wMzMTNKxVNxpduLEiYiIiEDTpk0xZ84c/Pvvv5JjR44cifz8fHh4eGDq1Kk4fPiwpOpelocPH6Jbt25S27p164aHDx8qfH2sXNNQTTp37izVybRLly54+vQphEJhpX3v378PoVAIb29vmJiYSL4uXLggaXKLiIhAUFCQQuWdPn069u/fDz8/P/zvf/9DWFhYtfvn5+dXasIxNjbGiRMn8OzZMyxcuBAmJib45JNP0LFjR+Tl5QFQ7h4fOHAA3bp1g4ODA0xMTLBw4cJKnaZdXV1ha2sr9/VOnjwZO3fuxPPnz3H16lWMGzeu0j5CoRDLly9Hy5YtYWVlBRMTE/zzzz+S527dujWCgoLQsmVLjBw5Elu3bkV6ejqA0n5NEydORHBwMAYNGoS1a9ciISFBcu67d+/iyy+/lPoZTp06FQkJCcjLy5PrtWtoaAiRSITCwkK5r5sQRVCQIaQG4g+TXbt2YfLkyTL3GTRoENLS0rB161aEh4cjPDwcwH8dOtu2bYuYmBgsX74c+fn5GDVqFEaMGAGgtFPx48ePsXHjRhgaGmLGjBno3r27Wjuf2trawsLCosoP5ocPH4LD4cDLy0up8+fk5IDH4+HWrVuIiIiQfD18+BBr164FUPoBp6h+/frhxYsX+OijjxAfH4+goCDMmzevyv1tbGwkH9oVeXp64r333sMvv/yC27dvIyoqCgcOHFC4TAAkIaN///44fvw47ty5gy+++KJSh15jY2OFztuvXz/k5+djypQpGDRoEKytrSvts3r1aqxduxaffvopzp07h4iICAQHB0uem8fj4fTp0/j777/RvHlzrF+/Hk2bNkVMTAwAYMeOHbh69Sq6du2KAwcOwNvbG9euXQNQ+nNctmyZ1M/w/v37ePr0KQwMDOR67aalpcHY2Fipnzch8qAgQ0gNQkJCUFRUhOLiYgQHB1d6PDU1FY8fP8bChQsRFBQEHx8fmR+eZmZmGD16NLZu3YoDBw7gzz//RFpaGoDSD/VBgwZh3bp1OH/+PK5evYr79+/LLI+Pjw+uXLkite3KlSto3ry53NfE5XIxatQo7N27F4mJiVKP5efnY+PGjQgODoaVlZVkuziciV27dg1NmjQBj8erdP42bdpAKBQiOTkZXl5eUl8ODg4AgFatWiE0NLTKMvL5fJm1Pba2tpgwYQJ+++03/Pjjj9iyZUuV52jTpg2ioqKqfFzMzc0NRkZGkg6wit7jsLAwuLq64osvvkD79u3RpEkTqQ6zytLT08O7776L8+fPVxmir1y5giFDhuCdd95B69at4eHhUWnIPYfDQbdu3bBs2TLcuXMHfD5fakRUmzZtsGDBAoSFhcHX1xd79+4FUBrAHz9+XOln6OXlBS639OOjptduZGRkpc7xhKiSnqYLQIi24/F4kpoLWR/alpaWsLa2xpYtW+Do6IiXL1/is88+k9rn+++/h6OjI9q0aQMul4uDBw/CwcEBFhYW2LlzJ4RCITp16gQjIyP89ttvMDQ0hKurq8zyzJ8/H6NGjUKbNm3Qu3dvHDt2DIcOHcKZM2cUuq5vvvkGoaGh6NOnD1atWgVfX1/ExMRg4cKFKC4urjSK5+XLl/j444/xwQcf4Pbt21i/fj3WrFkj89ze3t4YN24c3n33XaxZswZt2rRBSkoKQkND0apVKwwYMAALFixAy5YtMWPGDEybNg18Ph/nzp3DyJEjYWNjAzc3N4SHhyM2NhYmJiawsrLC0qVL0a5dO7Ro0QKFhYU4fvw4fHx8qrzG4OBgLFiwAOnp6bC0tARQOnooLy8P/fv3h6urKzIyMrBu3ToUFxejT58+St3jJk2a4OXLl9i/fz86dOiAEydOVBo6razly5dj/vz5MmtjxM/9xx9/ICwsDJaWlvj++++RlJQkCV3h4eEIDQ1F3759YWdnh/DwcKSkpMDHxwcxMTHYsmULBg8eDCcnJzx+/BhPnz7Fu+++CwBYvHgxBg4ciMaNG2PEiBHgcrm4e/cuIiMj8dVXX8n12r106RL69u2rkntBiEya7qRDiDYSd/atSsXOtKdPn2Y+Pj5MIBCwVq1asfPnz0t1Bt2yZQvz8/NjxsbGzMzMjAUFBbHbt28zxhg7fPgw69SpEzMzM2PGxsasc+fO7MyZM9WWb+PGjczDw4Pp6+szb29v9uuvv1ZbvqqkpKSw2bNnMxcXF6avr8/s7e3ZxIkTJaNWxAIDA9mMGTPYtGnTmJmZGbO0tGSff/65VOffip1zi4qK2OLFi5mbmxvT19dnjo6ObOjQoezevXuSfc6fP8+6du3KBAIBs7CwYMHBwSw9PZ0xxtjjx49Z586dmaGhIQPAYmJi2PLly5mPjw8zNDRkVlZWbMiQIez58+fVXmPHjh3Z5s2bJd+fPXuWDR8+nLm4uDA+n8/s7e1ZSEgIu3TpkkL3GBU6+86fP59ZW1szExMTNnr0aPbDDz8wc3NzyeNLlixhrVu3rrasjEl39pWlYmff1NRUNmTIEGZiYsLs7OzYwoUL2bvvvit5/UZFRbHg4GBma2vLBAIB8/b2ZuvXr2eMlXb6fuutt5ijoyPj8/nM1dWVLV68WKpj7qlTp1jXrl2ZoaEhMzMzYx07dpSMTKrptRsXF8f09fXZq1evarxuQpTFYUyB3n6EEFKFLl26ICgoCF999ZWmiyLlxIkTmD9/PiIjIyXNIaRufPrpp0hPT6+2+Y+Q2qLfakJIrRQWFuLmzZt48OABWrRooeniVDJgwAC8//77eP36taaL0uDY2dmpdNkKQmShGhlCSK0cOXIE7777LgYPHowdO3ZAX19f00UihDQgFGQIIYQQorOoaYkQQgghOouCDCGEEEJ0FgUZQgghhOgsCjKEEEII0VkUZAghhBCisyjIEEIIIURnUZAhhBBCiM6iIEMIIYQQnUVBhhBCCCE66/+QGooU3A3vogAAAABJRU5ErkJggg==", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#use scipy curve fit to create a general power law\n", - "popt, pcov = curve_fit(power_law, sorted_masses, sorted_powers,sigma=sorted_errors, absolute_sigma=True) #popt gives fit value for a\n", - "avg_a = popt\n", - "avg_aerr = np.sqrt(np.diag(pcov)) #gives the error in a\n", - "\n", - "#create an array with mass values using fitted a\n", - "avg_power = power_law(sorted_masses, avg_a)\n", - "\n", - "#plot combined mass and power law datasets\n", - "plt.figure()\n", - "plt.scatter(sorted_masses, sorted_powers, label='raw data')\n", - "plt.plot(sorted_masses, avg_power, color='r', label='curve fit')\n", - "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", - "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", - "plt.ylabel(\"M^(-a)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "400886ad-ad9a-47a9-8fa2-e513ef125d45", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.5836496] [7.20652905e-05]\n" - ] - } - ], - "source": [ - "print(avg_a, avg_aerr)" - ] - }, - { - "cell_type": "markdown", - "id": "9bc14680-1eb4-45ec-abed-4b399d7693a4", - "metadata": {}, - "source": [ - "#### Converting into a log function to match the curve fit" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "76bfab00-53a2-4a18-9cee-b4f11cfdde98", - "metadata": {}, - "outputs": [], - "source": [ - "#take the log of each of the values in sorted_powers\n", - "log_power = []\n", - "for n in sorted_powers:\n", - " log_power.append(math.log(n))\n", - "\n", - "#take the log of each of the values in sorted_masses\n", - "log_masses = []\n", - "for a in sorted_masses:\n", - " log_masses.append(math.log(a))\n", - "\n", - "#make sure both are arrays\n", - "log_power = np.array(log_power)\n", - "log_masses = np.array(log_masses)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "527b824d-29a7-4715-810b-01b626eae7fa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitted parameters: m = -0.5450\n", - "RMSE: 0.5058\n", - "MAE: 0.4105\n", - "R-squared: 0.2151\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#define a linear model (no intercept since no coefficient in power law)\n", - "def linear_model(x, m):\n", - " return m * x\n", - "\n", - "#curve fit\n", - "params, covariance = curve_fit(linear_model, log_masses, log_power)\n", - "\n", - "#get the fitted values\n", - "power_fitted = linear_model(log_masses, *params)\n", - "\n", - "#calculate residuals\n", - "residuals = log_power - power_fitted\n", - "\n", - "#calculate error metrics\n", - "rmse = np.sqrt(mean_squared_error(log_power, power_fitted))\n", - "mae = mean_absolute_error(log_power, power_fitted)\n", - "r2 = r2_score(log_power, power_fitted)\n", - "\n", - "#output the results\n", - "print(f\"Fitted parameters: m = {params[0]:.4f}\")\n", - "print(f\"RMSE: {rmse:.4f}\")\n", - "print(f\"MAE: {mae:.4f}\")\n", - "print(f\"R-squared: {r2:.4f}\")\n", - "\n", - "#plot the results\n", - "plt.figure(figsize=(10, 6))\n", - "plt.scatter(log_masses, log_power, label='Data', color='blue', s=10)\n", - "plt.plot(log_masses, linear_model(log_masses, *params), label='Fitted line', color='red')\n", - "plt.legend()\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Linear Fit of Multiple Linear Functions')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "23e78450-e261-4fe7-b307-41204c800ed3", - "metadata": {}, - "outputs": [], - "source": [ - "linpower_fit = []\n", - "for l in log_masses:\n", - " linpower_fit.append(linear_model(l, -0.5450))" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "2d6df51d-54d3-48d2-bfcd-49f0dceed8bc", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(log_masses, log_power, '.')\n", - "plt.plot(log_masses, linpower_fit)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "69324962-a5fb-44b7-a734-8d17dbe84c98", - "metadata": {}, - "source": [ - "#### Attempting a more accurate error" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "40a444f8-9de7-4d39-8b40-2fa6272acf78", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-0.545\n" - ] - } - ], - "source": [ - "print(params[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "cff2e9d0-e55a-4dbb-83e1-ccf940f84d97", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.15556349186104046\n" - ] - } - ], - "source": [ - "#find the difference between fit and upper/lower bounds\n", - "m = params[0]\n", - "up_diff = R_a - m\n", - "up_differr = R_aerr\n", - "low_diff = m - M1_a\n", - "low_differr = M1_aerr\n", - " #the errors in up_differr and low_differr are the same because m is a fixed value in this case\n", - "\n", - "#calculate error using sqrt function\n", - "fit_err = np.sqrt((up_differr)**2 + (low_differr)**2) #uncertainty formula\n", - "print(fit_err)" - ] - }, - { - "cell_type": "markdown", - "id": "b71d92d4-1658-46b1-b4a5-31aa601a29fa", - "metadata": {}, - "source": [ - "#### Replot our function with our new " - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "93f528bc-5570-46b2-902c-1c8137fb9c46", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#calculate error margins with RMSE value\n", - "logerr_up = linear_model(log_masses, -0.5450 + fit_err)\n", - "logerr_low = linear_model(log_masses, -0.5450 - fit_err)\n", - "\n", - "#plot new errors with function\n", - "plt.figure(figsize=(10, 6))\n", - "plt.scatter(log_masses, log_power, label='Data', color='blue', s=10)\n", - "plt.plot(log_masses, linear_model(log_masses, *params), label='Fitted line', color='red')\n", - "plt.fill_between(log_masses, logerr_up, logerr_low, color='red', alpha=0.2, label='error margin')\n", - "plt.legend()\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('Linear Fit of Multiple Linear Functions')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b90191d6-3b08-42c9-9b7e-b925f79c30cb", - "metadata": {}, - "source": [ - "This margin of error makes a lot more sense, as it encompasses many more values represented by the function. " - ] - }, - { - "cell_type": "markdown", - "id": "9412765c-6aba-4280-8c97-0f3c7a6f7daa", - "metadata": {}, - "source": [ - "#### Graphing our new error in power law space" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "a1ff5709-854c-46db-8dc5-822cfd65b212", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plotting different IMFs based on literature\n", - "plt.plot(BD_masses, power_law(BD_masses, M1_a), color = 'y', label = \"Mužić 2017\")\n", - "plt.plot(BD_masses, power_law(BD_masses, K_a), color = 'c', label = \"Kirkpatrick 2021\")\n", - "plt.plot(BD_masses, power_law(BD_masses, R_a), color = 'b', label = \"Ryan 2022\")\n", - "plt.plot(BD_masses, power_law(BD_masses, B_a), color = 'm', label = \"Best 2024\")\n", - "\n", - "#average our two fit parameters (one from power law, one from linear fit)\n", - "best_a = (avg_a + (-1*m)) / 2\n", - "\n", - "#finding upper and lower bounds using best_a\n", - "up_a = best_a + fit_err\n", - "down_a = best_a - fit_err\n", - "power_up = np.array(power_law(BD_masses, up_a))\n", - "power_down = power_law(BD_masses, down_a)\n", - "\n", - "#plotting our fit line and error margin\n", - "plt.plot(BD_masses, power_law(BD_masses, best_a), color = \"black\", label = \"fit for functions\")\n", - "plt.fill_between(BD_masses, power_up, power_down, color = \"black\", alpha = 0.2, label = \"fit error\")\n", - "\n", - "#organizing our graph\n", - "plt.title(\"Final Power-Law Curve Fit for Brown Dwarf Mass Objects\")\n", - "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", - "plt.ylabel(\"$M^{0.56 ± 0.16}$\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "a0e7f65f-d511-4840-937f-4fd2c57a2bd7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.5643248]\n" - ] - } - ], - "source": [ - "print(best_a)" - ] - }, - { - "cell_type": "markdown", - "id": "706598df-cec8-495d-9fbc-cfd29c9193d3", - "metadata": {}, - "source": [ - "### Conclusions" - ] - }, - { - "cell_type": "markdown", - "id": "40494bed-857e-4527-b10b-d72856d034b2", - "metadata": {}, - "source": [ - "From this process, we have determined that the best IMF model for brown-dwarf mass objects is given by a power law, $M^{-\\alpha}$, where ${\\alpha} = 0.56 ± 0.16$. This fits within our expected output because, in average, brown dwarf mass objects have been represented with $\\alpha$ values between 0.5-1." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7df524b5-524e-49f3-a183-c547afd36419", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/changes/Exoplanet_IMF.ipynb b/changes/Exoplanet_IMF.ipynb deleted file mode 100644 index 0bd50ba4..00000000 --- a/changes/Exoplanet_IMF.ipynb +++ /dev/null @@ -1,608 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "23642516", - "metadata": {}, - "source": [ - "## Plotting Studied Power-Law Functions For Exoplanets" - ] - }, - { - "cell_type": "markdown", - "id": "ad57fd40", - "metadata": {}, - "source": [ - "#### Importing Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "d6503a21", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from scipy.optimize import curve_fit" - ] - }, - { - "cell_type": "markdown", - "id": "4f72f6f7", - "metadata": {}, - "source": [ - "#### Creating the general power-law function for small masses" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d51b38dc", - "metadata": {}, - "outputs": [], - "source": [ - "#define function\n", - "def power_law(M, a):\n", - " return M**(-1*a)\n", - "\n", - "# M is a defined mass range according to a specified paper, and a is the associated alpha value" - ] - }, - { - "cell_type": "markdown", - "id": "3fe49d7b", - "metadata": {}, - "source": [ - "#### Defining different mass/alpha values based on published papers" - ] - }, - { - "cell_type": "markdown", - "id": "b7736fad", - "metadata": {}, - "source": [ - "***Masses are in solar masses!***" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "4145bc16", - "metadata": {}, - "outputs": [], - "source": [ - "#Moraux 2007 (0.03 - 0.6) a = 0.69 ± 0.15\n", - "M_mass = np.linspace(0.03, 0.6, 1000)\n", - "M_a = 0.69\n", - "M_aerr = 0.15\n", - "\n", - "#Suárez 2009 (0.02 - 0.50) a = 0.60 ± 0.13\n", - "S_mass = np.linspace(0.02, 0.5, 1000)\n", - "S_a = 0.60\n", - "S_aerr = 0.13\n", - "\n", - "#Lodieu 2009 (0.01 - 0.5) a = 0.5 ± 0.2\n", - "L_mass = np.linspace(0.01, 0.5, 1000)\n", - "L_a = 0.5\n", - "L_aerr = 0.2\n", - "\n", - "#Béjar 2011 (0.013 - 0.1) a = 0.7 ± 0.3\n", - "B_mass = np.linspace(0.013, 0.1, 1000)\n", - "B_a = 0.7\n", - "B_aerr = 0.3\n", - "\n", - "#Peña Ramírez 2012 (0.006 - 0.25) a = 0.6 ± 0.2\n", - "P_mass = np.linspace(0.006, 0.25, 1000)\n", - "P_a = 0.6\n", - "P_aerr = 0.2" - ] - }, - { - "cell_type": "markdown", - "id": "f2a24e63", - "metadata": {}, - "source": [ - "#### Plotting all of the curves together (w/o accounting for error)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "60cab6f2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#plot all of the power law functions based on mass ranges\n", - "plt.plot(M_mass, power_law(M_mass, M_a), color='r', label='Moraux (a=0.69)')\n", - "plt.plot(S_mass, power_law(S_mass, S_a), color='b', label='Suárez (a=0.60)')\n", - "plt.plot(L_mass, power_law(L_mass, L_a), color='m', label='Lodieu (a=0.50)')\n", - "plt.plot(B_mass, power_law(B_mass, B_a), color='c', label='Béjar (a=0.70)')\n", - "plt.plot(P_mass, power_law(P_mass, P_a), color='y', label='Peña Ramírez (a=0.60)')\n", - "plt.title(\"Power Law Mass Functions for Planetary Masses According to Published Papers\")\n", - "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", - "plt.ylabel(\"M^(-a)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f5494d3d", - "metadata": {}, - "source": [ - "#### Accounting for error in a" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "1e6c80f4", - "metadata": {}, - "outputs": [], - "source": [ - "#Moraux\n", - "M_aup = M_a + M_aerr\n", - "M_alow = M_a - M_aerr\n", - "M_outup = power_law(M_mass, M_aup)\n", - "M_outlow = power_law(M_mass, M_alow)\n", - "\n", - "#Suárez\n", - "S_aup = S_a + S_aerr\n", - "S_alow = S_a - S_aerr\n", - "S_outup = power_law(S_mass, S_aup)\n", - "S_outlow = power_law(S_mass, S_alow)\n", - "\n", - "#Lodieu\n", - "L_aup = L_a + L_aerr\n", - "L_alow = L_a - L_aerr\n", - "L_outup = power_law(L_mass, L_aup)\n", - "L_outlow = power_law(L_mass, L_alow)\n", - "\n", - "#Béjar\n", - "B_aup = B_a + B_aerr\n", - "B_alow = B_a - B_aerr\n", - "B_outup = power_law(B_mass, B_aup)\n", - "B_outlow = power_law(B_mass, B_alow)\n", - "\n", - "#Peña Ramírez\n", - "P_aup = P_a + P_aerr\n", - "P_alow = P_a - P_aerr\n", - "P_outup = power_law(P_mass, P_aup)\n", - "P_outlow = power_law(P_mass, P_alow)" - ] - }, - { - "cell_type": "markdown", - "id": "041954f2", - "metadata": {}, - "source": [ - "#### Graphing Individual Power-Law Functions (Accounting for Error in a)" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "id": "dc7a858e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Setting up subplots\n", - "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(10,10))\n", - "plt.subplots_adjust(hspace=0.75, wspace=1.25)\n", - "fig.delaxes(ax[2, 1])\n", - "\n", - "#Moraux\n", - "ax[0,0].plot(M_mass, power_law(M_mass, M_a), color='r')\n", - "ax[0,0].fill_between(M_mass, M_outup, M_outlow, color='r', alpha=0.2, label=\"error in a\")\n", - "ax[0,0].set_title(\"Moraux Mass Function for Planetary Masses in Blanco 1\")\n", - "ax[0,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[0,0].set_ylabel(\"M^(-0.69 ± 0.15)\")\n", - "ax[0,0].legend()\n", - "\n", - "#Suárez\n", - "ax[0,1].plot(S_mass, power_law(S_mass, S_a), color='b')\n", - "ax[0,1].fill_between(S_mass, S_outup, S_outlow, color='b', alpha=0.2, label=\"error in a\")\n", - "ax[0,1].set_title(\"Suárez Mass Function for Planetary Masses in 25 Ori Group\")\n", - "ax[0,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[0,1].set_ylabel(\"M^(-0.60 ± 0.13)\")\n", - "ax[0,1].legend()\n", - "\n", - "#Lodieu\n", - "ax[1,0].plot(L_mass, power_law(L_mass, L_a), color='m')\n", - "ax[1,0].fill_between(L_mass, L_outup, L_outlow, color='b', alpha=0.2, label=\"error in a\")\n", - "ax[1,0].set_title(\"Lodieu Mass Function for Planetary Masses in UKIDSS Galactic Clusters\")\n", - "ax[1,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[1,0].set_ylabel(\"M^(-0.5 ± 0.2)\")\n", - "ax[1,0].legend()\n", - "\n", - "#Béjar\n", - "ax[1,1].plot(B_mass, power_law(B_mass, B_a), color='c')\n", - "ax[1,1].fill_between(B_mass, B_outup, B_outlow, color='c', alpha=0.2, label=\"error in a\")\n", - "ax[1,1].set_title(\"Béjar Mass Function for Planetary Masses in σ Orionis\")\n", - "ax[1,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[1,1].set_ylabel(\"M^(-0.7 ± 0.3)\")\n", - "ax[1,1].legend()\n", - "\n", - "#Peña Ramírez\n", - "ax[2,0].plot(P_mass, power_law(P_mass, P_a), color='y')\n", - "ax[2,0].fill_between(P_mass, P_outup, P_outlow, color='y', alpha=0.2, label=\"error in a\")\n", - "ax[2,0].set_title(\"Peña Ramírez Mass Function for Planetary Masses in σ Orionis\")\n", - "ax[2,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[2,0].set_ylabel(\"M^(-0.6 ± 0.2)\")\n", - "ax[2,0].legend()\n", - "\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f63bb9fc", - "metadata": {}, - "source": [ - "### Creating a General Power-Law Curve Fit" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "ed848735", - "metadata": {}, - "outputs": [], - "source": [ - "#combine all mass data\n", - "all_masses = []\n", - "for a in M_mass:\n", - " all_masses.append(a)\n", - "for b in S_mass:\n", - " all_masses.append(b)\n", - "for c in L_mass:\n", - " all_masses.append(c)\n", - "for d in B_mass:\n", - " all_masses.append(d)\n", - "for e in P_mass:\n", - " all_masses.append(e)" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "df52ba0f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5000\n" - ] - } - ], - "source": [ - "print(len(all_masses)) #to make sure the correct amount of entries were appended" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "dfc306a1", - "metadata": {}, - "outputs": [], - "source": [ - "#combine all power_law output data\n", - "all_power = []\n", - "for f in M_mass:\n", - " all_power.append(power_law(f, M_a))\n", - "for g in S_mass:\n", - " all_power.append(power_law(g, S_a))\n", - "for h in L_mass:\n", - " all_power.append(power_law(h, L_a))\n", - "for k in B_mass:\n", - " all_power.append(power_law(k, B_a))\n", - "for l in P_mass:\n", - " all_power.append(power_law(l, P_a))" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "15d0484d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5000\n" - ] - } - ], - "source": [ - "print(len(all_power))" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "6a6629e9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#use scipy curve fit to create a general power law\n", - "popt, pcov = curve_fit(power_law, all_masses, all_power) #popt gives fit value for a\n", - "avg_a = popt\n", - "\n", - "#create an array with mass values using fitted a\n", - "avg_power = power_law(all_masses, avg_a)\n", - "\n", - "#plot combined mass and power law datasets\n", - "plt.figure()\n", - "plt.scatter(all_masses, all_power)\n", - "plt.plot(all_masses, avg_power, color='r')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "3739e5d8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.64439915]\n" - ] - } - ], - "source": [ - "print(popt)" - ] - }, - { - "cell_type": "markdown", - "id": "109097ab", - "metadata": {}, - "source": [ - "#### Cleaning Up the Curve Fit" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "7475e6d3", - "metadata": {}, - "outputs": [], - "source": [ - "#combining all mass and output data sets to fit\n", - "all_masses = np.array(all_masses)\n", - "all_power = np.array(all_power)\n", - "\n", - "#put combined mass data set into increasing order and reorder output array to match new indices \n", - "sorted_indices = np.argsort(all_masses)\n", - "sorted_masses = all_masses[sorted_indices]\n", - "sorted_powers = all_power[sorted_indices]" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "id": "acc33225", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#use scipy curve fit to create a general power law\n", - "popt, pcov = curve_fit(power_law, sorted_masses, sorted_powers,sigma=sorted_errors, absolute_sigma=True) #popt gives fit value for a\n", - "avg_a = popt\n", - "avg_aerr = np.sqrt(np.diag(pcov)) #gives the error in a\n", - "\n", - "#create an array with mass values using fitted a\n", - "avg_power = power_law(sorted_masses, avg_a)\n", - "\n", - "#plot combined mass and power law datasets\n", - "plt.figure()\n", - "plt.scatter(sorted_masses, sorted_powers, label='raw data')\n", - "plt.plot(sorted_masses, avg_power, color='r', label='curve fit')\n", - "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", - "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", - "plt.ylabel(\"M^(-a)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "bd3e02dd", - "metadata": {}, - "source": [ - "Based on the curve fit, our a value for the entire mass range is 0.60524093 ± 0.00211972" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "id": "0e0bc7ca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.00211972]\n" - ] - } - ], - "source": [ - "print(avg_aerr)" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "id": "7037fa8c", - "metadata": {}, - "outputs": [], - "source": [ - "#calculate the low and high outputs based on error of a\n", - "avg_aup = avg_a + avg_aerr\n", - "avg_alow = avg_a - avg_aerr\n", - "avg_outup = power_law(sorted_masses, avg_aup)\n", - "avg_outlow = power_law(sorted_masses, avg_alow)" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "id": "1cb19a53", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#fit plot including error\n", - "plt.figure()\n", - "plt.scatter(sorted_masses, sorted_powers, color='k', label='data created from all papers')\n", - "plt.plot(sorted_masses, avg_power, color='c', label='calculated curve fit')\n", - "plt.fill_between(sorted_masses, avg_outup, avg_outlow, color='c', alpha=0.2, label=\"error in a\")\n", - "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", - "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", - "plt.ylabel(\"M^(-0.605 ± 0.002)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "id": "cf3ff96a", - "metadata": {}, - "outputs": [], - "source": [ - "#create an error array for all values in sorted_masses\n", - "power_errors = []\n", - "for m in M_mass:\n", - " power_errors.append(power_law(m, M_a) - power_law(m, M_alow))\n", - "for n in S_mass:\n", - " power_errors.append(power_law(n, S_a) - power_law(n, S_alow))\n", - "for o in L_mass:\n", - " power_errors.append(power_law(o, L_a) - power_law(o, L_alow))\n", - "for p in B_mass:\n", - " power_errors.append(power_law(p, B_a) - power_law(p, B_alow))\n", - "for q in P_mass:\n", - " power_errors.append(power_law(q, P_a) - power_law(q, P_alow))\n", - "\n", - "#sort errors based on sorted indices\n", - "\n", - "# convert sorted_indices to a NumPy array for indexing\n", - "sorted_indices = np.array(sorted_indices)\n", - "\n", - "# Sort errors based on sorted indices\n", - "sorted_errors = [power_errors[i] for i in sorted_indices]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30d890fa", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c0206cbc", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/changes/Test_BD_Cluster.ipynb b/changes/Test_BD_Cluster.ipynb deleted file mode 100644 index d5a5da69..00000000 --- a/changes/Test_BD_Cluster.ipynb +++ /dev/null @@ -1,1326 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4257f594-b092-40bc-85cf-2397fe17724c", - "metadata": {}, - "source": [ - "# SPISEA: Making a Cluster with BD Candidates" - ] - }, - { - "cell_type": "markdown", - "id": "08de775e-f1bb-464b-8148-48b51e6d8822", - "metadata": {}, - "source": [ - "This is a document to generate a synthetic cluster in SPISEA with the addition of brown dwarf candidates. It will follow the Quick Start Guide closely, but with changes due to a lower mass limit and ability to consider smaller objects." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "b7536665-00cf-49ad-9028-930f56cf3204", - "metadata": {}, - "outputs": [], - "source": [ - "# Import necessary packages. \n", - "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", - "from spisea.imf import imf, multiplicity\n", - "import numpy as np\n", - "import pylab as py\n", - "import pdb\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "60ee38e8-ba5c-4c23-b9a2-22773bd91a62", - "metadata": {}, - "source": [ - "### 1: Making an Isochrone Object for our Cluster" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "0cd4c0a3-39e8-4bbf-9eba-e1883b6dacec", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Changing to logg=4.00 for T= 32651 logg=3.99\n", - 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"Changing to logg=5.00 for T= 43152 logg=5.94\n", - "Changing to logg=5.00 for T= 43271 logg=5.95\n", - "Changing to logg=5.00 for T= 43411 logg=5.95\n", - "Changing to logg=5.00 for T= 43561 logg=5.96\n", - "Changing to logg=5.00 for T= 43692 logg=5.96\n", - "Changing to logg=5.00 for T= 43833 logg=5.96\n", - "Changing to logg=5.00 for T= 43985 logg=5.97\n", - "Changing to logg=5.00 for T= 44147 logg=5.97\n", - "Changing to logg=5.00 for T= 44310 logg=5.97\n", - "Changing to logg=5.00 for T= 44484 logg=5.97\n", - "Changing to logg=5.00 for T= 44638 logg=5.97\n", - "Changing to logg=5.00 for T= 44802 logg=5.98\n", - "Changing to logg=5.00 for T= 44957 logg=5.98\n", - "Changing to logg=5.00 for T= 45144 logg=5.98\n", - "Changing to logg=5.00 for T= 45300 logg=5.99\n", - "Changing to logg=5.00 for T= 45478 logg=5.99\n", - "Changing to logg=5.00 for T= 45509 logg=5.99\n", - "Changing to logg=5.00 for T= 45646 logg=5.99\n", - "Changing to logg=5.00 for T= 45825 logg=5.99\n", - "Changing to logg=5.00 for T= 45983 logg=6.00\n", - "Changing to logg=5.00 for T= 46142 logg=6.00\n", - "Changing to logg=5.00 for T= 46313 logg=6.00\n", - "Changing to logg=5.00 for T= 46473 logg=6.00\n", - "Changing to logg=5.00 for T= 46623 logg=6.00\n", - "Changing to logg=5.00 for T= 46784 logg=6.00\n", - "Changing to logg=5.00 for T= 46946 logg=6.00\n", - "Changing to logg=5.00 for T= 47109 logg=6.00\n", - "Changing to logg=5.00 for T= 47261 logg=6.00\n", - "Changing to logg=5.00 for T= 47413 logg=6.01\n", - "Changing to logg=5.00 for T= 47566 logg=6.01\n", - "Changing to logg=5.00 for T= 47720 logg=6.01\n", - "Changing to logg=5.00 for T= 47863 logg=6.01\n", - "Changing to logg=5.00 for T= 48029 logg=6.01\n", - "Changing to logg=5.00 for T= 48173 logg=6.01\n", - "Changing to logg=5.00 for T= 48317 logg=6.02\n", - "Changing to logg=5.00 for T= 48473 logg=6.02\n", - "Changing to logg=5.00 for T= 48618 logg=6.02\n", - "Changing to logg=5.00 for T= 48753 logg=6.02\n", - "Changing to logg=5.00 for T= 48910 logg=6.02\n", - "Changing to logg=5.00 for T= 49057 logg=6.02\n", - "Changing to logg=5.00 for T= 49193 logg=6.02\n", - "Changing to logg=5.00 for T= 49329 logg=6.03\n", - "Changing to logg=5.00 for T= 49431 logg=6.03\n", - "Changing to logg=5.00 for T= 49465 logg=6.03\n", - "Changing to logg=5.00 for T= 49614 logg=6.03\n", - "Changing to logg=5.00 for T= 49751 logg=6.03\n", - "Changing to logg=5.00 for T= 49888 logg=6.03\n", - "Changing to T= 50000 for T= 50026 logg=6.03\n", - "Changing to logg=5.00 for T= 50026 logg=6.03\n", - "Changing to T= 50000 for T= 50176 logg=6.03\n", - "Changing to logg=5.00 for T= 50176 logg=6.03\n", - "Changing to T= 50000 for T= 50304 logg=6.03\n", - "Changing to logg=5.00 for T= 50304 logg=6.03\n", - "Changing to T= 50000 for T= 50431 logg=6.04\n", - "Changing to logg=5.00 for T= 50431 logg=6.04\n", - "Changing to T= 50000 for T= 50559 logg=6.04\n", - "Changing to logg=5.00 for T= 50559 logg=6.04\n", - "Changing to T= 50000 for T= 50699 logg=6.04\n", - "Changing to logg=5.00 for T= 50699 logg=6.04\n", - "Changing to T= 50000 for T= 50839 logg=6.04\n", - "Changing to logg=5.00 for T= 50839 logg=6.04\n", - "Changing to T= 50000 for T= 50957 logg=6.04\n", - "Changing to logg=5.00 for T= 50957 logg=6.04\n", - "Changing to T= 50000 for T= 51086 logg=6.04\n", - "Changing to logg=5.00 for T= 51086 logg=6.04\n", - "Changing to T= 50000 for T= 51215 logg=6.04\n", - "Changing to logg=5.00 for T= 51215 logg=6.04\n", - "Changing to T= 50000 for T= 51345 logg=6.04\n", - "Changing to logg=5.00 for T= 51345 logg=6.04\n", - "Changing to T= 50000 for T= 51475 logg=6.04\n", - "Changing to logg=5.00 for T= 51475 logg=6.04\n", - "Changing to T= 50000 for T= 51606 logg=6.04\n", - "Changing to logg=5.00 for T= 51606 logg=6.04\n", - "Changing to T= 50000 for T= 51737 logg=6.05\n", - "Changing to logg=5.00 for T= 51737 logg=6.05\n", - "Changing to T= 50000 for T= 51868 logg=6.05\n", - "Changing to logg=5.00 for T= 51868 logg=6.05\n", - "Changing to T= 50000 for T= 52000 logg=6.05\n", - "Changing to logg=5.00 for T= 52000 logg=6.05\n", - "Changing to T= 50000 for T= 52119 logg=6.05\n", - "Changing to logg=5.00 for T= 52119 logg=6.05\n", - "Changing to T= 50000 for T= 52240 logg=6.05\n", - "Changing to logg=5.00 for T= 52240 logg=6.05\n", - "Changing to T= 50000 for T= 52384 logg=6.05\n", - "Changing to logg=5.00 for T= 52384 logg=6.05\n", - "Changing to T= 50000 for T= 52505 logg=6.05\n", - "Changing to logg=5.00 for T= 52505 logg=6.05\n", - "Changing to T= 50000 for T= 52626 logg=6.05\n", - "Changing to logg=5.00 for T= 52626 logg=6.05\n", - "Changing to T= 50000 for T= 52747 logg=6.05\n", - "Changing to logg=5.00 for T= 52747 logg=6.05\n", - "Changing to T= 50000 for T= 52759 logg=6.05\n", - "Changing to logg=5.00 for T= 52759 logg=6.05\n", - "Changing to T= 50000 for T= 52857 logg=6.05\n", - "Changing to logg=5.00 for T= 52857 logg=6.05\n", - "Changing to T= 50000 for T= 52991 logg=6.05\n", - "Changing to logg=5.00 for T= 52991 logg=6.05\n", - "Changing to T= 50000 for T= 53125 logg=6.06\n", - "Changing to logg=5.00 for T= 53125 logg=6.06\n", - "Changing to T= 50000 for T= 53235 logg=6.06\n", - "Changing to logg=5.00 for T= 53235 logg=6.06\n", - "Changing to T= 50000 for T= 53358 logg=6.06\n", - "Changing to logg=5.00 for T= 53358 logg=6.06\n", - "Changing to T= 50000 for T= 53481 logg=6.06\n", - "Changing to logg=5.00 for T= 53481 logg=6.06\n", - "Changing to T= 50000 for T= 53592 logg=6.06\n", - "Changing to logg=5.00 for T= 53592 logg=6.06\n", - "Changing to T= 50000 for T= 53728 logg=6.06\n", - "Changing to logg=5.00 for T= 53728 logg=6.06\n", - "Changing to T= 50000 for T= 53864 logg=6.06\n", - "Changing to logg=5.00 for T= 53864 logg=6.06\n", - "Changing to T= 50000 for T= 53976 logg=6.07\n", - "Changing to logg=5.00 for T= 53976 logg=6.07\n", - "Changing to T= 50000 for T= 54100 logg=6.07\n", - "Changing to logg=5.00 for T= 54100 logg=6.07\n", - "Changing to T= 50000 for T= 54225 logg=6.07\n", - "Changing to logg=5.00 for T= 54225 logg=6.07\n", - "Changing to T= 50000 for T= 54363 logg=6.07\n", - "Changing to logg=5.00 for T= 54363 logg=6.07\n", - "Changing to T= 50000 for T= 54475 logg=6.07\n", - "Changing to logg=5.00 for T= 54475 logg=6.07\n", - "Changing to T= 50000 for T= 54588 logg=6.07\n", - "Changing to logg=5.00 for T= 54588 logg=6.07\n", - "Changing to T= 50000 for T= 54714 logg=6.08\n", - "Changing to logg=5.00 for T= 54714 logg=6.08\n", - "Changing to T= 50000 for T= 54853 logg=6.08\n", - "Changing to logg=5.00 for T= 54853 logg=6.08\n", - "Changing to T= 50000 for T= 54967 logg=6.08\n", - "Changing to logg=5.00 for T= 54967 logg=6.08\n", - "Changing to T= 50000 for T= 55093 logg=6.08\n", - "Changing to logg=5.00 for T= 55093 logg=6.08\n", - "Changing to T= 50000 for T= 55208 logg=6.08\n", - "Changing to logg=5.00 for T= 55208 logg=6.08\n", - "Changing to T= 50000 for T= 55322 logg=6.09\n", - "Changing to logg=5.00 for T= 55322 logg=6.09\n", - "Changing to T= 50000 for T= 55450 logg=6.09\n", - "Changing to logg=5.00 for T= 55450 logg=6.09\n", - "Changing to T= 50000 for T= 55590 logg=6.09\n", - "Changing to logg=5.00 for T= 55590 logg=6.09\n", - "Changing to T= 50000 for T= 55719 logg=6.09\n", - "Changing to logg=5.00 for T= 55719 logg=6.09\n", - "Changing to T= 50000 for T= 55834 logg=6.09\n", - "Changing to logg=5.00 for T= 55834 logg=6.09\n", - "Changing to T= 50000 for T= 55873 logg=6.09\n", - "Changing to logg=5.00 for T= 55873 logg=6.09\n", - "Changing to T= 50000 for T= 55963 logg=6.09\n", - "Changing to logg=5.00 for T= 55963 logg=6.09\n", - "Changing to T= 50000 for T= 56092 logg=6.10\n", - "Changing to logg=5.00 for T= 56092 logg=6.10\n", - "Changing to T= 50000 for T= 56234 logg=6.10\n", - "Changing to logg=5.00 for T= 56234 logg=6.10\n", - "Changing to T= 50000 for T= 56364 logg=6.10\n", - "Changing to logg=5.00 for T= 56364 logg=6.10\n", - "Changing to T= 50000 for T= 56507 logg=6.10\n", - "Changing to logg=5.00 for T= 56507 logg=6.10\n", - "Changing to T= 50000 for T= 56624 logg=6.11\n", - "Changing to logg=5.00 for T= 56624 logg=6.11\n", - "Changing to T= 50000 for T= 56754 logg=6.11\n", - "Changing to logg=5.00 for T= 56754 logg=6.11\n", - "Changing to T= 50000 for T= 56885 logg=6.11\n", - "Changing to logg=5.00 for T= 56885 logg=6.11\n", - "Changing to T= 50000 for T= 57030 logg=6.11\n", - "Changing to logg=5.00 for T= 57030 logg=6.11\n", - "Changing to T= 50000 for T= 57161 logg=6.11\n", - "Changing to logg=5.00 for T= 57161 logg=6.11\n", - "Changing to T= 50000 for T= 57293 logg=6.11\n", - "Changing to logg=5.00 for T= 57293 logg=6.11\n", - "Changing to T= 50000 for T= 57425 logg=6.11\n", - "Changing to logg=5.00 for T= 57425 logg=6.11\n", - "Changing to T= 50000 for T= 57570 logg=6.11\n", - "Changing to logg=5.00 for T= 57570 logg=6.11\n", - "Changing to T= 50000 for T= 57703 logg=6.11\n", - "Changing to logg=5.00 for T= 57703 logg=6.11\n", - "Changing to T= 50000 for T= 57850 logg=6.12\n", - "Changing to logg=5.00 for T= 57850 logg=6.12\n", - "Changing to T= 50000 for T= 57983 logg=6.12\n", - "Changing to logg=5.00 for T= 57983 logg=6.12\n", - "Changing to T= 50000 for T= 58117 logg=6.12\n", - "Changing to logg=5.00 for T= 58117 logg=6.12\n", - "Changing to T= 50000 for T= 58251 logg=6.13\n", - "Changing to logg=5.00 for T= 58251 logg=6.13\n", - "Changing to T= 50000 for T= 58398 logg=6.13\n", - "Changing to logg=5.00 for T= 58398 logg=6.13\n", - "Changing to T= 50000 for T= 58546 logg=6.13\n", - "Changing to logg=5.00 for T= 58546 logg=6.13\n", - "Changing to T= 50000 for T= 58695 logg=6.13\n", - "Changing to logg=5.00 for T= 58695 logg=6.13\n", - "Changing to T= 50000 for T= 58844 logg=6.14\n", - "Changing to logg=5.00 for T= 58844 logg=6.14\n", - "Changing to T= 50000 for T= 58993 logg=6.14\n", - "Changing to logg=5.00 for T= 58993 logg=6.14\n", - "Changing to T= 50000 for T= 59143 logg=6.14\n", - "Changing to logg=5.00 for T= 59143 logg=6.14\n", - "Changing to T= 50000 for T= 59293 logg=6.15\n", - "Changing to logg=5.00 for T= 59293 logg=6.15\n", - "Changing to T= 50000 for T= 59334 logg=6.15\n", - "Changing to logg=5.00 for T= 59334 logg=6.15\n", - "Changing to T= 50000 for T= 59457 logg=6.15\n", - "Changing to logg=5.00 for T= 59457 logg=6.15\n", - "Changing to T= 50000 for T= 59621 logg=6.16\n", - "Changing to logg=5.00 for T= 59621 logg=6.16\n", - "Changing to T= 50000 for T= 59800 logg=6.16\n", - "Changing to logg=5.00 for T= 59800 logg=6.16\n", - "Changing to T= 50000 for T= 59965 logg=6.16\n", - "Changing to logg=5.00 for T= 59965 logg=6.16\n", - "Changing to T= 50000 for T= 60159 logg=6.17\n", - "Changing to logg=5.00 for T= 60159 logg=6.17\n", - "Changing to T= 50000 for T= 60353 logg=6.17\n", - "Changing to logg=5.00 for T= 60353 logg=6.17\n", - "Changing to T= 50000 for T= 60534 logg=6.18\n", - "Changing to logg=5.00 for T= 60534 logg=6.18\n", - "Changing to T= 50000 for T= 60758 logg=6.18\n", - "Changing to logg=5.00 for T= 60758 logg=6.18\n", - "Changing to T= 50000 for T= 60968 logg=6.18\n", - "Changing to logg=5.00 for T= 60968 logg=6.18\n", - "Changing to T= 50000 for T= 61235 logg=6.19\n", - "Changing to logg=5.00 for T= 61235 logg=6.19\n", - "Changing to T= 50000 for T= 61504 logg=6.20\n", - "Changing to logg=5.00 for T= 61504 logg=6.20\n", - "Changing to T= 50000 for T= 61844 logg=6.21\n", - "Changing to logg=5.00 for T= 61844 logg=6.21\n", - "Changing to T= 50000 for T= 62287 logg=6.23\n", - "Changing to logg=5.00 for T= 62287 logg=6.23\n", - "Changing to T= 50000 for T= 63038 logg=6.27\n", - "Changing to logg=5.00 for T= 63038 logg=6.27\n", - "Changing to T= 50000 for T= 64239 logg=6.32\n", - "Changing to logg=5.00 for T= 64239 logg=6.32\n", - "Changing to T= 50000 for T= 65464 logg=6.37\n", - "Changing to logg=5.00 for T= 65464 logg=6.37\n", - "Changing to T= 50000 for T= 66696 logg=6.42\n", - "Changing to logg=5.00 for T= 66696 logg=6.42\n", - "Changing to T= 50000 for T= 67967 logg=6.47\n", - "Changing to logg=5.00 for T= 67967 logg=6.47\n", - "Changing to T= 50000 for T= 69263 logg=6.53\n", - "Changing to logg=5.00 for T= 69263 logg=6.53\n", - "Changing to T= 50000 for T= 71367 logg=6.63\n", - "Changing to logg=5.00 for T= 71367 logg=6.63\n", - "Isochrone generation took 112.722160 s.\n", - "Making photometry for isochrone: log(t) = 6.70 AKs = 0.80 dist = 4000\n", - " Starting at: 2024-08-02 09:54:23.120123 Usually takes ~5 minutes\n", - "Starting filter: wfc3,ir,f127m Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2928 K m_hst_f127m = 22.41\n", - "M = 1.175 Msun T = 4611 K m_hst_f127m = 18.72\n", - "M = 1.650 Msun T = 5355 K m_hst_f127m = 17.71\n", - "M = 1.973 Msun T = 6311 K m_hst_f127m = 16.34\n", - "M = 2.292 Msun T = 9669 K m_hst_f127m = 16.68\n", - "M = 2.648 Msun T = 11771 K m_hst_f127m = 16.64\n", - "M = 2.819 Msun T = 12291 K m_hst_f127m = 16.51\n", - "M = 3.075 Msun T = 13014 K m_hst_f127m = 16.33\n", - "M = 3.332 Msun T = 13709 K m_hst_f127m = 16.16\n", - "M = 3.482 Msun T = 14099 K m_hst_f127m = 16.07\n", - "M = 3.615 Msun T = 14438 K m_hst_f127m = 15.99\n", - "M = 8.977 Msun T = 23502 K m_hst_f127m = 14.07\n", - "M = 49.000 Msun T = 32344 K m_hst_f127m = 9.23\n", - "M = 50.757 Msun T = 31046 K m_hst_f127m = 9.25\n", - "M = 52.540 Msun T = 41937 K m_hst_f127m = 10.13\n", - "M = 54.406 Msun T = 48753 K m_hst_f127m = 10.85\n", - "M = 56.318 Msun T = 65464 K m_hst_f127m = 12.01\n", - "Starting filter: wfc3,ir,f139m Elapsed time: 25.91 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2928 K m_hst_f139m = 22.11\n", - "M = 1.175 Msun T = 4611 K m_hst_f139m = 18.14\n", - "M = 1.650 Msun T = 5355 K m_hst_f139m = 17.18\n", - "M = 1.973 Msun T = 6311 K m_hst_f139m = 15.86\n", - "M = 2.292 Msun T = 9669 K m_hst_f139m = 16.28\n", - "M = 2.648 Msun T = 11771 K m_hst_f139m = 16.25\n", - "M = 2.819 Msun T = 12291 K m_hst_f139m = 16.12\n", - "M = 3.075 Msun T = 13014 K m_hst_f139m = 15.94\n", - "M = 3.332 Msun T = 13709 K m_hst_f139m = 15.77\n", - "M = 3.482 Msun T = 14099 K m_hst_f139m = 15.68\n", - "M = 3.615 Msun T = 14438 K m_hst_f139m = 15.60\n", - "M = 8.977 Msun T = 23502 K m_hst_f139m = 13.70\n", - "M = 49.000 Msun T = 32344 K m_hst_f139m = 8.87\n", - "M = 50.757 Msun T = 31046 K m_hst_f139m = 8.88\n", - "M = 52.540 Msun T = 41937 K m_hst_f139m = 9.77\n", - "M = 54.406 Msun T = 48753 K m_hst_f139m = 10.50\n", - "M = 56.318 Msun T = 65464 K m_hst_f139m = 11.66\n", - "Starting filter: wfc3,ir,f153m Elapsed time: 51.87 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2928 K m_hst_f153m = 21.45\n", - "M = 1.175 Msun T = 4611 K m_hst_f153m = 17.51\n", - "M = 1.650 Msun T = 5355 K m_hst_f153m = 16.63\n", - "M = 1.973 Msun T = 6311 K m_hst_f153m = 15.37\n", - "M = 2.292 Msun T = 9669 K m_hst_f153m = 15.89\n", - "M = 2.648 Msun T = 11771 K m_hst_f153m = 15.87\n", - "M = 2.819 Msun T = 12291 K m_hst_f153m = 15.75\n", - "M = 3.075 Msun T = 13014 K m_hst_f153m = 15.57\n", - "M = 3.332 Msun T = 13709 K m_hst_f153m = 15.40\n", - "M = 3.482 Msun T = 14099 K m_hst_f153m = 15.31\n", - "M = 3.615 Msun T = 14438 K m_hst_f153m = 15.23\n", - "M = 8.977 Msun T = 23502 K m_hst_f153m = 13.34\n", - "M = 49.000 Msun T = 32344 K m_hst_f153m = 8.52\n", - "M = 50.757 Msun T = 31046 K m_hst_f153m = 8.53\n", - "M = 52.540 Msun T = 41937 K m_hst_f153m = 9.42\n", - "M = 54.406 Msun T = 48753 K m_hst_f153m = 10.15\n", - "M = 56.318 Msun T = 65464 K m_hst_f153m = 11.31\n", - " Time taken: 78.43 seconds\n" - ] - } - ], - "source": [ - "# Define isochrone parameters\n", - "logAge = np.log10(5*10**6.) # Age in log(years)\n", - "AKs = 0.8 # extinction in mags\n", - "dist = 4000 # distance in parsec\n", - "metallicity = 0 # Metallicity in [M/H]\n", - "\n", - "# Define evolution/atmosphere models and extinction law\n", - "evo_model = evolution.MergedBaraffePisaEkstromParsec() \n", - "atm_func = atmospheres.get_merged_atmosphere\n", - "red_law = reddening.RedLawHosek18b()\n", - "\n", - "# Also specify filters for synthetic photometry (optional). Here we use \n", - "# the HST WFC3-IR F127M, F139M, and F153M filters\n", - "filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f139m', 'wfc3,ir,f153m']\n", - "\n", - "# Specify the directory we want the output isochrone\n", - "# table saved in. If the directory does not already exist,\n", - "# SPISEA will create it.\n", - "iso_dir = 'isochrones/'\n", - "\n", - "# Make IsochronePhot object. Note that this will take a minute or two, \n", - "# unless the isochrone has been generated previously.\n", - "#\n", - "# Note that this is not show all of the user options \n", - "# for IsochronePhot. See docs for complete list of options.\n", - "my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n", - " evo_model=evo_model, atm_func=atm_func,\n", - " red_law=red_law, filters=filt_list,\n", - " iso_dir=iso_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a6e081f-84d3-4efb-a6d5-59d5f60d845f", - "metadata": {}, - "outputs": [], - "source": [ - "# The stars in the isochrone and associated properties \n", - "# are stored in an astropy table called \"points\" \n", - "# within the IsochronePhot object. \n", - "print(my_iso.points)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1205c450-d7f2-4ad7-86b0-006762426d5c", - "metadata": {}, - "outputs": [], - "source": [ - "# Example case:\n", - "# Identify a 1 M_sun star, print F127M, F139M, and F153M mags\n", - "idx = np.where( abs(my_iso.points['mass'] - 1.0) == min(abs(my_iso.points['mass'] - 1.0)) )[0]\n", - "f127m = np.round(my_iso.points[idx[0]]['m_hst_f127m'], decimals=3)\n", - "f139m = np.round(my_iso.points[idx[0]]['m_hst_f139m'], decimals=3)\n", - "f153m = np.round(my_iso.points[idx[0]]['m_hst_f153m'], decimals=3)\n", - "print('1 M_sun: F127M = {0} mag, F139M = {1} mag, F153M = {2} mag'.format(f127m, f139m, f153m))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a8169db9-486e-4d20-9a09-dfcd7ebbd3e1", - "metadata": {}, - "outputs": [], - "source": [ - "# Make a color-magnitude diagram from the isochrone\n", - "py.figure(1, figsize=(10,10))\n", - "py.clf()\n", - "py.plot(my_iso.points['m_hst_f127m'] - my_iso.points['m_hst_f153m'], \n", - " my_iso.points['m_hst_f153m'], 'r-', label='_nolegend_')\n", - "py.plot(my_iso.points['m_hst_f127m'][idx] - my_iso.points['m_hst_f153m'][idx], \n", - " my_iso.points['m_hst_f153m'][idx], 'b*', ms=15, label='1 $M_\\odot$')\n", - "py.xlabel('F127M - F153M')\n", - "py.ylabel('F153M')\n", - "py.gca().invert_yaxis()\n", - "py.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "7e9599c3-e7f7-4f1b-8eda-8148058b16c3", - "metadata": {}, - "source": [ - "### 2: Bringing in an IMF" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "493b8648-1c3b-4aab-858f-2f955195e634", - "metadata": {}, - "outputs": [], - "source": [ - "# Make multiplicity object Here, we use the MultiplicityUnresolved object, \n", - "# based on Lu+13. This means that star systems will be unresolved, i.e., \n", - "# that all components of a star system are combined into a single \"star\" in the cluster\n", - "imf_multi = multiplicity.MultiplicityUnresolved()\n", - "\n", - "# Make IMF object; we'll use a broken power law with the parameters from Kroupa+01\n", - "\n", - "# NOTE: when defining the power law slope for each segment of the IMF, we define\n", - "# the entire exponent, including the negative sign. For example, if dN/dm $\\propto$ m^-alpha,\n", - "# then you would use the value \"-2.3\" to specify an IMF with alpha = 2.3. \n", - "\n", - "my_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)" - ] - }, - { - "cell_type": "markdown", - "id": "0e42984d-bf09-4e71-a823-aa698672cfe6", - "metadata": {}, - "source": [ - "### 3: Generating Cluster" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0bf0fed8-2b70-41be-b950-83f1362af9f0", - "metadata": {}, - "outputs": [], - "source": [ - "# Define total cluster mass\n", - "mass = 10**5.\n", - "\n", - "# Make cluster object\n", - "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c8de1a27-0b07-404c-a13d-8434e204017d", - "metadata": {}, - "outputs": [], - "source": [ - "# Look at star systems table\n", - "print(cluster.star_systems)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6b10a958-a0f8-443c-be6b-e07c5de2d4dc", - "metadata": {}, - "outputs": [], - "source": [ - "# The companions table is accessed in a similar way\n", - "print(cluster.companions)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "095a963e-c0de-4bbc-a03f-471f3cdb7b91", - "metadata": {}, - "outputs": [], - "source": [ - "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", - "# multiple systems on the photometry\n", - "clust = cluster.star_systems\n", - "iso = my_iso.points\n", - "\n", - "py.figure(2, figsize=(10,10))\n", - "py.clf()\n", - "py.plot(clust['m_hst_f127m'] - clust['m_hst_f153m'], clust['m_hst_f153m'],\n", - " 'k.', ms=5, alpha=0.1, label='__nolegend__')\n", - "py.plot(iso['m_hst_f127m'] - iso['m_hst_f153m'], iso['m_hst_f153m'],\n", - " 'r-', label='Isochrone')\n", - "py.xlabel('F127M - F153M')\n", - "py.ylabel('F153M')\n", - "py.gca().invert_yaxis()\n", - "py.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d694ed41-c732-4093-9f0a-ad021fe8397a", - "metadata": {}, - "outputs": [], - "source": [ - "print(\"minimum primary mass: \" + str(np.min(clust['mass'])))\n", - "print(\"minimum companion mass: \" + str(np.min(cluster.companions['mass'])))\n", - "print(\"maximum primary mass: \" + str(np.max(clust['mass'])))\n", - "print(\"maximum companion mass: \" + str(np.max(cluster.companions['mass'])))" - ] - }, - { - "cell_type": "markdown", - "id": "fa4c660d-ea4e-491a-95db-278617ee1757", - "metadata": {}, - "source": [ - "### Creating a histogram to compare Weidner Kroupa and Muzic" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2540680-d69f-427f-b0d1-55130826ad8a", - "metadata": {}, - "outputs": [], - "source": [ - "#generate clusters for each class\n", - "w_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)\n", - "m_imf = imf.Muzic_2017(multiplicity=imf_multi)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4d1c647e-8e1a-4a24-8b20-26a8b14f4660", - "metadata": {}, - "outputs": [], - "source": [ - "# Define total cluster mass\n", - "mass = 10**5.\n", - "\n", - "# Make cluster objects for each\n", - "w_cluster = synthetic.ResolvedCluster(my_iso, w_imf, mass)\n", - "m_cluster = synthetic.ResolvedCluster(my_iso, m_imf, mass)" - ] - }, - { - "cell_type": "markdown", - "id": "f55b6d0a-ecc2-4bbb-aa5b-d8f23acfe441", - "metadata": {}, - "source": [ - "#### Comparing Weidner Kroupa and Muzic clusters data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eb3847e7-c184-4d49-a7ec-69d0a1e50fea", - "metadata": {}, - "outputs": [], - "source": [ - "#show total numbers for each cluster\n", - "print(\"primary masses in Weidner Kroupa cluster: \" + str(len(w_cluster.star_systems)))\n", - "print(\"companion masses in Weidner Kroupa cluster: \" + str(len(w_cluster.companions)))\n", - "print(\"primary masses in Muzic cluster: \" + str(len(m_cluster.star_systems)))\n", - "print(\"companion masses in Muzic cluster: \" + str(len(m_cluster.companions)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b2656bf-6a7e-4ca5-ad09-6ae59e8c6d0d", - "metadata": {}, - "outputs": [], - "source": [ - "#Comparing max/min primary/companion masses\n", - "#Weidner Kroupa\n", - "print('\\033[1m' + \"Weidner Kroupa\" + '\\033[0m')\n", - "print(\"minimum primary mass: \" + str(np.min(w_cluster.star_systems['mass'])))\n", - "print(\"minimum companion mass: \" + str(np.min(w_cluster.companions['mass'])))\n", - "print(\"maximum primary mass: \" + str(np.max(w_cluster.star_systems['mass'])))\n", - "print(\"maximum companion mass: \" + str(np.max(w_cluster.companions['mass'])))\n", - "print('\\n')\n", - "\n", - "#Muzic\n", - "print('\\033[1m' + \"Muzic\" + '\\033[0m')\n", - "print(\"minimum primary mass: \" + str(np.min(m_cluster.star_systems['mass'])))\n", - "print(\"minimum companion mass: \" + str(np.min(m_cluster.companions['mass'])))\n", - "print(\"maximum primary mass: \" + str(np.max(m_cluster.star_systems['mass'])))\n", - "print(\"maximum companion mass: \" + str(np.max(m_cluster.companions['mass'])))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17d1685d-0f63-4f6e-ad62-4ba3a03f0780", - "metadata": {}, - "outputs": [], - "source": [ - "#compare total cluster masses\n", - "#create combined arrays of primary/companion masses\n", - "all_m_masses = np.concatenate((m_cluster.star_systems['mass'], m_cluster.companions['mass']))\n", - "all_w_masses = np.concatenate((w_cluster.star_systems['mass'], w_cluster.companions['mass']))\n", - "\n", - "#find total of added masses\n", - "print(\"total mass of WD cluster: \" + str(np.sum(all_w_masses)))\n", - "print(\"total mass of M cluster: \" + str(np.sum(all_m_masses)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e2fe19b-9da8-4388-92bd-c1390444e8c4", - "metadata": {}, - "outputs": [], - "source": [ - "#define mass range of histogram\n", - "bin_edges = [0.01, 0.08, 0.5, 1, 2]\n", - "\n", - "#setting up subplots\n", - "fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(8,8))\n", - "plt.subplots_adjust(hspace=0.5, wspace=0.75)\n", - "\n", - "#create histograms to compare\n", - "counts_m, edges_m, _ = ax[0].hist(all_m_masses, bins=bin_edges, alpha=0.5, label='Muzic_2017', color='blue', edgecolor='black', histtype='bar')\n", - "counts_w, edges_w, _ = ax[1].hist(all_w_masses, bins=bin_edges, alpha=0.5, label='Weidner_Kroupa_2004', color='red', edgecolor='black', histtype='bar')\n", - "\n", - "#compute bin centers\n", - "bin_centers_m = (edges_m[:-1] + edges_m[1:]) / 2\n", - "bin_centers_w = (edges_w[:-1] + edges_w[1:]) / 2\n", - "\n", - "#generate x values for the broken power law curve\n", - "x_fit_m = np.linspace(bin_centers_m.min(), bin_centers_m.max(), 100)\n", - "x_fit_w = np.linspace(bin_centers_w.min(), bin_centers_w.max(), 100)\n", - "\n", - "\n", - "\n", - "#labels and legend\n", - "plt.xlabel('Stellar Mass (Solar Masses)')\n", - "plt.ylabel('Number of Stars')\n", - "plt.title('Comparison of Stellar Mass Distributions')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "04216fed-7f8b-4249-9c6c-29c82cc18bce", - "metadata": {}, - "source": [ - "## Generating a cluster with compound objects (using an IFMR)\n", - "This is before code is modified to allow for brown dwarves" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "25b1247a-0eb8-40bc-bf56-f89e788283cd", - "metadata": {}, - "outputs": [], - "source": [ - "# Create isochrone object \n", - "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", - "my_ifmr = ifmr.IFMR_Raithel18()\n", - "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", - " evo_model = evolution.MergedBaraffePisaEkstromParsec(),\n", - " filters=filt_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "01f6f94c-1b23-4ae1-bfc2-96e2d4cacd27", - "metadata": {}, - "outputs": [], - "source": [ - "# Create IMF objects \n", - "imf_multi = multiplicity.MultiplicityUnresolved()\n", - "massLimits = np.array([0.01, 0.08, 0.5, 1, np.inf])\n", - "powers = np.array([-0.3, -1.3, -2.3, -2.35])\n", - "k_imf = imf.IMF_broken_powerlaw(massLimits, powers, multiplicity=imf_multi)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "1a914c4c-ac7e-4c61-9e40-1b0d5dd5568f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 580447 stars out of mass range\n", - "Found 113559 companions out of stellar mass range\n" - ] - } - ], - "source": [ - "# Make clusters\n", - "cluster_mass = 10**6\n", - "k_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", - "\n", - "# Get outputs\n", - "k_out = k_cluster.star_systems\n", - "k_comp = k_cluster.companions" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "62293dea-989b-4870-a77b-ba677010da81", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs and WDs\n", - "p_bh = np.where(k_out['phase'] == 103)[0]\n", - "c_bh = np.where(k_comp['phase'] == 103)[0]\n", - "k_bh = np.concatenate([p_bh, c_bh])\n", - "p_ns = np.where(k_out['phase'] == 102)[0]\n", - "c_ns = np.where(k_comp['phase'] == 102)[0]\n", - "k_ns = np.concatenate([p_ns, c_ns])\n", - "p_wd = np.where(k_out['phase'] == 101)[0]\n", - "c_wd = np.where(k_comp['phase'] == 101)[0]\n", - "k_wd = np.concatenate([p_wd, c_wd])\n", - "\n", - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 1.4, 16)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(1, 2, 1)\n", - "plt.hist(k_out[p_bh]['mass_current'], histtype = 'step',\n", - " bins = bh_bins, label = 'Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.hist(k_out[p_wd]['mass_current'], histtype = 'step',\n", - " bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "d36bba0d-3037-46c0-91f3-321e47241204", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "831012.4509070338\n", - "5.311329909891375\n" - ] - } - ], - "source": [ - "all_masses = np.concatenate([k_out['mass'], k_comp['mass']])\n", - "tot_mass = np.sum(all_masses)\n", - "print(tot_mass)\n", - "print(np.min(k_out[p_wd]['mass']))" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "1f999373-557e-42f1-9c0b-43d1cfab198e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0700003309744075\n", - "0.010000236016655941\n" - ] - } - ], - "source": [ - "print(np.min(k_out['mass']))\n", - "print(np.min(k_comp['mass']))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "d7c2fa5e-49cd-4021-869a-6f9701677c68", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current smallest mass of generated black holes: 0.07006468311672817\n", - "Current largest mass of generated black holes: 15.65838834836072\n" - ] - } - ], - "source": [ - "print(\"Current smallest mass of generated black holes: \" + str(np.min(k_out[k_bh]['mass_current'])))\n", - "print(\"Current largest mass of generated black holes: \" + str(np.max(k_out[k_bh]['mass_current'])))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e056d1f-be87-43ee-9899-2a1859d5e2c5", - "metadata": {}, - "outputs": [], - "source": [ - "print('The cluster table contains these columns: {0}'.format(k_cluster.star_systems.keys()))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "c02d7dbd-9690-49f4-931d-e7f66172ba12", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass isMultiple ... m_hst_f153m N_companions\n", - "------------------ ---------- ... ------------------- ------------\n", - "34.858558890643366 True ... nan 1\n", - " 24.21988253820487 True ... 0.19920518920036126 1\n", - " 53.76008089560359 True ... -0.2181337523917536 4\n", - "15.882675891462688 True ... -1.810589141056601 2\n", - " 57.37043948819449 True ... nan 3\n", - "31.414432201808793 True ... nan 1\n", - "22.819575828477465 True ... 1.0957723191576627 5\n", - " ... ... ... ... ...\n", - "23.712544384283426 True ... 1.6681099957862053 1\n", - " 52.89729960707116 True ... 1.3625884002383177 3\n", - " 82.84997226207857 True ... nan 2\n", - " 32.27796062878089 True ... 0.43046734619105526 3\n", - " 26.92983956402475 True ... nan 2\n", - " 27.13236574281697 True ... nan 2\n", - " 36.6006664768621 True ... 2.5063372504019394 3\n", - "Length = 1671 rows\n" - ] - } - ], - "source": [ - "print(k_out[p_bh])" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "ddc351e5-e465-4f2e-9703-cd5c819a75f2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass \n", - "------------------\n", - "34.858558890643366\n", - " 24.21988253820487\n", - " 53.76008089560359\n", - "15.882675891462688\n", - " 57.37043948819449\n", - "31.414432201808793\n", - "22.819575828477465\n", - " ...\n", - "23.712544384283426\n", - " 52.89729960707116\n", - " 82.84997226207857\n", - " 32.27796062878089\n", - " 26.92983956402475\n", - " 27.13236574281697\n", - " 36.6006664768621\n", - "Length = 1671 rows mass_current \n", - "------------------\n", - "12.446576185012097\n", - " 8.78166110167993\n", - " 8.806507411947209\n", - " 5.424709536731119\n", - " 7.981409529281804\n", - "11.216283709433917\n", - " 8.283995574428168\n", - " ...\n", - " 8.603336271797449\n", - " 9.056339719938071\n", - " 6.152701918097497\n", - "11.515219186451843\n", - " 9.707197419320156\n", - " 9.775168357448042\n", - "13.119673398892417\n", - "Length = 1671 rows\n" - ] - } - ], - "source": [ - "print(k_out[p_bh]['mass'], k_out[p_bh]['mass_current'])" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "f5ce39b9-2c6d-4e7c-a6f6-ffd7235ac9e8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial mass of smallest generated black hole: 15.000468103060483\n", - "Initial mass of largest generated black hole: 117.48601718195576\n" - ] - } - ], - "source": [ - "print(\"Initial mass of smallest generated black hole: \" + str(np.min(k_out[p_bh]['mass'])))\n", - "print(\"Initial mass of largest generated black hole: \" + str(np.max(k_out[p_bh]['mass'])))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "1c41b219-7a85-4519-af99-77145daaa418", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial mass of smallest generated comp black hole: 15.037154672016358\n", - "Initial mass of largest generated comp black hole: 111.30489194158751\n" - ] - } - ], - "source": [ - "print(\"Initial mass of smallest generated comp black hole: \" + str(np.min(k_comp[c_bh]['mass'])))\n", - "print(\"Initial mass of largest generated comp black hole: \" + str(np.max(k_comp[c_bh]['mass'])))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "3566ea45-31f6-4015-bf84-d34ff93ec18e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 1.4, 16)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(1, 2, 1)\n", - "plt.hist(k_comp[c_bh]['mass_current'], histtype = 'step',\n", - " bins = bh_bins, label = 'Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.hist(k_comp[c_wd]['mass_current'], histtype = 'step',\n", - " bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "3b4167fc-fd67-4c11-9cb4-f46b1e8cb9a6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9729527582037262\n" - ] - } - ], - "source": [ - "print(np.min(k_comp[c_wd]['mass_current']))" - ] - }, - { - "cell_type": "markdown", - "id": "c17cd2c5-164a-49be-b118-8023b6f3a3b4", - "metadata": {}, - "source": [ - "## New Cluster after addition of BD criterion" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "bd81a9c8-bc0c-46e3-a20d-bc2112a50f5c", - "metadata": {}, - "outputs": [], - "source": [ - "#locate brown dwarfs\n", - "p_bd = np.where(k_out['phase'] == 99)[0]\n", - "c_bd = np.where(k_comp['phase'] == 99)[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f49be47c-dc94-467e-bf4d-41b6f926ace5", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/changes/evolution_gaussian_fit.ipynb b/changes/evolution_gaussian_fit.ipynb deleted file mode 100644 index 957d09b3..00000000 --- a/changes/evolution_gaussian_fit.ipynb +++ /dev/null @@ -1,1195 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9aaeb072-9ae6-4362-978f-4d9fd5802cd7", - "metadata": {}, - "source": [ - "## Generating Intermediary Data for Non-Overlapping Evolutionary Models" - ] - }, - { - "cell_type": "markdown", - "id": "5459271a-7e3f-4723-91eb-b8735a2a5ff1", - "metadata": {}, - "source": [ - "Oh no! Unfortunately our new evolutionary models for brown dwarf masses do not align with those previously implemented, leaving a mass gap. To address this, we have decided to use Gaussian processes to fit the two data regimes (Phillips and Pisa) and generate the associated data in this range. The parameters in question are mass, luminosity, effective temperature, and log gravity. This notebook shows an example case for solar metallicity and a log age of ~7.13." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "1cad6635-a51f-426b-a207-6e2be5710586", - "metadata": {}, - "outputs": [], - "source": [ - "# importing necessary packages\n", - "import matplotlib.pyplot as plt\n", - "from astropy.table import Table\n", - "import numpy as np\n", - "from sklearn.gaussian_process import GaussianProcessRegressor\n", - "from sklearn.gaussian_process.kernels import ConstantKernel as C, Matern\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.model_selection import KFold" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d22dab8a-4834-48ed-99b0-47295b685e38", - "metadata": {}, - "outputs": [], - "source": [ - "# importing compatible age fits files \n", - "phillips = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/iso/z00/iso_7.128205127364955.fits'\n", - "pisa = '/System/Volumes/Data/mnt/g/lu/models/evolution/Pisa2011/iso/z015/iso_7.13.fits'\n", - "\n", - "# loading tables and extracting data\n", - "phil_tbl = Table.read(phillips, format='fits')\n", - "pisa_tbl = Table.read(pisa, format='fits')\n", - "\n", - "phil_mass = phil_tbl['Mass']\n", - "pisa_mass = pisa_tbl['col3']\n", - "\n", - "phil_Teff = phil_tbl['Teff']\n", - "pisa_Teff = pisa_tbl['col2']\n", - "\n", - "phil_L = phil_tbl['Luminosity']\n", - "pisa_L = pisa_tbl['col1']\n", - "\n", - "phil_logg = phil_tbl['Gravity']\n", - "pisa_logg = pisa_tbl['col4']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fd004b17-6528-4633-a5a1-2ab841c41bb9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# creating graphs of mass vs. luminosity, Teff, and gravity\n", - "fig, axs = plt.subplots(3, 1, figsize=(6, 10))\n", - "\n", - "# create array for mass gap\n", - "mass_gap = np.linspace(np.max(phil_mass), np.min(pisa_mass), 1000)\n", - "\n", - "# mass vs. luminosity\n", - "axs[0].plot(phil_mass, phil_L, color='c', label='Phillips')\n", - "axs[0].plot(pisa_mass, pisa_L, color='pink', label='Pisa')\n", - "axs[0].set_title('Mass vs. Luminosity') # for logAge=7.13')\n", - "y_min0 = axs[0].get_ylim()[0]\n", - "y_max0 = axs[0].get_ylim()[1]\n", - "axs[0].fill_between(mass_gap, y_min0, y_max0, color='gray', label='mass gap', alpha=0.3)\n", - "axs[0].set_xlabel('Mass ($M_{\\odot}$)')\n", - "axs[0].set_ylabel('Luminosity (L/$L_{\\odot}$)')\n", - "axs[0].set_xlim(0,1)\n", - "axs[0].set_ylim(-4,0)\n", - "axs[0].legend()\n", - "\n", - "# mass vs. Teff\n", - "axs[1].plot(phil_mass, phil_Teff, color='c', label='Phillips')\n", - "axs[1].plot(pisa_mass, pisa_Teff, color='pink', label='Pisa')\n", - "y_min1 = axs[1].get_ylim()[0]\n", - "y_max1 = axs[1].get_ylim()[1]\n", - "axs[1].fill_between(mass_gap, y_min1, y_max1, color='gray', label='mass gap', alpha=0.3)\n", - "axs[1].set_title('Mass vs. Effective Temperature') # for logAge=7.13')\n", - "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", - "axs[1].set_ylabel('log(Teff) (log(K))')\n", - "axs[1].set_xlim(0,1)\n", - "axs[1].set_ylim(3,4)\n", - "axs[1].legend()\n", - "\n", - "# mass vs. logg (problem child)\n", - "axs[2].plot(phil_mass, phil_logg, color='c', label='Phillips')\n", - "axs[2].plot(pisa_mass, pisa_logg, color='pink', label='Pisa')\n", - "y_min2 = axs[2].get_ylim()[0]\n", - "y_max2 = axs[2].get_ylim()[1]\n", - "axs[2].fill_between(mass_gap, y_min2, y_max2, color='gray', label='mass gap', alpha=0.3)\n", - "axs[2].set_title('Mass vs. Gravity') # for logAge=7.13'\n", - "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", - "axs[2].set_ylabel('logg')\n", - "axs[2].set_xlim(0,1)\n", - "axs[2].set_ylim(4,4.5)\n", - "axs[2].legend()\n", - "\n", - "fig.tight_layout()\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "35e9b3a9-8efe-4668-a359-c8332e34fbba", - "metadata": {}, - "source": [ - "From these zoomed in graphs it becomes clear that though a mass gap is present, luminosty and effective temperature still seem to line up if a model connects them. In contrast, the graph for log gravity is very clearly disconnected, which is worth refining as new models continue to be generated. To best address this problem, we decided to use GaussianProcessRegressor with the Matern kernel, as through trial and error this appeared to be the best fit. We then decided to generate a chi-squared heat map for luminosity to find the ideal parameters for length_scale and nu. This was done with and without the use of the kFold class from sklearn, with a determination that using it produces a much better fit." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8df930e4-19df-4651-960b-91c7aa522482", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.075 0.2019\n" - ] - } - ], - "source": [ - "# creating arrays\n", - "## mass\n", - "phil_mass = phil_tbl['Mass']\n", - "pisa_mass = pisa_tbl['col3']\n", - "combined_masses = np.concatenate([phil_mass, pisa_mass])\n", - "\n", - "## Teff\n", - "phil_Teff = phil_tbl['Teff']\n", - "pisa_Teff = pisa_tbl['col2']\n", - "combined_Teff = np.concatenate([phil_Teff, pisa_Teff])\n", - "\n", - "## Luminosity\n", - "phil_L = phil_tbl['Luminosity']\n", - "pisa_L = pisa_tbl['col1']\n", - "combined_L = np.concatenate([phil_L, pisa_L])\n", - "\n", - "## logg\n", - "phil_logg = phil_tbl['Gravity']\n", - "pisa_logg = pisa_tbl['col4']\n", - "combined_logg = np.concatenate([phil_logg, pisa_logg])\n", - "print(np.max(phil_mass), np.min(pisa_mass))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "38a5d619-8cb2-40d6-962b-6f1491366962", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "### USING SKLEARN\n", - "\n", - "# creating input array (mass) and output array (luminosity)\n", - "X = combined_masses.reshape(-1, 1)\n", - "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", - "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", - "\n", - "# Define grid\n", - "length_scales = np.logspace(-1, 2, 20)\n", - "nus = [0.5, 1.5, 2.5] # available Matern values\n", - "\n", - "chi2_map = np.zeros((len(nus), len(length_scales)))\n", - "N_data = len(y)\n", - "N_params = 2\n", - "\n", - "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n", - "chi2_map = np.zeros((len(nus), len(length_scales)))\n", - "\n", - "for i, nu in enumerate(nus):\n", - " for j, ls in enumerate(length_scales):\n", - " kernel = Matern(length_scale=ls, nu=nu)\n", - " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", - "\n", - " residuals = []\n", - " try:\n", - " for train_idx, test_idx in kf.split(X):\n", - " gp.fit(X[train_idx], y[train_idx])\n", - " y_pred = gp.predict(X[test_idx])\n", - " residuals.append((y[test_idx] - y_pred)**2)\n", - "\n", - " residuals = np.concatenate(residuals)\n", - " chi2 = np.sum(residuals) # Assume unity residuals\n", - " chi2_red = chi2 / (len(y) - N_params) # Reduced chi^2\n", - " chi2_map[i, j] = np.log10(chi2_red)\n", - " except Exception as e:\n", - " chi2_map[i, j] = np.nan\n", - "\n", - "# Plotting\n", - "plt.figure(figsize=(10, 5))\n", - "im = plt.imshow(chi2_map, aspect='auto', origin='lower',\n", - " extent=[length_scales[0], length_scales[-1], 0, len(nus)-1],\n", - " cmap='viridis')\n", - "plt.colorbar(im, label=r'$\\chi^2$')\n", - "plt.xticks(length_scales, labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", - "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", - "plt.xlabel('Length Scale')\n", - "plt.ylabel(r'$\\nu$')\n", - "plt.xscale('log')\n", - "plt.title('Chi-Squared Heatmap for Luminosity Fit Parameters')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "7a4edb19-1411-44f6-a50d-d1f1fc5b48e6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.5 1.2742749857031335\n" - ] - } - ], - "source": [ - "i_best, j_best = np.unravel_index(np.nanargmin(chi2_map), chi2_map.shape)\n", - "best_nu = nus[i_best]\n", - "best_length_scale = length_scales[j_best]\n", - "print(best_nu, best_length_scale)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "79f3a7d1-ecd9-4d77-8923-6f729f9f66e3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Looking at physical interpretation of heatmap parameters\n", - "\n", - "# creating input array (mass) and output array (luminosity)\n", - "X = combined_masses.reshape(-1, 1)\n", - "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", - "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", - "\n", - "# trying different kernel types\n", - "kernel = Matern(length_scale=best_length_scale, nu=best_nu)\n", - "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", - "gp.fit(X, y)\n", - "y_pred_1, sigma_1 = gp.predict(x, return_std=True)\n", - "#chi2 = np.sum((y - y_pred_1)**2) #unit test for 2d function\n", - "#print(chi2)\n", - "\n", - "plt.figure()\n", - "plt.scatter(X, combined_L, color='blue', label='actual values')\n", - "plt.plot(x, y_pred_1, color='orange', label='predicted values')\n", - "plt.xlabel('Mass ($M_{\\odot}$)')\n", - "plt.ylabel('Luminosity')\n", - "plt.title(' Reduced $\\chi^2$ Determination of Luminosity Best Fit Using kFold')\n", - "plt.xlim(0, 1)\n", - "plt.ylim(-4,0)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "80feb1ea-eee1-489c-9fa1-05111ba6b86d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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FC3rwwQdd2ggMDNStt95qeqvK5d+v9evXV7Vq1TRmzBjNnDlTO3fuLPKYAGAVBLaAB0VERCg4OFh79+4tVb0aNWoU2Ge323Xu3LlC63z11VfOH3z52+X3q7Vp00ZjxozRRx99pEOHDunJJ5/Uvn37nPcIHjt2TJIUFRXlUs/Pz8+0TyVx+vRpdejQQevXr9cLL7ygNWvWaOPGjfrkk08kqcA5BQcHKywszGXfL7/8ohMnTiggIKDAOWZmZjoDc0/1v2bNmmrTpk2B7dprry3QL0kaNWpUgX4NHTpUkpx9+/Of/6xHH31UN910kxYtWqTvvvtOGzduVLdu3Yr872qmevXqLq8DAgIUHBxcIFU0ICDA5b7ITz75RP3791edOnU0b948rVu3Ths3btTDDz9sev/k5dfx0n3517o0jh07ZppqWrt2bdM2K3ol5bJe56Jc/jnMv+8+/zNw7Ngx+fn5FUg/t9lsioqKcl6jevXqacWKFapVq5aGDRumevXqqV69enr99ddLfoKXadmypTp06OC89/Wzzz7Tvn37Cn38lZkbbrjBZcxcf/31Ze6PmaCgIGfQvH37dp04cUJLly51LhpV2nFm9hkr7Tgx+5wUtT+/jfzvj759+xb4/pgyZYoMw3B5FJyZ/Dbatm1boI2FCxc6v3/ymX2/hoeH66uvvlKLFi30pz/9SU2bNlXt2rWVkpKi3NzcIo8PAJUZ99gCHuTr66tOnTrpn//8pw4cOODy2A1Pa926tTZu3OiyLz9gMOPv76+UlBS99tpr2rFjh6T//ejOzMx0WV30woULBYKO/B/32dnZLotiXf5DatWqVTp06JDWrFnjnKWVVOjzGM0WpIqIiFCNGjUKXT26SpUqpe6/J+Sv/jp27NhC769s1KiRpIv30iUmJurNN990eb8872GbN2+e4uPjtXDhQpfrnJ2dbVo+MzOz0H1l+UNHjRo1dPjw4QL7Dx06JEkFVtM1+yyU1aWf10t543Phjho1aujChQs6evSoS3BrGIYyMzPVtm1b574OHTqoQ4cOysvL06ZNmzR9+nSNGDFCkZGRGjhwYJmO//jjj6tfv376/vvv9cYbb6hhw4bq3Lmz2+flKT4+PmrTpk2h75d2nJl9xko7Tsoq//M+ffr0QleMvjRjoqg2Pv74Y8XGxhZ7zMLGVPPmzbVgwQIZhqHt27crLS1NEydOVFBQkJ555pli2wWAyogZW8DDxo4dK8MwlJycrJycnALv5+bm6h//+Ifbx6lSpUqBGcb8GQKzYEL6XxpwfgCcv7DK/PnzXcp9+OGHBRZTyk9N3L59u8v+y88l/4fU5StCz5o1qySnJeliSuixY8eUl5dnOpOaHzyWpv+e0KhRIzVo0EDbtm0z7VebNm2cQbfNZitwDbZv3+6ycI5UcAbPk2w2mwICAlx+3GZmZpqu9ipJK1eudM4ISRcXBlq4cKHq1atXpj/SdOrUSTt37tT333/vsn/u3Lmy2Wzq2LFjqdssqcjISAUGBhb4vBZ27hUlf5XaefPmuexftGiRzpw543z/Ur6+vrrpppucM62XX99LFff56tOnj+rWrauRI0c6F8ny5B8YStOXsijpOCuujdKMk7Jq3769qlatqp07dxb6/ZH/HV7Yteratav8/Py0Z8+eQtsoDZvNphtuuEGvvfaaqlatWuRnCQAqO2ZsAQ9r166d3nzzTQ0dOlStW7fWo48+qqZNmyo3N1dbtmzR7Nmz1axZM/Xs2dNrfejatauuueYa9ezZU40bN5bD4dDWrVv16quvKjQ0VE888YSki/eA3X///Zo2bZr8/f11++23a8eOHc4VQy91xx13qHr16ho8eLAmTpwoPz8/paWlFXhcSEJCgqpVq6Y//vGPSklJkb+/v+bPn69t27aVuP8DBw7U/Pnzdccdd+iJJ57QjTfeKH9/fx04cECrV69Wr1691KdPn1L131NmzZql7t27q2vXrkpKSlKdOnX022+/6d///re+//575/2Kd955p55//nmlpKTo1ltvVXp6uiZOnKj4+HiXoLtKlSqKjY3V3//+d3Xq1EnVq1dXREREie9xLMqdd96pTz75REOHDlXfvn2VkZGh559/XtHR0frpp58KlI+IiNBtt92m8ePHO1dF3rVrV7GP/CnMk08+qblz56pHjx6aOHGiYmNjtXTpUs2YMUOPPvqoGjZs6Nb5ZWZm6uOPPy6wPy4uTm3atNH999+vd999V/Xq1dMNN9ygDRs26IMPPnDrmJ7WuXNnde3aVWPGjNHJkyfVvn1756rILVu21AMPPCDp4n2kq1atUo8ePVS3bl2dP3/eubLy7bffXmj7zZo1kyTNnj1bVapUUWBgoOLj450z8L6+vho2bJjGjBmjkJCQAvf6elLz5s0lSVOmTFH37t3l6+ur66+/3hnMlUVJx1lxbZRmnJRVaGiopk+frkGDBum3335T3759VatWLR09elTbtm3T0aNHnTPP+dfq9ddf16BBg+Tv769GjRopLi5OEydO1LPPPqv//Oc/6tatm6pVq6ZffvlFGzZsUEhIiCZMmFBkPz777DPNmDFDvXv31rXXXivDMPTJJ5/oxIkTlWq2HgBKrQIXrgKuaFu3bjUGDRpk1K1b1wgICDBCQkKMli1bGs8995xx5MgRZ7nCVne99dZbjVtvvdX5ujSrIi9cuNC49957jQYNGhihoaGGv7+/UbduXeOBBx4wdu7c6VI2OzvbGDlypFGrVi0jMDDQuPnmm41169YZsbGxBVYV3rBhg5GQkGCEhIQYderUMVJSUoy33367wOqda9euNdq1a2cEBwcbNWvWNIYMGWJ8//33BVaOzV/11Exubq7xyiuvGDfccIMRGBhohIaGGo0bNzYeeeQR46effipT/81IMoYNG2b6XmErym7bts3o37+/UatWLcPf39+IiooybrvtNmPmzJku/Ro1apRRp04dIzAw0GjVqpWxZMkSY9CgQUZsbKxLeytWrDBatmxp2O12l9Wc81eePXr0qEv5wq7brbfeajRt2tRl30svvWTExcUZdrvduO6664y33nrLdIXr/OswY8YMo169eoa/v7/RuHFjY/78+UVdPqfCPsc///yzce+99xo1atQw/P39jUaNGhkvv/yyy+rR+SvWvvzyyyU6Vv7xVMgq1vnXLysryxgyZIgRGRlphISEGD179jT27dtX6KrIZb3ORa2KfHmbZquInzt3zhgzZowRGxtr+Pv7G9HR0cajjz5qHD9+3Flm3bp1Rp8+fYzY2FjDbrcbNWrUMG699Vbj008/dWn/8nMzjIur88bHxxu+vr6mK0XnX5M//vGPBc61MIWdXz6z76vs7GxjyJAhRs2aNQ2bzVbkauqGUfT3w6VtlmScFfcZK+04uVRhbedfg48++shl/1dffWX06NHDqF69uuHv72/UqVPH6NGjR4FyY8eONWrXrm34+PgUuJZLliwxOnbsaISFhRl2u92IjY01+vbta6xYsaLY67dr1y7jnnvuMerVq2cEBQUZ4eHhxo033mikpaWZXhsAsAqbYRiG16NnAJYTFxenxMREpaWlVXRXUA5sNpuGDRumN954o6K7gnI2ffp0Pf7449qxY4eaNm1a0d0BAKBMSEUGAOAqtGXLFu3du1cTJ05Ur169CGoBAJZGYAsAwFWoT58+yszMVIcOHTRz5syK7g4AAG4hFRkAAAAAYGkV+rifyZMnq23btqpSpYpq1aql3r17Kz09vcg6a9askc1mK7Dt2rWrnHoNAAAAAKhMKjSw/eqrrzRs2DB99913Wr58uS5cuKAuXbrozJkzxdZNT0/X4cOHnVuDBg3KoccAAAAAgMqmUqUiHz16VLVq1dJXX32l3/3ud6Zl1qxZo44dO+r48eOqWrVq+XYQAAAAAFDpVKrFo7KysiRJ1atXL7Zsy5Ytdf78eTVp0kTjxo1Tx44dTctlZ2crOzvb+drhcOi3335TjRo1ZLPZPNNxAAAAAAUYhqFTp06pdu3a8vGp0GTRUjt//rxycnI80lZAQIACAwM90hbMVZoZW8Mw1KtXLx0/flzffPNNoeXS09P19ddfq3Xr1srOztb777+vmTNnas2aNaazvKmpqZowYYI3uw4AAACgCBkZGbrmmmsquhsldv78ecXHhirzSJ5H2ouKitLevXsJbr2o0gS2w4YN09KlS/Xtt9+W+kPfs2dP2Ww2ffrppwXeu3zGNisrS3Xr1lXr7s/K158PFgCgYpyrYa2Zi/J2voZ1sqqyazgq7NhGDc/MJpmpXq34NU9KIybsuFv1I+2ny1Qv3P9sqeuE+Z0rcdlqviW7TqE+54stU923+HMML6KdQFvRQZiPCv/Zb7eZf46r+JiPxVCfgELa8Xd5fTroK8XExOjEiRMKDw8vsn+VycmTJxUeHq6fN8cprIp739cnTzkU23qfsrKyFBYW5qEe4nKVIhX5scce06effqqvv/66TH/JufnmmzVv3jzT9+x2u+x2e4H9vv6B8iOwBQBUEN8AAtui+NqtE9j6BFZgYBvkvc+Rb/AFj7bnH2IeCJVUQKB/8YVM2P1LXy/QL7fEZYN8S/ZzOtjXt9gyISUoE1pEOm+grej5qqIC28BChlxhgW2VQvpht7nu9wm+GMhZ9RbA0Co2hVZxr+8OWfPcraZCA1vDMPTYY49p8eLFWrNmjeLj48vUzpYtWxQdHe3h3gEAAAC4muUZDuW5md+aZ1TcH7+uJhX65+Jhw4Zp3rx5+uCDD1SlShVlZmYqMzNT5879L/1j7NixevDBB52vp02bpiVLluinn37Sjz/+qLFjx2rRokUaPnx4RZwCAAAAAFSIffv2afDgwYqPj1dQUJDq1aunlJSUYhe9SkpKks1mc9luvvnmcuq1d1TojO2bb74pSUpMTHTZP2fOHCUlJUmSDh8+rP379zvfy8nJ0ahRo3Tw4EEFBQWpadOmWrp0qe64447y6jYAAACAq4BDhhxFpHCXtA1v2bVrlxwOh2bNmqX69etrx44dSk5O1pkzZ/TKK68UWbdbt26aM2eO83VAgHu3C1S0Ck9FLk5aWprL69GjR2v06NFe6hEAAAAAXOSQQ+4mErvfQuG6deumbt26OV9fe+21Sk9P15tvvllsYGu32xUVFeW1vpU3Vq4AAAAAAC87efKky3bpk1s8KSsrS9WrVy+23Jo1a1SrVi01bNhQycnJOnLkiFf6U14IbAEAAADARJ5heGSTpJiYGIWHhzu3yZMne7y/e/bs0fTp0/XHP/6xyHLdu3fX/PnztWrVKr366qvauHGjbrvtNq8F2+WhUjzuBwAAAAAqG0/eY5uRkeHyHFuzR5LmS01N1YQJE4psd+PGjWrTpo3z9aFDh9StWzf169dPQ4YMKbLugAEDnP/erFkztWnTRrGxsVq6dKnuvvvuIutWVgS2AAAAAOBlYWFhLoFtUYYPH66BAwcWWSYuLs7574cOHVLHjh3Vrl07zZ49u9R9i46OVmxsrH766adS160sCGwBAAAAwIRDhvIqYFXkiIgIRURElKjswYMH1bFjR7Vu3Vpz5syRj0/p7zY9duyYMjIyFB0dXeq6lQX32AIAAACAifxUZHc3bzl06JASExMVExOjV155RUePHlVmZqYyMzNdyjVu3FiLFy+WJJ0+fVqjRo3SunXrtG/fPq1Zs0Y9e/ZURESE+vTp47W+ehsztgAAAABgQV9++aV2796t3bt365prrnF579JHq6anpysrK0uS5Ovrqx9++EFz587ViRMnFB0drY4dO2rhwoWqUqVKufbfkwhsAQAAAMDEpasau9OGtyQlJSkpKanYcpcGuUFBQfriiy+81qeKQmALAAAAACYc/93cbQPexz22AAAAAABLY8YWAAAAAEzkeWBVZHfro2QIbAEAAADARJ5xcXO3DXgfqcgAAAAAAEtjxhYAAAAATLB4lHUQ2AIAAACACYdsypPN7TbgfaQiAwAAAAAsjRlbAAAAADDhMC5u7rYB7yOwBQAAAAATeR5IRXa3PkqGVGQAAAAAgKUxYwsAAAAAJpixtQ4CWwAAAAAw4TBschhurorsZn2UDKnIAAAAAABLY8YWAAAAAEyQimwdBLYAAAAAYCJPPspzM8k1z0N9QdFIRQYAAAAAWBoztgAAAABgwvDA4lEGi0eVCwJbAAAAADDBPbbWQSoyAAAAAMDSmLEFAAAAABN5ho/yDDcXjzI81BkUicAWAAAAAEw4ZJPDzSRXh4hsywOpyAAAAAAAS2PGFgAAAABMsHiUdRDYAgAAAIAJz9xjSypyeSAVGQAAAABgaczYAgAAAICJi4tHuZdK7G59lAyBLQAAAACYcMhHeayKbAmkIgMAAAAALI0ZWwAAAAAwweJR1kFgCwAAAAAmHPKRg1RkSyAVGQAAAABgaczYAgAAAICJPMOmPMO9VY3drY+SIbAFAAAAABN5HlgVOY9U5HJBKjIAAAAAwNKYsQUAAAAAEw7DRw43V0V2sCpyuSCwBQAAAAATpCJbB6nIAAAAAABLY8YWAAAAAEw45P6qxg7PdAXFILAFAAAAABMO+cjhZpKru/VRMlxlAAAAAIClEdgCAAAAgIk8w8cjmzfFxcXJZrO5bM8880yRdQzDUGpqqmrXrq2goCAlJibqxx9/9Go/vY3AFgAAAABMOGTzyOZtEydO1OHDh53buHHjiiw/depU/fnPf9Ybb7yhjRs3KioqSp07d9apU6e83ldvIbAFAAAAAAurUqWKoqKinFtoaGihZQ3D0LRp0/Tss8/q7rvvVrNmzfTee+/p7Nmz+uCDD8qx155FYAsAAAAAJjyZinzy5EmXLTs722P9nDJlimrUqKEWLVpo0qRJysnJKbTs3r17lZmZqS5dujj32e123XrrrVq7dq3H+lTeWBUZAAAAAEzkyUd5bs4F5tePiYlx2Z+SkqLU1FS32pakJ554Qq1atVK1atW0YcMGjR07Vnv37tXbb79tWj4zM1OSFBkZ6bI/MjJSP//8s9v9qSgEtgAAAADgZRkZGQoLC3O+ttvthZZNTU3VhAkTimxv48aNatOmjZ588knnvuuvv17VqlVT3759nbO4hbHZXO/9NQyjwD4rIbAFAAAAABMOwyaH4V6wl18/LCzMJbAtyvDhwzVw4MAiy8TFxZnuv/nmmyVJu3fvNg1so6KiJF2cuY2OjnbuP3LkSIFZXCshsAUAAAAAEw4PpCI7ylA/IiJCERERZTreli1bJMklaL1UfHy8oqKitHz5crVs2VKSlJOTo6+++kpTpkwp0zErAxaPAgAAAAALWrdunV577TVt3bpVe/fu1YcffqhHHnlEd911l+rWress17hxYy1evFjSxRTkESNG6MUXX9TixYu1Y8cOJSUlKTg4WPfee29FnYrbmLEFAAAAABMOw0cOw80ZWzfrF8Vut2vhwoWaMGGCsrOzFRsbq+TkZI0ePdqlXHp6urKyspyvR48erXPnzmno0KE6fvy4brrpJn355ZeqUqWK1/rqbQS2AAAAAGAiTzblyb17bN2tX5RWrVrpu+++K7acYRgur202m1JTUz2yKnNlQSoyAAAAAMDSmLEFAAAAABOVPRUZ/0NgCwAAAAAm8uR+KnGeZ7qCYvDnAwAAAACApTFjCwAAAAAmSEW2DgJbAAAAADCRZ/goz83A1N36KBmuMgAAAADA0pixBQAAAAAThmxyuLl4lOHF59jifwhsAQAAAMAEqcjWwVUGAAAAAFgaM7YAAAAAYMJh2OQw3Esldrc+SobAFgAAAABM5MlHeW4mubpbHyXDVQYAAAAAWBoztgAAAABgglRk6yCwBQAAAAATDvnI4WaSq7v1UTJcZQAAAACApTFjCwAAAAAm8gyb8txMJXa3PkqGwBYAAAAATHCPrXWQigwAAAAAsDRmbAEAAADAhGH4yGG4NxdouFkfJUNgCwAAAAAm8mRTnty8x9bN+igZ/nwAAAAAALA0ZmwBAAAAwITDcH/xJ4fhoc6gSAS2AAAAAGDC4YF7bN2tj5LhKgMAAAAALI0ZWwAAAAAw4ZBNDjcXf3K3PkqGwBYAAAAATOQZNuW5eY+tu/VRMqQiAwAAAAAsjRlbAAAAADDB4lHWQWALAAAAACYcsrn/uB/usS0X/PkAAAAAAGBpzNgCAAAAgAnDA6siG8zYlgsCWwAAAAAw4TA8kIrMqsjlglRkAAAAAIClMWMLAAAAACZYFdk6CGwBAAAAwASpyNbBnw8AAAAAAJbGjC0AAAAAmHB4YFVknmNbPghsAQAAAMAEqcjWQSoyAAAAAMDSCGwBAAAAwET+jK27m7esWbNGNpvNdNu4cWOh9ZKSkgqUv/nmm73Wz/JAKjIAAAAAmKjsqcgJCQk6fPiwy77x48drxYoVatOmTZF1u3Xrpjlz5jhfBwQEeKWP5YXAFgAAAAAsKCAgQFFRUc7Xubm5+vTTTzV8+HDZbEUH1Ha73aWu1ZGKDAAAAAAmKnsq8uU+/fRT/frrr0pKSiq27Jo1a1SrVi01bNhQycnJOnLkiPc76EXM2AIAAACACUPuP67H+O8/T5486bLfbrfLbre71fbl3nnnHXXt2lUxMTFFluvevbv69eun2NhY7d27V+PHj9dtt92mzZs3e7xP5YUZWwAAAADwspiYGIWHhzu3yZMnF1o2NTW10EWh8rdNmza51Dlw4IC++OILDR48uNi+DBgwQD169FCzZs3Us2dP/fOf/9T//d//aenSpW6fZ0VhxhYAAAAATHhy8aiMjAyFhYU59xc1Mzp8+HANHDiwyHbj4uJcXs+ZM0c1atTQXXfdVeo+RkdHKzY2Vj/99FOp61YWBLYAAAAAYMKTgW1YWJhLYFuUiIgIRURElPgYhmFozpw5evDBB+Xv71/qPh47dkwZGRmKjo4udd3KglRkAAAAALCwVatWae/evYWmITdu3FiLFy+WJJ0+fVqjRo3SunXrtG/fPq1Zs0Y9e/ZURESE+vTpU57d9ihmbAEAAADARGV/jm2+d955RwkJCbruuutM309PT1dWVpYkydfXVz/88IPmzp2rEydOKDo6Wh07dtTChQtVpUoVr/fVWwhsAQAAAMCEVQLbDz74oMj3DcNw/ntQUJC++OILb3ep3JGKDAAAAACwNGZsAQAAAMCEYdhkuDnj6m59lAyBLQAAAACYcMgmh9xMRXazPkqGVGQAAAAAgKUxYwsAAAAAJqyyeBQIbAEAAADAFPfYWgepyAAAAAAAS2PGFgAAAABMkIpsHQS2AAAAAGCCVGTrIBUZAAAAAGBpV+2MbZX04/LztVd0NwAAV6ngKoEV3YVK7UKVgIruQonlhFfcz6mcUO/9lsnx8Gd0V2iEW/V/CDXKVO9CGeo5QvNKXNY/NKdE5aqEniu2TETw2WLLRAafLPS9mgGni6xbzb/w9sN9zftXpbD9PudN94f4ZLu8viW4yC5VeoYHUpGZsS0fV21gCwAAAABFMSQZZfubiksb8D5SkQEAAAAAlsaMLQAAAACYcMgmm9xcFdnN+igZAlsAAAAAMMGqyNZBKjIAAAAAwNKYsQUAAAAAEw7DJpubM67urqqMkiGwBQAAAAAThuGBVZFZFrlckIoMAAAAALA0ZmwBAAAAwASLR1kHgS0AAAAAmCCwtQ5SkQEAAAAAlsaMLQAAAACYYFVk6yCwBQAAAAATrIpsHaQiAwAAAAAsjRlbAAAAADBxccbW3cWjPNQZFInAFgAAAABMsCqydZCKDAAAAACwNGZsAQAAAMCE8d/N3TbgfQS2AAAAAGCCVGTrIBUZAAAAAGBpzNgCAAAAgBlykS2DwBYAAAAAzHggFVmkIpcLUpEBAAAAAJbGjC0AAAAAmDCMi5u7bcD7CGwBAAAAwASrIlsHqcgAAAAAAEtjxhYAAAAAzBg29xd/Ysa2XBDYAgAAAIAJ7rG1DlKRAQAAAACWxowtAAAAAJgx/ru52wa8jhlbAAAAADCRvyqyu5s3TZo0SQkJCQoODlbVqlVNy+zfv189e/ZUSEiIIiIi9PjjjysnJ6fIdrOzs/XYY48pIiJCISEhuuuuu3TgwAEvnIFnENgCAAAAgEXl5OSoX79+evTRR03fz8vLU48ePXTmzBl9++23WrBggRYtWqSRI0cW2e6IESO0ePFiLViwQN9++61Onz6tO++8U3l5ed44DbeRigwAAAAAhankqcQTJkyQJKWlpZm+/+WXX2rnzp3KyMhQ7dq1JUmvvvqqkpKSNGnSJIWFhRWok5WVpXfeeUfvv/++br/9dknSvHnzFBMToxUrVqhr167eORk3MGMLAAAAACaskIpcnHXr1qlZs2bOoFaSunbtquzsbG3evNm0zubNm5Wbm6suXbo499WuXVvNmjXT2rVrvd7nsmDGFgAAAAC87OTJky6v7Xa77Ha714+bmZmpyMhIl33VqlVTQECAMjMzC60TEBCgatWqueyPjIwstE5FY8YWAAAAAMwYHtokxcTEKDw83LlNnjy50MOmpqbKZrMVuW3atKnEp2GzFZw1NgzDdH9RylKnvDBjCwAAAACmbP/d3G1DysjIcLmftajZ2uHDh2vgwIFFthoXF1eio0dFRWn9+vUu+44fP67c3NwCM7mX1snJydHx48ddZm2PHDmihISEEh23vBHYAgAAAICXhYWFmS7UZCYiIkIREREeOW67du00adIkHT58WNHR0ZIuLihlt9vVunVr0zqtW7eWv7+/li9frv79+0uSDh8+rB07dmjq1Kke6ZenkYoMAAAAAGY8mIrsLfv379fWrVu1f/9+5eXlaevWrdq6datOnz4tSerSpYuaNGmiBx54QFu2bNHKlSs1atQoJScnOwPtgwcPqnHjxtqwYYMkKTw8XIMHD9bIkSO1cuVKbdmyRffff7+aN2/uXCW5smHGFgAAAADMeCIw9XJg+9xzz+m9995zvm7ZsqUkafXq1UpMTJSvr6+WLl2qoUOHqn379goKCtK9996rV155xVknNzdX6enpOnv2rHPfa6+9Jj8/P/Xv31/nzp1Tp06dlJaWJl9fX++eUBkR2AIAAACARaWlpRX6DNt8devW1WeffVbo+3FxcTIM1wg8MDBQ06dP1/Tp0z3RTa8jsAUAAAAAM4bt4uZuG/A6AlsAAAAAMGEYFzd324D3sXgUAAAAAMDSmLEFAAAAADMWWDwKFxHYAgAAAIAZ7rG1DFKRAQAAAACWxowtAAAAAJiwGRc3d9uA9xHYAgAAAIAZ7rG1DFKRAQAAAACWxowtAAAAAJhh8SjLILAFAAAAADOkIlsGqcgAAAAAAEtjxhYAAAAAzDBjaxkEtgAAAABghsDWMkhFBgAAAABYGjO2AAAAAGCGVZEtgxlbAAAAADBhMzyzXW3OnTungwcPFtj/448/eu2YBLYAAAAAAI/4+OOP1bBhQ91xxx26/vrrtX79eud7DzzwgNeOW6GB7ddff62ePXuqdu3astlsWrJkSZHl16xZI5vNVmDbtWtX+XQYAAAAwNXD8NB2FXnhhRf0/fffa9u2bXr33Xf18MMP64MPPpAkGYb3Lkap77F1OBz617/+paNHjyohIUFRUVFlPviZM2d0ww036KGHHtLvf//7EtdLT09XWFiY83XNmjXL3AcAAAAAgGfk5uY647M2bdro66+/1t13363du3fLZvPe/calDmxr1aql7OxsBQYG6sSJE7r//vs1ffp0hYaGlvrg3bt3V/fu3Utdr1atWqpatWqp6wEAAAAAvKdWrVravn27rr/+eklSjRo1tHz5cg0aNEjbt2/32nFLnYr80Ucf6dSpUzp69Kg2btyon3/+WTfeeKMyMzO90T9TLVu2VHR0tDp16qTVq1cXWTY7O1snT5502QAAAACgODZ5YPGoij6Jcvb++++rVq1aLvsCAgL0t7/9TV999ZXXjlvqwLZjx47Of2/RooVWrlypHj166JZbbtHhw4c92rnLRUdHa/bs2Vq0aJE++eQTNWrUSJ06ddLXX39daJ3JkycrPDzcucXExHi1jwAAAACuEPmP+3F3u4pcc801BW5XXbZsmSSpffv2Xjuu24tHnTlzRoMHD1bDhg3VpUsXT/SpUI0aNVJycrJatWqldu3aacaMGerRo4deeeWVQuuMHTtWWVlZzi0jI8OrfQQAAAAA/E/v3r31xBNPKDs722vHKHVg+9BDD6lLly5q0qSJcxa0adOmWrZsmfbs2eONPhbp5ptv1k8//VTo+3a7XWFhYS4bAAAAABSLVZE94ttvv9UXX3yh1q1bF3qf7aFDh9SrV68yH6PUi0f98ssvio2NVUJCgurUqeOyRURElLkjZbVlyxZFR0eX+3EBAAAAXOE8EZgS2KpNmzbasmWLnnjiCd10002aNGmSnnrqKUkXn7qza9cu/fnPf9a6devKfIxSB7aff/55mQ92udOnT2v37t3O13v37tXWrVtVvXp11a1bV2PHjtXBgwc1d+5cSdK0adMUFxenpk2bKicnR/PmzdOiRYu0aNEij/UJAAAAAOBZQUFBmjRpkgICAvT000/rb3/7mxwOh3bu3Kns7GzFxsZq8uTJZW6/1IGtJ23atMllMar8qH3QoEFKS0vT4cOHtX//fuf7OTk5GjVqlA4ePKigoCA1bdpUS5cu1R133FHufQcAAABwZctf2djdNq52s2bN0oQJE/TLL78oKChIbdu2lc1m0/r16zVs2DC98MILCg8Pd+sYFRrYJiYmyjAK/y+dlpbm8nr06NEaPXq0l3sFAAAAACIV2UPGjRunvn37asSIEWrYsKFstosrRb/22mv605/+pDNnzuiNN95QcHBwmY/h9qrIAAAAAAAUJjExUampqWrUqJEzqJWkJ598Uhs2bNCmTZt0/fXXa/369WU+BoEtAAAAAJhhVWSP+OijjxQZGWn6XvPmzbVx40bdeeed+t3vflfmY1RoKjIAAAAAVFbcY1s+7Ha7pk2bph49epS5DWZsAQAAAAAVrnPnzmWuy4wtAAAAAJgxbBc3d9uA1xHYAgAAAIAZVkW2DFKRAQAAAACWxowtAAAAAJhg8SjrILAFAAAAADOkIlsGqcgAAAAAAEtjxhYAAAAAzHggFZkZ2/JBYAsAAAAAZkhFtgxSkQEAAADAoiZNmqSEhAQFBweratWqBd7ftm2b7rnnHsXExCgoKEjXXXedXn/99WLbTUxMlM1mc9kGDhzohTPwDGZsAQAAAMCMBWZsc3Jy1K9fP7Vr107vvPNOgfc3b96smjVrat68eYqJidHatWv1hz/8Qb6+vho+fHiRbScnJ2vixInO10FBQR7vv6cQ2AIAAACACSs87mfChAmSpLS0NNP3H374YZfX1157rdatW6dPPvmk2MA2ODhYUVFRHumnt5GKDAAAAABXkaysLFWvXr3YcvPnz1dERISaNm2qUaNG6dSpU+XQu7JhxhYAAAAAvOzkyZMur+12u+x2e7n3Y926dfrwww+1dOnSIsvdd999io+PV1RUlHbs2KGxY8dq27ZtWr58eTn1tHSYsQUAAAAAM4aHNkkxMTEKDw93bpMnTy70sKmpqQUWbrp827RpU6lP58cff1SvXr303HPPqXPnzkWWTU5O1u23365mzZpp4MCB+vjjj7VixQp9//33pT5ueWDGFgAAAAC8LCMjQ2FhYc7XRc3WDh8+vNgViOPi4kp1/J07d+q2225TcnKyxo0bV6q6ktSqVSv5+/vrp59+UqtWrUpd39sIbAEAAADAhCcXjwoLC3MJbIsSERGhiIgI9w58iR9//FG33XabBg0apEmTJpW5jdzcXEVHR3usX55EKjIAAAAAFMYDacjetH//fm3dulX79+9XXl6etm7dqq1bt+r06dOSLgakHTt2VOfOnfXUU08pMzNTmZmZOnr0qLONgwcPqnHjxtqwYYMkac+ePZo4caI2bdqkffv26fPPP1e/fv3UsmVLtW/f3vsnVQbM2AIAAACART333HN67733nK9btmwpSVq9erUSExP10Ucf6ejRo5o/f77mz5/vLBcbG6t9+/ZJknJzc5Wenq6zZ89KkgICArRy5Uq9/vrrOn36tGJiYtSjRw+lpKTI19e3/E6uFAhsAQAAAMCMJ2ZdvTxrm5aWVugzbKWLC1GlpqYW2UZcXJwM438djYmJ0VdffeWhHpYPAlsAAAAAMOHJe2zhXdxjCwAAAACwNGZsAQAAAMCMBVKRcRGBLQAAAACYIBXZOkhFBgAAAABYGjO2AAAAAGCGVGTLILAFAAAAADMEtpZBKjIAAAAAwNKYsQUAAAAAEyweZR0EtgAAAABghlRkyyAVGQAAAABgaczYAgAAAIAZZmwtg8AWAAAAAExwj611kIoMAAAAALA0ZmwBAAAAwAypyJZBYAsAAAAAJkhFtg5SkQEAAAAAlsaMLQAAAACYIRXZMghsAQAAAMAMga1lkIoMAAAAALA0ZmwBAAAAwITtv5u7bcD7CGwBAAAAwAypyJZBKjIAAAAAwNKYsQUAAAAAEzzH1joIbAEAAADADKnIlkEqMgAAAADA0pixBQAAAIDCMONqCQS2AAAAAGCCe2ytg1RkAAAAAIClMWMLAAAAAGZYPMoyCGwBAAAAwASpyNZBKjIAAAAAwNKYsQUAAAAAM6QiWwaBLQAAAACYIBXZOq7awDYvfY9sNv+K7gYAADDhZ7NVdBdKzN/Xt8KOHernxZ9y/p79nWQLcK89W1n7U5Z6/iW/rkYJyxr24ssZ/uHFlvnFv3qh7x32L/qz6PAv/C5Eh7/5mCusTqHlLzvNL94qskuAx1y1gS0AAAAAFIlUZMsgsAUAAAAAMwS2lsGqyAAAAAAAS2PGFgAAAABMsHiUdRDYAgAAAIAZUpEtg1RkAAAAAIClEdgCAAAAgAmbYXhk86ZJkyYpISFBwcHBqlq1qvl52GwFtpkzZxbZbnZ2th577DFFREQoJCREd911lw4cOOCFM/AMAlsAAAAAMGN4aPOinJwc9evXT48++miR5ebMmaPDhw87t0GDBhVZfsSIEVq8eLEWLFigb7/9VqdPn9add96pvLw8T3bfY7jHFgAAAAAsasKECZKktLS0IstVrVpVUVFRJWozKytL77zzjt5//33dfvvtkqR58+YpJiZGK1asUNeuXd3qszcwYwsAAAAAJvJXRXZ3qwyGDx+uiIgItW3bVjNnzpTD4Si07ObNm5Wbm6suXbo499WuXVvNmjXT2rVry6O7pcaMLQAAAACY8eCqyCdPnnTZbbfbZbfb3Wy8ZJ5//nl16tRJQUFBWrlypUaOHKlff/1V48aNMy2fmZmpgIAAVatWzWV/ZGSkMjMzy6PLpcaMLQAAAAB4WUxMjMLDw53b5MmTCy2bmppquuDTpdumTZtKfOxx48apXbt2atGihUaOHKmJEyfq5ZdfLvU5GIYhm81W6nrlgRlbAAAAADDhiVTi/PoZGRkKCwtz7i9qtnb48OEaOHBgke3GxcWVuU8333yzTp48qV9++UWRkZEF3o+KilJOTo6OHz/uMmt75MgRJSQklPm43kRgCwAAAABmPJiKHBYW5hLYFiUiIkIRERFuHrhwW7ZsUWBgYKGPB2rdurX8/f21fPly9e/fX5J0+PBh7dixQ1OnTvVav9xBYAsAAAAAFrV//3799ttv2r9/v/Ly8rR161ZJUv369RUaGqp//OMfyszMVLt27RQUFKTVq1fr2Wef1R/+8AfnrPHBgwfVqVMnzZ07VzfeeKPCw8M1ePBgjRw5UjVq1FD16tU1atQoNW/e3LlKcmVDYAsAAAAAJjyZiuwtzz33nN577z3n65YtW0qSVq9ercTERPn7+2vGjBl66qmn5HA4dO2112rixIkaNmyYs05ubq7S09N19uxZ577XXntNfn5+6t+/v86dO6dOnTopLS1Nvr6+3j2hMrIZhlFJFqAuHydPnlR4eLgS1Ut+Nv+K7g4AADBTSRcnMWOrwB95Nj8vzlH4e/Z3ki3AvfZsZe1PWer5l/y6GiUsa9iLL2f4F/9ZKqqMo5j6Dv/C1411+JuPucLqFFr+stP84q0/KDw8XFlZWSVOw60M8mOG1v0nyTcg0K228nLOa/OHz1ruGlgNqyIDAAAAACyNVGQAAAAAKIS3U4nhGQS2AAAAAGDGMC5u7rYBryMVGQAAAABgaczYAgAAAIAJK6yKjIsIbAEAAADAjPHfzd024HWkIgMAAAAALI0ZWwAAAAAwYXNc3NxtA95HYAsAAAAAZkhFtgxSkQEAAAAAlsaMLQAAAACYYFVk6yCwBQAAAAAzhnFxc7cNeB2pyAAAAAAAS2PGFgAAAABMkIpsHQS2AAAAAGCGVZEtg1RkAAAAAIClMWMLAAAAACZIRbYOAlsAAAAAMMOqyJZBKjIAAAAAwNKYsQUAAAAAE6QiWweBLQAAAACYYVVkyyAVGQAAAABgaczYAgAAAIAJUpGtg8AWAAAAAMw4jIubu23A60hFBgAAAABYGjO2AAAAAGCGxaMsg8AWAAAAAEzY5IF7bD3SExSHVGQAAAAAgKUxYwsAAAAAZgzj4uZuG/A6AlsAAAAAMMHjfqyDVGQAAAAAgKUxYwsAAAAAZlgV2TIIbAEAAADAhM0wZHPzHll366NkSEUGAAAAAFgaM7YAAAAAYMbx383dNuB1BLYAAAAAYIJUZOsgFRkAAAAAYGnM2AIAAACAGVZFtgwCWwAAAAAwYxgXN3fbgNeRigwAAAAAsDRmbAEAAADAhM24uLnbBryPGVsAAAAAMJOfiuzu5kWTJk1SQkKCgoODVbVq1QLvp6WlyWazmW5HjhwptN3ExMQC5QcOHOjFM3EPM7YAAAAAYFE5OTnq16+f2rVrp3feeafA+wMGDFC3bt1c9iUlJen8+fOqVatWkW0nJydr4sSJztdBQUGe6bQXENgCAAAAgAmb4+LmbhveNGHCBEkXZ2bNBAUFuQSkR48e1apVq0yD4MsFBwcrKirKI/30NlKRAQAAAMCMB1ORT5486bJlZ2dXyCnNnTtXwcHB6tu3b7Fl58+fr4iICDVt2lSjRo3SqVOnyqGHZcOMLQAAAAB4WUxMjMvrlJQUpaamlns/3n33Xd17773FphXfd999io+PV1RUlHbs2KGxY8dq27ZtWr58eTn1tHQIbAEAAADAjPHfzd02JGVkZCgsLMy52263F1olNTXVmWJcmI0bN6pNmzal6sq6deu0c+dOzZ07t9iyycnJzn9v1qyZGjRooDZt2uj7779Xq1atSnXc8kBgCwAAAAAmbIYhm5urGufXDwsLcwlsizJ8+PBiVyCOi4srdV/efvtttWjRQq1bty513VatWsnf318//fQTgS0AAAAAoGgRERGKiIjwaJunT5/Whx9+qMmTJ5ep/o8//qjc3FxFR0d7tF+ewuJRAAAAAGDGAs+x3b9/v7Zu3ar9+/crLy9PW7du1datW3X69GmXcgsXLtSFCxd03333FWjj4MGDaty4sTZs2CBJ2rNnjyZOnKhNmzZp3759+vzzz9WvXz+1bNlS7du39+r5lBUztgAAAABgxpDk7uN6vBvX6rnnntN7773nfN2yZUtJ0urVq5WYmOjc/8477+juu+9WtWrVCrSRm5ur9PR0nT17VpIUEBCglStX6vXXX9fp06cVExOjHj16KCUlRb6+vt49oTIisAUAAAAAi0pLSyv0GbaXWrt2baHvxcXFybhkZjkmJkZfffWVJ7pXbghsAQAAAMCEJxePgncR2AIAAACAGUPu3yNLXFsuWDwKAAAAAGBpzNgCAAAAgBlPrGpMKnK5ILAFAAAAADMOSTYPtAGvIxUZAAAAAGBpzNgCAAAAgAlWRbYOAlsAAAAAMMM9tpZBKjIAAAAAwNKYsQUAAAAAM8zYWgaBLQAAAACYIbC1DFKRAQAAAACWxowtAAAAAJjhObaWQWALAAAAACZ43I91kIoMAAAAALA0ZmwBAAAAwAyLR1kGgS0AAAAAmHEYks3NwNRBYFseSEUGAAAAAFgaM7YAAAAAYIZUZMsgsAUAAAAAUx4IbEVgWx5IRQYAAAAAWBoztgAAAABghlRkyyCwBQAAAAAzDkNupxKzKnK5IBUZAAAAAGBpzNgCAAAAgBnDcXFztw14HYEtAAAAAJjhHlvLIBUZAAAAAGBpzNgCAAAAgBkWj7IMAlsAAAAAMEMqsmWQigwAAAAAsDRmbAEAAADAjCEPzNh6pCcoBoEtAAAAAJghFdkySEUGAAAAAFgaM7YAAAAAYMbhkOTwQBvwNgJbAAAAADBDKrJlkIoMAAAAALA0ZmwBAAAAwAwztpZBYAsAAAAAZhyG3H5ej4PAtjyQigwAAAAAsDRmbAEAAADAhGE4ZBjurWrsbn2UDIEtAAAAAJgxDPdTibnHtlyQigwAAAAAFrRv3z4NHjxY8fHxCgoKUr169ZSSkqKcnByXcvv371fPnj0VEhKiiIgIPf744wXKXC47O1uPPfaYIiIiFBISorvuuksHDhzw5um4hRlbAAAAADBjeGDxKC/O2O7atUsOh0OzZs1S/fr1tWPHDiUnJ+vMmTN65ZVXJEl5eXnq0aOHatasqW+//VbHjh3ToEGDZBiGpk+fXmjbI0aM0D/+8Q8tWLBANWrU0MiRI3XnnXdq8+bN8vX19do5lRWBLQAAAACYcTgkm5v3yHrxHttu3bqpW7duztfXXnut0tPT9eabbzoD2y+//FI7d+5URkaGateuLUl69dVXlZSUpEmTJiksLKxAu1lZWXrnnXf0/vvv6/bbb5ckzZs3TzExMVqxYoW6du3qtXMqK1KRAQAAAMDLTp486bJlZ2d75ThZWVmqXr268/W6devUrFkzZ1ArSV27dlV2drY2b95s2sbmzZuVm5urLl26OPfVrl1bzZo109q1a73Sb3cR2AIAAACAGcPwzCYpJiZG4eHhzm3y5Mke7+6ePXs0ffp0/fGPf3Tuy8zMVGRkpEu5atWqKSAgQJmZmabtZGZmKiAgQNWqVXPZHxkZWWidikZgCwAAAAAmDIfDI5skZWRkKCsry7mNHTu20OOmpqbKZrMVuW3atMmlzqFDh9StWzf169dPQ4YMcXnPZrMVPDfDMN1f5PUoQ53ywj22AAAAAOBlYWFhpvezmhk+fLgGDhxYZJm4uDjnvx86dEgdO3ZUu3btNHv2bJdyUVFRWr9+vcu+48ePKzc3t8BM7qV1cnJydPz4cZdZ2yNHjighIaFE51DeCGwBAAAAwEwFrYocERGhiIiIEpU9ePCgOnbsqNatW2vOnDny8XFNym3Xrp0mTZqkw4cPKzo6WtLFBaXsdrtat25t2mbr1q3l7++v5cuXq3///pKkw4cPa8eOHZo6dWqpz6c8kIoMAAAAAGYchmc2Lzl06JASExMVExOjV155RUePHlVmZqbLfbBdunRRkyZN9MADD2jLli1auXKlRo0apeTkZOcM8sGDB9W4cWNt2LBBkhQeHq7Bgwdr5MiRWrlypbZs2aL7779fzZs3d66SXNkwYwsAAAAAFvTll19q9+7d2r17t6655hqX94z/zhT7+vpq6dKlGjp0qNq3b6+goCDde++9zscBSVJubq7S09N19uxZ577XXntNfn5+6t+/v86dO6dOnTopLS2tUj7DVpJshuHFJwZXQidPnlR4eLgS1Ut+Nv+K7g4AADBTSRcnMWOrwB95Nj8vzlH4e/Z3ki3AvfZsZe1PWer5l/y6GiUsa9iLL2f4F/9ZKqqMo5j6Dv/CkzUd/uZjrrA6hZa/7DS/eOsPCg8PV1ZWVonvL60M8mOG2wL6uR0zXDBytSrnI8tdA6thxhYAAAAATBgOQ4bNvXnAq2wescJwjy0AAAAAwNIqPLCdMWOG4uPjFRgYqNatW+ubb74ptOyaNWtMn+G0a9eucuwxAAAAgKuC4fDMBq+r0FTkhQsXasSIEZoxY4bat2+vWbNmqXv37tq5c6fq1q1baL309HSX/PSaNWuWR3cBAAAAXEVIRbaOCp2x/fOf/6zBgwdryJAhuu666zRt2jTFxMTozTffLLJerVq1FBUV5dwq68pcAAAAAADvq7AZ25ycHG3evFnPPPOMy/4uXbpo7dq1RdZt2bKlzp8/ryZNmmjcuHHq2LFjoWWzs7OVnZ3tfJ2VlSVJuqBct5+1DAAAvMVCqyJXYJqhV4/t4Vkmm5vP8ixzfUcZrpEjr8RFjbySlS1JOcOnBKsi24pY2dhWzKrIRdYtZJXjQubBHIWM0cv/M508eVKSdWctLxjZbqcSX1Cuh3qDolRYYPvrr78qLy9PkZGRLvsjIyNdHih8qejoaM2ePVutW7dWdna23n//fXXq1Elr1qzR7373O9M6kydP1oQJEwrs/1afu38SAADAO6z0G/jCFXrs815sG1eNmMXjJUnHjh1TeHh4Bfem5AICAhQVFaVvMz0TM0RFRSkgIMAjbcFchT3H9tChQ6pTp47Wrl2rdu3aOfdPmjRJ77//fokXhOrZs6dsNps+/fRT0/cvn7E9ceKEYmNjtX//fksNrorQtm1bbdy4saK7USIV2VdvH9uT7bvbljv1S1u3pOVPnjypmJgYZWRk8Gy4YjCmK8exGdNFY0yXHGO6chybMV20rKws1a1bV8ePH1fVqlXL1LeKcv78eeXk5HikrYCAAAUGBnqkLZirsBnbiIgI+fr6FpidPXLkSIFZ3KLcfPPNmjdvXqHv2+122e32AvvDw8P5H2YxfH19LXONKrKv3j62J9t3ty136pe2bmnLh4WFWebzWlEY05Xj2IzpkmFMF48xXTmOzZguGR+fCn8YS6kFBgYSjFpIhX3CAgIC1Lp1ay1fvtxl//Lly5WQkFDidrZs2aLo6GhPdw+Shg0bVtFdKLGK7Ku3j+3J9t1ty536pa1rpc+fVVjpmjKmy6ctxrS1WemaMqbLpy3GNK5mFZaKLF183M8DDzygmTNnql27dpo9e7beeust/fjjj4qNjdXYsWN18OBBzZ07V5I0bdo0xcXFqWnTpsrJydG8efP00ksvadGiRbr77rtLdMyTJ08qPDxcWVlZlvkrJ4DCMaaBKwtjGriyMKZRXir0ObYDBgzQsWPHNHHiRB0+fFjNmjXT559/rtjYWEnS4cOHtX//fmf5nJwcjRo1SgcPHlRQUJCaNm2qpUuX6o477ijxMe12u1JSUkzTkwFYD2MauLIwpoErC2Ma5aVCZ2wBAAAAAHCX9e7iBgAAAADgEgS2AAAAAABLI7AFAAAAAFgagS0AAAAAwNIIbAEAAAAAlnbVB7YXLlzQ2bNnK7obADzM4XBUdBcAeABjGbhy8DAWeNNVHdhmZmZqwIABuvfee/Xqq6869zPoAGv69ddf9eqrr+rMmTPy8fHhBzFgUVlZWVq7dq0OHTokH5+r+qcKcEXIycmRJNlsNv7fDK+5av9vcezYMfXu3VsdOnTQ888/r7/97W+aNWuWpIuDDoC1HD9+XO3bt9fHH3+sMWPGENwCFnXkyBG1bNlSf/3rX9WtWzfNnDlTP/zwQ0V3C0AZ/fLLL+rTp4/+8pe/SBL/b4bXXLWB7dq1a9WhQweNGDFCzZs317Rp07RixQpJzNgCVrRv3z5169ZNaWlpCg4O1tNPP01wC1jQ559/rgEDBmj+/Pl67bXXlJmZqUWLFmnbtm0V3TUApXT+/HkNGDBAdrtd27dvJ7iFV121ge3tt9+ukSNHSpJyc3MVFhamvXv36tSpU7LZbAS3gMW0bNlSkyZNUsOGDTV48GCFhITo6aefVlZWlnx8fJSbm1vRXQRQArm5udqyZYskqVOnTurTp498fHy0cuVK5eTk8P9nwEICAwM1YsQIvfzyy3rggQe0fv16l+CW8QxPumoD26CgIEVFRUmS/P391aRJE0VGRqpKlSratWuXZs6cyQ9hwGJCQ0Nls9nUoEEDDR48WEFBQXrppZf03Xff6S9/+Yuys7MruosAipGcnKzY2Fi9/PLLkqQbbrhBt99+u/7+979rz5493C4EWET+jGzv3r1Vr149JSQkKDk5WRs2bNBrr70m6eKMbv79t4C7rsrA1uyvQz4+PgoPD9fSpUt1zz33yG63y9/fvwJ6B6C0Lh/TPj4+ql+/vsaPH6+MjAwlJCQoKChIdru9gnoIoCQMw5BhGBo4cKAOHTrk/PGbkJCgdu3aad26dRXcQwAldfnCb/7+/mrXrp0eeughpaen66mnnlKHDh2UkZFRQT3EleaqC2wNw3D+tffLL7/U/v375XA4lJ2drZ07d+qRRx7R2LFj9fDDD1dwTwGUhNmYliQ/Pz8dOXJEq1ev1jvvvKOhQ4dWZDcBFCF/Zsdms8lms+mmm25S9+7d9dNPP6lfv35avXq15s2bp2uuuaaCewqgJC6/fzb/D9B2u12dOnVS27ZtNWPGDA0ePFj16tWriC7iCmQzrqLk9kt/AH/44YcaOXKkVqxYoUaNGkmSRo0apUaNGik5OblAeQCVT3Fj+l//+pcyMjI0cODAAuUBVA4XLlyQn5+fJOnEiRM6d+6coqOjJUnnzp3T+PHjZbfb1aBBAyUlJVVgTwGUxOVjOjs7W5GRkc73jx07psTERD3++OP85oZHXTWB7aUDZuHChXrppZc0Z84ctWjRQnl5efL19dXZs2cVHBxcoDyAyqeoMe1wOAqkQDGmgcrn0rHav39/HThwQD/88IP69++vbt26qV+/fpJcfygzloHKq7AxPXDgQHXt2lV9+/aVJG3atElt2rSRxJiG51w1gW2+hQsXasqUKXr33XedP4DzU58AWI/ZmL48qAVQufXq1UsHDhxQSkqKfvnlF33yySc6cOCA7rnnHv3pT3+SJMY2YCGFjekBAwZo3LhxksRvcHicX0V3oDx9//33GjNmjJYsWcIPYOAKwJgGrG/37t3av3+/Zs2apRtvvFGSlJiYqLlz52r27Nny9/fX008/zdgGLKKoMf3222/LbrczpuEVV2xga/YDt1WrVvr6669Vt25dfgADFsOYBq5Mvr6+2rNnj/bs2eP8EdygQQM98sgjysvL0wcffKAmTZqoR48eFdxTACXBmEZFuSJ/BV64cMH5A/fEiRP65ZdfnO/VrVvX5X0AlR9jGrgyXH73k2EYCgsLU+vWrbVx40adPn3a+d4111yjBx98UEFBQfrmm2/Ku6sASoAxjcrkivsl6HA4nAtM9O/fX3fccYfq16+v5ORkffzxx5IuPgbk8mXIAVROjGngynDhwgXnvXRnz56VdPHxPjVq1ND999+vadOmacGCBS4/lBs3bqxOnTpp0aJFOnPmTIX0G4A5xjQqmysuFTl/1sbspvUJEyZo9+7deuaZZ+Tj40PqImABjGnA+i79A9VDDz2k5s2bKzk5WVWqVJEkDR48WD///LOGDRsmh8Oh/v37q2rVqpKkkJAQNWnSRAEBARXVfQCXYUyjMrriAlup6JvWZ86cKV9fX25aByyEMQ1YW/7YTEpK0ty5cxUYGCi73a6kpCSFhIRIkiZOnChfX18NHTpUmzZtUv369RUZGakXX3xRzz//vPz9/SvyFABcgjGNyuiKDGy5aR24sjCmAetbvXq19u7dqzVr1mj16tV6/PHHZRiGkpKSFBoaKklKSUlR/fr1tWTJEv3lL39R/fr1NX78eD3xxBOSeN4lUJkwplHZWD6wvXxAXH7Tes+ePZ2DK/+m9TVr1uibb77hRzBQCTGmgSvTNddco6SkJDVv3ly/+93vJMn543bQoEHOFMb77rtPAwYM0OnTp5WXl6caNWpI4jm2QGXDmEZlY+nA9sKFC878/rNnzyo4ONjlpvXk5GQ1adJEgwcPdv5Qzr9pfcGCBRo/frwzXQJAxWNMA1euBg0aKDY21nlfXUpKiqT//RB+6KGHFBISou+//15VqlRRgwYNnHUNw+AHMFDJMKZR2Vg2sOWmdeDKwpgGrnz5YzR/pubSH8K+vr4KCwvTk08+qdmzZ7v8CCZVEaicGNOoTCwb2HLTOnBlYUwDV49LVzFPSUmRn5+fhg0bJkkaN26cevfuXbEdBFAqjGlUBpYNbCVuWgeuNIxp4Opx6Q/hW265RZL0+uuv67HHHpPE/XeA1TCmUdEsHdhy0zpwZWFMA1cXHx8f7dy5U506ddIzzzzDD2DA4hjTqEiWDmy5aR24sjCmgatPZGSkVqxYocTEREn8AAasjjGNimLpwFbipnXgSsOYBq4uNWrU4AcwcAVhTKOiWD6wzcdN68CVhTENXH34AQxcWRjTKE9XTGArcdM6cKVhTAMAAKAkbIZhGBXdCU/buXOnrr/+eo0ePVovvviiJH4AA1bGmAYAAEBRrsjA9tixY/rhhx/I7weuEIxpAAAAFOWKDGwvxQ9g4MrCmAYAAMDlrvjAFgAAAABwZWPaAwAAAABgaQS2AAAAAABLI7AFAAAAAFgagS0AAAAAwNIIbAEAAAAAlkZgCwAAAACwNAJbAAAAAIClEdgCAAAAACyNwBYAUCklJSWpd+/eFd2NAlJTU9WiRYuK7gYAALgEgS0AXMUqQ/C4b98+2Ww2bd261e228vLyNHnyZDVu3FhBQUGqXr26br75Zs2ZM8f9jgIAgErLr6I7AACAp6Smpmr27Nl644031KZNG508eVKbNm3S8ePHK7prAADAi5ixBQAUaufOnbrjjjsUGhqqyMhIPfDAA/r111+d7ycmJurxxx/X6NGjVb16dUVFRSk1NdWljV27dumWW25RYGCgmjRpohUrVshms2nJkiWSpPj4eElSy5YtZbPZlJiY6FL/lVdeUXR0tGrUqKFhw4YpNze30P7+4x//0NChQ9WvXz/Fx8frhhtu0ODBg/XUU085yzgcDk2ZMkX169eX3W5X3bp1NWnSJOf7Y8aMUcOGDRUcHKxrr71W48ePL/KYkjRnzhxdd911CgwMVOPGjTVjxowiywMAAM8isAUAmDp8+LBuvfVWtWjRQps2bdKyZcv0yy+/qH///i7l3nvvPYWEhGj9+vWaOnWqJk6cqOXLl0u6GET27t1bwcHBWr9+vWbPnq1nn33Wpf6GDRskSStWrNDhw4f1ySefON9bvXq19uzZo9WrV+u9995TWlqa0tLSCu1zVFSUVq1apaNHjxZaZuzYsZoyZYrGjx+vnTt36oMPPlBkZKTz/SpVqigtLU07d+7U66+/rrfeekuvvfZaoe299dZbevbZZzVp0iT9+9//1osvvqjx48frvffeK7QOAADwLJthGEZFdwIAUDGSkpJ04sQJ5+zppZ577jmtX79eX3zxhXPfgQMHFBMTo/T0dDVs2FCJiYnKy8vTN9984yxz44036rbbbtNLL72kZcuWqWfPnsrIyFBUVJSkiwFs586dtXjxYvXu3Vv79u1TfHy8tmzZ4rIoU1JSktasWaM9e/bI19dXktS/f3/5+PhowYIFpuezc+dO9e3bV+np6WratKkSEhLUq1cvde/eXZJ06tQp1axZU2+88YaGDBlSomv08ssva+HChdq0aZOki+nOS5Yscd4TXLduXU2ZMkX33HOPs84LL7ygzz//XGvXri3RMQAAgHu4xxYAYGrz5s1avXq1QkNDC7y3Z88eNWzYUJJ0/fXXu7wXHR2tI0eOSJLS09MVExPjDGqli4FvSTVt2tQZ1Oa3/cMPPxRavkmTJtqxY4c2b96sb7/9Vl9//bV69uyppKQkvf322/r3v/+t7OxsderUqdA2Pv74Y02bNk27d+/W6dOndeHCBYWFhZmWPXr0qDIyMjR48GAlJyc791+4cEHh4eElPk8AAOAeAlsAgCmHw6GePXtqypQpBd6Ljo52/ru/v7/LezabTQ6HQ5JkGIZsNluZ+1BU24Xx8fFR27Zt1bZtWz355JOaN2+eHnjgAT377LMKCgoqsu53332ngQMHasKECeratavCw8O1YMECvfrqq6bl8/vy1ltv6aabbnJ579KAHAAAeBeBLQDAVKtWrbRo0SLFxcXJz69s/7to3Lix9u/fr19++cV5H+vGjRtdygQEBEi6+Kgeb2jSpIkk6cyZM2rQoIGCgoK0cuVK01Tkf/3rX4qNjXW5D/jnn38utO3IyEjVqVNH//nPf3Tfffd5vvMAAKBECGwB4CqXlZVV4Bmy1atX17Bhw/TWW2/pnnvu0dNPP62IiAjt3r1bCxYs0FtvvVWiGcnOnTurXr16GjRokKZOnapTp045g8b8mdxatWopKChIy5Yt0zXXXKPAwMAyp/H27dtX7du3V0JCgqKiorR3716NHTtWDRs2VOPGjeXn56cxY8Zo9OjRCggIUPv27XX06FH9+OOPGjx4sOrXr6/9+/drwYIFatu2rZYuXarFixcXeczU1FQ9/vjjCgsLU/fu3ZWdne18xNClqzEDAADvYVVkALjKrVmzRi1btnTZnnvuOdWuXVv/+te/lJeXp65du6pZs2Z64oknFB4eLh+fkv3vw9fXV0uWLNHp06fVtm1bDRkyROPGjZMkBQYGSpL8/Pz0l7/8RbNmzVLt2rXVq1evMp9L165d9Y9//EM9e/ZUw4YNNWjQIDVu3Fhffvmlc9Z5/PjxGjlypJ577jldd911GjBggPOe4F69eunJJ5/U8OHD1aJFC61du1bjx48v8phDhgzR22+/rbS0NDVv3ly33nqr0tLSnI8xAgAA3seqyACAcvWvf/1Lt9xyi3bv3q169epVdHcAAMAVgMAWAOBVixcvVmhoqBo0aKDdu3friSeeULVq1fTtt99WdNcAAMAVgntsAQBederUKY0ePVoZGRmKiIjQ7bffXugqwwAAAGXBjC0AAAAAwNJYPAoAAAAAYGkEtgAAAAAASyOwBQAAAABYGoEtAAAAAMDSCGwBAAAAAJZGYAsAAAAAsDQCWwAAAACApRHYAgAAAAAsjcAWAAAAAGBp/w+fWjfZNDWepQAAAABJRU5ErkJggg==", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "### WITHOUT KFOLD\n", - "X = combined_masses.reshape(-1, 1)\n", - "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", - "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", - "\n", - "# Define grid\n", - "length_scales = np.logspace(-2, 1, 20)\n", - "nus = [0.5, 1.5, 2.5] # available Matern values\n", - "\n", - "chi2_map = np.zeros((len(nus), len(length_scales)))\n", - "N_data = len(y)\n", - "N_params = 2\n", - "\n", - "chi2_map = np.zeros((len(nus), len(length_scales)))\n", - "\n", - "for i, nu in enumerate(nus):\n", - " for j, ls in enumerate(length_scales):\n", - " kernel = Matern(length_scale=ls, nu=nu)\n", - " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", - "\n", - " try:\n", - " gp.fit(X, y)\n", - " y_pred = gp.predict(X)\n", - " residuals = (y - y_pred)**2\n", - "\n", - " chi2 = np.sum(residuals) # assume unity residuals\n", - " chi2_red = chi2 / (N_data - N_params) # reduced chi^2\n", - " chi2_map[i, j] = np.log10(chi2_red)\n", - "\n", - " except Exception as e:\n", - " print(f\"Error at nu={nu}, length_scale={ls}: {e}\")\n", - " chi2_map[i, j] = np.nan\n", - "\n", - "# Plotting\n", - "plt.figure(figsize=(10, 5))\n", - "im = plt.imshow(chi2_map, aspect='auto', origin='lower',\n", - " extent=[length_scales[0], length_scales[-1], 0, len(nus)-1],\n", - " cmap='viridis')\n", - "plt.colorbar(im, label=r'$\\chi^2$')\n", - "plt.xticks(length_scales, labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", - "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", - "plt.xlabel('Length Scale')\n", - "plt.ylabel(r'$\\nu$')\n", - "plt.xscale('log')\n", - "plt.title('Chi-Squared Heatmap for Luminosity Fit Parameters')\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "aab48c6d-6d83-4ed6-9b70-6cf8d2842cd6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.5 0.01\n" - ] - } - ], - "source": [ - "i_best, j_best = np.unravel_index(np.nanargmin(chi2_map), chi2_map.shape)\n", - "best_nu = nus[i_best]\n", - "best_length_scale = length_scales[j_best]\n", - "print(best_nu, best_length_scale)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "3fec5172-ea8b-4f28-810f-c5577edfceb3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Looking at physical interpretation of heatmap parameters\n", - "\n", - "# creating input array (mass) and output array (everything else)\n", - "X = combined_masses.reshape(-1, 1)\n", - "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", - "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", - "\n", - "# trying different kernel types\n", - "kernel = Matern(length_scale=best_length_scale, nu=best_nu)\n", - "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", - "gp.fit(X, y)\n", - "y_pred_1, sigma_1 = gp.predict(x, return_std=True)\n", - "#chi2 = np.sum((y - y_pred_1)**2) #unit test for 2d function\n", - "#print(chi2)\n", - "\n", - "plt.figure()\n", - "plt.scatter(X, combined_L, color='blue', label='actual values')\n", - "plt.plot(x, y_pred_1, color='orange', label='predicted values')\n", - "plt.xlabel('Mass ($M_{\\odot}$)')\n", - "plt.ylabel('Luminosity')\n", - "plt.title('Reduced $\\chi^2$ Determination of Luminosity Best Fit Without kFold')\n", - "plt.xlim(0, 1)\n", - "plt.ylim(-4,0)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "99653c01-83e7-44c1-8a70-a04c185b0c3e", - "metadata": {}, - "source": [ - "Through the use of kFold we produced a pretty good fit! We can then repeat the process to find the best parameters for effective temperature and log gravity." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "53552603-6dce-4c96-9d22-0facf0c28767", - "metadata": {}, - "outputs": [], - "source": [ - "#assume unity uncertainties and focus on gap between model and data -- gives small value but scale by relative \n", - "#number of data points -- should give value close to 1" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "2b89c29a-9c7e-4f31-94a6-094df81e01c1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# creating input array (mass) and output array (teff)\n", - "X = combined_masses.reshape(-1, 1)\n", - "y_teff = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", - "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", - "\n", - "# Scale X and y\n", - "#X_scaler = StandardScaler()\n", - "#y_scaler = StandardScaler()\n", - "\n", - "#X = X_scaler.fit_transform(combined_masses.reshape(-1, 1))\n", - "#y = y_scaler.fit_transform(combined_L.reshape(-1, 1)).ravel()\n", - "\n", - "# Define grid\n", - "length_scales = np.logspace(-1, 2, 20)\n", - "nus = [0.5, 1.5, 2.5] # available Matern values\n", - "\n", - "chi2_map_teff = np.zeros((len(nus), len(length_scales)))\n", - "N_data_teff = len(y_teff)\n", - "N_params = 2\n", - "\n", - "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n", - "chi2_map_teff = np.zeros((len(nus), len(length_scales)))\n", - "\n", - "for i, nu in enumerate(nus):\n", - " for j, ls in enumerate(length_scales):\n", - " kernel = Matern(length_scale=ls, nu=nu)\n", - " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", - " \n", - " residuals_teff = []\n", - " try:\n", - " for train_idx, test_idx in kf.split(X):\n", - " gp.fit(X[train_idx], y_teff[train_idx])\n", - " y_pred_teff = gp.predict(X[test_idx])\n", - " residuals_teff.append((y_teff[test_idx] - y_pred_teff)**2)\n", - "\n", - " residuals_teff = np.concatenate(residuals_teff) \n", - " chi2_teff = np.sum(residuals_teff)\n", - " chi2_red_teff = chi2_teff / (N_data_teff - N_params)\n", - " chi2_map_teff[i, j] = np.log10(chi2_red_teff)\n", - " except Exception as e:\n", - " chi2_map_teff[i, j] = np.nan\n", - "# Plotting\n", - "plt.figure(figsize=(10, 5))\n", - "im = plt.imshow(chi2_map_teff, aspect='auto', origin='lower',\n", - " extent=[length_scales[0], length_scales[-1], 0, len(nus)-1],\n", - " cmap='viridis')\n", - "plt.colorbar(im, label=r'$\\chi^2$')\n", - "plt.xticks(length_scales, labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", - "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", - "plt.xlabel('Length Scale')\n", - "plt.ylabel(r'$\\nu$')\n", - "plt.xscale('log')\n", - "plt.title('Chi-Squared Heatmap for Luminosity Fit Parameters')\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b29c1755-4b8e-404b-a8f5-024a759179af", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.5 1.2742749857031335\n" - ] - } - ], - "source": [ - "i_best_Teff, j_best_Teff = np.unravel_index(np.nanargmin(chi2_map_teff), chi2_map_teff.shape)\n", - "best_nu_Teff = nus[i_best_Teff]\n", - "best_length_scale_Teff = length_scales[j_best_Teff]\n", - "print(best_nu_Teff, best_length_scale_Teff)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "926cbb27-cf76-4259-bff4-63cdad0fce02", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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JRCL5+5M5evQo4uPj4eHhIV+mSisNAFhaWqJt27YICAjA999/D3NzcwCARCLBzp074eLiopFr6lTUfjRFne+LKueuqsdDZsSIEZgzZw62bduG+/fvw9nZGT179ixV/cpCG+fNrl270Lp1a/nzffv2ITc3Vz6bT1N10sS5pMq/78Up789XdmFQBweHMu2rTp06mDFjBk6fPo2LFy8CUP87XVrluR+9D0LVq1fH/PnzMW/ePOzevRvvv/8+1qxZg44dO6JTp0748MMP4ebmhrS0NNy9exd//vmn/GJjS5cuRe/evdGjRw988sknEIvF+Pbbb2FpaYkXL14o7GfYsGFYtGgRhg8fjk8//RSZmZlYu3YtxGKxQrlVq1ahY8eOaNu2LT7//HN4eHggMTERR44cwaZNm2BtbQ0A5VLHogQFBWH+/PmwsbFBeHh4WT/yQl2/fl3eT52UlITg4GBs3boVhoaGOHTokHzcharvGwCaNWsmX2fs2LEwNjZGo0aN1NpGZdW/f39s27YNjRs3hre3N8LCwrBy5UqlrpmiPoPCLF++HD169ECXLl0wd+5cmJiYYMOGDbh+/Tr27NmjsZax8tyP7HtUUP369Us9dkfV74sq525Rx0N2XhdUrVo1vPPOO9i2bRtevXqFuXPnwsBAcWhnRX2fK/q8CQgIgJGREXr06CGfNda8eXMMHTpUo3Uq67lkbW2t8r/vxdHU55v/HEhOTkZAQABOnTqFd955R97So+q+UlJS0KVLF4wcORKNGzeGtbU1rl27hsDAQPksP3W/06VVrvvR+PDrSqq42UkZGRlCnTp1hAYNGgi5ubmCIAhCbGysMGHCBMHZ2VkwNjYWHBwchPbt2wtff/21wrpHjhwRvL29BRMTE6FOnTrCihUrlGYKyBw7dkxo0aKFYG5uLtSrV0/43//+V2jZ6Oho4b333hPs7e3l2x03bpyQmZmpUK486lhQfHy8ULNmTaFbt27CqlWrBJFIJLx8+bLE9VRVcAaBiYmJ4OjoKPj5+QnffPNNoTNwVH3fgiAI8+fPF2rXri0YGBgIAISzZ8+qvA3ZZ/Ts2TOl7Rb1WlGzbvz8/AQvLy+l913YrLGC2yys7MuXL4WJEycKjo6OgoWFhdCxY0chODhY8PPzE/z8/Er8DArbpiAIQnBwsNC1a1fB0tJSMDc3F9q1ayf8+eefKr33orZZGFX2o6lZYwCEzZs3l7jd4van6ndOlXO3qO9kUZ/fyZMn5e/j9u3bhb5/dc6J4hQ3a0zV/ZT13JCtHxYWJgwYMECwsrISrK2thREjRgiJiYkarZMglP1cklHl3/fi6qHqeylKYeeAra2t0KJFC2HVqlWl+v3IzMwUpk6dKnh7ews2NjaCubm50KhRI2Hx4sVCenq6Sp9LUfUsbqZw/nL5zwd19qMO3nSVipSTk4MuXbrgwYMHCA8Px+3bt9GxY0ecOnWqVLe8ICIqyVdffYUlS5bg2bNnlWq8GOkuve8ao6LNnTsX165dQ1BQkLxv2djYGMuWLYOhoSF8fX2VZoAQERFVJXp3HSFSzd69e7F27VqsWrVKft8hS0tLfP/997h16xZ69eoFNiYSEVFVx64xIiIi0ltsESIiIiK9xSBEREREeotBiIiIiPSW3s0ak0gkePLkCaytrSvlrROIiIhImSAISEtLQ+3atZUuLFoWeheEnjx5AldXV21Xg4iIiErh0aNHGr3Bsd4FIdnluB89egQbGxst14aIiIhUkZqaCldXV43fvkPvgpCsO8zGxoZBiIiIqIrR9LAWDpYmIiIivcUgRERERHqLQYiIiIj0lt6NESIiImVisRg5OTnargbpORMTE41OjVcFgxARkR4TBAFPnz7Fq1evtF0VIhgYGMDd3R0mJiYVtk8GISIiPSYLQY6OjrCwsOCFZklrZBc8TkhIQJ06dSrsu8ggRESkp8RisTwE2dvba7s6RHBwcMCTJ0+Qm5sLY2PjCtknB0sTEekp2ZggCwsLLdeESErWJSYWiytsnwxCRER6jt1hVFlo47vIIERERER6i0GIiIhIQ8aNG4fBgweX6z6++uortGjRolz3oU8YhIiISK8wSFB+nDVGRERlIhYDwcFAQgLg5AR06gQYGmq7VkSqYYsQERGVWkAA4OYGdOkCjBwp/a+bm3R5eQkMDETHjh1RrVo12Nvbo3///rh3755CmcePH2P48OGws7ODpaUlfHx8EBISgm3btmHJkiX4559/IBKJIBKJsG3bNsTFxUEkEiEyMlK+jVevXkEkEuHcuXMApDOZJk6cCHd3d5ibm6NRo0ZYs2aNyvVOSUmBubk5AgMDFZYHBATA0tISr1+/BgB89tlnaNiwISwsLFCvXj0sXLiw2Kt+d+7cGbNmzVJYNnjwYIwbN07+PDs7G/PmzYOzszMsLS3Rtm1b+fsCgAcPHmDAgAGoXr06LC0t4eXlhWPHjqn83qoytggREVGpBAQAQ4YAgqC4PD5euvzAAcDfX/P7TU9Px5w5c9CsWTOkp6dj0aJFeOeddxAZGQkDAwO8fv0afn5+cHZ2xpEjR1CrVi2Eh4dDIpFg2LBhuH79OgIDA/H3338DAGxtbZGYmFjifiUSCVxcXLBv3z7UqFEDly5dwuTJk+Hk5IShQ4eWuL6trS369euHXbt2oXfv3vLlu3fvxqBBg2BlZQUAsLa2xrZt21C7dm1ERUXhgw8+gLW1NebNm1fKTwwYP3484uLisHfvXtSuXRuHDh1C7969ERUVhQYNGmD69OnIzs7G+fPnYWlpiejoaHl9dB2DEBERqU0sBmbOVA5BgHSZSATMmgUMGqT5brJ3331X4fmWLVvg6OiI6OhoNG3aFLt378azZ89w7do12NnZAQA8PDzk5a2srGBkZIRatWqptV9jY2MsWbJE/tzd3R2XLl3Cvn37VApCADBq1CiMGTMGb968gYWFBVJTU3H06FEcPHhQXmbBggXyv93c3PDJJ5/g999/L3UQunfvHvbs2YPHjx+jdu3aAIC5c+ciMDAQW7duxTfffIOHDx/i3XffRbNmzQAA9erVK9W+qiJ2jRERkdqCg4HHj4t+XRCAR4+k5TTt3r17GDlyJOrVqwcbGxu4u7sDAB4+fAgAiIyMRMuWLeUhSJN++ukn+Pj4wMHBAVZWVti8ebN8v6ro168fjIyMcOTIEQDAwYMHYW1tjZ49e8rLHDhwAB07dkStWrVgZWWFhQsXqrWPgsLDwyEIAho2bAgrKyv5IygoSN6l+PHHH+Prr79Ghw4dsHjxYvz777+l3l9VwyBERERqS0jQbDl1DBgwAMnJydi8eTNCQkIQEhICQDoOBgDMzc3V3qbsjudCviauguNy9u3bh9mzZ2PChAk4efIkIiMjMX78ePl+VWFiYoIhQ4Zg9+7dAKTdYsOGDYORkbSD5sqVKxg+fDj69OmDv/76CxEREfjyyy+L3YeBgYFCvQvWXSKRwNDQEGFhYYiMjJQ/YmJi5GOcJk2ahPv372P06NGIioqCj48P1q1bp/L7qsoYhIiISG1OTpotp6rk5GTExMRgwYIF6NatGzw9PfHy5UuFMt7e3oiMjMSLFy8K3YaJiYnSLRwcHBwAAAn5klv+gdMAEBwcjPbt22PatGlo2bIlPDw8lAZpq2LUqFEIDAzEjRs3cPbsWYwaNUr+2sWLF1G3bl18+eWX8PHxQYMGDfDgwYNit+fg4KBQb7FYjOvXr8uft2zZEmKxGElJSfDw8FB45O8edHV1xdSpUxEQEIBPPvkEmzdvVvu9VUUMQkREpLZOnQAXF+lYoMKIRICrq7ScJlWvXh329vb4+eefcffuXZw5cwZz5sxRKDNixAjUqlULgwcPxsWLF3H//n0cPHgQly9fBiAddxMbG4vIyEg8f/4cWVlZMDc3R7t27bBixQpER0fj/PnzCmN1AOk4o9DQUJw4cQK3b9/GwoULce3aNbXfg5+fH2rWrIlRo0bBzc0N7dq1U9jHw4cPsXfvXty7dw9r167FoUOHit1e165dcfToURw9ehQ3b97EtGnT8OrVK/nrDRs2lI9NCggIQGxsLK5du4Zvv/1WPjNs1qxZOHHiBGJjYxEeHo4zZ87A09NT7fdWFTEIERGR2gwNAdnM8YJhSPZ89WrND5Q2MDDA3r17ERYWhqZNm2L27NlYuXKlQhkTExOcPHkSjo6O6Nu3L5o1a4YVK1bA8L/KvPvuu+jduze6dOkCBwcH7NmzBwDw66+/IicnBz4+Ppg5cya+/vprhe1OnToV/v7+GDZsGNq2bYvk5GRMmzZN7fcgEokwYsQI/PPPPwqtQQAwaNAgzJ49GzNmzECLFi1w6dIlLFy4sNjtTZgwAWPHjsWYMWPg5+cHd3d3dOnSRaHM1q1bMWbMGHzyySdo1KgRBg4ciJCQELi6ugKQtiJNnz4dnp6e6N27Nxo1aoQNGzao/d6qIpFQsGNRx6WmpsLW1hYpKSmwsbHRdnWIiLQmMzMTsbGxcHd3h5mZWam2ERAgnT2Wf+C0q6s0BJXH1HnSbcV9J8vr95vT54mIqNT8/aVT5HllaaqqGISIiKhMDA2Bzp21XQui0uEYISIiItJbDEJERESktxiEiIiISG8xCBEREZHeYhAiIiIivcUgRERERHpLq0Fo48aN8Pb2ho2NDWxsbODr64vjx48XWX7cuHEQiURKDy8vrwqsNREREekKrQYhFxcXrFixAqGhoQgNDUXXrl0xaNAg3Lhxo9Dya9asQUJCgvzx6NEj2NnZ4b333qvgmhMRkb5wc3PD6tWr5c9FIhEOHz5c4fX46quv0KJFi3Ldx7Zt21CtWrVy3Udlo9ULKg4YMEDh+bJly7Bx40ZcuXKl0FYeW1tb2Nrayp8fPnwYL1++xPjx48u9rkRERID0DvXVq1dXqexXX32Fw4cPK93JniqPSnNlabFYjP379yM9PR2+vr4qrbNlyxZ0794ddevWLbJMVlYWsrKy5M9TU1PLXFciIqpasrOzYWJiopFt1apVSyPbocpB64Olo6KiYGVlBVNTU0ydOhWHDh1CkyZNSlwvISEBx48fx6RJk4ott3z5cnlLkq2trfxOu0REVDV17twZM2bMwIwZM1CtWjXY29tjwYIFyH8PcTc3N3z99dcYN24cbG1t8cEHHwAALl26hLfffhvm5uZwdXXFxx9/jPT0dPl6SUlJGDBgAMzNzeHu7o5du3Yp7b9g19jjx48xfPhw2NnZwdLSEj4+PggJCcG2bduwZMkS/PPPP/Ixrdu2bQMApKSkYPLkyXB0dISNjQ26du2Kf/75R2E/K1asQM2aNWFtbY2JEyciMzOzyM9EIpHAxcUFP/30k8Ly8PBwiEQi3L9/HwCwatUqNGvWDJaWlnB1dcW0adPw+vXrIrc7btw4DB48WGHZrFmz0DnfPVUEQcB3332HevXqwdzcHM2bN8eBAwfkr798+RKjRo2Cg4MDzM3N0aBBA2zdurXIfVY0rQehRo0aITIyEleuXMGHH36IsWPHIjo6usT1ZP2YBQ9QQfPnz0dKSor88ejRIw3VnIhIxwgCkJuunUe+EKOK7du3w8jICCEhIVi7di1+/PFH/PLLLwplVq5ciaZNmyIsLAwLFy5EVFQUevXqBX9/f/z777/4/fffceHCBcyYMUO+zrhx4xAXF4czZ87gwIED2LBhA5KSkoqsx+vXr+Hn54cnT57gyJEj+OeffzBv3jxIJBIMGzYMn3zyCby8vORjW4cNGwZBENCvXz88ffoUx44dQ1hYGFq1aoVu3brhxYsXAIB9+/Zh8eLFWLZsGUJDQ+Hk5IQNGzYUWQ8DAwMMHz5cKbjt3r0bvr6+qFevnrzc2rVrcf36dWzfvh1nzpzBvHnz1PrsC1qwYAG2bt2KjRs34saNG5g9ezbef/99BAUFAQAWLlyI6OhoHD9+HDExMdi4cSNq1KhRpn1qlFDJdOvWTZg8eXKxZSQSieDh4SHMmjVL7e2npKQIAISUlJTSVpGISCdkZGQI0dHRQkZGhnRBzmtB2AXtPHJeq1xvPz8/wdPTU5BIJPJln332meDp6Sl/XrduXWHw4MEK640ePVrp9yU4OFgwMDAQMjIyhFu3bgkAhCtXrshfj4mJEQAIP/74o3wZAOHQoUOCIAjCpk2bBGtrayE5ObnQui5evFho3ry5wrLTp08LNjY2QmZmpsLy+vXrC5s2bRIEQRB8fX2FqVOnKrzetm1bpW3lFx4eLohEIiEuLk4QBEEQi8WCs7OzsH79+iLX2bdvn2Bvby9/vnXrVsHW1lb+fOzYscKgQYMU1pk5c6bg5+cnCIIgvH79WjAzMxMuXbqkUGbixInCiBEjBEEQhAEDBgjjx48vsg75KX0n8ymv32+ttwgVJAiCwpiewgQFBeHu3buYOHFiBdWKiIgqk3bt2kEkEsmf+/r64s6dOxCLxfJlPj4+CuuEhYVh27ZtsLKykj969eoFiUSC2NhYxMTEwMjISGG9xo0bFzuLKjIyEi1btoSdnZ3KdQ8LC8Pr169hb2+vUJfY2Fjcu3cPABATE6M0Xrak8bMtW7ZE48aNsWfPHgDS38qkpCQMHTpUXubs2bPo0aMHnJ2dYW1tjTFjxiA5OVmhe1Ad0dHRyMzMRI8ePRTey44dO+Tv5cMPP8TevXvRokULzJs3D5cuXSrVvsqLVgdLf/HFF+jTpw9cXV2RlpaGvXv34ty5cwgMDAQg7daKj4/Hjh07FNbbsmUL2rZti6ZNm2qj2kREusnQAhha9HiRct+3hllaWio8l0gkmDJlCj7++GOlsnXq1MGtW7cAQCFglcTc3FztekkkEjg5OeHcuXNKr5V16vqoUaOwe/dufP7559i9ezd69eol74Z68OAB+vbti6lTp2Lp0qWws7PDhQsXMHHiROTk5BS6PQMDA4WxVwAUykokEgDA0aNH4ezsrFDO1NQUANCnTx88ePAAR48exd9//41u3bph+vTp+P7778v0XjVFq0EoMTERo0ePRkJCAmxtbeHt7Y3AwED06NEDgHRA9MOHDxXWSUlJwcGDB7FmzRptVJmISHeJRICRZcnlKoErV64oPW/QoAEMDQ2LXKdVq1a4ceMGPDw8Cn3d09MTubm5CA0NRZs2bQAAt27dwqtXr4rcpre3N3755Re8ePGi0FYhExMThVYqWT2ePn0KIyMjuLm5FVmXK1euYMyYMQrvsSQjR47EggULEBYWhgMHDmDjxo3y10JDQ5Gbm4sffvgBBgbSDqF9+/YVuz0HBwdcv35dYVlkZCSMjY0BAE2aNIGpqSkePnwIPz+/Yrczbtw4jBs3Dp06dcKnn37KIARIW3aKIxtdn5+trS3evHlTTjUiIqKq4NGjR5gzZw6mTJmC8PBwrFu3Dj/88EOx63z22Wdo164dpk+fjg8++ACWlpaIiYnBqVOnsG7dOjRq1Ai9e/fGBx98gJ9//hlGRkaYNWtWsa0+I0aMwDfffIPBgwdj+fLlcHJyQkREBGrXrg1fX1+4ubkhNjYWkZGRcHFxgbW1Nbp37w5fX18MHjwY3377LRo1aoQnT57g2LFjGDx4MHx8fDBz5kyMHTsWPj4+6NixI3bt2oUbN27IBz0Xxd3dHe3bt8fEiRORm5uLQYMGyV+rX78+cnNzsW7dOgwYMAAXL15UmmVWUNeuXbFy5Urs2LEDvr6+2LlzJ65fv46WLVsCAKytrTF37lzMnj0bEokEHTt2RGpqKi5dugQrKyuMHTsWixYtQuvWreHl5YWsrCz89ddf8PT0LHa/FanSjREiIiIqyZgxY5CRkYE2bdpg+vTp+OijjzB58uRi1/H29kZQUBDu3LmDTp06oWXLlli4cCGcnJzkZbZu3QpXV1f4+fnB399fPsW9KCYmJjh58iQcHR3Rt29fNGvWDCtWrJC3TL377rvo3bs3unTpAgcHB+zZswcikQjHjh3D22+/jQkTJqBhw4YYPnw44uLiULNmTQDAsGHDsGjRInz22Wdo3bo1Hjx4gA8//FClz2bUqFH4559/4O/vrxDiWrRogVWrVuHbb79F06ZNsWvXLixfvrzYbfXq1QsLFy7EvHnz8NZbbyEtLU2hlQoAli5dikWLFmH58uXw9PREr1698Oeff8Ld3V3+Gc2fPx/e3t54++23YWhoiL1796r0XiqCSCjY+afjUlNTYWtri5SUFNjY2Gi7OkREWpOZmYnY2Fi4u7vDzMxM29VRWefOndGiRQuF216QbijuO1lev99sESIiIiK9xSBEREREeqvS3GuMiIhIFYVNOycqLbYIERERkd5iECIiIiK9xSBERKTnZFcHJtI2bUxk5xghIiI9ZWJiAgMDAzx58gQODg4wMTFR6/YSRJokCAKePXsGkUgkv3J1RWAQIiLSUwYGBnB3d0dCQgKePHmi7eoQQSQSwcXFpdhbpWgagxARkR4zMTFBnTp1kJubq3RPLKKKZmxsXKEhCGAQIiLSe7KuiIrsjiCqLDhYmoiIiPQWgxARERHpLQYhIiIi0lsMQkRERFTplddYfgYhIiIiqtQOHADq1i2fbTMIERERUaUjFgOnTwPt2wPvvQekpZXPfjh9noiIiCqVAweAiROB1NTy3xdbhIiIiEjrCrYAFQxB7g73ymW/DEJERESkFbLw8957gJUV0L07cPmyYpnW7qHY9/F7CF/WqlzqwK4xIiIiqnDFd38J6NHsFD7r/y26NT0DAEh9Uz71YBAiIiKiCiEWA+fOAQsXKrf8AIChQS6GtDmAef2/Qyv3CABATq4R9lwegRV/fgigvcbrxCBERERE5UosBpYtA1auBF6/Vn7dzDgD4/224pO+P6B+zfsAgPRMC2w++wFWHZ+DR8l1AJTPyGkGISIiIioXYjGwdCnw7bdAZqby69UtX2B6j/X4qOc6ONo+AwA8T7PH2hMfY/2p6Xjx2r7c68ggRERERBoj6/766Sfgjz+AnBzlMm4OsZjZaw0mdfkFVmbpAIDYJDf8cOwT/Bo0ARnZFhVWXwYhIiIiKrOSur8AoK3HFczpswrvtjkIQwMJACDyQXN899c87LsyFGJJxccSBiEiIiIqtZK6vwxEYgz2OYw5fVehQ8NL8uUno3rgh6Of4GRUTwCiYvdhbg7MnAmsWKHhyoNBiIiIiEpB1gK0fHnhAcjS9DUmdP4Vs3qvRj3HWABAdq4xdl0chR+Pz0bUI+8S92FlBXz6KfDll0B6OoMQERERaZFYDAQHA4cPA5s3A28KubZP7erx+KjnOkzptgnVLV8BAJLT7LDx9IdYf2o6nr5yKnYfxsbAoEHA1KlA586AoaHG34YCBiEiIiIq0YEDwLRpwLNnhb/evG4kPun7A4a32wtjo1wAwO2EBvjx+GzsuDAGb7Isi92+uTkwb570GkPlHX7yYxAiIiKiQpV0AUSRSII+zY/jk74/oKvXWfnyoJi3ser4HPwZPgCCUPzdvPJ3f1VkAJJhECIiIiIFqlwAcXTH3zC7z4/wdL4JAMgVG2JfyFCsOjYHYbE+Je6jXTvg668rpvurOAxCREREpPL4n2ndN2BKt02oYZ0MAEh5Y4Ofz0zGupMf/XcF6OLZ2AC//CK90WplwCBERESkx2TT31etAtLSCi/zVr2rmNl7DYa23Scf/xP3rC7WBM7ElqCJSMuwKXYfZmZA//4VNwBaHQxCREREeqik6e9Ghjl4x+cQZvVejfYN8wYInb/ZCauPz8KR8IElXgCxsnR/FYdBiIiISE+o0v1lZ5WMyV1/xrTuG+Bq/xiA9Po/ey6NwJoTMxER16rE/VS27q/iMAgRERHpsPzhZ9s2ICWl8HLedf7BjJ7/w/sddsLcRNpElJTigI2nP8TGvz9EYkqtEvdVFVqACmIQIiIi0kGyrq81a4AXLwovY2KUhXfbHMT0HusVbn8RFtsKawJn4vcrw5Cda1rivhwcgPXrq0YLUEEMQkRERDpCla4vAHC1f4ip3X7CpM6/wNFWeoXEnFwjBIT6Y92Jj3DxdgeUdP8vCwtg0iTgnXeATp2qTgtQQQxCREREVZjsooc//QScOFH0zC8Toyz0bXEM497ehv4t/5Lf/f3xC2dsOj0Fv5ybVOLtLwDtXwBR0xiEiIiIqqCSLnooJaCtRwhGd/wNw9vthb11Xh/Z39e7YcOpaSrN/gKq5vgfVTAIERERVRH5W3/++qvwae8AULdGHN7vuBNjOu5AQ6c78uVPXjph58X3sTVoPG4+8VRpn1V5/I8qGISIiIgqOVVaf2zMUzCkzQGM6bQDfp7n5cvTMy0QEOqPHcFjcOZGV0iEkptzdGX8jyoYhIiIiCohVVp/jAxz0LPZSYzu+BsGtf5DPu1dIhHhTHRX7Ageg0Oh7+B1prVK+9TV7q/iMAgRERFVArIZX/HxwOnTwP79RbX+CGhRNxJjOu3AyPa7UdM2Sf5KdLwntp8fi12XRiH+hYvK+9b17q/iMAgRERFpiaozvgDpDU9HddiFMR13oKnrDfnypBQH7Lk8AjuCxyA8rhVKmvYuY2sLjB2rH91fxWEQIiIiqmCqzfgCLE1f4523DmFMxx3o5nUaBgYCACAz2xRHwgdix4UxOPFvL+SKjVXab5cuwMSJgLOzfoef/BiEiIiIylH+Lq9nz4D794EtW4q+2KGBSIwuTc5iTKcd8H8rAFZm6fLXgm92xI4LY7A/5D2kvKmmch30ueurJAxCRERE5eTAAWDaNGkAKp4An3qhGNLmAN7vsBPOdk/kr9x56oHfLozGzgvvI/ZZPZX2a2kpDT3du7P1pyQMQkRERBqQv+UnMRHYtw8ICSm6vLFhNvw8gzDY5zAGtf4DLnbx8tdeplfD3svDsSN4DK7cbQdVxv2YmQH9+wNTp+rXrK+yYhAiIiIqJVXv7C5jZZaG3t6BGOxzGP1aHEU1y7wVXmdaIvDf3th9cSSORvZT6WangO7d8qKiMQgRERGpQZ2ZXgDgaJOIga2PYHDrw+je9G+YGmfLX0tMccSR8IE4HDoYp290Q1aOmUp1YOuP5jAIERERlUDVW1vIeNS8g8E+hzHY5zB8PS7LZ3sBwN2n9XEo9B0cDhuMK3faqXSlZxm2/mgegxAREVEBql/cUEY62Hlwa2n48XKJVnj12j0fHA4bjMOhgxEd3wSqXusHAKytgV692PpTXhiEiIiI/iO7vs+aNcCLF8WXLW6wc06uEc7FdMbhsME4EjYQj1+4qlwHzviqWAxCRESkt9Rt+SlpsPPxf/rgcOhgHIvsi1dvqqtVF328z1dlwCBERER6QRZ6EhIAR0fp3+vWldzyUx6DnfPjxQ61i0GIiIh0mjrdXYD0ys6t3cPQ0/sk+jQ/rtHBzhYWwIQJQP360gDEri/tYxAiIiKdov5AZ8DZ7jF6NjuJXt4n0N3rb9hbKyamsgx2BjjbqzJjECIioiqtYPD544+SW37MTd7g7cbn0cv7BHo2O6k0yyvljQ1O3+iGk1E9cTSin1qDnQHAxkY62LlJE+mYH477qbwYhIiIqEpSr8tLQDPXKHnw6dQoGGYmWXnbkhjg2v23cOLfXjgZ1RMhd9tCLFH9J5IzvaouBiEiIqr0Ct7BPS4O2LoVSE0teh0HmyT0aHoKPZudRM9mJ+FU/anC6w+fu+JEVC+c/LcnTt/ohpfpdmrViVd31g1aDUIbN27Exo0bERcXBwDw8vLCokWL0KdPnyLXycrKwv/93/9h586dePr0KVxcXPDll19iwoQJFVRrIiKqCLLw88cfwK5dJd/B3cQoC+0bXkKvZtJWn1buEQqvp2da4FxMZ3n4uZXQCOqO9eHFDXWPVoOQi4sLVqxYAQ8PDwDA9u3bMWjQIERERMDLy6vQdYYOHYrExERs2bIFHh4eSEpKQm5ubkVWm4iINKx0U9sFNHS6Le/u6ux5DlZm6QolIuJayLu7Lt7uoPKNTGXY5aX7RIIgCCUXqzh2dnZYuXIlJk6cqPRaYGAghg8fjvv378POTr0mTJnU1FTY2toiJSUFNjY2Za0uERGpqbTX8wGAahYv0a3pafkMr7o1Hiq8/vRVTZyM6omTUT1xKqoHklJrlqqOdnbAzJmc5VWZlNfvd6UZIyQWi7F//36kp6fD19e30DJHjhyBj48PvvvuO/z222+wtLTEwIEDsXTpUpibmxe6TlZWFrKy8gbEpRbXoUxEROVG3ev5ANLuLt8Gl9G1yRn0aHYKbepfhaGBRP56Vo4Jgm91krf6/PvQG+p2dwFA9erAoEFs+dFHWg9CUVFR8PX1RWZmJqysrHDo0CE0adKk0LL379/HhQsXYGZmhkOHDuH58+eYNm0aXrx4gV9//bXQdZYvX44lS5aU51sgIqICSjO4GZAGnzb1r8LPMwh+jYPQsdEFmJso3uo9Ot5THnyCYvyQkW2hVt2srIBPPpGGnaQkwMmJwUefab1rLDs7Gw8fPsSrV69w8OBB/PLLLwgKCio0DPXs2RPBwcF4+vQpbG1tAQABAQEYMmQI0tPTC20VKqxFyNXVlV1jREQaVJpr+QCAmXEG2jW4Ar/GQfDzDEI7jytKwSfhZS2cie6KMze64mRUT7Wv6SPD7q6qTWe7xkxMTOSDpX18fHDt2jWsWbMGmzZtUirr5OQEZ2dneQgCAE9PTwiCgMePH6NBgwZK65iamsLUVL3BcUREVLT8oScxEbh4ETh1CkhLK3ldC9N0tG9wSd7i06b+VYV7dwHS+3cFxfgh6KYfzkZ3QUy8J9jdReVF60GoIEEQFFpw8uvQoQP279+P169fw8rKCgBw+/ZtGBgYwMXFpSKrSUSkN0rb2gNI79beoeFF+HkGobPnOfi4h8LYSHGmb/yL2gi66ScNPzF+pZrWDjD4UOloNQh98cUX6NOnD1xdXZGWloa9e/fi3LlzCAwMBADMnz8f8fHx2LFjBwBg5MiRWLp0KcaPH48lS5bg+fPn+PTTTzFhwoQiB0sTEZH6xGLg3Dngp5+AEydUa+0BpLO6OjS6KO/qauUWDiNDsUKZB8/ryENP0E0/3Eusj9IEH0B649JRo6QBiMGHSkOrQSgxMRGjR49GQkICbG1t4e3tjcDAQPTo0QMAkJCQgIcP86ZGWllZ4dSpU/joo4/g4+MDe3t7DB06FF9//bW23gIRUZVWcFCzvT1w9qxqNyoFBLg7xKJDo4vw9biMDg0voplrlMKd2gHgXmI9eegJivHDg+dupaqrjQ0wbhzg7s47t5PmaH2wdEXjdYSISJ+VpZvL0CAX3nX+RcdGF9Cx4QV0bHQBtasnKJW79aQhzt96W97qU9rBzTIc5EyADg+WJiKi8qXurSpkLEzT0bZ+iDT4NLoAX4/LsDZXbCbKzjVGWGxrXLrTHpdut8eFWx1LfRFDQBp6PvqIU9up4jAIERHpmPxXbr5zB9i8GXj8uOT1HG0S0aHhRXnwKWx8T8obG1y83QEXbnXEhdsdce3eW8jMKd0YTYYeqgwYhIiIqqD8YcfJCWjfHrh0SZ1WHwENat1R6OZq6HRHqdSjZBcE3+okDT63OuLGYy9IhNInFQ5upsqGQYiIqIoorovL0FD6elGMDHPQsm6EvLWnY8MLcLRVTEsSiQjXHzeVt/ZcuNURj5LrlKnOnNJOlR2DEBFRJaVOF1fBEGRr8Qpt64fIu7ra1g+BpdkbhTKZ2aa4er+NvLXn8h1fvHpTvUx15t3aqaphECIi0qKyd3FJZ3M1c41CO48raOsRgnYeV9C49i2lcslpdtLxPf+19oTFtkZ2bumvvF+9OjBgAODiAhgYAJ07Sx8MPlSVMAgREVWwsnRxAQJc7B6jrUcI2taXhp7W7mGwMM1QKnn3aX1cvuuL4JudcOF2R9x80hiCYFDqerO1h3QRgxARUTnI39Lj6ChdlpSkfheXhWk6fNxD5a09beuHwNnuidJ6L9Or4eq9Ngi52xZX7rbD1XttkPy6Rpnfh7U10KsXMHUqW3tINzEIERFpSGmv1yMjEknQuPZNaeipH4K2HiFo5hoFQwOJQrlcsSH+feiNkHvS0BNyty1uP21YptYe2aDmrl2B5GReuZn0B4MQEVEplfZ6PVICald/gtbuYWhT/yraeVzBW/WuwdYiVanko2QXeeAJudcWYbGtkZFtUep6OzgAI0bwVhVEAIMQEVGJCuvm+usvdVp9BLjaP0Jr9zC0cguX/7dWtUSlkumZFrh2/y2E3GsrDz5PXjqXqf4uLsAHHwANGvCihUQFMQgREf2n7IEHAATUrfEAb9W7hlbueaGnhnWy8v4kBoiOb4Jr996Stvjca4sbj70glqj/T3PBQda8cCGRahiEiEivlXVcT61qCWjtHobW7mHwcQ9Fm/pXUdM2SalcTq4RbsR7ISy2NcLjWiEstjX+fehd5i4uWdiRTbuXTcNn+CFSDYMQEek8TVyrBwCcqj2Rhx7Zo7C7r+fkGuHfR94Ive+D8LhWCI9rhahHzZCVY1am91FSF1fnzmXaPJFeYhAiIp2hatdWydfqyWvp8XEPLTb0iCUGiIn3RFhsa4TFtcbVe20Q+aBFqUMPu7iIKhaDEBFVWbLgEx8PnD4tbeF58UK19fKraftUoXurtXtYodfqKRh6Qu/74J+HzfEmy7JM74NdXETawyBERFVCwdae4GBg3TrVgk9+DjZJ8rDjU0/6Xxe7eOX9FQg9YbGtEfmgRZlDD8AuLqLKhEGIiCql0rb25FfD+lle4HGT/tfVXvlCPxKJCDcTGiP0vg/CYlsjNNZHI6FH1tLTv7/0eVISW3mIKhsGISLSOk209thbPVdo5WntHoa6NR4qlZNIRLiV0Aihsf+FnvvS0JOeZVXm98Hr9RBVPQxCRFThytraY27yBi3dItC2fgja1L+KNvWvop5jrFI5iUSE208bygNPWFxrRMS1xOtMa429Fw5mJqraGISIqFyVtbXHQCSGp3OMPPC0rS+9/5aRofK0r1tPGsoHMYfFtkbEg5ZIy7DRyPtgNxeRbmIQIiKN0NRVmZ3t4hVaenzcQ2Ft/lqpZMLLWtLbUNxri6v32iD0vg9SM2zL/D4YeIj0C4MQEZVK2W44KmVjngKfeqHylp429a8Weq2etAwrhMb64Oq9Ngi52xZX77dB/AtnAKJS1Z3X6iEiGQYhIiqWZlp6AGPDbDRzjUJbj7zWnsZON2FgICiUyxUbIupRM3lLz9V7bRAT7wmJULZ0wmv1EFFhGISISElZ779lZJgDL5cb8HEPxVv1rsGnXiiauUbBxChHqez9JHd54Am51xYRcS3LdP8tALCzAz76SBpwiura4rV6iAhgECKi/5Q2/BiIxGhc+ybeqn8NPu6h8KkXihZ1ImFmkqVUNjnNDlfvt5EHn6v32uB5mkOZ6169urSlp3t3wNmZLTxEpDoGISI9VPAmpM+fA7NnlzzGRySSwKPmXXkrj497KFq5hcPS7I1S2VfptgiN9VG4SGHcMzeUdlyPjCqtPUREqmIQItJhBQNPp07SFp+ZM1UZ2CzA3SFWGnj+Cz2t3cNga5GqVPJ1pqU87ITe98G1+2/hflI9CIJBmd8DW3uIqDwxCBHpAFUDj709kJxc2BYE1K3xQOGqzK3dwmBvrXyxn4xsM0TEtVQIPbcTGpZ5MDPA1h4iqngMQkRVjCrdWkUFHukyaeiR3239v/twFRZ6snON8c+D5giNlQae0Ps+iI5vArFEM/90sLWHiLSNQYiokivN9XryhyCnak/QzuOKQmtPDWvllJSda4yoR82kd1v/7xH1qBmyc03L/B54kUIiqqwYhIgqifz333r2TNqqc/asevfhMjXORCu3cLTzuCJ/1KnxSKlcdq4xrj9qKr/xqCZCT/4bjsquN8TAQ0SVHYMQkZaU9cajgAA3hziF0NPSLULpWj1iiQGiHjWTd22VR0sPr8hMRFUVgxBRBSlr8LE0fQ2feqEKwadWtUSlcokpjrh8xxdX7rbDlbvtEHrfB+lZVmWqO7u2iEhXMQgRlQOxGDh3TvqQSKTh588/VQ8+IpEEDWrdUQg93nX+haGBRKFcdq4xIuJaykPPlbvtNHKtHoCtPUSkHxiEiDSgYGvP/v3Aa+UbphfJ1uIV2tS/inYeV+Db4DLa1g+BndVLpXIPn7sqhJ6IuJbIzDFXeT8iESAIyrPKXF2BH36Qhh/ef4uI9AmDEJGaCt6ENDgYWLdO9dYeA5EYTVyipaHH4zLaNbiCJs4xSuUyss0Qet9HIfg8eems0j6KCjwuLsDq1dJWnoLXHWLoISJ9xCBEpIKy3IS0hvUztPUIkYYejytoU/8qrM2Vm4vuPq2vEHr+feiNHLFJqeqrSuDhTUeJiBiEiJSUrcVHQCOnW/DzDEKnRsFo53EFHrXuKZVKy7DC1Xtt5KEn5F5bPEt1LFV9S+rWYuAhIioagxARytLiIw0+nZucQ2dP6aOwmVzR8Z64cievtefGY69S35LCzk5664wGDditRURUVgxCpHfK0uIjEknQ1OU6/DyD8Hbj83i78XnUtE1SKJOZbYord9vh/M23cfF2B1y91wav3lQvc71lAejLLxl8iIg0hUGI9IZYDCxbBqxZo/rAZkODXLR0i8Dbjc/Dr3EQOja6oDSbKyPbDCF32+JsTBecje6Cq/faICvHrNT1lN1/q2tX6UBnBwfeh4uIqLwwCJFOKqzV54cfSp7SLhJJ4F3nX3TzOo2uTc7g7cbnlQY2v860xMXbHRAU44fzN9/GtftvlekqzbzxKBGR9jAIkc6QhZ/Dh4Ft24CUFFXWEtCg1h109TqDbl6n0aXJWaUbkr5Mr4bgm50QdFMafCLiWpbp7usMPkRElQeDEFVZpb1lhYNNEno2O4nuTf9GN6/TcLVXvJV7WoYVzt98G6dvdMOZ6K7496E3BMGgVHW0tgZ69gR8fYFatRh8iIgqGwYhqjJKG3yMDbPRvuEl9PI+gV7NTqCVe4TC61k5Jrh0pz3O3OiK0ze64dr9t5ArNi5VHdnaQ0RUtTAIUaVWuu4uoH7Nu+jb4hh6NjuJzp7nYGWWrvB6eGxLnIzqidM3uuHi7Q7IyLZQq142NsCECbwJKRFRVccgRJWSujO8jA2z8Xbj8+jX8ij6Nj+GRrVvK7yemOKIU1E9EPhvb5yK6oGk1JqlqhensBMR6RYGIao08rf+bN4MvHlTfHmnak/Qp/lx9Gt5FD2anlKY3ZWTa4TgW50Q+G9vnIzqqfY4H7b4EBHpBwYh0iqxGDh3DvjpJ+DECSAtreiyBiIx3qp/Df1aHEW/FkeVxvo8fVUTxyL74mhkP5y63gNpGTZq14ctPkRE+oVBiLQmIACYPFnx7ugFVbN4iZ7eJ9GvxVH0aX4cDjbPFV6/eu8tHI3sh6MR/RAe10qtVh8rK+CTT6StPGzxISLSTwxCpBUBAcC77xb2igAvlxvSVp+WR9G+wSUYGYrlr6a8scGJf3vhaGQ/BP7Tu1RjfdjqQ0REMgxCVOHEYuDjj/Oem5u8QVevM+jb/Bj6tTyKujUeKpS/8bgJjkb2w7HIvrh4u4PaU9tr1ACmTwcaNWKrDxERKWIQogoXfF6Aee5dTOsh7fLq0uQszE0y5a9nZJvhbHQXefiJe+au1vZ5LR8iIlIVgxCVP0EAUmOApCAgMQhtEs7jzqoEhSIPntfB0Yh+OBrZD2eju6h1XR8GHyIiKi0GIdI8QQK8ipIGn6QgIOk8kJU3yNkC0qs5X7nbTj7L68ZjLwAilXdhawuMHQu88w6DDxERlZ5KQcjOzg63b99GjRo1MGHCBKxZswbW1tblXTeqKjKTgOdXgOSreY+cApeANjQHavgCjn4Q13gbXr5tce+BuVq76dIFmDiRrT5ERKQ5IkEQhJIKWVlZ4d9//0W9evVgaGiIp0+fwsHBoSLqp3GpqamwtbVFSkoKbGzUv86M3pPkAq/+BZ5f/u9xBXh9T7mckRXg0AFw9JM+7HwAQxP5y0XPGlPm4ACsXw+8956G3gMREVU55fX7rVKLkK+vLwYPHozWrVtDEAR8/PHHMDcv/P/mf/31V41VjiqBzGf5Qs9lIPkaIC7kks+2TQD7tv892gDVmgEGRX+9/P2BgwcLv46QpaU09HDMDxERlTeVgtDOnTvx448/4t496f/5p6SkIDMzs4S1qEp6fR9IOAk8uygNPoW19hjbSgNPDd//Hm0Bk2pq78rfXzrI+dw56QMAOneWPhh8iIioIqjUNZafu7s7QkNDYW9vX151KlfsGisgNx1IPAsknACeBAKv7yqXsW0iDTz27aT/tfUERKpfwZmIiKistNo1ln+wdJcuXWBiYlLySlR55bwG4v8EHv4uDT+SrLzXREaAQ3vAsUuZWnuIiIiqAg6W1hfibCD+CPDgd+DJUUCckfeapRvg1Btw6gXU6goY69HnQkREVYJODpbeuHEjNm7ciLi4OACAl5cXFi1ahD59+hRa/ty5c+jSpYvS8piYGDRu3Fjl/eqVtHvA3Z+B+1uBrGd5y608gLrDgDpDpQObRapfw4eIiEhXqD1YWiQSaWywtIuLC1asWAEPDw8AwPbt2zFo0CBERETAy8uryPVu3bqlkAarautUuXp2EbjxDfDkWN4y89qA+2igzjCgeguGHyIi0nuVbrC0nZ0dVq5ciYkTJyq9JmsRevnyJapVq1aq7et015ggSGd8RX8jvZozAEAk7fLymAI49y92SjsREVFlpdWusfxiY2Plf2dmZsLMzEwjFRGLxdi/fz/S09Ph6+tbbNmWLVsiMzMTTZo0wYIFCwrtLpPJyspCVlbeYODU1FSN1LfSeXoaiPwMeBEmfW5gDLiPAzw/BWwaaLVqRERElZXac6AlEgmWLl0KZ2dnWFlZ4f79+wCAhQsXYsuWLWpXICoqClZWVjA1NcXUqVNx6NAhNGnSpNCyTk5O+Pnnn3Hw4EEEBASgUaNG6NatG86fP19oeQBYvnw5bG1t5Q9XV1e161ippUQD5/oBZ7pLQ5ChBdB4DjAwFmj7M0MQERFRMdTuGvu///s/bN++Hf/3f/+HDz74ANevX0e9evWwb98+/Pjjj7h8+bJaFcjOzsbDhw/x6tUrHDx4EL/88guCgoKKDEMFDRgwACKRCEeOHCn09cJahFxdXat+11huOvDPAuD2OkAQS6e9N5gGNF0AmHHMFBER6Zby6hpTu0Vox44d+PnnnzFq1CgY5rv8r7e3N27evKl2BUxMTODh4QEfHx8sX74czZs3x5o1a1Rev127drhz506Rr5uamsLGxkbhUeUlnASONgVurZaGIJfBQL8bgM8ahiAiIiI1qD1GKD4+Xj7LKz+JRIKcnJwyV0gQBIUWnJJERETAycmpzPutErJfAWGzgNjt0ucWdYA2PwG1C7/cABERERVP7SDk5eWF4OBg1K1bV2H5/v370bJlS7W29cUXX6BPnz5wdXVFWloa9u7di3PnziEwMBAAMH/+fMTHx2PHjh0AgNWrV8PNzQ1eXl7Izs7Gzp07cfDgQRw8eFDdt1H1PA8BLg4H0uMAiICGHwHNvwaMrbVdMyIioipL5SA0YcIErFmzBosXL8bo0aMRHx8PiUSCgIAA3Lp1Czt27MBff/2l1s4TExMxevRoJCQkwNbWFt7e3ggMDESPHj0AAAkJCXj48KG8fHZ2NubOnYv4+HiYm5vDy8sLR48eRd++fdXab5UiSICYH4B/vgCEXMDSHWi/U3obDCIiIioTlQdLGxoaIiEhAY6Ojjhx4gS++eYbhIWFQSKRoFWrVli0aBF69uxZ3vUtsyp1HaGcNODS+9JbYwDSq0C3+RkwsdVuvYiIiCqY1q8jlD8v9erVC7169dJYJagQr+OA8wOBV1GAgSnQeg3gMZlXgyYiItIgtcYIifgjrHFiMXDunPQBAJ07A529LsLw4mAg6zlgVgt4+7D0LvBERESkUWoFoYYNG5YYhl68eFGmCumTAweACROAtLS8ZZFH/0T7j4fC3CQTqN4K8PsDsHDRXiWJiIh0mFpBaMmSJbC15fgUTZg3D1i5UnHZ2Le34ZdJk2BkKMaf4f0h8d2LQRaW2qkgERGRHlB5sLSBgQGePn0KR0fH8q5TuaoMg6X37weGDlVcNrnrJmyaOBUAsO38WEza/AucahshLg7Id91KIiIivaT1K0tzfJBmiMXAqFGKyyZ2/kUegn48PgsTfv4VYokRHj8GgoO1UEkiIiI9UapZY1R6TZoA+S/APe7trfh54mQA0hA0Z+cqAHmhMyGhgitIRESkR1QOQhKJpDzroRdatwZu3857PqzdXmz5YCIMDASsOzFDKQQBgL7cPYSIiEgb1L7pKpXOgAFAeHje857NTuC3D0fDwEDAxr+n4uMda1EwBDk4AJ06VWw9iYiI9AmDUAWYMwfIf/eRlm7hODjrXRgb5WLPpeGYvm09CoYgANiwgQOliYiIyhODUDnbvx/48ce859UtXyBglj+szNJxMqoHxv60HYKgfBjmzgWGDKnAihIREekhBqFyVHCGmEgkwY4Px8DN4QHuPq2PoWv3IUdsorTerFnK1xgiIiIizWMQKkedOinOEPt8wAr0b3kUGdlmGLL2AFLeVFNap39/xRYkIiIiKj8MQuVkzhzg8uW8512anMHS9xYCAKZvW49/HrRQWqd1a+DPPyuogkRERMQgVB4KjgtysEnC7ukjYWggwa/nxmNr0ASldRo0AEJDK7CSRERExCCkaWIxMGZM/iUCtk4ej1rVEhH1qOl/M8QUGRkBMTEVVkUiIiL6D4OQho0cCWRm5j2f0fN/6NfyGDKzTTHif3uQmWOutM6ePZwmT0REpA0MQhq0fz+wb1/ecy+X61g54lMAwKd7VuLG46ZK68yZw2nyRERE2sIgpCEFu8QMRGJsnTweZiZZOBrRF/87OUNpHV9f4IcfKrCSREREpIBBSEOWLlXsEvu411q8VT8Ur9Jt8cEvm1HwytHGxryzPBERkbYxCGmAWAx8803ecwebJHz93gIA0i6xhFe1ldbZvZvjgoiIiLSNQUgDli5VvHDirN6rYWn2Btfu+eCXs5OUyg8dynFBRERElQGDUBmJxcB33+U9tzFPwfQe0inyy/74EgW7xMzMpK1BREREpH0MQmV07hyQkZH3fFqPDbC1SMWNx01wJHygUvnffmOXGBERUWXBIFRGP/2U97e5yRvM7i29pPSKPz9Xuqs8u8SIiIgqFwahMhCLgT/+yHs+we9XONo+Q2ySG/ZeHq5Q1tiYXWJERESVDYNQGeQfJG1smI15/aWDhVYe/RS5YmOFsl98wS4xIiKiyoZBqJQKTpkf2WE36tR4hKevamJr0HiFsiYmwMKFFVxBIiIiKhGDUCnlbw0yEInx+YAVAIBVx+co3U9s4EC2BhEREVVGDEKlULA1aLDPYTSufQsv06vhp9NTlcpPVV5ERERElQCDUCkoXkBRwBeDpKnofydnIC3DRqGsuTnQuXOFVo+IiIhUxCCkpoKtQd28TqO1ezjSMy2wJnCmUvl589gtRkREVFkxCKmp4O00ZvT8HwDg16AJSH5dQ6EsB0kTERFVbgxCahCLgVWr8p672j/EgFZ/AgDWn5quVH7+fLYGERERVWYMQmoIDgbS0vKeT+76MwwNJDh9vStuJTRWKMvWICIiosqPQUgNhw7l/W1okItJnX8BAGz4e5pSWbYGERERVX4MQioSi4Ft2/Ked/M6jVrVEvEstYbSzVXZGkRERFQ1MAipKDgYSE3Nez6i/R4AwP6Q95Rup/Hhh2wNIiIiqgoYhFSUv1vM1DgT7/hIF+y5PEKp7ODBFVQpIiIiKhMGIRUU7Bbr5X0CthapeJTsgou3OyiUrVYN6NSpQqtHREREpcQgpIKC3WIDWx0BAARc84cgKH6EY8eyW4yIiKiqYBBSQXx83t8ikQR9mx8DAPwZPkCpLLvFiIiIqg4GIRX8/Xfe363cwuFU/SnSMqxw/ubbCuXYLUZERFS1MAiVQCwG9u/Pe96/5V8AgJNRPZEjNlEoy24xIiKiqoVBqATnzgHp6XnP+7U4CgD4K6K/Ull2ixEREVUtDEIl+OmnvL9rWD9Da/cwAEDgv70VytnYsFuMiIioqmEQKoZYDJw4kfe8e9O/YWAg4J8H3nj6ykmhbM+e7BYjIiKqahiEilHwJqs9m50EAJyI6qVUdurUiqoVERERaQqDUDHyT5sHBHkQOvlvT4VyVlZA584VVi0iIiLSEAahYuSfNu/lcgPOdk/wJsscF253VCj33nvsFiMiIqqKGISKUHDavKw1KOimH7JyzBTKdutWkTUjIiIiTWEQKkLBafO9vKWjpgt2iwGAs3MFVYqIiIg0ikGoCPmnzVuavoZf4yAAygOlOW2eiIio6mIQKkTBafM9mp2CmUkW7j6tj5h4T4WynDZPRERUdTEIFaLgtHnZ3eaPhA8EIFIoy2nzREREVReDUCEOHcr720Aklt9fTBqE8nDaPBERUdXGIFSAWAxs3Jj3vH+rv+Bg8xzJaXa4eLuDQllOmyciIqraGIQKWLoUyMnJez6v/3cAgE1npiBXbKxQltPmiYiIqjYGoXzEYmDVqrzn7RteRIeGl5CZbYq1Jz5WKs9p80RERFUbg1A+BQdJz+y1BgCwPXgsElNqKZTltHkiIqKqj0Eon/z3FjMxypLPFtt4+kOlsrNnc3wQERFRVccglM+zZ3l/e9S8CzOTLKRmWOOfB80VypmYAAsXVnDliIiISOMYhPJxcMj7u5HTLQDArSeNUPDaQR9+yNYgIiIiXaDVILRx40Z4e3vDxsYGNjY28PX1xfHjx1Va9+LFizAyMkKLFi00Vp979/L+blT7vyCU0Eip3ODBGtslERERaZFWg5CLiwtWrFiB0NBQhIaGomvXrhg0aBBu3LhR7HopKSkYM2YMumlw/rpYDPz8c95zd4dYAMDdRI8CdeYgaSIiIl2h1SA0YMAA9O3bFw0bNkTDhg2xbNkyWFlZ4cqVK8WuN2XKFIwcORK+vr4aq0twsOJgaTeHOABA3HM3hXIffMBuMSIiIl1RacYIicVi7N27F+np6cUGnK1bt+LevXtYvHixStvNyspCamqqwqMwCQmKz2UtQrFJ7grLGzRQabdERERUBRhpuwJRUVHw9fVFZmYmrKyscOjQITRp0qTQsnfu3MHnn3+O4OBgGBmpVvXly5djyZIlJZZzdMz7WySSoG6NBwCUW4TylyMiIqKqTestQo0aNUJkZCSuXLmCDz/8EGPHjkV0dLRSObFYjJEjR2LJkiVo2LChytufP38+UlJS5I9Hjx6VuI5TtQSYGOUgV2yI+Be8fDQREZGu0nqLkImJCTw8pAOSfXx8cO3aNaxZswabNm1SKJeWlobQ0FBERERgxowZAACJRAJBEGBkZISTJ0+ia9euSts3NTWFqalpifVISsr7WzY+6FGyK8QSoyLLERERUdWm9SBUkCAIyMrKUlpuY2ODqKgohWUbNmzAmTNncODAAbi7uyutow4np7y/XeweAwAeJtcpthwRERFVbVoNQl988QX69OkDV1dXpKWlYe/evTh37hwCAwMBSLu14uPjsWPHDhgYGKBp06YK6zs6OsLMzExpeWl06gTY2wPJyYCjjbTZJzGlpvx1kYhT54mIiHSNVoNQYmIiRo8ejYSEBNja2sLb2xuBgYHo0aMHACAhIQEPHz6skLr88Yc0BAF5QSgpNW9ktCAAq1dz6jwREZEuEQmCIGi7EhUpNTUVtra2SElJgY2NDQDpxRTd3IDH0h4x/DRhCqZ0+xmLDizB0kOLAEhbixITGYSIiIi0obDfb03Q+qyxyiA4OC8EAYW3CCUnS8sRERGR7mAQgvLFFGvaJgIAklIciy1HREREVRuDEJRnghXWIlRYOSIiIqraGIQgnQnm4pL33NFWOQi5unLGGBERka5hEIJ0APSIEdK/jQxzYGOeBgBIfm0vLzN8OAdKExER6RoGIUhnje3ZI/3b1jxFvjzlja387717peWIiIhIdzAIQXHWmK2FNAi9zrRUuL3Go0ecNUZERKRrGISgOBtMFoRSM5SvUcBZY0RERLqFQQiKs8FkQSh/t1hh5YiIiKjqYxBC3qwxkShvjFBKRl4QEok4a4yIiEgXMQhBOhtszRrp39UsFVuERCLpct5njIiISPcwCP3H3x/Ytw+o7aAYhFxcgAMHpK8TERGRbmEQ+k9AADB7NmAozusaq1ED+OEHhiAiIiJdxSAEaQgaMkQ6hT7/YOnkZGDYMOnrREREpHv0PgiJxcDMmYAgSJ/nD0KyZbNm8WKKREREukjvg1D+iykCyrPGBIEXUyQiItJVeh+ECl4k0fq/+4wVvKAiL6ZIRESke/Q+CBW8SKKlaToAID3TsthyREREVPXpfRDKfzFFALAweQMASM+SBiFeTJGIiEh36X0Qyn8xRZEor0XoTbYFL6ZIRESk4/Q+CAHS6wQdOAA4OwMWpnktQryYIhERkW5jEPqPvz8QFwc4OUqD0PqfLBAbyxBERESky4y0XYHKxNAQMBRJu8ba+FoC7A4jIiLSaWwRykecKwHEGQCAiyEWvIgiERGRjmMQ+k9AAODZMEP+vFc/C7i58fYaREREuoxBCHn3Gnv5/I182ZtsC8THS5czDBEREekmvQ9C+e81JpsxlpFtBkEw4L3GiIiIdJzeB6H89xqTX1U6K++q0rzXGBERke7S+yCU/x5isqtKv8myKLYcERER6Qa9D0L57yFmbiIdLJ2ZY1ZsOSIiItINeh+E8t9rzMQoGwCQlWsqf533GiMiItJdeh+E8t9rzPS/IJSdawIAvNcYERGRjtP7IATk3WvMqeZ/LUI50hYh3muMiIhIt/EWG//x9wcGt8wCLgP1G5rg7FlpdxhbgoiIiHQXg1A+BpC2CNWsZYKanbVbFyIiIip/7BrLTyINQjAwLb4cERER6QQGofxkQcjQRLv1ICIiogrBIJSfOEv6XwMGISIiIn3AIJSfvGuMQYiIiEgfMAjlxzFCREREeoVBKD8Ju8aIiIj0CYNQfuwaIyIi0isMQv8Ri4FHD6RB6GG8CcRiLVeIiIiIyh2DEICAAMDNDfjjkDQIbd1hCjc36XIiIiLSXXofhAICgCFDgMePAVNj6Rih7FwTxMdLlzMMERER6S69DkJiMTBzJiAI0ucmhnl3n5ctmzUL7CYjIiLSUXodhIKDpS1BMiZG/919Plc6fV4QgEePpOWIiIhI9+h1EEpIUHwuC0LZuSbFliMiIiLdoNdByMlJ8Xn+MULFlSMiIiLdoNdBqFMnwMUFEImkzwu2CIlEgKurtBwRERHpHr0OQoaGwJo10r9ForzB0lk5pvJwtHq1tBwRERHpHr0OQgDg7w8cOAA4O+drERKbwMVFutzfX8sVJCIionJjpO0KVAb+/sCgQcCbgCwgB1j6tQm8erAliIiISNfpfYuQjKEhYG0hbRHybmHCEERERKQHGITyk9901VS79SAiIqIKwSCUH+8+T0REpFcYhPKTSK8jBEMGISIiIn3AIJQfW4SIiIj0CoNQfpIc6X9FxtqtBxEREVUIBqH8ZEHIgEGIiIhIHzAI5ccgREREpFcYhGQEARBypX8zCBEREekFBiEZWQgCGISIiIj0hFaD0MaNG+Ht7Q0bGxvY2NjA19cXx48fL7L8hQsX0KFDB9jb28Pc3ByNGzfGjz/+qJnKyGaMARwsTUREpCe0eq8xFxcXrFixAh4eHgCA7du3Y9CgQYiIiICXl5dSeUtLS8yYMQPe3t6wtLTEhQsXMGXKFFhaWmLy5Mllq4xsfBDAFiEiIiI9IRIEQdB2JfKzs7PDypUrMXHiRJXK+/v7w9LSEr/99ptK5VNTU2Fra4uUlBTY2NjkvZD5DAhwlP49QgyI2GtIRERUWRT5+11GlebXXiwWY+/evUhPT4evr69K60RERODSpUvw8/MrskxWVhZSU1MVHoXuP1faIiSBAc4FGUAsVv89EBERUdWi9SAUFRUFKysrmJqaYurUqTh06BCaNGlS7DouLi4wNTWFj48Ppk+fjkmTJhVZdvny5bC1tZU/XF1dlcoEBAAdfKVBKDvbGF26AG5u0uVERESku7QehBo1aoTIyEhcuXIFH374IcaOHYvo6Ohi1wkODkZoaCh++uknrF69Gnv27Cmy7Pz585GSkiJ/PHr0SOH1gABgyBDg+bP/gpBYenuN+HjpcoYhIiIi3VXpxgh1794d9evXx6ZNm1Qq//XXX+O3337DrVu3VCqfv4/R0tIGbm7A48dA49oxiFnZBMlpdqgxNRkAIBIBLi5AbCxgaFjad0RERERlpfNjhGQEQUBWVla5lc8vOFgaggDA2FDaIpQjzpsxJgjAo0fSckRERKR7tDp9/osvvkCfPn3g6uqKtLQ07N27F+fOnUNgYCAAabdWfHw8duzYAQBYv3496tSpg8aNGwOQXlfo+++/x0cffVSq/Sck5P1dWBAqrBwRERHpDq0GocTERIwePRoJCQmwtbWFt7c3AgMD0aNHDwBAQkICHj58KC8vkUgwf/58xMbGwsjICPXr18eKFSswZcqUUu3fySnvb2OjooNQ/nJERESkOyrdGKHyVtgYofh4oEPDYAQvehu3njRE40+l4404RoiIiKhy0JsxQhXJ0BBYs0b6t0mBFiGRSLp89WqGICIiIl2l10EIAPz9gQMHAKeaikHIxUW63N9fm7UjIiKi8qTVMUKVhb8/MOitHCAYqONmjLNngU6d2BJERESk6xiE/mMI6d3n7WsYo3Nn7daFiIiIKobed43Jye4+zzvPExER6Q0GIRkGISIiIr3DICQjC0IiBiEiIiJ9wSAkI7BFiIiISN8wCMmwa4yIiEjvMAjJMAgRERHpHQYhGY4RIiIi0jsMQjIcI0RERKR3GIRk5F1jJtqtBxEREVUYBiEZjhEiIiLSOwxCMgxCREREeodBSIZBiIiISO8wCMkInDVGRESkbxiEZCTSu8+zRYiIiEh/MAgBEIuBhHhpi9D9OGOIxVquEBEREVUIvQ9CAQGAmxvw9ylpENrwkzHc3KTLiYiISLfpdRAKCACGDAEePwaMDaVBKEdsjPh46XKGISIiIt2mt0FILAZmzgQEQfo8fxCSLZs1C+wmIyIi0mF6G4QuXZK2BMnkD0KANCA9egQEB2ujdkRERFQR9DYIPX2q+NzYSDEIySQkVFSNiIiIqKLpbRCqVUvxubxFKFcxCDk5VVSNiIiIqKLpbRBq3x5wcQFEIunzgl1jIhHg6gp06qStGhIREVF509sgZGgIrFkj/VskUgxCsnC0erW0HBEREekmvQ1CAODvDxw4ADg7KwYhFxfpcn9/LVeQiIiIypWRtiugbf7+wKBBQMbBHCAXWPaNMZr2YEsQERGRPtC7ICT8d5Gg1NRUxRdEmUh9A7i/lY309NRC1iQiIiJtkf1uy37HNUXvglBycjIAwNXVtYgSAyuuMkRERKSW5ORk2Nraamx7eheE7OzsAAAPHz7U6AdJpZOamgpXV1c8evQINjY22q6OXuOxqDx4LCoPHovKIyUlBXXq1JH/jmuK3gUhAwPp+HBbW1t+qSsRGxsbHo9Kgsei8uCxqDx4LCoP2e+4xran0a0RERERVSEMQkRERKS39C4ImZqaYvHixTA1NdV2VQg8HpUJj0XlwWNRefBYVB7ldSxEgqbnoRERERFVEXrXIkREREQkwyBEREREeotBiIiIiPQWgxARERHpLZ0MQhs2bIC7uzvMzMzQunVrBAcHF1s+KCgIrVu3hpmZGerVq4effvqpgmqq+9Q5FgEBAejRowccHBxgY2MDX19fnDhxogJrq/vUPTdkLl68CCMjI7Ro0aJ8K6hH1D0WWVlZ+PLLL1G3bl2Ympqifv36+PXXXyuotrpN3WOxa9cuNG/eHBYWFnBycsL48ePlt2+i0jt//jwGDBiA2rVrQyQS4fDhwyWuo5Hfb0HH7N27VzA2NhY2b94sREdHCzNnzhQsLS2FBw8eFFr+/v37goWFhTBz5kwhOjpa2Lx5s2BsbCwcOHCggmuue9Q9FjNnzhS+/fZb4erVq8Lt27eF+fPnC8bGxkJ4eHgF11w3qXs8ZF69eiXUq1dP6Nmzp9C8efOKqayOK82xGDhwoNC2bVvh1KlTQmxsrBASEiJcvHixAmutm9Q9FsHBwYKBgYGwZs0a4f79+0JwcLDg5eUlDB48uIJrrnuOHTsmfPnll8LBgwcFAMKhQ4eKLa+p32+dC0Jt2rQRpk6dqrCscePGwueff15o+Xnz5gmNGzdWWDZlyhShXbt25VZHfaHusShMkyZNhCVLlmi6anqptMdj2LBhwoIFC4TFixczCGmIusfi+PHjgq2trZCcnFwR1dMr6h6LlStXCvXq1VNYtnbtWsHFxaXc6qiPVAlCmvr91qmusezsbISFhaFnz54Ky3v27IlLly4Vus7ly5eVyvfq1QuhoaHIyckpt7rqutIci4IkEgnS0tI0foM9fVTa47F161bcu3cPixcvLu8q6o3SHIsjR47Ax8cH3333HZydndGwYUPMnTsXGRkZFVFlnVWaY9G+fXs8fvwYx44dgyAISExMxIEDB9CvX7+KqDLlo6nfb5266erz588hFotRs2ZNheU1a9bE06dPC13n6dOnhZbPzc3F8+fP4eTkVG711WWlORYF/fDDD0hPT8fQoUPLo4p6pTTH486dO/j8888RHBwMIyOd+qdCq0pzLO7fv48LFy7AzMwMhw4dwvPnzzFt2jS8ePGC44TKoDTHon379ti1axeGDRuGzMxM5ObmYuDAgVi3bl1FVJny0dTvt061CMmIRCKF54IgKC0rqXxhy0l96h4LmT179uCrr77C77//DkdHx/Kqnt5R9XiIxWKMHDkSS5YsQcOGDSuqenpFnXNDIpFAJBJh165daNOmDfr27YtVq1Zh27ZtbBXSAHWORXR0ND7++GMsWrQIYWFhCAwMRGxsLKZOnVoRVaUCNPH7rVP/m1ejRg0YGhoqJfmkpCSl1ChTq1atQssbGRnB3t6+3Oqq60pzLGR+//13TJw4Efv370f37t3Ls5p6Q93jkZaWhtDQUERERGDGjBkApD/GgiDAyMgIJ0+eRNeuXSuk7rqmNOeGk5MTnJ2dYWtrK1/m6ekJQRDw+PFjNGjQoFzrrKtKcyyWL1+ODh064NNPPwUAeHt7w9LSEp06dcLXX3/NXoQKpKnfb51qETIxMUHr1q1x6tQpheWnTp1C+/btC13H19dXqfzJkyfh4+MDY2PjcqurrivNsQCkLUHjxo3D7t272eeuQeoeDxsbG0RFRSEyMlL+mDp1Kho1aoTIyEi0bdu2oqquc0pzbnTo0AFPnjzB69ev5ctu374NAwMDuLi4lGt9dVlpjsWbN29gYKD402loaAggrzWCKobGfr/VGlpdBcimQm7ZskWIjo4WZs2aJVhaWgpxcXGCIAjC559/LowePVpeXjb9bvbs2UJ0dLSwZcsWTp/XEHWPxe7duwUjIyNh/fr1QkJCgvzx6tUrbb0FnaLu8SiIs8Y0R91jkZaWJri4uAhDhgwRbty4IQQFBQkNGjQQJk2apK23oDPUPRZbt24VjIyMhA0bNgj37t0TLly4IPj4+Aht2rTR1lvQGWlpaUJERIQQEREhABBWrVolREREyC9lUF6/3zoXhARBENavXy/UrVtXMDExEVq1aiUEBQXJXxs7dqzg5+enUP7cuXNCy5YtBRMTE8HNzU3YuHFjBddYd6lzLPz8/AQASo+xY8dWfMV1lLrnRn4MQpql7rGIiYkRunfvLpibmwsuLi7CnDlzhDdv3lRwrXWTusdi7dq1QpMmTQRzc3PByclJGDVqlPD48eMKrrXuOXv2bLG/AeX1+y0SBLblERERkX7SqTFCREREROpgECIiIiK9xSBEREREeotBiIiIiPQWgxARERHpLQYhIiIi0lsMQkRERKS3GISIiIhIbzEIERERkd5iECIivZGcnAxHR0fExcWV2z6GDBmCVatWldv2iUizGISISGPGjRsHkUiEqVOnKr02bdo0iEQijBs3ruIr9p/ly5djwIABcHNzky97++23IRKJsHTpUoWygiCgbdu2EIlEWLRokcr7WLRoEZYtW4bU1FRNVZuIyhGDEBFplKurK/bu3YuMjAz5sszMTOzZswd16tTRWr0yMjKwZcsWTJo0Sb5MEARERkaibt26iIqKUii/fft2PHnyBADQqlUrlffj7e0NNzc37Nq1SzMVJ6JyxSBERBrVqlUr1KlTBwEBAfJlAQEBcHV1RcuWLeXLAgMD0bFjR1SrVg329vbo378/7t27p7CtAwcOoFmzZjA3N4e9vT26d++O9PT0El8rzPHjx2FkZARfX1/5sjt37iAtLQ3jxo1TCEJpaWmYP3++vPWqdevWan0GAwcOxJ49e9Rah4i0g0GIiDRu/Pjx2Lp1q/z5r7/+igkTJiiUSU9Px5w5c3Dt2jWcPn0aBgYGeOeddyCRSAAACQkJGDFiBCZMmICYmBicO3cO/v7+EASh2NeKcv78efj4+CgsCwsLg5mZGUaMGIE7d+4gKysLALB06VK0aNECTk5OqFGjBlxdXdV6/23atMHVq1fl2yOiystI2xUgIt0zevRozJ8/H3FxcRCJRLh48SL27t2Lc+fOycu8++67Cuts2bIFjo6OiI6ORtOmTZGQkIDc3Fz4+/ujbt26AIBmzZoBAG7fvl3ka0WJi4tD7dq1FZaFh4fD29sbDRs2hKWlJWJiYmBpaYkNGzYgNDQU33//vdqtQQDg7OyMrKwsPH36VF4/Iqqc2CJERBpXo0YN9OvXD9u3b8fWrVvRr18/1KhRQ6HMvXv3MHLkSNSrVw82NjZwd3cHADx8+BAA0Lx5c3Tr1g3NmjXDe++9h82bN+Ply5clvlaUjIwMmJmZKSwLCwtD69atIRKJ4O3tjevXr2P27NmYPHkyGjdujLCwMLXGB8mYm5sDAN68eaP2ukRUsRiEiKhcTJgwAdu2bcP27duVusUAYMCAAUhOTsbmzZsREhKCkJAQAEB2djYAwNDQEKdOncLx48fRpEkTrFu3Do0aNUJsbGyxrxWlRo0aSmEpIiJCHnSaN2+ONWvW4OrVq1i8eDGys7Nx48YNhSD05s0bfPrpp2jfvj3at2+PDz74AMnJyUr7evHiBQDAwcFBzU+NiCoagxARlYvevXsjOzsb2dnZ6NWrl8JrycnJiImJwYIFC9CtWzd4enoW2qIjEonQoUMHLFmyBBERETAxMcGhQ4dKfK0wLVu2RHR0tPz5/fv38erVK3nXV4sWLRAaGoply5bB1tYWUVFRyMnJUegamzFjBpo3b45Lly7h0qVLGD58OMaMGaM0Nun69etwcXFRagUjosqHY4SIqFwYGhoiJiZG/nd+1atXh729PX7++Wc4OTnh4cOH+PzzzxXKhISE4PTp0+jZsyccHR0REhKCZ8+ewdPTs9jXitKrVy/Mnz8fL1++RPXq1REWFgYTExM0bdoUADB27FgMHjwY9vb2AKTjh6pXry7vssvIyMDLly/x/vvv46uvvgIAfPXVV/jjjz9w9+5dNGjQQL6v4OBg9OzZs2wfIBFVCAYhIio3NjY2hS43MDDA3r178fHHH6Np06Zo1KgR1q5di86dOyuse/78eaxevRqpqamoW7cufvjhB/Tp0wcxMTFFvlaUZs2awcfHB/v27cOUKVMQHh6Opk2bwtjYGABgbGys0IITHh6uMN0/f6vPjBkzitxPZmYmDh06hBMnTpT4+RCR9omE4uabEhHpkGPHjmHu3Lm4fv06DAzUHxkwbtw49OjRA6NGjQIAnD59Gt9//z2OHTsGkUgEAFi/fj3++OMPnDx5UqN1J6LywRYhItIbffv2xZ07dxAfH6/2tYEAYMOGDViwYAHWrl0LkUgET09P7Ny5Ux6CAGnL0rp16zRZbSIqR2wRIiIiIr3FWWNERESktxiEiIiISG8xCBEREZHeYhAiIiIivcUgRERERHqLQYiIiIj0FoMQERER6S0GISIiItJbDEJERESktxiEiIiISG8xCBEREZHe+n+60fftJYtzxQAAAABJRU5ErkJggg==", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Looking at physical interpretation of heatmap parameters\n", - "\n", - "# creating input array (mass) and output array (everything else)\n", - "X = combined_masses.reshape(-1, 1)\n", - "y_teff = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", - "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", - "\n", - "# trying different kernel types\n", - "kernel = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", - "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", - "gp.fit(X, y_teff)\n", - "y_pred_teff, sigma_teff = gp.predict(x, return_std=True)\n", - "\n", - "plt.figure()\n", - "plt.scatter(X, combined_Teff, color='blue', label='actual values')\n", - "plt.plot(x, y_pred_teff, color='orange', label='predicted values')\n", - "plt.xlabel('Mass ($M_{\\odot}$)')\n", - "plt.ylabel('Teff')\n", - "plt.title('Reduced $\\chi^2$ Determination of Effective Temperature Best Fit')\n", - "plt.xlim(0, 1)\n", - "plt.ylim(3.25,3.75)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "fb250ffb-e8f5-4b66-a9d0-ae79190d1d0d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# creating input array (mass) and output array (luminosity)\n", - "X = combined_masses.reshape(-1, 1)\n", - "y_logg = combined_logg #np.array([combined_Teff, combined_L, combined_logg])\n", - "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", - "\n", - "# Define grid\n", - "length_scales = np.logspace(-1, 2, 20)\n", - "nus = [0.5, 1.5, 2.5] # available Matern values\n", - "\n", - "chi2_map_logg = np.zeros((len(nus), len(length_scales)))\n", - "N_data_logg = len(y_logg)\n", - "N_params = 2\n", - "\n", - "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n", - "\n", - "for i, nu in enumerate(nus):\n", - " for j, ls in enumerate(length_scales):\n", - " kernel = Matern(length_scale=ls, nu=nu)\n", - " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", - " \n", - " residuals_logg = []\n", - " try:\n", - " for train_idx, test_idx in kf.split(X):\n", - " gp.fit(X[train_idx], y_logg[train_idx])\n", - " y_pred_logg = gp.predict(X[test_idx])\n", - " residuals_logg.append((y_logg[test_idx] - y_pred_logg)**2)\n", - "\n", - " residuals_logg = np.concatenate(residuals_logg) \n", - " chi2_logg = np.sum(residuals_logg)\n", - " chi2_red_logg = chi2_logg / (N_data_logg - N_params)\n", - " chi2_map_logg[i, j] = np.log10(chi2_red_logg)\n", - " except Exception as e:\n", - " chi2_map_logg[i, j] = np.nan\n", - "\n", - "# Plotting\n", - "plt.figure(figsize=(10, 5))\n", - "im = plt.imshow(chi2_map_logg, aspect='auto', origin='lower',\n", - " extent=[length_scales[0], length_scales[-1], 0, len(nus)-1],\n", - " cmap='viridis')\n", - "plt.colorbar(im, label=r'$\\chi^2$')\n", - "plt.xticks(length_scales, labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", - "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", - "plt.xlabel('Length Scale')\n", - "plt.ylabel(r'$\\nu$')\n", - "plt.xscale('log')\n", - "plt.title('Chi-Squared Heatmap for log g Fit Parameters')\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "850e9dc3-5120-46ff-a15b-ccecc379e33d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.5 0.20691380811147897\n" - ] - } - ], - "source": [ - "i_best_logg, j_best_logg = np.unravel_index(np.nanargmin(chi2_map_logg), chi2_map_logg.shape)\n", - "best_nu_logg = nus[i_best_logg]\n", - "best_length_scale_logg = length_scales[j_best_logg]\n", - "print(best_nu_logg, best_length_scale_logg)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "ce7d8969-3bb4-4154-8d1a-baaa1975baa6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Looking at physical interpretation of heatmap parameters\n", - "\n", - "# creating input array (mass) and output array (everything else)\n", - "X = combined_masses.reshape(-1, 1)\n", - "y = combined_logg #np.array([combined_Teff, combined_L, combined_logg])\n", - "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", - "\n", - "# trying different kernel types\n", - "kernel = Matern(length_scale=best_length_scale_logg, nu=best_nu_logg)\n", - "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", - "gp.fit(X, y_logg)\n", - "y_pred_logg, sigma_logg = gp.predict(x, return_std=True)\n", - "\n", - "plt.figure()\n", - "plt.scatter(X, combined_logg, color='blue', label='actual values')\n", - "plt.plot(x, y_pred_logg, color='orange', label='predicted values')\n", - "plt.xlabel('Mass ($M_{\\odot}$)')\n", - "plt.ylabel('Log Gravity')\n", - "plt.title('Reduced $\\chi^2$ Determination of Gravity Best Fit')\n", - "plt.xlim(0, 1)\n", - "plt.ylim(4.0,4.5)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "147e614a-4e8f-485e-abc3-195630b144ae", - "metadata": {}, - "source": [ - "### Generating Data in the Interpolated Region" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "d78bbc34-8c01-46a2-83da-43ffde446c5d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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9/Prrr3Bzc8Obb76JevXqoV69elixYkWpfVX/j0Xh/5lo2LAhwsPDER4ejgkTJmjti4mJQceOHXH//n2sWLECJ06cQHh4uCboLPz3Vdy/C+r3VJe/Y6p4nONE5Uoul+OFF17Azz//jHv37ul8d1dx/wfq6uqK5s2b4+OPPy72GC8vLwDA9u3bYWVlhZ9++glKpVKz39j1aUri4uKCvLw8PHr0SOuLVN8vLW9vb7Rp06bEawDSXJ/CYmNjteYDlZdmzZph+/btEELgr7/+wqZNm/Dhhx/CxsYG77//vl7nKvh6Cv+OFPd6jF3vR6lUIiUlpcj2ylSfp6zP3MLCAs7OzgCkv5Xly5dj+fLliImJwd69e/H+++8jISGhzDtYSzJy5EjMmDEDq1evRvv27REfH48333xT7/M899xzaNiwIT788EN07dq1xLmPR48eRWxsLMLCwjRZJgCaeUsF+fr6Yv369QCAGzdu4Pvvv0dISAhycnKwZs0aAEDHjh3RsWNHqFQqnDt3DqtWrUJwcDDc3d0xfPjwYvsQFBQES0tL7N27VyuzZmNjo/nb/Omnn7SO2bNnD9LT0xEaGgpfX1/N9oiIiGKv8c8//xTZpv63orj/uSHTY8aJyt3MmTMhhMCECROQk5NTZH9ubi727dtX5nn69OmDy5cvo169emjTpk2RhzpwkslksLS01Br2yMzMxP/+978i59Q1w6IP9T/yO3bs0Nq+fft2o12jQ4cOsLGxKXL79r1793D06FG88MILRrlO4axHcWQyGVq0aIHPP/8cNWrUwIULF/S+jnrYrfDrCQ8Px9WrV432ekri5+eHGzduIDs7W7MtMTERJ0+eLNfr6qNhw4aoXbs2tm7dCiGEZnt6ejp27dqFDh06FDvJuU6dOpg8eTK6du1a5mdT2t+DUqnUDHUtW7YMLVu2xHPPPWfQa5k9ezb69u2Ld955p8Q26uC48A0bX3/9dannDggIwOzZs9GsWbNiX69cLke7du00GaDS3hNPT0+MGzcO+/fv1/nvt7h+CyFKLCGSlpZW5OaBrVu3wsLCAv/97391uiZVLGacqNx16NABX331FSZNmoTWrVvjjTfeQJMmTZCbm4uLFy9i7dq1aNq0Kfr27VvqeT788EMcPnwYgYGBmDJlCho2bIisrCxER0fjwIEDWLNmDby9vdG7d28sW7YMI0eOxGuvvYbExER89tlnxd4xp86a7NixA3Xr1oVSqUSzZs2e6vX26NEDzz33HN555x2kpqaidevWOHXqFDZv3gwARrkDrEaNGpgzZw4++OADjBkzBiNGjEBiYiLmz58PpVKJefPmPfU1AKBp06YAgLVr18LBwQFKpRL+/v44deoUvvzySwwYMAB169aFEAKhoaFITk7WqtGjq4YNG+K1117DqlWrYGFhgZ49e2ruqvPx8cHUqVOf+rXs27cPDg4ORbYPGTIEo0ePxtdff42XXnoJEyZMQGJiIpYsWQJHR8envq6xWFhYYMmSJRg1ahT69OmD119/HdnZ2fj000+RnJyMTz75BACQkpKCzp07Y+TIkWjUqBEcHBwQHh6OgwcPYtCgQaVeo1mzZggLC8O+ffvg6ekJBwcHNGzYULN/0qRJWLJkCc6fP49vvvnG4Nfy0ksv4aWXXiq1TWBgIJydnTFx4kTMmzcPVlZW2LJlC/7880+tdn/99RcmT56MoUOHokGDBlAoFDh69Cj++usvTeZzzZo1OHr0KHr37o06deogKytLc3dbly5dSu3H8uXLERUVhVGjRmHv3r3o378/vLy8kJGRgWvXrmH79u1QKpWwsrICINWmUygUGDFiBKZPn46srCx89dVXmmHUwlxcXPDGG28gJiYGAQEBOHDgANatW4c33nhDaw4iVSImnZpO1UpERIR4+eWXRZ06dYRCoRB2dnaiVatWYu7cuVoVutU1horz4MEDMWXKFOHv7y+srKxEzZo1RevWrcWsWbO0arts2LBBNGzYUFhbW4u6deuKRYsWifXr1wsAIioqStMuOjpadOvWTTg4OOhcx6nw3VnqekcFz/vo0SPxyiuviBo1aghbW1vRtWtXcfr0aQFArFixotT3qaQ6TsX55ptvRPPmzYVCoRBOTk6if//+Re4uLK2OU2HF3V22fPly4e/vL+RyueY9uXbtmhgxYoSoV6+esLGxEU5OTqJt27Zi06ZNZfa5pPdRXccpICBAWFlZCVdXV/HSSy+VWMdJV+rrlfRQ+/bbb0Xjxo2FUqkUzzzzjNixY0eJd9UV/mzUd5398MMPWtvVvxsFq6OXVsepMBRzF+aePXs0NX/s7OzECy+8IP744w/N/qysLDFx4kTRvHlz4ejoKGxsbETDhg3FvHnztGoWFXdXXUREhHjuueeEra2tpo5TYUFBQaJmzZoiIyOjyL7i6Pr7XNxddSdPnhQdOnQQtra2olatWuLVV18VFy5c0Prb/Oeff8TYsWNFo0aNhJ2dnbC3txfNmzcXn3/+ueYuxVOnTomBAwcKX19fYW1tLVxcXESnTp3E3r17dXoNKpVKbN68WXTt2lW4uroKS0tLze/8nDlzxL1797Ta79u3T7Ro0UIolUpRu3Zt8d5774mff/65yB2q6t/lsLAw0aZNG2FtbS08PT3FBx98UOqdemRaMiEK5HyJqNyoa/D88ccfCAwMNHV3iPSWkJAAX19fvPXWW1iyZImpu2P2goKC8PDhQ001fTIPHKojKgfbtm3D/fv30axZM1hYWOD06dP49NNP8d///pdBE5mde/fu4fbt2/j0009hYWGBt99+29RdIjIZBk5E5cDBwQHbt2/HggULkJ6eDk9PT4wdOxYLFiwwddeI9PbNN9/gww8/hJ+fH7Zs2YLatWubuktEJsOhOiIiIiIdmV05gi+//BL+/v6aasonTpwotf3x48fRunVrKJVK1K1bV1PTg4iIiEhfZhU47dixA8HBwZg1axYuXryIjh07omfPniUuZxAVFYVevXqhY8eOuHjxIj744ANMmTKl2MUkiYiIiMpiVkN17dq1w7PPPouvvvpKs61x48YYMGAAFi1aVKT9jBkzsHfvXs2aRgAwceJE/Pnnnzh16lSx18jOztYqgpefn49Hjx7BxcXF6NWKiYiIqHwIIZCWlgYvLy+j1M9TM5vJ4Tk5OTh//nyRpRy6detWYnXfU6dOadYsU+vevTvWr1+P3NxcTcGyghYtWoT58+cbr+NERERkMnfv3tV5uS9dmE3g9PDhQ6hUqiILIrq7u5e4Blh8fHyx7fPy8vDw4cNiF8ucOXMmpk2bpnmekpKCOnXq4O7du5WqijARERGVLDU1FT4+PsWuGPA0zCZwUis8XCaEKHUIrbj2xW1Xs7a2LnZpDkdHRwZOREREZsbY02zMZnK4q6sr5HJ5kexSQkJCkaySmoeHR7HtLS0tueo0ERER6c1sAieFQoHWrVvj8OHDWtvVi74Wp0OHDkXaHzp0CG3atCl2fhMRERFRacwmcAKAadOm4ZtvvsGGDRtw9epVTJ06FTExMZg4cSIAaX7SmDFjNO0nTpyIO3fuYNq0abh69So2bNiA9evX49133zXVSyAiIiIzZlZznIYNG4bExER8+OGHiIuLQ9OmTXHgwAH4+voCAOLi4rRqOvn7++PAgQOYOnUqVq9eDS8vL6xcuRKDBw82et9UKhVyc3ONfl4iKl9WVlaQy+Wm7gYRmQmzquNkCqmpqXByckJKSkqxk8OFEIiPj0dycnLFd46IjKJGjRrw8PBgrTaiKqSs729DmVXGqTJSB01ubm6wtbXlP7xEZkQIgYyMDCQkJABAsSVKiIgKYuD0FFQqlSZo4l16RObJxsYGgHTHrZubG4ftiKhUZjU5vLJRz2mytbU1cU+I6Gmo/4Y5T5GIysLAyQg4PEdk3vg3TES6YuBEREREpCMGTkREREQ6YuBElcrYsWMxYMCAcr1GSEgIWrZsWa7XICKiqomBUyWgUgFhYcC2bdJ/VSpT96h0DDyIiKi6YjkCEwsNBd5+G7h3799t3t7AihXAoEGm6xcREREVxYyTCYWGAkOGaAdNAHD/vrQ9NLR8rnvw4EH85z//QY0aNeDi4oI+ffogMjJSq829e/cwfPhw1KxZE3Z2dmjTpg3OnDmDTZs2Yf78+fjzzz8hk8kgk8mwadMmREdHQyaTISIiQnOO5ORkyGQyhIWFAZDqXo0fPx7+/v6wsbFBw4YNsWLFCp37nZKSAhsbGxw8eFBre2hoKOzs7PD48WMAwIwZMxAQEABbW1vUrVsXc+bMKfU286CgIAQHB2ttGzBgAMaOHat5npOTg+nTp6N27dqws7NDu3btNK8LAO7cuYO+ffvC2dkZdnZ2aNKkCQ4cOKDzayMiIvPAjJOJqFRSpqm4BW+EAGQyIDgY6N8fMHY9vvT0dEybNg3NmjVDeno65s6di4EDByIiIgIWFhZ4/PgxOnXqhNq1a2Pv3r3w8PDAhQsXkJ+fj2HDhuHy5cs4ePAgfv31VwCAk5MT/vnnnzKvm5+fD29vb3z//fdwdXXFyZMn8dprr8HT0xMvvvhimcc7OTmhd+/e2LJlC3r06KHZvnXrVvTv3x/29vYAAAcHB2zatAleXl64dOkSJkyYAAcHB0yfPt3Adwx45ZVXEB0dje3bt8PLywu7d+9Gjx49cOnSJTRo0ABvvvkmcnJy8Ntvv8HOzg5XrlzR9IeIiKoOBk4mcuJE0UxTQUIAd+9K7YKCjHvtwoscr1+/Hm5ubrhy5QqaNm2KrVu34sGDBwgPD0fNmjUBAPXr19e0t7e3h6WlJTw8PPS6rpWVFebPn6957u/vj5MnT+L777/XKXACgFGjRmHMmDHIyMiAra0tUlNTsX//fuzatUvTZvbs2Zqf/fz88M4772DHjh0GB06RkZHYtm0b7t27By8vLwDAu+++i4MHD2Ljxo1YuHAhYmJiMHjwYDRr1gwAULduXYOuRURElRsDJxOJizNuO31ERkZizpw5OH36NB4+fIj8/HwAQExMDJo2bYqIiAi0atVKEzQZ05o1a/DNN9/gzp07yMzMRE5Ojl4TzXv37g1LS0vs3bsXw4cPx65du+Dg4IBu3bpp2uzcuRPLly/HrVu38PjxY+Tl5T3VAo8XLlyAEAIBAQFa27OzszVL7UyZMgVvvPEGDh06hC5dumDw4MFo3ry5wdckIqLKiXOcTETXtUTLY83Rvn37IjExEevWrcOZM2dw5swZANI8HuDftbv0YWEh/SqJAmOPhecVff/995g6dSrGjRuHQ4cOISIiAq+88ormurpQKBQYMmQItm7dCkAaphs2bBgsLaX/Bzh9+jSGDx+Onj174qeffsLFixcxa9asUq9hYWGh1e/Cfc/Pz4dcLsf58+cRERGheVy9elUzR+vVV1/F7du3MXr0aFy6dAlt2rTBqlWrdH5dRERkHhg4mUjHjtLdcyWt9CCTAT4+UjtjSkxMxNWrVzF79my88MILaNy4MZKSkrTaNG/eHBEREXj06FGx51AoFFAVqplQq1YtAEBcgRRZwYniAHDixAkEBgZi0qRJaNWqFerXr19kUrouRo0ahYMHD+Lvv//GsWPHMGrUKM2+P/74A76+vpg1axbatGmDBg0a4M6dO6Wer1atWlr9VqlUuHz5suZ5q1atoFKpkJCQgPr162s9Cg5X+vj4YOLEiQgNDcU777yDdevW6f3aiIiocmPgZCJyuVRyACgaPKmfL19u/Inhzs7OcHFxwdq1a3Hr1i0cPXoU06ZN02ozYsQIeHh4YMCAAfjjjz9w+/Zt7Nq1C6dOnQIgzRuKiopCREQEHj58iOzsbNjY2KB9+/b45JNPcOXKFfz2229ac40AaZ7UuXPn8Msvv+DGjRuYM2cOwsPD9X4NnTp1gru7O0aNGgU/Pz+0b99e6xoxMTHYvn07IiMjsXLlSuzevbvU8z3//PPYv38/9u/fj2vXrmHSpElITk7W7A8ICNDMrQoNDUVUVBTCw8OxePFizZ1zwcHB+OWXXxAVFYULFy7g6NGjaNy4sd6vjYiIKjcGTiY0aBCwcydQu7b2dm9vaXt51HGysLDA9u3bcf78eTRt2hRTp07Fp59+qtVGoVDg0KFDcHNzQ69evdCsWTN88sknkD+J4gYPHowePXqgc+fOqFWrFrZt2wYA2LBhA3Jzc9GmTRu8/fbbWLBggdZ5J06ciEGDBmHYsGFo164dEhMTMWnSJL1fg0wmw4gRI/Dnn39qZZsAoH///pg6dSomT56Mli1b4uTJk5gzZ06p5xs3bhxefvlljBkzBp06dYK/vz86d+6s1Wbjxo0YM2YM3nnnHTRs2BD9+vXDmTNn4OPjA0DKUr355pto3LgxevTogYYNG+LLL7/U+7UREdHTUReVnjGjfM4vE4Und5CW1NRUODk5ISUlpcgE46ysLERFRcHf3x9KpdLga6hU0t1zcXHSnKaOHY2faSKikhnrb5mITEelAj76CFi2DEhLA4BUAMV/fz8N3lVXCcjlxi85QEREVNWps0tr1gA//giUUuvYaBg4ERERkdkoGCz99BOQlVWx12fgRERERJWaekrLnj3AunVARobp+sLAiYiIiCqdgsHSpk1ASoqpeyRh4ERERESVRtFJ3pULAyciIiIyCXVW6f594J9/gD/+APbtq5hJ3oZi4EREREQVqrJnlUrDwImIiIjKnSlKB5QHBk5ERERULkxdOqA8cMkVqvaCgoIQHBxs6m5UW9HR0ZDJZEUWhSYi86RSAUeOAEOHAvb2QJcu0jJiFRU0KZXAkCFSVqs8MONUGQgBZOcAeSrAUg5YK4qu/EtagoKC0LJlSyxfvtzUXSEiqtbUWaWwMODKFeDgwYqvs2RlBfTvD0ycKK3EIZcDqanlcy0GTqaWngk8TAIysoD8fMDCArBVAq7OgJ2NqXtX4XJzc2FlZWXqbhARUSkqyxCcgwMwbRowZ07FrfHKoTpTSs8E7icAjzMAK0spYLKylJ7fT5D2l4O0tDSMGjUKdnZ28PT0xOeff15kuConJwfTp09H7dq1YWdnh3bt2iEsLEyzf9OmTahRowZ++eUXNG7cGPb29ujRowfi4uK0rrVx40Y0btwYSqUSjRo1wpdffqnZpx6i+f777xEUFASlUonvvvsOiYmJGDFiBLy9vWFra4tmzZph27ZtmuPGjh2L48ePY8WKFZDJZJDJZIiOjgYAXLlyBb169YK9vT3c3d0xevRoPHz4UHNseno6xowZA3t7e3h6emLp0qVlvl8hISFo2bIlNmzYgDp16sDe3h5vvPEGVCoVlixZAg8PD7i5ueHjjz/WOm7ZsmVo1qwZ7Ozs4OPjg0mTJuHx48ea/Xfu3EHfvn3h7OwMOzs7NGnSBAcOHAAAJCUlYdSoUahVqxZsbGzQoEEDbNy48ak+0++++w5t2rSBg4MDPDw8MHLkSCQkJGj2h4WFQSaTYf/+/WjRogWUSiXatWuHS5culXjdESNGYPjw4VrbcnNz4erqqunvwYMH8Z///Ac1atSAi4sL+vTpg8jIyBLPqf7dKmjPnj2QFcrC7tu3D61bt4ZSqUTdunUxf/585OXlafaHhISgTp06sLa2hpeXF6ZMmVLiNYmobKYeglNzcgKmTAGOHQOSkoCQkIoLmgAAgkqVkpIiAIiUlJQi+zIzM8WVK1dEZmam/ifOzxci+r4Ql28KcfuuEFH3/n3cvittj46V2hnZq6++Knx9fcWvv/4qLl26JAYOHCgcHBzE22+/rWkzcuRIERgYKH777Tdx69Yt8emnnwpra2tx48YNIYQQGzduFFZWVqJLly4iPDxcnD9/XjRu3FiMHDlSc461a9cKT09PsWvXLnH79m2xa9cuUbNmTbFp0yYhhBBRUVECgPDz89O0uX//vrh375749NNPxcWLF0VkZKRYuXKlkMvl4vTp00IIIZKTk0WHDh3EhAkTRFxcnIiLixN5eXkiNjZWuLq6ipkzZ4qrV6+KCxcuiK5du4rOnTtr+vTGG28Ib29vcejQIfHXX3+JPn36CHt7e63XXti8efOEvb29GDJkiPj777/F3r17hUKhEN27dxdvvfWWuHbtmtiwYYMAIE6dOqU57vPPPxdHjx4Vt2/fFkeOHBENGzYUb7zxhmZ/7969RdeuXcVff/0lIiMjxb59+8Tx48eFEEK8+eabomXLliI8PFxERUWJw4cPi7179z7VZ7p+/Xpx4MABERkZKU6dOiXat28vevbsqdl/7NgxAUA0btxY6/3x8/MTOTk5xV533759wsbGRqSlpWltUyqVmr+ZnTt3il27dokbN26Iixcvir59+4pmzZoJlUql9Xtw8eJFIYT0u+Xk5KR1nd27d4uC/1wdPHhQODo6ik2bNonIyEhx6NAh4efnJ0JCQoQQQvzwww/C0dFRHDhwQNy5c0ecOXNGrF27tsT376n+lomqsLw8IX79VYghQ4RQKoWQ5pZU/MPWVogpU4Q4dkzqky5K+/5+GgycylBugVNmlhBXIoW4eUc7aFI/bt6R9mdmGeFV/Cs1NVVYWVmJH374QbMtOTlZ2Nraar5kb926JWQymbh//77WsS+88IKYOXOmEEL6cgMgbt26pdm/evVq4e7urnnu4+Mjtm7dqnWOjz76SHTo0EEI8e8X5vLly8vsd69evcQ777yjed6pU6ciwc6cOXNEt27dtLbdvXtXABDXr18XaWlpQqFQiO3bt2v2JyYmChsbmzIDJ1tbW5GamqrZ1r17d+Hn56f58hdCiIYNG4pFixaVeJ7vv/9euLi4aJ43a9ZM80VfWN++fcUrr7xS4rkK0uUzLc7Zs2cFAE3Qow6cint/duzYUew5cnJyhKurq9i8ebNm24gRI8TQoUNLvG5CQoIAIC5duiSEMCxw6tixo1i4cKFWm//973/C09NTCCHE0qVLRUBAQIkBX2EMnIj+VVmCJaVS6sOvv+oeLBVUXoET5ziZSp5KmtMkL2G0VG4BZAupnRHdvn0bubm5aNu2rWabk5MTGjZsqHl+4cIFCCEQEBCgdWx2djZcXFw0z21tbVGvXj3Nc09PT83Qz4MHD3D37l2MHz8eEyZM0LTJy8uDk5OT1nnbtGmj9VylUuGTTz7Bjh07cP/+fWRnZyM7Oxt2dnalvrbz58/j2LFjsLe3L7IvMjISmZmZyMnJQYcOHTTba9asqfXaS+Ln5wcHBwfNc3d3d8jlclhYWGhtKzj0dezYMSxcuBBXrlxBamoq8vLykJWVhfT0dNjZ2WHKlCl44403cOjQIXTp0gWDBw9G8+bNAQBvvPEGBg8ejAsXLqBbt24YMGAAAgMDi+2bLp8pAFy8eBEhISGIiIjAo0ePkJ+fDwCIiYnBM888o2lX3Ptz9erVYq9tZWWFoUOHYsuWLRg9ejTS09Px448/YuvWrZo2kZGRmDNnDk6fPo2HDx9qXbdp06YlvOOlO3/+PMLDw7WGR1UqFbKyspCRkYGhQ4di+fLlqFu3Lnr06IFevXqhb9++sLTkP3lEhRWs3n3kCPDDD0CBWQUVxsYG6NkTeOYZaYK3epJ3ZcN/RUzFUi5NBFflSz8XpsoHLGTF73sKQggAKDJfRL0dAPLz8yGXy3H+/HnIC/3WFgxKCk/ilslkmvOovxzXrVuHdu3aabUrfM7CAdHSpUvx+eefY/ny5Zo5QsHBwcjJySn1teXn56Nv375YvHhxkX2enp64efNmqceXprjXWtw29eu+c+cOevXqhYkTJ+Kjjz5CzZo18fvvv2P8+PHIfVL17dVXX0X37t2xf/9+HDp0CIsWLcLSpUvx1ltvoWfPnrhz5w7279+PX3/9FS+88ALefPNNfPbZZ0X6pstnmp6ejm7duqFbt2747rvvUKtWLcTExKB79+5lvq/FnbugUaNGoVOnTkhISMDhw4ehVCrRs2dPzf6+ffvCx8cH69atg5eXF/Lz89G0adMSr2thYaHVdwCa90wtPz8f8+fPx6BBg4ocr1Qq4ePjg+vXr+Pw4cP49ddfMWnSJHz66ac4fvw4bz4gQuVZQFepBPr00b4brrIzm8nhSUlJGD16NJycnODk5ITRo0cjOTm51GPGjh2rmTysfrRv375iOlwWa4U0GTw7R8pKFqQuT2BrI7Uzonr16sHKygpnz57VbEtNTdUKKlq1agWVSoWEhATUr19f6+Hh4aHTddzd3VG7dm3cvn27yDn8/f1LPfbEiRPo378/XnrpJbRo0QJ169YtEvQoFAqoVNrZuGeffRZ///03/Pz8ilzTzs4O9evXh5WVFU6fPq05JikpCTdu3NDpNenj3LlzyMvLw9KlS9G+fXsEBAQgNja2SDsfHx9MnDgRoaGheOedd7Bu3TrNvlq1amHs2LH47rvvsHz5cqxdu7bYa+nymV67dg0PHz7EJ598go4dO6JRo0Za2bGCint/GjVqVOJrDQwMhI+PD3bs2IEtW7Zg6NChUCik39vExERcvXoVs2fPxgsvvIDGjRsjKSmpxHOpX3daWhrS09M12wrXeHr22Wdx/fr1Ip9z/fr1NVlAGxsb9OvXDytXrkRYWBhOnTpV6kR3oqpKfQfcli3SEieDBwPOzkDnzsCKFRUfNKnrLP36q5TZ+uEH4IUXzCNoAswo4zRy5Ejcu3cPBw8eBAC89tprGD16NPbt21fqcT169NC6G0n9D7rJyWRSyYHsXKkUgbVCGp5T5UtBk5UV4FrD6PWcHBwc8PLLL+O9995DzZo14ebmhnnz5sHCwkKTVQgICMCoUaMwZswYLF26FK1atcLDhw9x9OhRNGvWDL169dLpWiEhIZgyZQocHR3Rs2dPZGdn49y5c0hKSsK0adNKPK5+/frYtWsXTp48CWdnZyxbtgzx8fFo3Lixpo2fnx/OnDmD6Oho2Nvbo2bNmnjzzTexbt06jBgxAu+99x5cXV1x69YtbN++HevWrYO9vT3Gjx+P9957Dy4uLnB3d8esWbO0htuMpV69esjLy8OqVavQt29f/PHHH1izZo1Wm+DgYPTs2RMBAQFISkrC0aNHNa9x7ty5aN26NZo0aYLs7Gz89NNPWq+/IF0+0zp16kChUGDVqlWYOHEiLl++jI8++qjY83344Yda74+rqysGDBhQ4muVyWQYOXIk1qxZgxs3buDYsWOafc7OznBxccHatWvh6emJmJgYvP/++6W+d+3atYOtrS0++OADvPXWWzh79iw2bdqk1Wbu3Lno06cPfHx8MHToUFhYWOCvv/7CpUuXsGDBAmzatAkqlUpzrv/973+wsbGBr69vqdcmqioKlgv45RfTrwdXXJ0ls2XUGVPl5MqVKwKA5q4qIYQ4deqUACCuXbtW4nEvv/yy6N+//1Ndu9wmh6s9zpDurrsSKcTlW9J/o2Ol7eUkNTVVjBw5Utja2goPDw+xbNky0bZtW/H+++9r2uTk5Ii5c+cKPz8/YWVlJTw8PMTAgQPFX3/9JYTQbQKvEEJs2bJFtGzZUigUCuHs7Cz++9//itDQUCFE0UnBaomJiaJ///7C3t5euLm5idmzZ4sxY8ZofZbXr18X7du3FzY2NgKAiIqKEkIIcePGDTFw4EBRo0YNYWNjIxo1aiSCg4NF/pO7E9PS0sRLL70kbG1thbu7u1iyZEmxE80LmjdvnmjRooXWtuJ+twqfZ9myZcLT01PY2NiI7t27i82bNwsAIikpSQghxOTJk0W9evWEtbW1qFWrlhg9erR4+PChEEKaRN+4cWNhY2MjatasKfr37y9u375dYh91+Uy3bt0q/Pz8hLW1tejQoYPYu3ev1vuvnhy+b98+0aRJE6FQKMT//d//iYiIiBKvq/b3338LAMLX11fzXqsdPnxYNG7cWFhbW4vmzZuLsLAwAUDs3r1bCFH878Hu3btF/fr1hVKpFH369BFr164t8rt18OBBERgYKGxsbISjo6No27at5s653bt3i3bt2glHR0dhZ2cn2rdvL3799dcS+8/J4WTu8vKkO86++06IV14Rwt7edJO6Cz4cHISYN8+wyd1Pq7wmh8uEKDxOVPls2LAB06ZNKzI0V6NGDXz++ed45ZVXij1u7Nix2LNnDxQKBWrUqIFOnTrh448/hpubW4nXUk9EVktNTYWPjw9SUlLg6Oio1TYrKwtRUVHw9/eHUqk0/AWauHJ4eno6ateujaVLl2L8+PEVdl0qP4Z8pmFhYejcuTOSkpKK1FGq6oz2t0xUgSpbVkmpBHr3Bjp0ADw8gNq1gY4dTZddSk1NhZOTU7Hf30/DLIbq4uPjiw123NzcEB8fX+JxPXv2xNChQ+Hr64uoqCjMmTMHzz//PM6fPw9ra+tij1m0aBHmz59vtL7rRCYDlMX3pzxcvHgR165dQ9u2bZGSkoIPP/wQANC/f/8K6wMZFz9Toqqvstz9VpA5Tu5+WiYNnEJCQsoMUsLDwwEUf1ePEKLUu32GDRum+blp06Zo06YNfH19sX///mLvxgGAmTNnas2/UWecqprPPvsM169fh0KhQOvWrXHixAm4urqaulv0FPiZElUthdeAO3LEdHe/FVQdg6WCTBo4TZ48uchyDYX5+fnhr7/+wj///FNk34MHD+Du7q7z9Tw9PeHr61vqbenW1tYlZqOqilatWuH8+fOm7gYZkTE+06CgoCJlAIio4lTGjJJadQ+WCjJp4OTq6qrT/xF36NABKSkpOHv2rKbI35kzZ5CSklJiUcDiJCYm4u7du/D09DS4z0RERMZQWTNKag4OQPfuDJYKM4s5To0bN0aPHj0wYcIEfP311wCkcgR9+vTRqo7cqFEjLFq0CAMHDsTjx48REhKCwYMHw9PTE9HR0fjggw/g6uqKgQMHGrV/6qKHRGSe+DdMFaXghO6ffqr4BXLL4uQEvPwyMHCgaSd2V2ZmETgBwJYtWzBlyhR069YNANCvXz988cUXWm2uX7+OlCfhulwux6VLl7B582YkJyfD09MTnTt3xo4dO7SWzngaCoUCFhYWiI2NRa1ataBQKEqdc0VElYsQAjk5OXjw4AEsLCwqT503qhIKZpTy86UhuF27Ks/wGwDY2QFDhwJdupj+LjhzYRblCEyprNsZc3JyEBcXh4yMDBP0joiMwdbWFp6engyc6KlV5oySnZ1UNdzbW1rxqzKvB2cM1bocQWWmUChQp04d5OXlFVkChIgqP7lcDktLS2aLySCVeUI3wHlK5YGBkxGoF3zl4qFERFVXwSDpwQMgOhrYulX6ubLg0Fv5Y+BERERUjMLZpB9/BB49MnWvimJWqWIxcCIiIoL5BEoA734zJQZORERU7aiDpLg4wM1N+nnVqsoZKDk4AN26VZ414Ko7Bk5ERFTlmVM2CWBGqTJj4ERERFWWSgV8/DGwYkXlDZSUSqB3b2aUzAUDJyIiqhKKu+tt40YgNdXUPSuKE7rNFwMnIiIyO+ZQGkCNGaWqhYETERFVauYUJKkxo1R1MXAiIqJKwxyDpFq1gBEjAH9/6WdmlKo2Bk5ERGQS5hgkAYCzM9C/P6tzV1cMnIiIqMKog6U9e4BNm4CUFFP3qGwMlKggBk5ERFQuVCogLEx65OdLmaV9+ypvWQA1BkpUGgZORERkFIWLTP7wA/D4sal7VbqaNYG33pKCo4QEwNOTgRKVjoETERHpjdkkqq4YOBERUZnMMZukVqsWMGqUFDAxUKKnxcCJiIg0Ci5+6+kJBAYCn3xSuZcsKYilAai8MXAiIqrm1MHSjz8CW7ZolwOwsJCG4iojBklkCgyciIiqmYJZpZs3gXXrgHv3im9bWYImBklUWTBwIiKqBkrLKlU2DJKoMmPgRERURRTMJLm5SdsSEsrOKlUGTk7Ayy8DAwcySKLKjYETEZGZ0mfIrbJhWQAyVwyciIjMiDkNuak5OADdugEdOgAeHgyUyLwxcCIiqmTMecgNYDaJqjYGTkRElYA5ZpIAZpOo+mHgRERkAuYyP0kul/qqxmwSVXcMnIiIylFxw24//VS5s0oFlygJDAROnvy3kjgDJaruGDgRERmRuWSSCvL2BiZMABo0KD44CgoyWdeIKh0GTkREBjDHTFJBXPiWyDAMnIiIdGSuE7iBsrNKRKQbBk5ERKUwp2CpYHBUsIwBAyUi42HgREQE8x1645AbUcVi4ERE1ZY5ZZPUOORGZFoMnIioWiicUTpxAli1Cnj0yNQ9K546k9Snj/ScQ25ElYPZBE4ff/wx9u/fj4iICCgUCiQnJ5d5jBAC8+fPx9q1a5GUlIR27dph9erVaNKkSfl3mIhMSh0o3b8PHDkiZZUqa5AEMJNEZC7MJnDKycnB0KFD0aFDB6xfv16nY5YsWYJly5Zh06ZNCAgIwIIFC9C1a1dcv34dDg4O5dxjIqoo5pZNUuP8JCLzIxNCCFN3Qh+bNm1CcHBwmRknIQS8vLwQHByMGTNmAACys7Ph7u6OxYsX4/XXX9fpeqmpqXByckJKSgocHR2ftvtEZATmlE3inW5EplFe399mk3HSV1RUFOLj49GtWzfNNmtra3Tq1AknT54sMXDKzs5Gdna25nlqamq595WIdKNSAR9/DKxYUXkDJYCZJKKqrMoGTvHx8QAAd3d3re3u7u64c+dOicctWrQI8+fPL9e+EVHZiht+W7oUePzY1D37FydwE1U/Jg2cQkJCygxSwsPD0aZNG4OvIZPJtJ4LIYpsK2jmzJmYNm2a5nlqaip8fHwMvj4R6aeyZ5WYTSKq3kwaOE2ePBnDhw8vtY2fn59B5/bw8AAgZZ48PT012xMSEopkoQqytraGtbW1QdckIt0UV2wyIUFaFHflSiAx0bT9K4zBEhGpmTRwcnV1haura7mc29/fHx4eHjh8+DBatWoFQLoz7/jx41i8eHG5XJOISmYOxSYdHYFx4zj0RkQlM5s5TjExMXj06BFiYmKgUqkQEREBAKhfvz7s7e0BAI0aNcKiRYswcOBAyGQyBAcHY+HChWjQoAEaNGiAhQsXwtbWFiNHjjThKyGqPswhWAKAmjWBt98GZs1igEREpTObwGnu3Ln49ttvNc/VWaRjx44hKCgIAHD9+nWkpKRo2kyfPh2ZmZmYNGmSpgDmoUOHWMOJqBxV9mCpZk3grbekLBIzSkSkL7Or41TRWMeJqHQF5yvdvAmsWwfcu2fqXv3L2Vmam9SlC1C7NoMkouqCdZyIqNKorFklZpOIqLwxcCKiUhXMKHl6Ag8fAlOnVo6skp0dMHQos0lEVHEYOBFRiUJDpUnTlSFIKoiTuYnIVBg4ERGA4jNLL74ImHoWJIffiKgyYeBEVA3pMvwml5smaCq4KC6DJCKqbBg4EVUzug6/qVTl2w+u80ZE5oiBE1EVVtmG37h0CRGZOwZORFVIWTWVTDH8xmCJiKoSBk5EVYBKBXz8MbBiBfDoUentyhvnKBFRVcbAicgMqTNL9+8DR44AP/wAPH5suv4wq0RE1QUDJyIzoVIBYWHAmjXAL78AaWkVc125XDtT5eMDLF0qBUvquVMMloioumDgRFTJqYfhPv20YrNKMpn0323bGCQREakxcCKqZCrLMJy3N7B8OTBoUMVfm4iosmLgRFRJ6DrB2xg4/EZEZBgGTkQmYorMEoffiIieDgMnogpWkZmlwjj8RkT0dBg4EVUQU0zyrllTWl6FNZWIiIyDgRNROTNFwGRvD7z3HjBrFgMlIiJjYuBEZCQFlztxc5O2/fQTsGEDkJpaMX1QZ5gYMBERlQ8GTkRPyZRzluzsgKFDgS5dgNq1ORRHRFTeGDgRGUCdXfrxx4rNKAGAUgn06QNMnAgEBTFQIiKqSAyciPRgyuwSh+GIiEyPgRNRGUyVXeIwHBFR5cPAiagEpsouMbNERFR5MXAiKsAU2SVmloiIzAcDJyKYJrvEWktEROaHgRNVa6as5s2AiYjI/DBwomrFFAvrAtKCuqNGAf37cyiOiMicMXCiamPnTmDSJODBg/K9Ts2awFtvSQFSQgLXiCMiqkoYOFGVp1JJ2Z4dO8rvGo6OwLhxzCgREVV1DJyoylLPX1qyBEhPL59rcL4SEVH1wsCJqhSVCggLA9askRbYzcoy/jWYXSIiqr4YOFGVUBF3xzG7REREDJzIrJV3wMTsEhERFcTAicxORQzHsTglEREVx8LUHdDVxx9/jMDAQNja2qJGjRo6HTN27FjIZDKtR/v27cu3o1RuVHkCKxZnI7BFBsa/lI1du4TRgyZ7e2D+fCA5GZg7l0ETERFpM5uMU05ODoYOHYoOHTpg/fr1Oh/Xo0cPbNy4UfNcoVCUR/eonB3+MROn9yehjmsW3hmSj8wcC1y7o8Se351xLcbmqc/P+UtERKQLswmc5s+fDwDYtGmTXsdZW1vDw8OjHHpEFUIInNiXikcXHuLZuipcv6tEepYF7JT5eDYgA3Xcc7Fyl5tBwZOtLfDqq8DAgZy/REREujGboTpDhYWFwc3NDQEBAZgwYQISEhJKbZ+dnY3U1FStB5lIeibyo2NR49FdBDZ9jNq1stG8XgacHVRIzZDj72glXJ1y0f8/yZDJhM6nVQ/HpaZKi/oGBTFoIiIi3VTpwKlnz57YsmULjh49iqVLlyI8PBzPP/88srOzSzxm0aJFcHJy0jx8fHwqsMcEABACSEoBomORcicNFjKBew8tkZkth6dLLto1ToerUx4AGe4mKNDYNxN13HPKPC3nLxER0dMyaeAUEhJSZPJ24ce5c+cMPv+wYcPQu3dvNG3aFH379sXPP/+MGzduYP/+/SUeM3PmTKSkpGged+/eNfj6ZID0TOBOLBB5F0h9DIu8bLg65sFSBmTnWuBBsiVslSo0qpMFGYD0LAsoFQIONqoST8mAiYiIjMWkc5wmT56M4cOHl9rGz8/PaNfz9PSEr68vbt68WWIba2trWFtbG+2apAMhgOwcIC0DeJgE5OYC+QJQWEKWLWCtUMHbPQd3/7FGRrYFUtPlcHXKhZO9Cvn5QFaODGmZ2tGQUgn06QNMnMihOCIiMh6TBk6urq5wdXWtsOslJibi7t278PT0rLBrUhnSM6VgKT0TSM8AVPmApaUUOFlZwMERiHkgRw3bPLg65SImwRo5eTI42AIKSxVq1VDh/A07xPwj3S3J+ktERFSezGaOU0xMDCIiIhATEwOVSoWIiAhERETgcYFy0Y0aNcLu3bsBAI8fP8a7776LU6dOITo6GmFhYejbty9cXV0xcOBAU70MUiswjwkpjwGZBQAZYCkH8lSah8xCBgdnK2TmyFHTQQVH2zxYW+VDJhPwdc/FgxQr/Ph7DdjZyTgcR0RE5c5syhHMnTsX3377reZ5q1atAADHjh1DUFAQAOD69etISUkBAMjlcly6dAmbN29GcnIyPD090blzZ+zYsQMODg4V3n8qID0TePDoybCcCpBbSMNzqnzA2gqwsJDKg+fkAnI5arpaIAlWyE3Pga0yH7WU+UhItkL4DXskWzrjiw02HI4jIqIKIRNC6H4fdzWUmpoKJycnpKSkwNHR0dTdMW9CAMmpQOxDKSjKzZWCJpkMyMmTskzWVoCVJZCbJ7WxtASs5IAQELkqJGcpkJpphSQbVzT7jyPkljJTvyoiIqqEyuv722wyTmTmCmeZZJAyTBYKKXhSWGplmWApfzLf6cl/8/Igs7KCs78jnF2d4Wv39NXCiYiI9MXAicpPKXfLQQgpgMrOAWTWT4InqyeZqDxALgMsZICtUjrO1gbwdAWcHaUMFRERkQkwcKLyUcbdcgCkITj1kJzSukCWyUIaurN8MmnJyRFwrQEwy0RERCbGwImMLz0TuJ/wZA6THEXulpNbSPOYrKykQEqlAlR5UiZJJpMCLBsboJYz4GALWCuYZSIiokqBgRMZV34+EP8QyMyShtnyhTQsZ2lZ5G45zfBcdo6UacrPl4KpGg6AqzMzTEREVOkYFDilp6fDzs7O2H0hc5eeCcQ9AB4mS5O/c57cNScgBU8WheYxWcmldnI5oFBI+ziPiYiIKjGDCmC6u7tj3Lhx+P33343dHzJHBYtZpqVLwZCV5ZP6THlSJikn70nmSa5911xOrvRzTUfAzwuo6cSgiYiIKi2DAqdt27YhJSUFL7zwAgICAvDJJ58gNjbW2H0jc1BoUV5k5z6Zs5T/b4ZJhid30eVJ+9R3y1lbAY4OQF1voI4nh+aIiKjSMyhw6tu3L3bt2oXY2Fi88cYb2LZtG3x9fdGnTx+EhoYiLy/P2P2kykg9CTz18b9lBqzk0tBcdq40EVwmk4IniycTxHNV/2aUnJhlIiIi82K0yuGrVq3Ce++9h5ycHLi6umLixIl4//33YWtra4zTmwwrhxdDCCArG7j3D5CZLQVGqY+l4TmZTAqYsrKln5XWT+Y7qQCllTSXiXfLERFROauUlcPj4+OxefNmbNy4ETExMRgyZAjGjx+P2NhYfPLJJzh9+jQOHTpkrL5SZaCuz5SWLhW2tLAA8vL+nQAue5JZslY8WX9OJW0HAAc7wMOVQ3JERGS2DAqcQkNDsXHjRvzyyy945pln8Oabb+Kll15CjRo1NG1atmypWYiXqojC9ZnkFgUmgAsgR0jzltTBU76QgqQ8FWBnC/jXlgItIiIiM2VQ4PTKK69g+PDh+OOPP/B///d/xbapW7cuZs2a9VSdo0qkcH0m4N+ClepaTOoJ4Jbyf7NMqnzARimVGWDQREREZs6gOU4ZGRlmP3dJV5zjhKL1mSws/s0oqVTS3CZ10KSwelIhPE8qZlnLmcUsiYiowpXX97dBKQAHBwckJCQU2Z6YmAi5XP7UnaJKRD08l55ZfH0mmUz6WQgAMmmojmUGiIioijJoqK6kJFV2djYUCsVTdYgqkYLDc+qK34CUcbJ6EjCp5zrl5Ert88FFeYmIqMrSK3BauXIlAEAmk+Gbb76Bvb29Zp9KpcJvv/2GRo0aGbeHZBqFh+eyc54MzeVrTwBX5UulBSxk0sK83m5PShCwzAAREVU9egVOn3/+OQAp47RmzRqtYTmFQgE/Pz+sWbPGuD2kiqcensvM+nd4DpCySiohFbdUPNmmElItJxsl4FVL+i8REVEVpVfgFBUVBQDo3LkzQkND4ezsXC6dIhMSQqrTlJsL2FprD89ZK6TMEyBlmvLzpZ/tbVmfiYiIqgWD5jgdO3bM2P2gykAIqQJ46mNpTpPcUso25eRKc5rUpQfyVFKwlJPL+kxERFSt6Bw4TZs2DR999BHs7Owwbdq0UtsuW7bsqTtGFUxdETz1MfA4Q5r0rbCSSgqo8v+tzwRIE8BzclmfiYiIqh2dA6eLFy8iNzdX83NJZJwUbH4KVgS3sgIsLZ+sL5crBU1Ka2mfugQBwOE5IiKqlnQOnAoOz3GorgopXBFcLv93eM5SLg3L5eYBjvaAKg/IyObwHBERVVtG+eZLTU3Fnj17cO3aNWOcjipKeiZw+x7wIAnIygaS06ShOisrKYDKU0llBnJypEAqJ4/Dc0REVK0Z9O334osv4osvvgAAZGZmok2bNnjxxRfRrFkz7Nq1y6gdpHJSUkXwnFwpiFJaS3OcBJ7MccoF7O2A2m4cniMiomrLoMDpt99+Q8eOHQEAu3fvhhACycnJWLlyJRYsWGDUDlI5KFxyQJ09srCQAihVgeE5BzvA3kZaOqWOB4MmIiKq1gwKnFJSUlCzZk0AwMGDBzF48GDY2tqid+/euHnzplE7SOUgKxtIy5CG4SCTgqU8lRRQqSuC5+ZKAZRKJa0752jPauBERFTtGRQ4+fj44NSpU0hPT8fBgwfRrVs3AEBSUhKUSlaOrtTSM4F7/wBp6UBqujSvSVVgsV71XXMqAWRkSfOdXGswaCIiIoKBBTCDg4MxatQo2Nvbw9fXF0FBQQCkIbxmzZoZs39kTOp5TVlZ0nwmuYUUEOWppP/K5awITkREVAqDAqdJkyahbdu2uHv3Lrp27QqLJ3Nk6tatyzlOlVXBeU32ttKCvTm50jCdlaWUbbKwkBbszWTJASIiouLIhBDiaU6gPryqFr5MTU2Fk5MTUlJS4OjoaOruGEa9lEpMnHSnnEIB5OUCqRnSHCZLudQmLx+wsZbuqOPdc0REZMbK6/vb4HTC5s2b0axZM9jY2MDGxgbNmzfH//73P6N1jIwkPVMKmGLipKVUUh4DKWmAkAGOtlIgpcr/d4jORsmgiYiIqAQGDdUtW7YMc+bMweTJk/Hcc89BCIE//vgDEydOxMOHDzF16lRj95MMUdZSKg52gJP9k/IDT+6g83aTgiciIiIqwqChOn9/f8yfPx9jxozR2v7tt98iJCQEUVFRRuugqZntUJ0Q/2aZbJ8EQimPtZdSUSikwAlP7qCzt5NqNVXRYVciIqo+yuv726CMU1xcHAIDA4tsDwwMRFxc3FN3ioxAXatJLpOyS3I5YKeUfi64lEp2jpRpYtkBIiKiMhk0x6l+/fr4/vvvi2zfsWMHGjRo8NSdoqdUuFZTUmrReU1cSoWIiEhvBmWc5s+fj2HDhuG3337Dc889B5lMht9//x1HjhwpNqB6WtHR0fjoo49w9OhRxMfHw8vLCy+99BJmzZoFhUJR4nFCCMyfPx9r165FUlIS2rVrh9WrV6NJkyZG72OlUVKtpsLzmrJzpaCpjierghMREenIoIzT4MGDcebMGbi6umLPnj0IDQ2Fq6srzp49i4EDBxq7j7h27Rry8/Px9ddf4++//8bnn3+ONWvW4IMPPij1uCVLlmDZsmX44osvEB4eDg8PD3Tt2hVpaWlG72OlULhWk/qOOZns3zXoMrKeZJu4lAoREZG+nrqOk6l8+umn+Oqrr3D79u1i9wsh4OXlheDgYMyYMQMAkJ2dDXd3dyxevBivv/56scdlZ2cjOztb8zw1NRU+Pj7mMTk8MwuIvCfNa7KyksoLpLFWExERVT+VanI4AOTn5+PWrVtISEhAvnqJjif++9//PnXHylJwoeHiREVFIT4+XrOOHgBYW1ujU6dOOHnyZImB06JFizB//nyj97fcpWcCsQnSvCYLmVTx28pSCpByc6XK4EL8W6vJqxaDJiIiIj0ZFDidPn0aI0eOxJ07d1A4YSWTyaBSqYzSuZJERkZi1apVWLp0aYlt4uPjAQDu7u5a293d3XHnzp0Sj5s5cyamTZumea7OOFVqZc1rsrcF7GWs1URERPSUDJrjNHHiRLRp0waXL1/Go0ePkJSUpHk8evRI5/OEhIRAJpOV+jh37pzWMbGxsejRoweGDh2KV199tcxrFF4KRghR6vIw1tbWcHR01HpUarrMa8rMBizkUrbJwV7KQhEREZHeDMo43bx5Ezt37kT9+vWf6uKTJ0/G8OHDS23j5+en+Tk2NhadO3dGhw4dsHbt2lKP8/DwACBlnjw9PTXbExISimShzFrBek35Qip2qcqXhuYs5VL2KTsHeCyTAibWaiIiIjKYQYFTu3btcOvWracOnFxdXeHq6qpT2/v376Nz585o3bo1Nm7cCAuL0pNl/v7+8PDwwOHDh9GqVSsAQE5ODo4fP47Fixc/Vb8rDc5rIiIiqlAGBU5vvfUW3nnnHcTHx6NZs2awsrLS2t+8eXOjdE4tNjYWQUFBqFOnDj777DM8ePBAs0+dWQKARo0aYdGiRRg4cCBkMhmCg4OxcOFCNGjQAA0aNMDChQtha2uLkSNHGrV/JsF5TURERBXOoMBp8ODBAIBx48ZptslkMs38IWNPDj906BBu3bqFW7duwdvbW2tfwcnp169fR0pKiub59OnTkZmZiUmTJmkKYB46dAgODg5G7V+FKzyvKV9IAZOVpfTIzZPmNTnaAfm5nNdERERkJAbVcSrtrjQA8PX1NbhDlU2lXOQ3KxuIui8FSZZyKYBKZb0mIiIitUpVx6kqBUbmRpUncPl0Ftyyc6GylsOrDmBhZSWtQZeexXlNRERE5UjnwGnv3r3o2bMnrKyssHfv3lLb9uvX76k7RkX9sjsT4T8noZF3OpwaZiE9MwfRf1vBs74N6jW0ApyelB/gvCYiIqJyofNQnYWFBeLj4+Hm5lbqHW0VUQCzIlWWobrPPsyEXXICXJ1ycTfBCi3rZ8K7Vi5yVQIZWZao6WeHeg0tpWxTRhZgbwfU8WDpASIiqpZMPlRXcFmVwkusUPn64QeBxOtJ8A3Ixd/RSgAyXLmjhINtPmyVKjja5eH+zUz4+9vCIjdHWqeO9ZqIiIiMzqDK4VRxVCpg9ns5aOSbhbsJCgBSMPQwxQpnrtoiLtEKeSoZfGpl4+H9HCnTxMngRERE5cLgRX7Pnj2LsLCwYhf5XbZs2VN3jCQjRwIKCxVsFPlIz9KOcx+mWOGPS5ZwdshDgE8O/ol1xctBTsw0ERERlRODAqeFCxdi9uzZaNiwIdzd3bXWfittHTjSzw8/AN9/D/h6yJGZYwE7ZT5SM+RabQRkyFNZ4J8kSzy0s2HQREREVI4MCpxWrFiBDRs2YOzYsUbuDqmpVMCYMdLPMf8ocO2OEs8GZGjmOP1LwMctB+dv2KHrOIUpukpERFRtGDTHycLCAs8995yx+0IFfPSRtJoKAAghw57fnfEwxQpN/LLgaKuC3ELA0VaFJn5ZeJBihSMRNRAUxGwTERFReTIocJo6dSpWr15t7L7QEyoVsGCB9rZrMTZYucsNF27YwsUpDwE+2XBxysP5G3ZYtcsNM+bbQC4v/nxERERkHAYtuZKfn4/evXvjxo0beOaZZ4os8hsaGmq0DpqaKeo4PfcccPJk8ftkMoE67jlwsFEhLVOOmH8UGDpUhh07KqRrREREZsHkdZwKeuutt3Ds2DF07twZLi4unBBuRDt2lBw0AdKw3Z34fxfsVSqBrVsroGNERERkWOC0efNm7Nq1C7179zZ2f6o1lQoYPVq/Y/73P3CIjoiIqIIYNMepZs2aqFevnrH7Uu199BGQm6t7+xdfBIYMKb/+EBERkTaDAqeQkBDMmzcPGRkZxu5PtaVSAQsX6t7eyopDdERERBXNoKG6lStXIjIyEu7u7vDz8ysyOfzChQtG6Vx1om+26bvvOERHRERU0QwKnAYMGGDkblRv+mabAgOlYToiIiKqWAYFTvPmzTN2P6q1kSN1zzbJ5cBvv5Vvf4iIiKh4Bs1xIuNRr0enq9mzOURHRERkKgYVwLSwsCi1dpNKpXqqTlUm5VkAU6UC7O3/XVqlLAoFkJHBwImIiKgslaoA5u7du7We5+bm4uLFi/j2228xf/58o3SsOii4Hp0uZs5k0ERERGRKBmWcSrJ161bs2LEDP/74o7FOaXLlFbGqVICDA5CZqVt7ZpuIiIh0V17f30ad49SuXTv8+uuvxjxllRUWpnvQBDDbREREVBkYLXDKzMzEqlWrULt2bWOdskpbs0b3tjY2wJw55dcXIiIi0o1Bc5ycnZ21JocLIZCWlgZbW1t89913RutcVaVSAfqMZm7ezGwTERFRZWBQ4LR8+XKt5xYWFqhVqxaaNGmCefPmoV+/fsboW5WlT5VwrkdHRERUeRh1cviff/6JZ599luUISqFSSUNvugROVlbSPChmm4iIiPRjFpPDqWz6ZJv692fQREREVJkwcKpAKhWwZInu7SdOLL++EBERkf4YOFUgfUoQ2NgAQUHl2RsiIiLSl16TwwcNGlTq/uTk5KfpS5WnTwmC6dM5TEdERFTZ6BU4OTk5lbl/zJgxT9WhqkqfEgQKBes2ERERVUZ6BU4bN24sr35UefpMCmeVcCIiosrJqOUIqiJj3M6oTwkCrklHRET09FiOwIzpk23q149BExERUWVlFoFTdHQ0xo8fD39/f9jY2KBevXqYN28ecnJySj1u7NixkMlkWo/27dtXUK8lLEFARERUdRi05EpFu3btGvLz8/H111+jfv36uHz5MiZMmID09HR89tlnpR7bo0cPrblZCoWivLurhSUIiIiIqg6zCJx69OiBHj16aJ7XrVsX169fx1dffVVm4GRtbQ0PDw+dr5WdnY3s7GzN89TUVP07XABLEBAREVUdZjFUV5yUlBTUrFmzzHZhYWFwc3NDQEAAJkyYgISEhFLbL1q0CE5OTpqHj4+PwX1kCQIiIqKqxSwDp8jISKxatQoTy5gQ1LNnT2zZsgVHjx7F0qVLER4ejueff14ro1TYzJkzkZKSonncvXvX4H6yBAEREVHVYtJyBCEhIZg/f36pbcLDw9GmTRvN89jYWHTq1AmdOnXCN998o9f14uLi4Ovri+3bt5dZBV3N0NsZVSrA2RlISyu7LUsQEBERGVd5lSMw6RynyZMnY/jw4aW28fPz0/wcGxuLzp07o0OHDli7dq3e1/P09ISvry9u3ryp97H6OnFCt6AJYAkCIiIic2HSwMnV1RWurq46tb1//z46d+6M1q1bY+PGjbCw0H+UMTExEXfv3oWnp6fex+pr927d27IEARERkXkwizlOsbGxCAoKgo+PDz777DM8ePAA8fHxiI+P12rXqFEj7H4SsTx+/BjvvvsuTp06hejoaISFhaFv375wdXXFwIEDy7W/KhWg6yiirS1LEBAREZkLsyhHcOjQIdy6dQu3bt2Ct7e31r6CU7SuX7+OlJQUAIBcLselS5ewefNmJCcnw9PTE507d8aOHTvg4OBQrv0NC5PmLOliwgQO0xEREZkLrlVXBkMmlw0dCuzcqdv5jx1jxomIiMjYuFadmVCpgJ9+0q2toyPQsWP59oeIiIiMh4GTkYWFAVlZurWdOpXDdEREROaEgZOR6brECiuFExERmR+zmBxe2alUUqbp6FEgNFS3Y1i7iYiIyPwwcHpKoaHAa68BiYn6HcfaTUREROaHgdNTCA0FBg/W/zgbG95JR0REZI44x8lAKhUwZYphx/buzWE6IiIic8TAyUAnTgD37xt2LIfpiIiIzBMDJwPFxRl2HIfpiIiIzBcDJwMZuk7w9OkcpiMiIjJXDJwM1LEjoO+Sd6zdREREZN4YOD0FKyv92s+cyWwTERGROWPgZKATJ4BHj3Rvb2/PbBMREZG5Y+BkoB9/1K/9t98y20RERGTuGDgZQKUCvvtOt7ZKJbBrFzBoUPn2iYiIiMofK4cb4MQJ4OHDsts5OgIPHkiTwomIiMj8MeNkAF1rOI0bx6CJiIioKmHgZABdazj171++/SAiIqKKxcDJAB07Ai4upbdxcZHaERERUdXBwImIiIhIRwycDHDiBJCYWHqbxESpHREREVUdDJwMoOvkcEMXAiYiIqLKiYGTAXSdHG7oQsBERERUOTFwMkDHjoC3NyCTFb9fJgN8fDg5nIiIqKph4GQAuRwYMQIQouQ2y5dziRUiIqKqhoGTAUJDgc8+K3n/u+9yiRUiIqKqiIGTnlQq4O23S882bd8utSMiIqKqhYGTnk6cAO7dK73N3bssRUBERFQVMXDSE0sREBERVV8MnPTEUgRERETVFwMnPbEUARERUfXFwElPcjmwYoX0c+HgSf2cpQiIiIiqJgZOBhg0SCo5YFHo3bOwYCkCIiKiqoyBkwHUdZwKlxxQqaTtoaGm6RcRERGVLwZOetKljlNwMOs4ERERVUVmEzj169cPderUgVKphKenJ0aPHo3Y2NhSjxFCICQkBF5eXrCxsUFQUBD+/vvvp+pHWXWchGAdJyIioqrKbAKnzp074/vvv8f169exa9cuREZGYsiQIaUes2TJEixbtgxffPEFwsPD4eHhga5duyItLc3gfrCOExERUfUlE6K0QafKa+/evRgwYACys7NhZWVVZL8QAl5eXggODsaMGTMAANnZ2XB3d8fixYvx+uuv63Sd1NRUODk5ISUlBY6OjggLAzp3Lvu4Y8eAoCA9XhAREREZTeHvb2Mxm4xTQY8ePcKWLVsQGBhYbNAEAFFRUYiPj0e3bt0026ytrdGpUyecPHmyxHNnZ2cjNTVV61EQ6zgRERFVX2YVOM2YMQN2dnZwcXFBTEwMfvzxxxLbxsfHAwDc3d21tru7u2v2FWfRokVwcnLSPHx8fLT2s44TERFR9WXSwCkkJAQymazUx7lz5zTt33vvPVy8eBGHDh2CXC7HmDFjUNZIo6xQdCOEKLKtoJkzZyIlJUXzuHv3bpE2gwYBO3cCtWtrb/f2lrazjhMREVHVZGnKi0+ePBnDhw8vtY2fn5/mZ1dXV7i6uiIgIACNGzeGj48PTp8+jQ4dOhQ5zsPDA4CUefIssHBcQkJCkSxUQdbW1rC2ti6z74MGAf37S3fPxcVJa9N17MhMExERUVVm0sBJHQgZQp1pys7OLna/v78/PDw8cPjwYbRq1QoAkJOTg+PHj2Px4sWGdbgAlYpBExERUXVjFnOczp49iy+++AIRERG4c+cOjh07hpEjR6JevXpa2aZGjRph9+7dAKQhuuDgYCxcuBC7d+/G5cuXMXbsWNja2mLkyJFP1Z/QUMDPT7q7buRI6b9+fqwYTkREVNWZNOOkKxsbG4SGhmLevHlIT0+Hp6cnevToge3bt2sNq12/fh0pKSma59OnT0dmZiYmTZqEpKQktGvXDocOHYKDg4PBfQkNBYYMKVo5/P59aTvnOBEREVVdZlvHqaIUrANhZ+cIP7+SK4fLZNIE8agoDtsRERGZEus4VQJcboWIiKh6Y+CkBy63QkREVL0xcNJDgaoGRmlHRERE5oWBkx643AoREVH1xsBJD1xuhYiIqHpj4KQnLrdCRERUfZlFHafKhsutEBERVU8MnAwklwNBQabuBREREVUkDtURERER6YiBExEREZGOGDgRERER6YiBExEREZGOODncQCoV76ojIiKqbhg4GSA0FHj7be0Ff729peKYrONERERUdXGoTk+hocCQIdpBEwDcvy9tDw01Tb+IiIio/DFw0oNKJWWahCi6T70tOFhqR0RERFUPAyc9nDhRNNNUkBDA3btSOyIiIqp6GDjpIS7OuO2IiIjIvDBw0oOnp3HbERERkXlh4KSHjh2lu+dksuL3y2SAj4/UjoiIiKoeBk56kMulkgNA0eBJ/Xz5ctZzIiIiqqoYOOlp0CBg506gdm3t7d7e0nbWcSIiIqq6WADTAIMGAf37s3I4ERFRdcPAyUByORAUZOpeEBERUUXiUB0RERGRjhg4EREREemIgRMRERGRjhg4EREREemIgRMRERGRjnhXnQFUKpYiICIiqo4YOOkpNBR4+23g3r1/t3l7SxXFWfySiIioauNQnR5CQ4EhQ7SDJgC4f1/aHhpqmn4RERFRxWDgpCOVSso0CVF0n3pbcLDUjoiIiKomBk46OnmyaKapICGAu3eluU9ERERUNTFw0lF8vG7t4uLKtx9ERERkOmYTOPXr1w916tSBUqmEp6cnRo8ejdjY2FKPGTt2LGQymdajffv2Bl3fw0O3dp6eBp2eiIiIzIDZBE6dO3fG999/j+vXr2PXrl2IjIzEkCFDyjyuR48eiIuL0zwOHDhg0PUDA6W752Sy4vfLZICPj1SagIiIiKomsylHMHXqVM3Pvr6+eP/99zFgwADk5ubCysqqxOOsra3hoWu6qBRyuVRyYMgQKUgqOElcHUwtX856TkRERFWZ2WScCnr06BG2bNmCwMDAUoMmAAgLC4ObmxsCAgIwYcIEJCQklNo+OzsbqampWg+1QYOAnTuB2rW1j/H2lrazjhMREVHVJhOiuBvsK6cZM2bgiy++QEZGBtq3b4+ffvoJLi4uJbbfsWMH7O3t4evri6ioKMyZMwd5eXk4f/48rK2tiz0mJCQE8+fPL7I9JSUFjo6OAFg5nIiIqLJLTU2Fk5OT1ve3MZg0cCopSCkoPDwcbdq0AQA8fPgQjx49wp07dzB//nw4OTnhp59+gqykiUeFxMXFwdfXF9u3b8egEtJD2dnZyM7O1jxPTU2Fj4+P0d94IiIiKj/lFTiZdI7T5MmTMXz48FLb+Pn5aX52dXWFq6srAgIC0LhxY/j4+OD06dPo0KGDTtfz9PSEr68vbt68WWIba2vrErNRREREVL2ZNHBSB0KGUCfKCmaHypKYmIi7d+/CkzUDiIiIyABmMTn87Nmz+OKLLxAREYE7d+7g2LFjGDlyJOrVq6eVbWrUqBF2794NAHj8+DHeffddnDp1CtHR0QgLC0Pfvn3h6uqKgQMHmuqlEBERkRkzi3IENjY2CA0Nxbx585Ceng5PT0/06NED27dv1xpWu379OlJSUgAAcrkcly5dwubNm5GcnAxPT0907twZO3bsgIODg6leChEREZkxs7qrzhTKa3IZERERlZ/y+v42i6E6IiIiosrALIbqKhvWcSIiIqqeGDjpKTQUePtt4N69f7d5e0vLsbByOBERUdXGoTo9hIZKa9UVDJoA4P59aXtoqGn6RURERBWDgZOOVCop01TcVHr1tuBgqR0RERFVTQycdHTyZNFMU0FCAHfvSnOfiIiIqGpi4KSj+Hjd2sXFlW8/iIiIyHQYOOnIw0O3dlzNhYiIqOpi4KSjwEDp7jmZrPj9Mhng4yOVJiAiIqKqiYGTjuRyqeQAUDR4Uj9fvpz1nIiIiKoyBk56GDQI2LkTqF1be7u3t7SddZyIiIiqNhbA1NOgQUD//qwcTkREVB0xcDKAXA4EBZm6F0RERFTROFRHREREpCMGTkREREQ64lCdnlQqzm8iIiKqrhg46SE0VFqvruDSK97eUpkC3lFHRERU9XGoTkd79wJDhhRdr+7+fWl7aKhp+kVEREQVh4GTjmbMkBbyLUy9LThYGsYjIiKiqouBk45iY0veJwRw964094mIiIiqLgZORhQXZ+oeEBERUXli4GREnp6m7gERERGVJwZOOvLyKrq4r5pMBvj4SKUJiIiIqOpi4KSjxYul/xYOntTPly9nPSciIqKqjoGTjvr1A3buBGrX1t7u7S1tZx0nIiKiqo8FMPUwaBDQvz8rhxMREVVXzDgRERER6YgZJz1wyRUiIqLqjRknHXHJFSIiImLgpCMuuUJEREQMnHTEJVeIiIiIgZMRcckVIiKiqo2BkxFxyRUiIqKqjYGTjrjkChERETFw0hGXXCEiIiKzC5yys7PRsmVLyGQyRERElNpWCIGQkBB4eXnBxsYGQUFB+Pvvvw26LpdcISIiIrMrgDl9+nR4eXnhzz//LLPtkiVLsGzZMmzatAkBAQFYsGABunbtiuvXr8PBwUHva/fvDzg5AWFh0vOgIOnBTBMREVH1YFYZp59//hmHDh3CZ599VmZbIQSWL1+OWbNmYdCgQWjatCm+/fZbZGRkYOvWrXpfe+9ewM8P6NIFWLBAeowdC/z4o/6vg4iIiMyT2WSc/vnnH0yYMAF79uyBra1tme2joqIQHx+Pbt26abZZW1ujU6dOOHnyJF5//fVij8vOzkZ2drbmeUpKCgBg9OjUIm3v3QMGDwb+9z9pKI+IiIgqh9RU6XtbFFe9+imYReAkhMDYsWMxceJEtGnTBtHR0WUeEx8fDwBwd3fX2u7u7o47d+6UeNyiRYswf/78Yvb4lHjM6NFldoeIiIhMIDExEU5OTkY7n0kDp5CQkBKClH+Fh4fj5MmTSE1NxcyZM/W+hqzQbXBCiCLbCpo5cyamTZumeZ6cnAxfX1/ExMQY9Y0n/aWmpsLHxwd3796Fo6OjqbtTrfGzqFz4eVQe/Cwqj5SUFNSpUwc1a9Y06nlNGjhNnjwZw4cPL7WNn58fFixYgNOnT8Pa2lprX5s2bTBq1Ch8++23RY7z8PAAIGWePAtUpkxISCiShSrI2tq6yHUAwMnJiX8ElYSjoyM/i0qCn0Xlws+j8uBnUXlYWBh3OrdJAydXV1e4urqW2W7lypVYsGCB5nlsbCy6d++OHTt2oF27dsUe4+/vDw8PDxw+fBitWrUCAOTk5OD48eNYrC7KRERERKQHs5jjVKdOHa3n9vb2AIB69erB29tbs71Ro0ZYtGgRBg4cCJlMhuDgYCxcuBANGjRAgwYNsHDhQtja2mLkyJEV2n8iIiKqGswicNLV9evXNXfBAVLNp8zMTEyaNAlJSUlo164dDh06pFcNJ2tra8ybN6/Y4TuqWPwsKg9+FpULP4/Kg59F5VFen4VMGPs+PSIiIqIqyqwKYBIRERGZEgMnIiIiIh0xcCIiIiLSEQMnIiIiIh0xcCIiIiLSEQMnAF9++SX8/f2hVCrRunVrnDhxotT2x48fR+vWraFUKlG3bl2sWbOmgnpa9enzWYSGhqJr166oVasWHB0d0aFDB/zyyy8V2NuqTd+/C7U//vgDlpaWaNmyZfl2sBrR97PIzs7GrFmz4OvrC2tra9SrVw8bNmyooN5Wffp+Hlu2bEGLFi1ga2sLT09PvPLKK0hMTKyg3lZdv/32G/r27QsvLy/IZDLs2bOnzGOM8v0tqrnt27cLKysrsW7dOnHlyhXx9ttvCzs7O3Hnzp1i29++fVvY2tqKt99+W1y5ckWsW7dOWFlZiZ07d1Zwz6sefT+Lt99+WyxevFicPXtW3LhxQ8ycOVNYWVmJCxcuVHDPqx59Pwu15ORkUbduXdGtWzfRokWLiulsFWfIZ9GvXz/Rrl07cfjwYREVFSXOnDkj/vjjjwrsddWl7+dx4sQJYWFhIVasWCFu374tTpw4IZo0aSIGDBhQwT2veg4cOCBmzZoldu3aJQCI3bt3l9reWN/f1T5watu2rZg4caLWtkaNGon333+/2PbTp08XjRo10tr2+uuvi/bt25dbH6sLfT+L4jzzzDNi/vz5xu5atWPoZzFs2DAxe/ZsMW/ePAZORqLvZ/Hzzz8LJycnkZiYWBHdq3b0/Tw+/fRTUbduXa1tK1euFN7e3uXWx+pIl8DJWN/f1XqoLicnB+fPn0e3bt20tnfr1g0nT54s9phTp04Vad+9e3ecO3cOubm55dbXqs6Qz6Kw/Px8pKWlGX0l7OrG0M9i48aNiIyMxLx588q7i9WGIZ/F3r170aZNGyxZsgS1a9dGQEAA3n33XWRmZlZEl6s0Qz6PwMBA3Lt3DwcOHIAQAv/88w927tyJ3r17V0SXqQBjfX9XqSVX9PXw4UOoVCq4u7trbXd3d0d8fHyxx8THxxfbPi8vDw8fPoSnp2e59bcqM+SzKGzp0qVIT0/Hiy++WB5drDYM+Sxu3ryJ999/HydOnIClZbX+Z8WoDPksbt++jd9//x1KpRK7d+/Gw4cPMWnSJDx69IjznJ6SIZ9HYGAgtmzZgmHDhiErKwt5eXno168fVq1aVRFdpgKM9f1drTNOajKZTOu5EKLItrLaF7ed9KfvZ6G2bds2hISEYMeOHXBzcyuv7lUrun4WKpUKI0eOxPz58xEQEFBR3atW9Pm7yM/Ph0wmw5YtW9C2bVv06tULy5Ytw6ZNm5h1MhJ9Po8rV65gypQpmDt3Ls6fP4+DBw8iKioKEydOrIiuUiHG+P6u1v9r6OrqCrlcXuT/FBISEopEpWoeHh7Ftre0tISLi0u59bWqM+SzUNuxYwfGjx+PH374AV26dCnPblYL+n4WaWlpOHfuHC5evIjJkycDkL68hRCwtLTEoUOH8Pzzz1dI36saQ/4uPD09Ubt2bTg5OWm2NW7cGEII3Lt3Dw0aNCjXPldlhnweixYtwnPPPYf33nsPANC8eXPY2dmhY8eOWLBgAUcpKpCxvr+rdcZJoVCgdevWOHz4sNb2w4cPIzAwsNhjOnToUKT9oUOH0KZNG1hZWZVbX6s6Qz4LQMo0jR07Flu3buWcASPR97NwdHTEpUuXEBERoXlMnDgRDRs2REREBNq1a1dRXa9yDPm7eO655xAbG4vHjx9rtt24cQMWFhbw9vYu1/5WdYZ8HhkZGbCw0P6qlcvlAP7NdlDFMNr3t15Tyasg9a2l69evF1euXBHBwcHCzs5OREdHCyGEeP/998Xo0aM17dW3M06dOlVcuXJFrF+/nuUIjETfz2Lr1q3C0tJSrF69WsTFxWkeycnJpnoJVYa+n0VhvKvOePT9LNLS0oS3t7cYMmSI+Pvvv8Xx48dFgwYNxKuvvmqql1Cl6Pt5bNy4UVhaWoovv/xSREZGit9//120adNGtG3b1lQvocpIS0sTFy9eFBcvXhQAxLJly8TFixc1pSHK6/u72gdOQgixevVq4evrKxQKhXj22WfF8ePHNftefvll0alTJ632YWFholWrVkKhUAg/Pz/x1VdfVXCPqy59PotOnToJAEUeL7/8csV3vArS9++iIAZOxqXvZ3H16lXRpUsXYWNjI7y9vcW0adNERkZGBfe66tL381i5cqV45plnhI2NjfD09BSjRo0S9+7dq+BeVz3Hjh0r9TugvL6/ZUIwV0hERESki2o9x4mIiIhIHwyciIiIiHTEwImIiIhIRwyciIiIiHTEwImIiIhIRwyciIiIiHTEwImIiIhIRwyciIiIiHTEwImIiIhIRwyciIiKkZiYCDc3N0RHR5fbNYYMGYJly5aV2/mJyPgYOBGRSYwdOxYymQwTJ04ssm/SpEmQyWQYO3ZsxXfsiUWLFqFv377w8/PTbPvvf/8LmUyGjz76SKutEALt2rWDTCbD3Llzdb7G3Llz8fHHHyM1NdVY3SaicsbAiYhMxsfHB9u3b0dmZqZmW1ZWFrZt24Y6deqYrF+ZmZlYv349Xn31Vc02IQQiIiLg6+uLS5cuabX/9ttvERsbCwB49tlndb5O8+bN4efnhy1bthin40RU7hg4EZHJPPvss6hTpw5CQ0M120JDQ+Hj44NWrVppth08eBD/+c9/UKNGDbi4uKBPnz6IjIzUOtfOnTvRrFkz2NjYwMXFBV26dEF6enqZ+4rz888/w9LSEh06dNBsu3nzJtLS0jB27FitwCktLQ0zZ87UZMdat26t13vQr18/bNu2Ta9jiMh0GDgRkUm98sor2Lhxo+b5hg0bMG7cOK026enpmDZtGsLDw3HkyBFYWFhg4MCByM/PBwDExcVhxIgRGDduHK5evYqwsDAMGjQIQohS95Xkt99+Q5s2bbS2nT9/HkqlEiNGjMDNmzeRnZ0NAPjoo4/QsmVLeHp6wtXVFT4+Pnq9/rZt2+Ls2bOa8xFR5WZp6g4QUfU2evRozJw5E9HR0ZDJZPjjjz+wfft2hIWFadoMHjxY65j169fDzc0NV65cQdOmTREXF4e8vDwMGjQIvr6+AIBmzZoBAG7cuFHivpJER0fDy8tLa9uFCxfQvHlzBAQEwM7ODlevXoWdnR2+/PJLnDt3Dp999pne2SYAqF27NrKzsxEfH6/pHxFVXsw4EZFJubq6onfv3vj222+xceNG9O7dG66urlptIiMjMXLkSNStWxeOjo7w9/cHAMTExAAAWrRogRdeeAHNmjXD0KFDsW7dOiQlJZW5rySZmZlQKpVa286fP4/WrVtDJpOhefPmuHz5MqZOnYrXXnsNjRo1wvnz5/Wa36RmY2MDAMjIyND7WCKqeAyciMjkxo0bh02bNuHbb78tMkwHAH379kViYiLWrVuHM2fO4MyZMwCAnJwcAIBcLsfhw4fx888/45lnnsGqVavQsGFDREVFlbqvJK6urkWCq4sXL2oCoxYtWmDFihU4e/Ys5s2bh5ycHPz9999agVNGRgbee+89BAYGIjAwEBMmTEBiYmKRaz169AgAUKtWLT3fNSIyBQZORGRyPXr0QE5ODnJyctC9e3etfYmJibh69Spmz56NF154AY0bNy42YySTyfDcc89h/vz5uHjxIhQKBXbv3l3mvuK0atUKV65c0Ty/ffs2kpOTNUNxLVu2xLlz5/Dxxx/DyckJly5dQm5urtZQ3eTJk9GiRQucPHkSJ0+exPDhwzFmzJgic6suX74Mb2/vIlk2IqqcOMeJiExOLpfj6tWrmp8LcnZ2houLC9auXQtPT0/ExMTg/fff12pz5swZHDlyBN26dYObmxvOnDmDBw8eoHHjxqXuK0n37t0xc+ZMJCUlwdnZGefPn4dCoUDTpk0BAC+//DIGDBgAFxcXANL8J2dnZ80QYmZmJpKSkvDSSy8hJCQEABASEoIff/wRt27dQoMGDTTXOnHiBLp16/Z0byARVRgGTkRUKTg6Oha73cLCAtu3b8eUKVPQtGlTNGzYECtXrkRQUJDWsb/99huWL1+O1NRU+Pr6YunSpejZsyeuXr1a4r6SNGvWDG3atMH333+P119/HRcuXEDTpk1hZWUFALCystLKEF24cEGrfELBrNLkyZNLvE5WVhZ2796NX375pcz3h4gqB5ko7Z5cIqJq6sCBA3j33Xdx+fJlWFjoP6th7Nix6Nq1K0aNGgUAOHLkCD777DMcOHAAMpkMALB69Wr8+OOPOHTokFH7TkTlhxknIqJi9OrVCzdv3sT9+/f1rs0EAF9++SVmz56NlStXQiaToXHjxvjuu+80QRMgZa5WrVplzG4TUTljxomIiIhIR7yrjoiIiEhHDJyIiIiIdMTAiYiIiEhHDJyIiIiIdMTAiYiIiEhHDJyIiIiIdMTAiYiIiEhHDJyIiIiIdMTAiYiIiEhHDJyIiIiIdPT/8yf9CE1FE68AAAAASUVORK5CYII=", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# starting with luminosity\n", - "\n", - "best_kernel_L = Matern(length_scale=best_length_scale, nu=best_nu)\n", - "gp_best_L = GaussianProcessRegressor(kernel=best_kernel_L, optimizer=None, normalize_y=True)\n", - "gp_best_L.fit(X, y)\n", - "\n", - "mass_vals = np.linspace(np.max(phil_mass), np.min(pisa_mass), 20).reshape(-1, 1)\n", - "L_pred = gp_best_L.predict(mass_vals)\n", - "\n", - "plt.figure()\n", - "plt.scatter(X, combined_L, color='blue', label='actual values')\n", - "plt.scatter(mass_vals, L_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", - "plt.title('Creating Points for Luminosity Mass Gap')\n", - "plt.xlabel('Mass ($M_{\\odot}$)')\n", - "plt.ylabel('Luminosity')\n", - "plt.xlim(0, 1)\n", - "plt.ylim(-4,0)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "5aee3424-7efa-4bed-8030-4e70eb1f47c2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# moving to effective temperature\n", - "\n", - "best_kernel_teff = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", - "gp_best_teff = GaussianProcessRegressor(kernel=best_kernel_teff, optimizer=None, normalize_y=True)\n", - "gp_best_teff.fit(X, y_teff)\n", - "\n", - "mass_vals = np.linspace(np.max(phil_mass), np.min(pisa_mass), 20).reshape(-1, 1)\n", - "teff_pred = gp_best_teff.predict(mass_vals)\n", - "\n", - "plt.figure()\n", - "plt.scatter(X, combined_Teff, color='blue', label='actual values')\n", - "plt.scatter(mass_vals, teff_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", - "plt.title('Creating Points for Teff Mass Gap')\n", - "plt.xlabel('Mass ($M_{\\odot}$)')\n", - "plt.ylabel('Teff')\n", - "plt.xlim(0, 1)\n", - "plt.ylim(3.25,3.75)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "53238900-30e4-4015-b033-d81dd763d773", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# moving to effective temperature\n", - "\n", - "best_kernel_logg = Matern(length_scale=best_length_scale_logg, nu=best_nu_logg)\n", - "gp_best_logg = GaussianProcessRegressor(kernel=best_kernel_logg, optimizer=None, normalize_y=True)\n", - "gp_best_logg.fit(X, y_logg)\n", - "\n", - "mass_vals = np.linspace(np.max(phil_mass), np.min(pisa_mass), 20).reshape(-1, 1)\n", - "logg_pred = gp_best_logg.predict(mass_vals)\n", - "\n", - "plt.figure()\n", - "plt.scatter(X, combined_logg, color='blue', label='actual values')\n", - "plt.scatter(mass_vals, logg_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", - "plt.title('Creating Points for logg Mass Gap')\n", - "plt.xlabel('Mass ($M_{\\odot}$)')\n", - "plt.ylabel('logg')\n", - "plt.xlim(0, 1)\n", - "plt.ylim(4.0,4.5)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "3da35d6f-d88c-4582-be4f-7b65f069e784", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# creating graphs of mass vs. luminosity, Teff, and gravity\n", - "fig, axs = plt.subplots(3, 1, figsize=(6, 10))\n", - "\n", - "# plotting generated data for luminosity\n", - "axs[0].scatter(combined_masses, combined_L, color='blue', label='actual values')\n", - "axs[0].scatter(mass_vals, L_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", - "axs[0].set_title('Creating Points for Luminosity Mass Gap')\n", - "axs[0].set_xlabel('Mass ($M_{\\odot}$)')\n", - "axs[0].set_ylabel('Luminosity')\n", - "axs[0].set_xlim(0, 1)\n", - "#axs[0].set_ylim(-4,0)\n", - "axs[0].legend()\n", - "\n", - "# mass vs. Teff\n", - "axs[1].scatter(combined_masses, combined_Teff, color='blue', label='actual values')\n", - "axs[1].scatter(mass_vals, teff_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", - "axs[1].set_title('Creating Points for Teff Mass Gap')\n", - "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", - "axs[1].set_ylabel('Teff')\n", - "axs[1].set_xlim(0, 1)\n", - "#axs[1].set_ylim(3.25,3.75)\n", - "axs[1].legend()\n", - "\n", - "# mass vs. logg (problem child)\n", - "axs[2].scatter(combined_masses, combined_logg, color='blue', label='actual values')\n", - "axs[2].scatter(mass_vals, logg_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", - "axs[2].set_title('Creating Points for logg Mass Gap')\n", - "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", - "axs[2].set_ylabel('logg')\n", - "axs[2].set_xlim(0, 1)\n", - "#axs[2].set_ylim(4.0,4.5)\n", - "axs[2].legend()\n", - "\n", - "fig.tight_layout()\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d3d88985-6d1f-4aa2-bd88-5f69b73897ea", - "metadata": {}, - "source": [ - "### Testing Fit Parameters for Another Cluster Age" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "c8631679-e803-4d74-a549-68f41ba1196c", - "metadata": {}, - "outputs": [], - "source": [ - "# importing compatible age fits files \n", - "phillips2 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/iso/z00/iso_8.051282050278353.fits'\n", - "pisa2 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Pisa2011/iso/z015/iso_8.00.fits'\n", - "\n", - "# loading tables and extracting data\n", - "phil2_tbl = Table.read(phillips2, format='fits')\n", - "pisa2_tbl = Table.read(pisa2, format='fits')\n", - "\n", - "phil2_mass = phil2_tbl['Mass']\n", - "pisa2_mass = pisa2_tbl['col3']\n", - "combined_masses2 = np.concatenate([phil2_mass, pisa2_mass])\n", - "\n", - "phil2_teff = phil2_tbl['Teff']\n", - "pisa2_teff = pisa2_tbl['col2']\n", - "combined_teff2 = np.concatenate([phil2_teff, pisa2_teff])\n", - "\n", - "phil2_L = phil2_tbl['Luminosity']\n", - "pisa2_L = pisa2_tbl['col1']\n", - "combined_L2 = np.concatenate([phil2_L, pisa2_L])\n", - "\n", - "phil2_logg = phil2_tbl['Gravity']\n", - "pisa2_logg = pisa2_tbl['col4']\n", - "combined_logg2 = np.concatenate([phil2_logg, pisa2_logg])" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "a95f9710-bc1b-40ae-9b4b-73dec1e9e6a6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# creating graphs of mass vs. luminosity, Teff, and gravity\n", - "fig, axs = plt.subplots(3, 1, figsize=(6, 10))\n", - "\n", - "\n", - "# generated data for mass vs. luminosity\n", - "best_kernel_L2 = Matern(length_scale=best_length_scale, nu=best_nu)\n", - "gp_best_L2 = GaussianProcessRegressor(kernel=best_kernel_L2, optimizer=None, normalize_y=True)\n", - "gp_best_L2.fit(combined_masses2.reshape(-1, 1), combined_L2)\n", - "\n", - "mass_vals2 = np.linspace(np.max(phil2_mass), np.min(pisa2_mass), 20).reshape(-1, 1)\n", - "L_pred2 = gp_best_L2.predict(mass_vals2)\n", - "\n", - "# plotting generated data for luminosity\n", - "axs[0].scatter(combined_masses2, combined_L2, color='blue', label='actual values')\n", - "axs[0].scatter(mass_vals2, L_pred2, color='pink', alpha=0.5, label='generated mass gap values')\n", - "axs[0].set_title('Creating Points for Luminosity Mass Gap')\n", - "axs[0].set_xlabel('Mass ($M_{\\odot}$)')\n", - "axs[0].set_ylabel('Luminosity')\n", - "axs[0].set_xlim(0, 1)\n", - "#axs[0].set_ylim(-4,0)\n", - "axs[0].legend()\n", - "\n", - "# generated data for mass vs. teff\n", - "best_kernel_teff2 = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", - "gp_best_teff2 = GaussianProcessRegressor(kernel=best_kernel_teff2, optimizer=None, normalize_y=True)\n", - "gp_best_teff2.fit(combined_masses2.reshape(-1, 1), combined_teff2)\n", - "\n", - "teff_pred2 = gp_best_teff2.predict(mass_vals2)\n", - "\n", - "# mass vs. Teff\n", - "axs[1].scatter(combined_masses2, combined_teff2, color='blue', label='actual values')\n", - "axs[1].scatter(mass_vals2, teff_pred2, color='pink', alpha=0.5, label='generated mass gap values')\n", - "axs[1].set_title('Creating Points for Teff Mass Gap')\n", - "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", - "axs[1].set_ylabel('Teff')\n", - "axs[1].set_xlim(0, 1)\n", - "#axs[1].set_ylim(3.25,3.75)\n", - "axs[1].legend()\n", - "\n", - "# generated data for mass vs. logg\n", - "best_kernel_logg2 = Matern(length_scale=best_length_scale_logg, nu=best_nu_logg)\n", - "gp_best_logg2 = GaussianProcessRegressor(kernel=best_kernel_logg2, optimizer=None, normalize_y=True)\n", - "gp_best_logg2.fit(combined_masses2.reshape(-1, 1), combined_logg2)\n", - "\n", - "logg_pred2 = gp_best_logg2.predict(mass_vals2)\n", - "\n", - "# mass vs. logg (problem child)\n", - "axs[2].scatter(combined_masses2, combined_logg2, color='blue', label='actual values')\n", - "axs[2].scatter(mass_vals2, logg_pred2, color='pink', alpha=0.5, label='generated mass gap values')\n", - "axs[2].set_title('Creating Points for logg Mass Gap')\n", - "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", - "axs[2].set_ylabel('logg')\n", - "axs[2].set_xlim(0, 1)\n", - "#axs[2].set_ylim(4.0,4.5)\n", - "axs[2].legend()\n", - "\n", - "fig.tight_layout()\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "95d0dfdc-6f4d-49be-89c7-cc8337b466c3", - "metadata": {}, - "source": [ - "### Now for logAge = 6.00!" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "2a136d24-1b35-4164-882f-a78d70c3583a", - "metadata": {}, - "outputs": [], - "source": [ - "# importing compatible age fits files \n", - "phillips3 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/iso/z00/iso_6.0.fits'\n", - "pisa3 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Pisa2011/iso/z015/iso_6.00.fits'\n", - "\n", - "# loading tables and extracting data\n", - "phil3_tbl = Table.read(phillips3, format='fits')\n", - "pisa3_tbl = Table.read(pisa3, format='fits')\n", - "\n", - "phil3_mass = phil3_tbl['Mass']\n", - "pisa3_mass = pisa3_tbl['col3']\n", - "combined_masses3 = np.concatenate([phil3_mass, pisa3_mass])\n", - "\n", - "phil3_teff = phil3_tbl['Teff']\n", - "pisa3_teff = pisa3_tbl['col2']\n", - "combined_teff3 = np.concatenate([phil3_teff, pisa3_teff])\n", - "\n", - "phil3_L = phil3_tbl['Luminosity']\n", - "pisa3_L = pisa3_tbl['col1']\n", - "combined_L3 = np.concatenate([phil3_L, pisa3_L])\n", - "\n", - "phil3_logg = phil3_tbl['Gravity']\n", - "pisa3_logg = pisa3_tbl['col4']\n", - "combined_logg3 = np.concatenate([phil3_logg, pisa3_logg])" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "e6040f03-c6e6-4b09-90d5-8d89f593822d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# creating graphs of mass vs. luminosity, Teff, and gravity\n", - "fig, axs = plt.subplots(3, 1, figsize=(6, 10))\n", - "\n", - "\n", - "# generated data for mass vs. luminosity\n", - "best_kernel_L3 = Matern(length_scale=best_length_scale, nu=best_nu)\n", - "gp_best_L3 = GaussianProcessRegressor(kernel=best_kernel_L3, optimizer=None, normalize_y=True)\n", - "gp_best_L3.fit(combined_masses3.reshape(-1, 1), combined_L3)\n", - "\n", - "mass_vals3 = np.linspace(np.max(phil3_mass), np.min(pisa3_mass), 20).reshape(-1, 1)\n", - "L_pred3 = gp_best_L3.predict(mass_vals3)\n", - "\n", - "# plotting generated data for luminosity\n", - "axs[0].scatter(combined_masses3, combined_L3, color='blue', label='actual values')\n", - "axs[0].scatter(mass_vals3, L_pred3, color='pink', alpha=0.5, label='generated mass gap values')\n", - "axs[0].set_title('Creating Points for Luminosity Mass Gap')\n", - "axs[0].set_xlabel('Mass ($M_{\\odot}$)')\n", - "axs[0].set_ylabel('Luminosity')\n", - "axs[0].set_xlim(0, 1)\n", - "#axs[0].set_ylim(-4,0)\n", - "axs[0].legend()\n", - "\n", - "# generated data for mass vs. teff\n", - "best_kernel_teff3 = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", - "gp_best_teff3 = GaussianProcessRegressor(kernel=best_kernel_teff3, optimizer=None, normalize_y=True)\n", - "gp_best_teff3.fit(combined_masses3.reshape(-1, 1), combined_teff3)\n", - "\n", - "teff_pred3 = gp_best_teff3.predict(mass_vals3)\n", - "\n", - "# mass vs. Teff\n", - "axs[1].scatter(combined_masses3, combined_teff3, color='blue', label='actual values')\n", - "axs[1].scatter(mass_vals3, teff_pred3, color='pink', alpha=0.5, label='generated mass gap values')\n", - "axs[1].set_title('Creating Points for Teff Mass Gap')\n", - "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", - "axs[1].set_ylabel('Teff')\n", - "axs[1].set_xlim(0, 1)\n", - "#axs[1].set_ylim(3.25,3.75)\n", - "axs[1].legend()\n", - "\n", - "# generated data for mass vs. logg\n", - "best_kernel_logg3 = Matern(length_scale=best_length_scale_logg, nu=best_nu_logg)\n", - "gp_best_logg3 = GaussianProcessRegressor(kernel=best_kernel_logg3, optimizer=None, normalize_y=True)\n", - "gp_best_logg3.fit(combined_masses3.reshape(-1, 1), combined_logg3)\n", - "\n", - "logg_pred3 = gp_best_logg3.predict(mass_vals3)\n", - "\n", - "# mass vs. logg (problem child)\n", - "axs[2].scatter(combined_masses3, combined_logg3, color='blue', label='actual values')\n", - "axs[2].scatter(mass_vals3, logg_pred3, color='pink', alpha=0.5, label='generated mass gap values')\n", - "axs[2].set_title('Creating Points for logg Mass Gap')\n", - "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", - "axs[2].set_ylabel('logg')\n", - "axs[2].set_xlim(0, 1)\n", - "#axs[2].set_ylim(4.0,4.5)\n", - "axs[2].legend()\n", - "\n", - "fig.tight_layout()\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "01fbb0b7-0d7c-4e04-b458-5635d4c0fcb5", - "metadata": {}, - "source": [ - "### Creating a Function to Apply Model to All Pisa/Phillips Files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "accb8141-1888-49cf-84be-161bc3dbc197", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/Quick_Start_Make_Cluster_w_BDs.ipynb b/docs/Quick_Start_Make_Cluster_w_BDs.ipynb new file mode 100644 index 00000000..dea97223 --- /dev/null +++ b/docs/Quick_Start_Make_Cluster_w_BDs.ipynb @@ -0,0 +1,997 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SPISEA Quick Start: Making A Cluster with Brown Dwarfs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a quick start guide to making a synthetic cluster using the SPISEA package, with the recent addition of brown dwarf capabilities. The cluster is constructed using a user-specified isochrone and initial mass function (IMF). Detailed documentation is provided in the ReadtheDocs page (https://spisea.readthedocs.io/en/latest/index.html).\n", + "\n", + "Before starting this tutorial, it is assumed that SPISEA has been installed and the user's python path has been altered to include the SPISEA top-level directory" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary packages. \n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.table import vstack\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step 1: Make a SPISEA isochrone object" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cluster is made from a theoretical isochrone at a given age, extinction, and distance from Earth. These parameters MUST be specified by the user. Other inputs (e.g. stellar evolution/atmosphere models, extinction law, and photometric filters used) are optional keywords. See documentation for all keywords and their default values.\n", + "\n", + "Important Note: The IsochronePhot class saves its output as a FITS table, which it will read on subsequent calls for the same isochrone rather than regenerating it from scratch. We highly recommend reading the \"Tips and Tricks: The IsochronePhot Object\" section of the Isochrone object documentation for details on how this process works.\n", + "\n", + "Here, we create a 5 Myr cluster isochrone at an extinction of 0.8 mags and distance of 4000 pc from Earth. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Define isochrone parameters\n", + "logAge = np.log10(5*10**6.) # Age in log(years)\n", + "AKs = 0.8 # extinction in mags\n", + "dist = 4000 # distance in parsec\n", + "metallicity = 0 # Metallicity in [M/H]\n", + "\n", + "# Define brown dwarf supported evolution/atmosphere models and extinction law\n", + "evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() \n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "red_law = reddening.RedLawHosek18b()\n", + "\n", + "# Also specify filters for synthetic photometry (optional). Here we use \n", + "# the HST WFC3-IR F127M, F139M, and F153M filters\n", + "filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f139m', 'wfc3,ir,f153m']\n", + "\n", + "# Specify the directory we want the output isochrone\n", + "# table saved in. If the directory does not already exist,\n", + "# SPISEA will create it.\n", + "iso_dir = 'isochrones/'\n", + "\n", + "# Make IsochronePhot object. Note that this will take a minute or two, \n", + "# unless the isochrone has been generated previously.\n", + "#\n", + "# Note that this is not show all of the user options \n", + "# for IsochronePhot. See docs for complete list of options.\n", + "my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n", + " evo_model=evo_model, atm_func=atm_func,\n", + " red_law=red_law, filters=filt_list,\n", + " iso_dir=iso_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once calculated, the isochrone will be written as a fits file to a location set by the \"iso_dir\" keyword (note: if iso_dir is not defined, the isochrone is saved in the current working directory by default). In the future, the IsochronePhot function will read this file directly rather than recalculating the isochrone again. \n", + "\n", + "The output file is named as: \"iso_logAge_AKs_distance_metallicity.fits, using the specified values" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " L Teff R mass logg isWR mass_current phase m_hst_f127m m_hst_f139m m_hst_f153m \n", + " W K m solMass solMass \n", + "---------------------- ------------------ ------------------ ------- ------ ----- ------------ ----- ------------------ ------------------ ------------------\n", + "2.5969998772673413e+20 432.41425250986447 102099864.14758782 0.0005 2.8027 False 0.0005 1 33.96148101996776 41.349910373359705 34.0979568030492\n", + " 8.992323640770438e+20 594.8397740692212 100398023.76104526 0.001 3.1183 False 0.001 1 31.543816736848385 37.485693950053815 31.74912432063749\n", + "3.7824389676866323e+21 848.3987109859956 101222058.29958254 0.002 3.412 False 0.002 1 29.766073130622274 33.63286970536957 29.89254633083153\n", + " 8.832261707714201e+21 1038.2453626533147 103282124.85442652 0.003 3.5708 False 0.003 1 29.02543484155902 31.81705248352881 28.90711516172416\n", + " 1.637087638152397e+22 1197.8432778258546 105639214.73169859 0.004 3.6762 False 0.004 1 28.50411100931251 30.631805143722218 28.20569350209261\n", + "2.7044062385807246e+22 1344.3117666713051 107801589.44807644 0.005 3.7555 False 0.005 1 28.743636291005714 29.26628300083259 27.46562164782375\n", + " 4.309946994929829e+22 1495.2024500170564 110008226.74654458 0.006 3.817 False 0.006 1 28.967396030125585 28.47721532076903 27.304549861471813\n", + " 6.123108133536956e+22 1614.3585568264868 112479962.84627353 0.007 3.8649 False 0.007 1 28.313354773527784 27.875356378312524 26.7119534651289\n", + " 8.504953218239384e+22 1734.2032668438671 114874904.73113203 0.008 3.9041 False 0.008 1 27.643694437524978 27.46206350340845 26.27499584127883\n", + " 1.139126431658312e+23 1844.1659537910111 117564219.30211641 0.009 3.9355 False 0.009 1 26.899838148339686 27.054189808173756 25.813918901177253\n", + "1.4567060673103695e+23 1939.0990786148488 120247253.09559214 0.01 3.9614 False 0.01 1 26.434795746099354 26.70402266370232 25.505200218109437\n", + "1.8204213869346178e+23 2025.816014501357 123161555.19497135 0.011 3.9823 False 0.011 1 26.08431494331709 26.515390202963896 25.460851589219537\n", + "2.2211195212106118e+23 2102.80983703774 126262726.80063727 0.012 3.9985 False 0.012 1 25.830417769249294 26.261911250846772 25.24675323138816\n", + "2.7737794679666444e+23 2191.7953438441314 129874881.47435205 0.013 4.0085 False 0.013 1 25.61017010667633 25.920886898678226 24.97583514579402\n", + " 3.348666743779798e+23 2257.3557110214474 134531865.43699867 0.014 4.0101 False 0.014 1 25.390425928750485 25.63927034298637 24.73498924387469\n", + "4.0688523367793376e+23 2321.132860663976 140257199.84967285 0.015 4.0039 False 0.015 1 25.211513303103406 25.378874895430037 24.518086721694612\n", + " ... ... ... ... ... ... ... ... ... ... ...\n", + "2.0154495387748317e+32 60158.91570534644 4647026662.010959 56.096 6.1667 True 0.014 1 11.766159725472992 11.413999520380655 11.06582056408738\n", + "2.0182359095265675e+32 60353.158050326085 4620353072.632374 56.116 6.1668 True 0.014 1 11.778659725472991 11.426499520380656 11.07832056408738\n", + " 2.021491544497126e+32 60534.08747539136 4596477771.565344 56.136 6.1756 True 0.014 1 11.78990972547299 11.437749520380654 11.089570564087376\n", + "2.0256850788490884e+32 60757.514610574115 4567464328.358439 56.157 6.1844 True 0.014 1 11.803659725472986 11.45149952038065 11.103320564087374\n", + "2.0298873125846324e+32 60967.726445724344 4540724620.555787 56.177 6.1845 True 0.014 1 11.816409725472989 11.464249520380653 11.116070564087378\n", + " 2.035972600517817e+32 61235.039172477336 4507909246.527953 56.197 6.1934 True 0.014 1 11.83215972547299 11.479999520380654 11.131820564087375\n", + "2.0430167585847428e+32 61503.52393069763 4476361627.706634 56.217 6.2024 True 0.014 1 11.84740972547299 11.495249520380654 11.147070564087379\n", + "2.0524468954776198e+32 61844.34582271275 4437365109.317572 56.237 6.2114 True 0.014 1 11.866409725472986 11.514249520380648 11.166070564087374\n", + "2.0666737776929845e+32 62287.37089440294 4389602193.657228 56.258 6.2297 True 0.014 1 11.889909725472995 11.537749520380656 11.189570564087381\n", + "2.1036424651269878e+32 63037.64788294872 4323895249.627832 56.278 6.2672 True 0.014 1 11.922659725472986 11.57049952038065 11.222320564087374\n", + "2.1760653272980036e+32 64239.1816824387 4234724526.2221713 56.298 6.3165 True 0.014 1 11.967909725472992 11.615749520380657 11.267570564087382\n", + " 2.250981517613893e+32 65463.61740672747 4147392750.6757283 56.318 6.3686 True 0.014 1 12.013159725472986 11.660999520380651 11.312820564087374\n", + "2.3279407848949313e+32 66696.03248243559 4063265158.5149813 56.339 6.4241 True 0.014 1 12.057659725472986 11.70549952038065 11.357320564087372\n", + "2.4080856466771184e+32 67967.29719504561 3979469351.1107726 56.359 6.4713 True 0.014 1 12.10290972547299 11.750749520380653 11.402570564087378\n", + "2.4909896846856737e+32 69262.79294373626 3897401645.8767776 56.379 6.5339 True 0.014 1 12.148159725472985 11.795999520380649 11.447820564087374\n", + "2.6240489308607902e+32 71367.42051224748 3767690015.125812 56.4 6.6299 True 0.014 1 12.22165972547299 11.869499520380655 11.521320564087379\n", + "Length = 1678 rows\n" + ] + } + ], + "source": [ + "# The stars in the isochrone and associated properties \n", + "# are stored in an astropy table called \"points\" \n", + "# within the IsochronePhot object. \n", + "print(my_iso.points)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OrderedDict([('REDLAW', 'H18b'), ('ATMFUNC', 'get_merged_atmosphere'), ('EVOMODEL', 'MergedPhillipsBaraffePisaEkstromParsec'), ('LOGAGE', 6.698970004336019), ('AKS', 0.8), ('DISTANCE', 4000), ('METAL_IN', 0), ('METAL_ACT', 0.0), ('WAVEMIN', 3000), ('WAVEMAX', 52000)])\n" + ] + } + ], + "source": [ + "# The isochrone table has meta keywords describing its input properties:\n", + "# REDLAW: which redlaw object was used\n", + "# ATMFUNC: atmosphere grid was used\n", + "# EVOMODEL: evolution model grid used\n", + "# LOGAGE: log(Age) of isochrone\n", + "# AKS: total extinction used\n", + "# DISTANCE: distance used\n", + "# METAL_IN: metallicity requested by user, in [M/H]\n", + "# METAL_ACT: actual metallicity of model, in [M/H] \n", + "# (only relevant if user chooses metallicity other than defined grid-points)\n", + "# WAVEMIN, WAVEMAX: the minimum and maximum wavelengths of the stellar spectra (angstroms)\n", + "print(my_iso.points.meta)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The columns in the isochrone table are: ['L', 'Teff', 'R', 'mass', 'logg', 'isWR', 'mass_current', 'phase', 'm_hst_f127m', 'm_hst_f139m', 'm_hst_f153m']\n" + ] + } + ], + "source": [ + "# See Isochrone Object documentation for column definitions\n", + "print('The columns in the isochrone table are: {0}'.format(my_iso.points.keys()))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 M_sun: F127M = 19.051 mag, F139M = 18.453 mag, F153M = 17.785 mag\n" + ] + } + ], + "source": [ + "# Example case:\n", + "# Identify a 1 M_sun star, print F127M, F139M, and F153M mags\n", + "idx = np.where( abs(my_iso.points['mass'] - 1.0) == min(abs(my_iso.points['mass'] - 1.0)) )[0]\n", + "f127m = np.round(my_iso.points[idx[0]]['m_hst_f127m'], decimals=3)\n", + "f139m = np.round(my_iso.points[idx[0]]['m_hst_f139m'], decimals=3)\n", + "f153m = np.round(my_iso.points[idx[0]]['m_hst_f153m'], decimals=3)\n", + "print('1 M_sun: F127M = {0} mag, F139M = {1} mag, F153M = {2} mag'.format(f127m, f139m, f153m))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make a color-magnitude diagram from the isochrone\n", + "py.figure(1, figsize=(10,10))\n", + "py.clf()\n", + "py.plot(my_iso.points['m_hst_f127m'] - my_iso.points['m_hst_f153m'], \n", + " my_iso.points['m_hst_f153m'], 'r-', label='_nolegend_')\n", + "py.plot(my_iso.points['m_hst_f127m'][idx] - my_iso.points['m_hst_f153m'][idx], \n", + " my_iso.points['m_hst_f153m'][idx], 'b*', ms=15, label='1 $M_\\odot$')\n", + "py.xlabel('F127M - F153M')\n", + "py.ylabel('F153M')\n", + "py.gca().invert_yaxis()\n", + "py.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2: Make an Initial Mass Function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SPISEA offers a range of initial mass functions (IMFs) a user can use from to make the cluster. In addition to the parameters defining the IMF, the user can input a SPISEA multiplicity object, which defines the multiplicity properties of the population. The default multiplicity is None (e.g. all stars are single).\n", + "\n", + "Here we define a Kirkpatrick and Salpeter IMF using the Multiplicity properties defined in Lu+13 and Begbie+26. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Make multiplicity object Here, we use the MultiplicityUnresolved object, \n", + "# based on Lu+13. This means that star systems will be unresolved, i.e., \n", + "# that all components of a star system are combined into a single \"star\" in the cluster\n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "\n", + "# Make IMF object; we'll use a broken power law with the parameters from Kroupa+01\n", + "\n", + "# NOTE: when defining the power law slope for each segment of the IMF, we define\n", + "# the entire exponent, including the negative sign. For example, if dN/dm $\\propto$ m^-alpha,\n", + "# then you would use the value \"-2.3\" to specify an IMF with alpha = 2.3. \n", + "\n", + "massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) # Define boundaries of each mass segement\n", + "powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) # Power law slope associated with each mass segment\n", + "my_imf = imf.IMF_broken_powerlaw(massLimits, powers, imf_multi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3: Make the Cluster \n", + "#### Option 1: Resolved Cluster without compact objects\n", + "\n", + "Here we make a resolved cluster using the ResolvedCluster object. \n", + "\n", + "To create the cluster, the user passes in an isochrone object, and imf object, and specifies the total cluster mass. Here we will make a 10^5 M_sun cluster using the isochrone and imf we have defined. \n", + "\n", + "###### Some Notes\n", + "1. Unless an IFMR object is defined, no compact objects are included. Stars that have evolved into compact objects will be dropped from the cluster. \n", + "2. Stars generated by the IMF object that have masses below the lowest mass in the evolution model are dropped from the cluster.\n", + "2. If you wish to create a cluster with differential extinction included in the output photometry, then you can use the ResolvedClusterDiffRedden object. It is the same as ResolvedCluster, but with an additional parameter to define dAKs, which characterizes the spread of extinction within the cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 30 stars out of mass range\n" + ] + } + ], + "source": [ + "# Define total cluster mass\n", + "mass = 10**5.\n", + "\n", + "# Make cluster object\n", + "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The individual stars (or star systems) in the cluster are stored in an astropy table called \"star_systems\" within the cluster object. If a multiplicity object is used, then an additional \"companions\" table is created that contains the properties of the companions to the primary star within each star system. See cluster object documentation for a description of the columns in these tables. \n", + "\n", + "If a multiplicity object is used, then the photometry in the star_systems table is the COMBINED photometry of the system; it includes the contributions from all companions." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass isMultiple systemMass Teff L ... metallicity m_hst_f127m m_hst_f139m m_hst_f153m N_companions\n", + "-------------------- ---------- -------------------- ------------------ ---------------------- ... ----------- ------------------ ------------------ ------------------ ------------\n", + " 0.8945070753124857 False 0.8945070753124857 4225.696580422644 2.0017761931081406e+26 ... 0.0 19.243056683615986 18.637173365678077 17.95084609292932 0\n", + " 0.9214480579938956 False 0.9214480579938956 4261.931211189814 2.116376266341599e+26 ... 0.0 19.19230771149491 18.588166309349045 17.906424455244924 0\n", + " 0.10024865234106253 False 0.10024865234106253 3027.3930442939845 8.920168492066747e+24 ... 0.0 22.126796830827757 21.771742516715804 21.121481298455976 0\n", + " 0.10331038960864716 False 0.10331038960864716 3033.2987146970418 9.299161930812652e+24 ... 0.0 22.0895560136715 21.731299701031396 21.081202065802547 0\n", + " 1.076851234267687 True 2.0546110053672764 4475.247283026874 2.915277577875829e+26 ... 0.0 18.238384604284892 17.64362975490658 16.98251770953915 1\n", + " 0.04550634271811754 False 0.04550634271811754 2907.073026736088 4.2987876632153534e+24 ... 0.0 22.86985622025449 22.578455896796655 21.922735680260743 0\n", + " 0.02057279229829472 False 0.02057279229829472 2592.6489001923687 1.0686433670147005e+24 ... 0.0 24.269977725835812 24.15885180995034 23.43422668084687 0\n", + " 0.1164879299290811 False 0.1164879299290811 3058.7163789360357 1.0930327796391296e+25 ... 0.0 21.929273688065674 21.55723615874269 20.90784257542883 0\n", + " 0.3011656908091307 False 0.3011656908091307 3381.005777927854 3.6465258290587674e+25 ... 0.0 20.77351977574311 20.262461248743314 19.595109800059674 0\n", + " 0.17103385046662112 False 0.17103385046662112 3163.96714800829 1.7678874696828524e+25 ... 0.0 21.455525612758095 21.036515054199743 20.384316470660114 0\n", + " 0.08647234805282597 False 0.08647234805282597 2980.1468856715474 8.453356916162971e+24 ... 0.0 22.159572961352055 21.832592634028575 21.17882282533773 0\n", + " 0.5438845821920302 True 0.6984912843595693 3773.589684576896 9.078463730803296e+25 ... 0.0 19.743652920498597 19.183873404619415 18.49368284927326 1\n", + " 0.06570331399723098 False 0.06570331399723098 3023.1716403002797 5.657113527948109e+24 ... 0.0 22.618813470993725 22.265343298536244 21.615534582925605 0\n", + " 0.6656753065815819 False 0.6656753065815819 3937.3176263869022 1.2365543288808776e+26 ... 0.0 19.687068904336645 19.083097570925787 18.379919030232507 0\n", + " 0.5089672130684594 False 0.5089672130684594 3729.542689305321 8.25392928841976e+25 ... 0.0 20.04904065871442 19.46279957797426 18.7680612269903 0\n", + " 0.2057786669731296 True 0.35015551288124436 3228.841994104164 2.213027864160729e+25 ... 0.0 20.676981288410097 20.249552004654582 19.595841215176918 1\n", + " ... ... ... ... ... ... ... ... ... ... ...\n", + " 0.20825958860927216 False 0.20825958860927216 3232.815912724316 2.2501626185589996e+25 ... 0.0 21.224069212493763 20.775357718194957 20.118298918902617 0\n", + " 0.14087954414741297 False 0.14087954414741297 3105.5526305019907 1.3946863955552297e+25 ... 0.0 21.688268213061907 21.292796845536593 20.644176998812117 0\n", + " 0.07469065773536471 False 0.07469065773536471 2951.534998904973 7.086107228344845e+24 ... 0.0 22.341435926066605 22.02694876760282 21.37263650179408 0\n", + " 0.6657824647823649 False 0.6657824647823649 3937.46312032627 1.2368621911866713e+26 ... 0.0 19.686839820664634 19.082861990648198 18.37968422643919 0\n", + " 1.1119472097955938 True 2.1819957339746914 4524.801113161184 3.1325420707808415e+26 ... 0.0 18.239939341376857 17.65784149495578 16.99412258354396 3\n", + " 0.20714722227773794 False 0.20714722227773794 3231.0341340446043 2.233512576122893e+25 ... 0.0 21.231135068941178 20.783288811238673 20.126380476131036 0\n", + " 0.11944545321920931 False 0.11944545321920931 3064.4210348991 1.129642123418031e+25 ... 0.0 21.893300456381454 21.518169916654795 20.868934348409407 0\n", + " 0.24800411202852105 False 0.24800411202852105 3295.9889087357333 2.8455882476473613e+25 ... 0.0 21.000088980176024 20.52252909657682 19.860623823459612 0\n", + " 0.08959718269185585 False 0.08959718269185585 2990.880928723694 8.787193864898941e+24 ... 0.0 22.120992869255 21.789321321865096 21.135885857980387 0\n", + "0.021559700900544373 False 0.021559700900544373 2620.3285372568107 1.203778611185518e+24 ... 0.0 24.15137366130518 24.017512390221583 23.303451105142077 0\n", + " 0.3654503647802998 False 0.3654503647802998 3488.358949105219 4.850834207222937e+25 ... 0.0 20.52038048863164 19.974444670523486 19.301358822464927 0\n", + "0.025273405877510015 False 0.025273405877510015 2702.510134767554 1.781147306062996e+24 ... 0.0 23.754965643144065 23.564033377191404 22.87585796983192 0\n", + " 0.24659794294040255 False 0.24659794294040255 3293.745676576533 2.824519426688407e+25 ... 0.0 21.006909892684053 20.53029949412168 19.86854728251577 0\n", + " 0.3370293445772329 True 0.6355153702785177 3440.836171686819 4.318269544740905e+25 ... 0.0 19.947508542307638 19.426118967061818 18.75711998661427 1\n", + " 0.8172248515214722 False 0.8172248515214722 4129.6093471768845 1.7156083342455475e+26 ... 0.0 19.383974807973964 18.775945032430535 18.080475905838 0\n", + " 0.01654650185255516 False 0.01654650185255516 2417.767065117449 5.58918923392165e+23 ... 0.0 24.90422715448032 24.96822186613111 24.162155489913683 0\n", + " 17.630115661052674 True 20.769088438035276 31468.54394656097 1.5193931809647468e+31 ... 0.0 12.32702290744279 11.969042567673034 11.617455775398914 1\n", + "Length = 134627 rows\n" + ] + } + ], + "source": [ + "# Look at star systems table\n", + "print(cluster.star_systems)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The cluster table contains these columns: ['mass', 'isMultiple', 'systemMass', 'Teff', 'L', 'logg', 'isWR', 'mass_current', 'phase', 'metallicity', 'm_hst_f127m', 'm_hst_f139m', 'm_hst_f153m', 'N_companions']\n" + ] + } + ], + "source": [ + "print('The cluster table contains these columns: {0}'.format(cluster.star_systems.keys()))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "system_idx mass Teff L logg isWR ... phase metallicity m_hst_f127m m_hst_f139m m_hst_f153m \n", + "---------- -------------------- ------------------ ---------------------- ------------------ ---- ... ----- ----------- ------------------ ------------------ ------------------\n", + " 4 0.9777597710995893 4338.028423554631 2.376440000333853e+26 4.144613121985373 0.0 ... 1.0 0.0 19.086957934184962 18.486987528104535 17.81521684302705\n", + " 11 0.15460670216753916 3132.2066605872137 1.5646612433306536e+25 3.965782010650262 0.0 ... 1.0 0.0 21.575780039801344 21.16920821774943 20.518870336074574\n", + " 15 0.14437684590811475 3112.343343205245 1.437991304956611e+25 3.9627130537724344 0.0 ... 1.0 0.0 21.65960932490563 21.261309867319465 20.61225231261144\n", + " 17 0.4650204014154078 3654.3377635407696 7.089917157017561e+25 4.045880610389237 0.0 ... 1.0 0.0 20.18069477498541 19.60485943995035 18.91589665728705\n", + " 18 0.21611829149024636 3245.4039143983646 2.367792694867138e+25 3.983874304532171 0.0 ... 1.0 0.0 21.174149986384823 20.719325724071265 20.06120391766671\n", + " 24 0.019251769432724716 2545.0517403588783 8.916886525425982e+23 3.9323450965869524 0.0 ... 90.0 0.0 24.447240624904143 24.38001047476367 23.634375576570488\n", + " 27 0.061399105243014986 2998.6063816271535 5.300514940142659e+24 3.9461766893991554 0.0 ... 90.0 0.0 22.674800431423606 22.335929893121737 21.68493380391131\n", + " 28 0.07586618186354736 2946.3336034173467 7.240598634041845e+24 3.8718905600099807 0.0 ... 90.0 0.0 22.31659287751744 22.004930985326748 21.35028028555419\n", + " 29 0.011279987308020363 2047.3733076075164 1.9326117788793354e+23 3.98683579438993 0.0 ... 90.0 0.0 26.01322695703587 26.4444193135208 25.400906766358748\n", + " 33 0.030747220674507657 2780.4846621353186 2.6698057497113615e+24 3.812589174560011 0.0 ... 90.0 0.0 23.340125524685607 23.100765619185168 22.430858171279134\n", + " 41 0.1899395949467709 3200.3102116434984 2.0018990400453213e+25 3.9762321805092973 0.0 ... 1.0 0.0 21.335481404290615 20.901601265503523 20.247057833568675\n", + " 43 0.17370286184077677 3169.1166434990073 1.8008924120018483e+25 3.9714423442430293 0.0 ... 1.0 0.0 21.43712755304723 21.01616311553559 20.36367771974419\n", + " 43 0.08988731510379509 2991.8775554768927 8.818189719182378e+24 3.886842241145313 0.0 ... 1.0 0.0 22.117410812453294 21.785303698017433 21.13189927714019\n", + " 44 0.11493495166390834 3055.7208972941844 1.0738094263081045e+25 3.9537298350158117 0.0 ... 1.0 0.0 21.94816302263832 21.57774961507522 20.92827305896864\n", + " 45 0.10921744100612697 3044.6926050144025 1.0030359132375865e+25 3.9519574067118994 0.0 ... 1.0 0.0 22.01770679807904 21.653272826081725 21.003490793821804\n", + " 56 0.17921144789136326 3179.744713241384 1.8690114790913798e+25 3.9730673771279523 0.0 ... 1.0 0.0 21.399155704720734 20.974158646572544 20.32108129752664\n", + " ... ... ... ... ... ... ... ... ... ... ... ...\n", + " 134568 0.019225820797705587 2544.0290143144557 8.880303425842793e+23 3.9327836285187754 0.0 ... 90.0 0.0 24.451109451721695 24.384862166462295 23.638750638409665\n", + " 134579 0.9105591799205729 4247.109006199656 2.069295157626062e+26 4.136927642811795 0.0 ... 1.0 0.0 19.212729002183476 18.607888422031078 17.924298222857573\n", + " 134584 2.5983636084266126 11615.076492887843 1.9347372795972835e+28 4.369187004568884 0.0 ... 1.0 0.0 16.67444748487556 16.281989752966407 15.901843222779457\n", + " 134586 0.13520743874798025 3094.654090962411 1.324538155931306e+25 3.9599382688181337 0.0 ... 1.0 0.0 21.741263743980408 21.35106844597512 20.70239185675216\n", + " 134591 0.05924781160596321 2986.9173416223575 5.096124050865475e+24 3.940889766856008 0.0 ... 90.0 0.0 22.71310841542965 22.379300532008862 21.72780517170673\n", + " 134593 0.2756279064559549 3340.298843201504 3.2593715060641265e+25 4.000875813807668 0.0 ... 1.0 0.0 20.874753464501353 20.37908637910061 19.714261946333085\n", + " 134593 0.033931438676622946 2815.1226643949935 3.178566974129362e+24 3.8008714033084425 0.0 ... 90.0 0.0 23.16235232828712 22.907014765554077 22.242486764196556\n", + " 134598 0.01365547347589503 2234.768425598801 3.150602828891717e+23 4.009548757561432 0.0 ... 90.0 0.0 25.46613362656358 25.7362947160493 24.81796704530788\n", + " 134602 0.06175905869223863 3000.595002880443 5.329553967912544e+24 3.9475085171612827 0.0 ... 90.0 0.0 22.66976905273162 22.329907579832305 21.679012057011885\n", + " 134606 0.10518387013167073 3036.912401146452 9.53106844786149e+24 3.950706999740818 0.0 ... 1.0 0.0 22.066768315215985 21.706552696027686 21.056555157441903\n", + " 134607 0.06595297956077562 3024.561478938733 5.678834305674499e+24 3.9623448325505595 0.0 ... 90.0 0.0 22.615497518054603 22.261201029492877 21.611459944695717\n", + " 134608 0.05357984927260094 2954.364382789203 4.724557235607795e+24 3.9110891709993054 0.0 ... 90.0 0.0 22.783575035804013 22.46537907115991 21.812070559399526\n", + " 134614 0.05350104453859063 2953.935579562626 4.719419229341913e+24 3.9106557449622485 0.0 ... 90.0 0.0 22.784605903103657 22.466628802592354 21.813297076407856\n", + " 134614 0.8143279945366201 4126.131564052948 1.7055764580864243e+26 4.122227458611192 0.0 ... 1.0 0.0 19.389364903246538 18.78128947374045 18.08551995697676\n", + " 134614 0.20221948510388665 3223.1409276550503 2.1597535717979043e+25 3.9798436506801274 0.0 ... 1.0 0.0 21.262436522872264 20.818423227763923 20.16218144281632\n", + " 134623 0.29848602570128485 3376.6883990671927 3.602000596222681e+25 4.007183657067854 0.0 ... 1.0 0.0 20.784583849392742 20.275102250818385 19.608008027386852\n", + " 134626 3.138972776982603 13189.7781963926 3.9271090984804426e+28 4.3648125078236415 0.0 ... 1.0 0.0 16.286117145293822 15.899794361248118 15.524214462670491\n", + "Length = 36781 rows\n" + ] + } + ], + "source": [ + "# The companions table is accessed in a similar way\n", + "print(cluster.companions)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", + "# multiple systems on the photometry\n", + "\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points\n", + "\n", + "# specify mask to only extend isochrone to brown dwarf masses\n", + "bds = iso['mass'] >= 0.01\n", + "\n", + "py.figure(2, figsize=(10,10))\n", + "py.clf()\n", + "py.plot(clust['m_hst_f127m'] - clust['m_hst_f153m'], clust['m_hst_f153m'],\n", + " 'k.', ms=5, alpha=0.1, label='__nolegend__')\n", + "py.plot(iso['m_hst_f127m'][bds] - iso['m_hst_f153m'][bds], iso['m_hst_f153m'][bds],\n", + " 'r-', label='Isochrone')\n", + "py.xlabel('F127M - F153M')\n", + "py.ylabel('F153M')\n", + "py.gca().invert_yaxis()\n", + "py.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Option 2: Resolved cluster with compact objects (white dwarfs, neutron stars, and black holes)\n", + "This is quite similar to the above, but includes compact objects. The additional piece of information required is to choose an initial-final mass relation (IFMR.)\n", + "\n", + "The output is the same as if we were making a cluster without using an IFMR. However, you can tell that compact objects are made by looking at the 'phase' keyword. Black holes have 'phase' = 103, neutron stars have 'phase' = 102, and white dwarfs have 'phase' = 101. Though not classified as a compact object, brown dwarfs have 'phase' = 90. For these compact objects (black holes, neutron stars, white dwarfs), the luminosity and temperature will return values of zero, and photometry will return nan, since we are assuming they are totally dark.\n", + "\n", + "Here, we make 4 different clusters, each of mass $10^6 M_\\odot$, by taking different combinations of age (either 100 Myr or 10 Gyr) and IMF (top-heavy or Kroupa). We then look at the different distributions of BH and WD masses. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Create isochrone object \n", + "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "my_iso_young = synthetic.IsochronePhot(8, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(),\n", + " filters=filt_list)\n", + "\n", + "my_iso_old = synthetic.IsochronePhot(10, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(),\n", + " filters=filt_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "massLimits = np.array([0.01, 0.05, 0.1, 0.5, 120])\n", + "powers_kroupa = np.array([-0.6, -0.25, -1.3, -2.3])\n", + "powers_theavy = np.array([-0.6, -0.25, -1.3, -1.3]) # top heavy\n", + "trunc_kroupa = imf.IMF_broken_powerlaw(massLimits, powers_kroupa)\n", + "trunc_theavy = imf.IMF_broken_powerlaw(massLimits, powers_theavy)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/caitlinbegbie/.local/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:501: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + } + ], + "source": [ + "# Make clusters \n", + "cluster_mass = 10**6\n", + "cluster_young_theavy = synthetic.ResolvedCluster(my_iso_young, trunc_theavy, cluster_mass, ifmr=my_ifmr)\n", + "cluster_old_theavy = synthetic.ResolvedCluster(my_iso_old, trunc_theavy, cluster_mass, ifmr=my_ifmr)\n", + "cluster_young_kroupa = synthetic.ResolvedCluster(my_iso_young, trunc_kroupa, cluster_mass, ifmr=my_ifmr)\n", + "cluster_old_kroupa = synthetic.ResolvedCluster(my_iso_old, trunc_kroupa, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get the outputs\n", + "young_theavy = cluster_young_theavy.star_systems\n", + "old_theavy = cluster_old_theavy.star_systems\n", + "young_kroupa = cluster_young_kroupa.star_systems\n", + "old_kroupa = cluster_old_kroupa.star_systems" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "young_theavy_bh_idx = np.where(young_theavy['phase'] == 103)[0]\n", + "old_theavy_bh_idx = np.where(old_theavy['phase'] == 103)[0]\n", + "young_kroupa_bh_idx = np.where(young_kroupa['phase'] == 103)[0]\n", + "old_kroupa_bh_idx = np.where(old_kroupa['phase'] == 103)[0]\n", + "\n", + "bh_bins = np.linspace(5, 16, 16)\n", + "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(young_theavy[young_theavy_bh_idx]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = '100 Myr, TH IMF', color = 'red', linestyle = ':', lw = 2)\n", + "plt.hist(old_theavy[old_theavy_bh_idx]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = '10 Gyr, TH IMF', color = 'gray', linestyle = ':', lw = 2)\n", + "plt.hist(young_kroupa[young_kroupa_bh_idx]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = '100 Myr, Kr IMF', color = 'red', lw = 2)\n", + "plt.hist(old_kroupa[old_kroupa_bh_idx]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = '10 Gyr, Kr IMF', color = 'gray', lw = 2)\n", + "plt.title('BH Mass Function')\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "young_theavy_wd_idx = np.where(young_theavy['phase'] == 101)[0]\n", + "old_theavy_wd_idx = np.where(old_theavy['phase'] == 101)[0]\n", + "young_kroupa_wd_idx = np.where(young_kroupa['phase'] == 101)[0]\n", + "old_kroupa_wd_idx = np.where(old_kroupa['phase'] == 101)[0]\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(young_theavy[young_theavy_wd_idx]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = '100 Myr, TH IMF', color = 'red', linestyle = ':', lw = 2)\n", + "plt.hist(old_theavy[old_theavy_wd_idx]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = '10 Gyr, TH IMF', color = 'gray', linestyle = ':', lw = 2)\n", + "plt.hist(young_kroupa[young_kroupa_wd_idx]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = '100 Myr, Kr IMF', color = 'red', lw = 2)\n", + "plt.hist(old_kroupa[old_kroupa_wd_idx]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = '10 Gyr, Kr IMF', color = 'gray', lw = 2)\n", + "plt.yscale('log')\n", + "plt.title('WD Mass Function')\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotted above are the distributions of BH and WD masses for clusters of different ages (100 Myr or 10 Gyr), with either a top-heavy or Kroupa IMF. For BHs, since those are formed relatively early on, the age of the cluster does not significantly change the mass distribution as most BHs have already formed by 100 Myr. However, the top heavy IMF allows the creation of many more massive compact objects. For WDs, both the age and IMF make significant differences in the distribution (note y-axis is logscaled)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explicitly Showing Brown Dwarfs in Simulated Clusters\n", + "\n", + "Here we are creating a cluster of log(age) = 8.4 with brown dwarfs. We show where they fall on the generated isochrone and how they incorporate into clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Create isochrone object \n", + "filt_list = ['wfc3,ir,f153m'] # Only 1 filter for plotting purposes\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "my_iso = synthetic.IsochronePhot(8.4, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(), #our new evolution model for BDs\n", + " filters=filt_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Brown Dwarfs in Simulated Isochrone\n", + "We have expanded the capabilites of isochrones to generate down to 0.005 solar masses, but masses below 0.01 solar masses are not yet supported by physical constraints. This will be expanded in later versions of the codebase." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brown dwarf (mass < 0.08 M_sun): F153M = 15.849 mag\n" + ] + } + ], + "source": [ + "# specifying brown dwarfs by mass\n", + "bd_idx = np.where((my_iso.points['mass'] > 0.01) & (my_iso.points['mass'] < 0.08) )[0]\n", + "if len(bd_idx) > 0:\n", + " f153m = np.round(my_iso.points[bd_idx[0]]['m_hst_f153m'], decimals=3)\n", + " mass = my_iso.points[bd_idx[0]]['mass']\n", + " print('Brown dwarf (mass < 0.08 M_sun): F153M = {0} mag'.format(f153m))\n", + "else:\n", + " print('No brown dwarf with mass < 0.08 M_sun found.')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make a mass-magnitude diagram from the isochrone and plot brown dwarfs\n", + "py.figure(1, figsize=(6,6))\n", + "py.clf()\n", + "py.plot(my_iso.points['mass'], my_iso.points['m_hst_f153m'], 'r-', label='generated isochron')\n", + "py.plot(my_iso.points['mass'][bd_idx], my_iso.points['m_hst_f153m'][bd_idx], 'b*', ms=15, label='BD mass')\n", + "py.title('Generated Isochron Containing Brown Dwarf Masses')\n", + "py.xlabel('Mass')\n", + "py.ylabel('Magnitude in F153M filter')\n", + "py.gca().invert_yaxis()\n", + "py.legend()\n", + "py.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Brown Dwarfs in Simulated Clusters\n", + "\n", + "We will now use our generated isochrone to pinpoint brown dwarfs in clusters. An easy way to do this is through the 'phase' column, where brown dwarfs are assigned a value of 90. We will see how their numbers and evolution compares to compact objects." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "kc_imf = imf.Salpeter_Kirkpatrick_2024(multiplicity=imf_multi)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 12259 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "kc_cluster = synthetic.ResolvedCluster(my_iso, kc_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "kc_out = kc_cluster.star_systems\n", + "kc_comp = kc_cluster.companions" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs, WDs, and BDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", + "\n", + "# Define bins for histograms\n", + "bh_bins = np.linspace(14, 100, 20)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(0, 30, 20)\n", + "\n", + "# Create subplots\n", + "plt.figure(figsize=(14,6))\n", + "\n", + "# Plot BHs\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(kc_out[p2_bh]['mass'], histtype='step', bins=bh_bins, label='Primary Black Holes', color='blue')\n", + "plt.hist(kc_comp[c_bh]['mass'], histtype='step', bins=bh_bins, label='Companion Black Holes', color='orange')\n", + "plt.title(\"Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(kc_out[p2_wd]['mass'], histtype='step', bins=wd_bins, label='Primary White Dwarves', color='blue')\n", + "plt.hist(kc_comp[c_wd]['mass'], histtype='step', bins=wd_bins, label='Companion White Dwarves', color='orange')\n", + "plt.title(\"White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(kc_out[p2_bd]['mass'], histtype='step', bins=bd_bins, label='Primary Brown Dwarves', color='blue')\n", + "plt.hist(kc_comp[c_bd]['mass'], histtype='step', bins=bd_bins, label='Companion Brown Dwarves', color='orange')\n", + "plt.title(\"Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(kc_out[p2_ns]['mass'], histtype='step', bins=ns_bins, label='Primary Neutron Stars', color='blue')\n", + "plt.hist(kc_comp[c_ns]['mass'], histtype='step', bins=ns_bins, label='Companion Neutron Stars', color='orange')\n", + "plt.title(\"Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "# Show the plots\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotted above are the number of generated compact objects (black holes, white dwarfs, and neutron stars) against the generated number of brown dwarfs." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs, WDs, and BDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "k_bh = vstack([kc_out[p2_bh], kc_comp[c_bh]])\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "k_ns = vstack([kc_out[p2_ns], kc_comp[c_ns]])\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", + "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", + "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", + "\n", + "# Create subplots\n", + "plt.figure(figsize=(14,6))\n", + "\n", + "# Plot BHs\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(k_bh['mass'], histtype='step', bins=bh_bins, label='Progenitor Black Hole Masses', color='blue')\n", + "plt.hist(k_bh['mass_current'], histtype='step', bins=bh_bins, label='Current Black Hole Masses', color='orange')\n", + "plt.title(\"Black Holes by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(k_wd['mass'], histtype='step', bins=wd_bins, label='Progenitor White Dwarf Masses', color='blue')\n", + "plt.hist(k_wd['mass_current'], histtype='step', bins=wd_bins, label='Current White Dwarf Masses', color='orange')\n", + "plt.title(\"White Dwarves by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(k_bd['mass'], histtype='step', bins=bd_bins, label='Progenitor Brown Dwarf Masses', color='blue')\n", + "plt.hist(k_bd['mass_current'], histtype='step', bins=bd_bins, label='Current Brown Dwarf Masses', color='orange')\n", + "plt.title(\"Brown Dwarves by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(k_ns['mass'], histtype='step', bins=ns_bins, label='Progenitor Neutron Star Masses', color='blue')\n", + "plt.hist(k_ns['mass_current'], histtype='step', bins=ns_bins, label='Current Neutron Stars Masses', color='orange')\n", + "plt.title(\"Neutron Stars by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "# Show the plots\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above we have plotted the initial masses of compact objects (black holes, neutron stars, and white dwarfs) against their current masses. We have done the same for brown dwarfs. As expected, brown dwarf masses do not change over time." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/changes/mass_to_age.py b/spisea/bd_merging_code.py similarity index 99% rename from changes/mass_to_age.py rename to spisea/bd_merging_code.py index 0ac86c9c..3eded9ea 100644 --- a/changes/mass_to_age.py +++ b/spisea/bd_merging_code.py @@ -1,3 +1,6 @@ +### Code used to incorporate new brown dwarf evolution/atmosphere models into pre-existing code. +### Messy, ask Caitlin Begbie if there are questions + import math import logging from numpy import genfromtxt @@ -632,10 +635,6 @@ def merge_isochrone_baraffe_pisa(logAge, metallicity='solar'): return iso - -import matplotlib.pyplot as plt -import numpy as np - """def plot_merged_isochrone(iso): "" Function to plot the merged isochrone model to visualize the transition @@ -697,9 +696,6 @@ def merge_isochrone_baraffe_pisa(logAge, metallicity='solar'): plt.show() """ -import numpy as np -import matplotlib.pyplot as plt - def plot_merged_isochrone(iso): """ Function to plot the merged isochrone model to visualize the transition From f4f80b2101dd2fe3bb560e08a4a79593b062c6e6 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Mon, 20 Apr 2026 14:35:42 -0700 Subject: [PATCH 116/191] updating documentation with new BD functions/models --- docs/atmo_models.rst | 4 ++++ docs/contributors.rst | 2 ++ docs/evo_models.rst | 5 +++++ docs/imf.rst | 3 +++ 4 files changed, 14 insertions(+) diff --git a/docs/atmo_models.rst b/docs/atmo_models.rst index 4fc8cb2d..3ab2a0c6 100644 --- a/docs/atmo_models.rst +++ b/docs/atmo_models.rst @@ -58,3 +58,7 @@ Model Atmosphere Classes .. autofunction:: atmospheres.get_phoenix_atmosphere +.. autofunction:: atmospheres.get_Phillips2020_atmosphere + +.. autofunction:: atmospheres.get_Meisner2023_atmosphere + diff --git a/docs/contributors.rst b/docs/contributors.rst index 40edaee7..e62072d7 100644 --- a/docs/contributors.rst +++ b/docs/contributors.rst @@ -34,3 +34,5 @@ Javier Moldón -- installation using Docker containers and Singularity Winston Zhang -- bugfix to make redlaw paths correct regardless of what operating system is used + +Caitlin Begbie -- added brown dwarf physics and capabilities diff --git a/docs/evo_models.rst b/docs/evo_models.rst index 98d3541c..99074c8f 100644 --- a/docs/evo_models.rst +++ b/docs/evo_models.rst @@ -78,4 +78,9 @@ Specific Evolution Model Classes .. autoclass:: evolution.Pisa :show-inheritance: + +.. autoclass:: evolution.MergedPhillipsBaraffePisaEkstromParsec + :show-inheritance: +.. autoclass:: evolution.Phillips2020 + :show-inheritance: \ No newline at end of file diff --git a/docs/imf.rst b/docs/imf.rst index a99b8fe1..ed601775 100644 --- a/docs/imf.rst +++ b/docs/imf.rst @@ -43,3 +43,6 @@ Broken Power-Law IMFs .. autoclass:: imf.imf.Weidner_Kroupa_2004 :show-inheritance: + +.. autoclass:: imf.imf.Salpeter_Kirkpatrick_2024 + :show-inheritance: \ No newline at end of file From e0d48f34119069452210c0741b37c8ecb8c3de78 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 27 Apr 2026 13:38:34 -0700 Subject: [PATCH 117/191] debug SODC --- spisea/reddening.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spisea/reddening.py b/spisea/reddening.py index 80c028a3..220f76ab 100755 --- a/spisea/reddening.py +++ b/spisea/reddening.py @@ -64,7 +64,7 @@ def get_red_law(str): 'H18b': RedLawHosek18b, 'NL18': RedLawNoguerasLara18, 'NL20': RedLawNoguerasLara20, - 'SODC': SODC + 'SODC': RedLawSODC } # Make reddening law object, including params if necessary. From 9b703e783a479b2662a4d4067dc7e6019f7ff442 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 27 Apr 2026 13:46:13 -0700 Subject: [PATCH 118/191] debug VIS wavelength, add SODC citations --- spisea/filters.py | 3 ++- spisea/reddening.py | 4 ++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/spisea/filters.py b/spisea/filters.py index e2a2aee9..63580ea2 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -465,7 +465,8 @@ def get_euclid_filt(name): transmission = t[t.keys()[1]] # Convert wavelength to Angstroms - wavelength = wavelength * 10 + if name.lower() != 'vis': + wavelength = wavelength * 10 # Make spectrum object spectrum = pysynphot.ArrayBandpass(wavelength, transmission, waveunits='angstrom', diff --git a/spisea/reddening.py b/spisea/reddening.py index 220f76ab..b0d23c19 100755 --- a/spisea/reddening.py +++ b/spisea/reddening.py @@ -415,6 +415,10 @@ class RedLawSODC(pysynphot.reddening.CustomRedLaw): r""" Defines the SODC extinction law from SynthPop, described by `Klüter & Huston et al. (2025) `_. + It is based on the `Cardelli et al. (1989) `_ + formulation with an updated optical end from `O'Donnell et al (1994) + `_ and infrared side adjusted to match + `Surot et al. (2020) `_. The law is defined from 0.25 - 3.5 microns, and in terms of :math:`A_{\lambda} / A_{Ks}`, where Ks is 2.174 microns. From efc2a140eaf4095f5447f1c100aa03897d04d5d4 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 27 Apr 2026 13:52:38 -0700 Subject: [PATCH 119/191] add test for SODC red law --- spisea/tests/test_reddening.py | 1 + 1 file changed, 1 insertion(+) diff --git a/spisea/tests/test_reddening.py b/spisea/tests/test_reddening.py index 99bbf78b..67d4842a 100644 --- a/spisea/tests/test_reddening.py +++ b/spisea/tests/test_reddening.py @@ -254,5 +254,6 @@ def test_all_EL(): red_law = reddening.RedLawSchoedel10() red_law = reddening.RedLawNoguerasLara18() red_law = reddening.RedLawNoguerasLara20() + red_law = reddening.RedLawSODC(2.5) return From ca7e8d28a1c64738c23cc01145dbb8353288b192 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 30 Apr 2026 11:12:33 -0700 Subject: [PATCH 120/191] add SODC extinction law to docs --- docs/extinction.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/extinction.rst b/docs/extinction.rst index 6b93be6b..0dc9d172 100755 --- a/docs/extinction.rst +++ b/docs/extinction.rst @@ -36,6 +36,7 @@ Available extinction laws: * RedLawHosek18b * RedLawNoguerasLara18 * RedLawNoguerasLara20 +* RedLawSODC Extinction Law Classes @@ -90,4 +91,7 @@ Extinction Law Classes .. autoclass:: reddening.RedLawNoguerasLara20 :members: NoguerasLara20 +.. autoclass:: reddening.RedLawSODC + :members: SODC + From 16f08c66205c0b7d248e566bd092c24ee3d7a273 Mon Sep 17 00:00:00 2001 From: Jessica Lu Date: Tue, 5 May 2026 16:11:37 -0700 Subject: [PATCH 121/191] Add test_atmospheres_grid_timing for k93models extraction benchmark. Mirrors synphot_update timing test; kept standalone without SPISEA imports so dev branch (which dropped test_atmospheres.py) still carries the check, and the pysynphot-only branch can run when stsynphot is not loaded. Co-authored-by: Cursor --- spisea/tests/test_atmospheres_grid_timing.py | 62 ++++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 spisea/tests/test_atmospheres_grid_timing.py diff --git a/spisea/tests/test_atmospheres_grid_timing.py b/spisea/tests/test_atmospheres_grid_timing.py new file mode 100644 index 00000000..afbecd66 --- /dev/null +++ b/spisea/tests/test_atmospheres_grid_timing.py @@ -0,0 +1,62 @@ +""" +Timing benchmark for Kurucz ``k93models`` grid extraction (stsynphot / pysynphot). + +Kept in a separate module (no ``spisea`` imports) so the pysynphot-only branch can +run when ``stsynphot`` is not imported elsewhere. +""" +import importlib.util +import os +import time + +import numpy as np +import pytest + + +def test_pysynphot_vs_stsynphot_timing(): + """ + Time Kurucz ``k93models`` spectrum extraction via stsynphot or pysynphot. + """ + temperature = 20000 + metallicity = 0.0 + gravity = 4.0 + + has_stsyn = importlib.util.find_spec("stsynphot") is not None + has_pysyn = importlib.util.find_spec("pysynphot") is not None + + if has_stsyn: + if not os.environ.get("PYSYN_CDBS"): + pytest.skip("PYSYN_CDBS not set; grid_to_spec needs CDBS tree") + + from stsynphot.catalog import grid_to_spec + + t0 = time.perf_counter() + try: + sp = grid_to_spec("k93models", temperature, metallicity, gravity) + w = sp.waveset + _ = sp(w) + except Exception as exc: + pytest.skip(f"stsynphot grid_to_spec failed: {exc}") + elapsed = time.perf_counter() - t0 + + assert elapsed >= 0 + assert np.isfinite(elapsed) + assert w.size > 0 + + elif has_pysyn: + import pysynphot + + t0 = time.perf_counter() + try: + sp = pysynphot.Icat("k93models", temperature, metallicity, gravity) + wave = sp.GetWaveSet() + _ = sp.sample(wave) + except Exception as exc: + pytest.skip(f"pysynphot Icat failed: {exc}") + elapsed = time.perf_counter() - t0 + + assert elapsed >= 0 + assert np.isfinite(elapsed) + assert np.asarray(wave).size > 0 + + else: + pytest.skip("Neither stsynphot nor pysynphot is installed") From bc250854bee9855b203ef36b9095ab9f9fb528c7 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 21 May 2026 17:46:53 -0700 Subject: [PATCH 122/191] do some filter cleanup --- filt_func/ukirt/Y.dat | 603 ++++++++++++++++++++++++++++++++++++++++++ filt_func/ukirt/Z.dat | 466 ++++++++++++++++++++++++++++++++ spisea/filters.py | 3 +- 3 files changed, 1071 insertions(+), 1 deletion(-) create mode 100644 filt_func/ukirt/Y.dat create mode 100644 filt_func/ukirt/Z.dat diff --git a/filt_func/ukirt/Y.dat b/filt_func/ukirt/Y.dat new file mode 100644 index 00000000..dc140f51 --- /dev/null +++ b/filt_func/ukirt/Y.dat @@ -0,0 +1,603 @@ +# Table 3 System throughput curve for the WFCAM Y filter +# 1.0mm water vapour, 1.3 airmass +# Col 1: wavelength (microns) +# Col 2: throughput +# + 0.9005 0.0000 + 0.9010 0.0000 + 0.9015 0.0000 + 0.9020 0.0000 + 0.9025 0.0000 + 0.9030 0.0000 + 0.9035 0.0000 + 0.9040 0.0000 + 0.9045 0.0000 + 0.9050 0.0000 + 0.9055 0.0000 + 0.9060 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0.0000 + 1.0285 0.0000 + 1.0290 0.0000 + 1.0295 0.0000 + 1.0300 0.0000 diff --git a/spisea/filters.py b/spisea/filters.py index 63580ea2..c1a048d1 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -103,7 +103,7 @@ def get_decam_filt(name): # Read in filter info try: t = Table.read('{0}/decam/DECam_filters.txt'.format(filters_dir), format='ascii') - t.rename_column('Y', 'y') + t['y'] = t['Y'] cols = np.array(t.keys()) idx = np.where(cols == name)[0][0] @@ -139,6 +139,7 @@ def get_PS1_filt(name): t.rename_column('col5', 'i') t.rename_column('col6', 'z') t.rename_column('col7', 'y') + t.rename_column('col8', 'w') cols = np.array(t.keys()) idx = np.where(cols == name)[0][0] From 9a4a35ddc7616bc26c77a7067a6bc945800c3b3c Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 21 May 2026 21:44:43 -0700 Subject: [PATCH 123/191] adding more filters --- docs/filters.rst | 43 +- filt_func/hipparcos/Hp.dat | 115 +++++ filt_func/kepler/Kp.dat | 625 ++++++++++++++++++++++++++ filt_func/tess/tess.dat | 857 ++++++++++++++++++++++++++++++++++++ filt_func/tycho/B.dat | 34 ++ filt_func/tycho/V.dat | 47 ++ filt_func/washington/C.dat | 24 + filt_func/washington/M.dat | 21 + filt_func/washington/T1.dat | 21 + filt_func/washington/T2.dat | 20 + spisea/filters.py | 90 ++++ spisea/synthetic.py | 22 +- spisea/tests/test_models.py | 5 +- 13 files changed, 1921 insertions(+), 3 deletions(-) create mode 100644 filt_func/hipparcos/Hp.dat create mode 100644 filt_func/kepler/Kp.dat create mode 100644 filt_func/tess/tess.dat create mode 100644 filt_func/tycho/B.dat create mode 100644 filt_func/tycho/V.dat create mode 100644 filt_func/washington/C.dat create mode 100644 filt_func/washington/M.dat create mode 100644 filt_func/washington/T1.dat create mode 100644 filt_func/washington/T2.dat diff --git a/docs/filters.rst b/docs/filters.rst index 3112d4a1..56186ed9 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -44,6 +44,7 @@ Available filters: * NACO * PanStarrs 1 * Roman Space Telescope +* TESS * UKIRT * Vera C. Rubin Observatory * VISTA @@ -116,6 +117,14 @@ Filters: J, H, Ks Example: ``'hawki,J'`` +**Hipparcos** + +`Hipparcos Hp filter `_ + +Filters: Hp + +Example: ``'hipparcos,Hp'`` + **Hubble Space Telescope** HST filters are defined by their `pysynphot OBSMODE strings @@ -171,6 +180,14 @@ Filters: J, H, Hcont, K, Kp, Ks, Kcont, Lp, Ms, Brgamma, FeII Example: ``'nirc2,Ks'`` +**Kepler** + +`Kepler Kp filter `_ + +Filters: Kp + +Example: ``'kepler,Kp'`` + **NACO** @@ -206,11 +223,27 @@ Filters: F062, F087, F106, F129, F158, W146, F184, F213 Example: ``'roman,wfi,f062'`` +**TESS** + +TESS filter: a single wide, red-optical `bandpass `_ + +Filters: tess + +Example: ``'tess,tess'`` + +**Tycho** + +`Tycho filters `_ + +Filters: B, V + +Example: ``'tycho,B'`` + **UKIRT** `UKIRT Telescope filters `_ -Filters: J, H, K +Filters: Z, Y, J, H, K Example: ``'ukirt,K'`` @@ -231,6 +264,14 @@ Filters: Z, Y, J, H, K Example: ``'vista,Y'`` +**Washington** + +Washington filter system from `Bessell et al. 2001 `_ + +Filters: C, M, T1, T2 + +Example: ``'washington,C'`` + **ZTF** `ZTF Telescope `_ diff --git a/filt_func/hipparcos/Hp.dat b/filt_func/hipparcos/Hp.dat new file mode 100644 index 00000000..87376f59 --- /dev/null +++ b/filt_func/hipparcos/Hp.dat @@ -0,0 +1,115 @@ +# Hipparcos HP filter from ADPS +# wavelength (Angstrom), transmission +3350.0 0.0000000000 +3400.0 0.0060000000 +3450.0 0.0230000000 +3500.0 0.0470000000 +3550.0 0.0780000000 +3600.0 0.1140000000 +3650.0 0.1540000000 +3700.0 0.1980000000 +3750.0 0.2480000000 +3800.0 0.3050000000 +3850.0 0.3690000000 +3900.0 0.4420000000 +3950.0 0.5230000000 +4000.0 0.6080000000 +4050.0 0.6940000000 +4100.0 0.7740000000 +4150.0 0.8450000000 +4200.0 0.9010000000 +4250.0 0.9410000000 +4300.0 0.9670000000 +4350.0 0.9840000000 +4400.0 0.9930000000 +4450.0 0.9980000000 +4500.0 1.0000000000 +4550.0 1.0000000000 +4600.0 0.9980000000 +4650.0 0.9930000000 +4700.0 0.9870000000 +4750.0 0.9790000000 +4800.0 0.9690000000 +4850.0 0.9580000000 +4900.0 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+# Source: Roland Vanderspek, POC, Kavli/MIT +# Version: 2.0 +# Date: 2020/07/30 +# Feedback: tesshelp@bigbang.gsfc.nasa.gov +# Wavelength (nm), lambda Transmission +300,0 +302,0 +304,0 +306,0 +308,0 +310,0 +312,0 +314,0 +316,0 +318,0 +320,0 +322,0 +324,0 +326,0 +328,0 +330,0 +332,0 +334,0 +336,0 +338,0 +340,0 +342,0 +344,0 +346,0 +348,0 +350,0 +352,0 +354,0 +356,0 +358,0 +360,0 +362,0 +364,0 +366,0 +368,0 +370,0 +372,0 +374,0 +376,0 +378,0 +380,0 +382,0 +384,0 +386,0 +388,0 +390,0 +392,0 +394,0 +396,0 +398,0 +400,0 +402,0 +404,0 +406,0 +408,0 +410,0 +412,0 +414,0 +416,0 +418,0 +420,0 +422,0 +424,0 +426,0 +428,0 +430,0 +432,0 +434,0 +436,0 +438,0 +440,0 +442,0 +444,0 +446,0 +448,0 +450,0 +452,0 +454,0 +456,0 +458,0 +460,0 +462,0 +464,0 +466,0 +468,0 +470,0 +472,0 +474,0 +476,0 +478,0 +480,0 +482,0 +484,0 +486,0 +488,0 +490,0 +492,0 +494,0 +496,0 +498,0 +500,0 +502,0 +504,0 +506,0 +508,0 +510,0 +512,0 +514,0 +516,0 +518,0 +520,0 +522,0 +524,0 +526,0 +528,0 +530,0 +532,0 +534,0.001 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+718,0.784 +720,0.783 +722,0.785 +724,0.784 +726,0.782 +728,0.78 +730,0.776 +732,0.773 +734,0.771 +736,0.768 +738,0.771 +740,0.77 +742,0.773 +744,0.771 +746,0.771 +748,0.774 +750,0.771 +752,0.772 +754,0.772 +756,0.774 +758,0.773 +760,0.774 +762,0.774 +764,0.772 +766,0.767 +768,0.765 +770,0.757 +772,0.754 +774,0.754 +776,0.753 +778,0.754 +780,0.755 +782,0.756 +784,0.757 +786,0.762 +788,0.763 +790,0.766 +792,0.763 +794,0.768 +796,0.769 +798,0.772 +800,0.777 +802,0.778 +804,0.78 +806,0.78 +808,0.779 +810,0.78 +812,0.778 +814,0.775 +816,0.771 +818,0.772 +820,0.769 +822,0.767 +824,0.764 +826,0.762 +828,0.763 +830,0.761 +832,0.761 +834,0.762 +836,0.763 +838,0.763 +840,0.763 +842,0.763 +844,0.762 +846,0.763 +848,0.761 +850,0.761 +852,0.758 +854,0.759 +856,0.758 +858,0.758 +860,0.759 +862,0.758 +864,0.759 +866,0.76 +868,0.76 +870,0.761 +872,0.762 +874,0.763 +876,0.763 +878,0.764 +880,0.764 +882,0.765 +884,0.765 +886,0.765 +888,0.765 +890,0.763 +892,0.761 +894,0.759 +896,0.756 +898,0.752 +900,0.748 +902,0.742 +904,0.737 +906,0.732 +908,0.725 +910,0.719 +912,0.713 +914,0.709 +916,0.704 +918,0.697 +920,0.694 +922,0.691 +924,0.687 +926,0.685 +928,0.682 +930,0.677 +932,0.675 +934,0.671 +936,0.666 +938,0.662 +940,0.656 +942,0.65 +944,0.641 +946,0.635 +948,0.626 +950,0.616 +952,0.605 +954,0.594 +956,0.582 +958,0.571 +960,0.557 +962,0.544 +964,0.53 +966,0.514 +968,0.499 +970,0.484 +972,0.469 +974,0.453 +976,0.438 +978,0.424 +980,0.411 +982,0.399 +984,0.386 +986,0.373 +988,0.362 +990,0.351 +992,0.342 +994,0.333 +996,0.321 +998,0.309 +1000,0.299 +1002,0.287 +1004,0.276 +1006,0.263 +1008,0.252 +1010,0.24 +1012,0.227 +1014,0.217 +1016,0.205 +1018,0.194 +1020,0.184 +1022,0.174 +1024,0.164 +1026,0.156 +1028,0.147 +1030,0.139 +1032,0.132 +1034,0.125 +1036,0.117 +1038,0.111 +1040,0.104 +1042,0.097 +1044,0.09 +1046,0.083 +1048,0.077 +1050,0.07 +1052,0.063 +1054,0.057 +1056,0.051 +1058,0.044 +1060,0.038 +1062,0 +1064,0 +1066,0 +1068,0 +1070,0 +1072,0 +1074,0 +1076,0 +1078,0 +1080,0 +1082,0 +1084,0 +1086,0 +1088,0 +1090,0 +1092,0 +1094,0 +1096,0 +1098,0 +1100,0 +1102,0 +1104,0 +1106,0 +1108,0 +1110,0 +1112,0 +1114,0 +1116,0 +1118,0 +1120,0 +1122,0 +1124,0 +1126,0 +1128,0 +1130,0 +1132,0 +1134,0 +1136,0 +1138,0 +1140,0 +1142,0 +1144,0 +1146,0 +1148,0 +1150,0 +1152,0 +1154,0 +1156,0 +1158,0 +1160,0 +1162,0 +1164,0 +1166,0 +1168,0 +1170,0 +1172,0 +1174,0 +1176,0 +1178,0 +1180,0 +1182,0 +1184,0 +1186,0 +1188,0 +1190,0 +1192,0 +1194,0 +1196,0 +1198,0 +1200,0 +1202,0 +1204,0 +1206,0 +1208,0 +1210,0 +1212,0 +1214,0 +1216,0 +1218,0 +1220,0 +1222,0 +1224,0 +1226,0 +1228,0 +1230,0 +1232,0 +1234,0 +1236,0 +1238,0 +1240,0 +1242,0 +1244,0 +1246,0 +1248,0 +1250,0 +1252,0 +1254,0 +1256,0 +1258,0 +1260,0 +1262,0 +1264,0 +1266,0 +1268,0 +1270,0 +1272,0 +1274,0 +1276,0 +1278,0 +1280,0 +1282,0 +1284,0 +1286,0 +1288,0 +1290,0 +1292,0 +1294,0 +1296,0 +1298,0 +1300,0 +1302,0 +1304,0 +1306,0 +1308,0 +1310,0 +1312,0 +1314,0 +1316,0 +1318,0 +1320,0 +1322,0 +1324,0 +1326,0 +1328,0 +1330,0 +1332,0 +1334,0 +1336,0 +1338,0 +1340,0 +1342,0 +1344,0 +1346,0 +1348,0 +1350,0 +1352,0 +1354,0 +1356,0 +1358,0 +1360,0 +1362,0 +1364,0 +1366,0 +1368,0 +1370,0 +1372,0 +1374,0 +1376,0 +1378,0 +1380,0 +1382,0 +1384,0 +1386,0 +1388,0 +1390,0 +1392,0 +1394,0 +1396,0 +1398,0 +1400,0 +1402,0 +1404,0 +1406,0 +1408,0 +1410,0 +1412,0 +1414,0 +1416,0 +1418,0 +1420,0 +1422,0 +1424,0 +1426,0 +1428,0 +1430,0 +1432,0 +1434,0 +1436,0 +1438,0 +1440,0 +1442,0 +1444,0 +1446,0 +1448,0 +1450,0 +1452,0 +1454,0 +1456,0 +1458,0 +1460,0 +1462,0 +1464,0 +1466,0 +1468,0 +1470,0 +1472,0 +1474,0 +1476,0 +1478,0 +1480,0 +1482,0 +1484,0 +1486,0 +1488,0 +1490,0 +1492,0 +1494,0 +1496,0 +1498,0 +1500,0 +1502,0 +1504,0 +1506,0 +1508,0 +1510,0 +1512,0 +1514,0 +1516,0 +1518,0 +1520,0 +1522,0 +1524,0 +1526,0 +1528,0 +1530,0 +1532,0 +1534,0 +1536,0 +1538,0 +1540,0 +1542,0 +1544,0 +1546,0 +1548,0 +1550,0 +1552,0 +1554,0 +1556,0 +1558,0 +1560,0 +1562,0 +1564,0 +1566,0 +1568,0 +1570,0 +1572,0 +1574,0 +1576,0 +1578,0 +1580,0 +1582,0 +1584,0 +1586,0 +1588,0 +1590,0 +1592,0 +1594,0 +1596,0 +1598,0 +1600,0 +1602,0 +1604,0 +1606,0 +1608,0 +1610,0 +1612,0 +1614,0 +1616,0 +1618,0 +1620,0 +1622,0 +1624,0 +1626,0 +1628,0 +1630,0 +1632,0 +1634,0 +1636,0 +1638,0 +1640,0 +1642,0 +1644,0 +1646,0 +1648,0 +1650,0 +1652,0 +1654,0 +1656,0 +1658,0 +1660,0 +1662,0 +1664,0 +1666,0 +1668,0 +1670,0 +1672,0 +1674,0 +1676,0 +1678,0 +1680,0 +1682,0 +1684,0 +1686,0 +1688,0 +1690,0 +1692,0 +1694,0 +1696,0 +1698,0 +1700,0 +1702,0 +1704,0 +1706,0 +1708,0 +1710,0 +1712,0 +1714,0 +1716,0 +1718,0 +1720,0 +1722,0 +1724,0 +1726,0 +1728,0 +1730,0 +1732,0 +1734,0 +1736,0 +1738,0 +1740,0 +1742,0 +1744,0 +1746,0 +1748,0 +1750,0 +1752,0 +1754,0 +1756,0 +1758,0 +1760,0 +1762,0 +1764,0 +1766,0 +1768,0 +1770,0 +1772,0 +1774,0 +1776,0 +1778,0 +1780,0 +1782,0 +1784,0 +1786,0 +1788,0 +1790,0 +1792,0 +1794,0 +1796,0 +1798,0 +1800,0 +1802,0 +1804,0 +1806,0 +1808,0 +1810,0 +1812,0 +1814,0 +1816,0 +1818,0 +1820,0 +1822,0 +1824,0 +1826,0 +1828,0 +1830,0 +1832,0 +1834,0 +1836,0 +1838,0 +1840,0 +1842,0 +1844,0 +1846,0 +1848,0 +1850,0 +1852,0 +1854,0 +1856,0 +1858,0 +1860,0 +1862,0 +1864,0 +1866,0 +1868,0 +1870,0 +1872,0 +1874,0 +1876,0 +1878,0 +1880,0 +1882,0 +1884,0 +1886,0 +1888,0 +1890,0 +1892,0 +1894,0 +1896,0 +1898,0 +1900,0 +1902,0 +1904,0 +1906,0 +1908,0 +1910,0 +1912,0 +1914,0 +1916,0 +1918,0 +1920,0 +1922,0 +1924,0 +1926,0 +1928,0 +1930,0 +1932,0 +1934,0 +1936,0 +1938,0 +1940,0 +1942,0 +1944,0 +1946,0 +1948,0 +1950,0 +1952,0 +1954,0 +1956,0 +1958,0 +1960,0 +1962,0 +1964,0 +1966,0 +1968,0 +1970,0 +1972,0 +1974,0 +1976,0 +1978,0 +1980,0 +1982,0 +1984,0 +1986,0 +1988,0 +1990,0 +1992,0 +1994,0 +1996,0 +1998,0 +2000,0 diff --git a/filt_func/tycho/B.dat b/filt_func/tycho/B.dat new file mode 100644 index 00000000..04a3bf2b --- /dev/null +++ b/filt_func/tycho/B.dat @@ -0,0 +1,34 @@ +# Tycho B filter from ADPS +# wavelength (Angstrom), transmission +3500.0 0.0000000000 +3550.0 0.0140000000 +3600.0 0.0580000000 +3650.0 0.1230000000 +3700.0 0.2060000000 +3750.0 0.3050000000 +3800.0 0.4160000000 +3850.0 0.5300000000 +3900.0 0.6360000000 +3950.0 0.7240000000 +4000.0 0.7870000000 +4050.0 0.8300000000 +4100.0 0.8610000000 +4150.0 0.8890000000 +4200.0 0.9200000000 +4250.0 0.9530000000 +4300.0 0.9820000000 +4350.0 1.0020000000 +4400.0 0.9760000000 +4450.0 0.8610000000 +4500.0 0.6850000000 +4550.0 0.4890000000 +4600.0 0.3170000000 +4650.0 0.2020000000 +4700.0 0.1360000000 +4750.0 0.1010000000 +4800.0 0.0800000000 +4850.0 0.0590000000 +4900.0 0.0360000000 +4950.0 0.0160000000 +5000.0 0.0030000000 +5050.0 0.0000000000 diff --git a/filt_func/tycho/V.dat b/filt_func/tycho/V.dat new file mode 100644 index 00000000..4313338b --- /dev/null +++ b/filt_func/tycho/V.dat @@ -0,0 +1,47 @@ +# Tycho V filter from ADPS +# wavelength (Angstrom), transmission +4550.0 0.0000000000 +4600.0 0.0220000000 +4650.0 0.1150000000 +4700.0 0.3010000000 +4750.0 0.5300000000 +4800.0 0.7370000000 +4850.0 0.8700000000 +4900.0 0.9400000000 +4950.0 0.9730000000 +5000.0 0.9900000000 +5050.0 0.9960000000 +5100.0 0.9910000000 +5150.0 0.9750000000 +5200.0 0.9490000000 +5250.0 0.9160000000 +5300.0 0.8780000000 +5350.0 0.8370000000 +5400.0 0.7940000000 +5450.0 0.7490000000 +5500.0 0.7040000000 +5550.0 0.6580000000 +5600.0 0.6120000000 +5650.0 0.5650000000 +5700.0 0.5180000000 +5750.0 0.4710000000 +5800.0 0.4240000000 +5850.0 0.3790000000 +5900.0 0.3350000000 +5950.0 0.2930000000 +6000.0 0.2540000000 +6050.0 0.2180000000 +6100.0 0.1860000000 +6150.0 0.1590000000 +6200.0 0.1350000000 +6250.0 0.1140000000 +6300.0 0.0970000000 +6350.0 0.0820000000 +6400.0 0.0690000000 +6450.0 0.0580000000 +6500.0 0.0470000000 +6550.0 0.0380000000 +6600.0 0.0280000000 +6650.0 0.0180000000 +6700.0 0.0080000000 +6750.0 0.0000000000 diff --git a/filt_func/washington/C.dat b/filt_func/washington/C.dat new file mode 100644 index 00000000..005d6e6b --- /dev/null +++ b/filt_func/washington/C.dat @@ -0,0 +1,24 @@ +# C filter profile from Bessell et al. (2001) +# wavelength (nm), transmission +300,0.000 +310,0.030 +320,0.157 +330,0.364 +340,0.591 +350,0.762 +360,0.860 +370,0.935 +380,0.984 +390,1.004 +400,0.987 +410,0.920 +420,0.819 +430,0.682 +440,0.542 +450,0.366 +460,0.168 +470,0.049 +480,0.000 +750,0.042 +760,0.024 +770,0.000 \ No newline at end of file diff --git a/filt_func/washington/M.dat b/filt_func/washington/M.dat new file mode 100644 index 00000000..8f61e17a --- /dev/null +++ b/filt_func/washington/M.dat @@ -0,0 +1,21 @@ +# M filter profile from Bessell et al. (2001) +# wavelength (nm), transmission +430,0.000 +440,0.015 +450,0.158 +460,0.448 +470,0.708 +480,0.866 +490,0.950 +500,0.993 +510,0.996 +520,0.944 +530,0.886 +540,0.800 +550,0.682 +560,0.518 +570,0.339 +580,0.190 +590,0.084 +600,0.016 +610,0.000 diff --git a/filt_func/washington/T1.dat b/filt_func/washington/T1.dat new file mode 100644 index 00000000..115ee779 --- /dev/null +++ b/filt_func/washington/T1.dat @@ -0,0 +1,21 @@ +# T1 filter profile from Bessell et al. (2001) +# wavelength (nm), transmission +560,0.000 +570,0.037 +580,0.103 +590,0.197 +600,0.663 +610,0.968 +620,1.011 +630,0.937 +640,0.842 +650,0.744 +660,0.656 +670,0.556 +680,0.448 +690,0.359 +700,0.291 +710,0.241 +720,0.164 +730,0.118 +740,0.069 diff --git a/filt_func/washington/T2.dat b/filt_func/washington/T2.dat new file mode 100644 index 00000000..ba9834a7 --- /dev/null +++ b/filt_func/washington/T2.dat @@ -0,0 +1,20 @@ +# T2 filter profile from Bessell et al. (2001) +# wavelength (nm), transmission +720,0.000 +730,0.130 +740,0.540 +750,0.698 +760,0.803 +770,0.875 +780,0.911 +790,0.932 +800,0.954 +810,0.975 +820,0.987 +830,0.999 +840,0.999 +850,0.979 +860,0.945 +870,0.844 +880,0.620 +890,0.000 \ No newline at end of file diff --git a/spisea/filters.py b/spisea/filters.py index c1a048d1..40261546 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -492,3 +492,93 @@ def get_nsfcam_filt(name): spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='nsfcam_{0}'.format(name)) return spectrum + +def get_tess_filt(name): + + """ + Define the TESS filter as pysynphot object + """ + try: + t = Table.read('{0}/tess/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find tess filter {0} in {1}/tess'.format(name, filters_dir)) + + # Wavelength from nanometers to angstroms and and transmission in fraction + wave = t['col1']*10 + trans = t['col2'] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='tess,{0}'.format(name)) + + return spectrum + +def get_washington_filt(name): + + """ + Define the Washington filters as pysynphot object + """ + try: + t = Table.read('{0}/washington/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find washington filter {0} in {1}/washington'.format(name, filters_dir)) + + # Wavelength from nanometers to angstroms and and transmission in fraction + wave = t['col1']*10 + trans = t['col2'] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='washington,{0}'.format(name)) + + return spectrum + +def get_hipparcos_filt(name): + + """ + Define the Hipparcos filter as pysynphot object + """ + try: + t = Table.read('{0}/hipparcos/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find hipparcos filter {0} in {1}/hipparcos'.format(name, filters_dir)) + + # Wavelength in angstroms and and transmission in fraction + wave = t['col1'] + trans = t['col2'] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='hipparcos,{0}'.format(name)) + + return spectrum + +def get_tycho_filt(name): + + """ + Define the Tycho filters as pysynphot object + """ + try: + t = Table.read('{0}/tycho/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find tycho filter {0} in {1}/tycho'.format(name, filters_dir)) + + # Wavelength in angstroms and and transmission in fraction + wave = t['col1'] + trans = t['col2'] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='tycho,{0}'.format(name)) + + return spectrum + +def get_kepler_filt(name): + + """ + Define the Kepler filters as pysynphot object + """ + try: + t = Table.read('{0}/kepler/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find kepler filter {0} in {1}/kepler'.format(name, filters_dir)) + + # Wavelength in angstroms and and transmission in fraction + wave = t['col1'] + trans = t['col2'] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='kepler,{0}'.format(name)) + + return spectrum diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 4dec9340..7463b525 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1787,6 +1787,21 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('nsfcam'): filt = filters.get_nsfcam_filt(filterName) + elif name.startswith('tess'): + filt = filters.get_tess_filt(filterName) + + elif name.startswith('washington'): + filt = filters.get_washington_filt(filterName) + + elif name.startswith('hipparcos'): + filt = filters.get_hipparcos_filt(filterName) + + elif name.startswith('tycho'): + filt = filters.get_tycho_filt(filterName) + + elif name.startswith('kepler'): + filt = filters.get_kepler_filt(filterName) + else: # Otherwise, look for the filter info in the cdbs/mtab and cdbs/comp files try: @@ -1934,7 +1949,12 @@ def get_obs_str(col): 'euclid_Y':'euclid,Y', 'euclid_J':'euclid,J', 'euclid_H':'euclid,H', - 'nsfcam_L':'nsfcam,L'} + 'nsfcam_L':'nsfcam,L', + 'tess_tess':'tess,tess', + 'washington_C':'washington,C', 'washington_M':'washington,M', + 'washington_T1':'washington,T1', 'washington_T2':'washington,T2', + 'hipparcos_Hp':'hipparcos,Hp', 'tycho_B':'tycho,B', 'tycho_V':'tycho,V', + 'kepler_Kp':'kepler,Kp'} obs_str = filt_list[name] diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 4ba0e683..e322dbad 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -189,7 +189,10 @@ def test_filters(): 'roman,wfi,f184', 'rubin,g', 'rubin,i', 'rubin,r', 'rubin,u', 'rubin,z', 'rubin,y', 'euclid,VIS', 'euclid,Y', 'euclid,J', 'euclid,H', - 'nsfcam,L'] + 'nsfcam,L', 'tess,tess', + 'washington,C', 'washington,M', 'washington,T1', 'washington,T2', + 'hipparcos,Hp', 'tycho,B', 'tycho,V', + 'kepler,Kp'] # Loop through filters to test that they work: get_filter_info for ii in filt_list: From 19e68296a9d7f1d551ae7ba1d8f7e8bb59db7566 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 25 May 2026 05:42:05 -0700 Subject: [PATCH 124/191] add OGLE R-wide filter --- docs/filters.rst | 8 + filt_func/ogle/Rw.dat | 805 ++++++++++++++++++++++++++++++++++++ spisea/filters.py | 18 + spisea/synthetic.py | 5 +- spisea/tests/test_models.py | 2 +- 5 files changed, 836 insertions(+), 2 deletions(-) create mode 100644 filt_func/ogle/Rw.dat diff --git a/docs/filters.rst b/docs/filters.rst index 56186ed9..b11316c5 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -198,6 +198,14 @@ IB_2.30, IB_2.33, IB_2.36 Example: ``'naco,H'`` +**OGLE** + +OGLE R-wide filter, provided by Andrzej Udalski + +Filters: Rw + +Example: ``'ogle,Rw'`` + **PanStarrs1** diff --git a/filt_func/ogle/Rw.dat b/filt_func/ogle/Rw.dat new file mode 100644 index 00000000..2c697527 --- /dev/null +++ b/filt_func/ogle/Rw.dat @@ -0,0 +1,805 @@ +# OGLE wide filter, new in 2026 +# Filter profile provided by Andrzej Udalski +# wavelength (nm), throughput (percent) +1100 0.00515 +1099 0.0056 +1098 0.00611 +1097 0.00668 +1096 0.00732 +1095 0.00805 +1094 0.00888 +1093 0.00983 +1092 0.01091 +1091 0.01215 +1090 0.01357 +1089 0.01522 +1088 0.01712 +1087 0.01933 +1086 0.0219 +1085 0.02491 +1084 0.02845 +1083 0.03261 +1082 0.03754 +1081 0.04339 +1080 0.05037 +1079 0.05873 +1078 0.06878 +1077 0.08095 +1076 0.09571 +1075 0.11373 +1074 0.13583 +1073 0.16308 +1072 0.19689 +1071 0.23903 +1070 0.29186 +1069 0.35853 +1068 0.44326 +1067 0.55144 +1066 0.69076 +1065 0.87104 +1064 1.10617 +1063 1.41536 +1062 1.82399 +1061 2.36891 +1060 3.09886 +1059 4.08453 +1058 5.41906 +1057 7.23521 +1056 9.70458 +1055 13.04807 +1054 17.52164 +1053 23.40456 +1052 30.89285 +1051 39.97982 +1050 50.3469 +1049 61.24691 +1048 71.67737 +1047 80.69016 +1046 87.7437 +1045 92.75309 +1044 96.00485 +1043 97.9376 +1042 98.9795 +1041 99.47313 +1040 99.65676 +1039 99.68338 +1038 99.64305 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+++ b/spisea/filters.py @@ -582,3 +582,21 @@ def get_kepler_filt(name): spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='kepler,{0}'.format(name)) return spectrum + +def get_ogle_filt(name): + + """ + Define the OGLE filters as pysynphot object + """ + try: + t = Table.read('{0}/ogle/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find ogle filter {0} in {1}/ogle'.format(name, filters_dir)) + + # Wavelength in nm->angstroms and and transmission in fraction + wave = np.flip(t['col1'])*10 + trans = np.flip(t['col2'])/100 + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ogle,{0}'.format(name)) + + return spectrum diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 7463b525..fabfe9ca 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1802,6 +1802,9 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('kepler'): filt = filters.get_kepler_filt(filterName) + elif name.startswith('ogle'): + filt = filters.get_ogle_filt(filterName) + else: # Otherwise, look for the filter info in the cdbs/mtab and cdbs/comp files try: @@ -1954,7 +1957,7 @@ def get_obs_str(col): 'washington_C':'washington,C', 'washington_M':'washington,M', 'washington_T1':'washington,T1', 'washington_T2':'washington,T2', 'hipparcos_Hp':'hipparcos,Hp', 'tycho_B':'tycho,B', 'tycho_V':'tycho,V', - 'kepler_Kp':'kepler,Kp'} + 'kepler_Kp':'kepler,Kp', 'ogle_Rw':'ogle,Rw'} obs_str = filt_list[name] diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index e322dbad..2a9e468d 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -192,7 +192,7 @@ def test_filters(): 'nsfcam,L', 'tess,tess', 'washington,C', 'washington,M', 'washington,T1', 'washington,T2', 'hipparcos,Hp', 'tycho,B', 'tycho,V', - 'kepler,Kp'] + 'kepler,Kp', 'ogle,Rw'] # Loop through filters to test that they work: get_filter_info for ii in filt_list: From 22e734b5fcf170a318cf5131650ba84d7afede1b Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 26 May 2026 15:47:34 -0700 Subject: [PATCH 125/191] doc cleanup --- spisea/filters.py | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/spisea/filters.py b/spisea/filters.py index 7fd2eafc..6db21a25 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -476,7 +476,6 @@ def get_euclid_filt(name): return spectrum def get_nsfcam_filt(name): - """ Define irtf nsfcam filters as pysynphot object """ @@ -494,7 +493,6 @@ def get_nsfcam_filt(name): return spectrum def get_tess_filt(name): - """ Define the TESS filter as pysynphot object """ @@ -512,7 +510,6 @@ def get_tess_filt(name): return spectrum def get_washington_filt(name): - """ Define the Washington filters as pysynphot object """ @@ -530,7 +527,6 @@ def get_washington_filt(name): return spectrum def get_hipparcos_filt(name): - """ Define the Hipparcos filter as pysynphot object """ @@ -548,7 +544,6 @@ def get_hipparcos_filt(name): return spectrum def get_tycho_filt(name): - """ Define the Tycho filters as pysynphot object """ @@ -566,7 +561,6 @@ def get_tycho_filt(name): return spectrum def get_kepler_filt(name): - """ Define the Kepler filters as pysynphot object """ @@ -584,7 +578,6 @@ def get_kepler_filt(name): return spectrum def get_ogle_filt(name): - """ Define the OGLE filters as pysynphot object """ @@ -593,7 +586,7 @@ def get_ogle_filt(name): except: raise ValueError('Could not find ogle filter {0} in {1}/ogle'.format(name, filters_dir)) - # Wavelength in nm->angstroms and and transmission in fraction + # Wavelength in nm->angstroms and and transmission in percent->fraction wave = np.flip(t['col1'])*10 trans = np.flip(t['col2'])/100 From ec0284e959e721eb2d9ffeeac5a156691a3b6444 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 28 May 2026 12:00:03 -0700 Subject: [PATCH 126/191] Enable all Gaia passband versions, adding EDR3 as recommended version --- docs/filters.rst | 14 +- filt_func/gaia/edr3/Gaia_passbands.txt | 781 +++++++++++++++++++++++++ filt_func/gaia/edr3/ReadMe | 134 +++++ filt_func/gaia/edr3/zeropt.dat | 2 + spisea/filters.py | 24 +- spisea/synthetic.py | 16 +- spisea/tests/test_models.py | 3 + 7 files changed, 950 insertions(+), 24 deletions(-) create mode 100644 filt_func/gaia/edr3/Gaia_passbands.txt create mode 100644 filt_func/gaia/edr3/ReadMe create mode 100644 filt_func/gaia/edr3/zeropt.dat diff --git a/docs/filters.rst b/docs/filters.rst index b11316c5..998624c3 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -95,17 +95,19 @@ Example: ``'euclid,Y'`` **GAIA** The `GAIA Space Telescope filters `_. -Note that three sets are available: the pre-launch passbands used in DR1 +Note that four sets are available: the pre-launch passbands used in DR1 (`Jordi+10 `_), -the passbands used for the DR2 published photometry, and -the *revised* DR2 passbands based on the DR2 data (October 2017). -ONLY THE REVISED DR2 PASSBANDS ARE SUPPORTED BY SPISEA. +the passbands used for the DR2 published photometry, +the *revised* DR2 passbands based on the DR2 data (October 2017), +and the `(E)DR3 passbands `_. Filters: G, Gbp, Grp -Example (gaia G filter from revised DR2 passbands): -``'gaia,dr2_rev,G'`` +Versions: dr1, dr2, dr2_rev, edr3 + +Example (gaia G filter from (E)DR3 passbands): +``'gaia,edr3,G'`` **HAWK-I** diff --git a/filt_func/gaia/edr3/Gaia_passbands.txt b/filt_func/gaia/edr3/Gaia_passbands.txt new file mode 100644 index 00000000..cc733830 --- /dev/null +++ b/filt_func/gaia/edr3/Gaia_passbands.txt @@ -0,0 +1,781 @@ + 320.00 2.37366962e-08 2.34103325e-11 99.99 99.99 99.99 99.99 + 321.00 1.57774443e-07 1.54036935e-10 99.99 99.99 99.99 99.99 + 322.00 9.08554755e-07 8.78053229e-10 99.99 99.99 99.99 99.99 + 323.00 4.78072331e-06 4.57325370e-09 99.99 99.99 99.99 99.99 + 324.00 2.39428071e-05 2.26698640e-08 99.99 99.99 99.99 99.99 + 325.00 1.03648276e-04 9.71310542e-08 3.87054116e-05 6.46801629e-08 99.99 99.99 + 326.00 3.72280534e-04 3.45277531e-07 1.75771984e-04 2.88785224e-07 99.99 99.99 + 327.00 1.09381843e-03 1.00397934e-06 6.49485416e-04 1.04897855e-06 99.99 99.99 + 328.00 2.65174642e-03 2.40864304e-06 1.96690820e-03 3.12246976e-06 99.99 99.99 + 329.00 5.45098195e-03 4.89953932e-06 5.01083084e-03 7.81780663e-06 99.99 99.99 + 330.00 9.76875472e-03 8.68837731e-06 1.09458069e-02 1.67813576e-05 99.99 99.99 + 331.00 1.56275855e-02 1.37527326e-05 2.07035425e-02 3.11867542e-05 99.99 99.99 + 332.00 2.27741233e-02 1.98296421e-05 3.46967871e-02 5.13454545e-05 99.99 99.99 + 333.00 3.07613294e-02 2.64991906e-05 5.25319303e-02 7.63595340e-05 99.99 99.99 + 334.00 3.93617092e-02 3.35454579e-05 7.35388438e-02 1.04983900e-04 99.99 99.99 + 335.00 4.81051591e-02 4.05565884e-05 9.60352312e-02 1.34629478e-04 99.99 99.99 + 336.00 5.65974619e-02 4.72013222e-05 1.18775774e-01 1.63484887e-04 99.99 99.99 + 337.00 6.48652284e-02 5.35098603e-05 1.42278579e-01 1.92249270e-04 99.99 99.99 + 338.00 7.25998465e-02 5.92378341e-05 1.65557648e-01 2.19575501e-04 99.99 99.99 + 339.00 7.99799035e-02 6.45450034e-05 1.88569139e-01 2.45441174e-04 99.99 99.99 + 340.00 8.68837415e-02 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99.99 99.99 99.99 99.99 99.99 +1092.00 99.99 99.99 99.99 99.99 99.99 99.99 +1093.00 99.99 99.99 99.99 99.99 99.99 99.99 +1094.00 99.99 99.99 99.99 99.99 99.99 99.99 +1095.00 99.99 99.99 99.99 99.99 99.99 99.99 +1096.00 99.99 99.99 99.99 99.99 99.99 99.99 +1097.00 99.99 99.99 99.99 99.99 99.99 99.99 +1098.00 99.99 99.99 99.99 99.99 99.99 99.99 +1099.00 99.99 99.99 99.99 99.99 99.99 99.99 +1100.00 99.99 99.99 99.99 99.99 99.99 99.99 diff --git a/filt_func/gaia/edr3/ReadMe b/filt_func/gaia/edr3/ReadMe new file mode 100644 index 00000000..bd3e2f27 --- /dev/null +++ b/filt_func/gaia/edr3/ReadMe @@ -0,0 +1,134 @@ +J/A+A/649/A3 Gaia Early Data Release 3 photometric passbands (Riello+, 2021) +================================================================================ +Gaia Early Data Release 3: Photometric content and validation. + Riello M., De Angeli F., Evans D.W., Montegriffo P., Carrasco J.M, + Busso G., Palaversa L., Burgess P., Diener C., Davidson M., Rowell N., + Fabricius C., Jordi C., Bellazzini M., Pancino E., Harrison D.L., + Cacciari C., van Leeuwen F., Hambly N.C., Hodgkin S.T., Osborne P.J., + Altavilla G., Barstow M.A., Brown A.G.A., Castellani M., Cowell S., + De Luise F., Gilmore G., Giuffrida G., Hidalgo S., Holland G., Marinoni S., + Pagani C., Piersimoni A.M., Pulone L., Ragaini S., Rainer M., Richards P.J., + Sanna N., Walton N.A., Weiler M., Yoldas A. + + =2021A&A...649A...3R (SIMBAD/NED BibCode) +================================================================================ +ADC_Keywords: Surveys ; Photometry, G band +Keywords: catalogues - surveys - instrumentation: photometers - + techniques: photometric - Galaxy: general + +Abstract: + Gaia Early Data Release 3 (Gaia EDR3) contains astrometry and + photometry results for about 1.8 billion sources based on + observations collected by the European Space Agency Gaia satellite + during the first 34 months of its operational phase. + + In this paper, we focus on the photometric content, describing the + input data, the algorithms, the processing, and the validation of the + results. Particular attention is given to the quality of the data and + to a number of features that users may need to take into account to + make the best use of the Gaia EDR3 catalogue. + + The processing broadly followed the same procedure as for Gaia DR2, + but with significant improvements in several aspects of the blue and + red photometer (BP and RP) preprocessing and in the photometric + calibration process. In particular, the treatment of the BP and RP + background has been updated to include a better estimation of the + local background, and the detection of crowding effects has been used + to exclude affected data from the calibrations. The photometric + calibration models have also been updated to account for flux loss + over the whole magnitude range. Significant improvements in the + modelling and calibration of the Gaia point and line spread functions + have also helped to reduce a number of instrumental effects that were + still present in DR2. + + Gaia EDR3 contains 1.806 billion sources with G-band photometry and + 1.540 billion sources with GBP and GRP photometry. The median + uncertainty in the G-band photometry, as measured from the standard + deviation of the internally calibrated mean photometry for a given + source, is 0.2mmag at magnitude G=10 to 14, 0.8mmag at G~17, and + 2.6mmag at G~19. The significant magnitude term found in the Gaia DR2 + photometry is no longer visible, and overall there are no trends + larger than 1mmag/mag. Using one passband over the whole colour and + magnitude range leaves no systematics above the 1% level in magnitude + in any of the bands, and a larger systematic is present for a very + small sample of bright and blue sources. A detailed description of the + residual systematic effects is provided. Overall the quality of the + calibrated mean photometry in Gaia EDR3 is superior with respect to + DR2 for all bands. + +Description: + These tabular data describes the photometric system defined by the G, + G_BP_ and G_RP_ Gaia bands for Gaia Early Data Release 3. + + The tables provide the full passband and the corresponding zero point + for each of the photometric bands. The zero points are available both + in the VEGAMAG and AB systems. + + The passband calibration is based on the modelling of a set of + corrections applied to pre-launch knowledge of the instrument, to find + the best match between observed and synthetic photometry for a set of + calibrators. For EDR3 a large set of calibrators covering a wide range + of spectra types was used for the passband calibration. + + For these sources reconstructed SEDs were obtained from externally + calibrated Gaia BP/RP spectra. + +File Summary: +-------------------------------------------------------------------------------- + FileName Lrecl Records Explanations +-------------------------------------------------------------------------------- +ReadMe 80 . This file +passband.dat 103 781 G, G_BP_ anf G_RP_ passbands used to generate + the magnitudes and astrophysical parameters + included in Gaia EDR3 +zeropt.dat 97 2 G, G_BP_ and G_RP_ zero points used to generate + the magnitudes and astrophysical parameters + included in Gaia EDR3 +-------------------------------------------------------------------------------- + +See also: + I/350 : Gaia EDR3 (Gaia Collaboration, 2020) + J/A+A/649/A6 : Gaia Catalogue of Nearby Stars - GCNS (Gaia collaboration, 2021) + J/A+A/649/A7 : MC structure and properties (Gaia Collaboration+, 2021) + +Byte-by-byte Description of file: passband.dat +-------------------------------------------------------------------------------- + Bytes Format Units Label Explanations +-------------------------------------------------------------------------------- + 1- 7 F7.2 nm lambda Wavelength + 10- 23 E14.9 mag GPb ?=99.99 G transmissivity curve at the + corresponding wavelength (1) + 26- 39 E14.9 mag e_GPb ?=99.99 Uncertainty on the G transmissivity + curve (1) + 42- 55 E14.9 mag BPPb ?=99.99 BP transmissivity curve at the + corresponding wavelength (1) + 58- 71 E14.9 mag e_BPPb ?=99.99 Uncertainty on the BP transmissivity + curve (1) + 74- 87 E14.9 mag RPPb ?=99.99 RP transmissivity curve at the + corresponding wavelength (1) + 90-103 E14.9 mag e_RPPb ?=99.99 Uncertainty on the RP transmissivity + curve (1) +-------------------------------------------------------------------------------- +Note (1): In correspondence to wavelength values where the passband is not + defined, both transmissivity and uncertainty are set to 99.99. +-------------------------------------------------------------------------------- + +Byte-by-byte Description of file: zeropt.dat +-------------------------------------------------------------------------------- + Bytes Format Units Label Explanations +-------------------------------------------------------------------------------- + 2- 14 F13.10 --- GZp G band zero point + 18- 29 F12.10 --- e_GZp G band zero point uncertainty + 32- 44 F13.10 --- BPZp G_BP_ band zero point + 48- 59 F12.10 --- e_BPZp G_BP_ band zero point uncertainty + 62- 74 F13.10 --- RPZp G_RP_ band zero point + 78- 89 F12.10 --- e_RPZp G_RP_ band zero point uncertainty + 91- 97 A7 --- System [VEGAMAG, AB] Photometric system +-------------------------------------------------------------------------------- + +Acknowledgements: + Marco Riello, mriello(at)ast.cam.ac.uk + Francesca De Angeli, fda(at)ast.cam.ac.uk + +================================================================================ +(End) Francesca De Angeli [IoA, UK], Patricia Vannier [CDS] 05-Jan-2021 diff --git a/filt_func/gaia/edr3/zeropt.dat b/filt_func/gaia/edr3/zeropt.dat new file mode 100644 index 00000000..0f4c7cca --- /dev/null +++ b/filt_func/gaia/edr3/zeropt.dat @@ -0,0 +1,2 @@ + 25.6873668671 0.0027553202 25.3385422158 0.0027901700 24.7478955012 0.0037793818 VEGAMAG + 25.8010446445 0.0027590522 25.3539555559 0.0023065687 25.1039837393 0.0015800349 AB diff --git a/spisea/filters.py b/spisea/filters.py index 6db21a25..33c7d80b 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -337,17 +337,14 @@ def get_keck_osiris_filt(name): def get_gaia_filt(version, name): """ Define Gaia filters as pysynphot object. - To avoid confusion, we will only support - the revised DR2 zeropoints from - Evans+18. - version: specify dr1, dr2, or dr2_rev + version: specify dr1, dr2, dr2_rev, or edr3 name: filter name """ - # Assert that we are using the revised DR2 zeropoints - if version != 'dr2_rev': - msg = 'Gaia version {0} not supported, use dr2_rev instead'.format(version) - raise ValueError(msg) + # Warn if not using latest version + if version != 'edr3': + msg = 'Gaia version {0} not recommended, use edr3 for the latest version'.format(version) + warnings.warn(msg) # Set the filter directory if version == 'dr1': @@ -356,8 +353,10 @@ def get_gaia_filt(version, name): path = '{0}/gaia/dr2/'.format(filters_dir) elif version == 'dr2_rev': path = '{0}/gaia/dr2_rev/'.format(filters_dir) + elif version == 'edr3': + path = '{0}/gaia/edr3/'.format(filters_dir) else: - raise ValueError('GAIA filter version {0} not understood. Please use dr1, dr2, or dr2_rev'.format(version)) + raise ValueError('GAIA filter version {0} not understood. Please use dr1, dr2, dr2_rev, or edr3'.format(version)) # Get the filter info try: @@ -371,17 +370,14 @@ def get_gaia_filt(version, name): t.rename_column('col4', 'Gbp') t.rename_column('col6', 'Grp') - cols = np.array(t.keys()) - idx = np.where(cols == name)[0][0] - - trans = t[cols[idx]] + trans = t[name] # Change 99 values where filters are undefined into 0, to ensure that # it doesn't mess up our flux values bad = np.where(trans > 90) trans[bad] = 0 except: - raise ValueError('Could not find Gaia filter {0}'.format(name)) + raise ValueError('Could not find Gaia filter {0} for version {1}'.format(name, version)) # Convert wavelengths to angstroms (from nm) wave = t['LAMBDA'] * 10 diff --git a/spisea/synthetic.py b/spisea/synthetic.py index fabfe9ca..055fe515 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1772,7 +1772,7 @@ def get_filter_info(name, vega=vega, rebin=True): filt = filters.get_ztf_filt(filterName) elif name.startswith('gaia'): - version = tmp[1] + version = tmp[1] if len(tmp)==3 else 'edr3' filt = filters.get_gaia_filt(version, filterName) elif name.startswith('hawki'): @@ -1858,8 +1858,14 @@ def get_filter_col_name(obs_str): if len(tmp) == 3: # Catch Gaia filter cases. Otherwise, it is HST filter - if 'dr2_rev' in tmp: + if 'dr1' in tmp: + filt_name = 'gaiaDR1_{0}'.format(tmp[-1]) + elif 'dr2' in tmp: + filt_name = 'gaiaDR2old_{0}'.format(tmp[-1]) + elif 'dr2_rev' in tmp: filt_name = 'gaiaDR2_{0}'.format(tmp[-1]) + elif 'edr3' in tmp: + filt_name = 'gaiaEDR3_{0}'.format(tmp[-1]) elif 'roman' in tmp: filt_name = 'roman_{0}'.format(tmp[-1]) else: @@ -1929,8 +1935,10 @@ def get_obs_str(col): 'ukirt_J':'ukirt,J', 'ukirt_H':'ukirt,H', 'ukirt_K':'ukirt,K', 'ctio_osiris_H': 'ctio_osiris,H', 'ctio_osiris_K': 'ctio_osiris,K', 'ztf_g':'ztf,g', 'ztf_r':'ztf,r', 'ztf_i':'ztf,i', - 'gaiaDR2_G': 'gaia,dr2_rev,G', 'gaiaDR2_Gbp':'gaia,dr2_rev,Gbp', - 'gaiaDR2_Grp':'gaia,dr2_rev,Grp', + 'gaiaDR1_G': 'gaia,dr1,G', 'gaiaDR1_Gbp':'gaia,dr1,Gbp', 'gaiaDR1_Grp':'gaia,dr1,Grp', + 'gaiaDR2old_G': 'gaia,dr2,G', 'gaiaDR2old_Gbp':'gaia,dr2,Gbp', 'gaiaDR2old_Grp':'gaia,dr2,Grp', + 'gaiaDR2_G': 'gaia,dr2_rev,G', 'gaiaDR2_Gbp':'gaia,dr2_rev,Gbp', 'gaiaDR2_Grp':'gaia,dr2_rev,Grp', + 'gaiaEDR3_G': 'gaia,edr3,G', 'gaiaEDR3_Gbp':'gaia,edr3,Gbp', 'gaiaEDR3_Grp':'gaia,edr3,Grp', 'hawki_J': 'hawki,J', 'hawki_H': 'hawki,H', 'hawki_Ks': 'hawki,Ks', diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 2a9e468d..7fc61187 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -165,7 +165,10 @@ def test_filters(): 'ubv,I', 'jg,J', 'jg,H', 'jg,K', 'decam,y', 'decam,i', 'decam,z', 'decam,u', 'decam,g', 'decam,r', + 'gaia,dr1,G', 'gaia,dr1,Gbp', 'gaia,dr1,Grp', + 'gaia,dr2,G', 'gaia,dr2,Gbp', 'gaia,dr2,Grp', 'gaia,dr2_rev,G', 'gaia,dr2_rev,Gbp', 'gaia,dr2_rev,Grp', + 'gaia,edr3,G', 'gaia,edr3,Gbp', 'gaia,edr3,Grp', 'jwst,F070W', 'jwst,F090W', 'jwst,F115W', 'jwst,F140M', 'jwst,F150W', 'jwst,F150W2', 'jwst,F162M', 'jwst,F164N', 'jwst,F182M', 'jwst,F187N', 'jwst,F200W', 'jwst,F212N', From d9777f8d8c3994b4a5793291af465c6cdb2f5e81 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 28 May 2026 14:35:16 -0700 Subject: [PATCH 127/191] clean up ps1 and decam filter names --- docs/filters.rst | 2 +- spisea/synthetic.py | 4 ++-- spisea/tests/test_models.py | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/filters.rst b/docs/filters.rst index 998624c3..66fbdf30 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -213,7 +213,7 @@ Example: ``'ogle,Rw'`` PanStarrs 1 filters from `Tonry et al. 2012 `_ -Filters: g, r, i, z, y +Filters: g, r, i, z, y, w Example: ``'ps1, g'`` diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 055fe515..aea8c048 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1886,12 +1886,12 @@ def get_obs_str(col): # Define dictionary for filters filt_list = {'hst_f127m': 'wfc3,ir,f127m', 'hst_f139m': 'wfc3,ir,f139m', 'hst_f153m': 'wfc3,ir,f153m', 'hst_f814w': 'acs,wfc1,f814w', 'hst_f125w': 'wfc3,ir,f125w', 'hst_f160w': 'wfc3,ir,f160w', - 'decam_y': 'decam,y', 'decam_i': 'decam,i', 'decam_z': 'decam,z', + 'decam_y': 'decam,y', 'decam_Y': 'decam,Y', 'decam_i': 'decam,i', 'decam_z': 'decam,z', 'decam_u':'decam,u', 'decam_g':'decam,g', 'decam_r':'decam,r', 'vista_Y':'vista,Y', 'vista_Z':'vista,Z', 'vista_J': 'vista,J', 'vista_H': 'vista,H', 'vista_Ks': 'vista,Ks', 'ps1_z':'ps1,z', 'ps1_g':'ps1,g', 'ps1_r': 'ps1,r', - 'ps1_i': 'ps1,i', 'ps1_y':'ps1,y', + 'ps1_i': 'ps1,i', 'ps1_y':'ps1,y', 'ps1_w': 'ps1,w', 'jwst_F090W': 'jwst,F090W', 'jwst_F164N': 'jwst,F164N', 'jwst_F212N': 'jwst,F212N', 'jwst_F323N':'jwst,F323N', 'jwst_F466N': 'jwst,F466N', 'jwst_F070W': 'jwst,F070W', diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 7fc61187..2f8db359 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -163,7 +163,7 @@ def test_filters(): 'ctio_osiris,K', 'ctio_osiris,H', 'ubv,U', 'ubv,B', 'ubv,V', 'ubv,R', 'ubv,I', 'jg,J', 'jg,H', 'jg,K', - 'decam,y', 'decam,i', 'decam,z', + 'decam,y', 'decam,Y', 'decam,i', 'decam,z', 'decam,u', 'decam,g', 'decam,r', 'gaia,dr1,G', 'gaia,dr1,Gbp', 'gaia,dr1,Grp', 'gaia,dr2,G', 'gaia,dr2,Gbp', 'gaia,dr2,Grp', @@ -182,7 +182,7 @@ def test_filters(): 'nirc1,K', 'nirc1,H', 'nirc2,J', 'nirc2,H', 'nirc2,Kp', 'nirc2,K', 'nirc2,Lp', 'nirc2,Hcont', 'nirc2,FeII', 'nirc2,Brgamma', 'ps1,z', - 'ps1,g', 'ps1,r','ps1,i', 'ps1,y', + 'ps1,g', 'ps1,r','ps1,i', 'ps1,y', 'ps1,w', 'ukirt,J', 'ukirt,H', 'ukirt,K', 'vista,Y', 'vista,Z', 'vista,J', 'vista,H', 'vista,Ks', 'ztf,g', 'ztf,r', 'ztf,i', From 52791bc3f511df9e0fcef83956523d458a5950f3 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 28 May 2026 15:38:39 -0700 Subject: [PATCH 128/191] make filter handling more generic so any synphot filters can be used --- docs/add_filters.rst | 6 +-- spisea/synthetic.py | 102 ++++++++----------------------------------- 2 files changed, 22 insertions(+), 86 deletions(-) diff --git a/docs/add_filters.rst b/docs/add_filters.rst index 4fe10c88..60dfde03 100644 --- a/docs/add_filters.rst +++ b/docs/add_filters.rst @@ -11,9 +11,9 @@ If the user wants to add new photometric filters to SPISEA, there are 4 main ste 3) Edit the ``get_filter_info()`` function in ``synthetic.py`` to call the new function in ``filters.py`` when the new filter string is called. - 4) Edit the ``get_obs_str()`` function in ``synthetic.py`` to - convert between column name and filter string (e.g input string - for get_filter_info) for the new filters. + 4) If needed (if converting between the ``'_'`` and ``','`` separators is not sufficient), + edit the ``get_obs_str()`` and ``get_filter_col_name()`` functions in ``synthetic.py`` to + convert between column name and filter string for the new filters. 5) Add the filter to the ``filt_list`` in the ``test_filters()`` function in ``tests/test_models.py`` to ensure the filter can be loaded properly. diff --git a/spisea/synthetic.py b/spisea/synthetic.py index aea8c048..dd2fcf1f 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1883,91 +1883,27 @@ def get_obs_str(col): # Remove the trailing m_ name = col[2:] - # Define dictionary for filters - filt_list = {'hst_f127m': 'wfc3,ir,f127m', 'hst_f139m': 'wfc3,ir,f139m', 'hst_f153m': 'wfc3,ir,f153m', - 'hst_f814w': 'acs,wfc1,f814w', 'hst_f125w': 'wfc3,ir,f125w', 'hst_f160w': 'wfc3,ir,f160w', - 'decam_y': 'decam,y', 'decam_Y': 'decam,Y', 'decam_i': 'decam,i', 'decam_z': 'decam,z', - 'decam_u':'decam,u', 'decam_g':'decam,g', 'decam_r':'decam,r', - 'vista_Y':'vista,Y', 'vista_Z':'vista,Z', 'vista_J': 'vista,J', - 'vista_H': 'vista,H', 'vista_Ks': 'vista,Ks', - 'ps1_z':'ps1,z', 'ps1_g':'ps1,g', 'ps1_r': 'ps1,r', - 'ps1_i': 'ps1,i', 'ps1_y':'ps1,y', 'ps1_w': 'ps1,w', - 'jwst_F090W': 'jwst,F090W', 'jwst_F164N': 'jwst,F164N', 'jwst_F212N': 'jwst,F212N', - 'jwst_F323N':'jwst,F323N', 'jwst_F466N': 'jwst,F466N', - 'jwst_F070W': 'jwst,F070W', - 'jwst_F115W': 'jwst,F115W', - 'jwst_F140M': 'jwst,F140M', - 'jwst_F150W': 'jwst,F150W', - 'jwst_F150W2': 'jwst,F150W2', - 'jwst_F162M': 'jwst,F162M', - 'jwst_F182M': 'jwst,F182M', - 'jwst_F187N': 'jwst,F187N', - 'jwst_F200W': 'jwst,F200W', - 'jwst_F210M': 'jwst,F210M', - 'jwst_F250M': 'jwst,F250M', - 'jwst_F277W': 'jwst,F277W', - 'jwst_F300M': 'jwst,F300M', - 'jwst_F322W2': 'jwst,F322W2', - 'jwst_F335M': 'jwst,F335M', - 'jwst_F356W': 'jwst,F356W', - 'jwst_F360M': 'jwst,F360M', - 'jwst_F405N': 'jwst,F405N', - 'jwst_F410M': 'jwst,F410M', - 'jwst_F430M': 'jwst,F430M', - 'jwst_F444W': 'jwst,F444W', - 'jwst_F440W': 'jwst,F440W', - 'jwst_F460M': 'jwst,F460M', - 'jwst_F470N': 'jwst,F470N', - 'jwst_F480M': 'jwst,F480M', - 'nirc2_J': 'nirc2,J', 'nirc2_H': 'nirc2,H', 'nirc2_Kp': 'nirc2,Kp', 'nirc2_K': 'nirc2,K', - 'nirc2_Lp': 'nirc2,Lp', 'nirc2_Ms': 'nirc2,Ms', 'nirc2_Hcont': 'nirc2,Hcont', - 'nirc2_FeII': 'nirc2,FeII', 'nirc2_Brgamma': 'nirc2,Brgamma', - '2mass_J': '2mass,J', '2mass_H': '2mass,H', '2mass_Ks': '2mass,Ks', - 'ubv_U':'ubv,U', 'ubv_B':'ubv,B', 'ubv_V':'ubv,V', 'ubv_R':'ubv,R', - 'ubv_I':'ubv,I', - 'jg_J': 'jg,J', 'jg_H': 'jg,H', 'jg_K': 'jg,K', - 'nirc1_K':'nirc1,K', 'nirc1_H':'nirc1,H', - 'naco_J':'naco,J', 'naco_H':'naco,H', 'naco_Ks':'naco,Ks', - 'naco_IB_2.00': 'naco,IB_2.00', 'naco_IB_2.03':'naco,IB_2.03', 'naco_IB_2.06':'naco,IB_2.06', - 'naco_IB_2.24':'naco,IB_2.24', 'naco_IB_2.27':'naco,IB_2.27', - 'naco_IB_2.30':'naco,IB_2.30', 'naco_IB_2.33':'naco,IB_2.33', - 'naco_IB_2.36':'naco,IB_2.36', - 'ukirt_J':'ukirt,J', 'ukirt_H':'ukirt,H', 'ukirt_K':'ukirt,K', - 'ctio_osiris_H': 'ctio_osiris,H', 'ctio_osiris_K': 'ctio_osiris,K', - 'ztf_g':'ztf,g', 'ztf_r':'ztf,r', 'ztf_i':'ztf,i', - 'gaiaDR1_G': 'gaia,dr1,G', 'gaiaDR1_Gbp':'gaia,dr1,Gbp', 'gaiaDR1_Grp':'gaia,dr1,Grp', + # This mostly follows a standard form, but we'll account for some special cases + if name[:4]=='hst_': + hst_filts = {'hst_f127m': 'wfc3,ir,f127m', 'hst_f139m': 'wfc3,ir,f139m', 'hst_f153m': 'wfc3,ir,f153m', + 'hst_f814w': 'acs,wfc1,f814w', 'hst_f125w': 'wfc3,ir,f125w', 'hst_f160w': 'wfc3,ir,f160w'} + obs_str = hst_filts[name] + elif name[:6]=='roman_': + tmp = name.split('_') + obs_str = 'roman,wfi,'+tmp[1] + elif name[:4]=='gaia': + gaia_filts = {'gaiaDR1_G': 'gaia,dr1,G', 'gaiaDR1_Gbp':'gaia,dr1,Gbp', 'gaiaDR1_Grp':'gaia,dr1,Grp', 'gaiaDR2old_G': 'gaia,dr2,G', 'gaiaDR2old_Gbp':'gaia,dr2,Gbp', 'gaiaDR2old_Grp':'gaia,dr2,Grp', 'gaiaDR2_G': 'gaia,dr2_rev,G', 'gaiaDR2_Gbp':'gaia,dr2_rev,Gbp', 'gaiaDR2_Grp':'gaia,dr2_rev,Grp', - 'gaiaEDR3_G': 'gaia,edr3,G', 'gaiaEDR3_Gbp':'gaia,edr3,Gbp', 'gaiaEDR3_Grp':'gaia,edr3,Grp', - 'hawki_J': 'hawki,J', - 'hawki_H': 'hawki,H', - 'hawki_Ks': 'hawki,Ks', - 'roman_f062': 'roman,wfi,f062', - 'roman_f087': 'roman,wfi,f087', - 'roman_f106': 'roman,wfi,f106', - 'roman_f129': 'roman,wfi,f129', - 'roman_f158': 'roman,wfi,f158', - 'roman_f146': 'roman,wfi,f146', - 'roman_f213': 'roman,wfi,f213', - 'roman_f184': 'roman,wfi,f184', - 'rubin_g':'rubin,g', - 'rubin_i':'rubin,i', - 'rubin_r':'rubin,r', - 'rubin_u':'rubin,u', - 'rubin_z':'rubin,z', - 'rubin_y':'rubin,y', - 'euclid_VIS':'euclid,VIS', - 'euclid_Y':'euclid,Y', - 'euclid_J':'euclid,J', - 'euclid_H':'euclid,H', - 'nsfcam_L':'nsfcam,L', - 'tess_tess':'tess,tess', - 'washington_C':'washington,C', 'washington_M':'washington,M', - 'washington_T1':'washington,T1', 'washington_T2':'washington,T2', - 'hipparcos_Hp':'hipparcos,Hp', 'tycho_B':'tycho,B', 'tycho_V':'tycho,V', - 'kepler_Kp':'kepler,Kp', 'ogle_Rw':'ogle,Rw'} - - obs_str = filt_list[name] + 'gaiaEDR3_G': 'gaia,edr3,G', 'gaiaEDR3_Gbp':'gaia,edr3,Gbp', 'gaiaEDR3_Grp':'gaia,edr3,Grp'} + obs_str = gaia_filts[name] + elif name[:8]=='naco_IB_': + obs_str = name.replace('_', ',', 1) + elif name[:12]=='ctio_osiris_': + tmp = name.split('_') + obs_str = tmp[0]+'_'+tmp[1]+','+tmp[2] + else: + obs_str = ','.join(name.split('_')) return obs_str From 2e99d0c57505994194cd9004398c1924977e15b2 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 28 May 2026 15:41:49 -0700 Subject: [PATCH 129/191] file cleanup --- docs/filters.rst | 5 +++++ filt_func/gaia/edr3/zeropt.dat | 2 -- 2 files changed, 5 insertions(+), 2 deletions(-) delete mode 100644 filt_func/gaia/edr3/zeropt.dat diff --git a/docs/filters.rst b/docs/filters.rst index 66fbdf30..0fffc9fe 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -34,7 +34,9 @@ Available filters: * Euclid * GAIA * HAWK-I +* Hipparcos * Hubble Space Telescope +* Kepler * IRTF * Johnson-Cousins * Johnson-Glass @@ -42,12 +44,15 @@ Available filters: * Keck NIRC * Keck NIRC2 * NACO +* OGLE * PanStarrs 1 * Roman Space Telescope * TESS +* Tycho * UKIRT * Vera C. Rubin Observatory * VISTA +* Washington * ZTF diff --git a/filt_func/gaia/edr3/zeropt.dat b/filt_func/gaia/edr3/zeropt.dat deleted file mode 100644 index 0f4c7cca..00000000 --- a/filt_func/gaia/edr3/zeropt.dat +++ /dev/null @@ -1,2 +0,0 @@ - 25.6873668671 0.0027553202 25.3385422158 0.0027901700 24.7478955012 0.0037793818 VEGAMAG - 25.8010446445 0.0027590522 25.3539555559 0.0023065687 25.1039837393 0.0015800349 AB From 329e4d878a2737bec5b1d01de7ca528c69964057 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 28 May 2026 15:50:14 -0700 Subject: [PATCH 130/191] update my constributions --- docs/contributors.rst | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/contributors.rst b/docs/contributors.rst index 1944147e..1ab42fc8 100644 --- a/docs/contributors.rst +++ b/docs/contributors.rst @@ -43,8 +43,9 @@ functions for primary and companion star mass generation (v2.3), updated random state generators (v2.4) Macy Huston -- New metallicity bound + isochrone filter checks, -imf_mass_lim bugfix, roman filter bugfix, added Euclid filters, Synthpop compatibility -updates (v2.2), improvements for reading in/updated existing isochrone -files, Vega mag to ST mag conversion function (v2.4) +imf_mass_lim bugfix, roman filter bugfix, Synthpop compatibility +updates (v2.2), improvements for reading in/updating existing isochrone +files, Vega mag to ST mag conversion function (v2.4), several new filter sets +and increased filter name flexibility for synphot Anna Pusack -- Added IRTF L-band filter support From 6f6ca6aafd1f2cf03ced11eb430fba77ed8a8306 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 28 May 2026 16:25:06 -0700 Subject: [PATCH 131/191] add subaru hsc filters --- docs/filters.rst | 11 + filt_func/subaru/hsc/Y.dat | 96 ++++ filt_func/subaru/hsc/g.dat | 187 +++++++ filt_func/subaru/hsc/i.dat | 566 +++++++++++++++++++ filt_func/subaru/hsc/nb387.dat | 38 ++ filt_func/subaru/hsc/nb468.dat | 54 ++ filt_func/subaru/hsc/nb515.dat | 35 ++ filt_func/subaru/hsc/nb527.dat | 40 ++ filt_func/subaru/hsc/nb656.dat | 52 ++ filt_func/subaru/hsc/nb718.dat | 74 +++ filt_func/subaru/hsc/nb816.dat | 74 +++ filt_func/subaru/hsc/nb921.dat | 961 +++++++++++++++++++++++++++++++++ filt_func/subaru/hsc/nb926.dat | 94 ++++ filt_func/subaru/hsc/nb973.dat | 99 ++++ filt_func/subaru/hsc/r.dat | 582 ++++++++++++++++++++ filt_func/subaru/hsc/z.dat | 65 +++ spisea/filters.py | 17 + spisea/synthetic.py | 6 + spisea/tests/test_models.py | 6 +- 19 files changed, 3056 insertions(+), 1 deletion(-) create mode 100644 filt_func/subaru/hsc/Y.dat create mode 100644 filt_func/subaru/hsc/g.dat create mode 100644 filt_func/subaru/hsc/i.dat create mode 100644 filt_func/subaru/hsc/nb387.dat create mode 100644 filt_func/subaru/hsc/nb468.dat create mode 100644 filt_func/subaru/hsc/nb515.dat create mode 100644 filt_func/subaru/hsc/nb527.dat create mode 100644 filt_func/subaru/hsc/nb656.dat create mode 100644 filt_func/subaru/hsc/nb718.dat create mode 100644 filt_func/subaru/hsc/nb816.dat create mode 100644 filt_func/subaru/hsc/nb921.dat create mode 100644 filt_func/subaru/hsc/nb926.dat create mode 100644 filt_func/subaru/hsc/nb973.dat create mode 100644 filt_func/subaru/hsc/r.dat create mode 100644 filt_func/subaru/hsc/z.dat diff --git a/docs/filters.rst b/docs/filters.rst index 0fffc9fe..667d78a0 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -47,6 +47,7 @@ Available filters: * OGLE * PanStarrs 1 * Roman Space Telescope +* Subaru * TESS * Tycho * UKIRT @@ -238,6 +239,16 @@ Filters: F062, F087, F106, F129, F158, W146, F184, F213 Example: ``'roman,wfi,f062'`` +**Subaru** + +Subaru filters from the `SVO Filter Profile Service `_. + +Filters: g, r, i, z, Y, nb387, nb468, nb515, nb527, nb656, nb718, nb816, nb921, nb926, nb973 + +Instruments: hsc + +Example: ``'subaru,hsc,i'`` + **TESS** TESS filter: a single wide, red-optical `bandpass `_ diff --git a/filt_func/subaru/hsc/Y.dat b/filt_func/subaru/hsc/Y.dat new file mode 100644 index 00000000..11e17836 --- /dev/null +++ b/filt_func/subaru/hsc/Y.dat @@ -0,0 +1,96 @@ +9000.00 0.0075 +9020.00 0.0033 +9040.00 0.0003 +9060.00 0.0031 +9080.00 0.0015 +9100.00 0.0050 +9120.00 0.0040 +9140.00 0.0076 +9160.00 0.0117 +9180.00 0.0141 +9200.00 0.0264 +9220.00 0.0373 +9240.00 0.0541 +9260.00 0.0860 +9280.00 0.1191 +9300.00 0.1495 +9320.00 0.1717 +9340.00 0.1991 +9360.00 0.2426 +9380.00 0.2940 +9400.00 0.3865 +9420.00 0.3959 +9440.00 0.3757 +9460.00 0.4018 +9480.00 0.4130 +9500.00 0.4044 +9520.00 0.4078 +9540.00 0.4048 +9560.00 0.4011 +9580.00 0.4026 +9600.00 0.4064 +9620.00 0.4153 +9640.00 0.4210 +9660.00 0.4148 +9680.00 0.4221 +9700.00 0.4226 +9720.00 0.4129 +9740.00 0.3978 +9760.00 0.3902 +9780.00 0.3902 +9800.00 0.3825 +9820.00 0.3814 +9840.00 0.3749 +9860.00 0.3663 +9880.00 0.3570 +9900.00 0.3481 +9920.00 0.3376 +9940.00 0.3269 +9960.00 0.3168 +9980.00 0.3055 +10000.00 0.2956 +10020.00 0.2817 +10040.00 0.2679 +10060.00 0.2544 +10080.00 0.2411 +10100.00 0.2290 +10120.00 0.2169 +10140.00 0.2036 +10160.00 0.1901 +10180.00 0.1763 +10200.00 0.1628 +10220.00 0.1536 +10240.00 0.1438 +10260.00 0.1346 +10280.00 0.1251 +10300.00 0.1154 +10320.00 0.1058 +10340.00 0.0961 +10360.00 0.0866 +10380.00 0.0771 +10400.00 0.0678 +10420.00 0.0647 +10440.00 0.0614 +10460.00 0.0581 +10480.00 0.0550 +10500.00 0.0516 +10520.00 0.0484 +10540.00 0.0451 +10560.00 0.0416 +10580.00 0.0381 +10600.00 0.0343 +10620.00 0.0302 +10640.00 0.0259 +10660.00 0.0213 +10680.00 0.0168 +10700.00 0.0125 +10720.00 0.0088 +10740.00 0.0057 +10760.00 0.0034 +10780.00 0.0018 +10800.00 0.0007 +10820.00 0.0005 +10840.00 0.0003 +10860.00 0.0001 +10880.00 0.0001 +10900.00 0.0000 diff --git a/filt_func/subaru/hsc/g.dat b/filt_func/subaru/hsc/g.dat new file mode 100644 index 00000000..8ab27c4a --- /dev/null +++ b/filt_func/subaru/hsc/g.dat @@ -0,0 +1,187 @@ +3840.00 0.0000 +3850.00 0.0001 +3860.00 0.0001 +3870.00 0.0001 +3880.00 0.0001 +3890.00 0.0001 +3900.00 0.0001 +3910.00 0.0003 +3920.00 0.0004 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0.0076 +8976.80 0.0073 +8981.30 0.0070 +8985.70 0.0070 +8990.20 0.0073 +8994.70 0.0072 +8999.20 0.0070 diff --git a/filt_func/subaru/hsc/nb387.dat b/filt_func/subaru/hsc/nb387.dat new file mode 100644 index 00000000..b0541787 --- /dev/null +++ b/filt_func/subaru/hsc/nb387.dat @@ -0,0 +1,38 @@ +3812.00 0.0000 +3814.00 0.0159 +3818.00 0.0652 +3822.00 0.1579 +3826.00 0.2902 +3830.00 0.4441 +3834.00 0.5945 +3838.00 0.7168 +3842.00 0.7966 +3846.00 0.8373 +3850.00 0.8542 +3854.00 0.8618 +3858.00 0.8671 +3862.00 0.8706 +3866.00 0.8673 +3870.00 0.8476 +3874.00 0.7990 +3878.00 0.7136 +3882.00 0.5934 +3886.00 0.4510 +3890.00 0.3050 +3895.00 0.1756 +3899.00 0.0801 +3903.00 0.0257 +3907.00 0.0046 +3911.00 0.0012 +3915.00 0.0030 +3919.00 0.0043 +3923.00 0.0041 +3927.00 0.0032 +3931.00 0.0025 +3935.00 0.0024 +3939.00 0.0026 +3943.00 0.0023 +3947.00 0.0018 +3951.00 0.0015 +3955.00 0.0017 +3960.00 0.0020 diff --git a/filt_func/subaru/hsc/nb468.dat b/filt_func/subaru/hsc/nb468.dat new file mode 100644 index 00000000..6a6f8372 --- /dev/null +++ b/filt_func/subaru/hsc/nb468.dat @@ -0,0 +1,54 @@ +4569.80 0.0001 +4574.00 0.0016 +4578.10 0.0017 +4582.20 0.0008 +4586.40 0.0009 +4590.50 0.0011 +4594.60 0.0006 +4598.80 0.0009 +4602.90 0.0038 +4607.00 0.0083 +4611.20 0.0140 +4615.30 0.0237 +4619.40 0.0420 +4623.60 0.0738 +4627.70 0.1236 +4631.90 0.1922 +4636.00 0.2738 +4640.10 0.3632 +4644.30 0.4602 +4648.40 0.5643 +4652.50 0.6704 +4656.70 0.7694 +4660.80 0.8487 +4665.00 0.8999 +4669.10 0.9249 +4673.30 0.9324 +4677.40 0.9301 +4681.50 0.9230 +4685.70 0.9141 +4689.80 0.9067 +4694.00 0.9035 +4698.10 0.9031 +4702.30 0.9000 +4706.40 0.8886 +4710.50 0.8629 +4714.70 0.8191 +4718.80 0.7612 +4723.00 0.6978 +4727.10 0.6312 +4731.30 0.5558 +4735.40 0.4654 +4739.60 0.3613 +4743.70 0.2542 +4747.90 0.1601 +4752.00 0.0911 +4756.20 0.0494 +4760.30 0.0276 +4764.50 0.0163 +4768.60 0.0099 +4772.80 0.0067 +4776.90 0.0055 +4781.10 0.0045 +4785.30 0.0030 +4789.40 0.0013 diff --git a/filt_func/subaru/hsc/nb515.dat 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+++ b/filt_func/subaru/hsc/z.dat @@ -0,0 +1,65 @@ +8280.00 0.0061 +8300.00 0.0034 +8320.00 0.0040 +8340.00 0.0045 +8360.00 0.0056 +8380.00 0.0089 +8400.00 0.0210 +8420.00 0.0333 +8440.00 0.0544 +8460.00 0.0824 +8480.00 0.1281 +8500.00 0.1891 +8520.00 0.2546 +8540.00 0.3355 +8560.00 0.4037 +8580.00 0.4649 +8600.00 0.5147 +8620.00 0.5471 +8640.00 0.5697 +8660.00 0.5809 +8680.00 0.5872 +8700.00 0.5888 +8720.00 0.5897 +8740.00 0.5902 +8760.00 0.5902 +8780.00 0.5910 +8800.00 0.5901 +8820.00 0.5883 +8840.00 0.5864 +8860.00 0.5876 +8880.00 0.5874 +8900.00 0.5853 +8920.00 0.5845 +8940.00 0.5770 +8960.00 0.5627 +8980.00 0.5424 +9000.00 0.5335 +9020.00 0.5416 +9040.00 0.5564 +9060.00 0.5466 +9080.00 0.5276 +9100.00 0.5348 +9120.00 0.5416 +9140.00 0.5336 +9160.00 0.5326 +9180.00 0.5228 +9200.00 0.5299 +9220.00 0.5135 +9240.00 0.4886 +9260.00 0.4487 +9280.00 0.3889 +9300.00 0.3104 +9320.00 0.2159 +9340.00 0.1541 +9360.00 0.1108 +9380.00 0.0766 +9400.00 0.0570 +9420.00 0.0330 +9440.00 0.0176 +9460.00 0.0111 +9480.00 0.0074 +9500.00 0.0038 +9520.00 0.0021 +9540.00 0.0012 +9560.00 0.0001 diff --git a/spisea/filters.py b/spisea/filters.py index 33c7d80b..ece4a7ce 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -589,3 +589,20 @@ def get_ogle_filt(name): spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ogle,{0}'.format(name)) return spectrum + +def get_subaru_filt(instrument, name): + """ + Define the subaru filters as pysynphot object + """ + try: + t = Table.read('{0}/subaru/{1}/{2}.dat'.format(filters_dir, instrument, name), format='ascii') + except: + raise ValueError('Could not find Subaru filter {0} in {1}/subaru/{2}'.format(name, filters_dir, instrument)) + + # Wavelength in nm->angstroms and and transmission in percent->fraction + wave = t['col1'] + trans = t['col2'] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='subaru,{0},{1}'.format(instrument, name)) + + return spectrum diff --git a/spisea/synthetic.py b/spisea/synthetic.py index dd2fcf1f..ee3ac5ce 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1805,6 +1805,10 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('ogle'): filt = filters.get_ogle_filt(filterName) + elif name.startswith('subaru'): + inst = tmp[1] + filt = filters.get_subaru_filt(inst, filterName) + else: # Otherwise, look for the filter info in the cdbs/mtab and cdbs/comp files try: @@ -1868,6 +1872,8 @@ def get_filter_col_name(obs_str): filt_name = 'gaiaEDR3_{0}'.format(tmp[-1]) elif 'roman' in tmp: filt_name = 'roman_{0}'.format(tmp[-1]) + elif tmp[0] == 'subaru': + filt_name = '_'.join(tmp) else: filt_name = 'hst_{0}'.format(tmp[-1]) else: diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 2f8db359..be6315b1 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -195,7 +195,11 @@ def test_filters(): 'nsfcam,L', 'tess,tess', 'washington,C', 'washington,M', 'washington,T1', 'washington,T2', 'hipparcos,Hp', 'tycho,B', 'tycho,V', - 'kepler,Kp', 'ogle,Rw'] + 'kepler,Kp', 'ogle,Rw', + 'subaru,hsc,g','subaru,hsc,r','subaru,hsc,i','subaru,hsc,z','subaru,hsc,Y', + 'subaru,hsc,nb387', 'subaru,hsc,nb468', 'subaru,hsc,nb515', 'subaru,hsc,nb527', + 'subaru,hsc,nb656', 'subaru,hsc,nb718', 'subaru,hsc,nb816', 'subaru,hsc,nb921', + 'subaru,hsc,nb926', 'subaru,hsc,nb973'] # Loop through filters to test that they work: get_filter_info for ii in filt_list: From 459141cc98bb9e461173cda45856ede8757b863f Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 1 Jun 2026 13:22:37 -0700 Subject: [PATCH 132/191] debug calc_multi no companions case --- spisea/imf/imf.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index df6a8887..eb14f55f 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -266,7 +266,10 @@ def calc_multi(self, newMasses, newIsMultiple, CSF, MF): # so we can put in uniform arrays in _multi_props.random_q. comp_unique = np.unique(comp_nums) comp_indices = [np.where(comp_nums == i)[0] for i in comp_unique] - compMasses = np.zeros((len(newMasses), max(comp_unique))) + if np.any(newIsMultiple): + compMasses = np.zeros((len(newMasses), max(comp_unique))) + else: + compMasses = np.zeros((len(newMasses), 1)) for comp_num, comp_index in zip(comp_unique, comp_indices): # Calculate masses of companions From dd184f0bf5a999a14db3ac3f4aa9691dfbe79af8 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 1 Jun 2026 14:06:41 -0700 Subject: [PATCH 133/191] debug case where cluster generates no companions --- spisea/synthetic.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index ee3ac5ce..3e8aa2e2 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -416,7 +416,8 @@ def _make_companions_table_new(self, star_systems, compMass): if self.verbose and sum(companions_teff_non_nan > 0) != N_comp_tot: print(f'Found {N_comp_tot - sum(companions_teff_non_nan > 0):d} companions out of stellar mass range') - assert companions['mass'][companions_teff_non_nan > 0].min() > 0, "Companion mass is not positive" + if len(companions)>0: + assert companions['mass'][companions_teff_non_nan > 0].min() > 0, "Companion mass is not positive" return star_systems, companions From 85ee83eee15b3dc0b12dc3427fa5e89ba6c883ee Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 1 Jun 2026 14:13:06 -0700 Subject: [PATCH 134/191] debug case where cluster generates no companions --- spisea/synthetic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 3e8aa2e2..e4b0aab7 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -416,7 +416,7 @@ def _make_companions_table_new(self, star_systems, compMass): if self.verbose and sum(companions_teff_non_nan > 0) != N_comp_tot: print(f'Found {N_comp_tot - sum(companions_teff_non_nan > 0):d} companions out of stellar mass range') - if len(companions)>0: + if len(companions['mass'][companions_teff_non_nan > 0])>0: assert companions['mass'][companions_teff_non_nan > 0].min() > 0, "Companion mass is not positive" return star_systems, companions From 4c8677a93a5cd09690f78a2ed4ee27354fa5f181 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Mon, 1 Jun 2026 16:47:53 -0700 Subject: [PATCH 135/191] Fixes to all merge conflicts --- spisea/atmospheres.py | 4 +- spisea/atmospheres_BACKUP_22350.py | 2524 ++++++++++++++++++++++ spisea/atmospheres_BACKUP_58456.py | 2524 ++++++++++++++++++++++ spisea/atmospheres_BASE_58456.py | 2165 +++++++++++++++++++ spisea/atmospheres_LOCAL_58456.py | 2508 +++++++++++++++++++++ spisea/atmospheres_REMOTE_58456.py | 2172 +++++++++++++++++++ spisea/ifmr.py | 6 +- spisea/imf/imf.py | 3 +- spisea/imf/multiplicity.py | 11 +- spisea/synthetic.py | 28 +- spisea/tests/test_data/companions.fits | Bin 236160 -> 236160 bytes spisea/tests/test_data/star_systems.fits | Bin 1258560 -> 1258560 bytes spisea/tests/test_models.py | 4 +- spisea/tests/test_multiplicity.py | 12 +- spisea/tests/test_synthetic.py | 11 +- 15 files changed, 11947 insertions(+), 25 deletions(-) create mode 100755 spisea/atmospheres_BACKUP_22350.py create mode 100755 spisea/atmospheres_BACKUP_58456.py create mode 100644 spisea/atmospheres_BASE_58456.py create mode 100644 spisea/atmospheres_LOCAL_58456.py create mode 100644 spisea/atmospheres_REMOTE_58456.py diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index 2d34e0e1..1cf94682 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -657,7 +657,7 @@ def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, reb sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) except: # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, + (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, metallicity=metallicity, temperature=temperature, gravity=gravity) @@ -695,7 +695,7 @@ def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, re sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) except: # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, + (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, metallicity=metallicity, temperature=temperature, gravity=gravity) diff --git a/spisea/atmospheres_BACKUP_22350.py b/spisea/atmospheres_BACKUP_22350.py new file mode 100755 index 00000000..e3d2233c --- /dev/null +++ b/spisea/atmospheres_BACKUP_22350.py @@ -0,0 +1,2524 @@ +import logging +import numpy as np +import pysynphot +import os +import glob +from astropy.io import fits +from astropy.table import Table, Column +import pysynphot +import time +import pdb +import warnings + +log = logging.getLogger('atmospheres') + +def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4, verbose=False): + """ + Given atmosphere model, get temperature and gravity bounds + """ + # Open catalog fits file and break out row indices + catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) + + teff_arr = [] + z_arr = [] + logg_arr = [] + for cur_row_index in range(len(catalog)): + index = catalog['INDEX'][cur_row_index] + tmp = index.split(',') + teff_arr.append(float(tmp[0])) + z_arr.append(float(tmp[1])) + logg_arr.append(float(tmp[2])) + teff_arr = np.array(teff_arr) + z_arr = np.array(z_arr) + logg_arr = np.array(logg_arr) + + # Filter by metallicity. Will chose the closest metallicity to desired input + metal_list = np.unique(np.array(z_arr)) + metal_idx = np.argmin(np.abs(metal_list - metallicity)) + metallicity_new = metal_list[metal_idx] + + z_filt = np.where(z_arr == metal_list[metal_idx]) + teff_arr = teff_arr[z_filt] + logg_arr = logg_arr[z_filt] + + # # Now find the closest atmosphere in parameter space to + # # the one we want. We'll find the match with the lowest + # # fractional difference + # teff_diff = (teff_arr - temperature) / temperature + # logg_diff = (logg_arr - gravity) / gravity + # + # diff_tot = abs(teff_diff) + abs(logg_diff) + # idx_f = np.argmin(diff_tot) + # + # temperature_new = teff_arr[idx_f] + # gravity_new = logg_arr[idx_f] + + # First check if temperature within bounds + temperature_new = temperature + if temperature > np.max(teff_arr): + temperature_new = np.max(teff_arr) + if temperature < np.min(teff_arr): + temperature_new = np.min(teff_arr) + + # If temperature within bounds, then check if metallicity within bounds + teff_diff = np.abs(teff_arr - temperature) + sorted_min_diffs = np.unique(teff_diff) + + ## Find two closest temperatures + teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] + teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] + + logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] + logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] + + ## Switch to most conservative bound of logg out of two closest temps + gravity_new = gravity + if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): + gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) + if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): + gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) + + if verbose: + # Print out changes, if any + if temperature_new != temperature: + teff_msg = 'Changing to T={0:6.0f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' + print( teff_msg.format(temperature_new, metallicity, temperature, gravity)) + + if gravity_new != gravity: + logg_msg = 'Changing to logg={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' + print( logg_msg.format(gravity_new, metallicity, temperature, gravity)) + + if metallicity_new != metallicity: + logg_msg = 'Changing to met={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' + print( logg_msg.format(metallicity_new, metallicity, temperature, gravity)) + + return (temperature_new, gravity_new, metallicity_new) + +def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): + """ + Return atmosphere from the Kurucz pysnphot grid + (`Kurucz 1993 `_). + + Grid Range: + + * Teff: 3000 - 50000 K + * gravity: 0 - 5 cgs + * metallicity: -5.0 - 1.0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + Always false for this particular function + """ + try: + sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('k93models', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Kurucz 1993 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): + """ + Return atmospheres from the pysynphot ATLAS9 atlas + (`Castelli & Kurucz 2004 `_). + + Grid Range: + + * Teff: 3500 - 50000 K + * gravity: 0 - 5.0 cgs + * [M/H]: -2.5 - 0.2 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('ck04models', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 5000) + gravity = log gravity (def = 4.0) + """ + try: + sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('nextgen', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find NextGen atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 5000) + gravity = log gravity (def = 4.0) + """ + sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, + rebin=False): + """ + Return atmosphere from the pysynphot + `PHOENIX atlas `_. + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + """ + try: + sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 24000) + gravity = log gravity (def = 4.3) + + rebin=True: pull from atmospheres at ck04model resolution. + """ + # Take care of atmospheres outside the catalog boundaries + logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' + if gravity > 4.3: + if verbose: + print( logg_msg.format(4.3, temperature, gravity)) + gravity = 4.3 + + if rebin: + sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) + else: + sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, + verbose=False): + """ + For a given stellar atmosphere, get extract the closest possible match in + Teff/logg space. Note that this is different from the normal routine + which interpolates along the input grid to get final spectrum. We can't + do this here because the Fierro+15 atmosphere grid is so sparse + + rebin=True: pull from atmospheres at ck04model resolution. + + If verbose, print out the parameters of the match + """ + # Set up the proper root directory + if rebin == True: + root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' + else: + root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' + + # Read in catalog, extract atmosphere info + cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') + teff_arr = [] + z_arr = [] + logg_arr = [] + for ii in range(len(cat)): + index = cat['INDEX'][ii] + tmp = index.split(',') + teff_arr.append(float(tmp[0])) + z_arr.append(float(tmp[1])) + logg_arr.append(float(tmp[2])) + teff_arr = np.array(teff_arr) + z_arr = np.array(z_arr) + logg_arr = np.array(logg_arr) + + # Now find the closest atmosphere in parameter space to + # the one we want. We'll find the match with the lowest + # fractional difference + teff_diff = (teff_arr - temperature) / temperature + logg_diff = (logg_arr - gravity) / gravity + + diff_tot = abs(teff_diff) + abs(logg_diff) + idx_f = np.where(diff_tot == min(diff_tot))[0][0] + + # Extract the filename of the best-match model and read as + # pysynphot object + infile = cat[idx_f]['FILENAME'].split('.') + spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) + + # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the + # ATLAS models are in FLAM at the surface. So, we need to multiply the + # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. + # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file + t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') + tmp = np.where(t['col1'] == infile[0]) + + lum = t['col3'][tmp] * (3.839*10**33) # cgs + sigma = 5.6704 * 10**-5 # cgs + teff = teff_arr[idx_f] # cgs + + radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm + radius /= 3.08*10**18 # in pc + + + # Make the pysynphot spectrum + w = spec['Wavelength'] + f = spec['Flux'] * (1000 / radius)**2. + sp = pysynphot.ArraySpectrum(w,f) + + #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) + + # Print out parameters of match, if desired + if verbose: + print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) + print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) + + return sp + +def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 24000) + gravity = log gravity (def = 4.3) + + rebin=True: pull from atmospheres at ck04model resolution. + """ + if rebin: + sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) + else: + sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 30000) + gravity = log gravity (def = 4.14) + """ + sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): + """ + Return PHOENIX v16 atmospheres from + `Husser et al. 2013 `_. + + Models originally downloaded via `ftp `_. + Solar metallicity and [alpha/Fe] is used. + + Grid Range: + + * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K + * gravity: 0.0 - 6.0 cgs, steps of 0.5 + * [M/H]: -4.0 - 1.0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + """ + atm_model_name = 'phoenix_v16' + if rebin == True: + atm_model_name = 'phoenix_v16_rebin' + + + # Extract atmosphere. If that fails, then check bounds and try again + try: + sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_model_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): + """ + Return atmosphere from CIFIST2011_2015 grid + (`Allard et al. 2012 `_, + `Baraffe et al. 2015 `_ ) + + Grid originally downloaded from `website `_. + + Grid Range: + + * Teff: 1200 - 7000 K + * gravity: 2.5 - 5.5 cgs + * [M/H] = 0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + """ + if rebin == True: + atm_name = 'BTSettl_2015_rebin' + else: + atm_name = 'BTSettl_2015' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) +<<<<<<< HEAD + #print(dir(obj)) + + +======= + + +>>>>>>> upstream/dev + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find BTSettl_2015 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): + """ + Return atmosphere from CIFIST2011 grid + (`Allard et al. 2012 `_) + + Grid originally downloaded `here `_ + + Notes + ------ + Grid Range: + + * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 + + Teff and gravity ranges depend on metallicity: + + [M/H] = -2.5 + + * Teff: 2600 - 4600 K + * gravity: 4.5 - 5.5 + + [M/H] = -2.0 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.5 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.0 + + * Teff: 2600 - 7000 + * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 + + [M/H] = -0.5 + + * Teff: 1000 -7000 + * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 + + [M/H] = 0 + + * Teff: 750 - 7000 + * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 + + [M/H] = 0.5 + + * Teff: 1000 - 5000 + * gravity: 3.5 - 5.0 + + + Alpha enhancement: + + * [M/H]= -0.0, +0.5 no anhancement + * [M/H]= -0.5 with [alpha/H]=+0.2 + * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + **PRINT STATEMENTS TO DEBUG + **check get_atmosphere_bounds + **comment out try/except clause and check break + """ + if rebin == True: + atm_name = 'BTSettl_rebin' + else: + atm_name = 'BTSettl' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) +<<<<<<< HEAD + +def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): + """ + Return atmosphere from Meisner2023 grid + (`Meisner et al. 2023 `_) + + Grid originally downloaded `here `_ + + Grid range: + * Teff = 250 - 1200 K + * gravity: 2.5 - 5.5 cgs (in steps of 0.5) + * [M/H] = -1.0, -0.5, 0, +0, +0.3 + + """ + if rebin == True: + atm_name = 'Meisner2023_rebin' + else: + atm_name = 'Meisner2023' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) +======= + +>>>>>>> upstream/dev + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Meisner2023 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): + """ + Return atmosphere from Phillips et al., 2020 using ATMO model + (`Phillips et al. 2020 `_) + + Grid originally downloaded `here `_ + + Grid Range: + * Teff: 200 - 3000 K + * gravity: 2.5 - 5.5 cgs + * [M/H] = 0 + """ + if rebin == True: + atm_name = 'Phillips2020_rebin' + else: + atm_name = 'Phillips2020' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Phillips2020 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): + """ + Return white dwarf atmospheres from + `Koester et al. 2010 `_ + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + """ + sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. + + Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 + """ + try: + sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_atlas_phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model + and phoenixV16. + + Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 + """ + try: + sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_BTSettl_phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_meisner_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model + and Meisner2023. + + Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 + """ + try: + sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_meisner', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find BTSettl-Meisner merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +#---------------------------------------------------------------------# +def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, + rebin=True): + """ + Return a stellar atmosphere from a suite of different model grids, + depending on the input temperature, (all values in K). + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + + Notes + ----- + The underlying stellar model grid used changes as a function of + stellar temperature (in K): + + * T > 20,000: ATLAS + * 5500 <= T < 20,000: ATLAS + * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge + * 3800 <= T < 5000: PHOENIXv16 + + For T < 3800, there is an additional gravity and metallicity + dependence: + + If T < 3800 and [M/H] = 0: + + * T < 3800, logg < 2.5: PHOENIX v16 + * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge + * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 + * 1200 < T <= 1000, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge + * 1000 < T <= 250, logg > 2.5: Meisner2023 + + Otherwise, if T < 3800 and [M/H] != 0: + + * T < 3800: PHOENIX v16 + + References: + + * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) + * PHOENIXv16 (`Husser et al. 2013 `_) + * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) + * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) + + LTE WARNING: + + The ATLAS atmospheres are calculated with LTE, and so they + are less accurate when non-LTE conditions apply (e.g. T > 20,000 + K). Ultimately we'd like to add a non-LTE atmosphere grid for + the hottest stars in the future. + + HOW BOUNDARIES BETWEEN MODELS ARE TREATED: + + At the boundary between two models grids a temperature range is defined + where the resulting atmosphere is a weighted average between the two + grids. Near one boundary one model + is weighted more heavily, while at the other boundary the other + model is weighted more heavily. These are calculated in the + temperature ranges where we switch between model grids, to + ensure a smooth transition. + """ + # For T < 3800, atmosphere depends on metallicity + gravity. + # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is + # currently available here, so if non-solar metallicity, just stick with + # the Phoenix grid + if (temperature < 1000): + if verbose: + print( 'Meisner2023 atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature <= 1200) & (temperature >= 1000): + if (gravity >= 3.5): + if verbose: + print( 'BTSettl/Meisner2023 merged atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + if (gravity < 3.5) & (gravity >=2.5): + if verbose: + print( 'Meisner2023 atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature <= 3800) & (metallicity == 0): + # High gravity are in BTSettl regime + if (temperature <= 3200) & (gravity > 2.5): + if verbose: + print( 'BTSettl_2015 atmosphere') + return get_BTSettl_2015_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): + if verbose: + print( 'BTSettl/Phoenixv16 merged atmosphere') + return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + # Low gravity is PHOENIX regime + if gravity <= 2.5: + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature <= 3800) & (metallicity != 0): + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + # For T > 3800, no metallicity or gravity dependence + if (temperature >= 3800) & (temperature < 5000): + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature >= 5000) & (temperature < 5500): + if verbose: + print( 'ATLAS/Phoenix merged atmosphere') + return get_atlas_phoenix_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + if (temperature >= 5500) & (temperature < 20000): + if verbose: + print( 'ATLAS merged atmosphere') + return get_castelli_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + if temperature >= 20000: + if verbose: + print( 'Still ATLAS merged atmosphere') + return get_castelli_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + #print('CMFGEN') + #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, + # temperature=temperature, + # gravity=gravity) + + + + +def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): + """ + Return the white dwarf atmosphere from + `Koester et al. 2010 `_. + If desired parameters are + outside of grid, return a blackbody spectrum instead + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + if verbose: + print('wdKoester atmosphere') + + return get_wdKoester_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + except pysynphot.exceptions.ParameterOutOfBounds: + # Use a black-body atmosphere. + bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) + return bbspec + + +def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): + """ + Return the brown dwarf atmosphere from + `Meisner et al. 2023 `_. + If desired parameters are + outside of grid, return a blackbody spectrum instead + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + if verbose: + print('Meisner2023 atmosphere') + + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + except pysynphot.exceptions.ParameterOutOfBounds: + # Use a black-body atmosphere + bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) + return bbspec + +def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, + verbose=False, rebin=None, + wave_min=500, wave_max=50_000, wave_num=20_000): + """ + Return a blackbody spectrum + + Parameters + ---------- + temperature: float, default=20_000 + The stellar temperature, in units of K + wave_min: float, default=500 + Sets the minimum wavelength (in Angstroms) of the wavelength range + for the blackbody spectrum + wave_max: float, default=50_000 + Sets the maximum wavelength (in Angstroms) of the wavelength range + for the blackbody spectrum + wave_num: int, default=20_000 + Sets the number of wavelength points in the wavelength range + Note: the wavelength range is evenly spaced in log space + """ + if ((metallicity is not None) or (gravity is not None) or + (rebin is not None)): + warnings.warn( + 'Only `temperature` keyword is used for black-body atmosphere' + ) + + if verbose: + print('Black-body atmosphere') + + # Modify pysynphot's default waveset to specified bounds + pysynphot.refs.set_default_waveset( + minwave=wave_min, maxwave=wave_max, num=wave_num + ) + + # Get black-body atmosphere for specified temperature from pysynphot + bbspec = pysynphot.spectrum.BlackBody(temperature) + + # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` + bbspec.convert('flam') + + # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. + # Need to remove this normalization for SPISEA by multiplying bbspec + # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 + bbspec *= (1000 * 3.086e18 / 6.957e10)**2 + + return bbspec + + +#--------------------------------------# +# Atmosphere formatting functions +#--------------------------------------# + +def download_CMFGEN_atmospheres(Table_rot, Table_norot): + """ + Downloads CMFGEN models from + https://sites.google.com/site/fluxesandcontinuum/home; + these contain continuum as well as lines. + + Table_rot, Table_norot are tables with the file prefixes + and model atmosphere parameters, taken by hand from the + Fierro+15 paper + + Website addresses are hardcoded + + Puts downloaded models in the current working directory. + """ + print( 'WARNING: THIS DOES NOT COMPLETELY WORK') + print( '**********************') + t_rot = Table.read(Table_rot, format='ascii') + t_norot = Table.read(Table_norot, format='ascii') + + tables = [t_rot, t_norot] + filenames = [t_rot['col1'], t_norot['col1']] + + # Hardcoded list of webiste addresses + web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' + web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' + web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', + web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', + web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', + web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', + web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] + # Array of masses that matches the website addresses + mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) + + # Loop through rotating and unrotating case. First loop is rot, second unrot + for i in range(2): + # Extract masses from filenames + masses = [] + for j in filenames[i]: + tmp = j.split('m') + mass = float(tmp[1][:-1]) + masses.append(mass) + + # Download the models webpage by webpage. A bit tricky because masses + # change slightly within a particular website. THIS IS WHAT FAILS + for j in range(len(web)): + if j == 0: + good = np.where( (masses <= mass_arr[j]) ) + else: + g = j - 1 + good = np.where( (masses <= mass_arr[j]) & + (masses > mass_arr[g]) ) + # Use wget command to pull down the files, and unzip them + for k in good[0]: + full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) + os.system('wget ' + full) + os.system('unzip '+ filenames[i][k] + '.flx.zip') + + return + +def organize_CMFGEN_atmospheres(path_to_dir): + """ + Organize CMFGEN grid from Fierro+15 + (http://www.astroscu.unam.mx/atlas/index.html) + into rot and noRot directories + + path_to_dir is from current working directory to directory + containing the downloaded models. Assumed that models + and tables describing parameters are in this directory. + + Tables describing parameters MUST be named Table_rot.txt, + Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. + These are located in same original directory as atmosphere files + + Will separate files into 2 subdirectories, one rotating and + the other non-rotating + + *Can't have any other files starting with "t" in model directory to start!* + """ + # First, record current working directory to return to later + start_dir = os.getcwd() + + # Enter atmosphere directory, collect rotating and non-rotating + # file names (assumed to all start with "t") + os.chdir(path_to_dir) + rot_models = glob.glob("t*r.flx*") + noRot_models = glob.glob("t*n.flx*") + + # Separate into different subdirectories + if os.path.exists('cmfgenF15_rot'): + pass + else: + os.mkdir('cmfgenF15_rot') + os.mkdir('cmfgenF15_noRot') + + for mod in rot_models: + cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) + os.system(cmd) + + for mod in noRot_models: + cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) + os.system(cmd) + + # Also move Tables with model parameters into correct directory + os.system('mv Table_rot.txt cmfgenF15_rot') + os.system('mv Table_noRot.txt cmfgenF15_noRot') + + # Return to original directory + os.chdir(start_dir) + + return + +def make_CMFGEN_catalog(path_to_dir): + """ + Create cdbs catalog.fits of CMFGEN grid from Fierro+15 + (http://www.astroscu.unam.mx/atlas/index.html). + THIS IS STEP 2, after organize_CMFGEN_atmospheres has + been run. + + path_to_dir is from current working directory to directory + containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, + needs to be a Table*.txt file which contains the parameters for all of the + original models, since params in filename are not precise enough + + Will create catalog.fits file in atmosphere directory with + description of each model + + *Can't have any other files starting with "t" in model directory to start!* + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere + # Note: can't rely on filename for this because not precise enough!! + + #---------OLD: GETTING PARAMS FROM FILENAME-------# + # Collect file names (assumed to all start with "t") + #files = glob.glob("t*") + #for name in files: + # tmp = name.split('l') + # temp = float(tmp[0][1:]) * 100.0 # In kelvin + + # lumtmp = tmp[1].split('_') + # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun + + # mass = float(lumtmp[0][5:-1]) # In M_sun + + # Need to calculate log g from T and L (cgs) + # lum_sun = 3.846 * 10**33 # erg/s + # M_sun = 2 * 10**33 # g + # G_si = 6.67 * 10**(-8) # cgs + # sigma_si = 5.67 * 10**(-5) # cgs + + # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ + # (lum * lum_sun) + # logg = np.log10(g) + #---------------------------------------------------# + + # Read table with atmosphere params + table = glob.glob('Table_*') + t = Table.read(table[0], format = 'ascii') + names = t['col1'] + temps = t['col2'] + logg = t['col4'] + + # Create catalog.fits file + index_str = [] + name_str = [] + for i in range(len(names)): + index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) + + #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# + #path = path_to_dir + '/' + names[i] + path = names[i] + '.fits[Flux]' + + index_str.append(index) + name_str.append(path) + + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits') + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): + """ + Code to put cmfgen models into cdbs format and adds proper unit keyword in + fits header. Save as fits file + + path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot + directory with the *.flx models. Note that these files have already been + organized using organize_CMFGEN_atmospheres code. + + path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or + cmfgen_norot directory. Will copy new fits files to this directory. + This directory must already exist! + """ + # Save starting directory for later, move into path_to_dir directory + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Collect the filenames, make necessary changes to each one + files = glob.glob('*.flx') + + # Need to make brand-new fits tables with data we want. + counter = 0 + for i in files: + counter += 1 + # Open file, extract useful info + t = Table.read(i, format='ascii') + wave = t['col1'] + flux = t['col2'] # Flux is already in erg/cm^2/s/A + + # Need to eliminate duplicate entries (pysynphot crashes) + unique = np.unique(wave, return_index=True) + wave = wave[unique[1]] + flux = flux[unique[1]] + + # Make fits table from individual columns. + c0 = fits.Column(name='Wavelength', format='D', array=wave) + c1 = fits.Column(name='Flux', format='E', array=flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + #Adding unit keywords + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + prihdu = fits.PrimaryHDU() + + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(i[:-4]+'.fits', overwrite=True) + + print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) + + # Return to original directory, copy over new .fits files to cdbs directory + os.chdir(start_dir) + cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) + os.system(cmd) + + return + +def rebin_cmfgen(cdbs_path, rot=True): + """ + Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; + this makes spectrophotometry MUCH faster + + cdbs_path: path to cdbs directory + rot=True for rotating models (cmfgen_rot), False for non-rotating models + + makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing cmfgen model; we will steal the header. + # Also define paths to new rebin directories + if rot == True: + tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' + path = cdbs_path+'/grid/cmfgen_rot_rebin/' + orig_path = cdbs_path+'/grid/cmfgen_rot/' + else: + tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' + path = cdbs_path+'/grid/cmfgen_norot_rebin/' + orig_path = cdbs_path+'/grid/cmfgen_norot/' + + cmfgen_hdu = fits.open(tmp) + header0 = cmfgen_hdu[0].header + # Create rebin directories if they don't already exist. Copy over + # catalog.fits file from original directory (will be the same) + if not os.path.exists(path): + os.mkdir(path) + cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) + os.system(cmd) + + # Read in the catalog.fits file + cat = fits.getdata(orig_path + 'catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + # First column in new files will be for [atlas] wavelength + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + + # For each catalog.fits entry, read the unbinned spectrum and rebin to + # the atlas resolution. Make a new fits file in rebin directory + count = 0 + for ff in range(len(files_all)): + count += 1 + # Extract the temp, Z, logg + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + grav = float(vals[2]) + + # Fetch the spectrum + if rot == True: + sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) + else: + sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) + + # Rebin + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + # Make the FITS file from the columns with header + cols = fits.ColDefs([c0,c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + # Write hdu to new directory with same filename + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(path+files_all[ff]) + + print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) + return + + +def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): + """ + Construct the Phoenix Husser+13 atmopsheres for each model. Combines the + fluxes from the *HiRES.fits files and the wavelengths of the + WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. + + path_to_dir is the path to the directory containing all of the downloaded + files + + met_str is the name of the current metallicity + + Creates new fits files for each atmosphere: phoenix_.fits, + which contains columns for the log g (column header = g#.#). Puts + atmospheres in new directory phoenixm00 + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # If it doesn't already exist, create the current metallicity subdirectory + sub_dir = '../phoenix{0}'.format(met_str) + if os.path.exists(sub_dir): + pass + else: + os.mkdir(sub_dir) + + # Extract wavelength array, make column for later + wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') + wave = wavefile[0].data + wavefile.close() + wave_col = Column(wave, name = 'WAVELENGTH') + + # Create temp array for Husser+13 grid (given in paper) + temp_arr = np.arange(2300, 7001, 100) + temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) + + print( 'Looping though all temps') + # For each temp, build file containing the flux for all gravities + i = 0 + for temp in temp_arr: + files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) + files.sort() + # Start the table with the wavelength column + t = Table() + t.add_column(wave_col) + for f in files: + # Extract the logg out of filename + logg = f[9:13] + + # Extract fluxes from file + spectrum = fits.open(f) + flux = spectrum[0].data + spectrum.close() + + # Make Column object with fluxes, add to table + col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) + t.add_column(col) + + # Now, construct final fits file for the given temp + outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) + t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) + + # Progress counter for user + i += 1 + print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) + + # Return to original directory + os.chdir(start_dir) + return + +def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): + """ + Makes catalog.fits file for Husser+13 phoenix models. Assumes that + organize_PHOENIXv16_atmospheres has been run already, and that the models lie + in subdirectory phoenix[met_str]. + + path_to_directory is the path to the directory with the reformatted + models (i.e. the output from construct_atmospheres, phoenix[met_str]) + + Puts catalog.fits file in directory the user starts in + """ + # Save starting directory for later, move into working directory + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Extract metallicity from metallicity string + met = float(met_str[1]) + (float(met_str[2]) * 0.1) + if 'm' in met_str: + met *= -1. + + # Collect the filenames. Each is a unique temp with many different log g's + files = glob.glob('phoenix*.fits') + files.sort() + + # Create the catalog.fits file, row by row + index_arr = [] + filename_arr = [] + for i in files: + # Get log g values from the column header the file + t = Table.read(i, format='fits') + keys = t.keys() + logg_vals = keys[1:] + + # Extract temp from filename + name = i.split('_') + temp = float(name[1][:-5]) + for j in logg_vals: + logg = float(j[1:]) + index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) + filename = path_to_dir + i + '[' + j + ']' + # Add row to table + index_arr.append(index) + filename_arr.append(filename) + + catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) + + # Return to starting directory, write catalog + os.chdir(start_dir) + + if os.path.exists('catalog.fits'): + from astropy.table import vstack + + prev_catalog = Table.read('catalog.fits', format='fits') + joined_catalog = vstack([prev_catalog, catalog]) + + joined_catalog.write('catalog.fits', format='fits', overwrite=True) + else: + catalog.write('catalog.fits', format='fits', overwrite=True) + + return + +def cdbs_PHOENIXv16(path_to_cdbs_dir): + """ + Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily + consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] + and adding the appropriate keywords to the fits header. + + path_to_cdbs_dir goes from current working directory to phoenix[met] directory + in cdbs/grids/phoenix_v16. Note that these files have already been organized + using organize_PHOENIXv16_atmospheres code. + + Overwrites original files in directory + """ + # Save starting directory for later, move into working directory + start_dir = os.getcwd() + os.chdir(path_to_cdbs_dir) + + # Collect the filenames, make necessary changes to each one + files = glob.glob('phoenix*.fits') + + ## Need to sort filenames; glob doesn't always give them in order + files.sort() + + # Need to make brand-new fits tables with data we want. + counter = 0 + for i in files: + counter += 1 + + # Read in current FITS table + cur_table = Table.read(i, format='fits') + + cur_table.columns[0].name = 'Wavelength' + + num_cols = len(cur_table.colnames) + + # Multiplying each flux column by 10^-8 for conversion + for cur_col_index in range(1, num_cols, 1): + cur_col_name = cur_table.colnames[cur_col_index] + cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 + + + # Construct new FITS file based on old one + hdu = fits.open(i) + header_0 = hdu[0].header + header_1 = hdu[1].header + sci = hdu[1].data + + tbhdu = fits.table_to_hdu(cur_table) + + # Copying over the older headers, adding unit keywords + prihdu = fits.PrimaryHDU(header=header_0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + tbhdu.header['TUNIT3'] = 'FLAM' + tbhdu.header['TUNIT4'] = 'FLAM' + tbhdu.header['TUNIT5'] = 'FLAM' + tbhdu.header['TUNIT6'] = 'FLAM' + tbhdu.header['TUNIT7'] = 'FLAM' + tbhdu.header['TUNIT8'] = 'FLAM' + tbhdu.header['TUNIT9'] = 'FLAM' + tbhdu.header['TUNIT10'] = 'FLAM' + tbhdu.header['TUNIT11'] = 'FLAM' + tbhdu.header['TUNIT12'] = 'FLAM' + tbhdu.header['TUNIT13'] = 'FLAM' + tbhdu.header['TUNIT14'] = 'FLAM' + + # Construct and write out final FITS file + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(i, overwrite=True) + + hdu.close() + print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) + + # Change back to starting directory + os.chdir(start_dir) + + return + +def rebin_phoenixV16(cdbs_path): + """ + Rebin phoenixV16 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: phoenix_v16_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing phoenix model; we will steal the header + ## (This assumes that at least 'm00' metallicity exists) + tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') + phoenix_hdu = fits.open(tmp) + header0 = phoenix_hdu[0].header + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/phoenix_v16_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + temp_arr = np.zeros(len(files_all), dtype=float) + logg_arr = np.zeros(len(files_all), dtype=float) + metal_arr = np.zeros(len(files_all), dtype=float) + + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + + temp_arr[ff] = float(vals[0]) + metal_arr[ff] = float(vals[1]) + logg_arr[ff] = float(vals[2]) + + + metal_uniq = np.unique(metal_arr) + temp_uniq = np.unique(temp_arr) + + for mm in range(len(metal_uniq)): + metal = metal_uniq[mm] # metallicity + + # Construct str for metallicity (for appropriate directory name) + met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) + if metal > 0: + met_str = 'p' + met_str + else: + met_str = 'm' + met_str + + # Make directory for current metallicity if it does not exist yet + if not os.path.exists(path + 'phoenix' + met_str): + os.mkdir(path + 'phoenix' + met_str) + + for tt in range(len(temp_uniq)): + temp = temp_uniq[tt] # temperature + + # Pick out the list of gravities for this T, Z combo + idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] + logg_exist = logg_arr[idx] + + # All gravities will go in one file. Here is the output + # file name. + outfile = path + files_all[idx[0]].split('[')[0] + + ## If the rebinned file already exists, continue + if os.path.exists(outfile): + continue + + # Build a columns array. One column for each gravity. + cols_arr = [] + + # Make the wavelength column, which is first in the cols array. + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + cols_arr.append(c0) + + for gg in range(len(logg_exist)): + grav = logg_exist[gg] # gravity + + # Fetch the spectrum + sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Store the spectrum + name = 'g{0:3.1f}'.format(grav) + col = fits.Column(name=name, format='E', array=flux_rebin) + cols_arr.append(col) + + + # Make the FITS file from the columns with header. + cols = fits.ColDefs(cols_arr) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + for gg in range(len(logg_exist)): + tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' + + # Write hdu + finalhdu = fits.HDUList([prihdu, tbhdu]) + # don't have overwrite to protect original files. + finalhdu.writeto(outfile) + + print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) + + + return + + +def rebin_spec(wave, specin, wavnew): + """ + Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: + http://www.astrobetter.com/blog/2013/08/12/ + python-tip-re-sampling-spectra-with-pysynphot/ + """ + spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) + f = np.ones(len(wave)) + filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') + obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') + + return obs.binflux + +def organize_BTSettl_2015_atmospheres(path_to_dir): + """ + Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. + Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] + + path_to_dir is the path to the directory containing all of the downloaded + files + + Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 + (assumes this directory exists) + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # If it doesn't already exist, create the BTSettl subdirectory + if not os.path.exists('BTSettl_2015'): + os.mkdir('BTSettl_2015') + + # Process each atmosphere file independently + print( 'Creating cdbs-ready files') + files = glob.glob('*.spec.fits') + + for i in files: + hdu = fits.open(i) + spec = hdu[1].data + header_0 = hdu[0].header + header_1 = hdu[1].header + + wave = spec.field(0) + flux = spec.field(1) + + # Get units right: convert wave from microns to Angstroms, + # flux from W /m^2/ micron to erg/s/cm^2/A + wave_new = wave * 10**4 + flux_new = flux * 10**(-1) + + # Make new fits table + c0 = fits.Column(name='Wavelength', format='D', array=wave_new) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Copy over headers, update unit keywords + prihdu = fits.PrimaryHDU(header=header_0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) + + hdu.close() + hdu_new.close() + + # Return to original directory + os.chdir(start_dir) + return + +def make_BTSettl_2015_catalog(path_to_dir): + """ + Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. + THIS IS STEP 2, after organize_CMFGEN_atmospheres has + been run. + + path_to_dir is from current working directory to the cdbs directory. + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere from the filename, + # construct columns for catalog file + files = glob.glob("*spec.fits") + index_str = [] + name_str = [] + for name in files: + tmp = name.split('-') + temp = float(tmp[0][3:]) * 100.0 # In kelvin + logg = float(tmp[1]) + + index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) + name_str.append('{0}[Flux]'.format(name)) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): + """ + Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: BTSettl_2015_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing phoenix model; we will steal the header + tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' + phoenix_hdu = fits.open(tmp) + header0 = phoenix_hdu[0].header + phoenix_hdu.close() + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/BTSettl_2015_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + print( 'Rebinning BTSettl spectra') + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the BTSettl spectrum, rebin flux + sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + return + +def make_wavelength_unique(files, dirname): + """ + Helper function to go through each BTSettl spectrum and ensure that + each wavelength point is unique. This is required for rebinning to work. + + + files: list of files to run this analysis on + """ + # Loop through each file, find fix repeated wavelength entries if necessary + for i in files: + t = Table.read('{0}/{1}'.format(dirname,i), format='fits') + test = np.unique(t['Wavelength'], return_index=True) + + if len(t) != len(test[0]): + t = t[test[1]] + + c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) + c1 = fits.Column(name='Flux', format='E', array=t['Flux']) + cols = fits.ColDefs([c0, c1]) + + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) + + # Also make sure wavelength is monotonic. If it is not, then it is + # a sign that the wavelengths are out of order + diff = np.diff(t['Wavelength']) + bad = np.where(diff < 0) + if len(bad[0]) > 0: + t.sort('Wavelength') + + c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) + c1 = fits.Column(name='Flux', format='E', array=t['Flux']) + cols = fits.ColDefs([c0, c1]) + + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) + + print('Done {0}'.format(i)) + + return + +def organize_BTSettl_atmospheres(): + """ + Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). + The code expects tp be run in cdbs/grid/BTSettl, and expects that the + individual model files have been downloaded from online + (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) + and processed into python-readable ascii files. + """ + orig_dir = os.getcwd() + dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] + #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] + + + # Go through each directory, turning each spectrum into a cdbs-ready file. + # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, + # for faster access later + for ii in dirs: + print('Starting {0}'.format(ii)) + os.chdir(ii) + + files = glob.glob('*.txt') + count=0 + for jj in files: + t = Table.read(jj, format='ascii') + # First, trim the wavelengths to a more reasonable wavelength range + good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) + t = t[good] + + # Convert flux units to Flam (Ergs/sec/cm**2/A) + flux_new = 10**(t['col2'] - 8.0) + + # Save the file as a fits file + c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Add unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) + hdu_new.close() + count += 1 + print('Done {0} of {1}'.format(count, len(files))) + + # Now, clean up all the files made when unzipping the spectra + cmd1 = 'rm *.bz2' + cmd2 = 'rm *.tmp' + #cmd3 = 'rm *.txt' + os.system(cmd1) + os.system(cmd2) + #os.system(cmd3) + print('==============================') + print('Done {0}'.format(ii)) + print('==============================') + + # Go back to original directory, move to next metallicity directory + os.chdir(orig_dir) + + return + +def make_BTSettl_catalog(): + """ + Create cdbs catalog.fits of BTSettl grid. + THIS IS STEP 2, after organize_BTSettl_atmospheres has + been run. + + Code expects to be run in cdbs/grid/BTSettl + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] + #dirs = ['btp05'] + + # Construct the catalog.fits file input. The input consists of + # and index string that specifies the stellar paramters, and a + # name string that points to the file + # Loop over all the metallicity directories to construct these inputs + index_str = [] + name_str = [] + for ii in dirs: + os.chdir(ii) + files = glob.glob('*.fits') + + # Construct the metallicity val + if 'm' in ii: + metal_flag = -1 * float(ii[3:])*0.1 + else: + metal_flag = float(ii[3:])*0.1 + + # Now collect the info from the files + for jj in files: + tmp = jj.split('-') + + if metal_flag >= 0: + temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin + try: + logg = float(tmp[1]) + except: + logg = float(tmp[1].split('+')[0]) + else: + temp = float(tmp[0][3:]) * 100.0 # In kelvin + logg = float(tmp[1]) + + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) + name_str.append('{0}/{1}[Flux]'.format(ii, jj)) + + # Go back to original directory to move to next metallicity + print('Done {0}'.format(ii)) + os.chdir(start_dir) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_BTSettl(make_unique=False): + """ + Rebin BTSettle models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory: BTSettl_rebin + + Code expects to be run in cdbs/grid directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Create cdbs/grid directory for rebinned models + path = 'BTSettl_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata('BTSettl/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + #==============================# + #tmp = [] + #for ii in files_all: + # if ii.startswith('btp00'): + # tmp.append(ii) + #files_all = tmp + #=============================# + + print( 'Rebinning BTSettl spectra') + if make_unique: + print('Making unique') + make_wavelength_unique(files_all, 'BTSettl') + print('Done') + + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the BTSettl spectrum, rebin flux + try: + sp = pysynphot.Icat('BTSettl', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + except: + pdb.set_trace() + orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) + outfile = path + files_all[ff].split('[')[0] + cmd = 'cp {0} {1}'.format(orig_file, outfile) + os.system(cmd) + + print('Done {0} of {1}'.format(ff, len(files_all))) + + return + +def organize_all_Meisner2023_atmospheres(): + """ + Construct cdbs-ready atmospheres for the Meisner2023 grid. + The code expects tp be run in cdbs/grid/Meisner2023, and expects that the + individual model files have been downloaded from online + and processed into python-readable ascii files. + """ + orig_dir = os.getcwd() + dirs = ['mm10', 'mm05', 'mp00', 'mp03'] + + # Go through each directory, turning each spectrum into a cdbs-ready file. + # Save as a fits file, for faster access later + for ii in dirs: + print('Starting {0}'.format(ii)) + os.chdir(ii) + + files = glob.glob('*.fits') + count=0 + for jj in files: + # Open each .fits file and read the data + with fits.open(jj) as hdul: + data = hdul[1].data + wavelength = data['Wavelength'] + flux = data['Flux'] + + # Make flux independent of R&D + flux_new = flux / 5e-20 + + # Create new columns with desired format + c0 = fits.Column(name='Wavelength', format='D', array=wavelength) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Add unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write the new fits table in the cdbs directory + output_filename = '{0}.fits'.format(jj[:-5]) # Removing the original .fits extension + hdu_new.writeto(output_filename, overwrite=True) + hdu_new.close() + count += 1 + print('Done {0} of {1}'.format(count, len(files))) + + # Go back to original directory, move to next metallicity directory + os.chdir(orig_dir) + + return + +def make_Meisner2023_catalog(): + """ + Create cdbs catalog.fits of Meisner2023 grid. + THIS IS STEP 2, after organize_Meisner2023_atmospheres has + been run. + + Code expects to be run in cdbs/grid/Meisner2023 + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + dirs = ['mm10', 'mm05', 'mp00', 'mp03'] + + # Construct the catalog.fits file input. The input consists of + # and index string that specifies the stellar paramters, and a + # name string that points to the file + # Loop over all the metallicity directories to construct these inputs + index_str = [] + name_str = [] + for ii in dirs: + os.chdir(ii) + files = glob.glob('spec_jwst_*.fits') + + for jj in files: + # Parse temperature, log(g), and metallicity from filename + temp_str = jj.split('_')[2] + logg_str = jj.split('_')[3] + metal_str = jj.split('_')[4] + + # Extract temperature, surface gravity, and metallicity + temp = float(temp_str[1:]) # Temperature in Kelvin + logg = float(logg_str[1:]) # Surface gravity log(g) + + # Build metallicity value + if metal_str.startswith('m'): + metallicity = -1 * float(metal_str[1:]) + else: + metallicity = float(metal_str[1:]) + + # Construct index and filename strings + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metallicity, logg)) + name_str.append('{0}/{1}[Flux]'.format(ii, jj)) + + print('Processed directory:', ii) + os.chdir(start_dir) + + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_Meisner2023(make_unique=False): + """ + Rebin Meisner2023 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory: Meisner2023_rebin + + Code expects to be run in cdbs/grid directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Create a directory for rebinned Meisner2023 models + rebin_path = 'Meisner2023_rebin/' + if not os.path.exists(rebin_path): + os.mkdir(rebin_path) + + # Load the catalog.fits file and extract all spectra file paths + cat = Table.read('Meisner2023/catalog.fits') + files_all = [cat[ii]['FILENAME'].split('[')[0] for ii in range(len(cat))] + + print('Rebinning Meisner2023 spectra') + if make_unique: + print('Making unique') + make_wavelength_unique(files_all, 'Meisner2023') + print('Done') + + for ff, file in enumerate(files_all): + vals = cat[ff]['INDEX'].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the Meisner2023 spectrum and rebin its flux + try: + sp = pysynphot.Icat('Meisner2023', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Create the output FITS file + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + # Write the new rebinned file in the Meisner2023_rebin directory + outfile = os.path.join(rebin_path, os.path.basename(file)) + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + except Exception as e: + print(f"Error processing {file}: {e}") + orig_file = os.path.join('Meisner2023', file) + outfile = os.path.join(rebin_path, os.path.basename(file)) + os.system(f'cp {orig_file} {outfile}') + + print('Done {0} of {1}'.format(ff + 1, len(files_all))) + + return + + + +def organize_WDKoester_atmospheres(path_to_dir): + """ + Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) + Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] + + path_to_dir is the path to the directory containing all of the downloaded + files + + Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter + (assumes this directory exists) + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Process each atmosphere file independently + print( 'Creating cdbs-ready files') + files = glob.glob('*.dk.dat.txt') + + for i in files: + data = Table.read(i, format='ascii') + + wave = data['col1'] # angstrom + flux = data['col2'] # erg/s/cm^2/A + + # Make new fits table + c0 = fits.Column(name='Wavelength', format='D', array=wave) + c1 = fits.Column(name='Flux', format='E', array=flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Copy over headers, update unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) + + hdu_new.close() + + # Return to original directory + os.chdir(start_dir) + return + +def make_WDKoester_catalog(path_to_dir): + """ + Create cdbs catalog.fits of wdKoester grid. + THIS IS STEP 2, after organize_WDKoester_atmospheres has + been run. + + path_to_dir is from current working directory to the cdbs directory. + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere from the filename, + # construct columns for catalog file + files = glob.glob("*dk.dat.fits") + index_str = [] + name_str = [] + for name in files: + tmp = name.split('.') + tmp2 = tmp[0].split('_') + temp = float(tmp2[0][2:]) # Kelvin + logg = float(tmp2[1]) / 100.0 # log(g) + + index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) + name_str.append('{0}[Flux]'.format(name)) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): + """ + Rebin wdKoester models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: wdKoester_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing model; we will steal the header + tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' + wdkoester_hdu = fits.open(tmp) + header0 = wdkoester_hdu[0].header + wdkoester_hdu.close() + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/wdKoester_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + print( 'Rebinning wdKoester spectra') + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the wdKoester spectrum, rebin flux + sp = pysynphot.Icat('wdKoester', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + return + + diff --git a/spisea/atmospheres_BACKUP_58456.py b/spisea/atmospheres_BACKUP_58456.py new file mode 100755 index 00000000..e3d2233c --- /dev/null +++ b/spisea/atmospheres_BACKUP_58456.py @@ -0,0 +1,2524 @@ +import logging +import numpy as np +import pysynphot +import os +import glob +from astropy.io import fits +from astropy.table import Table, Column +import pysynphot +import time +import pdb +import warnings + +log = logging.getLogger('atmospheres') + +def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4, verbose=False): + """ + Given atmosphere model, get temperature and gravity bounds + """ + # Open catalog fits file and break out row indices + catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) + + teff_arr = [] + z_arr = [] + logg_arr = [] + for cur_row_index in range(len(catalog)): + index = catalog['INDEX'][cur_row_index] + tmp = index.split(',') + teff_arr.append(float(tmp[0])) + z_arr.append(float(tmp[1])) + logg_arr.append(float(tmp[2])) + teff_arr = np.array(teff_arr) + z_arr = np.array(z_arr) + logg_arr = np.array(logg_arr) + + # Filter by metallicity. Will chose the closest metallicity to desired input + metal_list = np.unique(np.array(z_arr)) + metal_idx = np.argmin(np.abs(metal_list - metallicity)) + metallicity_new = metal_list[metal_idx] + + z_filt = np.where(z_arr == metal_list[metal_idx]) + teff_arr = teff_arr[z_filt] + logg_arr = logg_arr[z_filt] + + # # Now find the closest atmosphere in parameter space to + # # the one we want. We'll find the match with the lowest + # # fractional difference + # teff_diff = (teff_arr - temperature) / temperature + # logg_diff = (logg_arr - gravity) / gravity + # + # diff_tot = abs(teff_diff) + abs(logg_diff) + # idx_f = np.argmin(diff_tot) + # + # temperature_new = teff_arr[idx_f] + # gravity_new = logg_arr[idx_f] + + # First check if temperature within bounds + temperature_new = temperature + if temperature > np.max(teff_arr): + temperature_new = np.max(teff_arr) + if temperature < np.min(teff_arr): + temperature_new = np.min(teff_arr) + + # If temperature within bounds, then check if metallicity within bounds + teff_diff = np.abs(teff_arr - temperature) + sorted_min_diffs = np.unique(teff_diff) + + ## Find two closest temperatures + teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] + teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] + + logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] + logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] + + ## Switch to most conservative bound of logg out of two closest temps + gravity_new = gravity + if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): + gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) + if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): + gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) + + if verbose: + # Print out changes, if any + if temperature_new != temperature: + teff_msg = 'Changing to T={0:6.0f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' + print( teff_msg.format(temperature_new, metallicity, temperature, gravity)) + + if gravity_new != gravity: + logg_msg = 'Changing to logg={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' + print( logg_msg.format(gravity_new, metallicity, temperature, gravity)) + + if metallicity_new != metallicity: + logg_msg = 'Changing to met={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' + print( logg_msg.format(metallicity_new, metallicity, temperature, gravity)) + + return (temperature_new, gravity_new, metallicity_new) + +def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): + """ + Return atmosphere from the Kurucz pysnphot grid + (`Kurucz 1993 `_). + + Grid Range: + + * Teff: 3000 - 50000 K + * gravity: 0 - 5 cgs + * metallicity: -5.0 - 1.0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + Always false for this particular function + """ + try: + sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('k93models', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Kurucz 1993 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): + """ + Return atmospheres from the pysynphot ATLAS9 atlas + (`Castelli & Kurucz 2004 `_). + + Grid Range: + + * Teff: 3500 - 50000 K + * gravity: 0 - 5.0 cgs + * [M/H]: -2.5 - 0.2 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('ck04models', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 5000) + gravity = log gravity (def = 4.0) + """ + try: + sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('nextgen', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find NextGen atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 5000) + gravity = log gravity (def = 4.0) + """ + sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, + rebin=False): + """ + Return atmosphere from the pysynphot + `PHOENIX atlas `_. + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + """ + try: + sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 24000) + gravity = log gravity (def = 4.3) + + rebin=True: pull from atmospheres at ck04model resolution. + """ + # Take care of atmospheres outside the catalog boundaries + logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' + if gravity > 4.3: + if verbose: + print( logg_msg.format(4.3, temperature, gravity)) + gravity = 4.3 + + if rebin: + sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) + else: + sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, + verbose=False): + """ + For a given stellar atmosphere, get extract the closest possible match in + Teff/logg space. Note that this is different from the normal routine + which interpolates along the input grid to get final spectrum. We can't + do this here because the Fierro+15 atmosphere grid is so sparse + + rebin=True: pull from atmospheres at ck04model resolution. + + If verbose, print out the parameters of the match + """ + # Set up the proper root directory + if rebin == True: + root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' + else: + root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' + + # Read in catalog, extract atmosphere info + cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') + teff_arr = [] + z_arr = [] + logg_arr = [] + for ii in range(len(cat)): + index = cat['INDEX'][ii] + tmp = index.split(',') + teff_arr.append(float(tmp[0])) + z_arr.append(float(tmp[1])) + logg_arr.append(float(tmp[2])) + teff_arr = np.array(teff_arr) + z_arr = np.array(z_arr) + logg_arr = np.array(logg_arr) + + # Now find the closest atmosphere in parameter space to + # the one we want. We'll find the match with the lowest + # fractional difference + teff_diff = (teff_arr - temperature) / temperature + logg_diff = (logg_arr - gravity) / gravity + + diff_tot = abs(teff_diff) + abs(logg_diff) + idx_f = np.where(diff_tot == min(diff_tot))[0][0] + + # Extract the filename of the best-match model and read as + # pysynphot object + infile = cat[idx_f]['FILENAME'].split('.') + spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) + + # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the + # ATLAS models are in FLAM at the surface. So, we need to multiply the + # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. + # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file + t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') + tmp = np.where(t['col1'] == infile[0]) + + lum = t['col3'][tmp] * (3.839*10**33) # cgs + sigma = 5.6704 * 10**-5 # cgs + teff = teff_arr[idx_f] # cgs + + radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm + radius /= 3.08*10**18 # in pc + + + # Make the pysynphot spectrum + w = spec['Wavelength'] + f = spec['Flux'] * (1000 / radius)**2. + sp = pysynphot.ArraySpectrum(w,f) + + #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) + + # Print out parameters of match, if desired + if verbose: + print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) + print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) + + return sp + +def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 24000) + gravity = log gravity (def = 4.3) + + rebin=True: pull from atmospheres at ck04model resolution. + """ + if rebin: + sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) + else: + sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 30000) + gravity = log gravity (def = 4.14) + """ + sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): + """ + Return PHOENIX v16 atmospheres from + `Husser et al. 2013 `_. + + Models originally downloaded via `ftp `_. + Solar metallicity and [alpha/Fe] is used. + + Grid Range: + + * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K + * gravity: 0.0 - 6.0 cgs, steps of 0.5 + * [M/H]: -4.0 - 1.0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + """ + atm_model_name = 'phoenix_v16' + if rebin == True: + atm_model_name = 'phoenix_v16_rebin' + + + # Extract atmosphere. If that fails, then check bounds and try again + try: + sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_model_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): + """ + Return atmosphere from CIFIST2011_2015 grid + (`Allard et al. 2012 `_, + `Baraffe et al. 2015 `_ ) + + Grid originally downloaded from `website `_. + + Grid Range: + + * Teff: 1200 - 7000 K + * gravity: 2.5 - 5.5 cgs + * [M/H] = 0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + """ + if rebin == True: + atm_name = 'BTSettl_2015_rebin' + else: + atm_name = 'BTSettl_2015' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) +<<<<<<< HEAD + #print(dir(obj)) + + +======= + + +>>>>>>> upstream/dev + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find BTSettl_2015 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): + """ + Return atmosphere from CIFIST2011 grid + (`Allard et al. 2012 `_) + + Grid originally downloaded `here `_ + + Notes + ------ + Grid Range: + + * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 + + Teff and gravity ranges depend on metallicity: + + [M/H] = -2.5 + + * Teff: 2600 - 4600 K + * gravity: 4.5 - 5.5 + + [M/H] = -2.0 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.5 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.0 + + * Teff: 2600 - 7000 + * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 + + [M/H] = -0.5 + + * Teff: 1000 -7000 + * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 + + [M/H] = 0 + + * Teff: 750 - 7000 + * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 + + [M/H] = 0.5 + + * Teff: 1000 - 5000 + * gravity: 3.5 - 5.0 + + + Alpha enhancement: + + * [M/H]= -0.0, +0.5 no anhancement + * [M/H]= -0.5 with [alpha/H]=+0.2 + * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + **PRINT STATEMENTS TO DEBUG + **check get_atmosphere_bounds + **comment out try/except clause and check break + """ + if rebin == True: + atm_name = 'BTSettl_rebin' + else: + atm_name = 'BTSettl' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) +<<<<<<< HEAD + +def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): + """ + Return atmosphere from Meisner2023 grid + (`Meisner et al. 2023 `_) + + Grid originally downloaded `here `_ + + Grid range: + * Teff = 250 - 1200 K + * gravity: 2.5 - 5.5 cgs (in steps of 0.5) + * [M/H] = -1.0, -0.5, 0, +0, +0.3 + + """ + if rebin == True: + atm_name = 'Meisner2023_rebin' + else: + atm_name = 'Meisner2023' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) +======= + +>>>>>>> upstream/dev + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Meisner2023 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): + """ + Return atmosphere from Phillips et al., 2020 using ATMO model + (`Phillips et al. 2020 `_) + + Grid originally downloaded `here `_ + + Grid Range: + * Teff: 200 - 3000 K + * gravity: 2.5 - 5.5 cgs + * [M/H] = 0 + """ + if rebin == True: + atm_name = 'Phillips2020_rebin' + else: + atm_name = 'Phillips2020' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Phillips2020 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): + """ + Return white dwarf atmospheres from + `Koester et al. 2010 `_ + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + """ + sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. + + Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 + """ + try: + sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_atlas_phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model + and phoenixV16. + + Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 + """ + try: + sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_BTSettl_phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_meisner_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model + and Meisner2023. + + Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 + """ + try: + sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_meisner', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find BTSettl-Meisner merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +#---------------------------------------------------------------------# +def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, + rebin=True): + """ + Return a stellar atmosphere from a suite of different model grids, + depending on the input temperature, (all values in K). + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + + Notes + ----- + The underlying stellar model grid used changes as a function of + stellar temperature (in K): + + * T > 20,000: ATLAS + * 5500 <= T < 20,000: ATLAS + * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge + * 3800 <= T < 5000: PHOENIXv16 + + For T < 3800, there is an additional gravity and metallicity + dependence: + + If T < 3800 and [M/H] = 0: + + * T < 3800, logg < 2.5: PHOENIX v16 + * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge + * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 + * 1200 < T <= 1000, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge + * 1000 < T <= 250, logg > 2.5: Meisner2023 + + Otherwise, if T < 3800 and [M/H] != 0: + + * T < 3800: PHOENIX v16 + + References: + + * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) + * PHOENIXv16 (`Husser et al. 2013 `_) + * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) + * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) + + LTE WARNING: + + The ATLAS atmospheres are calculated with LTE, and so they + are less accurate when non-LTE conditions apply (e.g. T > 20,000 + K). Ultimately we'd like to add a non-LTE atmosphere grid for + the hottest stars in the future. + + HOW BOUNDARIES BETWEEN MODELS ARE TREATED: + + At the boundary between two models grids a temperature range is defined + where the resulting atmosphere is a weighted average between the two + grids. Near one boundary one model + is weighted more heavily, while at the other boundary the other + model is weighted more heavily. These are calculated in the + temperature ranges where we switch between model grids, to + ensure a smooth transition. + """ + # For T < 3800, atmosphere depends on metallicity + gravity. + # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is + # currently available here, so if non-solar metallicity, just stick with + # the Phoenix grid + if (temperature < 1000): + if verbose: + print( 'Meisner2023 atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature <= 1200) & (temperature >= 1000): + if (gravity >= 3.5): + if verbose: + print( 'BTSettl/Meisner2023 merged atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + if (gravity < 3.5) & (gravity >=2.5): + if verbose: + print( 'Meisner2023 atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature <= 3800) & (metallicity == 0): + # High gravity are in BTSettl regime + if (temperature <= 3200) & (gravity > 2.5): + if verbose: + print( 'BTSettl_2015 atmosphere') + return get_BTSettl_2015_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): + if verbose: + print( 'BTSettl/Phoenixv16 merged atmosphere') + return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + # Low gravity is PHOENIX regime + if gravity <= 2.5: + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature <= 3800) & (metallicity != 0): + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + # For T > 3800, no metallicity or gravity dependence + if (temperature >= 3800) & (temperature < 5000): + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature >= 5000) & (temperature < 5500): + if verbose: + print( 'ATLAS/Phoenix merged atmosphere') + return get_atlas_phoenix_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + if (temperature >= 5500) & (temperature < 20000): + if verbose: + print( 'ATLAS merged atmosphere') + return get_castelli_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + if temperature >= 20000: + if verbose: + print( 'Still ATLAS merged atmosphere') + return get_castelli_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + #print('CMFGEN') + #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, + # temperature=temperature, + # gravity=gravity) + + + + +def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): + """ + Return the white dwarf atmosphere from + `Koester et al. 2010 `_. + If desired parameters are + outside of grid, return a blackbody spectrum instead + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + if verbose: + print('wdKoester atmosphere') + + return get_wdKoester_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + except pysynphot.exceptions.ParameterOutOfBounds: + # Use a black-body atmosphere. + bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) + return bbspec + + +def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): + """ + Return the brown dwarf atmosphere from + `Meisner et al. 2023 `_. + If desired parameters are + outside of grid, return a blackbody spectrum instead + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + if verbose: + print('Meisner2023 atmosphere') + + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + except pysynphot.exceptions.ParameterOutOfBounds: + # Use a black-body atmosphere + bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) + return bbspec + +def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, + verbose=False, rebin=None, + wave_min=500, wave_max=50_000, wave_num=20_000): + """ + Return a blackbody spectrum + + Parameters + ---------- + temperature: float, default=20_000 + The stellar temperature, in units of K + wave_min: float, default=500 + Sets the minimum wavelength (in Angstroms) of the wavelength range + for the blackbody spectrum + wave_max: float, default=50_000 + Sets the maximum wavelength (in Angstroms) of the wavelength range + for the blackbody spectrum + wave_num: int, default=20_000 + Sets the number of wavelength points in the wavelength range + Note: the wavelength range is evenly spaced in log space + """ + if ((metallicity is not None) or (gravity is not None) or + (rebin is not None)): + warnings.warn( + 'Only `temperature` keyword is used for black-body atmosphere' + ) + + if verbose: + print('Black-body atmosphere') + + # Modify pysynphot's default waveset to specified bounds + pysynphot.refs.set_default_waveset( + minwave=wave_min, maxwave=wave_max, num=wave_num + ) + + # Get black-body atmosphere for specified temperature from pysynphot + bbspec = pysynphot.spectrum.BlackBody(temperature) + + # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` + bbspec.convert('flam') + + # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. + # Need to remove this normalization for SPISEA by multiplying bbspec + # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 + bbspec *= (1000 * 3.086e18 / 6.957e10)**2 + + return bbspec + + +#--------------------------------------# +# Atmosphere formatting functions +#--------------------------------------# + +def download_CMFGEN_atmospheres(Table_rot, Table_norot): + """ + Downloads CMFGEN models from + https://sites.google.com/site/fluxesandcontinuum/home; + these contain continuum as well as lines. + + Table_rot, Table_norot are tables with the file prefixes + and model atmosphere parameters, taken by hand from the + Fierro+15 paper + + Website addresses are hardcoded + + Puts downloaded models in the current working directory. + """ + print( 'WARNING: THIS DOES NOT COMPLETELY WORK') + print( '**********************') + t_rot = Table.read(Table_rot, format='ascii') + t_norot = Table.read(Table_norot, format='ascii') + + tables = [t_rot, t_norot] + filenames = [t_rot['col1'], t_norot['col1']] + + # Hardcoded list of webiste addresses + web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' + web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' + web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', + web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', + web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', + web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', + web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] + # Array of masses that matches the website addresses + mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) + + # Loop through rotating and unrotating case. First loop is rot, second unrot + for i in range(2): + # Extract masses from filenames + masses = [] + for j in filenames[i]: + tmp = j.split('m') + mass = float(tmp[1][:-1]) + masses.append(mass) + + # Download the models webpage by webpage. A bit tricky because masses + # change slightly within a particular website. THIS IS WHAT FAILS + for j in range(len(web)): + if j == 0: + good = np.where( (masses <= mass_arr[j]) ) + else: + g = j - 1 + good = np.where( (masses <= mass_arr[j]) & + (masses > mass_arr[g]) ) + # Use wget command to pull down the files, and unzip them + for k in good[0]: + full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) + os.system('wget ' + full) + os.system('unzip '+ filenames[i][k] + '.flx.zip') + + return + +def organize_CMFGEN_atmospheres(path_to_dir): + """ + Organize CMFGEN grid from Fierro+15 + (http://www.astroscu.unam.mx/atlas/index.html) + into rot and noRot directories + + path_to_dir is from current working directory to directory + containing the downloaded models. Assumed that models + and tables describing parameters are in this directory. + + Tables describing parameters MUST be named Table_rot.txt, + Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. + These are located in same original directory as atmosphere files + + Will separate files into 2 subdirectories, one rotating and + the other non-rotating + + *Can't have any other files starting with "t" in model directory to start!* + """ + # First, record current working directory to return to later + start_dir = os.getcwd() + + # Enter atmosphere directory, collect rotating and non-rotating + # file names (assumed to all start with "t") + os.chdir(path_to_dir) + rot_models = glob.glob("t*r.flx*") + noRot_models = glob.glob("t*n.flx*") + + # Separate into different subdirectories + if os.path.exists('cmfgenF15_rot'): + pass + else: + os.mkdir('cmfgenF15_rot') + os.mkdir('cmfgenF15_noRot') + + for mod in rot_models: + cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) + os.system(cmd) + + for mod in noRot_models: + cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) + os.system(cmd) + + # Also move Tables with model parameters into correct directory + os.system('mv Table_rot.txt cmfgenF15_rot') + os.system('mv Table_noRot.txt cmfgenF15_noRot') + + # Return to original directory + os.chdir(start_dir) + + return + +def make_CMFGEN_catalog(path_to_dir): + """ + Create cdbs catalog.fits of CMFGEN grid from Fierro+15 + (http://www.astroscu.unam.mx/atlas/index.html). + THIS IS STEP 2, after organize_CMFGEN_atmospheres has + been run. + + path_to_dir is from current working directory to directory + containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, + needs to be a Table*.txt file which contains the parameters for all of the + original models, since params in filename are not precise enough + + Will create catalog.fits file in atmosphere directory with + description of each model + + *Can't have any other files starting with "t" in model directory to start!* + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere + # Note: can't rely on filename for this because not precise enough!! + + #---------OLD: GETTING PARAMS FROM FILENAME-------# + # Collect file names (assumed to all start with "t") + #files = glob.glob("t*") + #for name in files: + # tmp = name.split('l') + # temp = float(tmp[0][1:]) * 100.0 # In kelvin + + # lumtmp = tmp[1].split('_') + # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun + + # mass = float(lumtmp[0][5:-1]) # In M_sun + + # Need to calculate log g from T and L (cgs) + # lum_sun = 3.846 * 10**33 # erg/s + # M_sun = 2 * 10**33 # g + # G_si = 6.67 * 10**(-8) # cgs + # sigma_si = 5.67 * 10**(-5) # cgs + + # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ + # (lum * lum_sun) + # logg = np.log10(g) + #---------------------------------------------------# + + # Read table with atmosphere params + table = glob.glob('Table_*') + t = Table.read(table[0], format = 'ascii') + names = t['col1'] + temps = t['col2'] + logg = t['col4'] + + # Create catalog.fits file + index_str = [] + name_str = [] + for i in range(len(names)): + index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) + + #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# + #path = path_to_dir + '/' + names[i] + path = names[i] + '.fits[Flux]' + + index_str.append(index) + name_str.append(path) + + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits') + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): + """ + Code to put cmfgen models into cdbs format and adds proper unit keyword in + fits header. Save as fits file + + path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot + directory with the *.flx models. Note that these files have already been + organized using organize_CMFGEN_atmospheres code. + + path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or + cmfgen_norot directory. Will copy new fits files to this directory. + This directory must already exist! + """ + # Save starting directory for later, move into path_to_dir directory + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Collect the filenames, make necessary changes to each one + files = glob.glob('*.flx') + + # Need to make brand-new fits tables with data we want. + counter = 0 + for i in files: + counter += 1 + # Open file, extract useful info + t = Table.read(i, format='ascii') + wave = t['col1'] + flux = t['col2'] # Flux is already in erg/cm^2/s/A + + # Need to eliminate duplicate entries (pysynphot crashes) + unique = np.unique(wave, return_index=True) + wave = wave[unique[1]] + flux = flux[unique[1]] + + # Make fits table from individual columns. + c0 = fits.Column(name='Wavelength', format='D', array=wave) + c1 = fits.Column(name='Flux', format='E', array=flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + #Adding unit keywords + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + prihdu = fits.PrimaryHDU() + + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(i[:-4]+'.fits', overwrite=True) + + print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) + + # Return to original directory, copy over new .fits files to cdbs directory + os.chdir(start_dir) + cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) + os.system(cmd) + + return + +def rebin_cmfgen(cdbs_path, rot=True): + """ + Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; + this makes spectrophotometry MUCH faster + + cdbs_path: path to cdbs directory + rot=True for rotating models (cmfgen_rot), False for non-rotating models + + makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing cmfgen model; we will steal the header. + # Also define paths to new rebin directories + if rot == True: + tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' + path = cdbs_path+'/grid/cmfgen_rot_rebin/' + orig_path = cdbs_path+'/grid/cmfgen_rot/' + else: + tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' + path = cdbs_path+'/grid/cmfgen_norot_rebin/' + orig_path = cdbs_path+'/grid/cmfgen_norot/' + + cmfgen_hdu = fits.open(tmp) + header0 = cmfgen_hdu[0].header + # Create rebin directories if they don't already exist. Copy over + # catalog.fits file from original directory (will be the same) + if not os.path.exists(path): + os.mkdir(path) + cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) + os.system(cmd) + + # Read in the catalog.fits file + cat = fits.getdata(orig_path + 'catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + # First column in new files will be for [atlas] wavelength + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + + # For each catalog.fits entry, read the unbinned spectrum and rebin to + # the atlas resolution. Make a new fits file in rebin directory + count = 0 + for ff in range(len(files_all)): + count += 1 + # Extract the temp, Z, logg + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + grav = float(vals[2]) + + # Fetch the spectrum + if rot == True: + sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) + else: + sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) + + # Rebin + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + # Make the FITS file from the columns with header + cols = fits.ColDefs([c0,c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + # Write hdu to new directory with same filename + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(path+files_all[ff]) + + print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) + return + + +def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): + """ + Construct the Phoenix Husser+13 atmopsheres for each model. Combines the + fluxes from the *HiRES.fits files and the wavelengths of the + WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. + + path_to_dir is the path to the directory containing all of the downloaded + files + + met_str is the name of the current metallicity + + Creates new fits files for each atmosphere: phoenix_.fits, + which contains columns for the log g (column header = g#.#). Puts + atmospheres in new directory phoenixm00 + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # If it doesn't already exist, create the current metallicity subdirectory + sub_dir = '../phoenix{0}'.format(met_str) + if os.path.exists(sub_dir): + pass + else: + os.mkdir(sub_dir) + + # Extract wavelength array, make column for later + wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') + wave = wavefile[0].data + wavefile.close() + wave_col = Column(wave, name = 'WAVELENGTH') + + # Create temp array for Husser+13 grid (given in paper) + temp_arr = np.arange(2300, 7001, 100) + temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) + + print( 'Looping though all temps') + # For each temp, build file containing the flux for all gravities + i = 0 + for temp in temp_arr: + files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) + files.sort() + # Start the table with the wavelength column + t = Table() + t.add_column(wave_col) + for f in files: + # Extract the logg out of filename + logg = f[9:13] + + # Extract fluxes from file + spectrum = fits.open(f) + flux = spectrum[0].data + spectrum.close() + + # Make Column object with fluxes, add to table + col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) + t.add_column(col) + + # Now, construct final fits file for the given temp + outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) + t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) + + # Progress counter for user + i += 1 + print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) + + # Return to original directory + os.chdir(start_dir) + return + +def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): + """ + Makes catalog.fits file for Husser+13 phoenix models. Assumes that + organize_PHOENIXv16_atmospheres has been run already, and that the models lie + in subdirectory phoenix[met_str]. + + path_to_directory is the path to the directory with the reformatted + models (i.e. the output from construct_atmospheres, phoenix[met_str]) + + Puts catalog.fits file in directory the user starts in + """ + # Save starting directory for later, move into working directory + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Extract metallicity from metallicity string + met = float(met_str[1]) + (float(met_str[2]) * 0.1) + if 'm' in met_str: + met *= -1. + + # Collect the filenames. Each is a unique temp with many different log g's + files = glob.glob('phoenix*.fits') + files.sort() + + # Create the catalog.fits file, row by row + index_arr = [] + filename_arr = [] + for i in files: + # Get log g values from the column header the file + t = Table.read(i, format='fits') + keys = t.keys() + logg_vals = keys[1:] + + # Extract temp from filename + name = i.split('_') + temp = float(name[1][:-5]) + for j in logg_vals: + logg = float(j[1:]) + index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) + filename = path_to_dir + i + '[' + j + ']' + # Add row to table + index_arr.append(index) + filename_arr.append(filename) + + catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) + + # Return to starting directory, write catalog + os.chdir(start_dir) + + if os.path.exists('catalog.fits'): + from astropy.table import vstack + + prev_catalog = Table.read('catalog.fits', format='fits') + joined_catalog = vstack([prev_catalog, catalog]) + + joined_catalog.write('catalog.fits', format='fits', overwrite=True) + else: + catalog.write('catalog.fits', format='fits', overwrite=True) + + return + +def cdbs_PHOENIXv16(path_to_cdbs_dir): + """ + Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily + consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] + and adding the appropriate keywords to the fits header. + + path_to_cdbs_dir goes from current working directory to phoenix[met] directory + in cdbs/grids/phoenix_v16. Note that these files have already been organized + using organize_PHOENIXv16_atmospheres code. + + Overwrites original files in directory + """ + # Save starting directory for later, move into working directory + start_dir = os.getcwd() + os.chdir(path_to_cdbs_dir) + + # Collect the filenames, make necessary changes to each one + files = glob.glob('phoenix*.fits') + + ## Need to sort filenames; glob doesn't always give them in order + files.sort() + + # Need to make brand-new fits tables with data we want. + counter = 0 + for i in files: + counter += 1 + + # Read in current FITS table + cur_table = Table.read(i, format='fits') + + cur_table.columns[0].name = 'Wavelength' + + num_cols = len(cur_table.colnames) + + # Multiplying each flux column by 10^-8 for conversion + for cur_col_index in range(1, num_cols, 1): + cur_col_name = cur_table.colnames[cur_col_index] + cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 + + + # Construct new FITS file based on old one + hdu = fits.open(i) + header_0 = hdu[0].header + header_1 = hdu[1].header + sci = hdu[1].data + + tbhdu = fits.table_to_hdu(cur_table) + + # Copying over the older headers, adding unit keywords + prihdu = fits.PrimaryHDU(header=header_0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + tbhdu.header['TUNIT3'] = 'FLAM' + tbhdu.header['TUNIT4'] = 'FLAM' + tbhdu.header['TUNIT5'] = 'FLAM' + tbhdu.header['TUNIT6'] = 'FLAM' + tbhdu.header['TUNIT7'] = 'FLAM' + tbhdu.header['TUNIT8'] = 'FLAM' + tbhdu.header['TUNIT9'] = 'FLAM' + tbhdu.header['TUNIT10'] = 'FLAM' + tbhdu.header['TUNIT11'] = 'FLAM' + tbhdu.header['TUNIT12'] = 'FLAM' + tbhdu.header['TUNIT13'] = 'FLAM' + tbhdu.header['TUNIT14'] = 'FLAM' + + # Construct and write out final FITS file + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(i, overwrite=True) + + hdu.close() + print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) + + # Change back to starting directory + os.chdir(start_dir) + + return + +def rebin_phoenixV16(cdbs_path): + """ + Rebin phoenixV16 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: phoenix_v16_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing phoenix model; we will steal the header + ## (This assumes that at least 'm00' metallicity exists) + tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') + phoenix_hdu = fits.open(tmp) + header0 = phoenix_hdu[0].header + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/phoenix_v16_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + temp_arr = np.zeros(len(files_all), dtype=float) + logg_arr = np.zeros(len(files_all), dtype=float) + metal_arr = np.zeros(len(files_all), dtype=float) + + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + + temp_arr[ff] = float(vals[0]) + metal_arr[ff] = float(vals[1]) + logg_arr[ff] = float(vals[2]) + + + metal_uniq = np.unique(metal_arr) + temp_uniq = np.unique(temp_arr) + + for mm in range(len(metal_uniq)): + metal = metal_uniq[mm] # metallicity + + # Construct str for metallicity (for appropriate directory name) + met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) + if metal > 0: + met_str = 'p' + met_str + else: + met_str = 'm' + met_str + + # Make directory for current metallicity if it does not exist yet + if not os.path.exists(path + 'phoenix' + met_str): + os.mkdir(path + 'phoenix' + met_str) + + for tt in range(len(temp_uniq)): + temp = temp_uniq[tt] # temperature + + # Pick out the list of gravities for this T, Z combo + idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] + logg_exist = logg_arr[idx] + + # All gravities will go in one file. Here is the output + # file name. + outfile = path + files_all[idx[0]].split('[')[0] + + ## If the rebinned file already exists, continue + if os.path.exists(outfile): + continue + + # Build a columns array. One column for each gravity. + cols_arr = [] + + # Make the wavelength column, which is first in the cols array. + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + cols_arr.append(c0) + + for gg in range(len(logg_exist)): + grav = logg_exist[gg] # gravity + + # Fetch the spectrum + sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Store the spectrum + name = 'g{0:3.1f}'.format(grav) + col = fits.Column(name=name, format='E', array=flux_rebin) + cols_arr.append(col) + + + # Make the FITS file from the columns with header. + cols = fits.ColDefs(cols_arr) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + for gg in range(len(logg_exist)): + tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' + + # Write hdu + finalhdu = fits.HDUList([prihdu, tbhdu]) + # don't have overwrite to protect original files. + finalhdu.writeto(outfile) + + print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) + + + return + + +def rebin_spec(wave, specin, wavnew): + """ + Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: + http://www.astrobetter.com/blog/2013/08/12/ + python-tip-re-sampling-spectra-with-pysynphot/ + """ + spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) + f = np.ones(len(wave)) + filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') + obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') + + return obs.binflux + +def organize_BTSettl_2015_atmospheres(path_to_dir): + """ + Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. + Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] + + path_to_dir is the path to the directory containing all of the downloaded + files + + Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 + (assumes this directory exists) + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # If it doesn't already exist, create the BTSettl subdirectory + if not os.path.exists('BTSettl_2015'): + os.mkdir('BTSettl_2015') + + # Process each atmosphere file independently + print( 'Creating cdbs-ready files') + files = glob.glob('*.spec.fits') + + for i in files: + hdu = fits.open(i) + spec = hdu[1].data + header_0 = hdu[0].header + header_1 = hdu[1].header + + wave = spec.field(0) + flux = spec.field(1) + + # Get units right: convert wave from microns to Angstroms, + # flux from W /m^2/ micron to erg/s/cm^2/A + wave_new = wave * 10**4 + flux_new = flux * 10**(-1) + + # Make new fits table + c0 = fits.Column(name='Wavelength', format='D', array=wave_new) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Copy over headers, update unit keywords + prihdu = fits.PrimaryHDU(header=header_0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) + + hdu.close() + hdu_new.close() + + # Return to original directory + os.chdir(start_dir) + return + +def make_BTSettl_2015_catalog(path_to_dir): + """ + Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. + THIS IS STEP 2, after organize_CMFGEN_atmospheres has + been run. + + path_to_dir is from current working directory to the cdbs directory. + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere from the filename, + # construct columns for catalog file + files = glob.glob("*spec.fits") + index_str = [] + name_str = [] + for name in files: + tmp = name.split('-') + temp = float(tmp[0][3:]) * 100.0 # In kelvin + logg = float(tmp[1]) + + index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) + name_str.append('{0}[Flux]'.format(name)) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): + """ + Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: BTSettl_2015_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing phoenix model; we will steal the header + tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' + phoenix_hdu = fits.open(tmp) + header0 = phoenix_hdu[0].header + phoenix_hdu.close() + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/BTSettl_2015_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + print( 'Rebinning BTSettl spectra') + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the BTSettl spectrum, rebin flux + sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + return + +def make_wavelength_unique(files, dirname): + """ + Helper function to go through each BTSettl spectrum and ensure that + each wavelength point is unique. This is required for rebinning to work. + + + files: list of files to run this analysis on + """ + # Loop through each file, find fix repeated wavelength entries if necessary + for i in files: + t = Table.read('{0}/{1}'.format(dirname,i), format='fits') + test = np.unique(t['Wavelength'], return_index=True) + + if len(t) != len(test[0]): + t = t[test[1]] + + c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) + c1 = fits.Column(name='Flux', format='E', array=t['Flux']) + cols = fits.ColDefs([c0, c1]) + + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) + + # Also make sure wavelength is monotonic. If it is not, then it is + # a sign that the wavelengths are out of order + diff = np.diff(t['Wavelength']) + bad = np.where(diff < 0) + if len(bad[0]) > 0: + t.sort('Wavelength') + + c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) + c1 = fits.Column(name='Flux', format='E', array=t['Flux']) + cols = fits.ColDefs([c0, c1]) + + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) + + print('Done {0}'.format(i)) + + return + +def organize_BTSettl_atmospheres(): + """ + Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). + The code expects tp be run in cdbs/grid/BTSettl, and expects that the + individual model files have been downloaded from online + (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) + and processed into python-readable ascii files. + """ + orig_dir = os.getcwd() + dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] + #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] + + + # Go through each directory, turning each spectrum into a cdbs-ready file. + # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, + # for faster access later + for ii in dirs: + print('Starting {0}'.format(ii)) + os.chdir(ii) + + files = glob.glob('*.txt') + count=0 + for jj in files: + t = Table.read(jj, format='ascii') + # First, trim the wavelengths to a more reasonable wavelength range + good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) + t = t[good] + + # Convert flux units to Flam (Ergs/sec/cm**2/A) + flux_new = 10**(t['col2'] - 8.0) + + # Save the file as a fits file + c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Add unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) + hdu_new.close() + count += 1 + print('Done {0} of {1}'.format(count, len(files))) + + # Now, clean up all the files made when unzipping the spectra + cmd1 = 'rm *.bz2' + cmd2 = 'rm *.tmp' + #cmd3 = 'rm *.txt' + os.system(cmd1) + os.system(cmd2) + #os.system(cmd3) + print('==============================') + print('Done {0}'.format(ii)) + print('==============================') + + # Go back to original directory, move to next metallicity directory + os.chdir(orig_dir) + + return + +def make_BTSettl_catalog(): + """ + Create cdbs catalog.fits of BTSettl grid. + THIS IS STEP 2, after organize_BTSettl_atmospheres has + been run. + + Code expects to be run in cdbs/grid/BTSettl + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] + #dirs = ['btp05'] + + # Construct the catalog.fits file input. The input consists of + # and index string that specifies the stellar paramters, and a + # name string that points to the file + # Loop over all the metallicity directories to construct these inputs + index_str = [] + name_str = [] + for ii in dirs: + os.chdir(ii) + files = glob.glob('*.fits') + + # Construct the metallicity val + if 'm' in ii: + metal_flag = -1 * float(ii[3:])*0.1 + else: + metal_flag = float(ii[3:])*0.1 + + # Now collect the info from the files + for jj in files: + tmp = jj.split('-') + + if metal_flag >= 0: + temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin + try: + logg = float(tmp[1]) + except: + logg = float(tmp[1].split('+')[0]) + else: + temp = float(tmp[0][3:]) * 100.0 # In kelvin + logg = float(tmp[1]) + + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) + name_str.append('{0}/{1}[Flux]'.format(ii, jj)) + + # Go back to original directory to move to next metallicity + print('Done {0}'.format(ii)) + os.chdir(start_dir) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_BTSettl(make_unique=False): + """ + Rebin BTSettle models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory: BTSettl_rebin + + Code expects to be run in cdbs/grid directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Create cdbs/grid directory for rebinned models + path = 'BTSettl_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata('BTSettl/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + #==============================# + #tmp = [] + #for ii in files_all: + # if ii.startswith('btp00'): + # tmp.append(ii) + #files_all = tmp + #=============================# + + print( 'Rebinning BTSettl spectra') + if make_unique: + print('Making unique') + make_wavelength_unique(files_all, 'BTSettl') + print('Done') + + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the BTSettl spectrum, rebin flux + try: + sp = pysynphot.Icat('BTSettl', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + except: + pdb.set_trace() + orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) + outfile = path + files_all[ff].split('[')[0] + cmd = 'cp {0} {1}'.format(orig_file, outfile) + os.system(cmd) + + print('Done {0} of {1}'.format(ff, len(files_all))) + + return + +def organize_all_Meisner2023_atmospheres(): + """ + Construct cdbs-ready atmospheres for the Meisner2023 grid. + The code expects tp be run in cdbs/grid/Meisner2023, and expects that the + individual model files have been downloaded from online + and processed into python-readable ascii files. + """ + orig_dir = os.getcwd() + dirs = ['mm10', 'mm05', 'mp00', 'mp03'] + + # Go through each directory, turning each spectrum into a cdbs-ready file. + # Save as a fits file, for faster access later + for ii in dirs: + print('Starting {0}'.format(ii)) + os.chdir(ii) + + files = glob.glob('*.fits') + count=0 + for jj in files: + # Open each .fits file and read the data + with fits.open(jj) as hdul: + data = hdul[1].data + wavelength = data['Wavelength'] + flux = data['Flux'] + + # Make flux independent of R&D + flux_new = flux / 5e-20 + + # Create new columns with desired format + c0 = fits.Column(name='Wavelength', format='D', array=wavelength) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Add unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write the new fits table in the cdbs directory + output_filename = '{0}.fits'.format(jj[:-5]) # Removing the original .fits extension + hdu_new.writeto(output_filename, overwrite=True) + hdu_new.close() + count += 1 + print('Done {0} of {1}'.format(count, len(files))) + + # Go back to original directory, move to next metallicity directory + os.chdir(orig_dir) + + return + +def make_Meisner2023_catalog(): + """ + Create cdbs catalog.fits of Meisner2023 grid. + THIS IS STEP 2, after organize_Meisner2023_atmospheres has + been run. + + Code expects to be run in cdbs/grid/Meisner2023 + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + dirs = ['mm10', 'mm05', 'mp00', 'mp03'] + + # Construct the catalog.fits file input. The input consists of + # and index string that specifies the stellar paramters, and a + # name string that points to the file + # Loop over all the metallicity directories to construct these inputs + index_str = [] + name_str = [] + for ii in dirs: + os.chdir(ii) + files = glob.glob('spec_jwst_*.fits') + + for jj in files: + # Parse temperature, log(g), and metallicity from filename + temp_str = jj.split('_')[2] + logg_str = jj.split('_')[3] + metal_str = jj.split('_')[4] + + # Extract temperature, surface gravity, and metallicity + temp = float(temp_str[1:]) # Temperature in Kelvin + logg = float(logg_str[1:]) # Surface gravity log(g) + + # Build metallicity value + if metal_str.startswith('m'): + metallicity = -1 * float(metal_str[1:]) + else: + metallicity = float(metal_str[1:]) + + # Construct index and filename strings + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metallicity, logg)) + name_str.append('{0}/{1}[Flux]'.format(ii, jj)) + + print('Processed directory:', ii) + os.chdir(start_dir) + + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_Meisner2023(make_unique=False): + """ + Rebin Meisner2023 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory: Meisner2023_rebin + + Code expects to be run in cdbs/grid directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Create a directory for rebinned Meisner2023 models + rebin_path = 'Meisner2023_rebin/' + if not os.path.exists(rebin_path): + os.mkdir(rebin_path) + + # Load the catalog.fits file and extract all spectra file paths + cat = Table.read('Meisner2023/catalog.fits') + files_all = [cat[ii]['FILENAME'].split('[')[0] for ii in range(len(cat))] + + print('Rebinning Meisner2023 spectra') + if make_unique: + print('Making unique') + make_wavelength_unique(files_all, 'Meisner2023') + print('Done') + + for ff, file in enumerate(files_all): + vals = cat[ff]['INDEX'].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the Meisner2023 spectrum and rebin its flux + try: + sp = pysynphot.Icat('Meisner2023', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Create the output FITS file + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + # Write the new rebinned file in the Meisner2023_rebin directory + outfile = os.path.join(rebin_path, os.path.basename(file)) + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + except Exception as e: + print(f"Error processing {file}: {e}") + orig_file = os.path.join('Meisner2023', file) + outfile = os.path.join(rebin_path, os.path.basename(file)) + os.system(f'cp {orig_file} {outfile}') + + print('Done {0} of {1}'.format(ff + 1, len(files_all))) + + return + + + +def organize_WDKoester_atmospheres(path_to_dir): + """ + Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) + Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] + + path_to_dir is the path to the directory containing all of the downloaded + files + + Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter + (assumes this directory exists) + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Process each atmosphere file independently + print( 'Creating cdbs-ready files') + files = glob.glob('*.dk.dat.txt') + + for i in files: + data = Table.read(i, format='ascii') + + wave = data['col1'] # angstrom + flux = data['col2'] # erg/s/cm^2/A + + # Make new fits table + c0 = fits.Column(name='Wavelength', format='D', array=wave) + c1 = fits.Column(name='Flux', format='E', array=flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Copy over headers, update unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) + + hdu_new.close() + + # Return to original directory + os.chdir(start_dir) + return + +def make_WDKoester_catalog(path_to_dir): + """ + Create cdbs catalog.fits of wdKoester grid. + THIS IS STEP 2, after organize_WDKoester_atmospheres has + been run. + + path_to_dir is from current working directory to the cdbs directory. + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere from the filename, + # construct columns for catalog file + files = glob.glob("*dk.dat.fits") + index_str = [] + name_str = [] + for name in files: + tmp = name.split('.') + tmp2 = tmp[0].split('_') + temp = float(tmp2[0][2:]) # Kelvin + logg = float(tmp2[1]) / 100.0 # log(g) + + index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) + name_str.append('{0}[Flux]'.format(name)) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): + """ + Rebin wdKoester models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: wdKoester_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing model; we will steal the header + tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' + wdkoester_hdu = fits.open(tmp) + header0 = wdkoester_hdu[0].header + wdkoester_hdu.close() + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/wdKoester_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + print( 'Rebinning wdKoester spectra') + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the wdKoester spectrum, rebin flux + sp = pysynphot.Icat('wdKoester', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + return + + diff --git a/spisea/atmospheres_BASE_58456.py b/spisea/atmospheres_BASE_58456.py new file mode 100644 index 00000000..8df2fbe1 --- /dev/null +++ b/spisea/atmospheres_BASE_58456.py @@ -0,0 +1,2165 @@ +import logging +import numpy as np +import pysynphot +import os +import glob +from astropy.io import fits +from astropy.table import Table, Column +import pysynphot +import time +import pdb +import warnings + +log = logging.getLogger('atmospheres') + +def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4): + """ + Given atmosphere model, get temperature and gravity bounds + """ + # Open catalog fits file and break out row indices + catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) + + teff_arr = [] + z_arr = [] + logg_arr = [] + for cur_row_index in range(len(catalog)): + index = catalog['INDEX'][cur_row_index] + tmp = index.split(',') + teff_arr.append(float(tmp[0])) + z_arr.append(float(tmp[1])) + logg_arr.append(float(tmp[2])) + teff_arr = np.array(teff_arr) + z_arr = np.array(z_arr) + logg_arr = np.array(logg_arr) + + # Filter by metallicity. Will chose the closest metallicity to desired input + metal_list = np.unique(np.array(z_arr)) + metal_idx = np.argmin(np.abs(metal_list - metallicity)) + + z_filt = np.where(z_arr == metal_list[metal_idx]) + teff_arr = teff_arr[z_filt] + logg_arr = logg_arr[z_filt] + + # # Now find the closest atmosphere in parameter space to + # # the one we want. We'll find the match with the lowest + # # fractional difference + # teff_diff = (teff_arr - temperature) / temperature + # logg_diff = (logg_arr - gravity) / gravity + # + # diff_tot = abs(teff_diff) + abs(logg_diff) + # idx_f = np.argmin(diff_tot) + # + # temperature_new = teff_arr[idx_f] + # gravity_new = logg_arr[idx_f] + + # First check if temperature within bounds + temperature_new = temperature + if temperature > np.max(teff_arr): + temperature_new = np.max(teff_arr) + if temperature < np.min(teff_arr): + temperature_new = np.min(teff_arr) + + # If temperature within bounds, then check if metallicity within bounds + teff_diff = np.abs(teff_arr - temperature) + sorted_min_diffs = np.unique(teff_diff) + + ## Find two closest temperatures + teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] + teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] + + logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] + logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] + + ## Switch to most conservative bound of logg out of two closest temps + gravity_new = gravity + if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): + gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) + if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): + gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) + + # Print out changes, if any + if temperature_new != temperature: + teff_msg = 'Changing to T={0:6.0f} for T={1:6.0f} logg={2:4.2f}' + print( teff_msg.format(temperature_new, temperature, gravity)) + + if gravity_new != gravity: + logg_msg = 'Changing to logg={0:4.2f} for T={1:6.0f} logg={2:4.2f}' + print( logg_msg.format(gravity_new, temperature, gravity)) + + return (temperature_new, gravity_new) + +def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): + """ + Return atmosphere from the Kurucz pysnphot grid + (`Kurucz 1993 `_). + + Grid Range: + + * Teff: 3000 - 50000 K + * gravity: 0 - 5 cgs + * metallicity: -5.0 - 1.0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + Always false for this particular function + """ + try: + sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('k93models', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Kurucz 1993 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): + """ + Return atmospheres from the pysynphot ATLAS9 atlas + (`Castelli & Kurucz 2004 `_). + + Grid Range: + + * Teff: 3500 - 50000 K + * gravity: 0 - 5.0 cgs + * [M/H]: -2.5 - 0.2 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('ck04models', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 5000) + gravity = log gravity (def = 4.0) + """ + try: + sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('nextgen', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find NextGen atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 5000) + gravity = log gravity (def = 4.0) + """ + sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, + rebin=False): + """ + Return atmosphere from the pysynphot + `PHOENIX atlas `_. + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + """ + try: + sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 24000) + gravity = log gravity (def = 4.3) + + rebin=True: pull from atmospheres at ck04model resolution. + """ + # Take care of atmospheres outside the catalog boundaries + logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' + if gravity > 4.3: + print( logg_msg.format(4.3, temperature, gravity)) + gravity = 4.3 + + if rebin: + sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) + else: + sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, + verbose=False): + """ + For a given stellar atmosphere, get extract the closest possible match in + Teff/logg space. Note that this is different from the normal routine + which interpolates along the input grid to get final spectrum. We can't + do this here because the Fierro+15 atmosphere grid is so sparse + + rebin=True: pull from atmospheres at ck04model resolution. + + If verbose, print out the parameters of the match + """ + # Set up the proper root directory + if rebin == True: + root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' + else: + root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' + + # Read in catalog, extract atmosphere info + cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') + teff_arr = [] + z_arr = [] + logg_arr = [] + for ii in range(len(cat)): + index = cat['INDEX'][ii] + tmp = index.split(',') + teff_arr.append(float(tmp[0])) + z_arr.append(float(tmp[1])) + logg_arr.append(float(tmp[2])) + teff_arr = np.array(teff_arr) + z_arr = np.array(z_arr) + logg_arr = np.array(logg_arr) + + # Now find the closest atmosphere in parameter space to + # the one we want. We'll find the match with the lowest + # fractional difference + teff_diff = (teff_arr - temperature) / temperature + logg_diff = (logg_arr - gravity) / gravity + + diff_tot = abs(teff_diff) + abs(logg_diff) + idx_f = np.where(diff_tot == min(diff_tot))[0][0] + + # Extract the filename of the best-match model and read as + # pysynphot object + infile = cat[idx_f]['FILENAME'].split('.') + spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) + + # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the + # ATLAS models are in FLAM at the surface. So, we need to multiply the + # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. + # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file + t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') + tmp = np.where(t['col1'] == infile[0]) + + lum = t['col3'][tmp] * (3.839*10**33) # cgs + sigma = 5.6704 * 10**-5 # cgs + teff = teff_arr[idx_f] # cgs + + radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm + radius /= 3.08*10**18 # in pc + + + # Make the pysynphot spectrum + w = spec['Wavelength'] + f = spec['Flux'] * (1000 / radius)**2. + sp = pysynphot.ArraySpectrum(w,f) + + #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) + + # Print out parameters of match, if desired + if verbose: + print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) + print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) + + return sp + +def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 24000) + gravity = log gravity (def = 4.3) + + rebin=True: pull from atmospheres at ck04model resolution. + """ + if rebin: + sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) + else: + sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 30000) + gravity = log gravity (def = 4.14) + """ + sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): + """ + Return PHOENIX v16 atmospheres from + `Husser et al. 2013 `_. + + Models originally downloaded via `ftp `_. + Solar metallicity and [alpha/Fe] is used. + + Grid Range: + + * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K + * gravity: 0.0 - 6.0 cgs, steps of 0.5 + * [M/H]: -4.0 - 1.0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + """ + atm_model_name = 'phoenix_v16' + if rebin == True: + atm_model_name = 'phoenix_v16_rebin' + + + # Extract atmosphere. If that fails, then check bounds and try again + try: + sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_model_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): + """ + Return atmosphere from CIFIST2011_2015 grid + (`Allard et al. 2012 `_, + `Baraffe et al. 2015 `_ ) + + Grid originally downloaded from `website `_. + + Grid Range: + + * Teff: 1200 - 7000 K + * gravity: 2.5 - 5.5 cgs + * [M/H] = 0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + """ + if rebin == True: + atm_name = 'BTSettl_2015_rebin' + else: + atm_name = 'BTSettl_2015' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find BTSettl_2015 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): + """ + Return atmosphere from CIFIST2011 grid + (`Allard et al. 2012 `_) + + Grid originally downloaded `here `_ + + Notes + ------ + Grid Range: + + * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 + + Teff and gravity ranges depend on metallicity: + + [M/H] = -2.5 + + * Teff: 2600 - 4600 K + * gravity: 4.5 - 5.5 + + [M/H] = -2.0 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.5 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.0 + + * Teff: 2600 - 7000 + * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 + + [M/H] = -0.5 + + * Teff: 1000 -7000 + * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 + + [M/H] = 0 + + * Teff: 750 - 7000 + * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 + + [M/H] = 0.5 + + * Teff: 1000 - 5000 + * gravity: 3.5 - 5.0 + + + Alpha enhancement: + + * [M/H]= -0.0, +0.5 no anhancement + * [M/H]= -0.5 with [alpha/H]=+0.2 + * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + """ + if rebin == True: + atm_name = 'BTSettl_rebin' + else: + atm_name = 'BTSettl' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find BTSettl_2015 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): + """ + Return white dwarf atmospheres from + `Koester et al. 2010 `_ + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + """ + sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. + + Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 + """ + try: + sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('merged_atlas_phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model + and phoenixV16. + + Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 + """ + try: + sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +#---------------------------------------------------------------------# +def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, + rebin=True): + """ + Return a stellar atmosphere from a suite of different model grids, + depending on the input temperature, (all values in K). + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + + Notes + ----- + The underlying stellar model grid used changes as a function of + stellar temperature (in K): + + * T > 20,000: ATLAS + * 5500 <= T < 20,000: ATLAS + * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge + * 3800 <= T < 5000: PHOENIXv16 + + For T < 3800, there is an additional gravity and metallicity + dependence: + + If T < 3800 and [M/H] = 0: + + * T < 3800, logg < 2.5: PHOENIX v16 + * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge + * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 + + Otherwise, if T < 3800 and [M/H] != 0: + + * T < 3800: PHOENIX v16 + + References: + + * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) + * PHOENIXv16 (`Husser et al. 2013 `_) + * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) + + LTE WARNING: + + The ATLAS atmospheres are calculated with LTE, and so they + are less accurate when non-LTE conditions apply (e.g. T > 20,000 + K). Ultimately we'd like to add a non-LTE atmosphere grid for + the hottest stars in the future. + + HOW BOUNDARIES BETWEEN MODELS ARE TREATED: + + At the boundary between two models grids a temperature range is defined + where the resulting atmosphere is a weighted average between the two + grids. Near one boundary one model + is weighted more heavily, while at the other boundary the other + model is weighted more heavily. These are calculated in the + temperature ranges where we switch between model grids, to + ensure a smooth transition. + """ + # For T < 3800, atmosphere depends on metallicity + gravity. + # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is + # currently available here, so if non-solar metallicity, just stick with + # the Phoenix grid + if (temperature <= 3800) & (metallicity == 0): + # High gravity are in BTSettl regime + if (temperature <= 3200) & (gravity > 2.5): + if verbose: + print( 'BTSettl_2015 atmosphere') + return get_BTSettl_2015_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): + if verbose: + print( 'BTSettl/Phoenixv16 merged atmosphere') + return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + # Low gravity is PHOENIX regime + if gravity <= 2.5: + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature <= 3800) & (metallicity != 0): + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + # For T > 3800, no metallicity or gravity dependence + if (temperature >= 3800) & (temperature < 5000): + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature >= 5000) & (temperature < 5500): + if verbose: + print( 'ATLAS/Phoenix merged atmosphere') + return get_atlas_phoenix_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + if (temperature >= 5500) & (temperature < 20000): + if verbose: + print( 'ATLAS merged atmosphere') + return get_castelli_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + if temperature >= 20000: + if verbose: + print( 'Still ATLAS merged atmosphere') + return get_castelli_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + #print('CMFGEN') + #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, + # temperature=temperature, + # gravity=gravity) + + + + +def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): + """ + Return the white dwarf atmosphere from + `Koester et al. 2010 `_. + If desired parameters are + outside of grid, return a blackbody spectrum instead + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + if verbose: + print('wdKoester atmosphere') + + return get_wdKoester_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + except pysynphot.exceptions.ParameterOutOfBounds: + # Use a black-body atmosphere. + bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) + return bbspec + +def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, + verbose=False, rebin=None, + wave_min=500, wave_max=50_000, wave_num=20_000): + """ + Return a blackbody spectrum + + Parameters + ---------- + temperature: float, default=20_000 + The stellar temperature, in units of K + wave_min: float, default=500 + Sets the minimum wavelength (in Angstroms) of the wavelength range + for the blackbody spectrum + wave_max: float, default=50_000 + Sets the maximum wavelength (in Angstroms) of the wavelength range + for the blackbody spectrum + wave_num: int, default=20_000 + Sets the number of wavelength points in the wavelength range + Note: the wavelength range is evenly spaced in log space + """ + if ((metallicity is not None) or (gravity is not None) or + (rebin is not None)): + warnings.warn( + 'Only `temperature` keyword is used for black-body atmosphere' + ) + + if verbose: + print('Black-body atmosphere') + + # Modify pysynphot's default waveset to specified bounds + pysynphot.refs.set_default_waveset( + minwave=wave_min, maxwave=wave_max, num=wave_num + ) + + # Get black-body atmosphere for specified temperature from pysynphot + bbspec = pysynphot.spectrum.BlackBody(temperature) + + # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` + bbspec.convert('flam') + + # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. + # Need to remove this normalization for SPISEA by multiplying bbspec + # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 + bbspec *= (1000 * 3.086e18 / 6.957e10)**2 + + return bbspec + + +#--------------------------------------# +# Atmosphere formatting functions +#--------------------------------------# + +def download_CMFGEN_atmospheres(Table_rot, Table_norot): + """ + Downloads CMFGEN models from + https://sites.google.com/site/fluxesandcontinuum/home; + these contain continuum as well as lines. + + Table_rot, Table_norot are tables with the file prefixes + and model atmosphere parameters, taken by hand from the + Fierro+15 paper + + Website addresses are hardcoded + + Puts downloaded models in the current working directory. + """ + print( 'WARNING: THIS DOES NOT COMPLETELY WORK') + print( '**********************') + t_rot = Table.read(Table_rot, format='ascii') + t_norot = Table.read(Table_norot, format='ascii') + + tables = [t_rot, t_norot] + filenames = [t_rot['col1'], t_norot['col1']] + + # Hardcoded list of webiste addresses + web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' + web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' + web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', + web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', + web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', + web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', + web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] + # Array of masses that matches the website addresses + mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) + + # Loop through rotating and unrotating case. First loop is rot, second unrot + for i in range(2): + # Extract masses from filenames + masses = [] + for j in filenames[i]: + tmp = j.split('m') + mass = float(tmp[1][:-1]) + masses.append(mass) + + # Download the models webpage by webpage. A bit tricky because masses + # change slightly within a particular website. THIS IS WHAT FAILS + for j in range(len(web)): + if j == 0: + good = np.where( (masses <= mass_arr[j]) ) + else: + g = j - 1 + good = np.where( (masses <= mass_arr[j]) & + (masses > mass_arr[g]) ) + # Use wget command to pull down the files, and unzip them + for k in good[0]: + full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) + os.system('wget ' + full) + os.system('unzip '+ filenames[i][k] + '.flx.zip') + + return + +def organize_CMFGEN_atmospheres(path_to_dir): + """ + Organize CMFGEN grid from Fierro+15 + (http://www.astroscu.unam.mx/atlas/index.html) + into rot and noRot directories + + path_to_dir is from current working directory to directory + containing the downloaded models. Assumed that models + and tables describing parameters are in this directory. + + Tables describing parameters MUST be named Table_rot.txt, + Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. + These are located in same original directory as atmosphere files + + Will separate files into 2 subdirectories, one rotating and + the other non-rotating + + *Can't have any other files starting with "t" in model directory to start!* + """ + # First, record current working directory to return to later + start_dir = os.getcwd() + + # Enter atmosphere directory, collect rotating and non-rotating + # file names (assumed to all start with "t") + os.chdir(path_to_dir) + rot_models = glob.glob("t*r.flx*") + noRot_models = glob.glob("t*n.flx*") + + # Separate into different subdirectories + if os.path.exists('cmfgenF15_rot'): + pass + else: + os.mkdir('cmfgenF15_rot') + os.mkdir('cmfgenF15_noRot') + + for mod in rot_models: + cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) + os.system(cmd) + + for mod in noRot_models: + cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) + os.system(cmd) + + # Also move Tables with model parameters into correct directory + os.system('mv Table_rot.txt cmfgenF15_rot') + os.system('mv Table_noRot.txt cmfgenF15_noRot') + + # Return to original directory + os.chdir(start_dir) + + return + +def make_CMFGEN_catalog(path_to_dir): + """ + Create cdbs catalog.fits of CMFGEN grid from Fierro+15 + (http://www.astroscu.unam.mx/atlas/index.html). + THIS IS STEP 2, after organize_CMFGEN_atmospheres has + been run. + + path_to_dir is from current working directory to directory + containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, + needs to be a Table*.txt file which contains the parameters for all of the + original models, since params in filename are not precise enough + + Will create catalog.fits file in atmosphere directory with + description of each model + + *Can't have any other files starting with "t" in model directory to start!* + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere + # Note: can't rely on filename for this because not precise enough!! + + #---------OLD: GETTING PARAMS FROM FILENAME-------# + # Collect file names (assumed to all start with "t") + #files = glob.glob("t*") + #for name in files: + # tmp = name.split('l') + # temp = float(tmp[0][1:]) * 100.0 # In kelvin + + # lumtmp = tmp[1].split('_') + # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun + + # mass = float(lumtmp[0][5:-1]) # In M_sun + + # Need to calculate log g from T and L (cgs) + # lum_sun = 3.846 * 10**33 # erg/s + # M_sun = 2 * 10**33 # g + # G_si = 6.67 * 10**(-8) # cgs + # sigma_si = 5.67 * 10**(-5) # cgs + + # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ + # (lum * lum_sun) + # logg = np.log10(g) + #---------------------------------------------------# + + # Read table with atmosphere params + table = glob.glob('Table_*') + t = Table.read(table[0], format = 'ascii') + names = t['col1'] + temps = t['col2'] + logg = t['col4'] + + # Create catalog.fits file + index_str = [] + name_str = [] + for i in range(len(names)): + index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) + + #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# + #path = path_to_dir + '/' + names[i] + path = names[i] + '.fits[Flux]' + + index_str.append(index) + name_str.append(path) + + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits') + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): + """ + Code to put cmfgen models into cdbs format and adds proper unit keyword in + fits header. Save as fits file + + path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot + directory with the *.flx models. Note that these files have already been + organized using organize_CMFGEN_atmospheres code. + + path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or + cmfgen_norot directory. Will copy new fits files to this directory. + This directory must already exist! + """ + # Save starting directory for later, move into path_to_dir directory + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Collect the filenames, make necessary changes to each one + files = glob.glob('*.flx') + + # Need to make brand-new fits tables with data we want. + counter = 0 + for i in files: + counter += 1 + # Open file, extract useful info + t = Table.read(i, format='ascii') + wave = t['col1'] + flux = t['col2'] # Flux is already in erg/cm^2/s/A + + # Need to eliminate duplicate entries (pysynphot crashes) + unique = np.unique(wave, return_index=True) + wave = wave[unique[1]] + flux = flux[unique[1]] + + # Make fits table from individual columns. + c0 = fits.Column(name='Wavelength', format='D', array=wave) + c1 = fits.Column(name='Flux', format='E', array=flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + #Adding unit keywords + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + prihdu = fits.PrimaryHDU() + + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(i[:-4]+'.fits', overwrite=True) + + print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) + + # Return to original directory, copy over new .fits files to cdbs directory + os.chdir(start_dir) + cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) + os.system(cmd) + + return + +def rebin_cmfgen(cdbs_path, rot=True): + """ + Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; + this makes spectrophotometry MUCH faster + + cdbs_path: path to cdbs directory + rot=True for rotating models (cmfgen_rot), False for non-rotating models + + makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing cmfgen model; we will steal the header. + # Also define paths to new rebin directories + if rot == True: + tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' + path = cdbs_path+'/grid/cmfgen_rot_rebin/' + orig_path = cdbs_path+'/grid/cmfgen_rot/' + else: + tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' + path = cdbs_path+'/grid/cmfgen_norot_rebin/' + orig_path = cdbs_path+'/grid/cmfgen_norot/' + + cmfgen_hdu = fits.open(tmp) + header0 = cmfgen_hdu[0].header + # Create rebin directories if they don't already exist. Copy over + # catalog.fits file from original directory (will be the same) + if not os.path.exists(path): + os.mkdir(path) + cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) + os.system(cmd) + + # Read in the catalog.fits file + cat = fits.getdata(orig_path + 'catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + # First column in new files will be for [atlas] wavelength + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + + # For each catalog.fits entry, read the unbinned spectrum and rebin to + # the atlas resolution. Make a new fits file in rebin directory + count = 0 + for ff in range(len(files_all)): + count += 1 + # Extract the temp, Z, logg + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + grav = float(vals[2]) + + # Fetch the spectrum + if rot == True: + sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) + else: + sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) + + # Rebin + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + # Make the FITS file from the columns with header + cols = fits.ColDefs([c0,c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + # Write hdu to new directory with same filename + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(path+files_all[ff]) + + print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) + return + + +def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): + """ + Construct the Phoenix Husser+13 atmopsheres for each model. Combines the + fluxes from the *HiRES.fits files and the wavelengths of the + WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. + + path_to_dir is the path to the directory containing all of the downloaded + files + + met_str is the name of the current metallicity + + Creates new fits files for each atmosphere: phoenix_.fits, + which contains columns for the log g (column header = g#.#). Puts + atmospheres in new directory phoenixm00 + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # If it doesn't already exist, create the current metallicity subdirectory + sub_dir = '../phoenix{0}'.format(met_str) + if os.path.exists(sub_dir): + pass + else: + os.mkdir(sub_dir) + + # Extract wavelength array, make column for later + wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') + wave = wavefile[0].data + wavefile.close() + wave_col = Column(wave, name = 'WAVELENGTH') + + # Create temp array for Husser+13 grid (given in paper) + temp_arr = np.arange(2300, 7001, 100) + temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) + + print( 'Looping though all temps') + # For each temp, build file containing the flux for all gravities + i = 0 + for temp in temp_arr: + files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) + files.sort() + # Start the table with the wavelength column + t = Table() + t.add_column(wave_col) + for f in files: + # Extract the logg out of filename + logg = f[9:13] + + # Extract fluxes from file + spectrum = fits.open(f) + flux = spectrum[0].data + spectrum.close() + + # Make Column object with fluxes, add to table + col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) + t.add_column(col) + + # Now, construct final fits file for the given temp + outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) + t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) + + # Progress counter for user + i += 1 + print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) + + # Return to original directory + os.chdir(start_dir) + return + +def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): + """ + Makes catalog.fits file for Husser+13 phoenix models. Assumes that + organize_PHOENIXv16_atmospheres has been run already, and that the models lie + in subdirectory phoenix[met_str]. + + path_to_directory is the path to the directory with the reformatted + models (i.e. the output from construct_atmospheres, phoenix[met_str]) + + Puts catalog.fits file in directory the user starts in + """ + # Save starting directory for later, move into working directory + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Extract metallicity from metallicity string + met = float(met_str[1]) + (float(met_str[2]) * 0.1) + if 'm' in met_str: + met *= -1. + + # Collect the filenames. Each is a unique temp with many different log g's + files = glob.glob('phoenix*.fits') + files.sort() + + # Create the catalog.fits file, row by row + index_arr = [] + filename_arr = [] + for i in files: + # Get log g values from the column header the file + t = Table.read(i, format='fits') + keys = t.keys() + logg_vals = keys[1:] + + # Extract temp from filename + name = i.split('_') + temp = float(name[1][:-5]) + for j in logg_vals: + logg = float(j[1:]) + index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) + filename = path_to_dir + i + '[' + j + ']' + # Add row to table + index_arr.append(index) + filename_arr.append(filename) + + catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) + + # Return to starting directory, write catalog + os.chdir(start_dir) + + if os.path.exists('catalog.fits'): + from astropy.table import vstack + + prev_catalog = Table.read('catalog.fits', format='fits') + joined_catalog = vstack([prev_catalog, catalog]) + + joined_catalog.write('catalog.fits', format='fits', overwrite=True) + else: + catalog.write('catalog.fits', format='fits', overwrite=True) + + return + +def cdbs_PHOENIXv16(path_to_cdbs_dir): + """ + Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily + consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] + and adding the appropriate keywords to the fits header. + + path_to_cdbs_dir goes from current working directory to phoenix[met] directory + in cdbs/grids/phoenix_v16. Note that these files have already been organized + using organize_PHOENIXv16_atmospheres code. + + Overwrites original files in directory + """ + # Save starting directory for later, move into working directory + start_dir = os.getcwd() + os.chdir(path_to_cdbs_dir) + + # Collect the filenames, make necessary changes to each one + files = glob.glob('phoenix*.fits') + + ## Need to sort filenames; glob doesn't always give them in order + files.sort() + + # Need to make brand-new fits tables with data we want. + counter = 0 + for i in files: + counter += 1 + + # Read in current FITS table + cur_table = Table.read(i, format='fits') + + cur_table.columns[0].name = 'Wavelength' + + num_cols = len(cur_table.colnames) + + # Multiplying each flux column by 10^-8 for conversion + for cur_col_index in range(1, num_cols, 1): + cur_col_name = cur_table.colnames[cur_col_index] + cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 + + + # Construct new FITS file based on old one + hdu = fits.open(i) + header_0 = hdu[0].header + header_1 = hdu[1].header + sci = hdu[1].data + + tbhdu = fits.table_to_hdu(cur_table) + + # Copying over the older headers, adding unit keywords + prihdu = fits.PrimaryHDU(header=header_0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + tbhdu.header['TUNIT3'] = 'FLAM' + tbhdu.header['TUNIT4'] = 'FLAM' + tbhdu.header['TUNIT5'] = 'FLAM' + tbhdu.header['TUNIT6'] = 'FLAM' + tbhdu.header['TUNIT7'] = 'FLAM' + tbhdu.header['TUNIT8'] = 'FLAM' + tbhdu.header['TUNIT9'] = 'FLAM' + tbhdu.header['TUNIT10'] = 'FLAM' + tbhdu.header['TUNIT11'] = 'FLAM' + tbhdu.header['TUNIT12'] = 'FLAM' + tbhdu.header['TUNIT13'] = 'FLAM' + tbhdu.header['TUNIT14'] = 'FLAM' + + # Construct and write out final FITS file + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(i, overwrite=True) + + hdu.close() + print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) + + # Change back to starting directory + os.chdir(start_dir) + + return + +def rebin_phoenixV16(cdbs_path): + """ + Rebin phoenixV16 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: phoenix_v16_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing phoenix model; we will steal the header + ## (This assumes that at least 'm00' metallicity exists) + tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') + phoenix_hdu = fits.open(tmp) + header0 = phoenix_hdu[0].header + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/phoenix_v16_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + temp_arr = np.zeros(len(files_all), dtype=float) + logg_arr = np.zeros(len(files_all), dtype=float) + metal_arr = np.zeros(len(files_all), dtype=float) + + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + + temp_arr[ff] = float(vals[0]) + metal_arr[ff] = float(vals[1]) + logg_arr[ff] = float(vals[2]) + + + metal_uniq = np.unique(metal_arr) + temp_uniq = np.unique(temp_arr) + + for mm in range(len(metal_uniq)): + metal = metal_uniq[mm] # metallicity + + # Construct str for metallicity (for appropriate directory name) + met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) + if metal > 0: + met_str = 'p' + met_str + else: + met_str = 'm' + met_str + + # Make directory for current metallicity if it does not exist yet + if not os.path.exists(path + 'phoenix' + met_str): + os.mkdir(path + 'phoenix' + met_str) + + for tt in range(len(temp_uniq)): + temp = temp_uniq[tt] # temperature + + # Pick out the list of gravities for this T, Z combo + idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] + logg_exist = logg_arr[idx] + + # All gravities will go in one file. Here is the output + # file name. + outfile = path + files_all[idx[0]].split('[')[0] + + ## If the rebinned file already exists, continue + if os.path.exists(outfile): + continue + + # Build a columns array. One column for each gravity. + cols_arr = [] + + # Make the wavelength column, which is first in the cols array. + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + cols_arr.append(c0) + + for gg in range(len(logg_exist)): + grav = logg_exist[gg] # gravity + + # Fetch the spectrum + sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Store the spectrum + name = 'g{0:3.1f}'.format(grav) + col = fits.Column(name=name, format='E', array=flux_rebin) + cols_arr.append(col) + + + # Make the FITS file from the columns with header. + cols = fits.ColDefs(cols_arr) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + for gg in range(len(logg_exist)): + tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' + + # Write hdu + finalhdu = fits.HDUList([prihdu, tbhdu]) + # don't have overwrite to protect original files. + finalhdu.writeto(outfile) + + print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) + + + return + + +def rebin_spec(wave, specin, wavnew): + """ + Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: + http://www.astrobetter.com/blog/2013/08/12/ + python-tip-re-sampling-spectra-with-pysynphot/ + """ + spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) + f = np.ones(len(wave)) + filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') + obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') + + return obs.binflux + +def organize_BTSettl_2015_atmospheres(path_to_dir): + """ + Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. + Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] + + path_to_dir is the path to the directory containing all of the downloaded + files + + Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 + (assumes this directory exists) + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # If it doesn't already exist, create the BTSettl subdirectory + if not os.path.exists('BTSettl_2015'): + os.mkdir('BTSettl_2015') + + # Process each atmosphere file independently + print( 'Creating cdbs-ready files') + files = glob.glob('*.spec.fits') + + for i in files: + hdu = fits.open(i) + spec = hdu[1].data + header_0 = hdu[0].header + header_1 = hdu[1].header + + wave = spec.field(0) + flux = spec.field(1) + + # Get units right: convert wave from microns to Angstroms, + # flux from W /m^2/ micron to erg/s/cm^2/A + wave_new = wave * 10**4 + flux_new = flux * 10**(-1) + + # Make new fits table + c0 = fits.Column(name='Wavelength', format='D', array=wave_new) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Copy over headers, update unit keywords + prihdu = fits.PrimaryHDU(header=header_0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) + + hdu.close() + hdu_new.close() + + # Return to original directory + os.chdir(start_dir) + return + +def make_BTSettl_2015_catalog(path_to_dir): + """ + Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. + THIS IS STEP 2, after organize_CMFGEN_atmospheres has + been run. + + path_to_dir is from current working directory to the cdbs directory. + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere from the filename, + # construct columns for catalog file + files = glob.glob("*spec.fits") + index_str = [] + name_str = [] + for name in files: + tmp = name.split('-') + temp = float(tmp[0][3:]) * 100.0 # In kelvin + logg = float(tmp[1]) + + index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) + name_str.append('{0}[Flux]'.format(name)) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): + """ + Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: BTSettl_2015_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing phoenix model; we will steal the header + tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' + phoenix_hdu = fits.open(tmp) + header0 = phoenix_hdu[0].header + phoenix_hdu.close() + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/BTSettl_2015_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + print( 'Rebinning BTSettl spectra') + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the BTSettl spectrum, rebin flux + sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + return + +def make_wavelength_unique(files, dirname): + """ + Helper function to go through each BTSettl spectrum and ensure that + each wavelength point is unique. This is required for rebinning to work. + + + files: list of files to run this analysis on + """ + # Loop through each file, find fix repeated wavelength entries if necessary + for i in files: + t = Table.read('{0}/{1}'.format(dirname,i), format='fits') + test = np.unique(t['Wavelength'], return_index=True) + + if len(t) != len(test[0]): + t = t[test[1]] + + c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) + c1 = fits.Column(name='Flux', format='E', array=t['Flux']) + cols = fits.ColDefs([c0, c1]) + + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) + + # Also make sure wavelength is monotonic. If it is not, then it is + # a sign that the wavelengths are out of order + diff = np.diff(t['Wavelength']) + bad = np.where(diff < 0) + if len(bad[0]) > 0: + t.sort('Wavelength') + + c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) + c1 = fits.Column(name='Flux', format='E', array=t['Flux']) + cols = fits.ColDefs([c0, c1]) + + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) + + print('Done {0}'.format(i)) + + return + +def organize_BTSettl_atmospheres(): + """ + Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). + The code expects tp be run in cdbs/grid/BTSettl, and expects that the + individual model files have been downloaded from online + (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) + and processed into python-readable ascii files. + """ + orig_dir = os.getcwd() + dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] + #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] + + + # Go through each directory, turning each spectrum into a cdbs-ready file. + # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, + # for faster access later + for ii in dirs: + print('Starting {0}'.format(ii)) + os.chdir(ii) + + files = glob.glob('*.txt') + count=0 + for jj in files: + t = Table.read(jj, format='ascii') + # First, trim the wavelengths to a more reasonable wavelength range + good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) + t = t[good] + + # Convert flux units to Flam (Ergs/sec/cm**2/A) + flux_new = 10**(t['col2'] - 8.0) + + # Save the file as a fits file + c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Add unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) + hdu_new.close() + count += 1 + print('Done {0} of {1}'.format(count, len(files))) + + # Now, clean up all the files made when unzipping the spectra + cmd1 = 'rm *.bz2' + cmd2 = 'rm *.tmp' + #cmd3 = 'rm *.txt' + os.system(cmd1) + os.system(cmd2) + #os.system(cmd3) + print('==============================') + print('Done {0}'.format(ii)) + print('==============================') + + # Go back to original directory, move to next metallicity directory + os.chdir(orig_dir) + + return + +def make_BTSettl_catalog(): + """ + Create cdbs catalog.fits of BTSettl grid. + THIS IS STEP 2, after organize_BTSettl_atmospheres has + been run. + + Code expects to be run in cdbs/grid/BTSettl + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] + #dirs = ['btp05'] + + # Construct the catalog.fits file input. The input consists of + # and index string that specifies the stellar paramters, and a + # name string that points to the file + # Loop over all the metallicity directories to construct these inputs + index_str = [] + name_str = [] + for ii in dirs: + os.chdir(ii) + files = glob.glob('*.fits') + + # Construct the metallicity val + if 'm' in ii: + metal_flag = -1 * float(ii[3:])*0.1 + else: + metal_flag = float(ii[3:])*0.1 + + # Now collect the info from the files + for jj in files: + tmp = jj.split('-') + + if metal_flag >= 0: + temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin + try: + logg = float(tmp[1]) + except: + logg = float(tmp[1].split('+')[0]) + else: + temp = float(tmp[0][3:]) * 100.0 # In kelvin + logg = float(tmp[1]) + + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) + name_str.append('{0}/{1}[Flux]'.format(ii, jj)) + + # Go back to original directory to move to next metallicity + print('Done {0}'.format(ii)) + os.chdir(start_dir) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_BTSettl(make_unique=False): + """ + Rebin BTSettle models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory: BTSettl_rebin + + Code expects to be run in cdbs/grid directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Create cdbs/grid directory for rebinned models + path = 'BTSettl_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata('BTSettl/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + #==============================# + #tmp = [] + #for ii in files_all: + # if ii.startswith('btp00'): + # tmp.append(ii) + #files_all = tmp + #=============================# + + print( 'Rebinning BTSettl spectra') + if make_unique: + print('Making unique') + make_wavelength_unique(files_all, 'BTSettl') + print('Done') + + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the BTSettl spectrum, rebin flux + try: + sp = pysynphot.Icat('BTSettl', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + except: + pdb.set_trace() + orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) + outfile = path + files_all[ff].split('[')[0] + cmd = 'cp {0} {1}'.format(orig_file, outfile) + os.system(cmd) + + print('Done {0} of {1}'.format(ff, len(files_all))) + + return + +def organize_WDKoester_atmospheres(path_to_dir): + """ + Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) + Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] + + path_to_dir is the path to the directory containing all of the downloaded + files + + Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter + (assumes this directory exists) + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Process each atmosphere file independently + print( 'Creating cdbs-ready files') + files = glob.glob('*.dk.dat.txt') + + for i in files: + data = Table.read(i, format='ascii') + + wave = data['col1'] # angstrom + flux = data['col2'] # erg/s/cm^2/A + + # Make new fits table + c0 = fits.Column(name='Wavelength', format='D', array=wave) + c1 = fits.Column(name='Flux', format='E', array=flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Copy over headers, update unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) + + hdu_new.close() + + # Return to original directory + os.chdir(start_dir) + return + +def make_WDKoester_catalog(path_to_dir): + """ + Create cdbs catalog.fits of wdKoester grid. + THIS IS STEP 2, after organize_WDKoester_atmospheres has + been run. + + path_to_dir is from current working directory to the cdbs directory. + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere from the filename, + # construct columns for catalog file + files = glob.glob("*dk.dat.fits") + index_str = [] + name_str = [] + for name in files: + tmp = name.split('.') + tmp2 = tmp[0].split('_') + temp = float(tmp2[0][2:]) # Kelvin + logg = float(tmp2[1]) / 100.0 # log(g) + + index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) + name_str.append('{0}[Flux]'.format(name)) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): + """ + Rebin wdKoester models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: wdKoester_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing model; we will steal the header + tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' + wdkoester_hdu = fits.open(tmp) + header0 = wdkoester_hdu[0].header + wdkoester_hdu.close() + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/wdKoester_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + print( 'Rebinning wdKoester spectra') + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the wdKoester spectrum, rebin flux + sp = pysynphot.Icat('wdKoester', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + return + + diff --git a/spisea/atmospheres_LOCAL_58456.py b/spisea/atmospheres_LOCAL_58456.py new file mode 100644 index 00000000..0f4c90e5 --- /dev/null +++ b/spisea/atmospheres_LOCAL_58456.py @@ -0,0 +1,2508 @@ +import logging +import numpy as np +import pysynphot +import os +import glob +from astropy.io import fits +from astropy.table import Table, Column +import pysynphot +import time +import pdb +import warnings + +log = logging.getLogger('atmospheres') + +def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4): + """ + Given atmosphere model, get temperature and gravity bounds + """ + # Open catalog fits file and break out row indices + catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) + + teff_arr = [] + z_arr = [] + logg_arr = [] + for cur_row_index in range(len(catalog)): + index = catalog['INDEX'][cur_row_index] + tmp = index.split(',') + teff_arr.append(float(tmp[0])) + z_arr.append(float(tmp[1])) + logg_arr.append(float(tmp[2])) + teff_arr = np.array(teff_arr) + z_arr = np.array(z_arr) + logg_arr = np.array(logg_arr) + + # Filter by metallicity. Will chose the closest metallicity to desired input + metal_list = np.unique(np.array(z_arr)) + metal_idx = np.argmin(np.abs(metal_list - metallicity)) + + z_filt = np.where(z_arr == metal_list[metal_idx]) + teff_arr = teff_arr[z_filt] + logg_arr = logg_arr[z_filt] + + # # Now find the closest atmosphere in parameter space to + # # the one we want. We'll find the match with the lowest + # # fractional difference + # teff_diff = (teff_arr - temperature) / temperature + # logg_diff = (logg_arr - gravity) / gravity + # + # diff_tot = abs(teff_diff) + abs(logg_diff) + # idx_f = np.argmin(diff_tot) + # + # temperature_new = teff_arr[idx_f] + # gravity_new = logg_arr[idx_f] + + # First check if temperature within bounds + temperature_new = temperature + if temperature > np.max(teff_arr): + temperature_new = np.max(teff_arr) + if temperature < np.min(teff_arr): + temperature_new = np.min(teff_arr) + + # If temperature within bounds, then check if metallicity within bounds + teff_diff = np.abs(teff_arr - temperature) + sorted_min_diffs = np.unique(teff_diff) + + ## Find two closest temperatures + teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] + teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] + + logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] + logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] + + ## Switch to most conservative bound of logg out of two closest temps + gravity_new = gravity + if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): + gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) + if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): + gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) + + # Print out changes, if any + if temperature_new != temperature: + teff_msg = 'Changing to T={0:6.0f} for T={1:6.0f} logg={2:4.2f}' + print( teff_msg.format(temperature_new, temperature, gravity)) + + if gravity_new != gravity: + logg_msg = 'Changing to logg={0:4.2f} for T={1:6.0f} logg={2:4.2f}' + print( logg_msg.format(gravity_new, temperature, gravity)) + + return (temperature_new, gravity_new) + +def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): + """ + Return atmosphere from the Kurucz pysnphot grid + (`Kurucz 1993 `_). + + Grid Range: + + * Teff: 3000 - 50000 K + * gravity: 0 - 5 cgs + * metallicity: -5.0 - 1.0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + Always false for this particular function + """ + try: + sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('k93models', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Kurucz 1993 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): + """ + Return atmospheres from the pysynphot ATLAS9 atlas + (`Castelli & Kurucz 2004 `_). + + Grid Range: + + * Teff: 3500 - 50000 K + * gravity: 0 - 5.0 cgs + * [M/H]: -2.5 - 0.2 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('ck04models', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 5000) + gravity = log gravity (def = 4.0) + """ + try: + sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('nextgen', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find NextGen atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 5000) + gravity = log gravity (def = 4.0) + """ + sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, + rebin=False): + """ + Return atmosphere from the pysynphot + `PHOENIX atlas `_. + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + """ + try: + sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 24000) + gravity = log gravity (def = 4.3) + + rebin=True: pull from atmospheres at ck04model resolution. + """ + # Take care of atmospheres outside the catalog boundaries + logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' + if gravity > 4.3: + print( logg_msg.format(4.3, temperature, gravity)) + gravity = 4.3 + + if rebin: + sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) + else: + sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, + verbose=False): + """ + For a given stellar atmosphere, get extract the closest possible match in + Teff/logg space. Note that this is different from the normal routine + which interpolates along the input grid to get final spectrum. We can't + do this here because the Fierro+15 atmosphere grid is so sparse + + rebin=True: pull from atmospheres at ck04model resolution. + + If verbose, print out the parameters of the match + """ + # Set up the proper root directory + if rebin == True: + root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' + else: + root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' + + # Read in catalog, extract atmosphere info + cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') + teff_arr = [] + z_arr = [] + logg_arr = [] + for ii in range(len(cat)): + index = cat['INDEX'][ii] + tmp = index.split(',') + teff_arr.append(float(tmp[0])) + z_arr.append(float(tmp[1])) + logg_arr.append(float(tmp[2])) + teff_arr = np.array(teff_arr) + z_arr = np.array(z_arr) + logg_arr = np.array(logg_arr) + + # Now find the closest atmosphere in parameter space to + # the one we want. We'll find the match with the lowest + # fractional difference + teff_diff = (teff_arr - temperature) / temperature + logg_diff = (logg_arr - gravity) / gravity + + diff_tot = abs(teff_diff) + abs(logg_diff) + idx_f = np.where(diff_tot == min(diff_tot))[0][0] + + # Extract the filename of the best-match model and read as + # pysynphot object + infile = cat[idx_f]['FILENAME'].split('.') + spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) + + # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the + # ATLAS models are in FLAM at the surface. So, we need to multiply the + # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. + # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file + t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') + tmp = np.where(t['col1'] == infile[0]) + + lum = t['col3'][tmp] * (3.839*10**33) # cgs + sigma = 5.6704 * 10**-5 # cgs + teff = teff_arr[idx_f] # cgs + + radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm + radius /= 3.08*10**18 # in pc + + + # Make the pysynphot spectrum + w = spec['Wavelength'] + f = spec['Flux'] * (1000 / radius)**2. + sp = pysynphot.ArraySpectrum(w,f) + + #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) + + # Print out parameters of match, if desired + if verbose: + print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) + print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) + + return sp + +def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 24000) + gravity = log gravity (def = 4.3) + + rebin=True: pull from atmospheres at ck04model resolution. + """ + if rebin: + sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) + else: + sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 30000) + gravity = log gravity (def = 4.14) + """ + sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): + """ + Return PHOENIX v16 atmospheres from + `Husser et al. 2013 `_. + + Models originally downloaded via `ftp `_. + Solar metallicity and [alpha/Fe] is used. + + Grid Range: + + * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K + * gravity: 0.0 - 6.0 cgs, steps of 0.5 + * [M/H]: -4.0 - 1.0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + """ + atm_model_name = 'phoenix_v16' + if rebin == True: + atm_model_name = 'phoenix_v16_rebin' + + + # Extract atmosphere. If that fails, then check bounds and try again + try: + sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_model_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): + """ + Return atmosphere from CIFIST2011_2015 grid + (`Allard et al. 2012 `_, + `Baraffe et al. 2015 `_ ) + + Grid originally downloaded from `website `_. + + Grid Range: + + * Teff: 1200 - 7000 K + * gravity: 2.5 - 5.5 cgs + * [M/H] = 0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + """ + if rebin == True: + atm_name = 'BTSettl_2015_rebin' + else: + atm_name = 'BTSettl_2015' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + #print(dir(obj)) + + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find BTSettl_2015 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): + """ + Return atmosphere from CIFIST2011 grid + (`Allard et al. 2012 `_) + + Grid originally downloaded `here `_ + + Notes + ------ + Grid Range: + + * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 + + Teff and gravity ranges depend on metallicity: + + [M/H] = -2.5 + + * Teff: 2600 - 4600 K + * gravity: 4.5 - 5.5 + + [M/H] = -2.0 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.5 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.0 + + * Teff: 2600 - 7000 + * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 + + [M/H] = -0.5 + + * Teff: 1000 -7000 + * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 + + [M/H] = 0 + + * Teff: 750 - 7000 + * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 + + [M/H] = 0.5 + + * Teff: 1000 - 5000 + * gravity: 3.5 - 5.0 + + + Alpha enhancement: + + * [M/H]= -0.0, +0.5 no anhancement + * [M/H]= -0.5 with [alpha/H]=+0.2 + * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + **PRINT STATEMENTS TO DEBUG + **check get_atmosphere_bounds + **comment out try/except clause and check break + """ + if rebin == True: + atm_name = 'BTSettl_rebin' + else: + atm_name = 'BTSettl' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + +def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): + """ + Return atmosphere from Meisner2023 grid + (`Meisner et al. 2023 `_) + + Grid originally downloaded `here `_ + + Grid range: + * Teff = 250 - 1200 K + * gravity: 2.5 - 5.5 cgs (in steps of 0.5) + * [M/H] = -1.0, -0.5, 0, +0, +0.3 + + """ + if rebin == True: + atm_name = 'Meisner2023_rebin' + else: + atm_name = 'Meisner2023' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Meisner2023 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): + """ + Return atmosphere from Phillips et al., 2020 using ATMO model + (`Phillips et al. 2020 `_) + + Grid originally downloaded `here `_ + + Grid Range: + * Teff: 200 - 3000 K + * gravity: 2.5 - 5.5 cgs + * [M/H] = 0 + """ + if rebin == True: + atm_name = 'Phillips2020_rebin' + else: + atm_name = 'Phillips2020' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Phillips2020 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): + """ + Return white dwarf atmospheres from + `Koester et al. 2010 `_ + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + """ + sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. + + Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 + """ + try: + sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('merged_atlas_phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model + and phoenixV16. + + Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 + """ + try: + sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_meisner_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model + and Meisner2023. + + Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 + """ + try: + sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_meisner', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find BTSettl-Meisner merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +#---------------------------------------------------------------------# +def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, + rebin=True): + """ + Return a stellar atmosphere from a suite of different model grids, + depending on the input temperature, (all values in K). + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + + Notes + ----- + The underlying stellar model grid used changes as a function of + stellar temperature (in K): + + * T > 20,000: ATLAS + * 5500 <= T < 20,000: ATLAS + * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge + * 3800 <= T < 5000: PHOENIXv16 + + For T < 3800, there is an additional gravity and metallicity + dependence: + + If T < 3800 and [M/H] = 0: + + * T < 3800, logg < 2.5: PHOENIX v16 + * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge + * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 + * 1200 < T <= 1000, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge + * 1000 < T <= 250, logg > 2.5: Meisner2023 + + Otherwise, if T < 3800 and [M/H] != 0: + + * T < 3800: PHOENIX v16 + + References: + + * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) + * PHOENIXv16 (`Husser et al. 2013 `_) + * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) + * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) + + LTE WARNING: + + The ATLAS atmospheres are calculated with LTE, and so they + are less accurate when non-LTE conditions apply (e.g. T > 20,000 + K). Ultimately we'd like to add a non-LTE atmosphere grid for + the hottest stars in the future. + + HOW BOUNDARIES BETWEEN MODELS ARE TREATED: + + At the boundary between two models grids a temperature range is defined + where the resulting atmosphere is a weighted average between the two + grids. Near one boundary one model + is weighted more heavily, while at the other boundary the other + model is weighted more heavily. These are calculated in the + temperature ranges where we switch between model grids, to + ensure a smooth transition. + """ + # For T < 3800, atmosphere depends on metallicity + gravity. + # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is + # currently available here, so if non-solar metallicity, just stick with + # the Phoenix grid + if (temperature < 1000): + if verbose: + print( 'Meisner2023 atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature <= 1200) & (temperature >= 1000): + if (gravity >= 3.5): + if verbose: + print( 'BTSettl/Meisner2023 merged atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + if (gravity < 3.5) & (gravity >=2.5): + if verbose: + print( 'Meisner2023 atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature <= 3800) & (metallicity == 0): + # High gravity are in BTSettl regime + if (temperature <= 3200) & (gravity > 2.5): + if verbose: + print( 'BTSettl_2015 atmosphere') + return get_BTSettl_2015_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): + if verbose: + print( 'BTSettl/Phoenixv16 merged atmosphere') + return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + # Low gravity is PHOENIX regime + if gravity <= 2.5: + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature <= 3800) & (metallicity != 0): + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + # For T > 3800, no metallicity or gravity dependence + if (temperature >= 3800) & (temperature < 5000): + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature >= 5000) & (temperature < 5500): + if verbose: + print( 'ATLAS/Phoenix merged atmosphere') + return get_atlas_phoenix_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + if (temperature >= 5500) & (temperature < 20000): + if verbose: + print( 'ATLAS merged atmosphere') + return get_castelli_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + if temperature >= 20000: + if verbose: + print( 'Still ATLAS merged atmosphere') + return get_castelli_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + #print('CMFGEN') + #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, + # temperature=temperature, + # gravity=gravity) + + + + +def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): + """ + Return the white dwarf atmosphere from + `Koester et al. 2010 `_. + If desired parameters are + outside of grid, return a blackbody spectrum instead + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + if verbose: + print('wdKoester atmosphere') + + return get_wdKoester_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + except pysynphot.exceptions.ParameterOutOfBounds: + # Use a black-body atmosphere. + bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) + return bbspec + + +def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): + """ + Return the brown dwarf atmosphere from + `Meisner et al. 2023 `_. + If desired parameters are + outside of grid, return a blackbody spectrum instead + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + if verbose: + print('Meisner2023 atmosphere') + + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + except pysynphot.exceptions.ParameterOutOfBounds: + # Use a black-body atmosphere + bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) + return bbspec + +def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, + verbose=False, rebin=None, + wave_min=500, wave_max=50_000, wave_num=20_000): + """ + Return a blackbody spectrum + + Parameters + ---------- + temperature: float, default=20_000 + The stellar temperature, in units of K + wave_min: float, default=500 + Sets the minimum wavelength (in Angstroms) of the wavelength range + for the blackbody spectrum + wave_max: float, default=50_000 + Sets the maximum wavelength (in Angstroms) of the wavelength range + for the blackbody spectrum + wave_num: int, default=20_000 + Sets the number of wavelength points in the wavelength range + Note: the wavelength range is evenly spaced in log space + """ + if ((metallicity is not None) or (gravity is not None) or + (rebin is not None)): + warnings.warn( + 'Only `temperature` keyword is used for black-body atmosphere' + ) + + if verbose: + print('Black-body atmosphere') + + # Modify pysynphot's default waveset to specified bounds + pysynphot.refs.set_default_waveset( + minwave=wave_min, maxwave=wave_max, num=wave_num + ) + + # Get black-body atmosphere for specified temperature from pysynphot + bbspec = pysynphot.spectrum.BlackBody(temperature) + + # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` + bbspec.convert('flam') + + # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. + # Need to remove this normalization for SPISEA by multiplying bbspec + # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 + bbspec *= (1000 * 3.086e18 / 6.957e10)**2 + + return bbspec + + +#--------------------------------------# +# Atmosphere formatting functions +#--------------------------------------# + +def download_CMFGEN_atmospheres(Table_rot, Table_norot): + """ + Downloads CMFGEN models from + https://sites.google.com/site/fluxesandcontinuum/home; + these contain continuum as well as lines. + + Table_rot, Table_norot are tables with the file prefixes + and model atmosphere parameters, taken by hand from the + Fierro+15 paper + + Website addresses are hardcoded + + Puts downloaded models in the current working directory. + """ + print( 'WARNING: THIS DOES NOT COMPLETELY WORK') + print( '**********************') + t_rot = Table.read(Table_rot, format='ascii') + t_norot = Table.read(Table_norot, format='ascii') + + tables = [t_rot, t_norot] + filenames = [t_rot['col1'], t_norot['col1']] + + # Hardcoded list of webiste addresses + web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' + web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' + web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', + web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', + web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', + web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', + web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] + # Array of masses that matches the website addresses + mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) + + # Loop through rotating and unrotating case. First loop is rot, second unrot + for i in range(2): + # Extract masses from filenames + masses = [] + for j in filenames[i]: + tmp = j.split('m') + mass = float(tmp[1][:-1]) + masses.append(mass) + + # Download the models webpage by webpage. A bit tricky because masses + # change slightly within a particular website. THIS IS WHAT FAILS + for j in range(len(web)): + if j == 0: + good = np.where( (masses <= mass_arr[j]) ) + else: + g = j - 1 + good = np.where( (masses <= mass_arr[j]) & + (masses > mass_arr[g]) ) + # Use wget command to pull down the files, and unzip them + for k in good[0]: + full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) + os.system('wget ' + full) + os.system('unzip '+ filenames[i][k] + '.flx.zip') + + return + +def organize_CMFGEN_atmospheres(path_to_dir): + """ + Organize CMFGEN grid from Fierro+15 + (http://www.astroscu.unam.mx/atlas/index.html) + into rot and noRot directories + + path_to_dir is from current working directory to directory + containing the downloaded models. Assumed that models + and tables describing parameters are in this directory. + + Tables describing parameters MUST be named Table_rot.txt, + Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. + These are located in same original directory as atmosphere files + + Will separate files into 2 subdirectories, one rotating and + the other non-rotating + + *Can't have any other files starting with "t" in model directory to start!* + """ + # First, record current working directory to return to later + start_dir = os.getcwd() + + # Enter atmosphere directory, collect rotating and non-rotating + # file names (assumed to all start with "t") + os.chdir(path_to_dir) + rot_models = glob.glob("t*r.flx*") + noRot_models = glob.glob("t*n.flx*") + + # Separate into different subdirectories + if os.path.exists('cmfgenF15_rot'): + pass + else: + os.mkdir('cmfgenF15_rot') + os.mkdir('cmfgenF15_noRot') + + for mod in rot_models: + cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) + os.system(cmd) + + for mod in noRot_models: + cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) + os.system(cmd) + + # Also move Tables with model parameters into correct directory + os.system('mv Table_rot.txt cmfgenF15_rot') + os.system('mv Table_noRot.txt cmfgenF15_noRot') + + # Return to original directory + os.chdir(start_dir) + + return + +def make_CMFGEN_catalog(path_to_dir): + """ + Create cdbs catalog.fits of CMFGEN grid from Fierro+15 + (http://www.astroscu.unam.mx/atlas/index.html). + THIS IS STEP 2, after organize_CMFGEN_atmospheres has + been run. + + path_to_dir is from current working directory to directory + containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, + needs to be a Table*.txt file which contains the parameters for all of the + original models, since params in filename are not precise enough + + Will create catalog.fits file in atmosphere directory with + description of each model + + *Can't have any other files starting with "t" in model directory to start!* + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere + # Note: can't rely on filename for this because not precise enough!! + + #---------OLD: GETTING PARAMS FROM FILENAME-------# + # Collect file names (assumed to all start with "t") + #files = glob.glob("t*") + #for name in files: + # tmp = name.split('l') + # temp = float(tmp[0][1:]) * 100.0 # In kelvin + + # lumtmp = tmp[1].split('_') + # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun + + # mass = float(lumtmp[0][5:-1]) # In M_sun + + # Need to calculate log g from T and L (cgs) + # lum_sun = 3.846 * 10**33 # erg/s + # M_sun = 2 * 10**33 # g + # G_si = 6.67 * 10**(-8) # cgs + # sigma_si = 5.67 * 10**(-5) # cgs + + # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ + # (lum * lum_sun) + # logg = np.log10(g) + #---------------------------------------------------# + + # Read table with atmosphere params + table = glob.glob('Table_*') + t = Table.read(table[0], format = 'ascii') + names = t['col1'] + temps = t['col2'] + logg = t['col4'] + + # Create catalog.fits file + index_str = [] + name_str = [] + for i in range(len(names)): + index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) + + #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# + #path = path_to_dir + '/' + names[i] + path = names[i] + '.fits[Flux]' + + index_str.append(index) + name_str.append(path) + + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits') + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): + """ + Code to put cmfgen models into cdbs format and adds proper unit keyword in + fits header. Save as fits file + + path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot + directory with the *.flx models. Note that these files have already been + organized using organize_CMFGEN_atmospheres code. + + path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or + cmfgen_norot directory. Will copy new fits files to this directory. + This directory must already exist! + """ + # Save starting directory for later, move into path_to_dir directory + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Collect the filenames, make necessary changes to each one + files = glob.glob('*.flx') + + # Need to make brand-new fits tables with data we want. + counter = 0 + for i in files: + counter += 1 + # Open file, extract useful info + t = Table.read(i, format='ascii') + wave = t['col1'] + flux = t['col2'] # Flux is already in erg/cm^2/s/A + + # Need to eliminate duplicate entries (pysynphot crashes) + unique = np.unique(wave, return_index=True) + wave = wave[unique[1]] + flux = flux[unique[1]] + + # Make fits table from individual columns. + c0 = fits.Column(name='Wavelength', format='D', array=wave) + c1 = fits.Column(name='Flux', format='E', array=flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + #Adding unit keywords + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + prihdu = fits.PrimaryHDU() + + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(i[:-4]+'.fits', overwrite=True) + + print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) + + # Return to original directory, copy over new .fits files to cdbs directory + os.chdir(start_dir) + cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) + os.system(cmd) + + return + +def rebin_cmfgen(cdbs_path, rot=True): + """ + Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; + this makes spectrophotometry MUCH faster + + cdbs_path: path to cdbs directory + rot=True for rotating models (cmfgen_rot), False for non-rotating models + + makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing cmfgen model; we will steal the header. + # Also define paths to new rebin directories + if rot == True: + tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' + path = cdbs_path+'/grid/cmfgen_rot_rebin/' + orig_path = cdbs_path+'/grid/cmfgen_rot/' + else: + tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' + path = cdbs_path+'/grid/cmfgen_norot_rebin/' + orig_path = cdbs_path+'/grid/cmfgen_norot/' + + cmfgen_hdu = fits.open(tmp) + header0 = cmfgen_hdu[0].header + # Create rebin directories if they don't already exist. Copy over + # catalog.fits file from original directory (will be the same) + if not os.path.exists(path): + os.mkdir(path) + cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) + os.system(cmd) + + # Read in the catalog.fits file + cat = fits.getdata(orig_path + 'catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + # First column in new files will be for [atlas] wavelength + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + + # For each catalog.fits entry, read the unbinned spectrum and rebin to + # the atlas resolution. Make a new fits file in rebin directory + count = 0 + for ff in range(len(files_all)): + count += 1 + # Extract the temp, Z, logg + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + grav = float(vals[2]) + + # Fetch the spectrum + if rot == True: + sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) + else: + sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) + + # Rebin + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + # Make the FITS file from the columns with header + cols = fits.ColDefs([c0,c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + # Write hdu to new directory with same filename + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(path+files_all[ff]) + + print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) + return + + +def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): + """ + Construct the Phoenix Husser+13 atmopsheres for each model. Combines the + fluxes from the *HiRES.fits files and the wavelengths of the + WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. + + path_to_dir is the path to the directory containing all of the downloaded + files + + met_str is the name of the current metallicity + + Creates new fits files for each atmosphere: phoenix_.fits, + which contains columns for the log g (column header = g#.#). Puts + atmospheres in new directory phoenixm00 + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # If it doesn't already exist, create the current metallicity subdirectory + sub_dir = '../phoenix{0}'.format(met_str) + if os.path.exists(sub_dir): + pass + else: + os.mkdir(sub_dir) + + # Extract wavelength array, make column for later + wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') + wave = wavefile[0].data + wavefile.close() + wave_col = Column(wave, name = 'WAVELENGTH') + + # Create temp array for Husser+13 grid (given in paper) + temp_arr = np.arange(2300, 7001, 100) + temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) + + print( 'Looping though all temps') + # For each temp, build file containing the flux for all gravities + i = 0 + for temp in temp_arr: + files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) + files.sort() + # Start the table with the wavelength column + t = Table() + t.add_column(wave_col) + for f in files: + # Extract the logg out of filename + logg = f[9:13] + + # Extract fluxes from file + spectrum = fits.open(f) + flux = spectrum[0].data + spectrum.close() + + # Make Column object with fluxes, add to table + col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) + t.add_column(col) + + # Now, construct final fits file for the given temp + outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) + t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) + + # Progress counter for user + i += 1 + print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) + + # Return to original directory + os.chdir(start_dir) + return + +def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): + """ + Makes catalog.fits file for Husser+13 phoenix models. Assumes that + organize_PHOENIXv16_atmospheres has been run already, and that the models lie + in subdirectory phoenix[met_str]. + + path_to_directory is the path to the directory with the reformatted + models (i.e. the output from construct_atmospheres, phoenix[met_str]) + + Puts catalog.fits file in directory the user starts in + """ + # Save starting directory for later, move into working directory + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Extract metallicity from metallicity string + met = float(met_str[1]) + (float(met_str[2]) * 0.1) + if 'm' in met_str: + met *= -1. + + # Collect the filenames. Each is a unique temp with many different log g's + files = glob.glob('phoenix*.fits') + files.sort() + + # Create the catalog.fits file, row by row + index_arr = [] + filename_arr = [] + for i in files: + # Get log g values from the column header the file + t = Table.read(i, format='fits') + keys = t.keys() + logg_vals = keys[1:] + + # Extract temp from filename + name = i.split('_') + temp = float(name[1][:-5]) + for j in logg_vals: + logg = float(j[1:]) + index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) + filename = path_to_dir + i + '[' + j + ']' + # Add row to table + index_arr.append(index) + filename_arr.append(filename) + + catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) + + # Return to starting directory, write catalog + os.chdir(start_dir) + + if os.path.exists('catalog.fits'): + from astropy.table import vstack + + prev_catalog = Table.read('catalog.fits', format='fits') + joined_catalog = vstack([prev_catalog, catalog]) + + joined_catalog.write('catalog.fits', format='fits', overwrite=True) + else: + catalog.write('catalog.fits', format='fits', overwrite=True) + + return + +def cdbs_PHOENIXv16(path_to_cdbs_dir): + """ + Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily + consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] + and adding the appropriate keywords to the fits header. + + path_to_cdbs_dir goes from current working directory to phoenix[met] directory + in cdbs/grids/phoenix_v16. Note that these files have already been organized + using organize_PHOENIXv16_atmospheres code. + + Overwrites original files in directory + """ + # Save starting directory for later, move into working directory + start_dir = os.getcwd() + os.chdir(path_to_cdbs_dir) + + # Collect the filenames, make necessary changes to each one + files = glob.glob('phoenix*.fits') + + ## Need to sort filenames; glob doesn't always give them in order + files.sort() + + # Need to make brand-new fits tables with data we want. + counter = 0 + for i in files: + counter += 1 + + # Read in current FITS table + cur_table = Table.read(i, format='fits') + + cur_table.columns[0].name = 'Wavelength' + + num_cols = len(cur_table.colnames) + + # Multiplying each flux column by 10^-8 for conversion + for cur_col_index in range(1, num_cols, 1): + cur_col_name = cur_table.colnames[cur_col_index] + cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 + + + # Construct new FITS file based on old one + hdu = fits.open(i) + header_0 = hdu[0].header + header_1 = hdu[1].header + sci = hdu[1].data + + tbhdu = fits.table_to_hdu(cur_table) + + # Copying over the older headers, adding unit keywords + prihdu = fits.PrimaryHDU(header=header_0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + tbhdu.header['TUNIT3'] = 'FLAM' + tbhdu.header['TUNIT4'] = 'FLAM' + tbhdu.header['TUNIT5'] = 'FLAM' + tbhdu.header['TUNIT6'] = 'FLAM' + tbhdu.header['TUNIT7'] = 'FLAM' + tbhdu.header['TUNIT8'] = 'FLAM' + tbhdu.header['TUNIT9'] = 'FLAM' + tbhdu.header['TUNIT10'] = 'FLAM' + tbhdu.header['TUNIT11'] = 'FLAM' + tbhdu.header['TUNIT12'] = 'FLAM' + tbhdu.header['TUNIT13'] = 'FLAM' + tbhdu.header['TUNIT14'] = 'FLAM' + + # Construct and write out final FITS file + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(i, overwrite=True) + + hdu.close() + print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) + + # Change back to starting directory + os.chdir(start_dir) + + return + +def rebin_phoenixV16(cdbs_path): + """ + Rebin phoenixV16 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: phoenix_v16_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing phoenix model; we will steal the header + ## (This assumes that at least 'm00' metallicity exists) + tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') + phoenix_hdu = fits.open(tmp) + header0 = phoenix_hdu[0].header + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/phoenix_v16_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + temp_arr = np.zeros(len(files_all), dtype=float) + logg_arr = np.zeros(len(files_all), dtype=float) + metal_arr = np.zeros(len(files_all), dtype=float) + + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + + temp_arr[ff] = float(vals[0]) + metal_arr[ff] = float(vals[1]) + logg_arr[ff] = float(vals[2]) + + + metal_uniq = np.unique(metal_arr) + temp_uniq = np.unique(temp_arr) + + for mm in range(len(metal_uniq)): + metal = metal_uniq[mm] # metallicity + + # Construct str for metallicity (for appropriate directory name) + met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) + if metal > 0: + met_str = 'p' + met_str + else: + met_str = 'm' + met_str + + # Make directory for current metallicity if it does not exist yet + if not os.path.exists(path + 'phoenix' + met_str): + os.mkdir(path + 'phoenix' + met_str) + + for tt in range(len(temp_uniq)): + temp = temp_uniq[tt] # temperature + + # Pick out the list of gravities for this T, Z combo + idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] + logg_exist = logg_arr[idx] + + # All gravities will go in one file. Here is the output + # file name. + outfile = path + files_all[idx[0]].split('[')[0] + + ## If the rebinned file already exists, continue + if os.path.exists(outfile): + continue + + # Build a columns array. One column for each gravity. + cols_arr = [] + + # Make the wavelength column, which is first in the cols array. + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + cols_arr.append(c0) + + for gg in range(len(logg_exist)): + grav = logg_exist[gg] # gravity + + # Fetch the spectrum + sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Store the spectrum + name = 'g{0:3.1f}'.format(grav) + col = fits.Column(name=name, format='E', array=flux_rebin) + cols_arr.append(col) + + + # Make the FITS file from the columns with header. + cols = fits.ColDefs(cols_arr) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + for gg in range(len(logg_exist)): + tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' + + # Write hdu + finalhdu = fits.HDUList([prihdu, tbhdu]) + # don't have overwrite to protect original files. + finalhdu.writeto(outfile) + + print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) + + + return + + +def rebin_spec(wave, specin, wavnew): + """ + Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: + http://www.astrobetter.com/blog/2013/08/12/ + python-tip-re-sampling-spectra-with-pysynphot/ + """ + spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) + f = np.ones(len(wave)) + filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') + obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') + + return obs.binflux + +def organize_BTSettl_2015_atmospheres(path_to_dir): + """ + Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. + Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] + + path_to_dir is the path to the directory containing all of the downloaded + files + + Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 + (assumes this directory exists) + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # If it doesn't already exist, create the BTSettl subdirectory + if not os.path.exists('BTSettl_2015'): + os.mkdir('BTSettl_2015') + + # Process each atmosphere file independently + print( 'Creating cdbs-ready files') + files = glob.glob('*.spec.fits') + + for i in files: + hdu = fits.open(i) + spec = hdu[1].data + header_0 = hdu[0].header + header_1 = hdu[1].header + + wave = spec.field(0) + flux = spec.field(1) + + # Get units right: convert wave from microns to Angstroms, + # flux from W /m^2/ micron to erg/s/cm^2/A + wave_new = wave * 10**4 + flux_new = flux * 10**(-1) + + # Make new fits table + c0 = fits.Column(name='Wavelength', format='D', array=wave_new) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Copy over headers, update unit keywords + prihdu = fits.PrimaryHDU(header=header_0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) + + hdu.close() + hdu_new.close() + + # Return to original directory + os.chdir(start_dir) + return + +def make_BTSettl_2015_catalog(path_to_dir): + """ + Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. + THIS IS STEP 2, after organize_CMFGEN_atmospheres has + been run. + + path_to_dir is from current working directory to the cdbs directory. + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere from the filename, + # construct columns for catalog file + files = glob.glob("*spec.fits") + index_str = [] + name_str = [] + for name in files: + tmp = name.split('-') + temp = float(tmp[0][3:]) * 100.0 # In kelvin + logg = float(tmp[1]) + + index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) + name_str.append('{0}[Flux]'.format(name)) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): + """ + Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: BTSettl_2015_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing phoenix model; we will steal the header + tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' + phoenix_hdu = fits.open(tmp) + header0 = phoenix_hdu[0].header + phoenix_hdu.close() + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/BTSettl_2015_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + print( 'Rebinning BTSettl spectra') + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the BTSettl spectrum, rebin flux + sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + return + +def make_wavelength_unique(files, dirname): + """ + Helper function to go through each BTSettl spectrum and ensure that + each wavelength point is unique. This is required for rebinning to work. + + + files: list of files to run this analysis on + """ + # Loop through each file, find fix repeated wavelength entries if necessary + for i in files: + t = Table.read('{0}/{1}'.format(dirname,i), format='fits') + test = np.unique(t['Wavelength'], return_index=True) + + if len(t) != len(test[0]): + t = t[test[1]] + + c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) + c1 = fits.Column(name='Flux', format='E', array=t['Flux']) + cols = fits.ColDefs([c0, c1]) + + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) + + # Also make sure wavelength is monotonic. If it is not, then it is + # a sign that the wavelengths are out of order + diff = np.diff(t['Wavelength']) + bad = np.where(diff < 0) + if len(bad[0]) > 0: + t.sort('Wavelength') + + c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) + c1 = fits.Column(name='Flux', format='E', array=t['Flux']) + cols = fits.ColDefs([c0, c1]) + + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) + + print('Done {0}'.format(i)) + + return + +def organize_BTSettl_atmospheres(): + """ + Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). + The code expects tp be run in cdbs/grid/BTSettl, and expects that the + individual model files have been downloaded from online + (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) + and processed into python-readable ascii files. + """ + orig_dir = os.getcwd() + dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] + #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] + + + # Go through each directory, turning each spectrum into a cdbs-ready file. + # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, + # for faster access later + for ii in dirs: + print('Starting {0}'.format(ii)) + os.chdir(ii) + + files = glob.glob('*.txt') + count=0 + for jj in files: + t = Table.read(jj, format='ascii') + # First, trim the wavelengths to a more reasonable wavelength range + good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) + t = t[good] + + # Convert flux units to Flam (Ergs/sec/cm**2/A) + flux_new = 10**(t['col2'] - 8.0) + + # Save the file as a fits file + c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Add unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) + hdu_new.close() + count += 1 + print('Done {0} of {1}'.format(count, len(files))) + + # Now, clean up all the files made when unzipping the spectra + cmd1 = 'rm *.bz2' + cmd2 = 'rm *.tmp' + #cmd3 = 'rm *.txt' + os.system(cmd1) + os.system(cmd2) + #os.system(cmd3) + print('==============================') + print('Done {0}'.format(ii)) + print('==============================') + + # Go back to original directory, move to next metallicity directory + os.chdir(orig_dir) + + return + +def make_BTSettl_catalog(): + """ + Create cdbs catalog.fits of BTSettl grid. + THIS IS STEP 2, after organize_BTSettl_atmospheres has + been run. + + Code expects to be run in cdbs/grid/BTSettl + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] + #dirs = ['btp05'] + + # Construct the catalog.fits file input. The input consists of + # and index string that specifies the stellar paramters, and a + # name string that points to the file + # Loop over all the metallicity directories to construct these inputs + index_str = [] + name_str = [] + for ii in dirs: + os.chdir(ii) + files = glob.glob('*.fits') + + # Construct the metallicity val + if 'm' in ii: + metal_flag = -1 * float(ii[3:])*0.1 + else: + metal_flag = float(ii[3:])*0.1 + + # Now collect the info from the files + for jj in files: + tmp = jj.split('-') + + if metal_flag >= 0: + temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin + try: + logg = float(tmp[1]) + except: + logg = float(tmp[1].split('+')[0]) + else: + temp = float(tmp[0][3:]) * 100.0 # In kelvin + logg = float(tmp[1]) + + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) + name_str.append('{0}/{1}[Flux]'.format(ii, jj)) + + # Go back to original directory to move to next metallicity + print('Done {0}'.format(ii)) + os.chdir(start_dir) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_BTSettl(make_unique=False): + """ + Rebin BTSettle models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory: BTSettl_rebin + + Code expects to be run in cdbs/grid directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Create cdbs/grid directory for rebinned models + path = 'BTSettl_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata('BTSettl/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + #==============================# + #tmp = [] + #for ii in files_all: + # if ii.startswith('btp00'): + # tmp.append(ii) + #files_all = tmp + #=============================# + + print( 'Rebinning BTSettl spectra') + if make_unique: + print('Making unique') + make_wavelength_unique(files_all, 'BTSettl') + print('Done') + + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the BTSettl spectrum, rebin flux + try: + sp = pysynphot.Icat('BTSettl', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + except: + pdb.set_trace() + orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) + outfile = path + files_all[ff].split('[')[0] + cmd = 'cp {0} {1}'.format(orig_file, outfile) + os.system(cmd) + + print('Done {0} of {1}'.format(ff, len(files_all))) + + return + +def organize_all_Meisner2023_atmospheres(): + """ + Construct cdbs-ready atmospheres for the Meisner2023 grid. + The code expects tp be run in cdbs/grid/Meisner2023, and expects that the + individual model files have been downloaded from online + and processed into python-readable ascii files. + """ + orig_dir = os.getcwd() + dirs = ['mm10', 'mm05', 'mp00', 'mp03'] + + # Go through each directory, turning each spectrum into a cdbs-ready file. + # Save as a fits file, for faster access later + for ii in dirs: + print('Starting {0}'.format(ii)) + os.chdir(ii) + + files = glob.glob('*.fits') + count=0 + for jj in files: + # Open each .fits file and read the data + with fits.open(jj) as hdul: + data = hdul[1].data + wavelength = data['Wavelength'] + flux = data['Flux'] + + # Make flux independent of R&D + flux_new = flux / 5e-20 + + # Create new columns with desired format + c0 = fits.Column(name='Wavelength', format='D', array=wavelength) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Add unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write the new fits table in the cdbs directory + output_filename = '{0}.fits'.format(jj[:-5]) # Removing the original .fits extension + hdu_new.writeto(output_filename, overwrite=True) + hdu_new.close() + count += 1 + print('Done {0} of {1}'.format(count, len(files))) + + # Go back to original directory, move to next metallicity directory + os.chdir(orig_dir) + + return + +def make_Meisner2023_catalog(): + """ + Create cdbs catalog.fits of Meisner2023 grid. + THIS IS STEP 2, after organize_Meisner2023_atmospheres has + been run. + + Code expects to be run in cdbs/grid/Meisner2023 + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + dirs = ['mm10', 'mm05', 'mp00', 'mp03'] + + # Construct the catalog.fits file input. The input consists of + # and index string that specifies the stellar paramters, and a + # name string that points to the file + # Loop over all the metallicity directories to construct these inputs + index_str = [] + name_str = [] + for ii in dirs: + os.chdir(ii) + files = glob.glob('spec_jwst_*.fits') + + for jj in files: + # Parse temperature, log(g), and metallicity from filename + temp_str = jj.split('_')[2] + logg_str = jj.split('_')[3] + metal_str = jj.split('_')[4] + + # Extract temperature, surface gravity, and metallicity + temp = float(temp_str[1:]) # Temperature in Kelvin + logg = float(logg_str[1:]) # Surface gravity log(g) + + # Build metallicity value + if metal_str.startswith('m'): + metallicity = -1 * float(metal_str[1:]) + else: + metallicity = float(metal_str[1:]) + + # Construct index and filename strings + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metallicity, logg)) + name_str.append('{0}/{1}[Flux]'.format(ii, jj)) + + print('Processed directory:', ii) + os.chdir(start_dir) + + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_Meisner2023(make_unique=False): + """ + Rebin Meisner2023 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory: Meisner2023_rebin + + Code expects to be run in cdbs/grid directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Create a directory for rebinned Meisner2023 models + rebin_path = 'Meisner2023_rebin/' + if not os.path.exists(rebin_path): + os.mkdir(rebin_path) + + # Load the catalog.fits file and extract all spectra file paths + cat = Table.read('Meisner2023/catalog.fits') + files_all = [cat[ii]['FILENAME'].split('[')[0] for ii in range(len(cat))] + + print('Rebinning Meisner2023 spectra') + if make_unique: + print('Making unique') + make_wavelength_unique(files_all, 'Meisner2023') + print('Done') + + for ff, file in enumerate(files_all): + vals = cat[ff]['INDEX'].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the Meisner2023 spectrum and rebin its flux + try: + sp = pysynphot.Icat('Meisner2023', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Create the output FITS file + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + # Write the new rebinned file in the Meisner2023_rebin directory + outfile = os.path.join(rebin_path, os.path.basename(file)) + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + except Exception as e: + print(f"Error processing {file}: {e}") + orig_file = os.path.join('Meisner2023', file) + outfile = os.path.join(rebin_path, os.path.basename(file)) + os.system(f'cp {orig_file} {outfile}') + + print('Done {0} of {1}'.format(ff + 1, len(files_all))) + + return + + + +def organize_WDKoester_atmospheres(path_to_dir): + """ + Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) + Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] + + path_to_dir is the path to the directory containing all of the downloaded + files + + Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter + (assumes this directory exists) + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Process each atmosphere file independently + print( 'Creating cdbs-ready files') + files = glob.glob('*.dk.dat.txt') + + for i in files: + data = Table.read(i, format='ascii') + + wave = data['col1'] # angstrom + flux = data['col2'] # erg/s/cm^2/A + + # Make new fits table + c0 = fits.Column(name='Wavelength', format='D', array=wave) + c1 = fits.Column(name='Flux', format='E', array=flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Copy over headers, update unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) + + hdu_new.close() + + # Return to original directory + os.chdir(start_dir) + return + +def make_WDKoester_catalog(path_to_dir): + """ + Create cdbs catalog.fits of wdKoester grid. + THIS IS STEP 2, after organize_WDKoester_atmospheres has + been run. + + path_to_dir is from current working directory to the cdbs directory. + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere from the filename, + # construct columns for catalog file + files = glob.glob("*dk.dat.fits") + index_str = [] + name_str = [] + for name in files: + tmp = name.split('.') + tmp2 = tmp[0].split('_') + temp = float(tmp2[0][2:]) # Kelvin + logg = float(tmp2[1]) / 100.0 # log(g) + + index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) + name_str.append('{0}[Flux]'.format(name)) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): + """ + Rebin wdKoester models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: wdKoester_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing model; we will steal the header + tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' + wdkoester_hdu = fits.open(tmp) + header0 = wdkoester_hdu[0].header + wdkoester_hdu.close() + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/wdKoester_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + print( 'Rebinning wdKoester spectra') + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the wdKoester spectrum, rebin flux + sp = pysynphot.Icat('wdKoester', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + return + + diff --git a/spisea/atmospheres_REMOTE_58456.py b/spisea/atmospheres_REMOTE_58456.py new file mode 100644 index 00000000..9867db81 --- /dev/null +++ b/spisea/atmospheres_REMOTE_58456.py @@ -0,0 +1,2172 @@ +import logging +import numpy as np +import pysynphot +import os +import glob +from astropy.io import fits +from astropy.table import Table, Column +import pysynphot +import time +import pdb +import warnings + +log = logging.getLogger('atmospheres') + +def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4, verbose=False): + """ + Given atmosphere model, get temperature and gravity bounds + """ + # Open catalog fits file and break out row indices + catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) + + teff_arr = [] + z_arr = [] + logg_arr = [] + for cur_row_index in range(len(catalog)): + index = catalog['INDEX'][cur_row_index] + tmp = index.split(',') + teff_arr.append(float(tmp[0])) + z_arr.append(float(tmp[1])) + logg_arr.append(float(tmp[2])) + teff_arr = np.array(teff_arr) + z_arr = np.array(z_arr) + logg_arr = np.array(logg_arr) + + # Filter by metallicity. Will chose the closest metallicity to desired input + metal_list = np.unique(np.array(z_arr)) + metal_idx = np.argmin(np.abs(metal_list - metallicity)) + metallicity_new = metal_list[metal_idx] + + z_filt = np.where(z_arr == metal_list[metal_idx]) + teff_arr = teff_arr[z_filt] + logg_arr = logg_arr[z_filt] + + # # Now find the closest atmosphere in parameter space to + # # the one we want. We'll find the match with the lowest + # # fractional difference + # teff_diff = (teff_arr - temperature) / temperature + # logg_diff = (logg_arr - gravity) / gravity + # + # diff_tot = abs(teff_diff) + abs(logg_diff) + # idx_f = np.argmin(diff_tot) + # + # temperature_new = teff_arr[idx_f] + # gravity_new = logg_arr[idx_f] + + # First check if temperature within bounds + temperature_new = temperature + if temperature > np.max(teff_arr): + temperature_new = np.max(teff_arr) + if temperature < np.min(teff_arr): + temperature_new = np.min(teff_arr) + + # If temperature within bounds, then check if metallicity within bounds + teff_diff = np.abs(teff_arr - temperature) + sorted_min_diffs = np.unique(teff_diff) + + ## Find two closest temperatures + teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] + teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] + + logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] + logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] + + ## Switch to most conservative bound of logg out of two closest temps + gravity_new = gravity + if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): + gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) + if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): + gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) + + if verbose: + # Print out changes, if any + if temperature_new != temperature: + teff_msg = 'Changing to T={0:6.0f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' + print( teff_msg.format(temperature_new, metallicity, temperature, gravity)) + + if gravity_new != gravity: + logg_msg = 'Changing to logg={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' + print( logg_msg.format(gravity_new, metallicity, temperature, gravity)) + + if metallicity_new != metallicity: + logg_msg = 'Changing to met={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' + print( logg_msg.format(metallicity_new, metallicity, temperature, gravity)) + + return (temperature_new, gravity_new, metallicity_new) + +def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): + """ + Return atmosphere from the Kurucz pysnphot grid + (`Kurucz 1993 `_). + + Grid Range: + + * Teff: 3000 - 50000 K + * gravity: 0 - 5 cgs + * metallicity: -5.0 - 1.0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + Always false for this particular function + """ + try: + sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('k93models', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Kurucz 1993 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): + """ + Return atmospheres from the pysynphot ATLAS9 atlas + (`Castelli & Kurucz 2004 `_). + + Grid Range: + + * Teff: 3500 - 50000 K + * gravity: 0 - 5.0 cgs + * [M/H]: -2.5 - 0.2 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('ck04models', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 5000) + gravity = log gravity (def = 4.0) + """ + try: + sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('nextgen', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find NextGen atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 5000) + gravity = log gravity (def = 4.0) + """ + sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, + rebin=False): + """ + Return atmosphere from the pysynphot + `PHOENIX atlas `_. + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + """ + try: + sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 24000) + gravity = log gravity (def = 4.3) + + rebin=True: pull from atmospheres at ck04model resolution. + """ + # Take care of atmospheres outside the catalog boundaries + logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' + if gravity > 4.3: + if verbose: + print( logg_msg.format(4.3, temperature, gravity)) + gravity = 4.3 + + if rebin: + sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) + else: + sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, + verbose=False): + """ + For a given stellar atmosphere, get extract the closest possible match in + Teff/logg space. Note that this is different from the normal routine + which interpolates along the input grid to get final spectrum. We can't + do this here because the Fierro+15 atmosphere grid is so sparse + + rebin=True: pull from atmospheres at ck04model resolution. + + If verbose, print out the parameters of the match + """ + # Set up the proper root directory + if rebin == True: + root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' + else: + root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' + + # Read in catalog, extract atmosphere info + cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') + teff_arr = [] + z_arr = [] + logg_arr = [] + for ii in range(len(cat)): + index = cat['INDEX'][ii] + tmp = index.split(',') + teff_arr.append(float(tmp[0])) + z_arr.append(float(tmp[1])) + logg_arr.append(float(tmp[2])) + teff_arr = np.array(teff_arr) + z_arr = np.array(z_arr) + logg_arr = np.array(logg_arr) + + # Now find the closest atmosphere in parameter space to + # the one we want. We'll find the match with the lowest + # fractional difference + teff_diff = (teff_arr - temperature) / temperature + logg_diff = (logg_arr - gravity) / gravity + + diff_tot = abs(teff_diff) + abs(logg_diff) + idx_f = np.where(diff_tot == min(diff_tot))[0][0] + + # Extract the filename of the best-match model and read as + # pysynphot object + infile = cat[idx_f]['FILENAME'].split('.') + spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) + + # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the + # ATLAS models are in FLAM at the surface. So, we need to multiply the + # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. + # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file + t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') + tmp = np.where(t['col1'] == infile[0]) + + lum = t['col3'][tmp] * (3.839*10**33) # cgs + sigma = 5.6704 * 10**-5 # cgs + teff = teff_arr[idx_f] # cgs + + radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm + radius /= 3.08*10**18 # in pc + + + # Make the pysynphot spectrum + w = spec['Wavelength'] + f = spec['Flux'] * (1000 / radius)**2. + sp = pysynphot.ArraySpectrum(w,f) + + #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) + + # Print out parameters of match, if desired + if verbose: + print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) + print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) + + return sp + +def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 24000) + gravity = log gravity (def = 4.3) + + rebin=True: pull from atmospheres at ck04model resolution. + """ + if rebin: + sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) + else: + sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): + """ + metallicity = [M/H] (def = 0) + temperature = Kelvin (def = 30000) + gravity = log gravity (def = 4.14) + """ + sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): + """ + Return PHOENIX v16 atmospheres from + `Husser et al. 2013 `_. + + Models originally downloaded via `ftp `_. + Solar metallicity and [alpha/Fe] is used. + + Grid Range: + + * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K + * gravity: 0.0 - 6.0 cgs, steps of 0.5 + * [M/H]: -4.0 - 1.0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + """ + atm_model_name = 'phoenix_v16' + if rebin == True: + atm_model_name = 'phoenix_v16_rebin' + + + # Extract atmosphere. If that fails, then check bounds and try again + try: + sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_model_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): + """ + Return atmosphere from CIFIST2011_2015 grid + (`Allard et al. 2012 `_, + `Baraffe et al. 2015 `_ ) + + Grid originally downloaded from `website `_. + + Grid Range: + + * Teff: 1200 - 7000 K + * gravity: 2.5 - 5.5 cgs + * [M/H] = 0 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + """ + if rebin == True: + atm_name = 'BTSettl_2015_rebin' + else: + atm_name = 'BTSettl_2015' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find BTSettl_2015 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): + """ + Return atmosphere from CIFIST2011 grid + (`Allard et al. 2012 `_) + + Grid originally downloaded `here `_ + + Notes + ------ + Grid Range: + + * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 + + Teff and gravity ranges depend on metallicity: + + [M/H] = -2.5 + + * Teff: 2600 - 4600 K + * gravity: 4.5 - 5.5 + + [M/H] = -2.0 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.5 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.0 + + * Teff: 2600 - 7000 + * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 + + [M/H] = -0.5 + + * Teff: 1000 -7000 + * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 + + [M/H] = 0 + + * Teff: 750 - 7000 + * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 + + [M/H] = 0.5 + + * Teff: 1000 - 5000 + * gravity: 3.5 - 5.0 + + + Alpha enhancement: + + * [M/H]= -0.0, +0.5 no anhancement + * [M/H]= -0.5 with [alpha/H]=+0.2 + * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + """ + if rebin == True: + atm_name = 'BTSettl_rebin' + else: + atm_name = 'BTSettl' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find BTSettl_2015 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): + """ + Return white dwarf atmospheres from + `Koester et al. 2010 `_ + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + """ + sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. + + Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 + """ + try: + sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_atlas_phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model + and phoenixV16. + + Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 + """ + try: + sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_BTSettl_phoenix', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp + +#---------------------------------------------------------------------# +def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, + rebin=True): + """ + Return a stellar atmosphere from a suite of different model grids, + depending on the input temperature, (all values in K). + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + + Notes + ----- + The underlying stellar model grid used changes as a function of + stellar temperature (in K): + + * T > 20,000: ATLAS + * 5500 <= T < 20,000: ATLAS + * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge + * 3800 <= T < 5000: PHOENIXv16 + + For T < 3800, there is an additional gravity and metallicity + dependence: + + If T < 3800 and [M/H] = 0: + + * T < 3800, logg < 2.5: PHOENIX v16 + * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge + * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 + + Otherwise, if T < 3800 and [M/H] != 0: + + * T < 3800: PHOENIX v16 + + References: + + * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) + * PHOENIXv16 (`Husser et al. 2013 `_) + * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) + + LTE WARNING: + + The ATLAS atmospheres are calculated with LTE, and so they + are less accurate when non-LTE conditions apply (e.g. T > 20,000 + K). Ultimately we'd like to add a non-LTE atmosphere grid for + the hottest stars in the future. + + HOW BOUNDARIES BETWEEN MODELS ARE TREATED: + + At the boundary between two models grids a temperature range is defined + where the resulting atmosphere is a weighted average between the two + grids. Near one boundary one model + is weighted more heavily, while at the other boundary the other + model is weighted more heavily. These are calculated in the + temperature ranges where we switch between model grids, to + ensure a smooth transition. + """ + # For T < 3800, atmosphere depends on metallicity + gravity. + # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is + # currently available here, so if non-solar metallicity, just stick with + # the Phoenix grid + if (temperature <= 3800) & (metallicity == 0): + # High gravity are in BTSettl regime + if (temperature <= 3200) & (gravity > 2.5): + if verbose: + print( 'BTSettl_2015 atmosphere') + return get_BTSettl_2015_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): + if verbose: + print( 'BTSettl/Phoenixv16 merged atmosphere') + return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + # Low gravity is PHOENIX regime + if gravity <= 2.5: + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature <= 3800) & (metallicity != 0): + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + # For T > 3800, no metallicity or gravity dependence + if (temperature >= 3800) & (temperature < 5000): + if verbose: + print( 'Phoenixv16 atmosphere') + return get_phoenixv16_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + + if (temperature >= 5000) & (temperature < 5500): + if verbose: + print( 'ATLAS/Phoenix merged atmosphere') + return get_atlas_phoenix_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + if (temperature >= 5500) & (temperature < 20000): + if verbose: + print( 'ATLAS merged atmosphere') + return get_castelli_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + if temperature >= 20000: + if verbose: + print( 'Still ATLAS merged atmosphere') + return get_castelli_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + #print('CMFGEN') + #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, + # temperature=temperature, + # gravity=gravity) + + + + +def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): + """ + Return the white dwarf atmosphere from + `Koester et al. 2010 `_. + If desired parameters are + outside of grid, return a blackbody spectrum instead + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + + verbose: boolean + True for verbose output + """ + try: + if verbose: + print('wdKoester atmosphere') + + return get_wdKoester_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + except pysynphot.exceptions.ParameterOutOfBounds: + # Use a black-body atmosphere. + bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) + return bbspec + +def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, + verbose=False, rebin=None, + wave_min=500, wave_max=50_000, wave_num=20_000): + """ + Return a blackbody spectrum + + Parameters + ---------- + temperature: float, default=20_000 + The stellar temperature, in units of K + wave_min: float, default=500 + Sets the minimum wavelength (in Angstroms) of the wavelength range + for the blackbody spectrum + wave_max: float, default=50_000 + Sets the maximum wavelength (in Angstroms) of the wavelength range + for the blackbody spectrum + wave_num: int, default=20_000 + Sets the number of wavelength points in the wavelength range + Note: the wavelength range is evenly spaced in log space + """ + if ((metallicity is not None) or (gravity is not None) or + (rebin is not None)): + warnings.warn( + 'Only `temperature` keyword is used for black-body atmosphere' + ) + + if verbose: + print('Black-body atmosphere') + + # Modify pysynphot's default waveset to specified bounds + pysynphot.refs.set_default_waveset( + minwave=wave_min, maxwave=wave_max, num=wave_num + ) + + # Get black-body atmosphere for specified temperature from pysynphot + bbspec = pysynphot.spectrum.BlackBody(temperature) + + # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` + bbspec.convert('flam') + + # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. + # Need to remove this normalization for SPISEA by multiplying bbspec + # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 + bbspec *= (1000 * 3.086e18 / 6.957e10)**2 + + return bbspec + + +#--------------------------------------# +# Atmosphere formatting functions +#--------------------------------------# + +def download_CMFGEN_atmospheres(Table_rot, Table_norot): + """ + Downloads CMFGEN models from + https://sites.google.com/site/fluxesandcontinuum/home; + these contain continuum as well as lines. + + Table_rot, Table_norot are tables with the file prefixes + and model atmosphere parameters, taken by hand from the + Fierro+15 paper + + Website addresses are hardcoded + + Puts downloaded models in the current working directory. + """ + print( 'WARNING: THIS DOES NOT COMPLETELY WORK') + print( '**********************') + t_rot = Table.read(Table_rot, format='ascii') + t_norot = Table.read(Table_norot, format='ascii') + + tables = [t_rot, t_norot] + filenames = [t_rot['col1'], t_norot['col1']] + + # Hardcoded list of webiste addresses + web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' + web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' + web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', + web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', + web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', + web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', + web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] + # Array of masses that matches the website addresses + mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) + + # Loop through rotating and unrotating case. First loop is rot, second unrot + for i in range(2): + # Extract masses from filenames + masses = [] + for j in filenames[i]: + tmp = j.split('m') + mass = float(tmp[1][:-1]) + masses.append(mass) + + # Download the models webpage by webpage. A bit tricky because masses + # change slightly within a particular website. THIS IS WHAT FAILS + for j in range(len(web)): + if j == 0: + good = np.where( (masses <= mass_arr[j]) ) + else: + g = j - 1 + good = np.where( (masses <= mass_arr[j]) & + (masses > mass_arr[g]) ) + # Use wget command to pull down the files, and unzip them + for k in good[0]: + full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) + os.system('wget ' + full) + os.system('unzip '+ filenames[i][k] + '.flx.zip') + + return + +def organize_CMFGEN_atmospheres(path_to_dir): + """ + Organize CMFGEN grid from Fierro+15 + (http://www.astroscu.unam.mx/atlas/index.html) + into rot and noRot directories + + path_to_dir is from current working directory to directory + containing the downloaded models. Assumed that models + and tables describing parameters are in this directory. + + Tables describing parameters MUST be named Table_rot.txt, + Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. + These are located in same original directory as atmosphere files + + Will separate files into 2 subdirectories, one rotating and + the other non-rotating + + *Can't have any other files starting with "t" in model directory to start!* + """ + # First, record current working directory to return to later + start_dir = os.getcwd() + + # Enter atmosphere directory, collect rotating and non-rotating + # file names (assumed to all start with "t") + os.chdir(path_to_dir) + rot_models = glob.glob("t*r.flx*") + noRot_models = glob.glob("t*n.flx*") + + # Separate into different subdirectories + if os.path.exists('cmfgenF15_rot'): + pass + else: + os.mkdir('cmfgenF15_rot') + os.mkdir('cmfgenF15_noRot') + + for mod in rot_models: + cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) + os.system(cmd) + + for mod in noRot_models: + cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) + os.system(cmd) + + # Also move Tables with model parameters into correct directory + os.system('mv Table_rot.txt cmfgenF15_rot') + os.system('mv Table_noRot.txt cmfgenF15_noRot') + + # Return to original directory + os.chdir(start_dir) + + return + +def make_CMFGEN_catalog(path_to_dir): + """ + Create cdbs catalog.fits of CMFGEN grid from Fierro+15 + (http://www.astroscu.unam.mx/atlas/index.html). + THIS IS STEP 2, after organize_CMFGEN_atmospheres has + been run. + + path_to_dir is from current working directory to directory + containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, + needs to be a Table*.txt file which contains the parameters for all of the + original models, since params in filename are not precise enough + + Will create catalog.fits file in atmosphere directory with + description of each model + + *Can't have any other files starting with "t" in model directory to start!* + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere + # Note: can't rely on filename for this because not precise enough!! + + #---------OLD: GETTING PARAMS FROM FILENAME-------# + # Collect file names (assumed to all start with "t") + #files = glob.glob("t*") + #for name in files: + # tmp = name.split('l') + # temp = float(tmp[0][1:]) * 100.0 # In kelvin + + # lumtmp = tmp[1].split('_') + # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun + + # mass = float(lumtmp[0][5:-1]) # In M_sun + + # Need to calculate log g from T and L (cgs) + # lum_sun = 3.846 * 10**33 # erg/s + # M_sun = 2 * 10**33 # g + # G_si = 6.67 * 10**(-8) # cgs + # sigma_si = 5.67 * 10**(-5) # cgs + + # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ + # (lum * lum_sun) + # logg = np.log10(g) + #---------------------------------------------------# + + # Read table with atmosphere params + table = glob.glob('Table_*') + t = Table.read(table[0], format = 'ascii') + names = t['col1'] + temps = t['col2'] + logg = t['col4'] + + # Create catalog.fits file + index_str = [] + name_str = [] + for i in range(len(names)): + index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) + + #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# + #path = path_to_dir + '/' + names[i] + path = names[i] + '.fits[Flux]' + + index_str.append(index) + name_str.append(path) + + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits') + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): + """ + Code to put cmfgen models into cdbs format and adds proper unit keyword in + fits header. Save as fits file + + path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot + directory with the *.flx models. Note that these files have already been + organized using organize_CMFGEN_atmospheres code. + + path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or + cmfgen_norot directory. Will copy new fits files to this directory. + This directory must already exist! + """ + # Save starting directory for later, move into path_to_dir directory + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Collect the filenames, make necessary changes to each one + files = glob.glob('*.flx') + + # Need to make brand-new fits tables with data we want. + counter = 0 + for i in files: + counter += 1 + # Open file, extract useful info + t = Table.read(i, format='ascii') + wave = t['col1'] + flux = t['col2'] # Flux is already in erg/cm^2/s/A + + # Need to eliminate duplicate entries (pysynphot crashes) + unique = np.unique(wave, return_index=True) + wave = wave[unique[1]] + flux = flux[unique[1]] + + # Make fits table from individual columns. + c0 = fits.Column(name='Wavelength', format='D', array=wave) + c1 = fits.Column(name='Flux', format='E', array=flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + #Adding unit keywords + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + prihdu = fits.PrimaryHDU() + + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(i[:-4]+'.fits', overwrite=True) + + print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) + + # Return to original directory, copy over new .fits files to cdbs directory + os.chdir(start_dir) + cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) + os.system(cmd) + + return + +def rebin_cmfgen(cdbs_path, rot=True): + """ + Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; + this makes spectrophotometry MUCH faster + + cdbs_path: path to cdbs directory + rot=True for rotating models (cmfgen_rot), False for non-rotating models + + makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing cmfgen model; we will steal the header. + # Also define paths to new rebin directories + if rot == True: + tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' + path = cdbs_path+'/grid/cmfgen_rot_rebin/' + orig_path = cdbs_path+'/grid/cmfgen_rot/' + else: + tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' + path = cdbs_path+'/grid/cmfgen_norot_rebin/' + orig_path = cdbs_path+'/grid/cmfgen_norot/' + + cmfgen_hdu = fits.open(tmp) + header0 = cmfgen_hdu[0].header + # Create rebin directories if they don't already exist. Copy over + # catalog.fits file from original directory (will be the same) + if not os.path.exists(path): + os.mkdir(path) + cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) + os.system(cmd) + + # Read in the catalog.fits file + cat = fits.getdata(orig_path + 'catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + # First column in new files will be for [atlas] wavelength + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + + # For each catalog.fits entry, read the unbinned spectrum and rebin to + # the atlas resolution. Make a new fits file in rebin directory + count = 0 + for ff in range(len(files_all)): + count += 1 + # Extract the temp, Z, logg + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + grav = float(vals[2]) + + # Fetch the spectrum + if rot == True: + sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) + else: + sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) + + # Rebin + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + # Make the FITS file from the columns with header + cols = fits.ColDefs([c0,c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + # Write hdu to new directory with same filename + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(path+files_all[ff]) + + print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) + return + + +def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): + """ + Construct the Phoenix Husser+13 atmopsheres for each model. Combines the + fluxes from the *HiRES.fits files and the wavelengths of the + WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. + + path_to_dir is the path to the directory containing all of the downloaded + files + + met_str is the name of the current metallicity + + Creates new fits files for each atmosphere: phoenix_.fits, + which contains columns for the log g (column header = g#.#). Puts + atmospheres in new directory phoenixm00 + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # If it doesn't already exist, create the current metallicity subdirectory + sub_dir = '../phoenix{0}'.format(met_str) + if os.path.exists(sub_dir): + pass + else: + os.mkdir(sub_dir) + + # Extract wavelength array, make column for later + wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') + wave = wavefile[0].data + wavefile.close() + wave_col = Column(wave, name = 'WAVELENGTH') + + # Create temp array for Husser+13 grid (given in paper) + temp_arr = np.arange(2300, 7001, 100) + temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) + + print( 'Looping though all temps') + # For each temp, build file containing the flux for all gravities + i = 0 + for temp in temp_arr: + files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) + files.sort() + # Start the table with the wavelength column + t = Table() + t.add_column(wave_col) + for f in files: + # Extract the logg out of filename + logg = f[9:13] + + # Extract fluxes from file + spectrum = fits.open(f) + flux = spectrum[0].data + spectrum.close() + + # Make Column object with fluxes, add to table + col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) + t.add_column(col) + + # Now, construct final fits file for the given temp + outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) + t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) + + # Progress counter for user + i += 1 + print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) + + # Return to original directory + os.chdir(start_dir) + return + +def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): + """ + Makes catalog.fits file for Husser+13 phoenix models. Assumes that + organize_PHOENIXv16_atmospheres has been run already, and that the models lie + in subdirectory phoenix[met_str]. + + path_to_directory is the path to the directory with the reformatted + models (i.e. the output from construct_atmospheres, phoenix[met_str]) + + Puts catalog.fits file in directory the user starts in + """ + # Save starting directory for later, move into working directory + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Extract metallicity from metallicity string + met = float(met_str[1]) + (float(met_str[2]) * 0.1) + if 'm' in met_str: + met *= -1. + + # Collect the filenames. Each is a unique temp with many different log g's + files = glob.glob('phoenix*.fits') + files.sort() + + # Create the catalog.fits file, row by row + index_arr = [] + filename_arr = [] + for i in files: + # Get log g values from the column header the file + t = Table.read(i, format='fits') + keys = t.keys() + logg_vals = keys[1:] + + # Extract temp from filename + name = i.split('_') + temp = float(name[1][:-5]) + for j in logg_vals: + logg = float(j[1:]) + index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) + filename = path_to_dir + i + '[' + j + ']' + # Add row to table + index_arr.append(index) + filename_arr.append(filename) + + catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) + + # Return to starting directory, write catalog + os.chdir(start_dir) + + if os.path.exists('catalog.fits'): + from astropy.table import vstack + + prev_catalog = Table.read('catalog.fits', format='fits') + joined_catalog = vstack([prev_catalog, catalog]) + + joined_catalog.write('catalog.fits', format='fits', overwrite=True) + else: + catalog.write('catalog.fits', format='fits', overwrite=True) + + return + +def cdbs_PHOENIXv16(path_to_cdbs_dir): + """ + Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily + consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] + and adding the appropriate keywords to the fits header. + + path_to_cdbs_dir goes from current working directory to phoenix[met] directory + in cdbs/grids/phoenix_v16. Note that these files have already been organized + using organize_PHOENIXv16_atmospheres code. + + Overwrites original files in directory + """ + # Save starting directory for later, move into working directory + start_dir = os.getcwd() + os.chdir(path_to_cdbs_dir) + + # Collect the filenames, make necessary changes to each one + files = glob.glob('phoenix*.fits') + + ## Need to sort filenames; glob doesn't always give them in order + files.sort() + + # Need to make brand-new fits tables with data we want. + counter = 0 + for i in files: + counter += 1 + + # Read in current FITS table + cur_table = Table.read(i, format='fits') + + cur_table.columns[0].name = 'Wavelength' + + num_cols = len(cur_table.colnames) + + # Multiplying each flux column by 10^-8 for conversion + for cur_col_index in range(1, num_cols, 1): + cur_col_name = cur_table.colnames[cur_col_index] + cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 + + + # Construct new FITS file based on old one + hdu = fits.open(i) + header_0 = hdu[0].header + header_1 = hdu[1].header + sci = hdu[1].data + + tbhdu = fits.table_to_hdu(cur_table) + + # Copying over the older headers, adding unit keywords + prihdu = fits.PrimaryHDU(header=header_0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + tbhdu.header['TUNIT3'] = 'FLAM' + tbhdu.header['TUNIT4'] = 'FLAM' + tbhdu.header['TUNIT5'] = 'FLAM' + tbhdu.header['TUNIT6'] = 'FLAM' + tbhdu.header['TUNIT7'] = 'FLAM' + tbhdu.header['TUNIT8'] = 'FLAM' + tbhdu.header['TUNIT9'] = 'FLAM' + tbhdu.header['TUNIT10'] = 'FLAM' + tbhdu.header['TUNIT11'] = 'FLAM' + tbhdu.header['TUNIT12'] = 'FLAM' + tbhdu.header['TUNIT13'] = 'FLAM' + tbhdu.header['TUNIT14'] = 'FLAM' + + # Construct and write out final FITS file + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(i, overwrite=True) + + hdu.close() + print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) + + # Change back to starting directory + os.chdir(start_dir) + + return + +def rebin_phoenixV16(cdbs_path): + """ + Rebin phoenixV16 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: phoenix_v16_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing phoenix model; we will steal the header + ## (This assumes that at least 'm00' metallicity exists) + tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') + phoenix_hdu = fits.open(tmp) + header0 = phoenix_hdu[0].header + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/phoenix_v16_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + temp_arr = np.zeros(len(files_all), dtype=float) + logg_arr = np.zeros(len(files_all), dtype=float) + metal_arr = np.zeros(len(files_all), dtype=float) + + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + + temp_arr[ff] = float(vals[0]) + metal_arr[ff] = float(vals[1]) + logg_arr[ff] = float(vals[2]) + + + metal_uniq = np.unique(metal_arr) + temp_uniq = np.unique(temp_arr) + + for mm in range(len(metal_uniq)): + metal = metal_uniq[mm] # metallicity + + # Construct str for metallicity (for appropriate directory name) + met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) + if metal > 0: + met_str = 'p' + met_str + else: + met_str = 'm' + met_str + + # Make directory for current metallicity if it does not exist yet + if not os.path.exists(path + 'phoenix' + met_str): + os.mkdir(path + 'phoenix' + met_str) + + for tt in range(len(temp_uniq)): + temp = temp_uniq[tt] # temperature + + # Pick out the list of gravities for this T, Z combo + idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] + logg_exist = logg_arr[idx] + + # All gravities will go in one file. Here is the output + # file name. + outfile = path + files_all[idx[0]].split('[')[0] + + ## If the rebinned file already exists, continue + if os.path.exists(outfile): + continue + + # Build a columns array. One column for each gravity. + cols_arr = [] + + # Make the wavelength column, which is first in the cols array. + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + cols_arr.append(c0) + + for gg in range(len(logg_exist)): + grav = logg_exist[gg] # gravity + + # Fetch the spectrum + sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Store the spectrum + name = 'g{0:3.1f}'.format(grav) + col = fits.Column(name=name, format='E', array=flux_rebin) + cols_arr.append(col) + + + # Make the FITS file from the columns with header. + cols = fits.ColDefs(cols_arr) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + for gg in range(len(logg_exist)): + tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' + + # Write hdu + finalhdu = fits.HDUList([prihdu, tbhdu]) + # don't have overwrite to protect original files. + finalhdu.writeto(outfile) + + print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) + + + return + + +def rebin_spec(wave, specin, wavnew): + """ + Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: + http://www.astrobetter.com/blog/2013/08/12/ + python-tip-re-sampling-spectra-with-pysynphot/ + """ + spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) + f = np.ones(len(wave)) + filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') + obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') + + return obs.binflux + +def organize_BTSettl_2015_atmospheres(path_to_dir): + """ + Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. + Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] + + path_to_dir is the path to the directory containing all of the downloaded + files + + Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 + (assumes this directory exists) + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # If it doesn't already exist, create the BTSettl subdirectory + if not os.path.exists('BTSettl_2015'): + os.mkdir('BTSettl_2015') + + # Process each atmosphere file independently + print( 'Creating cdbs-ready files') + files = glob.glob('*.spec.fits') + + for i in files: + hdu = fits.open(i) + spec = hdu[1].data + header_0 = hdu[0].header + header_1 = hdu[1].header + + wave = spec.field(0) + flux = spec.field(1) + + # Get units right: convert wave from microns to Angstroms, + # flux from W /m^2/ micron to erg/s/cm^2/A + wave_new = wave * 10**4 + flux_new = flux * 10**(-1) + + # Make new fits table + c0 = fits.Column(name='Wavelength', format='D', array=wave_new) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Copy over headers, update unit keywords + prihdu = fits.PrimaryHDU(header=header_0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) + + hdu.close() + hdu_new.close() + + # Return to original directory + os.chdir(start_dir) + return + +def make_BTSettl_2015_catalog(path_to_dir): + """ + Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. + THIS IS STEP 2, after organize_CMFGEN_atmospheres has + been run. + + path_to_dir is from current working directory to the cdbs directory. + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere from the filename, + # construct columns for catalog file + files = glob.glob("*spec.fits") + index_str = [] + name_str = [] + for name in files: + tmp = name.split('-') + temp = float(tmp[0][3:]) * 100.0 # In kelvin + logg = float(tmp[1]) + + index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) + name_str.append('{0}[Flux]'.format(name)) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): + """ + Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: BTSettl_2015_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing phoenix model; we will steal the header + tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' + phoenix_hdu = fits.open(tmp) + header0 = phoenix_hdu[0].header + phoenix_hdu.close() + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/BTSettl_2015_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + print( 'Rebinning BTSettl spectra') + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the BTSettl spectrum, rebin flux + sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + return + +def make_wavelength_unique(files, dirname): + """ + Helper function to go through each BTSettl spectrum and ensure that + each wavelength point is unique. This is required for rebinning to work. + + + files: list of files to run this analysis on + """ + # Loop through each file, find fix repeated wavelength entries if necessary + for i in files: + t = Table.read('{0}/{1}'.format(dirname,i), format='fits') + test = np.unique(t['Wavelength'], return_index=True) + + if len(t) != len(test[0]): + t = t[test[1]] + + c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) + c1 = fits.Column(name='Flux', format='E', array=t['Flux']) + cols = fits.ColDefs([c0, c1]) + + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) + + # Also make sure wavelength is monotonic. If it is not, then it is + # a sign that the wavelengths are out of order + diff = np.diff(t['Wavelength']) + bad = np.where(diff < 0) + if len(bad[0]) > 0: + t.sort('Wavelength') + + c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) + c1 = fits.Column(name='Flux', format='E', array=t['Flux']) + cols = fits.ColDefs([c0, c1]) + + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) + + print('Done {0}'.format(i)) + + return + +def organize_BTSettl_atmospheres(): + """ + Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). + The code expects tp be run in cdbs/grid/BTSettl, and expects that the + individual model files have been downloaded from online + (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) + and processed into python-readable ascii files. + """ + orig_dir = os.getcwd() + dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] + #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] + + + # Go through each directory, turning each spectrum into a cdbs-ready file. + # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, + # for faster access later + for ii in dirs: + print('Starting {0}'.format(ii)) + os.chdir(ii) + + files = glob.glob('*.txt') + count=0 + for jj in files: + t = Table.read(jj, format='ascii') + # First, trim the wavelengths to a more reasonable wavelength range + good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) + t = t[good] + + # Convert flux units to Flam (Ergs/sec/cm**2/A) + flux_new = 10**(t['col2'] - 8.0) + + # Save the file as a fits file + c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) + c1 = fits.Column(name='Flux', format='E', array=flux_new) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Add unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) + hdu_new.close() + count += 1 + print('Done {0} of {1}'.format(count, len(files))) + + # Now, clean up all the files made when unzipping the spectra + cmd1 = 'rm *.bz2' + cmd2 = 'rm *.tmp' + #cmd3 = 'rm *.txt' + os.system(cmd1) + os.system(cmd2) + #os.system(cmd3) + print('==============================') + print('Done {0}'.format(ii)) + print('==============================') + + # Go back to original directory, move to next metallicity directory + os.chdir(orig_dir) + + return + +def make_BTSettl_catalog(): + """ + Create cdbs catalog.fits of BTSettl grid. + THIS IS STEP 2, after organize_BTSettl_atmospheres has + been run. + + Code expects to be run in cdbs/grid/BTSettl + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] + #dirs = ['btp05'] + + # Construct the catalog.fits file input. The input consists of + # and index string that specifies the stellar paramters, and a + # name string that points to the file + # Loop over all the metallicity directories to construct these inputs + index_str = [] + name_str = [] + for ii in dirs: + os.chdir(ii) + files = glob.glob('*.fits') + + # Construct the metallicity val + if 'm' in ii: + metal_flag = -1 * float(ii[3:])*0.1 + else: + metal_flag = float(ii[3:])*0.1 + + # Now collect the info from the files + for jj in files: + tmp = jj.split('-') + + if metal_flag >= 0: + temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin + try: + logg = float(tmp[1]) + except: + logg = float(tmp[1].split('+')[0]) + else: + temp = float(tmp[0][3:]) * 100.0 # In kelvin + logg = float(tmp[1]) + + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) + name_str.append('{0}/{1}[Flux]'.format(ii, jj)) + + # Go back to original directory to move to next metallicity + print('Done {0}'.format(ii)) + os.chdir(start_dir) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_BTSettl(make_unique=False): + """ + Rebin BTSettle models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory: BTSettl_rebin + + Code expects to be run in cdbs/grid directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Create cdbs/grid directory for rebinned models + path = 'BTSettl_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata('BTSettl/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + #==============================# + #tmp = [] + #for ii in files_all: + # if ii.startswith('btp00'): + # tmp.append(ii) + #files_all = tmp + #=============================# + + print( 'Rebinning BTSettl spectra') + if make_unique: + print('Making unique') + make_wavelength_unique(files_all, 'BTSettl') + print('Done') + + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the BTSettl spectrum, rebin flux + try: + sp = pysynphot.Icat('BTSettl', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + except: + pdb.set_trace() + orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) + outfile = path + files_all[ff].split('[')[0] + cmd = 'cp {0} {1}'.format(orig_file, outfile) + os.system(cmd) + + print('Done {0} of {1}'.format(ff, len(files_all))) + + return + +def organize_WDKoester_atmospheres(path_to_dir): + """ + Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) + Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] + + path_to_dir is the path to the directory containing all of the downloaded + files + + Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter + (assumes this directory exists) + """ + # Save current directory for return later, move into working dir + start_dir = os.getcwd() + os.chdir(path_to_dir) + + # Process each atmosphere file independently + print( 'Creating cdbs-ready files') + files = glob.glob('*.dk.dat.txt') + + for i in files: + data = Table.read(i, format='ascii') + + wave = data['col1'] # angstrom + flux = data['col2'] # erg/s/cm^2/A + + # Make new fits table + c0 = fits.Column(name='Wavelength', format='D', array=wave) + c1 = fits.Column(name='Flux', format='E', array=flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Copy over headers, update unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write new fits table in cdbs directory + hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) + + hdu_new.close() + + # Return to original directory + os.chdir(start_dir) + return + +def make_WDKoester_catalog(path_to_dir): + """ + Create cdbs catalog.fits of wdKoester grid. + THIS IS STEP 2, after organize_WDKoester_atmospheres has + been run. + + path_to_dir is from current working directory to the cdbs directory. + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + + # Enter atmosphere directory + os.chdir(path_to_dir) + + # Extract parameters for each atmosphere from the filename, + # construct columns for catalog file + files = glob.glob("*dk.dat.fits") + index_str = [] + name_str = [] + for name in files: + tmp = name.split('.') + tmp2 = tmp[0].split('_') + temp = float(tmp2[0][2:]) # Kelvin + logg = float(tmp2[1]) / 100.0 # log(g) + + index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) + name_str.append('{0}[Flux]'.format(name)) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): + """ + Rebin wdKoester models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory in cdbs/grid: wdKoester_rebin + + cdbs_path: path to cdbs directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Open a fits table for an existing model; we will steal the header + tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' + wdkoester_hdu = fits.open(tmp) + header0 = wdkoester_hdu[0].header + wdkoester_hdu.close() + + # Create cdbs/grid directory for rebinned models + path = cdbs_path+'/grid/wdKoester_rebin/' + if not os.path.exists(path): + os.mkdir(path) + + # Read in the existing catalog.fits file and rebin every spectrum. + cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') + files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] + + print( 'Rebinning wdKoester spectra') + for ff in range(len(files_all)): + vals = cat[ff][0].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the wdKoester spectrum, rebin flux + sp = pysynphot.Icat('wdKoester', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + + # Make new output + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU(header=header0) + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + outfile = path + files_all[ff].split('[')[0] + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + return + + diff --git a/spisea/ifmr.py b/spisea/ifmr.py index eb841498..ff387e95 100755 --- a/spisea/ifmr.py +++ b/spisea/ifmr.py @@ -48,15 +48,11 @@ def Kalirai_mass(self, MZAMS): # for white dwarfs result = 0.109*MZAMS + 0.394 final = np.zeros(len(MZAMS)) - - bad_idx = np.where((MZAMS < 0.5) | (MZAMS >= 9)) - final[bad_idx] = -99 - good_idx = np.where((MZAMS >= 0.5) & (MZAMS < 9)) + good_idx = (MZAMS >= 0.5) & (MZAMS < 9) final[good_idx] = result[good_idx] final[~good_idx] = -99 - return final diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 3f1c1a81..e82944c8 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -82,7 +82,7 @@ class IMF(object): a GNU General Public License. """ - def __init__(self, massLimits=np.array([0.01,150]), multiplicity=None): + def __init__(self, massLimits=np.array([0.01,150]), multiplicity=None, seed=None): self._multi_props = multiplicity self._mass_limits = np.atleast_1d(massLimits) self.rng = np.random.default_rng(seed) @@ -303,6 +303,7 @@ def calc_multi(self, newMasses, newIsMultiple, CSF, MF): return compMasses, newSystemMasses, newIsMultiple + class IMF_broken_powerlaw(IMF): """ Initialize a multi-part power-law with N parts. Each part of the diff --git a/spisea/imf/multiplicity.py b/spisea/imf/multiplicity.py index 5166e07c..4258cb4c 100755 --- a/spisea/imf/multiplicity.py +++ b/spisea/imf/multiplicity.py @@ -294,11 +294,12 @@ def log_semimajoraxis(self, mass): logm = np.log10(mass) # Stellar mean and std (Duchene & Kraus 2013) - a_mean_func = astropy.modeling.powerlaws.BrokenPowerLaw1D(amplitude=self.a_amp, x_break=self.a_break, alpha_1=self.a_slope1, alpha_2=self.a_slope2) - log_a_mean_star = np.log10(a_mean_func(mass)) #mean log(a) + a_mean_func = astropy.modeling.powerlaws.BrokenPowerLaw1D(amplitude=self.a_amp, x_break=self.a_break, + alpha_1=self.a_slope1, alpha_2=self.a_slope2) + log_a_mean_star = np.log10(a_mean_func(mass)) # mean log(a) log_a_std_func = astropy.modeling.models.Linear1D(slope=self.a_std_slope, intercept=self.a_std_intercept) - log_a_std_star = log_a_std_func(logm) #sigma_log(a) - log_a_std_star[mass >= 2.9] = log_a_std_func(np.log10(2.9)) #sigma_log(a) + log_a_std_star = log_a_std_func(logm) # sigma_log(a) + log_a_std_star[mass >= 2.9] = log_a_std_func(np.log10(2.9)) # sigma_log(a) log_a_std_star = np.clip(log_a_std_star, 0.1, None) # BD mean and std (Fontanive+18): interpolated over substellar range @@ -316,7 +317,7 @@ def log_semimajoraxis(self, mass): # Sigmoid blend: smoothly transitions from BD to stellar regime at 0.08 M_sun w = 1.0 / (1.0 + np.exp(-(logm - np.log10(0.08)) / 0.15)) log_a_mean = (1 - w) * log_a_mean_bd + w * log_a_mean_star - log_a_std = (1 - w) * log_a_std_bd + w * log_a_std_star + log_a_std = (1 - w) * log_a_std_bd + w * log_a_std_star # Trunc normal distribution between log10(0.01) AU and log10(2000) AU log_a_lower = np.log10(0.01) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index e1d7de1c..125bf257 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -300,7 +300,7 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) & (star_systems_phase_non_nan != 9) & - (star_systems_phase_non_nan != 90) & # exclude BD phase + (star_systems_phase_non_nan != 90) & # exclude BD phase (star_systems_phase_non_nan != -99) ) star_systems['phase'][bad] = 5 @@ -383,6 +383,10 @@ def _make_companions_table_new(self, star_systems, compMass): companions['metallicity'] = np.ones(N_comp_tot) * self.iso.metallicity + bd_mask = (companions['mass'] >= 0.01) & (companions['mass'] <= 0.08) + companions['phase'][bd_mask] = 90 + companions['mass_current'][bd_mask] = companions['mass'][bd_mask] + # For a very small fraction of stars, the star phase falls on integers in-between # the ones we have definition for, as a result of the interpolation. For these # stars, round phase down to nearest defined phase (e.g., if phase is 71, @@ -393,6 +397,7 @@ def _make_companions_table_new(self, star_systems, compMass): (companions_phase_non_nan > 5) & (companions_phase_non_nan < 101) & (companions_phase_non_nan != 9) & + (companions_phase_non_nan != 90) & (companions_phase_non_nan != -99) ] = 5 @@ -530,7 +535,7 @@ def _make_companions_table(self, star_systems, compMass): # Convert nan_to_num to avoid errors on greater than, less than comparisons # reinforce BD phase of 90 and invariant masses - bd_mask = (companions['mass'] >= 0.01) & (companions['mass'] < 0.08) + bd_mask = (companions['mass'] >= 0.01) & (companions['mass'] <= 0.08) companions['phase'][bd_mask] = 90 companions['mass_current'][bd_mask] = companions['mass'][bd_mask] @@ -641,12 +646,25 @@ def _remove_bad_systems(self, star_systems, compMass, keep_low_mass_stars): star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) if (self.ifmr == None) and (not keep_low_mass_stars): print('Remove low mass stars below grid and compact objects') - # Keep only those stars with Teff assigned. - idx = star_systems_teff_non_nan > 0 + # Keep only those stars with Teff assigned and masses in grid + min_iso = np.min(self.iso.points['mass']) + max_iso = np.max(self.iso.points['mass']) + mass = star_systems['mass'] + on_grid = (mass >= min_iso) & (mass <= max_iso) & (star_systems_teff_non_nan > 0) + idx = on_grid + elif not keep_low_mass_stars: print('Remove low mass stars, keep compact objects') # Keep stars (with Teff) and any other compact objects (with phase info). - idx = (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) + min_iso = np.min(self.iso.points['mass']) + max_iso = np.max(self.iso.points['mass']) + mass = star_systems['mass'] + on_grid = (mass >= min_iso) & (mass <= max_iso) & (star_systems_teff_non_nan > 0) + above_grid = (mass > max_iso) & ( + (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 101) + ) + idx = on_grid | above_grid + elif self.ifmr == None: print('Remove compact objects, keep low mass stars below grid') # Keep stars (with Teff) and objects below mass grid diff --git a/spisea/tests/test_data/companions.fits b/spisea/tests/test_data/companions.fits index 916e273db0f13a4c0427d1b28b47d57e9a8c0fa2..c964bcf4483664580d09c56ac96726dc9196804e 100644 GIT binary patch literal 236160 zcmeFac{o*V-}s#jMP+XZ70DEp687GPy<>gDA}NHBCPRoQi4qwql|&RtX;j(fA!I0u z%wuLrMU)~!DI$LBx?1ac?&s)Uzx#Qg`#Rp^c;DmpPp&@SKG)Z|*KnTSb6~2!#dtF+ zA)z7hPrl3~)=AhMJm7H9-Sx19$3Y2QeKS*u!yYyV_S(4b{U`C*Uuo%^8S7h;_|!0; zjk~+eF^Ro49ySsl$K34yx#hq9)^LNRzA5&f|1>{E%y+=k)z;o!;-CXzc5<~paM_Y* z`~PbHS^58Y|M}1UxY9rGKdb%Gzjv`e;OMdMkN)^xq9-na#gl#g=~Mn^K4pfQ3W*Pk zC;KwiHri&0_2d5s;{`|egPv{@ZZ_^VuJ#`G?*Dwep!a|CDgC$h`_uQ&E&r9RnXW#% zS;zFB=BM=EeZMXl2Pb=%z2sIiv+c&{pPz@^;bVtA>|OUb?e+S{EAYSm*Z-sM%yf-R zw*2xL;Q!#?ut`Y#ncttEubivR;Xi+lf6wj z-R%!}{I5UbKktuUzP~R&)j#udI&5L`_q5+H-`|&?+MoHk?Xx-jN4@5M-FAQcaDTSn 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a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -132,7 +132,7 @@ def test_atmosphere_models(): print('Done {0}'.format(atm_func)) # Test get_merged_atmospheres at different temps - temp_range = [200, 1000, 2000, 3500, 4000, 5250, 6000, 12000] + temp_range = [250, 1000, 2000, 3500, 4000, 5250, 6000, 12000] atm_func = atmospheres.get_merged_atmosphere for ii in bd_metals_range: for jj in temp_range: @@ -159,7 +159,7 @@ def test_atmosphere_models(): # Test get_bd_atmosphere at different temps # This func only requests temp temp_range = [250, 400, 500, 750, 950, 1200] - atm_func = atm.get_bd_atmosphere + atm_func = atmospheres.get_bd_atmosphere for jj in temp_range: try: test = atm_func(temperature=jj, verbose=True) diff --git a/spisea/tests/test_multiplicity.py b/spisea/tests/test_multiplicity.py index a7eb4ed0..53226651 100755 --- a/spisea/tests/test_multiplicity.py +++ b/spisea/tests/test_multiplicity.py @@ -226,10 +226,20 @@ def test_resolvedmult(): mean_log_a = np.mean(bd_companions['log_a']) std_log_a = np.std(bd_companions['log_a']) + bd_masses = clust_Mult.star_systems['mass'][bd_idx] + expected_sigma = np.mean( + np.interp( + np.log10(bd_masses), + [np.log10(0.01), np.log10(0.08)], + [0.25, 0.5] + ) + ) + #expect lognormal centered near log10(2.9 AU), width ~0.21 assert abs(mean_log_a - np.log10(2.9)) < 0.25, \ f"BD mean log(a) off: {mean_log_a:.2f}" - assert abs(std_log_a - 0.21) < 0.15, \ + + assert abs(std_log_a - expected_sigma) < 0.15, \ f"BD sigma log(a) off: {std_log_a:.2f}" return diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 04b371bb..7762fd93 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -535,7 +535,7 @@ def test_ifmr_multiplicity(): startTime = time.time() - evo = evolution.MergedBaraffePisaEkstromParsec() + evo = evolution.MergedPhillipsBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere ifmr_obj = ifmr.IFMR_Raithel18() @@ -657,6 +657,7 @@ def test_ifmr_multiplicity(): comp_non_bd_idx = np.where((comps2['phase'] != 90) & (comps2['mass'] >= BD_MIN_MASS) & (comps2['mass'] <= BD_MAX_MASS)) + print(comps2[comp_non_bd_idx]['phase', 'mass']) assert len(comp_non_bd_idx[0]) == 0 # asserting no non-brown dwarf companions in BD mass range # Ensure BD temperature assignment is working correctly @@ -854,7 +855,8 @@ def test_keep_low_mass_stars(): red_law=red_law, filters=filt_list, mass_sampling=mass_sampling, - iso_dir=iso_dir + iso_dir=iso_dir, + recomp=True ) # Get the minimum mass in the isochrones. This should be the lowest @@ -1293,7 +1295,8 @@ def test_ResolvedCluster_random_state(): red_law=red_law, filters=filt_list, mass_sampling=10, - iso_dir=iso_dir + iso_dir=iso_dir, + recomp=True ) imf_limits = np.array([0.07, 0.5, 150]) @@ -1325,4 +1328,4 @@ def test_ResolvedCluster_random_state(): # np.testing.assert_array_equal(cluster1.companions[key], old_companion[key]) np.testing.assert_allclose(cluster1.companions[key], old_companion[key], rtol=1e-5, atol=1e-8) - return + return \ No newline at end of file From df1a1163893d4dfacefbb2ab76ade5029bdf78f0 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Mon, 1 Jun 2026 16:48:40 -0700 Subject: [PATCH 136/191] Committing changes from dev --- docs/contributors_BACKUP_57328.rst | 54 ++++++++++++++++++++++++++++++ docs/contributors_BASE_57328.rst | 36 ++++++++++++++++++++ docs/contributors_LOCAL_57328.rst | 38 +++++++++++++++++++++ docs/contributors_REMOTE_57328.rst | 50 +++++++++++++++++++++++++++ 4 files changed, 178 insertions(+) create mode 100644 docs/contributors_BACKUP_57328.rst create mode 100644 docs/contributors_BASE_57328.rst create mode 100644 docs/contributors_LOCAL_57328.rst create mode 100644 docs/contributors_REMOTE_57328.rst diff --git a/docs/contributors_BACKUP_57328.rst b/docs/contributors_BACKUP_57328.rst new file mode 100644 index 00000000..741f832f --- /dev/null +++ b/docs/contributors_BACKUP_57328.rst @@ -0,0 +1,54 @@ +.. _contributors: + +============ +Contributors +============ +Jessica Lu -- Lead Developer; developed initial package framework; collaboratively developed all aspects of code + +Matthew Hosek Jr -- Lead Developer; collaboratively developed all aspects of code + +Casey Lam -- implemented first IFMR module (IFMR_Raithel18) + +Abhimat Gautam -- implemented non-solar metallicity evolution model +support for MIST models as well as blackbody atmosphere function + +Kelly Lockhart -- helped develop UnresolvedCluster class + +Dongwon Kim -- code testing/debugging + +Siyao Jia -- helped with early code and documentation development + +Natasha Abrams -- developed resolved multiplicity capabilities +(ResolvedMultiplicityDK class) + +Michael Medford -- developed resolved multiplicity capabilities +(ResolvedMultiplicityDK class) + +Sam Rose -- developed metallicity-dependent IFMRs (IFMR_Spera15 and IFMR_N20_Sukhbold) + +Rebecca Lewis -- added HAWK-I filter support, code testing/debugging + +Manuel Parra-Royón -- installation using Docker containers and Singularity + +Javier Moldón -- installation using Docker containers and Singularity + +Winston Zhang -- bugfix to make redlaw paths correct regardless of +what operating system is used + +<<<<<<< HEAD +Caitlin Begbie -- added brown dwarf physics and capabilities +======= +Sage Hironaka Remulla -- added Rubin Observatory filters + +Lingfeng Wei -- bugfix to improve creation of iso_dir in +IsochronePhot, implemented faster cluster generation and test +functions for primary and companion star mass generation (v2.3), +updated random state generators (v2.4) + +Macy Huston -- New metallicity bound + isochrone filter checks, +imf_mass_lim bugfix, roman filter bugfix, added Euclid filters, Synthpop compatibility +updates (v2.2), improvements for reading in/updated existing isochrone +files, Vega mag to ST mag conversion function (v2.4) + +Anna Pusack -- Added IRTF L-band filter support +>>>>>>> upstream/dev diff --git a/docs/contributors_BASE_57328.rst b/docs/contributors_BASE_57328.rst new file mode 100644 index 00000000..40edaee7 --- /dev/null +++ b/docs/contributors_BASE_57328.rst @@ -0,0 +1,36 @@ +.. _contributors: + +============ +Contributors +============ +Jessica Lu -- Lead Developer; developed initial package framework; collaboratively developed all aspects of code + +Matthew Hosek Jr -- Lead Developer; collaboratively developed all aspects of code + +Casey Lam -- implemented first IFMR module (IFMR_Raithel18) + +Abhimat Gautam -- implemented non-solar metallicity evolution model +support for MIST models as well as blackbody atmosphere function + +Kelly Lockhart -- helped develop UnresolvedCluster class + +Dongwon Kim -- code testing/debugging + +Siyao Jia -- helped with early code and documentation development + +Natasha Abrams -- developed resolved multiplicity capabilities +(ResolvedMultiplicityDK class) + +Michael Medford -- developed resolved multiplicity capabilities +(ResolvedMultiplicityDK class) + +Sam Rose -- developed metallicity-dependent IFMRs (IFMR_Spera15 and IFMR_N20_Sukhbold) + +Rebecca Lewis -- added HAWK-I filter support, code testing/debugging + +Manuel Parra-Royón -- installation using Docker containers and Singularity + +Javier Moldón -- installation using Docker containers and Singularity + +Winston Zhang -- bugfix to make redlaw paths correct regardless of +what operating system is used diff --git a/docs/contributors_LOCAL_57328.rst b/docs/contributors_LOCAL_57328.rst new file mode 100644 index 00000000..e62072d7 --- /dev/null +++ b/docs/contributors_LOCAL_57328.rst @@ -0,0 +1,38 @@ +.. _contributors: + +============ +Contributors +============ +Jessica Lu -- Lead Developer; developed initial package framework; collaboratively developed all aspects of code + +Matthew Hosek Jr -- Lead Developer; collaboratively developed all aspects of code + +Casey Lam -- implemented first IFMR module (IFMR_Raithel18) + +Abhimat Gautam -- implemented non-solar metallicity evolution model +support for MIST models as well as blackbody atmosphere function + +Kelly Lockhart -- helped develop UnresolvedCluster class + +Dongwon Kim -- code testing/debugging + +Siyao Jia -- helped with early code and documentation development + +Natasha Abrams -- developed resolved multiplicity capabilities +(ResolvedMultiplicityDK class) + +Michael Medford -- developed resolved multiplicity capabilities +(ResolvedMultiplicityDK class) + +Sam Rose -- developed metallicity-dependent IFMRs (IFMR_Spera15 and IFMR_N20_Sukhbold) + +Rebecca Lewis -- added HAWK-I filter support, code testing/debugging + +Manuel Parra-Royón -- installation using Docker containers and Singularity + +Javier Moldón -- installation using Docker containers and Singularity + +Winston Zhang -- bugfix to make redlaw paths correct regardless of +what operating system is used + +Caitlin Begbie -- added brown dwarf physics and capabilities diff --git a/docs/contributors_REMOTE_57328.rst b/docs/contributors_REMOTE_57328.rst new file mode 100644 index 00000000..1944147e --- /dev/null +++ b/docs/contributors_REMOTE_57328.rst @@ -0,0 +1,50 @@ +.. _contributors: + +============ +Contributors +============ +Jessica Lu -- Lead Developer; developed initial package framework; collaboratively developed all aspects of code + +Matthew Hosek Jr -- Lead Developer; collaboratively developed all aspects of code + +Casey Lam -- implemented first IFMR module (IFMR_Raithel18) + +Abhimat Gautam -- implemented non-solar metallicity evolution model +support for MIST models as well as blackbody atmosphere function + +Kelly Lockhart -- helped develop UnresolvedCluster class + +Dongwon Kim -- code testing/debugging + +Siyao Jia -- helped with early code and documentation development + +Natasha Abrams -- developed resolved multiplicity capabilities +(ResolvedMultiplicityDK class) + +Michael Medford -- developed resolved multiplicity capabilities +(ResolvedMultiplicityDK class) + +Sam Rose -- developed metallicity-dependent IFMRs (IFMR_Spera15 and IFMR_N20_Sukhbold) + +Rebecca Lewis -- added HAWK-I filter support, code testing/debugging + +Manuel Parra-Royón -- installation using Docker containers and Singularity + +Javier Moldón -- installation using Docker containers and Singularity + +Winston Zhang -- bugfix to make redlaw paths correct regardless of +what operating system is used + +Sage Hironaka Remulla -- added Rubin Observatory filters + +Lingfeng Wei -- bugfix to improve creation of iso_dir in +IsochronePhot, implemented faster cluster generation and test +functions for primary and companion star mass generation (v2.3), +updated random state generators (v2.4) + +Macy Huston -- New metallicity bound + isochrone filter checks, +imf_mass_lim bugfix, roman filter bugfix, added Euclid filters, Synthpop compatibility +updates (v2.2), improvements for reading in/updated existing isochrone +files, Vega mag to ST mag conversion function (v2.4) + +Anna Pusack -- Added IRTF L-band filter support From 75f2e04ee70935f3c2ea1332f24bc89c6e0839a3 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Mon, 1 Jun 2026 16:57:36 -0700 Subject: [PATCH 137/191] Adding UKIRT Z and Y filters --- filt_func/ukirt/README.txt | 2 +- filt_func/ukirt/Y.dat | 603 ++++++++++++++++++++++++++++++++++++ filt_func/ukirt/Z.dat | 466 ++++++++++++++++++++++++++++ spisea/synthetic.py | 2 +- spisea/tests/test_models.py | 2 +- 5 files changed, 1072 insertions(+), 3 deletions(-) create mode 100644 filt_func/ukirt/Y.dat create mode 100644 filt_func/ukirt/Z.dat diff --git a/filt_func/ukirt/README.txt b/filt_func/ukirt/README.txt index b4f3b253..87a63d3f 100755 --- a/filt_func/ukirt/README.txt +++ b/filt_func/ukirt/README.txt @@ -1,2 +1,2 @@ -These are the UKIRT filters (JHK) from this site: +These are the UKIRT filters (ZYJHK) from this site: http://www.ukidss.org/technical/photom/photom.html diff --git a/filt_func/ukirt/Y.dat b/filt_func/ukirt/Y.dat new file mode 100644 index 00000000..a440de18 --- /dev/null +++ b/filt_func/ukirt/Y.dat @@ -0,0 +1,603 @@ +# Table 3 System throughput curve for the WFCAM Y filter +# 1.0mm water vapour, 1.3 airmass +# Col 1: wavelength (microns) +# Col 2: throughput +# Wavelength(microns) Transmission + 0.9005 0.0000 + 0.9010 0.0000 + 0.9015 0.0000 + 0.9020 0.0000 + 0.9025 0.0000 + 0.9030 0.0000 + 0.9035 0.0000 + 0.9040 0.0000 + 0.9045 0.0000 + 0.9050 0.0000 + 0.9055 0.0000 + 0.9060 0.0000 + 0.9065 0.0000 + 0.9070 0.0000 + 0.9075 0.0000 + 0.9080 0.0000 + 0.9085 0.0000 + 0.9090 0.0000 + 0.9095 0.0000 + 0.9100 0.0000 + 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a/spisea/synthetic.py b/spisea/synthetic.py index 125bf257..c327b415 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1961,7 +1961,7 @@ def get_obs_str(col): 'naco_IB_2.24':'naco,IB_2.24', 'naco_IB_2.27':'naco,IB_2.27', 'naco_IB_2.30':'naco,IB_2.30', 'naco_IB_2.33':'naco,IB_2.33', 'naco_IB_2.36':'naco,IB_2.36', - 'ukirt_J':'ukirt,J', 'ukirt_H':'ukirt,H', 'ukirt_K':'ukirt,K', + 'ukirt_Z':'ukirt,Z','ukirt_Y':'ukirt,Y','ukirt_J':'ukirt,J', 'ukirt_H':'ukirt,H', 'ukirt_K':'ukirt,K', 'ctio_osiris_H': 'ctio_osiris,H', 'ctio_osiris_K': 'ctio_osiris,K', 'ztf_g':'ztf,g', 'ztf_r':'ztf,r', 'ztf_i':'ztf,i', 'gaiaDR2_G': 'gaia,dr2_rev,G', 'gaiaDR2_Gbp':'gaia,dr2_rev,Gbp', diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 920ed525..35970431 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -200,7 +200,7 @@ def test_filters(): 'nirc2,Kp', 'nirc2,K', 'nirc2,Lp', 'nirc2,Hcont', 'nirc2,FeII', 'nirc2,Brgamma', 'ps1,z', 'ps1,g', 'ps1,r','ps1,i', 'ps1,y', - 'ukirt,J', 'ukirt,H', 'ukirt,K', + 'ukirt,Z','ukirt,Y','ukirt,J', 'ukirt,H', 'ukirt,K', 'vista,Y', 'vista,Z', 'vista,J', 'vista,H', 'vista,Ks', 'ztf,g', 'ztf,r', 'ztf,i', 'hawki,J', 'hawki,H', 'hawki,Ks', 'roman,wfi,f062', From 0a682416a4c6fb38dc5907bcba639fd9548a6751 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Tue, 2 Jun 2026 12:11:08 -0700 Subject: [PATCH 138/191] Updated documentation, including rst table replacements for evo and atmo models --- docs/atmo_models.rst | 105 +++++++++++++++++++++++++++++++++++++++++-- docs/evo_models.rst | 67 +++++++++++++++++++++++++-- docs/filters.rst | 2 +- 3 files changed, 165 insertions(+), 9 deletions(-) diff --git a/docs/atmo_models.rst b/docs/atmo_models.rst index 3ab2a0c6..fda94778 100644 --- a/docs/atmo_models.rst +++ b/docs/atmo_models.rst @@ -31,10 +31,107 @@ resolution of the atmosphere model grid. These are available in the uses has degraded the resolution of all atmosphere grids to R = 250 (the `spisea_cdbs.tar.gz` file). -.. figure:: images/atm_models.png - :width: 900 - :height: 196 - :align: center +.. list-table:: Atmosphere Models + :header-rows: 2 + :widths: 25 15 12 15 12 18 20 + + * - Model Name + - T\ :sub:`eff` Range (K) + - log *g* Range (cgs) + - Metallicity Range [Fe/H] + - λ Range (μm) + - Resolution\ :sup:`a` λ/Δλ + - Ref + * - ``get_merged_atmosphere`` + - 250 – 50000 + - \ :sup:`b` + - \ :sup:`b` + - \ :sup:`b` + - \ :sup:`b` + - Appendix B; Hosek et al. (2020) + * - ``get_castelli_atmosphere`` + - 3500 – 50000 + - 0 – 5.0 + - -2.5 – 0.2 + - 0.1 – 10 + - ~250 + - Castelli & Kurucz (2004) + * - ``get_phoenixv16_atmosphere`` + - 2300 – 12000 + - 0.0 – 6.0 + - -4.0 – +1.0 + - 0.05 – 5.5 + - 100,000 – 500,000 + - Husser et al. (2013) + * - ``get_BTSettl_2015_atmosphere`` + - 1200 – 7000 + - 2.5 – 5.5 + - 0 + - 0.01 – 30 + - 2000 – 700,000 + - Baraffe et al. (2015) + * - ``get_BTSettl_atmosphere``\ :sup:`d` + - 2600 – 7000 + - 4.5 – 5.5 + - -2.5 – 0.5 + - 0.1 – 6.9 + - 20,000 – 250,000 + - Allard et al. (2012b,a) + * - ``get_kurucz_atmosphere`` + - 3000 – 50000 + - 0 – 5.0 + - -5.0 – 1.0 + - 0.1 – 10 + - ~250 + - \ :sup:`c` + * - ``get_phoenix_atmosphere`` + - 2100 – 69000 + - + - -4.0 – 0.5 + - 0.001 – 995 + - ~280 + - Allard et al. (2003, 2007) + * - ``get_Phillips2020_atmosphere`` + - 200 - 3000 + - 2.5 - 5.5 + - 0 + - 0.2 - ~1980.2 + - 0.5 - 5000 + - Phillips et al. (2020) + * - ``get_Meisner2023_atmosphere`` + - 250 - 1200 + - 2.5 - 5.5 + - -1.0 - 0.3 + - 0.2 - 30 + - ~3000 + - Meisner et al. (2023) + * - ``get_wd_atmosphere``\ :sup:`e` + - – + - – + - – + - 0.1 – 3.0 + - 200 – 500,000 + - Koester (2010) + * - ``get_bb_atmosphere`` + - – + - – + - – + - – + - – + - Blackbody Spectrum + +.. rubric:: Footnotes + +:sup:`a` Resolution column reports the original resolution of the atmosphere model grid. +The default SPISEA grid degrades all atmosphere grids to R = 250 (``spisea_cdbs.tar.gz``). + +:sup:`b` See Appendix B; values depend on the underlying model selected by ``get_merged_atmosphere``. + +:sup:`c` Kurucz (1993); see CDBS documentation. + +:sup:`d` Solar metallicity only for BTSettl 2015. + +:sup:`e` White dwarf atmospheres only. Model Atmosphere Classes ------------------------- diff --git a/docs/evo_models.rst b/docs/evo_models.rst index 99074c8f..4cabc35c 100644 --- a/docs/evo_models.rst +++ b/docs/evo_models.rst @@ -14,10 +14,69 @@ The evolution object is an input for the :ref:`isochrone_objects`. Below is a table of the evolution model grids currently supported by SPISEA. -.. figure:: images/evo_models_f2.png - :width: 900 - :height: 210 - :align: center +.. list-table:: Evolution Models + :header-rows: 2 + :widths: 30 15 18 30 25 + + * - Model Name + - Mass Range + - log(Age) Range + - Metallicity Values + - Ref + * - + - M\ :sub:`⊙` + - Years + - [Fe/H] + - + * - ``MISTv1`` + - 0.10 – 300 + - 5.01 – 10.30 + - -4.0, -3.5, -3.0, -2.5, -2.0, -1.75, -1.5, -1.25, -1.0, -0.75, -0.5, -0.25, 0, 0.25, 0.5 + - v1.2; Choi et al. (2016) + * - ``MergedBaraffePisaEkstromParsec`` + - 0.08 – 120 + - 6.00 – 10.09 + - 0 + - Appendix B; Hosek et al. (2020) + * - ``MergedPhillipsBaraffePisaEkstromParsec`` + - 0.01 – 120 + - 6.00 – 10.00 + - 0 + - Appendix; Begbie et al. (2026) + * - ``Parsec`` + - 0.10 – 65 + - 6.60 – 10.12 + - 0 + - Bressan et al. (2012) + * - ``Baraffe15`` + - 0.07 – 1.4 + - 5.70 – 10.0 + - 0 + - Baraffe et al. (2015) + * - ``Ekstrom12`` + - 0.80 – 300 + - 6.00 – 8.0 + - 0 + - Ekström et al. (2012) + * - ``Pisa`` + - 0.20 – 7 + - 6.00 – 8.0 + - 0 + - Tognelli et al. (2011) + * - ``Phillips2020`` + - 0.0005–0.075 + - 6.00 - 10.00 + - 0 + - Phillips et al. (2020) + * - ``Marley2021`` + - 0.0005–0.083\ :sup:`a` + - 6.00 - 10.00 + - -0.5, 0, 0.5 + - Marley et al. (2021) + +.. rubric:: Footnotes + +:sup:`a` Actual maximum value given by the Sonora models (Marley et al., 2021) relies on the age of the cluster. For example, for log(Age)=6.0, the mass range is limited to 0.0005 - 0.011 M\ :sub:`⊙`. Please note the stellar mass range, age range, and metallicity values of the evolution model grid you choose: diff --git a/docs/filters.rst b/docs/filters.rst index 3112d4a1..9f006bda 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -210,7 +210,7 @@ Example: ``'roman,wfi,f062'`` `UKIRT Telescope filters `_ -Filters: J, H, K +Filters: Z, Y, J, H, K Example: ``'ukirt,K'`` From 76b85334837cf0b955c908dfb6c504acaae7c2e4 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 2 Jun 2026 16:27:37 -0700 Subject: [PATCH 139/191] Add test case for cluster with multiplicity on but no generated companions --- spisea/tests/test_synthetic.py | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 7694ebfd..028e8f52 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -1274,3 +1274,36 @@ def test_ResolvedCluster_random_state(): assert np.all(np.isclose(cluster1.companions[key], old_companion[key], rtol=1e-05, atol=1e-08)) return + +def test_ResolvedCluster_no_companions(): + """ + Test case where no companions get generated to + make sure we don't get any errors. This relies on using + a specific seed that results in no companions being generated. + """ + + # Define cluster parameters + logAge = 6.7 + AKs = 2.4 + distance = 4000 + cluster_mass = 10**2 + iso_dir = f'{spisea_path}/tests/isochrones' + + # Test filters + filt_list = ['nirc2,J', 'nirc2,Kp'] + evo = evolution.MergedBaraffePisaEkstromParsec() + atm_func = atmospheres.get_merged_atmosphere + red_law = reddening.RedLawNishiyama09() + + iso = syn.IsochronePhot(logAge, AKs, distance, metallicity=0, + evo_model=evo, atm_func=atm_func, + red_law=red_law, filters=filt_list, + iso_dir=iso_dir) + + imf_multi = multiplicity.MultiplicityResolvedDK() + my_imf = imf.Kroupa_2001(multiplicity=imf_multi) + + cluster = syn.ResolvedCluster(iso, my_imf, cluster_mass, + keep_low_mass_stars=True, seed=1074) + + assert(~np.any(cluster.star_systems['isMultiple'])) From ed63fca405648ca37e116eb00bef0cd6b7f18924 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 2 Jun 2026 17:32:10 -0700 Subject: [PATCH 140/191] resolve a couple easy numpy DepracationWarnings --- spisea/imf/imf.py | 4 ++-- spisea/tests/test_synthetic.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index eb14f55f..98de1fa3 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -159,7 +159,7 @@ def generate_cluster(self, totalMass): # start_while = time.time() while totalMassTally < totalMass: # Generate a random number array. - uniX = self.rng.random(int(newStarCount)) + uniX = self.rng.random(newStarCount.astype(int)) # Convert into the IMF from the inverted CDF newMasses = self.dice_star_cl(uniX) @@ -176,7 +176,7 @@ def generate_cluster(self, totalMass): MF = self._multi_props.multiplicity_fraction(newMasses) CSF = self._multi_props.companion_star_fraction(newMasses) - newIsMultiple = self.rng.random(int(newStarCount)) < MF + newIsMultiple = self.rng.random(newStarCount.astype(int)) < MF # Function to calculate multiple systems more efficiently # start_calc = time.time() diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 028e8f52..d34f643d 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -38,7 +38,7 @@ def test_isochrone(plot=False): plt.figure(2) iso.plot_mass_luminosity() - return iso + #return iso def test_iso_wave(): """ From 9ded344293efa4053035f66dab012ce92ff2e05a Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 4 Jun 2026 17:20:30 -0700 Subject: [PATCH 141/191] update DECam filters --- docs/filters.rst | 2 +- filt_func/decam/DECam_filters.txt | 803 +++++++++++++++++++++++++++++- spisea/filters.py | 16 +- 3 files changed, 808 insertions(+), 13 deletions(-) diff --git a/docs/filters.rst b/docs/filters.rst index 667d78a0..05f29103 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -83,7 +83,7 @@ Example: ``'ctio_osiris,H'`` **DeCam** -`Dark Energy Camera `_ +`Dark Energy Camera `_ Filters: u, g, r, i, z, Y diff --git a/filt_func/decam/DECam_filters.txt b/filt_func/decam/DECam_filters.txt index f0f38fa8..c3091a2a 100755 --- a/filt_func/decam/DECam_filters.txt +++ b/filt_func/decam/DECam_filters.txt @@ -1 +1,802 @@ -wavelength u g r i z Y atm 300 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00313 301 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00562 302 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00855 303 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.01480 304 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.01870 305 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.02910 306 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.03670 307 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.04870 308 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.05870 309 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.06840 310 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.09020 311 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.10000 312 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.11700 313 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.13300 314 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.14000 315 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.16300 316 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.17100 317 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.18400 318 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.19500 319 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.22300 320 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.20200 321 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.24600 322 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.22900 323 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.25000 324 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.27200 325 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.25600 326 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.28200 327 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.28900 328 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.27400 329 0.00000 0.00000 0.00000 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0.0011768966 + 1088 0.00643614 5.3907E-4 9.6931E-4 -0.0832711 -0.08200075 0.05956583 0.0013241379 + 1089 0.00485679 0.00169103 -3.0229E-4 -0.08398607 -0.08291194 0.04362047 0.0012 + 1090 0.00581852 4.7808E-4 -2.486E-4 -0.08335748 -0.08277959 0.03182469 0.0013403448 + 1091 0.00499214 0.00115179 -2.9229E-4 -0.08333294 -0.08277964 0.02313808 0.0012282759 + 1092 0.00660648 0.00239823 1.4854E-4 -0.08310545 -0.08262309 0.01797817 0.0015865517 + 1093 0.00758583 0.001444 -1.4845E-4 -0.08324569 -0.0830761 0.01293725 0.0014841379 + 1094 0.00849155 0.00101636 5.44E-5 -0.08321065 -0.08242526 0.01119921 0.0018937931 + 1095 0.0081559 0.00193852 4.4341E-4 -0.08283342 -0.08253679 0.00833828 0.0016365517 + 1096 0.01044786 0.00172939 1.5171E-4 -0.08410514 -0.08277018 0.00633391 0.0015962069 + 1097 0.01061531 0.00109917 -1.556E-4 -0.08388924 -0.08298165 0.00537765 0.0019762069 + 1098 0.00994707 0.0024647 -4.1038E-4 -0.0834014 -0.0825786 0.00369164 0.0022103448 + 1099 0.01076214 0.00215096 -4.8863E-4 -0.08313621 -0.08194089 0.00318824 0.0022258621 + 1100 0.01077938 0.00270941 -5.9364E-4 -0.08334281 -0.08360283 0.00208123 0.002392069 \ No newline at end of file diff --git a/spisea/filters.py b/spisea/filters.py index ece4a7ce..4f8a64e1 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -105,22 +105,16 @@ def get_decam_filt(name): t = Table.read('{0}/decam/DECam_filters.txt'.format(filters_dir), format='ascii') t['y'] = t['Y'] - cols = np.array(t.keys()) - idx = np.where(cols == name)[0][0] - - trans = t[cols[idx]] + trans = t[name] except: raise ValueError('Could not find DECAM filter {0} in {1}/decam/DECam_filters.txt'.format(name, filters_dir)) - # Limit to unmasked regions only - mask = np.ma.getmask(trans) - good = np.where(mask == False) + # Don't allow negative transmission + trans[trans<0] = 0.0 # Convert wavelengths from nm to angstroms, while eliminating masked regions - wave = t['wavelength'][good] * 10. - trans = trans[good] - wave = np.ma.filled(wave) - trans = np.ma.filled(trans) + wave = t['wavelength'] * 10. + trans = trans spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='decam_{0}'.format(name)) From c5f87c6c049e0aa1625dd028b4646447fcba9cef Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Thu, 4 Jun 2026 22:21:42 -0700 Subject: [PATCH 142/191] Figure changes for Begbie+26 --- docs/paper_examples/Begbie+26/Figure 2.ipynb | 12 +- docs/paper_examples/Begbie+26/Figure 3.ipynb | 23 +- docs/paper_examples/Begbie+26/Figure 4.ipynb | 34 +- docs/paper_examples/Begbie+26/Figure 5.ipynb | 43 +- docs/paper_examples/Begbie+26/Figure 6.ipynb | 176 +++-- docs/paper_examples/Begbie+26/Figure 7.ipynb | 200 +++-- docs/paper_examples/Begbie+26/Figure 8.ipynb | 735 +++++-------------- 7 files changed, 459 insertions(+), 764 deletions(-) diff --git a/docs/paper_examples/Begbie+26/Figure 2.ipynb b/docs/paper_examples/Begbie+26/Figure 2.ipynb index 39920ecc..5589c42f 100644 --- a/docs/paper_examples/Begbie+26/Figure 2.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 2.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "3d80653b-a8ee-422d-b008-14dc490ad520", "metadata": {}, "outputs": [], @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "7e967d89-695f-4e5a-a5b4-0dc84b6cf04e", "metadata": {}, "outputs": [], @@ -54,13 +54,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "9570acfa-4cf2-42da-ab38-32b7ea02837a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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WoWCBfkBAgDp+/Phi7WRkZJh/hs2bN7fY199//60ajcYSY/n111/VkJAQFVBfeOEFi2Xs/cSiLOvXrzf/3Lp06aLm5uYWuW76nfT19VU3btxosY3ExES1SZMmKqB27Nix2PUJEyaY47P0iVNubq55QWtZv7dlKfx7e/XvzqFDh9TVq1cX+fTlzjvvLLEt088VUJs1a6YmJSVZLBcXF2dehBwdHa3++++/xcr89NNP5neyq1evXuz7PG7cOBVQq1atWqzuAw88oALqwIEDzferq+8xb7/9tgqoXl5exeIs/PsKqEuWLCnWR3Z2ttq0aVMVCj49y8vLK/H7Uhp7PrFQVVW9++67zfV27dplfjw/P1+tUKGCCqjPPvtskTq5ubnm77vpe9OvX79ibbdo0UIF1CFDhhS7dvLkyWI/i8LOnDljvjfdfffdFssUzrno6Ohi/5eUVF7rRgy2fmKhqv9t7BEYGKimpKQUKX/LLbeogPrII4+oqqra9InF7bffXur/W2fPni1S98SJE1Z9ImE0GtXExETbvjHC4WRgITyWwWBQ77///hI/Eq1SpYo6fPhw9auvvir1hVpJ/7kZjUY1Li5OXbp0qVqjRg1zmREjRhRro6yBRXx8vNqmTZsSY23QoIH62GOPqYcOHSr1ORd+8WLNV2kvHJ966ikVCqZcXP3iIikpyfwx99VTJWxlaWChqqp5FxFvb2/1+PHjReqUNrAovIvVrFmzSux36dKl5nKvvfZaseuma15eXmW+qDGVDQoKUhMSEsp8njVr1izxBciUKVPM5ZKTk0vttyTjx49XAbVp06YWrztjYHHkyBHzi7a6desW2xktNzfXPJh78sknS21r48aN5hj++usv8+PZ2dlqxYoVzQMvg8Fgsf6ZM2fMu7Q5amBR2leDBg3U+fPnlxiPqhb93Sz8Yvdqr776qrlcaTuvzZgxw1xuxYoVRa59/vnn5mu///57kWv16tVTAXXlypVq3bp1zX8vbNCgQSqg3njjjcX6LZzHll5cm3z44YfmcmXtdFcSewcWhacZfvnll0Wu3XbbbSqgtmvXrsjje/bsUQE1NDTU/CI4LCysyJsEiYmJ5oHzu+++a9dzeuedd8z9WPp/p3DOLV68uMz2XDmwyMrKUsPCwlQouvNi4Rf633//vaqqtg0syvq6eqrU999/rznXhH5kKpTwWF5eXixYsIBNmzbRq1cvvLyKpvOlS5f44osvGDhwIDfddBMnTpwos806deoUOXk0KiqKu+++m7NnzwIFC7g/+ugjm2OtXLkyP/zwAx988IHFXaGOHz/O3Llzad26Nffcc0+xRcCOZjAYWLZsGVCw8O7qE7rDw8Pp27cvAMuWLbP7UMDSTJs2DW9vbwwGg0170pv2dVcUpci0nKsNHTqUsLCwInUsueWWW6xeCNmrV68SFykW/rkOGTKkxF1cWrRoYf67NdPAkpKSOHHiBL/99htHjx7l6NGj5p/XsWPHyMvLsyp2LS5fvsyAAQNIS0ujQoUKfPXVV0RERBQpU3j3s7J2iOncubP573v37jX//dChQ+YTrkePHl3sd9qkRo0a9O7d267nYo/jx4+zcOFC80FlpalZsyadOnUq8bopF8PDw7njjjtKLPfggw8Wq2NSePpS4ek8586d4++//0ZRFLp06WIuV7iMqqrs2rUL+G/qUElMGzlY0rp1a/Pf//nnn1LbcbSQkBDz39PS0opcMz3nQ4cOkZ6ebn7c9D3o1KkTHTp0IDAwkJSUFA4fPmwus2vXLvOU2rK+NwCpqamcPHmyyO9mUFBQkWsl8fPzY+jQoWX24UoBAQHceeedQNHpUEuXLgXg+uuvp0OHDk6Po1q1aua/WzrVW7gXGVgIj3fbbbexdetWLl++zPr165k6dSr9+/c3v6gEOHjwIJ06dbJqN5qr+fr60rZtW2JjY9m9ezehoaF2xenr68sjjzzCL7/8wunTp/nss8946qmn6NSpU5EXoUuXLmXgwIGlvpivXbs2asEnjqV+Xb3rj8nWrVvN3wvTblBXMz1+4cKFUl+Y26thw4aMGjUKKNh5pKw5ySZHjx4FCnbhKe0QJT8/P/P6FlMdS8ra/rew0ubwFh6cWVvu6hdFJqa1DNWqVaNSpUrUq1ePpk2b0qxZM5o1a2b+uRqNRvMLcWfJzc1lyJAhnDp1Ci8vL5YvX15s7jkU/I6ZtG/fvtTdpAq/MCw8R77wTl5X71Z0tZtuuknL0yrm6t8dg8HApUuXWL16NS1atOCHH36gZ8+erF27ttR2ysonUy62atWq1C1Eq1SpYh7wXp2/VapU4YYbbgCKDhpMf2/cuDGRkZEWBxa//vqrVesrAPNaBksKD7BLymNnKdzf1ffjktZZmL4HXbt2xc/Pz7xOxNL3r1KlSjRr1sxi36dPn+Z///sf1113HWFhYdStW7fI7+bDDz9sLlva7m3169cnICCg7CfrYvfeey9Q8L0x7eJnGliU9H9HWUaPHl3q/1tXDx7q1KljHqy//fbbNGnShClTpvDtt9/qvjOZKJsMLES5UbFiRfr378+0adNYv349ly5dYuHChVSsWBEoeIH84osvltrGli1bzIvjfvvtN06fPk1aWho//vgjjz76qHkPda1q1arFqFGjeOONN9i1axcXL15k0qRJ5ndov/32W5YvX+6QviwxLcoODw8vceFd4U8ynLGIG2Dq1Kn4+PhgNBpLHARdzfSiqEqVKmWWrVq1apE6lpjywxqmdyMtKfzuurXlLA0eFyxYwI033sgnn3xi1T79WVlZZZbRYty4cezevRuA2bNnl5gv9i4kL/zCoPAgqayTd635+Wth+sRyyJAh7NmzhwYNGpgXVJc2mCsrnxyVv6ZBgWk7Uij64hmgW7duQMEnW/Hx8UXKeHl5lfrJCmjLY2cq/IL96k8Q27RpYx64mp5rXl6e+dMm0/fG0qDL9PfOnTtb3B5406ZNNG7cmLlz51q15Xhpv5u23HdcqVOnTlx33XWoqspnn33G3r17+euvvwD7Bxb2WL58uXkweOzYMV5++WV69OhBeHg4Xbp04cMPP7T65G/hXDKwEOWWv78/Y8aMKfICfc2aNaXuHtWgQQOaNm1K06ZNady4MbVq1cLf39/psVaqVImZM2cWOYjLWfv1p6am8uWXXwIF+4Kb9ti/+isgIIDk5GQA1q1b55R3Ja+//nrzO2IrV64ssvNWWSz9x381VVXLLOPt7W11n872xx9/8Mgjj5Cfn09UVBSvv/46hw4dIiEhgdzcXPM7egsWLDDXseY52uutt95i4cKFQMG7jM8880yJZQu/uNyxY0eZu0uZvsaNG2fxuZT183Xm875aSEiIOc7U1FRWrVpVYllr80lr/premb948SJ//PEH8N8gw/SiuUaNGtStWxdVVYuchwAFn6x4yovbqxWevnT1p4M+Pj7m6TmmgcKBAwfIzMwkNDTU/Cmm6Xu0e/duDAYDycnJ5vuPpU9yEhISGDVqFJmZmYSEhDBt2jT27t1LXFwcOTk55t/N7du3m+uU9vNzp/tOaRRFMU+JW7JkiXlKVPv27XU9l6l69er88MMPfPPNNzz66KM0adIERVHIy8tj165djBs3jqZNm3L8+HHdYhKWycBClHu33norNWvWBAreEU1ISHBxRCV76KGHzH//+++/ndLHihUrbH6XOzMzs9QXU1q8+OKL+Pr6oqoqU6dOLbO86R1Ka97NNx3wp8fhTY7w6aefkp+fj7e3Nzt27ODpp5/mxhtvpFKlSkWmzTh7+hMUvDtrGui2b9++zLVFhddc+Pn5mQfoZX0V/mSi8M+prMMZ9T4Iq/C0oLIOXyyNo/L36nUW58+f56+//jKvr7i63I4dO2xaX+Guzpw5Yz4Y8IYbbiAyMrJYmavXWRReX2F6Qd+uXTsCAwNJTU3l8OHDZa6vWLlypfmNljVr1jB16lRuvvlmIiMj8fPzM5fT43dTb6Y3f44dO8Ynn3wC/HeAnt569OhBbGwsR48eJT4+ns8//5zu3bsDcOLECYYPH+6SuMR/ZGAhrgmFz50oaUGoO9AjTtO0pmrVqrF8+fIyv0znWzhrOtR1113HAw88ABR8MvLTTz+VWr5p06ZAwanbpb24zMvLM7+zaarj7n777TegYIG36awCSwqvZ3CG33//nREjRmAwGKhZsyZr164t85O7wue12LvHfuF57QcOHCi1bFnXHa3w6cNaFsybcvHw4cOlthMXF2eebmMpf6tVq0b9+vWBgkHD1esrTAoPLI4cOWJ+Y6Ws9RXu6t133zUPAEzn4Vzt6nUWV08RA4qtszCVqVixosV1MqbfzUqVKtGrV68S43P276YrNGjQwLymKTs7Gz8/P7d4AR8REcHw4cPZvn27+XyZn3/+2TxVS7iG+77CEsJBMjMzOXbsGFCw0E/vd69tmbJR+D+lOnXqODyWkydPmhc03nHHHYwYMaLML9POJTt37uTff/91eEwAzz//vPmF65QpU0ot27NnT6Dg+2qapmPJqlWrSElJKVLH3ZlevJa2IPHixYvmqWzOkJCQwIABA0hNTSU4OJivvvrKqvUAHTt2NP9uffjhh6Smptrcd+vWrc3Tc5YsWVLi7865c+ecfkDY1QoPZEyfgNrDlIvJycmsXr26xHILFiwwP/+S8rfwOgtLL56h6DoL0/RKRVHKXF/hjvbs2cM777wDFOxYNH78eIvl2rZtS3BwMADbtm0rtr7CpPCgq/CnGpbe1DH9bubk5JQ4nTYzM9Npb8C42ujRo/H398ff35+BAwe63afAPXr0MP+9tEXzwvlkYCE8Unp6Ou3atWPDhg2lrpkwGo3873//M68PGDhwoFVzmx1p06ZNDBs2rMi8YEsSExN5/PHHzf++/fbbHR5L4Rdrpm0Ey2Iqp6qqxRNYHaFGjRrm3VS+/vpr87uDlgwePNj8yc7MmTP55ZdfipU5c+YMTz/9NFCwAHXMmDFOiNrxTO9AHz9+nH379hW7bjrV11kLtvPy8rjzzjs5ceIEiqKwePFiWrZsaVXdgIAA8/f84sWLjBgxotRtk9PS0pg7d26Rx0zroqDgncfXX3+9WL38/HweeughcnNzrXxW2p0+fbrISd+mrZjtMWbMGPOi6Keeesq8005hv/zyCzNnzgQK5pYPGjTIYluF11msWLECKP7iufA6i/feew8o+GTo6u2C3ZnRaOTTTz/l1ltvNb/AnzNnTokDXl9fX/OnEQsWLCAjI6PI+gqTwgMz032kpE9yTL+bGRkZFqeFGgwGHnzwQc6fP2/z8/MEjz76KNnZ2WRnZztt/V9Jfv75Z37++ecSr6uqWmQbcmu3DxfO4ZgtboRwgR9//JEBAwaY/+Nt3749tWvXpkKFCiQnJ3P48GEWLlxong8dFhbGyy+/rHucRqORlStXsnLlSlq0aEG/fv1o27Yt1apVw8/Pj7i4OPbs2cO8efPMU3tat27N6NGjS2wzLy+v1C1UC6tXr555W0PTwCAqKsrqdyzbtWtHjRo1OHv2LEuWLOH555+3qp6tJk+ezPz588nKyir1HSdfX1/mzZtnPlehY8eOPPPMM/To0QMfHx9++OEHZs+ebf5evvHGG1SuXNkpMTvaPffcw3vvvYfRaKRv375MnDiRDh06EBAQwKFDh3j77bf566+/uOWWW/j+++8d3v/LL79sfuf27rvvpkGDBqXmWcWKFalevbr53xMnTmT79u1s377dvIPOI488Qvv27QkPDyctLY0///yTHTt2sG7dOgICAnjssceKtDllyhRWrFjB2bNnefbZZ/n555+59957iYqK4vjx47z11lscOHCAtm3bOnQ61NXP02g0kpCQwO7du3n33XfNU4juuusuqwdblkRGRvL6668TExPD+fPnadOmDc899xwdOnTAYDDwzTff8Prrr5Oeno6iKMybN6/EbWkLvwhOSUkptr6icLl//vnH/AmeO66vOH78uPncCaPRSEpKChcvXmT//v2sXbvWfFaGl5cXU6dOLbKtqyVdu3blm2++MT/nwusrTEzrLAqfd1HS92bYsGFMnjzZvDPYzz//TM+ePQkNDeW3337jvffe49ChQ0773byW/fzzz4wZM4a2bdsyYMAAbrzxRqpWrUpeXh4nT57kk08+Ydu2bUDBG3KFz70QLuCkg/eEcKqsrCy1atWqVp/kWb9+ffXgwYMW27L39NfCSjt5e8+ePWpwcLDVsfbq1avYqcYmtp68DaiHDx82x2F6bOzYsTY9v8cff9xcd9++fTbVLenkbUtMp4GbviydvG3y6aefmk8Ht/Tl7e2tzpw5s8T6pnLWnD5tTdnCJ8taOnnWpPAJtZa+H9OnTy/15/nUU08V+Z5ayll7T94u/LtgzdfVJ+SqqqpmZmaq9957r1X169SpYzG+o0ePlvr7PWbMmDK/B9aw9uRt09fw4cPV7Oxsi22ZfjctfU8seeWVV8ynPFv68vf3VxctWlRmO9dff725TpMmTSyWWbx4cZG2V69eXWJ71n5frc330tiabzfddJO6Y8cOq9revXt3kbqvv/66xXLdu3c3lwkLCyv1dPWFCxeW+jMbPny4+s0335T6+23rSdquPHnbFracvG3t74il2Ev76tixo5qQkGBz7MKxZCqU8EgBAQGcO3eO77//nunTp9OnTx/q1q1LcHAw3t7ehIaG0rBhQ4YPH86yZcs4evRokZNi9XTLLbcQHx/PV199xYQJE+jSpQvR0dH4+/vj4+NDpUqVuPHGGxk7dizfffcdW7dudco0hcJzf0s78deSwuWdOYf42WefNc+NLsvo0aP5448/eOKJJ2jUqBHBwcEEBgZy/fXX89BDD3H48GEmTZrktFidZcqUKXz99df07t2bihUr4ufnR40aNRgyZAhbt27ljTfecHWIpQoMDGTRokUcPHiQcePG0aRJE8LCwvDx8SE8PJyWLVvywAMPsGrVqhIPRWzSpAm//fYbEydOpH79+vj7+1O5cmW6devGsmXLSl1b4yiKolChQgUaN27MAw88wM6dO/n8888dtv305MmTOXz4MA899BDXX389gYGBBAcH06hRI5544gn++OMP8248pSn8qUVJ03hM6yyg4HkVPvncHfn5+REZGUn9+vW5/fbbmTFjBocPH2b//v1Wf9py0003FTmHw5rvTUnrK0zGjBnD7t27GTRoEJGRkfj6+lKtWjVuu+02vvjiCz7//HOP2UbWk4waNYrvvvuOyZMn06lTJ+rUqUNQUJD53jhw4ECWLVvGzp073W7tx7VIUVUdNwMXQgghhBBClEvyiYUQQgghhBBCMxlYCCGEEEIIITSTgYUQQgghhBBCMxlYCCGEEEIIITSTgYUQQgghhBBCMxlYCCGEEEIIITSTk7edyGg0cv78eSpUqICiKK4ORwghhBBCCJuoqkpaWhrR0dGlnvUCMrBwqvPnz1OzZk1XhyGEEEIIIYQmZ86coUaNGqWWkYGFE1WoUAEo+EGEhoYWuWY0GomPjycyMrLE0V9ZZUq7npyZw7afjuMTGI6/r2edBKqqKmSnQkCo0z/pcWRfWtqyta4t5a0pm5NnID8rmV43NiA8qOjJwlry0J3pGbcj+9LSlj11ra3j7Hua5Jn+femZa5Jn2ugdt6fe0yTP7JOamkrNmjXNr2tLIwMLJzK9iAsNDbU4sMjOziY0NLTUpC2tTGnXjT45BAWHEBQaRrC/r4OekT5UVcWQoeAdHK7LwMJRfWlpy9a6tpS3pmxGTh6ZXvkFuWphYGFvHrozPeN2ZF9a2rKnrrV1nH5PkzzTvS89c03yTBu94/bUe5rkmTbWvD7xnGcjhBBCCCGEcFvyiYUOjEYjRqOx2GOqqhZ73JYypV1XjUZQVVDVgqkwHkS9ErMecTuyLy1t2VrXlvJWlTXlih25ak0uuyM943ZkX1rasqeutXWcfU+TPNO/Lz1zTfJMG73j9tR7muSZfWyJUwYWThAbG0tsbCwGgwGA+Ph4srOzi5QxGo2kpKSgqmqpH7OVVqa06+k5eXjnZ0FWCoZ8z/oxq6gYczJAAQUnT4VyYF9a2rK1ri3lrSqbl493fhYJCZfJSS86dU5LHrozPeN2ZF9a2rKnrrV1nH1PkzzTvy89c03yTBu94/bUe5rkmX3S0tKsLutZrzg9RExMDDExMaSmphIWFkZkZKTFNRaKopS5MKi0MqVd98/MwXAyAQLD8PbANRao4B2kzxoLR/WlpS1b69pS3qqyOXkY8vKJiKhMmIU1FvbmoTvTM25H9qWlLXvqWlvH2fc0yTP9+9Iz1yTPtNE7bk+9p0me2ScgIMDqsjKw0IGXl5fFxFEUpcRr1pYp6bri5QWKAorikWdoKFfi1iN2R/alpS1b69pSvsyyplyxM1etyWV3pGfcjuxLS1v21LW2jjPvafbG7g48Nc+0tmdrXckzbfSO21PvaZJntrMlRvd/NkIIIYQQQgi3JwMLIYQQQgghhGYysBBCCCGEEEJoJgMLIYQQQgghhGayeFsHRjvOBrCmTGnX5RwL/fuScyw8Yz9uE089X0DPPd9tqSP7vlvmqXmmtT05X0Bfesftqfc0yTP72BKnDCycQM6x0EbOsZBzLPTgqecL6Lnnuy11ZN93yzw1z7S2J+cL6EvvuD31niZ5Zh85x8LF5BwLbeQcCznHQg+eer6Annu+21JH9n23zFPzTGt7cr6AvvSO21PvaZJn9pFzLNyMlj2M5RwLOcfCEeXlHAvLPPV8ATnHQvJMr77kfAHPIedYSJ45iy0xuv+zEUIIIYQQQrg9GVgIIYQQQgghNJOBhRBCCCGEEEIzGVgIIYQQQgghNJPF2+WQqqos+HgeFzOhZr1GXHddHYKCQ1wdlhBCCCGEKMdkYFEOJSYm8vT4x4s8ViGsIlWia1Clek2qVCv4s2r1mkRVq0HV6jUJCQ1zUbRCCCGEEKI8kIFFOXTq1Klij6WlJJGWksTfvx+xWCcopAJVo2sSFV0w0IiKrlHk36HhlTxy21ohhBBCCKEPGVjowGg0FjsO3ZnHxVevXp333v+Q7/YfJikhgcRL57l4/gzxF89jvHIa+NUy09P45/gx/jl+zOL1gMCggk88Cg8+rnzyUSW6BhUjIh028FBV1fzlbI7sS0tbtta1pbxVZVUVVBXVjly1JpfdkZ5xO7IvLW3ZU9faOs68p9kbuzvw1DzT2p6tdSXPtNE7bk+9p0me2ceWOGVg4QSxsbHExsZiuPIiPj4+nuzs7CJlnH1c/G19+xJVvzn+gRXw9y34MRvy80m4HMelC+eIu3C+4M+L583/jrt4gfz8PIuxZGdlcvrEcU6fOG7xup+/P5FVqlGlWnWqVKtOVLXogj+rFvxZqbL1p0uqqBhzMkABBSefvO3AvrS0ZWtdW8pbVTYvH+/8LBISLpOTXvSkdi156M70jNuRfWlpy5661tZx9j1N8kz/vvTMNckzbfSO21PvaZJn9klLS7O6rAwsnCAmJoaYmBhSU1MJCwsjMjKS0NDQImWcfVy8f2YOhpMJEBiGt3/BC0VvoFpYZapd37jE/hLjL3Hp/FkunT9j/jPu/Fkunj9D3Plz5OZkW6ybm5PDuX9Pce7fUxav+/j4ElWtOlFXPvWoUr2Gea1HlegaVI6qhrdPQTqqqgoqeAeFO336lSP70tKWrXVtKW9V2Zw8DHn5RERUJizIv8glLXnozvSM25F9aWnLnrrW1nH2PU3yTP++9Mw1yTNt9I7bU+9pkmf2CQgIsLqsDCx0oOU4d3uPi1e8vEBRQFGsfpHr7e1NZNVoIqtG0/TGm4pdV1WV5ITLVwYZZ7h47qrBx7kzZGdlWmw7Pz+P82dOcf7MKYvXvby9Cz7xuDLFKioykqq161G1ei2qVq9J5SrV8PXzs+p52Eq58j1yxCBGS1u21rWlfJllTbliZ65ak8vuSM+4HdmXlrbsqWttHWfe0+yN3R14ap5pbc/WupJn2ugdt6fe0yTPbGdLjDKwEFZTFIWKlSOpWDmSRs1vLHZdVVXSUpIKBhyFBh2Xzp/l0rkzXDx/hsx0yx+nGQ2GK+XPlth35SrVzLtYXb24PKpadfz8rR9RCyGEEEIIx5KBhXAYRVEIDa9EaHglGjRpYbFMemqKecBx8VzhaVYFn3ikpSRZrKeqKvEXzxN/8Ty/Hf7RYplKlaPMU6uqRNcssr1uVHQNAoOCHfZchRBCCCFEUTKwELoKCQ0jJDSM6xs2sXg9MyOdi+fOcPGfP4hPTCryicelC2dJTrhcYtuJl+NIvBzH778csng9rGKlK+s7ahbZ4SqyYjjVrm9ESIVQi/WEEEIIIUTZZGAh3EpQcAh16jekVnRVvIOLLzbOzsok7sI589Qq0ycdcRcK/kyMv1Ri2ylJiaQkJXL8t18sXq8QGl5kK92q1YsOQkJCw+QsDyGEEEKIEsjAQniUgMAgatWtT6269S1ez83JJu7i+f8GHFemWpnWfCTEXShxP+a01GTSUpM58cdRi9eDgkOKnFx+9ZSr0PBKDnueQgghhBCeRgYWolzx8w+gRu261Khd1+L1/Lw84i+dNw80Lp47w6V//yEu7lLB7lalHSKYkc7J479z8vjvFq/7BwYSVSWaqjVrFxp01CxyiKAn7P4ghBBCCGEPGViIa4qPry/VatSmWo3aQMGicENGsnnalSE/n4S4i1y6cNbi4vK482dLPEQwJyuLM6dOcObUCYvXff38iapWnSrVaxbZ0cq0uDwsyB9vpz1zIYQQQgjnkoGFEIV4+/gQFV3wQr9Z65uLXTcajSRejrO4nW7B38+Qm5Njse283BzOnf6Hc6f/sdy3t0/BwCP6qqlWV/4eWSXafIigEEIIIYS7kVcpQtjAy8uLylFVqRxVlSat2ha5pqoq+elJpGXnE3fhv0GHaXH5pXNnSj1E0GDI58LZ01w4e9py397eVI6qal7nEWk6RLBGLapG1ySyarTTDhEUQgghhCiLDCyEcCDTIYKVIqNoWNYhgoW20b107gyXzp7m0sXzZKSlWmzbaDAQd+EccRfOcaSEviOiqlo4RLA6kRXDqVq3IQGBQQ5+xkIIIYQQBWRgUYquXbuyb98+fK5MP7npppv49ttvXRyV8GQlHSJYeK1HRlqqhWlW//07NTnRYtuqqnL50gUuX7rAsZ8PWCxTMSKy0FSr/3a0CousRsWK4c54ykIIIYS4RsjAogzz58/n7rvvdnUYtlFVQjp2oGOFiiS360zGzR1Jb9AIZEcij1DWIYJZGRlcPH+GCyd+Jz4xqegC8/NnSj1EMCkhnqSEeP44crjYtciq0aS9/DIPjRmNt7csIxdCCCGEbWRgUR6dPo3P4Z+oCdTctR2AvNAwkm9sR1Lb9iS3vpm0Rs1QZSGwRwoMDua6ejdQs1qV0g8RvPIpx8Wz/3LpzEniLl3i0oWzJMRdtNhu/MXzjHvoAd5/9x1mzpxJv3795EBAIYQQQlit3LyyTEtL4+WXX+bnn3/m8OHDXL58malTpzJt2rRiZdPT03nhhRdYsWIFiYmJNGzYkOeee44RI0YUK/vkk0/y5JNP0rx5c958801atmzp/Cej1YkTqGFhKCkp5od8U1OI3LGVyB1bAcgPCia51U0kt21PUpv2pDZpgSoLf8uFqw8RvHpL3dzcHOIvnC8y1erXQ/s5cvAHAI4cOcKAAQPo2LEjs2fPpn379q58OkIIIYTwEOVmYJGQkMC8efNo0aIFgwYNYv78+SWWHTJkCAcOHGD27Nk0aNCAZcuWMXLkSIxGI6NGjTKXe+2112jcuDHe3t588MEH3Hbbbfz555+EhYXp8ZTs16MHKWfOs/eL9dT48w+q/Pwj4Qf34Vdobr5PZgaVv/+Oyt9/B4AhIJCUFq1JatOe5DY3k9L8RowBga56BsKJ/Pz8qV67DtVr1zE/lpGTx4Edm9j02Tx+OnQQgD179tCxY0f69+/PhAkTiIqKclXIQgghhPAA5WZgUbt2bZKSklAUhcuXL5c4sNi4cSPbtm0zDyYAunXrxunTp3nmmWcYPny4eX75TTfdZK43YcIEFi5cyA8//ECfPn0stp2Tk0NOoTMMUlMLdvcxGo0YjcYiZY1GI6qqFnvcljKlXVcVheR6N5B7Ywcu3jcWjEaC//mLigf3UfHgXioe3Iv/5Thzee/sLCrt30Ol/XsK2vb1I6V5K5Ja30xSm/aktGyDISi4xFgdSVVV85cn9aWlLVvr2lLeqrKqSpNWbZnw4F18u2UjL7zwAn/++ScAGzZs4Ouvv2bUqFG89NJLXHfddUWqWpPL7kjPuB3Zl5a27KlrbR1n39Mkz/TvS89ckzzTRu+4PfWeJnlmH1viLDcDC2vngq9du5aQkBCGDh1a5PExY8YwatQo9u/fT4cOHSzW9fLyKvXF2axZs5g+fXqxx+Pj48nOzi7ymNFoJCUlBVVV8SphUXVZZUq7np6Th3d+FmSlYMgv+DGnVqtC6oDbOT3gdlBVgs6cptLhA1T86QCVDh8k8NKF/55rXi4VD+2n4qH9MG8ORm8fUhs2JvHGtiS1bENSy9bkh1Qo8XuhhYqKMScDFFBw7hx/R/alpS1b69pS3qqyefl452eRmJhAx44d+eabb1ixYgVvvPEGFy5cQFVVPvvsM1asWMG9997L+PHjqVy5MmBdLrsjPeN2ZF9a2rKnrrV1nH1PkzzTvy89c03yTBu94/bUe5rkmX3S0tKsLltuBhbWOnr0KI0aNTJvIWvSvHlz8/UOHTqQnJzMgQMH6Ny5M4qi8OGHH3Lx4sVS55tPmjSJCRMmmP+dmppKzZo1iYyMJDQ0tEhZo9GIoihERkaWmrSllSntun9mDoaTCRAYhre/r8X2cxpV5EKjllwY9RAAAefOXPk0o+BTjaAzp8xlvQz5hP/2K+G//QpLFqB6eZHWsIn5E43k1u3IC69U4vfGFqqqggreQcUXJjuaI/vS0patdW0pb1XZnDwMeflERFQmLMgfgPHjxzN27FhiY2OZNWsWycnJ5OXlsWDBAr744gsmTJjAk08+SUhISJm57I6s+R10x760tGVPXWvrOPuepufPy5E8Nc+0tmdrXckzbfSO21PvaZJn9gkICLC67DU3sEhISKBu3brFHq9UqZL5OkBeXh6TJk3ijz/+wM/PjxYtWrBx40YqVqxYYtv+/v74+/s7J3AdZFevyYXqNblw+zAA/C9doOKhfVQ8sJfwQ/sI+ecvc1nFaCT02BFCjx2h9pKPAUir3/DKIONmktrcTG5lmZPv6QIDA5kwYQIDBw5k0aJFvPvuu2RmZpKens5LL73E+++/z3PPPccdd9zh6lCFEEII4WLX3MACSp82ZboWGRnJwYMH7Wo/NjaW2NhYDAYD4D5ToWyVGRJIZpdunOvSDQC/xAQq/nzIPH2qwonjKIWmhlX46w8q/PUHtZZ/UhBD7ToktWxD4o1tSWzVlpwqVa3qV6ZCuW4qVELCZXLSi37CZZoL+r///Y8RI0bw9ttv89lnn5Gfn8/ly5d5+umnefPNN5k4cSJDhw71mDMwPHWKikyF8oypAyaemmda25MpKvqSqVCSZ84kU6FKERERYf5UorDExIIdk0yfXGgRExNDTEwMqamphIWFue1UKFsZgsO5XPN6Lg8o+ETDJyWJij/9SMWDewk/uI/Q34+gFFrgE3L6JCGnT1Lzy5UAZNaoVegTjfZk1agFFgZ5MhXKPaZCmRTOs6pVq7Jw4UImT57M1KlT+fzzzwG4cOECTz75JPPnz2fGjBkMGDDA7c/A8NQpKjIVyjOmDph4ap5pbU+mqOhLpkJJnjmTTIUqRbNmzVi+fDn5+flF1lkcOXIEgKZNmzq8Ty8vL4uJoyhKidesLVPSdcXLq+BFu6I47QWeIbwSl7vfxuXutwHgnZ5G+OGCrW0rHtxL6NGf8crPN5cPOvsvQWf/pfq6LwDIrhpNUuubSW7TnqS27cm87nrzQEO5ErceL04d2ZeWtmyta0v5MsuacsXKXG3QoAHLly9n4sSJTJo0iS1btgDw22+/MXjwYDp06MDs2bPp1KmTVc/FVaz5HXTHvrS0ZU9da+s4855mb+zuwFPzTGt7ttaVPNNG77g99Z4meWY7W2K85gYWgwcP5uOPP2b16tUMHz7c/PiiRYuIjo6mXbt2Du/TaHTBdrNGI6gq6LRtK0B+cAiXO3bncsfuAHhlZhL+6yHzJxphv/6Ed+5/2/EGXDxPta/XUO3rNQDkRESS1OZmkm5sx+UmTclq1gacPK1Gtps1FyrIFRtztUWLFmzYsIEvv/yS1157jR9//BGAH374gc6dO9OnTx9mzpxp3hzBnVjzO+iOfWlpy5661tZx9j1Nz5+XI3lqnmltz9a6kmfa6B23p97TJM/sY0uc5WpgsWnTJjIyMsxzwY4dO8aqVasA6Nu3L0FBQfTp04devXoxbtw4UlNTqVevHsuXL2fz5s0sXbrUIfPDy8saC60MQHyzZsQ3awZjHsYrJ4ewY79S8fBBKh0+QPivP+OTnWUu758QT9Ut66m6ZT0AuaFhV9ZotCGpVVtS6zd0+EBD1lhcUcYai7LysHHjxqxZs4atW7cya9Ys/vqrYKH/pk2b2Lx5M4MHD2bixInUrl27zOemF0+d+67nfGRb6sicZMs8Nc+0tidz3/Wld9yeek+TPLOPLWssFFWvt7N1cN1113H69GmL106ePGk+2Cs9PZ3nn3+eFStWkJiYSMOGDZk0aRIjRoxwaDymNRZJSUkW11jEx8eXOX+vtDKlXU/JzGHzwT8JCo0gyEFrLBxNycsj9Niv5k80wg//iG96ycmbF1KB5FY3kdS2YJ1GauPmqL7anpuqqhgykvEOdswaC3vbsrWuLeWtKZuZk0dmagK3tbnB4hoLW/LQYDCwePFipk+fzpkzZ8zlfH19efjhh3n++eepUqVKmc/R2az5HXTHvrS0ZU9da+s4+56m58/LkTw1z7S2Z2tdyTNt9I7bU+9pkmf2SU1NpWLFiqSkpBR7PXu1cvWJxalTp6wqFxISwpw5c5gzZ45zAxKlUn19SWnRmpQWreGBx8BgoMKfvxF+YC8V9++i4i+H8UtJMpf3TU8jcvd2IndvB8AQGEhyi7Yktb1yOnizVqh+nrvdb3nh7e3NmDFjGDlyJB988AGzZs0iISGBvLw8YmNj+fTTT3nyySeZMGECYWFhrg5XCCGEEA5SrgYW7kKmQtkvuXYtkmrX5ET/fnj5BVLhnxMF29sePkilnw7gn/Tfjl7eWVlE7NtFxL5dABj8/Ehp2oLEVm1JbNWG5GYtMQYEltqfTIW6QuNUqJKu33XXXQwYMIAPP/yQjz76iMzMTDIyMpgxYwaxsbE8/vjj3HfffTbtOOEonjpFRaZCecbUARNPzTOt7ckUFX3JVCjJM2e6ZqdCuRuZCmWfEqfuqCpBp05cOR284ITwgEsXSmzH6ONLatOWJLVuVzB9qmVbDCEVrOvLkXE7oa47T4UqyaVLl3jllVeYN28eeXl55sdr1qzJ1KlTueeee4rs1OZsnjpFRaZCecbUARNPzTOt7ckUFX3JVCjJM2e6ZqdCuSsvr/K53awzWdweVVHIqlufrLr1OT/sXlBVAs/+S/jBH6h4cB/hB/cSdPZfc3Gv/DzCfz5A+M8HqLNgLqqXF6mNmpHctj1JrduT3LodeaFhst1sQQGbtpu19TpAtWrVmDt3LhMmTGDKlCksW7YMVVU5c+YMDz74IG+++SYzZ87k9ttv1y1nPXUbUNlu1v3/Iy7MU/NMa3uyDai+ZLtZyTNnsSVGGVgIz6UoZNWsTVbN2lwYPBIA/wvnqHhoX8E6jUP7CD7593/FjUbCfvuFsN9+ofanH6IqCukNGpPYohXJ7buQ3KY9eZUqu+rZXDPq1q3L0qVLeeaZZ5g8eTIbN24E4Pfff2fw4MG0a9eO2bNn07VrV9cGKoQQQgibyMBCB0bjtXGOhaNoOQ8iu2o0F/oN4UK/IQD4XY4r+DTjUMGhfRX++sNcVlFVKvz5GxX+/I3aK5YCkF63fsHp4G0KFoTnRFXVJW5PPcfCmusladasGevXr2fXrl1MnjyZvXv3ArB//366devGrbfeyiuvvEKrVq1satda9sbt6r60tGVPXWvrOPuepufPy5E8Nc+0tmdrXckzbfSO21PvaZJn9rElThlYOIEs3tbGkQuqswL9yOrUmfOdOgPgm5JExZ8PUemnggXhoX/9gVLoFybkn78I+ecvaq5YDEBGjVoktWpD4o1tSWzVluxq1Z0Sd3ldvG2Nhg0bsnr1avMZGH/++ScAW7ZsYcuWLQwaNIiJEydSp04dm9sujacuqtVzoaMtdWSxo2Wemmda25NFtfrSO25PvadJntnHlsXbnvWK00PExMQQExNjXrwdGRlpcfG2oihlLgwqrUxp1/0zczCcTIDAMLw9cPE2KngHaV9QfTVjcDgJ0XVI6HsnAN6pKYTu/Y6Io0eoeGgfob/9gteVASFA8Nl/CT77LzXWF5wOnlWtOklt2ps/1cisVadgfYLGuG2ta0t5q8rm5GHIyyciorLFxdv25qEt7rnnHkaNGsXSpUuZNm0a//5bsF5m3bp1bNiwgQcffJAXXniBatWq2d2HM+LWuy8tbdlT19o6zr6n6fnzciRPzTOt7dlaV/JMG73j9tR7muSZfWzZuVEGFjrQsnBHFm87N3ZDaBiXO3Yl6dZBKIqCd0YGYb8cpOKBvYQf2kfYrz/hlZdrLh944RyB61cRvb7gRPecyCpXBho3k9TmZlKrRMnibQ28vLwYM2YMo0aN4sMPP2TGjBlcvnyZ/Px8PvzwQxYvXsz48eN55plnCA8P19SXI+PWuy9ZvO3+/xEX5ql5prU9WVSrL1m8LXnmLLbEKAMLIQoxBAeT2KELiR26AOCVnUXYrz8RfrBgjUbYL4fwzs4yl/ePv0TVTeuoumkdALnhFQs+zWjbgaTWN5N+Q2PwgJuGu/H39+eJJ55gzJgxvPXWW7z55pukp6eTmZnJzJkz+eCDD5g8eTIxMTEEBpZ+VokQQggh9CEDCx0Y7VgQa02Z0q5fq4u3Hd2XwT+AxLYdSGzbAQAlL5fQo78UnKNxaB/hP/2IT2aGubxfchJVvtlIlW8KdjrKCw0judVN5ulTaY2aolo4q+FaXLxtjZCQEKZMmcIjjzzCzJkz+fDDD8nLyyMpKYlnnnmGd955hylTpnDffffZfAaGpy6q1XOhoy11ZLGjZZ6aZ1rbk0W1+tI7bk+9p0me2ceWOGVg4QSyeFsbRy7edkZfiQ3qk9igPoy6FyU/nwrHf6fS4YNUPHyASocP4pv+3yIn39QUInduI3LnNgDyg4JIan4jSVdOB09p3BTV1++aXrxtrcmTJ3PPPffw+uuvs2rVKlRV5dy5c4wdO5bXXnuN5557jn79+lk9lcxTF9XqudDRljqy2NEyT80zre3Jolp96R23p97TJM/sI4u3XUwWb2vjzMXbzugro20nMtp24l9VxZCaQNiFi1Q6uI+Khwq+/BITzGV9MjOJ3LeHyH17ADAEBJDcog1JrduR0KQZGS3bYQgNMy8Id0TcnrJ42xpRUVF8/vnnHDlyhBdeeIENGzYAcOLECR566CHatm3LzJkz6d69e5lteeqiWj0XOtpSRxY7Wuapeaa1PVlUqy+94/bUe5rkmX1k8bab0bJwRxZvOz92R/al+PiQ0bApmY2acfaeh0BVCT5xvOAcjQN7qXhwL/7xl8zlvbOzidi/h4j9e6h35TFDQCA5kVXIqVKVnMiq5ERVISeqKjlR1Qr+HlmV7MgojOVw8ba1WrRowfr169mzZw/PPfcc33//PQAHDhygV69e9OrVi1mzZtG6detS2/HURbWyeNv9/yMuzFPzTGt7sqhWX7J4W/LMWWyJUQYWQjiTopBR7wYy6t3AueGjQVUJ/PcUFQ/uJfxgwUAj8PzZIlW8s7MIOnOKoDOnSm06r0LolQFH1SsDkWoFf0ZVNf89t3KUxTUd5UXHjh3ZvXs3X3/9NZMmTeLo0aMAbNu2jW3btjFs2DBefvllGjRo4OJIhRBCiPKv/L7iEMIdKQpZteuQVbsO5+8YBUDA+TOEH9hL2I97CLp0Ef/4S/jHXcQ3NaXUpnzTUvFNSyXkxPESy6iKQm7FCLIjI8mtUp2cqKrkmj71qFKV3KiCT0UygkNLbMPdKYpC//796dOnD8uXL+fFF1/k1KlTAKxYsYLVq1fz4IMPMmXKFKKjo10brBBCCFGOycBCB0Y7dtqxpkxp12VXKP37sretrGo1yBxwJ2e698Q7+L91EF5ZmfjHx+EffxH/SxcL/oy7REDcRfziLuJ/6TwBl+PwvmpjgMIUVcU/8TL+iZfhz99LLGf09iErojL+tWqi1qgO1aujRkdDtWqo1arhHRiI0ccHKlYstv7DXXa3UBSFUaNGcccdd/Dxxx8zY8YM4uPjMRgMfPTRRyxevJj//e9/TJw4kYoVK3rsbj167qBiSx3ZRcUyT80zre3Jbj360jtuT72nSZ7Zx5Y4ZWDhBLIrlDbuviuUM9qyVNcA5EWEkx4RDg0bFi+fnYGXfxC+6en4x8cRcDkO/8txBMQV/Gl+LP4S/pfji5wofjUvQz7BcRch7iIcPABQ5BlEmvoNCMBQtSrGqlUL/qxShfyoKIxhYSTXqYNarRqGKlUgKMim5+9ow4YNo2/fvsybN48PPviA9PR0srKyeO211/joo4947LHHGDNmDLm5uR63W4/sCuUZu6iYyK5QsluPHmRXKMkzZ7JlVyhF9bS3sz2IaVeopKQki7tCxcfHl7njQGllSruekpnD5oN/EhQaQZAH7gplyEgu8u69J/SlpS1b69pSXlVVDGmJBOYa8b98iYBCn36YPg3xvXQR/7gLBCQnojji05uwMKheHapVg+hoiI5GverfVKsGvs7Pzfj4eGbNmsUHH3xAbu5/p6hHR0czfvx4HnvsMfz9/UtpQTtrft/1aMueutbWcfY9zZHfQz3pGbej+9Iz1yTPtNE7bk+9p0me2Sc1NZWKFSuSkpJS7PXs1TzrrWwPJbtC2c5jd4XS0JatdW0pr3h7k1c5gvzIKDIaNSt2PSMnj8zUBPq0vJ7w1CQ4f978pZ49S/Y//xCQmIhy4QKcOwfJyaX3l5ICKSlw7Nh/j1kqGBX130CjenXLf4+M1HR6eZUqVXjnnXd48sknmTZtGosXL8ZoNHL+/HkmTpzI/PnzmTFjBnfccYdTb/CeuoOKLXVkFxXLZFco2a1HD7IrlOSZs8iuUEII+/j6Qs2aBV9XqEYjKXFx+EdFFQxYATIz4cIFOH8e45kzpB8/ToW0NJRCAxLOnYOsrNL7i4sr+Pr555LL+PgU/aTj6oGH6Ss8vNTzP2rXrs0nn3zC008/zfPPP8+XX34JwPHjxxk2bBitW7dm9uzZ9OzZ08pvlhBCCCEKk4GFEMJ2QUFw/fUFX0YjmXFxhBQeeEDB5gEpKUU+/eDcueL/vnAB8vNL7is/H86cKfgqTWBgyQOPQv9u0qQJ69at4/vvv+fpp59m3759ABw6dIhevXrRo0cPZs2aRdu2bR3wjRJCCCGuHTKwEEI4h6IUfIoQHg6NG5dczmiEy5dLHniY/h4XVzBYKUlWFpw4UfBVmivrPzpUq8b2mjU5X6MGq374gR/Pn+c8cHz7dm656SZuv/NOZsyYwQ033GDHkxdCCCGuPTKwEEK4lpdXwVqLqCho2bLkcnl5cPFi2Z+AlLH+gyvrP5RjxwgC6gHPXVUkB1i6ahW3rllD7wceYMqUKdSoUUPLsxRCCCHKPRlY6EDOsbDNtXaOhT11bSlvVVlTrtiRq7rtx+3tXTClqXr10ssVWv9hmmqlFJ52deXvSinrP/yBB4C7jEbmfvwx7RYvZtT//sezzz5LpUqVbA7dU/d8t6WO7PtumZxjIecL6EHOsZA8cyY5x8LF5BwLbeQcCyu2m7WhvFVl8/Lxzs8iIeEyOelFt4DVkocuU6EC3HBDwZclqoqanEzm338TlpGBT1wc3hcv4nXpEt4XLuC7Zw/eaWkEAE8DD+Xk8Nobb9Dso48Y89hjPPjggwTZcFaHp+75bksd2ffdMjnHQs4X0IOcYyF55ky2nGPhWa84PURMTAwxMTHmcywiIyMtnmOhKEqZeySXVqa06/6ZORhOJkBgGN4eeI4FKngH6XOOhaP60tKWrXVtKW9V2Zw8DHn5RERUJiyo6JkOWvLQnRkjI4kPDyfcUtyJiaivvQbvvYeSnU0Y8Arwv7Q0Xp41i64LF/LclCk88MAD+FpxFocjv0da2rKnrrV1nH1P89g80zFuR/elZ65Jnmmjd9yeek+TPLNPQECA1WVlYKEDOcfCdnKOhWPLl1nWlCt25qon7cddWIlxV64Mr70GTzwB06ejLlyIYjBQFYgFJly6xIsxMbz95pu8/MorDBs2zCF7omuO20l1Zd93beQcCzlfQA9yjoXkmbPYEqP7PxshhHCV6tVh3jyU336DoUPND18PLANW/vMPi0aOpE3r1mzZssXj1jMJIYQQjiQDCyGEKMsNN8CKFfDjj1DoAL2WwCbg7Z9/Ztptt9G9e3f279/vqiiFEEIIl5KBhRBCWKttW9i2DbZtQ23d2vxwF2Av8MSOHdx/880MGTKE33//3WVhCiGEEK4gAwshhLBVz54oBw7AypWoDRqYHx4EHAEGrl1L3yZNeOCBBzhT1onhQgghRDkhAwshhLCHosCddxasv5g3DzU6Gii4qd4H/KGqNF24kPb16vHMM8+QmJjoymiFEEIIp5OBhRBCaOHjAw89hPL33/Dqq6jh4UDBIXtPAsdycwl+6y16tmvHK6+8Qnp6uiujFUIIIZxGBhZCCOEIgYEwcSLKP//ApEmogYEAhAIvAT+npxM/ZQqNr7+e2NhYcnNzXRquEEII4WhyjoUOjEZjsePQnX1cvGo0gqoWnDDsYVtgqldi1iNuR/alpS1b69pS3qqyplyxI1etyWV35LS4w8Jgxgx49NGCP+fPRzEYiALeBZ6Mi2PKY4/xzptvMvWllxgxYoRNe4RridueutbWcfY9TfJM/770zDXJM230jtuR/UmeuT9b4pSBhRPExsYSGxuLwWAAID4+nuzs7CJlnH1cfHpOHt75WZCVgiHfs37MKirGnAxQQMHJJ287sC8tbdla15byVpXNy8c7P4uEhMvkpBc9SVpLHrozp8ft4wPTpuF9772EvPoqgV99BUAdYAnw68mTTL7nHmbPmsWkyZPp3r27VYcdaonbnrrW1nH2PU3yTP++9Mw1yTNt9I7bkf1Jnrl/nqWlpVld1rNecXqImJgYYmJiSE1NJSwsjMjISEJDQ4uUcfZx8f6ZORhOJkBgGN7+vsXqujNVVUEF76Bwp5+87ci+tLRla11byltVNicPQ14+ERGVCQvyL3JJSx66M93ijorCuHo18du3E/HWW3ht3QpAc2ADsOfYMZ67+27mde7MzJkzad++vdPitqeutXWcfU+TPNO/Lz1zTfJMG73jdmR/kmfun2cBAQFWl5WBhQ60HOdu73HxipdXwa41iuL0F+fOoFyJW4/YHdmXlrZsrWtL+TLLmnLFzly1JpfdkZ5xG1q0gE2bYOdOeO65gsP2gI7AHmD9rl080rEj199+O6+88gpNmjRxStz21LW2jjPvafbG7g70jNvRfemZa5Jn2ugdtyP7kzxzb7bE6P7PRgghypNu3WDfPlizBrVhQ/PDA4BfgCFffsnAZs0YM2YMp0+fdlmYQgghhK1kYCGEEHpTFBg8GOXIEViwALVGDaDghnwv8Luq0urTT7mlfn2efPJJ4uPjXRquEEIIYQ0ZWAghhKv4+MD996P89Re88QZqpUoA+AGPA3/k5RH+zju0rFuXl156yaYFdEIIIYTeZGAhhBCuFhAATz1VcAbGCy+gBgUBEAJMpeAMjMSpU2lcty5z584lJyfHpeEKIYQQlsjAQggh3EVYGLz8MsqJExATg+pTsL9GJPAOsOfyZX564gm6dOzI0qVLzVtaCyGEEO5ABhZCCOFuqlaFuXNR/vgDRo0yP1wb+BT4+uxZVo8eTauWLdmwYYPHHYIphBCifJKBhRBCuKvrr4fPPoPDh6FvX/PDTYAvgQ+PHuW1AQPo3Lkz33//vcvCFEIIIUAGFkII4f5atoSvv4adO1ELHaDXAdgFTNqzh5iOHRk4cCBHjhxxVZRCCCGucTKwEEIIT9G5M+ru3SR9+ilqoQP0+gI/A8PXr2dw8+bce++9nDx50lVRCiGEuEbJwMIKe/fuxcvLixkzZrg6FCHEtU5RyLn1VtTDh+HTT1Fr1TJfugv4HWi3ZAmdGzTgiSeeIC4uzmWhCiGEuLbIwKIMRqOR8ePHc9NNN7k6FCGE+I+3N4wejXL8OLz9NmrlygD4AjHAH/n5RL77Lq3q1mXatGmkpqa6NFwhhBDlnwwsyvDRRx9xyy230LBhQ1eHIoQQxfn7w/jxBVvUTp2KGhICQDDwAvBrRgZp06fTuG5d5syZI2dgCCGEcJpyM7BIS0tj4sSJ9O7dm8jISBRFYdq0aRbLpqenM378eKKjowkICKBly5Z8/vnnxcolJCQwZ84cpk6d6uTohRBCo9BQmDatYIDx+OOovr4ARABvAnsTEjg6YQKdO3Rg8+bNLg1VCCFE+VRuBhYJCQnMmzePnJwcBg0aVGrZIUOGsGjRIqZOncqmTZto27YtI0eOZNmyZUXKTZo0iQkTJhAWFubEyIUQwoGiomDOnIIpUvfcg6ooANQEFgCbz59n4YABLPvsM5eGKYQQovwpNwOL2rVrk5SUxM6dO5k1a1aJ5TZu3Mi2bdt4//33GTt2LN26dePjjz+mV69ePPPMM+aTbA8dOsRPP/3Egw8+qNdTEEIIx7nuOli8GOWXX2DAAPPDjYBVRiPX33036596ymXhCSGEKH98XB2AoyhX3pUry9q1awkJCWHo0KFFHh8zZgyjRo1i//79dOjQgd27d3Ps2DGioqKAgulT3t7eHD9+nMWLF1tsOycnp8j8ZdNiSaPRiNFoLFLWaDSiqmqxx20pU9p11WgEVQVV9bhTedUrMesRtyP70tKWrXVtKW9VWVOu2JGr1uSyO9Izbkf2ZXNbTZrAunWwZw889xxee/cC0A7grbc4uWEDtT/7DG68UVN/zr6nSZ7p35eW9mytK3mmjd5xu/SepqGu5Jl9bImz3AwsrHX06FEaNWqEj0/Rp968eXPz9Q4dOvDggw9y5513mq9PmDCB6667jmeffbbEtmfNmsX06dOLPR4fH092dnaRx4xGIykpKaiqipeX5Q+OyipT2vX0nDy887MgKwVDvmf9mFVUjDkZoICCdQNGd+hLS1u21rWlvFVl8/Lxzs8iIeEyOem+RS5pyUN3pmfcjuzL7rYaNMC4ciX5X36J94svUj0xEYA6x49D27ZkDRxI+qRJGK67zq7+nH1PkzzTvy8t7dlaV/JMG73jdot7mh11Jc/sk5aWZnVZz3rF6QAJCQnUrVu32OOVKlUyXwcICQkh5MruKgBBQUGEhoYSERFRYtumNRkmqamp1KxZk8jISEJDQ4uUNRqNKIpCZGRkqUlbWpnSrvtn5mA4mQCBYXj7+xar685UVQUVvIPCrf4kyh360tKWrXVtKW9V2Zw8DHn5RERUJizIv8glLXnozvSM25F9aWnLaDQSP3gwle+/n3V3302L1aupc+Va4FdfEfDtt6jr1kG3bjb35+x7muSZ/n1pzTVb6kqeaaN33O50T5M8c76AgACry15zAwsofdpUSdc+/fTTMtv19/fH39+/zHJCCOFKio8PA1es4P233+bPp5/mRSAKUNLToV8/1C++KLIuQwghhLDGNTewiIiIMH8qUVjilWkBpk8utIiNjSU2Nta8EFymQtlGpkLJVCg9eOoUFUdOG7jzrrv43Nub+hMmsERVGQgoOTlw552kvPce2YMGydQBjTw1z7S2J1NU9CVToSTPnEmmQpWiWbNmLF++nPz8/CLrLI4cOQJA06ZNNfcRExNDTEwMqamphIWFyVQoG8lUKJkKpQdPnaLi6GkDjz/+ONWqVWPE3XczPz+fUYCSn0/Yo48SChgffFCmDmjgqXmmtT2ZoqIvmQoleeZMtkyFcv9n42CDBw8mPT2d1atXF3l80aJFREdH065dOxdFJoQQrjF06FBWrlvHQ/7+fHTlMUVV8Ro3DuWtt1wamxBCCM9Rrj6x2LRpExkZGeaPbI4dO8aqVasA6Nu3L0FBQfTp04devXoxbtw4UlNTqVevHsuXL2fz5s0sXboUb29vzXHIVChtZCqUTIXSg6dOUXHWtIHWrVuzZNkyRt97LykZGUy88rj3s8/ic+oUcc8/j1cp90eZOmCZp+aZ1vZkioq+ZCqU5JkzXbNTocaNG8fp06fN/165ciUrV64E4OTJk1x3ZRvFNWvW8PzzzzNlyhQSExNp2LAhy5cvZ8SIEQ6JQ6ZCaSNToWQqlB48dYqKM6cNDBo0iG+2b6dvnz6kJCXxypXHIz/4AKO3N7z9Nth5v7KmTHmaOmDiqXmmtT2ZoqIvmQoleeZM1+yuUKdOnbKqXEhICHPmzGHOnDnODUgIITxM27Zt+W7HDm699VbSLl7k3SuPe82di5qejjpvHjjgk10hhBDlT7kaWLgLmQqljUyFkqlQevDUKSp6TBuIiopizZo1DBs2jNSzZ1kAeAPKp5+Sc/kyybGx4Odnc9vX0tQBE0/NM63tyRQVfclUKMkzZ7pmp0K5C5kKpY1MhZKpUHrw1Ckqek0biIqKYs+ePfTu3Zthx4+zHPADAjZsoEpeHuqqVRAUZFPb19LUARNPzTOt7ckUFX3JVCjJM2e6ZqdCuSsvLy+LiaMoSonXrC1T0nXFywsUBRTF6S/OnUG5ErcesTuyLy1t2VrXlvJlljXlip25ak0uuyM943ZkX1rasqVu7dq12blzJz179mTgb7+xBggClC1bUPr2hfXrISzMpra15JLkmf596ZVrtpSXPLNM77g98Z5mS3nJs//YEqP7PxshhBAuExUVxapVq0ht355bgRTThd27oUcPuHzZhdEJIYRwJ/KJhQ6MRiNGo7HYY6qqFnvcljKlXVeNRlBVUNWCqTAeRL0Ssx5xO7IvLW3ZWteW8laVNeWKHblqTS67Iz3jdmRfWtqyp67RaCQsLIyNGzdy55130n37drYAlQEOHULt0gV182aM1ao59Z4meaZ/X3rmmrXlnf1/p+SZ/v1Jnrk/W+KUgYUTyOJtbWTxtize1oOnLqrVc6Hj1XXmz5/PI488QuctW9gGVAeUY8cwdOxIwuefkxweLosdr+Kpeaa1PVlUqy9ZvC155kyyeNvFZPG2NrJ4WxZv68FTF9XqudDRUp0vv/ySMWPG0HH5crYDdQGff/8l6s478Vq2jIr168tix0I8Nc+0tieLavUli7clz5xJFm+7GS0Ld2TxtizedkR5WbxtmacuqtVzoePVdfz9/VmyZAmPVqhAp3nz2Ao0AZTz54kYPBi2bMGrbVu7+y8vix0L89Q809qeLKrVlyzeljxzFltidP9nI4QQwq14e3vz4Ycfctczz9AFOHjlca+kJJSePQsWdgshhLjmyCcWOpDF27aRxduOLS+Lty3z1EW1rli8XVKdWbNmERoaSo8XX2Q90BlQUlNRb7214JyL226zqf/ytNjRxFPzTGt7sqhWX7J4W/LMmWTxtovJ4m1tZPG2LN7Wg6cuqnXl4m1LdR588EEAbnvxRVYDfQAlKwsGDSL5/ffJ6d/f6rbK02JHE0/NM63tyaJafcnibckzZ5LF2y4mi7e1kcXbsnhbD566qNbVi7ctmTx5MlWrVmXwww+zRFUZCih5eYSPHYv68cdw331WtVWeFjuaeGqeaW1PFtXqSxZvS545kyzedjOyeNt2snjbseVl8bZlnrqo1pWLt0ty//33o6oq9z76KGn5+dwPKEYjygMPQHo6PP64VW2Vl8WOhXlqnmltTxbV6ksWb0ueOYss3hZCCKG7AQMGsGrdOh7z92dO4QtPPAEzZhSs5RFCCFFuycBCCCGEw/Tp04dNW7bwYkgILxW+8OKLKM8+K4MLIYQox2QqlA5kVyjbyK5Qji0vu0JZ5qm79bjTrlAllevUqRPbvvmGvn37kpKYyJtXyihvvkmFS5cwzp8PvsXXfpWnXVRMPDXPtLYnu/XoS3aFkjxzJtkVysVkVyhtZFco2RVKD566W4+77QpVUrnatWuzevVqhg8fTlpcHB9S8BF58NKlZKank/ruu8UGF+VpFxUTT80zre3Jbj36kl2hJM+cSXaFcjHZFUob2RVKdoXSg6fu1uOOu0KVVC4qKordu3fTu3dv7jp9msWALxC0bh2B+fmoX3wBhXYbKU+7qJh4ap5pbU9269GX7AoleeZMsiuUm5FdoWwnu0I5trzsCmWZp+7W4467QpVUrkGDBuzevZuePXsy+PhxVgEBgLJhA0r//vDll1ChglV9SZ7p35fs1uM5ZFcoyTNnkV2hhBBCuI2aNWuye/duzrZoQR/A/KH6d99Br16QmOjC6IQQQjiKDCyEEEI4XVRUFNu3bye9TRt6AuahxP790LUrXLzouuCEEEI4hAwshBBC6KJixYp8/vnnVOjRgy6AeShx5Ah06gSnT7swOiGEEFrJwEIIIYRugoOD+eqrr6g7cCCdAPNQ4u+/Ubp0wfvECRdGJ4QQQgtZvK0DOcfCNnKOhWPLyzkWlnnq+QKecI5FWWX8/PxYsWIFY8aMoePy5XwD3AAoZ85QadAgjJs3Q6tWmmN3B56aZ1rbk/MF9CXnWEieOZOcY+Fico6FNnKOhZxjoQdPPV/AU86xsKbM66+/znPe3nRaupStQEvA+/JljD16kLR0KXlt2miK3R14ap5pbU/OF9CXnGMheeZMco6Fi8k5FtrIORZyjoUePPV8AU86x8KaMp9++inPVqlCtzff5GugA+CVkkKlESNQ166FHj00P29X8tQ809qenC+gLznHQvLMmeQcCzcj51jYTs6xcGx5OcfCMk89X8CTzrGwpszrr79OeHg4vV98kXVAT0DJyID+/VFWrIDbb7c7dnfgqXmmtT05X0Bfco6F5JmzyDkWQgghPIaiKLzwwgu88s479AfWmR7PzUW94w747DMXRieEEMJa8omFEEIIt/C///0PVVUZPmEC81WVewDFYEC95x5ISYEhQ1wdohBCiFLIJxZCCCHcxogRI1i8fDkP+vjw/pXHFFXFKyaG4LlzXRqbEEKI0sknFkIIIdzK0KFDCQ0NZcjgwaTm5PDclccrvPJKwVbar7xSsC5ICCGEW5FPLIQQQridPn36sHnLFmZWqMCkQo8rs2bB44+Dh+z/LoQQ1xIZWAghhHBLXbp0Yfv27cyrVImYwhfmzoUxYyA/31WhCSGEsEAGFkIIIdxW27Zt2blzJ2uqVuUewDyUWLwYhg2DnBwXRieEEKIwWWOhA6PRWOw4dGcfF68ajaCqoKoFB6R5EPVKzHrE7ci+tLRla11byltV1pQrduSqNbnsjvSM25F9aWnLnrrW1nHmPa1x48Z899139OrVi6Fnz/I54A+wdi3qgAGoq1dDcLDVz0lPnppnWtuzta475Jk9cbsLveP21Hua5Jl9bIlTBhZOEBsbS2xsLAaDAYD4+Hiys7OLlHH2cfHpOXl452dBVgqGfM/6MauoGHMyQAEFJ5+87cC+tLRla11byltVNi8f7/wsEhIuk5Ne9KR2LXnozvSM25F9aWnLnrrW1nH2PS00NJRFixbx0EMP0f+ff1gHBAPKtm3k9ehB0pIlqGFhVj0nPXlqnmltz9a67pJncj/Tvz/JM/fPs7S0NKvLetYrTg8RExNDTEwMqamphIWFERkZSWhoaJEyzj4u3j8zB8PJBAgMw9vft1hdd6aqKqjgHRTu9JO3HdmXlrZsrWtLeavK5uRhyMsnIqIyYUH+RS5pyUN3pmfcjuxLS1v21LW2jrPvaaZru3fvpm/fvvT+5Rc2AmGA34EDRI0cibppE0RGWvW89OKpeaa1PVvrulueyf1Mv/4kz9w/zwICAqwuKwMLHWg5zt3e4+IVL6+C7RgVxekvzp1BuRK3HrE7si8tbdla15byZZY15YqduWpNLrsjPeN2ZF9a2rKnrrV1nHlPM12rUqUK3333Hf369aPr3r1sBSIB5fBhlK5dYds2qFHD6uemB0/NM63t2VrXnfJM7mf69id55t5sidH9n40QQghRSMWKFdm6dSsRPXrQCThruvDHH9CxI5w44cLohBDi2iUDCyGEEB4nJCSEDRs2cMPAgXQE/jZdOH0aOnWCo0ddGJ0QQlybZGAhhBDCIwUEBLBq1SrajxhBJ8A8lLhwAbp0gQMHXBidEEJce2RgIYQQwmP5+vqydOlSbrvvProAP5ouJCZCjx6wc6cLoxNCiGuLDCyEEEJ4NG9vbxYsWMCIRx+lJ7DDdCEtDW67DTZudF1wQghxDZGBhRBCCI/n5eXF3Llzefipp+gDbDBdyM6G22+HFStcGJ0QQlwbZGAhhBCiXFAUhddff51nXnyRIcAXpgv5+agjR8KCBS6MTgghyj85x0IIIUS5oSgKL730EoGBgYyaPJlU4CFAMRrhwQcLpkeNH+/iKIUQonySgYUQQohyZ9KkSQQFBfHw+PGkAk+ZLjz5JKSkwJQpBQdDCiGEcBiZClWKESNGUKVKFUJDQ2nevDkbNmwou5IQQgi38MQTT/DRRx/xDDCl8IVp0+Cpp0BVXROYEEKUUzKwKMWLL77ImTNnSE1NZf78+dx1110kJCS4OiwhhBBWevjhh/l00SJe8fJifOELb78NDz8MBoOLIhNCiPJHBhalaNKkCX5+fgD4+PiQm5vLuXPnXByVEEIIW9x77718/vnnxPr48ABgNF2YPx/uugtyc10YnRBClB/lZmCRlpbGxIkT6d27N5GRkSiKwrRp0yyWTU9PZ/z48URHRxMQEEDLli35/PPPLZa96667CAgIoHXr1nTv3p1mzZo58VkIIYRwhqFDh7J69WqW+vkxAsgzXfjiCxg8GLKyXBidEEKUD+VmYJGQkMC8efPIyclh0KBBpZYdMmQIixYtYurUqWzatIm2bdsycuRIli1bVqzsZ599Rnp6Olu2bKF3794osthPCCE80sCBA1m/fj0bAgO5HTAPJTZuhD59IDXVhdEJIYTnKzcDi9q1a5OUlMTOnTuZNWtWieU2btzItm3beP/99xk7dizdunXj448/plevXjzzzDMYLMy39fHxoXfv3mzbto2NcoKrEEJ4rN69e7Np0yZ2BQdzG2AeSuzcCT17gqyjE0IIu5Wb7Wat/SRh7dq1hISEMHTo0CKPjxkzhlGjRrF//346dOhgsa7BYODvv/8use2cnBxycnLM/0698u6X0WjEaDQWKWs0GlFVtdjjtpQp7bpqNBbseKKqqB6284l6JWY94nZkX1rasrWuLeWtKmvKFTty1Zpcdkd6xu3IvrS0ZU9da+s4+57myO9hp06d2LJlC/369aNHSgqbgQiAAwdQu3RB3bIFqlXT3A94bp5pbc/WuuUxz/Skd9yeek+TPLOPLXGWm4GFtY4ePUqjRo3w8Sn61Js3b26+3qFDBy5evMj333/Pbbfdhr+/P2vWrOG7775j9uzZJbY9a9Yspk+fXuzx+Ph4srOzizxmNBpJSUlBVVW8vCx/cFRWmdKup+fk4Z2fBVkpGPI968esomLMyQAFFJw79cyRfWlpy9a6tpS3qmxePt75WSQkXCYn3bfIJS156M70jNuRfWlpy5661tZx9j3N0T+v66+/nhUrVjBixAi6JCWxDagGKL/9hqFjR5JWrMBQs6bmfjw1z7S2Z2vd8ppnetE7bk+9p0me2SctLc3qsp71itMBEhISqFu3brHHK1WqZL5u8s4773D//fejKAr169dnxYoVtGjRosS2J02axIQJE8z/Tk1NpWbNmkRGRhIaGlqkrNFoRFEUIiMjS03a0sqUdt0/MwfDyQQIDMPb37dYXXemqiqo4B0U7vQ1LY7sS0tbtta1pbxVZXPyMOTlExFRmbAg/yKXtOShO9Mzbkf2paUte+paW8fZ9zRn/Lx69uzJjh076N27Nx0vXeIboA7gc+oUlQcPRt26FRo21NSHp+aZ1vZsrVue80wPesftqfc0yTP7BAQEWF32mhtYQOnTpkzXqlatyu7du21q19/fH39//7ILCiGEcAtNmzblu+++o1evXnQ6d45tQCNAOXcOunZF3bQJWrVydZhCCOERrrmBRUREhMVD7hITE4H/PrnQIjY2ltjYWPNCcJkKZRuZCiVTofTgqVNUZCqU439eFStWZPXq1QwdOpTOZ86wBbgRUOLjUbt3J2nJEvJuusmutj01z7S2J1NU9CVToSTPnEmmQpWiWbNmLF++nPz8/CLrLI4cOQIUvHulVUxMDDExMaSmphIWFiZToWwkU6FkKpQePHWKikyFcs7PKyoqit27d9OrVy+6//UXG4COgFdqKpVGjkRdswZ69bK5XU/NM63tyRQVfclUKMkzZ7JlKpT7PxsHGzx4MOnp6axevbrI44sWLSI6Opp27dq5KDIhhBCuVLNmTXbs2EGNJk24Fdhy5XElMxNl4EBYu9aV4QkhhNsrV59YbNq0iYyMDPNHNseOHWPVqlUA9O3bl6CgIPr06UOvXr0YN24cqamp1KtXj+XLl7N582aWLl2Kt7e35jhkKpQ2MhVKpkLpwVOnqMhUKOf+vLy8vPjiiy8YMWIEA48eZTkwBFByc2H4cFLefpvsq7YrL42n5pnW9mSKir5kKpTkmTNds1Ohxo0bx+nTp83/XrlyJStXrgTg5MmTXHfddQCsWbOG559/nilTppCYmEjDhg1Zvnw5I0aMcEgcMhVKG5kKJVOh9OCpU1RkKpTzf15RUVHs2LGDfv36MWz/fhYAowHFYCD88ccxennBuHFWteWpeaa1PZmioi+ZCiV55kzX7K5Qp06dsqpcSEgIc+bMYc6cOc4N6AovLy+LiaMoSonXrC1T0nXFywsUBRTF6S/OnUG5ErcesTuyLy1t2VrXlvJlljXlip25ak0uuyM943ZkX1rasqeutXWceU+zN3Z7RUREsG3bNvr378+YXbtIBf535ZrXY49BaipMmmRVW56aZ1rbs7XutZhnjqR33J56T5M8s50tMZargYW7Mhrl5G1bqKqcvO3I8laVNeWKHblqTS67Iz3jdmRfWtqyp661dZx9T3NFngUHB/P1118zePBgHv/mG1KB500XJ09GTU5GnTmzYGBeAk/NM63t2Vr3Ws4zR9A7bk+9p0me2ceWOGVg4QSyxkIbWWMhayz04Klz3/Wcj2xLnfI8J/njjz9m7NixvLB1KynAa1ceV157jaxLl0idORPsfM6O5Oi+ZO673M/06E/yzP3z7JpdY+EuZI2FNrLGQtZY6MFT577rOR/ZljrlfU7yl19+yT333MPrq1aRBsRSsK1i0KJFBObloS5cCL7F77Wemmda25O57/rSO25PvadJntnnml1j4a60zK+TNRayxsIR5WWNhWWeOvdd1ljon2cBAQEsX76cwMBAPlyyhFRgEQX/iSrLlqFkZMDnn4OF/4A9Nc+0tidz3/Ulaywkz5zFlhhlYKEDox3z1q0pU9p1WWOhf1+yxsIz5oqaeOrcdz3nI9tS51qYk+zl5cXChQsJCAjg448/Jh1YAfgDfPklav/+BQfphYSY63hqnmltT+a+60vvuD31niZ5Zh9b4pSBhRPIGgttZI2FrLHQg6fOfddzPrItda6lOcnTp0/HaDSyYMEC+gJfAiGAsn07eT16kLR0KWpYGOC5eaa1PZn7ri+94/bUe5rkmX1kjYWLyRoLbWSNhayx0IOnzn3Xcz6yLXWutTnJH330EZUrV+bVV1+lF7ARqAj4HTxI1PDhqJs3Q1SUx+aZ1vZk7ru+9I7bU+9pkmf2kTUWbkbL/DpZYyFrLBxRXtZYWOapc99ljYV75NmsWbMIDg5mypQpdAW2AlUA5ZdfULp0gW++gerVPTbPtLYnc9/1JWssJM+cxZYY3f/ZCCGEEG5IURRefPFFXn/9dX4FOgH/mi4ePw4dO8Jff7kuQCGE0Jl8YqEDox0LYq0pU9p1Wbytf1+yeNszFqGZeOqiWj0XOtpS51pe7DhhwgQCAgL43//+R0dgO1Af4N9/Ubp0wXvZMoyVKzs9Dkd/j2RRrefQO25PvadJntnHljhlYOEEsnhbG1m8LYu39eCpi2r1XOhoS51rfbHjnXfeSV5eHk899RSdVJWtQHNAuXSJSoMHk/jZZxjatHFqDI7+HsmiWvfLs5LoHben3tMkz+wji7ddTBZvayOLt2Xxth48dVGtngsdbakjix3hiSeeICoqitGjR9PVYGAjcDPgnZpK5ZEjUdetg27dnNa/LN62royn55klsnhb8syZZPG2m9GycEcWb8vibUeUl8XblnnqolpZvO2+eXbXXXcRGBjIiBEj6JWXx5dAd0BJT0fp1w9WroQBA5zWvyzetq6Mp+eZJbJ4W/LMWWTxthBCCOEiQ4YMYd26deT5+9MX+Mp0IScHhgwpOKFbCCHKIRlYCCGEEA7Wt29fvv76a7yDgrgDWGa6kJ8Po0bBvHkujE4IIZxDpkLpQHaFso3sCuXY8rIrlGWyK5Rj68guKsV169aNr7/+mgEDBnBPejppwFgo+H0bOxZjaipMmOCw/mRXKOvKlLc8A9kVSvLMuWRXKBeTXaG0kV2hZFcoPciuULIrlB4aNGjAggULGDt2LI8kJ5MCTLxyzeuZZ0g/f570Z54pWOekkewKZV2Z8phnsiuU5Jkzya5QLia7Qmkju0LJrlB6kF2hZFcoPRiNRjp16sQ333xDnz59eDY+nhTglSvXQ95+m+D8fNS33gIX5oaj25PdevSld9yeek+TPLOP7ArlZmRXKNvJrlCOLS+7Qlkmu0I5to7somKZoii0atWKnTt30qNHD2ZeuEAa8K7p+nvvoaSnw8cfg7e35r5kV6hrN89kVyjHlZc8+4/sCiWEEEK4mUaNGrFr1y5q1arFe8B9gMF08ZNPYMQIyM11WXxCCKGVDCyEEEIIndSrV49du3Zx/fXXswgYDpiHEqtWwe23Q2am6wIUQggNZGAhhBBC6Kh27drs2rWLhg0bshoYCJiHEps3w223QWqq6wIUQgg7ycBCCCGE0Fl0dDQ7d+6kefPmbAFuBVJNa6B274bu3eHyZVeGKIQQNpPF2zow2nE2gDVlSrsu51jo35ecY+EZ+3Gb6Bm3p+75bksd2ffdstLirly5Mtu3b6dPnz7sOXiQbqrKVkUhQlXh0CHULl1Qt2yB6GjNfTk6dkfXlTzTRu+4PfWeJnlmH1vilIGFE8g5FtrIORZyjoUe9IzbU/d8t6WO7PtumTVxf/bZZ9x9990cOHCATqrKN4pCtKqiHDuG4ZZbSFqxAkPt2g7py9GxO6qu5Jk2esftqfc0yTP7yDkWLibnWGgj51jIORZ60DNuT93z3ZY6su+7ZdbEHRUVxTfffMPgwYP59ttv6aiqbFcU6qgqPv/+S+UhQwo+uWjcWHNfjo7dUXUlz7TRO25PvadJntlHzrFwM1r2MJZzLOQcC0eUl3MsLJNzLBxbR/Z9t8yauENDQ9mwYQN33HEHmzZtoqOqsk1RaKyqKOfPo3TtClu2QOvWmvtydOyOqit5po2cYyF55iy2xOj+z0YIIYS4BgQGBrJ27VoGDx7MeaCzqnLIdDEhoWBB9+7dLoxQCCFKp3lgER8fz6RJk2jVqhVhYWF4e3uX+OXjIx+QCCGEECXx9/fniy++YOTIkSQA3YFdpoupqXDrrQVb0gohhBvS9Er/zz//pEuXLsTHx1u9O40QQgghSubr68uSJUsICAjgk08+4TZgNdAHICsLBg6E5cvhjjtcG6gQQlxF0ycWTz31FHFxcbRt25bNmzdz6dIljFe2qyzpSwghhBCl8/b2Zv78+Tz66KNkAbcDK00X8/Jg2DD49FOXxSeEEJZo+sRi165dBAcHs3Xr1mK7HgkhhBDCfl5eXsydO5fAwEDefPNNRgJpwP0ARiOMGVMwPerxx10bqBBCXKHpEwtfX18aNmwogwohhBDCCRRF4fXXX+fFF1/EADwIzClc4IknYMaMgkMuhRDCxTQNLNq2bcvZs2cdFYsQQgghrqIoCi+99BIzZ85EBcYDLxUu8OKLMHGiDC6EEC6naSrU888/T48ePZgzZw5PPPGEo2IqdyytL3H2cfGq0Vjwn4yqetyiefVKzHrE7ci+tLRla11byltV1pQrduSqNbnsjvSM25F9aWnLnrrW1nH2PU3yDJ599lkCAwN58sknmQqkAm+YLr7xBqSkoE6Z4rDvkZ65Jnmmjd5xe+o9TfLMPrbEqWlg0alTJz777DPGjh3Lrl27uP/++7n++usJCgoqsU6tWrW0dOkRYmNjiY2NxWAwAAVb8mZnZxcp4+zj4tNz8vDOz4KsFAz5nrXNr4qKMScDFFBw8snbDuxLS1u21rWlvFVl8/Lxzs8iIeEyOelFT2rXkofuTM+4HdmXlrbsqWttHWff0yTPCowYMYK8vDyeffZZ3lRVUoEPKZh+4PXxxwSdP09cbCxe/v6a+9Iz1yTPtNE7bk+9p0me2SctLc3qsppfcTZt2pSWLVuybt061q1bV2pZRVHIz8/X2qXbi4mJISYmhtTUVMLCwoiMjCy2DsXZx8X7Z+ZgOJkAgWF4+/sWq+vOVFUFFbyDwp1+8rYj+9LSlq11bSlvVdmcPAx5+UREVCYsqOgLEi156M70jNuRfWlpy5661tZx9j1N8uw/Tz31FFFRUdx///18bDSSBixRFHxUlbCvv6aCosAXX0BAgMtit7Wu5Jk2esftqfc0yTP7BNhwL9E0sDh06BA9evQgLS0NVVUJCgqicuXKTn8x6Gm0HOdu73HxipcXKAooikf+PJQrcesRuyP70tKWrXVtKV9mWVOu2Jmr1uSyO9Izbkf2paUte+paW8eZ9zR7Y3cHzoh79OjRBAUFMWrUKD7PzydNVVnj5YWf0YjXhg3Qvz98+SVUqKCpHz1zTfJMG73j9tR7muSZ7WyJUdPA4rnnniM1NZXbb7+d1157jfr162tpTgghhBBWGjp0KAEBAdx55518nZvLrUYjX3t7E2QwwHffQa9esHEjVKrk6lCFENcITcOkH3/8kfDwcFasWCGDCiGEEEJnAwYMYMOGDQQGBrID6GYwkOpz5T3D/fuha1e4eNGFEQohriWaBhZ+fn7Uq1cPX1/PmsMvhBBClBe9evVi06ZNhISE8CNwS34+Cab/l48cgU6d4PRpl8YohLg2aBpYdOjQgRMnTlwTC7KFEEIId9WlSxe2bNlCaGgoR4Gb8/K44OdXcPHvvwsGF8ePuzRGIUT5p2lg8fLLL5Odnc3kyZMdFY8QQggh7HDzzTezcuVKIiIi+Bu4KTeXU6bBxZkzBYOLX35xaYxCiPJN0+Lt5ORkpk6dyosvvsh3333HfffdV+Y5Fp07d9bSpRBCCCFK0Lx5c7799lt69+7N2UuXaJeby05/fxrm5EBcXMGai40boX17V4cqhCiHNA0sunbtiqIoqKrKoUOH+Omnn0otf62cYyGEEEK4StOmTdm1axc9evTg7NmztM/JYbu/Pzfm5EBycsFuUV9+CT16uDpUIUQ5o2lg0blzZ488I0EIIYQozxo0aMCuXbvo3r07p06donNODpv9/emYkwMZGdC3L6xYAbff7upQhRDliKaBxY4dOxwUhhBCCCEcqU6dOuzevZsePXpw/PhxeubksM7fn9tyciA3F+64AxYtgrvucnWoQohywv2P+3ORnJwcxowZQ82aNQkNDeXmm2/mhx9+cHVYQgghhNVq1KjBzp07adKkCTnAgJwcVvn7F1w0GOCee+DDD10aoxCi/JCBRQny8/OpU6cO33//PcnJyYwbN46BAweSmZnp6tCEEEIIq1WtWpUdO3bQqlUr8oFhOTksNO0Wpaowbhy89ppLYxRClA9WT4XatWuXQzr0lF2hgoODmTJlivnfo0eP5sknn+Svv/6iRYsWLoxMCCGEsE3lypX59ttv6dOnD/v27eOB3FxS/Px4Mje3oMCzz0JKCsyYAbJ2UghhJ6sHFqYdoLRw5q5QaWlpvPzyy/z8888cPnyYy5cvM3XqVKZNm1asbHp6Oi+88AIrVqwgMTGRhg0b8txzzzFixIgS2//jjz/Iysri+uuvd0r8QgghhDOFh4ezdetW+vfvz65du5iQm0uyry/T8/IKCsycCampMGcOeMmEBiGE7aweWJS0A5Sqquzdu5e8vDz8/PyoXr06VapUIS4ujrNnz5Kbm4ufnx8333yzU3eQSkhIYN68ebRo0YJBgwYxf/78EssOGTKEAwcOMHv2bBo0aMCyZcsYOXIkRqORUaNGFSufmZnJPffcwwsvvEBISIjTnoMQQgjhTBUqVGDTpk0MGjSIbdu28VJeHik+PrxjetNv7tyCwcWCBTK4EELYzOqBhaUdoPLz8xkyZAgBAQG8+uqrPPjgg0VeeGdkZPDxxx8zffp0QkNDWbt2rUOCtqR27dokJSWhKAqXL18ucWCxceNGtm3bZh5MAHTr1o3Tp0/zzDPPMHz4cLy9vc3l8/LyGDZsGI0bN5YTxoUQQni8oKAgvvrqK4YNG8b69euZk59Pqrc3C1QVxWiExYshPR2WLnV1qEIID6Npu9nZs2fz9ddfs337drp27VrsenBwMOPHj6dly5Z0796dV1991Wkvzq39NGTt2rWEhIQwdOjQIo+PGTOGUaNGsX//fjp06ACA0Wjk3nvvxdvbmwULFpTZR05ODjk5OeZ/p6ammtsxGo1FyhqNRlRVLfa4LWVKu64ajQWL8lQVVVVLjdvdqFdi1iNuR/alpS1b69pS3qqyplyxI1etyWV3pGfcjuxLS1v21LW2jrPvaZJnju3Lz8+PlStXcvfdd7Nq1So+MRhI8fJihY8P3vn5sGZNwRkXH3ygS65Jnmmjd9yeek+TPLOPLXFqGlgsXbqU+vXrWxxUFNa1a1caNGjA4sWLXf6u/9GjR2nUqBE+PkWfevPmzc3XTQOLsWPHcuHCBTZv3lysvCWzZs1i+vTpxR6Pj48nOzu7yGNGo5GUlBRUVcWrhI+byypT2vX0nDy887MgKwVDvqYfs+5UVIw5GaCAgnMXETqyLy1t2VrXlvJWlc3Lxzs/i4SEy+Sk+xa5pCUP3ZmecTuyLy1t2VPX2jrOvqdJnjmnr7fffhtFUVi5ciVrjEb6GI187eeHb24uXtu2ETpsGPGffYYSHu7UWCTPtNE7bk+9p0me2SctLc3qsppecZ4+fZrGjRtbVTY4OJjff/9dS3cOkZCQQN26dYs9XqlSJfN1KHhu8+fPJyAggMqVK5vLbdq0iU6dOllse9KkSUyYMMH879TUVGrWrElkZCShoaFFyhqNRhRFITIystSkLa1Madf9M3MwnEyAwDC8/X2L1XVnqqqCCt5B4U4/2d2RfWlpy9a6tpS3qmxOHoa8fCIiKhMW5F/kkpY8dGd6xu3IvrS0ZU9da+s4+54meea8vpYtW0bFihWZN28e24Cuubl8GxCAf3Y2QT/9RODIkaibN0NkpNNikTzTRu+4PfWeJnlmn4CAAKvLahpYREREcPToUc6fP090dHSJ5c6dO8fRo0eJtOGm5EylvRAzXatdu7bNU1r8/f3x9/cvu6AQQgjhJry8vHj//fcJCAjg3Xff5Qfg5uxsvg8OJigjA+Xnn6FrV9QtW6BGDVeHK4RwY5oGFgMHDuTDDz9k0KBBLF68mIYNGxYr88cffzB69Gjy8/O5/fbbtXTnEBEREeZPJQpLTEwE/vvkQovY2FhiY2MxGAyATIWylUyFkqlQenD3KSrOaEumQunPk/LsueeeQ1VV3nvvPX4GbszIYF9wMOEZGSh//IGhY0eSVqzAcN11Do9F8kwbmQoleeZMuk2Fevnll9myZQsHDx6kSZMmdO7cmUaNGhEZGUl8fDx//PEHu3btwmg0UqdOHV566SUt3TlEs2bNWL58Ofn5+UXWTRw5cgSApk2bau4jJiaGmJgYUlNTCQsLk6lQNpKpUDIVSg+eMEXF0W3JVCj9eVqevfPOO0RGRjJlyhT+BFpmZHAwPJzKycn4nDlD5SFDCj65aNLEobFInmkjU6Ekz5zJlqlQmp5NREQEP/zwA4MGDQJg586dfPjhh8yYMYMPP/yQHTt2YDQauf3229mzZw8RERFaunOIwYMHk56ezurVq4s8vmjRIqKjo2nXrp2LIhNCCCFc7/nnn+f1118H4DTQLDmZC1c+zVcuXEDp2hUOHHBZfEII96V5jkyVKlVYs2YNJ06cYOvWrRw/fpz09HRCQkJo0KABvXv31u206k2bNpGRkWH+yObYsWOsWrUKgL59+xIUFESfPn3o1asX48aNIzU1lXr16rF8+XI2b97M0qVLi5xhYS+ZCqWNTIWSqVB68KQpKo5qS6ZC6c9T8+zuu+8mNzeX559/notA08REfoyI4PqEBJTERNSePUlatIi8K7soao1F8kwbmQoleeZMTpsKZTAYSnzhff311zNu3DhbmnO4cePGcfr0afO/V65cycqVKwE4efIk112ZF7pmzRqef/55pkyZQmJiIg0bNmT58uWMGDHCIXHIVChtZCqUTIXSg6dNUXFEWzIVSn+emmcAEydOJDAwkKeeeopEVaVVQgL7o6JoFBeHV3o6le66C3XlSujbV3MskmfayFQoyTNnctquUJGRkdx2223069ePPn36OGShsyOdOnXKqnIhISHMmTOHOXPmODegK7y8vCwmjqIoJV6ztkxJ1xUvL1AUUBSnvzh3BuVK3HrE7si+tLRla11bypdZ1pQrduaqNbnsjvSM25F9aWnLnrrW1nHmPc3e2N2Bp+YZwMiRI4mKimL06NGkGQzcGBfH91WrcuPFiyjZ2SiDB8Nnn8GwYZpjkTzTRu+4PfWeJnlmO1titGlgUaFCBT7//HO++OILvLy8aN++Pf3796dfv340KWMh17XMaJSTt22hqnLytiPLW1XWlCt25Ko1ueyO9IzbkX1pacueutbWcfY9TfJM/75M7Q0fPpyAgABGjhxJdl4eN1+8yPZq1eh04QLk56OOHImakgIPPGB3LJJn2ugdt6fe0yTP7GNLnDYNLE6fPs2vv/7K+vXr2bBhAz/88AN79uxh0qRJ1KpViwEDBtCvXz+6deuGn5+fzYGXF7LGQhtZYyFrLPTgqXPf9ZyPbEsdmZNsmafm2dXt3XLLLXzyySc8+OCDZGdn0/XCBb6sWpX+Fy+iGI0oDz9M6oULZD78sF2xSJ5po3fcnnpPkzyzj1O3m23evDnNmzfn+eefJyEhgQ0bNrBhwwa2bdvG3LlziY2NJSgoiF69etG/f3/69u1L1apVbe3Go8kaC21kjYWssdCDp85913M+si11ZE6yZZ6aZ5baGzlyJFWqVOH2228nMzOTARcv8kX16gw7dw6A0KlTCTEa4cUXMaqqzH3Xkd5xe+o9TfLMPrqevD169GjzAXi7du1i/fr1bNy4kXXr1vHll1+iKAqtWrUyf5rRunVrLV16JC3z62SNhayxcER5WWNhmafOfZc1FpJnevV1dXs9e/Zk69at9OnTh7S0NIafO0dKjRo8dPYsAF7Tp0NaGrz2msx915mssZA8cxZbYnTYHBkfHx+6d+9O9+7defvtt/nrr7/46quvzFOmDh06xPTp06latSr9+vVj3rx5jura7RntmLduTZnSrssaC/37kjUWnjFX1MRT577rOR/ZljoyJ9kyT82z0tpr374927Zto0+fPiQlJfHw2bOkVK/O01c+ueCttyA5GXX6dJn7rhO94/bUe5rkmX1sidNpk+/r16/PU089xVNPPUVqaiqbNm1iw4YNbN68mQULFpTrgYWssdBG1ljIGgs9eOrcdz3nI9tSR+YkW+apeVZWe7Vr12blypUMHz6chIQEnjl3juSqVXn50iUUVcVr4UKCLlwg7v338bJiGoXkmTZ6x+2p9zTJM/s4dY2FPUJDQxk+fDjDhw9HVVX27dunR7cuI2sstJE1FrLGQg+eOvddz/nIttSROcmWeWqeWdNeVFQUO3fupFevXly4cIFXLl4kvVo13o6PR8nPJ2zTJio8+iisXAmBgQ6JXfLMMr3j9tR7muSZfXRbY2EPRVFo37693t26lJb5dbLGQtZYOKK8rLGwzFPnvssaC8kzvfoqq70mTZqwa9cuevTowb///sucCxdIrVKF+cnJeOXk4LVpE/TrB199BVe9wWZv7JJnlskaC1lj4SxOW2Px0ksv2RzM1aZMmaK5DSGEEEK4h3r16pkHFydOnOCTS5dIi4zk8/R0vLOyYOdO6NkTNm2CiAhXhyuEcCKbBhbTpk3T/K7utTiwMNqxINaaMqVdl8Xb+vcli7c9YxGaiacuqtVzoaMtdWSxo2Wemme2tlezZk127NhB7969+f3331kVH0+f8HA2+vnhk5ICBw6gdu2KunkzVKtmd1+SZ5bJ4m1ZvO1MTlu8ff/999s8sEhISGD9+vUYDAaPnJJjD1m8rY0s3pbF23rw1EW1snhb8kyvvmxtz8fHhxUrVjBixAh+++03tiUn0yU0lG8rVcI/MRHl6FEMHTuStGIFhpo17epL8swyWbwti7edyWmLt+fPn2912ZSUFN544w1WrlxpfoE9cOBAW7rzWLJ4WxtZvC2Lt/XgqYtqZfG25JlefdnTXlRUFDt27KBPnz4cPHiQH1JTaRcSwr5q1Qi4cAGfU6eoPHgw6tat0LChzX1Jnlkmi7dl8bYzuXTxdmpqKm+//TbvvPMOqampqKpK3759eemll7jxxhsd3Z1HkMXbtpPF244tL4u3LfPURbWyeFvyTK++7GmvcuXKbNu2jVtvvZUff/yRX9LTaWE08lOtWgT/+y/KuXMoXbvCli3QqpXNfUmeWSaLt2XxtrPYEqPDnk16ejozZsygTp06vPTSS6SkpNC7d2/279/Phg0brtlBhRBCCHGtCQ0NZfny5XTv3h2A45mZNIqLI7VevYIC8fHQrRt8/70LoxRCOJrmgUVmZiazZ8+mTp06TJ06laSkJHr06MH333/Ppk2baNu2rSPiFEIIIYQHCQoK4quvvqJv374AnMnOpt7p0yQ2alRQICUFeveGbdtcGKUQwpHsngqVlZXF3Llzef3110lISEBVVbp27cpLL71Ex44dHRmjx5NdoWwju0I5trzsCmWZp+7WI7tCSZ7p1Zcjcs3f35/Vq1czatQo1q5dS3xeHnWOH+eP5s2p9uuvkJmJ2r8/6mefod5yi+SZnWRXKNkVypmctisUQHZ2Nu+//z6vvfYa8fHxqKpK586dmT59Ol26dLG1uXJJdoXSRnaFkl2h9OCpu/XIrlCSZ3r15chce/fddwFYu3YtqQYDdY8c4Wjz5lz/668oubl4jRiB8ZVXiLvnHskzO8iuULIrlDM5bVeod999l9mzZ3Pp0iVUVaVDhw5Mnz6dHj162BxkeSa7Qmkju0LJrlB68NTdemRXKMkzvfpydK598cUXjB07lk8++YRsVaXhr79ytF07bti/H8VgoPpzz2EICkKJidEUk+SZ7AqltbzkWVFO2xVq/PjxKIqCt7c3I0eO5NZbb+XSpUssW7bM6jZGjRplS5flgpYdAWRXKNkVyhHlZVcoyzx1tx7ZFUryTK++HJlrXl5ezJ8/n6CgIGJjY8kHGu3fz+GOHWmxZw8A3o8/DunpMGmSppgkzzyrP9kVyr3ZEqNdc2Ty8/NZunQpS5cutbnutTiwEEIIIUTBC5T33nuPwMBA3njjDVSg5Z497OnalVt27CgoNHlywcLuWbMK3vQQQngMmwYWnTt39sh3v4UQQgjhHhRF4bXXXiMoKIiXXnoJgI47dvBVx44MuPLJBa++CqmpMHcueMA7ukKIAjYNLHaY3k0QQgghhLCToihMnz6dwMBAJl2Z9jRwzx5W9ejBkG+/RVFV+OADSEuDTz4BH8/ahESIa5W8DSCEEEIIl3juueeYM2eO+d93bt/Ooh49UL29Cx5YuhTuvBOu2llRCOGerH4L4N9//3VIh7Vq1XJIO55EzrGwjZxj4djyco6FZZ56voCcYyF5pldfeuXaY489hq+vLzExMaiqyphvviG1e3f+t2cPSm4ufPllwVkXa9diDAyUPLNAzrGQcyycySnnWFx33XWa11coikJ+fr6mNjyBnGOhjZxjIedY6MFTzxeQcywkz/TqS89cu/3220lNTWXy5MkYjUae+PZbEm65hamHD+OVmYmyfTt53buTsHgxySB5dhU5x0LOsXAmp5xjUatWrRIHFufOnTMPGHx8fKhcuTIJCQnk5eUB4OvrS3R0tNVBeTo5x0IbOcdCzrHQg6eeLyDnWEie6dWX3ucL3HvvvdSuXZt77rmH/Px8Xvr+e3I6d2bWr7+iJCfjd/AgVUeMwGvpUiKioiTPCpFzLOQcC2dyyjkWp06dsvj4Y489xscff8zjjz/Oo48+Sv369VEUBVVV+fvvv4mNjeXDDz+kf//+vPfee1YHVp5o2cNYzrGQcywcUV7OsbDMU88XkHMsJM/06kvv8wWGDRtGcHAwd955J7m5uby6axfpHTvy3vHjKHFxKL/+SsTgwXht345X7dp29y155l79yTkW7s2WGDU9m/fff58PPviAJUuW8M4779CgQQPzCxdFUahfvz7vvPMOixcvNpcVQgghhCjJgAED2LBhA4GBgQDE7tnDfXXqYKxeHQCfEydQOneGv/5yZZhCCAs0DSw++ugjatWqxbBhw0otN2zYMGrVqsVHH32kpTshhBBCXAN69erF5s2bCQkJAWDx/v0MrVoVQ926ACj//gudOsGRI64MUwhxFU0Di7///pvIyEirykZGRvKXvLsghBBCCCt07tyZb775hvDwcADWHDpE/9BQshs0KChw6RJ06QI//ui6IIUQRWgaWISEhPDbb7+RnJxcarnk5GR+++03goODtXQnhBBCiGtIu3bt+Pbbb4mIiABg888/011RyL3xxoICSUnQowd8950LoxRCmGgaWPTq1YusrCzuuusuEhMTLZZJSkrirrvuIjs7m1tvvVVLd0IIIYS4xrRq1YqdO3dSpUoVAPb++Sft09LI6dChoEB6OvTpAxs2uDBKIQTYsCuUJTNnzmTz5s1s3ryZWrVqMXToUBo1akRkZCTx8fH88ccfrFy5koyMDCIiIpgxY4aj4hZCCCHENaJJkybs2rWLHj16cPbsWX766y9a5edzsGdPgr75BnJyYPBgWLIEylj3KYRwHk0Di1q1arF7927uvvtuDh8+zKJFi4psZ2k66bdVq1YsWbKE2qVsDSeEEEIIUZIGDRqwY8cOunfvzr///svvJ0/SLD+fn/v3p8KGDZCfD6NGQXIyDBrk6nCFuCZpPpK5UaNGHDp0iG+//ZYtW7Zw/Phx0tPTCQkJoUGDBvTu3ZsePXo4IlaPZTQaix2H7uzj4lWjEVQVVNU8wPMU6pWY9YjbkX1pacvWuraUt6qsKVfsyFVrctkd6Rm3I/vS0pY9da2t4+x7muSZ/n3pmWvWlq9duzZr1qxh1KhRHD9+nH/OnKFRXh6/Dh1KpZUrQVXxGjeOoAsXML74os19SZ7p35875pncz4qyJU7NAwuT7t270717d0c159FiY2OJjY3FYDAAEB8fT3Z2dpEyzj4uPj0nD+/8LMhKwZDvsB+zLlRUjDkZoICCk0/edmBfWtqyta4t5a0qm5ePd34WCQmXyUkvelK7ljx0Z3rG7ci+tLRlT11r6zj7niZ5pn9feuaaLXkWFBTEihUrGDFiBH/88QfnLl6kwfbtHBo6lNorVwIQ+tJLpKWmkjFxYsEBoFb2JXmmf3/ummdyP/tPWlqa1WU96xWnh4iJiSEmJobU1FTCwsKIjIwkNDS0SBlnHxfvn5mD4WQCBIbh7e9brK47U1UVVPAOCnf6yduO7EtLW7bWtaW8VWVz8jDk5RMRUZmwIP8il7TkoTvTM25H9qWlLXvqWlvH2fc0yTP9+9Iz1+zJs507d9KnTx9++uknEhITabVtG788/DA1580DoMI77xBiNKK++SYUalPyzL36c/c8k/sZBAQEWF1WBhY60HKcu73HxSteXgXv0iiK01+cO4NyJW49YndkX1rasrWuLeXLLGvKFTtz1Zpcdkd6xu3IvrS0ZU9da+s4855mb+zuwFPzTGt7tta1Nc+ioqLYvn07ffr0Yd++fSQlJ9Nk+XJ++t//qPfeewVl330XJS0NPv4YvL2t6kvyTP/+3DnP5H6GTTG6/7MRQgghhLAgPDycrVu30qVLF6BgykaLBQs48OijqKYXQ598AiNGQG6uCyMV4togAwshhBBCeKwKFSqwceNGevfuDUBmZiadFizgp4kTwffKVOBVq+D22yEz04WRClH+ycBCCCGEEB4tKCiIr776ioEDBwKQk5ND+zff5Ptnn4XAwIJCmzfDbbdBaqoLIxWifJOBhRBCCCE8nr+/P6tWreLOO+8EIC8vjy6zZvHNM89AhQoFhXbvRunZEyUhwYWRClF+ycBCCCGEEOWCr68vn332GUOHDgXAYDDQ++WX+XL8eIiIAEA5dIhKQ4bAxYsujFSI8kkGFkIIIYQoN3x8fHjnnXd4+OGHgYIttwe9/DLLHnkEoqMB8D1+HKVrVzhzxnWBClEOycBCCCGEEOWKl5cX77//Pk888YT5sbteeYX5o0ej1q4NgPLXX9C5M/zzj6vCFKLckYGFEEIIIcodRVF4++23mTx5svmxh2bNYs6QIeTXqVPwwKlTBYOLP/90TZBClDMysBBCCCFEuaQoCq+88gozZswwP/bk228zrVs31MaNCx44d65gcHHkiIuiFKL8kIFFKT744ANuvPFGfH19mTZtmqvDEUIIIYQdnn/+ed58803zv19ZuJDnO3RAbdmy4IG4OOjaFX76ySXxCVFeyMCiFNWqVWP69OkMGjTI1aEIIYQQQoMJEybw/vvvm/89a/58JrRogXrTTQUPJCai9OyJ78GDLopQCM8nA4tSDBo0iAEDBhAWFubqUIQQQgih0bhx41iwYAFeXgUvf95ZtIixdeqgduwIgJKSQsXhw2HHDhdGKYTnKjcDi7S0NCZOnEjv3r2JjIxEUZQSpy+lp6czfvx4oqOjCQgIoGXLlnz++ef6BiyEEEII3d13333Exsbi7e0NwMdffMG9kZEYu3cHwCszE6VfP9iyxZVhCuGRys3AIiEhgXnz5pGTk1Pm1KUhQ4awaNEipk6dyqZNm2jbti0jR45k2bJl+gQrhBBCCJcZNGgQK1aswNfXF4Cla9cyNCAAw223AaBkZ8PAgfDVV64MUwiPU24GFrVr1yYpKYmdO3cya9asEstt3LiRbdu28f777zN27Fi6devGxx9/TK9evXjmmWcwGAw6Ri2EEEIIVxg0aBBfffUVAQEBAKzZuJH+ubmkXxlckJsLd9wBK1a4MEohPIuPqwNwFEVRrCq3du1aQkJCGDp0aJHHx4wZw6hRo9i/fz8dOnSwK4acnBxycnLM/05NTQXAaDRiNBqLlDUajaiqWuxxW8qUdl01GkFVQVVRVdWep+My6pWY9YjbkX39n73zjovi2gLwNwtIEQVRNGrssXeNGiv2gr2XWDExMcQSTbMi9hJLYowxid3Ye0csFLu+RGOJscTeBQREqXvfH7gryy6wy8LCmvv93j7M3HPvOTN7dmbuzDnnmjOWqX1NkTdKVuMr6fBVY3w5O2JJuzNSlzljpaevsX0y+5wm/czyuizpa1nlZy1btmTXrl106tSJqKgo9h8+TOs6dTjUvTv2mzZBfDyid2/Ey5fQv79R+5IVWPr3Ya3nNHk+Sx+m2PnWTCyM5eLFi5QvXx5bW91dr1KlirZdM7GIj48nPj6ehIQE4uPjiY6Oxs7OThuXmZwZM2bg6+urt/3p06dER0frbFOr1YSHhyOE0CaRJSctmdTaX8TEYRP/Cl6FkxBvXV+zQKCOiQIFFIybMGYHXeaMZWpfU+SNko2Lxyb+FSEhz4h5YafTZI4fZmcsaXdG6jJnrPT0NbZPZp/TpJ9ZXpclfS0r/axSpUqsXbuWvn37EhkZybFTp6gfE8PB7t1x3bQJRa1GGTSI8CdPeJVNJxeW/n1Y6zlNns/SR2RkpNGy1nXHmQGEhIRQsmRJve1ubm7adg1Tp07VmShMmzaN5cuXM3DgQINjjxkzhlGjRmn/OyIigiJFiuDu7k7u3Ll1ZNVqNYqi4O7unqrTpiaTWrv9yxgSboaAows29nZ6fbMzQggQYOPkavSbqOygy5yxTO1rirxRsjFxJMTFkzdvPlyc7HWazPHD7Iwl7c5IXeaMlZ6+xvbJ7HOa9DPL67Kkr2W1n7Vr146DBw/SunVrwsLC+N+5czQDggcPxmnpUgBcvvmGXHZ2MGKEEUfAslj692Gt57Ss9rP02p3VaMIFjSH7700mkNqNWNK2SZMm6YSRCCFSnFQA2Nvbkzt3bp2PRCKRSCSS7M/777/PwYMHyZs3LwB/nDtH7RMniPzsM62MatQomDkzq0yUSLI9/7k3Fnnz5tV5K6EhNDQUePPmwhwWLVrEokWLtIngMhTKNGQolAyFsgTWGqIiQ6Gkn1lK138xROWdd95h5cqVfPTRRzx69IhLly9T7dUrgocModAvvwCgGjeOF0+f8uLrryGT36obiwyFsi4/s7bzmQyFSoXKlSuzbt064uPjdfIsLly4AEClSpXM1uHt7Y23tzcRERG4uLjIUCgTkaFQMhTKElhriIoMhZJ+Zild/9UQlRo1ahAQEEDLli25c+cO/968Sf0DBzj79dfknT0bAOcFC8ipUiFmz84WkwsZCmV9fmZN5zMZCpUKnTt35sWLF2zZskVn+8qVKylUqBB16tTJIsskEolEIpFkB0qVKkVgYCClSpUC4NatW1T9/XcejR+vlVHmzUMZNgyspLKPRGIJ3qo3Fvv27SMqKkr7yuby5cts3rwZAE9PT5ycnGjTpg0tWrRg6NChRERE8N5777Fu3Tr279/PmjVrUqz4ZAoyFMo8ZCiUDIWyBNYaoiJDoaSfWUrXfz1ExcHBgc2bN9OjRw+uXbvG/fv3qbJkCcFffkmZuXNRhEBZvJhXYWFEfPcdZMD9Q3qRoVDW62fWcD77z4ZCDR06lNu3b2v/e9OmTWzatAmAmzdvUrx4cQC2bt3KuHHjmDhxIqGhoZQrV45169bRq1evDLFDhkKZhwyFkqFQlsBaQ1RkKJT0M0vpkiEqKvLnz09QUBCtW7fm/PnzPH36lPrLl3Pa15cSvr4oCQk4rV+PoxCIlSvBLmuutzIUyrr9LLtjSijUWzWxuHXrllFyzs7OfP/993z//feZa9BrVCqVQcdRFCXFNmNlUmpXVKrEuE9FyfSb88xAeW23JWzPSF3mjGVqX1Pk05TV+Eo6fdUYX86OWNLujNRlzljp6Wtsn8w8p6XX9uyAtfqZueOZ2je7+tk777zD4cOHad26NWfOnCEkJIQac+dyesoUykycCPHxKBs2oMTEwPr1YG+vN6YlsPTvw1rPadnVz7Izptj4Vk0ssitqtVx52xSSlve1Jl3mjGVqX1PkjZLV+Eo6fNUYX86OWNLujNRlzljp6Wtsn8w+p0k/s7wuS/padvczV1dXDhw4QPv27Tl69Cjh4eHUnD6dY5MnU9nXN3FSsX07omNHxJYt4OhoxF5nHJb+fVjrOS27+1l2xRQ75cQiE5A5FuYhcyxkjoUlsNbYd0vGI5vSR8YkG8Za/czc8d7W2PeVK1cycOBAgoODefHiBXUmT2bv6NE0njcPJToaxc+P2FateL5yJSJnzjT3O6Ow9O/DWs9p1uJn2Y3/bI5FdkHmWJiHzLGQORaWwFpj3y0Zj2xKHxmTbBhr9TNzx3ubY9/3799P9+7d2bt3L9HR0bT+7jsO+vjQYMYMlBcvsD92jPz9+yN27wYXlzT3PSOw9O/DWs9p1uRn2Yn/bI5FdsWc+DqZYyFzLDJCXuZYGMZaY99ljoX0M0vpkrHv+jg5ObFt2zb69OnDli1biI2NpamPD/t9fWk2Zw48f45y/DhKixbg5wevV/LObGSOxdvlZ9kJU2yUEwsLoE5H3LoxMqm1yxwLy+uSORbWESuqwVpj3y0Zj2xKHxmTbBhr9TNzx3vbY99tbW1Zu3YtAwcO1C6623LCBLb5+NB+4UKUZ8/gf/9DNGmCOHAA8udPdTxzsfTvw1rPadbmZ9kFU+yUE4tMQOZYmIfMsZA5FpbAWmPfLRmPbEofGZNsGGv1M3PH+6/Evs+ZMwdFUVi7di1qtZpOkybx26hRDFi9GpsnT1AuXCChYUNCN25EXbBgmuOlF0v/Pqz1nGatfpbVyByLLEbmWJiHzLGQORaWwFpj3y0Zj2xKHxmTbBhr9TNzx/svxb6vXLmSPHnysGjRIoQQDJ47F9XYsQxYvRrl7l1sr1/HvVs3xMGDUKyYUWOaiqV/H9Z6TrNmP8tKZI5FNsOc+DqZYyFzLDJCXuZYGMZaY99ljoX0M0vpkrHvaaNSqVi4cCFOTk7MmTMHgEHTp/Pyq6/4bMsW+PdflH//RfHwgMOH4b33jBrXVGSOxdvtZ1mJKTZm/72RSCQSiUQiycYoisKsWbPw8fHRbvOeM4fZ7dohypZN3HD3LjRqBJcvZ5GVEknmI99YWAB1OhJijZFJrV0mb1tel0zeto4kNA3WmlRryURHU/rIZEfDWKufmTvefzWpduLEiTg4ODBmzBgAvvnhB6I++YRJOXKgXLgADx8iPDwQfn5QrVq6dBjC0r8Paz2nvS1+ZmlMsVNOLDIBmbxtHjJ5WyZvWwJrTaq1ZKKjKX1ksqNhrNXPzB3vv5xUO3DgQBISEhg/fjwAk5csIaRXL+YpCjn++gvl2TNE06aErVtHXPXq6dKRHEv/Pqz1nPY2+ZklkcnbWYxM3jYPmbwtk7ctgbUm1Voy0dGUPjLZ0TDW6mfmjvdfT6odM2YM+fLlY+jQoQghWLR+Peo+ffgxZ05UJ06gCg/HrWfPxEX0GjRIt56Mtjsr9Ek/y/7nM5m8nc0wJ3FHJm/L5O2MkJfJ24ax1qRambwt/cxSumRSbfr55JNPyJkzJwMGDECtVrN47Vpedu3KMg8PVIGBKJGRKG3awM6d0KyZWboy0u6s0Cf9LHtjio3Zf28kEolEIpFIrJC+ffuyYcMGbG0Tn+Ou3LKFPi4uqFu2TBR4+RLatoW9e7PQSokk45ATC4lEIpFIJJJMolu3bmzbto0cOXIAsGHnTjoBCe3aJQrExECnTrB1a1aZKJFkGDIUygKo01FpxxiZ1NplVSjL65JVoayjuoUGa63WY8kKKqb0kVVUDGOtfmbueLJajy6enp7s3LmTzp078+rVK3YdOEBrDw/2dumC3datEBeH6NEDsWIF9Olj8viW/n1Y6zntbfezzMIUO+XEIhOQVaHMQ1aFklWhLIG1VuuxZAUVU/rIKiqGsVY/M3c8Wa1Hn6pVq/L777/Tr18/oqKiOBgYSMP338evSxdctm5FSUiA/v2JePKEVyZOLiz9+7DWc9p/wc8yA1kVKouRVaHMQ1aFklWhLIG1VuuxZAUVU/rIKiqGsVY/M3c8Wa3HMB07dsTf3x9PT0+eP3/OqbNnaSEEgYMG4bh8OYoQuIweTS47O/D2NnpcS/8+rPWc9l/xs4xGVoXKZphTEUBWhZJVoTJCXlaFMoy1VuuRVaGkn1lKl6zWk/HUrVuXw4cP07JlS549e8aZ//2PD2JjOfHJJzgtWQKAavhwiI6Gr74yelxZFUr6WWZhio3Zf28kEolEIpFI3iKqV69OQEAA77zzDgB/XbhAzYAAIocPfyP09dfg65uYAyeRWAlyYiGRSCQSiURiYSpWrEhQUBBFihQB4Mo//1B9zx7CvvzyjdCkSTBmjJxcSKwGObGQSCQSiUQiyQJKly5NUFAQJUqUAODGjRtU27iRp2PHvhGaNQtGjAArqSAk+W8jJxYSiUQikUgkWUTx4sUJDg6mbNmyANy5c4eqy5fzcOLEN0ILF8Inn8DrapMSSXZFJm9bAHU61gYwRia1drmOheV1yXUsrOtpmrWuL2DJmu+m9JF13w1jrX5m7nhyfQHTKFiwIEeOHKFVq1ZcuHCBhw8fUnXxYs74+lLU1xdFrYbffkO8eoVYtgxsdW/fLG23tZ7T/ut+ll5MsVNOLDIBuY6Fech1LOQ6FpbAWtcXsGTNd1P6yLrvhrFWPzN3PLm+gOkoisL69evp1asXFy5c4OnTp1SbP58jX39N1TlzUBISUH7/nZjnz3n+00/weiXvrLDbWs9p0s/Sh1zHIouR61iYh1zHQq5jYQmsdX0BS9Z8N6WPrPtuGGv1M3PHk+sLpI/8+fMTEBBAu3btOHHiBM+fP6fRokWcmDyZCr6+KLGxOOzZQ4HPPkNs3Aiv1xewtN3Wek6TfpY+5DoW2QxzahjLdSzkOhYZIS/XsTCMta4vINexkH5mKV1yfQHL4+bmxoEDB2jfvj0BAQFERkZSe9o0jvr6Ut3XF6KjUfbsQenYEbZvh5w5s8Ruaz2nST8zHVNszP57I5FIJBKJRPIfwtnZmT179tCqVSsAXr58Sd1Jkzjp46OdSHDwILRpAyaEqUgkmY2cWEgkEolEIpFkM5ycnNixYwcdO3YEICYmhkYTJxIwbhxowquDg6FFCwgLy0JLJZI3yImFRCKRSCQSSTbE3t6eTZs20aNHDwDi4uJoPmEC+77+GtzcEoVOnUJp3hzl2bMstFQiSUROLCQSiUQikUiyKXZ2dqxdu5YBAwYAkJCQQNsJE9g2YgTkzw+Acu4cbl27wsOHWWmqRCInFhKJRCKRSCTZGRsbG5YtW8ann34KJFb76+Ljw++ffAKFCwNgd/UqSpMmcPduVpoq+Y8jJxYSiUQikUgk2RyVSsVPP/3EyJEjtdv6TpnCr/36IYoXB0C5dg0aNYJ//80aIyX/eeTEQiKRSCQSicQKUBSFefPmMXbsWO22ITNnsqBzZ+JLlEjccOtW4uTin3+yxkjJfxq5joUFUKvVesuhZ/Zy8UKtBiFAiMQF0qwI8dpmS9idkbrMGcvUvqbIGyWr8ZV0+KoxvpwdsaTdGanLnLHS09fYPpl9TpN+ZnldlvQ16WemMWXKFBwdHZkwYQIAo+bP59mgQUx1cED5+2+4fx/RqBHiwAGoXDnD9VvrOU36WfowxU45scgEFi1axKJFi0hISADg6dOnREdH68hk9nLxL2LisIl/Ba/CSYi3rq9ZIFDHRIECCpm88nYG6jJnLFP7miJvlGxcPDbxrwgJeUbMC92V2s3xw+yMJe3OSF3mjJWevsb2yexzmvQzy+uypK9JPzOdjz76iPj4eHx9fQGYvnw5UX36MEelwu7SJZQnTxBNmhC6bh3xVatmqG5rPadJP0sfkSaslWJdd5xWgre3N97e3kRERODi4oK7uzu5NTWnX5PZy8Xbv4wh4WYIOLpgY2+n1zc7I4QAATZOrpm+8nZG6jJnLFP7miJvlGxMHAlx8eTNmw8XJ3udJnP8MDtjSbszUpc5Y6Wnr7F9MvucJv3M8ros6WvSz9LHxIkTcXd35/PPPwfg+7VrEf36scDJCeXMGVRhYeTt0QOxZw/Uq5dheq31nCb9LH04ODgYLSsnFhbAnOXc07tcvKJSgaKAomT6zXlmoLy22xK2Z6Quc8Yyta8p8mnKanwlnb5qjC9nRyxpd0bqMmes9PQ1tk9mntPSa3t2wFr9zNzxTO0r/Sx9eHt74+joyMcff4xareaH1at51bMnS+ztUY4eRYmIQGndGnbvhsaNM0yvtZ7TpJ+Zjik2Zv+9kUgkEolEIpGkyMCBA/npp5+wsbEB4NcNG+iXLx/qpk0TBaKioE0b8PPLQisl/wXkxEIikUgkEonEyunYsSObNm0iR44cAPy+fTvdHRxIaNMmUSA6Gjp0gB07stBKyduOnFhIJBKJRCKRvAV07NiRHTt2aGPit+7dS/vYWOI7dUoUiI2Fbt1gw4asM1LyViMnFhKJRCKRSCRvCa1bt2bv3r3kzJkTgH2HDtEyJIS4Hj0SBeLjoU8fWLkyC62UvK3IiYVEIpFIJBLJW0STJk04cOCAtiLlkeBgPG7dIqZ//0QBtRoGDoSff846IyVvJXJiIZFIJBKJRPKWUa9ePQ4fPoybmxsAJ06fpt5ff/Hqo4/eCA0dCgsWZI2BkrcSObGQSCQSiUQieQupWbMmR44cIX/+/AD8ce4ctY4f54W39xuhL76A6dOzyELJ24acWEgkEolEIpG8pVSpUoXAwEAKFSoEwKXLl6nh50f4qFFvhMaNg/HjQYgsslLytiAnFhKJRCKRSCRvMeXKlSMoKIhixYoBcO36dapt3UrIN9+8EZo2Db78Uk4uJGYhJxap8PTpU9q2bUvOnDkpU6YM/v7+WW2SRCKRSCQSicmUKlWKoKAg3nvvPQBu3bpF1TVreDx+/BuhefPA2zsxuVsiSQdyYpEK3t7evPPOOzx9+pTvvvuOHj16EBISktVmSSQSiUQikZhM0aJFCQoKokKFCgDcv3+fKr/8wr1Jk0BREoUWL4bBgyEhIesMlVgtcmKRAi9evGD79u1MmjQJJycnOnToQNWqVdkhV6yUSCQSiURipRQsWJCAgACqVq0KwJMnT6j6ww/cnDIFbGwShVasgA8/hLi4rDNUYpW8NROLyMhIvv76a1q2bIm7uzuKojBp0iSDsi9evGDkyJEUKlQIBwcHqlWrxvr163Vkrl27hrOzM0WKFNFuq1y5MpcuXcrM3ZBIJBKJRCLJVNzd3Tly5Ai1a9cGIDQ0lOpz5vDP5MlgZ5cotGED9OgBMTFZaKnE2nhrJhYhISH88ssvxMTE0EmzdH0KdOnShZUrV+Lj48O+ffuoVasWvXv3Zu3atVqZFy9eaBeW0ZA7d25evHiRGeZLJBKJRCKRWIw8efLg7+9PgwYNAAgPD6fm9On85esL9vaJQtu3Q6dO8OpVltkpsS7emolFsWLFCAsLIzAwkBkzZqQot3fvXvz9/fnpp5/45JNPaNKkCb/++istWrTgq6++IuF1TKGzszMRERE6fSMiInB2ds7U/ZBIJBKJRCKxBLlz52b//v00a9YMgKioKOpMnsyZSZPAySlRaP9+aNsW5INViRHYZrUBGYWiSTpKg23btuHs7Ez37t11tg8aNIg+ffpw6tQp6tWrR+nSpXnx4gX37t3j3XffBeDixYv069cvxbFjYmKISfLKUDMxUavVqJNVWFCr1Qgh9LabIpNau1CrE0vGCYGwstJx4rXNlrA7I3WZM5apfU2RN0pW4yvp8FVjfDk7Ykm7M1KXOWOlp6+xfTL7nCb9zPK6LOlr0s/Mwxy7HR0d2blzJ927d2fv3r1ER0fTwMeHgz4+NJg5EyUyEo4cQbRqhdi9G1xcrPacJv0sfZhi51szsTCWixcvUr58eWxtdXe9SpUq2vZ69erh7OxMx44dmTRpEgsXLuTQoUOcO3eOTZs2pTj2jBkz8PX11dv+9OlToqOjdbap1WrCw8MRQqBSGX5xlJZMau0vYuKwiX8Fr8JJiLeur1kgUMdEgQIKxk0Ys4Muc8Yyta8p8kbJxsVjE/+KkJBnxLyw02kyxw+zM5a0OyN1mTNWevoa2yezz2nSzyyvy5K+Jv3MPDLC7sWLF/PZZ5+xZ88eYmNjaerjw8Yvv6Tj4sWowsNRjh8nvnFjQtetI8HV1SrPadLP0kdkZKTRstZ1x5kBhISEULJkSb3tbm5u2nYNP/30EwMGDCBv3rwULlyYDRs2kC9fvhTHHjNmDKOSrGQZERFBkSJFcHd318vXUKvVKIqCu7t7qk6bmkxq7fYvY0i4GQKOLtjY2+n1zc4IIUCAjZOr0W+isoMuc8Yyta8p8kbJxsSREBdP3rz5cHGy12kyxw+zM5a0OyN1mTNWevoa2yezz2nSzyyvy5K+Jv3MPDLK7q1btzJo0CDWrl1LfHw83WbPZpuPD+0XLkR59gy7v/4if69eJOzbh+LqanXnNOln6cPBwcFo2ey/N5lAajdiSdvc3d3Zu3cvL1++5Nq1a7Rs2TLVce3t7cmdO7fORyKRSCQSicQasLW1ZcWKFQwePBhIvAHu6OPDhqFDEe+8A4By4QI2zZqhevgwK02VZFP+c28s8ubNa3CRu9DQUODNmwtzWLRoEYsWLdImgstQKNOQoVAyFMoSWGuIigyFkn5mKV0yROW/62eTJ08GYOnSpQD0njKFZ8OHM3TzZmwePED55x/ydOpEyKZNiGLFssx26WeWQYZCpULlypVZt24d8fHxOnkWFy5cAKBSpUpm6/D29sbb25uIiAhcXFxkKJSJyFAoGQplCaw1REWGQkk/s5QuGaLy3/azJUuW4Obmxpw5cwAY9sMPKF9+yWdbt6L8+y857t4lf/fuCH9/KF06S2yXfmYZZChUKnTu3JkXL16wZcsWne0rV66kUKFC1KlTJ4ssk0gkEolEIskeKIrCjBkz8PHx0W77/LvvmN22LaJMmUSZu3dRGjeGy5ezxkhJtuOtemOxb98+oqKitK9sLl++zObNmwHw9PTEycmJNm3a0KJFC4YOHUpERATvvfce69atY//+/axZswYbzXL2ZiBDocxDhkLJUChLYK0hKjIUSvqZpXTJEBXpZwCffvopCQkJTJ06FYBvFy7kWb9+TAEcrl5FefQI4eFB6IYNxKcj6kP6Wfb3s/9sKNTQoUO5ffu29r83bdqkLQ978+ZNihcvDiRWPRg3bhwTJ04kNDSUcuXKsW7dOnr16pUhdshQKPOQoVAyFMoSWGuIigyFkn5mKV0yREX6mQZfX1/y58/P8OHDAfhu9Wpe9uzJQmdnVH/8gSo0lLzduyP27YPatS1mu/Qzy2BKKNRbNbG4deuWUXLOzs58//33fP/995lr0GtUKpVBx1EUJcU2Y2VSaldUKlAUUJRMvznPDJTXdlvC9ozUZc5YpvY1RT5NWY2vpNNXjfHl7Igl7c5IXeaMlZ6+xvbJzHNaem3PDlirn5k7nql9pZ+ZR2bbPWzYMBwdHRkyZAhCCH7asAHRpw+L7O1RTpxAef4cpWVL2LMHGja0mO3SzzIfU2x8qyYW2RW1Wq68bQpCyJW3M1LeKFmNr6TDV43x5eyIJe3OSF3mjJWevsb2yexzmvQzy+uypK9JPzMPS9nt5eWFvb09gwYNIiEhgcVr1xLVpQvLGzVCFRQEkZGI1q0R27ZB8+aZbrv0M8tgip1yYpEJyBwL85A5FjLHwhJYa+y7zLGQfmYpXTL2XfqZIVq0aKFdpTs+Pp5VW7cS3rIl6zw8cAwMRHn5Ejp04PlvvxFjxORC+ln297P/bI5FdkHmWJiHzLGQORaWwFpj32WOhfQzS+mSse/Sz1Ji0KBBODg48PHHHxMTE8OOAwfo2bIlW9u1w3b3bpSYGFy9vBC//w5du2aa7dLPLMN/Nsciu2JKfF1CQgJxcXFAouPFx8cTGxubotOm1B4XF4edCmxJwEZYV46FEAJFEahEfOa/schAXeaMZWpfU+SNkbUlATtVot/ExurKmOOH2RlL2p2RuswZKz19je1jjJw5viT9zPK6LOlrWeVndnZ22NnpPnyzptj3pFja7hYtWrBz5046derEq1ev2HXgAG08PNjbrRt2mzejxMWh9O4NK1fChx9mmu0yxyLzkTkW2Qy1Ou24dSEEjx8/1r4aSyqX2iuolNqFgOK5FBRVJIrauiYWCCCHGtThZPK8ImN1mTOWqX1NkTdC1sVGIHIpPH1wj2cGZNLrh9kdS9qdkbrMGSs9fY3tY4ycOb4k/czyuizpa1nlZ/b29uTNm5fcuXNbXey7BkvbrdHXtGlT9u3bR7t27Xjx4gUHAwNpWrcuB3v3xn7dOkhIQPTrh3j5EgYPznDbZY6FZZA5FllMenIsXr16RVRUFO7u7jg5OQFonU6lUhkMYUmtXS0Er2LjUCk2WGFRKIRQoyiWmcVnpC5zxjK1rynyackKAWqRgGMOO1TJHMYcP8zOWNLujNRlzljp6WtsH2PkzPEl6WeW12VJX8sqP4uPjyciIoJ79+7h7OxMjhw5rCr2XYOlY/aT6itbtiwbNmygT58+hIeHc/TECeq8eMHhnj1x27ABRQiUIUOIePqUl15eGWq7zLGwDKY8IFBEekrYSIxCk2MRFhZmMMfi6dOnuLu7oygKN27cIGfOnBQqVEhHLi4uTu81rTHtCWpB+MtobFS2qFTWcxGGxGRjEhLAxsYiydsZpcucsUzta4q8MbJqtSBBHY+LkwM2BvwlvX6Y3bGk3Rmpy5yx0tPX2D7GyJnjS9LPLK/Lkr6WVX4mhODevXvEx8dTtGhR7bXZGm74NCS9p7DUxCK5vnPnztGqVSuePXsGQOVKlThRvz45lyx502/WLPjyywyz3dS+xsobI5eWTGrtlv6+zCUiIoI8efIQHh6udz+bHPnGwgKkFV+nVqtJSEggd+7cOk9XEmPjFa1sclJvfz1ftK45BZBYuUhYoCJURusyZyxT+5oib5RskiZDT4rT74fZF0vanZG6zBkrPX2N7WOMnDm+JP3M8ros6WtZ6WeKouDq6sr9+/dJSEiwqtj3pFja7uT6atSoQUBAAM2bN+fRo0dcuHiR9+PiODViBLlfrxum+uYbePUKJk4kaTiFzLHI3phiY/bfm/8A8fHxANjaynmeRCKRSCSWRvP2QhPCLEkfFStWJCgoiCJFigBw5Z9/qL5rF2FfffVGaNIk+PbbxBhcyVuHvJO1AGklb2v+DZA8Mi2l7ca2I7C6txaCxPKoAmGZUKgM0mXOWKb2NUXeKNkk7mPIl8z2w2yKJe3OSF3mjJWevsb2MUbOHF+SfmZ5XZb0taz2M8012ZqSajVkVfK2IX2lSpXSvrm4efMm//77L1XXr+fsmDHknzEjUWj2bERUFGLBAtSQbttl8rZlkMnbWYypydsJCQna8neatxeAtg1Sfp2bUnuCWqAINYgEhHX4rQ5CqFHUOve7VqHLnLFM7WuKfJqyAhSR6INCpR+Ckl4/zM5Y0u6M1GXOWOnpa2wfY+TM8SXpZ5bXZUlfy2o/i4+PR61WExoaSlRUlNUk1WrIyuRtQ/qcnJzYsmUL3bt358aNG9y9e5cqy5YRNGoUZebNA0BZtIhXYWE8nzmT8BcvZPJ2NkYukJfFmLpAXmxsLJGRkdja2hoMh0orSc1Qu6IWiNh4UGxQrDB5WxGAyjLJ2xmly5yxTO1rirwxskItEIrA1tbWYPI2pM8PrQFL2p2RuswZKz19je1jjJw5viT9zPK6LOlrWeVntra2qFQq3NzcsLOzs5qkWg1J7yksNbFIS1/+/PkJDg6mVatWXLhwgcePH1N/1SrOTJpEscmTUdRqnNauxUEIlFmzcM+fP10TC7lAXuZjygJ52X9v3gI0yTnJP0kTdzQJZEk/gM5fU9q1WNecAnidZGzB5O2M0mXOWHdu38Y1lxOfDflIZ/vQIYNxccrB7du30q3LKNlkyduG/KxEiRKUKFHCZD+15CcwMBCVSoWvr2+ashp7mzRpkuJvMKM+GXmMzBkrPX2N7WOMXFoyqbVnJz/Lqu/e0ros6WvZxc+SX5ut6WNpu43RV7BgQY4cOULNmjUBePbsGdUXLODGlCnw+kGqat068gwdiio+3iL7bay8MXJpyaTWbm1+ZixyYiHJcm7fvoWLUw5cnHLQo0sngzLBQYG4OOVg5DBvyxonMZmpU6eiKApz58412F62bFkUReGLL74w2N6sWTMUReH06dOZaeZbwa1bt1AUhdatWxtsnz17NoqiULJkSW7cuGFh67I3586dY8KECXzwwQfkz58fe3t7SpYsyWeffcb9+/dT7Hft2jV69OiBu7s7jo6OVKlShR9//DHFGOSYmBimTp1K2bJlcXBwoGDBgnz00Uc8evTIKDs/++wz7U2vsX0AGjdurNdH4y+KolC4cOEUE5UvXLigvempVKmSTtuKFStSvfFv3Lix0TZK/hvkzZuXQ4cOUa9ePQCeP39OjZkzuTx5MuTIAYDDnj0oXbtCsrBxifUhQ6Ek2Qq//Xs5djSYevXqZrUp2QIf36l8MforChUqnNWmGE3Tpk2ZMGECR44cYfTo0TptDx8+5OrVqyiKwpEjR/T6xsbGcuLECXLlyqV9wmUKtWvX5u+//yZfvnzptv9tYcyYMcycOZOKFSty4MABChUqZHVJz5nJp59+yunTp6lVqxa9evXC3t6eU6dOsXjxYjZt2kRwcDDlypXT6XP58mXq1avHy5cv6dGjB4ULF2bfvn0MGzaMv/76i19++UVHXq1W06lTJ/z8/KhTpw5dunThxo0bLF++HH9/f06dOsU777yToo2HDh3i559/JmfOnERFRWXYvtva2vLgwQP8/Pzw9PTUa1+6dCm2trY6OX/JadasGQ0aNNDbXrx48QyzU/L24OLigp+fH+3btycgIIDIyEhqTZ3KUV9fqvn6okRHo+zdC+3bw/btkDNnVpssSSdyYmEBjK0Kpfkk5b9UFaposeLcu3sHn/Fj8T905L9XFQr95Op3ChbknYIFzdKVkVWh0moXQvD++++TM2dOgoODiY+Px8bGRiunmUx07tyZbdu2ERISgpubm7b95MmTvHr1irZt26JSqUy+EXZ0dKRs2bIp2mnqfmUkmV0VSvNvtVrNZ599xi+//EKdOnXYs2cPbm5uen3+61WhPvzwQ9asWUOpUqV0ts+aNYsxY8YwevRodu/erdM2dOhQwsPD2b17t/aGfMqUKXh6evLrr7/Sq1cvmjRpopVfsWIFfn5+9OjRg3Xr1mnDb5YvX87gwYP55ptvWLFihUH7IiMjGTx4MJ07dyYkJITAwECD14jkGLqGJD9u9erV4/z58yxbtow2bdroyMfGxvL777/j6enJzp079cbV/G3WrBnffvttqjZYU1UozXGytmo9GrJTVaiUcHJyYvfu3XTt2hU/Pz9evnxJ3UmTODxxIh9Mm4bq5Us4eBDRpg1i505IYyG29Nghq0KlD1kVKouRVaFMQ6gT96F06feo36AB635fw84d2+jQqbP2flcjg1Aj1LpP0e7evcusGdM45O/Ps2fPcHd3p2nz5nw7djzvvvuujmzb1q04djSYxyFhfDd7Fps3beTunTuM/uprxowbj6uzE/UbNOTXZcuYOG4chw8dIjY2hnr16zP7u3kUL1GCa1evMmniBI4dO0p8XBzNmrfgu3nzcc+fX6f60upVK9m7ZzcXL1zgyePHODo5UaNGTUaOGk0jDw/dg6D50Qqhs39DPxnCut/XcP7S3xQrVgyAyhXKcffOnRSP56Kfl/Bh337a/7516xZzZ8/iyOFDPHnyhDx58tC0eQvGjBtP0aJFX+t9UxVqx+5dzJgxg0uXLpE7d27atWvHlClTtOMlf4qZ3A8VRaF+/focOHCAM2fO8P7772tljxw5Qq5cuRg+fDhbt27l8OHDdOrUSdt++PBhABo1aqSjZ+fOnSxatIg///yTV69eUapUKfr378+IESN0Ji6BgYG0aNGC8ePHM3HiRB07jx07xsSJE/nf//6Hg4MDTZs2Zfr06dobi6T6Jk+ezNSpU/H39+fJkyfMmTOHK1eu4OrqSteuXZk+fTqOjo56xz44OJi5c+dy6tQpIiMjKVq0KN26deObb77B3t5ee4yS2tmyZUumTJnC6dOnCQ8PJzY2NsXvNvnx1tgshCA+Pp64uDgGDhzIpk2baNasGZs2bcLZ2VlHbvDgwaxZs4YrV66wa9culi1bxo0bN+jZsydLly4F4NKlS0ybNo3AwEDCw8MpWLAgHTp0YNy4cToTQYAcOXLQqFEj/P399c5HpUuXBhLDh5Lr//vvv9m8eTMrVqzg3r17FClSBC8vL4YPH64zRmxsLL/++it79uzh77//5unTp7i4uFCvXj3Gjh1L9erVUz1eKfHpp5/qHEMNI0eOZMqUKQQGBuq0Xb16laCgIBo3bkzLli21bYqi4Ovry6FDh/jll19o2LChts+vv/4KJPpTfHy8dp/69evHnDlz2LBhA/PnzydXrlx69o0aNYrIyEi+//57PvzwQ62tqb1FSOobSf1a00fz18HBge7du7Nq1SoePXqk84Zv27ZtPHv2jH79+mknFnFxcVrbNTcYmmuWMbYYun6lV15WhTJMdqsKlRpLlizhk08+wc/Pj5iYGBpPmsQab2+6LV2KKjISJTiYuKZNCVu7FuHqmqF2yKpQ6UNWhcpiZFUo01BUmhtDFeMmTmLr5k1M8fWlbcfO2NrY6sooKhTVm2N04/o1WjVvwtMnT2jj2ZZyFSpw5fJl1qxahd/+/fgdCqBUqffe6NJc2Pv04eKFv2javAV5XF0pXrKUdtzw8Oe0btGCAgUK0KdvP65fu8r+fXu5erU96zdtoXWLplStVp2+/Qdy/s8/2LF9G+EREWzfvVen+tJXo76gUuUqNG7SjHzu+Xj44AF7du2kU/u2rFm3kbbtO7w5CJoTi6Lo7J82wVBlo93+2efDef78OQh14vF4Lbty+TIePnyAU05nrezZ06fp3LEtL6OiaO3ZlpKlSnHn9m02bVjPQf8D+B8JokSJktqqUOvWrcVr0CBy585N3759cXV1Zc+ePbRr147Y2Fhy5MiR4kKOSf2wSZMmHDhwgKCgID744APt9sDAQOrXr0+9evVwdnYmKCiIbt26aduDgoKAxKehGj1jx45l5syZvPvuu3Tp0oXcuXMTHBzMt99+y9mzZ9m4caO2v2aSoVKpdOw8dOgQnp6eqFQqevbsScGCBTl8+DDNmjUjT548gO4ClZoT/ZIlS9i3bx8dO3akcePG+Pn5sWjRIsLCwlizZo3O/v/88894e3uTJ08e2rdvT758+Th79iwzZ84kKCgIPz8/bWUNjZ2nTp1i1qxZNGnShI8//pi7d+8avVCmnZ2dVlZRFGJjY+nevTv79u2jS5curF27lhyv45eTovGpUaNGcfLkSdq2bUu7du0oUKAAtra2HD9+nFatWhETE0O3bt0oVqwYJ0+eZOHChezfv5/jx4+TN29evTE137+h81HSfdLo/+qrrzh58iTdu3fHwcGBbdu2MXbsWG7cuKG9IYfEZM/Ro0fTsGFDWrduTd68ebl58yY7d+7Ez8+PwMBAatWqZdQxMwaVSoWNjY2eDx09ehSAli1b6n1HdevWxdXVleDgYG1bdHQ0p0+fpmzZspQoUULvuLRs2ZIffviBs2fP0qJFC522AwcOsHTpUlauXEnhwoW1xyyla0Ry7OzsDPZJ6i+DBw/mt99+Y8OGDYwYMULbd9WqVeTPn5+OHTvqjJf0+Gj+GmuLKciqUOkjO1aFSo0dO3bQv39/Nm7cSFxcHH0WLiTnxIl4fv89SmgoOf78k/y9eyP27wd39wyzQ1aFSh+mVIVCSDKN8PBwAYjw8HC9toSEBPHw4UORkJAgXr16JS5fvixevXqlI6NWq0VsbKxQq9UGx0+tPS4+QTyLiBJhUTEi/GVstv789fdVAYhmzVuK8Jex4vPhIwUgFiz8SSuze7+/AMSgwR/r9G3UuImebPjLWLFg4U8CEB5Nmupsb9CwkQBE5SpVxc17j0T4y1jx/MVLbTuJLxuE97AROv28PhoiAOHi6ipmzpmr3f48Kka0bNVGACLo+Cmdsc5f/kdvX/+5cVsULFhIlHrvPYPHoE/ffjrb+/TtJwDx199X9cZKquu7ed8LQLTxbCvCXkSL8Jex4ll4lCharLjIlSuXCD5xUqfv/oNHhI2NjWjdxlOEv4wVYVEx4ua9hyJ37twiZ86c4p9//tH6UkxMjGjYsKEARLFixYzyw1OnTiXa06aNdtuDBw8EIGbOnCmEEKJly5aicuXKOnocHR2Fq6urSEhIEEIIceDAAe04UVFROjo//fRTAYjNmzdrtx85ckQAwsfHR+e3VrJkSaEoiggODtbZ3qtXL+13nhQfH5/E79vFRVy5ckW7/eXLl6JMmTJCURRx//597fZLly4JW1tbUb16dRESEqIz1owZMwQgZs2apT1GGjsBsXTpUr1jmhpJj/fNmzcFID744ANRv359AQgvLy8RHx+fYt9+/RJ96t133xW3b9/WaU9ISBClS5cWgNi/f7+Ovm+//VYAYvDgwTp9AOHh4WHQD4oVK6bjM0n1FyhQQOcYRkZGisqVKwtABAYGardHR0eLe/fu6Y1/8eJF4ezsLJo3b27S8UuLDRs2CEB0795dZ/uXX36p529Jef/99wWg9dOLFy8KQLRr187gefrHH38UgFi0aJHO9vDwcFGkSBHh6emp3ebh4SEA8fDhw1RtT3qMDPXR+EurVq2EEEJUrFhRVKlSRdt+7949YWNjI0aPHi2ESPxuy5Qpo2P78uXLE8/XzZoJHx8fvY9GX1rXr9RsN1cuPddOzXU4KipKe222JpLeU1iLvvj4eDFw4EDtuVBRFLHV11eI/PmFgMRPhQpCPHiQYXYYK2+MXFoyqbVb+vsyl9TuZ5OT/adJ/3E++OADihQpwrvvvqv3KVKkCCVKlDDYXrxYUSqXK03F0iUp/16JTP141P8g7R0xgdFff0tuFxdmTZ/Ky5cvU5S7d+8uQQFHKFe+PAO9Buu0DfQaTNly5Qg8cph79+7q9R07fqJeSIcGZ2dnxk2cpLOte89eALi55eXTzz7XblcUha7duwNw8cIFnT7Fi5fQG/udggXp0KkzN65f586d2ynumykcOujPt1+PpmKlSvy2YrX26cf+vXu4c/sWI74YTeXKVXT61K1XH8927Tngt5+IiAgA9u7eRUREBF5eXpQpU0Yra2dnx+TJk02yqWbNmto3C5pwCU1+hcfrMDAPDw8uXrzIs2fPgDf5FR4eHtp9+PHHH4HENwdOTk7a8RVFYebMmSiKwrp161K15ejRo/z777+0a9dOJ9lUURSmTJmiE0qVnBEjRmhzNiAxh6N3794IIfjf//6n3b5kyRLi4+P54Ycf9Pzq66+/xt3dnQ0bNuiNX716dby8vFK13xhOnjzJsWPHqFu3LkuXLk11nzR89dVXb0LhXnPs2DGuXbtGmzZtaNWqlU7buHHjyJs3L2vXrk0zXMsYhg8fTqFChbT/7ezszIQJEwBYuXKldru9vT2FC+sXL6hYsSJNmjQhKCiIuLg4s+2BxLDK4cOH4+joqBP+BxAeHg4kJqEaQvNWWiNnqryGkSNHEh4ezpIlS9K5F8YzaNAg/vrrL60vr1ixgoSEBKN88tChQ/j6+up9TKlcJflvY2Njw9KlS7VhiUIIuvj48Psnn4DmN3/5MjRqBKmE/0qyFzIUKpvz+PHjVEsfvo24ubkx8otRTJ7kw08//sCXXxtOEPzr3DkA6jdopBdHqygK9eo35J8rV7j411+8+24Rnfaa76ccOlGy1HvkTFaRosDryi0VK1XS01XgncTk6gcPdL+nmzf/Zd6c2QQFBvDwwX1iYmJ02h89fEjRosVStMMYrv5zhUH9+pDHzY0NW7bj7OysbTtz5lSizNV/mDFtKoqi+xzhyePHqNVqrl+7RrUaNbh4MXFilDRGXMMHH3xgdIgOJF4wGjZsyJ49ezh79iwffPABAQEB5MyZU5tz4eHhgRCCgIAAunXrRkBAAJBYVUrDyZMnyZkzpzb2PzmOjo5cuXIlVVvOnz+f4n4VK1aMIkWKcOvWLYN9a9SoobdNk7fz/PlzHTsB9u/fz8GDB/X62NnZ8c8//+htr127dqq2G0uFChV4/vw5J06cYPLkyXr5JYYwpPvPP/8EMFgyVPPd+fn5cfXqVb0ypKZi6PvQbNN8ZxrOnTvH7NmzOXr0KI8ePdKbSDx79oyCBoocmEJoaCienp48efKEVatW6UwoLcW+fftYvnw5P//8s15+WGbQr18/xowZw7Jly6hZsyYrVqygTp06VKhQIc2+M2bMSDF5WyIxFpVKpS3XrKmq1nfKFF5++y0fr18Pt27B9euJk4vDh6Fkyaw1WJImcmKRzSlQoEC6+6pF5ldVAshvho0pMdT7c35dsoQf5s/Fa/DHBmUiIxOftOfPn99gu+bYRUSE67WlZnPyfBh4E5ucWlt8kpudGzeu07RRfSIjImjo0Zg2nm3JlSsXKpWKo8FBHA0O0ptomEpoSAg9unYmOjqazdt3UaSI7tPnsNAwADauT/2J/suXiWUsNW8uDB1PGxsbvbj6tGjatCl79uwhICCADz74gCNHjlC/fn3t8apVqxZOTk7aiYXmjUbSqjqhoaHEx8fj6+ubop60ynBqngin5icpTSwMPW3W2J90DYDQ0FAApk2blqothnRnBEWKFGHHjh00adIEHx8f1Go1kyZNMlm3xgdSsktTGjX5U/b0YOj7yP965d2k4x8/flw72WzevDndunUjV65cKIrC9u3bOX/+vNm/pbCwMJo3b86lS5dYvHgxffv21ZPR+EJK+645dppzhLHyGrmXL1/y8ccf06RJE4YMGWLG3hhP/vz58fT0ZN26dXTo0IHr16/z5ZdfWkS3RKJBURQmTZpEvnz5mD59OgBDZs4katQoRuzciXL9Oty+DQ0bwqFDkKwMtCR7IScW2ZyTJ09ia2ubYmWL+Ph4g+3xCWrCX0ZjY2OLyoiKHNkNR0dHvh03gRGfD2XunFm09myrJ5MrV+IF/MmTJwbH0GzXyCXFmCol5vDTwh94HhbGr8tW0KNXH522kcO8ORocZNb4cXFx9PuwDzf/vcEvS5dTu45+OJrmBmfD5m20at1KJyk8OWohtPKGjmdCQgIhISEGQ1JSQjNBOHLkCP379+f69es6IRY5cuSgbt26HDlyhJiYGE6ePEm+fPl0noTnzp0bRVG04VLpQXPjlpKfPH78ON1ja9Acu4iICIMVfjS/1eRkpB++9957BAQE0KRJE3x9fRFCpDohM6Rbsx8pHRPN9qQTbEVRUqwOFB4enmIo0JMnT/TeCjx58gS1Wq3TZ9q0acTExBAcHEydOnV0zncnT57Ue7thKqGhoTRv3pw///yTRYsW8cknnxiUS17hKilCCK5fv06hQoW0bztLlSqFSqUyKJ90HM24T5484f79+9y/fz/FZE7NW5k///yTatWqGb+TqeDl5cWOHTsYPHiwNtRPIrE0mtBUJycnxo8fD8AX8+YR5e3N2Bw5UC5fhgcPwMMDDh6EypWz2GJJSsgcCwugWasi+UeksI6F5gP6NbaNbdeSuaX5MwXNugt9BwygTNmy/LpkMffu6sdXVq5aFYDjx4IN1m4/cfyojlxqukQGHCjx+v8Egpv//gtAm7btdWTUajWnTh43bAfGf10jh3lz7GgwX379LT17f2hQpubrSjmnT51Mex8FVKqUeKIOCgrS86UTJ07olCw1xg+rVKlCnjx5OHbsGH5+fkBiGdmkMo0aNeLy5cvs2LGD6OhobQiOpr1OnTqEhIRw9epVg7+B1H4XSe2AxFKwyeVu3brF3bt3U90vY3RowopOnDhh9G81tfGN2b+kfzX/LlmyJAEBARQrVozJkyczfvx4w+eFFHRrblYDAgL09L18+ZKzZ8/i6OhImTJltO158uTRhmsmlb9586Y2XMyQ/uR+JoQgODgYgKpVq2q33bhxAzc3N+rXr68zflRUFH/88Ue6j6MQgpCQEO2k4ocffmDo0KEpympygw4cOKDXdurUKZ4/f64N7xNCYG9vT+3atfnnn3+0b8SS9jlw4IBWRgiBs7MzXl5eBj+aN0V9+vTBy8tLuy5Jar6R0vecfHubNm145513uH//Pl27diVXrlxp+osxx9yQz2eEvDFyacmk1p782mxNH0vbnZH6NGONGTOGuXPnav1u/KJFjKlbF6G5jj95gmjcGPWZM+m2w1h5Y+TSkkmt3dr8zFjkG4tMQK5jYRraNSp4s0aFEGpsVCom+EyiX5/ezJoxTSOslXm3cCEaNvIgOCiQVSuW0n/AQO2Yq1au4O/Ll2nk0ZjChQomGff1xUSd9Di/WXtCI5N8rQyNjam1IdTasYoUSYyPPnEsiBYt3yTBzv9uDpcvXdL2046l+dEmG/+NvW9kf1gwnzWrVtChY0fGTZigZ48GT09P3i1ShEULv6dJ0yY0aNhIZ2oRFxfH2TNnqFuvHohE+dy5c7N8+XI+/fRTbQJ3bGwsPj4+2n5prWORlIYNG7Jz507mzp2Lk5MT1atX1+mvSabWJIcnX7/is88+Y9++fXh5ebF582a9cKxHjx4RFhZG+fLlgTfhSZrfEyTmh5QoUYLdu3dry91qZCZMmKC3JoSmTTNe8v3VtCXVMWTIEH777TeGDRvGvn37KFJEN6cnLCyMGzduULNmTRRFMWinsSQ93snXsYDEsCh/f39atmzJtGnTiI+P1yYiJ72xMrQmQp06dShVqhT79u3Dz8+PZs2aafVNmzaNZ8+eMXDgQFQqlbZvzZo18ff35+DBg9rvMy4uji+++EI7bvJJKcDChQvp27evNoH7xYsXWj/o06ePtk/RokW5evUq58+f177hUKvVjB49mqdPn6a4L2kRGhpKq1atOH/+PPPmzePTTz9NdYySJUvSsGFDjhw5wq5du7QLy8XFxWmfsA4aNEhnjMGDB3Py5EnGjRvH6tVvCiusXLmSv//+m759++Lk5ER8fDwuLi78/PPPBnU3b96cR48eMXPmTO0kw5CtSX3D0PdsyF8Atm7dyv379/V+nxrkOhbZn6T3FNl9HYu0xurTpw9xcXHaHJ5ZS5cS0q0bi1Qqcvz5J0poKKJZM8J+/52YmjXlOhYWQK5jkcXIdSxMI+k6ForKNnGFaAGobOjQqSu163yQ+NQ9UVgnpGf+Dz/SqnkTRnzujd++fZQtV55/rvzN3j27yefuzvwffkxhXYjEbUl1afJRlGRrSSS1MbU2FFVigrTKBq+PP+H3Navp16c3Xbp1x80tL2dOn+L8uT9p1doTv/17ddamMHYdi8ePHjFp4gRsbGwoXrwkM2fM0Muiadu+A1WqVsPB0ZZVv2+gW+f2tGvTGo8mTahQITHM6O7dO5w4fgw3NzfOnruIUAtyueZh/oIFDPbyol69evTs2RMXFxf27NmDg4ODNgzDmHUsNDRt2pSdO3dy6dIlmjdvrreoXL169XBwcODS68lW0vUrANq2bcv48eOZOnUq5cuXp3Xr1hQtWpSQkBBu3LhBcHAwU6ZMofLr1+IprWOxZMkS2rZtS+vWrbXrWBw5coSHDx9SpUoV/vrrL4PrWNjY2Ojtr6E6/tWqVWPRokV89tlnVKpUCU9PT0qWLElERAQ3b94kMDCQfv36adf0SMlOU0i+jkXScTRhUU2bNmXWrFlAYrKtRhZSXhNh+fLltG7dmg4dOtC9e3eKFi3KyZMnCQgIoFSpUsyaNUun3xdffIG/vz+dOnWiZ8+e5MyZk4MHD+Lq6mrQZzT6a9Wqxfvvv0+PHj2wt7dn27Zt3Lp1Cy8vL50E/mHDhuHv70+TJk3o2rUrTk5OBAYGcv/+fRo3bkxAQIDR6zskpWfPnpw/f55y5crx/Plzpk6dqiczcuRIXJMs0LV48WLq169P9+7d6dGjBwULFsTPz4+//vqLwYMH07x5c53+AwcOZPPmzWzatIk7d+7g4eHBzZs32bJlC0WKFGH27NlG2Z0Z61gkHSfpWjMpjadBrmORPUl6T2EN61ikNdZXX31F/vz5+eijj1Cr1fy2eTOiRw9+cXZGFRyMKjISt969Ue/YgVKhglzHIpMxZR0LObGwACqVyqDjKIqibdOsWJz06YoQ4s2NZQpPXVJuf/1sOnvPKQyioCAUtDf6vlOn06ZFU4OypcuUJeDoCWZNm8pB/wP47d9HvnzufNi3P9+MG59m1aXkusyzO/H/FBSqVqvOtl17merrw64d21HZ2FCnTl38DgWyb88u/Pbv1bNDO0YqRMdEa58Y/vD9fIMyRYsVp0rVagDUfP99jp08y/fzv8P/gB8njx/H3t6egoUK0bZdB7r16JnEeOjffwBuefIwdepUVq1ahYuLC+3bt2f69OnacJ/kvpaaHya9QWzcuLFeu4ODA3Xq1CEwMJB33nnHYDWaKVOm4OHhwQ8//MChQ4d4/vw5efPmpUSJEkyaNIm+ffvq6U/+W2rRogWHDh1i/PjxbNq0CUdHR5o1a8batWsZPHiwnu0pjZNa25AhQ6hevTrz5s0jKCiInTt34uLiQtGiRRk5cqR29eSk/QyNnxYpHe/k4xQrVkybczFr1iyEEMycOVNH3pDuhg0bcvLkSSZPnsyBAwcIDw+nUKFCDBs2jIkTJ+qs0gzQpk0bNmzYwNSpU/n9999xc3Oje/fuTJ8+XZsvo9GTNLxmwYIFbNy4kd9++0278vbMmTO1i7Vp+rRv357Nmzczffp01q1bh5OTE02bNmXbtm3aNxzpOY6a8KQrV66kWE550KBB2gUUIbHE7enTpxk3bhz79u3jxYsXvPfee/zwww94e3vr2WBra8v27duZMWMGa9euZcGCBeTJk4eBAwcydepUkytZpbWfSX3DUJ/U/MUY3cn/GmuLsW8sjJE3Ri49107N/miuxyldt7MzlrY7I/UZGmvQoEE4OTnx4YcfkpCQwNKNG3nZsSNrmjVDdegQSlQUqnbtsF+6FFWvXkbbYazdxsilJZNauzX5mSk2KiJ5IKUkw9C8sQgPDzf4xuLJkyfkz5+f2NhYbt68SYkSJXRmhZrX1f/F5G2hjk812Ti76jJnLFP7miKflqxaCBIS4nFxcsDWRvcEYo4fZmcsaXdG6jJnrPT0NbaPMXJCCAYMGMDq1au5efMmxYsXN3oM6WeW12VJX8toPzP1nBUdHc3NmzcpVqwYERER2gpl1kLSewpLvbHIKH1pjbVjxw569OihXT+nc5s2bFIUbPYmPqATdnaI9etRdemSYXYbI5eWTGrtlv6+zCW1+9nkZP+9kUgkEolEIpH8J+nYsSM7duzQPnjdtm8f7aKjie/UCQAlLg6lRw8wsAipxPLIiYVEIpFIJBKJJNvSunVr9u3bpy3nvP/wYVo8e0Zsjx4AKAkJ0KcPrFiRhVZKQOZYSCQSSbYirYXtNCUKR40apRP//1/n1q1brDDipsLV1ZWRI0dmuj0SiSRjady4Mf7+/rRu3ZqIiAgCjh7Fo3Zt9nXvjuumTYnVFQcNglevYOjQrDb3P4ucWEgkEkk2IrVF7ZLi5eVllROLpUuXsnLlygzPN7h165ZRx65YsWJyYiGRWCl169bl8OHDtGzZktDQUE6ePk3DqChOffQRTr/9lij02WcQHQ1JSl5LLIcMhZJIJJJsRFoLianVamJjY/USn//rNG7c2KiF2DTVoCQSiXVSs2ZNAgICyJ8/PwAXL12i1rFjvPD2fiM0ahRMm5ZFFv63kRMLiUQikUgkEonVULlyZYKCgihcuDAAl//+mxp+foSPGvVGaPx4GDcOZPFTiyJDoSyAoeXQky/nnvSJWlK0Ky+n8MNIqx2B1a1lIRAk/k9kyPoSltJlzlim9jVF3ijZJO5jyJfM9sNsiiXtzkhd5oyVnr7G9jFGzhxfkn5meV2W9LWs9LOkbwQ1f60JS9udkfrSO1bp0qU5fPgwzZs35+7du1y7fp1qW7dy5uuvyTd7dqLQ9OmIly8R330HimK0LmPk0pJJrd3a/MwUO+XEIhNYtGgRixYtIiEhAYCnT58SHR2tI5N0OfeEhATUajXx8fHEx8drZTRtkPIiPym1J6gFilCDSEBYh9/qIIQaRa1zv2sVuswZy9S+psinKStAEYk+KFT66wek1w+zM5a0OyN1mTNWevoa28cYOXN8SfqZ5XVZ0tey2s/i4+NRq9WEhoYSFRWFEMIq1hfQkPSewlLrWGSUPnPGcnZ2ZsWKFQwePJhbt25x69YtqqxeTfDIkZRasAAAZcECXoWGEjFjBmowSpcxNqUlk1q7pb8vc4mMjDRaVk4sMgFvb2+8vb21C4q4u7sbXCBPs5x7bGwskZGR2NraYmur/5XY2dmlqs9Qu6IWiNh4UGxQVNZzEYbXT9YFoLKxyBuLjNJlzlim9jVF3hhZoRYIRWBra4tNCv6SHj+0Bixpd0bqMmes9PQ1to8xcub4kvQzy+uypK9llZ/Z2tqiUqlwc3PDzs4Od3d3q7jh05D0nsJSE4uM0mfOWJq+QUFBtG7dmsuXL/Pw4UPqrV3LWR8f3p08GUUInFatwhFI+OUXo3QZY1NaMqm1W/r7MpekizenhZxYWABjlnNXFEX70SCE0P53Sk9dUm5//WzauuYUACgoCIVMn1RktC5zxjK1rynyRskmaTL0pDj9fph9saTdGanLnLHS09fYPsbImeNL0s8sr8uSvpbVfqa5/mquxyldt7MzlrY7I/WZM5aiKBQuXJiAgABatmzJuXPnePLkCdUWLuTslCmU8PGBhASUVauwiY5G+e47o3QZY1NaMqm1W5OfmWJj9t8biUQikUgkEokkFdzd3Tl8+DC1a9cGIDQ0lOpz5vDP5Mnw+u2UsnEjrh9/DDExWWnqW42cWEgkEolEIpFIrJ48efLg7+9Pw4YNgcR8iprTp/OXry/Y2wPg4OeH0qkTvHyZhZa+vciJhUQikUgkEonkrSB37tzs27eP5s2bAxAVFUWdyZM5M2kSwskJAOXAAWjbFl68yEpT30rkxEIikUgkEolE8taQM2dOdu3aRdu2bQGIjo6mgY8PR8eNQ+3snCgUEAAtW0J4eNYZ+hYiJxYSSTbg9u1buDo7MXTIYJ3tQ4cMxsUpB7dv38oaw5JQokSJbLPa861bt1AUhYEDB2a1KVaLoig0btzYpD6TJk1CURQCAgIyxSZLkhE+lJ5jKJFILIODgwNbt26la9euAMTGxtLUx4ft3t4IV9dEoRMnoFkzCAnJOkPfMuTEQpLl3L59CxenHLg45aBHl04GZYKDAnFxysHIYd6WNU6Sbu7cucNnn31G6dKlcXBwwNnZmZIlS9K2bVtmzZpFVFRUVptoMitWrEBRFFasWJHVpugwcOBAFEXh5MmTem13796lXLlyKIrCxIkTs8C6zONtu7GPi4tjy5YtDBw4kPLly5MzZ05y5cpFnTp1+Omnn7RrLxhi7dq11KlTB1dXV9zc3PD09OTs2bN6ciEhIfzyyy906NCBkiVLYm9vT758+WjTpg1+fn5G2fn8+XMKFy6Moii0bt3a6P3TTOaS95k0aRIqlYocOXLw7bffpth/1KhRWrmZM2fqtDVu3FinslOOHDl0Ki5mt9+sxDLkyJGD9evX8+GHHwKJ65V0nzOHHSNHQr58iUL/+x80aQKPH2edoW8RstysJFvht38vx44GU69e3aw2JVvg4zuVL0Z/RaFChbPaFJM4f/48jRs35vnz59SvX582bdqQI0cObt68ydmzZ9m7dy9du3blvffey2pT32quXr1KixYtuHv3LgsWLGD48OHaRTj//vtvnF7HG/8XKVy4MH///TcuLi5ZbYqWGzdu0K1bN3LlykXTpk3p0KED4eHh7Nq1C29vb/bv38+OHTv0SqhOnz6dcePGUbRoUYYMGUJUVBQbNmygfv36+Pn56Uy+Nm3axNChQylcuDBNmzalcOHC3Lt3jy1btrB//37mzJnDl19+maqdw4cPJzwTwkdsbW1ZvXo106dPx8bGRqctLi6ONWvWYGtrq7OQbHJGjx5Nzpw5UavV2okFQLVq1TLcXol1YGtry8qVK3F0dOS3335DrVbTedIk1k+cSM9ff4WHD+HCBfDwgIMH4d13s9pkq0ZOLCTZhqLFinPv7h18xo/F//CRrDYnW/BOwYK8U7BgVpthMqNGjeL58+esWrWKfv366bWfOHGCfJqnRZJM4c8//6R169aEhISwYsUK+vfvjxBv1l4vV65cFlqX9djZ2WW7Y5ArVy5++uknBgwYoDPpmzt3Lo0bN2bXrl1s3ryZ7t27a9uuXbuGj48PZcqU4dSpU+TMmRNbW1tGjBhB7dq1+eijj7hy5Yp28dUyZcqwe/du2rRpo1Obfvz48dSpU4exY8fSp08fChUqZNDGXbt2sXr1an744QeGDx+eofvfqlUr9uzZw759+2jXrp2e3qdPn9KhQwd27tyZ4hhffvklBQoUID4+HltbW6ta80SSedjY2LBkyRIcHBz48ccfAeg1eTIvx45l0Jo1cOcO/PMPNGoEhw9D0aJZbLH1IkOhJNmG0qXL0KvPh5w5fYqdO7Yb3e/u3Tt4fzqEcqWKk88lJ+XfK4H3p0O4d++unmzbVs1xccpBTEwM0yZPolql8uRzzc2MqZMBcHHKQdtWzXlw/z6DB/ajRJGCFM7vRvfOHbl5818Arl39hw97dqNY4QIUzu9G/w978fTJEz1dq1euoHf3LlQuV5r8eXJRrHABOndoS1BggNH7ZijHonK50rg45cDV2UkbQpb08/vqVTpj3Lp1k2HeQ6lYphTurs6UKVGUoUMGc+fObYM6d+zYQa1atXB0dKRAgQJ8/PHHhIWFGW0zJE4cXF1dDU4qAOrWrYurJsb1NcuWLaNjx44UL14cBwcH3NzcaNWqFUeOGD/J1IRDREdH8/XXX1OkSBEcHByoXLkyy5YtS7Hfjh07aNasGXny5MHBwYFKlSrx3Xff6YSeDBw4kEGDBgEwaNAgg4taAkRGRuLj40PFihVxdHTE1dWVNm3acOzYsRTtjYmJYeLEibz33nvY2dkxadIko/fZEMHBwTRp0oTw8HC2bNlC//799WQMhRFpQqr+/fdf5s+fT8WKFXFwcGDw4MF6/ZPy119/UbhwYQoWLMipU6cACAgIQFEUJk2aRFBQEB4eHuTKlYsCBQrw4Ycfcu/ePb1xjhw5wscff0y5cuVwdnbG2dmZ999/n19++UVHTjM2QGBgoM53oQl5SRq2tmfPHho2bEiuXLm0eUKp5VhERkYyefJkqlSpQs6cOXFxcaF69epMmDCBuLi4VI+FEILhw4ejKAqDBg1K9el6cgoXLszQoUP13iTlzJmTUaNGafc3KcuXLyc+Pp5x48bpvH2pWLEi/fv358aNGxw+fFi7vWnTprRt21ZvwauyZcvSs2dP4uLiOH78uEH7QkNDGTJkCH369KF9+/ZG75exdO7cGVdXV4O/1WXLluHu7q5NxpVITEWlUrFgwQK8vd+EVHtNn86PPXpAyZKJG27ehIYN4dq1LLLS+pFvLCTZirETfNiyaSNTJk2iXYfOeq/Dk3Pj+jVaNW/C0ydPaOPZlnIVKnDl8mXWrFqB3/69+B0KoFQp/XCbvr16cPHCXzRt3oI8edpTvEQJbdvz52G0at6EAgUK0OfDfly/dpX9+/Zytd0/rN+0hdYtmlK1WnX69h/I+T//YMe2rYSHh7Nj9z4dHV9+MZxKlavg0aQp+dzz8fDBA/bs2knHtq1Zs24jbdt3SNcxGvr5MMKfP0cINYry5uZg5fJlPHz4AEcnR+22s6dP06VjW6Kiomjt2ZaSpUpx5/ZtNq5fh/8BP/yPBFGiREmt/OrVq/AaNIjcuXPTr18/XF1d2b17N61btyY2NpYcOXIYZaObmxuPHz/m0aNHvPPOO0b18fb2pmrVqjRv3hx3d3fu37/P9u3bad68OVu3bqVjx45GHiHo3r07f/31F927dycuLo6NGzcyePBgHj9+zJgxY3Rkx44dy8yZM3n33Xfp2rUruXPnJigoiK+++opTp06xadMmADp16sTz58/ZsWMHHTt2NBhaERoaSqNGjbh06RINGzakVatWib6xYwctWrRg48aNdO7cWa9fly5dOH/+PK1atcLNzY2SJUvqyRjL3r176datG7a2tuzbt48mTZqYPMawYcM4efIkbdu2pW3btri7u6coGxwcTPv27cmdOzd+fn5UqlRJp/3kyZPMmDGDtm3bMmzYMP744w/WrVvH0aNHOXPmDAUKFNDKzp49m+vXr/PBBx/QuXNnnj9/zv79+/nkk0+4cuUKs2bNAqB48eL4+Pjg6+tLsWLFdCYHyb+XTZs2ceDAAdq1a8dnn31GZGRkqvv+7NkzPDw8uHz5MtWqVePTTz9FrVZr9Y8ePVpvUqwhNjaWAQMGsH79er766itmz56NEMKkyUVK2L1e4Evz5kGDJpG+ZcuWen1atWrFzz//TGBgoMF2Y3Vo+Pzzz0lISOCHH35I8zimB3t7e3r27MmyZct4+vSp1u8ePHjA/v37GT58uNZGiSQ9KIrCuHHjcHd31z7AGfbdd0QNG8bX/v4oV67AvXsojRtju3495M+fpfZaJUKSKj/99JOoXr26sLW1FT4+Pib1DQ8PF4AIDw/Xa0tISBAPHz4UCQkJ4tWrV+Ly5cvi1atXOjJqtVrExsYKtVptcPzU2uPiE8SziCgRFhUjwl/GZuvPX39fFYBo1rylCH8ZKz4fPlIAYsHCn7Qyu/f7C0AMGvyxTt9GjZvoyYa/jBULFv4kAOHRpKnO9gYNGwlAVK5SVdy890iEv4wVz1+81LYDAhDew0bo9PP6aIgAhIurq5g5Z652+/OoGNGyVRsBiKDjp3TGOn/5H719/efGbVGwYCFR6r33DB6DPn376Wzv07efAMRff1/VGyupru/mfS8A0cazrQh7ES3CX8aKZ+FRomix4iJXrlwi+MRJnb77Dx4RNjY2onUbTxH+MlaERcWIm/ceity5c4ucOXOKf/75R+tLMTExomHDhgIQxYoVM8oPR45M/A5LlSol5s6dK06fPq3n38n5999/9bY9ePBAFCpUSJQuXVpn+82bNwUgBgwYoLPdw8NDAKJChQoiIiJCu/3hw4eiYMGCwtbWVty4cUNr9969exOPW5s2IioqSmefPv30UwGIzZs3a7cvX75cAGL58uUG96FPnz4CEMuWLdPZ/vDhQ1GkSBHh7u6ucxw09larVk2EhISkenyS2pb8eA8YMEAAYsSIEcLOzk7ky5dPnDlzJtW+gPDw8NBp14zz7rvvitu3b+v18fHxEYA4cuSIEEKI7du3CwcHB1GhQgVx584dHbuOHDmi/T399ttvOmNNmjRJAMLLy0tH/40bN/T2LS4uTrRo0ULY2NiI69ev67QZ2gcNmu9KURTh7++v156SD3Xv3l0AYuzYsXp9Hj16JOLi4gzqj4yMFC1atBCKoojvvvtOK5PWedxY2rRJPM/s2bNHZ3u+fPmEs7OzQV0XL14UgOjevXua40dERIgCBQoIBwcH8ezZM73xtm7dKgCxYcMGIcSb49eqVSuD4xna75T6aPxq9erV4tSpUwIQ8+bN07ZPnz5dAOLChQti2bJlAhDTp0/XGUPzWxo9erSYOHGiGD9+vJg4caLw8fERixcvTtM2zXU4KipKe222JpLeU1ibPnPGMrVvUvnZs2drz1GAmPjpp0JdpYoQIASIBDc3kXD2bLp1p9Zu6e/LXFK7n02OnFikwbZt28TOnTtFt27dsmRikVCjhlAXLiyEgY86ycdQW3yhQiKhUOFM/8RXr5GhE4ub9x6J3C4uomDBQuLhs+cpTiwuXb0hAFGufHnxPNkE6nlUjChbrpwAxKWrN/QmFus2bjF4gw4IZ2dn8eBpmM54+/wPC0CUKFlKT9eS3xIvdD8t+U1nrJQ+nwz1TrxIXrmWIROLrTv3CFtbW1GxUiVx/0motn3Nuo0CEOMnTjJoV/uOnYRKpRJ3Hz0TYVExYtHPvwhADBs2TM8PNTeJxk4sXr58Kfr37y9UKpX2pG1jYyNq1KghpkyZIsLCwoz+HQ0bNkwA4tatW9ptaU0sfv/9d71x5syZIwAxZcoUrd3t2rUTgLhz546e/PPnz4WiKKJr167abalNLJ4+fSpsbGxEs2bN9NrUarWYP3++AMSuXbv07N2xY0eaxyHpWClNLDSf/fv3p9k3tYnF999/b7BP0onFb7/9JmxsbETdunVFSEiInl0anylbtqx2m0YmKipKuLu7C0dHRxETE5PqvgkhxJYtW7QTFFMnFp07dzbYbsiHHj16JBRFEaVKlRKxsbEG+yVFo//Jkyfi/fffF7a2tmLVqlU6MhkxsViyZIkARNOmTfXa7OzsROHChQ3qunPnjgBEy5Yt09ShmRRPnjxZz/YnT56I/Pnzi06dOmnbMmtioVarReXKlUWlSpW07WXKlBG1atUSQog0JxaGPlWrVk3TNjmxyDp9WTWxEEKIhQsX6vjK6EGDhPr997WTC7WrqxAnT6ZL9391YiFDodKgU6dOQGIMdlagPH6Mcv++4bbU+gGpBxFlHOoMHs/NzY2RX4xi8iQffvrxB7782nD5wb/OnQOgfoNGenHuiqJQr35D/rlyhYt//cW77xbRaa/5fq0U9Zcs9R45c+bU2VbgdThPxUqV9HQVeCcxufrBA93v6ebNf5k3ZzZBgQE8fHCfmJgYnfZHDx9StGixFO0whqv/XGFQvz7kcXNjw5btOGsW/gHOnEmMdb969R9mTJuqEzYF8OTxY9RqNdevXaNajRpcvHgBgIYNG+rp+eCDD1IMjzCEo6MjK1euZNq0aezdu5fTp09z+vRp/vjjD/744w+WLFlCYGCgTsjPv//+y4wZMzh8+DD37+sfrwcPHlCsmHHHy9A+aLade+03AKdPnyZnzpwsXbo0xf24cuWKUTrPnDlDQkIC0dHRejkSQgiuXr0KwJUrV/QSU2vXrm2UjrRo0aIF/v7+DB06lCNHjhh9vJKTlj3z589n586deHp6smnTJpycnHQSw5NSv359vd+Mo6MjNWvWZP/+/Vy9elUbPhUZGcns2bPZtWsXN27c0CtJ/PDhwwzfl6ScPXsWIQRNmjQxOuTm8ePHNGjQgHv37rFjxw48PT1NtjE19uzZw+eff06xYsVYs2ZNho6tYezYsaxdu5bWrVszduxYvXZvb2/i4uJYvHhxpuhPzqBBgxg1ahRnzpwhOjqaq1evGq374cOHMnlbYjSff/45jo6OfPzxxwghmLt8OS/79OFHOztUJ06gPH8OzZvDnj2Jid2SNLGKiUVkZCRTpkzh3Llz/Pnnnzx79gwfHx+DCY4vXrxg/PjxbNy4kdDQUMqVK8e3335Lr169LG94BiBexx8bOjUmvYwnbxeAWgiUVKcfGYNIEiOdUQz1/pxflyzhh/lz8Rr8sUGZyMgIAPKnEAOpid2OiNAvi5g/FZtz586tt01zU51aW3ySpM4bN67TtFF9IiMiaOjRmDaebcmVKxcqlYqjwUEcDQ7Su3E2ldCQEHp07Ux0dDSbt++iSBHdKhZhoYkJ1xvXr0t1nJcvE2/eIiJSPp42NjbkzZvXZBvfffddhgwZwpAhQ4DEcppeXl4EBQXxxRdfaCfs169fp3bt2kRERNCkSRNtzL5KpSIgIIDAwECTjpehfdD4Q9IymaGhocTHx+Pr65viWMautxEaGgrAsWPHDCZqpzZegQz6DU2ePJkaNWowa9YsGjduzJEjR9K1qGFa9gQHBwPQunXrNEvWpvX71HwfsbGxNGnShD/++IPq1avTr18/8ubNi62tLbdu3WLlypXp+s2YcmyfP38OJCZRG8vDhw+JiIigTJky1KqV8gOL9ODn50fXrl0pUKAAhw8fpqCBCnEuLi4pln7V/KZTK6nr6+vLjBkzaNq0KVu3btXLa9u5cyebNm1ixYoVRudLmUvfvn355ptvWLZsGdHR0Tg4OFjtNVyS/Rk8eDCOjo7079+fhIQEFq9dy4vOnfmlXj0cjh+HFy+gdWvYuTNxkiFJFauYWGgW9KlatSqdOnXit99+S1G2S5cunDlzhpkzZ1KmTBnWrl1L7969UavV9OnTx4JWZwwJJ08m3rgaeuryOinQUHtCgprwl9HY2NiissInNo6Ojnw7bgIjPh/K3DmzaO2pXwkkV67Em/wnBioyJd2ukUtKZj/F+mnhDzwPC+PXZSvo0UvX70YO8+ZocJBZ48fFxdHvwz7c/PcGvyxdTu06H+jJaCZBGzZvo1XrViiqlH/uaiG08oaOZ0JCAiEhISbdcBmiVKlSrFixgpIlS+pUqpk/fz5hYWGsWbNGu5CRhk8//VSvEk5aPHnyhCJFdN9SPX69+FHSm6zcuXOjKArPnj0zdVf00By/0aNH89133+m0iSS/VUO+l5H+OHPmTGxsbJg+fbp2clEiSXECY0jLnqVLlzJ16lRGjBiBjY0Nn332WYqyKf0+k38fO3bs4I8//sDLy4vffvtNx4b169ezcuVKk/ZBgynHVpOUfT+Ft8SGqFatGgMGDOCjjz6iadOmHD58ONVkd2PZv38/nTt3Jl++fBw5ciTFhP7SpUtz4sQJHj16pDeJuva6sk3p0qUN9vX19WXSpEnaUraOjo56Mpo3fAMHDjRYQcvPzw9FUahatarO20BzcHd3p127dqxbt474+Hi6dOmSYsK8RJIR9OnTB3t7e3r37k1cXByrt20jvEULtrZogY2/P7x6Be3awebNiX8lKWIV5WaLFStGWFgYgYGBzJgxI0W5vXv34u/vz08//cQnn3xCkyZN+PXXX2nRogVfffWVTunIZs2a4eDgYPAzYcKEdNkZExNDRESEzgdArVYb/AghdP6d/APo/DWlXYvh6IRsjUCAgL4DBlCmbFl+XbKYe3fv6MlVrloVgOPHgvXCMIQQnDh+VEcuNV0iAw6UeP1/AsHNfxNL07Zpq1uSUa1Wc+qkfilHjX5jrRg5zJtjR4P58utv6dn7Q4MyNV8/PT196mTa+yigUqXKAAQFBen50okTJ7SVbUz10+SfpE+4Ndtu3LgBQPv27XVkExIStE//U/JxQ9uS74MQgqCgxMlc1apVtbK1atUiJCSEq1evGmW7pkRnfHy8Xtv777+PoiicOHHC6GNkaB/S+hgaK/k4U6dOZfz48dy+fZvGjRtz48YNvb6pHb/U9EHiDbi/vz81atTA29tbWxfekF3Hjh3TnuM0ba9eveJ///sfjo6OlC5dGiEE169fB9CWEzX03SW3T6VSkZCQkOaxSq09qUzNmjVRqVQcOXKE2NhYo74LSLzpXrp0KZcvX6ZJkyY8fvw43b8PIQT79u2jU6dO5MmTh8OHD1OqVKkUZRu9Ds/QrJqdVNf+/fsBaNSokV4/zVt/Dw8Pdu/ejaOjo8H9q1atGl5eXnqfnj17AolvJb28vOjcubNZPp/8+x00aBDh4eFERUUxaNCgFOUMjZvWMU+tPfm12Zo+lrY7I/WZM5apfVOS79y5M1u2bMHe3h6Anf7+dALiNROJmBhE586oN240Wndq7dbmZ8ZiFW8sjH3itG3bNpydnXUWD4LEeM0+ffpw6tQp6tWrB8ChQ4cy3M4ZM2YYDKl4+vQp0dHROtvUajXh4eEIkXjzpFariY+P1ylLqGkDw8cgtfYEtUARahAJiIxOgshghFoz4VMj1JqbVzU2KhUTfCbRr09vZs2YphHWyrxbuBANG3kQHBTIqhVL6T9goHbMVStX8PflyzTyaEzhQgWTjPv6YqJOepzVKOo3N/VCCJ32pDam1oZQa8cqUiRx5c4Tx4Jo0bKVVnb+d3O4fOmStp92LM2PNtn4b+x9I/vDgvmsWbWCDh07Mm7CBD17NHh6evJukSIsWvg9TZo2oUHDRjpTi7i4OM6eOUPdevVAJMrnzp2b5cuX8+mnn1KmTBkgMUTFx8dH2y956UxDfjh16lQGDBig99ZACMH06dOBxNh7zVgaucDAQFq3bq2VnzVrFhcvXgQS35po5DV/Nb+b5Mdr2rRptGnThly5cgGJT8fnz5+Pra0tPXr00E4Mhg4dip+fH15eXmzevFkv3OvRo0eEhYVRvnx54M3T9Tt37ugdh3z58tGtWzc2bdrErFmzGDVqlPZ4aI7R6dOnqVy5snZypbHXlHKkho635qSf9BhNnDgRRVGYMmUKTZo04cCBA5QsWVLnAYsQuqVQNeMkPRcl1ZdUT65cudi3bx9t2rRh2LBhxMXF8emnn2rt0vT5559/+O2337Q3hwkJCcyaNYunT58ycOBAVCoV8fHxvPt6tdtjx47Rrl077b4FBQVp31Kr1Wri4uK0bW5ubty7d8/g8dPYmtxHNBjyobx582pvLnx8fJg8ebJOnydPnuDm5qaTb6Q5hh9++CFqtZqPP/6Ypk2bcuDAAfLnz5/meTw5fn5+dOvWjTx58nDgwAFKlCiRqn/069ePuXPnMm3aNDw9PbV5VpcvX2b16tWUKlWKRo0a6Yzh6+vLtGnTaNCgAdu3bydHjhwGdQgh8PT0pH379nq237p1iw0bNlChQgV+/vlnnWOq6Zt8v5P6lCG/S/r9tmjRgs2bNwPo2J/UFw399uPj44mLizP52hkfH49arSY0NJSoqCjtxNVaSHpPYQm7M1KfOWOZ2jct+Vq1arF69WoGDBjAq1ev2O3vT7O6ddndrh25du9GiY+H3r0Jf/KEl126pDpWaros/X2Ziynlpa1iYmEsFy9epHz58npJplWqVNG2ayYWxqK5wGou2NHR0djZ2RlcX2HMmDHaRYwgMb61SJEiuLu768Xmq9VqFEXB3d2d2NhYIiMjsbW1NZggm1YSoaF2RS0QsfGg2KCosncolKLSHEsVisoWgUARgMqGDp26UrvOB4lP3ROFdUJ65v/wI62aN2HE59747dtH2XLl+efK3+zds5t87u7M/+FHHXnNRUSzLakuTT6Koih6YUMaG1NrQ1ElJkirbPD6+BN+X7Oafn1606Vbd9zc8nLm9CnOn/uTVq098du/F0Vl82YszYkl2fhv7E2UffzoEZMmTsDGxobixUsyc8YMvSyatu07UKVqNRwcbVn1+wa6dW5Puzat8WjShAoVEpNk7969w4njx3Bzc+PsuYsItSCXax7mL1jAYC8v6tWrR8+ePXFxcWHPnj04ODho47tTSuJO6offf/89U6ZM4f3336dGjRq4ubkREhLCkSNHuHbtGnnz5mXu3LnasYYOHcrKlSvp0aMHPXv2xM3NjVOnTvHHH3/Qtm1b9uzZg42NjVZe81elUunYozlepUqVonr16nTp0oW4uDg2bdrEkydPmDp1qnbCBNCmTRvGjx/P1KlTKV++PK1bt6Zo0aKEhIRw48YNgoODmTJlCpUrJ77NadCgAY6OjixcuJAXL15oQ16+/TaxwMDixYu5du0aY8aMYe3atXzwwQe4uLhw7949zp49y/Xr13nw4IH2fKCx15TEeEPHW3NhSnqMIPEG0tbWFh8fH1q0aMHhw4cpVqyYtq+iKDrymnEMnYvs7Oz09OTLlw9/f39atWrFqFGjEELwxRdfaGUgcX2FYcOGsX//fsqWLcv//vc//P39KVKkCDNmzNDq6dSpE8WLF2fevHlcuXKFihUrcvXqVXbv3k2nTp3YsmULKpVKZ7+bNm3Kxo0b6d27N9WqVcPGxoa2bdtSuXJlra3JfURDSj60ePFiLl++zMyZM/Hz86NJkyYIIbh27RoHDhzg0aNHOmE5SY/hoEGDsLW1ZdCgQdrjrQlPMiYZ/MqVK3Tr1o2YmBgaN26sXUMlKcWLF9cJSapQoQI+Pj5MmDCB999/n86dO/Pq1SvWr19PXFwcv/zyCw4ODlr5FStWMG3aNGxtbalduzbz58/X09G4cWOdxRMN2a7Z5+Q+lJykfVPqk/S7SrqWRpcuXfTG0/hVSr99W1tb7RimXDttbW1RqVS4ublhZ2eHu7u7VdzwaUh6T2GpiUVG6TNnLFP7GiPftWtX3N3dad++PS9evCDoxAk869blYO/e2K9bh6JW4zJ8OLns7FA6dEhxrNR0Wfr7Mpek55C0eKsmFiEhIQbjUN3c3LTtpjJ16lSdtxDTpk1j+fLlBmNN7e3tta/QJBmH79TptGnR1GBb6TJlCTh6glnTpnLQ/wB++/eRL587H/btzzfjxptddSm9VK1WnW279jLV14ddO7ajsrGhTp26+B0KZN+eXfjt35uucaNjorVP9374Xv+GAKBoseJUqVoNgJrvv8/Rk2f4Yf5c/A/4cfL4cezt7SlYqBBt23WgW4+eOn379x9AHldXpk2bxqpVq3BxcaF9+/ZMmzaNOnXqGG3nzp072bt3L0FBQezatYunT59ib29PyZIlGT16NKNGjdJJRK1evTp+fn5MmDBBm0Bar149jh49ys6dO9mzZ49Jx2nDhg1MnDiR9evX8/TpU0qXLs20adMMriA9efJkGjVqxMKFCzl06BDPnz8nb968lChRAh8fH52cDzc3NzZt2oSvry8///wzr169At5MLNzc3Dh27Bg//vgjGzduZO3atajVat555x0qV67MhAkTyJcvn0n7Yi4TJiRORMePH0+TJk3w8/OjQoUKGTa+q6srBw4coFWrVowePRqVSsWIESO07R988AFjx45lwoQJ+Pn5kSNHDnr16sWsWbN0cgKcnZ05dOgQX375JUePHiUgIICKFSuyZs0aChQowJYtW/R0L1iwAIDDhw+zbds2nWOdXvLly8eJEyf47rvv2Lx5M4sWNazBPAAAPMlJREFULcLBwYESJUrwzTff6FWMS06/fv20q243bdqUQ4cOGV344NGjR9oE9fXr1xuU8fDw0Lv+jBs3juLFi/P999/zyy+/kCNHDurVq4evr69eQvmtW7eAxIdm8+bNS9GW5KuySyT/JRo0aMCGDRv48MMPef78OUdPnKBBTAxBAwfiuGIFihDYfPopTk+fwreGK1f+V1FE8qDFbM6zZ89wd3c3WBWqTJkylCpVin37dFdAfvjwIYUKFWLGjBnaG4DMZNGiRSxatIiEhASuXr3K1atXtSEZGjSvwVxcXEhISCA8PJxixYrpzAo1r2ttbGxSfZ1rqD1BLXgZE4tKZWMw7zu7I9RqFAvN4jNSlzljmdrXFPm0ZIUAtToBJ/sc2CR7w2WOH1qa5s2bExQURGxsbJqylrQ7I3WZM1Z6+hrbJ7lcYGAgLVq0YPz48UycONGosVJrz05+ZgrW6mfmjmdq3/T6WXpkDLVHR0dz+/ZtcufOTVRUFC4uLlbxJFlD0nsKS4ZCZYQ+c8Yyta+x8hq5u3fv0rt3b23Vv/LlyhFUuzb5Vq3SykaMG8fLzz83SZelvy9ziYyMpEyZMoSHhxusjpmUt+qNRd68eQ2+ldA4hObNRWbj7e2Nt7c3ERERuLi4yFAoEzEUnmQNuswZy9S+psgbIyvUAqEIbG1t9SYWGtLjh5YmPaFFlrQ7I3WZM1Z6+hrbRyOXUsiKMWOl1p4d/Cw9WKufmTueqX1N9TNzZGQoVPbQl91CoZLKlS5dWvuQ5NGjR/x95QoNheDk8OG4/PADALmnTSOXjQ1i4kSdCp0yFOotoHLlytrydEkvZBcuJC78pVmEydKoVCqDjqMoirZNURTtR4MQ4k2MfQpPXVJuf/0iyrrmFAAoKAgFi6zBkZG6zBnL1L6myBslm6TJ0JPi9Pth1mDsE1JL2Z2RuswZKz19je2TXM7Qv83xpezoZ8ZgrX5m7nim9k2vn6VHxlC7xk811+OUrtvZGUvbnZH6zBnL1L7GymvkKlWqRFBQEM2aNePu3btc+ecfasTFcWb0aNzmzk2UnTwZJToaZs7UmVykpsua/MwUG9+qiUXnzp359ddf2bJli7YUHsDKlSspVKiQSTHiGYmhUl3JS42lVnYv6d/kpNWOwOomFyJJCVhLvLHIKF3mjGVqX1PkjZJNWqHYgC+Z7YcWxlg7LGm3KboCAgIICAhIsV2tVqNSqahWrRqdOnXKNDtM7ZO8hGfyfxszVmrt2c3PjGH79u388ccfaV6YkydMp5eMPkbmjGdq3/T4WXplkrdr/DTptdmasLTdGanPnLFM7WusfHK5UqVKERAQQIsWLfj333/5999/qbZxI4HDh1Pi9ZsLZs9GREUhFiwAlSpVXdbmZ29duVmAffv2ERUVpS15dfnyZW0pOk9PT5ycnGjTpg0tWrRg6NChRERE8N5777Fu3Tr279/PmjVrDFZyygyS5liALDebHpKXgLUWXeaMZWpfU+TTlBWgiEQfFCnkWIDpfmhp/P39AePKt1rSblN1HT58mKlTp6Yp169fP9qZsFhTevbZ2D7J5Ro0aKDNdTFUutZUX8pOfmYK27ZtY/Xq1WnKqdVqGjRoYJaujD5G5oxnat/0+ll6ZAy1y3KzWacvO5WbTU3OycmJzZs30717d27cuMHdu3eps24dwV98QdnX1dWURYt4FRZGxOzZqBXlP1lu1mqSt4sXL87t27cNtt28eZPixYsD8OLFC8aNG8fGjRsJDQ2lXLlyjBkzhl69elnQ2kQ0ORZhYWEGcyyePn2qzbG4desWJUqU0Itji4uLSzVONKX2BLVIXHlbZYvKCnMsSEgAG8vkWGSULnPGMrWvKfLGyKrVggR1PC5ODgZzLNLrh9kdS9qdkbrMGSs9fY3tY4ycOb4k/czyuizpa1npZ9HR0dy8eZOiRYsSGRlpNbHvGpLeU1hqYpFR+swZy9S+xsqnJvf48WNatWqlDbPPly8fZz7/nGKTJ6O8frIvevcmYdkynoaFpZhjYcnvy1wiIiLIkyePUcnb2X9vXnPr1i2dcKGkH82kAhJLFn7//fc8fPiQmJgYzp8/nyWTColEIpFIJBLJ20WBAgU4dOgQNWrUABKrlVZfsIBrvr4IzVot69ah6tMHjKhQ+LZhNaFQ1oQMhTIfGQqVsfL/lVAoU8jOoVCZNZYlQ6HSI/M2hkJZq5+ZO54MhbIsMhQq80OhkvP777/Tp08f/vzzT54/f07NWbPw/+or6sydixIbi2rbNnKFhPBk+XJUTk4Zts9ZwVsZCmWNyFCo9CFDoWQolKWw1hAVGQplXVirn5k7ngyFshwyFMpyoVBJZW7dusXHH3+sLbbh5ORE0Pjx1JgyBeX1Aqrqpk1h+3ZIsrjm2xwKJd9YWABZbtY0ZLnZjJX/L5abNQZL2p2RuswZKz19je1jjJw5viT9zPK6LOlrWe1nstxs1urLzuVmU5NzdnZm9+7ddOvWjf379/Py5Uvq+/pyZNIkPpg6FSUqCtXhw+DpCXv2QJKbcmvys/9sudnsiiw3axqy3KwsN2spLGl3RuoyZ6z09DW2jzFy5viS9DPL67Kkr2Wln2muv9ZWBlSDLDebueVmU5Oxt7dn69at9O7dmx07dhATE4PHxInsmziRJnPmoIqIgKNHEc2bI/buBTc3q/Ozt7LcrDUhcyzMR+ZYZKy8zLHQx1pj32WOhfQzS+mSORbZ/0myBpljYfkci+QyCxcuRFEUtm/fTlxcHK0mTWKptzd9V6/GJiwM5cwZ4hs3JnT9ehLc3N7aHAs5scgEvL298fb21uZYuLu7G8yxUBRFm2MRGRmJra2tzorhGtKKJTXUrqgFIjYeFBsUK8yxUASgskyORUbpMmcsU/uaIm+MrFALhCKwtbU1mGMB6fNDa8CSdmekLnPGSk9fY/sYI2eOL0k/s7wuS/paVvmZra0tKpUKNzc37OzsrCb2XUPSewpLTSwySp85Y5na11h5Y+QMyWzcuJGPP/6YlStXkpCQwKCFC3GaMIFuS5agPH6M3aVL5O/RgwQ/PxRXV6vxs+T5v6khJxYWQOZYmIbMschYeZljYRhrjX2XORbSzyylS+ZYZP8bvqTIHAvL51gkl1GpVCxbtgwnJycWL16MEIIekyezevx4+i5fDvfvo/z9NzZNm2Kzbh2qAgWsws9MsTH7741EIpFIJBKJRGIFqFQqFi1axBdffKHd1m/qVH7p2xder7umXL9O3s6d4caNLLIy85BvLCyATN42DZm8LZO3LYW1JtXK5G3pZ5bSJZO3rQOZvJ11ydspycyaNQshBAsWLADgk1mzeDFyJF/s2YNy7Ro29+4hGjdG7e8P5coZZX9WIZO3s5iMTt6OjksgXm34IpuQoMbGJs5g8vbLmDhUqgQy8+27nY2Cg51Nho8rk7czVj4rk7djY2OZOnUqGzdu5O7du8TFxeHv74+Hh4cRlqeNWq2mVq1aFC5cmJ07dxrdz1qTamXytvG2X79+ncqVKzN//nw+/fRTo/pkNNbqZ+aOJ5O3LYtM3s765G1D7UOGDMHe3p5Zs2YBMHrBAp4NGoSvSoXdP/+gPHiAulEjwjZuJL5CBSP32vLI5O0sJiOTt6PjEgi4Fkr4qziDuhISErCx0b+xVwvBq9h4VIoqUy9muR1taVI2v9mTi9u3b1GlfBm97U5OTpQoWZIOHTvz+YgvcHZ2NktPcrJj8vad27f1joWjoyMuLq6UKVeODz6oS+++fSlRrLhVJG/Pnj2bmTNn0rhxY3r16oWtrS2lSpUyWKggPSxbtowLFy7w66+/6ox569YtSpYsCUChQoW4ffu2wd/KlStXqFq1KgBly5bl77//zhC7DGFsYuqZM2eYNGkSJ06cIDY2looVKzJixAj69Olj9lgVKlRg5MiROmNpEEKwbds2fvzxR65cuUJ4eDhFihShYcOGjBkzRns8jd3HqlWrcuHCBQACAwNp2LChnkxYWBhlypQhJCQEgAsXLlCxYkWjxk+LcuXK8eGHHzJlyhT69++f5sJOmYlM3s5YeZm8rYtM3s4eyduG2qdOnUqBAgUYNWoUADOWLyfBy4sptrbkuHQJm5AQ8nbvjti3D95/38g9tywyeTubYU7ydrxaEP4qDgc7G+xtdccQgDpBhcrGRu92Ua0W2KlApdhk2huLmPgEIl7FE5egzrC3FiVKlqJnr94IoQYUnj17hv8BP2ZMm8Khg/7sP3jE4M1hesnOyduaYwEQExvD0ydP+eN/Z5g9czpz58xixBdfMNF3WpqhblmdvL1v3z6cnZ05cOBAht/wJCQkMHnyZDw8PKhTp45Om8YWW1tbHjx4wIEDB/D09NSze+nSpdja2mrfFmbGRNyURNaAgABatWpFjhw56NWrFy4uLmzdupW+ffty+/ZtxowZk+6xcufOzbZt27RjjR07Vkf+yy+/ZN68eRQsWJBOnTqRO3duzp8/z7Jly9i4cSPHjx+nUqVKRu1jdHQ0f//9t/bYXrx4kYYNG+rZ7uPjQ1hYGAC5cuWiTJkyBvctvYnEX331FatWrWLhwoWMHz/e6H4ZhUzelsnblkImb2d98nZK7V988QVOTk7aN6ezly0jtFs3luTMier0aZTQUJQWLWDfPqhXz6j9sCRygby3EHtbFU45dL8ugSAhAWxs9J9EJ6jVCKFGpcq8iQVATJLQrYygZMlSjBk/EaGOR1El7m9MTAzNGzfk9KmTHDsaTCOPxhmqM7uiORbJOX7sKJ985MW8777DxsaO8T6+WWCd8Tx48IC8efNmyhPbvXv3cufOHSZO1D9OGurVq6e9OU46sYDEMK21a9fi6elpUhiVqfzzzz+4ubnh7u6eqlx8fDwfffQRiqIQFBRE9erVgcSb77p16+Lj40O3bt0oUaJEmjoNjSWEYNy4cTRq1AgfHx+6d+9O6dKlAXj06BELFiygePHinD9/Xvt0XwjBvHnztJOOZcuWGbXP58+fJz4+nvbt27Nnzx7tm4ukXLx4kcWLF+Pp6cnu3bupVq1aht98V6pUiapVq/Lrr78yduxYq7tZlEgkbweffPIJDg4OeHl5oVar+W3zZkSPHvxqb48SHAwREdCyJezaBU2aZLW56UaeYSXZHnt7e+1k4tmzp9rtwUGBuDjlYMbUyZw+dZLOHdpStKA7Lk45dPqvXbOKZh4NKOSeh0LueWjm0YC1a1bpyISGhpLH2YE+PbrqbP/f2bO4OOXAxSkH9+/d02lr2qg+hfO7aZ90BwcF4ursxIypkzn355907tCWwvndKPJOPj7s2Y3bt29lyPGoV78B23buxt7enu/nz+Xevbtp7oOrs5NR+xAbG8vChQtp1aoVRYoUwd7engIFCtC9e3f+/PNPPVsCAgJQqVRMnjyZEydO0KpVK1xdXVEUhUmTJqEoCjdv3uT27dvaJ4KNGzfW9t+yZQseHh7kz58fBwcHihQpQuvWrdm+fbtRx2LFihUoikLXrl1TlHF0dKRnz57s2rWLZ8+e6bRptg0aNMgofabw7NkzfvzxR+rUqUP58uW5fft2mn0OHz7MjRs36NOnj3ZSAYlP8idMmEB8fDzLly83Sn9qY40fP15vrFu3bqFWq6lfv75eyFCbNm0AePLkiVG6Af744w8AGjVqRKlSpQxOLEaOHEmuXLno3LkzADVq1DB6fFPo0aMHd+7c4dChQ5kyvkQikRjDgAEDWLdunTZsd+nGjXzo5oa6WbNEgago8PSE/fuz0ErzkBMLSbYnNjaW4KBAFEWhSpWqeu2nTp3Es2Xij3KA12C6dOuubRvz9ZcMHfIRD+7fp9+AQfQf6MXDBw8YOuQjxn7zlVbOzc2NipUqc/RosE71g6PBgdp/BwcFaP8dGRnJ+XN/UrdeA71cgT//+IM2LZpga2PLwMEfU61GTXbv2knHtm30kvjTy3uly9C5azdiY2PZs2tnhu1DaGgoI0eOJCYmBk9PT7744gsaN27M/v37qV+/PmfOnDFoz8mTJ7UThiFDhtCzZ08aN26Mj48PLi4uuLi44OPjg4+PDwMHDgRg8eLFdOvWjWvXrtG5c2dGjRpF8+bNuXv3rlETCyEEAQEBlCtXDldX11Rlvby8iI2N5ffff9fZvnLlSvLnz0+7du3S1GcMMTExbNmyhY4dO1KoUCGGDRvGjRs3+OSTTyhatGia/QMCAgBo2bKlXptmW1BQkFG2GDNWYOAb3yhdujQ5cuTg2LFjeol6+19f5Jo2bWqUboD//e9/QOJkoVq1anoTi61bt3Lo0CF8fX21k67MmljUrVsXSJxsSSQSSVbSo0cPNm7cSI4ciQ9B1+3YQbccOUjQvFGPjoYOHcDIB2zZDRkKZQEypNwsr8uGJkdo/lhX+cWU+PffG0yfOhmEGoFCaEgIhw768/DBfSZPm8F7pfUTvI8cOsiPi3+h34CBOtuPHzvKTz/+QNly5fA/EoyLiwsAY8ZPpHnjBixa+D3tOnakbp0PEAgaeTRm0cLv+ev8eaq9fsIbHBhIxUqVePLkCcGBgfTq0xeAE8eOEh8fT8MklY0034Hf/r0sW7WGrt16aNs++WgQ69f+zu5dO+jWvafePiQtCWsMAkH9+g1Yv/Z3/vjfWe12w/sQQIWKlXj6NJV9eK02T5483L59m8KFC+voO3/+PA0bNmTs2LEcOHDgjR2v/fPgwYP89ttveHl56fTz8PBgxYoVQGI4T9J+v/32Gzly5ODcuXN6IUIhISFplpy8fPkyoaGhtGnTJtXSpAC1a9emYsWKLFu2jOHDhwNw//59/P39GTFihE7eTnrKah47dozVq1ezadMmwsLCcHBwoGPHjnz44Yd4enpiZ2dHXFxcmuNfu3YNgPfee09PztXVlXz58nHt2jWjym2mNJYQgjx58uiN5ebmxtSpU/n6668pX7487du3J1euXFy8eJGDBw/y8ccf8/nnnxtd4lPzxqJ69epUrVqVTZs2cefOHQoWLEhMTAxffvkl5cuXZ+jQoXTp0gVInFhkRrnZmjVrAnD8+PEsKVUry83KcrOZjSw3m/3KzabW3r59e1asWIGXlxfR0dFs27ePtk2asKtTJ+y2b4e4OES3bojVq6Gn/j2DpZHlZrOYjC03q05sT1Dxerg3iNdfdjx6CbyJ1WnVIJRMm3IIoQaRgFAnINRmVlRSJ+7czX9vMGv6VL32Nm3b0rJlS4Q6Xq9PlapV6duvr04bwO+rVwDw7Zhx5M6VU9ueO1dOvvl2LIMHDWDt6pV8ULs2ihrqN6jPooXfExRwiKpVKxMfH8/JE8f4sF9/Hj96RGDgEe0YQYFHAGjQoP4bva9/ePXqN6BLly469nzYt1/iJODsmRTDdrQlYdWaL1qtt09JeeeddwAIefZUK2d4H47zYd9+PH78OMV9QCSgCDU2NjkoUKCAnh+WLVsWDw8P/P39efXqlTZfQuPjVatWpV+/fjr9kpO8TQiBnZ0diqLotbm4uKQ6FqB9yu3u7m5QVrNNCEF8fDz9+/fnm2++4fTp09SoUYNly5aRkJBA//799WSN4caNG/z++++sXbuWf//9F5VKRaNGjejTpw9dunTRCSeKi4szqpTm8+fPAciZM6dBO3Lnzs29e/fSPVbSMpuasZLqGTlyJAUKFMDb25uff/5Zu/2DDz6gV69eBr8rDUnHjouL4+LFi5QsWRJnZ2cqV64MwJ9//kn+/PmZM2cON2/eZM+ePUDi2w0nJydKlSqVoeVmNTg6OuLg4KC3v5ZAlpuV5WYtgSw3mz3LzabUrlarqVatGqtXr2bAgAG8fPkSvyNHaFy7Nvs6diT3jh0oCQnQty8RT57wKosnF7LcbBaTkeVmbWzisLGxQWVjY7gaUjzY2OpvV9QCUIGiyrTkbUURoNigqGy0idbpHkuVuA/Nmrdky85dvM5K59mTpwQGHObrL0fRsnlTDgce1b610PSp+X5tg/r/Ov8XAA09mui1N/RITIy68NcFFEUFKhvqN2yMSqUiOCiY4V98yblz/yMyMpJGHk149OgR27Zu4faduxQvXoLgoCBy585NtRq1tHbw+sRRtXp1PX2FiySGwYSHRxi0VackrGY8VCkeV903G2/kUtqHho08ePzkSYr7gKLSlpu98Nd55syZw9GjR3n06JH2SbuG58+fU7BgQQCtT9aqVSvN5Ozkvt2jRw/GjBlD9erVtaFTDRo0SDOsKakdkPik3VDpWs02RVGwtbVlwIABjB8/npUrV1K7dm1Wr15N7dq1qVKliraPRtYYypcvD0CJEiWYNWsWffr00XvTk5y0jlHSSlap2WFjY2PWWMnLbGqYOnUqU6ZMwcfHh/79+5MnTx7OnTvHqFGjaNmyJRs2bNC+XUgJOzs7zp8/T1xcHDVq1MDW1lYb4vT3339TtWpVZs+eTYcOHWjTpg2PHj3i4cOH1K1bFwcHB+Li4lLdt/QWAXBzc+PZs2cZVubYVGS52YyVl+VmdZHlZrNvuVlD7Zq2Tp06aSsWRkREcPz0aVrWqkVAv344rF6NolbjMnIkuWxtYejQNPcvs5DlZrMZ5pSb1ZbCQ79cqEBo31TolxK13tCopOVR3fPnp1uPXrx6Fc3nQ4cw/7s5LFryq458/vz5DY4TGRmJSqUin4FKPPkLFEClUhEREQGvdbm6ulKlajVOHE8MEQoKTExMrtegIc+eJiatBgcGkiePG3+dP0fLVq11Jnua78Alt4uePs3NjFqd/LWT/j4bg4LCo8ePAMjnnk+7PaV9qN+gAc9CQlPcB/XrcIDjx4/TskVzIDEOv3Tp0uTMmRMhBLt27eL8+fPExsbqlWosUKCAzn8btDlZ2zfffEO+fPn4+eefmT9/PvPmzcPW1hZPT08WLFiQZuUjJycnAKKjow3qTbpNURQKFCiAp6cn69evp2PHjly/fp1FixYZlDWGypUrc+HCBW7fvs2BAwdwd3ena9euBtdKMLaUpiZcLyIiwqCc5mFFesdKakfysQ4fPszEiRP54osvGDdunHac+vXrs337dsqWLcuoUaNSeeP2ZmxNon/NmjVRFIV3330Xd3d3Ll68yNixY4mLi2Pu3LkoiqINmdJMPlLat2XLlvH9999rK2x169aNSZMm4ebmluIxSMqrV69wcnLK9LcGyZHlZmW5WUshy81m33Kzqd0D1q9fn8OHD9OyZUtCQ0M5deYMdWNjOfbxxzj9mni/o/r8c4iJgddrYVgaU74X6/rVSP6z1Hy9aMz5c/qViVK6uOTKlQu1Ws2zp0/12p4+eYJarSZXrlw62xt6NCYyMpJzf/zB0eAgKlepSp48eShdpiwFCxYiOCiA46+Toxtmcdnbo8HBANSoobugjqF9cDVyH2bOmE5MTAyHDh1i586dzJ07F19fXyZOnKgNvTJEem5iFEXho48+4uzZszx9+pRt27bRpUsXdu7cSdu2bbWhCimhycsIDQ01WqeXlxdhYWEMHjxYWy0qvfz111/88ccfjBgxgkuXLuHl5UWBAgXo0aMHO3bsIDY21uQxNaVfNfkRSQkLC+PZs2damYweSxOW1MRAmUN3d3cqV67MnTt39CprGSJp4raGqlWrsm/fPtavX8/IkSN57733APQmFob44osvWLRoEfPnz+f58+ecOHECGxsb6taty1MDv+/kaEIS0ir3K5FIJFlBzZo1CQgI0D4oPXf+PLWOHuXF55+/ERo9Gqbqh4pnN+TEQmIVaBbQUgvjE4iqVK0GQHCSqkgajh5NrKxTOVmVqYYNGwFw6OABTp04TqPGb26yGnp4EBwYSHBQ4ngNGnqQVVy/dpXtW7dgb29Puw4dddrM2Yd///0XNzc36tevr7P95cuX2hvAzCBv3rx06tSJDRs20LRpU/7++2+uX7+eap+KFSuiUqkM3jinhKenJ++88w73799P8e2CKVSvXp158+Zx79499u7dS6dOndi9ezedOnXinXfe4ZNPPiE4ONjo5FWP18UAkibIa9Bsa9SoUYaN5ZGk+IBmIpTSjbpmu729fZq6DU0WqlWrRlhYGO+8847OGxHNJESTYJ2cQ4cOsWfPHg4fPkzDhg2xt7enWLFizJ8/n5YtW/LFF1+kac+1a9dQq9XaXA+JRCLJblSuXJmgoCBtSO3lv/+m+r59PE/6lmLCBHhd8S+7IicWkmyPWq1myeLEkJW69RoY3a9P334AzJo+NTHk6TURERHaBHGNjIa69RtgY2PDL0sWExUVRaNGb268GjZqzIMH99mwfi0urq5Uqapf+tYSnDh+jM4d2hETE8OoL7+mULK4fnP2oWjRooSFhXHp0iXttoSEBL755hujngybgp+fn14ibVxcnPYNhKOjY6r9XV1dqVKlCmfPnjX6xt3W1padO3eybds2pmbgkx8bGxvatGnDunXrePz4MUuXLtUuytaoUSNKlCjBrVu30hynWbNmlCxZkrVr13Lu3Dnt9sjISKZMmYKtra22XK+GGzducOXKFb1cmNTGmjp1qt5YmsnkvHnzCA8P1xlr1apVXL9+nZo1a+q95UtOXFwcFy5coGjRouTL9yZM77PPPmPr1q3s2bNHZ4w//vgDe3t7KlSoYHC833//naFDhxqcBPr6+rJly5Y03w6dOnUK0J1ISSQSSXajbNmyBAUFUbx4cQCu37hBtS1bCPn220SBsWMhyVpQ2RGZYyHJVvz77w1mTJ2cWCFJUfHs2TOCgwL458oV3n23CF99M8boseo3aMgnQ71ZsngRdd+vTodOnRNzBXZs5969u3z62efUb9BQp/JS7ty5qVa9Bv87eyYx1KL+m4mMprTss6dPaduufabHsWqOBSQ+TX769Cn/O3uay5cuYWNjw5dff8M3Y8fr9TNnH7y9P8ff358GDRrQo0cPHBwcCAgI4P79+zRu3Fi7NkJG0LNnT5ycnGjQoAHFihUjLi4Of39/Ll++TM+ePY1a96FTp05MmjSJM2fOULt2baP01qpVi1q1aplUAcoUcuXKhZeXF15eXty5c4c1a9awevVqbbJ5atja2vLbb7/RqlUrGjZsSO/evcmdOzdbt27l5s2bTJ06lTJlyujY3axZM27fvs3Nmze1F6OUxsqVKxfbtm3TGUtD9+7dWbJkCQEBAZQuXZoOHTqQJ08ezp8/j7+/P/b29ixYsCDNfbh06RIxMTF6oU0lSpSgePHiOrY/e/aMu3fv8v7772NnZ2dwgnjv3j2dvA6VSsXixYv59NNPcXNzI2fOnISEhGgLChjC398fGxubDFuvRCKRSDKLkiVLEhQURLNmzbh27Rq3b9+myqpVHF+zhmJ9+mS1eWkiJxYWICPWsYiOTzCYjq2OT8BQ4SChFryKS0ClkGlVoWLiU4+BTw83/73BzCTlZu3t7SlarBifDx/JqC+/Jm+SJ6DGMHvufKpUrcbSX5ewYtlvAJQrX4Ex4yfSt/8AnfUjNInTDT08+N/ZM1Svoft0tkSJkhQtWow7d27ToJH+k09Npab0pM0bWsci6bFwdHTExcWV0mXL8vW3Y+ndty8lihXXKzOsIek+OOdyhoQEBCLlfXittm27dmzatIkZM2awZs0anJycaNq0KRs2bGDmzJmJokn8NCV/NbiPydqmT5+On58fp0+fZteuXeTMmZP33nuPn3/+GS8vL6PeQgwePJgpU6awevVqatWqlaI+Y2vZp7UPplKkSBHGjBnDmDFjiImJMWr8xo0bExwczKRJk9i4cSOxsbFUrFiRyZMn8+GHH6Zqd/KxDY1VoUIF7VhJ5VUqFfv27eP7779n48aNrFu3jtjYWAoUKECvXr0YO3YslSpVSvM71oQ2Va9ePc21KM6ePasnm/xv4cKFuXr1Kp6entrSx66urgghCAsLIyoqCjc3txTtevnyJdu3b6d9+/YULFhQrmNhwfHkOhaWQ65jYV3rWKTVt3DhwgQEBNCyZUsuXbrEgwcPqD1qFAcqVcqSkE5TvhtFZMVZ9i0n6ToWV69e5erVq3rhA5pkQhcXFxISEggPD6dYsWI6Jb2EEERFx3LkWgiRr/Rv4gUCoVajqFR61YTUCGLjElBUxtYZSh+5HexoXDYfDnYGSuGagWa/LEFG6jJnLFP7miKflqwQiRWrnOxzYKMyvH6AjY1NqjXhU2rPbPr168fBgwe5fv06OXPmNLqfJe3OSF3mjJWevsb2MUYuPb508OBBRowYwYkTJ3ByctJpGz16NE+ePGH16tUp2rVixQqGDBnCoUOHaNiwoVH7nJFYq5+ZO56pfbPaz6Kjo7l9+za5c+cmKioKFxcXq6oKlfSewpLrWGSEPnPGMrWvsfLGyKUlk1q7sXaEhITQq1cvLl68CCQuYrt58+YUQ0czi8jISMqUKUN4eHiauYlyYpGJaMo5hoWFGVzH4unTp9p1LG7dukWJEiX0agXHxcWRgIrYBMOzxfi4OGwN1OpWqwXhL2OwUdmgUmXexczORpXxkwqEdh2LzJ0WZawuc8Yyta8p8sbIqtWCBHU8Lk4OehMLIM21BdJqz0xu3rxJhQoV8PX15euvvzapryXtzkhd5oyVnr7G9jFGLj2+NGzYMAICAli4cCENGzbk8ePHTJkyhYMHD3Ls2LEUS07Hx8dTvnx5KlasyPbt29O0P7OwVj8zdzxT+2aln0VHR3Pz5k2KFi1KZGSkVa5jobmnsNTEIqP0mTOWqX2NlTdGLi2Z1NpNsTssLIy2bdty6tQpqlSpwqFDh4wus51RREREkCdPHqMmFjIUygKYu46Fg60NjjkMLKomBP9v796Do6ruOIB/dzebZdPN0oRXEpMJQiKYNgEptLRDgaKQUaZImGpBEhikFEKEokxxYDIlFXAYx75G8EHpTFEJpNJJ2lrByqMTDJURhfJIEBSDyiOGwGY3r0127+0fdJcs2ce9ubt3926+n5FR9vzOPWfh58397dl7jyvh9oZed38q43ILEAQ3DIYE6KPwKbIScvd0iJWxlByrP/tYSI2XFNuryd+Ox3KfCa+mUaNGYdeuXbhx44as8bW6v4CaewvI6ROp/QUAYNu2bdixYwdWr16NixcvIiUlBUVFRfjggw98bhC/25UrV1BcXIySkpKo5Cag3TxTejzuY6E+7mOhzX0sQs1jyJAheO+99/D000/j+eefD3rOixQ5fy8sLIhI85TsR0HasGzZMixZssTvBymBjBw5EhUVFZGdGBFRhCUnJ2Pnzp3RnoYk2irHiYiIiIgoJrGwICIiIiIixVhYEBERERGRYiwsiIiIiIhIMRYWRERERESkGAuLGMItRYiIiNTHn79E4cHHzapAEIQ+26H33s5dr9dDFEX09PT02SDPc7ILdNIL1Q4RUGE7iLASIeL2P6I6G+SFaSwlx5LbV068pNhe6eMvlxTnYYxSc97hHEvJsfrTV2ofKXFKcol5pv5YauZaNPOsu7vbu7+F52ezlvS+ptDaeEqOJbev1HgpcaFigrWr/fellJx5srCIgO3bt2P79u1wu90AgObmZnR1dfnEeLZz730is9lsMJvN3hhRFL3HCLTJT6B2tyBCJwqA6Iaojbz1IYoCdILP9a4mxlJyLLl95cSHjBUBnSjA5XJBvGvnbSV5GMvUnHc4x1JyrP70ldpHSpySXGKeqT+WmrkWzTwTRRG3bt0CcHuXY8/PZi1tkNf7mkKtnbfDNZ6SY8ntKzVeSlyomGDtav99KeVwOCTHsrCIgLKyMpSVlcFut2Pw4MEYNmxYny3QBUGATqfzbuduMplw/fp1tLa2IikpyXsy7OnpCTpWoHa3IMLZ0wODToBer50fwsD/P2F3C4BBr86KRZjGUnIsuX3lxEuJFQQRbtGNRIO+T2EB9D8PY52a8w7nWEqO1Z++UvtIiVOSS8wz9cdSM9fUzjPPNwVaW1vR2dmJjIwMWCwWn5/NWnH3NYWWxlNyLLl9pcZLiQsVE6xd7b8vpe7+Nk0wLCxUIGU795SUFDidTty4ccNnidbzValAn7oEahcEEZ3dPdDp9dBr6NM94P+fqgsCoNdH/Ftc4RxLybHk9pUTLyVWEEWIggBzorFPIaokD2OZmvMO51hKjtWfvlL7SIlTkkvMM/XHUjPXoplnJpMJ99xzD6xWq/eCL9DP7Vim9rzDOZ6SY8ntKzVeSlyomGDtWsozOXNkYREjdDod0tPTMXz4cO8nLYIgoKWlBUOGDAlYDQdqt3d2o/bMJZgtyTAnauuvWRRFCN0O6M3J6vwgDtNYSo4lt6+ceCmxnd0udLbZMDU/E1Zzok+bkjyMZWrOO5xjKTlWf/pK7SMlTkkuMc/UH0vNXItWnhmNRhiNRulvjIiC0tYV5wBgMBhgMBgA3D75GY1GDBo0KODJMVB7l6BDj6iDUZcAQa+tk6YoinBDD+iN6nwnOUxjKTmW3L5y4qXEunRAj6hDosmEQYNMPm1K8jCWqTnvcI6l5Fj96Su1j5Q4JbnEPFN/LDVzLVbyjIiU4f9RRERERESkGAsLIiIiIiJSjIUFEREREREpxsKCiIiIiIgUY2FBRERERESKsbAgIiIiIiLF+LjZCPJsdGe32/u0CYIAh8MR8pF5wWKCtds7nOhob0O3kIBOoyEM70Y9oigCXXbAKaryuNlwjaXkWHL7yomXEuvsccPV2Qa73Q69q+/jZvubh7FMzXmHcywlx+pPX6l9In1OY56pP5aaucY8U0bteWv1nMY86x/PdaznujYYFhYR5HA4AABZWVlRngkRERERUf85HA4MHjw4aIxOlFJ+UL8IgoCrV68iOdn/jseTJk3Chx9+GPQYoWICtdvtdmRlZeHLL7+E1WqVP/kok/JnE4tjKTmW3L5y4pXmWrA2Leca8yy8fXhO80+reab0eJE6pzHP/FMzz8I9HvMstomiCIfDgYyMjJArLFyxiCC9Xo/MzMyA7QaDIWRChYoJ1W61WjWRtHeT8mcTi2MpOZbcvnLileaalP5azDXmWXj78Jzmn1bzTOnxInVOY575p2aehXs85lnsC7VS4RH7X+yKY2VlZYpjpBxDi9R8X+EcS8mx5PaVE68015hnsTWWmnkmpw/Paf5pNc+UHi9S5zTmmX9qvyetntOYZ5HFr0LFKbvdjsGDB6O1tVUz1TBpE3ON1MA8IzUwz0gN8ZxnXLGIUyaTCRs3boTJZAodTKQAc43UwDwjNTDPSA3xnGdcsSAiIiIiIsW4YkFERERERIqxsCAiIiIiIsVYWBARERERkWIsLIiIiIiISDEWFuTD6XRiyZIlyMrKgtVqxeTJk3Hs2LFoT4vi0CuvvIIJEybAaDSioqIi2tOhONDc3IzZs2fjG9/4Bu677z6899570Z4SxSGeu0gNWr0eY2FBPlwuF+69917U1dXBZrOhtLQUc+bMQUdHR7SnRnEmPT0dv/71rzF37txoT4XiRFlZGdLS0tDc3IwXX3wRjz/+OFpaWqI9LYozPHeRGrR6PcbHzVJIqampOHLkCMaNGxftqVAc+tnPfobMzEx+8keKtLW1ITU1FZ999hmysrIAANOnT8eiRYvw5JNPRnl2FI947iK1aeF6jCsWMcrhcGDdunWYNWsWhg0bBp1OF/Dk1dbWhjVr1iAjIwODBg3C+PHjsXfv3rDM4/z58+js7MTo0aPDcjyKLbGSZzTwhDv3Ll68CIvF4i0qACA/Px/nzp2L5NugGMdzHKkl0rmmlesxFhYxqqWlBTt27IDT6Qy53Dpv3jzs2rULGzduxP79+zFp0iQsWLAAlZWViubQ0dGBkpISlJeXw2KxKDoWxaZYyDMamMKde21tbbBarT79rFYr2traIjF90gie40gtkcw1TV2PiRSTBEEQBUEQRVEUm5ubRQDixo0b+8T985//FAGIlZWVPq/PnDlTzMjIEF0ul/e1GTNmiCaTye+v8vJyn/7d3d3i7NmzxUWLFnnnQfEn2nkmiqK4dOlSv2NSfAt37n388cdiSkqKT8xTTz0lPvPMM5F5A6QJkTjHefDcRb1FKte0dj3GFYsYpdPpoNPpQsZVV1fDYrHgscce83l9yZIluHr1Ko4fP+597dChQ+jq6vL7a9OmTd44QRCwaNEiGAwG/OlPf5I0D9KmaOYZDWzhzr3c3Fy0tbXhq6++8sacPXsW3/rWt8I7cdKUSJzjiPyJRK5p8XqMhYXGnT17Fvfffz8SEhJ8Xi8oKPC2y7V8+XJcu3YNVVVVfY5LA1Mk8szlcqGrqwtut9vnv4l6k5p7FosFjz76KCoqKtDZ2Ym3334bp06dwpw5c1SfM2mPnHMcz12khJxc0+L1GAsLjWtpaUFqamqf1z2vyX3U4uXLl7Fz504cP34cQ4cOhcVigcViwdGjR8MyX9KmcOcZAGzevBlmsxl//vOfsWXLFpjNZrzxxhuK50rxRU7uvfzyy7h69SqGDBmCp59+GlVVVRg6dKhqcyXtkpNnPHeRElJzTavXY9oofyioYEtjcpfNsrOzIfIJxORHOPMMACoqKviYRpJEau4NGzYM77zzjhpTojgkNc947iKlpOSaVq/HuGKhcUOGDPH7afHNmzcBwG9VTCQX84yihblHamCekVriPddYWGhcfn4+Ghoa4HK5fF4/c+YMAODb3/52NKZFcYZ5RtHC3CM1MM9ILfGeaywsNK6oqAhtbW3461//6vP6rl27kJGRge9973tRmhnFE+YZRQtzj9TAPCO1xHuu8R6LGLZ//360t7fD4XAAAOrr67Fv3z4AwCOPPIKkpCQ8/PDDmDlzJkpLS2G325GTk4M9e/bgwIEDePPNN2EwGKL5FkgDmGcULcw9UgPzjNTCXAM3yItl2dnZIgC/vz7//HNvnMPhEFevXi2mpaWJiYmJYkFBgbhnz57oTZw0hXlG0cLcIzUwz0gtzDVR1ImiBm85JyIiIiKimMJ7LIiIiIiISDEWFkREREREpBgLCyIiIiIiUoyFBRERERERKcbCgoiIiIiIFGNhQUREREREirGwICIiIiIixVhYEBERERGRYiwsiIiIiIhIMRYWREQU186ePQuDwYAVK1ZEeyqS2O12pKSkYMqUKdGeChGRLCwsiIji2MiRI6HT6aDT6bB27dqgsX/4wx+8sTqdTqUZRt6zzz4Lg8GA9evX92nbu3evz3tubGwMeJwrV64gKSnJG7tly5aIzNdqtWL16tWoq6vD3/72t4iMQUQUCSwsiIgGiMrKSrjd7oDtb775poqzUcfRo0fxzjvvYOHChcjOzu7TfurUKZ/fnzt3LuCxysvL0dnZ6f39uHHjwjbPu61ZswZJSUlYv349RFGM2DhEROHEwoKIaAAYM2YMrl+/joMHD/pt/+STT3DixAmMGTNG5ZlF1rZt2wAAixcv9tv+3//+FwBw//33AwDq6+v9xp05cwavv/46cnNzvas548ePD/Ns70hJScGPf/xjNDQ04PDhwxEbh4gonFhYEBENAMXFxQACr0q88cYbAICSkhLV5hRpzc3NqKmpQUZGBqZOneo3xrNi4fnzCVRYrFu3DoIgoLi4GKIoIjU1FZmZmRGZt8f8+fMBADt37ozoOERE4cLCgohoAJg2bRqysrJQXV2N9vZ2nzZRFLF7926YzWbMmzcv4DHOnj2LjRs34vvf/z7S09ORmJiI9PR0zJs3D8eOHfPb5/Lly1i+fDlGjRoFk8mE5ORkjBo1CkVFRdi7d6/i+GCqq6vR3d2Nhx9+GHp93x93X3/9Na5fvw69Xo8nnngCgP+vQh0+fBgHDhzAQw89hLS0NACRXa3wKCwsREJCAmpqauB0OiM+HhGRUiwsiIgGAJ1Oh4ULF6K9vR3V1dU+be+//z4aGxsxd+5cJCcnBzzGmjVr8Nxzz+H8+fNISUlBfn4+XC4XqqurMXXqVFRWVvrENzY2YuLEidixYweampowZswY5OTkoLW1FTU1Ndi6daui+FBqa2sBAN/97nf9tntWK3JzczFy5EiMGDECDQ0NPvc0iKKIX/7yl9DpdHjhhRe8X52K5P0VHmazGfn5+ejq6sKHH34Y8fGIiJRiYUFENEB4vubk+dqTh9SvQa1YsQKnT5/GrVu3UF9fj48++ghff/01ampqYDabUVpaCofD4Y3/zW9+gxs3bmDx4sVoamrC6dOncfLkSbS0tKChoQErV670Ob7c+FA8qyjf+c53/LbfXSTk5+ejra0NX375pTemsrISH3/8MRYuXIgHHnjA20eNFQsAmDRpEoDbxR8RUaxjYUFENEDk5eXhgQcewKFDh3Dt2jUAgNPpxFtvvYXhw4dj5syZQfv/5Cc/QX5+vs9rOp0Ojz76KNasWQO73Y5//OMf3raLFy8CAJ555hlYLBaffmPHjsXPf/5zn9fkxgcjiqK3QEhPT/cb41mx6F1YAHfus3A6nSgvL4fJZMLmzZshiiJOnz7t0yfSPHO/fPmyKuMRESnBwoKIaAApKSmB2+3Gnj17AABvv/02bDYbFixYgISEhJD9v/jiC2zduhWPP/44ZsyYgSlTpmDKlCmoqqoCcGcVAACysrIAAPv27ZP0yFS58cHYbDa4XC4AQGpqqt8YfysWwJ37LLZt24bGxkasWrUK2dnZuHTpEhwOB4xGI/Ly8hTNTyrP3Jubm1UZj4hICRYWREQDyIIFC2AwGLxff/L82/NUpGB27dqFMWPGYP369Xjrrbdw5MgR1NXVoa6uzrvacPPmTW98WVkZjEYjNm3ahHvvvRcrVqzA7t27cfXqVb/HlxsfTFdXl/e/ExMT/bZ/8sknAO4UFgUFBQBur1jYbDY8//zzSElJwYYNGwDcKUTy8vJgNBplz6k/zGYzAPjsn0FEFKtYWBARDSBpaWl46KGHcOrUKdTW1mL//v0YO3YsJk6cGLTfZ599hmXLlqGrqwtr167FyZMnYbfbIQgCRFHEH//4RwBAT0+Pt8/48eNRW1uLWbNm4cqVK3jttddQXFyMzMxMFBYWoqGhwWcMufHB9F6laG1t7dN+7tw5uFwun8fG5uXlQa/Xo76+Hlu2bMHNmzexYcMGpKSkAICs+yuuXbuGp556CllZWTCZTMjJycGGDRtgs9kkvwfgTqE2dOhQWf2IiKKBhQUR0QDjuUm7pKQE3d3dkvau+Mtf/oKenh7Mnz8fL774IsaPH4/k5GTvZnG9b3jubfLkyXj33Xdx69YtHDhwAM8++ywyMzPxr3/9CzNnzuxzoS03PhCTyQSr1QrAdxXF4+77K4DbqwM5OTk4ffo0XnrpJWRnZ2PVqlXedqlPhDp16hTGjRuHr776Cjt37sTJkyfx29/+FseOHcOECRPQ2Ngo6T30nvuwYcMk9yEiihYWFkREA0xRUREsFgu++OIL72NoQ/FcDP/gBz/w29773gp/LBYLCgsLsXXrVpw/fx6jR4/GlStXsH///rDE++NZWfC30hGoSMjPz0dHRwecTic2b94Mk8nkbfMUI8FWLJxOJ4qKirBs2TLU1NSgsLAQeXl5mDNnDg4fPoxp06bhpz/9qeR7SDw3kk+YMEFSPBFRNLGwICIaYJKSkrB27Vo8+OCDWL58ObKzs0P28XzXv6mpqU/b+fPnfZ4GJWV8z43SUu6fkBvvMWXKFADAiRMn+rT5W7EAgNmzZ+PBBx/EE0884VNwtba2ep/MFGzFYt++fUhMTMSmTZv6tOn1erz00ku4cOGCd4+NUDz7V/zwhz+UFE9EFE0sLIiIBqCKigocPHgQr7zyiqR4z0X6yy+/7L0oB4ALFy7gscce83uDdGlpKaqqqtDR0eHzem1tLQ4dOgTA95N4ufGhzJo1C4D/PSACPTZ2yZIlOHjwIHbv3u39mhdwZ4UjKysr4FOmAOD48eOYNWuWd6fvffv2YejQoairqwNweyVm2rRpOH78eMj5f/rpp2hqasLYsWO9T8wiIoploZ8tSEREA97cuXMxefJkfPDBB5g4cSLuu+8+GAwGnDt3DmlpaSgvL0d5eblPn//85z949dVXkZCQgNzcXCQnJ6Opqcn7yX9xcTF+9KMf9Ts+lKlTpyInJwf//ve/0dTUhBEjRgAAPv/8c7S2tiIhIUHyY2Ol3l/hcrkwaNAg7++7urrQ0tLic1O70WiE2+0OOabnEb5PPvmkpDkSEUUbVyyIiCikhIQEvPvuu1i1ahVGjBiBTz/9FDabDUuXLsVHH32Ee+65p0+f3/3ud/jFL36BgoIC3Lhxw7vSUVhYiL///e94/fXXFcWHotPpsGzZMrjdbu9FOnCnSBg7dqzPPRTBSH0iVEFBAY4ePer9fXFxMURRxPTp0wHcfmrWsWPHvI+2DWbPnj0wGo1YvHixpDkSEUWbTlS6CxEREVGMstvtGD16NFJTU9HQ0OD9ilKk2Gw2jB49Gi+88AKWLl3ap72iogK7d+9GfX190L0wjhw5ghkzZmDlypXYvn17JKdMRBQ2XLEgIqK4ZbVaUV5ejgsXLmDv3r0RH++b3/wmKisrsWrVKqxcuRKXLl1CT08PGhoaMH/+fPz+979HVVVVyA32nnvuOVgsFvzqV7+K+JyJiMKF91gQEVFcKy0t9W7mp4bCwkK8//77WLduHXJzcyEIAhITE/HII4/gxIkTyMnJCdrfbrdj+vTpWL16tfe+ECIiLeBXoYiIiCKkvb0dLS0tGD58uM9N3URE8YiFBRERERERKcZ7LIiIiIiISDEWFkREREREpBgLCyIiIiIiUoyFBRERERERKcbCgoiIiIiIFGNhQUREREREirGwICIiIiIixVhYEBERERGRYiwsiIiIiIhIMRYWRERERESkGAsLIiIiIiJSjIUFEREREREp9j8oir+B1mZ3pwAAAABJRU5ErkJggg==", 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", "text/plain": [ "
      " ] @@ -73,8 +73,8 @@ "# Plot the figure\n", "plt.figure(figsize=(8,6))\n", "\n", - "plt.loglog(m_grid, o_xi_vals, lw=2, color='black', label='Normalized Weidner_Kroupa_2004 IMF')\n", - "plt.loglog(m_grid, s_xi_vals, lw=2, color='red', label='Normalized Salpeter_Kirkpatrick_2024 IMF')\n", + "plt.loglog(m_grid, o_xi_vals, lw=2, ls='-.', color='black', label='Normalized Weidner_Kroupa_2004 IMF')\n", + "plt.loglog(m_grid, s_xi_vals, lw=3, color='green', label='Normalized Salpeter_Kirkpatrick_2024 IMF')\n", "plt.axvspan(0.01, 0.08, alpha=0.3, color='tab:blue', label='Brown Dwarfs (M < 0.08 $M_\\\\odot$)')\n", "\n", "plt.xlabel(r'Mass $(M_\\odot)$', fontsize=16)\n", diff --git a/docs/paper_examples/Begbie+26/Figure 3.ipynb b/docs/paper_examples/Begbie+26/Figure 3.ipynb index 383f5af0..049968a1 100644 --- a/docs/paper_examples/Begbie+26/Figure 3.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 3.ipynb @@ -87,7 +87,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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      " ] @@ -100,9 +100,9 @@ "# Plot the figure\n", "fig, axs = plt.subplots(1, 3, figsize=(15,6))\n", "\n", - "axs[0].semilogy(wave_scaled, wave * merged_flux_1000, label='Merged', lw=4, color='gray')\n", - "axs[0].semilogy(wave_scaled, wave * bt_flux_1000, label='BT-Settl', lw=2, color='red', linestyle='--')\n", - "axs[0].semilogy(wave_scaled, wave * meis_flux_1000, label='Meisner', lw=2, color='blue', linestyle=':')\n", + "axs[0].semilogy(wave_scaled, wave * merged_flux_1000, label='Merged', lw=3, color='black')\n", + "axs[0].semilogy(wave_scaled, wave * bt_flux_1000, label='BT-Settl', lw=2, color='mediumseagreen', linestyle='--')\n", + "axs[0].semilogy(wave_scaled, wave * meis_flux_1000, label='Meisner', lw=2, color='m', linestyle=':')\n", "\n", "axs[0].set_xlabel('Wavelength ($10^4$ Å)', fontsize=20)\n", "axs[0].set_ylabel(r'$\\lambda F_\\lambda$', fontsize=20)\n", @@ -114,9 +114,9 @@ "axs[0].grid()\n", "\n", "\n", - "axs[1].semilogy(wave_scaled, wave * merged_flux_1100, label='Merged', lw=4, color='gray')\n", - "axs[1].semilogy(wave_scaled, wave * bt_flux_1100, label='BT-Settl', lw=2, color='red', linestyle='--')\n", - "axs[1].semilogy(wave_scaled, wave * meis_flux_1100, label='Meisner', lw=2, color='blue', linestyle=':')\n", + "axs[1].semilogy(wave_scaled, wave * merged_flux_1100, label='Merged', lw=3, color='black')\n", + "axs[1].semilogy(wave_scaled, wave * bt_flux_1100, label='BT-Settl', lw=2, color='mediumseagreen', linestyle='--')\n", + "axs[1].semilogy(wave_scaled, wave * meis_flux_1100, label='Meisner', lw=2, color='m', linestyle=':')\n", "\n", "axs[1].set_xlabel('Wavelength ($10^4$ Å)', fontsize=20)\n", "axs[1].set_ylabel(r'$\\lambda F_\\lambda$', fontsize=20)\n", @@ -128,14 +128,15 @@ "axs[1].grid()\n", "\n", "\n", - "axs[2].semilogy(wave_scaled, wave * merged_flux_1200, label='Merged', lw=4, color='gray')\n", - "axs[2].semilogy(wave_scaled, wave * bt_flux_1200, label='BT-Settl', lw=2, color='red', linestyle='--')\n", - "axs[2].semilogy(wave_scaled, wave * meis_flux_1200, label='Meisner', lw=2, color='blue', linestyle=':')\n", + "axs[2].semilogy(wave_scaled, wave * merged_flux_1200, label='Merged', lw=3, color='black')\n", + "axs[2].semilogy(wave_scaled, wave * bt_flux_1200, label='BT-Settl', lw=2, color='mediumseagreen', linestyle='--')\n", + "axs[2].semilogy(wave_scaled, wave * meis_flux_1200, label='Meisner', lw=2, color='m', linestyle=':')\n", "\n", "axs[2].set_xlabel('Wavelength ($10^4$ Å)', fontsize=20)\n", "axs[2].set_ylabel(r'$\\lambda F_\\lambda$', fontsize=20)\n", "axs[2].set_xlim(1, 4)\n", "axs[2].set_ylim(5e6, 3e8)\n", + "axs[2].tick_params(axis='both', labelsize=16)\n", "axs[2].legend(loc='upper right', fontsize=18)\n", "axs[2].set_title(r'T$_{eff}$=1200 K', fontsize=24)\n", "axs[2].grid()\n", @@ -143,7 +144,7 @@ "plt.suptitle('Merging BT-Settl and Meisner Atmospheric Grids from 1000 - 1200 K ($\\log g$=4.5)',\n", " fontsize=24, fontweight='bold')\n", "plt.tight_layout()\n", - "plt.savefig('atmo_merge.png')\n", + "#plt.savefig('atmo_merge.png')\n", "plt.show()" ] } diff --git a/docs/paper_examples/Begbie+26/Figure 4.ipynb b/docs/paper_examples/Begbie+26/Figure 4.ipynb index e3e5117a..de06325e 100644 --- a/docs/paper_examples/Begbie+26/Figure 4.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 4.ipynb @@ -64,7 +64,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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"text/plain": [ "
      " ] @@ -78,9 +78,9 @@ "fig, axs = plt.subplots(1, 3, figsize=(15,5))\n", "plt.suptitle('Interpolating from Phillips to Pisa for a 1 Myr Cluster', fontsize=30, fontweight='bold')\n", "\n", - "axs[0].scatter(philpisa_m[phil1], philpisa_l[phil1], label='Phillips', color='red', marker='d', s=70)\n", - "axs[0].scatter(philpisa_m[pisa], philpisa_l[pisa], label='Pisa', color='blue', marker='s', s=70)\n", - "axs[0].scatter(philpisa_m[philpisa_interp], philpisa_l[philpisa_interp], label='Interpolated Values', color='black', s=100)\n", + "axs[0].scatter(philpisa_m[phil1], philpisa_l[phil1], label='Phillips', color='mediumseagreen', marker='d', s=65)\n", + "axs[0].scatter(philpisa_m[pisa], philpisa_l[pisa], label='Pisa', color='m', marker='s', s=55)\n", + "axs[0].scatter(philpisa_m[philpisa_interp], philpisa_l[philpisa_interp], facecolors='none', edgecolors='black', lw=2, label='Interpolated Values', s=90)\n", "axs[0].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", "axs[0].set_title('Mass vs. Luminosity', fontsize=24)\n", "axs[0].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", @@ -91,9 +91,9 @@ "axs[0].grid()\n", "axs[0].legend(loc='lower right', fontsize=16)\n", "\n", - "axs[1].scatter(philpisa_m[phil1], philpisa_t[phil1], label='Phillips', color='red', marker='d', s=70)\n", - "axs[1].scatter(philpisa_m[pisa], philpisa_t[pisa], label='Pisa', color='blue', marker='s', s=70)\n", - "axs[1].scatter(philpisa_m[philpisa_interp], philpisa_t[philpisa_interp], label='Interpolated Values', color='black', s=100)\n", + "axs[1].scatter(philpisa_m[phil1], philpisa_t[phil1], label='Phillips', color='mediumseagreen', marker='d', s=65)\n", + "axs[1].scatter(philpisa_m[pisa], philpisa_t[pisa], label='Pisa', color='m', marker='s', s=55)\n", + "axs[1].scatter(philpisa_m[philpisa_interp], philpisa_t[philpisa_interp], label='Interpolated Values', facecolors='none', edgecolors='black', lw=2, s=90)\n", "axs[1].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", "axs[1].set_title('Mass vs. Effective Temperature', fontsize=24)\n", "axs[1].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", @@ -104,9 +104,9 @@ "axs[1].grid()\n", "axs[1].legend(loc='lower right', fontsize=16)\n", "\n", - "axs[2].scatter(philpisa_m[phil1], philpisa_g[phil1], label='Phillips', color='red', marker='d', s=70)\n", - "axs[2].scatter(philpisa_m[pisa], philpisa_g[pisa], label='Pisa', color='blue', marker='s', s=70)\n", - "axs[2].scatter(philpisa_m[philpisa_interp], philpisa_g[philpisa_interp], label='Interpolated Values', color='black', s=100)\n", + "axs[2].scatter(philpisa_m[phil1], philpisa_g[phil1], label='Phillips', color='mediumseagreen', marker='d', s=65)\n", + "axs[2].scatter(philpisa_m[pisa], philpisa_g[pisa], label='Pisa', color='m', marker='s', s=55)\n", + "axs[2].scatter(philpisa_m[philpisa_interp], philpisa_g[philpisa_interp], label='Interpolated Values', facecolors='none', edgecolors='black', lw=2, s=90)\n", "axs[2].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", "axs[2].set_title('Mass vs. Surface Gravity', fontsize=24)\n", "axs[2].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", @@ -130,7 +130,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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gpKamIi0tDYwxWFtbo27dumjdujUcHBw0XobXr1/jzp07eP36NXJzc2FgYIBPPvmE94kToiRGNCI6OpoB4H1169ZNcH0XFxfedXft2sWl++uvv1jjxo1503p4eLDDhw/zbsff31+wnMq8oqOjBT9TXl4e2759O2vfvj3T1dUVzMvQ0JB99NFH7NKlS2r7zsvKl5eXx5YvX84sLS0rpGnWrJlUnitWrFDoNwwKCmL9+vVjOjo6MtPa2tqyVatWsfz8fIU/j6SYmBj25ZdfMldXV7m/g4WFBRs5ciS7efOm2r87We7du8e+/fZb1qtXL1anTh2F9xctLS3Wrl07tmnTJpaRkSFYvm7duqllH50wYUKFvBU91sqbMGGCQtu5efMm69mzJ9PS0pKZ1tLSkq1evVqlfePs2bOsRYsWvOVo2rQp27FjByspKWGMCe/PLi4uSm+fz65duyr9W5U/Ryp6LIaFhbFBgwbJ/L7XrVsnWO47d+6wMWPGMGtra7nlq1evHps3bx57+fKl2r6XMvfu3WPdunVjIpGoQhptbW02atQoFh8fXyH/pKQkNmnSJKanp6f2fU0Zla3/GGNs3759gnm0b99e5npVVXdKklePChFaz9/fn3c9oXPiihUreNdT9DhS9TMmJiayKVOmMFNTU5npjIyM2IQJE1hCQoLg91Ken58fGzZsGDMxMVHo/KGrq8saN27MpkyZwnbv3s1SUlKU2h4fefu2Mi+h35cxxvLz89nOnTtZhw4d5LaZdHR0WNu2bdkff/zB8vLy1PJZ5RE61uTt94wxFhsbK/c74jtX9ezZk3ed8ePHs/379/MuNzMzY7m5uVX2WYOCggTX7dOnj+D6//3vf3nX7dixo1KfQ1lC7RxF9mFl9evXT3B7Xl5eVbZ/C1HXeUBIVRz/itYj586d421rPnr0SMVvUdrr16/Zzz//zIYNG8ZcXV1ltn/4Xg0aNGDffvutUu0xTRL6XmVdh2hS//79Bb87Dw8Plp2drZFtv337lunr6/Nu+/79+wrn5eHhwZvPli1bpNJqIh6gqsDAQLn77549e9SyrfKErjmErvlUXa+Mqu1TxhgTi8Xsn3/+Yb6+voL7juTLwMCAtWzZks2aNYsdPHiwwv6syRhCeU+ePGFTp05l9vb2cvOrX78+W7lyJUtOTpabbxlFYw937tzh/dwnTpxQeHtEGAXSNUTTgfTExETWvXt3hQ/+BQsWyNxOVQXSjxw5wurWratSvh07dpQbpFfkO4+Ojmbx8fGsWbNmvGlUCaR///33vAH08i8PDw8WGRkp97OUycvLY3PmzGHa2toqfXfdu3dnr169Ust3x0fehZYiL3t7e3bq1CnebdTGQHpRURGbPXu2whcD3bp1Y+/evZP7WzHGWGFhIRs3bpzCn9vX15e9e/fuvQ+kHzt2jBkYGPCm4wukx8XFsd69e6tUTm1tbTZz5kyFAjOKBNL/+9//KnQ+cXJykrqAvXnzJnN0dFSozF27dlV4X1OFOgLply5dEszDw8ND5npVVXdKokB66Wc8ffq0QjehADBzc3N25swZwe+GsdI6cPTo0Sodm5Kvffv2yd2WIqoqkH7y5Elma2urUr516tRhx44dU8vnFVLZQHphYaHczyLrhuGrV694b0wDYBcuXGBZWVnM0NCQN83+/fur7LNGREQIrjt06FDB9X/++WfB9Xfu3KnUZ1FGVQbSk5KS5LZ1Dx06pLbtVYamA+lVdfwrUo98/fXXgttTVyBdHe1GfX19tnr1aq7zSHWpKYH05ORkucfU33//rdEyTJkyhXfbkyZNUiiPBw8e8OZhZGRUoTOWJuIBqpo7d65gWby8vNSyHVlqWyA9NTWV9ejRo9LngRs3bihcHmVeQsduSkoKGz58uEr5Ghoasg0bNsj9XhlTLJD++++/Cx73FEhXH/WMNUGqVGxsLLp27Ypr164pvM769evxxx9/aK5QPMRiMT7//HOMGjUKSUlJKuVx+/ZtNGvWDH5+fpUqS2ZmJvr27Yvg4OBK5SNpyZIlWL58OYqLixVK/+LFC3Tt2hWRkZFy08bFxaFdu3bYtGmTyjOkX7t2Dc2bN6/xY9clJCRg6NChgkM51CYFBQUYMWIENm/erPAEVwEBAQoNVVFSUoJhw4Zh//79Cpfn4sWL8PX1lTvRUG12+fJlfPLJJ8jPz1dqPX9/fzRr1kxwGAEhJSUl2LZtG9q2bYvXr1+rlEeZ33//Hd98841C55O4uDj069cP6enpePjwIfr06YM3b94otJ3r16/X+PHF+cYELmNhYaF0nrWp7qxtzp49i+HDhyM1NVWh9O/evcPHH3+MU6dOCaabPHkyDh8+rI4i1hoLFy7E0KFD8fbtW5XWT05OxogRIzB//nw1l0y95B3jAGBubl7hvT179vAOpVGnTh307t0bJiYmGDhwIG++qkxUqqqMjAzB5Q0aNBBcXrduXcHlU6ZMQYcOHbBhwwaEhISo3F6sbpcvXxYse926dfHxxx9XYYmqR006/lesWIGff/650vlUlYKCAixbtgzz5s2r7qLUCIocUyNHjtRoGb788kveZYcOHZI7HwIAHDhwgHfZqFGjZNYTQjQRD+Ajb7z8mTNnarwMtYFYLMaQIUPg7+9f3UVR2v3799GsWTMcP35cpfXz8vIwf/58fPzxxwrHk/js2rUL8+bNq7XtgNqGAum10MqVKwXHTOTz9ddfIzs7WwMl4vfll19i27Ztlc4nMzMTQ4cOxaNHj1TOY/78+QgNDa10WcoEBgbiP//5j9LrxcfH45NPPhE8WWZmZmLAgAEICQmpTBEBAOnp6Rg2bFiVNBgqgzGG8ePHvxczwB85ckRugEiWQ4cO4caNG4JpfvjhB7njHcoSGBiIjRs3Kr1ebZCSkoKxY8cq3QB58uQJhg4dqnDwT0hoaCg++ugjwUnH5FmwYIFS6d++fYvp06dj6NChSt8kUWRfq07yxrK1s7NTOs/aVHfWNiNGjEBRUZFS6xQVFQme8wMDA3Hw4EF1FK/WWLlypdrG3v3999+xfPlyteSlCfKOcTMzM5nj6e/du5d3nZEjR3JjUo8ZM4Y3nZ+fX5WNKxsYGCi4vFu3boLL27Vrp9A2FixYgGbNmsHMzAwdOnTAnDlzsG/fvkrf4BXSo0cPiEQihV9CE5PevXtXcFvdunXT+NxJ1a0mHf/Xrl3DDz/8oJayVLXNmzfj0KFD1V2MaifvmOratavGjylvb2/069dP5rK8vDzs2rVLcH2xWCx4M33atGlKl0nd8QA+aWlpiIiIEEzTu3dvjZejNjhy5Ahu3rxZ3cVQWmRkJAYMGKBwRyYhJ06cwOTJkxXugFfe8+fPBedjIepX82cdIxVI9sSxsLBAp06dYGxsjPv37yM6Opp3vczMTBw8eFClSkcVBw8exIYNGwTTODg4oFGjRqhTpw7i4+MREhKCd+/eyUyblZWFsWPHIiQkRKXJe5TphaiIgoIC7m8dHR106NABTk5OSE9Px61bt3gnwgKAoKAg/Prrr/jmm29kLp81axaePHkiuH0nJye0bt0aenp6ePHiBR4/fsybNisrC0OHDkV4eDj09fWFP1gl6evro0GDBnB2doaxsTGMjIxQUFCApKQkPHv2TPDJhIKCAqxZs6bW9wCVPEb19PTQqVMn2NraIjIyEg8ePBBcd9u2bejSpYvMZc+ePcOPP/4ouL5IJELr1q3RoEEDZGVl4datW1yPuPe1R/rTp0+VXqewsBBDhw6VG/hu0qQJGjVqhKKiIjx69EjwRk9oaChmzpwp2HtGSFnjSV9fH127doWNjQ2Cg4Px7Nkz3nWOHTsm9b+DgwPat28PxhiuXbsm2NtHaF+rTrdv38a+ffsE08gLPslSW+rO2kiyPmzatCkaNWqEwsJC3L9/X/ACIzMzEzNnzsSFCxcqLCu/b5fn5eUFT09PmJqaIi8vD+/evcPLly8RExNTK3vjXL58GatWrRJMY2BggI4dO8LW1pab3Dw3N5c3/Q8//IDOnTujb9++6i5upSQnJ8sN8sk6xm/cuCH4RJ9k8HzAgAEwMzOTeY4Xi8XYu3cvli1bpkSplZeeni4YGHV2dkb//v0F82jUqBGaNGkit01YJjc3F4GBgQgMDMTmzZsBlPZ6/+STTzB+/HilJrSrSvJucipyQ6E2q2nHf0BAgNLrqJO2tjacnZ3h5uYGMzMzmJiYoLi4GBkZGYiIiEBUVJTg+qtWrcKoUaOUnkDzfSLvmGrfvn2VlOPLL7/k7Zm9bds2fPHFFxCJRDKXBwQE8LYhvLy80LFjR6XLo+54AB95QXQzMzN4eXlVSVlqOqH2nkgkQrNmzVC/fn0YGxsjJycHGRkZiIyMRGxsrMqB58oqKSnBkCFDkJKSwptGR0cHzZo1Q7169SAWixEZGSl4Tbdv3z7069cPn376qdLlkXfTnmhAdY4r8z7T5BjpZa9FixZJTcRUUlLCli9fLrjOJ598IrWdvLw8Fh0dzb2ExncaPny4VNryr6KiIi7fgoIC5ubmxpuXg4MDO3XqVIVx7DIzM9l3330nOK701q1bVfrOJV/W1tZs1KhR7KuvvmJffPEFGzVqFHN0dFRqjPSyV69evVhsbKzUetnZ2Wz69OmC69nZ2bGCgoIKn+Px48eCn9/AwEDmxCQPHjxgDRo0ENzm+vXrVfruhMZInzhxImvZsiX75ZdfWFBQkNyxCa9fv858fHx4t2Vubs6Ki4ul1klISOD2sxs3bgiW9caNG7z7qKwJPTQxRnrZq2/fvhXGeD137pzgBCp2dna82xQaaxAA8/T0ZMHBwVLr5Ofns0WLFsktqzrHSM/KypL63tu1a8e73fnz58v8rcpPRqjIsVj2aty4MZs+fTpbsmQJmzFjBuvbty/T19eXGiN9w4YNgnm4ubmxwMDACp/twIEDgmPvikQi3jFDFRkDtG3btiwuLo5bRywWs9mzZyv0uZctWyZ17KSkpLBGjRqptK9Vhir1X35+Pnv27BlbsWKF4PcLlE5UHBUVJXPbVVV3SqIx0v93Drlz547UemKxmG3btk3uuP8hISEVtjlkyBCZaU1NTVlAQABvWQsKCtitW7fYmjVrWJcuXZiWlpbaxkgvKiqSOk/Nnz+f9zO1a9dOsM0kOSmgWCxmLVu2FPyOPv30U5aWliZVnoyMDDZ+/HjB9Vq2bMnEYrFaPr8kZccNLyoqYq9fv2bbt29n9erVk7s/7d69u0IekydP5k3v7Oxc4XMK1dPu7u5q+6ySv2tkZCQLDAxk69atE1xPJBIpNE8AY4ydOHFC7velyEtLS4uNGzeOvXnzRqHtqmMOHEXPMULjFQNgBw8eVKjMVUFd54Ey1XX8Kzp2sI6ODuvatSubO3cuW7x4MZs4cSJr166dYHtHWbt27WL16tVjixcvZlevXpU7aWpkZCQbNmyYYLmVmcxSnWrKGOnNmzcX/H4OHDgguH5RUZHSxzjf9WLTpk1517l48SJvGaZOncq73m+//SZzHU3EA1Qh77zt6elZ6W0IqU1jpPOd/x0dHdmTJ094t5WTk8OuXr3Kli9fzp1Dy4+RrqkYwh9//CGY17Rp06Su58rcvHmTNW7cmHc9Z2dnmfEhxhSvk0UiEWvTpg2bNWsWW7JkCZsyZQrr1q0b09HRoTHS1YgC6Rqi6UD6jBkzeNdt1aqVyidtRWcDlmf//v28+ZiamrKIiAjB9b/66ive9du3by9zHUUqTpFIxFatWiWzgSYWi1lYWJjUe/KCd15eXoKNPb5AQNlL1oRA8k6SQhPDREVFMSMjI6UrwMoE0lNTU3mX8YmJiRGcLOzBgwe861amrLJoKpDerFkz3n1j4cKFguuWDyIzVnrTS2giTVNTU/b69Wve8soLwqszkF5eZWZwL6NIIN3JyYn5+fnJXD8zM1PqhpfQ725oaCh4jjp06JBgOfjOlfIC6WZmZuzt27cV1nvz5o3cz84X6BU6F/Pta5WlzEWMKq+ZM2fybrs66k4KpJdO8hYeHs67/m+//Sa4/pw5cyqs4+vrKzPtoEGDeLcjS3x8vOC5sTJU/T7Lk/f9+vr68gbDxGIx69+/v8r7kqoUuWml6qtx48ZSnTMYK71gNjU15V3n66+/rlDGf//9V3A7N2/erJbPqqOjw7Zt26bU9y2v3aDMy9bWtsJNL1mqMpDesGFDwXUvXLig1PdVlSp7Hqiu41+RQHq3bt1YZGSkzPVjY2PVNnF5RkaG0pOEFhcXM09PT96yr127Vi1lU1ZNCaRX9phSZyBdqP3LN+FyQUEBs7S0lLmOvr4+S0lJkbmeJuIBqpDX/m7Xrl2ltyGkNgXS+Y7juXPnKvWZo6KiZHaaK6POGIK7uztvPrNmzRJcNyEhQbA98++//8pcT5E6uXHjxuzhw4cy109OTpZ5nUlU8+E+71SLGRoaCo7N3bVrV95lqk74qazz58/zLhs7diwaNmwouL7QIy337t0TfIxGyG+//Ybly5fDwMCgwjKRSIRGjRopld+KFStk5lVm9erVguvLerzs4sWLvOmbNWsm+N3Ur18f06dP513+6tUrhIeHC5ZJWVZWVkqv4+LiAnt7e97lYWFhlSlSjfDf//6Xd98QOkYB2cfpvXv3BCfSnD17NurVq8e7fPXq1SoNiVRbmJub48aNG+jRo4fM5aampnBycgIAhIeHCw7RMn36dMFz1OjRo9G8eXPe5ULHsJBp06bJnFjOwcFB7oRzfEMUyBu6parqBHXx9vbGmjVrVFq3NtSdtdWkSZPg6enJu3zu3LmC49rLqgv50t+9e1fuI9OS7O3tBc+NNYG8Ccn+85//8D76LhKJ8NNPP1Uq/5rE1NQUu3btqlBfHT9+XHDIPFljovfq1Qt16tThXacqJx0t06FDB9y8eVPpCZ/Xrl2LtWvXqmV4vrdv32LQoEEaHT9d3fj2//dBTT3+27Zti0uXLvFOiOvk5AQzMzOV8i7P3Nxc6WFYtLW10bp1a97l78O1hCZV5TH16aef8l73nTlzBnFxcRXev3DhAu/whMOGDYO1tbXK5VF3PEAV7/M5TVl87b3Lly8rNfFy/fr1YWNjo65i8YqIiOBth2pra2Pp0qWC69vZ2aFnz568y4XiaEJcXV1x48YNtGjRQuZyGxsbudeTRHEUSK+F+vbtCwsLC97lDg4OvMsqMxGeMoTGH9u2bZvcCYn4TgBA6diWqkwY17hxY7XMYl9GW1sbgwYNEkzj4+OD+vXr8y4vP57V8+fPkZiYyJt+2LBhcsslL83169fl5qEKsViM+/fv48cff8SoUaPQsmVL2Nvbw9zcHDo6OhV+Y6Fxc9PS0jRSxqpiZWWFPn368C4XOkYB2cfp/fv3BdcZOnSo4HJbW1uVxhKsLb799lu4uroqlFbe2J+VPc4SExNVOkcNGDCAd5lQELJevXpo3LixzGW2traC26yqOkEdWrRoAT8/P1haWqq0fm2oO2sreceMjo6O4P799OnTCkFSvnHwk5KS4O3tja5du2Lu3LlYt24dzp49ixcvXig94XBNIXROcnV1FbxxB5TeZHdzc+Ndrql6X93q1KmDCxcuyAyOCQW9GzVqJPM70tHRwYgRI3jXO3LkSJXNHSISibBkyRJcu3ZN5fG+Fy5ciPDwcEyePBnGxsaVKk9KSgq+/fbbSuWhTrImlpUkNN9HbVdTj/9NmzZBT09PpXUrIyoqCps2bcLEiRPRqVMn1KtXD5aWltDX169wLfH333/z5lPbryUqqyYdU3p6epg9e7bMZSUlJTLnxhKab6gyc9aoOx7ApyZ9/zUdX3svPDwcrq6u6NOnD7744gts3LgRFy9eRHR0tNS8R1VNKM5VUlICJycnubGuU6dO8eah6k3AX375RfA6h6gXBdJroVatWgkuNzU15V1WFRNwicViwWCwOihzd7LMZ599pta7vw0aNJBbSQLgDXABQEJCgtT/8fHxgnk1bdpU7vaaNGkiuFzeNpTFGMPu3bvh4eGBtm3b4rvvvsPRo0fx6NEjJCYmIjMzU+n9jm/C2dqiefPmgj1rhI5RADKDQfL2eR8fH7nlEtoXa7vx48crnLamHmfe3t68y4SCJkLryeu9WBsCj1ZWVvjPf/6De/fuyb0xIKSm1521mbzjARA+/zDGKpzjRo8ezT1FUl5xcTFu3LiBTZs24csvv8SgQYPg6ekJQ0NDtGzZEnPnzsWFCxdqze8mdL5Q5HwECP8G6q731U1PTw9TpkxBWFgYOnXqVGH569evBS9chZ7Uk9VTvUxmZib++ecfpcqqKsYYfvrpJ/j4+ODRo0cq5+Pq6oqdO3ciISEBf//9NyZPnizYYUPIwYMHK9Xe8vf3BysdJlShl9BvKK+DQU3fhyujJh7/Xl5eaNOmjdLrVcaNGzfQvXt3NGzYEHPnzsWePXtw+/ZtxMXFISMjA4WFhUrlV9uvJSpL3jFV/hpU0z7//HPe6+YdO3agqKiI+z87Oxtnz56VmbZ+/fq8T58qQt3xAD6KfP+smibKrGlmzpzJe52Tn5+PK1euYP369Zg3bx769euH+vXrw8jICB06dMBXX32F69evV+l3KdQZUB1UiXOZmZnJ7VRH1IsC6bWQ0GOqAKCrq1tFJZEtNTVV43cJVekdqGoPID6KPlImNPRJ+d4SycnJld6mhYWFYBBX3jaUUVhYiDFjxmDSpEmIiopSW761JfjBRxPHaEZGBu8ybW1tmJiYyM3jfb1L7erqqtSjakLHgJaWlkLfk7whjVQ5zoTyFOoVpmoP7ZpKX18fLVq0wJQpU3Ds2DEkJCTgm2++qfTQRPKOy/d56CNNU6RuknfMlK8PjYyMcPz4caXOW8XFxXj06BE2bdqEAQMGoF69evjrr78UXr+6CJ0v1NHWUGe9rw5aWlpwd3fHqFGjsHnzZiQkJGDHjh28n3XPnj2C7cpPPvmEd1nnzp0Fh/ap6uFdIiMj0alTJ9y+fbtS+ZiamuLTTz/Fzp07ERUVhYSEBBw/fhwLFy5Ey5YtFQoUFRcX49atW5Uqh7q4u7sLLr97924VlaTq1cTjX93XTPKsW7cO3bt3l/vEoDJq+7VEZck7pu7duye4XEdHB9HR0VKvgwcPqlweKysrTJgwQeayxMREqZuaJ06cQG5ursy0U6dOrVQgvKr2bXnD2GZmZqp9uNXayt7eHocOHVJq6LKCggIEBgZi7dq16NatGzw8PHD69GkNlvJ/NN2mUiXO1bJlS7qOqWIUSK+FhMbkBqD0GHPqVhV3BFXZBo0JpX7fffcdDh8+XN3FqHE0cYwKNS5KSkoUunmlbG+e2uJ9ObZVfYRaHWPmVqV27dpVuDiLiYlBQkICcnJykJ+fj4cPH2LHjh0YPny42h4tl3dcamtrq2U7RH3atm2LZ8+eYfLkyQo9AVZeQkICpkyZgsWLF2ugdERI+WM8Ojoab968QWZmJoqLi/HixQscPnwYs2bNknuTZe/evYLLPTw8eB+f1tLSQmxsLO+6fn5+gssVUdbjOjc3F8+fP8cvv/wi+PRMXl4eRowYofJ8P7LY2dnh448/xtq1axEUFITo6GjMmTNH7nrR0dFqK0NlyAtuBQQE1IonqN4XVdmuunDhAr788stqHarhfaTIMSXvZoOrq6vUS2iYQUV88cUXvNdAW7Zs4f7mG9ZFR0cHEydOrFQZqmrftra2lhtMv3LlSpWURV0UOUaF5vMSMnDgQISEhGDEiBEqdTiLjIzEkCFDsG3bNpW2rwxNx7oozlU7UCCdqJ21tbVgoPDPP/9U6lFQWS9VKlFDQ8NKfKqKUlNTFUonNEZf+YtHeT0mFdlmRkaGYEUnbxuKSkpKwq+//sq73NbWFuvXr8ezZ8+Qk5NT4Td0cXFRSzk+FPJ6JSkynJKsyXzeB8oe20LHgFgsFuz9X0be2JvqOs7eRwYGBhUuzlxcXGBnZ6dSsJRUP0XqJnnHDF8w1d7eHjt37uR6rC1YsAA9evSAo6Ojwr3Sfv75Zzx48EChtNVB6HyhjrZGdZyPyh/jrq6ucHBwgKmpqVK9CW/cuIHIyEiNlVMsFssN1CvK0NAQHh4eWLRoEYKCggTbOQkJCVi4cKFatiuLi4sLNm7ciHnz5gmmqynDX/Tp00fwZubbt29x4sSJKixR1amJx7+6r5mEfPPNN7zLdHV18dVXX+Hu3bvIyMhASUmJ1LUEXw9nIv+YSkhIqPJjyt3dHQMHDpS57Pr163j69ClSUlJ4A8wDBw7knbRUUVW5b/fr109weVUEfdVJcvgdPpXpre3h4YGjR48iPj4eBw4cwOeff44uXbooFST+8ssvNX69K3ROdXBwqHScKyYmRukyVeV+TUpRIJ2onba2tuAJ786dO1VYGs2JiorifexMUmhoKO+y8o0BeeOphYSEyN2evDSVbYCUOXXqFG9PBj09Pdy8eRPz58+Hl5dXheAYY0zhiwNSSl6vBnmPaDLGKv0o+fuiNh1nhNQGT548kZtGqC4UiURyx783NTXFsGHDsG7dOvj5+SEuLg65ubkIDw/HmTNnsHTpUtjY2MhclzGmtmCpJgidkxQ5H8lLV5vPR1Ux9MqePXvUnqejoyMOHTok2LFk3759CAoKUvu2JcmbP8TMzEyj21dU3bp10bt3b8E0q1atQkFBQRWVqOp8yMd/RESEYP2xc+dO/Pzzz2jbti3Mzc0rHE/qfKrjfVNTjymhG4hbt27FkSNHeJ8+qcwko9Vh7NixgsufPXuG/fv3V1FpFCN086X8pPDlZWRkqOUpJxsbG4wZMwZbtmzB9evX8fbtW2RnZyM0NBT//PMP5s+fzzumel5eHo4cOVLpMggROmfHx8fj9evXGt0+qRkokE6kCJ08lalo+WZfBoB//vlH5ZnUo6KiND7Bg6JKSkpw5swZwTShoaF4+fIl7/L27dtL/e/h4SH42Jwik2LJ613QtWtXuXkoIiIigndZ8+bNBQO/d+7cQXZ2tkrblTf8wvt4kQXI/93kjQN87tw5qtj/n7zvUpHj7OTJk7zLbG1t4eHhoWyxCKm15NU7xcXFOHfuHO9yHx8fuZMwy2JgYABPT08MHDgQq1evFtzG/fv3lc5fHnW1mYTOSTExMXInp3z8+LFgDyZ11ftVLTc3F0ePHtX4diIiIjQyVnj79u0Fn6BkjGHp0qWCeYSEhOA///mP3AAGH3mBcr6bT9Xhyy+/FFz+9OnTSvXiv3z5ssrratKHfPwLXUsAwIgRI3iX5ebm1pgx/msqecdUaGio3DTq1rVrV97J3/ft24edO3fKXObk5ARfX19NFk3t2rdvjw4dOgimmTNnjspPXaWmplZq8mpZhOqMnJwcpKen8y4/fPiwxoY9MTY2ho+PD4YNG4b169cLXvfytffUFUMQinMBpZPnqqKkpITOabUIBdKJFKEL2WfPnimcz4ABA3iXvXv3DjNnzlRqLLwXL15gypQpaNSokdxGV1VatWqV4Fhg3377reD63bt3l/pfJBKhb9++vOlDQkJ4x40DSse63L59O+9yZ2dneHl5CZZJUULDX7x69Yq3Ii0oKKhUo01esEWZ/bQ2cXZ2RtOmTXmXnzlzBvv27ZO5LDY2FrNnz9ZU0WodLy8vODs78y7fvn27YKP28OHDePz4Me9yX1/fSk2EREhts2vXLjx//px3+caNG/H27Vve5eXrQqC0l/vTp0+VKofQDSxVb+ALEaqPIiMjFXoMGpD/+PeSJUt461RFgrHy8q+pjh8/rnIAWVma6vm+cuVKwXksLl26hBs3bvAuz8zMxJIlS+Di4oLly5cr/ci3vMkBW7ZsqVR+mtS3b1/BNjAAbN68GbNnz1ZqvPSysXNHjhxZ2SJqxId8/MsbSk+od+uyZcsUGorvQ6bIMbVlyxZMnTq1SudR4rshlpmZiYcPH8pcNnny5Fo5l83atWsFrwnevXuHLl268H5uWYqLi7Fjxw54enqqdYJeQP5QUFevXpX5flpaGr7//nuVthkYGKh0T3ZV2nvqiiE0atQIbm5uvMvXrl2rVOeNwsJC7Nu3D15eXli2bJnC65HqRYF0IkWoN3RISAi+/PJL3L17Fy9fvkRMTAz3Kt+QGTVqFJycnHjzOnr0KHx9fQWDUS9evMC2bdvQqVMneHp64q+//qpxEw2FhYVh4MCBFXrJ5+TkYObMmTh16hTvunZ2dhg0aFCF9xcsWCBY4U6ZMkVmwDQoKAh9+vQRHG5mwYIFvMuUJdSL6e3bt/jiiy8q3NmNjIxE3759cffuXZW3a2ZmJjgO2Ndff43jx48jPDxcah9VZbyxmkbe7zdx4kRMmjQJly9fxosXLxAUFIRffvkFLVu2pN7o5XzxxRe8y/Ly8nj300OHDmHSpEm864pEIsyfP18tZSSktigoKICvr2+FY4Yxhj/++ANff/214PqyHte+desWGjdujDZt2mDVqlW4efOm4I3rrKwswfGgLSwshD+ECoTaTCkpKZg8eTJu3LiBqKgoqbqo/HAE3bt3R4sWLXjzunjxIj777LMKPcEyMzMxadIkXLhwgXfd5s2by7xRURsIBbednJxkTmgq9Pr000958zty5Ajy8vLU/hnq1auH6dOnC6ZR5MI5PT0dP/zwA+rXr4+uXbti06ZNCAkJ4e2UkpWVhR9++EEwsOHi4lKpp6d69OjBO8kr30vecfjnn3/C0tJSMM2WLVvQuHFjHDlyBDk5OTLTZGZm4syZMxgyZAg8PT1x+vRpVT+mxn3Ix7+8JyJmzpyJpKQkqfcyMjIwe/ZsrF+/XoMlE5aSklLhGqPsJVRPZWdn866nqWFqFDmmdu7cCU9PT+zcuZM3CFlUVISwsDC1lGnkyJGoV6+ewum1tLQwefJktWy7qnXs2FHu9VtiYiLatWuH2bNn837HjDE8e/YMa9asgaurK6ZNm6aRYVKbNWsmeMPiyy+/xIsXL6Tee/HiBfr27Yv4+HiVtnny5Ek0aNAA3bt3x88//4ygoCDBjgjJycmCcyvw1TPqjCEIdQrMy8tDr169sHHjRt46Kjs7G1evXsWsWbNgb2+P8ePH16jOokQ+neouAKlZWrduLbh83bp1WLduXYX3V6xYgZUrV3L/GxgY4McffxR8pPXKlSto0aIF6tevDx8fH5iZmSErKwupqakIDw+vNWNoX716FW5ubujQoQMcHR2Rnp6OW7duye1FNX/+fOjp6VV4v0WLFhg9ejQOHTokc738/HyMHz8ey5YtQ+vWraGnp4cXL17IfbTL2dkZs2bNUvyDySFvNvgNGzbg4MGDaNWqFSwsLPDy5Us8ePBA7gzx8mhpaaFly5a8jz5FRkbyPgqq6Vm2NW3s2LFYv34971iYYrEYu3fvrpIxZWu7zz//HOvXr8erV69kLo+Ojkb79u3RrFkzeHp6ori4GEFBQbzpy4wcObJG9fAjpKq8evWKO2YaNWqEgoIC3L9/X+5wbL6+voJP2zx48AAPHjzAypUroaWlBRcXF7i5ucHc3BzGxsYoLi7GmzdvEBQUJHgjuUmTJip/Nj7y2kz79++XOf7phAkTpM7TIpEIP/30k2DP0b///hv//PMPOnXqhLp16yIlJQU3b96UO1fLmjVrauUTMq9fv8a1a9d4l48YMQKurq5K5Tl27Fjep/oyMzPxzz//yB3TVhVLly7Fjh07eAP1N27cwKVLl+T2HAVK2zE3btzgerGbmZnB09MTNjY2sLKyQlFREV6/fo1Hjx7JfUx97ty5yn8YDXNxccGRI0cwcOBAwfI/f/4co0ePhr6+Plq0aAE7OzsYGhoiNTUVCQkJePbsWaXbm1XlQz7+W7duDW1tbd7f6saNG3B1dUX79u1hZ2eH5ORk3L59W6E5qjRp0aJFKs2tcPz4cRw/flzmsvL1grooekzFxMRg6tSpmD59Opo0aQIHBwdYWVkhJycHaWlpePz4MTIzM9VSJh0dHcybNw9fffWVQun79u0rOHlzTfff//4Xz549w8WLF3nTFBcXY8uWLdiyZQtcXV3h7u4OGxsb5OXlISUlBeHh4VUyJ4CxsTHatm3LO6ddbGwsmjRpwk0CGhMTg7t37yo10oAsjDEEBARwPex1dHTg5uYGFxcXmJmZwdjYGAUFBXj16hUePXok+AQFX3tPnTGEGTNm4Pfff+cNfpd17li8eDFatmwJBwcHaGlpITU1FfHx8QgLC6v0d0aqFwXSiZQuXbqgTp06lZpxucyECRNw584d/PHHH4LpXr58KTiOeE2lr6/PNUiKiopw/fp1hddt1aqV4DiP27Ztk/tYe2xsLGJjYxXanrGxMU6cOCH4eLGy+vbtCwcHB8G7z0lJSTJ7ytja2qKoqEjlR+2HDx/+QY4hpqenhwMHDqBNmzZK95zT0tJC+/btacLR/6evr48TJ06ga9euguP1BwcHIzg4WKE8vby85J7vCHkfSdaHyhwzZmZmSh0zYrGY612sLKEb+6pq2LAhmjVrpvDnFeLr64tly5Zh9erVvGny8vJw5coVhfNcsmQJ+vfvX+myVYc9e/YIXmQKjZ3Mp3fv3jA3N8e7d+9kLt+9e7dGAul2dnaYNWsWfv31V9403377rUKB9PIyMzNVGv/f3d0dc+bMUXq9qtC7d2+cOHECo0aNkjufTkFBAQIDA6uoZJrzoR7/VlZWGDx4sOA8G3l5efD396/wvpaWFjw8PBAeHq7JIr4XlDmmxGKxUvW4qqZNm4bvv/9eoeG7pk6dqtGyaJquri73/Z89e1Zu+up+knrmzJm8gXSgdCgSviFeRCKRWjquFRcXIyIiQule2rq6uoL1uLpiCLq6ujh58iQ6d+4sOG58bm4ubt68WentkZqHhnYhUvT19bF8+XK15Vc27tr7qH379iqNO+3g4IBDhw5BV1eXN425uTnOnz8PHx+fyhQRQOnjTf/884/ae8kaGhpiw4YNSvd00dfXx5EjR1SaWK7MlClTBMcme5/5+Pjg/PnzMDExUXgdLS0t7NixA3369OFNU5N7LGlKixYtcOLECbmPvCrCy8sL58+f18jwEYTUdHv27IGWlnJNSl1dXezZs6dKepnNnj0bnTt31kjeP/zwg9ry+vHHHwWHp1HGrFmzBINyNd3evXt5lzk4OKBjx45K56mnp4eBAwfyLvfz81O4g4KyFi9eLFhv379/X3ASa3VycHDAv//+q9bOFerWv39/3L59WyNPktRUH+rx/8svv6jUdvrpp5/kPh1L/qcqjill2gHm5uaYMmWK3HR169bF4MGDK1OsGsHQ0BCnTp3C8uXLoaNTs/uyjh07VqU6dsqUKRpraylqzZo1gjECdcYQvL29cf78edjb26slP1K7UCCdVDBnzhx89913Sl8Uy6KlpYXt27fj2LFjsLW1rVReBgYGGD58OBo2bFjpcqnLxo0bsXjxYoWDkO7u7rh+/bpCn8HZ2Rn379/HzJkzVZ5cpUuXLnj06JFKvZwUMWLECGzatEnhBoGpqSlOnTqFrl27Vmq7ZmZmOHfunNomTq1tunfvjps3b6Jt27Zy07q4uODSpUuYNGmS4COZxsbG6ixirdG7d288fvxY5TFEtbS0MHXqVDx48EDpYQYIeV+MHj0aR44cUfgGn5mZGf755x8MHTpUo+XS1tbG0qVL8fvvv2tsG4MGDcK2bdtgYGCglvw2bNiAY8eOyZ3wi4+1tTUOHTqEzZs319obpDdu3BCc8Hn48OEqf7bhw4fzLhOLxYIB/MqwsbGRGyRdvnx5hV74jo6O6Natm9oCL0OGDMHdu3dRv359teSnSU2aNMGDBw/w22+/oW7dupXKS19fX/C3ryk+xOO/QYMGOHv2rNzx0suIRCL8+OOPcufeIBWp85gqY29vj6+//hphYWFwdnZWat358+fLvcadOHGiYOez2kRLSwurVq3Cw4cPBW/qKqpRo0YaGU5SW1sbR44cUbhDn46ODr799lts375dLfEjVRgYGGDjxo1YtGiRYDp1xxDat2+PJ0+eYNSoUZU+53p6emLMmDFqKRfRPAqkE5m+//57hIeHY9myZejevTvs7e0FJ2eQZ/jw4Xj9+jX27duH7t27K5xXgwYNMGXKFBw4cABv377FsWPHBCcxrWpl4xr6+/ujd+/evI0BW1tbrFy5EiEhIWjQoIHC+RsaGmLr1q2IiIjAggULFGqgmJubY/jw4QgICMD169c1HtybNWsWbt26JRisNzExwYQJE/DkyRP4+vqqZbteXl54/PgxDh06hE8//RReXl6wsLColTO6q6JZs2YIDAzEqVOnMHbsWLi7u8PExAQmJiZwd3fH8OHDceTIEYSHh6NXr14AIPiYoLoa1LWRs7Mz/P39cevWLYwePVqhHupOTk6YM2cOXrx4ge3bt8PIyKgKSkpIzTV8+HCEhITgs88+4w2oGxkZYfz48QgPD5d7ETllyhRcv34dK1asQK9evWBlZaVwWZydnTF79myEh4dj9erVGr+wmzFjBiIjI7F69Wr07dsXTk5Olbo5WdZm+vPPP9GuXTu5QVRtbW20bt0aW7ZsQWxsLEaPHq3ytmsCeWMPqzKsS5l+/foJ/jaqjHusqEWLFsHc3Jx3+ZMnT3D48GGp99zc3HDt2jUkJyfj8OHDmDt3Ltq2bStzjh0+1tbWmDJlCm7cuIGTJ0/WqHa0PHp6evjiiy8QGxuLgwcPYvjw4YLfoSRTU1MMHjwYW7duRXx8PHbu3Knh0qrHh3j8d+rUCSEhIZgyZQrvkxLa2tro27cvAgICFJqgl8hWmWMKKL3O7NGjB3788UcEBgYiNjYW//3vf9GoUSOly+Lq6oqPP/5YMM37+GR7kyZNcObMGYSHh2Pp0qVo2rSpwkFYHx8ffPnll7h+/TrCwsIq3TmNj6OjI27duoVFixbx1pk6OjoYMGAAHjx4gB9++EHlQPKKFStw6dIlfPPNN+jSpYtST617enrim2++wcuXLxUerkzdMQRra2scPnwYYWFhmDdvnsJPWhoaGqJXr1748ccfERQUhPDwcMyYMUOlMpCqJ2K1ffY9UisVFRUhODgYkZGRSE9PR0ZGBkQiEUxNTWFhYYGGDRuiUaNGSlXsmrBy5UqsWrVK5rJu3bpVmAjr7du3uHPnDt68eYPMzEzUrVsXnp6e6Nixo9ou5OPi4hAUFITk5GSkp6ejuLgYFhYWsLKygpeXFxo3blxtd4MTEhJw69YtxMbGIj8/H3Xr1oWTkxO6dOlCgcZqlp+fD0dHR95x6cePH6/RAEJtwhjDs2fP8PTpU6SlpSE9PR3a2tqwtLSEjY0NWrZsWasnPSJEWdeuXUOPHj14l5dvSubl5eH69et4/fo1UlJSYGFhAWdnZ3Tv3r1SAea4uDi8fPkSsbGxSElJQW5uLkpKSmBkZAQTExO4urrC09PzvTs+c3Nzce/ePcTGxiI9PR1ZWVkwMTGBpaUlnJyc0LZtW6WG+yK1X3FxMV6/fo2YmBjExcUhMzMTOTk5KCoqgrGxMUxNTWFnZ4emTZsq3Uu0pmOMISIiAs+fP+c+e2FhIQwNDWFmZgYHBwd4eXnBzc2t2trD6vShHf9ZWVm4ffs2IiIikJmZCUtLSzg4OKBdu3aws7Or7uK9l/iOKQMDA5iamsLU1BT16tWDh4eH2n8DPz8/rsNPed27d5c5Pv77KDMzEyEhIYiJiUFSUhLy8vKkYiPu7u7w9vaGmZlZlZetoKAAt27dwvPnz5Geng5zc3M4OTmhc+fOsLa2Vvv2GGOIiYlBdHQ0d97Lzc0FYwxGRkYwMzODm5sbvLy8auywKomJiQgKCkJSUhIyMjKQk5MDIyMjmJqawtHREZ6enu9NHfWhokA6IQKUDaQTUhWKioqUfszx559/xjfffMO7fN++fRg3blxli0YIeQ8pG0gnhBBCCJHnt99+w8KFC2Uu279/v0YmgCaEkMqiWyCEEFLL9OzZE99++63Ck6Lt3LkTS5cu5V2uq6uL/v37q6t4hBBCCCGEEMIrPT0dGzZskLnMxsamVsxrQAj5MNXsKYMJIYRUkJ6ejtWrV2PNmjXo2LEjevXqhVatWsHJyQnm5ubIy8vjHik7dOgQHj9+LJjfvHnzNPJoHiGEEEIIIeTDlpKSguzsbDDGkJ6ejidPnuDXX3/F69evZaafPn262ibxJoQQdaNAOiGE1FKMMdy6dQu3bt1SOQ9nZ2csX75cjaUihBBCCCGEkFKLFi1SeC4mMzMzLFiwQLMFIoSQSqChXQgh5ANlb2+Pq1evVsvENYQQQgghhBAi6fvvv0edOnWquxiEEMKLAumEEPIB6tmzJ27duoWGDRtWd1EIIYQQQgghH7ihQ4di3rx51V0MQggRRIF0QgipZTp16gRDQ0OV1m3fvj3279+Pq1evws3NTc0lI4QQQgghhBDlTJkyBYcPH4ZIJKruohBCiCAaI50QQmqZP/74A+vWrUNAQADu3LmDx48fIzo6GgkJCcjOzkZxcTFMTU1hZmaGOnXqoGnTpmjZsiX69OkDT0/P6i4+IYQQQggh5AMlEolgYmICFxcXdOnSBZMnT0br1q2ru1iEEKIQEWOMVXchCCGEEEIIIYQQQgghhJCaioZ2IYQQQgghhBBCCCGEEEIEUCCdEEIIIYQQQgghhBBCCBFAgXRCCCGEEEIIIYQQQgghRAAF0gkhhBBCCCGEEEIIIYQQARRIJ4QQQgghhBBCCCGEEEIEUCCdEEIIIYQQQgghhBBCCBFAgXRCCCGEEEIIIYQQQgghRAAF0gkhhBBCCCGEEEIIIYQQARRIJ4QQQgghhBBCCCGEEEIEUCCdEEIIIYQQQgghhBBCCBFAgXRCCCGEEEIIIYQQQgghRAAF0gkhhBBCCCGEEEIIIYQQARRIJ4QQQgghhBBCCCGEEEIEUCCdEEIIIYQQQgghhBBCCBFAgXRCCCGEEEIIIYQQQgghRAAF0gkhhBBCCCGEEEIIIYQQARRIJ4QQQgghhBBCCCGEEEIEUCCdEEIIIYQQQgghhBBCCBFAgXRCCCGEEEIIIYQQQgghRAAF0gkhRA0mTpwIkUgEkUiE3bt3V3dxqt3u3bu572PixInVXRxCyAciLS0N33//Pdq1awdLS0toa2sLnpufP3+O2bNnw8fHB6amplxakUiEmJiYKi9/VVi5ciX3GVeuXFndxSGEEEJIFVC2jUSIurxvbU8KpL9HunfvLnUBKBKJcPr0aaXyWLRoUYU83ocdnaiHq6srVbaEvOeoLiGaJlmXKPsS2o9iYmLQvHlzrFixAvfu3UNGRgbEYjFv+rNnz6J58+bYsmULnj17huzsbA18WlIdJG9uq+Pl6upa3R+JkA8WtUtIVRGLxTh37hymTJmCpk2bwtraGrq6ujAyMoK9vT3atWuH8ePHY+PGjQgJCanu4ipF2TYSUUx0dDS2bNmC4cOHw8fHB7a2ttDT04OZmRmcnZ3RvXt3fPHFF/jnn3+Ql5dX3cUlaqJT3QUgmrVnzx4MHjxYobQlJSU4cOCAhktECCGlYmJi4ObmBgBwcXF5b3t/vg+oLiG1wYwZMxAbGwsAMDQ0RO/eveHo6AhtbW0AgJeXF5c2JycHEyZMQH5+PgDA3t4enTt3Rp06dSASiQAAZmZmVfwJlLdy5UqsWrUKALBixQoKDBGiZmXnAwBgjFVjSYgkapcQdbt79y4mT56MZ8+eVVhWXFyMvLw8JCYm4t69e9i3bx8AoFu3brh27VoVl1Q1yrSRiHwvXrzA999/j4MHD8q8IVFUVISsrCzExsYiICAA69evh7GxMcaMGYNly5bRDXoZrl27hh49egCo+ccWBdLfc2fPnkV6ejosLS3lpr18+TISEhKqoFSEEEJqE6pLiKb06tULjRo1Ujh927ZtZb6fmJiIS5cuAQD09fURHBwMd3d33nzOnDmDtLQ0AICPjw/u378PQ0NDJUpOarLevXvDxMREMM3mzZu5v4cOHQpHR0fetNbW1morGyGk8qhdQtTpypUrGDRoEHdzHQCcnJzQsmVL1K1bF4wxJCcn48mTJ4iOjubSZGRkVENpladsG4kIO3r0KCZOnIjc3FzuPZFIBB8fHzRs2BDW1tYoKipCUlISXrx4gZcvXwIo7cSxY8cO7NmzB7GxsbC1ta2uj0AqiQLp7ylvb288e/YMhYWFOHToED7//HO56+zdu7fC+oQQxezevZuGu5EwceJEGhv9PUB1CdG0cePGqeVc8fDhQ+7vLl26yL1AlEw/ZsyYDyqIvnLlyve+5/q4ceMwbtw4wTSSgfT58+eje/fuGi4VIaSyqF1C1C09PR2ffvopF0R3d3fHli1b0Lt3b5np4+PjcfLkSezevRuFhYVVWVSVKdtGIvy2bduGWbNmcU8oWVlZYdGiRZg6dSrq1Kkjc53o6GgcP34cGzZsQFxcHIqKilBUVFSVxa5271vbk8ZIf0+NGTMGurq6AKQbD3wyMzNx8uRJAEDz5s3RpEkTTRaPEEJILUB1Cakt0tPTub/t7e3Vnp4QQkj1o3YJUbedO3ciOTkZAFCnTh3cuHGDN4gOAA4ODpg1axbu3buH48ePV1UxK4XaPOpx+/ZtzJs3jwuit2zZEk+fPsWSJUt4g+gA4ObmhkWLFuHly5fYsGEDjIyMqqrIREMokP6esrGxQf/+/QEAgYGBiIiIEEx/9OhRbvKDCRMmaLx8hBBCaj6qS0htIdmzR0tLfvNW2fSEEEKqH7VLiLqVDXkCAJMmTVJquI0GDRpookhqR22eyhOLxZg4cSL3Xbq4uMDPzw92dnYK56Grq4t58+YhODgY5ubmmioqqQJ0FL3Hxo8fz/0t74592XIdHR18+umnSm0nKCgIP/30EwYOHIj69evDxMQEenp6sLW1RceOHbFs2TK8fv1a4fxSUlKwdu1a9O7dGw4ODjAwMICRkRFcXFzQqlUrjBkzBrt27cKbN29482CM4eTJk/j000/h6ekJMzMzrkw+Pj7o06cPfvzxR9y/f1/l2aqPHz/OzfquzPiur169gpaWFkQiEfT09JCamlohTXZ2NrZt24aPPvoIzs7OMDIygoGBAZycnNC8eXMMGzYMW7ZsQWRkpEplr067d+/mvjdFHuePiYnh0vNNysGXxt/fH2PGjEH9+vVhYGAAS0tL9O3bF2fOnKmQR0lJCU6cOIH+/fvDxcWF+77HjRuHJ0+eyC3nxIkTuTLwDfGycuVKLk3Zo03FxcXYu3cvN+GLvr4+7O3tMXToUJw9e1budiUVFRVh165dGDp0KFxcXGBoaAgzMzN4enpiypQpuHz5ssJ5VfY4FPqdy5aVTTQKlB4XZenLvwDg7du30NPTg0gkgo6OjuDxL0ksFsPZ2ZnLS5nvgJSiuoTqkprq2rVr3Hc3adIk7v09e/ZUOI+UPVJa9v+ePXu49JMmTaqQXmiCo6tXr2LmzJnw8fGBlZUV9PX14eDgAF9fX2zatIkL2igqMzMTGzduxKBBg+Dq6goTExMuz169emHVqlV4+vSp1Drdu3eHSCTiJhoFgFWrVsk8h5Y/B8uqi8oEBQVxyywsLKTGixWSm5sLU1NTbl2hepMxhhMnTmDChAnw8PCAubk5DAwMUK9ePQwdOhR79uxBcXGxQtutSjk5Odi6dSsGDRoEFxcXGBkZwdTUFO7u7pg8eTL8/Pzk5iGrbiwpKcHevXvRp08fODo6Qk9PDw4ODhg7dqzM7zErKwsbN25Ex44dYWtrC0NDQ3h4eGDhwoVISUmRW4by9StQ+rtPnz4dnp6eMDExgaWlJVq3bo3Vq1fj3bt3in9JKG2L7Nu3D6NGjUL9+vVhamoKY2NjuLm5YcyYMThx4oTcSTslj23JIXfOnz+PMWPGwN3dHSYmJhCJRFi/fn2F7V+8eBFff/01evTowZ3/DQ0N4eTkhAEDBmDDhg3Izs5WaPuS+NopkpOl85VdiKzfRJE0wcHBmD9/Pho3bgwrKyuIRCIMHTpU5vqpqan49ddf0adPH9SrVw8GBgawsLCAt7c3Zs+ejQcPHihU1pqA2iXULlGnuLg47m/Ja5PKUuS4llRWrwu1QWSlSUhIwJo1a9C2bVvY2dlBW1sbFhYWSreRylP3/l8mPz8ff/31F0aNGoUGDRpw+3DdunXRpUsXLF68GHfv3lUoL3W3x4QcPXpU6sbdn3/+qXIwvGHDhjA1NZW5TFYbLS8vDzt37kTfvn3h7OzMXRM/fvxYat13797h4MGDmDFjBtq1awcbGxvo6enBzMwMDRs2xKeffoqjR48Knhs01Q4UanuWLSubaBQAAgICZNa3ZbGeR48ece9ZWloq/Fu/e/cORkZG3LovXrxQaL0KGHlvdOvWjQFgANjWrVtZQUEBs7KyYgCYq6srE4vFMteLjo5mIpGIAWAfffQRY4yx0aNHc3mtWLGCd5tt2rTh0gm9dHV12X//+1+5n+HkyZPM0tJSoTwdHR1l5pGYmMg6dOigUB4A2OXLl+V/uTLk5+czCwsLLp/79+8rtN7q1au5dQYPHlxh+e3bt5mjo6PC5S8qKlKp/KpwcXHhtrtr1y6V8ti1axeXx4QJE+Smj46O5tK7uLgolKa4uJjNmTNH8Hv79ttvufXfvn3L2rdvL7j/Hj16VLCcEyZMkPvdrFixQuq4iouLYx07dhQs56RJk1hJSYnc7ykwMJA1aNBA7v7Sp08flpSUJJiXOo5Dod9ZcpkirzLDhw/n3vvxxx/lfieMMXbhwgWpfUOR7/JDR3VJKapLNEcddQljjPn7+yv8+VasWCF1Dpb38vf3r7C9169fs+7du8td18HBgV2/fl2hz7B161aF99ULFy5w60kep/Je5c/B5eui8ry8vLjl8uq+Mn///Te3TtOmTXnTBQcHs+bNm8sts6enJ3v69KlC21aVvN9b0pEjR5idnZ3ccg8cOJBlZGTw5lO+bnz79i3r2rUrb356enrs/Pnz3Pp37txhDg4OvOnt7OxYeHi4wp+bMcZWrVrFtLS0ePO0t7dnAQEBCn2n/v7+CrVF2rdvz+Li4gTzKUvbrVs3lpGRwYYNGyYzr3Xr1nHrvX79mllbWyt0XNjY2LBLly7J3b4ir+joaN6yK6L8b6JImhUrVjBtbe0KZRkyZEiFdTdt2sTMzc0FP4NIJGKTJ09mBQUFCpW5KlG7pBS1SzTD29ub2+bXX3+ttnwVOa4lSe7nfHVS+TR8+5W5ubnSbSRJ6t7/yxw/flzhfWDr1q28+WiiPSaP5Pa8vb3Vkqcs5dtoz549Yz4+PjI/36NHj7j1jh8/zvT19RX6bps3by5Vb5WniXagUNtTmfa5ZDyoVatW3Pv79+9XqJxbt27l1unSpYtC68hCk42+x/T09DBq1Chs27YNMTExuH79Orp161Yh3d69e7meIZJ3+BVRdhdSX1+fm6XY3NwcjDEkJCTg7t27SElJQVFREb755hsAwNdffy0zrwcPHmDEiBFcLyRDQ0O0b98erq6u0NfXR2ZmJqKiovDkyROpGZIllZSU4KOPPkJQUBD3XuPGjdG4cWPujlpiYiKCg4MrPXu7vr4+RowYgR07dgAA/v77b7Ru3Vruen///Tf3d/mJsGJjY+Hr64usrCwApY//tGnTBg0bNoSRkRFycnIQExOD4OBgZGZmVqr877OlS5di06ZN0NLSQocOHeDp6Yn8/Hz4+/tzv/uPP/4ILy8vDB06FH369EFISAiMjIzQrVs3ODk5ITk5GVeuXEF2djaKioowbtw4tGjRQm2P8GVnZ6Nfv34IDQ2FkZERunTpgnr16iErKwv+/v5ISkoCAOzatQuenp7c8SPL9evX0b9/f6njom3btvDx8UFhYSECAwMRFRUFALh8+TI6d+6MmzdvyhzLTR3HoTxeXl6YPXs2srKyuJ5Cpqamcs8/06dP58Yi/Ouvv7B06VK5vTx27tzJ/T1p0iR6nFEFVJeUorqk5nF0dMTs2bMBAOHh4bh69SoAoFGjRujVq5dU2rZt2wIAl/7q1asIDw8HAPTq1atCrztHR0ep/8PCwtCrVy/u9xaJRGjevDl8fHxgZGSEN2/e4Pr168jKykJ8fDz69OmDCxcuSPWuKW/evHnYuHEj97+2tjbatGkDd3d3GBgYIDk5GY8fP+Z6u0r2Cho2bBgaN26Me/fu4f79+wCANm3acJ9TUvv27XnLIMvYsWPx7bffAijdz0aMGCF3HaH9scz169cxaNAgbp/T0dFB69at4enpCV1dXcTExODmzZvIz8/H8+fP0bFjR9y5cwdeXl5KlV/d1q1bh4ULF3LnN1NTU3To0AH16tVDSUkJnj17hvv374MxhrNnz6Jbt264ffu23DFQi4uL8fHHH+PWrVswMjJC9+7d4ejoiKSkJFy5cgU5OTkoLCzExx9/jNDQUBQWFqJv377IysqCra0tunTpAktLS0RFReHatWsQi8VITEzEsGHDEBwczI0jLeT333/HihUrAJQOUdCuXTvo6+vj6dOnuHfvHoDS3o4DBgyAn5+fzP2rzNGjRzF27FjusXcDAwPu3KutrY0XL17gzp07KC4uRmBgIDp06ID79+/LHUaBMYZx48bh7NmzEIlEaNOmDby8vMAYQ2hoqFQ7ICcnh+sFa2lpCR8fH7i4uMDExASFhYWIjo5GYGAg8vPzkZKSggEDBiAgIAAdO3aU2qbkuUVyUtqy98ozMzMT/Azq9ssvv3BPozRo0ABt27aFkZERYmJiKvzuX3zxhVSvfWtra7Rv3x4ODg7Iz8/Ho0ePEBoaCsYY/vrrL8THx+PcuXM1ur1E7ZJS1C5Rj4YNG3IT0O7ZswcLFy5E3bp1q7QMqrh9+zZWrlyJoqIiWFtbo2vXrrCxsUFSUhIePXqkUhupjDr3/zK//vorvvrqK+6YFIlEaNasGby9vWFiYoK0tDQ8efIEz58/BwDentCaaI/Jk5+fjzt37nD/jx49WuW8lJGamop+/frh9evXMDAwQJcuXeDi4oKsrCwEBgZKpU1KSkJBQQEAwMnJCd7e3rCzs4ORkRGys7MRFhaGhw8fgjGGx48fo0uXLnj8+DGsra0rbFdT7UA+bdu2xezZs/HmzRtuTgsHBwcMGzasQlrJ8k6fPh0zZswAUHrNP3bsWLnbkowNTJkyRalySlE5BE9qnPJ36xkrvfNb9t7kyZNlrtewYUMGgFlYWLC8vDzGmOJ36z///HN27tw5lpubK3N5cXEx27VrFzM2NubuWr58+VJm2iFDhnDbHD58OEtLS5OZLj8/n507d47NmDGjwrITJ05wedjb27PAwEDesoeGhrJvvvmG3b17lzeNPNeuXeO2Z2dnx4qLiwXTP3z4kEtvZmbGfd9l5s+fL3WH7M2bNzLzKSoqYteuXWNjx46Vu011qg090nV1dZlIJGI+Pj4sNDRUKl1eXh4bM2YMl9bd3Z3ruT5q1CiWkpIilT4+Pl7qDvDEiRN5y6lsj/SyO8YTJkxgqampUulycnKkymliYsKys7Nl5pmWliZ1Z79BgwYye47s37+fGRoacukGDRokMz91HIeMKfY7K/LbShKLxczNzY1bx8/PTzB9SkoK09PTYwCYlpYWe/XqldxtEKpLGKO6RNPU1SNdkrJ1iyLn7DI5OTlSvXN69+7Nnj9/XiHdu3fv2MyZM6X2Hb7eyZI9YsrqoNjYWJlpnzx5wubNm8cuXrxYYZm83uWyyFsnJiaG68Wpp6fHewyVSUpKYjo6Oty5VlZP44SEBGZra8ttd8yYMTLTJSYmSvU8btKkicb2Tcnvn6/335UrV7je2rq6uuzHH3+UWR8/evRIqlfj559/LjM/yf20rH4aPnx4hfbHmzdvpNof48ePZ61atWIikYitXr2aFRYWSqW/desWMzU15dLv3r1boc+tp6fHDAwM2L59+yqkCwwMlDpWPT09K5xryoSGhjIjIyMu7YIFCyq0bRhjLCoqinXu3JlL179/f5n5SfakLNu3mjRpwkJCQiqkzc/P5/6OiYlhc+fOZXfv3uV9Au3du3ds0aJFUm1BoafVJL8vRVRFj3QdHR1mbm7OTpw4USGd5Pexc+dOqbZkWW/u8vz8/KTaksr0MK0K1C6hdokmlX9S1s3NjW3fvl3mOUwZyp47lO2RrqOjw0QiEfvhhx8q1AmS5wHGlG8jqXP/Z4yxc+fOce0KAKxnz54sLCxMZtqXL1+y7777TmY9pon2mCKuX78u9Xv++++/Kuclj2Qbraz+GzFiBEtOTpZKV1JSIvW7nz59mv30008sIiKCN++XL18yX19fLv8pU6bITKeJdqAi7VVl68+srCxmYmLCgNKnqiIjIwXTh4SESJ2ncnJy5G6DDwXS3yOyGhmMMebh4cHtLOVPhrdu3eLWmT59Ove+oo0MRR06dIjLj++RqbJHMfX19VlWVpZK21m4cCG3ne3bt1emyAoRi8XM2dmZ26asi1y+8slq9Ek+niJ0EqwutSGQDoDVrVuXvX37VmbarKysCo/99unTh/ciSrKhbmJiwvuYobKBdKA0kMAnLy+P1atXj0t76NAhmemWL1/OpbGwsBAMFv/zzz9S25f1qLY6jkPGNBNIZ4yxH3/8kVtn7NixgmnXrVvHpfX19VXyE3y4qC6hukTTJOuSXr16sdmzZyv84qPJQPr333/Ppe3Xr5/cx83Hjx/Ppf/Pf/5TYXlaWppU0HPmzJlyy8tHE4F0xphUsPOPP/4QzG/jxo1SF8eyTJ48mUszdepUwfyKi4tZjx495NZ/lSVZH8oKWpSUlDB3d3cujbzHhhMSEljdunW5oIKsGyPlAzY9e/bkbX/cuXNHKi0A9sMPP/Buf82aNVL7qSKfW973+/z5c6kA+ebNm2Wm69mzJ5dG3tBr2dnZUoEQWQHB8kMS2NnZVQgiVJZkkEVy+JzyJMuhiKoIpItEIrnD7WRmZnLDc2hra8tN/+zZM2ZgYMAAMGtr60oFGNSN2iXULtGkoqIi1rp16wrnRm1tbda0aVM2depUtm3bNhYcHMw7jJAsyp47lA2kK3K+LaNsG0lRiuz/RUVFzNXVlUs3cOBAlYftUXd7TFH79u2T+t412TmrfLygb9++ah2atLCwkDVt2pQBYAYGBrxBcnW3AzURSGeMsalTp3LrLFu2TDCt5A09vs6Aiqq5z2wRtfnss88AlE5mVfaoRBnJCVqUfeRNGSNGjICJiQkA4MqVKzLTlD3GZWRkxKVVluSjYLKGrVA3kUgkNXGN5CMt5YnFYhw8eJD7X9YjL1Vd/vfVsmXLeB/JMzExwUcffST13tq1a3kfYe3QoQOcnZ0BlA7HUjYUQGXp6enht99+411uYGCAMWPGcP+XPboviTGGP//8k/v/u+++48oqy7Bhw9C/f3/u/61bt1ZIo47jUJMmT54MHZ3SUcmOHz+OjIwM3rR//fUX9/fUqVM1XbT3HtUlmvMh1yVXr17F5s2bFX5VtaKiImzatAkAoKWlhW3btnHnID4//fQTN9yErN/yzz//5B5vd3FxqTBZYk0guV8J7Y8AsH//fpnrlUlOTubyMDc3x7p16wTz09bWxpo1axTevqacOXOGm1isV69ech8ZtrOzwxdffAGgdL85cuSI3G389ttvvO2P9u3bw8XFRSp/oWHePvnkE+5vWW0GWbp27Sr4iLqHhwf3mQBg+/btFdIEBwdzE616eHhg8eLFgts0NjbG8uXLuf8V+X2XL18OGxsbuemUITn5Hl99UlONHDkSXbt2FUzz119/cW2kiRMnyk3v5eWFCRMmACgdTuDff/9VS1k1idolmvMhtUt0dHRw7ty5CpMDl5SUICQkBDt27MDMmTPRrFkz2NjYYNq0aXj06FH1FFaCo6OjYJ1QFRTZ/48fP84NUWdsbIxdu3bJbUfJoon2mKLS0tKk/rewsJC7zooVKzBnzhze14YNGxTa9vr169U61Jauri7XnsnPz8fNmzdlplNnO1CTpk+fzv29e/dulJSUyExXWFgoVc7KxgZojPQPwGeffYbly5eDMYa9e/dywbmCggIcPnwYQOn4ep06darUdp48eYKHDx8iJiYGmZmZ3BhNZcpOYk+ePIFYLK5wQnB2dkZUVBTS09Nx4MABpWdWL8ujzB9//IGPPvpIpRO1Mj777DP85z//AQCcOHEC27Ztg6GhYYV0/v7+iI+PB1A6bpWssfycnZ25i7bNmzdj6dKlGiz5+2v48OGCyxs3bsz97e7ujqZNm8pNXzZWXHR0tNT6qurcuTPs7OwE07Ro0YL7u6wBIiksLAyJiYkAShsUZRdAQqZOnYoLFy4AgMwZ4dVxHGqSvb09Bg4ciJMnTyI/Px8HDhzArFmzKqS7f/8+N1u4jY0NBg8eXNVFfe9QXUJ1yYfowYMH3JwVHTp0kAps8nFwcECjRo0QFhaG0NBQZGRkSF10SQaopk2bBn19fbWXu7JGjRqFefPmobCwEDdu3MDr169l3qiNiorC3bt3AZTeAJZV/165coU7jgcOHKhQIKldu3YwMjJCbm4u70Wepp0/f577WzJILaRnz57c3zdv3sSXX37Jm7Zhw4Zo1qyZYH4+Pj549eoVAGDQoEGC4567ubnB2NiYGyc8KysLpqamgvkrEmCcMGECVq9eDaA0aJ6eng5LS0tuueT3NHLkSGhra8vNs/z3JEQkEqk0Hm1RURHu3r2L4OBgJCYmIisrixujGgB3MwsAHj9+rHT+1UmR/VHV/fePP/4AUPq7fPzxx6oVsIpQu4TaJepSt25d+Pn54dixY/j9999x69YtbixvSWlpadixYwd27tyJKVOmYOPGjTAwMKiGEpde72p6HwAqv/9LtnnGjBmj8k1RTbTHFCVZXwClNwTk2bNnD1d/y9KtWzfMnz9fMI+mTZuqNE9MRkYGAgMD8fTpU6SmpiI7OxtisZhbLtkx8PHjxxg0aFCFPNTZDtSkNm3aoHnz5nj8+DHevHmDixcvYsCAARXSnTp1iptDpWnTpgrN+yCEAukfABcXF3Tt2hUBAQG4fPkyEhMTYWdnh9OnT3M9Fcru6Ktiz549WLNmDV68eKFQ+qKiIrx7906qEQ6UTtpQ1gNp3LhxOHToEEaPHo2ePXvC3t5eobxHjBiBFStWQCwW48KFC/D29sakSZPQv39/NG3aVCMT53h7e3MHb1ZWFk6dOiWzwSp5B+zTTz+VWZbRo0dzE4EsW7YMly5dwtixY9GnTx+4urqqvezvI3Nz8wqTxJUnue95e3vLzVMyvbomwGnSpIncNJKTabx7967CcsneEI0aNZI5WUh5khcTiYmJiI+Ph4ODA/eeOo5DTZs2bRrX82jnzp0yA+mSE4mMHz8eenp6VVW89xbVJVSXaMKuXbswceLE6i4GL8nJpVJSUjBnzhyF1is7JhhjePPmjdSFW9kFB4BKTX6lSZaWlhgwYABOnjwJxhgOHDggs6ex5P44ePBgmZMuSn6HL168UPg7LLs4T09PR05OjkIXruokWe5z584pFGyVrKtjY2MF0/r4+MjNT9n2ioWFBXJycgCUtlfkBdIVmYjW3d0d1tbWSE1N5SYpk9xvJb+nO3fuKPT7Sgao5H1Prq6usLKykptnmby8PKxZswbbtm1DSkqKQusomq6maNWqldw0kr/L3r17K/TYliUuLo77W97vUhNQu4TaJeokEokwcuRIjBw5EgkJCbh27Rru3LmDoKAgPH78WGoiWMYYduzYgZcvX+LixYtVEtAuT5HzQGWoa/+XnBSzMm0eTbTHFFW+Ls3JyamSSaaV/Y3j4uKwePFiHDt2rMINDz589Z8624GaNm3aNG5i3Z07d8oMpEvGBtTypHqlBoYhNQrf+HGMSU82s3btWsYYYwMHDuTG2YuKipJKr8j4cWKxmE2aNKnCeGKKvGSNK5WTk8M6deokM72bmxsbP34827t3L3v37p3g97Bu3TqpySzKXmZmZszX15f99NNPLDw8XIlvVr61a9dKjftVXl5eHjMzM+PSyJosibHS8ThHjBgh8ztwcHBgo0ePZn/88QdLSkpSa/kVVRvGSHd2dlaqDOPHj5ebXpGxdJUdI3358uVytytvnDDJMcD5JuySpWwMTAAsODhYapm6jkNNjZHOWOlxIjlu4+PHj6WW5+bmMnNzc27506dPFc6bUF1ShuoSzalNk41Kjvuq6uvGjRtcfu/evZNalpCQUKnPrakx0hlj7NixY1y6xo0by0xTNkYxAHb69GmZaYYPH17p75BvItbKkMxf1ni05edTUfbVoEGDCnlWpv3x119/yU0veWxFR0fL/dyZmZly82SMsebNm3PrHD58WGqZ5NjHqry0tbUrbE+y/dO2bVuFyshY6fwDkmVV9OXm5sabp2Q6RVTFGOl8EwCWycrKqvQx16tXL4XKXhWoXVKK2iXVp7CwkAUEBLCJEycybW1tqfL/+uuvMtdR9tyh7BjpQnM7lKdMG0nd+7+lpSW3/M6dOwqXuTx1t8eUoa4x0iXbX3z1g2QavnHnZXn48KHUd63oa9KkSbx5qqsdWP5zqXOMdMZK29Zl87no6upWOI/ExsZyE8fr6+tXeiJhxmiM9A/GyJEjYWRkBKC0V0JSUhL3mE3nzp1Rv359pfPcvn07du3axf0/cOBA7Nu3D6GhoUhPT0dBQQFY6YS2YIxJPX4j+WhJGSMjI/j7+2PdunVo0KCB1LLo6Gjs3bsX48ePh729Pb766ivk5eXJLNeCBQtw/fp1+Pr6St0Rz8zMxMWLF7FkyRI0atQIvXr14oZ+qCzJu+8XL17kHhspc/r0aa4nc9OmTXl7I2tpaeHIkSPYvXt3heFG4uPjcfjwYcyYMQMODg6YOnVqhfG6yP96sGkqvbqoY7vZ2dnc38r01JNMW/5RNXUdh5qkpaWFKVOmcP9L3mEGgGPHjnG9Ajt06KBQLz6iGKpLqC750Mh6GkhZfMNJAKiRc1GUGThwINdzKzQ0FCEhIVLL7927x/VUs7a2Rr9+/WTmo+7vsKpUttzyylwT2itl53N5hNoNlf2e+MYzLSNrKAk+s2fP5p4c0NfXx4wZM3D69GlERERwQ7uU1SXR0dHcerLqkppM3ndSW485VVC7hNolVUFXVxddu3bFrl274O/vL3VOVHSsa3VT5tyoDHXv/5J1RmXaPNV5Xis/jExYWFily6IIRX/jgoICDB8+HOnp6QAAW1tbrFixAteuXUNsbCxycnIgFou530zy9xWq/9TVDtQ0MzMzbgi4oqIi7Nu3T2r5rl27uM/58ccfK/WUGx8KpH8gTE1NMXToUABASEgIvvnmG+5EouoELGvXruX+Xr16Nc6cOYNx48bBx8cHFhYWFYZSKN/wlkVXVxcLFixAZGQknjx5gs2bN+OTTz6RGqojNzcXa9euRY8ePXgbGp07d8a///6Lt2/f4tixY5g/fz5atmwp1ejw8/NDu3btcOvWLWU/egX29vbceI+yJpiSnKBB3gQMIpEIEyZMQHBwMCIjI7Fz505MmDBBqiFYXFyMnTt3om3btkhOTq50+Wuy2nZxU5UkGyNlj3IrQjKtrMe+1XUcatKUKVO4cVj//vtvqcfXJAPrkgF3UnlUl1Bd8qGRvFhesGCB1MWjoi/JCczKn3Mlb4jWNPr6+hgxYgT3f/nJpiT/Hz16NO/43ZLf4fr161X6Dqvj0X/Jcj9+/FjpMsua26SmkRyqQIhQu0Hyeyp7BFzZlzq8efMGhw4dAlA6Ye2lS5ewbds2DBo0CA0bNoSJiYnU+O2K1CVVQRPt3PKdKzIyMpT+TWTNo1MTUbuE2iVVrUuXLlJjvL9+/ZqbS6syaso1r7r3f8k6ozJtHnW3x5TRpk0bqfls7t27p/Ln0ITjx49zN4ednJwQHByMlStXolu3bnBycoKRkZHUzXhF6z91tQOrguSko5KxgPI3DtQVG6BA+gdEsjGxe/duAKUTAowcOVLpvGJjY7kJQywtLfH1118Lps/MzOTukCmqcePGmDVrFg4ePIi4uDg8evRIase/e/cuNm/eLJiHjY0Nhg8fjvXr1yMoKAhv377F77//zk1ykZeXhxkzZihVLj6SjQfJsaLS0tK4nhFaWlpKTTDToEEDTJ48Gbt370ZUVBRevHiBRYsWceOwRUVFYdWqVWopf1WRPMEqcldYHXef31eSs94r2oBLSkpCfn4+97+8CV/UcRxqgqOjI/r37w+g9Bg7ceIEgNJj4vr16wBKbzSoMkEZEUZ1CdUlHxJbW1vu77J9tTLMzMykehhJ9oqtiST3xwMHDnBBz5KSEm4yv/LpylP3d1hVamu5laFo20FyvOzy7Yaa8j35+flx++eAAQPQtWtXwfRCk8BVRk1o51pYWEgFfd7X/bcMtUuoXVLVyq5ByiQkJFRIIzluem255tXE/i9ZR1SmzVOddY2BgQE6dOjA/S/Z/qkJyuYfAIAvvvhC6ruSRZn6Tx3twKrQvn177omYZ8+ecWPz+/v7c/udm5ub1GTnlUGB9A9I7969pSYVBIAhQ4bA3Nxc6bzKZuYGAE9PT7kTbNy8ebPSPU6aN2+OHTt2SN1tOn36tFJ52NjYYO7cuVLrPX36FC9fvqxU2YDSmbPLHi28ffs2d8AeOXIEhYWFAIDu3bvLnQhTiLu7O3755Rd8//333HvKfgfVTfKudPnHA2VR16OJ76MWLVpwf4eHhyv0GKRk7xQ7O7sK5wR51HEclqnsY+qy7jz/9ddf3Llm9OjRNXrYhNqK6hKqSz4k7dq14/4OCAhQePImRfP08/OrVF6aHp6sa9eucHZ2BlA6iVVAQAAA4PLly3j79i2A0oCI5AVmeZKf9+LFixosrXrV1nIrQ3LyNj4RERFce00kEkm1PYCa8z1J1ieKTORadtNd3WpKO7dt27bc3+/r/luG2iXULqlqBgYGUv9L3rgqo8y5oLCwUOFJPTVJE/u/5KTWlWnzaKI9pgzJG1NPnz7F5cuXq3T7QjRZ/6mjHaioyrZpp02bxv1dFhuQ7J0+efJktbWbKZD+AdHW1q5wp1jVR94kHx9T5LHQrVu3qrQdWQYOHMj9XXbwKqtDhw5SYyOpmo8kExMTDB48mPv/wIEDAKTv3KvrTp06voPq4ubmxv0dHBwst/It/wgh+R8vLy/Y2dkBKL0rLLmv8ZF8tKkyM6erYx+UbIQWFRUpvf6AAQO4RvvVq1cRFRWFPXv2cMtpWBfNoLrkf6guef916tSJGx8yOzsb27dvr3Sekj3Ztm/fXqmLwcqeR+URiURSx3vZY7yS++PYsWMF8/D19eUuxiMjI3H27Fm1l1MTJI+PAwcOICkpqRpLoxnlxxGVpayHLwA0a9YMlpaWUsslv6erV69WWwcIZeqT3Nxc7N27V6F8lT3GXF1duQv1yMhIuUMZaKqdK/m7bNu2TeppxPcNtUv+h9olVaNsLgagtJ50cnKqkEbymlcyvSynT5+uEceoJvZ/yTbPoUOHkJKSolLZNNEeU8bIkSPRsGFD7v/p06dz8wNUN2V+t6CgINy/f1/hvNXRDlRUZdu0n332GffU5+HDh/HmzRvuqXVtbW1MnDhRLeUEKJD+wVm2bBnu37/PvXx9fVXKx83NjWskhoaGIioqijft4cOH5V40FRQUKDxmluRjqJJDWwBQ+MScnp4utb3y+ahKshHx999/IyYmBrdv3wZQemIYPny44PqKll/oO6jpvLy8uDv0CQkJuHTpEm/ac+fO4dy5c1VVtFpHJBJJ9V75/vvv8ebNG970586dw5kzZ7j/Z86cKbVcXcehoiwsLLiKPykpSekKU1tbG5MnTwZQOv7ZZ599xn1+b29vtdwZJ7JRXVKK6pL3n76+PhYsWMD9v3TpUqUChbICAdOmTeOelnn16pVU/sqytrbm/hY6/1fGZ599xv197NgxpKWl4eTJk9x78gIojo6OUmlmzpypcFnFYnG1jZM7fPhw7qI5NzcX48aNU7ieys7OVmrukupy/fp1wUfUIyIisH79eu7/qVOnVkjTtm1bbtxZxhjGjRuncHChsLBQ6WEx+EiOs3zu3DnBoRQWLlyocJBO2WPMzMwMnp6eAEqHcyg/pqykR48eaSwYNGPGDC7oFBcXh1mzZincczolJUXuJLA1DbVLSlG7RHkrVqxQKrCYk5OD1atXc/+3bt1a5lCZkk+FSN6QLC8zMxOLFy9WePuapO79Hyid3LFsss7s7GxMmjRJpUk/NdEeU4a2tjZ27drFdQyIiYlB7969a8RNdsn679SpU7zpcnNzpWIHiqpsO1BRlW3TWlhYcGO6Z2VlYeTIkdx8E76+vjJveKmKAukfGAsLC7Ru3Zp7SU66owwbGxvu8RqxWIyRI0fi+fPnUmnEYjE2b96Mzz77DNra2hUegZKUkJCAevXqYeHChbyTNzDG8O+//2L58uXcewMGDJBKM2rUKHz00Uc4evQo7wXM69ev8cknn3CPorm7u0vdXawMX19frtIPCwvDwoULuUbr4MGDYWZmJri+s7Mzpk+fjmvXrvE2YAMDAzFnzhzu//LfQZnu3btDJBJBJBKp9e5bZeno6EiNWTht2jQ8e/ZMKg1jDPv27cOoUaNkPipH/mfBggVcr+zU1FT06tVLZq+Hw4cPS40XPmjQoArjh6rrOFSUvr4+PDw8AJRebJbdMVbG1KlTuWC85CPq1Btds6guobrkQ7Jw4ULuUdmsrCx07twZ27dv53778lJTU7Fjxw60atUKv/zyS4XllpaW+O9//8v9v23bNowePRpxcXEy83v69Cnmz58v88Zz2XiQQOnwDZoYY9Xb2xvNmzcHUDpp4bRp07jjom3btnB3d5ebx5o1a2Bvbw+g9OKoTZs2OHbsGO/kam/evMGGDRvQqFGjahuLVFtbG1u3buXOb5cvX0bXrl0FAy4hISFYsmQJnJ2da/z49wCgp6eHiRMnynyi7d69e+jTpw/Xu83d3Z23bt24cSN3cygkJARt27bFlStXeLcbGRmJ1atXw83NTS0TIgJAz549ueEnoqKiMHHiRGRkZEilyczMxPTp07Ft27YKE3LykTzGFO09Ltl7b/Hixbh582aFNBcuXEDfvn01NjyTubk51q1bx/2/a9cuDBo0COHh4TLTM8Zw584dzJkzBy4uLtUykXxlULuE2iWqunjxIndDcNeuXYJDZd6+fRtdu3bF06dPufeWLFkiM63keeDQoUPYtGlThTTh4eHo2bMnoqKiasQ1r7r3f6D02n/z5s3cue7s2bPw9fXlPRfFxMRg+fLlMp8aUnd7TFmdO3eWOq/ev38fPj4++PnnnwVv+mdkZGDbtm0au3Eq+STH3r178euvv1Y4ziIjI9G3b188fPhQ4fqvjDragYqoX78+V7ZXr16pNKmr5I0CTcYGhAc9IkTAjz/+iL59+0IsFuPRo0do0qQJOnXqhPr16yM7Oxs3btzgJt5YvXo1/vzzT8GJDTIyMvDbb7/ht99+g5WVFVq0aAFHR0fo6+sjKSkJISEhUhclHh4emD9/vlQeYrEY58+fx/nz56Grq4vGjRvDw8MD5ubmyMrKwqtXrxAYGMhduGlra+P3339X23eio6OD0aNHcxXlP//8wy1T5E5dXl4etm/fju3bt8PU1BTNmzeHs7MzjI2NkZKSgvDwcKmgc506dbBy5Uq1lV8Zy5cvl+qlJM+OHTvQunVrAMB3332Hw4cPIycnB7GxsWjevDm6deuG+vXrIzMzE7dv38br16+hra2NP/74Q2YPKFLK0tISBw4cQP/+/ZGbm4vnz5+jZcuWaNeuHby9vVFYWIi7d+9KTcri7u4uNV6YJHUch8oYPnw416tj3Lhx2LNnDxo2bCg1WZfk7PHlOTs7w9fXFxcuXODe09PTU/mRXlL1qC6p6EOqS/bv348HDx4onN7JyanKe26ZmJjg9OnT6N27N6Kjo7lg3FdffYUOHTrA0dERIpEIaWlpCAsLw/Pnz7l9g28IrVmzZiE0NJR7LPrIkSM4fvw42rRpAw8PDxgYGCA5ORmPHj1CTEwMb15t2rSBs7MzXr9+jcTERDRq1Ah9+/aFjY0Nd9Hapk2bSk+8PG7cOO4mrbL7IwDY29vj1KlTGDBgAFJSUpCQkICRI0eibt26aNeuHWxtbSEWi5GamorQ0FC8fPmy0uMOq0Pv3r2xdetWfP755ygpKUFgYCB30diiRQtYWloiLy8PiYmJePz4cY3omaaMX375BfPnz8dnn32GlStXokOHDtDT08PTp09x9+5dLp2RkRH27t3LGzBp3LgxDh48iNGjR3NtkT59+qBevXpo06YNbGxsUFhYiOTkZAQHB/PeNKoMS0tLLFq0iBtj+e+//8aFCxfQrl07ODo6IiEhAdeuXUNOTg60tbWxZcsWTJgwQW6+w4cP5yZUXLx4Mf7991/4+PhIBb2WLVsmNeTNvHnzsG3bNsTHxyMjIwNdu3ZFp06d0KhRI+Tn5+PBgwdcEGnXrl2YNGmSOr8KzsSJE/Hy5Uv88MMPAEp76p8/fx6NGzdG48aNYWZmhpycHLx58waPHj2qcOPhQ0Xtkoo+hHZJQEAAAgICIBKJ4OHhAS8vL1hbW0NLS4urj8v/znPnzsWwYcNk5telSxd89NFH3NPVc+fOxebNm9G+fXuIRCI8f/6c+80mTpyI6Ohobvzp6qTu/R8APvroI/z0009c+83Pzw/e3t5o1qwZfHx8YGJigrS0NISEhHCBe8mAdRlNtMeUNWfOHFhbW2PKlCnIy8tDSkoKvvnmGyxevBhNmjRBgwYNYG1tDZFIhHfv3iEyMhJPnjyReqJNX18fgwYNUkt5gNIbXd26dUNAQAAYY1i0aBE2b96Mli1bwtzcHBEREbh9+zZKSkrg6OiI+fPny51ItrzKtgMVoaWlhaFDh3JPcvXo0QP9+vWDs7Mzd3PUysoKS5cu5c2jc+fO8Pb2ljqf1K1bV63fNwCAkfdGt27dGAAGgG3durVSeY0ePZrLa8WKFbzptm7dynR0dLi05V9aWlps+fLlTCwWMxcXF+796OhoqXzi4uKYvr4+bz7lX927d2cJCQkVyjNw4ECF86hbty47efJkpb4nWQIDAytsy9ramhUWFspd18TEROHyN2vWjIWFhfHmJbk/TJgwQS2fTfI3VPbl7+8vldeFCxeYkZERb3ozMzN2/PhxFh0dzb3n4uIis1yKpJG0a9cupb6bCRMmcOl37dqlcpoVK1YodFyV8ff359J369ZNMO2dO3dY/fr15f4OvXv3ZklJSTLzUNdxyJji3/G7d++Yt7e34HbkOXHihFT6ESNGyF2H8KO6hOoSSTWtLmnWrJnMPDVxXi8vNTWVjRw5kolEIoXKamFhwXbv3i2Y5/r165mZmZncvEQiEbt48aLMPM6dOye435f/PpStixhjLD4+nmlpaUnlq6Ojw96+favQ+mViYmJYr169FP69bW1t2b///qvUNhQluZ3ybZTy/Pz8mLu7u8Ll9vHxYW/evKmQj6b3U6Hzo6zPzVjp/iC0T9vZ2cn9fso8fvyYtWrVSuHvydXVlT169KhCPsq0fyQVFxez8ePHyz0uT5w4oXDbsaioiPXo0UMwT1nfdVBQELOxseFdR09Pj23evJkxVvE3kUWRNHwOHz7MHBwcFP5d2rZty/Lz85XejqZQu4TaJZLU3S75/vvvmZ2dncLlA8AsLS3Zli1b5OadmprKWrduLZjX5MmTWX5+vtTn4jvnKpJGFmXrHnXt/+UdOnSI2draKvQd//nnn7z5aKI9pqywsDD2ySefVGgbCb1MTU3ZlClT2MuXL3nzVaWNxhhjiYmJrGXLloLb9/b2Zk+fPlV6f2Cs8u1ART/X69evBesrRWI969atk1pn0aJFCpVRGdQjnVTKzJkz0alTJ6xbtw7+/v6Ij4+HoaEhHB0d0bNnT0yePBktWrSQm4+joyNSU1Ph5+eHGzduICgoCJGRkUhOTkZhYSFMTU3h4uLC9arq3bu3zHxOnz6NR48e4erVq7h79y7CwsIQFxeHnJwc6Ovro06dOmjatCkGDBiATz/9VO5jaKpo164dPDw8pGbeHj16tFQPWz6pqam4fv06AgICcP/+fURERODt27fIz8+HkZERnJyc0KpVKwwfPhyDBw+WmliitunXrx/Cw8Oxdu1aXLx4EbGxsdDW1oazszMGDRqEzz//HM7OzlxPPCKsffv2CAsLw/79+3Hy5EmuZ5yuri7s7OzQuXNnjBkzBn379uXNQ13HoTLMzMxw7949bN26FWfOnEFYWBgyMjKUGi/9o48+gr6+PjdhHz3BUPtQXVIR1SU1j5WVFY4cOYLQ0FAcPHgQ165dQ3R0NFJTU6GlpQULCws0bNgQLVu2RO/evdGnTx+5jzzPnz8f48aNw+7du3Hx4kU8e/aMG0vWxsYGXl5e6NatG0aPHs376OyAAQMQFBSETZs24ebNm3j16hWys7PV2qPb3t4evXr1wuXLl7n3+vbti7p16yqVj4uLC65cuYI7d+7g6NGjuH79OmJjY5Geng4dHR1YW1vD3d0drVu3Rt++fdG9e3duPNLq1KNHD4SHh+PEiRM4d+4cAgMDkZiYiMzMTBgZGcHW1haNGjVCx44d0b9/f+4R6Npg5cqV6N+/P/744w/cuHED8fHx0NXVRYMGDTBs2DDMmTOHG2tbnmbNmuHBgwe4dOkSTp48iVu3bnG9ssvOnR4eHmjfvj18fX3RoUMHtQ5toq2tjT179mDkyJH4888/cffuXaSnp8PS0hLOzs4YMmQIJk+eDAcHB4Xblzo6Orh48SL++usvHD9+HE+ePEFaWhrvUAJlWrZsifDwcPz22284c+YMoqOjIRaL4eTkhD59+mDWrFnw9vZWw6eWb9SoURgyZAgOHTqEixcv4v79+0hOTkZ2djaMjY3h6OgILy8vdOnSBQMGDOCG3fuQUbukove1XfLdd9/h22+/xYMHD3D9+nXcu3cPz58/R1xcHDIzMyESiWBmZgYnJyc0bdoUvr6+GDJkiELDY1hZWeH27dvYsWMHDh48iKdPnyI7Oxv29vZo06YNpk+fjj59+lTBp1SOuvb/8kaPHo2BAwdi7969uHDhAoKDg5GcnIySkhJYWlrC09MTnTt3xogRIwTz10R7TFmNGjXCwYMHsXr1apw/fx5+fn4ICwtDamoq0tPTYWhoCEtLS9SrVw+tW7dGx44dMXDgQG4IMnWztbXl9rVDhw4hNDQUubm5qFu3Ljw9PTF69GiMHTsWRkZGKg2Zoq52oDz16tVDcHAwNm7ciEuXLuH58+fIyspSalz94cOH44svvuD+18SQryKmzlZ2LZabm4uAgAAEBQXh4cOHCAoK4ia7WLFiRbU98kwIIUTYtWvXuMf16tWrh5iYGAoMEkIIITJIBq7pMpDIs3v3boWGnrl8+bLSHSxWrlyJVatWyU0XERGhtrG+CSGEvN8k661OnTrJnKeksqq/i0cNce/ePZUnzCOEEFJ9duzYwf09efJkCqITQgghhKiRlpYWN9mkLJWZKFFXVxdWVla8y2vCUymEEEJqB8nYgKaeVKdaSYKlpSVatmzJvb744gskJiZWd7EIIYTwiI+Px7FjxwCUPtJNw7oQQgghhKhX2RN/mtCxY0dcu3ZNI3kTQgj5cAQFBeHWrVsASuO7o0eP1sh2KJD+/7p06YK0tDSp98pmFSaEEFLzlJSUYMGCBdzY6KNGjYKTk1M1l4oQQgghhBBCCCFVJT8/H/PmzeP+nzlzJgwNDTWyLQqk/z9tbe3qLgIhhBA5Dh8+jPv37yM7OxsBAQEIDw8HUPpIMc1lQQghhBBCCCGEvP+2bt2KqKgoZGRk4PLly9w8lzY2Nli0aJHGtkuBdEIIIbXGhQsXsGfPngrv//rrr/Dw8KiGEhFCCCGEEEIIIaQqHT58GAEBAVLvaWtrY+fOnYJzb1QWzchGCCGkVjI1NUX37t1x+vRpzJ49u7qLQwghhBDyXkpOTkarVq1gYmICQ0ND1K9fH+PGjVPL2OZPnz5F48aNYWhoCBMTE3h6emLatGl49OhR5QtOCCHkvScSiWBpaYkBAwYgICAAgwcP1uj2qEe6BhQUFHBj9gKAWCxGWloarK2tIRKJqrFkhBBSu/3+++/4/fffK7yfmZlZDaX5H8YYsrKy4ODgAC0tukf9vhCLxYiPj4epqSnV34SQ98K7d++4v6u77qwJqP5WTG5uLh4+fAhLS0vk5OQgOjoa0dHR+PvvvzFp0iT8+eef0NFRLbSQkpKCtLQ0WFhYIDMzEy9evMCLFy+wc+dOLF26FD/++KPcPOj6mxBCPiyMMezfv79a6m8KpGvATz/9hFWrVlV3MQghhFSx2NhYmvD0PRIfH4969epVdzEIIYRoGNXfsjk4OGDFihX4+OOP4enpCX19fZSUlODu3btYsWIFrly5gl27dsHY2BgbN25UKm93d3f8/PPPGDJkCNzc3KCrq4vCwkJcu3YNS5cuRVBQEFavXg1LS0ssXLhQMC+6/iaEkA9TddTfIsYYq9Itqsnu3bsxadIklde/cOEC+vXrJ5jG1dUVr169wooVK5SaxK78HfF3797B2dkZL1680Og4PbVRQUEBwsPDoa2trXIvBiFisRhxcXFwcnKiXiZVqLCwEImJiejUqROMjY2ruzgflKKiIvj7+6NHjx7Q1dWt7uJ8MNLS0uDh4YGMjAyYm5tXd3GImrx79w4WFhaIjo6m+ruc/Px8PH36FDo6Ohqrv1+/fg1nZ2eqv6tQYWEh4uPj0a1bN5iYmFR3cT4oRUVFuHTpEvr27Uv1dxVKS0uDm5sb1d8qEIvF+Pjjj3Hq1CloaWkhPDwc7u7uask7Pz8fXbt2xf3792FiYoK4uDjB34euvxVH19/vJ7r+rj50/V09qvP6m3qka4C+vj709fUrvG9lZQVra+tqKFHNlZ+fD1NTUxgYGEBPT0/t+ZeUlHCP9Wlra6s9fyJbfn4+MjMzYWVlRRfiVayoqAhGRkawtramirwa0OPD75ey39PU1BRmZmbVXJqaRU9PDyYmJhqtv1NSUmBpaUn1dxXKz89HRkYGzMzMqP6uYmX1t5mZGdXfVaioqAgA1d+q0NLSwtq1a3Hq1CmIxWKcOXMGX375pVryNjAwwJo1a9CnTx9kZ2fj6tWr+Pjjj3nT0/W34uj6+/1E19/Vh66/q1d11N+1NpA+ZswYDBw4UOX1qccBIYQQQgghhBCimoYNG8LGxgYpKSl4+fKlWvPu0KED97e68yaEEEJUVWsD6Xx3nQkhhBBCCCGEEEIIIYQQdaJBqwghhBBCCCGEEKKUqKgopKSkAADc3NzUmndgYCD3t7rzJoQQQlRFgXRCCCGEEEIIIYRwGGNyl3/11VcASsdLV2bYVXl5FxQUYNmyZQAAY2Nj9OrVS+G8CSGEEE2iQLqE9PR0pKSkcC+xWAwAyM3NlXo/Ozu7mktKCCGEEEIIIYRoxqtXr9C2bVv88ccfePnyJRf8FovFCAwMRP/+/XHixAkAwIwZM+Dp6Sm1/sqVKyESiSASiRATEyO17Pr16+jduzf279+PuLg47v2ioiJcvXoVXbp0wd27dwEAy5cvh4WFheY+KCGEEKKEWjtGuia0aNECr169qvD+L7/8gl9++YX7f8KECdi9e3cVlowQQgghhBBCCKk69+/fx/379wGUzlFmamqKrKwsFBQUcGkmTZqE33//Xal8GWO4evUqrl69CgAwNDSEsbEx3r17h6KiIgClvdwXL16Mr7/+Wk2fhhBCCKk8CqQTQgghhBBCCCGEY2tri40bN+LOnTt4/PgxkpOTkZ6eDgMDA7i5uaFjx46YPHkyOnXqpHTeTZo0wdq1a3Hnzh08efIEKSkpyMjIgJGREby9vdGlSxdMnz4dTZo00cAnI4QQQlRHgXQJ5R85I4QQQgghhBBCPjSGhoaYM2cO5syZo9L6K1euxMqVK2Uus7a2xsKFCytROkIIIaR60BjphBBCCCGEEEIIIYQQQogACqQTQgghhBBCCCGEEEIIIQIokE4IIYQQQgghhBBCCCGECKBAOiGEEEIIIYQQQgghhBAigALphBBCCCGEEEIIIYQQQogAneouACGEEEIIIYQQQghRrxImxoPk5xAzBgAoyC9AaFIYOjs1reaSEUJI7USBdEIIIYQQQgghhJD3gGTw/ObbUJx6dQdZYfFIvvECeFcAdzsXXLa4gJnDxqN+/fpgjKGgoAAlJSUwNDSElhYNXEAIIXwokE4IIYQQQgghhBBSi5UF0K8nPsHp14EAgPSHMYjZfRM50ckAAH19feQ0NkJqfiwWBCyAlZUViouLkZmZCQDQ1dVFu3btMGDAAPj4+EAkElXb5yGEkJqIAumEEEIIIYQQQgghtdjRl9exOew093/i5VBEbroCiBnvOmlpaVL/FxUV4ebNm7h58yZMTU2hpaWFgoICGBsbo2nTphgwYAA8PDwowE4I+WBRIJ0QQgghhBBCCCGklkrKy8DWsDPc/xmPX0kF0U0a1oX9oBaw8XGG4Y0k5D+IlFpfJBLB3d0dcXFxyM3NBQBkZWVxy/Pz8+Hv7w9/f394eHjA19cXAGBgYIAGDRrAwcFB0x+REEJqBAqkE0IIIYQQQgghhNRCJUyMObc3Qoz/9Tx/tf82F0S3H9Qc9ad2h0hLhKyg1ygMjePS6VuboCA1G4wx6OjooKSkpEL+RkZGKCkpQUFBAQDgxYsXePHihVSapk2bYtCgQWjVqpUmPiIhhNQYNIsEIYQQQgghhBBCSC1SwsS4mxSGRXf/QEJeOvd+duRbZD1PBAAYudpwQXQASDgfzKWz7eMDt+ndYWBmDAB49uwZFyx3dXWFnp4eAKCgoACjRo0SHM4lJCQEq1evxpo1awAAfn5+CA0NhVgsVuMnJoSQ6keBdEIIIYQQQgghhJBaooSJse7JcSy6tx0PUiKklqXc+t//9gOackH0vIQMvAuOBQDomBvCqm0DaOlow6S5k9T6TZs2xa+//opBgwaVbqukBPv27QNjpT3cJQPqvXv3Rp06dbj/nzx5AgD4888/sXTpUsycORMnT55EYWGhuj46IYRUKwqkE0IIIYQQQgghhNQCJUyMnx4fxKnXd2QuL0zP4f4283bk/s4Ki+f+Nm/syAXYjepZSa3/+eefQ1dXF717966Q95AhQzB79mzu/7dv3yI7O5u3rImJifjrr7+wdOlSxMbGIj09HcXFxXI+ISGE1Fw0RjohhBBCCCGEEEJILbDnxWVcfBPEu1yk9b/+kqz4f0OrFOf9r1e4rpkR93dRZh73d506deDoWBp8r1u3rlS+Xbp0weTJk1FUVIS//voLubm5XA90ANDV1eX+btiwIXR0dBAcXDqUzIsXL7gAvK6uLjp27IgBAwagUaNGgkPGEEJITUM90gkhhBBCCCGEEEJquKS8DOyKuCiYxsDWjPs77UE097e2oR73t2TwPCcqifvb1taW+zs5OVkq3xEjRkAkEkFPT08qHQC0bdsW27dvh7Fx6XjrMTExsLKS7unObbuoCAEBAfjmm2/w008/IT8/X/DzEEJITUKBdEIIIYQQQgghhJAarISJMf3Gernp6vTwAv6/k3fi+WCU/H9PdLNG9lyad0/fgIkZinMKkBXxlnvfyOh/PdX9/Py4v01MTODm5gYAYIwhNTWVW1avXj188803sLKyQosWLQAAxcXF8Pf3r1A2fX19mJiYcP8HBgZi3rx52L9/P86ePYuEhAS5n48QQqoTDe1CCCGEEEIIIYQQUkOVMDEWBv6B1MJMuWkN6prBqk19pN17icK0HDxbfRpeiwfC0MESZo1Lh20pzshFyu0XyApPBCsq4dZ9/PgxsrOzkZWVhbNnz3LvW1pacn8/ffoUmZn/K8fQoUO5YV309fWlyqKrq4sJEyYgMDAQoaGhKCgowFdffYXLly/j3r17YIwhMTERR44c4dZp0aIFRo0aBR8fHyW/JUII0TwKpBNCCCGEEEIIIYTUQCVMjNWPDiAoNULhdVwndsG7p29QklOAd8GxuD9lJ+r28IJJA1sgqrSHerJ/OJdeX18fBQUFKCwsxPr16xEZGYnc3FxueWJiInJycmBgYIADBw5Ibat9+/bc35GRkejevTv3/5IlS9C6dWtoaWkhNDQUAPDnn38iKSkJfB49eoTHjx/j888/R79+/RT+zIQQUhUokE4IIYQQQgghhBBSA+15cRmX4x8qtY5RPSv4rBiKZ9+fRHF2AUpyC5FwLhj6+vqwaty4QnoHBwdER5eOp37v3j3ufRMTE2RnZ6OoqAinT59GZGQkFxAvU9YL/fnz53j16hX3frNmzdC6dWupNAC4ILpIJIKVlRU3TEynTp0QERGBpKQkMMawZcsW+Pn5wdnZGd7e3ujcuTP09P43zjshhFQHGiOdEEIIIYQQQgghpIZRZHJRPmZeDmi+7lPY9m0MLT3hPpRlQXRJIpEIDRo04P4/ePAg7t+/zy0r8/z5c7x79w6///671Pq9evWSSiPJ0tIS69evx/z587n3TExM0KlTJ26YGAAIDw/HpUuXsH79ekycOBH79+9HcXGx4GchhBBNoh7phBBCCCGEEEIIITWIopOLCjGws4D73D5wm9QFqfdegqXlwdrEHmITHXzWdiBCAx/i6tWryM7OBlAaIGeMASidVDQ4OLhCnjo6OujVqxcuXiwN8G/fvh1ZWVlITU2V6nlep04dAEB2djYCAgKk8vj666/h5uaGuLg47r3AwEC8e/eO97NkZ2fjyJEjeP78Ob777jvqnU4IqRYUSCeEEEIIIYQQQgipIZSZXFQROiYGsO3pDe1iwDJNFw2sHNHOqwXa+bTApEmTkJOTg6KiIpiamiIxMRHnzp2Dn58f8vPzK+RVXFzMBdEBICYmhvvb0NCQ+/vt27fw8PDAunXrUFBQwL3v4eHBTSQqOVZ6WRBdW1sbTZs2xaNHj7j0jo6OuHbtGhfcnz17Nlq1agVXV1d06dIFxsbGavmeCCFEHgqkE0IIIZWRGQFkvKnuUhBCCCGEkPfErucXlZpcVMjgeu3R2a50XPSC/AKEhj5BZ6em3HItLS2Ymppy/9erVw8zZ87EpEmTkJCQgPz8fOjq6uL8+fPw8/NDSUmJzO1YWFhITQ569OhRHD9+HLGxsVLpWrVqxf195coVqWXGxsb44Ycf0KBBAwwfPhzFxcXIz89Hs2bNEBoaiuTkZAClQfrz588DAHbu3ImePXti3LhxUp+DEEI0gQLphBBCiKoyI4CzHkCuQXWXhBBCCCHKoBvhpIZKysvAnsjLlcqjr0NL9HZsCS2RFlrX8YC2qHR6vPz8fBgnFUNbS1tuHvr6+nB1deX+nzt3Lj777DNcvnwZISEhyM7ORkZGBjdZaEZGBk6cOIGWLVsCAN68+d/xpaOjw41trqNTGoZ6+vQpbt26JbXNadOmoWHDhgBKe6YXFxcjLS0N69ev5y1nQUEBLly4gCdPnuCHH36AtbW13M9GCCGqokA6IYQQoqrirOouASGEEEKURTfCSQ1VwsSYev23SuXR16EVlrYYwwXP1cnCwgIjR47EyJEjAZSOo37u3DkcOnQImZmyh6GxtrbGiBEj8McffwAAQkJCYGhoiD179nDjsQOAmZkZunTpAgCIiorihoMpG78dALy8vBAVFYXCwkLo6uqiV69e8Pf3R0FBAeLi4rBo0SIMGzYMTk5OaNq0KRe0J4QQdaGzCiGEEEIIIYSQDwfdCCc11E+PDiK9KFt+Qh6+jq2wpLlmguiyiEQiDBw4EL6+vrhz5w7u3bsHoDTg/vbtWwBAamoq/vrrL+jq6qKoqAjBwcEyJzFt1KgRdHV1AQBnzpypsHz27Nnw9fXFd999h+DgYBQVFWHQoEEoLCyEv78/GGNITU3Fjh07AABWVlbw9fXF4MGDaQx1QojaUCCdEEIIUUZmxP8uwN+FVW9ZCCGEkOokWScCgI4pYObO/z4hhFd8TiouxgepvH5VB9El6erqomvXrujUqRMiIiKwefNm7N69G2fPngUAFBUVyVyvQ4cOuHPnDgBwY69fuXIFfn5+Uul69+4NX19fAOCGiAGAxYsXIytL9o2xtLQ0HDx4ENevX8eqVatQt27dyn1IQggBBdIJIYQQxZU9Ck4IIYR86PjqxO6XgGt9K74/8EX1BtPpRjipwUqYGBMD1qq8/hDnDviiyfBqCaLLoq2tjenTp6N///74999/4e/vLzVEC1A6saiTkxP09PRQWFiIJ0+ecL3Nyxs8eDAAICsrCy9evABQ2hu+LIiup6cHAwMDbniZpk2b4smTJ2CM4c2bN1i2bBmWLFkCe3t7GBoaavKjE0LecxRIJ4QQUnPVtB5tH/Cj4A8fPsSZM2cQFBSEFy9eIDk5GZmZmTAzM0OjRo0wYMAAfP7557CyslI675UrV2LVqlVy00VERHATUMkSFRWFn3/+GZcuXUJCQgLMzMzQokULTJ8+HcOHD1e6XIQQUiUyI4D8d6V/pwcDBuby67qaUD/y1YkFScqlrwp0I5zUcCse7EGeuEDp9VpZN8SYBj2lJhStSerVq4dp06Zh6tSp3Djmq1evRmpqKnJycnD06FEubWFhoVQQ3dDQEHl5eTAzM+MmPT1//jzXu71sfPUmTZrg66+/xqlTp3Ds2DEAwIgRI/DRRx9hw4YNyM3Nxdu3b7FgwQIA+D/27js6irIL4PBvN70QSCCQhBp6lQ+U3kvoKIggioh0UQEFQUCkSFFQBEUQkd4VEOk1EDqINOkltEACpADpbXe+P9YMWdLDbup9zuG4mXln9u6a7OzceedeatWqRYcOHahTpw5abc57z4QQOZsk0oUQQmS/5BICkPxJb3bPaMunlixZwrx589SfbW1tsbOzIyQkhGPHjnHs2DHmzJnDli1baNCgQaaew8rKKtVEfGoNo3bs2EH37t2JjIwEDA2rgoOD2bNnD3v27KFv374sXrwYjUaTqdiEEMIs1ASvHTishX1NgajUj3UpJYXl+JiyfHwhXOR8/hHBHHx0IcPbFbFxYlb9D3NkAv1FGo0GW1tbypcvz3fffceSJUs4duwYer0+yVgnJyd69+7N+vXriYqKUsccPnyYtWvXGo0tXLgw48ePx87Ozqhx6bZt29R67S86e/YsZ8+epXLlynz55ZcULFjQhK9UCJHXSSJdCCFE9kopIdBkU/Lj5WQ4W9StW5cyZcrQuHFjKleuTKFChQAIDw9n48aNjBo1isDAQLp06cL169czdVLSsGFDfHx8Mrzd7du36dGjB5GRkTRq1IglS5ZQsWJFwsPD+e677/j6669ZunQplStXZvTo0RnevxBCmE1Kx7TUjnWZ2cZU0lMeJfBo8ssTj8/uO8yEyEH6H5qVqe0WNP40VyTRX1SkSBFGjx5NSEgI3t7e+Pn5cfXqVR4+fAhAaGgoPj4+6gSK8PBwhg4dyt27d5Psq2PHjmoS/ezZs+ryxEl0CwsLtf66k5OTWv7l6tWrjBs3jpkzZ0ozUiFEukkiXQghRPZKMSEQkbVxpEfCTPl86P333092uaOjI3369MHd3Z22bdvy+PFjtm3bRq9evbIstgkTJhAREYGbmxvbtm1Tk/yOjo5MnjyZhw8fsnDhQqZNm8bAgQNxdnbOstiEEFkgJ5Q5yQ/SWx7l5i/JLz/+nvHPMoNeCDbdOUa4LjrD2/Wr2JZidoVMH1AWcnFxoXv37oChgeicOXM4dOgQAJcuXTIamziJ7ubmpibd69Wrp46/deuW0TYWFhb06dOHggULMnv2bAA6d+6Mp6cnP/30E6Ghofj5+TFw4EA8PT2pWbMmbdq0Ub9HCiFEciSR/p/g4GC2bNmCt7c3Z86c4e7du8THx+Pq6sprr71Gnz596Nq1a3aHKYQQ+UfE7eSXZ+eMNqcKhhP/xLPxXkwM5FP169dXH9+/fz/LnjciIoKNGzcCMGTIkGRPfsaOHcvChQsJDQ3lr7/+om/fvlkWnxDCzHJjmZP0zOp+8VgHGd/G1K/f1DPes/IOs3x8IVzkXPF6HT9c3JDh7dp4vMr7FbzMEFH2sbS0ZMSIEdSoUYPNmzcn+13S2dmZ9957jzNnzqiJdBsbG/z8/Pj++6SNWj/77DOaNm3KP//8oy57+vQpf/75pzorHQwz3i9cuMCFCxdYu3YtrVu3ZuDAgVhbW5vhlQohcjtJpP/Hzc2N+Ph49WdbW1usrKx48OABDx48YPPmzbRv354NGzZgb2+fjZEKIUQekJ4kwr9fJb88u2e05dTETDY7fPiw+rhcuXJZ9rxHjhwhKioKgPbt2yc7pkyZMlSpUoUrV66o9dKFEHlEdpY5yYz0zurOzEXa7D4+5mRyIVzkQN+cW5v2oBe8VqQC42q9kytLuqRFq9XStm1b2rRpw6VLl7hy5QqXLl3izJkzADx58oRt27YZlWGZNWsW169fV0u3JChbtixNmjQBDCVcEuzbt4+YmJSbuup0Onbv3o2fnx+TJ0/GxsbGlC9RCJEH5L1P30yKj4+nbt26zJ8/H19fX6KioggPD+f27dv0798fgJ07dzJ48OBsjlQIIXK5hCTCrlcN/172RDanJkvygZiYGO7cucPPP/9M7969AShfvjydO3fO1P4uXbpE9erVsbOzw9HRkUqVKjFw4ECjmpcvunjxovq4WrVqKY6rXr26+hxCCGOBgYH8/vvvLF68GICpU6dy/Phxo8ZtwkSy8pglx0djThXApbbhX8Eq2R2NyOceRoawx/9MhrZxsS7A9/UG58kkemIajYbq1avTvXt3Jk2axLvvvquuu337ttF3vytXrqhJ9CJFiqjLW7ZsiUajITY2lr1796rLE5Lobm5uvP322+ryWrVq0a1bN3UW+uXLl/n888/ZvHkzV65ckeOhEEIlM9L/s3//flq0aJFkeZkyZVi0aBGWlpb8+uuvrFq1iunTp1OyZMlsiFIIIUwsO+rK5qUT+3x6q7itrW2ys3kaNWrEmjVrMj17JygoiJCQEAoVKkRoaCjXr1/n+vXrLF68mHHjxjF16tQk2/j7+wOG231Tu2OsePHiRuOTExMTY/S6Em77jYuLIy4uLlOvKa+Kj49Hr9ej1+uTzAIzhYR9mmPf+YFer+f+/ftERERgaWmJh4cHsbGxHDhwgGPHjvHkyRO0Wq1aCun+/fsoioKDgwOtWrVi+fLl/PTTT7zyyiuMHz+etm3bZu8LelGYL8SHGx6HXgPsko4JuQLxesNjS0cokHV3yqQqXk9y8cb9tywuudfyMs9l0s8ue5J9r19mf9n12frf/4c4bIGM16cW4mWN+XtxhrdZ2OSzPJ9ET07Pnj0pXbo0GzZs4MaNG0nWW1tb07lzZ+zs7Fi1ahVgqL+u0+mYN28eT548MRpfvnx5pkyZgqIo/P777+ryihUr4uHhwZ07dwBDXfaEC8ylSpWic+fOeHl5odXmv/8HQojnJJH+n+SS6In179+fX3/9FYB//vlHEulCiNwvN9aVzWkSbhUPfACkfhzJS9zc3IiOjiY8PJyICENT2BYtWjBz5kxKlSqV4f1VqFCBmTNn8sYbb+Dp6YmVlRWxsbH4+Pgwbtw4Tp8+zbRp03B2dmbkyJFG24aFGS7MpFV2LWF9wvjkfPPNN0yePDnJ8gMHDkhZt2zyYuMwkTGWloav+gkXkGrUqEGNGjXS3G7BggXqY51Ox44dO8wToEk4gkMy5REuATxItOBaFsWTDsnF+5+9DktM9zzHH2D8HphAKrFn2OFrZOv/F4e1RGoigXfTHCqEKQVEhuAbHpChbT6o0CbXNxd9GQ0aNKBBgwbcuHGDM2fOcPXqVU6fPg1AbGwsvr6+6qQJgKNHj7Jhwwb1e4RGo1FnlQ8fPhwHBwdu3rypjn/8+DHffPNNis9/79495s2bx/nz5xkxYoR6fBVC5D/y159Otra26mOZHSWEyJFCb0D0M8PjJ+fBtmDqCfHcVlc2Jdk9K9ypAsS5ZG8MWSxhpg4YTjxWrlzJtGnTqFu3LuPHj+frr7/O0P569eqVZJm1tTVt2rShadOmNG3alFOnTjFp0iQGDBhAwYIFX/YlJGvs2LGMGDFC/Tk0NJSSJUvSokULChcubJbnzK1iYmK4dOmS2lPG1HQ6Hbdu3aJs2bJYWFiYfP85lU6nIzw8HEVRKFCgAFqtlosXL3LgwAH8/PyIj4/HwcEBR0dH7t27R0hISKaep3DhwoSEhCS5Vb19bQcadh1DyP4PWbj7GefvPV+3evVqOnXq9DIvzzSenId9TTO+XetD4FzT9PFkVJgv7KqdZHEcdux1WIJXRD+siDLNc7U7Y96Z+Cn9v6j7G/w9MOnynPL/IEGYL8GBKd+lJIS5zPz3jwyNr+lclg8qtjFTNLlLhQoVqFDBcH6zYcMGVqxYAcC5c+c4d+6cOu7o0aPqYwsLCzWHU7VqVUqXLg0Y6qUnePDg+UXHEiVK8ODBAxRFwcnJieLFi3PliqGn05EjR4iNjeW9996jaNGiMtFCiHxIEunp5OPjoz5Oz0weIYTIUurscjvDbLF9TYGonDm7/GUT3w1WPa9tmhWlaESqihYtysiRI2nSpAkNGjRgypQp1K1b12QJN1tbW6ZPn46Xlxfh4eF4e3vz5ptvqusLFDD8PkVGRqa6n4T1CeOTY2Njk2xZGisrK7Mki3MznU6HVqtFq9WaNdFtYWGR5xLper0eX19fnj17hoWFBR4eHkRERLBjxw4OHjyolheysLDAxsYmzd/tlDg7O+Ph4ZFsXwAbGxuiow3lLAoVKsTTp08B2HfBioZdoftr0XxQN4pxRzrz7YKtAHzxxRe88cYb2X9Lu6UWMpNottRCTvg7dqkMnc6n2PTSiihDIv3FYx2k3Sgzq4+PKf2/sCD55Tnl/0ECl8pYKa7ZHYXIZ+L08fwTdD3d4+0tbPix4Uf5sqRLWt566y1cXV1ZsWIFgYGByY4pU6YMbdq0YeHChYChrAvAzZs3jWqnJxg8eDAdOnRgyJAh+Pv7o9Pp+Prrr1m6dCk7d+5EURT+/vtv/v77bywtLWncuDEdO3akUqVK5nuhQogcRRLp6fD06VP1Np8mTZqk+SEpNVbTT2qs5k16vaEmaXx8vPzOZ5XoZxhqfb5QYzX6Gdgl+n+QE+rK2pWBdleM40hu5lq18XApaU1sHCpBgUQXNHPA75j8nkPdunVp3Lgxhw4dYuHChSadudqgQQP18YulPjw8PAB48uQJkZGRKc4MSphplDBeCHMLDg7m0qVLREREYGNjg7u7OxcvXmTXrl0pnvAnptPpjJLoFhYWaDQa4uPjjcaVKlWKqKioJPtM+JtI0Lp1a7y9vVEUhdu3bwNgY23F3AnvsM97P8u3G5fY0Ghg+gfFOfJPRY78c50bN26wf/9+WrduneH3QrwgPQnuglUMTTEzIjPbvIyULozbFM3YeCHykc13jmVo/KLGn0oSPRXNmjWjSZMm/PPPPxw5coS7d++qxzgwTIIsVqyY+nNYWBjbt29n5cqVSb6/v/HGG3Ts2BFFUdTjp1arZejQoTx8+DDJc8fHx+Pj44OPjw8dO3Zk4MCB2X+xWQhhdpJIT4Ner6d3794EBARgY2PD3Llz09xGaqzmPFJjNXscPHgwu0PIXxLVLVVrrKZaHzWn1JVNIY47JL/cHDVfX1JmZ4zmNQm1KRPXnDS36tWrq48vXbpEnTp1kh138eJFAKpVq5YlcYn8486dO9y4cYOYmBgcHBywtbXFx8eHkydPqheW08vCwoLKlStz9erVJJMASpcurX6fSZilr9PpuHfvef0VT09PevbsyXfffUd8fLw6saN+/foMGzaMMmXKsGjRInV8s0pxFHzwC29WAu/j8DTWsPxaALxaAjS+CxjWEI78Y1i+bsms7E+kZzYZK0lc00voE5Jc0/KUlguRj8Xrdfx4+a90jy9qU4iSBYqlPTCf02q11K1bl7p16wKwcuVK1q9fD8DWrVvZtm2bOtbHx4cDBw6oP1tZWREXF4dGo+GNN94A4OrVq+qdWlFRUUb9dbRaLXq9Ho1Gg4ODA+HhholB27dvJzY2liFDhkj9dCHyuFz7F75s2TL69u2b6e137txJu3bt0hw3fPhw9YN3/vz51KyZdl0/qbGaflJjNW+KiYnh3r17NGvWDAcHh+wOJ+96cXb53wOT1lit+xs4/XcXTeR9OJaJhlrmrmmaUo3VhmuSj9fcNV8zITg4OLtDyBESknyplU/JjBMnTqiPPT09jdY1btwYOzs7oqKi2LVrV7KJ9Lt376q1Ldu0kRqjInP8/f3V27ydnJx49OgR27dv59q1jF9sLFy4MKVLl+bMmTNGy3U6HcHBwWoSvUyZMjx69IioqCijSQH9+/enXLlyjB8/3mhGXY8ePWjQoAG9evVi+fLl6vK2bdsC0KFDB1atWqWWdSn738RhjQba1IA/DH3bOHTNkEgHaF4l8XuQseZ4ZvFikjanlDnJrJQS/Kkl/jOzjbmk9L7m1PdbiGw08fTytAcl8lPDj8wUSd723nvvYW9vz6pVq9DpdEb9QBI/btiwIcePHwcM3y+LFCmCXq/njz+e17BPuAusdOnSDB8+nA0bNnDs2DEURWHGjBkcPHiQ9evXoygKe/fuZe/evTg7O9OsWTPat2+Pu7t7Fr1qIURWybWJ9Kzw+eef8/PPPwMwe/Zs+vXrl67tpMZq+kmN1bwp4ZY2S0tL+Z03l9AbsKtKiqvVGqt/J5NcyChz1zS1LUiytVRdqhnXkYUcmwzJ67/nCZ/VGo0mxTHe3t78/fffADRv3jzd+1YUJdX9xsTE8OWXXwLg4OBAq1atjNY7ODjQrVs3Vq1axS+//MKwYcOSNCOdMWMGYEjwd+nSJd2xifxJr9cTFRWFRqPBxsaGo0ePsn37dvViTEa88sorhISEcP/+faPlwcHBREU9/9zr3r0727dvJzIyUr193NLSkq+//ppHjx4xevRo9eTf3t6e1q1bY2trS+fOnfnzzz8Bw3edevXqAdCuXTtWrlypzogvWbKkus+yZcty+fJlAIISfbyWSjTX41mim2x0iSbVa7Up/61mKXOVRskOCRcGop8Z7rhqfSjtZuEy41uIXOdhZAiHHl1M9/gy9sUo7lDEjBHlXRqNhm7dutGiRQv27NnDvn37ePz4sdH6nj170r17d7XvjqWlJdHR0SxatIjTp08b7a9IkSJMnz6dAgUKGH3n37hxIwcOHEjSuPvJkyf89ddf/PXXX3Tu3Jl+/fpJLkKIPCTXJtLfeeedl6q/+uJJ9otGjx7NrFmzAPjuu+/49NNPM/1cQghhcolPnnM7SQjkeH5+fnTp0oUhQ4bg5eWFp6enmvz28/Nj9erVTJ06FUVRcHFx4bPPPjPaftKkSWrJs9u3b1OmTBl13aFDh5gyZQoffPABzZs3p0QJwzTYuLg4Dh06xNixYzl16hQAEyZMoFChQkni+/rrr9m0aRMBAQF07tyZxYsXU6FCBSIiIpg1axYLFiwAYPz48Tg7O5v67RF5hK+vLzt27ODw4cPqjG0LC4s0+6xYWFjg5eWFt7d3knqr//77r/rY1dWVnj178ssvvxAfH6+WhKpZsya9e/emWrVqTJo0SR3foEEDChUqRKFChWjQoAHHjhnq6hYtWhRbW1vAMMM8IZEOqLeTOzg44OTkpN6a/vjxY4oWNUw/T3x35FX/57EmTp7bJbo2uOv888elPOTOSrNwqvBfP5MHhjvA0nNxVo6RQuQqQ4/Ny9D4b+v2N1Mk+YeLiws9e/akZ8+e6PV65s+fz549e1AUhbVr1+Lj44ONjQ0xMTH4+vrSp08fo4vcCbp06UKBAgXQ6/XqhWiA/fv3J/u8ib87bN26lWfPnjFixAipny5EHpFrE+kpzfo2hVGjRvH9998DMHPmTD7//HOzPI8QQuQKWXGruCQEcrzz58/z4YcfAmBtbY2TkxNRUVFERESoYzw9Pdm4cSNubm7p3q+iKHh7e+Pt7Q2AnZ0dDg4OPHv2TE1KarVaxowZw+jRo5Pdh6enJ3/88Qfdu3fn8OHDVKxYkYIFCxIeHq6eyHzwwQeMGjUqU69d5C16vZ7Tp0/zzz//EBoaioWFBX5+fkbNyRIkTqI7Ojry2muv4ePjk2TM/v371d/XJk2a4O7ubnRrOBiamHl5eRETE8PChQvV5S1btgSgdu3aeHh44O9vyG4nLpHUpEkTNZGe+G+uaNGiaDQaFEVBp9Px7NkzdbKInZ2dmkg/evSo2k8gcbPSKw/g8TMoWhAOXn0ea9X/yrrE6+DH3c+Xv//260neo2yXk8qcCCFEMvzCA3kY/STd44vbuVDcUWajm5JWq2XIkCFqCRaAgIDn5cp0Op2aRLewsMDNzU1tVN+iRQsATp06laSxt4WFBW+//TZRUVFs2rQJgCFDhvDo0SM2bNiAoigcOnSIw4cPU6hQIerUqUOHDh0oW7as2V+zEMI8cm0i3Vw+//xzdSb6zJkz5aRbiPwk9EbenxXdZBM4lDI8zu11ZUWW8fDw4I8//lAbKAYEBBAUFISFhQWlSpWiZs2avPHGG7z77rvY2dllaN81atTg+++/5/jx41y4cIGgoCCePn2Kvb09VatWpUmTJgwaNIgaNWqkup8OHTrw77//MmPGDPbu3Yu/vz+FChWidu3aDB48mG7dur3MWyBysdjYWKKiotQmoH/88UeSE+HEbG1tKVy4sHoCnSA8PJxLly6pP7/77rucPXuWK1euEBsbqy7v3r07ZcqUITAw0KihWePGjQFDnf6lS5eqiffECfPq1aurifRHjx6pyxPu1ADDLeOxsbFYW1sbJcUB9u3bR7du3YiKiiIkJERdvn//frp27Yq1tbVagglAp8CUnUV4t3VJztw5i6OjYXnTShATB4PXV+LcXUMN+P9VK0e9TkNTfN+yjdzVJITI4Uae/DVD439s8LGZIsnfLCws+OSTT6hbty7btm3j/PnzSca89tpr9OnThzlz5gCGMjCOjo48ePCA+fPnJxk/evRoGjRowM6dO9VlN27c4NChQ0lqsz958oQ9e/awZ88e6taty4gRI7C3tzf9CxVCmJUk0hNJnET//vvvGTlyZDZHJITIMqE3YFvFpMs7Xc+ZJ+OZnWlXsFrarye31JUVWcba2pru3bvTvXv3TG0/adIko5IViRUuXNhkx9ty5coZzfQV+Vd0dDSHDh1i586d+Pr6ZmjbFy8GtW7dmgMHDqDT6dQEvJOTE2+++SZvvPEGw4YNU5PeRYoUUUsXdevWzSiR7vhfltra2poSJUqoM+Bv3brFa6+9BkDx4sXV8Tdu3ECv16PVao0aGsfHx3PgwAHatm3LyZMnjU7U169fT926dTl37hwxMTHq8qioKL788ksKFy6szrK3t7cnMjKSuw+C+GZ5kNFr/mk3/LoLHjwxJNGtrKyYM29xqv0MslVOPE4LIQSG2egBUSFpD/xPc/dXKGYvZejMRaPRUK9ePerVq0dAQAB37tzhr7/+UvugnD171ugCt6IozJkzh2PHjhkdV8FQmq1BgwYARr1Qdu/eTXKsrKzUi+h///03Y8aM4dNPP8XFxYWCBQvm3GOsEMKIJNL/88UXX6hJ9B9++CFJfVchRB6XUs3xnFqL/MUZeDK7XAiRjwUHB3Pv3j1iY2MJCQlh7dq1almT5FhYWNCjRw+2bdtGWFgYGo0GOzs7IiMjefLkCU+eGG7Br1y5MsOGDaNu3bpMnz5d3b5WrVpYW1sDhpItCRdwEs9ML1WqlFp7FeDOnTtUrGi4YFukSBE1kX78+HF69OgBYDSLPDQ0lEOHDtG8eXO19FGCxYsX4+7uzoYNG4yWR0ZGMnLkSKNa7a6urgQGBvLo0SM14W9hYUH16tU5ffp0sjXgf9gJCW+ftbU1q1evplmzZim+n0IIIZKX0dnoE2v1NlMk4kXu7u64u7vz6quvMm3aNM6ePYtOpzO6CA4Y/ezk5ERoaCgAXl5egOHY/+I2YCjLVr58eZYuXQoYGoE7Ozuzdu1a4uLiuHPnjtqLz8PDgw4dOtCqVSscHBzM8XKFECYiiXTg3r17zJw5EzDUzpoxYwYzZsxIcfznn38uddOFENkvPUnx1GaXS11ZIUQupigKZ8+eZfv27fzzzz9GM7MTK1GiBAEBAUYJY51Ox++//45erwcMZVcGDRrEV199xZ07d9RxjRo1AqB+/fqUL1+emzdvAoYyLwkaN26sJtLDwsIIDw9XZ587OjqqifRdu3ZRsWJFFEUxKi3j6+vL8ePHefXVV5MkzOfOncv9+/c5cuQI8Hw2W3R0NOPHj1fHubq6YmFhwcOHD9VGqWAoU5O4pnri15+4xEtytBro5NWAr6b9pM6YF0IIkX4ZnY3+adWuWGotzBiRSI61tTVfffUVa9euZceOHckeNy0tLWnVqhWKorBnzx7AkIhXFIVly5YZfS8A6Ny5MwMGDDDqv+Ln58eePXuSNCYH8Pf3Z9GiRaxbt45x48apfU2EEDmPJNJBPYlKeJy4JmVyXvyQFELkUolroj+7kvyYxMvz2oxuqSsrhMhl/Pz8ePz4MfHx8Xh7e3PixIlUx9vY2FCrVi31luu6desSGxvLuXPnjL7/tWrVioIFCzJ69Gg++ugjdXni2qUtWrRQE+m+vr4oioJGozGaOaYoCvv27aNLly6Eh4fz7NkzdZ23tzeNGzfGzs7OKFkPhr48np6e6nfMokWL8vjxY+Li4oyallauXBk/P78ks+1TqvmeOKkOhkTAi3XVa9WqRatWrYh9cpPAIEMJmUnD36RLhxaUrpvMnU5CCCHSJSOz0a00FnQr28SM0YjUWFpa0rt3b3r06MGhQ4e4ePEiFy9eVI+vVlZWlChRgsePH6vbXLp0iY0bN6rNwBM4OjrSp08fNBqNUWm2c+fOJfvchQsXVseFh4czfvx4GjVqhLu7O56entSrVw9LS0ndCZFTyF8jUKZMmRRnMQkh8qiUaqK/6MVyKbmtZnpas8tz4msRQohE4uPj8fHxYceOHWoi+0WFCxemVKlSnD17FjDUQFUUhZiYGLZt26aOe+eddyhTpgxff/21OhYMiWswzF739PRUZ5CdPXtWvXW7bNmy6vinT59y+fJlqlWrxq1bt4xiWbNmDdWqVePixYtGSWtFUZgyZYpRDfZKlSpx7do1dDqd+to0Gg1OTk4EBwcnKbty4cKFNN8vJycnPDw8CA0NJSwsDGtra0qXLk3btm2pW7cuQUFBhISEoNVqcXV1xcXF5b8tmxIdHc2dO3fo//mP6qx6IYQQGZfR2ehz6g0xYzQivWxsbPDy8sLLy4uYmBi++uorrl69SlRUFIsXL8bC4vkdA0uWLEl2H82bN1fLv+3fvz/J+iZNmtCxY0fGjBkDGGa2d+nShTVr1hAVFYVer+fw4cPq+EKFCtGuXTveeustdb9CiOwjiXQhRP6U2drnOb1mevQzOP4AWh8C24KSKBdC5EoJs8WDgoKYPXs2ly9fTnW8paWlUX3xCRMmsHPnTv7++291soSNjQ3lypUDoG/fvkaJ9MDAQEqUKAFAnTp11ET6P//8Q3R0NLa2tklmfc+dO5fp06cnaSoWHR3NF198YTRJo1q1aly6dAmdTqfOOtdqtUlmpoMh4Z7SBYMETk5OtGzZkmLFiuHv7090dDR2dnZUqVIlzZlrxYoVo1ixYqnuXwghxMvJyGx0Jyt7XilSNu2BIkvZ2Njw9ddf8+OPP3L06FGAZPuK2Nvb07x5c3bs2AEY6p0DXLt2Lcls9SZNmvD555+j0WiwsLBAp9Nx//59Ll68mGIcT58+Zd26dZw8eZL3338fBwcHihYtmuhCuBAiK0kiXQgh8gqnCmAXBzwA55pgZZXdEQkhRLopisLly5fZuXMnZ86cYcKECXzyySdqjXEwzAqPjo7G39/faNvEZflKlChB7dq1qVmzJmPHjuXatWuAYWZ7QjmWMmXKqOVTAPbs2UOtWrUAjGabRUdHs2DBAoYNG5akfrm/vz8fffQRUVFRANjZ2eHu7s6tW7eSlE+5dOlSkter1+uNXpuNjQ0ajUYtx2Jra0vTpk1p3749jo6O3L9/n9jYWJycnKhQoYLMShNCiBwqo7PRFzQcZsZoxMuwtbXliy++wM/Pj507d3LixAmePHmiJtStrKzo27cvDg4OaiI9JCSEAwcO8OuvvxpdVNdoNGrJl/DwcHUficu1FS9enAcPHqiPS5YsycmTJ1EUhdu3bzN58mR1bO3atenQoQN16tQx99sghEhEEulCCCGEECJbRUZG8t1333H69GnAkFROTvXq1dm6dSsALi4uTJw4kR9++IG7d++qY1xdXdFoNFhaWtK9e3emTp0KGGaRXbhwgVdeeQUAT09PNZF+/Phxrl27RqVKldQYEuzfvx9fX1/1OVxdXdHr9QQHBxMZGamOi4qKSlLmJTklSpTA2tqa8PBwLC0tcXd3x8vLi7p162JhYUFMTAx6vR47Ozs0Go26ncwiF0KI3GH037+le6ybrTMlCxQ1YzTCFEqWLMmgQYMYNGgQ8fHxTJ48mfPnzxMXF8e8efNwcnJSx/75559GfVgSVK9eXS0ld+DAgSTrBw4cSMeOHfnwww95+PAhAQEBaqm65Jw5c4YzZ87wv//9j44dO1KwYEGuXr3Kq6++avT9QQhhWpJIF0LkT2nVDjf1dkIIIVSKonDp0iXOnTvH06dPOXXqFE+ePFHXJ27gmdiWLVvUxw0bNsTT05MpU6bw0UcfqSVTHj58qM48f+2117CysiIuLg6ATZs2UaNGDTQaDUFBQeq+9Ho9EyZMoH379ly/fh0wJMyDg4PR6/VGifpnz54RGxub6uuztramfPny2NnZERUVpZaVadOmDW5ubqlua2trm+p6IYQQOZdfeCD3I4PSHvifOfWlNnpuY2lpyZdffskPP/ygNj0PDQ1V1ydOor/yyiv8+++/AGopltDQUDZt2mS0Ty8vLzp37qyOe/jwIXq9Xm2WnpiLiwsWFhZqyblz585x8+ZNJkyYgJeXF25FCvDRsFH07dtX+p0IYQaSSBdCmIRer+fUqVPs3buXkJAQBg0aRNeuXWnTpg39+vWjcOHC2R2isYSa4gk1z59dSdpYFKDBKihYxfDYsoDUHBdCiJegKAoHDhxg48aN+Pn5JVmv0WioXbs2LVu2VJfVrl2b8uXLs379eqNZWQknh4UKFaJ9+/asX78egICAAK5fv06lSpXQarU4OjqqSfrTp0+zbNkymjVrhq+vL2CY/R4TE0NUVBR//vmnun87OzssLS2TJM0T/2xlZUW5cuVwcHAgJiYGBwcHatSoQatWrVK8GCCEECLvmnhmRbrHutk6U9yxiBmjEeZia2vL2LFjuXDhglryJXH9dI1Gg5eXF927d2fgwIGA4UL/tWvX+Pnnn40u5gO88cYbgOGcOnG5OjCUePn000+ZM2cODx48ICQkhCpVqiTp3ZLg6k0/hg0bxs8//8zOnTuNmqULIV6eJNKFEJmm1+vRaDScO3eOn3/+WT2YJyQ3/vnnH/bt28dXX33Fp59+yrRp04xqz2a79CTFC1YBl9rmj0UIIfI4RVFYvHix0azy5MacPn0af39/PvvsMwBatGhBs2bNcHV1Zd68eerYhIagYGjelZBIB1i6dClTp04lLCzMqPYoGGalb9u2Tf25QYMG/Pvvv0bNSgHu3btn9LOrqyu2trbodDoKFixIgwYNaNWqFQUKyJ1KQgghICAyhBuhD9I9Xmaj524ajYZXXnmFV155hejoaJ48ecK6des4cOAAiqKwZ88eDh8+rN6ddu3aNUaNGmW0vaIoFC5cmFKlSgGG2eXBwcHqmAIFCjBt2jRcXFyoVauWWj/9ypUrycZUpyzsPWN4fP36dVq0aMGJEydwd3c307sgRP4jiXQhRIYEBASwc+dODh06pM7we7Fum+ULufKYmBhmzJjBrVu3WLt2bc5KpgshhDAbPz8/du3axY0bN3j06JFR+ZaqVavi6urKwYMHAcOMq4cPH6LT6YyS2gkXZ9u0acPmzZvV25xPnTpFUFAQRYoUSTL7+/Lly0yYMIESJUqox6hatWpx7tw5FEVRS70A+Pj4GG2buB6pjY0NzZo1o0OHDjKjSwghRKoWXNmW9qD/yGz0vMXW1hZ3d3eGDx9OoUKF1NItCQ3JX+Tu7k5YWBjh4eFqPfOoqCiWLVtmNK5Dhw5qSRitVmu0rkCBAnz2QWs2/LmJe88My77oBDO7wbtLPbhy05979+4xYsQI1q5da8JXK0T+pk17iBAiP4uKiuLx48cEBQXxyy+/MHjwYP766y9CQkJQFCVJEr1JJfjlA8PjQ1/B8HZg+V9mff369Xz99ddZ/ArSKaXa51ITXQghMiwoKIiJEyfy8ccfs3XrVq5evWqURNdoNLi6uhIdHa0uGzVqFLNnz6ZQoUJG+0po4Jlwm3QCvV7P3LlziY2NNWrymXCiefHiRXbt2qUuDwwMTLFhF4CzszPvvPMOa9asYcWKFaxcuZJ169bxySefSBJdCJEvLVu2DI1Gk+a/ffv2Zfo5Hj16xMiRI6lUqRJ2dna4uLjQpEkTFi1alOpndk4Tp49nf8C5dI+X2eh5k1arpW/fvvzwww+0bt0aKyurJGM8PDz4+OOPcXV1BQzfmfbu3csXX3zBnTt3jMY2a9YMMExcO3/+vNG6CZ904rXS0TSv8nzZpQfwvzLg/WNrXAoZJiJs2LCegIAA071IIfI5mZEuhEgiPj6eEydOsGPHDi5evJjsGEtLS+zs7AgLCzNafvgaOBWAZj3A0xXm9IY23T+j88Af0Ov1zJkzh1GjRuW8xicv1kwHqYkuhBCZ8OjRI8aMGWN0a/KLFEXh4MGDRo01ixYtiqOjI59++inffPONunzPnj1069YNrVZL0aJFjfZz9uxZRo0apc7mAujWrRtbtmwhJibGaGzihl0ODg40b96cAgUKYGVlRcmSJXnttdewtJSvxkII8SKtVqsm/ZJjY2OTqf2ePn2atm3bqscLR0dHwsLCOHLkCEeOHGH9+vVs2bIl0/vPSsHRoWkP+o/MRs/7ypcvz7Bhwxg8eDBBQUFcvXqV+fPnExcXh7+/P+PHjzc6H547d6762MLCQq237uTkBBgmByRufP6qJ1SKXQsB4GT3/Hmj/7vhzv3pCj5sBtM3Q3y8jmU/fcnYb5aY8RULkX/I2YIQAr1eT2BgIJGRkURERPDLL78k2wQugUaj4fXXX2fr1q0A2NtZ06ZJDTbvPWNIjlyFZonGd6gewQfdmrBk/UFCQ0NZvXo1gwcPNvOrygRJmgshRKbcvn2bffv2ce/ePa5evaomsV1cXHjzzTfZu3evegLYqVMn9u/fT2RkpNGM9MDAQBwdHalVqxbFixdXlz969IgVK1bQp08fo8ZalpaWxMfHG9VK12g0RrXSX1SmTBnat29P8+bNsbOzS3GcEEKI50qWLJlkpuzLevbsGZ06dSI4OJjKlSuzcuVKXnvtNWJjY/ntt9/47LPP2LNnD5999hnz58836XObw7h/lqZ77M8NPzFjJCInsbGxoXjx4hQvXhwPDw++/fZb9Q698PDwJOM9PDwoXrw4p06dAgwl8lxcXJg9e7bRuCaVnj/2SzRvoaD988c96xsS6QAXLl01zQsytdAbMpFN5DqSSBciH3v69Cm7d+9m165dKc4cdHNzIzg42KierKIo/Pnnn+rPLSvH0q/Gacpawg87SDTuvwc3f2FwDVjyX25j56blOTORLoQQIkPu3bvHb7/9lmLTq9jYWPR6vVrz3NXVlUGDBtGxY0e++OILQkOfz+Dbv38//fv3R6PRUL16daP9/Pnnn/j5+Rk1AO3VqxcbNmwgIiJCXZa4DIBGo6Fly5b069cPvV6Pra1trpjVKIQQ+cH333/Pw4cPsbOzY8eOHXh6egJgbW3Nxx9/TGhoKOPGjWPhwoV8+umnVKxYMZsjTllGm4y62ErpyPyoSpUq/Pbbbxw9epSdO3dy48YNdeZ5AldXV6O7P3766SeCg4OJjY01Glfgvxv69ArsSXQDeaNEfyYuiW4Aj4ox3j5HCL0B25L5u+50XZLpIkeTGulC5CM6nY7Hjx/z4MEDDh48yKBBg1i9enWKSXRLS0vq16+vJtFr165N165dk4wr+9+d9s2rQBWP58v/9n3++H+lnz8OCkr5dn8hhBA5X6w+nq2nDzBx4sQUk+hgmG21ZMkSo5nnYGgs2qdPH6Nlu3btSvVuqFOnTvHo0SPAUGZg+fLlRkl0R0dHihUrRvny5enRoweLFy9m+PDhFChQgIIFC0oSXQghcpAVK1YA0LNnTzWJntjQoUNxdHREp9OxevXqrA4vQzLSZLSley2stDKfMb+ytramRYsWzJw5kz///JMNGzbQtm1bdf358+fZseP5zLSAgAA1ie7s7KwuvxNk+O+aoxCYqKpQmUQVgy4k+kpVuFAOK6sKxjPR07NciBxCEulC5AMBAQEsXryY3r17M2DAAIYMGcKsWbPUxIZGo6FSpUpJtouPj+evv/5Sf+7QoQN9+/alV69eRuPCE+VHvGo8f3z85vPHT5/nOrCztX6p1yOEECJ76HQ6Dh48SI8JH7Jz+Ub1OOLh4UHfvn3Vca6urjRu3Fj9OeGCbGBgoNrwqmnTptjbP78HOSYmhrFjx3Lw4EGj/hwffPBBkiS4Xq9XH9vY2NC7d29Wr17Nb7/9xg8//MB7771HkSJSf1YIIXKia9euqXcYtW/fPtkxjo6ONGnSBDD0ysipMtpkdEiVjuYLRuQqGo0Ga2trPvroIz766CPc3NxSHFuuXDmGDh2q/rzjHMzYCn+cTNhX0m1+3f/8cduWTUwUtRBCEulC5FF6vV5NhH/44Yds3rw52TpsYKhhm7iBW69evdQO4Ym5u7sD8Oabb+Lg4KAuP5hoMqJ7oeePwxIl2BMO8gBVKySati6EECLHe/bsGdOnT6ds2bI0b94cn583oMQZbke2tLKiWbNm1Kjx/EpqtWrVGD16NCNHjkyyry1btgCGBHipUqWM1oWGhjJr1iwePDDcIm9vb8/q1auNGoc6ODjg4eFB1apVGTx4MMuXL6d79+5GDUeFEEKYTmBgIK+++iqOjo7Y2dlRtmxZ3nvvPXx8fDK1v8QXS18s5ZVYwrrLly9n6nmyQkaajFZ0KoGbvYsZoxG5kUajoV27dixYsIBJkybx+uuv06JFC6PEuq+vL1OmTMHa2jAhLTgcjl5/vo+e9Y33uf5uG/76x/DYvZgLXfpNMPfLSJ/QGxByxvDvWQp3ND678nxM6I2sjU+IdJB7ioTIQ4KCgti9ezf79+83asiWwMrKCnd3d6MaswDBwcEcPXpU/blTp07Y29uj0+k4cuSIujwkJISSJUtiZWVFixYt2LbNcBuj72M4cwdql4EniWae2/838Twsyrh2+sDe3V76tQohhMgavr6+tGvXjps3bya7Pj4ujrVr1+Lt7a0ui4qKAqBZs2acOXOGAwcOqOt27NhB+fLladWqFZGRkYDhJPKVV17h/PnzRvuOjIw06tHx5ptv8v7776PVylwQIYTIKpGRkZw5cwZnZ2ciIiK4ffs2t2/fZvXq1fTt25eFCxdiaZn+1IK/v7/6OHFz6RclrAsNDSU8PBxHx+TLU8TExBhdcE3ovxEXF2d0DDGHCaeWYa1YpGvs1//rbfZ40hIfH49er0ev1yepz20KCfs0x77zg5o1a1KzZk0AtazRrl27ktyJl8BCq+H12gota1jjB9wMsmWZtx2LfI6oTdVHj/kKINt/9wjzhV21X1iYTOP34wONf253BgqUM1tYLyXMl7j/LqbFBZ4FW6ecG2sek52/z5JIFyIPUBSF33//nXXr1hkdZF9Ut25d7t+/r/48YsQINm/ejK+vr7qdnZ2dOtu8d+/eRol0Hx8f9cD+4i3zM7db81nP6uy/dEZdVrMU3AmE95a4cTvwIQDNGrxC9ZaDXvIVCyGEyAqPHz+mdevW3Llzx7BAo8GxfFHi7j0FwMLBmrhncaAYxmo0GhRF4dy5c0RERODg4EDHjh2NEumKovDjjz+ybds29cJukSJFcHFxQavVJjmOOTg40LJlS9q3b0+JEiWy4mULIYTAULZr4sSJvPnmm1SqVAkbGxt0Oh0nT55k4sSJ7Nu3j6VLl+Lg4MDcuXPTvd+wsOc1kBOX+HpR4nVhYWEpJtK/+eYbJk+enGT5gQMHUt2/KXSkHJC+xNk/B46ZNZac5NatW9kdQp7QoEEDGjRokOa4hHLod8suoEVZaPFCLjpx3fVs5bA249scvgZcM3koprb3VAAQQG6INS9ImIyTHSSRLkQuFB8fz/Hjxzl69ChPnz7l0aNHRg1DtVotDg4ORl9SAaNZ56VLl6Z58+bUrVuXkSNHqrfRR0VFERYWRoECBXB3d6dixYpcv264b8zHx4fOnTtTtmxZHj58aLTvyOhYpi0zJNETvuOuOgLdjoNeMYx1dnZmwZLfTftmCCGEMJvx48erSfQKlStRYFh9wm485N58HwAK1y+PracL99efIiYoHEVRAMPswC1btvDOO+9QoUIFNcHu5OSkzhT09X3ekTowMNAo2V66tKFD9aJFiyhQoIDMQBdCiGzQpk0b2rRpY7TMwsKChg0bsnv3bt588002b97M/PnzGTZsGBUqVMiWOMeOHcuIESPUn0NDQylZsiQtWrSgcOHCZnve6efWcujhhXSNber2CuP+19NssaRXTEwMly5dwtbWFisrK5PvX6fTcevWLcqWLYuFRfpm6ouMO3z4MMuWLVObrjs4ODBmzBg+/PBDnj17hq2tNcOHf8aYMWNyzneoJ+dhX9OMb9f6EDjXNH08L+u/1xOHHXsdluAV0Q8ronJuvHlM4vxXVpNEuhC5zK5du1izZg1Pnz5Ndr2TkxMDBgxQZ4XY29vTvXt31q1bZ3TLo4uLi7r+7bff5ocfflDX7du3j65duwKGWYIJiXSdTsdXX33FwIEDOXToEACWlpZUr16dc+fOJYnF+zLoDTkVihUrxvbt26lcufJLvX4hhBBZ48mTJ6xatQowNH0rOqYFmsJ2RNx6rI7Rx+uwKeJEqV4NufXLAXSxz2+zXLduHQ4ODrRp00ZNsLu7u9O7d2+WL1+ebN8OR0dH2rRpQ48ePbh37x6Ojo455wRQCCGESqvV8v3337N582b0ej1bt241SmanpkCBAurjyMhInJyckh2XeMZh4m1eZGNjk6QpNRjKWpojWQyGJqP7Hp2DdLbnGFyto9liyQidTodWq0Wr1Zo10W1hYSGJdDNq3rw5DRs25MiRI3jv2kxohKGkXqUyRens9TofDJ9u1otImWKpBaIyt10O+NtJ4oXXY0WUIZGeVryhNyA+0YRHywLglD0XIXOz7Pw8lUS6EDlcREQEV65cITw8nGPHjnHixIlUx4eGhvLjjz+qdekaNWpEt27dqFKlCmPHjlWTGQ8ePEBRFDQaDQ0bNuSnn34iPj4egI0bN9KoUSNcXV3VmeoJwsLCjJLuLi4uqd665+EMg/u/z5DR3+Pq6pqp90AIIUTWW7dunVrrvEL7OmgKG+pY2ibqKh1x4zHODcph5WSHU40SPDl9W12nKAqLFi3i99+f34kUHh7OihUrjJLoNWrUoGzZsnh6etKoUSO1dIAQQojUPXv2jMDAQIKDg7Gzs8PV1ZUiRYpkWYKhfPnyFClShKCgoAyV8vDw8FAfP3jwIMVEesJ5iJOTU4plXbKLNBkV2c3a2pqWLVvSsmVLoqOjuXPnDtv2Hs1xfysqy5QvhpllO3NInARPrVlqgheT5KE3YFvFpNt0ui7J9FxEEulC5FD37t1j8+bNHDx4kNjY2CTrK1WqRMuWLfnll18AcHV1xdHRkdu3bxslIBJmnletWpVmzZrh4+MDGGrZ/vvvv9SsWRNra2sKFiyo3h4TGhrKF198Qbt27bh79y4Anp6e2NracuWK8QHj8ePHRj/Xql6Otk1fAeCvXz+jUf26WJZqZ4J3RAghRFa6du15jce4Ws9nNTlWKIZdCcOxJcr/CRH3gnAoVQSnKu5qIr18+fJqc9LEZcYSX5zVaDQMHjyYDh06mPV1CCFEXnHkyBF8fHw4fPgwx48fV8s6vKhixYo0adKEJk2a0LZtW4oWLZrFkaauevXq6uOLFy9SpUqVZMddvHgRMJzH5DTj/lma7rHTX+trxkiEyCWcKhgSxokT0cffSzquwSoo+N9nQk6arZ1SEvxFL76mxEny+LCk41NbLnIkSaQLkQMdOnSI2bNnpzoj79q1a+rscoCuXbvSrl07Zs+ezeHDh9XliWuZt2nTRk2kAyxZsoRvv/0WRVHUUjGWlpbEx8cTHBzM6tWr1bHh4eHcvv18pmFiFhYW1K9fn/bt21OjRg1iYmK4c+cOr3YYgWVOvSIuhBAiVYnLgVnaWauPNRoNbm2rw8kQAO6vP0XJt+uhtX7+tbJSpUq8/fbb/PLLL4SEhBjt19LSkkaNGvHGG29Qvnx5M78KIYTI3QICAvjll19YtmyZ0cXIxOcBL7p27RrXr19n8eLFWFhY4OXlxUcffUTHjh1NGpuvry9BQUGAYdJNelWqVIlSpUpx7949du3aRffu3ZOMiYiIUM9pXqzTnt0CIkO4Efog7YH/cbHNQTNqhchO6UmKF6wCLrXNH0tGZTbZLUnyPEcS6ULkAI8ePWL37t3cuHGDwMBA/P391XX29vZUqlSJs2fPAoYagAnJjYTa5QBubm5YWloydOhQzp49q942f+zYMQYPHoyjoyNubm5Gz3v79m1Gjx5NlSpV1KR948aN8fX1xc/Pz2hsYGCg+tjKyor+/ftTs2ZNtFotzs7O2NramvAdEUIIkd0Sl+MKu/EIx/LF1J+LtqiC7t5Fom9Ho4uM5c7Sw1gXfn7h9Pr16xw4cECtb2thYUG3bt2oWLEilSpVomDBgln3QoQQIhd68OABU6dOZenSpcTFxamJcwsLC6pVq8arr75K0aJFcXFxwdnZmaioKEJCQnjy5AnXr1/nn3/+ISgoiPj4eHbu3MmuXbuoXLkyEydOpEePHmk+f0IJyNTWjxo1CjDUS+/UqVOGXt/777/P1KlTWbduHV999RVlypQxWj9v3jzCw8OxsLCgV69eGdq3uS24si3dY1u618JKK2kXIfKtiHvPH2emHIzIcUz2if7hhx/Sv39/6tSpY6pdCpHnPX36lPnz53Py5MlkZ5VoNBrq1auHXq9Xl40aNYrw8HDmzp1rNGM9Ycafra0trVq1YvPmzQDExcWxePFihg0bZjQrMGHm+d27d9XyLQBnzpwhNDT5mn/u7u60adMGLy+vFGsZCiGEyBvadezAlClTAHi481/c2tVQkyoaSwvcO9QkZtMp4u4aSnzFBj+ve37jxg31sbW1NV988YV8RxRCiHSaPHky3333HVFRUSiKQtGiRXn77bfp1q0bderUwc7OLl37uX37Nt7e3qxZs4ZDhw5x5coV3nnnHWbPns1vv/1mVGLlRXfv3qVHjx70798fLy8vPD090Wg06PV6/v77byZNmsTu3bsBGDx4MJUqVTLaftKkSUyePFmN48VE+eeff86iRYt4+PAhHTt2ZMWKFbz66qvExsayePFivvrqKwAGDRpExYrpKKeQReL08ewPOJfu8UOqmPYuACHyjJRqn+ekmuimcLhr2mNSKwdjLtL0NNNMlkhfuHAhv/32G1WqVGHAgAG89957FClSxFS7FyJPURSFoKAgxo0bx6NHj1Idd+DAAezt7dVllStXxsnJiZiYGLU+OsD+/fvV2x5f/CLr7e1NREQElpbP/+S7dOnC/v37k9xynziJ7ubmxpgxY/Dw8MDS0tJoeyGEEHnbr1GHcaxQjPAbj4i4Hci9tSco9U59NZluYWNFiZ51CT5zm6DD19FFxxltb2VlRbNmzejatSslS5bMjpcghBC5UkIC2svLi5EjR9K6dWu0Wm2G9+Pp6cmAAQMYMGAA/v7+LFmyhDlz5nDy5En+/PPPVBPpAKdOneLUqVOA4a7YAgUKEBYWZlT6q2/fvvz0008Zjq1gwYJs27aNtm3bcvnyZV577TUKFChAdHQ0cXGG40mbNm2YPXt2hvdtTs9iI9CiQU/KpXUSSJNRIVLxYs10yNnJ3KxM8Ju7HExqTU9ffP6c/P8km5gsK2ZlZUVcXByXL19m5MiRjBkzhk6dOtG3b1/at2+fqQO/EHlJREQE+/fvZ+/evfj5+RnNJnd0dKRr166cOnWKq1evAtCsWTNOnDhBTEyMems8QHR0NE5OTnh5ebF69Wo18X358mX+/vtv6tatS3R0tDpeo9GgKAonTpwwWrZr1y61/EvCMjDMaK9cuTLt27enTp06WFhYmOcNEUIIkWNdDLnDrfAASr1Tn8tfG+5w8lt7gvDrD/F4vTaFq5dE0StE3Q8m4lagURI9oV9GzZo1KVAgj80qEkKILNC+fXsmTJhAvXr1TLZPDw8Pxo8fz2effca8efPS/HwuVqwYc+fO5fjx45w7d47AwECePHmCra0tnp6eNGzYkH79+tGoUaNMx/Tqq69y6dIlZsyYwbZt2/Dz88PBwYHq1avTp08f+vXrl+PyCPMub0lHCt1AmowKkYbclKDNTLPUiHvpm5Ge1VJK1D+7lHy8WTFDPhcxWSI9ICCAVatWsWzZMs6dO0dsbCybNm1i06ZNuLu706dPH/r27StNpUS+tH//fn755Rej2RuJhYeHc/XqVbUxaMGCBRkxYgT37t1j7NixRgnvEydO8Prrr2NpaUn9+vXZs2ePum7GjBn069ePf/75R1321ltvsWXLFqPnVhTFaJ+VK1dm7NixFCpUKNVaiEIIIfKHT479DIBLnbJ49m/K7cWHAHhy+g5PTt/BxsaG6tWrG80cBBgwYACvv/56tsQshBB5xfbt2822bwcHB0aPHp3mODs7Oz755BM++eSTTD3PpEmTmDRpUprjihUrxg8//MAPP/yQqefJSnH6ePb5n033eGkyKkQek5ubpaZHfEQKy6VhamImu7zr4uLCsGHDOHPmDGfPnuWTTz7BxcUFRVHw9/fn22+/pVKlSjRt2pQVK1YYzbAVIq9SFIUdO3YwZ84co0S2tbV1krGnTp1SZ5fb2dmh0WgoXbo0ffr0MRq3detWdcb5i/UR4+Li+PXXXzl9+rT6PPv37082gW9vb0+jRo2YNm0aM2bMwNnZWZLoQggh2HDrEDqe9+Yo3uVVKo5sj7WLQ4rbuLi48Pnnn0sSXQghRJ5lpbWkcbHUy+EkaFysujQZFSK/y2w5GHOUkQm9ASFnDP9Sanoaftv0z5sHmeWTvWbNmvz000/MmjWLLVu2sGTJEvbs2YNOp+Po0aMcPXqUoUOH8vbbb9OvXz/q169vjjCEyBaPHz9m165dHDx4kKCgIKMmovXq1aNbt27qLBA3Nze6du3KypUrCQ8PV5uKBgYGEhoaipOTE82bN2fJkiVERUUB8OjRI6ZNm8bnn3/OzZs31X03aNCA48ePG8USGxtLcHCw+nP16tUZO3Ys9vb2UrJFCCFEEvF6HT9e/ivJ8qLNK1OkcQVCTt4i8OBVlGcx2BQtgKVNQZo0aULfdj3kuCKEECLPS3xulxoNMkFJiDwtPc1SM1MOxhw1yVOqif6iC18lvzxx4l1qppsnkZ7AysqKbt260a1bNwICAli+fDnLly/n2rVrhIWFsXjxYhYvXkylSpXo378/vXv3pmjRouYMSQiz0ev1LF++nL/++ivFL1gnT57EyclJ/blSpUq0b9+eatWqMWrUKDVZrtPp2Lt3L926dcPGxobSpUurtdMBzp8/T79+/dTb6Z2cnNRtX6TRaHj11Vfp0KEDtWvXznF1BoUQQuQck86sSHGd1tKCIo0qUKRRBSziwS3ECiy1DKj3ThZGKIQQ+VeVKlXo168f77//PsWKFcvucPKdOH08Rx9fStfYw48uEKePl1npQuRVCUny6Gdw/AG0PgS2BZMmmXNCOZiXLc3yYvI/n9dMz7KMmru7O2PGjOHKlSvs2LEDNzc3wHBF99q1a4wePZqSJUvSs2dPzpw5k1VhCWESiqLw008/sWnTJjWJrtVqky2VsnfvXvVxQomWUqVK0blzZ6Nxv//+uzrjPHHzUEdHRwCjmrShoaGcO3dO/dnT05NZs2bx66+/sm7dOiZMmMBrr70mSXQhhBApehgZwsGHFzK0zYdVO5kpGiGEEC+6du0aY8aMoWTJknTp0oUtW7ag0+myO6x841lsRLpmmmuA1h61JYkuRF7nVAGcaxoeO9fMP8nl7K6ZHnoDnmbsnMWUsjSrdujQIfr27Uv37t159OiRmnB0cnJCURTi4uJYv349devWZdiwYWqZCyFyotjYWLy9vZk+fTqDBw9m//79gCGB3qNHD4YOHar+jrdp04YePXokSWSfP39e7RfQpk0bo3XR0dGMGTOGn3/+mTt37gCGC1Jdu3bF3t4+2ZiKFSvGBx98wIwZM6hQoQLu7u5J6qgLIYQQyfn0+C8ZGl/BqTiFbZzSHiiEEMIkatWqhaIoxMfHs3XrVrp27Urx4sUZPXo0V66kUPNWmMzcS5tRSLu0i0aj5eOq0jNECPGC9JSDEalLKFNzoH22hWD2S6R+fn4sX76cZcuWcfu2oXC9oihotVratm3LgAEDeP3117l79y5Llizht99+IygoiHnz5lG+fHmGDRtm7hCFyBBFUfjzzz/ZuHEj4eHhSdbr9Xr+/fdfrKys1GWenp507NgRT09PZsyYoS6Pjo5m+/btdO/eHVdXVywsLNDpdNjY2BATE0NsbCx79uxRxwcEBLBy5Ur1Zzs7O15//XWKFi2Km5sb1apVk1nnQgghMswvPJAHUcFpD/yPBVo6lZIeN0IIkZVOnz7NhQsXWLJkCWvWrCEwMJDHjx8za9YsZs2aRb169ejfvz9vv/22eherMI04fTz7A86la6xe0VPQOvmJT0KIfOzFmumQNTXH81KiPrtnw2OmRHpMTAx//vknS5cuZf/+/SiKos7MLVWqFP369aNfv36UKFFC3aZcuXJMmzaNMWPG0LVrV/bv38/ChQuzLJF+5swZtm7dyunTp7l+/bpRs8fKlSvToUMHhgwZgouLS5bEI3ImRVGYO3cu+/btS3Xc1atX8fX1VX9+8uQJAI0aNaJmzZqcP39eXbdq1SqcnJyoX7++emump6cnpUqVYu/evSnWW69atSpDhw6lePHiL/uyhBBC5HMTTi/P0PhZ9QYRd/+ZmaIRQgiRkho1ajB79my+++47tm7dytKlS9m1axfx8fGcPHmSkydP8umnn9K9e3f69u1LkyZNsjvkPOFZbARaNOjTMSO9pXstKesihEhedpR/SW/T0/TKS4n5TDDpp/vff//N0qVLWbduHaGhoYAh8WhlZcXrr7/OgAEDaNOmTbJ1oxMUKFCASZMmsX//fqNEpLktWbKEefPmqT/b2tpiZ2dHSEgIx44d49ixY8yZM4ctW7bQoEGDLItL5AyxsbHcu3ePHTt2GCXRmzVrhkajwcfHBzCUZ7l48SL+/v5GNcwPHjzIu+++i1arxcvLyyiRrigK8+bNM5ppHh8fz6lTp4yS6IULF8bV1ZUyZcrQrl07ypYta8ZXLIQQIr8IiAzhZph/useXc3KnsnMpLtzPvtqEQgiR31laWtK1a1e6du3Ko0ePWL58OcuXL+fKlStERESoP5crV47+/fvz/vvv4+7unt1h51rzLm9JX1kXNAyt9kYWRCSEEBmQngT+K1Pg36+SLm+wytAQFbJmBv2LQm8YXwTIZiZLpFerVo2rV68CqMm/SpUq0b9/f/r06YOrq2u695VwgI+NjTVVeGmqW7cuZcqUoXHjxlSuXJlChQoBEB4ezsaNGxk1ahSBgYF06dKF69evU7BgwSyLTWSfR48esX37dvbt25ekjEvNmjXp1q0bO3bsUJe1a9eOgQMHMnXqVKNk+aNHj9i9ezft27fHw8NDXV66dGnu3r0LoF58AtQmown69u1L165dTfrahBBCCICZ//6RofG/NvoUJU6a2wkhRE5RrFgxRo8ezejRozl58iSLFy/mjz/+IDQ0lJs3bzJu3Di++uor2rZtS//+/encuTMWFhbZHXauEaePZ5//2XSNVVCkrIsQIndy8Ex+ecEq4FI7a2NJkFATPQcxWSI9obmJnZ0d3bt3Z8CAATRu3DhT+3JycuL9999Pdea6qb3//vvJLnd0dKRPnz64u7vTtm1bHj9+zLZt2+jVq1eWxSayx9GjR/nhhx+MZpYndv78eYYPH0716tXVZU+ePKF8+fKMHDmSfv36ER8fr65bsGAB4eHhRjNB/ve//9G8eXPWrVtHTEyM0f41Gg316tWjS5cuVK1a1cSvTgghhDAkB/4Jup7u8Z9W7YqNhRXRkkgXQogcqV69etSrV4833niDgQMH8ujRI8Bwx+uOHTvYsWMH7u7ufP7553zyySdYWkoJkrRYaS2p4FScG6EP0hwrZV2EEDleik1PHbI2jvTIATXRX2SyT/hatWoxYMAAevXqhZOT00vty9XVlWXLlpkmMBOpX/95Q6379+9nYyQiK5w8eZKZM2eqd1dYWVnh6Oio1jpPaAaqKAoXLjy/td3Hx4c6depQqFAhGjVqxMGDB9V1iqKwcuVKo9kfR48eJSgoSP3Z0dGRbt26UaxYMSpXrkyRIkXM/VKFECJdzNlL5MGDB2zevJkDBw5w9uxZHjwwnKi6ublRv359Bg4cSMuWLVPc/oMPPmD58rRrfMfFxUnC4AWb7xzL0PjXy0h5OyGEyKnu3r3L8uXLWbZsmXrXq6IoWFpa4uXlxaVLl7h37x7+/v6MHDmSVatWsXfvXpydnbM58pwtKPoZd8IepjnOSmMhZV2EEDlfSk1PU5LPa6K/yGRnk6dPnzbVrnKkw4cPq4/LlSuXjZEIc1EUhUuXLnHt2jXWrFmjJtFbtGhB//79mThxIk+ePEGj0bB8+XL++OMP/vzzT6N9HD16lDfeeIOKFStSqVIlNZH+6quvqn8jCQ1FAaMkevHixZk4cSJubm7mfqlCCJFh5uol4ufnR+nSpY16Qtjb26MoCnfu3OHOnTusW7eOfv36sXDhwlRvRbe1tU219FpW3umWG8To4vjx8l/pHl/HpaLMshNCiBwmOjqajRs3snTpUnx8fFAURT2mVqhQgf79+/PBBx9QtGhRAPbu3ct3333Hvn37OHv2LJMnT2bOnDnZ+ApyvrmXNhOnpH0nlmcBd4rYvtykQiGEyBIp1TlPLsGeHQ1SczCTnQ19/fXXAHz00UfpnkX75MkT5s6dC8CECRNMFYrJxMTEEBAQwLZt29T4ypcvT+fOnbM5MmFKOp2OXbt2sW3bNnUWZGJPnz7lzp07avImYVbHBx98QFxcHFu3blXH6vV6Jk6cyJAhQ4iMjFSXN2/enKpVq7J27Vqjci8AFStWpEOHDjRu3Bhra2szvUohhHg55uolotPpUBSFVq1a8f7779O6dWs8PDzQ6/VcvXqVcePGsXnzZpYsWYKHhwdTpkxJcV9vv/12jrujLScbdHhOhsaP/l8P8wQihBAiw06cOMHSpUv5/fffCQszJD0URcHW1pZu3boxcOBAmjZtmmQ7Ly8vvLy8GD58OHPnzmXLli2SSE9FnD6e/QHn0jX2euh94vTxctFZCJF75bSkeQ6cDW+yT/hJkyah0Wh466230p1IDwkJUbfLSYl0W1vbJPWqARo1asSaNWuwsbHJhqiEOcTExPDtt9+mekfF2bNnOXv2LOXLl1eXnTx5kiZNmtCjRw927NiBTqfDwsICnU5HREQE33//vdGsyd9++82omWiFChX4+OOPcXFxUZNRQgiRk5mrl4izszOnT5+mdm3jBjZarZaqVauyadMmOnTowK5du5gzZw5ffvkltra2L/168ruLIXe4FR6Q7vHlHT1ws8942R4hhBCmExAQwMqVK1m6dCnXrxv6WyTMPq9ZsyYDBgzgvffeS9fF7A8++IC5c+fi5+dn1phzu2exEWjRoEdJdZwGaOVRW5LoQghhSi+WoXl2BY6/l60hyad8Mtzc3IiOjiY8PJyIiAjAUN5j5syZlCpVKs3tY2JijBLxCQnUuLi4FBtX5lfx8fHo9Xr0er1RyRNTSdhncvtWFIUff/yRixcvqhdHqlSpwtWrV1EUhQIFCmBra0tgYCBgKD+QMG7r1q3Uq1cPR0dHXn31Vc6fPw8YmodevHhRfY6EWrwxMTHqtv/73//49NNP1USQOV53dtPr9YDh/6/8zmethPdb3vesJe935nuJFCxYMEkSPTGNRkO/fv3YtWsX4eHhXLlyhVq1ar1UrPmdTtHz8bG5Gdpm6msfmCcYIYQQ6VaqVCn0er2aPHdycqJnz54MGDCA1157LUP7SuhrlvC9XSRv3uUtKGkk0QE0Gi0fV309CyISQoh8JofNks/WRHpC4sHKyirD2y5btoy+fftm+rl37txJu3btkl13584d9fHjx49ZuXIl06ZNo27duowfP14tY5OSb775hsmTJydZfuDAAezt7TMds8i8W7duJbv89ddf5/XXM/eFJ2Gfb731Fm+99Za6/O23305z2/wy8yNxs1WRtfbu3ZvdIeQriUs55Vfm7CWSeAZ6Xrz4mNW+OrU0zZl1iZWwL0xxR2l+LYQQ2S3hGNigQQMGDhxIjx49Mn1+WaxYMZYuXWrK8PKcOH08+/zPpmusXtFT0FrO9YUQIq/L1kT6uXPnAHB1dc3OMFJVtGhRRo4cSZMmTWjQoAFTpkyhbt26dOrUKcVtxo4dy4gRI9SfQ0NDKVmyJC1atKBw4cJZEXauERMTw6VLl7C1tc3UBZW06HQ6bt26RdmyZVEUBW9vb/bs2ZOkFrqdnR1t2rShffv2DB8+nJiYGAoUKMCCBQuwsLDgp59+4tixYwBqCZeE7SwsLAgPDwcMJVtu3Lih7rdcuXIMHjyYIkWK5KuLKDExMdy7d49mzZrh4OCQ3eHkK3FxcezduxcvLy+z/E2J5AUHB2d3CNkiq3qJ+Pj4AGBtbU3FihVTHOft7U3FihW5d+8e1tbWlC5dmlatWvHxxx9ToULOmsmQXfwjgjn8+FKGtplT/yMzRSOEECIjPvvsMwYMGECVKlVeel8JpdlEyqy0llRwKs6N0KR9tF7U0r2WlHURQghzywE10zP9Sb9ixYpkl2/evJl//vkn1W1jYmLw9fVlyZIlaDQa6tSpk+Hnf+edd1JNZqclvU3QEtStW5fGjRtz6NAhFi5cmOpz29jYJFtH3crKShJbL9DpdGi1WrRarVFNcVOLiopi6tSpXLt2Ldn1MTEx/PHHH+zZs4fSpUtz4cIFYmJiOHXqFI0aNeL111/nwIEDABQvXpygoKAkJXwAo7Iu1atX58svv8yXiWStVgsYStvI73z2kM+brJXf3uus7CVy+/ZtFixYABju+Em4FT059+/fx8LCAicnJ0JDQ7l48SIXL17kl19+Yc6cOQwZMsRkceVGOkVPH5/vMrRNc/dXKGbvbKaIhBBCZMSsWbOyO4R8JSj6GXfCHqY5zkpjwdBqb2RBREIIkc8l1EwPfAC0yJYQMp1I/+CDD9BoNEbLFEVh/Pjx6d6HoihotVqGDx+e4edPKVltTsWLFwfg5s2bWfq84uV9++23Rkn0MmXKqCV83NzcCAwMRKfT8fTpU6Pk0JIlS6hSpQrlypXD2dmZJ0+eEB4ezpw5c1i/fj0HDhxQaxQmKFGiBB06dKBdu3ZqjXQhhMhLXraXSHpFRUXRvXt3IiMjKVy4MN98802y42rXrk2dOnXo1KkTJUqUwMLCgsjISHbt2sXo0aPx9fXlo48+wtXV1agU14vyeo+TKWdWodfrsCb9F67HVe+Z7GvPzh4nwnykx0n2kR4n2SO3vd8tW7ZEo9GwZMkSSpcuna5t/P39ee+999BoNHh7e5s5wrxl7qXNxClpH4c8C7hTxDblC/1CCCFMyKkCxLlk29O/VJbvxQRiSsuSY21tTZ06dRg7dizNmjV7mTCyTEJN7AIFsv9WApExCRc/nJ2dGTNmDABffPEFYLjboEuXLnz//fdcvnyZqKgo7O3tiYyMJDAwkJEjR9KzZ081KZ5QLubGjRvq77ujoyO9e/emTJkyVK5cOclFJiGEyEtetpdIesTHx/Puu+9y+vRprKysWLNmjXpB+0XDhg1Lssze3p4333yTZs2a8dprr3Hnzh0+//xzunXrluJndF7vcfIqLrxK4wxts2fXbjNFkz4p9TgR5iU9TrKP9DjJWrmtx4mPjw8ajUa9iJ0eUVFR6nYi/eL08ewPOJeusddD7xOnj5fSLkIIkQ9k+pP+9u3b6mNFUShbtiwajYbdu3enWodUo9Fga2tL4cKFzVrKIyMSyouk9uXC29ubv//+G4DmzZtnUWQis548ecKePXs4efIkgwYNUpe3atWKMmXK8PTpU3XZlStXGDBgABMmTOCTTz4hKChInf0YHBxMcHAw8+bNU8dHRETw3XfPb413cHBg+vTplClTJitemhBCGImPjyc4OBg7O7tUy56YS2Z6iaRFp9Px3nvv8ddff2FpacmaNWto06ZNpvZVuHBhvvzySwYOHMjdu3c5e/YstWvXTnZsXu5x0m3v10TootM93kpjwda2U1Jcn5U9TnLK98X8QHqcZB/pcZI98muPE5G2Z7ERaNGk2ZxbA7TyqC1JdCGEyCcy/Wmf0q1kHh4e6b7NLKfw8/OjS5cuDBkyBC8vLzw9PdWkup+fH6tXr2bq1KkoioKLiwufffZZNkcsUqLX61mxYgWbN29Gp9MlKf+zYcMGtm/fTr9+/fD09OT27dvcuHGD69evU7FiRTp37qx2r69fvz537941qnsOxnddlClThlGjRlGyZEnzvzghRL7n5+fHwYMHOXz4MMeOHeP+/ftq+REw1GovUqQINWrUoEmTJmpyOyvKTGWkl0hqEpLov//+OxYWFqxatSrVcizp0aBBA/XxrVu3Ukyk59UeJ5vuHOGJPsJwtp9Ocxt+nOprzqoeJxYWFpJIz0LS4yT75fbPm9wmP7zXCbPXbW1tszmS3GXe5S0oaSTRATQaLR9XfT0LIhJCCJETmOzMOqGmYm51/vx5PvzwQ8BQdsbJyYmoqCij2+Y8PT3ZuHEjbm5u2RWmSIVer2f27Nlp3o4cFRXFvHnzqF+/vnpnxQ8//MD06dNp2LChmkgPDg5m+vTpbN26lUWLFqkJ9CJFilC5cmXatWtHjRo15DZJIYRZxcTEsHr1ahYvXsyJEyfU5cmVUouNjcXf35+AgAD27NkDGEpavf322wwZMoTq1aubNdaX7SWi0+no1auXURL97bffNmWI+U6MLo4fLv6ZoW3KOblT1aWMeQISQgiRpXbu3AkY+jiJ9InTx7PP/2y6xuoVPQWtc3/5NyGEEOkj9x9hmEX/xx9/4OPjw8mTJwkICCAoKAgLCwtKlSpFzZo1eeONN3j33Xexs7PL7nBFCjZv3qwm0bVaLW+88YbRLJMPPviA27dvq2NOnDhBkSJFCAoKwt/fn08++YRWrVqp40NCQpg1axaHDh1SE1adO3dm4MCBWfiqhBD5VWRkJHPnzmXWrFkEBwcbJc4LFSpErVq1KFq0KC4uLjg7OxMVFUVISAhPnjzh+vXrXLt2DUVRCAkJYcGCBSxYsIA2bdowefJk6tata5aYX6aXSHJJ9J49e5okrsQXIDw9PU2yz9xi0OE5Gd7m10afmjwOIYQQGdevX79kl48fP55ChQqlum1MTAy+vr6cOnUKjUaTa/qS5QRWWksqOBXnRuiDNMe2dK8lZV2EECIfkU98DDPQu3fvTvfu3bM7FJFJz549Y+PGjerPY8eOpV69euzfv19dFhYWxsiRIylRogSrV68GDLVztVotjx8/Jjw8nM2bN6vjr1+/zvXr19WfGzZsmOKXWSGEMKXly5czbtw4Hj58iKIoWFlZ0a5dO7p160b9+vWpWLFimvsICwvjn3/+wdvbm7Vr13L79m12797Nnj17eOutt5g1a1a6Z6eZu5eITqfj3Xff5Y8//sDS0jJDM9EVRUk1rpCQEKZPnw4YZuPVqlUrQ7HlZhdD7nArPCBD24ys1g0bi7xf6kAIIXKDZcuWJTnGKYpidM6SmoSL8C4uLowdO9bk8eVVQdHPuBf+KM1xVhoLhlZ7IwsiEkIIkVNkOJGekEjUaDQsXrw4yfLMeHFfQqRHbGwsR48eZceOHVy7dk1dbmVlxbVr1/D09OSVV14hKCgIgH379vHOO+/QvXt3Dhw4gL+/P9euXeObb75hx44dHD16NNkSRY6Ojrzxxht0795drR0qhBDm1LdvXwAqV67Mp59+Svfu3XF2ds7QPgoUKECLFi1o0aIFU6dO5cSJEyxatIiVK1eyYcMGqlWrxoQJE9K1r5ftJTJp0iQmT54MGJqVJ27OrNPp6N27t5pEX7NmTYYubK9atYpNmzbRq1cvmjRpQtGiRQFDGa/du3czevRodab8999/n68+xz859nOGxjtZ2dPFs5GZohFCCJFRpUqVMkqk3717F41Gg7u7e6r13TUaDba2tri7u9OwYUOGDBmCh4dHVoScJ8y7vIU4vS7NcZ4F3Clim/WN3oUQQmSfDCfSE18VT5z8Tu5qeXokzCSTRLrIiPv37/P111/z8OHDJOvi4uLYsGEDmzZtYuDAgZQtWxYwzFpfsGABn3zyCW3atGHZsmWAIakzatQoKlasqP4eWllZ0aBBA2rXrk3jxo2xtrbOstcmhBDVq1fnyy+/pEePHibrw1C/fn3q16/PxIkT+eabbzLcdMxcvUSOHj3K2rVrAcOJ/9ChQxk6dGiK43/88Uej2eo6nY5NmzaxadMmABwcHLC1teXp06fodIaTYBsbG3744Yd8VW99w61D6MhY/5rfGkszdSGEyEnu3Llj9HPCxeA9e/ZQtWrVbIgo78tIffTrofeJ08dLaRchhMhHMvyJ/+JV8bSWC2FqAQEBjBkzhtDQUHWZnZ0dUVFRgOELpl6vR6fTsXTpUqZMmYKlpSUxMTHs27eP+/fv88orr6jb3r59m1mzZhk1Ke3Xrx8dO3bMuhclhBCJ/Pvvv2bbd8mSJZk/f36GtjFnL5HEdwLFxcXx6FHqt1InfNYnaNGiBdOmTeP48eNcuXKF4OBgnj17hpOTE+XLl6dly5YMHjw4X9VGj9HF8ePlvzK0TQu3mng4FDZPQEIIIUyiadOmaDQaHBwcsjuUPCu99dE1aGjlIfXRhRAiv8nwp/6LV8XTWi6EKSmKwvfff68m0cuUKcOHH37Izp071UT4pEmT+Pvvv9m2bZu6XY8ePVi+fDl6vZ6rV69y9epVdd3evXuNnqNTp0506NAhC16NEEJknfDwcBwdHTO17cv2Epk0aRKTJk1Kdl3z5s2NGqlmVOnSpRk3blymt8+LMtpg1N7Chomv9jZPMEIIIUzGx8cnu0PI89JbH91aa8nHVV/PgoiEEELkJPmnUKjIE65evcqNGzcAcHd355tvvqFq1apGMw3PnTvHoEGD6Natm7rs4cOHTJo0KdVyAw4ODvTr14+BAwfK3RVCiGx38eJFk+3r6dOntG7d2mT7EzlXZhqMLm36ORYa+UoohBBCpLc+emnHYlIfXQgh8iE5axI5nl6v58yZM0ybNo3x48eryx0dHfH19UVRFFq1aoWFhQUAu3btIigoiLfffht7e3sAjhw5QoUKFViwYIFRY1xLS0tq167N0KFDWbZsGV26dJEkuhAiR2jdurVRI+XMCg4OpkWLFpw6dcoEUYmcLqMNRpu7vSIlXYQQQgie10fXk/adcgn10YUQQuQvWV7QKyYmhqdPn+Lq6qo2SxEiJffu3ePbb7/l/v37SdbduHGD8ePH4+npyZgxY2jatCkHDhwgMjKSL7/8khEjRlC3bl0AYmNjuXPnDqGhofz+++/qPt5//326dOmSVS9HCCHS7fHjx7Rq1YqDBw9Srly5TO3j4cOHtG7dmsuXL8tFwnxgw60jGWowaoGWSa++b8aIhBBCZEbZsmUBQxNuX1/fJMsz48V9iaSkProQQoi0mOyTPzw8nEOHDgGGJigv1mENCgpi8ODBbNu2jfj4eBwdHRk4cCDTp0/H2traVGGIPOTOnTuMHTuWiIgIdZlGo0FRFPW/YGgWOmrUKL766iuuXLnCw4cPCQgIYNSoURQrVoxWrVoBMHPmTJ48eaLu65VXXqFTp05Z+6KEECKdNBoNAQEBtGzZkoMHD1KmTJkMbX///n1atmypnjRLaZe8LV6v46fLf2Zom/kNP5GSLkIIkQMl9B978SL4y/QlkwvqaZP66EIIIdJiskT6xo0b6du3L6VKleLWrVtG6/R6Pe3bt+fMmTNq8jMsLIzZs2dz7949/vjjD1OFIfKI2NhYpkyZoibRS5cuTa9evVixYgX3799Hq9UydOhQ/vjjD/z9/QkNDWXOnDlMmTKFqVOncvfuXcBQFzhB4iT6q6++yujRo7G0lFkEQoic6ccff2TYsGFqQvzQoUOUKFEiXdveunWLVq1aqZ+FnTp1YsOGDeYMV2Szb86tTceN6M+Vc3KnqksZc4UjhBDiJfTp0ydDy4VpzLu8hTgl7Tu7pD66EELkXybLIu7evRuAbt26JSnZ8vvvv3P69Gk0Gg21a9emWbNmHDx4kDNnzrBx40Z27dpFu3btTBWKyAOOHDlCYGAgAOXLl2f69OnY2tpy+vRp7t+/j06nIzY2lu+//54vvvgCPz8/Hjx4wK1bt5g1axY+Pj5s27aNgADjhms1a9akQ4cO1KtXT0oLCSFytE8++YS4uDhGjhzJ3bt3adGiBQcPHsTDwyPV7a5evUrr1q3x9/cH4K233mLNmjVy4TAPexgZwh7/Mxna5tdGn5onGCGEEC9t6dKlGVouXl5CffT0SKiPLqVdhBAi/zFZJvHixYtoNBoaNGiQZN3KlSsBwyzgEydOMGvWLI4fP67Wr16xYoWpwhB5wO3bt1m3bp368/vvv4+trS0A7du3V5f//vvvxMbG0r9/f3XZjh07sLa2xsvLi4oVK6rL69Wrx9KlS5kyZQoNGjSQJLoQIlf47LPP+Oabb1AUhVu3btGyZUseP36c4vh///2X5s2bq0n03r17s27dOkmi53FjTi3O0PhPq3bFxsLKTNEIIYQQuY+V1pKW7v9Lc5wGDa09aksSXQgh8imTZRMTZg+XLl3aaHlcXBwHDx5Eo9Hw0UcfqSfzVlZWfPjhhyiKwsmTJ00Vhsil9Ho93t7ejBgxguHDh/Pw4UN13bRp05g/fz4PHjygbNmy1KhRA4Dg4GBGjx5NVFQURYoUAeDChQvcvXuX7777jj179qj76NmzJ4ULF87aFyWEECbwxRdfMGnSJBRF4caNG7Rs2VI95iZ26tQpo0T7oEGDWLZsmVw4zOMCIkPwDQtIe+B/rDQWdCvbxIwRCSGEELlTKceiaY6xsZD66EIIkZ+Z7Ow6JCQEMCTIE/vnn3+IiooCjGcTA+qM4cRJU5H/JJRo+fHHH7l582aS9TExMezatYtPP/2UU6dOMXLkSFxdXQF4/PgxM2bMUGuh6/V6hg4dypEjR4z24e7ubvbXIYQQ5jJhwgTGjRuHoihcuXIFLy8v9bgLhnJYiZcNHz6cBQsWSGOxfODHS5syNP7nBh+bKRIhhBDm1r17dzZv3kxcXFx2h5LnBEU/Y92tA2mOe7dcS6mPLoQQ+ZjJEul2dnYASW45P3jwIADlypWjWLFiyW4j8i+dTseiRYs4e/Z5PbrEdzUUKlRILesSExPDN998Q0BAADNmzKB8+fLquPj4+CT7trW1Zfjw4WaMXgghss7UqVP5/PPPURSFCxcu4OXlxbNnz/D29qZ9+/aEhoYCMHbsWGbPnp3N0YqsEKeP5+ijS+keLw1GhRAid9u4cSNvvvkmxYoVY/Dgweq5tnh58y5vIVavS3PcnbCUS+wJIYTI+0yWSC9XrhwAPj4+Rss3bdqERqOhWbNmSbZJuDW9aNG0b6ESedOmTZu4cOECYLiwMmHCBH788Ud1xvmzZ8+YOXMmjRs3BgwJ87lz5+Li4sL333/P5MmT1VIvCUqVKsXAgQNZunRpsjX7hRAit5o5cybDhw9HURTOnTtHo0aN6Ny5MxEREQB8/fXXTJs2LZujFFnlYeSTDI2f1+ATM0UihBAiKxQqVAhFUXj69CmLFi2iZcuWlCpVijFjxvDvv/9md3i5VkKjUb2iT3Ps/oCzxOmTTuISQgiRP5gske7l5YWiKMyfP5+dO3cSHh7O3LlzOXXqFACdO3dOsk3Cwd7Dw8NUYYhc5rffflMfjxgxgtdeew2tVkvbtm0BUBSFTZs2MWLECLUUkL+/P+fPn0er1VKzZk0cHBzUfQwYMICff/6Zzp07Gy0XQoi8Yvbs2QwZMkQt8xIdHQ3Ad999x/jx47M5OpGVRp78Nd1jKxUogYOV3AkohBC52aNHj9i0aRNvvfUWNjY2KIrC/fv3+e6776hVqxbVq1dnxowZ3L17N7tDzVXS22hUq9FKo1EhhMjnTJZIHz58OE5OToSFhdGpUycKFizIp59+CkCVKlWSTaRv374djUYjs4bzIZ1Ox6pVq9RGErTv5QAAzcdJREFUs3aFHHn11VfV9W3atFHr7R84cIAFCxbQrl07df2ePXsICQlh9uzZnDhxAgB7e3tatWqVha9CCCGyx7x58xgwYACKoqDVapk3bx4jR47M7rBEFvILDyQgKiTtgf+ZWucD8wUjhBAiS1hZWfHGG2/wxx9/8OjRI5YuXYqXlxdarRZFUbh8+TLjxo2jbNmyNG3alIULFxr1VBEpS0+jUWuthTQaFUKIfM5kiXR3d3e2bt2Km5sbiqKo/8qWLcuGDRuSNDzz9fXl8OHDgGE2u8gfwsLC+PbbbylXrhy9e/dWl0c9DeeD/v1Yu3YtYWFhFCpUiGHDhqnr9+zZw/z589Wfz549S//+/dW6gBqNhs8++0xmoQsh8gwLC4tU/y1evBiNRoOiKHzyySdpjre0lNlTeUlGZqOXsS+Gm72LGaMRQgiR1QoUKECfPn3YvXs39+/fZ/bs2dSpU0c9Dz969ChDhgzBw8NDTb6L5EmjUSGEEOll0rPqJk2acPv2bY4ePcrDhw9xd3encePGyZ68BwQE8NVXXwEkWz9d5D13796lffv2XLlyJdn1oU+esnbtWvbt28fkyZNp1qwZer2en376CZ1OZ9RQNDIyUn1sZWXFyJEjqVevntlfgxBCZBVFUbI7BJFDZXQ2+rd1+5sxGiGEENmtWLFiDB8+nOHDh+Pr68uqVatYu3Yt169fJzY2lq1bt7Jt2zZ69OiR3aHmSNJoVAghRHqZfHqatbU1LVq0SHNc48aN1QaSIu8LCQnBy8uLGzduAIYZ5A7lixF3z9AozcLBmrhn8aAoBAYG8tVXX/H999/TokULqlatys6dO9m1a5dRAr1gwYK0bduWdu3aUaRIkWx5XUIIYS5NmzZNcjeXEJCx2ejF7YpQ3FGOkUIIkV+UK1eOiRMnMnHiRNatW8dHH33E06dPszusHCuh0Wh67A84y3j9O1IjXQgh8jE5Aogs8fXXX6tJ9PLly1Pg0wbYeRTi38ErANBHxVG6T2Me7jxPzKNQgoODWbZsGSNHjqRYsWJ88MEHREdHs2PHDgDefPNN3nvvPSlVIITIs3x8fLI7BJED3Q17nKHZ6LPqDTJjNEIIIXKawMBAfv/9d1avXs3ff/+d3eHkeAmNRvcHnEt1nFajpaX7/ySJLoQQ+ZzZjgJxcXGcOXOGixcvqg1OXFxcqF69OrVr11YbSYq8LyIigqVLlwJga2tLuQmdiHA2zLJ0bVkVbsai6BUCD16hRI+63Ft0mLioGI4ePcqAAQMoWLAgZ86cYffu3YChlEu3bt0kiS6EECLfGXL0x3SPdbdzkdnoQgiRD0RERPDnn3+yZs0avL290el0aok4jUZD06ZN6dWrVzZHmXNJo1EhhBDpZfJMZGRkJFOmTOG3337jyZMnyY5xdnZm0KBBjB8/Hnt7e1OHIHKYjRs3EhoaCkCrru155vy8VIFb2xrE3/8XoqOJvB2E35oTWHs4EecbSHx8PBs2bCA6Opq9e/ei1+sB6Ny5MwUKFMiW1yKEEEJkl3NBvoTFR6V7/Ox6H5oxGiGEENkpPj6enTt3smbNGrZu3UpUlOH4kJBAr169Or169aJXr16UKFEiO0PN0aTRqBBCiIwwaSL93r17tG7dGl9f31SbpIWEhDBjxgw2btyIt7e3HNjzuGvXrqmPb1dUcEm0ztrZAdcONbm9/jjExREbHE5scLi6fvPmzUb7atiwIb179zZ3yEIIIUSO89nJBeke62brLLPRhRAiDzp8+DCrV69mw4YN6sS1hHPvkiVL8s4779CrVy9q1KiRnWHmGtJoVAghREaYLJEeFxdH+/btuXnzJgCVK1emb9++1KtXDzc3NxRF4dGjR/z9998sW7aMy5cvc+PGDdq3b8/Zs2elTEceFhMToz62sE1a0sfOrSClejXAb/s5ov2Tv4vB1taW119/nXfffRetVmu2WIUQIieIjIw0+x1bWfEcwnQ23DpEvJL2iX6COfWHmDEaIYQQ2aFMmTL4+fkBz5PnhQoV4q233qJXr140a9YsO8PLdaTRqBBCiIwyWUZy0aJFXLlyBY1Gw5dffsnFixcZNWoUTZs2pWLFilSqVImmTZvy+eef8++//zJ+/HgALl++zKJFi0wVhsiBXF1d1cdhNx4lO8bG1Ymy/Zvi2a8p9qULq8uLFSvGoEGDWLZsGe+9954k0YUQ+YKnpyffffcdkZGRJt/3yZMn6dChA7NmzTL5voV5xOt1/Hj5r3SPl9noQgiRN927dw9FUbC2tubNN99k48aNPHz4kIULF0oSPRMSGo2mRavR0tqjtiTRhRBCmC6Rvn79ejQaDV26dGHKlCmpJjy1Wi1ff/01Xbt2RVEU1q9fb6owRA70+uvPm7I83PUvik6f4lhbj0Lo456vHzFiBJ06dZJZk0KIfCUwMJAxY8ZQpkwZJkyYYFQiKzOio6P5/fffadOmDQ0bNlSbN4vcYeLp5RkaL7PRhRAib2revDmLFi3i0aNHbNiwga5du2JtbZ3dYeVq0mhUCCFERpgskX7x4kUA+vXrl+5t+vfvD8CFCxdMFYbIga7aBVPwlZIARPs/5c6yIynW0A86ekMt7+Lp6UnlypWzLE4hhMgpDhw4QI0aNQgKCmLatGlUrVqVOnXq8O233+Lj40NYWFia+7hy5QrLly/n/fffp1ixYrz77rvs27cPKysrRo0axWeffZYFr0S8rIeRIRx6dDHd42U2uhBC5F379++nX79+ODlJ00tTkEajQgghMspk9yY9e/YMAA8Pj3Rv4+7uDkBoaKipwhA5TIwujh8u/knJt+vx7OJ90Cs8+Os04bceU/yN2hR5pTSK3pJIv0Aen/IlPFHpl549e6LRaLIxeiGEyB7NmjXj7NmzrFu3jmnTpnH58mVOnz7NmTNnANBoNJQtW5aiRYvi7OyMs7MzUVFRhISE8OTJE3x9fQkPf964WVEUbG1t6dOnD19++aU0+c5Fhh6bl6HxMhtdCCGESB9pNCqEECKjTJZId3Fx4fHjx9y+fZtatWqla5tbt26p24q8adDhOQAUeqUk5Ye05OY8bwCe/evHs3/9sLGxoXr16kRHRxMXF6du9/7779OgQYPsCFkIIXIEjUbDO++8wzvvvMO+fftYtGgRW7duJSoqCkVRuHnzJr6+vkm2e/GOn6pVq9K7d2/69+9PkSIyUzk38QsP5GF08k24k+NmW0hmowshhBDpII1GhRBCZIbJjgS1a9dm586dzJs3jzfffDNd28ybNw+NRpPuxLvIXS4/ucet8AD1Z7d2r2BZ0J7bv/kQE5h8WYJCzs580KcPLVu2zKowhRAix2vdujWtW7cmJiaGkydPcvjwYY4dO8b9+/cJDAwkJCQEW1tbXF1dcXV1pUaNGjRp0oQmTZpQunTp7A5fZNLov3/L0PifGw41UyRCCCFyGl9fX7Zs2cL58+cJCgpSL7SnRKPR4O3tnYUR5mxWWktae9Riv/859KT8vmk1Wlq6/0+S6EIIIQATJtLfeecddu7ciY+PD/369WPu3Lk4ODgkOzYiIoKhQ4fi4+ODRqOhV69epgpD5CDDjye9Hb1Ig/IUrluWkNO3Cdx/BeVZDDZFnbC0KUiFV6sxputgLC3lS4oQQiTHxsaGpk2b0rRp0+wORZiZX3gg9yOD0j2+mVsNitk7mzEiIYQQOUFkZCQff/wxK1euTJI4VxQlSWnMhDFSMjOpwZU74p3GrHRpNCqEECIxk2Use/XqxYIFCzh27BjLly9n+/bt9OjRg3r16lGsWDE0Gg0PHz7k5MmTrF+/nsDAQAAaNWrEu+++a6owRA6x2+8U0fq4ZNdpLLQUrluOwnXLYREPbiFWxFkojK3bFwutyfrfCiGEELnWxDMrMjR+Uu33zRSJEEKInEJRFLp27cq+fftQFIUiRYpQokQJzp07h0ajoUmTJjx58oRr164RFxeHRqOhUqVKuLm5ZXfoOdLBhxdSmYtuMLBSB2k0KoQQQmWyrKVGo2Hr1q3Ur18fRVEIDAxk/vz59OnTh3bt2tG2bVv69OnD/Pnzefz4MYqi0KBBAzZv3myqEMzi22+/RaPRqP9E2uL1Or79948MbdOxRF1JogshhBBAQGQIN0IfpHv8p1W7Yqm1MGNEQgghcoL169ezd+9eACZOnMjDhw9ZseL5hdeDBw/y77//EhISwuzZs3FwcCAkJIQpU6Zw4MABk8VhinPkSZMmGe0jpX83b940WdyJBUU/Y9G1HamO0QDN3F4xy/MLIYTInUyauXR2dubIkSPMnTuXKlWqoChKsv+qVKnCzz//zOHDh3F2zrm3IV+7do3Jkydndxi5zpJru4lX0u5+nsDWwpo6RSuZMSIhhBAi91hwZVu6x1ppLOhWtokZoxFCCJFTrFmzBoAGDRowceJEtFptsolsBwcHhg8fjre3N2FhYbz55pv4+/ubJAZTnyNbWVlRrFixFP+Zq+znvMtbiNWndc6qYcHV7WZ5fiGEELmTyY9KWq2Wjz/+mI8//piAgAAuXrxISEgIAC4uLlSvXh13d3dTP63J6fV6+vfvT3R0NA0aNOD48ePZHVKuEBT9jJW++zK0zXvlW5kpGiGEECJ3idPHsz/gXLrHz6k3xHzBCCGEyFH++ecfNBoNAwcOTNf4OnXqMGTIEGbPns1PP/3Et99++1LPb45z5IYNG+Lj4/PS+8mIOH08+9KojQ6goLDP/wzj/tdTmo0KIYQATDwj/UXu7u54eXnx9ttv8/bbb+Pl5ZUrkugAc+fO5ejRo/Tq1Ys2bdpkdzi5xszzGSvp0qRYdQpaJ9+UVgghhMhvnsVGoCV9t8k7WdnzSpGyZo5ICCFEThEUZGhCXbbs889+Kysr9XFUVFSSbTp27AjAtm3pv9spJXnlHNlKa0lrj1poNamnQ7QaLa09aksSXQghhEqKUifj9u3bfPnllxQuXJjZs2dndzi5RrQuluOBV9I93gItY/7X04wRCSGEELnLvMtbUNJsfWbwW+PPzByNEEKInCShzEmBAgXUZYkfP3z4MMk2BQsWBMDPz++lnjuvnSN/XPV1LNNIpFtrLfi46utZFJEQQojcQBLpyRg4cCARERH88MMPuLq6Znc4ucbUM6szNH5+w0+wSOPLixBCCJFfJNxqnr40OrjaFTRrPEIIIXIWDw8PAAIDA9Vlbm5u2NnZAXDmzJkk2yQ064yPj3+p585r58iFrB2xt7RNdczASh0oYuuURREJIYTIDUx+j1J8fDzbt2/n8OHD3Lp1i7CwMHS61Jt4aDQavL29TR1Kpvz22294e3vTunVr3n///ewOJ9fwjwjm4KMLGdqmQqES6GJf7gudEEIIkVdYaS2p4FScG6EP0hzb0r2W3GouhBD5TM2aNfH19eXChQt4eXkBhnPpevXqcfDgQebPn0+3bt3U8fHx8ers8QoVKmT6ec15jnzp0iWqV6+Or68vFhYWFC9enKZNm/LRRx9Rq1Ytkz5XYhvvHOFpbHiK652tHXmzTGOzPb8QQojcyaRnYEeOHKF3797cu3dPXaYoKc+r0mg0KIqSbKfx7PDgwQNGjRqFnZ0dv/76a3aHk2voFD19fL7L0DbN3V7BSmuJDkmkCyGEEGBo2H07LCDNcRZoGFrtjSyISAghRE7SsmVLNm7cyK5duxgxYoS6vF+/fvj4+ODj40OzZs3o0aMHkZGRrFu3jrNnz6LRaOjRo0emntPc58hBQUGEhIRQqFAhQkNDuX79OtevX2fx4sWMGzeOqVOnmv45o5+x6NqOVMdExEfxNDZCZqQLIYQwYrJE+tWrV2nXrh1RUVEoioK1tTUVKlTAxcUFrTZ3lO8YPHgwz549Y8aMGUYNXDIqJiaGmJgY9efQ0FAA4uLiiIuLe+k4c5opZ1ah1+uwxiLd23xc+XXi4uKIj49Hr9ej1+vTvHMhMxL2aY59i5Tp9XrAMAsmL/7O52QJ77e871nL1O/3li1bAGjVqhUODtKQOb+Ye2kz8Yo+zXHlnIrLib0QQuRDXbt25ZNPPuHAgQPcunVLPWd97733WLNmDbt27eLIkSMcOXLEaLv//e9/Ron3jDDVOfKLKlSowMyZM3njjTfw9PTEysqK2NhYfHx8GDduHKdPn2batGk4OzszcuTIVPeV0fPvXy5uAR1YKymfv2r1Wn65uIUxNd/O5CvMmeT8O2+S8+/sI+ff2SM732+TJdKnT59OZGQkFhYWTJ48mWHDhuHo6Giq3SexbNky+vbtm+ntd+7cSbt27dSfV61axfbt21/qS0aCb775hsmTJydZfuDAAezt7V9q3znRq7jwKmnf9qbVatWLKqcPHjd3WCpLS0ujuyRE1rC0tOTo0aPZHUa+ZGlpyYEDB7I7jFwr4eQiIyIjI00aQ5cuXdBqtfz7779UrVpVXd6vXz80Gg1Tp07F3d3dpM8pslecPp79AefSNfZ66H3i9PFZVtolLi4uyQlxQsJCUZQM/72kh16vx9LSEr1en2PuXMwPFEXB0tKSmJgYtamhyBpxcXFYWloSHR0tCahMsrCwwMrKKrvDMCs3Nzfi4uJQFAULC+Mk8KZNm5g6dSqLFy9Wm44WKlSIXr16MW3aNGxtU68HnhxTniO/qFevXkmWWVtb06ZNG5o2bUrTpk05deoUkyZNYsCAAWrT1ORk9Pz7FZx4hYZpB/kAdjxIfeZ6Tpb4/Dsryfl39pDz7+wj598vJyecf2eEyb4h79+/H41Gw/Dhwxk3bpypdpslHj9+zKeffoqFhQW//fbbS584jB071uiLRmhoKCVLlqRFixYULlz4ZcPNMXSKnjf3fE2MEpvquAo2xWjoUIHiNoWw0FhQ1K6Quk5RFOLi4tBoNGY5UVYUhfj4eCwtLeVEPAslvO+2trbyvmcxRVGIjo6W9/4lWVtb4+LiQoECBdI1Pjg42OQxJFcabdmyZWg0GkaOHCmJ9DwmODo0XeM0QCuP2lmSRA8NDSUoKMholl8CRVGwsrJSj+Pm4Obmlqkv1iLzNBoNbm5uPHz4MNfcUZpXKIqCm5sbfn5+cvx+CTY2NhQpUgQnp7x7105Kf5s2NjZMmTKFKVOmEBISQnx8PK6urpn+fTL1OXJG2NraMn36dLy8vAgPD8fb25s333wzxfEZPf/+9vw6DgVcQJ9Ke2+tRktTtxq5ckZ6WFgYISEhxMYmPU+X8++8Sc6/s4+cf5tGTjj/Ti+THQ2DgoIAw+1mWeGdd96hU6dOmd4+8RXtL774guDgYIYMGULlypUJDzduOpL4AJSwztraGmtr62T3bWNjg42NTZLlVlZWeWaWhE7RM/zYAsKIMpzVp6CitRvdXOrhWqgwVvY2FHV0oYC1nbper9cTHR2NRqMxywmboijExsZibW0tH2pZSK/XExcXh4ODQ5LZMsK89Ho94eHhODo6ShIkExJOLp49e8ajR4+wtLRM18m4qT/bbWxsiI2NTXI8EnnXuH+WpmucRqPl46qvmzkaQxLiwYMHODo6UqRIEaysrIyOo3L8zpvk+J195Pj9chIfvx88MDRszovJ9IRZvo6Ojri4uKQ4LrV16WXqc+SMatCggfr41q1bqY7N6Pn3wKod2fvwbCppdLC10DKk+uu57vw9NDSUR48e4ejoSNGiReX4nU/I8Tv7yPH75eSU8++MMFki3dXVFX9/f+zs7NIebAIpHSwz4/bt2wD88ssv/PLLL6mOTbg6Mnz4cObMmWOS58+Nll7bzYUnt9Mc19ixIq6FCmNT2BFrC0tcnZyN1uv1erXhrLkO5GD4fZEDedZJuBXf1tZWDuRZTK/XExsbi62trRzIM8nOzo4CBQpw//59goKCsuVEvHjx4ty+fZvDhw9Tt27dLH/+5Jw5c4atW7dy+vRprl+/TmBgIKGhoTg5OVG5cmU6dOjAkCFDXurk/dGjR8ycOZNt27Zx79497OzsqFatGn369KF///5pfo77+voyc+ZM9uzZQ0BAAE5OTtSqVYtBgwbRrVu3TMdlbgGRIdwIfZCusXpFT0Fr85eJCwoKwtHRkRIlSiT7vsvxO2+S43f2keP3y8sJx29zK1OmDBqNhrlz5/LRRx+Z9bny8jnywYcXUk2iAwys1CFX9iOR43f+JMfv7CPH75eX247fJkukN27cmD/++IOLFy9Su3ZtU+1W5ECPo56y/ObeNMc5am0oaW2Yia7RaCjlUDQLohNCiJen0WgoWLAgDx48IC4uLsuveLdq1YrffvuNcePG8ffff1OxYkWjGObPn0/Rohn/TJ0wYUKmY1qyZAnz5s1Tf7a1tcXOzo6QkBCOHTvGsWPHmDNnDlu2bDGaRZZep0+fpm3btupteo6OjoSFhalN09avX8+WLVtSvIi+Y8cOunfvrtbLc3JyIjg4mD179rBnzx769u3L4sWLc+RJ3YIr29I9tqV7LbOXdYmLiyMmJoYiRYrkyPdLCCFSkt3Hb3Ozs7MjOjqaOnXqZHcoZnfixAn1saenp8n2GxT9jEXXUq97rgGaub1isufMKnL8FkLkVrnp+G2yM7ERI0awceNGfvzxR959991c1aDIx8cn1fWTJk1Sm5ckV7M2P9EpegYc+iFdY+011lhotWgstRSwssPKIvf8TgghRMLBW6fTZfmBfPz48fz5558EBwezYcMGo3WKoqQ5MywlL5NIr1u3LmXKlKFx48ZUrlyZQoUKAYbbuTdu3MioUaMIDAykS5cuXL9+PdWmYC969uwZnTp1Ijg4mMqVK7Ny5Upee+01YmNj+e233/jss8/Ys2cPn332GfPnz0+y/e3bt+nRoweRkZE0atSIJUuWULFiRcLDw/nuu+/4+uuvWbp0KZUrV2b06NGZfg/MISNNRgGGVOlovmD+k9DoMCd/gRVCiJRk5/Hb3IoXL46vr2+WNKQ15zlywozolMTExPDll18C4ODgQKtWrTK0/9TMu7yFWH1a75+GBVe3M7H2eyZ73qwgx28hRG6WW47fJrvvoE6dOvzwww+cO3eON998U62ZLvKWb86u5Ulcemv2Gr4caTQaPOzzTpNVIUT+kJ0zeUqWLMmZM2cYMGAAZcqUUZs6JsSkKEqm/r2M999/n88//5z69eurSXQwzBzv06cPq1atAgzNybZtS/8Ma4Dvv/+ehw8fYmdnx44dO3jttdcAQ63Vjz/+WD1RX7hwIdevX0+y/YQJE4iIiMDNzY1t27ZRsWJFNbbJkyczaNAgAKZNm8aTJ08y/NrNKb1NRgEqOpXAzf7l696ml8xmE0LkRnn5s6tNmzYAHDlyJJsjSdukSZPUhpZ37twxWnfo0CFat27NqlWruH//vro8Li4Ob29vmjRpwsmTJwHDMT7x946XEaePZ5//WfRK6k2sFRT2+Z8hTh9vkufNann5b0AIkXflls8uk00R/vrrrwGoV68e27Zto3Tp0nh5eVG5cmXs7dOu5fkys+RE1vCPCGa3/+kMb1fcXm4tE0KIjCpZsiQLFy40WqbVatFoNFy4cIGqVatmU2TJq1+/vvo48UlxeqxYsQKAnj17Jnv79tChQ5k+fTrh4eGsXr1aTawDREREsHHjRgCGDBmS7Mn22LFjWbhwIaGhofz111/07ds3Q/GZU3qbjAJMfy3nxC2EECLrDR8+nGXLlvH999/zzjvvULx48ewOKVMURcHb2xtvb2/AULLGwcGBZ8+eERcXBxi+84wZM8akd5JZaS1p7VGL/f7n0KdSJV2r0dLS/X9mL6UmhBAi9zHZkSHhijMYriJERUWxdetWtm7dmq7tJZGes+kUPR8c/D7D29lqrbGWki5CCJHnHT58WH1crly5dG937do17t27B0D79u2THePo6EiTJk3YuXMne/bsMUqkHzlyhKioqFS3L1OmDFWqVOHKlStqvfScICNNRgFcbAuYMRohhBA5XYUKFVizZg3vvfce9evXZ8aMGbz11ltYW1tnd2gZUqNGDb7//nuOHz/OhQsXCAoK4unTp9jb21O1alWaNGnCoEGDqFGjhsmfe3Dljnj7n011jLXWgo+rvm7y5xZCCJH7mTTD+eJt43mlnvikSZOYNGlSdoeRbXSKnqHH5hGlj8nQdnYW1hS0cTBTVEIIkXeNGDECgDFjxhg1FV26dCkajYYSJUpkV2hGYmJiCAgIYNu2beoF8fLly9O5c+d07+PixYvq4+rVq6c4rnr16uzcuZPLly+nuH21atVS3f7KlStcunQp3bGZ28Kr29M9NiuajAohhMjZWrZsCYCrqyu3b9+md+/e9O/fnwoVKuDs7IyFhUWK22o0GnUGuCmkdY6c2vrChQszcuRIk8WSEQcfXkhlLrrBwEodKGLrlCXxCCGEyF1MViNdr9e/1D+Rcy29tpsLT25neLvpr/VFg5R0MbU2bdpgZ2fHoUOHMrTdwIEDsbOzY+XKlUbLV65ciZ2dHQMHDjRafvfuXezs7KhUqVKSfVWqVAk7Ozvu3r2b8RcghEjTnDlz+PHHH5P0G0lo7PX48eNsiszA1tYWjUaDra0tnp6eDB06lCdPntCoUSO8vb2xsbFJ9778/f3Vx6ndop6wLjQ0lPDw5706ErZ3dnZOtZRcwvaJny87JdRpTa+saDIqzEuO30KIl+Xj48PBgwfVmuOKohATE8PFixc5fPgwPj4+Sf4dPHhQfZzfBUU/Y9G1HamO0QDN3F7JmoBEriDHbyFEYjK1SaTqcdRTlt/cm+HtWrjVpKi9M7d5avqg8phKlSqpZQ0S2Nra4u7uTpMmTRg+fHiOq4UshMged+/eRaPREBsbm61xuLm5ER0dTXh4OBEREQC0aNGCmTNnUqpUqQztKywsTH2cWiI88bqwsDAcHR2Ntk+rH0vC+sTP96KYmBhiYp7ffRUaamgEGhcXp9ZsNaWmrtU58fhKmuPKFHCjsFUBs8SQnLi4OBRFSXWyQ0IDW41GY5Y7EBP2mZPvbqxcuXKKx+/GjRunePzObPPfF7fL6OOMxJDw/19kncS/8/Levxy9Xo+iKMTFxaU6QxvIss9VU2natKn0nnoJ8y5vIVavS2OUhgVXtzOx9ntZEpPIenL+LYR4GZJIFynSKXoGHPohw9vZW9gw8dXexMVkb6Intylfvjyurq4APHv2jJs3b7JixQp+//13Vq9eTceOLzcb0c3NjYoVK+Lk9PK3KZYtWxZbW1ssLeUjRAhzsLe3JyoqKsmM9JwiYSYcwOPHj1m5ciXTpk2jbt26jB8/Xm1Antt88803RvXXExw4cCBdjdMzqi6FqUvjtAc+gx07Up9BZ0qWlpa4ubkRHh6e7Rdtsvv5U5OQ+CxXrhxFihQBDMfvW7dusXLlSv744w+WLl2q1u5PSI7GxcUZXbBJS5EiRahQoQL29vZG28XHxwOg0+mMlie8ZwkzVRMrU6YMNjY26PX6VGNIfNeHyFqpXewT6RMbG0tUVBSHDh1S/05SEhkZmUVRmYbMKs+89N4JpqCwz/8M4/7XU0qq5XFy/i2EyAz5KxQpmn52DU/iMn4itbTp51hotGTn/A6doudciC81nctmYxQZM3r0aHr37q3+/OjRI/r168f+/fsZPHgwV69eVWdgZsaUKVOYMmWKKUJl586dJtmPECJ55cuX58KFC6xYsYImTZrk6NlnRYsWZeTIkTRp0oQGDRowZcoU6tatS6dOndK1fYECzxtoRkZGpniykTjZkXibhMdpJUMS1ife9kVjx45V69ODYUZ6yZIladGiBYULF051/xn1KOoJfQ5+l+7xW9t8nWUn9NHR0fj5+eHo6IitrW2yYxRFISoqCq1Wa9LfT8Px+xY1nT3R/Z+9+w6L4uriAPybXZalI4qCIAoWUGyI3QB2TTTR2InG3pKY2IKaaFTs0Rijscae2BJbIrH33ruIooIoUlSQ3nbZme8Pvp2wshW2Aed9nn1cZu7M3B1hz8yde8+V5sPS0tJsf//l9VIWv0eOHInTp0/jm2++waNHj2BnZweBoCCjokgk0in90cKFC7Fw4cIiy+U300KhUGF/8kkHGYYpcpyjR4+qPRbHcZBIJAr1JcbBcRwyMjJgb29vtr/zpUVubi6sra0RFBSk8jtMLjk52Ui1IqYmEligk1sTnE64B5ZTPepDwAjQoaofNaLriO6/i6L7b0LKJooORKnE7BQcj7+t83YfVmsGN1v9NjYUxz8vL2Pd08P4ok43dHdpZurqFIuLiws2bdqEunXrIjk5GadOnULPnj1NXS1CiBH06tUL9+/fx5YtW3DkyBHUrFkTIpGIXz98+HDY2uo2mbO+Jxl7X4sWLRAQEIDz589j/fr1Wjeku7m58e/j4uJUNqTHxcUBABwcHBRuauTbp6SkIDs7W2XPcfn2hY/3PrFYrLSBUyQSKZx/fdhw/ygkjKbh5QU6ufnDRmyt1+OrI5PJwDAMBAKBysZUlmX5xj59NvodeHlFIX4zDGP2jYrv19HV1VUhfp8+fVohfuvrMxXehzbvtSHvZS///yfGIx+xQOe+5OQP+LT57tb3dzsxb+N8e+Di63DkylQ3pFsKhBjn28OItSob6P6bEFJe6HyVVrNmTdSsWRO1atVSurw4r/f3RUxv3OWVOm9T0dIe3zUONkBtdJOcl44tUQV53bdEncA7SekdIuvq6oratWsDAJ49e1ZkfWRkJAYOHIhq1arByckJbdq0wd69e5XuS9VkJ8WharKTwhOx3Lp1C5988gkqVqwIW1tbtGnTBv/884/S/eXn52PFihVo0aIF7O3tIRaL4ebmhjZt2mD27NlITU0tcZ0JKU2mTZuG1q1bg+M4JCQk4NKlS/xwbo7jcOPGDaUTipl6kjH5hJ7Kvq9UadCgAf8+PDxcZTn5uvdzVhbe/uHDhxq3r1+/vtZ1MxQpm4/TCXe1Lj+2bjfDVcaMUPym+E0IIYbkbOWIEd4fqi0z0vsjOFuVPBVHeULxm+I3IeWJzj3S5XlR3+/hUjhfqq7MvadReXPi1S28yU3Vebv1gZMgZEzfg+a3p0cg5Qp6+UnZfGx+fgIzGn1m4loVn6rJwG7fvo0FCxaAYRjUqVMHsbGxuHPnDgYPHgypVIrPPjPdZ758+TIWL14MS0tL1K1bF3Fxcbhy5Qp69eqFn3/+WSF1AgAEBwdj3759AApyzVasWBGJiYm4fv06v52fn58JPgkhpmFlZYVz585hz549OHnyJOLi4pCXl4dz586BYRg0bdpU5x7pxhAdHQ1AffqU9/n4+KB69ep4+fIljh49in79+hUpk5WVhQsXLgAouGEoLCAgANbW1sjJycHRo0fRvHnzItu/ePECjx49Urq9KaRJssCAAQfNE056O1SDq01FI9TK9Ch+U/wmhKh3/vx5nbdhGAZWVlZwdHSEp6cnn/6pvNIcec13kmtzRfGb4jch5YnODelDhw7VaTkpXfJkUsy9u0Pn7YbV6QIX6wr6r5CO7qVE40ziPf5nFhzOvQ3HJynP0bhi6cnXJpeYmIioqCgAKDJyY9asWRg5ciQWLVoEKysrcByHmTNn4ueff8aMGTPQv39/CIVCU1QbixYtQo8ePbBlyxY4ODiA4zisWrUK48ePx7Rp09CxY0c0btwYAHDr1i3s27cPHh4eOHbsGOrVq8fvJz09Hbt379Z7bmJCSgMLCwt89tlnChfl8uH+W7duLdIz25BkMpnGXNinTp3C9evXAQDt2rXTaf9DhgzB/Pnz8eeff2LmzJnw9PRUWL969WpkZmZCKBRi0KBBCutsbW3Rp08fbN++HWvXrsX48ePh6OioUGbx4sUAChr4P/30U53qZggrHx7QqhEdABY2G27g2pgHit8UvwkhmrVr165EndAsLCzg5+eHYcOGYdSoUeUutU1Sbhq2PFE/V8SmJ0fRyb0p9UrXEsVvit+ElDc6dx/esmUL/1K1vDgvYh4mXlmn8zaNnWpimLfpe/jlszKsePQPBFC8uBSAwYrHB5DPapeL1ly8efMGI0eORF5eHpycnNCxY0eF9fXq1cPPP//MT6LEMAxmz54NV1dXJCQk4MGDB6aoNgDAyckJq1ev5nvMMgyDb775Br1790Z+fj6WLVvGl3369CkAoG/fvgpBHCjIhTxq1Ch4eHgYr/KEkCJiY2PRpEkT/Pbbb4iOjlboqRMbG4sff/wRPXv2BMdxqFixIiZNmqSwfWhoKJ8XWtkItpCQELi6uiI7Oxvdu3fHrVu3AAASiQRr167FzJkzAQBjxoyBt7d3ke3nzp0LW1tbJCQk4JNPPuG/V7KysjB37lysW1cQ23744Qc4OTnp5ZwUl65pXSpaad+7v7Si+E3xmxCiPY7jiv2SSqW4ceMGvv76azRt2hQvX7409ccxqtURYZBoiCkSVobVEWFGqlHpRvGb4jch5RFNNkp4j1NjEZ76XKdtbIRirGjzlVmkdDkQewWx2UlFlrPgEJv9FmGvrqJ39Q9MUDPtLFmyhH+olJaWhmfPnkEikUAkEmH16tVFUiUMGTKkyGRUIpEIDRs2RGJiIp4/f26y4VhDhw7lLzAK++qrr7B//34cO3aMXyYP0qdOncK7d+9QsWL5SGFASHE8f17wHS3PRW5M9+7dwxdffAEAsLS0hIODA3JycpCVlcWX8fLywr59++Dq6qrTvh0dHXHw4EF07doVERERaNasGezt7ZGbmwupVAqgICXLL7/8onR7Ly8v7N69G/369cOFCxfg7e0NR0dHZGZmQiYruIkbNmwYpkyZUpyPrle6pHXpULUJRIKyf6lG8ZviNyFEO2fOnIFUKsXMmTNx7do1uLm5oV+/fmjWrBkqV64MAHj79i1u3ryJPXv2ID4+Hi1btsScOXOQk5OD8PBw/PXXXwgPD0d4eDi6deuGu3fvwsKi7McaKZuPk/F3NJZjORYn429jul9wuYjBJUHxm+I3IeURRQbCG3dJ9wlGtwSFmEUjelKhCU5U2fzsONq6NEQlsXkO03v27Bk/oYmlpSVcXFwQEBCACRMm8MOwCqtZU/lQOflFdOHGLWPz8fFRulz+xPv169dIT0+Hg4MDWrdujZYtW+LatWvw8PBA586dERQUhLZt28Lf35/mUCCkkBo1apjkuG5ubti9ezfOnj2La9euISEhAUlJSRAKhahevToaN26Mnj17YuDAgbC2ti7WMZo2bYqHDx9i8eLFOHjwIGJjY2Fra4sGDRpg6NChGDFiRJGbl8K6deuG+/fvY/HixThx4gTi4+NRoUIF+Pv7Y+zYsejTp09xP75e6ZLW5Zv6PQ1cG9Oj+P0fit+EEE3atm2LHj164Pr16/jmm2+wZMkSiMXiIuUGDRqEH3/8ESEhIVi9ejWWL1+Ow4cPo2fPnpgxYwZmzpyJBQsW4NGjR9iyZQtGjx5tgk9jXCKBBTq5NcHphHtgOVZlOQEjQIeqftSIrgHF7/9Q/CakfKHoQAAAx2JvQMLl67TNh9Wawc3WPHJnrX9ymJ/gRBUpK8NvT49geoMBRqqVbtavX4/BgwdrXV7VRIPyhiZVk6QYg/xi4n1VqlTh32dkZMDBwQECgQBHjhzBnDlzsH37dhw4cAAHDhwAUNBoGBoaimHDhhmj2oSUKunp6di7dy+uXLmCxMREZGdnY/PmzQqN7fHx8UhNTYWVlZXKi39tWFpaol+/fkonAtVGaGgoQkNDNZZzcXHBsmXLFIaf6qJWrVpYv359sbY1Bl3Tujha2hiuMmaC4vd/KH4TQjTZsmULDh48iO7du2PFihVqy4rFYqxcuRLPnz/HkSNHsH79eowZMwYAMG/ePFy8eBHnzp3D/v37y0VDOgCM8+2Bi6/DkStT3ZBuKRBinG8PI9aqdKL4/R+K34SULwZpSGdZFhEREYiOjkZGRgY/rFqdIUOGGKIqRAt5Minm39ul0zYVLe3wXeNgA9VIN1I2H2de39dYjgWLM4n3MMW3D/UwMLCkpKJD/ICCoaZyhYfKOTk5Yfny5fjll19w7949nD9/Hv/88w/OnDmD4cOHw87ODn379jV4vQkpLVavXo0ZM2YgIyMDQMGFO8MwRXrCnDt3DoMGDYKVlRVevXpFQzdNjNK6KKL4bX4ofhNi3jZv3gyGYfgGcW2MHTsWhw8fxu+//66w3bBhw3Du3Dncu3dPzdZli7OVI0b5dMOqiAMqy4z26UYTjWpA8dv8UPwmxHj0mpMjOzsb06dPh6urKxo3boxevXphyJAhGD58uNrXiBEj9FkNoqOxF9X3ZlBmfeBks0jpAhQM02vv0ggCDfURQID2ro0piBtBZGSk0uWPHj0CUNDr1MGh6AUqwzDw8/PD+PHjcfr0aXz33XcAgA0bNhiusoSUMqGhoRg/fjzS09NhaWmJpk2bqiw7YMAAVK1aFXl5edi3b58Ra0mU0TatCwOmXKR1ofhtfih+E2Le5H+L1apV03obednHjx8rLJenfHj37p2ealc61LJXPo8LAwY17Kqgt2eAkWtU+lD8Nj8UvwkxHr21hGZmZqJt27ZYvHgxkpKSdJ5BnJhG+LsYRGXE67RNX89AuFhXMEyFimmMdzeIGKHaMiKBEGPrfGSkGpVvv//+O/Ly8oosX7NmDYCCSQO10apVKwAF6SkIIcCdO3cwb948AMDnn3+OxMREXL9+XWV5gUCAfv36geM4nDihPo8lMSxd0rpw4MpFWheA4re5ofhNiHnLzc0FAMTGxmq9zatXrwCgyN+2SCQCgGLPbVIa5bMy/BL+N5RlgObAYVKDPrAQqI9JpADFb/NC8ZsQ49FbQ/r8+fNx69YtcByHVq1aYfPmzbh16xaioqLw/Plzta/o6Gh9VYPo6OvLq3Qqby0Q4Wsz7CXnLHbA8Fqd1ZYZUbuL2U50Uta8e/cO33zzDZ9mguM4rFmzBvv374dQKMTkyZP5sjt27MC8efMQExOjsI/k5GT8+uuvAAB/f3+j1Z0Qc7Zy5UpwHIfWrVvjjz/+gKOjo8ZtWrduDQB48OCBoatH1JCnddFGeUjrIkfx27xQ/CbEvMnnO9Glt+hvv/0GoGAekcLkDWWqciuXRftiLuJl1huVY8Oi0qnxUFsUv80LxW9CjEdvd2l79+4FwzDo1q0bDhw4wE+4QMzX3ujzkEH1RCvKbG071WxSuryvp0drHIq7jrjsZLCFLo8EYOBu44ye1VqZsHbly/fff4/Fixfj6NGj8PHxQXx8PH+xvmjRIvj5+fFl3759i1mzZmHWrFlwd3eHm5sbcnJy8OTJE0gkEri7u/M9cAkp786dOweGYfD1119rvY2npycAIC4uzkC1ItqgtC6qUfw2HxS/CTFvffv2RXh4OA4fPoyJEydiyZIlsLS0VFpWIpFgypQpOHz4MBiGKTJh+KVLlwAAtWvXNni9zUFSbho2Rh5WW2ZD5GF0cGtCOdK1RPHbfFD8JsR49NYiKr9BHz9+PDWilwJ5MilWRPyj0zbtXRvBzbaSYSqkBxYCISbU+1QhiAMACw4T6vaEkIbpGU2bNm1w+PBhfPDBB3j27BlSUlLQqlUr7N+/H1OmTFEo26dPHyxevBidO3eGUCjEgwcPkJCQgAYNGmD+/PkIDw9H9erVTfRJCDEvCQkJAAAfHx+ttxGLxQCKDukmxkNpXdSj+G0+KH4TYt5CQkJQq1YtcByHlStXolatWvj222+xc+dOnDx5EidPnsTOnTsxefJk1KpVC6tWFYw+lpeTk8lk2LlzJxiGQdeuXU31cYxqdUQYJKxMbRkJK8PqiDAj1aj0o/htPih+E2I8DKenBOU1atTAq1evcPPmTTRp0kQfuywz0tPT4ejoiKSkJFSqZPqGaBnH4rNTC5CQm6L1NgIwONVtiU4543Jzc/H8+XN4eXnByspKaRmWZZGTkwOGYfT2AGZh+F849/oBWI6FAAwCK9fHjEafgWG0G1JPiq9Lly64cOECjhw5glatWsHe3h5CIV1AGRPLskhPT4eDgwM91Cwhbb7D5JKTk+Hs7Iy0tDSlE/noS4UKFZCRkYErV66gRYsW/HKBQACGYfDgwQP4+voqbHPkyBF0794dlStXxuvXrw1Wt7JIX/E7KTcNfU7OLXKjqUyHqk0wp+ngYh9LHyh+lz8Uv02P4rf+mGP81qfY2Fh069YNDx8+BACV35Hy2/wGDRrg0KFD8PDw4Ne9ePECW7duBQCMGjUK7u7uhq20kb0fv6VsPjocnqr19qe7LSmVKdYofpc/FL9Nj+K3/pSW+K23/2X5Db2q2YKJ+Vj98IBOjegAMLPJoFIz8crYOh/xE5+IBBYY4aU+dxshhJQW1apVAwD+5lkbx48fB1B+hm6bo9URYZTWRQsUvwkhRDMPDw/cvn0bP//8M3x8fMBxnNKXt7c3fv75Z9y6dUuhER0o6AQ3e/ZszJ49u8w1oisjEligk1sTCDSkKBUwAnRy8y+VjeimRPGbEFKe6K0hfdKkSQCAVatWQU+d3IkBvMlJxZ6YCzpt42pVAZ3cS89kE5UKTXwyvFZnVLS0N3GNCCFEPzp06ACO47BlyxatykdHR2PTpk1gGAadO9NNjSlI2XycjL+jRTN6+UzrUhjFb0II0Y5IJMKkSZPw6NEjvHr1CkePHsWuXbuwa9cuHD16FLGxsXj8+DEmTZoEkUhk6uqahXG+PWCpoWOYpUCIcb49jFSjsoPiNyGkPNHbo9Y2bdpg8eLFmDp1KoKDg/Hbb7+hQoUK+to90ZMvL/6q8zarPhhvgJoY1qfV28DTzgWNnWoiXyI1dXUIIURnAoEAAoEA9+/f59O1fP3111i3bh0uXbqE0NBQhIaGqtz+5s2bCA4ORmZmJqysrDB27Fgj1ZwUJhJYoI6DO56ma57stUPVJuW+FxzFb0II0Y2bmxvc3NxMXQ2z52zliFE+3bAq4oDKMqN9utFEo8VE8ZsQUl7o9W4tJCQEtWvXxqhRo+Dh4YHOnTvD29sbNjaae1fNmjVLn1UhSpyMu403eak6bTPAKwgu1hUMUh9DEjICNK1UBxzHId/UlSGEkGJ6f4SXt7c3Zs6cidmzZ2PevHk4cuQI+vTpw68/evQo/v33Xxw/fhxnz54FUJA79ccff0TVqlWNWXXyf0m5aXiZqTk3vYgRluu0LnIUvwkhhBhKLXtXpcsZMKhuVxm9PQOMXKOyg+I3IaS80GtD+ps3b7B//36kpaWBZVkcOKD6ae/7qCHdsPJZGebc2a7TNlVtKuJLGtpGdCDPxcyyLCQSiYlrQ0jZNHPmTEilUixcuBA3btzAzZs3+cmcpkyZwpfjOA4Mw2DWrFkYP770jSwqK1ZHhEHKyjSW87KvSr3giMlQ/CaElHX5rAy/hP8NBiiSbo0Dh0kN+pSaOcEIkaP4TYjx6S1HenJyMoKCgrBjxw7IZDKVk56oehHDmnN7m87bbGs7DUINE7IQQggxvrlz5+Lq1avo3bs3rK2ti8RUkUiEjz76CBcuXMDs2bNNXd1yS54fndUiQ/qT9FeQstSHixBCCDBu3DjExWlOCVZcu3fvxs6dOw22f3O0L+YiXma9URmRo9LjjVofQgghpZPeWkkXLlyIJ0+egOM49OvXD2fOnEFycjJkMhlYltX4IoaTmP0OZxPv67TNRN9eEAtpYhpCCDFXzZo1w969e5Gamoq7d+/i+PHjOHLkCK5fv46UlBQcOnQIbdq0MXU1yzV5fnRNGDDo5OZf7vOjE0IIKbB27VrUrl0b48aNQ1RUlF72KZFIsHPnTtSvXx+fffYZnj17ppf9lgZJuWnYGHlYbZkNkYeRlJtupBoRQggprfR2xxYWFgaGYfD555/j999/19duiR58c3m1TuVFjBB9agYaqDaEEEL0ycLCAo0aNTJ1NYgSSblpiMlI1FjOUmCBcZRKjRBCyP8NHToUf/zxB9atW4d169ahRYsWGDx4MHr37g1XV+V5vpWRSqW4cOECdu7ciX379iE9PR0cx6FGjRro1KmTAT+BeVkdEQaJhjRrElaG1RFhmO3/uZFqRQghpDTSW0O6fOjZiBEj9LVLogexmW+RmJui0zarWo8zUG0IIYToSp7/nJQ+Kx8egJTTnB+9hp0L5UcnhBDC27JlC8aNG4cZM2bgxIkTuHbtGq5fv45vvvkGHh4eaN68OZo0aYIqVarAyckJTk5OyMnJwbt375CSkoInT57gxo0buH//Pp83meM4VKpUCVOnTsX48eMhFotN/CmNQ55mTROWY3Ey/jam+wXTCDFCCCEq6S1CODs7Iy4uDvb29vraJdGDqdc36FS+joMbfCt6GqYyhBBCdNalSxeIRCVPtcUwjN6GhxPNpGw+Tifc1aqsPD863bgTQgiRa9asGY4dO4abN2/il19+wf79+5GXl4eXL18iNjYW+/fvV7t94XnI6tati6+++grDhw+Hra2toatuVkQCC3Rya4LTCffAcqpTygoYATpU9aNYTAghRC29RYnAwED8+eefCA8Ph7+/v752S0ogNvMtXmUnaV2eAbD2gwmGqxAhhBCd6WuyMerZblxpkiwIwGicaJQB0JHyoxNCCFGhWbNm2LFjB9LT03HgwAGcOXMGFy5cUPtw3MbGBq1atUJgYCC6d++OZs2aGbHG5mecbw9cfB2OXJnqhnRLgZDSrBFCCNFIb3dt3377Lfbt24elS5eif//+sLKy0teuSTHNvv2HTuVnNfmcJhglhBAz4+bmppce6cS4VkeEaWhCL8AwArpxJ4QQopGDgwMGDx6MwYMHAwDevn2LV69e4e3bt3j37h2srKxQuXJlVK5cGbVq1YJQKDRxjc2Hs5UjRvl0w6qIAyrLjPbpRmnWCCGEaKS3hnR/f39s3LgRo0aNQpcuXbBx40Z4e3vra/dERwnZ7/A0XftejFWtndDJnUYSEEKIuTl+/Dh8fX1NXQ2iA23zsQIFOVkdLW0MXCNCCCFljbzRnGinj2cANkceRbYsT2G5AAw87Cqjt2eAiWpGCCGkNNFbQ7p8klFfX19cvHgR9erVQ+PGjeHt7Q0bG/U3iAzDYNOmTfqqCgGw7tFBncqvbPONgWpCCCGElC8igQU6VPXTKkd6h6pNKK0LIYQQYmAPUp4XaUQHABYcQhr2g4WAevATQgjRTG93blu3buXzrzIMA47jcO/ePdy7d0/tdhzHmUVD+tatWzF8+HCN5U6cOIFOnToZoUbFp8sEZwDQ1zMQLtYVDFYfQgghpLypbldFYxlLgRDf1O9phNoQQggh5Vc+K8PS+3uVzl1iIxSjgZOnaSpGCCGk1NFbQ3r16tXLxERmAoFA7RA5sVhsxNoUT3JuutZlbYRifE038YQQQojeJOWm4c/oMxrLfV67E+VjJYQQQgxsX8xFvMx6o3RdtiwP+2Muon/NtkauFSGEkNJIbw3pMTEx+tqVSXl4eJT6zzL+yhqty24JCoGQERiwNkQTHx8fvHz5UmGZlZUVqlatisDAQEyYMEFpfmSJRIKaNWsiOTkZ7u7uePLkCQQC1f+XXbp0wYULFxSWiUQiVK5cGc2aNcPYsWPRoUMHldvn5+dj69at2LNnD8LDw5Geng4nJydUqVIFjRo1QlBQEHr27AknJyd+m99//x0jR47UeA5q1Kih8u8uIyMDGzZswKFDhxAREYHk5GRYW1vDy8sLQUFBGDZsGPz9Kb8/IcR8rI4Ig0SWr7FcTIbym3pSOlD8pvhNiLGp+1tXhWEYWFlZwdHREXXq1EGrVq3QtWtXtd87ZUlSbho2Rh5WW2ZD5GF0cGtCD7fLCYrfFL8JKQlKylnGxGa+RULOO63LV7Z2NGBtiC5q167Nj4ZIS0vDs2fP8Mcff+Cvv/7Cjh070L17d4XyR48eRXJyMgAgLi4O58+fR7t27TQep1q1avDw8AAAZGdnIyoqCmFhYQgLC8OcOXMwderUItukp6ejR48euHbtGgDA2dkZDRo0AMuyiIqKwsOHD7Fr1y5UqVIF3bp1K7K9WCxGs2bNVNapatWqSpcfOXIEQ4YMQVJSEgDA3d0djRs3RlZWFiIjI3Hv3j2sXLkS48aNw6pVqzR+dkIIMTRdJho9nXAHP7CfUY70Uo7id1EUvwkxjLNnz/JpVN8fDc5xBSlLtFnu4uKCn3/+GZ999pmBa2x6qyPCIGFlastIWBlWR4Rhtv/nRqoVMQcUv4ui+E2IZnTnVsZMvb5B67I0wZl5mTp1KgYPHsz//Pr1a4wYMQKnT5/G2LFj8fjxY9jZ2fHrd+7cCQCoUKECUlNTsXPnTq0C+dChQ/HDDz/wP+fk5GD69OlYt24d5syZg08//RTe3t4K20yfPh3Xrl2Ds7MzNm3ahC5duvDrZDIZrly5gm3btsHKykrpMV1dXXHx4kWtzoPcv//+i169ekEmkyE4OBihoaHw8fHh12dlZeHAgQOYO3euzvsmhBBDEQksUMfBHU/T49SWEzACdKjqR3G4DKD4rYjiNyGGExQUBIZhkJCQgCdPngAoaCCvWbMm3yD49u1bREdH843t3t7ecHFxQXp6Op48eYKcnBwkJibi888/R2xsrNJGvLJC24fbLMfiZPxtTPcLprhcjlD8VkTxmxDtlI/xXOVEbOZbvMpO0rr8l/W6ay5ETMbFxQWbNm2CWCxGcnIyTp06xa9LSUnB0aNHAQDLly8HAPzzzz/Izs7W+TjW1tb46aefUKNGDbAsi7CwMIX1+fn5+PPPPwEAS5YsUQjiACAUChEQEIDffvutWMNNlXnz5g2GDh0KmUyGqVOnYteuXQpBHABsbW0xcOBA3Lt3T6uJggkpbZ4/f47o6OgiF9bEvCXlpuFl5muN5USMEON8exihRsTYKH5T/CbEUM6ePYvp06fj7du3qFixIlasWIGkpCQ8ffoUly9fxuXLl/H06VMkJSVh+fLlcHJywtu3b/H999/jzp07SEtLw19//YVq1aqB4zjMmDEDERERpv5YBiMSWKCTWxMINKQyFTACdHLzp0b0co7iN8VvQrSht4b0ly9fluhlLt6+fYumTZvCzs4O1tbWqFmzJj7//HOcPXvW1FXTaPbtP7Qu6+1QDa42FQ1YG9NhMp+BSblT8Eq9A0FWlKmrVGyurq6oXbs2AODZs2f88n379iEvLw9NmzbFgAEDUKdOHWRkZODff/8t1nEsLCzQuHFjACjy9/jmzRtkZWUBAF/G0FatWoWUlBTUr18fCxYsUFtWLBZjwoQJRqkXIcZUo0YN1KhRAxYWdFNXmqyOCINUwxByAKhh50K5WN9D8Vt3FL8JKV+ioqLQt29fMAyDK1eu4JtvvlHIjyzn5OSE8ePH48qVK2AYBv3798eTJ09gYWGBfv364fz586hQoQJYlsWaNdrPr1UajfPtAUuBUG0ZSwE93C4Jit+6o/hNSOmlt7tzLy+vYm/LMAzy8zVPymUM2dnZuH37NpycnJCVlYXnz5/j+fPn2LFjB4YPH47169drbNTIy8tDXl4e/3N6ejoAQCqVQiqVGqTer3NS8CItEZZQf5EgN7vRQIPVRU4qlYLjOLAsC5ZllZbhOI4fdijP31cSTOYziE80UlhmBSCn0z1w9nVKvH9Dkp8LZcvff79jxw4AwIABA8BxHPr3748FCxZg586d6N+/f7GOI3+abm1trbDezs6O//+5ceMG6tWrp9NnklP1O6CM/An86NGjIRAIdNq2vJOfc/nfHik+lmXBcRykUimEQvXfrYb+PiWlgy750Z+kv4KUzafeb//HZD6D+HhDhWVWAHI73wfMPH6roizWFo7f8n/nz5+PXbt28ct0lZOTA6Agfhdmb2/Px++bN28qnThN3+Txe8yYMfQQkBADWLp0KTIyMrBkyRLUqaP5u7FOnTqYOnUqvvvuOyxduhTr168HAHh6emLs2LFYvHgxzpw5Y+hqm5SzlSNG+XTDqogDKsuM9ulGD7eLieI3xW9Cyhu9/YXooxHUlNzc3DB79mz07t0bPj4+EIvFkMlkuHbtGmbPno2TJ09iy5YtsLW1xcqVK9Xua9GiRZgzZ06R5WfOnIGNjY2hPgLGI0DrsnfOXYN2t/rFZ2FhAVdXV2RmZkIikRj4aAWE2e+gLEtYfk4KZJZ5StaYnvxvJz8/X+EBDFCQpy0qquCJfvXq1ZGXl4eYmBhcvXoVQqEQPXr0QF5eHnr37o0FCxbg1KlTiI2NRZUqVYocR96oKpPJihwnJSUFN2/eBADUq1dPYb2lpSVatGiBa9euYcqUKYiPj0fPnj1Rs2ZNjZ9Nvh+WZfkHSpokJyfj6dOnAAB/f3+ttyOKMjIyTF2FUk8ikSAnJwfnz5/X+LC3OMM6SdmjbX50Bgw6utE8JQqkKr6z8kvnd1liYiIfv2vVqgWgIF2TPH7369cPABAcHIz58+fj5MmTeP36NVxcXHQ6zrt37/j4/X6vNXt7e7Rq1QpXrlxBSEgIXr9+jd69e/P10Td5egkAaNu2rUGOQUh5d/z4cTAMg8DAQK23kf89njx5UmF5hw4dsHjxYsTFqY9ZZUEfzwD8HXMRcdnJCssFYOBhVxm9PbW/jybvofhN8ZuQckZvd3BbtmzRWEY+0+++ffsQHx+PNm3aYPTo0cU63tatW0uUl+nIkSP48MMP+Z+7dOmiNPdUmzZtcOzYMfTu3RsHDhzAmjVrMH78eLU9AL7//ntMnjyZ/zk9PR0eHh5o3749KlWqVOw6qyJl8/HJ8Vlal98S+C2q2uq/Hu/Lzc1FbGws7OzsVE6CwXEccnJyIBAIiswwXxyMpUjpcguRBSzE4hLv3xDkn9vCwgLiQnV88+YNvvrqK+Tl5cHJyQldu3aFWCzG3r17ARQEuerVqwMoaPxu2rQpbt26hQMHDuDrr78uchyBoCCTk1Ao5I+TlZWF8PBwTJ8+HampqfDy8kJwcLBCPQDg119/Rbdu3ZCcnIy5c+di7ty5cHZ2hr+/P4KCgtC/f39Uq1aNL89xHCQSCb+f2NhYpcNO5caPH49ffvkFQMGFilzDhg3h4EC9Q3TBcRwyMjL4ngyk+HJzc2FtbY2goCCV32FyycnJateT8kHb/OiWAgsaQl6GvXnzBiNHjuTjd8eOHQH8N0lZ27Zt4erqCqDgJl0ev3fv3o1vvvlGq2NkZWXhwYMHmD59OlJSUuDl5YU+ffoUKbdixQp89NFHSE5OxqxZszBr1iw4OzujadOmCAwMRP/+/eHh4aHyOC9evFAbSyZMmMDnii3cGFeSkaqEENXi4+OLvW1iYqLCz/KON+93sCmLLARCuFpXLNKQzoJDSMN+sNCQ+oWUDxS/KX4Tog29NaQPHTpU67JLly7F+PHjsX79erRp0wZLlizRVzUMQiAQYOnSpThw4ABYlsW///6r0FD+PrFYXKQhEgBEIhFEIuUNvSUhggjNXevi0uuHGstWsaqA6hVc9V4HZWQyGRiGgUAg4Btx38eyLP8FX9xGPybzGf8knMmIVFpGmPkEnHySGZE9OLvaxTqWIf3000/YunUrACAtLQ3Pnj2DRCKBSCTC6tWr+QZl+bCrAQMGKJyz4OBg3Lp1C7t27VIbyBcuXIiFCxcWWd6pUyesWrVKaYNho0aNcPPmTSxbtgy7d+/G69evkZSUhOPHj+P48eOYM2cOJk2ahNmzZ0MgEPC97OX1E4vFaNasmco61apVi/8dkeeDAwqexqv63SHKyUceyP/2SPHJH/Bp891tiO92UvpQfnTdKMbvx0rLCDIiweH/sc5M4/eSJUv4DiXK4re9vT0AYNeuXQBQZAi4tvF7wYIFSvOWyuO3smvPhg0bKo3fx44dw7Fjx4rE7/dpit+FR6cVHglla2urchtCSPE5Ojri7du3uHDhAlq2bKnVNufPn+e3LUx+zW2Ijl7m5k7yM9xKflpkeTNnb/hVMkwv37KM4ncBit+ElE8mGVMsEomwdu1aPH78GD///DM6duyIrl276rSPzz77DB9//HGx6/D+hYQmtWvXhrOzM5KSkhAdHV3s4xrK0zTthuR5O1TTXKgUUZaTTRnLmyMUfs7r8sDsgvmzZ8/4CU0sLS3h4uKCgIAATJgwgR/udfXqVURFRUEsFqNnz54K2/fr1w/fffcd7ty5g0ePHqnMZV6tWjX+6bU8jQrDMKhfvz7fw10ZV1dXLFmyBEuWLMHjx49x69YtnDlzBocOHUJqaiqWLFkCsViM6dOnK9324sWLWp0H+QULUHCBTz3SCSGlAeVH1w3F7/9Q/CaEaOuDDz7A33//jR9//BG9evXSmOrh2bNn+PHHH8EwDNq0aaOw7uHDgk5YuqakKG3yWRmW3t8LARiwUExHm5CdjHxWRj3SdUDx+z8Uvwkpn0zaXfHLL78Ex3Eac44rIxaL4ezsXOxXWeo9GJv5Fm9yU7Uqe/FNOKSseUzsqheqcrIZajsDWr9+PXJycpCTk4O0tDQ8efIEmzdvVsiZJn8a/uGHHxZ5GOTi4oL27dsD+G/4mTJDhw7F6dOncfr0ady7dw+3bt2Cp6cnVqxYgcWLF2tV17p162LQoEHYuHEjwsPD0a5dOwDAsmXLSjw81N3dnX9fOM0LIYSYM3l+dE0YMOjk5l+uG9EBUPwuhOI3IURbEydOBMMwSElJQatWrbB69WqkpaUVKZeamopVq1ahdevWSElJAcMwRUZUHzx4UGkDe1mzL+YiXma9KdKIDgBx2cnYH6NdYyP5P4rfPIrfhJRPJm1Il+cZl0+yYM6ioqKQlJQEwPzyRn177Tety9LNe+klkUj4/OgHDhyAtbV1kZd8EqE///xT6wmA69Wrh23btkEgEGDhwoWIiYnRqV6VKlXCsmXLABQ8wX706JFO27/P2dmZ/244d+5cifZFCCHGQvnRiSoUvwkh+hIYGIi5c+eC4zi8e/cO48ePh7OzM7y9vREQEICAgAB4e3ujcuXKmDBhAj+Hy7x58/DBBx/w+4mKisKhQ4fAcRw++ugjU30cg3uXl4GNkYfVltkQeRhJuelGqhEpTSh+E0KUMWlDuvzpubKn6Mak6QuP4zhMmTIFQEHO3JKklNG32My3SMh5p3X5sXW7GbA2xJCOHDmCd+/ewcLCAi4uLipfQqEQr1694vMhasPf3x99+vSBVCpVmr9Nk8IPlyQSic7bv0+ef279+vWQyTTnGyaEEFOj/OhEFYrfhBB9mjFjBnbs2IHKlSuD4zjIZDI8e/YMV65cwZUrV/Ds2TPIZDJwHIcqVapg586d+P777xX2UatWLeTn54NlWXTrVnbvD7c+OQ6JhtgsYWVYHRFmpBqR0oTiNyFEGZM2pP/+++8AgKpVq5qyGnjx4gVatGiB3377DdHR0XzDOsuyuHr1Kj766CP8/fffAICxY8fCx8fHlNVVMPX6Bq3LejtUg6tNRQPWxgRE9prL6HM7E5IPFwsODkZMTIzKl3zWb3XDy5QJCQkBUPA0/cWLF/zy/Px8pKSkqN326tWrAAoeNBWeuKS4vv76a1SoUAEPHz7EjBkz1JbNy8vDr7/+WuJjEkJIccnzoysbNv4+eX70co/iN8Vvit+EFNtnn32Gly9fYteuXRg1ahRatWoFb29veHt7o1WrVhg5ciR27tyJFy9eIDg42NTVNZmLr8PBcqzaMizH4mT8bYrN2qL4TfGb4jcp50zSkP706VN88cUX+P3338EwjFk8Bb9x4wa++OIL1KpVC9bW1qhcuTJsbGzQunVrHDt2DAAwfPhws/rCiM18i1fZSVqXn+s/xIC1MQ3OrjbyujxAXvvLyGt/GZJmm5WWkzTbzJcxx4lONElJScHRo0cBAAMHDlRb9rPPPgMA/P3338jJydH6GI0aNULnzp2Rn5+PX375hV+emZmJunXrYvr06QgPD1cYwcFxHA4fPoxRo0YBALp16wZnZ2etj6mKi4sLtmzZAqFQiMWLF2PgwIGIjIxUKJOTk4Pdu3ejSZMm2LxZ+f87IYQYA+VH1x3F76IofhNCdGFpaYkBAwZg/fr1uHz5Mh49eoRHjx7h8uXL2LBhA4KDgyEWi01dTZMKcGkAAaO+yUPACCg264Did1EUvwkpX/QWLbR5CsayLFJTU5GR8d9EE1WqVNH4xMvQXFxcsHLlSly5cgV3797F27dvkZKSAisrK3h5eaFNmzYYMWKEQl45czD79h9al61mUxnudiX/gjVH2gRl1t4HcGpihNoYxt69eyGRSODm5oa2bduqLdupUydUqVIFb968wb///ov+/ftrfZzJkyfjxIkT+P333/Hdd9/B1dUVDMMgPT0dv/zyC3755Rc4OTmhRo0aYFkWsbGx/NPy+vXrq5w4ODExEQEBAWqPffToUdjZ2fE/f/rpp/jnn38wbNgw7Nq1C7t27YKHhwdcXV2RlZWF6Oho5ObmgmEYfPPNN1p/RkII0bek3DS8yKD86Lqi+K2I4jchhOjXMO8uuH4vCrky1b3SLQVCis06ovitiOI3IeWL3hrSdZ0gAQBatWqFLVu2mDy1i7W1Nb7++mt8/fXXJq2HLhKy3+FpepzW5Ze2GG3A2hBDkw8TGzBgAAQC9b0qLCws0KdPH6xduxa7du3SKZC3a9cO/v7+uH37Nn799VcsXLgQjo6OePDgAY4ePYrTp08jKioKUVFRyM3NRcWKFdGxY0f07NkTQ4cOhaWlpdL95uXl4dKlS2qPnZ9fdDjlxx9/jOjoaKxfvx6HDx9GREQE7t69CysrK9StWxdt27bFiBEj0KhRI60/IyGE6NvqiDBIOM1Dwik/evlD8ZviNyHGUDgVhJOTEywsqHc1AFQU22OUTzesijigssxon24Um0kRFL8pfhOiCsNpO7WwBsOHD9dYRiAQwN7eHl5eXmjbti38/Pz0cWizl56eDkdHRyQlJaFSpUp62efsW3/gdMJdrcpWta6I3R1/0MtxdZGbm4vnz5/Dy8sLVlZWSsuwLIucnBwwDKMxQGmDyXwG8fGGRevS+T5gX6fE+yfaYVkWEokE9vb2EAqFpq5OucKyLNLT0+Hg4KCXv6nyTJvvMLnk5GQ4OzsjLS0NDg50M1ZWaBO/pWw+OhyeqvU+T3dbYvbDxyl+l18Uv02H4rf+lJf4/ejRI6xZswYnT57E06dP+fQPDMOgTp066Ny5M7744gv4+vqauKbGVzh+OzpVwNBzP+FV1luFuUwEYOBhVxlbg6bAQlA2vu8ofpdfFL9Nh+K3/pSW+K23O7ktW7bovE10dDQuXrwIABgypOzl7zYUKZuvdSM6APzS8gvDVcbMyHO2QVqQPogDBwkrhsiuNhgT140QQkqz5ORkhIWF4dSpU7h9+zZevHiB/Px8VK5cGc2aNcPQoUPRq1cvnfcbExMDLy8vrcsPGzasyDXHsGHD+AnM1ZFKpXrvpceAAQNonGaUAYOObk3MvhHdVCh+E0KI9r7//nssXboULMvi/X5xHMchMjIST548wdq1azFlyhQsXLjQRDU1PQuBECGN+mL8lTUKy1lwCGnYr8w0opsKxW9CSHlj0ru5CxcuYPjw4RAIBNSQroPk3HSty7paOZXZ3OiqFM7ZxnEc2Lw8E9aGEELKBldXV4UhoFZWVhCJRIiLi0NcXBwOHDiAjz76CHv37oWNjY3W+xUKhXBxcVFbJjc3F2lpaQCA5s2bqyxnZWUFR0dHlesZRv+3dIvv/aWxER2g/OjaoPhNCCGaffPNN1izZg3fgF6vXj20bNkSrq6u4DgOr1+/xvXr1xEREQGZTIbFixcjKysLK1asMHHNTcfDtgoEYPge6QIw6OjeBH6Vapm4ZmUDxW9CSHliFt2i9JRdptyYflP73v+r2pSevO+EEELMV35+Plq0aIFhw4aha9eu/CTjMTExmD9/PjZt2oQjR45g7Nix2LZtm9b79fDwQGJiotoy33zzDVatWgVra2sMHDhQZbkBAwZg69atWh+7pBKz3+Fo3E2tylJ+dEIIISV16dIlrF69GgzDwNfXF+vXr0ebNm2Ulr1y5Qq++OILPHjwAKtWrcKAAQNUli3rVkeEKT70Zhh8VY8ebhNCCNEdJfApZXSdZLSilb0Ba0MIIaS8OH36NK5du4Yvv/ySb0QHAE9PT2zcuBFjx44FAGzfvh2xsbF6O25ubi527NgBAOjTpw8qVKigt32X1Pc3Nmtd9kn6K0hZzROSEkIIIar89ttvAAAvLy9cunRJbcN469atcf78eT5mr1u3zih1NDd3kp/hZPxtcIWa0lmOxaustyasFSGEkNKKGtJLmXWPDmpdtkNVysVKCCFEP9q3b692/ciRI/n3N29q10tbG/v370dKSgoAYNSoUXrbb0klZL/Ds4x4rct3dPOjmEwIIaRELly4AIZh8N1336lNZSbn6OiIadOmgeM4XLhwwQg1NC/5rAxL7++F4L1s3QyApQ/2IJ+VmaZihBBCSi1qSC9FdJ1k9Mt63Q1XGUIIIaSQwjOry2T6uzHdtGkTAKBOnTpo27at3vZbUusfH9K6rIAR4GvfTw1XGUIIIeWCPBVakyZNtN7G398fAPD69WuD1MmcHX51Ay+z3vC50eU4AC8y32B/zEXTVIwQQkipRQ3ppYguk4x6O1SDq01FA9aGEEII+c/Zs2f59w0bNtTLPqOjo3HmzBkAij3eVTl16hS8vb1hZWUFBwcHNGzYEBMnTsTTp0/1Uh85KZuPk/F3tC4/pHZHyo9OCCGkxOQPrbOysrTeJjMzEwAgFov1Vo8ff/wRDMPwr5J4/fo1vv32W/j4+MDa2hoVK1ZEYGAgNm7cWOK51HZGnVa7fkPkYSTpcI9NCCGE0BjjUkSXSUYXNhtuwJoQQggh/0lNTcWiRYsAAIGBgfDx8dHLfjdv3gyO42BhYYGhQ4dqLP/q1SsIhUI4ODggPT0d4eHhCA8Px9q1a7F8+XJ8+eWXarfPy8tDXl4e/3N6esHNtVQqhVQqVSjbxbUJzieGg+VYtfsUCSww0LN9ke3NmVQqBcdxYFkWLKv883EcB47jwDCMQSaNl++TJqQ3Dfn/PzGewr/zdO5LhmVZcBwHqVQKoVCotmxp+m4GCnKj37t3D2FhYQgKCtJqm3///RcAFOY3KYnIyEjMmTNHL/u6desWunbtiuTkZACAnZ0dMjIycPHiRVy8eBF79uxBWFhYsR8CFKRuUd13UMLKsDoiDLP9Py/W/gkhhJQ/1JBeStAko4QQQswRy7IYPHgwEhISIBaLsXLlSr3sVyaTYevWrQCA7t27w9XVVWVZf39/NG/eHB9//DGqVasGoVCI7OxsHD16FFOnTkVUVBS++uorVK5cGX379lW5n0WLFiltHDhz5gxsbGwUljWCAxpB9SRvPBY4fvSY5nJmxMLCAq6ursjMzIREIjFpXUx9/PJK3oOVGF9GRoapq1DqSSQS5OTk4Pz588jPVz/Jc3Z2tpFqpR/dunXD3bt3sWrVKnz00Ufo2LGj2vKnTp3CypUrwTAMunXrVuLjsyyLkSNHIjc3F61bt8aVK1eKva+0tDR8/PHHSE5ORt26dbFt2zY0a9YMEokEGzZswKRJk3D8+HFMmjQJa9asKV59ORbqGtJZjsXJ+NuY7hdM85gQQgjRCkWLUoImGSWEEGKOJkyYgIMHC2LUmjVr0LhxY73s9+jRo4iLK3iArGmS0fHjxxdZZmNjg969e6Nt27Zo1qwZYmJiEBISgj59+qgchv79999j8uTJ/M/p6enw8PBA+/btUalSpSLl98dcUpsr3d3GGZuCJqtcb65yc3MRGxsLOzs7hdz3hXEch5ycHAgEghIP61e1f4lEAktLS4PsnygnP+92dnYQCCgDpDFxHIeMjAzY29vT73wJ5ebmwtraGkFBQSq/w+TkPaFLi4kTJ2LVqlXIyMjARx99hNGjR2PEiBFo0qQJ/zfLsizu3LmDTZs2YePGjcjPz4ejoyMmTpxY4uOvXLkSly5dwqBBg1C7du0SNaQvXboUiYmJsLa2xuHDh+Hl5QUAsLS0xLhx45Ceno7p06dj/fr1mDhxIry9vXU+hoARQN34DgEjQIeqNBk4IYQQ7RUrYsydO1cvB797965e9lPW0SSjhBBCzFFISAhWrVoFAPjll18wYsQIve1748aNAAB3d3d89NFHxd5PpUqVMGPGDIwePRovXrzAnTt3+InX3icWi5UOHxeJRBCJREWW96kViLBXV/Eq622RicwA4KfWY5RuZ+5kMhkYhoFAIFDZmMqyLN/YZ8hGP33k3yXak6cXkf//E+ORp3Ohc19y8gd8qr67Cytt39HOzs7YvXs3evToAYlEgnXr1mHdunWwtLRExYoVwTAMkpOT+dE8HMfB0tISe/bsUfpAWBfPnz/HjBkzUKlSJfzyyy9YvXp1ifb3xx9/AACCg4P5RvTCvvnmGyxcuBCZmZnYsWNHsdLJWAiEyFcSn+UsBUKM8+2h834JIYSUX8VqSA8NDaWbGiNKk2RBAEbpTfr7aJJRQgghxjB16lT8/PPPAICffvpJLz3d5N68eYNDhwp6eg8bNkxjjltNWrduzb+Pjo5W2ZCuKwuBECGN+mL8laJDzrtVaw4XGye9HIcQQgiR69KlC65evYoxY8bg5s2bAArm+EhISChStnnz5li/fr1eRouNHj0aWVlZWLNmDSpXrlyifUVGRuLly5cAoPJhuZ2dHQIDA3HkyBEcP368WA3pA2t1wOa4UyrXj/bpRpOBE0II0UmxxzDR5E/GszoiDJwWjegATTJKCCHE8KZMmYKlS5cCAJYsWYKQkBC97v/333+HVCoFwzB67eVuCE0q1YarlRMSc1P4ZdZCS0xtPMCEtSKEEFKW+fn54fr167hx4wZOnjyJ8PBwvHv3DgBQsWJFNGjQAJ06dULz5s31crwNGzbg1KlT6NSpE4YMGVLi/YWHh/PvGzRooLJcgwYNcOTIEURERBTrON2qNcfJ1AdFRo4JwMDDrjJ6ewYUa7+EEELKr2I1pJ85c0bf9SAqSNl8nIy/o3V5mmSUEEKIIYWEhPA90ZcsWYIpU6bo/RibNm0CALRv3x41a9Ys8f6uXr3Kv1c2fLwk7iQ/U2hEB4AcmQQP3j2HX6Vaej0WIYQQUljz5s311liuSlxcHKZMmQJra2v89ttvetlnfHw8/97d3V1lOfm69PR0ZGZmws7OTmm5vLw85OXl8T+np6cDADgZi8m+vTD1+sYi20yu1xucjIVUpi6LeukilUrBcRxYluXTRb2P4zhwHAeGYQzSOVK+T+p4aRry/39iPIV/5+nclwzLsuA4DlKpVOOIZKlUaqRaFVWshvS2bdvqux5EBZHAAnUc3PE0PU5jWZpklBBCiCEVbkRfunQpvv32W70f4+LFi4iMjASgeZJRAPzNoCrv3r3DwoULAQDVqlVDkyZN9FNRAPmsDEvv7y2Sfk0ABksf7MHWoCmwEJQsLQ0hhBBiSmPHjkVaWhoWL16sl4fbAJCRkcG/t7GxUVmu8LqMjAyVDemLFi1SmvrlzJkzsLGxwXgU7Xkee/URYvFIl2qbPQsLC7i6uiIzM5PPk28qpj5+eZWZmWnqKpRbhb/XSPFIJBLk5OTg/PnzyM/PV1s2OzvbSLUqilpdzVxSbhpiMhI1lrOAAN/U72mEGhFiXuQNaKW910O7du1w7tw5nDlzBu3atTN1dYrYunUrhg8fjqFDh2Lr1q2mrg4xgWnTpvGN6MuWLcOkSZO03jY0NJS/wX3+/Dk8PT1VlpVPMlqxYkX07t1b4763b9+Ov//+G4MGDUJgYCCqVKkCAMjJycGxY8cwdepUREdHAyho/NfnJH77Yi7iZdabIstZcHiR+Qb7Yy6if03qfECIMhS/jYPid+kkzx+ub9WrV9ep/Pbt23Ho0CH4+flh8uTJBqmTPnz//fcK9UtPT4eHhwfat2+PSpUqYfvTU9geVZArXSwUYXNQCCqJy95I7tzcXMTGxsLOzg5WVlZKy3Ach5ycHH5SXn3jOA4SiQSWlpY0r54Ryc+7nZ2dwSeslvcUlslkBj2OoXXo0AHnzp3DqVOnShS/OY5DRkYG7O3t9fo7v3XrVowcORJDhgzBli1b9LZfc5abmwtra2sEBQWp/A6TS05ONlKtiqKGdDO38uEBSDnNX1A1HdxoopRSysfHBy9fvsT69esxePDgEu/v3r17+Pfff9GoUSP06EGz0OsiNTUVy5cvR4UKFfQ6caK+HT9+HF27doWVlRVev34NBwf1f/tv3ryBu7s78vPzcf36dYMPASZlz8uXL7FkyRIAgEAgwOLFi7F48WKV5UNCQoqVNz0jIwN79uwBAHz++ecQi8Uat5HJZPj777/x999/AwBsbW1hZWWF1NRU/gJfLBZj2bJlGDBAf3nLk3LTsDHysNoyGyIPo4NbE4rPZRTFb/NB8ZuUNfpOQwYUPLzS1MOvsDdv3mDixIkQCoXYsGEDLCz013Rgb/9fI3Z2drbKv4XCPQ4Lb/M+sVis9JpBJBIhTZaDnS/OQMIUXBN8XrsTXO0qFrfqZk0mk4FhGAgEApWNqSzL8o19hmzoZhjGbBvSy2L8lj+Ulv//G4M+jmMO8Vvd34ucuvgtT+dS+NzrI37L92XM/1NTkz/gE4lEEIlEastqWm9I5eN/o5SSsvk4nXBXq7JP0l9Bymp/YUTKrvv372PBggX4999/TV2VUic1NRVz5szB8uXLTV0VtTp16gQ3Nzfk5uZi3759Gsv/+eefyM/Ph4+PD92Ek2IpnO+PZVm8fv1a7au4w0p37drF3zRrk9YFKMijvmDBAnz88ceoVatWwU1zWhocHBzQvHlzTJs2DY8ePcJXX31VrDqpsjoiDBJW/YNuCSvD6ogwvR6XlF0Uv4uP4jcpa+R5rPX90sW0adOQnJyMMWPGoG7dusjMzFR4FU7doWyZOm5ubvz7uDjVKUzl6xwcHFSmddGkcLxmwOBlZtGRZISUBMXv4qP4TUoj6pFuxtIkWWDAgIP6ix4GQEc3f8qPTkg5IRAIMHDgQCxduhTbt2/H8OHD1Zbfvn07AOilxwUpnzw9PUuUfiE0NBShoaEay40ZMwZjxozRad81atTA9OnTi1mz4tF2InCWY3Ey/jam+wVTjCaEUPwmWjOHYfzPnz8HAKxduxZr165VW1beW3zChAlaNYg1aNCAfx8eHo569eopLRceHg4A8PX11abKRbdPicHJ+Nv8zxw4nIy/g5412tCE4IQQrVH8JoXRXZ0ZW/nwgMZGdABgGAHG+dIQYAB4/fo1jh07hjdv3kAkEqFmzZpo27atVukBCClNBg8ejKVLl+Ls2bOIj49X6NlT2JMnT3Djxg0wDINBgwYZuZaElE0igQU6uTXB6YR7YDlWZTkBI0CHqn7UiK6F+Ph4HD9+HG/fvoVYLEbt2rURFBRE8ZuUORS/iTaGDh1q6ioYlI+PD6pXr46XL1/i6NGj6NevX5EyWVlZuHDhAgCgS5cuxTrOukeHIGBoQnBDio2NxcmTJ5GUlASxWIy6devigw8+MHW1CNE7it9EjlK7mCld0rqwHAtHS9WznZcH4eHhGDp0KOrUqYOxY8di5syZ+O6779C/f380atQIc+fORXp6uqmrqZP58+fD2toa8+fPR1paGkJCQlCnTh04Ojqifv36WLRoUZE8hz4+Pnxvzu3bt8Pa2pp/KbsAPXHiBPr27YsaNWrA0dERtWrVwpgxY/iJ+Qp78eIFrK2t4ePjAwDYvHkzPvjgA1SuXBnW1tYKdbC1tcXLly9x7NgxtGvXDo6OjnBwcEDnzp35C2JlpFIpVq5ciRYtWsDBwQG2trZo3LgxFixYoPOszNHR0Vi8eDHatWsHDw8PiMViVK5cGR9++CEOHTpUpPywYcP4fJQvXrzg8/qpyu/3+PFjjBgxAp6enhCLxahUqRK6d++O06dPq6xTUlISvvrqK7i7u8PKygo+Pj6YN28epFKpTp8NABo1aoRGjRqBZVns3LlTZTn50/DAwEB+gserV69i6tSpaNasGapUqQKxWAwPDw8MHjwYDx8+1KkeW7duBcMwGDZsmNL1Z8+eBcMwKidweffuHWbMmIEGDRrA1tYW9vb2aNWqFTZs2KCQTkQuPz8fK1asQIsWLWBvbw+xWAw3Nze0adMGs2fPRmpqqk71J6S4xvn2gAWj/jLKUiCkB90a3LlzB4MGDYKPjw++/PJLzJo1C9OmTUOfPn3QuHFjLFq0CFlZWaaupk4oflP8VofiN8Xv0uLs2bNq08TMnj2bLytfpkt6hiFDhgAoSIEQExNTZP3q1auRmZkJoVBY7Mao+OwkhUZ0QHFCcFJ8169fR//+/VG3bl189dVXfPzu2bMn/Pz88NNPPyEnJ8fU1dQJxW+K3+pQ/Kb4LUcN6WZKntZFGx2qNinXvd0OHTqEoKAg7N69W+kX4uvXr/Hjjz+iQ4cOanPwmau0tDS0a9cOv/32GypVqoSqVasiOjoac+fOxfjx4xXKNm3aFLVr1wYAVKlSBa1bt+Zf9evXVygbEhKCHj168EHN19cXmZmZ2LZtG1q3bo0rV66orNP48eMxbtw4vHnzBt7e3qhQoUKRMvv27cPHH3+MBw8ewNvbG5aWljh58iTatWvHTyZYWE5ODj788EOMHz8eN27cQLVq1VC7dm2Eh4fjhx9+wAcffKDTzMwLFy7Ed999h1u3bsHGxgaNGjWCSCTCsWPH8PHHHxeZKNHb2xvNmjUDUDBh0QcffKDwKmz37t1o3LgxtmzZgnfv3sHX1xeWlpY4fPgwOnXqhJUrVxapT2JiIlq2bIm1a9fizZs3qFevHjiOw6xZs9C7d+9ipc2QDxWTB2tlduzYoVAWKJjE8aeffkJMTAxcXFxQr149ZGRkYPv27WjevDnOnj2rc12K4+HDh2jUqBEWLlyIp0+fwtPTEy4uLrh+/TrGjBmDAQMGFDkvwcHBmDhxIm7cuAEXFxc0btwYFhYWuH79OubOnav0RowQQ6hgaQcbC/WzyY/26UYTjaqxb98+tGvXDvv371c6AV58fDzmzZuHzp07482b0pfTluI3xW9VKH5T/C4PQkND+QYxZf+/ISEhcHV1RXZ2Nrp3745bt24BACQSCdauXYuZM2cCKEj75u3trff6bYg8jKTc0tXRylzs2LEDHTt2xL///qu04e3Vq1eYPXs2PvzwQ7x7984ENSwZit8Uv1Wh+E3xGwDAEYNLS0vjAHBJSUlabzPr5u9cwL+TNL4C/53Mvc1JM2Dtiy8nJ4eLiIjgcnJyVJaRyWRcZmYml5WVxeXk5Oj8OnnyJGdpackB4ABwzs7O3KRJk7hdu3ZxmzZt4j799FNOKBTy6xs0aMC9efOmWMcy1Kt69eocAG79+vUKy2fMmMEB4EQiERcQEMBFRUXx6/bu3ct/rrt37ypst379eg4A9/nnn6s85sqVKzkAnKenJ3fs2DF+eWZmJhcaGsoB4Nzd3bmUlBR+3ePHjzkAnFAo5Gxtbbk9e/bw6969e1fk81hYWHATJ07kJBIJx3EcJ5VKualTp3IAOAcHBy4+Pl7hd+Hbb7/lAHBubm7crVu3+OVPnz7l6tatywHg+vfvX+R3SP5/+77Dhw9zV69e5ViWVVh+/vx5rmrVqpxQKOSePXumsO758+ccAK5GjRoqf2fv3bvHicVizsrKilu/fj0nk8n4dWFhYZyDgwMnFAq5u3fvKmzXq1cvDgDn7+/PvXz5kl9+6tQpzt7enhOJRBwA7syZMyqP/b74+Hj+9yA8PJzjuIK/qZSUFE4mk3GXLl3iAHBWVlZcamoqv93vv//ORUVFKexLKpVyGzdu5CwsLLiaNWsqfC6O47gtW7ZwALihQ4dqtVzuzJkzHACubdu2CsszMzO5WrVqcQC48ePHc2lp/32PPXz4kKtfvz4HgFu1ahW//ObNmxwAzsPDg4uIiFDYX1paGrdhwwaFc1tS2nyHySUlJXEAFD4HKf3Uxe8/o86qjc+fHJvJSWX5Jqh1yRkjfh86dEghPru4uHAhISHcrl27uI0bN3Iff/wxJxAI+PX+/v4KscYcXhS/C1D8pvgtR/G7fJk9e7bKv+P31z9//lxpmZs3b3KVKlXiyxX+mwLAdenShcvNzdW5bvL4HfTnNyrjdNDBb7nQW9t03rc5M0b83rdvn0J8rlq1Kvfdd99xu3bt4tavX899+OGHHMMw/Po2bdpwaWlpJo/ZFL8pfstR/Kb4XVLUI90M6ZLWhQNXbtO6cByHyZMn87PD9+vXD5GRkVi4cCE+/fRTDBw4EDt37sTly5f5ITXh4eFYt26dCWutOwsLC2zZskUhB1f37t3x8ccfAwCOHTum0/4kEgkWLFgAoVCIXbt2ISgoiF8nFAoxbdo0fPrpp4iLi8P+/fuLbC+TyTBz5kz++AAUhpbJ1a1bF0uXLoVIJOI/x+LFi+Hv74/09HSF/4f09HR+EqPVq1fD39+fX1e7dm388ccfAIA9e/YgKipKq8/50UcfoWXLlkWGhQUGBmLevHmQyWT466+/tNpXYXPmzEFeXh4WL16M0aNHQyD472v0k08+wYIFCyCTyfDrr7/yy589e4Z//vkHAPDHH3/Aw8ODX9ehQwfMmTOnWMPLqlatio4dOwJQ/lRcvuyTTz6Bo6Mjv3zIkCGoWbOmQlkLCwuMHDkSwcHBiI6OxtWrV3Wujy42b96MqKgo9OrVCytWrICDw3+9dn19fbFz504wDINly5bxy58+fQoA6Nu3b5FJqRwcHDBq1CiFc0uIoSTlpmFj5GG1ZbLyc5AqKV0pSYyFZVlMnDgRMpkMQMF30uPHjzFv3jx8+umnGDRoEHbv3o1z587xse/27dvYtGmTKautM4rfFL9Vofj9H4rf5VvTpk3x8OFDTJo0CXXq1IFUKoWtrS0CAgKwYcMGHDlyxGBzZcgnBJeyRUdEEeXy8/MxYcIEvhf6mDFj8PjxY8yePRuffvopBg8ejP379+PUqVOoUqUKAODy5ct8D93SguI3xW9VKH7/pzzHb2pIN0NpkiwIKK2LRlevXsX9+/cBAI0bN8amTZtgY1P0oYK3tzf27NnDf6Fv2LCBv3kvDTp37oxq1aoVWd60aVMAwPPnz3Xa37Vr15CYmAg/Pz/4+fkpLSMP0qryqWmTp3DkyJFKl3/11VcAFC9ALl68iOzsbFSvXh09e/Yssk3z5s3RunVrcByHEydOaDy23Nu3b7FixQoMHDgQnTp1QkBAAAICAvj8jffu3dN6X0DBRdDhw4chFApV5iTr0aMgH/K5c+f4ZcePHwfHcQgKCioyxA8ARo0aBUtLS53qIicfMrZz506FYVhSqRS7d+9WKFOY/KK3d+/eaNeuHX9u5PXW9dzoSn6ROGrUKKXrGzVqBE9PT0RHR+PVq1cAwAfpU6dOlcphoqTsWB0RBgmrPo7kcxxWR4QZqUaly5kzZ/gL85YtW2LNmjWwsiqaJqdBgwb4888/+Z/Xr19frGG4pkLxm+K3OhS/SWkXGhrK50bXtF7eqUkZFxcXLFu2DE+ePEFOTg5SUlJw4cIFjBo1SqHBTN8EjACd3PzL7b10cRw+fJj/u27Xrh1++eUXpd+BTZo0wbZt2/iff/vtN4rfFL8BUPym+F02UNQwQ6sjwsBBc6BhwOCb+kW/9MqLwk8Av/76a/7JqzL169dH165dcfToUcTGxuLcuXPo0KGDMapZYu8/uZSTP+XXdRK28PBwAMDLly9VnoO0tDQABflp3+fs7AxnZ2eNx1GVy1D+JPPJkyf8Mvn7unXrKp1YBCj4P7xy5YrCduocP34c/fv35z+LMroGgydPniA3NxeWlpbo1q2b0jLyYFo4H7+8zu8/xZWzt7eHu7u7zhdlANCrVy/Y2dnh5cuXuHDhAgICAgAAR44cQXJyMpydnfHhhx8qbLNo0SL88MMPSnMayhk6UD548AAAMGvWLCxcuFBpmaSkJAAF57JatWpo3bo1WrZsiWvXrsHDwwOdO3dGUFAQ2rZtC39/f5W/O4Tok5TNx8n4OxrLyXu6TfcLppv09xSO3+PHj4dQKFRZtlmzZggMDMSFCxfw9OlTXLt2Da1atTJGNUuM4ncBit/KUfym+E1MiyYE113h+D1x4kS1DzoCAwPRtGlT3Lp1C/fu3cODBw/QqFEjY1SzxCh+F6D4rRzFb4rfdGdnZrS9QQfKd1oXQPFJsKov1cK6deuGo0ePAkCpmhDB1tZW6XL5hYuuT/fT0wsm1Xn79i3evn2rtmyOkpnWlfX6V6Zy5cpKl7u4uAAAMjIy+GWZmZkA/rs40XY7VVJTUxEcHIy0tDQMGTIEX331FXx8fODg4ACBQICTJ0+ic+fOOg/nkl8USCQSXLp0SW3Z3Nxc/r3886k6J0DB5ytOILe1tUWvXr2wbds2bN++nQ/k8iGUwcHBCg+Zzp8/j+nTp0MoFGLRokXo0aMHatSoARsbGzAMgx9++AELFiwo1lA3XcjPpXxiKXXkv4cCgQBHjhzBnDlzsH37dhw4cAAHDhwAANSoUQOhoaEqeyoQoi8igQU6uTXB6YR7YDnVF8MCRoAOVf2oEV2Jwt913bt311i+W7dufA+t58+fl5qGdIrfqrdTheI3xW9CjIUmBNed/B7awsICnTt31li+W7du/HdFTExMqWlIp/itejtVKH5T/C5PKLWLmREJLFDHwV2rsuU5rQsAhfQs2uTOK1wmP7/85sKTXxgEBwcjJydH7ev48ePFPo78aeb73rx5A6DgKbCcnZ2dwjplXr9+XWQ7VY4cOYKUlBS0bt0aW7duRcuWLVGhQgX+4ic2Nla7D/EeeT3d3d35oarqXu9vp+7CSd1n10Q+dGzv3r3Iy8tDeno6Dh48qLBOTh7gp0yZgu+++w6+vr6wtbXlnybrem7k26m6oFTVY0N+Tp4+farxPLZr147fzsnJCcuXL8fbt29x584drFixAu3bt8eLFy8wfPhw7N27V6f6E1Ic43x7wIJRfwlFPd1UKxy/1Y0mkyscv0tTajZ9o/hN8Rug+E2ILtxsnIukTBWAQQ27KujtGWCiWpVe8hgsEom0SrtD998FKH5T/AYofpcl1JBuZpJy0xCVXnQ4z/tEjLBcp3UB/ntCChRMYqJJ4TKFty1rNA2vkQ9vioiIMGg9VA0Be/ToEQDFoWfy948ePVIZEB4+fFhkO1XkvSVat26t9Hyoyj+m6dzVqVMHIpEICQkJOg29ktf58ePHStdnZmbyeciKo2PHjnB3d0dKSgoOHz6MsLAw5ObmwtvbGy1atFAoKz83bdq0UbovXXOzyS8MVV2kPHv2TOlyX19fAP8NddQVwzDw8/PD+PHjcfr0aXz33XcACuZAIMTQKljawcaiaE7vwqinm2qurq78+ytXrmgsT/G7AMVvit8AxW9CdPFFve5g30uZyoJDSMN+sBCoTitGlJPH4JycHK2+cwrHeIrfFL+VofhN8bs0ooZ0M7P0/p4iwV4ZL/uq5f4GvXfv3vz7wjNQK5OUlMRP/ODg4MDPtFwWySdsUzYsDAA++OADODs74/79+zh//rzB6rFp0yaly9esWQMA6NKlC78sICAANjY2iI2N5YcKFXbz5k1cuXIFDMNoNYxQPou5/Cl6YcnJySrrJt9O1bmzsbFB165dwbKswqzgmsg/6/nz55VeQG3cuBESiUTr/b1PIBBg4MCBAAqeeKub5ETduTl+/LjOgVyeQ/Du3btFepqwLIstW7Yo3U7+9/vrr7/qZfIheaoHZXkFCdG3fTEXkSrJVLneydKOerqp0atXL/69pvgdFxfHxwVnZ2cEBQUZtG6mRPGb4jfFb0L0q4GTJzq5+UPw/1FkAkaAzu7+8KtUy8Q1K50Kx295TFDl+fPnOHLkCADAzc0NLVu2NGjdTIniN8Vvit/lCzWkmxEpm49Lb7R7Svkk/RWkbPkdHgUUzGxdtWpVAAUziK9cuVJpuaysLAwbNoz/ch40aBA/rKUs8vLyAlCQ+yo7O7vIeisrK8ycORNAwbk4cOBAkS/Shw8fYsaMGVr19Ffl0aNHmDZtGp/rKz8/H9OnT8etW7dgb2+PL774gi/r4OCAL7/8EkDBxLF37vw3T0BUVBSGDh0KAOjfvz9q1dJ84RsYGAgA2L17N06ePMkvT0hIQJ8+fVQOLaxcuTLs7e3x5s0b/sn9++bNmwexWIz58+fjxx9/LBL0ExISsGLFCoXGodq1a6Nnz57gOA5Dhw5VePp99uxZhIaGapXeQB150D506BAuXboEhmGUzu4uz+H2448/KuSEu3HjBkaMGMFfCGqrcePGcHNzQ0JCAmbPns3/LuXm5mLixIkqe16MHTsWNWvWxJkzZzBo0CAkJCQorM/MzMTu3bsxefJkftmOHTswb968InMcJCcn8xdW/v7+OtWfEF0l5aZhY+RhtWWy8nOQKtFtIqrypHfv3qhUqRKAgiGxqm6u0tPTMXToUD6ODBs2TKtUbqUVxW+K3xS/CdG/cb49YPn/3ueWAiG+qkdp14orODiYT/Pxxx9/YOfOnUrLpaSkYMiQIfzEiqNGjYKFRdlNSUvxm+I3xe9yhiMGl5aWxgHgkpKS1JY7EHOFC/h3ksZX4L+TuNBb24xU++LLycnhIiIiuJycHJVlZDIZl5mZyWVlZXE5OTk6v9atW8cB4F8dOnTg/vrrL+758+dcREQEt3jxYq569er8+ooVK3KPHz8u1rEM9ZLXb/369QrLZ8yYwQHgZsyYoXS79evXcwC4zz//XGF5VlYWV7t2bQ4AV6lSJa5ly5ZcYGAgN27cOIVyISEhCueladOmXJMmTbiKFSvyyw8cOMCXf/z4MQeAq169ulafZ9asWRzDMFylSpW45s2bc87OzhwATiAQcLt27Sryu5Cdnc21b9+eP7avry/XuHFjTigUcgC4xo0bK/0bkpd/X9++ffl1tWvX5vz8/DgLCwvO3t6eW758OQeAa9u2bZHtRowYwQHgrKysuGbNmnFt27YtUm7//v2cjY0NX87Pz49r0aIF5+HhwR9z2rRpCtvExcVxnp6eHABOJBJxTZo04by9vTkAXPfu3bmgoCAOAHfmzBmVfy+aNG7cmD9+YGCg0jJpaWlczZo1OQCcpaUl17BhQ87Hx4c/55MnT+YAcLNnz1bYbsuWLRwAbujQoUX2uW3bNv64lStX5po1a8Y5ODhwdnZ23NKlS1We60ePHnFeXl7870W9evW4li1bct7e3vz/e8uWLfnyv/zyC38cd3d3rnnz5lyDBg04S0tLftmLFy+Kff7ep813mFxSUhIHgEtLS9Pb8YnpKYvfobf+4IIOfqs2Tgcd/LZUxGlVjBG/ly1bphC/u3btyu3du5d7/vw5Fx4ezs2fP59zc3Pj11epUoWLjo42ecym+E3xm+I3xW9i/t6P339GneUC/p3E/RV11sQ1MyxjxO958+YpxO+PP/6Y++eff7iYmBju/v37XGhoKOfq6sqvd3Nz4169emXymE3xm+I3xW+K3/pCPdKVSE9Px+LFi9GmTRtUrlwZYrEY1apVQ/v27REaGorU1FS9HzOfleHnB3u0KsswAprA7P+GDh2K6dOn8z+fPn0aAwYMgJeXF3x9fTFt2jS8fPkSQMFT171796JGjRqmqq5RCAQC/P333+jVqxeEQiFu3ryJCxcu4P79+wrl5s2bx58vGxsbPHjwAC9evIC7uzuGDh2Kv//+G+3bty92Pfr06YODBw+ifv36ePz4MXJzc9GhQwecOXMGwcHBRcpbW1vj2LFjWLFiBZo1a4YXL17gyZMn8PX1xfz583H58mW+B6M2duzYgZkzZ8LT0xMvXrxAYmIi+vbtixs3bqBx48Yqt1uxYgUmTJgAV1dX3Lt3D+fOncO5c+cUyvTq1QsRERGYMGECPD09ERkZiYiICNjY2KBXr174/fff+Zxhcm5ubrh+/Tq++OILODs7IyIiAhzHYe7cufj777815ofTRuGhZPKhZu9zcHDAxYsXMWTIEDg4OCAyMhISiQSTJ0/GlStXtJpM5n2ff/45du/ejaZNmyIjIwPR0dHo2LEjrl27hqZNm6rcrm7durh37x5+/PFHNG/eHHFxcbh79y4kEgnatm2LpUuX4s8//+TL9+nTB4sXL0bnzp0hFArx4MEDJCQkoEGDBpg/fz7Cw8NRvXp1netPiLakbD5Oxt8By7Fqy7Eci5Pxt8v9yDF1vvjiC0ycOJH/+dixY+jbty+8vLzQoEED/PDDD/xQ0YoVK2L//v38KLSyiuJ3AYrfFL8J0ScZx8LTrgqWthiDPl6Bpq5Oqfftt99i7Nix/M8HDx7Ep59+Ck9PTzRq1AihoaFITEwEAFSpUgUHDhzQKQaURhS/C1D8pvhdXjAcp4fkOGXImTNn8Nlnn/H5iywsLGBnZ6fQeH7nzh34+flpvc/09HQ4OjoiKSlJ5RfR0vt7cOCl5gm35E53WwKRwLyHR+Xm5uL58+fw8vJSOVyFZVnk5OSAYRitZv5WRT7k5MWLF0rXt2/fHkuXLuUnWCCG4+Pjg5cvX+LevXuoX78+hEKayMeYWJZFeno6HBwcSvQ3RbT7DpNLTk6Gs7Mz0tLS4OBQvuevKEuUxe85t7fhdMI9tY3pAkaADlX9MNv/c2NVVa+MGb83b96MRYsWqZzwqVOnTli2bBnq1KlT7GMQ7VD8Ni2K3/pD8ZsUjt8n08KxKuIAvvHtif4125q6agZlrPjNcRzWrVuHn376qUh6CKBgUsIPP/wQy5Ytg6enZ7GOQbRH8du0KH7rT2mJ3+bdEmtkly5dQvfu3ZGTk4NOnTohNDQUrVu3hkAgQE5ODiIiIvD333/D0dFRr8dNzH6nUyN6h6pNzL4R3dgGDRqE4OBgnDhxAmFhYXjz5g1EIhFq1aqF/v37o2HDhnp54kgIIaR8G+fbA+cTH0CipiHdUiCkkWNaGjFiBIYMGYIjR47g0KFDSEpKgqWlJWrXro3g4GDUq1eP4jchhJBieZeXwc9rsiHyMDq4NYGzFT0wKSmGYfDll19i1KhROHjwII4ePYrk5GRYWlrCx8cHwcHB8Pb2pvhNCCmTqDX2/7KzszFkyBDk5OSgT58+2L17t8LTJGtrazRt2lTtMInimn5zi9ZlGTD4pn5PvdehLBAKhfjwww/x4Ycf8ss4jkNeXp4Ja0UIIaQsqWBpBxsLK0gkmSrLjPbpRjfqOrCwsMAnn3yCTz75hF9G8ZsQQkhJbX1yAhJWBgCQsDKseRSGWU1K52gxcyQSidCrVy/06tWLX0bxmxBS1tG4g//btm0boqOjYW1tjXXr1hltSEZC9js8TY/TujwHDo6WNgasESGEEEJU2RdzEalqGtGdLO3Q2zPAiDUihBBCiDIXXz/gU7GxHIsTcbdxNznKxLUihBBSmlFD+v/98ccfAICePXvC2dnZaMdd//iQTuUprQshhBBiGkm5afwQcVWy8nOQKskyUo0IIYQQoooATJGflz7Yg/z/91InhBBCdEUN6QDy8vJw8+ZNAEDbtm0RHR2NkSNHolq1ahCLxXB1dUXPnj1x5MgRvR5XyubjZPwdrcuLGCGldSFmLzIyEllZWeVy9mZCSNm2OiKMHyKuSj7HYXVEmJFqRIj+UPwmhJQ1LLgiP7/IfIP9MRdNVCNC9I/iNyHGRV2bAcTExEAikQAAXr16hUaNGiErKwuWlpawsbHB69evERYWhrCwMHzxxRdYu3at2v3l5eUp5AVLT08HAEilUkilUoWyXVyb4HxiOD/kTJ2xPt3hKLQusg9zJZVKwXEcWJYFyyr/fBzHgeM4MAwDjuOUlikJ+T4NsW+imfz/nxhP4d95Ovclw7IsOI6DVCqFUChUW7a0fC+T4tP24TfLsTgZfxvT/YJpBBkhhBBihmjiUUIIIcVFd3gAUlJS+PeLFi2Cg4MDdu3ahT59+kAkEiE2NhZTp07Fn3/+iXXr1qFu3bqYMGGCyv0tWrQIc+bMKbL8zJkzsLFRzG/eCA5ohDbaVTQiDYcj1A8pNycWFhZwdXVFZmYm/6DCVEx9/PIqM1N1HmFiWBkZGaauQqknkUiQk5OD8+fPIz8/X23Z7OxsI9WKmIpIYIFObk1wOuGe2offAkaADlX9qBGdEEIIMVMSVobVEWGY7U8TjxJCCNFNqb3L27p1K4YPH17s7Y8cOYIPP/wQABR6bbIsi3Xr1mHAgAH8Mg8PD+zYsQORkZG4c+cO5s+fj3HjxsHCQvnp+/777zF58mT+5/T0dHh4eKB9+/aoVKlSkfL7Yy5pzJX+U4vRaFjRS6fPaGq5ubmIjY2FnZ0drKyslJbhOA45OTkQCARgGEZpmZLgOA4SiQSWlpYG2T9RTn7e7ezsjDZxLynAcRwyMjJgb29Pv/MllJubC2trawQFBan8DpNLTk42Uq2IKY3z7YHziQ8gUdOQbikQYpxvDyPWihBCCCG6oNFjhBBCiouiBgB7e3v+vYeHh0IjupxAIMC3336Lzz//HElJSbh16xZatmypdH9isRhisbjIcpFIBJFIVGR5n1qB2P3iPN7kpirdX8eqfvB38dby05gPmUwGhmEgEAhUNqayLMs39hmy0Y9hGGpUNCJ5ehH5/z8xHvmDQTr3JSd/wKfqu7swTetJ2VDB0g42FlaQSFSPthnt042GihNCCCFmjEaPEUIIKa5SGzk+++wzfPzxx8Xe3tHRkX/v7u7Ov69bt67KberVq8e/f/HihcqGdH37st4nRjkOIYQQQlTbF3MRqWoa0Z0s7dDbM8CINSKEEEKIrmj0GCGEkOIqtQ3pqnp9F0fFihXh7u6OuLg4tb2WC09Yqc/ezftiLqrsjQ4A5xLvo3/Ntno7HiGEEEJ0k5Sbho2R6ucpycrPQaoki3qkE0IIIWaMRo8RQggpLhr3/39dunQBADx69EihwbywR48e8e+9vPSTr1ybG/MNkYeRlJuul+MRQgghRHerI8IgYWVqy+RzHFZHhBmpRoQQQghRRwCmyM817KrQ6DFCCCHFRg3p/yefuDQ2NhZ//fVXkfUsy2LZsmUAClLB+Pv76+W42tyYy2cVJ4QQQojxSdl8nIy/A1bNJKPAf5OXSdl8I9WMEEIIIaqw4Ir8HNKwHywEQhPViBBCSGlHDen/FxgYiL59+wIAvvzyS/z111+QSqUAChrXBw0ahDt37gAAFixYoJdJ/OjGnBBCCDF/IoEFOrk1gYBRH/sFjACd3Pxp8jJCCCHEDAS4NORjt4ARoLO7P/wq1TJxrQghhJRm1JBeyNatWxEUFITU1FQEBwfD3t4eFStWRPXq1fHnn38CAGbNmoWhQ4fq5Xh0Y04IIYSUDuN8e8BSQw82mryMEEIIMR/DvDvzsdtSIMRX9ShGE0IIKRlqSC/E1tYWZ86cwYYNGxAUFARbW1tkZmbC3d0dwcHBuHTpEubMmaPXY47z7QELDQ3pdGNOiGoMw+h18l9Ntm7dCoZhMGzYMKMds6yic0lKE2crR3xUrYXaMjR5GSHao/hdetG5JKVFRbE9Rvl0A0AxmhB9ofhdetG51A9qSH+PQCDAqFGjcO7cOSQnJ0MikeDVq1fYtWsX2rRpo/fjVbC0g42FldoyFPTLNh8fH1hbW2Pbtm162d+9e/cwf/58hIVRXn1dpaamIjQ0FMuXLzd1VUpk586dEAqF/EUOwzAQCARwcnJC69atsXTpUuTm5pq6moSUKvmsDDffPlG5vrotTV5W3lD8Nh8UvwkhqvT1CsSylmPRxyvQ1FUhZoLit/mg+E1KI8oVYmL7Yi4iVZKpcr2TpV2ZvzGXSqXgOM5gTzXz8vL0uj+hUAiRSKTXferT/fv3sWDBAnz++efo0YNGMugiNTUVc+bMQY0aNTBx4kSlZRwdHeHj44OqVasat3LFIBaL0axZMwCATCbDy5cvcfXqVVy9ehW7du3C2bNnYW9vb7L6laZzSci+mIuIzX6rcn0z5zrlbvIyit/6RfG7+Ch+G1dpOpeECBkBmlf2MXU1zArFb/2i+F18FL+NqzSdS3NGDekmlJSbho2Rh9WWycrPQaokq8z2SJdKpXj27Bny8vIMEsg5jkN+fj4sLCz0tn8rKyvUqVPHrIM5MZxevXqhV69epq6GVlxdXXHx4kWFZceOHUOfPn1w+/Zt/Pjjj1iwYIGJale6ziUp397lZWiM14dfXcfgOp3LbLx+H8VvUtqUpphD8ZsQYigUv0lpU5piDsXv8oEa0k1odUQYJKxMbZl8jsPqiDDM9v/cSLUyLplMhry8PFhYWBgsMEqlUr3tOz8/H7m5uZDJZBTISanUtWtXTJo0CfPnz8f+/ftNGsgJKS22PjmuMV5LWFmZjtfvo/hNiHFR/CaE6APFb0KMi+J32UM50k1EyubjZPwdsByrthzLsTgZfxtSNt9INTMNeSA395eFhfGePc2fPx/W1taYP38+0tLSEBISgjp16sDR0RH169fHokWLkJ+v+Hvh4+ODMWPGAAC2b98Oa2tr/tWlS5cixzhx4gT69u2LGjVqwNHREbVq1cKYMWMQHR1dpOyLFy9gbW0NH5+CoZGbN2/GBx98gMqVK8Pa2lqhDra2tnj58iWOHTuGdu3awdHREQ4ODujcuTMuXLig8jNLpVKsXLkSLVq0gIODA2xtbdG4cWMsWLAA2dnZOp2/6OhoLF68GO3atYOHhwfEYjEqV66MDz/8EIcOHSpSftiwYfDy8uI/a+H8ZoV7U2iaoOPhw4cYPHgwqlWrBktLS7i4uKBPnz64evWq0vLDhg0DwzDYunUr4uPjMWLECFStWhVWVlaoX78+Vq9erdPn1kbz5s0BADExMUrXv3v3DjNmzECDBg1ga2sLe3t7tGrVChs2bADLKv/OkkgkWLhwIXx8fGBlZQV3d3d88cUXePv2LUJDQ8EwDEJDQxW2KQvnkpQPF1+HU7xWgeJ3URS/KX5T/Db/c0lIeUfxuyiK3xS/KX6b/7k0B9Qj3UREAgt0qOqH0wl31ZYTMAJ0qOoHkYD+q8qrtLQ0tGvXDs+ePUP9+vUhFAoRHR2NuXPnIjY2FmvWrOHLNm3aFJaWlnj27BmqVKmCWrVq8evq16+vsN+QkBD+i61KlSrw9fVFdHQ0tm3bhgMHDuCff/5B69atldZp/Pjx2LBhA6pVqwZvb2+lgX/fvn2YN28enJyc4O3tjefPn+PkyZM4ffo0/vzzT/Tr10+hfE5ODj7++GOcPn0aAFCvXj2IRCKEh4fj/v372Lt3L06ePIlKlSppdd4WLlyITZs2wc7ODm5ubmjUqBHi4uJw7NgxHDt2DD/++COmTZvGl/f29kazZs1w8+ZNhdxmuggLC0P//v2Rl5eHChUqoHHjxnjx4gX279+Pf/75B+vWrcPo0aOVbvvixQs0bdoUqamp8PX1hUAgQEREBL7++mukpqZixowZOtdHFflFkY2NTZF1Dx8+RNeuXREXFwdLS0vUrl0beXl5uH79Oq5du4bjx49j9+7dChc3+fn56NGjB44dOwbgvwl8Nm3ahGPHjuGTTz7RuY6l5VyS8iHApQEuZj1R25hO8Zq8j+I3xW+K3+Z7LgkhRBWK3xS/KX6b77k0B9Qj3YSq21XRWMZSIMQ4X5qwojz77bff4OzsjMjISFy9ehWPHz/G3r17IRQKsWXLFkRGRvJld+7cialTpwIAunTpgtOnT/OvX375hS+3ceNGrF69Gp6enjh27BhevHiBK1euID4+HqGhoUhPT8fgwYOVziwdFxeHnTt3Ys+ePXj69CkuXbqkNJAvXLgQEyZMQGJiIm7cuIHExERMnToVLMti1KhRSEhIUCg/c+ZMnD59Gm5ubrh16xYiIiJw7949REZGom7durh79y6++uorrc+b/Mlpeno6IiMjcePGDcTHx+P8+fOoWrUqZsyYgaioKL789OnTsWfPHgD/5TYr/NIkPj4egwcPRl5eHiZMmIDXr1/zn3vBggVgWRbjxo3D/fv3lW6/YMECBAQEICEhAbdu3UJcXBx/kTZ//nykpqZq/dk1OXLkCADAz89PYXlWVhZ69uyJuLg4jB8/Hm/fvsXDhw/x7NkzhIeHo379+ti7d6/CxSMALF++HMeOHUPFihVx6dIlPH78GHfu3EF0dDScnJywbt06nepXms4lKR+GeXeBBaP+koniNXkfxW+K3xS/zfdckvJBxrG48TYSMg2jyggpjOI3xW+K3+Z7Ls0BNaSbSFJuGv6MPqOx3MBaHcrNxGVEOQsLC2zZsgVubm78su7du+Pjjz8GAP4ppLYkEgkWLFgAoVCIXbt2ISgoiF8nFAoxbdo0fPrpp4iLi8P+/fuLbC+TyTBz5kz++AAUhpbJ1a1bF0uXLuVz2VlYWGDx4sXw9/dHenq6wpd7eno61q5dCwBYvXo1/P39+XW1a9fGH3/8AQDYs2ePQvBV56OPPkLLli2LTHITGBiIefPmQSaT4a+//tJqX9pYs2YN0tPT4efnh+XLl8PS0hIAIBAIMH36dHTr1g1SqRRLly5Vun2lSpWwdetWVKhQgV/25Zdfwt/fH7m5uThzRvP3hToymQzPnz/HjBkzsG3bNggEAv6iT27z5s2IiopCr169sGLFCjg4/Pfd4+vri507d4JhGCxbtoxfzrIsli9fDgD49ddf0aZNG36dh4cHdu/erXI4mirmfi5J+eMgsoGNhZXaMqN9ulG8JgooflP8pvhtXueSlD+HYq9j8rXfsO+56tQWhLyP4jfFb4rf5nUuzQ01pJuINhONAkBMxhsj1IaYs86dO6NatWpFljdt2hQA8Pz5c532d+3aNSQmJsLPz6/IE1E5eZBWlU9t0KBBGo8zcuRIpcvlT7ULX4BcvHgR2dnZqF69Onr27Flkm+bNm6N169bgOA4nTpzQeGy5t2/fYsWKFRg4cCA6deqEgIAABAQE8IHn3r17Wu9Lk+PHjwMAvv76a6XrJ0yYoFDufZ999hlsbW2LLJfnU1PW60CTwrnmLCwsULNmTSxcuBAeHh7YtWsXunbtqlBefuE2atQopftr1KgRPD09ER0djVevXgEAIiIiEBcXB1tb2yLDBYGCC7HAwECd6m2O55KUb4df3UCqJFPleidLO/T2DDBijUhpQPGb4jdA8bswit/E2HZGFaSs2BB5GEm56SauDSktKH5T/AYofhdG8VsRJfI0AflEo9o4nXAHP7CfUc7VcqxmzZpKl1epUpAaKCsrS6f9hYeHAwBevnyJDh06KC2TlpYGoGCIz/ucnZ3h7Oys8Tje3t5Kl9erVw8A8OTJE36Z/H3dunWLPMGWq1+/Pq5cuaKwnTrHjx9H//79+c+izLt377Talzbk9fL19VW6Xp4j7/Xr10hPT1d42gxAIZ9eYfL/58xM1Y14qhTONZeTk4OnT58iIyMDzs7OaNWqVZHyDx48AADMmjULCxcuVLrPpKQkAAVDDKtVq4anT58CKPi/kz+5fl+jRo1w9uxZrettjueSlG87o04DajqkZ+XnIFWSRT3SiQKK3wUoflP8lqP4TYwtn5UBEEDCyrDmURhmNfnc1FUipQDF7wIUvyl+y1H8VkStsyYgEligk1sTnI6/CxacynI0cRkBoPTJHlAwzAYAOE7175Ay6ekFvTHevn2Lt2/fqi2bk5NTZJmyCTKUqVy5stLlLi4uAICMjAx+mfyLVf5Fq+12qqSmpiI4OBhpaWkYMmQIvvrqK/j4+MDBwQECgQAnT55E586dIZVKtfos2tD0GeT1Bwo+w/vBR9//z8B/ueYK13Hy5MnYsGEDunXrhps3b8LK6r/WQflFz61btzTuW/67Ib+QtLe3V1lW3TplzPFcEiA5ORlhYWE4deoUbt++jRcvXiA/Px+VK1dGs2bNMHToUPTq1atY+w4NDcWcOXM0lnv69Clq166tcn1UVBSWLFmC48ePIyEhAQ4ODmjSpAnGjBmDPn36FKtuwH834irXcxxWR4Rhtj/doJP/UPxWvZ0qFL8LUPxWRPGbFFfBJOECsByLE3G30aN6a/hVUt7gQ4gcxW/V26lC8bsAxW9FZTV+U2oXExlbtzs4NY3oAE1cRgxD/iUXHByMnJwctS9VQ3e0IX9y+r43bwrSFRX+crezs1NYp8zr16+LbKfKkSNHkJKSgtatW2Pr1q1o2bIlKlSowH+Rx8bGavchdKDpM8jrD+ge2PTFzs4Oa9euhb+/Px4+fFgkx5n8Mzx9+hQcx6l9tWvXDsB/v0/qnjJrc/GlrB7mfC7LI1dXV4wYMQI7duzAo0ePwLIsRCIR4uLicODAAfTu3RvdunXjZ6UvDpFIBBcXF5UvCwvVD5YPHz6MRo0aYf369YiJiYFYLEZycjKOHz+Ovn37YsSIEcW+iGM1TFLGcixOxt+GlM0v1v4J0QbFb4rfFL8JKT4BGCx9sOf/D8cJMR6K3xS/KX6XLdSQbiLnEh9oaEanictI8agamiUnH9oVERFh0HqoGgL26NEjAIpDz+TvHz16pLKh6+HDh0W2UyUmJgYA0Lp1a6XnQ1VuNk3nTh15vVSdV3n9XVxcijzBNSahUMgPG1u6dKnC0Dv5UC758ENtyD/348ePVfYwkA9Z03Wf5n4uy5v8/Hy0aNECa9asQVRUFHJycpCZmYnnz5/zORmPHDmCsWPHFvsYbdq0QWJiosqXp6en0u2eP3+O/v37Izs7Gx988AEiIyORlpaGtLQ0zJo1CwCwZcsW/PTTT8Wql4BRf7kkYATo5OZPI8hIiVD8pvitDsVvQkqGBYcXmW+wP+ai5sKE6IDiN8VvdSh+lz3UkG4CSblp2Bh5WG0ZBkBb10bGqRApU+RDhZQNCwOADz74AM7Ozrh//z7Onz9vsHps2rRJ6fI1a9YAALp06cIvCwgIgI2NDWJjY3HgwIEi29y8eRNXrlwBwzDo3LmzxmPLZzEv/ORULjk5WWXd5NupOnfqyCcOWbVqldL1v/76q0I5U+ratSuaNGmCtLQ0hfr27t0bQEFdte25W69ePbi7uyMzMxN79+4tsj46OlrlpDnq6geUjnNZnpw+fRrXrl3Dl19+qZA70tPTExs3buQb0Ldv326QXifqzJo1C1lZWXB1dcXBgwf5i0E7OzvMmTMHY8aMAQAsWLAAKSkpOu/fQiBUu55GkBF9oPhN8VsTit+ElBxNPEr0jeI3xW9NKH6XLdSQbgKrI8Ig0TikjMG6x4eMUh9Stnh5eQEoyLOlLMWClZUVZs6cCaBg9u8DBw4U+dJ++PAhZsyYgcuXLxe7Ho8ePcK0adP4p6T5+fmYPn06bt26BXt7e3zxxRd8WQcHB3z55ZcACmaKvnPnv8l4o6KiMHToUABA//79VU5kUZh8lurdu3fj5MmT/PKEhAT06dMH+fnK0y9UrlwZ9vb2ePPmDf/kXltffvklHBwccPfuXUyaNAkSiQQAwLIslixZgkOHDkEkEuHbb7/Vab+GMnXqVADA8uXL+d+TsWPHombNmjhz5gwGDRqEhIQEhW0yMzOxe/duTJ48mV8mEAgwceJEAMD48eNx9epVft2rV6/Qv39/nXsalLZzWV60b99e7Xp5r3Sg4OLbWLKysrBv3z4ABb87FSpUKFLm+++/B1CQo/Kff/7R+RgdqvqpXU8jyIg+UPym+K0Nit+ElIyElWF1RJipq0HKEIrfFL+1QfG77KCGdCOTsvk4GX9HY75VDhzlWyXF0qRJE9SuXRsxMTHw9vZGu3bt0KVLF4SEhPBlxowZg5CQECQlJSE4OBjVqlVDQEAA2rRpA3d3dzRr1gzLli0r0ezK06dPx7Jly1C1alW0aNECVatWxaJFiyAQCLB+/Xq4ubkplJ83bx7at2+PuLg4+Pv7o379+vDz84OPjw8iIiLQuHFjrF69WqtjN23aFH379oVUKkXnzp1Rp04dNGnSBNWrV8ft27fx448/Kt2OYRj069cPAODv74/mzZujXbt2fD4yddzc3LBt2zZYWlpi+fLlcHV15T/3tGnTIBAIsGrVKjRqZB4jTfr16wcvLy8kJSVh/fr1AAp67x46dAheXl7YtWsXqlWrBl9fX7Rq1Qo+Pj6oUKECBgwYUOQCb+LEiejSpQuSkpLQunVr1KtXD/7+/vDy8kJycjJ/0SYUqu/VK1faziUpUHjiHJnMePlHL168yPdi+eijj5SW8fT05IfVFif35L3kaJXrqttWQW/PAJ33Scj7KH5T/NYGxW9CSobmNSH6RvGb4rc2KH6XHdSQbmQigQU6uTWBAOqfEJW3fKv5+fmQSqVm/1L1JNWcCAQC/P333+jVqxeEQiFu3ryJCxcu4P79+wrl5s2bh9OnT2PAgAGwsbHBgwcP8OLFC7i7u2Po0KH4+++/NfZAVadPnz44ePAg6tevj8ePHyM3NxcdOnTAmTNnEBwcXKS8tbU1jh07hhUrVqBZs2Z48eIFnjx5Al9fX8yfPx+XL19GpUqVtD7+jh07MHPmTHh6euLFixdITExE3759cePGDTRu3FjlditWrMCECRPg6uqKe/fu4dy5czh37pxWx+zRowdu3bqFQYMGwcrKCnfv3gXHcejVqxcuXrzIp5cwB0KhkH+i/PPPP/NPnevWrYt79+7hxx9/RPPmzREXF4e7d+9CIpGgbdu2WLp0Kf7880+FfVlYWODff//F/PnzUadOHURHRyMxMRFDhw7FtWvXIBaLAeg2MUlpOpekwNmzZ/n3DRs2LNY+Hj58iAYNGsDa2hp2dnbw8fHB6NGjFXrJvK9wTsH69eurLNegQQP+GLpKyElWua6Zcx2NqV/KMorf+kPxuwDFb/UofhNSMuXtPlsVit/6Q/G7AMVv9Sh+lx0Mp20iHlJs6enpcHR0RFJSEipVqoTE7Hfof3q+2slGrYQi7Go/o1QPFc/NzcXz58/h5eWl0FOxsLy8PDx48AB5eXklmmhCFY7jkJ+fDwsLC73t38rKCnXq1IFIJNLL/soaHx8fvHz5Evfu3UP9+vW1fgpK9INlWaSnp8PBwYGfJd3UPvnkExw8eBB///03Pv30U1NXR2vafIfJJScnw9nZGWlpaeVyApbU1FT4+voiISEBgYGBOud/DA0NxZw5cwAU3IxUqFAB6enp/M0TwzCYPn065s+fX2Tbb7/9FsuWLYOTkxPevXun8hiTJk3C8uXLUalSJSQlJSktk5eXh7y8PP7n9PR0eHh4oNOuiWBtld9wi4UibA4KQSVx2ZnBPjc3F7GxsfD09FT5uy+RSPDgwQP+JsAQ5PFbX6ytrVG7dm2K3yrUrVuXj9++vr5mE0PKC47jkJGRAXt7e4NcExdHjx49cOjQIezbt6/Uxe+YmBh4eHhoFb+rVq1abuN3WSW//w7685si8bss3GerQvff5RPdf5sW3X/rT2m5/y7fj2FN5FziA7WN6ED5ybcqEolQu3ZtcBxnsJuGvLw8/omcPgiFQgrihGjp1atXOHHiBIRCIVq1amXq6hADYFkWgwcPRkJCAsRiMVauXKnzPurUqYMlS5agZ8+e8PLygkgkgkQiwdmzZ/ncjgsWLICTk1OR3HwZGRkAABsbG7XHkK+Xl1dm0aJFfIN+YSO4FrBhVeyfBa6d0m1CH3NnYWEBV1dXZGZmqm0o9/LyMmoan5ISCoVgWVbhYQn5T+G+NSUZWk5KRt13lDHFxcXh5MmTEAqFqF+/PtLTS8/kjBKJBDk5OTh//rzG3qzK8hmTsq283GerQvffhJRtdP9teNSQbmRJuWnYGHlYbRkGQFvX8pN7SCQSgWEYgzy9k98UisVis+ndQ0hZNH/+fAwYMAB16tThl0VGRmLgwIHIy8tD79694erqasIaEkOZMGECDh48CABYs2aN2qGbqgwaNKjIMktLS3Tp0gVBQUEICgrCjRs3EBoailGjRsHR0bHE9Vbm+++/V5jMR94jfTNzHayGIeD/dplbZoaJy3uk29nZqewNwnEccnJyIBAIDNajTSKRwNLSkuK3kRQ+z3Z2dmbTq6q8MFWP9AULFqB///5F4veQIUOQl5eHXr16KawrDXJzc2FtbY2goCCterSR8kEABh52lWleE9D9NyFlAd1/m07ZuOMrRVZHhEHCauq9xWDd40OY7f+5UepECCEltXHjRsycORPOzs7w9PREWloanj59CgCoWbMmfv31VxPXkBhCSEgIVq1aBQD45ZdfMGLECL0fw8rKCgsXLkTnzp2RmZmJU6dOoXfv3vx6ee4/Tb0K5evV5QoUi8VKe1BJGBlYFTeDAkaADlX9YCO21vhZSguZTMbfYKu6yWZZlr9BNuSNMsMwdCNuAoZqYCGqsSwLwPjnftOmTZg1a5bK+L1y5cpS97sgf8AnEok09mKlXq7lBwsOIQ37let5TQghZQfdf5tO6boqKuWkbD5Oxt8By7Fqy3HgaCZxQkipMnPmTHTt2hVisRjh4eGIi4tD/fr1MWPGDNy8eRPu7u6mriLRs6lTp+Lnn38GAPz000+YOHGiwY7VunVr/n10dLTCOjc3NwBASkqK2sb0uLg4hfL6YikQYpxvD73ukxBCjIXiNynLBIyA/7ezuz/8KtUycY0IIUQ/KH6bDvVINyKRwAKd3JrgdPxdsGqypMt7t5WVIeKkfImMjATLsgadgI6Yn5EjR2LkyJGmrgYxkilTpmDp0qUAgCVLliAkJMRkdWnQoAH//uHDh2jevLnScuHh4QCA+vXr6/X45T3XKik7KH6XTxS/SVlmIRAiHxwsBUJ8VY8eepOyieJ3+UTx23SoR7qRja3bHZyGqUapdxshhBBzFRISotCIPmXKFIMf8+rVq/x7Ly8vhXUBAQGwti5Iq3L06FGl27948QKPHj0CAHTp0kXn41sJi6Z7EYBBDbsqlGuVEEIIMVMDa3UAQA+9CSGE6A81pBvZucQHGprRKdATQggxTyEhIXw6l6VLl+qlEV0+KZUqeXl5mDFjBgDA1tYWHTt2VFhva2uLPn36AADWrl2LtLS0IvtYvHgxgIL86J9++qnOdcyV5RVZRrlWCSGEEPPW3aMFlrUciz5egaauCiGEkDKCGtKN6F1eBjZGHlZbhgHQ1rWRcSpECCGEaGnatGl8I/qyZcvw7bffar1taGgoP2lkTEyMwrrz58+jU6dO2L59O169esUvl0qlOHXqFAIDA3Ht2jUAwKxZs1ChQoUi+587dy5sbW2RkJCATz75hJ9oJysrC3PnzsW6desAAD/88AOcnJx0+dgACnqfv89GKEYDJ0+d90UIIYQQ4xAyAjSv7AMhQ80ehBBC9IOScBvR1ifHIWFlGkoxWPf4EGb7f26UOhFCCCGavHz5EkuWLAEACAQCLF68mO/lrUxISIjWedM5jsOpU6dw6tQpAIC1tTVsbW2RlpYGqVTKH/O7777D1KlTle7Dy8sLu3fvRr9+/XDhwgV4e3vD0dERmZmZkMkK4u6wYcOK3YNe2bwm2bI87I+5iP412xZrn4QQQgghhBBCShdqSDeii6/DwdqqP+UcOJyMv43pfsFlZrJRTcP2CSHEHNF3139YllV4//r1a7XlMzMztd53w4YNsXTpUly5cgUPHjxAUlISUlNTYWNjA19fXwQGBmLMmDFo2LCh2v1069YN9+/fx+LFi3HixAnEx8ejQoUK8Pf3x9ixY/n0L/q0IfIwOrg1KbPp2OhvgBBSGtF3Fynv6G+AEFIalZbvrrLRUltKBLg0wMXMSKU92+QEjAAdqvqViUZ0obAgb6xUKuUngiOEkNJC3hta/l1Wnnl6epbowiY0NBShoaFK11WqVEmnNDHq1KpVC+vXr9fLvrQhYWVYHRFW5kaRUfwmhJRmFL9JeUXxmxBSmpWW+E3Jwozo89odwWmYatRSIMQ43x5GqpFhiUQiiMVipKWllZonS4QQAhQ8DU9LS4NYLIZIJDJ1dYiZYjkWJ+NvQ8rmm7oqekXxmxBSWlH8JuUZxW9CSGlVmuJ36e/2XIpcfftYQzM6MNqnW5kaIu7s7Iy4uDi8evUKjo6OEIlEYJj/Jm1jWRZ5eXlgGAYCgf6f63AcB4lEAgAKxyWGxbIspFIpcnNzzf5pYlnDsiwkEglyc3MN8jdV1nEcB6lUirS0NGRmZsLd3d3UVSJmrCyNInsfxe/yieK36VD8LhmK34QUoPhdPlH8Nh2K3yVTGuN32bvzM2M7o04DVqrXMwDaujYyWn2MwcGh4KFAUlIS4uLiiqyXB1qGYQwWaKVSqdk/0SprOI5Dfn4+rKysKJgYGcdxyMnJgbW1NV28loBYLIa7uzv/HUaIMmVpFNn7KH6XTxS/TYfit35Q/CblHcXv8onit+lQ/NaP0hS/qSHdiPJZGdRn02Gw7vGhMpdr1cHBAQ4ODpBKpZDJZArr8vLy8OTJE4MN35DJZEhMTESNGjXoyawR5eXlITExEa1atYKtra2pq1OuSKVSnD9/HkFBQXQBW0xCoZDOHdFKWRtF9j6K3+UPxW/TofhdchS/CSlA8bv8ofhtOhS/S660xW9qSDcilmOhriGdA4eT8bcx3S+4TA4TF4lEKv84DDm0LD8/HwKBgJ7MGhHDMMjPz4dYLIaVlZphGETvhEIh3xuhNAUjQsyZAAzY9372sKuM3p4BJquTMVH8Lj8ofpsOxW9CiL5R/C4/KH6bDsXv8oe+2f5PPrRJm1f79u2LdQwBo/50CxgBOrn5l8lGdEIIIaS0Yt+b4YQFh5CG/WAhoJ5WhBBCyq7bt29jzpw56NGjB+rWrYtKlSpBJBKhUqVK+OCDD7BgwQK8e/euWPsODQ3V6t772bNnev5UhBBCSPFRi+3/ubi4qF0vlUr5i4TmzZsX6xgWjAD5ataX5VyrhBBCSGkV4NIQF7MiwXIsBIwAHd384FeplqmrRQghhBjU5s2bsXr1av5nKysrWFtb4927d7h8+TIuX76M5cuXIywsDK1bty7WMUQiESpWrKhyvYUFNVkQQggxHxSV/i8xMVHt+p9//hkhISEAgJEjRxbrGFZCMXKRp3J9Wc+1SgghhJRGQ+p0wpV7T8FyLCwFQnxVjx56E0IIKftatGgBT09PBAQEoG7duqhQoQIAIDMzE/v27cOUKVPw9u1bfPrpp3jy5AkcHR11PkabNm1w9uxZ/VacEEIIMRBqSNfSpk2bAAABAQHw8fEp1j7SpVmApfJT7mRpV25yrRJCCCGlyeU3EZCyBWPK6KE3IYSQ8mLIkCFKl9vZ2WHo0KGoWrUqunbtijdv3uDgwYMYNGiQkWtICCGEGBflSNfC5cuX8ejRIwDAqFGjDHKMrPwcpEqyDLJvQgghhBTfzqjTAACRQIi2VRubuDaEEEKIeWjVqhX//tWrVyasCSGEEGIc1JCuBXlvdAcHB/Tr188gx8jnOKyOCDPIvgkhhBBSfPmsDAAg4zj89vigiWtDCCGEmIcLFy7w72vVorlDCCGElH3UkK5BZmYmdu/eDQAYOHAgbGxsDHIclmNxMv42P3ScEEIIIeaB5Vj+3xNxt3E3OcrENSKEEEJMIy8vDzExMVi1ahUGDx4MAKhduzY++eSTYu3v4cOHaNCgAaytrWFnZwcfHx+MHj0ad+7c0We1CSGEEL2gHOka/Pnnn8jMzASgfVqXvLw85OX9N6loWloaAECQpbqRXMAI0KZKfaSnpJWgtqVPXl4eMjIykJ2dbZAZ2VmWRXZ2NpKTkyEQ0HMjY5FIJMjOzsa7d+8U/haI4UmlUv53XiQSmbo65ca7d+8AABzHmbgmRJ/k/5/52RKwTEGvdAEYLLq6DWs/mAALgdCU1TOp3NxcZGZmIjc316DxOyUlheK3Ecnjd3p6OliWNXV1yhV5/E5PT6f4bUQZGRkAKH5rw8rKSul1/QcffICdO3dCLBYXa79JSUl49+4dKlSogPT0dDx58gRPnjzBpk2bMH36dMyfP1/jPlTdf8uvz8h/6P67bKL7b9Oh+2/TMOn9N0fUatmyJQeAa9y4sdbbzJ49mwNAL3rRi170KmevqKgowwUkYnRRUVEm/52iF73oRS96Gf5F8VuzGjVqcC4uLpytrS1/3tq3b8/duHGjWPvbvn07t2TJEi4yMpKTSCQcx3FcXl4ed+zYMa5p06b8MZYuXapxX3T/TS960Yte5fNlivjNcFzpfPy+detWDB8+vNjbHzlyBB9++KHaMvJhZgCwcuVKfP3111rt+/0n4qmpqahRowZevnwJR0fHYteZ6C49PR0eHh6IjY2Fg4ODqatTbtB5Nx0696aRlpaG6tWrIyUlBRUqVDB1dYiepKamwsnJieK3CdB3mWnQeTcdOvemQfG7eN68eYNt27ZhwYIFSE1NxQ8//IC5c+fqbf+5ubkICgrCjRs3YGdnh1evXqmNw3T/bT7ou8w06LybDp170zBl/KbULmps3LgRQMEwtkGDBmm9nVgsVjq0zdHRkf6wTMTBwYHOvQnQeTcdOvemQUNYyxb5/yfFb9Oh7zLToPNuOnTuTYPit26qVKmCb7/9FoGBgWjdujXmzZuHFi1a4OOPP9bL/q2srLBw4UJ07twZmZmZOHXqFHr37q2yPN1/mx/6LjMNOu+mQ+feNEwRv0ttQ/pnn31WokCt6cm0RCLB9u3bAQB9+vSBk5NTsY9FCCGEEEIIIYSUJS1atEBAQADOnz+P9evX660hHQBat27Nv4+OjtbbfgkhhJCSKLUN6aqeOuvLgQMHkJSUBED7SUYJIYQQQgghhJDywt3dHQDw7NkzE9eEEEIIMTwaw6aCPK1L7dq10bZt2xLtSywWY/bs2QZt+CfK0bk3DTrvpkPn3jTovJdN9P9qOnTuTYPOu+nQuTcNOu8lJ+8tbm9vr9f9Xr16lX/v5eWl07b0/2o6dO5Ng8676dC5Nw1TnvdSO9moIb18+RJeXl5gWRYLFy7E999/b+oqEUIIIYQQQgghRiGTySAQCMAwjMoyp06dQufOncFx/2vv3oOjKu8/jn82980FCqgk3BKkSI1EDKC1I1FBRVOtWOWOgRqdVmxnirWlUhi01XITp1NURCtGEblVqoxtpVwKAUWUiyBBhDIGCVBUxEggF3aT5/dHftkmkGw2yZ48u+H9msnMZs95vjznm835hGf37BpNnjxZs2fPDqi2McZv3YqKCt1www364IMPlJCQoCNHjvBhsACAkMAr0uvx8ssvq6qqSlFRUfrJT35iezoAAAAAALSaoqIiZWZm6oUXXtBnn32m2q+/Kyoq0qxZszRs2DAZY9SxY0c9/PDDdcY//vjjcrlccrlcOnToUJ1tmzZt0s0336zFixfryJEjvvs9Ho/Wr1+vrKwsffDBB5Kk6dOns4gOAAgZYfse6U6pqqrSK6+8Ikn64Q9/qJSUFLsTAgAAAACgle3evVsPPvigJCkmJkbt2rVTWVmZzpw549unZ8+eWrlypZKTkwOua4zR+vXrtX79ekmS2+1WQkKCvv32W3k8HklSRESEHn30UU2ePDmIRwQAQMuwkH6OdevW6fPPP5fEh4wCAAAAAC48Xbp00YoVK7Rx40Z98MEH+u9//6sTJ04oMjJSPXr0UL9+/TRs2DCNHTtWbre7SbUzMjI0d+5cvf/++9qzZ49OnDih4uJixcfHKz09XVlZWfrpT3+qjIwMh44OAIDm4T3SAQAAAAAAAADwg/dIb4KSkhI9/vjjysjIUGJiotq3b6+rr75aTz/9tM6ePdui2l988YUeeeQR9enTR263Wx07dlRWVpZeeuklXejPdTjR9+LiYq1atUrTp0/XHXfcoZSUFN97+NW8tQ+c6f3Ro0c1f/58jRgxQt/97nfldrvldrvVs2dPjRkzRv/+97+DfBThx4m+5+fna+rUqbr11lvVu3dvdejQQdHR0brkkks0ePBgzZs3T2VlZUE+kvDj5Hn+XA8++KDvvJOWlhbU2qiL/LaD/LaH/LaD/LaH/G6byG87yG97yG87yG97wjK/DQJy6NAhk5aWZiQZSSY+Pt7Exsb6vs/MzDQnT55sVu3t27ebTp06+WolJiaaqKgo3/dDhw415eXlQT6i8OBU3/Py8nw1zv3Ky8sL/oGEISd6f/jwYeNyuer0Oz4+3rjd7jr35ebmGq/X69CRhTanHvO33357nR4nJCSYhISEOvf17NnT7N+/34GjCg9OnufPtWHDhjq/C6mpqUGpi/OR33aQ3/aQ33aQ3/aQ320T+W0H+W0P+W0H+W1PuOY3C+kB8Hq9JiMjw0gyKSkpZu3atcYYYyorK82yZctMUlKSkWSys7ObXLu4uNgkJycbSeZ73/ue2bZtmzHGmIqKCvPss8+a6OhoI8lMnDgxqMcUDpzse15enklOTjbZ2dlm6tSpZuXKlQR5LU71vrCw0EgyN910k3n11VfN0aNHfXX37t1rhg0b5vs5TJs2LejHFeqcfMz/6U9/MvPmzTM7d+40p06d8t1/4sQJM2/ePN8fU+np6aaysjJoxxQunOz9uc6cOWN69eploqOjzcCBA/mPuIPIbzvIb3vIbzvIb3vI77aJ/LaD/LaH/LaD/LYnnPObhfQAvPTSS76Ty5YtW87bvmTJEt/2devWNan2tGnTjCTjdrvNZ599dt72GTNmGEkmMjLygnumysm+ezye8+4jyP/Hqd4XFxebHTt2NLi9qqrK3Hbbbb5XhpSVlTVr/uHKycd8Y1544QVf7XfffTeotcNBa/Z+0qRJRpKZOnWqmTBhAv8RdxD5bQf5bQ/5bQf5bQ/53Ta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"text/plain": [ "
      " ] @@ -144,9 +144,9 @@ "fig, axs = plt.subplots(1, 3, figsize=(15,5))\n", "plt.suptitle('Interpolating from Phillips to PARSEC for a 1 Gyr Cluster', fontsize=30, fontweight='bold')\n", "\n", - "axs[0].scatter(philpars_m[phil2], philpars_l[phil2], label='Phillips', color='red', marker='d', s=70)\n", + "axs[0].scatter(philpars_m[phil2], philpars_l[phil2], label='Phillips', color='mediumseagreen', marker='d', s=70)\n", "axs[0].scatter(philpars_m[parsec], philpars_l[parsec], label='PARSEC', color='orange', marker='P', s=70)\n", - "axs[0].scatter(philpars_m[philpars_interp], philpars_l[philpars_interp], label='Interpolated Values', color='black', s=100)\n", + "axs[0].scatter(philpars_m[philpars_interp], philpars_l[philpars_interp], label='Interpolated Values', facecolors='none', edgecolors='black', lw=2, s=80)\n", "axs[0].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", "axs[0].set_title('Mass vs. Luminosity', fontsize=24)\n", "axs[0].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", @@ -157,9 +157,9 @@ "axs[0].grid()\n", "axs[0].legend(loc='lower right', fontsize=16)\n", "\n", - "axs[1].scatter(philpars_m[phil2], philpars_t[phil2], label='Phillips', color='red', marker='d', s=70)\n", + "axs[1].scatter(philpars_m[phil2], philpars_t[phil2], label='Phillips', color='mediumseagreen', marker='d', s=70)\n", "axs[1].scatter(philpars_m[parsec], philpars_t[parsec], label='PARSEC', color='orange', marker='P', s=70)\n", - "axs[1].scatter(philpars_m[philpars_interp], philpars_t[philpars_interp], label='Interpolated Values', color='black', s=100)\n", + "axs[1].scatter(philpars_m[philpars_interp], philpars_t[philpars_interp], label='Interpolated Values', facecolors='none', edgecolors='black', lw=2, s=80)\n", "axs[1].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", "axs[1].set_title('Mass vs. Effective Temperature', fontsize=24)\n", "axs[1].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", @@ -170,9 +170,9 @@ "axs[1].grid()\n", "axs[1].legend(loc='lower right', fontsize=16)\n", "\n", - "axs[2].scatter(philpars_m[phil2], philpars_g[phil2], label='Phillips', color='red', marker='d', s=70)\n", + "axs[2].scatter(philpars_m[phil2], philpars_g[phil2], label='Phillips', color='mediumseagreen', marker='d', s=70)\n", "axs[2].scatter(philpars_m[parsec], philpars_g[parsec], label='PARSEC', color='orange', marker='P', s=70)\n", - "axs[2].scatter(philpars_m[philpars_interp], philpars_g[philpars_interp], label='Interpolated Values', color='black', s=100)\n", + "axs[2].scatter(philpars_m[philpars_interp], philpars_g[philpars_interp], label='Interpolated Values', facecolors='none', edgecolors='black', lw=2, s=80)\n", "axs[2].axvspan(0.075, 0.2, alpha=0.3, color='tab:gray', label='Interpolation Region')\n", "axs[2].set_title('Mass vs. Surface Gravity', fontsize=24)\n", "axs[2].set_xlabel('Mass (M$_\\odot$)', fontsize=20)\n", diff --git a/docs/paper_examples/Begbie+26/Figure 5.ipynb b/docs/paper_examples/Begbie+26/Figure 5.ipynb index 08e34412..a3ad8da2 100644 --- a/docs/paper_examples/Begbie+26/Figure 5.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 5.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "3d80653b-a8ee-422d-b008-14dc490ad520", "metadata": {}, "outputs": [], @@ -26,10 +26,20 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone loaded from existing file: .//iso_6.00_0.00_00010_p0.00.fits\n", + "Isochrone loaded from existing file: .//iso_8.00_0.00_00010_p0.00.fits\n", + "Isochrone loaded from existing file: .//iso_10.00_0.00_00010_p0.00.fits\n" + ] + } + ], "source": [ "# Defining three different isochrones (logage = 6, 8, 10)\n", "my_ifmr = ifmr.IFMR_Raithel18()\n", @@ -48,16 +58,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/u/caitlinbegbie/.local/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:501: RuntimeWarning: invalid value encountered in divide\n", - " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + "Remove low mass stars, keep compact objects\n", + "Remove low mass stars, keep compact objects\n", + "Remove low mass stars, keep compact objects\n" ] } ], @@ -79,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "1c3f73bf-52ac-446a-897c-cdf5a91dde17", "metadata": {}, "outputs": [], @@ -109,13 +120,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "cbbf0bea-0d3a-47fa-9bfb-c6ac0167bc0c", "metadata": {}, "outputs": [ { "data": { - "image/png": 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      " ] @@ -130,8 +141,8 @@ "\n", "plt.suptitle('Cluster Evolution over Time (with Brown Dwarfs)', fontsize=30, fontweight='bold')\n", "\n", - "axs[0].scatter(log_teff_young[~bd_idx_young], log_lum_young[~bd_idx_young], s=50, color='black')\n", - "axs[0].scatter(log_teff_young[bd_idx_young], log_lum_young[bd_idx_young], marker='d', s=50, color='red', label='Brown Dwarfs')\n", + "axs[0].scatter(log_teff_young[~bd_idx_young], log_lum_young[~bd_idx_young], s=40, color='black', alpha=0.6)\n", + "axs[0].scatter(log_teff_young[bd_idx_young], log_lum_young[bd_idx_young], marker='d', s=40, color='mediumseagreen', label='Brown Dwarfs')\n", "axs[0].invert_xaxis()\n", "axs[0].set_title('1 Myr Cluster', fontsize=25)\n", "axs[0].set_xlabel('log(Teff) [K]', fontsize=20)\n", @@ -140,8 +151,8 @@ "axs[0].legend(loc='lower left', fontsize=18)\n", "axs[0].grid()\n", "\n", - "axs[1].scatter(log_teff_med[~bd_idx_med], log_lum_med[~bd_idx_med], s=50, color='black')\n", - "axs[1].scatter(log_teff_med[bd_idx_med], log_lum_med[bd_idx_med], marker='d', s=50, color='red', label='Brown Dwarfs')\n", + "axs[1].scatter(log_teff_med[~bd_idx_med], log_lum_med[~bd_idx_med], s=40, color='black', alpha=0.6)\n", + "axs[1].scatter(log_teff_med[bd_idx_med], log_lum_med[bd_idx_med], marker='d', s=40, color='mediumseagreen', label='Brown Dwarfs')\n", "axs[1].invert_xaxis()\n", "axs[1].set_title('100 Myr Cluster', fontsize=25)\n", "axs[1].set_xlabel('log(Teff) [K]', fontsize=20)\n", @@ -150,8 +161,8 @@ "axs[1].legend(loc='lower left', fontsize=18)\n", "axs[1].grid()\n", "\n", - "axs[2].scatter(log_teff_old[~bd_idx_old], log_lum_old[~bd_idx_old], s=50, color='black')\n", - "axs[2].scatter(log_teff_old[bd_idx_old], log_lum_old[bd_idx_old], marker='d', s=50, color='red', label='Brown Dwarfs')\n", + "axs[2].scatter(log_teff_old[~bd_idx_old], log_lum_old[~bd_idx_old], s=40, color='black', alpha=0.6)\n", + "axs[2].scatter(log_teff_old[bd_idx_old], log_lum_old[bd_idx_old], marker='d', s=40, color='mediumseagreen', label='Brown Dwarfs')\n", "axs[2].invert_xaxis()\n", "axs[2].set_title('10 Gyr Cluster', fontsize=25)\n", "axs[2].set_xlabel('log(Teff) [K]', fontsize=20)\n", diff --git a/docs/paper_examples/Begbie+26/Figure 6.ipynb b/docs/paper_examples/Begbie+26/Figure 6.ipynb index 3c0759d1..86df8fc0 100644 --- a/docs/paper_examples/Begbie+26/Figure 6.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 6.ipynb @@ -12,19 +12,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "id": "3d80653b-a8ee-422d-b008-14dc490ad520", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "# Importing necessary packages.\n", "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", @@ -43,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", "metadata": {}, "outputs": [], @@ -54,51 +45,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "id": "b91cb1a8-8fc6-4939-9f3b-420082b20340", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Changing to T= 250 for T= 248 logg=3.06\n", - "Changing to logg=5.00 for T= 1542 logg=4.64\n", - "Changing to logg=5.00 for T= 1587 logg=4.66\n", - "Changing to logg=5.00 for T= 1632 logg=4.68\n", - "Changing to logg=5.00 for T= 1677 logg=4.70\n", - "Changing to logg=5.00 for T= 1725 logg=4.71\n", - "Changing to logg=5.00 for T= 1775 logg=4.73\n", - "Changing to logg=5.00 for T= 1820 logg=4.74\n", - "Changing to logg=5.00 for T= 1858 logg=4.75\n", - "Changing to logg=5.00 for T= 1895 logg=4.76\n", - "Changing to logg=5.00 for T= 1936 logg=4.78\n", - "Changing to logg=5.00 for T= 1977 logg=4.79\n", - "Isochrone generation took 13.853408 s.\n", - "Making photometry for isochrone: log(t) = 8.10 AKs = 0.00 dist = 134\n", - " Starting at: 2026-04-03 17:07:51.819141 Usually takes ~5 minutes\n", - "Starting filter: gaia,dr2_rev,G Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.001 Msun T = 248 K m_gaiaDR2_G = 37.41\n", - "M = 0.316 Msun T = 3192 K m_gaiaDR2_G = 16.60\n", - "M = 4.612 Msun T = 11585 K m_gaiaDR2_G = 3.29\n", - "M = 4.833 Msun T = 3785 K m_gaiaDR2_G = 2.11\n", - "Starting filter: gaia,dr2_rev,Gbp Elapsed time: 2.96 seconds\n", - "Starting synthetic photometry\n", - "M = 0.001 Msun T = 248 K m_gaiaDR2_Gbp = 41.58\n", - "M = 0.316 Msun T = 3192 K m_gaiaDR2_Gbp = 18.02\n", - "M = 4.612 Msun T = 11585 K m_gaiaDR2_Gbp = 3.24\n", - "M = 4.833 Msun T = 3785 K m_gaiaDR2_Gbp = 2.95\n", - "Starting filter: gaia,dr2_rev,Grp Elapsed time: 9.01 seconds\n", - "Starting synthetic photometry\n", - "M = 0.001 Msun T = 248 K m_gaiaDR2_Grp = 35.84\n", - "M = 0.316 Msun T = 3192 K m_gaiaDR2_Grp = 15.20\n", - "M = 4.612 Msun T = 11585 K m_gaiaDR2_Grp = 3.37\n", - "M = 4.833 Msun T = 3785 K m_gaiaDR2_Grp = 1.00\n", - " Time taken: 11.95 seconds\n" - ] - } - ], + "outputs": [], "source": [ "# Define isochrone parameters\n", "logAge = 8.10 # Age in log(years)\n", @@ -132,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "0eb22c21-cc15-4611-b9df-dcdf0473c365", "metadata": {}, "outputs": [], @@ -154,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "id": "c631060e-4f68-4534-9326-a5bf5cbef20a", "metadata": {}, "outputs": [ @@ -162,7 +112,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 12 stars out of mass range\n" + "Remove low mass stars below grid and compact objects\n", + "Found 9 stars out of mass range\n" ] } ], @@ -188,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "id": "1d4cf2ed-92fb-4a72-b64e-ecbb3ce47d11", "metadata": {}, "outputs": [ @@ -206,7 +157,7 @@ "2000" ] }, - "execution_count": 14, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -220,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "id": "1d18cfcf-df2b-4785-a2f3-9b8b9a74cd45", "metadata": {}, "outputs": [], @@ -244,13 +195,45 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, + "id": "9dbf8948-07b3-4302-bafa-09e760e45591", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0 Msun -> actual mass = 1.0000\n", + "0.5 Msun -> actual mass = 0.5000\n", + "0.08 Msun -> actual mass = 0.0816\n" + ] + } + ], + "source": [ + "marker_masses = [1.0, 0.5, 0.08]\n", + "\n", + "marker_points = {}\n", + "\n", + "for m in marker_masses:\n", + " idx = np.argmin(np.abs(iso['mass'] - m))\n", + "\n", + " marker_points[m] = {\n", + " 'color': iso['m_gaiaDR2_Gbp'][idx] - iso['m_gaiaDR2_Grp'][idx],\n", + " 'mag': iso['m_gaiaDR2_G'][idx]\n", + " }\n", + "\n", + " print(f\"{m} Msun -> actual mass = {iso['mass'][idx]:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "id": "514d3a68-227c-43a6-b2f8-e4355c4abe4f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
      " ] @@ -268,21 +251,59 @@ "axs[0].plot(color[phillips], mag[phillips], '-', color='purple', label='Phillips (BD)', linewidth=3)\n", "axs[0].plot(color[phillips_baraffe], mag[phillips_baraffe], '-', color='violet', label='Phillips/Baraffe transition', linewidth=3)\n", "axs[0].plot(color[baraffe], mag[baraffe], '-', color='orange', label='Baraffe', linewidth=3)\n", - "axs[0].plot(color[baraffe_pisa], mag[baraffe_pisa], '-', color='green', label='Baraffe/Pisa transition', linewidth=3)\n", + "axs[0].plot(color[baraffe_pisa], mag[baraffe_pisa], '-', color='red', label='Baraffe/Pisa transition', linewidth=3)\n", "axs[0].plot(color[pisa], mag[pisa], '-', color='blue', label='Pisa', linewidth=3)\n", "#axs[0].plot(color[ms], mag[ms], '-', color='red', label='Ekstrom MS', linewidth=3)\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[0].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[0].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", "axs[0].invert_yaxis()\n", "axs[0].set_xlabel(r'$G_{BP}-G_{RP}$', fontsize=20)\n", "axs[0].set_ylabel('G', fontsize=20)\n", "axs[0].set_title('100 Myr Expected Pleiades Isochrone \\n (Colored by Evolutionary Model)', fontsize=24)\n", - "axs[0].legend(markerscale=4, fontsize=18, loc='upper right')\n", + "axs[0].legend(markerscale=4, fontsize=16, loc='upper right')\n", "axs[0].tick_params(axis='both', labelsize=18)\n", "axs[0].grid()\n", "\n", "axs[1].plot(clust['m_gaiaDR2_Gbp'] - clust['m_gaiaDR2_Grp'], clust['m_gaiaDR2_G'],\n", " 'k.', ms=10, alpha=0.1, label='Simulated Pleiades Data')\n", "axs[1].plot(iso['m_gaiaDR2_Gbp'] - iso['m_gaiaDR2_Grp'], iso['m_gaiaDR2_G'],\n", - " 'r-', linewidth=3, label='Theoretical Pleiades Isochrone')\n", + " color='mediumseagreen', linewidth=3, label='Theoretical Pleiades Isochrone')\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[1].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[1].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", "axs[1].set_xlabel(r'$G_{BP}-G_{RP}$', fontsize=20)\n", "axs[1].set_ylabel('G', fontsize=20)\n", "axs[1].invert_yaxis()\n", @@ -292,9 +313,28 @@ "axs[1].grid()\n", "\n", "axs[2].plot(real_bp_rp, real_g,\n", - " 'm.', ms=10, alpha=0.3, label='real Gaia Pleiades Data')\n", + " 'm.', ms=10, alpha=0.3, label='Real Gaia Pleiades Data')\n", "axs[2].plot(iso['m_gaiaDR2_Gbp'] - iso['m_gaiaDR2_Grp'], iso['m_gaiaDR2_G'],\n", - " 'r-', linewidth=3, label='Theoretical Pleiades Isochrone')\n", + " color='mediumseagreen', linewidth=3, label='Theoretical Pleiades Isochrone')\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[2].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[2].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", "axs[2].set_xlabel(r'$G_{BP}-G_{RP}$', fontsize=20)\n", "axs[2].set_ylabel('G', fontsize=20)\n", "axs[2].invert_yaxis()\n", diff --git a/docs/paper_examples/Begbie+26/Figure 7.ipynb b/docs/paper_examples/Begbie+26/Figure 7.ipynb index a63c322a..35a2c8f3 100644 --- a/docs/paper_examples/Begbie+26/Figure 7.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 7.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "3d80653b-a8ee-422d-b008-14dc490ad520", "metadata": {}, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", "metadata": {}, "outputs": [], @@ -45,51 +45,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "b91cb1a8-8fc6-4939-9f3b-420082b20340", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Changing to T= 250 for T= 248 logg=3.06\n", - "Changing to logg=5.00 for T= 1542 logg=4.64\n", - "Changing to logg=5.00 for T= 1587 logg=4.66\n", - "Changing to logg=5.00 for T= 1632 logg=4.68\n", - "Changing to logg=5.00 for T= 1677 logg=4.70\n", - "Changing to logg=5.00 for T= 1725 logg=4.71\n", - "Changing to logg=5.00 for T= 1775 logg=4.73\n", - "Changing to logg=5.00 for T= 1820 logg=4.74\n", - "Changing to logg=5.00 for T= 1858 logg=4.75\n", - "Changing to logg=5.00 for T= 1895 logg=4.76\n", - "Changing to logg=5.00 for T= 1936 logg=4.78\n", - "Changing to logg=5.00 for T= 1977 logg=4.79\n", - "Isochrone generation took 13.891200 s.\n", - "Making photometry for isochrone: log(t) = 8.10 AKs = 0.00 dist = 134\n", - " Starting at: 2026-04-03 17:08:33.466620 Usually takes ~5 minutes\n", - "Starting filter: ukirt,J Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.001 Msun T = 248 K m_ukirt_J = 34.45\n", - "M = 0.316 Msun T = 3192 K m_ukirt_J = 13.27\n", - "M = 4.612 Msun T = 11585 K m_ukirt_J = 3.50\n", - "M = 4.833 Msun T = 3785 K m_ukirt_J = -0.44\n", - "Starting filter: ukirt,H Elapsed time: 1.63 seconds\n", - "Starting synthetic photometry\n", - "M = 0.001 Msun T = 248 K m_ukirt_H = 34.18\n", - "M = 0.316 Msun T = 3192 K m_ukirt_H = 12.72\n", - "M = 4.612 Msun T = 11585 K m_ukirt_H = 3.52\n", - "M = 4.833 Msun T = 3785 K m_ukirt_H = -1.24\n", - "Starting filter: ukirt,K Elapsed time: 3.91 seconds\n", - "Starting synthetic photometry\n", - "M = 0.001 Msun T = 248 K m_ukirt_K = 37.76\n", - "M = 0.316 Msun T = 3192 K m_ukirt_K = 12.45\n", - "M = 4.612 Msun T = 11585 K m_ukirt_K = 3.55\n", - "M = 4.833 Msun T = 3785 K m_ukirt_K = -1.45\n", - " Time taken: 6.20 seconds\n" - ] - } - ], + "outputs": [], "source": [ "# Define isochrone parameters\n", "logAge = 8.10 # Age in log(years)\n", @@ -103,7 +62,7 @@ "red_law = reddening.RedLawHosek18b()\n", "\n", "# Specify Gaia filters\n", - "ukidss_filts = ['ukirt,J', 'ukirt,H', 'ukirt,K']\n", + "ukidss_filts = ['ukirt,Z', 'ukirt,Y', 'ukirt,J', 'ukirt,H', 'ukirt,K']\n", "\n", "# Specify the directory we want the output isochrone\n", "# table saved in. If the directory does not already exist,\n", @@ -123,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "0eb22c21-cc15-4611-b9df-dcdf0473c365", "metadata": {}, "outputs": [], @@ -139,13 +98,13 @@ "pisa = (m >= 0.5) & (m < 7.0)\n", "ms = m >= 7.0 # Ekstrom at this age\n", "\n", - "color = my_iso.points['m_ukirt_J'] - my_iso.points['m_ukirt_K']\n", - "mag = my_iso.points['m_ukirt_J']" + "color = my_iso.points['m_ukirt_Z'] - my_iso.points['m_ukirt_J']\n", + "mag = my_iso.points['m_ukirt_Z']" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "c631060e-4f68-4534-9326-a5bf5cbef20a", "metadata": {}, "outputs": [ @@ -153,7 +112,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 12 stars out of mass range\n" + "Remove low mass stars below grid and compact objects\n", + "Found 16 stars out of mass range\n" ] } ], @@ -179,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "1d4cf2ed-92fb-4a72-b64e-ecbb3ce47d11", "metadata": {}, "outputs": [ @@ -196,24 +156,56 @@ "other = '/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/docs/paper_examples/Begbie+26/cluster_data/asu (2).fit'\n", "tab = Table.read(other, format='fits', hdu=2)\n", "\n", - "focus = np.where((tab['Jmag'] - tab['K2mag'] < 10))\n", - "minus = tab['Jmag'] - tab['K2mag']\n", - "J_K = minus[focus]\n", + "focus = np.where((tab['Zmag'] - tab['Jmag'] < 10) & (tab['Zmag'] > 0))\n", + "minus = tab['Zmag'] - tab['Jmag']\n", + "Z_J = minus[focus]\n", "\n", + "z_err = tab['e_Zmag']\n", "j_err = tab['e_Jmag']\n", - "k_err = tab['e_K1mag']\n", - "jk_err = np.sqrt(j_err**2 + k_err**2)" + "zj_err = np.sqrt(z_err**2 + j_err**2)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, + "id": "64ea3fad-4e9a-4d57-ae55-dbc33931a09c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0 Msun -> actual mass = 1.0000\n", + "0.5 Msun -> actual mass = 0.5000\n", + "0.08 Msun -> actual mass = 0.0816\n" + ] + } + ], + "source": [ + "marker_masses = [1.0, 0.5, 0.08]\n", + "\n", + "marker_points = {}\n", + "\n", + "for m in marker_masses:\n", + " idx = np.argmin(np.abs(iso['mass'] - m))\n", + "\n", + " marker_points[m] = {\n", + " 'color': color[idx],\n", + " 'mag': mag[idx]\n", + " }\n", + "\n", + " print(f\"{m} Msun -> actual mass = {iso['mass'][idx]:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "id": "514d3a68-227c-43a6-b2f8-e4355c4abe4f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
      " ] @@ -231,38 +223,94 @@ "axs[0].plot(color[phillips], mag[phillips], '-', color='purple', label='Phillips (BD)', linewidth=3)\n", "axs[0].plot(color[phillips_baraffe], mag[phillips_baraffe], '-', color='violet', label='Phillips/Baraffe transition', linewidth=3)\n", "axs[0].plot(color[baraffe], mag[baraffe], '-', color='orange', label='Baraffe', linewidth=3)\n", - "axs[0].plot(color[baraffe_pisa], mag[baraffe_pisa], '-', color='green', label='Baraffe/Pisa transition', linewidth=3)\n", + "axs[0].plot(color[baraffe_pisa], mag[baraffe_pisa], '-', color='red', label='Baraffe/Pisa transition', linewidth=3)\n", "axs[0].plot(color[pisa], mag[pisa], '-', color='blue', label='Pisa', linewidth=3)\n", "#axs[0].plot(color[ms], mag[ms], '-', color='red', label='Ekstrom MS', linewidth=3)\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[0].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[0].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", "axs[0].invert_yaxis()\n", - "axs[0].set_xlabel('J - K1', fontsize=20)\n", - "axs[0].set_ylabel('J', fontsize=20)\n", + "axs[0].set_xlabel('Z - J', fontsize=20)\n", + "axs[0].set_ylabel('Z', fontsize=20)\n", "axs[0].set_title('100 Myr Expected Pleiades Isochrone \\n (Colored by Evolutionary Model)', fontsize=24)\n", "axs[0].legend(markerscale=4, fontsize=18, loc='upper right')\n", "axs[0].tick_params(axis='both', labelsize=18)\n", "axs[0].grid()\n", "\n", - "axs[1].plot(clust['m_ukirt_J'] - clust['m_ukirt_K'], clust['m_ukirt_J'],\n", + "axs[1].plot(clust['m_ukirt_Z'] - clust['m_ukirt_J'], clust['m_ukirt_Z'],\n", " 'k.', ms=10, alpha=0.1, label='Simulated Pleiades Data')\n", - "axs[1].plot(my_iso.points['m_ukirt_J'][mask] - my_iso.points['m_ukirt_K'][mask], \n", - " my_iso.points['m_ukirt_J'][mask],\n", - " 'r-', linewidth=3, label='Theoretical Pleiades Isochrone')\n", - "axs[1].set_xlabel('J - K1', fontsize=20)\n", - "axs[1].set_ylabel('J', fontsize=20)\n", + "axs[1].plot(my_iso.points['m_ukirt_Z'][mask] - my_iso.points['m_ukirt_J'][mask], \n", + " my_iso.points['m_ukirt_Z'][mask],\n", + " 'mediumseagreen', linewidth=3, label='Theoretical Pleiades Isochrone')\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[1].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[1].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", + "axs[1].set_xlabel('Z - J', fontsize=20)\n", + "axs[1].set_ylabel('Z', fontsize=20)\n", "axs[1].invert_yaxis()\n", "axs[1].set_title('Simulated Pleiades Cluster', fontsize=24)\n", "axs[1].legend(fontsize=18, loc='upper right')\n", "axs[1].tick_params(axis='both', labelsize=18)\n", "axs[1].grid()\n", "\n", - "axs[2].plot(J_K, tab['Jmag'][focus],\n", - " 'm.', ms=10, alpha=0.3, label='real Gaia Pleiades Data')\n", - "axs[2].errorbar(J_K, tab['Jmag'][focus], xerr=jk_err[focus], yerr=j_err[focus], color='m', fmt='none', ms=5, alpha=0.3, label='Data Errors')\n", - "axs[2].plot(my_iso.points['m_ukirt_J'][mask] - my_iso.points['m_ukirt_K'][mask], \n", - " my_iso.points['m_ukirt_J'][mask],\n", - " 'r-', linewidth=3, label='Theoretical Pleiades Isochrone')\n", - "axs[2].set_xlabel('J - K1', fontsize=20)\n", - "axs[2].set_ylabel('J', fontsize=20)\n", + "\n", + "axs[2].errorbar(Z_J, tab['Zmag'][focus], xerr=zj_err[focus], yerr=z_err[focus], fmt='mo', ms=8, alpha=0.3, label='Real UKIDSS Data/Errors')\n", + "axs[2].plot(my_iso.points['m_ukirt_Z'][mask] - my_iso.points['m_ukirt_J'][mask], \n", + " my_iso.points['m_ukirt_Z'][mask],\n", + " 'mediumseagreen', linewidth=3, zorder=50, label='Theoretical Pleiades Isochrone')\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[2].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[2].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", + "axs[2].set_xlabel('Z - J', fontsize=20)\n", + "axs[2].set_ylabel('Z', fontsize=20)\n", "axs[2].invert_yaxis()\n", "axs[2].set_title('Real Pleiades Cluster vs. \\n Theoretical Isochrone', fontsize=24)\n", "axs[2].legend(fontsize=18, loc='upper right')\n", diff --git a/docs/paper_examples/Begbie+26/Figure 8.ipynb b/docs/paper_examples/Begbie+26/Figure 8.ipynb index e3a77265..a267e83a 100644 --- a/docs/paper_examples/Begbie+26/Figure 8.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 8.ipynb @@ -13,6 +13,32 @@ { "cell_type": "code", "execution_count": 1, + "id": "aa61aecf-9b3a-458d-8188-62668d8dde28", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA_merged/mergeconflicts/spisea/__init__.py\n" + ] + } + ], + "source": [ + "import sys\n", + "\n", + "sys.path.insert(\n", + " 0,\n", + " \"/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA_merged/mergeconflicts\"\n", + ")\n", + "\n", + "import spisea\n", + "print(spisea.__file__)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "3d80653b-a8ee-422d-b008-14dc490ad520", "metadata": {}, "outputs": [], @@ -34,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", "metadata": {}, "outputs": [], @@ -45,554 +71,26 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "b91cb1a8-8fc6-4939-9f3b-420082b20340", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Changing to logg=5.00 for T= 1614 logg=3.86\n", - "Changing to logg=5.00 for T= 1734 logg=3.90\n", - "Changing to logg=5.00 for T= 1844 logg=3.94\n", - "Changing to logg=5.00 for T= 1939 logg=3.96\n", - "Changing to logg=4.00 for T= 32651 logg=3.99\n", - "Changing to logg=4.00 for T= 32840 logg=3.98\n", - "Changing to logg=4.00 for T= 33037 logg=3.97\n", - "Changing to logg=4.00 for T= 33144 logg=3.96\n", - "Changing to logg=4.00 for T= 33205 logg=3.95\n", - "Changing to logg=4.00 for T= 33358 logg=3.94\n", - "Changing to logg=4.00 for T= 33504 logg=3.93\n", - "Changing to logg=4.00 for T= 33651 logg=3.91\n", - "Changing to logg=4.00 for T= 33713 logg=3.91\n", - "Changing to logg=4.00 for T= 33775 logg=3.90\n", - "Changing to logg=4.00 for T= 33892 logg=3.89\n", - "Changing to logg=4.00 for T= 34002 logg=3.87\n", - "Changing to logg=4.00 for T= 34111 logg=3.86\n", - "Changing to logg=4.00 for T= 34182 logg=3.85\n", - "Changing to logg=4.00 for T= 34222 logg=3.85\n", - "Changing to logg=4.00 for T= 34332 logg=3.83\n", - "Changing to logg=4.00 for T= 34443 logg=3.82\n", - "Changing to logg=4.00 for T= 34546 logg=3.81\n", - "Changing to logg=4.00 for T= 34658 logg=3.80\n", - "Changing to logg=4.00 for T= 34674 logg=3.80\n", - "Changing to logg=4.00 for T= 34746 logg=3.79\n", - "Changing to logg=4.00 for T= 34842 logg=3.78\n", - "Changing to logg=4.00 for T= 34930 logg=3.77\n", - "Changing to logg=4.00 for T= 35019 logg=3.76\n", - "Changing to logg=4.00 for T= 35108 logg=3.75\n", - "Changing to logg=4.00 for T= 35124 logg=3.74\n", - "Changing to logg=4.00 for T= 35229 logg=3.74\n", - "Changing to logg=4.00 for T= 35351 logg=3.73\n", - "Changing to logg=4.00 for T= 35465 logg=3.72\n", - 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"Changing to logg=5.00 for T= 33504 logg=5.08\n", - "Changing to logg=5.00 for T= 33558 logg=5.07\n", - "Changing to logg=5.00 for T= 33605 logg=5.08\n", - "Changing to logg=5.00 for T= 33651 logg=5.08\n", - "Changing to logg=5.00 for T= 33674 logg=5.07\n", - "Changing to logg=5.00 for T= 32870 logg=5.05\n", - "Changing to logg=5.00 for T= 30054 logg=5.01\n", - "Changing to logg=5.00 for T= 29819 logg=5.01\n", - "Changing to logg=5.00 for T= 35785 logg=5.39\n", - "Changing to logg=5.00 for T= 39683 logg=5.62\n", - "Changing to logg=5.00 for T= 39701 logg=5.62\n", - "Changing to logg=5.00 for T= 39655 logg=5.63\n", - "Changing to logg=5.00 for T= 39518 logg=5.63\n", - "Changing to logg=5.00 for T= 39373 logg=5.63\n", - "Changing to logg=5.00 for T= 39518 logg=5.65\n", - "Changing to logg=5.00 for T= 40031 logg=5.69\n", - "Changing to logg=5.00 for T= 40096 logg=5.70\n", - "Changing to logg=5.00 for T= 40495 logg=5.74\n", - "Changing to logg=5.00 for T= 41381 logg=5.82\n", - "Changing to logg=5.00 for T= 41976 logg=5.87\n", - "Changing to logg=5.00 for T= 42199 logg=5.90\n", - "Changing to logg=5.00 for T= 42228 logg=5.90\n", - "Changing to logg=5.00 for T= 42286 logg=5.90\n", - "Changing to logg=5.00 for T= 42345 logg=5.91\n", - "Changing to logg=5.00 for T= 42394 logg=5.92\n", - "Changing to logg=5.00 for T= 42462 logg=5.92\n", - "Changing to logg=5.00 for T= 42530 logg=5.92\n", - "Changing to logg=5.00 for T= 42619 logg=5.92\n", - "Changing to logg=5.00 for T= 42717 logg=5.93\n", - "Changing to logg=5.00 for T= 42825 logg=5.93\n", - "Changing to logg=5.00 for T= 42934 logg=5.94\n", - "Changing to logg=5.00 for T= 43053 logg=5.94\n", - "Changing to logg=5.00 for T= 43152 logg=5.94\n", - "Changing to logg=5.00 for T= 43271 logg=5.95\n", - "Changing to logg=5.00 for T= 43411 logg=5.95\n", - "Changing to logg=5.00 for T= 43561 logg=5.96\n", - "Changing to logg=5.00 for T= 43692 logg=5.96\n", - "Changing to logg=5.00 for T= 43833 logg=5.96\n", - "Changing to logg=5.00 for T= 43985 logg=5.97\n", - "Changing to logg=5.00 for T= 44147 logg=5.97\n", - "Changing to logg=5.00 for T= 44310 logg=5.97\n", - "Changing to logg=5.00 for T= 44484 logg=5.97\n", - "Changing to logg=5.00 for T= 44638 logg=5.97\n", - "Changing to logg=5.00 for T= 44802 logg=5.98\n", - "Changing to logg=5.00 for T= 44957 logg=5.98\n", - "Changing to logg=5.00 for T= 45144 logg=5.98\n", - "Changing to logg=5.00 for T= 45300 logg=5.99\n", - "Changing to logg=5.00 for T= 45478 logg=5.99\n", - "Changing to logg=5.00 for T= 45509 logg=5.99\n", - "Changing to logg=5.00 for T= 45646 logg=5.99\n", - "Changing to logg=5.00 for T= 45825 logg=5.99\n", - "Changing to logg=5.00 for T= 45983 logg=6.00\n", - "Changing to logg=5.00 for T= 46142 logg=6.00\n", - "Changing to logg=5.00 for T= 46313 logg=6.00\n", - "Changing to logg=5.00 for T= 46473 logg=6.00\n", - "Changing to logg=5.00 for T= 46623 logg=6.00\n", - "Changing to logg=5.00 for T= 46784 logg=6.00\n", - "Changing to logg=5.00 for T= 46946 logg=6.00\n", - "Changing to logg=5.00 for T= 47109 logg=6.00\n", - "Changing to logg=5.00 for T= 47261 logg=6.00\n", - "Changing to logg=5.00 for T= 47413 logg=6.01\n", - "Changing to logg=5.00 for T= 47566 logg=6.01\n", - "Changing to logg=5.00 for T= 47720 logg=6.01\n", - "Changing to logg=5.00 for T= 47863 logg=6.01\n", - "Changing to logg=5.00 for T= 48029 logg=6.01\n", - "Changing to logg=5.00 for T= 48173 logg=6.01\n", - "Changing to logg=5.00 for T= 48317 logg=6.02\n", - "Changing to logg=5.00 for T= 48473 logg=6.02\n", - "Changing to logg=5.00 for T= 48618 logg=6.02\n", - "Changing to logg=5.00 for T= 48753 logg=6.02\n", - "Changing to logg=5.00 for T= 48910 logg=6.02\n", - "Changing to logg=5.00 for T= 49057 logg=6.02\n", - "Changing to logg=5.00 for T= 49193 logg=6.02\n", - "Changing to logg=5.00 for T= 49329 logg=6.03\n", - "Changing to logg=5.00 for T= 49431 logg=6.03\n", - "Changing to logg=5.00 for T= 49465 logg=6.03\n", - "Changing to logg=5.00 for T= 49614 logg=6.03\n", - "Changing to logg=5.00 for T= 49751 logg=6.03\n", - "Changing to logg=5.00 for T= 49888 logg=6.03\n", - "Changing to T= 50000 for T= 50026 logg=6.03\n", - "Changing to logg=5.00 for T= 50026 logg=6.03\n", - "Changing to T= 50000 for T= 50176 logg=6.03\n", - "Changing to logg=5.00 for T= 50176 logg=6.03\n", - "Changing to T= 50000 for T= 50304 logg=6.03\n", - "Changing to logg=5.00 for T= 50304 logg=6.03\n", - "Changing to T= 50000 for T= 50431 logg=6.04\n", - "Changing to logg=5.00 for T= 50431 logg=6.04\n", - "Changing to T= 50000 for T= 50559 logg=6.04\n", - "Changing to logg=5.00 for T= 50559 logg=6.04\n", - "Changing to T= 50000 for T= 50699 logg=6.04\n", - "Changing to logg=5.00 for T= 50699 logg=6.04\n", - "Changing to T= 50000 for T= 50839 logg=6.04\n", - "Changing to logg=5.00 for T= 50839 logg=6.04\n", - "Changing to T= 50000 for T= 50957 logg=6.04\n", - "Changing to logg=5.00 for T= 50957 logg=6.04\n", - "Changing to T= 50000 for T= 51086 logg=6.04\n", - "Changing to logg=5.00 for T= 51086 logg=6.04\n", - "Changing to T= 50000 for T= 51215 logg=6.04\n", - "Changing to logg=5.00 for T= 51215 logg=6.04\n", - "Changing to T= 50000 for T= 51345 logg=6.04\n", - "Changing to logg=5.00 for T= 51345 logg=6.04\n", - "Changing to T= 50000 for T= 51475 logg=6.04\n", - "Changing to logg=5.00 for T= 51475 logg=6.04\n", - "Changing to T= 50000 for T= 51606 logg=6.04\n", - "Changing to logg=5.00 for T= 51606 logg=6.04\n", - "Changing to T= 50000 for T= 51737 logg=6.05\n", - "Changing to logg=5.00 for T= 51737 logg=6.05\n", - "Changing to T= 50000 for T= 51868 logg=6.05\n", - "Changing to logg=5.00 for T= 51868 logg=6.05\n", - "Changing to T= 50000 for T= 52000 logg=6.05\n", - "Changing to logg=5.00 for T= 52000 logg=6.05\n", - "Changing to T= 50000 for T= 52119 logg=6.05\n", - "Changing to logg=5.00 for T= 52119 logg=6.05\n", - "Changing to T= 50000 for T= 52240 logg=6.05\n", - "Changing to logg=5.00 for T= 52240 logg=6.05\n", - "Changing to T= 50000 for T= 52384 logg=6.05\n", - "Changing to logg=5.00 for T= 52384 logg=6.05\n", - "Changing to T= 50000 for T= 52505 logg=6.05\n", - "Changing to logg=5.00 for T= 52505 logg=6.05\n", - "Changing to T= 50000 for T= 52626 logg=6.05\n", - "Changing to logg=5.00 for T= 52626 logg=6.05\n", - "Changing to T= 50000 for T= 52747 logg=6.05\n", - "Changing to logg=5.00 for T= 52747 logg=6.05\n", - "Changing to T= 50000 for T= 52759 logg=6.05\n", - "Changing to logg=5.00 for T= 52759 logg=6.05\n", - "Changing to T= 50000 for T= 52857 logg=6.05\n", - "Changing to logg=5.00 for T= 52857 logg=6.05\n", - "Changing to T= 50000 for T= 52991 logg=6.05\n", - "Changing to logg=5.00 for T= 52991 logg=6.05\n", - "Changing to T= 50000 for T= 53125 logg=6.06\n", - "Changing to logg=5.00 for T= 53125 logg=6.06\n", - "Changing to T= 50000 for T= 53235 logg=6.06\n", - "Changing to logg=5.00 for T= 53235 logg=6.06\n", - "Changing to T= 50000 for T= 53358 logg=6.06\n", - "Changing to logg=5.00 for T= 53358 logg=6.06\n", - "Changing to T= 50000 for T= 53481 logg=6.06\n", - "Changing to logg=5.00 for T= 53481 logg=6.06\n", - "Changing to T= 50000 for T= 53592 logg=6.06\n", - "Changing to logg=5.00 for T= 53592 logg=6.06\n", - "Changing to T= 50000 for T= 53728 logg=6.06\n", - "Changing to logg=5.00 for T= 53728 logg=6.06\n", - "Changing to T= 50000 for T= 53864 logg=6.06\n", - "Changing to logg=5.00 for T= 53864 logg=6.06\n", - "Changing to T= 50000 for T= 53976 logg=6.07\n", - "Changing to logg=5.00 for T= 53976 logg=6.07\n", - "Changing to T= 50000 for T= 54100 logg=6.07\n", - "Changing to logg=5.00 for T= 54100 logg=6.07\n", - "Changing to T= 50000 for T= 54225 logg=6.07\n", - "Changing to logg=5.00 for T= 54225 logg=6.07\n", - "Changing to T= 50000 for T= 54363 logg=6.07\n", - "Changing to logg=5.00 for T= 54363 logg=6.07\n", - "Changing to T= 50000 for T= 54475 logg=6.07\n", - "Changing to logg=5.00 for T= 54475 logg=6.07\n", - "Changing to T= 50000 for T= 54588 logg=6.07\n", - "Changing to logg=5.00 for T= 54588 logg=6.07\n", - "Changing to T= 50000 for T= 54714 logg=6.08\n", - "Changing to logg=5.00 for T= 54714 logg=6.08\n", - "Changing to T= 50000 for T= 54853 logg=6.08\n", - "Changing to logg=5.00 for T= 54853 logg=6.08\n", - "Changing to T= 50000 for T= 54967 logg=6.08\n", - "Changing to logg=5.00 for T= 54967 logg=6.08\n", - "Changing to T= 50000 for T= 55093 logg=6.08\n", - "Changing to logg=5.00 for T= 55093 logg=6.08\n", - "Changing to T= 50000 for T= 55208 logg=6.08\n", - "Changing to logg=5.00 for T= 55208 logg=6.08\n", - "Changing to T= 50000 for T= 55322 logg=6.09\n", - "Changing to logg=5.00 for T= 55322 logg=6.09\n", - "Changing to T= 50000 for T= 55450 logg=6.09\n", - "Changing to logg=5.00 for T= 55450 logg=6.09\n", - "Changing to T= 50000 for T= 55590 logg=6.09\n", - "Changing to logg=5.00 for T= 55590 logg=6.09\n", - "Changing to T= 50000 for T= 55719 logg=6.09\n", - "Changing to logg=5.00 for T= 55719 logg=6.09\n", - "Changing to T= 50000 for T= 55834 logg=6.09\n", - "Changing to logg=5.00 for T= 55834 logg=6.09\n", - "Changing to T= 50000 for T= 55873 logg=6.09\n", - "Changing to logg=5.00 for T= 55873 logg=6.09\n", - "Changing to T= 50000 for T= 55963 logg=6.09\n", - "Changing to logg=5.00 for T= 55963 logg=6.09\n", - "Changing to T= 50000 for T= 56092 logg=6.10\n", - "Changing to logg=5.00 for T= 56092 logg=6.10\n", - "Changing to T= 50000 for T= 56234 logg=6.10\n", - "Changing to logg=5.00 for T= 56234 logg=6.10\n", - "Changing to T= 50000 for T= 56364 logg=6.10\n", - "Changing to logg=5.00 for T= 56364 logg=6.10\n", - "Changing to T= 50000 for T= 56507 logg=6.10\n", - "Changing to logg=5.00 for T= 56507 logg=6.10\n", - "Changing to T= 50000 for T= 56624 logg=6.11\n", - "Changing to logg=5.00 for T= 56624 logg=6.11\n", - "Changing to T= 50000 for T= 56754 logg=6.11\n", - "Changing to logg=5.00 for T= 56754 logg=6.11\n", - "Changing to T= 50000 for T= 56885 logg=6.11\n", - "Changing to logg=5.00 for T= 56885 logg=6.11\n", - "Changing to T= 50000 for T= 57030 logg=6.11\n", - "Changing to logg=5.00 for T= 57030 logg=6.11\n", - "Changing to T= 50000 for T= 57161 logg=6.11\n", - "Changing to logg=5.00 for T= 57161 logg=6.11\n", - "Changing to T= 50000 for T= 57293 logg=6.11\n", - "Changing to logg=5.00 for T= 57293 logg=6.11\n", - "Changing to T= 50000 for T= 57425 logg=6.11\n", - "Changing to logg=5.00 for T= 57425 logg=6.11\n", - "Changing to T= 50000 for T= 57570 logg=6.11\n", - "Changing to logg=5.00 for T= 57570 logg=6.11\n", - "Changing to T= 50000 for T= 57703 logg=6.11\n", - "Changing to logg=5.00 for T= 57703 logg=6.11\n", - "Changing to T= 50000 for T= 57850 logg=6.12\n", - "Changing to logg=5.00 for T= 57850 logg=6.12\n", - "Changing to T= 50000 for T= 57983 logg=6.12\n", - "Changing to logg=5.00 for T= 57983 logg=6.12\n", - "Changing to T= 50000 for T= 58117 logg=6.12\n", - "Changing to logg=5.00 for T= 58117 logg=6.12\n", - "Changing to T= 50000 for T= 58251 logg=6.13\n", - "Changing to logg=5.00 for T= 58251 logg=6.13\n", - "Changing to T= 50000 for T= 58398 logg=6.13\n", - "Changing to logg=5.00 for T= 58398 logg=6.13\n", - "Changing to T= 50000 for T= 58546 logg=6.13\n", - "Changing to logg=5.00 for T= 58546 logg=6.13\n", - "Changing to T= 50000 for T= 58695 logg=6.13\n", - "Changing to logg=5.00 for T= 58695 logg=6.13\n", - "Changing to T= 50000 for T= 58844 logg=6.14\n", - "Changing to logg=5.00 for T= 58844 logg=6.14\n", - "Changing to T= 50000 for T= 58993 logg=6.14\n", - "Changing to logg=5.00 for T= 58993 logg=6.14\n", - "Changing to T= 50000 for T= 59143 logg=6.14\n", - "Changing to logg=5.00 for T= 59143 logg=6.14\n", - "Changing to T= 50000 for T= 59293 logg=6.15\n", - "Changing to logg=5.00 for T= 59293 logg=6.15\n", - "Changing to T= 50000 for T= 59334 logg=6.15\n", - "Changing to logg=5.00 for T= 59334 logg=6.15\n", - "Changing to T= 50000 for T= 59457 logg=6.15\n", - "Changing to logg=5.00 for T= 59457 logg=6.15\n", - "Changing to T= 50000 for T= 59621 logg=6.16\n", - "Changing to logg=5.00 for T= 59621 logg=6.16\n", - "Changing to T= 50000 for T= 59800 logg=6.16\n", - "Changing to logg=5.00 for T= 59800 logg=6.16\n", - "Changing to T= 50000 for T= 59965 logg=6.16\n", - "Changing to logg=5.00 for T= 59965 logg=6.16\n", - "Changing to T= 50000 for T= 60159 logg=6.17\n", - "Changing to logg=5.00 for T= 60159 logg=6.17\n", - "Changing to T= 50000 for T= 60353 logg=6.17\n", - "Changing to logg=5.00 for T= 60353 logg=6.17\n", - "Changing to T= 50000 for T= 60534 logg=6.18\n", - "Changing to logg=5.00 for T= 60534 logg=6.18\n", - "Changing to T= 50000 for T= 60758 logg=6.18\n", - "Changing to logg=5.00 for T= 60758 logg=6.18\n", - "Changing to T= 50000 for T= 60968 logg=6.18\n", - "Changing to logg=5.00 for T= 60968 logg=6.18\n", - "Changing to T= 50000 for T= 61235 logg=6.19\n", - "Changing to logg=5.00 for T= 61235 logg=6.19\n", - "Changing to T= 50000 for T= 61504 logg=6.20\n", - "Changing to logg=5.00 for T= 61504 logg=6.20\n", - "Changing to T= 50000 for T= 61844 logg=6.21\n", - "Changing to logg=5.00 for T= 61844 logg=6.21\n", - "Changing to T= 50000 for T= 62287 logg=6.23\n", - "Changing to logg=5.00 for T= 62287 logg=6.23\n", - "Changing to T= 50000 for T= 63038 logg=6.27\n", - "Changing to logg=5.00 for T= 63038 logg=6.27\n", - "Changing to T= 50000 for T= 64239 logg=6.32\n", - "Changing to logg=5.00 for T= 64239 logg=6.32\n", - "Changing to T= 50000 for T= 65464 logg=6.37\n", - "Changing to logg=5.00 for T= 65464 logg=6.37\n", - "Changing to T= 50000 for T= 66696 logg=6.42\n", - "Changing to logg=5.00 for T= 66696 logg=6.42\n", - "Changing to T= 50000 for T= 67967 logg=6.47\n", - "Changing to logg=5.00 for T= 67967 logg=6.47\n", - "Changing to T= 50000 for T= 69263 logg=6.53\n", - "Changing to logg=5.00 for T= 69263 logg=6.53\n", - "Changing to T= 50000 for T= 71367 logg=6.63\n", - "Changing to logg=5.00 for T= 71367 logg=6.63\n", - "Isochrone generation took 91.105072 s.\n", - "Making photometry for isochrone: log(t) = 6.70 AKs = 0.05 dist = 145\n", - " Starting at: 2026-04-17 10:05:57.727481 Usually takes ~5 minutes\n", - "Starting filter: ukirt,J Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.001 Msun T = 432 K m_ukirt_J = 25.22\n", - "M = 0.500 Msun T = 3720 K m_ukirt_J = 10.68\n", - "M = 1.332 Msun T = 4834 K m_ukirt_J = 9.04\n", - "M = 1.747 Msun T = 5551 K m_ukirt_J = 7.97\n", - "M = 2.047 Msun T = 7076 K m_ukirt_J = 6.74\n", - "M = 2.392 Msun T = 10998 K m_ukirt_J = 7.19\n", - "M = 2.699 Msun T = 11926 K m_ukirt_J = 7.14\n", - "M = 2.877 Msun T = 12459 K m_ukirt_J = 7.02\n", - "M = 3.211 Msun T = 13384 K m_ukirt_J = 6.78\n", - "M = 3.375 Msun T = 13820 K m_ukirt_J = 6.68\n", - "M = 3.520 Msun T = 14194 K m_ukirt_J = 6.59\n", - "M = 3.646 Msun T = 14514 K m_ukirt_J = 6.52\n", - "M = 18.000 Msun T = 31681 K m_ukirt_J = 2.84\n", - "M = 49.494 Msun T = 37653 K m_ukirt_J = 0.12\n", - "M = 51.234 Msun T = 41812 K m_ukirt_J = 0.65\n", - "M = 53.034 Msun T = 33651 K m_ukirt_J = 0.13\n", - "M = 54.918 Msun T = 52240 K m_ukirt_J = 1.70\n", - "Starting filter: ukirt,H Elapsed time: 9.43 seconds\n", - "Starting synthetic photometry\n", - "M = 0.001 Msun T = 432 K m_ukirt_H = 25.71\n", - "M = 0.500 Msun T = 3720 K m_ukirt_H = 9.91\n", - "M = 1.332 Msun T = 4834 K m_ukirt_H = 8.46\n", - "M = 1.747 Msun T = 5551 K m_ukirt_H = 7.55\n", - "M = 2.047 Msun T = 7076 K m_ukirt_H = 6.52\n", - "M = 2.392 Msun T = 10998 K m_ukirt_H = 7.14\n", - "M = 2.699 Msun T = 11926 K m_ukirt_H = 7.10\n", - "M = 2.877 Msun T = 12459 K m_ukirt_H = 6.98\n", - "M = 3.211 Msun T = 13384 K m_ukirt_H = 6.75\n", - "M = 3.375 Msun T = 13820 K m_ukirt_H = 6.65\n", - "M = 3.520 Msun T = 14194 K m_ukirt_H = 6.56\n", - "M = 3.646 Msun T = 14514 K m_ukirt_H = 6.49\n", - "M = 18.000 Msun T = 31681 K m_ukirt_H = 2.88\n", - "M = 49.494 Msun T = 37653 K m_ukirt_H = 0.16\n", - "M = 51.234 Msun T = 41812 K m_ukirt_H = 0.69\n", - "M = 53.034 Msun T = 33651 K m_ukirt_H = 0.17\n", - "M = 54.918 Msun T = 52240 K m_ukirt_H = 1.74\n", - "Starting filter: ukirt,K Elapsed time: 22.66 seconds\n", - "Starting synthetic photometry\n", - "M = 0.001 Msun T = 432 K m_ukirt_K = 26.87\n", - "M = 0.500 Msun T = 3720 K m_ukirt_K = 9.66\n", - "M = 1.332 Msun T = 4834 K m_ukirt_K = 8.34\n", - "M = 1.747 Msun T = 5551 K m_ukirt_K = 7.46\n", - "M = 2.047 Msun T = 7076 K m_ukirt_K = 6.47\n", - "M = 2.392 Msun T = 10998 K m_ukirt_K = 7.12\n", - "M = 2.699 Msun T = 11926 K m_ukirt_K = 7.09\n", - "M = 2.877 Msun T = 12459 K m_ukirt_K = 6.97\n", - "M = 3.211 Msun T = 13384 K m_ukirt_K = 6.75\n", - "M = 3.375 Msun T = 13820 K m_ukirt_K = 6.65\n", - "M = 3.520 Msun T = 14194 K m_ukirt_K = 6.57\n", - "M = 3.646 Msun T = 14514 K m_ukirt_K = 6.50\n", - "M = 18.000 Msun T = 31681 K m_ukirt_K = 2.95\n", - "M = 49.494 Msun T = 37653 K m_ukirt_K = 0.23\n", - "M = 51.234 Msun T = 41812 K m_ukirt_K = 0.77\n", - "M = 53.034 Msun T = 33651 K m_ukirt_K = 0.24\n", - "M = 54.918 Msun T = 52240 K m_ukirt_K = 1.83\n", - " Time taken: 35.83 seconds\n" - ] - } - ], + "outputs": [], "source": [ "# Define isochrone parameters\n", - "logAge = np.log10(5*1e6) # Age in log(years)\n", - "AKs = 0.05 # extinction in mags\n", - "dist = 145 # distance in parsec\n", - "metallicity = 0 # Metallicity in [M/H]\n", + "logAge = np.log10(5e6) # 5 Myr\n", + "dist = 145 # pc\n", + "metallicity = 0\n", + "AKs = 0.05\n", + "\n", + "red_law = reddening.RedLawCardelli(3.1)\n", "\n", "# Define evolution/atmosphere models and extinction law\n", "evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() \n", "atm_func = atmospheres.get_merged_atmosphere\n", - "red_law = reddening.RedLawHosek18b()\n", + "#red_law = reddening.RedLawHosek18b()\n", "\n", "# Specify UKIDSS filters\n", - "ukidss_filts = ['ukirt,J', 'ukirt,H', 'ukirt,K']\n", + "ukidss_filts = ['ukirt,Z', 'ukirt,Y', 'ukirt,J', 'ukirt,H', 'ukirt,K']\n", "\n", "# Specify the directory we want the output isochrone\n", "# table saved in. If the directory does not already exist,\n", @@ -612,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "0eb22c21-cc15-4611-b9df-dcdf0473c365", "metadata": {}, "outputs": [], @@ -628,13 +126,13 @@ "pisa = (m >= 0.5) & (m < 7.0)\n", "ms = m >= 7.0 # Ekstrom at this age\n", "\n", - "color = my_iso.points['m_ukirt_J'] - my_iso.points['m_ukirt_K']\n", - "mag = my_iso.points['m_ukirt_J']" + "color = my_iso.points['m_ukirt_Z'] - my_iso.points['m_ukirt_J']\n", + "mag = my_iso.points['m_ukirt_Z']" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "c631060e-4f68-4534-9326-a5bf5cbef20a", "metadata": {}, "outputs": [ @@ -642,7 +140,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 1 stars out of mass range\n" + "Remove low mass stars below grid and compact objects\n" ] } ], @@ -658,7 +156,7 @@ "clust = cluster.star_systems\n", "iso = my_iso.points\n", "\n", - "mask = (my_iso.points['mass'] >= 0.009)\n", + "mask = (my_iso.points['mass'] >= 0.009) & (my_iso.points['mass'] < 7.0)\n", "\n", "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", "# multiple systems on the photometry\n", @@ -668,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "1d4cf2ed-92fb-4a72-b64e-ecbb3ce47d11", "metadata": {}, "outputs": [ @@ -686,24 +184,66 @@ "tab = Table.read(other, format='fits', hdu=2)\n", "tab\n", "\n", - "focus = np.where((tab['Jmag'] - tab['Kmag'] < 10))\n", - "minus = tab['Jmag'] - tab['Kmag']\n", - "J_K = minus[focus]\n", + "focus = np.where((tab['Zmag'] - tab['Jmag'] < 10) & (tab['Zmag'] > 0))\n", + "minus = tab['Zmag'] - tab['Jmag']\n", + "Z_J = minus[focus]\n", "\n", + "z_err = tab['e_Zmag']\n", "j_err = tab['e_Jmag']\n", - "k_err = tab['e_Kmag']\n", - "jk_err = np.sqrt(j_err**2 + k_err**2)" + "zj_err = np.sqrt(z_err**2 + j_err**2)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, + "id": "a2582947-4bae-4ee5-bfb6-9b43a49b4ab5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0 Msun -> actual mass = 0.9969\n", + "0.5 Msun -> actual mass = 0.5000\n", + "0.08 Msun -> actual mass = 0.0800\n" + ] + } + ], + "source": [ + "marker_masses = [1.0, 0.5, 0.08]\n", + "\n", + "marker_points = {}\n", + "\n", + "for m in marker_masses:\n", + " idx = np.argmin(np.abs(iso['mass'] - m))\n", + "\n", + " marker_points[m] = {\n", + " 'color': color[idx],\n", + " 'mag': mag[idx]\n", + " }\n", + "\n", + " print(f\"{m} Msun -> actual mass = {iso['mass'][idx]:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8d82b7de-58fe-43ee-85e9-7fb3f4c8ee09", + "metadata": {}, + "outputs": [], + "source": [ + "clust_mask = (clust['mass'] < 7)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "id": "514d3a68-227c-43a6-b2f8-e4355c4abe4f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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      " ] @@ -721,38 +261,93 @@ "axs[0].plot(color[phillips], mag[phillips], '-', color='purple', label='Phillips (BD)', linewidth=3)\n", "axs[0].plot(color[phillips_baraffe], mag[phillips_baraffe], '-', color='violet', label='Phillips/Baraffe transition', linewidth=3)\n", "axs[0].plot(color[baraffe], mag[baraffe], '-', color='orange', label='Baraffe', linewidth=3)\n", - "axs[0].plot(color[baraffe_pisa], mag[baraffe_pisa], '-', color='green', label='Baraffe/Pisa transition', linewidth=3)\n", + "axs[0].plot(color[baraffe_pisa], mag[baraffe_pisa], '-', color='red', label='Baraffe/Pisa transition', linewidth=3)\n", "axs[0].plot(color[pisa], mag[pisa], '-', color='blue', label='Pisa', linewidth=3)\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[0].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[0].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", "#axs[0].plot(color[ms], mag[ms], '-', color='red', label='Ekstrom MS', linewidth=3)\n", "axs[0].invert_yaxis()\n", - "axs[0].set_xlabel('J - K', fontsize=20)\n", - "axs[0].set_ylabel('J', fontsize=20)\n", + "axs[0].set_xlabel('Z - J', fontsize=20)\n", + "axs[0].set_ylabel('Z', fontsize=20)\n", "axs[0].set_title('5 Myr Expected Upper Sco Isochrone \\n (Colored by Evolutionary Model)', fontsize=24)\n", "axs[0].legend(markerscale=4, fontsize=18, loc='upper right')\n", "axs[0].tick_params(axis='both', labelsize=18)\n", "axs[0].grid()\n", "\n", - "axs[1].plot(clust['m_ukirt_J'] - clust['m_ukirt_K'], clust['m_ukirt_J'],\n", + "axs[1].plot(clust['m_ukirt_Z'][clust_mask] - clust['m_ukirt_J'][clust_mask], clust['m_ukirt_Z'][clust_mask],\n", " 'k.', ms=10, alpha=0.1, label='Simulated Upper Sco Data')\n", - "axs[1].plot(my_iso.points['m_ukirt_J'][mask] - my_iso.points['m_ukirt_K'][mask], \n", - " my_iso.points['m_ukirt_J'][mask],\n", - " 'r-', linewidth=3, label='Theoretical Upper Sco Isochrone')\n", - "axs[1].set_xlabel('J - K', fontsize=20)\n", - "axs[1].set_ylabel('J', fontsize=20)\n", + "axs[1].plot(my_iso.points['m_ukirt_Z'][mask] - my_iso.points['m_ukirt_J'][mask], \n", + " my_iso.points['m_ukirt_Z'][mask],\n", + " 'mediumseagreen', linewidth=4, label='Theoretical Upper Sco Isochrone')\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[1].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[1].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", + "axs[1].set_xlabel('Z - J', fontsize=20)\n", + "axs[1].set_ylabel('Z', fontsize=20)\n", "axs[1].invert_yaxis()\n", "axs[1].set_title('Simulated Upper Sco Cluster', fontsize=24)\n", "axs[1].legend(fontsize=18, loc='upper right')\n", "axs[1].tick_params(axis='both', labelsize=18)\n", "axs[1].grid()\n", "\n", - "axs[2].plot(J_K, tab['Jmag'][focus],\n", - " 'm.', ms=10, alpha=0.3, label='real Upper Sco Data (substellar)')\n", - "axs[2].errorbar(J_K, tab['Jmag'][focus], xerr=jk_err[focus], yerr=j_err[focus], color='m', fmt='none', ms=5, alpha=0.3, label='Data Errors')\n", - "axs[2].plot(my_iso.points['m_ukirt_J'][mask] - my_iso.points['m_ukirt_K'][mask], \n", - " my_iso.points['m_ukirt_J'][mask],\n", - " 'r-', linewidth=3, label='Theoretical Upper Sco Isochrone')\n", - "axs[2].set_xlabel('J - K', fontsize=20)\n", - "axs[2].set_ylabel('J', fontsize=20)\n", + "axs[2].errorbar(Z_J, tab['Zmag'][focus], xerr=zj_err[focus], yerr=z_err[focus], fmt='mo', ms=8, alpha=0.3, label='Real UKIDSS Data/Errors')\n", + "axs[2].plot(my_iso.points['m_ukirt_Z'][mask] - my_iso.points['m_ukirt_J'][mask], \n", + " my_iso.points['m_ukirt_Z'][mask],\n", + " 'mediumseagreen', linewidth=4, zorder=50, label='Theoretical Upper Sco Isochrone')\n", + "for m, pt in marker_points.items():\n", + "\n", + " axs[2].plot(\n", + " pt['color'],\n", + " pt['mag'],\n", + " marker='*',\n", + " markersize=18,\n", + " color='black',\n", + " zorder=100\n", + " )\n", + "\n", + " axs[2].annotate(\n", + " f'{m:.2f} $M_\\\\odot$',\n", + " (pt['color'], pt['mag']),\n", + " xytext=(10, 10),\n", + " textcoords='offset points',\n", + " fontsize=16,\n", + " fontweight='bold'\n", + " )\n", + "axs[2].set_xlabel('Z - J', fontsize=20)\n", + "axs[2].set_ylabel('Z', fontsize=20)\n", "axs[2].invert_yaxis()\n", "axs[2].set_title('Real Upper Sco Cluster vs. \\n Theoretical Isochrone', fontsize=24)\n", "axs[2].legend(fontsize=18, loc='upper right')\n", From 2aa4829000fcfaa0690195960c0b5177968ce2c2 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Fri, 5 Jun 2026 00:00:11 -0700 Subject: [PATCH 143/191] Change to Figure 12 --- docs/paper_examples/Begbie+26/Figure 12.ipynb | 36 ++++++++++++++++--- 1 file changed, 32 insertions(+), 4 deletions(-) diff --git a/docs/paper_examples/Begbie+26/Figure 12.ipynb b/docs/paper_examples/Begbie+26/Figure 12.ipynb index aa7ca1d8..d7021e98 100644 --- a/docs/paper_examples/Begbie+26/Figure 12.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 12.ipynb @@ -13,6 +13,32 @@ { "cell_type": "code", "execution_count": 1, + "id": "38f7fd03-45f5-4283-a9d1-223747ea45af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA_merged/mergeconflicts/spisea/__init__.py\n" + ] + } + ], + "source": [ + "import sys\n", + "\n", + "sys.path.insert(\n", + " 0,\n", + " \"/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA_merged/mergeconflicts\"\n", + ")\n", + "\n", + "import spisea\n", + "print(spisea.__file__)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "3d80653b-a8ee-422d-b008-14dc490ad520", "metadata": {}, "outputs": [], @@ -26,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", "metadata": {}, "outputs": [], @@ -39,13 +65,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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3upbOzk6+8pWvUFdXh9/vZ9q0aRx//PE0NjbS2NjITTfdpI+drBJxMrZv3w5oFYoHMmzKy8v1qtrCPhMx2qB9p9x3330AWCwWPvOZz4zbbrFYuPLKKwGtqjUWix1wvqlTp/I///M/ALS0tPDGG28AcNdddzFr1qwjWuOSJUuYNm0aAF/72tc4+eST+elPf8orr7xCLpc7ojn3p1Cl+9prr9Ha2jpmWy6X47HHHgMY9z8Jmqh44R779Kc/zdlnn81///d/s2XLFhRFeVfWNxHXXnstgiCwd+9eXn75ZeDtathLL71U/zJyKKiqygMPPMCyZcvweDxUVVUxe/Zs/f5vbGzkzTffBMbf/3a7Xb9HHnvsMWbMmMGtt97KqlWrDnq/XHXVVVitVkRR5PTTT+eiiy7if//3f9mxY4dRBfshwjDsDD7SBAIBPvGJT3DHHXfwxBNPMDAwwIoVKwgEAoAmS7C/SPGBZEUOxGR9QA/2gVDwKh5o3GjP4zv5cDvUY7yTtaxevZq5c+fy29/+dpyUy0RkMpmDjhlNQUakvLz8oGMrKirG7DMRhXvhnTJao+68886jtLR0wnEFz1A2m+WRRx456Lyf+cxn+NjHPqb/fc455/ClL33piNdptVp54okndK9aU1MT3/nOdzj99NMpKiriggsu4C9/+cs7us+uvvpq/fH+XrtVq1YRiUQAxsm0gOaxevzxx6mqqkJVVdatW8c3vvENFi5cSHFxMZdeeqmuV/luUltbq7cVu//+++np6WHdunXA4WnXZbNZLrzwQq699lpeeOGFg97fE23/7W9/q+vrdXZ2cuedd3LhhRdSUlLCySefzM9//vMJpWhmz57NQw89RCAQQJZlnnzySb785S9z/PHHU1ZWxrXXXnvIfXENjl0Mw87AYBR2u50bbriBhx56SH9u5cqVYzSlCh9o9fX1NDc3H/LPwoUL3/fzOdYIh8NcffXVpNNpPB4Pd9xxBxs3bmRwcBBRFFG1vF+ee+45fZ8j9SQcyAt6OHObzeYjOv7+jO4qsWrVqkm9vKeccoq+z8E07QDefPPNMR/GW7duZWBg4B2tde7cuTQ3N/P3v/+dG2+8kenTpwOakbFmzRquueYaTjnllCP+klNfX89pp50GjDfsCn8XFRWxfPnyCfc/44wzaGtr44EHHuDqq6/Ww5bxeJyVK1dy0UUXcf7550/6ZepIKRhwjzzyCCtWrCCfz1NVVcWyZcsOeY4f//jHrF69GoClS5fyyCOP0NbWRjKZRFEU/X+gYEROdI/6fD4ef/xxXn31Vb75zW9y0kknYTabURSFpqYmbrnlFhoaGti4ceO4fS+99FLa29u55557uOSSS/QvGKFQiAceeIAzzzyT66+//qA6egbHMEcruc/A4FinpqZGT1IeXWlakJJwuVwHrVw8EKN17A7EaI25A8EBErYPR8fuSI9R4ECJ+f/zP/+jb3vmmWcmneOxxx7Tx61bt27c9gMVTxQ0xZYtW3bAc1FVVa2rq9MrgQ/1HI6UQvXk4fwIgjBp1bWqahWSBT0zt9utS1lceOGFB1zLkZxfb2+v+sc//lEv/gDUT33qU4dzCcbwu9/9Tp+nqalJVVVVjcfjqsPhUAH1i1/84mHNt2fPHvXXv/61rvEHqF/72teOaG2F/ff/f4hGo/r6Cr9vvfXWMWMOVDyRz+fViooKFVCXLFlywMrT448//pDeHwrE43H18ccfVz/96U/rx6+srDykyuAdO3aoP/3pT3UpGg5Q2GJw7GN47AwMJmHKlCn649HhxRNPPBHQQquFXBuDQ2PHjh0AFBcXc+655046rpDHeCQcf/zxALzxxhtIkjTpuMHBQT0UXNjnvSKZTPL3v/8dgI997GM89NBDB/z5wx/+AGjemgMVUdxyyy289dZbANx9993ccsstADz11FN67t27RWVlJTfeeCMbN27kpJNOAuDJJ5887FB5gSuuuELPgSx46VauXKl3a5koDHsgpk2bxle+8hWampp0D96hhLIPB7/fz8UXXwygr/NwwrDDw8N6ru0VV1wxrnirQDKZpKWl5bDW5vV6ueiii1i5ciVf/epXAS2VZMOGDQfdd+7cuXz7299m06ZNuN1u4N2/dgbvH4ZhZ2AwAel0Wv/A9Pl8YypARzf5/q//+q/3fW0fZAoVfKIoThrqSafThxSCnIxzzjkHgGg0yt/+9rdJx/3xj3/Uw1yFfd4rHnvsMT0s+OUvf5nPfOYzB/z5/Oc/r7fRmsywW7NmDb/97W8BuPzyy7nuuuv44Q9/qBtd3/rWtw7bODgUrFYrS5cuBbTXMxqNHtE8JSUlejX4X//61zE5iNXV1XpF+eHi8/lYtGgRcPiFN4fC5z73Oex2O3a7nUWLFh3Wl4LRFawHChP/8Y9/POCXkoMxOufycK5BTU2NXhj1Xlw7g/cHw7Az+MiQTCY55ZRTePLJJw+YP5LP5/nKV76iV3RefPHFY/K1Fi1axHnnnQdouVK33377AY/b0dExJmfvo0xDQwMAqVRKr3wcjaIofOELX6C3t/eIj3HDDTfoxR3f/OY3J5SZ2bp1Kz/5yU8AqKqqOmKpikOlYKi6XC4uuOCCQ9rnsssuA2DPnj3jPMOhUIgbbrgB0NZ/zz33AJrR9eCDD+J0Okmn01xzzTWHbSC89NJLtLW1Tbo9l8uxfv16ADwez6RFIIdCwSvX19fHQw89xPPPPw9oxRWT5Ug+/fTT4yrVRxOLxXQpj/r6+iNe22RceOGFZLNZstnsYUuGlJaW6hW9f/3rXyesMm5qauJ73/vepHPs3btXv/6T8cwzz+iPR1+Df/zjHwc0xLu6uti1a9e4/Qw+WFiO9gIMDN5PXnvtNS666CL9w3zx4sXU1tbi9XqJRqO88cYbrFixgubmZkALvfzwhz8cN8+f/vQnFi5cSF9fHz/4wQ94+umnufHGG2lsbMThcBAOh9m2bRtr1qzh+eef51Of+hRXXXXV+326xxxXXHEF3/nOdxBFkeuvv54333yTc845B5/Px44dO/jNb37Dli1bOP300484zF1aWsqdd97JzTffTG9vLwsXLuTb3/42p512GoqisHbtWu68806SySSCIPD73//+iPTeDpV9+/bxwgsvAHDBBRccsizGpZdeym233QZohuHpp5+ub/vCF75Af38/giBw3333jancnT17NnfeeSf/+q//ypYtW7jjjjv48Y9/fMjrfe655/jhD3/IGWecwYUXXsi8efMoLS0lk8mwe/du/vd//5fXX39dX4fFcuQfI5/85Cfxer0kEgn+9V//VS9MOlAY9qGHHuKiiy7i3HPP5bzzzuP444+nuLiYRCLB9u3b+e1vf6sXqXz5y18+4rW9F5hMJq655hruvvtu3nzzTc444wy+/vWvM2PGDGKxGKtWreJ3v/sdHo+HKVOmsHv37nFz7Nu3j2XLljF37lw+/elPs3DhQqqqqgDNMHv44Yf1MOqJJ544phjnl7/8Jddccw0XXnghZ599NnPmzMHv9xOJRNi8eTO/+c1v9ND6sXbtDA6Do53kZ2DwfpHJZPTE5UP5aWhoUDdv3jzpfB0dHeqiRYsOaa4bbrhh3P4fxeIJVdU6UxyoCfmVV145pln84RZPFPjxj398wOPY7Xb1vvvuO6JzOBxGd7g4UCeMiSj0qS0qKlKz2ayqqqr6+9//Xp/v61//+qT7XnDBBSpovVZfeumlMdsOdH6j75UD/VxyySVqJpM5rPOZiM997nNj5j3uuOMOOL7w2h/s5+abbz7itliFOQ72/zARB+s8EY1G1RNOOGHSdRcXF6vr16+f9P1hdFeOA/3MmTNHbW9vH7Pv6G43k/2YzWb1Jz/5yWGft8Gxg+GxM/jI4HA46OnpYdOmTaxdu5ZNmzbR0tLCwMAA2WwWt9vNlClTmD9/Pp/85Ce59NJLdYX3iaitreXVV1/ln//8Jw8//DCvvvoqAwMDSJJEUVERDQ0NLF68mIsvvliXLjDQQqWzZs3izjvv5OWXXyYajRIMBpk/fz433HADV1xxhe7heid85zvf4ROf+AS//e1vef755+nt7cVkMjF16lTOO+88vva1r+kCxe8lhRw5u91+0E4l+3PppZeybds2otEojz/+OCeccAJf//rXAWhsbOSnP/3ppPv+6U9/orGxkaGhIa699lq2bt2qdzE5ELfeeiunnHIKzz77LBs3bqS3t1eXNamoqOCUU07hc5/73KRSJIfLNddcMyan8mBFE7/85S+5+OKLefbZZ9m8eTN9fX0MDQ1hNpupqanhtNNO4wtf+MIYD+exhN/v5+WXX+auu+7ikUceobW1FYvFQk1NDRdeeCH/7//9P734YyLOOOMMNm7cyLPPPssLL7zAvn379Pew4uJi5s+fz6WXXsr1118/7v3rkUceYe3atTz77LO8+eab9Pf3EwqFcDgc1NXVceaZZ/Iv//IvNDY2vteXweA9RFBVQ27awMDgg8XnPvc57r//fqZPn37AfDADAwODjxpG8YSBgcEHjoKq/v7tpgwMDAw+6hiGnYGBwQcKVVX1xugH6llrYGBg8FHEyLEzMDD4QNDe3k5nZyf33XcfHR0dAIcsHWJgYGDwUcHIsTMwMPhAcNZZZ43R7zrppJPYtGnTeypVYmBgYPBBwwjFGhgYfGCwWq1MmzaNb3zjGzz33HOGUWdgYGCwH4bHzsDAwMDAwMDgQ4KRY/ceks/n6e3txev1Ttoex8DAwMDAwMDgQKiqSiKRYMqUKZhMBw62Gobde0hvby81NTVHexkGBgYGBgYGHwK6uroOKGANhmH3nuL1egHthTgUxXcDg/ebdE7m5B8/B8Br3/0YLpvxlmBgYGBwrBGPx6mpqdHtigNhvIu/hxTCrz6fzzDsDI5JLDkZk11rSu/z+QzDzsDAwOAY5lDSuoyqWAMDAwMDAwODDwnG13MDg48wJkFgeWOF/tjAwMDA4IONYdgZGHyEcVjN/O6aBUd7GQYGBgYG7xJGKPY94O6772bu3LksWrToaC/FwMDAwMDA4COEIVD8HhKPx/H7/cRiMaN4wsDAwMDAwOCIOBx7wvDYGRh8hEnnZOq+/RR1336KdE4+2ssxMDAwMHiHGDl2xxiKoiBJ0tFehsFHBDEnU+U1a4+zWUz5o/uWYDabjf6vBgYGBu8Aw7A7RlBVlf7+fmKxGEZ03OD9Iq+q3LGsDIDe7n3HRGWs3W4nGAwa6QsGBgYGR4Bh2B0jxGIxotEopaWluN1uo7eswfuCkleRBxMA1JV5MZuO3n2nqiqSJBGLxejp6QEwjDsDAwODw8Qw7I4BVFVlcHAQn89HMBg82ssx+Aih5FUEiwiAw+E4qoYdgNPpxOv10t3dTSgUMgw7AwMDg8PEKJ44BlAUBUVRjA8xAwO0ljl+vx9RFI18UwMDA4PDxDDsjgFkWatGtFgMB6qBAaAXUCiKcpRXYmBgYPDBwrAkjiGMvDqD9xsB8Dqs+uNjBeN/wcDAwODIMAw7A4OPMCaTQH3QfbSXYfABojuSpnUgSUO5h+qAa9LnDmeOd2PO/ef2OiwksvKEc060bfT+G/eEAIHF00sOeT0HO6dDOef92dwxzIa2EEtmBFlYVzxmH2DMuXgdFtpDSX3dhe0HGhtJ5Qi47fr4Vdt66Y+LLG+sHHe8ia7T/ucz+rr1x7Ksau7DaTXjd1rGHGf/tey//vqgm/ZQClBZPD045tj7v4Zeh4XXOyP0xzMsqivBYTVP+tqv2tZHfzzL8sZKKvwONu4JEUlJBNzWcceZ7J4qrNFjt9AykNBfm2MJw7B7D7j77ru5++67jTCSgYHBB46DGRytA0m2dEYA9O37P3cwg2DFhr3s6k9SXeRgUX0xkZTEtu4YS2eVcvlCbfzGPSFe3B2iscpPQ7n3oAZe4ZihZJbOcAaLWUBW1AnXWdgWSmYJehxjjJPHtnTT3B3DaTUT9NjH7LuuZZCt3VGWzAiOMxxGb79sQfW4a7L/9ode3cc/t/ZQ4bPz+SXTSYoyHaEUHaEUbruZKxZNZVVzH09v7+OFlgG+d+FxJLKyPofLaubV9mFMgorXaUPJ5+kOp4lntXmsFhO7+uLYrSaOq/QTz8r6eVvMAk17w+wNpyh223hmRz82s4kdfTFQwee0srCuWD+HUDJLJCWNMYpWbGinI5zi1PoSAm4rO/sStPTFkUaueU80w0u7h0CAgMuGx2FhZ18MEIhmJPaFU4STInMr/dSXerCYBfYMJlFVbd9t3VH6olme2THAJSdV69dn90ACn9OK32llMC5S5rOzvTvGYDLLjr44VUUuEhkJJa9y6rRiGsp9eB0WNrSFWLdrkJQok8zKRDIi3eEMNouJqSVugh7HpEZ84bWLZXIkszJOqwWzGXoiWQAq/I4j+hLyXmEYdu8BN998MzfffLPeAsTgg4MgCCxdupQXXnjhaC/lPeHDfn4GR+7pKjCR4Taaggeo8Hv0Y6/Dwrpdg7pxtf8cBcOppS/BUEIkJUpkpDylXhta9F3Vx+3sS5ASZfrjWeJZecxco8+xP5ZlQ1sIl9VMPCtTW+KitsSpeaOK7GPW6XVYsJgFZpV7cVjNhJLiGGNtQ1uIfcNpStyaIeJ1WPRjZSWFWCZHSpTZ0BbSjcbCNStsT4ryGE9Z4Zp4HRaK3TaGUzlaB5K81j5MbyRLOCFy78Z2zIJAS3+CRFbC47AxJeAiksoRSopE0xL3vNjGl86cQTYns64rSnWRY8SAyRMXZRxmM72xLElR4rX2MHOm+OmPZYimJexm04gBrV0Tj93CC7sGyeYUesQ0A3ERk6BiFUzYrWaCbtuY69URSrOquZekqDkrzmgopSOcIpmV6Y9neLV9mJ5ImqQokRQVVm3r42Nzyij3O1DyeYpddtx2C53hNKoKZhPsHkiQzil47FYuW1hDVlKIZyQqfHZOqi2mN5Jha1eUwYQIqNgtFnYPxhGlPCdMLaLC52AoIeK0mnE7zChxFVRIZHJ0R7LYLAL9cZF4VjPk9w2nsZlNWF0WdvXH6Y6ksVvMTC/zjHx58Iz6ciDSGU7r91xDuYet3VGSokxtiZs5lb4xHruNe8Ksbxka88XkaGIYdgZHnY6ODurr68c973K5mD59Opdeeinf/OY38Xg8Y7bX1dXR2dmp/22z2fD5fNTX17No0SKuuuoqlixZ8p6v/4OMklfZ2RcHYE6l76jLnRi8cw5mmB2MiQy30VQHXOPmLTy3btcgWzoj1Ja4WFAbGDdH60CS4VSOWZVels4qA1QCbhv1QY/uASuMk/Mq82qKmFXupWUggdfx9sdVwZt35swgPdEsz+0coMJn57zjKlk8vYTWgSSd4QwN5fYxa01kZYZTOVoGEly2oHrEkzNEcsRYG07lmFrswmU1s607RnsoRSIr614+s8mEnM/rhmFDuWeMF9Ayst3rsIy7Jj6HmXhGorrIOXIuKk6rgMtuRZJVrDYBk6DluvrsZlxWM0PJLIIgkFehazhDeyiJx2ElJcq47Vbm1wRo6ghjNpmwW024bGayskKZ105jlZ9wUkRV08wo8wIC27rjLJ1VisNqpsLvoC+WQZLzOK0m/E4b6ZxWhb5+9yB2qxlQkRWVoYRIVsqTV1Ui6RwvtQ4xp8JHXdCFKOV5vTOClM+TEhUSWZk3urS/ExmJnKKSzilUB1yUuO3MKHMDAm92xcjmFDwOC6GkyM6+GNGMZsC3h1KUem04rSYQBGaW+/A7LYRTWYYSOV1I/cyZpQA0dQxjEgQq/A6Oq/SjqGGCbjuRdI7XOyMsqgug5PMoqkoiI5NXVVx2Mz6HDY/dQlpSxtxXjVX+MfdvdcClG/6jQ68XNFaOrDc55ovJ0cYw7AyOGaZPn85nP/tZQNP2GxoaYvXq1dxxxx08/fTTvPTSS5jN5jH7mM1mvve97wFadXEkEqG5uZl77rmH3/3ud1x00UXcd999BAKB9/18PijkjU4nHyoayj2EkllCSZHuSPqIct6ONJw02iicKK8tlBRprPJNmDfVHgoTSmZZPD04Zp7WgSSyopLIvt3LOJLKMRDPEknlWDIjSHsoidlkIuixAxBKZqktcY0zLAuel33DmuewqsiB32nDYhKQZAWLCX2+wge1dj1FIqkcRU4rsYxEy0CCWeVeHtvSzaxyLwtqA3gdFlY197GzP86fNuxlVXMfyxsr9TWsbu7j5T0hnFYTL7YO4rZbOOe4CnwOKy19CeqDHkAglBSZHvTQ3BOj1GtndqUXp9WM02oBBJY3VuJzWvVwcMtAgngmR3WRB5/DQnsoRV3QQySVIyspzKrwEXBbiaRy+jl5HRbsVhOlXgeipHDecRVkJJmtXTGUvEoqp+geqAW1AVoHEuwaiCMAyazEW71xitxWls0uY0NbiHAqR1pS8DosmjYmKv2xLFaTgGACUVbpjmQo8diYIjlZMiPI8Xt9NHfHSIoyK1/vJp2TKfM6iKVzDMZFXDYzxR47s8t9XHXKVFoHkqx9a5BQUuSZHf20DiQ5Z245s8q9lHrt2CwCM0o9tA0mCbptzCjz8OiWLkIJEbvNxKxyH2m3jNNioshlZTCRI5LKEU7meHVveMRb6EBVIeC26fceaIZdIisjKyrtoeSonEAtD7A+6NFD+scChmFncMwwY8YM7rjjjjHPiaLI4sWL2bhxIy+++CLLli0bs91isYzbB6Czs5PPf/7zPPHEE3z605/m+eefx2Qy1H0MPvxUB1wEPQ62dEbG5IgdiHfq5Rt97EKu2rpdg+M8WrKisqBW+5JV2F7IYVq9vY/kiPF2+cKp+jr6Y1ksZkH32HVH0vTHRTx2LSG/wu9gyYwgIOjH6wxnWFAbmDCp/7IF1Ty2pZvhVI6qIifLZpURSmZZvb2fZFZmTqVmeGq+M81A6YlmGE7l8Not7OiLkRJl2kNJ9g6laA8lueXjs6kOuGgPJdneE+OtvgS7R4yCMxpKaSj3sLnDTkaSCScUuobTlPkcfGf5XJKiRG80Q38sg0UQ8LusFHts9EQyJLIyoqQScGn5fpGUqJ9DwVj2Oy20DSZQ1DjzqovYM5Tin1u7cVjMlPoclADrW0LUljiZXuZBlPL8cm0L/VGRWRVe5tf4Cbi1uQf9OeZVa3qqW7uivNAyyPxqPyfVFhNw2wAVj93KPev30BFKc8/6PVxyUjVOm5lIOofPbuWU6cUkRYWaIhceh4VoRsIiCKRyMrFMjn3DaRJZmfqgh+09MSKpHH6nhaDHzqK6wMhxBDpCKVoGICXKrNrWB6jMqvSwN5QkI+Xpj2V4qWWQHT0x0qJMIiezensf/XERl1V7rz91WjHtoRT1QQ+NVT4unj+FRFYmlMyyYkMH4ZRIkdtCNK2wbtcgyxsruXTEk/vYlm72DaeZWuzisgXVutFWCN/HMjkSWRm3zULQ42DZ7LIj/r95tzE+6QyOaex2u27MDQ0NHfJ+tbW1PPHEE8ydO5f169fz2GOPHdZxu7q6uPLKKykpKcHtdnPWWWfxyiuvTDg2HA7z9a9/nfr6eux2O2VlZVx55ZW89dZb48aeddZZk0p5XH/99QiCQEdHh/7cvffeiyAI3HvvvTz33HMsWbIEt9tNSUkJ1113HeFweMK5/vCHP3D88cfjcDioqanh1ltvJZvNTjj2rW1v8pPv3cL8eY34/X6cTieNjY387Gc/m1AguK6ujrq6OqLRKF/96lepqanBYrFw7733snTpUqxWK319fRMe64orrkAQBN54440Jtxu8OzSUeyYMhb5b4wuGW3ckPeH2gjFXMD4KeW2FY4zeXjh+XYkbj0PzSo1mtKdk3a5BNu4J6WHa0WHXghG7/7m0DiR5cXeI1c19PLalG9C8csVuG/VBN8tml7F4epC6Ejdms8DOPq3FXtBjpzOc0UO0xW4bGUkhLSrYrSbOP66SaaVuLCaTfh71QQ/HV/lZ0hBkZoWX3QNx7n25nW//bSuvtocpddtx2kyoQDqn0NQxDAjUlrjwOW1kFG3+4VSOlCTTG03TEU6yrTvGq+1h/vRyB/es38NjW7pZ1zKoV2haTCbMJphV7sUkwHBSZDAukldVSr12MpLCa+0RVr7exS+eaWFLxzChlEi5307AbePF3dp766ULqqkpdtPUEWHLvggv7Brk/za083rnMJcvrOHyhVNprPZTVeREVfMMp7ScwnPnVHBCTRHLGyv50pkzuOmM6Xz1nAaWN1ZS5NTklKKpHP1RkV29cTZ3DLOorpgTphZRU+LizIYyzj++koDbxuLpQS5fWIN/xDu6tTvK39/oYe3OQaxmMwvriqktcVNR5CAuyoSTIqmcjCTnyUkqVpMAgkA4JXH27Ao+v2Q6fqeNoMfBwrpi/fVurPJR4XdQ7nNS4rGTU/LsHkiQlRR+ubaFJ7f2MBjP6jmR1QHXyL4lFLttmE0m6krcnDkzeMx46goYHjuDY5pcLscLL7yAIAiccMIJh7Wv0+nkW9/6FjfeeCMPP/wwV1xxxSHtF4lEOP3006msrOSmm26ip6eHhx9+mGXLlvH0009z1lln6WPD4TCnnnoqbW1tnHXWWXzmM5+ho6ODxx57jKeeeopnn32WxYsXH9a6J+KJJ57gySef5KKLLuLLX/4yL774In/+85/Zs2cPGzZsGDP2hz/8Id///vcpLy/ni1/8IlarlYcffpidO3dOOPffHrqP9c8+zdlnLWX58uWk02leeOEFbrvtNpqamvjb3/42bh9RFDn77LNJJBJcdNFF2Gw2ysvL+dKXvsSLL77In/70J77zne+M2ScUCvHPf/6TBQsWcOKJJ77ja2IwOYcbTj3c8ZN5+EaHXUcbcbKi4rCax3k1Rucw3bikfkzRwegxWg5WHDkfp7HKz7JZZXgdFv1YtSXOMaHn0V5Dr8PCmTOD7OxL6B/SwJjwbuH4j23pZldfnDuf3sX5x1WyoDZAVlL0JHnQKkaDbhstAwkW1RazN5SkdSBBQ7mWJ+iyWfA5LOweTNAbyaKoebJSHrNJoKrIQZXZTUKUKPPacVpNdIbTVPic+JwKJ9YUsXek8jOaknDaLLhkBYfVREqUycp5mrujzKsJUDzi2ZLzKsUeK0lRMxTrg1o4UsmDLKvEsxLJnMRgPENvLIucB6sJqj12FtWVkBRlUqLM1u4YbYMp2kMp9oaS5PMqCJDMSPzjzV5Oqi3WK2Wj6Rw2kwmnzcy2rigtAwnOmVPO8nmV+mtSKEIYjItkJBmX3UJWTtMby/BwUxcd4SSLaktGjLkSVm3rY+3OAV5pC5EHTIJ2bnI+j8UkEM9KDMQynDQ1wElTi4hltCrZWDaH127FJAgMJkRKfXYcVhMOi4nWgTgn1RaP+9JSHXDx1XNm6p7b+dVFOK1mwimR3zy3m65IBhWVIpcVi2mst3jjnjAuq5nF00oOSw7n/cQw7D4ApHPypNtMgoDDan7Px2ZyCuoEiaEu27t3C7W1telhVVVVCYVCPP300/T09PBf//VfzJw587DnXLp0KQBNTU2HvM+2bdu49tprue+++3Tv2uc//3mWLVvGF7/4RVpaWvSw7q233kpbWxu33XYbP/nJT/Q5rr/+es4//3yuu+46du3a9Y7DwI8//jgvvPACp59+OqB1ZDjnnHN44YUX2LRpE6eeeiqgXcMf/OAHVFVV8frrr1NWpn2Q3nHHHZx88skTzv35m7/Od370c+bVFOvFE6qq8oUvfIEVK1bw8ssv68ct0N/fz7x583j55ZdxOp3686Io8tWvfpUVK1Zw2223jfFO3n///eRyOb7whS+8o2th8P4x2YfV6By40WMKBt+C2sCERlxhbE8kTVPnMOcfV6nPO9q43P+4QY9WTFE8YgSMLkpYUBvQQ88AQc/4tRRCq5GUSCiZpT74tldv9LEuW1DNnU/vYteI1+6Wj88ek+O3bHYZC+uK+eXa3WxoDRFwWxiMi6hqZKQIxI3FLNA2mCSakrCawGG24LKq1ARcuOxmuiNZ/C4rFX4ndSMakjv7Esh5lfnVRVQFXMQyEiUeG9VFLnb2xTGbBOIZiaFElvpSj56nqKGSzSl0D8fY0hlhVrmXqcVuwkmRhCixqy/BUCJLmc+JIAj0RDOYBLBbzbrBOq+miHW7BuiLZsjKeSRFodhlZ3aJl7ahFLGMxKrmPj23bFall2ROxm4x0Z/QwtRtg0ndULKYBLbui5CR88yq8OKy2bFbBaJpJ/GMRCKb45W2EHuHUiyeHqQ+6KapQ6usbR1IICp5qvxOit02BGBWuY+OcIqMnCctKciKitViIiMrhJM5nFYzNrOZznCSrJRHMIHfYWUoKepewNH5coXfhZB2Q7mHWEbisS1RREnBYhIo8TjIq7C9J8acSp9u1K5vGUIQ4JKTqsfcr4VzH32Mo4Vh2H0AmPv9pyfdtmxWKX+64e0P7AU/XEtGmlg/75T6Yh7+0tveoyX/uU6/EfdnXrWfx//17YrSc+5aT080M25cx88uPOj6D5U9e/bwH//xH+Oev/jii7nwwiM7zpQpUwDNW3SomM1mfvzjH48xSpYu1bxZTz31FK+88gpLliwhl8vx0EMPUVJSohdwFPj4xz/Oxz/+cZ5++ml9/Dvh6quvHmNcmc1mrrvuOl544QWampp0w+4vf/kLsizzjW98QzfqAHw+H9/73ve49tprx809pXrquOcEQeDmm29mxYoVrF27dpxhB3DnnXeOMepAC51fd9113HXXXaxbt46zzz5b3/bHP/4Rl8vF1VdfffgXwOCoMJlnbrQRVjCwQklNlmL/ooX9x65rGeSl1kHCyRzpnEyp1z6u2nB/zbdCQcjoMO1oOZH2UEqXOHlx9xBnzgzqhk9DuUeXoyjz2Yln335/bB2I0x8XkfOqPva4Sh9pUUGU8npxhMUskJUUPS+w4L2T5DyD8SEEQSCSyrGzL05HOMWcCh8XNFYiyXma2oeRR6pJY1kTs8o9lHrtDCVydIRSZKQ8vdEMdqsJr8NChd/BJ+ZNGRNKzkoKa3b0YTabkGRV11zTZGHidEczeOxmckoeu1Xg+CofAzGRSCZHTs7jd1k5ub4Yv9PKP97sZiguks5J7BqpiF8yI4jLaubF1iHaBhPE0ipJUcZhNVNb7ETAhKTkWfl6N41VfuZU+oikJVr649QEXJw5s5QKn51dA0mUfB6fw8re4RQ5KU9GkvE6tMpfm0WgLuimaziFogokRJmd/XHCKRGLSaC6xEV3KE0mJyMpec6bUYHfaSGWkQGVGWVeaopdtAwkCLpt5GSFaFpCUlQCLhs2q4WUmEVWwGrSdPtAGFNFffnCqWOM+cIXkIykIOdVpHxeF1NuD6XoCqd4uGkfkVSOmmIXZT47Fb63CyUKRl0hH+9YCMsahp3BMcPHP/5x1qxZo/89ODjIc889x1e/+lVOO+00Xn311cP22qlHUPFZW1tLTU3NuOfPOOMMnnrqKd58802WLFnCrl27yGQynHXWWbhc47+hnXXWWTz99NP6+HfCSSedNO656upqAKLRqP7c1q1b9bVOtP79EQCrkOf+P97D2if/zq5du0gmk2OuW29v77j9HA4HjY2NE671pptu4q677uIPf/iDbtht2rSJHTt2cP311+Pz+SY/UYP3hck8cfs/fzDpk8K2glyFnIdls8om7VJQ0HLzWK2Ijjwzy3xsaAvx3M4BdvTEaA8lWTw9OBL+jBNw2mgdSI58+Aqsb9FywS5f6BojJ9IZTo8UZQhot66wX3g5hCBAhc9OQ7mPUFLkH693szecotLv4MyZZbqXL55VOO+4cnqiWfYNp2kPJfE7bTR1DNMRThF025lR5qGqyEl90D3idVMBgXW7BwklRE6tL2b5vCms2tZHqddOezjJ8IhnyVNThN9pZUNbmKaOHFP8ToI+O2aTSTdwQ0mR1oGE7gXc2h3FYjJRXeTA47DqoUEtvzBNJCkimAT8TiuipBJN55hfU0RaUtjYFiIuKcSzEoIg8KkTqtnRF6M/mmV3v6Yn6LKaaSj34rKZWYNKKCEiIJDISvREs5R57YhSntbBBHuGEsyu9NMby9AdyRBO5rCYTSSzMqKkEB2pNC2y2wjJGdKijMNiocRjRVFBkhXcdit2i4kFtZoh3x1NM7vCy+fPmMbrncP8480e4hmZjCSzsK6Yv23pRhCgodwLwHAqx46eGKFkDjmfZyiRwW41c9q0YoYSOQYSWbx2CyZBoKk9TKnXrt8XE3nXuiNpnFYz5T47SdGC22amPZQklMyRUfLsDaX488Z2HFYLVrPAp0+sGiNMXZDJuWxB9VH31oFh2H0geOsHH590m2m/RPwt/37OIY/d8G/LJhk5fuzabyydMBT7XlJWVsZVV11FJpPh85//PD/72c9YsWLFYc1RSOIvLS09rONORHl5OQCxWAyAeDw+5vn9qaioGDP+nTCR0LXFov37ju5wUjjWROcw0TpNJoF/+/L1PPHEE8ycOZMrr7ySsrIyrFYr0WiUX/3qV4iiOG6/srKySYtAZs2axdKlS1m5ciXDw8MUFxfzhz/8AYAvfvGLh3C2Bu81k3nijqQ69u1QKRSPyERMdrwFtQGWzAjq0hLL51XSH8uyozdKVzjNmu39BD0OWgYSpEUFnyNPKJkdKdJQJ9QKKxRneB0WGso9BD32cSHWxdODBD0OspLCS62DSEqeSEYim1OIpiRmlXtp7o6x8vVuKvwOwDVGsLjYbUOStWrW3miGjnCKMq+DoMfO5Qtr9NyrYpeVaCpH22CKjXvCvNo+zL7hFDaziQq/g6oiLcy6eyBBOCXisJioCriYUeahP66FNDe0hWgbSDKY0KqB0zkZWYFKv4NFdQG6o5ogM2gtx0RZweOwEs9KlPnslHrtNHUM01jl47IF1WzcGyKUyNHcHeWaU+tYPL2E5fMqufPpXXQMp8lEM7QNJtk1kODNzmFEBU6fXsyi+mJe2DVEUlQodtvpjaVJigqqqqLk8yyZHqR3OENWUnitfRg5n6fc6yCWlfDarVitAnaLVgzTWOXnikU1bGgLad0oUKksclJT7OS5nYNI+TwFb2xDuY9ZFQk27QkjKZrczLxqP22DSTZ3hPE7bWRzMt2RNAJQ6nUQz0hM8dtZVF9CZzjN8pIKdvbFWbdriK7hNB+bU87SWaWAysY9YfYNp1FG9AYLht4bXREsZhONUzzkFIXhlITdYmZRbTGxjPaadkfS2Cwm+uNvvydOJPFztDEMuwPwmc98hnXr1pHJZKirq+MnP/kJn/jEJ973dRxOHtt7NdZpMx980HtEITfs9ddfP+x9Cx0WFi1adMj7DA4OTvj8wMAA8LaRVfA8FZ6fbPxoD1Uh106WZd0wK/BuGoCDg4PU1tZOuJ7RNDU18cQTT/Dxj3+cp556aoxO4KZNm/jVr3414XEmM+oKfOlLX2L9+vU88MADevHK3LlzOe200w73lAzeAybzxO3//KEaeqPDovvnMu2/fUObVtXaUO7VvWrtoQrWbB+gxG0jlBQJum1MK3VTXeQaqXhNUh/00BPNjui9aXRHtI4IneE0VUVa1eP+IeLWgQT98SwVPgf98SwvtYaQlTx2q5kitxWr2UTLQEKXKomL0kgVpV0zjPaEiaRytEUylHnsVAVceqXp/p6zcp+TPUMpXtkzxFAyi6RomnElbjuVRU4aq3z0RLO0h5JYTSZmlXsBlXW7B6kpcrGoPkB90EM4KbI3lEDJQyiRxWw2Ue6zA4Le3WJDW4imjmEG41mCXjs+h5V4WqJDTuFxaFIwADPLvAzFstit5pGWXrB4egnnH6cVOqSysi5FMpyWsJkFZpR5iaRydEfTzKvy43FYaOqIUFfiZIpfM0RjGQmL2YRTAElRUfJasYOah2hWpL7Yg0UwISoKQ8ksjzR1Ueq1M73Ug5TPY7eYeW7XAHtDKXxOCzv743RFUlT6XSSzEh6H5h0DSEsK4VSOjnAKj92C2SQQcNuYXeFjWtBNU+cwi0a8f7UlTj2ncmtXjIykhXV39sWJZSTMAiQyEj6nlURWJpHVPG5umwWn1UK5346ch5pimFPp1/vb/nrtbl5tH8ZqFnBazXqhTn8sy9buqC5KfSxgGHYH4N///d/585//jM1m47XXXuPcc89l7969lJSUHO2lfaQYHh4GIJ/PH9Z+mUyGX/ziFwBcddVVh7xfZ2cnXV1d48KxL730EoBenTt79mwcDgdNTU2k0+lx4dj169ePGQ/oQsk9PT1jDK98Pq+HUd8J8+fPZ+XKlbz00kvjjNnC+kezZ88eAC688MJx4s8TjT9ULr30UoLBIH/4wx9wu90kk0mjaOIYYrIq2P2fP5RQ7Oj9RufbFYoYCtsK2wvSIaNzlEDg/OPLAYHOcJp0TiYx0vPUbTfTOmAGtF6iLqtJ71JRkDpJZGQiKWmMNl5h/pdah3hx9xDFbhvnzCnjjIYgA7EsWVnF57DQG88QdNuYVV5JWlSo8DvwOSy0DsRHeqTmeKq5D1HOc9r0IMsbK9nQFiIhynpVbcFruKiumK5Iiq37RNqHUlQVOakpdnH+cZV6c3qAGaVewi6Rcr+Dlr4Ee0IJBmNZPA4Li6cHiWclhhI58vk8OSXPVL+D2hLN25fIygTddiRZodSjhRgjKZFIVkKWFUo9DupLNYHiQpP7+lIPHoeFXf1JtnfHR141FatZwGTSiuOm+F2EEzlKvHZOqg2wqrmPtKh5AxfVFRNO5UaqgFO82h5GHClkUFGZ4nOSUxT8ThuykieUzNEby3DWrDJa+hK0DiZ5vXMYl82qdaBw2SlxW/E5zKRzClV+J6mcMuIdtDGzzIOrc5iAy8Z/PL6DcEpkRqmH2RUetnbHyGQUppd6tFy/VI58XusaMZTMcc4cLRVg8fS3e85u646iAuU+O4oCggA1++XDeR0WElmZnkia9buHmFnuHVP1esWiqUTSEl3DaXb1J3QJlFXNfby4e4h4RtJzRI82ho7dATjuuOOw2bSeeRaLhVwuR09Pz1Fe1UeLfD7Pb37zG2DiHLHJ6Ozs5KKLLuKtt95i2bJlXHLJJYe8r6IofPe73x2TZ7Z+/XpWrVrFjBkzdK+TzWbjqquuIhQK8dOf/nTMHGvXrmX16tXMmDFjTOHBwoULAU2fbjR33XUX7e3th7zGybj66qsxm83cddddYzyP8XicH/3oR+PGV9dohROrnl2Hkn/7fHfs2DHunA4Hm83GddddR3NzM9///vex2Wx87nOfO+L5DN4fChIhmzuGWbdLu3+WzR6fMzfRPt2RtK4hB+oYnboCDeVaRWdVkZP+WJZHN+9jxYZ2Nu0Nj3jj3CyoDVDhc5DMyuwZSvJGV5RX24fpj2dRVeiPi/rcDeUezj++nMsX1RBwW9nSGWHjntCYtS+qK6a+1MWsci8Bt50bl0zjtgvn8tlTa7GaTQxEs6zfPURjtZ+rT6nVWmtJCtu647y4O0TbYJJ4RkKWVUDl9c4I27qjWEyCbhgUtPYcVjNfOnMGZ80q47gqP0UuK4mMTFLUKmoTWZnOcIZF9QHOP76SCp+DqoALr92qVbD2x9m4J0x1kRO/00I6J5OTVTK5PHIeMpKMgEAkLbKzP0E0rfWuHUqKxNMSZpOJiiInw6kcj27uoiOUZl61nxK3DZ/dSk5WSIgSkVQOECjx2Jlb6SPosWO3mKgucVLmc5DIyiyqK2ZulY+Ay8qaHX2IUp6mzmF29sdJZGXmVxdRF3RhN5uIZ3MMp3Ls7o+TyimIsko8q2A1C1y+qIaGMg9mk5lUTqZlIE57OEk6l+e0GaV88YxpzJ3io9Rro6ZIC4FXBVzUBNy82h7mtfYQg/Esi+qL8Ttt9ETTpHIyHoeFjXvD/OPNbt7qi9EykGQwnqVtMKW//ktmaPqExS4bAgIzy7SilvOPr5g0H+65XZrkyiObu/j12lYe3dxFd0QTVc6rWjqASVB1I91pNWMyab+PFT40HrtEIsEPf/hD3nzzTd544w1CoRC33377hF0Jkskk3/ve93jkkUcYHh5m9uzZfPvb3+Yzn/nMuLHXXHMNf/vb3xBFkeXLl0+aMG7wzhktdwKaIPG6devYuXMnNTU14ypPQQtpFvZRFEVvKfbyyy+jKAqf/OQndZHfQ2XevHm88MILnHrqqZx99tn09vby17/+FavVyv/93/+NkS75z//8T9avX8+PfvQjXnnlFU455RRdx87lcvGnP/1pzPgbbriB//qv/+KOO+7gzTffZPr06WzevJnt27ezdOlS3ct3pMyYMYPvf//73H777cybN48rrrgCi8XC3/72NxobG2lpaRkz/uSTT+b4Exaw5om/c9ZZS1l86qns27ePxx9/nAsvvPCwhZ1Hc9NNN/GLX/yC3t5eXezZ4Nhm/y4RcPBcu9Hh2oKh47FbSecStA7Ex+Qeje6K0RPN0NwdYyiRpdznYDiVoz2UIujRPEYAL7YOMZQQCSdzLJtVxhkNpXp16urmXryOqXqV48Y9IWpLtOMU1gPQMpCg0u+i2G2jM5zWc/AASr12rBYTyZyse7fSOYlFdSVUFTmJpETaBlMEXDbyKiO9TDMII10pQDNsR7cwK7Sf2tIZIZ3TPG+FsaM9oBv3hGju0XT5PA4LLX1xZlV46QileHJrn1ZlLJggn9c07HKKnqe2qy8Oah7BJBBOi2QlBUEQKHLauOaUWh58tYOuSI6OUFLzCg4kRq6GgNeuCQZHUiJ1JW6cVhMvtYUYTuZwWMxUFClsHikU2TuUHKnKzWMxw/SghzkVPip8DgJuG8dX+Vmzo4+9gyliGRlBMGESwGYxUeS0MC2o5TzOrfQxPOJZk/N5ppV6WDqrlPqgmw1tId7cF2UwKVJT7KI9lCKSyjEYzxJKiMh5cFot1Ac9NLXvI5WVKSmxUeFzsHukqlkQwGO1EPBYKfXa9IrqqiInLpuFUq+DVK6Qi6wVuvTHsrQOaBqEWvs2G3Ie2gaT5OQ8JpNmhKZzCqDSEUoTz8i47RYsI4UuFX4HdUE31QEXGUk55BZ+7zUfGsMuHA7z+9//nvnz5/OpT31KT9aeiEsuuYSmpiZ+9rOfMXPmTP7yl79w1VVXkc/nx0kxPPjgg9x33308//zz7Ny587AMBIPDY3+5E7vdTl1dHd/4xje47bbbCAaD4/ZRFEXfx2az4fP5qK+v50tf+tI4iZBDJRAI8MQTT/Ctb32Le+65h2w2y6mnnspPfvKTcfOVlpby6quv8sMf/pB//vOfvPTSS/j9fj75yU9y++23c/zxx48ZX1FRwfPPP88tt9zCM888g8ViYdmyZWzatIkf/ehH79iwA/j+97/PlClT+O///m/uueceysrK+MxnPsMPfvCDceFis9nMb+79K7/66X/w2kvPs7mpiYaGBn7+859zwQUXvCPDbubMmXo7OKNo4tjhQEKqo3PhCuHOgwmv7t/XtWAYDiU0cdqGct+48K7We1WkZKRN1aK6YhrKvbQOxFnd3Edd0M2cSh9lPgeDiSwZSSGUynHW7DIe29JNS3+ctKQwJeDS9cUKbcRGF1C0DiT1RPnF00r0cGhBnqLIaeXyBTUjbaxgW7eWk+Vz2nSNs+5olpoSFzkpj5LPs6iuBJ9TEzteta2Xpo4IaVFm6ayyMZ00CtexPZQkMuJBWzy9RF9XJJUjlZPpj2dZ3liptx5bsaGdvniGnAIuKzjsFrKKQiQtUuF3cNmCan69djctAwnmVRfhtJloG0iSkmSmFrtwWM2c2VBGTlZx27U+tLGMxHFTfMyvLhrJvVNZs72foYSIzWJiOJVDlBUkJY8lKWi5ewktDO20mnFYzUSyCkMOkc+fMU03XGtLNA+b3WzG47RQU+SiN5ampT+By2Zm/e5BUrk8OVkLWZ85M8jCuhL9Xnp0cxev7g0Ty0jYLFr3jEgqR3NPjH3DKZJZGbfdTKnPrnfZqPA7mRb0Es/K+J02/A4rHocFWVFRVYhnZHYPJSh22qgqcoz0utUMtIyk8Pc3ehiKi1QHXKRETW4nIys4s2Y8DgvTSz2UuO00VvuoCbj1e6OpfZjuaJoyjx2b1aT3G55V7sUsCOwc8bZevtAw7N41amtriUQiCIJAKBSa1LBbtWoVzz77rG7MASxbtozOzk5uueUWrrzyynG5RhaLhfPOO49f//rXNDQ0sHz58vf8fD5K1NXVHZEsyejWW+8Wo9fxyCOPHNI+wWCQX/3qV5MWGuzPiSeeyNq1a8c9f++9944L0V5//fVcf/31E85z1llnTXrdvvCFL0yY0zbR+OKSIP/x899w3BS/LlB8oPGHet2z2SwtLS1MmzZtjJ6dwdHlQAURE+XeFfLmJho/2T5eh4WqIgeF/q37U+i9uqi+WDfCqgMuQsksSVGmM5xiTqWXqcUuMqKMnM8jyXlWbNhLXzSL12llVoWPJTOCIx4zkdoS55icvgJTi10Mp3I4rGYayj16d4n+WBaX3UyFz07QY9dDazv7YnqHisLaeyJpnmjuJYANh9WsG32rm/t4vXMYBM25tmsgocteFPTRElmZF3eHUFWtTRmgG0XzqovGeCr7Y1kqfHbqg5pu38LaABaTmfZwCklReWxLN5ctqMbjsJIWFaxmE7dfdPw4HbWGcg8Bt5VISiKVk0aKA6wsnzdFl/d4oWWIgXgWh8VMiduG12Elk1Nw2kzMr9Y8plu7I+RVgcF4hnhWoj+eHTFUNc9mJJVj10CCwXiWmiIX86r9pESZCp8Tu9VEOCWRyEha9wanFb/TRutAgpdah1jeWDkiGC2i5FVSokxOzhNw2mms8uG0mthjMmESBKqLXERSORw2M2fPKWfJjCCJrIzPYUEYacm2tTtKOCFqYV5RwWfP6/ef9uPD67DQNZyiV1EZSGRJ52RsVoGZ5T7kvEqFz47PaR2jqdgdSY/I1tgo9dmZX13ESbUBNrSF6BqRwxlOisRFmUhqvILA0eBDY9gdqift73//Ox6Ph8svv3zM8zfccANXX301r7766qSVe4qi0NbWNuncoiiOkYYoyGEYGHwUWbFiBcPDw/zbv/2b4el+nzkUr9zh9JE91PGjjar9E8kLawols3oBRaHisODpKlQzgqoLDBdaU61vGWI4LaKoKlVFLi5orGRhXfF+OnaMKaAY3V2g4KnrGk5jt5owmwSG4lqoNZ5VWFAbGJEuKRlz7fpjWf7y2j56oxkayryEkqJuBNotJixmLfw4FM+xezBJwGElnpG4cUk9AKFklqnFLoYSmjbdSbWBkZCxypIZQdpDKV0DsNhtQ1ZUTq4vZvdgguOrilBUsNvMDMYzrNs1gCTnNc01vx1JybNxT5ig28bmjmEiKRGX1cTyeVMAgW3dMVAFZFUd6eqQ1F+fnKKAAFaLZjjVBd0MJUS6o2kyksLyxkr8TittgwnUkXCw3WziTy93ICl5BAFOqS+hJ5pmz0CK3QNxtvfGiGUkVFTqSrSq5pSoeevm1xQB8OiWLnJyHp/TSlWRA5/dQutggoyUJ5bJYTGZWFQf4IpFU7l/UwdDCXEkHCpQ7LaNMbo0g81LKJnlzX0RZFWlPuihxGMnnBR5tKmLSCrHTUun6wbtoroSaordejWtxWRizkieYetAgraBJFVFzjFi2du6Y5hMJi45qVr3uI6Rw/HYKXLbde/e0eZDY9gdKtu3b2fOnDnjpCbmzZunbz/ttNPo7+/n5Zdf5vzzz8dut7Ny5UrWrVvHz372s0nn/ulPfzph5wQDg48SP/vZzxgaGtLDwP/yL/9ytJf0keNwvXL7s79heDj9MEeLERfCudUBl76m2hLXmF6voWSWzrDW1UZrsv62oTe67yto3rRoRtY9U5s7hnlscxexbI7jpvjGdawYXZH76OYutnVFqQu6Wd5YqcuklHrtpHMyrQMJvYsFqLohu6q5j/ZQkpysIsoKneE0PdEMsqIiynkq/A6qi1y0h1PEUjkSaRmlZRBQmVPpozOcISMpdIRShFM5Gsq9BD12ntzWyzNv9WMzm4mkc8yu8DKr3EtTR5gXW4YYSokkMjK1xS6GMzlcVrNmZL7agd1qIeCykchG2dYdQ5Lz7IukkEdkPZo6Isws9yII4LKZCXrsuOyWMfIsFsGE32nFY7cylBDJynlMgkokJfHKnhBdkRRKHpJZrXPE8dV+tnbF6ItnsZnBbtWKVTTNUxVRUkllZewWAYtJEwdOiRLd0Qxuu4W2wQS9sQx2i5lynwOX1YzHrokW51UVi0nAY7dgs5iIpHL0REOIUh63zUxtiYu2wQShpHYdRt9X1QEXq5v79C4bw6kc86qL6I2kiWRytA0meHSzZuD1x7PIeZVls8pYNruM2ZWaQHZ90M3CumJCyewYrcRC/uS8aj8Bt3VMukFtiZOqIu08QNVblx0LfOQMu3A4zLRp08Y9X1xcrG8v8Mtf/pIbb7wRQRBoaGjgkUceYf78+ZPOfdttt/GNb3xD/zsej0/YwcDA4MPMbbfdhs1mY/78+fz61782Ok0cBQ7XK7c/hypgvL+hN1rVv+B9KozdX8j10c1drG7uI+jROjmEklk2dwyzoS00ptVhYf79vWkA92/qoKkjDILAmh19XHtqHcVuG10j+U+XLaimP6YJ+kpyHrfdQoXPQXsoRYXPgdNqYWt3hP6oiNUi8GLrkCYn4rER9DgAGIhlMSFgt0BNwMWC2sBIZ4wEdqsJu8XM/Bo/dUEXQ0kRRc4TFyVa+hLMqfTrOV67LCa9mrLCr4nqNrUPo6hQFXAyp9KPw2qmM5wmltWkTpS8Sl8sQ0bKU1ut9ZoNJ3NkpTwzyjxM8Ttp6Y+TFCVUFQQEREmhdSBBTbGLS06q1tuRWUymMfIsCVEmr6hU+uzMrwnQH88Qzci40xL7IiksJhMn1xdT4dPCx1u7Y1jNAhYBLIIZm1nAJGjHqy12UeyxE3DZGIxn6Yyk6I9mCFvMWM0CbpuFloEksXSO+VOLWDIjSGdY80CGUiIeh5UFtQGK3XZ29cV55q0BPDYLGUWhusiF1WKiJ5KhN5YlkZV5tX1Yq4YeKZxZ+Xo3A3GRfD6veS7TOc6aWYbHYSWZlVm9vY9wUitiKeRhwtvVzIXr8rZhJuj3diF3c9nssjFhfy0XME6p14bLZiXgFibUcDwafOQMOzhw2LawraKi4rB1vOx2O3a7/R2tzcDg/UTgbfHpdytYeiT5kgbvLofilTsQRypgXGivZDGBy2omUGTVx+6/pkhKpCuSJisrTAk4iYdleqJZdvXFiWRyLJ5WMm7+0R68x7Z0I0p5ppa4SedkRClPe0jTjotntLyyjXvCbGgbYu9QikV1xVxyUrUe1hUEMJmgI5Qilslhs5iRlDxum4XaErfunclIClOLXVSXOPnE/Cm0DiRo6gzjddgocmqJ+22DKexWgdpiJzlZxWIWqApo4daGcg/eEYHfvliWe15s40tnzsDjsJASFQQBgh6tddjrnRFEWaE64CavqoiSQjKvUh90c8WiqdgtZlZn+qgscvK1c2ZS4XewcU+YpvYw23piyIpKwGWlPuhh+ahQtd9pG6Md2B5K6TIuNSUuGsrf7sEaSYmgCjisJuZUelk8PUjrQJJX24dxWS0oeUhkJYpcVk6uD1DhcxJwa8d8vTPC9p4YkWQOp81CtdtOmc9GXYmbmeVehhIiM8rcusj0pr0h0jmFaUE386sDvNg6yO6BBGaTiWK31mHCBJgFgSK3jXAqR18sjShrYs2rm/u4d2M7w8kcPocmXJyR8rjtZpbPq9Q9uHUOty4qXOHTDHZNDiapC00Xuofs7EvovYNH9z7ujqT59dpWtvVEqfQ7NFFmVaWxyq+Hgw+3a8t7xUfOsCspKRnjlStQEMEteO7eCXfffTd33333mFZPBgbHIiaTQEOZ92gvw+AY40ACxvB2qHR/Q6/wO5QU6Qyn9e4SExFw25ha7KbEbcVlNREosuGxW9m4N0Q0JdEykOCyBVo/ZK/Dwrpdg3p4t5CnN7vSR1WRg+aeOMUjFbad4QxzKr0EPQ5CySwWk4lppW4WjeRM1QfdIytQNVkWUaZ9KI3FIjCvqohF9cW6MG1zdwyzGc6eU85Vp0yldSDJ2p0DDMSznDC1SBMkHk6zrSdKOClqrdL8Dj42pwJQ2bh3mJ19cUBgOJWjN5YmktbahiWzEmazoFexJrIyzT0xbBYz58wtwWU18VRzP6By3nHlLKwr1qRA0hJLZ5XqOWCXL3SxeHoJq7b18WLrEHkVZpS5aQ8laQ+lqA+6aazy0xFKsmLDXpY3TgE049NqMSEpql7JPJzKISkqJS4bopTngU2dRFISy+dp2m+RlMS6lgF2DyQoLXg8u6K47WYiKYmnd/TTGU5hNmmFIjlZoTuSJSPlmVddxIwyD809MUDQxI1V8DusCAj8/Y0eoukcLpuZ6WVe4hlpRLNOoS8qUh90IedV0jmFCr9AfdDN/Zs62DuYotRj58SaImIZia5ImgqvU79vCnl57aEU61uGCLjtbNwTZsVLewmnRWaU+TijQetAsbq5j75ohuoSJ9VFTrqjGSymgrSNwLaeqOa1S4jYrWaqi52cVBtgYV0x3ZG0Lsp9tPnIGXaNjY089NBD41o6NTc3A4yTpzgSbr75Zm6++Wbi8fiEPT4NDAwMPghs7hhmVXOf3td1dK4cTC5eXB90j9GL259Ct4lT6ovpj2fpjmZpKNdC9tVFLip8eWaVe3Vtuf54Ri8uGE7lyOYUPA4zQbeNnX0JqoucLJ9XCTCm44X2YevQW5kNp3I0Vvn05xJZmS8tncHrnRHaBhPMKPPoBR3rdg3S1DFMf0zEZdNy/BrKPZwzp5z+eJZFdcW0DGiyJ8msjChpLQ1sZi3nKpqRUfL5kRw7mUxOwWu3UFOsiSVLcp5IWhrJe9NCtGfODBJJSfp1WjIjSEZSdA/X4uklE/bCBeiPZwknRawWE22DSTrCaTwOC9eeWkckJbLyjR4Keg9zKv0sm1VGf0zLH1NUmFXupWUgwWDcgpTPExopWmgbTNA64KU+6CHokWkbTBJK5PDYLTR1DNM6kMBhM+NxWFHyKrKq4rFZmVPpJTxSjRzLSLy0W/OS5pQ8+bzKcCaH02Zirt9HS3+ccDKHxSxQV+LhzIZSuobThEa8h1azQEKUGEpkyMkq4aREeyjJ+cdV6vfMpr1hdg3EMSMg58PM2+Mn6HEgK+qIVArMq/ajadKlCKdzSLKKSYDWEZ2/oMfOnqEkW7uiVPiclHjsbOuK0hlOUVviotLnJCnK1ASceBwWvA6bHsZ9p17yd5OPnGH36U9/mv/7v//jb3/7G1deeaX+/H333ceUKVM45ZRTjuLqDAwMDN59DqXoYSI2tIX0dlwF79uB8vcKoa9it22csn9BRLhQ9doZzmhCyHl1TJjwE/Om6GHQVc19DMQ0eZPGar9eibgtEiWcgh19cdKiwsfmlFMdcLG5Y5it3VGykqIXcISSIi+1DtEbzaDkVVr7E/THM8yt9FHidWAxCSSzMi0DCUIjxQ2hZJYXd4eYWuzC77SwozvGr9fu5qvnzOSmpdPpjqRZsaGdnf1xnDYzxW4bgqCSzClMK3Mh5zWZFc1TlKSpfZhIKkeRy8YnT6gmKUrsG9a6QgzGtVythnIfly+cyqOb9/Ho5m7MZoEyrx2LyaQXcCyeHmTZ7DI2dwxzz/o20qLCp06qIpLK8fyuASyCwOLpQSp8DkKpHHUlbrwOCy+2hrS2YUVOnFYza7b3YRKgssiFooKsqCRFGZfVRDiZYzglUuZ1YDYLpERZf02HU1qni7qgJm3SNZzBbbNQ6rWxvLGSRXXFrHyji5llPr3TxuaOYVZt69X7w4pynoyooAIuu5kpfhdW80gPbSVP62CCXf0xKnwOipw2ZlV4OW16kI5QimhaZjgljhSjpLnl/Nlc0FipdT9pGUSW89hsFko8dt1ALohWN/fEiGVy+J02LCaoDjiJZ7QQ/rqWQYIeO41VfjbuDRHPKLSHknxp6XSqipx61XJ9qQuPw0JjlVZMAQJeh4VHN+8DhDEtyI4mHyrDbvXq1aRSKRIJzfp+6623dIHV5cuX43K5uOCCCzj33HP58pe/TDweZ8aMGTz00EOsWbOGBx54YJyG3ZFwpKFYIzfJ4P0mn1fZPfJtdWa5F5Pp2JAlMf4X3l1Ge9kKf+9v5E1k/C2ZEdTCiz7HhLly++/TUO5ha3dU14EbXWARSmZZs72fRFbmnDllLKgNjKlCLRBKZgklRTx2C06riVKvnfk1/hEJD6gqciLJCrsHkjjMZipKHSyZoSW9b2gLsaE1RHsoSU3AjcUs0DaQJCNpLai0NmUJUjmFoCeHxWKiazjN4Eh3iFnlPl1AWVWhLugmIynsGUzRMvB2f9DWgSQd4RSKorJsThkBt40HN3WSlTLk8wKNVT40MV3HSMhUoCuSwWwSRtp5gapChU8rmih47H6/fg/PvNVHNCVTUWRHlPJ0xlMMJ3M889YATe0RrlhUwz3r97BlXxS/3QII9MdFUKEi4GROpY/6oJuuSJrXOyNIcp68qlLqtbNsVikZacQbJ+XpHE6ztKEURdUEeENJUQtzpyWSokSp10kiK1Pud+hGdXckjTAipaLk1RHRZa9+rhc0al601c19rNnRR1c4RSQjUVPsYk6Fj6aOMPGshNtmxmrSumBMK/UQTUtkJIVEVkZRITmUxm034XVaqA+6iaRyVPjtpEQJRVXJSLJ+z1QHXCyoLSKSzjGv2j9yTRkjWt0TzZAUZYrdNmaVa97E3f1JhhJZGsq9nDlTu4dKvXZEWcHv0KRLLl9Yw+YOrUPGrHIvDqtZTzWoLXHySFMXTR3DVPgdBD12w7B7t/nyl79MZ2en/vejjz7Ko48+CkB7ezt1dXUArFy5ku9+97t8//vf11uKPfTQQxO2FDsSDjcUWwgJy7J8kJEGBu8uKlpopPD4WEGStG/b78YXrY8CB/PIFZrUFyRGDlbxWvi7odzD9y86Tj/G6Dy30dIPhblG68btX2BRW+KitsRNRyhFwG3XBXy13CfNkxf02FmzvV/XiktkZUpG9MG0xPa47uGrLHKO8wwWDLzCB7DXYcFlNdEfF1lUV0xSlJhS5KQ/lmFmuZe+uJYXl8pKlLhtLKoP6A3kC6LFkZSotThTVTZ3DBNKZvHYrdSVuMeEqD12Cw++2oHNYqIjlKYznCaSyhFw24ikcsyp9NERTtHcE+PMmUEuXVD9tqTKtj6e2bFHr7oMuG2c2VBGWlKIZyV65Cw5SWFbdxRRUeiLZbGYYO4UP4unl9Afy6CoKr2RDH9/o5uzZpbxanuYruEMCCrXnFJLwVPa3BNnQV0xr3cO0xPN8vKeEJKiYreaqPQ5OaEmwEutg8QzMiZBpMxnp8KnGW0Vfk3eo6kjzHAyh9lsYmt3lLU7B1hUV0xd0E1Bg3DNjj5e2zuMxaz1UZ1T4eOr5zSwapubpo4IZpOA3WoaCeNa8LusiHEt7w7ADARcNoqdNlY197K9J04iK2nePUWlfSjN5o5hhhIia3b04XNYNYN2OMNQIkelz06536F7bwuixoX71u+00Vjlw+OwsryxUi9EOf+4CvrjItGMpFdWF6pnu4bTpCWFWeVeFtQGCCVFtvVEiaRzVAWcx0R+HXzIDLtDVcT3eDyH1SngvcZsNmM2m4nH43i9RiK7wUcbVVWJxWLY7XasVuvRXs4xw4GMt8mMtbe9ZaIu63AoFa8TzTdRH9nRBuNkaxw97+LpY+VKNDTvy86+OMsbK6ktcY/0h5VAVUe8XJLe7quxukgPcRZquQsVjaASdNtY+Xo3M8u9XHXKVEBge7cW2rtp6XQWT0+P9D9V6I72AQIWs0B9mVuXuygYqet2DRLPKpT7HKzfPciGtiHKvU4CXhtVRS7mVHpHjhvShXF39SVo6Y+TlRQG4hksZhNKXuWcOeU4rWZ2D8QRpfyY127tWwPsG05jtQgcP8XP3ClaqO+kYACX1YzdokmVeO0WusNp8mqehlIvZ80upTrgom1Iq0ZW8nkykkLAGcFqNo1U22rXuj7opj2U0owZu5WBWBaPw4LVZGZ7TxRJyVPishMotlHksmMyScyu9CIImkdwc8fwSD6ZSiqXx2k3c2ZDKW2DKd6MRAglswRcdr17TSH/rcxjp2M4RalXU4y4aekMTqrVZG2CbhvbRvoFe+wWhgShYH/isJmYUeahciR87LFbKPXaaQ+l6BpOs6M3yqrmPnb0xWgdSDI14KLUa2PvoOahHYxbmSVrntPBuEhtiSYSvbpZZdnscpbNKqOh3KPL4bisJrqjWYrdthGdwz62jfSbXTy9hFBS5JkdffTHRXojaaYEXATdNjx2zTCdX110THjr4ENm2H1QEQSBsrIy+vr6sNvtuN1uQ6nf4H1ByauoshYaymaz41qKvZ+oqookScRiMZLJJFVVVUdtLcciBxIdnsxYGy2mWghJHajidaJ5RwsOF8Knoz0fBYOxIEFSaG21v0Dw6OPA20ZgfdBDY3UR+4bTrGruxWk1UxNwgQBumxmHzULAbWXprFIK3qDqgEvvVxr02AklRe7f1IGAgADsDaXY0RejLqgJ23ZF07QNJtjcMcz9mzqwmEz4nZqRlJEU/E4b86qK9BzAQq5UVlLoiqTwjZyzKEMsKyGYBCp8Wsuq1dv72DdibJV6HBS5rYTjomYECSKVPgc5RSGWkeiOpumPi6zfPYTdYmbprFIWTy/hnLnl/OPNHmLpHPWlHk2mpGWQnmgWl9XMcEoCVK2ZvahQ5DKzqL5EN0TPP66ScDLHUELEJGgVp26bheMq/aRzCi/uDtETzSIrKrUlTtbs6GNfJM2iumIqfA46hpMMxUVahxJMLXFR6rNTX+rikhNrWLdrkFfaQuweSDCt1EM2pzAUz+J1WkhLeeZV+3mzO0I2l0dR8/gdmhRJqdeO3WxiZ3+CdE6mKT1MXdDF5Qun6h6wUCpHc0+UjnAKj93ClCInDqvWu1ZFpSucISvn8djMnDqtRGuTlpboi2XIq1pfWSEPKiqgYjObKPbYSGXNuGxmHBYTuwe043dHU/RGMpjNJqYEtPzHx7Z00xtJszek5TtmczJPtobY0DpIuc+JmFPY2RenPuimJ5rBPFJhDQLP7RzAZTdjs5iZ4nOOeCuPDQzD7j3gSHLs/H4/mUyGUCjE0NDQe7g6A4O3yasqg9EsAJa0Y0RF/uhit9upqqoyhI3340BFC5MZa/uLAk/GRJ0mCuMf3byPF3eHOHNmkMsXTp10bRv3hNnWHcVsguGURZdE2T8sWzAMWwcSbOuOsXRWKZctqOaxLd007Q0zmBBx2S1MLXaxqL5kTC/Z0esNJbPUlrjwOiysbu5lMC4iAJV+O36nBb/TRsHAcFhMDCVy/HLtbjpCSeZU+qjw2emKpklkZLwOC9t6oqzdWWgJqXL5wqm0DCToiWQpqfZx+YJqdg8k9SrSJTOCVPgdNLUPs7l9GFFWcFgsfOrEKmKZHK+2D5MUZcq8dgRBYCiRZSgh4ndakJU8g4ksHSEnQY+d5fMqiWUknt3ZjyTnCSVFLCaBfcNpEtkc0VSOjKIwr7qIyxfVMLrl2rpdgzRW+3mg8VR+v34PTzb3EkrmkOQ8KVFifk2AgFvrMNEykKAjlGYwIeJ3WmkPJXmxZQi7yYzXYSWvqIhSnlPqS+gaTtMykCCVkxhIZJGVPAvrinEVmeiJaDIkXcNatfC0oIdIJsdFjVOoCmivyf2bOtiwe4icolLkslJd7KIg/Pv8rn5e2h3CYTGBIGAWNC+wAJxQHeC1zmEyooyoKEhynv6sTFpSWD5d8wKmRZmeaIauSJrhpIhFMJGTVbqjWQRBIOCxcvr0UjKSzL5IBJfdTMCpyelMGZUvuKE1xLSgmyUNQZbMCPJIUxc9sTTdEZVosUyJ10Y0I+kV1bMrtf7Er3dG6I1liKY0vb76oHuUjM7RxzDs3gOORO5EEAQqKyspKyvT84sMDN5rMjmZm/6+AYAnv7IEp+3oviWYzWYj/DoJh9vaa/999mf0HBPl1xUMsI5QmoFYlo5Qekwf1v3nj6T6SGZlPddKK4J4u11YodLUaTWTkRScVvOY9k1VRU5aXVbC6Rwuq4msJOOxW3TF/9GVhxv3hFizfYDaEiegEkrmcFrNIKgoKlQXuyjz2kc+bDVPV188Q1JUsJpMzCz3aWuwWMiaFXxOGx6blZ2pBORVOkLa8cJJkYBLa9+lqHDJSdUkRZlIKjdS9AEeh7Y9LSvUl7qIZ2UW1pVQF3Tz4u4QjVU+Am4bL+waIinKzC73EU6J2C3adXhiWy9KPk91kYuZZT6GUzkebepiUX0AsNIX01pykQNRUsYYuut2De7nxVVJZGTt3Gwm+uJZLH0xrj21TveSZSSZoNtOPq+yqSNMPC3hdVipCjgIJXPEsjmIwEA8Q0qUSYoSxW47FX67bsympTz7htPUjFT+AuwbThNK5Sjx2PnHm91EUxIeh5VQQiSvqBS7rOzsixFJ5di0d5juaAaTIFBb7OKEqQHSokRfLEs8I+GxWUhnZWIZieqAk6qAm1nlWppS0OPgsgXVrNnRz0AsS1xUsJoEHDYT7UMpRFnBZjFT5U9RF3RR4XOwdGYpVaMqulsHkvp8hXPauCfMUCKLSRCQBZVMTkaWrVgEAUlWCCdFsjmF1zsjxLMyU/xOuofTDCREhpJZSr22Ma3OjiaGYXeMUci3MzB4P8ibZHoSmmfZ7nDgOMqGncGhcaCw7OHMUeirumRGUA/V7p9Ll5FkvE4ruwfiJMS3Nbv2J+C2UeZ1UDeiYbelM4LPYSGdk2gdSBDLSAzEs5R6bGQkrVKz3OegPqh5+9a3DOG2W6gqctEXS9MXy9LUMcwFjVoHgRd3h1BVraghksqxbzhFRpKp8GnHnFPpw++0Air9cRE5r4WIC56tpvYIe4aSFHus+J1aS62aEicmE/idVpKijMNiJisrvNY+zIa2IVSgttjNq+3D7BlMMr3MQ5HTRlckjd1i4vgqH3IeTplWgsdhYVFdsV60EUrCmTNLRwzRMHtCSURZwW4xUVvipsJnp6bYzT0vttEXy1ITSDMt6KE9lKY9nKIrksbvtCAIAgvriinx2JHkPL94poV0TuLTJ1ZT4XfonREKr4HPacFuNTGvSqsS3TuU4pGmLjwOM5KidbNorPLRNphCVcFjtzC91ENWVkjnFDa2hVFVlXK/k+Oq7AhY6SdLR1gz7G85fzazyr3s6I0iyQ4q/A7d4zqcyrFmRx+7+hIUua1MK3VruoOKwkBcZEdPnOpiJ8dX+ZHkPOpINXDAZcNpNdMeTpOT85R47VjNJkRZoTuSobLIRVKU9GMsm1XGwrpiHn+zB5/dzOkNQbZ0REmKMnIeVBTawykimRzySGFI4QvCig176QynOf/4Ci5bUM3GPSEeadqneSezMlaT1iotr0JC1ELgneEMrYMJ8nnNkD+joZTWgQQtAwkSYpp0TqvSFgTTO/qffLcw3sUNDD7CCAg0lHn0xwYfDA61F+yBPHujpUnaQ0m9N2phzoLHriDuu284PUZvDtB7uy6ZERwjnlsglMzS3BNjMJ6jzGenzOugtsTJ7sGEluSeU/SE/IykoKIJxpoEE1aLitNqGmn9lMJpNTGjzKvLkdQEXCN5TVpy/LxqrbXT6IR4r8NCdUDL61o8PagbBgG3jYZyH9VFTl5tD2MSwOu0UuG30x3JEEnncNnNlLjsRNIiQwmtMMFqMlHms6OS1yRBssqIZ02gM5zWjEOrmfaQ1mO0tsSpSaOEUiNGnabj1hZK0hUxAwIumwVU6I+LSIqK227FZhEYiokMJUUqfA7mVRdRH3Tzy7W7aRvUWl49sKmDGaVeZlV68dgtrNjQjtNq5sLGKfr1j2VyBFw29oYSDCVyuG0WJCVPKqfgtplZXF+iS7q82RXBZIKslNdyCP12rj21jg1tIZp7okTSErsHEnRH0qzZ0cfu/iSDcXHEuHYiyXmtBZgg4LSaCDhtmAUQBK1bRCQpIgig5KG2xE1VkYvaEicdoZQmXWMxaQUSHhu1QQ92S5JQQkRAQMlr+YyFHsSFNm3toSSilCeelSn12kmIEvm8it9lpdRjJ5aRRlomau9thd6viYwMaL1d12zvp6Vfk3yq8Dvwu2wjKSkqFotWwBFw26ktcZGRFL1VW0O5h4DbSkcoTUaSWVRXgsNqPiYqYw3D7j3AaClm8EHBaTPz7DeWHu1lGByEA+XAHYgDefZGS5OEkuIBu0lU+B0TGoiFPCWAr50zUz9mQ7lH95BEUjldbqSgAbazP4HNaqYuqPVk1XTGsjR3R0nnFCr9Dr0F2B837CWSyjG9zMMFjVOoDrioD2aZV/N2dWxGkmnq0MJ7BYZTuRFhX62tViIrj6mmLRwXoG0wSanXjkUQSOcUXDYz9UEPWzqH6Y2KI4K2LuZO8ZEQZcp9Dtw2C+ERTbqCURtKZlnXMojFJDCn0gsI3PtyOzt6Y9jMJuZWaKk5/dEseVWlJuDm0ydWs6q5j9bBBGZBQJLzVHjtpLIy6ZxCsdvG4uklPLalm+5IGq/DgmACn8PGYEJEFTSh5u7hDDaLwKLaYjwOC53hjO4RU1VNWqTcbyclKsQyOdoGcxS5rdThYmd/HJfNwsm1AfYMpakpdnLzsgZd4iSczNLcHWfpzFJWbetlR08Mq1nAZTPzzFsDAHjtVuR8HqfNjNdmQVHziJKKklcRJRhOS5w7t4IZZVouWqDIzuLpJfREs0TSEvGs5l3LqwI7e+MMxLNYzCamlzk5/7hKuoZTZHMKkl1h454Qi6cHufbUOu5Zv4c9gymKXBYunl9FXdBFfdDDhrYQXSPh4vqgm0c3dxFJ5bCYoS+e4ZU9IRZMDRBO5lABm9nEKfUlLKwrJpQUae6JaRWy86ZMWmxUyDk9UgHw9wrDsHsPMFqKGRgYvJscaej1YJ69goGotd6auAXYaK/c/scu5FcVfu+fq7dxT4j+uEgs83bv18IxChpvAP2xLPFMjtkVXjJSHjmvjnSA0PKaZEXFbX/bG/J65zCvjEhULJ83hZ19CbZ2RUHQDJkKv4Ot3VGau6MMxkUt0X44TTyTI+Cy47Bpbb+CHgdtg0ne6IpQ7nOQV9WRELGT/liWrkiGbE5rEq+i4ndasVq0qk0QGEpmaWqPjJIU0QzUwnZQ2TuUZDgt4bCA227BYTOzqC4wRj/tmR39qCpEMzliGZmcLGMxmXFazciKSn8sy6xyLw1lHmaW+6j0a/IrFX4nVrNAXzRLwikRSUm8sidEudeB024hKynERoziRfXFeOxWmjq0Lhid4TTRdA5FUYlltNZmyazmObWYtJDio5v3EUnlECWVEo+NvliWl9tChBIiDpsFUc6QzMo4LCYEh+blrfQ5iWUl9g6mSOdk8nkti1LJ50mJsh4mD7htY3LdzAK0DSXxOSxsaA2RkRXsap6Z5T4cVjPNPXEt1zOcYld/kp19mpctmZNJiRIpUULNg9Vioj7owWU1YxbANeJBXd8SQhA03cTBhMimvWGSWZloWsvPdNnM+J3WkXSEONmcgqvIdEj/Z6PTGvbvuHI0MAw7AwMDg2OcQw297s+hePYO5m0Y7ZUrNJ4vsLCuWG+Avm7XIFlJGSOEvGZ7P0MJkXK/neGUhY17wgQ9dl3PriBXsrU7yrbu+EjXAM3TVQi3nVxfAmjaY4VK27bBJF2RNG2DWieIOZVetvfEUPKa0ZeWtF6zVUUOQGsYf8/6PbzZHaPEZeP8xkpA4MltvWztipASFRxWCZMg4LJb8DutZHMyNrMJk12gwq9VrwbcVpZP13L+tByrOC0DCVY19+KyWaktcRFOijR3R1FVFb/TxoxyD9FMDofFTKnXTl3Qpbe7ag+laA8l8Tq10GE0nSOfVxEEk2YMobX6WtXci89pw+e0kZFk/vlmD53DaWwWE/OrtVBw0GNn3e5BZEXFade07+IZCYfVzIwyL5GUxMNNXXRHMij5PJKi4nFYKPfZUVWIpEVAIJnNsaM3xo+efAu3zUwypzAQy5AHhhIigyOeNAGQZJW6Ehczy30oqorfqXXCiKZzKKpWYZuVtWpXk8lEdzRNRlYIuu0881Y/XoeNxdNKqCrSQrIlbjuL6oqpCbjZ2h0FVcVpNdM6kKCxykdjlZ9YRgsJ7+yP47KaqSlyIcsqkZRITlF5rT2sa+7lJIWOsCbrUhBanlnuYfX2fvxOC0lRQhAY6f2qFW39eu1umjoi2K0mMpKihewPWlGeoC+qheIL3UmOJoZhZ2DwESaTU7j4t1pV7OP/umQkH8XgWOPdajA+kRF3MG/g/l65wjyrtvXSNpgayUGy0RlO6wUXhepArU+nFh4s9GAtHGu0uPGsci87emIMxLPIeZXGKt+I8RQnnVNYOqsUh9XMls4IoaRmgJR67cwo07w9hQIJENjZF2dbdwx4OzwMsG7XAG2DCSr9TqqKHHjsFrqjaaIZrUWVqkKZz8aiuhJiGYm9QymqipzMrtD6nhbypwqvhdaDdYhwQsRpteBzmHlmRz/be2LEshI5Jc8Np9dz1qxSVjX3sqs/QUZSiKQkVr6h5fpNL/OwbGYZRU4rVUUOQsksqqpS5nVgswgksjLFHisD8SxvdkXJ57V+qntCCSQZtvfEiaRlZDlPXdBNidsGKpzZUKZpvqWkkZ6m8OjmbloHEoiS1qfVYbNQ4bPjtllx2iQiaQFU8LtsxDI5+mMqjdVFVAWc9ETTI4UFJiwjHSNKvQ6mBT1csaiGCr+DVdt6aeqIkJPz5BSVoEczGFM5BasZ5lR6OWmqVjHd1B7mrd4EpV4blT4HTR3DDCWy5BSVHX0xbjlvNsvnVfLrta2s2tZLwGNjRqkXzfcnYDYJeGwWZpZ7yUgKVotAscdOXYmLXQMJhlMiHruVoNeOoqps7Y6iqKpWtV1fzA2n17Fmez97hpI4rWZKvQ49V3JTe5j+aJZppR7OnBmkodyj9btt7kOS81gtJj3PDjSv9NqdA1jMAsdVuYwcOwMDg6OLikrrYFJ/bPDhZiIjbnSxxP5yJt2RNImsPC681DqQ5KlmTZi3ZcDNZ0+dOk68uDrg4sYl08YYkqub+2jpH0BVVeqCbt0IdFjNI/IfWi/PSEpidbPWVzan5ImkciyeXjJy7DihlMjC2mKWz6vUz6WQ77S5Yxif08qSGcExhqzfaaPU66DUa6e5J64VFjhtlHnt5GTNM5TMKfTHsySzEuF0DttIc/qkKNFYraXVFK5RIiuj5lXkEU9VWsrTH8/itpvJKQrJrMzOvgT1QQ8VPiddEc2IBK0yVc6ryLJKU0eYZE4hl1NQ8iqCIOB3Wvn4cRWkJU3HbUNbiHBKxAQkshKSDHkglMjq8jGDiSz1Qa27R6HNWeH8eyJpbBaBuVP8JEWZ4YSI322j3OtkbyjFtJEilP5oljKPg1KPg2K3jS8tnU6F30FvLMvWrgg5Ja95MyUFu9nEBaMMnP64SOtAgiKXDVFWCCVFKvxO3DYzqZyWL6gJFNcQSeVo6hwmK+XpCKVIZDRplExOk9e5Z/0ezjuugtc7hxlIiljMJloG4vRFNemXGWVeLCaBzfuGEVSIZ2TqS93Mqy6iN5YhnZWZHnRTF9QKhLqG05qnr18Tq142swyTIIwUs2ht20BLD0iKCnlVC+kW7qnHtnTz4u4h0pJm3HZFUrSHKka+UAh47Bbqgu5jIgwLhmH3nmAUTxgYGBxrFER9fQ6tgKE7kh5TiDFeE21yb15DuYdZ5V5yssqscq/eDUIzBJP0x7JjiigKx1+zo4+3+uJ0hFNcvqCG2hIXoaRmkCydVUokJeq9VcMpkXBSpNht19fidVhoG0wRToqcWl8y7kN0tCEKsGJDOx2hFBc0VupyLDPKPKQlhZQoU1JkxywI7AklUVGJpiQ2dw4zv7qIKX4HQ3GR53YN8HpnhJ19ceZU+ukMp/VrUBVwMZyW2NoV0yRXKnyUeu0MJURNDDicYkNbiD2DSZQ8TC12MavcS/tQir2hFIOJDOGUiellbjIC5EZyCefXFOnG2Z1rdhFOiVgEAVlVUfICLpsJScmTkVQiaQlRkkmJClk5j2XE6b58XqWu9xdOZlFUWFSneaU2dw5T6tHCwjklj90qIEoKkYyW93jlydVjXtMzGzSPaHhE+NhqEXQh4kc3dwEqkqIgCFpfWKvZREKU8TgszKnwsaUzgigpNLVH6AilGErkqPA56I9nGU6LZGUFhwA1ATe7BuIMp3JaJbGiYDMJzCz3Mr+miBdbh1DyKovqinmxdYhdfQmsJgGz2YQ8mKfcZ8duseCyW8gpKrv6EyQyEqaRTmU5WcEsQMBtpbLISVyUiKa0/L+m9gjbeqLkJAWzCYJuu24Yzyr3Ep9ZqoklJzIMJyVWbGjnmR0DXHJSNdcurjtmCifAMOzeE4ziCQMDg6PN/mHXgtSDxSwQD2vFEvsba6N/Fx6HkiKhZFbPWyrM99VzZurG1uguE6M18EJJkaAnOdKZIkR3OK1JizgsI4UT6ojQcJzljVPoiWZo2TuM36nJlORkldkVPgJuqz5vIRTbNpjg9+v3EHBb9VDsig3tdIZTnH98BUGPg5a+OL2xDE3tYeZVFyHKMl2RFCAQz+Rw2cx0R7UCiRKXjUDASV4VEKU8JR6tfVcslqEnl+aZHf1IsoooK6xuzpKVyvA4LNjMAt3RNCaTQKnXxr7hNI1VPhbVB4ikJGIZibyqEnTbR6pyU3RHMsQympeozGfnkhNreGVPiJ19CewWEwOxDKu29XFSbYDdg3FEScHrsdNY5ac3lkVRVfaFUvTFRFKihNVkGhH0UGkdSBJLyzSUa7mKiYyElFexmgR29iYYTGYpdtlHClUU+mJZNncO47KasVlMKPm8XpiweHqQ1oEk8azC/Ooidg/EkRUYSops742S2iCTkRXEnEIqp+CwWkhJMqUeO/VB7TWR5DzFLisDiTxtQwne6JJBBbfDjFkwYTaZ8Dm1e6HUa8dk8lFb4qIjlCKZkfE6rCyeXkJVwEVNsYt1uwbY2hUlFM8iSjJmm5UiuxlFURmIicyu8GIyqUhSHlnNUx1wUeyx8ea+KENJEb9D658b9DhYPK2EJ7f20B5O0xnKEBVFXDYLLrtZzwNd1zKo948tyP880tTFC7sHSWQkmopdnNFQ+t79Ix8BhmFnYGBg8CFk457QmFZg++vT7Z8LNFEeX3XARdCjGWuFXqOjx+7v6SsYgpFUjkCR9mG9rmWQl1oHGYiJDCRFHBYTJ9QEdOHenmiaPUMJrbrSZmEgnqFtUKbYZcNrtzCjzMPi6UGCHs2IdFnNPPvWAK/sDbGtJ0ZVkYudfdr+zd1RbFYToOXuuewWHFYz4ZHQX0c4TUc4jSTnSecUeqIZilw2qov8lPvtLKrTWmm92DpEOifjsJmwW81IskJGytPcG0WU8kQzEntDCSRFHTEcrPTHskiKlVROq/xcPm8KrQNJHtjUwb5hzZDe0BZCkrUCk3KfA7NJYGqxC8eIl8tmMRHPKmzaG2ZzZ4Rn39I05kq9dpbNKueqU6bS3B1j5RtdlPsdxLMy+bxKWpIRTOCyWXBYTZjN0DqQ4KTaAOfMLaOpI0JfPMNbfVHyI6LEVouZhKiQFrUcPb9f6yfc3B3jza4IQwmRoMcxUiWaYNX2XjK5PPOri2gbStATzbJnMMlJtQFiGZm+WBaLCSr8Wl6i32nhqeY+BuIi6ZyMy6a9lpU+B267lXnVfrZ1xxhKZil2a8Ujo3MZf7pqJxlZBVFiW3eM/riIxSzQMpBgXziNw2rC57BS7LEzr8pPy4hgcE8sTYXPicttYjAhsjeUYvdAgqQoYTGb6I9n+dnqnTisZs4/roJQOkd/PEuR00J1kQuXzcyZDWV6mL+g9bihLYSsqCyoDbCovpiuaBqzIDAQy/LApk7OP7580pZ77zeGYWdgYGDwIWG0lw4EVBUK4qwTtSTrj2UP2AZJC9+K1JY4qQ96dI/F6Fy80UUQBUOwM5we8cgJZHMKLQNxREkT+HXbLcyp9FIdcLF4Ojyzo5/WgQQgsGxWGdmcwhuxCPl8jqysaa6BJnbcOqBp4iVFGVHKU+GzYBJU3uyK/P/s/XuQZIdZ341/zq3POX3vnp7bzszOzN5m19KuzEprW0aWLTBgC0jAr23A5lKhkhcoCipQlQovRQiQ4pek8quQXyXkDQQciA0OtrjkNa9sjEGWtLCSR1prb9qdnd2dmZ2e6e7p++Xcb78/TndrV5Zsy9iWwfOt2tq59Jw+3dM9/fTzPN/vh7bhIgkC80WdtBp3VbbbJodLad5xfJK0qjCRigkHm02TF3e7iIhMZlTumc3RG+76VXsW5ZZJPpkgqyoIeYGFQhI3CJBEkebAwfICHD+ib3lktBjDVW5bJGQJSYSr1R7nbjZ58PAEixPJODpDlWkZLrIIR6YyTGYS+CF3Be7GI82I5zfbVPs22y0DL4xISAKbTWNsULheG5BPKhyfybK21yPwBGRJIKXK2G5AtWvzJ58vs7rZ4sxSkdm8Tn1o8iimVL775AFOLxbYaBj0bY+1ap/7F/MokkAQRaQTMpMZdXhOcKXSpTnwUCQwHA/XDwkj8EOodGy6lofp+shSzJF97naLBw4Wx3Eikigwk9V4+Ogk5Y5FMZXg+GyW1a0WG3UDRRZx/JA/OV/mzFJMQLlvPs/qRpMgAsPxkSWBlekM67U+tZ5NKa1xZDrNwPZYKCZpWy6Xyz0afRsi+Im3H2F1s8VfX6tR7zsxtq2QQk+IXCx3CEIY2D6iCAeLOu89vcCthsFmM0bFnbvZpG3EeLu5uRxpVWat1iejyeM3RVcrPa5Ve/h+NH6efSNov7Db1772ta9/ILpzJ+6VSBAjjRBeU1k1Jh/wyo7YeHxrsjiRHBeAL9+7GzFINxoDNhoGmw0DywvYbBjcbpmEUfyid3gyNQTSJ8ZGCICZXLyTdmo+xyPHp8hoMgcKOuvVPtf3YifpCCl2uxWPUQ9Npjg5lwNiNuzoRTUMIaMnWKv1uVbr0bU8Uqo87jxNZuKu2YOHJpjOagxsDy0hD0kUmWGWW0A2qfCtR0rkdDnuvg3z5tZrA57bbLK62WZlJiZclNs2puthuiF7PQdZFgiCCIi7eY+ePADsoivyEHkGJ2azOF7Ik9frZNSXnOhZPUEplcDxApbd2FF8YbtN3/a4stPlZt2ga7qkVJmHjkyS0xWCKKTSsUlIAqWUyuSMiuH4vFjpsV7rs1DUeWRlimNTaVa3WrzrnllOzufGRWKlY9McOKxutjmzVODQZIr5fBJBENhoGDx+qcKLuz3cwCeX1LD8kIl0AsPx0BQZSRAwXB9NEVkspmM+cN2klFIRhrttpXSCk/M5buwNMByfctukZ3k4Xkg+pXByLs+1So9nyg0u7XSp9mwePXmAf8phLpTbdC2P2y2T++bzvGNlinrPIaMrDByPjYYJAszmklhuSLllkkrIbLdMBrbP8kQaVZJoGDH5wvFix67jhfRtF0EQOH2wyPHZLLcaAwQEqj2Li+Uue32bqYzG/zHc2RyZfR5Yit/A+GHEfF4HBNrGS3urr7f2C7uvgfbNE/v6+yIBgbm8Pv54X3+/dee49dWy6cptk6uV3hAFleXodPZVIxpG3bi24dxlGhh974lre2Q0mfsXCzQGDk+u1ccc1aWJJFEEqYTMQiHJ21de4qaOyAHrtQEXyl2qXZtPXaliugGFlMJ7758fY8FGMSu6IqJI4pgxO5NV+ez1OqIgcN98HstLcqs+YKdjcnohz/ecPMCF7Q67XYtPXNzle08d4EK5w9n1BoWkjKbInJzLAnHnZbRDpSUkvu349JjK0bPbbDQG9G0f24uZoJIkcGq+wELBpdbfZVLV8IKQhCSwMpNlJqvRNtyxsaDedzHceHQXhBHvPjnLrYbB5Z24eFgqpTl7o86tukFSlQgCeOhoiYeOlPjYqky974AQsd2ycIKQqYQ0xKlFvHGhALSpdG1cP+DdJ2c5Op0extEMmMnq499ZteegKRLnbjb4+PNlTDcgIQv4YcTAjoskVY6dwC/93g06lkcYROT1BO++d5a0KvOpKxVkUcR2A0JgqZRiuZTihe02fdsnlZCRJRFdlZjK6Dx9PR5vH5vJosoSA9vnQF5nKqtxci7HRiPulHl+wFqlD1Q4MZthOqtzu9UC4On1PQ6V0iyVUmOnbUqVODaVZamUIqPKHMjpHJlKUe1ZfH67TWGYJejuBhBBvW+jyCLJhEh94OAFIVcqXT78TNyRnUglGNg+hZRCMiGhKiKfvLSLqkg4Xsh6rT+mljQGNlcrfbaaBmb5S2fefb20X9h9DbRvntjX3xfpCYm/+YVve71PY19fJX0xh+tI67W403ByPv+quKSRNhoDbu4NODmX4/7Fwl05bqPruH+xcBc+rGM5BBFDrmvmrp2+9dqAJ9fqCAKU0hq2F+AHIVEYsdkcUO3YiBJsNkyWSknm8vq4U2Z5IVldIaPJdC2P7ZZJy/CwXR9VlnjXvTOxk7bu0jBc/vk7j/GfPnOdv7paYyJ957gzXuiPx8UqVytd/vraHtstg594+xEeWZkaF8ajwOWY1GCx3Tao9mwOTaZIqzKfvlIhrSq849jk2AwyKlg/8swmfcvnnW+Y5uRcjtWtJrebBn3H58RsFl2RkCQBRRK4uN3herXPRFrlzctFLpa7eH7A+a02phuwPJmi3nfI6h5pVeZbj8ShzVtNi5mshijEY/d8KjE+99OLRao9h2c2mhRSCZZLqfHIvDEQKGVU0gmZpYkkq5stCkkVO4jZvZYX0Hd8dt14PD2hJxh4AcsTqXEX+OR8bnwfTec0BrbPbsdCEkSmsyoLRR3D8Sml45iZSs9CFEVyusx0VsMPGTpjLT57bQ/LDbhvIc998wXqfYe/vdng+a0Wp+byHJpM4fshq5ttrlR6pBIyA9tHkgSWSinuncuxutni+a0WSTXu+J5ZirvCM1mdG3sDwgi0hMS0rpJSJZqDeKTs+AGKGJs4DhaT9CyX1c02SVViZTpL13J5cacHAhT0xDC0OGYSX630uVrpsVBIcma5+A2RYQf7hd2+9rWvfX3dVW6bnLvZAAQePPyFsR1/V426CXfGmoyutzGwmc/r4+DaVzu/9dqAtuEO9/S+MOPwTsfs6DqOTmfGO1R3ZqltNBo0Bg7LpRSn5rNUew4ZTeZCuYMkiswVdUQhRmO5fsCFcofbLRPD8dnpWKxMZ5jMqJzMZjm9WOTsjQYvbLVxvYCUJjORUoCIM4txptrKdIZyOzZJzGRV3nVPfC7Vrg3AvXO5Mcj9YrlD1/So9OJ9w0eOT42LVtP12WwYlNIqZ5aLzGTj6JUzi0X+5HyZFyt93jCbHd/W5zZbPPZ8mZXpDJoicqse49QeWCpS7ljstC0cL+BCuc3DR6coJhN4QcRG08ALIo5NZ7h3Ls/1oYM5mXDYbhsYjsdkRmU2q9OyXOp9l5yeYHFCp214NIadJ9cP+INnt3jmVpPj01kEMd4jaxsuOx2LluFyfqtFtWdzZDKNIgl0LZ8gAlmCRw7HwcYj1urn9vo0Bg5BFCGLsNE0+POLu+R0mROzubFztN532OmYdAyXvhMgEMeeqLJMIalSSCZYLCQx3IBG32WtOqCQVMioEltNi1t7fbLJxDhYud6Pb8/A8bla7ZHVFebyOg3TRZMkliZSeH7I87dbcYzKZourlR69oYt2u2Wy27ZIaxKnFwskExIvlNuEUUQhqRIT03zefKjIgUJy+PuSyGgy57fazORMJEGkmEpwbCqN6QTM5DQWCkkKKWX8JmWrGWPT0pr0NXkef6XaL+z2ta997evrrBFuq2/5ALzvgVd+QfhK4eKxiUEbI7tGPxvv1jWYyqr0bJ9S+pXxR6OYB1kUxoXY+du3URWRH3nLEg8sFe9yzJbScdxJY+Bwci7Lcik9jkA5d7PBx58rk9Zk3n3vLOaQBdu3/fGYdWU6w1qtz9PX6/Rsj/vmc+T0BKubLbZb8Qg4mZA5Op0dg+k3GgOUnsiRIQd1q2lhuh5hGAcKr9cGXK32GNg+A8ej3Db58DOb3Kob9CyPE7Mxi9bxAlIJmTCMWK/1hh0th6wms1Hvs9Ew6Fgu6WG3L6cnaBguhuuTTEgcm37ptt6JX0sl4h2wC+UO987lKKYS3L9Y5JmbTaIQupaHKIIkiNw3n+eBJZGHjpSYyWk8fCwOvm0bLptNk6bpktYUpnMq9YHDWq1HY+BwaiFPs+/gh1BIJsjoCV7YatMyXGp9i3/yrYeAiLbh8exGk7QqYbs+txomfhDSMV0mMxo5XcEYjsFHzs6ZnEbP8riw3aFrefhhiOn6+GHc7dzruePu3cFikiu7HXp2QERERpU5Np0dj4uXS7HJ4eJOl52ORXPgIssCqiJxIKdhFXTm80mqPZtnN1pERMzmNDabJuW2xXQQo8tWprPIQyPG6laTruXjBRG6IpJWZWbzGvfN5yl3TK5U+wwsnyuVHouFFAICiihyci6L48dh0qfm8/zwg0vjx/3Hn7vNpZ0uZxYnxuSOi+UuQRixMHzjcudz8V33zsTZfE2Tczebr/o8/nprv7Db176+iWV7Ae//rXMAfOwnHkRT9pFiXw+NcFtbTYtX6oaN9EoBwaNi7+WUh1e6jjv/jxUhCPEIbPQi9WrnN0rsbxoOkijStlzMTsDjlypsNAaMEvfvZMP+1dUabcvlgYNFBGG0symQ1mSWJlK0DZdL5S6LE/r4vEfkgpPzOTw/5Ppej3vn8miKNO7+PXSkNL6to9v/rntmmUip6IpE2/DIajJXdrus1/oMHJ/5gs7tponrx8G4MYpK5NBkipmsxqcuV9lqGPhhhCRB1/T4n+e2+PSLVRRZIp2QGbg+WT3etTp3s0EEvPVwvPs2l9doGx7VnsUTa3tAXKBuNAaUUgku3G7jBRH1nj3eR5MEgW85WBjugDlYboieiO+lubxGve/w+KUKuiKyVEpxerEAxGzcI1NpTi8WODGbZbNh8rmNFn9xuUJKldBkkZXpDHld4eh0Ck2RWJmJEVwPHi7x+MVdGn0HUDmzmCGtKWzUDbqWi+n5eEG8C9c2vLscz2eWilypdIlT8gSSCZl33TPLwPEY8XznC8mxS3gqm+CRlSmWSmmWS6nxXuLjlypcrfSpdW28YYiyIkAYRZxZLvLutMZ6rc8zt5roCREigXrPwQ8iVFkko0noijwuFJ+63mDgBCyXUhzIxTzgIIozD5dKKfqOz+FSmquVLpW2jSIKHJzQkQSRc7eaXNzpIkRwa9Z42SM/HmkXUglKaZUn1xrD3EN4ar3OMxtN3rI8MX7ujIrgT12ucbXSpdz+xuja7Rd2+9rXN7HCKBpzNWP34r6+Hno5buvV9ErF2ctDgEfHe6XrGF1+9HmcB6d9QTH4SsXie++f50NnN7ha7bGQT/LAwQKWFzCT1XjqeoOG4RBGEXP5lxyzbcvl1p6BIoo8sFTE9gIg4sRMls3GgEvlDrIscmI23j3++HO3AWFcBMQsVZ+1Wp+V6Qxdy+XBQxPj4i/+mW2eXKvz9pVJ3nZ0kj9+vowgwGQmwWZjQNfyeOF2m/O3m4iRiK5JNA0HiPjeUwfuMBLY8aK/FaAqAglZIggjyu0QURSYzeocnkyTHhYVT9+o4/khEDGT05jJaTz2fJndjs3A8dhumnhhyFRW41bDYL3exw9DJCkuKHuWS7ltMyrkJzMJDhaTDFyfszfqrG5K5HSZzYaJH4ZMZjTeeWKaC9sdXqx0mcwkmMlp9O0Yu7XZNHD8gKmMxspMGiJ4fqvFRFrl3rkcn99us9e1aRseq5stMprMQj45HsGvVXqU0iqGG9CzfRZL8ePhT86XOVhMosgiu20L0wkIice85nDn7t0nZ+967NS6FoIA0xl9bKJ4/FKFzaaB64fc3BsQAX4Y4hNysBg/FhVJIK2+tBIgCOD6EXt9h+22CVHEQiGD5YY8fqnCmaUCjxyfRlckFvI6kxmN89ttLu92KegJZrIaDDuGlY7JQjGFHQRMZXUePFRkp2Pz0We3aA88tITIwI4dupfKXT51pcKRyTRTWZWL2x0cP2BxIsmp+RzVnj3u/t7Y63Ox3OXUfI5CKsFmw0AUIjqWz3rtlTvgX2/tF3b72te+9vU66JUCge/U3Zl0LzFKv1TQ8J0axZq8fWXyC8ZEdxZzZ280aBkuxVRiXCw+cnyKE7OZ2JEJ3G5ZPHysxIOHSxRSCp9+scrNPWN8LvOFJN978gB/+OxtumYcTwHQMlzWaj1u7Q0QhHinMHbHNvjIM1sMbJ+MLnN4MsPKdAbmI5KKxOpmk522zepmcxxaG99fcdcRIo5OxygyiNhsmIhC3EX0wxDbg2xS4h0r0ywU4ugQ2wt47PkyDx0p8eMPLbNRH/DcVgs9IXOwmGQ6q0EELcvlxEyWE7MZtpoWixM6S6Ulrla6dC2fx54vM5fXaRkuAyd+QR/YPoosckaRmM3GXaSMKjOd0cjrCqmExKWdLgPHx3QCcimF49MZziwVubE3oGE4HJvOslBMUetaWMNx4YWdDns9mz+/WOH6Xp+MlmCvZxGGEUQR9YFDoiGQ02M3pyBAFILrhyRVmRt7fa7XBiwUY/f7py5XY+enG9Ac2GiKxOHJFD/yliU2GgYNI6ZKJCSJ++bzfPuJadarfS7vdscmlDsfQ489X6bSswmjiJv1Pr/9lMm3LBS4Ue9T7dksFlPkkgnSqoTrq1S7NivTWcotkxcrPTYaBvccyLM4kWRxIsXVao9iUqFlxFiw5VKKtuWy2RjwtzcbtE1vyNSF3a7Ntd0epuszldXYbpv8xeUu5Y6JKst8y2KeH3nL0bueJ82+w/O3W7GBI4p47PkyZ9frXN3tsV7qc6iU4Uqlw8D2eePBPP/6e+8F4PGLFao9C12Rud0yxwVey3TI6yqFtD98I/P6a7+w29e+9rWvb0DdOYYFxh8/cnzqNXQFXiqCXu2YsiSMg3OTikQhr4xfBEcdvvVaf9jZFcadv82GSb3nUtAT4zgQVRGRJYGdjoUkwXecmGZ1s4nvRyxOpJjOasznk+Pw14h416xteaRVhbccit2el3a6HCzqnJrPMrADPnFxl7yucGI2O8ZBjQq9UcH620/eiDtteY3ZnM5Ox2Q2q/Ntx6fHt3W7bbDTjg0U//ydx3j01AHsIMRyAtKqjBsEzGR0pnMaZ5aKDByfxQl9jCwDuFrp0jJc5vIa83mdv36xSstw0RQRbYjlAsgkZARRwAnifLupbAJdkbG9ENMLGLR9bCfECULOLBZpGC4r05lx/tpSKUHP8pGEODLG9UNe3O0zX9Q5kNXYSVp4vkjP9od4MgFBgJ4VkFZDvv3ENGeWijxxbY98UiGnJbhY7tCxPOYLOvN5HdcPUSSBM0sT9G2f5VKKqYxKreuQTym8/8wCDywVeW6zxeOXKsOO2N2P0e2Wie0FCCH0PR932NVMJiT8IKKYSrBQSHJxp8PskEphuB7Pb7UZOAHbLZPlUpq24TKTVan3VSYzCcIIKj2LU/N5JtIq9Z7LwPYxHZ+5gk7b9DCdOGA4jGKTyKWdDtsti77tUUgKvOuel+gRGU3mgaUi/+b7Tw7NS83x71IA1ITIdEbj7SuTGI7HC9tdXrjd4d8+fpV3rMSmjp7tk9VkDk+l8fyQgdPEcgMkwYtH/putu7qZr5f+zoVdFEU0Gg3q9TqWZVEqlZicnCSZfP3bka+X9nPs9rWvff1d9Wrs1teiUWH28sy50XFi/JfD3FwWENhqmhydzozdrKOO4dHp9DjO45OXKvzJ+W3KrZcIAiDw/FabxYkkCwWdas9GFAQ0ReLEbI69njseaZ29UedqpcdbDk1wfDpLFMXBrzld4anrdXRFIopgqZSmlFb5o9Xb3GoMyCRk9nrOeGH/5a7iQirBVFolqcadJlWRCMJwHHEiSwJnFossl1weOlKi3DYZjYk7lsdOx2SjYXO1MkBXRAa2x1T2JczW753bQJckprIaaU1iuZRmp9Oga/txlIYioysi1WGeHMMdsvl8kiNTKUDgoSMlLu122Ou5mK6P5ftcq8SdrZXpLE9c2+PszRh0P5vVaAxcTC9gvhiH4PYsF88Pcf0QWRRJJRWWSilahkfP9FATIrIEfhDSNBwu73R5sdqla3okExKCIBBGIZWujeOHCEQcnsrEtI2OxXxexw9CiimFBw7G4+9RFt+J2SxbTfOucaPtBZQ7ZnxcAEFAlAQWinGHtGf73Defp9qz6ZoeqiyxXEpxcaeDKEAxpXByPs9mw+D5rRbFVIK+7dEY2NT7Dn3b589e2EFTREQRTi/mObNcBAQu7XQpJBVyeoLdjoXtB/gBHMjr1HoCcwWdgePzqSuVoWHGHZNTRh3qk3M5SmmVY1Npnrxe59h0ehyevT50BP/tjQY39wacmh9dN5R3usgi3LeQ41bdRJGEuNCzvW+IkOKvqLBbX1/nj/7oj3jqqac4d+4cpml+wWWOHj3K2972Nr7zO7+T7/u+70NRXt1a/w9N+zl2+9rXvv6uevmo9st9sXi5k/aVMudGlxshkY7PZDmzVBwbIeDu7t7R6fQ4LqNruazvDSCCldks7x2m8rcNl6uVLkulNJYfsjSRGheFpbTKeq3Hc1stPD8cLsHDWq2HF0S89XCRmazOxXKXI1Mpjk5nyWgyG41BPHZDQFdlprLq2Kjx1PUGUcTY9fvg4dLw9gy4sdcnr8v4IfTt2HnsBxGqEhd9o2y8raaFF4Tc2hswnVUpHVC5XutjOT4gsDiRpDGw+fSVGuu1AZoi4oQhpZRK3/ZZmc4wlU5Q7duoclzIWl5A04iYSKtEYURakyikVLaaJhMZlVPzBW7VB/Rtj0OlNBsNA8MOsN2Aq9Uu7YE7PE6I5QUkJJEgBNPxCKL4fvb8CEkUeMNsliNTKT57vU4xleBATsePInY6JteGwbl7PZu+FSO5HlmJ99Ou7/W4VO7RtzwSskRmIU8xleDG3oDVzTYQoUoi5Y7JXt8hCCLOLBWxPJ/1IVarb/usbrYwnYADOZ3TBwv8zXoDP4z4q6t76KrEA4tFHj01y6Vyl+2WybHpDDldRpVF9ISMJAm4fkgpnaBpuFyr9GgZLglZJJdUEUWBatfG8uLw53fdM8v7HjjIc5stdjoWybyO6XbREiI926PatTg5l+N7T81SSKlsNgyuVwek1ZgoMnJ6r1V6JFWZBw/FRVyla3O10uNqpUtOT3B6scBSKU0Qxp1uxw/YaZscKOisTGeodEwMN+AdxyY5Op3ldsukb3k0Bu43hDv2NRV2H//4x/kv/+W/cPbsWSDu1gFx6GAuh67rtFotbNvm+vXrXL9+nQ996EMUi0V+9Ed/lJ//+Z9nbm7uq38r9rWvfe3r76G+lMP1K4k7ebmTdpRdtziRHHfqRrtRF7Y7VHoWEZDVlTEy6bnNFk+v742pBeduNuO4jITM249Nokqxe/rR4dhpvTag2rO5WO5xaj7Lj7xlaXzOcWcMupaPABwqpTkxm6VtuDjDLsdG3aTWs0klZBw/5HfP3qJruWRVhTceLHDPXI6kItKzg/H9dHIuRlT99bUqT6/vDQNpBQzbo2nEMSimF2J7AQMnds2OduQWinF22V7P5tytBo2+y1bL4I0LeWRJYGkyzSPHp1ir9WkZLsemM/QcD02SmM5qpDUZe5if1nd9BEGklFE5fbDAhe0uzYHD7IF4pOuHUVxg1Pqk1Rhf9sE3LzJwPK4Ou3WjAqLej52goQwtw0EUBBYKOsmETFcWKCRVWoaD44dkNYXJjMrqZhvbC3jjQoEzS0XWan1OL+RZ3WrheiFzuSRd2+O++TyPHJ9io2GQ0+OA3xt1g+XJFI+sTHF0Os1Hn72N54cEYYQbREiiSFqVCEKwvIB632Wv51Lt2Vyr9unbHjld4T2n59luGYgCRET4YUS9a2M5PoWkwsVyl6bhsNkQx4/LMIrYaBh8bqPF4kSS+YJOFEVYXkgprXJyLkt94CACG00TXR5yfNvmGGOHEjGVVYmIaA4z9DZbBo+emuV9Dyzwa5+4gun6qIpAz/ZwgoCsprA3cMh6AZ+6UiGnJ6h0TLqWRxCG3Njrc3Q6w9Rw7JzTZIrpBBsNk6eu73Flt4PpBPhBRNeKDT/HpzMAXNrp8sVc7l8vfVmF3V/91V/xC7/wC5w/f54oirjvvvv4nu/5Ht70pjdx5swZpqen77C2g+M4XLlyhc997nOcPXuWT3ziE/zGb/wG/+2//Td+9md/ll/4hV/Y72Tta1/fICqmEq/3KXzT6ks5XF+pSHt5offyr718hDvqTN2/WBj/zOMXKzxxrYYiiSxNpDg+kx1nydlewJ+9UOZW3eDbT8SPjauVLqbjoysSc4Ukc4Ukz2/F6Ki+HZ/jTFYje1QZZ9ONTAp92+eJtT0qHYtSOk79f+p6nZNzOd46RIxdqXQAgdmcxgvlDuWWRTRkreZ0hUIqMQxLjnh6vT4EsWd4ZqPJ1UoPVRJpGnFo7/1LRU7MZgCB81drPH5pl0OlNAfyGltNE8eP89t6lsdmw8B2AwQhRBIktlsWPctlIq0ycHxut0yCMOTYVJp7ZrPMZONg562mxVqtz9VqjygSKCQV3nt6geOzWbZbFobrY/vB8Dzg01dqvFjpYXsBuiJR69lMZzU6lkcxmaBru2y3DYIwHhlbrk/fAU0W40iPQpK1So/6wCYhSZyaz5PVFJ5Yq9EaeMzkVTqWN45VAfiRtyyNi+ARnu2Ja3usbrTI6DILhRRBCCdmsuPAXQBZFsELURWBBw8VSavK2KU8ijnZbAzYqA8wnLjj96GzG/Rsj3LXJJ1QODWX5GK5Ta1uU+s5mF6ALMa5fLYXO4cFYrxaZ+BiOh5eEPI9pw5Q7VljWkS165DWJJIJmYi4kLrTUNQY2PTsgHesTHHffJ6n1vew3JDVjRZtw0NXJI5NZ5BFgYQiIovxqDyjygQROF6InBKYyenMZDUiYmJKRpMJwrjAFUWRibTGbsem1vNRFYliUqFr+zy70cDxI779RIyg+2IRQl9PfVmF3Xd8x3eQy+X4l//yX/JjP/ZjrKysfNHLq6rK6dOnOX36ND/5kz+J4zh84hOf4D//5//Mv//3/x5d1/nlX/7lr8oN2Ne+9vWVK5mQOf+vvuP1Po1vWr2Sw/Xlu213Xu6Vcu1e/rWXj3BfaVev2rMxnIDTBzO8++SB8Qu7H0Ss1frIw7y3h46UOHezwVbTYr4Qu0YzmsxMThsfs9q1kSWB04uFcSzJf/rM9XFQ70NHSnSHu2G5lMJkRhtzZ0/MxuaIG3t9JFFEVUQ0WSLMx+SCNx7Mc6Hc5sXdPgdyGqWsOjY/nJjNUEqpzOY0JlLqXflqoz2px85vs9eLXb2HJ9PoisxESsJ0A2ayKjNvmGZ1M2axmsMol67tUelYtA2Hg8Ukt1smf/i5LXaHhenJ+TwD2+ftxyY5MZPFdHySCZmG4fK/XyhztRojrzRFom24FFIJMrrCZFql2rPpmC7PbbaYymgEUYTp+HQsD0kU0GURNZ2gbbr4bkx7ODWf5x3Hp/gPf3GNraaJLIl869G4eN7rOzHSK5MjCEO2GgZ7A4ebeypXdrocm86MeaZ/c6OB5wcUUioTqZigIUkC1Z7NuZtNtpomlhdwZDIdd68igHi87AcRmiIxmVE5e6PBbscePpIEDNfn8m4HTZYQAEUScIOAXFLFD0FPiJiux4F8ijcuFIYdQ4VL5S5eEJt79IQy3pPsWh6fuVrD9wN6jotAAtv30WWFnK7c9aZmvdbHdD2WSyne98ACpxcL/NaTNzi/1eFvbzXRZDEOfD46OSaWXKv0UBQRXYrjbWpdm3LHpJRVKSaVYfCxR05PoEoSaU1iZTrDXF5jsxHfR7Wezc36gIQoUkgrNAfOmH/8eu/XwZdZ2P3qr/4qP/uzP/sVd9lUVeW9730v733ve3n66afpdDpf0XH2ta997esfkl4p8uROzuvLHbCvhAp75SBixs6/UfL/qCMDcUDxw8cmefTk7F0ZccAXjIU3GgYREW4QjIkRDywl7yosRyPc0fUmFYlT81keOlJioxEjp0wnoJhRyenxrtyNvT5aQubIVJoDBZ3kHYHEc3mNraaFLAlcLHcYOD5JNQ7HXd1sMbA9Lu30OLMch/3e2DO4vNPBGlItIN69+96TB4BdCnqCnC5zZDo9xkeNbt+jp2b51U9c5mK5h+UY5FSFlCYPx2w+siBgOAEDJ8ByTXY7JnpCYaGo8+jJA0DEwA5oGS7nbraodGxEAQzHw/YC3nXvLN976sAYV/XpF6vsdmwqPRPfj/CjCEmI89wWiknum8+z27X42xsNVFniVsNgbhjI7PsRXdsbI9Y+u1aj3ncx3ICB7Y9NEXs4dMy4I3lwIoWmiJiuj+2GHJ5K4IUhf7Nep5hU2VJMZrIa9y8W2GnHgdRHptJ0TI/2MP8vq0k0Bg6fvFTh/O02CVkAAfJJZRhOHHJgiKnL6QlURaKQVElrMp4fcnGnw8p0llPzOQRBQBahlk3Qs1xKGY3vumeGzYbBR85tkpAldrsxei0EuraL40cMxLjbOXqOPLfZ4tNXqmR0hYHtj127YTTMywtCaqZLc+ACAoWUOu4mj3bi9voOrhegKCInZrJcr/W5stOlY3h869FJknmRcsdmdbPFidksOT2OOjFdH0kUUIa7gmvVPh3To5TW/v4Udv/qX/2rr9oVvu1tb/uqHWtf+9rXvv4h6It16V5eoO10bG63THY6Fu+9f35cHJbb5l3UgPXagCfX6ggC7HTs8agXGBdQo87bq414n7i2h+MFtAwHP4CCro7ZsMAYAj8yXYx291qGyyMrU8zkNB6/VKHWtXGCkGNChkIqwbMbLRp9h5WZLNWezc29OFj45HyWpCKOSRIADx2ZxPICziwVhy7bLJd2uhRTCR48XOKx58t8bqOJE4QcyOnMF3XahstW02RxQueBgwUulLucu9VEFASSisTR6QzVrj3ebyzoCYIgouv6TGViikO973C12iOnK0xlVIjADeIdP9cL2GiYfGz1Njtti3wqQVqTyapx1wogCEEUYLmUGhfPo/9/79wGQiRyoKCyPJGMDSdeQMfyUOS4yKj2bNIJmZmsyhNrexRTCd5xfJJLOz3Wan0A5vJJ0qpMShX5/O0OQQDFZII3LRe5WTfo2S71vo0bhAgCKIrAsekMf3OjTsfyCaOIA8N8O4BbjTgOZmJe5T2n53lus8Vnru6xNJEkiAZcrXRRJJH7FwsoksDADri4EyPHDhQ0VqZzLE4kKaVVGgOHraZJNiux0TS4uNNBVUSqXYuW4TGT0zk1n+fRkwdiN/CNPaJI4NhMmsmhmWIipXJyPsfzW21aA5frtT6PX6xwaadLtWvRtT0UWeBSuUvH9ji9EOfhyZLAi7s9ZFHE8X026gM+/MxmbOY4OUvf9lmv9XjiejzWf/hoiUdPHeCjz26x047XAFqGy9xcFtMLWb3V5PmtFhlNoWN6dC0XVRbJ6QrphMyx6TRLpdQ3xBgW9nPs9rWvb2rZXsCPfehzAPz+j79pHyn2OunOcerLu3QvL9BahksQhrQM967oiZePZO8M710upe/at+paLwXrjorAJ9b2uFDujIvF0fG22wYdIy440prMVtMas2Hv3A8c7dvFmXjCsLNo44cxFqpjetxqDFAVMR7FRhEMF+1jdJkLQMf0iSI4Mp3GDyKWSqlhoWBzZbfH4oTOfF6j2nOGYbcZ8skEfhihJ0T6w1w3y/P57LU+L1b6GI5HUpWZymhUezY928d0Pep9l8lMAi0hc3w2dv7Kw329iVQ8+msOHPKpBN/+hil0ReJCuUO167DZGHCrHtExXfSEwj0HshyZzmL6IYTgRxGVrsPHVm9zfqtFIZUgrSo8tV6HECazKj/zbUcBxtSDpuFwu2UiC2A6Pgt5nYViir98cY/ttsmJmSzLkylahsufnN/mQrlLKiFTSidw/QgBSOsyyWEm317PQVMkJBF0RSarSVwot5nOqfQsn6msyqn5PIWU8gV7kjM5jT/83Ca1ns3SRAo/DAhCOD6b5h+/cY7zW21u2APOLBVQJJFDpTQNw2W5lBq6jvvs9Wz2enGsTtNweep6nZ7lAyEJReS99y8wk9N44toehhMii7HJ5th0htOLRQ6VUlwsd5nOaBRTCSRR4Kn1Pfp2gCTEj5+crqApMnY9AEEgmVDoWD0MNyAiRJZkWqZD1/LoGh4AZ5aKVHs2C/kkWkLi6HSW+UKSe+fyPHe7jSZJ4zcO1a7NlZ0uLdMliOLOteEGqENSSd/yKHcsHjk+/Q3RrYP9wm5f+/qmVhhFPLvRGn+8r9dHrzZOHX3t5QXay6kTdzpfR5l1MctyYfz9vh0XY33bJ6cnKKYS452+xsBBFrmrWIzHvg5RFKFKEkemUpxeLN5VIMqSMF6qbwwc0qpMMZUgqYhsNS2yWvwCef9SgWdutugYHp95scbA8WMIvCzxyMoUfdvjmVtNlidTvPVw7G5NqzJrtT5tY9j50SRM12ezYXCh3KU8DPI9UNA5NJUmryvUehbltk29H0dklDsWqiIyV8hy33weiJ2nM1mVruVyrdpDlnIsTqQ4NjXLxXKXet/G8X38SOHYdIZNOTaYnJjN8ldXawzsgIVikp7psjdwiCIBZziOfOT4FO8+OcvT63v89bU9mgOfSzshn9/uMJlR6Zge2y0DSRTIhQqrmy1WN1tcH3YGD09nWMgnMRyfnuWx07ZY3Wyy0TDoWC7Xql2WJ1OcnMsSRSHPbbZpug6iAJoiocoiiiAwsH2KqQTNgRuHGwcBWV1BlgXKLZupjMoPvukgpheMx5OrG232egydswP+x9kNNvZMFoo67z8TP45ijq3E45cqPL/VxnB8Th/MM5HWqHRjp2zPcjkxm+Niuct22yQi4nApTSntcnG7S88JUIBb9T6/8ZdrLBSSceZhQkRAYODEBIeZrMrv/+0mG00DVRJ506EibcvFsHz8KKI5cOhZHht1g4ePTZJW85wc5jEm5bjTmZBEttompg2uENKzff72ZoPzt1tD40iGYioxjvhZq/UxnQBFjxFw1a5N3/aZzessFHVOzOZwvIDVrRa+H3G7bdKyXAwv4OyNxhesNbxe+rILux//8R//si4nSRK5XI6VlRXe+c53sry8/BWf3L72ta99fTPoS+HFRqG8r3aZO52vfdv/ogaLlxeRo9HpKKz1TgMHgCAInFkuUErfTR3o2/4QF9ZnLq8PF/vj7l0hr1JIRaxutNnumCzkk7zvgQVu7A24UR8Mx4MR1b7F0ek0bx12Rga2T1pV0BQp7vYFEYV8gqPTGuu1Hpd3ujh+QKPv0LE9btR6NA2XxQmdmazK85stEODIVBoQCMOQlKrwyPEpNEXi6fU6txoGBwo6iiTi+iEbjQEZLc5ZNd24G5PRYsTYynSGge1R69roShy3sdOxmcyovG1lirVKjxt7A9qmh+UG49299VqfNy0Vmcxo1Ps2O22LZEIaGzkSkkjf8bla7WG5Pn4AfSegY7j4fkSlayGJAklVxgsiFAmSikhKVeha8S7XqfkCf/niHi0jLnDyw4BmQYzzAZOKhCIK+EGI54dERARBPBoemUsulTvsti3Smsz5222ahsNGY4AbRHGGYBCxUEyy0RgAAidmM3zqcpWtpokArEzH9/NfXa0RBBE922O3E2O3oihiuzXAcAI0SSSXjAsoNwgIQ2j0PRp9j72ezXxRZz6XJKGIHJ5KUUjG4+dbdYMAUOWIWs/G9iI0WRxeV3y/BWGE4QaYTsAnLlTo2x5+EPLWI5McKOi8cLvNWrWP48W/VwEBSRCZzCYwnIBL5Q5zeY0HloqsTGd4brNFtWtR7ztk9QSlVIK1Wo/vPXlg/Ebphx9c4rnNFmdvNCilEjQMd1wgfyPoyy7sfu/3fg9BEMbZda+mO2NPBEHgB3/wB/mv//W/ks1mv/Kz3Ne+9rWvf8D6Ynl1r+SEfbm+FKVi1H1br/VZr/UopNSxE3bEiB0RHOAlA8fiRExeaAwcPnFxl52OyVw+yfeeip20n7xU4YntPU7N5VBkES8IUaQ4omOjYfD87RaNYWflQCHu/PTtuCPzqSsVJFHk3M0GbcOla3lst0z+5HyZlZksixPJMSWj2rVZ3WxjuD6FZALTCehaHnXDJZ9S0RWZ1c02siRy30KOR08dYL024GK5i+UF4zw62/U5NZ8bvwhvtw0qbYdK1+TBQxPM5XXahkNhOIY7d7PJ57c7tAYO6/U+98zmUBWJU/M5ZnI6c3kN2wvZ67doGy6/+cR1BAQWJlI8eGiCUlodd1dH5omn1vfYblkEUcRCPsl33zvLY+e3sewQVZbY7Zi0DJeprEoYRThewPJkGtMJmMlpLBST413M+YLOwPGYyes8fHSSet/marXPbtsiIYu4QYjrB0iiwGCvD4LIymyGR0/N8vjFXW63THY7NrN5jYQsDnfSQvq2B1EcfVLpmvyHT68hCwIH8jrCkLkriwJHZ2KDzH/6zHVu7g2IgIQsUO87XK/16ZixAeLFSpdkQubgRIpvOzHF09cb1Ps2ghB3bbcaJoYXcLCYpJCMg4Vv7g0QBMiqEvccyLE3pJnEJSqkVRklGecVdgyXF2s9DDtAFCIyegLD9WgOBBRJ5J1vmOZiuYPrR5ycyw0JEhGfvFRlMAyjLrdNVjdb7LRNLDdkJieTVCQ+daXK9UqfJ7U9fvjBpfHOa9twSCqxk3sU6/ONQJ2A11DY/eiP/uhdRduryfd9Op0Oly5d4vbt23z0ox9lc3OTz372s8jyN8fkdx8ptq997eu16IsVb19sTDvSl6JUzBeSlNIDnrpep9azmcpo46KjmErw0JHSF7hvR/+PjBlnb9TpGB5Z1WO91qcxiCMvDMen2rdQJZm9vk1alXn8UoWN+oB6zwZEFFGkZbhsNAaU0hqTmZFDMX5NeXajRc90UWSJmZyGLAl3mQ4+dHaDFys9ErLAQiFJbjqNIouIgsBSKYXlxZlz+ZTCoycPMF9IUu3aTGVVZrIapxcLnL3R4Lbjk9WVsWlkoZBkr+cycOLib7RfCAxdl5UhcF5AiBgG5grxyHYQG0SSCRE/CCm3TbaakJDjF/vVjSZNw+XNyxMcnc4wk9N49NQsN/YGbDZNNFkkrUm84/gUx2ez/NaTN2NuqRB3oZqGw4SpciAfx6KoskgQhSSHe7DzhSTf+YYZREHgnSemOTqd4SPPbNI2XSQJMrqMH4Qokjrs6vmoSty9i804sZFkvqjzjpXp8ejb80OuVnssTcSZerdbBu2BO+zGhszmdd56uAREJJU4M8/xQnq2R0aTyGgKqiwymY3P23IDIKJtetzcG3BiNksxlaBjuShS/GbA9HyESCAhiVyv9Wj2XfSETFEQOLNYIKUqNAYO01mNIIzwg2gc3LxUSrHZMFAEkYQUUkjFv/OO4XG9FhNSFopJ7l8s8sLtNm0z3gOEuBNeSqssl1Ks1wZsNgw0RebIlM6ZpSLlTuzOzacUjk1leW6zxYef2WSv52C6PqYbDDF2MkEU8e57Z1936gS8xo7da9WTTz7Jj/7oj3Lu3Dl+7/d+j3/6T//paz7G30ftI8X2ta99vRZlNPkunNederUx7WulUhydTvPwsdIwW+2ljt2dUSWvdp3zhSQ/8pYlzt5okFRELpa7CAKcnMuNMUvbLZML2xF7fYdzNxr03QBRFMloMsuTqXFA7VbTwnR96n2Hk3Px30fHD5nK6bxxIceJ2RznbjX5rSdv8sS1PZZKKXRFIqVKZFSFIApxA7j3QA7TDTgxm8XxAj67tsfAFjg/DE5uDBySiTjI+IGlIjM5jceeL3O7ZfKhs7cAgd1OvG+WkEUulbvM5TXe98BBym2T33ryJi9W+oRhhD8sPizHp9aPSRGWF/DgoQlSqkIIuCGIQEqTAIEb9QF920eTJZ64vkdalTg2lWWt1ieTkJkv6vgh45F3EMaYsGJKpdp1MJ2ItunSMjx2WhaCAF1bJgxbHB2SDiBiaSIFRGQ0mcWJFG3TJZmQOZDT6dkeAye+r4MgQlZlgiEJo953QBC4b77A+x5YoNw20RSJjCazVEqOx+gJSWIirZJKiBRSKm9enuCH3nxw/Gbk7I0GO524ywWwVo0du+84NkkhpQACf/r5HVobTbqWz5+ev42uKHh+RBCGhDJktQTzhSSqIlDvuxyc0Dk2leVCuc1my0JXbL5lIc+p+Tx/9kKZm3sGsiSQTsjYfsjihE4uqRBEEY4fR9OcnEtzaCrF1UqfjfqAfCp2tHYsn7M3Gnh+yNPrdQ7ktPHe6LtPzgIRDx6OO7ofOrtBQhJ5ZGWaH3rzQR57Pg7unslqLBR0tjsWgiCw3TZRZZFvBOoEfI3NE29/+9v5+Mc/zlve8hY++tGPftMUdvva17729Vo0QiS9vMD6YvpSI9pXwpW974GDr3Cc/pjmcOfy98tjVvq2P+bCFlIqL3fbaorEMxtNGoaNKkvcN5/DDQJMO+BWfYAqS8xkp7h/scB6rcdez6Has9nrOaiyxANLxfHxz96os17rs9k0WJnO8vaVSd68NMGl3Q7phIIfRhyZSo+T/h97vozrRwwch6fW91ir9ZEESGsKthfw8edu0zY8koqELAg8v9UhqUpMZVQkUWSvZ9GzfS6WO+x0bJKKSNNwCKOIrB5ntfkBHJ7KIIoi16o9NhsGT1zbo2m4pFUZz/fIaCJTGRXfDwmjuNjq2i7VroXrR+y0LcIoDku+byEPRKyPoksKGhsNE0mEbFLGcgJSikIqERf9thciAIsT+rgov7TTo9a1sbyAo9NZHj05S9NwkESRM8sFdjo2f32thh+EyDKIYry/99R6HcsN8MNw/Ps+d7PBU9cbnJzLYXoB2x2TatdCRECV444rxB3SOx9vthcQRXG0iwCoikgURphewNqtPrWeRUFXePPhCT53q8nABcf3KCQVCkkVYfhzUxmVjukymVE5szhBIaVwrdqnNTBQZZFT8wq3GkbM1nV8NEWklNZYnNA5szTBwPa51TDomC6KLHBkKoXphQycNrcag5g+IcU/k1Qk/nK9Qcf0xs+Bl5uNzt1sslEfsNU2ySdjAstohD96I5PWFLwgpNl34jcwpW+SuJM3velNLC4ucunSpa/1Ve1rX/v6CqTvR5y87vpyxq0j3VmwjXbQXkmjwm/UHXv4WIkHD5e+oNgzh8zTpuEwk9PuMlw8uVbH8nzSmkxOj1/cHjk+NR43PXFtjyfW9nh6vc5MVkOXJUQESmmV7zl1gO2WyY29GD3VMj2qPYdHT6WHO2JZMprMRsMYExrgpe6g72+w13dYnEjy4OEJPn2lwq26geuHfN8b54bn2Oe5zRabDYPZnE7LcNhumey2bfIphbl8ktXNJs9vdehaLocn04QRlNsGB4b7batbLUw3QJMlql2HF7a7zGQ1jkzGMSpxcG0Pww05s1xgMqOy1YpjW+p9m3Lb4sRshmrPwXBiY4frR7GbWJGYzmi8YTbB5Z0OtxsmaV0hpcr0bB9ZErhRi3fJbC/g5t6AfDLB/QeL8Sg2pXJqPkfbcmgOXI5MZjgxmxs/Vg4Wk7RNl3xSYb3Wo9pzcL0QP/RpGx4PHSnh+SE906NluthOSAB4vkFKlZlIaxRSSrxfttFmq2UQhhGWH1DUEwxsn62GgeUFBBEEkcOnr1TGY/L5QpKPP3eblCphuiLzxRT3zObGtIYrO13WagMmkglWZjNM53R2OxayKBAhUOlZBGHIREqNw4KDgPm8zqWdLgeLSRYndLK6TL1vs9GIEWOqEiPGHlgs8tYjJR48PMHjFytsNk3evDzBUikJxN3hZzea+EFIJqEgSQJBGGJ7Pn9xucLACUjIcUH655d2qfZsHj05y/mtNqtbTZoDl3LbwvVDtjsmj1+sYHrBGL331PU6ez0LTZFRJIGELL2mN2ZfS31dlt5mZmY4f/781+Oq9rWvfb0GJRMyV//Nu17v0/gHp9c6Jv1Srtg7NSrY7l8s8MjxqVe97lHht17rsdk0+Oy1Op++UhtzM0eF2ogOIYviXbl4GS0mRAxsET8CWWRMvAA4d7PJxXKHczebDByfQ5Npjs+kqQ/i0ebqZpPLOz3SmsxblidY3WyyVulx7maD9z1wcLy7t9EYxFD5Wn8cuvzAUox/qvUdFFlkvpDk2HSWyzs9dEXG9EI+t9Fks2ngDWkTbz08wVRG5eyNPSRBREuI6IoYOzSJkEWRnhUvuPcsnyA0OXerScdwSSYkHj46RTIh8YlLu8hi3PE54MVj5pwuU+05OF7IhXKHjKZw/8EihuszcH3CSOMth0rcqvdpDlz8IKLn+CRkkZXZOHT5mVtNLD9EDcJxtzGjyczldSAej+7141y+R45P8wfPblFuW5y71WTgBEiCyNVql0vlDpuNAUulFNf3etT7DoNhtzGZkElrEnt9h2duNTk6nWGplMSPiJm4ooBAhCwpHJ3OsFxKs1xK8/jFXVY3W8Oxs0E+qbA0keJWPQ6gJoJUQqSYTLBeG/D4pV1mchrnbjZZ3WgzmdU4fbDAmeUJ2obLpZ0uA8fjzFKB7baJKAh0DY+FYpJ7D+RomQ4Xt7sYwy5kVotISAICUrxPZzhc3+sxm0uyOJGiZ/ncrPeRBIG8niCfVHjrkQkeHLKGn1qPd0frfYcHloocnU5z7mYD0wmQRZGZCZXNxoCW4SAIIglZZCKZYDaXRBQFPD9is2nw+KUKT1zbo2t7zGRV5gs6khB3YZ9ar1PvO3z22h5JVaJneTQGLpLo8YbZHEsTqVdcpXg99HU5i1arRTr9jdGi3Ne+9rWvr7W+HCfrl1I8DmpwJ/v0tXTqRpdpDOKA2ee3WnRMj5m8xpyaHOfYzReSw0DZwV3H7Ns+fhhhuD5uEDKbVbm002WnYzGX13lyrc5arUe1F48XRQEePXmAE7NZ2obHjb0Bfhji+iEXtttst21USWBkmBid66cuV2kMHBKSxNXdLuu1Pu9YmWJluEc2Gn/90JsPslRK0TZcrlZ7XK/1CAKQJRE/DLjdtsjpCookI4kRuiLTNGJ27PHpLIbr8+JuD9sLEEXw/JDzW22SqgQRrG7FZIqN+oDkbJ7ttsUzt5qsT2domy6VjsUzCZnmwEEcFn4Q5+LdN5/ngaUi/+i+A2MeaRBGnFkq8OipA5y72SSfjCNVjk1nuDEkbQBYns+ZpQmWSimWSmmWSynO3mjQ6Dt0bY+upRAEEY2BQ71vE0QRn75SI4pg4HoUUyqSKOCHESdms8xkNZ5c26Nv+zz23DYIoEkCxXSCjKoQEZFU47FhMiGz0RiwutnG8wMSw9DdyYzKoydnGdg+Xhgza0sZlcmMxs36gIEdcO5mgyfXGliez5uXJ8bGk99+8ga1nk3bcHn01AFML+R2yyQIQ3J6gkdWpnh6vc6V3R6CF79hmMyovPFgYUwYWd1scWG7g+UOyKg5tGGBnlZlBo7PwInzDP/3C7tsNg3ymsIbD+Y5MpXiExd3ud0c4HgRbhAiCpDTFNKqgh9CRpVJKCKFpEJOS9CyXBbyOkemMtzY6xOEcVDy/YtFvu34NI2BzZNrDaIwQldE2qZL14KZnEZz4BARMZVJkEzI3zwdu+vXr7O+vs6DDz74tb6qfe1rX/v6htBrGa2+mtZrA5663iCKYvceMI4geXmm3Muve8STbQxstprxqHAmG1MXziwVhxEPLy163zl+HX1+dDrN0+t1XtztE0YRC4XYALF6q8luIcmp+RyFlMITV2sYrj8ezz2wVOSJa3tcq/VRZJEwiuIlc2KnZttw7+LcLk6kML2AStui2rOpdG1apntXsTDS6H544XaHvh2QT8q8cSHH52/HiCnT8Zkv6OT00SK9z4u7FoYbj5PdII7+mMvptG2PEFDlOF/u+a0OItCzfG7U+9T6Frsdi4Htk5AFKh2bMIKEAgVN5cJ2Ny7GJpJ3welHRfJo3A1xdtzx6Sxd2yOnKXz+djuubyNAgKbhjkOjGeKsDk+m6douk2kVRYpHm5/bbLHbiVmlth87XE/MaCxPJpnJ6iQTEk9er5NQRLZafa5Wu+STCd56aIIzyxMsl1I8cW2P63s9CkmF7ZbFM7fq2G7ETF4no8bUintms8zkNH72nUdZrw147LnbrG626Fo+M1kVP4pPfHEiyec2WqxX+1S7sUu62nNIDztX67UBK9MZepaHrkgsleKulq5IpBISYQiFlMKBvM6Fcoe+HV9uYHu0DBfVFbndskglZDKqTEqV6DseWU1hs2lyqdwhjODETJZ/8V3HAVj9fy5zZaeHG8SZd5NZlaVSinccn6JtuGy3TS7tdPD8iJ2OSVJVUBWJv1qrQRjvZSY8iXLb5On1Pc4sTYwDwtuGx/97cRcEKKZUNMXEDQKKaZXFCf0uhvPrqa9pYbe9vc0HPvABBEHgPe95z9fyqva1r319BbK9gJ/6yPMA/N8/fP8+UuyrpNcyWn01jVysINxVIDYGzniv7cRsZuzgu3P0W0prPLG2hywKnJjN3pVRBy/l1JXSdyPJXtqXUwGBjYYx5GJKFJIKbdOj7/gxKJ50vMg+dL86XjAmXthewE7HRJclDk2m8IKIS+UuphvwzK0mpheMi7Yff2iZx54v8+e9XYQhQSGdkF8VmbY4obM0kaRl2GS1BGEIE6kEfccnIUscm06T1hS2mjH1QJbi0GHbi/8PowjFEzk+nWGhkGIyo/JX12oMbH+4wO9hewFbDRNJirNbg0hAAIIIPB8iEW42DK7X+kiSwGbTpJBSeN8DB6l2bZ5erzOwfbTES8+n+sChZbgookBKjZ3CB3I69b6DqohUOiaG45NURGRRIKVKWL7EtWrc+fzON8yQ0STCKEKWRE7NZGkZDpOZBH4I1Z7NM7eabDfj2BdNkVG9gExC5szyxNgYEEfRxOP0cstiszlgIq1xIp/hdsvCCwKqXZtqz+bHHzrEI8en+OSlCm4QsduxyKoyCSng01cq+GHEtWqfS+U2Xdvj4aOTbDYM9ITI6laLtdqAnC6PR/IPLBXZaBisbrQQEVAUgQeWiiiiyLO3GgQhZDSFhCSiKSITaXVMk2gaLl3LxXEDTDfE9iwUWSSvK7zn9EtvAM4sTfDCdoee5SENH5cXyh3unctxtdLj7HoDNwhRZQHHj1jWYpzaZtMgo0nM55JYbsDlnR4bdZOsnuC998+zXhuwXEpTSCVoGw439gZIkgA+VLs2p+ZzbDVNSmn1709h92u/9mtf1uWCIKDT6XD58mXOnj2L53msrKzwUz/1U1/xSe5rX/v62iiMIp5Yq48/3tc3hkZ7cg8eLo130UaF29HpmGl6sdyh3nfG3bvnt9rjbLm24SGL4IfRK77QvFqg8YVyh4vlzpjwsN2y6Ns+kQpnbzaYzyd5YKnIidkMqxstXthu4/kRh6dSHJlKj3mzu22LWsfm6HSGH3/oEI89XyaIItIJGUkU2G6ZdxVtc3mN77pnmnrfYTKjkdNlCkPU06hYzGgypuux2QhJqhIH8ikm0gpZPcGDxSS6InO91qNv+6Q1hTcvxw7fruVR79vU+zGSa+AEuH6A6QbjqJZ632G3azKV1Xnw0AT/92fXqfVCNAROHCzyhgMZdrs2l8tdMqrMbEHHdHy2W+aYfrC6EY/er1b6/PXVGn4UsTSRJIoiHC9kZSaD4wXsDl2sYRTxyPEpzt5ocHG7gyDEeWuFlMoT1+tUuxZLE2kEIcJ0/JhR23OIENAViUeH0RyjsffFcoem4SCKETk9wZuWC8xkdQopZTzKh9gsJQgwk9WpdhxAIJ2QmMnp3KwP6NkBlhej2B57vsx775/n/WcWaJsx73VlNsN2y+LyTo+UKgMhQQSbTYOcFo9K3UCk3nfIp3ySiQy2H7CUSrHTNvnDz21hOAG6KqEEccbhbtfE9qPxWFYRRdwgYCqrjR3YixNJrlZ9sskEEyklJoSosRFlu2WMnc8Ab1qa4FZ9gBOEVLsmL+70+L1zGzG6TIlDjdOazAu32zFD1vIgCkkqGroqMyEK8WMsp/HQkdJd6w0PHp7gQ2c32Glb3Defp2t5GK5P23C/6IrE11NfdmH3K7/yK19WQPFII0LFW97yFv7X//pfJJOvf2jfvva1r329XnothoqX7+iNPm8MHErpONx3tHT/8m7eU9frRBG8fWVyjAh7+Xk8fnGXG3sD1msxhWB0naPjxh0JA8OOR5eaIsZg+zDk0ZOzPLBUpG145JMtIOKRlWkKKZUbewaX2l00WWAqq3FmqcB8ITnekxuF30qCMAw5doCIrabFA0sTHJ1O86GzG3x+u83xmZhWtNW0aAxsrlZ6PL/VASJcP8RyA3JairccKrJcSnP2RoOMniAIYzPF0ekMjYHDxXKXt69M0jYcWqbD8ZTK5HCk2zJcVjdbrNVi5NSx6SzLpRT3LRSodS0EUSCblPkX7zoxvv/+w6eu8ZdXq8zldYpplSCMCKJ4X2+t1uOhI5Mcnc4MGbwu1/cqEAm849gUBwpJeraPF4bIosjHVm+Po1nCSKDasXhus8V2w8AcEiB0RSaRFZnMqDQGDilFJq3JXCx3cLyQpuGgyRID10cSRU4fzDGZ0ZjJ6iwUk6xutsb7bvOFJEulJAeLSRRJpJSOHb6WF3NnRUSOz+jcN1/A8mJk3GjP88zSBIVUYjzOhYiZnM6tvQFty6OgKzCsEZYn4uxB0/FxvJDFYoozy8OQ6JaFKMD9iwUm0xoQYboBGU2imFRx/ZDLtS5ZTSGvy1yt9PDDiPm8zsD2MNyAhCxieSFLpSS2H7K62SaMoG95+FFIpWPjhSFCBIIgosigSRJBFJMn3n/mIOe32lwud+lZHl4QcCCfZGaY/aeIcfH3rntmx9SQ+xcLZDSZx54v88J2nJX49pVJCimVJ9fqFFLqK5qZXg992YXdww8//GUVdiNW7LFjx/iu7/ou3vGOd/xdzm9f+9rXvv5B6OXF2hcr9EbF2KhjNXphaQzsu/bsRpe7s5s36uJAhO0FPPZ8mYeOlMYZdeu1AZ+5usd222SzYY4LoCfX6pyaz3F0OsODhyeodm3SmszA9rjdMrk9DLAd7Y09emqWQkphs2Fyvdaj2rPJ6QqmG1BIybhBSHfoQh3t3v32kzcY2D6lVIJLO12iCE7N54Y5bfG5Xq32aA4cNptxEKwsCbQNLx6tRtGw4ItYqw1oWy47HRsQxgv6ZxaLNAwX2wu4WulhefE+YSGV4GAxxcPHSrzvgYNj1qfnh0AEAtS6Nr/15E0Gjs/KbI4whKym8PHntlkupTi/1eL/eWGHtuUyldF43wPzPH6xwsWdLqIASU1nqZTi3rkcf3K+jB9EeEFIGEZAxPbwHI9NZVAkgVrPxnTiMfHA8dntWdhuCFHIRFplOqtS77skJJmB7TOXTyIKxBl6a3vYbogiwXwhhSZL2MThvJVeh0s7XQSgMbCZzesUUglKaY3lUprFiSRbTYOkKpHVFZoDm0YE+WQCXUnyyPGpsev1aiV2VacSMg8fK3H2RoO+43NsOsuFcod8UuGHH1xCUySe22xR7zksFJOcWsjz5Fr9Lgfw395soskiqiTieiFt02Xg+hyZzPCue2YppBKsbjRx/RA9IQICHcuLUWOpBJYXcrM+oNa3ScoS33XvLAPb4+JOl8OlNMeXi3zy0u6Q4BExldU4NJ3me08eoGG4fG6jyfmtDpd3e0xnVaYyKoIoYLo+oiDgeAFhEGEFAXs9hyeu7WG6AW9fmeTBwxM89nyZa5UeYRRRysTrChCNv/+Noi+7sPvsZz/7NTyNbzw5jsNP/uRP8pnPfIZut8sb3vAG/uN//I+89a1vfb1PbV/72tffQ718/Dkq9NZrfUwvuKv4Gu3ojXbhRtEm5bZJKT2gMXDGRSIw/viR41M8eLjEh85u8MytJpIEbSMuxEbHzmgySxNJJjMJ7pt/yTkrCFDtWZQ7Fk+v14GIjuWT1+OQX8MJ2G6ZPLfZGofqAlwot9lomLzhQIYfetNBNhoDPn2lxnptQNf0WSqlKKXjwrOQSjCd1TgylaGQUmgbHtWehR8y5rmemImdnQA39vpjLNebl4txDt7J2WHR0eBqJf6ZpCIShCGSKNIw4riR1WG+3VIpxXIp3geMSRcCz222+NjqNmu1Hqfmc8wXYtD9WrWPH4Ycnc7wyMokF8pdnlirce5Gk+WpJOWmRdfy0GSJ5eFx28OdvGIqwfecPDAuANqmx6FSiuXJVEwqKCb5g2c32WwYbLdjZ/FESuVbDubZbptUuw6TaZXN9oCUovLI8WkePTnLRmPA1Up/XOBIQuzEFUWw3AA3iGJebE6lOYjNAV4YoMsymiwhiSKGE/DZtTpBGKEqIvP5eHS9OJFkp23SHniIooAoClyt9PlPn1njH79xnrbhslbp4QQBpZRK24hNDc2+wydeKNOzA1IJiWJKRZFFaj2bjK6Mx78jbN2IkbvZMEAQECUBJwjpdC0Mx6c4dAxDxCPHp0lrCrWexQvbbSRBQBYEVqYzPHysRLVrU25ZOIQcKqW4WO7SNT0QoN63MbwARRKYzui86+QsP/TmOJT73M0m8/kkV3d79GyfcsvgzYcmePuxKVa3Wryw1aZleuSTCpOZuBNruB7ltsXqhsTI1BJEIcmEzPGZLIWUwqWd3tD08o2jr1voiu/7f69Ysb7vs7y8zN/8zd8wPz/Phz/8Yf7RP/pH3L59e3+svK997es16+WGilGB9/T6HhfLPYC7yA+jy4wcriO33ajb9/Ix66i7t17rDwu9iIeOTKLI4ngUCnGMyVRWv8td++DhEqW0RkaT452vcgcBgbQm0bE8CkmVnuWhyCKPX9qNr09X0GSJnu0hCNEYKRUvujskVXkMWx/t3q1MZ5jMvEStWN3Y5tJOh5NzeVYOxcGvK9MZNEUaunpdBo5P1/K4XuthOgEnZmNE2IOHS8PCMO7YjVylDx0pcX6rzQvbLSwnoNaT+K0nbyCJMcM1pye4WunxuY0mhheQ0RR22hZt06WUTnDPXI6liSTVnsOtekwyEPGIhIhCUmEinUCTJXY7Fh9+ZpOcrjBX0DmQ1an2LM7dbFJKJSgklZiQkJDGCLBm36VreciiiOMH9G0PPwwRBIEzSwVuNQz8ACYKMe/0/FabQkoZF3ggcLXSJYgiErLEdFZFiAQMz2e5lObYtMBT1xvYvsDRyfi+3u1abLfMGBmWkDA7AaokMZVV2WwYmE5IVldYKiXRZInNpkm1a/Px1W0mM3FwsCwJNAYOaU1iPq9zcbuN4cShxbYX8NnrNcIwnsTO5nQ+e61O2/B49NRs3CF+scb6Xh8/CElrCm4QUzhkMaZarFUHnL/dYSKZ4PB0mvl8knLbptm3ccPYtNE2Xb7znhmOz2a40egjiQINw+XIVGrYUZS4uNOjY3ooksBERh2HKD9xbY+tpsmZ5QJXKz3WqvF49/JOj52OzWIxiSKLeEGA7UkcmUwzmVG5UO7ieuFwZUDgkZUp1mt9nt1oArBcSnO10udSuTPG0X0j6GteaZ07d44Pf/jDfPzjH6der3+tr+6rplQqxS//8i+PP/+xH/sxfu7nfo719XXuu+++1/HM9rWvff1D0KhIy2gyWT1xV/F1Z8jwTsce4pyi8fj1lViuH39umyfX6kRRNB5Z/tCbXwoCHpkQRsXgnV2/R45P3XW8UTzFKJi3Y7osT6Zx/JAbtT6CILA0oTKZif9dr/W43YoLnfm8Tt/2KQ1fWGOzR3wb1oaYsMs7Xao9m/NbbXa7BilV4oxTpGW4fOpKhZyeYD6vsVRKMZNVqfZs1vcGKGI8ngOGSK14nHtiNjse953favP45V1aAw8tIdKt9Og5HgfyOocmM8iiwMD2SOsy+aRCSo2L0zAKiSJwg4DPb3cwnIBDkykm0gmqXRtZFDhUyvCdb0gBAtWeRdfy+ZbFAj84fZBPXalwrTpgs2mS0WQM12fbcHHDgIwmM5PTODmfx/ICZEmkb8f7Z5WujSqLpBJxFIwfhJiuz2ev71HvORycSPLDb1milNZ4fqvNTFZnJmtytdInAkQiDC9kozHgn79zBYC1Sh9Zgp7txQW5JHJqLs9bj5RY3WwxsH02mwbNgUvXdnH8kGrP5mAhxZGpFAlJxPYjUqrMVEbFDaLxaD2rB8zm9LhTaLvUBy4dwxs6gAVuNQxu1Qes1/sUUgrLpfTQZAFaQub0Yh7Xj7i006XveKiKikBcIJY7Bj3b58pOD9sPUGUJx/EII4GW4fLJyxUkEQ7kdKIo3tk8faQ0ppgYzgZd06GU0Xjniem7VhmymszqRou0KlNMJ7DcuGAzHI+5vM7KdJaB4yMP9+tML6RreaQ1BUmCatdip2OxMp3B9AJahstGw6DWtblVH7DZML9Wfypes74mhd3Nmzf5yEc+wkc+8hFu3bpFFEWvyXjxlajf7/Nv/s2/4YUXXuDzn/88jUaDf/2v/zW/8iu/8gWXHQwG/NIv/RIf+9jHaLVaHD9+nF/4hV/gB3/wB1/1+NeuXcOyLA4fPvw1vBX72te+vtk02j+7U6Poka7lIolxpMPVSh8/jDt7oyLszgLwaqWL5floijT8e/uSy/nczQZ/+vldUgmRn3j7ER45PsVzmy12OtYXpOX3bZ/kkE/as+MO2YnZeK+tbXj07dh5uDyZIpmQx2Pi//SZNa5X+/hBNMRBxZll67UBpVSCjcaAY1NpliZSw/252LG50xXoGPEenCwK7PVjlFYhlaBnB4DA1d0ehhOPhf9o9TYXyx0WCjon53IUUolxnMvHn7vNY+e36Vo+qYRIQpJwwgBdkVFEiePDcfClnR6HJ9NIositvQGyJJJMSCRkidbAi8dtqsw7VqbGyKrVrSZBFHF0Ojsei492G9drA3J6At8P4yJ2EDtIbS/uYm40DB5YKvKz7zzKuZtFVjealDsWez2HnuUhiiCJAoVU3HUMI2gOXFqmy3xRv6sYbxsOhWSCqWzcRe1YHlEY0Ri4nN9qcWI2x8D2KXcsOqYVd8YkkZ7jMZlRyeoKXcujlFKJwojbzQFdK8ByfXJaglMLeebz+l34rM9tNOlbHjldZr3ax/YD7j2QZbttUek6OGEUR/IJkFUVcskE+ZTCKDKn3DaHt0/hwUMlVEUiIYu8WOkiSyLFlIogQt/y8aMQwY8IwghfCDHdgJwmUkwpJCSJSs8iisCwfR6/tEtOl/k/335kfD8LgjDeefvQ2Q02mwbvvncW0wv43GaLrukREMUjXkkkIUukNZme5XFkKkMQRVzY7oIQ5+MZjsdabUC97yCLcXzNQ0dKfGx1m//9QplK18b2Qywv+Or9Ufg76qtW2LXbbf7oj/6ID3/4wzzzzDNA7IxVVZVHH32UH/7hH/5qXdUrqtls8tu//dvcd999fN/3fR+/8zu/86qXfc973sPq6ir/7t/9O44dO8Yf/uEf8kM/9EOEYcgHPvCBL7i8aZr8yI/8CL/0S7+0T9DY1z8oJRMym//uu1/v0/im1JcyT1wodzAcn4Vikrm8Nt7luXP8OtrTkyUBP4TFiSS1rkPXdCl3rDsiRQTqPZstL+DDz2xS7zt86koFSRS/IC1/dHzbC1ir9Vkupcehu3fmeAHjSJK+7XMgl2SjYbJW7XH6YJFHT8aOwue32my3DXbaNqrUJa1JnBg6XqcyGosTPrNZjauVProiDXe5HP7H3zSZzmpMphOs7fXpWz5d2yNsmKzX+twzl+NNyxM8ekdG32bDxHQD8rpCRpPoGF7c1RLj7pXphTw6zP0b7ffldAXT8ynocUFluB5TGY03HiyMC8aj0xmu1eL9uzsL4dGYfLmU4uRcjk9fMbG8gIgo7nL5PgLx6BnigvzBYW8gXenGrlbTIQph4PrMF5OUUirXaj16lofjxwaDS+Uua7U+SUXiYrlH24gNCr4fcWG7TSCCH4Q8tV4nrSrcbpl0DBfT9QnCkLyeQBbFoSvVpG+7FPQEla5N1wqIgCCMI4/m80N+7I7FE9dqAKiSiCEI1HoOW00DNwgp6AmSqoymSHjDsGRZlsglFR45PkVOV4b3s8tURkWRBfLJBLcaA5IJhWIqQU5TWJ5Mc2Imy9Vqj+c2WyQVmQN5nb7jUe3aKJJIPqXg+hGXd9rYXkBWl+nZHk4QcaHcAeCTlyp8+kqFY9NxbuN6bcDVao9q1+Kza3skZDEuFsMI1w8opTVmc3GQdb1v07d95gtJprMaz221sNyAtx0durmDkLYRcO9cnoeOlOgPO547HYucLnP/8PH+jaK/U2HneR5//ud/zoc//GEef/xxPM8bx5wkk0l+4zd+g/e///3kcrmvysl+MS0uLtJutxEEgUaj8aqF3eOPP85f/uVfjos5gEceeYStrS3+xb/4F/zAD/wAkvRSqKTnebz//e/nDW94A7/4i7/4Nb8d+9rXvv5ha1TQNQYOW814fPPywm4UEXL2RoOHjpSYyWl3uWBH46XGwGFxQme5lKZv+8NjVsglExyfyYxHUculFO97YIHVzRaSKPKpKxVu1Q0OTaa+IA5lNOb97Sdv8MS1Guu1HkRwqxFf/h0r0xRSCbaaFkenNfq2zxNrewBMplXKbZOzN2M35ChK5Z4DWdZqfXbbJhfLPQpJma7lI0kCp+byXK/12b3R4L75PIen0nz6SoWbdYPGwOYdx6a5b77ArXqfruXiiRFpTaZjejy70WQur/O+B+L7z/ICNFlCT4g4fkRCGY5ZA5DUiAu3OyQViUJKoWf7nJjNUes6tA2XvuWTUmUs1yejKzx0JM4QfG6zxScvVdjrWaRVhf/xN7f4raducGwqy+e329T7Dt/5hmmUoVHh+GyGM4tFnry+R9PwODKZZrNh8mufuMyjJw+w0TD45OUKuiyS1WUW8klkASRJZC6v0zLcYcyJSBhJ+H7En5yPzRin5nO8fWWS1Y0WjYHDidkMXdvjerVP24zHoXt9h5bhMbBd/BBUWeTeuSw5XcHzQ/K6Qsd0aRoepYw6RpcJwFqlR0IW+YV3n2CnY/PEdpta18EPQ1KqTFZTUBURXYn3BpcmkvwN0Bo4DBwvRn8lZG63TA5Ppek1TRYnkvzThw+zXutzsdxlJqtzdDrD7569xVbLZDKjUkgpXNntYrgBekJieTLFc5stBs4oaqTIpXKHlhHvus0P41qafZdaz+Hjz93m0y9WubzTAyHuULcNl4V8EsOJndmmG+D5IX4Q4gdxsfbI8al4t84PObte59begFRC5r75HNWujReElDsWtudjuiEvVrqc32pzerHAO09MsbrZRhIFDhR0ZnKvToP5eusrKuz+9m//lg9/+MN87GMfo9PpjIu5e+65hw9+8IP84i/+Itlsln/2z/7ZV/Vkv5i+3FHvn/7pn5JOp3nf+95319f/yT/5J3zgAx/g2WefHTtfwzDkR3/0R5Ekid/93d/9ktfhOA6O44w/7/V6r/FW7Gtf+/qHrjsJCl8s0HSjMeDm3oC5vDZeAoeXqBGyJOAHEfcvFsaj3HLbZMSpGo0QP3O1xjtPTPN/vv0wj56a5dzNJhfLHWayGmcWi6zXBlS7ccfizu5hteew13NoGh6uH2C5Hk3DxXJD3nJoAlkSxrtjT6/X2e6YFFIJmoaNG8SO1scvxk7bR08e4N0nZ/nkpQrOlQq2F7DbtVAlkc36ANsPWZxIcmQqTc/2uf9gkYQkUUgl0BISj56c4WolxYVhkO9oLGw68ch5xNX1goDlIeZrs2niBxFhFOH4Abbn80K5TbVr8eCREidm48y6P+iadK0YGzZf0ElI4pgju9Ew+PSVKut7fbK6wkRa5eJ2l6bhsNO2MR2f5sDhqfUGUxmNiVSCI1NpVCUeeW80DK5WevzVtRpdy2NgB0DERt1Ak0UsP6ZhZBISCEK8lB8JGI7PwWKKQhTvO87kNO6Zy433MD99pYLthyyVUlheyPVqH0UUkAURRRHoii6llIaWkFiZTnPfQp6L5R6CACfnctS6Nl3b4133zABwYbuN7YWEUTzO3GjE43PPj3CDIGbGBiGltMqJmRzHptMslVKsbrRpmy4IAhktwYG8zn0LeXRFit3ejs9ez+b9ZxY4vVjA9AIWivHja7mUpNa1mcnprG60CII4qPrETDbG3tk+fdtDU2Tahse3HpnEDUIcL+TETJa9no3pBfRMn48/V47ZwnMCfhDykWe2mEirvGUYUm24HglZ4sJ2O877CyMSishSKTV8nuyS0RW2myaf22zypqUJVmZy7PVs6j2bfFJFkjxMN+T/vVSh2rOYyeqcWSpS7dlst8xxmPPrTZ2A11DY3bhxY7w3t7GxMS7mFhYW+MEf/EE++MEPcurUKYBv6M7W5cuXOXHixBc4dEfnfvny5XFh9xM/8RNUKhU+9alPfVmO3n/7b/8tv/qrv/rVP+l97etrJNsL+PmPvQDAf3z/G/eRYl8H3Rl78kovAqOOXtvwiP/MCnd93fYCZEkYu0fvXBAfOSdHI8RqLzYtxM7RWDudeAlcQGB1q8X57Q59yyWMBN68XODodBbbC6j1LOYKOooksNO2kARl2I0x6Ns+E+k44uG9989zYjbD81ttqh0bTZZRxIh63+XiTnkMu5/JaazV+siiSK1rYrkBLdfGD0GRRU4eyBOH1fr842+Z4998/8lx1lzb8OKQ2qJO3/KpdCycIGRWi5fzR1zdWtdmOqeR0pR4T810mM1qeGFIEMbnJIgClY4FRFyt9MhpCQ4Wk+STMm9anuDcrQbVns2FcgfLC6h0LOYLOg8fneL0YoE/YJOnb9TpWh5hGMZjXsvDdHwqHYntjkkppQ4dxxFrlR5+EJJMSGw0DAaOhyqLHJpMc7tt0DU9DAAiREEkjEIUSWI6o8boK0UkOxz/1vsOf3J+m4vlLrM5nbSqMJONi75b9QH1no0oCYiIJDWJn3vnCu8+OTss+EeGD4/rtT5eENEwXA7kdNaqPQ7kdSQxRnkxfGy0TZeMpiBLAocnMxRTCcotEz+MWN1sk1IlpjIqxVQC2w2pDxw8P2Rg+6xutLA8H334NyWtyWw2DHpW/Lt0vIi3HZtkJqvx5NoeogAnZnJMZTUGjs+Rqfh54gUBA9fn3rncmNRRSiX4xCUDSRDwogBJSrBQTDKRUYeOVYGlidjkstkwkGWRVCIin4x3GDOawmRaZbmUYr024LPX61Q6ceRP3/K51egzmVG5Vu3Rd3zuXyzwPacOjI0nW02Ty+Ue0zmNk3M5tlsmT13fo2d5/PhDy697cfdlF3bHjh1DEGJuXqFQ4L3vfS8f/OAHefjhh7+W5/dVV7PZ5NChQ1/w9WKxOP4+wNbWFr/zO7+DpmmUSi+51T75yU/ytre97RWP/X/9X/8XP//zPz/+vNfrsbCw8NU8/X3t66uqMIp4/FIVgP/v+/aRYl8P3elovdP8MOqY3dnR+z/un/+C3LtRp05TJB45PsUT1/Z4Ym2PSsdir28zkVbHGLFHT86SHY4VR8doGe7wRS/OqQvCEMMN8f2QG3sDLu30aJsuN+sDpjIqGTXBwPWZTGroqhgHxPZsFoopvCBkLh+H3k5lEkgihGHMRV2v95GGrxm1rsO5mw1ahosfhuOxVc/2aBsuGU3G8UOe3WjRt/1xpMmoa3lyLscjK1PD4jVmjV7ciaHxG414928yExdoS6Uky6U0u+2YWDFwAup9m/likoePTdKxPPqWy7mbLTw/YCavcfpgHi0hs1RKcr2mUus51Ho2fcunYcTO1GrP4vxWvFeoSRJuEHDPbJ6+47HVMLD9gFRCYa6o8fCxErYX8OkXq1T7NkSQ0WXCKDYZLBST3LeQw/LjYOIogolkAlkR0SSJfErhntkc12p9kmpIve9wqxFnCG42DRw/YOB4fOjsLVw/JKMrWF7cWcvqCook0DU9fvfsLZ64tseRqRSFlMq1Wp/zWx0QIJ+UkYZDqEdWpnj/mYPjXcqj02kcL6Das8lrCtWeQzGVoNazqHRtDNdHQGAynWF5Mc3A9vncRpP6wOGJ63vosoTtxbdLHCLDwg4Yjk8prZLWZModk4Htc3wmSz45fIxl1HEQ97MbTRoDZ8zoXav10RSJluFyZbeD70dMpBPMZnVWZuNMuWu3+qRViUeOTfHoqVk++uwW9YFNKa3Scn36js/77p/n6HSW57faY6qE6fqYnk9GlcnqMoosUe6YVLsWXhiRSsS7hD/+0DLVrs3HVm8ThhBG8XNm4PoMnHjv7k5U3uul1zyKLRQK/Pqv/zof+MAHyGazX4tz+prri41UR99bXFwcdyW/XKmqiqqqf6dz29e+9vXNoXI7Ht+0DDd2g/YcTs3nKKQUslocHXFnV+/lpobREv/IaLFe69Mx412xv71R5+n1PWay+l3joTuP8cS1GrIgcHqxyK3GgJmsDsDFchfT9jAdj2QxiReGOG5IE4ej6TQpVUEWBZYnUsO/lwIbjQFhJPDGhTxnlib4k/PbbDRMbC+IX4wtl9WNNmlNHpMh/vEb51ir9Xn6ep2W6VDv23hhhCTGx3xus8X/fmGH5sDjYFEf32+ltMr7zyzEHaCmAQhDJ298v+10bNKqQlqTSSZkDCd+0bacuGAEWN1os922CKKIvh1wwx2gyCLHptKcWSoAUOnYlNsmQQTr1QG36gYFPcE73zDNiQM5TMfnwcNF/vLFGn3XR5NEZnIqbz8aY6X+5PPb3G5aZFQFxw+xvZDZXGKM6tpuW0iCwENHJslqCpbnc6iUpmG4rExnGDg+eT3eRawPnDgGJQhwvXjxP5mQqPQcHD9gJoKDRZ2+HcTdQVWmbcZxHLcaBp/fTvC++xfoWx71gUNSkYgQeHajhe2FPHT07nDsctvkViOmTeT0xLiD6Qfx70cSRCazKsV0gss7XRRR4J65HLdbJo2+Q8f1UGQJPRGHYUtCHDEzn9fREhIzWY3tlkHb8GgYDqWUiipL5HRlmN0YY9J6pkdKUzg8lWJlOsN6rU+la9IaeEiiwHedOMDR6Qy2F7C62UQWBGZzSQophXM3m1wod4mA5VLcvVur9YGYcrLdNpjJqjQMl5msjqpIFPTEsDsq4AUhBydSw0Dt1JjFvNOxaRoeTcOh3AmYSqucmI1DtQsvMze9XvqyC7vv/u7v5i/+4i9ot9v89E//ND/3cz/Ho48+ygc+8AG+53u+5+9NQTMxMTHuyt2pVqsFvNS5+7voN3/zN/nN3/xNguAbx/68r33t6xtLo+5ZMZUgqUjU+w7VnkXP9uOokaY57rzB3TQKP4jGbtb5QpL33j/PXF7nD57ZZKdj8+R6zIst6AmOTme+4Bi/9okrPL3eYCqrcqCg44dQ7dnMZDXevjLJ/37BRRRE0prCe07Ps9dfo2v5mF7AoydnmMnqdC0PywuGqK029YHNxDCBf6GYJCGJdEyPkIhax6bZd8knFQa2h+WFJBWJubzGTFajaThUew6zeY1T8/lxVMXV3XhP+dmNFi/c7iCIkJAk3nJoghOzWU7MZsbXb7oeez2bW8MdMVkUCYkIicN8RVHkYrlLtWfhehELhSTHptPk9ASrmy1ut0weO79NMaVyYiZLfWAjiQIpVeTbT0wRRBEzWZ1HT82Og3fXa30qPRuJeN/w0FSGhuFyqdxhp2MxmVFZLiW5VhkwcDy8IMRwAiw34PmtNubQ9ZzTFc7eqHOp3Isdn5ZHMiFzYjY3ZOS2IIL5YpKlksSZpSI5XeHcrQbbLZOe7ZEXFQ7kNNb2+uiKzInZDM2BS8/ykcV4F/HYdIar1R4dy8N0fYrJDHpKxvPDcQB2uW3yobO3ePJ6HccP0edEVqazXNrp4voBUQQDxyPnKzheSM/2SKsSSxMpXD/EcHyCMN5rzGsKDx+dZHWzRaNvYw3flEiiiCwJTKSV8R5cjKJLjPMJ3SDkQF5nZTbDjz90aPz1IIT7FvJDOklEY+BwtdJldbMd741OFfjsWp0Xd7qkdJmjU2kmMxr1vsNEOkEhlWCt1udapc/VSo/DpTQH8hqnD8YFfbVns9k0xt3Po9MZTi8Wx53iG3t9kqrEREql0rNIyPGo+fQdu66vt77swu4Tn/gEjUaDj370o3z4wx/mueee40//9E/5sz/7MzKZDO95z3v4wAc+wLd/+7d/Lc/376yTJ0/y0Y9+9AtIGJcuXQLg3nvv/Ttfx0//9E/z0z/90/R6va+LI3hf+9rXV19fLI7kK7n8y79/567dpXKXK5Uuh0pp5oahxaPR7MuP0RjYZDWZxsC+i0bxvgeSpFWZT12pcGQyTduMOxGvtIOnKxJTWY1iSmG3He+bNYb4qKQqMZmJCQzHptOcnM/x//n+U3xs9TaGE2fCVXs2F7Y74yBjy4tHcw0jjlHZaducms/yjuNT/MEzt+nYHqmEhGTF+LO+4zOwPe6ZyzOdU7H9NJoiMZ3Vxm7UmaxGVlcYOAEbjQEJSRon81V7Fsdnczx4aIKzNxo8e6tJEEacWSpwoKCzMp1hdbPFRiMkDCAKIq7Xulyv9XF8n0JS5Y0LeRRZ4vRiga7lUR/Y+AEMbB/LC5jJ6phuwIGsTkpTxjEX8FKB3Bg45LUEhu0TIuC4PklFHDJ1Pcotkxdux85JWYrzCN+8HDNFr1S6mH5ArWczsH32+g5RBBkrjvuQJYG0GrN6wzDOgg3CkMmMSm6I7UqrCook0rMckgkpBt8jEA6L0IVCirbp8PnbHc7dbJFMSFhuzKYVVZn5os5Ox2Z1s8VSKcn7HjjIem3AteqAvhUbDlqWR0pVODmXxXDi8ayekHj46CTAeDT89I06nh8XY62BS8f0MJWYGpKQRBAEdjsmthcHQSuywOFSHAQcrwoIXK30OLNUpJhKMHB87pnLjTvO1a6NrojoisqZpSKrm02e3+owkU7wluUJZrImhhtwY2/Ai7td2qZHLqlwZqnIxXLMDC6lVK4On2f54WO/ZbkcmkxzsdxlKqvih1G8I5lKYDgBl8pdkopItedwfqtF1/E5mNfR09KY3Xux3CWrK3//CjuAUqnEz/zMz/AzP/MzXL9+nd///d/nD//wD9na2uL3fu/3+P3f/32mpuI29GsdY3699P3f//389//+3/njP/5jfuAHfmD89d///d/nwIEDvPnNb34dz25f+9rXN4pGO23whXEkX8nlX/79O3ftHnu+zE7bZrnk8sMPLo1/5k5ixHwhGbtcX6yxVEqhJeKx43vvn6fatcfRKP+/Hzz9BeG5zw87Wpd3eqQ1mXffO8tSKcmnX6xytdrjxEyWpYkU12s91vcGaEqcR1bpxbtxpbTGmeUJPnmpwuOXdlFlCT+M6NoeF8odprIaCUmgNwyxPVRKoSvyEC3lMpONd6fWqoMxG7cxcDFdDy+I0GSRvZ4Ts2Jns8zk4iy1H3rTIk+t19moD8glY0NEve9Q6zkUUxZXK112OzaWGyCJApYX8ENvXmS+kGTgeFze6ZJLSdheQMf28H2fEfgsoYi0DJezNxpcq/awvZCEJJKQRQ6VUgxsH8cNudkYoCUkepbL1jC+48cfOjTMpJvg7I06lZ6F6fi4QTTkp8qxC3aIWdNk8MOIm81BzIA9NcuHzm7w3FaLjuHG3bi8ju3H51DtWQiCwOpmk0vlLo4fokgim40Y91XvOZxZLjKZSRCGcVC0noj5tboSUy/Wqn2mMhqOH2C6AVcqHSZSKros4gcBgiBQ7dqUUvG0rW14/PaTN7mxN6CQVDg6lSGfSuAGATfrA/wwPjfD9Tk8+VKUTcz8dbmw3WW7bcYmlYGNKMTF2+MXd4f7iyk6ZoLmwMFwfCLAdH0+v9VmozHA9yMu78bF98NHSzx4aGJsAoLYJb7ZNElrMmu1PltNk4iIpYkUpxcLcUG3U+fyThsQIIpQZYlCSuXtK5NAxOpGm6eHJpvFQgrLCeiZHptNA1EQmMlqHB2OfC/tdEmpMoIQcKHc5cVhSLYqiwiigOGGHJ9Jc2ZpYozCu/O5+nrqK86xO3bsGL/+67/Or//6r/PUU0/xP//n/+SP//iPqdXiQMO9vT0OHz7Mj/zIj/DBD36Qo0ePftVO+tX0yU9+EsMw6PdjQPWLL77IY489BsCjjz5KMpnk3e9+N9/xHd/BT/3UT9Hr9Thy5Agf/ehH+dSnPsVHPvKRuzLsvlLtj2L3ta+//7qzozbSlwoVhpeYrS+/zCtxX0fHWxmyRO/EisEXFoPVnkXbclkiiSwKXNzuMJfX2elYnF1vADHJ4tzNJp+8VGGplOLRk7Pcv1hgvdbnajU2NDhewOpWC8+PODSZYjKjcrtlct98gQM5ncs7Peo9h6yqxPtoHZOirtC3fWwvRJMlVmbS1Hsuy6UUG40Btxqxt3Pg+ARB/OLu+xGCKLAwkecfv3Ge81ttupZHY+BQyqhsNU1u1gfDIF2GMPWIczcbPHW9wcm5HA8fneTYdCaOwHB8Pnttj5v1AV3L56m1BgiQ1WX0RFz4nLvZ5H0PxAaKe+eyzGR1kgmJx85vU+vaeEHEWw5P8J1vmOH/z96fB8l1n/fd6OesfXpfZ8PMYGawgyRAmiQk0aFI0VFkCVnkKJJz/cZOKkrFLt8312VfX6fyJinfyrVz71v13pTjVKUqihPZ1/IbJ6LiJLZM0QptihQkkAJIEQBBLDODmcH0LN3Te5/TZz/n/vHrboIUIFEUZVIynioUBo2e7tNn6fP9Pc93efZqneerdTbbtgACQ0D23PU9bD+gY7uoikJCk5nOJTm/3majOWCzZfNzjx/k4cUSP/OBRRLD+8YTxyYxNIXlWo/FUobt7gBdkdlXMKj1RcrEhc22sJ4ZGtqajk/D9PDCCFkCWZK4tttnq+3w0EIBJAlNkTk8mSU19KlTVZm9vkPD8qikE9w/b7DeEPvSCyL6TiBsW6KQ9sBlKmegyxIHJ7McmszQtT2u10yySY1Dkxn2+YLD+MyVGrWew5HpDD/x4CwgcWmrS9vqUm3byJLoIjYsl7OrTUbWOg8ulDi7KpJMwjBi4IeU00LMstawcPyApUqBcsbg6FSWV7e6fG11j8QwSULwDcFyQ1brJroq8zMfWHzTNSaRMVQWy2kePVRhtmAwUoAv10walkvH9XG9CGJI6DJtW5yf03mDs6sNwVWMItEd7QshT8f2IY75xEPzHJ7KsNsVHcym5TKTN5jKJal1HbwgHGcNDzxxHWy2B2MrnyfP3+SpSzs8dqTyrmfGviPJE4899hiPPfYY//bf/lv+8A//kN/93d/lT/7kT1hbW+PXfu3X+LVf+zXe9773cfbs2Xfi7e5YP//zP8/Gxsb4308++SRPPvkkAGtraywuLgLwB3/wB/yzf/bP+NVf/dVxpNjv//7vf9tIse+m7o5i79bd+sGvN+exwrfvyt3KgXvzc0YADmDjFu7c6PUeWijyix8+8i3bMCKSj8aup0/sI5fUh6PGJpIk0bY8UprCybn8LcAwpmm53GwO2GwN+LnHD5LSFZqmSyIn89z1Ost1k8OTGX7lx49xdrXJld0eth9yaqlEGEPP8SgmNTY7AzabFqthTM5QmRhmwJ5aLHGt1scPIrbaDmEUM5FJEIQRN5sDvAgSKhiSjO0G9J2AYlrD0BQmsglSmkzXCYiimCAURrofOFBiqZLhqUs7WG7Abs8hiGJKaZ0Tc/nxZ5vKC4uM3Z5DztBIJYTYxAlCrux0qbbL9J2Ajh3QtLr8zAcWOX3fPn7n7BoKEqYT8OzVGs8v77HX94hiyCRERqjtR7QsD12TmcmnsLyAQ8MO1Urd5CvXa1ys+nzuhXWm8wbTeYNDk5mxpUzDFH//jQf2sVI3ubjVwfZjUpqKFwgg9OzVOg3THfLosrQtj+eXG9heSHvg0ei7tAdihHmgkqJtByxNpJjOJfmTV3cYuCEJVXQVAU4tzTCdS/Lfv1llsy38+7JJjebAxfEjpnMJ7l+scPrEzBjkiHGuSPm4uNmhkkkwWzDYbA24XO1RTOr85Kn9rDeECtoPIsEty2hU0gnWG+bYoHcyl2C53qfv+MiAMuRCPn5kkvmSxUbTQlNkNlsD1homOUPD9SI8KUaTJPZsh7SmIMsgA7sdm/PrrbFQSCx8Yj5238y4i/fwYukNqvKP3TdDPqnxarWLrsh4oehyfv7cJhlDYaM5YLcj4tWOzeQoZxI0TZcLmx12ujb/x9NXeWixSMvyeOlmm4QiC0uegU8pqZNP6ciSEF60LQ9dlWkPPD7z/ApwiCs7fSw3YGRP9G7WO5oVm0gk+NSnPsWnPvUpGo0G/+k//Sd+7/d+j/Pnz/ONb3zjnXyr29b6+vpbel4mk+E3f/M3+c3f/M3v7wbdrbv1Hq+kpvDa/+vHxz/frW9ft+vivZXnvG5hknqDKfF3er25YopKRvxuJWPyxLFJpvMGX3ipSscOODGXp2v7nFtr8eF7ppjOGzx5fpP1xgBNkWhaLnvW66bpbcunmNaZL2dZbww4MpUbx1xd2emy0bRIajLzpRQpLUu1Y3N8OociiczP6XySjz8wO+6SBGHMXt8V7vuFJE8cneDlzQ5N08OIY4opnXJG5/75AllD5avLgng+mdNp2wG1ro0fxoRRhBfEaIrweltvWiQ1ZQxcbrpCQewHEefWWhydyTJTMJCQODotup2rDZNMQuXCZpf/6++9hK5KSMg4QciZlQYpTSZn6MhD0LtSN7HckCgWfacwjtjpOpQyCQ5OZDi1VOJ/vLLFdtXmT6/W+JsPznFoMsPLN9sizaBl89TFbQZ+xJ+8uktn4HG91scLIlRFIq0rVFs2puuDJA27nFmQ4Le+eoO5gkFCU/jy5R7TeQNdlUnqMsQxTlpHkiQhTHBUZCnmym4f0wnpDY17kTqc2FfADSLWGya2H5LUhVWHREQppaFIEtu+6JzWey5rDYszKw0uVbvExJycK5DSFEw3QJKE15wXRpiOz8sbHfYVU1yvmVheSCUjAP2V7R5nVvdYKKXoOwGSFFNIatw/n2etoWG5geBcSiKtZDpnYDo+SU1ls2VzszVg4Amgnxva3Hh+REZTmC+maA1ceo44p6udAVtth7WGyXwxzUMLQuDw5PlNhEm1x6WtHidm8xyeyvLLHznK2dUmz13bI6UrIud2vclsXogwyukEW+0Bri9seh49VKGcSfD/+9qaOB/7NvsKSQauD7qKH6hstiz2FBdNlQjiGMv1CeMYWRJmyLttl888v4LlhhybFnFm73a9o8Du1qpUKvzCL/wCv/ALv8DVq1f5vd/7ve/XW92tu3W33mZJkkRK/759DfzQ1e26eHd6zq38uDuZEt/p9W4d+QqfLZ/locXJKO9zf0moYT975gZt22O3J7Jhn7u2R70vRo7phIIXRWx3bZKazGI5zZHJLK4fMpETSsEnz2/yyMEyx2fy1Hseth8hSTLFglDUjsZTIw7frQTxhulS79mkhyrBnhNgewEfOjqJpshUOzbHpjNjVWMQxRyfzpHUFC5U2wy8AMsNUWTIJjWmcyJXNa2rwiS5YXFyLkcuqbPZGrBa79Pou3hhSD6pU87p/LX79/Hs1Rp7PZemLAQIpuMjyRJLFWFXcX69xUQ2QS6hcmqphO2HfONGk44soQ/XM0EItZ5LEMdCfGK5aIoQmiiSzHLNpJgWBreaLKHIEit1i6bloSoSCU1GkyV0Q0WRxVgRYCafwgkC/BD25Q222jZrDYuRGfNK3cRQhfL08aOTQ86az5XdLufX29yom8SAokiUUjofuXear63s0bMDru/1yejq2IRXkkTHC8DQFTIJja4TMJk3ODmX58pOl44dUE7rtG2Xm60Bjxwo86mH54EY14+4vNVDAgppjZblcWR47h6ZyvLEsUl+/Y8v07N9qu0BpVSCtKExM1Q3L5Yz7PVd/CjEdEL8MOK/fXOLet8moSrCPiUlDI/jGHRVIQhjUgmVDx2bopxJ8MpGm47tcWqpxH2zec6tt0hqCgvl5Jgz+qVXdzCdgMVymjgWStaeE7Bc63FuvYXlRZycq5AxFK7u9jk6LZS1u12Hz72wzmZnQPNVl4Vyaiww6jkBaV10fjVFZraQYrqQ4GK1hx/4PDxTomt7tAc+pZROIa0zcAMUFXY6LpbnM1d491Mn4PsI7G6tY8eO8eu//ut/Hm/1nqi7HLu7dbfu1q1j2yeOTb5lQvXI324E3mYLSfb6HvWeSJAQylV1rB69dTy7XOuR0kd2GMKPC+DceovdnkM+KfHCjSYJVUFV4NKWx2ZnwFbH5uhUlslcgqQmkzMU2pYHCDD68GLpWxR/4vM02GrbZA2NDxwoc269Ra3rMpExmC+lMDSZ4zP54f7osVzrUW0PyCcTTOYSTGQNvNDmyFSGTzw4xyMHX+cYXqx2h8kF6jg3t9F3GfghhZSOHYQMXDGqOzSZ5cxqA9MW/mmzxZRIbVBkXtvuMnAjkUiRELYyP3lqns2WRcN0yRgG+aRG3/Gx3BBDldlqD1hvWmQSKn/15D6Oz+TGIPu+2RxJTQVivrrcoDXwODwhcnlNNxRKVk/4yU0XDGHKHIgIK8sNkGSJfXmDY9NZ/CDmZmsAkkQqIV5zvTHgQrXNenMwFIWAIomx4LXdHlO5BJ98aJ4/urSNFAtO3qmlIrYf8tJGB0WRyGgq07kkOUPD8yMeOzzB4SnRgR2dU5e2ujieGF2fPrEPgM88t4oXhGQNjePTOVQZ8kmdU4slLm11WWuYfPLBeX73hXVKSZ17ZvNDb0CJp1/d5WbTYn85zYm5PC3L47XtruiQBSG9OKAt+0zlDf72qf2cW2txaatDSldZmkjxowcrmG5Az/YIojQPL5bGnEXRsTbG11AlnWCnbWO5Ph86NjXkeYr4t2u7JoYuM19Kc/rkvvECCeCpSzvs9V0MRdASan2HlKYymTOw/ZCMoaPLkohOm8nyxLEpbG+Dhuly/1wekPD8GqeWSixWUpxba7PVHuDJopNX7Qx+cAyKq9Uqc3Nz79ibbm9vs2/fvnfs9d5rdZdjd7d+UMoNQv7pH7wKwP/7E/eRUO+OY9+JErYk7rjL8Ob/+3a2KCN/uzASXK/ZgkgyAIm25fLqVkwYxeNMz7blMVtIYrr+OA90dFMc1YMLRc6sNFiu9am2RIctqau4noftBWy2BoBQnMYxHJpSWK2bxDFv8NIbbf+IOL/esKj3XSZyQsV6ZCqD7YcEUTRW247Gtn9yucbFakdYXcgKx6cneLZTx9AUjs/k30A4r2QMOoMGXhBzZbeHpso8eqhCz/bpuz6aIvPh41M0LI9HD1XGSRbPL+9huwGlTIJcUqPaGRBGoCoSE1mdpiWAbN8JOLVYpjsIOLUkQOtyrc8zr9VQVZnZQhKIOTSZZb6U4lqtz27XGY6JB+Pkjq2OzcANMVQxpt7rCy6k6fpEERzfl0OVZXw5opzWudEY4AchhbSO6YScnMvjBiKvdSKr88eXdtjp2vh+hKLI7MsnqWQSYqTeFykao8XCbCHF1e0eG60BW22b/+X9+5nOJdntORyopG8xOvZpWz5/drXGbtfhkQPlIV8x5suXa1yoDmhaHuV0guVaHzuI2JfXcYOIi1t9iNvMFVNstgd85WrMVD7BwUqGvusL5bAfcbHapWt7ZJMq5bRG03S5VO3QHvjIEqR0lTCKiYlZGuazXtnpUUjrZBKiI/o7Z9eYLaR45ECZSiaB44f862euc3Qqy0MLxTeIkg5NpvnmZpuW7VPJJJjOG5xZaeAHEUEc0bcjfufsGr/ykWPj6+DZq3XWmxZBGJPWJWw/wg8FiD01X6SY0tloWdgBTOeTwwVDhscOT/DFS9us1C1+8tT8uIs9V0zRtjzWmxaTWZ2sIZS/PzAGxQcPHuTTn/40/+Sf/BMWFhbe1htFUcR//s//mX/5L/8lf/tv/21+9Vd/9W29zt26W3frnaswivmvL1cB+LWfuPdd3pr3Tn23HnZvrhHP5/GjE7cVYDx7rc6Faue2oeG3qmvfnP16fth5S2rqkBNn03d80aXR1XE4e9ZQh9YVopt1+uQMv/jhI5xfb/HUpR2SmnD53+3ZdOyAfFLDD0KSY2GDgheELNf7HN0So+SzqyKzdbfnsN6wSCeEkGJ/OUU5nRirZzO6Ssvy6Dk+yXmFs6tN2pYrMmMjSGgyf+lwBdsPCeOYiaxOUlN48vwmmYSwsqik9WFEWHIsZug7AdM5Q2xD12WrY/PQQpG1hsl03hBK05Mz427nfCnFYjlF3w6YyglwalU7zBeET2AxrfHhe6bo2h5fXa5zarHMh++ZYqXex/ICbC/k1JIwsx0pjmcLBqYjoqNmC0lSmowfRtheyL58kiNTGfww5uxqk9bAx/ZCqp0BMhKltBgz91wfJ4jo2j5uGI65Y8u1PrWei+OHFFPCOPr4dI71pkVr4KEoCsWkSLGodV2m8gm8IGSv79K2PM5ttPjN/8uDnF9v8ZnnVzDdkNlCkkrG4LlrDa7VejCM5vrYUJFreQFJTaZj+Wy3HSrZBB+cyXFyLk/b8rm01aXWs7nZskhpKn3Hp20nmcgmaDY9ru/2OXtjDzeIyRoa983mODSZ4YsXd6h2bEopjcNTOdoDDycIKacTnJzLj8fy718q8+ihCp97YZ1OwydneKw3LK7s9Kh1bV7d7jFfErzOhimx0RQLppW6RRTFKEMxw+iYa6rEdC7JXs9htWbyq//9Ej/xI3Ocn0N2fQABAABJREFUPjnD4akMH1gqs9uzOVDJcG6jRcfyKaQ17pstYPuROPZBgOMHRBGs1C0yhoIENCyXvhPwxLHJMc0CYDJrsFBOjRcf73a3Dt4isPv4xz/OZz7zGX7rt36Lxx9/nJ/6qZ/i9OnT37Hr5vs+586d47/8l//C5z//eer1OjMzM3fMWr1bd+sHsb5XEHC33t263fF7qx52dz72MZIEbcv7FuuTUQRYy/JuO7a5laN3ZkXkq466Zn0nIIig2hmgyDIL5STTuSLPL4tu3HxJmMw+e7XOf/vmFjebFsVMc/i+otNwfCbL06/uslBOc/rEPvpOQMN0ePrVXfp2QMZQkSSJtaZFredyZrXBfbMFnh/6f2WTojNxfCZLJqGRS4oOzbNX69h+yGIphRfGdGyfsyt7fPnyLhO5BEenMoRRRCVjIA09wz52YgY/iDizssdXl/fIJQXBPp1Qmckn+eh9U7h+xB9d2ubIMBSeOKbveLQH4k9n4AOiy5c1VFKajOuHLO/2qfXtIf/Kww8jJjJCwfrUpW2u7vbp2wE3W0KokUvqzBaSXNzqst120FSJ4zM57pvNc3mrix+ELFUyfOrhOdqWz/PLdSIkjkxlOFDJDo2Dxfg7d6+K7Ye8cKPFwA3RFAlNlgTnMdCYzCbG8WqzQ7PqhumS1GSiWOW+2TxTOYOkppBUZWHfoQoeX9/0WW9ZeFHIRC5B1wnIJ1U+eq8Aa2dWGlzfNUlo8hAkdVkoi5it3a4zttZpWz6WEzBXSuEFERtNi3xKo5xJsFTJABaFlMq1XeH9Z7ohByYyzBVSTGQT3NiziGIIIyEUmcknyCdFssN8McVWx8YLIhKaRD6pUZBUDlSyLFUyTOcNGqZD2/J5eaNNztDYX0pxZFIYS9d6Droii+ivps3vvXCTuaKwHmlbLqt7Jk3TJYhi/s8XN4jimGPTWY5NZblY7TKTT/DNzQ67fZcvXtpm4Ic8eqjCwA8JIpgtpvjQsUk+e2aN9abFufUWQRRzarFMe+DhBxGaKmgMXTvgoYUix2fyHJ7KCOD83CpN0+XQZJbHj04MRRxdZgvGe8Kk+C0Bu89//vOcO3eOf/JP/gnPPvssX/nKVwCYmZnhoYceYmZmhlKpRCKRoNPp0Gq1uHLlCpcuXcLzPOI4plgs8uu//uv84i/+Islk8tu/4Q943eXY/cWq79bI9m69t+p2x++tqF/v9LsAjxysUMkYNEx3mDHpUsmY40SJUYrBtxvT3ho5duv2XKh2sIZRVKPX6dpCZWg6gtR+eCpDOaOz3ujTG/g8e63GH1/appRO0DBdtrsDaj2hlh0BNFmCMI6p9RzSuspSJY0qi7SEL5zfZOAFBKFQjj5xdJJHDlb47JkbbDQHQtkYRiR1hYOTGV5ca9EdeBDHpHWNtK7w9x89wFOXdriy00OVJU6fnGGumOLJ8zc5swJ+GOP6Ysw8ys8Fiacv7/BqVeSRPnZ4goSuMKul0BQZTYb2wOPrq01W98ToeMSVs1x/6AsnEygyUSxxYi5PMa3z3LU6K3vCnsMPQFdllnd7LNf6mK6PFwZIkgBnhqaQUBVutmz6TsAjByt84aUqtheRTih85J4ZimmNJ89X6Tk+rheSS2n89AcWOVDJ8IWXNymmEsiShBfGlDMJ5ktpbF/4oo1GhY8chPYwWq098Pgfr2yhKTIP7S9wZDqLF0Q0TQ9VEkbQnYHPUiXFibk8i0NeGwgvRJEmIkbrNxoWjx6u8GPHpnhpo42hKVTbA1bqfbqOT8byeGAYp6UrwrT58+dust4c0B4eBy+KkWUhqDi1VOK5a3vYbogsw0IpxXwpyUTWYLEi/APPJdokNUVsc99jrpwkikRsWN8JeHhRXCvPXKnhBiEJVWEyK8bpuiqTS6rYQ75iQpOxnIDL2z06lg/kSOoihaRju6gd4W8n/PSKFNM668NOd8v06DoeV3dENN3ovGqYzniRs9d3x8bEtwqF/EDQCRbLaT796BKXql3+xR9epjPw2O46+KHoOJ9aKlFM61hewJWd/tij8t2styyeOHXqFH/6p3/K1atX+cxnPsOTTz7J9vY229vbAMMw6DcmTmiaxuOPP84/+Af/gE9+8pM/MHmy32vd5dj9xaq3CgLu1nuzbnf83or69U6/e+vvV9uCu3Zlp0sQgSpL7PXdO5qYjoDiyAvtxGz+De77o1zYs6sNQOLljTYvrDWppBM8tFAgiMQYuJJJ8Hfev0BrCBT2+i6WF7LTsfHCmCCKaJoOT13a4exqg0cOVpgppEglPOp9l+V6n7liio/cO83XVxqsNUw0VcZ2Q2w/5OnLuyQ0mbOrLWw/4Ef2F0lqCkemsixWUry80UGVIWtoHJzI8PiRCdYaFrWuw8ALhupXwXtaqmT4qydmOLfexgsiKlmdIIzJJUWigSYr6IpM3hDdoJyhkTFUTs4V+PpKg92ug+n4bLVtTNtHViRkwAtjdEWikNY5PJlmqZIe8/G+crVOGMVoioohNAu8cKNFMaVTTCUggmI6wYFKmuVan5SucGgyPR5JX6p2mM4Z3J8vMF8S3oUt0wMpJgJ6tk/bcvnZxw9xbCbH58/dZM90mS8kuX++yGZ7wDdvttlfet3n8KmLO+z2HE6f2Me/fuYaXdtHlSGd0PjkwzMs13o8e30P25MwQnD9kPvnimiqPO7+AoI/uFRko2kPA+1huz0Yc9VGi4am5aFIEtW2Ta3vMFtIMV9KcXwmx7m1FhutAVEUc3QmS1KV2WzbyIj3ncwlqPV0gjgiYyg0LI+Xb3YAKKZ07EDY1GQMjWJG58hkjtd2etxomGwNrwkQZseL5TSHJtOASNoY2edoqkImoaKpErIkCQW1H9CxfR47PEFSU2hZHgcnMkDMtZ0ephOQMRSu7PYZuCLmLhyO/x89VOHljRbPL4ukEXh9ATYWV1zc4fnlPeIoRldl1lsWcSwMs7/82i7n19ukEyr3zGRFvnLX4SvX6iyW0ygydGz/B0c8cWsdO3aM3/iN3+A3fuM3WF1d5etf/zobGxs0Gg0cx6FUKjE5OckDDzzA+9///h/67tzd+sGrd3p0+lZBwPej7o6Bv/f6Xo7fW7ErqWQSBMNUhZQmorPuZGI6usE0TJcXbjQJoohMQn3DMRbedsY4JmyUbXp8OseJ2RwQ80cXt+k7HlM5A02ROVDJsGe6hFGEoan4YcjN5oC9vouqSCQ1GdsPmSskxykKI4f/lKbw/LKE7YmgeNMJ2O7YnFtrEROT1ESe6UwhRT6pcm6tzXw5RSGtYrkRYRxzsdrlG2tNLC9ElSW+eHGHL17cwQsjPvEjcxyeynG1ZrLVGTCdS4qRox1waavLIwdLLE2kmc6JEeGpxRLX6z0USWyjIkF74BOGEUEMahSTTKioSsRE1mB/Oc1iWcRurTUskfcqxUQRpHWFuWKSmy2bIAhww5DYi/GiGNMVCRQDN8APRWcG4Nxam0bfZa6Yot4TI+iLWx3atkcmoZBOaCRVkS06Gqd/c7MDMRybyVFMa5xbdwQ3zw/GKuhnr9QY+CG5pM79c0Wu7vTRFJmTc3meODbJ4SmRq3qzNUCVBGga+RqOzo+nLm7zzJU6pxaLLFYEWMoYCufW27QHPqcWS5xfb/Hadg/TFYbRK3WR9RpFoCoy0zmDQ5Npzq+36Dg+BybSlNMJruz2eOVml72+x0I5zfuWimiqMs7lvbDZpm0FyJLF0pDn6QeRsDhJalTbA3a6Dr/7wjrHZoSoppJJjLvYDdMhl9RRZJuW5VJM6SxV0jQsl6SqkNRkvECM4QF+7vGD9J2ArfaA//TiTdoDl+bAI60ptGwPBZmELrwB5woppvMGAz/i6m4f2xexaCMV9m7X4alL23z5tRpt0yVtaOwvJXG8iOWayZNulaNTWR5elJjOG5ycK9C2XP740i4XNttcqLYxFJWlScGNfbfre9qCgwcPcvDgwXdqW+7W3fpzqR+m0ekP02d5t+t7Bcm3y2eFb+3qHZ7KfVtD4tf5dXtc3emz23M4OpUb///otRqmy3rDYrGcAiQ6ts9Wxx5bg1zfNSmkdJ44NsVsISnGZ3449O4yeNrZHgbfR6w3Bzh+yMAPyegKTxyZHI9KH14scfrkDGdXm/zu2XU6tke95/D1G00OT2ZQZQlVlimldXZ7Lt+82RYmxFkDTRXB61d3e+z2bFRZJh7mvUqSGLut1PucPjlDIalS60jkDI2JrM56OMAcZtGmdY1qFFNMW1yv9VmumdhehK7KVDsD9vqe6LIoMhlDoZzSCWO4by7H8ekcm22ba7s94jhmozkgrYsEDEWSxiNAYS8SUUjqJFQFNwgx3QBFlnC9gPWGxVbHZrUusm4BbD9kt2djugGyBLYfkVBj2p7Hi2tCOXygkmEqZxCGEVEk8koVWeLwUGjxS//5Fep9B1WGhKZSSet86Ngk12t9rmx3+R+vbHGjYVFMaZxfb6HJwntydc/ieq3HY4cneXChyHLNZKVu0baEF+EI/E/nkkznRQJJtT2gY/sM3ICUrvLBwxNM5ZJ0h9FbbUukfaR0lY+dmKbasfnovTNstgZin/giDk6TJRbKqXEu74m5PLVh9yqMwQ8jCqkEJiGmJ17zvn15uo6H7QnD6E8+JJw21hoWZ2806TseRyZz1Ho2PTtgrpjkiWOTPH15h3rfJaWpFNICBF7a6lJMa1QyBk9f3mW9aaGrMrMJhYEXEYSQNGRhcu0GXN3tcXa1gR+EEMdEUcylrQ5nVxtsNG1URQgziIX/Xyml8/6lCvacsIPZbAve5yMHSqzULb706g4fWCrz0EKBs6st+q5PSEQYiY7pu13vPrS8W3frz7ne6dHpu9k1uzsGfufqewXJdwJzb+7qjX4eKVSncwkeXCiNTWuXKhkxTlsosdtzMBSFUlq/TQZtg3PrLTLDOKWtjj1Wj4r8UuHOP7K8ODmXZ7dnc2Wnx6tbXXpuiB9AZxBQbQsfu2a9R8v0KKb6fPaMw3TO4PTJmSHvaA8vCFElCcePaPRFluxkNkEvFNYaE9kEtZ7N2t6Anu2jqTJrDYtqa0AcSySHBrBB7CEDlYzORDbBcs1kOpek3vPIGCpnbzRZa1oQgRdGTOcM/voDs0CM6QUgQaPv0B74BGEMxCQ0mcmcwXwpzdqeSd8NeOVmB9MJqPc9BkNe4gcPT5A11GEMmM10Lskzr9WIYgEMj8/kMHQFxxPgzXIDdFVmr+8KgUNCoZBOUErrXN/r4/jCyiRraNhuQN106doBkuViqArNYfzUjumy3hpQTic4OJnm3pkif3xxm+u1PkEUDwUGMg3LY66Y4shUhgvVNit1S3C6ogjTDkgbKuW0Tr3vEEWw1RbZpjOFpMh9LSbHI+eGKRSzxWSCQSpEkiVkWUIfGi9brs/ff/TAOGasbQkhStf2sf2Qn/nA4ji6C+B/vrZL2/IpZ3WCKB6PHeeKKf63v3qc++cLPHVph92uzU7XpphK0B14rNZNwjgmm9CYyYu82M+eWeOVzTapoRl1o+9BDJO5JOVMgp/5wCJ9J0CRZRRJopzWODlX4GK1y57p8D9e2WYyZ5BPqkzlDDIJFT+MkQFNZuiJ55M1NBaGi5+bLZv95RR7PZe+HXCx2kFTFJKawvuXSrx/2JUtphNjCsT/+nsvjc+jm60BfdujnBGLgeMzeaZzr08li7fwYd/Nugvsvg91Vzzx3q53enT6ZkDwVoHeOwEIv9fPktQUXvrnHx7//Be57gSS3+pxcvyQzbbFvftyb+m4nFlp8NXrexTTOgM/YqVmIkli1LfetJjIJsjoGgvl5LgLd22nh66JgPS25eMEIROqsNE4OpXl2jCd4uFFARS/9OoOf/DNTXKGznxJmBmbzibXaj0msgkcT3i+PXqoIhIDwnh4ww95ca1FQpMppjW2Og5Xd/oMvBBFlkASIocgEurXju1xbr3Jr/71+ziz0mClbnGjYYrsUFlGikFXJSZzBovlFBeqHRw/GooIIp69VufEbI7Hj05wsdphbc/CckN0VRI+aAmVjabJly/vMF9KoRSTrNQt/ChmOp/AclS6jk8+qbNUSeEGEXs9B9sL2OmK7qCmShRTGsDYHmXEbZvIJpjIJVgsp+k5Pq9td3H9iCASgpD5klCCVjsDZEni/UsCdA/ckEJKY19exIOtNSxkUyRWFFMGpbSO60d4QUgQxvhBJNIKZLFfj07nqPddIGYmb+AEMRtNkyfP3wQk4ljC9r3xuNxTZeYLBqmERhSCHfhEkRiRF1OCg3j6Fk5mJWPy9Ku7NPoODy2WOH1ihqcu7fD1VWH23LJaTOWTfPDwBJe2eux0bExPpIeEEZhOMF58nD65j2Ja50uv7lBJJ5grCGXr+fUWL2+02O25nBq+x/PLdXY6DvW+A5JEEMW4XkTP9Xlof5HN1oA/u1Kj7wZkEypzpRST2QTTeQHqRgkn1faAQlJltR7QUD0alsfAC1lviIVDy3J54ugUHzo6SdvyefZ6ne5AnHOe66MqCidm8+PUiSs7XU7M5rmw2eHlm23WGgMKSR1Jgk88ODfuto+u9Wp7QL3vEsbgBhFBGKIrMnNFg2JaZ6M5GIJGkQF8Kx/23ay7wO77UHfFEz/8devN/lZAMOLLtIau/d/uIn8vjFElSaKc+YshavpOdScwdutxGv37VpA3OhfOrbfYajtv8An7djUy3B117EamuOfW2iKEXpUoJBNM55LjGLGW7THohDx1aQfT8fGDmK4j8jJVWaLec5ktJIeWCzGmEzBwQzaiAdXWgMvbHXRFoZIxeP9SaawEXK6ZfO6FdfwwYipncP98Hi8KaZk+V3b6nFoscXmry409k1RCww89wjgmoUkU0xoN06HWdTm/3iKlKewvJ9np2LgBpBMxU3nR1SimNNK6xlQ2Sd/12ZdPEkQxqgwgsd4wx0pJTZWYySfpOT7E8LXlBl0nwPGFqWw6oXBiIscnfmSeP/jmJherXcI44kAlQy6p0zRdLlY7Ii7KFvvhzGqDtaZI9BiB5XM3mvhhzIfvmWLgh1ysdrjZHBDD2Pql0XOZK6QI45imKeLbkprKdC7BkakcG80B12p9mpZPGMbMFpLMF5MsVoRh83ozYiJnUMrqlJI6qiTxlWt7tCyPuWKS9y2Vsf2AL1+u8cxrNb623CSX1AjCkDAExw+YzmdIJVRSCY2pnEE5I0DjzeaAaCha3GgOxtY44rzs4wYhaUNlOmew1rAwHaFudvwQSRJj06yhstMRUWdhFFNK60zkNNYaFhutATN5g8NTghc36gwP/Ihe0+bKTp+vLu/Rt33OrOxx32yBjz8wx5WdHhc2O9h+gCxLxBFMJwwmsgbn1pvYQUQxpfPg/uI4YWK51uPZa3tc3uoCDI2nJVRZQpHh6FSW2UKSOI55badLHIsR6mIlTTGtYSgKl9sdiCUWKmkmsglOLZWYK6ZYrpmkdI1iWiOdEGkTS+U0mipGtss18Tm6to8ivc6BDeMIQ5PRFEmcWymNUwvi+loop1hvWHz5tV0q2cS3mHm/W3UX2N2tH/r6foxK7xQX9ezV+rdYVNyp7o5RfzDq1uN0OzA+emw6Z5A7rPHoocodX2t0Lo4I459+dGlsPDzix2USGm4YMplJDMd6Pn03YH8pxVI5xZnVBn4Ysdmx6ToeigJd2yNnqGy2Lc6tyTxysEwmoZFPqkRxhO9IwzxZh0JK44ljU5w+OTPe/uxwnLvesLD9kAcXSjy4UOJ//9IV/ujCNpeqXd63VCKKYwxN4dpun/bAw1BVdrsuth9xdbfHZ55bFTYlscR8SXiZvX+pxEMLJZ5f3mOna7PbEePMyawwq01oMld2+vzplRrXd/s4XkhKV9lfShFEMdsdGz8YMJEzSOoquiyEHtmExid+ZH4MontOQCmp07A8WpbHK5sddnsOj05UeOxwjueX63QGATfqJrYbcHmrQ9Py6dkeqqKwUu8TxkLJu1RO03cDKpkEbdsTXSw/pJjUAWHN8mq1haZI2H7IybkcKV3hpY0WvUB05vZMDyfoUU7rVNIJJrIJeo7HbtfFcn1e3uzQs31SukIhrXFkMktSU6j3I2zPw/J8CkmdMBZCD8cLSSVUwliMbZOazPWaSVJXUGSJiWwCVZHG5P2nLu7w5Eub5AyNDx6ZABDgPYiZzidxggjbDceJGqYbEEYRi5UMSxUhVvnK9QYpTaGUTvClSzss1zJji52Rgfa5YWe3Z4PjR4RRNOTreTCfJ6kpnFlpEMUxc0MVcMv00If5ugCbrQHFtA5I7HZtLC/kqUvbHJ/Js96whlQEjWev1skYKn/t/n3cP59npW5yaDI79pGr9W0cP0aVY/YXUxyezpJJCAqD44eoisQzr9U4v9FmIqPTsjzsIGSxnObqbp9G32EyZ1DvO2x3HDIJlXtmcuQNHT8KmSum2O7aPH15h4Sq8tH7prD9ENsNsHX1PSGcgLvA7m79Bai30xn7TmDwTqDsTmHvt6t3U007KjcI+fUvXgHgn/+143cjxW5TtztOtx73b3fM33wejRIpJnMJgigep0+cWWnwp1dqrDVMHj1UIZ/UObfRom35JBSFj50QzvlfeKlKGIKmyByfFoKKjK6ST+rDrhdcrfU5u9pkq2PTHgQcqAhD2r3hyC+VUEkNx+5nVxs8/WqNhXKSTz96gEomwUsb7TEBXIzrPCw3YKmS5qc/sEjDdEloCmEUMVdI8dWVPZKaQhRD03LpWD5eELI4kWGpkuHxowJUNEyXvaF3Xsob8qqGcWjrDYvdnkPb9oljEeV1z74cF6sdJFkiiCJhs5E12Os7yJKETcjTl3c4MZfH0BSOTuUopfVxJ65teQycANMJ+NnHhcjvyZc20RMqfTdgpd7HD4TqWJYlJrIiL7Q98EhmE8iKTMZQSSVUFspJTp+YGRtGO15A3w2QJAE8c0mNnzw1T8ZQuXCzTTKhcv9cnr2+x9XdHhlDxQ1D1hsWAzfkR/YXeXB/gdW6RRCJMfzFzS7FlMZ8UYySLTfADSLSukYYR2STKg/MFzg+k6dtufzBN7cw3YD7Z/N86NgkILpXfScY8zd7tj9WNz91aZuBG+L4IQcqKT5+/z4uVLu0TJdzay3miylSusJCWQDq3Z7Lh4ZRdufWm7y41uRarc+FahsviDk5l2dlz8T1Iw4OjYsTmsxH7xX76WK1Q1pXOTGbJ2toNC2PlulzwWuz3bUZeBGmE9Ad+Fyr9Zkvpjg5l2e2mKJhukPuWky959B1fOaKSXa7DlEMuaTGbCFJw2pB3cRyA1b3TIppjZNzeaayoutc7Tg8fXkHRZYJo4h8Uue1nR5ty8cLQ3RVwdAEx06WYtKGyqnFIiCNbU+QhCF4JZNAU2Uapkut56DKMuW0zhPHJtlsDWhZnhhb5413/Xv9LrC7Wz80dScw9nY6Y98JDN4JlL0XwNp3U2EU87kXNgD4304fe5e35r1f3+3x/dbzSCRSTOcMdnsOl6odZgsGjx6qsNYw8fyIKzt9VFl4dyU0mUOTmbGJ7evjW4P5UopcUuPoVBZDU8bdgo2mTdvySGkKJ+dynD6xj6cu7fCNtSaphIo2TGwY+CF+EHKzZdGxXL7wUpVHD1VYKCdZrvXp2j5LE2ligBj2TIcvXdomoSmokkQsSVyodjg4kebYUDix3rDYbA7wowhNlnj8aIVHDpY5u9ogoYrOkhdGJFSJH5kvjj93OqEylU2w07XRFJmpvMF0LskVtc8D8wUcP6BhesRE5JIa5YyOqki4fsRnz6wxnTO+xfNPfAaPTzz4es55SleYKSSp9xxMNyCpqaQTGvm0NrQHidnruxTGXLwEOUNnr+/y7NUa+aTO7KwAVtdqfdwgxnQDLm52SGkKx2cEiA6imMVKethZVEnpCqosbDe8MBqCAYvXtnsokozjB7QsjyiGHz1UJmdonFluYDpCzLC/lOb9SyUWhzmrT13coTVwCUKYyif51MP7x13frKFyZqVBzwnIpzQ+8eAcfSegYwuPN02R2Ok5qKrMUiXFC6stVvdM7p/Pc2iyBEjDqDl/DKC8ICaMI1w/4OyNFkEQsbLXJ4rgyHSGn/nAAfrDMfnTl3fY7Ti4obDgKab18ajY9gNiFIIIojhCVWSyhspc0WAyl6Br+yRUmR89WBl3lH/rqzdomB6aIjFdSKJIkNIU2pbHzabFS+st0dkkxg1CTp/Yx2JF5Lg6Xki955JJqOSSGqW0zj0zWboDn0JSY76U4v65PMV0gr4b4Hghuz2HU4tl7p8rcKHaYaVm0rJE93Q6ZzBXSNE0BdWmYbmYboCuyGy2hefdcu1bYwT/vOsusLtbP5D13cRAvR2wdXdMerfean27RcDo/Lk10HxE0n7q4javbnVZb1jidwsprtd6dGyfY1MZHpgvkhyKF0Zu9tN5Yxhub4t4pFAAi83WgN2ezanFMh88rNAwXTaaAz54WADCS9UOfdfHj4RpbxTDs1drzBVSZBMq7YHP1Z0e988VqGQMvnRpl832gP2lNI8dmeBryw1e3eoiAfmUTjGls1zr03cDlsppfvy+GRbKybHCMyamYXp8+XKN9caA7a6NH0bsKxjsWS7ljOA+9Z1gGF8lcW6txWwxiSrJnFoocaNhktIUPnrfNEsVEQOW1FRe2+6y3rR49OAE5WyCF9eavLrV5VMPv56723cCypkEh6ey40QGEXWVZjKXYL1pIcsS5bROIa1xbFrwx3a7Dlsdh5SmsN2xOb/eFl0l2yMC5gspPv3BA8yX0vhhjO2GuImQxNC2xfYj9pdS7PWFalOVJCq5BB+9dwZDe/24GJrChWpXjMaTGsdm8izv9kCC67smBybSHJhI4wYRfUeoi6sdm92eO96+gxNZlOEiodoWnbogjOk7gVAo53SKSR3TDTDdgEJSZTqXJIwi2gOP82st8imdbFIjjCI6dkAuGRGEMdM5g81Wi69et5jOG+yZDpYd4ngx2YRCeqhGHXgRpxbKTOcN1hpNvnx5h9U9C9Pz0WQF2w9ZqqTxgxjHCykYOg/MF7l3X561hhAIFVMCMPWcgHrPoe8E+GHIv3lmGYh5cF6kYjx+eIIfPTTBcq3HCzeECTdIuEGMpsrM5w02WwMuVNucW2+hKkJ4M/ACHtxfYLGSoW155JMJlob7ttq2SesqXbvNcr1PIanTcwKu1/pU2zZN00ORRbJFQpN4+Wab3Z7NVM7g4ERm6G0YU+0MAGl4zr3794y7wO77UHdVsd//+l5ioN5K3QoGv91Y9q5B8N3KGuobuE231ug8+tfPXB8HyX/yoblhSoDwULP96JYcVo1CUmO35xBEoKkKl7Z6bHWc8Yjx6k6PMIrpOz57fZfLW13WmiZBGNO0PH7lx4+NjZFH49u9vkvW0Jgrphh4ATsdB9sXGablTALLE/FWjh/y7NU6e6ZLHMeU0zprwzEpsRjjHphIc2giy7XdHkEEPccfKgMhm9SZKSQxXZ+G6bDVGbBcF9FdfScknxSdk5Sust6weP76Ho8dEe7/hq4wkzOIYrjRMAkiODFXGNu/HJ/Jc2mry8pen3rX5Rlvl088OE8pqXOjYfKVa3uMlIlZQ6Vre5huMLbkeORgmfWGyYVqBxmJnK4SRCJCzQsinrq4Q9f2ObPSoJTW8cOIvhNgOT5hDGEQUe3YPHVpG1WW6No+fhBiusKz70bDpGMFtAcefcen2XdJGSqlbAJDU8bh8QDLtZ4wGh6OgB85UGIik+Drq3s0TIcDlTQn5gocncqy2bJ4frnBZnNAOZPgZmtAIanxxJFJVuomL6w16drCu84PI750aQeA2UJqmGwRc2mrh+OFhFFENqmjqwrbXQdZgvlSEkWW2V9KjZXVm+0BV3Z6WG7AVscWCug4wlAV5ktp9uUNghj2l1Jjj8Mnz20SxjETuQQpR8V0fUzH5+WNFg3LIYgiVBVOn5hhrWFyo25ybbeHH8ZoisQTR6eYyOpcrfU4u9pir2+jKgrzxRQT6QRhPPJvFN1WSRJiCkmCj9wzzRPHJvn8uU3WhjxRVYEgBMv1eW27x/Van4EbktAVHlooYToBV2s9rtV6rDdNLDeiobuU0wZhGOP4wnewmNY4OpUbLqgGNE2PpC6O58dOzFBti+i13Z7L6RMz74n7wPcN2NVqNba3tzl69Cip1Lv/Qf88664q9vtf30sM1HcLxpZrJs9eq/PV5T2Oz2R55GDluw6Lf6e25W699+rWTsmdjudohJrSZM6uNrm01UUdjiqXKhnWGubQQyxmt+fQsYVYopLWOXujQSmp07N9ruz2sL2AtKayUjfpDDxhtKtAUlOp91zOrjZZqqS5MFSEiuxQ0VEYGb6uNSwGbsBqfcAD8zr3zxU4NJnh3HqLF9eaOH44JLPHhMMMWFkC2wt5cbVJz/bRVZmELEQMowSBKztdlsppqp0BlhuiyELBGgQRFgGiwxKx3rBwfaHGFJ9b4sRsjrlCkovVLtO5JMW0xnpjwL9+5hpxBA/sL9K1PZKaiqr59J2AJ1/aZDpn4Pgh13b6Yx5hJWOgyjI9x+P8eovz6y1sP+R6rcdq3aKSTZAyVCSg74b0mhbPXKmRT2psdwbs9GwOTmT40YMVICahKrx0szUWGpTSOhOZBMWUhjQcLzf6HgM/QDNFeoOERD4pwu1v9SCsZBJ86dUd2gOfSjrBsekcPSckY6jMl9NkEsqYH3at1me2YBBHMX034N7ZPD3HZ7VucnmnSxDEKLLE9ZrJbs/BcgI6jk9CkblvNsejh/YznTe4stPnaq2HJgsipq4oTOcMUrrKXCHFYkVErn3+3CZXd3skVAXbjwiiGAMRyVXJiO/XpuXy9dUmi5UU/8v79rPbdfgfr1RpDTyOTmdZLKdZb5jU+zJbbRuQKKUSBEGMjDT0a5SomyLqznYDdFWh2hnw6KEK59bbdC0fWZKFoXTLIo6hOfA4PpMbpkVIQEwmIQQVr+30hscenCAkbSjMFlLcbFrU+wFXdnsYmsLhyQynFssU0xq7XYfr9R5L5QwpXeXyVgdVFpSGUibBbs8loUocmcrx+JEJbjRMojjJXtfB8SP+4OXqWPRx+uS+99T399sGdi+++CL/5b/8F/7yX/7L/NW/+lfHj/d6PX7mZ36GL37xiwCk02l+8zd/k7//9//+9761d+sHqr6foOV74bJ9t2BsFLx+sdphr+9SyRjvWJfwvWB5cre+u3rzef2dVLOjMdnxGWGLUUwLO4mW5VHJGDy8WBrak8CT52/y4lqbclojpSk8d32PjiU4dUlNoWm6SJEEcUgxqeFHEcWURsbQUCSG4peYMysNziw36NkeHzw8yamlMpe2ujx7tU4QxpTTCTw/wg0DmgOXpYkUL641iaKYdEIhZ2hMFxJstW3WmqLLNF9Mcm23T8eJeHmoAp6YNHhooUjDFOa9e32P1sBjMpfgyGSOvb7LtVqPQipBJWtwYi7PWsPk2m4f0/VZqqQ5t95EkWX2FZJM5wwWyklW6iaWF3Ch2qVtOiiyxOXtHmEcM1sw+Mg905xfb9E0XZZtj8lsktmyQThMdpjICiBseiHn1lt0bI8oEh2e+2YVOoMAL4iYyOrsK6ToOz6zBQMvEBYWcSxxfDrH6RMzvLzR4sJml6msQd7QCCKYzCZI6AqFoTp1vTng3n05Bn5Ey3SZyCYoZXRqPZvtts2zV+tsNAc8fnSCpUoaRZIoJDUKKZGysVBOkklo5JLa2Fz4s2fWeHGtyXwhRSGt0xx4rDUspnIGThjSsXwmsgmOTGU4UMlwbqPFas2k7wUoioTlRWNlaVJTkABZkkSKQ0JlvpSi3nc5t94in9TY6ti8vNGmYbnjzNiBF5A3NPwoZqmSZr6Y5PnlBl4woO+E9J2AC9UO1ZaNpspM5Qyu7PTYbAsLFlmSODCZoZTW6Q58urbPly/v8IkH5/nIPdN88cL28JxTObVQYqmS4b7ZPBOZBJudAfWeS2fgocggIY3TP0Y+d0+e3+Qb6012ezYJRebB/WWmc8Y49sv1Qm42LfIpnf2lJJ94cB7T9Xn+eoPNlkWt5zKRSfDE0UkUWaIzEBy/Ws+hlNYpZTRm8kkalsdGcyC6pWkdTZVpWi5feHlzGPH33rA5GdXbBnb/4T/8Bz772c/yEz/xE294/Fd+5Vf4oz/6I2RZJp/P0263+Yf/8B/y8MMPc+LEie91e+/WD1C9F0FLtT2gYboslJNvGYyNgtdTmsxK3WS51n9DosD38tnucvl+8Gp0XjdMZxwgPgJ1WUPloYXimFM3soZ4aaNNzlBQFYmlSoZMQuPpyzs4fvgGoAgiESBjaFza6mJ6onN370yOEYen1rXBF2OwpK4gyRJ5Q2W355LUVJYqQo3as0WO7Mj8t5TW+bPNGrYX8uD+ItZUlnrf4X1LgjB/szlAVWRmCklOzhXGSsqN9gDLjWgP/HGqQ9rQmC+n0VUZTZWHRq1JHjtS4cqO4Nk9vFgSgM9yUWTIGTra8PODxFIlzdWdHjcaA7IJhW+sNXGDiEo6QRDHuH6IF4QgQRjH9Fyh1K2bHh8tp5Elieeu1elYPqYf8P6lEit7Jq/tdqlfdzE0mf2lNA8uCo6W7YecWixxrdbn6k6PQkqlmEpQ7dj4QcS1mjBgft9iiVNLJdpDleNLGy1uNgcYusJU1hgqhFNYXsiX11oogCTB+5bKpBMxu117mMELu12X1brFXDGFF4acWxPAe6aQJJVQ2Ou7/OnVOvOlJKcWygRhzFrDYq1hUus67HZs5BhmiyncIOR6rc9Gw+Ke2Sz3zuRIaipXdnqAxM98YJG1hsnFaocrO338IOTqbp96TwDt/aU0URxxY89CV2LmCimCIGazPWClbmLoCoW0Stt2GbgBjx2eYK/v8up2hzCCckan5+g8drjCkaks0zlx7gufwNf9GJ+6tIPrh0gyhDHs9gRXLYwjFFnitZ0+9ovr7MuncMOIcAjsGpbHyxtt6j2XxUqaqbzB2p7J1d0eMXCgkuZ6vcfNps1awwTg3FoT14/QZZlsUljK2H447n6nEyozhSRzhRT2kGYAIk3kxFyBBcdnrpDiYrVLQpU5OJlmvWHRHfjcv78w3Kcipq2cTjDwQjEazibG6mAviFiu9d5TU5e3Dey+9rWvkU6neeyxx8aPmabJ5z73ObLZLC+++CLHjh3j3/ybf8Mv/uIv8q/+1b/id37nd96Jbb5bPyD1XgQtyzWTjeaAhxaK39VFOFdMUUwnWG/WaZgeh6eyb/j9t9ud/EFT0d6t18/nhumOFy4N0+H56w0eO1LhUw/v/xZO3eg5vaZN3wmG4ELkwP7lo1P0hvYitwajj8azuz2bqzWTQlLl+HSOvKGNbSX+4OUqy7U+lFIoskTTcvncC+scmshweaeLFIOqSDT74mb5wUMTrDct9kwXP4woZxLYfoTp+ExmE8wWUxyazFBMa0znDT796AGmcwb/87U6rYE7Bn2HJjNAzItrbWpdh3RCJY4jQMJ0fA5NZskaKg3T5WND8cOZlQaXqh1hl6IouH7ITtfB8wMsKaY1EPsgjGxmCklUScXQFAxNRpUlEasVRBydynB0SqhPP3h4gueW66iyzMVqhygGx4uGyQkyUSyI9SP17bn1Ftsdm4QmM1cQ3mV+ELHdEXmvhibTdXx++2vr7PUdcoYQVswVUjAk4sfETGQTvHx5l92ujaEKntx2V+Swbnds2gOffQVjfM70HB8viLi83WW7a5M3hAo3ocjsdG2u75rMF9N88PDE+FzabA+E6nkmx3TO4OJWh+2Ogx/EDPyAD39sBog5t95ioynAYCVjoCki/iyMYSZnEMcRSU3hsSMV2pbPXs/DDSORmZtQkCSwPF8Y+RaS/LeXqzhBxHrTYrku1NHllE4QxuQMhdMn9wHiu1SMpk2Oz+RYqqTpOwGnT8wwnTNYqZtATBhDZzhun8gmaFoeO12HrbYDxGSTKroqsdkakE+qSJIA4UEUszSRGSZ+CMuTantAZ+BSSGo8dWmHa7U+hZTOBw9PMJFNCEVzUkNTZdqWj6ErPHFsCj+IeOrSDq/t9CgmNU7MFTg5lx+OdeH3X7zJ9VqPUwtiH1yodpkrCLFS3wnYaA44tVTk1FKJKztdru720VWFmbxKve/ywlqLYjpBJfPeoNW8bWBXq9WYn59/w2PPPfccjuPw0z/90xw7JqwT/tE/+kf8+q//Oi+++OL3tqV36weu3inQ8k6OdL83sBmTNVQWymkOT2U4v97izErjDV0ZYGw4O/q/0ZjtvViGqvDVf/zE+Oe79Z1rdF5X24MxCPvqch/LExyyantASlM4UEmP/eJGxPnRF3/WUPnK1RpbTdEtGfnU3crdfORgheWayTdvtlltmByoZHhwf+ENFidHpjJ0bZ/75/IsVtJ8+bVdbuxZQwDlU8mIMe3qnkW1M+ChhRJd2+PVrR6yBIensyI2yXQ4tVDmFz58eOy1NxIijPhMAzdkt2Nz+sQ0jxyscHa1wUI5xYXNDjs9G1WW6DsB6WHH8alLO6w3LD5woEwlI5SaPdvD0GR2uy5fW23Stz2RsBBH4/2b0lWKKZ2UrjKRTXBtt48XRGR1lYEccmhSfPaWJSww7p8r0Hd8ghCcMCSpKeRTCeaLBn4Q88VXtnn60jbtgc9ExmCmYGA6IXt90eEspxOstyyCKEaWZV7b7rHTdYkiwa0rZXR2uza6ohDHwpz4G2stBm6IKkM+qZMxBGcLQFVkVEWinE6Q0BRkU3T0FEkSHmh9F1mKqXZsTs7mmS2kqHWFlcxW22IyJ/zayukEjxwsMV9KC2PehEJKVzEjwT+7stPl9Il9fOrhedqWx7m1NpudAYYqk04oWE7AZnvARsukbfs8vFjikYMVimldmAcjfAzFsZOBmGJa58BEhs32gIEXEoVQTiWYyie4XuszlRMjx2ev1nn2Wl0IVBxhG7PVsQlCMcq+vNPlxp4lFiJJFTeIMN2AUirHJx6c4yvX9ri202e+KLh9Pcen53hD2xqDri2UqaosiZQRXaZpeXRtHzeAtZZFarg/5ooaGUNlr+9wZbePIkMpnWA6J0ash6cynF1tkjNU/ChC10TX9PnrYuH1qYf3o6kyN1s2v/P1dSQgAvb6Nf7nlV0enC9S7dis7ZncP18giGChnBz7Cb6w1qKSTnBlp0sQvf4d8W7W2wZ2/X6fAwcOvOGxM2fOIEkSf+Wv/JXxY7Iss7i4yGuvvfb2t/Ju/cDVncDYWwFpb37OOznSfatq19vVIwcr49EbCBf3G3vCqmLUlRn934jjBCIW570qkJBlkRDwF73ezuJhdC49e7VOEMWcnCvwyMEyyzWTasfGDUOqHfsNQenweiTZXzo0wZcv7zKRTYx96kb/PzrfHT9ktWFiD1WrxXSC6laXpy/v4A3J7R8+PsmDCyIbNqOrKLKIv1JkhwOVFE3LI5fQ0BWFVzbFqEtTJCYyBo8erPDfvlnFtAO2OoNhnmaPtuXylat1Pnd2nbU9EyQZXYW+C+fWhHHypS2Ru3ldl3H8CGKwvYgwjLiy06UzEAKLC9UOf3xph6yhcmAiw2I5zXLdpGW6hEBeloh8CU2GSibBBw6W+ebNNr1BgOn6dAceuqogyxJhGLPbdTg8leFLl3Z4frmBH4RM5gx0NRx26CT25Q2Oz+R5aaNFc+DiB2JfRXGME4QYmkI5rY85b2K01iEMI17b6RNGMZoMaV3lpY0WG80BiiwxV0yx23UIo4jDU1n2FQ222jbV1gBNkbh3Ns9SRRDu75vN8+zVOoYqQyxR6zvInkQYRQSRMHNea5p07YDtns3AjWhf32Mml8QJI+YLKYppnT94ucqFaodySuevHJ9ir+9S6zu8vNHm8k6Pk/vyXNzuUu+4dF2PbEIlqatoqkLPcYkRn2N0bmcSXc6s7JEzNJbrJgM3JKkrtC2fi9UuAy+g5/jEMaQNAZ5GGcLTueSQzuLgeAGNnstsMcWppRJLlTRrDZMrOz26A59UQiGhyby61R1n6t4/L7pk59baOGGI6QZMZHXcMKTe97nZGpDUZL65KYQ/5XSCa7s90obGoYkseUPn6m4P2w/Y7Tr8+H0z9Gyx3QcqwiJmtWaxtmcOs68l0dEGTs4XWGuYLFUyuH6E5QVcrHY5s/Iyk5kEfhCx2R4QxpDWZRKqguUJqxsviJGAWt+mmNJ5/MjoepX42H0zAuDdaLFYSb8nJlRvG9iVy2U2NjaI4xhpmKv2zDPPAPD444+/4bm+76Pr+vewmT9Yddfu5M78ujuRy78dkHunR7qj9xt5St26baOop+8ESJ88v8lma4AiSRwdjmXfrIIc/f1e5BrerTfW93KM3pw8sdt12OkKW4Ty/Ou+VtX2gM+eucFG0+aj902xWElxbCbHYuX19zu/3hraVcRkjQm+dGmHvZ6LoSnkEhrrDZOdjo0ii4iwgRsy8IUtx3PX9rhe7+H6ERMZg5997IBQQ+6aAiA5ghhuqAqLlTQHJjK0Bx6phEpCFTf23z6zhh0IftSFaodazyEGCkmFE3MFWqZL0/KhLjhgJ2bzPHZ4kp4d0LV9Bp6PG8JaY4ChyZyYLWC5ATebFildwXR9wgi6dsBkLkE6oTKRET5pSPDBwxUOTWZ45Wab1sCjboYkNZV9GZ2DExmu1npM5w12uw7bXZum6UIME9mY9NAEeKaQ5PhMlqVKBn+Y4JBQFGIppmV51Lo22ZRGGMdUMgmm80LA8lPv389nz9zg4laHGPAjqPdtZotpdEXGCQJcPxxboRyoiOO60bRRFYl9xST3zOSQJJmFcpJz6y2u1XqAELUUkxrZpIofRLQt0am8f65ItTMgDGOqbWsIYCGB6LqtNwZstgfoisSJuTy/8OHDfOGlKn/4yjYbLRtNllnfs7C8EJmYZEIlratoqowfBoJjlk9yZNi1euQgPH15h6+vNMkmVAxNpiDp5A1ViCDaNlEc07V9VEnmsaMVTCek2hnw8GKJ+VKK/+NPrqLKMook4YdiLA0xL2+0Wan32ezYKJJEWlOptgbs9R0WKxlOn5gZmyt/dXmPnuuhSBLXa31ySZ3j0zmOz2T5ytU9ugOPfFJDV2UsLySb1JjIJljZ6xNEEdpQ1TtbSHJ0KjvuYpuuSGq5VusP31ucH1N5A1kGx4/pOT75pE4Fna+t7NGyfCZzCZIJBV2FKJYopnRmh7w8XYHVhuiE7nZddjuOsAOKoJJN8NMfWGTE5zs+k31PfMe/bWD3gQ98gD/8wz/kt37rt/jZn/1ZnnnmGV566SUeeOABJidfX33GcczKygr79+9/Rzb4B6Hu2p28tcitUX0nIPedRrpvx77kpY02C+UkDy0UyRoqX3ipOs54DcL4Dc+9vdoxRpVlsinBA3rzNtyqdBx5V303wPTPywbFCyL+v1++BsD/4yNH0Ue5VH/B6u0uHm53nPpOQBiBKsvjL/pqe8AXXqpydbeP6QZc2elzephxOhrfzhVTnFlp8M2bbYppfRzrBaDIEhe3Ogw8kSF76kAZRYIzqw0USfD3Ts7lWSinuF7vkTU0KhmD0ycyXK/1uLzl0/HE6LKYEuHxjhcykdVJqSppQ2HgBaw1TZKqQs8RcVYpTUFTRfrFUiXNIweE0OLsaoPtrs3ZGw0eOVDm4cUSOx2btK7QtX2iWCh/D00K25WEJhPFAgTZXkAhpfN33r/I6ZMzLNfMsX8ewGbbxtAUOgMf148JQo+9vsK+YpK5QprOwOepS9s0+y6aIhNFERlDY6aQQpWlMd9rrWGy27fp2AFh6PHX7t+H6fp8faVJ0dDo2T7Ltd54gbdc62M6IZWMgdex8SPB9TI0mQfmi7yy2WbghaR0BUUSvLkH9xex3IBXNtvUui49J+DHjk2xXOtzfr2FH0acmC1waDJNMa2zVMkMAZDJockMp0/OsNt1eOrSDrVeimrLptZ36ds+qiLhRzFBELNYyXByrsAXXqpSSetM5HRsLyCbVJnOGVza6pJP6tw/XxibO6/tDdhsDbA8n29uCkHFVsfm1EKJjcaAII6YyCRoD4QHWxBFxCJBi4SqkEwo+GHMdneA64sZ47n1Jld3+hTSGg/vL4qcVD/g+esNNlqWGI/LMosVYYvSHfgM/JCJTIJPPSyoW7s9wWcMQwGGFFmmZ/scncrwyMEK640By3t9sobKtd0+e32bvKGKLl3PoecEZBIqbdvjT6/UODaTGwp9dthoWrx/qcyhyQwrdZOEJtMyXZKawsm5POW0SVJTWaykuLLTByRSCYVSSsfxQ5YmshSSOvvySQxd4cRsji+/tkum72GoCnumS9ZQCcIYyw05Op0ja6gs13wmsvpQGPTu19sGdr/8y7/MH/3RH/HzP//z/NN/+k/pdDpIksQv//Ivv+F5zz//PJZlcerUqe95Y+/WD069FX7drYHoDy0U3zKQe3PdqdtyJ3Dk+CGbbYt79+V44tgkz16tj0HdiC/3ZjA3MsZsmC7V9uANXkp3srn4Tvvi24G3W19v9O/vB8gLooh///wNAH7xw4fR+YsJ7L6b8/XbpZ2MVNd5Q2O3a/P1lSaj8+Rma0BaV4QaMRK+d23L45krddqWy+mT+0hpCkemsqQTwk9rlD+a1ISCcqXep+cGnF9vEkZwo27St32Oz+SZL6X4qfcLX7EzKw222gNuNCzCKCahyaiKjCxLmE7AbrdNrWfzf/vLR1isZFhvWLy41mSnY1MP3OHiJmZ/OU0mqZLR1XHElB9EXN7pYjkhr233kCQxjprOGWQNje3ugL2+x3xJjBIdX5D3vTDC9nyiWCKlyzw4FDDtdh2+urzHWsNkr++OlY2aKpOSYhRkUgmFUwsldro2F6pdwetKKMwWkqiKxPuXyixW0lzZ6XJpq8tWx+ZStcN224Y4JgiFWvXe2QKvbYuEiJElx/uXygw8Ac66ts/7l8RY+9WtLhFgOgEfuWeapYk0tZ7Dhc02lhuy27WZL81yeacrxBFhzNWdHpoi7DIyhsZcweDQZBqQhkpNoea0/ZBiWsSWnVlpsN4USkxJhkJSjD4lJJqmRxBFJFSZp4epDh88VOFXPnJM5JLmRPqCpsgslNPjPNvtjkOt79C0RMeqlNZZrKS52RKLzNMnZnhhrUnLcrG9iP1lYU9zodphsyX2ix/GfPX6Hn3XJ6WrvLLZZiaXFL58SY2eIxJPVEXixKywVLm41SGhKqQ0haoX4gVCCTuRfX1id/rEPq7XTJqmyxNHJ9FUmeev1/nCy1VAYrGSZq6Yomf7tAceMbIwXw5iFopp0ppYTNteRNv2aFkeZ1YavLLZZrfncGw6NwaWsiRSVuzApWF5QxDcA2JMx+f4dJbFSoa9vku1I67PmUKKuUKSgR+y3hiw03YJ4wjLi/HCEE3RKKR0skY8Tk954UYT0xUm2u8FTvXbBnaPPvoo//W//lf++T//56ysrHDgwAF+6Zd+ib/zd/7OG5737/7dvwPgIx/5yPe2pXfrh6LeDFhe2mjz0ELxDRwjEDfRs6sNXidw3xnc3Knbcqex79OXd7i6I3gXE9kEDdP5lpzJN7++8CoyePZana2OzScfmhvzqb7dNoze83bb/laiqL4TaLxbf351O7B966JEjFrXWG9atCyXnY5Dw/LYatt8+J4p9pdSbLYgY4iRzeGpDF9d3qPec3h+uTFOm5jKG6w3LZ66tMOnH13iV//6vcProQnEfPm1Gq/ZAZoCsSQDLrMFj5alslwzWa71+PpKA12V2e069BwfRZZ431KRw1NZnjy/SWcQ4IfxsLNlsVhJsdd3ubItRrkpQ0ZXVTq2RzapcmqxxG7P4fnrdXY6Dn1bdBMVWSapitzOc+stbrYGZA2ViazOXCFFJqFhDLt1phMgIWHoMjlD43MvrLPXd/k/X1znYrVDQlXJJBRihJDHckM8P0aRI6IYEpowzV2tm8IcWZOZLSZ5YL/InP38uc0x7+/ogSzn15v4UcThqQyKLDOdT9I0hfWKHUXIIbhBNPRhG6AoMgcm0vzkqf1CkTwQwOLgRIYHF4r0nYCsofL5cyrfWG+iSBJPX97B9SOWKhmCKCJjqDx/fY90QuWhhQKmE/AH39wSZsxJlYWS4PPFMbQtjy+8VOVma0AlrXOzKTpe988V+dFDaWpdh52eTTmT4v1LJc7eaOF4IZYb8vBiaSzYWiin+OkPLI4zYm+2BFey1hOq03xa57HDk5w+OcO/eeY6z16t8+ihCifnCnxzo01CC/nEj8xjugF/dqVGz/ZRFZlMQial67QsF4sAxRLd3o/eN00lY/CfXtxgo2lheQF/4/5ZTp+c4amLO8MRvk3O0MgnNSw3ZL054MnzN3nkoBCT/e9/6+T4O/FStcv/eKVKx/J56tVtPvngvEgOGfiiuxyEuEHIxa0uCUVmIpdAloT582I5TT4pYIwsSfhBxF7fJZ9Ux+fiA/MFal13HKEnSRIXqt2hubBGQlep9xzapkdxMsNcQSh6NzsDgiBmtye6zKmExmQ2QT6pk1DlYSqKuH4qGR3BSIt5L9T3lDzx8Y9/nI9//OPf9jn//t//e/7dv/t3ZLPZ7+Wt7tZ7sN4MWG7twN2Opwa3B0C3A0PLNZPnrzeIY4aZgNyxg3Wnbsvt3uvsapN6X3y51/suT13aIaWrt7U/efPrjoyKW5Y3BnSjbXri2OQd48YapsNG0x6/5qjeShTV7T7P3frzq1vPcdG1dYedW3FMb12UPHu1znrTwnQCTuwrCGuHvst21+ZCtcNfO7mPtYZJxxbWD8s1kwOVNJcLBgMvGHrBpTCdYBwyPhJenF1t8vvf2GCv7+L6ISEQh5BOiOtDuOnrnF9v8ey1Gl074IH5PEemJvDDCNcXatLTJ2domi5/cnmHY9M5Xt5o88yVGllD5dh0Dl2VhZdcxkBVJDEyi2NSusL1Wp+VuhByyLKEKkkkVImpvMELN5qs1k1MP8DxAgppnZc323xtdY9G38V0AqJYjN6mc0l6js/59TZN02W9OSCMIJ9UOTqdo2N5HJjL882bHToDDx0ZCSHa2Os7KIqErsgi2SGl8cmH5ji72uQba00GXkDGKGNoCnEElheSD2JKaZnz620cPyChKRTTCSazOk3L48qwy/bY4Ql+4cOHx9fdUiVN3tKYyhs8e7XGubU2H75naqgcLnFurclmx+b4dI6DE2nOrbWZK6aYGGaYTucMXmy2cTyh1D02lWMimxDZwFLMhc0uLdtjviCoHU3Lo+8ELNd7TGR1DF1hvpQin9Q5PJWjawu7jxEnc/R9MPq+XWsIYLi/lGImZ3BmZY+5Qor75wsU0/qQl+iw0xHcs1/48GEANlsDnr1aZ7trM/ACwhgCPyCKhXfdekNGRubefTk+dHRynLyz1Ravc2Iuj+OHfOGlKqlhZ3mv7/HA/gL3zuT440u7XN7q4vjivAKhxm1bPsu1Ps8v7zFwQ3RVxvYinru+h+kFJHWFgxNptto2phdwdCrDRNbgaysNek6A6QQ4fshCOUVK1zhQEd6GfhTx1Ks7JFSFB+aLfPrRJT57Zo1Xqh38MOYnHthH1w5Ybw6odV00WSxMeq5HydIZ+BENy6XRd1FlGUNXiABNkQijGD+MODadG0cDXtrqkjE0Ti2VxvYp73Z937Ni7wK6H956cydp9O+B57PX98aeXrfWmwHLCBCO4nZuTXR47IgYdx6eyrDbdcYgaBTxdaHa4ZMPzX3HLtZu1xl3O9qWRyWdYH5IjBUmm9k3RP7c6fVGRsWvm8mKutPvvs7lS71h1AwCMJxZabDZEn9P54273bj3UN0OlD9xbJJK5vVjulBOjkfzAMu1PpW0zgeWyuMOz5cu7bDT3aPatrlW65NP6pSGUV0vbbRRFYm5Qopaz6acTjCdM3i500Ei5vh07pZzRthj9AY+qgwyIEtw374Cn3hodmyN8uT5TZqWT1qXWSxn+Kn3i+vvs2du8OJak2Ja4+ceP4imyKw3LVbqJoos0bU9LlQ7FFIakgwLZeHbeKNuIiPx3PU9qu0BcRyjqxIpXWS+HpvKMZ1LsNYwaQ08PD9ESkI5Azf2+vgBpBIK+wopDF3m0ESGjKHxymabvhOQN3Tmi1BJJzgwmaGQ1LD9kIyh8YGDZewgpJTSOTado2G6woRZVbhvLofphMwVxCj33FoTVZY4Mp0VPmp5g6MzOSIgk1CxPAE4o+FYdiavc2RKjB6jKCaSRArFyJvtqUs7WF4gOLdRzPWaSb3v8vzyHsW0Tiahst216Vg+SU3meq3PVtcmn1I5tVjmYrVL1w4wNIlcUmO+mOQnT82LkWG1g+0HFJM6GUNluzugafpEkTiuPTvg0laXI9PZIV/O4vx6k72+NxxTR2NOphBy3eTpV2uU0/oQXJS5VO1S7Qz46L0zGJrCSxtttjpCGJFP6UxkEyzXzDE37c+u1oRPXVLF9ELCSKacTvCJH5kTXopdh8WyML7+wktVHj1UYbaY4tHDEyyUUzx9eYcbexb7iyl0VeJAJc29Q3rAZFZHkaGc1sYLo1E2MhI0TRdZljg5lxciIUPj2m6flKEwkTVoD3we2F/gp96/wHLNZKfncGOvT73rsNW12VdIslBOkjNUTi2V+PLlGltthwOTaT796BJzxRSnFkucWdnD8UN2ey7TOYOcoVDvh+z0BkSRMFMuDOk4s4UkbcsVAHAIxF/d6rHZsqmrHg8vvk4j6Noeiiy/IZHo3a7vO7C7W++N+n6Q8d/cERv9vVwTbueCW/Sd63ajxrli6g2gcLlmjvM439w5+07qWlWRWKmZSBI8dqTCJx6cE9mStT4PLhR5eLHEs1frb2ncebtO2kh48ebffbNa8s2fuWUJ/sybP8db2T936+3VW70O7gTKR13WkRnrSxvtcUf5hbUmTdMjY2isNSw2mgMOTabJGILgPl9Kca3WHyumr+z0hedaUqMz8Bh4ISDUrrYfoQ2FLE+e36RteRyspLmxZ3F0Kku1Mxjniy5VXk+9+PDxKc5tNNnuOHz5tV3ySY1iWuOljQ5xHHOx2uHMSoOcoZHWVQ5NZthXTPLs1TqN/oBUQsFQFboDj7blMZVP0LYCpvMJ5ktJ2gOfa7s9ZEnmfUslkprCM1fqdG0fzw+HClodJ4gYuBGFlMbfuH8WgL2+w6HJDPOlNOZQGDJfSrKyZ3JoIkN74JPUFCrpBOtNi9lCkkpaR5GEsW3GUIljaA1cNFkmocLV3R7X6+KGmzY0Pv7A7Jjj9OlHlzi72qRtuYy+i55f3uN6rc9yvU/LErYw987mGXgh12smfTek2Xd5Ya2BLEncN1ug2RfCgolsgt2uzX88cwNZgp2Og6ZJvLbdY7NtE0UxG02brXaVYkpnMqez1/fYM10kBJeuktaREWkFkiRxdConhBc9h4MTaWbywgg5rSsYukhj2Ou71LqO6BgR07H9N31fSPRtH9sPyOyotC0BQDdbYrFw+sQMqiJRSQuA/KEjoqP4X1+q8vjRCY7PZHlls00UwYnZPDf2TCRZ4tRiiRNzeU7O5Wn0Xa7X+uz1HV5cE9m7jx2usFAW3UbPj8ZTkJ7jU0hrXK31ubzTJZfUuXe2wGzBYKM5IGcoTGQTFFIaLcslimOmVJnZIfe02XeJ4ghj2P1b3TPZaFncNyu64IWkxmw+Ra3r4vlCqQwSF6tdHj86wScenBtzMkej4a7tM5NLUkhrTOcSvHCjSRxBUpNwvJiYSPh4xoz9Rz/18DxPnr/JufUWWUMsZBTJRlMZ2758/twm13dNDkykx4u898J39FsCdp/+9KcBmJmZ4V/+y3/5hsfeakmSxH/8j//xu9y8u/VO1VsBB2/HY+7N3beRqvWtdsHgOysSR75JC+XU+LVG45eG6YwvpjdzoBqmS2445jw5l6OY1sdjhJFgYtQte/M23G5f3G70POLIvDno+9uNiG99n1vH1m93/9ytt15vFSTfCZT3nWC8wHjzKF2RwfND1hsWx2eyLJRTrDcsTMeHnIHpvv67y7U+X13eYzKT4PGjE0A8Dr6fLaSYzkUcncryhZeqXKx2kJDIGCr3zub5wDAC7PnlOpYX8PlzN2laPuW06MQEYUzL9IiHcU7FtE45o7NYTnOp2uG1nR4HKhn+8j1TzJdSbLYsSikNXZHJJFRalstGy8b2Q5qWx/5iCk2R+fSjB1iumfz7nkPTFF2P3Z5Dvecw8AJkWSKTUDk6nWG1bhETIUsS212bb95sM3BDzq23ObVYYuCFSBJcrHZZrvW5vNUjjGKmCwbzhSSmE4ix9cAfJynYfjgGeIcmsxTTOufWWlyqdgiiiPlicqxKPL/e4qlL29S6LnYQ8rH7ZsaqzJ2uzcANafRcmqbH/XMFjkxl2e7YdG2PlbrwlksoEi3Lo+oFEMNiJY0bhvQdH1mSkGUJXZbpuz5d2yOORfJHFEYgSfihAIMTGZ10Qhsv/vaX0rh7/TEdS1VkYiTKGYN/+9MPjbf/zEqDo1NZZgvGMIHEoWP7qJLEly5ts1zrcfrkvjEH+dxai5fWWyAJ7mBCk0lqCmdWGlzb6XHW9pgrpPjg4Qmx+O47tC2PBxeKzJdSKLLMyfkCP3ZcqHovVrsU0zqnFstc3unRs322OjbhEHxdrPb4Ww/NcXgqM1bchlJMUpMJ/IjN5gDT8TkxVxj7fEKTKzs9OrZPGEXYXkQ+qXNiLj/uHl7Y6mA6AR0rYLpg4IchTSvkM8+t8uBCkWs7PUwvYK4oYtomsiKf1vYDIObEsPPXMB3+8JVtlvf6aLLE/fMF7p3Js9kSma9+GFFIJjDlgLShcHAiQxDE/LeXq5xfb/LxB0S3MpMQpvTHpnMEw7SW+VKKL7xU5eWbgk5QzuhsNAfvmczYtwTsRlFgx44dGwO77zYe7C6we3frrYCD73TTG5HDN5oWH71v+lvGrKO61bj17XbBbn3PUUfsiaOv89iEmMEcdkzE6jVrqAy8gOVaj+VanxduNIUSzw0oZXR+7vFDgAhab1tipDXqlo04ctW2IPmO8i5v3fbbjZ5HnJbRiPbN6RNPXdpmOpfk9MmZ24Lgt1LfrUr4bt25bncd3A7EvxXe5uj4P3VpG5DIGjqPHEqPlYqjrNF638HQ6hyZzJIxFO7dl2Ol3qdj+xSSGheqXartASdnY3Z7EmldQVNUXt3q8vJmG0WCvKGP+VgrdYuG6bIvnxp3+fq2z0q9T6PvggSGKnNoOsvpEyL+abGcwg9F2oIkQUKTCcKYa7U+q3WTpuXRGvgEYUhKV4njGFWCpKoQENOxgyEvysMPBGADOLVY4vJOl3pXRFiVMwnWmhZt2yNGoj3weWG1Sdt2CUOG11TMQlnENoVRBBKYbiBsSxJpJrIG680BE1lDmOLmk7h+KPI9Z/PjReNaQ5iDFzI6QRBTbdl8/twm03mDMysN/uRyjZ7tkU6oQ7NamC+lqGQT7MUefihC7G8MEwWaluBU5ZIauiKR0BQUKcbQ5LHfXTGlMZE1yCc14dnWtimldAxNFZm1rk/H9mlZHherXSqZBAvl1Bi0L1UyzBYMzq21uTa0Wnni6CTX6z2mswZPnt/kkYPl8QLC0BROzOVZrpnj8f5Xl+t89XqD9eaAw1NC2f/IQfjKtTq1vkshKbzrJrIGImGhzlbbRpYkXC/i/HqLvb5LxlDHtjojisBIDHD2RpML1Q4vbbT48D1THJ3KcX4YxzaRSfBjxydZrGTG14Pp+PhBxHwxNcx9denZIjrM9BqcvJpndmgTFUQxYRShyjKLlTTHZ7I8crDCbtdhszUgjoQZsOUF7PVdTs4VaVguQRCz3rBIJVTsQBh2/5V7JgGJKzs9Fivp8ai4ZXmcmM1zaqlEz/Gw/XAoZKmKcziKUWVI6Qr3zORIJ0T3umv7LO/12em6PHlukw/fM8nHTsyMu76PHKwQROK6aVke0/kE6YTCUiV9R770u1FvaSt++7d/G+ANnmyjx+7WD0a9FXDwncDfcs0UHQhXRCfdqUYKvrbljbtst3vOt+sO3moi3LI8VJlvaXXf2vUagbH1hjW0TNAx3YCW5dG0XGo9YQFx/1xhLMp4/OgElUziWwLbn7/ewPICTs4Vxl24Ue7lQjl52xH07UawZ1Ya/NmVPeEdltb51MPvPXBmqApf/qXHxj//sNftroM7qV3fLACqtgc8dXGb3Z7wshpRAs7daNIceFQyOsemsoib6R5JTSGKxNgtimNWG31SmsbTl3eYK6SYyRskNIXVPZOu7XNpGLZuOT4RkE1oNCyXTELFdEIGbsD6nkkQgabIFFI2Dy2UmC8mOTSZ5r+/skXDFDFYU7kkJ/fleerSDqbj07R8brYsgjDi0ESW4zM5Bl7AqcXSEGgofHV5j+7AH9puSJTTOgldodFzWSimxtYslhOQSwqrjmu1PnOFFGEY0XM8trsDZElhXyHB0Ux2CL6EZYmuy9yzL8tPntrPmZUGu8OM2R87NoUfRux2HT7xI3OYbsDLm23WGiYJRWGj1WRfXqhfT58U3n+fPXOD56/v4QQRP3pAjLa/cr3GufUmZ1eFSvYrV2v07QDL9QV1Y+AxmU2QVBQmcsKr7MaeOcydjTGdgHJa59hMlqblMHAjdnoumSGfsG35SBI8vFhCxFTpZIwuOx2HpKawb5/BZttGkiCKhF2IoSpc2+lzfr3Nw4ulYUdR4tBkemzrct9sHtsPeGmjw1bXpm157PZskppKw3RYrvV59rqIFPu5xw5x+sQ+TCfE8kTnd7TIuLFn0rVdgmHH0PKEQX7b9PCCiGJaI60r/OnVGq4fcs9MHohxfBGNltIUXt5o8cxrdW62B7Qtnxh45rUa//ePHKXZd9hsWTRMj72+x32zKv+fp15jbW+ALEtkk0JoEkQxJ4sFmqbLld0+DdPjd19Y50Alg+n6YxCpKTKnFksYmsJu1+FzL6xzszVgIpcgisB0ffquz7HpLH/zweM8dXGH3Z7DqcXS2Nng3HqbYkooUqdzCeEDuSFSLUb+iufWW7Qsk9WGhS5LZBMqhbSOBILuIMErVRHH9okfmeMffvAAf/BylRt7Fpstm8NTOX7/xQ12eg4fPj7Fjx2bYqs94PJWl6WhvyCIMfyt3pPvZr0lYPf3/t7fe0uP3a0f7PpO4O/wVIaPnRDB06P2/+1quWby3LU9JAk+8eDtxQ3fyadtJJBQZcH7AL6l1X1rZ3AExkYrwKVKhuMzec6ttVip9ylnEjx6qMJ03hiLMkb2JqPA9p7tc3wmy4nZHMV0gkcOlse5mSldYeCFPH504ls+z27XGYOBW+voVJZCSkWWhY/ZqO6kJn4r/Md3mispyxJHpn44BU637iv49nY5I4/CkVmtqkhjo+pbz0nhOSdSA37xw0f45ENz9GyPlzba9BxBeh/ZWRyaTNO2XZwwopzWeGihxG7XxvUjbD/k4YUiHTtgXz5JxlA4UMlwbqPF6p6JaQf0h8HxnVB4dUmIzpYsiU7DVicAWrStLCfn8jxxdGrMJ+rbPl9bbbDX94iJmcwkODCRIookpvMGF6siXeH4TI5PPbyfpUqG7a7NSt1EBgxdJqWrwjIjBssNeX55j1rPZiJr8OHjUxTTOtWtLvOllLDE8EKIYyoZjZ94YI6BH7LTdVBkiWxCJ4pj7p8rstYw2WgKy5KTc/khF67Bdsfm3HqT0yf2MZlN8M0Nm+bAhFgsIv/RIaHi/MJLVa7t9OnZPkOPCSayCZKaijMchX/q4f388792L595boXrtT5hLBEEkVDES5DRVY5PZ/kb988OQVqTGNjuOvQdYaC8r6AShiL+K2dozBdTFNPaGEhtNAccn8nTtDxu7PSx/QTzxRQP7i9yvdbnZmuAF0YU0hpbXZtrOyKF4tVql2xSY6GcZK/vcW69yUZzgBsI37fnl+t07YDpnMErN9tDrzafKIr4/LmbnFoaGZ/b2F5IcSjGmc4n2WrbDNyApsQ4/3WxkmG53mOhnOaRA2WeX25QbQ3oOj7PXWswmUuw1/eo9zwmcwmR8uAnaJsu7tCk+dxak8VKmrWmxW7P5VqtR3vg8fXVBmEUD6PiUrxa7bJc7wkz64MTHJ3K0rEEv61te2w1B4SxRTqh4PgRL9xocGwmz/7hKHh/KcWRqSz5pMga7ndsXlxrEcaQ0sQ5aboB987kGbghpifUuyfmhPL32o0WOz0bCYlz602mc0kmsjpRnMb2QxRJIpdUuX+uSN8V4pjtto3lhqR0YXxsaAqKLGN5AV+5XgNgb6jsXmtYLNd6PHVplz3TpWv7/NixKQ5PZcY50O+Fem/0DX/I6r0WKfZ2gQN8q63Im7tOt3vtw1OZMW9o1E27FUiNnjP6+04dk1Jap2V546D1rY5921b3rQraW9/j4cUSrh+y0bZ4/MgEDy+WOL/eYqvj8Oihyvh5jx6q0BuGWV/a6vHE0clxaPuVnR6tgUvHhiiGKztdzq8L4vxIMakOuThd2yOfFEacc8UUhqZwYDJLGEVj7s+to+XR874bccRdIcVbr9t5JsLtDaQrGeNb0khGHbtbbXw+fHyS3Z7Lo4cq48dPn9jH8Znc0IAWurZPSpcBiU/8yDz37uuP447mi6mhX13MXCFJLhmOFxwjC52m6ZJUVXqOjxtG5IYpCYsVcfPLGxpBHHFxs0ve0LH9gHPrTbJJneSw+yFJkDc0aj0Xxw9pDnx0VaGc0XGHIgdpOC5+9mqdhulyYCLDkakM07kkXVt0uWKgmNLpOh7reyLFYOBHbLbFGFSVRaTe+fUmChK6pnD/fJFiWiPlKyyWU2QMjWpLpA3s9R2u7vZ5bbuLIkvsdm2eurjDSt1kzxTjrrWGyb0zeTYaFn4YIclwoJKh7wT0HTH+ni2mKKQEoLFcn4tbnSHolVhvDMYd+J97/BCfeX4F0w0pJXWqnQGOH9G1Pep9lwfmbb66vMeBSpqsobJaMzHdgPctlTm1VBTqzZ5D2/I5OaePhTK3TgvWGxYbjQHdgQDihyYznFosAiIxxAti9hWS6KrMC6sNJFnmvrkc07kk9Z7HdC7J8ZmcmDg0LRw/5MBEmpyh8Sev7eL5EfmkRjiMaqt2bG62RAxZ1lCFVYcTMJHRSeoKXTsUeaeGEGAsTQhVsOdH7PYclippXD+kZ/t0LI+O7bFUSaEpCgcqaXJJjaZp0B64mG5IUpP5xnqLA5UMP/HAHCv1PpYXMvACKmkdTVXIJ1XOrbfY7jq4QYy11ePQZJbHjkyiSAwFMkUuJjq0LB/bF7SAgR8xW/BJaTLHprKs1Ptc3e3x0fum+bnHD/L05R2CIOLMcoOTc/khP7DHC2st4ihGkST2FRLMFgwyCY0gim4RSSS5WO0iSzJ/+9Q859Za/OnVOjdb4uz/+AP7eORghUvVLq9ud+nbAZ8/t4nlBhQzGumEQtcOeOZKjZiYqVyCvKHxx5d22O25pDWFU4vFsVPD9zsl6Luptw3svva1r/GX/tJfesvP/43f+A1+6Zd+6e2+3Q9UvdcixUY3uIbpjlcVb0WBCW+8Gd4OxJ1dbfD89cYbrE1uBYDPXq3z9Ks1+ra46Y0ev5NP2+j9H1oocnQqy9OXd9hqD2hYHjfvYA0yUtCeX2/x2TM33sBpe265zvWdPs8ZdY7N5Pj1P77MXt9ju22Pb9wjs89nr9UppfXx+LVhihvwRDaB60fsdm2u7prANildGysmR8aglhugytJY0HF4KsP+UorW0J9qtH/FaFkacgF7gPSGES/cGYy/00IKL4j4t8+uAPC/PnHohypS7K14Jt4p/eTWxc2tvNKfHfI0QZzbo67y8Zk8p0/OcHa1yR9f2qZpulyt9fngoQk0VebLr+3ytZUGuYTKj983je1HrNRNgjjmzEqDlCZzsdrDDQPWGgP8KGKhmGa2kGS2KHzfPnrvDB87MTPOnA3CYWqCH3Jtp8eNep++GxCEEcW0znQhyWbbRpYgm1AxHTHacoOIDx6qkE/qrNRNvnK9zlwhxamlIo8crHB2tckzV2ooisS9M3naAw8/jFicSPPado+O5XF2tcV22R1f11O5JIenAxRJpmt7PP3qrhjlqiqmIwAdkvCV69oumiINLUMcfvtra8wUDI5N5cgYIrO25wT8pUMTXK/1mc4bb6BF7HQGHJnKYejKGKC5fkQxJThiSDH//eUql7e6QqjhhCiyxMAP6NkCVJhugO2ZNC2XtKbStFzySZ2ZQpJo1G21fKIoJq2rpHSFi5sdrtX6nBrGBY6u+6u7fZIJhb2eQ9v2uLDZYWkiw0funWKr43B1p0daF5zAna7NVNbg1GIZ0/V5/OjEG6Yfo4SRQ5Pi3CulBKVkMmvghRG6IiFJEkEY4QcRA0/EbB2fyZEzVDY7Nn4QcqCS5Yljk5iuT9sS4PHsjSZfvLRNVhc5uT0nQFXAj2LKGZ18Uubpy7s0+y5pQ2V/Oc1cIYXlBlzb7eOFESldwQ2Fkj+lK/zNh+aZLRj83gs3Md2QQkqjNxAJJZYXjhe8W22HpUqG/+ffuI/lmslWe8DTl3fRFIkgivjixR3miymqHZEUAhIfOzHDibk8v//iTbxQ7PesofL8coPVuokXRmQT6lC4YKMqDvmkiPX65ENz7HZFSkjHDsgkNDKGoA/EiP221XHY7Tpcq/WJYxj4IdX2ANML0LU0ByeEoIYY+m4gTLF1haNTOSazHtmkxsOL5bE6Ht47i+23Dew+9KEP8Y//8T/mX/yLf4Gq3vllqtUqf/fv/l2ee+65vzDA7r1WoxtUw3S+4wn47W6Gt/ePk8R1eAfO3eGpDAvlJBtNQZq+XY1A3pvVr194qcpW2wFa5JM64W2sQW69MX/uhXUu3OwwmRXedHPFFEcmc2w0B0znknzuhXWqbUfEw7hvvBizhooqS6Q0ZWxVsVBOcWJWdGJW6mL0EuGT1IQa0vVDLlQ7PHqoMhZPnF9v8eT5Km3L5/TJGfwgYqc7wPHDbzkWo27AZNbgbw1VYyNV7Z28+t5pIUUQRfzmny7D/5+9Pw+y5K7PvNFP7ifPvtXWVdVd1XtL6hZIaoSwEMgLL8gwthnkBfCM7Xvnjn0JfF/7xji8hce+mMERjvGM7w2Hw/YEtgGbd1jtAUsCg4WEQBLVEvQi9VbdVdW1nDp19iVP7pn3j1/WUbcQmxCLJ/hFKNSqLp2TZ8nM7+/7fZ7PA/zH1+z/3ypS7IVc29ev5xtzdkHDHz51bbxZqWZTrDYtWpbL+dpgrPHc6Iw4tdrmi8sNLDfk/PaAatagY3m0LA/LDcildE5vdFhpjhjYHo4f4/oBqy2LxsAjjGLCKKbWs5kvppEkODyZZ7Njs9V1iIl45f4K231xwxq6AQ9f2OFyfcDF2gAvjFneGXJirsAzUZwkO4AiyfTtgJWGNb7xHZnKsdmxqfUctrqOcMlWszQtl1rPpm15ZFMqdx2o0rE83CDkSEUUWqc3RpiazOGpPOvJyHC+bFLJaGx1R9R7NrfOl5jOp/jSSouL2yKj89a5IrIkRv2ltM5Wb0TX8hi6IRGQMTScIMTxQxxfdLr6TpCMO3M0hw7X2iOWG0P6js9q0+LTz2zjRxGltMg4bVkuBydy3Ht4Yux6/4tHrtBzfS7vDGgMHQZ2SBhHRFHMyIuoZHWOzeRJayrlrMjN9aOIWtfmNYcnmC2lx+dnY+DihiHtkTBbWG7IpR0RozbyAs5v9/GDkJSmYnsh3ZHPyAtv2CTsOptFvjS4QchfPLLM/skcR5PrXFpT2Oja1Lo2Gx2bC/U+bzw+w//t7v187uIOVxtDpgsmi9UMfhix2RmR1VUqCWevmjVYrGYY+eEYp7Orr+w7AYYm0xq69EchCjJHZ3Kcrw0IE+fu62+e4WJ9wNKKy1ZvRM7Ved3NU9x3fA8PnK1xtTlk4Pg8cmmHC9tDvCDkdTdPc/fBKitNixOzBXIJd27o+Ay9MNHuCU6joYiYMRDXwAfP1ug7Pken8lys99nsOnRGgtP3svkyi9XMeHP99HqH5sBlvW2xtNriYn1AEISoioymiBSTfRWTrKGNcUK7k5DGUGB7LtZFNvPQCVjviPPvwbNbfO5inUJKJ2MoZA2RNazYEposM5FL8drDE/TsgE8/s00QiMi4xUqGN98mtKDNoShY91VMLtcHNIfOmLzwvVwvurBTVZU/+qM/4qGHHuLv/u7vOHr06Ff9zgc+8AHe+c530uv1OHTo0Ld1oD9YL35dXzh9Ix3A17sZvhA/7q4DFapZg0NT2bFF/+6D1TFLaq6UHmMSvl635PrR5u37xBgjrYnRwETOYKGaZjEZx1z/ONez6hRZ5tBUlsNT+XHX7Ofu3DsOfPb8iLlSCkWSyRjquIB88GyNv3l8Bd+PmC2luedwlX0VcyxiDkT+NTHxOCan7wQsrbXZ7Dj0bY9XH5okl1K5tNOn3nfY7gso8qef3WbkBhBvMHR97jpQHY95QcQKlTLGV42kvxar7wfrpVu73dNyRr9h5NqxvPFmZVdXer7WJ4hiHr/SopoVpp7HLjdY74yQgANSZpxMUckYGIrMXMkkm1I5s9nDD2MyhkIla0AsulWKLDH0RBj6RM5AU2XuPlilYGp85Ol1HC/msxfrlEydPUWTpZU2LcslpSqstUe0LIfztR5XmwP8MAZJPG4xrWEHIQM7YL0zQldkjk3nqeYNgiimMXTYaI1YLVosVDLYXsCVpsXp9S6PXxHRZmlDBYQBQJGhPfI4s9nlltk8UwWT+47PsNIcstoa0R35LK21WG/bNAeuQJnICuWMPta2rjSHPHROvDfFtM7SWotad8TCRAYna4zHqiNPAGE7lkCImJqCIsHVhkUrMQEMXJ8nV5qkVAUvjLEKPqWM6NQ8fqXJStPCkBVsP0C2ZVKqxNCNaVquYJbFMT90sDqG1z58YYf//s8X2Rm6zJfTvP2uBTY6IzqWRxTFrHdGtEcuBVPjZXuLY+jvqdU2620RN7VQyVBI6/iRSOnY1WwuVjP4iXburgNVXD/ii1ebrLVGdEY+SystgjBmKp9itmiy0hjSsT0mcylAopo1yOgiO9YPRVyWqcksTmSpJngb24/GLuHl+pDZosn5Wp8Hz9bI6CqyAkEIYRihqwKbM5E1qGdcMfbXBFdu6Ab88NEpTm90CGPRhV5pDrmwLQrZtKFyeCrPWnuE5QX0HZ+VpsWD52p4gXAY9+0hE7kUx6bzHJvJsdoc8fnlBroic6UxZGm1xeGpPF+80mToBswXTX78+AwPnN2m7/jMl9P80t2L42thPqViuyF9J+DRy0LPF0Uxk3kTQ5Pxg5iLtQH5lMZGd4Tri4v1LsJqVzpxZCrHSnNINqXSHQXsDBxkwNBVRl5INWtguQEdKySKY3qOT8bQODQlIOEFU+MfvrLJesuiOXSYKgjH8VrL5s5FgaJ5aq1NNWt8X4CKX3Rh99RTT/HzP//zfPnLX+b222/nPe95D7/6q78KQLvd5pd/+Zf56Ec/ShzH/PIv/zL/9b/+15fsoH+wXtz6drs9u/y464u06x/zI09t8NjlJsBYM3S9Pu/5CRPXF2W748ljM3kcP+Q3P3qG5tBlJm+y2rQomBr331Ee33x31+V6/zp3n4lIlxCiYJC4/455qtkUQdTnyEyef/OyWR56psbAEUym7Z7D3zy+wuX6kOl8iomcuEGsNC0+82wdTZE4ub9Cuqiw2rLQVYVSRufQVI6b9+S5WB+QTsjuwu6u87K9xfGNbzJr0FEk1jsjPv7lTTa7zrgD97VwMbuajV2X7osZu34ngNT/u63nu5p38Tz7Kukxn2tXVrDr9D5f6xFEcHw2z5HpPDsJvBYkzm70APjx4zNjHZ0qS+wpmLiBcCFKCML+c5zHOqc3ujy71ccNQ/q2x33H99CzfT729AarbT+h5GtsdkYMvIB8SgNJ3KwjIlxfFIaTOQlFljA0mZ+5eS//5YFnGXoREPGl1RaL1Sx5U8NPOt+nN7pM5lM0Bh62GyBJYgQWRDFZXeHspmCDmZqKIkkYqsJrj06Ov7fTBVF8LK20eWqtTdvyURUopTUkWaI9FIaK9z+xylwxTQwJgy7LWkeE3k9kU9xzaJJSRgMknlhpi2B5WU7YdGnuOTTJdt+h3nNYjSKGboDlhUzmdFRJ5uxmj0v1IYencjSSz2PfRJqUolDMaMwV03z62W3CMCYGLM/nK9c6fOQpEUeWS6nkTA0vjJjOp8bfj+2+w0ZXMO9AaA1//pUL403r5foALxCA3I7l8VO3zSYif2UsP7llrsC5jR4d2+OOhTJHpnI8fa2DRUDLcvB8kCURSD+wfYZeQEYXndNSRufhizsA3HN4AlNTOF/rY2oGx6bznN7ocGZTsObypkZaU8Z5pdN5g0JaQ0bAnZ0wJPCFkWMyl6JleRRMDV0Vm4sPLa3TtFwWKhlef/MMj1zeGV93gzCmkNa4c7HCW26f45bZAh97eoN8SmNppS2yg12flKowXUhx52KZ+07MsN1z+MtHrnKtM8JQJVKaQssSJUcupSZDHomeHQjJASY3zxRuODcv1wV2yvNDLtT6qIqMrsgcnMgwVTCp9x0cPxx3xmVJGB36ts8v3b3I/+M1B8ed+bOJZjRrKKR1laKpUc0ZZA2VPQk78YtXmtheyJ6SKcDMQ2fMZRVAZY1yRsPUFC5s92kMXJbWWgwd8R3ZV8l8XxgoXnRhd9NNN/Hkk0/ye7/3e/zxH/8xv/Zrv8YnP/lJ/v2///f8xm/8BrVajenpad773vfy+te//qU85h+s78L6RiaKF1q7I4C7D1bHblKhIWGMLllriU7VLsgYGGtVhKEg5qFnajyz0cWLYsJQdA2WVlvcd2KGy/Uh/3PpmrhQ7i1zrT0ijoVbcLNrC/hka0StNyJO3Lu7nZRdofh6e0TfDrhUH3BqtU1KUYTWbm8ZSZISPVyMqsqkdXksbgfG4uPdwnVXy7ELE711rjh+n6YLKarZFKdWWzy23EKWnuPm7b6nm50RS2vtsX4KhKbrYk0UortdhW91/cBk8a2vXXfs7p+f/75tdm16dsB8Oc1dB6rcdaDKycUSn352m2stm795fIWSqRPGEYcn80k4udisnN3o0bZc7j0yNeaRNQYup651uFq38MKAXEpDiiFvNrH9ADeM8IKIzshnuTEgrasYisLICzg0mUVNkBbZlMJCNcP+aoaltTauHwkMSSlNreugyEK71Ry63H1wgsZA5/PdHS5uCxaX7QVEEUzlTLKGypevtdnq2EQx5E2NSlbj1rkJCgni5NRqewxoFuaFADcISWkyhbRGEEa0Rz7XOjaKJGH7IZWMTjVjcGq1zasPTXBgIsuTV1o8erlBY+jwf/7oERoDl2stUfDNFE1KGYNq1qCU0ShldLq2T0pXyaZCymmD43MFrjYsuiPhTqz3HfJJMPzJhfJ4LDddSFEwVR6/2ma9bSFLEk4QsXS1xWxRnNdiRCw6nKKIbwpk08gja6hkDQFwXmlarDQtzqx3efxqE02RyaVk3FB8Tr/3pps5tdpmabWFqkqYmsxEzkDXhMnkYn1AEEYU08Il3Bm5qLKKG4Q0LRc3CMfRcllDHcdVHZvJAzG2H7LVtfnKRkeYdJKi++xGlzsXy7z5NrEh2e45LK12aFkuByaz440miK7urmMU4MmVNlu90VgH3LJcVhojeqOA+++Y4/475tg1pgGkNIXX3TwlSARuQCWr4wci6zVrqGz3HT745DUevlhnozsiimLSus7te0tMFQz2V7NcbVqsNIc8u9Xj6WviOnXLbIGNrs1HntrgLbfPce/RSS7X++wMHCxXALBniykMVWXkRwRRzK1zBUZ+RGvoUu+7eEncmuUF47jIy/U+S1dbeGFM1hDnZMZQMDSRD/wcF3GI5fpsdG3uOVTl0FRuzEk9NJXltYcnyeht8qaY2lQyBjt98V7Nl02OzZS47/jM98X19ttyxaqqyn/5L/+FN77xjfy7f/fv+OxnP8tnP/tZAO6//37+/M//nHK5/JIc6A/Wi18vpntzfWGwqzd5oezX6x8/l1K5da4ICPeo7YdAfF08k3mD9uT6InG3UGoOxcmZTWkYqsIrD1TY7tlYXsjjV5osVrNcbQ6p91xcL+K+49OIrkGL9Y4tmF9ugOWGXG1YvPexFabzKR6/2uLMRhc/EEBUQ5UJohhdkbnrYHWMSNntkDm+0OU0Bz5//OkL/MJdixyaytF3AgZOwNNrNT7zbJ0fvWmKQ1O5MUx0V6d1/XsutEI2x2cLlJK8xF2y+0bXot5zAXjDcVG4nt/us9a2iCRe9Bj2B2kVL7yu/1yeX/xe747ddT/uZgyDKMrnExj1c9KGFG9++Twfe1po9bZ7Qiu00/dYqIoA962e0E61LJc794ux5KOXmjh+QNfykeUYU9ZYqGY4PJWnbXnEsei6hFkDVZFw/IiR5yIhoasSqixTzuqMOmIz4wYRz9R6lEyd5tDl755YRZElZkspEcNl+Rjarn4yxg/B9gLaMsRIKDLoqszF+oDL9QFOABlN5vBUlplimjsWxE39k2e2+OsvrBDHEvsnMjQGLu2hhyrLHJrM4kch6y0xnu5YLmldIwhDMSoG+o7P6Y0Oi9UMmiozsDwubg94bLlJPzFCxDEYqsJcURQeT1xtUzBFh2UiL96P+bLJnoLJtdYILxASiWMzeSpZg2pG56FnanQtn5XmkJ9/5QL3ndjDoan8eBP55NUWQy9kaUV8/jsDh4ET8qlnttEUmel8ihgYOgEDO+DEfJHZkslff2EFzxfj4K7tk9Ik9pWzSBJ0Rh7/r//rafIpjSiWODotsB3r3VGCqvETB76ITtsdZWYMhSgS2brX2jYDx+cz5+ssVNIJnkVc31K6yvHZPC3LJQhhppDivlsEgPqJlRbLO1YyTRDXsSiOUWWZYzM5pgupMVNw6AakdaFjPDSVZeSHXGuP2FOQsFxhMDkylWUhYe5lDZWL9QHbCQpmt6t9z+EqHcvn9HqXzXhEGMVcbQ5Fx8zx2ek7qLIk3LGHJliopseZxk+vd1lrWkkusErGUDk8lcX2Q7600qJv+5xcKLO01ialCulMSlMwNIWRG9JKxtzbfaEZDaOY1900RWPg8cSK0EZ+4swWuZSKLImJkB2ErDQDRn7IRMagWjGAmObQ4fOXRWfwtUcnqWZTN1w3dwvlZ2o9/CBKOvQxLctle+AgxTGqkiata/+6OHbfaCmKgiRJxEKYgqIovOxlL6NUKr0UD/+D9W2uF9O9ub6b9vnLA5rPE4+/0OPvsr9URSKISHIEwfFDRp5Px1LHwtLdXfHzESXNoUMupTGRM6hkDX7iZbOsNC0eudigY3mcr9XQVZm0riR5kxKHpnI8clEgG27bW+TkQpnlnSEQs9qyOLfZ48rOgJ4ToEhg6ioFU2O+nCatCVbXxVqf2ZLJyUXhvPq7J9e4vD0gjmPCGD725XX+85tuGYdYn17vsNoa8uhlmdv2lcYF6/Vg5bXWKOkASdxzuDq+qD18UTj7olji+GyRrGERBDGnVtscmsoyX0yz03NQpK9PMv96BfsP0ipeeD1fxyiivQSAehc8+9zfPcdjPD6bp5wEhD+ftVjO6JxcKCcRTBrbfRtdUXD9iB4eJ/YUGNgBU/kUR6ZyLK22aVouZVPn1rkihiahKfI4JeKx5SZbnRFpXePEnIDIfvb8DkPX5/BUDkWWEleiSj6l4fghfTuga3vkUiqGLNMaCrdqwVSRZXD8CDNUWG0NcfyIak4nCDWGbsDQFkDkxtDl2EyegqkTDD2m8wYnk4Lucn0AxEmslEMQRMJRGcY4fkAQi4IhTM6XrCHSK0a+D5HE6Y0uR6fyHJrM0rZ8nrjaJqUJHd5cMU1aU/ADldmCiSSLLt9jV5pM51OkdZXlHZ+7D05w7+FJ/uErm5zZ6NO1Anb6Nj3bZ6pgcGKuyNnNHo9fbdK1fEZegLsdYSgKbzg+M3bbv+X2OdKazNKqgNKmNYWb9xRZbgyQJDhf6wMxJ2aLrLdGWEkXyA8jLu8MUCWJfeUMuZSGF4b4QcRdB6ucSUbqBVMUmcs7Qz721DqSJDqZHcvn/jvK42zqjz61gRdEFE2df3tyjlOrbf7prChGFEViq+cwckOhX7N9bl8oc9eBKovVLB9auoblhvRsn4KpsVBJs9oa8eil5jh+bE/RxPUjOpY3nqDYfsC+SppjM4Wx5OPIVI6tjs1Wb0RzKEw9P35iD7fOFXlqrcPF7T7P1vp87sIOb7l9bpyVDPDpZ5dZ2RmR0lQMVcb2Q8oZnZv25HlqrYMsi3SShWqatZZNNTtM0FUjnCAin9b4d69cwNAUQGQZL+8MqfccLtUHbLRHFNM6eyvpxEAnUitOLlQoZTSeuNrm0vYAyxMF1X98zQFWmhbn+j1WdoZkUqq4FpbTtCzBqXS9CLOk8PZXLtAcOjx0bpvNrs18yeTf3LqHOxbKNzQrHr/SHHflixmNvWaGUsZgXyUzxs4slP8VJk98rRVFEX/wB3/Ae97zHoIg4LWvfS2vec1reM973sPv/u7v8k//9E+8733vY//+/S/V8f5gvYj1Yro318N/gyhmMieE19ePEXcLiuuLwN0RzY2cN2cMwCxl9ER87vAPT2+yM3RZbVrcsVDmcn3A2c0eURwxcANMTWHgBCxWM5yv9VnesVjvjlAkiVJGw3JDtvsO952Y4chMjngbbD/i5+6cGRePH3zyGqc3xA6z1nPJpzQm8jqTeZO95TRLV1tcagxRJdjs2bQsj+NzIvg7BqbyKdxAdPbEinno3DY7fRdJkrC9cNzle+DMFkurHfKmxiv3l9lXSY9zEYum0BDtjlf8IKaQUfmJl83ywNktPn2uzh9+8hl+9403c3KxlOj55Bt2gM8v5L6ZCLgXC0L+33U9X1c38sNkZOXdgNER8Vn+GCK7vGOR0sX3cfd9dPyQq40BSys+P3x0kn97+xzNoUuhpZNPqTxT69FNbrJHZ/K86YQo3Hq2TxTHpHSFPSWTtuUxdEL+7F8us96xSWkKE1mDUlrj5EKZ9fYoycEU3x/Li+jbPjftyWNqMpYXUUqrfHm9ixfEVAoqBVPFD0PypkZMzNB16NkRq80Re4opTiaMxzMbPQxFJpNSKaRUNro2rzk8yWrLYiJncGajx2TeYLVlMXQCjkznmMwKoX01Y9Ae+TQDFycIqfVtZvImmiKjSBJOGBKGMJHTBYvP8ZkvZ9BVZ5wVKyej2k+dqyHLsgDdVjM8udLC8UJkCRw/oNa1aQxc7lgoi26X7fKVvousgK4qHN9T5K4DFTa7NiVTR5Ml1tsBQSRGxc2hQxzH4yK+lBGj0PmiycHJHMs7wnlr6jKarPDFKy1KaTECji1PxHH5EfKu+V+Gn375HKc3ejy71cMPI9ykmyNJEifmi3zwyVV6TkRKjdhrZujZ/jjF5nJ9QFqXk6JHMPQWqmnece8hFqsZHltucq094vCUliSHCCPJ//czl7HcgPrA5lrb5vRGh0rG4Mh0nmPTeWw/4PR6l0s7AxYqaVKayhMrbaoZg7SuECeC/2Mz+XHneCKns9qy2OzaFEyVA5M50poiXK2KAFqf2+qx0bV53xOr3DJbZKU55ENLG5zd6JAzdd5068wYsL1rLmkMXJ7Z6vGpczXObHR53U3TYxi0LElM5lLsn8xiaArnaz3WWjZuID6zgeszGQlDUQzUeyJCT8CZ4zF/7nytR9cWiSBDT0xTsoZKEEZEsuhKr7YtDk1mSRsK9Z5DGHnj5JhDUyLntjlwiWLBUFxpDseRkqoM55Ic46Mz4rv/6WfrPHJhh9fdPIUmy+wMbL5wpTXGZn0/rBdd2F2+fJm3v/3tnDp1Cl3Xec973sOv//qvI0kSP/ETP8Hb3/52vvjFL3LrrbfyJ3/yJ/yH//AfXsrj/sH6Buv5N/Hn40GeH5X0tWKUnl+07Z6Yu/q5++9I39DBODSV5exGj4eeqXFyX3nMeRNaFgFEffRSg+OzeSRZojvyeHKlxYXtPm4QEcYxiiRI8kemc+POyWrCgKpkDcoZg2tti/mycOcBTOdN1jsjurafRM/Y+GHM2Q0xEr51rkglK3Q0u2LtjuXjhTGuF2LHMa4f0bcDjkzlSGsyj18V2YiKHBNG4vWd2ejy9LUOaU1hqpDi1nnB1/rIUxs8fKFOa+AyW0mzWN0r8h6THebp9S6fv9Tg8FQOOwjRNImCKXIap/MmYRzTGHo8tty8LjBbekEH8C6P8PnstW9U+L1QIWioCv/4jh8a//l/9/V8I8+uW06R5fHY+3J9OI6dOziVTXb6HrfvK96AounZHq2hR2vocmmnz71HJ8e4HohRZJla3yYIYTqfwvFDHr5Q59L2gJ7jEYWQ1mVWmiOWdwa0hz5BFGNoz+nShq5PzxZw4VJaQFdXWkMm8gaaIpEzdWzfZuSFHJnKk9ZkFqpZXnfTNMs7FlcaQ5HQEAvAdtvyMHWVtCGinV6VyBBA4vGrLXq2x8l9ZbKJ5qidxAIuVESx9YXLTTGZkWCubLI4kWVge7jDkLSqsljNkDNVupbLTsMmioS+b0/RpDl0ubjdZ6GSYaaYwlRlek7AdtehZbnYfkBKU7naHKLKMoYq0xsFGLpMIa1zcDKD44fYfkgQgg9IIewp6pyYL95g7nrwbI2V5oi9pTQL1QwPnavTGjrYfkTL8rh5Jo+pqZxcFK7+hy/tsN4doSEx8HxcP2ZLlTE0mYVqmumCybHpPNOFFM/W+jQHLqc3ehCDE0SsNIfoikwxrfHyeRFo//RaC9fvMVM0GbkBz271WWuNSOsKF+p9JCROLpa5/455Hr6ww9nN/jivdbZo4gfitZ6YK9K0BHvz85cbjLwAVZExdYWZvMnQ8blYH5BNKQQR7Axc/FDIWUppHdORx4XhVD7FasvifG2AqYlCZ285zY8em2JprYXnxzRHgsE48gWHTlNkfvTYFJ+7uEN7KEweq02Li9vCSDSR0zm5UOFjT28w9AKGbsDxuQJhFCfonAjLDTkx5zBXStOxaoShcCbfsVAWE5BrXa42h2R0layuJCkmEkem8sRxRGPocXAyQzVrJNewFo8tN9jqOnhBwE17irz55cII40cRuiYTRTG2HzHyIp5e7/DK/VV+6GCVTsLZ271WTudTHJnOJ+xAiUcvNcaRkmlNYbU1YqGS4ZfuXuQPPnGO9bYNEjxyuUEYCid3Y+CiytK31Dz5Tq4XXdi9/OUvZzQaceLECT7wgQ9wyy23jP/uZS97GU899RS/9Vu/xZ/+6Z/yy7/8y/zjP/4jn/zkJ1+Sg/7B+ur1zXZzXujnz3en7rLT4DmNUS713Be2Y3nsDBw6SXrC8x/3oWdqfOlqm5Eb8tY79wGMdQuPX2mNURIFUyNraOOiUZElJvMGqixTMFWOzRTY7jk0hyKL0fFD4ijGR3TJMnrAA2e3MDVBPQ+iKBnbDPjClRZdy8NQZap5Az+KOLfZG48Kbp0r8sRKi5gYUxd6l5Ef0BkJ5tFbbp/jmVqfJ1ZGEEeJBnDA5y81GToBGU3h6FQeU1N44GyNru0zVzSRJEgp4mfTeYPjs3myhsZfPLKM7YX0HJ9SWh+Hme/uGnu2z6V6nyNTObZ7DudrA6bzxjiubPf34Dke4e37SmNN3wt9ts/v1L5Q51aRJW6dL774L973yfpWu5PXg7D/0/9x9Aaj0PVJJovVDH3bJ07wD+L/HVDrjfD9GFWRMTQFz4/HBqDdke1m12HkBoRRzM0zBZZW2zy50qZn+8RxjIyEF4oxKjGkNIkwhqKpY6gSiiIBEo2BS5jkjp7Z7GE5AdNFEaEFsLwTsN1zKJgaL58vcn67TzUj9EN+FOGFotOUUmVOzOU5tqeYJGEIk1DW0Pjk6U0u1YdIksRac4RpKHQtHzu5ad17dJLPXdih1nPEY2kyrh/zqgMitzOKATkWTkLL459Ob2F7MboiMCyGJiMB9YGLKsu8fG+Jzc4ISZaYKBjYfoCVwJUHjg9IeEFEK/S4qZDjJ+6a464DFT7y1AYjN0BXZGQ5ZipncNOePKtNiw+fWk+MAW0aQ4dqVue+49OUMgZLK21kWaKY0VBkkQpycCrLpe0+f7fawdRkTEVhu2fjx6Akr7GSNbj3yNQYSLxQzdAZCY3s1Z0hx+eKzBcFimm75+AEIT0n4ENL19AUhbsPTeIGAc/WRKJC2lAppTVGbkgQiQ7qh09do2P5qLIovB9bbhKEMRtdm82Ow3pnhKGo7KuYvPrQBBe2+1hewNGpPLfOF3j8apvVxhA/jNEUiaErNHxDx0eSxGZ6vT1iIquzr5KmmjEEvieM8cKQgqmNM3j/4Ssb7PRdBqOAmXwKxwu40rAI44iCqZPSQg5P5bhUH6CrKtWcwltfsY+/e3KN0+tdCmmNXU1qKa0zXUgRhDGmpjCdN9jojNjuO2ST93IX/fTpZ7Z5djvCDXzSurjWB1HMa45MJEgo/QaQ8+X6gGbfZSJrECMMGbsRkGEUM1Mw6doeQfKeVDI6OUPh+Gwe149YWmvj+EK3/U9na8RxzMnF8ng6VAy1RGuYG0e2geBMfuVaD1WWeNOJPTy91mW7b7PrRP5+mYS86MLOcRx+4zd+g3e9611omvZVf28YBn/yJ3/Cm970Jn7hF36BBx988Ns60B+sr79e6Ka+qwfbZaa9EGH/eihw1lB56JkaQzcYj1wfPFdj6AhOk+2H3HNY2PAncymRFXndWEqIvEPyKY2pvMF0IfVVBaMiwUZHAF87I5G5edf+CqWMnkQyxcmJJLHWGnG+1qMx8MgYKvsnsuyrpPHDmKtJB++Tp22q1+VEbrRt8qZKFEXExMhJHFFj4NBzfPwg4okVMeYYuj4ZTWNvOcPOQGRimprCwxfq+EFIPqWS1UVepOvHSZfGR1NlShmD1abF2c0umiozXzS51rZpDMXObbVlsdayOD5XZLYosX8yx75qxOtvnmHoihvX9Y7jhWoaSRJIgAfO1njwbI3JvMHIj24otncvhC/EI3x+4fZCTMLvlwvPN1rf6tj4m+lOXr+eP5J9oSST69djy00Wqxkev9LkM+frhFGMqkr0Rz4DR2jL7j5Y5YGzNZ5cabHVsZnIGewpmkznU2x0bUH0NxQMVSZvqkzlTGp9kYCyt5Li4ERuzCXr2j57y2kWqxmWVtpM5g0mcynWuyOcMGCzE6En2jFTU3n44g5+GHFuq08cxzSHLrlEgzdyA1RFSUwXCrPFFMfnCuPO45n1DhcS4LEigyLDQiVLLqXRHDp88WoLyxOuRFUBTVFE0sLA4f/3L5exvYisoYzdjjt9F12RBfokY6BIsN6y6I4C4lh0zFaaFl4YktZV7txfIaXI9B2ffEqjlDGwvYBSJk0QRuwppMc63GpGp5jWmSmkkGQJWZK42hjx7Faf6aKJ7QVsdR3KGZ1XHRDFecfyOLlYojHwxkzM1abF0kqHWl/AmQ9N5pivpGlZHoYcs6eQJm0ozBRMbD8cbz7XWiOm8ibbfY9sSkvSHQLO10T+a9b12WiPGDg+t+8rM5036NmiOK71RVeyGGtkDZWMLlh7uxqvrKGyv5qhOinex3xKpTKXxw9izmz2SOsKP31yngfObrHWspkqGPSdEFUGVZXRFInpZBSeNzVyCdR9tWkJZ6kXUM0JpFMYCU1kzhBj6QfO1PjI0+u4XtLtImatNWIybzBIusYzeZN/c/scpYxBre/wQ4cqvP7mGR6+sMO5zZ7QV0sSSysdOpZPSld4462zVDM6S2tt5ssZLteHBFHMibkii9XMOG7yzbfNoysKOwOHMIzp2D5HZ3JsdpPPJwHO756fzaFLJMFUIUUhpdGyvESzLeKDdVWiaGqoikxKk2lbIjv5/PYALwip910MRSab0vACgSo5XxO4orSust6x2Ow4DJ0ANwxx/Wi82f6N1x9hsZrl4Qt1lhsDQCKKYXnHekEN+vdivejC7nOf+xx33333N/y9e++9lzNnzvDOd77zxT7V92z9+Z//OX/1V3/F2bNn+Z3f+R1+//d//3t9SF9zvdBNvZodju3awLhDsTtOghsjwVKaMuZH7YpAFZmkc8C403Y9lHhsBuiNsNyQZ7a6hCHMlzOcSByyHcujnwi+z2x0uFQfokgwV85wZEq0v3fXmY3+OGanmjW4XB+w2hphagpHp/PYfkg+Jf48sANqfRvLCSEGL4xpWyNWWzESkEtpzJXSHJ3Jc77WQ5FA0xXCEGpdh2pOZ6ZkEEWQSwltz5mNLtdaIz7z7A5918fUZBRDHeMAJCQOTGTZW0qz1rHojnyRKegEXOuMGLkBmiIQF9s9l2vtEbPFFHftLwMSx+cKPHBGOGpXm0NsP2K1ZfHKxcp1n02fYlpEm+0iEq4HFX+90fo3o7W7fnlBxF9/YQWAX/yhxe+bSLFv1fDzjbqTL2TW+WYed6MzGnfiBolZR0k2Kn4o3JFBFKEpUjJWT/HUWpsvrbYwNVVgIJIRWNvyKKR15otptno2a+0R11pDRn6E1pVYrOZoDATyZyafIq0p/PVjK1xpDjkxW+Teo5N88vQWrYFHc+QydANmSybzpTTFtMZ21yGMIg5MZJguGDy11mXo+uiyzHzZJI5IhPA9HjizzUTOwPICaj0HJ3GtyhFIskwYxXhhhB/GeIHLtbaFqkiUMzqaLPhw57f742ipOxYmuHWuwPKOhe2LrM30QKUxcIljF1kWBWEcI4xUI497Dlc5NlNgsZphq2NTSGkgCwNW2lA5OpUHBH7j8StN7r9jL1ebQywv4PBUkT2lNI9e2mG7ZxNGYvQdhjFuEJBWU6x3LC5sD4iJieKYwI+4uN3H9ssMnYCO7TFXSlPJ6ixW0nTtgHRKIaupeEFIRlfo2T7PbPa5VO9zz6FJ9lXS5FNCP7YbGJ/SxAi0aGpktByaLLNYTY8BvUsrbY7PFTnsi8SbjKESxKIIGjoBKU3BDUIafYfW0GOr5zBwfCpZgzfcIuDYcRyz0R3x/idWUWSZ43OF8bXhxFwRJDA1lfX2iIHrU8nozORFakpal0lrCmVTx9RkTi5UMDWF0xs9FqtCNnB6vSuixwomx6ZzGJow/5iawp2LFZ5aaxNEMdt9l/lyhr3l9Pj5z2x2kYCMoWB5AQ9frAMxbzi+h1xK5S8eXebS9pBKxuC+4zPjjvbAea6onS+b/Md7DjJwAj5xZov5hGG40bXHMY+7nc1SRmOxmiWrC60i5TRFUx+f248tN1lv22iyxEwpRS/RTgahOJ9niyYlU8fyQlqWz/5qFi+M6Nmig72vkiaOYyoZg6ETcKEmvkONgcs5WeL+O+YYOAEPnN2mOfBIaSIB48xGl8evtL4qT/17sV50YffNFHW7q1Ao8N73vvfFPtX3bM3MzPAHf/AHvO997/teH8o3XF8rexUYM3p2kxZuzIOVbqDsF0yN1YTVVM0azBTSN5yI1xcHux3Ackbn8s6A1tAlxhhTx+86UOFyXURxHZvJUc2miOOYra4QiN91oMyxmTyPXGwiSUKrIUmiENx9bBBjkTCOuVTvs9oacXgyi6kpbHVsiGIGfkDP8ZNsSoXOyIE4xtBk4jjCD0LW2yMkSThoU5rCdtfhxGyBnz65l5XmMEmZcHE8gTnZ6o5ww4hKxuDARIZTay2GbkRKlZjOp6jkDDa7NrYXEsYxdx1IoykyZzc6gMR2z6VluTQGLj92bIqUpvDwxR02uzan1ztcboiLRc7UEnfvc+u+E3tY3rG4WB+w3h6NdUO7WZnP/wyeXwB9KyaLIIp4z4MXAPj5u/Z930SKfSPDzwsVtF+vO3m9bq6a/fojk+cjUa5Ppzg0lWWza/OpczVaQx9VkclpIi5J5MYKXtlGV9ycV1s2siQQJZWswUIlw9ARDEXPD0XIPaCpIjNzuSk2C9PFFFld5UpjiBtEZFOigFjvjujYHo4XYXkRn3qmxsl9Fa61BXX/yHSON982x0PP1Kj1HCzHZyJnMF9JM3RCaj2bzsgbJ2RUcwY920MGVBkyKTVhOQpXaRxH2H6E44n8WT+IkBWYSsZqkiRhaqLAGfkRKV2hgpEUdOCFYnNYSOkEYYSETBTHxHHESsNi6ITjyLaZYpqh61NMi4Lw6bUO8xUTywnpWD4bnZHgq9kehqbwltvn2OqMWGuNcPyA1tBDU0CVFdojn9X2iHJGZ7pg0h56mJqMF0Y8tdbm7oMTvPrQBNP5FKWMxqOXRPE+nU/RsVyhw0prLFYzXNkZUus6nN3s8ebb5sYjwe2+Sy6ljhmXp1bbPHxhh5Qqc3gqx9nNPqdW2zSHLhN5g6l8is3OCCRhyqonOaVHprKc3FfmatOiawn9WjalslDJADFBFCdQ5HjMUbz7YHVssAijiDCCC9t9WpZLb+SzioUfRVzaHuAEEZqi0B55bHZsllYFBLoxcPEjoUHrOT57imnmSibrXZGmoUgyqy2LW2YLTBdMNjojzifPUTD18YbzQDWLLElYTsAo6ez6YcTpjS5pTcFyQwxNxtQUPrR0LYG6y9x3Yg+VjM7ZrS7Ngcsff/oCbzq+hzCKknE5ScyjzNNrHT5zvj7u6t5/xxwnF0r0bJ9b54rcsVDG8UPe+9hVZElCk8H2Q5YbQ7KGxtHpHCutIbqqctf+Coem8lyu9zmz0WcySWQpJ5F0l+tDzm72KGd09lcznNvqYjkR+VSc5M2Ke+VCJU3fEa5kLxCmma8Vm/ndXt9xb+7jjz/O+9//fj784Q/TaDS+00/3kq6f/MmfBOAf//Efv7cH8nXWN4O8ePjCzg1RXadW25ze6BLHEfcenRp33+ZKYpfZGLji4maJseL1EWG7a7fr9KM3Td3AZlqoZrjrQIXtnsN7H7uKqaljftF2z8H1Qw5MZFFksXOcyBljIe8uJgCk8Qh3uT7E8kLSRkQ2KfRGfsjF+mAsWDVUBVmWmCmaCY5BxID5YcRKa4SqdAgTEXclq+H5EMYRhiYnbCcxMrpQHyaiZBXX95AQpPyh6yNLMhldXJC7I5/pgrghrHcgb2icmCvyw0dTfPKMTt8WXZyrDQs3iPijB85zy2yejKFxzR2x3hbQzmxK5ZWLlUSjwbj4uvfoJNmUiuWKgO/dz3E3w/T5+jy4sQD6Rlq7r7U2OzaHpnLf6lfwO7K+UUftW3UEX6+bu/59eH4M3vX5sZfrA/H+F03uO/EcePQtt8/hB5GIXopidgYu9YFDxhDh6gVT47WHc/zzs3U0WaAu3nLbPLPJcTxwpsaF7T6VrE5aEwT+ckajPfRFR0yRCYKYSskgb6qsNEbsr4rP+uhUnubAo4eDH4DlhnzxSpMYiOMITZb52NPrXGvbhGGIosjIssRq02LohhiqRMZQMTWFuXIa1xcjXU0RLLiB6+P5Ed0ITFUmSoqza22LmJjJvCFuYLFE0dQZ2AGyBIokbsC+oXJqtcVmxxadN11mpmCiqzLbPQfTUPBDaFk+O/02qiIzV07TGXkoElSyYnS53XdQZJnpYoqp5Fx7/EqLldaQMJJYaVo8fqWJMU5bkPDDkBiJkqmjawqjvk0cSxyfK1LJ6hycyHJmo8fIDViois9SnE/5hMnmIUwkTVabIxYrGbZ7djJON5jI6Th+OJ52NAYuD1/YQVNljkzlOL3RpTkUub7TeYPtvovri85fRlfFSHToMvRE5z9jqONufyWX4s7FCl9YbpAKZV6xv8wv3b0IcANXbXeD99hyk4u1Pm3bI6Uo6KpMKS3wNaoqkdG0JANVw9QiMilRfKcNkaDTszxiYlKKQt/2iOKY2/cVmc6n+OCX1uk5PtN5g+mCeO3HZoSO8Usrbb5yrcN8OcPhyWlx/hVNnCCkobjMV0x0VaEx9Di3ucXL95b4qZfPsZsI9ECSEbu02uG+E3s4uVjmSsNio2vR6HssrYlc8HJGp5TR+dylHT5/yeXwVI5sSkVXxcbgfE1kvwoJjzhXl1ZbnN8aEMYxewopVC+gbXmEccxGx6Zr+RyaynLbPuFevW2fuKct71gcnMxwX+Jabw5dVBnW2yNWmkOiGOwkD/gNt8yMO/73nZihPRLZ0ClVYa5kslj9V26e+HrrypUrfOADH+ADH/gAV69eJY7jpJr9zq3BYMC73vUuvvKVr/DlL3+ZZrPJf/7P//kFx6fD4ZDf/d3f5UMf+hDtdpujR4/ym7/5m/zsz/7sd/QYvxPrm7nBNYcu+yrm2Lzw6We36driAn7HQuUGAf5dB0TweXPo8MhFQcB/bFloF66Pt3r0coPVtsXpjS6jROi6UBWupe2ew/ufWOX0tR55U0VTpwARSv355SZeEKGrMp88s4UXhlxr2vhRyN5Shv2TWUxN8JD2V7P08wYxMRttm77toyoSaU24wdKaynwpTRhFrHdHZHSF1tBDAQJEp2BPwSRjKLQtlyCMuLgtNB5dy+PjT2/y8ac3MHWVVyxUmCqkmC6kUBUJU1WQZBK93IggjJgpmuiq0M9Zni+0RbrKdDFFx/Jx/Yh636Fv+0IcHUY4fsRG12bg+hyYyCXwWpeYmGJaZ+SHdO2AoTMgmzDJHr6ww/5qhpblcvKGglp0V3ejjnZ1d7udpV2AaC4lhNbNoTvWfHwzY8crO8Pvm8LuG61vVKy+EHz4heDajy03xzF4ArkzHMfbLa212O65nFwoiV38Rm+cZrBQTfPl9Q49W8CwB7bPVM7Ei0K2eqJQV1WJqUKK+2+f4+13LQDifCxlNN54Ys94pDRwAh48u8XT1zpM5g2OTOU5u9ljpSmKqe7I48xGj9lSmnuPTnJpp4/tBhimhGX7WF5EVpep5gyGrk9j6NIbecRIzBVTvPJAhas7Q7wwZjKno6sK652RMHEQc2K+iCbLnNvsIksysiRGl4oscfN0ntNbHfxA3OiiOOaOfWWaQ4+hFxADUsIAObvZx/EDenaAhOiMHp7OcXKhzGcv1GkN4chUjiiW6Fo+ThDghzErjT5DD1IKTBdNJIQ+aqGa5fU3T3O1aQkDQMdOClGJkReMIc9hFGNoCuW00OcdmBCa2lrXJiZKMC15whhypkbaUFisZnl6rUO953B8tjDu1FyuD3CDiGrW4GJ9gOOL0fZUwSSI4iQ5Iman77DWtvjyWpuBKwwFYRyhqwrTBYP5coblHQsJidkknupq0+LYjICcrzQtSpkMt+8r0bUDHC/kzHqHtuUzU0pxcqFyg3Rg9zu9e65fa4/GRZ0ThlRzBgcns5zb6uG4EVEm4o237uG1RycACdcPeeRSg2xKjJk9Q2Uin+LoTB4/iFhaaTOdFxuY0xs9ntnqcfu+Mj98dGp8jm127cQ45xLGcLVpkdZV0KCS0alkDO49OklKU/iXC3XqfYeJnMH9d8wD8OFT19hTSGHqCkMv4IEzNUoZnQMTWdFVlOHgRJb1ts2WN6Ka0elaHu2R+Gcyb3ByX5mm5XGtLSQSaU3hn87W6I98Slmdak4XG4JCirwrtHGqIjBBEimmCyZ/8cgybcvn1vkCIHE16ZA+cKbGo5d3CGO49/AkYHNhe8CBaob5kknO1Mfg8ocv7LBYzXJkKs9Ta210TSZn/m8GKAbodDr8z//5P3n/+9/PE088AUAcxxiGwX333cfb3/72l+qpXnC1Wi3+8i//kltvvZWf/Mmf5H/8j//xNX/3zW9+M0tLS/zRH/0Rhw8f5u///u/5uZ/7OaIo4q1vfet39Dhf6nX9De56I4No8T+nz7r3yGRyc28ymTWYyhvcc2jyhv/v+rGWMFxInK/1xk6ttuXx+cui69oZeaiKxMgLObPRHY8NdjEQrh8xVxaIkwu1Pu99bIV6z6ZgamiKjCRBz/ZYbwsiP0gQw0rLom255E2diWyXmUKKgeuz3rbwwpiSqZExNH72FVO4fsjSWpvNjo3jRdQHDqWMRi6tE428cYtcZFjK5E2dhaoQ3tZ7DkleNEPP55FLO/zUbXOEsciinMgbTOQMdgYufScgihWcICSlauyfzJBWVZ7d7hMnweVnNnooiiiOhq6PKsvIkoShgJaQ09sjl3rfwXYjTENm6Phca4/YTNh85XTE0morEXnr43HH8bkCc6X0WHeYS6ljkX7f9jk2k0t4gdJ4bJjWFM4k2aXfrObjwOT3x27zm1nfqFjNpVRGXsDlev8GVA881/UYOAJtA8/F4e2ajoQmU2b/RIbpvMlTa52xoBpgtmhS6wjuoSILwfWl+oAoFuOa6XyK6XyK7b497gzsPvfZzT4926Ng6mx2Ha61R1yo9cnoKmld4dxWV2wABuK5TE1hpTnkA0+ssq+SZuSGjIKQnCIyTkPLRwIqaZ2jM3moDWgPXYIIMobQafYd4dqu9V2+stZhJ+G6pTSFoqmT1pXEvCMTRKHYGUmwUM3Qtly2+w5hLM6lc1t9jkxnSakKEzmdOxcrHJzM8MTVNrYfUDA1bpnNU0jpTOR0erbY9GiyTKPvkUmpFDMady5O8dnzda61RSGsqzJvOr6Hq80h0/lp5stpHnqmNs6O1VUZL0mOmUk4lKtNi6PTeRRZ4s7FMmEMfhDydFLUF0yN6UKKfRWTrKHxzKbo2D291mF5Z4gbiovA41daPHi2hhcKs1Jz4BGEIQvVLCcXKmOt164k5XJ9wMX6gJbl4gaw1R2xp2hiagpRzPi4LU847T9xdouSqbOnKBIgLtUHDByft79yAYj5x69sUeu7ZDSF2WKapdU2T611xDVupQ2ITtIbjouO0d5yWriM+w4bzRFbHZuh61Hr2jhBiOWEY3f/XCnNf//MJVZbFsRQyuhkUyIfdTbZlE4l4+TL9SELFZEokr8Otvv4lRZPrrRAgkJKI2doTOcNDk3laQ5dRl4kWHKuT0pTmC+l6Uz5LFQzN/BE909kcQOhbdvu2/SdQCRPaAqqKrHcGI6j7i7Vh5i6SiUrjB6rVy2CIOLW+RID2+NaG1QJdvoOlhtSzaX4D69ZJKUpfP5yg0cvNZAliUpGOKc1VWarY3N5Z4jri4lNydRJaTL1niANbHVHlDJ6kl0M5zb6LFSF/hpiHD/kjz91AUWWedOJPWPm6MgVTMtdyPz32kDxbRV2vu/zyU9+kve///088MAD+L4/Tp9Ip9P8t//23/jpn/5pCoXCS3KwX2/t27ePTqeDJEk0m82vWdg98MAD/PM///O4mANh8FhbW+M//af/xM/8zM+gKP96eF7bPYfTG93xjer6G9AzW13CCKoZY+yO3e3IXX+Taw5dzm72xh2g5zocaTY6lXGx+NAzNXYGLnIMcYQII49jhm7IdN4ga2icWm3TG3lUsgaGKlhCkGBT4pgDk1kyuoiGaVsuhT0aQRQLMKsqc7k+wPYiHM+ha3ls9W1KKQ1dU5kqiIy+viPGradWWzyz2aec1jB1mamcQcE0uLg9QELCC0KMZExycDI7Dhp/8FyNtaaN7z63u3J8ob3x/BgnDJgppFhvWay2RpTSGvOTWbp2wE17Cvzqjx7i7EaP/88nztEd+fR0lbQuirGFaprlHQvXD1FkEcBdTBtoqsRM3qScNtjqCRDoaw5P0rTEyHcX79KxBMh5Om+OOVIvZJpYaQ45t9ljtWVxbCY/5gXu6m4GCQT3es3HN3KazpbM79j39Lu9nl7r8PlLDSYSZ3EQCqforsuunNEJwnicRHC5Phx3NztWjVOrHeZLJncfnBhHKp3cV2ax6nH3wSqNgYtpqGB5hJGEG4jIr4yhcLSUY3lnMAZor7dtLtT6XG1amJrCenvEuc0uhiJzaDqHH0aEUcx0McWVnSGtoUs2pVLOGOLmk9ao9RyaQ5ej0znShkIUxfRsnz1FkyAKsVyRiqBrCk3LESNYKcYPIx651KAz8jEUhTPrHdbaI4iTVAoknq31mS2YHJ7O4voxVxoRuuRjKDKNgUMQi+6yIkErGT1d3B7Sc3wWyhkOTmbp2QEDx2d5Z4gkC5NFZ2hxsd7HCyIGTsBMMY0ThJzb7KIpYhwsyxIZXUKWJX7k2DQjL6Qx8OhYHn/zuDD1TGZT5NLC4XtZEUiYqYKA117rjFBkyGgqZza65Eyd9ZZFfeAQRWJSdDWBD988I9hqbcvjgXNbBCG4fph8Y0TxsBtXFgQRQazwQwcr46zarCE2VLuykSNTObojj97Iw9RUhq4gAyiSzEbbxvEj9hSFq3atMaKhC81tfeCgJ9elxWqGB87WqHVtTE3mDcenuWOhwuW6cG82hg4PX6wTxTGTuRS75/NsUpQtrXT48rUOcQyXty1iJAxVZiKv33DtEHIZj87IpzFwOTYtsowfPLdNNWNwfLYwngSosoAHNwYuH3t6Yxwdtt0VaTj3HJ7k5GLphhQhoVG2WW1aPHqpyd5ymsm8MU69+Ofz2zT6wvBTThssVDPcd3xmPGEwE41q3w7IGgqur+BHERP5FK/cX+FLKy3COGZn4PGZZ+uESZ2hqwpxgg1abVl87Mvr/Md7DnJyocyleh9NkblppoDti3NRkSResVBmInl9F+sDho7PZjxioZrhyNQUEzmDXUNfzhQbtoEbcO+RSU5vdLlQG1DMiOkKSGMG4KXtIUurbe5YKP/rLOy++MUv8v73v58PfehDdLvdcTF3880387a3vY3f/u3fJp/Pf1ehxN/sqPfjH/842WyW+++//4af/+Iv/iJvfetbefLJJ3nVq171oo7BdV1c1x3/d7/ff1GP862sB87WePRSg77tc9/xGVRF4uS+MpWMRb3v4ERCm7bWsulYNbb7zjgQHGCtNSKfUqh1bZ5aa3NqtcU9hya5bV+Jp9c6nN7oJteSmLbtk1JE96A5dDF1FSJB/D693mO777CdCLb7jo+mKhRSKrOGSkyMocp4YUi96RJEIV3LZ66cZk/BZLvv0hoKLp6mSQnLKMKyfTRJQlcVqlkDJxC5hhsdkakoyRLtkQjKvtoc8fJ5nZmCSRhaaKrosm31bO49OslEzuBDS+uYmszNszmeXuvghqDLUEjrBAFYnkBXDJyAZ7b6uCH0RgGSJOMFIee2enzuwg5NyyMGIsALReTQRkfEE4UxpA2VcpKOsd4ZkU0J0e5rj04mtPSApuV9lSlFjOqEbiStKczO5l9w3HjXgWrypxtHNdeLqvcmYfW768VEy/1rXcs7g3HU1O573Bw6N3Dmdt/3578v230b2w/xQoF82Ok7rDYtfvSmKf7PHz3MRke4E8MoZq5s0ncCXD9CkkQhUes4rLStRPspCuozGx1cP6aU0TB1he7IJ4yg74RMJmggTZFpDV28CJwgZCJvcOdihbSm8KFT6/Qdn6evdSmZBpNZAy+KyJuis2C7NrYfcWajgyLLaLKErMo4QcjQ9Rl5ouvdGnpEgi+Mqgi9kiLByA8wA4VXLJYwNJmL2wMUGba6DkEUM19Oo8gSuZROMaPRHAj9WEzM2c0+yztD1jsWnheRSSk4foCuqiiyxEqzhx8ISHjGUFlpDJNsWomDEzmKpo6pKWy2bTw/Yrvv8Ox2D8+LyWdU3nL7HLOlNB85tU4QRkwVTO47vofGwOVzF+u0LY+e5JMxNK7sDBm4AZIEeVNjsSLya682LCoZPdGKDWlaHrIE1ZzB8s6Ag5NiZNwYOEzkUhyeFFrjxsBj4Ibce2SSz19u8MkzWyDFHJjIcWw6z9vu3Ie4QEo8fqXJ5YaPm+AzJEmM449Ucpzb6KEnEpM4iknrKhO5FCvNIafXuwzdgP0T2TFa5b4TM2z3bR6/0hJGlCR1ZGmlw+cu7tAZiQ6sH4mimzimmhW8zkJa487FKpoqj6Udh6ay/NLd+/njT124Dtmk0xq61Do2nZFLztRvcLu2hg6rrVHCkRN6T2I4OJmlmk2Nz7W5UppSRufMhkCyxLEwLuz0RSF7fLZAJcFZDWyftK5xbCY31rQ+cKbGwxfrbHUdsobKqw5Ued1NGbb7Lltdm1PX2qR1VWjbiiYXtgfIkkQUx+PzWQK8MOTZrQF/8egy86UMuqKQMVRsXxh0lESHfe+RSe49OsmHT62z3h3RtlyiGBarGX7vTbfw4VPrPHKxwYm5AsfnilyoiQ57LqVy90GR9azK8ngsv6+S5vBkHtuLuHWu+H0BKf6mC7vl5eWxbm5lZWVczM3Pz/OzP/uzvO1tb+PEiRMA/PZv//Z35mhfgnXu3DmOHTuGqt740neP/dy5c+PCLggCgiAgDEOCIMBxHDRN+5odvfe85z38wR/8wXf2BTxvmYl42A+j8Q0dBOn+wrYY75xcKDN0Az79TI1nawOCMKKc1jk+V0giWqA9dLmyMyAIRYTKLbMFaj2bjfYIRZbIpjQqGQNDDWlaLrYfIUsBjWFEGMOV5pCMoVIyNYaujxTDbMEECba7DkemcyxUM6w2LfZXdfww5slBi1pPFGhuEBEREcUiZihrqBCHOIEYlViez6XtPgMnEBfOGAxNISQW/DkZPD9kpWlx00weVYa1zihJBvB46Jkai9UsX1hu4Ecxe8tpimmdoRMwXzbREm1MvSezvDOg1rVJaSpuGBAl78/ID6kPXN77hau8fG+Z2/eW2Bl44xFoa+gy8gKCIMJQVI5M53n8SpNhojlqDDy2+y36joeuKDx5VfDOTi6Wb7gYbHZtzm50MTWVf3td4Pzzx+W7uiB4DltzfLbAbDHFbNFksZr5Ku3d9QzDb3Z9o07f90tU2fVGiIOTOVZbI+45NMl0IcXAEe/DxXqfuw98tcMbnpM13Hd8D0MnxEpcoVs9m63uiKXVFvedmOHxKyJkfCJnsFhNs9K02Ol7bPdGNAceQydAkiXmS2nKGZUnV1qEkYQsCS3Ykek89b5gZIVxyE7fYbtnixikGOFLjiUUiQQk7FNKazSGLs9sdVFkmclsiliKyRri/N9TSNFzAra6I0ZuiCuRdIL1caxZPxWQT2n4UUgcCeakLEvsKZqUMzpxopMtmDqrTQs7COk5QlZxpTlgsZxlpmRQMg1sL6SS1ZnKp9hbTnO+1iMMhav24GSOKI6RJdgeuozckFiwljkxV+DSTj8ZgYlr8EI1Q73v0LE9JhJuGpGEqsYcnsyNtYUPnt1C1xQWK2kGTpBwKFXqgYNhaKQ0ia4ltIEFU0NXhXHk1j0lLu30MTVxk9dUBS2MSKkq2ZTKlYbF0lqb2WIaQ1VoDj0UWWJn4JA2VO5crOD4IZfqg/FnJMLgYw5N5bj36CSnVtv8w1c26FoeChLzlTTbPZvm0CMKYw5P5dgZOOwMXIaujxNEPHa5ge2XMXUZVZHZ6FhsdW1A4vfedDMnFyqst22OTucpZcS16ksrLSw/xFAkal0HPwyxXDEdWGtbqLLAMS1UM6y1RlysD8bO2bmiyc7AZTqf4u6DVaYLouP3pZUW7aGHrsg8s9mjNXTpjoQZTvBKDbKGymQuxeHkHNnt5O1egzqWhyQxnox0LGG+2GWShqFw4A8cuGVOTzhwIt/7409vcDVJf5mZTJHSFQ5N5bnvRJY/+F/PsNG2uWkmz3/6P44CjCdIS6stztf69G2fo9M55sppzm52qXVcdvouc6U0URzzheUmPdvn2HSOuaIp4MZDN3lNBnEUE0uMc5EhTjp89ji/eRd3dO/RyXEnszV0E6SX2ACW0vr3RbcOvoXC7vDhw0iSRBzHlEol3vKWt/C2t72Ne+655zt5fC/5arVaL5hdWy6Xx3+/u/7wD//whkLt3e9+N3/913/NL/zCL7zgY//Wb/0Wv/7rvz7+736/z/z8/Et05C+8CqZKKa3j+gLpcbUxoNazKZs6ta6DocljwnzL8pCJSSVhzRtdmz1dhyNJzFUUS6JbZQdca1tkDY20LkLEZUmMNjsjj3JaR8qAqSqESGiKxGIlQ0pXyKVV5J5EmJwcPdvDD2OOSFk2uiOudUb8yLGpRAPWJQgjFMTj5wyN3igQz2MJYbeuyewtZ2gOXXaGLn1X7IZlQArERTqly6R1lZEbcLE+ZLsvxjAjV1wAM4ZGrWNztWHhhRFuEuqtqzLZlEocS4RRRNbQ2MYliMWYa7pgMpmwvlxfwFQtz2ejbeP6TX7y5XNMFQLObPQxdWHPHzo+fgh+FLHSGDJfyiSC2pjl+oDNno2uyuyrCK1fd+Qx8sIxgmNXvL8b1O34If/9M5dIawp9JxjHiV3PENzFCsSx6FTZfsQ9h6vj0byqSOOx4/VGGRAxYh/8D68c//mF1jfq9H2/dAKvN0IIU0nuho7c0lqLa80Rn/XrXNwecmJOuIuvD+7e6AheYTalcLVp0bN9al0bSZZQk9ixjiWI9hld5okrbfKmRimrsd2X8cOASlbn9oUSP//KBd7/xCqypJBLK5yYLbJQFfqlhUo2SVsQQe59W3yvVQRKRFdkDFXhYn3AxVqf7Z5DMaURRhFDN8QJhAiuZ/vMFNIcnRYdpv7Iw3JF5FYMRFHM/qkssQQlU8P2I/wopmsLg8h00WQqZ9AYegwcn/+5dI1j0wUqWYOO5aKrMmHkM7QDdoYOfqSzXLdYqKQpZzQMTcH2Q2w3JJYiFitZFqsZLtT79Ho+zaE71iAuVDJcbVo0kuJ34AZ0LB9NEcXIqw9VMTWV7Z7DdDFibynDyxOJwcMXdrj36JRwqTshD1/c4fhsgSMzeTZ7Nn4Ysb+a48RckaETcqUxpGW5VDI6BVOjZwV8eb3LXDHFrXNFjIQ9dnKhzN89uUqj7mJ7IfccEo70izUxQp7MCY3ix55eZzXZvEax0CLOJTFpp1bb/MUjV6h1HTRFIpPoO4duyMj12eiIMW9z6DJwRJTboakcBVNlrTXi1rkSlazB2fUu7ZHPSlPIAi7WB3RGPjfPFnjL7XN88Mk1NEXGJGYqZ6IrEgM3wHIt/DAmq+vMV0zmimk6ljfWyO30bTbaNlsdG0mSOLlQGlMOTi6WaFku+yppLtUHPLPVp5LRKacNTi6WWKhmgZil1RadkZjCQMy1tui0ffqZbRRZJp+MU3e7lxfqA3b6DoWURjmjM5EzyBgqSDGGovDwhR3hDM8YZFMasiwJCoGhcnw2n+TpDjk8lUuuxQYfeWqDuw9WuffoJH/5yBXObfRpj1zalscz231u2lPgxFyJq40hrYGbRP75XGtbSEB75DPyQ85u9BjYPrfMFSiYOiA2QEPXH5sijs8542JuFzx+ud7n0FSWpdU2p9c7nK/JuH7IwckcJ+ZE8f390K2DFzGKLZVKvPvd7+atb30r+Xz+O3FM3/H19ca21//d7//+739LUGLDMDAM49s5tG95lTJi13xwMsvIF92FMIzJGAo5U0UCVpvCvbrTc5jKpzgwmWW9M2KlYdEfBQJVoirkUorITfVjogjecMs0y40h/VHAemeEE4gCp5xOoakKXhCyUDKZKaaZKwpN2I8cnYJY4lpnxHpbiHVVReLLax1sP2S+nOHIVI6HL+wwdANsT4wkTU1FVyUUSSKWRKFVzaWoZg3KGZ2ULlPr2SQbfzRFAJMlIIxgu2tjaCLkujl0E8AqicYv4MxmDz+MUSUxghLh4D6eH2J7AbIs0xi4DB2fKAQvjug7gpEURBGnNwTgOKurtEcBmz2XDz91jWomxeJEmqPTeT64dI2eLS5tGV1lpmCyfyKLH4YM3ICN3gjbE6OSxWqW1tBDVWTSujIuLq43w2z3HP740xdo9F0OTGZ43U3T46zd3d3y6Y0ubctjdjbPv719jsv1QWKaeA7r8XzzwPVLkaUbonpeaH0jB+o3i1P5ZteL7QDuGiB2/w0kkXTCFT6d38PSWhsZic2uzeNXAj7zbJ2FaobJvHCCb3bFBX2uaJI/pHN5u88VP6RoahyZETec7b6LIkvUBy59NyAmZiInNKVBKOC+c8WEkl80uX1ficNTOfEd6ArczVYCE9ZkmaKpYbkBfgSqIkZK1ZzB0ek8aU1hrS3YdQcns/zQgSpLqx2OTOfY7ttsd1zcIOT0RpeNtmCk5VMqfScgjGHgBmQMjVcdECOkSztDYWpThItvtphi6IW0Ry5dy6M+EK//1rkSX3E8vCCmaOpkDY27D1TZ7ju0hyJz2QsjSmmD6byBT8KokyClK9yxr8zZjR71vktaV5ktmzQGLpYnYsPafojwLcQMvZirjeFYwK/IEncfmKCSM6hm9DGU900n9vDqQ5M8fHFnnKnasTwWKxn8SMSZzZbSY3PRatMah8bnTBERJUxleQqmiNdKacIhe74mEgQOTma578QMH3zyGg9f3KFteSyttmhbYnR+Yj5P1/bFJrDvcGajx/maYLtpqsTecoaJbIpazyGri42SokioiozjR8QxzBRM/ujfnhhnbi9UM/zcnXv5g0+co3O1w7nNPg+cqY11cWlN4YEzgihg+6JT+mM3TSYstgEf//ImnZHHZD7FnoLJhW3RnZzM6zQGHoWUjpeLySQIpZ4d8JePXGG771BKa2RTagItVqn3RKdrN/KrmjV4+OIOjhdyYk5k4D58oc5Wz+Zaa4ihCoSKjADbtyyXOxfLbHZHLNeHRFFMMa0xkUvxo8cmWVpt86WVFnNlE88XReCP3TSFH0asd0a0LI9qNsXACcZcyPvvmON8rc9jl5tc3u7z3z9zkfW2TRDFHJ7KMvJDRm7IpZ0+r7tpRoyQm4LjV0rrzBZNFEkml1KpZnT2VUzWWjHT+dR4YzdINs27EP9dScvu3zUGrjBJJNDmjCE0do9ebrK00ua+E3s4NPX9Uw9904Xdj//4j/OpT32KTqfDO97xDn7t136N++67j7e+9a288Y1v/K4XNC92VSqVG7pyu6vdFs6j3c7dt7P+7M/+jD/7sz8jDMNv/Mvf5lqsZllaabO8Y3Hv0UnSmsx2X2AyDk4KttLDF+sC0KkrHJgUPKD1tsCgDF2ffdU0ByZzGLrQv3iRC5Jwvg6dgLShcGQqS3skLugjN2CjayfE+RKqDKc3Ojh+zOtvmeJ333gTv/nR0wxtcTEMohg3EJq0zshjabXNM7UeQRQjSxK2G+L5nig6p7I0BkJvN3DEbt5JOoUgxjuqDKosJ3DNkDAOiQENOD5bYLU5ot4XGsIYwfratRG4seimuUFEkBSGUhiRUxVGbogXQhiDEsHA9ri43aeQ1hjaPjGwt2zScwKCCBoDH9uPyaUFpHS+mGZoexi6xsGJDAPX5+m1Do2hixfETOYMShmDOxfLnNnoEQNBGI+B0NezAj93YYf3PbFKb+SjJILsajZFJ7H6P3C2xvlan5MLFVKacgOvTYxCRMTOrsD5a61vpoj6Rg7Ulzqq7MV2AO9YKI/fw4cv7NzQrdxXSTNbSvPao5M8cKbGZsdm6AZ0bY8F0uyrmCytdLjSGFLOavybW/dwx0KZU6ttMkvrQMyRqRyPJWOdybxBwdTImaoAwjZHQEwQijiwz16os9EdocgyN88Wxrv+jbZwcLt+yNANMHWF2XSaqZwBssBSdEY+fjCkMZ0XhZmuMJEzOL6nSMHUWaikmS+Jf/6pX2OjLbrPsiRxcrGCJst8YblB3/GZTqC4fiQcfY4fktaFXlWSJGp9h6Kp8yNHp1iuD3lmq8dOz+Uxp4kfhORMlQOTeY7O5DkyleNDp66RNlRUWaYz8gFYb9uYqkqpJI5R4EMqfPDJNZYbAxRZRpUlzm72II65aU+egeOTS+ncNJMXMOJrHT797DaltICUb3RGnNnsJsYS2FtO0xw6LFaz3HtkctyJPbvZw3LFcS6ttbm0M+T4bJ5jMwKSvljNsNK0eP0tU3zuwg5fWhNGg2xKpWN5Y6yMqasowNJqm9v2CafjamtIIaXzyv1lyhmNmBjLDVAkiXxKpd632eiI4qiSMTBVhZftLXGp3hdj9Ok8Pceja/mEoShwbE9mqqDz+JUmWUPj4JTocAKc3FdhpWHRHLr887PbQMzQCfnc9g6yJDFyQ5Bgrpged4cOTWXZ7ts8tdZhZ+Cw1h4yVzRxjRDL85GQeOX+MqWMwQNnajQGDk9fa7PWHiEhMVc2CUO4WB/wc3fuHWfiXr8RPL3RpY3Hqw9NcMdCmQfO1oiSz0VTBfdz5AaYusq+iji2uWIaTZYopATSqTV0Wd6xsLwITZG5da5EwdQS13iJ2/aVeODsFn4Y8/nLO5xcqIyjGUXubcj+apqtxL0axzFHZ/K8495DNAYuDz1TG8c1Xm2MsNwAUHjFYpmFaoZ//MqWmGg1LX7p7v03ZEM/fqVJx/I5X+ux0rKYzhsYmsJyfchs0eSuAxWOzxZ49HKDJ1da3Ht4kv/nvQdYWmnzxastem7AF5YbDBIz3r+qUewnPvEJms0mH/zgB3n/+9/PqVOn+PjHP84//MM/kMvlePOb38xb3/pWfuRHfuQ7ebzf9jp+/Dgf/OAHCYLgBp3d2bNnAbjlllu+7ed4xzvewTve8Q76/f533BE8cIKxwHVPyWS2aHJmo896e8TID9noiDZ8GEUcnhZFXTcZ3zgJ72OjPeLHT+xhT8lk5PqiexBGnN8aYPkBWUPkTXZHHmlDpTn0cINIZPANfTa6I+p9gWbo217i3hLjCssVeIKcoZA1NQppjdWWhetHuEEowrwlUUxFxNy5WOHRyw22uja2ByMvQpEi0oZGGMXEsbiYKJKE5d/4XliuyIrVFRldFbR/WZLo2s8V2Lv92CAxi2Z0mR+7eVo4pGp9dFVGUxXCOMRxIxqWx8AV3Q9DlTk6nafvhDSGHqoEuZRCyRTCYDcIMVSNnK7QGHg0EmeeE0bEEXhBwEIly6OXmlxtiouGF0RcbQ45s94dh5yvt0d8aaXFTt8hl1I5Oi1GR5udEUtrbdwg5HLd4ZmtHuudEf/5TbfckKNYzabGaR7wHOD0hS4452t93v/4GnvLaf7vr15kpTn6nmvlvt0O4G7+cT6l0rOFC3Cnb5PSVZpDl+2+Tc5UOTqdH8NlL9YHYyF1EEWsNIfjQnGlKVIrllZbBBHMJwLzpdU2Byay9B2flaZFrTui7wRoMmQNhfW2TRRFjFzRFa/3HeoDF0ORmC+n2ejYqDIcncmxp2DSGDgs7wzojnyGbsRnzteZLZnoisy9RyaZzqcS+n7AhfqAo1N55kpmQr0X+r1SWsBxt3ojoljitYerbPddVpsWE1kDPxCw2v3VLBtdm0bfwcjJnJgroCny2HQS2B4pXejl8imVrY7NVmckDEV+QHoqx217S1hewD8/20oSIzTObHaZL4tCf6Ga4abpPGlDZTqf4rHlJlEU0bF8ZBm2kxHqrXMFpgspnt3qYXsBc6U0fhQnHDqJ2aLQdq217PF3eRdZc3y2kIwAIYhjWkOXv3tyjWJKZ/+k6MQFUczx2QIZQyOjKfhhzE5faM16tsfIC6hkNCRJMCIfOFsTYNooRlclLu0M8IMYTZWxvYiRFzByQ0Q4QsxKy2LkBaR1ETXWsjw6Iw9ZlrGDgMbQRZEkkCGlylzdGfFxZ4swjpgtCr3gwBnST3h7/dU2VxoWVx+9iqaIZI+ZosliNU0UC/Hkh09tsNocsVBNM503ufugysee3qA3CnB8AdU1Ez3n7lWvPfJw/IjGQPx7Om9wYk+BM1s9qhl9fP5MF1JMFwSEfrtvU0rr9GwvcYHCyYUyT1xt0hh4GIrCzTN5JnIpbD8c67lfub883lT+5SPLfOa8yDL2gnCc9du0PIJIOPwBpvMmS6sttvsCd7ULyz+90aXed3j1oSpvO7qPtuXi+TH33bJnbMDY3dw+cGYLxw8xNIWUqnJ6Q2SNd0YefhThh9FYo/eRpzZIawqfu9RIGhoRURiztNbm7oNVbD/gfK3PYjXDyA+J4pgwjMV3xg/HkZkrzRFhHOF44Q3s0O/l+pZGsdVqlXe+8528853v5NKlS/zt3/4tf//3f8/a2hp/8zd/w9/+7d8yOSk0PLvmiu+39VM/9VP81V/9FR/96Ef5mZ/5mfHP//Zv/5Y9e/Zw5513fg+P7ltfjh+iqxKHp3LcfbDK08nO7fErIkdVFDkyQzdmoz1CU2QqGYNXLJZYrGZoDB3aSYV098Eqj18VFPuu5VFTbV6xWKEz8ugnTqqUKqMqEkVNo5wxGHkBfTtg5EXYyZ9rGYecoZJPa8T4OH6EJEvcsVCmlNaYzpvEcUytJwwKs6U0naFLxtD40kqbleaABFdFGEUJb05o1yIgjmK8675fMsnPgfWug5aEQL9srsjlHYu+HZIg68SY1JBx/QhZlnjZ3iL/79cd4S8eWeZKY8jd+yu86kCVMxtd/uXZOl3bY+QGRLGAcP7i3ftpWxcYOh7ljMGvvOYghqawtNJi6AZIckwprWMaKo4fiOJZEl1CJ4j4/OUdwlh0KvOmxkbCunr8qqDqj7xQoDkGgu23t5zGCUNcX2FprS3C2oMIUxd09yevtvjNj57m//zRI+OLXHPocGKuMBYxP3h2m2rW4ORi+atgpwuVDI8murS7D1Y5tyWc3C9k2PhurRfTARRZsC12cRC7rLihEzJwRNdzMmew2rRYa9nsq6T5uTsFsPgjT21wrT1ivphGkSTCSIyIdl//alM4zPeWTRaqGTqWz8MX6lzcHpA1VNKG0FT1nQBVkjg8neN1N80ILIMs07EFk26n72K5AYGmsLeSoTPyaFseK40hrzpQZa01IoyFm7Nn+3RHPpbrM1dKY2oK//xsndWWhaHIIEssNwakdQVNlTgwIQwLfhDxT2druEHEqw5UxjT9x6+06FguGUNluTFgq2czldMZOqLw/dhTG1zeGWJ5/th0kEupzJXSXNwWeqm5cpp95TTrnSSfGUH0D6MQWQLbF1FpG91l3CBkumDyk7fNkjU0/u7JNaIoJgL6TkDeVBi4AVtdh9bAZf9ElulCCkmSuH1feax9W22NSBsqC9XsmOG4u/mZL6eZLZoEcczA9ji5UGFptcVmx2GUCZEVods6ngjcB47gWUZRTIzQ0Q6dgEJKR1XgxFwx4eBFmJrK0Zk8s4U0y40hMTH7q1kUSeLMZpfGwOPoTI7DU1ku1gYJuDdmu+cgITF0QzpWH0WR0GSZ7HVa6MWJLCuNIX0nZDqfSiIfLXojj4vbAyHmjwWqxtR1bprJMVUwOblQJqUpPHi2xiVrwKXEGBHHcHAqSzmr03d84ljgm8II8X1RZe5cLHNkKsuRqdzY9fyjxyYZ+RFhCM0EjfLUWmfMcfyXCzsQw1QxNe7qHZ8rsLTawvMFxFpVRZd4d2T70DM1CqbO8dkCj19pstq0eHKljeUENFSZza7QquwmTKiyxPnagLWWxcAWqRn7JzJjcwdIfO7CDn3bZzpv8objMxyfK4yvS7tJMbvfh7SmsDiRwVQVNnsjVpoWA8dnT8FkOi+c59cjwU7M5cnoMrIssydrUEhr5FPiurmvkiGI4jHD9eh0jmMzBZZWxDi5mNY5MV9ksZoRjEAEZeIbRRZ+N9aL5tgdPnyYd7/73bz73e/m0Ucf5X3vex8f/ehHqdfrAOzs7HDgwAF+/ud/nre97W0cOnToJTvor7UefPBBLMtiMBgA8Oyzz/KRj3wEgPvuu490Os0b3vAGfuzHfoxf+ZVfod/vc/DgQT74wQ/y0EMP8YEPfOAlYdh9N0exgo4ec8eCsI8/vdaBWMAs631HuF9ni3RGLosTWXojEcC93BiIEPFiGkOVWe8I19/QCZnM6Yy8iCMzOYTpwqWY1rjn0AzLOxb1gYskxURRxIXtPpIkSqswApmIQ5M5FicybHVtLtX7Is5lFPDElSZ5U2fkBSgSyJLomPVtn1ZyXMWMjh/EiQSXZFcMOVMnHSEcpaqMlZgoJrMapq5S79m4iWDcj8VYdqvn4oYRmipBclMxVDlx3AbEcczl+pBf+7++jBuIHdfTax0aA4cwAi+K8QOhTTGiCEWWaAxckCR8cV/D0BQ+d6HOEystVElAkKeLJjJwIRCjLycQSAldkQGJmBhFkpgrmhRSGlpdpjXyuNa0SOkKeVMjBibzRjJO7+F6IVMFg97IwwshiCOiKMZyYi7Vhzy23BwnJ4jCxUxGtz5DN6BluWx0Rzy23ODmGdF1BLhz/3Pj3wOTWYxk5wvfP6aIF1ovlIf7yMUGkgTHZ/NjSLbQ1pVY3rFY7wqXtK7KHJsRnfSPPLUhOrWazNGZAildwfFCztd6nNnocnq9K7q4ioztR4DEmQ3RKUUSWJutns1KY0AYQS4lNK8920ugptB3fK61RoRRjKqI6KvLOwMUSRIda1kEiF9tDiiYGvcdn2ajY7O00sbyImq9EZ84vclWz4UI8mkVTZHxg5im52GoCqW0xlbX4QsbjWTsp3NspnDd59biH09vsZWMZSUk0oaM68fYXkAYQpyck8Ws+P65QUR76LJQSeP4Ids9J2GvZRNckkQlqzNXnABgvWNzcbtPZxjw8S9vcPehScoZnb49YKVpMXR9CimNAxMZ3nzbHA9fqPPE1RZDL2C1beH5EUgS9SRi73CSUnF0WowrB44ACz+50sJOUi9miynCKGK7Lzqx0wWT2YHHjxybxFAVlneGpDWFrKGx1h6x0bVQk5i3qXyKOIb6wGHkBpTSOicXyyytdCimNV5/yzSL1Sx/8cgVlneGXKr32VfOjgtDEE7KYzN5llba4+QNQ5PF9TEGTZKZzKe4db4oouKSTFLbCzF0j9ffPMNK0+KRiw02upaAUscCLD1TzHBwIsvBSZFWkdIU7j06SS6lsqdkcmQqx7mEP9oautw8I7SDfhBR648IwpicobKvkqGU0ZnMm2NX/G7X6uELdfZXM1QzOp+/3MDUFM7X+lysDYiS7+uJPQWBcNIUHr/SFNm8QYipyZyYLY4jJHfPuXJVB2IevdTkwnaf3sgjY2hoiiSSG2LBQD0+K7idT660qGQE6BpInLRiM3K+1iOlK/zwsSlu21fiw6eusYt42o3L3IW8x8Bd+8u84ZYZllZaKJLMYjXNPYcmmS8L6LOpKeyrpLl5T36cIgPwwNktTE1kFC+ttjmz0eXHj+95Dj1VFO7guw5U6FhCTjRwfNSEVbratJjIGS+KOvCdWC9J8sQ999zDPffcw5/92Z/xv/7X/+J973sfn/rUp1hZWeFd73oX73rXu3jFK17B448//lI83ddcv/Irv8La2tr4vz/84Q/z4Q9/GICVlRUWFhYA+NjHPsbv/M7v8Hu/93vjSLEPfvCDL1mk2HdzFPt8wXgpI3Q/ETHTORFWX87qLE5kmM6nWN4Z8ux2T8AmFZl8ykeWJOHEagtX1/6JHJ2RIIg3LaGrc4OIf/jyFoW0ShhGtIcekuQSAookzAKmJm50aV0e5wsemMjysac3aFsu9b5HrS9YdTLiJrLVtXGDmCCGlAZRBLIsoccxGUNGVcTjZXSVxtAljMDyhGZOlsT41dQUCmlxsey7IaosHJ5RHKMgRhIpVeaug1VKaZ3Pnq/T8DwkGeyB6EZmkq5Lxw7Y6tjkUipeKLpt1axwdF3rjPiLR66w2R2JDM0o4oEzWzxxtUUQxZiajBtGDNcCGn3x3uy+Vl2TKGQMJnM6y/UBI0+M2o5M5Xj53hKPXt4hGO+0XSTExa8xEMkGlhfypasd/ORie/N0gQjR0ZwvpceGlF2kya4QOJ9SuSUZV13a6XO1YWEoCtmUyuX6gLnroMSzJfOGSLGX2hTxUq4XysM9MVdgeWfI6fUeHdvj6HSeX7p7cZyzu3rKgqQzc9cBEdl0dqOLLMGEKYT7qgxbPZveto/jhWz2xJhnbzlNz/Y5s95lo2uxv5rhVQm8+C8euYIqKxiqxGReFxDYfoepQorjswWeqfWQZEhpCpWsieUE+FFMWlfIpzRm8iKNYKNj07V93nzbHO/6yRP84nuf5JHLTRwfaj3x3ZclcPyAOFYJkyJhrijGcU9cbbEz8JjJpzgyneeLyw3+8SsbYz3TRtti4EQoMmiKhBeIYHbbE0gfRQJdFc+hKrKAwEoSk3mTrZ7DSnNIZ+Ry3/E9LFQzmJrQ/tm+OAedIMJyTeoDl2LSjRFGlBSL1QwXtwf4YYyuyKy3RxycFNeZ3a7TIJF7XGtZPL3W4WXzJY5OZzk2U2ClKTYsO32b1tBFlxXCKGKxmiVraDz0TA1TU1ltCjnFdMEEYlZbFs0kOaPv+EkSgc5E1sDUFExNpp1Awrd6Dg+d2yaO4cR8kayh8dhyk+mCwam1NkEQsaeY5ocOTnBuqzcuAl59aIKfPrmXk4vCMd2zA1ZblkhQGAmtcHfkc89hAYffNUq1LY+Upgg8lATH9xTJGipXdixsX3A+W2mPCdtDU58zWF2vJb1YH1BPcEa37yvy2sMTyRkS07MDbD/gvuMzTBdSdKwaD56t8b9Oi5g1XZVZ3hliajJXmwMaA4/D01lmCmmOzORJpxS2e+64i3yhPkCVxDh5pmCiqRInF0vjjZUiyxiaQPSst0eYmswr91dw/QjLC7jasHB8gdzpJvrM7b5DDJxcrHD/HfNjbexm1+bMepeW5XJkKsds0RxLIiwvYLNrj+MUd1M4CqaWgJJd1rs2iizxEy+b5a4DVd772ApPrbap5gwWqmmGbkBaU1hpWtx1oMKrD03y0ac2hEnQC8eEgM2uzXp7RBBFKLLM+Vqf6XyKkwslWpbHdN4QQPowpGBqX0Ud+F6tlzQr1jAM7r//fu6//36azSZ///d/zwc+8AFOnTrFl770pZfyqV5wra6uflO/l81m+dM//VP+9E//9Dt7QN+Fdf1J/uDZGp9+dpuUpqApMrMlk9ffPMPSaptTq21yhookQ63rYHsBWVOjktGp9RwyhhjPTuRSbHVtGkOHyaxBEMSC7TRwaVsusiwBMd51oQaxBG4YMl9OE0Qxn19u8vnLDfZVsxyczHJgMou3FdIaCXHpbicujsQINYpBAbK6RhTHRKGIZZotmYxckQXZGLp0LX+sjcvqEiMvxvIj4qFLKa2LThgiIzaII7qWh5vgTSwJnt3sc3AqQxBGpHQJGYmhFyVFmULPFhcbP4auHYwNF9faFqW0TiWtCyOI/9xg9+n1LnYgnjeMxPvg+SHX92olhH5kYSKDIsO1lkLf9WlZPs/U+rxsb4l7Dk3w5NU2zaEr3HOILMaV5pA9xTS6KhFGkFIV9k9kedPL9tyQffrwhZ2xW3D3glfNisSRvhOQTymUTIOSqXNwMsOZjT6Ngcve8nOduEcvNrhlrnCDXu/7pVP3/A7d84tO8bMcZzd7rHdHpFSFYzO58fEvVsXY2XJ90lrSBkbol/ZV0th+wOn1DhtdB0WG/RMZ1psj0T2OIxRJ6Dofu9KgPfTxgphSRhQHGV3hxHyRPQWTel90LhYnspTSGhALYLAi0him8gaZJCj86bUOtb6N7Ylzx1AVfD/ii8stNrti9DlfNOm7HoaqEoQRfhjjBiFxHBGEUZLiIvOZZ+ukVOGwlmU4s9llqyPipTbaNu/44UPctrfCV9bbKIqCRMy+SoZjM3m+uNwijCIMTaFne4QxSLHo9rQtj81Og7ypEsUxQyfg9HoXLxTRe4okkBvltMbeSoaj0wVkaYCpKUwnqR/z5Qx7CsPxuOpKEqm1t5LhzsUypbTO1eaA0NSwXA/LE0VPEMVM500eudhgXyVNY+ByYXsgOpI5lYKpM0g6WfOlDPsqZqJxczmz0WW7bxNGMdWMzkpzSBjGzBczTOQFlPjLGx0qaYM4jomlOBnBKpxcLHHfiT185KkNHrvcpJRWmS2mGbkBr795mtlSmjCOCaKI6XyKT5zZIowifv6VC9x1IMVHntogpSkJdFpifzU7drHvfh+nC6nx91mcq89pB//mCytcbgxQFZmBHWD7oiP5/idWv+r/Fc5Zn6Hj07UDLtUbDByfHz8+wx0LZZ5a6yS4JYfPXqyz0bYZeQHEokMfE7Pdc1FVAYU/PJnnjoXy2JW/qzdcTww6xbSRIJ0i5tLpceh9LqUSJsXP0mqbcxs9VFXi6HSeE3NFVpsWz271xaSinGYip7PaHHJ+u0/WUMYGkuud/H1bFLzZlMZaa8S+Spp7Dlc5XxuMUzXuPTo5To65XO/zsS9v0ht5zJbSvO6m6TFnb61lQVLUn68NWG1atCwXQ1USALPOibk8J+aea8Rs9x22ujYd28PzQrZ6DoYqc2gqz+tvmaKaTY2j5dwErP39oK+Dl7iwu35Vq1V+9Vd/lV/91V/lwoULfOADH/hOPdX33fpujmKvv+F97OkNTq12ODCZZbZk4voRS6ttho5PNqUiSRIrO0OGjg9IjJyArZ6D44voGlWWsNyQZ2s9vCDizFYPxwvZ6gqRs65KSJKEIinYvijSlMSlGkRxEgfk4IjrBs9s9blY65M3NVFsSgFBDIYCuqpSSKuMvBA38AljaFr+c3o5x2e7J+jifhjjByHhdbLNKBbFYRSBF8TUei5iUAaqJOGHMUM/EMeWVGgXEzp+3tQpZQz6I+F0DWNwgxBVEdVmnDz+7tO5IWwPPObKJifmimx2RgJabIkXuotgGSUF2fNhOiHi8WcLJl+80sQPQwwFvBAGdsCztR4/fcdeOraHvxEBIkkDSbhmdVVmvmxS6ztMZFK8+vAEdx2ojqnt232bkwsVHC/kX9brpDWF2/aV+PzlHfwwRlNER/bS9oBXH57gtn1llncsICZ7HcPty+tdBq7/dc0W36v1/A7d7rHtAppByA+OzxY4Piv0hdenbgycQHw/hx5PrrQZ+RFHpnIJEkIUA82hSwwcnMhzcDLLhZqQdGiyRBhHNAYOAzsgloQ78tFLDQE/dgPmS2kmcgaXdvr0nYCtno0sSQwdi4EtIuKGriiKDFXh9n1lihmN9shF1ySkpNbs2T6PXGqQu9ZhfzXLdCnFopJBU2XaQ5eO7bPTd3CCiO2+jRuGXKj18SJIqRJeEImNmi7SXuIIymmduw5UxuacT3xlk52BgGnnUxo37ckzkTO4sN3H8gJMVcgByhmN89tCPybcjga9kS+0aW7AyAsIAwF3NRWBk5hLYsN2f//C1Rafu1Bn6IZkDZWZQop6z0HRZPZVTG7bV2JprUWt51IwVRYqeVqWm2j2BAdTkqAxcPjyepdG3yGdUpnIGvRsj83OCENT2Fcxx5+3nYyNr7VtihmNg5Oi4Hf8UCRfNMVn4oXxOBqxY4vOYdoQ2CWA1tAlpUlMF0xKaYP1rnBVHp3J88YTe8bauF1OXCWzxasPTdK2PPqORxjH7K9mmCqkyBra+Lv6/HjAXV1sc+iyWM0wlTcSkLDBRC6Fqcmst0Un6kNL67ihCLc/OpPnLbfP8XtvunmsNdvtci3vDMf6ykNTWT7y1AZdS0CHjcR8Nl9Os9YUUOPb95WZL5mUMjq5lMrjV5qAxHQ+xcMXdtjp25i6wDSVTJXTm0KOsOvmf3qtzWpzxJGpLH4QsdV3SKkCgn1uo8dETieMI4IIyhmNtK4x8oRbOIzE+fn8zdtuAft8l+5uV87xQ/74oQtcqvd5823zY4hzz/YppkMOTT23sbtzscx238XUFIGD0RVyoTAFPnq5ga6I7uL17MsvLDe5UO+hyQpDx8cNI7FxkmIWq1nuWCjTHLpUsrpoJkTxOMLte72+Y4Xd9evo0aP84R/+4Xfjqb4v1ndzFLsLqf385R2Gro8si9xKVZYZRSGPXmowdAMmcwZD16feFzcvWY6JkfADgT8YuSFPr7UJk4LJ8ULaI4+JjI6uyARhRD5tkDM0OtZzsWmmLqPJMk4QUO85uNFzxxbFomiyvYAoUjB1BdcPEwdsSM8WXCeuL9iSfwcR2J4/LtbCSOjUdrt7uiLjBqEAFSc/J3koyxe/GyO6aFldYuiJX3D8GFMLcX1h8JEATRLPl9ZlMlkBdn4h64/tCRSFkxS1YZygV5LXGl93DM9fWz2XD526hhvujq5lgXIAnlrrosoSQy+kkNaYLaWZzOmUMwYrLYs4irHcEF0RBXVr6PLBJ69xqT5gvT3C8SNalnCo2Z4gpj+2HPL5y02I4ehMnoyuUMoIx+TACbD9cGza2F0vny8ycIOv0tV9PyRLPL9Dt3sja1titN8cujxyscFrjkxw/x3ziZlC3JxEAkeftK5wZDrLStPiSyst+rZPY+DRtFyyusqJuQLtocdWz2azI0wtpq6gyRJXGxZF0yMippo2qGR1DE10Gk6ttvnilQafv9xAVSUMRaHec2j0XExDSW6cJYZOwKXtPo4fc2azy0whxf6JLLmUyrHpHJe2hWlo6PpEiTPXCyPCOGJ/NUsQx4m+C9xAjOEVO0DXZDK6gp0YmCSJsfDb9j36bsB2z2GlaZHWFPYm3a+N9ojTGx3uOTTJ+e0+Kw3B/prMGdw6X2Q6bzB0RQe6mjVI6wpBGNMauKiKiC0L5RhdlsiaKhe3B/RHARM5g+m8SdZQubozYK01IpNSedXeKp2R6AjuLaTGI1bLDckYgicnSxL/5mV7eKbW42pDjCTffNscp1ZbRHGMqavM5E2GXsB2T4xYj0zluffI5DiNpZpNjbOteyOfpdU204UUuqpQzmisNkeYmoofCl1vOvmM3SCia4+EiaE2ZBQEpFWVgSNivkauyCNdaQ6pZkUc2NlNYTbKp7UxqmOuaDJXNPncpQbnawOWG6I7pMjyuLN3Pdrocv25EeOu09IPI/woGneW7lysMPJDthLQupJsXmeLKe6/Y+84D/bUajsxLkps9wSpYLvnkNYUbp0rstWz2eiMqGYMSmmdTcVmJqMzX0rzTK2H60cYWpOhI8brk3nBEe27PooskdFFWodRH+AGIV+80uSxZRFpOXIDsikRcReGESgi01jTBSTe8UJ6TsBnnt3htr0ldE0Y+45Ni2Lq8SstHjxXo5rROblYGWc070KJAT586hr/dLY2fu8+f6lB03L5/7P3p0F23fd9N/g5+7l739u3N6Ab6MYOUgAtkpBEmxatSPYjMbGdspanMuO4Mp6aOJmZTFJOTdWkpiou58WUq/JiXE8qldgvnCdxnFRJcjbbtKLFFBeJpEDSAkCsDaC70evtvvs9+zov/ucedIOgRFO0JHv4q0JJIPree+7S93zP9/ddiobK3/3YInO1AlGScKxZOsDm10s6VzaHFDSZFBi4AWkCk2Wdrh2yYYtg8udv7XJ2tsqz5+bw4hjbi0mJKegqBV3k16myzErbYuRFLDVL/N2PLeaJBWPn8I96fijA7oP5y5uTM2UubfS5stFns+fiBhFdy8drFOk5AbtDDz9KGLkBXpjmQEmWhDhakWQUWaLvBoRxkvVbSoxikdXWcURP4JHJEpostD57oyB//KKu4gYZUEKALiBfRaoyhFGKG0ZC26NKJFGK46ckD4FA9wOIxapVk4XmLkzuAyZVhZKpYQcxknwfUD14PzKZmSJODzhne9lKeJyTHmfdnFVTxY8Swuz+ippMSoobpqiyYAa/eWsXN8qOQ4JGWcMPUgaeqA3TZAESAQo6+KFgDFME8wciXFlUqIlJkpi7ew5zEwbnDk/QsQMkSeZvnJ3h9dUuf/jmOookYarCaPI/r24jSzK2H9IoGUxXTNa7DnM1kw8vTDBbLVDUFRYni8zWCpyfr7HULB+46t2/0tg/S1ko6X5d3Y+DieLBtfC4oaORMQwvLe9lLLJ481650+FLr29QNkTQ6lsbAyoFjXPzNTb7Hh1LZDROFES/aBAmLDQK7FlB7gB97HANJFGxtzfKHK1JiiwLR3OaknXESnhRgh+maDEUy0KvGScSiiyRKBKtoU+jpHNksojlxvSdkIETMvJCgiihPQpYaBTp2iL82ItiEZ2iCH3lzsDPukx9TE2mY4ekaZql5NcI44QXbu6iKhISMo4fioL7VJzEfvvrNxl5kQBomoKmyoSRuN+vXdvhxs4QN0woGyqT2QXAn9/rs9ETIKhtBTh+jBuEuFECYUKzoqOpQsYgIXGvY7PVc5gsm7SzCr6ioVEyRdwJpARRwkRJ49RMNeupFrEz48iSa9sDXlze5aNLkyw1y7k78k+vbJEkKY2yTkFXiGKhqaoYKqoMf3ajxb9+/hYz1QL/p59ayoHA//frN4nilJ85Pc2vPDWVhRdv8fVru4RZ3/PQC5ksm2iKAEFRLNasUka/D12hcz09I+KiLm8MuLy+xnyjyEzVRJVkCrpC1/JpjQKqJ4Uc4ur2gI2ug0rKhaMNLq51ubtn5+vNsQng5EyZc4ervLjc5rWVDo8v1Plbjx0ijBL+fF1oQB8/WgfgixfXOTtbYapispbFXI21tS/fblMxVMKqQZwKN2cUpzhBxO7QZ7pqMJMKBvDC4iSPH60zWzXYGfpcWu9zdWtImqbEacp0RUcumxxrlqgWNKa2DQxN5sS0iMlpFHXaVsCbaz2GbsiJ6Qo/fWqK2arBQkOsVS0vIkpFfWOzpPPmvR52IC4qb7VEDI0XxZzKLhh79jYbPYd7bZsbrZGIwcq+JO8DYYkgTBj5Ea2hy8eOT7LStpifENV+1YLGEUr8zJnpAxem17dHtG2fRkFHkSScIEaVxOuwM/R4fbWL5UdEbsKqZjPyIv7eU0v8wWtrYoWfSVau7wyJkpSLKyKSplHS+LVnTtC2AzZ7HjdbIz5zbu4v62vwXc8HwO6vwRyeMClqDdY6Dn6UsjvyeTNrenCCGEWGFAlVgzQGWYEgFExXKgnBfs8J0RQ4OlGiWTLY6Dm5RqVj+Sjj1YgTHdCPRUmKoco4fowEmLqME9yn7cLkPgunAKaiYCjiitTa93MSUNIlZFmmYqh0LLFa9RMwZcHARakAU7Iko0gptYJGEMX4413r/kkFA+dHwhVbNhX8KMHy78PJnB0E4iBBQvS8jifOqHcvjBHnHeEgHE/FVPjwQp0313poGcBMEpit6miKjBPGlA1RudaxfdxQgMWpisFaVzj/NFnkzPWcgD3LY+hGHJ8qsWcFvL7aZbVt0XNCJITuS5IE0xhEorPSCYTgfOSHDF1xBbnRd2mUdE7P1jg6KcwRK22bpWYpjwQR2hjR8PHhIxNs9USK/oWlxtsEwD+OJoqKqdIo6Tx9QlSnRYnQjI1jSiBFkwWwKmiiheXoZIGnTzQ5PFHg+vaAKBGgZ+gF9Bz42LFJPnV2mp0s42yj77Ldd6gYKiVDJY6F1lOYcRQGXsj1bQH6NEWAvXpBAE1VlrDDmMmi+F26u2ezN/Sx/ZjWyCWIRRF8GCcECWwNHD422WS+UUQGNvsekgQ/eWKK27sWXhATJgm2H3G4XuSTZ6ts9B0uHG1wuF7k9dUOYZISJxJ+FHOv62KoMhVTQZEllncs3CimUTIA0fBSLhlEScJGzyXJ2ihqBZXtgceLy21aA0+wPik0qwZVUxP9yVFAmkpMlg2WJkvc2bOw/JAoc5HbvgCQT59oUtRkLq4qWEFExw6YKGkossHeyAMkdocppi7ciN9Z7bA39MVqV1P4jV/4EMsti5V2m7YdUC1oVEyV1Y5DEEb8zOlpPnPuEK+vdvmvb64x8iKqRZuTMyL/7F9+5TrtLJh9zOAstyyePXeIN+/1aI08ZFIkScLyI05Ol3Gzdb2hyZycKTNbK7DZd1nM9IhrHYebOwN2LZ8gTpipGmiaxPYwJI4TTE0ljBJ2Bh6PztVw/IhKQedwBjJ2hh5hlPDirTZpKoxZnzgjWiReuLlH2/KpFTT+/jPHeX21y0bfyRmil2+3ubtn88mzM7mubGySGvdV65pMHZ0oEVIDU1NYbonPaBgnREnCM6emOVwXrO3V7SGKLFPKelO7TsDQ8ukrMrIccrdtMVs1qWYdwI8frXNypkJn5LPRcynqcvb9qjJbNfjmrT1KRpdf+/gJZmtm/n1zfXtEkkKjJJz+8/UCl9b7uCPR7/2l19cBcQE3ckPaVkBJVzk+U8qNgePf63PzE9xsDfHChPPzov5OXKCm2crVyzV7Y2ZftIUo3GlbSEicO1w7EP90dq6aGV9C3FAwtJ84M43lR7xwc48T0/u/+1Lu7Frcbdu0BmoerBzF8YE8wB/lfADs/hLmh6mxE7U0bc7P1zg9W8n0NzF9JwApRZLFalVCwpRlJAUsT7hKi7pEraCy2XOIE2Fm2Oy5eYYaCLo/TFLWu54oq5bvM0+GAkEYE0RipSghwNB+iDWGSapEviYGERNSSFO8UKxDdQWQJNIUCppKmt5f94Zx5tRDVC5FcUJrKDK56iWD9sjDjwVDp0oQpAKs+ZFgIJaaReZqRV5b6WDt3xXvmxTBvqTp/Vw8ZMHECIZT4l7XyZ87wMCL+dZymyBOmCjqDJ0APwXLj5ipFmhZAboMh2cqFDSVtZ5DUVeYqZhs9l2iBAqaWMnaXoifwJ09Cy8Sxd6tocepmTILE0V0VeZ4s8TVrSETJQ0kCd3yaY98Rl6EoSmEcUC4E+OGMWlaRFMUVts2L98WJ5Ezs1VW23bGXAlmomqqOGHM6dlKHvL54PyoTBTjFfB+jc34OEZeRNcOePl2m9MzFRolXdRvZTlSTx0XjOT1HXFSG0dXjLwo15s9d3mbzshjribKwvdGPovNEo8fFbFB232Hrh0y9EL8OGayaFLSFTb7DittC1kGVVYYZdlhipSglWVaQx9Vlvipk5P4YcrQFWG1UxUDLxKVfZIkjA8FzcTyI07NVNBVmTSFo1NFZFkmiBIO1QpsDVx2Bh43d4ZoisJiSbT83NwZsdyyODtXpVYQFXZeEOcidk0R5fJlXcMOIoIwYb1rc3q2wmRZ1ISJz0rI8akKC/Ui20OXu7sW6z2XiYKKqcmZxlUE87qBCJg92izx8+cOcbdtcadt4UciPFxoU4V2c7ZmcnKmys2WBVLKhaOTLDSKfOXqNjsjn54d4oUxFxbr2IFgMdMMkI/1VW+s9Tg6WeQzH5oDUlbbDrd3LYJYyBMqpiqaHhSZekljcbJMs6Tzr/7sFvd6buYuV7nbtmm/scGN7SGGJvPJMzMYmoLtxVjZuqFr+2iKQslQkCSJJGNkFydLubO0WTZ49FCVhUZXMFIJnJoW7Rk9O0JTYi6udnGzbulTMwKAe2FM2xas587Q48JinXrJyHtwK6bK6bkqRtumXtJ5fbXL77+6ysAOqWVNMoosc2xKVDIeNF4Y+e+IF8b8wWtrrHUCqmZbdMtqCh8/NcXFlQ7XtoZ0rIB6UccLI3aGPsemSvwfPrbIyIt4a7PPN260mKmYqIrMrdaI6zsjsYZuOwCcnKmw2CyyZ/lMlXX2LBFefHG1x72ui+uH/E56h9/4hUdplg3eWOsJvWBVp2+HGegam1VkunbICzf3MvPCBGEU50zlo1ks0e++cIeLqx0qBZ0zGXM6WxVNPFc2+hydLOZGiY2+yxcvrueA6/JGn9MzVS4cnWR3uE1KmoO68feL+M4Qf//jy1u5UQVSJAlu71q4YcxUxRBmO0O4lMuGRkFTeWujS98NubjWPcAW/qjmA2D3lzA/TI3datvm6lafta5N2VR5YrHBetcWV2hRQsVQKGgqIz/CjyJSJCIEUKoYgk63/RhFGuvhYrwoIYzGwcBiEiAIkzz001Al6kWdtuXnPzfmzQwV4vh+u8MYkIVRSpAlQLlhgizdv00Qi0fRNRk3iDB1Fd+7r2Ubr1alDHYlKQzcCNuP0BTp/jHuQ5VhIn5ma+Ax9OI8iHk8ChxgH3VVANk0YwZLukipD6KYMBIgWeI+N5ik5KxjGEccmSyx0bORJJm9kUcQJkSyAGuPHa4x8APCMOVez0bJjtcJEyDE0FUMRLhxECVYXoAbyHTLBh8/3eTZc4f4f/3hZVY7DlVboV4yxNo4Ea7LI40iUZwy8kNWOyJUt2yoNCsGQZigZ6uu/czVyIv40ytbvLbSZaFR5Bd/4nBevr0/y+5Hpa0bn9jHtWBwfxVcMVURQJzV+ERxCpqIgvHCmFfudLC8iL4d0rf7jFmDKElpWx7Nssml9T5vrvc5M1MhTFKev7nLt++2eezwBHsj4RScrZnc3hth+zF+GKMpCpYXMSabZRI0VWjeem6EJLmkUkocpby20uVQtcAoiAijlLWuRaNoMFM1CCLBvnlRQkGXKeoaG12X1sAjiETtkhfFvLbSYeCGVAyVhUaRnhVyYlrUSLWGHmGc0HcD/s6FI/zEwgR2FsKrqzJBFGeRPSJaxfZFc8JGz6VsaCJQWxQZYGoKi80imwOHgiGifsIYsWqUZdqWz9ALMTSFQzWToqry37+7hRtF9OyAvhMQJ8KRXitouZD85Ew5WzGr1Es6N1sj8Z44AUM3wovE73DJ0JitmZQNkdGnKeLEeXSySM/2qZf0rNN1SFlXCeOAO22L565sUynofHihhq7IeFHK3baF7YnQ5IWJIs+cmc4Z2J4b0G+HOH7MJ09Ps951+e5GH9sTPdhzE0WeXKwLJ70fcbMliupXMof6eH16br7Gc5e32BkK00ejZJCmPhVTQ1dlCpoIcl9ujbi1YzFZMnj23BxXNwdsD1wursKvPXM870RtlHQuLDaoFjSWmqWcnSsaCg1ZrEufytoc9ksjxr+n47aI33v5LqsdByeI+ObNFnaQUCuo/F9/5gQnpiusth00WWatazNZNLiwWOfZc4cOaP7+fL1PzwmpFTQGbiRW3qaK60e8uLzL87d2aRQ0FidLTFUMGuWQK5t9DEVmcbLARg+6ls+X39jg9EwlM+ko/L2nlrjZGhFGCS/f3iOVRA7gielynhd3Z9fi3OEaH1ma5PJGnyubA5ww5s9utFjvONSLOnNVk6KucnKmkhueLC/ilTsdlpolvCDmmzdb6KqoYXT8mLKp8vjROrd3LWw/zCsXX7nbZbPvMD9R5G+dP8TJmTJRknB3T1wQf+6JeZplk9dXO1xcFfmGk2WDilEniFIURcINIy4sNbjVGqLK8o+FgeIDYPdXeDZ6QvzcHvl0LJ/Jsskjh6rIksTIE20LURIhSRKkCWVdAwl0JSaMEzRZ4tR0hWiqxLdvt4XGxlSpmiprHfdtjxemIEcphgqPzFWQZQnLDwkzFkxGnCD8SHypqpJYTUpAEN1n7+B+zMn+v0sSeH6CHwVvE85FqfiwyiQUdZV6UaM19PCjNGsKePgkQGsY0FeDA4+/H6CNJ4jTXB8npWLl6WdsZJxAQcmiVB5C+vXdBNe3qZV0vDBmkAUAJwmM3JjLGwOha4oF85E1AyEBBV2czE5OV5iq6Ly4vIcbCAh+Z1c0Tax3XfrOWBMU48ceSSoAbkkVlkpVlijrKn1X5HL5UYwTRMxPFrhwVGhq9uvsRp5Fo2TghTF3di2ev7HL9ZrB3ijIM7eev7nLpY0+n3ti/of+ZbU/+mB83GMWr215uRj99EwFyw+5uNKlYwcM3YDdYUDP9pEkEZ3x7TsdZrJMx85IiO7vdR28IGLkR9h+zMARq6x7PZueHVIvaYzciDhOKegy09VCJhBXc0lCCqiSjM8Y4MdUTI1QEt2su5aPhIQTBoy8mDQNKJsKMoigYAmiGLYHLjVTJU1T2pafZZZJ3N61SNKEqilK7O/1HP7gtTUeP1rPGlJ6DL2Yb9xocXiiSN8NCSNRf6QpMocrGmkCu1aQ9XoqxDGs9m1sP+RIvchHliY5MV1iZyiy8o5PlfHDhJutIV0npqJrxHGSyS5Sek6U1SillAwVy4vyi7uaofLUsWame9vhT6+khHGMFURUDIWRHzPwAtojYSaoGhoDL8LQVT79obm8fP1e1+Hl220OTxR4dUWwYx86XCNKYHaigB2Irt0wSjjSKFLUyry20mWz7zJwfWpFDVURbs9nz4kQYBDVhpeygOkkFcxia+gycEIKupq9VxH1gk6cJJR1NWsbCfiTK1tCo2v77Ax93ljrMlkWeZ1jRrFsaNxsjShqMkMvpqSruWlptmay0ChyfWfIVt/ld15MeebkNDdbQ+oFscKL4pSRF+Xrx3F13fUdYdJ46ngzWymPeGl5L4/biJKER7NVsaFIaAUVTZbx4zDLZJN4/Ghd9LaOfDYHDsMsHeH5Gy2eu7LNs+fm8jaa1Y7NqZkKjx6uUdRkNvoixmej67Led1hXZCrG/f7cIEqYmjf5vz9zkpW2zfXtAetdh28tt1nrWkxVCvzsI2KF/Nxl0YzSLOt84cJCzs72bD/72k85PFGgqMk5oN/quWz2XPpeyGpHrNu9LCV+cbLEWsfhT69ss9gsZY50mWbZ4HNPzOfr5JW2Rcf2udd1cMOEjx2bZLPvsNVz8+8ZEF28jh9xOnPVzteLWRvHSDSLSBIVQ6VWUNEVhe2+h67J/NLjC++49fhhzwfA7q/wvHKnw3rHJUhSJFI6tse3bwf03Ps6sCCBji00Ws2ywuKkODn2nICOE/DW5iBjPpLMLRTiBvFDDQmQAbJYhHmSwlTFIEk8khSmKyaqLLHWcRgfgQyED7+rt00Wr4QhpZQMDf8Bhi0ClBQKusKjh6ooksS9nkOaiFXuePOdkJk4MhCWZve9fx5U5UkcBGwp4AQx6b7/5n6fJ+InsDsK3vbfU2Dox+CJtPYoEbi1bMjoqnDkBXFC2/bpOCJbydQjggj6boi1NUBRxJpazgBhFIsnpsgQRAlrbRtFkZgo6JR1FSSJqYpOGKeirxehs1tt23nC/FrH4cxshVZ2zD9fN+k7EXYQAVJuzLnXFTqVHwa4e321y8u32zx9osmTi423Pd7vvnCHr19rcXqukq8cTU3B1BQ2+h73MrZ6oV7gcL1IIo1PYFAwVJJUYr3vCKY5TigaKjNVM9cqNssGpqoQxD47A5+hGwASP7k0ybPn5ujZIRdXO3z7Tgc7SMTFy77jc4OEOA0pagq7Ix83iFmcLDNT0VnrOvTtkB0vwNTE6rRkyNxqWdzZG/HoXJVm1WDkir5UJ4jY6sd5HMTeyGfkhnTTlIurXc7OiraZlT0HRRLaQkWWidMQFcEanZ6tcm1riB9F6IpMtaDiBQkDJyBMoDXyOTFd5tnzc/zn19YwVVm0pYQJaZoSRTBMRStD0xBZdkcmS5CWsPyIM3MV3lztcX1HhNeemK6gqTJrHZt7XUeAvjgRuW8xLDWL1AsGfphwbKpMSRcVZ7tDLw9qbpZ0Xrnbxs1CZIMoYZhlpwm2ZILOyMcNY7p2QM8JOT9f49MfmuWr13a4vDEkCEW8yu7Q43deuIMVRHnTx6GaSZqkyBJcONqgY/ms7NoUDBVNlVltO+xpHqdmq1w4NsHTJ5p88eI6HTugYmjsDH2ubw8ZuhGnZ6p5K8ZYwxfFKfUJnZMzJl4oGg4WGiV+7+UVkdumq/Rtn++u9enZAe1RQNXQchC33Bry+NEGj81PcHKmjOWHvLU5YLVj59ltt3ctXlre4/EjdYI4Ybk1IopSJkt6LkE5NCFqtOolPV8zRrG4WDVVkVIwfp/GX4Y/fXIqA8L32UkgX1k+d2UbO4iQkITruTPCciNMXeHUjPg8ijBkn/Vuj9WOxcCJCCOH11Y6hFHCrdYQCQldVRh5ESvtNi/eanPucJXPPjFP2/JZ6zgHWjLOz9d4a2uA5YfsDsXv1dANKeoqZ+cqzFYNXlxu841rLfw45sRMhX/yqVMAvHBrlxs7Iz68MIEfid7uZjkD0pFY/dezTMQ317b5k8tbqLLE8zd2We8KXd/AjQjiGNdNWGiI3MueE3GsqbPWc+i3Q5aa5fwxf9TznoHd0tIS1WqVixcvous/HoLB//+bFD9OCLOdZxAlJPtOM3L2Z+wVHHkhy7sjVFliJosy2Bl6+PsC4oIYsb7JRpcheIChClLYGQaoEqguSJKELEmEcUJr5LMfQz1c0XZwVEkYHbwoIYiFkzSO3xlFTZUNbu6M2Bo4OXArZAXbIz9ByqqRgkSshQ1Fwg7St1F0+1exD8OxBV3BUJUc+IxHgtzM8f3GUEWosJOB5YKuEMQJUQpJmlLSFZwwRpLIT2CLjRIlQ8RHxCkEpKgZa1QxFBRZxo/FyTdKQU6hUdQ4MVWhaMjc67rc61gM3DCLiFC4tK6yZwUiHiWKma6YYsW8Twd6ZXMIEpydreaVPZ97Yj6PFXnlTodm+S9nNTtm4l5a3uPyxgAg777dvw7eGYp12krbQlcVmiURwrzULHNmVnRw3tm1sIOIZ05N8/jChOh/TVMemxcr1iRJmSyOmVURgD1VNrHrQkMjWA9ZuKhTEQ1U1JQ8YHahIfSaIFjmQzWTjZ7QX6YpxFFCIIloHT8Msf1eXpLuZE5VoyiTJinrPQfHj4lS0XhRL5qUJzQkJGRZ4sR0GcsTuXHnDk9weibhzt6Ijh1ycbXHkYZYId3OnrPtx7StFC8M81yvrb6L7SdoBZkTUxVW2rbIbJQSvCjmi6+vc3vX4vbeiDt7Fmki1qlHGiX6ToATRByqmhRNlSSF+XqBW7sjSroIQpcVIc0omyplQ+SEzU8UODMrVn9vbQ3oOgG3d0cCVGgyTx1vcnauQs8OWO0IF+LF1W5mAIvY7Lj4lZgPM4GhylRNDT9jls8drnJuvsbN1ggniLMLkSqff/IIZUPjzu51LE84jreGLpoiszNwCWMhrzgzVyGIU7o9Edb7W599jN97eYXVtp01ccQYqozlhyzvjNjqCZaqYqjM10Vnq+WJHL8T0wLUjUOKP/3oHFVT5fr2iAuLgr3r2gE3WyO+u95jZ+jRLBkosowVRNzZtWmUdE7NVLjZEp2pu0PBKI+Z8/t5jNJBNiij/OtFjYKuMPACvEjBCSLhrpakvF8W2CevGOKGMecOVwHhti8ZGrNVoYd74mg9Z+uvbw85O1fNAV5BU9AUCVNVOTNXYeSHkPpMZ40O4v5FDEycCkPTPcmmoArz0a3WkJ2hz3y9wM89OsvJGVFPl6ZQLxnZ3z2qpspya8ifXtmmYwfIUooiS8xNFFARZpcwTljv2Tx6qEqzbPLCzT36XoAiSTnI/O2v32K5ZaMpEm6YYKiyWHtn9WVhkiDLMpYfsdwacWmjT88J0BSFG60hqx0723zEqLIAoyBWyFaWA2sqCmfmzJxl/XGY9wzsdnd3mZqa+gDUPWR+WOaJp443qRVW2Bn6D/13Oct3G68dRfZVxGTZ4FizTNsO2Og6+G700NvD20Hd/olSiCIY819u6L8rIPfgTBR1mmWN5V2xLkm+x+MmKdiZfX//YbshuNmjlzTBhA3ckCSBUkknjEVMgyplbuBEmEo0Cbx3eJtsNyYxYLFRYOgJ12maktcvJen3B65hBJosDBgxYr2ryJLIGAtSRmqUs6NSJhwXXybJgUDmcd2YpkhISYKElIE8iYKu8oUnF4hTsXZsDX3SVLzrUZLiBTG7owBVlbLqtJjdoUcCXNno54/RGngoisTsKSHOf/7GLidnyvsceN5fWuzJWDc0WzWpntTyL8kHo1aePXeIataHea/rUDYV1jouzbLJs+cO8erdbr4a2u67RElKmqQsZC7AK5sD3DDi+FSFsimxPfSIk4QT02U2ew4rHZueHRAlCSemKhiKYLDuth1+7+W7/OrTx7D8EDf7gEYJ6KrMVNlgzxInU1mGhBRTBS8SF0u2H1PSFTxNGJeEiP5+uLIqgSIJLZsqSxlYgSeOiPw7VZE5O1fl2fNz/Mv/eYMXb7UJI3Gfr9ztcHm9R9nQMLNVawq0bZ8njtSzXDMoGgonpktCj1tQmSoZ7Fo+PdsX2qc4wQ0SojilYMg8eqjJStvmbttiz/YphjFn5iqstC1ubI/QFYlr2yL4N0yEU/vy5oCCLvMzp6b5+88c5/kbuxiXFd7a7BPFKSV9HDHh8urdLooksTgpWje6to8dCEcksqgnrJcMzsxWubU7xNAU2pYIpz0xXcENRVOEbMPO0Of11S4XVztMFA1KhoYbROiKwkRRFR3LUYgTwtXNASkSQRTz4nKbZ88f4uxcJW9imamZWF7E5U0RATJbLfDU8UlaQ4+7ezZ/fHkTJ4gZuhEDVzB1cabLutkaMXRDXry1x3rPRpFkem7Az587RFFXieKUybLOmdkqLy3v5bpFN4zYGUaYqpCy9JyA1sCjZ4d5Pt8rd9q8cqfDU8fhCxcWKJsKlhcTpynz9QJ2kOQmoLbls951uNUaUdQV/ub5Q/z9Z44DIvNxDBiXWxZDLzrAjongZeHEvpmBG0jZ7Hu8dHuPjZ6LmWU4/uTxJr2sdrJsiAy7iqkyVTE4V61mRqQut3dFlyrAQiPi2XOHMtdsm9W2TVFX6Nk+z13e5srmQPSvZpmBhqpwvFnmwmKDC5mGfGfo0xp4vLU1oGP5fPykcPp6YUzBEMaKjZ7D0yea+ar1wmIjj3fxw5i7bYvJosHQtehYPq/e7RDECVNlg3rJYL5hcnvXpmf5KIrEfLOMqkhs9V1utkaUTY3BzohjUyX+n//LmR+5rm7/vGdgd/z4cfr9/vt4KH995odlntgZeAz37QfHjQdjPDBmlMa9rJIkXJgTBZU37vWxvAiZh8+YzXpQi1Y1ZSQkXD8+YFSAgyBHBqqmQv+dUBP3DRFRHLPWDQ4AmXeaMIG1toOu3n+c/WvVki56DCFl5IlKmoETZJEoUDSEW7A/rjeTJCTShzJ2IRD5MVUzpVEyCOOUUWaLDRMwdYkgTPPXWZNFrt/Qi+7fnwRukBJnx9p3ojxPL0GwdLIk3Hd7Iwk7CPHeAWeHiYiXkSXQ1ZRjjSpOEGci+y7TVZOJgkbF1Jgs65yaKbPec+naAXGacG62zuMLCt+6s0fHCtFk0BQZsv7emZqBJEnUSzrPXd7i69d3uZBV1t134Fk/kIbkncKO90eqvNN/3+iJENHPPTEP3F8P7V+DnZ2rUimomIrC9sDD8kJKpghVvbk9ZHfoZu7siKVmmYET5Zle37jZwgtj6iWdNIWeExDECdWCxsgPefGmCG69vTvK1/YVU+iXTE2hUTaywNOAkRujKwmqIhiR41NlNFWiWtQZOAF7o/sXY5osAoXPzVd5a3NIEMUMvZQwTLi6NaBa0DE1hZ2hyyt3OqLSLk1AkogSWG1bDLyIoSeE7s2KjuWFFBSFy5t9wlhcWIzciK+8tc3ynsjta5Z0vvDEAnvZOvb6zoBbOyMSoDcKubzRF/l9QYimiN+SC0cbvHGvS5Ik2JEIOZeBoiY6pL0oQldECfyXXl/n8kaP9Y4jDA9JiuXFfO1aizgVWtmRG9KsmBydFL3IW0OPIBRGpPmJIj07wA1jenZEvZCgyBI3t0XQ7PFp4YD9ytUdXri5y5WNAZYfiQ7TxQY3dobc69hIwMnpCkVD5V7XYavvoSkpSZqy3hVB1kvNcpZv6HNpo8/CRJG5qonlhRybKvGrTy/xey+v8OZaj0vrIUGSQCJA1HLL4tOPzuVhus/f2EWWYbZSYGckWileWN7lsfkaQZQwXy8wWTY5v1Dl8saQmYpBlECtIGq0doYepqPQdwMGbpB/1scRKWT9NpYXsd53OTtb5fRMhcsbA/woJknJ2CXBOlpBxM7Qze9nvOZ88Pds/Pffe/kuax2XyZIwCixOlujZ4vOwMCFq7pDE2SZKUiw/YrVtc3G1y2fOzTHyIqGDLokw9HrJYLoa5yaonz45zWxNVK99Z6XLVt9BksSFSLNksNaxaVseAzekrGsYmsTdtk3JUDk3X8PUFIZej5KhoqsyOwOfr19r8alHZvjMuTmWWyOubA6olzQ+/+QRRp4IXbf8CCcUWtEXbu2x2raZqZkcn6pgasKFHgXCINgs6ySpOMfafsh0xWRpqkyUpGz3Hb6z0qVe1LiwKDScP06gDn4AYPfLv/zL/LN/9s/4zne+w0c+8pH385g+mHc5L99uZxqg+6PtW52OxfkgnKVyKjLgOnbIMEMP72QoiBFr2CS93+wQp2B5ydv0aQ+bBB4K6hQyoKkKti9+h58ramK9G0TJ29i7GGHSCKL7nawSUNDEqqXvhoy8IBd0h/F94FY1NKwgzkKQZZAkNCkmybRr+53A49dua+CJSBZVetu/SdL9mJWJkuidlDOHsS5DuaDiBhFRBIYmESdpzjSmgCSlRIlEjAhKDd+BAhyzPynZelCSs8DchPbIw8nez54l+jRBVJ71nDBzdCa8eGsXkHjq2CSTFYPTMxW6dsD/+7+9BQiNpKHKXN8e0Rp69OyAW62RMN8An3gfbPwPMnD7gd7DCrT3R62MC8L3H8tGz2Gl3cnXseOqp+eubPHirT28KOHxmQrHmiXeXOtSMlTCOGW146ApMh+ar1HUFX7/1VVGboQdhJycLhNECS8tt4lSKBsSkyWDnhvw7Tt79DLtpy6JleWxZonFZinPCnvD6RLFMZoiYyhifVY2RH3R0AnpOgFlU0X2BXtXK2gcmSzyk8ebgMSrdzoosoSbiguBWkGnbChcWh/QGvrESUK1oNN3fIZeQMXUqJqh+JwlghFuVkzsIGR3KH5DZJmcVQqiNLvIE8aAJxcbVEyVF5cFICEGSRY1Xm4onkdRFyHEL9zao+f4RLEARpUMNMsSWH6M5YU0Kzq3d20ub2zScQK8ICJJoWyorIUxUZzSKGksNkuUTOHSnq2a3FBHFFSZimEwkzE2bcvnY8caVE82KWoKr94VeX17oyCrHpPY6XvsDEUnsqHJLJaL1Isau0OfnhMw8mNkGR47UqdR0vDCWBjMXIeu7bHadmiWTaI4ZSeLlhm5ERcW68iyxEzVBODZLHi2NXC5tSu6Zw1VPtDRPF8XQbmnpqucX5ig2ta4tD5gZdfmwlGRk/jclR069h4AEwWdRw7VeHKxwXJryKV7fZExaAoTlJs1GYgQ4xo7Qy8DWQPWezZG1oksTBXCULCw2iGMU7pWgBNGTGVs9vh+9v/vw4K/1zoOIzfio0sNPjNTybV1EvDYwkTuRr2yOUCVpTzQuaDJeT2acDMHfOWtbSaz1eeDWsSuHVDSZdECEiUoMhlTp7DecwmimMfmJ4jilLc22mwPHE5Ml/KqtIqpcmGpwcWVDp0sg+7SRp8wEtl4V7f67Aw8ZmsmRyeLQJqHmreGDm3b59h0kU+dneGbN/bY6DmZS1xsVI5NFJitmbQGMFURYc1tOyBNE56/uce9LvzKAy0iPy7znoHdP/2n/5SXXnqJX/zFX+Tf/tt/yy/8wi/kJ4AP5oczp2cqeQwE3GeuTFXo1VIOAr0EGHqR+PJ+yLzNJZq8/R/ey6p1/4ydhMQHo0ZA5OKN8+j8MKVkyMzWDLb7ft4GMZ6RFx+4fQo4IbhhSMVQ2L8F33/TkS/ChjVFhDO7Gcgr6aI2TZUzNk6V8UPRQjG+vSxLaHJKmIhjdzLKUkaEPrdH/gHQGyQi/85QFRRZnAQ69kE6zvFT4uwW4+aKJLnP6EkIDU2apnjRvtumYzCrZDVUEfc6NkMvwvIiirrM9sBh4AZEUcrmwMVyxXt/eqbCQqPAxdUuf2MfmPKimI7ts3qnQ6Oks9gs8cypKQ7Xi3ne1viEMK7rGmvx3m3t2IMnlu/XarE/y265NcIJwty9Nr79Czf3kCQOFKnPVgsUDRVNEc7HV+50uNd1aJR0pqsaK3uOaJQY+nzl6jZekGAHArD9+UYfBSlnYr0gJTBSdEXGUBXmGyo7A5cUib2Rz3czg4kTihXwzsAjTkBVUqoFPetDThi6EXu2hx+kVAyNx44IYN0sG3xsaZKyoWUVSWH+eZCllCCO6VgJYZzQtXzm6gU+dLjKyp7NnuUjZa/d7shj5Ib4UUzfDUgS8Zkt6oLZUBWJNAFNTTg7W+HETJXL631eX+sxdMWJUVdl5uumqI4yVExdYapssNAo8I0bLeEuTVOKhkohq6nas/zMAJSyN/IoGyrfvttmYGe5dAokqSSArioc+4aq0LECzh2e4FefPgbA7d0RuwOXybJBo2ywkexjJw0AAQAASURBVAUDj0/kz13eFq/VsUkgZfW6jSJLzE6YuctcU4QM4/aexdALkSSJwxOF/CLGD4Xm1A8SZFlCkuBSJkfYG3mUTU3o7MKIgSfYrq2+m+lLDX716SUAfvOP3uLWjiWMCqbK5fU+YZSgqTKnZyp89on57DOeMlcrkKYp9ZLOZt/FDcX6NI5TIjVhsVniE2emaVvCKTxRUDkzW6FaULmwOJn/bpycqbDRd9kZupyfr3F0sogbxnkP7fh37zPn5nj+xi5/+MYGZUPjM+fmcvCxM/C4tNHPIoHa9OyQQQYgnz0nLoomSwZJmnJ7d8TjR+ustC3WOnYGjuB3X7xDFCcsNcucmBYO2qmKyWKzlAFDl6OTBXaGHlt9N2OEK4yZxjH7D7DZc3jh1h66IjGZ3UetoPJHl7fpWT6nZkRGoCRJuEHMi8t7PH5UPJfZmsmTi408HmZ5RziET89U2Bu5dO2Q//rmBs+eP5wFtUucO1zlqeNNfvN/iPfc9hM2+x5XNvvsWT5HG0Uem68DKdtDn9mageuLLvVX7rSRJHGRo0oSThCxM/Te8bvuRznvGdj97M/+rLDmt9v80i/9ErVajZMnT1IqlR7685Ik8Y1vfOM9H+gH8/ax/Ayk7UdbKcjSPgOFdJCJUx9ihvh+8+BD/KDzYNTJ+DGCfa5WEE7Sof/wVe47GRdShOYpiBOi6OCKVQIGXoSagaex0SQFRplzdPxaOUFy4DmrkgCDYfSwoJTx/b19elYIGeO5Pxx5fC8PglNZllCkNAe49aLGsakSXVuI2P1YPI4fJfS8kJKmkGRA048SokR08bphAkQYskypqFDQZPwgRpJSbrVG3G0Lh1y4zyizOxTxIEMvxAkihl7IhcU6nzgzze++cJs/ubLD6ZkyF5YaB5Lz5+vFAwANHp5/9zDw9+CqdfzvOwOPl2+3KWoKQy9CVSQub/TpWD6WF/PmWo96SWdn4HK3bTFTMfIMPhFqW+DvfmyRb97c5ZU73aziS5xYjjVFGPNm12WlPSJJZRJEv7IiQZqkuHFCQZUwNQlD09AV8ZmSJPjI0iQL9QJfvbbDta0hXUswQ2kiIkYqpkqcpEwUdZ4+OUXV1PjWnYzpigQiL+oy9YJOGKX07YCLq10ubfQZZcyryJYTQCWKUlRVEvV/ioyhytzetbnXsUASTmjbi7LqKxUFkUGnKCklXaVoqFh+iOOLeBxdk+m7Ec/faBFECT3Hx4/IBOwaZ2er3GqN0FSZhXJBdIOqMo2iyGo7PVPJTtT3P7kDN+K1lTaqIrM38rG8CD8WsUdl0xC6VkBRZKoFGUOTsf2YnutnPbYiyX935KOqModqot5wqmLkDQuvrXRpjzwgxdAUdFWmZCh0rYB6UaNZNjg0ITpo/TCmY/nESUqjaGTAQ7BIN1tD7DBipmJSLiikWUXUmIYXshWJ1sAjTmG2KrPatvjSxXU+9cgMf/+Z41w4OsnAifJGgjfWejx/s0WcwNWtPj/3iDAGPJVp0HaGPmVDpajJ/NSJJvWixmsrXTRZomcHbPQclpolPjRfY7YqNF6SJET94wuqsUu9awecnKncf9wbu9xoDTkzU+X/8amTGQgs88zpKXq2z3JrRNvyeer4JC/fbvON6y1eudumrGuM3JBetvWpFnQem5/ACWI2ei63WhZvbQ0o6ippCpOlmNuexXrXwfIj/DjhwlI9j3q5b/IQBo21js1kWSdJJS6udnB8ocWElM8/eQSA//TaGsutEY8eqtEo6Xl4sSxBuaDjhhGfODPN1sBldc8iScWmqmsHvLS8x9m5CiBYw67jZ390JooGIy9koqCTpglfvdpCkaXc2LI4WWLoi6aeG9tDRpmJKorhUL1A1xZrcEWSSdKUKEoYeMIdfmK6zFTF4NJGjzCO+ZdfuZEB47kfG/buPQO7b37zmwf+3u/3uXjx4jv+/Ads3vs/PTtAUxXc6D48iFMI9xlKH9xyfo/It3ec93CTdz2qLPQgYw1cQYV38nJIwGRZo21979yRrh0+lJXMtYcPQWBjvd/45Ur2/XeRNadka+GHgLp3uM/xPEw7qCuiUePBmyVxmqX3Z0BPEm7JruULN6Uq1rkKMHBCLEk81yiBkRsiy0ILFSYQ+jFlXWaqpEEqYaiiR67j+MRxQtnQuNe1aRRUhn6EoYqg491RgO0FjFzhvHz2vJMBCWHquLA0ycdPibXh/qw5VZEOgCvgbWueP768RbSvCH3878stK9fGfPxUk82+xzeut5itmvzcozMsNcsM3ZC1tsNXr27zjRsyp2YruH7MWsfGyU6AlzZ6aLJMmibUCjoDN6RteRR1hcP1Aooss9lzeOb0NN+42coYr5SSphLFCQv1El4sWhCmqga1gtCLOV5M1wmwfJnnb7SYqxWpmDpnZivsDn3iVLz+SZoyXSkQRGKf6YcJG55DEAqd22zNIEHCChK+eXM3W9XHbA09Dk8UkCXRxFIxZFRVwQ8FM11UZfxIOEC3+x63W5aQJKgp8/Uid/eEg3q2rDNTM7i8MaBWMDg0YTBwImQk1KyJIk1T7u5ZIghZlTFUFV0VoPQji5M4YURr6GH5omnCCUQzxtm5KnsjL49HWW5ZuRvUUBR2+h4jX+j8Dk0UsL2QGInH5mssTha51RKPGacJcfZLEYQpv/PCHbHCG/loWcPKiekSThCzN/L4j6+uMlkyMFWJoRdyZXNIFCe4QYwdRXhBjK7IzFQFqBtHe8xUC1zZ7LPeFcX3mjrD6ZkK0xUTRZL4iSP1PNqjZ4es92xubI+YrZosNEosNArc3hP6uedv7LLatXlxeY9nz8/x7Pk56iUNkOjZPoYqEyUJQRxxa9tid7gGSHz+yQVOzlTZ6O/ylavb1Ao6nzgt2LmhG9K1fbqOcDA3y0auTYOUNE356lURwPzUscm8pm1/FiXAl99YZ6PnEETJAef6559c4Pkbu/zHV1fZs0REy4XFBlc3B3Qtn0RN0DWJDzVq1Ita3sv7zOkpjk4W+dadPda7LorkszRdZr3v4PgRszXRlrIwIViw/az9uEIMJJwwRpEk6kWNniO+m8qawhhBL7csHD9CU2WQRE1XUVPYGXqi4qwqthGmpvBbnz2f33fZ0PjK1W12Rz57I2EEOTpZ5M+ut/KL1aqpoasqAz/kVsviXtchjGKCRBjffuLIBKdnqqgy4vtUEbFJp+dEP/H4MzFm5AxN5sLRBm074PGjokZyd+TTtgKSNCVJoFrQ/uoDu+eff/79PI6/VvPDcsXWSzrTZQM/3Fd19XBCKZ93E9Hx4KQIxipO3z3I02QeqhebnzAYuGGemxclgiEZ36+mysRpkrN3+0eCLGzzewO7BMGgjY0VDxtVyqIpsr8rjHVIB39uDLCCMCZ9D9cm7/T4RV3UGT1oQAnT+/o9XVRtsNX37zOumcNRViWiKEVRoKSp9J0INxJ5hmOGVUUAw1L2ZekECbKc5pl9USIyoQxNQYsSOlbAE0cbnJopZ+YBoRn78hsbTFVEgf3pmcoBrcw7VXw5QcRya5j/jGDjRtzZHeGGCS/fbudxJuM4FS+Icyfg+MvVD8Wq5KnjTS4sNvjmDWFwkCLRH3xuvkbb8mlWDL6z0uXq9gBNztyucYqhyxyuFzg8UWRn4HFnbySqmabLnJoSFXyaLLNn+0QJ2GHEfL0gmLJYiOuTNKGgqeiqSlFXhOZnc4CiwHRZVC/FUYKuK6SkWURIjGnIrHdttgYeXiRCfFMkHjtc4zurXdwoQQ8TJgoaIy9i5IYUdI0wDnHCBMlPUDWJkq6In3Eldn2fju1T0EXYtTDrhJRNjbKpcnauiq5JrLRtwjjhzp5NEInHWJwooipwe0+4zw1NxIjEaUrZUPn4qSmePXeI3/rTawRRjKEpzFYNWiPhtl7t2FzbHnB5c0C9ZLDULLHZd9gb+kyWdExdxQkF0/3E0YmsvSFmcbKUrRtnsraTbb5xo5VJIDRaI4++ExIlCSVD6P7qJT1jnAJWr7UAicmSTrWgZfVxKisdi8RJSRPB7gVxnFfKnZwp0xp67PRdSoZGo6xz8W6Hq5t9wihF02QuZCfhzb7H0yeaOGFM3+1SL+hcWKpzfXuIlVWOGZpMGCX0nTDPkmuWzbzy7FNnZ3hxeRc/SAiJCaKUb95s5WCqUdKx/AhVlmhbQr8qSeIirWP7XFzp8oULCxydLGSaMbFSX2nb1EwVVZKI0pQjjWKu5Rv/TrWGLo2CQb2g8e++tYIkwUez3MW25WNqovbu+s6QgqawZ3kokoyqSoxsEUkyzl/b6Dl5vdaHDtf4g9dWqZk6f+uxQ1xc7bLasTk8IVIC5ieK+es9Zu3HsoiPn2pS1hWWWzY0BGt6fr7KyZnqAZb+bz9+mJ4dcmm9x/XtIXf2RhxplPjI0uQBEDtfL/L5J8V3zZdev4flxTSKGlMVUY13cbVDlIjQ47OzNV5f6+FlrO2RRpGpis6dPRvbi6iYKkMnIooFW54AmixRKmo8Nl9ntmby3JUt1jouH11q8NMnpzg5U+a5y9v8z7d2+NbyHinC6b7UFKtbN4z/esSdPPPMM+/ncfy1mh+WK7ZsqOiaqA/yx4jkB6DXHpZZNx4JwTK9w2b0bRMlIkrkQW2cF8Zvw57779IPEyZLOl07PJCvR3abgXPfLCJBHhD7MFbse62PFVkwZgfu7B1QsQLMN4oilPlB5JcdgyI//LV5p2Pw4/gdXcASUDFlTFXJ2bvxZJs8lDiloMkUdNEUEkQxViAAsgKUdXEiipKEnaGPE8aCbUzAVMgy8TQKmkw7SYnihLWOzYu39viVn1wE4BvXW9xqDdmzfB6dq/LLH1vM9SyidFvMWAPXKOl5ZtfuUFxNn5ypMl8v8sqdDl+/Jq6oJ0oijHUM6ta7DguNIsUJGTeMqZe0POj0916+y5WNAYcnTK5vDwnilHrJoJZ9CS82iwKEJSnLO0PSNEVVRKyE60dYPjTKOiVdRZKEKzNJY/7seotqQUNGrAUbBY3IgA8fmSCIUkZezFxNhxTcUMRQFE2VekHjrS3BGAURdCSfo5MlVFmiNfIo6xqqCqO2QxKLdok4TSnpGrqSEEQJe1bAhw7VuLw5wFCECcYNRcdrpaBj+2H+2VSyJoqdgcdMTQSAg8SRScE6tUYeXdtHVUTS/tWtAaoi1kd+mBJEwqHtRTE9O6DnCoZhsmwwVTHoWj6tkY8siauIlbaNpiqUChqqLBEmCRNFjShKaQ1d9kYCCF/McvzmJ4r4QYLli5yvWkGlWtDRFJmtvs+9js3dto2mSjx5tMGvPr3EcqvM87d2ieMEQ5PRFR1ZkrB9GVmCa9vD7AKhyhcuLDBwQ261hoQZMzhVNjF10elaNoV55fLGgL2Rx8qeRdVUOTlTplZQiVNxETVVNvn2nTZOECFLUC0Y3GyNuLja4aXlNls9h6mKSa0gwELPDnMTAUhoikStqDFR1GhbPhs9J2epl5olmmWD/3Fpiz3b45G5Gmfnqryx1uXWjoi0OTtX4fBEgZ4dZGG8NX75Y0e4uNLjZmvIeu9+00bHFs0cCSJnzfZF7Mh0xaBrq3ll1XLL4uvXWrStgMcWJnD8mGvbI2QFVjuiEiuKU2aqIkNxcbLErdaIzZ7HsekSz5yc5uJaN/9dHK+8X7nbIc6aLKarBeIkyZ9DQROuejcQ39M/fXI6l1G0LY/z8zXGnb49J8TU5Oz3VKxYx6BuvF7+/JNHeP7GbtYf7KLJMpWClgeUPzivr3b56rUd3DBmslRkbxTw/I3dPNNwsqSLCjvHZ3fkIUmwM/CZLGsosizajaKEGztDjk+XGQURmz2XIEqoKRo7Q4/nLm/zxlqPFEGejEHdH7y2ylbPQ9MkDlULzDcK/B8/ushnMlPNj9N80DzxV3i++Po9bresA2BMyfRj72W+l/YuTEH5CxCQKW8HdQBtO0L9HsxXGEOzYmKoCqvdg7VmKfe7WSELNjZUvDDKzRKNkkYUJ+IE/j2OT5Fgv584SqGqS5QVFT+K8YPkfnuGJGJMdEXOtGsHR5JE68W4/eLdvEyqrODs+0lVBk2WcbOdrpRKjLwIWd7HwGW4M0GYNaIkwQ1EUGq8DyWqitDbpZkDw8+aBGBs9BDgwI8S1joOVuZajFO4szfiv393g08/Oid6NR2xjrCyE9z17SF9N+RIQxgqxtEIH11qcHiiwOEJk6VmmcMTInLm/sooRVMkVEVifqKI5Yd8eV8p+3gNdHKmihfG/PbXb/H0iSYFTeVe12a17VDQhGD/Y8cn+RtnZmhbHlc2h6iyxPxEgetbQ3RVoagLsNYahqIsPggZuCH1gi66ZCPhjht6IYosMfQUHD+iXtIIwpRLm31IU87PVwnjmLc2RU9qLdFZ7zoizzB7VkGcsNnLXsME+kqIqkCSiLqtiqFyx4voJwkXjk5mWV4pC40imwOXza6LHyeiek+Csq5i6Spy5kZVFRk/jLPcw4jJoo4bibDrKE04NVPGC0XzyEbXyRzsornFKIKEhhOENIomqiqBSwZWRe1YmsUgKVLKWsdmtmrySx+e5+JKlyubfdpWyPFmkZlagZ4TsNJ2CKOEKxuDDAipArC4AfMTRU5Ml/IaqOeubNFzfEZuxMiL+c7dLutdh1MzZb7wxAJuGHOsWeLiWjfrtxWZjCMv5F7XwfZiDtULArC1HWZrJjM1kxPTZZwwpgu5xqrn+NzatfHCiEsbPW7vCh3ViWmX0zNVTkyXubjaxQ0SJDllvq7SLOm8vtolTVLeWOvStgNqpsZcrcB6zxHmjnnRLNGzAz6y2AAkrmwOANjsiyihMauUpClhJFzJv/r0Up6ZVtCUHMxdWu9zdXvAkUaRZ8/P0bMD7CCkNfT5zkqHDy/UMVWFMEmRZWgUdWSEAebC4iQnZyoHGK9PPTLD7d0RJ6Yr+FHM3sinWlAJooSO5XN+vsZSs5ytFgN6TsDhCZOfP3eIw/UiO0PB6O3Xpo7d05Mlg5Ebstwa0bGCzPzlM3QjpqrGgY7Z52/sstZxeeJoHYB/883bbPfd/DMCaZY3KYDufqnGyZkykyWDsu5RNGQOTxRzrel+3S3A77+6yr2Oy5m5CiemS1zeEFVrk2VdaKCz78HH5uts9EQ4teVHdKyAx+ZrkMK9nk0UpTw2X8MNE/r2DgCzVYMoSbm9a5ECZ2ZEMPMrd9r854trbPU9IX+QJNEmZIesd+23teX8OMz7Auxs2+Zb3/oWt27dYjQaUalUOHXqFD/1Uz/1jmaKD+YHn7t79gEwVtFlVBn6WSTJ9xpFejjL9b3m3eK672e2eKd18Jgva488bP+dQ5PHE6YQxlHOlE2VhbD16uYgD2fef0zyPsfpw7Ligjjl0ITOztAj2PckwhRW2lYe9Lr/WI1s5ZzAu3+BEGBr/2gymJoAdgnixCuMFDKynFDWRWvAWlfEVSRJSpgkhGmCLEkk6f3Xffx6aBLMVgtUixprbRvSWKwNk1TkXWkpmiQxfqlNVbiRL60PKGgqhyeKbPUc/FA0YnxntcOhiQIfWZrMQ4tvZMn8fhRxbKrCucNVRl6U627G89TxJte3Rfr+0BUl3F07wNBkalmdz5OLRa5sDPiXX7lB3wv45o1dDk8USFOR1RbEKdMVg/PzNT5xZpqNnsNmX3S+dmw/y0ZTmK6YNEo6u0MfLwwIopTOyMMPE3RV6DnHr5kKomXAE6uVy5si37Fe1PHDNC9U17IeXieIUeTxeybaIXpZNqEqAVKKE4jO4e2+yzBrjZAQWknLj+g6wgjQsTziNMVUxbHoWTjtVNlg5Ef4YUSjJKJOdi2fCVMTlX9hTJIIl66mKNQKGmsdBzs7zkpB9FiaqiI0hJIsApMVRQAFWaJjB+yNPJYmyxRUhVpBxwlFE81Co0jZVJkoamwPPJCyvDIvYroiji1IEr51Z4+iLh7nYauzcVbZ167tZK0IIt1/pW1RNlV+6vgUhqZkOsas6UWT8wgTJLjXdVAliZKhUDO1A00Mr9xpc317xGrbpu+KdoIwSvCjmDfX+ixNlfib5w7x7HnBqAzckD++vJUDnP/23Q26doiqSOwMhCmnqCmcO1zluSs7bPZdjjYFyBh6EYfqxVwy8NWrO6iyxOm5aq4rXWiYbA9cpio68/Uif/+ZE4CowWsNPAqawp22aIRZbVtZjluHe10bN0iYrpjsjXxu7Y5w/Yizh2osNIoM3JCFRjHver6yMeD3Xl5htmrw7PlDLLcqPH9zl4EbcGy6zGbfodX3CKKE8/MTjDyRM/fVa8JAcHiiQNsOODNX5Ymj9SyQ2OboZIGnjjdzDds40HfghtRM0VzjeCKb8eczUDcGNc2SnskvhJP2U2dnuLjaxfEjLm8MM91egbYlTCSqIuGFcc7c/dLj8xQNhQtHGxzOwN5+mUbb8tjsewRhwpm5Cn/3Y4vM1kRdWs8OOEGZ27sW6z2H27sCPE6WDT621OD2rs3N1jCraKvk8TRC7mFjeSEdO+DMbBU3FDrWqbLJhaVx1p9INigbAjAnKeyOPAqays7Q57krW7y0LKLH/loAuyAI+I3f+A3+9b/+19i2/bZ/L5VK/KN/9I/4jd/4jQ8aKv4SZrKks7aP1XLDBFl5d9vYvyioe9jsr+TaP+/1rnVFHNeeFXxPM8L+cTK5nQxMFnVut0b4schEGntKZKBaED2XdsbkpYgcMlm+bzCJ4pSOHYq2ggeehPUAnanKwiXrhe/NMfzg2taNwN0XZxJk1uEoTlFlkJFF96YfE0Vp/rpXDBlDE4xTkqYHwGytqFE0VI40ihiqzFrHIY4TUEXqvkSKotx/F5NEML6mKqMrMk8dazBXNfmzG62sUkemXtQpakoe6npmVtRCdUYBh2oR17dHRIm4in5YfEmcrSZBolESonBTU3KW7vXVDjtDlyBO2ZFdDk+YzNZMvrvexw5iPnS4dsB9V9QURq4I/G1l7MhszeTMrKjOGrkBYZTpJ+OEOBaf23HtnKGpLDQKaIqEIosV5mKzyLnDEwLMJ2nuCNVUGbLVXpQIQB1G9y+immWdoq6y3nWELjOB0BFgSyKlNXBZaVv51v/+tUuKLoOhgxXEmFpCEMYM3ZihazNfN5nLVmJ7lk8Qi9iikq7QdQIRrSKLizVNk5FliYVGgXttwSj5QYLtR2yRUjY1Hp2rMsraM3ZGHhMFnYKhoMhihbfes+nZEY4fZWHIUgaUPebrRU7NVLi1O+TOrg2pxM+cmubxo3Vevt3OgBiUTRHeenjCpKgpeEFCmoR8aL7G3T2La1tD2lbAP/7kST5xejrPHry5PUKSRfSFKkvEScLPPjpH2VRZbdv07DBncJplk7u729zrOhxtFjE1hZIR0Czr+GHCrdaIhcb9nLYnFxu8ud6jY/vsDgWobpZ1FElGqkn0VJ9PPTLLyZkqs/f6GLrMpx+dy2uuVtsOXhCzNRAhx1VT5cNHhJD+8sYA249plg3qRf0A01QvaczUTKYqBhVDw9JDnCDmykYfOZeAwHzdzBtQipMlfunxeSxfXASBxHNXtsQFw8DhrY0hE0WdnaHPbNUQ778sEycJjYJO1dA4NVPm+vaAgRtxeb3PztBlcbLM0clizjSOHe9fv77Lp85OZ7IJEcR9cbXDsWaZuQmTC0cbGJrMV6/tsDPwudu2ef7GLn96ZYs/v9enoCvsDFwKusrO0ONXn17i2fNzB2KRxqzgOKh4XLd2aaPP4QmThXqJw/VinmcpAq77LE6WAImuHTA3UeDsXJXZmsl8vUizbPLK3S6bfYdGQWe6atCxhSa5WRKM6VRFxwkEkzyWjIxB3Qs39zg/X+MzMxXalscLN9t4Ycy5+Yn8e+ap45P0bJ9L6wPu7I7YGfkYiszchKiX+/advbzi7Mdl3jOwi+OYX/iFX+BrX/saaZoyPz/PmTNnmJmZodVqcePGDTY2Nvit3/ot3njjDf7kT/4kO4l8MO/XjK90xxOlQPR9/RPfd8bvUrbJe+iMq7ke9ln+gTBj+t4MHglwY/d+RdMBU6wEUZyI9ST3X58kPaizk2UYugHvxsCdJOC9Q8TJoaohQlStID+W9/orH2WZKKM4OhDGnP97kmCiEoYpCUInqSgSqiS+5NsjDycIKRkaMmBHIgleU8Sq1/HvG1HiRLwGSBLbAxeQ+DsfPYKmynxnpUMUCb3Oqysdtvse83WTZ05Ns951sYKIsqkRJSmNTJey/+T2yp0Oq22b0zNVLiw18qT6oqbkNVl392yONIqcmq3g+AnTVVENtZGtvII4ZbVt8dzl7dyVOT6Wru0xcCOGskSlp9K2fUZ+dD9SJoUUoS3bG7m4QQb+U5+rW4IZU2QIQ8H6fP1aCz8S3ZLTFcEMbPZcwjjG1FQiP81XqOOPa2sUMFE4WAVXzDSwcQp+JLq+FEXKolfuz1gbaXsBSaKhq3L+mbnX87CDiI+fmqY98tkc+MQJ2EGMqUl4foqmSczXi6SkdJ2Q9a6HJItmkxiIooRyQWS03W3bWF6MIomLw8ePCJbhtZU2fVu4gU1NRlNUNFXmsYWJPM9MkeVMLwWbPQ9FlqiXNFbaFlc2BshSykbPozX0+Nq1HaYqorEliEWWXxin1Ao6OwOPNEnp2UG+KrS8mJEfUTbVfKXXd0NutkYUNLFSu707ysPVT86UcWNxG1WWeWShxq3dIc+cnOaVux3aVkAYJTx/YxcvFEClUdBZnCwz8kKmKjoLjRKbXZdPnZ3mycXJfOX3v144csAc1CybfOWtljDfSBLVggi6/tLr68zXC3SdgKKmsjhZ5FiznDNNy60RO0OPc4er1Es6N3ZGSBSZqpg4QcyZ2SqHakXsIGIxy4l75rQANsIQAI2Szu2WJfpd52ukScptzRau2Ws7VAyNv3FmmmpB5/SMiPIZ5zk+f3OXoRsw8oW28EijyNk5oYMbP9fbuzbrXeF8B1htO1zZ6LHWsVnvOfTsCEMZcKhe4JmT07TtgKKm8PzNXbYGHkVDwfYjwTJHCattOzeYfP7JIweyKMcM4SgzMTx3ZZsrG32KWiP/t/t5mSKuZ7Zq0LNF1d5s1eDK5oDNvsvnsqzAkRew0XGoHtL4P39skZW2BUh880aLl5b3ePxInccWJri+PRB1gXGarXpFUHe9pOUbgPHZYdwLPWYkwygReXqySA6YqZrMTRQwNQVNUSjrKn6Y5sf+o26ieM/A7nd+53f46le/yszMDP/qX/0rPvvZzx6INEnTlD/8wz/kH//jf8zXvvY1fvd3f5d/+A//4fty0B+MGC98+O5PAb7/IvOdp1ZQsfxI6NDeQTMWje2if4H5foDT0GSSRGTIjSvQouS9AcUDQCqFIAsb3j/7XyMZAWzilO+pARzPg6+JjGDwirrK0yenePNeLwd2DwN1DzOW5PclidXsmNV78L0chyiDiLZxJAH6EsRrdqReousEhLEIYXXDBC8SfY5+BEGU8vSJJrf3LLb7Lk4U5s8pSci+/BJ+/5VVnru8hRfFhHHC2bkas1WT1sCjY3sMvZCioVIpaGKNkhWOj7/YvvT6Oi/c3OOZ01OA0JxdWKrz+ScX+Bd/dJXW0OPiWod6SRdO1akSf/djiwD5l+mNnaFoPcikA7tDn3/3rRXWew6aItG3Arb6NhVD49hUmZmKyCwbeiF+LcZUZTqWjxckBLFwf3ZtmZiENIEoAEmKkfFJkQjjhJ2BWKVqCiiSxtBLhY4xTTF1lYW6yb2OR+wHpA98qK2MhlOAZlWjoKloiowdRDi+EMMbqmBYC6oAmyJgWHS4BjEkScjsRIGKoTLK7m/oRmx0HRYmi/ScEC8UoJJUQlEEIB35EUuTpdwRXi3qHJ0UwcWGqrI4WaJte7RHPkEkHLVxDIvNEi/f3hOMVBSDJJGmKUM/4sRUiZ2hx5trXWoFHVWGiytd3rzXFborTeHiSpcT0xX6ToAbRkwUNXYzJ60TRBR0lYqpk6QpAzfE8SOqBZ2CrvAnV7Z5cbnNKNM7VgsaNVNjtmrmLGDXDlBlmKmaTFVMVEVis+dwaaPP+UMis262Zoq+1YHP//7tVXZHLqQS393ocXfPwg4i4hQuLNb53JMLvLS8x+WNAZYXMVMz85BgIDvB3x9hDBDO37W2RJzCuUMTXNns0w8TwiTFUBWhSXVF/EitoDNR0vCCmLttm+rJJs8eb+YO0D3LY6psUCtoAhDFMTd2RgLgy9Aa+pQNjY8sNXj6RDPXrIrO2A49J6TvBnRaAVHk863bbY5Nl3lsfuJtDS7LrRFWIIB8yVB55W6HI43igec6XTXyTL69kUcQp3hRzKnpKpoqs9VzeXm5zdMnm/yTT53K16T32hKmrvCRxQbXd0ZoiszHjk3u09aKlfmLt9oH+mnH4G62arA79KmXhJP5/urVp2eHFDRFAM++QxynFLQGAzdgd+jx5Tc2ePpEkygW5iDLj1hpWyw1ywK4ZVlVJUPBCWOhx5uHs3M1llsjBm7IVMWgbGj7zBwLORAdfw+9vNymXlSpFIRje/w6jRnAgqawOCWct39ZXdp/0XnPwO4//If/gCRJ/Mmf/AmPP/742/5dkiQ+97nPcezYMZ588kn+/b//9x8Au/d5FOXhCOQHAXUSApT/RUOM3818P4Bmewm1oirWXFnulpWhm+912+8HGBN4W6zIw45NRmjmNFUmjNO81aOkC/FynEDXeXjUyljbNlvQ2Og7Isfse8w7sZKGIgBuvWgw9EJ6zn2gIEugqVIuEIbMRBHdD1P2Y1jpWJiazEylwERRp6DJLO+OCJMkX3X/z6s7VEzxWu8fGUACJ4y5tjUUK0tZRpXA1FQkSWK95xBEKTNVPe/I7NoBpqYcOKn07ID1nsPFlQ7n5yeQZfKi8HpRI0wSho64cv7584dyQPj8jV26dsB23xUmnDjBz1CwLIEiSbyx1mXgRvRtnzCGakHiX/zih3Ldz7/71gpxAsemSkxlFVe6KlErqsRtcXyKLPL3KllUyO7Aw1QVgliYUsqmYMQtLyRORIPC4YkSsiRj6jJRomYmlUQEG6cCcCtS1kksySiShCxBGCZoikS9aOCFCYWiTqWgYQfClHIg4kcSerNmdnFjexG6KkDjyI9IJaHHlGVRPVXW1Yy1AicUQEWRJQ7VREjzOMC2bKj0bFk0SpQVCrrK6TmxruvbISKMRUKVJFqWjxNEbHRlunZIQZf5qeNTuKHoym0NPfwwxQsjXlxuYwcxcZqwNwpEFltRY+BKzFZNUlISUwCz7YGHJEnZek08p7Y1rihLMjOKy3pfAI6ipjCUpfyiYbk15MrmkKthRM+JRCtLApc3+jx9YopjUyVeu9sRF4cg+mf7HmmaUjE1WgOfiqlm9WApBU2lVhB5dBs9J19F7gciX35jg3tdh4mCxunZKut9Bz9KmK0VMHSFTz86i6HJfPPGHq+utEkSONqEo80SFUPBj2NOz1Ty9oivX2+x3nWwGzEnpisM3AA/TDg6WcLyIi5v9pGQOHO0nOsWnzouGiheudNmte0wXdX52DERTXSrZbE3ElVzY0C1ny0Xfyost0a8ttJh4Ibc2R1R1GTqJZ2OHfDE0XquRTwxXc4bHP7OR0WQ8HOXtymbKqdnKjx/Y5eKqXJ4wuTl5ZB7XZdmSefUTIXVtk29pD8AbCQRn2QHOXBTZYnVtk2zbPDM6al8VTuu/IKUK5sDWgOPSkGjbCjEidAeK7JMzxXa2ueubLPatokzTeuLt9ps9j2iOGVxsoyqyHzizAxTFYOhGzJbNe7f99BjumJyszU60OD0ey+vsNqx+djSJGGUUC+pnD88QZyK1p7x993JGSH3ePn2Hn6YZDE5B0Htj2reM7C7fv06Z8+efSio2z+PP/44jzzyCNeuXXuvD/VXbn5YOXaffnSWS/d6jN5HFKar5EX38M46unczf9EVZIzQYKUpOHF6XyT3Ps04bPhhx6RKUCloSFLKyI0IEgGyFAXcICVJI/R3ISXoOz6bfffAuvRhwPOdcKYfC6dlELkHfkiWRX2Y48cHgHvWRX5ggvw+bFbbNqoKinSwZs0JE5zwYM9wSRNIQiIVBo0UzCxHLYjFSdwLIjazE1utoGFqykNDU0Fc0ULKzZ0RPSek54i12mfOzXF7zyKMUvzsoLwwzq/AK6bKwBXi/qErrrwlGaREaLdmJwzcIBHmg1h0+FYLGi/fbjNbMxl5Ede2hmz1HTYNlZKhcnauwidOz3BpvQ+Iz3VBVzjSKIoqrKGPE8TisaQUSZZQJSgYCkma0LMF47MzFC5WBaGpc0OhXytoCntZpZxwmSoM3JCmqqDIMk4YUzHFalPklyXc61pIkoymiIsaELrPx+Zr9JwIXVWoGAmKJDFfL/DxU1O8udbDDWJOTVdolHXu7mX1aboCEixOis7aby3v8fpKh+dv7vL4kQlMVQEpJU4TdEWmoCvUihp+mHJ3d4QdxMxOFPDDhGZVpzUSwazVrM5ub+hzqzWiWtDwogQzq8kDsP2Qa1tDnj7R5OhkSBSnbHRdojThJ45MMPRC7u7ZbPddtgYuh6omFVPFD2MmyxqaItMa+HhRiipLyLIIe760LmqeVEW8v597Yp7XV7vc2B7yofkajx6eIIxidkc+ILSRf+ejR/idF+7w6p0OzYpBo6Tx5lqfvit6ZW+2Rrx8u80/+dSpvBzeCWM2NgeifQJE/EwUA1IONuJElMfP1AxkWWKqYtBzfFQkDE3OmxTuti2h0To8keXtKUiSCL8dR4JcWKwzVTEo6QqQ4ocJPTdgoV7gVmuEKkl85FiDX3362NsaXb7yVot7HZsjk0V++uQ0v/zUIq+vdnnuyjazWactwCt3Ojlb/vknF5ivF2lb4vPZdwKGXsTtXYsLS5OkuUBFxInsDH2ePjHFYvN+ePhG3xVs7Wo3l1tEcYqpqSJ2Z+iz0Rvrve9/IY0ZwWdOi0Dzjc1B9rrIvLU5yKoADQDalp9XfgH07DBzlyt84sxMrsX9ytVtGgWdI40iRU1mdsLkcN3M44/Gq/2LKz1GXsR618bURKfuWselXpL4+Klmpl0EkKhPaFRMlS+/scH1nSGOH3FxtSuc48DtPYu+HfLclS3mqgVmqiYvLe9R0MQ5wQpirmwMKJtaDsJ/lPMDaew0TXtXP6tpGsl7zeD4Kzg/rBw7Q5MpmRqjwH9f7q9qjLVA+6qvJCiqEs477Q3Jctd04ejcnwZSNpR3rAR7p0nTlHfYML/zbb7PvyuAqQsAFDyEcFMQTJjjh6IHNrvDJNMsCpdqiht+fy504N1/AWQEK1Q2VAZu9K5B7sNcu+MmiQcni6bLZz+ITJLMORyCQoyuCoG1HTzcNR3GKUVD9GQGSRYIrSgsNUtsD7xstTZgsmSy0JBZapb4j6+ucnSyxLPn5rJk+HauT3nq+CTfvNHi2vaIY9Mqjx6u5SGen35UsANVQ+O/vbnJ9sBFUxW2ei4916drhQy8kL2RCFBezFaKQzfi+tYIKTMfKDLMN0o8OlfLNE2CpZitGbQtDzeIGGRVJo8frfPKnQ5JIhhOJ4jZ7Lv4YZLlyGVaN2n8micogSRaIzJGzo9Thq7I57LDBC2rKXKC+7mEqiwhSeKEUDFVPrrUAFLiBHRFZrqicmfXxo9AlRJ0VSNOxbt4bKrMXL3ItZ09FClhrlbECTz2LI9aQWWhUeRO2yLKAg69MGZ74BIlwvn71uaAY1Mlhn6AF4NnBVzbHnBypir0dokInh4FMUMvJoxEBp0ThBiKIkrhGwW6dsh232WQtSLommD6hlkXba2gUy9pWYWYR62gc35hgqeOT/Lc5S1W2jYDJ0BThAHhK1e3ef1uF9uP6Tgh5xcM9kY+Kx0LVRKK2GpB5ZNnZri8McDxI0qGihsJF/L4vXVDAfTHTQlfvHiPRlHPV2rLLYvz8zUkSco1WSBxc2eIKkk0qyanszouYYrw6dkBQ1nEmOwMXaqmxuyEiR/GLLdGqLLEpx+dw9SUfIXYtnxu7Awz9lx8YAQgEb+BPTvgyuaQc4drPHG0nmve1jouTy5O8uTiJG9ktXhxktLqe3xj6NF3IpoVnQuLgsHa7DncbA0YOAELjSKmJiJwxuzlcmtIvaTnoGWccTfWj42/DcYAa3GyyO7Qx/ZjpiomTx2f5Pr2kO+u9/jN/+GgKzJvbQ2olzRGfj3vX35peY831roUdZWFepGtQOT+LTWLFDQFWUq51bJ55FCFp443D+TirXUcqqbKztBlfqKQM4P1ksFq2+al5T2WWyM2+m7G1t3vxr28McANk3wb8KXX73F9a0TX9jF1hSeO1CnrKmVTxQ1jlpplnlxsiPfIFtV2t3dtLm8MOT9f2+cCtrKGD1jrOJycqeQh62dnq1hexHrfYWFCOMS3+y7LeyMcP6Ze0imbCpc3BhxrlpifKGR1aff1hX9lV7HHjx/nrbfeYnV1lcXFxXf8uZWVFd566y0eeeSR9/pQH8w7zGrbEVVT78NIiHBg/wH0EaWQfA9QB1nXapCgq+QoQ0LER/xFx3qgo/X9mBiwg4NNFJqcOQllCVmSGD74xBFrv3dLGj6MlUsQoLGoK4RJjO2nTJU1Rl74PWvT9t+PwsNNLOPVX5L9f1USq3lFknBExQG6KpGGKVEqXoMgAl1NMTUJN3tPxw7ROBUgsaDJWNmFdwrYYczVzQFRIuJgVBnOzlV59kNzXNroc3NnxGZ2pb479HHDmM2+l68sxnoty4vyxHyAqYrBUrNMx/KyL8yEeknj0nqfez0HVZb4yeOTFFQRvvy/PDrHpY0er690idIUFTkHYLYfcmK6xMCN+E+vrfLMqel8Xa0oEmmUEoQJv/lHV7m1M8xDnuNE6A3H9KqmijVkHCcEidC1DYmEqUSCw3WDMIYgEs0CAydEFkRY1iqRhUfLEk4gfqY18ri42sP2Iyw/FoDQjygbKl4cUzVUvCimVtRIkpgLxxqcnqnwreU2QSTTcwMBTF34o8vbHKkX0VWZ7YFDa+SKE3RZoT3y6HsxPc/Fj0R0hhsI1ncuY8iWd0Z0LJdEklCShCCMGLgKcVbPNYhCwiTBi2MaJY2SrrLVc7i7a1E0RHRKnIqYlShO2R64VEyNI5MlCqrCf//uBt+8sctkxeBv/8RhnCyNXxgjIioFFTuMKWgyTiBOhHYQM2HKFA2xGhYxMD7HpkQfZ9lU8zWsF8a0Bj4zVZMLi6L39M/X+5CKjuWvXN3GDxPiJKVe1Gjbwi29OFlipW3TswMmyynrXTvPSWyWDdY6DmfnxMk8jGL8OKFvh1xc6wpzTApvbQ7Y6Dt8+tE5PnNujo2eQ8/2ub0rWPEvvb7OU8cnc43W7728Qsf22Rl6PH60nuvKjk4WaVseS81yDjLqxS61otAVrnRsDtUKXFztsDcK8MKIa9sj3gh7nJ6tsjBRpGKokMIfX96mawdMlg3+5rlZjk4WWG4Ns0gRjePTZZaaZV5f7fL7r66iyDJPHRNs0lrHYbEpHMNn5yq8tLxH1wqYbxTzyBlVFizayZkyZ+cq3NweIskitmdv5DNRcijpKiVdxctyEYPMTTdeZ587XOPoZJGvXt1hZ+jxybP317Sff7LIb3/9Fq+tdGFesID3ukK7NzZGnJ+vsTN0qZhjqCKxZ3mM3IjXVjokacpq2xbmLV3Na71OzpTzDlth1OoB1cwFfIf//J01DE3hlz48v8+00eLunsVj8xPMZqHOF5bqlA2N//2VFabLBkZD4e89tQTAetfFDkI2+uICslkx36Yv/FHNewZ2n//85/nn//yf84u/+Iv8/u//PufPn3/bz1y6dIlf+ZVfIUkSvvCFL/xAB/rBvH3GzMr7MSm8DdSN5908RAokMeiI4N/cjfgXnCQVeWoPy5lT+cH0gylitZhk614/Bh1B9Uv+QRZLk0FTJIIfIBdGRrBm2wM/fw0Hbnj/3x5yGzWLYAnirMpNFWBh/+sxdr6OdWdxKv7IScqxZoHtgYcdJtiBAGK6JN7bGPJO3vEUdJlT02VWOg5uGNO1A0qmwjBzTSqAqsgEcUyKMLMM3ZDLG0Pu7jkEcYwbwK3WiKVmCX8Qca/rsNK2RMJ9U1x5bw88fvOP3uLXPn6CJxcbuSjZ1GQUSSYlxA4kuo5PGKbImshe+8SZaSHs7whR9MARsQL1koblxWwOHNwwYeCKYNo31/oi9Dhr7TBUhVJFZqKos9wa4cdZNE5Zx/IiJCklCGOiBAqKAJxtyycIEgqaLFjeSOjjjk9V2Rq43OsExGkqPov7PuS6IkCd+CSJ19nxQ9a7QjumyDIjN6QdiViWpWaJ9Z6bXQClTJR0upbPK1aQsy22f5/p3Rv5lE2VqqkRRKJ71pMT+m6Ava+Dz1BlJGR0VQRhW4EIru3YPmEiujsP1wvc2rEY+RZlQ7AksiQxVzMpZ5EVFxYbhEnMnhUwcEJ6TsBio4Trx3Rsl5EX0igZfOZDc/z3727w5/f61Es6P/fIDEAeVbPSttnsC+f0hxcmmKoYnJiusN5zcl3bI4eqXNrocWfXwo9iDE3GC2MKmpqzNf+3P3iDb97cpVrQsHxROzd0AwqaihtG9N2Inuvh+DELjSInpsvsZA0o610Hx4+ZqogIjMsbg1xz5QQCaF9YqlM2Ve7uWrhxnEd89LIV3GZfrKc/c24uY5SqXNkccnG1e6Baa+wAL+kqUZLmDRBPHK3TLBu8sdajWRZg+7f+9DprHZujk0WeONpgplbIV7i7w4BTM1X2LJ+NbowbRBiaLDSwQUTXCbGzjufbuzZ7ow5XtwfM14t8/NQ0UZyy0rZ4+XabKxtDpqo6S80juZ7NC2P+xR9dFaHfxxqstG2WmiU+frJJvWTQswNeuLkH3M+hfGOtiyJJFDSFKExo1nWmKiarHZuFRhFFkvnyGxsUNRFmfHGtw4Wjk1Qzg9XTJ5oH9H9jBv/0TAXLDxm64QFwVy9pXNkcsNK2eHKxwVPHJ/mZ09O8vtrlyaMNfu7RWSZLBmGcoCkyzZLOb3/9FqdnKnm13Grboe8GrHddvvT6Pb56bZu25VMz728cVzLwt94VnbufOjvN8ekyZUPjv7y5zmbX5exchV975kTO2JqacAPPT5hIwFzN5GSmpfxRz3sGdr/+67/OF7/4Ra5cucKHP/xhnn76aR555BGmp6fZ3d3l2rVrvPzyy6Rpyvnz5/n1X//19/O4PxigXtS/Zw3Y+zW6nJ2mMlbnnQBbmDlKpfTdO1lloGzIWP59pk6sAKUDlWJFTXSj/kUtsmMAJSEy3zRVEaAuigkCUQs1UxXRBU4QC80agoGJgvQAg1YxZNSMjdlPRo61ew8emmBvDr4/49uVNJlyQWN36B+4XfgAIpbSt7+/SYKoB3tgEsj0RmLS7HmMj2/8M/snihNamTbMj8T6MIgFsxQnCUVDuCkHbsC9jkOQQMcO6FgeqixRNTR0TWZv5NO1fdJU4lxR5/GjDRx/jzBJccOYvhsy8sK8I3Z8Um4NfUqGuOJ3sow+XZNoVgwUSVSMGarCRs/lzq7N0BdOuZ4TUjZE4O7Ii7jVGhEnKVGSZKGzEbosUTZUirowSJiaQhBFFHQZP4qIk4QwkokzEO2GKRs9Nze2OIE4WZi6zGTJBClltWPl+XMSwsSQxKKlxFBkagVNpNJ7EXEcU9A0klQEKzdKJlsDB38k9Hx39kYkKXjZ6+4NfL5htagUBHBLSYVpIwuvniioRJEAe7IEC/WicCxHSR7vU1AldEXGi2LqRQNJgo2uy94oYLpiIksBi80Sth9hB6Lej1RcbJiqwtm5Gs+em8s0Wwafe3yBfz24Td8VERczNYN7PQdDlcHUWKgXaZaFE7lkKpycrnB2rsYLN/dwwyjvYV1oFLi1I1i6th1QHnos1ItMV0z8OGaj7+D6MYos0SiJ997UFKarBps9h9/++i1R5B4nBLHQv41r58Zs2JtrPfwwplQX5oiRF7HR3wVSTE3BzQCuIgntbBjHXNkcYvsRHdunVtA5d7hKtaDRtYM8U+35G7vM3hsbJcQaceyUPXe4xpFGkb2Rz+ur3Xy1W8ryI90w4vr2gPWuw2bf4exslaOTBbww5t++eJu3NvsEMRia+L0dZ6zNZiChYoqw6YurPcIo4cWbe1hByNF6CU0WxfOVgspGXwTzjtyYvh7SLOm07YCeHaLIMlNVnXpB54sX1ymbCs+eO8SljT4v3tqjUdL5yRNNtvoeN3dGnJ+v8dTxSf63ry9ncScBOwOP9a6NKov4m4Km8PJtAfrcMOZe12G2anBookDXDihOFIjTlHsdl4W6x996wBz1/M1dvvz6PYI45ZlTU1xc7WQNNnWqBY31DNwVNSXLFBXmllfudKgXdT5xZoZPnBZ1ZmMnPsBv/o+rXNseitsBszUTTZbp2QGv3G1zebOP7cU0yyaPHKpwe3fEaytdPrrU4FNnp7m0Mci0j2L9/5Wr23TsgIKmcGFxMtdlVk2VqYrOucM1IMUNu8xNFH4s2Dr4AYBdsVjkz/7sz/gH/+Af8F//63/lpZde4qWXXkLKVi8gnLGf/exn+Tf/5t9QKBTet4P+YMTc3rPQVZnwHTRT73UeNBlE+9ZM3w9DvtsMujxLDhGsLO1DRn4E+5+RIgnWiDQhzE6CD5tx5dZ+Vq+gSUiSTJyIVYHlR0wUNHRVxQ0DFEmsoF0/JkoP9uVqcuY6jcfBvQpDLzyQcydLAnSChJ3dcHx8wgzy8GMNooTu6D6oM5T7LN2Dz9t74E6+F2vpR3Ee7rw/y0/i4YDciwUY3K+NFO91iiJJWF6IF0RUDS0/NjeMs/5PGU2WeOJIg52Ry3fvDfAjURPVtgWrdXVrwMAJ0VWZDx2aQJHg//IfvsOp6Spn52pEyYDZWogXCWNGBERhytALOTRR4Nlzc5ydEyHIf3R5m0E3xNNEpdZmkoomBUni4mpXVG/FWRBxpqX0opj5RokzcxVkBe61HewsnDfLSc5fl/FFQBblR5yClCQcrZUpGjKv3umIz0IWblzQFB49XCVJ4K3tAUGc4kbCnGD7Igh75AUoskzJUNkbeQzcEDmLEXKClMN1k8Oayu2WRZy9H5OKzFRZsBBbfVdUsWWmiyCOiVOh17u+MxStAAWxDm1bPtUMFLYtn4KqZJ950UOrTgi37LWtPqamYqriIiFKxAVMvaTz7Lk5nlxssNK2cmfoT5+c4qXlPaYrBrPVAmdnIwxVybS4Kc/f3EWTJeaqQszeswPOz9e4vWtxeaPP4YkCv/ThBb5ydRsZEUGy1Xf55JkZPnS4huVFRGlKwVCZTFPmJ4pIksR01aCoq7xwa4/Vts18o0Dt2CQ1U6NsiLyzca1co6TnbM9s1cjZJzVr2VioFxh6wn16eWtAHIuWlamKQUGTGXoR277D/EQhD69tWx4bPYeTM+W35drtNyecnKnwH19d5TurHUxN4fzhCT5+aoqeHWQxMhZhAj0nJIxSPvvEPJc2+vTtkIqp40cxJV3l9u4IUxf6r7EZSRgXRBvIes+m5wT4UYwXC1euG8TUTA1dk1hsFLi1O8LUFC5m8TSHD1c5M1OmZ4veXtuPCTK9+4XFSa5OFTk1Xc0utMI8BPrLb2yw0XcwFOGcffl2m52hnzmIx99ZCh075MR0mWNTJRRZziverm8PRFCyqTI7lgJkESJjzd6rK12cIGJv5DFZNhi5ImPz2eOTuRPZD2KCOKFsqLxyp82XXt9Ayxo/llsj/vTKFh074KNLDZwwYWvg4oQRbhBjeyEdy6dZNpBkiTBKCeSEiZLGsWaZE9NlXlvpMHKjrNElEixrmrIzFLl5QdZd/tQxwSwut0aZXtAjSkQG3vXtIZIkcXbux4Otgx+weaLZbPLlL3+Z27dv87WvfY1bt25hWRblcplTp07xcz/3cxw/fvz9OtYP5oEpqArO+wzq4D6IG0+y77+/H/NghluYaZgefDwJRMF9HOEFgkVKM+T4MIZsDCr3/1uUpMiSYAL8MCVMYXcUMFnS0CRAhpEvHI8yMF0xcMKEKBYC8YEXiS7aBPaihzgvUtFYUTZVvFDEiZiaaKQYz8OO9UHZYpzCTNYtmd91St7xun++F0s7ZgTfDQgf/9yDDyFgqoSTIcRbu5YI883y0Uq6StlQsf2ISlGI5hfsAm9tDEkUCSeM+M5KB1mCjy5NcmWzz+7AZ3lvRGskmLe1jsPnHl8QxfGmxpFGCcePcbN4l5Eb8d17Xa5sDFBlMHWVkRsiyaJ6K04SvDDCCSPiJKW/T7SoyiBnzGsQCQPD+fkJfvEnDvObf3SVvaFPhFj5T5UNNnrCxVwraiJyI4rRFcHwIIn4m+WWhRulKIhOyV1LsG6WFwt9VhAxdAWA7VpB/tp7EShSQtfyCZMULxLdvxKCjVVliUZRp2TI2L7QqcapWHebuoqhqySxeDITRY1PnJ7m0vqAb99pM3AjFBkmSwbNioGhijT8naHHRt8lCCNkWYDUhYao1/ratR0GrugnLRoqFUNj4IXoisyvPr20rxJJomP7fPXaDs+cFBE2WwOXb91u48UxBVURztEMCZ+arhCEMa+udLnZGvH4kbp4/mGcu03bQx8p07RavtBI1QoahyYK+F7IUrNIz444P1/L2aqVts3VrT5uGOOFMWVDI05TvnJ1O2PYajRKOl07IE1FVd5qx+FLF9f5VLYS7jsh2wMPQ1GZzXpSL28M2Bo4TKcFnjo2yWbf5cpGn1dXOuwMPUBkGTbLJp84M52H1f5vX18WdYfS+HczzVzcIQMnYCjJXAy7TFUM3DAmCBMmigYlQ+H0bJV6UccLBcs6VdE5VDPpOqJZ4vaeRZKmrHcd/ChmfqLIVMVge+CgSjL1gsETi6JW7OysyJcbuCFRklAr6Ay9CE1RiVKx9m+U9Dyu5ca2aOQp6aKCbrYqwnXnakXcMGalbeUsZ9vyuHF3RElXuHBqMu/KvbDYEHEsGwPOz1f5/IUFerYPSDw6V6Ne0vKcuiiB03NViprCaysdLq52qRQ0jjSKfO6JeWarJroig65ybn6C8/M1xg0V8/UiT59o8vuvrnKjNcLxIxYaRc7OVVFkiaIhwOKVzQG3WyOiNM00mAn1oka1oOWBwkkqmmNmDZMzM1XKpsL1nRGrHZupisFkSeQriiDnkEZZZ6lZIkrSnI1by2Qq97oOaSoy/1Y7Ns2SwcWVHrez0OqLK73cvPGjnvelK/bEiROcOHHi/birD+YvMDd2hu+70WA83+9+v1923MN+fsx0RMnbb//gZlFizJoILVGQQLwPzZR0+W01X/tHzv5EsQiaTRGRGQbijt0gQtcUTE2mqKu0Rz5+nGAHIW4oMsXc0BMnxnd4roYigJQXgx7HFA0ZJ0goqCpRFOVAM0UwfpnxcZ8C6/5EiQgl3T/JQ16X8Wvzfs3DnpeMeK3GEycHWds0Fa0LZVNjvlGgbKh882YL249R5BQvTFjviUDRQ7UCv/bxE/x/nrvGesdhoqihyKApMrd3hWtOkmB+oshmz6Wf9a5KElzfsQQrlIrXT1dkoUVarPPqnS4DN6Rvhxi6jJF1wALUChrHp0p8d61PKmWawPU+z13e5vbuMGc8vQhalouqSCRRSpQIZ2yYQKOkgSThhRGbPZckSVElmChoSLJEhrWyzECFOEmZKuu0hiKkdxyMEyMuOLxQ6AIjOyRKxcVNCiKzreOCJKGp4rXuWj6aJrNY0inqCkMnIU5i3CDm8aMNLq33GXridSrrMs2KcJgqssTdPQtVkZmvFwijBE2VKWgKnzw7w8mZCkVdwf5OzL226Bxemi9TLagosszh+v7Q2pQgSlhuWeiygqpKbPU9vCgSJfVxwsiLUWXRyemHMZIs4QYxQSSiH4qGysgPGXohfSckiGImCjppmpKmKSttcXKVJYmeE2I0i5yYKfP40Xp+clxpW3mf63rXIYhTmiWdgq7S0f2cYRt5ES8t72GoiujBThKeu7LFbK2AqkqESYosieYUQ5MpmwqDnQhwub494MLiJEVN4eJah+s7Q4qaiqnJ/KfvrGYVUzV+/9VVXrnTwQtiqkWNs3OVPAzX8iOiBFQ5JYxibrVGzE0UeOpEk7NzlRzwvLHW42ZrxL2uQxCmpEoi1q2mzsALWWnbbPc9irqC5UXcbEn4UYypKhQMlV/JhPtvrPU4OlmgWTbxwpibrRFPH28yWzXzvtWKqfLKnQ5hlHBmrkpr6DJVNrMLSMECNko6l9f77I38XPf30vIIVYJqQWdn6PLFixYd2+fTH5plqVk+UHj//I1d/subG6SpiDR55U6bnh0yP2ECUvY7HlA0FIqGkrub6yWds4eqLE6W+NWnxXMaM3obPYeXb7eFG50URZFyTep01UCVxZdTQVMomxqqIlHSVSRiPrrUyC8KnruyxaX1AbIksdQs8oULC8zWzDynzg1jnCBhM3N/l3SVnz4xxSfOTDPyIjZ7Dm+u9bGDiKEbUTaUjOFVKOkqkHKjJRy6lifeA+CvD7D7YH40Uy28u7iZdztlXcIJ0hzUfa8cOk0WrktrX/KvqdzvXd0/4yw8WSIvUBdfgkLonaQpbnhwxapKAtS4wf2KpjRj1STutzM8hNBCQoA5SRbMWRyTZ5gYGsxWzKwEPsELUwZuQExClMDITXIgF6aC+XkYsFMlqJgavi2oOcdPMDUhRhy50dvWpe+mRvCdQJaasWXju9hvcnmn9+hh9/VuwbgiZ2HNimCU3EiYTcbvyTg0OjYStvs+f/DaGqttEUWRJnBnd4QsSdhBxLfvtCmbGiemK1krg4QXCaeam+2eT86KUNntoRCnqxLoisgHCyOxptcVmXrZ4G9/eJ6TMxWubg1JYrGWVuKUwxMmm12XMBUu5JKhImV9wTtDjz+6vIXtR2/7vLgBVE0R9KvJosPSVEUNWOIlRBEMItGKUDYUJis69zpO/prHccqVzR5BJD6P0djIggCnUkreDSvJwsk7looKPaP42VpBHHPHDjIQq9C1AhEvEicYiigi/50X7vDWZv9ApI3thUyUNIqayp/f6xEnKWdmq0xXDGFGiVOubQ+5vjNkcbIkmG/EeztRVKkXDWarQmg+dlCqspxFa0gMvICyoaEqsFAuMjthcntnxHbPy5P3bT/C1BTKpkI1awHZHDi4gQCkUSwueNwwIojF+6sqMlGcYPkhIHpwO3bA4QnzAHOoyuKPoWqYcUrJEOaJ1ihi5G0BcHKmwoXsNgVN4VYGngxN5u/9xBL/5c/XWe96dC0/d2t+8qzOVs/l4mqPW60RBU1FlWXOzpY4O1fhv393k6ubovfY8iN2Rz5HG/eDleOEfGV6/vCE0PfpGnMTBqdmKiw2i3n0z/4Zh/v27JCdoctax0GSJD5+sklBl+laIcenSpyYrnBpo8+l9R6hlqBKwqW61CzlTs4313pcXO1SLWj8rfOH+EefPJWbE95c6/HHV7aQgCeO1nMgJ8xPA6oFERmzlVVpeWHMf/vuBnf3bC4sNqgWNK5s9GmPfJDg4koPgM89MQ+QBxV//NQ45gW+8tY2eyPhXB5nwD1xdIKzczXKhsrN1oiKKTS1FVPlWLOUx6Jc2RxwaaNPUVO4stHHj4TGtaiLxoeRF6HIMq2hSyvr+q0WBOM3Xp+Pq9Lm60XeXOvyktdGkSU6ttD3fu6JeX716aXcPHJxtUu9NMnOwKNa0FhsFvP39D+9tsbdto2pC111kqbIssyFpUmeXGxkES0iDNnxRb+1/bA8rR/BfADs/gpP8mBT/XuY/cDAClIBvLL/8L2wSJBAuA/UyTwc1MF9DZOUneHCJIvETAVAkyWJon5foyZ+WPRYDt2ANLuDWkE45Cw/JIhTJoo6fhjhhckB4AOZXu0hx+OHwpl6crrCes9mdxSIk5x08LXYrwF82GiqEDqHsYjySFIeyiCOgdn/j70/D5Lkvs874U/eWXdXd/U13TPdg7kBzIACMCJBQSAhUzYJ7Vo2TVDH+9K2ZK9lBcMOrV/LIYe8krxehe1whG/ZFqXQK1mSZZuHpJUIcinaEEGQIDQAyLkwR89Md093T191V2blnfn+8cvMrh4MSBCgCNmvfhEI9HTXkZVVlfnk832OOHlzHbgJUDZVMWq8x/2z9yuLVsmMIrL02uczXgd4jy4JUBWJQeoSzvLS0uxi0n9ieRFeEGF5EU5goEgSigQu0HNDzsyN4YYRm12XP7y6jamrvPu+CSqmxmcvbZIg0XcCbD9ku+8QJgl+tBe/MlbUmaoY3Ok7lHQxrntofoye4/OJl9ew3Sj/XHlhgiLJVAoaHTugaXn0hn7e5GB7MQkxuvza1ysDsiRT1lVMVYDHMEpY6wyx3JHonThh4EX47SFRkuQZhbDnWI4SwfQ5fogfCTA8VlA5UC9yp+vQtjySBAwVKqZO3/bxAVWBetEAKaFe0rmvUeLGrsXQE/2qYSI6Z6sFlc2ei+WFFDXRQBJEMV034GStwK2mnWbVxewMHHYHLk3bQ0ZioqwTJ4gTZiAiPXRVZr3t8NXVLsemKzy8MC7GX5sDDo2LoFc3iIjiBMsLONIoc2K2yocemed//89fQ1YlZIU8L+zhhTHe/8B0mg+W8N+v7NAfiiq2saJO2VDZ6Dhoisx7j0/S9wKubQ1YaQ0xVZnmwKdW1BjlpA83SkRxQsvyKZmii9XxReesF8S0bJ8bOxZ9N+SRhTrffWySZ6/toCsyh8YLnF0QUSkf/I6DnFtpEUQJmiLlrOBLK21+8Qs3uLLZhwQeOTyeM0jPXNhEScF+x/ZplIy8jUIwU6ILdmm7z9EpUTVVT3PYhEvW3Afqsp+Xtq08hPeFmy1mqgWRjWaobPU9zi4YPHXmQF7Lt9YZIiFRNrXcfZvVbz17dZvuMOD4dIWm5eXdplkNm4gbFw0bmirnLtQM1C03La5tW2SX1bsDj5mqyVOnZ5mpmSMA1OXKVp9rXxlwZbPPqdkqqy2HhYnCvvdKxLn4tGyPiZLB2cX6yGu5zc0dS1yEdV02Oi5DbxdTU/KR+u32kIHrMwyEK3+8qHNitpqH/h4aL3JjZ0DL8pms6Dx5fCrPxXvm4iYrLRuQePrRIvWSwcHxIo2SQdlU9uVcAlheQFFX9+UMLm33c23p8WlhrpipmaLtYugjSwleEHF+vcuJ6QrfeXica5t9SBJiJB6aH/v6B9hv03pDwE5JE/dPnjzJ5cuX9/3um1mKolCv13nHO97Bj/3Yj/HBD37wm36MP11766F5MZJ6M4AhW3dDkTfCLGVrVPhv6moeBDu6tCwjDQEKgmg/Mzfw4nsySUEMTcvHUGUUxAnWDUPcMCKOExGkHIjoi2ybFRCaKPn1AYwqgeWE3Ny1INnbFiUFLtHrgFpVhoohAFYCGIoYXx2dKtOyPHbTPtK7n7ZiKuiqjOUGhK/jelDvAcL2P4ZG/3UCjkcZqAjBmk6Uhb6nPdz/hN8I1IGI7NBV0ZQAe2HNSrKfaUoQ4D4OIpqWj65KyLKMFMUUNZnvvX+K3/3aBk3bp2mL96Rj+8zVTUxNYTw1BzTtQLRAGDK6Iqq1VFmmWtDxopggEm0km12HtfaQ5sDDDUUUiSbtVcVt9h00Wd4LkybJx/+jI/G793ucQM8JcYIQXZFYbtkEkXi/dRWCUETOqJIIJB4GCaYqcXBcMBICQCZIiTixjRV1tvtDdgc+MQleGDNwAubrJjf8EE2VOTBWxPVD+kNxADZVmfbQw49iSrqK48fIiWB2iroiEvIlwT6OFVV2ByoTZR0JidvtIUVNYbPnsNaxGQbCRRvFCYosRlSmJrPYKHNjZ0DTCoQ0QBIdmmVDY73rsNKy+WefuwqxiMDRFZnLd3rc2LYIUkb00XdM5FmE33m4TtNyiROhUUVLKJsaDy8I40XHDpgdM9keiGDrM3Nj2F7I8q7FdEnn+79jjpmayS9+4QYvr3aoFTRkJE7MVjjcKPHs1Z28ZUBkAsYkboCmyPhygqoIBrVa0Dg6VeLYdDU/YZ9f77LeHlIyVJq2z/Udi57j07UD1jp2WrOn5CPFs4sTrHUcGmUj7yr+xMvreJEA2m4Qc2PHyrMXRfi20K5dWO/SsjwMVeHpR+cpGyqfemWN4yPbA7wmsBcE+/apr65TNgTw/NQr6yxtD/ieU0Ib+OzVHQ43sv7kJB/7Hpsu88yFTZ69ukNRV5iqmByfrrDaGrLRdfJariyIuaCpKcj3mRsr8NiRCZHVlkbRZH3Fth8Qhglnj++5jTPGMcvme2lVRLvMVEVnbxbEnCSiQeJHH7+PU7NNrmyKLtatvscLN5scbpS5sjnIK/Qys8Z2z8H2Q2HcyU0TUNSFTjDT5GWgWDCMDndMh6mqcA5n79eVrT5dO+BzlzfT+jyfxTQ8PXs9GYATo+xiznxmzuobOzarbZuOXeOH3nkITZVp2z6n52psdB2+ttrh333hBook8/DCWP55SRCFAQ8vvP1jWHiDwC5zuY62RyRvgi0Kw5Dd3V3+4A/+gM9//vN87GMf46/9tb/2TT/On/T17aoUe3CuRtmU6TpvXmmXhfa+kXfz9UZ5sixCXbM1OiINEnK90SgIkRFhuE4QEyd7TtySIVEtGOx0XQKEe1RRxDhVRMCJLdBl0Qzgh/vdnkY6tsxWUZPww2QfcIoR46uZMRMrHc8ZuowfiWL1jJkaXWJkHOfbqaoyE2VRmdV3A7wgRlVBQ8ZPu1t1WTBe7jB4XbODjGDkdEWMKPt3Bfjpqbbsjerq3Ag2eh7f/GWXWH4EvrP/c6sAyT32CYh93Uu3WUaAIVWR+fyVba5sWvntghi6jkg49CMR2dGx/fw+tYJGy/JEILQa03d84iTBDSJkoO8GZFp9EMBltl6gbKqEUUJ76NNK2ddsmdr+phFJkpBJcuerIu8FUEsJaIoiQAoCtJZUBd2EIIxIiPPvimiPMJEl0c2qBDGSDJ2hz+20RxdSY0oUc7vtUBjISLLEeEnn1GyVL99o5qYhP0qE2zVOmCgqlE0VVZWZrZkcrJe4stnnVtPi5dUWCw3R+zlVNdnpu9zpCX3QwkSJuVqR7YEHSUzXCfADoYPUFOgPfTEOdwNsL0aREw7Wi8zXC/m4tmuLkW5JVrm1a9F1Alw/QpJhrKSlRfQCpNQKGo2yya2mRRjGGKbG19bESLM18NA1me87PcvRyQrX4gGTFZHyXzAU/CDmv55b4+zhOl4g2Lfxks6x6Sonpit5/6eiwFZPuBN1VU57jSUenKtR0OS0/UIwXV9c2mFpe8DDC3XmxkyK2gT1kpZrwnb6Igi7Nwxww5jr2xZbfZfnl3bpDQXYfeLYJDM1k0+8vM5ae8jJmTLvPd5gq++x2rIZ+pEol0ewblc3+7Rtj/GikWpSRUjypY0+WR1Ztvb6R8dzhqhpubQsj9vNkL67nGsiZ6pGDj4eWajz9KMH8/0+cMVjbvVdbC9kqlrkxHQ119tlrQogcWmjx5dvtgijhISEsqFxYb3LJ15Zo6AqvONQncePNvjIuxbTsGULtyAMIVmna9Ny8/YJ0a8LM1WTekmn3xpSHzM4PVfj/FqHT7y0xkMHx3jqzOw+4Ls78NjounSdAEWS6Ngejx2ZyNlVYfIQjNzcmMncWIHD6eh11E07XxdjUlNXOD5dYaYq2mWalpc3Rqy1bW63HX71hWXKukZCko9gR3usl7b7XNns54aRZ6/tpDWGHhJwY8fimQubBGGMKkscbpR47MgEL9xqMvQiDC1mLe2qPZu6yFVZzj8fb/d6Q8DuXnVgb6YiLEkSut0un/zkJ/mxH/sx/vk//+f/UwK7b1el2LXtASVdw/E8/Pjrg7PX02IVU13dNwJ2EiI65F7VYm6QoEgjY9m7UMi94G0M+EGMLAmtXs0UHZRJAmVdoaVAEJGbF7KlIlyKpiaA3t09FRL72a+CphJEQb5zdFWioCskiRDAl02VIBLVTc2BSycI0xw9cdJXM1FfIh7LD33hVvVDlpsWJU0jiJI0D0yAlDgRQFRVRPm7/3V2boxgjKYqOvNjIqbBGXkBQQw3du38/XmjOrmIPXPHN7Pu9djR6/zh7l/FCIarawf8kdXd9zfhptUo6irt4ZDuMECRRNtD1dRRJVlEbyQi7mTYEwdYQ03H6okA3Gk9qdB+DX2GfoSuSLQH/ms+32G099lTIGdoFWCyolPSRZtCEMVUizqOF+UsnqFJVEwFyw3wQvGZLhuyEOHLImooSSLiWMpBu2cHqCP7ZTQ2BSmhWtA5NlUhjEV8g4QwjqgyyLIIatZUWbCSQcRa20GSpLQJALwoZrvvcmqmStkUjMR01eRIo8xDB8foOQErLZvtvsPVrQFhQl7B1RkGTFZNTs7WuLDWJoglVtpDxksGhqJwZr7CRNnIS943eiJmZapmsNgo88SxBhc3emx0HebGCry43Ga1bdEbBun3zUNR04zHFKDUSwZHp2ClZbM7cDF1lcWJMistmy/fatKyPdFhmwCJlOeGNfvCQXx2cZwHDsgEYcRnL2/RHfo00xPvB07PcrhR4vkbTe50hnx1rUu9oDMMojwMODuRZ4L5jbbDWEHjyRMiZuWzlzd5ebVD2/bzsNpfef4WV7csTs6U+dHH78vZqmcubLLVd3GDKGcTO46P48c8fKjEdx+bpGKqrDRthnMRx6crvLzaoWm5bHTFGDNKGcBsuw43yrxjvs6dnkPFUJkdK+RGi62ey9APWUo1aQNX/Jw5UguawuHJImfmxlhrOyzvWjx0sMZTZw7kWWvXtwds9VzcQLQivOe4uO2tHZuKoaDIEnNjJk8/eoiPv3Sblu2zMFECJC6ud1mYKAISz17b4fx6VwBmXeXYdIVj0+V8JPzCzSZLuxbNgcf1nQH1ks7Tjx7kQ4/MpxcDSQ6wL6x3ubjRzxnWpuXl35isreKJ442089liuWlxcaPP+fVu3kZxfr3L7faQtfaQOEl45+EJTs9VAYmzi+N86pV17vQEg3+gZuYj2Iz1m68XGQYx51batGyPj7xrkfGSjuWFnEzrxC5sdPnyzSZIohIS4EcfP8zjR0TH7EzFYGvgcWWrT7WgUSvo6cXJ/+A5dm9mSZJEvV7nr//1v86/+Bf/ghs3bnw7n/5/unViuoKqSOJq8RvgbF0VjNDdS4xshAj7Gy3ndarFEvazOZoi2K/sV6q0N9IbfQQ/Ea0I9ZKOqci0LF9kyakyBUMjdALhUkQAGl0RJ8owjtFVHS8MkbkLOMoScpTkWjP/LtZUliXBEsRxnuReK+ocqBXY6TtiNyaCKSsZ4oBKInosawURtdCzA6woxvI9ynpAyVCI4gg/hL4nmB1Ng6Ku0Qy+vpg2e+scP2TDjwjjhKmyzsANcpfn6D77ZnjykqGCF74G3KUpL2+qGQS+/uhYkfbMAxkzFqf/2b6PpkgE4Z4W0JAlbN+n7937MyhS7lXiWOyPJAFZkYRpIIh47fA7vR+vHVNnpHIEbA98dAUMTeHYdJUEuGb1iRLxOTNVmaEX4oaic1NVxP3jOMF2I4Ikes3FkmByIQkFw1nSZQxVwQ0TpqsGf+6BGa5vDzh3S9RmKbJgrSfKBposs2u5bHRsFFkmihNkSTB8ZVNlulqgO/QIwpie69O0PdZaQ8qmyv/y0AEsL+T3L96hbYs6qcONMuvtoahCGytQ0BRuNcXJ1wvBUMVj99wATRHfiYyRu9NzUCWJE3M1Hjo4Rr0kIiC2+ne4uN4jCGO8MMZUFQwtIPIzx7RAxYuNEvfPVsl+q8gSd3ouTxxrUDFUbuxYDP2Q9baDrkmoisRiQ8RurLUdirrC+04I7dTF9R6femUNM60x2xkIjeG55RZXNntpVZQ45pyYreQOWVERtZML6UWXqs3CRIkfeuch5utFTs/X+K0Xb3N+vZtro15e7Yhg22qBpW2LrZ6bsjAJV7b6vHK7zZHJCgfHi9QLQk93dnGciqnmtV0//M6FdFx6h89d3kaRJU7NVHPQlungiprCVNXk7OHxHCSN6vB20taMDKwOfZGhmbVpGIpKlAjgvNK0uL4zILv0G/oiNmajO8R2Q9553wRnFyfY7t9hYUI4ZrPg3+zTW9BUTs1WAdKfRVBxNt4tagqqIlExVdEpnOsHJSbKhnCOp2HAIADU04/uaQxnaiZFTeHGjsXS9iAHh89e22Gj61LUFGw/5NxymyubgzxyRJUlLqx3KWoyx6ZF5t7zN5q8uNzKwXLW6PHIQp0/+8A0H39pHV0VRgcgzyQcHekuNy28IOb5G818bJ39/uJGDyeI0BQZyxNAeaZqcmPXwlAVDE1lvq6wOFGiUdJZblo8dt/E/xw5dm9l/d2/+3e5ffv22/X0/1MsywvY7rv3rN8aXZosssfce+SwjcriVMRJb/ScnX3t7z6P64rQALXskJGKWIA8UyhORGK+LCc5q+WHe7dVJXHbJAY3jgU7F4MkCR3TcARAiJwxnZ2BOGh0bJ+yqRLFCdFIQ4UfCKAqp/NlU1OoF3Vud9x0n+1taVY/6AUx650hiixTUCPqRZ04FfdnrN6RqQpXtwb52CZblh9j+XHe6Uq6L+IQ7CTax7DJCLdukI4UVaBe1vDSAno71bUN/ZCjUxWcIKJqKrx6p/+G9HHZflJlGC9rtAbBPQGYBDmYfz2g+PWYwbsfU5WEvsQP4zwWxUxf5+hmR6kDtFJQ8bNuybSYPkGws6MfJAkRuQApC5rpH6PkNVrNbMkI0JIkCRmmz1zFoytBsJleFLG0M+DBA2PIaSRNEEHfiXKJQta8kpBQLWj0h4IBzrRq2cdPk6FW0NEUiXrR4NB4kfGyznJTjPHOr3dE9p0XEMTis7AwUebQeIHbHQfLjYgSKBuALOUGjPmxAgMnZMMLCSJ49U5faPkGDpYnnIatgcfyrpXuI5mzC1VatsfQi5iuGhQNla4TiIo1I+bhQ+M8dKguxOpOwCurbb58c5dqQaNl+SSIqj1I+OylTSZKOrYvMs52Bxq6KjNTK3Bkssy1nQFJDDt9Vxhrgoi19hBVkfgzJ6coGypL2wO2+i4zVZM4Ecx80/ZEZqCqsNIcUjIUwjgmiYXGc6vn8m/++3WWmzZm2hijq8JE0rTEaHmm6nBls48qC3CRuWmzdoOM6TncKFM2Ve50HX7l+WVmqiaQUCtofP87DnC4UeaZi5sUDZVTM1V6TsC//8MbLE4UMXWVK3d6XN0aIJEwWTZ4/OghQLQTWF7AZy9vcnVzwMnZSj4+3Op73G4PKWgKRyb3mJysTu++RokDdTF2zLY7a1fo2D4LE6K54sR0BVNT8jHrStMmTmKGXszS9oATMxWhi/VDzq22sD0BBB+cq3JytkYUx3z47CGeubjJ9a0Bi40Ss2MFVFl8y9c7Qx47MkHH9rmy2efs4jh/KWXHsky552806TkBV7cG9B2fmdqeMaRsqExVDI5OlunYQW4gGa0OA9KYE42W7XP14h22+i5PnZ7F9SOeXdvm8aOTnJkf4+J6l7Wuw1TF4HCjBCTsDkQv74X1Pu85MXkXG1hiuWmxMFHk2HQ5f76V5pArmz1mqgX6bkijvMfazdRMHj/a4Nxyh4vrXeFwbQ+503H48NmDPHZknKtbA+bHith+yHpbNHsossx9kyXOLoxzq2lT0BQ+e3mLlV2LluVjeWGexfd2rrcN2P3Ij/zI2/XU/9OsleYwrzd6vaVKAhCZipQzDFVTpu/unekys2C5oFAxNdq2h+3vZ4tkoGIoDDwBVnRVwUpR4egm1AuqaABIhN5toqzTHHjE6VhVlcVYTVUkNEVmsqJj+xFuILQMcZIQhgn9INoX66Ep5KXvIDRQnWG4b0RpqBDHaYRDyWCjKyI1TFWhaoqMudETfBQl9EPB+nQtIVA3dYW5uog0uOx0kCWYLGtEcYLtBcSxcMTK8f6Q4IjU/TqiLQzCPcAnS5l5BIq6TJTE6Iqou5JliW56lQsCLK51hjTK4gBZNFTc4RvTbiQIMK1Jcg447u7YjfnGNXRvlBlUEK9LT1m0KI2xmR8vsdUfYnkjoDsWLReKIjpcExIcXwzTE6BmCKDeTynGGJHpVkoBvHwPgKZL4rMUI9oYMldvSRcl8MRi20R22r2JbTdIuNMdksT3liwkCMCnayL0NUFC9ULqRY2hHzLwIlGvVtSYKBk8eWKSxUaJixt9VBkO1Aq8fLvNaitgqmLwwFyN69sWpirj+KLFI4wS0a4Sx0xXTRIkLC/A0GTWuw7bAwdFljE0wU7d2h3ghwnTVeFu/Jefv4abShsquiKiZWIBRr0gJoxCJisGTxxrcGWrDxK0LI+xgkZJU7iw3hUuZS8SDKIiif23Y3Nz1+LadoKpKEgSBCnLf3ZxnAfnapxbaRNEMW3LZ2fgstlzudMbEifwucvb6IpEZ+jTGQbMVE2qpk5Ri1EkiV1bmKGubvUpmxoHxwr4UcLt9pBnLt6hYwdIJByeKDFRMZipmRysF6mnuqx6ScP2I0r6nuMTxEXnZtfh5vaAuTGTRtmkVtDZ9Ia8vNpOL0ISCppCxVCxfRFi/tDBMX708cOiTqs9ZLJiUDY1NvsucRJT1DXOLk7kdWYv3BRGAS+IOTlb4SPvWsw1W64fcmi8mLNQt9vCKXtiusJyU5gxwijZp8t64WaL//CHNxh4AQ/MjlEyVCwv5AOpvm3ghnzm0hbLuxa2H6IrCo8s1vmJ9x3n+RtNbreHgE8UC9atZYue31//ygpVU6Ne0pmpGXkf7WrLoVG2ePLkFMMgymNQPvTIfK5tW27a3Ni2KOoKAyfg6laYd7nO14tc2x5wcb0HJHzX0Uk6tsfHXxKkzWrLyV+bqOJS8MKQtuXztbVOHtlle6Lu7ezhOkVtnBs7Ni3b45XVNvWSzqHxAn+03KEz9FMHb5IbO569usNqy+GRhToX13t89vIm739gFieIeO56k4cPjXH28HjuGs5CmFdbDmVTZehHdB2PHcuD7T4DN+Sp0weoFpo5g/fZS9tAwsnpzPkssdKyWe8MiSPw4oSVls1nLm7mncFv53rLwO6LX/wiv/mbv8n58+dpt9sErzN6kiSJmzdvvtWn+9M1slZa9jc8AeuqTElXaA7c/ITVd/c7Ucup3kJVZAxVjIHuXhJC23aqXmTHcmlbwT2HYKoissdA5K3tDjziWJgXlNSYUNAVnCDCCSPcwKFkCJ2bH4qTd72kE/RdPAQLosgyigK9YbDPmHE3s6grMqgwUytQMhRu7YquTS90KRoqk2WN7tBLK8v2XJ4gxr0kEHgRN3ctpqsGuqogIRLoL230cIMEOQXKQRi/xnVKsp/pynCoKoGuSbiBcE8GYYyiiBy9MBR1ZnePS/tOiO0K4JokowOTPeChjMSPjC4viBlIe2zh3UBlFLxkGYNvdmWP33MiETysiqqhKIqRkVClBEUhvwARgBf8MKJsKKLOKUXwXSfk0HiBMHEYpjjXi0H2xZ2Te6CyKIEgFOHBSSLex8iPMdWEop4aWb4OqAOoF1XRPoIApaOgV5NBUySRWRjGrKauWVOV6XshThAhSUI/HISiYeL6tsWDcyKa5fxal92+CLrWVAVDlfm+0weYqrZZb4sRlzUMUsNFjKZI9N2AoiZGZb2hz/bAxQ9jyoZGraDRtl2GaY9xmNKYD83XuXKnTxgnuQ5QUSQSH7661oVEsFN9J2C1ZSFJok2joCnMp0BJloTW7+B4AVWWmamZeEEktKVRRBCJEeyl9R6mrvDAAZm1ts2ljR5nF8f5npPTuQbt/FqX9c6QOz0HErFNr6y22R14AngbOpYbECegyBKSJLLq/CjJGwqCMKZW1DgyVeL/9c5Fzq20ubrVz+NKlrYtDjfK/MXvOED2DfnYF26mzKAhYlr8kCubA546XebJE1MsbQ/4yq0WfhQz9EOKujgFbvYcEinhyKS4qJus6JiaAkkG/jTqRZ1TM1UeXqgDe/El1zb7SLLEw4cEsGxaLq4f0rIDTs6UcdJ9uNgo5q5MRZa5vj3g+HSFpe0+TctNI1ASBl6I5UVsD1yOmGVGv+EVU0WREfE+hkbF1FBS0f6HHpnP2T4BPhI2U4PNZs/lu442+PH3Hs1BTX1My8N8n726Q6OkM1cXwDMzbwB0bI+dgcvZxXHOHh7Pu2//9eeXKJsK9zXKqIpE0wq4vjMQYFyC03NVVEXk41lekGr2BCkwUdZRJEn08FZ1Jio6hirnAK1eMvjCtV1u7Fg4QcxWz2Vpe4CiSKy3hzx3vZnHyWQM3bHpMv/s/7nKH91qi/dnrEgUx9jpwTVzDYdRQtVU2ek72H7EQ/M1Do6X+P3zd9gZuLy00qZW0LixbaUu4gYbXeHu3uq7vLjcYaKk0yiJ9IGDY+J1bXSGLDZKfyJ0dm8J2H30ox/lP/yH//CGHLLSaMHmn65vySrpr337TCXNTMvGVklCy3bvaXrIXH6KLKEpIlC2P/TvyeZECIdq3wvoDcN9YCAb/2URJaN5YV6Y5E5SEnEiHnhRfqjyY5CCCE0WWkEjdb8pioyuxBiq0KFYXkDH9vOg3CgFNJnYHYS70FAlUR+kynk7gIbIE5so6iTAwAmwgziNLRFBxlG4V/PVdwMmywYHx0v0nYAwTvITKIkoe68aGgN3f+Dt6IhSlaBS0IiiGMuL8FMGVEXspyBO0JSETuLh7JF1+96bbHtk9hynowxtlO4LwaLsvRe6KoJfR/Vt2eNodxkq3uq3Mh75fxKLWJR+GNH39q7U7455ybbL8iLmxkzAp5/m5m12ndewcl50b0cu7IHS0Y93lEDTDtAVkZEo5AX3fgBVEuyh63jcfRgzVRE1U9Bk2nbA0N+z6nhhjI7MdNnAj2L6bkgQJdi+z8srbbb6LkMv5E7HEZ9xYEKVsP2IX/3yCkEUU0sDUdfaQzYdoe80ZPFt2R44SJKMF0QMvTj9vIe07WBfxd+dnsN/PXebs4fHedd9E3xluc1W38HyQhEHlF5EqIoMssRqy2bgxdQLMqossd0XbsVG2eBdRxrESUJ/GAoTTxBhqGL05Icx17cGDNwAQ5G4f05onZ65uEnH9rm+PUCSJIZ+SNPymKwahHGc1lipacdpwnp7SKOiY2oqcZKgWTJJkqBIAkB4YczB8QIfemSeZy5sYqgymqLwmy+ustl1iBKRyfbMxTvsDvw8HPdTX12nNxSl934YcepAjSeOTbKVvr7MGSlGdQLMZHlvSZyw0RXMS8sOWNq2WGyUmKwYtJ0Af6fP7JiZn8iXm1aegfe5y9u0bB9Dk7mw3ufGjs1K2qhR0BR2ByLTbWGitC+seOAELKVNBYYqLnQ3ui6PH23w9CPzXN+2eM/xSYZ+xLnlDh3bz40Rs7UiB+tFTs3WcvdoBiYyPVxmcNjoujheSBgneQh1xVTZ6Lp5/dWzV3d4ebWDqkjUCjrXtge5frtiqtRLOtNVM3/8s4sTXNse8OyVbYZBRLWg87+/7wSfemUdWU7oOh4PzQtw1m8NubY9yA0te6An4dxyh1dud1jrxARRzA0G4jugSulxX8TSrLYc3DAkIebAWJnvO3OAekm7J4B6/wOzDL2Q+bE9xuxm02KxWcIJwjTHUKZqqqy0hnTSffWB07OcW2nxylqHzqUtyqZKMZUizNeLOYO5tD3g0nofSHj/gzOcZZy7o2jebrYO3gKw+43f+A3+/b//99x///3863/9r/mpn/opXn75Za5fv87a2hrnz5/nX/7Lf8nOzg7/4l/8C/7cn/tz38rt/tMFPHlyit/52noexAoC0OmqjCzHIh4igTAFV6PnLVHNItG2RXH4ydkKf3SrhT+Sg5tFh0TsxUOUDZX12M0f50BVR1YUdnviRJ4AyDKNgkij98Ikj/7QVZkkgTCJ9z0PcYKiypiyggQ0Bz6qIlMtaERRwnbfJYkTirpGHMe4oSh7l4C5eoEoEvEOc/Ui4yWNGzs2XcffBx5blk/L9oniBF2RkYPU6asrFDQVL4pJ4oSeI6qBNrrDNFLCEc+V5uPJkgjPPVgv4oQR/aEYX93NehmqzLvvG2elZXN5JPZDpDiJx/MiXpcuk1K9YfYaIvbrIWEPqPkjv5cQ2yhJEt7Im57p6sK7nu+NDHhfr+Hj7pXwxkKYR7WIItdq/7j27nU3qMuAbsjXb97wIijrEId7sO7u74FEGqWSboYk791AVBdJtCxxITC6okTUVh2bEaLrtbaDKsN618WNRBSCH0b5aF6RBJvdc3wGTkAQgRdG+QVPtSAuOgxVwgki6kUDVZGxvRBNiSipKpoi0YuCPMYlTJlc24v47KUtvDAWgbpxTJIkyJJM0ZCZLBscmSxRMjTOr3cJ20N0VcHQ5DRrL6JpuXzxutDALTRKjJd0agWVdtqzaqoyk1WDgq5wZn6MD589yMAN84aCTAf20kqbW00LVZJ4ZGGcs4frHG6UeWW1zQu3WvmJVTQ9KByZLuP6MT3XY+BGxAmcmq3lJ8ehF9GyB/hBRMnQeGC2ynTNpKCpJImftzdspbEvkiTlhoKH5sc4NVvhyuYgd0Zmva9Anm32xaXdnA1dmCjgBhGZw/L69gBVkjg4UcByA5q2lzcwnFtuc317wMHxIk8cm6Re0jm33Kbj+Cw2Snzg9CxL231WrgiwN+rMrBc1akU9bagocWWzR9v2GbghP/TOhVyb9ivPL/PFpV2ubfcBYZyYHyvw1JnZ1wCIZ6/usNYeEsZxbnDIAEmWn5eNV0dHwBVTRVUkGiWdyxs9lnYG+QXtMxc3mamaPHF8kiubfV5Me38/9Mg8RU1hq+/k7/2ffWCGj59bE27hQFxOLUwUXxNdkmnQyoaGF0W4QcitHZH9aHshz9NksmQiSfDE8UkeXZzgpZU257Q27zs1zVNnZnnhZpMXbrZ47Aj72MUPnJ7F1BReXu0w9MN01CvhBCG7A5/tvstUxWSmarI4UWRxosTjRxusd4Zc2ewzcAPkRLDdC2lzCJCbRY7lo9gkD6p+7rqoUcsy9f4krDcN7H75l38ZSZL4z//5P/Pggw9iGCKn6MiRIxw5coT3vve9fPSjH+WHf/iH+dt/+2/z/PPPs7Cw8C3b8D9dotamZmrs2nvjbxEXIepnFFnoyHRDFCe3h3u3G3oBiiwTxwlOEHF1s88wZdJMRZzI5+pFqgWVq9t94kjC9iK2es6+k2OMhC6BlNUeAMQxuqpRMgxatkcQCX2eLEt8x6Exuk7I9c0BAzcULkdZwo9EnlXG1phJTFE3iJMYKw0FloMYQxM6NlmCmqlyoGZyfWeAoSk8ND+G7QdYfpc4TijpMrYvmCDLj3N2SyZGVyUUKUGVFRoVnVpBFLE/f72JHaR9p+3hHhOWMoSGItymbcdntmoyXhSOqMGoKUMRYZ3DIE41L3tLNB3sjVSzZopRQCND7lrrvE4wMen9g4g8wiNKf9f3YkqaTMVQsPyIgi5TLxo4fkTH9oneAPi6e70B4/W+dbeub3SNPr0XRNj3YJPvZXjIQKuqCHMKCGA7VRV9mbvWa2UgsiSTSFH+Gg6OF9jpu3mkjCTtAe0oEaxj9tyWH6OrMaam4oWi6kuRJYI0FzFJ4PxaT3TDShK1gsb8WIGiLrM98IiimKIuiZzHJMENQsaLBiTghjEH60WmKibnN7qUdIWD40Wht4thpmqI3DQvxNAUGiUR2LzYKHFfo8S5lTY7fY8zB2s8dLDG718UmVuKJCFLMvWiRqNiMnQDiqbKYqPMwAt5aL5GQZfpOyGFNA+sqKusNm02ei4dO6BiamnYrsavvrBM2/KoFDSefmQ+1xctN20ubvRQZYlTsxVOzwswlmmYdEWEdz/96CFeWmlzebNPzwlQJZmaqRFGMZ0g5OFDdQ7UC/zBq1t4ocsDB2p5ptlaZ0jb8RkzVR6am+Chg2MAvLjcYqJk8J4TDTp2wLXNAY2Syf0zNRYbJc6vd1jasfnMpTu849A4j90njAlL24N85DkKijJwWtQU+m6YM0xO2roRxjGv3G5TM0UbStMWtWQFTWG6avLEsUmeOjObA8cD9ULepZqxSlt9j0rq1lratjB1lUcWxlhslFKDgPhmjAbogqhHMzWF+bECW31PaOCOCV1a5vrNHrNiqhwcL3K7LbpWYa/yTNwu2VdJlt13uWlxcb0rchk98ZpnqmaquRM5dE8cbzBTNbAaJYIw4oWbzRxcZozfwkSB990/zY0dC8sNubjR58kTU/sMLdnrAji30mbghDTKOocmxHFdVeA9x6cY+hFbfSc3lhybLuf/f+Fmk4+/tE7ZVGmUDSqmytAPeWmlzUsrLXYHHpMVM5cZkAi94aFxlYImc3SqQr2kMVUt8EjaQPLs1R22eoK1P1Av8F3HJnn86P7PiTC2NAEp/wx17IDtnksh/ewA/2MzdhcuXODQoUM8+OCDwN6oNUmS/GdVVfmlX/olPv3pT/PzP//z/PZv//a3YJP/dGXrwno3ZTz2LwmRwxVGCSFgeQmGHOyLqRBAJMZU0hGrI0awiiw0EEkkxMRRIhCNIidpqn+EoYo6oygRBywzHfUoafiEF8OdnsfcmME7Do7TdwN0RaY3DJgbK/EX3jHOb764wnbPw/ICyoYmxNYjJ/IgFNqmkzNjnFtpYaVjMCc9d1cNhXccGmOrJ0JtdVXixVstum6A5ycoQLWqosqREOMnQucWRQnDtJe2qEnomsRE0UBTJbZ6PtM1k+2eyDbLQF1RE52zCcIcsDvwUsegJAwRcZwbUAq6hKaIK9mm5e65KpP9DRwSYgxYNESMgz9CS8Xpu6gp3xhQZe+nfNfvnSDOwYcbxGx1HVT19Ueao5+d7CbZcyeJyHDre28c2r0RJlAC3FQfl43WQYDne6k7ss/vKNsbxeJALCfQtDqvGbiGcZTfWFOE43isqGMEIUMvwo/BlKBkqnjDMNdqhumHMUrAUJL0OyWLXLP2EMsTRpyeE6Q6xwTH94iBsmnScwKiJEmbJCLsIE57jn0qBZUDY0V+9PH7sLyAa9sDOkMPOhIDN2CYjvtA7PzJki5crZ0hXvqhvH+2xuyYz8mZKgfHS0xXDdqW2BY3iHCCGMeP2LI8or6H5YUcn6oQJXB8SuTg1YsisLZsqgy9kJ2+S4QIvwVx4g3CmKPT5XS0JkBdBuhUGc6vdXn+xi4dO+BvvOcIuwMRyVLQxRjrY1+4wTMXt+i7IVVTQdMlSobCo4vj3NoRr/HEdIWXVlpYbkjP9dnquczXiyKDzYuIDY0PPXqQJ09O8fGX1mhaXiq0Hxd63DihYqr5bX7jhRVuNW/gBqJerWxofPbyJjt9j0bZoFEWY0XRcjDk0Lhwfr6y2mGn7zBZMVlsiJP2amvIyq5F1w05Nq3w/gcOcW6lzUzV5OGFeg6cRgOFf+J9oq/14y+tASKOo++KcGPhFB2gyiJSZVT39chCfR8oqJgqThBx32SJ956c4nCjnFeBLW1bPHtthy8u7UKqA37yxBQfemQ+D1j+xS/cIE4k3v/gNE8/eohG2UyBVZLvg+ybOHBCVFXm5GwljznZ6rn0nYCZqkHHDvjKchtFFm7mq1sWG103H29neXQZwM2Ch0djZ7LbNS2hl3vhRhMnjJirT4raRDugaGicnK0ycEP6bpizinfHq5RNlcWJUgr0Wlxa7zHwQrqOj6HIPHRojDiG3b7ouZUkiamqjpMz71LOzv6fv3eZgqbw+DGRH/jBh+c5PV/jhZtNlpt2zji+tNLimYubyLKUhxvf2LHwoojJinHXqPntXW8a2DmOw7Fjx/J/FwoiA6nb7VKv1/Pf12o17r//fr785S+/hc3803Wvtdy0cUfYjlEhvK4oSER4KT3jpY6/ekHG8gRokRCOS12VkVAZK8qUNBU3itB8idbQw+tGhLEYwXphhBcm6HLCwfECWz0HJxRu17KpESVeDrxAsFwPzY/x6p0eLdvn+IxIL3/26g43d2wASobGdM3ACSMMNSSMYwauKH6fqpo8dmSCnuvTd0I2OsOcaTE0maubA2KStLLLp2mJkZecjg7v9H3KukxRl0nihPm60MztDDyRtSZJ1Is67aHPRneIoSripK+FQkOliKwvQ5VpWh5h2q9paDKPLIzTsT3+29VtgiCLY1GZKBts9xy8SNSnLTYKLEyUuLI5YOD4OEGSu0DjRHSGWm6AGooxVHbYsdxgn+D/XuPQ0Ty6OP23oaSBvghwnLlxwxi8e2j57l6jwCh7ujARrQzfHGf3+iubdibsZd5NV3U6tp9GkNz7flKKOvdtYwJX7vREjIcuDCqj4+CMpJYR921ZAbIkRp6ip1hk2T28UBei+iDGDTJoDa4X0k9H4nIYIyEuYjK1nZwI7WNGvw79iKtbfYZegqlLNCoGm10HgqzxJESSoF7UWUvZXDeIRKdqLKVtLCG2J6rBNE0mTiQOjRfzANj19pDvfWCGYzMV2rbPte0Bs7UiRd1nve2wRUJn6OGHwiQVRRHbPfHv8ZLOVLXAqdkqz9/YZWnHwvZF72+1KMavVVPlF79wEz+MqZd0vv8dczTKJs9e20kBnUTXCVAlMeLqOQHPLe0AsDsQRqXZagGQ+PTFO6y1HQ6NF/mx9xzh2vaAq5t9ojjm9PwYYZKktWExfSdgpRnnbJOuytw3JeJgMlNGxjZd3bI4t9zG0EQrxanZSn5SvdW0kRKYrBqcXRjnV19YZrfvc2SyxBPHG1RMlV95fpkrW30USQRd3+kILVjfCTg8Kdihx4402Oq7bPUdqgWdv/rYYUxNoagL3dlo6fwoaMly6i6sdynpKk8cn9zXR3phvYckwanZKvWSzkqqZ8xe40ZnyLnVNvNjRcI4yYOCZ2omP/G+44Bgj86vd7mw1kWSRK/2F5d2BcBNwd0fLTvYbphq9MQXITMwZMyZqEebYKVpc369Q0FT81Hp0rZFzwlo2R5VU2OtPaRiqiLWZBhwuz3MWcpG2eL3Ltzh+RtN3v/ALKfnakDCctPKnbFC8+fkPba6ppAAWz2XSkHDjSJu7fp51Vum8fvMxc3c6ZoFU5+ZHxth1JpUCiq6JlPUFcZLOu9/YBbLC1hp2qy0bIq6QkETkTlbfTe90FD5xCvrrDRtagWN/+277+Mf/YXTgGAXn7veJEmErnCtPeSrt9t0hwGyDF++2WStPcT2I4y0XvX8ejcfgb/d600Du5mZGTqdPVp1dlbYsV999VW+67u+a99td3d36ff7b/ap3ra1u7vLX/2rf5U//MM/ZG5ujl/4hV/ge7/3e9/uzcrXQ/NjXN7oYnsRiiIYuiwTrOuErym2jxFmhlpRo52e4ExVYcfyUGRxEhor6Ky2LLwwZpiOMfXU1OBHAjH4EbRsHy11wOqKjK5KLIyLcc9uzyVOs82euXSHnZ5HLAlW7ZmLd/jcpS2alsdEWWemZhLFMRVD4933CT3D/31+gzC9Cq+XdA7Uirz/gSq/d2GTK5t9MUaVJbqp9d1QZYJ4z0wxXjbYGXjiZJxSWQHCnTVbNbH9AC8U0RA9x6c/DBh4MYoUYnkhcRJjqBL1kk61IJolirpCggimXJwQgae//kI7L4qXEWMPWZKYqhZQLDF6OTVT5X95aI61tsinOr/eyd20MeD4EZMVk7IpIgAy4Jq9j9m6l8mhpAuhvXB+ittkrKAmwXTVoO8K0fHAS+7xCG98tYZfP2j57vX13LZ3w8MY2Om/PuoU2XQwVjSw/RDb3dM0xpBG98SUdZmyqdyzVzdG7BtNFYxcApRTNs3UFK5tDpBJ9rV+yLCv0cUPESaB0W2TBDhXpATbD/O+ZFWGqbLJmbkxgjDC9h1RV6dIGKqM7Ub8ztfWKWgiky1IwPUD/HAvwFlVRGhsFAtABokIaY5D1toCVBydqlDUFS5v9CibKrIkoSsKScoWumGEhoIqS7TsgHb6X8f26Dsh8/UCmiIukiChXjRY7VgMhhEFXeHQhEbHDjjcKDNe0tPezCobXZeL611MTaGkK9xuD/lPL64ShDEDL2DXcnluaYc4SRgvCrflWntIUROBrytNm8VGzPHpMj0nFMciTWGmZtIo6fxfv3+Z5sDj1IEahxuiDWOlaXP2cJ1TszVARGlc2+5jp5KULFDYcoW26vhUlQvrPTZaQ6pFnbOLdR47ItiulZbN0IuomAJc7Fq+6BtGRpEEoNjquVzfHhDHCd91vIHlBay1h1RNhXPLba5t93MGMAM3L692cgdlVj4/mlMH8J4TIhIkW7fbQ5KEfLx+davHZtfl1IEqH33yGEvbA75wbRcgD/zN9HOZzm275wr3Mwk/878+uNep2htyc9fiq7c7HJoo8f4HRQ9tkiR8cWmHiqkyUzNZ7w5Zbzv4YcKji+O5niyMY27t2sxUTQ6NF1mYKHFqtsrFjd6+poVj02Wi9LbXtgc8ND+2r481A7VZj+3jRxucmq3mnbIHx4t876lprm0P8pDpTAf4my+u8vJqh6XtAafna/v+BqLB4/T8WK71GzUwfPylNXGhkDgcnTrAX3pkIm/y+OLSjmAJdYWJskF2lM1Y1YKmcHSqzMHxIn94dYemJdpyagXRUtG2/Tyf8MVlMQLuO8G+9/rtWm8a2J04cYLnn38+H70+/vjj/Mf/+B/5p//0n/LJT34STRM6gF//9V/n9u3bnDp16lu20d+u9dGPfpSZmRl2d3f5/Oc/z4c//GFu3LjBxMTE271pAPzQOw+xO3C5sNHnSKPIl2+26KQnloypUeT9Be7DICEMhbNOSsD2BSMXxglbvaGIOpGEMHzUfecEETOVAtt9h2EggmULmoKqxrhhTDT00Soi58v2A0xVoWho3G5ZRIkYQ+30PT7+0gauL068naHPq3d66KrCWFGjbKqcXRznldtttvsebhBzZbPHmXlxIE8SKKYMhhuIKIRaQaNq6rRtl9sth1iCgReIYGQJKoaGHyWEkZ+XvauKQs3UMFQVNwxxU4dsmIDrx0yUNY5MVdjqOWz1PcaLGgsTZXYGLlGcsN33+J1XNrjdsve3K8SCaSvoCnPjBXrDgJdWO/TckOPTFSRZHITC7UGe1dZPR3YygjnyQrFvMuCS6coyRm4UnomswSSNhEmDj5O9Oy5Olrm80aXjvDVQ92bWN/uMr8cF6mnQsRsKEF4yVCRTGA9UWcb143zsKyJzxCOZitDDjer3NFVibqxAZ+hjeRFlQ+JAvcB2z3nNmFlLLwgyM3RBFYyhH+534FZMlbGCRhDFdFKNX1GHhw+NM1k2+OLSLm3bE+7bdPxZLWhs9lycQYipKXjpNgcRECWYmkwxzXR0vBAvlDi3LEaV2WdguTXkVmvI19a7HJuqcHG9R9fx0BWVelGlVhRMc9tOaFo+83UTJxDjwO2ey67lcaBm8sSxaUAiiGK2ui6bfRcNmUZFnNRkSeLCeo9j06LV4b+eW+PccpsnT04zN2ZybrnNK7e72J5P2VAZBhFBkNC2PFw/RlWFKH+lNeTcSpuJosFMrUCUJAy9kJlqgWpBsFW9YcB9jQrnVtts9jySRLDmCxNFdvouHcfn+raIapmpFrDcCEUSUSk9N8j7PhVZ5vhMBVNXcP2QiYqBrsi8uNxOu2U1FBnm6yaGpnJovMidjoMmCTY+CGN+/SsrGIrCenuIJAlA/9W1LpYbMF0VNVU9J2C+XqBpeax3hnn/a8cOmJur0rF9XlxuCwNCTYw+M41WvaSz2nJYmCjwxHGhFbyxM8AJQgq6YJ+mqwIwNi1XsNV3fasE+KrQd0OGgY2VOrNBXGQ2bS81LWgYmszChGBRL270uLDWpTv0sdyIchqC7oURJUPNtYDz9SIfedciz99o0ijpNG2fx4+Kuq+7mzLm60Xe/8Asn728yYnpyj7AB0ID6AZRDuoeXRzn0cXxPMQ4A1sfemQegOVmMw8crhXE++UFMS/cbJGZMrIxb9NyCaMEU1P29cGKJfaHH8Zs9Z1cF5iBt+86Kho5MkC43hmmbKfo2D06VRaayzghStMakiShbKhimqPJfHFpF9sLKRtqGnz99q83Dey+7/u+j8997nM899xzvOc97+EHf/AH+dmf/Vk+/elPc+LECR555BG2t7f50pe+hCRJ/M2/+Te/ldv9x74sy+J3fud3uHnzJsVikT//5/88Dz30EL/7u7/Lj/7oj77dm5ev3YHP7sAlCCMcf6+M3VQlFFmhaIhsup2+j5OeQEbbHEZDf20/oexHTJQM3CDGSS2UokopxvZDFFlGU6L0Sq9A3xEnSVmS6A4DdvseYRIzWRJf/r7j0xn6kEh5AGr23F4IXhgyVoBGuUTPEXqjx45M8MWlJrsDl+2+x2RV59FD9bwezA8ioljivkaRoqHh+BGHxktIkkzbFiOnKBGNBl3HY7xkEkYqqgI1UxO9mRWDo1MVdi3B7DlehKGJ5Pvxkkg8b9s+hhJBItGxRSm9Ikls9RyQpNT5tX+Fsej03Og4RFGCokqstcQ4oKSrKLLEfL3Ila1BDiYHToimiFFLHCfYXpSPaxP29HN3g6WcSbqXkzSGG9sWfefrp9R9s6aIN7q+VY856vh2goQkiVLziYymyAxHnimO90CXpsqpZnNvr/lBwlprmAdf9xzBOnv3sPJWTAU/TAjSnRsn8MCBGmtth5bl54A+jJO0HzXC1BWkIOLkVI2feN8JfvG5G3RS81DJkFlsiJPRyq6NF4TEcSKcnGQ5kQAS1YJGGMd0bDGONyWZ7b74XGfmG0US4HanH1E1Rdm5H0IYhdQKKn6aqeem34XVlo2pa6JJQVPpOj5TFYOeE/L80i6WJ+r1TF0mjmPGSjpjRZ3lXZvDkxovrbQ4v95jZdciSOD6tsVTp2c4OlXh2vYATTaYHy+y2hrih0OqpoauyUiSRNHQGHpi1OyGMZMVAz8sUUw7OMMo4aH04u3SRo9qQeXMfJV60cDUFRplgw+fPciBeoET0xUubfR45uIdZFmmYmrIMkyWdVxfNF5MlAzec3ySpu3TmCrjpS7lOx2Hz726heWGXN0a8I6DY3zokUMcmy6z1XPxoggviOk4PrfXHUxN5vBkiSASbuOSLtOxE241LQxVZnasQEnXRti0g4DEV5ZbLE6UmKmaDNwwd8QC+XjvPSf2xrOZAWG963C6XuTEdIVzK8JB/H/+3iXOLk7wwYfn76nfEkzYgO2eyCnd6jm8tNJmabuPH8YUDZUnjk3m8ScgRothnOBFMbYf0LJFY0KUCKnAaGByFsT8iZfXc9fuo4vF3CjziZfXc6BmagoH6yVh9kg1cRlQypi6uwOZs9vdbay4uNFnvKSz1XN5aH6Mkq5ydKpMx/a5uNHjieNZcPAWEyWdo1OVHGCPjkIzV2tWUZY5k5e2LfpuyKnZWt7qkbmHb7eHDH0BsLO4HFWWWJwoUS/pTFYMlndt/DDm4nqPgRtR0GTOLk5QL+mv2Ya3Y71pYPfhD3+Yfr+fM3Plcpnf//3f58Mf/jA3b95kZWVFPIGq8hM/8RP8rb/1t74lG/x6azAY8I/+0T/ia1/7Gl/96ldpNpv87M/+LD/3cz/3mttalsU/+Af/gP/6X/8r7XabkydP8lM/9VP84A/+YH6bpaUlyuUyBw8ezH93+vRpLl++/Mf6Or6ZtbRtcXWrR2cYYLlBHgGiq2BoAsiUDZXJsiH0No4vwlU1CdtLSNKTw2TKsmmKzMnZKkVdYbvnYijiBDlW0HGDECcFdidmKmiKyIl75+EGR6fK3NixuLjRpTnwiPxEjEITqBcNvECAQndEJKbIe6HCE+m41fZDPntxk7KpUivoKLIolF9tioypubEifhCzFTlEccJmz2Xoi9T+RtlAU+T0ilOi7/gM3IjUECmy6uKEMBEutytbA1bbQ87M1zkxU00NJAFDP8KPYpabNlEshM9FXWYrZRBJEkqGylTFpDkQIvNRWBAlMBiKE/90xeT0XI2dgc9qy8bxPeQ0iFVBGAx0GVRVFi7m1KI5qkHLHlOW9jLrJEnElrweeMqw+tbAe51bpO8B4nGT5Jtn2O5+vm8HJ6gpUDQUbC/AD8HPqibSJx9l0oSu8K6IEvYYOBD36zkB/l02YQloD6N9Fz1eJCJNSBI0VUKTpTwv0HIDYbZJa/NWWjY/87uXGLgBqiLGw8emyzx5Ypq1js3NHRtdVXCdII/R0VWZkqkyXy+QJBJ3uk7u2C1oCkVNwQ9jzPRCrWJqTBsFNFXiQM1kZ+BSNkUA8lTFoFrU+NptFy+Kc9d1EIVMV0wWG2Wubw+oFDQxPnJDgjhmumYyWTG4sSOAyPKuTRgnGLrMRsfh5q5FHCcossRmz+HTFzc5MV1lvGgQxHHKrCfoiuh/nakVqJri/LDVd0jaMU4QEcQxJ2YruflEBPWW6Tk+690h81KB7zk5Tc/xWWkO+czFTT589mCuL/vs5U02ui6TFQNdlfDDhFtNm4qp5T2lX1jawfIEoxfFCUcnK3SGwpix1XPpDUMubfT39Z7uDjw+9co6B2oF2gOfja6DH8ZMVwwOjBV46vSsYCxXWxQ0lXcenqCoKXzh2g6fu7yV11+1LI+W5fN9p2d5+tF5OvZeZt3puVremgF7DN7hRil1vorX9d3Hpvj3z95gZ+DSsn1+8s+dfA1YyOrHzq20ccNY6CMLOs/faHJjW4DP0/NjHBwv8sWlXZa2+zx15gAfemSevuOz2nIYLxls9Vzedd8EmixxdGoPAGZs2tJ2n4vrXRYm9ofvPnNxky9e383Hj3ezdOudYa5lPDVTzUes9wKoWeRKNhr+4tJu3oYSxgkH6oU05qS1r+N24IgAqXLf5cJ6DyCPQMmep1E2ObuocW17kLOR2fN1bBEDs9N3WGkNObs4zqFxsZ8PjQuQ/amvrqHIEg8vTPCjjx/mEy+vc2PHwo0iZmomZVNMgGwv5DOXNgH2deS+HetNA7vp6Wl++qd/et/vvuM7voNr167xR3/0R6ysrFAoFHjXu97F9PT0W97Qb7RarRYf+9jHeOihh/gLf+Ev8Mu//Muve9sPfvCDnDt3jn/yT/4Jx48f5z/9p//ED/3QDxHHMT/8wz8MCPBXrVb33a9ardJsNv9YX8c3s7JIjDgBUk2bKsskxFheQBhDxxYnnZKhiTDOBFRZZaIsxoaCcQgZK+hMVk0eu2+Cz726he2HyLLMRNEASTgrxRWgxKkZcQC/ti1qceoljcmKwem5MWwv5Px6h+4wIExivutog92Bx4vLTXb6rhhxyTISMVMVk3ffN8HuwOfZazs4fkhCwkytwCOHhAFn1/RZadlYrjgJ1ooabigCLYu6qNsBsIMANZIhkRgvaxQ0BSSXiZLBowt1Lmz0hMs3TrjjC3ODHwit0uFGme4wEBVOqkLfCZAR+X5CcweLEyXalk/b9tBVme88XGdn4LLRdlBVcZDJTuxu6trc7gtR8FTF4NBEkY7tpfErCYYmUzcUpmsmQzeikxo4omSvr1dFOHnDNAbG0CRkJJwgRlMlVElKQ2D3fy4yQ8XdS5VgoqLjeBGDNHvvzUSffKvWKCDMWKtR3HU3mxhG4PrhXjdy8lo3MIiasXvtF10Cf+T1aqpMktzVwqLLeGFqLkqgoAkntoRg+CRJSg06EhMlg62+QxDFxHGSO3u7TsjAHRAke1Eq3WHIjR2L3YGLokjoqooTRgRBTMkUDJ0XiJaMYhpDMlcvMPQjCppCyxbVasenyzxxbDKXFZxdnOBTr6zhBDFTZZPDkyWOT1e4vtMnihOCKGa2WqBeMpCQWG07vLLWpWpqjJU0jk9VONwo0SgbnD08jhdEXFzrpt8vjemizoGaiR8kIi5n6FEr6NRLGm4Qc2Gji+WF+GGM5YbUChp+HAtneM/FC0VrwJ2ujeXGJIR86cYuf/3x+zg2XeWLS7usNG16jggFn66YHJ4o85XlFsu7Ft1hkDukRYVXi6qp8o6DNY5PV9nsu0xWfOoFg7XukGpBZ6JkYHshW10HN4jFiO7EJA8vHBTl8TebDFwfPxJGjSzP7txKm6XtAZoqUytqNG3hJh4v6ZyarfLo4jjLTZum7bE4UcrHhpc3e9zatXnm4ianZiucmK7StDzqJZ2nHz3Is1d3+KUv3mK37/JnH5jO886eubDJJ15Zo2JoHJkscX1ngJMyxKdmK5w9PM759W7e8nD2cH1fVMvStsUXru0y9EJOz9dyw0DH9nP5Sr2kc26lzRev71Iv6RybrvLkySmeOn2A5280udNx6AwDHpir5cA5W5nTd+iHFDSVmaqxD4habkDRUJipiqiz/c5Vci1jFCWcmq3sG72O3ubYdDmvLZsbK/Do4jinZivsDjwKmsJ6d4jlhXkGXjYGvrjeo1ZSOT5VoVZQ2R14dGx/n9t5bsxkteWgphr05aaVV4qFUUJ9TOfYtMknXhqw1hmyOFHkb7/veL5dS9uif1lJY30AiprC2cU6ZxcnsLyQK5s9VlsOu5aDqY62hr9961veFSvLMu9617t417ve9a1+6K+7FhYW6HQ6SJJEs9l8XWD3zDPP8Ad/8Ac5mAN48sknWV1d5Sd/8if5gR/4ARRFoVwuv8bw0e/3KZf/ZNiZQegoJEkSovkQxipqfhLJRlgKwiFreaH4gCoSB8bMnBFqWgFeFBPjMVU16DlBOnYEKY7puX5aJSTS4U1NxfZDSrrKRFk4uj736hZxqjs4MV3lgQM1XrndwVQVZmsmmipTuaNjG2LMe2DMpKirPHRwjIcX6vxfv//qvvGWJousoYsbwj12/4EKPUd0XXaHYiRqqiIKoFbQBRMXJamuT8XURKXVdNWkUda5fKfPdt/jvkaR7jDAT5nNkqEw9GKubPWZrZpMV8v0nYC1zpCBG1ApVEiSRGR+qQqPLNRZbtnUTJVaQePUbFWc0OOEoqEQhAk9z88rwvwIVpoWuwNhpIgTCVUSBpZSQeVIo8Ru3ydKElRFggSKuoKhKlheQBIJ/WoYZ1EzezyeLIkD6XrXyl2wEsLtGY5mh7DXWhHHQqcSxa/Xw7D/Pq/HCN79tzd7GBu9nyaTm3pGjRGjK4Z9mrn0GiGPyVER8TFBHO9zZ0N6fT+CeE1FjHOjOEaWklybqEiihQUSTs9VCWJ4daNPhACWhiZAnRfEbA8cHF/Eyqgpoxoke5l42WvUFBk3CLmw3qXt+IRRwmRZR5dlIiXGD0McX7wnV7csFupF5scLfNfRGQ6OF/OxnBdEHJ0q89SZA7xws8nvvLLBK6tdDE1mbsykXhSi7uvbA3YHIn6ioIosx9kxUUF1bWtAEov6s92+x8AJefCAONlbXsAXru9i+xEJCSdnyszVS6w0bUqGytnDdVZbw1xA/6mvruNZMfWihuWGyDK4kQgP3/Y8JCR6aVB4WddIkpA4SagaGvWSwZMnp3IGJYgEsH360XkeXhjnmYub3Ok6JAREScyd3pDfevE2n7u8xWTV4H/77iP5iTdjZp65sMlW38lPuOeW27x4q4UdCFPLTM3koVRkf/CqyI4bjQ5x/ZD58SJRkvDwwTpn5scIohhNkSkbau7K/ci7Fvdpx97/wCzXtgd59+rZw+KitGP7fPyl2xxulCnpMqtBxLPXdvnqWpeTM1UsN8ALYop6zMWNHjsDj+mqmWfAnZ6r4QQhL692uLbdxwmivEYLBDP2nhOTZGG5QD72fPKE0Jq9vNphpmrw3ccn8+YJIDcgHJ0q5bl72crAlxtEqIoIajY1habl8dz13dwpauoqT56c5qkzB/Zp5URUjwB/H3hwNt++0bEsQNPy+MylTRolAQydQKSEr3eGgJRqD32ubkUsTOj52Dp7/Z94eZ2OHaKpMk+dOcCxFFCvb/SI4pi27TM3JrLqMg1f0/JyU0emOayYKiVDY6psMFkx89fxwk0RPPyuNFYnM9703ZDvPjbFseky//rzS7y82qZe0nhofgxNkTjcePsxwrcc2L1d641Wlv32b/825XKZp59+et/vf+RHfoQf/uEf5sUXX+Td7343x44dw7Is1tfXmZ8XV2aXLl3iIx/5yOs+tud5eN7e+OuP2wl8bLrMyRlxZRMnIqMLhMA7i744Nl3l0HiBSxs9TF2mVtDSOAURcaCmep2JokF3GLLSspkdK+QjKgmoFQySJEFTJKaqJrd2LIIo5vBUiWEQstt3U4ApsdYdst4e0rQCusOAz17e4r7JMrN1A1mBybLB+x+cza+6lrYtagWNoi7nWXV3ug49J6CgKcRJQs8J8n8v7di0LIe5WomZMVFbpCkSlhtiqiLM0/UjakUVexBxaaNPEMVEUcxah7x3taiLZPxLmz08J6JqqHzv/TP0HJ/NvgskVEyNliWeu+8G6KroHnUCEQNxYMxktWXjBjGTVRNFgoKliLaMgRB/VwsaQy+i74j7x4kABmGUYLkRLVuMwgq6xHTVQJJkTh6o4Icxlzb6WK5PUZf31Vlln3SR2SfAg6lBQVVRZBkvDIhi8IIkj0IJQzH6jZwQVZXQpJQVlMG9B713N6gaZbXi9H5J/NZ6ZkeXL6bcVAtqbgD6ektBhJkmI6ydrIhR6z2iHXPgq0uQSCKrMYyiPNNQBoq6LHSWfkzZUHjsyCS7A48rd/r5i0/SzEHRRrIXrSLLCMNOGhJsaAqOFyIrErNVk3pZdJcOnIBhELLatIlyTaB4EE2CI40yh9Kg4p3BHR5ZqFPUNaqmyuXNHl++2eLyZp/5sSI7lsfQC5msGowXDRplnVdud7h8xyWOEyRJdB23LY+lOGF2zERVJWYKJmcXx/nC0i67fZdzfkRnGDD0QlpDnzCOCcKYpR2LsqmTkHBzd4CqVHn/gzMcbpRZblqMF3RsL0xPaHIa0urj+X0mVV0wX2k4spOCq1pqNPnc5S28IGKuXszHW88vNTlQL+S6rpmqyfn1Lj3Hp2JofOlGEyeIKOl7mqhRAX9mJDA1JY/FuL7TJ+yLEXDGQC1MFPnA6dn8vll0yG1PmJxOzVbzyI9M+5UHQs/XcsYtax144ngj72mtlzQeO9LgmQubfPylNaoFje8+PskHHz5I8fImN3asvOP7yeNTHEg1dc9e3eHqVp+TM1VMXbiYzy232OiIZoejU+WUcSvnIOrYdDnV9Yl1d/NEZtgYBaHZGh2bZvsgi1r5vYt3qBd0Ts5WxXHKCzA14e7t2B5bfY9GSafvBBRTl1G2LyYrBrupDKdRNnjsyET+vJkrVpWl1GTiY6Vga7Jscnp+jMMN0bax0rT5wOlZ6iVRzTbaRpKtDIxmsSfZ68hCi5ebdj6aFwHHe38fzR48t9zm4kY3dcYmfPLldaaqe6/jzHzmAu/lnbfZ/a9u92kPfSQkru8MmB8r7tMQvl3rLQO7JEm4cOECt27dwrKsr9sb+5f/8l9+q0/3ltelS5c4deoUqrr/pZ85cyb/+7vf/W7K5TLf//3fz8/93M/xb/7Nv+G//bf/xte+9jU+/vGPv+5j/+N//I/5h//wH/6xbv/d6/BkmZu7gl6erpokCay0LeIYjs9UOTlT4fJmn6EfoadNE0EkDvL1osjYOjJZZuiHLO1a3G7b6LLQqoWOT0FTeeBAlePTFa5u9ek7IZ1Ui+ZGMVNlERAaxyJPbrygc83r5xllPSfg9FyNP//QgTz3CRJ+/8Id+q7P8akq01WTiqkxTANekyTh2Ws7eIHIdhsv6dw3KTQoVzb7KLLMzJhIfP/8q9to6VXlTLXAc0u7rLRsWrZExVDFyaWgUtJFfc12z0WRhO7i/gNV/DBis+cSJTAMIn7onQvUChpbfY/O0Kfv9rC9AF1VsLwIXRFRK+KqTOJra136rjCNHJkq4YcJ9ZKGpkoMvYi27SPL4j5BFCMjkUiCfVlpDTA0lTgRFVGeLnR+W1ccygUNU5EpmzpVQ2V74OS9u3EMcZTQHfq5I9QPRPzJwPFJJFgYLzH0QzZ64kIju10EyGm4tCwL/ZYbfWN4dve3+u5WCNgz47yRWrF7rbYdoCqv/3dTJR/DylLqAk7/psnpc8cJsnTvEbOhKqIUPEgwkpiyqeGFPmGyF4eSpBDQDRN+//wdDowVODBm0HUCpESU1IehcFG7YaqPU2RmagZhnAiXc5wQhAl+BGoCzaGHrMhIQNcRUTuJtHeRAeIi7DsW6vx//uxJnr26w1duNZFlifmxIu89WeXccoeXl4WWylBlwoMJ33FQMEOTFYOv3e5wZXPAzsDDcsVeKRsxuipct34UszsQgeBHpioieBzBkkRxQsv2KOoqEhJjBU2AzjBmvTskihI2uq4YLxZ1AL5yq81yyyZJEtq2jxvE9ByPrZ6LoSrMzVTwowRTV3P9VQZG/uH/fZk/Wm5zc3fA48cEs3RiusJy00rDikUOXBBGgpU3xXi3VtBYaAinphDObzNR0jh7WGTO3a3xGrghRyYrLEyUOLs4TtPyWJgo0LEFU7Q4UeKp07MM3JDHjzby7thG2djHigEsbffZHXhs9R3Wuw7n17sU01y0jh3kTNTpORFs+7lXN2nbHpoi0bZ9TE3hI+9a5Be/cFNoZMOE82tdHjo4xun5Gqfna/vcoU3L5be/eoc7PYe5epGHF8ZzwDAKKJ9+9FD++T42LaI57jY5wP7WhwwEjQK6rHLs3GqLWzs2902Ra+KalsfvXbjDwBUjb1NXaNo+uwOPnb7HsekqIJEk4ngyWdGZqRZe06JRMYWTNGM2FyYKPP3oPCvNIU4g3oPlppWHKGdMXwbEgH2gNnPWjq7R1/XMxTu8cLONrorL0qcfPbTv79nnYafv0k3jnM6v9xi4ArxlPcTnltt89XaHy40Ss2OF3NTRtFyOTJYII6E77doBM9X4T0RI8VsCdr/2a7/GT//0T7O5ufmGbv8nAdi1Wi3uu+++1/x+fHw8/3u2/t2/+3f8lb/yV5iYmGBubo7/8l/+C41G4zX3zdbf//t/n7/zd/5O/u9+v7/PfPGtXkvbFmGc8K77GnSGHpWCLg7K1xL6rs9EyeDq9oDdgYsbJgRRSILKkXqRP3v/NAfHS+kIQeYPrzfxg4itXkScCEYijBIUU4j9nSBiYaLItc0BBU1m6Ie0Bp4Yz6oyQZQwVtSZrhnUSzqOLzLddFmiUTb2fQk/c3GTr652aNkeO31fsBKGQtkQovCCrrA78LB9EYJaNhUemK3x8EKdWkHj/FqPkqFS1BUenK8xUzU5OF7k2vaAw40SK7sWbiicfacP1lhvO5QNjaKhMPQi4sSnoIuPfqNqcP+BKjuWuApd2rZEebUbcXG9x9CL0rGtStlUUWWJSkHjldU2W30PRZJoWwFh7OGFEcenq7StgPWOg+UKd2tRA03RkWVRbi1L4HoRQQxlWeZA1SBGABM3DIRb1g4YL2scm67QHfr4ad+pJksomkyCANMZkybCb4WuUpZTBklXGDMVUU4/YlzRNIgD8XzR17kQ+2ZXwr37aN/oitnvgr175aCO/UYJEOHNQQwyCRl5f7epI0yiPLB46CcYWrxPo+cGMX4Qo6pC2nC743Cn51Av63lenCbL7PQ9rm0NcrA8VhR9lCVdQVMUgjCm7YjvhiQlJIkMiTAQeJHITxwv65iqQtN2iWPRFVvUNZabFte3+ySShCxJdBw/NQroFAwVRY4xdHHhJZgdBONtqFzfsYhiUZcnkZAgUdJVxtOx747loioqiiRzYb3HZje9WJASIY2Yr3FuucOAhJmagh9GdO2A8ZKGJkuEkWiaAKEf3eq51AoaJV1lvd3ndtvGCRIaFRk/Sri61SdKEr7j4NhdHa0GsgyKLOftBOfXu9QKOqam8PyNJs8vNfMR723LI0wSvnNxgr/9vmPM14ssN20GTsCdrggWPrfc2adBy07c77pvPB+hXb7TZ2GiyFbfo2V5OIFgzBVZztsn7hb3Z9vctFyeOD7J4UaJZy7e4eJ6j5MzFY6mVV0ZEyX0VkMsL2KiYqTHpT7Hp8rM1YvMjhUoGgorTZtX1jrsDjyOTVfy/fPSSpvz611OTFc4OSNcoBc2unQcj9naXvPCqIFgdFsz5lDUp3m5SSMDMXeDjj0WU4wsZ6oG51bbvP+BWR5dHOellTafuXiHq2klXKPs8eTJaR4/2kgz9Ny0W1cwW1t9jzAW7OkowzaaY9dP9WrZe5WBTgFcJYqGgiJJeajyaIRJNjY/v97lQ4/Mv677dGlbBCOLpAglD2nOjCuj4PjDZw9SNlUsV4TgG4rCYqOUs6Ed2+Padh9dlXH9iIudLnc6ThoqHVLUVaYrOqoq8/4HXtvh+3asNw3sfu3Xfo0f+ZEfAWBubo4zZ84wOTn5hkeib+f6ets4+rfJyUmeeeaZN/y4hmHknbnfjpXlJp1b7mD5EQfHNe5rlPjKrSZVSQcJhq4QXx+ZLLA78MQYsSgqbq5tD7jdHjJwfOoFlYmyycALCKOEIApAEsLyMIkJ44TTczVOzYp8pj94dYfVts19k2UON0oUNJXFRpHDjTKWGwkBehod8OUbu5xf7+a2+GvbA/wozl1Q9aJGGCZ5/lQcQ3fYy5mXO12XT1+8k77mKlt9l3MrHVY7NiemqxybrnB+vcvzS03OzFd599FJvnyrieWFWG7EzaaN6wt90sMLY5BILDaKPLe0y1p7yMF6kXJB5T/90SqyJDNWVHni2BRTFQM11WJVDJW11hBVlXj3+ATn13u8crvDZNlIQ3FFp+3y7oCxopbHVySxYAcHXkgcCr1VECfM1IogJaiSRNcNmCga7Ayc/DXHCBdvyVB4YLbBetsRhodUyxVGQhc2yk5lDFYcw61dm5KhcHCihCaL+IAc2yUSBU3C9mO86B7U21tY8ZsAdd9s5Mqoru41zw+5qaJWUFN9aIyqyEhJfgvBuAUxsgJKitCi9D89kQhS7i4BgiCmZwvt6VOnZynqQ241LYhE/mB3GFAVxTvMjhWIooSBF6CaMoU0JHi9Y2O7EWEinNBhlNALhN5OVwS87DsB55Y7KLLMIwtjzFQKLLcsnru+w9HJCk8/sv8icavvcHVrwJdvtigbKkenRDTHqZmaYDZ2LQ7UCxysF5msGPTdgK/d7rLWEU7ysqExVgBdVdNtkNA1mRmjwGTFYLvv4kYRpqqK2BJdZrysCZdizWTgBpyYrvDhswd55uJm6nyPePd9E5yZr3F5U1wYffbyJreaNgVNplbQ8YKE+2eqnJit5kzZtc0+uiZTMVVOTFe4fKdLWRetOG3bJwhFAFA21ssAy7nlFte2B6/RoIkT+/A1NV1L232ubPWpmFr6GkLajouE6EzNqraytoOzC+M0bT9n4wauyN7b6Yuie0mSUgF+lablcnFDgMeTMxWcIOb69oDbTdEk8d4UoFRMlVdWO9zYGTBZMWlabh6RkYFagKdOH+D69oCV1pCW5XOwXkrdtQnvOTGZ74PRNV8v0ihbPHd9F9sXwn4QOt0nT+yB61FN3GjsCsD/+7FFXlpp8y8/f507HYdXbnch/T7NjxXz43hW+3Vte5Dm+vl4QYyhyXmLxrHp8j5gdn69m7Ki5ggAd6maSt5j+87DE1xc73JhvUe9pNMo743cj02XhWN2rcvcWOF13afHpsu8/8FpOvY4W6njdeWKzVRFxHCNguNs9P/CzRZHpwTw7dg+H/vCTQB6TkjFVOnaPiVdBFrf6Q3RVYVTM1U0Vc57hk3t64wcvo3rTQO7f/bP/hmSJPHzP//z/L2/9/eQZfkb3+lPwJqYmNjHymWr3W4De8zdW1m/8Au/wC/8wi8QvYER11tZWQnxc0u7eGk67VduNVlvu5i6BBTT7Dl4530NHj/a4JmLmxQ0hYWJImVD5YVbTbp2wNGpEvP1AjO1Ol7qdusNRZCxriicnqtxuFFKx6kSJ2fLjBU1zszXiBIxSrG8kFdS2n26YrLetek6If/t6jZxDJc3hB29qCm8+8gEIGHqCjd2LdpDH8cXAapz9QKGpmDqCqaq0B36tCyPGztCuFrQVIqGgqkoefp5xVTzXsOeFqLKwlQyUzMp6QpeEOZjljMHx5gbK/C7X7uD7YlRg+WF9FxxOh+4GsMg4kceP0zPFbU0YrScUFIUCprK9a0mAzdgcbzIOw7WWW4Kx2PLDiiZGocnS8hIRAnc1yjy6maf3YFPHAnAMFc3ma8X+MzFLQZeRBInHJksM14UAMLyApwg4taOTb2go6syeFEaTxITx2JsqNw1dsx+DGJRb3VrZ5Dq+sjR0zBIKGjf2EDxZlbAXjj2G13fLLS81xh4dGWXZpIsMVEwGPoREyUBSHb6Hl4cQSSAXeZaLeiiai8hBc2kFXxFcb/bnSFhDL/6pWVmqiJgN2MExVg4JogEI7vas+g5ISVD4cR0hRs7A7b6QW6y0DWZKE6wvAgpgXrVoFbU6Aw9dldcwU4fmmCr7+L4IoNuZ+BxdKrE33jPUT7+0u08v8tQhfvU0GTedd8EiizT9wLuP1ClZGh4YcSVrT5eVOLxow1eXG5heSH1gmAuzsyP5TERK02b61sDdFVisjLJdNWkZfuYmsyBsQInpst8+OwhBq4AcGVTY6ZqMFMz+dHHD3NqtgoIwPipV9Yp60KQXjN1vnh9FyRRpVY2VM7eN8GHHplnadvidntI2/GpI6I6ippCzw7Z6Yvez6mqSUkXwPPZa6K67MmTUzz9aDFl0MQxLcsJfPbqzj7AAnsjupdW2qy2bCaKBgdqJmGcUC/oHBwXtV3Z2PCzlzf58o0WF9a7zNYKnJwRry1jt95zYpKVps3QDzncKDNTM2laHqfnqjlD+PJqh4fmx5go6zmTk4GnbHqRsVWNsgCsQRhTLwpwO3CFI1+TJWZrBSDhxWURtfKX7sFWjYK1J443uLI5YLU1ZBiElA2Fpe1BDpBGu21fG+pLDjCnKyLTU1dk3FAA2WwkXDFVhn5IEEVs9VxUWQQ81wq6CPVND0yjIcbZez4aBpyxnRfWhSb98aON/Fie7fPR93CmanJpo5fr50Zf+6jBIhtTv7TSHvmMlPIKuNERb3YhsDBRZKPrcHG9y8AJ8aKIoRelJITGZMXEi2K6dkJ6PcbcWCHvk/2TMIaFtwDsbt68yYEDB/ipn/qpb+X2/LGv06dP81u/9VuEYbhPZ3fx4kUAHnzwwbf8HB/96Ef56Ec/Sr/fp1arveXH+3rrxo5Fb+gDEle3BsJAkcBUtURRV1LBfkKjpOdXWc9e22Gj6zA3ZlJQFDr4DP2YpuVRNIQW5Ozh8fxquD30ubLZ48pmnyubfdY6Qxw/ZLIs0uzX2kPqJY3xkkHL8oGEqYpJ2RC9s2OmysU7fVq2xzMXN9kdeDxxvMFjRxq8cLPFStNmrlbgTs9h4AZUzQrfdaTB0akSPSfkd89v4AURy02byYqBE4SMF3TC1N33ws0Wjx2Z4NRshc9e2sILY+ZqBU6khda2HzFI9XzZ9vadgCdPTvHCzRYDJyBJEk5MlzE1laImrsBAXKVCwmRFZ7wo2Njz61066X1u7FqMFUU12un5MXYHLl6Q4IYR5TQoVlMU4kRkBoZxjKEpbPddlrYtBm4AiQDPEyWTd97X4MJ6l8sbPSw3oO+FrHWGIEHVUCgXVKREIkpiagUdTZa5sdvHCdlnrhgvCpalZYvRbkmT0Q2wvBhFAlX++jzZ3WNMidePUbl7vR6o+2aYOUMBVRFmlbs7ckE0QWQ6t9FV1CT8dE7rBRFhKJpVdFmiaIiKOlOVccM4DWpNq9hGNjoz1OoK3D9b46u32/k22EGCHYi6LcuNxGtKEtwgolJQOTlTEbE+ihiDFg0ZJ4hIEEzd/HiRQ+NF1jsOXijiTQ6MmTx8aJzPXd6iY/sst2w6TkDb9pAl0dM7cEN+52uCtV7rDHl1s8+BmtD7lAyVyYqeJ//f2rWZKBn8pUfmcYOIT72yxnrb4VMvrwsTjyIzWTFo2j4X1nustGzCKMYNo7xlZq075NRMlaEfcWa+xoceOZjrv7LRqcj/8vIIjacfFcDpn33uKqu7NgVd4eGFBmcXxzm30s4ZO4B6SeTbHZsu57lhmZOxL0OloLJYMlLTgKg1+8qtVn5i3stYG+THk6cfPcTHvnAzzdcTTGLG8GXgwgkiNFm4oa9tDygZKt95eCKPLgExdj27MM5W32XoRIRpXIfQewng9PyNJlc2+5QMsU8G7h4weD0m7G7wkb3+7P8ZuDA1NQ/NlVMjTklXCWNYmChR0JS8Dmw0QuSllTbnVtqcXRxnsSEA0EzVZKvvstKyubjRy0ekbhCx1rF54MD+SK9sCXAlminsgcuu5XJ4okS9ZORO5oEbstP32Bm4VEyV2TGTmWqBeknjcKO8D+iMvvbstTYtLw8uLmpK7uQeuGK8eWy6mgIw67WA6a5jTDai/eLSLjNVg3rJyA0wAzckjBM0Vc5/N3q/0fdhaXvAxfUuEyWddx6e4MaOxc1dCz+Kc62eIsu4UYQfS5xbabPaEp3Pggn8k7HeUo7d19Ob/Uldf/Ev/kV+6Zd+iU9+8pP8wA/8QP77X/u1X+PAgQO8853vfBu37ptfR6fKLDbKlA0hkr7VtCgbGg+nOXCSBHEs5aOAY9PlnA6fGytQK2ls9l2CKKJW0NgZeCw3bZ5+9GCuRbiyOeDqluhkrKSBo34YY/khlUBNk/BlFidKNEo6ax0HXZU4OF7mqdOzLDdtgpg0jd1gp++R8SobXYetvsvBiSJuFNEceHzlZpu/8Z4jPHZkgl95fpmyoeIFMbsDl/XukFpBR9dkfDfk+Ru7nFsWbGvHDriVhqgenBDj4Y7tMVs1USWhRXry5BTP32jmOUcnZir896vbWF7IWEljdqzIwPX5ynKLrb7LeschSqBsaIJBa1r4aVCxlEj0hoK9UGWJA7UCx6cr3Nq1kRVSF5lgQycrOufXe0xWdHYHPltdF9sP0wJvgwfnaxR1mdWWjR9GvPvIBLuWn2eRRbGIRImiBDcMMFWVM/M1PnD6AD/1yfM4A3H1WjVkKgWN6YrBpY1+7vg0tFTbF8RUTI16QWPoDyHZA2tj5h5rJUvsA1QJb90B+0ZAnQwUNHEys92Q5HXu5IRQUCUMNSFKcwPLpnBEh+njJEmCGybEMWwFHqoaUDMVpqtlDhgK620nf21ZBuHo+WKsqHN9e4BzV91Y2/LSg7zQFKqygqyKbtjr26JRZL5e4OB4kbW2I7IWEzAMhdkxE9uLxNixoOD5MUMvQlNlqqZK0/YIo4Q7nSEt22eyrHOwLoJrW5bHpy9uie1MY3bec3ySB+ZqKWMeMD9WwEh1fp+5uMnRqRJnFyf4/Kvb3GzZdCxh5tkZeKiKTKNkoMkSzYEI65sbK1AwFEjgK7eaPHJonIcX6gzckOWmxcWNfl7btTBR4NRsbd9J9/kbYgIwUytwfLpMGAtXZcbmjbJZHXuTYRDlDssgjFFlYYTK4iRAnHw7dkAprWwSoMBlteUw9ENsL+TK5kAAm9UWa22bvutTNlWK+t4pbmnb4uziuOh/DkXMUUFVuNNxeObCJg8v1POS+idPTPGTf/akqAOrmrkeLNOEtW2fxUaJU7OV3IEp4jOSezJhd0d9jIKLpuXRtFwON/biS/IMtUS8z5MVIzehfOLldV5c7lMtiIv1bH+eX++w1hmiq1LeP/ueEw3qJZ2CJi4wKqbKemfIp15ZY2nbYqKk84HTouc9M62MjlqfvbbDnZ5Dxw4wNIW5eiln7I5Nl1mYKNJ1PE7OVFhslFltDamX9BzUjWbujTZLZMynCGV2aJTKHJkq72M/m5b7mlHuemfIVt9BUyS2+qJlY5Bq465t9wnDhEuyRNlUubLZ59RslbKh0nN87DQLD8jPa10n4NC4YBJFfZtHQVPToOYKDy/Uef5Gk2evbrPd81gxhyy3bGwv5MEDNR46OEY97XEeZRbf7vWmgd33f//387GPfYxWq/Unpjv1M5/5DLZtMxiIlO9XX32VT3ziEwA89dRTFItFPvCBD/C93/u9/PiP/zj9fp+jR4/yW7/1W3z2s5/lN37jN1CUtz4j/3aNYgGeOjPLselKqttoc2PH5uiUqD65uNHnOxcnuNNz2B34vHCzydOPHsrp8IqpcmGty9AP2B3AZEVcOa00bf7l569zYrqSp3avtYckkKargzZVYbKs03fDnH6fqRo8fHqW5280ubjeZaVp5weKDzw4Q5ZtlF2JZWLaMI7zqi23HKKrgiXLzCHvPtIQXZY9h6OTZaIEHrtvgrW2zR+8uk176HNuuc3RKVFn07J9WgOP52/sAkJIvmv5fG29CyTMjZnc6Ti8uNwiCIXbLowEOI3imJbl07EDCqrCmbkxtgYOi+NFzq10SNL2ClNTGCvqWF4ghL5Dny/fbDJdMYkSKBgKB+tFvudk5TVX7b/y/DKOH2H7IUmcMFsvUNLVNMvKJQEeOaTw/gdmOLfaRgZsLyRJElq2j+PHRHHAZEV0Sd4/W6U5aKYdvwKgjGrqYoR+S5HFGLM/DAhjAUYFIyRqQJIkQUpES8e95rR/XPVjoyvLqrOD10YG3P38Tmq/VdNxdG8ktyXL/Rt9GXIYUzR0dvoulh/t6+MVeXPsCzXuOQFuuBeHIqeOXz8G3xf6uqqpUyto4v6qhOWH+FGEqsistGx6tmB2FUmM1GSkVMcaU9I1dCXmvqkSRU3hyFQZJJH9tmu5BGFC3xWh2VNlg4IhvhdhmFAtaowVNG41bYIw5hevbRPGCUVNZbKs8/zNLp4fsdKq8PSj8zx99iBfvrHLV261kGSJh+ZrvPfkFF4Qs9oRTHi1oHF8usJio8gvfuEmKykT8T2npnl5tSMSENY7DL2IqarByekpDjdK+clyq+dyp+Pw0LwwS6y1beEut30urPdTU4sAxAsTwuF+bqVD0RCMVMsSo9eZqplHimRMjCpLnJ6rcWPH4ivLLd51eIJHFuq4qQGi5wQ8f6NJ1dSZqRVI4oSCpuSs2TMXNvn0xTucmK7yt993DIAXbrb43OUtLi23WGnZDIMoZ5Cy+1za6DFTNfedrDNtM0h5nEem53s9hik71mWPPfr7L1zbxQnCNB+wsi+E+NRMlVXNZrGxF7HRKOnM1U0aJZ2Pv3Sbjh2wMFEgSeo0LeFQPjReZDE9Vq+2hmiqwsCLeObiHUBis+fhhTEF0WPHemfIr39lhaubgzSHTjCX82MFHpitstyyefxIg5laYZ8mUFNl3CBBU5Vc83dls0fXCXPAlGnjsn03uh8/8fI6F9b7tGyfWtqa8aFH5mmUjXSy5OZu2eyzE8bC9BbGov1ip+/hRSFDLxIsZc3k5dU2t5s2K02bMwfHqBX0fN8vbVs8d71Jy/ZySUVWNXa4UWKjKy74xOdOOH3nx4qUDAXbj9joOEDCdM3kqTOz+blU7LPX1pq9HetNA7uf/dmf5TOf+Qw/8AM/wG/+5m9+W9olvtH68R//cVZXV/N/f/zjH8/jSZaXl1lcXATgU5/6FD/90z/Nz/zMz+SVYr/1W7+1r1Lsraxv5yh29Cqy70Z84PQsFVPlmYubzI+ZaZhpi89c2uTcsriieOxIgydPTvHs1R2ubQ/wgpgwDpgo6yxMiOynrZ7HctPiYL2EqkjMjhVQZYnr2wPW2kP+zKlpnjo9y69/ZYW+C+vtociDWutRMhROzoixZ2a9b5TNVEsigkkz0Wym3cuiBubHCgyDaN+VW5ZCtjvw+O1XNiia4iq7bCpoqoKE6Ds8WxKuuCubfQ7Wi+xaLpc2+owVdSYrOook89xSE12VKagKLcujYmg8cWwS2xf5WE+enObcSpsXbrZY7w4pGQpRDF9Kt69sapQLGv1hwJMnJimZGudutbjTc1FkcMIYLx1pwd7VeHawfuFmk6tbAyG8Rph1esOAVzf7tGyRhu6FIde3LDrDgFtNm/GiYGDXug67lgeSRJJI/D+Xt+i7AfcfqNEb+ly+08cLE3pOyN2eCKHVASIRfdJ34zyeRJETkhi8MCYElOS1cSEyAvhkYch/HOsbAcfX0+7dK15FQWTthcleZVoiwWrL3bf9RV1ivGjQGfrCcSzFDNMHdEYeuGTIzI0Vublr5YBZU+Ds4XFKusqLyy16af5eGCXEcZw/b7bPvGHIpTt95scLDP2YOx2HIIEk6SBLImOyWtCEKxaJakGlZKj4Ucx8ucBMrcD59S5VU+XBuTGiOGalZXN1q0/XDtAVifsmy/TcEAmJ6VqB952ayj97jbJBJT3BZSfLLyzt0LUDDk0UqBc0rmz1qRU0UUSvStQKGk3LZWGiyJXNHps9D9sLiGO4sN5jq+/k0RUAV7b63DdZYq095NMXNyGB7ztzIGeiRrs+nzp9QHTv2h6NukGjZNCyRaRIP43ZgARVlug6gXDRDzyiSEQKZcDAC2LQhM53bqxA2/ZZ2RXgJGN7tvouOwMPkn4+EtzoOqiyxPGZMsenqgRhhOtHFMeU/D5t20+Z+2FeQC/CgCU+c2mTK5t9zi6O50awu9sXsnV3btzo78/M1zi30ubKlohUaZRNtnouz99ocjZtYQCJF2428xaFWkHn3GobK9Ucv+dEg1pBw1RlBm6Y9liL/b0wUcyPsRfXu3SHAUMvYHbMZLGxx6gpsowiS1zfGvD//dJymm0oLlx1WWGmVqBRNlJmqgU0CcKYM/PVPEuuUbYI472xegaY7t4v2c9ZzM2oSSV7f86vd7ndFoBz9HU+eWIqlwUsbQ9ED7KmcHZxnKdSYmHoCT1go2xQ1BTm5mr7xrCZBrHrBBwc3wOeWXAzWiIq7toOfVfc5kOPHmRpu8/NXYuyoTBTNfmtF29zbrnN++4XjSLZOe5/WGA3Pj7Ol770JT7ykY9w5MgRPvCBD3DkyBGKxXu/IEmS+D/+j//jTW/oG1lZP+03WuVymX/1r/4V/+pf/as/1u35dq3Mkp6laD9z8Q7//coux6ZLPHXmAI8dmWCj63Bhvctz15u582tpe8BkxWCybDBe1kWaft+lbQXUChrzYwWqpjjIzc0JLca1zT6yBJYbsty0qRV0Jko6lhtxcaPHuZUWRV3lLzw8zw+989C+ZPil7T6fubjJ0nafeklnteUIi33NZG6skNvnwzgRcQauSCLPThwdx2fH8tBcwXxMVkxOzpR57/HJPBg0G/Ucmy7zK8/fYnnXZnGiwAcfPshnL29yc9vGjyOOTQmtSaOkUzY1mmkW17mVNjNV0fG61hlyp+ey1h7SHIjxW10RIMDxInYsj5/8rsMEYYy03iGKEta6DoYqXI31ks4Xru0iSWJMPAwigjAWOWiJ6AcNkwTHD9FUifGSgeNHNK0Iyw9I+qIkfugGlA2NuTGT8ZKBhI/lB9zatdnpu5ycrdL3RLSKrkKjpOP4Tg6CZEQ/cEFTiaJA1GWRBhcnYPkxprIHkAxFIkqSfexVFkXyxwXqsuf4euv18vHuBQgVGYopKPID0Q5xr/urssTZw3W+ertLxw5QFInZos5238sNFIokXLiaKjNbM1nruGnItcJD86I9pTP0ubLVp2P7uH6Mqct8xyERz/MHl7dIm6Jwg5BHF8bTCyTBDOwOXL52u832wEOVJeIEZioGHzh9gCtbffqOz87ApzsMcfyIg/UiY2nQ707PRUZCU0Rvbd8NeeBAjePTFZ46PZu7/aDJ4UaZJ09M5azFZy9tsdF1qJgqjh/TsixUWeb8epcwSnj40DgPzY/lsRBPnT7AK7c7bPYSZsZM3nNiko7t89Jqh6blcmZegM1LG/1cOI8kpd/NCV642eLGzgDbC+nYAY1yyNnFcS5u9PIAXkjSAGSbK5s9whjmx0yub/fZ6nqcmKnw3pOT+fe8bftESczAjdN6wYSVpo0dRGmTgVhPnZ7FGhH9Z6aNME44uyiaBZ673mS7J+Qelzd7TJUNJio696WsZMasbXTFWPzqVp/u0Kda0NK6KjtnljIQIQBhc9/vspX97caOheWHHBwrcvZwPQesmTv2ofmxPFg5a1F4/oZw/WctICCAdklXWWgYzFRNPntpi4ET8vRZ4abuOwEnZ6qcX++y3YuZKBl5I0TT8jg5XeZ2y+JWc0gQiSSEIEqoFTQ0RYDEvfgUl+euN0kS+EuPzOdmkOxYnwGvb2QmsNKLBEOTc4d0xn7NjZn0nYA7XQdZkjgzX90XZzNwLR5eqOdM63cfm8y3IzNf1EsGFzd6DIO9g9lWz2Wj6+aNGqMtEwALE+I9qhV0hiUhm2iUDJqWy8ML47kDerUl8gzXukNu7Fg8dWZ23z54O9dbyrH7xV/8RZ5//nmGwyGf/OQn73kbSZLEiOfbAOz+/3U9f6PJV9e61At6bsfXFAnbF2nrT56c4kOPzDM3JjQg2YH9K8stNrsOE2WdoR+x3BIB07qicGSyjCTJDIOYMEo4Nm3mH9jsSucPr27jRwkffHie0/M1fuX5W5y/3WW8rO9LA8/WMIj56u0OKy2h4ctGFy/cbPGFa7sUdYW17hBFknD9CFMXbtzsdl4Q4/kxhydLnJqp3lOkOwokZ6om35MyiwM3pFbQGSt59F1YnCjy6OIETcvjhVst/DACJM6vdbkkS7zv1DQH6gXW2kNWWzZBHFPVDL7n5BR3ek5+lZmNI3pOSHcoTCizVZNTs9V8bA0JVzb7XFjvUzEV/Dji5EyFA2MFXt3sY7khqpLFZmgMfZEl6AQhSSJiTNq2z/HpCuNlkfYfOVlunISiSLh+KHLyNEXotDKQpgoXchiL2BpNlSESbN0ocNv3c5hQK2oUk4RhEBKGe/21b2apsmALv16hhMJ+DV/msTfUNNsuEYzbKGMnIQwOiizlLRvZ7yWJdNypEEYxyd29sXI6Uo1iNFnh8ESJ261mmhUYUTaUtLJOI4pjhn7M9c2+cGtrIl3Y0FRu7IiTi67IhFGCpsj4cpyOXmVKuiqAwa5NnMDRKQG4CprCZtdhd+AxXtbT8OA4N2z8mVNTdIY+7zo8wfm1Lq/c7nAy/UwtN21u7goAsTNwCNOTb28Y0LJcLmx0+b7TB1JdXJPPXNrCckPed0qwCiJc1WOiZNAdBrQtnzBOKJsKhxsFbrdtdgYOqpIahYY+naHPE8cm+dDDB/nC0g7Hp6r5ST4T52uKjKbI7A5ctvsu8/UiJV3lcKM8Aowizhwco17ScrDywYfneWmlzedf3ebs4XFA2jfOA4mNjoPtR0zXzH2hvJAK3jd6ZMx+vaRRK6qcXdyTCGWasSwvLTNtXFjvcnGjzxPHJzk0XhSh5E7AnY5L0VDww5hbTZv3npzizHyV55Z2+aPlFkEoRuJBKPSQIiYjycFOxtpkADq7SB3d9uxvN3dF+87Zw/X87xmT1SjpOWOa7e+lbWtf5l7m9r2y2efMfJWnzhxI3xeH82s9Pnd5i3pR41ZzyOPHGjxxrMHADagXtfzxVltDhn6I5QlDUBAmTFZ17mtUmKwYuXZuNOA3+7bdnft3t1lkq+fu29ZRI8Vo7EjGlmWf2+euNzk9V6Na0LjdHjIcEf2OunpHnbbZe50BvPXOMNdNZpKBX3zuBq/eGXD/gQo/+78+yAs3m/zGV1bpOQEHxkRUzUzVZH5MjPSXWxa2L2reGmUhf3lppc1G1+Vwo4gfxBydKr8uW/t2rDcN7P7tv/23/MzP/AwAjz32GO94xzv+h8mx++Ne3y6NXfYFOTFdoX+skad9H5veu/odtfs//ejel8oNIhQZBl5I0/bzztPJssGhiRJHp8q5di9zwokS5gZlQ+NXX1im1feRZLi2PeADp2f50cfv25eefrfW4PGjDZa2+vTckLKh5iPZK5s9nCCiqIs6rmEY0lRlztTH8hNZ0xKRDmcOjjFe0vcJuldbgvnIriKfON6gUTbpu1F+FZcdiObHClzc6LHYKOXPv9F12Oo5qTlEQVcV6iWdp1L9TJwkfP7Vrdw5uTBREp3Ets96Z8jjRxu8tNJiq+dS1GUON8pc3Oiz0XVzjclnLm7Ssn02OkO2ex6TZQNNLSFL4EcRmz0Py4tS56BKmMai9GXRhThbNzk6VSZMEnb7LrIMOvCOg2Mcnixxp+NQ1BSKmkJ3JA8kjsVYVZUlDjeKKLJCd+hxuz3cZxYYBW0R0B4GHKjqhLFM+I3yRb7OkhCg7l41X6Nr9JuiSXsBxBkYlCAvgx9dXgRECaPKWAkBhr0oJngd94WqSCAJ0DtZMbh0R0R+JAhjUMFQqabauZYd4KX9wqYEYwU9dcUGwh3asWn1fYJI1O5NlHXm6wVuNS1eXN6lVjCYKGlUTI3vPtbglbTGqGAozKsFFFkmimJiAhEHYqo8d73J7sDlOw+PA0LTef9sBU1V+NLNJm4QUVBF8PFUxeDJE1N86WaL5kBop7b6LuvpqDFJEnYH3v+PvX8Pjuu87/vx17ns/YbFLrAAARAASfAiGVR0oSWmsmW1TiIrdi6O5Thp3KTur80knvlOv+240zbfpnEuTVK3M5lM+2uTNk4at/H3FyluU6eybMuVJdGRbJKKRYjiBSABEAsCC+z9es6es7u/P559Ds4uFiRFyZac8jOjAQXsnvOc23M+z+fzvvDqWpGyYVNv2o49VqFuci1Xp4MYe7rQoGG2aXcgW2lSadi0Oh3WCw02iwZ3T0ZpteHiZpmnzqZ5+FDSMYEX7i8+Tq8UqDVtrm5Xu36kYR4/Pr7L11S08zpdbbItCo0mr6bFudF1xWGrvnQlx9hQgLBX58TMsKOPJl+kEb/eA99YLza4uFHmmfMbDukAdrdD3Ytd2dUwLGEDKDGnHk11Kn1zqSjfXC5QaViMRrwoCnh1havZOvviQR4/mGQlW+dypoJhtbpzbQXTFhjifkFhST4wmi2OdLF1Mvwejal4iGxNaB1OJwIOk3RhvYSu4hBX0oU6p5ayQrol5HWqT2NRP2uhBtfydQp1Db9Hx7Lb3HcoSd0SrVLZXp5OBCnUmkwMiZZ9xbCIBrwOtOfUUta1UBXvE1k13Stk8iUw3E2HuewmUpw8mHBZgFWZTojr+eJihVrTJh7ycl8y1MX/iXvuI/dP9lQG+5nG7uiXWFnMVKmawgu5Zra641foAAGPjqp0OLuSJxnxMz8Z41IXejQatZlLRZz9LmdrLGWqTCcCfP9ckvum4wNZz29XvKnETlEUPv/5z/PRj370rRzT93x8tzB27lXLL39oR6bF7YfXv0KSeDtNhYjfy/cfEKvA7UqTXN3kPYdGHPaV++Z0W9JcylQEUHXIx+FUxPHskxPtk2fWeP7SNo8cGekRkHxgZtjRR5LJoCBIwPxkrIv7Ef5+UiFcrvQVBeYnopQaTaqmLSayVsdpT4iHtkKmZLCSFSbSUb/eA2aVL4HNsmB5SUbVw4eSXC80OLdeZNIf4H1HhcL857+5yuVMlbBfJ+DTse02l7fKjMeCVBoWC+kSQY/GXCrCvliQ5WydqF93/BLP5YWI5smD4pzFAl4mh4Kk43Umh4T1TyLkw7Rb1IwWNdOmYbWZSfoYjfoZDft4cVGAfPdFA9w3HRfSD1Wh+ZeM+LlrX4ztiknAq6EqggzhbdrYmoLV6tDpQKPZIhH2kgj7sVptXr9edyp0IY9Crd/GoRubZcHg2yut8+vQJXzuGR1uXKkbFIOGM2gf/cmoO+SYDWvndaojRKMBWt3EeSwaENWuWhNNhbBH+MX6PCqm1SJXtZxtaQqoisJQ0Esi7GFlu02uagicTtyP16OwlheM8FQkQLbSpGFB3TKFVEy7w8XNCqoCuq5QKltkKyYhn4cDIyE+fjLF+Y0Sr14rYthtrFabV64V8Xs0PLpIDATZRyegq1SboqJ7JBUl5PcQ0FVCPo3jEzEHayRe6k3a3ay43rTJlBsU6hZLW1W8mkbIrwn5m06HYq2JosC+WIDRqB9dFT7Ml7Yq1JoWa/k6h0YihP26g3+qNAS7u2pYhP0ePnzfJFXT5s+/vU4632Cz3HCuS6FmOfJEssIFQow3GvCSq5qcS5eYSUacRdHJg6ICZlhC7FhXVT54fJ8zt0l8rrTR+sj9k3zmyxe5ui3IW2MxvzP/ScydnBPd7VE5jyVDAr9WrFkYrRatTsdZsE4nggQ8GrGAh3jII7BwV7Iku9Usj65SqFtcylTwezReXs5RMSzunx5mNhniyTPXcLdl5WJYJgxPnllDtqMHm9cLrPPZ1TwXNiqObJUkZoDCC5e3Wc3X8OsCdxb0aazlG2RKBhf1MjPJIJbdZqNYJ+ITskj3T8c5eTDhLOZlS1kmQ4Naze4ETWICj6QiTntzx8JNZ6sspHVOLb3CielhZ87eLBmOPVvZsJlOCJHmYsPi+OSQgw3UVBWfB6fyJq/jZ08ts5qr8eDssEPKGyQvI6tsLy5ucd9UnEcPjzreu/LcF2pNIW9iimf0SCqC1ZUkivh1xxdXfnarYhD0aoxG/Y7kzTuFGXvbid21a9eYmZm5k9S9jeFegbrBvYBTxXKLUWarBhc2KuSqJiGfzt37dlhLveXxHX2fQXpLEgMhafHu2KnA2bhfve7qovxu/3Yn48GeSVjuewd4bVExbGYSoV2tCBDaWKmYn4Zls5qro2sKZUdjSGyzYthsV5pslZsONkPvPq92q82r6QIhnw4ofOX1DNmKyXQixMERISnz4Xun8Hs0FjOitbpZNigbNodGQ5itFtsVUQ0LeDSqpk2hJtjIC+ki04kQ75qI4dFVZwI8s5LnUqbMkfEIdCKki8IJw+/VWcnVyJQNKqbNy1dzTCdDTAwFUFSYGg4xkwhxeiVPq90h7NPxaF3BXSARAhSFTLmB0WxTN20ubwrHD7Mr1ObtygLULKncJtqmtAXBQkEIGw/5dUyrtSsBbHcg6FF77MpuNW6XYasrN/ai1YFoUGjM2W3pOCnC71WZnxhiu2pwLVtHVQWT9f/91ioNu03AqxL1e0iEfRxJRfnWco6cInQGNcArOlckwh4eu3ucp15ZYy1vY1otjo3FmBoO8scvr1AzbK5kq4wP+blebDj4Ol1TWc3VURWIBjx4NQW/R2P/sJ+ZRAjo8Njd4yRCPpazVV6/XqZi2oT9GpGAj8tbZVotGI8F8Osqr62XURCVx3ytyXDYRywo7M/kguXphQ00ReXwWJiZpLhfPKrCSNRHrtakbtlMxoPEAjqlhs3r18u02m3mxsL84F1jrOYaRP06ozEf6XwDuy0cIMaifkAQdST78i+vZCl1Pdt++UN3Oy/lhw8lnQXaVsUg7NdZLzbEvdOVKplNhrlncojFTJm/ulbkzGqOr18UzF05Hzx1Ns3V7RpjUb+zYJNYuYrRdAR4Ae4ej+HThB7lS1eyvHw1T7nR5MRMwmEyyjlRhEhW/uH7D/PcxS0ub4n5z7Q07HbbSbCCXh1dU0gXGyxtVYUcjtVyqvdBj8bx7iJ1u2KSr5kOkemV1TzPXtgi7Bfs5mTY36frZvD8pSyKgtPyA3rcGUBhLCrcE1RFcWSrJHZSRIfTywKSMZMM8VMP7uezp65yaaPSxY8J/9NCo8nUcIj3zI30VECBnnl9s2Sga4pDfJGtZsEOFuSwFxcrnEuXegh3cuEdD3l45MgIXzm/wWvrZepNmx+8a4xs1eT0co6/ulbk3v1DfGB+n6MjN50IOZW2tXydVrvtECykZMtTZ9Nc2CjTanfYLJuUjZ2kSqoPrGRrPHRAJKwvLm5xLl3m4bkk/+CRgz3zxhMPCALixUwF3SMko/xdQePTy3k2Syb/a2GDpa0qJ2YTlBpNIWHkUdA1BcNqdTGPu23b3o647cRufHycaHSwuOGd+O6E+0F87uIWX1rYoGoKLM10IkC2anJmJc9iRggXF2oWdrvD/dPDHBuP9qxW3dt68sy1gSbTMvoxDDKhnE2GObWUpdSwmZ8ccloL8iGU2nFuj79+XEK/wbVsIQP8/vNXqBq2Q2lfzuZ6GKdCQNTvTMKGJWzTCrWmIyI5lwrz3sNJByh7vSk+I7xqPZQbNpcyZU7MxvnBu1JczlR55PAI9WaLzXKjR08qHvKykq2zVW4Q9Ye75uQ1BIC7zkq2BojVncA++njuohDRfM/cCP/X++f4k2+ukq2YjEb8/PwjB52296mlLFcyFTp00FXw6SoL3ZXtB9417uzjYqZM1LWqD3k0wn5BtMjVmqyX6tgdKJttGlaDVMRHLOQh5NMYjfjJVU2kJK+3m+D6fCqG1UZX4fumhvn+Qwm+8nqGC9fLPUlVswV2u03AIyRTTLuNeQvVuWBXp86wWhjWrTtgKEDEr1HpWnMNClWFuin+rgk4obP9arPNt9fyxAI+UBQUBa5uVak124T9KpPxEEfGIhwbE/PaWr4uNNs0jUbTFjIzWotSw+Z/vrrO+fUyna43759/O82hEYHNKWhNFBQ0VWEmGe62tTyUGk02yw1CXp16s0XFsIj4dII+Dxc3hYD3XCrKJx6e5XefvUzYrzMe8/PgbIJTV7KU63ZXkiTM5YzwhdVU2CqbhLw6H75vEr9H439fzPD5b13jrvEI90zFebXTYXm7zmbRxKMrzCTCjET8bFdMEnjJ1UwUVN47J57XXFWIeYPCdCLAbFK8qHya5mCNLmYqfHB+H8fGhU/ubDJE1bC4lKk6rVk5T4hKyTbTiQDTiSCXMxUubpTZNxRgJhnCbnecqpuuCrmXTNnkqbNpJuJBxxtULgqDHpEgF2omm2UDXVFotXEEeAHKhs2+eJCyIdp5drvN4laVUr23pbaYKTvJlhSYXcxU0FV47O7xrgCtqFTJ+UxWDi9tVqgaNhPDASd5LRs275kbYSzm53Mvr9Bothnqup4sbVUx7RYjuo8LGxXstnBayFZNnr+0zfHJWI+OHezMnWv5Ona73a36B5ifHOqpji1nayxmKt1ORy+JbDIe5MRMgrV8nYBHJ+zTmUmGCFUEnEAu1N0tbvdCX2Lf4kNe3ntYMIJlte3U0jaaqnI0FaGatKmZFlG/7kBlJMng/uk4jxwepWzajEUCPPNahkrDYiQivMUPjYYdHTkJuZBY0FhAiDPLtrSsjuVrTY6NRzk2HhmItV7N1aiatsOyHosGiM55OZKKOO4kcr6VP4cCHjJFhaGAp9sS3urCKzxUuvZp9Wab0aiPVMyPRxOWd194JU21aXNsLNrTUn+74rYTu49+9KP823/7b7l27Rr79+9++f+fHN9NHTsZcymxIl/N1YiHPI68yIWNEq+tlwn7dY6NR5xJ7cal4t0m0+6ye79yt2yVrheFPMBU11BbVv0c9lofBR4GqbHv3rf8DHQI+3U2y4ZDuJCr2x2z7h15kafPXecvl7KcmIkzkwyRrZrMpcI88cB+p7Uc9mskuzIvUiFfCpK6k9pf/eJrvLiYpWq0nAkkGfbz/KUsWxWDerPNXCrqGEc/eeYaFzfLbJYbQivKfUwK1Jo2T51NY9ltQn7hUCE1nGTlMujRHIN5gEJDSC/ISfPcWgnDanHPxBAocHY1T6stSBiHUxGy9SZqV8dPYtTMdoewTyMa8NLuYq9kPHQwyatrRWFlBNCB7arJn3a1stzEBY0d3Fu7I1p3W5Umpr1T/dsr2l22naYqhH0qTbvVw7i9UTWv3BD6cwoQ9mnoKhQaO89Zs8v49XvEcfd7yjZsaFaFo4OuqthtYR9mWG2B2dyu0bRaXM3Whd+rpmFYNqbV7jKDO6zlaqIiKMfbFonzt9MlUlEBmvd5VHRFIR72MB7zU2o0MSyBBJyKhwj7dSGm3emwVTawWh2OjkWc6k2uK8i7LxbgXLrEdtkUlUNNYSUnpIXGh4KYdovLmTKFmuk848vZGlsVA6vdJhULdP2WGygKTMQDjET8rObqKApdwoDJdCLI48f38fjxfV1HhzLPvLZBIuTl9HKBhfUizVYbn67SbLUBwWCPhzys5gT04/96/+Geqr+Mpxc2ePHyNu85PMKx8QgXN8uiKtzuMD8RdRZjp5aywsUm6MW0W8xPRh383UK6xMSQn3/4/sPOfPDi4jbn0oIwcGJWLHbc+5YtxULNZHIoiE8TeEq38G22Ks7bTCKEYbX4zJcvslUxCXp0ooHKLkF1EAl/1bA5mAyTivl4fH5fD45XQF6yZMsmIxEv90wKdvTSVpVESCQEdrvj6KplqwaKIjoOcv6A3gWxSOJVhkNe4iEP5ZzwJX306Ci///wVnjy7hq4qHByJOJJS7jn2mfMbTmLr0YVNm9kS9/mppazDvIXeLo88HvnTPfd/9tQyr14rMpeK8PjxcZ46m+ZrF8rousp903GyVcORW5HVvZGQn6nhIB5d4eJmi5BP431HJ51k6OTBBBc2Sqzm6jy9sEHQqzMWFRJYMpHt18Lrf5fI3z/m0k6VLeTHXS1kWVWUmNNHjoxwbDzKVtnk2Hi061gBJw4kHHkgYWMm0Mmiai3IQ9WmTa5qspKr9bzf3q54Uzp2L7zwAj/yIz/CH//xH3P8+PG3clzf0/Hd1LGTIfAasw61XlraLGYqXNisoKkwmwz3lNj7kyr5/7PJkANo7cd89E/ac6kdtXT3qmmn1SG0qOYnYoR9OpcyFaeULtsPC+tlXk0X+cj9k45ApATquhXb5ydizCRCrGRrjEV9Patb+blza0VCPl1oQXXFURtWu0dHzy2Y6ablb5YMogEPU8PBXa3osWiAeNcOyT3hSckHoEe4czYZJuzX2SgaopXTxeQ8elSwHel0uJav49EVRkI+vLqyK+ndLDco1i2arTbVrl/h2dU8VcNiNBoQuldlYcszNxZBU1WhxWULh4x7p4Yo1Jq021AxmnRQaDRtyvU22UoTj65guNqoVquF0bSdpMqjK2yUGtS7vUQHq9Zlp3a6/zZtASbeizalKyIhMbtlNmFr3HL04NS+L7pzMRVReWt3QOngJFNaF9xeNtq7EsEOQpxYfr/fUaLdAcnxanZlOZot2CgIlmqm3AAUaqZN027RagvNOq0DPl3D7ma4OpCMeCnWm9gd8HtUNE0IEFdNUWX16xoXt8u023BkPELM72GtWOfd48NEAzoXNyoEvCoBr07Yv2Oz9eDsMKdXcyxtV2k0W7TabcoNG0WV3rQdZpNBoZ+oqXi7wuLbFZN7JmMEPBpeTZi//41DCXRNYTVXo1y3WMlWScUCBDwqr6YLpPMNpoZDPVX0xUyFa7k614sGXl24FtQtG8PSuGs8SjwoNO+qhu1IdAxiBaYLdZa7XtAXNspkulUYAeNIOM/fWMzPw4eSfO7lFfYNBfibx1KOzVe1W42Wz5ncjxQnPjGTcNwT0oU6T58TLTNp6C4rch9/aAbA8WaVgrQzCWG/dXolL1q9MR/xgIdza0UeOpBw5pmXruS6NmqC5Xl0JuoYyLvHlS7U+fpF4dhw13gURVGoWwKrFwnoPTIbYr6Vc6rSQziTC+KhgIexqJ94yOuQydwtv82yQdNukxoO8siRkV1z9GJGyNjMjYY5MZNwFoZ374tyKVMRBJiuZuhipiKSXZ/eo80HQtC5UNtw8M9jUYEFPjETZzIedJIfTVVFx2Gr6sihiG1knQRWJoLX8nXWi4Zz7RYzVQIenYphOQLT2arRk8jKz2ar4l46eXA35CgZ3nELARxGtNsfVlY6Ty/nHd0/kWCK59uwWo7mo7tLJTtaIxEvdlvAVx49LMYlcXtvd9x2YveLv/iLzM7O8uSTT3Lfffdx77333lTH7g/+4A9ue6B34tZCVs3kqm0uFXawZNIKRkZ/Fa7fGPq5i1s9VbFBZtHuVqk7NksG9abN6eU8fq/Go0fEd92ldCl3MBzy9tDR87VmD+hZAoMl4Hq7YhIPeXtWt4Nsfsaift5zOOnoecHO6nPQS0hqR0mciPwc9Dp8uLF9s0mjixfSWM01HDNvCfj1eiCg6uwf3mGR+XSNhmWTinlEmzhfF7p6QwEHOyRb0tuVBl5dR1cV4iEvht0GBNj57GqOhtXi/PUSR8cjaIpCs9XGarVYylTI10xifg+Zqkky5CNft2jabZot0T4MeT3Cgqtb8Dq7WkASYH260m0fWj3MWYWdpM7tHWu2RAI3CAMnEredX47FfJSMJk1T/K5fDNkdHk0kYf1GFFJ/DwSLNuzTqJmtXdvyakImRbaIPapIFBWENIfcBoiksW23CXZbpabdckSGI34vQZ/WtbBqoWkwFQ/S7kDZsBgJeLlvv6gMv369zLl1QXrwaSrtdgddV5lNBjm7WiRTNtiuGDx2t0hGDo2EWdquUmxYDjB8LhUhXRQkH6Mp8IKmLTCU90zG2Rcz2a4abHSTh7/94HQX0ymejZ96cBrAAf03rJYgA9gtrhUapIsGR1IRinWbkmFh9Stad4WBQz6VB2eF+O1aoc5ytsbhVIRywxL2d1abD8yP71mBX8xU2SgbNKwWlzMVhgIeVFUI7Mo5xm01FQt4GU56ncr1k2fWOLdewmjKOu1O+D2aYzg/PxnrCoDnePLMGkXD4tJmmUjAQ6VhMZMI7armPXdxi2zVZDVX57V0mROzw5yYiRPw6GxXTCcJkaK804lAd7Fcpt5s0bBaA7sYL13J8vqG0OsL+TwOCeLphQ1Wuu3BD8yPO8d+/3ScZNjveHi7SWTzEzGkludcSmAnpcuF3OeJmWFyNZPH7h5nfjI2EB/9weP7dlXc0oU6fo/GWMzviAu/cHmbTgcOpcK75mGJkRyNCIzf48f3ddUXREIqk1ypuiC15OR532lRKmyWDCaGApQbzZ65/+xqge2KiWkJvLLEIEqPXtkulu4RbmmZiF/vYgEtB2Pufgbc3r3uhM/v1ZiPx5zxXdiocGGzTNinCaJc33uzUGuSKRvsHw7g0TWu5etEA60eiNHbHbed2P3RH/2Ro1EHcPbsWc6ePbvn5+8kdt/52Mu2ZmJICAC7cRv9Nzsw8Gc/5sMd7u30ExmkQXTDspmPDzkVMbkK7E+03C0c6WUrq4/zE9FdOLpBlUNp8+N2snj0yKiz0rrZQycJHe6Sfz+zyn3cgMPyPT4Zdfwt5XXYPxx0JmmZ2OxU+UyWtkTLq9SwWMlVmR0JUTbsLtZHwW610TWN6USAVDRA3RSSMEKKJsxUPMi5dIlSvcnXLmYwrDZBr8ZIxMuVrRobJYNMyaTdgbphY3d29OpUOuxPBBkKebi0WRGyKC0hwhsPejg0GsajKbyaLvYIvbrJCIoiCAkNU7QpURDYlICO2WyzUTbpgOPxuVaoE/J7eNdEjFeuFamZzT3xdVp3P7Yz3p2qXH8FLuBV8ekCF+hXxeLBaouEcnwoQNWwydmWIIMgqm5eXcWrqng04fzh0VWsVhtVUTDsFtWmLVjG3WphKuZn/3CQb18rEPTAockhhoNeVvN1EmGfSNbtFg/MDLNdMbqJUoexSIh98QAxv85yVvi/+jwqh0bDrOXrrGTrLG/XKNabhH0efuBYyrlPAIymTalhEfaIsQa8Ohc2yyS7+M5yo8mBZMhJbGQ1AXZepK+s5kXrSVc4NhphebtGripcXoaCOhslheVs1WGJS32xFsIqyu1Rul6U8hkmdquNonQcDN2JmWEuZSqOHyoIaMTBZJiqaaMhPIuHuvppEuckE5/lbM151kEwRE8v50Vr3auztFXh95+/AuBoWOqqwreWc5QbTT7x8AGgQzTgwaOrTMaDbJQb2J2OozMGYh6QSZXE/a3mGswkgyTDfv7sbJqG1erBCcNOdV+K1O4twqswPuQn5Avx0RNTzvyznK06RvfuayxbxrqqOMnUUqaKosCH75t0EptBielcKuxIo/g9mgNRkYoE/aQ4N47upStZnnltsweGIq3I3HOo/P7xyRhLW73t7P5OiDzHT51Nd+VXfI5Atpy7z64WWC8K/cVj47HufWI4XaHFTJmVXI1czXTeAdAhW90hBUqstOxOPXdxi8VMhStb1R7tU2lJNxzy9iRe7neXdLKQHaTVXI3NooFfV5nqJozu8xYP+Qj7dRpWm0ePpvjT02v874sZclWTRNg3kFT43Y7bTuz+8A//8K0cx514C2IQFkKASBs9KzxZmZtOBB3AMOyuYu1VjZPRj1WQ35FjcOtWydW7oM1XdyWC7v1KNpRcTbvHvpcIpFhx7kwagxLcQeGe+PpJITsP+g7D2H3c4LLEmUnsskqT5JSKYfPFc9c5tZTl4w/N8MQDUzx3cYtvLueJ+Dz4Pbp4abu8LSN+nXfPigrlR0/s509Pr/HKaoGDo2HSxQZPnU3z6NEUtabN8nadVku8NO+fHuLx+X38zrOXeXUtDx1QVSGmq9ht8cArcHA0wofvm2QlW6XSsCg3hMl3MizAzLICtLhVpVizB2LeNBVUVIaCKkMBL8VGE0VR8OsaY5EAHYTERyLkpWLaNFttOoaQPTDtFlG/TqvTpt5NDBVk21a8+trdNoclciTUbgs15FWx220MSyR7iqJSN22UTodIwIffo5KvmjRbbSJ+D4mwl2a7RqfTxrJF4qcqbawWBDwqYzE/0aCXSsNiu2pSqjfpdPFsrXYHVRXHVDdbVE0Lj66jqQolwyIa0IVXb810qkKLmQizmQqT8SDvOzJCoWbx/zt9jXzNZHI4yIeOT/D48XF+99lFrm6XaVpiH5WGxe89v8Ravs7jx8d59Kh44YT9HgIeje2KySurBZbNKgE9xtiQn1hQR1NVR99LYlQLNYvPnlrmwqYwRq+ZLeJBL2ORAIWahUfTuGdqCMBJ/uWLXMpPTMaDXYFck2xVtPxnEiHGoj5eTZcYjwY4kIxweiXPVtng/EaJiaEgQwGdxUyFFxe3sdsd3nd0hPcdHeXCRolSw2ZqOEg85N3VHVhYL3UlO3awu/m6KV6mQeEOc2lhg7ppsz8R5LF3iYpn1bS5tFFxdPXec3ikC92Icnq5wKXu4um5i1sOoSoZ8qJrCrPJcA/RANg1b8ln/7mLW45v7bHxKOuFOqdX85yYHmaiC+8Qi08pjCzmRJkUuMld8nc7x17GaLYI+zXHGq1Q22mNSk9eKSMiz62ca+XPbNWgYdlc2CiRLvRKkgAOPCYZrjqkrpWsSKLWCwYPzyWdTki/QsFcKkLZsLtt/QZSh1Bi6OScKRQDig7T+/lL2e6YyoxFAw4hx00UE10mv9NlAsWRpTqXLnXxoAEuZ4SIvsRKy/N7drXA8naFy1tV9g8HXHOxia6yC+YyqDsl34mPvWuM08viPB0bj1ExbIfA85H7Jx3Nw2t5gQO8XmqQqzb5xmKWUJeI8j2b2P3sz/7sWzmOv1bxdpAnYHDS01+FA7HaE23SHH6v7nz3jUY/VsGNMRuUFMrPS02m/v32V8ckGFlObv2WPDcaT3/iuFf0t6P7Ezq3Tt6g/byaLhILePF7NGd7/Yl0xK+zXqxTrFk8vbDhTGiPvWuMQq3Jq2tF0kWjZ1wVw2Y06mc6EaBi2NSaNigQC3ic1nXVtJhLCcHYbNVEQSSHAO+dG8G0WhTqAqtSaDTxezQOjUaY7Yoz+z0ahZqwzqo3WzRbHUJeHVUVOlwfuX+Sr18cwrRylOoWrq4lYZ94KWYrJkNBLz9+7ySvpou8eHmba7k6zVibu/ZFSYS8bFdNsusl2h2oNTuoik2bDh5VIaBrmFYbDQWfLqqUbm084S6hUO9i5pQONKw2Hl0h4BFYs3a7TdDnIehV2TcUYDjo4+JGmeulRhej5GUkLLyCy4aF3W7TtDuYlkXDEl2HomGhqwpNu42qKuiKIvB9mko04KFqCsHtTkch4FHZKDUo1m1SES/3TsUZifhoWDYL6RLxkIefeWjGwUMtbYk2q2F10BTVsR6CDl5do0Mbr6bS6YiKxJ98a5XNsuE4phwbj7CaE97IIb9GB417pmJO1chdWZGCr9mqwcvLOXJVE1VRaLXbbFcNmustUVWYG3HG4bbyktuamIjy8YdmnMVV1K9hNIUITqnLaL6/6835ymqeFxa3qTfbDAV0jo3HHPstWfWajAc5sxISgHiP2oPhks+I1KiUc8AjR0a4sFHukixiFGomLyxuoyuQCHm5sFHietEgFfURD/gcuEmvE4HS0zZdK9RYLxhMxP1MxUOO9p17npDzVrpQ76nUyDFmyyZbZZPtmpDN2SwbnJhOONguUcnydReZhpMsS2FdiRmWc47c7lZFQCb8Hs1x8jm7WiAR9jGTCLJdaTIS8bJVbjrn1t1aTBfqFGoWqgLFhu0kgv1dkmzVcJKY9x9LsVk2OJAMMZtsdsXW845Mjax6DZpfJT5aV5UuQ9nEbncwmq2ueoHP8dW9sFHi0kaF19JlnnBVMZ88c43TV3MEfbpzL8gkMl0UGojHJ2PEQx4ubFRYydYoNSwemBneJcW1vF2lati8fr3Cr37xNTIlk4bd4qFZkWi/uCgSe4lvvFG3Ss4+Jw8m2CwZlBpNVrM1zq8XOTGTcOzPzqWLNEybkE8jHvAyNuR3Oj9vZ7wpS7E7MTjeDvLEXjEo2RNabia1ps3xeO9qa69k6EZ/H4v5XeBUMQkM8kd0T0BuEKuM/iSrH/dxM3Plvap5ex2DrK7JFad7DNOJoGNs3n/M7nbDWr5OLLAjhDxogqgYtkO8CHg0V7XUDygYdhufpvZgdvqT4Hsmh5jrikHLlfRipsK5dBFNhZpps1kyaLZaeHQNuyVe1Eq2xkjEh1qEYs3Card49OgoVdPiqbNrDAc9xIMechUTVWkT8Anx1cXNMr/3/BVKhkUi7HPkYXRgKhng4YMjTA0H+H9Pr7FeqHN2NU8s4KVNm1ZHtF+nE0GsVpuLmxVMe4ehWjNb7E8E8OsaKzkhUJ0ICRmGa4VGT5vVboOidNAUgcVTEBU8q9kR2L0OeBAWcRXTZq1gsF1ukq01Me0OFcMi6BOEB6MlHE86QK5q0G6DqkHI56HdEePTVIVOuwO6Qjzo48EDCUYiPl66miVbbePrVrKqpkWxbmHaggRjWDYruTprecFWNZotvnJ+g3zNot602RcL0Oq0nWsn74tU1E/I66FiWlSMFj4NfB7Rbl3LCxuyfUMBNssNxiIBfnh+nHjIx2wy5JAO3ImMfAbOrOSZSQjrPcnIXCvUGQ55SUX9juUf0LMAk+OTL29JpMpWDVZydfK1JmMxv3Dn8GhUDLuLtfLx/KVtjo3HeipW0k5MHu9W2WS7YjrOB6eWsmxXTJ45v4FptTk6HnZVvgSeabNsOOO42JV5OTQaEVJGDeGWcGI27iRV7nkg7CuhaRAPemh14MT0MIlQjYBHvanmmGzjyaRM2hImQj6OjUcxrRanV/McGgmzlm/w7bUCYZ/OvqEAY91FmTtZdmOIdRVnzqgYNrqqEvYJC8WIX+eps2leXStRqlscSUUZiwbYKjcJeHSC3hadTlt4a5cM59ovZqqcS5dodxT2d43tJSxEMu7lglm2dqVEy0Q8yM+cnAHgd5697PjUTgwFXK3dqV0L8fWiwbl0kdfWS3hUhWTUx+RQsEtGUJzFxsmDCT576mpXdsr9hCtYXdcjN5ZtLhV2IDlzqYgjMAw4uL3+QsBipsJ21cRqt3hxMYvVEvfo6ZV8l+xT4fxGmSOpKI8eGe3BjLvvmecubvUsziX55FqhRqkh7uG5VMRxLnHb371TcHZ3Erv/A8ONT5CJlxvEPAgIPUjuZC9K/CBgq4wbJYiDkiL3WPsn4Tdq4TKILNJfXes/Djd+xL3PbNXk4oaQM5mIBbHbpV0yA+5jODoedcgdgGNwPj8R7aHluyuN/Ulw/3a/tLDBRtHgwEiIAyNhLFsopCdDXrK1ZreK1GJ+Iso9UzH+8BvLXNw0+Oypq8wmw1zdrlKJ+FlYrwDwsROTJEJevrGUYzFTpWm3CPo83LUvTKvVZiVXZyYR5D2HRyk2LJa2amRrJmWzzUvLOd5zKEkiJF76U8NBgl4P37yao1hvYtm9enUqCsmIl8sZMc0blk3QqzlYOoUuzk7Ysor/V0T1rtFlZ7Q7gjjRtNtc2a4S8XmEe0KjuSPZgmjphrwaTbtDsSFYxmGfECBNhPxMJ4OCkBP0kCkLl4h8Q7Ty98WFRtnlTIWr2zXa7RZrhToRr85oxE8i7GF+IkapYVGq2xxORWlYNn95ZZuG1SYV8ePVNYI+jWNjUcaifs6s5NmuGFRMC01VmZ8UL+6lrSojEV83EavwymoRu9PGtNtdz+I6U8OTJMM+Xlkt8PJyjplEiE88PMtcKtyzmJJiurLVdGYlz5+eXuN6qU6rqxsnQfPukPedG9gvnoUKIZ9Ou9PGoyl4Nb1nISIrhfI+lcmibF++mi7y8KGk0+YM+zw8c17obp5fL/H6RpmRsI//z3sO9MwvUhBX4lSHAh6KDYvNskEs4KFD0CEmyedFYveWs1W+cj7Dtbyoqk0Mie0eG4+ysF7qYWNKLc6d6qLADRrNFnl2cGZudu77jo7yMydnutqUWbbKBqkh4e6yVTb5ifsnewgFEb/OK6sFdFWw7KVMzFwqzNRwkGt5qFstlrM18rUmAa9GLOhxtN4eOSL0N0+v5IUGpN3m/EaJWHfROAj+AjuYZdmKdCf/Ly5uMxbtxSzLipNkuSp95Cf3vCuTG1mxKzYsGlbLmbPd74dj41ECHp0LGxXOrOR5YGbYIcTJ+b1/2+4WuYTKyOt1ejkvSCPvGuOJB/Zz33ScutVCU+BcusRYzMdmSQjGNywfYzEfmqKiq0pPd8kd8pwEuiL0sg0eC+ioKAS9OpPDwZ4u0mwyxNML1wl6NN4pcUuJ3a/+6q8CkEwm+cVf/MWe391qKIrCv/gX/+INDu9O3Gq8kSRHTLy92oODkip3ImRYLdYKNe7eFx34nf5q2fxEjM2y4ZTXB21zr5fKzcZ6K9saFDciibj3J7ftBna72VgSdO3zqBRqFp1OnbmuBpkblOu2R3M7ZSxmqhQbNq1225Gg2ev6uc9Jf6KdrZrCkzJf54eP72MuFSFfazpCnuWGxVbVIOgdZqPUoNywabc6XC8JNqVf1zkyFuFbK+IcNq0231jKkqs1CXlVRsJexof8fPhegQkM+HSOpqLY7Q4Vo8la3sCrqkR9bQ6NhB3yhlcXelv1pk3ZsIRheve9oCKYqQGvxkjEz1BQp2rYtNvCAm1iKMB2zaDd6hDxe2h1xGq+agj9OtPuENAVmnYHry5EgKvNNvm6TastsDceTeXsaoFmC1qdDrmqScCnMRLyUTVtsQ2PRizgR9cVTh5IUG+2eGFxm5BX4/v2x9mumEIK4WqOiSEh6bC0JYgBpXqT0ZCXRMRPJOBBakM+MDPssB+FLAoEu6zS7YrJpY0ylzMVVnJ1mrbNiZkED3TbOg90PVDPrhaYS0WIh7ycWy/RsmA2GSLgVclXLV5YzAqmuopT4ZDV26deWSMR9vVUw+XPimGzkquxXmwQ8okX0GdPXeXYeMxZULghDO4W3mKmyjeXc46zQtNqk4z6ODEzzFq+xouL2wKEPoA1726xVgzbwW89d3FLeOS2RRVzJVdDVelJOOWirlCznKrIUMDDUEDv0cCTz8yZlTy/98ISVbPFsbEo2xWTatNGUxRyZROPqhD26QQ9mjOml67kWC82OJcuEvLqjiam1N67d/+Q4+qwVwdhLhXm/cdGWdqqdRNznXhod0diOVt18GLHxqO4NUU/cv+k0wEoNyzmJ6JMDnU1DCsmr6wVCPs0xqIBTFvov+VqpuNjK2VJ+uEvk/Gg0zbsn4ufXtjghcvb3Lc/3sOkdWONx2L+XbjA/kqmtBe7b7oXLydb0vI7q7kG6WKd9YKQlZLtVPf8Lp8Bd1t70KL6+UvbrBXq+DQpaIQjpIymcGQs6szdn3t5BV0Vbj91q0XQo+7y8pYh2qslJuJ+ruVxrvOx8RgXNiuEvTr3TMZ6HDhAWHKu5OrUrXcGO/aWErtf+ZVfQVEUjhw54iR28neSFbtXyM/cSey+s/FGkpxBScTN8HlPnU2zXjAcj9e9viN/L4G2/VRxCfB1e7jebgzGRewdN2vVQq9unizXu6uZ7n2GfR7qZovDqR1fS/lZXVNYylR75AHcL779w8EeCZpbuX7uCfVIKsJMMkQH2Cw1WNqq8NET+3te0H/yrRWubtX44sJ12i2wOx38HhXoUKpbRBNefujuMT738jUA/ipdoNxoMRX387eOpXjhcpbNssEfv7TMWqGBXxMJwfxEjIhPZylTQ9dUkpGQ8FttWHg08bL++sUMXl1UddwzhE9XGO1WIJa2qlitdtcHtYOqCIuzZstLudHEbncI+XXKXZsqKXbRaneYSQbwahplw6LWFOxbIZHQZGpYiPDmqybjUT/NVptWu0Ms6MFqdxgO+7h7PEKh0eRarsHp1Tw+TePSZoWwT2eiYnbNv2vY7Q4HRkMcSUUJeHX0hk3dEpIhiioYxFL4WorFvnQly3DIx3BIJNGXMxVOzMRZydUo15pAh6BHiOLeMzkECKyRFHMVyVQFj6pSbbeoGhZT8RDxgMWVrSrXywbjMT/3T8cdI/gXF7cxrTZh344+mvwp79f3HxvlhcUs9aZIgl9dK/Hi5Sz37o8LX1gXCSpbNVnKVLtexwnHrH4iHiTsF0mr36NR7y4Gzm+U+NQPHt0FGpfty34iU8SvO6K7U/EgnjmVqmHvqixJMfFLmQrrxToKQR46kKBQa3Jho8Lj8zuwis+9vMLr1yuEfJpwp4n62SwbZMoNzq+XkQx7EHIruqo4CeNMItQVcBeLs6phE/RpjiOCnBuyVZP5iViPXtlkPMg/eOSQw0ien4j1kNIkU9XtLNFfYZTyIJoitAelDttcKsJipsK30wWuF9o0mqKeXarbtDodfCEh+SLnESmV5O7GyISnYtg9c/9Y1N8lq3R6ErVB889yttrDSHVXAQGnuiurbP33oGEJHc5DI+KcSReR/uiHoLgTPDn/6arC8cloF3vndVr/cjHiVjWYjO/YVGarJhev5qg0LOJBD/nunOo+XrcyQtW0KNQsh7H74GyiW/3duQd22NyikNFP0ni74pYSu3/5L/8lICp2/b+7E7vj7XKecP+8UdwsiRhEYhAeiNFdwNBBHrNzqXDPePoTyX6K/O3GrSRqe4U7gZPbkuPvZ9TuVZlczFQZHwrgkWazrs8K02iNpa0deRK538VMtaeC17+PvcabrRqOJMKlTAW7LbTG6MClTMWxPZLn+sP3TrFdNqkZLRQFwj6dkE8jFRFWOENBD6u5mrOPsWiAiN9mfjLG1y5sOYzKgE+4KFjeFltlodsHQpC33RFJ4nZFJFc+TaVmWtSbbbx6k1jAi0cHLNA1GI34OTwWodq0Wc1VqZltVEUwbHM1Q7ysdBW1a/mldc1vNQ3CXp2mZWO1IFez2Dek4dU1vLrQudNUBU2Fpa0qrTYkwj5SsYCDY7q0WUHXNMZjPmpNG7sFIxEfpZrFVrVCNKCzPx7iwmaZdL6OYbVotzt8YzHHt9eKXNkS7ch4yINltcnVTAJelS+9tsFKtoZHV3n1WpH1Yp1owMM9E3GKDYtql3TwrokoVquNaXUYiXjZrpj815dXSIR8XMqUMe0239+V11hYL1ExLVqtNsu5GkvbNWIBnYBPJxH0EvSK6o18eT8+P0404CEZ8vK5l1fQVHHNVnI13n9slMeP72MuJZKaby7niAc9NCybXE1Qju+fjjuMUcG8NMjXTb5yfpPZZGiXWb38d8Sv89LVLMWaxaml7C5WuVvkVSYyL13JcmGjgq6qDkO2bNgcG48OxN7KVmUHmBoOdqU4trm6Le7d5WxNJGg1i1hAZzYZdkR4y4bNPZND6JqKpqo9lSQJ/t8/HORISmD2NkuGo2326NEUjx/f54xDVJ3qPe3tp86mnURiJVsjUzIIdA3tpfzS1y9tkS4IEoCsbm2WjJ52omm3yZQN7p2K91QIQZAdfuDYmMBFmxZlwyLgURmOeDk8GmUmGeq5Lv0wGPec5J7zpDantEirdYkrvSQyk4X1UpcJ3FthdHcmol22rnxnuBe4dquzs2jQFEeaRcaZlTxPL1xnLBpw2OASguIm2vXi7qI9CbfUJJRz4yDbynShzqmlbac1e7irSzpIGWFH0krptsz9TlXVjfsTeDxxT0gP4lstNHwn4w0ldjf73Z0Q8XY5T9xqknOzJGIQFk14II7uWpEPotMDPVizfvzeG620fSdiUALnHpP86ZYlgN5EVlRKtlhIF5kY8jvetvK45YtlLhXZhUuULwcZ8u9Pn7vO0laNQ6NhHj8+3vO91VyDY+MRkmG/MxlrqsJkItA1Glec7Z9ZyXE5U0VFYatSZyQS4Nh4hIrR4uh4lNmu7cPLV3POGOYnYsylIpxezrFWEGb1kYCwqWo02+RqTa5kq2xWDA4mw0R8HsoNi2KjScTvJRn2sFUxMZrCw9TvFZP3RCxAsWHTtNvY7Q65qkHZsNEUDY/ewacrXXFlYXc2Fgs4lT9VVfDpGpoiNNzCHo26JSQXcpUm+bpw1uggLKoaTQHKVzowHPahq4pQ2N+ssl0x6Fg27Xabq9kaugKHx6KsFxtkyiaTwwFGIl7OpYsYdov9wyGstgCBS0u1oEfjJ0/s54XLWQp1k2LdRlNtnruUoVS30FUVr6bh13TumRrifUdHut9vcnGzQrFuoSjg2RKVmVrDphSxqBktKqbFxc0yx8YjvPdwkv3DAbYrQvdus2SQqzVptdqMDwXI15o8eXbNYQhKTNJnvnyRixsV9g8HKNZtVvM1lrZqzn0R9euYdpuF9TIPTMd5YCZBMuR1JE5kZefkQdG+u7hR4XMvr/CpHzrqJDOSMSnv9Z87Ocsz5zc4koo4zg+nV3NE/V5iAY+oVDYshySxlm9gt9o8MDPMR+6fZLMkFgth3+DXkTuRkJUbTVU5MCKkVwQDt8VQyIOmKaiK4lSudU3hvulhxyrNvTj70sIGpUaTkwcSXMpUHMLAR+6fFGSRbJ3fffYyh0Yj3SRoJ0F68swaXzm/yWbZYDlbJRbwslFsEAnoHBoNMZeKOi3DdKGOT9OIh7zOddA1hXNrRScpGg75uqbyas+xy+dezrvPXdoiFQs41ciKafcskAdhkt24Sfec5/69u6rqXvRODgk7PJkUu5MlwPnccMhLer3EhY2y490qW6H9fqz92n+nlrK8uJgl6NF6WpmDMMayrWxYrYHwGF1VOJcuMjEU6GE3y2v/8Ydm+NzLK6TzDV5Ll7BaLS5vVYkHvPzkif275mkpSj2XEjqs1wsN5x4QeLwtAh69i8czerCNb2fcIU/ciV1xK1i0vT476HP9n3kzlba3KvqrcDL62VE3Iow8enSUY+MxtspNBnnb9lPq+/fbH4uZKs9e2BLCtbka0tDc3cqQmDxnMk56d1X/AP7g1FVeWS3Q7rQJ+QTzNRbQqRjChWS7YhLx610fTBFrhTonDyY5NBrh4qZoaYX9Oh++d4qRiI/fe2GJhXSJzZJJq9UhGfY5bZ5kyMOv/ug8v/2li1y0SuwbCtJqd8jXTOIhDzRsITthttiqGMJKLewj6tex2238HrWrwzeMoijsiwWomRbXCg3yVSFkrCodqs0WIY+HaFBns2TQ6uwIF+uaQtCrkqspmO0O2arJhBng+w8myJRN0sU6tt2Bjk3IpxPy6RTqTfJ1E6vVolxvYrU7aKqoFIb8GjF/AKvdpm7aFLsvuKXtKoW6OIeHUz7mJ6O8ulYi2xZ6cEMBL0MhwUSVArVPnlnjelHoXalqh7BXgOL9Xo1iTey31enQtNuOxdR60WAk4mU11+BwKsLlTIW6aXNiJs4Li1mu5eu8mi6iKDv3nq6qHB2PcPd4jOcubxELeDg0GnKSHBALjmK9iUdX+YfvP8zvPHuZr13IMBb18YN3jzvts8fuHqfeFAz6p89dJx7y8ZXzG2x27xnZ4qqatlOFEfdwhkzZ4Pv2DzEW9fPlUSv6AACSiUlEQVTaeokLm2XOd6tqAY/KSMTPsfGIs3C0Wx1RhW51BuKr+rGvH+o6KQDEQz6H0CCB+NIzei/dTBBV7qvbNZ45v8Fjd49TblgO03S9aPDi0jbbFZOVXN3x35aJxPOXtqmbNgdGQjx29ziXMsJpYmpYeO5KzN+FjTLTiSCxwI6si6yOnl8vMRz0cM/+OGNRPwvrQs/SvVAWLhf2LlH3xUyVdLGBrioOyF+2JPvPnZyT+vU1B81J/QlgPOQd2EqX10JiAiM+4Y6zkhO2du5zdTPc98OHkpQbTarducndyux/V8j5RgphS/LHdGJHP3C7YrKSrfI7z1525kb3nD0W8/O7z17mUqbC2dUi2xWTA6OD32GShJOtmlzYKPPN5Rw+j8qJ2TgXNsq8uJhlJhHEp+u7fMvfzriT2P0fGG+UdHCjRKz/b3th7r5bN/utkkhuZUz9SdggeRQ3G1CGe7WXDPt7JAn69+seb8SvM5MIMhL2cc+U0KiS1mIBj0q92WZiKOBUaGSrZyzm54GZ3mtwZiXP5UwFj6IwOxpiXyzA+Y0y+arJcNBL2KcznQjxNw4mWbheBoRF1lNnroECHzy+TzAzrxV57uIWH5gf58R0gnzVYjUvql9Fw0JTFUI+ndmREGMxP0fHw6SLdfbF/BitNqoq8CpNu03VtIW9lqqgICyy4kEfxUaTYsPG22zzwuVtRiN+3j07TLZmUqhbtNodNLWrR2baqAoEW4KkIQF8KkJKJlM2HZyYeMXDN5fzrBfqKB0Fn0chGfYR8upUmzZBj8ZMIkzDshkK+qgaFigKHRTWCw02FIPRqJ9Op4OK8IFd2hK4Iq9HYzYZ5BMPH+ClK1lOLxdIF+uEfBqZktBc+3a6wP/9/iOcPJjg9HKec+tFxqMBZkdCHEiGeP7yNulCg1LVpNIQEioVw+bVdJFTi1niQcHebVgtxocCGM0WS1s1Wq02YZ/ObDLEdCJItmoQ9gmfY4kPum8q3nU6UFjO1riWr7NVbjAc8nAkFeFEd4EgRbZNq816sUGhJkSkHzkywonpBM9eyLC0VaPeLJOvNgUZoSrY1ZLZ7XaPmBgK0Gy1GA372CwL67LNSgPL6jA27uexu8fxd7XwfufZyxxJRXqqO3vpXLqr3RG/zmdPLTMW9TnVOPm3bNXghcvb7B8O4tFVCrWm40ftXgRJ1qeuqvg9GsfGIw6u1G53OJqKcjTV4dBoBFB68K2jUR/HJ2NOVV1aebnnHTczOVs1+cIracI+nZ9/5CAgXFHunog5nrjSslAmErJV3OmwS29vs2QwHPJi2W3HC1fi+vY6d1IBAHY6EfJv7nEbVsupZM5PxgYminJenBoOcm6tyLV8nfceHuHYeBTpVNTfzdlrXh6L+XnP3OgNnDx69wk4+o3n0kVWczWmE2Kbs0nhhvHi4hbfXBb7fvhQEl1Teqp8J2aHaVhthoIeyobFY3cPtsWTre1a0yYZ8jES8VI1WxRqluMfPhbz49G0XezitzPedGJXKpX4+te/ztWrV6lWq3uSKe6QJ9458UZbsd9LcStjv53kL12od3WYGjz2rtSuSoLbNqcfACzxJbISIV+AMitx+xqORgNOmzZdqHNho0KtaRPw+HpkByRWUfpLypeDPK6fenA/M8kgsg34zeUcnU4bn64S9KrMjQm85HDIy1cvbmFaLa5sVTl3vYTSAV1TUYG6ZbNdNXju0hZGs0XAqxH06Fhqm1jAQ9ivk4oG8Hk00ZapCqJD0KtzcmqIpa0KNbOFrqn4NBWTFqkuUL9mCnbfAzMpLmyUuHC9QsNqU2pYzCaDonIGWO0OdaslGHAKNKwW21VTeLF6oGEJjF6nA7Vm2yFr+DRhK1U1bYI+HY8mhHGPjEdZSJcoGRZ3jUd539FRh71sWi3Wcg1arTbJsJfRSACr3eJari6IIBYEGxY/es8EHl0w7YRWV9KR8Dg0EmYt0KBQz5KtNnnm/AYfmB/noyem2BcPcCQVYS1f5/lFwQw9eTDBQrrE1WyHsZjfSfJhx95O4t++uZnjlWt52h3YnwhyfHLIEcPVNaOn8uXRhTaikEUJUjGapPMGoxEff+ekIEDJZOhTP3TUaauVVZx7LR7yMhrxc2g0RDzk4/RyjoV1UakYDvmYGg4Q9gmLsOVsjdVcne2qSaZs8o0rOYJenYm4n4jfS8vb5uMPzThtRVkpXM5W+dQPHe151gZh7dxM3acXNvjSwgajUX9XYBlnwbWYqThYt9Gon/iQj7rV5uJGmZeuZpkYCvKh4/uc6s3T5zYciYuaaRPwqJyYTfTIDwFc2BCOCuWGRdCrO63VQXNFf8VeEsY2ig1HU04ekxvPDDuG9dOJ4ECpJ4EVExXbitFE0xRmEiFnXnHr87nHMp0IOC4iQk8O5x4AWEiXeOb8BnarQ6ZsOkS5Gy3qpdyJ0CwMDayMuokPgwgae8FT+qN/3h6L+R0dudVcg5VcneOTQ3zk/kkifp1owOsk7wvpIufXS4x3/bgB3nt4ZKDofb+M13sPJ4UTRc3kQDJCrmY6AuJzqYhzPt2Qm7c73lRi9+lPf5rf/u3fxjTFiRqU1N1hxb4zov+huNEN+E7AwN1u3MrY5YM7qOVzo++s5upUGgII3/+35y9tIzticpsSpC1X4Rc2KtjtMsMhr+MF+d7DI3u2axczVex2h+OTQ84EJQ23gR67nJeuZB2Ve3lcEu/x5Jk1Ah6dR4+kaHR1sp67mCHoUZlLRUkEvawV6lwvGcwMh/B5BNC8YjQJenRGwn50VbSumlaLeMiLV1PZrprEdY33HRlx9h3y6UzFg92KY4RvLue4lKnQarXRVYVWR0VTlW61TdhflQ2LY2MxVnMN2jSZGg5yaDQs3Dg6CttLBnYbWu02EZ9GqyO2NRLxogDttomiQizoxaNrmJZNqy2IGh5Npdq0iQe9+D0qjWab19Ilmi3R2qabwFzKVCg3hCvF9VIDq9WhaXe4Z/8Qa/k6+apFwGPRareZSQjnjldW83zhlTTRgIcPHt/HpUyFixsVLmyUOZAMc2gkQqbSIOr3kC7UWc5W+dZyjv/xSprtioGiCiHZ+/bHOfGw8FmVieJcKsw/fP9h55547uIWdqtDybBotkTiFfF5nKRwrVBzrK3ceKaJIT+nl/Os5hrsGwpABw53HSv6k4CgR+N6s4XPozIS8TKbDHflLnp1FM+tCw3GoFclFvA6iaTEIy1vV4XPsapwIBlkJOLvVnhtlrM1J7GT97Smqjdsv4GYv55eECSVoEejaljEgh6OpIS7RK/1YAez1Wa7YhL2i6rmyYMJPvPli2xnRMvW7XJQt1qcS5c4Phnj+NRQV67D51hJvbi4xbHxGGNRP6s5wZg9Nh6lUDP50msbjpbgXjha+bw+dGCYzZJJspsQ9le13Dg5OcZBidJLV3KOMHnU7+XufTsYxH4LxqfPbfDs6xmOjIeFHZiq8tCB4YFzzme+fJFvXc1zcDTEw3NJhyh3o4XwZLxXs/BGFbp+Jq1MOLNV84Zi0W4ih0xI5T0i/HATDq7zWn5nrPdMDjEW87OcrVExbDRNcZjQstLsTszd50L+FPsQciwvXM5yaDTEPivgKBrI61uoNXlxUegnvt12YvAmErvPfOYzfPrTnwbgoYce4t5772VkZKQH73En3jnxRqpw7wQM3O3ErVbi+itqcPNzMpcKO0LCO8r6O3+TMgZuUU5JIJEr7FLDIhbwdBnGsZ7v91cA3eOUEzzgiJrKfx8bjznsMYmLcY9hMh7saRfL++B60eD0SoH7poc5Phmj2DBpdTQ+fO+OLpWsEj18KMkrqwU6FDgwEuaeqRgvLGaxWh3CXp2TB5NslgxOLWU5cmBHagBgOhFivdgQ2ltenbIpyBaepsZwyEOp0WRzxWA4JNwfAh6BBTTtNqVGk0cOj2C327x8JQ8KpKL+rqBxi2TYR9Cno3Y13cI+nfcdGSXgUbmwKfxAoUOxZtGwbIZDXtrtDlG/TsDnY7MovFfX8nXsdodSXUisaIqC3y+Yua9eKxL0aSgqjEb9HEmF+bsPH6Bi2PyvhQ3S+TpTXYmShw8leelqlu2yyWbJwLRbGFabpa0qv/n0BZazwp8zX2vSbIGudmhaHRbWhQ3ZPZNDZKsG57utcfe9IGwALY6MhdFUBb+u8omHhZivlCKy7Ry6nuexu8eZn4zx0pWs4xjS6XTwaCrRgJd0sc4rqwXKhs10IuC8NM+lS6wV6l0v1pDzQpT3p0xQjk/ESBcb7IsFifh0Frt4TKlxefJggmzdZDjgZV88iN3qUGu2WN6u8vVLW47VWqFmcvd4r3TIXiGSlhpV02Zpq0K21uT4xBCHRkOOTMwO5k5U8JazVVRV4ZXVAnWrxYlp8cIVi5YdGaZeiQubiaGdltqr6SIL6RJb5SaPHBlhOhHkwmaZ8IbOWNRPtasP+NKVnOPmIL8rW81Bj0rZaJEI+4kFfGRrTTbLplM5GpTUZKsmX1rYYK1QZyounDVOdhnTFzZKKMCJ6YTTvt0L07tZblBoNFlIl2l1OhwYCe0C+Mt/P3b3OLmqSczv5Ugq0iOPcivzpDsh6v/OIAKMu5vR3yIeVNFzExncIQh5woJMYgHd+59NhnjXRIyxqJBZWlgXi+tCzeL5S9muq8bu8+FO+tw+v6+sFiirO3Z4AOc3SlzdrhENeL+3E7vf+73fQ1EU/tt/+2987GMfeyvHdCe+A/FGqnBv1NHhrY7b3f+tTkCDGFe3Mo69hJLdq9ZB25QM3KnhIBNDAWdVDezpbOGuupxaynJpo4zdaXcn896V9qBjkf/uP4bNksA8tTsd0oUGZ1fyjA0F2DcUZDzmd1oTcjxSs3A5W2V6OMR7D4sJ7pVrRSaHA3z4vskeALzfo/W0Uz7x8Gy3uiH0215dK/HaepE2QgetaXXodBNiTVGoWRavrhW5slXDq6skQj4++egcI2HhmpCKBjCaNq+ul1jL15kcCnbJBh3aHQGcfu/hJLGAlxcWt6g3WyTCHtodLyGv6ojiHk5FgSLn0kVqOZvRsI+RqI9UxEcbsFsdvn2tSKHe5J6pGHWzRc20SMX8jhzCkVSETNkkUzL5w1PL3DMVY180QKVhEfCqXV9KUaE8vZKn3rTZHw8S9XtZzVXRNYWp4WCPSn/U1W50XzthA9ik04F/9ANHuvjLHJ955iKvb5SQVcdSQyRyfo/GC5ezXMvVQBHaXgGPyiurVRpWi0TIx3vmRljMVHj+Upbjk1EeOTLC6eU8S9sVVKVDodZkNVd3KsCyYnJiNsG+ovDhTRfrXM5UCPo0zFbbAdofSUUZDnlJhrycXs2TrzYpNiyubFc5tZR1NB5TUb9zDw165tz3uVxYrWRrrOTqJEM+Xl7OUzVsnnhgZxsnDya5sFHm4maF6USApa0Kf7VW5D1zST71Q0ed1qusrsj/nru4xfnr5Z6K10funxSEiq6tGXQcMgjA+4+lHK9d94IO4NNffI3Lm1UenE3wgflxp3K/khVt/UKtSdmwdyU1sgOwVqhRb7aoN20aVqtrQShs9qYTIeIhT0+bU8rKuBOSx+f3EQ14HTcat+xM/3n+wLwggXztQoY/emmZyaEgsYCHQBfjKROZWxFTd1+3/nnNnYS6q5I7nRQT2HEC2YvoNmg/O4t2w9War3Zb5z4KNRNdFc/D6ZUcDcumUGvy+89fYbPc4PH5fTwwM+xgm+cnokhPZoBXVvP8r4VNTFv44b6yWmCzbGBabcZiPoIe7U3rs74VcduJ3fr6OjMzM3eSugHxdujY3SzeSBXu7cbY3e7+32gL+WbnxD3RyNX47VQ7+8fVn/gNIl7IlWy9KSoC+ZpJqy0shwZV99yrTPdKcjFT5YvnrtNqC3zTcla8LHy6SrbS4NJmhd985hIAX/2/3wvgWDK5qzVyxSrHKi3SqqbtfH46Iax2zqzkaVgtHp8f79oG7bRl4iEf21UTTVU4Nh6lathkaybHxqI0rFY3sRAVpETYx1jUz3MXt/jWco7ZkRAPHUiwkq3xyrUiJcvGtKuEfDp/62iKmWSoC/wv8vVL2xRqJh5N49BohB+4a5Sp4SDPnN9gu2KxmqtzT7dq6tGEy0arA3ZXNHer3GA47GU85ufD9wnnjUubZcaiAecl9dET+ynULV5NF3k1XeT1jTK1pqgEddpCc+1Ed/XeaLZoddr8/HsPsZav83svLNG0hZ7dEw/sd+l2GZRzQth7OZvlmdc2SYR8jER8DptU4rWev7TNpUyZqmkT9ur4dZW2XyPq9xDx68xPxCjUm3TaHcJ+ndVcg5BXYzopdO/GYn5eXNymYdk9Qq9hv0apYbO0VSXs1yjUNFZzjV3SD6eWshweDZMICdJJxbAp0eTwqLhHHj6U5OmF67x+vcxQQBA7jqYEtlPaUPVX6/Z69t0LqyfPrLGaq3NoNES4rLOSqyHhEfLaPD6/j/fMifv36XMbrGTrjEUDPa1X6X7g1tyrN20WM5UeyIpbbH2naiZ0JKWemdt/VR5H1Wyhqr3P+KmlLBc3KwQ8Amy/vF3l22t57pmMUzFth+BxfDJG0Ks6FnP950km2cmweN43inVeWy9xfr3I+NDO/NBrw5VlOVtz2MyDLCSDHpWxqB9NVbDbbVayoko6GvG/ITH1/nDPa7KzsJcYcrZq8PylrMMyHWTT6L7W/fAiKRT93sM7lcls1eT0cp6F9RIeXaFq2KwX6jRbbb76+iYbZQNdUVwVNwET2SyblI0CW2WB4RuJiCTetFqcvZbn64sZIj4Pjxwe5dh4hNVcowdW8HbFbSd2k5OThMPfexis70a8HTp2b2W83Ri729n/d6LK6F793U6i2S/0LGNQQiZDWredmB7G51E5vZxHQeFvHBzBo6u7BKLd0Z8UymNotduOHtnd4zEUBVIRP6qicGAk5Hx/Ih7gm1fznF0tUG8Ks+tHjoz0JGZytT8x5HdA07JaMDHkZ2G9xMVNweSLBjyMxfw9oqhub8iTBxM7LdwukzPgUYU4bq3p6Jp9+fwG10sN7E4H6LBdMQh5dVA6VE2brYrJ5UyZ8Zifr13KcDUjLNt0FXwehWv5Gv/j22mCXp1OBw6OCFYcdFAU1UlWnjqb5lq+TqvdJhUNOHZfFcPmoyemnGrLp//na0QDXh46kODETJx40EPNbHE1W0VVdcZjAWJ+D4VGk6WtCodGI3zs3VNOUvAXr16n3e4Q8ukcGt2RAJL3mqw0nFnJc2GzjEdRUVWFaEAn7Pc4SdbxyZiodnY6zCQFs/fVtRLfXM4R9Xvw6CqjUb8jwHt6Jc/xyagjuvvU2TTFhsX85JAjbC3FaGMBnZVcHaFfrTjaZK+sFnhxcYuxaAC71XHM4wW5aJmVbI2r2RpBr1gYSObgidlhh80tE45Bz4ob5J8u1J37Q9quyXvIvTiS3xMitYbD/pTPnPQQnRoOOixg2GnBup+brbLJdsXsAcK7yQ9zqXD3Ogq3lEFVcfmdRw+PcnolR8Ww+OypZaqGzVqxztRQgEOjETbLDV7fKFHsOqskwj62ysK/dmIo0EOkcoe7LS7GpdDuKKJS3LXz6x+XZHcKGLx4/qYTgV1WYWWjxQ/eneoheUlikRsaUm9aPQlw/znoT/7c35W2Y/0JkLuTIklfN3Io2nvhrXSPU3G2mwxXydWaNCwbr+4BOlitDttVk3qzhVdTmRsLO/eEvMfkAvdLCwZbZYN2py32MQRbFcHaN20h8i0Zue8EbPptJ3Yf+9jH+O3f/m1WV1eZnp5+K8d0J97meLsxdrez/+9ElbG3ZfvGFcX3GtNebh2T8SCnV3K8fr1MIuTlPXOj+L06036dmS4AfK9jc0ux9LPTpCinpqrEQ14+fN+k0xZqShNXYL3QcLZR6GrdQYenz13n2QtbvP/YKP/gkUPdBKDstH4kcWJiKNAV1g2yXTEJelSePnedc+kiM4kddfxCTUiGrGRrbFcM1gsNzqzkqBgtEmEv9+0f5vJWhcOjEc6lSzTMFqoiJE3+/NvrNJptQn6NiN8jKmHtNtGAlz/51jWWszUCHo1U1MdcKsJd41G+cWWbtXyDpl0jEvDyQ3eneOKBKee6rhfq/Mk3VxmL+Tl5YJjZZLinPSRtjKDDmdUC5YbF3GiYCxtl7HaHD8zvI+LX+dPTawAcn4zxzPlNrmVrFGrCheMnuizIp86muZKt4tGFI0k85OPMSr7rniC29+gR8SK/nKmgdCAU0Gh3oGrYXM5UuGcy5pzDhtXiA+8a54kHpjizkuflq3lyFZNvLeeIBrxMJwIOtslud6hbbSdZupavs3842FM5kQD3+YmY8MfcKFFqWKwXhbTOs69nKDSavGcuyXvmRvueiQ6KAmNRH6DwpYUNRiI+njgx5dyT/fd/P+FgOhFw7iegx2FiL09lSRB6/tI204kAHl3rqVq7tc8knstNTHEnHXKx4j4ukRjsuOUAPSQFt5WYxLzJY213oGLaXF/N07Q7RPw6J2aHSYb9pIsN7hqP0Wy1GYv6ubpdpdZsEfQJ0otsVT555hpui7CdMe2cs8felaJQs4iHPM4C4nefXeRipswH5/dx33SckYiXsWgAN9nErfE2qOXpTsB3HB5E8vxauoxgTvscQoIcm2Qwrxfqjp6cO0Ed5F3rvrZPPDDlkDHkOR+UPMPuhfdsMuQIXsukVbTyUxRqw8RDHmaTYZazNVayVVZyNepmi3jQ23PN+zGu10sNVnLimU6Efbx7Ns78ZIyqYWG3ccgU74S47cTul37pl/jqV7/Kj/7oj/LHf/zHHD9+/K0c1524E28oblTle7PVvNtNdPcaU7++k3tScrSRogHXxGU6pIm9xuHWqXLbOsmJ7eMPzXBqKctsMuS0YkBUs2Rc2drZxsmDApg9lwrz2VPLFLqAb3k8r6aFdpWspvSz9+SkXG/ahLy6I0b73MUtnn09w1qxjk9XsVttmq02Ub+XoE9jJhHi8flxKoaNYbU4vZJnXzyA1elg2G3W8g0Oj4WJB7ykiw3umYpxYkY4B1wv1PF7VO6ZGuLkgWGWtmrEAh4+ct8Uf/7tdVZyNRpmi7V8o+fcPb+4xZmVAsmIj795NMVYzM8rqxu8uLjFiZkEwyEv59aEG4VPU0mGfXg1jWLDYv/wDo6nYbXodOD0ap50vg6qQiLs4fhk1PlMvgv6D/s1rFaHJ0+vMZMMCY0+y2Z+csixfrJabSYTQT40v496s8XTCxtsV0zSxQYXNytc2qzg0QXm7Mkz17iwIVp80YAH026zkq1i2i0W0iWqpu3Y0cmKSX9SBziVWJlEpAsJp+I6MeTn/XelHCwSiERVsFsF7m0mEeK+6WE+9/IK375WJBUVmMTlbNVpj0mZHil9Ie/x6YRIOnZIQB3HYaK/uiafl50QSWXDaqMoak8SOWjBI1qTOacaJY/3RpXEQUxStyTRZtkgXWxQajQdL9qARycR0lhq2ihKmxMzw07iJbchE9NLmQoBn8b+4aBDcHju4laPRZg8B3OpsJM8SdFy95ifOpvmm1dzbNdMvvp6hnjIQ9DrcZ7pQXCQQfNcf9VPyrBMJwJc3LR5YTFLpWER8nt49+yws02ZTJ9ezbNeEFVIOUb5t35yjsS1vfdwkice2H9DMob8OWjhvZytcmWrSrnRJOgVUjyPHh11WvnymE4eTDgJ5H94bol0sci+7j3Q/854YGaYfbEAlzMVAt2Fw0wy5MAo3Av1d0LcdmLn9/t5/vnn+cmf/Enuu+8+7r33Xg4ePEgwOPjFoygKf/AHf3DbA70Td+JGcaPk6+3CDO41pkEJn/y39G90T7Q3I3nstc3+BFLaRVWMnZX+vVNDzt8Pjobxucyz5TZOzAwTDXicl+tkPNjjm+iubsm/u6sgbi0wOSGORH3UzRaLW2UiPi9HUmFSsYCD/aoYwtEg6NV592yCoa6ocjzk5fH5cYEVvLTNsfEYfo/WJaeEOLYvxuPz4zy9sMHXLmzy/GWVB2biNFttDKuF3eqwWWn0nJ/Do1EaTaHLJ8HdsjIltbCuFxpcLzXw6BpbpQbX8jXuHhcJ5GbJ6DGHN60WdVO0d3xeAbx/6UqO2WSIyaEAS1tVxqLi51bFxOtRuGcyTsNqOUlyvtbEoyuoqi4A9FaL8Zgfr0flsbvHOb2S43rRoEOHy1tl/mqtQM20OZKKcs/UEJc2ysJyKlPhC6+scWQs5tjR7SWj4V4cuF+ebjYj4GC1LmyUOJcWJIKJIT9Ktz0pxY7jIQ8nHDFtk06HLolGwWi2WCiUun7KVS5lyvzw/D4ePz7ukgiq9VRQ04V69z6r8OLiVpecojntUfexyRbjYqbiiC27k5/nLm45BAWA08t5TswO76qK9ycbMtwLGLck0amlLFtlgxJNR4g54tf53MsrXN2u0bBaA+EZJw8muosnwyE4yOfZrWW31zPtDrmACPk1Cg2omkKiSSam/fPSreCM5Vjkz5MHRcJ/+moOFDg6FnaSdIm5vX86Luz8usx6Gf2VNknOKdQsak2bCxsVp9Xc/51Bc2D/8RRqFpmSwf7hYE8yviObYjjaobK6OJMMMWJ6CXo0xzav30f80GiYlWyNI+MREmGf4xDzdne4BsVtJ3atVotPfvKT/MVf/AXtdpuzZ89y9uzZPT9/J7G7E29XvN2YQXfs1UqSMWiSuJWJo/8zolKxWx+q/9/DoZ32w0Q8gLQxg149LnfrSu7vSCrCM+c3MLovq35twP62hJR98Xs1PjA/TrZq8D9eaRH0aQyHfby2XmIs6mMuFe2RN8hWDRRFYToRJBn2MRbzO/pqUn5go1hHU1X2xQPi71EfqqYKGZOGTQeB+fPpKo/Mjfacn5MHBX7r7GrBSXjef1eKpa0KQY/GK6sFLm6WsVod5idiFOtNzGqTQt1kNdfg9HKelVy9m8REyFYNxyWiUBfSJ1tloY1Wt1p8cznHt9MFfuBYinv2xzCtNulinVjA6+w/WzUxmi3SxTqnV3JoqorPozoCv/OTMY6NR7mwUaHYsGg0jS7WBx6fHyfg0Uh3ZWbGYoGel7r7PlxIl5wXrzv57ifQyGv53MUtnnktQ6VhcWJ22NE7G4v5XS1UhaPjQvokXRQ4Odkek23AsF8krJtlg0uZCltlk6Wtak91aK92oZSWyNWaTMUFRrQfYC/bo5LReWGjRLqQ6EmYBAtY49x6kdOrObI1c1dVzI3X2gtLt5ipsFkWiYJ7weNmiEuf3IBHczE/s7hbrG6SRm9bcjcb313Nz1YNzqzknWsl/3byQILTK3nGon5HYuaN+pn2t2fd3xPixP6eY5AtcSEhMgXsMOtl9FfapOyUwI0O9WjdDfIev1nEQx5SMT+xgMfBRkqcsKw4uhO+5ayotqdifsqG7SR1/TZqctEtE8P+hPqdFLed2P36r/86n/3sZ/F6vfzET/wE3/d933dHx+5O3AlX3AhLBG++enijFrP7xSj/f1AyWW/uTE7rhQYBr+Z89mbt7WfOb3B1u8bplRxj0QD1pt2VyGj07MM9JklOiPh1Iv4wyagP02qzsF6kWLNY2hIVlK1yg06nDSgOKHkxU3ZeGpLBuZytcS5dom62sLG5lhcttlLDYno4SCrq54PH91E1bQfDNtEFd/eLucpjnYwH+QePHHS1ky0UBby6wolZofv3xYXrHN8XQ9cUtqsma4U6Xl2l0m156qrCWrFOsWYR9ukEvcI9IuhRmRoO0u4SHn7qwWk+e2qZi5tlEqEdIeBkuIrfqzES8aGrKuWGRaUh5BWkULWsGAigucmzF7bI1kwqho1HF0mtqipE/aIdtVkyeOmKSCagw2quwaXNMivZGuWGxS9/6G6g19hdknDk9ZPkhtVch1hAZy4VZSzm31XZ2ywZvHQ1SzFrcWopyz2TQ0Jg2bZIF+scGgkTDQhrM2loH/BofOGVNO89nHTalf0LkcVM1WnPnpge5mq21gPi3wmRkB0aDbPP2u1BCqK1+dETU4T9Oq+uCcFfWbGVz6iopAmf0ELNpGy0eu7tybhg2Z5eKZCrNfnUDx3tOQ/yGV3L19mqmOSqJu+dGwU6u1qs2apJ1K85yYhbjHdQuHF260XDuVbuJEgmVc9d3OJLr21Q7SYj/cnijSRMFtIlPvPlizx29zgfmB/fM+k8s5LnK+c3qJstHK+/AdFPKuvvSMj7bDlb7dEnvJXtTcZ3GPzZqskLl7edczwIQyhC3CsC/hLpqfouZqpsloyeBY679ft2S4PtFbed2P3RH/0Rqqry1a9+lfe85z1v5ZjuxJ14S+PtasUO2m9/svRmJoYbHdfN8CkyNFXhbxxMkKs1Wc7W8GjqLa2S3S/YsWiAhfUSnQ4cG48ylxrcNp5Lhdk/HHTatwCxgJcSTcaiAaYTIQ6NhllYL5EpG6zk6qzmGs6k/KWF61zcLFNsNFnpep/OT8Q4Phnj9EqOcsNmvVjnXFrj5as5Gs0WE/EAIxEffo/mtHAXM2VKDWFu3y9t4cYluitYY9EAm+WGg9M6MZ2g3rS4siU0sqaGgtwzGcOja13ygZByWctnqRhNIn7dAe//6PdNkAz7MKwWnz21TKab1Egcovv6yRfc6eVC18qo4Zzr9WLDIQPMJkNslg0Hmxnx65xZyZOtCpanZH1e2apSa9rMJEIcG4/Q6QgLN0F42GnjSWJMv8bY/dNxHp/fx6klkSD2i9DK8b90JYemKOxPBJw2nK4pXC8YrBcMclWTpt0h6FH5B48cAnZkTEBxtnVmJc/TC9ed45pLhfnQ8X3OmF6+mu+SfIQUkGTPDmLODoIp3D8d5xMPzzqMaIkZdVc461abc+kSTMZ4z9zIrnu730HD/dzIxcFWucFqrkarDYlwiY8/NNP99k6LdTVXR9cU0utldBWOjcd69nVmJc/nXl5BV1U+eHzfnpAHt3yLXHTdNx1nJhHqkYaBwa4O8vxIgfIzKzmudBdcH5gfdwhFbnuwdKHO515e4Vq+wdHxCCcPJvec2/rno0Gt4ecubvV420oCySDbskHzW7ZqUKhZDpFrMVPuSczcFenZZGjgvSK3W2/arGRrzCR3HEb6r6973++EuO3ELpPJcPjw4TtJ3Z14x8fb1YodtN/+SeylKzlX62Jv4PKtYvX22s9en/PpGr/9keO7Xnw3SzzdL1gQ7Y9+5t6gMX3k/kleupIjWzWYTYaF1poLwC63Vag1WSs0WM7WugmKQa7WpGw0uyK8HYYCQkk+GfaRLjYom2XqZovNkkEy7KPRbKGpKqeWsi51+w7/a2GDcsMSrFqrhVvaYhCmajFTJR7yOm0yCVq3Wh2u5etMDgV44sSUM35ZcbiwUSHs01GAmWRoF8nkd569zAuXt/FoKofHwq4KXO/1qxg2fq/GfLzXWs79klvMVB1wvPzOPZMxTq8UGIkIduxKtkbAo9Jua6zkahwbj/JTD07zwEyip/366JEdtmvFEC0xt0+rBL/Hh7zEQ16yVWOAJEWHgEdjKr5D1rFbHVFBiwdYzFS4vFlxCDmwW8YEBHvyxcUs8YAXiT1134PS8eXChsDSgQDpu8+fxGttloyB1Wh5X/7m/3qdP/+rNJbd4lOPHXP2cyQVYTkrsKaDWI8PzAw7x9j/jO0kCzpXtmsU6iam1XY08WTLUX7OsFo8c34DTVWdSp5caJxaynJ1u8aBkVCXLbuGFPF1n3uZbKwVarx+veycu088PLtnguvWKJRzkmmLVrlPV3nXRJTH7h53xirJU0+dTTsVSl1VOToe4eMPzTjJ2aCk52bzsYRJuKuXsropFzNuyIeELsj70C3tcigVpmG1OJcuM5eKdkka4vhGoz7sdqdH2NmNv5P3fMCjUjVtVrsOI+4W+jsJ5uOO207sZmZmUKX64p24E+/geLvArbe2345juN4fg1bGsLc+3u2OY9BL0D2GvQRj3f+/lyvHoH3J9lEyLHBy/Qbaclu/8+xlXr6S45SxTcMa5sHZYfwelavbNWIBL48cSRL26by4uMXkUICTBxKO36rfozkVB/n/8gVgWm2sNkzFheuDe1KWwOtCzXLYhbICJ198Mkkxu4K2hUazh7EsX2p2u8P3H0w6GnD97Z2gR+O+/XFqTZts2eTphQ2AXTZe2apBxKdRblhOdao/3ImXvG82inUHGJ4M+52XXSrmY7vSpNBtT4qXedaR7IiHPI7dlbuKIxm17oqWvD+S4d4258mDOzI47mRCHpcEqN/Mj/ThQ0nKjaZTsXPfj4LpKD57ZiXfQ/BxV61korR/ONij79h/XzZbHbIVky8tbPLo0ZRDtvB3E1S/R7vhfd2PcXU/o3OpMHWrxbV8nSEX9kueX1nle+7iluPeIdnREsTvtj47tZTlXLpIyKuTDPt3JU7Zqkmp3iQV9XHPZNy5L9z31SC272JG2N4pChxORQj6NDRV5UPH9zlzjUyE5djkdh86IDQqx2L+nnPsPte30qFwVy8vXc1TblgcTkWYSYYI+0Tl2w35mEuFWe86oUgCjZR2uW867uAA51JCG/LPv50mV7WYTgTx6GoPU1xXFcaiPhYzZTbLJna7w/xElJmuu01/C/2NYP++m3Hbid3P/uzP8s//+T9nYWGB+fn5t3JMd+JO/LWKG01m/Y4O7pAr43580JttLbvHMzEUoGEJ3FDAo+3a9ndiRTpom4MqhOKlbnV1ojrMpYS4rhTCBYXTKzleXMzynrkk/+CRgz1A7ecubpGvNbmUqTiJ8cmDggF5br1IyKc71Q6pzL+0VSES8BAPeXrakjIxk2PNVoVgaTLsIx7w9lhUQTfRUukC18OOblyr62zRagv7LUEiMXnyzBor2RpPL1xnNVfHr6ukYgECHo1r+TqG1aJQt8jVTNpteqoXMiRDUt43VdN2pDMAh10pK30StC5U+Qtcy4uKXsNq0+nAI0eSPS99eS+69bo2S8ZACzQ35q4/qQB6HBGePHON08sFsjWzq8m3c0+7P9efiKQLdZ4+t8HSVpVDo2GnIur2N10r1Li4UWEo5OEHjqWcBH+H7LMjbvvI4RHOb5Sw2h1OLWWdaylFw+/eF73hs+weYz+r0n0+ZEI3nQgwnQj2kB/cFVP3/Sf3JwW/r+WFpVrY36vZJ/eXDFcpNiyGAj4emBnuIaBMxncEhIXEjMD3FWom59Jljk/GHK3LV1bzbJbNG17fnXMhFgcgFmeDFpS3Mne528ufe3mFzbLB3RMxR5pEVIt9DuRDniddVRzBZlm9lvePjKfOpkkXGnhU1dEG7b9mm2WT19ZLaKrCgwcSfXNEzmG/v9OqdO647cTuU5/6FGfOnOGDH/wg/+7f/Ts+9KEPvZXj+p6Od6Kl2J347scg/Er/ZHajalr/5CnjzSZb7sl1OOTlrl/+MgCv/+oP7dr2d6La6cazidbGjoL/Z09dZTVX57F3jfHEA/sHakqNRQWDtlAze3T/ZLhfsMMhb0/LCCDs11FQWFgv8dlTV/nEwwec9k2taXO868SwkC5RajQ5eSCxi5l5YaPCWrHOsfEo0HHagDKxkt6uW+Um9S54v9Vu07Ta5BsGwwEveki8UGeTIZ54YBLpg3otV6dht+ikS/h0hVQswH1TQ3h0zXHoKNQsSg3L8fB0V0km40FHesMtnXHyYJKXruRYzlY5eTDZbReXOb2cZ61Qx6cL+7W92uqD7kXZlnXL6MhrLOOV1QIL66VdkiGAc95X8zX8ugZ0BmrMvXQlx5de22AmIXBOIF7Sz13MsFU2hchs9zxLWy4ptwGgqeouH2PoFbediAd56ECSVrvdU/mTJKFLmQpV0+b5S9scn+wVI5YJxnOXtig1mk6FcNCCJeLXWS82nMWCm/wgnSZkO3J+ItrTapVJzP7hIBND/j3ZmXMpIZMi3Rtmk6EeRw85LjfBYDTqQ1EEFOLRo6M8d3GLc+kyisLA67t7buh1fBj0PC5mKtSb1q5ktP8cyfP52N3jPXIpO9p9oV2ag7KNKtvKblcNOd8EPRrvnklwaDS8yxdbttMXMxVWczWmEwI+8fS5DTbLDQIenVfWCoR9Gj//3kPvyEqdjNtO7N7//vcDsLm5yY/92I8xPDx8Ux27r33ta7e7u++p+F63FLsTb00Mwq+80XAnQW5Mze0onPdXPSJ+nRcubQ/c33cjFjPC7Fx6QgLiRdUQulv9Y5YtzM2y4bwY4iGP42wgJ3F3W3FiyE+5YfVIKNjtDomwl3S+zqtrJZ46m+ZIKsJIxMd8dMdy65nzGyykhR7cz52cdc7ZU2fTXNgs02p1ODYeIezzkKs1CXjUnuSvv0ImqwLS8WFiKOAIT+/4oF5jfyKEX1dZzlXJVy00BccvdH4y5rTsvvBKmk5HvHQfmOm9bu6Ey32+JTOyUGtSt9qs5GooCN/eY13Q+40WGjci6fT/7ulzGzz7eoaQTydfMzm9rPW4esiK8PxElIBH49BoyMGdfem1DQe3J7BmHaqGzYXNMk+dTTMx5Cdfa3IkFeVICkYiwoC93BVhdlcV5ydju1iqbiC9W9xW4kbdiZRbJHk5W0VRYLPccMSINVVlKOBhLOrvMqIF89kNn3AvpqC3ugq79R77Wdvu58CNgZResf3uFJNx4a375Jk1JyE+Nh5hYb3EerHBR+6fdI5fWoy5MZvyOkoM41wq3FOdHRRS0mY2Ger5vZuEcGY1j4IgWblxie4qooxBUkuD7mv3HOm+lu42tsTWSU1DKcXiDrkdkVhGnORSalrOJILkqibXsjafe3nFYYO/E+O2E7uvf/3rPf+fy+XI5XJ7fv6ODMqd+D8t+nFFbyb6k6Db2Z470UyG/Sxnq468yHciboX84X5xADz2rhTyBeUeszQP3+hiaQJejaWtGn6vwaNHRqkYtoNHtOw2mbJBuyv3sX9YYGncla3JoQDfXM6hKjjtWmm5JferqSq6BsWaxaVMhX/4/sM8d3GLtXydsE/jxHTCabPGAl5iAS91q+Xo+rlbOMvZLNmqQjLk5fz1Ipbt72HjyXM1mwzz4KzFq2sF2i3QNYj5vZxLl8nXTU4tZXns7nGnzSordv3q93tpGEpm5GbZpNiwaNpt9sX8PD4/vqsC4r6GEq8oWaf9FSjpQPHo0VHOrOR56myaxc0yWxWTYNPGoynkaqZDZAH3izRK2WgRD/mc5GUmEcKwWo4VXcSv866JGNWuePPEUMBJcNxAfSnC7D7uW2Ux9mNN5VhO9mHH1osGR1IRp4LXaosEebsiRKol1u9mhAE59lshRrlZvDJhPbOS58XFbaqGRa5mEfBoA9xpREIsyTLDIe8uaMeOL6wg5bir9e62uMSW9lvDye9e2BDPUL8O384xVFBQhL8xyq52+CB3j36SiDsRdlfjBl3n3Vp0vXjmQfPToN9NDAfYWGmgKvADx8a4nKlQqlt85ssXHV3Jd1rcdmL33HPPvZXjuBN34q9dvJXVr0FJ0M0Sp/6/u1/6UqjT7TzxVsfN8DT9Lw6gx6IHdrMFc12dNrvVJqdrzMd3JCFeTRe5uFHmWr5Op91hJOyj0xHisNmaSblh8YmHZx0we79mlducfC4lWL8/cCzV0wqaS4WZGg5yLS/kNZazNecFEg95Sa+XeOb8BrGA13lhuVl9mgbL23VKdZsHuizL/hb0ZrnBX17NYbc6JMM+Qj6hZ3c1W+XKttBtG4sGmEmGCHr1XW0yYGC1Zy4VdpiR8nhLdeFl2/8y7q80rRVqPdZQ7mv7arrI1y5kWM5WGYv5ObWU5dRilgPJkCPAHPV7CPs1NAWWu3g1GTv3pdEjQdJfzQl2relk4rabMCD8bOX2+l/8sNPKM6xW1/fUZGG9tIug5E6kkmGfQxCBnYWAxPRJlwxZ+Srn6j3Jj4Qb9FdDpaag9DSFDh89sX8gq3dQon5qKcuLl7cJ+jTun44zFg3sYijv2Jf1LpbcCeMXz11nvVhHUxSGg749rQv3SjbdMjrHJ4d6MIywQwyJ+HU2yw3GogFnLP3XXT4PcozuKjPsYPduJjMyaFHdj2furxAOgs1MxoXmZNWwuZqt87F3z/BTD+7nM1++yNXtGk8vbAx0cXm747YTu0ceeeStHMeduBN34gYxKAm6WeLU//feloWoFLmdJ97quF0s4KCXgmQLJqZ8jEVF5UQanstj/8j9k3zmyxcpNywCXo17poaYS0U4s5Lnr9YKjp3TYHwQu1pD8u+SkCEJFkGPxlBAJ19rEvSojlyCcF9oUDNtx+tUVkhlWzbs00mExDH0tt1EC7pQa1I1WgyHvAwHfcwmg/i9Oh5dYyjopVBr0rQ6VE2bgEdzWmP9TET3vwedz3ShzsRQAMtuO1W/QfdTxK9Tb1pE/R4Sk16OpCI9llGymracFXIXL13JEfSoHJ8U1m4ATy9sOEbp59JFFreq1E3baSsPaqX1X5+bVb9Fy1Z40K4XGw7+zH1PSeyj3eo4moLTiUBPFUt+Vh6f9Ct2X69BJBK3PzPQI70hK+2Ak1i8dCXHk2fSaKpCmw5Xt2romuJ4lfY/D4PEtCW5aCzq4/Hj+1zXL9dzHvtxjfJcO1puDYt0vsFsMsgjR3br9Lm/N+iaRPx6jwOFPP5+ceHlbI2tcpNj47Ge7fQnre5KHnRIhnzdT+50/W5UueuvXrv/vtf4Zdt2ciiwiwx0YnqYutnisMvqUXpvBz3qQOWCtztuO7G7E3fiTvTGd1uF/GaJ015/d09wbueJtzput2J5I9yWe/IfNHF//KEZEiFBpnj8uEgsXlzcYiTi4+hY9IZJ5l54RncVR1beHjkyQjLsI1s1KHcB7A/M7Ca7uF+wMiTmy31sD87Gu5puCn6vxofumeAj90+yWTI4tZTlSCpC0KMyORTAp6t4dJVYwONo6/Vj7GCnIiSA88Geas7T567z7IUtZhJBRqOBXdgqGZIEUjNtjk8NUTVtXrqaYzjk7XmRPXxohEKt6bh7PHpklAdmhnnu4hZbZZOG1WLa78Gra9BB+ARneiVSbnS/uK/NoEqcHLdMuiaGdhin2arBwnrZIVUITb862xWTTkewkycmoj2VJglXOL2S51y6RHTO07OA6CeRAD2VZiGsLT5zfDLKZtnskeiADhG/jqoIlxLPmMq+mL/HU9V9XP3XBehhfEpZnOlEkEKtyQuXtx2ySv+81F8hPpyKsJKrcc9kfBf27EZzmvu5cD8nbnFh93cKtSZbFYNCrelUMi9slCk2hJBw//FKMsSJ2fie7fXnLm7tSqz6K3F7JV7ubbir7uVcneVszangz09E+ekHp3vOgZut7ZZ9+WuV2H3jG9/g+eefZ319HcMwejxhV1ZWaDabHD58+AZbuBN34ns/dlaaxsB20VsdN0uc3spW8FsZtyIZMehvUvsLevXV+r03P/HwgZ6XjN2Gd88mekzKb3Rt9sIzzqV2EyLCPg/1ZoVBDhZ7HcegSqrAmRWIhzwOcFu+pOxWB79HIx7y0rDKABQbFmNR/0D2n9zHC5ezZGsmoxEfd48LHFsyLAgoT7+2wXalyUxiJ+mbS+3WRZTHfGGj0k2Y/LtwWrKipGuKI/gqcYPZqsHxyZjDtLXbHf7msRRjUb/D0JTjvZXnZVD7zH3e3UnXTpIWRFdhIV1iYshPMuzn+UtZtioGK7kaoxE/P9H30pdwhbGon+icZ2DCNWhsz13aclwjZBVUiFsLDKGU6JhLhR1s281aebfyHMuqr3Rq6HREIiXazb2m9+4KMSjEAh4CXo3tirlLaPqlK1meeS3DdCLQ81wB3QRxmdVcjQdnh5lLRXswdf33ZTzkZTTiJx7yOs9Yw7IJ+/We+2mvCu6gcCfz8vvu/YtKssDnvnQlu2cb3/356UQQ6DjJnnRYEXNMjkLNJO6SQJJ4y71IJW9HvKmRLC0t8bf/9t/mzJkzAHQ6HRRF6Uns/vW//tf83u/9Hl//+tfvuFTcib/W0Y9hg3eWzcygUBWFx+fHnH9/N+JGbht7tZdfupLlyTNpwn6dD7xrbBfIOls1dnlvStC/bJXeCjYHboxnlJilp86mWcvXsdvtblXLRIosu1fxg/Zzs4pkb0upTL0pcFzZqkKtabNRqtNsdZifiDmyFIOs6+YnYjz92nUubjRJhHyOHdZTZ9M0mm0mhwN89MQUy9kaz18SSXH/9ZCtvP72lptlKltp/YxXWbmR1a+IX3cgAPI6y2t1q8+L+zwNulfcSUG2ahD160Cn6xJSYyVb5+TBJI8cERXGUqNJw2o7L+VBcIX+Fl8/DqtfXkcI5focKRNpOi+TOPd+BsXtVP7d50Ukjb4eLK37eREV4mE2y0aXwdrBr2ukizuyQHK/hVqTa/kaDcveVZFazFRZydaomjabZZOy0dvuf/LMWo80TL91l3zGZpNhlrNVJ9Hvv5Y3Oz97VVCXsyKBPTYe7Tkfi5lKj/2c3M+TZ67xzGubJEJeDo2GHakZwKnOywVBKup3JGp0TdnF1H27401Zij3yyCNsbGxw4sQJPvjBD/K5z32OK1eu9Hzu537u5/iP//E/8md/9md3Ers78dc6Br0U3unh92j8f//2/d/lve7ttrF3e1kh3GVL9oPQ5TmXn3O/9PsnXff2b2R8fqOEEwSA3u6KDUspCXfS4xZMleKz7srSoLbWoMqeW0fs5MEEFzbKvLTUwOvRulWwva3r5lIRDmQitNrtHtarrD7JF5uU8LiRcXv/+HrbbnWmE8Fdlae5lHBAEO3ZMo8eGR1YEZQxqPK4V2yWjB5sltvJQpI7VnMNdE2hnLPZLBk07TaXMxUAnnhgqsd7tf+lvNf1GCRf5CZauEWZX1nNU2/ajubaXouKfoLFIFu7myV7gyrFO3p5IadtuOODHHHa+CcPJinULE6v5riWF2ORlbJ4yMdUPMhMMrTrmZSuJmNRP1PDwV1VK9l6XdrSKBt2D6NXXINeeR6Z6N9q1dYtLi2fefl3UTlVuhI+vbi/CxslzqVF5dvdyr6wUWG7apKtimT/w/dNOtuT5/PCRoXphCAuud1k3FCGd0LcdmL3r/7Vv2JjY4NPfvKT/O7v/i6KovCVr3xlV2L37ne/m0gkwl/+5V++6cHeiTvxvRDv1Bbo2xX9L6UbuW3sde7cPqJ7vdjcL8FBVaT+7csX7a20zgclTzvAel+PtIkUlgWF1Zx4YWyVTadCeasVmf7K4WQ8yLHxqGNILqsJ/fgzN9O3X5cN2KXGL6+HlJbo9+69mWRNtmo42Do5np2fWcdlYRBGyv3//ddjUJtSVnuDXo16s8UjR0aYjAd56myaU4tZ5/h622s1OkI5l2jA41Se3N6r/ZVZNx7NLdvhZt7KMbnt3HaqP2s8+/oWkYDuMI4HEQrk+XUTLC5sVKg1d7Qc5Wf2akH3b6tfL285W3OcRhbWS7y4uM1Y1OeMRS4ChORPm5Vszamo3+i5qxg2Qa+O9Cfur1rJ1uuh0bBjU7fbU3jnPnL/vNEz4sbgSfxc0KNyLl3mkSMjzCZDlBpNwcYOeJzzIlvVj8/vIxoQVXx3FbbUsEhFfRwejTKTDO6qCgMEvfquBDVdqDueyu+UuO3E7i/+4i8IhUL8m3/zb26qUXfgwAHW1tZud1d34k7cie/huBE791YrNG8kWXazCN0r8v593WrrfNBLRiYiW2Wz24aN9uDNJE5HgNnNrserSC4GVWT22l8/mH02GeL41JBTdXMfk7tKKSuG7krizc7tk2eu8eSZNKbdYioe2tUmlee2/3qtFw0HAC8roZK4Uejqq/UzIQcda//1kNXW3uvScRIgRdnBkR3pJhfyvLjvl4phs5qr84N3j/VU0ndYpf6e43NXgyQ+TSabMll3V5YGieZCh0jA4yS0/Rg493WL+HVGoz7Goj4kDlG4n+xIgtysBb2X96s4n4ZTwdJVWM3V2CqbPbjCuZSQ8cnXmjSsVk8FN1s1yVaNXZXyG0EKoHcx1t96H/Q89bd5b/RMZqvCz1ni58qq4oy5YtjEAl5KNB3snVvmBnDwtm42bLlhUW22mEmGdj137mTSjQ196UqWCxsV5/5/pzBjbzuxW19f56677sLn8930sz6fj0KhcNPPvRPjP/yH/8B/+k//iYWFBX7pl36JX/mVX3m7h3Qn7sRbFvWm3WMpFvS+9QDgvdqrg1oqt5pY3egzg/a3Fx5L/K3i4NgG7a//u19a2OALr6QJ+3SCXuGYsIM3M5ykTuLL4iEv7z084npRD7ZeGjRW2GG3njyYYDlb5cpWlYkhf4/NGdBTpTy1lOXcWpHtitlDALmRKOtKtoZpt5iMB3bJXsiXs6yQSA23bNVwLK7kS+25i1uOPdtMIuhUfgZdLzdeTWKzzqzkWS82SIa8ZGvNnvaerC5KwWQpKSMdCtwJvDh3Oc6lC2yWDO7eF+2ptLg9VPuB91JbTeLTdvBqu11kBt1v7qr0ZDzY49rQfz8tZ6tsV0ynbTioQjZoYeNuXffiVqeccwwQ9nmcCta+oQBHx6JsV0wWM+UeeIC7jey2SesnErnb3vJ87kAhdmLQmN1JXv9zv9cz3I/vfOpsmoV0iYBH45EjyZ7qriRAzE/Eepw0JAlJytzIcLNh250OrVaHQs3cJZGyw3jeSeyzVYMnz6TRNYWRiO8dxYy97Vk8HA6zvb198w8C165dI5FI3PyD78AYHx/n05/+NH/8x3/8dg/lTtyJ78nYq9rW/xKVn+2Pm63e+z8jE4RB++p/Ubx0JcezFzKEfTvA9v6KWv+L+wuvpPnLK1lCPp13zyScap1b5kEA2KtO+0vqBUoLJ5ns9Kv3S607dxLQSwpRuoxHC1D6KkI7VcqxmJ+JoQBuAsigY5Pn7rlLW2wUGyRCPt53JOWYo8+lepmyLy5usZAuUm40CXo9TCeCPQ4QIBKOkYiXgKFhtxmIm9oLrwY7FbBsrbmnfdQgM3j3sbyaLjIxJNivlzICT3UpU3E0CQfdF+7t9zMyB5Ep+mMhXXIcOCQov/+YBsvK7CT6t1KZdp+DncR+N27VXcGVFSy73cGjq2SrJpcyFTbLJp94eHZP7CfA8ckYm+WGk2BLAWpgl+SHe9+DEjb39vuf+72eYUnCkBJD1/J1VKXD8ckYYZ+HFxe3CXg0ZpJBslWF1VyDqF9jvdhwSFMyse5fLBjNFtebdY6kIjz2rjHEYquza76R95XRbBH26w6ZSeJ+H58ff0fh7G47sbv33nv53//7f7OwsMD8/Pyen3v++efZ3Nzkx3/8x293V29r/NiP/RgAf/7nf/72DuRO3Im/ZjHoJToo9qr4vZHPuCdot/xDoWZi2sK6SlZAVrJ1MmWht+Uep4zDqTBLWxVmkqGeypb7p/vY1rs2aHLfMrFyv5zd6v2aAu2OwsSQn5MHk10zd4vFboVK4u5ky1eyf90+ngLLiAMo34nd1cK5lJCM2CobBH0as8nQnpiuY+MxVrJ1qkaLY+MxpxLn1sw7tZTFbsOJ2TjCOqriGNHLl58bA9dPXJBtsyOpCH6P1nNNB7Uc+9uDbi27R46MMJ0I0OiyIAfdF/3JjJvM0L8QGFT5lOeq353DHYPuDRniHO7YXN1qxUdg/kSbdDYZ3oVb7cUZVinULOIhD7PJMOWGRa5mspKr3bDKJPF3kmiRLtQdAWp5PiVZSMrcuJNrWQXcWyi9OvD56Y2dpHUuFWa/y/nluYtbvLi4jc+jMj8xxHsPJ7l/Os6Li9ucS5coN6weuzMQVbpTS1kmhvys5GoUak32xYOOJ23/uKR1m9FskauZ1JstKoYgxRyfHOJIKvKOc5+47cTu7/29v8fXvvY1PvGJT/A//+f/ZHx8fNdnrly5wic+8QkUReHv//2/f9uDrFQq/Nqv/Rrf/va3+au/+iuy2Sz/8l/+y4Ft0Wq1yv/z//w//Omf/in5fJ6jR4/yT//pP+VjH/vYbe//TtyJ77X4boslv5l4I/i5292Gu6rgVpePh7zsHw5xYjbutOUaVqurtzUYZvLo0RQeXXMqM3L7ezFsP3L/JC9dybGYqbDUTd5kYuV++U4M+bmwUeHiZgXbbiMrOE88sJ/nLm7xhVfSdDrwE/dPuiqCxkD2LwyudLpbff1jlOenv6rk3s7JgwknUZWVuCfPrAkpGp/O8akh5yUvW8KybbZebDhtMLejyHOXxMv52HiUkwcTTnXL79F2sWjdDNT+v7mP5Vbv/b1s1NxkhvWisUu6xn1OJKmi02mTCPl6QPnuSuJe45G/e+HyNtAZiLscNNbJeJBkuNp12zB68F1uj1/pn7yaq3N8MkYybHNiZhjoMBYNDEikeqP/XigbLd4zN8JYzO+Qdfqrtm5tub0Stv5ndq9nuL+t/ZH7J/nsqWXOpYsEPBqjUT+TceHuInGAEb9ONOAh6NF69BxPHkh0RaeLWHaEmUSQmUTISVL7k3oQFcpz6RLHJ2POYkVW+5cyVcoNy4GwvFPm2ttO7D72sY/xhS98gaeeeoq77rqLH/qhH+LatWsA/PIv/zKvvfYaTz/9NM1mk49//OM89thjtz3IXC7H7//+73PPPffwYz/2Y/zn//yf9/zshz/8YU6fPs1v/dZvcfjwYf7kT/6En/qpn6LdbvPTP/3Ttz2GO3EnbhTvtETqVtqX3yvxZo5lB8tluPA0Hsq5HamH/krHzeQLBoHlbzRG+QJ+4fK24+AgK13ys+JlQs8Ltx88v1O5KztgdpngDRrzoBequ1rSjyN6+FCSU0vZHnanOyJ+vWsZpjExEXNtV7goTCdCJENelrNVTh4QCVq+1mQ6EeDYuMA8LWdrPQ4YssK2kC6yVTadpLMfpC6rdAIPae0Sgx2U9AyKQXit/qStn8yw20x+dwUuGRYWZu+ZS+ySNoFe94Od6+12NumtpN7It9c9VneF0l15k5g7025RqFvEgzp+j/BqLRs29aY90N5rr3N5I6zcdCLokCLk2G50L73ZuXIyHuTYeITtimCff+T+qV3bknCEl67kmJ+IsVkWBJ9LmQpjUR/bFZOG1WY0GnCkemRS97mXVhxC6MmDSYIejeOTUUcy6MxKns+eWhZV/XqToFe9JZLSdzPeFFL6T/7kTzh48CC/8zu/w5/+6Z86v/+N3/gNOp0OXq+Xf/JP/gm/8Ru/8aYGOT09TaFQQFEUstnsnond008/zVe/+lUnmQN49NFHWV1d5VOf+hQ/+ZM/iaZpAPytv/W3+MY3vjFwO5/61Kf4tV/7tTc15jvxf1a80xKpW2lffq/EmzkWiSmbn4g6VQXotfrqrxrI2Iu1O2g8NxujOzGT+nMy3BWikYiPlVydfmKFrNxJ5qoQ+/UPxBPKsd/o5Tnofh3M7nSzZtd48vQakYCHn3lo2lUF3EmOnzqb5up2jWfOb/Dxh2Z2VXJ2CB85JyENerSuKGykJ1HqFzDWNYWlTNXR9RvUDpXhJpy4j38xU+Uvzl3Hbre5ezw2MGnbkfGIdhPnm5MZ9monyp/u5AvY5R8b9ukcSoW7gsGD5U0GMV77K5Ty2l/YKNGwWhwdi9CwbAIenZmkwJqdupJlX1S0py9slEgXdp+jGy1SduMly7sEx5ezNZYyVSaGAgO9b9/IXDmIbNFfxZPRi5/dwZM+fnzcSeInJqJ8+L5JRwrnwkaJYsNm/3CQoEdz2r6FWtP5jrTIA1HBe+HyNh5NJRrUaXV6HWreCfGmEjtd1/nN3/xN/vE//sc8/fTTLCwsUCqVCIfD3HXXXfzwD//wwBbtG42byanI+O///b8TDod54oknen7/d//u3+Wnf/qn+eY3v8n3f//3A/C1r33tTY+rP0zTxDRN5//L5fJbvo878c6Md1oi9Va0N98p4T6WN77aF5WQeMjX07p7Iy8U9+fdGC/5knaPz/27QbFZNkgXG7usyqQTwtJWhY1ig1xVsCR3t+UEuWI6sVsw1j0+N45Q+s26W8c3Sk73FgruEAnouzTpnGMrGVh2G02FpiUcOfYSJF7MVHjh8jYjES/blSadDkjnjr3GJ9rVO4SQ/nao/LmYqfLMa5tdyyx6tAMjfh273ebiRoW6afODd4/3JH9uDUR5jW/lXht0/d3bnBgKMDG0UxmWOElpVyUqaKaTCA1qhd+o/dyfmNlt4UksK2eide7n1FKWa9k6HlUlEfZSbPQ6Ssjjj/r1bmXY3JUc9+83WzUGCFzv4OL6n1l3RfbMSp7lbA3pQOHGqvXjKd1ki73m2d6EeIdotJipOkxuWd0eiwlmud2GSqPJQrrJg7PD/MxDMxRqJptlc4CMTw7LbnPffiFbY3c6TA3v1iZ8u+Mt0TZIJpP8nb/zd96KTb2peO211zh27Bi63ntYx48fd/4uE7tbDdu2sW2bVquFbdsYhoHH43Eqf+74zd/8TT796U/f/gHcie/Z+F5NpFRF4dEjI86/3+nxRlf7gzBltxqDkgvZ4hqN+nbhanYqC7sFjyW7VbZi+6suTzwg8GbpYoPJeJBWu0OhZvHkmWu4K083E2p2t8ekjdXnXl7h4kaF5WyVsZh/z/aa/J3EvX1pYYOwX3daUHtVSeQ+602L19bLBLw6R8ajA8+5/N6Li9vUmjbz0RjHxoWY86CWcf/3+qs/8vr0f346Eewmtp0efOWjR0b5+EMzfO7lFUyrzXqxsev8SRZzoWaxsF5ysFuDkjw3lu2Z8xtoqsqHju/rSUxkkn3/dNz5rqxsSbbtYqbCdsV0dPnmUr1sZEmSuBVyhfu+felKloV00REjfuzucepmC6+moKkq+4eDPYm8PP5So0nVEJW+vbTnZAyCM8wmw6wXBaljEHlCVmTXiw2nCistuuTn+hNaSWqI+HXnevY/a+5jl9VWmRDK7cj7+9V0kSOpCEZTtKs9mko85OOJB6Z47uIWlzLXabXbPXaEwt+25STN7zTShIx3jmvtWxC5XI4DBw7s+v3w8LDz9zcav/7rv96TrP3Gb/wGf/iHf8jP/dzP7frsP/tn/4x/9I/+kfP/5XKZqampXZ+7E3finRJ+j8Yf/t13v93DuOVwq/zfSryZhHvwd0UlYizqZy7Vi6sRlYgdCy25Dfm39x5OMqg96P4+7LAYL2xUeP7SFlb3ZffEA3tLUuy1jRcXK5TqFh06mFb7hgzIMyt5nl64TsCjo6twKVOmWBfsSXdCCIN1AxczFVZydWYSoYFCre5Ex253mEkEHYeLW2kn32q1djIe5BMPH+hp07pbrpPxIGMxv5McuM+JG7NWVndwb3tpr7kZsW4XC3cyOT8R29VCdR/Lo0dHu+cxMlAsWyRCUuD35jppvfdIL3bvA/Pj+D2a046WyYm7Evbi4jZbFZOpoSAnZod7qoaDNCcH3ZOyrS8kfNjluOG+T2UV1l2xG+TUIffx1Nk01/J19g8LbOJejFtZ7ctWlZ4qs/saX8pUOHstz2bR4Ni+qINrnUvtCDa/slpwnovjk1E2ywbX8nVOLWXfMYLE/fGWJnaXL1/m3/7bf8u3vvUtms0mc3NzfOITn+BHfuRH3srd3DBu1La91ZauO37lV37llkWJfT7fLQk234k7cSduLwbhwN5oe/bNgLf3qlrBzgvYbnd24bYkRu5G4X5BVgybYiNPrWkT9Akj+1s5FnfVTYoEx4IehsM+p6W013Y+9/IKr14rMhrx88SJKcaiAU6v5tBV1WkxDsK0SZyfTE5upZr46JFRR5x4L3/Qm8lm3CoWbLNkMBzyOpUXec7cFRf39wYJ9cro116Tf7t7X5RLmUpPdUcmk25rtr3G7k5I5H7c1bnbhXq4WcwygXUnLf0M6Mn4DjHhxGx8lwOD+/jlMQ2yfpPblNfYXa3svz6DqrBuTcj+6rBbEBsYWJF3a/jZrU7Pdvqv8VfOb2B3OmTKBpslwxnbw4eSPL2wwQuL26TzDUYjPn7h0UM8fnyfk7S/dCV7UzvCtyNuObH7yle+ws/8zM/w4IMP8sUvfnHX359//nl++Id/mEaj4fjyXbhwgS9+8Yt86lOf4rd+67feulHvEYlEYmBVLp/PAzuVu+90/Pt//+/59//+39Nqtb4r+7sTd+J7LW43uRr0grtdMLb78+4Vfv9L6lYZl4PG58aq3eiYB+GQ9g8HUYCp4aDjC3srxyL3/97DSVaydVZyNXya4iQde21HU1XmUmFOzCSYTQpLsfum4z3q/YMwbTLeyLmR5+NGbXJ3ZWWQbMZeyU7/uexfDLxRzJr79/0aZ+7PusWP3RWpfsxjf8uwfx+DqnO3W3l2JzHuMff/TmLeTh4c3FqV+xeuIEaPg8Yg67ebJao3i73OT/895N7noM/1J+f9lebFTJWffvc0f/zyCnarw6mlrJNoVgyblWyNXNVkctjPPZMC5zeXCjvn72Z2hG9X3HJi9+yzz5LL5fjoRz+662/NZpOf/dmfpV6vEwqF+MVf/EUOHDjAN77xDf7bf/tvfOYzn+FHfuRH3jC+7Y3G/Pw8n//857Ftuwdnt7CwAMC73vWu7+j+ZXzyk5/kk5/8JOVymVgs9l3Z5524E7cT9abN/b/2LABn/8X7vyOWYoPidlnEg15wb7SacaPk0P2Skr/vfyn3x16Jn1vy4kYVpn7lfrmNvfTYbmafJrfxxAP7+Z1nL3N+vYRHVzkxG++pjrg1u2aTYT50fJ+zLzn2QcmP+zhv5vd7o6T4ZsmKrJrsJZsh/91PWOk/z6JFviOfMuic7SVC3C9LcqsJlrttOKgde6Pt3G517kZj2auiKe89qTWYDPtuyLSWRAx3snQjeaC9EtWbLexulFxD7zV3exP3t/XHYn4emNmNB81WTaeS+eiRUf7Vjx93CEYyJEYvVzN579wIc6mocxzynirUmkT9+htOXL/Tccuz+De+8Q0UReFHf/RHd/3tf/yP/8G1a9dQVZUvf/nLTgL38z//88zMzPDrv/7r/Of//J+/44ndj//4j/Of/tN/4s/+7M/4yZ/8Sef3/+W//Bf27dvHgw8++B3d/524E9+L0bC++5Xlt/Ll9UarGYM+P8jtoJ+IsFcicKOq2Y1+yujHgLnHKf/e//83sk9zx8OHkpxfL1E1bfolVNwyK1I6Za+x7zWeXo/Svduv/efmViu27mrbG9F2cydyIrnYkU8ZdM4GyYtkq2YXLG/vEgC+leNwX9e9cISDYq/7eVCieatj2etzcoyJkNexytorBmEV+xM1OX53vJkq+61cc0lM2rHdo4dcIW3CpO4j0KNrKbc9SGx8JOLj2+kWpYbdg+91mNeGzbsmopQN+5bwj9+tuOXELp1Oc/DgQaLR6K6/PfPMMwC8733v25W8/eN//I/51//6X/OXf/mXb2qgX/rSl6jValQqFQBef/11nnrqKQAef/xxgsEgH/jAB/iBH/gBfuEXfoFyucyhQ4f4/Oc/zzPPPMN//a//dSCT9TsRd1qxd+JO3Dhut7X0nYobuR3s9dLub0v2y4TcaoVJJiL9iVf/ftyfd/+8UUihVvmi6q/2CTC46bzQ+0H9MgbJqEzGgxRqTbYqBivZ2i1r/gE3TQj7vy+ZkGv5OlPDopInbLJ2KiZnVvJO5UgmciJyFGrNXQB+d/S/tM+uFoh2hYqrhtajQedONG6UoET8Omv5OmdWciRDXn7m5MyNLtXAOLOSdypJFcPuScTfyFjc4a5aAcxPCFbyaq7OcrY2kOkprn8FXR3c0r9VvKOMW72H+3GWg9rYEnYgmdUyAdVVhdPLeS5lKiTDvh7dR9FS78XGDbrHG1YL02pzeavMTFLgEp9e2GAs6icR8tHpIMhGb4DQ9d2IWx7J9vY299xzz8C/vfTSSyiKwuOPP77rb7FYjOnpadbX129/lMAv/MIvsLq66vz/k08+yZNPPgnA8vIyMzMzAHzhC1/gl37pl/jlX/5lx1Ls85///HfVUuxOK/ZO3InvrXijmLF+rI+7fSm/OygGvQD7K0o3S4zcCeJmydhTcmFQkuYeo2BjRikbBV5ZLXBqKUvQo1E27D3Zn+7qZbpQZ7NsEPbrNKzWwGPfu2W727R+UHXGfW7ztSZ2u+14fV7ZqtLpwKFUmHLO7pHLEMmyyYWNEhc2K7RaHZ54YG8Go7syuFPVMSkbNsfGow4WsP/67cXSlm3L89eLlBo2X1y4fluJ3dMLG3ztQoYzKzneOzfK8ckY8ZBn4FhuNVlyV60kQWEuFaZQM/nK+U0iAY8j2SJDyqB0OuwSsJbbvFVJljeCr+3HWe7VDh9EYMhWDZ55LUOnwy7dx/5tDGIxR/w6Y1E/h///7Z17fFTlnf8/k8lMJpnJfSAJJAYM4WoCUvgpiiIqoHRL1V9VvABFa5GieOuNLr9Cl93V9tVt6662r1r9VRcFaXGxXQW7oqxcZPmBooAECHcCIcnkNpdkMjPJ8/tjOJMzZ55zm3uS7/v14gWZOec53+fMQ85nvs/3UmpDrsUMwIAiqxmHLnRc6XqRjxGF2aH/M7z7kio0C7uMjAw0NzdHvO50OnHixAkAkN3qLCwsDLUbi5azZ89qOs5ms+HFF1/Eiy++GNP1CIIYOsRjO1fLg1VLUDgg7zkTUApclx4jfk9uW1homl5bno+bqodFZH8KAma03RrastpxrBmBPhZqhH68ySXrtZDawgvQ53lnpPdHiOcSeusKBZuPN7nCttGD57bibGsXjAYDsi1G8Dyi4jp0UoFmy4qcs9QeoW3a7pMOlOb3e9Ec7h60eXyYVFaA9q4efKNmBPe+qFGaZ0GWKQMOtw/7zrSiprwA86vKuPdGaQ3zYh2liSBfNTpxvq0L46+0x5LGYIq9YtJxcy2ZuNjRjfNtXarb1ryYVSWxJy3uLIW3boT5AUC7xwfAgL2nHKEtbOn1eNvmO441w+kNYO7E0rC1KpRnaff4cKrZjZqReRGhGqlGs7AbPXo0jh8/joaGBpSXl4de3759OxhjyMrKwrRp07jntrS0oLS0NHZrCYIgdJJOfXy1Cki1bTUtgetKYlE65vRRRcjLNoW6U0gf+mccbpxqdmNkgYXbvaK+yc1tRyZcV+jmoCRCpN4Z4T7wEi8AhOwUPC3SbfR2T7DI7vRRRWHdHHj3WRDILm8ALm9QKHR2+5CfbQ6bk9QOns1Bz2Z2RDs18WfAy77mMb82mGl7sjk4tpLnSmlstfVU3+RGj78PBVYT7phUFhI2QoxhTXkBV6yJ71+bx4feKx5VwUaxbUp1/eS25uub3BHlUngZ5HLr5t5pV2HHsWa881lDaAtbum0dPN6FzAyEbTNLvfICgn1/PnCB29UmHdAs7ObMmYOvvvoKK1aswKZNm2CxWOB0OvH888/DYDDg9ttv59Zwa2trw5kzZ3DjjTfG1fB0hmLsCCJ9iCVQOxHX0XKumvdPi0CUHiPunSnU1BOX/nj69rEKo4UXupWzgdeOjPdwliPHZIQzIzzWTbgWD7nEEwAotJoxPNeCUXar7INXTiB/2dABd0+AOy4QvlbE2cuXO73o8vnR7snkJjhIhRDPOykmuBZy4fQGUFmco1geRvh8h+WakWM2hd03tQSG6hIbRhRY4O9lcPf4Q6/NGjcMdY3OCLEm9XQK3lK5LxpyiSTCOO0eH6ctGT8RRrouygvDM8h5/5eEFm48j3V9kxuHGjq5PYiF98XzENZ2sD4hC80j1V8axWgWds888wxee+01vPfeeygrK8PYsWNRX1+Pzs5OAMD3v/997nn/8R//AQBDSthRjB0xUMgwGHDd6KLQvwcjegK1oxFm0p6W0WzJSG2UE26xic9IcSZ3byK3TpVbsynFGSrdf2mG5uGLnfD4AqGEgcri7FBPUZ6HS86rAmhrJyd3n+VKzQg2i8vTiLNs65vcaHH50Oz0odBqjujSIMSitXt64MwA17slvZbg7Rxtt6rEcQU/39K8bEi7oojtE36W3rsJZflodga3LQXstizMrymLEGtSTycv6UiMdA2IW5gJsZv3TC2P+Kx48ada42GF7eT6JndEVrL4fuRaMsOEn9SbJ/63dMtXbJswXjrsDGgWdhUVFdiyZQvuv/9+tLa2Yv/+/QCCsXfr1q3DrFmzuOe99NJLMBgMuPPOO+NjMUEQccNiMmLTshmpNkOVWASN1u1PrQJQitjrFc2WjJ65SQWX+FwAiuUweEKH98AHorsXvDZQSteQzqe6JBjHVdfoCtVKs9ss2HG8GbtPtiA/2xx2ntQrKB1bHGulp9ae+FweSl5CYQ5CiQ259mDnWrswoSxfVnj2x6P1d24QWn8JiS3SLdfRdisudnRjamUht5sDz9MlnqNw/mi7Nez4PEsmuvy9oVqCwjyB/tZ1Yo8aD/G1xOJfSZiL76lwLV4fXUBoh9eI0rwsTK0sCiVyCNm+4s9AuLf1TS4caui8sv3b32FD8LrWN7lQUZQTir0szbdEJHJIPX/pUqxYV37urbfeitOnT2Pr1q04ffo08vLyMHfuXFRXV3OPb21txdKlS2EwGDBz5sy4GEwQxNAjGb80tQpAKdEKQgE9c5NeS7y9ardZFMthyM1PLvhcToApCR5eGyi5a0jnU14YLKosFatfNnSg2elFJ3zcLVpx+Q6ph4xX/FnL/LV+BtJjhTkI1xbHKYrPD5Ya6d8a/POBCxAL8n5PVnaEF1hIbJEmzvDa7YlR67MsPV+45q76ZhxqCPY+FgSjeH0IglOuNZwYqfhX80yLxavSGtx90oFdJ1pQaDWjy9+Hk01uGAzAzWPtEfdPGKfL5+du/7q8AZxr7cKRi05cMzIPOWYTXN4Apo2K7NhxudOLXfUtqG9yYWplYdokUeguvJKbmxtW/FeJ4uJiPPXUU7qNGuhQjB1BxJdYxVMiET+c9HjfeFu4etqOBenfXhViido9vpDHQqljhoA0+Jx3rPj+yxXKVfqMci2ZaOzoRn2TCyMLskMB6LwHu9gLJ8SwCeJM2ttULBR4HjKpd40nGHItmejs9sHdE8DeU61cb5jUPqWtU6WED+F8cTcGAGGCnLdNKj5XEIzSeDaeZ0t8rpLwE2/5ev29+M32E5g5xo7Z44cj15KJvGxzWFeG8PP6hZp0/rzsUznxL4aX9S393MXjzhxjh7PbH/LYCZmrPM91UCS6cKbFDWuWEaPtkcK7stiKsw5P2La29AvHjmPN2FXfgp0nWq6sr9y0SaJIn4p6gwiKsSMGCl2+AGb+fAcAYPePZietpZheovWmJRs93jfeFq5SLTze2OLt1aC3qD/OLU/FQyMgDT6XO6a8MNgrdP3/nEWzqwd2a1ZIiMi15BJec3kD6GMMXb4A6ho70dCu3IlBPNfZ44fL2icWCtUltggPmVQgCfdG2jouP9uMIqsZgjiUesO0Cnatn79UjEmD+5VQEoxycY7ia8kluBy+2IkiqxnO7uAWJRD00Al/pIi9obPHDedeWykpSEkEim0VC2zedm55YQ5K8y2YUJYHgF1pIxbeOk8qtrv8vfjqktBqrzjs+PLCHDwyc3TE573jWHPIszuyIBvnWruQbTJi9LAcjB2el1ZfOtPztzhBEElD2KYaiqgJEr1iUo9nUXqsXIya0vE8O4X3HW4vnK3aCqcqxaoJNLR3Yf3/nMWxRheuKs7GzWP7WzT1b4d6QzW/pPFzd1xTirpGFwJ9TDFhgDdXuYe6eHtRzvsn/lyF+yskIvBEIM8bFlkeg78+tH7+Ulul3TdiCT2Q23LlCT/hWrmWTGRmGHC4oQPjS/Mws9rO9dAB4fF/0lhDtb95n6OwhS7EE4qPL823oDRfOQZUEJiHGjpgNWeGfdnYe8oRWnPi88eV5KIgxwxjRjAWUjo3cRyf2MtbZDWjzePDyAILKouzUdfoQll+sB1ZOn3xJGFHEMSQhfcAjeWhqhYzpBSgr7ZNpTXuTThOGuOlJlh540mzVjMzMjC+LBeLrh8V5uUQxES7xx/a/tUSP6f1PvK8OYJN4gLBcuJc6l0CgrF7uZbMiIey3Fa0sL2ttMUdL8+ykkCU+xzFgksp1o4X8B/cygYYA0bZrWHJBFLE8X/SWn3S+SuFKeRaMtHlC2D/mVZYzJlhhX7FcY8jCywR91sqENs8PowqtmJCWX82cH1TsIespyeAUfacsAQPi8mI2oqCK+VX7BFzE19L7FmXllUJ9DllS+KkEhJ2BEEMWXgP0Gji+aIRTVps0Wu7GK1CUGk8qdft72pHcOcoxG8VFphCMUlaPGjRZAOLy4sAiChOy/PSSGPtNn/WgN31DgAI66UrZ4c4Lk7cVk0r8SySLfc5igWXUCZGnKmq1M3E4fbiVLMbjc5u9PiVY8Pl4v+02C1OUnF5A2hx9cDjC6C2MCciHk74XEcWRCaQaLGnukScoYwr3kAGu82CXEtmhCjlxRmOK8nFkYsdONHkxqQReZz/Uy5cdnbjcENn2pQ6AUjYJQRKniCIgYGa+NBKNKJJiy1KgkCvneK4JWkWptx40oemFlGqZpNSaynpMcJ4cvevvDCyOK3gpamtKAht1TncPagZmYfR9uAx40pyAQQ7Dah5KoXXeG3V5OyVwtuqVordk1tPweOdaHZ6ceBsKxxub+gz5HnixJmqvDGFvx3uHrS4vWjq7MEnJ1oU+9pG65WsLulP0tl7Kiiqa0bmodCaxa0zJ42rlG7HqrXdE7zEwrF2WxYc7p6IuFZpaZnK4hx88FUjTrd4cMbhRmOHF+0eH443uXBnTVnY+F3+XhxqcKLV40NFoTXMvlRCwi4BUPIEQQxMovWs6PWeab1+PMu8iGOspFmYaueo2annYS/e/uN5YnglSpTGl74neGnEPT+lteDEnTaEnqLiLWteiRSlrFItwt7h9l7xJDpDx8mdJ7ee6pvcONTgxIV2Dw6e78VVxTmhz5B3j8SJEuLtZLEnLxgC0IXRxTb4exnGxnFbUbpOBLEmrs8nl0kq3W6NJWQiPDwhvHagtLSMw+2FMSMDVw+z4o5JZbjQ5sHJZg9yTBkRtfrGleTijMON6ZVFGFnIj41NBSTsCIIgrhCtkIpXbJVSFmG8qC7pb7EU9N6dh1gIRWOnFnjlXeS8W3IFgNUQe2kElLbbhXOk3rI2jw+ZGYYwEaT0WWgR9oABZ1s9GFVslU0ukLNJfJ1Z44Zh/xkjLrZ3odhqjhBq4vPFgmj2+OFhZVaE44Vr3zJumGzf4Wi/8Eh7wCoJLKXr6Q2ZkLOXFwogrEchYWa03RbmVQX6haU06cdiMqKi0IosU4bme5IMSNgRxBAmw2BAbXl+6N9DnUQIKSlKD0mlLMJ4ERQ//Q+snSccYAyaCszK2akFXq9atbHLC4MlVoQWY9NGFekWGWIxIa1DpvQZCF4lQQTp8RryYbCaM1GaZwnbVlQ6j+cZvXdasMWbEEt4+KJT1vMqJyClwhYIz/SNn+eYcYsAq81bej21kAm99kqTaWaPHx4Sb1IvolK2uuAFrWt0hnliUw0JO4IYwlhMRvz1iZmpNiNtSISQkqL00JFeX03ExBqULw4w1yPSortPkb1qtYy9+6QjlOgwbVRRRBB+NF5GAKqfAc+rJFecWfy+nECaURXsDiLEefGurWazeGy7zaKYlSkWxOKYOjkvqdgmqact2i88wpz1nqfletKMbfF9EpIghBhLsaAXBHFHtx9XFUXOjbf9fa61G5XF2WGCXBgXYOjs9iPQ16daMzJZpIcVBEEQQwQ9D0k1z0OsMXi8rUu9SD1qcvB61WpBqKcm/C0Owlerhye2b1xJbkRMn1ZbxAH2SvGJ0s+Dl82rtg0ptntXfQtK84LHbj3UiO11Tbh9Qgm+O6tKNWFFKoh5XTek27H9NvE9bXqJ9ouSlvOkGdvC30LMoFA2SFq3b+cJBxyeHgzPzdIkesUeXN5nW1mcg/zsTJxr7cIZh1vx/0CyIGGXACgrliDSn3iWoNCDnoedWn/PaD0p8Zy7VEDIXSPah7y084E4CF/LvMX2CQkTwjg8OwWkXqAdx5uRmWFAbXkeCmW8ZFq3PrXch90nHTjU0Im86qD4uOz0os3jw2WnV9M4UkEszEfaVUM8VkN7F3Ycaw6LM5Pei2T9X1Fbo1JhK7elLv0/NCzXjGyTERazEZ+fa1f9UsLz4AZtc6HL54ctKxNuby8crh60e/zxvAVRQ8IuAVBWLDFQ6Pb14vZffQIA2P7sLGSbjSm2KHmk4mGlF5c3wC3AGytKW3x6kQoI3jWitVtLEHy09qnZKRVlgpewuiRPUyanXjt5dju7ffAH+vDnA+cxfVQR8rJNsvOQUppvweTyApTmW8LmIe2qIUZcjFc8x2i+QMT65UFp/egpAyTOZna4vWhx+VAzMh/VJbnYVd+M/WfbccYR3GIN/s1PJBKPu+NYMw41dMJgAI43udDq6UGWyYhCq0n3PBMBCTuCGMIwMFzs6A79eyiRjESJWFHbdoxWPIkf8rzSHnqQCgjpNbRmPvKIdn7isXm9TuU6WYiRCgQ9XkIle7TOY9qoIri8AbzzWQPOt3XhnqnlePr2sSGvGm9LVYyWBAQpWu9FIj87NVv0ji0ex+HuAWNAodWM2eOHI9eSiVaPDz3+Pqz/n7Nwe3uRbTKGEomU2vYJmeWj7TaMLLBAEITpAAk7giCGJPFIlEj0di5v21GLKNFir5AJKC4tEs18pA9ZtcKxcufxiFZ864lNFMe+8TJl1eaj1uJLGtyvV4ALAkK6LcrbUuWtDXEtO7VrS7dklbJk1RJYYv3iJPf/U62nstI4M6oQFt84bVQRSvMt2HxFPFcWmzGhLF91CzoYm9r/czrE1YkhYUcQBBElsXoltAgp6QOOJ0q0XCPXkondJx1h3jlpnBKvEKya3dIHuNZ7ouXBL5wv7TqghtrYvPfl7I42gUUuuF8L4s9MECLSRAfelipvbWj9TJXmxKuvqJbAEo8vTmKkHUvEPZW1/D8S31NpuRm5LhdKMa5KySiphoQdQRBElMTqlYhGGOr1xIg9PNLCv9KHr9b5KIlL8RjSh5/0IahlznrukVZvIW9rUVwiQzyWuKAyD7l7JhYFagJVrg6bUqKD+DzhdZ4t0axRqaCRjqE3gUUrSgJNnIUq/Tz01K0rspoR6GUR7d3E8XOCN3JkQbZqtxHeZ5RqSNgRBEFESaxeCZ4IUhNqwjXVPDFylfX1eAe12K00xp8PnMfOEw4MyzUjx2zS9BCU6zqgRchG60GVlsgQj6XU9kp8Halok7Ygk7ON18JMS6KDMOZ7hy4h0NeHRdePwrRRRbJB/+LtVQDcenxCaZgcU0aY7bx1EW+PnDAfuc9P6l2We09uXEHU2a1m7D/XBsYYt1ex2Bs5ssASJiLltrqVPqNUQMIuAVC5E4IgtCD1FOgRJdKHmdQ7Vt/kwuGLnaqdHrTCK1+iTrAocWleNqpLchUfgtJm7EB40L+W+xPLlqdcjTu1seR6y0q9Xl5/Ly60ezBpRF7Y+WLRIedJlaO6xIZAXx9Ot3iw+6QjFOvF+5IgzYTm1eMTSsPUlufjpuphSRcrcvdcqfAzoJ7VLR5382cNuNjuRbHVjJuqh3NFm9gbKd1eF8cWKgn+VELCLgFQuRNioGCAAdXDbaF/E6lDryiRi70TvGJdvoCmTg881ISBVk9NMEuQQdzZwuV1c4/t32rL5npJ1LZEBbu0xF1JRaTYK6d1O1dsN6+3rdRjd7zJhYvtXhxvcuHOmrLQcdUlwWxNPcWAxR0w7phUhuNNrrAyKLzPSrq+pEkZDe1dyDFloLY8H/NrykJFjdUSSuIZWyonaJVi/gD5LiK8cYUyMsEvG/z1LRe7qiW2MB0gYUcQQ5hssxEfPjsr1WYQiN+2Li+eTS9ahIEWyguDra+CjeezAMg/hKX2S21R2xLVMgfpezwRufmzBlxo60JFUY6mlmVyW4TS+yVXTy94j9xX7pE7wiMlJ3QEj5tQBkUML+hfur7EWZ3CmE5vL26qHhby/OlJKNGTwOBwe3H4olMxq1YpQUcpllDNFqGMzGfn2kPiTBpjyTtfeG3mGHvabb1KIWFHEAQxCIhnzBPvwRnt+GoPYen40i1XPVuiWmPzBOEz2m4LK1UheN8CfX2yXploO2rI1dPbe8qBdo8/LHFDKR5P8F5KPW5ipN5CLWhNvAja4ESXLxAmHLV4dMUJEEVWc9g9lksgASI9aNJ7Lifc5WwRvKQOtzd0Xbk2ZGKP3o7jzSiymjFzjF13pnYyIWFHEAQxxFDzrsRTJKo9hKV2SbNTtdoiPMzF2Y5CrT7pQ1pO+ORaMlFkNWPG1cWwmIxc0STdBlTL4lSac32TGztPOMAY8L9Fnis5MSv2Xt47rUL2XkTrXdWSeFHf5Mbhi04whrD7p+Wa0mOkcXLRCHot1xHTn0ksJE8EhbUgVMXtwsTCtbrEhl31LTjU0AFntx855nBRG+22dCIgYUcQQ5huXy8WvLQbAPDXJ2YOqZZiQ5lY6+8lCl52qlaEh7i0WbtWTxTQL/gsJqPstq/4XK1ZnGKkW4w3j7VDHIMojKXF06Qlpox3bb2116Q1+fTYzLNLHCMot72q58uFHi+qtGyKw92Dwxc74fEFsPukAyMLskPtwsTCtbwwBxPKctHi6kFpXhaqS/JU10CqIGFHEEMYBob6Znfo30Tq0dLJINYHSCzekHgit32qZJecOBGLBnF3ASVPFG9MPQkaArzMSrlEDukWo56MZV48nh6Ea3f5Amh29mDWuGFhsXZKLbSEv8sLc3DvtKsiaujptUOalSv3mWhZ73rElXQuB862oa6xE9leI863dcHZ7UNteT4KraaIeLsZVfaw2ne8cdMBEnYEQRBpgLSyPqCvA4Le68QqEOMxjnRO4ngm4TW5c+Rq4kWzjRxtgobUXi3JBkqxf2rwtqr10L/t6UKLKzITV85+3j2NZT1Wl0S2SpPC21qXS9TQ4h2VS0r5/Fw7jlx0YlxpLnq8fnR0B3BTdW5oHYi38qXt53ItmTjj8CjOIxWQsCMIgkgDlCrrA/HzssVLICo9eLXCm5OafULiw7iS3Ig4OK1iMxpPodz5PNGm9NnJJYloIZatauE6wnyrS3JjWmOx3LOg10+bZ026tQ7wvxAofQkQjuOtrctOL9o8Plzu9KKsIDuibI3SGs00GnCyyQ3DlYpC0f4/iDck7AiCINIA6cNfSrwSGuIlEAWB1e7xhTyMgPZAci3bfjyU4uDURKGcV1TvvZUTc3LjKG3f6vkc4vXZ6bFT7xhy7D3Vik+Ot0Rs/6ox2m4N21oHtN8Hufg9sbd0fk0Z8rJNYV8U1LyV4nFGFmRDqNWYLrF2JOwIgiDSgHhmovLQW3iXd57YPkFgFRZkobokS3cguVJ2qdK5Sg913nvi+LndJx1o8/hQMzJfNZZOCXESg977KRDN5612jp5actF6lqI/n13xbGmL5VXaHtd676TH8byls8cPj+jYIRwrfk08X/G44nOlAjRVkLBLANRSjCCIdENJdGlpvi49T8nDqHcrT48gVHqoK8WCZRoNuNDWhUBfH0bbrdyacnq2cQGEymUIW3DC9fTGzcXrHC2fsbRlm16k19Da3kuceKCFRCb4yI299VAjttc14fYJJZhfW6Z6v8TZvTOq7GnTYoyEXQKglmLEQMEAw5WtBGopNthRelBGU7ZDKdtUrhWV2vmJfIiLPXafnws2u585xq7aZUFKfyxkdqhchp72VnLj6Tln7ykHdp5wRPQBlvNY7j3lQF2jC4E+hpqReTF7K8V/Sz2vcnPR66GMJUtW79gCoXg7pzfic5ZmyAoxe7yeu6mGhB1BDGGyzUbs+fGtqTaDSAJKD1Yl0RdLlqlwfiy2AbE91MVjl+ZbUN/kvlJothNA/1aa0jau+LpSTyVvCy7RcXPtHj+aOr1o9/hl5yogFEH2+AKoLS/AjCq7ZvHNQ3wNuSzdRAh0ofODUhsyLSitJSHebuYYO0rzLQDCPdK8rihq2b2pgIQdQRDEEEecbSj+OVrivY0Wr0xeQZTkWjJDD3Dpe2rXlYvbEv+caAqtJpTkW1BoNakeW13SX1B4RlWxbGapVqQZwdIs3UTNv7rEhi8bOiJavekV/UoCUdr2TTqekDDk9feGRPGMquLQ/5t0gYQdQRAEoftBr/RAjWciSNAr5EVlcU7chCKvbyuPaAVqoorrCuiJVysvzOEWQY52boIw2lXfjNI8i6bPJR7bqOWFOfjW18rD4vkEe/TcPzmBqAUhYeh4kytUQ9Hh7okq2zeRkLAjiCGM19+L+36/FwDwp2UzYDFRS7F4EM94oGRdV++DPlYvmlZbg16h7qhrt8VCtAK1v8ZfD+w25TlGI7DisXWtFA+pdL4gjA43dKLZ6QvrcSucu/eUA2IPYbw9rlJ7tLRZE4/BE4hSpIkRwtYrgLCuJw63V1e2bzIgYUcQQ5g+xkKxRn0sfX4xDXSS1T+SF8wd7XX1iphYt1u12prI7MhEIdjqcHtV5xhP76ZAtGVntGzTCsJoZIEF0n6xwrk7TzjAGGC3ZYUJonh9htJ1r6fNmp4vFNuONMLtDaDd44dQ0FnIfBXGGW236cr2TQYk7AiCIOJMoh9kAtIHcLKuK1xPrqSGFo+hVlv1Ch8lG9Ts0+vxlDtesPnA2TZc7PAi16L8qI23h1fPOuAdq3a+3PaucI4Q0yecH2/xGsu61/OFYlSxFWdbPbjs9MLpDYSdE20bumRAwo4gCCLOxPIg4z3ktdSSi/W6PPR6AONVjy4WlGxQs0/u/WjqxgH9MVmuK6IgGpv1wrNVTaADwRIq7R4/Cq2mmGqyKYk+rXYDyvUAlda9mkjW84XikZmjQ8Wtha1XveOkAhJ2BEEQaQTvIa+nllw80fvwSvTDTotni5e5qHYfBeTe1yqslcZTi1tTGkcPerdWhfd3nnCgqdOLknyLYk22RMWPKtXDizYZRfhZKhb1itbSfAumjUpcglC8IWFHEASRRvAe8ql6iOi9rpbjYxEGWjxb4szFNo8vrKyFmn1y70crrMXvS2ug6RlHitw9VKsrpyRAbx5rD3nslARmouJHxUkQo+22sCLK0msqeVbF89daPFmMtJyL1rmmKmGKBwk7giCINCKdPQHxIBZhoMWzJc5cFDpNqJW1UHsox+MzETIoHe4eTdmbSsjdQ15dOeEYNQGqdfs0UV5ZcRIEYIDdliV7TSXPKm/+PJErh/jeJiJ2LxmQsFOgp6cHjz/+OLZv347Ozk5MnDgRv/rVr3DDDTek2jSCiBtFVnOqTSBSRDy9DFoTE3ItmVG3tNIisMTHCJ0m1K6191RrwmuRBYWL5Ur2ZlZM91tOcCQj7kuPyNW7vuSyiaXX1OpZ5Z2n1QbBZq2euljWdbwhYadAIBDA6NGjsWfPHpSXl2P9+vVYsGABzp8/j5ycwfuNmhg65Jgz8fn/mZNqM4gYiVagxdPLoDUxQUsWYbwEp9KDOTxYnyWlFlm8hJfcvOLhWYzXvW9o78LmzxrQ5vGFbFO7hmB/sE2buiDnjRXr/PUKV2GOs8cNT5vsWBJ2ClitVvz0pz8N/bxkyRI888wzqK+vx+TJk1NoGUEQRD/RCrR4eniiTUzgkYxtLfE19HRyEIhGAA2EbfZ43fv6JjfaPD4UWc3ItWSGJbKoXYN3n6LNTOah9bNTK52z+bMGHGt0IsuUoVrSJpmkjyUKuFwurFu3Dl988QUOHjwIh8OBNWvWYO3atRHHut1urF69Gn/605/Q1taG8ePH48c//jEWLlwYsx3Hjh1Dd3c3qqqqYh6LIAgiXvBEk57uA3rhjR1tYgIPLSIwVs+S3i03KfESQOkUdA/ET+yLx9Fbdy6Wkj9aUOoXqyV5QhB159u6kGXKQH62WbWkTTIZEMKutbUVr7zyCiZPnoy77roLr776quyx99xzD/bv348XXngBY8eOxYYNG/DAAw+gr68PDz74YNQ2dHV1YdGiRVi9ejVsttTvoRNEPPD6e7Hk//4/AMAbj/wvaik2QOEJk0R6vaL1kkhbNMmhRWjFOr9YvWfxEkCpCrpX2w5VO07P2Jc7vcg0GkJeLbV7H2vJHy217OT6xWpJnqhvcuN8Wxd6+/pwx6QyWEzGtIitExgQwq6yshLt7e0wGAxwOByywm7r1q348MMPQ2IOAGbPno1z587hBz/4Ae6//34YjcEH12233YY9e/Zwx/nBD36AdevWhX72+/247777MHHiRPzkJz+J8+wIInX0MYZ9Z9pC/yYGD4kMpI/WS/LJ8RYYDFCskyYQr0KziUJPYL2SKErVPLQWY45GeErP0VqoWUBLyR9x0oJQPFiIz5OL7ROPJe0Xy0uCUErSuKooB20eHywmY9rE1gkMCGFnCEa1qrJlyxbYbDbce++9Ya8vXboUDz74IPbt2xfKaP3oo480jdnX14fFixfDaDTitddeU7Slp6cHPT09oZ+dTqemaxAEQcSbRMZzRTN2dYkNs8YNA8DiEmc3WOLVUjUPJW9UrG3qtJYnkUOPxzbTaECgl4XOE8f2VZfIF4aWXkNPcg9PGKYTA0LYaeXIkSOYMGECMjPDp1VbWxt6X2+pkmXLlqGxsREffPBBxLhSnn/+efzsZz/TZzRBEMQQoLwwR1cpkVR75OJBOs8h2pIh0YydCPEqrlcobvcljZ1UKgzNGy+e4jNVDCph19raiquvvjri9aKiotD7ejh37hxeffVVWCwW2O320Ovbtm3DTTfdFHH8qlWr8Oyzz4Z+djqdqKio0HVNgiAIIr0fnFoZiHMYKDZrLfmiVbANlHlrYVAJO0B521brlq5AZWUlmI64o6ysLGRlZakfSBAEQRBEwhlMgk0rGak2IJ4UFxdzvXJtbcHgcMFzl2hefvllTJw4EdOnT0/K9QiCIAiCIIBBJuxqampQV1eHQCA88+bw4cMAgGuuuSYpdqxYsQJHjx7F/v37k3I9goiFbJMR2VTmhCAIYlAwqITd3XffDbfbjXfeeSfs9TfeeAMjRozAddddlyLLCCI9yTFnom7dHahbdwdyzIMuMoMgCGLIMWB+k2/btg0ejwculwsAcPToUWzevBkAMH/+fOTk5ODOO+/EnDlzsHz5cjidTowZMwYbN27EBx98gDfffDNUwy7RvPzyy3j55ZfR29ublOsRBEEQBEEAgIHpyQ5IIaNGjcK5c+e47505cwajRo0CEGwp9vd///dhLcVWrVoVl5ZienE6ncjPz0dnZyfy8vKSfn2CIAiCIAY+evTEgBF2AxESdkS64/X3YvmbnwEAfvfw16ilGEEQRBqiR08MmK3YgQRtxRIDhT7GsON4S+jfBEEQxMBmUCVPpAuUFUsQBEEQRCogYUcQBEEQBDFIIGFHEARBEAQxSCBhlwCo8wRBEARBEKmAhF0CoBg7giAIgiBSAWXFJhChkozT6UyxJQTBp8sXQF9PF4DgOg1Q9wmCIIi0Q9ARWirUUR27BNLQ0ICKiopUm0EQBEEQxCDgwoULKC8vVzyGhF0C6evrw6VLl5CbmwuDwcA9Zvr06apbtnLHOJ1OVFRU4MKFCwO6ALKWe5DO14t1PL3n6zle67Fqx9EaTO/rJXsN6j0nlt9zWt4fDOtwoK/BWMekNagMYwwulwsjRoxARoZyFB3tuySQjIwMVWVtNBpVF4HaMXl5eQP2lxmg7R6k8/ViHU/v+XqO13qs2nG0BtP7esleg3rPicfvOS1jDOR1ONDXYKxj0hpUJz8/X9NxlDyRYlasWBGXYwYyyZ5fvK8X63h6z9dzvNZj1Y6jNZje10v2GtR7Tjx+z9EaTP/rxTImrcH4QVuxAxjqRUukGlqDRDpA65BINem0BsljN4DJysrCmjVrkJWVlWpTiCEKrUEiHaB1SKSadFqD5LEjCIIgCIIYJJDHjiAIgiAIYpBAwo4gCIIgCGKQQMKOIAiCIAhikEDCjiAIgiAIYpBAwm6I0dPTg6VLl6KiogJ5eXm4/vrr8emnn6baLGKI8bvf/Q5Tp06FyWTC2rVrU20OMQRoaWnB17/+dVitVowdOxYffvhhqk0ihhjJ+r1Hwm6IEQgEMHr0aOzZswcdHR1Yvnw5FixYgK6urlSbRgwhysrK8LOf/Qx33XVXqk0hhggrVqxAaWkpWlpa8Mtf/hL33XcfWltbU20WMYRI1u89KndCoKioCDt27MDkyZNTbQoxxPjOd76D8vJy8toRCcXtdqOoqAinTp1CRUUFAOCWW27B4sWL8cgjj6TYOmKokejfe+SxSxEulws//OEPMXfuXAwbNgwGg0H2Q3a73Xj66acxYsQIWCwWTJkyBW+//XZc7Dh27Bi6u7tRVVUVl/GIgUO6rEGCkBLvtVlfXw+bzRYSdQBQU1ODr776KpHTIAYwA/n3Iwm7FNHa2opXXnkFPT09qm7Ze+65B2+88QbWrFmDbdu2Yfr06XjggQewYcOGmGzo6urCokWLsHr1athstpjGIgYe6bAGCYJHvNem2+2OaPOUl5cHt9udCPOJQcCA/v3IiJTQ19fH+vr6GGOMtbS0MABszZo1Ece9//77DADbsGFD2Otz5sxhI0aMYIFAIPTarbfeyrKysrh/Vq9eHXa+z+djX//619nixYtDdhBDi1SvQcYYe/TRR7nXJIY28V6bn3/+OSssLAw75oknnmDPPvtsYiZADHgS8ftRING/98hjlyIMBgMMBoPqcVu2bIHNZsO9994b9vrSpUtx6dIl7Nu3L/TaRx99BK/Xy/2zbt260HF9fX1YvHgxjEYjXnvtNU12EIOPVK5BglAi3muzuroabrcbDQ0NoWOOHDmCSZMmxddwYtCQiN+PyYKEXZpz5MgRTJgwAZmZmWGv19bWht7Xy7Jly9DY2IhNmzZFjEsQUhKxBgOBALxeL3p7e8P+TRB60Lo2bTYbvvnNb2Lt2rXo7u7Ge++9hy+++AILFixIus3E4ELP78dk/d4jYZfmtLa2oqioKOJ14TW96frnzp3Dq6++in379sFut8Nms8Fms2HXrl1xsZcYfMR7DQLAP/7jPyI7Oxuvv/46/umf/gnZ2dlYv359zLYSQws9a/O3v/0tLl26hOLiYjzzzDPYtGkT7HZ70mwlBid61mCyfu+Ru2YAoOQO1ruNWllZCUYVbgidxHMNAsDatWupxAkRF7SuzWHDhmHr1q3JMIkYYmhdg8n6vUceuzSnuLiY6xFpa2sDAO43BYKIJ7QGiXSF1iaRatJxDZKwS3NqampQV1eHQCAQ9vrhw4cBANdcc00qzCKGELQGiXSF1iaRatJxDZKwS3PuvvtuuN1uvPPOO2Gvv/HGGxgxYgSuu+66FFlGDBVoDRLpCq1NItWk4xqkGLsUsm3bNng8HrhcLgDA0aNHsXnzZgDA/PnzkZOTgzvvvBNz5szB8uXL4XQ6MWbMGGzcuBEffPAB3nzzTRiNxlROgRjg0Bok0hVam0SqGbBrMGEV8ghVKisrGQDunzNnzoSOc7lcbOXKlay0tJSZzWZWW1vLNm7cmDrDiUEDrUEiXaG1SaSagboGDYxRiiRBEARBEMRggGLsCIIgCIIgBgkk7AiCIAiCIAYJJOwIgiAIgiAGCSTsCIIgCIIgBgkk7AiCIAiCIAYJJOwIgiAIgiAGCSTsCIIgCIIgBgkk7AiCIAiCIAYJJOwIgiAIgiAGCSTsCIIgCIIgBgkk7AiCiJpRo0bBYDDg7NmzqTaFSEOOHDkCo9GIxx9/PNWmaMbpdKKwsBAzZ85MtSkEERUk7AhiCCAIMPGf7OxsVFVV4ZFHHsFXX32VahMHHeJ7/txzzyke++KLL4Z9NoOFH/3oRzAajVi1alXEe+l6f/Ly8rBy5Urs2bMHf/nLXxJ6LYJIBCTsCGIIUV1djRtvvBE33ngjqqqq0NDQgD/+8Y/42te+hv/8z//UPV5VVRXGjRsHk8mUAGsHDxs2bEBvb6/s+2+++WYSrUkOu3btwtatW/HQQw+hsrJS8dh0uz9PP/00cnJysGrVKjDGknptgogVEnYEMYT4yU9+gt27d2P37t04cuQIzp8/j9tvvx09PT1YunQp3G63rvE++ugjHDt2DCNHjkyQxQOfcePG4fLly9i+fTv3/ePHj+PAgQMYN25cki1LLC+99BIAYMmSJYrHpeP9KSwsxDe+8Q3U1dXh448/Ttp1CSIekLAjiCFMSUkJ1q9fj6ysLLS2tuLDDz9MtUmDjocffhiAvNdp/fr1AIBFixYlzaZE09LSgnfffRcjRozAzTffrHhsut6fhQsXAgBeffXVpF6XIGKFhB1BDHFKS0tRXV0NAKivrw+9Lo5neuedd3DzzTejoKAgLFlCLnlCfO6WLVtwww03wGazoaSkBEuWLMHly5dDxwpbwVarFcOHD8fjjz+Ozs5Orq1HjhzBmjVrMGPGDJSVlcFsNqOsrAz33HMPPv30U+45avM4fvw4DAYD7HY7fD6f7H2qqamBwWDA+++/r3A3I5k1axYqKiqwZcsWeDyesPcYY3jrrbeQnZ2Ne+65R3aMaOYNAOfOncOyZctw9dVXIysrC7m5ubj66qtx99134+233476WDW2bNkCn8+HO++8ExkZyo+ZeNyfRDBv3jxkZmbi3XffRU9PT1KvTRAxwQiCGPRUVlYyAOyPf/wj9/1JkyYxAOznP/956DUADAB74YUXGABWUlLCpk+fzoYNG8bOnDkTNq7ws/Tcf/3Xf2UAWHl5OZs8eTLLyspiANjEiRNZd3c3W7lyJQPArr76ajZp0iSWmZnJALBZs2axvr6+CDtvu+02BoAVFBSwCRMmsKlTpzK73c4AMKPRyN56662Ic7TMY8aMGQwAe+edd7j358CBAwwAKy0tZYFAQP2Gi+7Nrl272I9//GMGgK1fvz7smJ07dzIA7IEHHmAXLlwI2RqPeZ85cyZ0TE5ODqupqWFTpkxhRUVFDACbPHlyVMdq4aGHHmIA2O9///uk3J9Ece2114ZsJIiBAgk7ghgCKAm7xsbGkOASCxvhIWo2m9krr7wSElp+v5/5/f6wceWEndVqZRs2bAi9fuHCBTZmzBgGgN11110sPz+fbd++PfT+oUOHQmJi69atEbb++c9/ZocOHQp7ra+vj7377rvMZrOxvLw85nQ6ubYozeMPf/gDA8AWLFjAvX9PPvkkA8C+//3vc9/nIRYuX331FQPA5s6dG3bMY489FpqrknCJZt5PPPEEA8CWLFnCXC5X2Ht1dXVhokvPsVoYPXo0A8AOHDgge0w870+i+O53v8sAsOeffz5p1ySIWCFhRxBDADlh19TUxG6//XYGgBUWFoaJA+Eh+uSTT6qOKyfsnnrqqYhzfv/734fe//Wvfx3xvuC9WblypZ4pstWrVzMAEd4rLfNwOp3MarUyk8nEmpubw97z+Xwhb9aRI0c02yMWLowFvT9Go5FdunSJMcaY1+tlBQUFbPjw4czv90ctXOTmPW/ePAaAffnll6pj6DlWjb6+vpDn9eLFi7LHJev+xMKaNWsYAPb4448n7ZoEESsUY0cQQ4h//ud/xsyZMzFz5kxcc801qKiowPbt22EymfCHP/wBubm5EecsXrw46us9+uijEa9NmTIl9O9HHnkk4v1rr70WAHD69GnumOfPn8cLL7yA++67D7feemtoPps2bQIAfPnll9zzlOaRm5uLb33rW/D7/diwYUPYe++//z4cDgemTZuGSZMmyY6hxqJFi9Db24uNGzcCAN577z10dHTggQceQGZmpur5euddUVEBANi8ebNqyQ49x6rR0dGBQCAAACgqKtJ8Xqz3JxEI9re0tKTk+gQRFalWlgRBJB7BOyL+Yzab2VVXXcUefvhhdvDgwYhzhOPcbrfquHIeO4/HE3HO6dOnGQA2bNgw7pgff/wxA8BuueWWiPdef/11ZrFYIuYi/vOd73xH9zwYY+yTTz5hANi1114b9vpdd93FALCXXnpJ8XwpUo9UY2MjMxqNbMqUKYwxxr75zW8yAGz//v2MMabokYpm3gcPHmQmk4kBYJWVlWzZsmXszTff5HrR9ByrxqVLl0I29fb2JuX+JIpXXnmFAWDz589P2jUJIlZI2BHEEEAteYKHloeomrDjcebMmZCA4LFjxw6GKwkUYk6ePBkSH8899xw7ePAgczqdoZg5IU5uyZIluuchUF1dzQCE4tlaWlqYyWRiZrOZtba2ahpDQCpcGOvf8vzkk0+Y2Wxm48ePD70nJ1yinTdjjO3du5fNnTs3tDUKgBkMBjZ37lx29OjRqI9Vwuv1hs5va2tL+P3hcenSJbZixQpWXl7OzGYzq6qqYqtWrWLt7e2a58EYCyXcLF68WNd5BJFKaCuWIIgBwZ/+9Cf4/X4sXLgQv/zlLzFlyhTk5uaGSplcuHAh5mt8+9vfBgC88cYbAICNGzfC7/djwYIFurYV5RBqsS1atAg+n09TbbZY5n399dfjb3/7G9rb2/HBBx/gRz/6EcrLy/Ff//VfmDNnDjo6OqI6VomsrCzk5eUBANra2jSdIxDN/ZHyxRdfYPLkyWhoaMCrr76KgwcP4le/+hU+/fRTTJ06VVdfY8H+YcOG6baDIFIFCTuCIAYEwgP5hhtu4L4vF1unh29/+9swGo146623EAgE8Prrr4dejwd33303bDYbzp8/D4PBgIceekj1nHjM22azYd68eXjhhRdw7NgxVFVV4eLFi9i2bVtMx8ohxFHW1dVpPgeI7v6I6enpwd13343HHnsM7777LubNm4eJEydiwYIF+PjjjzFr1izcf//9muMIjx49CgCYOnWqLjsIIpWQsCMIYkCQnZ0NAGhqaop479ixY1H1upUyYsQIzJ07F5cvX8a//Mu/4PPPP0dpaSnuuOOOmMcGgJycHDz33HO47bbbsGzZMtUeqkD8552Tk4OamhoAwKVLl+J2rJiZM2cCAA4cOKDbNr33R8zmzZthNpuxbt26iPcyMjLwb//2bzhx4gR27typabz9+/cDAG666SZddhBEKiFhRxDEgEAQC7/97W/xxRdfhF4/ceIE7r33XpjN5rhcR8jUXb16NYBgyyuj0RiXsQFg7dq12L59O373u99pOj7aeS9fvhybNm1CV1dX2Os7d+7ERx99BKDfE6XnWC3MnTsXALB7927N5wjovT9i9u3bh7lz54a6XWzevBl2ux179uwBEPRGzpo1C/v27VMd6+TJk2hqasL48eNDWcMEMRAgYUcQxIDgrrvuwvXXX4/29nZMmzYNEydORE1NDcaPH4/W1taQEIuVBQsWwG63h0p2xGsbNlqinffevXuxcOFC5OfnY+LEibjuuuswatQozJo1Cy6XCw8//DBmz56t+1gt3HzzzRgzZgz++7//m+tpTBSBQAAmkyn0s9frRWtrK/x+f+g1k8mE3t5e1bGEMjK8kjwEkc6QsCMIYkCQmZmJv/3tb3jyySdRUlKCkydPoqOjA48++ig+++wzjBw5Mi7XMZvNePDBBwEg5tp18SDaef/617/GU089hdraWjgcjpC3b968efjrX/+Kf//3f4/qWC0YDAY89thj6O3tDQmkZFBbW4tdu3aFfn744YfBGMMtt9wCAPD7/fj0009RW1urOtbGjRthMpmwZMmSRJlLEAnBwLRGkRIEQQwRFi5ciE2bNuGll17CihUrUm3OgMTpdKKqqgpFRUWoq6sLbY8mko6ODlRVVeEXv/gFtzj22rVr8dZbb+Ho0aNhnj0pO3bswK233orvfe97ePnllxNpMkHEHfLYEQRBiGhtbcVf/vIXZGVl4YEHHki1OQOWvLw8rF69GidOnMDbb7+dlGsWFBRgw4YNePLJJ/G9730Pp0+fht/vR11dHRYuXIjf/OY32LRpk6KoA4B/+Id/gM1mw09/+tOk2E0Q8SQ1fVoIgiDSlLVr18Lr9WLJkiVxqV03lFm+fDmcTif6+vqSds158+Zh9+7d+OEPf4jq6mr09fXBbDZj/vz5OHDgAMaMGaN4vtPpxC233IKVK1eipKQkSVYTRPygrViCIIY8X3zxBZ5++mlcunQJ9fX1yM7OxuHDh1FVVZVq04gY8Hg8aG1txfDhw2GxWFJtDkEkBfLYEQQx5Ono6MAnn3yCrKwsTJ8+Hb/4xS9I1A0CrFYrrFZrqs0giKRCHjuCIAiCIIhBAiVPEARBEARBDBJI2BEEQRAEQQwSSNgRBEEQBEEMEkjYEQRBEARBDBJI2BEEQRAEQQwSSNgRBEEQBEEMEkjYEQRBEARBDBJI2BEEQRAEQQwSSNgRBEEQBEEMEkjYEQRBEARBDBJI2BEEQRAEQQwS/j95j26sM2XsQwAAAABJRU5ErkJggg==", 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", 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      " ] @@ -65,13 +91,15 @@ "plt.xlabel('Primary Mass (M$_\\odot$)', fontsize=16)\n", "plt.ylabel('Semimajor Axis (AU)', fontsize=16)\n", "#plt.plot(bd_mass, bd_sep, 'r--', label='Fontanive+18 (approx)')\n", + "plt.annotate('Brown Dwarf\\nRegime', xy=(0.01, 100), xytext=(0.025, 100), fontsize=14, fontweight='bold', ha='center', va='center')\n", + "plt.annotate('Stellar\\nRegime', xy=(0.01, 100), xytext=(0.2, 100), fontsize=14, fontweight='bold', ha='center', va='center')\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12)\n", "plt.title('Semimajor Axis vs Mass', fontsize=20)\n", "plt.axvline(0.08, linestyle='--', label='BD boundary')\n", "plt.legend(fontsize=14)\n", "plt.tight_layout()\n", - "plt.savefig('semimajoraxis.png')\n", + "#plt.savefig('semimajoraxis.png')\n", "plt.show()" ] } From e85347fa2d86ef9abd4d44a29772bbdeb626f407 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Fri, 5 Jun 2026 00:07:52 -0700 Subject: [PATCH 144/191] Last changes to figures --- docs/paper_examples/Begbie+26/Figure 10.ipynb | 12 ++++--- docs/paper_examples/Begbie+26/Figure 12.ipynb | 36 +++---------------- 2 files changed, 12 insertions(+), 36 deletions(-) diff --git a/docs/paper_examples/Begbie+26/Figure 10.ipynb b/docs/paper_examples/Begbie+26/Figure 10.ipynb index 1c6edfec..10180077 100644 --- a/docs/paper_examples/Begbie+26/Figure 10.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 10.ipynb @@ -5,14 +5,14 @@ "id": "51fdd39d-b433-4371-a487-fa5d855b79de", "metadata": {}, "source": [ - "## Below is the code to recreate Figure 9.\n", + "## Below is the code to recreate Figure 10.\n", "\n", "Topic: Comparing mass fraction as a function of primary mass. This shows the mass-dependent override imposed for brown dwarfs." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "3d80653b-a8ee-422d-b008-14dc490ad520", "metadata": {}, "outputs": [], @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", "metadata": {}, "outputs": [], @@ -39,13 +39,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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xYsQIBAYGomHDhpg9e7bRPbBkOkzqqEK4ublh0KBBAAp6ZDSXHl588UWDL3VVVqXdowXA7MtxlXX+ihAaGoqnnnoKQNHLPydPntTO62aKXrryKOtSqFwuxyuvvIK0tDRIpVLMnDkThw4dQkpKCvLy8rQ9gYUv75n7Z1te+rQNa3kOgwcPhrOzM4Cil2BVKpV2pHBYWJjO0bXTpk1DUlISvvrqK/Tv3197/9/du3exYsUKtG7dGgsWLDBpzKZ6/xs9ejRu3LgBQRAwYcIE7N27F7dv3y7S7gonZCX9zJo3b464uDhs3boVEyZMQIMGDQAUJIC7d+/GyJEj0aFDB9y/f7/YsR9//DGuXr2Kjz/+GD179tQm8deuXcPSpUvRtGlTk98TTIZhUkcVRnMJYu/evYiOjgZg+KUHzSd9tVpdarknL+1Zg9TU1FL3a95EBUEwetqUJ9WsWVP7ODk52SR1lpdmwMShQ4e0n+w1CZ6zs3OJg0msyV9//aWNe/ny5Vi6dCm6du0KX19fODo6asuV1tta+OeSkpJivmDLoLkcmpaWpr30rYum/ZqyfRrDw8NDO4hm48aN2suGf/31l/Zm/rIGHnh7e2PGjBnYuXMn0tPTcebMGcyfPx9eXl4QRREff/wxtm3bZtK4y/v+d/HiRe0k0HPnzsWqVavQp08f7STAGvr08kskErz44otYtWoVrl69iuTkZKxatUr7gevMmTOYMmVKiccGBQVh3rx52L9/PzIyMnD06FG89dZbcHJygkKhwOuvv17k9gKqWEzqqML06tULfn5+UCqV2mVxdI1A1EXzqTYjI6PUcpcuXTI2TLPRjDIsa3+jRo1MNkigdevW2l6YJy9VmYIxvX8jRoyAi4sLRFHEmjVrIJPJ8NtvvwEo6IUpaeSkNUlISNA+HjZsmM5yMTExOvcVnjvNmJ+LqXpdg4ODART0Ppb1h1izUoAp26exNEnbo0ePsHv3bgD/9dq5uroatIaqnZ0d2rRpg8WLF2P//v3a7b///rsJIy7/+58p2p0ufn5+mDBhAo4fP65tmzt27Ch2+fZJUqkUnTt3xtdff619/UVRNGjOSzItJnVUYSQSCUaPHg1HR0c4Ojpi1KhRBo/60yzAnp2drTNxk8vlJU6eaQgnJycABaPLTGXNmjU698XExGhvWu/du7fJzlm9enV06tQJQMEfKVP31mleJ0D/18rDw0N7v9OaNWuwadMmZGZmAih9BKa1KNyjpRnB+iS1Wq2dBqQkPXr0gKurKwDgm2++MfgeM2Ne95IUbmurVq3SWe748ePay+OmbJ/G6tevn7aX8ddff0VeXh62bNkCoOCSpua1NVSbNm20vZCmHuFZ3vc/fdodgHJd/pRKpejWrZv2fGV9eC6sV69e2sccHWs5TOqoQn366afIy8tDXl4evvjiC4OP17zhAMDSpUuL7RdFEW+99Va5kxfNzeumnPbgzz//LPHTf05ODiZPngygoNdA12UPY7377rsACv4QDBkyRJtAlcTQkWyFb/I35LXSXIK9efMm5syZA6AgYX9yfVVr1KhRI+1jXYn63LlzcfbsWZ11eHl5aX/OZ86cwYwZM3Tes6ZQKIrd32Ts6/6k9u3ba0dl//TTT9rLgoVlZmZqY7Wzs8PUqVONPp+pSKVSDBkyBEDBFBvr16/XrlVa2qXXDRs2lNr7FBMTo718qfkAaUrlef/Tp919//33+OOPP3TWceTIkVJH+srlcu1cem5ubkXuS1y3bl2pl+j37t2rfWyO1470w2XCqFJp3bo1nn76aZw4cQIrV66EXC7H2LFj4enpiStXruCHH37AwYMH0bFjx3It9dOpUyfcuHEDf/75J1asWIHOnTtre0c8PDzKHMlakrZt22LEiBE4dOgQXn75ZXh4eGiXCdP0Ok6bNk3vKTX0NWDAAEycOBGrVq3CsWPH0Lx5c7zxxhvo3LkzPDw88PDhQ8TExOD3339Hy5YtiywGX5Y6deogICAAd+7cwRdffIHatWujSZMm2pGUPj4+Jd4Irpn37MKFC9r7oMaPH28VgznK0rdvX3h7e+P+/fuYP38+bt68iRdeeAE1a9bE1atXsXLlSuzfvx+dO3fWOckyACxatAjR0dGIi4vDt99+i+PHj2PKlCkICQmBg4MD7ty5g6NHj2L9+vVYvHgxxo0bpz3W2Ne9JD/++CM6dOgAuVyO5557Dm+++SYGDBgANzc37TJhmnsIZ8+erb1ka2mjRo3CihUrIJPJMGvWLABArVq10KdPH53HvPvuu3jttdcwcOBAdO3aFY0bN4arqyvS0tJw9OhRfPPNNwAKetU0I7WtReElx77//ntkZGRg5MiR8PPzw+3bt7Fu3Tps2rSp1Ha3f/9+LFq0CM888wyee+45tGzZErVq1YJMJsPly5fxww8/aD+MvPrqq0VGRI8ePRqzZ8/G4MGD0alTJzRo0ABOTk5ITU1FdHQ0vv/+ewAFyeCoUaPM/4JQySpq6QqqGkpaJscQZS2TI4qieOHCBdHb21vngtIzZ84s9zJh//zzj+jo6Fhi/YUXvTdkmbDr16+L9erV0xn3Sy+9pHP5nfIua6ZUKsU33nhDFARB5/mffG761v3dd9/prK+0NvDFF19oy9nZ2Ym3bt3SWdYQmiWrylpKrrCyfo5P2r17t+jk5KTzeXfv3l2Mj48vs84HDx5ol08r7V9Jx+v7upf1uyCKorhnz54yF3mfNm1akWWuCtO85iW1n8L0XeZPH2q1usjyZADEN954o9Rjnixf0j8nJydxzZo1RsVk7ve/f/75R6xWrZrO2ENCQsTk5GSdv7NPLluo69/gwYNFmUxW5Fh9jvPy8ipxqTmqOLz8SpVO06ZNcfbsWUydOhVBQUFwcHBArVq18Oyzz2Lnzp0lXpY1VGhoKI4fP47hw4ejTp06RUaXGatevXo4c+YM5s2bh2bNmsHFxQWenp7o2rWr9lN2WXOFGUsikeCbb75BTEwMJk+erO2hcHFxQaNGjdC/f3+sXLlSu9i5IaZOnYrNmzcjLCwM3t7eej+HwiP/+vTpg8DAQIPPbSl9+/ZFTEwMRo0aBX9/f0ilUtSqVQvdunXDjz/+iP379+t1X1fNmjVx6NAhbNmyBS+//LJ2JGO1atUQHByMkSNHYtu2bcWWuwKMf91LEhYWhqtXr2LevHkIDQ2Fh4cHHB0dUadOHYwcORJHjhzBt99+qx19bg0EQSj2upT0OhV2+PBh/PTTTxg6dChCQkJQq1Yt7ZJYbdq0wTvvvIPExETtSFVrExoaitjYWLz22msICgqCVCpF9erV0b59e3zxxRc4depUqfMezpkzB1FRUXj77bfx9NNPo06dOnBycoKTkxPq1q2LoUOHYufOndi8eXOR+zaBgtG333zzDV588UU0b94cNWrUgL29PapVq4ann34aCxcuxKVLlwwe/EamJYiilUw+RGSDFi5ciA8//BAArGaeL2uxf/9+7U33GzZsKHGyWCIi0p/1fOwioirl559/BgDUqFHDoCkoiIioZEzqiKjCJSUlYePGjQAKBkiY4vI2EVFVx9GvRFQh7t69i9zcXNy4cQPvvfceFAoFnJycMGPGDEuHRkRkE5jUEVGFGDlypHYOLI2PPvoItWvXtlBERES2hUkdEVUoFxcXNG7cGDNmzMDYsWMtHQ4Rkc3g6FciIiIiG8CeOj2p1WokJyfD3d29Usx6T0RERNZJFEVkZ2fD39/fpPM/MqnTU3JycqWaHJWIiIis2+3btxEQEGCy+pjU6UmzjuLt27fh4eFh8voVCgX27t2LsLAwSKVSk9dPVVeuXIn2H+8HAJya3wsuDsb92rONkjVj+yRrV7iNymQyBAYG6r1Gs76Y1OlJc8nVw8PDbEmdi4sLPDw8+IZEJmUvV8LO0QVAQfstT1LHNkrWiu2TrF1JbdTUt3Nx8mEiIiIiG8CeOiIbZycI6B/iq31MRES2iUkdkY1zkkrw3cinLB0GERGZGS+/EhEREdkAJnVERERENoBJHZGNy5UrUfe9naj73k7kypWWDoeIiMyESR0RERGRDWBSR0RERGQDrD6py87Oxpw5cxAWFoZatWpBEAQsXLhQ7+Pv37+PcePGoWbNmnBxcUHHjh2xf/9+8wVMREREZpenUGHL2Tt4be0ZDFtxHK+tPYMtZ+8gT6GyqjorktVPaZKWloYff/wRrVq1wosvvoiffvpJ72Pz8/PRq1cvZGRkYNmyZfD29sby5cvx7LPPYt++fejWrZsZIyciIiJziE5MxayNsciSKWEnAGoRsBOA3Qn3sHB7Ar4cEorezX0sXmdFs/qkLigoCI8ePYIgCHj48KFBSd2qVasQHx+PY8eOoWPHjgCAHj16oFWrVpgzZw5OnjxprrCJiIjIDKITUzF5bQwgFnyvfuJrtkyJSWtj8OPotuijZxJmjjotweovvwqCYPTaaFu3bkWTJk20CR0A2NvbY9SoUTh16hTu3r1rqjCJiIjIzPIUKszaGAuI2vyrGPHf/2ZvjNXrsqk56rQUq++pK4/4+Hg888wzxba3bNkSAJCQkIDatWtXdFhEFcpOENCjSS3tYyKiyioqLgVZsrKnZhIBZMqUeGfjOQTX9iy1bNzdTIPq3BWfgkGtA/SMuGLZdFKXlpaG6tWrF9uu2ZaWlqbz2Pz8fOTn52u/z8rKAgAoFAooFAoTRwptneaom6o2CYAfR7X+9zs1FAq1UfWwjZI1Y/usGnbHp2jvd9PH9vMp2H4+xWTntxOAXXEpeD7Y8EuwhduoudqpTSd1AEq9dFvaviVLluDDDz8stn3v3r1wcXExSWwliY6ONlvdRKbANkrWjO3Tdj3MA05flUAt6n/FwV0qoplX6RnghQwB2Qr96lSLwPU79xAVFaV3DE+Kjo5Gbm6u0ceXxqaTuho1apTYG5eeng4AJfbiacydOxczZ87Ufp+VlYXAwECEhYXBw8PD5LEqFApER0ejT58+kEqlJq+fqLzYRsmasX3apjyFCnsS72Pz2bs4fj3doGPtBKBjIx8sHx5aarlpkbHYd+G+Xr1/dgJQP8AX/fuXXmdJCrdRmUxm8PH6sOmkLiQkBHFxccW2a7YFBwfrPNbR0RGOjo7FtkulUrO+YZi7fqp6cuVKPLVoHwDgzP96w8WhfL/2bKNkzdg+rVOeQoWouBTsTUhFRq4cXi4OCGvhg/4hfnCSSoqVj7+biQ2nb2Nb7F1k5RXc7yYIQCNvN1xOzdHrnGoR6BfiV2Z7eDbYD3sT75u0ztJIpVIoleZZstGmk7pBgwbh9ddfx8mTJ9GhQwcAgFKpxLp169ChQwf4+/tbOEKiiiGz4tFaRGTb9J3/LTNXgT9i72LD6dtITMnSHl/byxkvPxWAIW0DUNPNEe0/2YdsmVLnSFUAEAB4ONujX7BfmfH1D/HDwu0JJq3TUipFUrdr1y48fvwY2dnZAIDExERs2rQJANC/f3+4uLhg4sSJWLNmDa5du4agoCAAwIQJE7B8+XIMGTIE4eHh8Pb2xnfffYdLly5h3759Fns+REREVYFe87/9EoO2davj3J0MyJUFA7kcJHbo08IHQ9sGonPDmpDY/XfP25dDQjFpbQwEHVOQCP/+t3RIaIm9gE9ykkpMXqelVIqkburUqbh586b2+40bN2Ljxo0AgBs3bqBu3bpQqVRQqVQQxf9+HI6Ojti/fz/mzJmDN998E7m5uQgNDcWuXbu4mgQREZEZ6T3/G4DTSQX3yzX1dcfQdoF4MbQ2qrk6lHhM7+Y++HF0W8zeGIvMJ3r/1GJBb9pSA1d/MEedllApkrqkpKQyy0RERCAiIqLYdh8fH6xZs8b0QREREZFO+s4pp/F2n0aY3rORXgsO9Gnug5PzemNXfAr2xKciQyaHl7MD+gb7oF9wyffpWaLOilYpkjoiIiKqXPYmpOo9p5ydAFxIzjZoBSknqQSDWgeYdCJgc9RZkax+mTAiIiKqfDJy5XpPEqwWgQyZ3LwBVQHsqSOycXaCgA71qmsfExFVBC8XB4N66rycS76HjvTHpI7IxjlJJdgwpaOlwyCiKiashQ92J9zTq6xaBPoasfQWFcXLr0RERGRy/UP84OFcdt+RAMDTyud/qyyY1BEREZHJOUklWPSC7pWbgMoz/1tlwaSOyMblypVosygabRZFI1dunqVpiIhKcim1YNEAzdzBT371cLbHytFtrX7+t8qC99QRVQHpjzmqjIgq1v3sPKz+OwkA8O2INshXqirt/G+VBZM6IiIiMrnvDlyDTKFCaKAX+gX7QhCESjv/W2XBy69ERERkUnce5eLXkwXLe87p28SgSYXJeEzqiIiIyKSW7bsChUpEpwY10KlhTUuHU2UwqSMiIiKTufYgB5vP3gEAzO7bxMLRVC1M6oiIiMhkvoy+DLUI9G7mgzZ1qlk6nCqFAyWIbJydIKBlgKf2MRGRucTfzcTO8ykQBGBWWGNLh1PlMKkjsnFOUgn+fKOLpcMgoipg6d5LAIAXWvmjmZ+HhaOpenj5lYiIiMrtdFI6Dlx6AImdgLd7s5fOEpjUERERUbmIoojP9xT00r3SNgB1a7paOKKqiUkdkY2TyVXoHP4XOof/BZlcZelwiMgGHbnyEKdupMPB3g5v9mxk6XCqLN5TR2TjRIi4myHTPiYiMqXCvXSjnw6Cv5ezhSOquthTR0REREbbk3APcXcz4eogwevdG1g6nCqNSR0REREZRaUW8cXeywCAiV3qoYabo4UjqtqY1BEREZFR/vjnLq7ez4GnsxSvdq1v6XCqPCZ1REREZDC5Uo2v9hX00r3WrQE8nKQWjoiY1BEREZHBNsTcxp1HMtRyd8TYTkGWDofA0a9ENk+AgEbebtrHRETlJZOr8M3+KwCAN3s2hIsD0wlrwJ8CkY1zdpAgemY3S4dBRDbkl+NJuJ+dj4BqzhjWro6lw6F/8fIrERER6S0rT4HvD10DALzVqxEc7JlKWAv+JIiIiEhvPx25gYxcBRrUcsWg1rUtHQ4VwqSOyMbJ5Cr0+fIQ+nx5iMuEEVG5pOXkY9WR6wCAWWFNYC9hGmFNeE8dkY0TIeLK/RztYyIiY31/8Boey1UIru2BZ1v4WjocegJTbCIiIipTSqYMv5y4CQCYHdYEdnYcTW9tmNQRERFRmb756yrkSjXa162Obo1rWTocKgGTOiIiIipV0sPH+P30bQDA7L5NIAjspbNGTOqIiIioVF/vuwylWkS3xrXQvl51S4dDOjCpIyIiIp0u3svCtnPJAArupSPrxdGvRDZOgIDaXs7ax0REpclTqBAVl4K9CanIyJXj+sPHEEWgbwsfhAR4Wjo8KgWTOiIb5+wgwd/v9bR0GERUCUQnpmLWxlhkyZSwEwB1oVmQ/r6ahn2Jqejd3MdyAVKpePmViIiIEJ2YislrY5AtUwIomtABwON8JSatjUF0YqoFoiN9MKkjIiKq4vIUKszaGAuI0DlFufjvf7M3xiJPwdVprBGTOiIbl6dQ4YVvj+KFb4/yjZiIShQVl4IsmbLMNWdEAJkyJXbFp1REWGQgJnVENk4tijh/JxPn72RCLXKZMCIqbm9CKvRdIMJOAPbE8xKsNWJSR0REVMVl5MqL3UOni1oEMmRy8wZERmFSR0REVMV5uTgY1FPn5exg3oDIKEzqiIiIqriwFj4G9dT1Dea0JtaISR0REVEV1z/EDx7OZU9dKwDwdLZHv2A/8wdFBmNSR0REVMU5SSWY169ZqWWEf/9bOiQUTlJJhcRFhuGKEkRVQHVX3v9CRKX7+1oaAEBiJ0ClFrUrSmi+ejjbY+mQUK4oYcWY1BHZOBcHe5z9Xx9Lh0FEVuzsrUfYfi4ZggBseq0jktIeY098KjJkcng5O6BvsA/6Bfuxh87KMakjIiKqwkRRxOIdiQCAl9oEoHWdamhdpxoGtQ6wcGRkqHIndVlZWThw4ACuX7+OnJwciDomNxUEAf/73//KezoiIiIyoai4ezh7KwPOUglmhzWxdDhUDuVK6pYsWYLFixcjLy9Pu+3JpE4QBIiiyKSOyELyFCqM/fkUAGDNhPa8fEJEWvlKFcJ3XwAATOpaH76eThaOiMrD6KTuu+++w/z58wEAbdq0Qbt27eDt7Q07Ow6oJbImalHEyRvp2sdERBq/HLuJ2+ky1HJ3xJSu9S0dDpVTuZI6QRDw3XffYcqUKaaMqZicnBwsWLAAv//+O9LT09G0aVO89957GDZsWJnHHjhwAJ988gnOnTuH3Nxc1K9fH6+++iqmTZsGiYQ9FkREVDU9eizHN39dAQC8E9YEro68zb6yM/oneO3aNfj7+5s9oQOAwYMH4/Tp0wgPD0fjxo2xfv16DB8+HGq1GiNGjNB53L59+9C3b1907doVK1euhKurK/7880+89dZbuHbtGpYtW2b22ImIiKzRsv1XkJWnRFNfd7z0FAdF2AKjk7pq1arB19fXlLGUKCoqCtHR0dpEDgB69OiBmzdv4p133sHQoUN19rhFRERAKpVix44dcHV1BQD07t0bly5dQkREBJM6IiKqkq4/yMG6EzcBAAueaw6Jvgu/klUz+ga4Z599FomJicjKyjJlPMVs3boVbm5uGDJkSJHt48ePR3JyMk6ePKnzWKlUCgcHBzg7OxfZ7uXlBScn3gxKRERV05JdF6FUi+jRpBa6NKpp6XDIRIxO6j7++GN4enpi8uTJkMlkpoypiPj4eDRr1gz29kU7FVu2bKndr8trr70GuVyO6dOnIzk5GRkZGVi7di22bt2KOXPmmC1mIiIia3XiehqiE1MhsRMwr3/pS4NR5WL05dfo6Gi89tpr+Pjjj3HkyBEMHz4cDRo00F7mLMmYMWMMPk9aWhrq1y8+Iqd69era/bp06NABf/31F4YMGYLly5cDACQSCZYsWYJZs2aVet78/Hzk5+drv9f0SCoUCigUCoOfR1k0dZqjbqraFAolnKV2/z5WQCEYNwKWbZSsGdunftRqEYt2JAAAXnmqNupWd+JrVkEKt1FzveaCqGu24DLY2dlp56ADCuajK4tKpTL4PI0bN0aDBg2wa9euIttTUlLg7++PJUuW4L333ivx2DNnzqB///7o0KEDJk+eDFdXV/z111/47LPPsGDBglLnzVu4cCE+/PDDYtvXr18PFxcXg58HERGRpZ1+IGDdVQkcJSL+11oFd6mlI6qacnNzMWLECGRmZsLDw8Nk9RrdUzdmzBi9ErnyqlGjRom9cenpBfNuaXrsSjJt2jT4+Phg69at2sEUPXr0gJ2dHRYuXIiRI0eW2AsIAHPnzsXMmTO132dlZSEwMBBhYWEm/QFoKBQKREdHo0+fPpBK+VtG1odtlKwZ22fZZHIVliw7CiAfb/ZsjKFd61k6pCqlcBs1121rRid1ERERJgxDt5CQEERGRkKpVBa5ry4uLg4AEBwcrPPY2NhYDB8+vNjo2Hbt2kGtVuPChQs6kzpHR0c4OjoW2y6VSs36hmHu+onKi22UrBnbp24rjiThXlY+ans549WuDSDl6jIWIZVKoVQqzVK31S//MGjQIOTk5GDz5s1Ftq9Zswb+/v7o0KGDzmP9/f0RExNT7LLv8ePHAQABAZyXh2xfnkKF8atPYfzqU8hTGH4LBBFVfvez8/D9wWsAgDnPNuFygTbKpNNH5+TkIDs7G+7u7nBzczNJnf369UOfPn0wdepUZGVloWHDhoiMjMTu3buxbt06bS/cxIkTsWbNGly7dg1BQUEAgLfffhvTp0/HgAEDMGXKFLi4uGD//v1YunQpevfujVatWpkkRiJrphZFHLj0QPuYiKqer6Kv4LFchVYBnhjQ0t/S4ZCZlLunLj4+HuPGjYO/vz88PT0REBAAT09P+Pv7Y8KECaVOOaKvLVu2YPTo0Xj//ffx7LPP4uTJk4iMjMTIkSO1ZVQqFVQqFQqP+3jzzTexefNmZGdn49VXX8WgQYOwY8cOfPDBB/jjjz/KHRcREZG1u3QvGxtO3wIALHi+Oew40bDNKldP3apVqzBt2jQoFAo8OYj23r17iIiIwPr167F8+XJMnDjR6PO4ublh2bJlpa4AERERUeJ9foMHD8bgwYONPjcREVFl9nHUBahFoF+wL9rV1T24kCo/o3vqTp48iSlTpkAul6Nfv37Ys2cP7ty5A4VCgTt37mDPnj3o378/5HI5XnvtNZw6dcqUcRMREVEZDl1+gMOXH0AqEfBev6aWDofMzOik7vPPP4coivjkk0+wY8cO9OnTB/7+/pBIJPD390efPn2wY8cOhIeHQ6VS4fPPPzdl3ERERFQKlVrEJzsvAADGdKyLoBq6Fwcg22B0Unf06FHUqlVL58S/Gu+88w68vb1x5MgRY09FREREBvo95jYupWbD01mKN3s2tHQ4VAGMTuoePXqkHWVaGkEQEBQUhIyMDGNPRURERAbIyVdi6d7LAIDpvRrBy8XBwhFRRTB6oIS3tzeuXr1abFLgJykUCly9ehU1a9Y09lREVA4uDvZICn/O0mEQUQVacegaHubko24NF4x+uuwOGLINRvfUde/eHRkZGXj33XdLLffuu+8iIyMDPXv2NPZUREREpKfkDBlWHrkOAHivX1M42Fv9OgNkIkb31M2dOxebNm3C119/jUOHDmHatGlo3rw5vL29cf/+fSQmJuLbb79FbGwsHBwcyrz3joiIiMrviz2XkKdQo33d6ujbwtfS4VAFMjqpa968OTZs2IAxY8bg7NmzePXVV4uVEUURHh4eWLt2LZo3b16uQInIOHkKFWb+HgsA+PKVUC4PRGTD4u5kYss/dwEAC55vBkHgRMNVSbkmH37hhRdw4cIFLF++HNHR0bh8+TJycnLg5uaGxo0bo2/fvpg6dSr8/PxMFS8RGUgtioiKuwcA+GIIlwkjsiV5ChWi4lKwNyEVj3LluJyaDQAY0NIPLQO8LBscVbhyr/3q5+eHxYsXY/HixaaIh4iIiPQQnZiKWRtjkSVTwk4A1IU+sx28/AD7ElPRu7mP5QKkCse7J4mIiCqZ6MRUTF4bg2yZEkDRhA4AcvKUmLQ2BtGJqRaIjiyFSR0REVElkqdQYdbGWEAEdN1QIf773+yNschTqCouOLIovS6/TpgwAUDBpdaPP/64yDZ9CYKAVatWGRgeERERFRYVl4Ksf3voSiMCyJQpsSs+BYNaB5g/MLI4vZK6iIgIAEDTpk21SZ1mm76Y1BEREZXf3oTUYvfQ6WInAHviU5nUVRF6JXWrV68GAHh6ehbbRkRERBUnI1euV0IHFCR+GTK5eQMiq6FXUjd27Fi9thGR9XGWSpD4UV/tYyKq3LxcHAzqqfNy5rqvVQUHShDZOEEQ4OJgDxcHe05ESmQDwlr4GNRT1zeY05pUFUYndRKJBN26ddOrbI8ePWBvX+4p8YiIiKq8/iF+cHUsu9ddAODpbI9+wVwAoKowOqkTRRGiqP/s9IaUJSLTyVeqMOv3c5j1+znkKzm1AVFlJwiAp7O09DL//rd0CJcGrEoq5PLr48ePIZWW3gCJyDxUahGbz97B5rN3oNL3mg0RWa3lf11FckYe3J3s4e5UcBXM7t87KzRfPZztsXJ0W64oUcWY/ZropUuXEB8fj9q1a5v7VERERDYtITkT3x28BgD49KWW6NnUG7viU7AnPhUZMjm8nB3QN9gH/YL92ENXBemd1C1btgzLli0rsi0mJgb169fXeYxMJsP9+/cBAAMHDjQyRCIiIlKo1Jiz6TyUahH9gn3RP6TgXrlBrQM4Dx0BMCCpy8jIQFJSkvZ7QRCQl5dXZFtJ3N3dMWTIECxevNjYGImIiKq8Hw9fR0JyFrxcpPhwYAtLh0NWSO+kbsaMGRg3bhyAgkEP9evXR7t27fD777+XWF4QBDg7O6NWrVomCZSIiKiqupKajWX7rgAAPhjQHN7uThaOiKyR3kmdp6dnkRUlxo4di8aNGyMoKMgsgREREVHBYKd3Np2HXKVGz6beeDGU96hTyYweKMFlwoiIiMxv9d83EHs7A+6O9vh4UDAnESedjE7qUlNTsWfPHjRs2BCdOnXSWe7vv//GtWvX8Oyzz8Lb29vY0xGRkZylEpxZ0Fv7mIgqjxsPH+PzPZcAAPOfawY/T2cLR0TWzOh56r7//nuMHz8ed+7cKbXc3bt3MX78ePz444/GnoqIykEQBNRwc0QNN0d+wieqRNRqEe9uPo98pRqdG9bA0HaBlg6JrJzRSd2OHTvg6OiIl156qdRygwcPhqOjI/78809jT0VERFTl/HryJk7dSIeLgwThg1vyQxmVyejLr0lJSahXrx4kktIv59jb26NevXq4efOmsacionLIV6qweMcFAMCC55vB0Z6XYIms3e30XCzZdREA8O6zTRFY3cXCEVFlYHRPXW5uLlxc9Gtkzs7OyMrKMvZURFQOKrWItSduYu2Jm1wmjKgSEEUR87bGIVeuQru61TD6ac4yQfoxOqmrXbs2Lly4AJlMVmo5mUyGixcvwtfX19hTERERVRkbY+7gyJWHcLS3w6cvtYSdHS+7kn6MTup69OgBmUyGRYsWlVpu8eLFyM3NRa9evYw9FRERUZWQmpWHRTsTAQCzwhqjfi03C0dElYnRSd3s2bMhlUrx6aefYvLkybhy5UqR/VeuXMGUKVMQHh4OBwcHzJ49u9zBEhER2SpRFDF/axyy85RoFeiFiV10r61OVBKjk7rGjRtj1apVsLe3x6pVq9C0aVPUqFEDDRo0QI0aNdC0aVOsXLmyyH4iIiIq2Z/nkrHvwn1IJQI+f7klJLzsSgYyOqkDgJEjR+Lvv//Gs88+C3t7ezx69Ag3btzAo0ePIJVK8fzzz+PYsWMYOXKkqeIlIiKyOQ9z8rHwzwQAwPSejdDYx93CEVFlZPSUJhpt27bFzp07kZeXh6tXryIrKwvu7u5o1KgRnJy44DAREVFZPvgzAY9yFWju54HXujewdDhUSZU7qdNwcnJCcHCwqaojIhNxspfgyJwe2sdEZF12x6dg5/kUSOwEfPZyS0gl5bqIRlWYyZI6IrJOdnYCJy4lslIZuXIs+KPgsutr3eojuLanhSOiyswkSd2JEydw7tw5pKenQ6FQlFhGEAT873//M8XpiIiIbMJHOxLxMCcfDb3d8GbPRpYOhyq5ciV1hw8fxsSJE3H9+vVSy4miyKSOyELkSjW+2HsJADA7rAkc7Hlph6ii5SlUiIpLwd6EVGTkyuHl4oDA6s7YcvYuBAH47OWWcJLy9ggqH6OTusTERPTr1w8KhQIjR47EoUOHcOfOHcybNw+3b9/GuXPncO7cOTg7O2Pq1Klwd+dIHiJLUKrV+PFwwQevGb0bwaF8g96JyEDRiamYtTEWWTIl7ARALUL7FQB6N/VBmzrVLBsk2QSjk7rw8HDk5eXhp59+wvjx4/HMM8/gzp07RVaY2Lt3LyZOnIg9e/bg+PHjJgmYiIiosohOTMXktTHAvwmc+omvALDvQiqiE1PRp7lPxQdINsXoj+wHDx6Ep6cnxo4dq7NMWFgYtmzZgoSEBHz00UfGnoqIiKjSyVOoMGtjLCBqczqdZm+MRZ5CVRFhkQ0zOqm7f/8+6tatCzu7girs7Qs6/WQyWZFy7dq1Q5MmTbBly5ZyhElERFS5RMWlIEumLDOhEwFkypTYFZ9SEWGRDTM6qfP09IRK9d+niurVqwMAbt68Waysg4MD7t69a+ypiIiIKp29CanQd6UvOwHYE59q3oDI5hmd1NWpUwcpKf99qggJCQEAbN++vUi5pKQkXLp0CR4eHsaeioiIqNLJyJUXuXeuNGoRyJDJzRsQ2Tyjk7oePXogLS0NSUlJAIDhw4dDEATMnz8fCxYswM6dO/Hzzz8jLCwMCoUC/fv3N1XMREREVs/LxcGgnjovZwfzBkQ2z+jRry+99BK2bt2Ko0ePom7dumjSpAkWLVqE+fPnY8mSJdpyoiiifv36CA8PN0nARGQYJ3sJ9r7dVfuYiCpGWAsf7E64p1dZtQj0DeboVyofo5O6Dh064MqVK0W2zZ07F126dMGvv/6KpKQkODs7o0uXLpg8eTLnqSOyEDs7AY19+PtHVNH6h/hh4fYEZMmUpZYTAHg426NfsF/FBEY2y+Rrvz7zzDN45plnTF0tERFRpeIklWBUhyB8d/CazjLCv/8tHRLKFSWo3Iy+p65nz57o378/5HLe2ElkzeRKNb6Kvoyvoi9DrlRbOhyiKuPOo1ysO1EwI4RmeT7NPXaarx7O9lg5ui16c+JhMgGjk7rjx4/j/v37cHAw/42dOTk5mDFjBvz9/eHk5ITQ0FD89ttveh+/bds2dOvWDR4eHnB1dUWLFi3w448/mjFiIuuhVKuxbP8VLNt/BUo1kzqiiqBQqfFm5D/IylOiVaAXzszvja+GtkJYc188Xb86wpr74quhrXByXm8mdGQyRl9+rVOnDvLy8kwZi06DBw/G6dOnER4ejsaNG2P9+vUYPnw41Go1RowYUeqx4eHhmD9/Pl577TXMnTsXUqkUFy9eZA8jERGZzRd7LuGfWxlwd7LHt8Nbw91ZikGtAzCodYClQyMbVq7Rr5999hkuX76Mxo0bmzKmIqKiohAdHa1N5ICC6VRu3ryJd955B0OHDoVEUvJ9CGfOnNGOxp0zZ452e69evcwWLxERVW0HLt7HisPXAQCfv9wKgdVdLBwRVRVGX35dsGABQkNDMXDgQJw7d86UMRWxdetWuLm5YciQIUW2jx8/HsnJyTh58qTOY7/99ls4OjrizTffNFt8REREGimZMsz8PRYAMK5TXTwb7GvZgKhKMbqn7o033kCjRo2wadMmtGnTBi1atECzZs3g6upaYnlBELBq1SqDzxMfH49mzZpp15bVaNmypXZ/p06dSjz28OHDaNasGTZv3oxFixbh6tWr8PPzw6hRo/DRRx9VyP2ARERUNShVarwVGYtHuQoE1/bA3P5NLR0SVTFGJ3UREREQBAGiWLAGSnx8POLj43WWNzapS0tLQ/369Ytt16w1m5aWpvPYu3fv4sGDB5g+fToWLVqE5s2bY//+/QgPD8ft27fx66+/6jw2Pz8f+fn52u+zsrIAAAqFAgqFwuDnURZNneaom6o2hUJZ6LECCkHPdYuK1cM2StbLGtrnV/uu4lRSOlwdJfh6SEvYiWooFBycRAUKt1FztVOjk7rVq1ebMo5SCYLudVZK26dWq5GdnY3IyEgMGzYMQMH9eI8fP8bXX3+NDz/8EA0bNizx2CVLluDDDz8stn3v3r1wcTHf/RHR0dFmq5uqpnwVoPlV37NnLxzLORUW2yhZM0u1z0sZAr6/YAdAwMt15Eg4eRAJFomErF10dDRyc3PNUrfRSd3YsWNNGYdONWrUKLE3Lj09HcB/PXa6jr137x769u1bZHu/fv3w9ddf4+zZszqTurlz52LmzJna77OyshAYGIiwsDB4eHgY81RKpVAoEB0djT59+kAqlZq8fqq6VGoRDVoX9DS38PeARN/FKJ/ANkrWzJLt80F2Pj5afhwi5BjaNgALBjav0PNT5VC4jcpkMrOcQ++krnr16ujUqRN27NhRbN/hw4fh6emJVq1amTQ4AAgJCUFkZCSUSmWR++ri4uIAAMHBwTqPbdmyJe7dK77unuaSsZ2d7nEijo6OcHR0LLZdKpWa9Q3D3PVT1SMF8FS9mqarj22UrFhFt0+VWsTszWeQ9liOpr7u+HBgMKRcGYJKIZVKoVSWvnScsfQe/ZqRkaG9r+xJ3bt3x/Tp000WVGGDBg1CTk4ONm/eXGT7mjVr4O/vjw4dOug89qWXXgIA7Nq1q8j2qKgo2NnZoV27dqYPmIiIqozlB67i2LU0uDhI8O2INlzqiyzKZGu/anq/TK1fv37o06cPpk6diqysLDRs2BCRkZHYvXs31q1bp52jbuLEiVizZg2uXbuGoKAgAAXTnqxYsQKvv/46Hj58iObNm2Pfvn1Yvnw5Xn/9dW05IlsmV6qx+u8bAIDxnetplysiovI5cT0NX++7DABY/GIwGnq7WTgiqupMltSZ05YtWzB//ny8//77SE9PR9OmTYsMfgAAlUoFlUpVJLmUSqWIjo7GvHnz8MknnyA9PR316tVDeHh4kfvliGyZUq3Gkl0XAQCjOwbBwfjpKYnoX2k5+Zge+Q/UIvDyUwEY3IYrRZDlVYqkzs3NDcuWLcOyZct0lomIiEBERESx7dWrV8cPP/yAH374wYwREhFRVaFWi5j5+zncz85HQ283fDSwhaVDIgJQjhUliIiIqqIVh6/j0OUHcLS3w/IRbeDiUCn6R6gKYFJHRESkpzM30/HF3ksAgA9faIEmvu4WjojoPwZ9vIiJiSlxdQdBEHTuK1zm2rVrhkdIRERkBR49luPN9f9ApRYxMNQfQ9sFWjokoiIMSury8vKQlJRk8D6g9JUfiIiIrEWeQoWouBTsTUhFRq4cXi4O6NPcBzvOJyM5Mw/1arri40Eh/LtGVkfvpK4ilwUjIiKyhOjEVMzaGIssmRJ2AqAWATsB2J1QMJG9vZ2Ab0e0hpsj76Mj66N3q6yoZcGIyLQc7SWInPS09jERlSw6MRWT18YA/86MpX7iKwAo1SKSM/LQwt+z4gMkKgMHShDZOImdgI4NaqBjgxpGr/tKZOvyFCrM2hgLiNqcrkQCgNkbY5GnUFVQZET6Y1JHRERVXlRcCrJkylITOqAg4cuUKbErPqUiwiIyCJM6IhunUKnxy/Ek/HI8CQqV2tLhEFmlvQmp0Lcj204A9sSnmjcgIiPwTk8iG6dQqfH+tgQABcsZSSX8LEf0pIxceZF750qjFoEMmdy8AREZge/uRERU5Xm5OBjUU+fl7GDegIiMwKSOiIiqvLAWPgb11PUN9jFvQERGYFJHRERVXv8QP7g7lX1HkgDA09ke/YL9zB8UkYGY1BERUZUnCICfp1PpZf79b+mQUDhJOecjWR+jk7p3330XFy9eNGUsREREFU4URczZdB6XU3PgZG+nXS1Cc4+d5quHsz1Wjm6L3s156ZWsk9GjXz///HN88cUXaNeuHcaNG4dhw4bBy8vLhKERERGZ3xd7L2FbbDLs7QSsHNsW7epWx674FOyJT0WGTA4vZwf0DfZBv2A/9tCRVTM6qXv99dexYcMGnDp1CqdPn8bbb7+NgQMHYty4cejbty8XOiayEg4SO/w8rq32MRH9J/LULSw/cA0AsGRwCJ5pVAsAMKh1AAa1DrBkaEQGM/od/ttvv0VycjI2bdqE559/Hmq1Gr///juee+45BAQE4L333sOFCxdMGSsRGcFeYoeeTX3Qs6kP7JnUEWkduHQfC/6IBwC81asRhrQNtHBEROVTrnd4qVSKwYMHY9u2bbh79y6++uortGrVCikpKfjss88QHByMDh064IcffkBGRoaJQiYiIiqf+LuZeOPXs1CpRbzUJgAzejeydEhE5Wayj+01a9bEW2+9hbNnz+LcuXN4++234e3tjdOnT2PatGnw8/PDsGHDsGfPHlOdkoj0oFCpsTHmNjbG3OYyYUQA7mbIMCHiNB7LVejcsAaWDA7hLUNkE8xyLSYkJAQTJ07E8OHDYW9vD1EUkZ+fj99//x39+/dH48aN8fvvv5vj1ET0BIVKjXc2ncc7m84zqaMqLytPgQmrT+N+dj6a+Ljj+1FPwcGetyWQbTDp2q/p6emIjIzEmjVrcObMGQCARCLBgAEDMH78eKSmpuKnn37CmTNnMHz4cGRkZGDy5MmmDIGIiKhEcqUaU9edwaXUbHi7O2L1+HbwcJJaOiwikyl3UqdSqbBz506sWbMGO3fuhEKhgCiKaNq0KcaPH48xY8bAx+e/OX2mTJmCzZs345VXXsHSpUuZ1BERkdmJooj3tpzH31fT4Oogwc/j2sHfy9nSYRGZlNFJ3T///IM1a9YgMjISDx8+hCiKcHd3x+jRozFhwgR07NhR57EvvfQSWrdujfPnzxt7eiIiIr19ve8Ktpy9C4mdgOUj2yC4tqelQyIyOaOTuqeeegqCIEAURTzzzDOYMGEChgwZAhcXF72Od3Nzg1KpNPb0REREevk95jaW7b8CAFj8YjC6N/G2cERE5mF0Uufv74+xY8diwoQJaNCggcHHHzx40NhTExER6eXIlQeYtyUOADCtRwMMb1/HwhERmY/RSd2tW7dgZ8cRQ0REZJ0upGRh6rqzUKpFDAz1x+ywJpYOicisjE7qevfujZYtW+Lrr78us+zbb7+N8+fPY//+/caejoiM5CCxw/IRbbSPiWxJnkKFqLgU7I5PwfU7dtiZGYtng/3QJsgL41efRk6+Ek/Xr47PXm7JuejI5hmd1B08eFDve+JiY2Nx+PBhY09FROVgL7HDcy39LB0GkclFJ6Zi1sZYZMmUsBMAtWiH6xfuY2/i/X+/Bxp6u2HFqLZwtJdYOlwiszPpPHW6yOVySCT8hSIiItOITkzF5LUxgFjwvVrH14md68HThXPRUdVg9msxMpkMly9fRo0aNcx9KiIqgVKlxs7zKdh5PgVKrihBNiBPocKsjbGAqM3pSiQACN99AXkKVQVFRmRZevfUbdu2Ddu2bSuy7cqVK5gwYYLOY2QyGU6fPo309HS8/PLLxkdJREaTq9SYtv4sACDxo76w5311VMlFxaUgS1b27T8igEyZErviUzCodYD5AyOyML2TutjYWERERGi/FwQBqampRbbp0qhRI3zyySfGxEdERFTE3oRU7T1zZbETgD3xqUzqqErQO6l78cUXUbduXQAFy61MmDABjRs3xty5c0ssLwgCnJ2dUb9+fbRp04ajjoiIyCQycuV6JXRAQeKXIZObNyAiK6F3UteqVSu0atVK+/3ChQvRqlUrjB071iyBERERlcTLxcGgnjovZwfzB0VkBYwe/ZqUlGTCMIiIiPQT1sIHuxPu6VVWLQJ9g33MHBGRdeAd00REVKn0D/GDs7TsabIEAJ7O9ugXzHkaqWrQq6ful19+AQB4enpi4MCBRbYZYsyYMQYfQ0REVNif55IhK2OaEuHf/5YOCYWTHgkgkS3QK6kbN24cBEFAkyZNtEmdZpshmNQRVTypxA6fv9xS+5ioMvv99G28u+U8AKBHk1o4c+tRoRUloP3q4WyPpUNC0bs5L71S1aFXUjdmzBgIggA/P79i24jIukkldhjSNtDSYRCV24bTt/Du5jgAwNiOQVj4QgvkK9XYFZ+CXXEpuH7nHuoH+KJfiB/6Bfuxh46qHL2SupLmotNnfjoiIiJTiDx1C3O3FCR04zrVxQcDmkMQBDhJJRjUOgDPB/sgKioK/fuHQirlsmBUNVXI2q9EZDlKlRqHrzwAAHRtVIsrSlCls/7kLczbWpDQje9cF+8/35xXiohKwKSOyMbJVWpMiIgBwGXCqPL59eRNzN8aDwCY0Lke/vd8MyZ0RDoY/e7+559/on79+li6dGmp5ZYuXYr69esjKirK2FMREVEVtPbEfwndq12Y0BGVxeik7pdffsHNmzcxaNCgUssNHDgQSUlJRk2BQkREVdPa40n43x8FCd2kZ+ph/nNM6IjKYvTl13/++Qfe3t6oX79+qeUaNmwIHx8fxMTEGHsqIiKqQn45noT3tyUAAKZ0rY/3+jVlQkekB6N76pKTk1GnTh29ygYGBiIlJcXYUxERURUR8feN/xK6bkzoiAxhdE+dq6srHjx4oFfZhw8fwtHR0dhTERFRFbD67xv4cHsiAGBq9waY07cJEzoiAxid1IWEhODw4cOIiYlB27ZtdZaLiYlBUlISunTpYuypiIjIBuQpVIiKS8HehFRk5Mrh5eKAsBY+6B/ih19P3sKiHQUJ3evdG+AdJnREBjM6qRsxYgQOHTqEkSNHYteuXSXeW3fjxg2MHDkSgiBgxIgR5QqUiIwjldjho4EttI+JLCE6MRWzNsYWW9Jrd8I9zNsShzylGgDwRo+GmBXWmAkdkRGMTuomTJiANWvW4NixYwgODsbgwYPRoUMHeHl5ISMjAydOnMAff/wBmUyGTp06YdKkSaaMm4j0JJXYYUzHupYOg6qw6MRUTF4bA4gF36uf+KpJ6J4L8WNCR1QORid1EokEO3bswPjx47Ft2zasX78ekZGR2v2iWPDbOmjQIKxatQoSCdfgIyKqavIUKszaGAuI2pxOpyNXHyBfqeaarURGKte1GC8vL2zduhWnTp3C/PnzMWjQIPTq1QsvvvgiFixYgJiYGGzevBleXl7lCjInJwczZsyAv78/nJycEBoait9++83gehYsWABBEBAcHFyueIgqE5VaxPFraTh+LQ0qdVl/VolMKyouBVkyZZkJHQBkyZTYFc+ZEoiMZZJlwtq2bVvqYInyGjx4ME6fPo3w8HA0btwY69evx/Dhw6FWq/W+Vy82NhZffPEFfHx8zBYnkTXKV6owfOUJAAXLhLk4cHVAqjh7E1K199CVxU4A9sSnYlDrAPMHRmSDrP7dPSoqCtHR0dpEDgB69OiBmzdv4p133sHQoUPLvLSrVCoxfvx4TJkyBefOncPDhw8rInQioiovI1euV0IHFCR+GTK5eQMismFWPxRu69atcHNzw5AhQ4psHz9+PJKTk3Hy5Mky6wgPD0d6ejo+/vhjc4VJREQl8HJxgJ2e4x7sBMDL2cG8ARHZML166jTTlTRs2BB79+4tsk1fgiDg2rVrBoYHxMfHo1mzZrC3Lxpqy5Yttfs7deqk8/jExEQsXrwYW7ZsgZubm8HnJyIi44W18MHuhHt6lVWLQN9g3iJDZCy9krqkpCQAgJOTU7Ft+jJ2iHpaWlqJCWT16tW1+3VRq9WYMGECBg8ejP79+xt03vz8fOTn52u/z8rKAgAoFAooFAqD6tKHpk5z1E1Vm0KhLPRYAYVg3GAJtlEyRhNvFwgCIJbR7AQA7k726NOkplFtjO2TrF3hNmqudqpXUnfjxg0AgFQqLbatIpSWEJa278svv8SVK1fw559/GnzOJUuW4MMPPyy2fe/evXBxcTG4Pn1FR0ebrW6qmvJVgOZXfc+evXAs52wRbKOkrxvZwMqLEoiigP8mNCnpPbtg39CgfOyP3lOuc7J9krWLjo5Gbm6uWerWK6kLCgrSa5s51KhRo8TeuPT0dAD/9dg96datW3j//fcRHh4OBwcHZGRkACgYNKFWq5GRkQFHR0c4OzuXePzcuXMxc+ZM7fdZWVkIDAxEWFgYPDw8yvmsilMoFIiOjkafPn2KJM9E5ZUrV2LOqb8AAH37hhk9+pVtlAyxNzEV32+MQ75SjZDaHhjdIRCLoy4hK6/oihJqEfBwkuKzl4LRq6m30edj+yRrV7iNymQys5zD6ke/hoSEIDIyEkqlssh9dXFxcQCgc86569evQyaT4a233sJbb71VbH+1atXw1ltv4euvvy7xeEdHRzg6OhbbLpVKzfqGYe76qepxFiSY269pwWNHR0jtyzc+im2UyrL67xv4aEciRBHo1dQb34xoDRcHe7zQOhC74lOwJz4VGTI5vJwd0DfYB/2C/Uw24TDbJ1k7qVQKpVJZdkEjWH1SN2jQIKxcuRKbN2/G0KFDtdvXrFkDf39/dOjQocTjQkNDceDAgWLbZ8yYgczMTKxevRoBAZwLiWyfg70dpnRrYOkwqApQq0V8EnUBPx0tuD1nZIc6+PCFFrD/d81hJ6kEg1oHcB46IjPRK6mbMGFCuU8kCAJWrVpl8HH9+vVDnz59MHXqVGRlZaFhw4aIjIzE7t27sW7dOu0cdRMnTsSaNWtw7do1BAUFwcvLC927dy9Wn5eXF5RKZYn7iIjIOHkKFWb9fg474wpWhJjzbBNM7daA67gSVSC9krqIiIhyn8jYpA4AtmzZgvnz5+P9999Heno6mjZtisjISAwbNkxbRqVSQaVSadecJaICKrWI+LuZAIDg2p6Q6DtpGJGeMnLlmPRLDE4nPYJUIuCLIa0wMLS2pcMiqnL0SupWr15t7jhK5ebmhmXLlmHZsmU6y0REROiVfB48eNB0gRFVAvlKFQYu/xsAlwkj07udnouxq0/h+oPHcHeyx4rRT6FTg5qWDouoStLr3X3s2LHmjoOIiCqZ83cyMCEiBg9z8uHv6YTV49ujia+7pcMiqrL4kZ2IiAx24OJ9vP7rWcgUKjTz80DE+Hbw8XAq+0AiMhuTJXUymQzXrl1DdnY23N3d0aBBA51zwBERkXXLU6gQFZeCvQmpyMiVw8vFAWEtfNA/xA9b/7mLBX/EQ6UW8UyjmvhuZBu4O3EaESJLK3dSt2vXLnz22Wf4+++/oVKptNvt7OzQpUsXvPPOOwYv0UVERJYTnZiKWRtjkSUrOlHw7oR7mLulYEJhAHj5qQAsGRwCqaR8cx8SkWmU6zdxxowZeP7553Ho0CEolUo4ODigdu3acHBwgEqlwqFDhzBgwABMnz7dVPESEZEZRSemYvLaGGTLCiZHVf87oYDmqyahG9DSD5+/3JIJHZEVMfq3cfXq1fi///s/2NvbY+bMmbh69SpkMhlu3bqlvRQ7c+ZMSKVSLF++HD///LMp4yYiIhPLU6gwa2MsIP63Uqsuh6880CZ4RGQdjE7qvvnmGwiCgMjISHzxxReoX79+kf316tXDF198gcjISIiiiOXLl5c7WCIynL2dHd7q1Qhv9WoEezv2qpBuUXEpyJIpy0zoACBTpsSu+BSzx0RE+jP6nrqLFy8iKCgIgwcPLrXcoEGDULduXVy4cMHYUxFROTjY2+HtPo0tHQZVAnsTUrX30JXFTgD2xKdyyS8iK2J0Uufu7o4aNWroVbZGjRrIzc019lRERFQBMnLleiV0QEHilyGTmzcgIjKI0ddiunfvjoSEBKSnp5daLi0tDQkJCVxrlchC1GoRl1OzcTk1G2p9/2JTleTl4gB9V5GzEwAvZwfzBkREBjE6qVu8eDEcHR0xdOhQPHjwoMQyDx48wLBhw+Dk5ITFixcbHSQRGS9PqULYV4cR9tVh5ClVZR9AVZavp5NBPXV9g33MGxARGcToy6/Hjx/HtGnT8Nlnn2nvrWvWrBm8vb3x4MEDXLhwAZs3b4ZKpcI777yDY8eO4dixY8XqGTNmTLmeABERlU+eQoVPoi7gl+M39SovAPBwtke/YD/zBkZEBjE6qRs3bhwEQYAoilAqlVi/fj0E4b9+e1H87+PekiVLdNbDpI6IyHKuPcjBG+v/wYWULABA3xY+2JuYqnNaE+Hf/5YOCYWTVFKRoRJRGYxO6saMGVMkiSMiospl05k7eH9bPHLlKtRwdcDSV1qhexNvRCemYvbGWGQ+saKEWizooVs6JBS9m/PSK5G1MTqpi4iIMGEYRERUUXLylXj/j3hs+ecuAKBTgxr4amgofDycAAB9mvvg5Lze2BWfgj3xqciQyeHl7IC+wT7oF+zHHjoiK1XutV+JiKjyiL+biTcj/8GNh49hJwAz+zTG1O4NIXli2KuTVIJBrQM4Dx1RJcKkjoioChBFEWuOJeGTqIuQq9Tw83TC/w1vjXZ1q1s6NCIyEb2SusOHDwMAXFxc0LZt2yLbDNG1a1eDjyGi8rG3s8PkrvW1j8n25ClUiIpLwd6EVGTkyuHl4oCwFj7oH1JwqTQjV453Np1HdGIqAKB3Mx98/nJLVHPlPHNEtkSvpK579+4QBAFNmjRBYmJikW36EgQBSqXSuCiJyGgO9naY17+ZpcMgM4lOTMWsjbHIemJQw+6Ee1i4PQGvd2uAX47fRHJmHhwkdpjXvynGdqrLgW5ENkivpK5r164QBAF16tQpto2IiCwjOjEVk9fGaOceUT/xNUumRPjuSwCAujVc8O2INgiu7WmBSImoIuiV1B08eFCvbURkfdRqEXczZACA2l7OsNN3HSiyankKFWZtjNU5n1xhUomAzVM7oYabY0WERkQWwhtsiGxcnlKFZz47gGc+O8BlwmxIVFwKsmTKMhM6AFCoRBy+UvJyjkRkO4xO6iZMmIDw8HC9yn766aeYMGGCsaciIqIn7E1Ihb6drnYCsCc+1bwBEZHFGZ3URUREICoqSq+yu3fvxpo1a4w9FRERPSEjV669d64sahHIkMnNGxARWVyFXH5VKpWw41QKREQm4+XiYFBPnZczpy8hsnVmz7RUKhWuX78OLy8vc5+KiKhKEEURNd0cDOqp6xvMtVqJbJ3eK0ocPny42IjXW7du4aOPPtJ5jEwmw7Fjx3Dv3j3069fP6CCJiKjAnUe5WPBHPA5e0m/ggwDAw9ke/YL9zBsYEVmc3kndgQMH8OGHH0IQBIhiwcfDW7duYeHChWUeW61atVKTPyIiKp1KLSLiWBKW7r2EXLkKDhI79AvxxZ/nknVOayL8+9/SIaFwkkoqOGIiqmh6J3Xdu3fXPhZFER999BHq1KmD8ePHl1heEAQ4Ozujfv36CAsLg7u7e7mDJSLDSewEjH46SPuYKp/E5Cy8t+U8zt/JBAC0r1sdnwwOQUNvNzzf0h+zN8Yi84kVJdRiQQ/d0iGh6N2cl16JqgK9k7pu3bqhW7du2u81Sd0HH3xglsCIyDQc7SVY9GKwpcMgI+QpVPh63xWsPHIdKrUIdyd7zO3XDMPaBWonke7T3Acn5/XGrvgU7IlPRYZMDi9nB/QN9kG/YD/20BFVIXondU9Sq9WmjIOIiAr5++pDzNsah5tpuQCA/iG+WDigBbw9nIqVdZJKMKh1AAa1DqjoMInIihid1BFR5SCKItIfF8xRVt3VgWs2W1ieQoWouBTsTUhFRq4cXi4OCGvhg/4hBb1qjx7L8XHUBWw6cwcA4OvhhEUvBqMPL6ESURn0SuoOHz5skpN17drVJPUQkf5kChWeWrwPAJD4UV+4OPCznKVEJ6Zi1sZYZD1x/9vuhHtY+GcChrYLxJazd5H2WA5BAEY/HYR3+jaBu5PU0qETUSWg17t79+7dy/3pXhAEKJXKctVBRFRZRSemYvLaGO0wVfUTX7PylFh55AYAoJG3G8JfCsFTQdUtECkRVVZ6JXVdu3blJRsiIiPlKVSYtTFW59QjhTna22HL1E5wd2bvHBEZRq+k7slJh4mISH9RcSnIkul3pSJfqca+i6kc9EBEBuOCrEREZrY3IdWgdVr3xKeaNyAisklM6oiIzCwjV27QOq0ZMrl5AyIim2T0MLhbt24ZfEydOnWMPR0RUaWkVot4LFfpXd5OALycHcwYERHZKqOTurp16xo0eIKjX4ksQ2In4KU2AdrHVHFOXk/D4p0XEHc3U+9j1CLQN5hz0hGR4YxO6urUqaMzqXv8+DEePnwIAJBKpfD39zf2NERUTo72Eix9pZWlw6hSbjx8jPBdF7AnoeDeOFcHCZRqEXKlutTRrwIK1mvtF+xXIXESkW0xOqlLSkoqdX9WVhZWrlyJRYsWYcSIEfj444+NPRURUaWQkSvHsv1XsPb4TSjVIuwEYHj7Oni7T2PE3srApLUxEHRMayL8+9/SIaFcr5WIjGK2qeU9PDwwa9YstGjRAs899xyaNm2K0aNHm+t0RKSDKIqQKQru6XKWSjjnpBnIlWr8cjwJ3/x1FZkyBQCge5NamNe/GRr7uAMAejf3wY+j22L2xlhkPrGihFos6KFbOiQUvbkcGBEZyezrBT377LMICgrCsmXLmNQRWYBMoULz9/cA4DJhhiprnVZRFLE7/h7Cd1/EzbRcAEBTX3fMf64ZnmlUq1h9fZr74OS83tgVn4I98anIkMnh5eyAvsE+6Bfsxx46IiqXCnl39/LywsWLFyviVEREJlHqOq3bE/Bmj0bYm3gPp5MeAQBquTtiVp/GGNI2sNQBKU5SCQa1DuDkwkRkcmZP6u7fv48LFy7A1dXV3KciIjKJMtdplSnxcdQFAICT1A6Tn6mPKd0awNWRvaBEZDlmewd6+PAhTp8+jQULFkAul2PgwIHmOhURkckYsk6rVCJg94xnULeGW0WERkRUKqOTOolEv3s/RFGEr68vwsPDjT0VEVGFMWSdVoVKxD+3MpjUEZFVMHqZMFEUS/3n4uKC4OBgzJkzB+fPn0fdunVNGDYRkXlwnVYiqqyM7qlTq9WmjIOIyCqkPc7nOq1EVCnxrl4iG2cnCOgf4qt9TCXLylPgl2NJiL2dofcxXKeViKwJkzoiG+ckleC7kU9ZOgyr9eixHKv/voHVx5KQnWfY+tRcp5WIrInR99RVpJycHMyYMQP+/v5wcnJCaGgofvvttzKP27JlC4YPH46GDRvC2dkZdevWxciRI3HlypUKiJqIrNmD7HwsibqAzp/+hf/76yqy85Ro5O2GL4a0hIeTPcrq0xQAeHKdViKyInr31Ok72lUXQRCgVBr2KVhj8ODBOH36NMLDw9G4cWOsX78ew4cPh1qtxogRI3Qe9+mnn8LX1xfz589H/fr1cfv2bXzyySdo06YNTpw4gRYtWhj7dIjIypS1+oNGSqYMKw5dR+SpW8hXFtwb3NzPA2/2bIi+LXxhZyfAy9mB67QSUaWjd1IninreOWzi46OiohAdHa1N5ACgR48euHnzJt555x0MHTpUZ8K5fft2eHt7F9nWs2dP1K1bF1999RV++ukno2Iiqkxy5UqbXyasrNUfvhwSiia+7vj+0DVsirkDuaogmQsN9ML0Xg3Ro4l3kTVxuU4rEVVGBr27C4KAJk2aYPTo0Rg8eDDc3Mw/N9PWrVvh5uaGIUOGFNk+fvx4jBgxAidPnkSnTp1KPPbJhA4A/P39ERAQgNu3b5slXiKqWGWt/pAtU+LVX2K0CRkAdKhXHW/2bITODWsUSeYK4zqtRFTZ6J3UffXVV/j1118RExODBQsW4OOPP8agQYMwevRo9O7dG3Z25rk9Lz4+Hs2aNYO9fdFQW7Zsqd2vK6kryfXr13Hz5k28+OKLpgyTiCxAn9UfNNvVItC5QQ281bsx2terrlf9XKeViCoTvZO6t956C2+99RYuX76MX375BevXr8evv/6K9evXw9vbGyNGjMDIkSPRpk0bkwaYlpaG+vXrF9tevXp17X59KZVKTJw4EW5ubnj77bdLLZufn4/8/Hzt91lZWQAAhUIBhUKh9zn1panTHHVT1aZQKAs9VkAhGHcrhDW20e2xyXqv/gAAg0L90DrA3aqeA5mGNbZPosIKt1FztVNBLMfNckePHsUvv/yCTZs2ISMjA4IgoGnTphgzZgxGjBiBwMDAcgfYuHFjNGjQALt27SqyPSUlBf7+/liyZAnee++9MusRRRHjxo3Dr7/+is2bN5e5Fu3ChQvx4YcfFtu+fv16uLi4GPYkiCwoXwXMOVXw+e2z9ko42tBVw1WX7BCXLkAsc6wqIEBESHURE5tw4nQisqzc3FyMGDECmZmZ8PDwMFm95UrqNORyObZv3461a9di9+7dUCgUEAQBr732Gr799tty1d2xY0eoVCqcOnWqyPaEhAQEBwdjxYoVmDx5cql1iKKIV199FREREVizZg1GjRpV5nlL6qkLDAzEw4cPTfoD0FAoFIiOjkafPn0glUpNXj9VXblyJVot+gsAcO5/PY0eKGGNbXTwDycQdzdL7/Id6lbDuontzBgRWYo1tk+iwgq3UZlMhpo1a5o8qTPJMDgHBwe89NJLeOmll3DkyBGMHj0at27dwuXLl8tdd0hICCIjI6FUKovcVxcXFwcACA4OLvV4TUK3evVqrFq1Sq+EDgAcHR3h6OhYbLtUKjXrG4a566eqRyr+14tV0L7K92tv6Taar1Rhd/w9/HrylkEJnZ0AVHN15O+XjbN0+yQqi1QqNXqKt7KYJKlLTU1FZGQk1q5di9jYWIiiCDc3N3Tp0qXcdQ8aNAgrV67E5s2bMXToUO32NWvWwN/fHx06dNB5rCiKmDRpElavXo0VK1Zg/Pjx5Y6HqLKxEwT0aFJL+9ga6DunXGFJDx8j8tQtbDxzB+mPC9ZbFQRA32sNXP2BiGyd0UmdTCbD1q1bsXbtWuzfvx9KpRISiQRhYWEYPXo0Bg0aBGdn53IH2K9fP/Tp0wdTp05FVlYWGjZsiMjISOzevRvr1q3TzlE3ceJErFmzBteuXUNQUBAAYPr06Vi1ahUmTJiAkJAQnDhxQluvo6MjWrduXe74iKydk1SC1ePbWzoMLX3mlNPM/6ZQqbH/Qip+PXkLR6481Nbh5+mEYe3q4MXW/hjw7VFky5Q6R78CBZMFe3D1ByKycQYldaIoYt++fVi3bh22bt2Kx48fQxRFtG7dGqNHj8bw4cPh42P6T8JbtmzB/Pnz8f777yM9PR1NmzZFZGQkhg0bpi2jUqmgUqmKTHK8fft2AMDPP/+Mn3/+uUidQUFBSEpKMnmsRKSbPnPKTVobgyWDQ3D3kQy/nb6NB9kF97YKAtCtcS2M7BCEHk1qwV5SMI3Sl0NCufoDEREMSOreeecdrF+/Hvfu3YMoiggMDMQbb7yB0aNHo1mzZuaMEW5ubli2bBmWLVums0xERAQiIiKKbGPSRmQ99J5TTgTe2xyn3VbTzRFD2wVgWLs6CKxefOQ5V38gIiqgd1K3dOlS7YoSo0aNQrdu3SAIAh49eoRjx47pVYchkwQTkWnkypV4atE+AMCZ//W22DJhUXEpBs0p18jHDW/3bozezXzgYF/65OZc/YGIyIh76i5duoT//e9/Bp9IEASzjfYgotLJFCpLh4C9CalFluoqjSAADWq6oX+I/vfAcfUHIqrq9E7q6tSpo3ONRCKisjzKleuV0AEFI1ozZHLzBkREZGP0Tup4fxoRGeN2ei7++Ocu4u5m6n2MnQB4OTuYMSoiIttjmZtriKjS0Mwptzs+Bdfv2GFnZiyeDfYrdU65TJkCu+JSsOXsXZxKSjf4nJxTjojIcEzqiCzAmMl3LaH4nHJ2uH7hPvYm3i9xTrlDlx5g6z93EX0hFXJlwRqrggB0alADA1r645OoC8jO45xyRETmwKSOqIIZMvmupePUZ065uf2aIjkjD9vPJSPt8X/3wTX2ccPgNgEYGOoPP8+CichrujlyTjkiIjNhUkdUgfRNlH4c3RZ9TJTY2QkCOtSrrn2sD0PmlPsk6qJ2W003BwwMrY1BrWujhb9HscFVnFOOiMh8mNQRVRB9EyVBBGZvjMXJeb0N7q3SdVl3zYT2BtVl6Jxyret4YXqvRnimYU3tSg+6cE45IiLzYFJHVEH0TZREAJkyJXbFpxg055qpLusqVWpsOXsXAnQnn4XZCYCPuxN6NPHWO1bOKUdEZHpM6ogqiEGT7wJY/XcSAqq5IKCaM7zdnSCx033p1JjLukqVGjfTc3ElNQdXUrNx+X7B1+sPHkOuUuv9vNScU46IyCowqSOqIBmGTL4L4PydTAz54TgAQCoR4O/ljIBqzgjwKkj0Aqo7I6CaC2q5O2DW77F63f827dcz6NPcF9ce5OD6w8faEapP0jf51JTlnHJERJbHpI6ogni5OBiULFV3dYCboz2SM2RQqETcTMvFzbRcAGlGxyBXidgZl6L93klqh0be7mjk44ZG3u5o7OOGxj7uOHUjHbM2ntOrTs4pR0RkHZjUEVWQsBY+2J1wT+/y/3u+GQa1DoBKLSI1Kw93Hslw51HuE19luJ2eq9e9bxqNfdwwp29TNPZxR0A1Z9iVcFm3lrsjPtyRgGwZ55QjIqosmNQRVZBnW/jiXcl5KFSlp2BPJkoSu4JLr/5ezmj/79QkhQ1dcRwnb+i/akN1V4cyB0w4SSX4ckgo55QjIqpESp97gIhMQqlS470tcXoldIYmStX+vayrDwH63/+mmVPOw7ngs5/mHJqvHs72WDm6LeeUIyKyEuypIzIzhUqNGb/FYmdcCuztBEx6ph7Wn7plssl3DbmsK8Kw+98Kzym3Ky4F1+/cQ/0AX/QL8eOcckREVoZJHZEZyZVqvBl5FnsSUiGVCPhu5FPo09wHb/VubLLJd/uH+GHh9rLvfwMADyeJwfe/aeaUez7YB1FRUejfPxRSqdSgOoiIyPyY1BGZSb5ShdfXncX+i/fhYG+HFaOeQo+mBRP0mnLyXX3uf9MIH9yKvWtERDaK99QRmUGeQoXJv5zB/ov34Whvh5/GtNUmdOZQ1v1vns72+GlMW/RvyVGqRES2ij11RCYmk6sw6ZcYHL36EM5SCVaNbYtODWua/bxcU5WIqGpjUkdkQo/zlZi45jROXE+Hq4MEq8e3L3EaEnPhmqpERFUXkzoiE8nJV2L86lM4nfQIbo72WDOhHZ4KqriETheZXIXeXx4CAOyb2Q3ODuyxIyKyRUzqiEwgK0+BsT+fwj+3MuDuZI+1EzsgNNDL0mEBAESIuJsh0z4mIiLbxKSOqJwycxUY/fNJnL+TCU9nKX59tQOCa3taOiwiIqpimNQRlcOjx3KMWnUSCclZqO7qgHUTO6C5v4elwyIioiqISR1ZTJ5Chai4FOxNSEVGrhxeLg4Ia+GD/iHGj9SsyDqfrl8dEyJicPFeNmq6OeDXV59GE193o85BRERUXkzqyCKiE1Mxa2Mssp5YKmt3wj0s3J6ALw1cKssSdWq+r+XuiMhJHdDQmwkdERFZDicfpgoXnZiKyWtjkC1TAihIjAp/zZYpMWltDKITU626Ts3X6T0bMqEjIiKLY08dVag8hQqzNsYCpSxnJQIQRGD2xlicnNe7zMumlqoTAAQAX+y9hCFtA612cl8BAhp5u2kfE5mDKIpQqVRQKpUWOb9CoYC9vT3y8vKgUqksEgNVXfb29pBIJBAEy77HMqmjChUVl4IsWdlv+iKATJkSszbGooV/6SNJE+5mWrzOXfEpVjvhr7ODBNEzu1k6DLJRoigiIyMDDx48sGgyJYoifH19cfv2bYv/YaWqSSKRwNvbG56enhZrg0zqqELtTUjV3oumj53n72Hn+XsmjcHUddoJwJ74VKtN6ojM6d69e8jIyICHhwc8PDxgb29vkT9oarUaOTk5cHNzg50d7yyiiiOKIpRKJbKyspCSkgKZTAY/P8uss82kjipURq5c74QOKBiE0KNJrVLLHLh4Hw9y5BarUy0CGTL9z09kK1QqFTIzM1GrVi3UrGn+9Y1Lo1arIZfL4eTkxKSOLMLd3R2Ojo54+PAhvL29IZFU/C05TOqowsTfzcS1h4/1Lm8nAE/VqYbPXm5VarnX1p7B3sR7eiWL5qrTy9mh7IIWIpOr8MK3RwEAf77RhcuEkckoFAqIoghXV1dLh0JkFVxdXfHgwQMoFAqLJHX8OENml5CciUm/xOD5b47iQXa+3sepRaBvcNlTkIS18NG798+SdVqKCBFX7ufgyv0cLhNGZsF72IgKWPp3gUkdmc2FlCxMWRuD5/7vKKITC+6lG9DSD26O9mWOwRQAeDrbo19w2fcl9A/xg4ez9ddJRERkTrz8SnoxZKWGi/eysGzfFeyKLxiMIAjAgJb+mN6rERp6u2FfYiomrY2BoGO6EOHf/5YOCdVrmhAnqQRfDgm1+jqJiIjMiT11VKboxFS0/2QfZv5+DnsT7+HEjXTsTbyHmb+fQ/tP9mHfvxP6Xk7NxrRfz+LZr49gV/w9CALwfEs/7J3RFf83vDUa/jtXWu/mPvhxdFt4OBd8prD7tztM89XD2R4rR7c1aPWHylInEZG5CIKA7t27WzoMs7H152cK7KmjUmlWVdB0VZW4UsMvMXgqqBrO3HoE8d/tz4X44a3ejdDYp+SVFvo098HJeb2xKz4Fe+JTkSGTw8vZAX2DfdAv2Lh1WitLnURkO5KSklCvXr1i211cXNCgQQO89NJLmDVrFtzc3Irsr1u3Lm7evKn93sHBAR4eHqhXrx7atWuH4cOHo0uXLmaPn2wLkzrSSd+VGgAg5uYjAEC/YF+81bsRmvp6lFm/k1SCQa0DTDq/W2Wpk4hsS4MGDTBq1CgABfOWPXjwALt27cLChQuxZ88eHDlypNhoSIlEggULFgAAlEolHj16hLi4OKxYsQLfffcdBgwYgDVr1qBatWoV/nyocmJSRzrpu/qDxuywJnijZ0MzRkTGECCgtpez9jERmV7Dhg2xcOHCItvy8/PRsWNHHD9+HIcPH0aPHj2K7Le3ty92DADcvHkTEydOxPbt2zFo0CD89ddfnHuP9MJWQjppVn/Qh51QMA8dWR9nBwn+fq8n/n6vJ+eoI6pAjo6O2kTuwYMHeh8XFBSE7du3o3nz5jh06BA2bdpk0Hlv376NoUOHokaNGnB1dUX37t1x7NixEsumpaXh7bffRr169eDo6Ahvb28MHToUiYmJxcp2795d55Qd48aNgyAISEpK0m6LiIiAIAiIiIjA/v370aVLF7i6uqJGjRoYO3Ys0tLSSqzrp59+QnBwMJycnBAYGIg5c+YgLy+vxLJnzpzBG2+8geDgYHh6esLZ2RkhISEIDw+HQqEoVr5u3bqoW7cuMjIyMH36dAQGBsLe3h4RERHo1q0bpFIpUlJSSjzXK6+8AkEQ8M8//5S43xowqSOdDFn9gasqEBEVJZfLcfDgQQiCgNDQUIOOdXZ2xuzZswEAGzZs0Pu4R48eoXPnzkhKSsLkyZPx0ksv4fjx4+jRowcOHjxYpGxaWhqefvppfP3116hbty5mzpyJXr16YcuWLWjfvj2OHz9uUMy6bN++Hf3794evry+mTp2KBg0a4JdffsHAgQOLlV20aBEmTZqEhw8fYtKkSRgyZAg2bNiAIUOGlFj3ypUrsXXrVoSEhGDKlCmYOHEiRFHE3LlzMWzYsBKPyc/PR8+ePbFr1y4MGDAAb7zxBnx8fDBlyhQolUqsXr262DEPHz7Etm3b8NRTT6F169ble0HMiJdfSScvFwe912m19lUViIjM6erVq9pLqaIo4uHDh9izZw/u3r2Lzz77DI0bNza4zm7dugEATp8+rfcx58+fx+jRo7FmzRptr9rEiRPRo0cPTJo0CZcuXdJeyp0zZw6uXr2KuXPn4pNPPtHWMW7cODz77LMYO3YsLl68WO5Lv3/++ScOHjyIzp07AyhYXq537944ePAgTpw4gaeffhpAwWv40UcfoXbt2jh79iy8vb0BAAsXLkT79u1LrHvu3LlYvnx5kfsVRVHEq6++ip9//hl///239rwa9+7dQ8uWLfH333/D2dlZuz0/Px/Tp0/Hzz//jLlz5xbplVy7di3kcjleffXVcr0W5saeOtKpS6MaNrOqQlWWpyhYJuyFb48iT6GydDhUxeTKlTr/Pdkey1tWJleVWFaz/cl/pnTt2jV8+OGH+PDDD/HRRx/hu+++w7Vr1xAWFobnnnvOqDr9/f0BFPQS6UsikeDjjz8ukpB069YN/fv3x9WrV7WXYeVyOSIjI1GjRg3tYA2Nvn37om/fvrhy5YrOy7aGGDFiRJHESiKRYOzYsQCKJqzr16+HUqnEzJkztQkdAHh4eBSLUSMoKKjYABRBEDBt2jQAwL59+0o87vPPPy+S0AEFl8vHjh2La9eu4cCBA0X2rVq1Ci4uLhgxYkRZT9ei2FNHJTp5PQ3f/nVVr7ICCuZs46oK1kktijh/J1P7mKgiNX9/j859PZrUwurx//XAPLVoH2Q6Pnh0qFcdG6Z01H7f5dMDSH9c8i0fLQM88ecb/00H0vvLQ7ibIStWLincuGSrJH379sXu3bu139+/fx/79+/H9OnT0alTJ5w8edLg3jrRiN/XoKAgBAYGFtv+zDPPYOfOnYiNjUWXLl1w8eJFyGQydO/eHS4uLsXKd+/eHXv27NGWL482bdoU2xYQUDCbQEZGhnbbuXPntLGWFH9J5HI5vv32W/z222+4ePEicnJyirxuycnJxY5xcnJCSEhIifVNnjwZX375JX766Sf07NkTAHDixAkkJCRg3Lhx8PAoe2YHS2JSR0Wo1CK++esK/m//FahFwMfdEff/Xa+VqyoQEenH29sbw4cPh0wmw8SJExEeHo6ff/7ZoDo0N+zXqlXLoPOWxMen4EpKZmbBB7ysrKwi25/k6+tbpHx5eHp6Fttmb1+QfqhU/yXxmnOV9Bx0xfnyyy9j+/btaNy4MYYOHQpvb29IpVJkZGRg2bJlyM8vvt64t7e3zgEfTZo0Qbdu3bBlyxakp6ejevXq+OmnnwAAkyZNKuOZWh6TOtJKzpBhxoZYnLqRDgB4qU0APhzYAsevpWH2xlhkypTae+w0Xz2c7bF0SChXVSCiEiV+1FfnPrsn/rCe+V9vvcsefbfo9CBqtRrZWdlw93CH/ROX4/bN7AZR52yb5qW5F+zs2bMGH6sZ2NCuXTu9j7l//36J21NTC1b+0SRYmh4nzXZd5Qv3TGnurVMqldqkTMOUyd/9+/cRFBRUYjyFnT59Gtu3b0ffvn2xc+fOIpdhT5w4gWXLlpV4Hl0JncaUKVNw6NAhrFu3DhMmTMCGDRvQvHlzdOrUydCnVOGY1BEAYG/CPczZfB4ZuQq4Okjw8aAQvNi6NgCuqkBExnNx0P/PTHnKqtVqKB0kcHGwL3ZjvyWn8klPL/iQrFarDTpOJpNh6dKlAIDhw4frfdzNmzdx+/btYpdgjxw5AgDaUbhNmzaFk5MTTp8+jdzc3GKXYA8dOlSkPADtJMh3794tknSp1WrtpdPyaNWqFbZs2YIjR44US2Q18Rd27do1AMBzzz1X7L66ksrr66WXXkLNmjXx008/wdXVFTk5OVY/QEKDAyWquDyFCu9vi8fktWeQkatAywBP7Jz+jDah09CsqvDD6Kfw2+SO+GH0UxjUOoAJHRGRDmq1Gt988w0A3feEleTmzZsYMGAAEhMT0aNHDwwePFjvY1UqFebPn1/kvrJDhw4hKioKDRs21PY2OTg4YPjw4Xj48CGWLFlSpI59+/Zh165daNiwYZEBDm3btgVQMP9cYV9++SVu3Lihd4y6jBgxAhKJBF9++WWRHsesrCwsXry4WHlNYnn06NEi2xMSEoo9J0M4ODhg7NixiIuLw/vvvw8HBweMGTPG6PoqEnvqqrCr97Pxxvp/cPFeNgBgctf6mB3WBA72zPWJiAxReEoToGCy4QMHDuDChQsIDAwscfSmUqnUHqNSqbTLhP39999QqVQYOHCgdgJffbVs2RIHDx7E008/jZ49eyI5ORm//fYbpFIpVq5cWaQX89NPP8WhQ4ewePFiHDt2DB06dEBSUhI2bdoEFxcXrF69ukj58ePH47PPPsPChQsRGxuLBg0aICYmBvHx8ejWrZu2d89YDRs2xPvvv48PPvgALVu2xCuvvAJ7e3ts3rwZISEhuHTpUpHy7du3R/v27fH7778jJSUFTz/9NG7duoU///wTzz33nMGTNhc2efJkLF26FMnJydqJnCsDJnVVkCiK2HD6NhZuT0CeQo2abg74YkgrdG9S8g22VPlVd+UcgkTmpJnSRMPR0VE7oe/cuXNRs2bNYseoVCrtMQ4ODvDw8EC9evUwZcqUYtOA6KtatWrYvn07Zs+ejRUrViAvLw9PP/00Pvnkk2L11apVCydPnsSiRYuwbds2HDlyBJ6enhg4cCA++OADBAcHFynv6+uLv/76C++88w727t0Le3t79OjRAydOnMDixYvLndQBwPvvvw9/f3989dVXWLFiBby9vTFs2DB89NFHxS4RSyQS7NixA++99x52796N06dPo1GjRvjiiy/Qr1+/ciV1jRs31i7xVhkGSGgIojFjpitYTk4OFixYgN9//x3p6elo2rQp3nvvPZ2zRRd2//59zJkzBzt27EBubi5atWqFxYsXo1evXgbFkJWVBU9PT2RmZpp0SHOeQoWouBTsjk/B9Tv3UD/AF88G+6F/iPH3qmnq3JuQioxcObxcHBDWwgf9Q/yQr1Rj3tY47DxfMKrqmUY1sfSVVvB2dzLZcyLbpFAoEBUVhf79+0MqlVo6HLICeXl5uHHjBurVqwcnJ8u+h6jVamRlZcHDw4PrpFK55eXloXbt2vDy8sLVq1f17i0t7Xei8HuoTCYzS05RKXrqBg8ejNOnTyM8PByNGzfG+vXrMXz4cKjV6lInAszPz0evXr20Q5u9vb2xfPlyPPvss9i3b592tm5LiU5MxayNscjSjiq1w/UL97E38T4Wbk/Al0aMKi1eZ8FI1d0J9/C/bfFwspcg7bEc9nYCZvdtgsnP1Iedvgu8EhERVQE///wz0tPT8e677xp0+dvSrD6pi4qKQnR0tDaRA4AePXrg5s2beOeddzB06NBio140Vq1ahfj4eBw7dgwdO3bUHtuqVSvMmTMHJ0+erLDn8aToxFRMXhujnfxN/cTXbJkSk9bG4MfRbdFHz8SurDof56vwOF+Fmm4OWDmmLVrXqWaiZ0NERFT5hYeH48GDB9pLv6+99pqlQzKI1fdRb926FW5ubsUW8x0/fjySk5NLTcy2bt2KJk2aaBM6oGDCw1GjRuHUqVO4e/eu2eIuTZ5ChVkbYwGx5Al9gX+3i8DsjbF6Le2kT50acqUazfyse1ZsMp08hQpDVxzH0BXHuUwYEVEp5s6di2+//RbNmzfHtm3brH4FiSdZfU9dfHw8mjVrVmyiw5YtW2r365oQMD4+vsRh5JpjExISULt27WL7zS0qLgVZsrLXHRQBZMqUmLUxFi38i8/IXVjC3Uy96gSArDwldsWnYFDrAL3KU+WmFkWc/HdCaS4TRkSkWyUYZlAqq0/q0tLSUL9+/WLbq1evrt1f2rGacoYem5+fX2R5Ec2SKgqFAgqFQr/gddgdn6K9300fO8/fw87z98p1zsLsBGBXXAqeD+YqEFWBQqEs9FgBhWDcm5am3Ze3/ZPtUCgUEEURarXa4Ml1TU3zx1gTD5ElqNVqiKIIhUJR7Nawwu+h5noftfqkDih9SY+ybmA09tglS5YUGZ6usXfv3hIXPzbE9Tt2UIv6X/l2l4po7lX6H+LEDAHZCv1u5lSLwPU79xAVFaV3DFR55asAza/6nj174VjO+aKjo6PLHRPZBnt7e/j6+iInJwdyudzS4QAAsrOzLR0CVWFyuRwymQyHDx+GUlny1bPo6Gjk5uaa5fxWn9TVqFGjxB41zdIrJfXEmeLYuXPnYubMmdrvs7KyEBgYiLCwsHJfY9+ZGYvrF+7r1VNnJwAdG/lg+fDQUstNi4zFPgPqrB/gi/79S6+TbEOuXIk5p/4CAPTtG2bQUkyFKRQKREdHo0+fPpzShAAUTN9w+/ZtuLm5WXxKE1EUkZ2dDXd390o1WpFsS15eHpydndG1a9cSpzTRvIfKZDKznN/qk7qQkBBERkYWW0A4Li4OAIpNjvjksZpyhelzrKOjIxwdHYttl0ql5f6D9mywH/Ymlrzo8pPUItAvxK/Mc5qjTrINUvG/P3AF7bd8v/am+B0g26BSqSAIAgRBsPjccJpLrtYQC1Vdmt+H0t4npVKpzl688rL6lj9o0CDk5ORg8+bNRbavWbMG/v7+6NChQ6nHXrx4scgIWaVSiXXr1qFDhw7w9/c3W9yl6R/iBw9ne5T1WVIA4Olsj37Bfhapk4ioNJp7hnifJVEBze+CrqnWzM3qk7p+/fqhT58+mDp1KlauXIkDBw5g8uTJ2L17Nz777DPtCzdx4kTY29vj5s2b2mMnTJiAFi1aYMiQIVi/fj327duHV155BZcuXcKnn35qqacEJ6kEXw4JBQToTMKEf/9bOiRUr5UlzFEn2Q5nqQTO/JmTiUmlUjg6OiIzM7PSjxokKi9RFJGZmQlHR0eLXc2w+suvALBlyxbMnz8f77//vnaZsMjIyCLLhKlUKqhUqiJvLI6Ojti/fz/mzJmDN998E7m5uQgNDcWuXbssvppE7+Y++HF0W8zeGIvMJ1Z/UIuAh7M9lhq4ooQ56qTKz8XBHhcWPWvpMMhG1axZE3fv3sWdO3fg6ekJqVRqkXva1Go15HI58vLyePmVKpRmtGtmZiZycnIsMlWaRqVY+9UamHPt113xKdgV99/ar/1C/NAvuHxrv+6KT8Ge+FRkyOTwcnZA32CfctVJxLVfSZesrCw8fPiwyDRQFU0URchkMjg7O3OgBFmEo6MjatasqTNH4NqvVYCTVIJBrQPwfLDPvz/s0HL/wdTUycmFiagieHh4wMPDAwqFAiqVZVYtUSgUOHz4MLp27coPHVThJBKJVbQ7JnVENi5PocLUdWcAAN+Peoq9tWQ2lhwZLZFIoFQq4eTkZBV/XIksgUkdkY1TiyIOXHqgfUxERLaJd5MSERER2QAmdUREREQ2gEkdERERkQ1gUkdERERkA5jUEREREdkAjn7Vk2aO5qysLLPUr1AokJubi6ysLA7HJ5PKlSuhzs8FUNB+lQ7G/dqzjZI1Y/ska1e4jcpkMgAw+fJ6XFFCT3fu3EFgYKClwyAiIiIbcfv2bQQEmG6hACZ1elKr1UhOToa7u7vOJWjatWuH06dP66yjtP1ZWVkIDAzE7du3TbpkiKWU9VpUpnOWt15jjjf0GH3Kl7eMLbVRS7RPc53XFHWau43qW7Y8bdSW2ifANlre483xHqpPOX3fQ93d3ZGdnQ1/f3+TrlXMy696srOzKzOblkgkpb6ZlLUf+G+5ncpOn+daWc5Z3nqNOd7QY/Qpb6oyttBGLdE+zXVeU9Rp7jaqb1lTtFFbaJ8A22h5jzfHe6g+5Qx5D/X09NQ7Pn1xoIQJTZs2rVz7bYklnqu5zlneeo053tBj9ClvqjK2wFLP0xznNUWd5m6j+pZlG/0P22j5jjfHe6g+5SzdPnn51UpkZWXB09MTmZmZNvEpk2wP2yhZM7ZPsnYV0UbZU2clHB0d8cEHH8DR0dHSoRCViG2UrBnbJ1m7imij7KkjIiIisgHsqSMiIiKyAUzqiIiIiGwAkzoiIiIiG8CkjoiIiMgGMKmrxPLz8zF+/HgEBgbCw8MDTz/9NI4dO2bpsIiK+P7779GmTRtIpVIsXLjQ0uFQFfbgwQM899xzcHV1RePGjREdHW3pkIiKKO/7JZO6SkypVKJevXr4+++/kZGRgalTp+KFF15Abm6upUMj0vLz88OHH36IF1980dKhUBU3bdo0+Pr64sGDB/jiiy/wyiuvIC0tzdJhEWmV9/2SU5rYmOrVq+PAgQNo1aqVpUMhKuLVV19FQEAAe+vIInJyclC9enVcu3YNgYGBAIDu3btjzJgxmDBhgoWjIyrK2PdL9tSZQHZ2NubMmYOwsDDUqlULgiDo/EHk5ORgxowZ8Pf3h5OTE0JDQ/Hbb7+ZJI6LFy9CJpOhQYMGJqmPbIe1tFEifZm6zV65cgVubm7ahA4AQkJCkJCQYM6nQTbMGt9XmdSZQFpaGn788Ufk5+eX2WU6ePBgrFmzBh988AF27dqFdu3aYfjw4Vi/fn25YsjNzcXo0aOxYMECuLm5lasusj3W0EaJDGHqNpuTk1NsaSYPDw/k5OSYI3yqAqzyfVWkclOr1aJarRZFURQfPHggAhA/+OCDYuV27twpAhDXr19fZHufPn1Ef39/UalUarf17NlTdHR0LPHfggULihwvl8vF5557ThwzZow2DqLCLN1GRVEUJ06cWOI5iUpi6jZ79uxZsVq1akXKvPHGG+LMmTPN8wTI5pnjfVXD2PdL9tSZgCAIEAShzHJbt26Fm5sbhgwZUmT7+PHjkZycjJMnT2q37d+/H3l5eSX+W7RokbacWq3GmDFjIJFIsGrVKr3ioKrHkm2UyBimbrONGjVCTk4O7ty5oy0THx+PFi1amDZwqjLM8b5aXkzqKlB8fDyaNWsGe3v7Ittbtmyp3W+oKVOmICUlBRs2bChWL5GhzNFGlUol8vLyoFKpijwmMgV926ybmxsGDhyIhQsXQiaTYceOHYiNjcULL7xQ4TFT1WLI+2p53y+Z1FWgtLQ0VK9evdh2zTZDh9bfvHkTP/30E06ePImaNWvCzc0Nbm5uOHLkiEniparH1G0UABYvXgxnZ2dERETg448/hrOzM9auXVvuWIkAw9rsd999h+TkZNSoUQNvv/02NmzYgJo1a1ZYrFQ1GdJGy/t+ya6dClZaV62hl06DgoIgckYaMjFTtlEAWLhwIacxIbPSt83WqlULUVFRFRESURH6ttHyvl+yp64C1ahRo8SejvT0dAAoMZMnqkhso1TZsM2StavINsqkrgKFhITgwoULUCqVRbbHxcUBAIKDgy0RFpEW2yhVNmyzZO0qso0yqatAgwYNQk5ODjZv3lxk+5o1a+Dv748OHTpYKDKiAmyjVNmwzZK1q8g2ynvqTGTXrl14/PgxsrOzAQCJiYnYtGkTAKB///5wcXFBv3790KdPH0ydOhVZWVlo2LAhIiMjsXv3bqxbtw4SicSST4FsHNsoVTZss2TtrK6NGjyzHZUoKChIBFDivxs3bmjLZWdni9OnTxd9fX1FBwcHsWXLlmJkZKTlAqcqg22UKhu2WbJ21tZGBVHk8EkiIiKiyo731BERERHZACZ1RERERDaASR0RERGRDWBSR0RERGQDmNQRERER2QAmdUREREQ2gEkdERERkQ1gUkdERERkA5jUEREREdkAJnVERERENoBJHRFp1a1bF4IgICkpydKhkJVISkqCIAhF/i1cuNDSYRkkNDS0SPzdu3e3dEhEZsGkjqgS0iRfhf85OzujQYMGmDBhAhISEiwdos0p/JrPmjWr1LLLli0r8rOxBY6OjujcuTM6d+6MOnXqFNtvza9P69at0blzZwQHB5v9XESWJIiiKFo6CCIyTN26dXHz5k00atQI3t7eAICMjAxcuXIFcrkcjo6O2LhxIwYMGGBQvb169cLdu3exf/9+1K5d2xyhV1qa1xwAfH19cefOHUgkkhLLtmvXDjExMdrvK/PbbFJSEurVq4egoKBSe3Arw+tz8OBB9OjRA926dcPBgwcr5JxEFYk9dUSV2Lx583D06FEcPXoU8fHxuHXrFnr37o38/HyMHz8eOTk5BtW3f/9+XLx4kQldKZo0aYJ79+5h3759Je6/dOkSYmJi0KRJkwqOzDrw9SGyHCZ1RDbEx8cHa9euhaOjI9LS0hAdHW3pkGzOqFGjAADr1q0rcf/atWsBAKNHj66wmKwJXx8iy2FSR2RjfH190ahRIwDAlStXtNsL37+0efNmdO3aFV5eXkUGRugaKFH42K1bt6JTp05wc3ODj48Pxo4di3v37mnLrl69Gk899RRcXV3h7e2N1157DZmZmSXGGh8fjw8++AAdO3aEn58fHBwc4Ofnh8GDB+PYsWMlHlPW87h06RIEQUDNmjUhl8t1vk4hISEQBAE7d+4s5dUsrlu3bggMDMTWrVvx+PHjIvtEUcSvv/4KZ2dnDB48WGcdxjxvALh58yamTJmC+vXrw9HREe7u7qhfvz4GDRqE3377zeiypmSK14eIjCQSUaUTFBQkAhBXr15d4v4WLVqIAMRPP/1Uuw2ACEAMDw8XAYg+Pj5iu3btxFq1aok3btwoUq/m+yeP/b//+z8RgBgQECC2atVKdHR0FAGIzZs3F2UymTh9+nQRgFi/fn2xRYsWor29vQhA7Natm6hWq4vF2atXLxGA6OXlJTZr1kxs06aNWLNmTRGAKJFIxF9//bXYMfo8j44dO4oAxM2bN5f4+sTExIgARF9fX1GpVJb9ghd6bY4cOSK+9957IgBx7dq1RcocPnxYBCAOHz5cvH37tjZWUzzvGzduaMu4uLiIISEhYmhoqFi9enURgNiqVSujypblxo0bIgAxKCiowl4fczlw4IC2PRLZIiZ1RJVQaUldSkqKNtkqnNRo/oA6ODiIP/74ozbJUigUokKhKFKvrqTO1dVVXL9+vXb77du3xYYNG4oAxBdffFH09PQU9+3bp91//vx5bSIRFRVVLNaNGzeK58+fL7JNrVaLf/zxh+jm5iZ6eHiIWVlZJcZS2vNYuXKlCEB84YUXSnz93nzzTRGAOHv27BL3l6Rw0pKQkCACEMPCwoqUmTRpkva5lpa0GPO833jjDRGAOHbsWDE7O7vIvgsXLogrVqwwqmxZjEnqyvv6mAuTOrJ1TOqIKiFdSV1qaqrYu3dvEYBYrVq1IomB5g/om2++WWa9upK6t956q9gxK1as0O7/6quviu3X9NpMnz7dkKcoLliwQARQrNdKn+eRlZUlurq6ilKpVLx//36RfXK5XNuLFR8fr3c8hZMWURTF1q1bixKJRExOThZFURTz8vJELy8v0dvbW1QoFEYnLbqed9++fUUA4rlz58qsw5CyZTEmqRNF870+5cGkjmwd76kjqsQ++eQTdOnSBV26dEFwcDACAwOxb98+SKVSrFy5Eu7u7sWOGTNmjNHnmzhxYrFtoaGh2scTJkwotr9169YAgOvXr5dY561btxAeHo5XXnkFPXv21D6fDRs2AADOnTtX4nGlPQ93d3e8/PLLUCgUWL9+fZF9O3fuxMOHD9G2bVu0aNFCZx1lGT16NFQqFSIjIwEAO3bsQEZGBoYPHw57e/syjzf0eQcGBgIANm3aVOYUIIaUNZfyvj5EZARLZ5VEZDhNr0jhfw4ODmKdOnXEUaNGif/880+xYzTlcnJyyqxXV0/d48ePix1z/fp1EYBYq1atEuv866+/RABi9+7di+2LiIgQnZycij2Xwv9effVVg5+HKIrioUOHRABi69ati2x/8cUXRQDit99+W+rxT3qyJyolJUWUSCRiaGioKIqiOHDgQBGAePr0aVEUxVJ7oox53v/8848olUq1vWZTpkwR161bJ969e7dY/YaULYuxPXXleX3MhT11ZOuY1BFVQmUNlCiJPn9Ay0rqSlLWH31df0ivXr2qTTxmzZol/vPP/7d3PyFR9HEcx99aO+afKEIqyrBYBBHcRIokgk2i3U7SHqKtjKyICImCggg6bHQJ6c+hqEuHCiwMD/05pFGi1ipCplCIrB7CajFq2a0o3HZjnkO4Tz1PPenu1uo8n9dtZn478/3NZT/M7ze/6TPfv3+fmCM3Pi9ux44dk+7HuJKSEhNIzF978+aNabPZTMMwzFAoNKFzjPtnaDHNv4c5Ozo6TMMwzNLS0sSxn4WWZPttmqbZ3d1tulyuxAsogJmVlWW6XC5zYGAg6bb/JdlQl+z9+ZFgMGjW19ebRUVFpmEYpt1uN48ePWqGw+EJ98M0FerE+jT8KiIZcePGDWKxGF6vl1OnTlFRUcHs2bMTy5W8ePEi5WvU1dUBcOXKFQCuX79OLBajpqaGefPmpXz+8bXWtm/fzufPnye09loq/a6qqqK1tZVwOExLSwtHjhyhqKiIe/fusX79eiKRSFJtf5dk7s8/9ff3s3z5cl6+fMmlS5fo6+vjzJkzdHV1UVlZqe8Ui3xDoU5EMmL8z3j16tU/PP6zuXSTUVdXx4wZM2hsbCQej3P58uXE/nTweDwUFBQwMjJCVlYW27Zt++Vv0tHvgoIC3G43J0+eZHBwELvdzqtXr7h7925KbdMtmfvzrWg0isfjYc+ePdy8eRO3201ZWRk1NTW0tbXhdDrZvHnztP4Mm0g6KdSJSEbk5uYC8Pr1638dGxwc5M6dOylfY9GiRbhcLkZHRzl9+jRPnjxh4cKFbNiwIeVzA+Tl5XHo0CHWrVvH3r17KS4u/uVv0t3vvLw8ysvLAQgGg2lrmw7J3J9vNTc3YxgGJ06c+Nex7Oxszp07RyAQoLOzM10li0xrCnUikhFr1qwB4MKFC/T39yf2BwIBNm3ahGEYabnO+Bu5x44dA75+xupnH5pPhs/n4/79+1y8eHFC7ZPt9759+2hqauLTp0/f7e/s7OTBgwcAVFZWTrrt7zbZ+/Otnp4eXC4X2dlf/6qam5spLCzE7/cDX59COp1Oenp60lqzyHSlUCciGbFx40aqqqoIh8OsWLGCsrIyysvLKS0tJRQKJUJYqmpqaigsLCQejwPpG3pNVrL97u7uxuv1MmfOHMrKyli1ahVLly7F6XTy4cMHamtrqa6unnTbqSwej2Oz2RLbY2NjhEIhYrFYYp/NZuPLly+ZKE9kylGoE5GMmDlzJq2trezfv58FCxYwPDxMJBJh9+7d9Pb2snjx4rRcxzAMtm7dCpDy2nTpkGy/z549y4EDB3A4HLx9+zbxlM/tdnP79m2uXr2aVNupzOFw8PDhw8R2bW0tpmmydu1aAGKxGF1dXTgcjgxVKDK1ZJmaYSoiFuf1emlqauL8+fPU19dnupxp5fnz5yxbtozi4uI//qZpJBLBbrfT0NDww4WvfT4fjY2NDAwMfPdE72fa29uprq7G6XTS3t7+GyoWySwt6y0ilhYKhbh16xY5OTls2bIl0+VMW6Ojo4n5gLt27frh10PSbe7cuVy7dg2Px0Nvby+HDx9myZIlDA8Pc/z4cVpaWmhra/tloNu5cydDQ0O8e/fut9cskkkafhURS/P5fIyNjeH1etOyNt3/VTQaxe/34/f7GRkZ+WPXdbvdPHr0iEAgQElJCYZhUFFRQTQa5fHjxxN64aOvrw+/38+zZ8/+QMUimaPhVxGxnP7+fg4ePEgwGGRoaIjc3FyePn2K3W7PdGmSgo8fPxIKhZg/fz6zZs3KdDkiU46GX0XEciKRCB0dHeTk5LBy5UoaGhoU6CwgPz+f/Pz8TJchMmXpSZ2IiIiIBWhOnYiIiIgFKNSJiIiIWIBCnYiIiIgFKNSJiIiIWIBCnYiIiIgFKNSJiIiIWIBCnYiIiIgFKNSJiIiIWIBCnYiIiIgFKNSJiIiIWIBCnYiIiIgF/AWEMknLChFXgwAAAABJRU5ErkJggg==", + "image/png": 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9+gGpNTJptx6eeOKJXN/qKqqya6MF2Hw6rpyOXxAaNWpE06ZNAfPbP4cOHTKN65YftXR5kdOt0JSUFJ566inu3LmDRqPh1VdfZdeuXURFRZGUlGSqCUx/e8/Wv9u8suTaKCzn0L9/f1xdXQHzW7AGg8HUU7hbt25Z9q59/vnniYyM5LPPPqNXr16m9n/Xr19n/vz5NG7cmDfffDNfY86vz78RI0Zw6dIlVCoVY8aMYdu2bVy9etXsukufkGX2O6tXrx4nTpxg/fr1jBkzhurVqwOpCeCWLVsYNmwYLVu2JCYmJsO+77//PufPn+f999+nU6dOpiT+woULzJ07lzp16uR7m2CRO5LUiQKTdgti27ZthISEALm/9ZD2Td9oNGa73cO39gqD6OjobNenfYiqVCqrh015WNmyZU2Pb9y4kS9l5lVah4ldu3aZvtmnJXiurq6ZdiYpTP766y9T3F9//TVz587l0UcfpXz58jg7O5u2y662Nf3vJSoqynbB5iDtduidO3dMt76zknb95uf1aQ0vLy9TJ5rVq1ebbhv+9ddfpsb8OXU88PX1ZeLEiWzcuJHY2FiOHDnCG2+8gY+PD4qi8P777/Pbb7/la9x5/fw7ffq0aRDoadOmsXDhQrp27WoaBDiNJbX8Dg4OPPHEEyxcuJDz589z48YNFi5caPrCdeTIEZ577rlM9w0MDGT69Ols376duLg49u7dy8svv4yLiws6nY7//e9/Zs0LRMGSpE4UmM6dO+Pv749erzdNi5NVD8SspH2rjYuLy3a7M2fOWBumzaT1Msxpfc2aNfOtk0Djxo1NtTAP36rKD9bU/g0dOhQ3NzcURWHp0qVotVp+/vlnILUWJrOek4VJRESE6fHgwYOz3C40NDTLdenHTrPm95Jfta5BQUFAau1jTn+I02YKyM/r01ppSdvdu3fZsmUL8F+tnbu7e67mUFWr1TRp0oRZs2axfft20/JVq1blY8R5//zLj+suK/7+/owZM4YDBw6Yrs0//vgjw+3bh2k0Gtq0acPnn39uev0VRcnVmJcif0lSJwqMg4MDI0aMwNnZGWdnZ4YPH57rXn9pE7Dfu3cvy8QtJSUl08Ezc8PFxQVI7V2WX5YuXZrlutDQUFOj9S5duuTbMUuXLk3r1q2B1D9S+V1bl/Y6geWvlZeXl6m909KlS1mzZg3x8fFA9j0wC4v0NVppPVgfZjQaTcOAZKZjx464u7sD8OWXX+a6jZk1r3tm0l9rCxcuzHK7AwcOmG6P5+f1aa2ePXuaahl/+uknkpKSWLduHZB6SzPttc2tJk2amGoh87uHZ14//yy57oA83f7UaDS0b9/edLycvjyn17lzZ9Nj6R1rP5LUiQL14YcfkpSURFJSEp988kmu90/7wAGYO3duhvWKovDyyy/nOXlJa7yen8Me/P7775l++79//z7jxo0DUmsNsrrtYa3XXnsNSP1DMHDgQFMClZnc9mRL38g/N69V2i3Yy5cvM3XqVCA1YX94ftXCqGbNmqbHWSXq06ZN4+jRo1mW4ePjY/o9HzlyhIkTJ2bZZk2n02Vo32Tt6/6wFi1amHpl//DDD6bbgunFx8ebYlWr1UyYMMHq4+UXjUbDwIEDgdQhNlasWGGaqzS7W6+//PJLtrVPoaGhptuXaV8g81NePv8sue6+/fZbfv311yzL2LNnT7Y9fVNSUkxj6Xl4eJi1S1y+fHm2t+i3bdtmemyL105YRqYJE0VK48aNadWqFQcPHmTBggWkpKQwcuRIvL29OXfuHN999x07d+7kkUceydNUP61bt+bSpUv8/vvvzJ8/nzZt2phqR7y8vHLsyZqZZs2aMXToUHbt2sWTTz6Jl5eXaZqwtFrH559/3uIhNSzVp08fxo4dy8KFC9m/fz/16tXjhRdeoE2bNnh5eXH79m1CQ0NZtWoVDRo0MJsMPieVK1emYsWKXLt2jU8++YQKFSpQu3ZtU09KPz+/TBuCp417durUKVM7qNGjRxeKzhw56d69O76+vsTExPDGG29w+fJlHn/8ccqWLcv58+dZsGAB27dvp02bNlkOsgwwc+ZMQkJCOHHiBF999RUHDhzgueeeIzg4GCcnJ65du8bevXtZsWIFs2bNYtSoUaZ9rX3dM/P999/TsmVLUlJS6N27Ny+++CJ9+vTBw8PDNE1YWhvCyZMnm27Z2tvw4cOZP38+Wq2WSZMmAVCuXDm6du2a5T6vvfYa48ePp2/fvjz66KPUqlULd3d37ty5w969e/nyyy+B1Fq1tJ7ahUX6Kce+/fZb4uLiGDZsGP7+/ly9epXly5ezZs2abK+77du3M3PmTNq1a0fv3r1p0KAB5cqVQ6vVcvbsWb777jvTl5FnnnnGrEf0iBEjmDx5Mv3796d169ZUr14dFxcXoqOjCQkJ4dtvvwVSk8Hhw4fb/gURmSuoqStEyZDZNDm5kdM0OYqiKKdOnVJ8fX2znFD61VdfzfM0Yf/884/i7OycafnpJ73PzTRhFy9eVKpWrZpl3AMGDMhy+p28Tmum1+uVF154QVGpVFke/+Fzs7Tsb775JsvysrsGPvnkE9N2arVauXLlSpbb5kbalFU5TSWXXk6/x4dt2bJFcXFxyfK8O3TooISHh+dY5q1bt0zTp2X3k9n+lr7uOb0XFEVRtm7dmuMk788//7zZNFfppb3mmV0/6Vk6zZ8ljEaj2fRkgPLCCy9ku8/D22f24+LioixdutSqmGz9+ffPP/8opUqVyjL24OBg5caNG1m+Zx+etjCrn/79+ytardZsX0v28/HxyXSqOVFw5ParKHLq1KnD0aNHmTBhAoGBgTg5OVGuXDl69OjBxo0bM70tm1uNGjXiwIEDDBkyhMqVK5v1LrNW1apVOXLkCNOnT6du3bq4ubnh7e3No48+avqWndNYYdZycHDgyy+/JDQ0lHHjxplqKNzc3KhZsya9evViwYIFpsnOc2PChAmsXbuWbt264evra/E5pO/517VrVypVqpTrY9tL9+7dCQ0NZfjw4QQEBKDRaChXrhzt27fn+++/Z/v27Ra16ypbtiy7du1i3bp1PPnkk6aejKVKlSIoKIhhw4bx22+/ZZjuCqx/3TPTrVs3zp8/z/Tp02nUqBFeXl44OztTuXJlhg0bxp49e/jqq69Mvc8LA5VKleF1yex1Sm/37t388MMPDBo0iODgYMqVK2eaEqtJkyZMmTKFkydPmnqqFjaNGjUiLCyM8ePHExgYiEajoXTp0rRo0YJPPvmEv//+O9txD6dOncqmTZt45ZVXaNWqFZUrV8bFxQUXFxeqVKnCoEGD2LhxI2vXrjVrtwmpvW+//PJLnnjiCerVq0eZMmVwdHSkVKlStGrVihkzZnDmzJlcd34T+UulKIVk8CEhiqEZM2bw7rvvAhSacb4Ki+3bt5sa3f/yyy+ZDhYrhBDCcoXna5cQokRZtGgRAGXKlMnVEBRCCCEyJ0mdEKLARUZGsnr1aiC1g0R+3N4WQoiSTnq/CiEKxPXr10lMTOTSpUu8/vrr6HQ6XFxcmDhxor1DE0KIYkGSOiFEgRg2bJhpDKw07733HhUqVLBTREIIUbxIUieEKFBubm7UqlWLiRMnMnLkSHuHI4QQxYb0fhVCCCGEKAakps5CRqORGzdu4OnpWSRGvRdCCCFE4aQoCvfu3SMgICBfx3+UpM5CN27cKFKDowohhBCicLt69SoVK1bMt/IkqbNQ2jyKV69excvLK9/L1+l0bNu2jW7duqHRaPK9fFFyJaboafH+dgD+fqMzbk7Wve3lGhWFmVyforBLf41qtVoqVapk8RzNlpKkzkJpt1y9vLxsltS5ubnh5eUlH0giXzmm6FE7uwGp129ekjq5RkVhJdenKOwyu0bzuzmXDD4shBBCCFEMSE2dEMWcWqWiV3B502MhhBDFkyR1QhRzLhoHvhnW1N5hCCGEsDG5/SqEEEIIUQxIUieEEEIIUQxIUidEMZeYoqfK6xup8vpGElP09g5HCCGEjUhSJ4QQQghRDEhSJ4QQQghRDBT6pO7evXtMnTqVbt26Ua5cOVQqFTNmzLB4/5iYGEaNGkXZsmVxc3PjkUceYfv27bYLWAghhBA2l6QzsO7oNcYvO8Lg+QcYv+wI645eI0lnKFRlFqRCP6TJnTt3+P7772nYsCFPPPEEP/zwg8X7Jicn07lzZ+Li4pg3bx6+vr58/fXX9OjRgz///JP27dvbMHIhhBBC2ELIyWgmrQ4jQatHrQKjAmoVbIm4yYwNEXw6sBFd6vnZvcyCVuiTusDAQO7evYtKpeL27du5SuoWLlxIeHg4+/fv55FHHgGgY8eONGzYkKlTp3Lo0CFbhS2EEEIIGwg5Gc24ZaGgpD43PvT/Pa2eZ5eF8v2IZnS1MAmzRZn2UOhvv6pUKqvnRlu/fj21a9c2JXQAjo6ODB8+nL///pvr16/nV5hCCCGEsLEknYFJq8NAMeVfGSj//jN5dZhFt01tUaa9FPqaurwIDw+nXbt2GZY3aNAAgIiICCpUqFDQYQlRoNQqFR1rlzM9FkKIomrTiSgStDkPzaQA8Vo9U1YfI6iCd7bbnrgen6syN4dH0a9xRQsjLljFOqm7c+cOpUuXzrA8bdmdO3ey3Dc5OZnk5GTT84SEBAB0Oh06nS6fI8VUpi3KFiWbA/D98Mb/PjOi0xmtKkeuUVGYyfVZMmwJjzK1d7PEhuNRbDgelW/HV6tg84koHgvK/S3Y9Neora7TYp3UAdneus1u3ezZs3n33XczLN+2bRtubm75EltmQkJCbFa2EPlBrlFRmMn1WXwl6uHoBQeMiuV3HDw1CnV9ss8AT8WpuKezrEyjAhev3WTTpk0Wx/CwkJAQEhMTrd4/O8U6qStTpkymtXGxsbEAmdbipZk2bRqvvvqq6XlCQgKVKlWiW7dueHl55XusOp2OkJAQunbtikajyffyhcgruUZFYSbXZ/GkKApHr8Tx8+FrbI6IJllv+Z0GtQoeqenH10MaZbvd8yvD+PNUjEW1f2oVVKtYnl69si8zM+mvUa1Wm+v9LVGsk7rg4GBOnDiRYXnasqCgoCz3dXZ2xtnZOcNyjUZj0w8MW5cvSp7EFD1NZ/4JwJG3uuDmlLe3vVyjojCT67NwStIZ2HQiim0R0cQlpuDj5kS3+n70CvbHReOQYfv4RB3r/rnGyr+vcDb6vml5gLcLN+KTLDqmUYGewf45Xg89gvzZdjImX8vMjkajQa+3zZSNxTqp69evH//73/84dOgQLVu2BECv17N8+XJatmxJQECAnSMUomBoC3FvLSFE8Wbp+G9ptXIrDl3hj+M3TLVyrhoH+jT0Z2jLQOr4edBi9nbuafVZ9lQFUAFero70DPLPMb5ewf7M2BCRr2XaS5FI6jZv3syDBw+4d+8eACdPnmTNmjUA9OrVCzc3N8aOHcvSpUu5cOECgYGBAIwZM4avv/6agQMHMmfOHHx9ffnmm284c+YMf/75p93ORwghhCgJLBr/7cdQBreoxD9X4jh9855p3zrlPRnasjJPNK6Al8t/NWOfDmzEs8tCUWUxBInq33/mDmyUaS3gw1w0Dvlepr0UiaRuwoQJXL582fR89erVrF69GoBLly5RpUoVDAYDBoMBRfnv1+Hs7Mz27duZOnUqL774IomJiTRq1IjNmzfLbBJCCCGEDVk8/huw8u+rALho1DzWIIChLSvTuJJPph0au9Tz4/sRzZi8Ooz4h2r/jEpqbdrcXM7+YIsy7aFIJHWRkZE5brNkyRKWLFmSYbmfnx9Lly7N/6CEEEIIkSVLx5RL069xADMeD8LbNef2al3r+XFoehc2h0exNTyaOG0KPq5OdA/yo2dQ5u307FFmQSsSSZ0QQgghipZtEdEWjymnVoE2xWhRQpfGReNAv8YV83UgYFuUWZAK/TRhQgghhCh64hJTLB4k2KhAnDbFtgGVAFJTJ0Qxp1apaFm1tOmxEEIUBB83p1zV1Pm4Otk+qGJOkjohijkXjQO/PPeIvcMQQpQw3er7sSXipkXbGhXobsXUW8Kc3H4VQgghRL7rFeyPl2vOdUcqwLuQj/9WVEhSJ4QQQoh856Jx4MMBDbLdpqiM/1ZUSFInRDGXmKKnycwQmswMITHFNlPTCCFEZqLiUqf0SmvNq1aZ/+/l6siCEc0K/fhvRYW0qROiBIh9IL3KhBAF616Sjq92nAdgxuP18XJ1LLLjvxUVktQJIYQQIt8t2HOJ2AcpVCvrzrCWlXF0UBfZ8d+KCrn9KoQQQoh8deteMj/suQjA5O61cXSQdKMgyKsshBBCiHz15V/nSEwx0LCSDz2Dyts7nBJDkjohhBBC5JvLdx6w4tAVAF7rURuVDHpeYCSpE0IIIUS+mbvtLHqjwqO1ytG6ell7h1OiSEcJIYo5tUpFg4repsdCCGEr4dfj+f3YDQCmdq9t52hKHknqhCjmXDQO/P5CW3uHIYQoAT7aegaAxxsGEFTB287RlDxy+1UIIYQQebb/wm12n72Fo1rFpG617B1OiSRJnRBCCCHyRFEUPtySWks3tGVlAsu42zmikkmSOiGKOW2KgTZz/qLNnL/QphjsHY4QohjaEn6TY1fjcHNy4MVONe0dToklbeqEKOYUFK7HaU2PhRAiP+kNRj7ellpL90zbqpTzdLZzRCWX1NQJIYQQwmqrj1zj4q0HlHZ34tlHq9k7nBJNkjohhBBCWEWbYuDzP88C8HzHGni6aOwcUckmSZ0QQgghrLJkfyTRCclU8HFleKvK9g6nxJOkTgghhBC5Fp+o49ud5wGY1K0Wzo4Odo5ISFInhBBCiFz7Ztd5EpL01CnvSd9GFewdjkB6vwpR7KlQUdPXw/RYCCHyKipey5J9kQBM7VEbB7V8thQGktQJUcy5OjkQ8mp7e4chhChG5v15jmS9kRZVStOxtq+9wxH/ktuvQgghhLDY+Zj7rAq9CsBrPeugUkktXWEhSZ0QQgghLPbx1tMYFehaz4+mgaXsHY5IR5I6IYo5bYqBrp/uouunu2SaMCFEnhy9cpetEdGoVTC1e217hyMeIm3qhCjmFBTOxdw3PRZCCGsoisKHm08DMKBJRWr6edo5IvEwqakTQgghRI52nr3FoUuxODmqeaVrLXuHIzIhSZ0QQgghsmU0Kny05QwAo1pXIcDH1c4RicxIUieEEEKIbP1+7AanohLwdHHkfx2q2zsckQVJ6oQQQgiRpRS9kbkhqbV049tXx8fNyc4RiaxIRwkhhBBCmCTpDGw6EcW2iGjiElOI1+q4GqulnIcTY9pUtXd4IhuS1AlRzKlQUeHf9i8yTZgQIjshJ6OZtDqMBK0etQqM6TrM30vWs+/8bbrU87NfgCJbktQJUcy5Ojmw7/VO9g5DCFHIhZyMZtyyUNJGPjI+NAJSss7Is8tC+X5EM7pKYlcoSZs6IQqpn376iRYtWuDu7o5KpUKlUtGhQwd7hyWKqQ4dOpius1GjRpmWR0ZGmparVCp27txptxiF7STpDExaHQYKWY5mqfz7z+TVYSTpZCDzwkiSOjvbuXMnKpUKJycnnnjiCZycnFCpVDg6OuLt7U3Dhg154YUXOHv2rL1DLTTS/4FJe608PDyoWLEibdq0YeLEifzzzz/2DjNPtm3bxvDhwzl8+DCJiYn2DkfYiKIorFixgm7duuHr64tGo8HHx4dq1arRqVMnJk2axO7du832SX/tL1myxD6Bi2Jn04koErT6HIcnV4B4rZ7N4VEFEZbIJbn9WkgZDAYSEhI4fvw4x48fZ/HixezcuZPmzZvbO7RCx2Aw8ODBAx48eMD169fZv38/8+bN46mnnuL777/H29vb3iHm2ooVK0yPS5cuzYsvvoiHhweVKlXKdVlJOgNPzT8AwKrnHsFF45BvcYq8GTFiBD/99JPZsvj4eOLj47l06RI7duwgPj6eRx991E4RipJi279Tfz18yzUzahVsDY+mX+OKtg9M5IokdYXMwIEDadGiBXq9nr///pv169cDkJiYyPvvv8+vv/5qUTl6vR6dToera/EeILJZs2YMGjSIxMREzp07x4YNG4iPjwdg1apVXLp0iV27dhWJ1+HevXt4eqZOu3PlyhXT8l69ejFjxgyryzUqCsevxZsei8Jh8+bNZgldy5Yt6dKlC87Ozly9epUzZ85w4MABO0ZoH+nfB6LgxCWmWJTQQWriF6dNsW1Awipy+7WQ6d69O5MnT+b1119n3bp1BAcHm9adPn3abNtRo0aZtbW6ePEigwYNomzZsjg5OXHo0CHTttevX2fy5MkEBQXh4eGBi4sL1atXZ+zYsZw4ccKs3M8//9xUbu3a5hM2N27c2LQuJCTEtHzhwoWm5X5+fij/Jg8Px3jjxg3Gjh2Ln58fLi4uNGzYkLVr11r9etWvX5/Jkyfz9ttvs2zZMi5fvkzv3r1N6w8fPszMmTNNzxs1amSKZ/bs2ablx44dMy2vUKGC2THatWtnWjd16lTT8vnz5zNw4EDq1KlD2bJl0Wg0eHl50bhxY15//XVu376dId4qVaqYypoxYwbbt2+nffv2eHl54eXlxYwZM1CpVOzYscO0z/Lly832EcVD+vdPzZo12b9/P7NmzeKtt97i+++/Z9euXURHRzNu3DjgvzZv6Y0ePdp0bVSpUsVsXVRUFK+//joNGjTA09MTFxcXatWqxauvvsrNmzfz5RzOnz/Pyy+/TNu2balUqRLu7u44OztTsWJFHn/8cf74448M+yxZssTsFvL9+/eZPHkygYGBODo6Mnfu3HyJTeSOj5sTags7x6tV4OMqY9UVRpLUFVIGg4GDBw9y+fJl07Ly5ctnuf2NGzd45JFHWLVqFXfu3DElVQC7d+8mKCiIuXPnEhERwYMHD0hOTubixYssWrSIpk2bsnTpUtP2nTr911Py7NmzREdHA6nfoNMngHv27Mn0cWZ/fACuXr1K06ZNWbRoETExMSQnJ3P8+HEGDhzItm3bLH1psuXt7c0vv/xi9lp99dVXpKSkZDi39DHv3bvX9PjGjRtcvHgRgOTkZA4fPmxa17FjR9PjL7/8kjVr1nDmzBnu3LmDXq/n3r17hIWF8eGHH9KoUSNu3LiRZaxbt26le/fu7N69m3v37uXhrEVRpNfrTY9jY2M5f/58hm1KlSpFixYtcl32vn37CAoK4sMPP+TEiRPcv3+f5ORkzp07x2effUbDhg0JCwvLS/gAhIWF8cUXX7Bv3z6uXbtGYmIiKSkpXL9+nQ0bNtCnTx/ee++9bMvo0aMHc+fO5cqVKxgM0vjeXrrV98tVTV33IOn9WhjJ7ddC5plnnuGZZ57JsFytVjNlypQs9zt37hwqlYqBAwcSHBxMZGQk7u7uxMXF0a9fP+Li4gBwd3dnzJgxuLq6smzZMqKiotDpdDzzzDM0adKE4OBggoODKVu2rKmmae/evQwYMID9+/ebfeimT4rSN+ZOn/ikd/HiRdzc3HjxxRcxGo189913GAwGFEVh7ty5dOvWLVevVVbc3d0ZPHgwn3/+OZCajIaGhtK6dWs6duzIZ599BsD+/fsxGo2o1WqzpC7t3KpVq8ahQ4dITk4GwNHRkXbt2pm28fPzo0aNGlSrVo3SpUujUqm4fv26KbG+fv06s2bN4ptvvsk0zoMHD+Lp6cmwYcMICAggNDSUbt264eHhwbfffmtKLNNuMQO0bt06X14jYX+NGjUyPb5z5w516tShQYMGNG/enObNm9OlSxeqVatm2mbChAk89thjZp8DgwYNolmzZgCmtqPx8fH069eP2NhYAKpVq8ZTTz2FRqNh1apVnDlzhpiYGPr378+pU6dwdna2+hw0Gg1NmjShadOmlCtXDi8vL+7fv8++fftMtc0zZ85k7NixGWrA0+zbt482bdrQuXNn7t27R8WK0k7LHnoF+zNjQwQJWn2226kAL1dHegb5F0xgIlckqSsiZs2aZXZbMTNff/01EyZMMFv2+eefmz7cAdatW2dKnp577jnq1KmDTqdDr9fzxRdfsGDBAlQqFe3btzfdFt2zZw8DBgwwJT5lypThzp07HDp0iJSUFG7dusWlS5dMx0hfG/awVatWmc5Do9GYEq/Q0FALXwnL1KpVy+z59evXAXj00UdxcHDAYDAQHx/P8ePHadSoEfv27QP+O7c9e/YwcuRIs8S1efPmeHh4mJ5v376dxMREDhw4wMWLF7l//z7VqlWjbdu2/Pbbb0BqbVxWHB0d2bdvn9ktdkhN3P744w9TUpd2i1kULyNGjOCLL77g2LFjQGpP2GPHjnHs2DF++OEHIPUL0rfffkvt2rVNiX36pK5Hjx5mw48ALF26lFu3bgHg6+vL0aNHTQnfq6++ir+/P0lJSVy6dIm1a9cydOhQq8+hb9++9O3bl7Nnz/LPP/9w69YtHB0d6dWrF4cOHSIxMRG9Xs9ff/3FiBEjMi1j8ODBrFixItPafVFwXDQOfDygAc8tP5rlNqp//5k7sJF0uCqkJKkrZNI6ShgMBsLDw/n555/R6/VMnz4dnU7H22+/nel+pUuXNrW9SW///v2mx76+vma1YWkJSNo36vTbduzY0ZTUpSVzaf+PHz+ejz76CK1Wy5EjR8xuEVeoUCFDQpV+XfrENH17vbt372bxilhHyaJDgLe3N40bNzYlkXv37qVUqVJcvXoVgBdffJEZM2aYkrn0Sd3DNZCffvop77zzDvfv388yjrRkMjO9e/fOkNCJkkOj0bBr1y5mzZrFkiVLMm2DuWPHDrp160Z4eLjFnQfSvqAAxMTE4OPjk+W2+/fvz1NSFxkZybBhw8w+OzJz7dq1LNe99tprktAVEnce6IDU5E0BU2/YtP+9XB2ZO7CRzChRiEmbukImraPEa6+9xrJly3jjjTdM62bOnJllklC9enUcHDJ+c0qfLPn6+mZY7+fnl+m26ROYsLAwYmNj+fvvvwHo2rUrTZs2BVKTIktuvQIEBgaaPU9/2yerJMxaD4/rl/7WT/oY9+zZY0pWq1SpwuDBg037R0VFmf2xSl8D+euvvzJp0qRsEzrAdOs2M1klv7ZQ2t2J0u7SsLmw8fb25uOPPyY6Oprjx4/z/fffM2TIELPe2leuXGHdunUWl5m+Zj4naTV61nriiSdyTOig8LwPRNYeJOv5NCT1c3N6rzp8Nqgh3eqVp1W10nSrV57PBjXk0PQuktAVclJTV8ilbySt1+s5fPhwpm1T3NzcMt2/VKlSpscxMTEZ1qd1gnh423r16uHn50d0dDQGg4GvvvqKxMRENBoNLVq0oF27dhw8eJA9e/aY3XrNLqnTaDRmz2317fzBgwf88ssvpueenp6mdkeQGuPHH38MpCZ1aefdtm1bateuja+vLzExMXz55ZemDgzOzs5m7dnSlx8QEMDatWtp3Lgxzs7OfPPNNzz//PM5xpnV7yy/uTk5cvStrgVyLGEdtVptas/67LPP8s8//9CkSRPT+sw6UWQl/fu4cuXKvPjii1lu+3Dv9tw4c+aM6dYxwCuvvMLrr79OuXLlUKlU+Pr6WpQ0FtT7QGTv+90XuX0/mSpl3BjZuipOjmoZh64Ikpq6Qi59z0sg173D0iciMTExZr1ML168aNZB4OFG+OmnpPryyy+B1Eb7rq6upg4DO3fuJCIiwrRddu3pCkJCQgKDBg0yG7LhhRdewMnpv1qqdu3a4eiY+n0mKirKdJs57Zzatm0LYNbBoVWrVma1J3fu3DE9btq0Ka1atcLZ2Rmj0cjq1attcGaiuFm6dCnz58/PtOdz+rabgNkt1LRrF8h0tpH07+Po6Gh69+7N5MmTzX4mTpxIjRo1TNe6NdK/BwCGDx+Or68vKpWKv/76K8+1gKLgxCQk8f3u1Da8U3vUwclRUoOiKs81dQkJCezYscPUUDyr22gqlYq33norr4cr9rZu3crdu3cxGAycPHnSbGYBBwcHWrZsmavyRo4cycyZM023ZPr372/W+1WnS21D4ejomOEbfceOHU01UmntfdL+CLRt2xaVSmX2B6lKlSoZxsqytYiICD755BOSkpI4e/YsGzZsMPX0hdQk9OHrzsPDg2bNmnHw4EEg47m1a9eOdevWmQYxhow1kLVr1zaNM7Zx40aeffZZKlSowMaNG/O904coni5dusS7777LxIkTadeuHY0aNaJUqVLExMSY1QSrVCqztrAVKlQwtWOdO3cud+7cwdXVlcaNG9O5c2dGjRrFrFmzuHPnDsnJybRq1YqnnnqKqlWrotVqOXnyJDt37iQ2NpZLly6Z1ezlRo0aNVCr1RiNRiA1qRs8eDBRUVEyfVkR89mfZ9HqDDSp7EPPoKyHzhKFX56SutmzZzNr1iySkpJMyx5O6lQqFYqiSFJnodWrV2dZ0/P222/nurt/qVKlWLNmDf369SM+Pp4HDx6Yat3SODo6Mn/+fBo0aGC2PLNbqWmJT6lSpahfvz7h4eGmdfaopQsNDc0yiRo4cCALFizIdDaJjh07mpI6SO1oUrduXQCzYUvSPHxuL7/8MkuXLuXevXsYjUZTb0VHR0eGDRuWYeone0rSGRi5KLU95NIxLaTXWiGTlJRESEiI2WDE6U2dOtWsQ03//v1Nw/JcvHjR1Hnq+eefp3Pnzvj4+PDrr7/St29fYmNjSUhIMF2f+cnX15dx48bx3XffAXDq1CneeecdADp37szp06ez7SgkCoez0ff45XBqR7E3eteVTitFnNVJ3TfffGNqxN+kSROaN2+Or68varVU2+YXZ2dnypcvT/PmzXnmmWfo3r27VeV07NiREydO8Omnn7J161YiIyMxGo34+/vToUMHJk6cSMOGDTPsV6tWLSpUqGD6YFapVLRp08a0vl27dmZJXXbt6WxJrVbj7OxMqVKlCAwMpFmzZowaNcqsTdLDOnbsaDajRJs2bUwfZo0aNcLT09NUC+nq6pqhhrRGjRrs3r2b1157jb1796JWq2natCnvvfceFy9eLFRJnVFROHQp1vRYFA4TJ04kODiYv/76i9DQUG7evMmtW7cwGAz4+fnRvHlzxo4dS69evcz2e//99zEYDKxdu5abN29m2iSjbdu2RERE8MUXX7BlyxbOnz9PcnIy5cuXp1KlSnTq1IknnngizzXrX375JQEBASxcuJAbN27g7+/PoEGDePfdd01fkkThNmfzaYwK9KhfnqaBpe0djsgrxUr169dX1Gq18t1331lbhMXu3bunvPzyy4q/v7/i7OysNGzYUFm5cqVF+/71119Kly5dlHLlyinu7u5KcHCwMm/ePEWv1+cqhvj4eAVQ4uPjrTmFHKWkpCi//vqrkpKSYpPyRcn1IFmnBL72hxL42h/Kg2Sd1eXINSoKM7k+c2/f+VtK4Gt/KNWnbVQu3rpv73CKvfTXqK1yCqtr6i5cuEBAQADPPfdc/mWYWejfvz+HDx9mzpw51KpVixUrVjBkyBCMRmO2Yyz9+eefdO/enUcffZQFCxbg7u7O77//zssvv8yFCxeYN2+ezWMXQgghChujUeGDTacAGNayMlXLuts5IpEfrE7qSpUqle1cpPll06ZNhISEmBI5SL11dvnyZaZMmcKgQYMyHZ8NUieO1mg0/PHHH7i7p16wXbp04cyZMyxZskSSOiGEECXS78duEH49AU9nR17qXNPe4Yh8YnUDuB49enDy5EkSEhLyM54M1q9fj4eHBwMHDjRbPnr0aG7cuMGhQ4ey3Fej0eDk5JShobyPjw8uLi42iVcIIYQozJJ0Bj7eegaA8R2qU8bD+vl/ReFidVL3/vvv4+3tzbhx49BqtfkZk5nw8HDq1q1rNjYTYOqpmb6h/sPGjx9PSkoKL730Ejdu3CAuLo5ly5axfv16pk6darOYhRBCiMJq6f5IrsdpKe/lwpg2Ve0djshHVt9+DQkJYfz48bz//vvs2bOHIUOGUL16ddNtzsw8/fTTuT7OnTt3qFatWoblpUuXNq3PSsuWLfnrr78YOHAgX3/9NZA61tvs2bOZNGlStsdNTk42m9omrUZSp9OZxnbLT2ll2qJsUbLpdHpcNep/H+vQqazrASvXqCjM5Pq0zN3EFL7akTpDycTO1XFUGdHpjHaOqmRIf43a6jpVKYp1Yxyo1WrTGHRg2ZRPuZ0NAVKH1ahevTqbN282Wx4VFUVAQACzZ8/m9ddfz3TfI0eO0KtXL1q2bMm4ceNwd3fnr7/+4qOPPuLNN9/Mdty8GTNm8O6772ZYvmLFCpnWRgghRJG0LlLNrig1AW4KUxoYUMuwdHaRmJjI0KFDiY+Px8vLK9/Ktbqm7umnny6QQQrLlCmTaW1c2gwJaTV2mXn++efx8/Nj/fr1ps4UHTt2RK1WM2PGDIYNG5ZpLSDAtGnTePXVV03PExISqFSpEt26dcvXX0AanU5HSEgIXbt2zTBHakm2a9cuunb9b97Ss2fPFvisFSKVXKPFW1F/r8n1mbPLsYlM/nsfoDBrYFPa1Shr75BKlPTXqK2arVmd1BXUNDDBwcGsXLkSvV5v1q7uxIkTAAQFBWW5b1hYGEOGDMnQO7Z58+YYjUZOnTqVZVLn7OyMs3PGxqMajcamHxi2Lj+9nTt3ZjpgsFqtxtPTk2rVqtG1a1deeeWVAunpnJmH21IW5OsjMie/g9yT91rBKapxF4TPtl9AZ1BoV7Msner62zucEkuj0aDX621Sdp7nfrW1fv36sWDBAtauXcugQYNMy5cuXUpAQEC2c6EGBAQQGhqKwWAwS+wOHDgAkOspt0oKo9FIfHw8//zzD//88w8//vgjf//9N5UqVSrwWKpXr87HH39sep5dzazIXJLOwITlRwD4dnhTmSasEJH3migo/1y5y8bjUahUML2XzPZRXOVrUnf//n3u3buHp6cnHh4e+VJmz5496dq1KxMmTCAhIYEaNWqwcuVKtmzZwvLly03J2tixY1m6dCkXLlwgMDAQgFdeeYWXXnqJPn368Nxzz+Hm5sb27duZO3cuXbp0yXRqrJJs0KBBNGvWjISEBH799VdTbejNmzf57LPP+PTTTws8pkqVKjF58uQCP25xYlQUdpy5ZXos7E/ea6IgKcp/Aw0PaFKRuv7534RIFA55nqg1PDycUaNGERAQgLe3NxUrVsTb25uAgADGjBmT7ZAjllq3bh0jRozg7bffpkePHhw6dIiVK1cybNgw0zYGgwGDwUD6fh8vvvgia9eu5d69ezzzzDP069ePP/74g3feeYdff/01z3EVNz169GDy5Mm899577NmzBycnJ9O6kydPZth+586dPPXUU1SqVAlnZ2e8vb1p164dCxcuxGjMvDfVDz/8QHBwMC4uLlSsWJFXXnmFe/fuUaVKFVQqFSqVihkzZpgdI225SqUiMjLStG7UqFGm5R06dODs2bP069cPb29vSpcuzdChQ4mOjgZgx44dtGvXDjc3N8qVK8fYsWO5e/dupjFac15C5Ia816w/L5F7205GczjyLi4aNZO61bJ3OMKW8jLH2A8//KA4OzsrarVaUalUmf44OzsrP/zwQz7MaGZfxXHu1x07diiA6Wfx4sVm60uXLm1aN3ToULN1r732mtm+D//07t07w7m8/vrrmW7bvHlzxc/Pz/T8nXfeyTLGS5cumdaNHDnStLxq1apKqVKlMpRdu3ZtZfny5Yparc6w7tFHH83wmlhzXoWdzP1qf/Jes/17Ta7PzKXoDUrHj3coga/9oXy05ZS9wynRCmLuV6tr6g4dOsRzzz1HSkoKPXv2ZOvWrVy7dg2dTse1a9fYunUrvXr1IiUlhfHjx/P3339beyhRwBISEvjiiy9MPYwBnnrqKdPjFStW8OGHH5qe9+7dm5kzZzJ+/HjT7B0bN27knXfeMW1z+PBhs318fX2ZMmUK48eP59ixY6Zv+da6dOkSarWaKVOmMGDAANPyM2fOMHz4cCpXrsz06dNp3769ad3u3bs5ePBgns5LiLyQ95rl5yWs8/Phq1y8/YAy7k6Mb1/d3uEIW7M2GxwwYICiVquV2bNnZ7vdhx9+qKhUKuXJJ5+09lCFQkmoqcvsx83NTfn444/N9mvcuLFp/bhx48zWfffdd6Z1Hh4eSnJysqIoijJu3DjTcrVarYSHh5v2Wbx4sdkxrak9AJS9e/cqiqIoRqNR8ff3Ny3XaDTKlStXFEVRlLi4OEWj0ZjWffHFF3k6r6JAaursT95rtn+vyfWZ0b0kndJ05jYl8LU/lKX7L9k7nBKvUNfU7d27l3LlymU58G+aKVOm4Ovry549e6w9lLCjfv36MWHCBNPzxMREwsLCTM+///57s3Y448ePN627f/8+x48fB1IHgk7TtGlT6tevb3o+fPjwDMMp5FZgYCBt2rQBUgfCTussA9CmTRtTb0Jvb298fX1N69La+lh7XkLkF3mvyXstv83fdYHb91OoWtadIS0q2zscUQCsTuru3r1r9mbOStqbPi4uztpDiQIyaNAgPvjgAx577DHTsp9++ol+/fqZOqDcvXvXrDNKTm7dSu11mf73//A4XI6OjpQtm7dBMCtUqGD2PP0Ygw+vS/9HLa0xtrXnJYQ15L0m7zVbuxmfxII9FwF4rUdtNA557hcpigCrv7L5+vpy/vz5DIMCP0yn03H+/Pk8f5AI2+vRowejRo0CYPz48cyfPx9Inef3p59+Yvjw4fj4+Jjt079/fx555JEsy6xduzaA2X4xMTFm2+j1em7fvp2n2LMbbNSSmglrz6socHNyJHJOb3uHIdKR99p/itN7rTD5NOQMSTojzQJL0b2+fQa0FgXP6qSuQ4cOrFixgtdee425c+dmud1rr71GXFwcvXvLH5WiZM6cOfz888/Ex8cD8O677zJkyBDc3d1p2LAhx44dA1K/db/yyisZZu24desW+/btM83Y0bx5c9NtodDQUM6fP0+NGjUAWL58uc1G17aUteclRF7Je03ea/nt9M0EVh+5BsC0XnULZEpPUThYndRNmzaNNWvW8Pnnn7Nr1y6ef/556tWrh6+vLzExMZw8eZKvvvqKsLAwnJyccmx7JwoXHx8fnn/+eT744AMAzp8/zy+//MLQoUOZPHkyI0aMAFLHpGrYsCGPPfYY3t7exMTEEBoayoEDB2jbti1PPPEEkDo49Pz581EUBYPBQPv27RkxYgQJCQksXLjQXqdpxprzEiKv5L0m77X8NnvTaRQFegWXp2lgKXuHIwqQ1UldvXr1+OWXX3j66ac5evQozzzzTIZtFEXBy8uLZcuWUa9evTwFKgrexIkT+fzzz0lMTATggw8+YMiQIQwfPpzjx4+bphSKiIggIiIi27KaNWvGa6+9xpw5cwC4ceOGaUiDJk2acP36ddNQC2q1fdp+WHNeRUGSzsCrq8IA+PSpRjJNWCEk77Xi8V4rDPaeu82us7fQOKiY2r2OvcMRBSxP7+jHH3+cU6dOMX36dJo1a4aXlxdqtRovLy+aNWvGm2++yalTp+jTp09+xSsKULly5cyS9YiICNavXw/ARx99xK5duxg8eDCVK1fG2dkZLy8v6tSpQ9++fVmwYAGrVq0yK2/27Nl8//331K9fHycnJ/z9/XnhhRfYvn079+7dM233cJubgmTNeRV2RkVh04mbbDpxU6YJK6TkvVY83mv2kKQzsO7oNcYvO8Kg+QeY8FPqrfdBzStRpay7naMTBU2l5KYbUgmWkJCAt7c38fHxeHnl/7x5Op2OTZs20atXr2wbIhdlWq3WNLBoen/88YdZ4r9v3z5at25dkKEVa4kpeuq9vRWAk+91x83Jugr6knCNFhcl8b1WEq/PkJPRTFodRoJWj1oFxnR/zT1dHPnsqUZ0qednvwCFmfTXqFartUlOkbcBi4TIhenTpxMWFkafPn2oWrUqer2ew4cP8+2335q2adasWbY94YQQOZP3WvEXcjKacctCU4dpxjyhA7ifpOfZZaF8P6IZXSWxKzEkqRMFRlEUdu7cyc6dOzNdX6NGDVavXi09tYTII3mvFW9JOgOTVoeBYsrpMlAAlQKTV4dxaHoXaUtbQliU1I0ZMwYAf39/3n//fbNlllKpVIWm55WwjyeeeILo6GgOHTrErVu3SEpKwsfHh6CgIPr168czzzyDm5ubvcMUosiT91rxtulEFAnanIemUYB4rZ7N4VH0a1zR9oEJu7MoqVuyZAkAderUMSV1acssJUmd6NChAx06dLB3GEIUe/JeK962RURnaEOXFbUKtoZHS1JXQliU1C1evBhIncvv4WVCCCGEKDhxiSkWJXSQmvjFaVNsG5AoNCxK6kaOHGnRMiFE4eOqceDke91Nj4UQRZuPm1Ouaup8XJ1sH5QoFGSGXyGKOZVKhZuTI25OjtIwXohioFt9v1zV1HUPkt6vJYXVSZ2DgwPt27e3aNuOHTtaNNGzEEIIIbLXK9gfd+eca91VgLerIz2D/G0flCgUrE7qFEUhN+MWyxjHQthHst7ApFXHmLTqGMl6g73DEULkkUoFXi7ZD7Cs+vefuQNlasCSpEBuvz548KDEjPAtRGFjMCqsPXqNtUevYbD0no0QotD6escFouKT8HR2xNMl9S6Y+t+WFWn/e7k6smBEM5lRooSx+T3RM2fOEB4eToUKFWx9KCGEEKJYOxWVwDc7zgMwe0AwXer6sTk8iq3h0cRpU/BxdaJ7kB89g/ylhq4EsjipmzdvHvPmzTNbFhoaSrVq1bLcR6vVEhMTA0Dfvn2tDFEIIYQQeoOR19YeR29U6FbPj97B/qhUKvo1rijj0AkgF0ldXFwckZGRpucqlYqkpCSzZZnx9PRk4MCBzJo1y9oYhRBCiBJv0b5LHL8Wj6eLIzOfCJLe7CIDi5O6iRMnMmrUKCC100O1atVo3rw5q1atynR7lUqFq6sr5cqVy5dAhRBCiJLq0u0HzN12FoC3etfDz8vFzhGJwsjipM7b29tsRomRI0dSq1YtAgMDbRKYEEIIIcBoVHh97XGS9Uba1ijLwGZyq1VkzuqOEjJNmBBCCGF7K/6+wqFLsbhqHJjdP1huu4osWZ3URUdHs3XrVmrUqEHr1q2z3G7fvn1cuHCBHj164Ovra+3hhBBWctU4cOTNLqbHQoii40acljmbTwMwpXttKpV2s3NEojCzepy6b7/9ltGjR3Pt2rVst7t+/TqjR4/m+++/t/ZQQog8UKlUlPFwpoyHs3zDF6IIURSFN9af4H6yniaVfRjZuoq9QxKFnNVJ3R9//IGzszMDBgzIdrv+/fvj7OzM77//bu2hhBBCiBLnt7Ab7DhzCycHNR892QAHtXwpE9mz+vZrZGQkVatWxcEh+9s5jo6OVK1alcuXL1t7KCFEHiTrDcz64xQAbz5WF2dHuQUrRGF3+34y726IAOClzjWo4etp54hEUWB1TV1iYiJubpbd23d1dSUhIcHaQwkh8sBgVFh28DLLDl6WacKEKCJm/B7B3UQddcp78lz76vYORxQRVid1FSpU4NSpU2i12my302q1nD59mvLly1t7KCGEEKLE2BZxkz+OR+GgVvHxkw3ROBTINO2iGLD6SunYsSNarZaZM2dmu92sWbNITEykc+fO1h5KCCGEKBHitTre/DUcgGfbVSO4oncOewjxH6uTusmTJ6PRaPjwww8ZN24c586dM1t/7tw5nnvuOebMmYOTkxOTJ0/Oc7BCCCFEcfbBxlPE3Eumall3Jnapae9wRBFjdVJXq1YtFi5ciKOjIwsXLqROnTqUKVOG6tWrU6ZMGerUqcOCBQvM1gshhBAic/vO3+aX0KsAfDigAS4yrqTIpTzdqB82bBj79u2jR48eODo6cvfuXS5dusTdu3fRaDQ89thj7N+/n2HDhuVXvEIIIUSxk5ii5/V1xwEY0SqQFlVL2zkiURRZPaRJmmbNmrFx40aSkpI4f/48CQkJeHp6UrNmTVxcZMJhIYQQIiefbD3L1VgtAd4uvNZT7mwJ6+Q5qUvj4uJCUFBQfhUnhMgnLo4O7Jna0fRYCFG4HL1yl8X7LwHwQf9gPJzz7U+zKGHkyhGimFOrVTJfpBCFVLLewNQ1x1EU6N+kAh1qyxzpwnr5ktQdPHiQY8eOERsbi06ny3QblUrFW2+9lR+HE0IIIYqFr/86z/mY+5T1cOKt3vXsHY4o4vKU1O3evZuxY8dy8eLFbLdTFEWSOiHsJEVv5JNtZwCY3K02To4ykKkQBS1JZ2DTiSi2RUQTl5iCj5sT9QO8+HrHeQDe6xtEKXcnO0cpijqrk7qTJ0/Ss2dPdDodw4YNY9euXVy7do3p06dz9epVjh07xrFjx3B1dWXChAl4esq8dULYg95o5PvdqV+8JnapiVPeOr0LIXIp5GQ0k1aHkaDVo1aBUQG1CrZE3ASgcWUfegbJrEsi76xO6ubMmUNSUhI//PADo0ePpl27dly7ds1sholt27YxduxYtm7dyoEDB/IlYCGEEKKoCDkZzbhlofDvtMvGh/4HCLsSx5+nYuhaz6/gAxTFitVf2Xfu3Im3tzcjR47Mcptu3bqxbt06IiIieO+996w9lBBCCFHkJOkMTFodBoopp8vS5NVhJOkMBRGWKMasTupiYmKoUqUKanVqEY6OqZV+Wq3WbLvmzZtTu3Zt1q1bl4cwhRBCiKJl04koErT6HBM6BYjX6tkcHlUQYYlizOqkztvbG4Phv28VpUunjn59+fLlDNs6OTlx/fp1aw8lhBBCFDnbIqJRqyzbVq2CreHRtg1IFHtWJ3WVK1cmKuq/bxXBwcEAbNiwwWy7yMhIzpw5g5eXl7WHEkIIIYqcuMQUs7Zz2TEqEKdNsW1AotizOqnr2LEjd+7cITIyEoAhQ4agUql44403ePPNN9m4cSOLFi2iW7du6HQ6evXqlV8xCyGEEIWej5tTrmrqfFxlSBORN1b3fh0wYADr169n7969VKlShdq1azNz5kzeeOMNZs+ebdpOURSqVavGnDlz8iVgIUTuuDg6sO2VR02PhRAFo1t9P9OwJTkxKtA9SHq/iryxOqlr2bIl586dM1s2bdo02rZty08//URkZCSurq60bduWcePGyTh1QtiJWq2ilp+8/4QoaL2C/ZmxIYIErT7b7VSAl6sjPYP8CyYwUWzl+9yv7dq1o127dvldrBBCCFGkuGgceKFjTT7YdCrLbVT//jN3YCNcNFKTLvLG6jZ1nTp1olevXqSkSMNOIQqzFL2Rz0LO8lnIWVL0RnuHI0SJEZ+oY+n+SAAc/21cl9bGLu1/L1dHFoxoRhcZeFjkA6uTugMHDhATE4OTk+0bdt6/f5+JEycSEBCAi4sLjRo14ueff7Z4/99++4327dvj5eWFu7s79evX5/vvv7dhxEIUHnqjkXnbzzFv+zn0RknqhCgIiqIwafUxrsdpqVzajQPTOvHZoIZ0q1eeVtVK061eeT4b1JBD07tIQifyjdW3XytXrkxSUlJ+xpKl/v37c/jwYebMmUOtWrVYsWIFQ4YMwWg0MnTo0Gz3nTNnDm+88Qbjx49n2rRpaDQaTp8+LTWMQgghbOaHPZf481Q0Tg5qvhnWhHKeLvRrXJF+jSvaOzRRjOWp9+tHH33E2bNnqVWrVn7GZGbTpk2EhISYEjlIHU7l8uXLTJkyhUGDBuHgkHk7hCNHjph6406dOtW0vHPnzjaLVwghRMl25PJdPtxyGoC3+tQjqIK3nSMSJYXVt1/ffPNNGjVqRN++fTl27Fh+xmRm/fr1eHh4MHDgQLPlo0eP5saNGxw6dCjLfb/66iucnZ158cUXbRafEEIIkebugxReXHEUvVHhsQb+DG9Z2d4hiRLE6pq6F154gZo1a7JmzRqaNGlC/fr1qVu3Lu7u7plur1KpWLhwYa6PEx4eTt26dU1zy6Zp0KCBaX3r1q0z3Xf37t3UrVuXtWvXMnPmTM6fP4+/vz/Dhw/nvffeK5D2gEIIIUoGo1Hh1VVh3IhPompZd2b3D0alsnD0YSHygdVJ3ZIlS1CpVChK6hwo4eHhhIeHZ7m9tUndnTt3qFatWoblaXPN3rlzJ8t9r1+/zq1bt3jppZeYOXMm9erVY/v27cyZM4erV6/y008/ZblvcnIyycnJpucJCQkA6HQ6dDpdrs8jJ2ll2qJsUbLpdPp0j3XoVBbOW5ShHLlGReFVGK7P+bsvsePMLZwd1cx7qgEuDvJ+Ef9Jf43a6rqwOqlbvHhxfsaRrey+6WS3zmg0cu/ePVauXMngwYOB1PZ4Dx484PPPP+fdd9+lRo0ame47e/Zs3n333QzLt23bhpubWy7PwHIhISE2K1uUTMkGSHurb926Dec8DoUl16gozOx1fV5IgK8iHAAV/SrruPTPHi79Y5dQRCEXEhJCYmKiTcq2OqkbOXJkfsaRpTJlymRaGxcbGwv8V2OX1b43b96ke/fuZst79uzJ559/ztGjR7NM6qZNm8arr75qep6QkEClSpXo1q0bXl5e1pxKtnQ6HSEhIXTt2hWNRpPv5YuSy2BUqN44taa5foAXDpZORvkQuUZFYWbP6/PO/WQ++OYgRpLp29Cf9wYEyW1XkUH6a1Sr1drkGBYndaVLl6Z169b88ccfGdbt3r0bb29vGjZsmK/BAQQHB7Ny5Ur0er1Zu7oTJ04AEBQUlOW+DRo04ObNjPPupd0yVquz7ifi7OyMs7NzhuUajcamHxi2Ll+UPBqgadWy+VeeXKOiECvo69NgVJiy7ijR95KpXs6dD/o3wMkp3ydrEsWIRqNBr89+6jhrWdz7NS4uztSu7GEdOnTgpZdeyreg0uvXrx/3799n7dq1ZsuXLl1KQEAALVu2zHLfAQMGALB582az5Zs2bUKtVtO8efP8D1gIIUSJ8fWO8+w5dxsXjZpvhzfF3VkSOmE/+Xb1pdV+5beePXvStWtXJkyYQEJCAjVq1GDlypVs2bKF5cuXm8aoGzt2LEuXLuXChQsEBgYCqcOezJ8/n//973/cvn2bevXq8eeff/L111/zv//9z7SdEMVZit7I4n2XABjdpipOjlaPZCSESGf/hdt8/udZAGY9EUwtP087RyRKuiLxlWLdunW88cYbvP3228TGxlKnTh2zzg8ABoMBg8FgllxqNBpCQkKYPn06H3zwAbGxsVStWpU5c+aYtZcTojjTG43M3pw6EOqIRwJxsn54SiHEv2LuJfHSyjCMCgxsWpEnm8pMEcL+ikRS5+Hhwbx585g3b16W2yxZsoQlS5ZkWF66dGm+++47vvvuOxtGKIQQoqQwGBVeXhnG7fvJ1Pbz5L2+WbftFqIgyVd2IYQQIhfm/XmWAxfv4ObkwNfDmuDqlMdxgoTIJ5LUCSGEEBbaffYWX+44D8Ds/sHU8PWwc0RC/CdXt19DQ0Mznd1BpVJluS79NhcuXMh9hEIIIUQhEJ2QxCu/hKEoMKRFZfo2qmDvkIQwk6ukLikpicjIyFyvg+xnfhBCCCEKiySdgU0notgWEU1cYgo+bk50ruvLL39f5c6DFOr5e/FOn3r2DlOIDCxO6gpyWjAhhBDCHkJORjNpdRgJWj1qFRgVUKtgS0TqQPYujmq+GdYEF420oxOFj8VJXUFNCyaEyF/Ojg6sfLaV6bEQInMhJ6MZtywU/h0Zy/jQ/wDJeiPnYu5Tpax7wQcoRA6ko4QQxZyDWsUj1cvwSPUyVs/7KkRxl6QzMGl1GCimnC5Lk1eHkaQzFERYQuSKJHVCCCFKvE0nokjQ6nNM6BQgXqtnc3hUQYQlRK5IUidEMaczGPnxQCQ/HohEZzDaOxwhCqVtEdFYWpGtVsHW8GjbBiSEFYrEjBJCCOvpDEbe/i0CgCebVkTjIN/lhHhYXGKKWdu57BgViNOm2DYgIawgn+5CCCFKPB83p1zV1Pm4Otk2ICGsIEmdEEKIEq9bfb9c1dR1D/KzbUBCWEGSOiGEECVer2B/PF1ybpGkArxdHekZ5G/7oITIJUnqhBBClHgGo0IpN02226j+/WfuwEYy+LAolKxO6l577TVOnz6dn7EIIYQQBc5gVHj553+4EqvF09kRD+fUGru0NnZp/3u5OrJgRDO61JNbr6Jwsrr368cff8wnn3xC8+bNGTVqFIMHD8bHxycfQxNCCCFs7/2Np/jzVAxOjmqWjGlB/QAvNodHsTU8mjhtCj6uTnQP8qNnkL/U0IlCzeqk7n//+x+//PILf//9N4cPH+aVV16hb9++jBo1iu7du6NSycj1QhQGTg5qFo1qZnoshPjPjwciWbTvEgCfPtWQpoGlAOjXuCL9Gle0Z2hC5JrVn/BfffUVN27cYM2aNTz22GMYjUZWrVpF7969qVixIq+//jqnTp3Kz1iFEFZwdFDTqY4fner44ShJnRAmO07HMOP31DEcp3SvzWMNAuwckRB5k6dPeI1GQ//+/fntt9+4fv06n332GQ0bNiQqKoqPPvqIoKAgWrZsyXfffUdcXFw+hSyEEELkzckbCbyw4ihGBZ5qVpH/dahu75CEyLN8+9petmxZXn75ZY4ePcqxY8d45ZVX8PX15fDhwzz//PP4+/szePBgtm7dml+HFEJYQGcwsjr0KqtDr8o0YUIAN+OTGLPkMA9SDLSuXob3+wVLkyFRLNjkXkxwcDBjx45lyJAhODo6oigKycnJrFq1il69elGrVi1WrVpli0MLIR6iMxiZsuY4U9Ycl6ROlHgPkvWMXXqYmwlJ1PD14NvhTWXqPFFs5Ovcr7GxsaxcuZKlS5dy5MgRABwcHOjTpw+jR48mOjqaH374gSNHjjBkyBDi4uIYN25cfoYghBBCZCpt6JKIGwmUcXdi8ajmeLtmPzadEEVJnpM6g8HAxo0bWbp0KRs3bkSn06EoCnXq1GH06NE8/fTT+Pn9N6bPc889x9q1a3nqqaeYO3euJHVCCCEKxKyNJ/nzVAzOjmoWjGxGpdJu9g5JiHxldVL3zz//sHTpUlauXMnt27dRFAVPT09GjBjBmDFjeOSRR7Lcd8CAATRu3Jjjx49be3ghhBDCYkv3R7J4XyQAnz7ViCaVS9k3ICFswOqkrmnTpqhUKhRFoV27dowZM4aBAwfi5mbZNx8PDw/0er21hxdCCCEs8tfpaN7dkDp0ydQetendQOZtFcWT1UldQEAAI0eOZMyYMVSvnvuu4Dt37rT20EIIIYRFIm7E88KKfzAqMKhZJSa0l6FLRPFldVJ35coV1GrpMSSEEKJwuhmfxNgloSSmGGhTowyz+gXJ0CWiWLM6K+vSpQsTJ060aNtXXnmFzp07W3soIUQeODmo+XpoE74e2kSmCRMlxsNDl3wzTIYuEcWf1TV1O3futLhNXFhYGLt377b2UEKIPHB0UEsbIlFsJekMbDoRxZbwKC5eU7MxPoxu9f3549gNGbpElDj5Ok5dVlJSUnBwcCiIQwkhhCghQk5GM2l1GAlaPWoVGBU1F0/FsO1kDACOapUMXSJKFJsndVqtlrNnz1KmTBlbH0oIkQm9wcjWiGgAutf3w1FuQYliIORkNOOWhYKS+tz40P+QOtjwnfspBR+cEHZicVL322+/8dtvv5ktO3fuHGPGjMlyH61Wy+HDh4mNjeXJJ5+0PkohhNVSDEaeX3EUgJPvdZekThR5SToDk1aHgWLK6bI0eXUYh6Z3wUUjd4tE8WdxUhcWFsaSJUtMz1UqFdHR0WbLslKzZk0++OADa+ITQgghzGw6EUWCNuc23QoQr9WzOTyKfo0r2j4wIezM4qTuiSeeoEqVKgAoisKYMWOoVasW06ZNy3R7lUqFq6sr1apVo0mTJtKNXAghRL7YFhH9bxu6nLdVq2BreLQkdaJEsDipa9iwIQ0bNjQ9nzFjBg0bNmTkyJE2CUwIIYTITFxiikUJHaQmfnFaaVcnSgarO0pERkbmYxhCCCGEZXzcnHJVU+fj6mT7oIQoBKTFtBBCiCKlW32/XNXUdQ/ys21AQhQSFtXU/fjjjwB4e3vTt29fs2W58fTTT+d6HyGEECK9iqUsG3dOBXi5OtIzSAbfFiWDRUndqFGjUKlU1K5d25TUpS3LDUnqhCh4Ggc1Hz/ZwPRYiKLs6JW7jF1y2PRcRebDmqj+/WfuwEYynIkoMSxK6p5++mlUKhX+/v4ZlgkhCjeNg5qBzSrZOwwh8uzI5VhGLjrM/WQ9LaqWZkSrQN789QTxphklMP3v5erI3IGN6FJPbr2KksOipC6zsegsGZ9OCCGEyA+HI2MZtehvHqQYeKRaGRaOaoabkyNd6/mxOTyKzSeiuHjtJtUqlqdnsD89g/ylhk6UOAUy96sQwn70BiO7z90C4NGa5WRGCVHkHLx4hzFLDpOYYqBNjTL88HRzXJ1SEzYXjQP9GlfksSA/Nm3aRK9ejdBoNHaOWAj7kKROiGIuxWBkzJJQQKYJE0XP/gu3GbskFK3OQLuaZVnwdDOpgRMiC1Z/uv/+++9Uq1aNuXPnZrvd3LlzqVatGps2bbL2UEIIIUqgfedvM2bJYbQ6A+1rlZOETogcWJ3U/fjjj1y+fJl+/fplu13fvn2JjIy0aggUIYQQJdPus7cYs+QwSTojHWuXY/6IppLQCZEDq2+//vPPP/j6+lKtWrVst6tRowZ+fn6EhoZaeyghhBAlyM4zMYxbdoQUvZEudX35elgTnB0loRMiJ1bX1N24cYPKlStbtG2lSpWIioqy9lBCCCFKiL9ORzPux9SErls9P74Z1lQSOiEsZHVNnbu7O7du3bJo29u3b+Ps7GztoYQQQpQAISej+d9PR9AZFHoGleeLIY1lwGwhcsHqpC44OJjdu3cTGhpKs2bNstwuNDSUyMhI2rZta+2hhBBCFANJOgObTkSxLSKauMQUfNyc6Fbfj17B/uw8c4sXVx5FZ1DoHezP54MbSUInRC5ZndQNHTqUXbt2MWzYMDZv3pxp27pLly4xbNgwVCoVQ4cOzVOgQgjraBzUvNe3vumxEPYQcjKaSavDSHho9octETd589dwknQGjAr0aRjAZ081lKF3hLCC1UndmDFjWLp0Kfv37ycoKIj+/fvTsmVLfHx8iIuL4+DBg/z6669otVpat27Ns88+m59xCyEspHFQ8/QjVewdhijBQk5GM25ZqGmSVuND/yemGABoWbW0JHRC5IHVSZ2DgwN//PEHo0eP5rfffmPFihWsXLnStF5RUt+t/fr1Y+HChTg4SENXIYQoaZJ0BiatDgPFlNNl6VRUAnqjgvSLEMI6efo65OPjw/r16/n7779544036NevH507d+aJJ57gzTffJDQ0lLVr1+Lj45OnIO/fv8/EiRMJCAjAxcWFRo0a8fPPP+e6nDfffBOVSkVQUFCe4hGiKDEYFQ5cuMOBC3cwGHP6sypE/tp0IooErT7HhA4gIUnP5nAZKUEIa+XLNGHNmjXLtrNEXvXv35/Dhw8zZ84catWqxYoVKxgyZAhGo9HitnphYWF88skn+Pn52SxOIQqjZL2BIQsOAqnThLk5yeyAouBsi4g2taHLiVoFW8Oj6de4ou0DE6IYKvSf7ps2bSIkJMSUyAF07NiRy5cvM2XKFAYNGpTjrV29Xs/o0aN57rnnOHbsGLdv3y6I0IUQosSLS0yxKKGD1MQvTpti24CEKMYKfWvU9evX4+HhwcCBA82Wjx49mhs3bnDo0KEcy5gzZw6xsbG8//77tgpTCCFEJnzcnFCrLNtWrQIfVyfbBiREMWZRTV3acCU1atRg27ZtZssspVKpuHDhQi7Dg/DwcOrWrYujo3moDRo0MK1v3bp1lvufPHmSWbNmsW7dOjw8PHJ9fCGEENbrVt+PLRE3LdrWqED3IGkiI4S1LErqIiMjAXBxccmwzFIqlYVf1R5y586dTBPI0qVLm9ZnxWg0MmbMGPr370+vXr1yddzk5GSSk5NNzxMSEgDQ6XTodLpclWWJtDJtUbYo2XQ6fbrHOnQq6zpLyDUqrNGggqdFbepUgKeLI11rl7XqGpPrUxR26a9RW12nFiV1ly5dAkCj0WRYVhCySwizW/fpp59y7tw5fv/991wfc/bs2bz77rsZlm/btg03N7dcl2epkJAQm5UtSqZkA6S91bdu3YZzHoeLkGtUWOrGA5h/2gGjouK/AU0y+8xOXTcoMJntIVvzdEy5PkVhFxISQmJiok3KtiipCwwMtGiZLZQpUybT2rjY2Fjgvxq7h125coW3336bOXPm4OTkRFxcHJDaacJoNBIXF4ezszOurq6Z7j9t2jReffVV0/OEhAQqVapEt27d8PLyyuNZZaTT6QgJCaFr165mybMQeZWYomfq338B0L17N6t7v8o1KnJj34U7vLHyGPdT9FQr686YNoF8tPUsCUnmM0oYFfBy0fDRgCA61/G1+nhyfYrCLv01qtVqbXKMQt/7NTg4mJUrV6LX683a1Z04cQIgyzHnLl68iFar5eWXX+bll1/OsL5UqVK8/PLLfP7555nu7+zsjLOzc4blGo3Gph8Yti5flDyuKgem9ayT+tjZGY1j3vpHyTUqcrI69CrT1p1Ab1RoUbU0C0Y0w9tNw5PNKrM5PIqt4dHEaVPwcXWie5AfPYP8cdHkz4jDcn2Kwk6j0aDX63Pe0AqFPqnr168fCxYsYO3atQwaNMi0fOnSpQQEBNCyZctM92vUqBE7duzIsHzixInEx8ezePFiKlaUsZBE8efkqOa59tXtHYYoARRFYd72c3z+5zkAHm8YwMcDG+D87xQRLhoH+jWuKOPQCWEjFiV1Y8aMyfOBVCoVCxcuzPV+PXv2pGvXrkyYMIGEhARq1KjBypUr2bJlC8uXLzeNUTd27FiWLl3KhQsXCAwMxMfHhw4dOmQoz8fHB71en+k6IYQQ1knRG5m+/gRrjlwD4H8dqjO5W23Ulo5nIoTIM4uSuiVLluT5QNYmdQDr1q3jjTfe4O233yY2NpY6deqwcuVKBg8ebNrGYDBgMBhMc84KIVIZjArh1+MBCKrgjYP8kRX5LCFJx/+WH2Xv+duoVTDziSCGtSyYdtdCiP9YlNQtXrzY1nFky8PDg3nz5jFv3rwst1myZIlFyefOnTvzLzAhioBkvYG+X+8DZJowkf+i4rWMXnyY0zfv4ebkwNdDm9AxDx0ehBDWs+jTfeTIkbaOQwghRBFz8kYCo5f8TXRCMuU8nVk8qjlBFbztHZYQJZZ8ZRdCCJFru8/e4n8/HeV+sp6avh4sHt2ciqVsN4anECJn+ZbUabVaLly4wL179/D09KR69epZjgEnhBCicEvSGdh0IoptEdHEJabg4+ZEt/p+9Ar25/ewG0xbfwKDUaFVtdLMH9EMb1cZRkQIe8tzUrd582Y++ugj9u3bh8FgMC1Xq9W0bduWKVOm5HqKLiGEEPYTcjKaSavDSNCaDxS8JeIm09adIFlvBOCJRgF8+OR/Q5YIIewrT6OQTpw4kccee4xdu3ah1+txcnKiQoUKODk5YTAY2LVrF3369OGll17Kr3iFEELYUMjJaMYtC+WeNnVw1LQ5W9P+T0voegf789mgRpLQCVGIWJ3ULV68mC+++AJHR0deffVVzp8/j1ar5cqVK6Zbsa+++ioajYavv/6aRYsW5WfcQggh8lmSzsCk1WGg/DdTa1b2nr9lSvCEEIWD1Undl19+iUqlYuXKlXzyySdUq1bNbH3VqlX55JNPWLlyJYqi8PXXX+c5WCFE7jmq1bzcuSYvd66JozpvU4SJ4m3TiSgStPocEzqAeK2ezeFRNo9JCGE5q9vUnT59msDAQPr375/tdv369aNKlSqcOnXK2kMJIfLAyVHNK11r2TsMUQRsi4g2taHLiVoFW8OjZcovIQoRq5M6T09PypQpY9G2ZcqUITEx0dpDCSGEKABxiSkWJXSQmvjFaVNsG5AQIlesvhfToUMHIiIiiI2NzXa7O3fuEBERIXOtCmEnRqPC2eh7nI2+h9HSv9iiRPJxc0Jl4SxyahX4uDrZNiAhRK5YndTNmjULZ2dnBg0axK1btzLd5tatWwwePBgXFxdmzZpldZBCCOsl6Q10+2w33T7bTZLekPMOosQq4+GEpdNnGxXoHuRn24CEELli9e3XAwcO8Pzzz/PRRx+Z2tbVrVsXX19fbt26xalTp1i7di0Gg4EpU6awf/9+9u/fn6Gcp59+Ok8nIIQQIm8eJOt567dw1h29btH2KsDL1ZGeQf62DUwIkStWJ3WjRo1CpVKhKAp6vZ4VK1agSldvr6T7ujd79uwsy5GkTggh7Cf8ejwvrvyHS7cfoFbBYw382XA8KsthTVT//jN3YCNcNDJGnRCFidVJ3dNPP22WxAkhhCg6FEVh0b5IPtx8mhSDEX9vFz4f1IiW1crQp2E0k1eHEf/QjBJGJbWGbu7ARnSpJ7dehShsrE7qlixZko9hCCGEKCh37iczZc1x/jodA0DXen58NKABpdydTM8PTe/C5vAotoZHE6dNwcfVie5BfvQM8pcaOiEKqTzP/SqEEKLo2H/hNhN/DiPmXjJOjmre7F2XEa0CM9x5cdE40K9xRRmHTogiRJI6IYQoAfQGI5//eY6vd55HUaB6OXe+HNKEegFe9g5NCJFPLErqdu/eDYCbmxvNmjUzW5Ybjz76aK73EULkjaNazbhHq5kei+InSWdg04kotkVEE5eYgo+bE93q+9ErOPVW6bW7ibz8cxhHLt8FYFCzSrzzeD3cnOR7vRDFiUXv6A4dOqBSqahduzYnT540W2YplUqFXq+3LkohhNWcHNVM71XX3mEIGwk5Gc2k1WEkPNSpYUvETWZsiGBYi8r8dOgKCUl6PJ0d+aB/MH0aBtg7bCGEDViU1D366KOoVCoqV66cYZkQQgj7CDkZzbhloaaxR4wP/Z+g1fPtrosANKrkw5dDGlOptJsdIhVCFASLkrqdO3datEwIUfgYjQrX47QAVPBxRa2WL2PFQZLOwKTVYVmOJ5eek6Oa5WNb4OGiKYjQhBB2Ig1shCjmkvQG2n20g3Yf7ZBpwoqRTSeiSNDqc0zoAFL0RkJORds8JiGEfVmd1I0ZM4Y5c+ZYtO2HH37ImDFjrD2UEEKIh2yLiMbSSle1CraGS1InRHFndVK3ZMkSNm3aZNG2W7ZsYenSpdYeSgghxEPiElNMbedyYlQgTpti24CEEHZXILdf9Xo9ahlKQQgh8o2Pm1Ouaup8XJ1sG5AQwu5snmkZDAYuXryIj4+PrQ8lhBAlgqIoeLk65qqmrnuQzNUqRHFn8ciTu3fvztDj9cqVK7z33ntZ7qPVatm/fz83b96kZ8+eVgcphBAi1dXYRF5fd5x95+9YtL0K8HJ1pGeQv20DE0LYncVJ3Y4dO3j33XdRqVQoSurXwytXrjBjxowc9y1VqlS2yZ8QQojsGY0KPx6I5KOtZ0hMMeDsqKZvowBWH7mW5bAmqn//mTuwES4ahwKOWAhR0CxO6jp06GB6rCgK7733HpUrV2b06NGZbq9SqXB1daVatWp069YNT0/PPAcrhMg9B7WKEa0CTY9F0XPx1n1eW3ucw5Gp03y1qFqaDwc0oGpZd7rWK8/k1WHEPzSjhFFJraGbO7ARXerJrVchSgKLk7r27dvTvn170/O0pO6dd96xSWBCiPzh7OjAzCeC7B2GsILeYGTh3kt8GnKWZL0RdycHXu9Zh2EtA02DSHet58eh6V3YHB7F1vBo4rQp+Lg60T3Ij55B/lJDJ0QJYvVszkajMT/jEEIIkc6Zm/eYuuYYx67FA9CuZllm9w+mYqmM03y5aBzo17gi/RpXLOgwhRCFiNVJnRCiaFAUhdgHqWOUlXZ3kjmb7SxJZ2DTiSi2RUQTl5iCj5sT3er70Ss4tVZNZzDy7c4LfPnXOXQGBU8XR956rB4Dm1aU350QIlsWJXW7d+/Ol4M9+uij+VKOEMJyWp2BprP+BODke91xc5LvcvYScjKaSavDSHio/duWiJvM2BDBy51rsubIdU5FJQDQpa4v7/cLxs/Lxc6RCyGKAos+3Tt06JDnb4gqlQq9Xp+nMoQQoqgKORnNuGWhpm6qxof+T9DqmfnHKQBKuWmY8Xh9Hm8YILVzQgiLWZTUPfroo/LBIoQQVkrSGZi0OizLoUfS0zio+OOltlTwydh2TgghsmNRUvfwoMNCCCEst+lEFAlay+5U6AwKf1+KpV9jSeqEELkjE7IKIYSNbYuIztU8rVvDo20bkBCiWJKkTgghbCwuMSVX87TGaVNsG5AQoliyuhvclStXcr1P5cqVrT2cEEIUWY4Oln9/VqvAx9XJhtEIIYorq5O6KlWq5KrzhPR+FcI+HNQqBjSpaHosCk7MvSQ+CznHvvO3Ld7HqED3IJnWSwiRe1YndZUrV84yqXvw4AG3b6d+iGk0GgICAqw9jBAij5wdHZj7VEN7h1GiPEjW8/3uiyzYc5HEFAMAjmoVBqOSbe9XFanztfYM8i+QOIUQxYvVSV1kZGS26xMSEliwYAEzZ85k6NChvP/++9YeSgghigS9wciq0Gt89udZbt1LBqBRJR+m96pLglbHs8tCUWUxrInq33/mDmwk87UKIaxis6Hlvby8mDRpEvXr16d3797UqVOHESNG2OpwQogsKIqCVpdaW+SqcZAxJ21AURS2n4phzpbTnI+5D0Dl0m681qMOvYLLm17z70c0Y/LqMOIfmlHCqKTW0M0d2Igu9eTWqxDCOjafL6hHjx4EBgYyb948SeqEsAOtzkC9t7cCMk1YbuU0TyvAsatxfLDpFIcuxQLg46bhpU41Gd4qECdH8w4SXev5cWh6FzaHR7E1PJo4bQo+rk50D/KjZ5C/1NAJIfKkQD7dfXx8OH36dEEcSggh8kVO87RO61GX/RfvsOHYDQCcHNWMaVOVCR2q4+2qybJcF40D/RpXpF/jigV1KkKIEsLmSV1MTAynTp3C3d3d1ocSQoh8Yck8rdPWnwBApYJ+jSswqVttKvi42iFaIYRIZbOk7vbt2xw+fJg333yTlJQU+vbta6tDCSFEvsnNPK2OahWrnnuEJoGlCiI0IYTIltVJnYODZW0/FEWhfPnyzJkzx9pDCSFEgcnNPK16o8Ll2AeS1AkhCgWrpwlTFCXbHzc3N4KCgpg6dSrHjx+nSpUq+Ri2EELYhszTKoQoqqyuqTMajfkZhxBCFAqxD2SeViFE0SRjGwhRzKlVKnoFlzc9Fpl7kKxnxaErhF2Ls3gfmadVCFGYSFInRDHnonHgm2FN7R1GoZWQpOPH/ZEs3HuJu4m6XO0r87QKIQoTq9vUFaT79+8zceJEAgICcHFxoVGjRvz888857rdu3TqGDBlCjRo1cHV1pUqVKgwbNoxz584VQNRCiMIs9kEKn2w9Q5s5f/HJtrPcTdQRWMaNWU/Ux8vFkZzqNFWAt8zTKoQoRCyuqbO0t2tWVCoVer1lPcoe1r9/fw4fPsycOXOoVasWK1asYMiQIRiNRoYOHZrlfh9++CHly5fnjTfeoFq1aly9epUPPviAJk2acPDgQerXr2/t6QghChlLZn8AiElIYsGeiyw/eMU0fVpNXw9e6FSD3sH+ODqoKe/lKvO0CiGKHIuTOkWxsOVwPu+/adMmQkJCTIkcQMeOHbl8+TJTpkxh0KBBWSacGzZswNfX12xZp06dqFKlCp999hk//PCDVTEJUZQkpuiL/TRhOc3+8OnARtQN8GL+rgv8fPgqKfrUjl71A7x4sVMNutUrjzpdl9cu9fxknlYhRJGTq093lUpF7dq1GTFiBP3798fDw8NWcZmsX78eDw8PBg4caLZ89OjRDB06lEOHDtG6detM9304oQMICAigYsWKXL161SbxCiEKVk6zP9zT6nnmx1BTQgbQpLIPL3aqSYfa5VBl0XlE5mkVQhQ1Fid1n332GT/99BOhoaG8+eabvP/++/Tr148RI0bQpUsX1GrbNM8LDw+nbt26ODqah9qgQQPT+qySusxcvHiRy5cv88QTT+RnmEIIO7Bk9oe05UYFWlUtzUtdavJItTJZJnPpyTytQoiixOKk7uWXX+bll1/m7Nmz/Pjjj6xYsYKffvqJFStW4Ovry9ChQxk2bBhNmjTJ1wDv3LlDtWrVMiwvXbq0ab2l9Ho9Y8eOxcPDg1deeSXbbZOTk0lOTjY9T0hIAECn06HT5a6HnCXSyrRF2aJk0+n06R7r0KmsawpRGK/RDWE3LJ79AeDJJgE0r+xtdfteUXgVxutTiPTSX6O2uk5VSh4ay+3du5cff/yRNWvWEBcXh0qlok6dOjz99NMMHTqUSpUq5TnAWrVqUb16dTZv3my2PCoqioCAAGbPns3rr7+eYzmKojBq1Ch++ukn1q5dm+NctDNmzODdd9/NsHzFihW4ubnl7iSEsKNkA0z9O/X720ct9DgXo7uGC8+oORGrQsmxryqoUAgurTC2tgycLoSwr8TERIYOHUp8fDxeXl75Vm6ekro0KSkpbNiwgWXLlrFlyxZ0Oh0qlYrx48fz1Vdf5ansRx55BIPBwN9//222PCIigqCgIObPn8+4ceOyLUNRFJ555hmWLFnC0qVLGT58eI7HzaymrlKlSty+fTtffwFpdDodISEhdO3aFY1Gk+/li5IrMUVPw5l/AXDsrU5Wd5QojNfok/MPcuxagsXbt6xSiuVjm9swImEvhfH6FCK99NeoVqulbNmy+Z7U5Us3OCcnJwYMGMCAAQPYs2cPI0aM4MqVK5w9ezbPZQcHB7Ny5Ur0er1Zu7oTJ04AEBQUlO3+aQnd4sWLWbhwoUUJHYCzszPOzs4Zlms0Gpt+YNi6fFHyaJT/arFSr6+8ve3tfY0ajAo7Tsew/NDlXCV0ahWUcneW91cxZ+/rU4icaDQamzUByZekLjo6mpUrV7Js2TLCwsJQFAUPDw/atm2b57L79evHggULWLt2LYMGDTItX7p0KQEBAbRs2TLLfRVF4dlnn2Xx4sXMnz+f0aNH5zkeIYoatUpFx9rlTI+Lqtv3k/nl8FVWHLrC9ThtrveX2R+EEMWd1UmdVqtl/fr1LFu2jO3bt6PX63FwcKBbt26MGDGCfv364erqmucAe/bsSdeuXZkwYQIJCQnUqFGDlStXsmXLFpYvX24ao27s2LEsXbqUCxcuEBgYCMBLL73EwoULGTNmDMHBwRw8eNBUrrOzM40bN85zfEIUdi4aBxaPbmHvMMxYOlCwoigcjrzL8oOX2Rwehc6Q2lrEx03DU80qMaBJBQbOP8A9rT7L3q+QOliwl8z+IIQo5nKV1CmKwp9//sny5ctZv349Dx48QFEUGjduzIgRIxgyZAh+fvn/TXjdunW88cYbvP3228TGxlKnTh1WrlzJ4MGDTdsYDAYMBoPZIMcbNmwAYNGiRSxatMiszMDAQCIjI/M9ViFE9iwZKLhV9TKs/+c6yw9c5kz0PdO+jSr5MKJVIL0b/Jf8fTqwkcz+IIQQ5CKpmzJlCitWrODmzZsoikKlSpV44YUXGDFiBHXr1rVljHh4eDBv3jzmzZuX5TZLlixhyZIlZsskaROicLF0oGBnRzXJ/8764KJR80SjCgxvFUhQBe8MZcrsD0IIkcripG7u3LmmGSWGDx9O+/btUalU3L17l/3791tURm4GCRZC5I/EFD1NZ/4JwJG3uthtmrDcDBScrDdSpawbIx+pQv8mFfF2zb7hu8z+IIQQVrSpO3PmDG+99VauD6RSqWTATyHsJG3ienvadCIqVwMFv9SpJv2bWD6Tg8z+IIQo6SxO6ipXrmzRtDpCCJGZbRHRZvOvZketSt0+N0mdEEKUdBYnddI+TQhhjYQkHVtO3GT/hdsWJXSQmvjFaVNsG5gQQhQz9mlcI4QoMtKGH9kSHsXFa2o2xofRI8g/w/Aj6SXrDew8c4vfwq7z56kYUvS5m5pLrQIfV6f8CF8IIUoMSeqEsANLx2mzt4zDj6i5eCqGbSdjTMOPpPUqNRoV/o6M5bew62w8HkVC0n/t52r6elDTz4NNJ25adFwZKFgIIXJPkjohCpgl47QVhuE3LBl+5Nllobz9WD1uJiSxIewGN+KTTPuX93Lh8UYB9G0UQD1/L5L1Rvae/1MGChZCCBuRpE6IAmRpovT9iGZ0tSKxy6wGsEPtcjSvUgq1SmXxNGEWDz+iwLsbTpqWebo40ivIn76NA2hZtQwO6v+O56JxkIGChRDChiSpE6KAWJooqRSYvDqMQ9O75Cqxya4G0MvVkU9zkSjldviR4ApePN+xBh1q+2Z7DBkoWAghbEeSOiEKiKWJkgLEa/VsDo+yeMy1/KwBjE/U8cvhq6jIOvlMT62CCj5u9LDwdqkMFCyEELYhSZ0QBSQ347QBfLL1LNditVQs7UqlUm5UKu1GOQ9n1GrzW6jW1AA6O6q5EZ/EhZj7nI+5z/lb97kQc58Lt+5z+37uhhKxZvgRGShYCCHynyR1QhSQuMQUixM6gOtxWuaGnDVb5uSopqKPKxVKuVKptBuVSrlxI06bqxrALp/uIvZBCokpWc8y4eKoJsnCYUhk+BEhhCgcJKkTooD4uDlZXFOnAqr7etC4kg/X7mq5ejeRqPgkUvRGLt5+wMXbD6yO49pdLQCOahVVyrpTo5wH1X3dqeHrQY1ynlQr587WiJu8uuqYReXJ8CNCCFE4SFInRAHpVt+PLRGWjdOmAM93rG52e1JvMBIVn8TVu4lci9Vy7W4iV+9qCTkZzf1kyzs11PLz4JthTQks44bGQZ3pNr2C/ZmxIUKGHxFCiCJEkjohCkjbmmVxUIEhh5q6rBIlRwd16i3X0m5Q/b/l45cdYdvJmxbXAFYr60ENX49st5PhR4QQoujJ/Gu6ECJfxSfqGLsk1JTQZTVanDWJUrf6fha31VOw/FZp2vAjXq6p3/3S+mek/e/l6siCEc1k+BEhhCgkJKkTwsbiElMYtvAgJ67HU9rdiXf61MvXRKlXsD9ero5ZJorpebk45OpWadrwI58NakiXur7U8DLSpa4vnw1qyKHpXSShE0KIQkRuvwphQ3cfpDDsh0OcjEqgjLsTK55tRe3yngxpUTnfxmmz5FZpmtn9GlhVfr/GFXksyI9NmzbRq1cjNBpNrsoQQghhe5LUCWEjsf8mdKeiEijrkZrQ1fLzBPJ/nLacZmpwUEGl0m50lpo1IYQotiSpE8IGYh+kMHTBQU7fvEdZD2dWPtuSmv8mdLYiMzUIIUTJJkmdEPnszv1khv1wiNM371HO05mVz7bKsbdpfpGZGoQQouSSpE6IfHT7fjJDFxzkbPR9fD2dWTmuFdXLFUxCJ4QQomSTpE6IfHLrXmpCdy7mPn5eqTV01QpBQqdNMdDl010A/Plqe1yd5DasEEIUR5LUCZEPYu4lMXTBIc7H3Ke8lwsrx7Wiall3e4cFgILC9Tit6bEQQojiSZI6IfIoJiGJIQsOcuHWA/y9XVj5bCuqFJKETgghRMkhSZ0QeRCdkMSQ7w9y8fYDArxTa+gCy0hCJ4QQouBJUifsJklnYNOJKLZFRBOXmIKPmxPd6vvRK9j64TcKssymgaUYtfgwl24/oIKPKz+Pa5U6L6sQQghhB5LUCbsIORnNpNVhJDw0UO6WiJvM2BDBpwMb5XoKqoIuM+25JHRCCCEKA5n7VRS4kJPRjFsWyj2tHsA0GX3a//e0ep5dFkrIyehCXWba/y92qiEJnRBCCLuTmjpRoJJ0BiatDoNs5ihVAJUCk1eHcWh6lxxvm9qrTAAVMHvzKZ5oXKHQztigQkXNfwc/VqGyczSiuFIUBYPBgF6vt8vxdTodjo6OJCUlYTAY7BKDKLkcHR1xcHBApbLvZ6wkdaJAbToRRYI25w99BYjX6pm0Ooz6Ad7ZbhtxPd7uZW4Ojyq0szi4OjkQ8mp7e4chiilFUYiLi+PWrVt2TaYURaF8+fJcvXrV7n9YRcnk4OCAr68v3t7edrsGJakTBWpbRLSpLZolNh6/ycbjN/M1hvwuU62CreHRhTapE8KWbt68SVxcHF5eXnh5eeHo6GiXP2hGo5H79+/j4eGBWi0ti0TBURQFvV5PQkICUVFRaLVa/P397RKLJHWiQN1NTLE4oQMo5+lMx9rlst1mx+kYbt1PsVuZRgXitJYfX4jiwmAwEB8fT7ly5ShbtqxdYzEajaSkpODi4iJJnbALT09PnJ2duX37Nr6+vjg4FHyTHEnqRIEwGBU2nYgi4kaCxfuoVdC0cik+erJhttuNX3aEbSdvWpQs2qpMH1ennDe0E22Kgce/2gvA7y+0lWnCRL7R6XQoioK7u4zNKASAu7s7t27dQqfTSVInih+dwcj6f67z3c4LXLz9IFf7GhXoHpTzECTd6vuxJcKy26n2LNNeFBTOxdw3PRYiv0kbNiFS2fu9IHXUwiaSdAZ+PBBJh493MnXNcS7efoCPm4YXO9bA08Uxxz6YKsDb1ZGeQTm3S+gV7I+Xa+EvUwghhLAlqakTFrF0pob7yXp+OniZBXsucft+MgBlPZwZ92hVhrYMxMPZkYaVfHh2WSiqLIYLUf37z9yBjSwaJsRF48CnAxsV+jKFEEIIW5KkTuTIkpkamlcpzZL9kSzad4l4rQ5InWnhufbVeKpZJbOkp0s9P74f0YzJq8OIf6hMowJero7MzeXsD0WlTCGEsBWVSkX79u3ZuXOnvUOxieJ+fvlBkjqRrbRZFdKqqjKbqeGZH0NxcVSTpDcCULWsOxM6VKdf4wpoHDK/w9+1nh+Hpndhc3gUW8OjidOm4OPqRPcgP3oGWTdPa1EpUwhRfERGRlK1atUMy93c3KhevToDBgxg0qRJeHh4mK2vUqUKly9fNj13cnLCy8uLqlWr0rx5c4YMGULbtm1tHr8oXiSpE1mydKYGgCS9kVp+HrzYqSa9gv1xUOfcWNRF40C/xhXzdXy3olKmEKJ4qV69OsOHDwdSxy27desWmzdvZsaMGWzdupU9e/Zk6A3p4ODAm2++CYBer+fu3bucOHGC+fPn880339CnTx+WLl1KqVKlCvx8RNEkSZ3IkqWzP6QZ3746fRoG2DAiYQ0VKir4uJoeCyHyX40aNZgxY4bZsuTkZB555BEOHDjA7t276dixo9l6R0fHDPsAXL58mbFjx7Jhwwb69evHX3/9JWPvCYvIVSKylDb7gyXUqtTtReHj6uTAvtc7se/1TjJGnRAFyNnZ2ZTI3bp1y+L9AgMD2bBhA/Xq1WPXrl2sWbMmV8e9evUqgwYNokyZMri7u9OhQwf279+f6bZ37tzhlVdeoWrVqjg7O+Pr68ugQYM4efJkhm07dOiQ5ZAdo0aNQqVSERkZaVq2ZMkSVCoVS5YsYfv27bRt2xZ3d3fKlCnDyJEjuXPnTqZl/fDDDwQFBeHi4kKlSpWYOnUqSUlJmW575MgRXnjhBYKCgvD29sbV1ZXg4GDmzJmDTqfLsH2VKlWoUqUKcXFxvPTSS1SqVAlHR0eWLFlC+/bt0Wg0REVFZXqsp556CpVKxT///JPp+sJAkjqRpbhczP4gsyoIIYS5lJQUdu7ciUqlolGjRrna19XVlcmTJwPwyy+/WLzf3bt3adOmDZGRkYwbN44BAwZw4MABOnbsmKGDwZ07d2jVqhWff/45VapU4dVXX6Vz586sW7eOFi1acODAgVzFnJUNGzbQq1cvypcvz4QJE6hevTo//vgjffv2zbDtzJkzefbZZ7l9+zbPPvssAwcO5JdffmHgwIGZlr1gwQLWr19PcHAwzz33HGPHjkVRFKZNm8bgwYMz3Sc5OZlOnTqxefNm+vTpwwsvvICfnx/PPfccer2exYsXZ9jn9u3b/PbbbzRt2pTGjRvn7QWxIbn9KrLk4+Zk8TythX1WBSGEsKXz58+bbqUqisLt27fZunUr169f56OPPqJWrVq5LrN9+/YAHD582OJ9jh8/zogRI1i6dKmpVm3s2LF07NiRZ599ljNnzphu5U6dOpXz588zbdo0PvjgA1MZo0aNokePHowcOZLTp0/n+dbv77//zs6dO2nTpg2QOr1cly5d2LlzJwcPHqRVq1ZA6mv43nvvUaFCBY4ePYqvry8AM2bMoEWLFpmWPW3aNL7++muz9oqKovDMM8+waNEi9u3bZzpumps3b9KgQQP27duHq6uraXlycjIvvfQSixYtYtq0aWa1ksuWLSMlJYVnnnkmT6+FrUlNnciSn5dzrmrqCvOsCiVZki51mrDHv9pLks5g73BECZOYos/y5+HrMa/balMMmW6btvzhn/x04cIF3n33Xd59913ee+89vvnmGy5cuEC3bt3o3bu3VWUGBKS2Ub59+7bF+zg4OPD++++bJSTt27enV69enD9/3nQbNiUlhZUrV1KmTBlTZ4003bt3p3v37pw7dy7L27a5MXToULPEysHBgZEjRwLmCeuKFSvQ6/W8+uqrpoQOwMvLK0OMaQIDAzN0QFGpVDz//PMA/Pnnn5nu9/HHH5sldJB6u3zkyJFcuHCBHTt2mK1buHAhbm5uDB06NKfTtSupqRMZJOsNfLDxFEsPXM55Y1IH4fWSWRUKLaOicPxavOmxEAWp3ttbs1zXsXY5Fo/+rwam6cw/0WbxxaNl1dL88twjpudtP9xB7IPMm3w0qOjN7y/8NxxIl093cT1Om2G7yDnWJVuZ6d69O1u2bDE9j4mJYfv27bz00ku0bt2aQ4cO5bq2TrHi/RoYGEilSpUyLG/Xrh0bN24kLCyMtm3bcvr0abRaLR06dMDNzS3D9h06dGDr1q2m7fOiSZMmGZZVrJg6mkBcXJxp2bFjx0yxZhZ/ZlJSUvjqq6/4+eefOX36NPfv3zd73W7cuJFhHxcXF4KDgzMtb9y4cXz66af88MMPdOrUCYCDBw8SERHBqFGj8PLyyuIsCwdJ6oSZK3cSeWHlUVMS0DOofOocqDKrghBCWMzX15chQ4ag1WoZO3Ysc+bMYdGiRbkqI63Bfrly5XJ13Mz4+aXeSYmPT/1sT0hIMFv+sPLly5ttnxfe3t4Zljk6pqYfBsN/SXzasTI7h6zifPLJJ9mwYQO1atVi0KBB+Pr6otFoiIuLY968eSQnJ2fYx9fXN8sOH7Vr16Z9+/asW7eO2NhYSpcuzQ8//ADAs88+m8OZ2p8kdcJkS/hNpqw5xr0kPT5uGj57qhEd6/gScjJaZlUQQljl5Hvds1ynfugP65G3uli87d7XzIcHMRqN3Eu4h6eXJ44P3Y7789X2KFmOtmlbaW3Bjh49mut90zo2NG/e3OJ9YmJiMl0eHZ06OkFagpVW45S2PKvt09dMpbWt0+v1pqQsTX4mfzExMQQGBmYaT3qHDx9mw4YNdO/enY0bN5rdhj148CDz5s3L9DhZJXRpnnvuOXbt2sXy5csZM2YMv/zyC/Xq1aN169a5PaUCJ0mdIEVvZPbmUyzeFwlA08BSfDmkMQH/jm0msyoIIazl5mT5n5m8bGs0GtE7OeDm5JihYb89h/KJjY0FUuPLDa1Wy9y5cwEYMmSIxftdvnyZq1evZrgFu2fPHgBTL9w6derg4uLC4cOHSUxMzHALdteuXWbbA6ZBkK9fv26WdBmNRtOt07xo2LAh69atY8+ePRkS2bT407tw4QIAvXv3ztCuLrPtLTVgwADKli3LDz/8gLu7O/fv3y/0HSTSSEeJEu5qbCIDv9tvSuiee7QaP49rZUro0qTNqvDdiKb8PO4RvhvRlH6NK0pCJ4QQWTAajXz55ZdA1m3CMnP58mX69OnDyZMn6dixI/3797d4X4PBwBtvvGHWrmzXrl1s2rSJGjVqmGqbnJycGDJkCLdv32b27NlmZfz5559s3ryZGjVqmHVwaNasGZA6/lx6n376KZcuXbI4xqwMHToUBwcHPv30U7Max4SEBGbNmpVh+7TEcu/evWbLIyIiMpxTbjg5OTFy5EhOnDjB22+/jZOTE08//bTV5RUkqakrwbZF3GTy6mMkJOnxdtXw6VMN6VxXbqMKIURupR/SBFIHG96xYwenTp2iUqVKmfbe1Ov1pn0MBoNpmrB9+/ZhMBjo27evaQBfSzVo0ICdO3fSqlUrOnXqxI0bN/j555/RaDQsWLDArBbzww8/ZNeuXcyaNYv9+/fTsmVLIiMjWbNmDW5ubixevNhs+9GjR/PRRx8xY8YMwsLCqF69OqGhoYSHh9O+fXtT7Z61atSowdtvv80777xDgwYNeOqpp3B0dGTt2rUEBwdz5swZs+1btGhBixYtWLVqFVFRUbRq1YorV67w+++/07t371wP2pzeuHHjmDt3Ljdu3DAN5FwUSFJXAqXojXy45TQL96Z+s2pc2YevhjYxTSUlip/S7jKGoBC2lDakSRpnZ2fTgL7Tpk2jbNmyGfYxGAymfZycnPDy8qJq1ao899xzGYYBsVSpUqXYsGEDkydPZv78+SQlJdGqVSs++OCDDOWVK1eOQ4cOMXPmTH777Tf27NmDt7c3ffv25Z133iEoKMhs+/Lly/PXX38xZcoUtm3bhqOjIx07duTgwYPMmjUrz0kdwNtvv01AQACfffYZ8+fPx9fXl8GDB/Pee+9luEXs4ODAH3/8weuvv86WLVs4fPgwNWvW5JNPPqFnz555Supq1aplmuKtKHSQSKNSrOkzXcDu37/Pm2++yapVq4iNjaVOnTq8/vrrWY4WnV5MTAxTp07ljz/+IDExkYYNGzJr1iw6d+6cqxgSEhLw9vYmPj4+X7s0J+kMbDoRxZbwKC5eu0m1iuXpEeRPr2Dr26qllbktIpq4xBR83JzoVt+PXsH+3L6fzAsr/iHsahwAz7StytQedXBylDvxIns6nY5NmzbRq1cvNBqNvcMRhUBSUhKXLl2iatWquLi42DUWo9HI/9u786gor7sP4N9xmBmRYQmiIEJQiTGCIBp91egRjRE0aYl46ta6RExiPEaTRmtq66nkpIvxZDltTXLUpNGqEKvWpImigvt2jCYSHXfrBqJGiTCMbDPMff+wM2GcAWblmXn8fs4hgee59z73Plye+Xmf57lXr9cjLCyM66SSx2pra9G5c2dERETg4sWLTo+WNvc30fgaWlNT45OYIiBG6saOHYujR49iyZIlePzxx5GXl4dJkybBbDY3OxFgXV0dRowYYX21uWPHjvjwww8xatQoFBUVWWfrlkrh6VuYt6EYeutbpW1w6cwP2HH6B+R+dQrvu/FWqX2Z999U3XbqJhZ9oQMAVNc3IKxtEN4d1xsZyTG+aBoREVHA+sc//oEff/wRb775pku3v6Xm90Hd1q1bUVhYaA3kAGD48OG4evUqfvOb32DChAl2b71YfPrpp9DpdDh06BAGDRpkzdu7d28sWLAAR44cabV2PKjw9C28vOaYdfI38wP/r6ox4aU1x7BiSj+MdDKwa6nM6vr78wF1iQrBmpz/Q3yk/YSTRERED6slS5bg9u3b1lu/r7zyitRVconfj1Fv3rwZWq3WbjHf6dOno6ysrNnAbPPmzejRo4c1oAPuT3g4efJkfPPNN7h+/brP6t2cWmMD5m0obnJCX+B/2wUwf0OxU0s7OVOmxY+GOnQI1bhUZwpctcYGTFh+GBOWH+YyYUREzVi4cCGWLVuGpKQkfPnll36/gsSD/H6kTqfToWfPnnYTHaamplr3NzUhoE6nc/gauSXvqVOn0LlzZy/XuGVbT96AvqbldQcFgMoaE+ZtKEZyrP2M3I2dul7pVJkAoK81oUB3A9l94pxKT4HNLASOXP7R+j0RETkWAK8ZNMvvg7ry8nJ069bNbntkZKR1f3N5LelczVtXV2ezvIhlSRWj0Qij0ehc5ZuwTXfD+rybM7acuIktJ256dMzG2iiAgpM38LNenL7kYWA0mhp9b4RR4d5Fy9LvPe3/JB9GoxFCCJjNZpcn1/U2y4expT5EUjCbzRBCwGg02j0a1vga6qvrqN8HdUDzS3q09ACju3n/8pe/2LyebrFjxw6Hix+74lJpG5iF83e+Q1UCSRHNfxCfrlCgyujcw5xmAVwqvYmtW7c6XQcKXHUNgOVPffv2HdB4OF90YWGhx3UieQgKCkJMTAwMBgPq6+ulrg4AoKqqSuoq0EOsvr4eNTU12LdvH0wmx3fPCgsLUV1d7ZPj+31Q1759e4cjapalVxyNxHkj78KFC/HGG29Yf9br9YiPj0dGRobH99i3VBbj0pkfnBqpa6MABnWPxoeT0ppNNzu/GEUulNktLgbPPtt8mSQP1fUmLPhmFwAgMzPDpaWYGjMajSgsLMTIkSM5pQkBuD99Q0lJCbRareRTmgghUFVVhdDQ0IB6W5Hkpba2FsHBwRg6dKjDKU0s19CamhqfHN/vg7qUlBTk5+fbLSB88uRJALCbHPHBvJZ0jTmTV6PRQKOxf5lApVJ5/IE2qlcn7DjteNHlB5kFMDqlU4vH9EWZJA8q8dMH3P3+69mfvTf+BkgeGhoaoFAooFAoJJ8bznLL1R/qQg8vy99Dc9dJlUrV5Ciep/y+52dnZ8NgMGDTpk0221evXo3Y2FgMGDCg2bxnz561eUPWZDJh7dq1GDBgAGJjY31W7+Y8m9IJYcFBaOnfkgoA4cFBGN2rkyRlEhE1x/LMEJ+zJLrP8rfQ1FRrvub3Qd3o0aMxcuRIzJo1CytXrsTu3bvx8ssvY9u2bVi6dKn1xM2YMQNBQUG4evWqNW9OTg6Sk5Mxbtw45OXloaioCOPHj8e5c+fwzjvvSNUktFUp8f64NECBJoMwxf/+8964NKdWlvBFmSQfwSolgvk7Jy9TqVTQaDSorKwM+LcGiTwlhEBlZSU0Go1kdzP8/vYrAPz73//G73//e/zhD3+wLhOWn59vs0xYQ0MDGhoabC4sGo0GO3fuxIIFCzBnzhxUV1cjLS0NBQUFkq8m8UxSNFZM6Yf5G4pR+cDqD2YBhAUH4T0XV5TwRZkU+Nqpg3Dm7VFSV4NkKioqCtevX0dpaSnCw8OhUqkkeabNbDajvr4etbW1vP1KrcrytmtlZSUMBoMkU6VZBMTar/7Al2u/FuhuoODkT2u/jk7phNG9PFv7tUB3A9t1t1BRU4+IYDUye0V7VCYR136lpuj1ety5c8dmGqjWJoRATU0NgoOD+aIESUKj0SAqKqrJGIFrvz4E2qqUyO4Th5/1iv7fLzvN4w9MS5mcXJiIWkNYWBjCwsJgNBrR0CDNqiVGoxH79u3D0KFD+Y8OanVKpdIv+h2DOiKZqzU2YNbabwEAH09+kqO15DNSvhmtVCphMpnQtm1bv/hwJZICgzoimTMLgd3nblu/JyIieeLTpEREREQywKCOiIiISAYY1BERERHJAIM6IiIiIhlgUEdEREQkA3z71UmWOZr1er1PyjcajaiuroZer+fr+ORV1fUmmOuqAdzvvya1e3/27KPkz9g/yd817qM1NTUA4PXl9biihJNKS0sRHx8vdTWIiIhIJkpKShAX572FAhjUOclsNqOsrAyhoaFNLkHTv39/HD16tMkymtuv1+sRHx+PkpISry4ZIpWWzkUgHdPTct3J72oeZ9J7mkZOfVSK/umr43qjTF/3UWfTetJH5dQ/AfZRT/P74hrqTDpnr6GhoaGoqqpCbGysV9cq5u1XJ7Vp06bFaFqpVDZ7MWlpP/DTcjuBzpm2BsoxPS3Xnfyu5nEmvbfSyKGPStE/fXVcb5Tp6z7qbFpv9FE59E+AfdTT/L64hjqTzpVraHh4uNP1cxZflPCi2bNne7RfTqRoq6+O6Wm57uR3NY8z6b2VRg6kaqcvjuuNMn3dR51Nyz76E/ZRz/L74hrqTDqp+ydvv/oJvV6P8PBwVFZWyuJfmSQ/7KPkz9g/yd+1Rh/lSJ2f0Gg0WLx4MTQajdRVIXKIfZT8Gfsn+bvW6KMcqSMiIiKSAY7UEREREckAgzoiIiIiGWBQR0RERCQDDOqIiIiIZIBBXQCrq6vD9OnTER8fj7CwMAwcOBCHDh2SulpENj7++GP07dsXKpUKubm5UleHHmK3b9/Gc889h5CQEDz++OMoLCyUukpENjy9XjKoC2Amkwldu3bFwYMHUVFRgVmzZiErKwvV1dVSV43IqlOnTnjrrbcwZswYqatCD7nZs2cjJiYGt2/fxrvvvovx48ejvLxc6moRWXl6veSUJjITGRmJ3bt3o3fv3lJXhcjGiy++iLi4OI7WkSQMBgMiIyPx3//+F/Hx8QCAYcOGYerUqcjJyZG4dkS23L1ecqTOC6qqqrBgwQJkZGSgQ4cOUCgUTf4iDAYDXn/9dcTGxqJt27ZIS0vD559/7pV6nD17FjU1NUhMTPRKeSQf/tJHiZzl7T574cIFaLVaa0AHACkpKTh16pQvm0Ey5o/XVQZ1XlBeXo4VK1agrq6uxSHTsWPHYvXq1Vi8eDEKCgrQv39/TJo0CXl5eR7Vobq6GlOmTMGiRYug1Wo9Kovkxx/6KJErvN1nDQaD3dJMYWFhMBgMvqg+PQT88roqyGNms1mYzWYhhBC3b98WAMTixYvt0m3ZskUAEHl5eTbbR44cKWJjY4XJZLJue/rpp4VGo3H4tWjRIpv89fX14rnnnhNTp0611oOoMan7qBBCzJgxw+ExiRzxdp/97rvvxCOPPGKT5tVXXxVvvPGGbxpAsueL66qFu9dLjtR5gUKhgEKhaDHd5s2bodVqMW7cOJvt06dPR1lZGY4cOWLdtnPnTtTW1jr8evvtt63pzGYzpk6dCqVSiU8//dSpetDDR8o+SuQOb/fZ7t27w2AwoLS01JpGp9MhOTnZuxWnh4YvrqueYlDXinQ6HXr27ImgoCCb7ampqdb9rpo5cyZu3LiB9evX25VL5Cpf9FGTyYTa2lo0NDTYfE/kDc72Wa1Wi+effx65ubmoqanB119/jeLiYmRlZbV6nenh4sp11dPrJYO6VlReXo7IyEi77ZZtrr5af/XqVXzyySc4cuQIoqKioNVqodVqsX//fq/Ulx4+3u6jAPDHP/4RwcHBWLVqFf70pz8hODgYa9as8biuRIBrffajjz5CWVkZ2rdvj1//+tdYv349oqKiWq2u9HBypY96er3k0E4ra26o1tVbpwkJCRCckYa8zJt9FAByc3M5jQn5lLN9tkOHDti6dWtrVInIhrN91NPrJUfqWlH79u0djnT8+OOPAOAwkidqTeyjFGjYZ8nftWYfZVDXilJSUnDmzBmYTCab7SdPngQA9OrVS4pqEVmxj1KgYZ8lf9eafZRBXSvKzs6GwWDApk2bbLavXr0asbGxGDBggEQ1I7qPfZQCDfss+bvW7KN8ps5LCgoKcO/ePVRVVQEATp8+jY0bNwIAnn32WbRr1w6jR4/GyJEjMWvWLOj1ejz22GPIz8/Htm3bsHbtWiiVSimbQDLHPkqBhn2W/J3f9VGXZ7YjhxISEgQAh1+XL1+2pquqqhJz584VMTExQq1Wi9TUVJGfny9dxemhwT5KgYZ9lvydv/VRhRB8fZKIiIgo0PGZOiIiIiIZYFBHREREJAMM6oiIiIhkgEEdERERkQwwqCMiIiKSAQZ1RERERDLAoI6IiIhIBhjUEREREckAgzoiIiIiGWBQR0RERCQDDOqIyKpLly5QKBS4cuWK1FUhP3HlyhUoFAqbr9zcXKmr5ZK0tDSb+g8bNkzqKhH5BIM6ogBkCb4afwUHByMxMRE5OTk4deqU1FWUncbnfN68ec2m/etf/2rzu5EDjUaDwYMHY/DgwXj00Uft9vvz+enTpw8GDx6MXr16+fxYRFJSCCGE1JUgItd06dIFV69eRffu3dGxY0cAQEVFBS5cuID6+npoNBps2LABP//5z10qd8SIEbh+/Tp27tyJzp07+6LqActyzgEgJiYGpaWlUCqVDtP2798fx44ds/4cyJfZK1euoGvXrkhISGh2BDcQzs+ePXswfPhwpKenY8+ePa1yTKLWxJE6ogD2u9/9DgcOHMCBAweg0+lw7do1PPPMM6irq8P06dNhMBhcKm/nzp04e/YsA7pm9OjRAzdv3kRRUZHD/efOncOxY8fQo0ePVq6Zf+D5IZIOgzoiGYmOjsaaNWug0WhQXl6OwsJCqaskO5MnTwYArF271uH+NWvWAACmTJnSanXyJzw/RNJhUEckMzExMejevTsA4MKFC9btjZ9f2rRpE4YOHYqIiAibFyOaelGicd7NmzfjqaeeglarRXR0NKZNm4abN29a03722Wd48sknERISgo4dO+KVV15BZWWlw7rqdDosXrwYgwYNQqdOnaBWq9GpUyeMHTsWhw4dcpinpXacO3cOCoUCUVFRqK+vb/I8paSkQKFQYMuWLc2cTXvp6emIj4/H5s2bce/ePZt9QgisW7cOwcHBGDt2bJNluNNuALh69SpmzpyJbt26QaPRIDQ0FN26dUN2djY+//xzt9N6kzfODxG5SRBRwElISBAAxGeffeZwf3JysgAg3nnnHes2AAKAWLJkiQAgoqOjRf/+/UWHDh3E5cuXbcq1/Pxg3r/97W8CgIiLixO9e/cWGo1GABBJSUmipqZGzJ07VwAQ3bp1E8nJySIoKEgAEOnp6cJsNtvVc8SIEQKAiIiIED179hR9+/YVUVFRAoBQKpVi3bp1dnmcacegQYMEALFp0yaH5+fYsWMCgIiJiREmk6nlE97o3Ozfv1/89re/FQDEmjVrbNLs27dPABCTJk0SJSUl1rp6o92XL1+2pmnXrp1ISUkRaWlpIjIyUgAQvXv3dittSy5fviwAiISEhFY7P76ye/dua38kkiMGdUQBqLmg7saNG9Zgq3FQY/kAVavVYsWKFdYgy2g0CqPRaFNuU0FdSEiIyMvLs24vKSkRjz32mAAgxowZI8LDw0VRUZF1/4kTJ6yBxNatW+3qumHDBnHixAmbbWazWXzxxRdCq9WKsLAwodfrHdaluXasXLlSABBZWVkOz9+cOXMEADF//nyH+x1pHLScOnVKABAZGRk2aV566SVrW5sLWtxp96uvvioAiGnTpomqqiqbfWfOnBHLly93K21L3AnqPD0/vsKgjuSOQR1RAGoqqLt165Z45plnBADxyCOP2AQGlg/QOXPmtFhuU0Hda6+9Zpdn+fLl1v0ffPCB3X7LqM3cuXNdaaJYtGiRAGA3auVMO/R6vQgJCREqlUr88MMPNvvq6+uto1g6nc7p+jQOWoQQok+fPkKpVIqysjIhhBC1tbUiIiJCdOzYURiNRreDlqbanZmZKQCI77//vsUyXEnbEneCOiF8d348waCO5I7P1BEFsD//+c8YMmQIhgwZgl69eiE+Ph5FRUVQqVRYuXIlQkND7fJMnTrV7ePNmDHDbltaWpr1+5ycHLv9ffr0AQBcunTJYZnXrl3DkiVLMH78eDz99NPW9qxfvx4A8P333zvM11w7QkND8Ytf/AJGoxF5eXk2+7Zs2YI7d+6gX79+SE5ObrKMlkyZMgUNDQ3Iz88HAHz99deoqKjApEmTEBQU1GJ+V9sdHx8PANi4cWOLU4C4ktZXPD0/ROQGqaNKInKdZVSk8ZdarRaPPvqomDx5sjh+/LhdHks6g8HQYrlNjdTdu3fPLs+lS5cEANGhQweHZe7atUsAEMOGDbPbt2rVKtG2bVu7tjT+evHFF11uhxBC7N27VwAQffr0sdk+ZswYAUAsW7as2fwPenAk6saNG0KpVIq0tDQhhBDPP/+8ACCOHj0qhBDNjkS50+7jx48LlUplHTWbOXOmWLt2rbh+/bpd+a6kbYm7I3WenB9f4UgdyR2DOqIA1NKLEo448wHaUlDnSEsf+k19kF68eNEaeMybN08cP35c6PV66zNylufipk2b5nI7LLp37y4AWJ9fu337tlCpVEKtVovy8nKnyrB4MGgR4qfbnHv37hVqtVo88cQT1n1NBS3utlsIIQ4fPiwyMjKsL6AAEAqFQmRkZIjTp0+7nbY57gZ17p4fR8rKysTs2bNFXFycUKvVIjExUSxcuFDcvXvX6XYIwaCO5I+3X4lIEv/6179gNBoxceJEvPvuu0hLS0NoaKh1upKSkhKPj/HCCy8AAFavXg0AyM/Ph9FoRFZWFiIjIz0u3zLX2pQpU1BfX+/U3GuetHvgwIHYvn077t69i23btuHNN99EXFwcduzYgZEjR6KiosKttL7izvl5UHFxMXr37o3S0lJ88sknOH78ON5//30cOnQIffv25TrFRI0wqCMiSVg+jJ966imH+5t6ls4VL7zwApRKJdatWweTyYRVq1ZZt3tDdnY2tFotrl27BoVCgV/96lct5vFGu7VaLTIzM7FkyRKcPXsWiYmJuH79OgoKCjxK623unJ/G6urqkJ2djZdeeglffPEFMjMzkZSUhKysLOzatQvp6emYMGFCQC/DRuRNDOqISBLBwcEAgFu3btntO3v2LL766iuPjxEbG4uMjAzcvHkT7733Hr777jvExMRg1KhRHpcNAO3atcO8efMwYsQIzJw5EwkJCS3m8Xa727Vrh5SUFABAWVmZ19J6gzvnp7GNGzdCrVbj7bffttvXpk0b/P3vf8f58+exb98+b1WZKKAxqCMiSQwZMgQA8NFHH6G4uNi6/fz58xg3bhzUarVXjmN5I3fRokUA7i9j1dRC8+7Izc1FUVERPv74Y6fSu9vuWbNmYf369aiurrbZvm/fPuzcuRMA0LdvX5fT+pqr56exI0eOICMjA23a3P+o2rhxI6KionDw4EEA90ch09PTceTIEa/WmShQMagjIkmMGTMGAwcOxN27d9GvXz8kJSUhJSUFTzzxBMrLy61BmKeysrIQFRUFk8kEwHu3Xt3lbrsPHz6MiRMnIjw8HElJSRgwYAC6dOmC9PR0VFVVYfLkyRg+fLjLaf2ZyWSCSqWy/lxbW4vy8nIYjUbrNpVKhYaGBimqR+R3GNQRkSSCgoKwfft2zJkzB9HR0bh48SIqKiowY8YMfPvtt+jcubNXjqNWq/HLX/4SADyem84b3G33Bx98gNdeew2pqam4c+eOdZQvMzMT//nPf/DPf/7TrbT+LDU1Ffv377f+PHnyZAghMGzYMACA0WjEoUOHkJqaKlENifyLQvAJUyKSuYkTJ2L9+vVYtmwZZs+eLXV1AsqVK1fQtWtXJCQktPqbphUVFUhMTMTSpUsdTnydm5uLdevW4fTp0zYjek3Zs2cPhg8fjvT0dOzZs8cHNSaSFqf1JiJZKy8vx5dffgmNRoNJkyZJXZ2AdfPmTevzgDk5OQ5XD/G2iIgI5OXlITs7G99++y3mz5+P+Ph4XLx4EW+99Ra2bduGXbt2tRjQTZ8+HRcuXEBlZaXP60wkJd5+JSJZy83NRW1tLSZOnOiVuekeVnV1dTh48CAOHjyIa9eutdpxMzMzceDAAZw/fx7du3eHWq1GWloa6urqcOzYMade+Dh+/DgOHjwInU7XCjUmkg5vvxKR7BQXF+P1119HWVkZLly4gODgYJw8eRKJiYlSV408cO/ePZSXl6Njx45o27at1NUh8ju8/UpEslNRUYG9e/dCo9Ggf//+WLp0KQM6GQgJCUFISIjU1SDyWxypIyIiIpIBPlNHREREJAMM6oiIiIhkgEEdERERkQwwqCMiIiKSAQZ1RERERDLAoI6IiIhIBhjUEREREckAgzoiIiIiGWBQR0RERCQDDOqIiIiIZIBBHREREZEM/D+7/5aPYxh0rQAAAABJRU5ErkJggg==", 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      " ] @@ -72,6 +72,8 @@ "plt.yticks(fontsize=12)\n", "plt.title('Multiplicity Fraction vs Mass', fontsize=20)\n", "plt.axvline(0.08, linestyle='--', label='BD boundary')\n", + "plt.annotate('Brown Dwarf\\nRegime', xy=(0.01, 0.8), xytext=(0.025, 0.8), fontsize=14, fontweight='bold', ha='center', va='center')\n", + "plt.annotate('Stellar\\nRegime', xy=(0.01, 0.8), xytext=(0.5, 0.8), fontsize=14, fontweight='bold', ha='center', va='center')\n", "plt.legend(fontsize=14)\n", "plt.grid()\n", "plt.tight_layout()\n", diff --git a/docs/paper_examples/Begbie+26/Figure 12.ipynb b/docs/paper_examples/Begbie+26/Figure 12.ipynb index d7021e98..929b6a92 100644 --- a/docs/paper_examples/Begbie+26/Figure 12.ipynb +++ b/docs/paper_examples/Begbie+26/Figure 12.ipynb @@ -5,40 +5,14 @@ "id": "51fdd39d-b433-4371-a487-fa5d855b79de", "metadata": {}, "source": [ - "## Below is the code to recreate Figure 11.\n", + "## Below is the code to recreate Figure 12.\n", "\n", "Topic: Showing the semimajor axis distribution as a function of initial mass (reflects changes induced for brown dwarfs)." ] }, { "cell_type": "code", - "execution_count": 1, - "id": "38f7fd03-45f5-4283-a9d1-223747ea45af", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA_merged/mergeconflicts/spisea/__init__.py\n" - ] - } - ], - "source": [ - "import sys\n", - "\n", - "sys.path.insert(\n", - " 0,\n", - " \"/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA_merged/mergeconflicts\"\n", - ")\n", - "\n", - "import spisea\n", - "print(spisea.__file__)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "3d80653b-a8ee-422d-b008-14dc490ad520", "metadata": {}, "outputs": [], @@ -52,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", "metadata": {}, "outputs": [], @@ -65,13 +39,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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      " ] From 3994949808a634f9e0271128a4adcd7483cbf560 Mon Sep 17 00:00:00 2001 From: nsabrams Date: Thu, 11 Jun 2026 17:50:15 -0700 Subject: [PATCH 145/191] switched to sum of rows for kicks --- spisea/evolution.py | 34 +++++++++++++++++++--------------- 1 file changed, 19 insertions(+), 15 deletions(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index f3d3c0bc..e2c406d5 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1716,26 +1716,30 @@ def evolve(self, star_systems, companions, logAge, metallicity): star_systems['L'] = final_binaries['lum_1'] star_systems['logg'] = self.calc_logg(final_binaries['mass_1'], final_binaries['rad_1']) - # Takes second row with priamry info for binaries - # This allows for if first object is kicked but not disrupted - multiples_mask = star_systems['isMultiple'] == 1 - star_systems['kick_x'][multiples_mask] = kick_info.groupby(level=0).nth(1).loc[multiples_mask, 'delta_vsysx_1_rot'] - star_systems['kick_y'][multiples_mask] = kick_info.groupby(level=0).nth(1).loc[multiples_mask, 'delta_vsysy_1_rot'] - star_systems['kick_z'][multiples_mask] = kick_info.groupby(level=0).nth(1).loc[multiples_mask, 'delta_vsysz_1_rot'] - # Takes first row with primary info for singles since second row is blank - singles_mask = star_systems['isMultiple'] == 0 - star_systems['kick_x'][singles_mask] = kick_info.groupby(level=0).nth(0).loc[singles_mask, 'delta_vsysx_1_rot'] - star_systems['kick_y'][singles_mask] = kick_info.groupby(level=0).nth(0).loc[singles_mask, 'delta_vsysy_1_rot'] - star_systems['kick_z'][singles_mask] = kick_info.groupby(level=0).nth(0).loc[singles_mask, 'delta_vsysz_1_rot'] + # Takes sum of the delta kicks in case there was a kick, no disruption, then second kick + # Even for isolated stars, take sum since second row is blank + primary_kick_sum = ( + kick_info + .groupby(level=0)[["delta_vsysx_1_rot", "delta_vsysy_1_rot", "delta_vsysz_1_rot"]] + .sum() + ) + star_systems["kick_x"] = primary_kick_sum["delta_vsysx_1_rot"].reindex(star_systems["system_idx"], fill_value=0).to_numpy() + star_systems["kick_y"] = primary_kick_sum["delta_vsysy_1_rot"].reindex(star_systems["system_idx"], fill_value=0).to_numpy() + star_systems["kick_z"] = primary_kick_sum["delta_vsysz_1_rot"].reindex(star_systems["system_idx"], fill_value=0).to_numpy() companions['mass_current'] = final_binaries['mass_2'][companion_system_idxs] companions['Teff'] = final_binaries['teff_2'][companion_system_idxs] companions['L'] = final_binaries['lum_2'][companion_system_idxs] companions['logg'] = self.calc_logg(final_binaries['mass_2'][companion_system_idxs], final_binaries['rad_2'][companion_system_idxs]) - # Takes second row with companion info - companions['kick_x'] = kick_info.groupby(level=0).nth(1)['delta_vsysx_2_rot'][companion_system_idxs] - companions['kick_y'] = kick_info.groupby(level=0).nth(1)['delta_vsysy_2_rot'][companion_system_idxs] - companions['kick_z'] = kick_info.groupby(level=0).nth(1)['delta_vsysz_2_rot'][companion_system_idxs] + # Also take sum of companion kicks + companion_kick_sum = ( + kick_info + .groupby(level=0)[["delta_vsysx_2_rot", "delta_vsysy_2_rot", "delta_vsysz_2_rot"]] + .sum() + ) + companions['kick_x'] = companion_kick_sum["delta_vsysx_2_rot"].reindex(companion_system_idxs, fill_value=0).to_numpy() + companions['kick_y'] = companion_kick_sum["delta_vsysy_2_rot"].reindex(companion_system_idxs, fill_value=0).to_numpy() + companions['kick_z'] = companion_kick_sum["delta_vsysz_2_rot"].reindex(companion_system_idxs, fill_value=0).to_numpy() loga = np.log10(final_binaries['sep'][companion_system_idxs]*u.Rsun.to('AU')) companions['log_a'] = loga From 809acc20e41b57005f164f09a0464b122cce391f Mon Sep 17 00:00:00 2001 From: nsabrams Date: Thu, 11 Jun 2026 17:56:31 -0700 Subject: [PATCH 146/191] switch recomp isochrone to false --- docs/Cluster_w_COSMIC.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/Cluster_w_COSMIC.ipynb b/docs/Cluster_w_COSMIC.ipynb index a9a21f89..00314a16 100755 --- a/docs/Cluster_w_COSMIC.ipynb +++ b/docs/Cluster_w_COSMIC.ipynb @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "90c33d5c-7c52-4dd1-9545-25b00db26032", "metadata": { "scrolled": true @@ -1132,7 +1132,7 @@ "iso = synthetic.IsochronePhotExternalEvolution(logAge, AKs, dist, metallicity=metallicity,\n", " evo_model=evo_merged, atm_func=atm_func,\n", " filters=filt_list, red_law=redlaw,\n", - " atm_grid_dir=iso_dir, mass_sampling=3, recomp=True)" + " atm_grid_dir=iso_dir, mass_sampling=3, recomp=False)" ] }, { From b6b678c2ccd546de11c3d1598771cef52dfe55bc Mon Sep 17 00:00:00 2001 From: nsabrams Date: Fri, 12 Jun 2026 14:06:04 -0700 Subject: [PATCH 147/191] fixed interpolator call issue --- spisea/atmospheres.py | 131 ++++++++++++------------------------------ spisea/synthetic.py | 5 +- 2 files changed, 40 insertions(+), 96 deletions(-) diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index 6451ebb9..051cc8c8 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -1176,9 +1176,9 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose * T < 3800, logg < 2.5: PHOENIX v16 * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge - * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - * 1200 < T <= 1000, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge - * 1000 < T <= 250, logg > 2.5: Meisner2023 + * 1200 < T <= 3200, logg > 2.5: BTSettl_CIFITS2011_2015 + * 1000 <= T <= 1200, logg >= 2.5: Meisner2023 + * 250 <= T < 1000: Meisner2023 Otherwise, if T < 3800 and [M/H] != 0: @@ -1406,7 +1406,9 @@ def get_merged_atmosphere_w_bb_supplement(metallicity=0, temperature=20000, grav * T < 3800, logg < 2.5: PHOENIX v16 * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge - * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 + * 1200 < T <= 3200, logg > 2.5: BTSettl_CIFITS2011_2015 + * 1000 <= T <= 1200, logg >= 2.5: Meisner2023 + * 250 <= T < 1000: Meisner2023 Otherwise, if T < 3800 and [M/H] != 0: @@ -1417,6 +1419,7 @@ def get_merged_atmosphere_w_bb_supplement(metallicity=0, temperature=20000, grav * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) * PHOENIXv16 (`Husser et al. 2013 `_) * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) + * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) LTE WARNING: @@ -1436,110 +1439,51 @@ def get_merged_atmosphere_w_bb_supplement(metallicity=0, temperature=20000, grav ensure a smooth transition. """ - if (temperature <= 1000): - print('BB atmosphere') - return get_bb_atmosphere(temperature=temperature, - metallicity=metallicity, - gravity=gravity, - verbose=verbose) if (gravity >= 9.8): - print('BB atmosphere') + if verbose: + print('BB atmosphere') return get_bb_atmosphere(temperature=temperature, metallicity=metallicity, gravity=gravity, verbose=verbose) if (temperature < 4.6e3) & (gravity >= 6.5): - print('BB atmosphere') + if verbose: + print('BB atmosphere') return get_bb_atmosphere(temperature=temperature, metallicity=metallicity, gravity=gravity, verbose=verbose) if (temperature < 3.5e3) & (gravity < 6.5) & (gravity > 6): - print('BB atmosphere') + if verbose: + print('BB atmosphere') return get_bb_atmosphere(temperature=temperature, metallicity=metallicity, gravity=gravity, verbose=verbose) - - - # For T < 3800, atmosphere depends on metallicity + gravity. - # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is - # currently available here, so if non-solar metallicity, just stick with - # the Phoenix grid - if (temperature <= 3800) & (metallicity == 0): - # High gravity are in BTSettl regime - if (temperature <= 3200) & (gravity > 2.5): - if verbose: - print( 'BTSettl_2015 atmosphere') - return get_BTSettl_2015_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): - if verbose: - print( 'BTSettl/Phoenixv16 merged atmosphere') - return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - # Low gravity is PHOENIX regime - if gravity <= 2.5: - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity != 0): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - # For T > 3800, no metallicity or gravity dependence - if (temperature >= 3800) & (temperature < 5000): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 5000) & (temperature < 5500): - if verbose: - print( 'ATLAS/Phoenix merged atmosphere') - return get_atlas_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if (temperature >= 5500) & (temperature < 20000): - if verbose: - print( 'ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if temperature >= 20000: - if verbose: - print( 'Still ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - # Returns BB if outside of WD defined atmospheres - else: + if gravity > 6: if verbose: print('WD or BB atmosphere') return get_wd_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) + temperature=temperature, + gravity=gravity, + verbose=verbose) + + return get_merged_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + verbose=verbose, + rebin=rebin) def get_merged_atmosphere_w_bb_supplement_grid(bb_supplement_tarr='default', bb_supplement_zarr='default', bb_supplement_loggarr='default', rebin=True): + """ + Return the atmosphere parameter grid used by + get_merged_atmosphere_w_bb_supplement. + This is the standard merged atmosphere grid plus the white dwarf grid and + blackbody supplement points for high-gravity regions. + """ super_tarr, super_zarr, super_loggarr = get_merged_atmosphere_grid(rebin=rebin) wd_tarr, wd_zarr, wd_loggarr = get_wdKoester_atmosphere_grid() @@ -1548,14 +1492,15 @@ def get_merged_atmosphere_w_bb_supplement_grid(bb_supplement_tarr='default', bb_ super_loggarr = np.concatenate((super_loggarr, wd_loggarr)) if bb_supplement_tarr == 'default': - X, Y = np.meshgrid(np.logspace(np.log10(2e3), np.log10(4.6e3), 20), np.linspace(6.5, 8.7, 10)) - X1, Y1 = np.meshgrid(np.logspace(np.log10(2e3), np.log10(3.5e3), 15), np.linspace(6, 6.25, 2)) - #X2, Y2 = np.meshgrid(np.logspace(np.log10(2e2), np.log10(1.1e3), 20), np.linspace(3, 4.5, 4)) - X3, Y3 = np.meshgrid(np.logspace(np.log10(8e3), np.log10(2e4), 25), np.linspace(9.8, 11.6, 8)) - bb_supplement_tarr = np.concatenate((X.ravel(), X1.ravel(), X3.ravel())) - bb_supplement_loggarr = np.concatenate((Y.ravel(), Y1.ravel(), Y3.ravel())) - #bb_supplement_tarr = np.concatenate((X.ravel(), X1.ravel(), X2.ravel(), X3.ravel())) - #bb_supplement_loggarr = np.concatenate((Y.ravel(), Y1.ravel(), Y2.ravel(), Y3.ravel())) + X_highg, Y_highg = np.meshgrid(np.logspace(np.log10(2e3), np.log10(2e4), 35), + np.linspace(9.8, 11.6, 8)) + X_cool_highg, Y_cool_highg = np.meshgrid(np.logspace(np.log10(2e3), np.log10(4.6e3), 20), + np.linspace(6.5, 9.7, 10)) + X_cool_midg, Y_cool_midg = np.meshgrid(np.logspace(np.log10(2e3), np.log10(3.5e3), 15), + np.linspace(6.1, 6.4, 4)) + + bb_supplement_tarr = np.concatenate((X_highg.ravel(), X_cool_highg.ravel(), X_cool_midg.ravel())) + bb_supplement_loggarr = np.concatenate((Y_highg.ravel(), Y_cool_highg.ravel(), Y_cool_midg.ravel())) bb_supplement_zarr = np.zeros(len(bb_supplement_tarr)) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 820a923d..12ee0e6a 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -215,8 +215,7 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, interp_keys = ['Teff', 'L', 'logg', 'isWR', 'mass_current', 'phase'] + self.filt_names self.iso_interps = {} for ikey in interp_keys: - self.iso_interps[ikey] = Interpolator(self.iso.points['mass'], self.iso.points[ikey], - kind='linear', bounds_error=False, fill_value=np.nan) + self.iso_interps[ikey] = Interpolator(self.iso.points['mass'], self.iso.points[ikey]) else: from scipy.interpolate import LinearNDInterpolator self.iso.points.sort(['Teff', 'logg', 'metallicity']) @@ -332,7 +331,7 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # Add columns for the Teff, L, logg, isWR, mass_current, phase, and filters. for key in ['Teff', 'L', 'logg', 'mass_current', 'phase']: star_systems.add_column(Column(np.empty(N_systems, dtype=float), name=key)) - star_systems.add_column(Column(np.zeros(N_systems, dtype=bool), name='isWR')) + star_systems.add_column(Column(np.zeros(N_systems, dtype=bool), name='isWR')) # for models with no WR designation, this remains 0 star_systems['metallicity'] = np.ones(N_systems) * self.iso.metallicity # Add the filter columns to the table. They are empty so far. From 252f2a65c94bb903229f3ebda38b102f7fa7711d Mon Sep 17 00:00:00 2001 From: nsabrams Date: Mon, 15 Jun 2026 16:03:43 -0700 Subject: [PATCH 148/191] fixed companion mass checks --- spisea/evolution.py | 2 -- spisea/synthetic.py | 6 +++--- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index e2c406d5..1129f54f 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1817,8 +1817,6 @@ def evolve(self, star_systems, companions, logAge, metallicity): companions['system_idx'] = mapping[companions['system_idx']] star_systems.remove_columns(['system_idx', 'system_idx_new']) - #FIXME add assertion about mass_current not being zero - # Preserve a scalar kick magnitude alongside the vector components. for table in (star_systems, companions): table['kick'] = np.sqrt(table['kick_x']**2 + table['kick_y']**2 + table['kick_z']**2) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 12ee0e6a..1eee1237 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -688,9 +688,9 @@ def _make_companions_table(self, star_systems, compMass): companions['phase'][low_mass_idxs] = 98 # FIXME SHOULD BE ADDED FOR EXTERNALEVOL TOO - # Double check that everything behaved properly. - if len(idx)>0: - assert companions['mass'][companions_teff_non_nan > 0].min() > 0, "Companion mass is not positive" + # Double check that everything behaved properly. + if len(idx)>0: + assert companions['mass'][companions_teff_non_nan > 0].min() > 0, "Companion mass is not positive" return star_systems, companions From bd69b57a7cd66a90fef8ba8472ead60b1fcb2c24 Mon Sep 17 00:00:00 2001 From: nsabrams Date: Tue, 16 Jun 2026 10:39:49 -0700 Subject: [PATCH 149/191] added unit and integration tests via claude --- spisea/tests/test_models.py | 76 ++++++++++++++++ spisea/tests/test_synthetic.py | 157 +++++++++++++++++++++++++++++++++ 2 files changed, 233 insertions(+) diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 35970431..c3cd7aa2 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -91,6 +91,82 @@ def test_synthpop_MIST_extension(): return +def test_COSMIC_init(): + """ + Test the COSMIC external evolution model constructor: default flags, + default BSEDict, and that user-supplied options are stored. + """ + # Default construction + evo = evolution.COSMIC() + assert evo.external_evol is True + assert evo.z_solar == 0.02 + assert evo.model_version_name == 'COSMIC' + assert evo.keep_disrupted_companions is True + assert evo.keep_COSMIC_tables is False + + # Default BSEDict should be a populated dictionary of BSE parameters + assert isinstance(evo.BSEDict, dict) + assert len(evo.BSEDict) > 0 + + # User-supplied options should be stored + custom_dict = {'windflag': 3, 'neta': 0.5} + evo2 = evolution.COSMIC(BSEDict=custom_dict, keep_disrupted_companions=False, + keep_COSMIC_tables=True) + assert evo2.BSEDict == custom_dict + assert evo2.keep_disrupted_companions is False + assert evo2.keep_COSMIC_tables is True + + return + +def test_COSMIC_calc_logg(): + """ + Test the COSMIC.calc_logg helper. For the Sun (M=1 Msun, R=1 Rsun) + the surface gravity should be logg ~ 4.438 (cgs). + """ + evo = evolution.COSMIC() + + # Scalar solar value + assert np.isclose(evo.calc_logg(1.0, 1.0), 4.438, atol=0.01) + + # Array input should be handled element-wise + masses = np.array([1.0, 2.0]) + radii = np.array([1.0, 2.0]) + logg = evo.calc_logg(masses, radii) + assert np.isclose(logg[0], 4.438, atol=0.01) + # logg scales as log10(M/R^2); doubling both M and R lowers logg by log10(2) + assert np.isclose(logg[0] - logg[1], np.log10(2.0), atol=0.01) + + return + +def test_COSMIC_get_kick_differential(): + """ + Test the COSMIC.get_kick_differential helper. The transformation is a + pure rotation (Rz(theta) * Rx(phi)) of the kick vector, so it must + preserve the vector magnitude. A zero kick must map to a zero kick. + """ + evo = evolution.COSMIC() + + # Zero kick in -> zero kick out + zeros = np.zeros(3) + phase = np.array([0.3, 1.1, 2.0]) + incl = np.array([0.5, 1.5, 2.5]) + kd_zero = evo.get_kick_differential(zeros, zeros, zeros, phase=phase, inclination=incl) + assert np.allclose(kd_zero.d_x.value, 0.0) + assert np.allclose(kd_zero.d_y.value, 0.0) + assert np.allclose(kd_zero.d_z.value, 0.0) + + # Magnitude is preserved under the rotation + vx = np.array([10.0, -5.0, 3.0]) + vy = np.array([2.0, 7.0, -1.0]) + vz = np.array([-4.0, 1.0, 8.0]) + kd = evo.get_kick_differential(vx, vy, vz, phase=phase, inclination=incl) + + mag_in = np.sqrt(vx**2 + vy**2 + vz**2) + mag_out = np.sqrt(kd.d_x.value**2 + kd.d_y.value**2 + kd.d_z.value**2) + np.testing.assert_allclose(mag_out, mag_in, rtol=1e-10) + + return + def test_atmosphere_models(): """ Test the rebinned atmosphere models used for synthetic photometry diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 7762fd93..f11582c6 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -2,6 +2,9 @@ import pdb import time import spisea +import pytest +import warnings +import importlib import numpy as np import pylab as plt from astropy.table import Table @@ -941,6 +944,160 @@ def test_compact_object_companions(): assert (len(nan_lum_companions) == 0) | (all(np.isnan(nan_lum_companions['phase'])) == False) +def _require_cosmic(): + """ + Skip the calling test if the optional `cosmic` package is not installed, + emitting a warning so the skip is visible (rather than silent). + """ + if importlib.util.find_spec('cosmic') is None: + msg = ('COSMIC integration test skipped: the optional `cosmic` package ' + 'is not installed. Run in an environment with COSMIC (e.g. ' + '`astro_cosmic`) to exercise these tests.') + warnings.warn(msg) + pytest.skip(msg) + + return + +def test_COSMIC_evolve(): + """ + Test the COSMIC external evolution model's evolve() method directly on a + small, hand-built set of star systems and companions. Uses a young, low-mass + population so no compact remnants/disruptions occur, keeping the run fast + and avoiding the merger/disruption branches. + + Skipped (with a warning) if the optional `cosmic` package is not installed. + """ + _require_cosmic() + + from astropy.table import Table + + # Build a minimal star_systems table: 2 binaries + 1 single + star_systems = Table() + star_systems['mass'] = np.array([1.0, 0.9, 0.5]) + star_systems['isMultiple'] = np.array([True, True, False]) + star_systems['N_companions'] = np.array([1, 1, 0]) + star_systems['systemMass'] = np.array([1.5, 1.3, 0.5]) + + # Companions for the first two systems + companions = Table() + companions['system_idx'] = np.array([0, 1]) + companions['mass'] = np.array([0.5, 0.4]) + companions['log_a'] = np.array([1.0, 1.5]) # log10(AU) + companions['e'] = np.array([0.1, 0.2]) + companions['i'] = np.array([30.0, 60.0]) # degrees + companions['Omega'] = np.array([0.0, 0.0]) + companions['omega'] = np.array([0.0, 0.0]) + + evo = evolution.COSMIC(keep_disrupted_companions=False, keep_COSMIC_tables=True) + ss, comp = evo.evolve(star_systems, companions, logAge=8.0, metallicity=0.0) + + # Check that evolve() populated the expected output columns + expected_cols = ['mass_current', 'Teff', 'L', 'logg', 'phase', + 'kick', 'kick_x', 'kick_y', 'kick_z'] + for col in expected_cols: + assert col in ss.colnames, 'star_systems missing column {0}'.format(col) + assert col in comp.colnames, 'companions missing column {0}'.format(col) + + # Core bookkeeping invariant: companion count must match companion table length + assert np.sum(ss['N_companions']) == len(comp) + + # Scalar kick must be the magnitude of the kick vector components + np.testing.assert_allclose( + ss['kick'], np.sqrt(ss['kick_x']**2 + ss['kick_y']**2 + ss['kick_z']**2)) + np.testing.assert_allclose( + comp['kick'], np.sqrt(comp['kick_x']**2 + comp['kick_y']**2 + comp['kick_z']**2)) + + # Young, low-mass stars should remain stellar (no compact remnants here). + # Compact-object phase codes are 101 (WD), 102 (NS), 103 (BH). + assert np.all(ss['phase'] < 100) + assert np.all(comp['phase'] < 100) + + # keep_COSMIC_tables=True should store the raw COSMIC output tables + for attr in ['bpp', 'bcm', 'initC', 'kick_info']: + assert hasattr(evo, attr), 'COSMIC model missing table {0}'.format(attr) + + return + +def test_COSMIC_ResolvedCluster(): + """ + Test the full COSMIC cluster pipeline, mirroring the Cluster_w_COSMIC + tutorial: build an IsochronePhotExternalEvolution, then a ResolvedCluster + with binary multiplicity (CSF_max=1, companion_max=True), and check the + output tables. + + Skipped (with a warning) if the optional `cosmic` package is not installed. + """ + _require_cosmic() + + # Cluster/isochrone parameters (kept small/cheap) + logAge = 9.0 + AKs = 0.0 + distance = 4000 + metallicity = 0.0 + cluster_mass = 10**3. + mass_sampling = 10 + atm_grid_dir = f'{spisea_path}/tests/atm_cosmic' + + filt_list = ['ubv,V'] + + # External evolution model (keep tables so we can verify them) + evo = evolution.COSMIC(keep_COSMIC_tables=True) + atm_func = atmospheres.get_merged_atmosphere_w_bb_supplement + red_law = reddening.RedLawCardelli(3.1) + + iso = syn.IsochronePhotExternalEvolution( + logAge, + AKs, + distance, + metallicity=metallicity, + evo_model=evo, + atm_func=atm_func, + red_law=red_law, + filters=filt_list, + atm_grid_dir=atm_grid_dir, + mass_sampling=mass_sampling, + recomp=False + ) + + # COSMIC only supports binaries: resolved multiplicity, no higher-order systems + clust_multiplicity = multiplicity.MultiplicityResolvedDK(CSF_max=1, companion_max=True) + my_imf = imf.Kroupa_2001(multiplicity=clust_multiplicity) + + cluster = syn.ResolvedCluster(iso, my_imf, cluster_mass) + star_systems = cluster.star_systems + companions = cluster.companions + + # Basic sanity: stars and companions were produced + assert len(star_systems) > 0 + assert len(companions) > 0 + + # Core bookkeeping invariant + assert np.sum(star_systems['N_companions']) == len(companions) + + # Kick columns present and self-consistent on both tables + for tab in (star_systems, companions): + for col in ['kick', 'kick_x', 'kick_y', 'kick_z']: + assert col in tab.colnames + np.testing.assert_allclose( + tab['kick'], np.sqrt(tab['kick_x']**2 + tab['kick_y']**2 + tab['kick_z']**2)) + + # Synthetic photometry column should exist + assert 'm_ubv_V' in star_systems.colnames + + # All phase codes should be in the allowed set: + # 0-9 stellar phases, 101 (WD), 102 (NS), 103 (BH) + allowed_phases = set(range(0, 10)) | {101, 102, 103} + ss_phases = set(np.unique(star_systems['phase']).astype(int).tolist()) + comp_phases = set(np.unique(companions['phase']).astype(int).tolist()) + assert ss_phases.issubset(allowed_phases), 'Unexpected star phases: {0}'.format(ss_phases - allowed_phases) + assert comp_phases.issubset(allowed_phases), 'Unexpected companion phases: {0}'.format(comp_phases - allowed_phases) + + # keep_COSMIC_tables=True should expose the raw COSMIC tables on the evo model + for attr in ['bpp', 'bcm', 'initC', 'kick_info']: + assert hasattr(iso.evo_model, attr), 'COSMIC model missing table {0}'.format(attr) + + return + #=================================# # Additional timing functions #=================================# From 5a5748103df9b525e7f131c23771a57c54acf721 Mon Sep 17 00:00:00 2001 From: nsabrams Date: Tue, 16 Jun 2026 10:45:43 -0700 Subject: [PATCH 150/191] updated to new cosmic default bsedict but kept zsolar = 0.02 --- spisea/evolution.py | 44 ++++++++++++++++++++++---------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index 1129f54f..a4d1a824 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1545,28 +1545,28 @@ class COSMIC(StellarEvolution): def __init__(self, BSEDict='default', keep_disrupted_companions=True, keep_COSMIC_tables=False): if BSEDict == 'default': self.BSEDict = { - "pts1": 0.001, "pts2": 0.01, "pts3": 0.02, "zsun": 0.02, "windflag": 3, - "eddlimflag": 0, "neta": 0.5, "bwind": 0.0, "hewind": 0.5, "beta": 0.125, - "xi": 0.5, "acc2": 1.5, "LBV_flag": 1, "alpha1": 1.0, "lambdaf": 0.0, - "ceflag": 1, "cekickflag": 2, "cemergeflag": 1, "cehestarflag": 0, - "qcflag": 5, - "qcrit_array": [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0], - "kickflag": 5, "sigma": 265.0, "bhflag": 1, "bhsigmafrac": 1.0, - "sigmadiv": -20.0, "ecsn": 2.25, "ecsn_mlow": 1.6, "aic": 1, "ussn": 1, - "polar_kick_angle": 90.0, - "natal_kick_array": [[-100.0, -100.0, -100.0, -100.0, 0.0], [-100.0, -100.0, -100.0, -100.0, 0.0]], - "mm_mu_ns": 400.0, "mm_mu_bh": 200.0, "remnantflag": 4, - "fryer_mass_limit": 0, "mxns": 3.0, "rembar_massloss": 0.5, - "wd_mass_lim": 1, "maltsev_mode": 0, "maltsev_fallback": 0.5, - "maltsev_pf_prob": 0.1, "pisn": -2, "ppi_co_shift": 0.0, - "ppi_extra_ml": 0.0, "bhspinflag": 0, "bhspinmag": 0.0, "grflag": 1, - "eddfac": 10, "gamma": -2, "don_lim": -1, "acc_lim": -1, "tflag": 1, - "ST_tide": 1, - "fprimc_array": [2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0], - "ifflag": 1, "wdflag": 1, "epsnov": 0.001, "bdecayfac": 1, - "bconst": 3000, "ck": 1000, "rejuv_fac": 1.0, "rejuvflag": 0, - "bhms_coll_flag": 0, "htpmb": 1, "ST_cr": 1, "rtmsflag": 0 - } + "pts1": 0.001, "pts2": 0.01, "pts3": 0.02, "zsun": 0.02, "windflag": 3, + "eddlimflag": 0, "neta": 0.5, "bwind": 0.0, "hewind": 0.5, "beta": 0.125, + "xi": 0.5, "acc2": 1.5, "LBV_flag": 1, "alpha1": [1.0, 1.0], + "lambdaf": 0.0, "ceflag": 1, "cekickflag": 2, "cemergeflag": 1, + "cehestarflag": 0, "qcflag": 5, + "qcrit_array": [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0], + "kickflag": 5, "sigma": 265.0, "bhflag": 1, "bhsigmafrac": 1.0, + "sigmadiv": -20.0, "ecsn": 2.25, "ecsn_mlow": 1.6, "aic": 1, "ussn": 1, + "polar_kick_angle": 90.0, + "natal_kick_array": [[-100.0, -100.0, -100.0, -100.0, 0.0], [-100.0, -100.0, -100.0, -100.0, 0.0]], + "mm_mu_ns": 400.0, "mm_mu_bh": 200.0, "remnantflag": 4, + "fryer_mass_limit": 0, "mxns": 3.0, "fryer_fmix": 1.0, + "fryer_mcrit_nsbh": 5.75, "rembar_massloss": 0.5, "wd_mass_lim": 1, + "maltsev_mode": 0, "maltsev_fallback": 0.5, "maltsev_pf_prob": 0.1, + "pisn": -2, "ppi_co_shift": 0.0, "ppi_extra_ml": 0.0, "bhspinflag": 0, + "bhspinmag": 0.0, "grflag": 1, "eddfac": 10, "gamma": -2, "don_lim": -1, + "acc_lim": [-1, -1], "smt_periastron_check": 0, "tflag": 1, "ST_tide": 1, + "fprimc_array": [2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0], + "ifflag": 1, "wdflag": 1, "epsnov": 0.001, "bdecayfac": 1, + "bconst": 3000, "ck": 1000, "rejuv_fac": 1.0, "rejuvflag": 0, + "bhms_coll_flag": 0, "htpmb": 1, "ST_cr": 1, "rtmsflag": 0 + } else: self.BSEDict = BSEDict From 07f3d53ec559b67dc81845654968b54287c4d31e Mon Sep 17 00:00:00 2001 From: nsabrams Date: Tue, 16 Jun 2026 11:04:48 -0700 Subject: [PATCH 151/191] added copy=True to accomidate more updated versions of numpy and pandas --- spisea/evolution.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index a4d1a824..be93e066 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1744,13 +1744,13 @@ def evolve(self, star_systems, companions, logAge, metallicity): loga = np.log10(final_binaries['sep'][companion_system_idxs]*u.Rsun.to('AU')) companions['log_a'] = loga - fixed_phases1 = final_binaries['kstar_1'].to_numpy() + fixed_phases1 = final_binaries['kstar_1'].to_numpy(copy=True) fixed_phases1[np.where((final_binaries['kstar_1'] >= 10) & (final_binaries['kstar_1'] <= 12))[0]] = 101 fixed_phases1[np.where(final_binaries['kstar_1'] == 13)[0]] = 102 fixed_phases1[np.where(final_binaries['kstar_1'] == 14)[0]] = 103 star_systems['phase'] = fixed_phases1 - fixed_phases2 = final_binaries['kstar_2'][companion_system_idxs].to_numpy() + fixed_phases2 = final_binaries['kstar_2'][companion_system_idxs].to_numpy(copy=True) fixed_phases2[np.where((final_binaries['kstar_2'][companion_system_idxs] >= 10) & (final_binaries['kstar_2'][companion_system_idxs] <= 12))[0]] = 101 fixed_phases2[np.where(final_binaries['kstar_2'][companion_system_idxs] == 13)[0]] = 102 fixed_phases2[np.where(final_binaries['kstar_2'][companion_system_idxs] == 14)[0]] = 103 From 008639dca0b6e8bbde093f1c0df9c72ea7ad75a3 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 16 Jun 2026 17:25:12 -0700 Subject: [PATCH 152/191] fix low mass companion handling in get_companions_table_new --- spisea/synthetic.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index e4b0aab7..264cf9d3 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -415,6 +415,12 @@ def _make_companions_table_new(self, star_systems, compMass): companions_teff_non_nan = np.nan_to_num(companions['Teff'], nan=-99) if self.verbose and sum(companions_teff_non_nan > 0) != N_comp_tot: print(f'Found {N_comp_tot - sum(companions_teff_non_nan > 0):d} companions out of stellar mass range') + + # For low-mass stars and substellar objects below isochrone, assume no mass loss and set phase to 98 + low_mass_idxs = (companions['mass'] 0])>0: assert companions['mass'][companions_teff_non_nan > 0].min() > 0, "Companion mass is not positive" @@ -586,8 +592,9 @@ def _make_companions_table(self, star_systems, compMass): # For low-mass stars and substellar objects below isochrone, assume no mass loss and set phase to 98 low_mass_idxs = (companions['mass'] 0: assert companions['mass'][companions_teff_non_nan > 0].min() > 0, "Companion mass is not positive" @@ -640,13 +647,6 @@ def _remove_bad_systems(self, star_systems, compMass, keep_low_mass_stars): star_systems['phase'][lm_idx] = 98 #pdb.set_trace() - if keep_low_mass_stars: - lm_idx = star_systems['mass'] < np.min(self.iso.points['mass']) - # Adjust the properties as needed - star_systems['mass_current'][lm_idx] = star_systems['mass'][lm_idx] - star_systems['phase'][lm_idx] = 98 - #pdb.set_trace() - star_systems = star_systems[idx] N_systems = len(star_systems) From 963bb2cb5e2c6cc73457701154418f10c70b9068 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 17 Jun 2026 12:56:14 -0700 Subject: [PATCH 153/191] allow metallicity slightly outside range for MIST so floating point differences are ok --- spisea/evolution.py | 4 ++-- spisea/synthetic.py | 1 - 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index 2f9dd760..192b128d 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1114,8 +1114,8 @@ def isochrone(self, age=1.e8, metallicity=0.0): if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') - if ((z_defined < np.min(self.z_list)) or - (z_defined > np.max(self.z_list))): + if ((z_defined < np.min(self.z_list)-0.1) or + (z_defined > np.max(self.z_list)+0.1)): raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') # Find nearest age in grid to input grid diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 264cf9d3..2ba947d1 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -419,7 +419,6 @@ def _make_companions_table_new(self, star_systems, compMass): # For low-mass stars and substellar objects below isochrone, assume no mass loss and set phase to 98 low_mass_idxs = (companions['mass'] 0])>0: From d5905b9cb472ecf473382eef2aab968494fd3ba6 Mon Sep 17 00:00:00 2001 From: nsabrams Date: Wed, 17 Jun 2026 14:38:00 -0700 Subject: [PATCH 154/191] added docs for cosmic --- docs/evo_models.rst | 10 ++++++++++ docs/make_isochrone.rst | 13 ++++++++++++- docs/multiplicity.rst | 5 +++++ 3 files changed, 27 insertions(+), 1 deletion(-) diff --git a/docs/evo_models.rst b/docs/evo_models.rst index 4cabc35c..d066ae2e 100644 --- a/docs/evo_models.rst +++ b/docs/evo_models.rst @@ -73,11 +73,18 @@ Below is a table of the evolution model grids currently supported by SPISEA. - 6.00 - 10.00 - -0.5, 0, 0.5 - Marley et al. (2021) + * - ``COSMIC`` :sup:`b` + - 0.08 - 150 + - - + - -2.3 - 0.18 + - Breivik et al. (2020) .. rubric:: Footnotes :sup:`a` Actual maximum value given by the Sonora models (Marley et al., 2021) relies on the age of the cluster. For example, for log(Age)=6.0, the mass range is limited to 0.0005 - 0.011 M\ :sub:`⊙`. +:sup:`b` COSMIC evolves the stars externally and does not use SPISEA's standard isochrone-grid architecture. Instead, it uses a custom atmosphere grid that is created on the fly. See Breivik et al. (2020) for more details on COSMIC. When COSMIC is used, the IFMR is ignored. + Please note the stellar mass range, age range, and metallicity values of the evolution model grid you choose: @@ -142,4 +149,7 @@ Specific Evolution Model Classes :show-inheritance: .. autoclass:: evolution.Phillips2020 + :show-inheritance: + +.. autoclass:: evolution.COSMIC :show-inheritance: \ No newline at end of file diff --git a/docs/make_isochrone.rst b/docs/make_isochrone.rst index 5f764c93..11ae9bd1 100644 --- a/docs/make_isochrone.rst +++ b/docs/make_isochrone.rst @@ -89,11 +89,18 @@ Tips and Tricks: The IsochronePhot Object reddening law have changed, the original file will be overwritten by the new isochrone. - *To avoid files from being unintentially overwritten, we recommend + *To avoid files from being unintentionally overwritten, we recommend that users specify different iso_dir paths when making isochrones with different evolution models, atmosphere models, or reddening laws.* +* For external evolution models (i.e. COSMIC), you should use + IsochronePhotExternalEvolution + instead of IsochronePhot. This is because those evolution models do not have isochrones but + instead evolve the stars externally. The first time you run a new AKs, metallicity, or distance, + this will take ~10-20 mins because it is creating a new atmosphere grid. This table is saved in the + specified iso_dir, under the filename atm___.fits. + Base Isochrone Class ---------------------------- .. autoclass:: synthetic.Isochrone @@ -108,6 +115,10 @@ Isochrone Sub-classes :show-inheritance: :members: make_photometry, plot_CMD, plot_mass_magnitude +.. autoclass:: synthetic.IsochronePhotExternalEvolution + :show-inheritance: + :members: make_photometry, plot_CMD, plot_mass_magnitude + Photometry Conversion Functions ----------------------------- diff --git a/docs/multiplicity.rst b/docs/multiplicity.rst index 69011c8c..1b7709f9 100644 --- a/docs/multiplicity.rst +++ b/docs/multiplicity.rst @@ -27,6 +27,11 @@ returned in the ``star_systems`` table off the cluster object is the same for both unresolved and resolved multiplicity classes: it represents the combined photometry of all stars within a given system. +For most selected evolution models, the multiples are evolved as single stars. +To evolve binaries (does not support higher order multiples), you should use one of the ``MultiplicityResolved`` classes +and the ``COSMIC`` evolution model. +See the example jupyter notebook `Cluster_w_COSMIC.ipynb `_ for an example. + Unresolved Multiplicity Classes ------------------------------------------ From c14fb3d4e514c9120e6574fe9b677155d864202c Mon Sep 17 00:00:00 2001 From: nsabrams Date: Wed, 17 Jun 2026 14:42:33 -0700 Subject: [PATCH 155/191] removed backup merge conflict files --- docs/contributors_BACKUP_57328.rst | 54 - docs/contributors_BASE_57328.rst | 36 - docs/contributors_LOCAL_57328.rst | 38 - docs/contributors_REMOTE_57328.rst | 50 - spisea/atmospheres_BACKUP_22350.py | 2524 ---------------------------- spisea/atmospheres_BACKUP_58456.py | 2524 ---------------------------- spisea/atmospheres_BASE_58456.py | 2165 ------------------------ spisea/atmospheres_LOCAL_58456.py | 2508 --------------------------- spisea/atmospheres_REMOTE_58456.py | 2172 ------------------------ 9 files changed, 12071 deletions(-) delete mode 100644 docs/contributors_BACKUP_57328.rst delete mode 100644 docs/contributors_BASE_57328.rst delete mode 100644 docs/contributors_LOCAL_57328.rst delete mode 100644 docs/contributors_REMOTE_57328.rst delete mode 100755 spisea/atmospheres_BACKUP_22350.py delete mode 100755 spisea/atmospheres_BACKUP_58456.py delete mode 100644 spisea/atmospheres_BASE_58456.py delete mode 100644 spisea/atmospheres_LOCAL_58456.py delete mode 100644 spisea/atmospheres_REMOTE_58456.py diff --git a/docs/contributors_BACKUP_57328.rst b/docs/contributors_BACKUP_57328.rst deleted file mode 100644 index 741f832f..00000000 --- a/docs/contributors_BACKUP_57328.rst +++ /dev/null @@ -1,54 +0,0 @@ -.. _contributors: - -============ -Contributors -============ -Jessica Lu -- Lead Developer; developed initial package framework; collaboratively developed all aspects of code - -Matthew Hosek Jr -- Lead Developer; collaboratively developed all aspects of code - -Casey Lam -- implemented first IFMR module (IFMR_Raithel18) - -Abhimat Gautam -- implemented non-solar metallicity evolution model -support for MIST models as well as blackbody atmosphere function - -Kelly Lockhart -- helped develop UnresolvedCluster class - -Dongwon Kim -- code testing/debugging - -Siyao Jia -- helped with early code and documentation development - -Natasha Abrams -- developed resolved multiplicity capabilities -(ResolvedMultiplicityDK class) - -Michael Medford -- developed resolved multiplicity capabilities -(ResolvedMultiplicityDK class) - -Sam Rose -- developed metallicity-dependent IFMRs (IFMR_Spera15 and IFMR_N20_Sukhbold) - -Rebecca Lewis -- added HAWK-I filter support, code testing/debugging - -Manuel Parra-Royón -- installation using Docker containers and Singularity - -Javier Moldón -- installation using Docker containers and Singularity - -Winston Zhang -- bugfix to make redlaw paths correct regardless of -what operating system is used - -<<<<<<< HEAD -Caitlin Begbie -- added brown dwarf physics and capabilities -======= -Sage Hironaka Remulla -- added Rubin Observatory filters - -Lingfeng Wei -- bugfix to improve creation of iso_dir in -IsochronePhot, implemented faster cluster generation and test -functions for primary and companion star mass generation (v2.3), -updated random state generators (v2.4) - -Macy Huston -- New metallicity bound + isochrone filter checks, -imf_mass_lim bugfix, roman filter bugfix, added Euclid filters, Synthpop compatibility -updates (v2.2), improvements for reading in/updated existing isochrone -files, Vega mag to ST mag conversion function (v2.4) - -Anna Pusack -- Added IRTF L-band filter support ->>>>>>> upstream/dev diff --git a/docs/contributors_BASE_57328.rst b/docs/contributors_BASE_57328.rst deleted file mode 100644 index 40edaee7..00000000 --- a/docs/contributors_BASE_57328.rst +++ /dev/null @@ -1,36 +0,0 @@ -.. _contributors: - -============ -Contributors -============ -Jessica Lu -- Lead Developer; developed initial package framework; collaboratively developed all aspects of code - -Matthew Hosek Jr -- Lead Developer; collaboratively developed all aspects of code - -Casey Lam -- implemented first IFMR module (IFMR_Raithel18) - -Abhimat Gautam -- implemented non-solar metallicity evolution model -support for MIST models as well as blackbody atmosphere function - -Kelly Lockhart -- helped develop UnresolvedCluster class - -Dongwon Kim -- code testing/debugging - -Siyao Jia -- helped with early code and documentation development - -Natasha Abrams -- developed resolved multiplicity capabilities -(ResolvedMultiplicityDK class) - -Michael Medford -- developed resolved multiplicity capabilities -(ResolvedMultiplicityDK class) - -Sam Rose -- developed metallicity-dependent IFMRs (IFMR_Spera15 and IFMR_N20_Sukhbold) - -Rebecca Lewis -- added HAWK-I filter support, code testing/debugging - -Manuel Parra-Royón -- installation using Docker containers and Singularity - -Javier Moldón -- installation using Docker containers and Singularity - -Winston Zhang -- bugfix to make redlaw paths correct regardless of -what operating system is used diff --git a/docs/contributors_LOCAL_57328.rst b/docs/contributors_LOCAL_57328.rst deleted file mode 100644 index e62072d7..00000000 --- a/docs/contributors_LOCAL_57328.rst +++ /dev/null @@ -1,38 +0,0 @@ -.. _contributors: - -============ -Contributors -============ -Jessica Lu -- Lead Developer; developed initial package framework; collaboratively developed all aspects of code - -Matthew Hosek Jr -- Lead Developer; collaboratively developed all aspects of code - -Casey Lam -- implemented first IFMR module (IFMR_Raithel18) - -Abhimat Gautam -- implemented non-solar metallicity evolution model -support for MIST models as well as blackbody atmosphere function - -Kelly Lockhart -- helped develop UnresolvedCluster class - -Dongwon Kim -- code testing/debugging - -Siyao Jia -- helped with early code and documentation development - -Natasha Abrams -- developed resolved multiplicity capabilities -(ResolvedMultiplicityDK class) - -Michael Medford -- developed resolved multiplicity capabilities -(ResolvedMultiplicityDK class) - -Sam Rose -- developed metallicity-dependent IFMRs (IFMR_Spera15 and IFMR_N20_Sukhbold) - -Rebecca Lewis -- added HAWK-I filter support, code testing/debugging - -Manuel Parra-Royón -- installation using Docker containers and Singularity - -Javier Moldón -- installation using Docker containers and Singularity - -Winston Zhang -- bugfix to make redlaw paths correct regardless of -what operating system is used - -Caitlin Begbie -- added brown dwarf physics and capabilities diff --git a/docs/contributors_REMOTE_57328.rst b/docs/contributors_REMOTE_57328.rst deleted file mode 100644 index 1944147e..00000000 --- a/docs/contributors_REMOTE_57328.rst +++ /dev/null @@ -1,50 +0,0 @@ -.. _contributors: - -============ -Contributors -============ -Jessica Lu -- Lead Developer; developed initial package framework; collaboratively developed all aspects of code - -Matthew Hosek Jr -- Lead Developer; collaboratively developed all aspects of code - -Casey Lam -- implemented first IFMR module (IFMR_Raithel18) - -Abhimat Gautam -- implemented non-solar metallicity evolution model -support for MIST models as well as blackbody atmosphere function - -Kelly Lockhart -- helped develop UnresolvedCluster class - -Dongwon Kim -- code testing/debugging - -Siyao Jia -- helped with early code and documentation development - -Natasha Abrams -- developed resolved multiplicity capabilities -(ResolvedMultiplicityDK class) - -Michael Medford -- developed resolved multiplicity capabilities -(ResolvedMultiplicityDK class) - -Sam Rose -- developed metallicity-dependent IFMRs (IFMR_Spera15 and IFMR_N20_Sukhbold) - -Rebecca Lewis -- added HAWK-I filter support, code testing/debugging - -Manuel Parra-Royón -- installation using Docker containers and Singularity - -Javier Moldón -- installation using Docker containers and Singularity - -Winston Zhang -- bugfix to make redlaw paths correct regardless of -what operating system is used - -Sage Hironaka Remulla -- added Rubin Observatory filters - -Lingfeng Wei -- bugfix to improve creation of iso_dir in -IsochronePhot, implemented faster cluster generation and test -functions for primary and companion star mass generation (v2.3), -updated random state generators (v2.4) - -Macy Huston -- New metallicity bound + isochrone filter checks, -imf_mass_lim bugfix, roman filter bugfix, added Euclid filters, Synthpop compatibility -updates (v2.2), improvements for reading in/updated existing isochrone -files, Vega mag to ST mag conversion function (v2.4) - -Anna Pusack -- Added IRTF L-band filter support diff --git a/spisea/atmospheres_BACKUP_22350.py b/spisea/atmospheres_BACKUP_22350.py deleted file mode 100755 index e3d2233c..00000000 --- a/spisea/atmospheres_BACKUP_22350.py +++ /dev/null @@ -1,2524 +0,0 @@ -import logging -import numpy as np -import pysynphot -import os -import glob -from astropy.io import fits -from astropy.table import Table, Column -import pysynphot -import time -import pdb -import warnings - -log = logging.getLogger('atmospheres') - -def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Given atmosphere model, get temperature and gravity bounds - """ - # Open catalog fits file and break out row indices - catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) - - teff_arr = [] - z_arr = [] - logg_arr = [] - for cur_row_index in range(len(catalog)): - index = catalog['INDEX'][cur_row_index] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Filter by metallicity. Will chose the closest metallicity to desired input - metal_list = np.unique(np.array(z_arr)) - metal_idx = np.argmin(np.abs(metal_list - metallicity)) - metallicity_new = metal_list[metal_idx] - - z_filt = np.where(z_arr == metal_list[metal_idx]) - teff_arr = teff_arr[z_filt] - logg_arr = logg_arr[z_filt] - - # # Now find the closest atmosphere in parameter space to - # # the one we want. We'll find the match with the lowest - # # fractional difference - # teff_diff = (teff_arr - temperature) / temperature - # logg_diff = (logg_arr - gravity) / gravity - # - # diff_tot = abs(teff_diff) + abs(logg_diff) - # idx_f = np.argmin(diff_tot) - # - # temperature_new = teff_arr[idx_f] - # gravity_new = logg_arr[idx_f] - - # First check if temperature within bounds - temperature_new = temperature - if temperature > np.max(teff_arr): - temperature_new = np.max(teff_arr) - if temperature < np.min(teff_arr): - temperature_new = np.min(teff_arr) - - # If temperature within bounds, then check if metallicity within bounds - teff_diff = np.abs(teff_arr - temperature) - sorted_min_diffs = np.unique(teff_diff) - - ## Find two closest temperatures - teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] - teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] - - logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] - logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] - - ## Switch to most conservative bound of logg out of two closest temps - gravity_new = gravity - if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): - gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) - if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): - gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) - - if verbose: - # Print out changes, if any - if temperature_new != temperature: - teff_msg = 'Changing to T={0:6.0f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( teff_msg.format(temperature_new, metallicity, temperature, gravity)) - - if gravity_new != gravity: - logg_msg = 'Changing to logg={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( logg_msg.format(gravity_new, metallicity, temperature, gravity)) - - if metallicity_new != metallicity: - logg_msg = 'Changing to met={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( logg_msg.format(metallicity_new, metallicity, temperature, gravity)) - - return (temperature_new, gravity_new, metallicity_new) - -def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmosphere from the Kurucz pysnphot grid - (`Kurucz 1993 `_). - - Grid Range: - - * Teff: 3000 - 50000 K - * gravity: 0 - 5 cgs - * metallicity: -5.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - Always false for this particular function - """ - try: - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('k93models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Kurucz 1993 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmospheres from the pysynphot ATLAS9 atlas - (`Castelli & Kurucz 2004 `_). - - Grid Range: - - * Teff: 3500 - 50000 K - * gravity: 0 - 5.0 cgs - * [M/H]: -2.5 - 0.2 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('ck04models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - try: - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('nextgen', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find NextGen atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, - rebin=False): - """ - Return atmosphere from the pysynphot - `PHOENIX atlas `_. - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - try: - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - # Take care of atmospheres outside the catalog boundaries - logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' - if gravity > 4.3: - if verbose: - print( logg_msg.format(4.3, temperature, gravity)) - gravity = 4.3 - - if rebin: - sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, - verbose=False): - """ - For a given stellar atmosphere, get extract the closest possible match in - Teff/logg space. Note that this is different from the normal routine - which interpolates along the input grid to get final spectrum. We can't - do this here because the Fierro+15 atmosphere grid is so sparse - - rebin=True: pull from atmospheres at ck04model resolution. - - If verbose, print out the parameters of the match - """ - # Set up the proper root directory - if rebin == True: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' - else: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' - - # Read in catalog, extract atmosphere info - cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') - teff_arr = [] - z_arr = [] - logg_arr = [] - for ii in range(len(cat)): - index = cat['INDEX'][ii] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Now find the closest atmosphere in parameter space to - # the one we want. We'll find the match with the lowest - # fractional difference - teff_diff = (teff_arr - temperature) / temperature - logg_diff = (logg_arr - gravity) / gravity - - diff_tot = abs(teff_diff) + abs(logg_diff) - idx_f = np.where(diff_tot == min(diff_tot))[0][0] - - # Extract the filename of the best-match model and read as - # pysynphot object - infile = cat[idx_f]['FILENAME'].split('.') - spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the - # ATLAS models are in FLAM at the surface. So, we need to multiply the - # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. - # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file - t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') - tmp = np.where(t['col1'] == infile[0]) - - lum = t['col3'][tmp] * (3.839*10**33) # cgs - sigma = 5.6704 * 10**-5 # cgs - teff = teff_arr[idx_f] # cgs - - radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm - radius /= 3.08*10**18 # in pc - - - # Make the pysynphot spectrum - w = spec['Wavelength'] - f = spec['Flux'] * (1000 / radius)**2. - sp = pysynphot.ArraySpectrum(w,f) - - #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Print out parameters of match, if desired - if verbose: - print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) - print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - if rebin: - sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 30000) - gravity = log gravity (def = 4.14) - """ - sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): - """ - Return PHOENIX v16 atmospheres from - `Husser et al. 2013 `_. - - Models originally downloaded via `ftp `_. - Solar metallicity and [alpha/Fe] is used. - - Grid Range: - - * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K - * gravity: 0.0 - 6.0 cgs, steps of 0.5 - * [M/H]: -4.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - atm_model_name = 'phoenix_v16' - if rebin == True: - atm_model_name = 'phoenix_v16_rebin' - - - # Extract atmosphere. If that fails, then check bounds and try again - try: - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_model_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): - """ - Return atmosphere from CIFIST2011_2015 grid - (`Allard et al. 2012 `_, - `Baraffe et al. 2015 `_ ) - - Grid originally downloaded from `website `_. - - Grid Range: - - * Teff: 1200 - 7000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_2015_rebin' - else: - atm_name = 'BTSettl_2015' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) -<<<<<<< HEAD - #print(dir(obj)) - - -======= - - ->>>>>>> upstream/dev - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): - """ - Return atmosphere from CIFIST2011 grid - (`Allard et al. 2012 `_) - - Grid originally downloaded `here `_ - - Notes - ------ - Grid Range: - - * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - - Teff and gravity ranges depend on metallicity: - - [M/H] = -2.5 - - * Teff: 2600 - 4600 K - * gravity: 4.5 - 5.5 - - [M/H] = -2.0 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.5 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.0 - - * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 - - [M/H] = -0.5 - - * Teff: 1000 -7000 - * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 - - [M/H] = 0 - - * Teff: 750 - 7000 - * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 - - [M/H] = 0.5 - - * Teff: 1000 - 5000 - * gravity: 3.5 - 5.0 - - - Alpha enhancement: - - * [M/H]= -0.0, +0.5 no anhancement - * [M/H]= -0.5 with [alpha/H]=+0.2 - * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - **PRINT STATEMENTS TO DEBUG - **check get_atmosphere_bounds - **comment out try/except clause and check break - """ - if rebin == True: - atm_name = 'BTSettl_rebin' - else: - atm_name = 'BTSettl' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) -<<<<<<< HEAD - -def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): - """ - Return atmosphere from Meisner2023 grid - (`Meisner et al. 2023 `_) - - Grid originally downloaded `here `_ - - Grid range: - * Teff = 250 - 1200 K - * gravity: 2.5 - 5.5 cgs (in steps of 0.5) - * [M/H] = -1.0, -0.5, 0, +0, +0.3 - - """ - if rebin == True: - atm_name = 'Meisner2023_rebin' - else: - atm_name = 'Meisner2023' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) -======= - ->>>>>>> upstream/dev - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Meisner2023 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): - """ - Return atmosphere from Phillips et al., 2020 using ATMO model - (`Phillips et al. 2020 `_) - - Grid originally downloaded `here `_ - - Grid Range: - * Teff: 200 - 3000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - """ - if rebin == True: - atm_name = 'Phillips2020_rebin' - else: - atm_name = 'Phillips2020' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Phillips2020 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): - """ - Return white dwarf atmospheres from - `Koester et al. 2010 `_ - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. - - Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 - """ - try: - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_atlas_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and phoenixV16. - - Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_BTSettl_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_meisner_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and Meisner2023. - - Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_meisner', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl-Meisner merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -#---------------------------------------------------------------------# -def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, - rebin=True): - """ - Return a stellar atmosphere from a suite of different model grids, - depending on the input temperature, (all values in K). - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - - Notes - ----- - The underlying stellar model grid used changes as a function of - stellar temperature (in K): - - * T > 20,000: ATLAS - * 5500 <= T < 20,000: ATLAS - * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge - * 3800 <= T < 5000: PHOENIXv16 - - For T < 3800, there is an additional gravity and metallicity - dependence: - - If T < 3800 and [M/H] = 0: - - * T < 3800, logg < 2.5: PHOENIX v16 - * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge - * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - * 1200 < T <= 1000, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge - * 1000 < T <= 250, logg > 2.5: Meisner2023 - - Otherwise, if T < 3800 and [M/H] != 0: - - * T < 3800: PHOENIX v16 - - References: - - * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) - * PHOENIXv16 (`Husser et al. 2013 `_) - * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) - * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) - - LTE WARNING: - - The ATLAS atmospheres are calculated with LTE, and so they - are less accurate when non-LTE conditions apply (e.g. T > 20,000 - K). Ultimately we'd like to add a non-LTE atmosphere grid for - the hottest stars in the future. - - HOW BOUNDARIES BETWEEN MODELS ARE TREATED: - - At the boundary between two models grids a temperature range is defined - where the resulting atmosphere is a weighted average between the two - grids. Near one boundary one model - is weighted more heavily, while at the other boundary the other - model is weighted more heavily. These are calculated in the - temperature ranges where we switch between model grids, to - ensure a smooth transition. - """ - # For T < 3800, atmosphere depends on metallicity + gravity. - # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is - # currently available here, so if non-solar metallicity, just stick with - # the Phoenix grid - if (temperature < 1000): - if verbose: - print( 'Meisner2023 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 1200) & (temperature >= 1000): - if (gravity >= 3.5): - if verbose: - print( 'BTSettl/Meisner2023 merged atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - if (gravity < 3.5) & (gravity >=2.5): - if verbose: - print( 'Meisner2023 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity == 0): - # High gravity are in BTSettl regime - if (temperature <= 3200) & (gravity > 2.5): - if verbose: - print( 'BTSettl_2015 atmosphere') - return get_BTSettl_2015_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): - if verbose: - print( 'BTSettl/Phoenixv16 merged atmosphere') - return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - # Low gravity is PHOENIX regime - if gravity <= 2.5: - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity != 0): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - # For T > 3800, no metallicity or gravity dependence - if (temperature >= 3800) & (temperature < 5000): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 5000) & (temperature < 5500): - if verbose: - print( 'ATLAS/Phoenix merged atmosphere') - return get_atlas_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if (temperature >= 5500) & (temperature < 20000): - if verbose: - print( 'ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if temperature >= 20000: - if verbose: - print( 'Still ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - #print('CMFGEN') - #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, - # temperature=temperature, - # gravity=gravity) - - - - -def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Return the white dwarf atmosphere from - `Koester et al. 2010 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('wdKoester atmosphere') - - return get_wdKoester_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere. - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - - -def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): - """ - Return the brown dwarf atmosphere from - `Meisner et al. 2023 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('Meisner2023 atmosphere') - - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - -def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, - verbose=False, rebin=None, - wave_min=500, wave_max=50_000, wave_num=20_000): - """ - Return a blackbody spectrum - - Parameters - ---------- - temperature: float, default=20_000 - The stellar temperature, in units of K - wave_min: float, default=500 - Sets the minimum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_max: float, default=50_000 - Sets the maximum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_num: int, default=20_000 - Sets the number of wavelength points in the wavelength range - Note: the wavelength range is evenly spaced in log space - """ - if ((metallicity is not None) or (gravity is not None) or - (rebin is not None)): - warnings.warn( - 'Only `temperature` keyword is used for black-body atmosphere' - ) - - if verbose: - print('Black-body atmosphere') - - # Modify pysynphot's default waveset to specified bounds - pysynphot.refs.set_default_waveset( - minwave=wave_min, maxwave=wave_max, num=wave_num - ) - - # Get black-body atmosphere for specified temperature from pysynphot - bbspec = pysynphot.spectrum.BlackBody(temperature) - - # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` - bbspec.convert('flam') - - # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. - # Need to remove this normalization for SPISEA by multiplying bbspec - # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 - bbspec *= (1000 * 3.086e18 / 6.957e10)**2 - - return bbspec - - -#--------------------------------------# -# Atmosphere formatting functions -#--------------------------------------# - -def download_CMFGEN_atmospheres(Table_rot, Table_norot): - """ - Downloads CMFGEN models from - https://sites.google.com/site/fluxesandcontinuum/home; - these contain continuum as well as lines. - - Table_rot, Table_norot are tables with the file prefixes - and model atmosphere parameters, taken by hand from the - Fierro+15 paper - - Website addresses are hardcoded - - Puts downloaded models in the current working directory. - """ - print( 'WARNING: THIS DOES NOT COMPLETELY WORK') - print( '**********************') - t_rot = Table.read(Table_rot, format='ascii') - t_norot = Table.read(Table_norot, format='ascii') - - tables = [t_rot, t_norot] - filenames = [t_rot['col1'], t_norot['col1']] - - # Hardcoded list of webiste addresses - web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' - web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' - web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', - web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', - web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', - web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', - web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] - # Array of masses that matches the website addresses - mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) - - # Loop through rotating and unrotating case. First loop is rot, second unrot - for i in range(2): - # Extract masses from filenames - masses = [] - for j in filenames[i]: - tmp = j.split('m') - mass = float(tmp[1][:-1]) - masses.append(mass) - - # Download the models webpage by webpage. A bit tricky because masses - # change slightly within a particular website. THIS IS WHAT FAILS - for j in range(len(web)): - if j == 0: - good = np.where( (masses <= mass_arr[j]) ) - else: - g = j - 1 - good = np.where( (masses <= mass_arr[j]) & - (masses > mass_arr[g]) ) - # Use wget command to pull down the files, and unzip them - for k in good[0]: - full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) - os.system('wget ' + full) - os.system('unzip '+ filenames[i][k] + '.flx.zip') - - return - -def organize_CMFGEN_atmospheres(path_to_dir): - """ - Organize CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html) - into rot and noRot directories - - path_to_dir is from current working directory to directory - containing the downloaded models. Assumed that models - and tables describing parameters are in this directory. - - Tables describing parameters MUST be named Table_rot.txt, - Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. - These are located in same original directory as atmosphere files - - Will separate files into 2 subdirectories, one rotating and - the other non-rotating - - *Can't have any other files starting with "t" in model directory to start!* - """ - # First, record current working directory to return to later - start_dir = os.getcwd() - - # Enter atmosphere directory, collect rotating and non-rotating - # file names (assumed to all start with "t") - os.chdir(path_to_dir) - rot_models = glob.glob("t*r.flx*") - noRot_models = glob.glob("t*n.flx*") - - # Separate into different subdirectories - if os.path.exists('cmfgenF15_rot'): - pass - else: - os.mkdir('cmfgenF15_rot') - os.mkdir('cmfgenF15_noRot') - - for mod in rot_models: - cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) - os.system(cmd) - - for mod in noRot_models: - cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) - os.system(cmd) - - # Also move Tables with model parameters into correct directory - os.system('mv Table_rot.txt cmfgenF15_rot') - os.system('mv Table_noRot.txt cmfgenF15_noRot') - - # Return to original directory - os.chdir(start_dir) - - return - -def make_CMFGEN_catalog(path_to_dir): - """ - Create cdbs catalog.fits of CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html). - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to directory - containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, - needs to be a Table*.txt file which contains the parameters for all of the - original models, since params in filename are not precise enough - - Will create catalog.fits file in atmosphere directory with - description of each model - - *Can't have any other files starting with "t" in model directory to start!* - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere - # Note: can't rely on filename for this because not precise enough!! - - #---------OLD: GETTING PARAMS FROM FILENAME-------# - # Collect file names (assumed to all start with "t") - #files = glob.glob("t*") - #for name in files: - # tmp = name.split('l') - # temp = float(tmp[0][1:]) * 100.0 # In kelvin - - # lumtmp = tmp[1].split('_') - # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun - - # mass = float(lumtmp[0][5:-1]) # In M_sun - - # Need to calculate log g from T and L (cgs) - # lum_sun = 3.846 * 10**33 # erg/s - # M_sun = 2 * 10**33 # g - # G_si = 6.67 * 10**(-8) # cgs - # sigma_si = 5.67 * 10**(-5) # cgs - - # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ - # (lum * lum_sun) - # logg = np.log10(g) - #---------------------------------------------------# - - # Read table with atmosphere params - table = glob.glob('Table_*') - t = Table.read(table[0], format = 'ascii') - names = t['col1'] - temps = t['col2'] - logg = t['col4'] - - # Create catalog.fits file - index_str = [] - name_str = [] - for i in range(len(names)): - index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) - - #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# - #path = path_to_dir + '/' + names[i] - path = names[i] + '.fits[Flux]' - - index_str.append(index) - name_str.append(path) - - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits') - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): - """ - Code to put cmfgen models into cdbs format and adds proper unit keyword in - fits header. Save as fits file - - path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot - directory with the *.flx models. Note that these files have already been - organized using organize_CMFGEN_atmospheres code. - - path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or - cmfgen_norot directory. Will copy new fits files to this directory. - This directory must already exist! - """ - # Save starting directory for later, move into path_to_dir directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('*.flx') - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - # Open file, extract useful info - t = Table.read(i, format='ascii') - wave = t['col1'] - flux = t['col2'] # Flux is already in erg/cm^2/s/A - - # Need to eliminate duplicate entries (pysynphot crashes) - unique = np.unique(wave, return_index=True) - wave = wave[unique[1]] - flux = flux[unique[1]] - - # Make fits table from individual columns. - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - #Adding unit keywords - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - prihdu = fits.PrimaryHDU() - - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i[:-4]+'.fits', overwrite=True) - - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Return to original directory, copy over new .fits files to cdbs directory - os.chdir(start_dir) - cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) - os.system(cmd) - - return - -def rebin_cmfgen(cdbs_path, rot=True): - """ - Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; - this makes spectrophotometry MUCH faster - - cdbs_path: path to cdbs directory - rot=True for rotating models (cmfgen_rot), False for non-rotating models - - makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing cmfgen model; we will steal the header. - # Also define paths to new rebin directories - if rot == True: - tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' - path = cdbs_path+'/grid/cmfgen_rot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_rot/' - else: - tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' - path = cdbs_path+'/grid/cmfgen_norot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_norot/' - - cmfgen_hdu = fits.open(tmp) - header0 = cmfgen_hdu[0].header - # Create rebin directories if they don't already exist. Copy over - # catalog.fits file from original directory (will be the same) - if not os.path.exists(path): - os.mkdir(path) - cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) - os.system(cmd) - - # Read in the catalog.fits file - cat = fits.getdata(orig_path + 'catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - # First column in new files will be for [atlas] wavelength - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - - # For each catalog.fits entry, read the unbinned spectrum and rebin to - # the atlas resolution. Make a new fits file in rebin directory - count = 0 - for ff in range(len(files_all)): - count += 1 - # Extract the temp, Z, logg - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - grav = float(vals[2]) - - # Fetch the spectrum - if rot == True: - sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) - else: - sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) - - # Rebin - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - # Make the FITS file from the columns with header - cols = fits.ColDefs([c0,c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write hdu to new directory with same filename - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(path+files_all[ff]) - - print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) - return - - -def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): - """ - Construct the Phoenix Husser+13 atmopsheres for each model. Combines the - fluxes from the *HiRES.fits files and the wavelengths of the - WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. - - path_to_dir is the path to the directory containing all of the downloaded - files - - met_str is the name of the current metallicity - - Creates new fits files for each atmosphere: phoenix_.fits, - which contains columns for the log g (column header = g#.#). Puts - atmospheres in new directory phoenixm00 - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the current metallicity subdirectory - sub_dir = '../phoenix{0}'.format(met_str) - if os.path.exists(sub_dir): - pass - else: - os.mkdir(sub_dir) - - # Extract wavelength array, make column for later - wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') - wave = wavefile[0].data - wavefile.close() - wave_col = Column(wave, name = 'WAVELENGTH') - - # Create temp array for Husser+13 grid (given in paper) - temp_arr = np.arange(2300, 7001, 100) - temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) - - print( 'Looping though all temps') - # For each temp, build file containing the flux for all gravities - i = 0 - for temp in temp_arr: - files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) - files.sort() - # Start the table with the wavelength column - t = Table() - t.add_column(wave_col) - for f in files: - # Extract the logg out of filename - logg = f[9:13] - - # Extract fluxes from file - spectrum = fits.open(f) - flux = spectrum[0].data - spectrum.close() - - # Make Column object with fluxes, add to table - col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) - t.add_column(col) - - # Now, construct final fits file for the given temp - outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) - t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) - - # Progress counter for user - i += 1 - print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) - - # Return to original directory - os.chdir(start_dir) - return - -def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): - """ - Makes catalog.fits file for Husser+13 phoenix models. Assumes that - organize_PHOENIXv16_atmospheres has been run already, and that the models lie - in subdirectory phoenix[met_str]. - - path_to_directory is the path to the directory with the reformatted - models (i.e. the output from construct_atmospheres, phoenix[met_str]) - - Puts catalog.fits file in directory the user starts in - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Extract metallicity from metallicity string - met = float(met_str[1]) + (float(met_str[2]) * 0.1) - if 'm' in met_str: - met *= -1. - - # Collect the filenames. Each is a unique temp with many different log g's - files = glob.glob('phoenix*.fits') - files.sort() - - # Create the catalog.fits file, row by row - index_arr = [] - filename_arr = [] - for i in files: - # Get log g values from the column header the file - t = Table.read(i, format='fits') - keys = t.keys() - logg_vals = keys[1:] - - # Extract temp from filename - name = i.split('_') - temp = float(name[1][:-5]) - for j in logg_vals: - logg = float(j[1:]) - index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) - filename = path_to_dir + i + '[' + j + ']' - # Add row to table - index_arr.append(index) - filename_arr.append(filename) - - catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) - - # Return to starting directory, write catalog - os.chdir(start_dir) - - if os.path.exists('catalog.fits'): - from astropy.table import vstack - - prev_catalog = Table.read('catalog.fits', format='fits') - joined_catalog = vstack([prev_catalog, catalog]) - - joined_catalog.write('catalog.fits', format='fits', overwrite=True) - else: - catalog.write('catalog.fits', format='fits', overwrite=True) - - return - -def cdbs_PHOENIXv16(path_to_cdbs_dir): - """ - Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily - consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] - and adding the appropriate keywords to the fits header. - - path_to_cdbs_dir goes from current working directory to phoenix[met] directory - in cdbs/grids/phoenix_v16. Note that these files have already been organized - using organize_PHOENIXv16_atmospheres code. - - Overwrites original files in directory - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_cdbs_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('phoenix*.fits') - - ## Need to sort filenames; glob doesn't always give them in order - files.sort() - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - - # Read in current FITS table - cur_table = Table.read(i, format='fits') - - cur_table.columns[0].name = 'Wavelength' - - num_cols = len(cur_table.colnames) - - # Multiplying each flux column by 10^-8 for conversion - for cur_col_index in range(1, num_cols, 1): - cur_col_name = cur_table.colnames[cur_col_index] - cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 - - - # Construct new FITS file based on old one - hdu = fits.open(i) - header_0 = hdu[0].header - header_1 = hdu[1].header - sci = hdu[1].data - - tbhdu = fits.table_to_hdu(cur_table) - - # Copying over the older headers, adding unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - tbhdu.header['TUNIT3'] = 'FLAM' - tbhdu.header['TUNIT4'] = 'FLAM' - tbhdu.header['TUNIT5'] = 'FLAM' - tbhdu.header['TUNIT6'] = 'FLAM' - tbhdu.header['TUNIT7'] = 'FLAM' - tbhdu.header['TUNIT8'] = 'FLAM' - tbhdu.header['TUNIT9'] = 'FLAM' - tbhdu.header['TUNIT10'] = 'FLAM' - tbhdu.header['TUNIT11'] = 'FLAM' - tbhdu.header['TUNIT12'] = 'FLAM' - tbhdu.header['TUNIT13'] = 'FLAM' - tbhdu.header['TUNIT14'] = 'FLAM' - - # Construct and write out final FITS file - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i, overwrite=True) - - hdu.close() - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Change back to starting directory - os.chdir(start_dir) - - return - -def rebin_phoenixV16(cdbs_path): - """ - Rebin phoenixV16 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: phoenix_v16_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - ## (This assumes that at least 'm00' metallicity exists) - tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/phoenix_v16_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - temp_arr = np.zeros(len(files_all), dtype=float) - logg_arr = np.zeros(len(files_all), dtype=float) - metal_arr = np.zeros(len(files_all), dtype=float) - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - - temp_arr[ff] = float(vals[0]) - metal_arr[ff] = float(vals[1]) - logg_arr[ff] = float(vals[2]) - - - metal_uniq = np.unique(metal_arr) - temp_uniq = np.unique(temp_arr) - - for mm in range(len(metal_uniq)): - metal = metal_uniq[mm] # metallicity - - # Construct str for metallicity (for appropriate directory name) - met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) - if metal > 0: - met_str = 'p' + met_str - else: - met_str = 'm' + met_str - - # Make directory for current metallicity if it does not exist yet - if not os.path.exists(path + 'phoenix' + met_str): - os.mkdir(path + 'phoenix' + met_str) - - for tt in range(len(temp_uniq)): - temp = temp_uniq[tt] # temperature - - # Pick out the list of gravities for this T, Z combo - idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] - logg_exist = logg_arr[idx] - - # All gravities will go in one file. Here is the output - # file name. - outfile = path + files_all[idx[0]].split('[')[0] - - ## If the rebinned file already exists, continue - if os.path.exists(outfile): - continue - - # Build a columns array. One column for each gravity. - cols_arr = [] - - # Make the wavelength column, which is first in the cols array. - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - cols_arr.append(c0) - - for gg in range(len(logg_exist)): - grav = logg_exist[gg] # gravity - - # Fetch the spectrum - sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Store the spectrum - name = 'g{0:3.1f}'.format(grav) - col = fits.Column(name=name, format='E', array=flux_rebin) - cols_arr.append(col) - - - # Make the FITS file from the columns with header. - cols = fits.ColDefs(cols_arr) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - for gg in range(len(logg_exist)): - tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' - - # Write hdu - finalhdu = fits.HDUList([prihdu, tbhdu]) - # don't have overwrite to protect original files. - finalhdu.writeto(outfile) - - print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) - - - return - - -def rebin_spec(wave, specin, wavnew): - """ - Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: - http://www.astrobetter.com/blog/2013/08/12/ - python-tip-re-sampling-spectra-with-pysynphot/ - """ - spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) - f = np.ones(len(wave)) - filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') - obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') - - return obs.binflux - -def organize_BTSettl_2015_atmospheres(path_to_dir): - """ - Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the BTSettl subdirectory - if not os.path.exists('BTSettl_2015'): - os.mkdir('BTSettl_2015') - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.spec.fits') - - for i in files: - hdu = fits.open(i) - spec = hdu[1].data - header_0 = hdu[0].header - header_1 = hdu[1].header - - wave = spec.field(0) - flux = spec.field(1) - - # Get units right: convert wave from microns to Angstroms, - # flux from W /m^2/ micron to erg/s/cm^2/A - wave_new = wave * 10**4 - flux_new = flux * 10**(-1) - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave_new) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) - - hdu.close() - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_BTSettl_2015_catalog(path_to_dir): - """ - Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*spec.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('-') - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: BTSettl_2015_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - phoenix_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/BTSettl_2015_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning BTSettl spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - -def make_wavelength_unique(files, dirname): - """ - Helper function to go through each BTSettl spectrum and ensure that - each wavelength point is unique. This is required for rebinning to work. - - - files: list of files to run this analysis on - """ - # Loop through each file, find fix repeated wavelength entries if necessary - for i in files: - t = Table.read('{0}/{1}'.format(dirname,i), format='fits') - test = np.unique(t['Wavelength'], return_index=True) - - if len(t) != len(test[0]): - t = t[test[1]] - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - # Also make sure wavelength is monotonic. If it is not, then it is - # a sign that the wavelengths are out of order - diff = np.diff(t['Wavelength']) - bad = np.where(diff < 0) - if len(bad[0]) > 0: - t.sort('Wavelength') - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - print('Done {0}'.format(i)) - - return - -def organize_BTSettl_atmospheres(): - """ - Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). - The code expects tp be run in cdbs/grid/BTSettl, and expects that the - individual model files have been downloaded from online - (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] - - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, - # for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.txt') - count=0 - for jj in files: - t = Table.read(jj, format='ascii') - # First, trim the wavelengths to a more reasonable wavelength range - good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) - t = t[good] - - # Convert flux units to Flam (Ergs/sec/cm**2/A) - flux_new = 10**(t['col2'] - 8.0) - - # Save the file as a fits file - c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Now, clean up all the files made when unzipping the spectra - cmd1 = 'rm *.bz2' - cmd2 = 'rm *.tmp' - #cmd3 = 'rm *.txt' - os.system(cmd1) - os.system(cmd2) - #os.system(cmd3) - print('==============================') - print('Done {0}'.format(ii)) - print('==============================') - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_BTSettl_catalog(): - """ - Create cdbs catalog.fits of BTSettl grid. - THIS IS STEP 2, after organize_BTSettl_atmospheres has - been run. - - Code expects to be run in cdbs/grid/BTSettl - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btp05'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('*.fits') - - # Construct the metallicity val - if 'm' in ii: - metal_flag = -1 * float(ii[3:])*0.1 - else: - metal_flag = float(ii[3:])*0.1 - - # Now collect the info from the files - for jj in files: - tmp = jj.split('-') - - if metal_flag >= 0: - temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin - try: - logg = float(tmp[1]) - except: - logg = float(tmp[1].split('+')[0]) - else: - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - # Go back to original directory to move to next metallicity - print('Done {0}'.format(ii)) - os.chdir(start_dir) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl(make_unique=False): - """ - Rebin BTSettle models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: BTSettl_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create cdbs/grid directory for rebinned models - path = 'BTSettl_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata('BTSettl/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - #==============================# - #tmp = [] - #for ii in files_all: - # if ii.startswith('btp00'): - # tmp.append(ii) - #files_all = tmp - #=============================# - - print( 'Rebinning BTSettl spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'BTSettl') - print('Done') - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - try: - sp = pysynphot.Icat('BTSettl', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - except: - pdb.set_trace() - orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) - outfile = path + files_all[ff].split('[')[0] - cmd = 'cp {0} {1}'.format(orig_file, outfile) - os.system(cmd) - - print('Done {0} of {1}'.format(ff, len(files_all))) - - return - -def organize_all_Meisner2023_atmospheres(): - """ - Construct cdbs-ready atmospheres for the Meisner2023 grid. - The code expects tp be run in cdbs/grid/Meisner2023, and expects that the - individual model files have been downloaded from online - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['mm10', 'mm05', 'mp00', 'mp03'] - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Save as a fits file, for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.fits') - count=0 - for jj in files: - # Open each .fits file and read the data - with fits.open(jj) as hdul: - data = hdul[1].data - wavelength = data['Wavelength'] - flux = data['Flux'] - - # Make flux independent of R&D - flux_new = flux / 5e-20 - - # Create new columns with desired format - c0 = fits.Column(name='Wavelength', format='D', array=wavelength) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write the new fits table in the cdbs directory - output_filename = '{0}.fits'.format(jj[:-5]) # Removing the original .fits extension - hdu_new.writeto(output_filename, overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_Meisner2023_catalog(): - """ - Create cdbs catalog.fits of Meisner2023 grid. - THIS IS STEP 2, after organize_Meisner2023_atmospheres has - been run. - - Code expects to be run in cdbs/grid/Meisner2023 - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['mm10', 'mm05', 'mp00', 'mp03'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('spec_jwst_*.fits') - - for jj in files: - # Parse temperature, log(g), and metallicity from filename - temp_str = jj.split('_')[2] - logg_str = jj.split('_')[3] - metal_str = jj.split('_')[4] - - # Extract temperature, surface gravity, and metallicity - temp = float(temp_str[1:]) # Temperature in Kelvin - logg = float(logg_str[1:]) # Surface gravity log(g) - - # Build metallicity value - if metal_str.startswith('m'): - metallicity = -1 * float(metal_str[1:]) - else: - metallicity = float(metal_str[1:]) - - # Construct index and filename strings - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metallicity, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - print('Processed directory:', ii) - os.chdir(start_dir) - - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_Meisner2023(make_unique=False): - """ - Rebin Meisner2023 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: Meisner2023_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create a directory for rebinned Meisner2023 models - rebin_path = 'Meisner2023_rebin/' - if not os.path.exists(rebin_path): - os.mkdir(rebin_path) - - # Load the catalog.fits file and extract all spectra file paths - cat = Table.read('Meisner2023/catalog.fits') - files_all = [cat[ii]['FILENAME'].split('[')[0] for ii in range(len(cat))] - - print('Rebinning Meisner2023 spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'Meisner2023') - print('Done') - - for ff, file in enumerate(files_all): - vals = cat[ff]['INDEX'].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the Meisner2023 spectrum and rebin its flux - try: - sp = pysynphot.Icat('Meisner2023', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Create the output FITS file - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write the new rebinned file in the Meisner2023_rebin directory - outfile = os.path.join(rebin_path, os.path.basename(file)) - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - except Exception as e: - print(f"Error processing {file}: {e}") - orig_file = os.path.join('Meisner2023', file) - outfile = os.path.join(rebin_path, os.path.basename(file)) - os.system(f'cp {orig_file} {outfile}') - - print('Done {0} of {1}'.format(ff + 1, len(files_all))) - - return - - - -def organize_WDKoester_atmospheres(path_to_dir): - """ - Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.dk.dat.txt') - - for i in files: - data = Table.read(i, format='ascii') - - wave = data['col1'] # angstrom - flux = data['col2'] # erg/s/cm^2/A - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) - - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_WDKoester_catalog(path_to_dir): - """ - Create cdbs catalog.fits of wdKoester grid. - THIS IS STEP 2, after organize_WDKoester_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*dk.dat.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('.') - tmp2 = tmp[0].split('_') - temp = float(tmp2[0][2:]) # Kelvin - logg = float(tmp2[1]) / 100.0 # log(g) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin wdKoester models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: wdKoester_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing model; we will steal the header - tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' - wdkoester_hdu = fits.open(tmp) - header0 = wdkoester_hdu[0].header - wdkoester_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/wdKoester_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning wdKoester spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the wdKoester spectrum, rebin flux - sp = pysynphot.Icat('wdKoester', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - - diff --git a/spisea/atmospheres_BACKUP_58456.py b/spisea/atmospheres_BACKUP_58456.py deleted file mode 100755 index e3d2233c..00000000 --- a/spisea/atmospheres_BACKUP_58456.py +++ /dev/null @@ -1,2524 +0,0 @@ -import logging -import numpy as np -import pysynphot -import os -import glob -from astropy.io import fits -from astropy.table import Table, Column -import pysynphot -import time -import pdb -import warnings - -log = logging.getLogger('atmospheres') - -def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Given atmosphere model, get temperature and gravity bounds - """ - # Open catalog fits file and break out row indices - catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) - - teff_arr = [] - z_arr = [] - logg_arr = [] - for cur_row_index in range(len(catalog)): - index = catalog['INDEX'][cur_row_index] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Filter by metallicity. Will chose the closest metallicity to desired input - metal_list = np.unique(np.array(z_arr)) - metal_idx = np.argmin(np.abs(metal_list - metallicity)) - metallicity_new = metal_list[metal_idx] - - z_filt = np.where(z_arr == metal_list[metal_idx]) - teff_arr = teff_arr[z_filt] - logg_arr = logg_arr[z_filt] - - # # Now find the closest atmosphere in parameter space to - # # the one we want. We'll find the match with the lowest - # # fractional difference - # teff_diff = (teff_arr - temperature) / temperature - # logg_diff = (logg_arr - gravity) / gravity - # - # diff_tot = abs(teff_diff) + abs(logg_diff) - # idx_f = np.argmin(diff_tot) - # - # temperature_new = teff_arr[idx_f] - # gravity_new = logg_arr[idx_f] - - # First check if temperature within bounds - temperature_new = temperature - if temperature > np.max(teff_arr): - temperature_new = np.max(teff_arr) - if temperature < np.min(teff_arr): - temperature_new = np.min(teff_arr) - - # If temperature within bounds, then check if metallicity within bounds - teff_diff = np.abs(teff_arr - temperature) - sorted_min_diffs = np.unique(teff_diff) - - ## Find two closest temperatures - teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] - teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] - - logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] - logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] - - ## Switch to most conservative bound of logg out of two closest temps - gravity_new = gravity - if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): - gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) - if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): - gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) - - if verbose: - # Print out changes, if any - if temperature_new != temperature: - teff_msg = 'Changing to T={0:6.0f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( teff_msg.format(temperature_new, metallicity, temperature, gravity)) - - if gravity_new != gravity: - logg_msg = 'Changing to logg={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( logg_msg.format(gravity_new, metallicity, temperature, gravity)) - - if metallicity_new != metallicity: - logg_msg = 'Changing to met={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( logg_msg.format(metallicity_new, metallicity, temperature, gravity)) - - return (temperature_new, gravity_new, metallicity_new) - -def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmosphere from the Kurucz pysnphot grid - (`Kurucz 1993 `_). - - Grid Range: - - * Teff: 3000 - 50000 K - * gravity: 0 - 5 cgs - * metallicity: -5.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - Always false for this particular function - """ - try: - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('k93models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Kurucz 1993 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmospheres from the pysynphot ATLAS9 atlas - (`Castelli & Kurucz 2004 `_). - - Grid Range: - - * Teff: 3500 - 50000 K - * gravity: 0 - 5.0 cgs - * [M/H]: -2.5 - 0.2 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('ck04models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - try: - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('nextgen', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find NextGen atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, - rebin=False): - """ - Return atmosphere from the pysynphot - `PHOENIX atlas `_. - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - try: - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - # Take care of atmospheres outside the catalog boundaries - logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' - if gravity > 4.3: - if verbose: - print( logg_msg.format(4.3, temperature, gravity)) - gravity = 4.3 - - if rebin: - sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, - verbose=False): - """ - For a given stellar atmosphere, get extract the closest possible match in - Teff/logg space. Note that this is different from the normal routine - which interpolates along the input grid to get final spectrum. We can't - do this here because the Fierro+15 atmosphere grid is so sparse - - rebin=True: pull from atmospheres at ck04model resolution. - - If verbose, print out the parameters of the match - """ - # Set up the proper root directory - if rebin == True: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' - else: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' - - # Read in catalog, extract atmosphere info - cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') - teff_arr = [] - z_arr = [] - logg_arr = [] - for ii in range(len(cat)): - index = cat['INDEX'][ii] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Now find the closest atmosphere in parameter space to - # the one we want. We'll find the match with the lowest - # fractional difference - teff_diff = (teff_arr - temperature) / temperature - logg_diff = (logg_arr - gravity) / gravity - - diff_tot = abs(teff_diff) + abs(logg_diff) - idx_f = np.where(diff_tot == min(diff_tot))[0][0] - - # Extract the filename of the best-match model and read as - # pysynphot object - infile = cat[idx_f]['FILENAME'].split('.') - spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the - # ATLAS models are in FLAM at the surface. So, we need to multiply the - # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. - # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file - t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') - tmp = np.where(t['col1'] == infile[0]) - - lum = t['col3'][tmp] * (3.839*10**33) # cgs - sigma = 5.6704 * 10**-5 # cgs - teff = teff_arr[idx_f] # cgs - - radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm - radius /= 3.08*10**18 # in pc - - - # Make the pysynphot spectrum - w = spec['Wavelength'] - f = spec['Flux'] * (1000 / radius)**2. - sp = pysynphot.ArraySpectrum(w,f) - - #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Print out parameters of match, if desired - if verbose: - print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) - print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - if rebin: - sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 30000) - gravity = log gravity (def = 4.14) - """ - sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): - """ - Return PHOENIX v16 atmospheres from - `Husser et al. 2013 `_. - - Models originally downloaded via `ftp `_. - Solar metallicity and [alpha/Fe] is used. - - Grid Range: - - * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K - * gravity: 0.0 - 6.0 cgs, steps of 0.5 - * [M/H]: -4.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - atm_model_name = 'phoenix_v16' - if rebin == True: - atm_model_name = 'phoenix_v16_rebin' - - - # Extract atmosphere. If that fails, then check bounds and try again - try: - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_model_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): - """ - Return atmosphere from CIFIST2011_2015 grid - (`Allard et al. 2012 `_, - `Baraffe et al. 2015 `_ ) - - Grid originally downloaded from `website `_. - - Grid Range: - - * Teff: 1200 - 7000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_2015_rebin' - else: - atm_name = 'BTSettl_2015' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) -<<<<<<< HEAD - #print(dir(obj)) - - -======= - - ->>>>>>> upstream/dev - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): - """ - Return atmosphere from CIFIST2011 grid - (`Allard et al. 2012 `_) - - Grid originally downloaded `here `_ - - Notes - ------ - Grid Range: - - * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - - Teff and gravity ranges depend on metallicity: - - [M/H] = -2.5 - - * Teff: 2600 - 4600 K - * gravity: 4.5 - 5.5 - - [M/H] = -2.0 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.5 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.0 - - * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 - - [M/H] = -0.5 - - * Teff: 1000 -7000 - * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 - - [M/H] = 0 - - * Teff: 750 - 7000 - * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 - - [M/H] = 0.5 - - * Teff: 1000 - 5000 - * gravity: 3.5 - 5.0 - - - Alpha enhancement: - - * [M/H]= -0.0, +0.5 no anhancement - * [M/H]= -0.5 with [alpha/H]=+0.2 - * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - **PRINT STATEMENTS TO DEBUG - **check get_atmosphere_bounds - **comment out try/except clause and check break - """ - if rebin == True: - atm_name = 'BTSettl_rebin' - else: - atm_name = 'BTSettl' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) -<<<<<<< HEAD - -def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): - """ - Return atmosphere from Meisner2023 grid - (`Meisner et al. 2023 `_) - - Grid originally downloaded `here `_ - - Grid range: - * Teff = 250 - 1200 K - * gravity: 2.5 - 5.5 cgs (in steps of 0.5) - * [M/H] = -1.0, -0.5, 0, +0, +0.3 - - """ - if rebin == True: - atm_name = 'Meisner2023_rebin' - else: - atm_name = 'Meisner2023' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) -======= - ->>>>>>> upstream/dev - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Meisner2023 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): - """ - Return atmosphere from Phillips et al., 2020 using ATMO model - (`Phillips et al. 2020 `_) - - Grid originally downloaded `here `_ - - Grid Range: - * Teff: 200 - 3000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - """ - if rebin == True: - atm_name = 'Phillips2020_rebin' - else: - atm_name = 'Phillips2020' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Phillips2020 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): - """ - Return white dwarf atmospheres from - `Koester et al. 2010 `_ - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. - - Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 - """ - try: - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_atlas_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and phoenixV16. - - Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_BTSettl_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_meisner_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and Meisner2023. - - Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_meisner', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl-Meisner merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -#---------------------------------------------------------------------# -def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, - rebin=True): - """ - Return a stellar atmosphere from a suite of different model grids, - depending on the input temperature, (all values in K). - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - - Notes - ----- - The underlying stellar model grid used changes as a function of - stellar temperature (in K): - - * T > 20,000: ATLAS - * 5500 <= T < 20,000: ATLAS - * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge - * 3800 <= T < 5000: PHOENIXv16 - - For T < 3800, there is an additional gravity and metallicity - dependence: - - If T < 3800 and [M/H] = 0: - - * T < 3800, logg < 2.5: PHOENIX v16 - * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge - * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - * 1200 < T <= 1000, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge - * 1000 < T <= 250, logg > 2.5: Meisner2023 - - Otherwise, if T < 3800 and [M/H] != 0: - - * T < 3800: PHOENIX v16 - - References: - - * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) - * PHOENIXv16 (`Husser et al. 2013 `_) - * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) - * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) - - LTE WARNING: - - The ATLAS atmospheres are calculated with LTE, and so they - are less accurate when non-LTE conditions apply (e.g. T > 20,000 - K). Ultimately we'd like to add a non-LTE atmosphere grid for - the hottest stars in the future. - - HOW BOUNDARIES BETWEEN MODELS ARE TREATED: - - At the boundary between two models grids a temperature range is defined - where the resulting atmosphere is a weighted average between the two - grids. Near one boundary one model - is weighted more heavily, while at the other boundary the other - model is weighted more heavily. These are calculated in the - temperature ranges where we switch between model grids, to - ensure a smooth transition. - """ - # For T < 3800, atmosphere depends on metallicity + gravity. - # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is - # currently available here, so if non-solar metallicity, just stick with - # the Phoenix grid - if (temperature < 1000): - if verbose: - print( 'Meisner2023 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 1200) & (temperature >= 1000): - if (gravity >= 3.5): - if verbose: - print( 'BTSettl/Meisner2023 merged atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - if (gravity < 3.5) & (gravity >=2.5): - if verbose: - print( 'Meisner2023 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity == 0): - # High gravity are in BTSettl regime - if (temperature <= 3200) & (gravity > 2.5): - if verbose: - print( 'BTSettl_2015 atmosphere') - return get_BTSettl_2015_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): - if verbose: - print( 'BTSettl/Phoenixv16 merged atmosphere') - return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - # Low gravity is PHOENIX regime - if gravity <= 2.5: - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity != 0): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - # For T > 3800, no metallicity or gravity dependence - if (temperature >= 3800) & (temperature < 5000): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 5000) & (temperature < 5500): - if verbose: - print( 'ATLAS/Phoenix merged atmosphere') - return get_atlas_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if (temperature >= 5500) & (temperature < 20000): - if verbose: - print( 'ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if temperature >= 20000: - if verbose: - print( 'Still ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - #print('CMFGEN') - #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, - # temperature=temperature, - # gravity=gravity) - - - - -def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Return the white dwarf atmosphere from - `Koester et al. 2010 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('wdKoester atmosphere') - - return get_wdKoester_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere. - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - - -def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): - """ - Return the brown dwarf atmosphere from - `Meisner et al. 2023 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('Meisner2023 atmosphere') - - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - -def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, - verbose=False, rebin=None, - wave_min=500, wave_max=50_000, wave_num=20_000): - """ - Return a blackbody spectrum - - Parameters - ---------- - temperature: float, default=20_000 - The stellar temperature, in units of K - wave_min: float, default=500 - Sets the minimum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_max: float, default=50_000 - Sets the maximum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_num: int, default=20_000 - Sets the number of wavelength points in the wavelength range - Note: the wavelength range is evenly spaced in log space - """ - if ((metallicity is not None) or (gravity is not None) or - (rebin is not None)): - warnings.warn( - 'Only `temperature` keyword is used for black-body atmosphere' - ) - - if verbose: - print('Black-body atmosphere') - - # Modify pysynphot's default waveset to specified bounds - pysynphot.refs.set_default_waveset( - minwave=wave_min, maxwave=wave_max, num=wave_num - ) - - # Get black-body atmosphere for specified temperature from pysynphot - bbspec = pysynphot.spectrum.BlackBody(temperature) - - # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` - bbspec.convert('flam') - - # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. - # Need to remove this normalization for SPISEA by multiplying bbspec - # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 - bbspec *= (1000 * 3.086e18 / 6.957e10)**2 - - return bbspec - - -#--------------------------------------# -# Atmosphere formatting functions -#--------------------------------------# - -def download_CMFGEN_atmospheres(Table_rot, Table_norot): - """ - Downloads CMFGEN models from - https://sites.google.com/site/fluxesandcontinuum/home; - these contain continuum as well as lines. - - Table_rot, Table_norot are tables with the file prefixes - and model atmosphere parameters, taken by hand from the - Fierro+15 paper - - Website addresses are hardcoded - - Puts downloaded models in the current working directory. - """ - print( 'WARNING: THIS DOES NOT COMPLETELY WORK') - print( '**********************') - t_rot = Table.read(Table_rot, format='ascii') - t_norot = Table.read(Table_norot, format='ascii') - - tables = [t_rot, t_norot] - filenames = [t_rot['col1'], t_norot['col1']] - - # Hardcoded list of webiste addresses - web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' - web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' - web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', - web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', - web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', - web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', - web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] - # Array of masses that matches the website addresses - mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) - - # Loop through rotating and unrotating case. First loop is rot, second unrot - for i in range(2): - # Extract masses from filenames - masses = [] - for j in filenames[i]: - tmp = j.split('m') - mass = float(tmp[1][:-1]) - masses.append(mass) - - # Download the models webpage by webpage. A bit tricky because masses - # change slightly within a particular website. THIS IS WHAT FAILS - for j in range(len(web)): - if j == 0: - good = np.where( (masses <= mass_arr[j]) ) - else: - g = j - 1 - good = np.where( (masses <= mass_arr[j]) & - (masses > mass_arr[g]) ) - # Use wget command to pull down the files, and unzip them - for k in good[0]: - full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) - os.system('wget ' + full) - os.system('unzip '+ filenames[i][k] + '.flx.zip') - - return - -def organize_CMFGEN_atmospheres(path_to_dir): - """ - Organize CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html) - into rot and noRot directories - - path_to_dir is from current working directory to directory - containing the downloaded models. Assumed that models - and tables describing parameters are in this directory. - - Tables describing parameters MUST be named Table_rot.txt, - Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. - These are located in same original directory as atmosphere files - - Will separate files into 2 subdirectories, one rotating and - the other non-rotating - - *Can't have any other files starting with "t" in model directory to start!* - """ - # First, record current working directory to return to later - start_dir = os.getcwd() - - # Enter atmosphere directory, collect rotating and non-rotating - # file names (assumed to all start with "t") - os.chdir(path_to_dir) - rot_models = glob.glob("t*r.flx*") - noRot_models = glob.glob("t*n.flx*") - - # Separate into different subdirectories - if os.path.exists('cmfgenF15_rot'): - pass - else: - os.mkdir('cmfgenF15_rot') - os.mkdir('cmfgenF15_noRot') - - for mod in rot_models: - cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) - os.system(cmd) - - for mod in noRot_models: - cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) - os.system(cmd) - - # Also move Tables with model parameters into correct directory - os.system('mv Table_rot.txt cmfgenF15_rot') - os.system('mv Table_noRot.txt cmfgenF15_noRot') - - # Return to original directory - os.chdir(start_dir) - - return - -def make_CMFGEN_catalog(path_to_dir): - """ - Create cdbs catalog.fits of CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html). - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to directory - containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, - needs to be a Table*.txt file which contains the parameters for all of the - original models, since params in filename are not precise enough - - Will create catalog.fits file in atmosphere directory with - description of each model - - *Can't have any other files starting with "t" in model directory to start!* - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere - # Note: can't rely on filename for this because not precise enough!! - - #---------OLD: GETTING PARAMS FROM FILENAME-------# - # Collect file names (assumed to all start with "t") - #files = glob.glob("t*") - #for name in files: - # tmp = name.split('l') - # temp = float(tmp[0][1:]) * 100.0 # In kelvin - - # lumtmp = tmp[1].split('_') - # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun - - # mass = float(lumtmp[0][5:-1]) # In M_sun - - # Need to calculate log g from T and L (cgs) - # lum_sun = 3.846 * 10**33 # erg/s - # M_sun = 2 * 10**33 # g - # G_si = 6.67 * 10**(-8) # cgs - # sigma_si = 5.67 * 10**(-5) # cgs - - # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ - # (lum * lum_sun) - # logg = np.log10(g) - #---------------------------------------------------# - - # Read table with atmosphere params - table = glob.glob('Table_*') - t = Table.read(table[0], format = 'ascii') - names = t['col1'] - temps = t['col2'] - logg = t['col4'] - - # Create catalog.fits file - index_str = [] - name_str = [] - for i in range(len(names)): - index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) - - #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# - #path = path_to_dir + '/' + names[i] - path = names[i] + '.fits[Flux]' - - index_str.append(index) - name_str.append(path) - - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits') - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): - """ - Code to put cmfgen models into cdbs format and adds proper unit keyword in - fits header. Save as fits file - - path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot - directory with the *.flx models. Note that these files have already been - organized using organize_CMFGEN_atmospheres code. - - path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or - cmfgen_norot directory. Will copy new fits files to this directory. - This directory must already exist! - """ - # Save starting directory for later, move into path_to_dir directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('*.flx') - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - # Open file, extract useful info - t = Table.read(i, format='ascii') - wave = t['col1'] - flux = t['col2'] # Flux is already in erg/cm^2/s/A - - # Need to eliminate duplicate entries (pysynphot crashes) - unique = np.unique(wave, return_index=True) - wave = wave[unique[1]] - flux = flux[unique[1]] - - # Make fits table from individual columns. - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - #Adding unit keywords - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - prihdu = fits.PrimaryHDU() - - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i[:-4]+'.fits', overwrite=True) - - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Return to original directory, copy over new .fits files to cdbs directory - os.chdir(start_dir) - cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) - os.system(cmd) - - return - -def rebin_cmfgen(cdbs_path, rot=True): - """ - Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; - this makes spectrophotometry MUCH faster - - cdbs_path: path to cdbs directory - rot=True for rotating models (cmfgen_rot), False for non-rotating models - - makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing cmfgen model; we will steal the header. - # Also define paths to new rebin directories - if rot == True: - tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' - path = cdbs_path+'/grid/cmfgen_rot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_rot/' - else: - tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' - path = cdbs_path+'/grid/cmfgen_norot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_norot/' - - cmfgen_hdu = fits.open(tmp) - header0 = cmfgen_hdu[0].header - # Create rebin directories if they don't already exist. Copy over - # catalog.fits file from original directory (will be the same) - if not os.path.exists(path): - os.mkdir(path) - cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) - os.system(cmd) - - # Read in the catalog.fits file - cat = fits.getdata(orig_path + 'catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - # First column in new files will be for [atlas] wavelength - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - - # For each catalog.fits entry, read the unbinned spectrum and rebin to - # the atlas resolution. Make a new fits file in rebin directory - count = 0 - for ff in range(len(files_all)): - count += 1 - # Extract the temp, Z, logg - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - grav = float(vals[2]) - - # Fetch the spectrum - if rot == True: - sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) - else: - sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) - - # Rebin - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - # Make the FITS file from the columns with header - cols = fits.ColDefs([c0,c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write hdu to new directory with same filename - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(path+files_all[ff]) - - print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) - return - - -def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): - """ - Construct the Phoenix Husser+13 atmopsheres for each model. Combines the - fluxes from the *HiRES.fits files and the wavelengths of the - WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. - - path_to_dir is the path to the directory containing all of the downloaded - files - - met_str is the name of the current metallicity - - Creates new fits files for each atmosphere: phoenix_.fits, - which contains columns for the log g (column header = g#.#). Puts - atmospheres in new directory phoenixm00 - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the current metallicity subdirectory - sub_dir = '../phoenix{0}'.format(met_str) - if os.path.exists(sub_dir): - pass - else: - os.mkdir(sub_dir) - - # Extract wavelength array, make column for later - wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') - wave = wavefile[0].data - wavefile.close() - wave_col = Column(wave, name = 'WAVELENGTH') - - # Create temp array for Husser+13 grid (given in paper) - temp_arr = np.arange(2300, 7001, 100) - temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) - - print( 'Looping though all temps') - # For each temp, build file containing the flux for all gravities - i = 0 - for temp in temp_arr: - files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) - files.sort() - # Start the table with the wavelength column - t = Table() - t.add_column(wave_col) - for f in files: - # Extract the logg out of filename - logg = f[9:13] - - # Extract fluxes from file - spectrum = fits.open(f) - flux = spectrum[0].data - spectrum.close() - - # Make Column object with fluxes, add to table - col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) - t.add_column(col) - - # Now, construct final fits file for the given temp - outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) - t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) - - # Progress counter for user - i += 1 - print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) - - # Return to original directory - os.chdir(start_dir) - return - -def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): - """ - Makes catalog.fits file for Husser+13 phoenix models. Assumes that - organize_PHOENIXv16_atmospheres has been run already, and that the models lie - in subdirectory phoenix[met_str]. - - path_to_directory is the path to the directory with the reformatted - models (i.e. the output from construct_atmospheres, phoenix[met_str]) - - Puts catalog.fits file in directory the user starts in - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Extract metallicity from metallicity string - met = float(met_str[1]) + (float(met_str[2]) * 0.1) - if 'm' in met_str: - met *= -1. - - # Collect the filenames. Each is a unique temp with many different log g's - files = glob.glob('phoenix*.fits') - files.sort() - - # Create the catalog.fits file, row by row - index_arr = [] - filename_arr = [] - for i in files: - # Get log g values from the column header the file - t = Table.read(i, format='fits') - keys = t.keys() - logg_vals = keys[1:] - - # Extract temp from filename - name = i.split('_') - temp = float(name[1][:-5]) - for j in logg_vals: - logg = float(j[1:]) - index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) - filename = path_to_dir + i + '[' + j + ']' - # Add row to table - index_arr.append(index) - filename_arr.append(filename) - - catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) - - # Return to starting directory, write catalog - os.chdir(start_dir) - - if os.path.exists('catalog.fits'): - from astropy.table import vstack - - prev_catalog = Table.read('catalog.fits', format='fits') - joined_catalog = vstack([prev_catalog, catalog]) - - joined_catalog.write('catalog.fits', format='fits', overwrite=True) - else: - catalog.write('catalog.fits', format='fits', overwrite=True) - - return - -def cdbs_PHOENIXv16(path_to_cdbs_dir): - """ - Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily - consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] - and adding the appropriate keywords to the fits header. - - path_to_cdbs_dir goes from current working directory to phoenix[met] directory - in cdbs/grids/phoenix_v16. Note that these files have already been organized - using organize_PHOENIXv16_atmospheres code. - - Overwrites original files in directory - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_cdbs_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('phoenix*.fits') - - ## Need to sort filenames; glob doesn't always give them in order - files.sort() - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - - # Read in current FITS table - cur_table = Table.read(i, format='fits') - - cur_table.columns[0].name = 'Wavelength' - - num_cols = len(cur_table.colnames) - - # Multiplying each flux column by 10^-8 for conversion - for cur_col_index in range(1, num_cols, 1): - cur_col_name = cur_table.colnames[cur_col_index] - cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 - - - # Construct new FITS file based on old one - hdu = fits.open(i) - header_0 = hdu[0].header - header_1 = hdu[1].header - sci = hdu[1].data - - tbhdu = fits.table_to_hdu(cur_table) - - # Copying over the older headers, adding unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - tbhdu.header['TUNIT3'] = 'FLAM' - tbhdu.header['TUNIT4'] = 'FLAM' - tbhdu.header['TUNIT5'] = 'FLAM' - tbhdu.header['TUNIT6'] = 'FLAM' - tbhdu.header['TUNIT7'] = 'FLAM' - tbhdu.header['TUNIT8'] = 'FLAM' - tbhdu.header['TUNIT9'] = 'FLAM' - tbhdu.header['TUNIT10'] = 'FLAM' - tbhdu.header['TUNIT11'] = 'FLAM' - tbhdu.header['TUNIT12'] = 'FLAM' - tbhdu.header['TUNIT13'] = 'FLAM' - tbhdu.header['TUNIT14'] = 'FLAM' - - # Construct and write out final FITS file - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i, overwrite=True) - - hdu.close() - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Change back to starting directory - os.chdir(start_dir) - - return - -def rebin_phoenixV16(cdbs_path): - """ - Rebin phoenixV16 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: phoenix_v16_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - ## (This assumes that at least 'm00' metallicity exists) - tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/phoenix_v16_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - temp_arr = np.zeros(len(files_all), dtype=float) - logg_arr = np.zeros(len(files_all), dtype=float) - metal_arr = np.zeros(len(files_all), dtype=float) - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - - temp_arr[ff] = float(vals[0]) - metal_arr[ff] = float(vals[1]) - logg_arr[ff] = float(vals[2]) - - - metal_uniq = np.unique(metal_arr) - temp_uniq = np.unique(temp_arr) - - for mm in range(len(metal_uniq)): - metal = metal_uniq[mm] # metallicity - - # Construct str for metallicity (for appropriate directory name) - met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) - if metal > 0: - met_str = 'p' + met_str - else: - met_str = 'm' + met_str - - # Make directory for current metallicity if it does not exist yet - if not os.path.exists(path + 'phoenix' + met_str): - os.mkdir(path + 'phoenix' + met_str) - - for tt in range(len(temp_uniq)): - temp = temp_uniq[tt] # temperature - - # Pick out the list of gravities for this T, Z combo - idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] - logg_exist = logg_arr[idx] - - # All gravities will go in one file. Here is the output - # file name. - outfile = path + files_all[idx[0]].split('[')[0] - - ## If the rebinned file already exists, continue - if os.path.exists(outfile): - continue - - # Build a columns array. One column for each gravity. - cols_arr = [] - - # Make the wavelength column, which is first in the cols array. - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - cols_arr.append(c0) - - for gg in range(len(logg_exist)): - grav = logg_exist[gg] # gravity - - # Fetch the spectrum - sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Store the spectrum - name = 'g{0:3.1f}'.format(grav) - col = fits.Column(name=name, format='E', array=flux_rebin) - cols_arr.append(col) - - - # Make the FITS file from the columns with header. - cols = fits.ColDefs(cols_arr) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - for gg in range(len(logg_exist)): - tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' - - # Write hdu - finalhdu = fits.HDUList([prihdu, tbhdu]) - # don't have overwrite to protect original files. - finalhdu.writeto(outfile) - - print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) - - - return - - -def rebin_spec(wave, specin, wavnew): - """ - Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: - http://www.astrobetter.com/blog/2013/08/12/ - python-tip-re-sampling-spectra-with-pysynphot/ - """ - spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) - f = np.ones(len(wave)) - filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') - obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') - - return obs.binflux - -def organize_BTSettl_2015_atmospheres(path_to_dir): - """ - Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the BTSettl subdirectory - if not os.path.exists('BTSettl_2015'): - os.mkdir('BTSettl_2015') - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.spec.fits') - - for i in files: - hdu = fits.open(i) - spec = hdu[1].data - header_0 = hdu[0].header - header_1 = hdu[1].header - - wave = spec.field(0) - flux = spec.field(1) - - # Get units right: convert wave from microns to Angstroms, - # flux from W /m^2/ micron to erg/s/cm^2/A - wave_new = wave * 10**4 - flux_new = flux * 10**(-1) - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave_new) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) - - hdu.close() - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_BTSettl_2015_catalog(path_to_dir): - """ - Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*spec.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('-') - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: BTSettl_2015_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - phoenix_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/BTSettl_2015_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning BTSettl spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - -def make_wavelength_unique(files, dirname): - """ - Helper function to go through each BTSettl spectrum and ensure that - each wavelength point is unique. This is required for rebinning to work. - - - files: list of files to run this analysis on - """ - # Loop through each file, find fix repeated wavelength entries if necessary - for i in files: - t = Table.read('{0}/{1}'.format(dirname,i), format='fits') - test = np.unique(t['Wavelength'], return_index=True) - - if len(t) != len(test[0]): - t = t[test[1]] - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - # Also make sure wavelength is monotonic. If it is not, then it is - # a sign that the wavelengths are out of order - diff = np.diff(t['Wavelength']) - bad = np.where(diff < 0) - if len(bad[0]) > 0: - t.sort('Wavelength') - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - print('Done {0}'.format(i)) - - return - -def organize_BTSettl_atmospheres(): - """ - Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). - The code expects tp be run in cdbs/grid/BTSettl, and expects that the - individual model files have been downloaded from online - (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] - - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, - # for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.txt') - count=0 - for jj in files: - t = Table.read(jj, format='ascii') - # First, trim the wavelengths to a more reasonable wavelength range - good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) - t = t[good] - - # Convert flux units to Flam (Ergs/sec/cm**2/A) - flux_new = 10**(t['col2'] - 8.0) - - # Save the file as a fits file - c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Now, clean up all the files made when unzipping the spectra - cmd1 = 'rm *.bz2' - cmd2 = 'rm *.tmp' - #cmd3 = 'rm *.txt' - os.system(cmd1) - os.system(cmd2) - #os.system(cmd3) - print('==============================') - print('Done {0}'.format(ii)) - print('==============================') - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_BTSettl_catalog(): - """ - Create cdbs catalog.fits of BTSettl grid. - THIS IS STEP 2, after organize_BTSettl_atmospheres has - been run. - - Code expects to be run in cdbs/grid/BTSettl - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btp05'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('*.fits') - - # Construct the metallicity val - if 'm' in ii: - metal_flag = -1 * float(ii[3:])*0.1 - else: - metal_flag = float(ii[3:])*0.1 - - # Now collect the info from the files - for jj in files: - tmp = jj.split('-') - - if metal_flag >= 0: - temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin - try: - logg = float(tmp[1]) - except: - logg = float(tmp[1].split('+')[0]) - else: - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - # Go back to original directory to move to next metallicity - print('Done {0}'.format(ii)) - os.chdir(start_dir) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl(make_unique=False): - """ - Rebin BTSettle models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: BTSettl_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create cdbs/grid directory for rebinned models - path = 'BTSettl_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata('BTSettl/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - #==============================# - #tmp = [] - #for ii in files_all: - # if ii.startswith('btp00'): - # tmp.append(ii) - #files_all = tmp - #=============================# - - print( 'Rebinning BTSettl spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'BTSettl') - print('Done') - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - try: - sp = pysynphot.Icat('BTSettl', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - except: - pdb.set_trace() - orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) - outfile = path + files_all[ff].split('[')[0] - cmd = 'cp {0} {1}'.format(orig_file, outfile) - os.system(cmd) - - print('Done {0} of {1}'.format(ff, len(files_all))) - - return - -def organize_all_Meisner2023_atmospheres(): - """ - Construct cdbs-ready atmospheres for the Meisner2023 grid. - The code expects tp be run in cdbs/grid/Meisner2023, and expects that the - individual model files have been downloaded from online - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['mm10', 'mm05', 'mp00', 'mp03'] - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Save as a fits file, for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.fits') - count=0 - for jj in files: - # Open each .fits file and read the data - with fits.open(jj) as hdul: - data = hdul[1].data - wavelength = data['Wavelength'] - flux = data['Flux'] - - # Make flux independent of R&D - flux_new = flux / 5e-20 - - # Create new columns with desired format - c0 = fits.Column(name='Wavelength', format='D', array=wavelength) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write the new fits table in the cdbs directory - output_filename = '{0}.fits'.format(jj[:-5]) # Removing the original .fits extension - hdu_new.writeto(output_filename, overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_Meisner2023_catalog(): - """ - Create cdbs catalog.fits of Meisner2023 grid. - THIS IS STEP 2, after organize_Meisner2023_atmospheres has - been run. - - Code expects to be run in cdbs/grid/Meisner2023 - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['mm10', 'mm05', 'mp00', 'mp03'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('spec_jwst_*.fits') - - for jj in files: - # Parse temperature, log(g), and metallicity from filename - temp_str = jj.split('_')[2] - logg_str = jj.split('_')[3] - metal_str = jj.split('_')[4] - - # Extract temperature, surface gravity, and metallicity - temp = float(temp_str[1:]) # Temperature in Kelvin - logg = float(logg_str[1:]) # Surface gravity log(g) - - # Build metallicity value - if metal_str.startswith('m'): - metallicity = -1 * float(metal_str[1:]) - else: - metallicity = float(metal_str[1:]) - - # Construct index and filename strings - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metallicity, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - print('Processed directory:', ii) - os.chdir(start_dir) - - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_Meisner2023(make_unique=False): - """ - Rebin Meisner2023 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: Meisner2023_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create a directory for rebinned Meisner2023 models - rebin_path = 'Meisner2023_rebin/' - if not os.path.exists(rebin_path): - os.mkdir(rebin_path) - - # Load the catalog.fits file and extract all spectra file paths - cat = Table.read('Meisner2023/catalog.fits') - files_all = [cat[ii]['FILENAME'].split('[')[0] for ii in range(len(cat))] - - print('Rebinning Meisner2023 spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'Meisner2023') - print('Done') - - for ff, file in enumerate(files_all): - vals = cat[ff]['INDEX'].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the Meisner2023 spectrum and rebin its flux - try: - sp = pysynphot.Icat('Meisner2023', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Create the output FITS file - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write the new rebinned file in the Meisner2023_rebin directory - outfile = os.path.join(rebin_path, os.path.basename(file)) - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - except Exception as e: - print(f"Error processing {file}: {e}") - orig_file = os.path.join('Meisner2023', file) - outfile = os.path.join(rebin_path, os.path.basename(file)) - os.system(f'cp {orig_file} {outfile}') - - print('Done {0} of {1}'.format(ff + 1, len(files_all))) - - return - - - -def organize_WDKoester_atmospheres(path_to_dir): - """ - Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.dk.dat.txt') - - for i in files: - data = Table.read(i, format='ascii') - - wave = data['col1'] # angstrom - flux = data['col2'] # erg/s/cm^2/A - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) - - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_WDKoester_catalog(path_to_dir): - """ - Create cdbs catalog.fits of wdKoester grid. - THIS IS STEP 2, after organize_WDKoester_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*dk.dat.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('.') - tmp2 = tmp[0].split('_') - temp = float(tmp2[0][2:]) # Kelvin - logg = float(tmp2[1]) / 100.0 # log(g) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin wdKoester models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: wdKoester_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing model; we will steal the header - tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' - wdkoester_hdu = fits.open(tmp) - header0 = wdkoester_hdu[0].header - wdkoester_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/wdKoester_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning wdKoester spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the wdKoester spectrum, rebin flux - sp = pysynphot.Icat('wdKoester', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - - diff --git a/spisea/atmospheres_BASE_58456.py b/spisea/atmospheres_BASE_58456.py deleted file mode 100644 index 8df2fbe1..00000000 --- a/spisea/atmospheres_BASE_58456.py +++ /dev/null @@ -1,2165 +0,0 @@ -import logging -import numpy as np -import pysynphot -import os -import glob -from astropy.io import fits -from astropy.table import Table, Column -import pysynphot -import time -import pdb -import warnings - -log = logging.getLogger('atmospheres') - -def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4): - """ - Given atmosphere model, get temperature and gravity bounds - """ - # Open catalog fits file and break out row indices - catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) - - teff_arr = [] - z_arr = [] - logg_arr = [] - for cur_row_index in range(len(catalog)): - index = catalog['INDEX'][cur_row_index] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Filter by metallicity. Will chose the closest metallicity to desired input - metal_list = np.unique(np.array(z_arr)) - metal_idx = np.argmin(np.abs(metal_list - metallicity)) - - z_filt = np.where(z_arr == metal_list[metal_idx]) - teff_arr = teff_arr[z_filt] - logg_arr = logg_arr[z_filt] - - # # Now find the closest atmosphere in parameter space to - # # the one we want. We'll find the match with the lowest - # # fractional difference - # teff_diff = (teff_arr - temperature) / temperature - # logg_diff = (logg_arr - gravity) / gravity - # - # diff_tot = abs(teff_diff) + abs(logg_diff) - # idx_f = np.argmin(diff_tot) - # - # temperature_new = teff_arr[idx_f] - # gravity_new = logg_arr[idx_f] - - # First check if temperature within bounds - temperature_new = temperature - if temperature > np.max(teff_arr): - temperature_new = np.max(teff_arr) - if temperature < np.min(teff_arr): - temperature_new = np.min(teff_arr) - - # If temperature within bounds, then check if metallicity within bounds - teff_diff = np.abs(teff_arr - temperature) - sorted_min_diffs = np.unique(teff_diff) - - ## Find two closest temperatures - teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] - teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] - - logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] - logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] - - ## Switch to most conservative bound of logg out of two closest temps - gravity_new = gravity - if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): - gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) - if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): - gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) - - # Print out changes, if any - if temperature_new != temperature: - teff_msg = 'Changing to T={0:6.0f} for T={1:6.0f} logg={2:4.2f}' - print( teff_msg.format(temperature_new, temperature, gravity)) - - if gravity_new != gravity: - logg_msg = 'Changing to logg={0:4.2f} for T={1:6.0f} logg={2:4.2f}' - print( logg_msg.format(gravity_new, temperature, gravity)) - - return (temperature_new, gravity_new) - -def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmosphere from the Kurucz pysnphot grid - (`Kurucz 1993 `_). - - Grid Range: - - * Teff: 3000 - 50000 K - * gravity: 0 - 5 cgs - * metallicity: -5.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - Always false for this particular function - """ - try: - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('k93models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Kurucz 1993 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmospheres from the pysynphot ATLAS9 atlas - (`Castelli & Kurucz 2004 `_). - - Grid Range: - - * Teff: 3500 - 50000 K - * gravity: 0 - 5.0 cgs - * [M/H]: -2.5 - 0.2 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('ck04models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - try: - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('nextgen', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find NextGen atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, - rebin=False): - """ - Return atmosphere from the pysynphot - `PHOENIX atlas `_. - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - try: - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - # Take care of atmospheres outside the catalog boundaries - logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' - if gravity > 4.3: - print( logg_msg.format(4.3, temperature, gravity)) - gravity = 4.3 - - if rebin: - sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, - verbose=False): - """ - For a given stellar atmosphere, get extract the closest possible match in - Teff/logg space. Note that this is different from the normal routine - which interpolates along the input grid to get final spectrum. We can't - do this here because the Fierro+15 atmosphere grid is so sparse - - rebin=True: pull from atmospheres at ck04model resolution. - - If verbose, print out the parameters of the match - """ - # Set up the proper root directory - if rebin == True: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' - else: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' - - # Read in catalog, extract atmosphere info - cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') - teff_arr = [] - z_arr = [] - logg_arr = [] - for ii in range(len(cat)): - index = cat['INDEX'][ii] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Now find the closest atmosphere in parameter space to - # the one we want. We'll find the match with the lowest - # fractional difference - teff_diff = (teff_arr - temperature) / temperature - logg_diff = (logg_arr - gravity) / gravity - - diff_tot = abs(teff_diff) + abs(logg_diff) - idx_f = np.where(diff_tot == min(diff_tot))[0][0] - - # Extract the filename of the best-match model and read as - # pysynphot object - infile = cat[idx_f]['FILENAME'].split('.') - spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the - # ATLAS models are in FLAM at the surface. So, we need to multiply the - # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. - # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file - t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') - tmp = np.where(t['col1'] == infile[0]) - - lum = t['col3'][tmp] * (3.839*10**33) # cgs - sigma = 5.6704 * 10**-5 # cgs - teff = teff_arr[idx_f] # cgs - - radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm - radius /= 3.08*10**18 # in pc - - - # Make the pysynphot spectrum - w = spec['Wavelength'] - f = spec['Flux'] * (1000 / radius)**2. - sp = pysynphot.ArraySpectrum(w,f) - - #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Print out parameters of match, if desired - if verbose: - print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) - print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - if rebin: - sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 30000) - gravity = log gravity (def = 4.14) - """ - sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): - """ - Return PHOENIX v16 atmospheres from - `Husser et al. 2013 `_. - - Models originally downloaded via `ftp `_. - Solar metallicity and [alpha/Fe] is used. - - Grid Range: - - * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K - * gravity: 0.0 - 6.0 cgs, steps of 0.5 - * [M/H]: -4.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - atm_model_name = 'phoenix_v16' - if rebin == True: - atm_model_name = 'phoenix_v16_rebin' - - - # Extract atmosphere. If that fails, then check bounds and try again - try: - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_model_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): - """ - Return atmosphere from CIFIST2011_2015 grid - (`Allard et al. 2012 `_, - `Baraffe et al. 2015 `_ ) - - Grid originally downloaded from `website `_. - - Grid Range: - - * Teff: 1200 - 7000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_2015_rebin' - else: - atm_name = 'BTSettl_2015' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): - """ - Return atmosphere from CIFIST2011 grid - (`Allard et al. 2012 `_) - - Grid originally downloaded `here `_ - - Notes - ------ - Grid Range: - - * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - - Teff and gravity ranges depend on metallicity: - - [M/H] = -2.5 - - * Teff: 2600 - 4600 K - * gravity: 4.5 - 5.5 - - [M/H] = -2.0 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.5 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.0 - - * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 - - [M/H] = -0.5 - - * Teff: 1000 -7000 - * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 - - [M/H] = 0 - - * Teff: 750 - 7000 - * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 - - [M/H] = 0.5 - - * Teff: 1000 - 5000 - * gravity: 3.5 - 5.0 - - - Alpha enhancement: - - * [M/H]= -0.0, +0.5 no anhancement - * [M/H]= -0.5 with [alpha/H]=+0.2 - * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_rebin' - else: - atm_name = 'BTSettl' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): - """ - Return white dwarf atmospheres from - `Koester et al. 2010 `_ - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. - - Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 - """ - try: - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_atlas_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and phoenixV16. - - Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -#---------------------------------------------------------------------# -def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, - rebin=True): - """ - Return a stellar atmosphere from a suite of different model grids, - depending on the input temperature, (all values in K). - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - - Notes - ----- - The underlying stellar model grid used changes as a function of - stellar temperature (in K): - - * T > 20,000: ATLAS - * 5500 <= T < 20,000: ATLAS - * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge - * 3800 <= T < 5000: PHOENIXv16 - - For T < 3800, there is an additional gravity and metallicity - dependence: - - If T < 3800 and [M/H] = 0: - - * T < 3800, logg < 2.5: PHOENIX v16 - * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge - * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - - Otherwise, if T < 3800 and [M/H] != 0: - - * T < 3800: PHOENIX v16 - - References: - - * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) - * PHOENIXv16 (`Husser et al. 2013 `_) - * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) - - LTE WARNING: - - The ATLAS atmospheres are calculated with LTE, and so they - are less accurate when non-LTE conditions apply (e.g. T > 20,000 - K). Ultimately we'd like to add a non-LTE atmosphere grid for - the hottest stars in the future. - - HOW BOUNDARIES BETWEEN MODELS ARE TREATED: - - At the boundary between two models grids a temperature range is defined - where the resulting atmosphere is a weighted average between the two - grids. Near one boundary one model - is weighted more heavily, while at the other boundary the other - model is weighted more heavily. These are calculated in the - temperature ranges where we switch between model grids, to - ensure a smooth transition. - """ - # For T < 3800, atmosphere depends on metallicity + gravity. - # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is - # currently available here, so if non-solar metallicity, just stick with - # the Phoenix grid - if (temperature <= 3800) & (metallicity == 0): - # High gravity are in BTSettl regime - if (temperature <= 3200) & (gravity > 2.5): - if verbose: - print( 'BTSettl_2015 atmosphere') - return get_BTSettl_2015_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): - if verbose: - print( 'BTSettl/Phoenixv16 merged atmosphere') - return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - # Low gravity is PHOENIX regime - if gravity <= 2.5: - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity != 0): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - # For T > 3800, no metallicity or gravity dependence - if (temperature >= 3800) & (temperature < 5000): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 5000) & (temperature < 5500): - if verbose: - print( 'ATLAS/Phoenix merged atmosphere') - return get_atlas_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if (temperature >= 5500) & (temperature < 20000): - if verbose: - print( 'ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if temperature >= 20000: - if verbose: - print( 'Still ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - #print('CMFGEN') - #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, - # temperature=temperature, - # gravity=gravity) - - - - -def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Return the white dwarf atmosphere from - `Koester et al. 2010 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('wdKoester atmosphere') - - return get_wdKoester_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere. - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - -def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, - verbose=False, rebin=None, - wave_min=500, wave_max=50_000, wave_num=20_000): - """ - Return a blackbody spectrum - - Parameters - ---------- - temperature: float, default=20_000 - The stellar temperature, in units of K - wave_min: float, default=500 - Sets the minimum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_max: float, default=50_000 - Sets the maximum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_num: int, default=20_000 - Sets the number of wavelength points in the wavelength range - Note: the wavelength range is evenly spaced in log space - """ - if ((metallicity is not None) or (gravity is not None) or - (rebin is not None)): - warnings.warn( - 'Only `temperature` keyword is used for black-body atmosphere' - ) - - if verbose: - print('Black-body atmosphere') - - # Modify pysynphot's default waveset to specified bounds - pysynphot.refs.set_default_waveset( - minwave=wave_min, maxwave=wave_max, num=wave_num - ) - - # Get black-body atmosphere for specified temperature from pysynphot - bbspec = pysynphot.spectrum.BlackBody(temperature) - - # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` - bbspec.convert('flam') - - # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. - # Need to remove this normalization for SPISEA by multiplying bbspec - # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 - bbspec *= (1000 * 3.086e18 / 6.957e10)**2 - - return bbspec - - -#--------------------------------------# -# Atmosphere formatting functions -#--------------------------------------# - -def download_CMFGEN_atmospheres(Table_rot, Table_norot): - """ - Downloads CMFGEN models from - https://sites.google.com/site/fluxesandcontinuum/home; - these contain continuum as well as lines. - - Table_rot, Table_norot are tables with the file prefixes - and model atmosphere parameters, taken by hand from the - Fierro+15 paper - - Website addresses are hardcoded - - Puts downloaded models in the current working directory. - """ - print( 'WARNING: THIS DOES NOT COMPLETELY WORK') - print( '**********************') - t_rot = Table.read(Table_rot, format='ascii') - t_norot = Table.read(Table_norot, format='ascii') - - tables = [t_rot, t_norot] - filenames = [t_rot['col1'], t_norot['col1']] - - # Hardcoded list of webiste addresses - web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' - web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' - web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', - web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', - web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', - web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', - web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] - # Array of masses that matches the website addresses - mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) - - # Loop through rotating and unrotating case. First loop is rot, second unrot - for i in range(2): - # Extract masses from filenames - masses = [] - for j in filenames[i]: - tmp = j.split('m') - mass = float(tmp[1][:-1]) - masses.append(mass) - - # Download the models webpage by webpage. A bit tricky because masses - # change slightly within a particular website. THIS IS WHAT FAILS - for j in range(len(web)): - if j == 0: - good = np.where( (masses <= mass_arr[j]) ) - else: - g = j - 1 - good = np.where( (masses <= mass_arr[j]) & - (masses > mass_arr[g]) ) - # Use wget command to pull down the files, and unzip them - for k in good[0]: - full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) - os.system('wget ' + full) - os.system('unzip '+ filenames[i][k] + '.flx.zip') - - return - -def organize_CMFGEN_atmospheres(path_to_dir): - """ - Organize CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html) - into rot and noRot directories - - path_to_dir is from current working directory to directory - containing the downloaded models. Assumed that models - and tables describing parameters are in this directory. - - Tables describing parameters MUST be named Table_rot.txt, - Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. - These are located in same original directory as atmosphere files - - Will separate files into 2 subdirectories, one rotating and - the other non-rotating - - *Can't have any other files starting with "t" in model directory to start!* - """ - # First, record current working directory to return to later - start_dir = os.getcwd() - - # Enter atmosphere directory, collect rotating and non-rotating - # file names (assumed to all start with "t") - os.chdir(path_to_dir) - rot_models = glob.glob("t*r.flx*") - noRot_models = glob.glob("t*n.flx*") - - # Separate into different subdirectories - if os.path.exists('cmfgenF15_rot'): - pass - else: - os.mkdir('cmfgenF15_rot') - os.mkdir('cmfgenF15_noRot') - - for mod in rot_models: - cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) - os.system(cmd) - - for mod in noRot_models: - cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) - os.system(cmd) - - # Also move Tables with model parameters into correct directory - os.system('mv Table_rot.txt cmfgenF15_rot') - os.system('mv Table_noRot.txt cmfgenF15_noRot') - - # Return to original directory - os.chdir(start_dir) - - return - -def make_CMFGEN_catalog(path_to_dir): - """ - Create cdbs catalog.fits of CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html). - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to directory - containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, - needs to be a Table*.txt file which contains the parameters for all of the - original models, since params in filename are not precise enough - - Will create catalog.fits file in atmosphere directory with - description of each model - - *Can't have any other files starting with "t" in model directory to start!* - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere - # Note: can't rely on filename for this because not precise enough!! - - #---------OLD: GETTING PARAMS FROM FILENAME-------# - # Collect file names (assumed to all start with "t") - #files = glob.glob("t*") - #for name in files: - # tmp = name.split('l') - # temp = float(tmp[0][1:]) * 100.0 # In kelvin - - # lumtmp = tmp[1].split('_') - # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun - - # mass = float(lumtmp[0][5:-1]) # In M_sun - - # Need to calculate log g from T and L (cgs) - # lum_sun = 3.846 * 10**33 # erg/s - # M_sun = 2 * 10**33 # g - # G_si = 6.67 * 10**(-8) # cgs - # sigma_si = 5.67 * 10**(-5) # cgs - - # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ - # (lum * lum_sun) - # logg = np.log10(g) - #---------------------------------------------------# - - # Read table with atmosphere params - table = glob.glob('Table_*') - t = Table.read(table[0], format = 'ascii') - names = t['col1'] - temps = t['col2'] - logg = t['col4'] - - # Create catalog.fits file - index_str = [] - name_str = [] - for i in range(len(names)): - index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) - - #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# - #path = path_to_dir + '/' + names[i] - path = names[i] + '.fits[Flux]' - - index_str.append(index) - name_str.append(path) - - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits') - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): - """ - Code to put cmfgen models into cdbs format and adds proper unit keyword in - fits header. Save as fits file - - path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot - directory with the *.flx models. Note that these files have already been - organized using organize_CMFGEN_atmospheres code. - - path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or - cmfgen_norot directory. Will copy new fits files to this directory. - This directory must already exist! - """ - # Save starting directory for later, move into path_to_dir directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('*.flx') - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - # Open file, extract useful info - t = Table.read(i, format='ascii') - wave = t['col1'] - flux = t['col2'] # Flux is already in erg/cm^2/s/A - - # Need to eliminate duplicate entries (pysynphot crashes) - unique = np.unique(wave, return_index=True) - wave = wave[unique[1]] - flux = flux[unique[1]] - - # Make fits table from individual columns. - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - #Adding unit keywords - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - prihdu = fits.PrimaryHDU() - - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i[:-4]+'.fits', overwrite=True) - - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Return to original directory, copy over new .fits files to cdbs directory - os.chdir(start_dir) - cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) - os.system(cmd) - - return - -def rebin_cmfgen(cdbs_path, rot=True): - """ - Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; - this makes spectrophotometry MUCH faster - - cdbs_path: path to cdbs directory - rot=True for rotating models (cmfgen_rot), False for non-rotating models - - makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing cmfgen model; we will steal the header. - # Also define paths to new rebin directories - if rot == True: - tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' - path = cdbs_path+'/grid/cmfgen_rot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_rot/' - else: - tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' - path = cdbs_path+'/grid/cmfgen_norot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_norot/' - - cmfgen_hdu = fits.open(tmp) - header0 = cmfgen_hdu[0].header - # Create rebin directories if they don't already exist. Copy over - # catalog.fits file from original directory (will be the same) - if not os.path.exists(path): - os.mkdir(path) - cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) - os.system(cmd) - - # Read in the catalog.fits file - cat = fits.getdata(orig_path + 'catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - # First column in new files will be for [atlas] wavelength - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - - # For each catalog.fits entry, read the unbinned spectrum and rebin to - # the atlas resolution. Make a new fits file in rebin directory - count = 0 - for ff in range(len(files_all)): - count += 1 - # Extract the temp, Z, logg - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - grav = float(vals[2]) - - # Fetch the spectrum - if rot == True: - sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) - else: - sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) - - # Rebin - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - # Make the FITS file from the columns with header - cols = fits.ColDefs([c0,c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write hdu to new directory with same filename - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(path+files_all[ff]) - - print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) - return - - -def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): - """ - Construct the Phoenix Husser+13 atmopsheres for each model. Combines the - fluxes from the *HiRES.fits files and the wavelengths of the - WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. - - path_to_dir is the path to the directory containing all of the downloaded - files - - met_str is the name of the current metallicity - - Creates new fits files for each atmosphere: phoenix_.fits, - which contains columns for the log g (column header = g#.#). Puts - atmospheres in new directory phoenixm00 - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the current metallicity subdirectory - sub_dir = '../phoenix{0}'.format(met_str) - if os.path.exists(sub_dir): - pass - else: - os.mkdir(sub_dir) - - # Extract wavelength array, make column for later - wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') - wave = wavefile[0].data - wavefile.close() - wave_col = Column(wave, name = 'WAVELENGTH') - - # Create temp array for Husser+13 grid (given in paper) - temp_arr = np.arange(2300, 7001, 100) - temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) - - print( 'Looping though all temps') - # For each temp, build file containing the flux for all gravities - i = 0 - for temp in temp_arr: - files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) - files.sort() - # Start the table with the wavelength column - t = Table() - t.add_column(wave_col) - for f in files: - # Extract the logg out of filename - logg = f[9:13] - - # Extract fluxes from file - spectrum = fits.open(f) - flux = spectrum[0].data - spectrum.close() - - # Make Column object with fluxes, add to table - col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) - t.add_column(col) - - # Now, construct final fits file for the given temp - outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) - t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) - - # Progress counter for user - i += 1 - print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) - - # Return to original directory - os.chdir(start_dir) - return - -def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): - """ - Makes catalog.fits file for Husser+13 phoenix models. Assumes that - organize_PHOENIXv16_atmospheres has been run already, and that the models lie - in subdirectory phoenix[met_str]. - - path_to_directory is the path to the directory with the reformatted - models (i.e. the output from construct_atmospheres, phoenix[met_str]) - - Puts catalog.fits file in directory the user starts in - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Extract metallicity from metallicity string - met = float(met_str[1]) + (float(met_str[2]) * 0.1) - if 'm' in met_str: - met *= -1. - - # Collect the filenames. Each is a unique temp with many different log g's - files = glob.glob('phoenix*.fits') - files.sort() - - # Create the catalog.fits file, row by row - index_arr = [] - filename_arr = [] - for i in files: - # Get log g values from the column header the file - t = Table.read(i, format='fits') - keys = t.keys() - logg_vals = keys[1:] - - # Extract temp from filename - name = i.split('_') - temp = float(name[1][:-5]) - for j in logg_vals: - logg = float(j[1:]) - index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) - filename = path_to_dir + i + '[' + j + ']' - # Add row to table - index_arr.append(index) - filename_arr.append(filename) - - catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) - - # Return to starting directory, write catalog - os.chdir(start_dir) - - if os.path.exists('catalog.fits'): - from astropy.table import vstack - - prev_catalog = Table.read('catalog.fits', format='fits') - joined_catalog = vstack([prev_catalog, catalog]) - - joined_catalog.write('catalog.fits', format='fits', overwrite=True) - else: - catalog.write('catalog.fits', format='fits', overwrite=True) - - return - -def cdbs_PHOENIXv16(path_to_cdbs_dir): - """ - Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily - consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] - and adding the appropriate keywords to the fits header. - - path_to_cdbs_dir goes from current working directory to phoenix[met] directory - in cdbs/grids/phoenix_v16. Note that these files have already been organized - using organize_PHOENIXv16_atmospheres code. - - Overwrites original files in directory - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_cdbs_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('phoenix*.fits') - - ## Need to sort filenames; glob doesn't always give them in order - files.sort() - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - - # Read in current FITS table - cur_table = Table.read(i, format='fits') - - cur_table.columns[0].name = 'Wavelength' - - num_cols = len(cur_table.colnames) - - # Multiplying each flux column by 10^-8 for conversion - for cur_col_index in range(1, num_cols, 1): - cur_col_name = cur_table.colnames[cur_col_index] - cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 - - - # Construct new FITS file based on old one - hdu = fits.open(i) - header_0 = hdu[0].header - header_1 = hdu[1].header - sci = hdu[1].data - - tbhdu = fits.table_to_hdu(cur_table) - - # Copying over the older headers, adding unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - tbhdu.header['TUNIT3'] = 'FLAM' - tbhdu.header['TUNIT4'] = 'FLAM' - tbhdu.header['TUNIT5'] = 'FLAM' - tbhdu.header['TUNIT6'] = 'FLAM' - tbhdu.header['TUNIT7'] = 'FLAM' - tbhdu.header['TUNIT8'] = 'FLAM' - tbhdu.header['TUNIT9'] = 'FLAM' - tbhdu.header['TUNIT10'] = 'FLAM' - tbhdu.header['TUNIT11'] = 'FLAM' - tbhdu.header['TUNIT12'] = 'FLAM' - tbhdu.header['TUNIT13'] = 'FLAM' - tbhdu.header['TUNIT14'] = 'FLAM' - - # Construct and write out final FITS file - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i, overwrite=True) - - hdu.close() - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Change back to starting directory - os.chdir(start_dir) - - return - -def rebin_phoenixV16(cdbs_path): - """ - Rebin phoenixV16 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: phoenix_v16_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - ## (This assumes that at least 'm00' metallicity exists) - tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/phoenix_v16_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - temp_arr = np.zeros(len(files_all), dtype=float) - logg_arr = np.zeros(len(files_all), dtype=float) - metal_arr = np.zeros(len(files_all), dtype=float) - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - - temp_arr[ff] = float(vals[0]) - metal_arr[ff] = float(vals[1]) - logg_arr[ff] = float(vals[2]) - - - metal_uniq = np.unique(metal_arr) - temp_uniq = np.unique(temp_arr) - - for mm in range(len(metal_uniq)): - metal = metal_uniq[mm] # metallicity - - # Construct str for metallicity (for appropriate directory name) - met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) - if metal > 0: - met_str = 'p' + met_str - else: - met_str = 'm' + met_str - - # Make directory for current metallicity if it does not exist yet - if not os.path.exists(path + 'phoenix' + met_str): - os.mkdir(path + 'phoenix' + met_str) - - for tt in range(len(temp_uniq)): - temp = temp_uniq[tt] # temperature - - # Pick out the list of gravities for this T, Z combo - idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] - logg_exist = logg_arr[idx] - - # All gravities will go in one file. Here is the output - # file name. - outfile = path + files_all[idx[0]].split('[')[0] - - ## If the rebinned file already exists, continue - if os.path.exists(outfile): - continue - - # Build a columns array. One column for each gravity. - cols_arr = [] - - # Make the wavelength column, which is first in the cols array. - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - cols_arr.append(c0) - - for gg in range(len(logg_exist)): - grav = logg_exist[gg] # gravity - - # Fetch the spectrum - sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Store the spectrum - name = 'g{0:3.1f}'.format(grav) - col = fits.Column(name=name, format='E', array=flux_rebin) - cols_arr.append(col) - - - # Make the FITS file from the columns with header. - cols = fits.ColDefs(cols_arr) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - for gg in range(len(logg_exist)): - tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' - - # Write hdu - finalhdu = fits.HDUList([prihdu, tbhdu]) - # don't have overwrite to protect original files. - finalhdu.writeto(outfile) - - print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) - - - return - - -def rebin_spec(wave, specin, wavnew): - """ - Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: - http://www.astrobetter.com/blog/2013/08/12/ - python-tip-re-sampling-spectra-with-pysynphot/ - """ - spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) - f = np.ones(len(wave)) - filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') - obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') - - return obs.binflux - -def organize_BTSettl_2015_atmospheres(path_to_dir): - """ - Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the BTSettl subdirectory - if not os.path.exists('BTSettl_2015'): - os.mkdir('BTSettl_2015') - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.spec.fits') - - for i in files: - hdu = fits.open(i) - spec = hdu[1].data - header_0 = hdu[0].header - header_1 = hdu[1].header - - wave = spec.field(0) - flux = spec.field(1) - - # Get units right: convert wave from microns to Angstroms, - # flux from W /m^2/ micron to erg/s/cm^2/A - wave_new = wave * 10**4 - flux_new = flux * 10**(-1) - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave_new) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) - - hdu.close() - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_BTSettl_2015_catalog(path_to_dir): - """ - Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*spec.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('-') - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: BTSettl_2015_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - phoenix_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/BTSettl_2015_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning BTSettl spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - -def make_wavelength_unique(files, dirname): - """ - Helper function to go through each BTSettl spectrum and ensure that - each wavelength point is unique. This is required for rebinning to work. - - - files: list of files to run this analysis on - """ - # Loop through each file, find fix repeated wavelength entries if necessary - for i in files: - t = Table.read('{0}/{1}'.format(dirname,i), format='fits') - test = np.unique(t['Wavelength'], return_index=True) - - if len(t) != len(test[0]): - t = t[test[1]] - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - # Also make sure wavelength is monotonic. If it is not, then it is - # a sign that the wavelengths are out of order - diff = np.diff(t['Wavelength']) - bad = np.where(diff < 0) - if len(bad[0]) > 0: - t.sort('Wavelength') - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - print('Done {0}'.format(i)) - - return - -def organize_BTSettl_atmospheres(): - """ - Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). - The code expects tp be run in cdbs/grid/BTSettl, and expects that the - individual model files have been downloaded from online - (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] - - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, - # for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.txt') - count=0 - for jj in files: - t = Table.read(jj, format='ascii') - # First, trim the wavelengths to a more reasonable wavelength range - good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) - t = t[good] - - # Convert flux units to Flam (Ergs/sec/cm**2/A) - flux_new = 10**(t['col2'] - 8.0) - - # Save the file as a fits file - c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Now, clean up all the files made when unzipping the spectra - cmd1 = 'rm *.bz2' - cmd2 = 'rm *.tmp' - #cmd3 = 'rm *.txt' - os.system(cmd1) - os.system(cmd2) - #os.system(cmd3) - print('==============================') - print('Done {0}'.format(ii)) - print('==============================') - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_BTSettl_catalog(): - """ - Create cdbs catalog.fits of BTSettl grid. - THIS IS STEP 2, after organize_BTSettl_atmospheres has - been run. - - Code expects to be run in cdbs/grid/BTSettl - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btp05'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('*.fits') - - # Construct the metallicity val - if 'm' in ii: - metal_flag = -1 * float(ii[3:])*0.1 - else: - metal_flag = float(ii[3:])*0.1 - - # Now collect the info from the files - for jj in files: - tmp = jj.split('-') - - if metal_flag >= 0: - temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin - try: - logg = float(tmp[1]) - except: - logg = float(tmp[1].split('+')[0]) - else: - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - # Go back to original directory to move to next metallicity - print('Done {0}'.format(ii)) - os.chdir(start_dir) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl(make_unique=False): - """ - Rebin BTSettle models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: BTSettl_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create cdbs/grid directory for rebinned models - path = 'BTSettl_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata('BTSettl/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - #==============================# - #tmp = [] - #for ii in files_all: - # if ii.startswith('btp00'): - # tmp.append(ii) - #files_all = tmp - #=============================# - - print( 'Rebinning BTSettl spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'BTSettl') - print('Done') - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - try: - sp = pysynphot.Icat('BTSettl', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - except: - pdb.set_trace() - orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) - outfile = path + files_all[ff].split('[')[0] - cmd = 'cp {0} {1}'.format(orig_file, outfile) - os.system(cmd) - - print('Done {0} of {1}'.format(ff, len(files_all))) - - return - -def organize_WDKoester_atmospheres(path_to_dir): - """ - Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.dk.dat.txt') - - for i in files: - data = Table.read(i, format='ascii') - - wave = data['col1'] # angstrom - flux = data['col2'] # erg/s/cm^2/A - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) - - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_WDKoester_catalog(path_to_dir): - """ - Create cdbs catalog.fits of wdKoester grid. - THIS IS STEP 2, after organize_WDKoester_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*dk.dat.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('.') - tmp2 = tmp[0].split('_') - temp = float(tmp2[0][2:]) # Kelvin - logg = float(tmp2[1]) / 100.0 # log(g) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin wdKoester models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: wdKoester_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing model; we will steal the header - tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' - wdkoester_hdu = fits.open(tmp) - header0 = wdkoester_hdu[0].header - wdkoester_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/wdKoester_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning wdKoester spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the wdKoester spectrum, rebin flux - sp = pysynphot.Icat('wdKoester', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - - diff --git a/spisea/atmospheres_LOCAL_58456.py b/spisea/atmospheres_LOCAL_58456.py deleted file mode 100644 index 0f4c90e5..00000000 --- a/spisea/atmospheres_LOCAL_58456.py +++ /dev/null @@ -1,2508 +0,0 @@ -import logging -import numpy as np -import pysynphot -import os -import glob -from astropy.io import fits -from astropy.table import Table, Column -import pysynphot -import time -import pdb -import warnings - -log = logging.getLogger('atmospheres') - -def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4): - """ - Given atmosphere model, get temperature and gravity bounds - """ - # Open catalog fits file and break out row indices - catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) - - teff_arr = [] - z_arr = [] - logg_arr = [] - for cur_row_index in range(len(catalog)): - index = catalog['INDEX'][cur_row_index] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Filter by metallicity. Will chose the closest metallicity to desired input - metal_list = np.unique(np.array(z_arr)) - metal_idx = np.argmin(np.abs(metal_list - metallicity)) - - z_filt = np.where(z_arr == metal_list[metal_idx]) - teff_arr = teff_arr[z_filt] - logg_arr = logg_arr[z_filt] - - # # Now find the closest atmosphere in parameter space to - # # the one we want. We'll find the match with the lowest - # # fractional difference - # teff_diff = (teff_arr - temperature) / temperature - # logg_diff = (logg_arr - gravity) / gravity - # - # diff_tot = abs(teff_diff) + abs(logg_diff) - # idx_f = np.argmin(diff_tot) - # - # temperature_new = teff_arr[idx_f] - # gravity_new = logg_arr[idx_f] - - # First check if temperature within bounds - temperature_new = temperature - if temperature > np.max(teff_arr): - temperature_new = np.max(teff_arr) - if temperature < np.min(teff_arr): - temperature_new = np.min(teff_arr) - - # If temperature within bounds, then check if metallicity within bounds - teff_diff = np.abs(teff_arr - temperature) - sorted_min_diffs = np.unique(teff_diff) - - ## Find two closest temperatures - teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] - teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] - - logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] - logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] - - ## Switch to most conservative bound of logg out of two closest temps - gravity_new = gravity - if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): - gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) - if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): - gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) - - # Print out changes, if any - if temperature_new != temperature: - teff_msg = 'Changing to T={0:6.0f} for T={1:6.0f} logg={2:4.2f}' - print( teff_msg.format(temperature_new, temperature, gravity)) - - if gravity_new != gravity: - logg_msg = 'Changing to logg={0:4.2f} for T={1:6.0f} logg={2:4.2f}' - print( logg_msg.format(gravity_new, temperature, gravity)) - - return (temperature_new, gravity_new) - -def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmosphere from the Kurucz pysnphot grid - (`Kurucz 1993 `_). - - Grid Range: - - * Teff: 3000 - 50000 K - * gravity: 0 - 5 cgs - * metallicity: -5.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - Always false for this particular function - """ - try: - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('k93models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Kurucz 1993 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmospheres from the pysynphot ATLAS9 atlas - (`Castelli & Kurucz 2004 `_). - - Grid Range: - - * Teff: 3500 - 50000 K - * gravity: 0 - 5.0 cgs - * [M/H]: -2.5 - 0.2 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('ck04models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - try: - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('nextgen', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find NextGen atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, - rebin=False): - """ - Return atmosphere from the pysynphot - `PHOENIX atlas `_. - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - try: - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - # Take care of atmospheres outside the catalog boundaries - logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' - if gravity > 4.3: - print( logg_msg.format(4.3, temperature, gravity)) - gravity = 4.3 - - if rebin: - sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, - verbose=False): - """ - For a given stellar atmosphere, get extract the closest possible match in - Teff/logg space. Note that this is different from the normal routine - which interpolates along the input grid to get final spectrum. We can't - do this here because the Fierro+15 atmosphere grid is so sparse - - rebin=True: pull from atmospheres at ck04model resolution. - - If verbose, print out the parameters of the match - """ - # Set up the proper root directory - if rebin == True: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' - else: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' - - # Read in catalog, extract atmosphere info - cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') - teff_arr = [] - z_arr = [] - logg_arr = [] - for ii in range(len(cat)): - index = cat['INDEX'][ii] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Now find the closest atmosphere in parameter space to - # the one we want. We'll find the match with the lowest - # fractional difference - teff_diff = (teff_arr - temperature) / temperature - logg_diff = (logg_arr - gravity) / gravity - - diff_tot = abs(teff_diff) + abs(logg_diff) - idx_f = np.where(diff_tot == min(diff_tot))[0][0] - - # Extract the filename of the best-match model and read as - # pysynphot object - infile = cat[idx_f]['FILENAME'].split('.') - spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the - # ATLAS models are in FLAM at the surface. So, we need to multiply the - # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. - # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file - t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') - tmp = np.where(t['col1'] == infile[0]) - - lum = t['col3'][tmp] * (3.839*10**33) # cgs - sigma = 5.6704 * 10**-5 # cgs - teff = teff_arr[idx_f] # cgs - - radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm - radius /= 3.08*10**18 # in pc - - - # Make the pysynphot spectrum - w = spec['Wavelength'] - f = spec['Flux'] * (1000 / radius)**2. - sp = pysynphot.ArraySpectrum(w,f) - - #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Print out parameters of match, if desired - if verbose: - print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) - print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - if rebin: - sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 30000) - gravity = log gravity (def = 4.14) - """ - sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): - """ - Return PHOENIX v16 atmospheres from - `Husser et al. 2013 `_. - - Models originally downloaded via `ftp `_. - Solar metallicity and [alpha/Fe] is used. - - Grid Range: - - * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K - * gravity: 0.0 - 6.0 cgs, steps of 0.5 - * [M/H]: -4.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - atm_model_name = 'phoenix_v16' - if rebin == True: - atm_model_name = 'phoenix_v16_rebin' - - - # Extract atmosphere. If that fails, then check bounds and try again - try: - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_model_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): - """ - Return atmosphere from CIFIST2011_2015 grid - (`Allard et al. 2012 `_, - `Baraffe et al. 2015 `_ ) - - Grid originally downloaded from `website `_. - - Grid Range: - - * Teff: 1200 - 7000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_2015_rebin' - else: - atm_name = 'BTSettl_2015' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - #print(dir(obj)) - - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): - """ - Return atmosphere from CIFIST2011 grid - (`Allard et al. 2012 `_) - - Grid originally downloaded `here `_ - - Notes - ------ - Grid Range: - - * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - - Teff and gravity ranges depend on metallicity: - - [M/H] = -2.5 - - * Teff: 2600 - 4600 K - * gravity: 4.5 - 5.5 - - [M/H] = -2.0 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.5 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.0 - - * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 - - [M/H] = -0.5 - - * Teff: 1000 -7000 - * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 - - [M/H] = 0 - - * Teff: 750 - 7000 - * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 - - [M/H] = 0.5 - - * Teff: 1000 - 5000 - * gravity: 3.5 - 5.0 - - - Alpha enhancement: - - * [M/H]= -0.0, +0.5 no anhancement - * [M/H]= -0.5 with [alpha/H]=+0.2 - * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - **PRINT STATEMENTS TO DEBUG - **check get_atmosphere_bounds - **comment out try/except clause and check break - """ - if rebin == True: - atm_name = 'BTSettl_rebin' - else: - atm_name = 'BTSettl' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - -def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): - """ - Return atmosphere from Meisner2023 grid - (`Meisner et al. 2023 `_) - - Grid originally downloaded `here `_ - - Grid range: - * Teff = 250 - 1200 K - * gravity: 2.5 - 5.5 cgs (in steps of 0.5) - * [M/H] = -1.0, -0.5, 0, +0, +0.3 - - """ - if rebin == True: - atm_name = 'Meisner2023_rebin' - else: - atm_name = 'Meisner2023' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Meisner2023 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): - """ - Return atmosphere from Phillips et al., 2020 using ATMO model - (`Phillips et al. 2020 `_) - - Grid originally downloaded `here `_ - - Grid Range: - * Teff: 200 - 3000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - """ - if rebin == True: - atm_name = 'Phillips2020_rebin' - else: - atm_name = 'Phillips2020' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Phillips2020 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): - """ - Return white dwarf atmospheres from - `Koester et al. 2010 `_ - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. - - Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 - """ - try: - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_atlas_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and phoenixV16. - - Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_meisner_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and Meisner2023. - - Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_meisner', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl-Meisner merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -#---------------------------------------------------------------------# -def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, - rebin=True): - """ - Return a stellar atmosphere from a suite of different model grids, - depending on the input temperature, (all values in K). - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - - Notes - ----- - The underlying stellar model grid used changes as a function of - stellar temperature (in K): - - * T > 20,000: ATLAS - * 5500 <= T < 20,000: ATLAS - * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge - * 3800 <= T < 5000: PHOENIXv16 - - For T < 3800, there is an additional gravity and metallicity - dependence: - - If T < 3800 and [M/H] = 0: - - * T < 3800, logg < 2.5: PHOENIX v16 - * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge - * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - * 1200 < T <= 1000, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge - * 1000 < T <= 250, logg > 2.5: Meisner2023 - - Otherwise, if T < 3800 and [M/H] != 0: - - * T < 3800: PHOENIX v16 - - References: - - * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) - * PHOENIXv16 (`Husser et al. 2013 `_) - * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) - * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) - - LTE WARNING: - - The ATLAS atmospheres are calculated with LTE, and so they - are less accurate when non-LTE conditions apply (e.g. T > 20,000 - K). Ultimately we'd like to add a non-LTE atmosphere grid for - the hottest stars in the future. - - HOW BOUNDARIES BETWEEN MODELS ARE TREATED: - - At the boundary between two models grids a temperature range is defined - where the resulting atmosphere is a weighted average between the two - grids. Near one boundary one model - is weighted more heavily, while at the other boundary the other - model is weighted more heavily. These are calculated in the - temperature ranges where we switch between model grids, to - ensure a smooth transition. - """ - # For T < 3800, atmosphere depends on metallicity + gravity. - # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is - # currently available here, so if non-solar metallicity, just stick with - # the Phoenix grid - if (temperature < 1000): - if verbose: - print( 'Meisner2023 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 1200) & (temperature >= 1000): - if (gravity >= 3.5): - if verbose: - print( 'BTSettl/Meisner2023 merged atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - if (gravity < 3.5) & (gravity >=2.5): - if verbose: - print( 'Meisner2023 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity == 0): - # High gravity are in BTSettl regime - if (temperature <= 3200) & (gravity > 2.5): - if verbose: - print( 'BTSettl_2015 atmosphere') - return get_BTSettl_2015_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): - if verbose: - print( 'BTSettl/Phoenixv16 merged atmosphere') - return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - # Low gravity is PHOENIX regime - if gravity <= 2.5: - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity != 0): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - # For T > 3800, no metallicity or gravity dependence - if (temperature >= 3800) & (temperature < 5000): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 5000) & (temperature < 5500): - if verbose: - print( 'ATLAS/Phoenix merged atmosphere') - return get_atlas_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if (temperature >= 5500) & (temperature < 20000): - if verbose: - print( 'ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if temperature >= 20000: - if verbose: - print( 'Still ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - #print('CMFGEN') - #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, - # temperature=temperature, - # gravity=gravity) - - - - -def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Return the white dwarf atmosphere from - `Koester et al. 2010 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('wdKoester atmosphere') - - return get_wdKoester_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere. - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - - -def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): - """ - Return the brown dwarf atmosphere from - `Meisner et al. 2023 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('Meisner2023 atmosphere') - - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - -def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, - verbose=False, rebin=None, - wave_min=500, wave_max=50_000, wave_num=20_000): - """ - Return a blackbody spectrum - - Parameters - ---------- - temperature: float, default=20_000 - The stellar temperature, in units of K - wave_min: float, default=500 - Sets the minimum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_max: float, default=50_000 - Sets the maximum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_num: int, default=20_000 - Sets the number of wavelength points in the wavelength range - Note: the wavelength range is evenly spaced in log space - """ - if ((metallicity is not None) or (gravity is not None) or - (rebin is not None)): - warnings.warn( - 'Only `temperature` keyword is used for black-body atmosphere' - ) - - if verbose: - print('Black-body atmosphere') - - # Modify pysynphot's default waveset to specified bounds - pysynphot.refs.set_default_waveset( - minwave=wave_min, maxwave=wave_max, num=wave_num - ) - - # Get black-body atmosphere for specified temperature from pysynphot - bbspec = pysynphot.spectrum.BlackBody(temperature) - - # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` - bbspec.convert('flam') - - # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. - # Need to remove this normalization for SPISEA by multiplying bbspec - # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 - bbspec *= (1000 * 3.086e18 / 6.957e10)**2 - - return bbspec - - -#--------------------------------------# -# Atmosphere formatting functions -#--------------------------------------# - -def download_CMFGEN_atmospheres(Table_rot, Table_norot): - """ - Downloads CMFGEN models from - https://sites.google.com/site/fluxesandcontinuum/home; - these contain continuum as well as lines. - - Table_rot, Table_norot are tables with the file prefixes - and model atmosphere parameters, taken by hand from the - Fierro+15 paper - - Website addresses are hardcoded - - Puts downloaded models in the current working directory. - """ - print( 'WARNING: THIS DOES NOT COMPLETELY WORK') - print( '**********************') - t_rot = Table.read(Table_rot, format='ascii') - t_norot = Table.read(Table_norot, format='ascii') - - tables = [t_rot, t_norot] - filenames = [t_rot['col1'], t_norot['col1']] - - # Hardcoded list of webiste addresses - web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' - web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' - web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', - web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', - web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', - web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', - web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] - # Array of masses that matches the website addresses - mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) - - # Loop through rotating and unrotating case. First loop is rot, second unrot - for i in range(2): - # Extract masses from filenames - masses = [] - for j in filenames[i]: - tmp = j.split('m') - mass = float(tmp[1][:-1]) - masses.append(mass) - - # Download the models webpage by webpage. A bit tricky because masses - # change slightly within a particular website. THIS IS WHAT FAILS - for j in range(len(web)): - if j == 0: - good = np.where( (masses <= mass_arr[j]) ) - else: - g = j - 1 - good = np.where( (masses <= mass_arr[j]) & - (masses > mass_arr[g]) ) - # Use wget command to pull down the files, and unzip them - for k in good[0]: - full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) - os.system('wget ' + full) - os.system('unzip '+ filenames[i][k] + '.flx.zip') - - return - -def organize_CMFGEN_atmospheres(path_to_dir): - """ - Organize CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html) - into rot and noRot directories - - path_to_dir is from current working directory to directory - containing the downloaded models. Assumed that models - and tables describing parameters are in this directory. - - Tables describing parameters MUST be named Table_rot.txt, - Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. - These are located in same original directory as atmosphere files - - Will separate files into 2 subdirectories, one rotating and - the other non-rotating - - *Can't have any other files starting with "t" in model directory to start!* - """ - # First, record current working directory to return to later - start_dir = os.getcwd() - - # Enter atmosphere directory, collect rotating and non-rotating - # file names (assumed to all start with "t") - os.chdir(path_to_dir) - rot_models = glob.glob("t*r.flx*") - noRot_models = glob.glob("t*n.flx*") - - # Separate into different subdirectories - if os.path.exists('cmfgenF15_rot'): - pass - else: - os.mkdir('cmfgenF15_rot') - os.mkdir('cmfgenF15_noRot') - - for mod in rot_models: - cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) - os.system(cmd) - - for mod in noRot_models: - cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) - os.system(cmd) - - # Also move Tables with model parameters into correct directory - os.system('mv Table_rot.txt cmfgenF15_rot') - os.system('mv Table_noRot.txt cmfgenF15_noRot') - - # Return to original directory - os.chdir(start_dir) - - return - -def make_CMFGEN_catalog(path_to_dir): - """ - Create cdbs catalog.fits of CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html). - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to directory - containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, - needs to be a Table*.txt file which contains the parameters for all of the - original models, since params in filename are not precise enough - - Will create catalog.fits file in atmosphere directory with - description of each model - - *Can't have any other files starting with "t" in model directory to start!* - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere - # Note: can't rely on filename for this because not precise enough!! - - #---------OLD: GETTING PARAMS FROM FILENAME-------# - # Collect file names (assumed to all start with "t") - #files = glob.glob("t*") - #for name in files: - # tmp = name.split('l') - # temp = float(tmp[0][1:]) * 100.0 # In kelvin - - # lumtmp = tmp[1].split('_') - # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun - - # mass = float(lumtmp[0][5:-1]) # In M_sun - - # Need to calculate log g from T and L (cgs) - # lum_sun = 3.846 * 10**33 # erg/s - # M_sun = 2 * 10**33 # g - # G_si = 6.67 * 10**(-8) # cgs - # sigma_si = 5.67 * 10**(-5) # cgs - - # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ - # (lum * lum_sun) - # logg = np.log10(g) - #---------------------------------------------------# - - # Read table with atmosphere params - table = glob.glob('Table_*') - t = Table.read(table[0], format = 'ascii') - names = t['col1'] - temps = t['col2'] - logg = t['col4'] - - # Create catalog.fits file - index_str = [] - name_str = [] - for i in range(len(names)): - index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) - - #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# - #path = path_to_dir + '/' + names[i] - path = names[i] + '.fits[Flux]' - - index_str.append(index) - name_str.append(path) - - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits') - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): - """ - Code to put cmfgen models into cdbs format and adds proper unit keyword in - fits header. Save as fits file - - path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot - directory with the *.flx models. Note that these files have already been - organized using organize_CMFGEN_atmospheres code. - - path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or - cmfgen_norot directory. Will copy new fits files to this directory. - This directory must already exist! - """ - # Save starting directory for later, move into path_to_dir directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('*.flx') - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - # Open file, extract useful info - t = Table.read(i, format='ascii') - wave = t['col1'] - flux = t['col2'] # Flux is already in erg/cm^2/s/A - - # Need to eliminate duplicate entries (pysynphot crashes) - unique = np.unique(wave, return_index=True) - wave = wave[unique[1]] - flux = flux[unique[1]] - - # Make fits table from individual columns. - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - #Adding unit keywords - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - prihdu = fits.PrimaryHDU() - - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i[:-4]+'.fits', overwrite=True) - - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Return to original directory, copy over new .fits files to cdbs directory - os.chdir(start_dir) - cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) - os.system(cmd) - - return - -def rebin_cmfgen(cdbs_path, rot=True): - """ - Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; - this makes spectrophotometry MUCH faster - - cdbs_path: path to cdbs directory - rot=True for rotating models (cmfgen_rot), False for non-rotating models - - makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing cmfgen model; we will steal the header. - # Also define paths to new rebin directories - if rot == True: - tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' - path = cdbs_path+'/grid/cmfgen_rot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_rot/' - else: - tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' - path = cdbs_path+'/grid/cmfgen_norot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_norot/' - - cmfgen_hdu = fits.open(tmp) - header0 = cmfgen_hdu[0].header - # Create rebin directories if they don't already exist. Copy over - # catalog.fits file from original directory (will be the same) - if not os.path.exists(path): - os.mkdir(path) - cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) - os.system(cmd) - - # Read in the catalog.fits file - cat = fits.getdata(orig_path + 'catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - # First column in new files will be for [atlas] wavelength - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - - # For each catalog.fits entry, read the unbinned spectrum and rebin to - # the atlas resolution. Make a new fits file in rebin directory - count = 0 - for ff in range(len(files_all)): - count += 1 - # Extract the temp, Z, logg - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - grav = float(vals[2]) - - # Fetch the spectrum - if rot == True: - sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) - else: - sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) - - # Rebin - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - # Make the FITS file from the columns with header - cols = fits.ColDefs([c0,c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write hdu to new directory with same filename - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(path+files_all[ff]) - - print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) - return - - -def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): - """ - Construct the Phoenix Husser+13 atmopsheres for each model. Combines the - fluxes from the *HiRES.fits files and the wavelengths of the - WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. - - path_to_dir is the path to the directory containing all of the downloaded - files - - met_str is the name of the current metallicity - - Creates new fits files for each atmosphere: phoenix_.fits, - which contains columns for the log g (column header = g#.#). Puts - atmospheres in new directory phoenixm00 - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the current metallicity subdirectory - sub_dir = '../phoenix{0}'.format(met_str) - if os.path.exists(sub_dir): - pass - else: - os.mkdir(sub_dir) - - # Extract wavelength array, make column for later - wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') - wave = wavefile[0].data - wavefile.close() - wave_col = Column(wave, name = 'WAVELENGTH') - - # Create temp array for Husser+13 grid (given in paper) - temp_arr = np.arange(2300, 7001, 100) - temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) - - print( 'Looping though all temps') - # For each temp, build file containing the flux for all gravities - i = 0 - for temp in temp_arr: - files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) - files.sort() - # Start the table with the wavelength column - t = Table() - t.add_column(wave_col) - for f in files: - # Extract the logg out of filename - logg = f[9:13] - - # Extract fluxes from file - spectrum = fits.open(f) - flux = spectrum[0].data - spectrum.close() - - # Make Column object with fluxes, add to table - col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) - t.add_column(col) - - # Now, construct final fits file for the given temp - outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) - t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) - - # Progress counter for user - i += 1 - print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) - - # Return to original directory - os.chdir(start_dir) - return - -def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): - """ - Makes catalog.fits file for Husser+13 phoenix models. Assumes that - organize_PHOENIXv16_atmospheres has been run already, and that the models lie - in subdirectory phoenix[met_str]. - - path_to_directory is the path to the directory with the reformatted - models (i.e. the output from construct_atmospheres, phoenix[met_str]) - - Puts catalog.fits file in directory the user starts in - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Extract metallicity from metallicity string - met = float(met_str[1]) + (float(met_str[2]) * 0.1) - if 'm' in met_str: - met *= -1. - - # Collect the filenames. Each is a unique temp with many different log g's - files = glob.glob('phoenix*.fits') - files.sort() - - # Create the catalog.fits file, row by row - index_arr = [] - filename_arr = [] - for i in files: - # Get log g values from the column header the file - t = Table.read(i, format='fits') - keys = t.keys() - logg_vals = keys[1:] - - # Extract temp from filename - name = i.split('_') - temp = float(name[1][:-5]) - for j in logg_vals: - logg = float(j[1:]) - index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) - filename = path_to_dir + i + '[' + j + ']' - # Add row to table - index_arr.append(index) - filename_arr.append(filename) - - catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) - - # Return to starting directory, write catalog - os.chdir(start_dir) - - if os.path.exists('catalog.fits'): - from astropy.table import vstack - - prev_catalog = Table.read('catalog.fits', format='fits') - joined_catalog = vstack([prev_catalog, catalog]) - - joined_catalog.write('catalog.fits', format='fits', overwrite=True) - else: - catalog.write('catalog.fits', format='fits', overwrite=True) - - return - -def cdbs_PHOENIXv16(path_to_cdbs_dir): - """ - Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily - consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] - and adding the appropriate keywords to the fits header. - - path_to_cdbs_dir goes from current working directory to phoenix[met] directory - in cdbs/grids/phoenix_v16. Note that these files have already been organized - using organize_PHOENIXv16_atmospheres code. - - Overwrites original files in directory - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_cdbs_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('phoenix*.fits') - - ## Need to sort filenames; glob doesn't always give them in order - files.sort() - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - - # Read in current FITS table - cur_table = Table.read(i, format='fits') - - cur_table.columns[0].name = 'Wavelength' - - num_cols = len(cur_table.colnames) - - # Multiplying each flux column by 10^-8 for conversion - for cur_col_index in range(1, num_cols, 1): - cur_col_name = cur_table.colnames[cur_col_index] - cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 - - - # Construct new FITS file based on old one - hdu = fits.open(i) - header_0 = hdu[0].header - header_1 = hdu[1].header - sci = hdu[1].data - - tbhdu = fits.table_to_hdu(cur_table) - - # Copying over the older headers, adding unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - tbhdu.header['TUNIT3'] = 'FLAM' - tbhdu.header['TUNIT4'] = 'FLAM' - tbhdu.header['TUNIT5'] = 'FLAM' - tbhdu.header['TUNIT6'] = 'FLAM' - tbhdu.header['TUNIT7'] = 'FLAM' - tbhdu.header['TUNIT8'] = 'FLAM' - tbhdu.header['TUNIT9'] = 'FLAM' - tbhdu.header['TUNIT10'] = 'FLAM' - tbhdu.header['TUNIT11'] = 'FLAM' - tbhdu.header['TUNIT12'] = 'FLAM' - tbhdu.header['TUNIT13'] = 'FLAM' - tbhdu.header['TUNIT14'] = 'FLAM' - - # Construct and write out final FITS file - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i, overwrite=True) - - hdu.close() - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Change back to starting directory - os.chdir(start_dir) - - return - -def rebin_phoenixV16(cdbs_path): - """ - Rebin phoenixV16 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: phoenix_v16_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - ## (This assumes that at least 'm00' metallicity exists) - tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/phoenix_v16_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - temp_arr = np.zeros(len(files_all), dtype=float) - logg_arr = np.zeros(len(files_all), dtype=float) - metal_arr = np.zeros(len(files_all), dtype=float) - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - - temp_arr[ff] = float(vals[0]) - metal_arr[ff] = float(vals[1]) - logg_arr[ff] = float(vals[2]) - - - metal_uniq = np.unique(metal_arr) - temp_uniq = np.unique(temp_arr) - - for mm in range(len(metal_uniq)): - metal = metal_uniq[mm] # metallicity - - # Construct str for metallicity (for appropriate directory name) - met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) - if metal > 0: - met_str = 'p' + met_str - else: - met_str = 'm' + met_str - - # Make directory for current metallicity if it does not exist yet - if not os.path.exists(path + 'phoenix' + met_str): - os.mkdir(path + 'phoenix' + met_str) - - for tt in range(len(temp_uniq)): - temp = temp_uniq[tt] # temperature - - # Pick out the list of gravities for this T, Z combo - idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] - logg_exist = logg_arr[idx] - - # All gravities will go in one file. Here is the output - # file name. - outfile = path + files_all[idx[0]].split('[')[0] - - ## If the rebinned file already exists, continue - if os.path.exists(outfile): - continue - - # Build a columns array. One column for each gravity. - cols_arr = [] - - # Make the wavelength column, which is first in the cols array. - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - cols_arr.append(c0) - - for gg in range(len(logg_exist)): - grav = logg_exist[gg] # gravity - - # Fetch the spectrum - sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Store the spectrum - name = 'g{0:3.1f}'.format(grav) - col = fits.Column(name=name, format='E', array=flux_rebin) - cols_arr.append(col) - - - # Make the FITS file from the columns with header. - cols = fits.ColDefs(cols_arr) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - for gg in range(len(logg_exist)): - tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' - - # Write hdu - finalhdu = fits.HDUList([prihdu, tbhdu]) - # don't have overwrite to protect original files. - finalhdu.writeto(outfile) - - print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) - - - return - - -def rebin_spec(wave, specin, wavnew): - """ - Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: - http://www.astrobetter.com/blog/2013/08/12/ - python-tip-re-sampling-spectra-with-pysynphot/ - """ - spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) - f = np.ones(len(wave)) - filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') - obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') - - return obs.binflux - -def organize_BTSettl_2015_atmospheres(path_to_dir): - """ - Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the BTSettl subdirectory - if not os.path.exists('BTSettl_2015'): - os.mkdir('BTSettl_2015') - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.spec.fits') - - for i in files: - hdu = fits.open(i) - spec = hdu[1].data - header_0 = hdu[0].header - header_1 = hdu[1].header - - wave = spec.field(0) - flux = spec.field(1) - - # Get units right: convert wave from microns to Angstroms, - # flux from W /m^2/ micron to erg/s/cm^2/A - wave_new = wave * 10**4 - flux_new = flux * 10**(-1) - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave_new) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) - - hdu.close() - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_BTSettl_2015_catalog(path_to_dir): - """ - Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*spec.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('-') - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: BTSettl_2015_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - phoenix_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/BTSettl_2015_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning BTSettl spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - -def make_wavelength_unique(files, dirname): - """ - Helper function to go through each BTSettl spectrum and ensure that - each wavelength point is unique. This is required for rebinning to work. - - - files: list of files to run this analysis on - """ - # Loop through each file, find fix repeated wavelength entries if necessary - for i in files: - t = Table.read('{0}/{1}'.format(dirname,i), format='fits') - test = np.unique(t['Wavelength'], return_index=True) - - if len(t) != len(test[0]): - t = t[test[1]] - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - # Also make sure wavelength is monotonic. If it is not, then it is - # a sign that the wavelengths are out of order - diff = np.diff(t['Wavelength']) - bad = np.where(diff < 0) - if len(bad[0]) > 0: - t.sort('Wavelength') - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - print('Done {0}'.format(i)) - - return - -def organize_BTSettl_atmospheres(): - """ - Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). - The code expects tp be run in cdbs/grid/BTSettl, and expects that the - individual model files have been downloaded from online - (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] - - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, - # for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.txt') - count=0 - for jj in files: - t = Table.read(jj, format='ascii') - # First, trim the wavelengths to a more reasonable wavelength range - good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) - t = t[good] - - # Convert flux units to Flam (Ergs/sec/cm**2/A) - flux_new = 10**(t['col2'] - 8.0) - - # Save the file as a fits file - c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Now, clean up all the files made when unzipping the spectra - cmd1 = 'rm *.bz2' - cmd2 = 'rm *.tmp' - #cmd3 = 'rm *.txt' - os.system(cmd1) - os.system(cmd2) - #os.system(cmd3) - print('==============================') - print('Done {0}'.format(ii)) - print('==============================') - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_BTSettl_catalog(): - """ - Create cdbs catalog.fits of BTSettl grid. - THIS IS STEP 2, after organize_BTSettl_atmospheres has - been run. - - Code expects to be run in cdbs/grid/BTSettl - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btp05'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('*.fits') - - # Construct the metallicity val - if 'm' in ii: - metal_flag = -1 * float(ii[3:])*0.1 - else: - metal_flag = float(ii[3:])*0.1 - - # Now collect the info from the files - for jj in files: - tmp = jj.split('-') - - if metal_flag >= 0: - temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin - try: - logg = float(tmp[1]) - except: - logg = float(tmp[1].split('+')[0]) - else: - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - # Go back to original directory to move to next metallicity - print('Done {0}'.format(ii)) - os.chdir(start_dir) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl(make_unique=False): - """ - Rebin BTSettle models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: BTSettl_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create cdbs/grid directory for rebinned models - path = 'BTSettl_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata('BTSettl/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - #==============================# - #tmp = [] - #for ii in files_all: - # if ii.startswith('btp00'): - # tmp.append(ii) - #files_all = tmp - #=============================# - - print( 'Rebinning BTSettl spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'BTSettl') - print('Done') - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - try: - sp = pysynphot.Icat('BTSettl', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - except: - pdb.set_trace() - orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) - outfile = path + files_all[ff].split('[')[0] - cmd = 'cp {0} {1}'.format(orig_file, outfile) - os.system(cmd) - - print('Done {0} of {1}'.format(ff, len(files_all))) - - return - -def organize_all_Meisner2023_atmospheres(): - """ - Construct cdbs-ready atmospheres for the Meisner2023 grid. - The code expects tp be run in cdbs/grid/Meisner2023, and expects that the - individual model files have been downloaded from online - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['mm10', 'mm05', 'mp00', 'mp03'] - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Save as a fits file, for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.fits') - count=0 - for jj in files: - # Open each .fits file and read the data - with fits.open(jj) as hdul: - data = hdul[1].data - wavelength = data['Wavelength'] - flux = data['Flux'] - - # Make flux independent of R&D - flux_new = flux / 5e-20 - - # Create new columns with desired format - c0 = fits.Column(name='Wavelength', format='D', array=wavelength) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write the new fits table in the cdbs directory - output_filename = '{0}.fits'.format(jj[:-5]) # Removing the original .fits extension - hdu_new.writeto(output_filename, overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_Meisner2023_catalog(): - """ - Create cdbs catalog.fits of Meisner2023 grid. - THIS IS STEP 2, after organize_Meisner2023_atmospheres has - been run. - - Code expects to be run in cdbs/grid/Meisner2023 - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['mm10', 'mm05', 'mp00', 'mp03'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('spec_jwst_*.fits') - - for jj in files: - # Parse temperature, log(g), and metallicity from filename - temp_str = jj.split('_')[2] - logg_str = jj.split('_')[3] - metal_str = jj.split('_')[4] - - # Extract temperature, surface gravity, and metallicity - temp = float(temp_str[1:]) # Temperature in Kelvin - logg = float(logg_str[1:]) # Surface gravity log(g) - - # Build metallicity value - if metal_str.startswith('m'): - metallicity = -1 * float(metal_str[1:]) - else: - metallicity = float(metal_str[1:]) - - # Construct index and filename strings - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metallicity, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - print('Processed directory:', ii) - os.chdir(start_dir) - - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_Meisner2023(make_unique=False): - """ - Rebin Meisner2023 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: Meisner2023_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create a directory for rebinned Meisner2023 models - rebin_path = 'Meisner2023_rebin/' - if not os.path.exists(rebin_path): - os.mkdir(rebin_path) - - # Load the catalog.fits file and extract all spectra file paths - cat = Table.read('Meisner2023/catalog.fits') - files_all = [cat[ii]['FILENAME'].split('[')[0] for ii in range(len(cat))] - - print('Rebinning Meisner2023 spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'Meisner2023') - print('Done') - - for ff, file in enumerate(files_all): - vals = cat[ff]['INDEX'].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the Meisner2023 spectrum and rebin its flux - try: - sp = pysynphot.Icat('Meisner2023', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Create the output FITS file - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write the new rebinned file in the Meisner2023_rebin directory - outfile = os.path.join(rebin_path, os.path.basename(file)) - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - except Exception as e: - print(f"Error processing {file}: {e}") - orig_file = os.path.join('Meisner2023', file) - outfile = os.path.join(rebin_path, os.path.basename(file)) - os.system(f'cp {orig_file} {outfile}') - - print('Done {0} of {1}'.format(ff + 1, len(files_all))) - - return - - - -def organize_WDKoester_atmospheres(path_to_dir): - """ - Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.dk.dat.txt') - - for i in files: - data = Table.read(i, format='ascii') - - wave = data['col1'] # angstrom - flux = data['col2'] # erg/s/cm^2/A - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) - - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_WDKoester_catalog(path_to_dir): - """ - Create cdbs catalog.fits of wdKoester grid. - THIS IS STEP 2, after organize_WDKoester_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*dk.dat.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('.') - tmp2 = tmp[0].split('_') - temp = float(tmp2[0][2:]) # Kelvin - logg = float(tmp2[1]) / 100.0 # log(g) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin wdKoester models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: wdKoester_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing model; we will steal the header - tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' - wdkoester_hdu = fits.open(tmp) - header0 = wdkoester_hdu[0].header - wdkoester_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/wdKoester_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning wdKoester spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the wdKoester spectrum, rebin flux - sp = pysynphot.Icat('wdKoester', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - - diff --git a/spisea/atmospheres_REMOTE_58456.py b/spisea/atmospheres_REMOTE_58456.py deleted file mode 100644 index 9867db81..00000000 --- a/spisea/atmospheres_REMOTE_58456.py +++ /dev/null @@ -1,2172 +0,0 @@ -import logging -import numpy as np -import pysynphot -import os -import glob -from astropy.io import fits -from astropy.table import Table, Column -import pysynphot -import time -import pdb -import warnings - -log = logging.getLogger('atmospheres') - -def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Given atmosphere model, get temperature and gravity bounds - """ - # Open catalog fits file and break out row indices - catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) - - teff_arr = [] - z_arr = [] - logg_arr = [] - for cur_row_index in range(len(catalog)): - index = catalog['INDEX'][cur_row_index] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Filter by metallicity. Will chose the closest metallicity to desired input - metal_list = np.unique(np.array(z_arr)) - metal_idx = np.argmin(np.abs(metal_list - metallicity)) - metallicity_new = metal_list[metal_idx] - - z_filt = np.where(z_arr == metal_list[metal_idx]) - teff_arr = teff_arr[z_filt] - logg_arr = logg_arr[z_filt] - - # # Now find the closest atmosphere in parameter space to - # # the one we want. We'll find the match with the lowest - # # fractional difference - # teff_diff = (teff_arr - temperature) / temperature - # logg_diff = (logg_arr - gravity) / gravity - # - # diff_tot = abs(teff_diff) + abs(logg_diff) - # idx_f = np.argmin(diff_tot) - # - # temperature_new = teff_arr[idx_f] - # gravity_new = logg_arr[idx_f] - - # First check if temperature within bounds - temperature_new = temperature - if temperature > np.max(teff_arr): - temperature_new = np.max(teff_arr) - if temperature < np.min(teff_arr): - temperature_new = np.min(teff_arr) - - # If temperature within bounds, then check if metallicity within bounds - teff_diff = np.abs(teff_arr - temperature) - sorted_min_diffs = np.unique(teff_diff) - - ## Find two closest temperatures - teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] - teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] - - logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] - logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] - - ## Switch to most conservative bound of logg out of two closest temps - gravity_new = gravity - if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): - gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) - if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): - gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) - - if verbose: - # Print out changes, if any - if temperature_new != temperature: - teff_msg = 'Changing to T={0:6.0f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( teff_msg.format(temperature_new, metallicity, temperature, gravity)) - - if gravity_new != gravity: - logg_msg = 'Changing to logg={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( logg_msg.format(gravity_new, metallicity, temperature, gravity)) - - if metallicity_new != metallicity: - logg_msg = 'Changing to met={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( logg_msg.format(metallicity_new, metallicity, temperature, gravity)) - - return (temperature_new, gravity_new, metallicity_new) - -def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmosphere from the Kurucz pysnphot grid - (`Kurucz 1993 `_). - - Grid Range: - - * Teff: 3000 - 50000 K - * gravity: 0 - 5 cgs - * metallicity: -5.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - Always false for this particular function - """ - try: - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('k93models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Kurucz 1993 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmospheres from the pysynphot ATLAS9 atlas - (`Castelli & Kurucz 2004 `_). - - Grid Range: - - * Teff: 3500 - 50000 K - * gravity: 0 - 5.0 cgs - * [M/H]: -2.5 - 0.2 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('ck04models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - try: - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('nextgen', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find NextGen atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, - rebin=False): - """ - Return atmosphere from the pysynphot - `PHOENIX atlas `_. - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - try: - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - # Take care of atmospheres outside the catalog boundaries - logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' - if gravity > 4.3: - if verbose: - print( logg_msg.format(4.3, temperature, gravity)) - gravity = 4.3 - - if rebin: - sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, - verbose=False): - """ - For a given stellar atmosphere, get extract the closest possible match in - Teff/logg space. Note that this is different from the normal routine - which interpolates along the input grid to get final spectrum. We can't - do this here because the Fierro+15 atmosphere grid is so sparse - - rebin=True: pull from atmospheres at ck04model resolution. - - If verbose, print out the parameters of the match - """ - # Set up the proper root directory - if rebin == True: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' - else: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' - - # Read in catalog, extract atmosphere info - cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') - teff_arr = [] - z_arr = [] - logg_arr = [] - for ii in range(len(cat)): - index = cat['INDEX'][ii] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Now find the closest atmosphere in parameter space to - # the one we want. We'll find the match with the lowest - # fractional difference - teff_diff = (teff_arr - temperature) / temperature - logg_diff = (logg_arr - gravity) / gravity - - diff_tot = abs(teff_diff) + abs(logg_diff) - idx_f = np.where(diff_tot == min(diff_tot))[0][0] - - # Extract the filename of the best-match model and read as - # pysynphot object - infile = cat[idx_f]['FILENAME'].split('.') - spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the - # ATLAS models are in FLAM at the surface. So, we need to multiply the - # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. - # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file - t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') - tmp = np.where(t['col1'] == infile[0]) - - lum = t['col3'][tmp] * (3.839*10**33) # cgs - sigma = 5.6704 * 10**-5 # cgs - teff = teff_arr[idx_f] # cgs - - radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm - radius /= 3.08*10**18 # in pc - - - # Make the pysynphot spectrum - w = spec['Wavelength'] - f = spec['Flux'] * (1000 / radius)**2. - sp = pysynphot.ArraySpectrum(w,f) - - #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Print out parameters of match, if desired - if verbose: - print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) - print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - if rebin: - sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 30000) - gravity = log gravity (def = 4.14) - """ - sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): - """ - Return PHOENIX v16 atmospheres from - `Husser et al. 2013 `_. - - Models originally downloaded via `ftp `_. - Solar metallicity and [alpha/Fe] is used. - - Grid Range: - - * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K - * gravity: 0.0 - 6.0 cgs, steps of 0.5 - * [M/H]: -4.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - atm_model_name = 'phoenix_v16' - if rebin == True: - atm_model_name = 'phoenix_v16_rebin' - - - # Extract atmosphere. If that fails, then check bounds and try again - try: - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_model_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): - """ - Return atmosphere from CIFIST2011_2015 grid - (`Allard et al. 2012 `_, - `Baraffe et al. 2015 `_ ) - - Grid originally downloaded from `website `_. - - Grid Range: - - * Teff: 1200 - 7000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_2015_rebin' - else: - atm_name = 'BTSettl_2015' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): - """ - Return atmosphere from CIFIST2011 grid - (`Allard et al. 2012 `_) - - Grid originally downloaded `here `_ - - Notes - ------ - Grid Range: - - * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - - Teff and gravity ranges depend on metallicity: - - [M/H] = -2.5 - - * Teff: 2600 - 4600 K - * gravity: 4.5 - 5.5 - - [M/H] = -2.0 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.5 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.0 - - * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 - - [M/H] = -0.5 - - * Teff: 1000 -7000 - * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 - - [M/H] = 0 - - * Teff: 750 - 7000 - * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 - - [M/H] = 0.5 - - * Teff: 1000 - 5000 - * gravity: 3.5 - 5.0 - - - Alpha enhancement: - - * [M/H]= -0.0, +0.5 no anhancement - * [M/H]= -0.5 with [alpha/H]=+0.2 - * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_rebin' - else: - atm_name = 'BTSettl' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): - """ - Return white dwarf atmospheres from - `Koester et al. 2010 `_ - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. - - Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 - """ - try: - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_atlas_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and phoenixV16. - - Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_BTSettl_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -#---------------------------------------------------------------------# -def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, - rebin=True): - """ - Return a stellar atmosphere from a suite of different model grids, - depending on the input temperature, (all values in K). - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - - Notes - ----- - The underlying stellar model grid used changes as a function of - stellar temperature (in K): - - * T > 20,000: ATLAS - * 5500 <= T < 20,000: ATLAS - * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge - * 3800 <= T < 5000: PHOENIXv16 - - For T < 3800, there is an additional gravity and metallicity - dependence: - - If T < 3800 and [M/H] = 0: - - * T < 3800, logg < 2.5: PHOENIX v16 - * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge - * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - - Otherwise, if T < 3800 and [M/H] != 0: - - * T < 3800: PHOENIX v16 - - References: - - * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) - * PHOENIXv16 (`Husser et al. 2013 `_) - * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) - - LTE WARNING: - - The ATLAS atmospheres are calculated with LTE, and so they - are less accurate when non-LTE conditions apply (e.g. T > 20,000 - K). Ultimately we'd like to add a non-LTE atmosphere grid for - the hottest stars in the future. - - HOW BOUNDARIES BETWEEN MODELS ARE TREATED: - - At the boundary between two models grids a temperature range is defined - where the resulting atmosphere is a weighted average between the two - grids. Near one boundary one model - is weighted more heavily, while at the other boundary the other - model is weighted more heavily. These are calculated in the - temperature ranges where we switch between model grids, to - ensure a smooth transition. - """ - # For T < 3800, atmosphere depends on metallicity + gravity. - # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is - # currently available here, so if non-solar metallicity, just stick with - # the Phoenix grid - if (temperature <= 3800) & (metallicity == 0): - # High gravity are in BTSettl regime - if (temperature <= 3200) & (gravity > 2.5): - if verbose: - print( 'BTSettl_2015 atmosphere') - return get_BTSettl_2015_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): - if verbose: - print( 'BTSettl/Phoenixv16 merged atmosphere') - return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - # Low gravity is PHOENIX regime - if gravity <= 2.5: - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity != 0): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - # For T > 3800, no metallicity or gravity dependence - if (temperature >= 3800) & (temperature < 5000): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 5000) & (temperature < 5500): - if verbose: - print( 'ATLAS/Phoenix merged atmosphere') - return get_atlas_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if (temperature >= 5500) & (temperature < 20000): - if verbose: - print( 'ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if temperature >= 20000: - if verbose: - print( 'Still ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - #print('CMFGEN') - #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, - # temperature=temperature, - # gravity=gravity) - - - - -def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Return the white dwarf atmosphere from - `Koester et al. 2010 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('wdKoester atmosphere') - - return get_wdKoester_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere. - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - -def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, - verbose=False, rebin=None, - wave_min=500, wave_max=50_000, wave_num=20_000): - """ - Return a blackbody spectrum - - Parameters - ---------- - temperature: float, default=20_000 - The stellar temperature, in units of K - wave_min: float, default=500 - Sets the minimum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_max: float, default=50_000 - Sets the maximum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_num: int, default=20_000 - Sets the number of wavelength points in the wavelength range - Note: the wavelength range is evenly spaced in log space - """ - if ((metallicity is not None) or (gravity is not None) or - (rebin is not None)): - warnings.warn( - 'Only `temperature` keyword is used for black-body atmosphere' - ) - - if verbose: - print('Black-body atmosphere') - - # Modify pysynphot's default waveset to specified bounds - pysynphot.refs.set_default_waveset( - minwave=wave_min, maxwave=wave_max, num=wave_num - ) - - # Get black-body atmosphere for specified temperature from pysynphot - bbspec = pysynphot.spectrum.BlackBody(temperature) - - # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` - bbspec.convert('flam') - - # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. - # Need to remove this normalization for SPISEA by multiplying bbspec - # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 - bbspec *= (1000 * 3.086e18 / 6.957e10)**2 - - return bbspec - - -#--------------------------------------# -# Atmosphere formatting functions -#--------------------------------------# - -def download_CMFGEN_atmospheres(Table_rot, Table_norot): - """ - Downloads CMFGEN models from - https://sites.google.com/site/fluxesandcontinuum/home; - these contain continuum as well as lines. - - Table_rot, Table_norot are tables with the file prefixes - and model atmosphere parameters, taken by hand from the - Fierro+15 paper - - Website addresses are hardcoded - - Puts downloaded models in the current working directory. - """ - print( 'WARNING: THIS DOES NOT COMPLETELY WORK') - print( '**********************') - t_rot = Table.read(Table_rot, format='ascii') - t_norot = Table.read(Table_norot, format='ascii') - - tables = [t_rot, t_norot] - filenames = [t_rot['col1'], t_norot['col1']] - - # Hardcoded list of webiste addresses - web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' - web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' - web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', - web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', - web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', - web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', - web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] - # Array of masses that matches the website addresses - mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) - - # Loop through rotating and unrotating case. First loop is rot, second unrot - for i in range(2): - # Extract masses from filenames - masses = [] - for j in filenames[i]: - tmp = j.split('m') - mass = float(tmp[1][:-1]) - masses.append(mass) - - # Download the models webpage by webpage. A bit tricky because masses - # change slightly within a particular website. THIS IS WHAT FAILS - for j in range(len(web)): - if j == 0: - good = np.where( (masses <= mass_arr[j]) ) - else: - g = j - 1 - good = np.where( (masses <= mass_arr[j]) & - (masses > mass_arr[g]) ) - # Use wget command to pull down the files, and unzip them - for k in good[0]: - full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) - os.system('wget ' + full) - os.system('unzip '+ filenames[i][k] + '.flx.zip') - - return - -def organize_CMFGEN_atmospheres(path_to_dir): - """ - Organize CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html) - into rot and noRot directories - - path_to_dir is from current working directory to directory - containing the downloaded models. Assumed that models - and tables describing parameters are in this directory. - - Tables describing parameters MUST be named Table_rot.txt, - Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. - These are located in same original directory as atmosphere files - - Will separate files into 2 subdirectories, one rotating and - the other non-rotating - - *Can't have any other files starting with "t" in model directory to start!* - """ - # First, record current working directory to return to later - start_dir = os.getcwd() - - # Enter atmosphere directory, collect rotating and non-rotating - # file names (assumed to all start with "t") - os.chdir(path_to_dir) - rot_models = glob.glob("t*r.flx*") - noRot_models = glob.glob("t*n.flx*") - - # Separate into different subdirectories - if os.path.exists('cmfgenF15_rot'): - pass - else: - os.mkdir('cmfgenF15_rot') - os.mkdir('cmfgenF15_noRot') - - for mod in rot_models: - cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) - os.system(cmd) - - for mod in noRot_models: - cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) - os.system(cmd) - - # Also move Tables with model parameters into correct directory - os.system('mv Table_rot.txt cmfgenF15_rot') - os.system('mv Table_noRot.txt cmfgenF15_noRot') - - # Return to original directory - os.chdir(start_dir) - - return - -def make_CMFGEN_catalog(path_to_dir): - """ - Create cdbs catalog.fits of CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html). - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to directory - containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, - needs to be a Table*.txt file which contains the parameters for all of the - original models, since params in filename are not precise enough - - Will create catalog.fits file in atmosphere directory with - description of each model - - *Can't have any other files starting with "t" in model directory to start!* - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere - # Note: can't rely on filename for this because not precise enough!! - - #---------OLD: GETTING PARAMS FROM FILENAME-------# - # Collect file names (assumed to all start with "t") - #files = glob.glob("t*") - #for name in files: - # tmp = name.split('l') - # temp = float(tmp[0][1:]) * 100.0 # In kelvin - - # lumtmp = tmp[1].split('_') - # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun - - # mass = float(lumtmp[0][5:-1]) # In M_sun - - # Need to calculate log g from T and L (cgs) - # lum_sun = 3.846 * 10**33 # erg/s - # M_sun = 2 * 10**33 # g - # G_si = 6.67 * 10**(-8) # cgs - # sigma_si = 5.67 * 10**(-5) # cgs - - # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ - # (lum * lum_sun) - # logg = np.log10(g) - #---------------------------------------------------# - - # Read table with atmosphere params - table = glob.glob('Table_*') - t = Table.read(table[0], format = 'ascii') - names = t['col1'] - temps = t['col2'] - logg = t['col4'] - - # Create catalog.fits file - index_str = [] - name_str = [] - for i in range(len(names)): - index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) - - #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# - #path = path_to_dir + '/' + names[i] - path = names[i] + '.fits[Flux]' - - index_str.append(index) - name_str.append(path) - - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits') - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): - """ - Code to put cmfgen models into cdbs format and adds proper unit keyword in - fits header. Save as fits file - - path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot - directory with the *.flx models. Note that these files have already been - organized using organize_CMFGEN_atmospheres code. - - path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or - cmfgen_norot directory. Will copy new fits files to this directory. - This directory must already exist! - """ - # Save starting directory for later, move into path_to_dir directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('*.flx') - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - # Open file, extract useful info - t = Table.read(i, format='ascii') - wave = t['col1'] - flux = t['col2'] # Flux is already in erg/cm^2/s/A - - # Need to eliminate duplicate entries (pysynphot crashes) - unique = np.unique(wave, return_index=True) - wave = wave[unique[1]] - flux = flux[unique[1]] - - # Make fits table from individual columns. - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - #Adding unit keywords - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - prihdu = fits.PrimaryHDU() - - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i[:-4]+'.fits', overwrite=True) - - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Return to original directory, copy over new .fits files to cdbs directory - os.chdir(start_dir) - cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) - os.system(cmd) - - return - -def rebin_cmfgen(cdbs_path, rot=True): - """ - Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; - this makes spectrophotometry MUCH faster - - cdbs_path: path to cdbs directory - rot=True for rotating models (cmfgen_rot), False for non-rotating models - - makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing cmfgen model; we will steal the header. - # Also define paths to new rebin directories - if rot == True: - tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' - path = cdbs_path+'/grid/cmfgen_rot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_rot/' - else: - tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' - path = cdbs_path+'/grid/cmfgen_norot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_norot/' - - cmfgen_hdu = fits.open(tmp) - header0 = cmfgen_hdu[0].header - # Create rebin directories if they don't already exist. Copy over - # catalog.fits file from original directory (will be the same) - if not os.path.exists(path): - os.mkdir(path) - cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) - os.system(cmd) - - # Read in the catalog.fits file - cat = fits.getdata(orig_path + 'catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - # First column in new files will be for [atlas] wavelength - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - - # For each catalog.fits entry, read the unbinned spectrum and rebin to - # the atlas resolution. Make a new fits file in rebin directory - count = 0 - for ff in range(len(files_all)): - count += 1 - # Extract the temp, Z, logg - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - grav = float(vals[2]) - - # Fetch the spectrum - if rot == True: - sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) - else: - sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) - - # Rebin - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - # Make the FITS file from the columns with header - cols = fits.ColDefs([c0,c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write hdu to new directory with same filename - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(path+files_all[ff]) - - print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) - return - - -def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): - """ - Construct the Phoenix Husser+13 atmopsheres for each model. Combines the - fluxes from the *HiRES.fits files and the wavelengths of the - WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. - - path_to_dir is the path to the directory containing all of the downloaded - files - - met_str is the name of the current metallicity - - Creates new fits files for each atmosphere: phoenix_.fits, - which contains columns for the log g (column header = g#.#). Puts - atmospheres in new directory phoenixm00 - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the current metallicity subdirectory - sub_dir = '../phoenix{0}'.format(met_str) - if os.path.exists(sub_dir): - pass - else: - os.mkdir(sub_dir) - - # Extract wavelength array, make column for later - wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') - wave = wavefile[0].data - wavefile.close() - wave_col = Column(wave, name = 'WAVELENGTH') - - # Create temp array for Husser+13 grid (given in paper) - temp_arr = np.arange(2300, 7001, 100) - temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) - - print( 'Looping though all temps') - # For each temp, build file containing the flux for all gravities - i = 0 - for temp in temp_arr: - files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) - files.sort() - # Start the table with the wavelength column - t = Table() - t.add_column(wave_col) - for f in files: - # Extract the logg out of filename - logg = f[9:13] - - # Extract fluxes from file - spectrum = fits.open(f) - flux = spectrum[0].data - spectrum.close() - - # Make Column object with fluxes, add to table - col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) - t.add_column(col) - - # Now, construct final fits file for the given temp - outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) - t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) - - # Progress counter for user - i += 1 - print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) - - # Return to original directory - os.chdir(start_dir) - return - -def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): - """ - Makes catalog.fits file for Husser+13 phoenix models. Assumes that - organize_PHOENIXv16_atmospheres has been run already, and that the models lie - in subdirectory phoenix[met_str]. - - path_to_directory is the path to the directory with the reformatted - models (i.e. the output from construct_atmospheres, phoenix[met_str]) - - Puts catalog.fits file in directory the user starts in - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Extract metallicity from metallicity string - met = float(met_str[1]) + (float(met_str[2]) * 0.1) - if 'm' in met_str: - met *= -1. - - # Collect the filenames. Each is a unique temp with many different log g's - files = glob.glob('phoenix*.fits') - files.sort() - - # Create the catalog.fits file, row by row - index_arr = [] - filename_arr = [] - for i in files: - # Get log g values from the column header the file - t = Table.read(i, format='fits') - keys = t.keys() - logg_vals = keys[1:] - - # Extract temp from filename - name = i.split('_') - temp = float(name[1][:-5]) - for j in logg_vals: - logg = float(j[1:]) - index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) - filename = path_to_dir + i + '[' + j + ']' - # Add row to table - index_arr.append(index) - filename_arr.append(filename) - - catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) - - # Return to starting directory, write catalog - os.chdir(start_dir) - - if os.path.exists('catalog.fits'): - from astropy.table import vstack - - prev_catalog = Table.read('catalog.fits', format='fits') - joined_catalog = vstack([prev_catalog, catalog]) - - joined_catalog.write('catalog.fits', format='fits', overwrite=True) - else: - catalog.write('catalog.fits', format='fits', overwrite=True) - - return - -def cdbs_PHOENIXv16(path_to_cdbs_dir): - """ - Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily - consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] - and adding the appropriate keywords to the fits header. - - path_to_cdbs_dir goes from current working directory to phoenix[met] directory - in cdbs/grids/phoenix_v16. Note that these files have already been organized - using organize_PHOENIXv16_atmospheres code. - - Overwrites original files in directory - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_cdbs_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('phoenix*.fits') - - ## Need to sort filenames; glob doesn't always give them in order - files.sort() - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - - # Read in current FITS table - cur_table = Table.read(i, format='fits') - - cur_table.columns[0].name = 'Wavelength' - - num_cols = len(cur_table.colnames) - - # Multiplying each flux column by 10^-8 for conversion - for cur_col_index in range(1, num_cols, 1): - cur_col_name = cur_table.colnames[cur_col_index] - cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 - - - # Construct new FITS file based on old one - hdu = fits.open(i) - header_0 = hdu[0].header - header_1 = hdu[1].header - sci = hdu[1].data - - tbhdu = fits.table_to_hdu(cur_table) - - # Copying over the older headers, adding unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - tbhdu.header['TUNIT3'] = 'FLAM' - tbhdu.header['TUNIT4'] = 'FLAM' - tbhdu.header['TUNIT5'] = 'FLAM' - tbhdu.header['TUNIT6'] = 'FLAM' - tbhdu.header['TUNIT7'] = 'FLAM' - tbhdu.header['TUNIT8'] = 'FLAM' - tbhdu.header['TUNIT9'] = 'FLAM' - tbhdu.header['TUNIT10'] = 'FLAM' - tbhdu.header['TUNIT11'] = 'FLAM' - tbhdu.header['TUNIT12'] = 'FLAM' - tbhdu.header['TUNIT13'] = 'FLAM' - tbhdu.header['TUNIT14'] = 'FLAM' - - # Construct and write out final FITS file - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i, overwrite=True) - - hdu.close() - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Change back to starting directory - os.chdir(start_dir) - - return - -def rebin_phoenixV16(cdbs_path): - """ - Rebin phoenixV16 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: phoenix_v16_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - ## (This assumes that at least 'm00' metallicity exists) - tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/phoenix_v16_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - temp_arr = np.zeros(len(files_all), dtype=float) - logg_arr = np.zeros(len(files_all), dtype=float) - metal_arr = np.zeros(len(files_all), dtype=float) - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - - temp_arr[ff] = float(vals[0]) - metal_arr[ff] = float(vals[1]) - logg_arr[ff] = float(vals[2]) - - - metal_uniq = np.unique(metal_arr) - temp_uniq = np.unique(temp_arr) - - for mm in range(len(metal_uniq)): - metal = metal_uniq[mm] # metallicity - - # Construct str for metallicity (for appropriate directory name) - met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) - if metal > 0: - met_str = 'p' + met_str - else: - met_str = 'm' + met_str - - # Make directory for current metallicity if it does not exist yet - if not os.path.exists(path + 'phoenix' + met_str): - os.mkdir(path + 'phoenix' + met_str) - - for tt in range(len(temp_uniq)): - temp = temp_uniq[tt] # temperature - - # Pick out the list of gravities for this T, Z combo - idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] - logg_exist = logg_arr[idx] - - # All gravities will go in one file. Here is the output - # file name. - outfile = path + files_all[idx[0]].split('[')[0] - - ## If the rebinned file already exists, continue - if os.path.exists(outfile): - continue - - # Build a columns array. One column for each gravity. - cols_arr = [] - - # Make the wavelength column, which is first in the cols array. - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - cols_arr.append(c0) - - for gg in range(len(logg_exist)): - grav = logg_exist[gg] # gravity - - # Fetch the spectrum - sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Store the spectrum - name = 'g{0:3.1f}'.format(grav) - col = fits.Column(name=name, format='E', array=flux_rebin) - cols_arr.append(col) - - - # Make the FITS file from the columns with header. - cols = fits.ColDefs(cols_arr) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - for gg in range(len(logg_exist)): - tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' - - # Write hdu - finalhdu = fits.HDUList([prihdu, tbhdu]) - # don't have overwrite to protect original files. - finalhdu.writeto(outfile) - - print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) - - - return - - -def rebin_spec(wave, specin, wavnew): - """ - Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: - http://www.astrobetter.com/blog/2013/08/12/ - python-tip-re-sampling-spectra-with-pysynphot/ - """ - spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) - f = np.ones(len(wave)) - filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') - obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') - - return obs.binflux - -def organize_BTSettl_2015_atmospheres(path_to_dir): - """ - Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the BTSettl subdirectory - if not os.path.exists('BTSettl_2015'): - os.mkdir('BTSettl_2015') - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.spec.fits') - - for i in files: - hdu = fits.open(i) - spec = hdu[1].data - header_0 = hdu[0].header - header_1 = hdu[1].header - - wave = spec.field(0) - flux = spec.field(1) - - # Get units right: convert wave from microns to Angstroms, - # flux from W /m^2/ micron to erg/s/cm^2/A - wave_new = wave * 10**4 - flux_new = flux * 10**(-1) - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave_new) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) - - hdu.close() - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_BTSettl_2015_catalog(path_to_dir): - """ - Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*spec.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('-') - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: BTSettl_2015_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - phoenix_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/BTSettl_2015_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning BTSettl spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - -def make_wavelength_unique(files, dirname): - """ - Helper function to go through each BTSettl spectrum and ensure that - each wavelength point is unique. This is required for rebinning to work. - - - files: list of files to run this analysis on - """ - # Loop through each file, find fix repeated wavelength entries if necessary - for i in files: - t = Table.read('{0}/{1}'.format(dirname,i), format='fits') - test = np.unique(t['Wavelength'], return_index=True) - - if len(t) != len(test[0]): - t = t[test[1]] - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - # Also make sure wavelength is monotonic. If it is not, then it is - # a sign that the wavelengths are out of order - diff = np.diff(t['Wavelength']) - bad = np.where(diff < 0) - if len(bad[0]) > 0: - t.sort('Wavelength') - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - print('Done {0}'.format(i)) - - return - -def organize_BTSettl_atmospheres(): - """ - Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). - The code expects tp be run in cdbs/grid/BTSettl, and expects that the - individual model files have been downloaded from online - (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] - - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, - # for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.txt') - count=0 - for jj in files: - t = Table.read(jj, format='ascii') - # First, trim the wavelengths to a more reasonable wavelength range - good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) - t = t[good] - - # Convert flux units to Flam (Ergs/sec/cm**2/A) - flux_new = 10**(t['col2'] - 8.0) - - # Save the file as a fits file - c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Now, clean up all the files made when unzipping the spectra - cmd1 = 'rm *.bz2' - cmd2 = 'rm *.tmp' - #cmd3 = 'rm *.txt' - os.system(cmd1) - os.system(cmd2) - #os.system(cmd3) - print('==============================') - print('Done {0}'.format(ii)) - print('==============================') - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_BTSettl_catalog(): - """ - Create cdbs catalog.fits of BTSettl grid. - THIS IS STEP 2, after organize_BTSettl_atmospheres has - been run. - - Code expects to be run in cdbs/grid/BTSettl - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btp05'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('*.fits') - - # Construct the metallicity val - if 'm' in ii: - metal_flag = -1 * float(ii[3:])*0.1 - else: - metal_flag = float(ii[3:])*0.1 - - # Now collect the info from the files - for jj in files: - tmp = jj.split('-') - - if metal_flag >= 0: - temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin - try: - logg = float(tmp[1]) - except: - logg = float(tmp[1].split('+')[0]) - else: - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - # Go back to original directory to move to next metallicity - print('Done {0}'.format(ii)) - os.chdir(start_dir) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl(make_unique=False): - """ - Rebin BTSettle models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: BTSettl_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create cdbs/grid directory for rebinned models - path = 'BTSettl_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata('BTSettl/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - #==============================# - #tmp = [] - #for ii in files_all: - # if ii.startswith('btp00'): - # tmp.append(ii) - #files_all = tmp - #=============================# - - print( 'Rebinning BTSettl spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'BTSettl') - print('Done') - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - try: - sp = pysynphot.Icat('BTSettl', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - except: - pdb.set_trace() - orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) - outfile = path + files_all[ff].split('[')[0] - cmd = 'cp {0} {1}'.format(orig_file, outfile) - os.system(cmd) - - print('Done {0} of {1}'.format(ff, len(files_all))) - - return - -def organize_WDKoester_atmospheres(path_to_dir): - """ - Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.dk.dat.txt') - - for i in files: - data = Table.read(i, format='ascii') - - wave = data['col1'] # angstrom - flux = data['col2'] # erg/s/cm^2/A - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) - - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_WDKoester_catalog(path_to_dir): - """ - Create cdbs catalog.fits of wdKoester grid. - THIS IS STEP 2, after organize_WDKoester_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*dk.dat.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('.') - tmp2 = tmp[0].split('_') - temp = float(tmp2[0][2:]) # Kelvin - logg = float(tmp2[1]) / 100.0 # log(g) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin wdKoester models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: wdKoester_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing model; we will steal the header - tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' - wdkoester_hdu = fits.open(tmp) - header0 = wdkoester_hdu[0].header - wdkoester_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/wdKoester_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning wdKoester spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the wdKoester spectrum, rebin flux - sp = pysynphot.Icat('wdKoester', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - - From 1293ea8ecf531e5f8ebe6f258ee940abc524fece Mon Sep 17 00:00:00 2001 From: nsabrams Date: Wed, 17 Jun 2026 14:43:44 -0700 Subject: [PATCH 156/191] updated contributors file --- docs/contributors.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/contributors.rst b/docs/contributors.rst index c3b51bdf..9cb44a45 100644 --- a/docs/contributors.rst +++ b/docs/contributors.rst @@ -19,7 +19,7 @@ Dongwon Kim -- code testing/debugging Siyao Jia -- helped with early code and documentation development Natasha Abrams -- developed resolved multiplicity capabilities -(ResolvedMultiplicityDK class) +(ResolvedMultiplicityDK class) and added COSMIC support. Michael Medford -- developed resolved multiplicity capabilities (ResolvedMultiplicityDK class) @@ -49,4 +49,4 @@ files, Vega mag to ST mag conversion function (v2.4) Anna Pusack -- Added IRTF L-band filter support -Caitlin Begbie -- added brown dwarf physics and capabilities \ No newline at end of file +Caitlin Begbie -- added brown dwarf physics and capabilities From 9b39f7ddcde637b84e31a24b0f02acd03151e981 Mon Sep 17 00:00:00 2001 From: nsabrams Date: Wed, 17 Jun 2026 15:04:34 -0700 Subject: [PATCH 157/191] fixed tiny path typo in docs --- docs/atmo_models.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/atmo_models.rst b/docs/atmo_models.rst index fda94778..b54056bc 100644 --- a/docs/atmo_models.rst +++ b/docs/atmo_models.rst @@ -4,7 +4,7 @@ Atmosphere Model Object ======================================== Stellar atmosphere models are defined as functions in -``popstar/atmospheres.py``. These can be called by:: +``spisea/atmospheres.py``. These can be called by:: from popstar import atmospheres atmo = atmospheres. From 342c0821e5648c9615b2b2650bc9c1e3542358b4 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 18 Jun 2026 12:17:27 -0700 Subject: [PATCH 158/191] clean out duplicate _make_companions_table and remove decam,y filter bug: --- spisea/filters.py | 6 +- spisea/synthetic.py | 198 +----------------------------------- spisea/tests/test_models.py | 2 +- 3 files changed, 7 insertions(+), 199 deletions(-) diff --git a/spisea/filters.py b/spisea/filters.py index 4f8a64e1..5ab9efec 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -103,11 +103,13 @@ def get_decam_filt(name): # Read in filter info try: t = Table.read('{0}/decam/DECam_filters.txt'.format(filters_dir), format='ascii') - t['y'] = t['Y'] trans = t[name] except: - raise ValueError('Could not find DECAM filter {0} in {1}/decam/DECam_filters.txt'.format(name, filters_dir)) + if name=='y': + raise ValueError('DECam has a /"Y/" filter, not /"y/". The /"y/" in SPISEA = cc)[0] - - # Get the location in the companions array for each system and - # the cc'th companion. - cdx = comp_index[idx, cc-1] - - # companions['mass'][cdx] = compMass[idx, cc-1] - companions['mass'][cdx] = [compMass[ii][cc-1] for ii in idx] - comp_mass = companions['mass'][cdx] - - if len(idx) > 0: - companions['Teff'][cdx] = self.iso_interps['Teff'](comp_mass) - companions['L'][cdx] = self.iso_interps['L'](comp_mass) - companions['logg'][cdx] = self.iso_interps['logg'](comp_mass) - companions['isWR'][cdx] = np.round(self.iso_interps['isWR'](comp_mass)) - companions['mass_current'] = self.iso_interps['mass_current'](companions['mass']) - companions['phase'] = np.round(self.iso_interps['phase'](companions['mass'])) - companions['metallicity'] = np.ones(N_comp_tot)*self.iso.metallicity #**** - - # For a very small fraction of stars, the star phase falls on integers in-between - # the ones we have definition for, as a result of the interpolation. For these - # stars, round phase down to nearest defined phase (e.g., if phase is 71, - # then round it down to 5, rather than up to 101). - # Convert nan_to_num to avoid errors on greater than, less than comparisons - - # reinforce BD phase of 90 and invariant masses - bd_mask = (companions['mass'] >= 0.01) & (companions['mass'] <= 0.08) - companions['phase'][bd_mask] = 90 - companions['mass_current'][bd_mask] = companions['mass'][bd_mask] - - companions_phase_non_nan = np.nan_to_num(companions['phase'], nan=-99) - bad = np.where( (companions_phase_non_nan > 5) & - (companions_phase_non_nan < 101) & - (companions_phase_non_nan != 9) & - (companions_phase_non_nan != 90) & - (companions_phase_non_nan != -99)) - # Print warning, if desired - verbose=False - if verbose: - for ii in range(len(bad)): - print('WARNING: changing phase {0} to 5'.format(companions['phase'][bad[ii]])) - companions['phase'][bad] = 5 - - for filt in self.filt_names: - # Magnitude of companion - companions[filt][cdx] = self.iso_interps[filt](comp_mass) - - mag_s = star_systems[filt][idx] - mag_c = companions[filt][cdx] - - # Add companion flux to system flux. - f1 = 10**(-mag_s / 2.5) - f2 = 10**(-mag_c / 2.5) - - # For dark objects, turn the np.nan fluxes into zeros. - f1 = np.nan_to_num(f1) - f2 = np.nan_to_num(f2) - - # If *both* objects are dark, then keep the magnitude - # as np.nan. Otherwise, add fluxes together - good = np.where( (f1 != 0) | (f2 != 0) )[0] - bad = np.where( (f1 == 0) & (f2 == 0) )[0] - - star_systems[filt][idx[good]] = -2.5 * np.log10(f1[good] + f2[good]) - star_systems[filt][idx[bad]] = np.nan - - - ##### - # Make Remnants with flux = 0 in all bands. - ##### - if self.ifmr != None: - # Identify compact objects as those with Teff = 0 or with masses above the max iso mass - # Exclude BDs from designation - highest_mass_iso = self.iso.points['mass'].max() - cdx_rem = np.where((np.isnan(companions['Teff'])) & - (companions['mass'] > highest_mass_iso) & - (companions['mass'] >= 0.08))[0] - - # Calculate remnant mass and ID for compact objects; update remnant_id and - # remnant_mass arrays accordingly - if 'metallicity_array' in inspect.getfullargspec(self.ifmr.generate_death_mass).args: - r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem], - metallicity_array=companions['metallicity'][cdx_rem]) - else: - r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem]) - - - # Drop remnants where it is not relevant (e.g. not a compact object or - # outside mass range IFMR is defined for) - good = np.where(r_id_tmp > 0) - cdx_rem_good = cdx_rem[good] - - companions['mass_current'][cdx_rem_good] = r_mass_tmp[good] - companions['phase'][cdx_rem_good] = r_id_tmp[good] - - # Give remnants a magnitude of nan, so they can be filtered out later when calculating flux. - for filt in self.filt_names: - companions[filt][cdx_rem_good] = np.full(len(cdx_rem_good), np.nan) - - - # Notify if we have a lot of bad ones. - # Convert nan_to_num to avoid errors on greater than, less than comparisons - companions_teff_non_nan = np.nan_to_num(companions['Teff'], nan=-99) - idx = np.where(companions_teff_non_nan > 0)[0] - if len(idx) != N_comp_tot and self.verbose: - print( 'Found {0:d} companions out of stellar mass range'.format(N_comp_tot - len(idx))) - - # For low-mass stars and substellar objects below isochrone, assume no mass loss and set phase to 98 - low_mass_idxs = (companions['mass'] 0: - assert companions['mass'][companions_teff_non_nan > 0].min() > 0, "Companion mass is not positive" - - return star_systems, companions - - def _remove_bad_systems(self, star_systems, compMass, keep_low_mass_stars): """ Helper function to remove stars with masses outside the isochrone diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 917fcaec..e527707d 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -183,7 +183,7 @@ def test_filters(): 'ctio_osiris,K', 'ctio_osiris,H', 'ubv,U', 'ubv,B', 'ubv,V', 'ubv,R', 'ubv,I', 'jg,J', 'jg,H', 'jg,K', - 'decam,y', 'decam,Y', 'decam,i', 'decam,z', + 'decam,Y', 'decam,i', 'decam,z', 'decam,u', 'decam,g', 'decam,r', 'gaia,dr1,G', 'gaia,dr1,Gbp', 'gaia,dr1,Grp', 'gaia,dr2,G', 'gaia,dr2,Gbp', 'gaia,dr2,Grp', From 9343ecdfece4b1b7c81be5b1050a16b7baaa6206 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 18 Jun 2026 12:51:50 -0700 Subject: [PATCH 159/191] Update grid_version and add some details to changelog for v3.0 --- docs/getting_started.rst | 6 +++--- docs/index.rst | 27 +++++++++++++++++++++++++++ spisea/atmospheres.py | 4 ++-- spisea/evolution.py | 6 +++--- 4 files changed, 35 insertions(+), 8 deletions(-) diff --git a/docs/getting_started.rst b/docs/getting_started.rst index dd2eeb96..868787de 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -98,15 +98,15 @@ STScI CDBS conventions and should be placed in the ``cdbs/grid`` directory. You will need to download 2 files: * `spisea_models.tar.gz - `_ (3.4 GB; 18 GB unzipped) + `_ (4.1 GB; 23 GB unzipped) -* `spisea_cdbs.tar.gz `_ (142 MB; 248 MB unzipped) +* `spisea_cdbs.tar.gz `_ (155 MB; 354 MB unzipped) You may **optionally** download a third file, which contains higher-resolution stellar atmospheres. Note that this file is quite large, and is not necessary for most SPISEA use cases: -* `spisea_cdbs_highres.tar.gz `_ (50 GB; 74 GB unzipped) +* `spisea_cdbs_highres.tar.gz `_ (54 GB; 80 GB unzipped) SPISEA uses the low-resolution atmospheres (R = 250) in ``spisea_cdbs.tar.gz`` by default, as diff --git a/docs/index.rst b/docs/index.rst index 048f5c8b..40c14047 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -82,6 +82,33 @@ releases will be co-authors in future SPISEA software papers. Change Log ---------- +3.0 (2026-**-**) + * Addition of brown dwarf models + * New evolution grids: Phillips2020, Marley2021, and + mergedPhillipsMergedPhillipsBaraffePisaEkstromParsec + * New atmosphere grids: Phillips2020, Meisner2023 + * get_merged_atmosphere now uses Meisner2023 for T < 1000 K + and interpolated BTSettl/Meisner2023 for 1000 < T <= 1200 + * Updated evolution and atmosphere grid data sets w/ grid_version=3.0 + * These include the new brown dwarf models + * The MISTv1.2-synthpop model extension was also modified to include + denser sampling in the gap between the base MISTv1.2 grids and 0.1Msun. + * Added SODC extinction law + * Filter handling improvements + * Fix bug where DECam "Y" filter was mislabeled "y", and updated + DECam filters to latest version + * Add new filters for Hipparcos, Kepler, OGLE, TESS, Tycho, Washington, + Subaru HSC + * Enable use of all Gaia filters with warning recommending latest (EDR3) + * All pysynphot filters can now be used + * Minor bugs and case handling + * Debug edge case for no companion stars + * Restore functionality where final mass = initial for companion + stars with lower mass than the isochrone's range + * MIST evolution allows input metallicities within 0.1 dex + of the allowed range, since the nearest grid value is adopted, + and this eliminates floating value precision issues. + 2.4 (2026-03-20) * Added backward compatibility for isochrone file names created before v2.3 diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index 1cf94682..489c0078 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -872,8 +872,8 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose * T < 3800, logg < 2.5: PHOENIX v16 * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - * 1200 < T <= 1000, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge - * 1000 < T <= 250, logg > 2.5: Meisner2023 + * 1000 < T <= 1200, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge + * 250 < T <= 1000, logg > 2.5: Meisner2023 Otherwise, if T < 3800 and [M/H] != 0: diff --git a/spisea/evolution.py b/spisea/evolution.py index feb69399..56fbae48 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -573,7 +573,7 @@ def __init__(self): self.age_list = np.arange(6.0, 10.0, 0.001) # define required evo_grid number - self.evo_grid_min = 1.0 + self.evo_grid_min = 3.0 def isochrone(self, age= 1.e8, metallicity=0.0): r""" @@ -722,7 +722,7 @@ def __init__(self): } # Define required evo_grid number - self.evo_grid_min = 1.0 + self.evo_grid_min = 3.0 def isochrone(self, age=1.e8, metallicity=0.0): r""" @@ -1592,7 +1592,7 @@ def __init__(self, rot=True): self.z_file_map = {0.015: 'z015_norot/'} # Define required evo_grid number - self.evo_grid_min = 1.0 + self.evo_grid_min = 3.0 def isochrone(self, age=1.e8, metallicity=0.0): From 1548f21f9d9724d56a746e1e3a29f78609985ca9 Mon Sep 17 00:00:00 2001 From: nsabrams Date: Thu, 18 Jun 2026 15:22:08 -0700 Subject: [PATCH 160/191] edited test to make sure cluster mass within 2 percent rather than specific number --- spisea/tests/test_imf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/spisea/tests/test_imf.py b/spisea/tests/test_imf.py index 969e550d..943dc0a1 100755 --- a/spisea/tests/test_imf.py +++ b/spisea/tests/test_imf.py @@ -19,8 +19,8 @@ def test_generate_cluster(): mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) # Make sure that the total mass is always within the expected - # range of the requested mass. - assert np.abs(M_cl - sysMass.sum()) < 120.0 + # range of the requested mass (2%). + assert np.abs(M_cl - sysMass.sum()) < M_cl*0.02 # Check that enough companions were generated. # Should be greater than 25% of the stars with companions. From 1b02b21dce59d4493594f0eef5e169b0f92874a6 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 30 Jun 2026 17:37:57 -0700 Subject: [PATCH 161/191] add bessell (1990) ubvri filters --- filt_func/bessell/B.dat | 21 + filt_func/bessell/I.dat | 23 + filt_func/bessell/R.dat | 24 + filt_func/bessell/README.txt | 4 + filt_func/bessell/U.dat | 25 + filt_func/bessell/V.dat | 24 + spisea/atmospheres_BACKUP_22350.py | 2524 ---------------------------- spisea/atmospheres_BACKUP_58456.py | 2524 ---------------------------- spisea/atmospheres_BASE_58456.py | 2165 ------------------------ spisea/atmospheres_LOCAL_58456.py | 2508 --------------------------- spisea/atmospheres_REMOTE_58456.py | 2172 ------------------------ spisea/filters.py | 16 + spisea/synthetic.py | 3 + spisea/tests/test_models.py | 3 +- 14 files changed, 142 insertions(+), 11894 deletions(-) create mode 100644 filt_func/bessell/B.dat create mode 100644 filt_func/bessell/I.dat create mode 100644 filt_func/bessell/R.dat create mode 100755 filt_func/bessell/README.txt create mode 100644 filt_func/bessell/U.dat create mode 100644 filt_func/bessell/V.dat delete mode 100755 spisea/atmospheres_BACKUP_22350.py delete mode 100755 spisea/atmospheres_BACKUP_58456.py delete mode 100644 spisea/atmospheres_BASE_58456.py delete mode 100644 spisea/atmospheres_LOCAL_58456.py delete mode 100644 spisea/atmospheres_REMOTE_58456.py diff --git a/filt_func/bessell/B.dat b/filt_func/bessell/B.dat new file mode 100644 index 00000000..9d2eb167 --- /dev/null +++ b/filt_func/bessell/B.dat @@ -0,0 +1,21 @@ +3600.0 0.0000000000 +3700.0 0.0300000000 +3800.0 0.1340000000 +3900.0 0.5670000000 +4000.0 0.9200000000 +4100.0 0.9780000000 +4200.0 1.0000000000 +4300.0 0.9780000000 +4400.0 0.9350000000 +4500.0 0.8530000000 +4600.0 0.7400000000 +4700.0 0.6400000000 +4800.0 0.5360000000 +4900.0 0.4240000000 +5000.0 0.3250000000 +5100.0 0.2350000000 +5200.0 0.1500000000 +5300.0 0.0950000000 +5400.0 0.0430000000 +5500.0 0.0090000000 +5600.0 0.0000000000 diff --git a/filt_func/bessell/I.dat b/filt_func/bessell/I.dat new file mode 100644 index 00000000..67ba0255 --- /dev/null +++ b/filt_func/bessell/I.dat @@ -0,0 +1,23 @@ +7000.0 0.0000000000 +7100.0 0.0240000000 +7200.0 0.2320000000 +7300.0 0.5550000000 +7400.0 0.7850000000 +7500.0 0.9100000000 +7600.0 0.9650000000 +7700.0 0.9850000000 +7800.0 0.9900000000 +7900.0 0.9950000000 +8000.0 1.0000000000 +8100.0 1.0000000000 +8200.0 0.9900000000 +8300.0 0.9800000000 +8400.0 0.9500000000 +8500.0 0.9100000000 +8600.0 0.8600000000 +8700.0 0.7500000000 +8800.0 0.5600000000 +8900.0 0.3300000000 +9000.0 0.1500000000 +9100.0 0.0300000000 +9200.0 0.0000000000 diff --git a/filt_func/bessell/R.dat b/filt_func/bessell/R.dat new file mode 100644 index 00000000..17882885 --- /dev/null +++ b/filt_func/bessell/R.dat @@ -0,0 +1,24 @@ +5500.0 0.0000000000 +5600.0 0.2300000000 +5700.0 0.7400000000 +5800.0 0.9100000000 +5900.0 0.9800000000 +6000.0 1.0000000000 +6100.0 0.9800000000 +6200.0 0.9600000000 +6300.0 0.9300000000 +6400.0 0.9000000000 +6500.0 0.8600000000 +6600.0 0.8100000000 +6700.0 0.7800000000 +6800.0 0.7200000000 +6900.0 0.6700000000 +7000.0 0.6100000000 +7100.0 0.5600000000 +7200.0 0.5100000000 +7300.0 0.4600000000 +7400.0 0.4000000000 +7500.0 0.3500000000 +8000.0 0.1400000000 +8500.0 0.0300000000 +9000.0 0.0000000000 diff --git a/filt_func/bessell/README.txt b/filt_func/bessell/README.txt new file mode 100755 index 00000000..b951df6c --- /dev/null +++ b/filt_func/bessell/README.txt @@ -0,0 +1,4 @@ +These are the Johnson-Cousin filters (UBVRI) from Bessell (1990). + +The wavelength is angstrom, the transmission is a fraction. + diff --git a/filt_func/bessell/U.dat b/filt_func/bessell/U.dat new file mode 100644 index 00000000..1fccf4f6 --- /dev/null +++ b/filt_func/bessell/U.dat @@ -0,0 +1,25 @@ +3000.0 0.0000000000 +3050.0 0.0160000000 +3100.0 0.0680000000 +3150.0 0.1670000000 +3200.0 0.2870000000 +3250.0 0.4230000000 +3300.0 0.5600000000 +3350.0 0.6730000000 +3400.0 0.7720000000 +3450.0 0.8410000000 +3500.0 0.9050000000 +3550.0 0.9430000000 +3600.0 0.9810000000 +3650.0 0.9930000000 +3700.0 1.0000000000 +3750.0 0.9890000000 +3800.0 0.9160000000 +3850.0 0.8040000000 +3900.0 0.6250000000 +3950.0 0.4230000000 +4000.0 0.2380000000 +4050.0 0.1140000000 +4100.0 0.0510000000 +4150.0 0.0190000000 +4200.0 0.0000000000 diff --git a/filt_func/bessell/V.dat b/filt_func/bessell/V.dat new file mode 100644 index 00000000..241fc922 --- /dev/null +++ b/filt_func/bessell/V.dat @@ -0,0 +1,24 @@ +4700.0 0.0000000000 +4800.0 0.0300000000 +4900.0 0.1630000000 +5000.0 0.4580000000 +5100.0 0.7800000000 +5200.0 0.9670000000 +5300.0 1.0000000000 +5400.0 0.9730000000 +5500.0 0.8980000000 +5600.0 0.7920000000 +5700.0 0.6840000000 +5800.0 0.5740000000 +5900.0 0.4610000000 +6000.0 0.3590000000 +6100.0 0.2700000000 +6200.0 0.1970000000 +6300.0 0.1350000000 +6400.0 0.0810000000 +6500.0 0.0450000000 +6600.0 0.0250000000 +6700.0 0.0170000000 +6800.0 0.0130000000 +6900.0 0.0090000000 +7000.0 0.0000000000 diff --git a/spisea/atmospheres_BACKUP_22350.py b/spisea/atmospheres_BACKUP_22350.py deleted file mode 100755 index e3d2233c..00000000 --- a/spisea/atmospheres_BACKUP_22350.py +++ /dev/null @@ -1,2524 +0,0 @@ -import logging -import numpy as np -import pysynphot -import os -import glob -from astropy.io import fits -from astropy.table import Table, Column -import pysynphot -import time -import pdb -import warnings - -log = logging.getLogger('atmospheres') - -def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Given atmosphere model, get temperature and gravity bounds - """ - # Open catalog fits file and break out row indices - catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) - - teff_arr = [] - z_arr = [] - logg_arr = [] - for cur_row_index in range(len(catalog)): - index = catalog['INDEX'][cur_row_index] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Filter by metallicity. Will chose the closest metallicity to desired input - metal_list = np.unique(np.array(z_arr)) - metal_idx = np.argmin(np.abs(metal_list - metallicity)) - metallicity_new = metal_list[metal_idx] - - z_filt = np.where(z_arr == metal_list[metal_idx]) - teff_arr = teff_arr[z_filt] - logg_arr = logg_arr[z_filt] - - # # Now find the closest atmosphere in parameter space to - # # the one we want. We'll find the match with the lowest - # # fractional difference - # teff_diff = (teff_arr - temperature) / temperature - # logg_diff = (logg_arr - gravity) / gravity - # - # diff_tot = abs(teff_diff) + abs(logg_diff) - # idx_f = np.argmin(diff_tot) - # - # temperature_new = teff_arr[idx_f] - # gravity_new = logg_arr[idx_f] - - # First check if temperature within bounds - temperature_new = temperature - if temperature > np.max(teff_arr): - temperature_new = np.max(teff_arr) - if temperature < np.min(teff_arr): - temperature_new = np.min(teff_arr) - - # If temperature within bounds, then check if metallicity within bounds - teff_diff = np.abs(teff_arr - temperature) - sorted_min_diffs = np.unique(teff_diff) - - ## Find two closest temperatures - teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] - teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] - - logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] - logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] - - ## Switch to most conservative bound of logg out of two closest temps - gravity_new = gravity - if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): - gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) - if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): - gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) - - if verbose: - # Print out changes, if any - if temperature_new != temperature: - teff_msg = 'Changing to T={0:6.0f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( teff_msg.format(temperature_new, metallicity, temperature, gravity)) - - if gravity_new != gravity: - logg_msg = 'Changing to logg={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( logg_msg.format(gravity_new, metallicity, temperature, gravity)) - - if metallicity_new != metallicity: - logg_msg = 'Changing to met={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( logg_msg.format(metallicity_new, metallicity, temperature, gravity)) - - return (temperature_new, gravity_new, metallicity_new) - -def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmosphere from the Kurucz pysnphot grid - (`Kurucz 1993 `_). - - Grid Range: - - * Teff: 3000 - 50000 K - * gravity: 0 - 5 cgs - * metallicity: -5.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - Always false for this particular function - """ - try: - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('k93models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Kurucz 1993 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmospheres from the pysynphot ATLAS9 atlas - (`Castelli & Kurucz 2004 `_). - - Grid Range: - - * Teff: 3500 - 50000 K - * gravity: 0 - 5.0 cgs - * [M/H]: -2.5 - 0.2 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('ck04models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - try: - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('nextgen', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find NextGen atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, - rebin=False): - """ - Return atmosphere from the pysynphot - `PHOENIX atlas `_. - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - try: - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - # Take care of atmospheres outside the catalog boundaries - logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' - if gravity > 4.3: - if verbose: - print( logg_msg.format(4.3, temperature, gravity)) - gravity = 4.3 - - if rebin: - sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, - verbose=False): - """ - For a given stellar atmosphere, get extract the closest possible match in - Teff/logg space. Note that this is different from the normal routine - which interpolates along the input grid to get final spectrum. We can't - do this here because the Fierro+15 atmosphere grid is so sparse - - rebin=True: pull from atmospheres at ck04model resolution. - - If verbose, print out the parameters of the match - """ - # Set up the proper root directory - if rebin == True: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' - else: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' - - # Read in catalog, extract atmosphere info - cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') - teff_arr = [] - z_arr = [] - logg_arr = [] - for ii in range(len(cat)): - index = cat['INDEX'][ii] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Now find the closest atmosphere in parameter space to - # the one we want. We'll find the match with the lowest - # fractional difference - teff_diff = (teff_arr - temperature) / temperature - logg_diff = (logg_arr - gravity) / gravity - - diff_tot = abs(teff_diff) + abs(logg_diff) - idx_f = np.where(diff_tot == min(diff_tot))[0][0] - - # Extract the filename of the best-match model and read as - # pysynphot object - infile = cat[idx_f]['FILENAME'].split('.') - spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the - # ATLAS models are in FLAM at the surface. So, we need to multiply the - # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. - # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file - t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') - tmp = np.where(t['col1'] == infile[0]) - - lum = t['col3'][tmp] * (3.839*10**33) # cgs - sigma = 5.6704 * 10**-5 # cgs - teff = teff_arr[idx_f] # cgs - - radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm - radius /= 3.08*10**18 # in pc - - - # Make the pysynphot spectrum - w = spec['Wavelength'] - f = spec['Flux'] * (1000 / radius)**2. - sp = pysynphot.ArraySpectrum(w,f) - - #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Print out parameters of match, if desired - if verbose: - print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) - print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - if rebin: - sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 30000) - gravity = log gravity (def = 4.14) - """ - sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): - """ - Return PHOENIX v16 atmospheres from - `Husser et al. 2013 `_. - - Models originally downloaded via `ftp `_. - Solar metallicity and [alpha/Fe] is used. - - Grid Range: - - * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K - * gravity: 0.0 - 6.0 cgs, steps of 0.5 - * [M/H]: -4.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - atm_model_name = 'phoenix_v16' - if rebin == True: - atm_model_name = 'phoenix_v16_rebin' - - - # Extract atmosphere. If that fails, then check bounds and try again - try: - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_model_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): - """ - Return atmosphere from CIFIST2011_2015 grid - (`Allard et al. 2012 `_, - `Baraffe et al. 2015 `_ ) - - Grid originally downloaded from `website `_. - - Grid Range: - - * Teff: 1200 - 7000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_2015_rebin' - else: - atm_name = 'BTSettl_2015' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) -<<<<<<< HEAD - #print(dir(obj)) - - -======= - - ->>>>>>> upstream/dev - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): - """ - Return atmosphere from CIFIST2011 grid - (`Allard et al. 2012 `_) - - Grid originally downloaded `here `_ - - Notes - ------ - Grid Range: - - * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - - Teff and gravity ranges depend on metallicity: - - [M/H] = -2.5 - - * Teff: 2600 - 4600 K - * gravity: 4.5 - 5.5 - - [M/H] = -2.0 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.5 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.0 - - * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 - - [M/H] = -0.5 - - * Teff: 1000 -7000 - * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 - - [M/H] = 0 - - * Teff: 750 - 7000 - * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 - - [M/H] = 0.5 - - * Teff: 1000 - 5000 - * gravity: 3.5 - 5.0 - - - Alpha enhancement: - - * [M/H]= -0.0, +0.5 no anhancement - * [M/H]= -0.5 with [alpha/H]=+0.2 - * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - **PRINT STATEMENTS TO DEBUG - **check get_atmosphere_bounds - **comment out try/except clause and check break - """ - if rebin == True: - atm_name = 'BTSettl_rebin' - else: - atm_name = 'BTSettl' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) -<<<<<<< HEAD - -def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): - """ - Return atmosphere from Meisner2023 grid - (`Meisner et al. 2023 `_) - - Grid originally downloaded `here `_ - - Grid range: - * Teff = 250 - 1200 K - * gravity: 2.5 - 5.5 cgs (in steps of 0.5) - * [M/H] = -1.0, -0.5, 0, +0, +0.3 - - """ - if rebin == True: - atm_name = 'Meisner2023_rebin' - else: - atm_name = 'Meisner2023' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) -======= - ->>>>>>> upstream/dev - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Meisner2023 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): - """ - Return atmosphere from Phillips et al., 2020 using ATMO model - (`Phillips et al. 2020 `_) - - Grid originally downloaded `here `_ - - Grid Range: - * Teff: 200 - 3000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - """ - if rebin == True: - atm_name = 'Phillips2020_rebin' - else: - atm_name = 'Phillips2020' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Phillips2020 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): - """ - Return white dwarf atmospheres from - `Koester et al. 2010 `_ - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. - - Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 - """ - try: - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_atlas_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and phoenixV16. - - Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_BTSettl_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_meisner_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and Meisner2023. - - Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_meisner', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl-Meisner merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -#---------------------------------------------------------------------# -def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, - rebin=True): - """ - Return a stellar atmosphere from a suite of different model grids, - depending on the input temperature, (all values in K). - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - - Notes - ----- - The underlying stellar model grid used changes as a function of - stellar temperature (in K): - - * T > 20,000: ATLAS - * 5500 <= T < 20,000: ATLAS - * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge - * 3800 <= T < 5000: PHOENIXv16 - - For T < 3800, there is an additional gravity and metallicity - dependence: - - If T < 3800 and [M/H] = 0: - - * T < 3800, logg < 2.5: PHOENIX v16 - * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge - * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - * 1200 < T <= 1000, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge - * 1000 < T <= 250, logg > 2.5: Meisner2023 - - Otherwise, if T < 3800 and [M/H] != 0: - - * T < 3800: PHOENIX v16 - - References: - - * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) - * PHOENIXv16 (`Husser et al. 2013 `_) - * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) - * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) - - LTE WARNING: - - The ATLAS atmospheres are calculated with LTE, and so they - are less accurate when non-LTE conditions apply (e.g. T > 20,000 - K). Ultimately we'd like to add a non-LTE atmosphere grid for - the hottest stars in the future. - - HOW BOUNDARIES BETWEEN MODELS ARE TREATED: - - At the boundary between two models grids a temperature range is defined - where the resulting atmosphere is a weighted average between the two - grids. Near one boundary one model - is weighted more heavily, while at the other boundary the other - model is weighted more heavily. These are calculated in the - temperature ranges where we switch between model grids, to - ensure a smooth transition. - """ - # For T < 3800, atmosphere depends on metallicity + gravity. - # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is - # currently available here, so if non-solar metallicity, just stick with - # the Phoenix grid - if (temperature < 1000): - if verbose: - print( 'Meisner2023 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 1200) & (temperature >= 1000): - if (gravity >= 3.5): - if verbose: - print( 'BTSettl/Meisner2023 merged atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - if (gravity < 3.5) & (gravity >=2.5): - if verbose: - print( 'Meisner2023 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity == 0): - # High gravity are in BTSettl regime - if (temperature <= 3200) & (gravity > 2.5): - if verbose: - print( 'BTSettl_2015 atmosphere') - return get_BTSettl_2015_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): - if verbose: - print( 'BTSettl/Phoenixv16 merged atmosphere') - return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - # Low gravity is PHOENIX regime - if gravity <= 2.5: - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity != 0): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - # For T > 3800, no metallicity or gravity dependence - if (temperature >= 3800) & (temperature < 5000): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 5000) & (temperature < 5500): - if verbose: - print( 'ATLAS/Phoenix merged atmosphere') - return get_atlas_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if (temperature >= 5500) & (temperature < 20000): - if verbose: - print( 'ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if temperature >= 20000: - if verbose: - print( 'Still ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - #print('CMFGEN') - #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, - # temperature=temperature, - # gravity=gravity) - - - - -def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Return the white dwarf atmosphere from - `Koester et al. 2010 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('wdKoester atmosphere') - - return get_wdKoester_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere. - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - - -def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): - """ - Return the brown dwarf atmosphere from - `Meisner et al. 2023 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('Meisner2023 atmosphere') - - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - -def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, - verbose=False, rebin=None, - wave_min=500, wave_max=50_000, wave_num=20_000): - """ - Return a blackbody spectrum - - Parameters - ---------- - temperature: float, default=20_000 - The stellar temperature, in units of K - wave_min: float, default=500 - Sets the minimum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_max: float, default=50_000 - Sets the maximum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_num: int, default=20_000 - Sets the number of wavelength points in the wavelength range - Note: the wavelength range is evenly spaced in log space - """ - if ((metallicity is not None) or (gravity is not None) or - (rebin is not None)): - warnings.warn( - 'Only `temperature` keyword is used for black-body atmosphere' - ) - - if verbose: - print('Black-body atmosphere') - - # Modify pysynphot's default waveset to specified bounds - pysynphot.refs.set_default_waveset( - minwave=wave_min, maxwave=wave_max, num=wave_num - ) - - # Get black-body atmosphere for specified temperature from pysynphot - bbspec = pysynphot.spectrum.BlackBody(temperature) - - # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` - bbspec.convert('flam') - - # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. - # Need to remove this normalization for SPISEA by multiplying bbspec - # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 - bbspec *= (1000 * 3.086e18 / 6.957e10)**2 - - return bbspec - - -#--------------------------------------# -# Atmosphere formatting functions -#--------------------------------------# - -def download_CMFGEN_atmospheres(Table_rot, Table_norot): - """ - Downloads CMFGEN models from - https://sites.google.com/site/fluxesandcontinuum/home; - these contain continuum as well as lines. - - Table_rot, Table_norot are tables with the file prefixes - and model atmosphere parameters, taken by hand from the - Fierro+15 paper - - Website addresses are hardcoded - - Puts downloaded models in the current working directory. - """ - print( 'WARNING: THIS DOES NOT COMPLETELY WORK') - print( '**********************') - t_rot = Table.read(Table_rot, format='ascii') - t_norot = Table.read(Table_norot, format='ascii') - - tables = [t_rot, t_norot] - filenames = [t_rot['col1'], t_norot['col1']] - - # Hardcoded list of webiste addresses - web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' - web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' - web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', - web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', - web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', - web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', - web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] - # Array of masses that matches the website addresses - mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) - - # Loop through rotating and unrotating case. First loop is rot, second unrot - for i in range(2): - # Extract masses from filenames - masses = [] - for j in filenames[i]: - tmp = j.split('m') - mass = float(tmp[1][:-1]) - masses.append(mass) - - # Download the models webpage by webpage. A bit tricky because masses - # change slightly within a particular website. THIS IS WHAT FAILS - for j in range(len(web)): - if j == 0: - good = np.where( (masses <= mass_arr[j]) ) - else: - g = j - 1 - good = np.where( (masses <= mass_arr[j]) & - (masses > mass_arr[g]) ) - # Use wget command to pull down the files, and unzip them - for k in good[0]: - full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) - os.system('wget ' + full) - os.system('unzip '+ filenames[i][k] + '.flx.zip') - - return - -def organize_CMFGEN_atmospheres(path_to_dir): - """ - Organize CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html) - into rot and noRot directories - - path_to_dir is from current working directory to directory - containing the downloaded models. Assumed that models - and tables describing parameters are in this directory. - - Tables describing parameters MUST be named Table_rot.txt, - Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. - These are located in same original directory as atmosphere files - - Will separate files into 2 subdirectories, one rotating and - the other non-rotating - - *Can't have any other files starting with "t" in model directory to start!* - """ - # First, record current working directory to return to later - start_dir = os.getcwd() - - # Enter atmosphere directory, collect rotating and non-rotating - # file names (assumed to all start with "t") - os.chdir(path_to_dir) - rot_models = glob.glob("t*r.flx*") - noRot_models = glob.glob("t*n.flx*") - - # Separate into different subdirectories - if os.path.exists('cmfgenF15_rot'): - pass - else: - os.mkdir('cmfgenF15_rot') - os.mkdir('cmfgenF15_noRot') - - for mod in rot_models: - cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) - os.system(cmd) - - for mod in noRot_models: - cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) - os.system(cmd) - - # Also move Tables with model parameters into correct directory - os.system('mv Table_rot.txt cmfgenF15_rot') - os.system('mv Table_noRot.txt cmfgenF15_noRot') - - # Return to original directory - os.chdir(start_dir) - - return - -def make_CMFGEN_catalog(path_to_dir): - """ - Create cdbs catalog.fits of CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html). - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to directory - containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, - needs to be a Table*.txt file which contains the parameters for all of the - original models, since params in filename are not precise enough - - Will create catalog.fits file in atmosphere directory with - description of each model - - *Can't have any other files starting with "t" in model directory to start!* - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere - # Note: can't rely on filename for this because not precise enough!! - - #---------OLD: GETTING PARAMS FROM FILENAME-------# - # Collect file names (assumed to all start with "t") - #files = glob.glob("t*") - #for name in files: - # tmp = name.split('l') - # temp = float(tmp[0][1:]) * 100.0 # In kelvin - - # lumtmp = tmp[1].split('_') - # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun - - # mass = float(lumtmp[0][5:-1]) # In M_sun - - # Need to calculate log g from T and L (cgs) - # lum_sun = 3.846 * 10**33 # erg/s - # M_sun = 2 * 10**33 # g - # G_si = 6.67 * 10**(-8) # cgs - # sigma_si = 5.67 * 10**(-5) # cgs - - # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ - # (lum * lum_sun) - # logg = np.log10(g) - #---------------------------------------------------# - - # Read table with atmosphere params - table = glob.glob('Table_*') - t = Table.read(table[0], format = 'ascii') - names = t['col1'] - temps = t['col2'] - logg = t['col4'] - - # Create catalog.fits file - index_str = [] - name_str = [] - for i in range(len(names)): - index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) - - #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# - #path = path_to_dir + '/' + names[i] - path = names[i] + '.fits[Flux]' - - index_str.append(index) - name_str.append(path) - - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits') - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): - """ - Code to put cmfgen models into cdbs format and adds proper unit keyword in - fits header. Save as fits file - - path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot - directory with the *.flx models. Note that these files have already been - organized using organize_CMFGEN_atmospheres code. - - path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or - cmfgen_norot directory. Will copy new fits files to this directory. - This directory must already exist! - """ - # Save starting directory for later, move into path_to_dir directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('*.flx') - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - # Open file, extract useful info - t = Table.read(i, format='ascii') - wave = t['col1'] - flux = t['col2'] # Flux is already in erg/cm^2/s/A - - # Need to eliminate duplicate entries (pysynphot crashes) - unique = np.unique(wave, return_index=True) - wave = wave[unique[1]] - flux = flux[unique[1]] - - # Make fits table from individual columns. - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - #Adding unit keywords - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - prihdu = fits.PrimaryHDU() - - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i[:-4]+'.fits', overwrite=True) - - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Return to original directory, copy over new .fits files to cdbs directory - os.chdir(start_dir) - cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) - os.system(cmd) - - return - -def rebin_cmfgen(cdbs_path, rot=True): - """ - Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; - this makes spectrophotometry MUCH faster - - cdbs_path: path to cdbs directory - rot=True for rotating models (cmfgen_rot), False for non-rotating models - - makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing cmfgen model; we will steal the header. - # Also define paths to new rebin directories - if rot == True: - tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' - path = cdbs_path+'/grid/cmfgen_rot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_rot/' - else: - tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' - path = cdbs_path+'/grid/cmfgen_norot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_norot/' - - cmfgen_hdu = fits.open(tmp) - header0 = cmfgen_hdu[0].header - # Create rebin directories if they don't already exist. Copy over - # catalog.fits file from original directory (will be the same) - if not os.path.exists(path): - os.mkdir(path) - cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) - os.system(cmd) - - # Read in the catalog.fits file - cat = fits.getdata(orig_path + 'catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - # First column in new files will be for [atlas] wavelength - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - - # For each catalog.fits entry, read the unbinned spectrum and rebin to - # the atlas resolution. Make a new fits file in rebin directory - count = 0 - for ff in range(len(files_all)): - count += 1 - # Extract the temp, Z, logg - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - grav = float(vals[2]) - - # Fetch the spectrum - if rot == True: - sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) - else: - sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) - - # Rebin - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - # Make the FITS file from the columns with header - cols = fits.ColDefs([c0,c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write hdu to new directory with same filename - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(path+files_all[ff]) - - print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) - return - - -def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): - """ - Construct the Phoenix Husser+13 atmopsheres for each model. Combines the - fluxes from the *HiRES.fits files and the wavelengths of the - WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. - - path_to_dir is the path to the directory containing all of the downloaded - files - - met_str is the name of the current metallicity - - Creates new fits files for each atmosphere: phoenix_.fits, - which contains columns for the log g (column header = g#.#). Puts - atmospheres in new directory phoenixm00 - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the current metallicity subdirectory - sub_dir = '../phoenix{0}'.format(met_str) - if os.path.exists(sub_dir): - pass - else: - os.mkdir(sub_dir) - - # Extract wavelength array, make column for later - wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') - wave = wavefile[0].data - wavefile.close() - wave_col = Column(wave, name = 'WAVELENGTH') - - # Create temp array for Husser+13 grid (given in paper) - temp_arr = np.arange(2300, 7001, 100) - temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) - - print( 'Looping though all temps') - # For each temp, build file containing the flux for all gravities - i = 0 - for temp in temp_arr: - files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) - files.sort() - # Start the table with the wavelength column - t = Table() - t.add_column(wave_col) - for f in files: - # Extract the logg out of filename - logg = f[9:13] - - # Extract fluxes from file - spectrum = fits.open(f) - flux = spectrum[0].data - spectrum.close() - - # Make Column object with fluxes, add to table - col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) - t.add_column(col) - - # Now, construct final fits file for the given temp - outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) - t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) - - # Progress counter for user - i += 1 - print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) - - # Return to original directory - os.chdir(start_dir) - return - -def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): - """ - Makes catalog.fits file for Husser+13 phoenix models. Assumes that - organize_PHOENIXv16_atmospheres has been run already, and that the models lie - in subdirectory phoenix[met_str]. - - path_to_directory is the path to the directory with the reformatted - models (i.e. the output from construct_atmospheres, phoenix[met_str]) - - Puts catalog.fits file in directory the user starts in - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Extract metallicity from metallicity string - met = float(met_str[1]) + (float(met_str[2]) * 0.1) - if 'm' in met_str: - met *= -1. - - # Collect the filenames. Each is a unique temp with many different log g's - files = glob.glob('phoenix*.fits') - files.sort() - - # Create the catalog.fits file, row by row - index_arr = [] - filename_arr = [] - for i in files: - # Get log g values from the column header the file - t = Table.read(i, format='fits') - keys = t.keys() - logg_vals = keys[1:] - - # Extract temp from filename - name = i.split('_') - temp = float(name[1][:-5]) - for j in logg_vals: - logg = float(j[1:]) - index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) - filename = path_to_dir + i + '[' + j + ']' - # Add row to table - index_arr.append(index) - filename_arr.append(filename) - - catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) - - # Return to starting directory, write catalog - os.chdir(start_dir) - - if os.path.exists('catalog.fits'): - from astropy.table import vstack - - prev_catalog = Table.read('catalog.fits', format='fits') - joined_catalog = vstack([prev_catalog, catalog]) - - joined_catalog.write('catalog.fits', format='fits', overwrite=True) - else: - catalog.write('catalog.fits', format='fits', overwrite=True) - - return - -def cdbs_PHOENIXv16(path_to_cdbs_dir): - """ - Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily - consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] - and adding the appropriate keywords to the fits header. - - path_to_cdbs_dir goes from current working directory to phoenix[met] directory - in cdbs/grids/phoenix_v16. Note that these files have already been organized - using organize_PHOENIXv16_atmospheres code. - - Overwrites original files in directory - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_cdbs_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('phoenix*.fits') - - ## Need to sort filenames; glob doesn't always give them in order - files.sort() - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - - # Read in current FITS table - cur_table = Table.read(i, format='fits') - - cur_table.columns[0].name = 'Wavelength' - - num_cols = len(cur_table.colnames) - - # Multiplying each flux column by 10^-8 for conversion - for cur_col_index in range(1, num_cols, 1): - cur_col_name = cur_table.colnames[cur_col_index] - cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 - - - # Construct new FITS file based on old one - hdu = fits.open(i) - header_0 = hdu[0].header - header_1 = hdu[1].header - sci = hdu[1].data - - tbhdu = fits.table_to_hdu(cur_table) - - # Copying over the older headers, adding unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - tbhdu.header['TUNIT3'] = 'FLAM' - tbhdu.header['TUNIT4'] = 'FLAM' - tbhdu.header['TUNIT5'] = 'FLAM' - tbhdu.header['TUNIT6'] = 'FLAM' - tbhdu.header['TUNIT7'] = 'FLAM' - tbhdu.header['TUNIT8'] = 'FLAM' - tbhdu.header['TUNIT9'] = 'FLAM' - tbhdu.header['TUNIT10'] = 'FLAM' - tbhdu.header['TUNIT11'] = 'FLAM' - tbhdu.header['TUNIT12'] = 'FLAM' - tbhdu.header['TUNIT13'] = 'FLAM' - tbhdu.header['TUNIT14'] = 'FLAM' - - # Construct and write out final FITS file - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i, overwrite=True) - - hdu.close() - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Change back to starting directory - os.chdir(start_dir) - - return - -def rebin_phoenixV16(cdbs_path): - """ - Rebin phoenixV16 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: phoenix_v16_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - ## (This assumes that at least 'm00' metallicity exists) - tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/phoenix_v16_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - temp_arr = np.zeros(len(files_all), dtype=float) - logg_arr = np.zeros(len(files_all), dtype=float) - metal_arr = np.zeros(len(files_all), dtype=float) - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - - temp_arr[ff] = float(vals[0]) - metal_arr[ff] = float(vals[1]) - logg_arr[ff] = float(vals[2]) - - - metal_uniq = np.unique(metal_arr) - temp_uniq = np.unique(temp_arr) - - for mm in range(len(metal_uniq)): - metal = metal_uniq[mm] # metallicity - - # Construct str for metallicity (for appropriate directory name) - met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) - if metal > 0: - met_str = 'p' + met_str - else: - met_str = 'm' + met_str - - # Make directory for current metallicity if it does not exist yet - if not os.path.exists(path + 'phoenix' + met_str): - os.mkdir(path + 'phoenix' + met_str) - - for tt in range(len(temp_uniq)): - temp = temp_uniq[tt] # temperature - - # Pick out the list of gravities for this T, Z combo - idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] - logg_exist = logg_arr[idx] - - # All gravities will go in one file. Here is the output - # file name. - outfile = path + files_all[idx[0]].split('[')[0] - - ## If the rebinned file already exists, continue - if os.path.exists(outfile): - continue - - # Build a columns array. One column for each gravity. - cols_arr = [] - - # Make the wavelength column, which is first in the cols array. - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - cols_arr.append(c0) - - for gg in range(len(logg_exist)): - grav = logg_exist[gg] # gravity - - # Fetch the spectrum - sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Store the spectrum - name = 'g{0:3.1f}'.format(grav) - col = fits.Column(name=name, format='E', array=flux_rebin) - cols_arr.append(col) - - - # Make the FITS file from the columns with header. - cols = fits.ColDefs(cols_arr) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - for gg in range(len(logg_exist)): - tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' - - # Write hdu - finalhdu = fits.HDUList([prihdu, tbhdu]) - # don't have overwrite to protect original files. - finalhdu.writeto(outfile) - - print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) - - - return - - -def rebin_spec(wave, specin, wavnew): - """ - Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: - http://www.astrobetter.com/blog/2013/08/12/ - python-tip-re-sampling-spectra-with-pysynphot/ - """ - spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) - f = np.ones(len(wave)) - filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') - obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') - - return obs.binflux - -def organize_BTSettl_2015_atmospheres(path_to_dir): - """ - Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the BTSettl subdirectory - if not os.path.exists('BTSettl_2015'): - os.mkdir('BTSettl_2015') - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.spec.fits') - - for i in files: - hdu = fits.open(i) - spec = hdu[1].data - header_0 = hdu[0].header - header_1 = hdu[1].header - - wave = spec.field(0) - flux = spec.field(1) - - # Get units right: convert wave from microns to Angstroms, - # flux from W /m^2/ micron to erg/s/cm^2/A - wave_new = wave * 10**4 - flux_new = flux * 10**(-1) - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave_new) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) - - hdu.close() - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_BTSettl_2015_catalog(path_to_dir): - """ - Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*spec.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('-') - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: BTSettl_2015_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - phoenix_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/BTSettl_2015_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning BTSettl spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - -def make_wavelength_unique(files, dirname): - """ - Helper function to go through each BTSettl spectrum and ensure that - each wavelength point is unique. This is required for rebinning to work. - - - files: list of files to run this analysis on - """ - # Loop through each file, find fix repeated wavelength entries if necessary - for i in files: - t = Table.read('{0}/{1}'.format(dirname,i), format='fits') - test = np.unique(t['Wavelength'], return_index=True) - - if len(t) != len(test[0]): - t = t[test[1]] - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - # Also make sure wavelength is monotonic. If it is not, then it is - # a sign that the wavelengths are out of order - diff = np.diff(t['Wavelength']) - bad = np.where(diff < 0) - if len(bad[0]) > 0: - t.sort('Wavelength') - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - print('Done {0}'.format(i)) - - return - -def organize_BTSettl_atmospheres(): - """ - Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). - The code expects tp be run in cdbs/grid/BTSettl, and expects that the - individual model files have been downloaded from online - (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] - - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, - # for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.txt') - count=0 - for jj in files: - t = Table.read(jj, format='ascii') - # First, trim the wavelengths to a more reasonable wavelength range - good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) - t = t[good] - - # Convert flux units to Flam (Ergs/sec/cm**2/A) - flux_new = 10**(t['col2'] - 8.0) - - # Save the file as a fits file - c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Now, clean up all the files made when unzipping the spectra - cmd1 = 'rm *.bz2' - cmd2 = 'rm *.tmp' - #cmd3 = 'rm *.txt' - os.system(cmd1) - os.system(cmd2) - #os.system(cmd3) - print('==============================') - print('Done {0}'.format(ii)) - print('==============================') - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_BTSettl_catalog(): - """ - Create cdbs catalog.fits of BTSettl grid. - THIS IS STEP 2, after organize_BTSettl_atmospheres has - been run. - - Code expects to be run in cdbs/grid/BTSettl - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btp05'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('*.fits') - - # Construct the metallicity val - if 'm' in ii: - metal_flag = -1 * float(ii[3:])*0.1 - else: - metal_flag = float(ii[3:])*0.1 - - # Now collect the info from the files - for jj in files: - tmp = jj.split('-') - - if metal_flag >= 0: - temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin - try: - logg = float(tmp[1]) - except: - logg = float(tmp[1].split('+')[0]) - else: - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - # Go back to original directory to move to next metallicity - print('Done {0}'.format(ii)) - os.chdir(start_dir) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl(make_unique=False): - """ - Rebin BTSettle models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: BTSettl_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create cdbs/grid directory for rebinned models - path = 'BTSettl_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata('BTSettl/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - #==============================# - #tmp = [] - #for ii in files_all: - # if ii.startswith('btp00'): - # tmp.append(ii) - #files_all = tmp - #=============================# - - print( 'Rebinning BTSettl spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'BTSettl') - print('Done') - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - try: - sp = pysynphot.Icat('BTSettl', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - except: - pdb.set_trace() - orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) - outfile = path + files_all[ff].split('[')[0] - cmd = 'cp {0} {1}'.format(orig_file, outfile) - os.system(cmd) - - print('Done {0} of {1}'.format(ff, len(files_all))) - - return - -def organize_all_Meisner2023_atmospheres(): - """ - Construct cdbs-ready atmospheres for the Meisner2023 grid. - The code expects tp be run in cdbs/grid/Meisner2023, and expects that the - individual model files have been downloaded from online - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['mm10', 'mm05', 'mp00', 'mp03'] - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Save as a fits file, for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.fits') - count=0 - for jj in files: - # Open each .fits file and read the data - with fits.open(jj) as hdul: - data = hdul[1].data - wavelength = data['Wavelength'] - flux = data['Flux'] - - # Make flux independent of R&D - flux_new = flux / 5e-20 - - # Create new columns with desired format - c0 = fits.Column(name='Wavelength', format='D', array=wavelength) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write the new fits table in the cdbs directory - output_filename = '{0}.fits'.format(jj[:-5]) # Removing the original .fits extension - hdu_new.writeto(output_filename, overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_Meisner2023_catalog(): - """ - Create cdbs catalog.fits of Meisner2023 grid. - THIS IS STEP 2, after organize_Meisner2023_atmospheres has - been run. - - Code expects to be run in cdbs/grid/Meisner2023 - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['mm10', 'mm05', 'mp00', 'mp03'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('spec_jwst_*.fits') - - for jj in files: - # Parse temperature, log(g), and metallicity from filename - temp_str = jj.split('_')[2] - logg_str = jj.split('_')[3] - metal_str = jj.split('_')[4] - - # Extract temperature, surface gravity, and metallicity - temp = float(temp_str[1:]) # Temperature in Kelvin - logg = float(logg_str[1:]) # Surface gravity log(g) - - # Build metallicity value - if metal_str.startswith('m'): - metallicity = -1 * float(metal_str[1:]) - else: - metallicity = float(metal_str[1:]) - - # Construct index and filename strings - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metallicity, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - print('Processed directory:', ii) - os.chdir(start_dir) - - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_Meisner2023(make_unique=False): - """ - Rebin Meisner2023 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: Meisner2023_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create a directory for rebinned Meisner2023 models - rebin_path = 'Meisner2023_rebin/' - if not os.path.exists(rebin_path): - os.mkdir(rebin_path) - - # Load the catalog.fits file and extract all spectra file paths - cat = Table.read('Meisner2023/catalog.fits') - files_all = [cat[ii]['FILENAME'].split('[')[0] for ii in range(len(cat))] - - print('Rebinning Meisner2023 spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'Meisner2023') - print('Done') - - for ff, file in enumerate(files_all): - vals = cat[ff]['INDEX'].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the Meisner2023 spectrum and rebin its flux - try: - sp = pysynphot.Icat('Meisner2023', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Create the output FITS file - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write the new rebinned file in the Meisner2023_rebin directory - outfile = os.path.join(rebin_path, os.path.basename(file)) - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - except Exception as e: - print(f"Error processing {file}: {e}") - orig_file = os.path.join('Meisner2023', file) - outfile = os.path.join(rebin_path, os.path.basename(file)) - os.system(f'cp {orig_file} {outfile}') - - print('Done {0} of {1}'.format(ff + 1, len(files_all))) - - return - - - -def organize_WDKoester_atmospheres(path_to_dir): - """ - Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.dk.dat.txt') - - for i in files: - data = Table.read(i, format='ascii') - - wave = data['col1'] # angstrom - flux = data['col2'] # erg/s/cm^2/A - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) - - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_WDKoester_catalog(path_to_dir): - """ - Create cdbs catalog.fits of wdKoester grid. - THIS IS STEP 2, after organize_WDKoester_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*dk.dat.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('.') - tmp2 = tmp[0].split('_') - temp = float(tmp2[0][2:]) # Kelvin - logg = float(tmp2[1]) / 100.0 # log(g) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin wdKoester models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: wdKoester_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing model; we will steal the header - tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' - wdkoester_hdu = fits.open(tmp) - header0 = wdkoester_hdu[0].header - wdkoester_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/wdKoester_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning wdKoester spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the wdKoester spectrum, rebin flux - sp = pysynphot.Icat('wdKoester', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - - diff --git a/spisea/atmospheres_BACKUP_58456.py b/spisea/atmospheres_BACKUP_58456.py deleted file mode 100755 index e3d2233c..00000000 --- a/spisea/atmospheres_BACKUP_58456.py +++ /dev/null @@ -1,2524 +0,0 @@ -import logging -import numpy as np -import pysynphot -import os -import glob -from astropy.io import fits -from astropy.table import Table, Column -import pysynphot -import time -import pdb -import warnings - -log = logging.getLogger('atmospheres') - -def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Given atmosphere model, get temperature and gravity bounds - """ - # Open catalog fits file and break out row indices - catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) - - teff_arr = [] - z_arr = [] - logg_arr = [] - for cur_row_index in range(len(catalog)): - index = catalog['INDEX'][cur_row_index] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Filter by metallicity. Will chose the closest metallicity to desired input - metal_list = np.unique(np.array(z_arr)) - metal_idx = np.argmin(np.abs(metal_list - metallicity)) - metallicity_new = metal_list[metal_idx] - - z_filt = np.where(z_arr == metal_list[metal_idx]) - teff_arr = teff_arr[z_filt] - logg_arr = logg_arr[z_filt] - - # # Now find the closest atmosphere in parameter space to - # # the one we want. We'll find the match with the lowest - # # fractional difference - # teff_diff = (teff_arr - temperature) / temperature - # logg_diff = (logg_arr - gravity) / gravity - # - # diff_tot = abs(teff_diff) + abs(logg_diff) - # idx_f = np.argmin(diff_tot) - # - # temperature_new = teff_arr[idx_f] - # gravity_new = logg_arr[idx_f] - - # First check if temperature within bounds - temperature_new = temperature - if temperature > np.max(teff_arr): - temperature_new = np.max(teff_arr) - if temperature < np.min(teff_arr): - temperature_new = np.min(teff_arr) - - # If temperature within bounds, then check if metallicity within bounds - teff_diff = np.abs(teff_arr - temperature) - sorted_min_diffs = np.unique(teff_diff) - - ## Find two closest temperatures - teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] - teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] - - logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] - logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] - - ## Switch to most conservative bound of logg out of two closest temps - gravity_new = gravity - if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): - gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) - if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): - gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) - - if verbose: - # Print out changes, if any - if temperature_new != temperature: - teff_msg = 'Changing to T={0:6.0f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( teff_msg.format(temperature_new, metallicity, temperature, gravity)) - - if gravity_new != gravity: - logg_msg = 'Changing to logg={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( logg_msg.format(gravity_new, metallicity, temperature, gravity)) - - if metallicity_new != metallicity: - logg_msg = 'Changing to met={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( logg_msg.format(metallicity_new, metallicity, temperature, gravity)) - - return (temperature_new, gravity_new, metallicity_new) - -def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmosphere from the Kurucz pysnphot grid - (`Kurucz 1993 `_). - - Grid Range: - - * Teff: 3000 - 50000 K - * gravity: 0 - 5 cgs - * metallicity: -5.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - Always false for this particular function - """ - try: - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('k93models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Kurucz 1993 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmospheres from the pysynphot ATLAS9 atlas - (`Castelli & Kurucz 2004 `_). - - Grid Range: - - * Teff: 3500 - 50000 K - * gravity: 0 - 5.0 cgs - * [M/H]: -2.5 - 0.2 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('ck04models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - try: - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('nextgen', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find NextGen atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, - rebin=False): - """ - Return atmosphere from the pysynphot - `PHOENIX atlas `_. - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - try: - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - # Take care of atmospheres outside the catalog boundaries - logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' - if gravity > 4.3: - if verbose: - print( logg_msg.format(4.3, temperature, gravity)) - gravity = 4.3 - - if rebin: - sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, - verbose=False): - """ - For a given stellar atmosphere, get extract the closest possible match in - Teff/logg space. Note that this is different from the normal routine - which interpolates along the input grid to get final spectrum. We can't - do this here because the Fierro+15 atmosphere grid is so sparse - - rebin=True: pull from atmospheres at ck04model resolution. - - If verbose, print out the parameters of the match - """ - # Set up the proper root directory - if rebin == True: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' - else: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' - - # Read in catalog, extract atmosphere info - cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') - teff_arr = [] - z_arr = [] - logg_arr = [] - for ii in range(len(cat)): - index = cat['INDEX'][ii] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Now find the closest atmosphere in parameter space to - # the one we want. We'll find the match with the lowest - # fractional difference - teff_diff = (teff_arr - temperature) / temperature - logg_diff = (logg_arr - gravity) / gravity - - diff_tot = abs(teff_diff) + abs(logg_diff) - idx_f = np.where(diff_tot == min(diff_tot))[0][0] - - # Extract the filename of the best-match model and read as - # pysynphot object - infile = cat[idx_f]['FILENAME'].split('.') - spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the - # ATLAS models are in FLAM at the surface. So, we need to multiply the - # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. - # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file - t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') - tmp = np.where(t['col1'] == infile[0]) - - lum = t['col3'][tmp] * (3.839*10**33) # cgs - sigma = 5.6704 * 10**-5 # cgs - teff = teff_arr[idx_f] # cgs - - radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm - radius /= 3.08*10**18 # in pc - - - # Make the pysynphot spectrum - w = spec['Wavelength'] - f = spec['Flux'] * (1000 / radius)**2. - sp = pysynphot.ArraySpectrum(w,f) - - #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Print out parameters of match, if desired - if verbose: - print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) - print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - if rebin: - sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 30000) - gravity = log gravity (def = 4.14) - """ - sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): - """ - Return PHOENIX v16 atmospheres from - `Husser et al. 2013 `_. - - Models originally downloaded via `ftp `_. - Solar metallicity and [alpha/Fe] is used. - - Grid Range: - - * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K - * gravity: 0.0 - 6.0 cgs, steps of 0.5 - * [M/H]: -4.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - atm_model_name = 'phoenix_v16' - if rebin == True: - atm_model_name = 'phoenix_v16_rebin' - - - # Extract atmosphere. If that fails, then check bounds and try again - try: - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_model_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): - """ - Return atmosphere from CIFIST2011_2015 grid - (`Allard et al. 2012 `_, - `Baraffe et al. 2015 `_ ) - - Grid originally downloaded from `website `_. - - Grid Range: - - * Teff: 1200 - 7000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_2015_rebin' - else: - atm_name = 'BTSettl_2015' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) -<<<<<<< HEAD - #print(dir(obj)) - - -======= - - ->>>>>>> upstream/dev - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): - """ - Return atmosphere from CIFIST2011 grid - (`Allard et al. 2012 `_) - - Grid originally downloaded `here `_ - - Notes - ------ - Grid Range: - - * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - - Teff and gravity ranges depend on metallicity: - - [M/H] = -2.5 - - * Teff: 2600 - 4600 K - * gravity: 4.5 - 5.5 - - [M/H] = -2.0 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.5 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.0 - - * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 - - [M/H] = -0.5 - - * Teff: 1000 -7000 - * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 - - [M/H] = 0 - - * Teff: 750 - 7000 - * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 - - [M/H] = 0.5 - - * Teff: 1000 - 5000 - * gravity: 3.5 - 5.0 - - - Alpha enhancement: - - * [M/H]= -0.0, +0.5 no anhancement - * [M/H]= -0.5 with [alpha/H]=+0.2 - * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - **PRINT STATEMENTS TO DEBUG - **check get_atmosphere_bounds - **comment out try/except clause and check break - """ - if rebin == True: - atm_name = 'BTSettl_rebin' - else: - atm_name = 'BTSettl' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) -<<<<<<< HEAD - -def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): - """ - Return atmosphere from Meisner2023 grid - (`Meisner et al. 2023 `_) - - Grid originally downloaded `here `_ - - Grid range: - * Teff = 250 - 1200 K - * gravity: 2.5 - 5.5 cgs (in steps of 0.5) - * [M/H] = -1.0, -0.5, 0, +0, +0.3 - - """ - if rebin == True: - atm_name = 'Meisner2023_rebin' - else: - atm_name = 'Meisner2023' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) -======= - ->>>>>>> upstream/dev - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Meisner2023 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): - """ - Return atmosphere from Phillips et al., 2020 using ATMO model - (`Phillips et al. 2020 `_) - - Grid originally downloaded `here `_ - - Grid Range: - * Teff: 200 - 3000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - """ - if rebin == True: - atm_name = 'Phillips2020_rebin' - else: - atm_name = 'Phillips2020' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Phillips2020 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): - """ - Return white dwarf atmospheres from - `Koester et al. 2010 `_ - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. - - Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 - """ - try: - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_atlas_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and phoenixV16. - - Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_BTSettl_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_meisner_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and Meisner2023. - - Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_meisner', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl-Meisner merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -#---------------------------------------------------------------------# -def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, - rebin=True): - """ - Return a stellar atmosphere from a suite of different model grids, - depending on the input temperature, (all values in K). - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - - Notes - ----- - The underlying stellar model grid used changes as a function of - stellar temperature (in K): - - * T > 20,000: ATLAS - * 5500 <= T < 20,000: ATLAS - * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge - * 3800 <= T < 5000: PHOENIXv16 - - For T < 3800, there is an additional gravity and metallicity - dependence: - - If T < 3800 and [M/H] = 0: - - * T < 3800, logg < 2.5: PHOENIX v16 - * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge - * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - * 1200 < T <= 1000, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge - * 1000 < T <= 250, logg > 2.5: Meisner2023 - - Otherwise, if T < 3800 and [M/H] != 0: - - * T < 3800: PHOENIX v16 - - References: - - * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) - * PHOENIXv16 (`Husser et al. 2013 `_) - * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) - * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) - - LTE WARNING: - - The ATLAS atmospheres are calculated with LTE, and so they - are less accurate when non-LTE conditions apply (e.g. T > 20,000 - K). Ultimately we'd like to add a non-LTE atmosphere grid for - the hottest stars in the future. - - HOW BOUNDARIES BETWEEN MODELS ARE TREATED: - - At the boundary between two models grids a temperature range is defined - where the resulting atmosphere is a weighted average between the two - grids. Near one boundary one model - is weighted more heavily, while at the other boundary the other - model is weighted more heavily. These are calculated in the - temperature ranges where we switch between model grids, to - ensure a smooth transition. - """ - # For T < 3800, atmosphere depends on metallicity + gravity. - # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is - # currently available here, so if non-solar metallicity, just stick with - # the Phoenix grid - if (temperature < 1000): - if verbose: - print( 'Meisner2023 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 1200) & (temperature >= 1000): - if (gravity >= 3.5): - if verbose: - print( 'BTSettl/Meisner2023 merged atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - if (gravity < 3.5) & (gravity >=2.5): - if verbose: - print( 'Meisner2023 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity == 0): - # High gravity are in BTSettl regime - if (temperature <= 3200) & (gravity > 2.5): - if verbose: - print( 'BTSettl_2015 atmosphere') - return get_BTSettl_2015_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): - if verbose: - print( 'BTSettl/Phoenixv16 merged atmosphere') - return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - # Low gravity is PHOENIX regime - if gravity <= 2.5: - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity != 0): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - # For T > 3800, no metallicity or gravity dependence - if (temperature >= 3800) & (temperature < 5000): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 5000) & (temperature < 5500): - if verbose: - print( 'ATLAS/Phoenix merged atmosphere') - return get_atlas_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if (temperature >= 5500) & (temperature < 20000): - if verbose: - print( 'ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if temperature >= 20000: - if verbose: - print( 'Still ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - #print('CMFGEN') - #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, - # temperature=temperature, - # gravity=gravity) - - - - -def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Return the white dwarf atmosphere from - `Koester et al. 2010 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('wdKoester atmosphere') - - return get_wdKoester_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere. - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - - -def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): - """ - Return the brown dwarf atmosphere from - `Meisner et al. 2023 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('Meisner2023 atmosphere') - - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - -def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, - verbose=False, rebin=None, - wave_min=500, wave_max=50_000, wave_num=20_000): - """ - Return a blackbody spectrum - - Parameters - ---------- - temperature: float, default=20_000 - The stellar temperature, in units of K - wave_min: float, default=500 - Sets the minimum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_max: float, default=50_000 - Sets the maximum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_num: int, default=20_000 - Sets the number of wavelength points in the wavelength range - Note: the wavelength range is evenly spaced in log space - """ - if ((metallicity is not None) or (gravity is not None) or - (rebin is not None)): - warnings.warn( - 'Only `temperature` keyword is used for black-body atmosphere' - ) - - if verbose: - print('Black-body atmosphere') - - # Modify pysynphot's default waveset to specified bounds - pysynphot.refs.set_default_waveset( - minwave=wave_min, maxwave=wave_max, num=wave_num - ) - - # Get black-body atmosphere for specified temperature from pysynphot - bbspec = pysynphot.spectrum.BlackBody(temperature) - - # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` - bbspec.convert('flam') - - # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. - # Need to remove this normalization for SPISEA by multiplying bbspec - # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 - bbspec *= (1000 * 3.086e18 / 6.957e10)**2 - - return bbspec - - -#--------------------------------------# -# Atmosphere formatting functions -#--------------------------------------# - -def download_CMFGEN_atmospheres(Table_rot, Table_norot): - """ - Downloads CMFGEN models from - https://sites.google.com/site/fluxesandcontinuum/home; - these contain continuum as well as lines. - - Table_rot, Table_norot are tables with the file prefixes - and model atmosphere parameters, taken by hand from the - Fierro+15 paper - - Website addresses are hardcoded - - Puts downloaded models in the current working directory. - """ - print( 'WARNING: THIS DOES NOT COMPLETELY WORK') - print( '**********************') - t_rot = Table.read(Table_rot, format='ascii') - t_norot = Table.read(Table_norot, format='ascii') - - tables = [t_rot, t_norot] - filenames = [t_rot['col1'], t_norot['col1']] - - # Hardcoded list of webiste addresses - web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' - web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' - web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', - web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', - web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', - web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', - web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] - # Array of masses that matches the website addresses - mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) - - # Loop through rotating and unrotating case. First loop is rot, second unrot - for i in range(2): - # Extract masses from filenames - masses = [] - for j in filenames[i]: - tmp = j.split('m') - mass = float(tmp[1][:-1]) - masses.append(mass) - - # Download the models webpage by webpage. A bit tricky because masses - # change slightly within a particular website. THIS IS WHAT FAILS - for j in range(len(web)): - if j == 0: - good = np.where( (masses <= mass_arr[j]) ) - else: - g = j - 1 - good = np.where( (masses <= mass_arr[j]) & - (masses > mass_arr[g]) ) - # Use wget command to pull down the files, and unzip them - for k in good[0]: - full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) - os.system('wget ' + full) - os.system('unzip '+ filenames[i][k] + '.flx.zip') - - return - -def organize_CMFGEN_atmospheres(path_to_dir): - """ - Organize CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html) - into rot and noRot directories - - path_to_dir is from current working directory to directory - containing the downloaded models. Assumed that models - and tables describing parameters are in this directory. - - Tables describing parameters MUST be named Table_rot.txt, - Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. - These are located in same original directory as atmosphere files - - Will separate files into 2 subdirectories, one rotating and - the other non-rotating - - *Can't have any other files starting with "t" in model directory to start!* - """ - # First, record current working directory to return to later - start_dir = os.getcwd() - - # Enter atmosphere directory, collect rotating and non-rotating - # file names (assumed to all start with "t") - os.chdir(path_to_dir) - rot_models = glob.glob("t*r.flx*") - noRot_models = glob.glob("t*n.flx*") - - # Separate into different subdirectories - if os.path.exists('cmfgenF15_rot'): - pass - else: - os.mkdir('cmfgenF15_rot') - os.mkdir('cmfgenF15_noRot') - - for mod in rot_models: - cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) - os.system(cmd) - - for mod in noRot_models: - cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) - os.system(cmd) - - # Also move Tables with model parameters into correct directory - os.system('mv Table_rot.txt cmfgenF15_rot') - os.system('mv Table_noRot.txt cmfgenF15_noRot') - - # Return to original directory - os.chdir(start_dir) - - return - -def make_CMFGEN_catalog(path_to_dir): - """ - Create cdbs catalog.fits of CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html). - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to directory - containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, - needs to be a Table*.txt file which contains the parameters for all of the - original models, since params in filename are not precise enough - - Will create catalog.fits file in atmosphere directory with - description of each model - - *Can't have any other files starting with "t" in model directory to start!* - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere - # Note: can't rely on filename for this because not precise enough!! - - #---------OLD: GETTING PARAMS FROM FILENAME-------# - # Collect file names (assumed to all start with "t") - #files = glob.glob("t*") - #for name in files: - # tmp = name.split('l') - # temp = float(tmp[0][1:]) * 100.0 # In kelvin - - # lumtmp = tmp[1].split('_') - # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun - - # mass = float(lumtmp[0][5:-1]) # In M_sun - - # Need to calculate log g from T and L (cgs) - # lum_sun = 3.846 * 10**33 # erg/s - # M_sun = 2 * 10**33 # g - # G_si = 6.67 * 10**(-8) # cgs - # sigma_si = 5.67 * 10**(-5) # cgs - - # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ - # (lum * lum_sun) - # logg = np.log10(g) - #---------------------------------------------------# - - # Read table with atmosphere params - table = glob.glob('Table_*') - t = Table.read(table[0], format = 'ascii') - names = t['col1'] - temps = t['col2'] - logg = t['col4'] - - # Create catalog.fits file - index_str = [] - name_str = [] - for i in range(len(names)): - index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) - - #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# - #path = path_to_dir + '/' + names[i] - path = names[i] + '.fits[Flux]' - - index_str.append(index) - name_str.append(path) - - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits') - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): - """ - Code to put cmfgen models into cdbs format and adds proper unit keyword in - fits header. Save as fits file - - path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot - directory with the *.flx models. Note that these files have already been - organized using organize_CMFGEN_atmospheres code. - - path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or - cmfgen_norot directory. Will copy new fits files to this directory. - This directory must already exist! - """ - # Save starting directory for later, move into path_to_dir directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('*.flx') - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - # Open file, extract useful info - t = Table.read(i, format='ascii') - wave = t['col1'] - flux = t['col2'] # Flux is already in erg/cm^2/s/A - - # Need to eliminate duplicate entries (pysynphot crashes) - unique = np.unique(wave, return_index=True) - wave = wave[unique[1]] - flux = flux[unique[1]] - - # Make fits table from individual columns. - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - #Adding unit keywords - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - prihdu = fits.PrimaryHDU() - - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i[:-4]+'.fits', overwrite=True) - - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Return to original directory, copy over new .fits files to cdbs directory - os.chdir(start_dir) - cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) - os.system(cmd) - - return - -def rebin_cmfgen(cdbs_path, rot=True): - """ - Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; - this makes spectrophotometry MUCH faster - - cdbs_path: path to cdbs directory - rot=True for rotating models (cmfgen_rot), False for non-rotating models - - makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing cmfgen model; we will steal the header. - # Also define paths to new rebin directories - if rot == True: - tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' - path = cdbs_path+'/grid/cmfgen_rot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_rot/' - else: - tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' - path = cdbs_path+'/grid/cmfgen_norot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_norot/' - - cmfgen_hdu = fits.open(tmp) - header0 = cmfgen_hdu[0].header - # Create rebin directories if they don't already exist. Copy over - # catalog.fits file from original directory (will be the same) - if not os.path.exists(path): - os.mkdir(path) - cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) - os.system(cmd) - - # Read in the catalog.fits file - cat = fits.getdata(orig_path + 'catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - # First column in new files will be for [atlas] wavelength - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - - # For each catalog.fits entry, read the unbinned spectrum and rebin to - # the atlas resolution. Make a new fits file in rebin directory - count = 0 - for ff in range(len(files_all)): - count += 1 - # Extract the temp, Z, logg - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - grav = float(vals[2]) - - # Fetch the spectrum - if rot == True: - sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) - else: - sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) - - # Rebin - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - # Make the FITS file from the columns with header - cols = fits.ColDefs([c0,c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write hdu to new directory with same filename - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(path+files_all[ff]) - - print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) - return - - -def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): - """ - Construct the Phoenix Husser+13 atmopsheres for each model. Combines the - fluxes from the *HiRES.fits files and the wavelengths of the - WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. - - path_to_dir is the path to the directory containing all of the downloaded - files - - met_str is the name of the current metallicity - - Creates new fits files for each atmosphere: phoenix_.fits, - which contains columns for the log g (column header = g#.#). Puts - atmospheres in new directory phoenixm00 - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the current metallicity subdirectory - sub_dir = '../phoenix{0}'.format(met_str) - if os.path.exists(sub_dir): - pass - else: - os.mkdir(sub_dir) - - # Extract wavelength array, make column for later - wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') - wave = wavefile[0].data - wavefile.close() - wave_col = Column(wave, name = 'WAVELENGTH') - - # Create temp array for Husser+13 grid (given in paper) - temp_arr = np.arange(2300, 7001, 100) - temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) - - print( 'Looping though all temps') - # For each temp, build file containing the flux for all gravities - i = 0 - for temp in temp_arr: - files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) - files.sort() - # Start the table with the wavelength column - t = Table() - t.add_column(wave_col) - for f in files: - # Extract the logg out of filename - logg = f[9:13] - - # Extract fluxes from file - spectrum = fits.open(f) - flux = spectrum[0].data - spectrum.close() - - # Make Column object with fluxes, add to table - col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) - t.add_column(col) - - # Now, construct final fits file for the given temp - outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) - t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) - - # Progress counter for user - i += 1 - print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) - - # Return to original directory - os.chdir(start_dir) - return - -def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): - """ - Makes catalog.fits file for Husser+13 phoenix models. Assumes that - organize_PHOENIXv16_atmospheres has been run already, and that the models lie - in subdirectory phoenix[met_str]. - - path_to_directory is the path to the directory with the reformatted - models (i.e. the output from construct_atmospheres, phoenix[met_str]) - - Puts catalog.fits file in directory the user starts in - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Extract metallicity from metallicity string - met = float(met_str[1]) + (float(met_str[2]) * 0.1) - if 'm' in met_str: - met *= -1. - - # Collect the filenames. Each is a unique temp with many different log g's - files = glob.glob('phoenix*.fits') - files.sort() - - # Create the catalog.fits file, row by row - index_arr = [] - filename_arr = [] - for i in files: - # Get log g values from the column header the file - t = Table.read(i, format='fits') - keys = t.keys() - logg_vals = keys[1:] - - # Extract temp from filename - name = i.split('_') - temp = float(name[1][:-5]) - for j in logg_vals: - logg = float(j[1:]) - index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) - filename = path_to_dir + i + '[' + j + ']' - # Add row to table - index_arr.append(index) - filename_arr.append(filename) - - catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) - - # Return to starting directory, write catalog - os.chdir(start_dir) - - if os.path.exists('catalog.fits'): - from astropy.table import vstack - - prev_catalog = Table.read('catalog.fits', format='fits') - joined_catalog = vstack([prev_catalog, catalog]) - - joined_catalog.write('catalog.fits', format='fits', overwrite=True) - else: - catalog.write('catalog.fits', format='fits', overwrite=True) - - return - -def cdbs_PHOENIXv16(path_to_cdbs_dir): - """ - Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily - consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] - and adding the appropriate keywords to the fits header. - - path_to_cdbs_dir goes from current working directory to phoenix[met] directory - in cdbs/grids/phoenix_v16. Note that these files have already been organized - using organize_PHOENIXv16_atmospheres code. - - Overwrites original files in directory - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_cdbs_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('phoenix*.fits') - - ## Need to sort filenames; glob doesn't always give them in order - files.sort() - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - - # Read in current FITS table - cur_table = Table.read(i, format='fits') - - cur_table.columns[0].name = 'Wavelength' - - num_cols = len(cur_table.colnames) - - # Multiplying each flux column by 10^-8 for conversion - for cur_col_index in range(1, num_cols, 1): - cur_col_name = cur_table.colnames[cur_col_index] - cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 - - - # Construct new FITS file based on old one - hdu = fits.open(i) - header_0 = hdu[0].header - header_1 = hdu[1].header - sci = hdu[1].data - - tbhdu = fits.table_to_hdu(cur_table) - - # Copying over the older headers, adding unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - tbhdu.header['TUNIT3'] = 'FLAM' - tbhdu.header['TUNIT4'] = 'FLAM' - tbhdu.header['TUNIT5'] = 'FLAM' - tbhdu.header['TUNIT6'] = 'FLAM' - tbhdu.header['TUNIT7'] = 'FLAM' - tbhdu.header['TUNIT8'] = 'FLAM' - tbhdu.header['TUNIT9'] = 'FLAM' - tbhdu.header['TUNIT10'] = 'FLAM' - tbhdu.header['TUNIT11'] = 'FLAM' - tbhdu.header['TUNIT12'] = 'FLAM' - tbhdu.header['TUNIT13'] = 'FLAM' - tbhdu.header['TUNIT14'] = 'FLAM' - - # Construct and write out final FITS file - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i, overwrite=True) - - hdu.close() - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Change back to starting directory - os.chdir(start_dir) - - return - -def rebin_phoenixV16(cdbs_path): - """ - Rebin phoenixV16 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: phoenix_v16_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - ## (This assumes that at least 'm00' metallicity exists) - tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/phoenix_v16_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - temp_arr = np.zeros(len(files_all), dtype=float) - logg_arr = np.zeros(len(files_all), dtype=float) - metal_arr = np.zeros(len(files_all), dtype=float) - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - - temp_arr[ff] = float(vals[0]) - metal_arr[ff] = float(vals[1]) - logg_arr[ff] = float(vals[2]) - - - metal_uniq = np.unique(metal_arr) - temp_uniq = np.unique(temp_arr) - - for mm in range(len(metal_uniq)): - metal = metal_uniq[mm] # metallicity - - # Construct str for metallicity (for appropriate directory name) - met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) - if metal > 0: - met_str = 'p' + met_str - else: - met_str = 'm' + met_str - - # Make directory for current metallicity if it does not exist yet - if not os.path.exists(path + 'phoenix' + met_str): - os.mkdir(path + 'phoenix' + met_str) - - for tt in range(len(temp_uniq)): - temp = temp_uniq[tt] # temperature - - # Pick out the list of gravities for this T, Z combo - idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] - logg_exist = logg_arr[idx] - - # All gravities will go in one file. Here is the output - # file name. - outfile = path + files_all[idx[0]].split('[')[0] - - ## If the rebinned file already exists, continue - if os.path.exists(outfile): - continue - - # Build a columns array. One column for each gravity. - cols_arr = [] - - # Make the wavelength column, which is first in the cols array. - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - cols_arr.append(c0) - - for gg in range(len(logg_exist)): - grav = logg_exist[gg] # gravity - - # Fetch the spectrum - sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Store the spectrum - name = 'g{0:3.1f}'.format(grav) - col = fits.Column(name=name, format='E', array=flux_rebin) - cols_arr.append(col) - - - # Make the FITS file from the columns with header. - cols = fits.ColDefs(cols_arr) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - for gg in range(len(logg_exist)): - tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' - - # Write hdu - finalhdu = fits.HDUList([prihdu, tbhdu]) - # don't have overwrite to protect original files. - finalhdu.writeto(outfile) - - print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) - - - return - - -def rebin_spec(wave, specin, wavnew): - """ - Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: - http://www.astrobetter.com/blog/2013/08/12/ - python-tip-re-sampling-spectra-with-pysynphot/ - """ - spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) - f = np.ones(len(wave)) - filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') - obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') - - return obs.binflux - -def organize_BTSettl_2015_atmospheres(path_to_dir): - """ - Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the BTSettl subdirectory - if not os.path.exists('BTSettl_2015'): - os.mkdir('BTSettl_2015') - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.spec.fits') - - for i in files: - hdu = fits.open(i) - spec = hdu[1].data - header_0 = hdu[0].header - header_1 = hdu[1].header - - wave = spec.field(0) - flux = spec.field(1) - - # Get units right: convert wave from microns to Angstroms, - # flux from W /m^2/ micron to erg/s/cm^2/A - wave_new = wave * 10**4 - flux_new = flux * 10**(-1) - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave_new) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) - - hdu.close() - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_BTSettl_2015_catalog(path_to_dir): - """ - Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*spec.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('-') - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: BTSettl_2015_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - phoenix_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/BTSettl_2015_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning BTSettl spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - -def make_wavelength_unique(files, dirname): - """ - Helper function to go through each BTSettl spectrum and ensure that - each wavelength point is unique. This is required for rebinning to work. - - - files: list of files to run this analysis on - """ - # Loop through each file, find fix repeated wavelength entries if necessary - for i in files: - t = Table.read('{0}/{1}'.format(dirname,i), format='fits') - test = np.unique(t['Wavelength'], return_index=True) - - if len(t) != len(test[0]): - t = t[test[1]] - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - # Also make sure wavelength is monotonic. If it is not, then it is - # a sign that the wavelengths are out of order - diff = np.diff(t['Wavelength']) - bad = np.where(diff < 0) - if len(bad[0]) > 0: - t.sort('Wavelength') - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - print('Done {0}'.format(i)) - - return - -def organize_BTSettl_atmospheres(): - """ - Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). - The code expects tp be run in cdbs/grid/BTSettl, and expects that the - individual model files have been downloaded from online - (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] - - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, - # for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.txt') - count=0 - for jj in files: - t = Table.read(jj, format='ascii') - # First, trim the wavelengths to a more reasonable wavelength range - good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) - t = t[good] - - # Convert flux units to Flam (Ergs/sec/cm**2/A) - flux_new = 10**(t['col2'] - 8.0) - - # Save the file as a fits file - c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Now, clean up all the files made when unzipping the spectra - cmd1 = 'rm *.bz2' - cmd2 = 'rm *.tmp' - #cmd3 = 'rm *.txt' - os.system(cmd1) - os.system(cmd2) - #os.system(cmd3) - print('==============================') - print('Done {0}'.format(ii)) - print('==============================') - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_BTSettl_catalog(): - """ - Create cdbs catalog.fits of BTSettl grid. - THIS IS STEP 2, after organize_BTSettl_atmospheres has - been run. - - Code expects to be run in cdbs/grid/BTSettl - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btp05'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('*.fits') - - # Construct the metallicity val - if 'm' in ii: - metal_flag = -1 * float(ii[3:])*0.1 - else: - metal_flag = float(ii[3:])*0.1 - - # Now collect the info from the files - for jj in files: - tmp = jj.split('-') - - if metal_flag >= 0: - temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin - try: - logg = float(tmp[1]) - except: - logg = float(tmp[1].split('+')[0]) - else: - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - # Go back to original directory to move to next metallicity - print('Done {0}'.format(ii)) - os.chdir(start_dir) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl(make_unique=False): - """ - Rebin BTSettle models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: BTSettl_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create cdbs/grid directory for rebinned models - path = 'BTSettl_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata('BTSettl/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - #==============================# - #tmp = [] - #for ii in files_all: - # if ii.startswith('btp00'): - # tmp.append(ii) - #files_all = tmp - #=============================# - - print( 'Rebinning BTSettl spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'BTSettl') - print('Done') - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - try: - sp = pysynphot.Icat('BTSettl', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - except: - pdb.set_trace() - orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) - outfile = path + files_all[ff].split('[')[0] - cmd = 'cp {0} {1}'.format(orig_file, outfile) - os.system(cmd) - - print('Done {0} of {1}'.format(ff, len(files_all))) - - return - -def organize_all_Meisner2023_atmospheres(): - """ - Construct cdbs-ready atmospheres for the Meisner2023 grid. - The code expects tp be run in cdbs/grid/Meisner2023, and expects that the - individual model files have been downloaded from online - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['mm10', 'mm05', 'mp00', 'mp03'] - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Save as a fits file, for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.fits') - count=0 - for jj in files: - # Open each .fits file and read the data - with fits.open(jj) as hdul: - data = hdul[1].data - wavelength = data['Wavelength'] - flux = data['Flux'] - - # Make flux independent of R&D - flux_new = flux / 5e-20 - - # Create new columns with desired format - c0 = fits.Column(name='Wavelength', format='D', array=wavelength) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write the new fits table in the cdbs directory - output_filename = '{0}.fits'.format(jj[:-5]) # Removing the original .fits extension - hdu_new.writeto(output_filename, overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_Meisner2023_catalog(): - """ - Create cdbs catalog.fits of Meisner2023 grid. - THIS IS STEP 2, after organize_Meisner2023_atmospheres has - been run. - - Code expects to be run in cdbs/grid/Meisner2023 - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['mm10', 'mm05', 'mp00', 'mp03'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('spec_jwst_*.fits') - - for jj in files: - # Parse temperature, log(g), and metallicity from filename - temp_str = jj.split('_')[2] - logg_str = jj.split('_')[3] - metal_str = jj.split('_')[4] - - # Extract temperature, surface gravity, and metallicity - temp = float(temp_str[1:]) # Temperature in Kelvin - logg = float(logg_str[1:]) # Surface gravity log(g) - - # Build metallicity value - if metal_str.startswith('m'): - metallicity = -1 * float(metal_str[1:]) - else: - metallicity = float(metal_str[1:]) - - # Construct index and filename strings - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metallicity, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - print('Processed directory:', ii) - os.chdir(start_dir) - - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_Meisner2023(make_unique=False): - """ - Rebin Meisner2023 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: Meisner2023_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create a directory for rebinned Meisner2023 models - rebin_path = 'Meisner2023_rebin/' - if not os.path.exists(rebin_path): - os.mkdir(rebin_path) - - # Load the catalog.fits file and extract all spectra file paths - cat = Table.read('Meisner2023/catalog.fits') - files_all = [cat[ii]['FILENAME'].split('[')[0] for ii in range(len(cat))] - - print('Rebinning Meisner2023 spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'Meisner2023') - print('Done') - - for ff, file in enumerate(files_all): - vals = cat[ff]['INDEX'].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the Meisner2023 spectrum and rebin its flux - try: - sp = pysynphot.Icat('Meisner2023', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Create the output FITS file - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write the new rebinned file in the Meisner2023_rebin directory - outfile = os.path.join(rebin_path, os.path.basename(file)) - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - except Exception as e: - print(f"Error processing {file}: {e}") - orig_file = os.path.join('Meisner2023', file) - outfile = os.path.join(rebin_path, os.path.basename(file)) - os.system(f'cp {orig_file} {outfile}') - - print('Done {0} of {1}'.format(ff + 1, len(files_all))) - - return - - - -def organize_WDKoester_atmospheres(path_to_dir): - """ - Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.dk.dat.txt') - - for i in files: - data = Table.read(i, format='ascii') - - wave = data['col1'] # angstrom - flux = data['col2'] # erg/s/cm^2/A - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) - - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_WDKoester_catalog(path_to_dir): - """ - Create cdbs catalog.fits of wdKoester grid. - THIS IS STEP 2, after organize_WDKoester_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*dk.dat.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('.') - tmp2 = tmp[0].split('_') - temp = float(tmp2[0][2:]) # Kelvin - logg = float(tmp2[1]) / 100.0 # log(g) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin wdKoester models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: wdKoester_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing model; we will steal the header - tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' - wdkoester_hdu = fits.open(tmp) - header0 = wdkoester_hdu[0].header - wdkoester_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/wdKoester_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning wdKoester spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the wdKoester spectrum, rebin flux - sp = pysynphot.Icat('wdKoester', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - - diff --git a/spisea/atmospheres_BASE_58456.py b/spisea/atmospheres_BASE_58456.py deleted file mode 100644 index 8df2fbe1..00000000 --- a/spisea/atmospheres_BASE_58456.py +++ /dev/null @@ -1,2165 +0,0 @@ -import logging -import numpy as np -import pysynphot -import os -import glob -from astropy.io import fits -from astropy.table import Table, Column -import pysynphot -import time -import pdb -import warnings - -log = logging.getLogger('atmospheres') - -def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4): - """ - Given atmosphere model, get temperature and gravity bounds - """ - # Open catalog fits file and break out row indices - catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) - - teff_arr = [] - z_arr = [] - logg_arr = [] - for cur_row_index in range(len(catalog)): - index = catalog['INDEX'][cur_row_index] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Filter by metallicity. Will chose the closest metallicity to desired input - metal_list = np.unique(np.array(z_arr)) - metal_idx = np.argmin(np.abs(metal_list - metallicity)) - - z_filt = np.where(z_arr == metal_list[metal_idx]) - teff_arr = teff_arr[z_filt] - logg_arr = logg_arr[z_filt] - - # # Now find the closest atmosphere in parameter space to - # # the one we want. We'll find the match with the lowest - # # fractional difference - # teff_diff = (teff_arr - temperature) / temperature - # logg_diff = (logg_arr - gravity) / gravity - # - # diff_tot = abs(teff_diff) + abs(logg_diff) - # idx_f = np.argmin(diff_tot) - # - # temperature_new = teff_arr[idx_f] - # gravity_new = logg_arr[idx_f] - - # First check if temperature within bounds - temperature_new = temperature - if temperature > np.max(teff_arr): - temperature_new = np.max(teff_arr) - if temperature < np.min(teff_arr): - temperature_new = np.min(teff_arr) - - # If temperature within bounds, then check if metallicity within bounds - teff_diff = np.abs(teff_arr - temperature) - sorted_min_diffs = np.unique(teff_diff) - - ## Find two closest temperatures - teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] - teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] - - logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] - logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] - - ## Switch to most conservative bound of logg out of two closest temps - gravity_new = gravity - if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): - gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) - if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): - gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) - - # Print out changes, if any - if temperature_new != temperature: - teff_msg = 'Changing to T={0:6.0f} for T={1:6.0f} logg={2:4.2f}' - print( teff_msg.format(temperature_new, temperature, gravity)) - - if gravity_new != gravity: - logg_msg = 'Changing to logg={0:4.2f} for T={1:6.0f} logg={2:4.2f}' - print( logg_msg.format(gravity_new, temperature, gravity)) - - return (temperature_new, gravity_new) - -def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmosphere from the Kurucz pysnphot grid - (`Kurucz 1993 `_). - - Grid Range: - - * Teff: 3000 - 50000 K - * gravity: 0 - 5 cgs - * metallicity: -5.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - Always false for this particular function - """ - try: - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('k93models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Kurucz 1993 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmospheres from the pysynphot ATLAS9 atlas - (`Castelli & Kurucz 2004 `_). - - Grid Range: - - * Teff: 3500 - 50000 K - * gravity: 0 - 5.0 cgs - * [M/H]: -2.5 - 0.2 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('ck04models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - try: - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('nextgen', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find NextGen atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, - rebin=False): - """ - Return atmosphere from the pysynphot - `PHOENIX atlas `_. - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - try: - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - # Take care of atmospheres outside the catalog boundaries - logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' - if gravity > 4.3: - print( logg_msg.format(4.3, temperature, gravity)) - gravity = 4.3 - - if rebin: - sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, - verbose=False): - """ - For a given stellar atmosphere, get extract the closest possible match in - Teff/logg space. Note that this is different from the normal routine - which interpolates along the input grid to get final spectrum. We can't - do this here because the Fierro+15 atmosphere grid is so sparse - - rebin=True: pull from atmospheres at ck04model resolution. - - If verbose, print out the parameters of the match - """ - # Set up the proper root directory - if rebin == True: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' - else: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' - - # Read in catalog, extract atmosphere info - cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') - teff_arr = [] - z_arr = [] - logg_arr = [] - for ii in range(len(cat)): - index = cat['INDEX'][ii] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Now find the closest atmosphere in parameter space to - # the one we want. We'll find the match with the lowest - # fractional difference - teff_diff = (teff_arr - temperature) / temperature - logg_diff = (logg_arr - gravity) / gravity - - diff_tot = abs(teff_diff) + abs(logg_diff) - idx_f = np.where(diff_tot == min(diff_tot))[0][0] - - # Extract the filename of the best-match model and read as - # pysynphot object - infile = cat[idx_f]['FILENAME'].split('.') - spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the - # ATLAS models are in FLAM at the surface. So, we need to multiply the - # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. - # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file - t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') - tmp = np.where(t['col1'] == infile[0]) - - lum = t['col3'][tmp] * (3.839*10**33) # cgs - sigma = 5.6704 * 10**-5 # cgs - teff = teff_arr[idx_f] # cgs - - radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm - radius /= 3.08*10**18 # in pc - - - # Make the pysynphot spectrum - w = spec['Wavelength'] - f = spec['Flux'] * (1000 / radius)**2. - sp = pysynphot.ArraySpectrum(w,f) - - #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Print out parameters of match, if desired - if verbose: - print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) - print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - if rebin: - sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 30000) - gravity = log gravity (def = 4.14) - """ - sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): - """ - Return PHOENIX v16 atmospheres from - `Husser et al. 2013 `_. - - Models originally downloaded via `ftp `_. - Solar metallicity and [alpha/Fe] is used. - - Grid Range: - - * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K - * gravity: 0.0 - 6.0 cgs, steps of 0.5 - * [M/H]: -4.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - atm_model_name = 'phoenix_v16' - if rebin == True: - atm_model_name = 'phoenix_v16_rebin' - - - # Extract atmosphere. If that fails, then check bounds and try again - try: - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_model_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): - """ - Return atmosphere from CIFIST2011_2015 grid - (`Allard et al. 2012 `_, - `Baraffe et al. 2015 `_ ) - - Grid originally downloaded from `website `_. - - Grid Range: - - * Teff: 1200 - 7000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_2015_rebin' - else: - atm_name = 'BTSettl_2015' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): - """ - Return atmosphere from CIFIST2011 grid - (`Allard et al. 2012 `_) - - Grid originally downloaded `here `_ - - Notes - ------ - Grid Range: - - * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - - Teff and gravity ranges depend on metallicity: - - [M/H] = -2.5 - - * Teff: 2600 - 4600 K - * gravity: 4.5 - 5.5 - - [M/H] = -2.0 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.5 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.0 - - * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 - - [M/H] = -0.5 - - * Teff: 1000 -7000 - * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 - - [M/H] = 0 - - * Teff: 750 - 7000 - * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 - - [M/H] = 0.5 - - * Teff: 1000 - 5000 - * gravity: 3.5 - 5.0 - - - Alpha enhancement: - - * [M/H]= -0.0, +0.5 no anhancement - * [M/H]= -0.5 with [alpha/H]=+0.2 - * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_rebin' - else: - atm_name = 'BTSettl' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): - """ - Return white dwarf atmospheres from - `Koester et al. 2010 `_ - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. - - Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 - """ - try: - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_atlas_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and phoenixV16. - - Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -#---------------------------------------------------------------------# -def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, - rebin=True): - """ - Return a stellar atmosphere from a suite of different model grids, - depending on the input temperature, (all values in K). - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - - Notes - ----- - The underlying stellar model grid used changes as a function of - stellar temperature (in K): - - * T > 20,000: ATLAS - * 5500 <= T < 20,000: ATLAS - * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge - * 3800 <= T < 5000: PHOENIXv16 - - For T < 3800, there is an additional gravity and metallicity - dependence: - - If T < 3800 and [M/H] = 0: - - * T < 3800, logg < 2.5: PHOENIX v16 - * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge - * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - - Otherwise, if T < 3800 and [M/H] != 0: - - * T < 3800: PHOENIX v16 - - References: - - * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) - * PHOENIXv16 (`Husser et al. 2013 `_) - * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) - - LTE WARNING: - - The ATLAS atmospheres are calculated with LTE, and so they - are less accurate when non-LTE conditions apply (e.g. T > 20,000 - K). Ultimately we'd like to add a non-LTE atmosphere grid for - the hottest stars in the future. - - HOW BOUNDARIES BETWEEN MODELS ARE TREATED: - - At the boundary between two models grids a temperature range is defined - where the resulting atmosphere is a weighted average between the two - grids. Near one boundary one model - is weighted more heavily, while at the other boundary the other - model is weighted more heavily. These are calculated in the - temperature ranges where we switch between model grids, to - ensure a smooth transition. - """ - # For T < 3800, atmosphere depends on metallicity + gravity. - # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is - # currently available here, so if non-solar metallicity, just stick with - # the Phoenix grid - if (temperature <= 3800) & (metallicity == 0): - # High gravity are in BTSettl regime - if (temperature <= 3200) & (gravity > 2.5): - if verbose: - print( 'BTSettl_2015 atmosphere') - return get_BTSettl_2015_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): - if verbose: - print( 'BTSettl/Phoenixv16 merged atmosphere') - return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - # Low gravity is PHOENIX regime - if gravity <= 2.5: - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity != 0): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - # For T > 3800, no metallicity or gravity dependence - if (temperature >= 3800) & (temperature < 5000): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 5000) & (temperature < 5500): - if verbose: - print( 'ATLAS/Phoenix merged atmosphere') - return get_atlas_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if (temperature >= 5500) & (temperature < 20000): - if verbose: - print( 'ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if temperature >= 20000: - if verbose: - print( 'Still ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - #print('CMFGEN') - #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, - # temperature=temperature, - # gravity=gravity) - - - - -def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Return the white dwarf atmosphere from - `Koester et al. 2010 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('wdKoester atmosphere') - - return get_wdKoester_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere. - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - -def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, - verbose=False, rebin=None, - wave_min=500, wave_max=50_000, wave_num=20_000): - """ - Return a blackbody spectrum - - Parameters - ---------- - temperature: float, default=20_000 - The stellar temperature, in units of K - wave_min: float, default=500 - Sets the minimum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_max: float, default=50_000 - Sets the maximum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_num: int, default=20_000 - Sets the number of wavelength points in the wavelength range - Note: the wavelength range is evenly spaced in log space - """ - if ((metallicity is not None) or (gravity is not None) or - (rebin is not None)): - warnings.warn( - 'Only `temperature` keyword is used for black-body atmosphere' - ) - - if verbose: - print('Black-body atmosphere') - - # Modify pysynphot's default waveset to specified bounds - pysynphot.refs.set_default_waveset( - minwave=wave_min, maxwave=wave_max, num=wave_num - ) - - # Get black-body atmosphere for specified temperature from pysynphot - bbspec = pysynphot.spectrum.BlackBody(temperature) - - # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` - bbspec.convert('flam') - - # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. - # Need to remove this normalization for SPISEA by multiplying bbspec - # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 - bbspec *= (1000 * 3.086e18 / 6.957e10)**2 - - return bbspec - - -#--------------------------------------# -# Atmosphere formatting functions -#--------------------------------------# - -def download_CMFGEN_atmospheres(Table_rot, Table_norot): - """ - Downloads CMFGEN models from - https://sites.google.com/site/fluxesandcontinuum/home; - these contain continuum as well as lines. - - Table_rot, Table_norot are tables with the file prefixes - and model atmosphere parameters, taken by hand from the - Fierro+15 paper - - Website addresses are hardcoded - - Puts downloaded models in the current working directory. - """ - print( 'WARNING: THIS DOES NOT COMPLETELY WORK') - print( '**********************') - t_rot = Table.read(Table_rot, format='ascii') - t_norot = Table.read(Table_norot, format='ascii') - - tables = [t_rot, t_norot] - filenames = [t_rot['col1'], t_norot['col1']] - - # Hardcoded list of webiste addresses - web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' - web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' - web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', - web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', - web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', - web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', - web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] - # Array of masses that matches the website addresses - mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) - - # Loop through rotating and unrotating case. First loop is rot, second unrot - for i in range(2): - # Extract masses from filenames - masses = [] - for j in filenames[i]: - tmp = j.split('m') - mass = float(tmp[1][:-1]) - masses.append(mass) - - # Download the models webpage by webpage. A bit tricky because masses - # change slightly within a particular website. THIS IS WHAT FAILS - for j in range(len(web)): - if j == 0: - good = np.where( (masses <= mass_arr[j]) ) - else: - g = j - 1 - good = np.where( (masses <= mass_arr[j]) & - (masses > mass_arr[g]) ) - # Use wget command to pull down the files, and unzip them - for k in good[0]: - full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) - os.system('wget ' + full) - os.system('unzip '+ filenames[i][k] + '.flx.zip') - - return - -def organize_CMFGEN_atmospheres(path_to_dir): - """ - Organize CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html) - into rot and noRot directories - - path_to_dir is from current working directory to directory - containing the downloaded models. Assumed that models - and tables describing parameters are in this directory. - - Tables describing parameters MUST be named Table_rot.txt, - Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. - These are located in same original directory as atmosphere files - - Will separate files into 2 subdirectories, one rotating and - the other non-rotating - - *Can't have any other files starting with "t" in model directory to start!* - """ - # First, record current working directory to return to later - start_dir = os.getcwd() - - # Enter atmosphere directory, collect rotating and non-rotating - # file names (assumed to all start with "t") - os.chdir(path_to_dir) - rot_models = glob.glob("t*r.flx*") - noRot_models = glob.glob("t*n.flx*") - - # Separate into different subdirectories - if os.path.exists('cmfgenF15_rot'): - pass - else: - os.mkdir('cmfgenF15_rot') - os.mkdir('cmfgenF15_noRot') - - for mod in rot_models: - cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) - os.system(cmd) - - for mod in noRot_models: - cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) - os.system(cmd) - - # Also move Tables with model parameters into correct directory - os.system('mv Table_rot.txt cmfgenF15_rot') - os.system('mv Table_noRot.txt cmfgenF15_noRot') - - # Return to original directory - os.chdir(start_dir) - - return - -def make_CMFGEN_catalog(path_to_dir): - """ - Create cdbs catalog.fits of CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html). - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to directory - containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, - needs to be a Table*.txt file which contains the parameters for all of the - original models, since params in filename are not precise enough - - Will create catalog.fits file in atmosphere directory with - description of each model - - *Can't have any other files starting with "t" in model directory to start!* - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere - # Note: can't rely on filename for this because not precise enough!! - - #---------OLD: GETTING PARAMS FROM FILENAME-------# - # Collect file names (assumed to all start with "t") - #files = glob.glob("t*") - #for name in files: - # tmp = name.split('l') - # temp = float(tmp[0][1:]) * 100.0 # In kelvin - - # lumtmp = tmp[1].split('_') - # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun - - # mass = float(lumtmp[0][5:-1]) # In M_sun - - # Need to calculate log g from T and L (cgs) - # lum_sun = 3.846 * 10**33 # erg/s - # M_sun = 2 * 10**33 # g - # G_si = 6.67 * 10**(-8) # cgs - # sigma_si = 5.67 * 10**(-5) # cgs - - # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ - # (lum * lum_sun) - # logg = np.log10(g) - #---------------------------------------------------# - - # Read table with atmosphere params - table = glob.glob('Table_*') - t = Table.read(table[0], format = 'ascii') - names = t['col1'] - temps = t['col2'] - logg = t['col4'] - - # Create catalog.fits file - index_str = [] - name_str = [] - for i in range(len(names)): - index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) - - #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# - #path = path_to_dir + '/' + names[i] - path = names[i] + '.fits[Flux]' - - index_str.append(index) - name_str.append(path) - - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits') - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): - """ - Code to put cmfgen models into cdbs format and adds proper unit keyword in - fits header. Save as fits file - - path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot - directory with the *.flx models. Note that these files have already been - organized using organize_CMFGEN_atmospheres code. - - path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or - cmfgen_norot directory. Will copy new fits files to this directory. - This directory must already exist! - """ - # Save starting directory for later, move into path_to_dir directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('*.flx') - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - # Open file, extract useful info - t = Table.read(i, format='ascii') - wave = t['col1'] - flux = t['col2'] # Flux is already in erg/cm^2/s/A - - # Need to eliminate duplicate entries (pysynphot crashes) - unique = np.unique(wave, return_index=True) - wave = wave[unique[1]] - flux = flux[unique[1]] - - # Make fits table from individual columns. - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - #Adding unit keywords - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - prihdu = fits.PrimaryHDU() - - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i[:-4]+'.fits', overwrite=True) - - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Return to original directory, copy over new .fits files to cdbs directory - os.chdir(start_dir) - cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) - os.system(cmd) - - return - -def rebin_cmfgen(cdbs_path, rot=True): - """ - Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; - this makes spectrophotometry MUCH faster - - cdbs_path: path to cdbs directory - rot=True for rotating models (cmfgen_rot), False for non-rotating models - - makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing cmfgen model; we will steal the header. - # Also define paths to new rebin directories - if rot == True: - tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' - path = cdbs_path+'/grid/cmfgen_rot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_rot/' - else: - tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' - path = cdbs_path+'/grid/cmfgen_norot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_norot/' - - cmfgen_hdu = fits.open(tmp) - header0 = cmfgen_hdu[0].header - # Create rebin directories if they don't already exist. Copy over - # catalog.fits file from original directory (will be the same) - if not os.path.exists(path): - os.mkdir(path) - cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) - os.system(cmd) - - # Read in the catalog.fits file - cat = fits.getdata(orig_path + 'catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - # First column in new files will be for [atlas] wavelength - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - - # For each catalog.fits entry, read the unbinned spectrum and rebin to - # the atlas resolution. Make a new fits file in rebin directory - count = 0 - for ff in range(len(files_all)): - count += 1 - # Extract the temp, Z, logg - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - grav = float(vals[2]) - - # Fetch the spectrum - if rot == True: - sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) - else: - sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) - - # Rebin - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - # Make the FITS file from the columns with header - cols = fits.ColDefs([c0,c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write hdu to new directory with same filename - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(path+files_all[ff]) - - print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) - return - - -def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): - """ - Construct the Phoenix Husser+13 atmopsheres for each model. Combines the - fluxes from the *HiRES.fits files and the wavelengths of the - WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. - - path_to_dir is the path to the directory containing all of the downloaded - files - - met_str is the name of the current metallicity - - Creates new fits files for each atmosphere: phoenix_.fits, - which contains columns for the log g (column header = g#.#). Puts - atmospheres in new directory phoenixm00 - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the current metallicity subdirectory - sub_dir = '../phoenix{0}'.format(met_str) - if os.path.exists(sub_dir): - pass - else: - os.mkdir(sub_dir) - - # Extract wavelength array, make column for later - wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') - wave = wavefile[0].data - wavefile.close() - wave_col = Column(wave, name = 'WAVELENGTH') - - # Create temp array for Husser+13 grid (given in paper) - temp_arr = np.arange(2300, 7001, 100) - temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) - - print( 'Looping though all temps') - # For each temp, build file containing the flux for all gravities - i = 0 - for temp in temp_arr: - files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) - files.sort() - # Start the table with the wavelength column - t = Table() - t.add_column(wave_col) - for f in files: - # Extract the logg out of filename - logg = f[9:13] - - # Extract fluxes from file - spectrum = fits.open(f) - flux = spectrum[0].data - spectrum.close() - - # Make Column object with fluxes, add to table - col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) - t.add_column(col) - - # Now, construct final fits file for the given temp - outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) - t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) - - # Progress counter for user - i += 1 - print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) - - # Return to original directory - os.chdir(start_dir) - return - -def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): - """ - Makes catalog.fits file for Husser+13 phoenix models. Assumes that - organize_PHOENIXv16_atmospheres has been run already, and that the models lie - in subdirectory phoenix[met_str]. - - path_to_directory is the path to the directory with the reformatted - models (i.e. the output from construct_atmospheres, phoenix[met_str]) - - Puts catalog.fits file in directory the user starts in - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Extract metallicity from metallicity string - met = float(met_str[1]) + (float(met_str[2]) * 0.1) - if 'm' in met_str: - met *= -1. - - # Collect the filenames. Each is a unique temp with many different log g's - files = glob.glob('phoenix*.fits') - files.sort() - - # Create the catalog.fits file, row by row - index_arr = [] - filename_arr = [] - for i in files: - # Get log g values from the column header the file - t = Table.read(i, format='fits') - keys = t.keys() - logg_vals = keys[1:] - - # Extract temp from filename - name = i.split('_') - temp = float(name[1][:-5]) - for j in logg_vals: - logg = float(j[1:]) - index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) - filename = path_to_dir + i + '[' + j + ']' - # Add row to table - index_arr.append(index) - filename_arr.append(filename) - - catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) - - # Return to starting directory, write catalog - os.chdir(start_dir) - - if os.path.exists('catalog.fits'): - from astropy.table import vstack - - prev_catalog = Table.read('catalog.fits', format='fits') - joined_catalog = vstack([prev_catalog, catalog]) - - joined_catalog.write('catalog.fits', format='fits', overwrite=True) - else: - catalog.write('catalog.fits', format='fits', overwrite=True) - - return - -def cdbs_PHOENIXv16(path_to_cdbs_dir): - """ - Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily - consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] - and adding the appropriate keywords to the fits header. - - path_to_cdbs_dir goes from current working directory to phoenix[met] directory - in cdbs/grids/phoenix_v16. Note that these files have already been organized - using organize_PHOENIXv16_atmospheres code. - - Overwrites original files in directory - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_cdbs_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('phoenix*.fits') - - ## Need to sort filenames; glob doesn't always give them in order - files.sort() - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - - # Read in current FITS table - cur_table = Table.read(i, format='fits') - - cur_table.columns[0].name = 'Wavelength' - - num_cols = len(cur_table.colnames) - - # Multiplying each flux column by 10^-8 for conversion - for cur_col_index in range(1, num_cols, 1): - cur_col_name = cur_table.colnames[cur_col_index] - cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 - - - # Construct new FITS file based on old one - hdu = fits.open(i) - header_0 = hdu[0].header - header_1 = hdu[1].header - sci = hdu[1].data - - tbhdu = fits.table_to_hdu(cur_table) - - # Copying over the older headers, adding unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - tbhdu.header['TUNIT3'] = 'FLAM' - tbhdu.header['TUNIT4'] = 'FLAM' - tbhdu.header['TUNIT5'] = 'FLAM' - tbhdu.header['TUNIT6'] = 'FLAM' - tbhdu.header['TUNIT7'] = 'FLAM' - tbhdu.header['TUNIT8'] = 'FLAM' - tbhdu.header['TUNIT9'] = 'FLAM' - tbhdu.header['TUNIT10'] = 'FLAM' - tbhdu.header['TUNIT11'] = 'FLAM' - tbhdu.header['TUNIT12'] = 'FLAM' - tbhdu.header['TUNIT13'] = 'FLAM' - tbhdu.header['TUNIT14'] = 'FLAM' - - # Construct and write out final FITS file - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i, overwrite=True) - - hdu.close() - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Change back to starting directory - os.chdir(start_dir) - - return - -def rebin_phoenixV16(cdbs_path): - """ - Rebin phoenixV16 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: phoenix_v16_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - ## (This assumes that at least 'm00' metallicity exists) - tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/phoenix_v16_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - temp_arr = np.zeros(len(files_all), dtype=float) - logg_arr = np.zeros(len(files_all), dtype=float) - metal_arr = np.zeros(len(files_all), dtype=float) - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - - temp_arr[ff] = float(vals[0]) - metal_arr[ff] = float(vals[1]) - logg_arr[ff] = float(vals[2]) - - - metal_uniq = np.unique(metal_arr) - temp_uniq = np.unique(temp_arr) - - for mm in range(len(metal_uniq)): - metal = metal_uniq[mm] # metallicity - - # Construct str for metallicity (for appropriate directory name) - met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) - if metal > 0: - met_str = 'p' + met_str - else: - met_str = 'm' + met_str - - # Make directory for current metallicity if it does not exist yet - if not os.path.exists(path + 'phoenix' + met_str): - os.mkdir(path + 'phoenix' + met_str) - - for tt in range(len(temp_uniq)): - temp = temp_uniq[tt] # temperature - - # Pick out the list of gravities for this T, Z combo - idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] - logg_exist = logg_arr[idx] - - # All gravities will go in one file. Here is the output - # file name. - outfile = path + files_all[idx[0]].split('[')[0] - - ## If the rebinned file already exists, continue - if os.path.exists(outfile): - continue - - # Build a columns array. One column for each gravity. - cols_arr = [] - - # Make the wavelength column, which is first in the cols array. - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - cols_arr.append(c0) - - for gg in range(len(logg_exist)): - grav = logg_exist[gg] # gravity - - # Fetch the spectrum - sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Store the spectrum - name = 'g{0:3.1f}'.format(grav) - col = fits.Column(name=name, format='E', array=flux_rebin) - cols_arr.append(col) - - - # Make the FITS file from the columns with header. - cols = fits.ColDefs(cols_arr) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - for gg in range(len(logg_exist)): - tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' - - # Write hdu - finalhdu = fits.HDUList([prihdu, tbhdu]) - # don't have overwrite to protect original files. - finalhdu.writeto(outfile) - - print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) - - - return - - -def rebin_spec(wave, specin, wavnew): - """ - Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: - http://www.astrobetter.com/blog/2013/08/12/ - python-tip-re-sampling-spectra-with-pysynphot/ - """ - spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) - f = np.ones(len(wave)) - filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') - obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') - - return obs.binflux - -def organize_BTSettl_2015_atmospheres(path_to_dir): - """ - Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the BTSettl subdirectory - if not os.path.exists('BTSettl_2015'): - os.mkdir('BTSettl_2015') - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.spec.fits') - - for i in files: - hdu = fits.open(i) - spec = hdu[1].data - header_0 = hdu[0].header - header_1 = hdu[1].header - - wave = spec.field(0) - flux = spec.field(1) - - # Get units right: convert wave from microns to Angstroms, - # flux from W /m^2/ micron to erg/s/cm^2/A - wave_new = wave * 10**4 - flux_new = flux * 10**(-1) - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave_new) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) - - hdu.close() - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_BTSettl_2015_catalog(path_to_dir): - """ - Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*spec.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('-') - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: BTSettl_2015_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - phoenix_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/BTSettl_2015_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning BTSettl spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - -def make_wavelength_unique(files, dirname): - """ - Helper function to go through each BTSettl spectrum and ensure that - each wavelength point is unique. This is required for rebinning to work. - - - files: list of files to run this analysis on - """ - # Loop through each file, find fix repeated wavelength entries if necessary - for i in files: - t = Table.read('{0}/{1}'.format(dirname,i), format='fits') - test = np.unique(t['Wavelength'], return_index=True) - - if len(t) != len(test[0]): - t = t[test[1]] - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - # Also make sure wavelength is monotonic. If it is not, then it is - # a sign that the wavelengths are out of order - diff = np.diff(t['Wavelength']) - bad = np.where(diff < 0) - if len(bad[0]) > 0: - t.sort('Wavelength') - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - print('Done {0}'.format(i)) - - return - -def organize_BTSettl_atmospheres(): - """ - Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). - The code expects tp be run in cdbs/grid/BTSettl, and expects that the - individual model files have been downloaded from online - (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] - - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, - # for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.txt') - count=0 - for jj in files: - t = Table.read(jj, format='ascii') - # First, trim the wavelengths to a more reasonable wavelength range - good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) - t = t[good] - - # Convert flux units to Flam (Ergs/sec/cm**2/A) - flux_new = 10**(t['col2'] - 8.0) - - # Save the file as a fits file - c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Now, clean up all the files made when unzipping the spectra - cmd1 = 'rm *.bz2' - cmd2 = 'rm *.tmp' - #cmd3 = 'rm *.txt' - os.system(cmd1) - os.system(cmd2) - #os.system(cmd3) - print('==============================') - print('Done {0}'.format(ii)) - print('==============================') - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_BTSettl_catalog(): - """ - Create cdbs catalog.fits of BTSettl grid. - THIS IS STEP 2, after organize_BTSettl_atmospheres has - been run. - - Code expects to be run in cdbs/grid/BTSettl - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btp05'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('*.fits') - - # Construct the metallicity val - if 'm' in ii: - metal_flag = -1 * float(ii[3:])*0.1 - else: - metal_flag = float(ii[3:])*0.1 - - # Now collect the info from the files - for jj in files: - tmp = jj.split('-') - - if metal_flag >= 0: - temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin - try: - logg = float(tmp[1]) - except: - logg = float(tmp[1].split('+')[0]) - else: - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - # Go back to original directory to move to next metallicity - print('Done {0}'.format(ii)) - os.chdir(start_dir) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl(make_unique=False): - """ - Rebin BTSettle models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: BTSettl_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create cdbs/grid directory for rebinned models - path = 'BTSettl_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata('BTSettl/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - #==============================# - #tmp = [] - #for ii in files_all: - # if ii.startswith('btp00'): - # tmp.append(ii) - #files_all = tmp - #=============================# - - print( 'Rebinning BTSettl spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'BTSettl') - print('Done') - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - try: - sp = pysynphot.Icat('BTSettl', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - except: - pdb.set_trace() - orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) - outfile = path + files_all[ff].split('[')[0] - cmd = 'cp {0} {1}'.format(orig_file, outfile) - os.system(cmd) - - print('Done {0} of {1}'.format(ff, len(files_all))) - - return - -def organize_WDKoester_atmospheres(path_to_dir): - """ - Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.dk.dat.txt') - - for i in files: - data = Table.read(i, format='ascii') - - wave = data['col1'] # angstrom - flux = data['col2'] # erg/s/cm^2/A - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) - - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_WDKoester_catalog(path_to_dir): - """ - Create cdbs catalog.fits of wdKoester grid. - THIS IS STEP 2, after organize_WDKoester_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*dk.dat.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('.') - tmp2 = tmp[0].split('_') - temp = float(tmp2[0][2:]) # Kelvin - logg = float(tmp2[1]) / 100.0 # log(g) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin wdKoester models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: wdKoester_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing model; we will steal the header - tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' - wdkoester_hdu = fits.open(tmp) - header0 = wdkoester_hdu[0].header - wdkoester_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/wdKoester_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning wdKoester spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the wdKoester spectrum, rebin flux - sp = pysynphot.Icat('wdKoester', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - - diff --git a/spisea/atmospheres_LOCAL_58456.py b/spisea/atmospheres_LOCAL_58456.py deleted file mode 100644 index 0f4c90e5..00000000 --- a/spisea/atmospheres_LOCAL_58456.py +++ /dev/null @@ -1,2508 +0,0 @@ -import logging -import numpy as np -import pysynphot -import os -import glob -from astropy.io import fits -from astropy.table import Table, Column -import pysynphot -import time -import pdb -import warnings - -log = logging.getLogger('atmospheres') - -def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4): - """ - Given atmosphere model, get temperature and gravity bounds - """ - # Open catalog fits file and break out row indices - catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) - - teff_arr = [] - z_arr = [] - logg_arr = [] - for cur_row_index in range(len(catalog)): - index = catalog['INDEX'][cur_row_index] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Filter by metallicity. Will chose the closest metallicity to desired input - metal_list = np.unique(np.array(z_arr)) - metal_idx = np.argmin(np.abs(metal_list - metallicity)) - - z_filt = np.where(z_arr == metal_list[metal_idx]) - teff_arr = teff_arr[z_filt] - logg_arr = logg_arr[z_filt] - - # # Now find the closest atmosphere in parameter space to - # # the one we want. We'll find the match with the lowest - # # fractional difference - # teff_diff = (teff_arr - temperature) / temperature - # logg_diff = (logg_arr - gravity) / gravity - # - # diff_tot = abs(teff_diff) + abs(logg_diff) - # idx_f = np.argmin(diff_tot) - # - # temperature_new = teff_arr[idx_f] - # gravity_new = logg_arr[idx_f] - - # First check if temperature within bounds - temperature_new = temperature - if temperature > np.max(teff_arr): - temperature_new = np.max(teff_arr) - if temperature < np.min(teff_arr): - temperature_new = np.min(teff_arr) - - # If temperature within bounds, then check if metallicity within bounds - teff_diff = np.abs(teff_arr - temperature) - sorted_min_diffs = np.unique(teff_diff) - - ## Find two closest temperatures - teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] - teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] - - logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] - logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] - - ## Switch to most conservative bound of logg out of two closest temps - gravity_new = gravity - if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): - gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) - if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): - gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) - - # Print out changes, if any - if temperature_new != temperature: - teff_msg = 'Changing to T={0:6.0f} for T={1:6.0f} logg={2:4.2f}' - print( teff_msg.format(temperature_new, temperature, gravity)) - - if gravity_new != gravity: - logg_msg = 'Changing to logg={0:4.2f} for T={1:6.0f} logg={2:4.2f}' - print( logg_msg.format(gravity_new, temperature, gravity)) - - return (temperature_new, gravity_new) - -def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmosphere from the Kurucz pysnphot grid - (`Kurucz 1993 `_). - - Grid Range: - - * Teff: 3000 - 50000 K - * gravity: 0 - 5 cgs - * metallicity: -5.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - Always false for this particular function - """ - try: - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('k93models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Kurucz 1993 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmospheres from the pysynphot ATLAS9 atlas - (`Castelli & Kurucz 2004 `_). - - Grid Range: - - * Teff: 3500 - 50000 K - * gravity: 0 - 5.0 cgs - * [M/H]: -2.5 - 0.2 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('ck04models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - try: - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('nextgen', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find NextGen atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, - rebin=False): - """ - Return atmosphere from the pysynphot - `PHOENIX atlas `_. - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - try: - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - # Take care of atmospheres outside the catalog boundaries - logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' - if gravity > 4.3: - print( logg_msg.format(4.3, temperature, gravity)) - gravity = 4.3 - - if rebin: - sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, - verbose=False): - """ - For a given stellar atmosphere, get extract the closest possible match in - Teff/logg space. Note that this is different from the normal routine - which interpolates along the input grid to get final spectrum. We can't - do this here because the Fierro+15 atmosphere grid is so sparse - - rebin=True: pull from atmospheres at ck04model resolution. - - If verbose, print out the parameters of the match - """ - # Set up the proper root directory - if rebin == True: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' - else: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' - - # Read in catalog, extract atmosphere info - cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') - teff_arr = [] - z_arr = [] - logg_arr = [] - for ii in range(len(cat)): - index = cat['INDEX'][ii] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Now find the closest atmosphere in parameter space to - # the one we want. We'll find the match with the lowest - # fractional difference - teff_diff = (teff_arr - temperature) / temperature - logg_diff = (logg_arr - gravity) / gravity - - diff_tot = abs(teff_diff) + abs(logg_diff) - idx_f = np.where(diff_tot == min(diff_tot))[0][0] - - # Extract the filename of the best-match model and read as - # pysynphot object - infile = cat[idx_f]['FILENAME'].split('.') - spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the - # ATLAS models are in FLAM at the surface. So, we need to multiply the - # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. - # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file - t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') - tmp = np.where(t['col1'] == infile[0]) - - lum = t['col3'][tmp] * (3.839*10**33) # cgs - sigma = 5.6704 * 10**-5 # cgs - teff = teff_arr[idx_f] # cgs - - radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm - radius /= 3.08*10**18 # in pc - - - # Make the pysynphot spectrum - w = spec['Wavelength'] - f = spec['Flux'] * (1000 / radius)**2. - sp = pysynphot.ArraySpectrum(w,f) - - #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Print out parameters of match, if desired - if verbose: - print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) - print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - if rebin: - sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 30000) - gravity = log gravity (def = 4.14) - """ - sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): - """ - Return PHOENIX v16 atmospheres from - `Husser et al. 2013 `_. - - Models originally downloaded via `ftp `_. - Solar metallicity and [alpha/Fe] is used. - - Grid Range: - - * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K - * gravity: 0.0 - 6.0 cgs, steps of 0.5 - * [M/H]: -4.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - atm_model_name = 'phoenix_v16' - if rebin == True: - atm_model_name = 'phoenix_v16_rebin' - - - # Extract atmosphere. If that fails, then check bounds and try again - try: - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_model_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): - """ - Return atmosphere from CIFIST2011_2015 grid - (`Allard et al. 2012 `_, - `Baraffe et al. 2015 `_ ) - - Grid originally downloaded from `website `_. - - Grid Range: - - * Teff: 1200 - 7000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_2015_rebin' - else: - atm_name = 'BTSettl_2015' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - #print(dir(obj)) - - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): - """ - Return atmosphere from CIFIST2011 grid - (`Allard et al. 2012 `_) - - Grid originally downloaded `here `_ - - Notes - ------ - Grid Range: - - * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - - Teff and gravity ranges depend on metallicity: - - [M/H] = -2.5 - - * Teff: 2600 - 4600 K - * gravity: 4.5 - 5.5 - - [M/H] = -2.0 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.5 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.0 - - * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 - - [M/H] = -0.5 - - * Teff: 1000 -7000 - * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 - - [M/H] = 0 - - * Teff: 750 - 7000 - * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 - - [M/H] = 0.5 - - * Teff: 1000 - 5000 - * gravity: 3.5 - 5.0 - - - Alpha enhancement: - - * [M/H]= -0.0, +0.5 no anhancement - * [M/H]= -0.5 with [alpha/H]=+0.2 - * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - **PRINT STATEMENTS TO DEBUG - **check get_atmosphere_bounds - **comment out try/except clause and check break - """ - if rebin == True: - atm_name = 'BTSettl_rebin' - else: - atm_name = 'BTSettl' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - -def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): - """ - Return atmosphere from Meisner2023 grid - (`Meisner et al. 2023 `_) - - Grid originally downloaded `here `_ - - Grid range: - * Teff = 250 - 1200 K - * gravity: 2.5 - 5.5 cgs (in steps of 0.5) - * [M/H] = -1.0, -0.5, 0, +0, +0.3 - - """ - if rebin == True: - atm_name = 'Meisner2023_rebin' - else: - atm_name = 'Meisner2023' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Meisner2023 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): - """ - Return atmosphere from Phillips et al., 2020 using ATMO model - (`Phillips et al. 2020 `_) - - Grid originally downloaded `here `_ - - Grid Range: - * Teff: 200 - 3000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - """ - if rebin == True: - atm_name = 'Phillips2020_rebin' - else: - atm_name = 'Phillips2020' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Phillips2020 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): - """ - Return white dwarf atmospheres from - `Koester et al. 2010 `_ - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. - - Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 - """ - try: - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_atlas_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and phoenixV16. - - Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_meisner_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and Meisner2023. - - Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_meisner', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl-Meisner merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -#---------------------------------------------------------------------# -def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, - rebin=True): - """ - Return a stellar atmosphere from a suite of different model grids, - depending on the input temperature, (all values in K). - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - - Notes - ----- - The underlying stellar model grid used changes as a function of - stellar temperature (in K): - - * T > 20,000: ATLAS - * 5500 <= T < 20,000: ATLAS - * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge - * 3800 <= T < 5000: PHOENIXv16 - - For T < 3800, there is an additional gravity and metallicity - dependence: - - If T < 3800 and [M/H] = 0: - - * T < 3800, logg < 2.5: PHOENIX v16 - * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge - * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - * 1200 < T <= 1000, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge - * 1000 < T <= 250, logg > 2.5: Meisner2023 - - Otherwise, if T < 3800 and [M/H] != 0: - - * T < 3800: PHOENIX v16 - - References: - - * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) - * PHOENIXv16 (`Husser et al. 2013 `_) - * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) - * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) - - LTE WARNING: - - The ATLAS atmospheres are calculated with LTE, and so they - are less accurate when non-LTE conditions apply (e.g. T > 20,000 - K). Ultimately we'd like to add a non-LTE atmosphere grid for - the hottest stars in the future. - - HOW BOUNDARIES BETWEEN MODELS ARE TREATED: - - At the boundary between two models grids a temperature range is defined - where the resulting atmosphere is a weighted average between the two - grids. Near one boundary one model - is weighted more heavily, while at the other boundary the other - model is weighted more heavily. These are calculated in the - temperature ranges where we switch between model grids, to - ensure a smooth transition. - """ - # For T < 3800, atmosphere depends on metallicity + gravity. - # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is - # currently available here, so if non-solar metallicity, just stick with - # the Phoenix grid - if (temperature < 1000): - if verbose: - print( 'Meisner2023 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 1200) & (temperature >= 1000): - if (gravity >= 3.5): - if verbose: - print( 'BTSettl/Meisner2023 merged atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - if (gravity < 3.5) & (gravity >=2.5): - if verbose: - print( 'Meisner2023 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity == 0): - # High gravity are in BTSettl regime - if (temperature <= 3200) & (gravity > 2.5): - if verbose: - print( 'BTSettl_2015 atmosphere') - return get_BTSettl_2015_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): - if verbose: - print( 'BTSettl/Phoenixv16 merged atmosphere') - return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - # Low gravity is PHOENIX regime - if gravity <= 2.5: - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity != 0): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - # For T > 3800, no metallicity or gravity dependence - if (temperature >= 3800) & (temperature < 5000): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 5000) & (temperature < 5500): - if verbose: - print( 'ATLAS/Phoenix merged atmosphere') - return get_atlas_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if (temperature >= 5500) & (temperature < 20000): - if verbose: - print( 'ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if temperature >= 20000: - if verbose: - print( 'Still ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - #print('CMFGEN') - #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, - # temperature=temperature, - # gravity=gravity) - - - - -def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Return the white dwarf atmosphere from - `Koester et al. 2010 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('wdKoester atmosphere') - - return get_wdKoester_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere. - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - - -def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): - """ - Return the brown dwarf atmosphere from - `Meisner et al. 2023 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('Meisner2023 atmosphere') - - return get_Meisner2023_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - -def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, - verbose=False, rebin=None, - wave_min=500, wave_max=50_000, wave_num=20_000): - """ - Return a blackbody spectrum - - Parameters - ---------- - temperature: float, default=20_000 - The stellar temperature, in units of K - wave_min: float, default=500 - Sets the minimum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_max: float, default=50_000 - Sets the maximum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_num: int, default=20_000 - Sets the number of wavelength points in the wavelength range - Note: the wavelength range is evenly spaced in log space - """ - if ((metallicity is not None) or (gravity is not None) or - (rebin is not None)): - warnings.warn( - 'Only `temperature` keyword is used for black-body atmosphere' - ) - - if verbose: - print('Black-body atmosphere') - - # Modify pysynphot's default waveset to specified bounds - pysynphot.refs.set_default_waveset( - minwave=wave_min, maxwave=wave_max, num=wave_num - ) - - # Get black-body atmosphere for specified temperature from pysynphot - bbspec = pysynphot.spectrum.BlackBody(temperature) - - # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` - bbspec.convert('flam') - - # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. - # Need to remove this normalization for SPISEA by multiplying bbspec - # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 - bbspec *= (1000 * 3.086e18 / 6.957e10)**2 - - return bbspec - - -#--------------------------------------# -# Atmosphere formatting functions -#--------------------------------------# - -def download_CMFGEN_atmospheres(Table_rot, Table_norot): - """ - Downloads CMFGEN models from - https://sites.google.com/site/fluxesandcontinuum/home; - these contain continuum as well as lines. - - Table_rot, Table_norot are tables with the file prefixes - and model atmosphere parameters, taken by hand from the - Fierro+15 paper - - Website addresses are hardcoded - - Puts downloaded models in the current working directory. - """ - print( 'WARNING: THIS DOES NOT COMPLETELY WORK') - print( '**********************') - t_rot = Table.read(Table_rot, format='ascii') - t_norot = Table.read(Table_norot, format='ascii') - - tables = [t_rot, t_norot] - filenames = [t_rot['col1'], t_norot['col1']] - - # Hardcoded list of webiste addresses - web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' - web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' - web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', - web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', - web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', - web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', - web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] - # Array of masses that matches the website addresses - mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) - - # Loop through rotating and unrotating case. First loop is rot, second unrot - for i in range(2): - # Extract masses from filenames - masses = [] - for j in filenames[i]: - tmp = j.split('m') - mass = float(tmp[1][:-1]) - masses.append(mass) - - # Download the models webpage by webpage. A bit tricky because masses - # change slightly within a particular website. THIS IS WHAT FAILS - for j in range(len(web)): - if j == 0: - good = np.where( (masses <= mass_arr[j]) ) - else: - g = j - 1 - good = np.where( (masses <= mass_arr[j]) & - (masses > mass_arr[g]) ) - # Use wget command to pull down the files, and unzip them - for k in good[0]: - full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) - os.system('wget ' + full) - os.system('unzip '+ filenames[i][k] + '.flx.zip') - - return - -def organize_CMFGEN_atmospheres(path_to_dir): - """ - Organize CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html) - into rot and noRot directories - - path_to_dir is from current working directory to directory - containing the downloaded models. Assumed that models - and tables describing parameters are in this directory. - - Tables describing parameters MUST be named Table_rot.txt, - Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. - These are located in same original directory as atmosphere files - - Will separate files into 2 subdirectories, one rotating and - the other non-rotating - - *Can't have any other files starting with "t" in model directory to start!* - """ - # First, record current working directory to return to later - start_dir = os.getcwd() - - # Enter atmosphere directory, collect rotating and non-rotating - # file names (assumed to all start with "t") - os.chdir(path_to_dir) - rot_models = glob.glob("t*r.flx*") - noRot_models = glob.glob("t*n.flx*") - - # Separate into different subdirectories - if os.path.exists('cmfgenF15_rot'): - pass - else: - os.mkdir('cmfgenF15_rot') - os.mkdir('cmfgenF15_noRot') - - for mod in rot_models: - cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) - os.system(cmd) - - for mod in noRot_models: - cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) - os.system(cmd) - - # Also move Tables with model parameters into correct directory - os.system('mv Table_rot.txt cmfgenF15_rot') - os.system('mv Table_noRot.txt cmfgenF15_noRot') - - # Return to original directory - os.chdir(start_dir) - - return - -def make_CMFGEN_catalog(path_to_dir): - """ - Create cdbs catalog.fits of CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html). - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to directory - containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, - needs to be a Table*.txt file which contains the parameters for all of the - original models, since params in filename are not precise enough - - Will create catalog.fits file in atmosphere directory with - description of each model - - *Can't have any other files starting with "t" in model directory to start!* - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere - # Note: can't rely on filename for this because not precise enough!! - - #---------OLD: GETTING PARAMS FROM FILENAME-------# - # Collect file names (assumed to all start with "t") - #files = glob.glob("t*") - #for name in files: - # tmp = name.split('l') - # temp = float(tmp[0][1:]) * 100.0 # In kelvin - - # lumtmp = tmp[1].split('_') - # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun - - # mass = float(lumtmp[0][5:-1]) # In M_sun - - # Need to calculate log g from T and L (cgs) - # lum_sun = 3.846 * 10**33 # erg/s - # M_sun = 2 * 10**33 # g - # G_si = 6.67 * 10**(-8) # cgs - # sigma_si = 5.67 * 10**(-5) # cgs - - # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ - # (lum * lum_sun) - # logg = np.log10(g) - #---------------------------------------------------# - - # Read table with atmosphere params - table = glob.glob('Table_*') - t = Table.read(table[0], format = 'ascii') - names = t['col1'] - temps = t['col2'] - logg = t['col4'] - - # Create catalog.fits file - index_str = [] - name_str = [] - for i in range(len(names)): - index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) - - #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# - #path = path_to_dir + '/' + names[i] - path = names[i] + '.fits[Flux]' - - index_str.append(index) - name_str.append(path) - - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits') - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): - """ - Code to put cmfgen models into cdbs format and adds proper unit keyword in - fits header. Save as fits file - - path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot - directory with the *.flx models. Note that these files have already been - organized using organize_CMFGEN_atmospheres code. - - path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or - cmfgen_norot directory. Will copy new fits files to this directory. - This directory must already exist! - """ - # Save starting directory for later, move into path_to_dir directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('*.flx') - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - # Open file, extract useful info - t = Table.read(i, format='ascii') - wave = t['col1'] - flux = t['col2'] # Flux is already in erg/cm^2/s/A - - # Need to eliminate duplicate entries (pysynphot crashes) - unique = np.unique(wave, return_index=True) - wave = wave[unique[1]] - flux = flux[unique[1]] - - # Make fits table from individual columns. - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - #Adding unit keywords - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - prihdu = fits.PrimaryHDU() - - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i[:-4]+'.fits', overwrite=True) - - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Return to original directory, copy over new .fits files to cdbs directory - os.chdir(start_dir) - cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) - os.system(cmd) - - return - -def rebin_cmfgen(cdbs_path, rot=True): - """ - Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; - this makes spectrophotometry MUCH faster - - cdbs_path: path to cdbs directory - rot=True for rotating models (cmfgen_rot), False for non-rotating models - - makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing cmfgen model; we will steal the header. - # Also define paths to new rebin directories - if rot == True: - tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' - path = cdbs_path+'/grid/cmfgen_rot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_rot/' - else: - tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' - path = cdbs_path+'/grid/cmfgen_norot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_norot/' - - cmfgen_hdu = fits.open(tmp) - header0 = cmfgen_hdu[0].header - # Create rebin directories if they don't already exist. Copy over - # catalog.fits file from original directory (will be the same) - if not os.path.exists(path): - os.mkdir(path) - cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) - os.system(cmd) - - # Read in the catalog.fits file - cat = fits.getdata(orig_path + 'catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - # First column in new files will be for [atlas] wavelength - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - - # For each catalog.fits entry, read the unbinned spectrum and rebin to - # the atlas resolution. Make a new fits file in rebin directory - count = 0 - for ff in range(len(files_all)): - count += 1 - # Extract the temp, Z, logg - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - grav = float(vals[2]) - - # Fetch the spectrum - if rot == True: - sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) - else: - sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) - - # Rebin - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - # Make the FITS file from the columns with header - cols = fits.ColDefs([c0,c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write hdu to new directory with same filename - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(path+files_all[ff]) - - print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) - return - - -def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): - """ - Construct the Phoenix Husser+13 atmopsheres for each model. Combines the - fluxes from the *HiRES.fits files and the wavelengths of the - WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. - - path_to_dir is the path to the directory containing all of the downloaded - files - - met_str is the name of the current metallicity - - Creates new fits files for each atmosphere: phoenix_.fits, - which contains columns for the log g (column header = g#.#). Puts - atmospheres in new directory phoenixm00 - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the current metallicity subdirectory - sub_dir = '../phoenix{0}'.format(met_str) - if os.path.exists(sub_dir): - pass - else: - os.mkdir(sub_dir) - - # Extract wavelength array, make column for later - wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') - wave = wavefile[0].data - wavefile.close() - wave_col = Column(wave, name = 'WAVELENGTH') - - # Create temp array for Husser+13 grid (given in paper) - temp_arr = np.arange(2300, 7001, 100) - temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) - - print( 'Looping though all temps') - # For each temp, build file containing the flux for all gravities - i = 0 - for temp in temp_arr: - files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) - files.sort() - # Start the table with the wavelength column - t = Table() - t.add_column(wave_col) - for f in files: - # Extract the logg out of filename - logg = f[9:13] - - # Extract fluxes from file - spectrum = fits.open(f) - flux = spectrum[0].data - spectrum.close() - - # Make Column object with fluxes, add to table - col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) - t.add_column(col) - - # Now, construct final fits file for the given temp - outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) - t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) - - # Progress counter for user - i += 1 - print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) - - # Return to original directory - os.chdir(start_dir) - return - -def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): - """ - Makes catalog.fits file for Husser+13 phoenix models. Assumes that - organize_PHOENIXv16_atmospheres has been run already, and that the models lie - in subdirectory phoenix[met_str]. - - path_to_directory is the path to the directory with the reformatted - models (i.e. the output from construct_atmospheres, phoenix[met_str]) - - Puts catalog.fits file in directory the user starts in - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Extract metallicity from metallicity string - met = float(met_str[1]) + (float(met_str[2]) * 0.1) - if 'm' in met_str: - met *= -1. - - # Collect the filenames. Each is a unique temp with many different log g's - files = glob.glob('phoenix*.fits') - files.sort() - - # Create the catalog.fits file, row by row - index_arr = [] - filename_arr = [] - for i in files: - # Get log g values from the column header the file - t = Table.read(i, format='fits') - keys = t.keys() - logg_vals = keys[1:] - - # Extract temp from filename - name = i.split('_') - temp = float(name[1][:-5]) - for j in logg_vals: - logg = float(j[1:]) - index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) - filename = path_to_dir + i + '[' + j + ']' - # Add row to table - index_arr.append(index) - filename_arr.append(filename) - - catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) - - # Return to starting directory, write catalog - os.chdir(start_dir) - - if os.path.exists('catalog.fits'): - from astropy.table import vstack - - prev_catalog = Table.read('catalog.fits', format='fits') - joined_catalog = vstack([prev_catalog, catalog]) - - joined_catalog.write('catalog.fits', format='fits', overwrite=True) - else: - catalog.write('catalog.fits', format='fits', overwrite=True) - - return - -def cdbs_PHOENIXv16(path_to_cdbs_dir): - """ - Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily - consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] - and adding the appropriate keywords to the fits header. - - path_to_cdbs_dir goes from current working directory to phoenix[met] directory - in cdbs/grids/phoenix_v16. Note that these files have already been organized - using organize_PHOENIXv16_atmospheres code. - - Overwrites original files in directory - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_cdbs_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('phoenix*.fits') - - ## Need to sort filenames; glob doesn't always give them in order - files.sort() - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - - # Read in current FITS table - cur_table = Table.read(i, format='fits') - - cur_table.columns[0].name = 'Wavelength' - - num_cols = len(cur_table.colnames) - - # Multiplying each flux column by 10^-8 for conversion - for cur_col_index in range(1, num_cols, 1): - cur_col_name = cur_table.colnames[cur_col_index] - cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 - - - # Construct new FITS file based on old one - hdu = fits.open(i) - header_0 = hdu[0].header - header_1 = hdu[1].header - sci = hdu[1].data - - tbhdu = fits.table_to_hdu(cur_table) - - # Copying over the older headers, adding unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - tbhdu.header['TUNIT3'] = 'FLAM' - tbhdu.header['TUNIT4'] = 'FLAM' - tbhdu.header['TUNIT5'] = 'FLAM' - tbhdu.header['TUNIT6'] = 'FLAM' - tbhdu.header['TUNIT7'] = 'FLAM' - tbhdu.header['TUNIT8'] = 'FLAM' - tbhdu.header['TUNIT9'] = 'FLAM' - tbhdu.header['TUNIT10'] = 'FLAM' - tbhdu.header['TUNIT11'] = 'FLAM' - tbhdu.header['TUNIT12'] = 'FLAM' - tbhdu.header['TUNIT13'] = 'FLAM' - tbhdu.header['TUNIT14'] = 'FLAM' - - # Construct and write out final FITS file - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i, overwrite=True) - - hdu.close() - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Change back to starting directory - os.chdir(start_dir) - - return - -def rebin_phoenixV16(cdbs_path): - """ - Rebin phoenixV16 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: phoenix_v16_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - ## (This assumes that at least 'm00' metallicity exists) - tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/phoenix_v16_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - temp_arr = np.zeros(len(files_all), dtype=float) - logg_arr = np.zeros(len(files_all), dtype=float) - metal_arr = np.zeros(len(files_all), dtype=float) - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - - temp_arr[ff] = float(vals[0]) - metal_arr[ff] = float(vals[1]) - logg_arr[ff] = float(vals[2]) - - - metal_uniq = np.unique(metal_arr) - temp_uniq = np.unique(temp_arr) - - for mm in range(len(metal_uniq)): - metal = metal_uniq[mm] # metallicity - - # Construct str for metallicity (for appropriate directory name) - met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) - if metal > 0: - met_str = 'p' + met_str - else: - met_str = 'm' + met_str - - # Make directory for current metallicity if it does not exist yet - if not os.path.exists(path + 'phoenix' + met_str): - os.mkdir(path + 'phoenix' + met_str) - - for tt in range(len(temp_uniq)): - temp = temp_uniq[tt] # temperature - - # Pick out the list of gravities for this T, Z combo - idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] - logg_exist = logg_arr[idx] - - # All gravities will go in one file. Here is the output - # file name. - outfile = path + files_all[idx[0]].split('[')[0] - - ## If the rebinned file already exists, continue - if os.path.exists(outfile): - continue - - # Build a columns array. One column for each gravity. - cols_arr = [] - - # Make the wavelength column, which is first in the cols array. - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - cols_arr.append(c0) - - for gg in range(len(logg_exist)): - grav = logg_exist[gg] # gravity - - # Fetch the spectrum - sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Store the spectrum - name = 'g{0:3.1f}'.format(grav) - col = fits.Column(name=name, format='E', array=flux_rebin) - cols_arr.append(col) - - - # Make the FITS file from the columns with header. - cols = fits.ColDefs(cols_arr) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - for gg in range(len(logg_exist)): - tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' - - # Write hdu - finalhdu = fits.HDUList([prihdu, tbhdu]) - # don't have overwrite to protect original files. - finalhdu.writeto(outfile) - - print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) - - - return - - -def rebin_spec(wave, specin, wavnew): - """ - Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: - http://www.astrobetter.com/blog/2013/08/12/ - python-tip-re-sampling-spectra-with-pysynphot/ - """ - spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) - f = np.ones(len(wave)) - filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') - obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') - - return obs.binflux - -def organize_BTSettl_2015_atmospheres(path_to_dir): - """ - Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the BTSettl subdirectory - if not os.path.exists('BTSettl_2015'): - os.mkdir('BTSettl_2015') - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.spec.fits') - - for i in files: - hdu = fits.open(i) - spec = hdu[1].data - header_0 = hdu[0].header - header_1 = hdu[1].header - - wave = spec.field(0) - flux = spec.field(1) - - # Get units right: convert wave from microns to Angstroms, - # flux from W /m^2/ micron to erg/s/cm^2/A - wave_new = wave * 10**4 - flux_new = flux * 10**(-1) - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave_new) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) - - hdu.close() - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_BTSettl_2015_catalog(path_to_dir): - """ - Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*spec.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('-') - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: BTSettl_2015_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - phoenix_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/BTSettl_2015_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning BTSettl spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - -def make_wavelength_unique(files, dirname): - """ - Helper function to go through each BTSettl spectrum and ensure that - each wavelength point is unique. This is required for rebinning to work. - - - files: list of files to run this analysis on - """ - # Loop through each file, find fix repeated wavelength entries if necessary - for i in files: - t = Table.read('{0}/{1}'.format(dirname,i), format='fits') - test = np.unique(t['Wavelength'], return_index=True) - - if len(t) != len(test[0]): - t = t[test[1]] - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - # Also make sure wavelength is monotonic. If it is not, then it is - # a sign that the wavelengths are out of order - diff = np.diff(t['Wavelength']) - bad = np.where(diff < 0) - if len(bad[0]) > 0: - t.sort('Wavelength') - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - print('Done {0}'.format(i)) - - return - -def organize_BTSettl_atmospheres(): - """ - Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). - The code expects tp be run in cdbs/grid/BTSettl, and expects that the - individual model files have been downloaded from online - (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] - - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, - # for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.txt') - count=0 - for jj in files: - t = Table.read(jj, format='ascii') - # First, trim the wavelengths to a more reasonable wavelength range - good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) - t = t[good] - - # Convert flux units to Flam (Ergs/sec/cm**2/A) - flux_new = 10**(t['col2'] - 8.0) - - # Save the file as a fits file - c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Now, clean up all the files made when unzipping the spectra - cmd1 = 'rm *.bz2' - cmd2 = 'rm *.tmp' - #cmd3 = 'rm *.txt' - os.system(cmd1) - os.system(cmd2) - #os.system(cmd3) - print('==============================') - print('Done {0}'.format(ii)) - print('==============================') - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_BTSettl_catalog(): - """ - Create cdbs catalog.fits of BTSettl grid. - THIS IS STEP 2, after organize_BTSettl_atmospheres has - been run. - - Code expects to be run in cdbs/grid/BTSettl - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btp05'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('*.fits') - - # Construct the metallicity val - if 'm' in ii: - metal_flag = -1 * float(ii[3:])*0.1 - else: - metal_flag = float(ii[3:])*0.1 - - # Now collect the info from the files - for jj in files: - tmp = jj.split('-') - - if metal_flag >= 0: - temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin - try: - logg = float(tmp[1]) - except: - logg = float(tmp[1].split('+')[0]) - else: - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - # Go back to original directory to move to next metallicity - print('Done {0}'.format(ii)) - os.chdir(start_dir) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl(make_unique=False): - """ - Rebin BTSettle models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: BTSettl_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create cdbs/grid directory for rebinned models - path = 'BTSettl_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata('BTSettl/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - #==============================# - #tmp = [] - #for ii in files_all: - # if ii.startswith('btp00'): - # tmp.append(ii) - #files_all = tmp - #=============================# - - print( 'Rebinning BTSettl spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'BTSettl') - print('Done') - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - try: - sp = pysynphot.Icat('BTSettl', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - except: - pdb.set_trace() - orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) - outfile = path + files_all[ff].split('[')[0] - cmd = 'cp {0} {1}'.format(orig_file, outfile) - os.system(cmd) - - print('Done {0} of {1}'.format(ff, len(files_all))) - - return - -def organize_all_Meisner2023_atmospheres(): - """ - Construct cdbs-ready atmospheres for the Meisner2023 grid. - The code expects tp be run in cdbs/grid/Meisner2023, and expects that the - individual model files have been downloaded from online - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['mm10', 'mm05', 'mp00', 'mp03'] - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Save as a fits file, for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.fits') - count=0 - for jj in files: - # Open each .fits file and read the data - with fits.open(jj) as hdul: - data = hdul[1].data - wavelength = data['Wavelength'] - flux = data['Flux'] - - # Make flux independent of R&D - flux_new = flux / 5e-20 - - # Create new columns with desired format - c0 = fits.Column(name='Wavelength', format='D', array=wavelength) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write the new fits table in the cdbs directory - output_filename = '{0}.fits'.format(jj[:-5]) # Removing the original .fits extension - hdu_new.writeto(output_filename, overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_Meisner2023_catalog(): - """ - Create cdbs catalog.fits of Meisner2023 grid. - THIS IS STEP 2, after organize_Meisner2023_atmospheres has - been run. - - Code expects to be run in cdbs/grid/Meisner2023 - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['mm10', 'mm05', 'mp00', 'mp03'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('spec_jwst_*.fits') - - for jj in files: - # Parse temperature, log(g), and metallicity from filename - temp_str = jj.split('_')[2] - logg_str = jj.split('_')[3] - metal_str = jj.split('_')[4] - - # Extract temperature, surface gravity, and metallicity - temp = float(temp_str[1:]) # Temperature in Kelvin - logg = float(logg_str[1:]) # Surface gravity log(g) - - # Build metallicity value - if metal_str.startswith('m'): - metallicity = -1 * float(metal_str[1:]) - else: - metallicity = float(metal_str[1:]) - - # Construct index and filename strings - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metallicity, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - print('Processed directory:', ii) - os.chdir(start_dir) - - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_Meisner2023(make_unique=False): - """ - Rebin Meisner2023 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: Meisner2023_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create a directory for rebinned Meisner2023 models - rebin_path = 'Meisner2023_rebin/' - if not os.path.exists(rebin_path): - os.mkdir(rebin_path) - - # Load the catalog.fits file and extract all spectra file paths - cat = Table.read('Meisner2023/catalog.fits') - files_all = [cat[ii]['FILENAME'].split('[')[0] for ii in range(len(cat))] - - print('Rebinning Meisner2023 spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'Meisner2023') - print('Done') - - for ff, file in enumerate(files_all): - vals = cat[ff]['INDEX'].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the Meisner2023 spectrum and rebin its flux - try: - sp = pysynphot.Icat('Meisner2023', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Create the output FITS file - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write the new rebinned file in the Meisner2023_rebin directory - outfile = os.path.join(rebin_path, os.path.basename(file)) - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - except Exception as e: - print(f"Error processing {file}: {e}") - orig_file = os.path.join('Meisner2023', file) - outfile = os.path.join(rebin_path, os.path.basename(file)) - os.system(f'cp {orig_file} {outfile}') - - print('Done {0} of {1}'.format(ff + 1, len(files_all))) - - return - - - -def organize_WDKoester_atmospheres(path_to_dir): - """ - Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.dk.dat.txt') - - for i in files: - data = Table.read(i, format='ascii') - - wave = data['col1'] # angstrom - flux = data['col2'] # erg/s/cm^2/A - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) - - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_WDKoester_catalog(path_to_dir): - """ - Create cdbs catalog.fits of wdKoester grid. - THIS IS STEP 2, after organize_WDKoester_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*dk.dat.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('.') - tmp2 = tmp[0].split('_') - temp = float(tmp2[0][2:]) # Kelvin - logg = float(tmp2[1]) / 100.0 # log(g) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin wdKoester models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: wdKoester_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing model; we will steal the header - tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' - wdkoester_hdu = fits.open(tmp) - header0 = wdkoester_hdu[0].header - wdkoester_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/wdKoester_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning wdKoester spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the wdKoester spectrum, rebin flux - sp = pysynphot.Icat('wdKoester', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - - diff --git a/spisea/atmospheres_REMOTE_58456.py b/spisea/atmospheres_REMOTE_58456.py deleted file mode 100644 index 9867db81..00000000 --- a/spisea/atmospheres_REMOTE_58456.py +++ /dev/null @@ -1,2172 +0,0 @@ -import logging -import numpy as np -import pysynphot -import os -import glob -from astropy.io import fits -from astropy.table import Table, Column -import pysynphot -import time -import pdb -import warnings - -log = logging.getLogger('atmospheres') - -def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Given atmosphere model, get temperature and gravity bounds - """ - # Open catalog fits file and break out row indices - catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) - - teff_arr = [] - z_arr = [] - logg_arr = [] - for cur_row_index in range(len(catalog)): - index = catalog['INDEX'][cur_row_index] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Filter by metallicity. Will chose the closest metallicity to desired input - metal_list = np.unique(np.array(z_arr)) - metal_idx = np.argmin(np.abs(metal_list - metallicity)) - metallicity_new = metal_list[metal_idx] - - z_filt = np.where(z_arr == metal_list[metal_idx]) - teff_arr = teff_arr[z_filt] - logg_arr = logg_arr[z_filt] - - # # Now find the closest atmosphere in parameter space to - # # the one we want. We'll find the match with the lowest - # # fractional difference - # teff_diff = (teff_arr - temperature) / temperature - # logg_diff = (logg_arr - gravity) / gravity - # - # diff_tot = abs(teff_diff) + abs(logg_diff) - # idx_f = np.argmin(diff_tot) - # - # temperature_new = teff_arr[idx_f] - # gravity_new = logg_arr[idx_f] - - # First check if temperature within bounds - temperature_new = temperature - if temperature > np.max(teff_arr): - temperature_new = np.max(teff_arr) - if temperature < np.min(teff_arr): - temperature_new = np.min(teff_arr) - - # If temperature within bounds, then check if metallicity within bounds - teff_diff = np.abs(teff_arr - temperature) - sorted_min_diffs = np.unique(teff_diff) - - ## Find two closest temperatures - teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] - teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] - - logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] - logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] - - ## Switch to most conservative bound of logg out of two closest temps - gravity_new = gravity - if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): - gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) - if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): - gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) - - if verbose: - # Print out changes, if any - if temperature_new != temperature: - teff_msg = 'Changing to T={0:6.0f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( teff_msg.format(temperature_new, metallicity, temperature, gravity)) - - if gravity_new != gravity: - logg_msg = 'Changing to logg={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( logg_msg.format(gravity_new, metallicity, temperature, gravity)) - - if metallicity_new != metallicity: - logg_msg = 'Changing to met={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' - print( logg_msg.format(metallicity_new, metallicity, temperature, gravity)) - - return (temperature_new, gravity_new, metallicity_new) - -def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmosphere from the Kurucz pysnphot grid - (`Kurucz 1993 `_). - - Grid Range: - - * Teff: 3000 - 50000 K - * gravity: 0 - 5 cgs - * metallicity: -5.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - Always false for this particular function - """ - try: - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('k93models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Kurucz 1993 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): - """ - Return atmospheres from the pysynphot ATLAS9 atlas - (`Castelli & Kurucz 2004 `_). - - Grid Range: - - * Teff: 3500 - 50000 K - * gravity: 0 - 5.0 cgs - * [M/H]: -2.5 - 0.2 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('ck04models', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - try: - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('nextgen', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find NextGen atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 5000) - gravity = log gravity (def = 4.0) - """ - sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, - rebin=False): - """ - Return atmosphere from the pysynphot - `PHOENIX atlas `_. - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - try: - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - # Take care of atmospheres outside the catalog boundaries - logg_msg = 'Changing to logg={0:3.1f} for T={1:6.0f} logg={2:4.2f}' - if gravity > 4.3: - if verbose: - print( logg_msg.format(4.3, temperature, gravity)) - gravity = 4.3 - - if rebin: - sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, - verbose=False): - """ - For a given stellar atmosphere, get extract the closest possible match in - Teff/logg space. Note that this is different from the normal routine - which interpolates along the input grid to get final spectrum. We can't - do this here because the Fierro+15 atmosphere grid is so sparse - - rebin=True: pull from atmospheres at ck04model resolution. - - If verbose, print out the parameters of the match - """ - # Set up the proper root directory - if rebin == True: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot_rebin/' - else: - root_dir = os.environ['PYSYN_CDBS'] + '/cmfgen_rot/' - - # Read in catalog, extract atmosphere info - cat = Table.read('{0}/catalog.fits'.format(root_dir), format='fits') - teff_arr = [] - z_arr = [] - logg_arr = [] - for ii in range(len(cat)): - index = cat['INDEX'][ii] - tmp = index.split(',') - teff_arr.append(float(tmp[0])) - z_arr.append(float(tmp[1])) - logg_arr.append(float(tmp[2])) - teff_arr = np.array(teff_arr) - z_arr = np.array(z_arr) - logg_arr = np.array(logg_arr) - - # Now find the closest atmosphere in parameter space to - # the one we want. We'll find the match with the lowest - # fractional difference - teff_diff = (teff_arr - temperature) / temperature - logg_diff = (logg_arr - gravity) / gravity - - diff_tot = abs(teff_diff) + abs(logg_diff) - idx_f = np.where(diff_tot == min(diff_tot))[0][0] - - # Extract the filename of the best-match model and read as - # pysynphot object - infile = cat[idx_f]['FILENAME'].split('.') - spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the - # ATLAS models are in FLAM at the surface. So, we need to multiply the - # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. - # We'll calculate radius from Teff and logL, which is given in the Table_*.txt file - t = Table.read('{0}/Table_rot.txt'.format(root_dir), format='ascii') - tmp = np.where(t['col1'] == infile[0]) - - lum = t['col3'][tmp] * (3.839*10**33) # cgs - sigma = 5.6704 * 10**-5 # cgs - teff = teff_arr[idx_f] # cgs - - radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm - radius /= 3.08*10**18 # in pc - - - # Make the pysynphot spectrum - w = spec['Wavelength'] - f = spec['Flux'] * (1000 / radius)**2. - sp = pysynphot.ArraySpectrum(w,f) - - #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) - - # Print out parameters of match, if desired - if verbose: - print('Teff match: Input: {0}, Output: {1}'.format(temperature, teff_arr[idx_f])) - print('logg match: Input: {0}, Output: {1}'.format(gravity, logg_arr[idx_f])) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, rebin=True): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 24000) - gravity = log gravity (def = 4.3) - - rebin=True: pull from atmospheres at ck04model resolution. - """ - if rebin: - sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) - else: - sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): - """ - metallicity = [M/H] (def = 0) - temperature = Kelvin (def = 30000) - gravity = log gravity (def = 4.14) - """ - sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): - """ - Return PHOENIX v16 atmospheres from - `Husser et al. 2013 `_. - - Models originally downloaded via `ftp `_. - Solar metallicity and [alpha/Fe] is used. - - Grid Range: - - * Teff: 2300 - 7000 K, steps of 100 K; 7000 - 12000 in steps of 200 K - * gravity: 0.0 - 6.0 cgs, steps of 0.5 - * [M/H]: -4.0 - 1.0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - """ - atm_model_name = 'phoenix_v16' - if rebin == True: - atm_model_name = 'phoenix_v16_rebin' - - - # Extract atmosphere. If that fails, then check bounds and try again - try: - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_model_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): - """ - Return atmosphere from CIFIST2011_2015 grid - (`Allard et al. 2012 `_, - `Baraffe et al. 2015 `_ ) - - Grid originally downloaded from `website `_. - - Grid Range: - - * Teff: 1200 - 7000 K - * gravity: 2.5 - 5.5 cgs - * [M/H] = 0 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_2015_rebin' - else: - atm_name = 'BTSettl_2015' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): - """ - Return atmosphere from CIFIST2011 grid - (`Allard et al. 2012 `_) - - Grid originally downloaded `here `_ - - Notes - ------ - Grid Range: - - * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - - Teff and gravity ranges depend on metallicity: - - [M/H] = -2.5 - - * Teff: 2600 - 4600 K - * gravity: 4.5 - 5.5 - - [M/H] = -2.0 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.5 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.0 - - * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 - - [M/H] = -0.5 - - * Teff: 1000 -7000 - * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 - - [M/H] = 0 - - * Teff: 750 - 7000 - * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 - - [M/H] = 0.5 - - * Teff: 1000 - 5000 - * gravity: 3.5 - 5.0 - - - Alpha enhancement: - - * [M/H]= -0.0, +0.5 no anhancement - * [M/H]= -0.5 with [alpha/H]=+0.2 - * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - if rebin == True: - atm_name = 'BTSettl_rebin' - else: - atm_name = 'BTSettl' - - try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): - """ - Return white dwarf atmospheres from - `Koester et al. 2010 `_ - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - """ - sp = pysynphot.Icat('wdKoester', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. - - Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 - """ - try: - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_atlas_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): - """ - Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model - and phoenixV16. - - Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 - """ - try: - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - except: - # Check atmosphere catalog bounds - (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_BTSettl_phoenix', - metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) - - # Do some error checking - idx = np.where(sp.flux != 0)[0] - if len(idx) == 0: - print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') - print( ' temperature = %d' % temperature) - print( ' metallicity = %.1f' % metallicity) - print( ' log gravity = %.1f' % gravity) - - return sp - -#---------------------------------------------------------------------# -def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, - rebin=True): - """ - Return a stellar atmosphere from a suite of different model grids, - depending on the input temperature, (all values in K). - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - - Notes - ----- - The underlying stellar model grid used changes as a function of - stellar temperature (in K): - - * T > 20,000: ATLAS - * 5500 <= T < 20,000: ATLAS - * 5000 <= T < 5500: ATLAS/PHOENIXv16 merge - * 3800 <= T < 5000: PHOENIXv16 - - For T < 3800, there is an additional gravity and metallicity - dependence: - - If T < 3800 and [M/H] = 0: - - * T < 3800, logg < 2.5: PHOENIX v16 - * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge - * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - - Otherwise, if T < 3800 and [M/H] != 0: - - * T < 3800: PHOENIX v16 - - References: - - * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) - * PHOENIXv16 (`Husser et al. 2013 `_) - * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) - - LTE WARNING: - - The ATLAS atmospheres are calculated with LTE, and so they - are less accurate when non-LTE conditions apply (e.g. T > 20,000 - K). Ultimately we'd like to add a non-LTE atmosphere grid for - the hottest stars in the future. - - HOW BOUNDARIES BETWEEN MODELS ARE TREATED: - - At the boundary between two models grids a temperature range is defined - where the resulting atmosphere is a weighted average between the two - grids. Near one boundary one model - is weighted more heavily, while at the other boundary the other - model is weighted more heavily. These are calculated in the - temperature ranges where we switch between model grids, to - ensure a smooth transition. - """ - # For T < 3800, atmosphere depends on metallicity + gravity. - # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is - # currently available here, so if non-solar metallicity, just stick with - # the Phoenix grid - if (temperature <= 3800) & (metallicity == 0): - # High gravity are in BTSettl regime - if (temperature <= 3200) & (gravity > 2.5): - if verbose: - print( 'BTSettl_2015 atmosphere') - return get_BTSettl_2015_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): - if verbose: - print( 'BTSettl/Phoenixv16 merged atmosphere') - return get_BTSettl_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - # Low gravity is PHOENIX regime - if gravity <= 2.5: - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature <= 3800) & (metallicity != 0): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - # For T > 3800, no metallicity or gravity dependence - if (temperature >= 3800) & (temperature < 5000): - if verbose: - print( 'Phoenixv16 atmosphere') - return get_phoenixv16_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity, - rebin=rebin) - - if (temperature >= 5000) & (temperature < 5500): - if verbose: - print( 'ATLAS/Phoenix merged atmosphere') - return get_atlas_phoenix_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if (temperature >= 5500) & (temperature < 20000): - if verbose: - print( 'ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - if temperature >= 20000: - if verbose: - print( 'Still ATLAS merged atmosphere') - return get_castelli_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - #print('CMFGEN') - #return get_cmfgenRot_atmosphere_closest(metallicity=metallicity, - # temperature=temperature, - # gravity=gravity) - - - - -def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): - """ - Return the white dwarf atmosphere from - `Koester et al. 2010 `_. - If desired parameters are - outside of grid, return a blackbody spectrum instead - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - If true, rebins the atmospheres so that they are the same - resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. - - verbose: boolean - True for verbose output - """ - try: - if verbose: - print('wdKoester atmosphere') - - return get_wdKoester_atmosphere(metallicity=metallicity, - temperature=temperature, - gravity=gravity) - - except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere. - bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) - return bbspec - -def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, - verbose=False, rebin=None, - wave_min=500, wave_max=50_000, wave_num=20_000): - """ - Return a blackbody spectrum - - Parameters - ---------- - temperature: float, default=20_000 - The stellar temperature, in units of K - wave_min: float, default=500 - Sets the minimum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_max: float, default=50_000 - Sets the maximum wavelength (in Angstroms) of the wavelength range - for the blackbody spectrum - wave_num: int, default=20_000 - Sets the number of wavelength points in the wavelength range - Note: the wavelength range is evenly spaced in log space - """ - if ((metallicity is not None) or (gravity is not None) or - (rebin is not None)): - warnings.warn( - 'Only `temperature` keyword is used for black-body atmosphere' - ) - - if verbose: - print('Black-body atmosphere') - - # Modify pysynphot's default waveset to specified bounds - pysynphot.refs.set_default_waveset( - minwave=wave_min, maxwave=wave_max, num=wave_num - ) - - # Get black-body atmosphere for specified temperature from pysynphot - bbspec = pysynphot.spectrum.BlackBody(temperature) - - # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` - bbspec.convert('flam') - - # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. - # Need to remove this normalization for SPISEA by multiplying bbspec - # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 - bbspec *= (1000 * 3.086e18 / 6.957e10)**2 - - return bbspec - - -#--------------------------------------# -# Atmosphere formatting functions -#--------------------------------------# - -def download_CMFGEN_atmospheres(Table_rot, Table_norot): - """ - Downloads CMFGEN models from - https://sites.google.com/site/fluxesandcontinuum/home; - these contain continuum as well as lines. - - Table_rot, Table_norot are tables with the file prefixes - and model atmosphere parameters, taken by hand from the - Fierro+15 paper - - Website addresses are hardcoded - - Puts downloaded models in the current working directory. - """ - print( 'WARNING: THIS DOES NOT COMPLETELY WORK') - print( '**********************') - t_rot = Table.read(Table_rot, format='ascii') - t_norot = Table.read(Table_norot, format='ascii') - - tables = [t_rot, t_norot] - filenames = [t_rot['col1'], t_norot['col1']] - - # Hardcoded list of webiste addresses - web_base1 = 'https://sites.google.com/site/fluxesandcontinuum/home/' - web_base2 = 'https://sites.google.com/site/modelsobmassivestars/' - web = [web_base1+'009-solar-masses/',web_base1+'012-solar-masses/', - web_base1+'015-solar-masses/',web_base1+'020-solar-masses/', - web_base1+'025-solar-masses/',web_base2+'009-solar-masses-tracks/', - web_base2+'040-solar-masses/',web_base2+'060-solar-masses/', - web_base1+'085-solar-masses/',web_base1+'120-solar-masses/'] - # Array of masses that matches the website addresses - mass_arr = np.array([9.,12.,15.,20.,25.,32.,40.,60.,85.,120.]) - - # Loop through rotating and unrotating case. First loop is rot, second unrot - for i in range(2): - # Extract masses from filenames - masses = [] - for j in filenames[i]: - tmp = j.split('m') - mass = float(tmp[1][:-1]) - masses.append(mass) - - # Download the models webpage by webpage. A bit tricky because masses - # change slightly within a particular website. THIS IS WHAT FAILS - for j in range(len(web)): - if j == 0: - good = np.where( (masses <= mass_arr[j]) ) - else: - g = j - 1 - good = np.where( (masses <= mass_arr[j]) & - (masses > mass_arr[g]) ) - # Use wget command to pull down the files, and unzip them - for k in good[0]: - full = web[j]+'{1:s}.flx.zip'.format(mass_arr[j],filenames[i][k]) - os.system('wget ' + full) - os.system('unzip '+ filenames[i][k] + '.flx.zip') - - return - -def organize_CMFGEN_atmospheres(path_to_dir): - """ - Organize CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html) - into rot and noRot directories - - path_to_dir is from current working directory to directory - containing the downloaded models. Assumed that models - and tables describing parameters are in this directory. - - Tables describing parameters MUST be named Table_rot.txt, - Table_noRot.txt. Made by hand from Tables 3, 4 in Fierro+15. - These are located in same original directory as atmosphere files - - Will separate files into 2 subdirectories, one rotating and - the other non-rotating - - *Can't have any other files starting with "t" in model directory to start!* - """ - # First, record current working directory to return to later - start_dir = os.getcwd() - - # Enter atmosphere directory, collect rotating and non-rotating - # file names (assumed to all start with "t") - os.chdir(path_to_dir) - rot_models = glob.glob("t*r.flx*") - noRot_models = glob.glob("t*n.flx*") - - # Separate into different subdirectories - if os.path.exists('cmfgenF15_rot'): - pass - else: - os.mkdir('cmfgenF15_rot') - os.mkdir('cmfgenF15_noRot') - - for mod in rot_models: - cmd = 'mv {0:s} cmfgenF15_rot'.format(mod) - os.system(cmd) - - for mod in noRot_models: - cmd = 'mv {0:s} cmfgenF15_noRot'.format(mod) - os.system(cmd) - - # Also move Tables with model parameters into correct directory - os.system('mv Table_rot.txt cmfgenF15_rot') - os.system('mv Table_noRot.txt cmfgenF15_noRot') - - # Return to original directory - os.chdir(start_dir) - - return - -def make_CMFGEN_catalog(path_to_dir): - """ - Create cdbs catalog.fits of CMFGEN grid from Fierro+15 - (http://www.astroscu.unam.mx/atlas/index.html). - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to directory - containing the rotating or non-rotating models (i.e. cmfgenF15_rot). Also, - needs to be a Table*.txt file which contains the parameters for all of the - original models, since params in filename are not precise enough - - Will create catalog.fits file in atmosphere directory with - description of each model - - *Can't have any other files starting with "t" in model directory to start!* - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere - # Note: can't rely on filename for this because not precise enough!! - - #---------OLD: GETTING PARAMS FROM FILENAME-------# - # Collect file names (assumed to all start with "t") - #files = glob.glob("t*") - #for name in files: - # tmp = name.split('l') - # temp = float(tmp[0][1:]) * 100.0 # In kelvin - - # lumtmp = tmp[1].split('_') - # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun - - # mass = float(lumtmp[0][5:-1]) # In M_sun - - # Need to calculate log g from T and L (cgs) - # lum_sun = 3.846 * 10**33 # erg/s - # M_sun = 2 * 10**33 # g - # G_si = 6.67 * 10**(-8) # cgs - # sigma_si = 5.67 * 10**(-5) # cgs - - # g = (G_si * mass * M_sun * 4 * np.pi * sigma_si * temp**4) / \ - # (lum * lum_sun) - # logg = np.log10(g) - #---------------------------------------------------# - - # Read table with atmosphere params - table = glob.glob('Table_*') - t = Table.read(table[0], format = 'ascii') - names = t['col1'] - temps = t['col2'] - logg = t['col4'] - - # Create catalog.fits file - index_str = [] - name_str = [] - for i in range(len(names)): - index = '{0:5.0f},0.0,{1:3.2f}'.format(temps[i], logg[i]) - - #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# - #path = path_to_dir + '/' + names[i] - path = names[i] + '.fits[Flux]' - - index_str.append(index) - name_str.append(path) - - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits') - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): - """ - Code to put cmfgen models into cdbs format and adds proper unit keyword in - fits header. Save as fits file - - path_to_dir goes from current directory to cmfgen_rot or cmfgen_norot - directory with the *.flx models. Note that these files have already been - organized using organize_CMFGEN_atmospheres code. - - path_to_cdbs_dir goes from current directory to cdbs/grid/cmfgen_rot or - cmfgen_norot directory. Will copy new fits files to this directory. - This directory must already exist! - """ - # Save starting directory for later, move into path_to_dir directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('*.flx') - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - # Open file, extract useful info - t = Table.read(i, format='ascii') - wave = t['col1'] - flux = t['col2'] # Flux is already in erg/cm^2/s/A - - # Need to eliminate duplicate entries (pysynphot crashes) - unique = np.unique(wave, return_index=True) - wave = wave[unique[1]] - flux = flux[unique[1]] - - # Make fits table from individual columns. - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - #Adding unit keywords - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - prihdu = fits.PrimaryHDU() - - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i[:-4]+'.fits', overwrite=True) - - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Return to original directory, copy over new .fits files to cdbs directory - os.chdir(start_dir) - cmd = 'mv {0:s}/*.fits {1:s}'.format(path_to_dir, path_to_cdbs_dir) - os.system(cmd) - - return - -def rebin_cmfgen(cdbs_path, rot=True): - """ - Rebin cmfgen_rot and cmfgen_norot models to atlas ck04 resolution; - this makes spectrophotometry MUCH faster - - cdbs_path: path to cdbs directory - rot=True for rotating models (cmfgen_rot), False for non-rotating models - - makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing cmfgen model; we will steal the header. - # Also define paths to new rebin directories - if rot == True: - tmp = cdbs_path+'/grid/cmfgen_rot/t0200l0008m009r.fits' - path = cdbs_path+'/grid/cmfgen_rot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_rot/' - else: - tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' - path = cdbs_path+'/grid/cmfgen_norot_rebin/' - orig_path = cdbs_path+'/grid/cmfgen_norot/' - - cmfgen_hdu = fits.open(tmp) - header0 = cmfgen_hdu[0].header - # Create rebin directories if they don't already exist. Copy over - # catalog.fits file from original directory (will be the same) - if not os.path.exists(path): - os.mkdir(path) - cmd = 'cp {0:s}catalog.fits {1:s}'.format(orig_path, path) - os.system(cmd) - - # Read in the catalog.fits file - cat = fits.getdata(orig_path + 'catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - # First column in new files will be for [atlas] wavelength - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - - # For each catalog.fits entry, read the unbinned spectrum and rebin to - # the atlas resolution. Make a new fits file in rebin directory - count = 0 - for ff in range(len(files_all)): - count += 1 - # Extract the temp, Z, logg - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - grav = float(vals[2]) - - # Fetch the spectrum - if rot == True: - sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) - else: - sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) - - # Rebin - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - # Make the FITS file from the columns with header - cols = fits.ColDefs([c0,c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - # Write hdu to new directory with same filename - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(path+files_all[ff]) - - print( 'Finished file {0} of {1}'.format(count, len(files_all)) ) - return - - -def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): - """ - Construct the Phoenix Husser+13 atmopsheres for each model. Combines the - fluxes from the *HiRES.fits files and the wavelengths of the - WAVE_PHONEIX-ACES-AGSS-COND-2011.fits file. - - path_to_dir is the path to the directory containing all of the downloaded - files - - met_str is the name of the current metallicity - - Creates new fits files for each atmosphere: phoenix_.fits, - which contains columns for the log g (column header = g#.#). Puts - atmospheres in new directory phoenixm00 - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the current metallicity subdirectory - sub_dir = '../phoenix{0}'.format(met_str) - if os.path.exists(sub_dir): - pass - else: - os.mkdir(sub_dir) - - # Extract wavelength array, make column for later - wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') - wave = wavefile[0].data - wavefile.close() - wave_col = Column(wave, name = 'WAVELENGTH') - - # Create temp array for Husser+13 grid (given in paper) - temp_arr = np.arange(2300, 7001, 100) - temp_arr = np.append(temp_arr, np.arange(7000, 12001, 200)) - - print( 'Looping though all temps') - # For each temp, build file containing the flux for all gravities - i = 0 - for temp in temp_arr: - files = glob.glob('lte{0:05d}-*-HiRes.fits'.format(temp)) - files.sort() - # Start the table with the wavelength column - t = Table() - t.add_column(wave_col) - for f in files: - # Extract the logg out of filename - logg = f[9:13] - - # Extract fluxes from file - spectrum = fits.open(f) - flux = spectrum[0].data - spectrum.close() - - # Make Column object with fluxes, add to table - col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) - t.add_column(col) - - # Now, construct final fits file for the given temp - outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) - t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) - - # Progress counter for user - i += 1 - print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) - - # Return to original directory - os.chdir(start_dir) - return - -def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): - """ - Makes catalog.fits file for Husser+13 phoenix models. Assumes that - organize_PHOENIXv16_atmospheres has been run already, and that the models lie - in subdirectory phoenix[met_str]. - - path_to_directory is the path to the directory with the reformatted - models (i.e. the output from construct_atmospheres, phoenix[met_str]) - - Puts catalog.fits file in directory the user starts in - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Extract metallicity from metallicity string - met = float(met_str[1]) + (float(met_str[2]) * 0.1) - if 'm' in met_str: - met *= -1. - - # Collect the filenames. Each is a unique temp with many different log g's - files = glob.glob('phoenix*.fits') - files.sort() - - # Create the catalog.fits file, row by row - index_arr = [] - filename_arr = [] - for i in files: - # Get log g values from the column header the file - t = Table.read(i, format='fits') - keys = t.keys() - logg_vals = keys[1:] - - # Extract temp from filename - name = i.split('_') - temp = float(name[1][:-5]) - for j in logg_vals: - logg = float(j[1:]) - index = '{0:5.0f},{1:2.1f},{2:2.1f}'.format(temp, met, logg) - filename = path_to_dir + i + '[' + j + ']' - # Add row to table - index_arr.append(index) - filename_arr.append(filename) - - catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) - - # Return to starting directory, write catalog - os.chdir(start_dir) - - if os.path.exists('catalog.fits'): - from astropy.table import vstack - - prev_catalog = Table.read('catalog.fits', format='fits') - joined_catalog = vstack([prev_catalog, catalog]) - - joined_catalog.write('catalog.fits', format='fits', overwrite=True) - else: - catalog.write('catalog.fits', format='fits', overwrite=True) - - return - -def cdbs_PHOENIXv16(path_to_cdbs_dir): - """ - Put the PHOENIXv16 (Husser+13) fits files into cdbs format. This primarily - consists of adjusting the flux units from [erg/s/cm^2/cm] to [erg/s/cm^2/A] - and adding the appropriate keywords to the fits header. - - path_to_cdbs_dir goes from current working directory to phoenix[met] directory - in cdbs/grids/phoenix_v16. Note that these files have already been organized - using organize_PHOENIXv16_atmospheres code. - - Overwrites original files in directory - """ - # Save starting directory for later, move into working directory - start_dir = os.getcwd() - os.chdir(path_to_cdbs_dir) - - # Collect the filenames, make necessary changes to each one - files = glob.glob('phoenix*.fits') - - ## Need to sort filenames; glob doesn't always give them in order - files.sort() - - # Need to make brand-new fits tables with data we want. - counter = 0 - for i in files: - counter += 1 - - # Read in current FITS table - cur_table = Table.read(i, format='fits') - - cur_table.columns[0].name = 'Wavelength' - - num_cols = len(cur_table.colnames) - - # Multiplying each flux column by 10^-8 for conversion - for cur_col_index in range(1, num_cols, 1): - cur_col_name = cur_table.colnames[cur_col_index] - cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 - - - # Construct new FITS file based on old one - hdu = fits.open(i) - header_0 = hdu[0].header - header_1 = hdu[1].header - sci = hdu[1].data - - tbhdu = fits.table_to_hdu(cur_table) - - # Copying over the older headers, adding unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - tbhdu.header['TUNIT3'] = 'FLAM' - tbhdu.header['TUNIT4'] = 'FLAM' - tbhdu.header['TUNIT5'] = 'FLAM' - tbhdu.header['TUNIT6'] = 'FLAM' - tbhdu.header['TUNIT7'] = 'FLAM' - tbhdu.header['TUNIT8'] = 'FLAM' - tbhdu.header['TUNIT9'] = 'FLAM' - tbhdu.header['TUNIT10'] = 'FLAM' - tbhdu.header['TUNIT11'] = 'FLAM' - tbhdu.header['TUNIT12'] = 'FLAM' - tbhdu.header['TUNIT13'] = 'FLAM' - tbhdu.header['TUNIT14'] = 'FLAM' - - # Construct and write out final FITS file - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(i, overwrite=True) - - hdu.close() - print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - - # Change back to starting directory - os.chdir(start_dir) - - return - -def rebin_phoenixV16(cdbs_path): - """ - Rebin phoenixV16 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: phoenix_v16_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - ## (This assumes that at least 'm00' metallicity exists) - tmp = '{0}/grid/phoenix_v16/phoenix{1}/phoenix{1}_02400.fits'.format(cdbs_path, 'm00') - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/phoenix_v16_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - temp_arr = np.zeros(len(files_all), dtype=float) - logg_arr = np.zeros(len(files_all), dtype=float) - metal_arr = np.zeros(len(files_all), dtype=float) - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - - temp_arr[ff] = float(vals[0]) - metal_arr[ff] = float(vals[1]) - logg_arr[ff] = float(vals[2]) - - - metal_uniq = np.unique(metal_arr) - temp_uniq = np.unique(temp_arr) - - for mm in range(len(metal_uniq)): - metal = metal_uniq[mm] # metallicity - - # Construct str for metallicity (for appropriate directory name) - met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) - if metal > 0: - met_str = 'p' + met_str - else: - met_str = 'm' + met_str - - # Make directory for current metallicity if it does not exist yet - if not os.path.exists(path + 'phoenix' + met_str): - os.mkdir(path + 'phoenix' + met_str) - - for tt in range(len(temp_uniq)): - temp = temp_uniq[tt] # temperature - - # Pick out the list of gravities for this T, Z combo - idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] - logg_exist = logg_arr[idx] - - # All gravities will go in one file. Here is the output - # file name. - outfile = path + files_all[idx[0]].split('[')[0] - - ## If the rebinned file already exists, continue - if os.path.exists(outfile): - continue - - # Build a columns array. One column for each gravity. - cols_arr = [] - - # Make the wavelength column, which is first in the cols array. - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - cols_arr.append(c0) - - for gg in range(len(logg_exist)): - grav = logg_exist[gg] # gravity - - # Fetch the spectrum - sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Store the spectrum - name = 'g{0:3.1f}'.format(grav) - col = fits.Column(name=name, format='E', array=flux_rebin) - cols_arr.append(col) - - - # Make the FITS file from the columns with header. - cols = fits.ColDefs(cols_arr) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - for gg in range(len(logg_exist)): - tbhdu.header['TUNIT{0:d}'.format(gg+2)] = 'FLAM' - - # Write hdu - finalhdu = fits.HDUList([prihdu, tbhdu]) - # don't have overwrite to protect original files. - finalhdu.writeto(outfile) - - print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) - - - return - - -def rebin_spec(wave, specin, wavnew): - """ - Helper routine to rebin spectra. TAKEN FROM ASTROBETTER BLOG FROM JESSICA: - http://www.astrobetter.com/blog/2013/08/12/ - python-tip-re-sampling-spectra-with-pysynphot/ - """ - spec = pysynphot.spectrum.ArraySourceSpectrum(wave=wave, flux=specin) - f = np.ones(len(wave)) - filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') - obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') - - return obs.binflux - -def organize_BTSettl_2015_atmospheres(path_to_dir): - """ - Construct cdbs-ready BTSettl_CIFITS_2011_2015 atmospheres for each model. - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/grid/BTSettl_2015 - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # If it doesn't already exist, create the BTSettl subdirectory - if not os.path.exists('BTSettl_2015'): - os.mkdir('BTSettl_2015') - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.spec.fits') - - for i in files: - hdu = fits.open(i) - spec = hdu[1].data - header_0 = hdu[0].header - header_1 = hdu[1].header - - wave = spec.field(0) - flux = spec.field(1) - - # Get units right: convert wave from microns to Angstroms, - # flux from W /m^2/ micron to erg/s/cm^2/A - wave_new = wave * 10**4 - flux_new = flux * 10**(-1) - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave_new) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU(header=header_0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) - - hdu.close() - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_BTSettl_2015_catalog(path_to_dir): - """ - Create cdbs catalog.fits of BTSettl_CIFITS2011_2015 grid. - THIS IS STEP 2, after organize_CMFGEN_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*spec.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('-') - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin BTSettle_CIFITS2011_2015 models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: BTSettl_2015_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing phoenix model; we will steal the header - tmp = cdbs_path+'/grid/phoenix_v16/phoenixm00/phoenixm00_02400.fits' - phoenix_hdu = fits.open(tmp) - header0 = phoenix_hdu[0].header - phoenix_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/BTSettl_2015_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/BTSettl_2015/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning BTSettl spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - sp = pysynphot.Icat('BTSettl_2015', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - -def make_wavelength_unique(files, dirname): - """ - Helper function to go through each BTSettl spectrum and ensure that - each wavelength point is unique. This is required for rebinning to work. - - - files: list of files to run this analysis on - """ - # Loop through each file, find fix repeated wavelength entries if necessary - for i in files: - t = Table.read('{0}/{1}'.format(dirname,i), format='fits') - test = np.unique(t['Wavelength'], return_index=True) - - if len(t) != len(test[0]): - t = t[test[1]] - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - # Also make sure wavelength is monotonic. If it is not, then it is - # a sign that the wavelengths are out of order - diff = np.diff(t['Wavelength']) - bad = np.where(diff < 0) - if len(bad[0]) > 0: - t.sort('Wavelength') - - c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) - c1 = fits.Column(name='Flux', format='E', array=t['Flux']) - cols = fits.ColDefs([c0, c1]) - - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto('{0}/{1}'.format(dirname,i), overwrite=True) - - print('Done {0}'.format(i)) - - return - -def organize_BTSettl_atmospheres(): - """ - Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). - The code expects tp be run in cdbs/grid/BTSettl, and expects that the - individual model files have been downloaded from online - (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) - and processed into python-readable ascii files. - """ - orig_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] - - - # Go through each directory, turning each spectrum into a cdbs-ready file. - # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, - # for faster access later - for ii in dirs: - print('Starting {0}'.format(ii)) - os.chdir(ii) - - files = glob.glob('*.txt') - count=0 - for jj in files: - t = Table.read(jj, format='ascii') - # First, trim the wavelengths to a more reasonable wavelength range - good = np.where( (t['col1'] > 1000) & (t['col1'] < 70000) ) - t = t[good] - - # Convert flux units to Flam (Ergs/sec/cm**2/A) - flux_new = 10**(t['col2'] - 8.0) - - # Save the file as a fits file - c0 = fits.Column(name='Wavelength', format='D', array=t['col1']) - c1 = fits.Column(name='Flux', format='E', array=flux_new) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Add unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) - hdu_new.close() - count += 1 - print('Done {0} of {1}'.format(count, len(files))) - - # Now, clean up all the files made when unzipping the spectra - cmd1 = 'rm *.bz2' - cmd2 = 'rm *.tmp' - #cmd3 = 'rm *.txt' - os.system(cmd1) - os.system(cmd2) - #os.system(cmd3) - print('==============================') - print('Done {0}'.format(ii)) - print('==============================') - - # Go back to original directory, move to next metallicity directory - os.chdir(orig_dir) - - return - -def make_BTSettl_catalog(): - """ - Create cdbs catalog.fits of BTSettl grid. - THIS IS STEP 2, after organize_BTSettl_atmospheres has - been run. - - Code expects to be run in cdbs/grid/BTSettl - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] - #dirs = ['btp05'] - - # Construct the catalog.fits file input. The input consists of - # and index string that specifies the stellar paramters, and a - # name string that points to the file - # Loop over all the metallicity directories to construct these inputs - index_str = [] - name_str = [] - for ii in dirs: - os.chdir(ii) - files = glob.glob('*.fits') - - # Construct the metallicity val - if 'm' in ii: - metal_flag = -1 * float(ii[3:])*0.1 - else: - metal_flag = float(ii[3:])*0.1 - - # Now collect the info from the files - for jj in files: - tmp = jj.split('-') - - if metal_flag >= 0: - temp = float(tmp[0].split('+')[0][3:]) * 100.0 # In kelvin - try: - logg = float(tmp[1]) - except: - logg = float(tmp[1].split('+')[0]) - else: - temp = float(tmp[0][3:]) * 100.0 # In kelvin - logg = float(tmp[1]) - - index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) - name_str.append('{0}/{1}[Flux]'.format(ii, jj)) - - # Go back to original directory to move to next metallicity - print('Done {0}'.format(ii)) - os.chdir(start_dir) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_BTSettl(make_unique=False): - """ - Rebin BTSettle models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory: BTSettl_rebin - - Code expects to be run in cdbs/grid directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Create cdbs/grid directory for rebinned models - path = 'BTSettl_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata('BTSettl/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - #==============================# - #tmp = [] - #for ii in files_all: - # if ii.startswith('btp00'): - # tmp.append(ii) - #files_all = tmp - #=============================# - - print( 'Rebinning BTSettl spectra') - if make_unique: - print('Making unique') - make_wavelength_unique(files_all, 'BTSettl') - print('Done') - - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the BTSettl spectrum, rebin flux - try: - sp = pysynphot.Icat('BTSettl', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - except: - pdb.set_trace() - orig_file = '{0}/{1}'.format('BTSettl/', files_all[ff].split('[')[0]) - outfile = path + files_all[ff].split('[')[0] - cmd = 'cp {0} {1}'.format(orig_file, outfile) - os.system(cmd) - - print('Done {0} of {1}'.format(ff, len(files_all))) - - return - -def organize_WDKoester_atmospheres(path_to_dir): - """ - Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) - Will convert wavelength units to angstroms and flux units to [erg/s/cm^2/A] - - path_to_dir is the path to the directory containing all of the downloaded - files - - Saves cdbs-ready atmospheres into os.environ['PYSYN_CDBS']/wdKoeseter - (assumes this directory exists) - """ - # Save current directory for return later, move into working dir - start_dir = os.getcwd() - os.chdir(path_to_dir) - - # Process each atmosphere file independently - print( 'Creating cdbs-ready files') - files = glob.glob('*.dk.dat.txt') - - for i in files: - data = Table.read(i, format='ascii') - - wave = data['col1'] # angstrom - flux = data['col2'] # erg/s/cm^2/A - - # Make new fits table - c0 = fits.Column(name='Wavelength', format='D', array=wave) - c1 = fits.Column(name='Flux', format='E', array=flux) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - - # Copy over headers, update unit keywords - prihdu = fits.PrimaryHDU() - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - hdu_new = fits.HDUList([prihdu, tbhdu]) - - # Write new fits table in cdbs directory - hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) - - hdu_new.close() - - # Return to original directory - os.chdir(start_dir) - return - -def make_WDKoester_catalog(path_to_dir): - """ - Create cdbs catalog.fits of wdKoester grid. - THIS IS STEP 2, after organize_WDKoester_atmospheres has - been run. - - path_to_dir is from current working directory to the cdbs directory. - Will create catalog.fits file in atmosphere directory with - description of each model - """ - # Record current working directory for later - start_dir = os.getcwd() - - # Enter atmosphere directory - os.chdir(path_to_dir) - - # Extract parameters for each atmosphere from the filename, - # construct columns for catalog file - files = glob.glob("*dk.dat.fits") - index_str = [] - name_str = [] - for name in files: - tmp = name.split('.') - tmp2 = tmp[0].split('_') - temp = float(tmp2[0][2:]) # Kelvin - logg = float(tmp2[1]) / 100.0 # log(g) - - index_str.append('{0:5.0f},0.0,{1:3.2f}'.format(temp, logg)) - name_str.append('{0}[Flux]'.format(name)) - - # Make catalog - catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) - - # Create catalog.fits file in directory with the models - catalog.write('catalog.fits', format = 'fits', overwrite=True) - - # Move back to original directory, create the catalog.fits file - os.chdir(start_dir) - - return - -def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): - """ - Rebin wdKoester models to atlas ck04 resolution; this makes - spectrophotometry MUCH faster - - makes new directory in cdbs/grid: wdKoester_rebin - - cdbs_path: path to cdbs directory - """ - # Get an atlas ck04 model, we will use this to set wavelength grid - sp_atlas = get_castelli_atmosphere() - - # Open a fits table for an existing model; we will steal the header - tmp = cdbs_path+'/grid/wdKoester/da70000_800.dk.dat.fits' - wdkoester_hdu = fits.open(tmp) - header0 = wdkoester_hdu[0].header - wdkoester_hdu.close() - - # Create cdbs/grid directory for rebinned models - path = cdbs_path+'/grid/wdKoester_rebin/' - if not os.path.exists(path): - os.mkdir(path) - - # Read in the existing catalog.fits file and rebin every spectrum. - cat = fits.getdata(cdbs_path + '/grid/wdKoester/catalog.fits') - files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] - - print( 'Rebinning wdKoester spectra') - for ff in range(len(files_all)): - vals = cat[ff][0].split(',') - temp = float(vals[0]) - metal = float(vals[1]) - logg = float(vals[2]) - - # Fetch the wdKoester spectrum, rebin flux - sp = pysynphot.Icat('wdKoester', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - - # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - - cols = fits.ColDefs([c0, c1]) - tbhdu = fits.BinTableHDU.from_columns(cols) - prihdu = fits.PrimaryHDU(header=header0) - tbhdu.header['TUNIT1'] = 'ANGSTROM' - tbhdu.header['TUNIT2'] = 'FLAM' - - outfile = path + files_all[ff].split('[')[0] - finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) - - return - - diff --git a/spisea/filters.py b/spisea/filters.py index 5ab9efec..1a579739 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -292,6 +292,22 @@ def get_ubv_filt(name): return spectrum +def get_bessell_filt(name): + """ + Define ubv (Johnson-Cousin filters, as defined in Bessell 1990) as pysynphot object + """ + try: + t = Table.read('{0}/bessell/{1}.dat'.format(filters_dir, name), format='ascii') + except: + raise ValueError('Could not find bessell filter {0} in {1}/bessell'.format(name, filters_dir)) + + wave = t[t.keys()[0]] + trans = t[t.keys()[1]] + + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='bessell_{0}'.format(name)) + + return spectrum + def get_ukirt_filt(name): """ Define UKIRT filters as pysynphot object diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 29d877b2..0a797e29 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1668,6 +1668,9 @@ def get_filter_info(name, vega=vega, rebin=True): inst = tmp[1] filt = filters.get_subaru_filt(inst, filterName) + elif name.startswith('bessell'): + filt = filters.get_bessell_filt(filterName) + else: # Otherwise, look for the filter info in the cdbs/mtab and cdbs/comp files try: diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index e527707d..c67d041f 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -219,7 +219,8 @@ def test_filters(): 'subaru,hsc,g','subaru,hsc,r','subaru,hsc,i','subaru,hsc,z','subaru,hsc,Y', 'subaru,hsc,nb387', 'subaru,hsc,nb468', 'subaru,hsc,nb515', 'subaru,hsc,nb527', 'subaru,hsc,nb656', 'subaru,hsc,nb718', 'subaru,hsc,nb816', 'subaru,hsc,nb921', - 'subaru,hsc,nb926', 'subaru,hsc,nb973'] + 'subaru,hsc,nb926', 'subaru,hsc,nb973', + 'bessell,U', 'bessell,B', 'bessell,V', 'bessell,R', 'bessell,I'] # Loop through filters to test that they work: get_filter_info for ii in filt_list: From ebcc2f367e21c06ab0c1df42db43ec3010023cdc Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 30 Jun 2026 17:49:22 -0700 Subject: [PATCH 162/191] document Bessell filters + add warning to existing ubv for I-filter --- docs/filters.rst | 8 ++++++++ spisea/filters.py | 5 +++++ 2 files changed, 13 insertions(+) diff --git a/docs/filters.rst b/docs/filters.rst index 05f29103..9d6b559b 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -29,6 +29,7 @@ directories. Available filters: * 2MASS +* Bessell * CTIO_OSIRIS * DeCam * Euclid @@ -69,6 +70,13 @@ Filters: J, H, Ks Example: ``'2mass,H'`` +**Bessell** + +`Bessel (1990) `_ Johnson-Cousins UBVRI filters + +Filters: U, B, V, R, I + +Example: ``'bessell,U'`` **CTIO_OSIRIS** diff --git a/spisea/filters.py b/spisea/filters.py index 1a579739..f3834167 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -288,6 +288,11 @@ def get_ubv_filt(name): bad = np.where(trans < 0) trans[bad] = 0 + if name=='I': + warnings.warn("Filter profile ubv,I uses the Johnson filter which extends further to long wavelengths than Cousins. " + "Here, it is improperly cut off at 1.1 microns where transmission is ~20%. " + "Consider using the Bessell UBVRI (bessell,I) filter system instead.") + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ubv_{0}'.format(name)) return spectrum From 40abd1575be06ca74d16a9716b0e5f0cc8ead24a Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Thu, 2 Jul 2026 14:50:52 -0700 Subject: [PATCH 163/191] small docs formatting bugfix --- docs/index.rst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index 40c14047..d1a7d75f 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -103,8 +103,7 @@ Change Log * All pysynphot filters can now be used * Minor bugs and case handling * Debug edge case for no companion stars - * Restore functionality where final mass = initial for companion - stars with lower mass than the isochrone's range + * Restore functionality where final mass = initial for companion stars with lower mass than the isochrone's range * MIST evolution allows input metallicities within 0.1 dex of the allowed range, since the nearest grid value is adopted, and this eliminates floating value precision issues. From 67b91755a370985da6452002d5d59af3be029b62 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Thu, 2 Jul 2026 14:54:04 -0700 Subject: [PATCH 164/191] more docs reformatting on changelog --- docs/index.rst | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index d1a7d75f..25faa516 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -84,15 +84,15 @@ Change Log ---------- 3.0 (2026-**-**) * Addition of brown dwarf models - * New evolution grids: Phillips2020, Marley2021, and - mergedPhillipsMergedPhillipsBaraffePisaEkstromParsec + * New evolution grids: Phillips2020, Marley2021, and mergedPhillipsMergedPhillipsBaraffePisaEkstromParsec * New atmosphere grids: Phillips2020, Meisner2023 * get_merged_atmosphere now uses Meisner2023 for T < 1000 K and interpolated BTSettl/Meisner2023 for 1000 < T <= 1200 - * Updated evolution and atmosphere grid data sets w/ grid_version=3.0 + * Updated evolution and atmosphere grid data sets w/ + grid_version=3.0 * These include the new brown dwarf models - * The MISTv1.2-synthpop model extension was also modified to include - denser sampling in the gap between the base MISTv1.2 grids and 0.1Msun. + * The MISTv1.2-synthpop model extension was also modified to + include denser sampling in the gap between the base MISTv1.2 grids and 0.1Msun. * Added SODC extinction law * Filter handling improvements * Fix bug where DECam "Y" filter was mislabeled "y", and updated @@ -103,7 +103,8 @@ Change Log * All pysynphot filters can now be used * Minor bugs and case handling * Debug edge case for no companion stars - * Restore functionality where final mass = initial for companion stars with lower mass than the isochrone's range + * Restore functionality where final mass = initial for companion + stars with lower mass than the isochrone's range * MIST evolution allows input metallicities within 0.1 dex of the allowed range, since the nearest grid value is adopted, and this eliminates floating value precision issues. From 3877917380d5b1dcc832b35ef1114e8d0bf910c0 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Thu, 2 Jul 2026 14:55:02 -0700 Subject: [PATCH 165/191] updated version to 3.0 --- docs/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index f10e26c1..d7bd8743 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,9 +25,9 @@ author = 'Matthew Hosek Jr, Jessica R. Lu, Casey Y. Lam' # The short X.Y version -version = '2.4' +version = '3.0' # The full version, including alpha/beta/rc tags -release = '2.4' +release = '3.0' From 07c4874c22308bf9bdb7a8102b61a61481a22f24 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 2 Jul 2026 15:39:21 -0700 Subject: [PATCH 166/191] debug Phillips2020 evo file age naming --- spisea/evolution.py | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index 56fbae48..ec670104 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -570,7 +570,16 @@ def __init__(self): self.z_solar = 0.015 # populate list of isochrone ages (log scale) - self.age_list = np.arange(6.0, 10.0, 0.001) + self.age_list = np.array([6.0, 6.102564101374986, 6.205128204112327, 6.307692307498837, + 6.41025641041525, 6.512820513399242, 6.615384615079123, 6.717948717593952, + 6.820512820432055, 6.923076923041517,7.025641027630915, 7.128205127364955, + 7.230769230806862, 7.333333333326906, 7.43589743645667, 7.538461537884313, + 7.64102564151456, 7.743589743588999, 7.846153846357851, 7.948717948583871, + 8.051282050278353, 8.153846153753816, 8.256410257173776, 8.35897435818861, + 8.461538460830697, 8.564102564448598, 8.666666666328634, 8.769230769062688, + 8.87179487160516, 8.974358974304664, 9.076923076299368, 9.179487179310316, + 9.282051282920115, 9.384615385138028, 9.487179487213739, 9.589743589709178, + 9.692307692157154, 9.794871794783068, 9.89743589751841, 10.0]) # define required evo_grid number self.evo_grid_min = 3.0 @@ -605,11 +614,9 @@ def isochrone(self, age= 1.e8, metallicity=0.0): iso_path = os.path.join(self.model_dir, 'iso', z_dir) # Find nearest age in grid to input grid by parsing through available files - p_files = glob.glob(os.path.join(iso_path, 'iso_*.fits')) - p_ages = np.array([float(f.split('_')[1].replace('.fits', '')) for f in p_files]) - close_age = np.argmin(abs(p_ages - log_age)) - close_file = p_files[close_age] - print(f"Found nearest age file as {close_file} for requested age of {log_age}") + close_age = self.age_list[np.argmin(abs(self.age_list - log_age))] + close_file = iso_path+f'/iso_{close_age:.15f}.fits' + print(f"Found nearest age {close_age} for requested age of {log_age}") # Make sure the closest file exists if not os.path.exists(close_file): From 8a9ad9153d077bb3a0d585ed5c5cfdb43f420c5b Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 2 Jul 2026 15:42:23 -0700 Subject: [PATCH 167/191] johnson-cousins I band note --- docs/filters.rst | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/filters.rst b/docs/filters.rst index 9d6b559b..330e990c 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -155,6 +155,12 @@ Example: ``'wfc3,ir,f125w'`` Johnson-Cousin filters (downloaded from http://www.aip.de/en/research/facilities/stella/instruments/data/johnson-ubvri-filter-curves). +Note: As of July 2026, the link for the source is broken. We note that the I filter here is that of +the Johnson standard filter, which has a long red tail. In the transmission curve here, it is cut off +at 1.1 microns. We recommend careful selection for a standard I filter. Note that the Bessell (1990) +filters are available as listed above. We also have specific filter profiles for many existing +instruments. + Filters: U, B, V, R, I Example: ``'ubv,B'`` From 0cb96a5ed2a42e2192968257325114dc4b78ecc2 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 6 Jul 2026 16:00:47 -0700 Subject: [PATCH 168/191] allow mag sys conversion for IsochronePhot object --- spisea/synthetic.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 0a797e29..fa2175aa 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1038,6 +1038,10 @@ class IsochronePhot(Isochrone): Define what filters the synthetic photometry will be calculated for, via the filter string identifier. + + mag_sys : string + Define magnitude system for synthetic photometry. Default + is 'Vega', with other options 'AB' and 'ST'. """ def __init__(self, logAge, AKs, distance, metallicity=0.0, @@ -1048,9 +1052,14 @@ def __init__(self, logAge, AKs, distance, min_mass=None, max_mass=None, rebin=True, recomp=False, filters=['ubv,U', 'ubv,B', 'ubv,V', 'ubv,R', 'ubv,I'], - verbose=False): + mag_sys='Vega', + verbose=False): self.metallicity = metallicity self.verbose=verbose + + if mag_sys not in ['Vega', 'AB', 'ST']: + raise ValueError(f'Invalid mag_sys={mag_sys}. Use Vega, AB, or ST.') + self.mag_sys = mag_sys # Make the iso_dir, if it doesn't already exist if not os.path.exists(iso_dir): @@ -1166,6 +1175,13 @@ def __init__(self, logAge, AKs, distance, drop_columns = [col for col in self.points.columns if (col[:2]=='m_' and (col not in all_filters))] self.points.remove_columns(drop_columns) + + if self.mag_sys == 'AB': + for i,filt in enumerate(filters): + self.points[all_filters[i]] += calc_ab_vega_filter_conversion(filt) + elif self.mag_sys == 'ST': + for i,filt in enumerate(filters): + self.points[all_filters[i]] += calc_st_vega_filter_conversion(filt) return From 122c51c2bec7fa00bb3b2d1b9fa8519f90219110 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 6 Jul 2026 16:20:13 -0700 Subject: [PATCH 169/191] clarify MAGSYS in metadata and docstring --- spisea/synthetic.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index fa2175aa..bc27def3 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -900,6 +900,7 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, tab.meta['METAL_ACT'] = evol.meta['metallicity_act'] tab.meta['WAVEMIN'] = wave_range[0] tab.meta['WAVEMAX'] = wave_range[1] + tab.meta['MAGSYS'] = 'Vega' self.points = tab @@ -1039,9 +1040,9 @@ class IsochronePhot(Isochrone): will be calculated for, via the filter string identifier. - mag_sys : string + mag_sys : string, optional Define magnitude system for synthetic photometry. Default - is 'Vega', with other options 'AB' and 'ST'. + is 'Vega', with alternative options 'AB' and 'ST'. """ def __init__(self, logAge, AKs, distance, metallicity=0.0, @@ -1179,9 +1180,11 @@ def __init__(self, logAge, AKs, distance, if self.mag_sys == 'AB': for i,filt in enumerate(filters): self.points[all_filters[i]] += calc_ab_vega_filter_conversion(filt) + tab.meta['MAGSYS'] = 'AB' elif self.mag_sys == 'ST': for i,filt in enumerate(filters): self.points[all_filters[i]] += calc_st_vega_filter_conversion(filt) + tab.meta['MAGSYS'] = 'ST' return From 22d3271791f30c3fa3acdd963695a01436c0144a Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 6 Jul 2026 16:21:21 -0700 Subject: [PATCH 170/191] clarify MAGSYS in metadata and docstring --- spisea/synthetic.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index bc27def3..1a462d14 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1180,11 +1180,11 @@ def __init__(self, logAge, AKs, distance, if self.mag_sys == 'AB': for i,filt in enumerate(filters): self.points[all_filters[i]] += calc_ab_vega_filter_conversion(filt) - tab.meta['MAGSYS'] = 'AB' + self.points.meta['MAGSYS'] = 'AB' elif self.mag_sys == 'ST': for i,filt in enumerate(filters): self.points[all_filters[i]] += calc_st_vega_filter_conversion(filt) - tab.meta['MAGSYS'] = 'ST' + self.points.meta['MAGSYS'] = 'ST' return From 0fed5ff6d5f8e9e1c11b322e7040eefede5e99a3 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 7 Jul 2026 10:39:55 -0700 Subject: [PATCH 171/191] Fix documentation typo --- spisea/atmospheres.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index 489c0078..a2c2b560 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -645,7 +645,7 @@ def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, reb Grid range: * Teff = 250 - 1200 K * gravity: 2.5 - 5.5 cgs (in steps of 0.5) - * [M/H] = -1.0, -0.5, 0, +0, +0.3 + * [M/H] = -1.0, -0.5, +0, +0.3 """ if rebin == True: From 1ce572ac384e1e65cda6dc49bb8006ac8e2e8978 Mon Sep 17 00:00:00 2001 From: nsabrams Date: Tue, 7 Jul 2026 14:44:14 -0700 Subject: [PATCH 172/191] added docstring to cosmic evo model --- spisea/evolution.py | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index 40ba9f71..5915fa91 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1541,7 +1541,27 @@ def format_isochrones(self): # COSMIC Breivik+ 2020 - not normal evo model #===========================================# class COSMIC(StellarEvolution): - + """ + Evolve objects using COSMIC + `Breivik et al. 2020 `_. + + See code website here: `_. + + Parameters + ---------- + BSEDict: dict or string, optional + Binary Stellar Evolution dictionary for COSMIC evolution. + Default is 'default' which uses the dictionary from COSMIC docs with zsun = 0.02. + + keep_disrupted_companions: bool, optional + When True, if the system is disrupted, the companions are added to the primary table. + When False, the companion is deleted. + Default is True. + + keep_COSMIC_tables : bool, optional + Allows COSMIC tables to be accessible on the SPISEA evolution object. + Default is False. + """ def __init__(self, BSEDict='default', keep_disrupted_companions=True, keep_COSMIC_tables=False): if BSEDict == 'default': self.BSEDict = { From d73f4cc146b071790b3b004acd43e842aba98082 Mon Sep 17 00:00:00 2001 From: nsabrams Date: Tue, 7 Jul 2026 14:45:16 -0700 Subject: [PATCH 173/191] updated default evo model --- spisea/synthetic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index b1156a03..28e0db50 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1508,7 +1508,7 @@ class IsochronePhotExternalEvolution(IsochronePhot): """ def __init__(self, logAge, AKs, distance, metallicity=0.0, - evo_model=default_evo_model, atm_func=default_atm_func, + evo_model=evolution.COSMIC(), atm_func=default_atm_func, wd_atm_func = default_wd_atm_func, wave_range=[3000, 52000], red_law=default_red_law, mass_sampling=1, atm_grid_dir='./', From e3c30eae5221588f34e9e70268f15ec670851dac Mon Sep 17 00:00:00 2001 From: nsabrams Date: Tue, 7 Jul 2026 16:10:57 -0700 Subject: [PATCH 174/191] refactored external evol in resolved clusters --- spisea/synthetic.py | 322 ++++++++++++++++++++++++++------------------ 1 file changed, 194 insertions(+), 128 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 28e0db50..ce3ba3ee 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -229,73 +229,43 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, ##### # Make a table to contain all the information about each stellar system. ##### - # start1 = time.time() - star_systems = self._make_star_systems_table(mass, isMulti, sysMass) - # end1 = time.time() - # print('Star systems table took {0:f} s.'.format(end1 - start1)) - - # Trim out bad systems; specifically, stars with masses outside those provided - # by the model isochrone (except for compact objects). - # Assumes external evolution software (i.e. COSMIC) will handle systems that fall outside of range if self.external_evol == False: + # start1 = time.time() + star_systems = self._make_star_systems_table(mass, isMulti, sysMass) + # end1 = time.time() + # print('Star systems table took {0:f} s.'.format(end1 - start1)) + + # Trim out bad systems; specifically, stars with masses outside those provided + # by the model isochrone (except for compact objects). + # Assumes external evolution software (i.e. COSMIC) will handle systems that fall outside of range star_systems, compMass = self._remove_bad_systems(star_systems, compMass, keep_low_mass_stars) + else: + # Makes initial table to be evolved externally + star_systems = self._make_star_systems_table_initial(mass, isMulti, sysMass) ##### # Make a table to contain all the information about companions. ##### if self.imf.make_multiples: # start3 = time.time() - star_systems, companions = self._make_companions_table(star_systems, compMass) + if self.external_evol == False: + star_systems, companions = self._make_companions_table(star_systems, compMass) + else: + # Makes initial table to be evolved externally + star_systems, companions = self._make_companions_table_initial(star_systems, compMass) # end3 = time.time() # print('Companion table new took {0:f} s.'.format(end3 - start3)) + + ##### + # Do external evolution if chosen and assign photometry + ##### # Assigns atmospheres based on grid for external evolution software # Must be done here instead of in Isochrone() since the systems are evolved after generation above if self.external_evol: star_systems, companions = iso.evo_model.evolve(star_systems, companions, iso.logAge, iso.metallicity) - for filt in self.filt_names: - filt_name = filt.split('_') - filt_val = get_filter_info(get_obs_str(filt), rebin=False, vega=vega) - - # Rescale magnitudes to correct radius - # Since original grid was done assuming 1 Rsun - star_systems[filt] = self.iso_interps[filt](star_systems['Teff'], star_systems['logg'], iso.metallicity) - flux_val = filt_val.flux0*(10**(-(star_systems[filt] - filt_val.mag0)/2.5)) - R_vals = np.sqrt((star_systems['L']*(units.Lsun)/(4*np.pi*c.sigma_sb.cgs*(star_systems['Teff']*units.K)**4)).to('pc^2')).value - flux_rescaled = flux_val*((R_vals / iso.distance)**2)/((float(1*units.Rsun.to('pc')) / iso.distance)**2) - m_rescaled = -2.5*np.log10(flux_rescaled/filt_val.flux0) + filt_val.mag0 - star_systems[filt] = m_rescaled - - companions[filt] = self.iso_interps[filt](companions['Teff'], companions['logg'], iso.metallicity) - flux_val = filt_val.flux0*(10**(-(companions[filt] - filt_val.mag0)/2.5)) - R_vals = np.sqrt((companions['L']*(units.Lsun)/(4*np.pi*c.sigma_sb.cgs*(companions['Teff']*units.K)**4)).to('pc^2')).value - flux_rescaled = flux_val*((R_vals / iso.distance)**2)/((float(1*units.Rsun.to('pc')) / iso.distance)**2) - m_rescaled = -2.5*np.log10(flux_rescaled/filt_val.flux0) + filt_val.mag0 - companions[filt] = m_rescaled - - # Add companions masses to primaries - N_comp_max = np.max(star_systems['N_companions']) - comp_index = np.zeros((len(star_systems), N_comp_max), dtype=int) - kk = 0 - for ii in range(len(star_systems)): - for cc in range(star_systems['N_companions'][ii]): - comp_index[ii][cc] = kk - kk += 1 - - # Find all the systems with at least one companion... add the flux - # of that companion to the primary. Repeat for 2 companions, - # 3 companions, etc. - for cc in range(1, N_comp_max+1): - # All systems with at least cc companions. - idx = np.where(star_systems['N_companions'] >= cc)[0] - - # Get the location in the companions array for each system and - # the cc'th companion. - cdx = comp_index[idx, cc-1] - star_systems = self._calc_system_mag(star_systems, companions, idx, cdx, filt) - - #star_systems, companions = self._remove_bad_systems_and_companions(star_systems, companions) + star_systems, companions = self._external_evol_add_photometry(star_systems, companions, iso) self.companions = companions if self.imf.make_multiples and not hasattr(self, 'companions'): @@ -324,63 +294,50 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): """ Make a star_systems table and get synthetic photometry for each primary star. """ - star_systems = Table([mass, isMulti, sysMass], - names=['mass', 'isMultiple', 'systemMass']) + star_systems = self._make_star_systems_table_initial(mass, isMulti, sysMass) N_systems = len(star_systems) - # Add columns for the Teff, L, logg, isWR, mass_current, phase, and filters. - for key in ['Teff', 'L', 'logg', 'mass_current', 'phase']: - star_systems.add_column(Column(np.empty(N_systems, dtype=float), name=key)) - star_systems.add_column(Column(np.zeros(N_systems, dtype=bool), name='isWR')) # for models with no WR designation, this remains 0 - star_systems['metallicity'] = np.ones(N_systems) * self.iso.metallicity + # Use our pre-built interpolators to fetch values from the isochrone for each star. + star_systems['Teff'] = self.iso_interps['Teff'](star_systems['mass']) + star_systems['L'] = self.iso_interps['L'](star_systems['mass']) + star_systems['logg'] = self.iso_interps['logg'](star_systems['mass']) + star_systems['isWR'] = ~(self.iso_interps['isWR'](star_systems['mass']) < 0.5) #round to 0 or 1 for speed + star_systems['mass_current'] = self.iso_interps['mass_current'](star_systems['mass']) + star_systems['phase'] = np.round(self.iso_interps['phase'](star_systems['mass'])) + star_systems['metallicity'] = np.ones(N_systems)*self.iso.metallicity - # Add the filter columns to the table. They are empty so far. - # Keep track of the filter names in : filt_names - for filt in self.filt_names: - star_systems.add_column(Column(np.empty(N_systems, dtype=float), name=filt)) + # For a very small fraction of stars, the star phase falls on integers in-between + # the ones we have definition for, as a result of the interpolation. For these + # stars, round phase down to nearest defined phase (e.g., if phase is 71, + # then round it down to 5, rather than up to 101). + # Note: this only becomes relevant when the cluster is > 10**6 M-sun, this + # effect is so small + # Convert nan_to_num to avoid errors on greater than, less than comparisons - # Use our pre-built interpolators to fetch values from the isochrone for each star. - if self.external_evol == False: - star_systems['Teff'] = self.iso_interps['Teff'](star_systems['mass']) - star_systems['L'] = self.iso_interps['L'](star_systems['mass']) - star_systems['logg'] = self.iso_interps['logg'](star_systems['mass']) - star_systems['isWR'] = ~(self.iso_interps['isWR'](star_systems['mass']) < 0.5) #round to 0 or 1 for speed - star_systems['mass_current'] = self.iso_interps['mass_current'](star_systems['mass']) - star_systems['phase'] = np.round(self.iso_interps['phase'](star_systems['mass'])) - star_systems['metallicity'] = np.ones(N_systems)*self.iso.metallicity - - # For a very small fraction of stars, the star phase falls on integers in-between - # the ones we have definition for, as a result of the interpolation. For these - # stars, round phase down to nearest defined phase (e.g., if phase is 71, - # then round it down to 5, rather than up to 101). - # Note: this only becomes relevant when the cluster is > 10**6 M-sun, this - # effect is so small - # Convert nan_to_num to avoid errors on greater than, less than comparisons - - # Define brown dwarf mass range - bd_mask = (star_systems['mass'] >= 0.01) & (star_systems['mass'] <= 0.08) - # hard code BDs as 90 and invariant masses - star_systems['phase'][bd_mask] = 90 - star_systems['mass_current'][bd_mask] = star_systems['mass'][bd_mask] - - # Identify bad phases (non-brown-dwarfs only) - star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) - bad = np.where( - (star_systems_phase_non_nan > 5) & - (star_systems_phase_non_nan < 101) & - (star_systems_phase_non_nan != 9) & - (star_systems_phase_non_nan != 90) & - (star_systems_phase_non_nan != -99) - ) - # Print warning, if desired - verbose=False - if verbose: - for ii in range(len(bad[0])): - print('WARNING: changing phase {0} to 5'.format(star_systems['phase'][bad[0][ii]])) - star_systems['phase'][bad] = 5 + # Define brown dwarf mass range + bd_mask = (star_systems['mass'] >= 0.01) & (star_systems['mass'] <= 0.08) + # hard code BDs as 90 and invariant masses + star_systems['phase'][bd_mask] = 90 + star_systems['mass_current'][bd_mask] = star_systems['mass'][bd_mask] - for filt in self.filt_names: - star_systems[filt] = self.iso_interps[filt](star_systems['mass']) + # Identify bad phases (non-brown-dwarfs only) + star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) + bad = np.where( + (star_systems_phase_non_nan > 5) & + (star_systems_phase_non_nan < 101) & + (star_systems_phase_non_nan != 9) & + (star_systems_phase_non_nan != 90) & + (star_systems_phase_non_nan != -99) + ) + # Print warning, if desired + verbose=False + if verbose: + for ii in range(len(bad[0])): + print('WARNING: changing phase {0} to 5'.format(star_systems['phase'][bad[0][ii]])) + star_systems['phase'][bad] = 5 + + for filt in self.filt_names: + star_systems[filt] = self.iso_interps[filt](star_systems['mass']) ##### # Make Remnants @@ -389,7 +346,7 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # # Remnants have flux = 0 in all bands if they are generated here. ##### - if self.ifmr != None and self.external_evol == False: + if self.ifmr != None: # Identify compact objects as those with Teff = 0 or with phase > 100 or BDs highest_mass_iso = self.iso.points['mass'].max() idx_rem = np.where((np.isnan(star_systems['Teff'])) & (star_systems['mass'] > highest_mass_iso))[0] @@ -416,6 +373,27 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): return star_systems + def _make_star_systems_table_initial(self, mass, isMulti, sysMass): + """ + Make intial star_systems table and add columns to be filled in. + """ + star_systems = Table([mass, isMulti, sysMass], + names=['mass', 'isMultiple', 'systemMass']) + N_systems = len(star_systems) + + # Add columns for the Teff, L, logg, isWR, mass_current, phase, and filters. + for key in ['Teff', 'L', 'logg', 'mass_current', 'phase']: + star_systems.add_column(Column(np.empty(N_systems, dtype=float), name=key)) + star_systems.add_column(Column(np.zeros(N_systems, dtype=bool), name='isWR')) # for models with no WR designation, this remains 0 + star_systems['metallicity'] = np.ones(N_systems) * self.iso.metallicity + + # Add the filter columns to the table. They are empty so far. + # Keep track of the filter names in : filt_names + for filt in self.filt_names: + star_systems.add_column(Column(np.empty(N_systems, dtype=float), name=filt)) + + return star_systems + def _make_companions_table(self, star_systems, compMass): """Make companions table for resolved clusters with multiplicity. @@ -429,35 +407,14 @@ def _make_companions_table(self, star_systems, compMass): Returns ------- + star_systems : astropy.table.Table + companions : astropy.table.Table """ + star_systems, companions = self._make_companions_table_initial(star_systems, compMass) N_systems = len(star_systems) N_companions = np.sum(~compMass.mask, axis=1) N_comp_tot = np.sum(N_companions) - star_systems.add_column(Column(N_companions, name='N_companions')) - system_index = np.repeat(np.arange(N_systems), N_companions) - companions = Table([system_index], names=['system_idx']) - companions.add_column(np.zeros(N_comp_tot, dtype=float), name='mass') - - if isinstance(self.imf._multi_props, multiplicity.MultiplicityResolvedDK): - companions.add_column(Column(self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx']]), name='log_a')) - companions.add_column(Column(self.imf._multi_props.random_e(self.rng.random(N_comp_tot)), name='e')) - companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters( - self.rng.random(N_comp_tot), - self.rng.random(N_comp_tot), - self.rng.random(N_comp_tot) - ) - - companions['mass'] = compMass.compressed() - for key in ['Teff', 'L', 'logg', 'mass_current', 'phase']: - companions[key] = np.empty(N_comp_tot, dtype=float) - companions['isWR'] = np.zeros(N_comp_tot, dtype=bool) - companions['metallicity'] = np.ones(N_comp_tot) * self.iso.metallicity - for filt in self.filt_names: - companions[filt] = np.empty(N_comp_tot, dtype=float) - - if self.external_evol: - return star_systems, companions for key in ['Teff', 'L', 'logg', 'mass_current']: companions[key] = self.iso_interps[key](companions['mass']) @@ -532,6 +489,115 @@ def _make_companions_table(self, star_systems, compMass): return star_systems, companions + def _make_companions_table_initial(self, star_systems, compMass): + """Make initial companions table for resolved clusters with multiplicity. + + Parameters + ---------- + star_systems : astropy.table.Table + Table containing the properties of the primary stars. + compMass : numpy.ma.MaskedArray + Masked array containing the masses of the companions. + + Returns + ------- + star_systems : astropy.table.Table + Table containing the properties of the primary stars. + companions : astropy.table.Table + Table containing the properties of the companion stars. + """ + N_systems = len(star_systems) + N_companions = np.sum(~compMass.mask, axis=1) + N_comp_tot = np.sum(N_companions) + star_systems.add_column(Column(N_companions, name='N_companions')) + system_index = np.repeat(np.arange(N_systems), N_companions) + companions = Table([system_index], names=['system_idx']) + companions.add_column(np.zeros(N_comp_tot, dtype=float), name='mass') + + if isinstance(self.imf._multi_props, multiplicity.MultiplicityResolvedDK): + companions.add_column(Column(self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx']]), name='log_a')) + companions.add_column(Column(self.imf._multi_props.random_e(self.rng.random(N_comp_tot)), name='e')) + companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters( + self.rng.random(N_comp_tot), + self.rng.random(N_comp_tot), + self.rng.random(N_comp_tot) + ) + + companions['mass'] = compMass.compressed() + for key in ['Teff', 'L', 'logg', 'mass_current', 'phase']: + companions[key] = np.empty(N_comp_tot, dtype=float) + companions['isWR'] = np.zeros(N_comp_tot, dtype=bool) + companions['metallicity'] = np.ones(N_comp_tot) * self.iso.metallicity + for filt in self.filt_names: + companions[filt] = np.empty(N_comp_tot, dtype=float) + + return star_systems, companions + + + def _external_evol_add_photometry(self, star_systems, companions, iso): + """ + Function to calculate the photometry for systems that were + evolved using an external software (i.e. COSMIC) + + Parameters + ---------- + star_systems : astropy.table.Table + Table containing the properties of the primary objects. + companions : astropy.table.Table + Table containing the properties of the companions. + iso : Ioschrone object + + Returns + ------- + star_systems : astropy.table.Table + Table containing the properties of the primary objects. + companions : astropy.table.Table + Table containing the properties of the companion companions. + """ + c = constants + for filt in self.filt_names: + filt_name = filt.split('_') + filt_val = get_filter_info(get_obs_str(filt), rebin=False, vega=vega) + + # Rescale magnitudes to correct radius + # Since original grid was done assuming 1 Rsun + star_systems[filt] = self.iso_interps[filt](star_systems['Teff'], star_systems['logg'], iso.metallicity) + flux_val = filt_val.flux0*(10**(-(star_systems[filt] - filt_val.mag0)/2.5)) + R_vals = np.sqrt((star_systems['L']*(units.Lsun)/(4*np.pi*c.sigma_sb.cgs*(star_systems['Teff']*units.K)**4)).to('pc^2')).value + flux_rescaled = flux_val*((R_vals / iso.distance)**2)/((float(1*units.Rsun.to('pc')) / iso.distance)**2) + m_rescaled = -2.5*np.log10(flux_rescaled/filt_val.flux0) + filt_val.mag0 + star_systems[filt] = m_rescaled + + companions[filt] = self.iso_interps[filt](companions['Teff'], companions['logg'], iso.metallicity) + flux_val = filt_val.flux0*(10**(-(companions[filt] - filt_val.mag0)/2.5)) + R_vals = np.sqrt((companions['L']*(units.Lsun)/(4*np.pi*c.sigma_sb.cgs*(companions['Teff']*units.K)**4)).to('pc^2')).value + flux_rescaled = flux_val*((R_vals / iso.distance)**2)/((float(1*units.Rsun.to('pc')) / iso.distance)**2) + m_rescaled = -2.5*np.log10(flux_rescaled/filt_val.flux0) + filt_val.mag0 + companions[filt] = m_rescaled + + # Add companions masses to primaries + N_comp_max = np.max(star_systems['N_companions']) + comp_index = np.zeros((len(star_systems), N_comp_max), dtype=int) + kk = 0 + for ii in range(len(star_systems)): + for cc in range(star_systems['N_companions'][ii]): + comp_index[ii][cc] = kk + kk += 1 + + # Find all the systems with at least one companion... add the flux + # of that companion to the primary. Repeat for 2 companions, + # 3 companions, etc. + for cc in range(1, N_comp_max+1): + # All systems with at least cc companions. + idx = np.where(star_systems['N_companions'] >= cc)[0] + + # Get the location in the companions array for each system and + # the cc'th companion. + cdx = comp_index[idx, cc-1] + star_systems = self._calc_system_mag(star_systems, companions, idx, cdx, filt) + + return star_systems, companions + def _calc_system_mag(self, star_systems, companions, idx, cdx, filt): From 1bb017687fc038c75d1ef4819a3b77fdfb139a65 Mon Sep 17 00:00:00 2001 From: nsabrams Date: Tue, 7 Jul 2026 16:13:36 -0700 Subject: [PATCH 175/191] added sentence about cosmic being slower --- docs/multiplicity.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/multiplicity.rst b/docs/multiplicity.rst index 1b7709f9..2eb597a1 100644 --- a/docs/multiplicity.rst +++ b/docs/multiplicity.rst @@ -31,6 +31,7 @@ For most selected evolution models, the multiples are evolved as single stars. To evolve binaries (does not support higher order multiples), you should use one of the ``MultiplicityResolved`` classes and the ``COSMIC`` evolution model. See the example jupyter notebook `Cluster_w_COSMIC.ipynb `_ for an example. +Note that currently COSMIC due to being external evolution is significantly slower than the other evolution options. Unresolved Multiplicity Classes From 94e439c2a848b626b3812b15e3e3ce7b2e9a4755 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 13 Jul 2026 21:23:46 -0700 Subject: [PATCH 176/191] mag_sys updates in COSMIC additions --- spisea/synthetic.py | 21 +++++++++++++++++++-- 1 file changed, 19 insertions(+), 2 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 4bdb3c69..13338213 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1590,6 +1590,10 @@ class IsochronePhotExternalEvolution(IsochronePhot): Define what filters the synthetic photometry will be calculated for, via the filter string identifier. + + mag_sys : string, optional + Define magnitude system for synthetic photometry. Default + is 'Vega', with alternative options 'AB' and 'ST'. """ def __init__(self, logAge, AKs, distance, metallicity=0.0, @@ -1599,7 +1603,12 @@ def __init__(self, logAge, AKs, distance, red_law=default_red_law, mass_sampling=1, atm_grid_dir='./', min_mass=None, max_mass=None, rebin=True, recomp=False, filters=['ubv,U', 'ubv,B', 'ubv,V', - 'ubv,R', 'ubv,I']): + 'ubv,R', 'ubv,I'], + mag_sys='Vega'): + + if mag_sys not in ['Vega', 'AB', 'ST']: + raise ValueError(f'Invalid mag_sys={mag_sys}. Use Vega, AB, or ST.') + self.mag_sys = mag_sys self.evo_model = evo_model self.atm_func = atm_func @@ -1724,6 +1733,7 @@ def __init__(self, logAge, AKs, distance, tab.meta['DISTANCE'] = distance tab.meta['WAVEMIN'] = wave_range[0] tab.meta['WAVEMAX'] = wave_range[1] + tab.meta['MAGSYS'] = 'Vega' self.points = tab @@ -1788,7 +1798,14 @@ def __init__(self, logAge, AKs, distance, self.make_photometry(rebin=rebin, vega=vega, comp_filters=comp_filters) - + if self.mag_sys == 'AB': + for i,filt in enumerate(filters): + self.points[all_filters[i]] += calc_ab_vega_filter_conversion(filt) + self.points.meta['MAGSYS'] = 'AB' + elif self.mag_sys == 'ST': + for i,filt in enumerate(filters): + self.points[all_filters[i]] += calc_st_vega_filter_conversion(filt) + self.points.meta['MAGSYS'] = 'ST' return From 2656cf4858e57a7fd4b638359eae2d875502fc06 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 13 Jul 2026 21:41:10 -0700 Subject: [PATCH 177/191] update docs for mag sys --- docs/make_isochrone.rst | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/docs/make_isochrone.rst b/docs/make_isochrone.rst index 11ae9bd1..5a7b3fb3 100644 --- a/docs/make_isochrone.rst +++ b/docs/make_isochrone.rst @@ -10,9 +10,10 @@ total extinction, and metallicity, along with the :ref:`atmo_models`, If the IsochronePhot sub-class is used then synthetic photometry will be produced. The :ref:`filters` are defined as additional -inputs. The output photometry is in terms of vega mags, but the user -can also calculate the required conversion to AB mags or ST mags using -the functions in :ref:`Photometry Conversion Functions `. +inputs. The output photometry is in Vega mags by default (and is always +saved to the iso file in Vega mag), but the user +can opt to return the IsochronePhot object in AB or ST mags. Either way, +the magnitude system is indicated in the MAGSYS isochrone table metadata. An example of making an IsochronePhot object:: From f0ac7bf192c603b74906ec270b37e5f96c48e676 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 14 Jul 2026 16:09:21 -0700 Subject: [PATCH 178/191] only keep requested filters in external evol isochrone --- spisea/synthetic.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 13338213..366b7fba 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1797,6 +1797,12 @@ def __init__(self, logAge, AKs, distance, self.spec_list.append(star) self.make_photometry(rebin=rebin, vega=vega, comp_filters=comp_filters) + + # Drop filters in the saved file that we don't actually want here + all_filters = ['m_'+get_filter_col_name(f) for f in filters] + drop_columns = [col for col in self.points.columns if (col[:2]=='m_' and + (col not in all_filters))] + self.points.remove_columns(drop_columns) if self.mag_sys == 'AB': for i,filt in enumerate(filters): From 1ab477467e3c5068e25a44c355e34ee6b67eca00 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 14 Jul 2026 16:13:17 -0700 Subject: [PATCH 179/191] updated contribution: --- docs/contributors.rst | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/docs/contributors.rst b/docs/contributors.rst index bfd4c49e..af28f0f4 100644 --- a/docs/contributors.rst +++ b/docs/contributors.rst @@ -42,11 +42,8 @@ IsochronePhot, implemented faster cluster generation and test functions for primary and companion star mass generation (v2.3), updated random state generators (v2.4) -Macy Huston -- New metallicity bound + isochrone filter checks, -imf_mass_lim bugfix, roman filter bugfix, Synthpop compatibility -updates (v2.2), improvements for reading in/updating existing isochrone -files, Vega mag to ST mag conversion function (v2.4), several new filter sets -and increased filter name flexibility for synphot +Macy Huston -- bug fixes, SynthPop compatibility updates, magnitude system flexibility, +new filter sets, data set maintenance Anna Pusack -- Added IRTF L-band filter support From 5690e203416bbb3a1d7d1fcb34fc4d85b1b8bc6d Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 14 Jul 2026 16:29:02 -0700 Subject: [PATCH 180/191] change MISTv1 synthpop_extension default to True --- docs/index.rst | 2 ++ spisea/evolution.py | 18 ++++++++++-------- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index 25faa516..cb0a5c90 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -83,6 +83,8 @@ releases will be co-authors in future SPISEA software papers. Change Log ---------- 3.0 (2026-**-**) + * Modified default for MISTv1.2 isochrones: synthpop_extension will be True by default + to keep a consistent lower mass limit of 0.1Msun across all ages and metallicities. * Addition of brown dwarf models * New evolution grids: Phillips2020, Marley2021, and mergedPhillipsMergedPhillipsBaraffePisaEkstromParsec * New atmosphere grids: Phillips2020, Meisner2023 diff --git a/spisea/evolution.py b/spisea/evolution.py index f01dc1e4..6ffe6e99 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1327,13 +1327,14 @@ class MISTv1(StellarEvolution): was downloaded on 8/2018 (solar metallicity) and 4/2019 (other metallicities). Default is 1.2. - synthpop_extension: boolean (default False) + synthpop_extension: boolean (default True) If True, the isochrones are extended down to a minimum initial - mass of 0.1Msun using grids interpolated via SynthPop. If False, - the web-downloaded MIST isochrones are used with their varying - lower mass limits. True option is only valid for version=1.2. + mass of 0.1Msun using grids interpolated via `SynthPop + `_. + If False, the web-downloaded MIST isochrones are used with their + varying lower mass limits. True option is only valid for version=1.2. """ - def __init__(self, version=1.2, synthpop_extension=False): + def __init__(self, version=1.2, synthpop_extension=True): # define metallicity parameters for MIST models self.z_list = [0.0000014, # [Fe/H] = -4.00 0.0000045, # [Fe/H] = -3.50 @@ -1357,11 +1358,12 @@ def __init__(self, version=1.2, synthpop_extension=False): # Set version directory self.version = version self.synthpop_extension = synthpop_extension - if (self.version == 1.0) and (not synthpop_extension): + if self.version == 1.0: self.model_version_name = 'MISTv1.0' version_dir = 'v1.0/' - elif (self.version == 1.0) and synthpop_extension: - raise ValueError('Synthpop isochrone extension not supported for MISTv1.0 isochrones') + if synthpop_extension: + warnings.warn('SynthPop isochrone extension not supported for MISTv1.0 isochrones') + self.synthpop_extension = False elif self.version == 1.2: self.model_version_name = 'MISTv1.2' version_dir = 'v1.2/' From a689650dc8dc6f07a555bc796a75e14cae78b559 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 17 Jul 2026 16:52:54 -0700 Subject: [PATCH 181/191] updating to v2.5 --- docs/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index d7bd8743..590e51da 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,9 +25,9 @@ author = 'Matthew Hosek Jr, Jessica R. Lu, Casey Y. Lam' # The short X.Y version -version = '3.0' +version = '2.5' # The full version, including alpha/beta/rc tags -release = '3.0' +release = '2.5' From ba03bd5b7b61739badbb0cbf95ef8b019e20d87a Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 17 Jul 2026 16:52:59 -0700 Subject: [PATCH 182/191] updating changelog --- docs/index.rst | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index cb0a5c90..1a4675a4 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -82,7 +82,7 @@ releases will be co-authors in future SPISEA software papers. Change Log ---------- -3.0 (2026-**-**) +2.5 (2026-07-17) * Modified default for MISTv1.2 isochrones: synthpop_extension will be True by default to keep a consistent lower mass limit of 0.1Msun across all ages and metallicities. * Addition of brown dwarf models @@ -94,7 +94,9 @@ Change Log grid_version=3.0 * These include the new brown dwarf models * The MISTv1.2-synthpop model extension was also modified to - include denser sampling in the gap between the base MISTv1.2 grids and 0.1Msun. + include denser sampling in the gap between the base MISTv1.2 + grids and 0.1Msun. + * Added SODC extinction law * Filter handling improvements * Fix bug where DECam "Y" filter was mislabeled "y", and updated From 58dad8ecd1adcd0d86a949564719ab60d19f3e72 Mon Sep 17 00:00:00 2001 From: mwhosek Date: Fri, 17 Jul 2026 17:23:45 -0700 Subject: [PATCH 183/191] Update index.rst --- docs/index.rst | 74 ++++++++++++++++++++++---------------------------- 1 file changed, 32 insertions(+), 42 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index 1a4675a4..be12aa56 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -83,35 +83,33 @@ releases will be co-authors in future SPISEA software papers. Change Log ---------- 2.5 (2026-07-17) - * Modified default for MISTv1.2 isochrones: synthpop_extension will be True by default - to keep a consistent lower mass limit of 0.1Msun across all ages and metallicities. - * Addition of brown dwarf models - * New evolution grids: Phillips2020, Marley2021, and mergedPhillipsMergedPhillipsBaraffePisaEkstromParsec - * New atmosphere grids: Phillips2020, Meisner2023 - * get_merged_atmosphere now uses Meisner2023 for T < 1000 K - and interpolated BTSettl/Meisner2023 for 1000 < T <= 1200 - * Updated evolution and atmosphere grid data sets w/ - grid_version=3.0 - * These include the new brown dwarf models - * The MISTv1.2-synthpop model extension was also modified to - include denser sampling in the gap between the base MISTv1.2 - grids and 0.1Msun. - - * Added SODC extinction law - * Filter handling improvements - * Fix bug where DECam "Y" filter was mislabeled "y", and updated - DECam filters to latest version - * Add new filters for Hipparcos, Kepler, OGLE, TESS, Tycho, Washington, - Subaru HSC - * Enable use of all Gaia filters with warning recommending latest (EDR3) - * All pysynphot filters can now be used - * Minor bugs and case handling - * Debug edge case for no companion stars - * Restore functionality where final mass = initial for companion - stars with lower mass than the isochrone's range - * MIST evolution allows input metallicities within 0.1 dex - of the allowed range, since the nearest grid value is adopted, - and this eliminates floating value precision issues. + +* *Major Changes* + * Addition of brown dwarf models (`see Begbie et al. (2026) `_) + * New evolution grids: `Phillips2020`, Marley2021, and mergedPhillipsMergedPhillipsBaraffePisaEkstromParsec + * New atmosphere grids: Phillips2020, Meisner2023 + * get_merged_atmosphere now uses Meisner2023 for T < 1000 K and interpolated BTSettl/Meisner2023 for 1000 < T <= 1200 + * Updated evolution and atmosphere grid data sets w/ grid_version=3.0 + * Added option to evolve binary systems using `COSMIC `_ via new COSMIC evolution model. + * When COSMIC is used when creating synthetic clusters, SPISEA creates binary systems normally (using the Multiplicity obj) which are then evolved using COSMIC. + * New COSMIC evolution class, which is used with new IsochronePhotExternalEvolution obj ('external evolution' referring to fact that stellar evolution is done outside of standard SPISEA isochrone-grid framework) + * New merged atmosphere model class get_merged_atmosphere_w_bb_supplement, which reverts to blackbody atmospheres for stars with parameters outside of existing grid support. + * New tutorial for creating SPISEA clusters using COSMIC: docs/Cluster_w_COSMIC.ipynb + +* *Minor Changes* + * The MISTv1.2-synthpop model extension was modified to include denser sampling in the gap between the base MISTv1.2 grids and 0.1Msun. + * Modified default for MISTv1.2 isochrones: synthpop_extension will be True by default to keep a consistent lower mass limit of 0.1Msun across all ages and metallicities. + * Added option to return synthetic photometry in terms of AB or ST mag units in IsochronePhot. Vega mag units remains the default. New meta keyword `MAGSYS` added to output tables to specify magnitude unit system. + * Added SODC extinction law + * Filter handling improvements + * Fix bug where DECam "Y" filter was mislabeled "y", and updated DECam filters to latest version + * Add new filters for Hipparcos, Kepler, OGLE, TESS, Tycho, Washington, Subaru HSC + * Enable use of all Gaia filters with warning recommending latest (EDR3) + * All pysynphot filters can now be used + * Minor bugs and case handling + * Debug edge case for no companion stars + * Restore functionality where final mass = initial for companion stars with lower mass than the isochrone's range + * MIST evolution allows input metallicities within 0.1 dex of the allowed range, since the nearest grid value is adopted, and this eliminates floating value precision issues. 2.4 (2026-03-20) * Added backward compatibility for isochrone file names created @@ -135,19 +133,11 @@ Change Log 2.2 (2026-01-16) - * Compatibility updates for SPISEA to work with `SynthPop - `_. Updates include: - * Low mass objects below the isochrone grid can optionally be kept - in clusters (off by default) and will have - ``current_mass=initial_mass`` and ``phase=98``, with no other - evolutionary information or photometry. - * Evolution model versions are now logged in IsochronePhot files - and checked if present. - * The option ``synthpop_extension`` is now available for MISTv1 - version=1.2 evolution. This fills in the missing parameter space - down to initial mass 0.1Msun in isochrones where needed. Use of - this option will require downloading updated isochrone files. - * Added support for Euclid filters. + * Compatibility updates for SPISEA to work with `SynthPop `_. Updates include: + * Low mass objects below the isochrone grid can optionally be kept in clusters (off by default) and will have ``current_mass=initial_mass`` and ``phase=98``, with no other evolutionary information or photometry. + * Evolution model versions are now logged in IsochronePhot files and checked if present. + * The option ``synthpop_extension`` is now available for MISTv1 version=1.2 evolution. This fills in the missing parameter space down to initial mass 0.1Msun in isochrones where needed. Use of this option will require downloading updated isochrone files. + * Added support for Euclid filters. 2.1.15 (2025-10-25) * Updated Roman filter name from outdated w146 to current f146. From From 4a5c4bb1273ec238025a23510a47359fb542de10 Mon Sep 17 00:00:00 2001 From: mwhosek Date: Fri, 17 Jul 2026 17:24:37 -0700 Subject: [PATCH 184/191] Update index.rst --- docs/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/index.rst b/docs/index.rst index be12aa56..223f102b 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -85,7 +85,7 @@ Change Log 2.5 (2026-07-17) * *Major Changes* - * Addition of brown dwarf models (`see Begbie et al. (2026) `_) + * Addition of brown dwarf models (see `Begbie et al. (2026) `_) * New evolution grids: `Phillips2020`, Marley2021, and mergedPhillipsMergedPhillipsBaraffePisaEkstromParsec * New atmosphere grids: Phillips2020, Meisner2023 * get_merged_atmosphere now uses Meisner2023 for T < 1000 K and interpolated BTSettl/Meisner2023 for 1000 < T <= 1200 From d3de62b1f20948686f45b8e48a70dc01bc3255f7 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 17 Jul 2026 17:32:14 -0700 Subject: [PATCH 185/191] changing installation page heading structure --- docs/getting_started.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 868787de..db20785a 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -1,5 +1,7 @@ .. _getting_started: +Installation +############ ========================== Install From Git From a0bbc520868397631fa253f1dc18b70cc353813c Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 17 Jul 2026 17:46:55 -0700 Subject: [PATCH 186/191] sphinx yaml stuff --- .readthedocs.yml | 2 +- docs/requirements.txt | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index 40ae08cb..437a8141 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -13,7 +13,7 @@ version: 2 build: os: ubuntu-22.04 tools: - python: "3.7" + python: "3.12" # Build documentation in the docs/ directory with Sphinx diff --git a/docs/requirements.txt b/docs/requirements.txt index 285b1e61..f2ac1325 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -4,4 +4,5 @@ numpydoc astropy pysynphot scipy +pandas docutils<0.18 From 8799070bd39f090ca2e37546bbec9f19fe5668f3 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 17 Jul 2026 17:52:29 -0700 Subject: [PATCH 187/191] adding setuptools for sphinx autodoc --- docs/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/requirements.txt b/docs/requirements.txt index f2ac1325..4697ca52 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -5,4 +5,5 @@ astropy pysynphot scipy pandas +setuptools docutils<0.18 From f70d7155fc52c2c23d05be169738a41c17d140f9 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 17 Jul 2026 18:03:02 -0700 Subject: [PATCH 188/191] moving python version to 3.10, looks like setuptools fails post 3.11 --- .readthedocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index 437a8141..a86dc535 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -13,7 +13,7 @@ version: 2 build: os: ubuntu-22.04 tools: - python: "3.12" + python: "3.10" # Build documentation in the docs/ directory with Sphinx From c430ca14ff53754c5d8c3586040fee1ea6635f6d Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 17 Jul 2026 18:06:29 -0700 Subject: [PATCH 189/191] looks like we need older setuptools --- docs/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 4697ca52..1a222608 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -5,5 +5,5 @@ astropy pysynphot scipy pandas -setuptools +setuptools<=82.0 docutils<0.18 From ccbe477b9ca1b6b2ec97eb7ade784182f335cb0b Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 17 Jul 2026 18:07:54 -0700 Subject: [PATCH 190/191] oops, need setup tools <= 81 --- docs/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 1a222608..a739c96a 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -5,5 +5,5 @@ astropy pysynphot scipy pandas -setuptools<=82.0 +setuptools<=81.0 docutils<0.18 From cf67f1e8aa50c5e49f29d21a016356f2386df0ee Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 17 Jul 2026 18:13:06 -0700 Subject: [PATCH 191/191] now fixing numpy - pysynphot compatibility issues --- docs/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index a739c96a..7a2f1159 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,5 +1,5 @@ matplotlib -numpy +numpy <= 1.24.4 numpydoc astropy pysynphot

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z-fH~_hx~|=Tw5x`k?FOKOMZsNr#LdIuROHHq5DFXC+THzWO~Bk)X;t+e36{z$9c8& z_P@F}V;h8FD2R^13RH~Z4QNs^0F-H&z-+;gQ1F{4o$g3%3856$o&UZ6W2X)CN~?L4 zPtLb>X+8V=yWabMfI6Si=RKOI=BBT{|E=>dN6r39SC03m=5?InsXF?u-tCiDnz{6S zm@A%J)10bPzU%AuXK}0VUOw-ocwE`9_RCLK=BQInT37s9(_C6dE_KQoUB@}7NB2{_ zv@gFy#iPG9M@~8?b51L+^2t+m%4dF-p51>o-<^x Date: Mon, 23 Feb 2026 22:50:45 -0800 Subject: [PATCH 083/191] Remove unnecessary checkings --- spisea/synthetic.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index a2bd7b98..cf6848db 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1141,14 +1141,12 @@ def __init__(self, logAge, AKs, distance, oldest_file = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}.fits' new_file_exists = check_save_file(self.save_file, evo_model, atm_func, red_law, verbose=verbose) - older_file_exists = check_save_file(older_file, evo_model, atm_func, red_law, verbose=verbose) - oldest_file_exists = check_save_file(oldest_file, evo_model, atm_func, red_law, verbose=verbose) if new_file_exists: self.save_file_legacy = None - elif older_file_exists: + elif check_save_file(older_file, evo_model, atm_func, red_law, verbose=verbose): self.save_file_legacy = older_file - elif oldest_file_exists and metallicity == 0.0: + elif metallicity == 0.0 and check_save_file(oldest_file, evo_model, atm_func, red_law, verbose=verbose): self.save_file_legacy = oldest_file else: self.save_file_legacy = None From e753199062dda23c4904c05c972bf8aefe031177 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 25 Feb 2026 15:03:25 -0800 Subject: [PATCH 084/191] Added documentation on random number generator behavior; Added support of overriding random number generator in generate_cluster and calc_multi function; Added testing of generate_cluster random reproducibility --- spisea/imf/imf.py | 79 ++++++++++++++++------ spisea/synthetic.py | 119 +++++++++++++++++++++++++++------ spisea/tests/test_synthetic.py | 6 ++ 3 files changed, 163 insertions(+), 41 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 4598705d..4f2e20a5 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -25,16 +25,6 @@ class IMF(object): The IMF base class. The mass sampling and multiplicity implementation is here. - Notes - ----- - Code author: J. Lu. - - Original code was taken from libimf package written by Jan Pflamm-Altenburg - (`Pflamm-Altenburg & Kroupa 2006 `_) - and has been modified only marginally, though more convinient and general purpose - functions have been added. The libimf code was licensed under - a GNU General Public License. - Parameters ---------- @@ -46,11 +36,53 @@ class IMF(object): multiplicity : Multiplicity object or None If None, no multiplicity is assumed. Otherwise, use multiplicity object to create multiple star systems. + + seed : int, optional + Seed for the random generator numpy.random.default_rng(seed). + All random functions in the class will use this generator, unless a different generator is passed in as an argument to the function. Default None. + Behavior: + :: + + imf = IMF(..., seed=42) + result1 = imf.generate_cluster() + result2 = imf.generate_cluster() + imf = IMF(..., seed=42) + result3 = imf.generate_cluster() + result4 = imf.generate_cluster() + + result1==result3, result2==result4, but result1≠result2, result3≠result4. + This is the same behavior as + :: + + rng = np.random.default_rng(seed=42) + result1 = rng.random(1) + result2 = rng.random(1) + rng = np.random.default_rng(seed=42) + result3 = rng.random(1) + result4 = rng.random(1) + + If identical output is desired over each run, the same generator can be passed in as an argument to the generate_cluster function, e.g. + :: + + result1 = imf.generate_cluster(rng=np.random.default_rng(42)) + result2 = imf.generate_cluster(rng=np.random.default_rng(42)) + + In this case, result1==result2 + + Notes + ----- + Code author: J. Lu. + + Original code was taken from libimf package written by Jan Pflamm-Altenburg + (`Pflamm-Altenburg & Kroupa 2006 `_) + and has been modified only marginally, though more convinient and general purpose + functions have been added. The libimf code was licensed under + a GNU General Public License. + """ def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None, seed=None): self._multi_props = multiplicity self._mass_limits = np.atleast_1d(massLimits) - self.seed = seed self.rng = np.random.default_rng(seed) if multiplicity: @@ -61,7 +93,7 @@ def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None, seed=None) return - def generate_cluster(self, totalMass): + def generate_cluster(self, totalMass, rng=None): """ Generate a cluster of stellar systems with the specified IMF. @@ -81,10 +113,10 @@ def generate_cluster(self, totalMass): totalMass : float The total mass of the cluster (including companions) in solar masses. - seed: int - If set to non-None, all random sampling will be seeded with the - specified seed, forcing identical output. - Default None + rng: numpy.random.Generator, optional + If not provided, will use the random number generator specified by the seed in the IMF instance. + If provided, will override the random generator in the IMF instance and use the specified generator, + by default None. Returns ------- @@ -102,6 +134,8 @@ def generate_cluster(self, totalMass): Array of total system masses (primary + companions) for each primary star. """ + rng = self.rng if rng is None else rng + initial_mass_limit = self._mass_limits[-1] if (self._mass_limits[-1] > totalMass): @@ -131,7 +165,7 @@ def generate_cluster(self, totalMass): # start_while = time.time() while totalMassTally < totalMass: # Generate a random number array. - uniX = self.rng.random(int(newStarCount)) + uniX = rng.random(int(newStarCount)) # Convert into the IMF from the inverted CDF newMasses = self.dice_star_cl(uniX) @@ -148,11 +182,11 @@ def generate_cluster(self, totalMass): MF = self._multi_props.multiplicity_fraction(newMasses) CSF = self._multi_props.companion_star_fraction(newMasses) - newIsMultiple = self.rng.random(int(newStarCount)) < MF + newIsMultiple = rng.random(int(newStarCount)) < MF # Function to calculate multiple systems more efficiently # start_calc = time.time() - newCompMasses, newSystemMasses, newIsMultiple = self.calc_multi(newMasses, newIsMultiple, CSF, MF) + newCompMasses, newSystemMasses, newIsMultiple = self.calc_multi(newMasses, newIsMultiple, CSF, MF, rng=rng) # end_calc = time.time() # print('Time taken for calc_multi: ', end_calc - start_calc) newTotalMassTally = newSystemMasses.sum() @@ -218,17 +252,18 @@ def generate_cluster(self, totalMass): return (masses, isMultiple, compMasses, systemMasses) - def calc_multi(self, newMasses, newIsMultiple, CSF, MF): + def calc_multi(self, newMasses, newIsMultiple, CSF, MF, rng=None): """ Helper function to calculate multiples more efficiently. We will use array operations as much as possible """ + rng = self.rng if rng is None else rng # Copy over the primary masses. Eventually add the companions. newSystemMasses = newMasses.copy() # Identify multiple systems, calculate number of companions for each multiple_idx = np.where(newIsMultiple)[0] - comp_nums = 1 + self.rng.poisson((CSF[multiple_idx] / MF[multiple_idx]) - 1) + comp_nums = 1 + rng.poisson((CSF[multiple_idx] / MF[multiple_idx]) - 1) if self._multi_props.companion_max: too_many = np.where(comp_nums > self._multi_props.CSF_max)[0] comp_nums[too_many] = self._multi_props.CSF_max @@ -242,7 +277,7 @@ def calc_multi(self, newMasses, newIsMultiple, CSF, MF): for comp_num, comp_index in zip(comp_unique, comp_indices): # Calculate masses of companions - q_values = self._multi_props.random_q(self.rng.random((len(comp_index), comp_num))) + q_values = self._multi_props.random_q(rng.random((len(comp_index), comp_num))) m_comp = np.multiply(q_values, np.transpose([primary[comp_index]])) compMasses[multiple_idx[comp_index], :comp_num] = m_comp diff --git a/spisea/synthetic.py b/spisea/synthetic.py index cf6848db..63938db8 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -92,9 +92,9 @@ class Cluster(object): no compact remnants are produced. seed: int - If set to non-None, all random sampling will be seeded with the - specified seed, forcing identical output. - Default None + Seed for the random number generator numpy.random.default_rng(seed). + All random functions in the class will use this generator, + unless a different generator is passed in as an argument to the function, by default None. vebose: boolean True for verbose output. @@ -133,24 +133,56 @@ class ResolvedCluster(Cluster): cluster_mass: float Total initial mass of the cluster, in M_sun - ifmr: ifmr object or None + ifmr: ifmr object, optional If ifmr object is defined, will create compact remnants produced by the cluster at the given isochrone age. Otherwise, no compact remnants are produced. + By default None. - keep_low_mass_stars: boolean (default False) + keep_low_mass_stars: boolean, optional If True, the cluster will not cut out stars below the isochrone grid on initial mass. They are assigned a current mass equal to their initial mass, a phase of 98, and no other evolutionary properties or photometry. If False, stars below the isochrone initial mass limit are cut out. + By default False. - seed: int - If set to non-None, all random sampling will be seeded with the - specified seed, forcing identical output. - Default None + seed: int, optional + Seed for the random number generator numpy.random.default_rng(seed). + All random functions in the class will use this generator, + unless a different generator is passed in as an argument to the function, by default None. - vebose: boolean + vebose: boolean, optional True for verbose output. + + Attributes + ---------- + star_systems: astropy.table.Table + Table containing the properties of the primary stars (or stellar systems, if multiplicity is used). The columns include: + mass: primary mass + isMultiple: boolean for whether the star is in a multiple system + systemMass: total initial mass of the stellar system (primary + companions) + Teff: effective temperature of the star + L: luminosity of the star in L_sun + logg: surface gravity of the star in cgs + isWR: boolean for whether the star is a Wolf-Rayet star + mass_current: current mass of the star + phase: evolutionary phase of the star, as defined by the isochrone model + metallicity: metallicity of the star + filter columns: magnitude of the star in each filter defined by the isochrone model + + companions: astropy.table.Table (only if multiplicity is used in the IMF object) + Table containing the properties of the companion stars. The columns include: + system_idx: index of the stellar system this companion belongs to, which can be used to match to the star_systems table + mass: initial mass of the companion star + Teff: effective temperature of the companion star + L: luminosity of the companion star in L_sun + logg: surface gravity of the companion star in cgs + isWR: boolean for whether the companion star is a Wolf-Rayet star + mass_current: current mass of the companion star + phase: evolutionary phase of the companion star, as defined by the isochrone model + metallicity: metallicity of the companion star + filter columns: magnitude of the companion star in each filter defined by the isochrone model + If multiplicity properties are defined in the IMF object, additional columns for those properties (e.g., log_a, e, i, Omega, omega) are included. """ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, seed=None, keep_low_mass_stars=False): @@ -659,21 +691,55 @@ class ResolvedClusterDiffRedden(ResolvedCluster): from which the delta_AKs values will be drawn from for each individual system. - ifmr: ifmr object or None + ifmr: ifmr object, optional If ifmr object is defined, will create compact remnants produced by the cluster at the given isochrone age. Otherwise, no compact remnants are produced. - seed: int - If set to non-None, all random sampling will be seeded with the - specified seed, forcing identical output. - Default None + seed: int, optional + Seed for the random number generator numpy.random.default_rng(seed). + All random functions in the class will use this generator, + unless a different generator is passed in as an argument to the function, by default None. - vebose: boolean + + vebose: boolean, optional True for verbose output. + + + Attributes + ---------- + star_systems: astropy.table.Table + Table containing the properties of the primary stars (or stellar systems, if multiplicity is used). The columns include: + mass: primary mass + isMultiple: boolean for whether the star is in a multiple system + systemMass: total initial mass of the stellar system (primary + companions) + Teff: effective temperature of the star + L: luminosity of the star in L_sun + logg: surface gravity of the star in cgs + isWR: boolean for whether the star is a Wolf-Rayet star + mass_current: current mass of the star + phase: evolutionary phase of the star, as defined by the isochrone model + metallicity: metallicity of the star + filter columns: magnitude of the star in each filter defined by the isochrone model, which have been perturbed by differential reddening according to the delta_AKs parameter + AKs_f: the final AKs value for each star, which is the original AKs value from the isochrone plus the differential reddening drawn from the Gaussian distribution defined by delta_AKs + + companions: astropy.table.Table, optional + If multiplicity is used, this table contains the properties of the companion stars. The columns include: + system_idx: index of the primary star's system in the star_systems table + mass: mass of the companion star + Teff: effective temperature of the companion star + L: luminosity of the companion star in L_sun + logg: surface gravity of the companion star in cgs + isWR: boolean for whether the companion star is a Wolf-Rayet star + mass_current: current mass of the companion star + phase: evolutionary phase of the companion star, as defined by the isochrone model + metallicity: metallicity of the companion star + filter columns: magnitude of the companion star in each filter defined by the isochrone model, which have been perturbed by differential reddening according to the delta_AKs parameter + If multiplicity properties are defined in the IMF object, additional columns for those properties (e.g., log_a, e, i, Omega, omega) are included. + """ def __init__(self, iso, imf, cluster_mass, deltaAKs, - ifmr=None, verbose=False, seed=None, keep_low_mass_stars=False): + ifmr=None, verbose=False, seed=None): ResolvedCluster.__init__(self, iso, imf, cluster_mass, ifmr=ifmr, verbose=verbose, seed=seed) @@ -743,8 +809,23 @@ class UnresolvedCluster(Cluster): Define the minumum and maximum wavelengths of the final output spectrum, in Angstroms. Array should be [min_wave, max_wave] - vebose: boolean - True for verbose output. + vebose: boolean, optional + True for verbose output, by default False. + + Attributes + ---------- + mass_all: numpy array + The mass of each star in the cluster, in M_sun. + spec_list: list of spectra + List of the spectra of each star in the cluster, resampled to a common wavelength grid + spec_list_trim: list of spectra + List of the spectra of each star in the cluster, resampled to a common wavelength grid and trimmed to the wavelength range defined by wave_range + spec_tot_full: numpy array + The total spectrum of the cluster, obtained by summing the spectra of all stars in spec_list, before trimming to wave_range + spec_trim: numpy array + The total spectrum of the cluster, obtained by summing the spectra of all stars in spec_list_trim, which have been trimmed to the wavelength range defined by wave_range + wave_trim: numpy array + The wavelength grid corresponding to spec_trim, which is the same as the wavelength grid of the individual spectra in spec_list_trim """ def __init__(self, iso, imf, cluster_mass, wave_range=[3000, 52000], verbose=False): diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index a39a6505..d37a8077 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -1244,6 +1244,12 @@ def test_ResolvedCluster_random_state(): imf_multi = multiplicity.MultiplicityUnresolved() imf_test = imf.IMF_broken_powerlaw(imf_limits, imf_powers, multiplicity=imf_multi) + # Test that the same random seed produces the same cluster + result1 = imf_test.generate_cluster(cluster_mass, rng=np.random.default_rng(seed=42)) + result2 = imf_test.generate_cluster(cluster_mass, rng=np.random.default_rng(seed=42)) + np.testing.assert_equal(result1, result2) + + # Test that two clusters generated with the same seed have the same star systems and companions cluster1 = syn.ResolvedCluster(iso, imf_test, cluster_mass, seed=42) cluster2 = syn.ResolvedCluster(iso, imf_test, cluster_mass, seed=42) np.testing.assert_array_equal(cluster1.star_systems, cluster2.star_systems) From 2b9e77ef74fb269f846b35b95de3c5c8b2759bd0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 25 Feb 2026 16:10:23 -0800 Subject: [PATCH 085/191] Moved IMF testing under spisea/tests --- spisea/imf/tests/__init__.py | 1 - spisea/{imf => }/tests/test_imf.py | 67 ++++++++----------- spisea/{imf => }/tests/test_multiplicity.py | 74 +++++++++------------ 3 files changed, 58 insertions(+), 84 deletions(-) delete mode 100755 spisea/imf/tests/__init__.py rename spisea/{imf => }/tests/test_imf.py (92%) rename spisea/{imf => }/tests/test_multiplicity.py (85%) diff --git a/spisea/imf/tests/__init__.py b/spisea/imf/tests/__init__.py deleted file mode 100755 index 8b137891..00000000 --- a/spisea/imf/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/spisea/imf/tests/test_imf.py b/spisea/tests/test_imf.py similarity index 92% rename from spisea/imf/tests/test_imf.py rename to spisea/tests/test_imf.py index 571f6edc..5f465747 100755 --- a/spisea/imf/tests/test_imf.py +++ b/spisea/tests/test_imf.py @@ -1,11 +1,10 @@ import numpy as np import time import pdb +import cProfile +from spisea.imf import imf, multiplicity def test_generate_cluster(): - from .. import imf - from .. import multiplicity - # Make multiplicity object imf_multi = multiplicity.MultiplicityUnresolved() @@ -32,8 +31,6 @@ def test_generate_cluster(): return def test_prim_power(): - from .. import imf - #mass_limits = np.array([0.1, 1.0, 100.0]) #powers = np.array([-2.0, -1.8]) mass_limits = np.array([1.0, 100.0]) @@ -60,9 +57,6 @@ def test_prim_power(): return def test_xi(): - from .. import imf - - import cProfile, pstats, io #from pstats import SortKey pr = cProfile.Profile() @@ -72,14 +66,14 @@ def test_xi(): ########## # - # Test validity of returned values. - # + # Test validity of returned values. + # ########## N_size = 10 m = np.linspace(0.2, 20, N_size) val_good = np.array([1.6206566 , 0.26895718, 0.10135922, 0.05639448, 0.03703704, 0.0243668 , 0.01613091, 0.01137139, 0.00839526, 0.00642142]) - + val_test = np.zeros(len(m), dtype=float) for ii in range(len(m)): val_test[ii] = imf_tmp.xi(m[ii]) @@ -90,26 +84,26 @@ def test_xi(): ########## # # Performance testing - # + # ########## t1 = time.time() # pr.enable() - + # Run a time test N_size = int(1e4) m = np.random.uniform(1.1, 99.0, size=N_size) foo1 = imf_tmp.xi(m) - + # pr.disable() t2 = time.time() print('test_xi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) - + # s = io.StringIO() # sortby = SortKey.CUMULATIVE # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) # ps.print_stats() - # print(s.getvalue()) + # print(s.getvalue()) return @@ -118,9 +112,6 @@ def test_xi2(): Test that xi() produces the correct probability for a given slope. """ - from .. import imf - - import cProfile, pstats, io #from pstats import SortKey pr = cProfile.Profile() @@ -173,13 +164,10 @@ def test_xi2(): return - -def test_mxi(): - from .. import imf - import cProfile, pstats, io +def test_mxi(): #from pstats import SortKey pr = cProfile.Profile() @@ -189,8 +177,8 @@ def test_mxi(): ########## # - # Test validity of returned values. - # + # Test validity of returned values. + # ########## N_size = 10 m = np.linspace(0.2, 20, N_size) @@ -207,11 +195,11 @@ def test_mxi(): ########## # # Performance testing - # + # ########## t1 = time.time() # pr.enable() - + # Run a time test N_size = int(1e4) m = np.random.uniform(1.1, 99.0, size=N_size) @@ -222,29 +210,26 @@ def test_mxi(): t2 = time.time() print('test_mxi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) - + # s = io.StringIO() # sortby = SortKey.CUMULATIVE # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) # ps.print_stats() - # print(s.getvalue()) + # print(s.getvalue()) return def test_theta_closed(): - from .. import imf - - import cProfile, pstats, io #from pstats import SortKey - + mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) powers = np.array([-0.3, -1.5, -2.3]) imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) ########## # - # Test validity of returned values. - # + # Test validity of returned values. + # ########## N_size = 10 m = np.linspace(0.2, 20, N_size) @@ -265,22 +250,22 @@ def test_theta_closed(): np.testing.assert_equal(val_test, val_good) - + ########## # # Speed tests and performance profiling. - # + # ########## N_size = 10000 m = np.linspace(1.1, 99, N_size) - + tmp = np.zeros((len(m), len(powers)), dtype=float) t1 = time.time() # pr = cProfile.Profile() # pr.enable() - + for ii in range(len(m)): tmp[ii] = imf.theta_closed(m[ii] - imf_tmp._m_limits_low) @@ -288,7 +273,7 @@ def test_theta_closed(): t2 = time.time() print('Runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) - + # s = io.StringIO() # sortby = SortKey.CUMULATIVE # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) @@ -297,4 +282,4 @@ def test_theta_closed(): return - + diff --git a/spisea/imf/tests/test_multiplicity.py b/spisea/tests/test_multiplicity.py similarity index 85% rename from spisea/imf/tests/test_multiplicity.py rename to spisea/tests/test_multiplicity.py index 3a9dc9b7..9df0776d 100755 --- a/spisea/imf/tests/test_multiplicity.py +++ b/spisea/tests/test_multiplicity.py @@ -1,12 +1,12 @@ import numpy as np import time - +import spisea +from spisea.imf import imf, multiplicity + def test_create_MultiplicityUnresolved(): """ Tests creating and accessing a MultiplicityUnresolved object. """ - from .. import multiplicity - # All default parameters -- check their values mu1 = multiplicity.MultiplicityUnresolved() assert mu1.MF_amp == 0.44 @@ -18,28 +18,26 @@ def test_create_MultiplicityUnresolved(): assert mu1.q_min == 0.01 # Test setting different parameters - mu2 = multiplicity.MultiplicityUnresolved(MF_amp=0.4, + mu2 = multiplicity.MultiplicityUnresolved(MF_amp=0.4, MF_power=0.4, - CSF_amp=0.4, - CSF_power=0.4, + CSF_amp=0.4, + CSF_power=0.4, CSF_max=4, - q_power=0.4, + q_power=0.4, q_min=0.04) - assert mu2.MF_amp == 0.4 + assert mu2.MF_amp == 0.4 assert mu2.MF_pow == 0.4 - assert mu2.CSF_amp == 0.4 - assert mu2.CSF_pow == 0.4 + assert mu2.CSF_amp == 0.4 + assert mu2.CSF_pow == 0.4 assert mu2.CSF_max == 4 - assert mu2.q_pow == 0.4 + assert mu2.q_pow == 0.4 assert mu2.q_min == 0.04 def test_multiplicity_fraction(): """ Test creating a MultiplicityUnresolved object and getting the multiplicity fraction out. - """ - from spisea.imf import multiplicity - + """ # First set of multiplicity parameters mu1 = multiplicity.MultiplicityUnresolved() @@ -57,14 +55,14 @@ def test_multiplicity_fraction(): CSF_amp=0.4, CSF_power=0.4, CSF_max=4, q_power=0.4, q_min=0.04) - mf2_1 = mu1.multiplicity_fraction(1.0) - np.testing.assert_almost_equal(mf2_1, 0.44, decimal=2) + mf2_1 = mu2.multiplicity_fraction(1.0) + np.testing.assert_almost_equal(mf2_1, 0.4, decimal=2) - mf2_2 = mu1.multiplicity_fraction(10.0) + mf2_2 = mu2.multiplicity_fraction(10.0) np.testing.assert_almost_equal(mf2_2, 1.0, decimal=2) - mf2_3 = mu1.multiplicity_fraction(0.1) - np.testing.assert_almost_equal(mf2_3, 0.136, decimal=2) + mf2_3 = mu2.multiplicity_fraction(0.1) + np.testing.assert_almost_equal(mf2_3, 0.159, decimal=2) def test_multiplicity_fraction_array(): @@ -72,8 +70,6 @@ def test_multiplicity_fraction_array(): Test multiplicity_fraction() on the MultiplicityUnresolved object where the inputs and outputs are in array form. """ - from spisea.imf import multiplicity - # First set of multiplicity parameters mu1 = multiplicity.MultiplicityUnresolved() @@ -83,14 +79,12 @@ def test_multiplicity_fraction_array(): np.testing.assert_almost_equal(mf_array[0], 0.44, decimal=2) np.testing.assert_almost_equal(mf_array[1], 1.0, decimal=2) np.testing.assert_almost_equal(mf_array[2], 0.136, decimal=2) - - + + def test_companion_star_fraction(): """ Test the companion_star fraction on the MultiplicityUnresolved object. """ - from spisea.imf import multiplicity - # First set of multiplicity parameters mu1 = multiplicity.MultiplicityUnresolved() @@ -120,12 +114,10 @@ def test_companion_star_fraction(): def test_resolvedmult(): """ - Test creating a MultiplicityResolvedDK object + Test creating a MultiplicityResolvedDK object and that the parameters it's populated with are correct. """ from spisea import synthetic, evolution, atmospheres, reddening, ifmr - from spisea.imf import imf, multiplicity - # Fetch isochrone logAge = 6.70 # Age in log(years) AKs = 1.0 # Ks filter extinction in mags @@ -135,56 +127,54 @@ def test_resolvedmult(): evo_merged = evolution.MISTv1() redlaw = reddening.RedLawCardelli(3.1) # Rv = 3.1 filt_list = ['nirc2,J', 'nirc2,Kp'] - + startTime = time.time() iso_merged = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=metallicity, evo_model=evo_merged, atm_func=atm_func, filters=filt_list, red_law=redlaw, - mass_sampling=3) + mass_sampling=3, iso_dir=f'{spisea.__path__[0]}/tests/isochrones') print('Constructed isochrone: %d seconds' % (time.time() - startTime)) - - # Now we can make the cluster. + + # Now we can make the cluster. clust_mtot = 10**4. clust_multiplicity = multiplicity.MultiplicityResolvedDK() # Multiplicity is defined in the IMF object clust_imf_Mult = imf.Kroupa_2001(multiplicity=clust_multiplicity) - + # Make clusters clust_Mult = synthetic.ResolvedCluster(iso_merged, clust_imf_Mult, clust_mtot) clust_Mult_ss = clust_Mult.star_systems - + print('Constructed cluster: %d seconds' % (time.time() - startTime)) - + #check if columns were created assert 'log_a' in clust_Mult.companions.colnames assert 'e' in clust_Mult.companions.colnames assert 'i' in clust_Mult.companions.colnames assert 'Omega' in clust_Mult.companions.colnames assert 'omega' in clust_Mult.companions.colnames - + #check values are in correct range assert all(10**i<= 2000 and 10**i>= 0 for i in clust_Mult.companions['log_a']) #max separation is 2000 AU assert all(i<= 1 and i>= 0 for i in clust_Mult.companions['e']) assert all(i<= 180 and i>= 0 for i in clust_Mult.companions['i']) assert all(i<= 360 and i>= 0 for i in clust_Mult.companions['omega']) assert all(i<= 360 and i>= 0 for i in clust_Mult.companions['Omega']) - + #checks sign for inclination is being randomly genarated assert any(i > 90 for i in clust_Mult.companions['i']) and any(i < 90 for i in clust_Mult.companions['i']) - + #checks eccentricity follows f(e) = 2e pdf n, bins = np.histogram(clust_Mult.companions['e'], density = True) bin_centers = 0.5*(bins[1:] + bins[:-1]) assert all(np.abs(i) < 0.3 for i in 2*bin_centers - n) - + #checks shape of inclination histogram is sin(i) n, bins = np.histogram(clust_Mult.companions['i']) bin_centers = 0.5*(bins[1:] + bins[:-1]) assert all(np.abs(i) < 0.15 for i in n/max(n) - np.sin(np.pi*bin_centers/180)) - - return - + return From 138ca290dc1e31a6ced5bbae710ae57ec667003d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 25 Feb 2026 16:12:29 -0800 Subject: [PATCH 086/191] Optimized import and removed unused imports --- spisea/synthetic.py | 46 +++++++++++++--------------------- spisea/tests/test_models.py | 24 ++++++++++-------- spisea/tests/test_synthetic.py | 2 +- 3 files changed, 31 insertions(+), 41 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index ac25af93..ad0c781a 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1,33 +1,21 @@ +import os +import time +import math +import scipy +import inspect +import warnings import numpy as np import pylab as plt -from spisea import reddening -from spisea import evolution +import matplotlib.pyplot as plt +from spisea import reddening, evolution, filters from spisea import atmospheres as atm -from spisea import filters -from spisea.imf import imf, multiplicity -from scipy import interpolate -from scipy import stats -from scipy.special import erf +from scipy.spatial import cKDTree as KDTree +from spisea.imf import multiplicity from pysynphot import spectrum from pysynphot import ObsBandpass from pysynphot import observation as obs -import pysynphot from astropy import constants, units -from astropy.table import Table, Column, MaskedColumn -import pickle -import time, datetime -import math -import os, glob -import tempfile -import scipy -import matplotlib -import matplotlib.pyplot as plt -import time -import warnings -import pdb -from scipy.spatial import cKDTree as KDTree -import inspect -import astropy.modeling +from astropy.table import Table, Column default_evo_model = evolution.MISTv1() default_red_law = reddening.RedLawNishiyama09() @@ -525,12 +513,12 @@ def _make_companions_table(self, star_systems, compMass): bad = np.where( (companions_phase_non_nan > 5) & (companions_phase_non_nan < 101) & (companions_phase_non_nan != 9) & - (companions_phase_non_nan != -99)) + (companions_phase_non_nan != -99))[0] # Print warning, if desired verbose=False if verbose: - for ii in range(len(bad[0])): - print('WARNING: changing phase {0} to 5'.format(companions['phase'][bad[0][ii]])) + for ii in range(len(bad)): + print('WARNING: changing phase {0} to 5'.format(companions['phase'][bad[ii]])) companions['phase'][bad] = 5 for filt in self.filt_names: @@ -550,8 +538,8 @@ def _make_companions_table(self, star_systems, compMass): # If *both* objects are dark, then keep the magnitude # as np.nan. Otherwise, add fluxes together - good = np.where( (f1 != 0) | (f2 != 0) ) - bad = np.where( (f1 == 0) & (f2 == 0) ) + good = np.where( (f1 != 0) | (f2 != 0) )[0] + bad = np.where( (f1 == 0) & (f2 == 0) )[0] star_systems[filt][idx[good]] = -2.5 * np.log10(f1[good] + f2[good]) star_systems[filt][idx[bad]] = np.nan @@ -576,7 +564,7 @@ def _make_companions_table(self, star_systems, compMass): # Drop remnants where it is not relevant (e.g. not a compact object or # outside mass range IFMR is defined for) - good = np.where(r_id_tmp > 0) + good = np.where(r_id_tmp > 0)[0] cdx_rem_good = cdx_rem[good] companions['mass_current'][cdx_rem_good] = r_mass_tmp[good] diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 5dc85282..5f81438d 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -1,5 +1,5 @@ # Test functions for the different stellar evolution and atmosphere models -from spisea import evolution +from spisea import evolution, atmospheres, synthetic import numpy as np import pdb @@ -10,8 +10,6 @@ def test_evo_model_grid_num(): are working). Try it on one evolution model here; we'll test on all evo models in another function. """ - from spisea import evolution - # Make MIST evolution model, check evo grid variables evo = evolution.MISTv1() assert isinstance(evo.evo_grid_min, float) @@ -93,11 +91,17 @@ def test_atmosphere_models(): """ Test the rebinned atmosphere models used for synthetic photometry """ - from spisea import atmospheres as atm - # Array of atmospheres - atm_arr = [atm.get_merged_atmosphere, atm.get_castelli_atmosphere, atm.get_phoenixv16_atmosphere, atm.get_BTSettl_2015_atmosphere, - atm.get_BTSettl_atmosphere, atm.get_kurucz_atmosphere, atm.get_phoenix_atmosphere, atm.get_wdKoester_atmosphere] + atm_arr = [ + atmospheres.get_merged_atmosphere, + atmospheres.get_castelli_atmosphere, + atmospheres.get_phoenixv16_atmosphere, + atmospheres.get_BTSettl_2015_atmosphere, + atmospheres.get_BTSettl_atmosphere, + atmospheres.get_kurucz_atmosphere, + atmospheres.get_phoenix_atmosphere, + atmospheres.get_wdKoester_atmosphere + ] # Array of metallicities metals_range = [-2.0, 0, 0.15] @@ -121,7 +125,7 @@ def test_atmosphere_models(): # Test get_merged_atmospheres at different temps temp_range = [2000, 3500, 4000, 5250, 6000, 12000] - atm_func = atm.get_merged_atmosphere + atm_func = atmospheres.get_merged_atmosphere for ii in metals_range: for jj in temp_range: try: @@ -135,7 +139,7 @@ def test_atmosphere_models(): # Test get_bb_atmosphere at different temps # This func only requests temp temp_range = [2000, 3500, 4000, 5250, 6000, 12000] - atm_func = atm.get_bb_atmosphere + atm_func = atmospheres.get_bb_atmosphere for jj in temp_range: try: test = atm_func(temperature=jj, verbose=True) @@ -150,8 +154,6 @@ def test_filters(): """ Test to make sure all of the filters work as expected """ - from spisea import synthetic - # Define vega spectrum vega = synthetic.Vega() diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 386faf58..94914add 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -161,7 +161,7 @@ def test_IsochronePhot(plot=False): ) endTime = time.time() print('IsochronePhot generated in: %d seconds' % (endTime - startTime)) - # Typically takes 120 seconds if file is regenerated. + # Typically takes 40 seconds if file is regenerated. # Limited by pysynphot.Icat call in atmospheres.py assert iso.points.meta['LOGAGE'] == logAge From bf01acb6a6468958ba0fe3fff9c278f45a8a3c4e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 25 Feb 2026 16:50:52 -0800 Subject: [PATCH 087/191] Removed the extra parameter rng in generate_cluster and calc_multi, as it can be set before running the function --- spisea/imf/imf.py | 39 +++++++++++++--------------------- spisea/tests/test_synthetic.py | 6 ++++-- 2 files changed, 19 insertions(+), 26 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 4f2e20a5..8e6e1454 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -39,7 +39,7 @@ class IMF(object): seed : int, optional Seed for the random generator numpy.random.default_rng(seed). - All random functions in the class will use this generator, unless a different generator is passed in as an argument to the function. Default None. + All random functions in the class will use this generator, by default None. Behavior: :: @@ -61,11 +61,12 @@ class IMF(object): result3 = rng.random(1) result4 = rng.random(1) - If identical output is desired over each run, the same generator can be passed in as an argument to the generate_cluster function, e.g. + If identical output is desired over each run, the random state can be reset before running the function, e.g. :: - - result1 = imf.generate_cluster(rng=np.random.default_rng(42)) - result2 = imf.generate_cluster(rng=np.random.default_rng(42)) + imf.rng = np.random.default_rng(seed=42) + result1 = imf.generate_cluster() + imf.rng = np.random.default_rng(seed=42) + result2 = imf.generate_cluster() In this case, result1==result2 @@ -92,8 +93,7 @@ def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None, seed=None) return - - def generate_cluster(self, totalMass, rng=None): + def generate_cluster(self, totalMass): """ Generate a cluster of stellar systems with the specified IMF. @@ -113,11 +113,6 @@ def generate_cluster(self, totalMass, rng=None): totalMass : float The total mass of the cluster (including companions) in solar masses. - rng: numpy.random.Generator, optional - If not provided, will use the random number generator specified by the seed in the IMF instance. - If provided, will override the random generator in the IMF instance and use the specified generator, - by default None. - Returns ------- masses : numpy float array @@ -134,8 +129,6 @@ def generate_cluster(self, totalMass, rng=None): Array of total system masses (primary + companions) for each primary star. """ - rng = self.rng if rng is None else rng - initial_mass_limit = self._mass_limits[-1] if (self._mass_limits[-1] > totalMass): @@ -165,7 +158,7 @@ def generate_cluster(self, totalMass, rng=None): # start_while = time.time() while totalMassTally < totalMass: # Generate a random number array. - uniX = rng.random(int(newStarCount)) + uniX = self.rng.random(int(newStarCount)) # Convert into the IMF from the inverted CDF newMasses = self.dice_star_cl(uniX) @@ -182,11 +175,11 @@ def generate_cluster(self, totalMass, rng=None): MF = self._multi_props.multiplicity_fraction(newMasses) CSF = self._multi_props.companion_star_fraction(newMasses) - newIsMultiple = rng.random(int(newStarCount)) < MF + newIsMultiple = self.rng.random(int(newStarCount)) < MF # Function to calculate multiple systems more efficiently # start_calc = time.time() - newCompMasses, newSystemMasses, newIsMultiple = self.calc_multi(newMasses, newIsMultiple, CSF, MF, rng=rng) + newCompMasses, newSystemMasses, newIsMultiple = self.calc_multi(newMasses, newIsMultiple, CSF, MF) # end_calc = time.time() # print('Time taken for calc_multi: ', end_calc - start_calc) newTotalMassTally = newSystemMasses.sum() @@ -252,18 +245,17 @@ def generate_cluster(self, totalMass, rng=None): return (masses, isMultiple, compMasses, systemMasses) - def calc_multi(self, newMasses, newIsMultiple, CSF, MF, rng=None): + def calc_multi(self, newMasses, newIsMultiple, CSF, MF): """ Helper function to calculate multiples more efficiently. We will use array operations as much as possible """ - rng = self.rng if rng is None else rng # Copy over the primary masses. Eventually add the companions. newSystemMasses = newMasses.copy() # Identify multiple systems, calculate number of companions for each multiple_idx = np.where(newIsMultiple)[0] - comp_nums = 1 + rng.poisson((CSF[multiple_idx] / MF[multiple_idx]) - 1) + comp_nums = 1 + self.rng.poisson((CSF[multiple_idx] / MF[multiple_idx]) - 1) if self._multi_props.companion_max: too_many = np.where(comp_nums > self._multi_props.CSF_max)[0] comp_nums[too_many] = self._multi_props.CSF_max @@ -277,7 +269,7 @@ def calc_multi(self, newMasses, newIsMultiple, CSF, MF, rng=None): for comp_num, comp_index in zip(comp_unique, comp_indices): # Calculate masses of companions - q_values = self._multi_props.random_q(rng.random((len(comp_index), comp_num))) + q_values = self._multi_props.random_q(self.rng.random((len(comp_index), comp_num))) m_comp = np.multiply(q_values, np.transpose([primary[comp_index]])) compMasses[multiple_idx[comp_index], :comp_num] = m_comp @@ -288,7 +280,6 @@ def calc_multi(self, newMasses, newIsMultiple, CSF, MF, rng=None): return compMasses, newSystemMasses, newIsMultiple - class IMF_broken_powerlaw(IMF): """ Initialize a multi-part power-law with N parts. Each part of the @@ -318,8 +309,8 @@ def __init__(self, mass_limits, powers, multiplicity=None, seed=None): if len(mass_limits) != len(powers) + 1: msg = 'Incorrect specification of multi-part powerlaw.\n' msg += ' len(massLimts) != len(powers)+1\n' - msg += ' len(massLimits) = \n' + len(mass_limits) - msg += ' len(powers) = \n' + len(powers) + msg += ' len(massLimits) = \n' + str(len(mass_limits)) + msg += ' len(powers) = \n' + str(len(powers)) raise RuntimeError(msg) mass_limits = np.atleast_1d(mass_limits) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 94914add..72331850 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -1249,8 +1249,10 @@ def test_ResolvedCluster_random_state(): imf_test = imf.IMF_broken_powerlaw(imf_limits, imf_powers, multiplicity=imf_multi) # Test that the same random seed produces the same cluster - result1 = imf_test.generate_cluster(cluster_mass, rng=np.random.default_rng(seed=42)) - result2 = imf_test.generate_cluster(cluster_mass, rng=np.random.default_rng(seed=42)) + imf_test.rng = np.random.default_rng(seed=42) + result1 = imf_test.generate_cluster(cluster_mass) + imf_test.rng = np.random.default_rng(seed=42) + result2 = imf_test.generate_cluster(cluster_mass) np.testing.assert_equal(result1, result2) # Test that two clusters generated with the same seed have the same star systems and companions From b2ea3fb3dbcd21e0ca56c80fba862fbe29b72b8c Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 26 Feb 2026 10:24:55 +0100 Subject: [PATCH 088/191] started interp_AKs_grid setup with kwargs and variables --- spisea/evolution.py | 66 ++++++++++++++++++++++++--------------------- spisea/synthetic.py | 11 +++++++- 2 files changed, 46 insertions(+), 31 deletions(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index fea75ab3..60e30887 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -66,8 +66,8 @@ class StellarEvolution(object): """ Base Stellar evolution class. - Parameters - ---------- + Subclasses must define the following parameters: + ------------------------------------------------ model_dir: path Directory path to evolution model files @@ -83,14 +83,11 @@ class StellarEvolution(object): model_version_name: string Name of the model class plus additional details like version numbers and rotation if relevant. + + AKs_grid_age_list: list + Sparser list of ages to use for AKs interpolation grid """ - def __init__(self, model_dir, age_list, mass_list, z_list): - self.model_dir = model_dir - self.z_list = z_list - self.mass_list = mass_list - self.age_list = age_list - self.model_version_name = "None" - + def __init__(self): return class Geneva(StellarEvolution): @@ -100,22 +97,22 @@ def __init__(self): """ self.model_version_name = "Geneva" # populate list of model masses (in solar masses) - mass_list = [(0.1 + i*0.005) for i in range(181)] + self.mass_list = [(0.1 + i*0.005) for i in range(181)] # define metallicity parameters for Geneva models - z_list = [0.01, 0.02, 0.03] + self.z_list = [0.01, 0.02, 0.03] # populate list of isochrone ages (log scale) age_list = [round(5.5 + 0.01*i, 2) for i in range(190)] age_list += [round(7.4 + 0.05*i, 2) for i in range(12)] age_list += [round(math.log10(1.e8*x), 2) for x in range(1, 10)] age_list += [round(math.log10(1.e9*x), 2) for x in range(1, 10)] - age_list = age_list + self.age_list = age_list + # Add AKs_grid_age_list after clarifying data set + #self.AKs_grid_age_list = np.linspace(5.5, , 0.05) # specify location of model files - model_dir = models_dir + 'geneva/' - - StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) + self.model_dir = models_dir + 'geneva/' self.z_solar = 0.02 self.z_file_map = {0.01: 'z01/', 0.02: 'z02/', 0.03: 'z03/'} @@ -186,6 +183,7 @@ def __init__(self, rot=True): # populate list of isochrone ages (log scale) self.age_list = np.arange(6.0, 8.0+0.005, 0.01) + self.AKs_grid_age_list = np.arange(6.0, 8.0+0.005, 0.05) # Specify location of model files self.model_dir = models_dir+'Ekstrom2012/' @@ -420,7 +418,9 @@ def __init__(self): # populate list of isochrone ages (log scale) self.age_list = np.arange(6.6, 10.12+0.005, 0.01) self.age_list = np.append(6.40, self.age_list) - + self.AKs_grid_age_list = np.arange(6.6, 10.12+0.005, 0.05) + self.AKs_grid_age_list = [6.40] + list(self.AKs_grid_age_list) + [10.12] + # Specify location of model files self.model_dir = models_dir+'ParsecV1.2s/' @@ -568,6 +568,7 @@ def __init__(self): # populate list of isochrone ages (log scale) self.age_list = np.arange(6.0, 8.01+0.005, 0.01) + self.AKs_grid_age_list = np.arange(6.0, 8.01+0.005, 0.05) # Specify location of model files self.model_dir = models_dir+'Pisa2011/' @@ -728,6 +729,7 @@ def __init__(self): # populate list of isochrone ages (log scale) self.age_list = np.arange(6.0, 8.0+0.005, 0.01) + self.AKs_grid_age_list = np.arange(6.0, 8.0+0.005, 0.05) # Specify location of model files self.model_dir = models_dir+'Baraffe15/' @@ -1048,6 +1050,8 @@ def __init__(self, version=1.2, synthpop_extension=False): # populate list of isochrone ages (log scale) self.age_list = np.arange(5.01, 10.30+0.005, 0.01) + self.AKs_grid_age_list = np.linspace(5.0,10.3,107) + self.AKs_grid_age_list[0] = 5.01 # Set version directory self.version = version @@ -1277,17 +1281,18 @@ def __init__(self, rot=True): else: self.model_version_name = "MergedBaraffePisaEkstromParsec-norot" # populate list of model masses (in solar masses) - mass_list = [(0.1 + i*0.005) for i in range(181)] + self.mass_list = [(0.1 + i*0.005) for i in range(181)] # define metallicity parameters for Geneva models - z_list = [0.015] + self.z_list = [0.015] # populate list of isochrone ages (log scale) - age_list = np.arange(6.0, 10.091, 0.01).tolist() + self.age_list = np.arange(6.0, 10.091, 0.01).tolist() + self.AKs_grid_age_list = np.arange(6.0, 10.091, 0.05).tolist() + self.AKs_grid_age_list += [10.09] # specify location of model files - model_dir = models_dir + 'merged/baraffe_pisa_ekstrom_parsec/' - StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) + self.model_dir = models_dir + 'merged/baraffe_pisa_ekstrom_parsec/' self.z_solar = 0.015 # Switch to specify rotating/non-rotating models @@ -1384,17 +1389,17 @@ def __init__(self, rot=True): else: self.model_version_name = "MergedPisaEkstromParsec-norot" # populate list of model masses (in solar masses) - mass_list = [(0.1 + i*0.005) for i in range(181)] + self.mass_list = [(0.1 + i*0.005) for i in range(181)] # define metallicity parameters for Geneva models - z_list = [0.015] + self.z_list = [0.015] # populate list of isochrone ages (log scale) - age_list = np.arange(6.0, 8.001, 0.01).tolist() + self.age_list = np.arange(6.0, 8.001, 0.01).tolist() + self.AKs_grid_age_list = np.arange(6.0, 8.001, 0.05).tolist() # specify location of model files - model_dir = models_dir + 'merged/pisa_ekstrom_parsec/' - StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) + self.model_dir = models_dir + 'merged/pisa_ekstrom_parsec/' self.z_solar = 0.015 #Switch to specify rot/notot @@ -1482,10 +1487,10 @@ def __init__(self): """ self.model_version_name = "MergedSiessGenevaPadova" # populate list of model masses (in solar masses) - mass_list = [(0.1 + i*0.005) for i in range(181)] + self.mass_list = [(0.1 + i*0.005) for i in range(181)] # define metallicity parameters for Geneva models - z_list = [0.02] + self.z_list = [0.02] # populate list of isochrone ages (log scale) age_list = np.arange(5.5, 7.41, 0.01).tolist() @@ -1506,10 +1511,11 @@ def __init__(self): age_list.append(9.60) age_list.append(9.70) age_list.append(9.78) + self.age_list = age_list + # TODO: define AKs_grid_age_list after clarifying isochrones # specify location of model files - model_dir = models_dir + 'merged/siess_meynetMaeder_padova/' - StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) + self.model_dir = models_dir + 'merged/siess_meynetMaeder_padova/' self.z_solar = 0.02 # Metallicity map diff --git a/spisea/synthetic.py b/spisea/synthetic.py index e6390844..82f87a30 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1110,6 +1110,14 @@ class IsochronePhot(Isochrone): Define what filters the synthetic photometry will be calculated for, via the filter string identifier. + + verbose : bool + Determines whether certain information gets printed + + interp_AKs_grid : bool + If true, interpolate over existing grid for AKs values, instead of + re-generating all photometry from scratch. If False, re-generate all + photometry from scratch if the file does not already exist. """ def __init__(self, logAge, AKs, distance, metallicity=0.0, @@ -1120,7 +1128,8 @@ def __init__(self, logAge, AKs, distance, min_mass=None, max_mass=None, rebin=True, recomp=False, filters=['ubv,U', 'ubv,B', 'ubv,V', 'ubv,R', 'ubv,I'], - verbose=False): + verbose=False, + interp_AKs_grid=False): self.metallicity = metallicity # Make the iso_dir, if it doesn't already exist From fc5b408c5fc23ba0b5bfc18e8c2f9391f139a394 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 26 Feb 2026 17:03:17 +0100 Subject: [PATCH 089/191] add Gaia EDR3 filters --- docs/filters.rst | 9 +- filt_func/gaia/edr3/Gaia_passbands.txt | 781 +++++++++++++++++++++++++ filt_func/gaia/edr3/ReadMe | 134 +++++ spisea/filters.py | 12 +- spisea/synthetic.py | 3 + spisea/tests/test_models.py | 1 + 6 files changed, 931 insertions(+), 9 deletions(-) create mode 100644 filt_func/gaia/edr3/Gaia_passbands.txt create mode 100644 filt_func/gaia/edr3/ReadMe diff --git a/docs/filters.rst b/docs/filters.rst index 918f27f4..b7254cb9 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -93,12 +93,13 @@ Example: ``'euclid,Y'`` **GAIA** The `GAIA Space Telescope filters `_. -Note that three sets are available: the pre-launch passbands used in DR1 +Note that four sets are available: the pre-launch passbands used in DR1 (`Jordi+10 `_), -the passbands used for the DR2 published photometry, and -the *revised* DR2 passbands based on the DR2 data (October 2017). -ONLY THE REVISED DR2 PASSBANDS ARE SUPPORTED BY SPISEA. +the passbands used for the DR2 published photometry, +the *revised* DR2 passbands based on the DR2 data (October 2017), +and the EDR3 passbands from Riello et al (2021). +ONLY THE REVISED DR2 and EDR3 PASSBANDS ARE SUPPORTED BY SPISEA. Filters: G, Gbp, Grp diff --git a/filt_func/gaia/edr3/Gaia_passbands.txt b/filt_func/gaia/edr3/Gaia_passbands.txt new file mode 100644 index 00000000..cc733830 --- /dev/null +++ b/filt_func/gaia/edr3/Gaia_passbands.txt @@ -0,0 +1,781 @@ + 320.00 2.37366962e-08 2.34103325e-11 99.99 99.99 99.99 99.99 + 321.00 1.57774443e-07 1.54036935e-10 99.99 99.99 99.99 99.99 + 322.00 9.08554755e-07 8.78053229e-10 99.99 99.99 99.99 99.99 + 323.00 4.78072331e-06 4.57325370e-09 99.99 99.99 99.99 99.99 + 324.00 2.39428071e-05 2.26698640e-08 99.99 99.99 99.99 99.99 + 325.00 1.03648276e-04 9.71310542e-08 3.87054116e-05 6.46801629e-08 99.99 99.99 + 326.00 3.72280534e-04 3.45277531e-07 1.75771984e-04 2.88785224e-07 99.99 99.99 + 327.00 1.09381843e-03 1.00397934e-06 6.49485416e-04 1.04897855e-06 99.99 99.99 + 328.00 2.65174642e-03 2.40864304e-06 1.96690820e-03 3.12246976e-06 99.99 99.99 + 329.00 5.45098195e-03 4.89953932e-06 5.01083084e-03 7.81780663e-06 99.99 99.99 + 330.00 9.76875472e-03 8.68837731e-06 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99.99 99.99 99.99 +1097.00 99.99 99.99 99.99 99.99 99.99 99.99 +1098.00 99.99 99.99 99.99 99.99 99.99 99.99 +1099.00 99.99 99.99 99.99 99.99 99.99 99.99 +1100.00 99.99 99.99 99.99 99.99 99.99 99.99 diff --git a/filt_func/gaia/edr3/ReadMe b/filt_func/gaia/edr3/ReadMe new file mode 100644 index 00000000..bd3e2f27 --- /dev/null +++ b/filt_func/gaia/edr3/ReadMe @@ -0,0 +1,134 @@ +J/A+A/649/A3 Gaia Early Data Release 3 photometric passbands (Riello+, 2021) +================================================================================ +Gaia Early Data Release 3: Photometric content and validation. + Riello M., De Angeli F., Evans D.W., Montegriffo P., Carrasco J.M, + Busso G., Palaversa L., Burgess P., Diener C., Davidson M., Rowell N., + Fabricius C., Jordi C., Bellazzini M., Pancino E., Harrison D.L., + Cacciari C., van Leeuwen F., Hambly N.C., Hodgkin S.T., Osborne P.J., + Altavilla G., Barstow M.A., Brown A.G.A., Castellani M., Cowell S., + De Luise F., Gilmore G., Giuffrida G., Hidalgo S., Holland G., Marinoni S., + Pagani C., Piersimoni A.M., Pulone L., Ragaini S., Rainer M., Richards P.J., + Sanna N., Walton N.A., Weiler M., Yoldas A. + + =2021A&A...649A...3R (SIMBAD/NED BibCode) +================================================================================ +ADC_Keywords: Surveys ; Photometry, G band +Keywords: catalogues - surveys - instrumentation: photometers - + techniques: photometric - Galaxy: general + +Abstract: + Gaia Early Data Release 3 (Gaia EDR3) contains astrometry and + photometry results for about 1.8 billion sources based on + observations collected by the European Space Agency Gaia satellite + during the first 34 months of its operational phase. + + In this paper, we focus on the photometric content, describing the + input data, the algorithms, the processing, and the validation of the + results. Particular attention is given to the quality of the data and + to a number of features that users may need to take into account to + make the best use of the Gaia EDR3 catalogue. + + The processing broadly followed the same procedure as for Gaia DR2, + but with significant improvements in several aspects of the blue and + red photometer (BP and RP) preprocessing and in the photometric + calibration process. In particular, the treatment of the BP and RP + background has been updated to include a better estimation of the + local background, and the detection of crowding effects has been used + to exclude affected data from the calibrations. The photometric + calibration models have also been updated to account for flux loss + over the whole magnitude range. Significant improvements in the + modelling and calibration of the Gaia point and line spread functions + have also helped to reduce a number of instrumental effects that were + still present in DR2. + + Gaia EDR3 contains 1.806 billion sources with G-band photometry and + 1.540 billion sources with GBP and GRP photometry. The median + uncertainty in the G-band photometry, as measured from the standard + deviation of the internally calibrated mean photometry for a given + source, is 0.2mmag at magnitude G=10 to 14, 0.8mmag at G~17, and + 2.6mmag at G~19. The significant magnitude term found in the Gaia DR2 + photometry is no longer visible, and overall there are no trends + larger than 1mmag/mag. Using one passband over the whole colour and + magnitude range leaves no systematics above the 1% level in magnitude + in any of the bands, and a larger systematic is present for a very + small sample of bright and blue sources. A detailed description of the + residual systematic effects is provided. Overall the quality of the + calibrated mean photometry in Gaia EDR3 is superior with respect to + DR2 for all bands. + +Description: + These tabular data describes the photometric system defined by the G, + G_BP_ and G_RP_ Gaia bands for Gaia Early Data Release 3. + + The tables provide the full passband and the corresponding zero point + for each of the photometric bands. The zero points are available both + in the VEGAMAG and AB systems. + + The passband calibration is based on the modelling of a set of + corrections applied to pre-launch knowledge of the instrument, to find + the best match between observed and synthetic photometry for a set of + calibrators. For EDR3 a large set of calibrators covering a wide range + of spectra types was used for the passband calibration. + + For these sources reconstructed SEDs were obtained from externally + calibrated Gaia BP/RP spectra. + +File Summary: +-------------------------------------------------------------------------------- + FileName Lrecl Records Explanations +-------------------------------------------------------------------------------- +ReadMe 80 . This file +passband.dat 103 781 G, G_BP_ anf G_RP_ passbands used to generate + the magnitudes and astrophysical parameters + included in Gaia EDR3 +zeropt.dat 97 2 G, G_BP_ and G_RP_ zero points used to generate + the magnitudes and astrophysical parameters + included in Gaia EDR3 +-------------------------------------------------------------------------------- + +See also: + I/350 : Gaia EDR3 (Gaia Collaboration, 2020) + J/A+A/649/A6 : Gaia Catalogue of Nearby Stars - GCNS (Gaia collaboration, 2021) + J/A+A/649/A7 : MC structure and properties (Gaia Collaboration+, 2021) + +Byte-by-byte Description of file: passband.dat +-------------------------------------------------------------------------------- + Bytes Format Units Label Explanations +-------------------------------------------------------------------------------- + 1- 7 F7.2 nm lambda Wavelength + 10- 23 E14.9 mag GPb ?=99.99 G transmissivity curve at the + corresponding wavelength (1) + 26- 39 E14.9 mag e_GPb ?=99.99 Uncertainty on the G transmissivity + curve (1) + 42- 55 E14.9 mag BPPb ?=99.99 BP transmissivity curve at the + corresponding wavelength (1) + 58- 71 E14.9 mag e_BPPb ?=99.99 Uncertainty on the BP transmissivity + curve (1) + 74- 87 E14.9 mag RPPb ?=99.99 RP transmissivity curve at the + corresponding wavelength (1) + 90-103 E14.9 mag e_RPPb ?=99.99 Uncertainty on the RP transmissivity + curve (1) +-------------------------------------------------------------------------------- +Note (1): In correspondence to wavelength values where the passband is not + defined, both transmissivity and uncertainty are set to 99.99. +-------------------------------------------------------------------------------- + +Byte-by-byte Description of file: zeropt.dat +-------------------------------------------------------------------------------- + Bytes Format Units Label Explanations +-------------------------------------------------------------------------------- + 2- 14 F13.10 --- GZp G band zero point + 18- 29 F12.10 --- e_GZp G band zero point uncertainty + 32- 44 F13.10 --- BPZp G_BP_ band zero point + 48- 59 F12.10 --- e_BPZp G_BP_ band zero point uncertainty + 62- 74 F13.10 --- RPZp G_RP_ band zero point + 78- 89 F12.10 --- e_RPZp G_RP_ band zero point uncertainty + 91- 97 A7 --- System [VEGAMAG, AB] Photometric system +-------------------------------------------------------------------------------- + +Acknowledgements: + Marco Riello, mriello(at)ast.cam.ac.uk + Francesca De Angeli, fda(at)ast.cam.ac.uk + +================================================================================ +(End) Francesca De Angeli [IoA, UK], Patricia Vannier [CDS] 05-Jan-2021 diff --git a/spisea/filters.py b/spisea/filters.py index 23c029b3..4b100d71 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -338,14 +338,14 @@ def get_gaia_filt(version, name): Define Gaia filters as pysynphot object. To avoid confusion, we will only support the revised DR2 zeropoints from - Evans+18. + Evans+18 and EDR3 from Riello+21. - version: specify dr1, dr2, or dr2_rev + version: specify dr1, dr2, dr2_rev, edr3 name: filter name """ # Assert that we are using the revised DR2 zeropoints - if version != 'dr2_rev': - msg = 'Gaia version {0} not supported, use dr2_rev instead'.format(version) + if version not in ['dr2_rev', 'edr3']: + msg = 'Gaia version {0} not supported, use dr2_rev or edr3 instead'.format(version) raise ValueError(msg) # Set the filter directory @@ -355,8 +355,10 @@ def get_gaia_filt(version, name): path = '{0}/gaia/dr2/'.format(filters_dir) elif version == 'dr2_rev': path = '{0}/gaia/dr2_rev/'.format(filters_dir) + elif version == 'edr3': + path = '{0}/gaia/edr3/'.format(filters_dir) else: - raise ValueError('GAIA filter version {0} not understood. Please use dr1, dr2, or dr2_rev'.format(version)) + raise ValueError('GAIA filter version {0} not understood. Please use dr1, dr2, dr2_rev, or edr3'.format(version)) # Get the filter info try: diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 82f87a30..bda6f9d7 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1765,6 +1765,8 @@ def get_filter_col_name(obs_str): # Catch Gaia filter cases. Otherwise, it is HST filter if 'dr2_rev' in tmp: filt_name = 'gaiaDR2_{0}'.format(tmp[-1]) + elif 'edr3' in tmp: + filt_name = 'gaiaEDR3_{0}'.format(tmp[-1]) elif 'roman' in tmp: filt_name = 'roman_{0}'.format(tmp[-1]) else: @@ -1836,6 +1838,7 @@ def get_obs_str(col): 'ztf_g':'ztf,g', 'ztf_r':'ztf,r', 'ztf_i':'ztf,i', 'gaiaDR2_G': 'gaia,dr2_rev,G', 'gaiaDR2_Gbp':'gaia,dr2_rev,Gbp', 'gaiaDR2_Grp':'gaia,dr2_rev,Grp', + 'gaiaEDR3_G': 'gaia,edr3,G', 'gaiaEDR3_Gbp':'gaia,edr3,Gbp', 'gaiaEDR3_Grp':'gaia,edr3,Grp', 'hawki_J': 'hawki,J', 'hawki_H': 'hawki,H', 'hawki_Ks': 'hawki,Ks', diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index be7b9307..05b7305f 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -164,6 +164,7 @@ def test_filters(): 'decam,y', 'decam,i', 'decam,z', 'decam,u', 'decam,g', 'decam,r', 'gaia,dr2_rev,G', 'gaia,dr2_rev,Gbp', 'gaia,dr2_rev,Grp', + 'gaia,edr3,G', 'gaia,edr3,Gbp', 'gaia,edr3,Grp', 'jwst,F070W', 'jwst,F090W', 'jwst,F115W', 'jwst,F140M', 'jwst,F150W', 'jwst,F150W2', 'jwst,F162M', 'jwst,F164N', 'jwst,F182M', 'jwst,F187N', 'jwst,F200W', 'jwst,F212N', From 6bcc97223cecea3776a603d9f8a785284988f8b9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 26 Feb 2026 11:08:50 -0800 Subject: [PATCH 090/191] Added missing blank line in docstring code block --- spisea/imf/imf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 8e6e1454..df6a8887 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -63,6 +63,7 @@ class IMF(object): If identical output is desired over each run, the random state can be reset before running the function, e.g. :: + imf.rng = np.random.default_rng(seed=42) result1 = imf.generate_cluster() imf.rng = np.random.default_rng(seed=42) From 345e6e4f5f76464eec0435136ce53eae09f2cd96 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 27 Feb 2026 14:06:53 -0800 Subject: [PATCH 091/191] MF=0 stipulation added for random companion counts --- spisea/imf/multiplicity.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/spisea/imf/multiplicity.py b/spisea/imf/multiplicity.py index 00c2b809..6b249fe7 100755 --- a/spisea/imf/multiplicity.py +++ b/spisea/imf/multiplicity.py @@ -219,6 +219,10 @@ def random_companion_count(self, x, CSF, MF): """ Helper function: calculate number of companions. """ + # bd stipulation since mf=0 + if MF <= 0: + return 0 + n_comp = 1 + np.random.poisson((CSF / MF) - 1) if self.companion_max == True: From a8bc0f416e40a566390db223cd72860fb65d5432 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 27 Feb 2026 14:27:56 -0800 Subject: [PATCH 092/191] fixing BD binarity --- spisea/imf/multiplicity.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/spisea/imf/multiplicity.py b/spisea/imf/multiplicity.py index 6b249fe7..82a8c765 100755 --- a/spisea/imf/multiplicity.py +++ b/spisea/imf/multiplicity.py @@ -183,6 +183,8 @@ def companion_star_fraction(self, mass): csf = self.multiplicity_fraction(mass) else: csf[csf > self.CSF_max] = self.CSF_max + bd = mass <= 0.08 + csf[bd] = self.multiplicity_fraction(mass[bd]) return csf From 1d9cdc2ba4fb8f6660b7064e6f7d2460a5466e7a Mon Sep 17 00:00:00 2001 From: Rocketpack23 Date: Thu, 5 Mar 2026 11:18:24 -0800 Subject: [PATCH 093/191] updated filters.rst and dictionary in synthetic.py --- docs/filters.rst | 26 +++++++++++++++++--------- spisea/synthetic.py | 5 +++-- 2 files changed, 20 insertions(+), 11 deletions(-) diff --git a/docs/filters.rst b/docs/filters.rst index 918f27f4..6efb28fc 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -7,16 +7,16 @@ Photometric Filters The user can specify what filters are used for synthetic photometry when defining the :ref:`isochrone_objects`. Each filter is identified by a unique string, and an array of such strings -are passed into the Isochrone call. +are passed into the Isochrone call. For example:: - + # Use the HST WFC3-IR F127M and F153M filters, along with NIRC2 Kp filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f153m', 'nirc2,Kp'] my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0, evo_model=evo_model, atm_func=atm_func, red_law=red_law, filters=filt_list) - + These strings follow the format ``,``. Note that there is no space after the comma, and case matters. @@ -40,7 +40,7 @@ Available filters: * JWST * Keck NIRC * Keck NIRC2 -* NACO +* NACO * PanStarrs 1 * Roman Space Telescope * UKIRT @@ -48,11 +48,11 @@ Available filters: * VISTA * ZTF - + Filter Sets ------------ - + **2MASS** `Two-Micron Sky Survey `_ @@ -83,7 +83,7 @@ Example: ``'decam,r'`` **Euclid** -Euclid space telescope `NISP filters `_ +Euclid space telescope `NISP filters `_ and `VIS single filter `_ Filters: VIS, Y, J, H @@ -146,10 +146,10 @@ Example: ``'jg,K'`` JWST NIRCam filters, downloaded from `NIRCam website `_. The filter functions in the nircam_throughputs/modAB_mean/nrc_plus_ote folder is used. -Filters: F070W, F090W, F115W, F140M, F150W, F150W2, F162M, F164N, F182M, F187N, F200W, F210M, F212N, F250M, F277W, F300M, F322W2, F323N, F335M, F356W, F360M, F405N, F410M, F430M, F444W, F460M, F466N, F470N, F480M +Filters: F070W, F090W, F115W, F140M, F150W, F150W2, F162M, F164N, F182M, F187N, F200W, F210M, F212N, F250M, F277W, F300M, F322W2, F323N, F335M, F356W, F360M, F405N, F410M, F430M, F444W, F460M, F466N, F470N, F480M Example: ``'jwst,F356W'`` - + **Keck NIRC** @@ -237,3 +237,11 @@ Example: ``'vista,Y'`` Filters: g, r, i Example: ``'ztf,g'`` + +**IRTF** + +`IRTF NSFCam `_ + +Filters: L + +Example: ``'nsfcam,L'`` diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 4c98dc57..171d0bda 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1554,7 +1554,7 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('nsfcam'): filt = filters.get_nsfcam_filt(filterName) - + else: # Otherwise, look for the filter info in the cdbs/mtab and cdbs/comp files try: @@ -1701,7 +1701,8 @@ def get_obs_str(col): 'euclid_VIS':'euclid,VIS', 'euclid_Y':'euclid,Y', 'euclid_J':'euclid,J', - 'euclid_H':'euclid,H'} + 'euclid_H':'euclid,H', + 'nsfcam_L':'nsfcam,L'} obs_str = filt_list[name] From 5da97c8fc8ac9dec85750893ba35d6cd1972cf38 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 20 Mar 2026 11:23:45 -0700 Subject: [PATCH 094/191] adding nsfcam,L to test_filters --- spisea/tests/test_models.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index be7b9307..68139ecb 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -186,7 +186,8 @@ def test_filters(): 'roman,wfi,f158', 'roman,wfi,f146', 'roman,wfi,f213', 'roman,wfi,f184', 'rubin,g', 'rubin,i', 'rubin,r', 'rubin,u', 'rubin,z', 'rubin,y', - 'euclid,VIS', 'euclid,Y', 'euclid,J', 'euclid,H'] + 'euclid,VIS', 'euclid,Y', 'euclid,J', 'euclid,H', + 'nsfcam,L'] # Loop through filters to test that they work: get_filter_info for ii in filt_list: @@ -205,4 +206,4 @@ def test_filters(): print('get_obs_str pass') print('Filters done') - return \ No newline at end of file + return From c958a0acbb38e012a74d71547e19beceeb774185 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 20 Mar 2026 12:54:37 -0700 Subject: [PATCH 095/191] resolve merge mistakes --- docs/filters.rst | 9 +- filt_func/gaia/edr3/Gaia_passbands.txt | 781 ------------------------- filt_func/gaia/edr3/ReadMe | 134 ----- spisea/evolution.py | 20 +- spisea/filters.py | 8 +- spisea/synthetic.py | 14 +- spisea/tests/test_models.py | 1 - 7 files changed, 18 insertions(+), 949 deletions(-) delete mode 100644 filt_func/gaia/edr3/Gaia_passbands.txt delete mode 100644 filt_func/gaia/edr3/ReadMe diff --git a/docs/filters.rst b/docs/filters.rst index 2d8eedf9..6efb28fc 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -93,13 +93,12 @@ Example: ``'euclid,Y'`` **GAIA** The `GAIA Space Telescope filters `_. -Note that four sets are available: the pre-launch passbands used in DR1 +Note that three sets are available: the pre-launch passbands used in DR1 (`Jordi+10 `_), -the passbands used for the DR2 published photometry, -the *revised* DR2 passbands based on the DR2 data (October 2017), -and the EDR3 passbands from Riello et al (2021). -ONLY THE REVISED DR2 and EDR3 PASSBANDS ARE SUPPORTED BY SPISEA. +the passbands used for the DR2 published photometry, and +the *revised* DR2 passbands based on the DR2 data (October 2017). +ONLY THE REVISED DR2 PASSBANDS ARE SUPPORTED BY SPISEA. Filters: G, Gbp, Grp diff --git a/filt_func/gaia/edr3/Gaia_passbands.txt b/filt_func/gaia/edr3/Gaia_passbands.txt deleted file mode 100644 index cc733830..00000000 --- a/filt_func/gaia/edr3/Gaia_passbands.txt +++ /dev/null @@ -1,781 +0,0 @@ - 320.00 2.37366962e-08 2.34103325e-11 99.99 99.99 99.99 99.99 - 321.00 1.57774443e-07 1.54036935e-10 99.99 99.99 99.99 99.99 - 322.00 9.08554755e-07 8.78053229e-10 99.99 99.99 99.99 99.99 - 323.00 4.78072331e-06 4.57325370e-09 99.99 99.99 99.99 99.99 - 324.00 2.39428071e-05 2.26698640e-08 99.99 99.99 99.99 99.99 - 325.00 1.03648276e-04 9.71310542e-08 3.87054116e-05 6.46801629e-08 99.99 99.99 - 326.00 3.72280534e-04 3.45277531e-07 1.75771984e-04 2.88785224e-07 99.99 99.99 - 327.00 1.09381843e-03 1.00397934e-06 6.49485416e-04 1.04897855e-06 99.99 99.99 - 328.00 2.65174642e-03 2.40864304e-06 1.96690820e-03 3.12246976e-06 99.99 99.99 - 329.00 5.45098195e-03 4.89953932e-06 5.01083084e-03 7.81780663e-06 99.99 99.99 - 330.00 9.76875472e-03 8.68837731e-06 1.09458069e-02 1.67813576e-05 99.99 99.99 - 331.00 1.56275855e-02 1.37527326e-05 2.07035425e-02 3.11867542e-05 99.99 99.99 - 332.00 2.27741233e-02 1.98296421e-05 3.46967871e-02 5.13454545e-05 99.99 99.99 - 333.00 3.07613294e-02 2.64991906e-05 5.25319303e-02 7.63595340e-05 99.99 99.99 - 334.00 3.93617092e-02 3.35454579e-05 7.35388438e-02 1.04983900e-04 99.99 99.99 - 335.00 4.81051591e-02 4.05565884e-05 9.60352312e-02 1.34629478e-04 99.99 99.99 - 336.00 5.65974619e-02 4.72013222e-05 1.18775774e-01 1.63484887e-04 99.99 99.99 - 337.00 6.48652284e-02 5.35098603e-05 1.42278579e-01 1.92249270e-04 99.99 99.99 - 338.00 7.25998465e-02 5.92378341e-05 1.65557648e-01 2.19575501e-04 99.99 99.99 - 339.00 7.99799035e-02 6.45450034e-05 1.88569139e-01 2.45441174e-04 99.99 99.99 - 340.00 8.68837415e-02 6.93449692e-05 2.09777042e-01 2.67921956e-04 99.99 99.99 - 341.00 9.34892114e-02 7.37919125e-05 2.28816153e-01 2.86708496e-04 99.99 99.99 - 342.00 9.95098198e-02 7.76711769e-05 2.43876762e-01 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99.99 99.99 99.99 -1097.00 99.99 99.99 99.99 99.99 99.99 99.99 -1098.00 99.99 99.99 99.99 99.99 99.99 99.99 -1099.00 99.99 99.99 99.99 99.99 99.99 99.99 -1100.00 99.99 99.99 99.99 99.99 99.99 99.99 diff --git a/filt_func/gaia/edr3/ReadMe b/filt_func/gaia/edr3/ReadMe deleted file mode 100644 index bd3e2f27..00000000 --- a/filt_func/gaia/edr3/ReadMe +++ /dev/null @@ -1,134 +0,0 @@ -J/A+A/649/A3 Gaia Early Data Release 3 photometric passbands (Riello+, 2021) -================================================================================ -Gaia Early Data Release 3: Photometric content and validation. - Riello M., De Angeli F., Evans D.W., Montegriffo P., Carrasco J.M, - Busso G., Palaversa L., Burgess P., Diener C., Davidson M., Rowell N., - Fabricius C., Jordi C., Bellazzini M., Pancino E., Harrison D.L., - Cacciari C., van Leeuwen F., Hambly N.C., Hodgkin S.T., Osborne P.J., - Altavilla G., Barstow M.A., Brown A.G.A., Castellani M., Cowell S., - De Luise F., Gilmore G., Giuffrida G., Hidalgo S., Holland G., Marinoni S., - Pagani C., Piersimoni A.M., Pulone L., Ragaini S., Rainer M., Richards P.J., - Sanna N., Walton N.A., Weiler M., Yoldas A. - - =2021A&A...649A...3R (SIMBAD/NED BibCode) -================================================================================ -ADC_Keywords: Surveys ; Photometry, G band -Keywords: catalogues - surveys - instrumentation: photometers - - techniques: photometric - Galaxy: general - -Abstract: - Gaia Early Data Release 3 (Gaia EDR3) contains astrometry and - photometry results for about 1.8 billion sources based on - observations collected by the European Space Agency Gaia satellite - during the first 34 months of its operational phase. - - In this paper, we focus on the photometric content, describing the - input data, the algorithms, the processing, and the validation of the - results. Particular attention is given to the quality of the data and - to a number of features that users may need to take into account to - make the best use of the Gaia EDR3 catalogue. - - The processing broadly followed the same procedure as for Gaia DR2, - but with significant improvements in several aspects of the blue and - red photometer (BP and RP) preprocessing and in the photometric - calibration process. In particular, the treatment of the BP and RP - background has been updated to include a better estimation of the - local background, and the detection of crowding effects has been used - to exclude affected data from the calibrations. The photometric - calibration models have also been updated to account for flux loss - over the whole magnitude range. Significant improvements in the - modelling and calibration of the Gaia point and line spread functions - have also helped to reduce a number of instrumental effects that were - still present in DR2. - - Gaia EDR3 contains 1.806 billion sources with G-band photometry and - 1.540 billion sources with GBP and GRP photometry. The median - uncertainty in the G-band photometry, as measured from the standard - deviation of the internally calibrated mean photometry for a given - source, is 0.2mmag at magnitude G=10 to 14, 0.8mmag at G~17, and - 2.6mmag at G~19. The significant magnitude term found in the Gaia DR2 - photometry is no longer visible, and overall there are no trends - larger than 1mmag/mag. Using one passband over the whole colour and - magnitude range leaves no systematics above the 1% level in magnitude - in any of the bands, and a larger systematic is present for a very - small sample of bright and blue sources. A detailed description of the - residual systematic effects is provided. Overall the quality of the - calibrated mean photometry in Gaia EDR3 is superior with respect to - DR2 for all bands. - -Description: - These tabular data describes the photometric system defined by the G, - G_BP_ and G_RP_ Gaia bands for Gaia Early Data Release 3. - - The tables provide the full passband and the corresponding zero point - for each of the photometric bands. The zero points are available both - in the VEGAMAG and AB systems. - - The passband calibration is based on the modelling of a set of - corrections applied to pre-launch knowledge of the instrument, to find - the best match between observed and synthetic photometry for a set of - calibrators. For EDR3 a large set of calibrators covering a wide range - of spectra types was used for the passband calibration. - - For these sources reconstructed SEDs were obtained from externally - calibrated Gaia BP/RP spectra. - -File Summary: --------------------------------------------------------------------------------- - FileName Lrecl Records Explanations --------------------------------------------------------------------------------- -ReadMe 80 . This file -passband.dat 103 781 G, G_BP_ anf G_RP_ passbands used to generate - the magnitudes and astrophysical parameters - included in Gaia EDR3 -zeropt.dat 97 2 G, G_BP_ and G_RP_ zero points used to generate - the magnitudes and astrophysical parameters - included in Gaia EDR3 --------------------------------------------------------------------------------- - -See also: - I/350 : Gaia EDR3 (Gaia Collaboration, 2020) - J/A+A/649/A6 : Gaia Catalogue of Nearby Stars - GCNS (Gaia collaboration, 2021) - J/A+A/649/A7 : MC structure and properties (Gaia Collaboration+, 2021) - -Byte-by-byte Description of file: passband.dat --------------------------------------------------------------------------------- - Bytes Format Units Label Explanations --------------------------------------------------------------------------------- - 1- 7 F7.2 nm lambda Wavelength - 10- 23 E14.9 mag GPb ?=99.99 G transmissivity curve at the - corresponding wavelength (1) - 26- 39 E14.9 mag e_GPb ?=99.99 Uncertainty on the G transmissivity - curve (1) - 42- 55 E14.9 mag BPPb ?=99.99 BP transmissivity curve at the - corresponding wavelength (1) - 58- 71 E14.9 mag e_BPPb ?=99.99 Uncertainty on the BP transmissivity - curve (1) - 74- 87 E14.9 mag RPPb ?=99.99 RP transmissivity curve at the - corresponding wavelength (1) - 90-103 E14.9 mag e_RPPb ?=99.99 Uncertainty on the RP transmissivity - curve (1) --------------------------------------------------------------------------------- -Note (1): In correspondence to wavelength values where the passband is not - defined, both transmissivity and uncertainty are set to 99.99. --------------------------------------------------------------------------------- - -Byte-by-byte Description of file: zeropt.dat --------------------------------------------------------------------------------- - Bytes Format Units Label Explanations --------------------------------------------------------------------------------- - 2- 14 F13.10 --- GZp G band zero point - 18- 29 F12.10 --- e_GZp G band zero point uncertainty - 32- 44 F13.10 --- BPZp G_BP_ band zero point - 48- 59 F12.10 --- e_BPZp G_BP_ band zero point uncertainty - 62- 74 F13.10 --- RPZp G_RP_ band zero point - 78- 89 F12.10 --- e_RPZp G_RP_ band zero point uncertainty - 91- 97 A7 --- System [VEGAMAG, AB] Photometric system --------------------------------------------------------------------------------- - -Acknowledgements: - Marco Riello, mriello(at)ast.cam.ac.uk - Francesca De Angeli, fda(at)ast.cam.ac.uk - -================================================================================ -(End) Francesca De Angeli [IoA, UK], Patricia Vannier [CDS] 05-Jan-2021 diff --git a/spisea/evolution.py b/spisea/evolution.py index b99373dd..2f9dd760 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -66,8 +66,8 @@ class StellarEvolution(object): """ Base Stellar evolution class. - Subclasses must define the following parameters: - ------------------------------------------------ + Parameters + ---------- model_dir: path Directory path to evolution model files @@ -83,9 +83,6 @@ class StellarEvolution(object): model_version_name: string Name of the model class plus additional details like version numbers and rotation if relevant. - - AKs_grid_age_list: list - Sparser list of ages to use for AKs interpolation grid """ def __init__(self, model_dir, age_list, mass_list, z_list): self.model_dir = model_dir @@ -117,6 +114,8 @@ def __init__(self): # specify location of model files self.model_dir = models_dir + 'geneva/' + + StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) self.z_solar = 0.02 self.z_file_map = {0.01: 'z01/', 0.02: 'z02/', 0.03: 'z03/'} @@ -1049,8 +1048,6 @@ def __init__(self, version=1.2, synthpop_extension=False): # populate list of isochrone ages (log scale) self.age_list = np.arange(5.01, 10.30+0.005, 0.01) - self.AKs_grid_age_list = np.linspace(5.0,10.3,107) - self.AKs_grid_age_list[0] = 5.01 # Set version directory self.version = version @@ -1289,7 +1286,8 @@ def __init__(self, rot=True): age_list = np.arange(6.0, 10.091, 0.01).tolist() # specify location of model files - self.model_dir = models_dir + 'merged/baraffe_pisa_ekstrom_parsec/' + model_dir = models_dir + 'merged/baraffe_pisa_ekstrom_parsec/' + StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) self.z_solar = 0.015 # Switch to specify rotating/non-rotating models @@ -1395,7 +1393,8 @@ def __init__(self, rot=True): age_list = np.arange(6.0, 8.001, 0.01).tolist() # specify location of model files - self.model_dir = models_dir + 'merged/pisa_ekstrom_parsec/' + model_dir = models_dir + 'merged/pisa_ekstrom_parsec/' + StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) self.z_solar = 0.015 #Switch to specify rot/notot @@ -1509,7 +1508,8 @@ def __init__(self): age_list.append(9.78) # specify location of model files - self.model_dir = models_dir + 'merged/siess_meynetMaeder_padova/' + model_dir = models_dir + 'merged/siess_meynetMaeder_padova/' + StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) self.z_solar = 0.02 # Metallicity map diff --git a/spisea/filters.py b/spisea/filters.py index ca295551..e2a2aee9 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -340,12 +340,12 @@ def get_gaia_filt(version, name): the revised DR2 zeropoints from Evans+18. - version: specify dr1, dr2, dr2_rev, edr3 + version: specify dr1, dr2, or dr2_rev name: filter name """ # Assert that we are using the revised DR2 zeropoints - if version not in ['dr2_rev', 'edr3']: - msg = 'Gaia version {0} not supported, use dr2_rev or edr3 instead'.format(version) + if version != 'dr2_rev': + msg = 'Gaia version {0} not supported, use dr2_rev instead'.format(version) raise ValueError(msg) # Set the filter directory @@ -355,8 +355,6 @@ def get_gaia_filt(version, name): path = '{0}/gaia/dr2/'.format(filters_dir) elif version == 'dr2_rev': path = '{0}/gaia/dr2_rev/'.format(filters_dir) - elif version == 'edr3': - path = '{0}/gaia/edr3/'.format(filters_dir) else: raise ValueError('GAIA filter version {0} not understood. Please use dr1, dr2, or dr2_rev'.format(version)) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 1f3f7453..4dec9340 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1179,14 +1179,6 @@ class IsochronePhot(Isochrone): Define what filters the synthetic photometry will be calculated for, via the filter string identifier. - - verbose : bool - Determines whether certain information gets printed - - interp_AKs_grid : bool - If true, interpolate over existing grid for AKs values, instead of - re-generating all photometry from scratch. If False, re-generate all - photometry from scratch if the file does not already exist. """ def __init__(self, logAge, AKs, distance, metallicity=0.0, @@ -1197,8 +1189,7 @@ def __init__(self, logAge, AKs, distance, min_mass=None, max_mass=None, rebin=True, recomp=False, filters=['ubv,U', 'ubv,B', 'ubv,V', 'ubv,R', 'ubv,I'], - verbose=False, - interp_AKs_grid=False): + verbose=False): self.metallicity = metallicity self.verbose=verbose @@ -1851,8 +1842,6 @@ def get_filter_col_name(obs_str): # Catch Gaia filter cases. Otherwise, it is HST filter if 'dr2_rev' in tmp: filt_name = 'gaiaDR2_{0}'.format(tmp[-1]) - elif 'edr3' in tmp: - filt_name = 'gaiaEDR3_{0}'.format(tmp[-1]) elif 'roman' in tmp: filt_name = 'roman_{0}'.format(tmp[-1]) else: @@ -1924,7 +1913,6 @@ def get_obs_str(col): 'ztf_g':'ztf,g', 'ztf_r':'ztf,r', 'ztf_i':'ztf,i', 'gaiaDR2_G': 'gaia,dr2_rev,G', 'gaiaDR2_Gbp':'gaia,dr2_rev,Gbp', 'gaiaDR2_Grp':'gaia,dr2_rev,Grp', - 'gaiaEDR3_G': 'gaia,edr3,G', 'gaiaEDR3_Gbp':'gaia,edr3,Gbp', 'gaiaEDR3_Grp':'gaia,edr3,Grp', 'hawki_J': 'hawki,J', 'hawki_H': 'hawki,H', 'hawki_Ks': 'hawki,Ks', diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 77c5945e..4ba0e683 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -166,7 +166,6 @@ def test_filters(): 'decam,y', 'decam,i', 'decam,z', 'decam,u', 'decam,g', 'decam,r', 'gaia,dr2_rev,G', 'gaia,dr2_rev,Gbp', 'gaia,dr2_rev,Grp', - 'gaia,edr3,G', 'gaia,edr3,Gbp', 'gaia,edr3,Grp', 'jwst,F070W', 'jwst,F090W', 'jwst,F115W', 'jwst,F140M', 'jwst,F150W', 'jwst,F150W2', 'jwst,F162M', 'jwst,F164N', 'jwst,F182M', 'jwst,F187N', 'jwst,F200W', 'jwst,F212N', From 8bfbbde1bb66357a332a4aaf46337d6dac959265 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 20 Mar 2026 13:16:54 -0700 Subject: [PATCH 096/191] fixed assert euqal to isclose --- spisea/tests/test_synthetic.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 72331850..7694ebfd 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -1266,7 +1266,8 @@ def test_ResolvedCluster_random_state(): old_companion = pickle.load(file) for key in old_star_systems.colnames: - np.testing.assert_array_equal(cluster1.star_systems[key], old_star_systems[key]) + #np.testing.assert_array_equal(cluster1.star_systems[key], old_star_systems[key]) + assert np.all(np.isclose(cluster1.star_systems[key], old_star_systems[key], rtol=1e-05, atol=1e-08)) for key in old_companion.colnames: # Require values are consistent within reasonable bounds From 4b3ebb0f84172a7de3b2a21ac0a561aed9cd0546 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 20 Mar 2026 14:48:43 -0700 Subject: [PATCH 097/191] updating version --- docs/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 18901890..f10e26c1 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,9 +25,9 @@ author = 'Matthew Hosek Jr, Jessica R. Lu, Casey Y. Lam' # The short X.Y version -version = '2.3' +version = '2.4' # The full version, including alpha/beta/rc tags -release = '2.3' +release = '2.4' From 83b2fd9e948941d09b38428c09be9d92f4a62699 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 20 Mar 2026 14:48:52 -0700 Subject: [PATCH 098/191] updating contributors --- docs/contributors.rst | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/contributors.rst b/docs/contributors.rst index 6dac7d1e..1944147e 100644 --- a/docs/contributors.rst +++ b/docs/contributors.rst @@ -39,8 +39,12 @@ Sage Hironaka Remulla -- added Rubin Observatory filters Lingfeng Wei -- bugfix to improve creation of iso_dir in IsochronePhot, implemented faster cluster generation and test -functions for primary and companion star mass generation (v2.3) +functions for primary and companion star mass generation (v2.3), +updated random state generators (v2.4) Macy Huston -- New metallicity bound + isochrone filter checks, imf_mass_lim bugfix, roman filter bugfix, added Euclid filters, Synthpop compatibility -updates (v2.2) +updates (v2.2), improvements for reading in/updated existing isochrone +files, Vega mag to ST mag conversion function (v2.4) + +Anna Pusack -- Added IRTF L-band filter support From 4cf9e283c0284e7b88fb5056a6a09f956384cecb Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 20 Mar 2026 14:49:03 -0700 Subject: [PATCH 099/191] adding irtf --- docs/filters.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/filters.rst b/docs/filters.rst index 6efb28fc..3112d4a1 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -35,6 +35,7 @@ Available filters: * GAIA * HAWK-I * Hubble Space Telescope +* IRTF * Johnson-Cousins * Johnson-Glass * JWST From e774edadf2147b04d096df2a1e4458c24c861b98 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 20 Mar 2026 14:49:30 -0700 Subject: [PATCH 100/191] updating changelog --- docs/index.rst | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/docs/index.rst b/docs/index.rst index 71878d34..048f5c8b 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -82,6 +82,17 @@ releases will be co-authors in future SPISEA software papers. Change Log ---------- +2.4 (2026-03-20) + * Added backward compatibility for isochrone file names created + before v2.3 + * When reading isochrone from existing file, only keep user + requested filters in resulting table. + * Changed the global random seed to a random generator within each class, + but still retaining the reproducibility. Test cluster files in + test_data also updated accordingly for testing purposes + * Added filter support for IRTF L-band + * Added conversion function between ST mags and Vega mags + 2.3 (2026-02-10) * Achieves faster cluster generation (factor of about 2x) by using replacing ragged arrays with masked arrays when calculating From 3c1440cef45df6f9caadfac35d5e2b9233ee5452 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 20 Mar 2026 14:49:58 -0700 Subject: [PATCH 101/191] adding conversion funcs, docs about mag systems --- docs/make_isochrone.rst | 27 ++++++++++++++++----------- 1 file changed, 16 insertions(+), 11 deletions(-) diff --git a/docs/make_isochrone.rst b/docs/make_isochrone.rst index c4f3270e..ab070538 100644 --- a/docs/make_isochrone.rst +++ b/docs/make_isochrone.rst @@ -9,7 +9,10 @@ total extinction, and metallicity, along with the :ref:`atmo_models`, :ref:`evo_models`, and :ref:`ext_law`. If the IsochronePhot sub-class is used then synthetic photometry -will be produced. The :ref:`filters` are defined as additional inputs. +will be produced. The :ref:`filters` are defined as additional +inputs. The output photometry is in terms of vega mags, but the user +can also calculate the required conversion to AB mags or ST mags using +the functions in :ref:`phot_conversions` An example of making an IsochronePhot object:: @@ -76,10 +79,9 @@ Tips and Tricks: The IsochronePhot Object * **WARNING**: When IsochronePhot checks to see if the desired isochrone table already exists, it checks all isochrone properties - except for the photometric filters (evolution models, atmosphere - models, and reddening law are encoded in the table meta-data). + (evolution models, atmosphere models, and reddening law are encoded in the table meta-data). If any of these parameters do not match, then the isochrone will - be re-calculated. + be re-calculated. However, to keep the isochrone filenames reasonable, only the age, extinction, distance, and metallicity are encoded in the @@ -91,13 +93,6 @@ Tips and Tricks: The IsochronePhot Object that users specify different iso_dir paths when making isochrones with different evolution models, atmosphere models, or reddening laws.* - - * **WARNING**: IsochronePhot does not check existing - isochrone tables to see if the photometric filters match - those specified by the user. *So, if the user wishes to generate an - isochrone with different filters, we recommend either using a - different iso_dir path or setting the keyword recomp=True (see - docs below).* Base Isochrone Class ---------------------------- @@ -112,3 +107,13 @@ Isochrone Sub-classes .. autoclass:: synthetic.IsochronePhot :show-inheritance: :members: make_photometry, plot_CMD, plot_mass_magnitude + + +Photometry Conversion Functions +----------------------------- +.. _phot_conversions: + +.. autofunction:: synthetic.calc_ab_vega_filter_conversion + +.. autofunction:: synthetic.calc_st_vega_filter_conversion + From 39bafec7056e9f7b44da2027d1fc0a6153a8325f Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 20 Mar 2026 14:54:49 -0700 Subject: [PATCH 102/191] fixing links --- docs/make_isochrone.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/make_isochrone.rst b/docs/make_isochrone.rst index ab070538..5f764c93 100644 --- a/docs/make_isochrone.rst +++ b/docs/make_isochrone.rst @@ -12,7 +12,7 @@ If the IsochronePhot sub-class is used then synthetic photometry will be produced. The :ref:`filters` are defined as additional inputs. The output photometry is in terms of vega mags, but the user can also calculate the required conversion to AB mags or ST mags using -the functions in :ref:`phot_conversions` +the functions in :ref:`Photometry Conversion Functions `. An example of making an IsochronePhot object:: From fb3cdc2b16d337223706b7cdb8923734dbd54f02 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 20 Mar 2026 15:06:20 -0700 Subject: [PATCH 103/191] update doc string --- spisea/synthetic.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 4dec9340..7cbca17c 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -2075,10 +2075,10 @@ def calc_ab_vega_filter_conversion(filt_str): Note: this conversion is just the vega magnitude in AB system - Parameters: - ----------- + Parameters + ---------- filt_str: string - Filter identification string + SPISEA filter identification string (see Photometric Filters doc page) """ # Get filter info filt = get_filter_info(filt_str) @@ -2138,10 +2138,10 @@ def calc_st_vega_filter_conversion(filt_str): Note: this conversion is just the vega magnitude in ST system - Parameters: - ----------- + Parameters + ---------- filt_str: string - Filter identification string + SPISEA filter identification string (see Photometric Filters doc page) """ # Get filter info filt = get_filter_info(filt_str) From e414eeebfa3a9ae88a4b6cc70375c2d04c6ee8f3 Mon Sep 17 00:00:00 2001 From: Jessica Lu Date: Wed, 1 Apr 2026 15:34:41 -1000 Subject: [PATCH 104/191] Working on new packaging. --- LICENSE.txt | 674 ++++++++++++++++++++++++++++++++ MANIFEST.in | 16 - licenses/LICENSE.rst | 708 ---------------------------------- licenses/README.rst | 9 - licenses/TEMPLATE_LICENCE.rst | 31 -- pyproject.toml | 111 +++++- setup.cfg | 77 ---- setup.py | 78 ---- spisea/__init__.py | 19 +- spisea/_astropy_init.py | 51 --- spisea/tests/coveragerc | 31 -- spisea/tests/setup_package.py | 3 - tox.ini | 93 ----- 13 files changed, 784 insertions(+), 1117 deletions(-) create mode 100644 LICENSE.txt delete mode 100644 MANIFEST.in delete mode 100644 licenses/LICENSE.rst delete mode 100644 licenses/README.rst delete mode 100644 licenses/TEMPLATE_LICENCE.rst delete mode 100755 setup.cfg delete mode 100755 setup.py delete mode 100755 spisea/_astropy_init.py delete mode 100755 spisea/tests/coveragerc delete mode 100755 spisea/tests/setup_package.py delete mode 100644 tox.ini diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 00000000..9cecc1d4 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 2c409d1c..00000000 --- a/MANIFEST.in +++ /dev/null @@ -1,16 +0,0 @@ -include README.md -include CHANGES.rst -include setup.cfg -include LICENSE.rst -include pyproject.toml - -recursive-include spisea *.pyx *.c *.pxd -recursive-include docs * -recursive-include licenses * -recursive-include scripts * - -prune build -prune docs/_build -prune docs/api - -global-exclude *.pyc *.o diff --git a/licenses/LICENSE.rst b/licenses/LICENSE.rst deleted file mode 100644 index c19dffca..00000000 --- a/licenses/LICENSE.rst +++ /dev/null @@ -1,708 +0,0 @@ -Copyright (C) 2020, Matthew Hosek Jr., Jessica Lu, Casey Lam, Abhimat Gautam, Kelly Lockhart, Dongwon Kim, Siyao Jia - -This program is free software: you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation, either version 3 of the License, or -any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -You should have received a copy of the GNU General Public License -along with this program. If not, see . - ---- - -****************************************************************************** -GNU General Public License -****************************************************************************** - -Version 3, 29 June 2007 - -Copyright (c) 2007 Free Software Foundation, Inc. <`http://fsf.org/`_> - -Everyone is permitted to copy and distribute verbatim copies of this license -document, but changing it is not allowed. - -.. contents:: - -Preamble -============================================================================== - -The GNU General Public License is a free, copyleft license for software and -other kinds of works. - -The licenses for most software and other practical works are designed to take -away your freedom to share and change the works. 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It is safest to attach -them to the start of each source file to most effectively state the exclusion -of warranty; and each file should have at least the ?copyright? line and a -pointer to where the full notice is found. - -:: - Copyright (C) - - This program is free software: you can redistribute it and/or - modify - it under the terms of the GNU General Public License as - published by - the Free Software Foundation, either version 3 of the - License, or - (at your option) any later version. - - This program is distributed in the hope that it will be - useful, - but WITHOUT ANY WARRANTY; without even the implied warranty - of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public - License - along with this program. If not, see - . - - -Also add information on how to contact you by electronic and paper mail. - -If the program does terminal interaction, make it output a short notice like -this when it starts in an interactive mode: - -:: Copyright (C) - This program comes with ABSOLUTELY NO WARRANTY; for details - type ``show w``. - This is free software, and you are welcome to redistribute it - under certain conditions; type ``show c`` for details. - - -The hypothetical commands ``show w`` and ``show c`` should show the appropriate -parts of the General Public License. Of course, your program's commands might -be different; for a GUI interface, you would use an ?about box?. - -You should also get your employer (if you work as a programmer) or school, if -any, to sign a ?copyright disclaimer? for the program, if necessary. For more -information on this, and how to apply and follow the GNU GPL, see -<`http://www.gnu.org/licenses/`_>. - -The GNU General Public License does not permit incorporating your program -into proprietary programs. If your program is a subroutine library, you may -consider it more useful to permit linking proprietary applications with the -library. If this is what you want to do, use the GNU Lesser General Public -License instead of this License. But first, please read -<`http://www.gnu.org/philosophy/why-not-lgpl.html`_>. - -.. _http://fsf.org/: http://fsf.org/ -.. _http://www.gnu.org/licenses/: http://www.gnu.org/licenses/ -.. _http://www.gnu.org/philosophy/why-not-lgpl.html: - http://www.gnu.org/philosophy/why-not-lgpl.html diff --git a/licenses/README.rst b/licenses/README.rst deleted file mode 100644 index fa84ca8e..00000000 --- a/licenses/README.rst +++ /dev/null @@ -1,9 +0,0 @@ -Licenses -======== - -This directory holds license and credit information for the package, -works the package is derived from, and/or datasets. - -Ensure that you pick a package license which is in this folder and it matches -the one mentioned in the top level README.rst file. If you are using the -pre-rendered version of this template check for the word 'Other' in the README. diff --git a/licenses/TEMPLATE_LICENCE.rst b/licenses/TEMPLATE_LICENCE.rst deleted file mode 100644 index a460a728..00000000 --- a/licenses/TEMPLATE_LICENCE.rst +++ /dev/null @@ -1,31 +0,0 @@ -This project is based upon the Astropy package template -(https://github.com/astropy/package-template/) which is licensed under the terms -of the following license. - ---- - -Copyright (c) 2018, Astropy Developers -All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, -are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. -* Redistributions in binary form must reproduce the above copyright notice, this - list of conditions and the following disclaimer in the documentation and/or - other materials provided with the distribution. -* Neither the name of the Astropy Team nor the names of its contributors may be - used to endorse or promote products derived from this software without - specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR -ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/pyproject.toml b/pyproject.toml index 6c989ab1..802b4051 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,10 +1,107 @@ -[build-system] +[project] +name = "SPISEA" +description = "SPISEA (Stellar Population Interface for Synthetic Evolution and Atmospheres) is a package that generates single-age, single-metallicity populations (i.e. star clusters)" +version = "2.4.1" +authors = [ + {name = "Matt Hosek Jr.", email = "mwhosek@astro.ucla.edu"}, + {name = "Jessica R. Lu", email = "jlu.astro@berkeley.edu"}, + {name = "Samantha Rose"}, + {name = "Casey Lam"}, + {name = "Natasha Abrams"}, + {name = "Michael Medford"}, + {name = "Lingfeng Wei"}, + {name = "Macy Huston"}, + {name = "Abhimat Gautam"}, + {name = "Kelly Lockhart"}, + {name = "Dongwon Kim"}, + {name = "Siyao Jia"} +] +requires-python = ">=3.11" +readme = "README.md" +license = "GPL-3.0-or-later" +license-files = ["LICENSE*"] + +dependencies = [ + "numpy>=1.24.3", + "scipy>=1.10.1", + "matplotlib", + "astropy", + "pysynphot" +] + +optional-dependencies.dev = [ + "setuptools>=61.0", + "setuptools-scm", + "wheel", + "numpydoc", + "sphinx_rtd_theme", + "pytest" +] + +classifiers = [ + "Development Status :: 5 - Production/Stable", + "Programming Language :: Python :: 3.11", + "Operating System :: OS Independent", + "Intended Audience :: Developers" +] -requires = ["setuptools", - "setuptools_scm", - "wheel", - "extension-helpers", - "oldest-supported-numpy", - "cython==0.29.14"] +[project.urls] +Homepage = "https://github.com/astropy/SPISEA" +Issues = "https://github.com/astropy/SPISEA/issues" +[build-system] +requires = [ + "setuptools>=61.0", + "setuptools-scm", + "wheel", + "numpydoc", + "sphinx_rtd_theme", + "pytest" +] build-backend = 'setuptools.build_meta' + +[tool.setuptools.package-data] +spisea = ["data/*"] + +[tool.pytest] +minversion = "9.0" +addopts = ["-ra", "-q"] +testpaths = [ + "spisea", + "docs", +] + +### Stuff from setup.cfg that hasn't been ported over. +### Not sure if anyone was using test coverage. +#[coverage:run] +#omit = +# spisea/_astropy_init* +# spisea/conftest.py +# spisea/*setup_package* +# spisea/tests/* +# spisea/*/tests/* +# spisea/extern/* +# spisea/version* +# */spisea/_astropy_init* +# */spisea/conftest.py +# */spisea/*setup_package* +# */spisea/tests/* +# */spisea/*/tests/* +# */spisea/extern/* +# */spisea/version* +# +#[coverage:report] +#exclude_lines = +# # Have to re-enable the standard pragma +# pragma: no cover +# # Don't complain about packages we have installed +# except ImportError +# # Don't complain if tests don't hit assertions +# raise AssertionError +# raise NotImplementedError +# # Don't complain about script hooks +# def main\(.*\): +# # Ignore branches that don't pertain to this version of Python +# pragma: py{ignore_python_version} +# # Don't complain about IPython completion helper +# def _ipython_key_completions_ diff --git a/setup.cfg b/setup.cfg deleted file mode 100755 index 4d69fa9c..00000000 --- a/setup.cfg +++ /dev/null @@ -1,77 +0,0 @@ -[metadata] -name = spisea -author = Matthew Hosek Jr., Jessica Lu, Casey Lam, Abhimat Gautam, Kelly Lockhart, Dongwon Kim, Siyao Jia -author_email = mwhosek@astro.ucla.edu -license = GNU GPL v3+ -license_file = licenses/LICENSE.rst -url = https://github.com/astropy/SPISEA -description = SPISEA is an python package that generates single-age, single-metallicity populations (i.e. star clusters). -long_description = file: README.rst -long_description_content_type = text/x-rst -edit_on_github = False -github_project = astropy/astropy - -[options] -zip_safe = False -packages = find: -python_requires = >=3.7 -setup_requires = setuptools_scm -install_requires = - astropy - pysynphot - scipy - numpy - matplotlib - -[options.entry_points] -console_scripts = - astropy-package-template-example = packagename.example_mod:main - -[options.extras_require] -test = - pytest-astropy -docs = - sphinx-astropy - -[options.package_data] -spisea = data/* - -[tool:pytest] -testpaths = "spisea" "docs" -astropy_header = true -doctest_plus = enabled -text_file_format = rst -#addopts = --doctest-rst - -[coverage:run] -omit = - spisea/_astropy_init* - spisea/conftest.py - spisea/*setup_package* - spisea/tests/* - spisea/*/tests/* - spisea/extern/* - spisea/version* - */spisea/_astropy_init* - */spisea/conftest.py - */spisea/*setup_package* - */spisea/tests/* - */spisea/*/tests/* - */spisea/extern/* - */spisea/version* - -[coverage:report] -exclude_lines = - # Have to re-enable the standard pragma - pragma: no cover - # Don't complain about packages we have installed - except ImportError - # Don't complain if tests don't hit assertions - raise AssertionError - raise NotImplementedError - # Don't complain about script hooks - def main\(.*\): - # Ignore branches that don't pertain to this version of Python - pragma: py{ignore_python_version} - # Don't complain about IPython completion helper - def _ipython_key_completions_ diff --git a/setup.py b/setup.py deleted file mode 100755 index 5f83aee4..00000000 --- a/setup.py +++ /dev/null @@ -1,78 +0,0 @@ -#!/usr/bin/env python -# Licensed under a 3-clause BSD style license - see LICENSE.rst - -# NOTE: The configuration for the package, including the name, version, and -# other information are set in the setup.cfg file. - -import os -import sys - -from setuptools import setup - - -# First provide helpful messages if contributors try and run legacy commands -# for tests or docs. - -TEST_HELP = """ -Note: running tests is no longer done using 'python setup.py test'. Instead -you will need to run: - - tox -e test - -If you don't already have tox installed, you can install it with: - - pip install tox - -If you only want to run part of the test suite, you can also use pytest -directly with:: - - pip install -e .[test] - pytest - -For more information, see: - - http://docs.astropy.org/en/latest/development/testguide.html#running-tests -""" - -if 'test' in sys.argv: - print(TEST_HELP) - sys.exit(1) - -DOCS_HELP = """ -Note: building the documentation is no longer done using -'python setup.py build_docs'. Instead you will need to run: - - tox -e build_docs - -If you don't already have tox installed, you can install it with: - - pip install tox - -You can also build the documentation with Sphinx directly using:: - - pip install -e .[docs] - cd docs - make html - -For more information, see: - - http://docs.astropy.org/en/latest/install.html#builddocs -""" - -if 'build_docs' in sys.argv or 'build_sphinx' in sys.argv: - print(DOCS_HELP) - sys.exit(1) - -VERSION_TEMPLATE = """ -# Note that we need to fall back to the hard-coded version if either -# setuptools_scm can't be imported or setuptools_scm can't determine the -# version, so we catch the generic 'Exception'. -try: - from setuptools_scm import get_version - version = get_version(root='..', relative_to=__file__) -except Exception: - version = '{version}' -""".lstrip() - -setup(use_scm_version={'write_to': os.path.join('spisea', 'version.py'), - 'write_to_template': VERSION_TEMPLATE}) diff --git a/spisea/__init__.py b/spisea/__init__.py index 80caa065..57871c83 100755 --- a/spisea/__init__.py +++ b/spisea/__init__.py @@ -1,14 +1,7 @@ -# Licensed under a 3-clause BSD style license - see LICENSE.rst +import importlib.metadata -# Packages may add whatever they like to this file, but -# should keep this content at the top. -# ---------------------------------------------------------------------------- -from ._astropy_init import * # noqa -# ---------------------------------------------------------------------------- - -__all__ = [] -from spisea import * # noqa -# Then you can be explicit to control what ends up in the namespace, -__all__ += ['do_primes'] # noqa -# or you can keep everything from the subpackage with the following instead -# __all__ += example_mod.__all__ +try: + __version__ = importlib.metadata.version(__package__) +except importlib.metadata.PackageNotFoundError: + # Fallback for development mode if not installed + __version__ = "2.4.1" diff --git a/spisea/_astropy_init.py b/spisea/_astropy_init.py deleted file mode 100755 index 58189c4b..00000000 --- a/spisea/_astropy_init.py +++ /dev/null @@ -1,51 +0,0 @@ -# Licensed under a 3-clause BSD style license - see LICENSE.rst - -__all__ = ['__version__'] - -# this indicates whether or not we are in the package's setup.py -try: - _ASTROPY_SETUP_ -except NameError: - import builtins - builtins._ASTROPY_SETUP_ = False - -try: - from .version import version as __version__ -except ImportError: - __version__ = '' - -if not _ASTROPY_SETUP_: # noqa - import os - from warnings import warn - #from astropy.config.configuration import ( - # update_default_config, - # ConfigurationDefaultMissingError, - # ConfigurationDefaultMissingWarning) - - # Create the test function for self test - from astropy.tests.runner import TestRunner - test = TestRunner.make_test_runner_in(os.path.dirname(__file__)) - test.__test__ = False - __all__ += ['test'] - - # add these here so we only need to cleanup the namespace at the end - #config_dir = None - - #if not os.environ.get('ASTROPY_SKIP_CONFIG_UPDATE', False): - # config_dir = os.path.dirname(__file__) - # config_template = os.path.join(config_dir, __package__ + ".cfg") - # if os.path.isfile(config_template): - # try: - # update_default_config( - # __package__, config_dir, version=__version__) - # except TypeError as orig_error: - # try: - # update_default_config(__package__, config_dir) - # except ConfigurationDefaultMissingError as e: - # wmsg = (e.args[0] + - # " Cannot install default profile. If you are " - # "importing from source, this is expected.") - # warn(ConfigurationDefaultMissingWarning(wmsg)) - # del e - # except Exception: - # raise orig_error diff --git a/spisea/tests/coveragerc b/spisea/tests/coveragerc deleted file mode 100755 index bec7c291..00000000 --- a/spisea/tests/coveragerc +++ /dev/null @@ -1,31 +0,0 @@ -[run] -source = {packagename} -omit = - {packagename}/_astropy_init* - {packagename}/conftest* - {packagename}/cython_version* - {packagename}/setup_package* - {packagename}/*/setup_package* - {packagename}/*/*/setup_package* - {packagename}/tests/* - {packagename}/*/tests/* - {packagename}/*/*/tests/* - {packagename}/version* - -[report] -exclude_lines = - # Have to re-enable the standard pragma - pragma: no cover - - # Don't complain about packages we have installed - except ImportError - - # Don't complain if tests don't hit assertions - raise AssertionError - raise NotImplementedError - - # Don't complain about script hooks - def main\(.*\): - - # Ignore branches that don't pertain to this version of Python - pragma: py{ignore_python_version} \ No newline at end of file diff --git a/spisea/tests/setup_package.py b/spisea/tests/setup_package.py deleted file mode 100755 index f2fd9ed4..00000000 --- a/spisea/tests/setup_package.py +++ /dev/null @@ -1,3 +0,0 @@ -def get_package_data(): - return { - _ASTROPY_PACKAGE_NAME_ + '.tests': ['coveragerc']} diff --git a/tox.ini b/tox.ini deleted file mode 100644 index 27d48961..00000000 --- a/tox.ini +++ /dev/null @@ -1,93 +0,0 @@ -[tox] -envlist = - py{36,37,38}-test{,-alldeps,-devdeps}{,-cov} - py{36,37,38}-test-numpy{116,117,118} - py{36,37,38}-test-astropy{30,40,lts} - build_docs - linkcheck - codestyle -requires = - setuptools >= 30.3.0 - pip >= 19.3.1 -isolated_build = true -indexserver = - NIGHTLY = https://pypi.anaconda.org/scipy-wheels-nightly/simple - -[testenv] -# Suppress display of matplotlib plots generated during docs build -setenv = MPLBACKEND=agg - -# Pass through the following environment variables which may be needed for the CI -passenv = HOME WINDIR LC_ALL LC_CTYPE CC CI TRAVIS - -# Run the tests in a temporary directory to make sure that we don't import -# this package from the source tree -changedir = .tmp/{envname} - -# tox environments are constructed with so-called 'factors' (or terms) -# separated by hyphens, e.g. test-devdeps-cov. Lines below starting with factor: -# will only take effect if that factor is included in the environment name. To -# see a list of example environments that can be run, along with a description, -# run: -# -# tox -l -v -# -description = - run tests - alldeps: with all optional dependencies - devdeps: with the latest developer version of key dependencies - oldestdeps: with the oldest supported version of key dependencies - cov: and test coverage - numpy116: with numpy 1.16.* - numpy117: with numpy 1.17.* - numpy118: with numpy 1.18.* - astropy30: with astropy 3.0.* - astropy40: with astropy 4.0.* - astropylts: with the latest astropy LTS - -# The following provides some specific pinnings for key packages -deps = - - numpy116: numpy==1.16.* - numpy117: numpy==1.17.* - numpy118: numpy==1.18.* - - astropy30: astropy==3.0.* - astropy40: astropy==4.0.* - astropylts: astropy==4.0.* - - devdeps: :NIGHTLY:numpy - devdeps: git+https://github.com/astropy/astropy.git#egg=astropy - -# The following indicates which extras_require from setup.cfg will be installed -extras = - test - alldeps: all - -commands = - pip freeze - !cov: pytest --pyargs spisea {toxinidir}/docs {posargs} - cov: pytest --pyargs spisea {toxinidir}/docs --cov spisea --cov-config={toxinidir}/setup.cfg {posargs} - -[testenv:build_docs] -changedir = docs -description = invoke sphinx-build to build the HTML docs -extras = docs -commands = - pip freeze - sphinx-build -W -b html . _build/html - -[testenv:linkcheck] -changedir = docs -description = check the links in the HTML docs -extras = docs -commands = - pip freeze - sphinx-build -W -b linkcheck . _build/html - -[testenv:codestyle] -skip_install = true -changedir = . -description = check code style, e.g. with flake8 -deps = flake8 -commands = flake8 spisea --count --max-line-length=100 From f9c8ed03d44fe3df901f320b718d1b73132297ba Mon Sep 17 00:00:00 2001 From: Jessica Lu Date: Wed, 1 Apr 2026 15:38:03 -1000 Subject: [PATCH 105/191] Explicitly specifying that there is only one package. --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 802b4051..a81dee03 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -60,6 +60,9 @@ requires = [ ] build-backend = 'setuptools.build_meta' +[tool.setuptools] +py-modules = ["spisea"] + [tool.setuptools.package-data] spisea = ["data/*"] From 6699843a7f90224562ef7c0a7902a8d17dfb12ef Mon Sep 17 00:00:00 2001 From: Jessica Lu Date: Wed, 1 Apr 2026 15:38:45 -1000 Subject: [PATCH 106/191] Explicitly specifying that there is only one package, again. --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index a81dee03..5b46d1d5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -61,7 +61,7 @@ requires = [ build-backend = 'setuptools.build_meta' [tool.setuptools] -py-modules = ["spisea"] +packages = ["spisea"] [tool.setuptools.package-data] spisea = ["data/*"] From 63ea1a438aec3313a2c67139b6db16320ab7a372 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 3 Apr 2026 10:56:22 -0700 Subject: [PATCH 107/191] Changed formatting of Hosek+20 figure notebooks and first few Begbie+26 figure notebooks --- docs/paper_examples/Begbie+26/Figure 10.ipynb | 122 ++++++++++ docs/paper_examples/Begbie+26/Figure 2.ipynb | 115 ++++++++++ docs/paper_examples/Begbie+26/Figure 3.ipynb | 172 ++++++++++++++ docs/paper_examples/Begbie+26/Figure 4.ipynb | 213 ++++++++++++++++++ docs/paper_examples/Begbie+26/Figure 5.ipynb | 190 ++++++++++++++++ docs/paper_examples/Begbie+26/Figure 9.ipynb | 104 +++++++++ docs/paper_examples/Begbie+26/README | 1 + .../{ => Hosek+20}/Figure 2.ipynb | 6 +- .../{ => Hosek+20}/Figure 3.ipynb | 0 .../{ => Hosek+20}/Figure 4.ipynb | 0 .../{ => Hosek+20}/Figure 5.ipynb | 0 .../{ => Hosek+20}/Figure 6.ipynb | 0 .../{ => Hosek+20}/Figure 7.ipynb | 0 docs/paper_examples/{ => Hosek+20}/README | 0 14 files changed, 920 insertions(+), 3 deletions(-) create mode 100644 docs/paper_examples/Begbie+26/Figure 10.ipynb create mode 100644 docs/paper_examples/Begbie+26/Figure 2.ipynb create mode 100644 docs/paper_examples/Begbie+26/Figure 3.ipynb create mode 100644 docs/paper_examples/Begbie+26/Figure 4.ipynb create mode 100644 docs/paper_examples/Begbie+26/Figure 5.ipynb create mode 100644 docs/paper_examples/Begbie+26/Figure 9.ipynb create mode 100644 docs/paper_examples/Begbie+26/README rename docs/paper_examples/{ => Hosek+20}/Figure 2.ipynb (98%) rename docs/paper_examples/{ => Hosek+20}/Figure 3.ipynb (100%) rename docs/paper_examples/{ => Hosek+20}/Figure 4.ipynb (100%) rename docs/paper_examples/{ => Hosek+20}/Figure 5.ipynb (100%) rename docs/paper_examples/{ => Hosek+20}/Figure 6.ipynb (100%) rename docs/paper_examples/{ => Hosek+20}/Figure 7.ipynb (100%) rename docs/paper_examples/{ => Hosek+20}/README (100%) diff --git a/docs/paper_examples/Begbie+26/Figure 10.ipynb b/docs/paper_examples/Begbie+26/Figure 10.ipynb new file mode 100644 index 00000000..0f9432ee --- /dev/null +++ b/docs/paper_examples/Begbie+26/Figure 10.ipynb @@ -0,0 +1,122 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51fdd39d-b433-4371-a487-fa5d855b79de", + "metadata": {}, + "source": [ + "## Below is the code to recreate Figure 10.\n", + "\n", + "Topic: Showing the brown dwarf companion count distribution to prove an enforced limit of one." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3d80653b-a8ee-422d-b008-14dc490ad520", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary packages.\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from spisea.imf import multiplicity\n", + "from matplotlib import ticker" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ef9ce616-14b5-4041-867f-05cc8f6596dd", + "metadata": {}, + "outputs": [], + "source": [ + "# synthetic mass distribution (log-uniform)\n", + "N = 20000\n", + "masses = 10**np.random.uniform(np.log10(0.01), np.log10(10), N)\n", + "mult = multiplicity.MultiplicityResolvedDK()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c2069789-7745-4609-91fe-2c81bfb67f4a", + "metadata": {}, + "outputs": [], + "source": [ + "# binarity (BD primaries have ≤ 1 companion)\n", + "mf = np.array([mult.multiplicity_fraction(m) for m in masses])\n", + "csf = np.array([mult.companion_star_fraction(m) for m in masses])\n", + "\n", + "rand = np.random.rand(len(masses))\n", + "is_mult = rand < mf\n", + "\n", + "n_comp = np.zeros(len(masses), dtype=int)\n", + "\n", + "# same process as in MultiplicityUnresolved\n", + "for i, m in enumerate(masses):\n", + " if not is_mult[i]:\n", + " n_comp[i] = 0\n", + " elif m <= 0.08:\n", + " n_comp[i] = 1 # hard BD limit\n", + " else:\n", + " n_comp[i] = mult.random_companion_count(1, csf[i], mf[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1bde3a03-d9c5-4762-a8be-7611f8668d1e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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z-fH~_hx~|=Tw5x`k?FOKOMZsNr#LdIuROHHq5DFXC+THzWO~Bk)X;t+e36{z$9c8& z_P@F}V;h8FD2R^13RH~Z4QNs^0F-H&z-+;gQ1F{4o$g3%3856$o&UZ6W2X)CN~?L4 zPtLb>X+8V=yWabMfI6Si=RKOI=BBT{|E=>dN6r39SC03m=5?InsXF?u-tCiDnz{6S zm@A%J)10bPzU%AuXK}0VUOw-ocwE`9_RCLK=BQInT37s9(_C6dE_KQoUB@}7NB2{_ zv@gFy#iPG9M@~8?b51L+^2t+m%4dF-p51>o-<^x Date: Thu, 19 Feb 2026 16:43:40 -0800 Subject: [PATCH 067/191] Updating to next version --- docs/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index c72f260d..18901890 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,9 +25,9 @@ author = 'Matthew Hosek Jr, Jessica R. Lu, Casey Y. Lam' # The short X.Y version -version = '2.2' +version = '2.3' # The full version, including alpha/beta/rc tags -release = '2.2' +release = '2.3' From fbdbb1128059a429cd34a9659896902d39f808cc Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Thu, 19 Feb 2026 16:43:55 -0800 Subject: [PATCH 068/191] updating changelog --- docs/index.rst | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/docs/index.rst b/docs/index.rst index 11e1e44e..71878d34 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -82,6 +82,16 @@ releases will be co-authors in future SPISEA software papers. Change Log ---------- +2.3 (2026-02-10) + * Achieves faster cluster generation (factor of about 2x) by using + replacing ragged arrays with masked arrays when calculating + multiplicity properties + * Added new test functions (and associated test data files) ensuring + that the primary mass and companion + mass distibutions remain the same as generated with SPISEA <= v2.2 + * Added support to Euclid VIS filter + + 2.2 (2026-01-16) * Compatibility updates for SPISEA to work with `SynthPop `_. Updates include: From 604319a5ec9a7afd1f6e8bb8f9ef3a9ef7c106be Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Thu, 19 Feb 2026 17:11:27 -0800 Subject: [PATCH 069/191] small cleanup, using np.isclose for reasonable value comparison tests --- spisea/tests/test_synthetic.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 349667c9..f02bbfa3 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -47,9 +47,10 @@ def test_iso_wave(): range (propagated through IsochronePhot) """ # Define isochrone parameters - logAge = np.log10(5*10**6.) # Age in log(years) - AKs = 0.8 # extinction in mags + logAge = 6.7 # Age in log(years) + AKs = 2.7 # extinction in mags dist = 4000 # distance in parsec + metal = 0.0 # metallicity iso_dir = f'{spisea_path}/tests/isochrones' # Define evolution/atmosphere models and extinction law (optional) @@ -72,6 +73,7 @@ def test_iso_wave(): wave_range1 = [3000, 52000] my_iso = syn.IsochronePhot( logAge, AKs, dist, + metallicity=metal, evo_model=evo_model, atm_func=atm_func, red_law=red_law, @@ -155,6 +157,7 @@ def test_IsochronePhot(plot=False): filters=filt_list, mass_sampling=mass_sampling, iso_dir=iso_dir + recomp=True ) endTime = time.time() print('IsochronePhot generated in: %d seconds' % (endTime - startTime)) @@ -309,7 +312,8 @@ def test_ResolvedCluster(): red_law=red_law, filters=filt_list, mass_sampling=mass_sampling, - iso_dir=iso_dir + iso_dir=iso_dir, + recomp=True ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) @@ -1254,10 +1258,11 @@ def test_ResolvedCluster_random_state(): old_companion = pickle.load(file) for key in old_star_systems.colnames: - # Equal to the 7th decimal - np.testing.assert_almost_equal(cluster1.star_systems[key], old_star_systems[key]) + # Require values are consistent within reasonable bounds + assert np.all(np.isclose(cluster1.star_systems[key], old_star_systems[key], rtol=1e-05, atol=1e-08)) for key in old_companion.colnames: - np.testing.assert_array_equal(cluster1.companions[key], old_companion[key]) + # Require values are consistent within reasonable bounds + assert np.all(np.isclose(cluster1.companions[key], old_companion[key], rtol=1e-05, atol=1e-08)) - return \ No newline at end of file + return From 4e2407f1aa4030132516fab4db49f85d8ff31f0c Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 20 Feb 2026 16:15:05 -0800 Subject: [PATCH 070/191] adding tests/isochrones/ so not tracked --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index ae231559..4fc5dfe3 100755 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +# SPISEA-generated isochrones from test functions +spisea/tests/isochrones/ + # Compiled files *.py[co] *.a From 5f36da8fe2c22d9db7b2f21bd8908e210101a260 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 20 Feb 2026 16:15:25 -0800 Subject: [PATCH 071/191] removing test isochrones from git tracking --- .../isochrones/iso_6.70_0.80_04000_m1.50.fits | 20 ---- .../isochrones/iso_6.70_0.80_04000_p0.00.fits | 14 --- .../isochrones/iso_6.70_1.67_08000_p0.00.fits | 2 - .../isochrones/iso_6.70_2.30_08000_p0.00.fits | 1 - .../isochrones/iso_6.70_2.40_04000_p0.00.fits | 93 ------------------- .../isochrones/iso_6.70_2.46_08000_p0.00.fits | 1 - .../isochrones/iso_6.70_2.62_08000_p0.00.fits | 1 - .../isochrones/iso_6.70_2.70_04000_p0.00.fits | 44 --------- .../isochrones/iso_9.70_0.00_01000_p0.00.fits | 53 ----------- 9 files changed, 229 deletions(-) delete mode 100644 spisea/tests/isochrones/iso_6.70_0.80_04000_m1.50.fits delete mode 100644 spisea/tests/isochrones/iso_6.70_0.80_04000_p0.00.fits delete mode 100644 spisea/tests/isochrones/iso_6.70_1.67_08000_p0.00.fits delete mode 100644 spisea/tests/isochrones/iso_6.70_2.30_08000_p0.00.fits delete mode 100644 spisea/tests/isochrones/iso_6.70_2.40_04000_p0.00.fits delete mode 100644 spisea/tests/isochrones/iso_6.70_2.46_08000_p0.00.fits delete mode 100644 spisea/tests/isochrones/iso_6.70_2.62_08000_p0.00.fits delete mode 100644 spisea/tests/isochrones/iso_6.70_2.70_04000_p0.00.fits delete mode 100644 spisea/tests/isochrones/iso_9.70_0.00_01000_p0.00.fits diff --git a/spisea/tests/isochrones/iso_6.70_0.80_04000_m1.50.fits b/spisea/tests/isochrones/iso_6.70_0.80_04000_m1.50.fits deleted file mode 100644 index 4f7413bb..00000000 --- a/spisea/tests/isochrones/iso_6.70_0.80_04000_m1.50.fits +++ /dev/null @@ -1,20 +0,0 @@ -SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 81 / length of dimension 1 NAXIS2 = 71 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 11 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'm_hst_f127m' TFORM9 = 'D ' TTYPE10 = 'm_hst_f139m' TFORM10 = 'D ' TTYPE11 = 'm_hst_f153m' TFORM11 = 'D ' REDLAW = 'H18b ' ATMFUNC = 'get_phoenixv16_atmosphere' EVOMODEL= 'MISTv1 ' HIERARCH EVOMODELVERSION = 'MISTv1.2' LOGAGE = 6.698970004336019 AKS = 0.8 DISTANCE= 4000 METAL_IN= -1.5 HIERARCH METAL_ACT = -1.4990758306077128 WAVEMIN = 3000 WAVEMAX = 52000 END E!L_@WO&A@%%o?@̱X2%F?$*@6YR*@5Aᚮ@5&SU6ZE(<i;@`Aр?4+Y[@XF?4*vb@56C@5m<@4ʂBmE306{@ 8.A$:?i`@]6h@F?iߠ4@5@4܋]@4P7E@*_@[Ag`?q@IF?ipX@4i V@4l%QB@3zn-EFgo6G@AxS%Y?֟aD @tnF?֟_AO@4\.@4@3oET{O.,@DֱZA~JC?{k -V@)(4F?{|@43T@3"g|@2u3E`ūi@sD[AFaY.O?}b9@%efF?{0(;Ŀ@3ph@3(L@2zWJEx@}[2Ao?UJ@,F?U:@2miS@2CJKc@1ZE F@)@Z'Aћ7!cn5?3T4@IwF?KGÿ@1^@11ؾ@0,rEGe9@%[Aʓ03?$C_@~CY(F? %8@1kC@1NB=JW@0v/F?'@1*@##@1t\zdd@1/EcG@ȱ3o(AE(_?Ml@A-F?W@1\?@1K H@08T~Eė3vW_@PfAffk@64q@}F@ƍ@1f":@1,Z@00 Eک7Qt`@Α -gBA09 d@ ۷@۸EF@A+}@1k :@1Q@0Պf=iEo! 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B4@Jm@QT?1&y@-@%YNE|KF>߲@2``BOq$-@J\(@͸T?1&y@.1@%uF'FU}ʉ@@\BK1ֻ@JƧ@wkPT?1&y@.2=,@%iF!`+@@"&ALl@J1&x@QT?1&y@.V7@%-8FԼU@I۬A@{@K I^5@>BT?1&y@.s-@%ņӹF|}p@5AT='6@KO;@T?1&y@.*@%'TF(?$Q@ɀA=$^v@K I^@$T?1&y@.!2@&dBXcF@stAfEp]@K'O;d@ ƧT?1&y@.ÿu@&T?1&y@/# -@&602bFpM@bD7A@KW+ I@%F -L0T?1&y@/Cγ@&V?EF' 5@&xWAَs@Kc -=p@-8YT?1&y@/bG\m/@&~FPU@c0\9A55@KpbM@.}VlT?1&y@/|Z@&# Fߵω@>AAJT$@K}V@6!.IT?1&y@/@'&F-Tp@#%A,m@K+ I@?|hsT?1&y@/MK@'lF+v;@;TwAg@K -=p@@4m9T?1&y@/z@'5sH kF$@RRQAێ{@K -=q@HT?1&y@/0k#@'PdqFc5Q@xA^E.|@KO;dZ@QuT?1&y@0T/m@'jPb)Fl4@$͟PmA+ޑE@K1'@ZQ_T?1&y@0BfS@'+3Fbn@c|,|3A38@KZ1@cS&T?1&y@0ot6@'ߖFɾ@N,AWG+@K&x@kC]T?1&y@0%,@'ÀmFbn@ -jA/2K@KE@tDT?1&y@02 ^@'̿g F]@`YC AvWyI@Kn@~ (T?1&y@0?yJ@'暊kF\,Q!9@vIA 3zD@KzH@͞T?1&y@0L/v@(;FU?3@ A[q@LR@@T?1&y@0WyJ@(kFc/ @x%A6O -4@L+@͞&T?1&y@0fs=@(4A5F%Lò@ -OvAN_@LƧ@AsT?1&y@0x &ִ@(Wt9gF2F@A՟@L(9Xb@yrGE8T?1&y@0{dk@(TS$ \ No newline at end of file diff --git a/spisea/tests/isochrones/iso_6.70_2.46_08000_p0.00.fits b/spisea/tests/isochrones/iso_6.70_2.46_08000_p0.00.fits deleted file mode 100644 index a582318f..00000000 --- a/spisea/tests/isochrones/iso_6.70_2.46_08000_p0.00.fits +++ /dev/null @@ -1 +0,0 @@ -SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 89 / length of dimension 1 NAXIS2 = 3 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 12 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'm_hst_f127m' TFORM9 = 'D ' TTYPE10 = 'm_hst_f153m' TFORM10 = 'D ' TTYPE11 = 'm_nirc2_H' TFORM11 = 'D ' TTYPE12 = 'm_nirc2_Kp' TFORM12 = 'D ' REDLAW = 'S10 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MISTv1 ' HIERARCH EVOMODELVERSION = 'MISTv1.2' LOGAGE = 6.698970004336019 AKS = 2.46 DISTANCE= 8000 METAL_IN= 0 HIERARCH METAL_ACT = -0.00616030870481844 WAVEMIN = 3000 WAVEMAX = 52000 END Eg!溑@%^A$}Y(3,?L҄@F٠aF?L7@7@6n@62wjH|@4,"2Ei16@chAͦ1 ?Waq+@9F?W_IG@7(зU@6!9V@6!k~@4r}EkVHC@(AG-@?&ѓ.N@pF?%#,@7$fZf@6@6 l,}@4 q \ No newline at end of file diff --git a/spisea/tests/isochrones/iso_6.70_2.62_08000_p0.00.fits b/spisea/tests/isochrones/iso_6.70_2.62_08000_p0.00.fits deleted file mode 100644 index dcd17c25..00000000 --- a/spisea/tests/isochrones/iso_6.70_2.62_08000_p0.00.fits +++ /dev/null @@ -1 +0,0 @@ -SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 89 / length of dimension 1 NAXIS2 = 3 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 12 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'm_hst_f127m' TFORM9 = 'D ' TTYPE10 = 'm_hst_f153m' TFORM10 = 'D ' TTYPE11 = 'm_nirc2_H' TFORM11 = 'D ' TTYPE12 = 'm_nirc2_Kp' TFORM12 = 'D ' REDLAW = 'F11 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MISTv1 ' HIERARCH EVOMODELVERSION = 'MISTv1.2' LOGAGE = 6.698970004336019 AKS = 2.62 DISTANCE= 8000 METAL_IN= 0 HIERARCH METAL_ACT = -0.00616030870481844 WAVEMIN = 3000 WAVEMAX = 52000 END Eg!溑@%^A$}Y(3,?L҄@F٠aF?L7@:EZ @7-@6b]@4EgEi16@chAͦ1 ?Waq+@9F?W_IG@:/لT@7m@6Q@@46չEkVHC@(AG-@?&ѓ.N@pF?%#,@:#oe@7ۇ@6H[@4-{Pm \ No newline at end of file diff --git a/spisea/tests/isochrones/iso_6.70_2.70_04000_p0.00.fits b/spisea/tests/isochrones/iso_6.70_2.70_04000_p0.00.fits deleted file mode 100644 index d8d7bbbe..00000000 --- a/spisea/tests/isochrones/iso_6.70_2.70_04000_p0.00.fits +++ /dev/null @@ -1,44 +0,0 @@ -SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 73 / length of dimension 1 NAXIS2 = 161 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 10 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'K ' TTYPE9 = 'm_nirc2_J' TFORM9 = 'D ' TTYPE10 = 'm_nirc2_Kp' TFORM10 = 'D ' REDLAW = 'N09 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MergedBaraffePisaEkstromParsec' HIERARCH EVOMODELVERSION = 'None ' LOGAGE = 6.7 AKS = 2.7 DISTANCE= 4000 METAL_IN= 0.0 HIERARCH METAL_ACT = 0.0 WAVEMIN = 3000 WAVEMAX = 52000 END E'| @c۳A$wv%4?Q@hr!F?Q@;_@6,2uZE/ޙC@ڈ@A=ˡX? -=p -@Q F? -=p -@:z@5_3EE0 @p]A|ʄ?QR@uF?QR@9$?@4ER/Y[a@^zAmnA?ݏ@@IQF?ݏ@9~~z~@3VEZyJ@aJAɅ ; ?dAג?O3@F?O3@7@2"X@4Ev3@܎0AOy?Yy@}!.HF?Yy@7T6{@2igE @[| @AЊqhi?a@qu!SF?a@7]<4@1GpEF\@^Y A]/xvH?'2@cwkF?'2@7jx5@1wMEIz@s4A+37?cs@SMjF?cs@7P6=@1|fjQE>~@mAԤE]?d8 @@NUF?d8 @74AkK@1 -tE 8I@4!Agm(?"$@)ᰉ'F?"$@7>4@1vEIA@|xMAMTЋ?8:6@TF?8:6@6+5+@11=.EBe@ǵ-AMAaJe?O@Q_F?O@6Ԓ?v|@1dq+E)Pج@$lP=pAؠF?ް@'/WF?ް@6:BE@1B}E?@} ˧A͸?*^o@?H˒:F?*^o@6"\@1EO8E@֙AۃreO;? 5@xF? 5@6^W}T@0WrEZ@4/vZA酘j?*Gfv@6CF?*Gfv@67~8>@0gEm2@ *mAe䌭Y? *@T*1F? 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Mb@Y@@)f$)E@)-) w@(igF+&@"],AƀπO?Sh @Jq`F?&@Y@@)&@)+)y@)F(DUӌ@)YAu+?S@Vإ*F?ӟ @Y@@* ;@*u8w)@*:e9AF#}w@CƷ;AA@7?S`@!`tIF?c@Y@@+Z|\8@+ {dc@*jߋFZ7@uհA3Em?SBHo@-F?VD@Y@@+@O@+2I[@+lj(AF7}@ڝX&Aӊr?S!}@G\eF?og@Y@@,N#@,@+ٚ>F -:9!@vj#@ADYYT'?S @4zF? w,"@Y@@,$6a@,6J@,D< \FyI@\BzkAeL??SH9w@{ZF? -.]@Y@@--=y@,Ul@,eb ZXF ݈)@. w_AFY?SrEK@7diCF?yK@Y@@-,ͼ;@-_P&@-$0Fcy;f@)At?SkM@ZF?^N9@Y@@.lk @.29@-oS(8EEqr@v4AhQ?S?21@᝸F?.|@Y@@/uMft@/IsX@/ ioED@޳0lA}l)?SS - @C2F?vA@Y@@0WD@0:U.@0g)EgDK,t@+Ax??S4)@(?SF?ߣ@Y@@0ƻr@0 G@0lE":@K0jAv --?SwFN@#T{F?@Y@@1&:@0A]D@0ܣģEJAF@2!ǀY\At?S^xK@afqF?kU@Y@@1eo0@#3 ?F? -Ifd:@Y@@3W\1d@2?n@2XJYEϓuuB@TyLgAjGjj?T@ZFUVF?RA @Y@@3: -ʫ@3!@3,Ek@cWLAh^l?T%saI@1F?)m@Y@@3kk}h@3OHJ !@34mQhEm@d=rAg؃#&?TSq @ÓF?Ք0@Y@@3F!T@3}wc@3b1>Es@z(bAAfZ?Ti@ya5F?&W@Y@@3F,@3:(2F@3r'Ei({z@gEAf7?Tl8@ǟF?3KGFg@Y@@3c`Z@3AZi@38 -/@7E`Y}U@dX `Ae5Y?UC_|@ fxXGF?DRB@Y@@4"b"@4 >@3c-EU d@ -2Ae ň_?U%Xx@ $vJzF?\Ց@Y@@4Na@44@4(EKX@hURAdHM?VK@4bxXF?%]@Y@@4QjC@4k@<@4U*EAtmc@[T,,Ad&b?W@F^F?Ỷ@Y@@44@4ʂǹ@4Oz>E6;@ -AcF.?ZVG@V F?%u=@Y@@4@4̟-EU@4*4 \ No newline at end of file From 984e34f4cf804cf0ed2f815d5c7c1d190f1769c7 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 20 Feb 2026 16:16:36 -0800 Subject: [PATCH 072/191] fixing typo bug --- spisea/tests/test_synthetic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index f02bbfa3..2a909585 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -156,7 +156,7 @@ def test_IsochronePhot(plot=False): red_law=redlaw, filters=filt_list, mass_sampling=mass_sampling, - iso_dir=iso_dir + iso_dir=iso_dir, recomp=True ) endTime = time.time() From 2beaf10f471096c84927bb5dd2b92627f608a015 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 20 Feb 2026 16:18:50 -0800 Subject: [PATCH 073/191] adding v2.3 work --- docs/contributors.rst | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/contributors.rst b/docs/contributors.rst index 714e4f94..6dac7d1e 100644 --- a/docs/contributors.rst +++ b/docs/contributors.rst @@ -37,7 +37,9 @@ what operating system is used Sage Hironaka Remulla -- added Rubin Observatory filters -Lingfeng Wei -- bugfix to improve creation of iso_dir in IsochronePhot +Lingfeng Wei -- bugfix to improve creation of iso_dir in +IsochronePhot, implemented faster cluster generation and test +functions for primary and companion star mass generation (v2.3) Macy Huston -- New metallicity bound + isochrone filter checks, imf_mass_lim bugfix, roman filter bugfix, added Euclid filters, Synthpop compatibility From e384cf19fda8d51d7e69dd207e11ee34ec53b228 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 23 Feb 2026 12:32:36 +0100 Subject: [PATCH 074/191] add ST mag conversion --- spisea/synthetic.py | 51 +++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 49 insertions(+), 2 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index d8f17d87..6ddd4ecb 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -238,7 +238,8 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # effect is so small # Convert nan_to_num to avoid errors on greater than, less than comparisons star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) - bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) + bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) & + (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) # Print warning, if desired verbose=False if verbose: @@ -324,7 +325,8 @@ def _make_companions_table(self, star_systems, compMass): companions['log_a'][ii] = self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx'][ii]]) companions['e'] = self.imf._multi_props.random_e(np.random.rand(N_comp_tot)) - companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters(np.random.rand(N_comp_tot),np.random.rand(N_comp_tot),np.random.rand(N_comp_tot)) + companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters( + np.random.rand(N_comp_tot),np.random.rand(N_comp_tot),np.random.rand(N_comp_tot)) # Make an array that maps system index (ii), companion index (cc) to @@ -1090,6 +1092,12 @@ def __init__(self, logAge, AKs, distance, self.make_photometry(rebin=rebin, vega=vega, comp_filters=comp_filters) + # Drop filters in the saved file that we don't actually want here + all_filters = ['m_'+get_filter_col_name(f) for f in filters] + drop_columns = [col for col in self.points.columns if (col[:2]=='m_' and + (col not in all_filters))] + self.points.remove_columns(drop_columns) + return def make_photometry(self, rebin=True, vega=vega, comp_filters=None): @@ -1892,3 +1900,42 @@ def calc_ab_vega_filter_conversion(filt_str): return vega_mag_ab +def calc_st_vega_filter_conversion(filt_str): + """ + Function to calculate the conversion between + ST and Vega magnitudes for a given filter: + m_ST - m_vega + + Note: this conversion is just the vega magnitude in + ST system + + Parameters: + ----------- + filt_str: string + Filter identification string + """ + # Get filter info + filt = get_filter_info(filt_str) + + # Interpolate the filter function to be the exact same sampling as the + # vega spectrum + c = 2.997*10**18 # A / s + filt_interp = scipy.interpolate.interp1d(filt.wave, filt.throughput, kind='linear', bounds_error=False, + fill_value=0) + s_interp = filt_interp(vega.wave) + + # Calculate the numerator + diff = np.diff(vega.wave) + numerator = np.sum(vega.flux[:-1] * s_interp[:-1] * diff) + + # Now we need to intergrate the filter response for the denominator + denominator = np.sum(s_interp[:-1] * diff) + # Fλ must be in erg cm–2 sec–1 Å–1 + + # Calculate vega AB magnitude. This is the conversion + vega_mag_st = -2.5 * np.log10(numerator / denominator) - 21.1 + + print('For {0}, m_st - m_vega = {1}'.format(filt_str, vega_mag_st)) + + return vega_mag_st + From 5f299902d4968f48fb5efccdb7895e89e59c1e5e Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 23 Feb 2026 17:01:52 +0100 Subject: [PATCH 075/191] store verbose variable in IsochronePhot --- spisea/synthetic.py | 1 + 1 file changed, 1 insertion(+) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 70b9b6e1..22958efe 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1118,6 +1118,7 @@ def __init__(self, logAge, AKs, distance, 'ubv,R', 'ubv,I'], verbose=False): self.metallicity = metallicity + self.verbose=verbose # Make the iso_dir, if it doesn't already exist if not os.path.exists(iso_dir): From b6c47af04eb750b8341b55d487ca858396f83ae2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 23 Feb 2026 18:29:38 -0800 Subject: [PATCH 076/191] Trim trailing white spaces --- spisea/atmospheres.py | 390 ++++++++++++++++---------------- spisea/conftest.py | 4 +- spisea/evolution.py | 340 ++++++++++++++-------------- spisea/exceptions.py | 4 +- spisea/filters.py | 62 ++--- spisea/ifmr.py | 248 ++++++++++---------- spisea/reddening.py | 390 ++++++++++++++++---------------- spisea/synthetic.py | 24 +- spisea/tests/test_exceptions.py | 2 +- spisea/tests/test_models.py | 26 +-- spisea/tests/test_reddening.py | 30 +-- spisea/tests/test_synthetic.py | 178 +++++++-------- 12 files changed, 849 insertions(+), 849 deletions(-) diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index 5c206ee8..9867db81 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -18,7 +18,7 @@ def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4 """ # Open catalog fits file and break out row indices catalog = Table.read('{0}/grid/{1}/catalog.fits'.format(os.environ['PYSYN_CDBS'], model_dir)) - + teff_arr = [] z_arr = [] logg_arr = [] @@ -36,11 +36,11 @@ def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4 metal_list = np.unique(np.array(z_arr)) metal_idx = np.argmin(np.abs(metal_list - metallicity)) metallicity_new = metal_list[metal_idx] - + z_filt = np.where(z_arr == metal_list[metal_idx]) teff_arr = teff_arr[z_filt] logg_arr = logg_arr[z_filt] - + # # Now find the closest atmosphere in parameter space to # # the one we want. We'll find the match with the lowest # # fractional difference @@ -52,38 +52,38 @@ def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4 # # temperature_new = teff_arr[idx_f] # gravity_new = logg_arr[idx_f] - + # First check if temperature within bounds temperature_new = temperature if temperature > np.max(teff_arr): temperature_new = np.max(teff_arr) if temperature < np.min(teff_arr): temperature_new = np.min(teff_arr) - + # If temperature within bounds, then check if metallicity within bounds teff_diff = np.abs(teff_arr - temperature) sorted_min_diffs = np.unique(teff_diff) - + ## Find two closest temperatures teff_close_1 = teff_arr[np.where(teff_diff == sorted_min_diffs[0])[0][0]] teff_close_2 = teff_arr[np.where(teff_diff == sorted_min_diffs[1])[0][0]] - + logg_arr_1 = logg_arr[np.where(teff_arr == teff_close_1)] logg_arr_2 = logg_arr[np.where(teff_arr == teff_close_2)] - + ## Switch to most conservative bound of logg out of two closest temps gravity_new = gravity if gravity > np.min([np.max(logg_arr_1), np.max(logg_arr_2)]): gravity_new = np.min([np.max(logg_arr_1), np.max(logg_arr_2)]) if gravity < np.max([np.min(logg_arr_1), np.min(logg_arr_2)]): gravity_new = np.max([np.min(logg_arr_1), np.min(logg_arr_2)]) - + if verbose: # Print out changes, if any if temperature_new != temperature: teff_msg = 'Changing to T={0:6.0f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' print( teff_msg.format(temperature_new, metallicity, temperature, gravity)) - + if gravity_new != gravity: logg_msg = 'Changing to logg={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' print( logg_msg.format(gravity_new, metallicity, temperature, gravity)) @@ -91,12 +91,12 @@ def get_atmosphere_bounds(model_dir, metallicity=0, temperature=20000, gravity=4 if metallicity_new != metallicity: logg_msg = 'Changing to met={0:4.2f} for met={1:4.2f} T={2:6.0f} logg={3:4.2f}' print( logg_msg.format(metallicity_new, metallicity, temperature, gravity)) - + return (temperature_new, gravity_new, metallicity_new) def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): """ - Return atmosphere from the Kurucz pysnphot grid + Return atmosphere from the Kurucz pysnphot grid (`Kurucz 1993 `_). Grid Range: @@ -115,7 +115,7 @@ def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=Fal gravity: float The stellar gravity, in cgs units - + rebin: boolean Always false for this particular function """ @@ -127,7 +127,7 @@ def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=Fal metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) # Do some error checking @@ -142,10 +142,10 @@ def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=Fal def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): """ - Return atmospheres from the pysynphot ATLAS9 atlas + Return atmospheres from the pysynphot ATLAS9 atlas (`Castelli & Kurucz 2004 `_). - Grid Range: + Grid Range: * Teff: 3500 - 50000 K * gravity: 0 - 5.0 cgs @@ -161,7 +161,7 @@ def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=F gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, @@ -178,9 +178,9 @@ def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=F metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) - + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: @@ -205,7 +205,7 @@ def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=Fal metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) # Do some error checking @@ -239,7 +239,7 @@ def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=F def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=False): """ - Return atmosphere from the pysynphot + Return atmosphere from the pysynphot `PHOENIX atlas `_. Parameters @@ -252,7 +252,7 @@ def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, @@ -267,7 +267,7 @@ def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) # Do some error checking @@ -294,12 +294,12 @@ def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebi if verbose: print( logg_msg.format(4.3, temperature, gravity)) gravity = 4.3 - + if rebin: sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) else: sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) - + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: @@ -313,7 +313,7 @@ def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebi def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4.3, rebin=True, verbose=False): """ - For a given stellar atmosphere, get extract the closest possible match in + For a given stellar atmosphere, get extract the closest possible match in Teff/logg space. Note that this is different from the normal routine which interpolates along the input grid to get final spectrum. We can't do this here because the Fierro+15 atmosphere grid is so sparse @@ -348,7 +348,7 @@ def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4 # fractional difference teff_diff = (teff_arr - temperature) / temperature logg_diff = (logg_arr - gravity) / gravity - + diff_tot = abs(teff_diff) + abs(logg_diff) idx_f = np.where(diff_tot == min(diff_tot))[0][0] @@ -356,7 +356,7 @@ def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4 # pysynphot object infile = cat[idx_f]['FILENAME'].split('.') spec = Table.read('{0}/{1}.fits'.format(root_dir, infile[0])) - + # Now, the CMFGEN atmospheres assume a distance of 1 kpc, while the the # ATLAS models are in FLAM at the surface. So, we need to multiply the # CMFGEN atmospheres by (1000/R)**2. in order to convert to FLAM on surface. @@ -370,13 +370,13 @@ def get_cmfgenRot_atmosphere_closest(metallicity=0, temperature=24000, gravity=4 radius = np.sqrt( lum / (4.0 * np.pi * teff**4. * sigma) ) # in cm radius /= 3.08*10**18 # in pc - + # Make the pysynphot spectrum w = spec['Wavelength'] f = spec['Flux'] * (1000 / radius)**2. sp = pysynphot.ArraySpectrum(w,f) - + #sp = pysynphot.FileSpectrum('{0}/{1}.fits'.format(root_dir, infile[0])) # Print out parameters of match, if desired @@ -398,7 +398,7 @@ def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, r sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) else: sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) - + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: @@ -429,9 +429,9 @@ def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin=True): """ - Return PHOENIX v16 atmospheres from - `Husser et al. 2013 `_. - + Return PHOENIX v16 atmospheres from + `Husser et al. 2013 `_. + Models originally downloaded via `ftp `_. Solar metallicity and [alpha/Fe] is used. @@ -451,7 +451,7 @@ def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin= gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, @@ -472,9 +472,9 @@ def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin= metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) - + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: @@ -487,14 +487,14 @@ def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin= def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebin=True): """ - Return atmosphere from CIFIST2011_2015 grid - (`Allard et al. 2012 `_, + Return atmosphere from CIFIST2011_2015 grid + (`Allard et al. 2012 `_, `Baraffe et al. 2015 `_ ) Grid originally downloaded from `website `_. Grid Range: - + * Teff: 1200 - 7000 K * gravity: 2.5 - 5.5 cgs * [M/H] = 0 @@ -509,11 +509,11 @@ def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebi gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, - which is often sufficient synthetic photometry in most cases. + which is often sufficient synthetic photometry in most cases. """ if rebin == True: atm_name = 'BTSettl_2015_rebin' @@ -528,10 +528,10 @@ def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebi metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - + + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: @@ -544,7 +544,7 @@ def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebi def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=True): """ - Return atmosphere from CIFIST2011 grid + Return atmosphere from CIFIST2011 grid (`Allard et al. 2012 `_) Grid originally downloaded `here `_ @@ -552,16 +552,16 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T Notes ------ Grid Range: - + * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - + Teff and gravity ranges depend on metallicity: [M/H] = -2.5 * Teff: 2600 - 4600 K * gravity: 4.5 - 5.5 - + [M/H] = -2.0 * Teff: 2600 - 7000 @@ -575,7 +575,7 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T [M/H] = -1.0 * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 + * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 [M/H] = -0.5 @@ -609,7 +609,7 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, @@ -628,10 +628,10 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - - + + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: @@ -644,7 +644,7 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): """ - Return white dwarf atmospheres from + Return white dwarf atmospheres from `Koester et al. 2010 `_ Parameters @@ -657,7 +657,7 @@ def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, @@ -672,14 +672,14 @@ def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): print( ' temperature = %d' % temperature) print( ' metallicity = %.1f' % metallicity) print( ' log gravity = %.1f' % gravity) - + return sp def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): """ Return atmosphere that is a linear merge of atlas ck04 model and phoenixV16. - Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 + Only valid for temps between 5000 - 5500K, gravity from 0 = 5.0 """ try: sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) @@ -689,7 +689,7 @@ def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) # Do some error checking @@ -707,7 +707,7 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model and phoenixV16. - Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 + Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 """ try: sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) @@ -717,7 +717,7 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): metallicity=metallicity, temperature=temperature, gravity=gravity) - + sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) # Do some error checking @@ -734,7 +734,7 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, rebin=True): """ - Return a stellar atmosphere from a suite of different model grids, + Return a stellar atmosphere from a suite of different model grids, depending on the input temperature, (all values in K). Parameters @@ -747,7 +747,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, @@ -758,7 +758,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose Notes ----- - The underlying stellar model grid used changes as a function of + The underlying stellar model grid used changes as a function of stellar temperature (in K): * T > 20,000: ATLAS @@ -769,14 +769,14 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose For T < 3800, there is an additional gravity and metallicity dependence: - If T < 3800 and [M/H] = 0: - + If T < 3800 and [M/H] = 0: + * T < 3800, logg < 2.5: PHOENIX v16 * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 Otherwise, if T < 3800 and [M/H] != 0: - + * T < 3800: PHOENIX v16 References: @@ -785,21 +785,21 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose * PHOENIXv16 (`Husser et al. 2013 `_) * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) - LTE WARNING: + LTE WARNING: The ATLAS atmospheres are calculated with LTE, and so they are less accurate when non-LTE conditions apply (e.g. T > 20,000 K). Ultimately we'd like to add a non-LTE atmosphere grid for the hottest stars in the future. - HOW BOUNDARIES BETWEEN MODELS ARE TREATED: + HOW BOUNDARIES BETWEEN MODELS ARE TREATED: - At the boundary between two models grids a temperature range is defined - where the resulting atmosphere is a weighted average between the two + At the boundary between two models grids a temperature range is defined + where the resulting atmosphere is a weighted average between the two grids. Near one boundary one model - is weighted more heavily, while at the other boundary the other - model is weighted more heavily. These are calculated in the - temperature ranges where we switch between model grids, to + is weighted more heavily, while at the other boundary the other + model is weighted more heavily. These are calculated in the + temperature ranges where we switch between model grids, to ensure a smooth transition. """ # For T < 3800, atmosphere depends on metallicity + gravity. @@ -815,7 +815,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose temperature=temperature, gravity=gravity, rebin=rebin) - + if (temperature >= 3200) & (temperature < 3800) & (gravity > 2.5): if verbose: print( 'BTSettl/Phoenixv16 merged atmosphere') @@ -831,7 +831,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose temperature=temperature, gravity=gravity, rebin=rebin) - + if (temperature <= 3800) & (metallicity != 0): if verbose: print( 'Phoenixv16 atmosphere') @@ -855,7 +855,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose return get_atlas_phoenix_atmosphere(metallicity=metallicity, temperature=temperature, gravity=gravity) - + if (temperature >= 5500) & (temperature < 20000): if verbose: print( 'ATLAS merged atmosphere') @@ -875,14 +875,14 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose # temperature=temperature, # gravity=gravity) - + def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False): """ - Return the white dwarf atmosphere from - `Koester et al. 2010 `_. - If desired parameters are + Return the white dwarf atmosphere from + `Koester et al. 2010 `_. + If desired parameters are outside of grid, return a blackbody spectrum instead Parameters @@ -895,7 +895,7 @@ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False gravity: float The stellar gravity, in cgs units - + rebin: boolean If true, rebins the atmospheres so that they are the same resolution as the Castelli+04 atmospheres. Default is False, @@ -911,7 +911,7 @@ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False return get_wdKoester_atmosphere(metallicity=metallicity, temperature=temperature, gravity=gravity) - + except pysynphot.exceptions.ParameterOutOfBounds: # Use a black-body atmosphere. bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) @@ -942,29 +942,29 @@ def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, warnings.warn( 'Only `temperature` keyword is used for black-body atmosphere' ) - + if verbose: print('Black-body atmosphere') - + # Modify pysynphot's default waveset to specified bounds pysynphot.refs.set_default_waveset( minwave=wave_min, maxwave=wave_max, num=wave_num ) - + # Get black-body atmosphere for specified temperature from pysynphot bbspec = pysynphot.spectrum.BlackBody(temperature) - + # pysynphot `BlackBody` generates spectrum in `photlam`, need in `flam` bbspec.convert('flam') - + # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. # Need to remove this normalization for SPISEA by multiplying bbspec # by (1000 * 1 parsec / 1 Rsun)**2 = (1000 * 3.08e18 cm / 6.957e10 cm)**2 bbspec *= (1000 * 3.086e18 / 6.957e10)**2 - + return bbspec - + #--------------------------------------# # Atmosphere formatting functions #--------------------------------------# @@ -980,7 +980,7 @@ def download_CMFGEN_atmospheres(Table_rot, Table_norot): Fierro+15 paper Website addresses are hardcoded - + Puts downloaded models in the current working directory. """ print( 'WARNING: THIS DOES NOT COMPLETELY WORK') @@ -1049,7 +1049,7 @@ def organize_CMFGEN_atmospheres(path_to_dir): """ # First, record current working directory to return to later start_dir = os.getcwd() - + # Enter atmosphere directory, collect rotating and non-rotating # file names (assumed to all start with "t") os.chdir(path_to_dir) @@ -1074,10 +1074,10 @@ def organize_CMFGEN_atmospheres(path_to_dir): # Also move Tables with model parameters into correct directory os.system('mv Table_rot.txt cmfgenF15_rot') os.system('mv Table_noRot.txt cmfgenF15_noRot') - + # Return to original directory os.chdir(start_dir) - + return def make_CMFGEN_catalog(path_to_dir): @@ -1099,10 +1099,10 @@ def make_CMFGEN_catalog(path_to_dir): """ # Record current working directory for later start_dir = os.getcwd() - + # Enter atmosphere directory os.chdir(path_to_dir) - + # Extract parameters for each atmosphere # Note: can't rely on filename for this because not precise enough!! @@ -1117,7 +1117,7 @@ def make_CMFGEN_catalog(path_to_dir): # lum = float(lumtmp[0][:-5]) * 1000.0 # In L_sun # mass = float(lumtmp[0][5:-1]) # In M_sun - + # Need to calculate log g from T and L (cgs) # lum_sun = 3.846 * 10**33 # erg/s # M_sun = 2 * 10**33 # g @@ -1145,18 +1145,18 @@ def make_CMFGEN_catalog(path_to_dir): #---NOTE: THE FOLLOWING DEPENDS ON FINAL LOCATION OF CATALOG FILE---# #path = path_to_dir + '/' + names[i] path = names[i] + '.fits[Flux]' - + index_str.append(index) name_str.append(path) - + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) # Create catalog.fits file in directory with the models catalog.write('catalog.fits', format = 'fits') - + # Move back to original directory, create the catalog.fits file os.chdir(start_dir) - + return def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): @@ -1192,23 +1192,23 @@ def cdbs_cmfgen(path_to_dir, path_to_cdbs_dir): unique = np.unique(wave, return_index=True) wave = wave[unique[1]] flux = flux[unique[1]] - - # Make fits table from individual columns. + + # Make fits table from individual columns. c0 = fits.Column(name='Wavelength', format='D', array=wave) c1 = fits.Column(name='Flux', format='E', array=flux) cols = fits.ColDefs([c0, c1]) tbhdu = fits.BinTableHDU.from_columns(cols) - #Adding unit keywords + #Adding unit keywords tbhdu.header['TUNIT1'] = 'ANGSTROM' tbhdu.header['TUNIT2'] = 'FLAM' prihdu = fits.PrimaryHDU() - + finalhdu = fits.HDUList([prihdu, tbhdu]) finalhdu.writeto(i[:-4]+'.fits', overwrite=True) - + print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) # Return to original directory, copy over new .fits files to cdbs directory @@ -1225,7 +1225,7 @@ def rebin_cmfgen(cdbs_path, rot=True): cdbs_path: path to cdbs directory rot=True for rotating models (cmfgen_rot), False for non-rotating models - + makes new directory in cdbs/grid: cmfgen_rot_rebin or cmfgen_norot_rebin """ # Get an atlas ck04 model, we will use this to set wavelength grid @@ -1241,7 +1241,7 @@ def rebin_cmfgen(cdbs_path, rot=True): tmp = cdbs_path+'/grid/cmfgen_norot/t0200l0007m009n.fits' path = cdbs_path+'/grid/cmfgen_norot_rebin/' orig_path = cdbs_path+'/grid/cmfgen_norot/' - + cmfgen_hdu = fits.open(tmp) header0 = cmfgen_hdu[0].header # Create rebin directories if they don't already exist. Copy over @@ -1256,7 +1256,7 @@ def rebin_cmfgen(cdbs_path, rot=True): files_all = [cat[ii][1].split('[')[0] for ii in range(len(cat))] # First column in new files will be for [atlas] wavelength - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) # For each catalog.fits entry, read the unbinned spectrum and rebin to # the atlas resolution. Make a new fits file in rebin directory @@ -1268,16 +1268,16 @@ def rebin_cmfgen(cdbs_path, rot=True): temp = float(vals[0]) metal = float(vals[1]) grav = float(vals[2]) - + # Fetch the spectrum - if rot == True: + if rot == True: sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) else: sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) # Rebin flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) # Make the FITS file from the columns with header cols = fits.ColDefs([c0,c1]) @@ -1302,7 +1302,7 @@ def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): path_to_dir is the path to the directory containing all of the downloaded files - + met_str is the name of the current metallicity Creates new fits files for each atmosphere: phoenix_.fits, @@ -1319,7 +1319,7 @@ def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): pass else: os.mkdir(sub_dir) - + # Extract wavelength array, make column for later wavefile = fits.open('WAVE_PHOENIX-ACES-AGSS-COND-2011.fits') wave = wavefile[0].data @@ -1342,7 +1342,7 @@ def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): for f in files: # Extract the logg out of filename logg = f[9:13] - + # Extract fluxes from file spectrum = fits.open(f) flux = spectrum[0].data @@ -1351,11 +1351,11 @@ def organize_PHOENIXv16_atmospheres(path_to_dir, met_str='m00'): # Make Column object with fluxes, add to table col = Column(flux, name = 'g{0:2.1f}'.format(float(logg))) t.add_column(col) - + # Now, construct final fits file for the given temp outname = 'phoenix{0}_{1:05d}.fits'.format(met_str, temp) - t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) - + t.write('{0}/{1}'.format(sub_dir, outname), format = 'fits', overwrite = True) + # Progress counter for user i += 1 print( 'Done {0:d} of {1:d}'.format(i, len(temp_arr))) @@ -1372,18 +1372,18 @@ def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): path_to_directory is the path to the directory with the reformatted models (i.e. the output from construct_atmospheres, phoenix[met_str]) - + Puts catalog.fits file in directory the user starts in """ # Save starting directory for later, move into working directory start_dir = os.getcwd() os.chdir(path_to_dir) - + # Extract metallicity from metallicity string met = float(met_str[1]) + (float(met_str[2]) * 0.1) if 'm' in met_str: met *= -1. - + # Collect the filenames. Each is a unique temp with many different log g's files = glob.glob('phoenix*.fits') files.sort() @@ -1396,7 +1396,7 @@ def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): t = Table.read(i, format='fits') keys = t.keys() logg_vals = keys[1:] - + # Extract temp from filename name = i.split('_') temp = float(name[1][:-5]) @@ -1409,20 +1409,20 @@ def make_PHOENIXv16_catalog(path_to_dir, met_str='m00'): filename_arr.append(filename) catalog = Table([index_arr, filename_arr], names=('INDEX', 'FILENAME')) - + # Return to starting directory, write catalog os.chdir(start_dir) - + if os.path.exists('catalog.fits'): from astropy.table import vstack - + prev_catalog = Table.read('catalog.fits', format='fits') joined_catalog = vstack([prev_catalog, catalog]) - + joined_catalog.write('catalog.fits', format='fits', overwrite=True) else: catalog.write('catalog.fits', format='fits', overwrite=True) - + return def cdbs_PHOENIXv16(path_to_cdbs_dir): @@ -1443,7 +1443,7 @@ def cdbs_PHOENIXv16(path_to_cdbs_dir): # Collect the filenames, make necessary changes to each one files = glob.glob('phoenix*.fits') - + ## Need to sort filenames; glob doesn't always give them in order files.sort() @@ -1451,28 +1451,28 @@ def cdbs_PHOENIXv16(path_to_cdbs_dir): counter = 0 for i in files: counter += 1 - + # Read in current FITS table cur_table = Table.read(i, format='fits') - + cur_table.columns[0].name = 'Wavelength' - + num_cols = len(cur_table.colnames) - - # Multiplying each flux column by 10^-8 for conversion + + # Multiplying each flux column by 10^-8 for conversion for cur_col_index in range(1, num_cols, 1): cur_col_name = cur_table.colnames[cur_col_index] cur_table[cur_col_name] = cur_table[cur_col_name] * 10.**-8 - - + + # Construct new FITS file based on old one hdu = fits.open(i) header_0 = hdu[0].header header_1 = hdu[1].header sci = hdu[1].data - + tbhdu = fits.table_to_hdu(cur_table) - + # Copying over the older headers, adding unit keywords prihdu = fits.PrimaryHDU(header=header_0) tbhdu.header['TUNIT1'] = 'ANGSTROM' @@ -1489,17 +1489,17 @@ def cdbs_PHOENIXv16(path_to_cdbs_dir): tbhdu.header['TUNIT12'] = 'FLAM' tbhdu.header['TUNIT13'] = 'FLAM' tbhdu.header['TUNIT14'] = 'FLAM' - + # Construct and write out final FITS file finalhdu = fits.HDUList([prihdu, tbhdu]) finalhdu.writeto(i, overwrite=True) - + hdu.close() print( 'Done {0:2.0f} of {1:2.0f}'.format(counter, len(files))) - + # Change back to starting directory os.chdir(start_dir) - + return def rebin_phoenixV16(cdbs_path): @@ -1524,7 +1524,7 @@ def rebin_phoenixV16(cdbs_path): path = cdbs_path+'/grid/phoenix_v16_rebin/' if not os.path.exists(path): os.mkdir(path) - + # Read in the existing catalog.fits file and rebin every spectrum. cat = fits.getdata(cdbs_path + '/grid/phoenix_v16/catalog.fits') @@ -1539,51 +1539,51 @@ def rebin_phoenixV16(cdbs_path): temp_arr[ff] = float(vals[0]) metal_arr[ff] = float(vals[1]) logg_arr[ff] = float(vals[2]) - + metal_uniq = np.unique(metal_arr) temp_uniq = np.unique(temp_arr) - + for mm in range(len(metal_uniq)): metal = metal_uniq[mm] # metallicity - + # Construct str for metallicity (for appropriate directory name) met_str = str(int(np.abs(metal))) + str(int((metal % 1.0)*10)) if metal > 0: met_str = 'p' + met_str else: met_str = 'm' + met_str - + # Make directory for current metallicity if it does not exist yet if not os.path.exists(path + 'phoenix' + met_str): os.mkdir(path + 'phoenix' + met_str) - + for tt in range(len(temp_uniq)): temp = temp_uniq[tt] # temperature - # Pick out the list of gravities for this T, Z combo + # Pick out the list of gravities for this T, Z combo idx = np.where((metal_arr == metal) & (temp_arr == temp))[0] logg_exist = logg_arr[idx] - + # All gravities will go in one file. Here is the output # file name. outfile = path + files_all[idx[0]].split('[')[0] - + ## If the rebinned file already exists, continue if os.path.exists(outfile): continue - + # Build a columns array. One column for each gravity. cols_arr = [] # Make the wavelength column, which is first in the cols array. c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) cols_arr.append(c0) - + for gg in range(len(logg_exist)): grav = logg_exist[gg] # gravity - # Fetch the spectrum + # Fetch the spectrum sp = pysynphot.Icat('phoenix_v16', temp, metal, grav) flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) @@ -1591,7 +1591,7 @@ def rebin_phoenixV16(cdbs_path): name = 'g{0:3.1f}'.format(grav) col = fits.Column(name=name, format='E', array=flux_rebin) cols_arr.append(col) - + # Make the FITS file from the columns with header. cols = fits.ColDefs(cols_arr) @@ -1607,7 +1607,7 @@ def rebin_phoenixV16(cdbs_path): finalhdu.writeto(outfile) print( 'Finished file ' + outfile + ' with gravities: ', logg_exist) - + return @@ -1622,7 +1622,7 @@ def rebin_spec(wave, specin, wavnew): f = np.ones(len(wave)) filt = pysynphot.spectrum.ArraySpectralElement(wave, f, waveunits='angstrom') obs = pysynphot.observation.Observation(spec, filt, binset=wavnew, force='taper') - + return obs.binflux def organize_BTSettl_2015_atmospheres(path_to_dir): @@ -1653,7 +1653,7 @@ def organize_BTSettl_2015_atmospheres(path_to_dir): spec = hdu[1].data header_0 = hdu[0].header header_1 = hdu[1].header - + wave = spec.field(0) flux = spec.field(1) @@ -1674,13 +1674,13 @@ def organize_BTSettl_2015_atmospheres(path_to_dir): tbhdu.header['TUNIT1'] = 'ANGSTROM' tbhdu.header['TUNIT2'] = 'FLAM' hdu_new = fits.HDUList([prihdu, tbhdu]) - + # Write new fits table in cdbs directory hdu_new.writeto(os.environ['PYSYN_CDBS']+'grid/BTSettl_2015/'+i, overwrite=True) hdu.close() hdu_new.close() - + # Return to original directory os.chdir(start_dir) return @@ -1697,10 +1697,10 @@ def make_BTSettl_2015_catalog(path_to_dir): """ # Record current working directory for later start_dir = os.getcwd() - + # Enter atmosphere directory os.chdir(path_to_dir) - + # Extract parameters for each atmosphere from the filename, # construct columns for catalog file files = glob.glob("*spec.fits") @@ -1719,10 +1719,10 @@ def make_BTSettl_2015_catalog(path_to_dir): # Create catalog.fits file in directory with the models catalog.write('catalog.fits', format = 'fits', overwrite=True) - + # Move back to original directory, create the catalog.fits file os.chdir(start_dir) - + return def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): @@ -1765,8 +1765,8 @@ def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): # Make new output c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + cols = fits.ColDefs([c0, c1]) tbhdu = fits.BinTableHDU.from_columns(cols) prihdu = fits.PrimaryHDU(header=header0) @@ -1775,7 +1775,7 @@ def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): outfile = path + files_all[ff].split('[')[0] finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) + finalhdu.writeto(outfile, overwrite=True) return @@ -1794,7 +1794,7 @@ def make_wavelength_unique(files, dirname): if len(t) != len(test[0]): t = t[test[1]] - + c0 = fits.Column(name='Wavelength', format='D', array=t['Wavelength']) c1 = fits.Column(name='Flux', format='E', array=t['Flux']) cols = fits.ColDefs([c0, c1]) @@ -1832,14 +1832,14 @@ def organize_BTSettl_atmospheres(): """ Construct cdbs-ready atmospheres for the BTSettl grid (CIFITS2011). The code expects tp be run in cdbs/grid/BTSettl, and expects that the - individual model files have been downloaded from online + individual model files have been downloaded from online (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011/SPECTRA/) - and processed into python-readable ascii files. + and processed into python-readable ascii files. """ orig_dir = os.getcwd() dirs = ['btm25', 'btm20', 'btm15', 'btm10', 'btm05', 'btp00', 'btp05'] #dirs = ['btm10', 'btm05', 'btp00', 'btp05'] - + # Go through each directory, turning each spectrum into a cdbs-ready file. # Will convert flux into Ergs/sec/cm**2/A (FLAM) units and save as a fits file, @@ -1871,13 +1871,13 @@ def organize_BTSettl_atmospheres(): tbhdu.header['TUNIT1'] = 'ANGSTROM' tbhdu.header['TUNIT2'] = 'FLAM' hdu_new = fits.HDUList([prihdu, tbhdu]) - + # Write new fits table in cdbs directory hdu_new.writeto('{0}.fits'.format(jj[:-4]), overwrite=True) hdu_new.close() count += 1 print('Done {0} of {1}'.format(count, len(files))) - + # Now, clean up all the files made when unzipping the spectra cmd1 = 'rm *.bz2' cmd2 = 'rm *.tmp' @@ -1888,7 +1888,7 @@ def organize_BTSettl_atmospheres(): print('==============================') print('Done {0}'.format(ii)) print('==============================') - + # Go back to original directory, move to next metallicity directory os.chdir(orig_dir) @@ -1924,7 +1924,7 @@ def make_BTSettl_catalog(): metal_flag = -1 * float(ii[3:])*0.1 else: metal_flag = float(ii[3:])*0.1 - + # Now collect the info from the files for jj in files: tmp = jj.split('-') @@ -1938,7 +1938,7 @@ def make_BTSettl_catalog(): else: temp = float(tmp[0][3:]) * 100.0 # In kelvin logg = float(tmp[1]) - + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_flag, logg)) name_str.append('{0}/{1}[Flux]'.format(ii, jj)) @@ -1951,10 +1951,10 @@ def make_BTSettl_catalog(): # Create catalog.fits file in directory with the models catalog.write('catalog.fits', format = 'fits', overwrite=True) - + # Move back to original directory, create the catalog.fits file os.chdir(start_dir) - + return def rebin_BTSettl(make_unique=False): @@ -1985,7 +1985,7 @@ def rebin_BTSettl(make_unique=False): # tmp.append(ii) #files_all = tmp #=============================# - + print( 'Rebinning BTSettl spectra') if make_unique: print('Making unique') @@ -2005,14 +2005,14 @@ def rebin_BTSettl(make_unique=False): # Make new output c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + cols = fits.ColDefs([c0, c1]) tbhdu = fits.BinTableHDU.from_columns(cols) prihdu = fits.PrimaryHDU() tbhdu.header['TUNIT1'] = 'ANGSTROM' tbhdu.header['TUNIT2'] = 'FLAM' - + outfile = path + files_all[ff].split('[')[0] finalhdu = fits.HDUList([prihdu, tbhdu]) finalhdu.writeto(outfile, overwrite=True) @@ -2022,9 +2022,9 @@ def rebin_BTSettl(make_unique=False): outfile = path + files_all[ff].split('[')[0] cmd = 'cp {0} {1}'.format(orig_file, outfile) os.system(cmd) - + print('Done {0} of {1}'.format(ff, len(files_all))) - + return def organize_WDKoester_atmospheres(path_to_dir): @@ -2048,7 +2048,7 @@ def organize_WDKoester_atmospheres(path_to_dir): for i in files: data = Table.read(i, format='ascii') - + wave = data['col1'] # angstrom flux = data['col2'] # erg/s/cm^2/A @@ -2064,12 +2064,12 @@ def organize_WDKoester_atmospheres(path_to_dir): tbhdu.header['TUNIT1'] = 'ANGSTROM' tbhdu.header['TUNIT2'] = 'FLAM' hdu_new = fits.HDUList([prihdu, tbhdu]) - + # Write new fits table in cdbs directory hdu_new.writeto(os.environ['PYSYN_CDBS']+'/grid/wdKoester/'+i.replace('.txt', '.fits'), overwrite=True) hdu_new.close() - + # Return to original directory os.chdir(start_dir) return @@ -2086,10 +2086,10 @@ def make_WDKoester_catalog(path_to_dir): """ # Record current working directory for later start_dir = os.getcwd() - + # Enter atmosphere directory os.chdir(path_to_dir) - + # Extract parameters for each atmosphere from the filename, # construct columns for catalog file files = glob.glob("*dk.dat.fits") @@ -2109,10 +2109,10 @@ def make_WDKoester_catalog(path_to_dir): # Create catalog.fits file in directory with the models catalog.write('catalog.fits', format = 'fits', overwrite=True) - + # Move back to original directory, create the catalog.fits file os.chdir(start_dir) - + return def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): @@ -2155,8 +2155,8 @@ def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): # Make new output c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) - c1 = fits.Column(name='Flux', format='E', array=flux_rebin) - + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + cols = fits.ColDefs([c0, c1]) tbhdu = fits.BinTableHDU.from_columns(cols) prihdu = fits.PrimaryHDU(header=header0) @@ -2165,8 +2165,8 @@ def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): outfile = path + files_all[ff].split('[')[0] finalhdu = fits.HDUList([prihdu, tbhdu]) - finalhdu.writeto(outfile, overwrite=True) + finalhdu.writeto(outfile, overwrite=True) return - - + + diff --git a/spisea/conftest.py b/spisea/conftest.py index 142ae511..fab9a899 100755 --- a/spisea/conftest.py +++ b/spisea/conftest.py @@ -18,10 +18,10 @@ ASTROPY_HEADER = True except ImportError: ASTROPY_HEADER = False - + else: # As of Astropy 5.1, the pytest plugins provided by Astropy have been removed - # and are instead provided by pytest-astropy-header + # and are instead provided by pytest-astropy-header # (https://github.com/astropy/pytest-astropy-header) from pytest_astropy_header.display import PYTEST_HEADER_MODULES, TESTED_VERSIONS ASTROPY_HEADER = True diff --git a/spisea/evolution.py b/spisea/evolution.py index fea75ab3..895b9bb6 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -30,7 +30,7 @@ def get_installed_grid_num(input_models_dir): """ # Define the installed model grid number file_name = input_models_dir + '/grid_version.txt' - + # Read in the file. In the case where it doesn't # exist, then grid version is assumed to be 1.0 # (since this didn't always exist) @@ -51,15 +51,15 @@ def check_evo_grid_number(required_num, input_models_dir): grid version number. Installed grid number must be greater than or equal to this number """ - + # Get installed gridnumber grid_num = get_installed_grid_num(input_models_dir) - + # Check: is installed grid number < required_num? # If not, raise mismatch error if grid_num < required_num: raise exceptions.ModelMismatch(required_num, grid_num, 'evolution') - + return grid_num class StellarEvolution(object): @@ -90,9 +90,9 @@ def __init__(self, model_dir, age_list, mass_list, z_list): self.mass_list = mass_list self.age_list = age_list self.model_version_name = "None" - + return - + class Geneva(StellarEvolution): def __init__(self): r""" @@ -101,17 +101,17 @@ def __init__(self): self.model_version_name = "Geneva" # populate list of model masses (in solar masses) mass_list = [(0.1 + i*0.005) for i in range(181)] - + # define metallicity parameters for Geneva models z_list = [0.01, 0.02, 0.03] - + # populate list of isochrone ages (log scale) age_list = [round(5.5 + 0.01*i, 2) for i in range(190)] age_list += [round(7.4 + 0.05*i, 2) for i in range(12)] age_list += [round(math.log10(1.e8*x), 2) for x in range(1, 10)] age_list += [round(math.log10(1.e9*x), 2) for x in range(1, 10)] age_list = age_list - + # specify location of model files model_dir = models_dir + 'geneva/' @@ -122,7 +122,7 @@ def __init__(self): # Define required evo_grid number self.evo_grid_min = 1.0 - + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the Geneva collection. @@ -131,10 +131,10 @@ def isochrone(self, age=1.e8, metallicity=0.0): # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - + # convert metallicity to mass fraction z_defined = self.z_solar*10.**metallicity - + # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') @@ -148,14 +148,14 @@ def isochrone(self, age=1.e8, metallicity=0.0): age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_' + str(self.age_list[age_idx]) + '.fits' - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - + # generate isochrone file string full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file - + # return isochrone data return genfromtxt(full_iso_file, comments='#') @@ -166,7 +166,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): class Ekstrom12(StellarEvolution): """ - Evolution models from + Evolution models from `Ekstrom et al. 2012 `_. Downloaded from `website `_. @@ -183,10 +183,10 @@ def __init__(self, rot=True): self.model_version_name = "Ekstrom12-norot" # define metallicity parameters for Ekstrom+12 models self.z_list = [0.014] - + # populate list of isochrone ages (log scale) self.age_list = np.arange(6.0, 8.0+0.005, 0.01) - + # Specify location of model files self.model_dir = models_dir+'Ekstrom2012/' @@ -199,7 +199,7 @@ def __init__(self, rot=True): # Define required evo_grid number self.evo_grid_min = 1.0 - + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the Ekstrom+12 Geneva collection. @@ -208,34 +208,34 @@ def isochrone(self, age=1.e8, metallicity=0.0): # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - + # convert metallicity to mass fraction z_defined = self.z_solar*10.**metallicity log_age = math.log10(age) - + # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') if ((z_defined < np.min(self.z_list)) or (z_defined > np.max(self.z_list))): - raise ValueError(f'Requested metallicity {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') + raise ValueError(f'Requested metallicity z_solar * 10^{metallicity} = {z_defined} is out of bounds between {np.min(self.z_list)} and {np.max(self.z_list)}.') # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - + # generate isochrone file string - if self.rot: + if self.rot: full_iso_file = self.model_dir + 'iso/' + z_dir + 'rot/' + iso_file else: full_iso_file = self.model_dir + 'iso/' + z_dir + 'norot/' + iso_file - + # Return isochrone data iso = Table.read(full_iso_file, format='fits') iso.rename_column('col4', 'Z') @@ -267,11 +267,11 @@ def format_isochrones(input_iso_dir): Parse iso.fits (filename hardcoded) file downloaded from Ekstrom+12 models, create individual isochrone files for the different ages. - input_iso_directory should lead to - Ekstrom2012/iso/ + input_iso_directory should lead to + Ekstrom2012/iso/ directory, where iso.fits file should be located. - Creates two new directories, rot and norot, which contain their + Creates two new directories, rot and norot, which contain their respective isochrones. """ # Store current directory for later @@ -279,13 +279,13 @@ def format_isochrones(input_iso_dir): # Move into metallicity direcotry, read iso.fits file os.chdir(input_iso_dir) - + print( 'Read Input: this is slow') iso = Table.read('iso.fits') print( 'Done' ) - + ages_all = iso['col1'] - + # Extract the unique ages age_arr = np.unique(ages_all) @@ -299,7 +299,7 @@ def format_isochrones(input_iso_dir): else: os.mkdir('rot') os.mkdir('norot') - + print( 'Making individual isochrone files') for age in age_arr: good = np.where(ages_all == age) @@ -310,7 +310,7 @@ def format_isochrones(input_iso_dir): tmp_r = iso[good][idx_r] tmp_n = iso[good][idx_n] - + # Write tables tmp_r.write('rot/iso_{0:4.2f}.fits'.format(age)) tmp_n.write('norot/iso_{0:4.2f}.fits'.format(age)) @@ -327,14 +327,14 @@ def create_iso(fileList, ageList, rot=True): iso.fits format for parse_iso code. fileList: list of downloaded isochrone files (could be one) - + ageList: list of lists of ages associated with each file in filelist. MUST BE IN SAME ORDER AS ISOCHRONES IN FILE! Also needs to be in logAge - + rot = TRUE: assumes that models are rotating, will add appropriate column - + This code writes the individual files, which is then easiest to combine by hand - in aquamacs + in aquamacs """ # Read each file in fileList individually, add necessary columns for i in range(len(fileList)): @@ -345,14 +345,14 @@ def create_iso(fileList, ageList, rot=True): start = np.where(t['M_ini'] == 0.8) # Now, each identified start is assumed to be associated with the - # corresponding age in ages + # corresponding age in ages if len(start[0]) != len(ages): print( 'Ages mismatched in file! Quitting...') return age_arr = np.zeros(len(t)) - + for j in range(len(start[0])): low_ind = start[0][j] # Deal with case at end of file @@ -371,9 +371,9 @@ def create_iso(fileList, ageList, rot=True): rot_val[:] = 'r' if not rot: rot_val[:] = 'n' - + col_rot = Column(rot_val, name='Rot') - + t.add_column(col_rot, index=0) t.add_column(col_age, index=0) @@ -387,7 +387,7 @@ def create_iso(fileList, ageList, rot=True): class Parsec(StellarEvolution): """ - Evolution models from + Evolution models from `Bressan et al. 2012 `_, version 1.2s. @@ -413,14 +413,14 @@ def __init__(self): # populate list of model masses (in solar masses) self.model_version_name = "Parsec1.2s" #mass_list = [(0.1 + i*0.005) for i in range(181)] - + # define metallicity parameters for Parsec models self.z_list = [0.005, 0.015, 0.04] - + # populate list of isochrone ages (log scale) self.age_list = np.arange(6.6, 10.12+0.005, 0.01) self.age_list = np.append(6.40, self.age_list) - + # Specify location of model files self.model_dir = models_dir+'ParsecV1.2s/' @@ -429,8 +429,8 @@ def __init__(self): self.z_file_map = {0.005: 'z005/', 0.015: 'z015/', 0.04: 'z04/'} # Define required evo_grid number - self.evo_grid_min = 1.0 - + self.evo_grid_min = 1.0 + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the Parsec version 1.2s @@ -440,12 +440,12 @@ def isochrone(self, age=1.e8, metallicity=0.0): # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - + # convert metallicity to mass fraction z_defined = self.z_solar*10.**metallicity log_age = math.log10(age) - + # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') @@ -457,14 +457,14 @@ def isochrone(self, age=1.e8, metallicity=0.0): # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - + # generate isochrone file string full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file - + # return isochrone data iso = Table.read(full_iso_file, format='fits') iso.rename_column('col1', 'Z') @@ -480,19 +480,19 @@ def isochrone(self, age=1.e8, metallicity=0.0): # Parsec doesn't identify WR stars, so identify all as "False" isWR = Column([False] * len(iso), name='isWR') iso.add_column(isWR) - + iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) return iso - + def format_isochrones(input_iso_dir, metallicity_list): r""" Parse isochrone file downloaded from Parsec version 1.2 for different metallicities, create individual isochrone files for the different ages. - + input_iso_dir: points to ParsecV1.2s/iso directory. Assumes metallicity subdirectories already exist with isochrone files downloaded in them (isochrones files expected to start with "output*") @@ -505,7 +505,7 @@ def format_isochrones(input_iso_dir, metallicity_list): # Move into isochrone directory os.chdir(input_iso_dir) - + # Work on each metallicity isochrones individually for metal in metallicity_list: # More into metallicity directory, read isochrone file @@ -515,7 +515,7 @@ def format_isochrones(input_iso_dir, metallicity_list): print( 'Read Input: this is slow') iso = Table.read(isoFile[0], format='fits') print( 'Done') - + ages_all = iso['col2'] # Extract the unique ages @@ -544,9 +544,9 @@ def format_isochrones(input_iso_dir, metallicity_list): class Pisa(StellarEvolution): """ - Evolution models from + Evolution models from `Tognelli et al. 2011 `_. - + Downloaded `online `_ Notes @@ -565,10 +565,10 @@ def __init__(self): self.model_version_name = "Pisa" # define metallicity parameters for Pisa models self.z_list = [0.015] - + # populate list of isochrone ages (log scale) self.age_list = np.arange(6.0, 8.01+0.005, 0.01) - + # Specify location of model files self.model_dir = models_dir+'Pisa2011/' @@ -578,7 +578,7 @@ def __init__(self): # Define required evo_grid number self.evo_grid_min = 1.0 - + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the Pisa (Tognelli+11) @@ -588,12 +588,12 @@ def isochrone(self, age=1.e8, metallicity=0.0): # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - + # convert metallicity to mass fraction z_defined = self.z_solar*10.**metallicity log_age = math.log10(age) - + # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') @@ -605,14 +605,14 @@ def isochrone(self, age=1.e8, metallicity=0.0): # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - + # generate isochrone file string full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file - + # return isochrone data iso = Table.read(full_iso_file, format='fits') iso.rename_column('col1', 'logL') @@ -625,7 +625,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): isWR = Column([False] * len(iso), name='isWR') iso.add_column(isWR) - # Add columns for current mass and phase. + # Add columns for current mass and phase. iso.add_column( Column(np.zeros(len(iso)), name = 'phase')) iso.add_column( Column(iso['mass'], name = 'mass_current')) @@ -643,7 +643,7 @@ def format_isochrones(input_iso_dir, metallicity_list): input_iso_dir: points to Pisa2011/iso directory. Individual metallicity directories with the downloaded isochrones are expected to already exist there - + metallicity_list is the list of metallicities on which function is to be run. @@ -665,7 +665,7 @@ def format_isochrones(input_iso_dir, metallicity_list): else: # Create a ReadMe with the original file names to preserve the # model details - + cmd = "ls *.FITS > ReadMe" os.system(cmd) @@ -699,14 +699,14 @@ def make_isochrone_grid(metallicity=0.015): while 0.0150 would not) """ logAge_arr = np.arange(6.0, 8.0+0.005, 0.01) - + count = 0 for logAge in logAge_arr: # Could interpolate using evolutionary tracks, but less accurate. make_isochrone_pisa_interp(logAge, metallicity=metallicity) count += 1 - + print( 'Done {0} of {1} models'.format(count, (len(logAge_arr)))) return @@ -716,7 +716,7 @@ def make_isochrone_grid(metallicity=0.015): #==============================# class Baraffe15(StellarEvolution): """ - Evolution models published in + Evolution models published in `Baraffe et al. 2015 `_. Downloaded from `BHAC15 site `_. @@ -725,10 +725,10 @@ def __init__(self): self.model_version_name = "Baraffe15" # define metallicity parameters for Baraffe models self.z_list = [0.015] - + # populate list of isochrone ages (log scale) self.age_list = np.arange(6.0, 8.0+0.005, 0.01) - + # Specify location of model files self.model_dir = models_dir+'Baraffe15/' @@ -738,7 +738,7 @@ def __init__(self): # Define required evo_grid number self.evo_grid_min = 1.0 - + def isochrone(self, age=5.e7, metallicity=0.0): r""" Extract an individual isochrone from the Baraffe+15 @@ -748,12 +748,12 @@ def isochrone(self, age=5.e7, metallicity=0.0): # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - + # convert metallicity to mass fraction z_defined = self.z_solar*10.**metallicity log_age = math.log10(age) - + # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') @@ -765,26 +765,26 @@ def isochrone(self, age=5.e7, metallicity=0.0): # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - + # generate isochrone file string full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file - + # Read isochrone, get in proper format iso = Table.read(full_iso_file, format='fits') iso.rename_column('Mass', 'mass') iso.rename_column('logG', 'logg') iso['logT'] = np.log10(iso['Teff']) - + # Pisa models are too low for WR phase, add WR column with all False iso['logT_WR'] = iso['logT'] isWR = Column([False] * len(iso), name='isWR') iso.add_column(isWR) - # Add columns for current mass and phase. + # Add columns for current mass and phase. iso.add_column( Column(np.zeros(len(iso)), name = 'phase')) iso.add_column( Column(iso['mass'], name = 'mass_current')) @@ -798,11 +798,11 @@ def tracks_to_isochrones(self, tracksFile): r""" Create isochrones at desired age sampling (6.0 < logAge < 8.0, steps of 0.01; hardcoded) from the Baraffe+15 tracks downloaded - online. + online. tracksFile: tracks.dat file downloaded from Baraffe+15, with format modified to be read in python - + Writes isochrones in iso/ subdirectory off of work directory. Will create this subdirectory if it doesn't already exist """ @@ -810,7 +810,7 @@ def tracks_to_isochrones(self, tracksFile): age_arr = np.arange(6.0, 8.0+0.005, 0.01) #age_arr = [6.28] - + # Loop through the masses, interpolating track over time at each. # Resample track properties at hardcoded ages masses = np.unique(tracks['col1']) @@ -835,13 +835,13 @@ def tracks_to_isochrones(self, tracksFile): # Interpolate Teff, logL, and logG using linear interpolator tck_Teff = interpolate.interp1d(tmp['col2'], tmp['col3']) tck_logL = interpolate.interp1d(tmp['col2'], tmp['col4']) - tck_logG = interpolate.interp1d(tmp['col2'], tmp['col5']) + tck_logG = interpolate.interp1d(tmp['col2'], tmp['col5']) Teff = tck_Teff(age_arr) logL = tck_logL(age_arr) logG = tck_logG(age_arr) - + # Test interpolation if desired test=False if test: @@ -868,9 +868,9 @@ def tracks_to_isochrones(self, tracksFile): py.xlabel('logAge') py.ylabel('logG') py.savefig('test_logG.png') - + pdb.set_trace() - + # Build upon arrays of interpolated values mass_interp = np.concatenate((mass_interp, np.ones(len(Teff)) * mass)) age_interp = np.concatenate((age_interp, age_arr)) @@ -905,11 +905,11 @@ def tracks_to_isochrones(self, tracksFile): def test_age_interp(self, onlineIso, interpIso): r""" Compare one of our interpolated ischrones with one - of the isochrones provided online by Baraffe+15. + of the isochrones provided online by Baraffe+15. """ true_iso = Table.read(onlineIso, format='ascii') our_iso = Table.read(interpIso, format='fits') - + # Compare the two isochrones using plots. Look at mass vs. Teff, # mass vs. logG, mass vs. logL. Ideally these isochrones should # be identical @@ -941,7 +941,7 @@ def test_age_interp(self, onlineIso, interpIso): Teff_diff = np.mean(abs(true_iso['col2'][7:] - our_iso['Teff'])) logL_diff = np.mean(abs(true_iso['col3'][7:] - our_iso['logL'])) logG_diff = np.mean(abs(true_iso['col4'][7:] - our_iso['logG'])) - + print( 'Average abs difference in Teff: {0}'.format(Teff_diff)) print( 'Average abs difference in logL: {0}'.format(logL_diff)) print( 'Average abs difference in logg: {0}'.format(logG_diff)) @@ -958,7 +958,7 @@ def compare_Baraffe_Pisa(BaraffeIso, PisaIso): name = BaraffeIso.split('_') age = name[1][:4] - + # Extract paramters we need b_mass = b['Mass'] b_logT = np.log10(b['Teff']) @@ -972,7 +972,7 @@ def compare_Baraffe_Pisa(BaraffeIso, PisaIso): m05_b = np.where( abs(b_mass - 0.5) == min(abs(b_mass - 0.5)) ) m05_p = np.where( abs(p_mass - 0.5) == min(abs(p_mass - 0.5)) ) - + # Comparison plots py.figure(1, figsize=(10,10)) py.clf() @@ -1000,7 +1000,7 @@ def compare_Baraffe_Pisa(BaraffeIso, PisaIso): #py.axis([4.4, 3.4, -3, 4]) #py.gca().invert_xaxis() py.legend() - py.savefig('BaraffePisa_comp_mass_{0}.png'.format(age)) + py.savefig('BaraffePisa_comp_mass_{0}.png'.format(age)) return @@ -1010,7 +1010,7 @@ def compare_Baraffe_Pisa(BaraffeIso, PisaIso): class MISTv1(StellarEvolution): """ Define intrinsic properties for the MIST v1 stellar - models. + models. Models originally downloaded from `online server `_. @@ -1021,11 +1021,11 @@ class MISTv1(StellarEvolution): was downloaded from MIST website on 2/2017, while Version 1.2 was downloaded on 8/2018 (solar metallicity) and 4/2019 (other metallicities). Default is 1.2. - + synthpop_extension: boolean (default False) If True, the isochrones are extended down to a minimum initial mass of 0.1Msun using grids interpolated via SynthPop. If False, - the web-downloaded MIST isochrones are used with their varying + the web-downloaded MIST isochrones are used with their varying lower mass limits. True option is only valid for version=1.2. """ def __init__(self, version=1.2, synthpop_extension=False): @@ -1045,7 +1045,7 @@ def __init__(self, version=1.2, synthpop_extension=False): 0.014, # [Fe/H] = 0.00 0.025, # [Fe/H] = 0.25 0.045] # [Fe/H] = 0.50 - + # populate list of isochrone ages (log scale) self.age_list = np.arange(5.01, 10.30+0.005, 0.01) @@ -1062,7 +1062,7 @@ def __init__(self, version=1.2, synthpop_extension=False): version_dir = 'v1.2/' else: raise ValueError('Version {0} not supported for MIST isochrones'.format(version)) - + # Specify location of model files self.model_dir = models_dir+'MISTv1/' + version_dir if self.synthpop_extension: @@ -1094,7 +1094,7 @@ def __init__(self, version=1.2, synthpop_extension=False): # Define required evo_grid number (now 1.2 for synthpop extension) self.evo_grid_min = 1.2 - + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the MISTv1 @@ -1104,7 +1104,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - + # convert metallicity to mass fraction z_defined = self.z_solar * (10.**metallicity) @@ -1121,16 +1121,16 @@ def isochrone(self, age=1.e8, metallicity=0.0): # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - + # generate isochrone file string full_iso_file = self.model_dir + 'iso/' + z_dir + iso_file if self.synthpop_extension: addl_iso_file = self.model_extension_dir + 'iso/' + z_dir + iso_file - + # return isochrone data. Column locations depend on # version iso = Table.read(full_iso_file, format='fits') @@ -1174,7 +1174,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) return iso - + def format_isochrones(self): r""" Parse isochrone file downloaded from MIST web server, @@ -1199,7 +1199,7 @@ def format_isochrones(self): # Move into isochrone directory os.chdir(input_iso_dir) - + # Work on each metallicity isochrones individually for metal in metallicity_list: # More into metallicity directory, read isochrone file @@ -1253,19 +1253,19 @@ class MergedBaraffePisaEkstromParsec(StellarEvolution): The model used depends on the age of the population and what stellar masses are being modeled: - + For logAge < 7.4: * Baraffe: 0.08 - 0.4 M_sun - * Baraffe/Pisa transition: 0.4 - 0.5 M_sun + * Baraffe/Pisa transition: 0.4 - 0.5 M_sun * Pisa: 0.5 M_sun to the highest mass in Pisa isochrone (typically 5 - 7 Msun) * Geneva: Highest mass of Pisa models to 120 M_sun For logAge > 7.4: * Parsec v1.2s: full mass range - + Parameters ---------- rot: boolean, optional @@ -1278,18 +1278,18 @@ def __init__(self, rot=True): self.model_version_name = "MergedBaraffePisaEkstromParsec-norot" # populate list of model masses (in solar masses) mass_list = [(0.1 + i*0.005) for i in range(181)] - + # define metallicity parameters for Geneva models z_list = [0.015] - + # populate list of isochrone ages (log scale) age_list = np.arange(6.0, 10.091, 0.01).tolist() - + # specify location of model files model_dir = models_dir + 'merged/baraffe_pisa_ekstrom_parsec/' StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) self.z_solar = 0.015 - + # Switch to specify rotating/non-rotating models if rot: self.z_file_map = {0.015: 'z015_rot/'} @@ -1298,23 +1298,23 @@ def __init__(self, rot=True): # Define required evo_grid number self.evo_grid_min = 1.0 - - + + def isochrone(self, age=1.e8, metallicity=0.0): r""" - Extract an individual isochrone from the Baraffe-Pisa-Ekstrom-Parsec + Extract an individual isochrone from the Baraffe-Pisa-Ekstrom-Parsec collection """ # Error check to see if installed evolution model # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - + # convert metallicity to mass fraction z_defined = self.z_solar*10.**metallicity log_age = math.log10(age) - + # check age and metallicity are within bounds if ((log_age < np.min(self.age_list)) or (log_age > np.max(self.age_list))): raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') @@ -1326,8 +1326,8 @@ def isochrone(self, age=1.e8, metallicity=0.0): # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - - + + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] @@ -1343,9 +1343,9 @@ def isochrone(self, age=1.e8, metallicity=0.0): # ASCII version of files (newer model evo grids iso_file = 'iso_{0:.2f}.dat'.format(self.age_list[age_idx]) full_iso_file = self.model_dir + z_dir + iso_file - + iso = Table.read(full_iso_file, format='ascii') - + iso.rename_column('col1', 'mass') iso.rename_column('col2', 'logT') iso.rename_column('col3', 'logL') @@ -1360,18 +1360,18 @@ def isochrone(self, age=1.e8, metallicity=0.0): idx_WR = np.where(iso['logT'] != iso['logT_WR']) isWR[idx_WR] = True iso.add_column(isWR) - + iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) - + return iso class MergedPisaEkstromParsec(StellarEvolution): """ Same as MergedBaraffePisaEkstromParsec, but without - the Baraffe models. + the Baraffe models. Parameters ---------- @@ -1385,13 +1385,13 @@ def __init__(self, rot=True): self.model_version_name = "MergedPisaEkstromParsec-norot" # populate list of model masses (in solar masses) mass_list = [(0.1 + i*0.005) for i in range(181)] - + # define metallicity parameters for Geneva models z_list = [0.015] - + # populate list of isochrone ages (log scale) age_list = np.arange(6.0, 8.001, 0.01).tolist() - + # specify location of model files model_dir = models_dir + 'merged/pisa_ekstrom_parsec/' StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) @@ -1405,13 +1405,13 @@ def __init__(self, rot=True): # Define required evo_grid number self.evo_grid_min = 1.0 - + # Error check to see if installed evolution model # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - - + + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the Pisa-Ekstrom-Parsec collection. @@ -1420,7 +1420,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): z_defined = self.z_solar*10.**metallicity log_age = math.log10(age) - + # check age and metallicity are within bounds if (log_age < self.age_list[0]) or (log_age > self.age_list[-1]): raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') @@ -1431,7 +1431,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] @@ -1451,7 +1451,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) - + return iso class MergedSiessGenevaPadova(StellarEvolution): @@ -1466,27 +1466,27 @@ class MergedSiessGenevaPadova(StellarEvolution): * Padova (`Marigo et al. 2008 `_) For logAge < 7.4: - + * Siess: 0.1 - 7 M_sun * Siess/Geneva transition: 7 - 9 M_sun * Geneva: > 9 M_sun For logAge > 7.4: - + * Padova: full mass range """ def __init__(self): """ - Define intrinsic properties for merged Siess-meynetMaeder-Padova + Define intrinsic properties for merged Siess-meynetMaeder-Padova stellar models. """ self.model_version_name = "MergedSiessGenevaPadova" # populate list of model masses (in solar masses) mass_list = [(0.1 + i*0.005) for i in range(181)] - + # define metallicity parameters for Geneva models z_list = [0.02] - + # populate list of isochrone ages (log scale) age_list = np.arange(5.5, 7.41, 0.01).tolist() age_list.append(7.48) @@ -1506,24 +1506,24 @@ def __init__(self): age_list.append(9.60) age_list.append(9.70) age_list.append(9.78) - + # specify location of model files model_dir = models_dir + 'merged/siess_meynetMaeder_padova/' StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) self.z_solar = 0.02 - + # Metallicity map self.z_file_map = {0.02: 'z02/'} # Define required evo_grid number self.evo_grid_min = 1.0 - + # Error check to see if installed evolution model # grid is compatible with code version. Also return # current grid num self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) - - + + def isochrone(self, age=1.e8, metallicity=0.0): r""" Extract an individual isochrone from the Siess-Geneva-Padova collection. @@ -1532,7 +1532,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): z_defined = self.z_solar*10.**metallicity log_age = math.log10(age) - + # check age and metallicity are within bounds if (log_age < self.age_list[0]) or (log_age > self.age_list[-1]): raise ValueError(f'Requested age {log_age} is out of bounds between {np.min(self.age_list)} and {np.max(self.age_list)}.') @@ -1543,7 +1543,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): # Find nearest age in grid to input grid age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] iso_file = 'iso_{0:.2f}.fits'.format(self.age_list[age_idx]) - + # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] @@ -1559,16 +1559,16 @@ def isochrone(self, age=1.e8, metallicity=0.0): iso.rename_column('col4', 'logg') iso.rename_column('col5', 'logT_WR') iso.rename_column('col6', 'model_ref') - + iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) - + return iso #================================================# - -def make_isochrone_pisa_interp(log_age, metallicity=0.015, + +def make_isochrone_pisa_interp(log_age, metallicity=0.015, tracks=None, test=False): """ Read in a set of isochrones and generate an isochrone at log_age @@ -1595,7 +1595,7 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015, if os.path.exists(rootDir+'iso_{0:3.2f}.fits'.format(log_age)): print( 'Isochrone at logAge = {0:3.2f} already exists'.format(log_age)) return - + # Name/directory for interpolated isochrone isoFile = rootDir+'iso_%3.2f.fits' % log_age outSuffix = '_%.2f' % (log_age) @@ -1616,7 +1616,7 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015, good = np.where(tmp == log_age) young_model_logage = tmp[good[0]-1] old_model_logage = tmp[good[0]+1] - + # Isolate younger/older isochrones young_ind = np.where(iso.log_ages == young_model_logage) old_ind = np.where(iso.log_ages == old_model_logage) @@ -1629,7 +1629,7 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015, if abs(young_model_logage - log_age) <= abs(old_model_logage - log_age): # Use young model mass grid young_iso, old_iso = interpolate_iso_tempgrid(young_iso, old_iso) - + else: # Use old model mass grid old_iso, young_iso = interpolate_iso_tempgrid(old_iso, young_iso) @@ -1637,25 +1637,25 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015, # Now, can interpolate in time over the two models. Do this star by star. # Work in linear time here!! numStars = len(young_iso.M) - + interp_iso = Isochrone(log_age) interp_iso.log_Teff = np.zeros(numStars, dtype=float) interp_iso.log_L = np.zeros(numStars, dtype=float) interp_iso.log_g = np.zeros(numStars, dtype=float) interp_iso.M = young_iso.M # Since mass grids should already be matched - + for i in range(numStars): # Do interpolations in linear space model_ages = [10**young_model_logage[0], 10**old_model_logage[0]] target_age = 10**log_age #model_ages = [young_model_logage[0], old_model_logage[0]] #target_age = log_age - + # Build interpolation functions Teff_arr = [10**young_iso.log_Teff[i], 10**old_iso.log_Teff[i]] logL_arr = [10**young_iso.log_L[i], 10**old_iso.log_L[i]] logg_arr = [10**young_iso.log_g[i], 10**old_iso.log_g[i]] - + f_log_Teff = interpolate.interp1d(model_ages, Teff_arr, kind='linear') f_log_L = interpolate.interp1d(model_ages, logL_arr, kind='linear') f_log_g = interpolate.interp1d(model_ages, logg_arr, kind='linear') @@ -1681,15 +1681,15 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015, py.legend() py.title('Pisa 2011 Isochrone at log t = %.2f' % log_age) py.savefig(rootDir + 'plots/interp_isochrone_at' + outSuffix + '.png') - + print( time.asctime(), 'Finished.') # Write output to file, MUST BE IN SAME ORDER AS ORIG FILES _out = open(isoFile, 'w') - - _out.write('%10s %10s %10s %10s\n' % + + _out.write('%10s %10s %10s %10s\n' % ('# log L', 'log Teff', 'Mass', 'log g')) - _out.write('%10s %10s %10s %10s\n' % + _out.write('%10s %10s %10s %10s\n' % ('# (Lsun)', '(Kelvin)', '(Msun)', '(cgs)')) for ii in range(len(interp_iso.M)): @@ -1713,7 +1713,7 @@ def get_orig_pisa_isochrones(metallicity=0.015): if not os.path.exists(pms_dir): print( 'Failed to find Siess PMS isochrones for metallicity = ' + metSuffix) return - + # Collect the isochrones files = glob.glob(pms_dir + '*.dat') count = len(files) @@ -1722,7 +1722,7 @@ def get_orig_pisa_isochrones(metallicity=0.015): data.isochrones = [] data.log_ages = [] - + # Extract useful params from isochrones for ff in range(len(files)): d = Table.read(files[ff], format='ascii') @@ -1730,7 +1730,7 @@ def get_orig_pisa_isochrones(metallicity=0.015): # Extract logAge from filename log_age = float(files[ff].split('_')[2][:-4]) - # Create an isochrone object + # Create an isochrone object iso = Isochrone(log_age) iso.M = d['col3'] iso.log_Teff = d['col2'] @@ -1739,13 +1739,13 @@ def get_orig_pisa_isochrones(metallicity=0.015): # If a log g column exist, extract it. Otherwise, calculate # log g from T and L and add column at end if len(d.keys()) == 3: - + # Calculate log g from T and L L_sun = 3.8 * 10**33 #cgs SB_sig = 5.67 * 10**-5 #cgs M_sun = 2. * 10**33 #cgs G_const = 6.67 * 10**-8 #cgs - + radius = np.sqrt( (10**d['col1'] * L_sun) / (4 * np.pi * SB_sig * (10**d['col2'])**4) ) g = (G_const * d['col3'] * M_sun) / radius**2 @@ -1754,7 +1754,7 @@ def get_orig_pisa_isochrones(metallicity=0.015): iso.log_g = np.log10(g.astype(np.float)) else: iso.log_g = d['col4'] - + data.isochrones.append(iso) data.log_ages.append(log_age) diff --git a/spisea/exceptions.py b/spisea/exceptions.py index 5b45baa1..e0587d90 100644 --- a/spisea/exceptions.py +++ b/spisea/exceptions.py @@ -8,12 +8,12 @@ class ModelMismatch(Exception): """ def __init__(self, required_num, grid_num, model_type): assert (model_type == 'evolution') | (model_type == 'atmosphere') - + if model_type == 'evolution': model_file = 'spisea_models.tar.gz' elif model_type == 'atmosphere': model_file = 'spisea_cdbs.tar.gz' - + # Compose error message str1 = 'WARNING: Desired {0} model requires model grid version >= {1},'.format(model_type, required_num) str2 = 'but model grid version {0} is installed.'.format(grid_num) diff --git a/spisea/filters.py b/spisea/filters.py index 23c029b3..d7d0b5e1 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -29,7 +29,7 @@ def get_nirc2_filt(name): while len(idx) != 0: wavelength[idx+1] += 1.0e-8 - + diff = np.diff(wavelength) idx = np.where(diff <= 0)[0] #print( 'Duplicate entry loop' ) @@ -68,7 +68,7 @@ def get_2mass_filt(name): name='2MASS_{0}'.format(name)) return spectrum - + def get_vista_filt(name): """ @@ -79,21 +79,21 @@ def get_vista_filt(name): t = Table.read('{0}/vista/VISTA_Filters_at80K_forETC_{1}.dat'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find VISTA filter file {0}/vista/VISTA_Filters_at80K_forETC_{1}.dat'.format(filters_dir, name)) + raise ValueError('Could not find VISTA filter file {0}/vista/VISTA_Filters_at80K_forETC_{1}.dat'.format(filters_dir, name)) # Wavelength must be in angstroms, transmission in fraction wave = t['col1'] * 10 trans = t['col2'] * 0.01 - + # Change any negative numbers to 0, as well as anything shortward # of 0.4 microns or longward of 2.9 microns # (no VISTA filter transmissions beyond these boundaries) bad = np.where( (trans < 0) | (wave < 4000) | (wave > 29000) ) trans[bad] = 0 - + # Now we can define the VISTA filter bandpass objects spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='VISTA_{0}'.format(name)) - + return spectrum def get_decam_filt(name): @@ -104,18 +104,18 @@ def get_decam_filt(name): try: t = Table.read('{0}/decam/DECam_filters.txt'.format(filters_dir), format='ascii') t.rename_column('Y', 'y') - + cols = np.array(t.keys()) idx = np.where(cols == name)[0][0] trans = t[cols[idx]] except: - raise ValueError('Could not find DECAM filter {0} in {1}/decam/DECam_filters.txt'.format(name, filters_dir)) + raise ValueError('Could not find DECAM filter {0} in {1}/decam/DECam_filters.txt'.format(name, filters_dir)) # Limit to unmasked regions only mask = np.ma.getmask(trans) good = np.where(mask == False) - + # Convert wavelengths from nm to angstroms, while eliminating masked regions wave = t['wavelength'][good] * 10. trans = trans[good] @@ -126,7 +126,7 @@ def get_decam_filt(name): return spectrum -def get_PS1_filt(name): +def get_PS1_filt(name): """ Define PS1 filter as pysynphot object """ @@ -145,11 +145,11 @@ def get_PS1_filt(name): trans = t[cols[idx]] except: - raise ValueError('Could not find PS1 filter {0} in {1}/ps1'.format(name, filters_dir)) + raise ValueError('Could not find PS1 filter {0} in {1}/ps1'.format(name, filters_dir)) # Convert wavelengths from nm to angstroms wave = t['wave'] * 10. - + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ps1_{0}'.format(name)) return spectrum @@ -161,7 +161,7 @@ def get_jwst_filt(name): try: t = Table.read('{0}/jwst/{1}.txt'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find JWST filter {0} in {1}/jwst'.format(name, filters_dir)) + raise ValueError('Could not find JWST filter {0} in {1}/jwst'.format(name, filters_dir)) # Convert wavelengths to angstroms wave = t['microns'] * 10**4. @@ -170,10 +170,10 @@ def get_jwst_filt(name): # Change any negative numbers to 0 bad = np.where(trans < 0) trans[bad] = 0 - + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='jwst_{0}'.format(name)) - return spectrum + return spectrum def get_Johnson_Glass_filt(name): """ @@ -182,7 +182,7 @@ def get_Johnson_Glass_filt(name): try: t = Table.read('{0}/Johnson_Glass/{1}.txt'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find Johnson-Glass filter {0} in {1}/Johnson_Glass'.format(name, filters_dir)) + raise ValueError('Could not find Johnson-Glass filter {0} in {1}/Johnson_Glass'.format(name, filters_dir)) # Convert wavelengths to angstroms wave = t['col1'] * 10. @@ -191,10 +191,10 @@ def get_Johnson_Glass_filt(name): # Change any negative numbers to 0 bad = np.where(trans < 0) trans[bad] = 0 - + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='jg_{0}'.format(name)) - return spectrum + return spectrum def get_nirc1_filt(name): """ @@ -203,12 +203,12 @@ def get_nirc1_filt(name): try: t = Table.read('{0}/nirc1/{1}.txt'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find NIRC1 filter {0} in {1}/nirc1'.format(name, filters_dir)) + raise ValueError('Could not find NIRC1 filter {0} in {1}/nirc1'.format(name, filters_dir)) # Convert wavelengths to angstroms wave = t['col1'] * 10**4 trans = t['col2'] - + # Lets fix wavelength array for duplicate values or negative vals; # delete these entries diff = np.diff(wave) @@ -222,14 +222,14 @@ def get_nirc1_filt(name): diff = np.diff(wave) idx = np.where(diff <= 0)[0] - + # Change any negative transmission vals to 0 bad = np.where(trans < 0) trans[bad] = 0 spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='nirc1_{0}'.format(name)) - return spectrum + return spectrum def get_ctio_osiris_filt(name): """ @@ -238,7 +238,7 @@ def get_ctio_osiris_filt(name): try: t = Table.read('{0}/CTIO_OSIRIS/{1}.txt'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find CTIO/OSIRIS filter {0} in {1}/CTIO_OSIRIS'.format(name, filters_dir)) + raise ValueError('Could not find CTIO/OSIRIS filter {0} in {1}/CTIO_OSIRIS'.format(name, filters_dir)) # Convert wavelengths to angstroms wave = t['col1'] * 10**4 @@ -247,7 +247,7 @@ def get_ctio_osiris_filt(name): # Change any negative numbers to 0 bad = np.where(trans < 0) trans[bad] = 0 - + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ctio_osiris_{0}'.format(name)) return spectrum @@ -259,7 +259,7 @@ def get_naco_filt(name): try: t = Table.read('{0}/naco/{1}.dat'.format(filters_dir, name), format='ascii') except: - raise ValueError('Could not find NACO filter {0} in {1}/naco'.format(name, filters_dir)) + raise ValueError('Could not find NACO filter {0} in {1}/naco'.format(name, filters_dir)) # Convert wavelengths to angstroms wave = t['col1'] * 10**4 @@ -268,7 +268,7 @@ def get_naco_filt(name): # Change any negative numbers to 0 bad = np.where(trans < 0) trans[bad] = 0 - + spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='naco_{0}'.format(name)) return spectrum @@ -282,7 +282,7 @@ def get_ubv_filt(name): except: raise ValueError('Could not find ubv filter {0} in {1}/ubv'.format(name, filters_dir)) - # Convert wavelength from nm to angstroms + # Convert wavelength from nm to angstroms wave = t[t.keys()[0]] * 10. # Convert transmission to ratio (from percent) trans = t[t.keys()[1]] / 100. @@ -336,8 +336,8 @@ def get_keck_osiris_filt(name): def get_gaia_filt(version, name): """ Define Gaia filters as pysynphot object. - To avoid confusion, we will only support - the revised DR2 zeropoints from + To avoid confusion, we will only support + the revised DR2 zeropoints from Evans+18. version: specify dr1, dr2, or dr2_rev @@ -357,7 +357,7 @@ def get_gaia_filt(version, name): path = '{0}/gaia/dr2_rev/'.format(filters_dir) else: raise ValueError('GAIA filter version {0} not understood. Please use dr1, dr2, or dr2_rev'.format(version)) - + # Get the filter info try: t = Table.read('{0}/Gaia_passbands.txt'.format(path), format='ascii') @@ -380,7 +380,7 @@ def get_gaia_filt(version, name): bad = np.where(trans > 90) trans[bad] = 0 except: - raise ValueError('Could not find Gaia filter {0}'.format(name)) + raise ValueError('Could not find Gaia filter {0}'.format(name)) # Convert wavelengths to angstroms (from nm) wave = t['LAMBDA'] * 10 diff --git a/spisea/ifmr.py b/spisea/ifmr.py index cafa66d6..b7cc7235 100755 --- a/spisea/ifmr.py +++ b/spisea/ifmr.py @@ -12,7 +12,7 @@ #https://ui.adsabs.harvard.edu/abs/2014ApJ...783...10S/abstract #BH/NS IFMR based on Sukhbold et al. 2016 for solar-Z models:: #https://ui.adsabs.harvard.edu/abs/2016ApJ...821...38S/abstract -#PPISN based on Woosley 2017: +#PPISN based on Woosley 2017: #https://ui.adsabs.harvard.edu/abs/2017ApJ...836..244W/abstract #PPSIN based on Woosley et al. 2020: #https://ui.adsabs.harvard.edu/abs/2020ApJ...896...56W/abstract @@ -38,7 +38,7 @@ def get_Z(self, Fe_H): return 10**(Fe_H - 1.85387) def Kalirai_mass(self, MZAMS): - """ + """ From Kalirai+08 https://ui.adsabs.harvard.edu/abs/2008ApJ...676..594K/abstract 1.16 < MZAMS < 6.5 But we use this function for anything between 0.5 and 9 depending on the IFMR. @@ -48,34 +48,34 @@ def Kalirai_mass(self, MZAMS): result = 0.109*MZAMS + 0.394 final = np.zeros(len(MZAMS)) - + good_idx = MZAMS >= 0.5 final[good_idx] = result[good_idx] final[~good_idx] = -99 return final - - + + class IFMR_Spera15(IFMR): """ The BH/NS IFMR (used for MZAMS>= 7 M_sun) comes from `Spera et. al. (2015) Appendix C `_. - The WD IFMR (used for MZAMS< 7_Msun) comes from + The WD IFMR (used for MZAMS< 7_Msun) comes from `Kalirai et al. (2008) `_. See Rose et al. (submitted) for more details. - + """ - + #The get_Mco functions come from C11 of Spera def get_Mco_low_metal(self, Z, MZAMS): """ C15 of Spera, valid for Z < 1.0e-3 - + """ - + B1 = 67.07 K1 = 46.89 K2 = 1.138e2 @@ -94,7 +94,7 @@ def get_Mco_med_metal(self, Z, MZAMS): C14 of Spera, valid for Z >= 1.0e-3 and Z <= 4.0e-3 """ - + B1 = 40.98 + 3.415e4*Z - 8.064e6*Z**2 K1 = 35.17 + 1.548e4*Z - 3.759e6*Z**2 K2 = 20.36 + 1.162e5*Z - 2.276e7*Z**2 @@ -113,7 +113,7 @@ def get_Mco_high_metal(self, Z, MZAMS): C13 of Spera, valid for Z > 4.0e-3 """ - + B1 = 59.63 - 2.969e3*Z + 4.988e4*Z**2 K1 = 45.04 - 2.176e3*Z + 3.806e4*Z**2 K2 = 1.389e2 - 4.664e3*Z + 5.106e4*Z**2 @@ -123,7 +123,7 @@ def get_Mco_high_metal(self, Z, MZAMS): g1 = 0.5/(1+10**((K1-MZAMS)*(d1))) #C12 of Spera g2 = 0.5/(1+10**((K2-MZAMS)*(d2))) #C12 of Spera - + return -2.0 + (B1 + 2.0)*(g1 + g2) #C11 of Spera @@ -132,8 +132,8 @@ def get_Mco(self, Z, MZAMS): This function uses Spera C11-C15 in order to reurn an array of core masses from an array of metallicities and ZAMS masses. It will be the same length as these two arrays with -99 entries where invalid (ie MZAMS< 7 M_sun) - Parameters: - + Parameters: + Z: an array with metallicities reported as Z where Z is metal_mass/total_mass MZAMS: an array of ZAMS masses in solar masses. The Spera functions are valid for MZAMS> 7 M_sun @@ -158,28 +158,28 @@ def get_Mco(self, Z, MZAMS): core_masses[high_metal_idx] = self.get_Mco_high_metal(Z[high_metal_idx], MZAMS[high_metal_idx]) return core_masses - + def M_rem_very_low_metal_low_mass(self, Z, Mco): """ C1 of Spera, valid for Z <= 5.0e-4 and Mco <= 5.0 - + Parameters: Z: an array of metallicities reported as metal_mass/total_mass Mco: an arrray of core masses in M_sun """ - + p = -2.333 + 0.1559*Mco + 0.2700*Mco**2 #C2 of Spera #need to return p or 1.27, whichever is greater final = np.zeros(len(p)) - + p_max_idx = np.where(p >= 1.27) final[p_max_idx] = p[p_max_idx] - + p_min_idx = np.where(p < 1.27) final[p_min_idx] = 1.27 @@ -217,7 +217,7 @@ def M_rem_very_low_metal_high_mass(self, Z, Mco): m = -6.476e2*Z + 1.911 #C3 of Spera q = 2.300e3*Z + 11.67 #C3 of Spera - + f = m*Mco + q #C2 of Spera #need to return either p or f, whichever is less @@ -228,7 +228,7 @@ def M_rem_very_low_metal_high_mass(self, Z, Mco): f_min_idx = np.where(f < p) final[f_min_idx] = f[f_min_idx] - + return final def M_rem_low_metal_low_mass(self, Z, Mco): @@ -252,15 +252,15 @@ def M_rem_low_metal_low_mass(self, Z, Mco): #need to return h or 1.27, whichever is greater final = np.zeros(len(h)) - + h_max_idx = np.where(h >= 1.27) final[h_max_idx] = h[h_max_idx] - + h_min_idx = np.where(h < 1.27) final[h_min_idx] = 1.27 return final - + def M_rem_low_metal_med_mass(self, Z, Mco): """ @@ -316,9 +316,9 @@ def M_rem_low_metal_high_mass(self, Z, Mco): f_max_idx = np.where(f > h) final[f_max_idx] = f[f_max_idx] - + return final - + def M_rem_med_metal_low_mass(self, Z, Mco): """ @@ -341,10 +341,10 @@ def M_rem_med_metal_low_mass(self, Z, Mco): #need to return h or 1.27, whichever is greater final = np.zeros(len(h)) - + h_max_idx = np.where(h >= 1.27) final[h_max_idx] = h[h_max_idx] - + h_min_idx = np.where(h < 1.27) final[h_min_idx] = 1.27 @@ -395,7 +395,7 @@ def M_rem_med_metal_high_mass_1(self, Z, Mco): q = -1.296e4*Z + 26.98 f = m*Mco + q #C5 of Spera - + #need to return either h or f, whichever is greater final = np.zeros(len(h)) @@ -404,7 +404,7 @@ def M_rem_med_metal_high_mass_1(self, Z, Mco): f_max_idx = np.where(f > h) final[f_max_idx] = f[f_max_idx] - + return final def M_rem_med_metal_high_mass_2(self, Z, Mco): @@ -431,7 +431,7 @@ def M_rem_med_metal_high_mass_2(self, Z, Mco): q = 1.061 f = m*Mco + q #C5 of Spera - + #need to return either h or f, whichever is greater final = np.zeros(len(h)) @@ -440,7 +440,7 @@ def M_rem_med_metal_high_mass_2(self, Z, Mco): f_max_idx = np.where(f > h) final[f_max_idx] = f[f_max_idx] - + return final @@ -465,10 +465,10 @@ def M_rem_high_metal_low_mass(self, Z, Mco): #need to return h or 1.27, whichever is greater final = np.zeros(len(h)) - + h_max_idx = np.where(h >= 1.27) final[h_max_idx] = h[h_max_idx] - + h_min_idx = np.where(h < 1.27) final[h_min_idx] = 1.27 @@ -519,7 +519,7 @@ def M_rem_high_metal_high_mass(self, Z, Mco): q = 1.061 f = m*Mco + q #C5 of Spera - + #need to return either h or f, whichever is greater final = np.zeros(len(h)) @@ -528,15 +528,15 @@ def M_rem_high_metal_high_mass(self, Z, Mco): f_max_idx = np.where(f > h) final[f_max_idx] = f[f_max_idx] - + return final def generate_death_mass(self, mass_array, metallicity_array): """ - The top-level function that assigns the remnant type - and mass based on the stellar initial mass. - + The top-level function that assigns the remnant type + and mass based on the stellar initial mass. + Parameters ---------- mass_array: array of floats @@ -547,20 +547,20 @@ def generate_death_mass(self, mass_array, metallicity_array): Notes ------ The output typecode tells what compact object formed: - + * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 - A typecode of value -1 means you're outside the range of - validity for applying the ifmr formula. - A remnant mass of -99 means you're outside the range of + A typecode of value -1 means you're outside the range of + validity for applying the ifmr formula. + A remnant mass of -99 means you're outside the range of validity for applying the ifmr formula. - Range of validity: MZAMS > 0.5 - + Range of validity: MZAMS > 0.5 + Returns ------- output_arr: 2-element array - output_array[0] contains the remnant mass, and + output_array[0] contains the remnant mass, and output_array[1] contains the typecode """ @@ -585,10 +585,10 @@ def generate_death_mass(self, mass_array, metallicity_array): Kal_idx = np.where(core_mass < 0) rem_mass_array[Kal_idx] = self.Kalirai_mass(mass_array[Kal_idx]) - + ##### very low metallicity Z < 5.0e-4 - + #remnant masses of stars with Z < 5.0e-4 and Mco < 5.0 very_low_metal_low_mass_idx = np.where((Z_array < 5.0e-4) & (core_mass < 5.0) & (core_mass >= 0)) rem_mass_array[very_low_metal_low_mass_idx] = self.M_rem_very_low_metal_low_mass(Z_array[very_low_metal_low_mass_idx], core_mass[very_low_metal_low_mass_idx]) @@ -602,7 +602,7 @@ def generate_death_mass(self, mass_array, metallicity_array): rem_mass_array[very_low_metal_high_mass_idx] = self.M_rem_very_low_metal_high_mass(Z_array[very_low_metal_high_mass_idx], core_mass[very_low_metal_high_mass_idx]) #### low metallicity 5.0e-4 <= Z < 1.0e-3 - + #remnant masses of stars with 5.0e-4 <= Z < 1.0e-3 and Mco < 5.0 low_metal_low_mass_idx = np.where((Z_array >= 5.0e-4) & (Z_array < 1.0e-3) & (core_mass < 5.0) & (core_mass >= 0)) rem_mass_array[low_metal_low_mass_idx] = self.M_rem_low_metal_low_mass(Z_array[low_metal_low_mass_idx], core_mass[low_metal_low_mass_idx]) @@ -614,9 +614,9 @@ def generate_death_mass(self, mass_array, metallicity_array): #remnant masses of stars with 5.0e-4 <= Z < 1.0e-3 and Mco > 10.0 low_metal_high_mass_idx = np.where((Z_array >= 5.0e-4) & (Z_array < 1.0e-3) & (core_mass > 10.0)) rem_mass_array[low_metal_high_mass_idx] = self.M_rem_low_metal_high_mass(Z_array[low_metal_high_mass_idx], core_mass[low_metal_high_mass_idx]) - + #### medium metallicity 1.0e-3 <= Z <= 4.0e-3 - + #remnant masses of stars with 1.0e-3 <= Z <= 4.0e-3 and Mco < 5.0 med_metal_low_mass_idx = np.where((Z_array >= 1.0e-3) & (Z_array <= 4.0e-3) & (core_mass < 5.0) & (core_mass >= 0)) rem_mass_array[med_metal_low_mass_idx] = self.M_rem_med_metal_low_mass(Z_array[med_metal_low_mass_idx],core_mass[med_metal_low_mass_idx]) @@ -632,9 +632,9 @@ def generate_death_mass(self, mass_array, metallicity_array): #remnant masses of stars with 2.0e-3 <= Z <= 4.0e-3 and Mco > 10.0 med_metal_high_mass_idx_2 = np.where((Z_array >= 2.0e-3) & (Z_array <= 4.0e-3) & (core_mass > 10.0)) rem_mass_array[med_metal_high_mass_idx_2] = self.M_rem_med_metal_high_mass_2(Z_array[med_metal_high_mass_idx_2], core_mass[med_metal_high_mass_idx_2]) - + #### high metallicity Z > 4.0e-3 - + #remnant masses of stars with Z > 4.0e-3 and Mco < 5.0 high_metal_low_mass_idx = np.where((Z_array > 4.0e-3) & (core_mass < 5.0) & (core_mass >= 0)) rem_mass_array[high_metal_low_mass_idx] = self.M_rem_high_metal_low_mass(Z_array[high_metal_low_mass_idx], core_mass[high_metal_low_mass_idx]) @@ -646,16 +646,16 @@ def generate_death_mass(self, mass_array, metallicity_array): #remnant masses of stars with Z > 4.0e-3 and MZAMS > 10.0 high_metal_high_mass_idx = np.where((Z_array > 4.0e-3) & (core_mass > 10.0)) rem_mass_array[high_metal_high_mass_idx] = self.M_rem_high_metal_high_mass(Z_array[high_metal_high_mass_idx], core_mass[high_metal_high_mass_idx]) - + #assign object types based on remnant mass bad_idx = np.where(rem_mass_array < 0) #outside the range of validity for the ifmr WD_idx = np.where((rem_mass_array <= 1.4) & (rem_mass_array >= 0 )) #based on the Chandresekhar limit - NS_idx = np.where((rem_mass_array > 1.4) & (rem_mass_array <= 3.0)) #based on figures 15-17 of Spera + NS_idx = np.where((rem_mass_array > 1.4) & (rem_mass_array <= 3.0)) #based on figures 15-17 of Spera BH_idx = np.where(rem_mass_array > 3.0) #based on figures 15-17 of Spera output_array[0][bad_idx] = rem_mass_array[bad_idx] output_array[1][bad_idx] = -1 - + output_array[0][WD_idx] = rem_mass_array[WD_idx] output_array[1][WD_idx] = codes['WD'] @@ -666,76 +666,76 @@ def generate_death_mass(self, mass_array, metallicity_array): output_array[1][BH_idx] = codes['BH'] return output_array - + class IFMR_Raithel18(IFMR): """ - The IFMR is a combination of the WD IFMR from + The IFMR is a combination of the WD IFMR from `Kalirai et al. (2008) `_ and the NS/BH IFMR from `Raithel et al. (2018) `_. - Note that the NS masses are NOT assigned based on the above results. + Note that the NS masses are NOT assigned based on the above results. We do take the NS/BH formation ratio and the BH masses. - NS masses are assigned based on random draws from a Gaussian (see NS_mass function). + NS masses are assigned based on random draws from a Gaussian (see NS_mass function). - See + See `Lam et al. (2020) `_ and Rose et al. (submitted) for more details. """ def BH_mass_core_low(self, MZAMS): - """ - Eqn (1) - Paper: 15 < MZAMS < 40 - Us extending: 15 < MZAMS < 42.22 + """ + Eqn (1) + Paper: 15 < MZAMS < 40 + Us extending: 15 < MZAMS < 42.22 """ return -2.024 + 0.4130*MZAMS def BH_mass_all_low(self, MZAMS): - """ - Eqn (2) - Paper: 15 < MZAMS < 40 - Us extending: 15 < MZAMS < 42.22 + """ + Eqn (2) + Paper: 15 < MZAMS < 40 + Us extending: 15 < MZAMS < 42.22 """ return 16.28 + 0.00694 * (MZAMS - 21.872) - 0.05973 * (MZAMS - 21.872)**2 + 0.003112 * (MZAMS - 21.872)**3 def BH_mass_high(self, MZAMS): - """ - Eqn (3) - Paper: 45 < MZAMS < 120 - Us extending: 42.22 < MZAMS < 120 + """ + Eqn (3) + Paper: 45 < MZAMS < 120 + Us extending: 42.22 < MZAMS < 120 """ return 5.795 + 1.007 * 10**9 * MZAMS**-4.926 def BH_mass_low(self, MZAMS, f_ej): - """ - Eqn (4) - Paper: 15 < MZAMS < 40 - Us extending: 15 < MZAMS < 42.22 + """ + Eqn (4) + Paper: 15 < MZAMS < 40 + Us extending: 15 < MZAMS < 42.22 """ return f_ej * self.BH_mass_core_low(MZAMS) + (1 - f_ej) * self.BH_mass_all_low(MZAMS) def NS_mass(self, MZAMS): - """ + """ Drawing the NS mass from a Gaussian distrobuton based on observational data. Gaussian fit by Emily Ramey and Sergiy Vasylyev of University of Caifornia, Berkeley using a - sample of NSs from Ozel & Freire (2016) — J1811+2405 Ng et al. (2020), - J2302+4442 Kirichenko et al. (2018), J2215+5135 Linares et al. (2018), - J1913+1102 Ferdman & Collaboration (2017), J1411+2551 Martinez et al. (2017), + sample of NSs from Ozel & Freire (2016) — J1811+2405 Ng et al. (2020), + J2302+4442 Kirichenko et al. (2018), J2215+5135 Linares et al. (2018), + J1913+1102 Ferdman & Collaboration (2017), J1411+2551 Martinez et al. (2017), J1757+1854 Cameron et al. (2018), J0030+0451 Riley et al. (2019), J1301+0833 Romani et al. (2016) The Gaussian distribution was fit using this data and a Bayesian MCMC method adapted from Kiziltan et al. (2010). - + """ return self.rng.normal(loc=1.36, scale=0.09, size=len(MZAMS)) def generate_death_mass(self, mass_array): """ - The top-level function that assigns the remnant type - and mass based on the stellar initial mass. - + The top-level function that assigns the remnant type + and mass based on the stellar initial mass. + Parameters ---------- mass_array: array of floats @@ -746,15 +746,15 @@ def generate_death_mass(self, mass_array): Notes ------ The output typecode tells what compact object formed: - + * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 - A typecode of value -1 means you're outside the range of - validity for applying the ifmr formula. + A typecode of value -1 means you're outside the range of + validity for applying the ifmr formula. - A remnant mass of -99 means you're outside the range of + A remnant mass of -99 means you're outside the range of validity for applying the ifmr formula. Range of validity: 0.5 < MZAMS < 120 @@ -762,7 +762,7 @@ def generate_death_mass(self, mass_array): Returns ------- output_arr: 2-element array - output_array[0] contains the remnant mass, and + output_array[0] contains the remnant mass, and output_array[1] contains the typecode """ @@ -774,7 +774,7 @@ def generate_death_mass(self, mass_array): random_array = self.rng.integers(1, 1001, size = len(mass_array)) codes = {'WD': 101, 'NS': 102, 'BH': 103} - + """ The id_arrays are to separate all the different formation regimes """ @@ -801,7 +801,7 @@ def generate_death_mass(self, mass_array): id_array4_BH = np.where((mass_array >= 17.8) & (mass_array < 18.5) & (random_array > 833)) output_array[0][id_array4_BH]= self.BH_mass_low(mass_array[id_array4_BH], 0.9) output_array[1][id_array4_BH] = codes['BH'] - + id_array4_NS = np.where((mass_array >= 17.8) & (mass_array < 18.5) & (random_array <= 833)) output_array[0][id_array4_NS] = self.NS_mass(mass_array[id_array4_NS]) output_array[1][id_array4_NS] = codes['NS'] @@ -809,7 +809,7 @@ def generate_death_mass(self, mass_array): id_array5_BH = np.where((mass_array >= 18.5) & (mass_array < 21.7) & (random_array > 500)) output_array[0][id_array5_BH] = self.BH_mass_low(mass_array[id_array5_BH], 0.9) output_array[1][id_array5_BH] = codes['BH'] - + id_array5_NS = np.where((mass_array >= 18.5) & (mass_array < 21.7) & (random_array <= 500)) output_array[0][id_array5_NS] = self.NS_mass(mass_array[id_array5_NS]) output_array[1][id_array5_NS] = codes['NS'] @@ -821,7 +821,7 @@ def generate_death_mass(self, mass_array): id_array7_BH = np.where((mass_array >= 25.2) & (mass_array < 27.5) & (random_array > 652)) output_array[0][id_array7_BH] = self.BH_mass_low(mass_array[id_array7_BH], 0.9) output_array[1][id_array7_BH] = codes['BH'] - + id_array7_NS = np.where((mass_array >= 25.2) & (mass_array < 27.5) & (random_array <= 652)) output_array[0][id_array7_NS] = self.NS_mass(mass_array[id_array7_NS]) output_array[1][id_array7_NS] = codes['NS'] @@ -837,7 +837,7 @@ def generate_death_mass(self, mass_array): id_array10_BH = np.where((mass_array >= 60) & (mass_array < 120) & (random_array > 400)) output_array[0][id_array10_BH] = self.BH_mass_high(mass_array[id_array10_BH]) output_array[1][id_array10_BH] = codes['BH'] - + id_array10_NS = np.where((mass_array >= 60) & (mass_array < 120) & (random_array <= 400)) output_array[0][id_array10_NS] = self.NS_mass(mass_array[id_array10_NS]) output_array[1][id_array10_NS] = codes['NS'] @@ -850,11 +850,11 @@ class IFMR_N20_Sukhbold(IFMR): `Sukhbold & Woosley (2014) `_. The BH/NS IFMR for solar metallicity progenitors comes from `Sukhbold et al. (2016) `_. - The PPISN models are from + The PPISN models are from `Woosley (2017) `_ and `Woosley et al. (2020) `_. - The WD IFMR is from + The WD IFMR is from `Kalirai et al. (2008) `_. Note that the NS masses are NOT assigned based on the above results. We do take the NS/BH formation ratio and the BH masses. @@ -869,7 +869,7 @@ def zero_BH_mass(self, MZAMS): #func = np.poly1d(zero_coeff) #result = func(MZAMS) return 0.46522639*MZAMS + -3.29170817 - + def solar_BH_mass(self, MZAMS): #solar_coeff = [-0.27079245, 24.74320755] #func = np.poly1d(solar_coeff) @@ -879,24 +879,24 @@ def solar_BH_mass(self, MZAMS): Zsun = 0.014 def NS_mass(self, MZAMS): - """ + """ Drawing the NS mass from a Gaussian distrobuton based on observational data. Gaussian fit by Emily Ramey and Sergiy Vasylyev of University of Caifornia, Berkeley using a - sample of NSs from Ozel & Freire (2016) — J1811+2405 Ng et al. (2020), - J2302+4442 Kirichenko et al. (2018), J2215+5135 Linares et al. (2018), - J1913+1102 Ferdman & Collaboration (2017), J1411+2551 Martinez et al. (2017), + sample of NSs from Ozel & Freire (2016) — J1811+2405 Ng et al. (2020), + J2302+4442 Kirichenko et al. (2018), J2215+5135 Linares et al. (2018), + J1913+1102 Ferdman & Collaboration (2017), J1411+2551 Martinez et al. (2017), J1757+1854 Cameron et al. (2018), J0030+0451 Riley et al. (2019), J1301+0833 Romani et al. (2016) The Gaussian distribution was fit using this data and a Bayesian MCMC method adapted from Kiziltan et al. (2010). - + """ if isinstance(MZAMS, np.ndarray): return self.rng.normal(loc=1.36, scale=0.09, size=len(MZAMS)) else: return self.rng.normal(loc=1.36, scale=0.09, size=1)[0] - - + + def BH_mass_low(self, MZAMS): """ 9 < MZAMS < 40 Msun @@ -912,7 +912,7 @@ def BH_mass_high(self, MZAMS, Z): """ # Solar metallicity (what Sam is using) Zsun = 0.014 - + zfrac = np.atleast_1d(Z/Zsun) # super-solar Z gives identical results as solar Z above_idx = np.where(zfrac > 1) @@ -935,15 +935,15 @@ def prob_BH_high(self, Z): """ # Solar metallicity (what Sam is using) Zsun = 0.014 - + Z = np.atleast_1d(Z) # Convert from [Fe/H] to Z zfrac = Z / Zsun - + # super-solar Z gives identical results as solar Z zfrac[zfrac > 1] = 1.0 zfrac[zfrac < 0] = np.nan - + pBH = 1 - 0.8*zfrac return pBH @@ -951,9 +951,9 @@ def prob_BH_high(self, Z): def generate_death_mass(self, mass_array, metallicity_array): """ - The top-level function that assigns the remnant type - and mass based on the stellar initial mass. - + The top-level function that assigns the remnant type + and mass based on the stellar initial mass. + Parameters ---------- mass_array: array of floats @@ -964,27 +964,27 @@ def generate_death_mass(self, mass_array, metallicity_array): Notes ------ The output typecode tells what compact object formed: - + * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 - A typecode of value -1 means you're outside the range of - validity for applying the ifmr formula. - A remnant mass of -99 means you're outside the range of + A typecode of value -1 means you're outside the range of + validity for applying the ifmr formula. + A remnant mass of -99 means you're outside the range of validity for applying the ifmr formula. - Range of validity: MZAMS > 0.5 - + Range of validity: MZAMS > 0.5 + Returns ------- output_arr: 2-element array - output_array[0] contains the remnant mass, and + output_array[0] contains the remnant mass, and output_array[1] contains the typecode """ #output_array[0] holds the remnant mass #output_array[1] holds the remnant type mass_array = np.atleast_1d(mass_array) metallicity_array = np.atleast_1d(metallicity_array) - + output_array = np.zeros((2, len(mass_array))) codes = {'WD': 101, 'NS': 102, 'BH': 103} @@ -1042,14 +1042,14 @@ def generate_death_mass(self, mass_array, metallicity_array): BH_or_NS = np.where((mass_array >= 60) & (mass_array < 120))[0] pBH = self.prob_BH_high(Z_array[BH_or_NS]) is_BH = random_array[BH_or_NS] > 100 * pBH - + id_array8 = BH_or_NS[is_BH] id_array9 = BH_or_NS[~is_BH] - + # Assign BH masses and types for BH-forming indices output_array[0][id_array8] = self.BH_mass_high(mass_array[id_array8], Z_array[id_array8]) output_array[1][id_array8] = codes['BH'] - + # Assign NS masses and types for NS-forming indices output_array[0][id_array9] = self.NS_mass(mass_array[id_array9]) output_array[1][id_array9] = codes['NS'] diff --git a/spisea/reddening.py b/spisea/reddening.py index 2754719b..677999a8 100755 --- a/spisea/reddening.py +++ b/spisea/reddening.py @@ -24,12 +24,12 @@ def get_red_law(str): ---------- str: str Reddening law name and additional params (comma-separated). - Name must match + Name must match """ # Parse the string, extracting redlaw name and other params tmp = str.split(',') name = tmp[0] - + # How we split this up changes for the broken power law EL # versus the other ELs (since we have arrays for broken power law EL) if name == 'broken_pl': @@ -86,12 +86,12 @@ def get_red_law(str): class RedLawNishiyama09(pysynphot.reddening.CustomRedLaw): """ - The extinction law towards the Galactic Center - from `Nishiyama et al. 2009 + The extinction law towards the Galactic Center + from `Nishiyama et al. 2009 `_, - combined with the Av / AKs value from `Nishiyama et al. 2008 - `_. - This law is defined between 0.5 - 8.0 microns. + combined with the Av / AKs value from `Nishiyama et al. 2008 + `_. + This law is defined between 0.5 - 8.0 microns. This law is constructed in 3 segments: @@ -107,7 +107,7 @@ class RedLawNishiyama09(pysynphot.reddening.CustomRedLaw): def __init__(self): # Fetch the extinction curve, pre-interpolate across 3-8 microns wave = np.arange(0.5, 8.0, 0.001) - + # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 wave_vals, Alambda_scaled = RedLawNishiyama09._derive_nishiyama09(wave) @@ -115,7 +115,7 @@ def __init__(self): # Convert wavelength to angstrom wave_vals *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave_vals, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave_vals, waveunits='angstrom', Avscaled=Alambda_scaled, name='Nishiyama09', @@ -128,10 +128,10 @@ def __init__(self): # Other info self.scale_lambda = 2.14 self.name = 'N09' - + @staticmethod def _derive_nishiyama09(wavelength): - """ + """ Calculate the N09 extinction law as defined in the paper: a A_lambda/AKs = power law of exponent -2.0 between JHK. Then use a *linear* interpolation in 1/lambda space to go from J to the V-band observation, @@ -148,7 +148,7 @@ def _derive_nishiyama09(wavelength): #-----Define power law extinction law between JHK----# jhk_idx = np.where( (wavelength >= 1.25) & (wavelength <= 2.14) ) #jhk_idx = np.where( (wavelength >= 1.25) & (wavelength <= 2.3) ) - + alpha = 2.0 wave_jhk = wavelength[jhk_idx] idx_scale = np.where(abs(wave_jhk - 2.14) == min(abs(wave_jhk - 2.14)) ) @@ -177,10 +177,10 @@ def _derive_nishiyama09(wavelength): #wave = np.array([0.551, 1.25, 1.63, 2.14, wave_jhk[-1], 3.545, 4.442, 5.675, 7.760]) #A_AKs = np.array([16.13, 3.02, 1.73, 1.00, A_Ks_jhk[-1], 0.500, 0.390, 0.360, 0.430]) #interp_idx = np.where(wave > 2.2) - + spline_interp = interpolate.splrep(wave[interp_idx], A_AKs[interp_idx], k=3, s=0) A_AKs_long = interpolate.splev(wavelength[long_idx], spline_interp) - + # Stitch together sections for the final law wave_vals = np.concatenate((wavelength[jv_idx[0]], wavelength[jhk_idx[0]])) A_AKs_vjhk = np.concatenate((A_Ks_jv, A_Ks_jhk)) @@ -192,8 +192,8 @@ def _derive_nishiyama09(wavelength): return wave_vals, A_AKs_final def Nishiyama09(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -213,7 +213,7 @@ def Nishiyama09(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -261,33 +261,33 @@ def plot_Nishiyama09(self): py.savefig('nishiyama09_el.png') return - + class RedLawCardelli(pysynphot.reddening.CustomRedLaw): """ - Defines the extinction law from - `Cardelli et al. 1989 `_. + Defines the extinction law from + `Cardelli et al. 1989 `_. The law is defined from 0.3 - 3 microns, and in terms of :math:`A_{\lambda} / A_{Ks}`, where Ks is 2.174 microns. Parameters ---------- Rv : float - Ratio of absolute to selective extinction, :math:`A(V) / E(B-V)`. + Ratio of absolute to selective extinction, :math:`A(V) / E(B-V)`. The standard value for the diffuse ISM is 3.1. """ def __init__(self, Rv): # Fetch the extinction curve, pre-interpolate across 0.3-3 microns wave = np.arange(0.3, 3.0, 0.001) - + # This will eventually be scaled by AKs when you - # call reddening(). Produces A_lambda for AKs = 1, which will be + # call reddening(). Produces A_lambda for AKs = 1, which will be # scaled later. Expects wavelength in microns Alambda_scaled = RedLawCardelli._derive_cardelli(wave, Rv) # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Cardelli89', @@ -298,7 +298,7 @@ def __init__(self, Rv): self.high_lim = max(wave) # other info - self.scale_lambda = 2.174 + self.scale_lambda = 2.174 self.name = 'C89,{0}'.format(Rv) @staticmethod @@ -317,7 +317,7 @@ def _derive_cardelli(wavelength, Rv): if (np.max(x) > 8.0): print( 'wavelength is shorter than applicable range for Cardelli law') return None - + # Set up some arrays for coefficients that we will need a = np.zeros(len(x), dtype=float) b = np.zeros(len(x), dtype=float) @@ -366,14 +366,14 @@ def _derive_cardelli(wavelength, Rv): k_ind = np.where(abs(x-0.46) == min(abs(x-0.46))) Aks_Av = a[k_ind] + b[k_ind]/Rv # Aks / Av Av_Aks = 1.0 / Aks_Av # Av / Aks - + output = extinction * Av_Aks # (A(lamb) / Av) * (Av / Aks) = (A(lamb) / Aks) return output def Cardelli89(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -393,7 +393,7 @@ def Cardelli89(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -409,12 +409,12 @@ def Cardelli89(self, wavelength, AKs): A_at_wave = np.array(A_AKs_at_wave) * AKs return A_at_wave - + class RedLawRomanZuniga07(pysynphot.reddening.CustomRedLaw): """ Defines extinction law from `Roman-Zuniga et al. 2007 `_ - for the dense cloud core Barnard 59. The law is a cubic spline fit + for the dense cloud core Barnard 59. The law is a cubic spline fit to the values of A_lambda / A_Ks derived using the color-color diagrams slopes in their Table 1. It is defined between 1.0 - 8.0 microns. @@ -424,7 +424,7 @@ class RedLawRomanZuniga07(pysynphot.reddening.CustomRedLaw): def __init__(self): # Fetch the extinction curve, pre-interpolate across 1-8 microns wave = np.arange(1.0, 8.0, 0.01) - + # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawRomanZuniga07._derive_romanzuniga07(wave) @@ -432,7 +432,7 @@ def __init__(self): # Convert wavelength to angstrom wave *= 10**4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='RomanZuniga07', @@ -452,7 +452,7 @@ def _derive_romanzuniga07(wavelength): wave = np.array([1.240, 1.664, 2.164, 3.545, 4.442, 5.675, 7.760]) A_AKs = np.array([2.299, 1.550, 1.000, 0.618, 0.525, 0.462, 0.455]) A_AKs_err = np.array([0.530, 0.080, 0.000, 0.077, 0.063, 0.055, 0.059]) - + # Interpolate over the curve spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0) A_AKs_at_wave = interpolate.splev(wavelength, spline_interp) @@ -460,8 +460,8 @@ def _derive_romanzuniga07(wavelength): return A_AKs_at_wave def RomanZuniga07(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -481,7 +481,7 @@ def RomanZuniga07(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -538,7 +538,7 @@ class RedLawRiekeLebofsky(pysynphot.reddening.CustomRedLaw): def __init__(self): # Define the wavelength range of the extinction law wave = np.arange(1.0, 5.0, 0.001) - + # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawRiekeLebofsky._derive_RiekeLebofsky(wave) @@ -546,7 +546,7 @@ def __init__(self): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='RiekeLebofsky', @@ -569,17 +569,17 @@ def _derive_RiekeLebofsky(wavelength): Data pulled from Rieke+Lebofsky 1985, Table 3. Note that their Table contains values from 0.3 - 13 microns, but only 1 - 5 microns - is measured directly. + is measured directly. Wavelengths for filters I - M are from Rieke+89, table 4. """ # Arrays with the values from the paper - filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M', - '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]', + filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M', + '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]', '[11.0]', '[11.5]', '[12.0]', '[12.5]', '[13.0]'] - wave = np.array([0.365, 0.445, 0.551, 0.658, 0.9, 1.25, 1.60, 2.2, + wave = np.array([0.365, 0.445, 0.551, 0.658, 0.9, 1.25, 1.60, 2.2, 3.50, 4.8, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, - 11.5, 12.0, 12.5, 13.0]) + 11.5, 12.0, 12.5, 13.0]) A_Av = np.array([1.531, 1.324, 1.00, 0.748, 0.482, 0.282, 0.175, 0.112, 0.058, 0.023, 0.02, 0.043, 0.074, 0.087, 0.083, 0.074, 0.060, 0.047, 0.037, 0.030, 0.027]) @@ -597,7 +597,7 @@ def _derive_RiekeLebofsky(wavelength): wave_interp = wave[idx1:idx2+1] A_Ak_interp = A_Ak[idx1:idx2+1] assert len(wave_interp) == 6 - + # Interpolate over the curve over desired wavelength range spline_interp = interpolate.splrep(wave_interp, A_Ak_interp, k=3, s=0) A_Ak_at_wave = interpolate.splev(wavelength, spline_interp) @@ -605,8 +605,8 @@ def _derive_RiekeLebofsky(wavelength): return A_Ak_at_wave def RiekeLebofsky85(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -626,7 +626,7 @@ def RiekeLebofsky85(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -657,12 +657,12 @@ def plot_RiekeLebofsky85(self): # Get the observed values from their Table 3. Note only JHKLM is # measured directly by RL85, other values come from elsewhere. # Wavelengths are from Rieke+89, Table 4 - filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M', - '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]', + filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M', + '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]', '[11.0]', '[11.5]', '[12.0]', '[12.5]', '[13.0]'] - wave_obs = np.array([0.365, 0.445, 0.551, 0.658, 0.9, 1.25, 1.60, 2.2, + wave_obs = np.array([0.365, 0.445, 0.551, 0.658, 0.9, 1.25, 1.60, 2.2, 3.50, 4.8, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, - 11.5, 12.0, 12.5, 13.0]) + 11.5, 12.0, 12.5, 13.0]) A_Av = np.array([1.531, 1.324, 1.00, 0.748, 0.482, 0.282, 0.175, 0.112, 0.058, 0.023, 0.02, 0.043, 0.074, 0.087, 0.083, 0.074, 0.060, 0.047, 0.037, 0.030, 0.027]) @@ -708,7 +708,7 @@ class RedLawDamineli16(pysynphot.reddening.CustomRedLaw): def __init__(self): # Fetch the extinction curve, pre-interpolate across 0.4-4.8 microns wave = np.arange(0.4, 4.8, 0.001) - + # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawDamineli16._derive_Damineli16(wave) @@ -716,7 +716,7 @@ def __init__(self): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Damineli16', @@ -730,7 +730,7 @@ def __init__(self): self.name = 'D16' return - + @staticmethod def _derive_Damineli16(wavelength): """ @@ -748,13 +748,13 @@ def _derive_Damineli16(wavelength): log_A_AKs = -0.015 + 2.33*x + 0.522*x**2. - 3.001*x**3. + 2.034*x**4. # Now to convert this back to linear space - A_AKs_at_wave = 10**log_A_AKs + A_AKs_at_wave = 10**log_A_AKs return A_AKs_at_wave def Damineli16(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -774,7 +774,7 @@ def Damineli16(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -821,7 +821,7 @@ def plot_Damineli16(self): py.legend() py.savefig('damineli16_el.png') return - + class RedLawDeMarchi16(pysynphot.reddening.CustomRedLaw): """ Defines extinction law from `De Marchi et al. 2016 @@ -831,7 +831,7 @@ class RedLawDeMarchi16(pysynphot.reddening.CustomRedLaw): def __init__(self): # Fetch the extinction curve, pre-interpolate across 1-8 microns wave = np.arange(0.3, 8.0, 0.001) - + # This will eventually be scaled by AK when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawDeMarchi16._derive_DeMarchi16(wave) @@ -839,7 +839,7 @@ def __init__(self): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='DeMarchi16', @@ -891,8 +891,8 @@ def _derive_DeMarchi16(wavelength): return A_AK_at_wave def DeMarchi16(self, wavelength, AK): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -912,7 +912,7 @@ def DeMarchi16(self, wavelength, AK): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -928,32 +928,32 @@ def DeMarchi16(self, wavelength, AK): A_at_wave = np.array(A_AKs_at_wave) * AK return A_at_wave - + class RedLawFitzpatrick09(pysynphot.reddening.CustomRedLaw): """ - Defines the extinction law from + Defines the extinction law from `Fitzpatrick et al. 2009 `_. The law is defined between 0.5 -- 3 microns. The extinction law is as defined in their equation 5, and has two free parameters: :math:`\alpha` and R(V). Averaged over 14 sight-lines, - the authors generally find either :math:`alpha` ~ 2.5, R(V) ~ 3, or - :math:`alpha` ~ 1.8, R(V) ~ 5 (their Figure 6). + the authors generally find either :math:`alpha` ~ 2.5, R(V) ~ 3, or + :math:`alpha` ~ 1.8, R(V) ~ 5 (their Figure 6). A_lambda / A_K = 1 at lambda = 2.18 Parameters ---------- alpha : float - alpha parameter for extinction law. + alpha parameter for extinction law. RV : float - R(V) parameter for extinction law. + R(V) parameter for extinction law. """ def __init__(self, alpha, RV): # Fetch the extinction curve, pre-interpolate across 1-8 microns wave = np.arange(0.5, 3.0, 0.001) - + # This will eventually be scaled by AK when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawFitzpatrick09._derive_Fitzpatrick09(wave, alpha, RV) @@ -961,7 +961,7 @@ def __init__(self, alpha, RV): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Fitzpatrick09', @@ -995,14 +995,14 @@ def _derive_Fitzpatrick09(wavelength, alpha, RV): """ alpha = float(alpha) RV = float(RV) - + # First we'll calculate k(lambda - V) = E(lambda - V) / E(B - V), # directly from equation 5 k = (0.349 + 2.087*RV) * (1.0 / (1.0 + (wavelength / 0.507)**alpha)) - RV # We'll calculate Alam/Av from K + Rv - Alam_Av = (k / RV) + 1. - + Alam_Av = (k / RV) + 1. + # Finally, to get A_lambda/Aks we need to divide Alam_Av by AKs_Av. # We'll assume a wavelength of 2.18 for Ks, since it is the wavelength # they report for K-band @@ -1013,8 +1013,8 @@ def _derive_Fitzpatrick09(wavelength, alpha, RV): return A_AKs_at_wave def Fitzpatrick09(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -1034,7 +1034,7 @@ def Fitzpatrick09(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1053,7 +1053,7 @@ def Fitzpatrick09(self, wavelength, AKs): class RedLawSchlafly16(pysynphot.reddening.CustomRedLaw): """ - Defines the extinction law from `Schlafly et al. 2016 + Defines the extinction law from `Schlafly et al. 2016 `_. The law is defined between 0.5 - 4.8 microns. @@ -1069,7 +1069,7 @@ class RedLawSchlafly16(pysynphot.reddening.CustomRedLaw): def __init__(self, AH_AKs, x): # Fetch the extinction curve, pre-interpolate across 0.5-4.8 microns wave = np.arange(0.5, 4.8, 0.001) - + # This will eventually be scaled by AK when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawSchlafly16._derive_Schlafly16(wave, AH_AKs, x) @@ -1077,7 +1077,7 @@ def __init__(self, AH_AKs, x): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Schlafly16', @@ -1091,7 +1091,7 @@ def __init__(self, AH_AKs, x): @staticmethod def _derive_Schlafly16(wavelength, AH_AKs, x): """ - Calculate Schalfly+16 extinction law according to + Calculate Schalfly+16 extinction law according to code provided in appendix of the paper. AH_AKs sets the gray component while x sets the shape of the law in an Rv-like way @@ -1102,20 +1102,20 @@ def _derive_Schlafly16(wavelength, AH_AKs, x): # Evaluate function for desired wavelengths (in angstroms) law = law_func(wavelength*10**4) - + # Now normalize to A_lambda/AKs, rather than A_lambda/A(5420) idx = np.where( abs(wavelength - 2.151) == min(abs(wavelength - 2.151)) ) law_out = law / law[idx] - + return law_out @staticmethod def _Schlafly_appendix(x, rhk): - """ + """ Schlafly+16 extinction law as defined in paper appendix. We've modified - the wrapper slightly so that the user has control of rhk and x. Here is + the wrapper slightly so that the user has control of rhk and x. Here is the comments from that code: - + Returns the extinction curve, A(lambda)/A(5420 A), according to Schlafly+2016, for the parameter "x," which controls the overall shape of the extinction curve in an R(V)-like way. The extinction curve returned @@ -1137,7 +1137,7 @@ def _Schlafly_appendix(x, rhk): lam: anchor wavelengths (angstroms), default to Schlafly+2016 Returns: the extinction curve E, so the extinction alam = A(lam)/A(5420 A) - is given by: + is given by: A = extcurve(x) alam = A(lam) """ @@ -1162,8 +1162,8 @@ def _Schlafly_appendix(x, rhk): return CubicSpline(lam, anchors/cs0(5420.), yp='3d=0') def Schlafly16(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -1183,7 +1183,7 @@ def Schlafly16(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1202,7 +1202,7 @@ def Schlafly16(self, wavelength, AKs): class RedLawIndebetouw05(pysynphot.reddening.CustomRedLaw): """ - Defines the extinction law from `Indebetouw et al. 2005 + Defines the extinction law from `Indebetouw et al. 2005 `_. The law is defined between 1.25 - 8 microns using Equation 4 in their paper. @@ -1217,7 +1217,7 @@ def __init__(self): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Indebetouw05', @@ -1244,8 +1244,8 @@ def _derive_Indebetouw05(wave): return Alambda_AK def Indebetouw05(self, wavelength, AK): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall extinction at AK (2.164 microns) Parameters @@ -1265,7 +1265,7 @@ def Indebetouw05(self, wavelength, AK): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1284,7 +1284,7 @@ def Indebetouw05(self, wavelength, AK): def plot_Indebetouw05(self): """ - Plot Indebetouw+05 extinciton curve versus their + Plot Indebetouw+05 extinciton curve versus their actual measured values (their Table 1). This is similar to their Figure 6. @@ -1296,13 +1296,13 @@ def plot_Indebetouw05(self): # Convert wave to microns for plot wave *= 10**-4 - + # Their average measurements across sight lines # from Table 1 wave_arr = [1.240, 1.664, 2.164, 3.545, 4.442, 5.675, 7.760] law_obs_arr = [2.50, 1.55, 1.0, 0.56, 0.43, 0.43, 0.43] law_obs_err_arr = [0.15, 0.08, 0.0, 0.06, 0.08, 0.10, 0.10] - + # Make plot py.figure(figsize=(10,10)) py.plot(wave, law, 'r-', label='EL Function') @@ -1317,14 +1317,14 @@ def plot_Indebetouw05(self): py.savefig('indebetouw05_el.png') return - + class RedLawPowerLaw(pysynphot.reddening.CustomRedLaw): """ - Extinction object that is a power-law extinction law: + Extinction object that is a power-law extinction law: :math:`A_{\lambda} \propto \lambda^{\alpha}`. - For example, to create an extinction law between - 0.8 and 3 microns where :math:`\alpha = 2.21`, + For example, to create an extinction law between + 0.8 and 3 microns where :math:`\alpha = 2.21`, where :math:`A_{\lambda} / A_{Ks} = 1` at 2.12 microns: >>> from spisea import reddening @@ -1349,7 +1349,7 @@ class RedLawPowerLaw(pysynphot.reddening.CustomRedLaw): def __init__(self, alpha, K_wave, wave_min=0.5, wave_max=5.0): # Fetch the extinction curve, pre-interpolate across wave_min to wave_max wave = np.arange(wave_min, wave_max, 0.001) - + # This will eventually be scaled by AK when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawPowerLaw._derive_powerlaw(wave, alpha, K_wave) @@ -1357,7 +1357,7 @@ def __init__(self, alpha, K_wave, wave_min=0.5, wave_max=5.0): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Power law') @@ -1379,8 +1379,8 @@ def _derive_powerlaw(wavelength, alpha, K_wave): in microns alpha: float - -1.0 * (power law exponent) - + -1.0 * (power law exponent) + K_wave: float Desired K-band wavelength, in microns """ @@ -1394,8 +1394,8 @@ def _derive_powerlaw(wavelength, alpha, K_wave): return A_AKs_at_wave def powerlaw(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -1415,7 +1415,7 @@ def powerlaw(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1434,22 +1434,22 @@ def powerlaw(self, wavelength, AKs): class RedLawBrokenPowerLaw(pysynphot.reddening.CustomRedLaw): """ - Extinction object that is a broken power-law extinction law: + Extinction object that is a broken power-law extinction law: :math:`A_{\lambda} \propto \lambda^{\alpha[n]}` - for: + for: :math: `\lambda_{limits}[n] < \lambda <= \lambda_{limits}[n+1]` - Note: lambda_limits must be continuous in wavelength and K_wave must be - within one of the section defined by the lambda_limits array. + Note: lambda_limits must be continuous in wavelength and K_wave must be + within one of the section defined by the lambda_limits array. Extinction law is only defined over lambda_limits - + Units of lambda_limits array is microns. Parameters ---------- lambda_limits : numpy array - Array of length (N + 1) with lower and upper wavelength limits of + Array of length (N + 1) with lower and upper wavelength limits of the power-law segments. Units are microns. alpha_vals : numpy array @@ -1480,7 +1480,7 @@ def __init__(self, lambda_limits, alpha_vals, K_wave): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Broken Power law') @@ -1502,8 +1502,8 @@ def _derive_broken_powerlaw(wave, lambda_limits, alpha_vals, K_wave): in microns alpha: float - -1.0 * (power law exponent) - + -1.0 * (power law exponent) + K_wave: float Desired K-band wavelength, in microns """ @@ -1529,14 +1529,14 @@ def _derive_broken_powerlaw(wave, lambda_limits, alpha_vals, K_wave): #print('wave_connect = {0}'.format(wave_connect)) #print('alph_num = {0}'.format(alpha_vals[jj])) #print('alpha_den = {0}'.format(alpha_vals[jj+1])) - + coeff *= val - + law[idx] = coeff * (wave[idx]**(-1.0 * alpha)) # Let's make sure we didn't miss updating any parts of the law assert np.sum(np.isnan(law)) == 0 - + # We'll identify K-band as 2.14 microns idx = np.where(abs(wave - K_wave) == min(abs(wave - K_wave))) A_AKs_at_wave = law / law[idx] @@ -1544,8 +1544,8 @@ def _derive_broken_powerlaw(wave, lambda_limits, alpha_vals, K_wave): return A_AKs_at_wave def broken_powerlaw(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -1565,7 +1565,7 @@ def broken_powerlaw(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1584,7 +1584,7 @@ def broken_powerlaw(self, wavelength, AKs): class RedLawFritz11(pysynphot.reddening.CustomRedLaw): """ - Defines extinction law from `Fritz et al. 2011 + Defines extinction law from `Fritz et al. 2011 `_ for the Galactic Center. The law is defined from 1.0 -- 26 microns. @@ -1613,12 +1613,12 @@ def __init__(self, scale_lambda=2.166): ext_scale = ext / ext[idx] # Make custom reddening law - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=ext_scale, name='Fritz11', litref='Fritz+2011') - + # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = min(wave) self.high_lim = max(wave) @@ -1628,13 +1628,13 @@ def __init__(self, scale_lambda=2.166): self.name = 'F11' return - + @staticmethod def _read_Fritz11(): """ - Return the interpolated extinction curve from Fritz+11, - as defined in their Table 8. - + Return the interpolated extinction curve from Fritz+11, + as defined in their Table 8. + Output: ------ wave: array @@ -1649,7 +1649,7 @@ def _read_Fritz11(): # Read in file with Table 8 info (published with Fritz+11 paper) inpath = os.path.dirname(os.path.abspath(__file__)) infile = os.path.join(inpath, 'el_files', 'fritz11_EL_table8.fits') - + t = Table.read(infile, format='fits') wave = t['lambda'] ext = t['A'] @@ -1661,7 +1661,7 @@ def _read_Fritz11(): def _read_Fritz11_obs(): """ Return the Fritz+11 observed values, from their Table 2 - + Output: ------- wave: array @@ -1703,7 +1703,7 @@ def plot_Fritz11(self): # extinction at 2.166 microns. Remember that this produces # throughput = 10^-0.4*Alambda ext_scaled = self.reddening(2.62) - + # Make plot py.figure(figsize=(10,10)) py.plot(wave, ext, 'r-', label='Interpolated EL') @@ -1723,8 +1723,8 @@ def plot_Fritz11(self): return def Fritz11(self, wavelength, A_scale_lambda): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and a total extinction at the scale_lambda (the wavelength where the extinction law = 1) @@ -1745,7 +1745,7 @@ def Fritz11(self, wavelength, A_scale_lambda): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/A_scale_lambda from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1768,18 +1768,18 @@ def Fritz11(self, wavelength, A_scale_lambda): #==============================================# #class RedLawHosek18(pysynphot.reddening.CustomRedLaw): # """ -# Defines extinction law from `Hosek et al. 2018 +# Defines extinction law from `Hosek et al. 2018 # `_ -# for the Arches Cluster and Wd1. The law is defined between +# for the Arches Cluster and Wd1. The law is defined between # 0.7 - 3.54 microns. # -# WARNING: DEPRECATED! This law has revised to RedLawHosek18b, which +# WARNING: DEPRECATED! This law has revised to RedLawHosek18b, which # should be used instead # """ # def __init__(self): # # Fetch the extinction curve, pre-interpolate across 3-8 microns # wave = np.arange(0.7, 3.545, 0.001) -# +# # # This will eventually be scaled by AKs when you # # call reddening(). Right now, calc for AKs=1 # Alambda_scaled = RedLawHosek18._derive_Hosek18(wave) @@ -1787,7 +1787,7 @@ def Fritz11(self, wavelength, A_scale_lambda): # # Convert wavelength to angstrom # wave *= 10 ** 4 # -# pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, +# pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, # waveunits='angstrom', # Avscaled=Alambda_scaled, # name='Hosek+18', @@ -1797,12 +1797,12 @@ def Fritz11(self, wavelength, A_scale_lambda): # self.low_lim = min(wave) # self.high_lim = max(wave) # self.name = 'H18' -# +# # @staticmethod # def _derive_Hosek18(wavelength): -# """ -# Derive the Hosek+18 extinction law, using the data from Table 4. -# +# """ +# Derive the Hosek+18 extinction law, using the data from Table 4. +# # Calculate the resulting extinction for an array of wavelengths. # The extinction is normalized with A_Ks. # @@ -1816,19 +1816,19 @@ def Fritz11(self, wavelength, A_scale_lambda): # # Extinction law definition # wave = np.array([0.8059, 0.962, 1.25, 1.53, 2.14, 3.545]) # A_AKs = np.array([9.66, 6.29, 3.56, 2.33, 1.0, 0.50]) -# +# # # # Following Hosek+18, Interpolate over the curve with cubic spline interpolation # spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0) # A_AKs_at_wave = interpolate.splev(wavelength, spline_interp) # # # This curve already assumes A_Ks = 1.0, so we can go straight to -# # output +# # output # return A_AKs_at_wave # # def Hosek18(self, wavelength, AKs): -# """ -# Return the extinction at a given wavelength assuming the +# """ +# Return the extinction at a given wavelength assuming the # extinction law and an overall `AKs` value. # # Parameters @@ -1848,7 +1848,7 @@ def Fritz11(self, wavelength, A_scale_lambda): # # extinction law # if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): # return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) -# +# # # Extract wave and A/AKs from law, turning wave into micron units # wave = self.wave * (10**-4) # law = self.obscuration @@ -1868,7 +1868,7 @@ def Fritz11(self, wavelength, A_scale_lambda): class RedLawHosek18b(pysynphot.reddening.CustomRedLaw): """ - Defines extinction law from `Hosek et al. 2019 + Defines extinction law from `Hosek et al. 2019 `_ for the Arches cluster and Wd1. The law is derived between 0.7 - 3.54 microns @@ -1876,7 +1876,7 @@ class RedLawHosek18b(pysynphot.reddening.CustomRedLaw): def __init__(self): # Fetch the extinction curve, pre-interpolate across 3-8 microns wave = np.arange(0.7, 3.545, 0.001) - + # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawHosek18b._derive_Hosek18b(wave) @@ -1884,7 +1884,7 @@ def __init__(self): # Convert wavelength to angstrom wave *= 10 ** 4 - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, + pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, waveunits='angstrom', Avscaled=Alambda_scaled, name='Hosek+18b', @@ -1894,12 +1894,12 @@ def __init__(self): self.low_lim = min(wave) self.high_lim = max(wave) self.name = 'H18b' - + @staticmethod def _derive_Hosek18b(wavelength): - """ - Derive the Hosek+18 extinction law, using the data from Table 4. - + """ + Derive the Hosek+18 extinction law, using the data from Table 4. + Calculate the resulting extinction for an array of wavelengths. The extinction is normalized with A_Ks. @@ -1913,18 +1913,18 @@ def _derive_Hosek18b(wavelength): # Extinction law definition wave = np.array([0.8059, 0.962, 1.25, 1.53, 2.14, 3.545]) A_AKs = np.array([7.943, 5.715, 3.142, 2.04, 1.0, 0.50]) - + # Following Hosek+18, Interpolate over the curve with cubic spline interpolation spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0) A_AKs_at_wave = interpolate.splev(wavelength, spline_interp) # This curve already assumes A_Ks = 1.0, so we can go straight to - # output + # output return A_AKs_at_wave def Hosek18b(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -1944,7 +1944,7 @@ def Hosek18b(self, wavelength, AKs): # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) - + # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) law = self.obscuration @@ -1967,15 +1967,15 @@ class RedLawSchoedel10(RedLawBrokenPowerLaw): `_ for the Galactic Center. It is defined between 1.5 - 3.8 microns. - Power law indices: + Power law indices: * 1.677 - 2.168 microns ---> alpha = 2.21 +/- 0.24 * 2.168 - 3.636 microns ---> alpha = 1.34 +/- 0.29 - Wavelengths come from effective wavelengths of observations (some buffer + Wavelengths come from effective wavelengths of observations (some buffer is added to either side of these values). - - Reddening law is scaled such that A_lambda / A_Ks = 1 at + + Reddening law is scaled such that A_lambda / A_Ks = 1 at lambda = 2.168 microns. """ def __init__(self): @@ -1983,7 +1983,7 @@ def __init__(self): alpha_vals = [1.34, 2.21] K_wave = 2.168 RedLawBrokenPowerLaw.__init__(self, lambda_limits, alpha_vals, K_wave) - + # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = np.min(lambda_limits)*10**4 self.high_lim = np.max(lambda_limits)*10**4 @@ -1995,8 +1995,8 @@ def __init__(self): return def Schoedel10(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and a total extinction at scale_lambda (the wavelength where the extinction law = 1) @@ -2016,7 +2016,7 @@ def Schoedel10(self, wavelength, AKs): # Return error if any wavelength is beyond interpolation range of # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): - return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) + return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) @@ -2032,21 +2032,21 @@ def Schoedel10(self, wavelength, AKs): # Now multiply by AKs (since law assumes AKs = 1) A_at_wave = np.array(A_AKs_at_wave) * AKs - return A_at_wave + return A_at_wave + - class RedLawNoguerasLara18(RedLawPowerLaw): """ - Defines extinction law from `Nogueras-Lara et al. 2018 + Defines extinction law from `Nogueras-Lara et al. 2018 `_ for the Galactic Center. It is defined between 1.0 - 3.0 microns. - Measurements were made in JHK, with effective wavelengths + Measurements were made in JHK, with effective wavelengths of 1.2685, 1.6506, and 2.1629 microns, respectively. - This extinction law is a single power law with exponent + This extinction law is a single power law with exponent of alpha = 2.3 +/- 0.08. - Reddening law is scaled such that A_lambda / A_Ks = 1 at + Reddening law is scaled such that A_lambda / A_Ks = 1 at lambda = 2.163 microns (the observed K-band) """ def __init__(self): @@ -2054,7 +2054,7 @@ def __init__(self): wave_max = 3.0 K_wave = 2.163 RedLawPowerLaw.__init__(self, 2.30, K_wave, wave_min=wave_min, wave_max=wave_max) - + # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = wave_min*10**4 self.high_lim = wave_max*10**4 @@ -2063,8 +2063,8 @@ def __init__(self): self.name = 'NL18' def NoguerasLara18(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and an overall `AKs` value. Parameters @@ -2083,7 +2083,7 @@ def NoguerasLara18(self, wavelength, AKs): # Return error if any wavelength is beyond interpolation range of # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): - return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) + return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) @@ -2106,18 +2106,18 @@ class RedLawNoguerasLara20(RedLawBrokenPowerLaw): Defines extinction law from `Nogueras-Lara et al. 2020 `_ for the Galactic Center. It is defined between 1.0 -- 3 microns. - Measurements were made in JHK, with effective wavelengths + Measurements were made in JHK, with effective wavelengths of 1.2685, 1.6506, and 2.1629 microns, respectively - Measured power law indices: - + Measured power law indices: + * 1.2685 - 1.6505 microns ---> alpha = 2.44 +/- 0.05 * 1.6505 - 2.1629 microns ---> alpha = 2.23 +/- 0.05 - Wavelengths come from effective wavelengths of observations (some buffer + Wavelengths come from effective wavelengths of observations (some buffer is added to either side of these values). - - Reddening law is scaled such that A_lambda / A_Ks = 1 at + + Reddening law is scaled such that A_lambda / A_Ks = 1 at lambda = 2.163 microns (the observed K-band) """ def __init__(self): @@ -2125,7 +2125,7 @@ def __init__(self): alpha_vals = [2.44, 2.23] K_wave = 2.163 RedLawBrokenPowerLaw.__init__(self, lambda_limits, alpha_vals, K_wave) - + # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = np.min(lambda_limits)*10**4 self.high_lim = np.max(lambda_limits)*10**4 @@ -2137,8 +2137,8 @@ def __init__(self): return def NoguerasLara20(self, wavelength, AKs): - """ - Return the extinction at a given wavelength assuming the + """ + Return the extinction at a given wavelength assuming the extinction law and a total extinction at scale_lambda (the wavelength where the extinction law = 1) @@ -2158,7 +2158,7 @@ def NoguerasLara20(self, wavelength, AKs): # Return error if any wavelength is beyond interpolation range of # extinction law if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))): - return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) + return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) # Extract wave and A/AKs from law, turning wave into micron units wave = self.wave * (10**-4) @@ -2174,7 +2174,7 @@ def NoguerasLara20(self, wavelength, AKs): # Now multiply by AKs (since law assumes AKs = 1) A_at_wave = np.array(A_AKs_at_wave) * AKs - return A_at_wave + return A_at_wave #---------------------------# # Cubic spline function from Schalfly+16 appendix diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 932198bf..69b61d2e 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -139,8 +139,8 @@ class ResolvedCluster(Cluster): no compact remnants are produced. keep_low_mass_stars: boolean (default False) - If True, the cluster will not cut out stars below the isochrone grid - on initial mass. They are assigned a current mass equal to their initial + If True, the cluster will not cut out stars below the isochrone grid + on initial mass. They are assigned a current mass equal to their initial mass, a phase of 98, and no other evolutionary properties or photometry. If False, stars below the isochrone initial mass limit are cut out. @@ -160,7 +160,7 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, if seed is not None and verbose: print('WARNING: random seed set to %i' % seed) # imf.rng = self.rng - + ##### # Sample the IMF to build up our cluster mass. ##### @@ -573,7 +573,7 @@ def _make_companions_table(self, star_systems, compMass): return star_systems, companions - + def _remove_bad_systems(self, star_systems, compMass, keep_low_mass_stars): """ Helper function to remove stars with masses outside the isochrone @@ -607,7 +607,7 @@ def _remove_bad_systems(self, star_systems, compMass, keep_low_mass_stars): idx = (star_systems_teff_non_nan > 0) | \ (star_systems_phase_non_nan >= 0) | \ (star_systems['mass'] < np.min(self.iso.points['mass'])) - + n_out_of_range = N_systems - sum(idx) if self.verbose and n_out_of_range > 0: print( 'Found {0:d} stars out of mass range'.format(n_out_of_range)) @@ -978,7 +978,7 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, tab.meta['WAVEMAX'] = wave_range[1] self.points = tab - + if self.verbose: t2 = time.time() print( 'Isochrone generation took {0:f} s.'.format(t2-t1)) @@ -1131,13 +1131,13 @@ def __init__(self, logAge, AKs, distance, # For solar metallicity case, allow for legacy isochrones (which didn't have # metallicity tag since they were all solar metallicity) to be read # properly - + # Set save file name metal_value = round(abs(metallicity), 2) metal_sign = 'm' if metallicity < 0 else 'p' self.save_file = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}_{metal_sign}{metal_value:.2f}.fits' - if metallicity == 0.0: + if metallicity == 0.0: self.save_file_legacy = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}.fits' else: self.save_file_legacy = self.save_file @@ -1216,9 +1216,9 @@ def __init__(self, logAge, AKs, distance, # Convert into flux observed at Earth (unreddened) star *= (R / self.points.meta["DISTANCE"])**2 # in erg s^-1 cm^-2 A^-1 # Redden the spectrum. This doesn't take much time at all. - red = red_law.reddening(AKs).resample(star.wave) + red = red_law.reddening(AKs).resample(star.wave) star *= red - # Save the final spectrum to our spec_list for later use. + # Save the final spectrum to our spec_list for later use. self.spec_list.append(star) self.make_photometry(rebin=rebin, vega=vega, comp_filters=comp_filters) @@ -1226,7 +1226,7 @@ def __init__(self, logAge, AKs, distance, return def make_photometry(self, rebin=True, vega=vega, comp_filters=None): - """ + """ Make synthetic photometry for the specified filters. This function udpates the self.points table to include new columns with the photometry. @@ -1700,7 +1700,7 @@ def get_filter_info(name, vega=vega, rebin=True): elif name.startswith('euclid'): filt = filters.get_euclid_filt(filterName) - + else: # Otherwise, look for the filter info in the cdbs/mtab and cdbs/comp files try: diff --git a/spisea/tests/test_exceptions.py b/spisea/tests/test_exceptions.py index 4fe73e5f..b20a132d 100644 --- a/spisea/tests/test_exceptions.py +++ b/spisea/tests/test_exceptions.py @@ -32,6 +32,6 @@ def test_grid_number_exception(): # Case 3: installed model grid is higher than required grid (no error) required_grid = installed_grid - 1.0 - evolution.check_evo_grid_number(required_grid, models_dir) + evolution.check_evo_grid_number(required_grid, models_dir) return \ No newline at end of file diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 2fd66f22..1225d682 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -5,17 +5,17 @@ def test_evo_model_grid_num(): """ - Make sure evolution models have both evo_grid_num + Make sure evolution models have both evo_grid_num and evo_grid_min (e.g., make sure these functions are working). Try it on one evolution model here; we'll test on all evo models in another function. """ from spisea import evolution - + # Make MIST evolution model, check evo grid variables evo = evolution.MISTv1() assert isinstance(evo.evo_grid_min, float) - + return def test_evolution_models(): @@ -32,7 +32,7 @@ def test_evolution_models(): metal_solar = [0] # Array of evolution models to test - evo_models = [evolution.MISTv1(version=1.2), evolution.MergedBaraffePisaEkstromParsec(), + evo_models = [evolution.MISTv1(version=1.2), evolution.MergedBaraffePisaEkstromParsec(), evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa()] @@ -68,12 +68,12 @@ def test_evolution_models(): raise Exception('EVO TEST FAILED: {0}, age = {1}, metal = {2}'.format(evo, kk, jj)) print('Done {0}'.format(evo)) - + return def test_synthpop_MIST_extension(): """ - Testing the synthpop MIST extension to consistently lower masses + Testing the synthpop MIST extension to consistently lower masses """ evo1_grid = evolution.MISTv1(version=1.2, synthpop_extension=False) evo2_grid = evolution.MISTv1(version=1.2, synthpop_extension=True) @@ -116,9 +116,9 @@ def test_atmosphere_models(): test = atm_func(metallicity=jj) except: raise Exception('ATM TEST FAILED: {0}, metal = {1}'.format(atm_func, jj)) - + print('Done {0}'.format(atm_func)) - + # Test get_merged_atmospheres at different temps temp_range = [2000, 3500, 4000, 5250, 6000, 12000] atm_func = atm.get_merged_atmosphere @@ -131,7 +131,7 @@ def test_atmosphere_models(): print('get_merged_atmosphere: all temps/metallicities passed') - + # Test get_bb_atmosphere at different temps # This func only requests temp temp_range = [2000, 3500, 4000, 5250, 6000, 12000] @@ -141,9 +141,9 @@ def test_atmosphere_models(): test = atm_func(temperature=jj, verbose=True) except: raise Exception('ATM TEST FAILED: {0}, temp = {2}'.format(atm_func, jj)) - + print('get_bb_atmosphere: all temps passed') - + return def test_filters(): @@ -154,7 +154,7 @@ def test_filters(): # Define vega spectrum vega = synthetic.Vega() - + # Filter list to test filt_list = ['wfc3,ir,f127m','acs,wfc1,f814w', '2mass,J', '2mass,H','2mass,Ks', @@ -193,7 +193,7 @@ def test_filters(): filt = synthetic.get_filter_info(ii, rebin=True, vega=vega) print('get_filter_info pass') - + # Loop through filters to test that they work: get_obs_str for ii in filt_list: # Test going from col_name to obs_str diff --git a/spisea/tests/test_reddening.py b/spisea/tests/test_reddening.py index 2df86a9a..99bbf78b 100644 --- a/spisea/tests/test_reddening.py +++ b/spisea/tests/test_reddening.py @@ -39,7 +39,7 @@ def test_RedLawBrokenPowerLaw(plots=False): # Compare law_test and the output from the redlaw object law_output = red_law.broken_powerlaw(wave_test, 1) - + assert len(law_test) == len(law_output) assert np.sum(np.isnan(law_output)) == 0 @@ -62,7 +62,7 @@ def test_RedLawBrokenPowerLaw(plots=False): idx2 = np.where(abs(wave_test-2.1) == np.min(abs(wave_test-2.1))) slope = (log_output[idx1] - log_output[idx2]) / (log_wave[idx1] - log_wave[idx2]) assert abs(slope - (-1.0 * alpha1)) < 10**-4 - + # If desired (debug only), make plot to see what law looks like if plots: # Test plot: these should match nearly exactly @@ -109,7 +109,7 @@ def test_RedLawBrokenPowerLaw(plots=False): idx = np.where( (wave_test >= 1.27) & (wave_test < 1.63)) coeff = (1.63 ** (-1*alpha1)) / (1.63 ** (-1*alpha2)) law_test[idx] = coeff * wave_test[idx] ** (-1*alpha2) - + # 1.27 - 0.8 idx = np.where( (wave_test >= 0.8) & (wave_test < 1.27)) coeff1 = (1.63 ** (-1*alpha1)) / (1.63 ** (-1*alpha2)) @@ -124,7 +124,7 @@ def test_RedLawBrokenPowerLaw(plots=False): coeff3 = (0.8 ** (-1*alpha3)) / (0.8 ** (-1*alpha4)) coeff_f = coeff1 * coeff2 * coeff3 law_test[idx] = coeff_f * wave_test[idx] ** (-1*alpha4) - + assert np.sum(np.isnan(law_test)) == 0 # Put in terms of A_lambda / A_Ks, like the reddening object @@ -133,7 +133,7 @@ def test_RedLawBrokenPowerLaw(plots=False): # Compare law_test and the output from the redlaw object law_output = red_law.broken_powerlaw(wave_test, 1) - + assert len(law_test) == len(law_output) assert np.sum(np.isnan(law_output)) == 0 @@ -166,7 +166,7 @@ def test_RedLawBrokenPowerLaw(plots=False): idx2 = np.where(abs(wave_test-0.7) == np.min(abs(wave_test-0.7))) slope = (log_output[idx1] - log_output[idx2]) / (log_wave[idx1] - log_wave[idx2]) assert abs(slope - (-1.0 * alpha4)) < 10**-4 - + # If desired (debug only), make plot to see what law looks like if plots: # Test plot: these should match nearly exactly @@ -195,9 +195,9 @@ def test_red_law_IsochronePhot(): metallicity = 0 # Metallicity in [M/H] # Define evolution/atmosphere models and extinction law - evo_model = evolution.MISTv1() + evo_model = evolution.MISTv1() atm_func = atmospheres.get_merged_atmosphere - + # Also specify filters for synthetic photometry. filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f153m', 'nirc2,H', 'nirc2,Kp'] @@ -206,7 +206,7 @@ def test_red_law_IsochronePhot(): 'RL85', 'D16', 'F09,2.5,3', 'S16,1.55,0', 'DM16', 'H18b', 'NL18', 'C89,3.1', 'pl,2.12,0.9,2.4', 'broken_pl,[2.3,1.63,0.9],[2.23, 3.0],2.12'] - + aks_arr = [2.62, 2.46, 1.67, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3] for ii in range(len(redlaw_arr)): redlaw = reddening.get_red_law(redlaw_arr[ii]) @@ -215,15 +215,15 @@ def test_red_law_IsochronePhot(): # Try to run isochrone phot iso_test = synthetic.IsochronePhot( - logAge, - aks, - dist, + logAge, + aks, + dist, metallicity=0, - evo_model=evo_model, + evo_model=evo_model, atm_func=atm_func, - red_law=redlaw, + red_law=redlaw, filters=filt_list, - min_mass=0.95, + min_mass=0.95, max_mass=1.05, iso_dir='isochrones/', recomp=True diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 349667c9..80284505 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -30,11 +30,11 @@ def test_isochrone(plot=False): assert iso.points.meta['AKS'] == AKs assert iso.points.meta['DISTANCE'] == distance assert len(iso.points) > 100 - + if plot: - plt.figure(1) + plt.figure(1) iso.plot_HR_diagram() - + plt.figure(2) iso.plot_mass_luminosity() @@ -42,7 +42,7 @@ def test_isochrone(plot=False): def test_iso_wave(): """ - Test to make sure isochrones generated have spectra with the proper + Test to make sure isochrones generated have spectra with the proper wavelength range, and that the user has control over that wavelength range (propagated through IsochronePhot) """ @@ -53,11 +53,11 @@ def test_iso_wave(): iso_dir = f'{spisea_path}/tests/isochrones' # Define evolution/atmosphere models and extinction law (optional) - evo_model = evolution.MergedBaraffePisaEkstromParsec() + evo_model = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawHosek18b() - # Also specify filters for synthetic photometry (optional). Here we use + # Also specify filters for synthetic photometry (optional). Here we use # the HST WFC3-IR F127M, F139M, and F153M filters filt_list = ['wfc3,ir,f127m'] @@ -72,13 +72,13 @@ def test_iso_wave(): wave_range1 = [3000, 52000] my_iso = syn.IsochronePhot( logAge, AKs, dist, - evo_model=evo_model, + evo_model=evo_model, atm_func=atm_func, - red_law=red_law, + red_law=red_law, filters=filt_list, - mass_sampling=10, + mass_sampling=10, wave_range=wave_range1, - recomp=True, + recomp=True, iso_dir=iso_dir ) @@ -91,14 +91,14 @@ def test_iso_wave(): # properly? wave_range2 = [1200, 90000] my_iso = syn.IsochronePhot( - logAge, - AKs, + logAge, + AKs, dist, - evo_model=evo_model, + evo_model=evo_model, atm_func=atm_func, - red_law=red_law, + red_law=red_law, filters=filt_list, - mass_sampling=10, + mass_sampling=10, wave_range=wave_range2, recomp=True, iso_dir=iso_dir @@ -113,8 +113,8 @@ def test_iso_wave(): wave_range3 = [1200, 1000000] try: my_iso = syn.IsochronePhot( - logAge, - AKs, + logAge, + AKs, dist, evo_model=evo_model, atm_func=atm_func, @@ -122,11 +122,11 @@ def test_iso_wave(): filters=filt_list, mass_sampling=10, wave_range=wave_range3, - recomp=True, + recomp=True, iso_dir=iso_dir ) print('WAVE TEST FAILED!!! Should have crashed here, wavelength range out of bounds') - raise ValueError() + raise ValueError() except: print('Wavelength out of bound condition passed. Test is good') pass @@ -146,9 +146,9 @@ def test_IsochronePhot(plot=False): startTime = time.time() iso = syn.IsochronePhot( - logAge, + logAge, AKs, - distance, + distance, evo_model=evo_model, atm_func=atm_func, red_law=redlaw, @@ -169,9 +169,9 @@ def test_IsochronePhot(plot=False): assert 'm_nirc2_J' in iso.points.colnames if plot: - plt.figure(1) + plt.figure(1) iso.plot_CMD('mag814w', 'mag160w') - + plt.figure(2) iso.plot_mass_magnitude('mag160w') @@ -195,16 +195,16 @@ def test_IsochronePhot(plot=False): ) assert iso_new.recalc == False - + # Check 2: Confirm that adding a new column to an existing isochrone works properly. # Does the new filter get added to the isochrone? And the old ones still there? # Does the computed data for the new filter match the same result if you fully regenerate the isochrone? iso_new_addfilt = syn.IsochronePhot( logAge, AKs, - distance, + distance, evo_model=evo_model, - atm_func=atm_func, + atm_func=atm_func, red_law=redlaw, filters=filt_list+['2mass,Ks'], mass_sampling=mass_sampling, @@ -214,11 +214,11 @@ def test_IsochronePhot(plot=False): assert iso_new_addfilt.recalc == False assert 'm_2mass_Ks' in iso_new_addfilt.points.colnames assert 'm_nirc2_J' in iso_new_addfilt.points.colnames - + iso_new_3filt = syn.IsochronePhot( - logAge, + logAge, AKs, - distance, + distance, evo_model=evo_model, atm_func=atm_func, red_law=redlaw, @@ -294,12 +294,12 @@ def test_ResolvedCluster(): filt_list = ['nirc2,J', 'nirc2,Kp'] startTime = time.time() - + evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawNishiyama09() - + iso = syn.IsochronePhot( logAge, AKs, @@ -324,7 +324,7 @@ def test_ResolvedCluster(): my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=None) print('Constructed IMF: %d seconds' % (time.time() - startTime)) - + cluster1 = syn.ResolvedCluster(iso, my_imf1, cluster_mass) clust1 = cluster1.star_systems print('Constructed cluster: %d seconds' % (time.time() - startTime)) @@ -352,7 +352,7 @@ def test_ResolvedCluster(): my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) - + cluster2 = syn.ResolvedCluster(iso, my_imf2, cluster_mass) clust2 = cluster2.star_systems print('Constructed cluster with multiples: %d seconds' % (time.time() - startTime)) @@ -362,7 +362,7 @@ def test_ResolvedCluster(): assert np.sum(clust2['N_companions']) == len(cluster2.companions) ########## - # Plots + # Plots ########## # Plot an IR CMD and compare cluster members to isochrone. plt.figure(1) @@ -382,7 +382,7 @@ def test_ResolvedCluster(): plt.gca().invert_yaxis() plt.xlabel('Mass (Msun)') plt.ylabel('J (mag)') - + # # Plot the spectrum of the most massive star # idx = cluster.mass.argmax() # plt.clf() @@ -406,14 +406,14 @@ def test_ResolvedClusterDiffRedden(): # Test filters filt_list = ['nirc2,J', 'nirc2,Kp'] - + startTime = time.time() - + evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawNishiyama09() - + iso = syn.IsochronePhot( logAge, AKs, @@ -437,13 +437,13 @@ def test_ResolvedClusterDiffRedden(): my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=None) print('Constructed IMF: %d seconds' % (time.time() - startTime)) - + cluster1 = syn.ResolvedClusterDiffRedden(iso, my_imf1, cluster_mass, deltaAKs) clust1 = cluster1.star_systems print('Constructed cluster: %d seconds' % (time.time() - startTime)) assert len(clust1) > 0 - + plt.figure(3) plt.clf() plt.plot(clust1['m_nirc2_J'] - clust1['m_nirc2_Kp'], clust1['m_nirc2_J'], 'r.') @@ -460,7 +460,7 @@ def test_ResolvedClusterDiffRedden(): my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) - + cluster2 = syn.ResolvedClusterDiffRedden(iso, my_imf2, cluster_mass, deltaAKs) clust2 = cluster2.star_systems print('Constructed cluster with multiples: %d seconds' % (time.time() - startTime)) @@ -470,7 +470,7 @@ def test_ResolvedClusterDiffRedden(): assert np.sum(clust2['N_companions']) == len(cluster2.companions) ########## - # Plots + # Plots ########## # Plot an IR CMD and compare cluster members to isochrone. plt.figure(1) @@ -492,7 +492,7 @@ def test_ResolvedClusterDiffRedden(): plt.ylabel('J (mag)') return - + def test_UnresolvedCluster(): log_age = 6.7 AKs = 0.0 @@ -500,7 +500,7 @@ def test_UnresolvedCluster(): metallicity=0 cluster_mass = 10**4. - startTime = time.time() + startTime = time.time() multi = multiplicity.MultiplicityUnresolved() imf_in = imf.Kroupa_2001(multiplicity=multi) evo = evolution.MergedBaraffePisaEkstromParsec() @@ -533,13 +533,13 @@ def test_ifmr_multiplicity(): filt_list = ['nirc2,Kp', 'nirc2,H', 'nirc2,J'] startTime = time.time() - + evo = evolution.MISTv1() atm_func = atmospheres.get_merged_atmosphere ifmr_obj = ifmr.IFMR_Raithel18() red_law = reddening.RedLawNishiyama09() - + iso = syn.IsochronePhot( logAge, AKs, @@ -563,13 +563,13 @@ def test_ifmr_multiplicity(): ########## my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=None) - print('Constructed IMF: %d seconds' % (time.time() - startTime)) - + print('Constructed IMF: %d seconds' % (time.time() - startTime)) + cluster1 = syn.ResolvedCluster(iso, my_imf1, cluster_mass, ifmr=ifmr_obj) clust1 = cluster1.star_systems print('Constructed cluster: %d seconds' % (time.time() - startTime)) - + ########## # Test with multiplicity and IFMR ########## @@ -577,7 +577,7 @@ def test_ifmr_multiplicity(): my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) - + cluster2 = syn.ResolvedCluster(iso, my_imf2, cluster_mass, ifmr=ifmr_obj) clust2 = cluster2.star_systems comps2 = cluster2.companions @@ -613,16 +613,16 @@ def test_metallicity(): Test isochrone generation at different metallicities """ # Define isochrone parameters - logAge = np.log10(5*10**6.) - AKs = 0.8 - dist = 4000 + logAge = np.log10(5*10**6.) + AKs = 0.8 + dist = 4000 evo_model = evolution.MISTv1() atm_func = atmospheres.get_phoenixv16_atmosphere red_law = reddening.RedLawHosek18b() filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f139m', 'wfc3,ir,f153m'] iso_dir = f'{spisea_path}/tests/isochrones' - # Start with a solar metallicity isochrone + # Start with a solar metallicity isochrone metallicity= 0. metal_sign = 'm' if metallicity < 0 else 'p' @@ -639,7 +639,7 @@ def test_metallicity(): mass_sampling=10, iso_dir=iso_dir ) - + # Test isochrone properties assert my_iso.points.meta['METAL_IN'] == 0.0 assert os.path.exists(f'{iso_dir}/iso_6.70_0.80_04000_p0.00.fits') @@ -695,12 +695,12 @@ def test_cluster_mass(): filt_list = ['nirc2,J', 'nirc2,Kp'] startTime = time.time() - + # Define evolution/atmosphere models and extinction law - evo = evolution.MISTv1() + evo = evolution.MISTv1() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawHosek18b() - + iso = syn.IsochronePhot( logAge, AKs, @@ -721,7 +721,7 @@ def test_cluster_mass(): # IFMR my_ifmr = ifmr.IFMR_Raithel18() - + ########## # Start without multiplicity @@ -729,7 +729,7 @@ def test_cluster_mass(): my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=None) print('Constructed IMF: %d seconds' % (time.time() - startTime)) - + cluster1 = syn.ResolvedCluster(iso, my_imf1, cluster_mass, ifmr=my_ifmr) clust1 = cluster1.star_systems print('Constructed cluster: %d seconds' % (time.time() - startTime)) @@ -755,7 +755,7 @@ def test_cluster_mass(): my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) - + cluster2 = syn.ResolvedCluster(iso, my_imf2, cluster_mass, ifmr=my_ifmr) clust2 = cluster2.star_systems print('Constructed cluster with multiples: %d seconds' % (time.time() - startTime)) @@ -769,7 +769,7 @@ def test_cluster_mass(): def test_keep_low_mass_stars(): """ - Test "keep_low_mass_stars = True" functionality introduced in v2.2 + Test "keep_low_mass_stars = True" functionality introduced in v2.2 """ # Define cluster parameters, pulling on an isochrone generated in an earlier test (since # we don't care about isochrone generation here @@ -782,12 +782,12 @@ def test_keep_low_mass_stars(): # Test filters filt_list = ['nirc2,J', 'nirc2,Kp'] - + # Define evolution/atmosphere models and extinction law - evo = evolution.MISTv1() + evo = evolution.MISTv1() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawHosek18b() - + iso = syn.IsochronePhot( logAge, AKs, @@ -805,7 +805,7 @@ def test_keep_low_mass_stars(): # Make sure this min mass is low enough for a reasonalbe test min_mass_iso = np.min(iso.points['mass']) assert min_mass_iso >= 0.05 - + # Define IMF + IFMR. Make sure IMF goes to really low masses, # below the 0.08 Msun limit of the MIST isochrones imf_min = 0.01 @@ -833,9 +833,9 @@ def test_keep_low_mass_stars(): return - + def test_compact_object_companions(): - + # Define cluster parameters logAge = 6.7 AKs = 2.4 @@ -848,12 +848,12 @@ def test_compact_object_companions(): filt_list = ['nirc2,J', 'nirc2,Kp'] startTime = time.time() - + evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawNishiyama09() - + iso = syn.IsochronePhot( logAge, AKs, @@ -867,7 +867,7 @@ def test_compact_object_companions(): ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) - + clust_multiplicity = multiplicity.MultiplicityResolvedDK() massLimits = np.array([0.2, 0.5, 1, 120]) # mass segments @@ -894,12 +894,12 @@ def time_test_cluster(): iso_dir = f'{spisea_path}/tests/isochrones' startTime = time.time() - + evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawNishiyama09() filt_list = ['nirc2,J', 'nirc2,Kp'] - + iso = syn.IsochronePhot( logAge, AKs, @@ -917,12 +917,12 @@ def time_test_cluster(): multi = multiplicity.MultiplicityUnresolved() my_imf = imf.IMF_broken_powerlaw(imf_limits, imf_powers, multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) - + cluster = syn.ResolvedCluster(iso, my_imf, cluster_mass) print('Constructed cluster: %d seconds' % (time.time() - startTime)) return - + def model_young_cluster_object(resolved=False): log_age = 6.5 AKs = 0.1 @@ -934,10 +934,10 @@ def model_young_cluster_object(resolved=False): evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere iso = syn.Isochrone( - log_age, - AKs, - distance, - evo_model=evo, + log_age, + AKs, + distance, + evo_model=evo, atm_func=atm_func, mass_sampling=10 ) @@ -980,13 +980,13 @@ def time_test_mass_match(): start_time = time.time() star_masses, isMulti, compMass, sysMass = imf_in.generate_cluster(cluster_mass) print('Generated cluster masses in {0:.0f} s'.format(time.time() - start_time)) - + def match_model_masses1(isoMasses, starMasses): indices = np.empty(len(starMasses), dtype=int) - + for ii in range(len(starMasses)): theMass = starMasses[ii] - + dm = np.abs(isoMasses - theMass) mdx = dm.argmin() @@ -997,12 +997,12 @@ def match_model_masses1(isoMasses, starMasses): indices[ii] = mdx return indices - + def match_model_masses2(isoMasses, starMasses): isoMasses_tmp = isoMasses.reshape((len(isoMasses), 1)) kdt = KDTree(isoMasses_tmp) - + starMasses_tmp = starMasses.reshape((len(starMasses), 1)) q_results = kdt.query(starMasses_tmp, k=1) indices = q_results[1] @@ -1011,7 +1011,7 @@ def match_model_masses2(isoMasses, starMasses): idx = np.where(dm_frac > 0.1)[0] indices[idx] = -1 - + return indices print('Test #1 START') @@ -1042,7 +1042,7 @@ def FeH_from_Z(Z): metal = np.array([2.0e-4, 1.0e-3, 2.0e-3, 2.0e-2]) #ensure that all Spera metallicity regimes are represented FeH = FeH_from_Z(metal) #generate death mass takes metallicty as [Fe/H] - #want to get a good range of masses for Spera, should expect 8 invalids, 8 WDs, 3 NSs, and 9 BHs + #want to get a good range of masses for Spera, should expect 8 invalids, 8 WDs, 3 NSs, and 9 BHs ZAMS = np.array([-0.2*np.ones(len(FeH)), 0.2*np.ones(len(FeH)), 4.0*np.ones(len(FeH)), 9.2*np.ones(len(FeH)), 15.0*np.ones(len(FeH)), 30.0*np.ones(len(FeH)), 150.0*np.ones(len(FeH))]) @@ -1134,7 +1134,7 @@ def test_Spera15_IFMR_7(): assert len(BH_idx) == 10 , "There are not the right number of BHs for the Spera15 IFMR" return - + def generate_Raithel18_IFMR(): """ Make a set of objects using the Raithel18 IFMR for the purposes of testing @@ -1143,7 +1143,7 @@ def generate_Raithel18_IFMR(): """ Raithel = ifmr.IFMR_Raithel18() - ZAMS = np.array([-0.2, 0.2, 1.0, 7.0, 10.0, 14.0, 16.0, 18.0, 18.6, 22.0, 26.0, 28.0, 50.0, 61.0, 119.0, 121.0]) + ZAMS = np.array([-0.2, 0.2, 1.0, 7.0, 10.0, 14.0, 16.0, 18.0, 18.6, 22.0, 26.0, 28.0, 50.0, 61.0, 119.0, 121.0]) #3 invalid indices, 2 WDs, cannot make statements about #of BHs and NSs because the Raithel IFMR has some randomness output_array = Raithel.generate_death_mass(ZAMS) @@ -1226,7 +1226,7 @@ def test_ResolvedCluster_random_state(): atm_func = atmospheres.get_merged_atmosphere red_law = reddening.RedLawNishiyama09() filt_list = ['nirc2,J', 'nirc2,Kp'] - + iso = syn.IsochronePhot( log_age, AKs, From 941dc92b9cdb420f5033b35f60a91392cc8d62a5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 23 Feb 2026 19:04:28 -0800 Subject: [PATCH 077/191] Fixed minor typos and missing import --- spisea/evolution.py | 2 +- spisea/reddening.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index 895b9bb6..be6a1d3f 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1602,7 +1602,7 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015, print( '*** Generating Pisa isochrone for log t = %3.2f and Z = %.3f' % \ (log_age, metallicity)) - + import time print( time.asctime(), 'Getting original Pisa isochrones.') iso = get_orig_pisa_isochrones(metallicity=metallicity) diff --git a/spisea/reddening.py b/spisea/reddening.py index 677999a8..a4eb7b92 100755 --- a/spisea/reddening.py +++ b/spisea/reddening.py @@ -1243,7 +1243,7 @@ def _derive_Indebetouw05(wave): Alambda_AK = 10**log_Alambda_AK return Alambda_AK - def Indebetouw05(self, wavelength, AK): + def Indebetouw05(self, wavelength, AKs): """ Return the extinction at a given wavelength assuming the extinction law and an overall extinction at AK (2.164 microns) From c42b10fcdaa87c46180c7e15a974bf898e80f808 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 23 Feb 2026 19:06:09 -0800 Subject: [PATCH 078/191] Added backward compatibility for isochrone file names --- spisea/synthetic.py | 119 ++++++++++++++++++++++++-------------------- 1 file changed, 64 insertions(+), 55 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 69b61d2e..9ae64359 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1135,20 +1135,31 @@ def __init__(self, logAge, AKs, distance, # Set save file name metal_value = round(abs(metallicity), 2) metal_sign = 'm' if metallicity < 0 else 'p' - self.save_file = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}_{metal_sign}{metal_value:.2f}.fits' - if metallicity == 0.0: - self.save_file_legacy = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}.fits' + oldest_file = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}.fits' + older_file = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}_{metal_sign}{metal_value*10:02.0f}.fits' + self.save_file = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}_{metal_sign}{metal_value:.2f}.fits' + + oldest_file_exists = check_save_file(oldest_file, evo_model, atm_func, red_law, verbose=verbose) + older_file_exists = check_save_file(older_file, evo_model, atm_func, red_law, verbose=verbose) + new_file_exists = check_save_file(self.save_file, evo_model, atm_func, red_law, verbose=verbose) + + if new_file_exists: + self.save_file_legacy = None + elif older_file_exists: + self.save_file_legacy = older_file + elif oldest_file_exists and metallicity == 0.0: + self.save_file_legacy = oldest_file else: - self.save_file_legacy = self.save_file + self.save_file_legacy = None # Expected filters self.filters = filters # Recalculate isochrone if save_file doesn't exist or recomp == True - file_exists = self.check_save_file(evo_model, atm_func, red_law, verbose=verbose) + file_exists = new_file_exists or (self.save_file_legacy is not None) - if (not file_exists) | (recomp==True): + if (recomp==True) or (not file_exists): self.recalc = True if verbose: print(f'Generating new isochrone of log(t)={logAge:.2f}, AKs={AKs:.2f}, d={distance} pc') @@ -1172,12 +1183,12 @@ def __init__(self, logAge, AKs, distance, print(f'Isochrone saved to {self.save_file}') else: self.recalc = False - try: + if new_file_exists: self.points = Table.read(self.save_file) - except: + print(f'Isochrone loaded from existing file: {self.save_file}') + else: self.points = Table.read(self.save_file_legacy) - # print(f'Isochrone loaded from existing file: {self.save_file}') - # Add some error checking. + print(f'Isochrone loaded from existing file: {self.save_file_legacy}') # Next: do we have all the filters we need? comp_filters = [] @@ -1285,51 +1296,6 @@ def make_photometry(self, rebin=True, vega=vega, comp_filters=None): return - def check_save_file(self, evo_model, atm_func, red_law, verbose=False): - """ - Check to see if save_file exists, as saved by the save_file - and save_file_legacy objects. If the filename exists, check the - meta-data as well. - - returns a boolean: True is file exists, false otherwise - """ - out_bool = False - - if os.path.exists(self.save_file) | os.path.exists(self.save_file_legacy): - try: - tmp = Table.read(self.save_file) - except: - tmp = Table.read(self.save_file_legacy) - - - # See if the meta-data matches: evo model, atm_func, redlaw - if ( (tmp.meta['EVOMODEL'] == type(evo_model).__name__) & - (tmp.meta['ATMFUNC'] == atm_func.__name__) & - (tmp.meta['REDLAW'] == red_law.name) ): - out_bool = True - else: - # If out_bool is false, print out what doesn't match - if verbose: - print(f'Isochrone file {self.save_file} exists, but meta-data does not match.') - if tmp.meta['EVOMODEL'] != type(evo_model).__name__: - print(f' EVOMODEL: {tmp.meta["EVOMODEL"]} != {type(evo_model).__name__}') - if tmp.meta['ATMFUNC'] != atm_func.__name__: - print(f' ATMFUNC: {tmp.meta["ATMFUNC"]} != {atm_func.__name__}') - if tmp.meta['REDLAW'] != red_law.name: - print(f' REDLAW: {tmp.meta["REDLAW"]} != {red_law.name}') - - # Check model version if it was logged - if 'EVOMODELVERSION' in tmp.meta: - if tmp.meta['EVOMODELVERSION'] != evo_model.model_version_name: - out_bool=False - if verbose: - print(f'EVOMODELVERSION does not match: The recorded {tmp.meta["EVOMODELVERSION"]} does not matched the existing version {evo_model.model_version_name}') - - else: - if verbose: - print(f'Isochrone file {self.save_file} or {self.save_file_legacy} does not exist, generating new one.') - - return out_bool def plot_CMD(self, mag1, mag2, savefile=None): """ @@ -1637,6 +1603,49 @@ def make_photometry(self, filters, rebin=True): return +def check_save_file(save_file_path, evo_model, atm_func, red_law, verbose=False): + """ + Check to see if save_file exists, as saved by the save_file + and save_file_legacy objects. If the filename exists, check the + meta-data as well. + + returns a boolean: True is file exists, false otherwise + """ + out_bool = False + + if not os.path.exists(save_file_path): + if verbose: print(f'Isochrone file {save_file_path} does not exist.') + return out_bool + + tmp = Table.read(save_file_path) + + # See if the meta-data matches: evo model, atm_func, redlaw + if ( (tmp.meta['EVOMODEL'] == type(evo_model).__name__) & + (tmp.meta['ATMFUNC'] == atm_func.__name__) & + (tmp.meta['REDLAW'] == red_law.name) ): + out_bool = True + else: + # If out_bool is false, print out what doesn't match + if verbose: + print(f'Isochrone file {save_file_path} exists, but meta-data does not match.') + if tmp.meta['EVOMODEL'] != type(evo_model).__name__: + print(f' EVOMODEL: {tmp.meta["EVOMODEL"]} != {type(evo_model).__name__}') + if tmp.meta['ATMFUNC'] != atm_func.__name__: + print(f' ATMFUNC: {tmp.meta["ATMFUNC"]} != {atm_func.__name__}') + if tmp.meta['REDLAW'] != red_law.name: + print(f' REDLAW: {tmp.meta["REDLAW"]} != {red_law.name}') + + # Check model version if it was logged + if 'EVOMODELVERSION' in tmp.meta: + if tmp.meta['EVOMODELVERSION'] != evo_model.model_version_name: + out_bool=False + if verbose: + print(f'EVOMODELVERSION does not match: The recorded {tmp.meta["EVOMODELVERSION"]} does not matched the existing version {evo_model.model_version_name}') + + + return out_bool + + def get_filter_info(name, vega=vega, rebin=True): """ Define filter functions, setting ZP according to From 02c0feb2a30c59be1e5f85e04ab99a97d815b4d5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 23 Feb 2026 19:06:40 -0800 Subject: [PATCH 079/191] Added isochrone folder into gitignore --- .gitignore | 2 +- .../isochrones/iso_6.70_2.70_04000_p0.00.fits | 44 ------------------- 2 files changed, 1 insertion(+), 45 deletions(-) delete mode 100644 spisea/tests/isochrones/iso_6.70_2.70_04000_p0.00.fits diff --git a/.gitignore b/.gitignore index ae231559..b29e4f96 100755 --- a/.gitignore +++ b/.gitignore @@ -54,4 +54,4 @@ distribute-*.tar.gz ._* # Test files generated -# spisea/tests/isochrones +spisea/tests/isochrones diff --git a/spisea/tests/isochrones/iso_6.70_2.70_04000_p0.00.fits b/spisea/tests/isochrones/iso_6.70_2.70_04000_p0.00.fits deleted file mode 100644 index d8d7bbbe..00000000 --- a/spisea/tests/isochrones/iso_6.70_2.70_04000_p0.00.fits +++ /dev/null @@ -1,44 +0,0 @@ -SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 73 / length of dimension 1 NAXIS2 = 161 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 10 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'K ' TTYPE9 = 'm_nirc2_J' TFORM9 = 'D ' TTYPE10 = 'm_nirc2_Kp' TFORM10 = 'D ' REDLAW = 'N09 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MergedBaraffePisaEkstromParsec' HIERARCH EVOMODELVERSION = 'None ' LOGAGE = 6.7 AKS = 2.7 DISTANCE= 4000 METAL_IN= 0.0 HIERARCH METAL_ACT = 0.0 WAVEMIN = 3000 WAVEMAX = 52000 END E'| @c۳A$wv%4?Q@hr!F?Q@;_@6,2uZE/ޙC@ڈ@A=ˡX? 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F3"@oL%A*Ǩ@!9XbN@S&F?1&y@3@-E&FJ 3%,@nAXXU![@(@"`F?1&y@2L@+p ?F_É @i IAȆ@/@kC,zF?1&y@20D@*<`Fpą*,@߲گAP]4n@3@)^F?1&y@1'v@)Zm1gF}!pi@v&u -aAiH?j@75?|h@EoiF?1&y@1 0a@(Y2 FQ% @7A@;@eO F?1&y@08k=@']<F3O@Q Bڥ7@?@ 2WF?1&y@0-v@&Y'lFe@~X|WB 1=@AޗO;@ m8YF?1&y@/ʐQ@&HshFSKnqn@.uÄBe6,@D@ (TɅT?1&y@/?ۿ@ߖ=BAlf@H@ F]cT?1&y@-llE@# FW@ ;,B@Htj@ 3|T?1&y@-r3.X@#7vFE"@jWB{U@H\(@ L/{JT?1&y@-j>M@#Շ&F+@˰߱BM@H -=p@˒:)zT?1&y@.9s@$BͧF.i@XBTC@Hڟvȴ?M:T?1&y@$@G Fjjt@̚rB9700@Hl@` quT?1&y@(07"[@oJwF#œ{@ҥ@B, Q]<@IE@rGT?1&y@)=C@ bb@Fj@֨))B"[Ri@Ip -=@oiT?1&y@+mKl}@!W(4F ߓb@hБB@I4S@T*1T?1&y@,%o>@">TF/@|$QBZT@IIx@QT?1&y@-8i 9"@#x.vCFh@Qf"@KB}-@I``A7@|j~#T?1&y@-s@Y@#=(3F -@gBńC(@IxQ@mT?1&y@-M-Z@#uTBPFGv@^$-B7VN_@Ip -=@][T?1&y@.kK.@$h|XF -f@N;Bª@,@IE@f,T?1&y@.jX@$赲OUFJh@ -B@I"`@~ (T?1&y@/H(@%3k؍F -f@OBqQI@J@E84T?1&y@/Q *TN@%[-F*i@DŽ7BZ @JKƧ@䎊rT?1&y@/:p&@%3Fmrp@䈩YsB %]@J/|hs@u%F T?1&y@/9/j)@%)FA!q@z((ÜBa_v@JEQ@MO;dZT?1&y@/8\Cv@% -F7@p1sB }`&@J]p -=@:>BT?1&y@.SiѶD@$_?F.sct@3FB 4A@Ju\(@!RT?1&y@.A2N@$UF<7@\>)Bi#`@JS@NTT?1&y@.@$ebdfmFKrM@`RBC[@J9XbN@x)T?1&y@/=\ -@%dC-XF;q=[@~xBD<@J@{5XyT?1&y@/ X=@%+:ה>FGSܴn@(K1 B4@Jm@QT?1&y@/1Go@% FU}ʉ@@\BK1ֻ@JƧ@wkPT?1&y@/v<@&6'FԼU@I۬A@{@K I^5@>BT?1&y@0E@&xm`OF(?$Q@ɀA=$^v@K I^@$T?1&y@07e@&zs2FcktN9@+Aʋi@K3@T?1&y@0U;.@&zځvF10+YE^@mOKA;S=@KJvȴ@>T?1&y@0n=aH@'*"F' 5@&xWAَs@Kc -=p@-8YT?1&y@0rfWj@'hIFߵω@>AAJT$@K}V@6!.IT?1&y@0ҧ"@'̿jF+v;@;TwAg@K -=p@@4m9T?1&y@04@'`heFc5Q@xA^E.|@KO;dZ@QuT?1&y@0ݣ@@( =q}Fbn@c|,|3A38@KZ1@cS&T?1&y@0,Zt@(7r_Fbn@ -jA/2K@KE@tDT?1&y@1@(k/ ^F\,Q!9@vIA 3zD@KzH@͞T?1&y@1)]@(KɏFc/ @x%A6O -4@L+@͞&T?1&y@1BE2@({$HF2F@A՟@L(9Xb@yrGE8T?1&y@1~v@)Kjry \ No newline at end of file From 9283afdf4c771377c5e194c99c13213106a9507e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 23 Feb 2026 19:14:13 -0800 Subject: [PATCH 080/191] Fixed r string warnings --- spisea/reddening.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/spisea/reddening.py b/spisea/reddening.py index a4eb7b92..0d25e5ae 100755 --- a/spisea/reddening.py +++ b/spisea/reddening.py @@ -253,7 +253,7 @@ def plot_Nishiyama09(self): py.errorbar(wave_obs, A_AKs, yerr=A_AKs_err, fmt='k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') - py.ylabel('Extinction (A$_{\lambda}$)') + py.ylabel(r'Extinction (A$_{\lambda}$)') py.title('Nishiyama+09 EL') py.gca().set_xscale('log') py.gca().set_yscale('log') @@ -263,7 +263,7 @@ def plot_Nishiyama09(self): return class RedLawCardelli(pysynphot.reddening.CustomRedLaw): - """ + r""" Defines the extinction law from `Cardelli et al. 1989 `_. The law is defined from 0.3 - 3 microns, and in terms @@ -519,7 +519,7 @@ def plot_RomanZuniga07(self): py.errorbar(wave_obs, A_AKs, yerr=A_AKs_err, fmt='k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') - py.ylabel('Extinction (A$_{\lambda}$)') + py.ylabel(r'Extinction (A$_{\lambda}$)') py.title('Roman-Zuniga+07 EL') py.gca().set_xscale('log') py.gca().set_yscale('log') @@ -687,7 +687,7 @@ def plot_RiekeLebofsky85(self): py.plot(wave_obs_f, A_Ak_f, 'k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') - py.ylabel('Extinction (A$_{\lambda}$)') + py.ylabel(r'Extinction (A$_{\lambda}$)') py.title('Rieke+Lebofsky+85 EL') py.gca().set_xscale('log') py.gca().set_yscale('log') @@ -814,7 +814,7 @@ def plot_Damineli16(self): py.errorbar(wave_obs, A_AKs, fmt='k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') - py.ylabel('Extinction (A$_{\lambda}$)') + py.ylabel(r'Extinction (A$_{\lambda}$)') py.title('Damineli+16 EL') py.gca().set_xscale('log') py.gca().set_yscale('log') @@ -1309,7 +1309,7 @@ def plot_Indebetouw05(self): py.errorbar(wave_arr, law_obs_arr, yerr=law_obs_err_arr, fmt='k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') - py.ylabel('Extinction (A$_{\lambda}$)') + py.ylabel(r'Extinction (A$_{\lambda}$)') py.title('Indebetouw+05 EL') py.gca().set_xscale('log') py.gca().set_yscale('log') @@ -1319,7 +1319,7 @@ def plot_Indebetouw05(self): return class RedLawPowerLaw(pysynphot.reddening.CustomRedLaw): - """ + r""" Extinction object that is a power-law extinction law: :math:`A_{\lambda} \propto \lambda^{\alpha}`. @@ -1433,7 +1433,7 @@ def powerlaw(self, wavelength, AKs): return A_at_wave class RedLawBrokenPowerLaw(pysynphot.reddening.CustomRedLaw): - """ + r""" Extinction object that is a broken power-law extinction law: :math:`A_{\lambda} \propto \lambda^{\alpha[n]}` @@ -1713,7 +1713,7 @@ def plot_Fritz11(self): label='Measured') py.plot(ext_scaled.wave*10**-4, np.log10(ext_scaled.throughput)/-0.4, 'b-', label='Scaled EL') py.xlabel('Wavelength (microns)') - py.ylabel('Extinction (A$_{\lambda}$)') + py.ylabel(r'Extinction (A$_{\lambda}$)') py.title('Fritz+11 EL') py.gca().set_xscale('log') py.gca().set_yscale('log') From 67228f16bf1e2c71388caa6ce26d6425043ad5ee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 23 Feb 2026 19:14:35 -0800 Subject: [PATCH 081/191] Adjusted check save file order --- spisea/synthetic.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 9ae64359..a91d81c2 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -1136,13 +1136,13 @@ def __init__(self, logAge, AKs, distance, metal_value = round(abs(metallicity), 2) metal_sign = 'm' if metallicity < 0 else 'p' - oldest_file = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}.fits' - older_file = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}_{metal_sign}{metal_value*10:02.0f}.fits' self.save_file = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}_{metal_sign}{metal_value:.2f}.fits' + older_file = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}_{metal_sign}{metal_value*10:02.0f}.fits' + oldest_file = f'{iso_dir}/iso_{logAge:.2f}_{AKs:4.2f}_{str(round(distance)).zfill(5)}.fits' - oldest_file_exists = check_save_file(oldest_file, evo_model, atm_func, red_law, verbose=verbose) - older_file_exists = check_save_file(older_file, evo_model, atm_func, red_law, verbose=verbose) new_file_exists = check_save_file(self.save_file, evo_model, atm_func, red_law, verbose=verbose) + older_file_exists = check_save_file(older_file, evo_model, atm_func, red_law, verbose=verbose) + oldest_file_exists = check_save_file(oldest_file, evo_model, atm_func, red_law, verbose=verbose) if new_file_exists: self.save_file_legacy = None From 21bc2b3831accc7ae9174cc3fcf51e85b3ee14fc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 23 Feb 2026 22:35:57 -0800 Subject: [PATCH 082/191] Changed global random seed to random generator; Updated star systems and companions files for future comparisons --- spisea/imf/imf.py | 16 +++++-------- spisea/synthetic.py | 30 ++++++++++++------------ spisea/tests/test_data/companions.pkl | Bin 251721 -> 231086 bytes spisea/tests/test_data/star_systems.pkl | Bin 1516177 -> 1253105 bytes spisea/tests/test_synthetic.py | 3 +-- 5 files changed, 22 insertions(+), 27 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index cb6866e6..4598705d 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -51,7 +51,7 @@ def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None, seed=None) self._multi_props = multiplicity self._mass_limits = np.atleast_1d(massLimits) self.seed = seed - # self.rng = np.random.default_rng(seed) + self.rng = np.random.default_rng(seed) if multiplicity: self.make_multiples = True @@ -61,7 +61,7 @@ def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None, seed=None) return - def generate_cluster(self, totalMass, seed=None): + def generate_cluster(self, totalMass): """ Generate a cluster of stellar systems with the specified IMF. @@ -128,13 +128,10 @@ def generate_cluster(self, totalMass, seed=None): totalMassTally = 0 loopCnt = 0 - if seed: - np.random.seed(seed=seed) - # start_while = time.time() while totalMassTally < totalMass: # Generate a random number array. - uniX = np.random.rand(int(newStarCount)) + uniX = self.rng.random(int(newStarCount)) # Convert into the IMF from the inverted CDF newMasses = self.dice_star_cl(uniX) @@ -151,7 +148,7 @@ def generate_cluster(self, totalMass, seed=None): MF = self._multi_props.multiplicity_fraction(newMasses) CSF = self._multi_props.companion_star_fraction(newMasses) - newIsMultiple = np.random.rand(int(newStarCount)) < MF + newIsMultiple = self.rng.random(int(newStarCount)) < MF # Function to calculate multiple systems more efficiently # start_calc = time.time() @@ -231,7 +228,7 @@ def calc_multi(self, newMasses, newIsMultiple, CSF, MF): # Identify multiple systems, calculate number of companions for each multiple_idx = np.where(newIsMultiple)[0] - comp_nums = 1 + np.random.poisson((CSF[multiple_idx] / MF[multiple_idx]) - 1) + comp_nums = 1 + self.rng.poisson((CSF[multiple_idx] / MF[multiple_idx]) - 1) if self._multi_props.companion_max: too_many = np.where(comp_nums > self._multi_props.CSF_max)[0] comp_nums[too_many] = self._multi_props.CSF_max @@ -245,7 +242,7 @@ def calc_multi(self, newMasses, newIsMultiple, CSF, MF): for comp_num, comp_index in zip(comp_unique, comp_indices): # Calculate masses of companions - q_values = self._multi_props.random_q(np.random.rand(len(comp_index), comp_num)) + q_values = self._multi_props.random_q(self.rng.random((len(comp_index), comp_num))) m_comp = np.multiply(q_values, np.transpose([primary[comp_index]])) compMasses[multiple_idx[comp_index], :comp_num] = m_comp @@ -912,4 +909,3 @@ def inv_error(x): return y else: return -y - diff --git a/spisea/synthetic.py b/spisea/synthetic.py index a91d81c2..a2bd7b98 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -107,7 +107,7 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=False, self.ifmr = ifmr self.cluster_mass = cluster_mass self.seed = seed - # self.rng = np.random.default_rng(self.seed) + self.rng = np.random.default_rng(self.seed) return @@ -159,13 +159,13 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, # Provide a user warning is random seed is set if seed is not None and verbose: print('WARNING: random seed set to %i' % seed) - # imf.rng = self.rng + imf.rng = self.rng ##### # Sample the IMF to build up our cluster mass. ##### # start0 = time.time() - mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass, seed=seed) + mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass) # end0 = time.time() # print('IMF sampling took {0:f} s.'.format(end0 - start0)) @@ -328,11 +328,11 @@ def _make_companions_table_new(self, star_systems, compMass): if isinstance(self.imf._multi_props, multiplicity.MultiplicityResolvedDK): companions.add_column(Column(self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx']]), name='log_a')) - companions.add_column(Column(self.imf._multi_props.random_e(np.random.rand(N_comp_tot)), name='e')) + companions.add_column(Column(self.imf._multi_props.random_e(self.rng.random(N_comp_tot)), name='e')) companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters( - np.random.rand(N_comp_tot), - np.random.rand(N_comp_tot), - np.random.rand(N_comp_tot) + self.rng.random(N_comp_tot), + self.rng.random(N_comp_tot), + self.rng.random(N_comp_tot) ) companions['mass'] = compMass.compressed() @@ -441,11 +441,11 @@ def _make_companions_table(self, star_systems, compMass): for ii in range(len(companions)): companions['log_a'][ii] = self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx'][ii]]) - companions['e'] = self.imf._multi_props.random_e(np.random.rand(N_comp_tot)) + companions['e'] = self.imf._multi_props.random_e(self.rng.random(N_comp_tot)) companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters( - np.random.rand(N_comp_tot), - np.random.rand(N_comp_tot), - np.random.rand(N_comp_tot) + self.rng.random(N_comp_tot), + self.rng.random(N_comp_tot), + self.rng.random(N_comp_tot) ) @@ -701,7 +701,7 @@ def __init__(self, iso, imf, cluster_mass, deltaAKs, # Perturb all of star systems' photometry by a random amount corresponding to # differential de-reddening. The distribution is normal with a width of # Aks +/- deltaAKs in each filter - rand_red = np.random.randn(len(self.star_systems)) + rand_red = self.rng.standard_normal(len(self.star_systems)) for filt in self.filt_names: self.star_systems[filt] += rand_red * delta_red_filt[filt] @@ -1527,18 +1527,18 @@ def apply_reddening(self, AKs, extinction_law, dAKs=0, dist='uniform', dAKs_max= # extinction law if dAKs != 0: if dist == 'gaussian': - AKs_act = np.random.normal(loc=AKs, scale=dAKs) + AKs_act = self.rng.normal(loc=AKs, scale=dAKs) # Apply dAKs_max if desired. Redo if diff > dAKs_max if dAKs_max != None: diff = abs(AKs_act - AKs) while diff > dAKs_max: print('While loop active') - AKs_act = np.random.normal(loc=AKs, scale=dAKs) + AKs_act = self.rng.normal(loc=AKs, scale=dAKs) diff = abs(AKs_act - AKs) elif dist == 'uniform': low = AKs - dAKs high = AKs + dAKs - AKs_act = np.random.uniform(low=low, high=high) + AKs_act = self.rng.uniform(low=low, high=high) else: print('dist {0} undefined'.format(dist)) return diff --git a/spisea/tests/test_data/companions.pkl b/spisea/tests/test_data/companions.pkl index a6852ef4abad97afe33e9c25d5e1798df2caf679..0395d75d828740e3bd040811cc8926271a26676c 100644 GIT binary patch literal 231086 zcmb5%2~hE7D4{4Nkp{{TA<9rxLS@Jh4MOug&+}ZG zXO&8`RPvnP&&OK-XRmdy|MO%muls$~-LUsL`|N#Pd*wRM z^)d@xy)*iM|8~7UZvX$3tBdO*Ti5#nC(fNYqkqD}=-gRzcI`Ghsc)u#a*NRki~sq6 zeXcV1`OU4(E%Z%}8J)Cpy)Smw(uDo_j-5DXroYU@@{EO%u9=yxwd?(f$ISFkus?@| zndSffulJ|?zrQy7%UGJ6b-h1+^WXn)aaH56bKT6T=X%GL{Z-UBcPjDQvA?(f{rqfC 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