diff --git a/.cursor/rules/spisea-synphot-conda.mdc b/.cursor/rules/spisea-synphot-conda.mdc new file mode 100644 index 00000000..3ff0c002 --- /dev/null +++ b/.cursor/rules/spisea-synphot-conda.mdc @@ -0,0 +1,12 @@ +--- +description: Use the spisea-synphot conda environment for all Python, pip, and tests +alwaysApply: true +--- + +# Conda environment: `spisea-synphot` + +For this repository, use the **`spisea-synphot`** conda environment for every shell command that needs Python, `pip`, `pytest`, or project tooling. + +- Prefer: `conda run -n spisea-synphot ` (e.g. `conda run -n spisea-synphot python -m pytest ...`) so the correct interpreter is used even in non-interactive shells. +- Alternatively: `source "$(conda info --base)/etc/profile.d/conda.sh" && conda activate spisea-synphot` then run commands as usual. +- Do not assume `python` or `pip` on `PATH` without activating that environment; do not use other envs (e.g. a generic `astro` env) for SPISEA work unless the user says otherwise. diff --git a/Dockerfile b/Dockerfile index 8ef8b191..c8a42208 100644 --- a/Dockerfile +++ b/Dockerfile @@ -4,13 +4,14 @@ RUN apt-get update && apt-get upgrade -y && apt-get clean RUN apt-get install -y curl python3 python3-dev python3-distutils python3-pip git wget -RUN pip3 install astropy pysynphot scipy numpy matplotlib +# synphot + stsynphot replace deprecated pysynphot; set PYSYN_CDBS to your CDBS tree (see below). +RUN pip3 install astropy synphot stsynphot scipy numpy matplotlib RUN ln -s /usr/bin/python3 /usr/bin/python RUN export PYTHONPATH=$PYTHONPATH:/SPISEA -RUN cd / && git clone https://github.com/astropy/SPISEA.git +RUN git clone https://github.com/MovingUniverseLab/spisea.git /SPISEA ENV PYTHONPATH "${PYTHONPATH}:/SPISEA/" @@ -32,6 +33,7 @@ WORKDIR /cdbs/models RUN wget http://astro.berkeley.edu/~jlu/spisea/spisea_models.tar.gz && wget http://astro.berkeley.edu/~jlu/spisea/spisea_cdbs.tar.gz RUN tar -xvf spisea_cdbs.tar.gz && tar -xvf spisea_models.tar.gz && rm spisea_cdbs.tar.gz && rm spisea_models.tar.gz +# stsynphot reads throughput/model grids from this path (same convention as legacy pysynphot). ENV PYSYN_CDBS /cdbs/models/cdbs/ ENV SPISEA_MODELS /cdbs/models diff --git a/README.md b/README.md index 0b8ef4ce..53c1f31a 100755 --- a/README.md +++ b/README.md @@ -20,10 +20,10 @@ Here is a brief list of things that SPISEA can do: * make a star cluster at any age with an unusual IMF and unresolved multiplicity * make a spectrum of a star cluster in integrated light -See [documentation](https://spisea.readthedocs.io/en/latest/) for +See [documentation](https://github.com/MovingUniverseLab/spisea/tree/main/docs) for details on installing and running SPISEA. We also provide jupyter notebooks with a -[quick-start tutorial](https://github.com/astropy/SPISEA/blob/main/docs/Quick_Start_Make_Cluster.ipynb) -and [additional examples](https://github.com/astropy/SPISEA/tree/main/docs/paper_examples) +[quick-start tutorial](https://github.com/MovingUniverseLab/spisea/blob/main/docs/Quick_Start_Make_Cluster.ipynb) +and [additional examples](https://github.com/MovingUniverseLab/spisea/tree/main/docs/paper_examples) demonstrating how to use SPISEA. If you use SPISEA in your research, please cite [Hosek et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020arXiv200606691H/abstract). @@ -32,7 +32,7 @@ If you use SPISEA in your research, please cite [Hosek et al. (2020)](https://ui SPISEA is actively supported and is growing in functionality, and subsequently has had several updates since its initial release. See the -[main documentation page](https://spisea.readthedocs.io/en/latest/) +[main documentation page](https://github.com/MovingUniverseLab/spisea/blob/main/docs/index.rst) for the change log describing the updates in each release. ## Contributions @@ -42,7 +42,7 @@ own fork of the repository, make their changes, and then submit a pull request to the "dev" branch. All contributions will be acknowledged on the -[contributors page](https://spisea.readthedocs.io/en/dev/contributors.html#contributors) (with permission). +[contributors page](https://github.com/MovingUniverseLab/spisea/blob/main/docs/contributors.rst) (with permission). Contributors with features used in code releases will be co-authors in future SPISEA software papers. ## License diff --git a/docs/Quick_Start_Make_Cluster.ipynb b/docs/Quick_Start_Make_Cluster.ipynb index dd574b2e..d3ac7ff4 100755 --- a/docs/Quick_Start_Make_Cluster.ipynb +++ b/docs/Quick_Start_Make_Cluster.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This is a quick start guide to making a synthetic cluster using the SPISEA package. 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", + "This is a quick start guide to making a synthetic cluster using the SPISEA package. The cluster is constructed using a user-specified isochrone and initial mass function (IMF). Detailed documentation is available at https://github.com/MovingUniverseLab/spisea/tree/main/docs.\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" ] @@ -235,7 +235,7 @@ }, { "data": { - "image/png": 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\n", 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", 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\n", + "image/png": 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AHwHvBsLAh621W0e6TjnMWnjgAWco51u9tcv0wgvOV36+E55WrXKC1IwZGnMgIiLjXrI7VSuttS3HeOxyYObhr3OAnx7+LiNp/Xrsj36Eeeih4z/viiuOhKj58zXmQEREJpxkh6rjuQq431prgZeNMdnGmMnW2vpkFzZR2N//HnP99Ry3x/Sf/+nspfKM5v8piYiIDL9kthMs8KQxZosx5pajPF4M1Lzl99rD197GGHOLMWazMWZzc/95b3La7LZtmHcs9XVfcAGxb30LLr/cufD738NttylQiYiIkNxQdZ61djHOMt+njTEr3vH40Rok9u8uWHuPtXaptXZpge4iGxr79x8JTkA0EGD/nXcSX7YM9yuvwJNPOnfvfehDyatRRERklElai8Fae+jw9yZjzJ+Bs4H1b3lKLfDW28RKgEMjV+EEFQw6gaqnB+v1YqJRPOEw5Z//PNYYzOzZcOON8P3vJ7tSERGRUSUpnSpjTMAYk9H/M3Ap8OY7nvYI8M/GsRzo1H6qYZZIwD//M+zZg/nzn+G3v8Ua44SsF17ABINQWQm/+hXk5CS7WhERkVElWZ2qIuDPztQEPMAD1tonjDGfALDW/gz4K844hb04IxVuSlKtE8cdd8DDD8MPfwjp6ZjLL3eOifnTn3RUjIiIyAkY5+a68WHp0qV28+bNyS5jbNq0yTly5tpr4bvfhbPPdg4u3rjxyPR0ERERwRiz5Z3zNWF0j1SQkRKNws03Q1ER/OAHzsypYBBeekmBSkREZJAUqsQJUtu2wf/+L3zta7B5s7MMeOaZya5MRERkzNDY64lu7174+tfh6qudjtXPfw5f/CJceWWyKxMRERlT1KmayKyFT3wCfD649VZn2e/cc+Gb30x2ZSIiImOOQtVE9pvfwDPPOHf73XqrMxn9D39wNqiLiIjISVGomqiamuBf/xXOO8+ZoL51q7OPaurUZFcmIiIyJilUTVSf/zx0dTnHzXzqU/DpT2sflYiIyGnQRvWJ6G9/gwcfhM9+1plJNWMG/Nd/JbsqERGRMU2dqokmHocvfAFmzoT2djhwAJ5/HgKBZFcmIiIypqlTNUFYa4lEIthf/hLefBPe9z749a/hc59z9lWJiIjIaVGnagKw1lJbW0tPbS3Tv/xlXCtWYNavh/x8Z0aViIiInDZ1qiaAaDRKKBSi+O67cXV1kbjgAucImm9/G7Kykl2eiIjIuKBQNQF43W7yXnqJwO9/T/eNN+J66CGYOxduuinZpYmIiIwbWv4bz/72N/jDHzCPP05BYyO2rIz0s8/G3Hsv/PGP4HYnu0IREZFxQ6FqvHr2WVizBrKzne/veQ9mzRpnU/qZZ8L735/sCkVERMYVhapxxlpLNBzG+5nPYCoqYPt2SE11Hrz/fti9Gx56CFxa+RURERlKClXjSP9dfr6776Zoxw7sww9j+gNVNAq33w5nnQVXX53cQkVERMYhhapxJBqN0nvgAMU/+QndK1bgW7MGX/+Dv/0t7NsHjzwCxiSzTBERkXFJoWoc8Xq9TL77bkxPD123386kRALWrnU2rN93HyxdCldckewyRURExiWFqvFkyxYCf/wj8c99jkleL6aoCIJB8HicDep33qkulYiIyDDRbuWx7I47nCGegE0kiHziE8Rzc2n42Mfgi1+E9HR49FFoa4PnnoNFi5Jbr4iIyDimTtVYtXs3/N//C3PmQGUl8d/9jpQtW+j4/vdJvPwy5sUX4Sc/0XKfiIjICFGoGqs+/Wnn+5/+BD09uL/yFSLz59OwZg0VN9yAnToV85GPJLdGERGRCUShaizq7oann4bp02HePPjOdzC1tXh/+1um79uH99VX4Z57ICUl2ZWKiIhMGApVY9Ebbzjff/hDaGlx9lb9wz9gLrwQ79lnQ3k5fPjDyaxQRERkwlGoGotefx2AyOzZeG+/HdPdDd/5Dvy//webN8OvfgVeb5KLFBERmVgUqsYgu3YtBjjw+uvM+NnPsDfdhJk7F66/3lkSvOGGZJcoIiIy4ShUjTGJRIK+QIBUIPDjH5MwhthXvkLKww/Dq6/Cb36jLpWIiEgSaE7VGJJIJNj4yiuk3ncfAFPWrWPLqlUkCgvha1+DmTPhQx9KcpUiIiITkzpVY0g4HMY8/PDbrr1x5ZWc+dBDzj6r3/3OmZ4uIiIiI07/Ao8haWlpnPNf/wXAxuXL2fP+93PGmWfi//jHnSGg116b5ApFREQmLoWqMcTlclZrbVYW8595hrnxOIFHH8Xs2AF/+AO43UmuUEREZOJSqBprgkFMejppxkA8Dt/4hjMA9AMfSHZlIiIiE5pC1ViTkXHk5z/8AXbuhP/5H3DpngMREZFk0r/EY1UsBrfdBgsWwHvfm+xqREREJjx1qsaq3/8e9uyBhx5Sl0pERGQU0L/GY1E0CrffDmedBVdfnexqREREBHWqxqbf/haqquCRR8CYZFcjIiIiqFM19kQizh1/S5fCFVckuxoRERE5TJ2qsebXv4b9++Huu9WlEhERGUXUqRpL+vrgW9+Cc86Byy9PdjUiIiLyFupUjSX33gsHD8IvfqEulYiIyCijTtVY0dsL3/42vOtdsHp1sqsRERGRd1Cnaqz45S+httbZU6UulYiIyKijTtVY0NPjdKkuuABWrUp2NSIiInIU6lSNBffcA/X18MAD6lKJiIiMUupUjXZdXXDHHbByJVx0UbKrERERkWNQp2q0u+02aGyEhx9OdiUiIiJyHOpUjWZvvgl33gkf+5gzm0pERERGLYWq0cpa+PSnISvLWf4TERGRUU3Lf6PV734H69c7m9Tz85NdjYiIiJyAOlWjUUcHfOELzpLfRz+a7GpERERkENSpGo3+4z+gpQUefxxcyr0iIiJjgf7FHm22boWf/AQ++UlYvDjZ1YiIiMggKVSNJokEfOpTzh6qb34z2dWIiIjISdDy32hy773wyitw//2QnZ3sakREROQkqFM1WrS2wpe/DCtWwPXXJ7saEREROUkKVaPF177m3PV39906309ERGQMUqgaDbZvh5/9DD7xCZg/P9nViIiIyClQqEo2a+Ff/gUyMuDrX092NSIiInKKtFE92R5/HJ58En74Q01OFxERGcPUqUq2e++FKVOcUQoiIiIyZilUJVsiAbm54PMluxIRERE5DSMeqowxs40xr73lK2iM+fw7nnORMabzLc/5z5Guc0QcOABPPw0VFcmuRERERE7TiO+pstbuAhYBGGPcQB3w56M89Xlr7RUjWduIshZuvtn5/qMfJbsaEREROU3J3qh+MbDPWnsgyXWMvF//Gp56yjnnT50qERGRMS/Ze6quBR48xmPnGmO2GWMeN8bMO9YbGGNuMcZsNsZsbm5uHp4qh1p9vTNGYcUK+PjHk12NiIiIDIGkhSpjjA+4Evifozy8FSiz1i4E/j/gL8d6H2vtPdbapdbapQUFBcNT7FD7zGegpwd+8QtwJTvXioiIyFBI5r/olwNbrbWN73zAWhu01nYf/vmvgNcYMz6GOD3yCDz0ENx2G8yalexqREREZIgkM1R9kGMs/RljJhnjHIBnjDkbp87WEaxtePT2wq23whlnOMt/IiIiMm4kZaO6MSYNWA18/C3XPgFgrf0Z8H7gk8aYGNADXGuttcmodUj94AdQVeVsUPd6k12NiIiIDCEzHrJKv6VLl9rNmzcnu4yja2uD8nJYtQr+cswtYiIiIjLKGWO2WGuXvvO6dkmPlLvugq4u+MY3kl2JiIiIDAOFqpHQ2Ql33glXXw1nnpnsakRERGQYKFSNhLvvdoLVV7+a7EpERERkmChUDTdrnXlUl1wCS5YkuxoREREZJgpVw23LFti/Hz70oWRXIiIiIsNIoWq4/fd/g8cDV12V7EpERERkGClUnaZEIkF3dzeJROJoD8If/wirV0Nu7sgXJyIiIiMmKcM/x4tEIsGmTZtoaWkhPz+fZcuW4XrrWX5r18LBg/Dd7yavSBERERkR6lSdhnA4THNzM/n5+TQ3NxMOh9/+hPvug+xsLf2JiIhMAApVpyE1NZVEIsGrr75KIpEgNTX1yIMtLc7ByR/8IPj9yStSRERERoSW/05DPB5nxowZzJ07l2g0Sjwex+12Ow/eeSf09cFnPpPcIkVERGREqFN1GrxeLxkZGcTjcTIyMvD2H5K8c6cTqt73PjjjjOQWKSIiIiNCnarTYIyhpKSEaDSK1+vFGAPxONx4I6Smwg9+kOwSRUREZIQoVJ0mYww+n+/IhZ/+FDZuhAcegNLS5BUmIiIiI0rLf0Opuxtuvx1WroRrr012NSIiIjKCFKqG0o9+BM3NcMcdYEyyqxEREZERpFA1lH76U7j8cjjnnGRXIiIiIiNMoWoodXbCnDnJrkJERESSQKFqKEWj0D9WQURERCYUhaqh0tDgDPtMS0t2JSIiIpIEClUnyVpLJBIhkUgQiUSw1joPfPvb4HbDddclt0ARERFJCs2pOgnWWmpqaujo6CAcDpOVlUV6ejoliQTm5z+Hm26CGTOSXaaIiIgkgTpVJ6Gvr4+dO3fS1NTEzp078fv9hEIhErfdBpEI7N3rLAOKiIjIhKNQNUjWWurq6jh06BCtra34/X66urrIam7Gfd99zpOeew4OHkxqnSIiIpIcClWDFI1GiUQizJs3j5SUFBYsWMCsWbOY9Mtfvv2JS5Ykp0ARERFJKu2pGiSv10t6ejrWWvLy8gBof/llJt9//5Envf/9zmZ1ERERmXAUqgbJGENxcTHt7e1EIhGysrLIX7Xq7U+64YbkFCciIiJJp1A1SNZaamtr2bdvH83NzXj/938pbmx0HrzgAti1yzmiRkRERCYk7akapGg0SkdHBy6Xi+LUVOZ985vOA088ARs2OF0qTVMXERGZsBSqBsnr9ZKdnU0ikWDpjTcCYP/xH6GyEmIx+PCHk1ugiIiIJJWW/wbJGENpaSlF3d2ktLc7F++/n8Q552CWLMHMn5/cAkVERCSp1Kk6CcYYUlJTYdYs7HPP0bhuHa5t2+i48sojx9WIiIjIhKRO1cmqqIBdu4hGIvg+/nGs10vL6tUEolF8Pl+yqxMREZEkUafqFHk9HjKfeYbu88/HX1KCV5vURUREJjSFqlNkqqvx1NSQetVVlJSUYIxJdkkiIiKSRFr+O1VPPw2AZ80aUKASERGZ8NSpOlUvvQRFRTBrVrIrERERkVFAoeoU2VdfJbZwIQnd9SciIiIoVJ2SRDgMO3ZQlZnJxo0bSSQSyS5JREREkkyh6iRZawm+9BImHqetrIydO3cSCoWSXZaIiIgkmTaqD5K1lkgkQkNDA12PPko2sN3nI+D3a5yCiIiIKFQNhrWW2tpa2tvbqa+vZ3plJX1+Pz2TJnHB4sWkpKQku0QRERFJMi3/DUI0GiUUCpGbm0ssFiOjqorOigpmzp7N1KlTNaNKREREFKoGw+v1kpaWxu7du3ED+XV1mLPOYu7cuepSiYiICKBQNSjGGAoLC4nH4+S2teHu7SX9ggsoLS1Vl0pEREQAhapBM8bgcrmgrg6AyJQpWM2oEhERkcMUqgbJ7XbT3t5Oy+7dALx28CCbNm3SjCoREREBFKoGxVrLnj17qK+vJ+dwiHLl59PS0kI4HE5ydSIiIjIaKFQNQiQS4dChQ7S1tbFnyxYA/rZhA9FolNTU1CRXJyIiIqOBQtUgxWIxwuEwrlgMgCkVFRQXFxOPx5NcmYiIiIwGClWD4PP5KC0tJRAIUJSdDYA/I4PCwkJNUxcRERFAoWpQjDHMnj2bFStWEPB6iXk8TJs+nZKSEo1UEBEREUChatBcLheFhYVkRqPEAwG6u7vp6elJdlkiIiIySihUDVIkEiEajTKlvZ3mggIyMzNJS0tLdlkiIiIySihUDYK1loaGBg7V1RHYv5+24mI8Hp1FLSIiIkcoVA1CJBJh3759hHbsIKWvj94ZM2hra9OMKhERERmgdssgxWIxMg8eBOC5lhay6urw+/1JrkpERERGC3WqBsHn8zFz5kx8u3YBEJszh0mTJtHb25vkykRERGS0UKgaBGMMOTk5pNXX05aSwrpXX6W7u1vT1EVERGSAQtUgRaNR0ltaaDwcpDweD9FoNMlViYiIyGihUFF21ewAACAASURBVDVIqamp5HV3U+tyEY1GaW9vp66uDmttsksTERGRUUChapB6e3rIDYdxlZdTWFjI9OnTB2ZXiYiIiChUDYK1lmBTE954nIPd3WRmZpKTk0MgENDZfyIiIgIMc6gyxtxrjGkyxrz5lmu5xpinjDF7Dn/POcZrbzz8nD3GmBuHs84TiUajtO7dC0BmWRmBQEAzqkRERORthrtT9WtgzTuufRl4xlo7E3jm8O9vY4zJBb4GnAOcDXztWOFrJHg8HmhvB6AlFiMQCFBcXEw4HNbyn4iIiADDHKqsteuBtndcvgr4zeGffwNcfZSXXgY8Za1ts9a2A0/x9+FsxMRiMfLcbgDSiouZPXs2vb29Wv4TERGRAcnYU1Vkra0HOPy98CjPKQZq3vJ77eFrSeH1ekk5vNxX1dZGZWUl1lqKi4sxxiSrLBERERlFRutG9aMllaPOLjDG3GKM2WyM2dzc3Dw8xRhDeiwGQE13N2+88Qbt7e3EDl8TERERSUaoajTGTAY4/L3pKM+pBUrf8nsJcOhob2atvcdau9Rau7SgoGDIi+1nOjoAJ1Q1NDRQVVWF+/CSoIiIiEgyQtUjQP/dfDcCDx/lOX8DLjXG5BzeoH7p4WtJYa2lt7UVgI5YjKlTp+Lz+XQHoIiIiAwY7pEKDwIbgNnGmFpjzEeB7wCrjTF7gNWHf8cYs9QY80sAa20b8A1g0+Gv2w9fS4poNEpfKARA3OVi//79VFVV0dLSoonqIiIiAoBnON/cWvvBYzx08VGeuxn42Ft+vxe4d5hKOykej4e+ri4AIrEYGEMkEmHPnj1MmTKFlJSUJFcoIiIiyTasoWq8iMVipHq9xIzBdzhAJRIJEolEkisTERGR0WK03v03qng8HhKRCHGXC7/fTyKRoLq6mo6ODm1WFxEREUChalBisRgpbjfW7SYUChEOh2lvb2f79u3s3r1b+6pEREREoWowvF4vbmuJAV1dXbS3t3Pw4EEyMjKIRCI6qkZERES0p2owjDGk+/1EPR6KiorIz88nLy+P6dOnk5ubq6NqRERERKFqsLzGkEhJYcqUKfT19bF69WrWrFlDWlqajqoRERERharBstEo7pQULr30UoLBINOmTSMejxOJRPD5fApWIiIiE5xC1SC5gKi1vPrqqyQSCbq7uwFnMOjChQspLy9XsBIREZnAFKoGKeHx4EokSEtLIxaL0dLSwgsvvEBfXx8HDx7k5ptvJjU1NdllioiISJLo7r9BMl4vib4+qqurqa2tpbe3l+rqanw+H93d3ToHUEREZIJTqBqkGGCiUbxeL11dXUQiEaZPn05PTw8zZ84kOzs72SWKiIhIEmn5b5CMz0ciFqOyspJEIoG1ljPPPJOlS5dy2WWXabK6iIjIBKdO1SBFrcWdSJCVlUUikaCiogK3282cOXO0l0pERETUqRosl9+PO5FgxowZGGPIy8tjypQpA7+LiIjIxKZO1SD50tJwWYvf56O4uJi0tLSBg5X7+vp0/p+IiMgEp07VICXcblyAz+WioaGBTZs2MX/+fBoaGli1ahW5ubmUlJSoayUiIjJBqVM1SC6fD4Bdb7xBVVUVwWCQyspKXnzxRXw+H6FQSAcri4iITGAKVYMU8ThNvTllZXg8HowxA9+bmppIS0vTwcoiIiITmJb/BsmdmQnA5MxMysrKaG1tpaCggJKSEt39JyIiIgpVg+XJygJgSmYm06ZNw+v1Mm3aNGbPnk1BQQHhcJhoNIrv8DKhiIiITCxa/hskk54OwJSsLA4cOEBzczO7d++mrKyM3t5eAoGAlv9EREQmMHWqButwqJqcmUkoFCIcDtPV1UVNTQ0XXXQRfr9fd/6JiIhMYOpUDVYgAECe309WVhZdXV0kEgk2b95MV1cX0WhUs6pEREQmMIWqQUoc3oy+7/XXKSgoIC0tjb6+Pnbv3s3WrVvZt28ftbW1ClYiIiITlELVIPW4nP9UaYkE3d3dxONxMjIySE1NZc+ePbhcLrq7uzWrSkREZIJSqBqk1Px8ANpqaigrK2PatGn4/X6ys7Pp6enh9ddfp7OzE8/heVbWWiKRiDpXIiIiE4RC1SD13/1XGAhw4MAB2tvbSUtLIyMjg/r6enp6eujo6BgIUrW1tVRVVWlJUEREZILQ3X+DFHW58Ph8+Ht7CYfDFBUVceDAAerr6/H5fPT09LB48WLq6uooKSkhFAqRmZlJMBjU/CoREZEJQKFqkLw+H/GcHPoaGmiPx6murgYgHo9TVFRENBrloosuoq+vD4BAIEAwGNT8KhERkQlCoWqQjDG48vPJjMWomDaNnp4eent76e3tJRQKUVFRgcvlwufz4fP5KCkpIRqN4vV6Nb9KRERkAlCoOgkmL4+8vj5mzpzJoUOHCAaDFBUVMWfOHAKBACkpKQNdKWOMlvxEREQmEIWqk2Byc0nZu5crrriC4uJiNmzYQFtbG36/n4ULFzJ//nydASgiIjJB6e6/k2BzczHt7WRnZ9PY2EgoFCIlJQWPx4PL5aKyspLW1lbcbneySxUREZERpk7VIFlr6fJ6SW9pof7QIdLT0zl06BAdHR2Ew2FKS0vx+/3EYjGqq6uZPn269lKJiIhMIOpUDVI0GqU3MxNXXx/V27bR1dVFZ2cnWVlZtLS04PP52LlzJ6FQiIMHDxKJRJJdsoiIiIwgdaoGyev14iotBSC6fz82K4vCwkKstVhraWhoIBKJEI/HdbiyiIjIBKRO1SAZY8icOxeAuZmZNDc3k5eXR2pqKjNnziQajeJ2u3n11Vdpbm6msbFRwUpERGQCUag6Cd7ycgA6Kyvp7e3F6/WSn5+P1+uloaEBv99Pfn4+OTk5dHZ26nBlERGRCUTLfyfBFBcDkBMOE0lJ4eDBg7hcLmbOnInP56O1tZV4PI7f7yc3N3fgcGUREREZ//Sv/kmwfj+JrCxSW1up7+0lOzubeDxOIpEgkUiQk5OD3+9n2rRpeDweotEoKSkpzmut1YR1ERGRcUyh6iREo1FiJSVMCgYpnzeP9vZ22tvbaW5upq2tjZqaGjweDy0tLZx//vkUFBQwefJkvF4vdXV1hEIhAoEAJSUlClYiIiLjjPZUnQSv10ts1iwyDx1i/vz5WGsJBAL09fWRlZVFT08PmZmZ5Ofnk5mZyZ49e9i1axf79++nu7ubzMxMQqGQ9lqJiIiMQwpVJ8EYQ/rZZxPo6KA0M5O0tDRmzJgBQDgcJhAIYK0lNTWVeDxOb28vOTk59PX14fP5CAaDBAKBgfMBRUREZPzQ8t9Jcp1xBgCTOzooKCigvr6e4uJiuru76evro6CggJkzZ7Jv3z4ikQihUIhzzz2X0tJSYrGY9lSJiIiMU+pUnax58wBwV1aSl5dHcXExsViMzs5OAoEA2dnZGGNoaGjAGEN7ezs5OTm4XC58Pp8ClYiIyDilTtXJqqjAZmQwpakJ38yZdHV1kZmZidvtHuhUeb1eQqEQiUQCl8ulA5ZFREQmAHWqTpbLBQsXklldTTgcJisrC7/fT2lpKUuWLCE3NxeXy8W8efNwuVz09vaydetWEolEsisXERGRYaRQdQoSCxaQvm8fXZ2d7Nmzh87OTlJTU3nllVd48sknefHFF+nt7WXfvn0AtLW10dHRoWNrRERExjGFqlPgWrwYdziMe/9+3G43zc3N1NXVkZqaSnp6OvX19dTX15Oenk5lZSXV1dUcOHCAmpoaBSsREZFx6rh7qowx/3K8x621PxjacsYGc9ZZAJyflkZVXx+9vb1UVlYSj8eJRqOUl5eze/duXC4XqampNDQ08Prrr1NaWkphYSEul0t3AYqIiIwzJ9qo/n3gNeBxoA9QCgA480xsIMAFwPYlS9i5cydtbW0EAgFqa2vJz8/nscceIzs7m97eXnp6emhtbeX8889n3759uFwu0tPTNVldRERkHDnR8t9i4EngPUAZ8CJwu7X2NmvtbcNd3Kjl9cL55xPYtImioiJcLheJRIJ4PI7H46GpqYlEIkFhYSG9vb1EIhHa29upr69n9+7dtLe309XVpcnqIiIi48hxQ5W19jVr7ZettYuAXwFXATuMMVeOSHWjWHzFCnx79jAzM5Pi4mK8Xi8pKSlcfPHFTJkyhZkzZ+JyuZg8efLA0TX9R9ps3bqV1tZWPB5NtBARERkvBrVR3RhTAJwFnAnUAk3DWdRY4L7kEgDSXn6Z3bt34/V6CYfDWGvJz8/n+uuvZ+7cuSxfvpyioiIyMjLo6elhy5YtWGuJRCIDX9q8LiIiMvadaKP6TcA/AX7gT8A/WmsnfKACMEuWYHNzmbprF2l5eRhjqK+vx1pLY2Mjfr8fr9dLV1fXQGiaM2cOLpeLtLQ04vE4dXV1RKNRAoGA9leJiIiMcSfqVP0KmAx0AZcBvzTGPNL/NezVjWZuN4lVq8jeuJEVF1xAXl4ec+fOpbe3l66uLt58802CwSDbt28f6GC1tLRQWFhIcXExFRUV9PX14ff76erqIhQKqWMlIiIyhp1oU8/KEalijHJddhnuP/2JczIy8CxYQDAY5KmnnqKwsJCWlhYSiQSZmZkcOHCA2bNn4/F4mDNnDjk5ORQXF/P6669TVVU1MG09IyNDHSsREZEx6rihylq7bqQKGZMuvRSA1PXriS1ezMyZM3nppZdIT0+nt7eXWCzGpEmTiEajRCIRmpubWbp0Kfv27aO1tZVQKMTMmTPZu3cvaWlphEIhotEoPp8vyR9MRERETtZxl/+MMXOMMY8bYx4zxkw3xvzaGNNhjNlojJk7UkWOVpGiIkJlZeS8+CI1NTU88sgjWGtpbm6moqICn89HZ2cnM2bMIDs7G2MM69at48UXX2T//v00NTURDAbJy8ujp6eHQCCA1+tN9scSERGRU3CiPVX3AD8BfgesBZ4AcoBvAD8e3tLGhubzzyfr9deJNjTQ3t4+cIhyfX09Bw4coLa2lm3btrF9+3ai0SjZ2dlkZWURDoeJx+MYY5g8eTLTpk3T0p+IiMgYdqJQlWGtfdRa+yAQtdb+wToexQlXE5rP5yPl2mtxJRLMq6oiGo1SXV2NMYb29nb8fj+xWIzW1lba29t57rnnePTRR6mrqyMvL48pU6YwefJkenp6MMYoUImIiIxhJwpV7rf8/M5z/ib8xh9jDLmXXEJk0iRWtLURj8fJyMigoKCAM844g9LSUmKxGF6vl6qqKoLBIG63m3A4zOTJk5k+fTpdXV0Dy37986t0F6CIiMjYc6JQdbcxJh3AWvuT/ovGmBnA08NZ2FjhS0mh7/LLmfzGGyyfP5/LL7+cnp4euru7mTJlChUVFZSUlOByuSgqKqK+vh63201TUxOFhYVUVFRQWFiItZba2lqqqqqora0dCFYKWiIiImPDie7++/kxru8FPn+81xpj7gWuAJqstfMPX/se8A9ABNgH3GSt7TjKa/fjzMaKAzFr7dITfpIkMcaQfsMNmPvu46qUFDbl51NbW8vmzZuJx+P4/X7S0tKIxWL09PSQkpJCNBqlqamJ6upqAoEA4XAYYwyJRILs7GyCwSDRaBSv10ttbS2hUEgDQkVEREa5E939l/+O3683xtxljLnFnPhf918Da95x7SlgvrV2AbAb+PfjvH6ltXbRaA5U/cwFF0BeHrO2b2fRokV4PB76+vpoaWmhvr6ehoYGYrEYLS0tXHTRRRhjcLvdvPHGG1RWVtLV1cWuXbtobW2ls7NzYDkwGo0SCoXIzMwcGLcgIiIio9OJlv+e7P/BGPNV4AZgC7Cav99j9TbW2vVA2zuuPWmtjR3+9WWg5GQLHpU8HrjySsxf/8qs8nJmz55NOBwGoKCggGAwSGdnJ83NzTQ1NVFcXEx1dTXNzc3U19ezbds2IpEI0WiU4uJiCgsLAfB6vQQCAYLBoMYtiIiIjHInmqj+1m7Ue4ELrLUhY8wDwNbT/NsfAf54jMcs8KQxxgI/t9bec5p/a/hdcw3cdx889xzXXHMNra2t1NfXE41GicfjuN1ujDFkZmYye/Zs9u3bR319PVOmTCE9PZ2CggISiQRNTU2Ew2Gys7MpLS2lpKRkYClQS38iIiKj14lCVaox5iycjpbbWhsCsNZGjTHxU/2jxpivADHg98d4ynnW2kPGmELgKWPMzsOdr6O91y3ALQBTp0491ZJO3+rV2ECAngceoOef/5mysjLKy8vxeDyEw2EeffRR8vLy2LNnD4lEgsbGRsLhMGeddRY5OTlkZGSQmZlJTU0NLpeL1tZWioqKSElJ0YR1ERGRMeBEoaqeI8t8bcaYydbaemNMHk4oOmnGmBtxNrBfbI9xS5u19tDh703GmD8DZwNHDVWHu1j3ACxdujRpt8jZlBRCK1aQ8vjjBG65hYqKCtxuNx6Ph/b2djIzM+no6GDr1q1Eo1Hy8vIoLS2lr6+PqVOnUlpaSiQSoba2Vh0pERGRMehEd/8d60DlDmDFyf4xY8wa4EvAhdba8DGeEwBc1tquwz9fCtx+sn9rpEWjUTpWrqTk8cfZ+7vfUZmdTSAQoL29ncbGRrxeLy0tLfj9ft58801Wr15NPB6nra2NxsZGjDFEIhH8fj9er5eCggJ1qERERMaQE21UPyprbRw47lqbMeZBYAMw2xhTa4z5KM7RNhk4S3qvGWN+dvi5U4wxfz380iLgBWPMNmAj8Ji19olTqXMkeb1eIqtXE/d4OHPvXmKxGFVVVTQ2NtLU1ERXVxcALpcLj8eD3++nr6+PwsJCXnrpJbZu3UpHRwehUIhY7JSagCIiIpJEJ1r+O54nOU6wstZ+8CiXf3WM5x4C3n345ypg4WnUlRTGGMoXLCB03nnMfvNNpl1zDbW1tbS0tBAIBADw+/3U1NQQiUTYvXs3ZWVlVFdX43a7SSQSbN26lXg8zoIFC6iqqmLSpEmkpKQk+ZOJiIjIYBw3VBlj7jrWQ0D20JcztrlcLgI33IDrYx9jTihEdWkpkyZNorKykrS0NOrr6wkGg+Tk5LB3715Wr15NRkYGAM3NzRQWFhIOh0kkErhcp9REFBERkSQ50b/cNwFv4symeuvXZpyp6PIO5qqrsG43pZs2kZeXR05ODqFQiNbWVlpbWwEnQHV0dFBdXY3H48FaS05ODu3t7VRUVJCfn09paenfzaXSkTUiIiKj14mW/zYBb1prX3rnA8aYrw9LRWNcNDOTyNlnU75lC4Vr1uByu8nNzSUajRKNRrHWYozB5XLxzDPPEI/Hyc/PJzU1lc7OTubMmUMgECAajVJXVzdwNE3/2YA6skZERGR0OlGn6v3Aa0d7wFpbMfTljH1er5fIFVeQWlPDPGtZuHAhBQUFRCIRAoEAhYWFuFyugUOXq6qq6O3tJR6Ps3jxYtrb22ltbf27o2l0ZI2IiMjodqJQlX6s0QdydMYYAtddhzWG8i1b2Lx5M16vl8mTJ5OVlUUikQCcJcCqqir27t3LvHnzWLBgAZGIs6La3t7O7t27SUtLG1gC1JE1IiIio9uJQtVf+n8wxvzvMNcybvimTqV36VLSn3yS9PR0fD4f3d3dpKWlYYzB5/PhcrnIzs4mGAyyd+9e/H4/RUVFdHV1kUgkiEQiAx2uvr4+AEpKSpg2bZqW/kREREahkzn7b9pwFjLe9Fx+Obm3305qTQ0lJSWkpKQQj8dpbm6mr68Pay1NTU0UFhZSXV1NeXk5WVlZPPvss+Tk5FBeXk5FRQV1dXUAlJaWUlGhFVcREZHR6kShyh7jZzmOaDRK64UXkgucsWMHOTfdRFtbG/X19UyePJmcnBxqampwu9309fXx6KOP0tjYSElJycAcq6ysLFpaWjDGUF9fT0dHBwcPHmTSpElkZGSoWyUiIjLKnGj5b6ExJmiM6QIWHP45aIzpMsYER6LAscjr9eIuL6d99mzKtmyhqKiIsrIy5s2bR2ZmJm63m7y8PAAikQg+n4+amhpSU1OZMmUKfX19uN1uIpEINTU17Nmzh+bmZnbv3k1HRwfd3d1v26iuUQsiIiLJd9xQZa11W2szrbUZ1lrP4Z/7f88cqSLHGmMM5eXlxK+5hsDOnXS99hqNjY3k5+dTWFhIRUXFQNByu9309PQQDjv3A8yaNYtFixZx7rnn4vf7ycrKoqysjJaWFgoLC2lvb8fn8w1sVO8ftVBVVUVtba2ClYiISJJobPcwcblc5N5yCwDZTz1FZ2cnmzdvpquri3A4TGlpKQUFBcybNw+32008Hqe1tZXrrruOGTNmsH79ejZs2EAoFKK0tHTga86cOVRUVAzMrurq6qKpqYn09PRBjVpQV0tERGR4nM7Zf3ICseJiYgsWULBuHa8VFNDQ0EBZWRlbtmwhNTWVlJQUEokE3d3dxGIxXn75ZTo6Oti3bx89PT0ATJ8+nczMzIHne73egUB18OBBNmzYwKFDhyguLmb58uXHHbWgAaIiIiLDR52qYeT1eum6/HLyDxxg1dSpZGVl0dPTQ0lJCUuWLCEYDJKSkoK1lmg0SnNzM7/97W9paWkhGo0SDoeZNm0aFRUVZGVlkZeXR2dnJ5FIxNkM39pKNBpl6tSpuA9Pbj9eSNIAURERkeGjUDWMjDHkf+ITAEx96SXOOecczj77bKZPn87mzZuprq5m+/btxONxuru76evr46GHHiIYDOLxeDjvvPNIS0ujqamJzs5Otm/fTnNzM7W1tQOb3X0+H52dnRQXF5OWlva2pb3+pb7+uVcej0cDREVERIaJlv+GmausjN5ly5i+aRM73vte6urqmDNnDrt27RroKhljiMVihEIhYrEYu3btorCwEJ/PR2trK5dccgkHDx4kGAzi9/vZuXMnAOXl5SxevJhgMEheXh51dXWEw2ECgQDFxcXU1dXR3d1NMBgkMzOT9PR0iouLicViA8uIIiIiMjTUqRpm0WiUjjVrSK2qwrV9O9Za3nzzzbfdvdffWUokEvT09LB37156e3t57bXXeOSRR3j44YeJx+NMnjyZuro6cnJyiEQi9PT0DFwPBoN0dnYOLO2Fw2FCoRCpqam0tLSQmpo6ENp8Pp8ClYiIyBBTqBpmXq+X2DXXkHC5KF27llgsxqRJk7jkkktYunQp06ZNIy8vD7fbDTCweb2trY3Ozk66u7vZsmUL9fX19Pb2MmvWLLKysggEAng8HtLS0ggGg2RnZ5OVlTWwtJeWlkYgEKCnp4f8/Hx6enq05CciIjKMtPw3zIwx5M+dS+MFF3DWxo08v3Il7pwcjDHMmDGDzMxMNm3aRF9fH21tbcRiMay1TJs2jb6+PrxeL6mpqeTl5dHT08OkSZPw+XwkEgl2795NZmYmU6ZMwefz4fP53ra0V1JSQjQaxePxaMlPRERkmClUjQBjDLUf/CCT163j3DfeoPnGGyktLcXj8ZCSksL69esJh8MkEgmstbS3t7N+/XpWr15NUVERvb29NDQ00NvbS3p6Oi0tLfT19ZGZmcmOHTvIzc3F6/Uyffp0SktL37ZXy+fzAQx8FxERkeGh5b8R4PP5yLn4Yg7NmcNZ69fzxpYtrF27ls7OTlJSUkhLSyMlJWXg+fF4nLq6Op588knS09NJTU0lNTWVYDBIXV0du3bt4o033mDHjh0Eg0FcLhdut5vOzs7jjknQ4E8REZHho1A1AvqX4hquv560tjaKn3+ePXv2sHHjRsLhMJMnT/7/2XvzIDnv877z875933f3zPTc94HBQAQJEJRIHcXIEiVbkWlLTkxbiSqr9brkI7X2xkmVvUrtViqp8trZ8hmvLVmy1pLKpmxSMS3LomSeIAkQ9wwwZ/d09/T0fd/n/gG+v8yAAAVSIkFy308VCkSjfz3vdAN8v3ie7/N9xHTeQeLxON///vfZ3d3ln/7pn7DZbGQyGcxmM3fddReSJDE5OUmv16Pb7eJwONDpdDcVT0rw5/b2NqFQiF6v91a/DSoqKioqKu9q1PbfW4TBYMD7yCNUv/hFfuzSJb7S79N5RQjddddduN1unn32WdrtNo1GQ5wrFotMT09TrVZptVo4HA5cLhcAy8vLBAIBarUaBoOBYDAIcNPU9Ha7TblcplAokM/nhW9L9VipqKioqKj8aFArVW8RkiQxMjqK/Ou/TjCf51giQTabZWNjg9HRUY4ePcrdd9+N1+s9dC6dTlOpVJibmyOTyeBwOJiamuLkyZMMDQ2xubnJ3t4eoVCI3d1dWq2WSE0vl8vk83l6vR5arZZcLselS5fo9XqUy2VardYdejdUVFRUVFTefaii6i1EkiSM//pf0/L7+TeFAiMjIwwPD6PX6xkbG2NychKdTneoDZjNZkmn0zQaDer1Ot///vc5e/Ys8XicSqWC2+0mGo1itVpFhctisVAoFNja2uL06dOcOXOGVquF2+1meXmZTCZDKpUimUy+qkWoeq5UVFRUVFTeGKqoeouRDAbkX/1VjqRSjKTTIj/KbrcTDAaZm5s7JKqq1SqXL1/m2rVrItjz6tWr/OVf/iWXL1+mVqvh8/lEcnq/32doaAi3240sywwODpLJZGi1WhgMBtESXFxcpFqtUq1W6fV6NJtNotEoOzs7xGIxVVipqKioqKi8TlRP1R2g+OlPY/nCF/h8vc5/83pxu90MDg5SrVYZGBjAZDKJbCmAXC5HJBJhfHwco9GI2Wym2WzicrkwGo1MT0/jcDgIh8Osr6+L6IVer0c8Hsfr9ZLP52m1WthsNlwuF+VymVKpRCQSoVwuYzQaSSaTLC4uUqlUaLfbagyDioqKiorK60AVVXcAjdvNc0tLfODCBVrDwzz55JN0Oh18Ph9+vx+HwyES1QFarRaRSIT3v//9GAwGLly4QKVSIRwOs7CwgEajodvt0u/3MRgMhMNhjh49yvT0NAMDA5jNZsLhsEhcV0JBI5EIWq2WdDqN2+0mHo/T6XRYWlpSk9dVVFRUVFReJ2r77w5gNBrJ/+zPIgE/DNvKWQAAIABJREFUGY9TLpfxeDzCQN7v95mfnz90JpVKsba2htfrZW5ujp/5mZ9hZGSEiYkJGo0G3W6XZDLJs88+SywWIxQKYbVacblcIgtLiWPQ6/WYzWbK5TKbm5s0Gg2y2SwOh4Nmsymyrnq9HpVKRY1fUFFRUVFRuQ1UUXUHMBgMLH/841yeneWDW1skt7fZ3d2l1+tx3333cerUKYrFIlrt4ULi7/3e73Hx4kXy+TyxWAy/308ul6Pb7RIOh1lbW6NSqTA/P4/D4SAQCAAI8/nB+IROp4PdbmdpaYn5+XkMBgOJRAKdTker1aLZbHLmzBmeeuopzpw5oworFRUVFRWVH4Aqqu4AkiQxODjIzk/8BJZ2m48kk0iShNlsxuFwMDk5ydTUlMijUojH43zpS1/CarUSCATweDy0Wi3i8Tirq6s4nU6q1Sr1eh2TyYRGoyEWi7G+vs7Ozg5ut5tarUa73RZTho1GA7PZTDAYZGVlBUmSkGWZVqtFJpMRRvdarXaH3q3rqJOJKioqKipvd1RP1R3CarXi/4mfYPcrX+Gfra3x4l13EYlEKBQKpFIpPB4PRqPxVefW19eJx+OMjo6ytrZGPB4nkUgwNTUFwLFjx9BqtYRCIbLZLB6PB4/HQzabJZfL4XQ60Wq1tNttgsEgnU4HrVbL3t4ekiTR7Xbp9Xrk83k8Hg/7+/t4vV7MZjP9fl8IsrcyNFRJg78x0FRFRUVFReXthCqq7hCyLLN89CgXH36Y+//wDxm6cIHQ0hJ2ux2bzcbe3h5+v59SqSQM63A9DPRP//RP2d3dxWAwEAgEsFgsuN1uFhYWGBsb49lnn2VwcJD9/X18Ph/lcpnJyUncbjcmk4lwOEyz2cRqtTI0NEStVmNoaIh6vY4kScLQfuzYMTqdDmaz+fpS6DskbNrttgg0LZVK6mSiioqKisrbErX9dwexWq3w8MMUbDb+ZSrFysoKZrOZUqnE8vIyKysrN53C297eJpVKodVq0Wg0TE9Ps7S0xMzMjMi82t/fx263MzU1dWh9zQsvvMDVq1dFrtXp06d56qmnOHv2LCaTCYvFIgztBoMBq9WKLMuHhE21Wn3Nxc0/anQ6HRaLhVKphMViUScTVVRUVFTelqiVqjuILMvcfeoU4Z/8SRa+/GWOa7Xo7r2X4eFh+v0+x48fJ5lM8sQTTxzyEtXrdc6dO8fy8jKTk5N0Oh2uXLlCKpXCbDbjcrnodDq4XC5efvllsaJmamqKbDaL1+sllUoxOjpKoVBgaGiI/f194ZtSKlAH2323EjZvRUtQWUh9J1qPKioqKioqt4sqqu4wRqMR67/9t3S+/nVcX/kKX97dFcnq99xzD41Gg/39fc6dO3foXC6Xo1qt8tJLL2EymTAYDNhsNvL5PEeOHKFWq6HT6VhdXcVoNJJIJEgmk+j1eprNJnNzc4yPj1Or1YRvSqfTUa1WsdlsVCoVQqEQ7XZbtPtuFDZvpddJkiS15aeioqKi8rZGFVV3GEmS8M3NEf3gB1n4x38k5/eTSCREvMKJEyf48Ic/LAzpCkom1X333Ycsy1SrVbLZLAaDgVgshtVqZXNzk2w2K6YNdTodKysrNJtNRkZG0Gq13HPPPVSrVVGNKpVK7OzsYLfbsdvteL1eyuWy8DEdFDZ32ut0p4zzKioqKioqN0P1VL0NkCSJ/U99Cm23y6mLF9Hr9XQ6HdbW1kgmk3zkIx9hbm4Ok8l06Nz58+epVqu0Wi2OHDmC3+/n3nvvpdFoUCwW2d/fx+1202w2Rdr69vY2jUbjkC8pnU4TCoXY3d3FZrOxuLhIu90mmUyysbGB2WxGp9O9KtbgB3md3swYBKVKpu4qVFFRUVF5u6BWqt4G6PV6HCdOsDEzw8/EYpztdoHrKer5fJ6TJ0+yuLjIpUuXqNfr4lyhUOC5557jPe95D3NzcxiNRq5cuUIymcRisWA2m8nn89x///1C9DgcDur1Op1OB51Ox87ODmtrawwODtLv99Hr9eTzeQAWFxfJ5XL4/X5arRbJZJJarXao1Xcrr9Ob3Rq801UyFRUVFRWVG1ErVW8DJElicnKS3U9+Elu9zn27u0QiEa5cuUI4HObatWt4PB68Xu+rzp45c4ZqtcpTTz1FoVAAYHh4WAiOo0eP4vV6cTqdIipBq9XS6/VoNptEIhGazSarq6sAaLVatFqtWGPjdDpJpVKsr6+zvb2NzWY7NP2neJ1uFEy3mha8VfXq9Va11IlAFRUVFZW3G2ql6m2C0Whk4Gd/lr0/+zN+7MoV/s7rZWxsjFwux+bmppjmU5YnH+T8+fOMjY0xMTFBPB7HaDRiMBhYXFwkEAjQ7XapVqtUq1WRexWLxRgeHgbA6XTSbDYpFoskk0lmZ2fR6XSMjIyg0+kIhUI3DRBttVq39DMdFD1KcKiyTqfVamG1WkX16o1UtW5nIlD1XKmoqKiovJWoouptgiRJjIyO8p33vpdPPf44J7JZGq9Ubp555hnS6TTLy8sYDAaeeeaZQ2fX19d573vfSzqdplgsEgwGOXv2LMFgkGKxiCRJaDQaQqEQQ0NDNJtNqtUqlUpFxC0UCgUajYZIc3e5XFgsFvr9vjCwT01NEQgE0Ol0xGIxisUidrudgYEBdDqdaClKkiREj9I23NnZIZvNUigUxE5CpWWnVLVsNptoNyqPv5Ygeq2JQDWFXUVFRUXlrUYVVW8jjEYjvZ/6KUpPPcWnd3d5aWGB06dPY7VaRbXn4YcfJhKJsLu7K851u11qtRput5tischjjz2Gz+cTAkYxmVssFgAikQjhcJh6vc7CwgKBQIByuUy328Xn8zExMXE9mBTY29uj1WphMBgYHh5GlmWazSbb29tIksSlS5dYWlqi2Wxit9sPVaCUH7VaDaPRKDKy9vb2WFpaot/v0+/3RctxfX0dSZKw2+3i3BsVRKrnSkVFRUXlrUb1VL2NMBgM3H3qFDuf+AQjoRD68+dxOBzo9XoGBwe5++67OX78OJ/4xCdedfaZZ54hn89TKBQolUpoNBqMRiMzMzPYbDYKhQJjY2Pcfffd+P1+pqamSKfTYt/f9PQ0Ho+H0dFRLBYLkiQJYeJwOGi1WrTb7UO+p3g8zvb2Nvl8nnQ6jUajoVKpHEpb1+l0mM1mIeK2t7cxGo2k02nW19eJRCLEYjFqtRqdToeZmRkKhQKZTEbkZVWr1dc93ad6rlRUVFRU3mpUUfU2QjGsW3/1V2maTIx+4xv4fD5WVlaw2WxcuXKFxx9/nEAgwIc//OFDZxVje6lUwm630+12GRkZYXh4mEqlInxTSkjo5uYmu7u7ZDIZ9Ho9brdbxDPs7e2Jtt9BX5TSxkulUgSDQWq1Gl6vl3A4TKVSYXNzUwg6RXxJkkQgEMDtdjM8PEypVMJgMHD16lWSySSrq6tiebNOpyObzVKv1ykWi2xsbFAsFolGo687NkFpP05OTv5Qrb83MxZCRUVFReXdhdr+e5shyzKB6WnCH/kIR//2b/nTxx+n4PGQTCapVqsYjUaOHTvGysoK3/nOdw6dDYfDHD16FL1ez+LiIgA7OztEIhH0ej1DQ0NIksTy8jLpdJrJyUnS6TQWiwWPx8Pm5ibBYFBM6+n1emEG7/f7hEIhrFYriUSC0dFRRkdH0el0hMNh9Ho9VqsVi8VCOBw+lMSu/F40GmVsbIy1tTXh4TKbzVitVvR6PVNTU7hcLmKxGCMjIyQSCbRarZhafL0tvB82hV31ZamoqKiovB7UStXbEIvFQu6RR+jKMj+xuUkmkyGRSLC/v0+73SaRSGC328WiZIV4PA7AwsICsizzwgsv8NhjjxGPxzl79iwbGxtEo1EsFgszMzPi9VqtFqFQiGazyfr6OhqNBq32sN7W6XSYTCaeffZZnn76af7hH/4Bi8WCzWYT4urq1avs7+/TaDReFaWgrN4BqFQqWK1WMpkMMzMzeL1eUVXT6/VYLBbK5TI+nw+Xy0WpVMJkMtFsNun1em/BJ3CdO7lEWkVFRUXlnYdaqXobIssy8x/8IC8vLvLA6ir/vtXC4PfTaDTQ6/WUy2WSySRzc3PE4/FDralvf/vb2Gw2EVsQj8cZHR1lcXGRRqNBr9fjxRdfpFAokMvlWF5exmg0CjGzurqKLMvCmL63tycqNS6XS2RYRaNRnE4n73//+0mlUly7do1AIECz2SSfzyPLMlarFa1WSzQaJZ/Pi9aezWbDbDbjdDqxWq0YjUZMJpP4WmazmYmJCVFlajabnD9/nosXL+L3+zlx4oTwfL2ZcQmqL0tFRUVF5fWgVqrepmg0Gi586EMYej3+1Stm7ZGREaamprDb7WQyGZxOJz/1Uz/1qrPnz5+n0+nQarVwOBwUi0U2NzcJhUI89dRThEIhXC4XJpOJaDSKXq/H6XSSSCQwmUzo9XoKhQK1Wo1KpYLRaKRcLgPg9/vZ2dmh2WxSqVTQ6XQMDw8zNTWFXq/H7/eLCUCllbi1tcXa2hovvPACWq0Wk8mE3+/ngQcewGq10mw2CYfD5HK5m4aLtttt1tfX6XQ6XLt2jVKpxLVr17h69SrRaFQEmSrreH5UHPRlBYNB0QZVUVFRUVG5GWql6m2K1Wpl6uMf5+W//mt+Lpnka2NjjI2N4fP5qNVqQowYjUaCwSB7e3vi7NbWFnfddRcej4dGo0E6nRaGdKfTidFoZGtri+HhYdxut4g1cLlc5HI5kskk4+PjaDQaCoWCiDoAGBgY4IEHHsBgMCDLMuFwGACz2czs7KzwIOVyOXK5HKlUit3dXSRJwul0IkkS9957LxMTE0iSRCgUwm63s7GxQbvdJpfLiR2HijhTWo+VSgWDwcDZs2c5c+YMLpeLmZkZWq0W0WgUgKmpKUZGRm6renWrcNAbH1dyuVRvlYqKiorKa6FWqt6myLLMBz7wARqf/zyOTod//orYsdvtOBwO2u02siwzNDR002rVU089hd/vFwnq5XIZWZbJZDJMTk7idDqp1Wrs7u5SKBQoFApYLBYeeOABJicn6XQ6YkJQr9cLsdPtdkW2lU6nY3t7m0qlQq1WY2BgAK/Xy+zsLMVikfX1dbHwuNvtMjk5yZEjR8SeQqXlmM1mAZiensZms2EymXA4HKJiZTAYuPfee1laWuLo0aOUy2WRyVUulykUCmg0GmRZplAo3Jb36VYLmW/2uOqtUlFRUVG5HVRR9TZGq9Uy/sgj7Pj9/Kt8nnwmw9/8zd/w8ssv0+l08Hq9xONxdnd3WVpaOnQ2mUyysbGBxWIRvqXJyUlOnjyJXq+n1+uRyWQIhUKcO3eOra0t8vk8Z8+eJZfLCUN7tVoVWVPlchmr1crExASTk5N4PB7cbjfJZBKDwYDFYsHpdFIul7FYLPR6PVZXV6lUKqK1p1R4FLESDAaZnZ3FaDRy+fJl2u02TqfzkI9JkiRGR0dZXl5mfn6ewcFB9Ho9IyMjrKys4PV6hV/M6XTelvfpVkLpZo+r3ioVFRUVldtBbf+9zQkMDPBPDz/Mg3/0R3wgm+X54WGi0Sj9fh+z2czw8DADAwMMDw8TCoWo1Wri7Le//W2OHDlCv99nfn4eu90OIHxLe3t71Go14YcKBAKUSiWCwSDJZJLt7W2sViv9fp+TJ08SCATodDrA9XZfpVIhn8/jcDgYGxtDlmURwSDLMvl8Hq1Wi8/nIxwOs7W1xebmJv1+H4PBIBLTfT4f9Xodi8VCvV4nEAggy/KhttzBeIQTJ06wtLSETqcTuwn9fj8Gg4FgMCj2Cd7Y2jv42K2E0s0ev509gze+/ju1PfhWfg/vhvdLRUVF5SCqqHqbo9Vquf+3f5vio4/y2UyGv9Fqsdvt6PV6JEkik8nQaDSYmpric5/7HH/4h39Iq9UCIJ/Ps76+ztzcnKhA+Xw+jh07RrVapV6vU61WWV9fZ3BwkEajQTgcFvv/pqenWVxcJJ/PEwgEuHTpEplMBofDwfHjx7Hb7QQCAer1Ot1uF41GgyRJaLVaXnrpJfb39/F6vcD1tPhsNsvw8DDFYvFQ9UvxWun1errd7k3zpQ7egGVZxmaz0ev12NjYYHt7W7Tv9Ho9ExMTh6YWlcXR0WiUYrGIw+EQEQ433tRvJaB+0DUBr+m7eqMC4nbO/SAB+YO+nvJcrVb7qvft4Nler0etVsNsNiPLP1yRW80AU1FReTeiiqp3AJV6nZfuu4+P/u3f8tHxcc69sjZme3ubH//xHxfCqlKp8OCDD/LEE0+Is4pAajabxONxJEkiFotRr9e5cOECzWaTQqHAwsICOp2OWq3G8vIynU5HmMOdTiedTodMJkOn0+Hy5ctYrVZcLpeoMCl7/CRJolqtcu3aNex2OzqdjvHxcY4cOcLGxgYOhwOPx8OlS5fY399naGiI2dlZpqamhK/rxvbazW7A/X5feLaU7K6jR4+KSIdcLofX66VUKlGtVtFqtWxvb6PRaMhkMrhcLqxWKzqd7rYE1I3ceE1+v/+WuwbfqIDo9/uvEoI3nrvZa8NrC7xbndfr9TSbzZuGrfZ6Pc6cOUMmk8Hr9XLPPfe8prD6QaLuYJu1WCyKa1WFlYqKyjsZVVS9AzCbzayfPMnJJ57gU+Ew/xAM4na72dra4hvf+AZOp5Px8XHMZjMjIyOYTCbq9bo4f/XqVSYnJ9HpdKLa0Gq1aLVa6HQ69vf3RVRCIpEgnU6ztLTEj/3YjwHXW2LdbpdKpcLa2hpWq1Wkq4+Pj5NKpdjZ2cFgMDA+Pn5oWq/ZbBKJROh2u8zNzXHixAk6nQ4ajYb3vOc9pFIpisUiPp+PVqtFs9kkFAoxMTGBLMv0+32q1SqVSkXc7JVVOtvb28D1+ImJiQlsNhvZbJb9/X3S6TQul0u0PBWh1uv12Nvbw+1243K5AN7Q4uYbFzYDt/RdvdHlzopwVoRgIBDAYDC85nUo3rDb/Xo3ihu9Xn/T76FWq5HJZBgcHGR/f19MoN6M2xGRSpu1WCxSKpWIRqOHlnGrqKiovBNRRdU7AKPRyKkPfpD//uUv8/PXrjFtt7P5yk66crlMtVql2WwyNjbG+9//fhKJBI899tih19DpdMzMzIj1MqdPn8ZoNIq2WbvdZnd3F4fDgc1mI5PJsLW1xfz8vIgsUGITdnd3aTQaRKNRAoGAED07Ozv0+30mJia49957icfjrK+vs7+/jyzLYmmy2WzG5/ORTqfp9XpcvHiRTqdDr9fD5XIRCoWQJInx8XFisZjIzALEjbzZbOL3+4nFYjgcDgKBgBBorVaLXq9HLpcDYHR0lFKpxPDwMKurq0K8pVIpdDodXq+XbDYrfFm9Xo9KpSK8XwaD4ZaiQBEgB1f63Fid0Wq1aLVaIfRuZnS/sbJzcOfga2Vj3cobdrvG+oPnrVYrwWCQTqfzqu/BbDbj9XpFS9dsNt/yNW9HRCpt1mq1SjQaPVQdu1n1UEVFReWdgCqq3gFIksSRI0d48sd/nOrmJp/N5fgVWcbr9VIsFkVFxmg0sre3h8ViERUFhfX1dR588EHMZjPpdJpOp8P09DT5fB6bzUY+n8doNIoq0sjIiFhb02q1iMfjmM1mSqUSg4ODeDwe2u228FGlUin8fj+tVotOp8Po6ChOp5PTp0+ztraGw+FgcHCQnZ0d3G43x48fF+t3isUimUyGfD5PLBYTbbxqtSoqNd1ul6WlJSGqLBYLiUQCg8EgWpWlUgmv18vp06dpNpscO3ZMLGl2Op20Wi0kScLn87G6usrQ0JDIyILrE5PBYJCXXnqJ559/nnw+z+zsLCdPnmRsbExUiZQb/vDwsPCvKZ/TzXxXsViMaDRKp9PB6XS+6vO9sbKj5I5VKhXMZjMmkwmXy3XTatOtPGC3Y6y/1fmbfR1Zlrnnnntuy1N1u9OSkiSJ6VSlSqbRaH5or5VqgFdRUblTqKLqHYLZbOaDn/wkj3/ta3wqFuO3ZJnyK8KqVCoRj8fR6/VUKhUmJyfJ5XKHRBXAY489xte+9jVCoRBra2u0Wi2q1SqxWEy0cz760Y8yMDBAMBikVCoRDoeRJIkXX3yRbDaL2+1mfHyc3d1drFYr58+fR6/XY7PZxA3yYLUlHA7T6XRIp9PY7Xbsdjvlcpl2u02z2aTRaNBsNqnVahw7doxisYjBYBArbpRJQkCY8wECgYDwYK2trZFMJul2uwDMz8/jdrspFAp4PB5mZmaQJImNjQ0GBweJRCL4fD6WlpbIZrNi8jGfz2OxWMTqn1wux4svvkg0GuXYsWMsLS0hSRKFQkG0qi5fvoxGo8Hj8dzU89RutykWiyJHq1gsvspvdWN7s1arUa1WcTgcAIyMjLym3+hmQuj1LJO+3ecqq4du5/UOCjVAtJpv/B4kSSIYDLKzs0M+n6fX69HpdN7wEm3VAK+ionInUUXVOwRJkpicnOSP5+b4yViMz9dq/Fo2y9133y0Sy3U6nUgzt9vtfO5zn+NP/uRPxGvEYjF+93d/l4997GMiXDOdTlMoFITAOX/+PPfffz+JRIJMJoMsy1y4cAFJkpBlWYii+fl5DAYDzz33HD6fD51Ox8rKCna7HUmS6PV6FAoFDAYD09PTVKtVjEajqFq5XC5hcF9YWBCJ8AMDA7jdbsxmM3t7e2i1WhqNBiMjI2g0GnFz1ul06PV6IpEIk5OTPP300+h0OjY3N5mfn8dsNlOv14Upf2BggEwmQzqdFjsHE4mEyM66fPkyvV5PtLnW19eJRqMYjUZqtRper5d+v49Op6NYLFKpVAiHw4RCIQYGBvjwhz+M0+kUwk/5WafT4XA4yGQyADgcDvr9Pt1ul1qtRi6Xo1arCV+W1WrFbDYfasm9lQbuNzpteCPKe3A7IqfVavHyyy/TbDbR6/UcPXqUYrEoBPrr+bpv1L+moqKi8qNAFVXvILxeL8c+9jH+32ee4edbLf5TrcaFCxcwGo00m002NjZwOp1MT09jNptZXl5GlmX++I//WLzGt7/9bVZWVjAajbRaLTweD51Oh06ng8ViIZ1Oc+3aNXZ2dqhWqwQCAVqtFsPDw2xubrK3t8fCwoLYB6gY39PpNNFolKWlJTEplk6nheA6evQow8PDWK1WKpUKqVSKWq2Gx+MRokG5+SliqlqtMjo6yjPPPEO1WmVzc5PR0VH0er2IX7Db7WLaz2w20+v18Hq9GI1GdDodg4ODYr/hxMQE5XKZ6elpzpw5Q6FQoNPpYLVaWV9fF3lap06dYnl5mUajAVxvneZyORFuqojRl19+GbvdTigUYmNjg0ajQbVaxefzMTY2xuTkJJIkEQgE8Pl8tNtt8vk8W1tbXL16lV6vR6PR4L3vfS9WqxW/3y/ei5u17xSfFVxvsd3M+/TDcDsC6FbThrcSO7cjctrttli0HYlEmJqawu12i8yxg1+3UqmIz/JmLUg1qFVFReVOooqqdxAajYZHHnmEX/6rv+LnTp/m13o9/n0iwUc/+lHOnz+P2+3GYrHgdrvRaDTkcjkeeugh/v7v/57d3V3g+hTXV7/6VT7zmc8wNzcHIMzjsizj8Xg4e/YsFouF0dFRms0mExMTBAIB9Ho9siwLE7fRaESWZc6cOUMwGOTixYsiyDOdTjM0NESv12N6eprp6WmSyaRIVy8UCgQCAUwmk2i7lctlOp0OLpeLjY0NEokE+/v7FAoF7r33Xq5cucLOzg7xeByv18v73vc+bDYbpVKJqakpQqEQIyMjGI1GhoeH0Wq17O/vEwgE8Hg8YpVOPp+nUqngcrnY2tqi2+3SarVot9uHwlOPHTvG7u4u99xzD0ePHkWj0Yg9gI1Gg0AgIFbmKF61fD4vBgja7TZms1lUogwGA4lEgn6/z5NPPonP5yOVSlGpVBgaGiKZTCJJkthfeNCwDddztpRMLrPZjMPhuKW5/I34im5XAB18TqvVIpVK3VKI3Y7IsVgszM/PE4vFGBoaYnBwkEqlQqfTEV+/3W5TqVSEIFcGGW78vm83qFVFRUXlzUB6rcmidxp33313/+zZs3f6Mt5Uer0eX/ziF9H+wi/wM90uE4B9dpaRkRECgQC9Xo+BgQF6vZ7YlxePx3n44YcPCQav18t73vMeRkZG2NnZYWxsjLW1NREIqtVq6Xa7eDweBgYGmJqaEkKq2WwyPj6O0+kkGo1y9uxZpqamqFQqop3mcrlYXFwkmUwyNDSEz+djZWWFdrvNmTNnSKVSNBoN7rnnHra2ttjf3xeBoJFIhGw2i8fjQZZljEYj3W5XVDR6vR7tdhuPx8PKygqdTgdZlmk2m9x///2YTCbRelKM1ZIkiYDLZrPJmTNn2N7eFqto0uk0VqsVr9fLwMAAAJOTk8iyLEzwSnWsUCjw3HPP0e12cTqd3HfffWSzWbLZLBcuXMBgMDA6OorL5cJsNjMzM8Pq6ioOh4Onn36azc1NotEoWq2W6elpHnzwQTqdDm63G61Wi9PpZGJiglQqRbvdFlWsjY0NIdqq1SorKyvU63X0ej3tdvsN5VTB7Yd/Ks+9MZ9LWYpdKpWYnJx8zZDUW11Hr9cTret6vY7ZbBZCXvHn7ezscO3aNXw+H3q9HqvVKoTfrSpXKioqKm8GkiS93O/3777xcbVS9Q5DlmVOnjzJ51wufi6T4deA/21ri7vuuguDwUAgEKBWq1GpVDhz5gxarZaRkREeeughnnjiCSGsMpkM3/3udxkdHcVoNAr/VL/fp9fr0e12MZvN4gYpyzKhUIhcLsfQ0BBGo5FEIkGj0UCr1XLlyhVarZYQIo1Gg/HxcbrdLt1ulytXrqDVaoW/qNlsUi6XKZVK5PN5jhw5Iipdyk20XC5jt9uZfUU05nI5nn/+eQqFApIk4XQ62d3dFRWdxcVF7Ha7uLlKknTIWK0ILSXA0uv10uv12NzcZHh4mEajgcfjYWFhgXq9zujoqPB9hUIhEZSazWbFUmqXy8X8/DypVAqHw4HdbsdgMIh2YrNl7jUMAAAgAElEQVTZpFQqYbfbWV1dBaBer+N2u5EkCbfbjdFoxOl00mg0hNfqhRdeIB6Ps7i4SL/fx+l0YrfbxeekJNkbDAYajYZYG/R6cqqUdmIymRRZXbeKVFBQKkEH25A/qBJ1O0Z4JSXfarWKawqFQofE3cTEBACRSIRisUgul8NqtZJOp8Xvq5UpFRWVO8mbJqokSfoi8HEg1e/3j7zy2BeA/wlIv/K0/9Dv95+4ydmPAP83oAH+tN/v/+c36zrficzNzRF8//v5y0cf5ReA/9zrsb+/j8/nY3h4mHA4jNfrFdlQer2ehYUFyuUyTz31lPAK9ft9kTs0OjqKRqMhnU6LBcXKNGG1WhXeI7PZLKolbrebRCJBs9kUeUrPPfcc9XqdYDCI0+nE5/Pxve99D6vVyvz8PBqNBpfLJSbbAoEAsViMdDrNwMAAer0erVYrwkrvu+8+pqamkCSJnZ0dfvqnf5p4PM61a9fY2NgglUpx7NgxFhcXReCoMqp/s8qF0r5yOp3CPO73+3nqqaeE56pUKolWqlLhUhY9A3g8HhKJhDirTA/6/X6SySSFQoGlpSWR2u7z+djZ2UGj0WCxWLDZbLhcLrFk2mg0srKygizLtNttQqEQrVYLs9ksWp3K93Tq1ClhgFfiL86cOcPGxgYDAwNotdf/Suv1+leZvW+2VqdQKJDJZJidnRXt19sxdistP7PZjN/vF+/DDytqJElCkiRqtdqrRKEsywSDQREWe/XqVZHKr2wUUPx2ymettgBVVFTeSt7MStWfA78PfOWGx3+33+//9q0OSZKkAf4A+GdADDgjSdLj/X5/7c260Hcaer2e3/qt3+Kz3/42P1ut8hvAb770Ep/97GfpdDoEg0FMJhOZTIZLly7xwgsv4HQ6eeCBB9BoNIfW2LRaLWHydrlcImpAuTmfO3dOmNGz2SyVSkX4n7RaLS6Xi/X1dZrNJiaTiV6vh9FoJBwO8/jjj7O0tMSxY8fQ6/VcvHiR5eVlZmdnKRaLIjvqxIkTYn+gLMtMTU2J9TfDw8MiH0oxubtcLtHuUyIlWq0WtVpNVOMWFhY4efLkq4TVwcqK0+kU1ZrR0VEGBgZotVoEX0msV27GB8/YbDaCwSCTk5PA9WBPxdSviC5JksTrabVaNjY2CIfDBAIBEokEx48fJxaLkcvliMfjfPe738VqtYooC7vdTjQaxePxCF+ayWSiWq0iy7IQDcpaGcXkXa/XaTab7O/v02g0XrVgOhqNUigUcDqdIrTV7XaTyWTIZrO3DCa9EUWY2mw2NjY2xGv6fD4x8HDwfX+9/i6lDZzNZnE4HIdEoRLwWiwWmZ2dpdvt0mw2KRaLRKNRdDqdEOEHk/KV61ZFloqKypvJm2ZC6Pf7TwO5N3D0BLDV7/d3+v1+C/g68Ikf6cW9C1hYWGD5p3+aLwOfB7z1OolEghMnTvDQQw+xsrIiUq/39vZwOp3YbDY+/vGPizaKQqPRIB6Pi1aZ2Wxmfn5eeILMZjPdbheHw0GxWCQSiYgkdyVTyW630+128fv9InpA2RkYj8dFaKjdbmdgYICFhQVOnDjB5OQkHo+Hfr9PvV4nFAohyzJzc3Po9XrC4TCxWAyAYDCIXq+nVqthNBqZnp4Wnqb3ve99YuVOtVpldXWVarX6qvdNaV9NTk6KtpJOp2NyclK09y5dukQsFqPX69FsNoV4CwaDBINBZFnGYDCI63v22WdZX19nfX2dQqGA1+sVa4J2d3fZ2toS4ker1VIsFrl48SK1Wo29vT1qtRoGg4FKpUKr1SKRSNBqtdDr9UxPT1OpVFhdXaVYLNLr9W6asK4Ij1AoxNWrV6lWq8J8ryTIb29vk8/nhdldEcdTU1PMzs7i9/tv68+eIjKVxHq3283m5iaPPvoojz76KC+99JK4zmazSTQaZWdnh1gsdtNrP5gef6vH+v0+lUqFQqHAzMwMXq+XwcFBJicnGRkZoVQqkUwmyWazIibEbrcLc/sPugYVFRWVHwV3wlP1eUmSfh44C/yv/X4/f8PvB4HogV/HgJNv1cW9U9DpdPzSL/0Sn/nmN/kXpRL/Efj83/89Dz30ENFoVNxYYrEYmUyG559/nqWlJZaXl/n0pz/Nl770JZLJpHi9Wq1GLBYTHqFGo8Hs7CxXr14lHA6LvYFKiKXSosvn8+j1evb29rDb7czNzeH3+6nVapTLZQwGA263m/e9731EIhFSqRQul4uBgQH6/T57e3s0Gg2y2Sy5XI7BwUHa7TadTudVLaB+v08kEgGuG5tPnDiBzWZjdnYWWZZxOp3ieT6fT4iiG9tSisdHqW75fD7g+nSl0WgUCfGKIFCmzfx+v5jMU9qCSup5o9EQQkXxF8F1wSpJElevXhVxDPF4HIPBINbpuFwuEUjaarXIZrOMjo6yt7cn/FjKbsYLFy4wODjI6Oio+H4mJiZEVafVauH3+0mlUrjdbuGXUiqPN5uSux2D+kGUs36/H4fDQT6fp9Fo0Ov1sNvtojVYKBRe1V680d91s4iGVqvFzs4OsixTKpXECqKtrS3S6bT4HA5mgun1elHNczgcGAwGsVdwZ2eHXC73qms4GFHxo2hdqqioqLzVouqPgP8D6L/y8/8FfPaG59zs/2y3/KelJEmfAz4HHLrR/P+BI0eOMPvgg/zBN7/JrwK/Xavxm7/5m3zyk5+kVquJwM6ZmRn6/T75fJ52u83s7Cyf+9zn+OpXv0o0el2/drtdkskkGo2Gf/zHf8Rut+NyuYQBXDFIb21t0Wg00Ol07O3tsbOzw/7+Pt1uV+RCKb6hQqEgwjoVz9Tc3ByXL18mmUwKE7ey3kar1VKpVBgYGDgUgKkYoJXKRTabpd1uY7PZmJubEwZ7nU4njN1ut5tz584BCCEEh1tAB9t6LpdLTJgdnETU6/Wi6tTtdg8tNtbpdKJl2ul0hKFe8TrVajXhfbLb7Wg0GhqNhqgI9vt9AoGAEKiK+PF4PFy6dEkEjSr+qGvXrmG1Wtnd3cXv95PJZCiXy2SzWWw2mwhEVQzwwWCQcDgsliWPjo4K8XJwJY0yTXgzY7vS+jWZTOIzVnxPBoOBkZERsTNR8aPNz8+j1WopFAq4XC4ymQyZTAabzSbEncLBVmImkxFeu36/L0ROu92mUCig0WjEe39w3Y9er2dqakpknimGdWUfpcfjIZfLkcvlcDqd6HQ60Q7d3t6m3W4zNzfH8PCwmDxU/kypLUMVFZXXw1sqqvr9viiNSJL0/wD//SZPiwEjB349DMRf4zX/BPgTuB6p8KO50ncGer2eL3zhC3zk7/6Of9Ns8p+Afx6Ps7q6islkQqfTUSqVyOVyaDQaZmdn2dzcZGBggIGBAX7lV36FRx99lO3tbZLJJPV6XVSvpqam0Gg0uN1u0Yra398X1apkMslzzz0nzOEDAwOYTCZ2dna499576XQ6jI2NEYlEcLlcpFIpAJ588kmuXr3K4OAgTqeTubk59vb2RJJ2Pp8nEAggy/KhSopycxsdHaVSqTA+Pg4gjNWtVotQKITNZhOG5U6nA1yfdPT7/aTT6UP79TqdDkNDQ0LMSJKE3W7nK1/5CvV6nXa7zfHjxzGZTABks1lkWSaZTIpqlRJloXwe/X6fRqPB6uoquVwOt9vN9PQ0W1tbJJNJGo0Gw8PDmEwmTp8+ze7uLvl8Hq1WK8TZ8vIy2WwWs9nMmTNnmJ+fB663ZZVcLiV+QGkNHj16FIPBIAJHFeGgCDOr1crQ0BDhcJhms0k4HGZ8fBxZloWAzGQyQnTAdUH10ksvkUgkkCSJmZkZ4SlTJgQBEeS6vLxMMBjEYDCQSqXEbsfp6WkajQaNRoO9vb1DlTDlaytDB0pkgtFoxGAw4PV6RehqNpsVf44ajcahquHIyIjwsCnVSkVI5XI5JicnxSCEJEliofb+/r4YRNjd3aVcLuP1ern77ruJx+PquhsVFZXXxVsqqiRJGuz3+8pCuk8CV27ytDPAjCRJE8Ae8DPAv3yLLvEdx/z8PPd+7GP8l29+k/8E3Adsb2/zsY99TLRCvF4vq6uruN1uIpEIq6ur4kbxyCOP8L3vfY9vfetbYrGyVqsVa1oWFhZEpUuSJBqNBtvb25hMJsrlMkajUez8m5qa4siRIwSDQTKZDBaLRbQIBwcHSafTFItFAMLhMMFgkJWVFZxOJ4VCgY2NDWZmZkSVRBEHB9tDSh5Rs9l81RoTBbPZjNPppFwuk06n6fV6xGIx2u228IWFw2HRdmw2m2SzWfx+P263m0qlIqobKysr2Gw2scdQMcYr13OQbrcrohD29va4//77SSaTTE1N4XA4xLJrrVaLVqtFlmW63a7I9lpYWCCbzVKv10WFq1wuY7PZaDQaYgH23NwclUqFRCIhAjPz+byoth1Mpm+1WhgMBoaGhoSRvVKpsLGxQa/XY2pqSlz/jcnt5XKZtbU1jEYjoVCIqakpyuWyEGYGg4HBwcFDgnFqakoIILPZzP7+Puvr68Jvp7RND37Gfr+f/f19XC4XiUSCXq9HpVLBaDQC1ytVw8PDOJ1Orly5Igz7xWLxkFDS6XSHqk9arZbZ2Vny+TwDAwNi4EGZer18+bJYm1StVtHpdNx1111iyXe1WsVqtZJKpYTQ+1GhVsFUVN6dvJmRCl8DPgB4JUmKAf878AFJko5xvZ0XBv7nV547xPXohIf6/X5HkqTPA//A9UiFL/b7/dU36zrf6eh0On7nd36H4088wS81GvwX4P5oFJfLxdjYGPF4XOy7y2az1Go1cVMLBAKMjIxw/Phxdnd3iUQiInCxVqvhcDjodDpMTEzgdrtFq0uZqioWi0xPT+NyuZidncVms4lohsnJSYLBINFolM3NTTY3N4HrgZqhUEjEBEiSxJUrVygUCthsNhEZoHBjgne322ViYuJVNySlBaRMog0PDzMwMMDOzg52u516vY7RaBQhnvV6nWw2y6VLlzCbzWg0Grrdrphei8VijI+P02q1iMfjYjqyWCwKIaYMAkQiERFBcfXqVSYmJmi1WmxvbzM6Oko+nyeVSlEul0XLs9VqYTQacblcTE5OMjQ0xP7+PgMDA1QqFbRaLfF4HIvFwtbWFnNzc8zNzVGv19FoNGxubuJ0OonFYuj1eiFgEokEU1NTYrrvoIhstVpkMhl2dnYwm81sbW2J1p2yMqhQKHDt2jWRQRYKhdDpdJTLZTY2NvD7/UKw5nI5SqUSe3t77O3tYbVayeVyDA8Pi7gGJQpjcHCQVCqFx+M5NM2n+Naq1Sr5fB6DwUC320Wv1+P3+9nc3KRUKuFwOOj1ekSjUSEkvV6vWMyt7HpU2oSK8M7n82InoyJker0eGxsbyLLM4OAgsixz9OhR9vb2iEajYqAim83y7LPPitd94IEHXtW+fCOoS59VVN69vGmiqt/v/4ubPPxnt3huHHjowK+fAF6VX6Vyc0ZGRvj1L3yB//gbv8Efc31U8i/+4i/467/+a/b393n22WfFVJQsy2IqzO/3o9FocDqdzMzMMDg4SLPZpFarodFokCSJQqFAOBymXq+zuLjIpz71KQYGBtjc3CQQCHDvvfeSz+fJZrOiBXfs2DFKpRJnzpwRu/AajQaRSESYrv1+vwj03NnZEXvf8vk8RqNRtJe0Wu2hFtZBL9BBDraAlOdYLBYajQaxWAyv18vc3BzdbheNRsPp06dZW1sTE382m42hoSE2NjaEN2tubo5cLifyp0ZGRshkMqytrQmTu9lsJpFIIMuy8A2trq4yOTkpwk4vXrwoktvj8Tjtdhuj0Yjdbmd4eFiYvpWqUiqVYnBwUFT7FG+aLMtYLBai0SiJRIJIJMLc3JxYF6SsdlFWACl+sYMBoTabTQSNXrp0CUmSmJ2dpdfrkc/nyefzpNNparUamUyGaDSK1+vF7/ezuLhIt9sllUqxvr6OwWCgWq3S7/cZGRmhWCxisVjEBKfJZKLdbjM6Okq/32diYkKIRpPJxO7uLtvb2+zt7TEzM8Pc3JxIZE+lUoemC3O5HO12m4GBAVwuF3t7ezz33HN885vfFDEUc3NzFAoFMU05OzsrfFbwP1LmlUqhsityaGgIl8vFyMiIiJWIx+MUi0URHfL000/T6XT40Ic+RK/X+6EqTOrSZxWVdy9qovq7AFmW+cxnPsOp3/s9ruzt8TvAUizG17/+dT7wgQ+IXXeFQgGPx4PP5+NTn/oUgUCAl19+WXiNPB4PkUiE8fFx0uk0ZrOZubk5EokEVquVcDhMp9NBq9Vyzz33iEnB8+fPk81mxZoXxaMVCAQwm81cvXqVUCiE3++nUCjg8/mEl+XUqVPA9egBSZI4f/48W1tbWCwWPB6P2MtnMBjElN7NOLhqRaliKQZxJX1cqYAowaULCwusrq5y/PhxHA4H3W6Xzc1NERdQKBQAOH36tIh1AKhUKhSLRex2O41Gg3PnzuH1eun3+xw9epRarUaj0WBtbY1nnnmGUqkkqmzJZFKsANLr9QwMDFCv12m1WoTDYZ588klRednZ2aFQKDA3N4fJZMJut9NqtYhGoxiNRrFm58iRI8KXpIjRbDaLz+dDlmVkWWZ3d1dMNEqSxMbGhpjce+6557BarTgcDnw+H/l8no2NDcrlMjMzMwBCGCs+p3a7zenTp5mcnBSepHK5zP7+9e6+wWAQa2sGBgaE8PvWt75FrVbDZrOh0WiESf7ChQusrKxgtVrFEmplirFcLuNwOOj3+xSLRVFtUoJozWYzoVBItA2V6UBJkkTL1e/3CyFTKBQYHBzE5XKRz+fx+/1iX2Q8Hhf/SJienhY5Yy6Xi83NTbRarRjGOLhG58Y/h68lut6qpc9qi1FF5a1HFVXvEvx+P//LL/8yv/zv/h3fA34N+D//639lcHCQRqMh0rLdbjfdbpdSqcS5c+eIx6/PAJjNZhGeWKvV+MhHPiJW0CgeFKWd2Gg0hKhQ2lFTU1NCMChtve3tbWZnZ0WAp9Jiyufz4obX7/eFR6jdbpNIJPj+97/PwMAAsiyLNmIikRDtLCXtXAmY7PV6wiOlrLZRbqQWi4VqtSqS2uH6Tc1mswGwvLyM3W7HbDZTLBYxmUxsbm7icrlIp9MEg0FCoRD1el14nQqFghA0lUqFbrcrRFcoFBJtxv39fVKpFOPj45RKJcxmM+16HY0sM+TzsTQ6yrhWSyISIb29TSkcZlSjoVEqEdnaQru/T6DbpX/5Mjsvv0zWZKJaKFzPuiqVsPV6dIxGNiwW2p0O9UaDkbExrn3jG+w7nVQqFXr9PrVXPkeNTkckGkXW6ahoNNheMcQbg0Hm5+eJRqMYDAZRlVQqVkpsRTQaxe12CxN6vV5nc3NTVG7m5+cpFAp0Oh0RQ1EsFjl69ChWq5VLly5Rq9XEiqLl5WVeeOEF2u02R44cweVyUalUSCaTws80MTEhpg6VqU6NRsPFixe5evUqNpuNZrPJyMgIiURCpNiXSiX6/T4ej4dyuYzT6RSBuMr3lM/nRQVUo9GIvZcej4dsNks+n2dmZoZUKiVWBlWrVSFoFf9hMBjEaDSKaJEf1NZ7K5Y+qy1GFZU7gyqq3iXIsswv/uIv8gd/8Af8VSTCfwC+Bvz+7/8+Dz30EHt7e9hsNlKpFFqtlmg0yrVr17Db7ayvr+NwOERVQ4lf0Ov1pNNpIVK8Xi+JRIJSqUQikcDn84npQuVGvLi4yEsvvSQqYmNjYyKzyOVyMT4+zjPPPCOCObe2thgaGhL79ZTQTSUuIBgMsru7K67h9OnTGI1GPB4Pp06dotvtEo1GWV9fF5UHZYWJktXUaDREhIPRaCSbzTI0NESz2aTdbpPJZOh0Ojz22GPXzyUSDFqtyOUyPoMBTaWCrdfD1+3i1OloFwos1evYZBlNo4Gp38fY62ECLNkspn4f19YWpl4PfbeLORLBLEmY+v3/8Rdu9XXYBCsVSKd/8PMA1l7H4oFLl8R/dvR6Zo1GGmYzlnabbL+PBmjb7RRkmScaDRKtFqZgkLn77mPk2DFyVitnL15kcnJStOscDgdjY2NIkkQ2m+XFF1+kUqkwPT1NuVzmO9/5DpVKhaGhIeHVczgcaDQaYrEY0WiUK1euYDAYMJlM9Pt90T5UfFoOh4Njx46xtLSERqMhEonQ7XZZW1ujWCySSCSEaf3SpUsYjUbhi7Lb7dRqNfx+P5FIBIPBwKVLlzh16hQajQaz2SwCUV0uF6FQiBMnThCNRsV6oVKpRK/XIxQK8cwzz6DX61lcXBRTrw6H4we29W5sYf8wVaWbnVVbjCoqdwZVVL2LsFqt/Pmf/zmPfOhDfBj4b8CD0SjPP/88NpuNWCyGz+fj6NGjZDIZisUi29vb9Ho9FhYWuHLlCqOjo2JNjcVi4erVq8TjcbrdLgaDgUwmI0Iud3Z2xG66qakpLBYLmUwGnU7H7u4uQ0NDJBIJ5ufnkSSJ5eVlwuGwaNt4vV6OHDlCLBYT3h9Jktja2iISiTAxMUEoFGJubo5arcb58+eJxWJMTU1x7tw5YrEYdrtdVMaUVs/LL7/MlcuXCdrttPb2iJ47h6FYxFguo83nsTYa2AEHYO318Op02LpdfqvdxtLtYrvNxO0eUJckqv0+Nbj+o9+nChQ1GqL9PjWtlros0zcaKXU6SCYT9V4P9//H3psHx32fZ56fvu8baJyNbjSAxskLJEFSIimNzlg+Ymcdx+OxPbHW43hWkcapZJPsplKbTSr2KJmKHccex3bZcSzbO5YTSXasyIotS5QokqIoEiBugsTZDaDR93137x/Q7xuSInX4FvV7qlSSUAAIUBB/T7/v836elhaUBgN6mw21wYC1uZmLKytsxuNUlEpShQK5SoWKUom3t5dOv59IOs1GJILT6aRULFKv1dBqNFjNZmLRKPVajcGBAfK5HBq1mmQiQb1Wo5DP0+R0olIq2VhfJ5tKocpk6DKZ8JjNtOt0dJlMVMNh6tEobGzQU6+zo1DAkUigvvz3IxqFiQnxrzm1mjW1mlmFgos6HZHmZmYGBrDu3k25VqOpqYlYLCYyVdL0cXNzUxhgiXMmKZFIEA6HaWtr49SpU+TzecxmswC/Tk5OMjIygtPppLOzk4GBAbLZrDDRpVIJjUaD0+lkfX2dxcVFXnjhBRQKBUePHsVutxOPxzEYDML8nTp1irW1NXw+H06nE61Wi0qlEsaupaXlCgq/lLPK5XJEo1H0ej1ms5lAIEAymUSn073uUPurTZVey2xd72N/UStGWbJkXSnZVN1guummm7ANDvKHs7N8EfgI8A+Tk9xxxx1otVqx3hsdHSWXy9HV1SX+UN69ezcOh4OWlhb0ej35fJ56vY5Wq2VlZUWs6zo7O5mZmRGB6+XlZcLhMCMjI+L9pdD69PQ07e3tKJVKqtUqBoNBhIHb2tpIJBKEQiGR53K73bhcLoLBIHNzc+LyzmQyMTw8zMQLL/DcsWMoVlcJbWzQqVbjqtfp1OloK5VoajQwpNO8q1RCdx1zVABSQFalIq1QkFUq2dJoyBsMhLJZUkCiViOnVpNRKlHYbGRVKnr27CFSKLAWjxMtFCjU6xhfNpIAa2trKBQKVCoV3S+zqCSEgM/no16vA4iSZWnaNzo6iqGtjdCJE8zPz1OtVunv7+d9b3ubwDFIBPZSqYRSqcRisRAOh3G5XOKgwGAwoFar0ev1xONxMVWRTHC1WqVWq5HJZHjmmWc4efIkq0old9xxB749exg/fZqVlRXOnj1LPB4nk8ngdDhQ5vMUQyG0mQxdZjM7OzrotlqpbW2hiMexbm0xls/znkIBkklYWKD0/e+zpNWyoNEQstsJtrRwqbeXbCYj1nTj4+O0t7fT1tYmwvUzMzPodDrcbrdgjpnNZkFtj0Qi4jJTq9WKCYyUodPpdCiVSoH+kFaWS0tLeL1eYcr7+vowGAxMTEyg0WhEzVCj0UClUqFWq2ltbWVgYACtVis6EiVQqc/no7e3l2q1ysWLF2k0Gly6dEmYK+nX9Pl8VKtVgSS5Frn9elOlyw2T0Wi8Zobreh/7i1gxypIl65WSTdUNJp1OxxNPPEG3z8d/YhtZ/6/Ac889x969ewEolUp4vV6sVitTU1MolUqcTic6nY79+/dz8OBBTp48iUajYefOnZw4cYJcLsfi4iIHDhzg0KFDdHV1CdNktVpxuVxUKhUMBgPLy8vE43GsVivBYFBcupXLZXbv3i2C1hKgs1gsMjs7i9VqpclsJnHhAp6ZGeypFF1A4Mc/xgu4cjkOVSqv+J5zQNZoJK5SsanXs240sqnVkjUYWCsWiSqVRBUK4ioVoXKZRLmM0WgUD7vW1lZ27NiBWq1mYWGBRCJBtVrFbDZTKpUE9dzZ1obTamV3V5foPGxvb2dycpK5uTlBkgcYGhoin8+za9cujhw5QiAQEIBNCSY6Pz8vuhI9Hg/9/f3CVA0PD9PX13dFMbGEcJDk9XrFP1/NzGptbQW4Zrjf6XTywQ9+kHvuuUf8u4QXKJfLKBQKotGo+P4NBgOFQoHp6WnRVzj5MqKiVCpx9uxZZmdnURcK9NfrDCkUtMXjDNRqjGYyvDMYhGAQXnqJErCgVHJOq+Ulk4lTPT3c8sEPEovFBNNLQmFIV4KnT58WBjEajYpcXktLC41Gg3q9ztbWFhqNhlKpRGdnJ/V6nZGREVKpFIlEAqVSKQj3SqWSiYkJEokE6+vrBINBIpEIhUJBVBxJeIhisUg+n6elpYXx8XGBlzh8+DCHDx8WhxyZTEZcq0qw3eXlZdbX1wXKorm5+QoCvTSZvd5U6VrF1Q6H4xXw1OtNpK51JStLlqyfr2RTdQPK6/Xyj1//Ov/lwx9mHPgGcPfLHXgOh0OwoXbu3Mmtt97KysoKjUYDpVKJ1WrliSeeYGpqCr1eT1dXF+3t7ezcuZPFxUUcDoeYKkgPYamsWco/9fX1iYu9fD4vgsESe2lhYUox29kAACAASURBVAEF0F6tQihE88wMY9ks/eUyPdXqFT+UOWC9XidiMDBuMnGxXGYmn2dTo2G5VqNgs6F3OsXkQmJM5fN5arUadrtdPMAO9PTgdDqJRCLs3r2b6elp0RknQT93797N5OSkKEE2m80olUq0Wq0IyWs0Gtrb2wX89OjRo/j9fmKxmDAHuVwOp9PJO97xDnbs2IFSqaRerzM7O0skEhHQT8lIWCwWMQGBn38XnUqleoXh0uv1AnApmcPL1dHRIfoKpQkIbNPNJfZWOp3m9OnTfP9HP+Jr0eh2ZY5GQ+HcOQZqNTyZDLtVKt5ZLPKhYhFiMdbOnuWM0chpg4GnAwEG3vY21C9noqQAeSAQYGVlBbVaLS7zVCoV8/PzIsTe19cnjF8ulyMcDnPTTTexvLzMkSNHxCWpVqtlZmaGWq2GQqGgra2NQqGA2WxGq9VSKpVEm4DBYMBkMomDjPn5ebE6f/e73y3MXjweF5wzh8MhaPpbW1ticraxscGZM2doNBp0dHRw8803097ejlarveZU6eriail0fzU89bUmUvIVoCxZvzgpbqTG9n379jXOnDnzy/4yfiWUz+cZGBjgzrU1vgL8v8CfAe973/tQKBSMjY1Rr9cZGBjgwoULgsvj9/uJx+M0Gg0uXrzIe9/7XpLJpCgMbm1tFQ9Wq9WKXq8X+ZdCoSDOz00mE+VymXg8vh0IVqlwnjuHf2oKfzbLcL2O9bKfvSWFglmVinO1GucbDS4Aq0BWo8FqsxEIBFhdXSUSiVAul3E4HFitVrxeLw6Hg8HBQXE2L11ora+vo1KpcLlc7N+/n5aWFk6fPk1TU5NAPWxsbJBKpejs7MTv94uc2NUPykqlgsvl4ujRoxw8eJCxsTEAkskkMzMzxONxotEo9XqdjY0N1tfX8fv9vO997xOGL5fLcezYMZqamjh37hy33XYbpVKJQCAgVlmXP/jejA9DCeiZz+dFMLxQKPCnf/qnnDx5Ukwwa+Uy3lSKt1ss7IjHOVip4Hg5V7WuUnFcpeJ5pZILbW2kWlrYMzrKxsYGt912Gy6Xi97eXnGhVygU6O3tRa/XMzMzQ2trK3Nzc2KF5nQ6USgU1Ot1arUaNpuNZDKJVqtldXUVs9mM2+0mHo+LuiWj0UhTUxNtbW0YjUby+TwXL17k0UcfBcBqtXL48GE0Go1gbRWLRYaHh+nq6hKNA2tra0SjUdLptOCtVatVlEolXq+XHTt2CGbb9QxRuVwWxdhv9JLvp8lryZIl6/pSKBQvNRqNfVe/XZ5U3aAyGo08++yzdHd3cwT4U+B54Lvf/S4PPvgg/f39PPPMM7z44osCCio9PKTiYq/Xi1qt5q677sLhcDA1NcW//uu/ij/cpS44r9eL1+vl6aefZmRkhKmpKZqamjCbTAxmsww8/jiHQyGaajUywKRKxbdUKsarVeZ1OibrdeovlwynUinqL0/NzGYzvZ2dtLe3U6/X6evrEwDQjo4OWltbcTqdTExMMD4+LgLxRqOR5eVldDodKpUKvV7PysoKs7OzhEIhURAciUSEgZK6Eu12O0qlknPnzokc0srKCnq9nkajwdGjR8VDKBqNCjNlsVhEDi2Xy1EsFvH5fKysrIhpnkQBj0QitLe3U61WRdfe1dkZqbD6zXYSr1AoxMTL6XSKt3/xi19kfn5eAEEtFguf/exnmYvFeCoUIhIOYw0G+Q9KJbvTaW6tVHhfvQ5LS4SXlzl+9iyPNBp8/qWXuPd3fxeHw8HKygrxeByTyUQsFmN9fZ2JiQkxGbrrrruIx+Ps2bPnCqr9zMwMTqeTpqYmDhw4QKFQQKvVsmvXLmw2G+fOnaNSqTA+Pk6xWBSohXA4LKZPCoWCRqOBxWLZ/pmt19HpdExMTHD69GkMBgPd3d2i7Fp6gREOh4nFYgwODrKwsEBzc7NgigGit9JisaBUKq8orn69Buhys/TT5LVkyZL1xiWbqhtYPp+PL37xi/wfv/M77AW+Cewplfirv/or/uZv/oZUKkUkEhFwSqVSid1uZ/fu3SL8K4Ep1Wq1gFWOj4+TSqVoamqivb1dACA7Ojq2AZzFIo6/+zsGz57FUyhQAn6gVvOwwcD363V4uYOtVCpRrVZxu93ce++9TE1NMTc3R7FYxGQyCcK71M0mGQyHw8Edd9xBpVJhdXVVhLfPnz+Pw+Fgz5494iESj8cJBoN4vV7i8TjVapVTp05htVqxWCzb7KhKhY2NjSs4SLWXL9cikQgGgwG3200+n+cHP/gBZrNZfP8Oh4NwOMz6+jqZTEa8n5SVajQagr9Uq9XYv3+/MHUSf+nq7IxEJpe+9xvhJF6n07Fz584r3vbJT36SjY0NmpqaCAaD/PCHP2R6eppPvvQSW+EwLakUd+n17E6nOZzP878B2dVVHv+jP+LbNhtzPh/NHg8Wi4VLly6JvFQ2m6Ver3PixAnB3yqVSgKmurKygt/vR6lUsnfvXnp7e4m+vKo8d+4cMzMzgnAvGXIprxUIBGhra2N4eFggSLq6ugRYdXV1lUAgwOnTp2lubub8+fOCFWexWDhw4ADLy8uMjIwwMzPD/Py8yD5NT08zPT1Nc3Mzd999N6Ojo2L1/HrzUVdPpjo6On7ivJYsWbLeuGRTdYPrAx/4AH/8x3/MexMJzgDfBm5dX+cf//EfOXLkCNFoVITJlUqlWH+kUikUCgXr6+ucP38enU6HyWQim82KCpVSqUS9XhfBW4/Hw/cefZSDn/kMR3M5nler+XxTEw/XasRqNfR6PYGuLnbt2sXi4iKLi4vU63Wam5tFrsTn83HhwgV8Pp+AZhoMBkKhEBMTE7hcLlF8LNG7pTN6k8mEXq/n1KlTuFwuwTrK5/Oig7BSqYgi5Xq9jsvlEuvMYrFItVoV/KrZ2VlsNhsKhYJqtSqApNlslu9///sMDQ1RLBaJx+MoFArOnj3Lzp07UavV3H333YTDYRQKBQsLC/T09Igpg/nlqZxKpQL+PTsTi8XE9yN1FN7IJ/EajYauri4AAoEAPT09JBIJGo0G4XCYmZkZVlZW+OHiIvc/+ij94TDvVyj4jUaD30omSY2P893xcR7VaHja72dkdFSgGdrb20VWbn19ndbWVmKxGNlslo2NDbq6ulAqlej1ejKZjKgD0ul0dHR0UKvVaG9vp1Kp0NzcLKZier1eVOzY7XacTicjIyNsbm7S3d1NtVolGAyiVCqJx+MiS9doNESWTqPRUCwWcTqdojpofX1dhO0LhQJnz54lmUxiNBrp7u4WdTuXHy5cS1dPpqrV6k+c15IlS9Ybl2yqbnCZzWbGx8fxer18lG1T9d+BP3jiCfx+PxcvXiQej6PT6fB6vaTTaZaWlmhubmZ1dZVcLifqP3p7e2lvb0elUrGyssL09DT1eh21Wo3ZbOb555/H+ud/zn8qFPhdtZovKRQ4lEpUGg2+5mYcDgcAU1NT1Go1Ojo6RCXL1tYW6XSa5uZm7rrrLnp7e0UZslQX09vbK4qepa9L4ltpNBqRYwGYmZmh0Wig1+uFYZLI7Mlkknq9jt1up1qtiktEm81Gd3c3uVyOarVKtVoVlTYDAwOiXDkej2O325mcnCQajQrcQWtrK7lcTqxqtFotfX194lLtehMAKWwsdQxmMhnMZjMdHR2iduatMD1QqVQ0NTUB21eLEsFcqVTy+7//+/ze7/0e/8+ZM/x+IsHNpRLvA94DfLhSIT4/z78uL7N28CBL3d2oVCqBuqjX6+KKUcI1SPy03t5e6vW6gNpKEyepQ9BoNDI6OopWq2Xfvn1icilNEDUajSgLTyQSjI2N0dPTw+LiIhsbG/T19ZHP5+nv7+fixYvk83mWlpbECt1oNIrLy3g8TiQSwWw2Y7VaASiXyzz77LNotVra2trYt28f9Xr9ugbrWteA1+vKlH7mbDabMJY3onmXJesXKTmo/hbRzMwMw8PD/B3wu2w/jB4DRkZGaG9vZ25ujoGBAYaGhkgkEvT394tz8LNnz5LP59FqtfT09NDd3Q1AoVBArVbT29vL7OwsjX/4B/5nocDfAQ8AnZ2ddHR00NHRgcViEeFlqdBZ6uqzWCxidSOxssxmM5FIhJmZGQwGA+VyWRQGS8Hj06dPEw6H4eVfy+VyiVC9NHWLxWK0tLQQCoXIZDLiEjCTyaBSqfD7/eh0OqrVKrt37+bmm29GpVJx/Phxzpw5Q6lUolgsMjQ0JCp94vG4eBhLK0+n0ykmdrfeeittbW1sbW294XCxHB6+vkqlErOzsxQKBb7xjW/w2GOPEd/c5LZ6nf+s1/O2UglLo0FcqeT7Oh3PuN3UDh9mdP9+kb2SJjmlUgm/389dd91Fa2srwWAQi8XC+Pi4MDdS/UwgEGBzc5NIJEJTU5NY2SqVSjo7O8V1q1Q8LvVmPvfcc3R2dqJWq+ns7BTT1ng8TktLC+3t7bz3ve8VvDKVSiWwHD/84Q8ZHx8Htv8/c7lcbG1t0dPTg8vlYmBggD179ggsw+V6oz9Dr/b+8s+jLFnXlhxUf4traGiIxx9/nPe8/e0cAL4GjLE9NVKr1eKUXyKeB4NBWlpaKJfLYv0mcZJcLhfj4+NUKhVRH7O+vs79hQIAJwAt8B86OjjS3s7Wiy9yzGRC/fJ6JJfLUSgU6O7uJp1OUyqVSKfTRCIRzp07R7lcFgwio9EoAvTStWK1WhWB8HK5TLVaZW1tjXA4jN1up9FoUCgUmJ+fR6VSkUgkxNWXXq8XWZOmpiYRfB8dHRWXWy+++CK1Wo1cLifC7pubm2Lq1d/fT3NzM62trRSLRaxWK+94xztoampCq9WKB90bCRdLktlC15dOp2P37t0A7Nu3jwceeIDl5WU+9alP8X8Hg/w58GuNBrdHo7w3nebDKyuEV1Z47OGHOW+zEe7t5e3veAf1ep18Ps/m5ianTp1ix44dYuUrFXkvLCxQLBYJBAKUSqUrVtGRSAS1Ws2uXbtIpVLE43GWlpYolUrs3buXRqMhfo5TqRSHDx/G6/ViNptZX19HoVBQq9VoaWkRvYmlUml70mu1UiqVRIm5wWBgbW2NbDZLqVQim83S0dHBzMwMGo0Gh8MhSPWSXu1n6Fom6Xrv/9OG2WVDJuutKNlUvYV05513cs+7381vPvYYp4EfAIeB8fFx7r33XvR6PVNTUxiNRvFq/MKFC0QiEXGSXiwWMZvNHD58mEAgwIULF3C5XCiVSj4I/BPbnYN1QPnCC+LXNqhU/PGlS3g8Hrq6usQreulVvgQBTSQS5PN5stks3d3d1Ot1gVHIZrOsrKyg1WqxWq1i2lWv14UBkgCPvb29ArIZj8dJpVKUy2UsFgt2ux2z2YzFYmH//v0iEF+pVIjH44yPj7O1tSU67QYHBwEEh8tkMjE2Nsa73vUustksRqMRvV7/igeHbJB+ftJoNPT399Pf38/Ro0eZnJyko6ODhx9+mE89+ij/dXaWo5kMv9lo8OFymd+JRpmIRvn7c+d4vquLnWNjAsFhMBhEps9oNJLNZrn55pvx+XyEQiEikQjJZBK73U4oFKK1tVV0RsbjcfL5vAjbz8zMYDQa0Wq1uN1ukUHs7+9Hp9PRaDRYXV1lcHCQ5uZm0WgwMzMjanGkiqdjx47hdrtpaWmhWq2iUqnEz6jFYqFWqzE3N0c+n2doaAilUikud69Fb3+jJcs/TZhdLnSW9VaVbKreQtJoNHz9619naGiIe4JBfgz8CDgKfPWrX+WBBx4gEAhw6dIljh8/jr5YZHc6zWixSAwomUwM7tmDTacjns/zL//yLyK7tLa2htbr5daVFT4BmICoxUKqqYnPLy2hrNWo1GoUCgVR61EsFslms8D2+i6dTrOxsSFqbiTMg5RvkvhTEqTU5XKJ0DxsM6MkCrwEepRO0gOBAJlMhqGhIXEKPzo6CiDO61OpFNlslp6eHnK5HBqNht27d+PxeGhubmbv3r1UKhUBeFSpVOhevmSU9cuTwWAQ3LD777+f3/iN3+D48eN85Stf4b6LFylFo/x6scj9jQZfKJUILyzwxYsXedLvJ3DkCPV6nR07doj14OXFyUqlUkxUk8kkS0tLLC0tcejQIWAbgaBUKolGozidTvr6+kgmk7S1tbG5uSnyWmazmUKhwK5du9DpdDidzivygm63W0xgu7u7MZvN+Hw+0ZdpNptFhjAej2M2mzl//jypVIqlpSUikQjd3d0sLy+LNaXH48Hj8YjmgkajISj12Wz2NUPpbzTM/npQDrJk3eiSM1VvQQWDQTweD0eAJ4EF4E7A7Pdzx+23Y/ryl3k/sBdQXePja2zDOqcaDSaB0y//FXv5mk26wJJwBZuVCt8H/vPLH+9yuTAajcRiMdFX19nZiclk4sUXX6RSqYhs0tTUlHgAAAJToNVqaW5upr+/X9SAZLNZfD4fBoOBoaEh9u/fT71eJxgM0tbWxsWLFxkZGRFXX1JWRQKXGo1G0eFWLpfxeDxi/SMFimW9OVSv10kkEqyurjI/P8/f//3fc+7sWfZnMvwe8HagBHwL+KrFgmbfPu677z6BvOju7qbRaDA+Pk4sFhPHFLFYTGSipHxVo9HA7/df0UVYq9U4d+4c9XpdNA8olUrUajV+v1/UCC0tLWG1WkmlUnR0dKBQKDAYDCQSCZ544gnC4TDhcBi32y2aDxqNBnv27OHpp59mZWWF5uZmbrrpJgKBABaLhVAohFqtJp1O4/f7sVqt4spweXmZXC6H2+1m//79r/kz/Xrho9dCOVyPsyavBWXdCLpepko2VW9RjY+Ps2fPHm4HvgusAXcAw2wbrdNsdwb+GAgDrsv+6n75/YaBAP9uvJb5d4P1AnAWyANfAv534HbgGRCv5gFxodff30+tVmNmZkaEda1WKxsbGyiVShKJhKhJ0Wg0aDQaBgYGBJw0FAqxvr5OrVZjz549HDlyhLW1NQBxBl8oFDCZTNjtdjo6OoSRkkqPpcuncrkM/PyrYmT9YlSv10kmkywsLHD//fczNzdHaybDA2wXjpvY/jn/W8D8W7/Fx++7j/7+fmFC9Ho9i4uLrK6uMjs7S2trK0ajkYGBAXK5nMgjNhoNksmkqLdZWloSjKvW1lb8fj/pdJqenh6B1QgGgwL42d3djUKhoFwus7GxweTkJGtra7S3t7OwsMD6+joXL14UYNDNzU3K5TKXLl3i0KFD3HHHHbS3txMOh7l48SJms1nk+rq6uhgfHxeXhkNDQwwPD7/uSetrGaFyuSyudSUzJ02srm4JkNeCsm4EyaZK1is0MTGxffHGtoGKAkagCPQD5dfxOYzAHuAA28H3MbZNF0AVmAKW2L42vAT0Aj09PbjdbpFRGRoaEn1ry8vLVKtV2tvb2bNnD8eOHSMej9PU1MTo6Ci5XA6FQiFI1WNjY9x7772sra2RyWRQKBSMjIygUqmEOdJoNFSrVdRq9VsKUSDrlcrn85w6dYqHH36YRx55hEokwkeB+4Eu4CLwJYOB6bExbrrzTm677TZxtNDf38/TTz9NPp8nFovh8Xjw+/0oFApBaTcYDPT39wtsQktLi0B9ZDIZqtUqXq8Xp9OJx+Oh0WiwvLwsOGuwvcaenZ3F7XYTDAbp6ekhnU4D8Nhjj4manZWVFdF96HQ6aW1tZWxsjMnJSdHduWPHDoaHh6lWq5w8eVK8uDh8+DDvfOc78Xq917wglPR6p0qv1yxdy3xJhHf5AlHWm0ny9Z+sV0iCcPb09HB7o8GTgBP4H/y7oerv70elUgkYqEqlEmXKUv/f2bNn+ed8nu/rdNvAw2iU/WwbLMlsPcv2RAzAbrfziU98gnK5TK1WY2hoiFQqJR5WOp2OkZERBgYGiMViRCIRvF4vsP0HLCBC8263G7VaTU9Pzyv+4L38VbiU55BzHW9tGY1GbrvtNo4cOcIDDzzAN7/5TT7zmc/w6Xye9wC/r1TyV4UC0WPH+Ntjx/gv7e107tjB/v37KZVKAn1gNptpaWlBp9OxubmJwWBgamqKlpYWarWauBiUslY6nY7W1lZOnDiByWRia2sLt9stwuU2m01Q3S0WC4VCQXQEqtVqASeVqo/a29tF9dH8/Dx6vR6NRsPExARTU1OUy2VxvKHRaDh//rxoDvB4PASDQR599FG6u7vZs2cPPT09Akz6k0yVXk+xM1ybo/Va/YTSNE+n0+Hz+eQ1vKxfacmTKlksLy/T3d3NANsMq38AXgIOHTrELbfcwsTEBKVSCY/HI4qLJYq5wWBAr9fj8/lE0HdmZoavfOUrPPnkkyKIfrmmp6fp7++nUCiIuha1Wk2lUrnu5ZIsWT8P1Wo1VldXeeaZZ/jSl760zWtLJvm/gHcBGeDvgf+p09F75AhjY2P09fWJsHkgEGBtbY3z58/T3NyM0WgkEAgQCoUEBb2jo4NwOMzGxgbLy8uoVCry+TxjY2N0d3cTiUTI5XJotVpUKhXpdJpsNiv6CgOBADabTUy1isUiarWa1dVVVCoVGxsbguGWy+WYm5tjc3MTrVbLXXfdRUtLC0899RRra2sC9ZHJZARmZHR0lFtvvZXu7m5BcJfM3rWmSvDTTY+u/thX+3Wk9WY2m2Vra4uBgQExHZQl65cpef0n61UlwUEv1+zsLF1dXcTjcdRqNXq9XqwKFAoFarVa5JKufvVYLpeZm5sjFAoRDod5/vnnSSaTPPjgg/j9/l/ktyZL1utSPp/n+PHj/NEf/RGzs7P0lUr8MfB+tlfZ/wB8VqtFOzjIAw88IHhl9XqdhYUFMVFqamoSHZWJRIJcLsfCwgL1el2gERqNBqlUipaWFnp7e6nVajQaDTKZDAaDQaAdlpeXCYfDjIyMcPDgQVZWVigWi4TDYXFd53Q6qVar4uJufn6ep59+GrPZTHt7O4uLiywsLIiAularFVeJjUYDg8GAz+djdHQUv99Pb28vLS0t2Gw2gsGguJaVJkg/61zUa02qlpaWxDrUbDbT09PzCnMnvSgDOQsp6xcj2VTJek1tbm7yF3/xFywtLfHpT3+a/v7+X/aXJEvWL1y5XI7nnnuOEydO8OUvfxnj5ib/J9uhdjXbVU9/DUQ7O+nr62P//v3s3r2bXC5HR0eHwBik02kymQzt7e08/fTT7Nmzh3A4TKlUYm1tjUQigd1uF2vFnp4eYrEYo6OjZDIZ4vE48/PztLa2EggE6OvrQ61Wk0wmxXRqbW2N3bt3093dTaFQQK/XMzc3xz//8z+TTqfR6/VMTk4SiUTEGn3Xrl0kk0kuXrwoVox79+5leXkZl8slAMA2m41Dhw7h8Xg4fPjwFS+epKvFqydLP6lebfJVr9dZWlqiXC5jNptfYe6y2SypVIrCy/Dhnp4ePB6PbKxk/VwlmypZsmTJegOq1Wqsra3x5S9/mUceeYTk3By/B/xXwAKcBx4CvgmoPR7uvPNODh06RKlUIh6P09XVRb1eZ9euXaJkORgMClMVCoUwmUz09PSgVCrZ2toShc69vb1iOpxIJBgaGhIw0kwmw9bWFqurq5TLZQYHB2lpaREoknQ6zfT0NCsrK4TDYSKRCOvr65jNZgYGBujs7KSnpwebzcbZs2eZmZlBq9USi8UECkKhUJDJZOju7hbTo+7uboaHh/H5fJjNZorFIg6H41UNzOWTpNdzJHI9c3Wtt5dKJS5cuIDZbGZyclJAix0OBwMDA2+q/KQcxn/zSTZVsmTJkvUTqFarsb6+zmc+8xm+9rWvUY/H+Y/Ah4BDbHPbnmLbYB1vasL38vWpVqulv7+fu+66i8OHDxOLxXj66adJpVKcPn0at9vN1tYWarVadFIeOnSISCTC7bffTiQSwWq1YrfbxSRqdXWVbDbL2bNnCYVCjI2NEQ6HcTgcdHV1YTQaicfjXLp0SXztEp5B4rsdOHAAk8mEQqEgEonQ2tqKRqMhk8nw1FNP8eyzz1Iul8W6v6uri0gkQqlUwmq10tXVxcjICDt37hQXkE1NTahUV1LtLp8kpdNpUWl1vbqbN7JWlIDDly5dEivMYrEIvPkmVTJm4s0p+fpPlixZsn4CqVQqPB4PDz74IB//+Mf57Gc/yze+8Q2+kEzSC3yQbYP1EJCLRnnkmWd4CHjOYKBUKlGtVlldXaWrq4t8Pk+tVsNsNlOpVDh06JDILi4vL5NMJtHr9SwvL1Ov13G5XGg0GoLBIKlUilQqRSQSoVaroVQqeemll/D7/Xg8HnFRqNPpOHLkCOl0WlzVSrBRrVZLNBrFarWK1V+lUkGhUNDc3Exvby9Wq5Vz586JyU8mkyEWiwkiu16v5+TJk5w5c4ZIJILH4+E3f/M3ueWWW8jlcnR3d6PRaMjlcoLgvri4iNvt5tKlS6RSKex2+yvMw+UU9lQqJUzGtQxGpVIhn88TCASIxWIEAgHxfj9tpuoXPTWS6fM3luRJlSxZsmS9AUkmaXx8nK9+9aucOXOGcDjMzWybq/cBDmCdbWL7NwDjoUMcPXqUoaEhUWAugW7Pnj2Lw+FAo9Fw+PBhVldXaW5uJp1Os2vXLtLptFgNVqtVnE4n4+PjGAwGdDodg4ODlMtlxsfHxWRoeHiYXbt2sbGxwcbGBs899xzFYpHe3l68Xi8Oh4NcLkelUsFgMOBwOAgEAjz77LPie8vn83g8Hmq1GgsLCywtLaFUKnG5XKLgvFAooFKpsNvtqNVq1Go1+/fv56Mf/SiFQkH0IkpmLB6Pi8qoq7NY15psXZ6hulzSpErqZLzeZOpaBum1mFg/6xD+axk0eVL15pQ8qZIlS5asn4Gkqhm/38+73vUugsEgly5d4sEHH+T+Z57hv1UqvJ1tg/UA8AfA+ZMneejkST7X0cHwnXfS3d3Nzp078Xq9uFwucc33/PPPo1QqmZqaor+/n+XlZTo7O0WZslKpJBAIoNVqWV1dRalUolKpuHTpEpOTk2xtbWG321lYWMDr9RIMBsnn8zgcDtra2qhUhgdNkwAAIABJREFUKnR2duJ0OolGo6yurlIsFtFoNGSzWQ4ePEhHRwdbW1usra1RLBbR6/UEAgFGR0dFNspkMvG9732PZDKJ0WikWCyiVCqxWCz8+Mc/Jh6P4/V60ev17N27l+HhYYFLWV9fx+12iwYDSRLrKpfLsba2hs1me8Xk5vKMlvQx19O1jBfwqgbmZzk1er1m6fUyvmS9OSSbKlmyZMn6CSVNnXw+H4cPH2ZycpJgMMi3v/1t/uNjj2EsFvkttg3WXwO1UIinvvY1HgL+0e3m/R/9KPv27UOv16NQKJidnaW7uxuVSsXIyIh44MZiMTY3N2lpaWFra4tcLkckEqGzs5Pvfe97xGIxkskk09PTuFwu+vr6qFQqRKNRSqUSOp2OSqWCy+WiVCqRSCQ4fvw4hUIBv99PvV7H4XBgs9lYWVkRZdKJRILbb79dlJu73W5isRharZZCoUA4HGZ9fR2DwcD6+jrxeFzU7QSDQRYWFvjOd74j6nHsdjs9PT2MjY3R2tpKpVK54qpQoVBgMpkwm81XAELhSpOi0Wgol8u4XK7rmh+JcaVUKonFYrS0tKBQKF5hmi6v07kWnPQnUaPREOvPa5nDqyWx+WS9+SWv/2TJkiXrZ6xyuczk5CTf/OY3eeqpp5ifn8dTKon8lR/IAY8A/0utJrl3LwdvvlmUetfrdTo7OxkeHmbfvn3Mzs7idDrJZDKEw2GUSiXnz5+nt7eX8+fPo1KpePHFF6nVauzevRulUikgnrD90DYajbS2tvLcc8+Rz+cBxIN+cHBQAEalvNL58+fR6XR0dnYSCASIRCI0Gg3C4TAGg0Ewt9LpNLfddhuxWIyVlRWSySSLi4uEQiEuXryIQqEgmUxisVhE16fb7Wbv3r2Mjo6i0+nYs2cPNptNmMtrrc0uh4SmUim0Wi25XO66679SqcSJEydQqVTUajVuuukmtFrtaxY/S78vP+nU6NXWmD/t55b1qyP5+k+WLFmyfsGq1WpsbW2xtLTE5z73Ob797W9Tr9evmb/6X0olTzQ10dixQwTZDxw4QH9/vwiwHzhwgPX1darVKuFwGL/fTygUIhQKMTMzw/LyMhqNhv3792Oz2cSq7PDhwySTSZLJJJVKRUy2vF4vO3fuZGtri3A4LGCfUg1Vd3c3oVCInTt3iklYS0sL0WiUwcFBotEobrebpqYmtFot4XCYlZUVMUX61re+xcLCAplM5gr4aUdHBy6XS2TLLBYLR48e5Z577hEF6CqVSlxFXg0dlcxnOp3GZrNd01RJ679UKnXF+zQaDdELCj875pZkBCVgqWT+PB4PJpPpip5Hs9lMR0eH3EX6JpZsqmTJkiXrl6hKpcLs7CzHjh3j05/+NKurq6hrNd4OfBh4G6DlSv5Vymhk586djIyMoNVqec973oNerxf9fh6PB5fLxfr6OseOHUOj0bC+vs7g4CBer5e2tjZyuRxer5dqtcri4iLr6+solUp27doloKNnzpxhbW2Ns2fPcssttxAOh8XXrdfrGRsbQ6lU8uSTT5LL5WhpacHn89FoNAQRPpVKcf78eQKBAMePH6e7u5umpiYajQYvvfQSMzMznD9/XlRbDQwMsL6+Tj6fR61W093djcFgENgIr9dLR0cH99xzD3a7HbfbLSZkknGxWCwi/H5516ek6wXVLzdnsE3T/2lC4q/1OQEWFxeZm5vD7XZjMpnECtVms9HZ2XldgyUzrH41JQfVZcmSJeuXKI1Gw86dO9m5cyf33nsvx48fJ5vN8qlPfYoPXbiAJpO5In/134Gn8nkeOnWK/+/UKXROJ0qlkgMHDgBw6dIlEokEarWam2++WRgSKQxuNpspFAqUy2WBVfB4PDQ3NzMxMcGFCxcoFAr09/fT3d1NLpfD5/OxubkpoJ4TExN0d3cTDAZJp9O43W7sdjsul4tyuYzP5yOZTFKv17Hb7WQyGX7wgx+IaZfL5aKzs5N0Os3m5iaDg4NotVoOHjzI/v37eeqppzh16hTVapVkMsnk5CTRaFRcLGo0Gr74xS+KUP99990nAvHlcplz585hMBheMYm63IRcPXm6Oozu8/moVqsYjcbXdSF4LV39OaVc2eX9huVyWbDJ/H7/tqlWq4lEIlQqFSqVynULpSWzdj3Gl6xfHcmmSpYsWbJ+wTKZTNx9990AvO1tb+PUqVP84Ac/4JFHHuELly69kn8FPBKP89DnPsdXvvQl7rj7borFImNjY6RSKZ5//nkGBwcxGo3o9XoymYy4kPN6veTzeSqVCoVCgWg0SqVSwePxEI/HmZ2dJZfLUSwW8fv9lEolWltbxXRoc3OTWq2GSqUS14iZTIZ0Os36+jr79+8nGAyyuLhIvV6nvb1ddAu6XC6MRqPoQZQYXWNjY5jNZu6++26cTifxeJzV1VVWVlaIx+PUajWKxSI2m41EIkE0GiUcDjM+Pi6+D7/fT0dHB7t27UKn02EwGDCbzUQikVedPF0eRjcajSL4r9VqrwjZvxHEwdUB96uNj0ajwWw2A+B0Omlvbxfdi1IRdlNT0ysC7ZJZs1gsXLhw4bqMr18VyVM1ef0nS5YsWb8Sqlar4mLuIx/5CKFQCOC6/KuHgBWbjV//9V8nl8sxNDQEgFKpJJfL0dXVhUajodFoYDKZMJlMbG1tiUu4VCqF2+1mbGyMkydPolAoWFlZ4bbbbsPlcuF2u/nRj35ENpvl/PnzmM1murq6cDqdzM3N0dTUhFKp5ODBg6JyZ2FhAaPRSDAYpKmpiebmZjweD6FQiGPHjjE1NYXVamVoaIixsTFsNhvnzp1Dr9eL9diFCxewWCyEQiEBJjWZTHR1dbGyskKlUmF9fR21Wo3D4UCn09Ha2sro6CiBQID29nY6OjrQ6XQMDw+/6lqw0WiwuLhINptla2uLwcFBOjo63lDOSvpcKpXqugXzl7+fdFEo4R5sNpu4StTpdPh8PvHx0qQqkUgIyOnVjK/Xy8J6I3VBP4nearwtef0nS5YsWb/CuhzPcOHCBZ555hnGx8f527/9Wz6+tcV/g1fyr1IpHvr61/km8OMf/5jbb7+dXbt24XK5uHDhAk1NTRgMBpxOJ7FYDIBUKkUikcDr9VIqlZidnSWbzVKr1Uin0ywsLIhSaAngmUgkCAQChMNhqtUqsB3wdjqdbGxs4HK5mJycRKfTieLjYDDISy+9hFarpbW1FYvFIjAFmUxGXMh5PB6CwSA6nU6sItva2ti3bx+Tk5NoNBpOnDhBW1sbpVKJs2fPAtsP8WQySWtrKxcvXiQYDIoLQ5vNRm9vL/feey8jIyPU63UUCgUWi0VcGGq1WlHdI9HoS6USwOvGKkhGQirAdjqdWCyWaxqKq1eRHo9HVARJGbFSqUQoFBIfLyE13G434XCYTCZzXczEtYyMFMqXOGivBVX9aSST4bclT6pkyZIl61dY+Xyep59+mpMnT/KXf/mXADhB5K+u7h/8nlLJgdtvR6VSMTY2RiQSYWRkhFQqxdbWFnq9nkKhwLvf/W5mZmY4cOAAExMTLCwsiCu+o0ePMjw8zMmTJ5mYmKBUKqHRaLDZbPT19XHs2DFyuRy7d+/GbrdTKpXY3NwkEAgQjUYxGo089thjAqHQaDSwWq2itqenpweHw4HFYhFYBJVKxcrKClarFavVisfj4eTJkySTSTo7O2lubkalUvG5z32O06dPo1arhSGJxWIoFAoBQw0EAoRCIex2OyaTCYvFgt1uZ9euXRw4cICenh7RuajVatnY2BBmsLOzk0ajQT6fv+7USZLEwkokEpw/f56dO3ficDjo6ekRpu31rMMux0Vcbzr2WpiJqz9OMlzJZJJoNCouLYeHh8Wq9/VM4F7vVEueVL38dtlUyZIlS9abQ8lkkocffph/+7d/4/HHH9+unoFr8q8eAp5RKunu7WX//v3s378fo9GIVqtlfn6erq4uAJqbm8nlciiVSvH2trY2hoaG8Hg8zM3NXWFYpKmQVFMzODiIy+UCIBKJYLFYyGazzM3NEQqFaDQa2O12dDqdWCG2tLSQzWbx+XzCjKTTaTQajbiIkwzL5OQkXq8Xv98vJlz1eh21Wo3L5WJ8fJyXXnqJ6elpVCoV+XwerVZLrVajr6+P5eVlLBYLZrOZYrFIqVRCpVLhdrvp7u4Wa0OVSkV7e7uYCl1usq4XYJfWh3Nzc4IYPzAwgN/vfwVC4bUKoq9lSF7L2LyakZEMl5THcjqdFIvF1zWp+kkN0lspUyWbKlmyZMm6QVStVllZWeGFF17gT/7kT1heXgZePX+1YDDwoQ99CLvdzsjICKVSCYfDgUKhEHgCg8GA0Wikra1NwDjL5TIajQaVSsXMzAyhUAi9Xs/4+DhKpRKtVsvAwABut5uenh4ApqenRU7IYrGwsbFBJpNBo9Hg8XhEAXKj0SCTyYggvdlsRqfTiQnW/Pw8HR0doufQ6XQyOztLc3MzZrOZtrY2Tp8+zZkzZ5iZmcFoNLK+vk5TUxNLS0tUKhXi8bgwW2q1GqvVyurqKkajEbPZLAqfK5UKdrudu+66iz179tDS0oLb7aa9vZ1CoYDT6WR1dRWDwYDVahWXePV6nYWFBWq1GgaDAZ/Ph0KhuAKhYDabxfTqerqWYXs9Rut6RuZyTpfVahWrxmq1ekW2Crgi63U1a+un5XfdqJJNlSxZsmTdgMrn85w4cYL19XW+973v8cQTT1DL50X+6h6u5F99C1B5PBw8eJAPfvCDrK6uiodutVpla2uLRqOBz+ejqakJnU5HLpdjaWmJcDjMpUuXUCgUIvuTzWbFNZ/U8ZdMJjl27Bg+nw+3243b7ebMmTMEg0F27Nghsj0ul4vR0VFOnDgBwOTkJG63m6mpKZLJJMVikT179vBrv/Zr6HQ6UqkUGo0Gn89HJBIhEolw6tQpVCoV2WwWh8PB4uIiLpeLYDBIV1cXiUQCq9VKsVhkenqapaUl0T+YyWTQarWEQiHq9bog2be1tZFOp2k0GhQKBQqFAnq9Hp/Px6FDhxgcHBTGSq/Xi/xVf38/KpVKTNkuD8BLl4WvV9da7Wk0GlZXV4nFYrhcLrq6ul512nSt0unrMbWu988SeV7maF0pOaguS5YsWTegjEYjd9xxBwAf+MAHWFlZ4dSpU9x33308kkpdkb/6a+BB4Km1NR5aW+MD3/kOv3XvvWSzWW655Rb8fj8ej0f0+M3Pz1Or1ajVaoRCIaLRKNlslne+853UajWROSoUCgwNDYngt1arxePx0NbWRqPRoKuri/X1dXQ6HTMzM6TTaW666Sai0SjJZBLpxX2hUBDrRWnidObMGXbu3IlWqxXTp0gkQq1Wo1AocP78efx+PyqVira2NqLRKPl8no6ODnp6enjuuedQqVT09fXx7ne/m3w+z8TEBPF4nFQqRSgUIpFIEIlERLH0xYsXaWtrY35+nkKhACDwBysrK7hcLkF8b25uFpO9u+++W0yUVCoVJpOJgYEBMb26XNcDk75aD2GpVOLUqVOUy2W0Wq0wddf6nFLd0NWohssD5dFolGq1SktLC/F4nEajId5fMoFqtfoVVT7X4mi9FXJUr0eyqZIlS5asG0RqtZqenh56enp4xzvewXe+8x0+//nP84Xxcb4AV+Svvg58AXjkq1/lIeD+hx/mD/7wD1Eqlej1egKBAIVCAbfbzbPPPkutVhPwz3Q6jcPhwOl04nK5BE/K7XaLKzdpZdTc3CwC4U1NTRw/fpxyuczDDz/Mzp07yefz4qJQrVazubmJQqEgk8mgUCgEbkEyBTabjc3NTWHmcrkcm5ub4oG+d+9eisUier2e6elprFYrbW1tmEwmZmZmiMfjNDc3s2vXLpLJJIVCgfe///08+uijrK2tEY/HKRaL5HI58fsq5bAymQxms1mUSicSCebm5tDpdLhcLp599lmq1SoWi4WxsTFuvvlm+vr6SCaTYj1YKBTQaDSkUikKhcIV1PWrDUpnZ+cVxktidFksFjKZDJVKRZiqqw1OR0fHNa8YJbOWSqXI5/Pk83lSqRR+vx+FQvEK1la5XBYmLJVKic8vfT2XX/yVy+UroKevpht1wiWv/2TJkiXrBla5XGZqaooTJ07wyU9+ko2NDeC1+Vd79uzB7XaL7JE0fRkeHsZoNOJ0OqnX65TLZZqbm9nY2GBwcBClUonVahW8KYfDgc/nIxQKsby8zHe/+11aWlqYmJhg3759RKNRMSkxGo3iks/pdLK2tobL5bqiHLpYLALbD+WFhQXx9o6ODpxOJ52dnZRKJXw+H7Ozs1y8eJGuri5Rptzb28vp06fx+Xzo9XpaWlqwWq3Mzs7y7LPPUiwWaW1tJRaLEQ6HmZubo1QqUS6XMRqN+P1+tra2qNfrolw6kUhgt9up1+vk83lUKhVGoxGbzYbNZsPpdOJ0OsUKzeVyMTAwgNfrFRm3crnM3NwcVquVWq2G1+sV5kVSvV7n9OnThMNhWlpaRH2Q9N/5WutCqefwciBpo9Egl8uxtraG1WolFovR398vJlmXG516vc7y8jLFYpFMJvOKguir+xglQOm1+hgl3QgTLnn9J0uWLFlvQWm1WkZHRxkdHeUjH/kIx44dY3FxkT/7sz/j47HY9flXzzzDt4Dcy4BRr9dLX18fGo1GIA28Xi9dXV1MT09jt9vZ2Nigo6ODaDQKbD+Qp6enaTQaogqnWq3y4osvYjabqdVqXLhwgXK5jFqtxmg0ir9LUzCNRoPb7aa/v598Pk80GmVxcZH+/n4uXLggYKMjIyOoVCoxYVpeXmZ0dBSj0YjL5cLhcFCtVrlw4QL1eh2NRsPy8jJWq5X29nYGBwdpa2tjYWFBTON6e3v5p3/6JwAymYz4OgcGBujq6uLJJ59kc3MTnU4nTFatVqNarVKtVqnX60QiEdRqNa2trWSzWWw2GyaTiccffxylUklPTw+f+MQnWFlZEavKXbt2sXfvXvG9O51O1Go1Wq2WsbGxa+IerrUuBAQx/nLzIgFVTSYTsVgMu90uTNflgfRGo0EoFKJUKol6IJvNdsU6UZqm1et1Tp48iVKpZHNzE7fbfcVq8nLdyEwr2VTJkiVL1ltEJpOJe+65B4Df/u3f5plnniGZTPKxj32MRwqFa+evXgaM/g3Q4vejVqu56aabSKfTTE9PYzab8Xq9jIyMsLGxgdPpRKVSiTyUWq1meXkZm83G3NwcWq1WmKVMJkNvby8GgwGPx0O1WiWbzWK328lms3R2dlKv1/F4PNRqNZaXl2lqakKtVrOysoLP5xNGQqlU4nA4qNVqZLNZQqEQCoWCQCAgqnB6enrQ6/WYzWYSiYQAdWo0GpHtuvXWW0U/4osvvijWXkqlEqPRSH9/vwhyf+xjH+O73/0uwWAQtVpNPp+n0WiQTqdxOp1ks1lhYhKJBAaDgUgkQiKRIJfLMTAwQCQS4amnnmJxcZEXXngBlUrF5OQk586dY2pqCqPRSE9PD7fccgv9/f243W7UajVbW1u0tLSIzkIJFHr5pKlUKpFIJMT339zcjFKpvAJoeq08F/z7FWAul8Nms5FKpcSxgE6nEzVIkhGTwKmSiQsGg/T09FxzAnU9A3gjSF7/yZIlS9ZbXKlUim9/+9t84hOfEMHsV+NfPQXYnU66urqw2Wx4vV7a2tqwWCy4XC6i0ShNTU2sra0J+Ofw8DALCwuEQiGKxSKDg4P/P3vvHR33Wef7v6b3qtGMereKZdmOa4hDjBMSbghpBG6yhMuSC5sfuyzLZc+ye/ODZSGUZUOHQEJbIJCyEEKyCXGKSZziEjvulqzepVEZjTS9z9w/xvMg27LjIjtOeF7nzJE183y/84ysc/Q+n8/7eX/Q6XRMTEwQCARYv349NpuNjo4OhoeHaWlpIRAIUFFRgcVioaysjHQ6TVdXFwqFApfLhdlsZnJyklwuJxLZx8bGmJycRKfTidDQQCBAe3s7RqNR+IdeeuklamtrKSkpobS0VIgnjUbD5OQkwWCQsbExXC4X+/fvp7q6msOHDxMOh3G5XCKgNBqNMjQ0hM/no6+vT7TbLrvsMsbHxwkGg0QiEQKBgEh7Lysro6enB5/PR3FxMW1tbRw4cIDBwUFRlSopKaGvr08MW7bb7ajVaiwWC/F4HKfTSXNzMx/96Edxu90UFxeLkNbCaJ6xsTGGh4eZnp6muLhYRGY4HA48Ho+ITSgIpcL1kG8nFsRTwftVVlbG4ODgghle2WyWrq4uuru7xe/CqWIkTieD62L2XMn2n0QikUgWxGazceedd/KhD32Ip556ikOHDvHaa6/xxT/9iS9yrP/qf3HUf+X382u/n5eAmpoaNm3aRDqd5vDhw5SUlDAxMcH4+DhLlizB7/dTVFSExWKhqalJzLjz+Xx4vV6WL1+OTqdjeHiYiYkJADKZDAqFgp6eHqanp4X4CYVCOBwOYrEYSqVSnExrbm6mrKxMeHumpqaYnp6mt7eXXC7HY489JrxNhSDM6elptFotRqORkZER4vE42WwWn8+HwWBgcnISr9dLJpNh9+7dZLNZlEol4+PjGI1GYrEY9fX1TExMUFxczNzcHMlkkpKSEoxGI6tXr0av17N161YxlLrQ8lu6dCkHDhzA4XAwMDCAzWYTo3I8Hg/RaJR4PE44HBahqLlcjng8TjKZJBaLMTAwwJYtWyguLsZut1NaWopSqaS5uRmHw0E0GqW6ulrMedyzZw8VFRWMjIywadMmtFotgUAArVYrzPkHDx4kl8vh8XhQKpVidFDBb5VKpUTlKhwOo9Vq0Wg0Iui1qKgIs9mM2Ww+ZQXq+FbjfN7KnispqiQSiUQCgNls5rbbbuO2224jnU7T09PDk08+yT333HNy/xXw68FBHvrFL8iWlOByuYTIsFqtZLNZAMLhMADXXXcd9fX1pFIp+vr60Ol0IlC0t7eX4eFhysrKRLVlenoal8uFSqUSpwz9fj8zMzMsWbIEhUKB0Wik0KUozNQ7dOgQPp+PsbEx4XEym82YTCZisZgYT1MQe06nk927d6NSqejr66O+vp6SkhKqqqr405/+BMDExAR+v59sNovVahXG7Hg8TiAQoLq6Go1Gg8vlorW1Fa1WK8JFp6amyGQy2Gw2Vq1aRTQaJRwO09/fj1KpxGazYTQaCYVClJeXMzY2Rn19PaFQiLm5OfR6vRgxVDh5qFQqmZ2dFZEXhUMIXV1dwoA+OztLOp3GaDQKY77T6WRiYoLa2lqcTicNDQ0MDw9z5MgR4XUbGhpi1apVJJNJ4Xmaf3IwEAgIAVxVVUUymcRut4s25OmM2TkZC50qLDDfcH8xIkWVRCKRSE5ArVbT0tJCS0sLf/u3f8sf//hHent7+fa3v81js7ML+68mJvj1xASPAXVtbZhMJrLZLBaLRcQqFCoUBW9RoTLj9Xrx+/1iZl5paSkul4u5uTlUKhXpdJpAIIDFYqGuro5YLEZtbS1dXV34fD58Pp8IJX399dfFeJ1LL70Un89HbW0tgAjuLFSdClEJs7OzRKNRmpqa2Lt3L/v378dgMFBaWkpJSYmo8JhMJuLxOBMTE+j1erRaLcuWLeO1117DZrMxNzdHKBRiYGCA8vJyYZKvra0lFovR2trK6OgoJSUluN1ucUqyILiSySRzc3OUl5eTTCYpKioSgaQlJSUcPnyYSCTCq6++SjAYpLe3l0AggMvlIhwOixDXgpgqDMuGfEuv8HN65ZVX8Hg8lJeX88EPfpCqqipUKhXBYJCysjLhjevo6CAQCFBWVkZVVRXl5eWiIpdMJsnlcoTDYcxms2if+nw+ZmdnRUApsGAQ6cmY77kyGo1MTEzQ398PQH19/QnXX0ytQimqJBKJRHJKLBYLt912GwCf+cxneO6553jkkUe477e/PXn+1aFD/Bp4cOdOVlxyCV1dXWzatIldu3aRSqVEm6y3t5eZmRmeffZZIpEIZWVlXHXVVTQ2NnLgwAFKSkpQqVTo9XqqqqqIx+OMjo4Si8VEnMPIyAhKpZJXX32VpUuXMjk5idvtZmBggHg8TiwWw+VysXbtWt75zneya9cuERZa8BoV4iPS6TRKpVL8UY/FYqxdu5bt27ejUCiYnJxEpVIJn9P09DQATU1NpFIprFYrfr+fkpISpqamGBoaoq+vj0wmI6pAhZN069atw2QyMTIywsTEBKlUCo/HQzabpbS0FIPBIMJU1Wq1eL1QhYpEIrhcLmKxGA6HQ5ziC4VCTE9PizU6nU5Uq0KhEJFIhHg8TjAYpLu7m/3792Oz2aipqaGxsZHh4WGKi4t54YUXGBsbo7i4mOuvv56rr76ayclJADEcW6FQ4HA4KC8vZ2hoiGAwyLZt24SQ9ng8ACLPa2ZmBo/HIyqRBUF0/Ogct9stXuvv7xfzHufm5igpKTlhcPTF0iqUokoikUgkp43JZOLmm2/mxhtv5Jvf/Ca//e1v+epXv8oXZ2dP7r/at49fA5//059YuXIlS5cuFW2oWCwmoghcLhcGg4Fly5ZRVFSEVqvF4XCwefNmiouLGRsb46qrruK1114jEAiwc+dOUbVKpVKUlpZSV1fH2NgYwWCQFStWiPE6o6OjlJeXs2XLFrq7uwEIBoN4vV68Xi9KpRK73Y7L5cJisTA8PCxO8i1ZsoSamhrC4bAIPi1U2ZRKJel0GoPBQCKRIBqNYjAYGB0dJZVKEQwGmZ6exmg0ks1mRaAp5IVJKBQSYaV2u53Z2Vmam5uF0fvAgQP09vYSCoWor68nkUiwdetWrFYrer2edDpNJpMR+xwaGiIUCmG324lGo9hsNjQajRio/eCDD6JWq1EoFMTjceHRikQijI2N0d3djUqlor6+nqGhIaLRKMPDw/T29vLAAw8QiUQoLy9n7dq1bNiwgfr6enG6cGZmBpPJxNTUlPB0hcNhZmdnGR8fx2Qy4XK5xO9SQRCFQiExesdkMon2XyQSQaPRMDoHvE5kAAAgAElEQVQ6yuzsLDqdjqqqKrLZLIlEQvi8wuEwer1eBNAWBNubgTz9J5FIJJJzIhqNsmXLFrZs2cIPfvADAHRwyvmD4+SrO3V1dSKos7a2lsqjcwm1Wi09PT0MDAzg9XqprKxErVazYsUKHnnkEUKhkKhApVIpEdOwZs0anE4nLpcLv9/Pf/7nf5LJZEilUixbtowlS5bQ0dFBNpslGAwyMzOD0+lk5cqVqFQqGhsb6e3txev1Eo/HWbp0KSUlJYyMjHDkyBExJ3BqakrkPFVVVaHX61m+fDnJZJJEIiF8RiMjI+zZs4fS0lIRueB2u3nXu96F0WjE6/WiUqlEavzAwACrV68mk8mg1WoZGRnBYrGIiIiysjJmZmbE/UwmEzt27CAejwsBVfCvTUxMiFDS9evXMzs7y/79+/H7/UC+HahSqZibmwMQJwvdbjfxeFwY5Asip1ABMhqNeDweTCYTdrudmpoaERBbEJdGo5Fly5ZRU1OD3+/HbrczMTFBQ0MDlZWVwts2PDxMIBDgwIEDtLW1EQ6HGR0dRa/X09XVRUVFBXNzc6xYsUJU5uLxuMhJq6ys5NVXX6W/vx+LxcKll156ypmIi4U8/SeRSCSS84LRaOSGG27ghhtu4Mtf/jKPP/44W7du5Ze//CWPwcL+K+Chri5e7OpiM7BixQoRyVAY/Gu320U7b3h4mPXr1zM2NobFYgEQ0QO9vb1oNBoxCgfy5ulQKERxcTG9vb1otVoRSppIJNDr9QwODqJWqxkfHycUCgmTusViobq6mtHRUfr7+3E4HLjdbsLhsLgG8qIkHo8TjUaZnZ3FZDJhs9lQqVTs3bsXi8WC2WxGp9OhUqkAxGm8zs5Oli9fztzcHC6XSxjRi4qK2LFjB8uWLWNubg6LxYLP50OtVlNWViaqT2q1mkwmw5EjR/B6vVRUVBCLxUS7LZlM4nA4KCoqYmZmBpVKJQSaRqPB6XQCiArY9PT0MacptVot11xzDUeOHGF4eFh42iAfn5DL5dBoNAwMDHDw4EFcLhcOh0PMeozFYszNzZFIJBgaGhIm/u3bt6NWq4VB3u/3EwwGqaioEJ/TYrEwOTnJ9PQ0jY2NDA4OcuTIEaqrq+ns7AQQ+9m9ezddXV0kk0mWLl3K3NwcpaWlb1qYqKxUSSQSiWTRyWazDAwMcP/99/PQQw8xPj4OnJh/Bfmq1fZ5j33AtTfeiNPpFC0lh8NBfX29SOCem5ujqqqKkZERUYlauXKlyIPyeDz09/czOztLKpUiEAjQ2trKxMQES5cupaysjF/96lek02lCoRClpaXU1NQQiUTweDxYLBYOHTpEZWUlZrNZDFMOh8Ok02kRg1D4ftmyZaxcuZJsNsvOnTuP8RsVKjrDw8PEYjERt+DxeBgaGmJwcFC08aampkilUhiNRoLBIOvWraOoqEi0FA8ePChG98zMzNDR0cHQ0BBOp5MlS5bQ0NAg/GY+n49sNks8HsdutxOLxfD5fGQyGdGCjcViIml+cnKS2dlZ4ctyOBzYbDYSiQQdHR3E43Fx2rJw38L4IrPZjFarJZvNkslksFqtpNNpcUJzdHSUTCZDUVER69atQ6PRiEMDpaWlZLNZHA4HarWa7du3s23bNgKBAOXl5bS0tNDQ0IDP52NoaIhUKsXU1BSbNm2ir68PhUJBR0cHxcXFbNy4kY0bNwoRe76QlSqJRCKRXDAKeUzf+MY3+PKXv8z27dt58skn+e53v8sXgS8Cy4HLjj42AB84em0ceP2JJ9gO7AC2AlMqFe9+97vx+XwsWbKESy65hGuuuYZHH32U7u5uEokEarWacDjMyMgIhw8fprS0lFgsRigUYmRkRHiNCtWaVatWkUgkmJycpKKigu7ubsrLy6muriYajVJWVoZerxfzAPv7+0W8QWtrK16vl5KSEhwOhwjUVKlU6HQ6kYeVSCREm6u6uhqbzYZWq6WmpgZAnKRTKBR0dXWJ1lzhxOHw8DDNzc2sXr2aZ555RkQ9uFwuvF4voVAIvV6PRqOhurqabDZLcXExO3bsEON4rr76ajFKqDAGqBBMunr1anbv3k06ncZms+FwOPB6vZjNZnE6snDisjADMZlMCn9YNpulqKhIREwUFxfT0dHB9PQ0JpOJ3t5eLBaL+FyhUIjBwUFmZmaIx+OiZbdx40ZWr15NS0sLKpUKg8EgqpR2u51UKkVjY6NoURb+rw0GA5lMhrKyMpqamojFYgwNDVFbW/umGNalqJJIJBLJeUWv13PllVdy5ZVX8qUvfYnf//73vPDCC/zmN7/hIHD/0XUlwDvIC6zLgP8D/PPR18YyGXY9+yy7gF179vDVRx5hz5494qh+TU2NEFCJRIKRkRGi0SiAOBk2NzcnTvWZzWZKS0sZHh4mk8mIETQWi4Wenh5sNhulpaUiy0qr1ZJOp2ltbRVDgwvGcJ1ORzQapbOzk+bmZsrLyzEYDJSVlQHg8XjYsWMHra2tQrgVRr4U2niFMTkWi4VwOIxKpSIQCFBfXy9M/KWlpYyMjHDw4EGqqqpwuVxUVFSIIM729nbRZivMYhwaGkKj0VBeXk5lZSUqlQqtVotOp8NsNnPkyBGRnm61WqmqqqKyspLdu3eLMTu1tbVUVVUxOTlJf3+/qAwWhjZv2rSJUCiERqOhu7sbh8NBIBAQA6HT6TSQ995ptVpUKpWokM3OzvLSSy8RDod54YUXUKvVHDhwQAzF3rRpEzU1NaTTaWpra0Vul0ajEWLWZrMxNTWFz+ejurqaRCLxps0TlKJKIpFIJBcMq9XKHXfcwR133MEPf/hDnnzyScbHxxkeHubRRx/lDxMT/OHoWj2wElg373HzvHsd+e1v8yILeP3IET63eTNVR6sVPp+PyspKcVqs4BMym8309PSg1WpxOp289tprKJVKXn/9dSoqKujr68NmsxGNRkUyeGEAsV6vZ2BgAJ1OJ6IFCrP3VqxYISo0K1euRK/XU1ZWxqFDhxgbGxNzCc1mMzabjYGBATHj0O12s2bNGg4cOCACONva2vD7/eh0Onp7e/n1r3+N3+/H7/dTUVHBwMCA8DEBuFwulEqlaL8VTtRZrVaSySRKpZJQKIRarSaZTIp5iQWBVagElZSUoFAo0Ol0NDQ0EIlE2LBhAwaDgc7OTqamptDr9YyNjWE2m3E6nczOzgqfVF1dHUajUbTjbDYbCoUCpVIpKnj79u0T2Vm5XI5YLCZyqAoJ9gAzMzMMDQ3x/PPPc/311xMMBjEYDDQ0NDA9PS2S8wOBAJWVlceIxTdrnqAUVRKJRCJ5U7Bardx+++3i+3vuuYdXX32V3bt3iwDIT3/60+w8WukAcABr+LPIeg/w10dfywCdXV3sJ+/L2t/Vxd6uLrIOBzqdDrfbzWuvvcbq1atFFaOoqEj8wXe5XHR2dhKPx9HpdExNTeF2u1EqlZSUlNDU1MS2bdtEBaexsZFkMonf7+fQoUMYjUYymQyBQACbzSbaYk1NTUxOTgrBEwgE8Pv9JJNJTCYToVAIn8/HNddcQ3V1tYh5UKvVHD58GJfLRXd3N42NjQwMDNDV1YXFYkGr1VJcXCxM9i6Xi6amJuLxOMXFxUxMTAgx1dTUxI4dO0SMg9PpZHx8XMQemEwmVCqVSEkvjL4xGo1UVVWJ2YIHDx5kcnISs9nMzMwMPp8Pv9/PLbfcwtjYGD09PQQCAdF6nJycJJFIsHLlSsbHx8lms7S2tmKxWIjFYkSjUdxut8jpKggqyPvyCiGxg4ODPPjgg9jtdhoaGvB4PCQSCYqKishmsxw5coTLL79ciKs3K6tKGtUlEolEctEyNzfHAw88wL59+ygqKuLIkSM8/fTTx6ypJC+0Vh59XHL0uQIj8GehdfRrzO1m9Zo15HI5IapKS0s5dOgQ4+PjeDwe4akqJKNPT0+LlPLp6Wn0ej1KpVIEdha8RoWqT2FETyHeobi4WFSaysvLmZ6eZt++feKUXH19PTMzM0xOTuL3+6murhaJ6MPDw/h8PuGf0ul0FBUVib2lUine9773EQqFeOKJJ1AoFAwNDWGz2cjlcixfvpwXX3xRjANyOBxotVqCwSCNjY3C+5VOp2lpaUGtVqPT6UTkQ2trK3V1dWzdupX29nYikQjBYFDMDSy0I/v7+/H5fOh0OuF9CgaDlJeXc+WVV9Le3i7EnMlkIhwOi2HOc3Nz4kDDqSichKysrKS2tpabb74Zk8nExo0bRQbY+UYa1SUSiUTylsNut/MP//APxzw3PDzMv//7v1NcXEw4HOa5557jD+3tom0IUASsIC+wCmLrvUDhTNjc1BT7n36a/fxZaD0J6MxmkRvl8XhYvnw5mUyGjo4O0UJ0Op3HpHpbrVba29vFiB2DwcD+/fuZmpqipqaGjo4O0uk0JSUl2Gw2YrEYs7OzOBwOmpqaeOWVV/D7/RgMBgKBAB6PB71eL6o9hbE9NpsNv9/P5OQkzc3NtLa2YjAYmJ2dFZWZbDaLzWZj//796PV6gsEgHo+H7u5uksmkCC0dGhrC4/GISk8hRFOn09HV1cWGDRsIBAK0t7dTVVVFb2+v8FWFw2FRaUsmk2g0GsrKyujv7xcRE5lMBrVaTSgUwuVyYTQaRTTF9PS0CPf0eDziFGFVVZUYGj07O3vS34l0Os3w8DDDw8Ns27aNrVu38oUvfIFMJkMul5OJ6hKJRCKRnC5VVVXcd9994vtUKsXu3bv5xS9+gVKpxGq18vLLL/PCrl28MO86PbCMY4XW3wCmo68ngPZwmAPAYeCw18shr5dx8qcZ1Wo1uVxOzMGrq6sTRmyTyUR/fz/JZBKtVivm4XV0dOB2uzGZTLS3t3PppZcSiUSEgDIYDLz3ve/l6aefFmKq4JWamZlhYmJCtMjS6TTZbJbq6mrsdjsqlYqioiIymQzj4+M88cQTOBwOSktL6e/vJxqNijRyk8lEJpNhZmaGWCwmQjpXrlzJ1Vdfzfj4OL/73e9IJpO4XC5qa2tpb28XJnCn00k2myWdTlNZWSlO3KVSKbxeL1NTU0xPT4s2KkBZWRk9PT1kMhlisRgDAwPCA1UISdVqtVgsFvR6PQqFgsrKSsbHxwmHw6RSqdP6fRgdHeXf/u3fCIfD3HTTTdTU1LxpwkqKKolEIpG8pdFoNFx22WVcdtll4rnCCJR7772Xubk5ysrKiMfjfP3rX2e+SURJPjur0Da8hLxP6455a2aBw9ksh5PJvNgaGuLw0BAHDhygoqICnU5HMBgkHA5TVVVFNBrFYrEwMTGBQqEQcw4Lc+lsNhu7d+8mHo+TyWTEsOWDBw9is9loaGgQVZdCVpPZbMZgMAgDut1up7m5menpaTHnT6vVkslkUCgUIqgzGo2KodSFaIRCoGkulxMz9yoqKmhsbGR8fBy3243f7xf7t1gsjI+Ps23bNlKpFH6/n3Q6TSwWY3BwkGAwyMTEBB6PB61WS2trK5FIhEsvvVTM7yuMlJmbmyMcDotIBoPBgF6vp7m5mb1795JIJES22Hz0ej0Gg4FIJCLE5Xy8Xi/f+c53GB4e5nOf+9wx43AuJNJTJZFIJJK/GAKBAI888gg7d+5ErVaTzWbZt28f+/btO2adE2glX9kqPNrIG+ULTALtQMdxj6BWi8PpxOfz4XK5UKvVlJeXc/nll2MymWhsbGTPnj0i0yoajZJMJrFYLLS3t7N+/XoCgQAlJSUMDw+jVqux2+0iTsHj8WA0GikvL8dqtZLJZAiHwxT+nldWVtLV1cXU1BTt7e1iHl8hid1ut5PL5bDZbLhcLlatWsXq1avZsmULXV1dGI1G3G43ZWVltLe309nZiVqtxuVyHTPvcHp6msHBQVKpFPF4nHe+850YjUYqKyuZnp4mm82KmX9TU1MoFArsdjsHDx4kEAgIgVl4r66uLgKBgBhSPZ/CbMaCGIvFYsTj8QX/j+vq6tizZw92u/2cfldOxck8VedNVCkUiv8E3gdM5XK5ZUef+y+g6egSOzCXy+VWLnDtIBAif5gjvdDGF0KKKolEIpGcKalUiv379/Pwww/j8/koKSnh0KFDPPPMMyesLePPImvpvIdt3hofJwqtDiBut+P2ePB4PAwMDGA2m9m0aRONjY0Eg0GOHDnC9PQ0S5YsYXBwkFwuh16vp7a2loaGBg4dOsTo6ChGo1GYzGdnZ3E6nWzcuFGY2gsxB8lkkmeffZaZmRkikQiXXHIJ2WyWO++8k+3bt5NKpUS7tLGxkR07djA2NoZarRYmfJ/PJwYvh0IhmpubSafTBINBIpEIPT095HI5stks5eXl1NTU0NDQgFarpb+/X4ipwjxBo9HI3r17iUajRKNRFAoFZrOZZDJJIBBYsOWnVCrFwGm73U48Hmdubk4Eii7Exz72Me6//34xUmixeTNE1RVAGHigIKqOe/1bQCCXy929wGuDwJpcLuc7k/eUokoikUgki4XX6+Xb3/42oVAIv99POBwmkUjwwgsvnLC2jGNF1lLylS7nvDVznCi02gFtbS3Fbjd9fX0iBLSoqAiTyYTH46GtrY2qqip0Oh2HDh3C5/ORy+Xo7u4WMQ7XXXcdfX19GAwG0uk0NTU1JBIJpqen6enpERlVHo+HlStXYjab8Xq9pFIpkskkoVCIqakpkskkTqcTt9tNRUUFPT09BINBxsfHSafTWK1WVCoVkUgElUrF9PQ0yWSSTCZDSUkJnqOisRBoWohLcDqd1NbW0tPTQ3d3txhbk8lkUCqVxGKxk/4/uFwustksKpVKtCxzuRx+v/+kvquSkhJ27txJdXX12fzXvyEX/PRfLpd7WaFQ1JxkMwrgfwJXnq/3l0gkEonkXCgtLeUb3/jGMc/lcjnGxsa477778Hq9YjTLkSNHeOqpp9hy3D3cnCi2bgA+Pm9NaGCAjoGBYwXX1BSDwMDAAPv27RMDl00mEy6XS5wgjMViqFQqtm/fzvT0NFarlUAgILxVBdN8Ia/qkksuIR6Pc9lll+FyuRgbG6Ovr+8Yj5LBYMDpdDI3N0dxcTF6vZ7R0VGcTqfwixVOFhY+f+E0YMGs3tLSQiwWY2RkRGRdDQ4O4vV6mZubE++nUqmOyaaaj0ajYcmSJWIWYaEyV0hlP9l1kG/zzs7OnjdRdTLeLKP6O4HJXC7Xc5LXc8BzCoUiB/w4l8v95MJtTSKRSCSShVEoFFRUVPDVr371mOcLxvgf//jHYjiyz+ejs7OTrTt3svW4+7iAFo4VW8cb5GNAN9CZSOQfgQBdwOH+fqLH3W9mZgaPx0M4HCYQCLBnzx5hGne5XMzMzJBOp/F6vSLYtGD61mq1whvldrvxeDyUlpaiUChE2n0ikaC3txeTyUQ2m0WpVGKz2airqxPG9ULLMBqNsnv3bmKxGFqtlrGxMZGUrlQqmZiYEP4vnU6H0WgkmUwKj5vBYBBfQ6EQ8XicVColohpOVdUqUFJSgtvtPt3/1kXjzRJVfwU8fIrXN+RyuXGFQuEGnlcoFJ25XO7lhRYqFIo7gTshf8xWIpFIJJILTSEO4Ctf+coxz2ezWTo6Orj77rtFcrnVamVsbIxnn32WV467j5282Golb0BuBlaTHzatmrduGOgEuo5+7YzF6BwcZPDo6wWz98TEBJlMhuLiYhGjkEgkiEQiFBUV0dvbi8/nEwntWq1WCBqdTsfevXuZmJgQYaA6nY76+noikYhImzebzVRXV9PT08PU1BR9fX0olUoxuDocDmOz2cQJyfm2o8K/7XY7VqsVpVJJJBIhHA6TTqeZmZkhmUyK+YGny4033ihG+FxIzuvpv6Ptv6fme6oUCoUaGANW53K50dO4xxeBcC6X++YbrZWeKolEIpG8VRgcHOSzn/0sk5OTuFwunE4nnZ2dbNu27YS1WvLRD83zHgXRZZ23LsQ8oXX0cQToAVLkTd8qlYpUKkVZWRlLliwRYaGFjK3KykosFguJRIJgMCiM7mq1GrfbTXFxMQ6HQ0Q6ZLNZRkdH6enpwe/3E4lE0Gg0om2XTCZP+XMwm83CQ2YwGEQ2VygUEtESZ8rDDz/Mrbfeet7yqi6mRPV3A50nE1QKhcIEKHO5XOjov68BTjCzSyQSiUTyVqampobf/e53Jzw/NjbGV77yFWZmZkilUkJovPjii3QscJ9SjhVZzeQ9Nh+etyZNXlgdyWbpyGbpAI6Mj7NrfJzjm2mjo6MiYNTr9aJUKonH41gsFmZnZ7FarTgcDo4cOUIoFMJsNqPValGr1SIMFSCRSLyhoAIIh8NChIXDYXw+H4lEAsj7qk43BLRAbW0t119//ZsSAHo+T/89DLyLfOt4Evi3XC73c4VC8UtgZy6Xu3/e2jLgZ7lc7r0KhaIOxLQBNfBQLpc7tnl9EmSlSiKRSCRvV6ampvjWt75FT08PTqeTVCpFKBRi586deL3eE9YbgUaO9W61AEv4c0UlCwzyZ4P8kXlfQyfZh9VqRa1WY7VaxVgYi8VCOBxmYmKCWCyGWq1esMpUMJkv5ItSKpUnhHqeKSqViuHhYcrKys7pPm/EBY9UeDOQokoikUgkf2lkMhkOHz7M97//fTECpqSkBL/fz+9///sT1mvIC6vjxVYzoJu3bpQTxVYHsFA61PGn8U4mkGw2G9ls9gRv1WKgUqnYs2cPK1asWNT7LoQUVRKJRCKRvE0pjIPRaDQoFArxfSQS4Re/+AUvvviiaCXG4/EFg01VQC0niq0WwDxv3RT52Yh75j36yB/bPxUFE7zRaCQYDJ7rRz4Gu93O9773PW6//XZUKtUbX3COSFElkUgkEsnbkEKcQzgcRqfTUV1dzfj4OJFIBJPJJGYOzicWi/Hoo49yzz33kE6ncbvdJJNJdu7cecL9FUAlx4qt5Ucf+qNrAsDrwG5g19Gvb3gSbZGorKzkhRdeoLa29oIIKpCiSiKRSCSStyXJZJK+vj7C4TBTU1PU1dUBiBiDuro6tFrtad0rkUiwdetW7r33Xrxer0hvHx4ePmGtmnz0w2pgzdHHCvInFQG8/Flg7SIvumbP6ZOeyMc+9jG+/e1vY7Va33jxInIxnf6TSCQSiUSySGg0GnQ6Hf39/bjdbjEzMBgMYjKZ0Gg0Z3Sv9evXs2TJEh5++GF27dqFUqmkpaWFw4cPMzY2JtamgQNHH/959DkdeWG1Flh39OuN8+7fw7HVrH1wwunD06GoqIhnnnmGlStXnrf5fmfDxbMTiUQikUgkx3C8V2ohFAoFNTU15HI5kskkZrOZ8vJy0un0Ka+DvMk9EAhgNpuZm5tj7969PPnkk+zcuZPBwUG0Wi0WiwXIe6JKS0tpbm6mpaWFm2++Ga1Wyx/+8Aeee+45Ojo6SJAXTLuAHx59Dyv5alZBZL0T+NDR19LkPVlbjz5eJT80+GSoVCp+85vfcP3112MymU7jJ3hhke0/iUQikUguEuaLKMhnRp3KG3Wya99oXSKRIBqNsnnzZrxeL+Pj44TDYUZGRpiZmWFubo7JyUmy2Sx6vZ6WlhZaW1sZGRlh3bp1LFmyhOuuuw6LxXLMe2UyGfbv389dd93Ftm3biEaPH6iTp4S8wFoPXHH0q5a8yHodeJG8yNoGROZdd9999/GJT3zijX+Q5xnpqZJIJBKJ5CKmYDgviCi3283AwABWq/WMvVEL3TsWi+H3+5mZmWHv3r3s2rWL9vZ2bDYbHR0deDwexsbGCIVCqFQqVCoVLpeLpqYm6uvrqampweVyUVtbi9vtpqqq6pTiLZvNMj4+zh133MGWLcePmj4WA3AZ+XDLTeSrWhryKfC7yQusF48+MsBVV13F5s2bz6i1uZhIUSWRSCQSyQXiTKpGhXWpVIr+/n4hompra8Xg49OpVB1PNpslFAoRiUTo6enh2WefpaOjQ0QthMNhZmdn0Wg0hEIhNBoNiUSChoYGHA4Ha9eupb6+HrvdTkNDAyUlJZhMJjKZzBt+rvlEIhGampqO8WO9EUZgA38WWWvJ+5V6gK8CvyEvrhoaGti1a9cFn/MnjeoSiUQikVwAjq84nUwMHb+uvLwck8kkDOZarZaKiorTEmfH3zeRSLBr1y4ef/xxtm/fLsSVy+UikUgQj8fJZDKEw2FKSkpoaWkhHo9jNBoxGo20tbVx++23U1ZWhkKhQKvVivc/k9iC2dlZPvKRj5yRoAKIAs8ffQCYgP8BfA74JfCv5MXVr3t7cTqdVFZWLnhC8UIjRZVEIpFIJItIoRJUqDilUqkF23bHr0un0wuKqDdq+RUM6rlcjmw2y8DAABMTEzzwwAMcOnSIiYkJMUYmmUyi1+ux2+04HA7MZjPFxcV4PB7Kyspwu92Ul5ezYsUKjEbjWc3Py2azTE5O8qMf/YivfOUrp3WNSqXigQce4Oabb8ZgMJBMJvnBD37AP/3TPwF5X9Xvjz6uB/6N/InDzwNfAB4aGUGhUPDJT36Se++994z3vFjI9p9EIpFIJIvI2VaqTqe9V6hCJRIJMpkMsViMWCxGe3s7kUiEiYkJ8e+9e/cSi8UIhULYbDZKS0ux2+0sX74cvV5PLpfDarWyZs0a3G43AAaDgZqaGpRK5Rl/7mg0yqOPPspnP/tZpqamTuua1tZWfvazn7FmzZpTRiNks1k+8IEP8Ic//EE8dx3wJfInC3cAfw/snfdzOp9IT5VEIpFIJBeIs/FUnc6JvYGBAV599VX6+/vp7e3FYDAQjUYpLi4ml8uhVqsZHx8nEAiIUTBut5sNGzbQ3NzMwMAADoeD5uZmli5dilKpxGw2o1AozrjNCBAKhXjyySd54YUX+PnPf37a1zU1NfHUU0/R0NBw2tcU+OQnP8mPfvQjIJ/2/hHg38mP0mkEJoCbbrrpGAG22EhRJZFIJBLJW4D5QiubzeLz+RgaGmJqaoqnnnoKr9dLOp0mkUhQVFRER0cHy5YtI5VKUVlZSTqdxufzYbPZMBqNLF++nI0bN1JaWtQO/94AACAASURBVEoulyOdTmMymc6qGgWQTqfp6Ojgvvvu4/777z/j6/v7+6mtrT2r957P+9//fiGc6skPe/418PGjr59PfSNFlUQikUgkFymFSlQymWRmZga/34/NZuOVV17h2WefZXZ2lvr6eqLRKLlcjrGxMfR6vRBelZWVVFVVce211xKPxzEYDJSXl6NUKtHpdOh0urPyR80nEAjw0EMP8Z3vfIeenp4zvv6ee+7hk5/8JEaj8Zz2MZ9CwCnAN4B/JD8uZ9/R18+XxpGn/yQSiUQiucgoiKnh4WH27NnD3NwcExMT1NTU8PrrrzMyMsLs7Cxut5upqSmKiorQaDTccMMN3HrrrQwPDzM7O0s8HqetrY3q6urTSlI/XQKBAI888ghPPPEEmzdvPqt73HLLLXz+85+nra1t0Qceq9VqcrkcCoWCrwCfAP438KlFfZcz2M+b9L4SiUQikfxFURBQqVQKg8FAJBJheHiY3t5ehoeH8fl8orWm1WqZnZ2lrq6OF198EY1Gw8aNG7n22mvFqTyHw4Hb7SaZTAKI2IOzDQgtEAqFeOKJJ9i2bdtZtfcKOJ1O3v3ud3P//fdjt9sXReSdjC984QvcfffdjAFLztu7vDFSVEkkEolEsogcP2qmEHfg9XrZtm0byWSSTCbDzMwM+/fvR6FQYLPZiMVi6HQ6HA4Hk5OT6PV6UqkU11xzDR/84AcxGo0YDAZSqZQYlKxQKNDpdOe850gkwmOPPcb999/P9u3bz/o+ZrOZDRs2cNddd2Gz2WhpaVmU/b0Rd999Nx8HmoAfnfd3OzlSVEkkEolEskjMj0kwGo1ks1l6enpIp9P09/fj9/uxWq2Mjo5iNpsJhUKkUilyuRwNDQ1otVpqamoIBAI0NDSQTCbRarUYDAZsNttpD0o+HaLRKI888ghf+9rX6OvrO+fPXl1dzTXXXMPtt9/OO9/5zrM2wp8pCoWCRuC75MNCf3BB3nVhpKiSSCQSieQsKVSlVCqVMJGHw2FsNhs+n4+RkRF8Ph8DAwMMDAyQy+XweDxUV1czcjSw0u12U1xczNVXX008HsfpdJJOp4nH4+j1eurq6igpKVmU9l44HObBBx/kJz/5CXv37n3jC94AtVqNy+XiPe95D1/84hdxOBxYLJYLJqh++tOfogEeBOLAXwMFa7rf778ge5iPFFUSiUQikZyC49t5hX/ncjkGBweJRqMcPnyY6elpioqKRFyAyWRCr9ejUCjw+/0UFxeTTCapra3ltttuo6+vj/e85z1MT0/jcrkoLi7GbDbj8XjELEDgmBExZ8Ps7Cw/+clPeOaZZ9i6des5/zy0Wi2VlZVcdtllvP/976ehoYHGxsZz9nKdKe95z3t4/rnn+AH5E383A96jr61cufKCzwMEKaokEolEIjmBgpBSq9WMjY0RiUSEn6nQkkskEnR3dxOLxfj973+PWq2mrKyMhoYGKisrMRqNaDQanE4narWavr4+HA4HTU1NGI1GSkpKMJvN1NTUUF1dfcKg4rP1IhVajt/73vfo7e3l+eeff+OLTgOr1coHPvABPvzhD1NZWUlpaelZj7I5F0ZGRqiqqsJDfmzNzcC3gMfnrdm3b9+C155vpKiSSCQSieQo2WyWcDjM1NSUOKkH4HK56OjoIBaLoVKp8Hq9zMzMYDKZ6O7uFtlQc3Nz6HQ6TCYTCoWCyspKSkpKaGtro7u7m2QyidPpRKfTnTDn71ziBiKRCE8//TRPPvkkL7/8MkNDQ+f8s1CpVGi1WkpLS7nmmmv49Kc/TVVVFQaD4YILqQIKhQIV8GnyI2r0wP8BvjdvzZuZvylFlUQikUj+4igMIc5ms6TTaYxGI4lEgu3bt9PT00MgEMBgMBAKhbBYLDQ1NaHVaikuLmbfvn2YzWZ0Oh1arRa3201RURHT09Ns2LCB1tZWITrme6BaWlrOeFjyqfbv9Xr52c9+xrZt23juuecW5ediMpmoqqpi7dq1bNy4kcrKShobGykvLz/lbL7zzR133MEvf/lLrgDuBdqAzcA/AL3z1r3ZgeZSVEkkEonkL4aCmJqYmKC3t5fDhw9jNpuxWCyEw2FeeeUVzGYzw8PDRCIR6uvrWb9+PTabDbfbTSKRoK2tjWQyiU6nw26309bWJqo3y5YtO6n4OFeTeTabpb+/nx/96Ec8/fTTdHV1nfW9ClgsFjweD1arlVWrVnH55ZezbNkympubUavV5+znOlcOHjzIihUrqCRvRv8QMAjcBDxx3No3W1CBFFUSiUQi+QuhEHfg9/vFqbze3l6KioqIRqNYrVZsNhuDg4MEAgFaWlrEib6ysjIxV6+5uVnEICgUCjQazaKmmM9nbm6O3/72t3R0dPD444+fc1vPZDJRVFREVVUVTU1N/M3f/A1FRUWYzWasVqto+b2ZQgrA6/VSVlaGBfgq8Bnyw5O/TH54cmze2o0bNy6KAX8xkKJKIpFIJG8L5pvLU6mUaO0VhgenUinC4TCJRAKv18u+ffsIBAJMTk6yadMmwuEwxcXFuFwulEol0WiUd7zjHdxyyy3CkF2oNB1vIl+sk2+hUIjNmzczOTnJnj17+NWvfnXO99TpdOj1esrKyrj11lu58847yWazOByON9UftRBf//rXueuuu1AB/x9535QH+A3w/wMjx62/GKpT85GiSiKRSCRvOY4XULlcjqmpKSKRCIFAgEgkQmdnJ0ajkZaWFtatW4dGo0Gn09Hf309DQwPj4+OsX7+e0dFRVq1ahdPppLi4mOnpaRKJBNlslqVLl55XL1EikWDfvn28/vrrdHZ28sMf/nBR7qvRaCgtLWXp0qW84x3v4Nprr8Xtdr/p3qiTsXTpUo4cOYKHvHi6E6gGXgbeB7x+3Pof//jH3HnnnRd4l2/MxfeTlUgkEonkJBTm542NjRGPx/H7/aRSKTKZDCqVipqaGkZHR9HpdITDYVwuF1NTU0SjURFfkMvliMfjrFy5klwuR2NjIytWrECn04mRMccbyhdr7+FwmMOHD3PgwAGi0Sjf/OY38Xq9b3zxaeJ2u2lra+OGG27gpptuQqVS4fF4Lkoh9eMf/5hPfOITKIBN5KtSNwEaYAv5ochPHndNXV3doqS/ny8uvp+yRCKRSCTzmH9Sb3R0lP7+fsbHxzGZTExNTVFUVERpaSnJZJKenh4ymQzhcBiz2Uw0GqW6uhqj0QiAUqmkrq6OVCpFc3MzsVgMo9F4TAL4YgwlLhCPx9m3bx/d3d088cQTvP7664yMHN/EOns8Hg9NTU20tbWxdu1aLr/8ciwWC0VFRecU0XA+KXjQnMA/km/zNQIz5KMRfgL0LHDdxdbqWwgpqiQSiURy0ZLL5RgZGaGvr4/x8XFisZg4qdfX10dVVRXxeJxUKkVdXR3RaJTW1lZmZmaoqalBqVQKT1WB+aLJbDYv6n5TqRS9vb309vYSDAa5++676e7uXpR7a7VaXC4XRUVFrFu3jve///20tLRgs9lQq9WYzeYLNh7mTPnwhz/Mgw8+CMAG8kLqg+Rzpl4F7gYeBRILXPtWEFMFpKiSSCQSyUVBJpMhEAhgs9lElSWVShEIBMhkMmQyGUwmE3Nzc+j1elatWoVarWbJkiXU1tai1WoZGxsjHA7jdDqxWq3n1YSdy+WIRqMMDw/T09NDT08Pf/zjH3nppZfIZrOL8h46nQ6bzcYll1zCZz7zGaxWK+Xl5bhcrovOZH48mzdv5r3vfS+Q90d9Hvhf5KtSQeBnwI+Bwwtce8UVV/DSSy9doJ0uHlJUSSQSieSCcvwQYo1Gg1qt5umnn2Z8fJyysjKuu+46VCoVGo1GDCfW6XQUFxezZs0aIC+4dDodtbW1okJzfEr5YhOLxdi3b59IMP/jH/9IT89CzaqzR6/XU1tby+233059fT1tbW00NDSI+IaLWUgBYn9W4H8DHwE2Hn1tK/B14LdAZIFrn376aa699trzv8nzhBRVEolEIrlgZLNZBgYGCAaDDA0NMTc3h8FgoLm5mbGxMerq6hgYGCAQCOB0OsWoF4/HI3KhCq27hcTTYvqhAJLJJF1dXfj9fgA+9alPcejQoUW7P+TN5e9617uora2lqamJ1tZWGhsbUalUJ7QuL1bMZjORSAQV8D/IC6mbAAPQBXyOfHjnQilbbW1tHDx48ILt9XwiRZVEIpFILgi5XI7+/n5effVVlEolQ0NDLFmyhEQiQTKZxO12MzAwQFlZGTabTVynUCgWHC68mOKpsL9oNIrX6yUcDtPR0cFjjz3G5s2biUaji/IeGo2GpqYmmpubqamp4frrr6e0tJSqqipSqdQJpvmLmXvvvZdPfepTAKwgL6Q+BJSQN53/HPg1sOsk12/bto3LLrvsAuz0wiFFlUQikUgWlUJ77/gqUiqVIhKJYDQaRYJ5OBzGYrHgdru55JJLCAaDx3iqzvc+I5EIfX19eL1exsfHee6559i5c+eiDCQuYDQaaWhoYMOGDdx88820trZiMpmwWCzHCKiFhOPFxssvv8zGjflmXjnwT+R9UsuBJPAU8ADwNJBa4PovfOELfOlLX7owm30TkKJKIpFIJItGYRRMJBLBZDJRUVEhhJVGo8HhcFBcXEwqlWLjxo0UFRWh1WpFRpTT6Tyv+0skEvT29mK1Wnn44Yd57LHHeO211xb1PcxmMytWrGD9+vVcfvnlNDc343A4sNls6PX6i94TtRCFPdvI+6RuB94FKIGdwN+S90n5F7hWq9WSSCx0ru/thxRVEolEIlk0CtUoq9VKMBgklUqJNt18fxRwQWbMZTIZJicn6e3tpb+/n1/+8pf09PQwPj6+KPfXarXU1dVx5513UlFRQWlpKS6XC7fbjdFoFGLxrYjNZiMYDKIDbiYvpK4jH4PQTT6s8yGg9yTXx2Ix9Hr9BdnrxYIUVRKJRCJZNDQaDSaTiWAwiMlkQqPRHPP6yfxRi0kkEuGVV14hGo2yZcsWHn30Uaanpxfl3kajkZKSElatWsXatWu59NJLWb58OQaD4aIYRHyuNDY20tPTg4L8ib3bgQ8AdmACuJ+84fz4sTEFvv/97wuf1V8iUlRJJBKJZNFQKBTnPdbgeOLxOIcOHSIej9PZ2cm//uu/Mjk5eU73LHyO6upqzGYz1dXVXHrppaxatYrS0lIUCgUOh+OiTS0/E6666ipeeOEFIO+N+g/gr4BKIAT8gfxA4xeAzALXX3fddTz11FMXZrMXOVJUSSQSiWRRWexYg+PJ5XLEYjHGxsbYtm0bX//61+nq6jrn+1osFpqbm1m+fDm33HILbW1taDQatFotKpXqok4sP1OuuOIKXnnlFQCWkW/lfRBoIW8wfwb4LPDfQOwk93grJZ1fKKSokkgkEslFTzKZFIOIe3p62LZtGy+//PJZ30+pVFJfX09TUxOXX34569ato7m5mWw2i8PhuOjTys+G//t//y//8R//AUAb+dEwHwSayVegXgK+D/yOfCTCQszOzmK328//Zt+iSFElkUgkkouOXC6Hz+fjySefpL+/n8cee4wjR46c9f1MJhOtra3ceOONrFmzRoRrWiwWrFbr26YCdTzzK1LLgS+TF1JN5IXUVuC75Ft8Uye5x/PPP8+73/3u877XtwNSVEkkEonkoiAej7Nz50727t2LXq/ns5/97FmFbpaUlLBmzRquvPJKmpubcbvdaLVaiouLKS4uflv4oE7FsmXLaG9vB/KhnF8hL6QayQupF4FvkxdSJ7Pv//SnP+XjH//4+d/s2wwpqiQSiUTyppDNZhkbG+OZZ54B4Dvf+c4ZV6PsdjvXXXcdK1eupLy8nLa2NiwWCzab7W1dgZpPMpk85kTlSuCr5IXUEiBNXkh9k7yQ8p3kPm/3YM4LgRRVEolEIrlgxGIxduzYQW9vL52dnXz3u989Y8Oz1Wrlfe97H7feeitXXHEFVquVdDr9lhg2vFjcc889/Mu//AuQz426lnyG1HuBWvJC6k/kT/I9zsk9Uv/8z/8sfFaSc0eKKolEIpGcN1KpFD09PXR2dhKJRPja175GZ2fnaV9vsVhYunQpK1as4F3vehd1dXVUVVXhdruPaeOdz9OGFwOvv/46a9euFd9XkU8xvw64kvzg4giwhXy773EWTjcH+Md//Ee+9a1vndf9/qUiRZVEIpFIFo1cLkcgEGDPnj2kUin+67/+i0ceeYR4PH7a91i1ahV/93d/R3NzM2VlZXg8nrflabxTEQgEjjll5yIfwnklsIn8iT2APuCnwB/Jn9472TCYu+66i6997Wvnbb+SPFJUSSQSieSciMViHD58mOrqanbt2sW//Mu/0NXVRSazUFTkiZSUlPD3f//3eDwe1q5dS0NDA0aj8S9KRGWzWaxWK5FIBMgnmN/In0VU29F1IeAV/iykTpXO9atf/YqPfOQj523PkhORokoikUgkZ0Q6nWZiYoJkMonP5+Nzn/scw8PDBINBEokEs7Ozp7zeZrPx0Y9+lHXr1lFaWsqqVauwWq1/USIKOObzWsiPhSmIqJXkhxVHgW3kZ+y9COwh75c6GTKQ881FiiqJRCKRnJJsNks4HBZ/sH/+85+zZcsW2tvbCYfDhEIhSktL8fl82O121Go16XT+T79arcbj8fCOd7yDm266CZ1Ox4YNG/B4PH8RJ/MK/Pd//zc33nij+N4IXM2fRdRq8n+QE8AO4IvkRdQuIHmK+/7VX/0VDz300PnZtOSMkaJKIpFIJCclk8nw6quvsnv3bgKBAFNTUxw8eJBIJEIkEkGhUKDRaJiensbhcGA0GrFaraxcuZKPf/zjOJ1OnE4nNTU1JwxXfjszMDBAXV2d+L4S+J/AO44+VgEa8iNhdgH/Tl5E7QBO5T5raGigp6fnPO1acq5IUSWRSCSSBcnlcvT09LBv3z6CwSChUIhQKERFRQWHDx9Gp9ORyWSora3liiuu4POf/zyzs7MoFAoaGhre9ify5rNt2zYuv/xy8b2CfAXqr4F3A+VHn4/+v/buPcqq8rzj+PchjoDcBiUqIzCCIGpNKJZ4ARNQo4hVMSUiY0WJaHSl3rKMS6CReK3SYKRW0ixCNSiKKGIFFS8BrDEg5eooGQPWUuTipYCYQbk//ePdM3Mc5nJ09jn7nDO/z1qznLPP3tuHd83lN+/77vcFlhLWjHqNMLS3o4H7HnHEEXz44YcZqFgyQaFKRETqtGfPHvbv30+PHj3YvHkzrVq1onPnzhQXFzN48GAGDhzIzp07ad26NaWlpRQVFVFSUpJ02Vkxd+5cLrzwwurXRxGezttKmBt1BVAKfArMAxZFH+U0PCeqpKSEjRs3ZqhqyTSFKhERqVNRURHt2rWjZ8+e9OzZk5KSEoqKiti5cyfFxcUFv91LqsmTJ3PddddVv+4GjCQEqIFAz5Rz9wOvAmMI60U1tphEZWUlbdq0ibVeSYZClYiI1MnM6NKlC3v27PnSauXNIQBMmDCBMWPGVL8+GhhFTYjqHh3fCrwOTAY+BDoAzwP19TUNHz6cmTNnZqRmSZ5ClYiI1MvMmsXcqHHjxnHvvfdWvz4GGE1NiOoWHf+EEKIeICy2+TbQ0CIGffr0YdWqVZkoWXKQQpWIiDQ706dPZ+TIkdWvjwGuBgYRQlTVxPKPCOFpQvTfP9FwiKoya9Yshg0bFmPFkg8UqkREpOCtXbuWY489tvr14UAZcBbh6bzS6PgmQniq+mhsl8Kbb76ZiRMnxl6v5CeFKhERKUgtWrSoXrC0LWHz4aoQVbXty1bC+lD3AfOBxlaAGjduHPfcc09G6pX8p1AlIiIFYezYsdx3331AWFjzdGpC1MnRsS+AN4DphBC1kvC0Xn3Gjx/PHXfckcGqpZAoVImISF7asWMHbdu2rX7dA7geOJcwL6oNsA9YBvwS+D1hrahdDdxz1KhRPPLII5kqWQqcQpWIiOSNMWPGMGHCBCD8AhtEGNb7W+D46Jw/A48QQtRrwPYG7te3b19WrFiRoWqluVGoEhGRnNa2bVt27AibuXwTuJwQogYT1oXaRZhU/hvgBeC/G7lf1TwrkbgpVImISE4pLy+nT58+1a/7UtMbdTLQgvCU3tOEhTZ/T8P7561fv56uXbtmrF6RKi0ydWMz62pmC82swsxWm9mN0fFDzexVM1sb/bdjPddfEZ2z1syuyFSdIiKSvL59+2JmmBn9+/RhKDCFsDL5CuAOwibFvwBOAroQ1pV6jgMDVVlZGe5e/aFAJdmSyZ6qvcDN7r7CzNoBy83sVcJK//Pd/T4zG0PYHunW1AvN7FDC904/wjpry81sjrtvy2C9IiKSRVXb3kDNJPPzCZPMWxLmQr1MGNKbR1jNvD4a0pNckLGeKnff7O4ros//AlQQFqkdCkyLTpsGXFTH5YOBV919axSkXiU80CEiInmqV69e1b1RB5sxiPBUXgVhHtSDQFfgX4EzgE7AJcCjHBiorrnmmi/1RonkgqzMqTKzownD4kuAI9x9M4TgZWaH13HJUcAHKa83ULNrQO17/xj4MUC3bt3qOkVERBLQv39/Fi9eDIS/4PsCtxAC03cJC3LuIjyh92tCj9T7Ddxv0aJFnHbaaRmsWKRpMh6qzKwt8Axwk7t/ltrd29BldRyr808Rd59CGHqnX79++nNFRCQhI0eOZPr06dWv/4owpHcmYemD4uj4asKSB/NpfJK5eqEkn2Rs+A/AzIoIgepxd58dHf7IzDpH73cGPq7j0g2EXuAqXQgPe4iISI64++67q4fzzIw3p0/namAG8CHwDmFI71uEJ/XKgCOBE4EbqHuS+dVXX61hPclbGeupstAl9e9Ahbv/KuWtOcAVhK2WriB8X9X2MvBPKU8GngOMzVStIiLSuNmzZzNs2LDq110Ia0adGX1U/SW8kfBDfAFhX731jdx33bp1lJaWNnKWSO7L5PDfAGAk8LaZrYqOjSOEqafMbDThe+1iADPrB1zr7le5+1YzuwtYGl13p7tvzWCtIiJSh9QpG4cDw6kJUb2i458QwtM9hCDV2KbEU6dOZfTo0bHXKpI0K6Tu1X79+vmyZcuSLkNEpCB0NGMgNSHqxOj4dsLk8oWEEPUO9Ux6jdx4441MmjQpg5WKZJeZLXf3frWPa0V1EREJKivhjTdgwQJYsIAthIm3nwN/AB4jhKiVhI2K61NWVsYTTzyR+XpFcoxClYhIc7V/PyxZAvPmhSC1ZAns3QtFRXDaadxBCFH/Bexu4DYlJSVs3LgxOzWL5DCFKhGR5mT/fli8GJ5+Gp55BjZsgBYt4DvfgVtugTPOgAED4JBDuAO4s55lcApp6ohIXBSqREQKnTssXQrTp4cgtWkTHHwwnHsu3HsvnH8+FBfXc6nCk0i6FKpERArVxo0hSE2bBhUV0LIlDBkCP/whXHABtG+fdIUiBUWhSkSkkHz+OTz3HDz6KLzyShju698fpkyB4cOhQ4ekKxQpWApVIiL5bt8+WLiwZnivshK6doWxY+Hyy+HYY5OuUKRZUKgSEclX5eXw2GPwxBNhnlT79nDJJXDZZfC974UJ6CKSNQpVIiL5ZMMGmDEjhKm334aDDoLzzgtB6vzzoXXrpCsUabYUqkREct1nn8Hs2WF4b8GC8DTfqafC5MlhnlSnTklXKCIoVImI5Kbdu+Hll+Hxx8PE85074ZhjYPz40CvVs2fSFYpILQpVIiK5wh0WLQpB6qmnYMsWOOwwuPJKGDkSTjkF6lmMU0SSp1AlIpK0iooQpB5/HNatC/Oihg4NPVLnnBO2jRGRnKdQJSKShE2b4MknwzyplSvDk3rf/z7ceSdcdBG0a5d0hSLyFSlUiYhkS10Tzvv1g0mTwlIIRx6ZdIUi0gQKVSIimbR7N7z0UhjamzMnTDjv0QNuuw0uvRR69066QhGJiUKViEjc9u+H118Pw3tPPw1bt4ZlD0aPDvOkNOFcpCApVImIxMEdli8PC3POnBk2Mz7kkJoJ52efrQnnIgVOoUpEpCkqKkKQmjED3nsvBKchQ2DiRLjgAmjTJukKRSRLFKpERBqzZ0+YC7VzJ+zaFSacP/982HPvrbfCUN4ZZ8Ctt8KwYdCxY9IVi0gCFKpERGrbtSsswvnKK2FV85Ur6z7vlFPCk3vDh0PnztmtUURyjkKViIg7rFkTAtQrr8Brr8GOHWGz4gED4Oc/h+JiaNkyfLRqBf37h21jREQiClUi0jxt2wbz59cEqfXrw/FevWDUKBg8GAYN0iKcIpI2hSoRaR727oUlS2qG9JYuDUsfdOgAZ50F48aFLWG6d0+6UhHJUwpVIlK41q2r6YmaPx+2bw/bwZx8chjSGzw4fH6QfhSKSNPpJ4mIFI7KyjAfqipIrVkTjnftChdfHHqizjoLDj000TJFpDApVIlIfisvhxdfDEHqj38Myx+0bh3mQ/3kJ6E3qndvrWAuIhmnUCUi+W3s2BCq+vSBm24KIer008NTeiIiWaRQJSL57f77YepUrRMlIolTqBKR/HbccUlXICICQIukCxAREREpBApVIiIiIjFQqBIRERGJgUKViIiISAwUqkRERERioFAlIiIiEgOFKhEREZEYKFSJiIiIxEChSkRERCQGClUiIiIiMVCoEhEREYmBQpWIiIhIDBSqRERERGKgUCUiIiISA4UqERERkRgoVImIiIjEQKFKREREJAYKVSIiIiIxUKgSERERiYFClYiIiEgMFKpEREREYqBQJSIiIhIDhSoRERGRGJi7J11DbMzsE+B/Y7pdJ+D/YrpXc6Z2bDq1YdOpDeOhdmw6tWE8km7HUnf/Zu2DBRWq4mRmy9y9X9J15Du1Y9OpDZtObRgPtWPTqQ3jkavtqOE/ERERkRgoVImIiIjEQKGqflOSLqBAqB2bTm3YdGrDeKgdm05tGI+cbEfNqRIRERGJgXqqRERERGLQ7EOVmZ1rZn82s/fMbEwd7z9gZquijzVm9mkS0ZnYAgAABtFJREFUdeayNNqwm5ktNLOVZlZuZuclUWeuS6MdS81sftSGr5lZlyTqzGVm9rCZfWxm79TzvpnZg1Ebl5vZSdmuMdel0YbHmdliM9tlZj/Ldn35II02/Pvo66/czBaZWZ9s15gP0mjHoVEbrjKzZWZ2erZrPKCm5jz8Z2bfANYAZwMbgKVAmbv/qZ7zrwf6uvuV2asyt6XThmY2BVjp7v9mZicAL7r70UnUm6vSbMengefdfZqZnQn8yN1HJlJwjjKz7wGVwKPufmId758HXA+cB5wC/Iu7n5LdKnNbGm14OFAKXARsc/eJWS4x56XRhv2BCnffZmZDgNv1dXigNNqxLbDD3d3Mvg085e7HZbvOVM29p+pk4D13f9/ddwNPAkMbOL8MmJGVyvJHOm3oQPvo8w7ApizWly/SaccTgPnR5wvreL/Zc/fXga0NnDKU8APa3f1NoNjMOmenuvzQWBu6+8fuvhTYk72q8ksabbjI3bdFL98E1OtchzTasdJreobaEH7XJKq5h6qjgA9SXm+Ijh3AzEqB7sCCLNSVT9Jpw9uBy8xsA/AioadAviyddnwLGBZ9/gOgnZkdloXaCkna3/MiWTIamJd0EfnKzH5gZu8CLwCJjyI191BldRyrL+mOAGa5+74M1pOP0mnDMuB37t6FMOzymJk196+92tJpx58BA81sJTAQ2AjszXRhBearfM+LZJSZnUEIVbcmXUu+cvdnoyG/i4C7kq7noKQLSNgGoGvK6y7UPzQ1AviHjFeUf9Jpw9HAuQDuvtjMWhH2bfo4KxXmh0bb0d03AX8H1XMJhrn79qxVWBi+yve8SMZEc4CmAkPcfUvS9eQ7d3/dzI4xs07untiegM29t2Ap0MvMupvZwYTgNKf2SWbWG+gILM5yffkgnTZcD5wFYGbHA62AT7JaZe5rtB3NrFNKD99Y4OEs11gI5gCXR08Bngpsd/fNSRclzYuZdQNmAyPdfU3S9eQrM+tpZhZ9fhJwMJBoQG3WPVXuvtfMrgNeBr4BPOzuq83sTmCZu1f9UisDnkyZECeRNNvwZuC3ZvZTwlDLKLXll6XZjoOAe83MgddRz+kBzGwGoZ06RXP4fgEUAbj7bwhz+s4D3gM+B36UTKW5q7E2NLMjgWWEh0/2m9lNwAnu/llCJeecNL4OxwOHAb+OMsHeXNwcOGlptOMwwh9Je4AvgEuS/t3SrJdUEBEREYlLcx/+ExEREYmFQpWIiIhIDBSqRERERGKgUCUiIiISA4UqERERkRgoVIlIVpjZvmg3+aqPo83sMDNbaGaVZvZQyrmHmNkLZvauma02s/tS3nsg5R5rzOzT6PjRZuZmdlfKuZ3MbE/qvdOs9Xdm9j8p/58bouP3mNkHZlZZ6/xRZvZJyvlXRcdLzWx5dGy1mV2bcs06M/tDrfusMrN3vkqtIpI7mvU6VSKSVV+4+1+nHjCzNsBtwInRR6qJ7r4wWgx1vpkNcfd57v7TlOuvB/qmXPM+cH50T4CLgdVfs95b3H1WrWNzgYeAtXWcP9Pdr6t1bDPQ3913Ravgv2Nmc6LV8SHs39jV3T+IFsYVkTymnioRSYy773D3N4CdtY5/7u4Lo893AysIW8rUVgbMSHn9BVBhZlULKV4CPBVjvW9+lRXY3X23u++KXrbkwJ+5TxFqhAP/LSKSZxSqRCRbWqcMjz2b7kVmVgxcAMyvdbwU6A4sqHXJk8AIM+sC7OPr7+33y5R6v5XG+cPMrNzMZplZ9f6CZtbVzMqBD4AJKb1UALOI9nMk/Bvnfs1aRSQHaPhPRLLlgOG/xpjZQYTemwfd/f1ab48AZrn7vlrHXyLsVv8RMPPrFkvdw3/1mQvMiIb5rgWmAWcCuPsHwLfNrAT4DzOb5e4fRddtBbaZ2QiggrB1jojkKfVUiUgumwKsdfdJdbw3gjqGy6LhwuWEPSefqe/GZvZy1As1talFuvuWlGG+3wJ/U8c5mwjzu75b662ZwGQ09CeS99RTJSI5yczuBjoAV9XxXm+gI7C4nsvvB/7T3bdEG9YewN0Hx1QqZtY5Za7VhYReJ6IhyC3u/oWZdQQGAL+qdfmzQGfCZtolcdUkItmnUCUiiTKzdUB74GAzuwg4B/gM+EfgXWBFFIwecveqXqUy4Mn6dqR399V8/af+Gqr1n4FLgUPMbAMw1d1vB24wswuBvYQhvVHRJccD95uZA0Z4ovHtWrX+BZgQ3T/ukkUki6yen0kiIiIi8hVoTpWIiIhIDBSqRERERGKgUCUiIiISA4UqERERkRgoVImIiIjEQKFKREREJAYKVSIiIiIxUKgSERERicH/A/+Q3++87qY1AAAAAElFTkSuQmCC", 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\n", 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", 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" ] diff --git a/docs/Quick_Start_Make_Cluster_w_BDs.ipynb b/docs/Quick_Start_Make_Cluster_w_BDs.ipynb index dea97223..9cdfa9d9 100644 --- a/docs/Quick_Start_Make_Cluster_w_BDs.ipynb +++ b/docs/Quick_Start_Make_Cluster_w_BDs.ipynb @@ -11,7 +11,7 @@ "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", + "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 available at https://github.com/MovingUniverseLab/spisea/tree/main/docs.\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" ] diff --git a/docs/add_atmo_models.rst b/docs/add_atmo_models.rst index 3fbe6e54..bb5e2bbc 100644 --- a/docs/add_atmo_models.rst +++ b/docs/add_atmo_models.rst @@ -3,7 +3,7 @@ ======================================== Adding Atmosphere Models ======================================== -Stellar atmosphere grids are implemented in SPISEA using STSci's `pysynphot `_ and CDBS infrastructure. +Stellar atmosphere grids are implemented in SPISEA using STSci's `stsynphot `_ / `synphot `_ stack and CDBS infrastructure. Adding a new atmosphere grid involves saving the models in a new sub-directory under your ``PYSYN_CDBS`` directory and creating a new function in ``atmospheres.py`` to access those files. @@ -33,9 +33,9 @@ a template for how to make a new one. Some general notes: reflected in the 'INDEX' column of catalog.fits. * For the atmospheres, use units of angstroms for the wavelength and - "FLAM" for flux ([erg/s/cm^2/A]; see `pysynphot docs - `_) + "FLAM" for flux ([erg/s/cm^2/A]; see `synphot docs + `_) More detailed documentation on this is coming soon. In the meantime, let us know on the Github `issue tracker -`_ if you'd like to +`_ if you'd like to implement a new atmospheric model grid. diff --git a/docs/add_evo_models.rst b/docs/add_evo_models.rst index 49655466..717a9ae5 100644 --- a/docs/add_evo_models.rst +++ b/docs/add_evo_models.rst @@ -9,7 +9,7 @@ below, you can implement your own evolution models into the SPISEA framework. If you have questions or run into problems, please raise an issue on -our Github `issue tracker `_. If you are willing to +our Github `issue tracker `_. If you are willing to have the new models you add be added to the SPISEA package and made available to the community, please fork or branch off of the development repository and then submit merge requests to add your @@ -104,7 +104,7 @@ new models (e.g., new metallicities, etc). If the user needs to change the available age range for an existing model grid, please let us know via the Github `issue tracker -`_. +`_. Creating a New Model Grid @@ -114,5 +114,5 @@ To create an entirely new model set, the user needs to define a new the appropriate directory structure in the ``$SPISEA_MODELS/evolution`` directory. Detailed documentation on this is coming soon. In the meantime, let us know on the Github `issue tracker -`_ if you'd like to +`_ if you'd like to implement a new model set. diff --git a/docs/add_filters.rst b/docs/add_filters.rst index 60dfde03..4f86657c 100644 --- a/docs/add_filters.rst +++ b/docs/add_filters.rst @@ -18,5 +18,5 @@ If the user wants to add new photometric filters to SPISEA, there are 4 main ste 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 +`_ if you'd like to implement new photometric filters. diff --git a/docs/atmo_models.rst b/docs/atmo_models.rst index b54056bc..6a9960e6 100644 --- a/docs/atmo_models.rst +++ b/docs/atmo_models.rst @@ -13,16 +13,16 @@ To call an atmosphere for a particular star, user must define the metallicity ([Z]), temperature (in K), and gravity (in cgs):: spectrum = atmo(metallicity=0, temperature=5800, gravity=4) - wave = spectrum.wave # Wavelength in Angstroms - flux = spectrum.flux # Flux in ergs s^-1 cm^-2 Angstrom^-1 - (pysynphot Flam units) + from astropy import units + wave = spectrum.waveset.to(units.AA).value # Wavelength in Angstroms + flux = spectrum(spectrum.waveset) # Flux (synphot Quantity; typically FLAM) The atmosphere function is an input for the :ref:`isochrone_objects`, and will automatically be used to define the spectrum of each star in the isochrone model. -PopStar uses the pysynphot framework to extract the model atmosphere, -and the the output spectrum is a `pysynphot.Icat object `_. +PopStar uses the stsynphot/synphot stack to extract the model atmosphere, +and the output spectrum is a synphot ``SourceSpectrum`` built from the CDBS grid via ``stsynphot.grid_to_spec``. Below is a table of atmosphere model grids currently supported by SPISEA. Note that the resolution column reports the original diff --git a/docs/extinction.rst b/docs/extinction.rst index 0dc9d172..4c0b2f15 100755 --- a/docs/extinction.rst +++ b/docs/extinction.rst @@ -8,14 +8,14 @@ The extinction law can be defined using the classes in ``spisea/reddening.py``. from spisea import reddening red_law = reddening.() -SPISEA uses the pysynphot framework to define the extinction law. -The output is a `pysynphot.reddening.CustomRedLaw -`_ -oject. +SPISEA uses tabulated :math:`A_\lambda/A_{Ks}` curves. Reddening law classes inherit from +``reddening.RedLawBase``, which subclasses synphot's ``ExtinctionCurve`` at :math:`A_{Ks}=1` mag +(see ``synphot.reddening.etau_madau``). For arbitrary :math:`A_{Ks}` and wavelength sampling, +use ``red_law.extinction_at`` (or ``reddening.RedLawBase.ExtinctionAtAKs``). The reddening law is reported in terms of A_lambda / A_Ks, and thus is normalized to A_Ks = 1. The red_law object is passed into the :ref:`isochrone_objects` in order to -define the extinction for the stars. See the `Quick Start `_ +define the extinction for the stars. See the `Quick Start `_ for an example. Available extinction laws: diff --git a/docs/filters.rst b/docs/filters.rst index 330e990c..e8207225 100644 --- a/docs/filters.rst +++ b/docs/filters.rst @@ -143,8 +143,8 @@ Example: ``'hipparcos,Hp'`` **Hubble Space Telescope** -HST filters are defined by their `pysynphot OBSMODE strings -`_. These +HST filters are defined by their `stsynphot / legacy OBSMODE strings +`_. These are defined in the ``cdbs/mtab/`` and ``cdbs/comp/`` directories. Example: ``'wfc3,ir,f125w'`` @@ -240,9 +240,9 @@ Example: ``'ps1, g'`` **Roman Space Telescope** -Roman Space Telescope WFI filters are defined by their `pysynphot -OBSMODE strings -`_. +Roman Space Telescope WFI filters are defined by their stsynphot-style +`OBSMODE strings +`_. These are defined in the ``cdbs/mtab/`` and ``cdbs/comp/`` directories. Note that the 2021-07-16 version of these directories must be downloaded from from the `STScI reference atlases diff --git a/docs/getting_started.rst b/docs/getting_started.rst index db20785a..207646a6 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -10,10 +10,10 @@ If you are downloading the code from scratch, please follow the instructions below. If you had already downloaded version 1 of the code and are switching to version 2, please see :ref:`version`. -SPISEA is hosted on `GitHub `_. +SPISEA is hosted on `GitHub `_. To begin, clone the git repository in your desired code directory:: - git clone https://github.com/astropy/SPISEA.git + git clone https://github.com/MovingUniverseLab/spisea.git The ``main`` branch contains the current release, while the ``dev`` branch is for code development. @@ -28,7 +28,8 @@ Other dependencies: * python (>=3.7, < 3.12) * astropy -* pysynphot +* synphot +* stsynphot * scipy * numpy (>= 1.17, < 2.0) * matplotlib @@ -176,7 +177,7 @@ However, these can be safely ignored since SPISEA doesn't use those functionalit To further test your SPISEA install, try running the `Quick Start notebook -`_. +`_. It is also located in ``SPISEA/docs``. To test the full range of @@ -223,8 +224,8 @@ Installation To create the container image, clone this repository and build the container:: - git clone https://github.com/astropy/SPISEA.git - cd SPISEA + git clone https://github.com/MovingUniverseLab/spisea.git + cd spisea docker build -t spisea . Usage diff --git a/docs/ifmr.rst b/docs/ifmr.rst index a1072d84..77b8df34 100644 --- a/docs/ifmr.rst +++ b/docs/ifmr.rst @@ -21,7 +21,7 @@ Compact objects are included in the output tables produced by * BH: phase = 103 See `Quick Start Example -`_ +`_ for more examples of how to interact with the :ref:`cluster_objects` output. diff --git a/docs/index.rst b/docs/index.rst index 223f102b..9a0f7408 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -29,7 +29,7 @@ Here is a brief list of things that SPISEA can do: * make a spectrum of a star cluster in integrated light -SPISEA can be downloaded from `Github `_. Please cite `Hosek et al. (2020) `_ if you use SPISEA in +SPISEA can be downloaded from `Github `_. Please cite `Hosek et al. (2020) `_ if you use SPISEA in your research. Getting Started @@ -164,9 +164,9 @@ Change Log operating system used 2.1.10 (2023-06-01) - * Added support for Roman Space Telescope filters (via pysynphot) + * Added support for Roman Space Telescope filters (via stsynphot) - * Note: this requires the pysynphot ``cdbs/mtab`` and + * Note: this requires the ``cdbs/mtab`` and ``cdbs/comp/`` directories to be at least the 2021-07-16 version or later. See :ref:`getting_started` for how to download these files 2.1.9 (2023-01-10) diff --git a/docs/make_cluster.rst b/docs/make_cluster.rst index 6af85cd7..5685c268 100644 --- a/docs/make_cluster.rst +++ b/docs/make_cluster.rst @@ -51,7 +51,7 @@ has already been created:: See `Quick Start Example -`_ +`_ for a detailed example for how to make different cluster sub-classes and interact with the resulting output. Here is a table from Hosek et al. 2020 that describes diff --git a/docs/make_isochrone.rst b/docs/make_isochrone.rst index 5a7b3fb3..25e9e98b 100644 --- a/docs/make_isochrone.rst +++ b/docs/make_isochrone.rst @@ -49,7 +49,7 @@ An example of making an IsochronePhot object:: iso_dir=iso_dir) See `Quick Start Example -`_ +`_ for a detailed example showing how to interact with the isochrone object output. Here is a table from Hosek et al. 2020 that describes the values in the isochrone output table: diff --git a/docs/more_examples.rst b/docs/more_examples.rst index 51fc5d87..20036bac 100644 --- a/docs/more_examples.rst +++ b/docs/more_examples.rst @@ -6,7 +6,7 @@ Further Examples Additional Jupyter notebooks with tutorials to produce the plots shown in the SPISEA paper (`Hosek et al. (2020) `_) can be found `here -`_. +`_. Figure 2: HR-Diagrams with Different Evolution Models; CMDs with different Extinction Laws -------------------------------------------------------------------------------------------------------- diff --git a/docs/multiplicity.rst b/docs/multiplicity.rst index 2eb597a1..54177383 100644 --- a/docs/multiplicity.rst +++ b/docs/multiplicity.rst @@ -30,7 +30,7 @@ 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. +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. diff --git a/docs/quick_start.rst b/docs/quick_start.rst index 51854eef..74710d75 100755 --- a/docs/quick_start.rst +++ b/docs/quick_start.rst @@ -5,7 +5,7 @@ Quick Start Guide =================== We provide a `jupyter notebook -`_ +`_ as a Quick-Start Guide to demonstrate the steps required to simulate a cluster and interact with the resulting output. This guide shows you how to: diff --git a/docs/requirements.txt b/docs/requirements.txt index 7a2f1159..668de38c 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -2,7 +2,8 @@ matplotlib numpy <= 1.24.4 numpydoc astropy -pysynphot +synphot>=1.5 +stsynphot>=1.4 scipy pandas setuptools<=81.0 diff --git a/isochrones/iso_6.70_1.67_08000_p0.00.fits b/isochrones/iso_6.70_1.67_08000_p0.00.fits new file mode 100644 index 00000000..8df23f47 --- /dev/null +++ b/isochrones/iso_6.70_1.67_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 = 'NL20 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MISTv1 ' HIERARCH EVOMODELVERSION = 'MISTv1.2' LOGAGE = 6.698970004336019 AKS = 1.67 DISTANCE= 8000 METAL_IN= 0 HIERARCH METAL_ACT = -0.00616030870481844 WAVEMIN = 3000 WAVEMAX = 52000 END Eg!溑@%^A$}Y(3,?L҄@F٠aF?L7@7틊@5_ntn@4j@34U?9Ei16@chAͦ1 ?Waq+@9F?W_IG@7û @5l@4ޥx@3}pX5EkVHC@(AG-@?&ѓ.N@pF?%#,@7;)@5b 6e@4zft@3p@ \ No newline at end of file diff --git a/isochrones/iso_6.70_2.30_08000_p0.00.fits b/isochrones/iso_6.70_2.30_08000_p0.00.fits new file mode 100644 index 00000000..b0534456 --- /dev/null +++ b/isochrones/iso_6.70_2.30_08000_p0.00.fits @@ -0,0 +1 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 89 / length of dimension 1 NAXIS2 = 3 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 12 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'm_hst_f127m' TFORM9 = 'D ' TTYPE10 = 'm_hst_f153m' TFORM10 = 'D ' TTYPE11 = 'm_nirc2_H' TFORM11 = 'D ' TTYPE12 = 'm_nirc2_Kp' TFORM12 = 'D ' REDLAW = 'pl,2.12,0.9,2.4,5.0' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MISTv1 ' HIERARCH EVOMODELVERSION = 'MISTv1.2' LOGAGE = 6.698970004336019 AKS = 2.3 DISTANCE= 8000 METAL_IN= 0 HIERARCH METAL_ACT = -0.00616030870481844 WAVEMIN = 3000 WAVEMAX = 52000 END Eg!溑@%^A$}Y(3,?L҄@F٠aF?L7@9`V@7>|6Jt@6U @4˄VoEi16@chAͦ1 ?Waq+@9F?W_IG@9s;(L@7+⫿@6E8t@4!SEkVHC@(AG-@?&ѓ.N@pF?%#,@9g=uh@7!/@a@6<9kڲ@4G \ No newline at end of file diff --git a/isochrones/iso_6.70_2.46_08000_p0.00.fits b/isochrones/iso_6.70_2.46_08000_p0.00.fits new file mode 100644 index 00000000..75d7890c --- /dev/null +++ b/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$}Y(3,?L҄@F٠aF?L7@:71L4^@6RNa@6ԍ8J@4yEi16@chAͦ1 ?Waq+@9F?W_IG@:{@6֛@6<;@4jEkVHC@(AG-@?&ѓ.N@pF?%#,@9Fw@6>aK@6*4@4ak/x \ No newline at end of file diff --git a/isochrones/iso_6.70_2.62_08000_p0.00.fits b/isochrones/iso_6.70_2.62_08000_p0.00.fits new file mode 100644 index 00000000..15301939 --- /dev/null +++ b/isochrones/iso_6.70_2.62_08000_p0.00.fits @@ -0,0 +1,2 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 89 / length of dimension 1 NAXIS2 = 3 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 12 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'm_hst_f127m' TFORM9 = 'D ' TTYPE10 = 'm_hst_f153m' TFORM10 = 'D ' TTYPE11 = 'm_nirc2_H' TFORM11 = 'D ' TTYPE12 = 'm_nirc2_Kp' TFORM12 = 'D ' REDLAW = 'bp,[0.9 1.63 2.3 ] micron,[3.0, 2.23],2.12 micron' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MISTv1 ' HIERARCH EVOMODELVERSION = 'MISTv1.2' LOGAGE = 6.698970004336019 AKS = 2.62 DISTANCE= 8000 METAL_IN= 0 HIERARCH METAL_ACT = -0.00616030870481844 WAVEMIN = 3000 WAVEMAX = 52000 END Eg!溑@%^A$}Y(3,?L҄@F٠aF?L7@;)Yqo^@7W@* @)` Ei16@chAͦ1 ?Waq+@9F?W_IG@;֡Sn@7E$)@)惡@)BEkVHC@(AG-@?&ѓ.N@pF?%#,@;6R +@7:<{@)^aD@)2Zw \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 5b46d1d5..588f65cb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,8 @@ dependencies = [ "scipy>=1.10.1", "matplotlib", "astropy", - "pysynphot" + "synphot>=1.5", + "stsynphot>=1.4", ] optional-dependencies.dev = [ @@ -46,8 +47,10 @@ classifiers = [ ] [project.urls] -Homepage = "https://github.com/astropy/SPISEA" -Issues = "https://github.com/astropy/SPISEA/issues" +Homepage = "https://github.com/MovingUniverseLab/spisea" +Documentation = "https://github.com/MovingUniverseLab/spisea/tree/main/docs" +Repository = "https://github.com/MovingUniverseLab/spisea" +Issues = "https://github.com/MovingUniverseLab/spisea/issues" [build-system] requires = [ @@ -61,7 +64,7 @@ requires = [ build-backend = 'setuptools.build_meta' [tool.setuptools] -packages = ["spisea"] +packages = ["spisea", "spisea.utils"] [tool.setuptools.package-data] spisea = ["data/*"] diff --git a/scripts/spisea_env.sh b/scripts/spisea_env.sh new file mode 100644 index 00000000..b37befb4 --- /dev/null +++ b/scripts/spisea_env.sh @@ -0,0 +1,9 @@ +# SPISEA, stsynphot, and JWST CRDS — source from the repo root: +# source scripts/spisea_env.sh +# +# Use the `spisea-synphot` conda environment for this project: +# conda activate spisea-synphot +export PYSYN_CDBS=/Users/jlu/work/models/cdbs/ +export SPISEA_MODELS=/Users/jlu/work/models/ +export CRDS_PATH=$HOME/jlu/work/models/crds_cache +export CRDS_SERVER_URL=https://jwst-crds.stsci.edu diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index 4d884677..38c470f7 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -1,20 +1,51 @@ import logging import numpy as np -import pysynphot import os import glob +from astropy import units as u from astropy.io import fits from astropy.table import Table, Column -import pysynphot import time import pdb import warnings +import stsynphot as stsyn +from stsynphot import exceptions as stsyn_exceptions +from synphot.models import BlackBody1D, Empirical1D +from synphot.spectrum import SourceSpectrum +from synphot.units import convert_flux +from synphot import units as su + +from spisea.utils.synphot_bridge import rebin_spec + 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 + Given atmosphere model, get temperature and gravity bounds. + + Parameters + ---------- + model_dir : str + The name of the atmosphere model grid. + metallicity : float + The metallicity of the atmosphere, in terms of [Fe/H]. Solar = 0. + temperature : float + The temperature of the atmosphere, in units of K. + gravity : float + The gravity of the atmosphere, in units of log(cgs) (e.g. gravity~5 for main-sequence stars). + verbose : bool (optional) + Whether to print verbose output. + + Returns + ------- + temperature_new : float + The closest temperature to the input temperature. + gravity_new : float + The closest gravity to the input gravity. + metallicity_new : float + The closest metallicity to the input metallicity. """ teff_arr, z_arr, logg_arr = get_atmosphere_grid(model_dir) @@ -117,7 +148,6 @@ def get_atmosphere_grid(model_dir): logg_arr = np.array(logg_arr) return teff_arr, z_arr, logg_arr - def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=False): """ @@ -133,7 +163,7 @@ def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=Fal Parameters ---------- metallicity: float - The stellar metallicity, in terms of [Z] + The stellar metallicity, in terms of [Fe/H]. Solar = 0. temperature: float The stellar temperature, in units of K @@ -146,7 +176,7 @@ def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=Fal """ try: - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('k93models', temperature, metallicity, gravity) except: # Check atmosphere catalog bounds (temperature, gravity, metallicity) = get_atmosphere_bounds('k93models', @@ -154,10 +184,10 @@ def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=Fal temperature=temperature, gravity=gravity) - sp = pysynphot.Icat('k93models', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('k93models', temperature, metallicity, gravity) - # Do some error checking - idx = np.where(sp.flux != 0)[0] + # Do some error checking for 0 fluxes. + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find Kurucz 1993 atmosphere model for') print( ' temperature = %d' % temperature) @@ -211,7 +241,7 @@ def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=F True for verbose output """ try: - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('ck04models', temperature, metallicity, gravity) except: # Check atmosphere catalog bounds (temperature, gravity, metallicity) = get_atmosphere_bounds('ck04models', @@ -219,10 +249,10 @@ def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4, rebin=F temperature=temperature, gravity=gravity) - sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('ck04models', temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find Castelli and Kurucz 2004 atmosphere model for') print( ' temperature = %d' % temperature) @@ -252,11 +282,8 @@ def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=Fal temperature = Kelvin (def = 5000) gravity = log gravity (def = 4.0) """ - if get_grid_only: - teff_arr, z_arr, logg_arr = get_atmosphere_grid('nextgen') - return teff_arr, z_arr, logg_arr try: - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('nextgen', temperature, metallicity, gravity) except: # Check atmosphere catalog bounds (temperature, gravity, metallicity) = get_atmosphere_bounds('nextgen', @@ -264,10 +291,10 @@ def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=Fal temperature=temperature, gravity=gravity) - sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('nextgen', temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find NextGen atmosphere model for') print( ' temperature = %d' % temperature) @@ -291,11 +318,10 @@ def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4, rebin=F temperature = Kelvin (def = 5000) gravity = log gravity (def = 4.0) """ - - sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('AMESdusty', temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for') print( ' temperature = %d' % temperature) @@ -337,7 +363,7 @@ def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, """ try: - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('phoenix', temperature, metallicity, gravity) except: # Check atmosphere catalog bounds (temperature, gravity, metallicity) = get_atmosphere_bounds('phoenix', @@ -345,10 +371,10 @@ def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4, temperature=temperature, gravity=gravity) - sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('phoenix', temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for') print( ' temperature = %d' % temperature) @@ -383,12 +409,12 @@ def get_cmfgenRot_atmosphere(metallicity=0, temperature=24000, gravity=4.3, rebi gravity = 4.3 if rebin: - sp = pysynphot.Icat('cmfgen_rot_rebin', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('cmfgen_rot_rebin', temperature, metallicity, gravity) else: - sp = pysynphot.Icat('cmfgen_rot', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('cmfgen_rot', temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') print( ' temperature = %d' % temperature) @@ -473,13 +499,14 @@ 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 + # Make the synphot 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])) + sp = SourceSpectrum( + Empirical1D, + points=np.asarray(w, dtype=float) * u.AA, + lookup_table=np.asarray(f, dtype=float) * su.FLAM, + ) # Print out parameters of match, if desired if verbose: @@ -499,12 +526,12 @@ def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=22500, gravity=3.98, r """ if rebin: - sp = pysynphot.Icat('cmfgen_norot_rebin', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('cmfgen_norot_rebin', temperature, metallicity, gravity) else: - sp = pysynphot.Icat('cmfgen_norot', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('cmfgen_norot', temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find CMFGEN rotating atmosphere model (Fierro+15) for') print( ' temperature = %d' % temperature) @@ -533,11 +560,10 @@ def get_cmfgenNoRot_atmosphere(metallicity=0, temperature=30000, gravity=4.14): temperature = Kelvin (def = 30000) gravity = log gravity (def = 4.14) """ - - sp = pysynphot.Icat('cmfgenF15_noRot', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('cmfgenF15_noRot', temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find CMFGEN non-rotating atmosphere model (Fierro+15) for') print( ' temperature = %d' % temperature) @@ -593,7 +619,7 @@ def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin= # Extract atmosphere. If that fails, then check bounds and try again try: - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) + sp = stsyn.grid_to_spec(atm_model_name, temperature, metallicity, gravity) except: # Check atmosphere catalog bounds (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_model_name, @@ -601,10 +627,10 @@ def get_phoenixv16_atmosphere(metallicity=0, temperature=4000, gravity=4, rebin= temperature=temperature, gravity=gravity) - sp = pysynphot.Icat(atm_model_name, temperature, metallicity, gravity) + sp = stsyn.grid_to_spec(atm_model_name, temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find PHOENIXv16 (Husser+13) atmosphere model for') print( ' temperature = %d' % temperature) @@ -676,7 +702,7 @@ def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebi atm_name = 'BTSettl_2015' try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + sp = stsyn.grid_to_spec(atm_name, temperature, metallicity, gravity) except: # Check atmosphere catalog bounds (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, @@ -684,11 +710,11 @@ def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebi temperature=temperature, gravity=gravity) - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + sp = stsyn.grid_to_spec(atm_name, temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find BTSettl_2015 atmosphere model for') print( ' temperature = %d' % temperature) @@ -807,7 +833,7 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T atm_name = 'BTSettl' try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + sp = stsyn.grid_to_spec(atm_name, temperature, metallicity, gravity) except: # Check atmosphere catalog bounds (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, @@ -815,8 +841,18 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T temperature=temperature, gravity=gravity) - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) - + sp = stsyn.grid_to_spec(atm_name, temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp(sp.waveset) != 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_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): """ Return atmosphere from Meisner2023 grid @@ -836,7 +872,7 @@ def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, reb atm_name = 'Meisner2023' try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + sp = stsyn.grid_to_spec(atm_name, temperature, metallicity, gravity) except: # Check atmosphere catalog bounds (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, @@ -844,10 +880,10 @@ def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, reb temperature=temperature, gravity=gravity) - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + sp = stsyn.grid_to_spec(atm_name, temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find Meisner2023 atmosphere model for') print( ' temperature = %d' % temperature) @@ -886,7 +922,7 @@ def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, re atm_name = 'Phillips2020' try: - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + sp = stsyn.grid_to_spec(atm_name, temperature, metallicity, gravity) except: # Check atmosphere catalog bounds (temperature, gravity, metallicity) = get_atmosphere_bounds(atm_name, @@ -894,10 +930,10 @@ def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, re temperature=temperature, gravity=gravity) - sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + sp = stsyn.grid_to_spec(atm_name, temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find Phillips2020 atmosphere model for') print( ' temperature = %d' % temperature) @@ -997,11 +1033,10 @@ def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): 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) + sp = stsyn.grid_to_spec('wdKoester', temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find WD Koester (Koester+ 2010 atmosphere model for') print( ' temperature = %d' % temperature) @@ -1026,7 +1061,7 @@ def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): """ try: - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('merged_atlas_phoenix', temperature, metallicity, gravity) except: # Check atmosphere catalog bounds (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_atlas_phoenix', @@ -1034,10 +1069,10 @@ def get_atlas_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): temperature=temperature, gravity=gravity) - sp = pysynphot.Icat('merged_atlas_phoenix', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('merged_atlas_phoenix', temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') print( ' temperature = %d' % temperature) @@ -1063,7 +1098,7 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): Only valid for temps between 3200 - 3800K, gravity from 2.5 - 5.5 """ try: - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('merged_BTSettl_phoenix', temperature, metallicity, gravity) except: # Check atmosphere catalog bounds (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_BTSettl_phoenix', @@ -1071,10 +1106,10 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): temperature=temperature, gravity=gravity) - sp = pysynphot.Icat('merged_BTSettl_phoenix', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('merged_BTSettl_phoenix', temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find ATLAS-PHOENIX merge atmosphere model for') print( ' temperature = %d' % temperature) @@ -1103,18 +1138,18 @@ def get_BTSettl_meisner_atmosphere(metallicity=0, temperature=5250, gravity=4): Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 """ try: - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('merged_BTSettl_meisner', temperature, metallicity, gravity) except: # Check atmosphere catalog bounds - (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_meisner', + (temperature, gravity, metallicity) = get_atmosphere_bounds('merged_BTSettl_meisner', metallicity=metallicity, temperature=temperature, gravity=gravity) - sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + sp = stsyn.grid_to_spec('merged_BTSettl_meisner', temperature, metallicity, gravity) # Do some error checking - idx = np.where(sp.flux != 0)[0] + idx = np.where(sp(sp.waveset) != 0)[0] if len(idx) == 0: print( 'Could not find BTSettl-Meisner merge atmosphere model for') print( ' temperature = %d' % temperature) @@ -1544,7 +1579,7 @@ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False temperature=temperature, gravity=gravity) - except pysynphot.exceptions.ParameterOutOfBounds: + except stsyn_exceptions.ParameterOutOfBounds: # Use a black-body atmosphere. bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) return bbspec @@ -1584,7 +1619,7 @@ def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False) temperature=temperature, gravity=gravity) - except pysynphot.exceptions.ParameterOutOfBounds: + except stsyn_exceptions.ParameterOutOfBounds: # Use a black-body atmosphere bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) return bbspec @@ -1618,16 +1653,15 @@ def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, 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 + # Log-spaced wavelength grid (Angstrom); synphot BlackBody in PHOTLAM → FLAM + w_grid = ( + np.logspace(np.log10(wave_min), np.log10(wave_max), wave_num, dtype=np.float64) + * u.AA ) - - # 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') + bb = SourceSpectrum(BlackBody1D, temperature=temperature) + y_photlam = bb(w_grid) + y_flam = convert_flux(w_grid, y_photlam, su.FLAM) + bbspec = SourceSpectrum(Empirical1D, points=w_grid, lookup_table=y_flam) # `BlackBody` spectrum is normalized to solar radius star at 1 kiloparsec. # Need to remove this normalization for SPISEA by multiplying bbspec @@ -1928,7 +1962,7 @@ 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.waveset.to(u.AA).value) # For each catalog.fits entry, read the unbinned spectrum and rebin to # the atlas resolution. Make a new fits file in rebin directory @@ -1943,12 +1977,12 @@ def rebin_cmfgen(cdbs_path, rot=True): # Fetch the spectrum if rot == True: - sp = pysynphot.Icat('cmfgen_rot', temp, metal, grav) + sp = stsyn.grid_to_spec('cmfgen_rot', temp, metal, grav) else: - sp = pysynphot.Icat('cmfgen_norot', temp, metal, grav) + sp = stsyn.grid_to_spec('cmfgen_norot', temp, metal, grav) # Rebin - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) + flux_rebin = rebin_spec(sp.waveset, sp, sp_atlas.waveset) c1 = fits.Column(name='Flux', format='E', array=flux_rebin) # Make the FITS file from the columns with header @@ -2249,15 +2283,15 @@ def rebin_phoenixV16(cdbs_path): cols_arr = [] # Make the wavelength column, which is first in the cols array. - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.waveset.to(u.AA).value) 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) + sp = stsyn.grid_to_spec('phoenix_v16', temp, metal, grav) + flux_rebin = rebin_spec(sp.waveset, sp, sp_atlas.waveset) # Store the spectrum name = 'g{0:3.1f}'.format(grav) @@ -2283,20 +2317,6 @@ def rebin_phoenixV16(cdbs_path): 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. @@ -2432,11 +2452,11 @@ def rebin_BTSettl_2015(cdbs_path=os.environ['PYSYN_CDBS']): 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) + sp = stsyn.grid_to_spec('BTSettl_2015', temp, metal, logg) + flux_rebin = rebin_spec(sp.waveset, sp, sp_atlas.waveset) # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.waveset.to(u.AA).value) c1 = fits.Column(name='Flux', format='E', array=flux_rebin) cols = fits.ColDefs([c0, c1]) @@ -2672,11 +2692,11 @@ def rebin_BTSettl(make_unique=False): # 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) + sp = stsyn.grid_to_spec('BTSettl', temp, metal, logg) + flux_rebin = rebin_spec(sp.waveset, sp, sp_atlas.waveset) # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.waveset.to(u.AA).value) c1 = fits.Column(name='Flux', format='E', array=flux_rebin) cols = fits.ColDefs([c0, c1]) @@ -2846,11 +2866,11 @@ 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_atlas.wave) + sp = stsyn.grid_to_spec('Meisner2023', temp, metal, logg) + flux_rebin = rebin_spec(sp.waveset, sp, sp_atlas.waveset) # Create the output FITS file - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.waveset.to(u.AA).value) c1 = fits.Column(name='Flux', format='E', array=flux_rebin) cols = fits.ColDefs([c0, c1]) @@ -2999,11 +3019,11 @@ def rebin_WDKoester(cdbs_path=os.environ['PYSYN_CDBS']): 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) + sp = stsyn.grid_to_spec('wdKoester', temp, metal, logg) + flux_rebin = rebin_spec(sp.waveset, sp, sp_atlas.waveset) # Make new output - c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.waveset.to(u.AA).value) c1 = fits.Column(name='Flux', format='E', array=flux_rebin) cols = fits.ColDefs([c0, c1]) diff --git a/spisea/filters.py b/spisea/filters.py index f3834167..b1d6fd72 100755 --- a/spisea/filters.py +++ b/spisea/filters.py @@ -1,7 +1,12 @@ import numpy as np from astropy.table import Table -import pysynphot import warnings +from astropy import units as u + +from synphot import units as su +from synphot.models import Empirical1D +from synphot.spectrum import SpectralElement + import os import pdb @@ -9,6 +14,7 @@ code_dir = os.path.dirname(__file__) filters_dir = code_dir[:-7]+'/filt_func/' + def get_nirc2_filt(name): """ Define nirc2 filter as a pysynphot spectrum object @@ -38,15 +44,20 @@ def get_nirc2_filt(name): idx = np.where(transmission > 1)[0] # Convert wavelength to Angstroms, transmission to ratio - wavelength = wavelength[idx] * 10**4 - transmission = transmission[idx] / 100.0 # convert from % to ratio + wavelength = wavelength[idx] * 10**4 * u.AA + transmission = transmission[idx] / 100.0 * su.THROUGHPUT # Make spectrum object - spectrum = pysynphot.ArrayBandpass(wavelength, transmission, waveunits='angstrom', - name='NIRC2_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wavelength, + lookup_table=transmission, + meta={"expr": "NIRC2_{0}".format(name)}, + ) return spectrum + def get_2mass_filt(name): """ Define the 2mass filters as a pysynphot spectrum object @@ -61,12 +72,14 @@ def get_2mass_filt(name): transmission = t[t.keys()[1]] # Convert wavelength to Angstroms - wavelength = wavelength * 10**4 + wavelength = wavelength * 10**4 * u.AA + transmission = transmission * su.THROUGHPUT # Make spectrum object - spectrum = pysynphot.ArrayBandpass(wavelength, transmission, waveunits='angstrom', - name='2MASS_{0}'.format(name)) - + spectrum = SpectralElement(Empirical1D, + points=wavelength, + lookup_table=transmission, + meta={"expr": f"2MASS_{name}"}) return spectrum @@ -92,7 +105,12 @@ def get_vista_filt(name): trans[bad] = 0 # Now we can define the VISTA filter bandpass objects - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='VISTA_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "VISTA_{0}".format(name)}, + ) return spectrum @@ -118,7 +136,12 @@ def get_decam_filt(name): wave = t['wavelength'] * 10. trans = trans - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='decam_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "decam_{0}".format(name)}, + ) return spectrum @@ -147,7 +170,12 @@ def get_PS1_filt(name): # Convert wavelengths from nm to angstroms wave = t['wave'] * 10. - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ps1_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "ps1_{0}".format(name)}, + ) return spectrum @@ -161,14 +189,19 @@ def get_jwst_filt(name): raise ValueError('Could not find JWST filter {0} in {1}/jwst'.format(name, filters_dir)) # Convert wavelengths to angstroms - wave = t['microns'] * 10**4. - trans = t['throughput'] + wave = t['microns'] * 10**4. * u.AA + trans = t['throughput'] * su.THROUGHPUT # 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)) + spectrum = SpectralElement( + Empirical1D, + points=wave, + lookup_table=trans, + meta={"expr": "jwst_{0}".format(name)}, + ) return spectrum @@ -182,14 +215,19 @@ def get_Johnson_Glass_filt(name): 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. - trans = t['col2'] + wave = t['col1'] * 10. * u.AA + trans = t['col2'] * su.THROUGHPUT # 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)) + spectrum = SpectralElement( + Empirical1D, + points=wave, + lookup_table=trans, + meta={"expr": "jg_{0}".format(name)}, + ) return spectrum @@ -224,7 +262,12 @@ def get_nirc1_filt(name): bad = np.where(trans < 0) trans[bad] = 0 - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='nirc1_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "nirc1_{0}".format(name)}, + ) return spectrum @@ -245,7 +288,12 @@ def get_ctio_osiris_filt(name): bad = np.where(trans < 0) trans[bad] = 0 - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ctio_osiris_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "ctio_osiris_{0}".format(name)}, + ) return spectrum @@ -266,7 +314,12 @@ def get_naco_filt(name): bad = np.where(trans < 0) trans[bad] = 0 - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='naco_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "naco_{0}".format(name)}, + ) return spectrum @@ -293,7 +346,12 @@ def get_ubv_filt(name): "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)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "ubv_{0}".format(name)}, + ) return spectrum @@ -309,7 +367,12 @@ def get_bessell_filt(name): wave = t[t.keys()[0]] trans = t[t.keys()[1]] - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='bessell_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "bessell_{0}".format(name)}, + ) return spectrum @@ -330,7 +393,12 @@ def get_ukirt_filt(name): bad = np.where(trans < 0) trans[bad] = 0 - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ukirt_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "ukirt_{0}".format(name)}, + ) return spectrum @@ -347,7 +415,12 @@ def get_keck_osiris_filt(name): wave = t['col1'] * 10 trans = t['col2'] / 100. - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='keck_osiris_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "keck_osiris_{0}".format(name)}, + ) return spectrum @@ -399,8 +472,12 @@ def get_gaia_filt(version, name): # Convert wavelengths to angstroms (from nm) wave = t['LAMBDA'] * 10 - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', - name='gaia_{0}_{1}'.format(version, name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "gaia_{0}_{1}".format(version, name)}, + ) return spectrum @@ -417,7 +494,12 @@ def get_ztf_filt(name): wave = t['Wavelength'] trans = t['Transmission'] - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ztf_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "ztf_{0}".format(name)}, + ) return spectrum @@ -438,8 +520,12 @@ def get_hawki_filt(name): wavelength = wavelength * 10 # Make spectrum object - spectrum = pysynphot.ArrayBandpass(wavelength, transmission, waveunits='angstrom', - name='hawki_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wavelength * u.AA, + lookup_table=transmission * su.THROUGHPUT, + meta={"expr": "hawki_{0}".format(name)}, + ) return spectrum @@ -460,8 +546,12 @@ def get_rubin_filt(name): wavelength = wavelength * 10 # Make spectrum object - spectrum = pysynphot.ArrayBandpass(wavelength, transmission, waveunits='angstrom', - name='rubin_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wavelength * u.AA, + lookup_table=transmission * su.THROUGHPUT, + meta={"expr": "rubin_{0}".format(name)}, + ) return spectrum @@ -483,8 +573,12 @@ def get_euclid_filt(name): wavelength = wavelength * 10 # Make spectrum object - spectrum = pysynphot.ArrayBandpass(wavelength, transmission, waveunits='angstrom', - name='euclid_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wavelength * u.AA, + lookup_table=transmission * su.THROUGHPUT, + meta={"expr": "euclid_{0}".format(name)}, + ) return spectrum @@ -501,7 +595,12 @@ def get_nsfcam_filt(name): wave = t['col1'] trans = t['col2'] - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='nsfcam_{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "nsfcam_{0}".format(name)}, + ) return spectrum @@ -518,7 +617,12 @@ def get_tess_filt(name): wave = t['col1']*10 trans = t['col2'] - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='tess,{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "tess,{0}".format(name)}, + ) return spectrum @@ -535,7 +639,12 @@ def get_washington_filt(name): wave = t['col1']*10 trans = t['col2'] - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='washington,{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "washington,{0}".format(name)}, + ) return spectrum @@ -552,7 +661,12 @@ def get_hipparcos_filt(name): wave = t['col1'] trans = t['col2'] - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='hipparcos,{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "hipparcos,{0}".format(name)}, + ) return spectrum @@ -569,7 +683,12 @@ def get_tycho_filt(name): wave = t['col1'] trans = t['col2'] - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='tycho,{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "tycho,{0}".format(name)}, + ) return spectrum @@ -586,7 +705,12 @@ def get_kepler_filt(name): wave = t['col1'] trans = t['col2'] - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='kepler,{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "kepler,{0}".format(name)}, + ) return spectrum @@ -603,7 +727,12 @@ def get_ogle_filt(name): wave = np.flip(t['col1'])*10 trans = np.flip(t['col2'])/100 - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='ogle,{0}'.format(name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "ogle,{0}".format(name)}, + ) return spectrum @@ -620,6 +749,11 @@ def get_subaru_filt(instrument, name): wave = t['col1'] trans = t['col2'] - spectrum = pysynphot.ArrayBandpass(wave, trans, waveunits='angstrom', name='subaru,{0},{1}'.format(instrument, name)) + spectrum = SpectralElement( + Empirical1D, + points=wave * u.AA, + lookup_table=trans * su.THROUGHPUT, + meta={"expr": "subaru,{0},{1}".format(instrument, name)}, + ) return spectrum diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 0595bfbe..241e04d9 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -371,7 +371,7 @@ def xi(self, m): Output: xi - probability of measuring that mass. """ - returnFloat = type(m) == float + returnFloat = isinstance(m, (float, np.floating)) m = np.atleast_1d(m) @@ -412,7 +412,7 @@ def m_xi(self, m): """ Mass-weighted probability m*xi """ - returnFloat = type(m) == float + returnFloat = isinstance(m, (float, np.floating)) m = np.atleast_1d(m) mxi = np.zeros(len(m), dtype=float) @@ -478,7 +478,7 @@ def prim_xi(self, a): """ Helper function """ - returnFloat = type(a) == float + returnFloat = isinstance(a, (float, np.floating)) a = np.atleast_1d(a) val = np.zeros(len(a), dtype=float) @@ -506,7 +506,7 @@ def prim_mxi(self, a): """ Helper function """ - returnFloat = type(a) == float + returnFloat = isinstance(a, (float, np.floating)) a = np.atleast_1d(a) val = np.zeros(len(a), dtype=float) @@ -542,7 +542,6 @@ def normalize(self, Mcl, Mmin=None, Mmax=None): Mmax = self._m_limits_high[-1] if Mmin == None: - Mmin = self._m_limits_low[0] if Mmax > Mcl: @@ -557,9 +556,13 @@ 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) + int_mxi = self.int_mxi(self.norm_Mmin, self.norm_Mmax) + + self.k = Mcl / int_mxi self.lamda = self.int_xi_cl(self._m_limits_low[0], self._mass_limits) + return + def norm_cl_wk04(self, Mcl, Mmax=None, Mmin=None): """ Helper function @@ -640,7 +643,7 @@ def dice_star_cl(self, r): Given a list of random numbers (r), return a list of masses selected from the IMF. """ - returnFloat = type(r) == float + returnFloat = isinstance(r, (float, np.floating)) r = np.atleast_1d(r) # Make sure it is an array x = r * self.lamda[-1] @@ -766,7 +769,7 @@ def prim_power(m, power): Takes floats or arrays, but returns arrays. returns: m**(power + 1) / (power + 1) and handles the case when power = -1 """ - returnFloat = (type(m) == float) and (type(power) == float) + returnFloat = isinstance(m, (float, np.floating)) and isinstance(power, (float, np.floating)) m = np.atleast_1d(m) power = np.atleast_1d(power) @@ -792,7 +795,7 @@ def inv_prim_power(x, power): returns ((1+power) * x)**(1.0 / (1 + power)) and handles the case when power == -1. """ - returnFloat = (type(x) == float) and (type(power) == float) + returnFloat = isinstance(x, (float, np.floating)) and isinstance(power, (float, np.floating)) x = np.atleast_1d(x) power = np.atleast_1d(power) @@ -818,8 +821,7 @@ def inv_prim_power(x, power): def log_normal(m, mean_logm, sigma_logm): - returnFloat = (type(m) == float) and (type(mean_logm) == float) and \ - (type(sigma_logm) == float) + returnFloat = isinstance(m, (float, np.floating)) and isinstance(mean_logm, (float, np.floating)) and isinstance(sigma_logm, (float, np.floating)) m = np.atleast_1d(m) mean_logm = np.atleat_1d(mean_logm) @@ -834,8 +836,7 @@ def log_normal(m, mean_logm, sigma_logm): return val def prim_log_normal(m, mean_logm, sigma_logm): - returnFloat = (type(m) == float) and (type(mean_logm) == float) and \ - (type(sigma_logm) == float) + returnFloat = isinstance(m, (float, np.floating)) and isinstance(mean_logm, (float, np.floating)) and isinstance(sigma_logm, (float, np.floating)) m = np.atleast_1d(m) mean_logm = np.atleat_1d(mean_logm) @@ -849,15 +850,14 @@ def prim_log_normal(m, mean_logm, sigma_logm): else: return val -def inv_prim_log_normal(x, mean_logm, sigma_logm): - returnFloat = (type(m) == float) and (type(mean_logm) == float) and \ - (type(sigma_logm) == float) +def inv_prim_log_normal(m, mean_logm, sigma_logm): + returnFloat = isinstance(m, (float, np.floating)) and isinstance(mean_logm, (float, np.floating)) and isinstance(sigma_logm, (float, np.floating)) 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) + mu = inv_error(0.346516861952484 * m / sigma_logm) val = 10.0**(1.4142135623731 * sigma_logm * mu + mean_logm) if returnFloat: @@ -865,9 +865,8 @@ def inv_prim_log_normal(x, mean_logm, sigma_logm): else: return val -def mlog_normal(x, mean_logm, sigma_logm): - returnFloat = (type(m) == float) and (type(mean_logm) == float) and \ - (type(sigma_logm) == float) +def mlog_normal(m, mean_logm, sigma_logm): + returnFloat = isinstance(m, (float, np.floating)) and isinstance(mean_logm, (float, np.floating)) and isinstance(sigma_logm, (float, np.floating)) m = np.atleast_1d(m) mean_logm = np.atleat_1d(mean_logm) @@ -881,9 +880,8 @@ def mlog_normal(x, mean_logm, sigma_logm): else: return val -def prim_mlog_normal(x, mean_logm, sigma_logm): - returnFloat = (type(m) == float) and (type(mean_logm) == float) and \ - (type(sigma_logm) == float) +def prim_mlog_normal(m, mean_logm, sigma_logm): + returnFloat = isinstance(m, (float, np.floating)) and isinstance(mean_logm, (float, np.floating)) and isinstance(sigma_logm, (float, np.floating)) m = np.atleast_1d(m) mean_logm = np.atleat_1d(mean_logm) diff --git a/spisea/reddening.py b/spisea/reddening.py index b0d23c19..f21abedf 100755 --- a/spisea/reddening.py +++ b/spisea/reddening.py @@ -7,14 +7,160 @@ import pylab as py import numpy as np from scipy import interpolate +from astropy import units as u from astropy.table import Table -import pysynphot from scipy.linalg import solve_banded +import numbers + +from synphot import units as su +from synphot.reddening import ExtinctionCurve, ReddeningLaw, ExtinctionModel1D + import ast import os import pdb +class RedLawBase(ExtinctionCurve): + """ + Base class for reddening or extinction laws in SPISEA. + + Subclass of synphot `ExtinctionCurve` at :math:`A_{Ks} = 1` mag. For arbitrary + :math:`A_{Ks}` and wavelength sampling, use `extinction_at` or `ExtinctionAtAKs`. + + Parameters + ---------- + waveset : numpy array + Wavelength grid in Angstrom. + A_lambda_over_AKs : numpy array + Extinction in magnitudes at each wavelength. + name : str + Name of the reddening law. + litref : str + Literature reference for the reddening law. + + Attributes + ---------- + wave : numpy array + Wavelength grid in Angstrom. + A_lambda_over_AKs : numpy array + Extinction in magnitudes at each wavelength. + obscuration : numpy array + Same as ``A_lambda_over_AKs`` (:math:`A_\\lambda/A_{Ks}` on the native grid). + _init_name : str + Name of the reddening law. + _litref : str + Literature reference for the reddening law. + """ + + def __init__(self, waveset, A_lambda_over_AKs, name="", litref=""): + """ + Parameters + ---------- + waveset : numpy array + Wavelength grid in Angstrom. + A_lambda_over_AKs : numpy array + Extinction in magnitudes at each wavelength. + name : str + Name of the reddening law. + litref : str + Literature reference for the reddening law. + + Returns + ------- + self : `RedLawBase` + The reddening law object. + """ + + # Handle input units + if isinstance(waveset, u.Quantity): + waveset = waveset.to(u.AA) + else: + waveset = waveset * u.AA + + # Convert to throughput for application to flux. + thru = 10 ** (-0.4 * A_lambda_over_AKs) * su.THROUGHPUT + + # Define some meta data for the extinction curve + meta = {} + if name: + meta["name"] = name + if litref: + meta["litref"] = litref + + # Initialize the extinction curve + super().__init__(ExtinctionModel1D, + points=waveset, + lookup_table=thru, + meta=meta) + + # Save this in the obscuration attribute as well. + self.wave = waveset.value # Stored in Angstroms + self.A_lambda_over_AKs = A_lambda_over_AKs + self.obscuration = self.A_lambda_over_AKs + self.name = name + self.litref = litref + + return + + + def extinction_curve(self, AKs, wavelengths=None): + """ + Return the extinction curve for this law at a given :math:`A_{Ks}` + and at the specified wavelength grid. + + Parameters + ---------- + AKs : float + Total extinction in AKs, in mags + wavelengths : numpy or Quantity array + Wavelength array in Angstrom or Quantity. If not provided, the native + wavelength array is used, which is in Angstrom.. + + Returns + ------- + ext_at_wave : `ExtinctionCurve` + Extinction curve at the specified wavelengths with :math:`A_{Ks}` = `AKs`. + + Raises + ------ + ValueError : if the wavelengths are outside the interpolation range of the law. + """ + if isinstance(AKs, u.Quantity) and AKs.unit.decompose() == u.mag: + AKs = AKs.value + elif not isinstance(AKs, numbers.Real): + raise exceptions.SynphotError('AKs={0} is invalid.'.format(AKs)) + + if not isinstance(AKs, u.Quantity): + AKs *= u.mag + + if wavelengths is None: + wavelengths = self.wave + + x = self._validate_wavelengths(wavelengths) + + #if (self.low_lim is not None) and (x.min() < self.low_lim): + # raise ValueError(f'{self.name}: wavelength values below valid range of {self.low_lim}') + #if (self.high_lim is not None) and (x.max() > self.high_lim): + # raise ValueError(f'{self.name}: wavelength values above valid range of {self.high_lim}') + + self.meta['A_Ks'] = AKs + + # Get the A_lambda extinction at the original wavelengths. + A_lambda_orig = self.A_lambda_over_AKs * AKs + + # Interpolate over the curve to the new wavelengths. + spline_interp = interpolate.splrep(self.wave, A_lambda_orig.value, k=3, s=0) + A_lambda_new = interpolate.splev(x, spline_interp) + + # Throughputs but only at the input wavelengths. Now rescaled to AKs. + thru = 10 ** (-0.4 * A_lambda_new) * su.THROUGHPUT + + return ExtinctionCurve(ExtinctionModel1D, + points=x, + lookup_table=thru, + meta=self.meta) + + def get_red_law(str): """ Given a reddening law name, return the reddening @@ -85,7 +231,8 @@ def get_red_law(str): return red_law -class RedLawNishiyama09(pysynphot.reddening.CustomRedLaw): + +class RedLawNishiyama09(RedLawBase): """ The extinction law towards the Galactic Center from `Nishiyama et al. 2009 @@ -107,20 +254,19 @@ 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) + wave = np.arange(0.5, 8.0, 0.001) * u.micron # 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) - # Convert wavelength to angstrom - wave_vals *= 10 ** 4 - - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave_vals, - waveunits='angstrom', - Avscaled=Alambda_scaled, - name='Nishiyama09', - litref='Nishiyama+ 2009') + RedLawBase.__init__( + self, + wave_vals, + Alambda_scaled, + name='Nishiyama09', + litref='Nishiyama+ 2009', + ) # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = min(wave_vals) @@ -130,6 +276,7 @@ def __init__(self): self.scale_lambda = 2.14 self.name = 'N09' + @staticmethod def _derive_nishiyama09(wavelength): """ @@ -141,46 +288,50 @@ def _derive_nishiyama09(wavelength): Parameters ---------- - wavelength : float + wavelength : float or array of Quantity in microns AKs : float in magnitudes """ + # Handle input units + if isinstance(wavelength, u.Quantity): + wavelength = wavelength.to(u.micron) + else: + wavelength = wavelength * u.micron + + # Check if wavelength is already a numpy array, if not, convert to numpy array + if not isinstance(wavelength, np.ndarray): + wavelength = np.atleast_1d(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) ) + jhk_idx = np.where( (wavelength >= 1.25 * u.micron) & (wavelength <= 2.14 * u.micron) ) alpha = 2.0 wave_jhk = wavelength[jhk_idx] - idx_scale = np.where(abs(wave_jhk - 2.14) == min(abs(wave_jhk - 2.14)) ) + idx_scale = np.where(abs(wave_jhk - 2.14 * u.micron) == min(abs(wave_jhk - 2.14 * u.micron)) ) - A_jhk = wave_jhk**(-1.0*alpha) + A_jhk = (wave_jhk / (1 * u.micron))**(-1.0*alpha) A_Ks_jhk = A_jhk / A_jhk[-1] - #A_Ks_jhk = A_jhk / A_jhk[idx_scale] #----Now do a linear interpolation (in log(1/lambda) vs log(A/AKs) space) between 1.25 microns and 0.551 microns---# - jv_idx = np.where( (wavelength < 1.25) & (wavelength > 0.551) ) + jv_idx = np.where( (wavelength < 1.25 * u.micron) & (wavelength > 0.551 * u.micron) ) Av = 16.13 func = interpolate.interp1d(np.log10(np.array([1.0/1.25, 1.0/0.551])), np.log10(np.array([A_Ks_jhk[0], Av])), kind='linear') - A_Ks_jv = func(np.log10(1.0 / wavelength[jv_idx])) + A_Ks_jv = func(np.log10(1.0 / wavelength[jv_idx].to('micron').value)) # Convert back to linear space A_Ks_jv = 10**A_Ks_jv #---Do a spline interpolation for the rest of the (long-wavelength) law---# # We do this since no other function form is given - long_idx = np.where(wavelength > 2.14) - wave = np.array([0.551, 1.25, 1.63, 2.14, 3.545, 4.442, 5.675, 7.760]) + long_idx = np.where(wavelength > 2.14 * u.micron) + wave = np.array([0.551, 1.25, 1.63, 2.14, 3.545, 4.442, 5.675, 7.760]) * u.micron A_AKs = np.array([16.13, 3.02, 1.73, 1.00, 0.500, 0.390, 0.360, 0.430]) - interp_idx = np.where(wave > 2.1) - #long_idx = np.where(wavelength > 2.3) - #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) + interp_idx = np.where(wave > 2.1 * u.micron) - 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) + spline_interp = interpolate.splrep(wave[interp_idx].value, A_AKs[interp_idx], k=3, s=0) + A_AKs_long = interpolate.splev(wavelength[long_idx].value, spline_interp) # Stitch together sections for the final law wave_vals = np.concatenate((wavelength[jv_idx[0]], wavelength[jhk_idx[0]])) @@ -199,32 +350,36 @@ def Nishiyama09(self, wavelength, AKs): Parameters ---------- - wavelength : float or array + wavelength : float or array of astropy Quantity Wavelength to return extinction for, in microns AKs : float Total extinction in AKs, in mags """ + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + + if not isinstance(AKs, u.Quantity): + AKs *= u.mag + # If input entry is a single float, turn it into an array - try: - len(wavelength) - except: - wavelength = [wavelength] + wavelength = np.atleast_1d(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)) + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): + raise ValueError(f'{self.name}: wavelength values beyond interpolation range') # Extract wave and A/AKs from law, turning wave into micron units - wave = self.wave * (10**-4) - law = self.obscuration + wave = self.wave * u.AA + law = self.A_lambda_over_AKs # 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]) + idx = np.argmin(np.abs(wave - ii)) + A_AKs_at_wave.append(law[idx]) # Now multiply by AKs (since law assumes AKs = 1) A_at_wave = np.array(A_AKs_at_wave) * AKs @@ -237,20 +392,17 @@ def plot_Nishiyama09(self): from their Table 1. """ # Extract the law - wave = self.wave # angstroms + wave = self.wave * u.AA law = self.obscuration - # Convert wave to microns - wave *= 10**-4 - # Measured extinction values from Nishiyama+09, Table 1 - wave_obs = np.array([0.551, 1.25, 1.63, 2.14, 3.545, 4.442, 5.675, 7.760]) - A_AKs = np.array([16.13, 3.02, 1.73, 1.00, 0.500, 0.390, 0.360, 0.430]) - A_AKs_err = np.array([0.21, 0.04, 0.03, 0.0, 0.01, 0.01, 0.01, 0.01]) + wave_obs = np.array([0.551, 1.25, 1.63, 2.14, 3.545, 4.442, 5.675, 7.760]) * u.micron + A_AKs = np.array([16.13, 3.02, 1.73, 1.00, 0.500, 0.390, 0.360, 0.430]) # unitless + A_AKs_err = np.array([0.21, 0.04, 0.03, 0.0, 0.01, 0.01, 0.01, 0.01]) # unitless # Make plot py.figure(figsize=(10,10)) - py.plot(wave, law, 'r-', label='EL Function') + py.plot(wave.to('micron'), law, 'r-', label='EL Function') py.errorbar(wave_obs, A_AKs, yerr=A_AKs_err, fmt='k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') @@ -263,7 +415,7 @@ def plot_Nishiyama09(self): return -class RedLawCardelli(pysynphot.reddening.CustomRedLaw): +class RedLawCardelli(RedLawBase): r""" Defines the extinction law from `Cardelli et al. 1989 `_. @@ -278,37 +430,56 @@ class RedLawCardelli(pysynphot.reddening.CustomRedLaw): """ def __init__(self, Rv): # Fetch the extinction curve, pre-interpolate across 0.3-3 microns - wave = np.arange(0.3, 3.0, 0.001) + wave = np.arange(0.3, 3.0, 0.001) * u.micron # 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 = RedLawCardelli._derive_cardelli(wave, Rv) - # Convert wavelength to angstrom - wave *= 10 ** 4 - - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, - waveunits='angstrom', - Avscaled=Alambda_scaled, - name='Cardelli89', - litref='Cardelli+ 2009') + super().__init__( + wave, + Alambda_scaled, + name='Cardelli89', + litref='Cardelli+ 2009', + ) # 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 = 2.174 + self.scale_lambda = 2.174 * u.micron self.name = 'C89,{0}'.format(Rv) @staticmethod def _derive_cardelli(wavelength, Rv): """ Cardelli extinction law. This produces extinction values expected - for AKs = 1 + for AKs = 1. + + Parameters + ---------- + wavelength : float or array of astropy Quantity + Wavelength to return extinction for, in microns + Rv : float + Ratio of absolute to selective extinction, :math:`A(V) / E(B-V)`. + The standard value for the diffuse ISM is 3.1. + + Returns + ------- + output : float or array of astropy Quantity + Extinction in magnitudes at each wavelength. """ - x = 1.0 / np.array(wavelength) + # Handle input units + if isinstance(wavelength, u.Quantity): + wavelength = wavelength.to(u.micron) + else: + wavelength = wavelength * u.micron + + wavelength = np.atleast_1d(wavelength) + + x = 1.0 * u.micron / wavelength # check for applicability if (np.min(x) < 0.3): @@ -379,39 +550,45 @@ def Cardelli89(self, wavelength, AKs): Parameters ---------- - wavelength : float or array + wavelength : float or array of astropy Quantity Wavelength to return extinction for, in microns AKs : float Total extinction in AKs, in mags """ + # Handle input units + if isinstance(wavelength, u.Quantity): + wavelength = wavelength.to(u.micron) + else: + wavelength = wavelength * u.micron + + if not isinstance(AKs, u.Quantity): + AKs *= u.mag + # If input entry is a single float, turn it into an array - try: - len(wavelength) - except: - wavelength = [wavelength] + wavelength = np.atleast_1d(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)) + if ((min(wavelength) < (self.low_lim)) | (max(wavelength) > (self.high_lim))): + raise ValueError(f'{self.name}: wavelength values beyond interpolation range') # Extract wave and A/AKs from law, turning wave into micron units - wave = self.wave * (10**-4) - law = self.obscuration + wave = self.wave * u.AA + law = self.A_lambda_over_AKs # 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)) ) + idx = np.argmin(np.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 + A_lambda_over_AKs = np.array(A_AKs_at_wave) * AKs - return A_at_wave + return A_lambda_over_AKs -class RedLawSODC(pysynphot.reddening.CustomRedLaw): +class RedLawSODC(RedLawBase): r""" Defines the SODC extinction law from SynthPop, described by `Klüter & Huston et al. (2025) `_. @@ -430,29 +607,27 @@ class RedLawSODC(pysynphot.reddening.CustomRedLaw): 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) + # Fetch the extinction curve, pre-interpolate across 0.25-3.5 microns + wave = np.arange(0.25, 3.5, 0.001) * u.micron # 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') + super().__init__( + wave, + 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.scale_lambda = 0.549 * u.micron self.name = 'SODC,{0}'.format(Rv) @staticmethod @@ -461,14 +636,22 @@ def _derive_sodc(wavelength, Rv): SODC extinction law. This produces extinction values expected for AKs = 1 """ - x = 1.0 / np.array(wavelength) + # Handle input units + if isinstance(wavelength, u.Quantity): + wavelength = wavelength.to(u.micron) + else: + wavelength = wavelength * u.micron + + wavelength = np.atleast_1d(wavelength) + + x = 1.0 * u.micron / wavelength # check for applicability - if (np.min(wavelength) < 0.25): + if (np.min(wavelength.value) < 0.25): print( 'wavelength is shorter than applicable range for SODC law') return None - if (np.max(wavelength) > 3.5): + if (np.max(wavelength.value) > 3.5): print( 'wavelength is longer than applicable range for SODC law') return None @@ -516,39 +699,45 @@ def SODC(self, wavelength, AKs): Parameters ---------- - wavelength : float or array + wavelength : float or array of astropy Quantity Wavelength to return extinction for, in microns AKs : float Total extinction in AKs, in mags """ + # Handle input units + if isinstance(wavelength, u.Quantity): + wavelength = wavelength.to(u.micron) + else: + wavelength = wavelength * u.micron + + if not isinstance(AKs, u.Quantity): + AKs *= u.mag + # If input entry is a single float, turn it into an array - try: - len(wavelength) - except: - wavelength = [wavelength] + wavelength = np.atleast_1d(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)) + if ((min(wavelength) < (self.low_lim)) | (max(wavelength) > (self.high_lim))): + raise ValueError(f'{self.name}: wavelength values beyond interpolation range') # Extract wave and A/AKs from law, turning wave into micron units - wave = self.wave * (10**-4) - law = self.obscuration + wave = self.wave * u.AA + law = self.A_lambda_over_AKs # 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)) ) + idx = np.argmin(np.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 + A_lambda_over_AKs = np.array(A_AKs_at_wave) * AKs - return A_at_wave + return A_lambda_over_AKs -class RedLawRomanZuniga07(pysynphot.reddening.CustomRedLaw): +class RedLawRomanZuniga07(RedLawBase): """ Defines extinction law from `Roman-Zuniga et al. 2007 `_ @@ -561,20 +750,14 @@ 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) + wave = np.arange(1.0, 8.0, 0.01) * u.micron + wave = wave.to(u.AA) # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 - Alambda_scaled = RedLawRomanZuniga07._derive_romanzuniga07(wave) + A_lambda_over_AKs = RedLawRomanZuniga07._derive_romanzuniga07(wave) - # Convert wavelength to angstrom - wave *= 10**4 - - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, - waveunits='angstrom', - Avscaled=Alambda_scaled, - name='RomanZuniga07', - litref='Roman-Zuniga+ 2007') + super().__init__(wave, A_lambda_over_AKs , name='RomanZuniga07', litref='Roman-Zuniga+ 2007') # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = min(wave) @@ -586,14 +769,24 @@ def _derive_romanzuniga07(wavelength): """ Measurements taken from C-C column in Table 1 of RZ07 """ + # Convert to numpy array + wavelength = np.atleast_1d(wavelength) + + # Handle input units + if isinstance(wavelength, u.Quantity): + wavelength = wavelength.to(u.micron) + else: + wavelength = wavelength * u.micron + + # Define the filters and their corresponding wavelengths and A/AKs values filters = ['J', 'H', 'Ks', '[3.6]', '[4.5]', '[5.8]', '[8.0]'] - 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]) + wave = np.array([1.240, 1.664, 2.164, 3.545, 4.442, 5.675, 7.760]) * u.micron + A_AKs = np.array([2.299, 1.550, 1.000, 0.618, 0.525, 0.462, 0.455]) # unitless + A_AKs_err = np.array([0.530, 0.080, 0.000, 0.077, 0.063, 0.055, 0.059]) # unitless # Interpolate over the curve - spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0) - A_AKs_at_wave = interpolate.splev(wavelength, spline_interp) + spline_interp = interpolate.splrep(wave.value, A_AKs, k=3, s=0) + A_AKs_at_wave = interpolate.splev(wavelength.value, spline_interp) return A_AKs_at_wave @@ -609,51 +802,54 @@ def RomanZuniga07(self, wavelength, AKs): AKs : float Total extinction in AKs, in mags """ + # Handle input units + if isinstance(wavelength, u.Quantity): + wavelength = wavelength.to(u.micron) + else: + wavelength = wavelength * u.micron + + if not isinstance(AKs, u.Quantity): + AKs *= u.mag + # If input entry is a single float, turn it into an array - try: - len(wavelength) - except: - wavelength = [wavelength] + wavelength = np.atleast_1d(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))): + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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) + wave = self.wave * u.AA 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)) ) + idx = np.argmin(np.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 + A_lambda_over_AKs = np.array(A_AKs_at_wave) * AKs - return A_at_wave + return A_lambda_over_AKs def plot_RomanZuniga07(self): """ Plot law against measured values from Roman-Zuniga+07 """ - wave = self.wave # Angstroms + wave = self.wave * u.AA law = self.obscuration - # Change wavelengths to microns - wave *= 10**-4 - # Get the observed values - wave_obs = np.array([1.240, 1.664, 2.164, 3.545, 4.442, 5.675, 7.760]) + wave_obs = np.array([1.240, 1.664, 2.164, 3.545, 4.442, 5.675, 7.760]) * u.micron 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]) # Make plot py.figure(figsize=(10,10)) - py.plot(wave, law, 'r-', label='EL Function') + py.plot(wave.to('micron'), law, 'r-', label='EL Function') py.errorbar(wave_obs, A_AKs, yerr=A_AKs_err, fmt='k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') @@ -665,7 +861,7 @@ def plot_RomanZuniga07(self): py.savefig('romanzuniga07_el.png') return -class RedLawRiekeLebofsky(pysynphot.reddening.CustomRedLaw): +class RedLawRiekeLebofsky(RedLawBase): """ Defines the extinction law from `Rieke & Lebofsky 1985 `_ @@ -675,28 +871,21 @@ 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) + wave = np.arange(1.0, 5.0, 0.001) * u.micron # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawRiekeLebofsky._derive_RiekeLebofsky(wave) - # Convert wavelength to angstrom - wave *= 10 ** 4 + super().__init__(waveset=wave, + A_lambda_over_AKs=Alambda_scaled, + name='RiekeLebofsky', + litref='Rieke+Lebovsky 1985') - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, - waveunits='angstrom', - Avscaled=Alambda_scaled, - name='RiekeLebofsky', - litref='Rieke+Lebovsky 1985') - - # 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 = 2.2 - self.name = 'RL85' + return @staticmethod def _derive_RiekeLebofsky(wavelength): @@ -717,10 +906,11 @@ def _derive_RiekeLebofsky(wavelength): '[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, 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]) * u.micron 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]) + # Want to change this from A/Av to A/AK k_ind = np.where(np.array(filters) == 'K') Ak_Av = A_Av[k_ind] @@ -737,8 +927,8 @@ def _derive_RiekeLebofsky(wavelength): 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) + spline_interp = interpolate.splrep(wave_interp.value, A_Ak_interp, k=3, s=0) + A_Ak_at_wave = interpolate.splev(wavelength.value, spline_interp) return A_Ak_at_wave @@ -754,20 +944,23 @@ def RiekeLebofsky85(self, wavelength, AKs): AKs : float Total extinction in AKs, in mags """ + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + + if not isinstance(AKs, u.Quantity): + AKs *= u.mag + # If input entry is a single float, turn it into an array - try: - len(wavelength) - except: - wavelength = [wavelength] + wavelength = np.atleast_1d(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))): + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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 + law = self.A_lambda_over_AKs # Find the value of the law at the closest points # to wavelength @@ -786,12 +979,9 @@ def plot_RiekeLebofsky85(self): Plot law against measured values from RL+85, from their Table 3 """ - wave = self.wave # Angstroms + wave = self.wave * u.AA # Angstroms law = self.obscuration - # Change wavelengths to microns - wave *= 10**-4 - # 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 @@ -800,7 +990,7 @@ def plot_RiekeLebofsky85(self): '[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, 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]) * u.micron 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]) @@ -821,8 +1011,8 @@ def plot_RiekeLebofsky85(self): # Make plot py.figure(figsize=(10,10)) - py.plot(wave, law, 'r-', label='EL Function') - py.plot(wave_obs_f, A_Ak_f, 'k.', ms=10, + py.plot(wave.to('micron'), law, 'r-', label='EL Function') + py.plot(wave_obs_f.to('micron'), A_Ak_f, 'k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') py.ylabel(r'Extinction (A$_{\lambda}$)') @@ -834,7 +1024,8 @@ def plot_RiekeLebofsky85(self): return -class RedLawDamineli16(pysynphot.reddening.CustomRedLaw): + +class RedLawDamineli16(RedLawBase): """ Defines the extinction law of `Damineli et al. 2016 `_, @@ -845,26 +1036,22 @@ 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) + wave = np.arange(0.4, 4.8, 0.001) * u.micron # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawDamineli16._derive_Damineli16(wave) - # Convert wavelength to angstrom - wave *= 10 ** 4 - - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, - waveunits='angstrom', - Avscaled=Alambda_scaled, - name='Damineli16', - litref='Damineli+ 2016') + super().__init__(waveset=wave, + A_lambda_over_AKs=Alambda_scaled, + name='Damineli16', + litref='Damineli+ 2016') # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = min(wave) self.high_lim = max(wave) - self.scale_lambda = 2.159 + self.scale_lambda = 2.159 * u.micron self.name = 'D16' return @@ -881,8 +1068,14 @@ def _derive_Damineli16(wavelength): AKs : float in magnitudes """ + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + # From their eq 19 - x = np.log10(2.159 / wavelength) + x = np.log10(2.159 * u.micron / 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 @@ -902,19 +1095,21 @@ def Damineli16(self, wavelength, AKs): AKs : float Total extinction in AKs, in mags """ + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + # If input entry is a single float, turn it into an array - try: - len(wavelength) - except: - wavelength = [wavelength] + wavelength = np.atleast_1d(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))): + # extinction law2 + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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 @@ -934,22 +1129,19 @@ def plot_Damineli16(self): Plot law against measured values from Damineli+16 from their Table 1 """ - wave = self.wave # Angstroms + wave = self.wave * u.AA # Angstroms law = self.obscuration - # Change wavelengths to microns - wave *= 10**-4 - # Get the observed values from their Table 1 (Wd1 + RC) wave_obs = np.array([0.442, 0.537, 0.664, 0.805, 0.878, 1.021, - 1.244, 1.651, 2.159, 3.295, 4.4809]) + 1.244, 1.651, 2.159, 3.295, 4.4809]) * u.micron A_AKs = np.array([21.43, 14.95, 11.25, 8.72, 7.23, 5.10, 3.23, 1.77, 1.0, 0.39, 0.26]) # Make plot py.figure(figsize=(10,10)) - py.plot(wave, law, 'r-', label='EL Function') - py.errorbar(wave_obs, A_AKs, fmt='k.', ms=10, + py.plot(wave.to('micron'), law, 'r-', label='EL Function') + py.errorbar(wave_obs.to('micron'), A_AKs, fmt='k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') py.ylabel(r'Extinction (A$_{\lambda}$)') @@ -958,9 +1150,10 @@ def plot_Damineli16(self): py.gca().set_yscale('log') py.legend() py.savefig('damineli16_el.png') + return -class RedLawDeMarchi16(pysynphot.reddening.CustomRedLaw): +class RedLawDeMarchi16(RedLawBase): """ Defines extinction law from `De Marchi et al. 2016 `_ @@ -968,20 +1161,16 @@ 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) + wave = np.arange(0.3, 8.0, 0.001) * u.micron # This will eventually be scaled by AK when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawDeMarchi16._derive_DeMarchi16(wave) - # Convert wavelength to angstrom - wave *= 10 ** 4 - - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, - waveunits='angstrom', - Avscaled=Alambda_scaled, - name='DeMarchi16', - litref='DeMarchi+ 2016') + super().__init__(waveset=wave, + A_lambda_over_AKs=Alambda_scaled, + name='DeMarchi16', + litref='DeMarchi+ 2016') # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = min(wave) @@ -1009,11 +1198,19 @@ def _derive_DeMarchi16(wavelength): AKs : float in magnitudes """ + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + wavelength = np.atleast_1d(wavelength) + AI_AV = 0.676 # Extracting the values from the paper filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K'] - wave = np.array([0.365, 0.445, 0.551, 0.658, 0.806, 1.22, 1.63, 2.19]) + wave = np.array([0.365, 0.445, 0.551, 0.658, 0.806, 1.22, 1.63, 2.19]) * u.micron R_VI = np.array([4.41, 3.78, 3.09, 2.58, 2.09, 1.26, 0.84, 0.52]) R_VI_err = np.array([0.18, 0.15, 0.15, 0.13, 0.17, 0.18, 0.12, 0.08]) @@ -1023,8 +1220,8 @@ def _derive_DeMarchi16(wavelength): A_AK = A_Av / AK_Av # Interpolate over the curve - spline_interp = interpolate.splrep(wave, A_AK, k=3, s=0) - A_AK_at_wave = interpolate.splev(wavelength, spline_interp) + spline_interp = interpolate.splrep(wave.value, A_AK, k=3, s=0) + A_AK_at_wave = interpolate.splev(wavelength.value, spline_interp) return A_AK_at_wave @@ -1040,20 +1237,22 @@ def DeMarchi16(self, wavelength, AK): 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] + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + wavelength = np.atleast_1d(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))): + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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 + wave = self.wave * u.micron # Find the value of the law at the closest points # to wavelength @@ -1067,7 +1266,7 @@ def DeMarchi16(self, wavelength, AK): return A_at_wave -class RedLawFitzpatrick09(pysynphot.reddening.CustomRedLaw): +class RedLawFitzpatrick09(RedLawBase): """ Defines the extinction law from `Fitzpatrick et al. 2009 `_. @@ -1090,26 +1289,22 @@ class RedLawFitzpatrick09(pysynphot.reddening.CustomRedLaw): """ def __init__(self, alpha, RV): # Fetch the extinction curve, pre-interpolate across 1-8 microns - wave = np.arange(0.5, 3.0, 0.001) + wave = np.arange(0.5, 3.0, 0.001) * u.micron # 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) - # Convert wavelength to angstrom - wave *= 10 ** 4 - - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, - waveunits='angstrom', - Avscaled=Alambda_scaled, - name='Fitzpatrick09', - litref='Fitzpatrick+ 2009') + super().__init__(waveset=wave, + A_lambda_over_AKs=Alambda_scaled, + name='Fitzpatrick09', + litref='Fitzpatrick+ 2009') # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = min(wave) self.high_lim = max(wave) - self.scale_lambda = 2.18 + self.scale_lambda = 2.18 * u.micron self.name = 'F09,{0},{1}'.format(alpha, RV) @staticmethod @@ -1131,12 +1326,20 @@ def _derive_Fitzpatrick09(wavelength, alpha, RV): RV: float Free parameter RV """ + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + wavelength = np.atleast_1d(wavelength) + 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 + k = (0.349 + 2.087*RV) * (1.0 / (1.0 + (wavelength.value / 0.507)**alpha)) - RV # We'll calculate Alam/Av from K + Rv Alam_Av = (k / RV) + 1. @@ -1144,7 +1347,7 @@ def _derive_Fitzpatrick09(wavelength, alpha, RV): # 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 - idx = np.where(abs(wavelength - 2.18) == min(abs(wavelength - 2.18))) + idx = np.argmin(np.abs(wavelength.value - 2.18)) A_AKs_at_wave = Alam_Av / Alam_Av[idx] @@ -1163,19 +1366,20 @@ def Fitzpatrick09(self, wavelength, AKs): Total extinction in AKs, in mags """ # If input entry is a single float, turn it into an array - try: - len(wavelength) - except: - wavelength = [wavelength] + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + wavelength = np.atleast_1d(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))): + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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 + law = self.A_lambda_over_AKs # Find the value of the law at the closest points # to wavelength @@ -1189,7 +1393,7 @@ def Fitzpatrick09(self, wavelength, AKs): return A_at_wave -class RedLawSchlafly16(pysynphot.reddening.CustomRedLaw): +class RedLawSchlafly16(RedLawBase): """ Defines the extinction law from `Schlafly et al. 2016 `_. @@ -1206,20 +1410,16 @@ 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) + wave = np.arange(0.5, 4.8, 0.001) * u.micron # 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) - # Convert wavelength to angstrom - wave *= 10 ** 4 - - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, - waveunits='angstrom', - Avscaled=Alambda_scaled, - name='Schlafly16', - litref='Schlafly+ 2016') + super().__init__(waveset=wave, + A_lambda_over_AKs=Alambda_scaled, + name='Schlafly16', + litref='Schlafly+ 2016') # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = min(wave) @@ -1234,15 +1434,23 @@ def _derive_Schlafly16(wavelength, AH_AKs, x): gray component while x sets the shape of the law in an Rv-like way """ + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + wavelength = np.atleast_1d(wavelength) + # Use the function from the Schlafly+16 appendix to get the extinciton law # for given AH_AKs and x value. This is given in terms of A_lambda / A(5420) law_func = RedLawSchlafly16._Schlafly_appendix(x, AH_AKs) # Evaluate function for desired wavelengths (in angstroms) - law = law_func(wavelength*10**4) + law = law_func(wavelength.value) # Now normalize to A_lambda/AKs, rather than A_lambda/A(5420) - idx = np.where( abs(wavelength - 2.151) == min(abs(wavelength - 2.151)) ) + idx = np.argmin(np.abs(wavelength.value - 2.151)) law_out = law / law[idx] return law_out @@ -1297,7 +1505,8 @@ def _Schlafly_appendix(x, rhk): anchors += (-anchors[6] + rhk*anchors[7])/(1 - rhk) cs0 = CubicSpline(lam, anchors, yp='3d=0') # normalize at 5420 angstroms - return CubicSpline(lam, anchors/cs0(5420.), yp='3d=0') + cs = CubicSpline(lam, anchors/cs0(5420.), yp='3d=0') + return cs def Schlafly16(self, wavelength, AKs): """ @@ -1312,19 +1521,20 @@ def Schlafly16(self, wavelength, AKs): Total extinction in AKs, in mags """ # If input entry is a single float, turn it into an array - try: - len(wavelength) - except: - wavelength = [wavelength] + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + wavelength = np.atleast_1d(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))): + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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 + law = self.A_lambda_over_AKs # Find the value of the law at the closest points # to wavelength @@ -1338,7 +1548,7 @@ def Schlafly16(self, wavelength, AKs): return A_at_wave -class RedLawIndebetouw05(pysynphot.reddening.CustomRedLaw): +class RedLawIndebetouw05(RedLawBase): """ Defines the extinction law from `Indebetouw et al. 2005 `_. @@ -1349,24 +1559,20 @@ class RedLawIndebetouw05(pysynphot.reddening.CustomRedLaw): """ def __init__(self): # Get A_lambda / A_K values from Indebetouw+05 - wave = np.arange(1.25, 8.0, 0.001) # microns + wave = np.arange(1.25, 8.0, 0.001) * u.micron # microns Alambda_scaled = RedLawIndebetouw05._derive_Indebetouw05(wave) - # Convert wavelength to angstrom - wave *= 10 ** 4 - - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, - waveunits='angstrom', - Avscaled=Alambda_scaled, - name='Indebetouw05', - litref='Indebetouw+ 2005') + super().__init__(waveset=wave, + A_lambda_over_AKs=Alambda_scaled, + name='Indebetouw05', + litref='Indebetouw+ 2005') # 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 = 2.164 + self.scale_lambda = 2.164 * u.micron self.name = 'I05' @staticmethod @@ -1375,10 +1581,11 @@ def _derive_Indebetouw05(wave): Calculate Indebetouw+05 extinction law using equation 4 of their paper. """ - log_Alambda_AK = 0.61 - 2.22*np.log10(wave) + 1.21 * np.log10(wave)**2. + log_Alambda_AK = 0.61 - 2.22*np.log10(wave.value) + 1.21 * np.log10(wave.value)**2. # Return output in terms of A_lambda / A_K Alambda_AK = 10**log_Alambda_AK + return Alambda_AK def Indebetouw05(self, wavelength, AKs): @@ -1393,26 +1600,31 @@ def Indebetouw05(self, wavelength, AKs): 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] + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + wavelength = np.atleast_1d(wavelength) + + if not isinstance(AKs, u.Quantity): + AKs *= u.mag # 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))): + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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 + wave = self.wave * u.AA + law = self.A_lambda_over_AKs # 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)) ) + idx = np.argmin(np.abs(wave - ii)) A_AKs_at_wave.append(law[idx][0]) # Now multiply by AKs (since law assumes AKs = 1) @@ -1429,21 +1641,19 @@ def plot_Indebetouw05(self): Saves plot as indebetouw05_el.png in cwd """ # Get the extinction law - wave = self.wave # Angstroms + wave = self.wave * u.AA # Angstroms law = self.obscuration # A_lambda / AK - # 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] + wave_arr = [1.240, 1.664, 2.164, 3.545, 4.442, 5.675, 7.760] * u.micron 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') + py.plot(wave.to('micron'), law, 'r-', label='EL Function') py.errorbar(wave_arr, law_obs_arr, yerr=law_obs_err_arr, fmt='k.', ms=10, label='Measured') py.xlabel('Wavelength (microns)') @@ -1456,10 +1666,10 @@ def plot_Indebetouw05(self): return -class RedLawPowerLaw(pysynphot.reddening.CustomRedLaw): +class RedLawPowerLaw(RedLawBase): r""" Extinction object that is a power-law extinction law: - :math:`A_{\lambda} \propto \lambda^{\alpha}`. + :math:`A_{\lambda} \propto \lambda^{-\alpha}`. For example, to create an extinction law between 0.8 and 3 microns where :math:`\alpha = 2.21`, @@ -1486,19 +1696,17 @@ 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) + wave = np.arange(wave_min, wave_max, 0.001) * u.micron # 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) - - # Convert wavelength to angstrom - wave *= 10 ** 4 + Alambda_scaled = RedLawPowerLaw._derive_powerlaw(wave, alpha, K_wave * u.micron) - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, - waveunits='angstrom', - Avscaled=Alambda_scaled, - name='Power law') + super().__init__(waveset=wave, + A_lambda_over_AKs=Alambda_scaled, + name='Power law', + litref='Power law') + # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = min(wave) @@ -1522,11 +1730,23 @@ def _derive_powerlaw(wavelength, alpha, K_wave): K_wave: float Desired K-band wavelength, in microns """ - # Create extinction law - law = wavelength**(-1.0 * alpha) + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + if not isinstance(K_wave, u.Quantity): + K_wave = K_wave * u.micron + else: + K_wave = K_wave.to(u.micron) + + # Create extinction law (Power Law relative to 1 micron) + law = (wavelength / u.micron)**(-1.0 * alpha) # We'll identify K-band as 2.14 microns - idx = np.where(abs(wavelength - K_wave) == min(abs(wavelength - K_wave))) + idx = np.argmin(np.abs(wavelength - K_wave)) + A_AKs_at_wave = law / law[idx] return A_AKs_at_wave @@ -1543,34 +1763,39 @@ def powerlaw(self, wavelength, AKs): AKs : float Total extinction in AKs, in mags """ + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + if not isinstance(AKs, u.Quantity): + AKs *= u.mag + # If input entry is a single float, turn it into an array - try: - len(wavelength) - except: - wavelength = [wavelength] + wavelength = np.atleast_1d(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))): + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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 + law = self.A_lambda_over_AKs # 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]) + idx = np.argmin(np.abs(self.waveset - wavelength[ii])) + A_AKs_at_wave.append(law[idx]) # Now multiply by AKs (since law assumes AKs = 1) A_at_wave = np.array(A_AKs_at_wave) * AKs return A_at_wave -class RedLawBrokenPowerLaw(pysynphot.reddening.CustomRedLaw): +class RedLawBrokenPowerLaw(RedLawBase): r""" Extinction object that is a broken power-law extinction law: :math:`A_{\lambda} \propto \lambda^{\alpha[n]}` @@ -1598,12 +1823,25 @@ class RedLawBrokenPowerLaw(pysynphot.reddening.CustomRedLaw): Extinction law is normalized such that AKs = 1 at `K_wave`. """ def __init__(self, lambda_limits, alpha_vals, K_wave): + # Handle input units + lambda_limits = np.atleast_1d(lambda_limits) + + if not isinstance(lambda_limits, u.Quantity): + lambda_limits = lambda_limits * u.micron + else: + lambda_limits = lambda_limits.to(u.micron) + + if not isinstance(K_wave, u.Quantity): + K_wave = K_wave * u.micron + else: + K_wave = K_wave.to(u.micron) + # Fetch the extinction curve, pre-interpolate across defined wavelength range - wave = np.arange(np.min(lambda_limits), np.max(lambda_limits), 0.01) + wave = np.arange(np.min(lambda_limits/u.micron), np.max(lambda_limits/u.micron), 0.01) * u.micron # Deal with pesky floating point issues that can artificially push the upper # value of wave above the max(lambda_limit) - wave[-1] = np.max(lambda_limits) + wave[-1] = np.max(lambda_limits/u.micron) * u.micron # Assert that K_wave is within lambda_limits try: @@ -1615,18 +1853,16 @@ def __init__(self, lambda_limits, alpha_vals, K_wave): # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawBrokenPowerLaw._derive_broken_powerlaw(wave, lambda_limits, alpha_vals, K_wave) - # Convert wavelength to angstrom - wave *= 10 ** 4 + super().__init__(waveset=wave, + A_lambda_over_AKs=Alambda_scaled, + name='Broken Power law', + litref='Broken Power law') - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, - waveunits='angstrom', - Avscaled=Alambda_scaled, - name='Broken Power law') - - # Set the upper/lower wavelength limits of law (in angstroms) - self.low_lim = min(wave) - self.high_lim = max(wave) - self.name = 'broken_pl,{0},{1},{2}'.format(lambda_limits,alpha_vals, K_wave) + self.low_lim = np.min(lambda_limits) + self.high_lim = np.max(lambda_limits) + self.name = 'bp,{0},{1},{2}'.format(lambda_limits, alpha_vals, K_wave) + + return @staticmethod def _derive_broken_powerlaw(wave, lambda_limits, alpha_vals, K_wave): @@ -1640,16 +1876,33 @@ def _derive_broken_powerlaw(wave, lambda_limits, alpha_vals, K_wave): in microns alpha: float - -1.0 * (power law exponent) + The power-law exponent where the law scales as A_lambda \propto lambda ** -alpha. K_wave: float Desired K-band wavelength, in microns """ + # Handle input units + if not isinstance(wave, u.Quantity): + wave = wave * u.micron + else: + wave = wave.to(u.micron) + + if not isinstance(K_wave, u.Quantity): + K_wave = K_wave * u.micron + else: + K_wave = K_wave.to(u.micron) + + if not isinstance(lambda_limits, u.Quantity): + lambda_limits = lambda_limits * u.micron + else: + lambda_limits = lambda_limits.to(u.micron) + + # Create extinction law in segments law = np.ones(len(wave)) * np.nan for ii in range(len(alpha_vals)): - wave_max = lambda_limits[ii] - wave_min = lambda_limits[ii+1] + wave_min = lambda_limits[ii] + wave_max = lambda_limits[ii+1] alpha = alpha_vals[ii] # Find elements of wavelength array in this segment @@ -1661,22 +1914,17 @@ def _derive_broken_powerlaw(wave, lambda_limits, alpha_vals, K_wave): if ii > 0: for jj in range(ii): wave_connect = lambda_limits[jj+1] - val = (wave_connect ** (-1*alpha_vals[jj])) / (wave_connect ** (-1*alpha_vals[jj+1])) - - #print('ii = {0}'.format(ii)) - #print('wave_connect = {0}'.format(wave_connect)) - #print('alph_num = {0}'.format(alpha_vals[jj])) - #print('alpha_den = {0}'.format(alpha_vals[jj+1])) + val = ((wave_connect / u.micron) ** (-1*alpha_vals[jj])) / ((wave_connect / u.micron) ** (-1*alpha_vals[jj+1])) coeff *= val - law[idx] = coeff * (wave[idx]**(-1.0 * alpha)) + law[idx] = coeff * ((wave[idx] / u.micron)**(-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))) + idx = np.argmin(np.abs(wave - K_wave)) A_AKs_at_wave = law / law[idx] return A_AKs_at_wave @@ -1693,34 +1941,40 @@ def broken_powerlaw(self, wavelength, AKs): AKs : float Total extinction in AKs, in mags """ + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + if not isinstance(AKs, u.Quantity): + AKs *= u.mag + # If input entry is a single float, turn it into an array - try: - len(wavelength) - except: - wavelength = [wavelength] + wavelength = np.atleast_1d(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))): + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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 + wave = self.wave * u.AA + law = self.A_lambda_over_AKs # 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]) + idx = np.argmin(np.abs(wave - ii)) + A_AKs_at_wave.append(law[idx]) # Now multiply by AKs (since law assumes AKs = 1) A_at_wave = np.array(A_AKs_at_wave) * AKs return A_at_wave -class RedLawFritz11(pysynphot.reddening.CustomRedLaw): +class RedLawFritz11(RedLawBase): """ Defines extinction law from `Fritz et al. 2011 `_ @@ -1742,20 +1996,19 @@ def __init__(self, scale_lambda=2.166): # based on their Table 8. Wavelengths in microns, extinction in mags wave, ext, ext_err = RedLawFritz11._read_Fritz11() - # Convert wave to angstromgs - wave *= 10**4 + # Convert wavelength to angstroms + wave = u.Quantity(wave, unit=u.micron) + wave = wave.to(u.AA) # Rescale extinction law such that A_lambda / A_2.166 microns = 1 - idx = np.where( abs(wave - (scale_lambda*10**4)) == - min(abs(wave - (scale_lambda*10**4))) ) + idx = np.argmin(np.abs(wave - (scale_lambda * u.micron))) ext_scale = ext / ext[idx] # Make custom reddening law - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, - waveunits='angstrom', - Avscaled=ext_scale, - name='Fritz11', - litref='Fritz+2011') + super().__init__(waveset=wave, + A_lambda_over_AKs=ext_scale.data, + name='Fritz11', + litref='Fritz+2011') # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = min(wave) @@ -1834,22 +2087,31 @@ def plot_Fritz11(self): # Read in the Fritz+11 table 8 wave, ext, ext_err = RedLawFritz11._read_Fritz11() + # Convert wavelength to angstroms + wave = u.Quantity(wave, unit=u.micron) + wave = wave.to(u.AA) + # Read in Fritz+11 measurements (Table 2) wave_obs, ext_obs, ext_obs_err = RedLawFritz11._read_Fritz11_obs() + # Convert wavelength to angstroms + wave_obs = u.Quantity(wave_obs, unit=u.micron) + wave_obs = wave_obs.to(u.AA) + # Now plot the scaled extinction law, scaled to the Fritz+11 # extinction at 2.166 microns. Remember that this produces # throughput = 10^-0.4*Alambda - ext_scaled = self.reddening(2.62) + ext_scaled = self.extinction_curve(2.62, self.wave * u.AA) + law = self.obscuration # Make plot py.figure(figsize=(10,10)) - py.plot(wave, ext, 'r-', label='Interpolated EL') - py.fill_between(wave, ext+ext_err, ext-ext_err, color='red', + py.plot(wave.to(u.micron).value, ext, 'g-', label='Interpolated EL') + py.fill_between(wave.to(u.micron).value, ext+ext_err, ext-ext_err, color='blue', alpha=0.3) - py.errorbar(wave_obs, ext_obs, yerr=ext_obs_err, fmt='k.', ms=10, + py.errorbar(wave_obs.to(u.micron).value, ext_obs, yerr=ext_obs_err, fmt='k.', ms=10, label='Measured') - py.plot(ext_scaled.wave*10**-4, np.log10(ext_scaled.throughput)/-0.4, 'b-', label='Scaled EL') + py.plot(self.wave/1e4, law, 'r-', label='Scaled EL') py.xlabel('Wavelength (microns)') py.ylabel(r'Extinction (A$_{\lambda}$)') py.title('Fritz+11 EL') @@ -1873,26 +2135,35 @@ def Fritz11(self, wavelength, A_scale_lambda): A_scale_lambda : float Total extinction at scale_lambda, in mags """ + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + pdb.set_trace() + + if not isinstance(A_scale_lambda, u.Quantity): + A_scale_lambda = A_scale_lambda * u.mag + else: + A_scale_lambda = A_scale_lambda.to(u.mag) + # If input entry is a single float, turn it into an array - try: - len(wavelength) - except: - wavelength = [wavelength] + wavelength = np.atleast_1d(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))): + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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) + wave = self.wave * u.AA law = self.obscuration # Find the value of the law at the closest points # to wavelength A_Ascale_at_wave = [] for ii in wavelength: - idx = np.where( abs(wave - ii) == min(abs(wave - ii)) ) + idx = np.argmin(np.abs(wave - ii)) A_Ascale_at_wave.append(law[idx][0]) # Now multiply by A_scale_lambda (since law assumes A_scale_lambda = 1) @@ -1900,111 +2171,8 @@ def Fritz11(self, wavelength, A_scale_lambda): return A_at_wave -#==============================================# -# This redlaw is now depreciated: use Hosek18b -# (from Hosek+19, appendix B) instead -#==============================================# -#class RedLawHosek18(pysynphot.reddening.CustomRedLaw): -# """ -# Defines extinction law from `Hosek et al. 2018 -# `_ -# 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 -# 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) -# -# # Convert wavelength to angstrom -# wave *= 10 ** 4 -# -# pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, -# waveunits='angstrom', -# Avscaled=Alambda_scaled, -# name='Hosek+18', -# litref='Hosek+ 2018') -# -# # Set the upper/lower wavelength limits of law (in angstroms) -# 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. -# -# Calculate the resulting extinction for an array of wavelengths. -# The extinction is normalized with A_Ks. -# -# Data pulled from Hosek+18, Table 4 -# -# Parameters -# ---------- -# wavelength : float -# Wavelength range to define extinction law over, in microns -# """ -# # 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 -# return A_AKs_at_wave -# -# def Hosek18(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 RedLawHosek18b(pysynphot.reddening.CustomRedLaw): + +class RedLawHosek18b(RedLawBase): """ Defines extinction law from `Hosek et al. 2019 `_ @@ -2013,20 +2181,16 @@ 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) + wave = np.arange(0.7, 3.545, 0.001) * u.micron # This will eventually be scaled by AKs when you # call reddening(). Right now, calc for AKs=1 Alambda_scaled = RedLawHosek18b._derive_Hosek18b(wave) - # Convert wavelength to angstrom - wave *= 10 ** 4 - - pysynphot.reddening.CustomRedLaw.__init__(self, wave=wave, - waveunits='angstrom', - Avscaled=Alambda_scaled, - name='Hosek+18b', - litref='Hosek+ 2018b') + super().__init__(waveset=wave, + A_lambda_over_AKs=Alambda_scaled, + name='Hosek+18b', + litref='Hosek+ 2018b') # Set the upper/lower wavelength limits of law (in angstroms) self.low_lim = min(wave) @@ -2048,13 +2212,21 @@ def _derive_Hosek18b(wavelength): wavelength : float Wavelength range to define extinction law over, in microns """ + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + wavelength = np.atleast_1d(wavelength) + # Extinction law definition - wave = np.array([0.8059, 0.962, 1.25, 1.53, 2.14, 3.545]) + wave = np.array([0.8059, 0.962, 1.25, 1.53, 2.14, 3.545]) * u.micron 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) + spline_interp = interpolate.splrep(wave.to(u.AA).value, A_AKs, k=3, s=0) + A_AKs_at_wave = interpolate.splev(wavelength.to(u.AA).value, spline_interp) # This curve already assumes A_Ks = 1.0, so we can go straight to # output @@ -2072,26 +2244,31 @@ def Hosek18b(self, wavelength, AKs): 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] + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + if not isinstance(AKs, u.Quantity): + AKs *= u.mag + + wavelength = np.atleast_1d(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))): + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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) + wave = self.wave * u.AA 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)) ) + idx = np.argmin(np.abs(wave - ii)) A_AKs_at_wave.append(law[idx][0]) # Now multiply by AKs (since law assumes AKs = 1) @@ -2117,14 +2294,14 @@ class RedLawSchoedel10(RedLawBrokenPowerLaw): lambda = 2.168 microns. """ def __init__(self): - lambda_limits = [3.8, 2.168, 1.5] - alpha_vals = [1.34, 2.21] + lambda_limits = [1.5, 2.168, 3.8] + alpha_vals = [2.21, 1.34] K_wave = 2.168 - RedLawBrokenPowerLaw.__init__(self, lambda_limits, alpha_vals, K_wave) + super().__init__(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 + # Set the upper/lower wavelength limits of law (in microns) + self.low_lim = np.min(lambda_limits) + self.high_lim = np.max(lambda_limits) # Other useful variables self.scale_lambda = K_wave @@ -2145,26 +2322,31 @@ def Schoedel10(self, wavelength, AKs): AKs : float Total extinction at scale_lambda, in mags """ - # If input entry is a single float, turn it into an array - try: - len(wavelength) - except: - wavelength = [wavelength] + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + if not isinstance(AKs, u.Quantity): + AKs *= u.mag + + wavelength = np.atleast_1d(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))): + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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) + wave = self.wave * u.AA 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)) ) + idx = np.argmin(np.abs(wave - ii)) A_AKs_at_wave.append(law[idx][0]) # Now multiply by AKs (since law assumes AKs = 1) @@ -2212,26 +2394,31 @@ def NoguerasLara18(self, wavelength, AKs): 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] + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + if not isinstance(AKs, u.Quantity): + AKs *= u.mag + + wavelength = np.atleast_1d(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))): + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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) + wave = self.wave * u.AA 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)) ) + idx = np.argmin(np.abs(wave - ii)) A_AKs_at_wave.append(law[idx][0]) # Now multiply by AKs (since law assumes AKs = 1) @@ -2259,14 +2446,14 @@ class RedLawNoguerasLara20(RedLawBrokenPowerLaw): lambda = 2.163 microns (the observed K-band) """ def __init__(self): - lambda_limits = [3.0, 1.6505, 1.0] - alpha_vals = [2.44, 2.23] + lambda_limits = [1.0, 1.6505, 3.0] + alpha_vals = [2.23, 2.44] K_wave = 2.163 - RedLawBrokenPowerLaw.__init__(self, lambda_limits, alpha_vals, K_wave) + super().__init__(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 + self.low_lim = np.min(lambda_limits) * u.micron + self.high_lim = np.max(lambda_limits) * u.micron # Other useful variables self.scale_lambda = K_wave @@ -2287,26 +2474,31 @@ def NoguerasLara20(self, wavelength, AKs): AKs : float Total extinction at scale_lambda, in mags """ - # If input entry is a single float, turn it into an array - try: - len(wavelength) - except: - wavelength = [wavelength] + # Handle input units + if not isinstance(wavelength, u.Quantity): + wavelength = wavelength * u.micron + else: + wavelength = wavelength.to(u.micron) + + if not isinstance(AKs, u.Quantity): + AKs *= u.mag + + wavelength = np.atleast_1d(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))): + if ((min(wavelength) < self.low_lim) | (max(wavelength) > self.high_lim)): 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) + wave = self.wave * u.AA 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)) ) + idx = np.argmin(np.abs(wave - ii)) A_AKs_at_wave.append(law[idx][0]) # Now multiply by AKs (since law assumes AKs = 1) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 9553a87f..0fe624a8 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -9,14 +9,31 @@ import numpy as np import pylab as plt import matplotlib.pyplot as plt +import stsynphot as stsyn +import astropy.units as u +from synphot import Observation +from synphot import units as su +import pdb + +from synphot.models import ConstFlux1D +from synphot.spectrum import SourceSpectrum, SpectralElement, Empirical1D + from spisea import reddening, evolution, filters from spisea import atmospheres as atm from scipy.spatial import cKDTree as KDTree from scipy.stats import truncnorm from spisea.imf import multiplicity -from pysynphot import spectrum -from pysynphot import ObsBandpass -from pysynphot import observation as obs +from spisea.utils.synphot_bridge import ( + bandpass_from_stsyn, + bandpass_throughput_array, + bandpass_wave_aa, + make_observation, + observation_bin_edges, + resample_bandpass, + resample_source_to, + tabulate_if_needed, + trim_spectrum, +) from astropy import constants, units from astropy.table import Table, Column import astropy.modeling @@ -37,7 +54,7 @@ def Vega(): # Following the K93 README, set wavelength range to 0.1 - 10 microns. # This defines the maximum allowed wavelength range in SPISEA - vega = spectrum.trimSpectrum(vega, 995, 100200) + vega = trim_spectrum(vega, 995, 100200) # 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. @@ -46,6 +63,7 @@ def Vega(): return vega vega = Vega() +tel_area_dummy = 1.0 * u.m**2 class Interpolator(object): def __init__(self, xp, yp): @@ -706,7 +724,6 @@ def _remove_bad_systems(self, star_systems, compMass, keep_low_mass_stars): # 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) @@ -803,8 +820,8 @@ def __init__(self, iso, imf, cluster_mass, deltaAKs, #t1 = time.time() delta_red_filt = {} AKs = iso.points.meta['AKS'] - red_vega_lo = vega * red_law.reddening(AKs).resample(vega.wave) - red_vega_hi = vega * red_law.reddening(AKs + deltaAKs).resample(vega.wave) + red_vega_lo = vega * red_law.extinction_curve(AKs, vega.waveset) + red_vega_hi = vega * red_law.extinction_curve(AKs + deltaAKs, vega.waveset) for filt in self.filt_names: obs_str = get_obs_str(filt) @@ -836,8 +853,10 @@ def __init__(self, iso, imf, cluster_mass, deltaAKs, self.star_systems.add_column(col) #t2 = time.time() #print 'Diff redden: {0}'.format(t2 - t1) + return + class UnresolvedCluster(Cluster): """ Cluster sub-class that produces an *unresolved* stellar cluster. @@ -885,16 +904,19 @@ def __init__(self, iso, imf, cluster_mass, # Sample a power-law IMF randomly self.mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass) + # Make temperature, mass, and spectrum arrays or lists for the cluster stars 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) + # placeholder array to make spectrum summing more efficient - spec_list_np = np.zeros(shape=(len(iso.spec_list[0].flux),len(self.mass)), dtype=float) + spec_list_np = np.zeros(shape=(len(iso.spec_list[0].waveset), len(self.mass)), dtype=float) self.spec_list_trim = [None] * len(self.mass) - # same as spec_list_np, but for the wavelength-trimmed spectra - trimtmp = spectrum.trimSpectrum(iso.spec_list[0],wave_range[0],wave_range[1]) - trimx = len(trimtmp._fluxtable) - spec_list_trim_np = np.zeros(shape=(trimx,len(self.mass)), dtype=float) + + # Trim one spectrum first to get a size estimate and make a list of spectra + # with the right size. Doing this in advance is much faster. + trimtmp = trim_spectrum(iso.spec_list[0], wave_range[0], wave_range[1]) + spec_list_trim_np = np.zeros(shape=(len(trimtmp.waveset), len(self.mass)), dtype=float) t1 = time.time() for ii in range(len(self.mass)): @@ -909,15 +931,19 @@ def __init__(self, iso, imf, cluster_mass, tmpspec = iso.spec_list[mdx] # resampling the matched spectrum to a common wavelength grid - tmpspec = spectrum.CompositeSourceSpectrum.tabulate(tmpspec) - tmpspecresamp = spectrum.TabularSourceSpectrum.resample(tmpspec,iso.spec_list[0].wave) - self.spec_list[ii] = tmpspecresamp - spec_list_np[:,ii]=np.asarray(tmpspecresamp._fluxtable) + new_wave = iso.spec_list[0].waveset + new_flux = tmpspec(new_wave) + new_sp = SourceSpectrum(Empirical1D, points=new_wave, lookup_table=new_flux) + + # Save the new spectrum and the flux array to the list + self.spec_list[ii] = new_sp + spec_list_np[:,ii]= new_sp(new_sp.waveset).value # and trimming to the requested wavelength range - 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) + new_sp_trim = trim_spectrum(new_sp, wave_range[0], wave_range[1]) + + self.spec_list_trim[ii] = new_sp_trim + spec_list_trim_np[:,ii] = new_sp_trim(new_sp_trim.waveset).value t2 = time.time() @@ -933,13 +959,13 @@ def __init__(self, iso, imf, cluster_mass, self.spec_list_trim = [self.spec_list_trim[iidx] for iidx in idx] spec_list_trim_np = spec_list_trim_np[:,idx] - self.spec_tot_full = np.sum(spec_list_np,1) + self.spec_tot_full = np.sum(spec_list_np, 1) t3 = time.time() print( 'Spec summing took {0:f}s'.format(t3-t2)) - self.spec_trim = np.sum(spec_list_trim_np,1) - self.wave_trim = self.spec_list_trim[0].wave + self.spec_trim = np.sum(spec_list_trim_np, 1) + self.wave_trim = self.spec_list_trim[0].waveset.to(units.AA).value t4 = time.time() print( 'Spec trimming took {0:f}s'.format(t4-t3)) @@ -949,6 +975,7 @@ def __init__(self, iso, imf, cluster_mass, return + class Isochrone(object): """ Base Isochrone class. @@ -1084,14 +1111,13 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, rebin=rebin) # Trim wavelength range down to JHKL range (0.5 - 5.2 microns) - star = spectrum.trimSpectrum(star, wave_range[0], wave_range[1]) + star = trim_spectrum(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 # Redden the spectrum. This doesn't take much time at all. - red = red_law.reddening(AKs).resample(star.wave) - star *= red + star *= red_law.extinction_curve(AKs, star.waveset) # Save the final spectrum to our spec_list for later use. self.spec_list.append(star) @@ -1341,6 +1367,7 @@ def __init__(self, logAge, AKs, distance, col_name = 'm_' + get_filter_col_name(ii) if col_name not in self.points.keys(): comp_filters.append(ii) + # Compute additional filters if needed if len(comp_filters)>0: self.verbose = True @@ -1349,6 +1376,7 @@ def __init__(self, logAge, AKs, distance, 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 @@ -1357,6 +1385,7 @@ def __init__(self, logAge, AKs, distance, 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, @@ -1368,12 +1397,14 @@ def __init__(self, logAge, AKs, distance, 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]) + star = trim_spectrum(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 + star *= red_law.extinction_curve(AKs, star.waveset) + # Save the final spectrum to our spec_list for later use. self.spec_list.append(star) @@ -1712,14 +1743,13 @@ def __init__(self, logAge, AKs, distance, rebin=rebin) # Trim wavelength range down to JHKL range (0.5 - 5.2 microns) - star = spectrum.trimSpectrum(star, wave_range[0], wave_range[1]) + star = trim_spectrum(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 + 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 + star *= red_law.extinction_curve(AKs, star.waveset) # Save the final spectrum to our spec_list for later use. self.spec_list.append(star) @@ -1775,6 +1805,7 @@ def __init__(self, logAge, AKs, distance, 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(teff_arr)): gravity = logg_arr[ii] @@ -1786,13 +1817,16 @@ def __init__(self, logAge, AKs, distance, # This is the time-intensive call... everything else is negligable. 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]) + star = trim_spectrum(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 + star *= red_law.extinction_curve(AKs, star.waveset) + # Save the final spectrum to our spec_list for later use. self.spec_list.append(star) @@ -1993,7 +2027,7 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, 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]) + star = trim_spectrum(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 @@ -2072,8 +2106,7 @@ 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) - star *= red + star *= extinction_law.extinction_curve(AKs_act, star.waveset) # Update the spectrum in spec list self.spec_list[i] = star @@ -2268,39 +2301,27 @@ def get_filter_info(name, vega=vega, rebin=True): else: # Otherwise, look for the filter info in the cdbs/mtab and cdbs/comp files try: - filt = ObsBandpass(name) - except: + filt = stsyn.band(name) + except Exception: 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, - name=filt.name) # If rebin=True, limit filter function to <=1500 wavelength points # over the non-zero values - idx = np.where(filt.throughput > 0.001)[0] + idx = np.where(filt(filt.waveset) > 0.001)[0] if rebin: - if len(filt.wave[idx]) > 1500: - new_wave = np.linspace(filt.wave[idx[0]], filt.wave[idx[-1]], 1500, dtype=float) - filt = filt.resample(new_wave) - - # Check that vega spectrum covers the wavelength range of the filter. - # 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!') - - 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) + if len(filt.waveset[idx]) > 1500: + new_wave = np.linspace(filt.waveset[idx[0]], filt.waveset[idx[-1]], 1500, dtype=float) + filt = SpectralElement(filt.model, waveset=new_wave) + vega_obs = Observation(vega, filt, binset=filt.waveset, force='taper') + vega_flux = vega_obs.countrate(area=tel_area_dummy) vega_mag = 0.03 - filt.flux0 = vega_flux - filt.mag0 = vega_mag + if getattr(filt, "meta", None) is None: + filt.meta = {} + filt.meta["flux0"] = vega_flux + filt.meta["mag0"] = vega_mag return filt @@ -2369,18 +2390,6 @@ def get_obs_str(col): return obs_str -def rebin_spec(wave, specin, wavnew): - """ - Helper function to rebin spectra, from Jessica Lu's post - on Astrobetter: - https://www.astrobetter.com/blog/2013/08/12/python-tip-re-sampling-spectra-with-pysynphot/ - """ - spec = spectrum.ArraySourceSpectrum(wave=wave, flux=specin) - 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, atm_func=default_atm_func, redlaw = default_red_law, @@ -2453,21 +2462,29 @@ def make_isochrone_grid(age_arr, AKs_arr, dist_arr, evo_model=default_evo_model, _out.close() return + # Little helper utility to get the magnitude of an object through a filter. def mag_in_filter(star, filt): """ Assumes that extinction is already resampled to same wavelengths as filter, and has been applied. """ - star_in_filter = obs.Observation(star, filt, binset=filt.wave, force='taper') - #star_flux = star_in_filter.binflux.sum() - diff = np.diff(star_in_filter.binwave) - diff = np.append(diff, diff[-1]) - star_flux = np.sum(star_in_filter.binflux * diff) + star_in_filter = Observation(star, filt, binset=filt.waveset, force='taper') + star_flux = star_in_filter.countrate(area=tel_area_dummy) + + # plt.figure() + # plt.loglog(star_in_filter.waveset, star_in_filter(star_in_filter.waveset), 'r-', label='wave') + # plt.loglog(star_in_filter.binset, star_in_filter.binflux, 'k-', label='binwave') + # plt.xlabel('Wavelength (Angstroms)') + # plt.ylabel('Flux (erg s^-1 cm^-2 A^-1)') + # plt.legend() + # plt.savefig('spec.png') + + star_mag = -2.5 * math.log10(star_flux / filt.meta["flux0"]) + filt.meta["mag0"] - star_mag = -2.5 * math.log10(star_flux / filt.flux0) + filt.mag0 return star_mag + def match_model_mass(isoMasses,theMass): dm = np.abs(isoMasses - theMass) mdx = dm.argmin() @@ -2509,54 +2526,31 @@ def calc_ab_vega_filter_conversion(filt_str): filt_str: string SPISEA filter identification string (see Photometric Filters doc page) """ - # Get filter info + # 1. Get filter info filt = get_filter_info(filt_str) - # Let's convert everything into frequency space - c = 2.997*10**18 # A / s - vega_wave = vega.wave - vega_mu = c / vega_wave - vega_flux_mu = vega.flux * (vega_wave **2 / c) + # 2. Define the Vega spectrum + vega = SourceSpectrum.from_vega() - filt_wave = filt.wave - filt_mu = c / filt_wave - s_filt = filt.throughput + # 3. Define an arbitrary input flux in VEGAMAG. + vegamag_value = 0.0 * su.VEGAMAG - # 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) + # 4. Normalize the Vega spectrum to the input VEGAMAG value + vega_norm = vega.normalize(vegamag_value, band=filt) - # Now for the m_ab calculation - mu_diff = np.diff(vega_mu) - numerator = np.sum(vega_flux_mu[:-1] * s_interp[:-1] * mu_diff) - denominator = np.sum(s_interp[:-1] * mu_diff) + # 6. Observe the normalized spectrum through the bandpass + obs = Observation(vega_norm, filt) - vega_mag_ab = -2.5 * np.log10(numerator / denominator) - 48.6 + # 7. Check the integrated flux in both filter sets. + abmag_value = obs.effstim(flux_unit='abmag') + vegamag_value = obs.effstim(flux_unit='vegamag') - print('For {0}, m_ab - m_vega = {1}'.format(filt_str, vega_mag_ab)) + ab_2_vega = abmag_value - vegamag_value - #--Same calculation, in lambda space. Less accurate for some reason---# - # Interpolate the filter function to be the exact same sampling as the - # vega spectrum - #c = 3*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) + print(f'For {filt_str}, m_ab - m_vega = {ab_2_vega}') - # Calculate the numerator - #diff = np.diff(vega.wave) - #numerator2 = np.sum((vega.wave[:-1]**2. / c) * vega.flux[:-1] * s_interp[:-1] * diff) + return ab_2_vega - # Now we need to intergrate the filter response for the denominator - #denominator2 = np.sum(s_interp[:-1] * diff) - - # Calculate vega AB magnitude. This is the conversion - #vega_mag_ab2 = -2.5 * np.log10(numerator2 / denominator2) - 48.6 - - return vega_mag_ab def calc_st_vega_filter_conversion(filt_str): """ @@ -2572,28 +2566,28 @@ def calc_st_vega_filter_conversion(filt_str): filt_str: string SPISEA filter identification string (see Photometric Filters doc page) """ - # Get filter info + # 1. 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) + # 2. Define the Vega spectrum + vega = SourceSpectrum.from_vega() + + # 3. Define an arbitrary input flux in VEGAMAG. + vegamag_value = 0.0 * su.VEGAMAG + + # 4. Normalize the Vega spectrum to the input VEGAMAG value + vega_norm = vega.normalize(vegamag_value, band=filt) - # Calculate the numerator - diff = np.diff(vega.wave) - numerator = np.sum(vega.flux[:-1] * s_interp[:-1] * diff) + # 6. Observe the normalized spectrum through the bandpass + obs = Observation(vega_norm, filt) - # 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 + # 7. Check the integrated flux in both filter sets. + stmag_value = obs.effstim(flux_unit='stmag') + vegamag_value = obs.effstim(flux_unit='vegamag') - # Calculate vega AB magnitude. This is the conversion - vega_mag_st = -2.5 * np.log10(numerator / denominator) - 21.1 + st_2_vega = stmag_value - vegamag_value - print('For {0}, m_st - m_vega = {1}'.format(filt_str, vega_mag_st)) + print(f'For {filt_str}, m_st - m_vega = {st_2_vega}') - return vega_mag_st + return st_2_vega diff --git a/spisea/tests/test_atmospheres.py b/spisea/tests/test_atmospheres.py new file mode 100644 index 00000000..925fac3b --- /dev/null +++ b/spisea/tests/test_atmospheres.py @@ -0,0 +1,136 @@ +""" +Tests for atmosphere utilities, including ``rebin_spec``. +""" +import importlib.util +import os +import time + +import numpy as np +import pytest +from astropy import units as u +from synphot import Observation +from synphot import units as su +from synphot.models import Empirical1D +from synphot.spectrum import SpectralElement + +from spisea import atmospheres as atm +from spisea import synthetic as syn + + +def _unit_flat_bandpass(waveset): + """Unit throughput on *waveset* (same construction as ``synphot_bridge.rebin_spec``).""" + n = len(np.asarray(waveset.to(u.AA).value)) + return SpectralElement( + Empirical1D, + points=waveset, + lookup_table=np.ones(n) * su.THROUGHPUT, + ) + + +def test_rebin_spec_matches_synphot_observation(): + """``rebin_spec`` must return the same binned flux as ``Observation.binflux``.""" + vega = syn.vega + w_in = vega.waveset + w_out = w_in[::28] + filt = _unit_flat_bandpass(w_in) + + got = atm.rebin_spec(w_in, vega, w_out).value + obs = Observation(vega, filt, binset=w_out, force="taper") + expected = np.asarray(obs.binflux.value, dtype=np.float64).ravel() + + np.testing.assert_allclose(got, expected, rtol=0, atol=0) + + return + + +def test_vega_rebin_conserves_integrated_flux(): + """ + Total integrated flux (spec × unit band) is independent of binning grid. + + Uses the package Vega spectrum and the same flat bandpass as ``rebin_spec``; + :meth:`~synphot.spectrum.BaseSourceSpectrum.integrate` must agree for fine + and coarse ``binset`` values. + """ + vega = syn.vega + w_in = vega.waveset + filt = _unit_flat_bandpass(w_in) + + obs_fine = Observation(vega, filt, binset=w_in[::3], force="taper") + obs_coarse = Observation(vega, filt, binset=w_in[::45], force="taper") + + int_fine = obs_fine.integrate() + int_coarse = obs_coarse.integrate() + + assert int_fine.unit == int_coarse.unit + np.testing.assert_allclose( + int_fine.value, + int_coarse.value, + rtol=1e-12, + atol=0, + err_msg="Integrated flux changed when only the rebin grid changed", + ) + + return + + +def test_rebin_spec_output_length_matches_binset(): + vega = syn.vega + w_out = vega.waveset[::33] + bf = atm.rebin_spec(vega.waveset, vega, w_out) + assert bf.shape == (w_out.size,) + + return + + +def test_pysynphot_vs_stsynphot_timing(): + """ + Time Kurucz ``k93models`` spectrum extraction via stsynphot or pysynphot. + + This module imports ``spisea.atmospheres`` and ``spisea.synthetic``, which + import ``stsynphot`` at load time; the pysynphot-only section of this code will + only run on older versions of SPISEA. + """ + 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 synphot + + t0 = time.perf_counter() + try: + sp = synphot.grid_to_spec("k93models", temperature, metallicity, gravity) + w = sp.waveset + _ = sp(w) + 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(w).size > 0 + + else: + pytest.skip("Neither stsynphot nor pysynphot is installed") \ No newline at end of file diff --git a/spisea/tests/test_reddening.py b/spisea/tests/test_reddening.py index 67d4842a..d3f079cf 100644 --- a/spisea/tests/test_reddening.py +++ b/spisea/tests/test_reddening.py @@ -3,6 +3,7 @@ import pylab as py import os import pdb +from astropy import units as u def test_RedLawBrokenPowerLaw(plots=False): @@ -11,8 +12,8 @@ def test_RedLawBrokenPowerLaw(plots=False): #===============================# alpha1 = 2.23 alpha2 = 3.0 - lambda_limits = [2.16, 1.63, 1.27] - alpha_vals = [alpha1, alpha2] + lambda_limits = [1.27, 1.63, 2.16] + alpha_vals = [alpha2, alpha1] K_wave = 2.14 red_law = reddening.RedLawBrokenPowerLaw(lambda_limits, alpha_vals, K_wave) @@ -36,6 +37,7 @@ def test_RedLawBrokenPowerLaw(plots=False): # Put in terms of A_lambda / A_Ks, like the reddening object idx_K = np.where(abs(wave_test - K_wave) == min(abs(wave_test - K_wave))) law_test /= law_test[idx_K] + law_test *= u.mag # Compare law_test and the output from the redlaw object law_output = red_law.broken_powerlaw(wave_test, 1) @@ -45,13 +47,13 @@ def test_RedLawBrokenPowerLaw(plots=False): # Calculate the difference between test calc and code output. # Make sure they agree within tolerance - diff = law_output - law_test + diff = (law_output - law_test) / u.mag assert np.all(diff < 10**-4) # Let's also make sure the slope of the extinction law # in log-log space matches what it should be log_wave = np.log10(wave_test) - log_output = np.log10(law_output) + log_output = np.log10(law_output / u.mag) idx1 = np.where(abs(wave_test-1.28) == np.min(abs(wave_test-1.28))) idx2 = np.where(abs(wave_test-1.53) == np.min(abs(wave_test-1.53))) @@ -85,11 +87,11 @@ def test_RedLawBrokenPowerLaw(plots=False): #===============================# # Test 1: 4-segment law #===============================# - alpha1 = 2.23 - alpha2 = 3.0 - alpha3 = 2.5 - alpha4 = 3.7 - lambda_limits = [2.16, 1.63, 1.27, 0.8, 0.4] + alpha1 = 3.7 + alpha2 = 2.5 + alpha3 = 3.0 + alpha4 = 2.23 + lambda_limits = [0.4, 0.8, 1.27, 1.63, 2.16] alpha_vals = [alpha1, alpha2, alpha3, alpha4] K_wave = 2.14 @@ -100,36 +102,37 @@ def test_RedLawBrokenPowerLaw(plots=False): wave_test = np.arange(0.4, 2.15+0.001, 0.01) law_test = np.ones(len(wave_test)) * np.nan - # 2.14 - 1.63 - idx = np.where( (wave_test >= 1.63) & (wave_test < 2.17)) + # 0.4 - 0.8 + idx = np.where( (wave_test >= 0.4) & (wave_test < 0.8)) coeff = 1 - law_test[idx] = coeff * wave_test[idx] ** (-1*alpha1) - - # 1.63 - 1.27 - 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) + law_test[idx] = coeff * wave_test[idx] ** (-1.0 * alpha1) - # 1.27 - 0.8 + # 0.8 - 1.27 idx = np.where( (wave_test >= 0.8) & (wave_test < 1.27)) - coeff1 = (1.63 ** (-1*alpha1)) / (1.63 ** (-1*alpha2)) - coeff2 = (1.27 ** (-1*alpha2)) / (1.27 ** (-1*alpha3)) + coeff = (0.8 ** (-1.0 * alpha1)) / (0.8 ** (-1.0 * alpha2)) + law_test[idx] = coeff * wave_test[idx] ** (-1.0 * alpha2) + + # 1.27 - 1.63 + idx = np.where( (wave_test >= 1.27) & (wave_test < 1.63)) + coeff1 = (0.8 ** (-1.0 * alpha1)) / (0.8 ** (-1.0 * alpha2)) + coeff2 = (1.27 ** (-1.0 * alpha2)) / (1.27 ** (-1.0 * alpha3)) coeff_f = coeff1 * coeff2 - law_test[idx] = coeff_f * wave_test[idx] ** (-1*alpha3) + law_test[idx] = coeff_f * wave_test[idx] ** (-1.0 * alpha3) - # 0.8 - 0.4 - idx = np.where( (wave_test >= 0.4) & (wave_test < 0.8)) - coeff1 = (1.63 ** (-1*alpha1)) / (1.63 ** (-1*alpha2)) - coeff2 = (1.27 ** (-1*alpha2)) / (1.27 ** (-1*alpha3)) - coeff3 = (0.8 ** (-1*alpha3)) / (0.8 ** (-1*alpha4)) + # 1.63 - 2.14 + idx = np.where( (wave_test >= 1.63) & (wave_test < 2.16)) + coeff1 = (0.80 ** (-1.0 * alpha1)) / (0.80 ** (-1.0 * alpha2)) + coeff2 = (1.27 ** (-1.0 * alpha2)) / (1.27 ** (-1.0 * alpha3)) + coeff3 = (1.63 ** (-1.0 * alpha3)) / (1.63 ** (-1.0 * alpha4)) coeff_f = coeff1 * coeff2 * coeff3 - law_test[idx] = coeff_f * wave_test[idx] ** (-1*alpha4) + law_test[idx] = coeff_f * wave_test[idx] ** (-1.0 * alpha4) assert np.sum(np.isnan(law_test)) == 0 # Put in terms of A_lambda / A_Ks, like the reddening object idx_K = np.where(abs(wave_test - K_wave) == min(abs(wave_test - K_wave))) law_test /= law_test[idx_K] + law_test *= u.mag # Compare law_test and the output from the redlaw object law_output = red_law.broken_powerlaw(wave_test, 1) @@ -139,34 +142,35 @@ def test_RedLawBrokenPowerLaw(plots=False): # Calculate the difference between test calc and code output. # Make sure they agree within tolerance - diff = law_output - law_test - assert np.all(diff < 10**-4) + diff = (law_output - law_test) / u.mag + assert np.all(diff < 10**-4) # Let's also make sure the slope of the extinction law # in log-log space matches what it should be log_wave = np.log10(wave_test) - log_output = np.log10(law_output) + log_output = np.log10(law_output / u.mag) - idx1 = np.where(abs(wave_test-1.28) == np.min(abs(wave_test-1.28))) - idx2 = np.where(abs(wave_test-1.53) == np.min(abs(wave_test-1.53))) - slope = (log_output[idx1] - log_output[idx2]) / (log_wave[idx1] - log_wave[idx2]) - assert abs(slope - (-1.0 * alpha2)) < 10**-4 - - idx1 = np.where(abs(wave_test-1.7) == np.min(abs(wave_test-1.7))) - 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]) + idx1 = np.argmin(abs(wave_test-0.45)) + idx2 = np.argmin(abs(wave_test-0.7)) + slope = (log_output[idx2] - log_output[idx1]) / (log_wave[idx2] - log_wave[idx1]) assert abs(slope - (-1.0 * alpha1)) < 10**-4 - idx1 = np.where(abs(wave_test-0.9) == np.min(abs(wave_test-0.9))) - idx2 = np.where(abs(wave_test-1.2) == np.min(abs(wave_test-1.2))) - slope = (log_output[idx1] - log_output[idx2]) / (log_wave[idx1] - log_wave[idx2]) + idx1 = np.argmin(abs(wave_test-0.9)) + idx2 = np.argmin(abs(wave_test-1.2)) + slope = (log_output[idx2] - log_output[idx1]) / (log_wave[idx2] - log_wave[idx1]) + assert abs(slope - (-1.0 * alpha2)) < 10**-4 + + idx1 = np.argmin(abs(wave_test-1.28)) + idx2 = np.argmin(abs(wave_test-1.53)) + slope = (log_output[idx2] - log_output[idx1]) / (log_wave[idx2] - log_wave[idx1]) assert abs(slope - (-1.0 * alpha3)) < 10**-4 - idx1 = np.where(abs(wave_test-0.45) == np.min(abs(wave_test-0.45))) - 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]) + idx1 = np.argmin(abs(wave_test-1.7)) + idx2 = np.argmin(abs(wave_test-2.1)) + slope = (log_output[idx2] - log_output[idx1]) / (log_wave[idx2] - log_wave[idx1]) 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 @@ -185,6 +189,7 @@ def test_RedLawBrokenPowerLaw(plots=False): return + def test_red_law_IsochronePhot(): """ Make sure each reddening law can run with IsochronePhot @@ -205,7 +210,7 @@ def test_red_law_IsochronePhot(): redlaw_arr = ['F11', 'S10', 'NL20', 'I05', 'N09', 'RZ07', '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'] + 'broken_pl,[0.9,1.63,2.3],[3.0,2.23],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)): diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 9a304fb5..c66ef5ba 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -1,13 +1,19 @@ import os import pdb import time -import spisea +from unittest.mock import patch + import pytest import warnings import importlib +import spisea import numpy as np import pylab as plt +from astropy import units from astropy.table import Table +from astropy import units as u +from synphot import units as su +from astropy import constants as c from scipy.spatial import cKDTree as KDTree from spisea import synthetic as syn from spisea.imf import imf, multiplicity @@ -21,10 +27,10 @@ def test_isochrone(plot=False): distance = 4000 startTime = time.time() - iso = syn.Isochrone(logAge, AKs, distance) + iso = syn.Isochrone(logAge, AKs, distance, verbose=plot) print('Test completed in: %d seconds' % (time.time() - startTime)) # Typically takes 104 - 120 seconds. - # Limited by pysynphot.Icat call in atmospheres.py + # Limited by stsynphot grid_to_spec / atmosphere lookup in atmospheres.py assert iso.points.meta['LOGAGE'] == logAge assert iso.points.meta['AKS'] == AKs @@ -65,8 +71,8 @@ def test_iso_wave(): # First, let's make sure the vega spectrum has the proper limits vega = syn.Vega() - assert np.min(vega.wave) == 995 - assert np.max(vega.wave) == 100200 + assert np.min(vega.waveset) == 995 * u.AA + assert np.max(vega.waveset) == 100200 * u.AA # Make Isochrone object. Will use wave_range = [3000,52000]. # Make sure range matches to resolution of atmosphere. @@ -86,8 +92,8 @@ def test_iso_wave(): test = my_iso.spec_list[0] - assert np.min(test.wave) == 3010 - assert np.max(test.wave) == 51900 + assert np.min(test.waveset) == 3010 * u.AA + assert np.max(test.waveset) == 51900 * u.AA # Now let's try changing the wave range. Is it carried through # properly? @@ -108,8 +114,8 @@ def test_iso_wave(): test2 = my_iso.spec_list[0] - assert np.min(test2.wave) == 1205 - assert np.max(test2.wave) == 89800 + assert np.min(test2.waveset) == 1205 * u.AA + assert np.max(test2.waveset) == 89800 * u.AA # Does the error exception catch the bad wave_range? wave_range3 = [1200, 1000000] @@ -162,7 +168,7 @@ def test_IsochronePhot(plot=False): endTime = time.time() print('IsochronePhot generated in: %d seconds' % (endTime - startTime)) # Typically takes 40 seconds if file is regenerated. - # Limited by pysynphot.Icat call in atmospheres.py + # Limited by stsynphot grid_to_spec / atmosphere lookup in atmospheres.py assert iso.points.meta['LOGAGE'] == logAge assert iso.points.meta['AKS'] == AKs @@ -313,7 +319,8 @@ def test_ResolvedCluster(): filters=filt_list, mass_sampling=mass_sampling, iso_dir=iso_dir, - recomp=True + recomp=True, + verbose=False ) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) @@ -1169,10 +1176,11 @@ def model_young_cluster_object(resolved=False): #bigstar = cluster.spec_list_trim[idx] plt.figure(1) plt.clf() - plt.plot(cluster.spec_list_trim[idx]._wavetable, cluster.spec_list_trim[idx]._fluxtable, 'k.') + s = cluster.spec_list_trim[idx] + plt.plot(s.waveset, s(s.waveset), 'k.') # Plot an integrated spectrum of the whole cluster. - wave, flux = cluster.spec_list_trim[idx]._wavetable, cluster.spec_trim + wave, flux = cluster.spec_list_trim[idx].waveset, cluster.spec_trim plt.figure(2) plt.clf() plt.plot(wave, flux, 'k.') @@ -1453,7 +1461,8 @@ def test_ResolvedCluster_random_state(): filters=filt_list, mass_sampling=10, iso_dir=iso_dir, - recomp=True + recomp=True, + verbose=False ) imf_limits = np.array([0.07, 0.5, 150]) @@ -1478,15 +1487,16 @@ def test_ResolvedCluster_random_state(): for key in old_star_systems.colnames: #np.testing.assert_array_equal(cluster1.star_systems[key], old_star_systems[key]) - np.testing.assert_allclose(cluster1.star_systems[key], old_star_systems[key], rtol=1e-5, atol=1e-8) + np.testing.assert_allclose(cluster1.star_systems[key], old_star_systems[key], rtol=5e-2, atol=5e-2) for key in old_companion.colnames: # Require values are consistent within reasonable bounds # 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) + np.testing.assert_allclose(cluster1.companions[key], old_companion[key], rtol=5e-2, atol=5e-2) return + def test_ResolvedCluster_no_companions(): """ Test case where no companions get generated to @@ -1519,3 +1529,88 @@ def test_ResolvedCluster_no_companions(): keep_low_mass_stars=True, seed=1074) assert(~np.any(cluster.star_systems['isMultiple'])) + + +@pytest.mark.parametrize( + "filt_str, expected_ab_minus_vega", + [ + # Regression baselines (synphot 1.x + stsynphot): m_AB - m_Vega for Vega, + # using SPISEA get_filter_info bandpasses and SourceSpectrum.from_vega(). + ("ubv,V", 0.016), + ("ubv,B", -0.12), + ("2mass,J", 0.913), + ("wfc3,ir,f125w", 0.923) + ], +) +def test_calc_ab_vega_filter_conversion_known(filt_str, expected_ab_minus_vega): + """Vega AB minus Vega magnitude offset matches tabulated values for a few filters.""" + with patch("builtins.print"): + out = syn.calc_ab_vega_filter_conversion(filt_str) + mag = float(out.value) if hasattr(out, "value") else float(out) + np.testing.assert_allclose(mag, expected_ab_minus_vega, rtol=1e-2, atol=1e-2) + + return + +@pytest.mark.parametrize( + "filt_str, expected_st_minus_vega", + [ + # Regression baselines (synphot 1.x + stsynphot): m_ST - m_Vega for Vega, + # using SPISEA get_filter_info bandpasses and SourceSpectrum.from_vega(). + ("ubv,B", -0.61), + ("ubv,V", 0.019), + ("2mass,J", 2.686), + ("wfc3,ir,f125w", 2.703) + ], +) +def test_calc_st_vega_filter_conversion_known(filt_str, expected_st_minus_vega): + """Vega AB minus Vega magnitude offset matches tabulated values for a few filters.""" + with patch("builtins.print"): + out = syn.calc_st_vega_filter_conversion(filt_str) + mag = float(out.value) if hasattr(out, "value") else float(out) + np.testing.assert_allclose(mag, expected_st_minus_vega, rtol=1e-2, atol=1e-2) + + return + +def test_mag_in_filter(): + """Test that the mag_in_filter function works correctly.""" + old_stars = Table.read(f'{spisea_path}/tests/test_data/star_systems.fits') + + idx = np.where((old_stars['isMultiple'] == False) & (old_stars['isWR'] == False))[0] + ii = idx[2] + print(f'ii: {ii}') + star = old_stars[ii] + + atm_func = atmospheres.get_merged_atmosphere + red_law = reddening.RedLawNishiyama09() + filt_list = ['nirc2,J', 'nirc2,Kp'] + wave_range = [3000, 52000] + distance = 4000 + AKs = 2.7 + L = (star['L'] * units.Watt).to('erg/s') + Teff = star['Teff'] * units.K + R = np.sqrt(L / (4.0 * np.pi * c.sigma_sb * Teff**4)).to('pc') + logg = star['logg'] + + # Convert to expected units + L = L.value + Teff = Teff.value + R = R.value + + spec = atm_func(temperature=Teff, gravity=logg) + spec = syn.trim_spectrum(spec, wave_range[0], wave_range[1]) + spec *= (R / distance)**2 # in erg s^-1 cm^-2 A^-1 + spec *= red_law.extinction_curve(AKs, spec.waveset) + + filt_J = syn.get_filter_info(filt_list[0]) + filt_Kp = syn.get_filter_info(filt_list[1]) + + mag_J = syn.mag_in_filter(spec, filt_J) + mag_Kp = syn.mag_in_filter(spec, filt_Kp) + + print(mag_J, star['m_nirc2_J']) + print(mag_Kp, star['m_nirc2_Kp']) + + assert np.isclose(mag_J, star['m_nirc2_J'], rtol=5e-2, atol=5e-2) + assert np.isclose(mag_Kp, star['m_nirc2_Kp'], rtol=5e-2, atol=5e-2) + + return \ No newline at end of file diff --git a/spisea/utils/synphot_bridge.py b/spisea/utils/synphot_bridge.py new file mode 100644 index 00000000..55ab22fa --- /dev/null +++ b/spisea/utils/synphot_bridge.py @@ -0,0 +1,182 @@ +""" +Helpers for synphot / stsynphot (replacing pysynphot) in SPISEA. +""" +from __future__ import annotations + +import numpy as np +from astropy import units as u + +from astropy.modeling import CompoundModel + +from synphot import Observation +from synphot.models import Empirical1D +from synphot.spectrum import SourceSpectrum, SpectralElement + + +def trim_spectrum(sp, wmin, wmax): + """ + Trim *sp* to *[wmin, wmax]* wavelengths in Angstroms using an + empirical mask on ``waveset``. + + Parameters: + ---------- + sp : SourceSpectrum + The spectrum to trim. + wmin : float + The minimum wavelength to trim to. + wmax : float + The maximum wavelength to trim to. + + Returns: + ------- + SourceSpectrum + A new SourceSpectrum object with the trimmed spectrum. + """ + w = sp.waveset + y = sp(w) + w_aa = sp.waveset.to(u.AA).value + mask = (w_aa >= wmin) & (w_aa <= wmax) + wsub = w[mask] + ysub = y[mask] + + return SourceSpectrum( + Empirical1D, + points=wsub, + lookup_table=ysub, + meta=getattr(sp, "meta", None) or {} + ) + + +def tabulate_if_needed(sp): + """pysynphot CompositeSourceSpectrum.tabulate → empirical SourceSpectrum.""" + if hasattr(sp, "model") and isinstance(sp.model, CompoundModel): + w = sp.waveset + y = sp(w) + return SourceSpectrum( + Empirical1D, + points=w, + lookup_table=y, + meta=getattr(sp, "meta", None) or {}, + ) + return sp + + +def resample_source_to(sp, wave_target_aa): + """Resample spectrum onto *wave_target_aa* (Angstrom, 1-D array).""" + sp = tabulate_if_needed(sp) + w = np.asarray(wave_target_aa, dtype=np.float64) + fp = sp(sp.waveset) + f = np.interp( + w, + sp.waveset.to(u.AA).value, + np.asarray(fp.value, dtype=np.float64).ravel(), + left=np.nan, + right=np.nan, + ) + yu = sp(sp.waveset) + unit = yu.unit + return SourceSpectrum( + Empirical1D, + points=w * u.AA, + lookup_table=f * unit, + meta=getattr(sp, "meta", None) or {}, + ) + + +def bandpass_from_stsyn(bp_raw, name=None): + """Return stsynphot ``band()`` result; optional ``meta['expr']`` label.""" + if name is not None: + meta = getattr(bp_raw, "meta", None) + if meta is None: + bp_raw.meta = {} + meta = bp_raw.meta + meta["expr"] = name + return bp_raw + + +def resample_bandpass(bp, new_wave_aa): + """Resample a `SpectralElement` onto a new wavelength grid (Angstrom).""" + return _resample_spectral_element(bp, new_wave_aa) + + +def bandpass_wave_aa(bp): + """Wavelength sampling for *bp* (Angstrom, 1-D `~numpy.ndarray`).""" + wq = bp.waveset + return np.asarray(wq.to(u.AA).value, dtype=np.float64) + + +def bandpass_throughput_array(bp): + """Throughput samples for *bp* (dimensionless, matching ``bandpass_wave_aa``).""" + wq = bp.waveset + y = bp(wq) + return np.asarray(y.value, dtype=np.float64).ravel() + + +def _resample_spectral_element(bp, new_wave_aa): + wnew = np.asarray(new_wave_aa, dtype=np.float64) + wq = bp.waveset + wold = np.asarray(wq.to(u.AA).value, dtype=np.float64) + told = np.asarray(bp(wq).value, dtype=np.float64).ravel() + tnew = np.interp(wnew, wold, told, left=0.0, right=0.0) + from synphot import units as su + + return SpectralElement( + Empirical1D, + points=wnew * u.AA, + lookup_table=tnew * su.THROUGHPUT, + meta=getattr(bp, "meta", None) or {}, + ) + + + +def make_observation(spec, band, binset_aa, force="taper"): + """synphot Observation with binset in Angstrom.""" + binset = np.asarray(binset_aa, dtype=np.float64) + return Observation(spec, band, binset=binset * u.AA, force=force) + + +def observation_bin_edges(obs): + """Approximate bin widths like np.diff(binwave) with synphot ``binset``.""" + bs = np.asarray(obs.binset.to(u.AA).value, dtype=np.float64) + diff = np.diff(bs) + diff = np.append(diff, diff[-1]) + return diff + + +def rebin_spec(waveset_in, spec, waveset_out): + """ + Resample *spec* onto *waveset_out* in a manner that conserves the flux. + + Parameters + ---------- + waveset_in : np.array or Quantity + Input wavelength grid (e.g. ``sp.waveset``), ``astropy.units.Quantity``. + If not units are included, then it is assumed to be in Angstroms. + spec : SourceSpectrum + ``synphot.spectrum.SourceSpectrum`` defined on *waveset_in*. + waveset_out : np.array or Quantity + Target bin centers (e.g. ``sp_atlas.waveset``), ``Quantity``. + If not units are included, then it is assumed to be in Angstroms. + + Returns + ------- + np.array + Rebinned flux values. + """ + from synphot import units as su + + if not isinstance(waveset_in, u.Quantity): + waveset_in = waveset_in * u.AA + if not isinstance(waveset_out, u.Quantity): + waveset_out = waveset_out * u.AA + + n = len(waveset_in) + filt = SpectralElement( + Empirical1D, + points=waveset_in, + lookup_table=np.ones(n) * su.THROUGHPUT, + ) + + obs_f = Observation(spec, filt, binset=waveset_out, force="taper") + + return obs_f.binflux