From a21402f2fe99c369292971221cef2cc829e0b094 Mon Sep 17 00:00:00 2001 From: "Moloney, Philip" Date: Tue, 15 Mar 2022 20:17:40 +0000 Subject: [PATCH 01/30] =?UTF-8?q?=F0=9F=8E=89=20Initial=20forecasting=20at?= =?UTF-8?q?empt?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Notebooks/forecast.ipynb | 324 +++++++++++++++++++++++++++++++++++++++ environment.yml | 1 + 2 files changed, 325 insertions(+) create mode 100644 Notebooks/forecast.ipynb diff --git a/Notebooks/forecast.ipynb b/Notebooks/forecast.ipynb new file mode 100644 index 0000000..bd80beb --- /dev/null +++ b/Notebooks/forecast.ipynb @@ -0,0 +1,324 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Attempt to do some Bayesian forecasting\n", + "\n", + "* Used the work done on:\n", + " * https://towardsdatascience.com/forecasting-with-bayesian-dynamic-generalized-linear-models-in-python-865587fbaf90" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"../\")\n", + "from Hack import load\n", + "from Hack.rl import get_expected_price as get_ep\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "import pybats\n", + "from pybats.loss_functions import MAPE\n", + "from pybats.analysis import analysis\n", + "from pybats.point_forecast import median\n", + "from pybats.plot import *" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#Load the data\n", + "epex = load.epex().load()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "forecast_start = 0\n", + "forecast_end = 500\n", + "length=forecast_end-forecast_start\n", + "indexs=np.arange(forecast_start,forecast_end)\n", + "prices=epex.values[forecast_start:forecast_end,0]\n", + "\n", + "mod, samples = analysis(Y = prices, X=indexs, family='poisson',\n", + " forecast_start=forecast_start, \n", + " forecast_end=forecast_end, \n", + " k=k,\n", + " ntrend=1, # Intercept and slope in model\n", + " nsamps=5000, # Number of samples taken in the Poisson process\n", + " seasPeriods=[48], # Length of the seasonal variations in the data - i.e. every 24hr here\n", + " seasHarmComponents=[[1,2]], # To pick out the half dayly and daily harmonics\n", + " prior_length=prior_size, # How many data points to use in defining prior - i.e. 48 = one day\n", + " deltrend=0.94, # Discount factor on the intercept parameter\n", + " delregn=0.90, # Discount factor on the regression parameters\n", + " delVar=0.98,\n", + " delSeas=0.98,\n", + " rho=.6, # Random effect to increase variance\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "def bayes_forecast(iv,dv,start,prior_size=48):\n", + " '''\n", + " This functions runs the Pybats algorithm by taking two parameters: an independent variable matrix and a dependent variable. \n", + " Both elements must be sequential time series. \n", + " '''\n", + " # first check if the iv = None, indicating this would be a univariate series\n", + " if iv is None:\n", + " x = None\n", + " else:\n", + " x = iv\n", + " y = dv\n", + "\n", + " if prior_size>start:\n", + " raise Exception('Warning: must start longer than the priorsize')\n", + " \n", + " # set the one-step-ahead value; by default we want 1\n", + " k = 1 \n", + " forecast_start = start \n", + " forecast_end = len(y)-1\n", + " mod, samples = analysis(Y=y, X=x, family='poisson',\n", + " forecast_start=forecast_start, \n", + " forecast_end=forecast_end, \n", + " k=k,\n", + " ntrend=1, # Intercept and slope in model\n", + " nsamps=5000, # Number of samples taken in the Poisson process\n", + " seasPeriods=[48], # Length of the seasonal variations in the data - i.e. every 24hr here\n", + " seasHarmComponents=[[1,2]], # To pick out the half dayly and daily harmonics\n", + " prior_length=prior_size, # How many data points to use in defining prior - i.e. 48 = one day\n", + " deltrend=0.94, # Discount factor on the intercept parameter\n", + " delregn=0.90, # Discount factor on the regression parameters\n", + " delVar=0.98,\n", + " delSeas=0.98,\n", + " rho=.6, # Random effect to increase variance\n", + " )\n", + " forecast = median(samples)\n", + " \n", + " # set confidence interval for in-sample forecast\n", + " credible_interval=95\n", + " alpha = (100-credible_interval)/2\n", + " upper=np.percentile(samples, [100-alpha], axis=0).reshape(-1)\n", + " lower=np.percentile(samples, [alpha], axis=0).reshape(-1)\n", + " print(\"MAPE:\", MAPE(y[-18:], forecast[-18:]).round(2))\n", + " \n", + " #Generate the Bayesian Future Forecast\n", + " return mod, forecast, samples, y\n" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "index_start=0\n", + "index_stop=500\n", + "length=index_stop-index_start\n", + "\n", + "indexs=np.arange(np.size(epex.values[index_start:index_stop,0]))\n", + "prices=epex.values[index_start:index_stop,0]\n", + "median_prices = np.zeros_like(prices)\n", + "for i,val in enumerate(median_prices):\n", + " median_prices[i]=get_ep(prices,i,mode='median')" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(indexs[index_start:index_stop],prices[index_start:index_stop],label='prices')\n", + "plt.plot(indexs[index_start:index_stop],median_prices[index_start:index_stop],label='median prices')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "beginning forecasting\n", + "MAPE: 12.89\n" + ] + } + ], + "source": [ + "for_start=50\n", + "mv_mod, mv_for, mv_samp, mv_y = bayes_forecast(indexs,prices,for_start)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 95.0)" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(indexs[index_start:index_stop],prices[index_start:index_stop],label='actual median')\n", + "plt.plot(indexs[index_start+for_start:index_stop],mv_for,label='Bayesian forecast')\n", + "upper=2*np.max(median_prices[index_start:index_stop])\n", + "plt.ylim([0,upper])" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " ...,\n", + " [ 0.00000000e+00],\n", + " [-2.14748365e+09],\n", + " [ 0.00000000e+00]],\n", + "\n", + " [[ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " ...,\n", + " [ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " [ 0.00000000e+00]],\n", + "\n", + " [[ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " ...,\n", + " [ 0.00000000e+00],\n", + " [-2.14748365e+09],\n", + " [-2.14748365e+09]],\n", + "\n", + " ...,\n", + "\n", + " [[ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " ...,\n", + " [ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " [ 0.00000000e+00]],\n", + "\n", + " [[ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " ...,\n", + " [ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " [ 0.00000000e+00]],\n", + "\n", + " [[ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " [ 0.00000000e+00],\n", + " ...,\n", + " [-2.14748365e+09],\n", + " [ 0.00000000e+00],\n", + " [ 0.00000000e+00]]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mv_samp" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "04a20cc0f25f2654a5fc5715c026c4c293afbc25926c593c301cab56769943bf" + }, + "kernelspec": { + "display_name": "Python 3.9.10 ('ml')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/environment.yml b/environment.yml index 92b6fdb..23b977a 100644 --- a/environment.yml +++ b/environment.yml @@ -28,3 +28,4 @@ dependencies: - pip - pip: - stable_baselines3 + - pybats From 8133b9c4bc437a16c7bf8290a306ed4c05ecf149 Mon Sep 17 00:00:00 2001 From: "Moloney, Philip" Date: Tue, 15 Mar 2022 20:18:51 +0000 Subject: [PATCH 02/30] Updating forecasting --- Notebooks/forecast.ipynb | 117 +++++++++++++++++++++++++++++++++++---- 1 file changed, 105 insertions(+), 12 deletions(-) diff --git a/Notebooks/forecast.ipynb b/Notebooks/forecast.ipynb index bd80beb..9d9db88 100644 --- a/Notebooks/forecast.ipynb +++ b/Notebooks/forecast.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 137, "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ "from pybats.loss_functions import MAPE\n", "from pybats.analysis import analysis\n", "from pybats.point_forecast import median\n", - "from pybats.plot import *" + "from pybats.plot import plot_data_forecast" ] }, { @@ -42,31 +42,124 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 172, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "beginning forecasting\n", + "MAPE: 86.55\n" + ] + } + ], "source": [ - "forecast_start = 0\n", - "forecast_end = 500\n", + "forecast_start = 100\n", + "forecast_end = 1000\n", "length=forecast_end-forecast_start\n", - "indexs=np.arange(forecast_start,forecast_end)\n", - "prices=epex.values[forecast_start:forecast_end,0]\n", + "indexs=np.arange(forecast_start,forecast_end+1)\n", + "prices=epex.values[:,0]\n", + "date_indexs = np.arange(np.size(prices))\n", "\n", - "mod, samples = analysis(Y = prices, X=indexs, family='poisson',\n", + "mod, samples = analysis(Y = prices[1:], X=date_indexs[1:], family='poisson',\n", " forecast_start=forecast_start, \n", " forecast_end=forecast_end, \n", - " k=k,\n", + " k=1,\n", " ntrend=1, # Intercept and slope in model\n", " nsamps=5000, # Number of samples taken in the Poisson process\n", " seasPeriods=[48], # Length of the seasonal variations in the data - i.e. every 24hr here\n", " seasHarmComponents=[[1,2]], # To pick out the half dayly and daily harmonics\n", - " prior_length=prior_size, # How many data points to use in defining prior - i.e. 48 = one day\n", + " prior_length=48, # How many data points to use in defining prior - i.e. 48 = one day\n", " deltrend=0.94, # Discount factor on the intercept parameter\n", " delregn=0.90, # Discount factor on the regression parameters\n", " delVar=0.98,\n", " delSeas=0.98,\n", " rho=.6, # Random effect to increase variance\n", - " )" + " )\n", + "\n", + "forecast = median(samples)\n", + "\n", + "# set confidence interval for in-sample forecast\n", + "credible_interval=66\n", + "alpha = (100-credible_interval)/2\n", + "upper=np.percentile(samples, [100-alpha], axis=0).reshape(-1)\n", + "lower=np.percentile(samples, [alpha], axis=0).reshape(-1)\n", + "print(\"MAPE:\", MAPE(prices[-18:], forecast[-18:]).round(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import lines\n", + "\n", + "# Plot the 1-quarter ahead forecast\n", + "h = 1\n", + "start = forecast_start + h\n", + "end = forecast_end + h + 1\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "#line1 = ax.plot(date_indexs[start:end],prices[start:end],label='Data',color='r')\n", + "ax.plot(date_indexs[start:end],prices[start:end],label='Data',color='r',alpha=0.5)\n", + "plot_data_forecast(fig, ax, y = prices[start:end],\n", + " samples = samples[:,:,h-1],\n", + " f = forecast[:,h-1],\n", + " dates = indexs,\n", + " xlabel='Time index', ylabel='EPEX', title='0.5 hour ahead forecast',credible_interval=50)\n", + "##handles, labels = ax.get_legend_handles_labels()\n", + "#handles.append(line1)\n", + "#labels.append(\"Data\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 500.0)" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(indexs,forecast)\n", + "plt.plot()" ] }, { From 4bb1c386169a28286f1ec765b7ed851ec71db3f6 Mon Sep 17 00:00:00 2001 From: "Moloney, Philip" Date: Tue, 15 Mar 2022 20:22:15 +0000 Subject: [PATCH 03/30] Update forecasting --- Notebooks/forecast.ipynb | 259 --------------------------------------- 1 file changed, 259 deletions(-) diff --git a/Notebooks/forecast.ipynb b/Notebooks/forecast.ipynb index 9d9db88..a8e7f23 100644 --- a/Notebooks/forecast.ipynb +++ b/Notebooks/forecast.ipynb @@ -128,265 +128,6 @@ "\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, 500.0)" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(indexs,forecast)\n", - "plt.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [], - "source": [ - "def bayes_forecast(iv,dv,start,prior_size=48):\n", - " '''\n", - " This functions runs the Pybats algorithm by taking two parameters: an independent variable matrix and a dependent variable. \n", - " Both elements must be sequential time series. \n", - " '''\n", - " # first check if the iv = None, indicating this would be a univariate series\n", - " if iv is None:\n", - " x = None\n", - " else:\n", - " x = iv\n", - " y = dv\n", - "\n", - " if prior_size>start:\n", - " raise Exception('Warning: must start longer than the priorsize')\n", - " \n", - " # set the one-step-ahead value; by default we want 1\n", - " k = 1 \n", - " forecast_start = start \n", - " forecast_end = len(y)-1\n", - " mod, samples = analysis(Y=y, X=x, family='poisson',\n", - " forecast_start=forecast_start, \n", - " forecast_end=forecast_end, \n", - " k=k,\n", - " ntrend=1, # Intercept and slope in model\n", - " nsamps=5000, # Number of samples taken in the Poisson process\n", - " seasPeriods=[48], # Length of the seasonal variations in the data - i.e. every 24hr here\n", - " seasHarmComponents=[[1,2]], # To pick out the half dayly and daily harmonics\n", - " prior_length=prior_size, # How many data points to use in defining prior - i.e. 48 = one day\n", - " deltrend=0.94, # Discount factor on the intercept parameter\n", - " delregn=0.90, # Discount factor on the regression parameters\n", - " delVar=0.98,\n", - " delSeas=0.98,\n", - " rho=.6, # Random effect to increase variance\n", - " )\n", - " forecast = median(samples)\n", - " \n", - " # set confidence interval for in-sample forecast\n", - " credible_interval=95\n", - " alpha = (100-credible_interval)/2\n", - " upper=np.percentile(samples, [100-alpha], axis=0).reshape(-1)\n", - " lower=np.percentile(samples, [alpha], axis=0).reshape(-1)\n", - " print(\"MAPE:\", MAPE(y[-18:], forecast[-18:]).round(2))\n", - " \n", - " #Generate the Bayesian Future Forecast\n", - " return mod, forecast, samples, y\n" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [], - "source": [ - "index_start=0\n", - "index_stop=500\n", - "length=index_stop-index_start\n", - "\n", - "indexs=np.arange(np.size(epex.values[index_start:index_stop,0]))\n", - "prices=epex.values[index_start:index_stop,0]\n", - "median_prices = np.zeros_like(prices)\n", - "for i,val in enumerate(median_prices):\n", - " median_prices[i]=get_ep(prices,i,mode='median')" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(indexs[index_start:index_stop],prices[index_start:index_stop],label='prices')\n", - "plt.plot(indexs[index_start:index_stop],median_prices[index_start:index_stop],label='median prices')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "beginning forecasting\n", - "MAPE: 12.89\n" - ] - } - ], - "source": [ - "for_start=50\n", - "mv_mod, mv_for, mv_samp, mv_y = bayes_forecast(indexs,prices,for_start)" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, 95.0)" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(indexs[index_start:index_stop],prices[index_start:index_stop],label='actual median')\n", - "plt.plot(indexs[index_start+for_start:index_stop],mv_for,label='Bayesian forecast')\n", - "upper=2*np.max(median_prices[index_start:index_stop])\n", - "plt.ylim([0,upper])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " ...,\n", - " [ 0.00000000e+00],\n", - " [-2.14748365e+09],\n", - " [ 0.00000000e+00]],\n", - "\n", - " [[ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " ...,\n", - " [ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " [ 0.00000000e+00]],\n", - "\n", - " [[ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " ...,\n", - " [ 0.00000000e+00],\n", - " [-2.14748365e+09],\n", - " [-2.14748365e+09]],\n", - "\n", - " ...,\n", - "\n", - " [[ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " ...,\n", - " [ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " [ 0.00000000e+00]],\n", - "\n", - " [[ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " ...,\n", - " [ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " [ 0.00000000e+00]],\n", - "\n", - " [[ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " [ 0.00000000e+00],\n", - " ...,\n", - " [-2.14748365e+09],\n", - " [ 0.00000000e+00],\n", - " [ 0.00000000e+00]]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mv_samp" - ] } ], "metadata": { From e9df5fc03ba0b7a5c84ed8818ce4452f791aa7c9 Mon Sep 17 00:00:00 2001 From: griffinfarrow Date: Tue, 15 Mar 2022 21:54:52 +0000 Subject: [PATCH 04/30] adding a tuning directory --- Tuning/hyperparam_tuning.ipynb | 128 +++++++++++++++++++++++++++++++++ 1 file changed, 128 insertions(+) create mode 100644 Tuning/hyperparam_tuning.ipynb diff --git a/Tuning/hyperparam_tuning.ipynb b/Tuning/hyperparam_tuning.ipynb new file mode 100644 index 0000000..379a5d3 --- /dev/null +++ b/Tuning/hyperparam_tuning.ipynb @@ -0,0 +1,128 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook for hyperparameter tuning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a very simple notebook to test out some algorithms for hyperparameter tuning, to find one that works well and apply it to our model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Necessary imports\n", + "\n", + "import sys\n", + "sys.path.append(\"../\")\n", + "\n", + "from Hack import load \n", + "from Hack import rl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import optuna\n", + "from stable_baselines3 import PPO\n", + "from stable_baselines3.ppo.policies import MlpPolicy\n", + "from stable_baselines3.common.vec_env import DummyVecEnv\n", + "from stable_baselines3.common.env_checker import check_env\n", + "\n", + "%matplotlib qt5" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# load epex data\n", + "epex = load.epex().load()\n", + "price_array = epex['apx_da_hourly'].values\n", + "\n", + "start_of_2020 = None\n", + "start_of_2021 = None\n", + "\n", + "for idx, (i, row) in enumerate(epex.iterrows()):\n", + " if i.year > 2019 and start_of_2020 is None:\n", + " start_of_2020 = idx\n", + " if i.year > 2020 and start_of_2021 is None:\n", + " start_of_2021 = idx\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (1556052740.py, line 13)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m Input \u001b[1;32mIn [10]\u001b[1;36m\u001b[0m\n\u001b[1;33m model =\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "def objective():\n", + " \"\"\"\n", + " Function to take in hyperparameters, train a reinforcement model, and output the \"profit\" of the model as a metric\n", + " \"\"\"\n", + "\n", + " start_idx = 0\n", + " end_idx = start_of_2020 #4 * 2*24*7 #start_of_2020 # 2019->2020 # 2*24*7\n", + " obs_price_array = price_array[start_idx:end_idx]\n", + "\n", + " power = 0.5\n", + " env = rl.energy_price_env(obs_price_array, window_size=24*2, power=power)\n", + " model = PPO(MlpPolicy, env, verbose=0) \n", + " model.learn(total_timesteps = 50000)\n", + "\n", + " test_start_idx = start_of_2020\n", + " test_end_idx = start_of_2021\n", + "\n", + " test_price_array = price_array[test_start_idx:test_end_idx]\n", + "\n", + " new_env = DummyVecEnv([lambda: rl.energy_price_env(test_price_array, power=power)])\n", + " mean_reward_after_train = rl.evaluate(model, new_env=new_env, num_episodes=100, index=epex.index[test_start_idx:test_end_idx])\n", + "\n", + " return mean_reward_after_train" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "040d274fdfca6ecc88f65f18dafc70b49547e52dd567f9545727ec9f8e0b0ee0" + }, + "kernelspec": { + "display_name": "Python 3.9.10 ('ml')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From a71669078a4e519711a8dc2ded0a934941e3add2 Mon Sep 17 00:00:00 2001 From: Ronan Laker Date: Wed, 16 Mar 2022 08:49:00 +0000 Subject: [PATCH 05/30] =?UTF-8?q?=F0=9F=8E=A8=20got=20prices=20from=20df?= =?UTF-8?q?=20the=20"proper"=20way?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Notebooks/forecast.ipynb | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/Notebooks/forecast.ipynb b/Notebooks/forecast.ipynb index a8e7f23..34f240f 100644 --- a/Notebooks/forecast.ipynb +++ b/Notebooks/forecast.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -50,7 +50,7 @@ "output_type": "stream", "text": [ "beginning forecasting\n", - "MAPE: 86.55\n" + "MAPE: 83.06\n" ] } ], @@ -59,7 +59,7 @@ "forecast_end = 1000\n", "length=forecast_end-forecast_start\n", "indexs=np.arange(forecast_start,forecast_end+1)\n", - "prices=epex.values[:,0]\n", + "prices=epex['apx_da_hourly'].values\n", "date_indexs = np.arange(np.size(prices))\n", "\n", "mod, samples = analysis(Y = prices[1:], X=date_indexs[1:], family='poisson',\n", @@ -90,12 +90,12 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -107,6 +107,7 @@ } ], "source": [ + "%matplotlib inline\n", "from matplotlib import lines\n", "\n", "# Plot the 1-quarter ahead forecast\n", From f207da4fcfd713684396f0f29eaf063785d87248 Mon Sep 17 00:00:00 2001 From: Ronan Laker Date: Thu, 17 Mar 2022 19:45:48 +0000 Subject: [PATCH 06/30] =?UTF-8?q?=E2=9C=85=20added=20tests=20for=20rl.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Hack/rl.py | 82 +++++++++++++++++--- Hack/tests/__init__.py | 0 Hack/tests/test_rl.py | 118 +++++++++++++++++++++++++++++ Notebooks/BatteryCharging/RL.ipynb | 80 +++++++++++++++---- setup.cfg | 6 +- tests/test_installation.py | 14 ---- 6 files changed, 261 insertions(+), 39 deletions(-) create mode 100644 Hack/tests/__init__.py create mode 100644 Hack/tests/test_rl.py delete mode 100644 tests/test_installation.py diff --git a/Hack/rl.py b/Hack/rl.py index 3652eb1..d80c795 100644 --- a/Hack/rl.py +++ b/Hack/rl.py @@ -17,15 +17,37 @@ def get_mode(arr, bin_number=10): return np.nan -def get_expected_price(price_array, idx, window_size=2 * 24, mode="mode"): +def get_expected_price(price_array, idx, window_size=2 * 24, mode="median"): + """Gets the expected price using the history of prices. + + Currently this is a rolling window, with some kind of averaging. + + In the future we want to implement a forecasting model instead. + + Parameters + ---------- + price_array : array + All the prices in the environment + idx : int + Current idx of the environment (time) + window_size : int, optional + size of the rolling window, by default 2*24 + mode : str, optional + type of averaging to use, by default "median" + + Returns + ------- + float + Expected price at this time index + """ idx = int(idx) if idx == 0: arr = price_array[idx] elif idx < window_size: - arr = price_array[:idx] + arr = price_array[: idx + 1] else: - arr = price_array[idx - window_size : idx] + arr = price_array[idx - window_size : idx + 1] if mode == "mean": return np.mean(arr) @@ -60,6 +82,7 @@ def __init__(self, obs_price_array, start_energy=1, window_size=1000, power=1): self.get_price(self.time), self.get_expected_price(self.time), start_energy, + self.time, ] ) @@ -74,7 +97,7 @@ def get_expected_price(self, idx, window_size=2 * 24, mode="median"): self.price_array, idx, window_size=window_size, mode=mode ) - def apply_action(self, mapped_action, current_energy): + def apply_action(self, human_action, current_energy): """Applies the mapped action. -1 for sell @@ -83,20 +106,20 @@ def apply_action(self, mapped_action, current_energy): Parameters ---------- - mapped_action : int + human_action : int Action to applly, has to be the mapped action current_energy : float Current energy in the battery """ - if mapped_action == -1: + if human_action == -1: # discharge === selling for 30 mins (0.5 hours) new_energy = current_energy - (self.power * 0.5) - elif mapped_action == 0: + elif human_action == 0: # hold === do nothing new_energy = current_energy - elif mapped_action == 1: + elif human_action == 1: # charge === buy energy for 30 mins (0.5 hours) new_energy = current_energy + (self.power * 0.5 * self.efficiency) @@ -115,8 +138,8 @@ def get_reward(self, delta_energy, current_price, expected_price): def step(self, action): current_price, expected_price, current_energy, current_time = self.state - mapped_action = env2human(action) - new_energy = self.apply_action(mapped_action, current_energy) + human_action = env2human(action) + new_energy = self.apply_action(human_action, current_energy) # want to save this to punish even if battery is empty/full @@ -164,11 +187,48 @@ def reset(self): return self.state -def humans2env(action): +def human2env(action): + """Needs because Gym env would only work with 0,1,2 as states + but this is confusing as a human. + + We have: + -1 == sell == 0 in env + 0 == hold == 1 in env + 1 == buy == 2 in env + + Parameters + ---------- + action : int + Human readable action + + Returns + ------- + int + Action that the environment accepts + """ return int(action + 1) def env2human(action): + """Needs because Gym env would only work with 0,1,2 as states + but this is confusing as a human. + + We have: + -1 == sell == 0 in env + 0 == hold == 1 in env + 1 == buy == 2 in env + + Parameters + ---------- + int + Action that the environment accepts + + Returns + ------- + action : int + Human readable action + + """ return int(action - 1) diff --git a/Hack/tests/__init__.py b/Hack/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Hack/tests/test_rl.py b/Hack/tests/test_rl.py new file mode 100644 index 0000000..92cef86 --- /dev/null +++ b/Hack/tests/test_rl.py @@ -0,0 +1,118 @@ +import numpy as np +import pytest +from stable_baselines3.common.env_checker import check_env + +from Hack import rl + + +@pytest.fixture +def price_array(): + return np.array([10, 20, 30, 20, 10]) + + +@pytest.fixture +def env(price_array): + return rl.energy_price_env(price_array, window_size=3, power=1) + + +@pytest.mark.parametrize("human_action, mapped_action", [(-1, 0), (0, 1), (1, 2)]) +def test_mapped_actions(human_action, mapped_action): + assert mapped_action == rl.human2env(human_action) + assert human_action == rl.env2human(mapped_action) + + +def test_get_mode(): + + # just give it lots of ones and see if it gives you one + data = np.ones(100) + + mode = rl.get_mode(data, 10) + + assert mode == pytest.approx(1, 0.2) + mode = rl.get_mode(np.array([np.nan]), 10) + assert np.isnan(mode) + + +@pytest.mark.parametrize( + "idx, expected_price", [(0, 10), (1, 15), (2, 20), (3, 20), (4, 20)] +) +def test_expected_price_median(price_array, idx, expected_price): + assert expected_price == rl.get_expected_price(price_array, idx, 3, "median") + + +def test_env(env): + # this should output a None if it passes + assert check_env(env, warn=True) is None + + +def test_sell(env): + expected_states = [ + np.array([20, 15, 0.5, 1]), + np.array([30, 20, 0, 2]), + np.array([20, 20, 0, 3]), + np.array([10, 20, 0, 4]), + ] + for i, expected_state in enumerate(expected_states): + state, reward, done, _ = env.step(rl.human2env(-1)) + assert np.allclose(state, expected_state) + if i == len(expected_states) - 1: + assert done + + +def test_buy(price_array): + env = rl.energy_price_env(price_array, start_energy=0) + expected_states = [ + np.array([20, 15, 0.425, 1]), + np.array([30, 20, 0.85, 2]), + np.array([20, 20, 1, 3]), + np.array([10, 20, 1, 4]), + ] + + for i, expected_state in enumerate(expected_states): + state, reward, done, _ = env.step(rl.human2env(1)) + assert np.allclose(state, expected_state) + if i == len(expected_states) - 1: + assert done + + +def test_hold(env): + expected_states = [ + np.array([20, 15, 1, 1]), + np.array([30, 20, 1, 2]), + np.array([20, 20, 1, 3]), + np.array([10, 20, 1, 4]), + ] + + for i, expected_state in enumerate(expected_states): + state, reward, done, _ = env.step(rl.human2env(0)) + assert np.allclose(state, expected_state) + if i == len(expected_states) - 1: + assert done + + +@pytest.mark.parametrize( + "human_action, current_energy, new_energy", + [ + (-1, 1, 0.5), + (0, 1, 1), + (1, 0, 0.425), + ], +) +def test_apply_action(env, human_action, current_energy, new_energy): + assert env.apply_action(human_action, current_energy) == new_energy + + +@pytest.mark.parametrize( + "delta_energy, current_price, expected_price, expected_reward", + [ + (1, 10, 10, 0), + (1, 20, 10, -10), + (-1, 20, 10, 10), + (1, 10, 20, 10), + (-1, 10, 20, -10), + ], +) +def test_reward(env, delta_energy, current_price, expected_price, expected_reward): + assert ( + env.get_reward(delta_energy, current_price, expected_price) == expected_reward + ) diff --git a/Notebooks/BatteryCharging/RL.ipynb b/Notebooks/BatteryCharging/RL.ipynb index 80d30ba..ea8c165 100644 --- a/Notebooks/BatteryCharging/RL.ipynb +++ b/Notebooks/BatteryCharging/RL.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -87,18 +87,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Ronan\\Anaconda3\\envs\\ml\\lib\\site-packages\\gym\\logger.py:34: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n", - " warnings.warn(colorize(\"%s: %s\" % (\"WARN\", msg % args), \"yellow\"))\n" - ] - } - ], + "outputs": [], "source": [ "start_idx = 0\n", "end_idx = 4*7*24*2 #start_of_2020 #4 * 2*24*7 #start_of_2020 # 2019->2020 # 2*24*7\n", @@ -110,6 +101,69 @@ "check_env(env, warn=True)" ] }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[20. 15. 0.425 1. ]\n", + "0.0\n" + ] + } + ], + "source": [ + "test_prices = np.array([10,20,30,20,10])\n", + "power = 1 # MW\n", + "env = rl.energy_price_env(test_prices, window_size=3, power=power,start_energy=0)\n", + "state, reward, _, _ = env.step(rl.human2env(1))\n", + "print(state)\n", + "print(reward)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10. 20. 1. 4.]\n", + "0.0\n" + ] + } + ], + "source": [ + "state, reward, _, _ = env.step(rl.human2env(1))\n", + "print(state)\n", + "print(reward)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env.get_reward(1,10,20)" + ] + }, { "cell_type": "code", "execution_count": 5, diff --git a/setup.cfg b/setup.cfg index 693d2e7..99cbae4 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,4 +1,8 @@ [options.extras_require] test = pytest - sklearn + stable-baselines3 + +[tool:pytest] +filterwarnings = + ignore::UserWarning diff --git a/tests/test_installation.py b/tests/test_installation.py deleted file mode 100644 index 8a57d98..0000000 --- a/tests/test_installation.py +++ /dev/null @@ -1,14 +0,0 @@ -# Run the Hello World! version of the ML packages -import numpy as np - - -def test_sklearn(): - # first example from sklearn docs - from sklearn.ensemble import RandomForestClassifier - - clf = RandomForestClassifier(random_state=0) - X = [[1, 2, 3], [11, 12, 13]] # 2 samples, 3 features - y = [0, 1] # classes of each sample - clf.fit(X, y) - prediction = clf.predict([[4, 5, 6], [14, 15, 16]]) - assert np.allclose(prediction, np.array([0, 1])) From 7de5eae8da3fc18a7fd15c88104ce24101152f70 Mon Sep 17 00:00:00 2001 From: Ronan Laker Date: Fri, 18 Mar 2022 13:08:32 +0000 Subject: [PATCH 07/30] removed test_installation --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f664de0..68c8f1c 100644 --- a/README.md +++ b/README.md @@ -40,7 +40,7 @@ If you need to install a new package, try using conda first. Then use pip, but r python -m pip install ``` -Test your install by running the ```test_installation.py``` script (just checks sklearn as an example module). I use pytest (installed in the environment) to run the tests. Either run `pytest` in the command line, or use the "Testing" extension of VSCode. +I use pytest (installed in the environment) to run the tests. Either run `pytest` in the command line, or use the "Testing" extension of VSCode. ## Development From ed21f2ca79c619c2fb344ed340fc1fb1b7ecb1f0 Mon Sep 17 00:00:00 2001 From: "Moloney, Philip" Date: Sat, 19 Mar 2022 17:44:56 +0000 Subject: [PATCH 08/30] =?UTF-8?q?=F0=9F=9A=A7=20Updating=20the=20forecasti?= =?UTF-8?q?ng=20with=20modular?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Forecasting/Bayesian_Forecast.py | 139 +++++++++++++++++++++++++++++++ Notebooks/forecast.ipynb | 131 +++++++++++------------------ 2 files changed, 188 insertions(+), 82 deletions(-) create mode 100644 Forecasting/Bayesian_Forecast.py diff --git a/Forecasting/Bayesian_Forecast.py b/Forecasting/Bayesian_Forecast.py new file mode 100644 index 0000000..8923460 --- /dev/null +++ b/Forecasting/Bayesian_Forecast.py @@ -0,0 +1,139 @@ +from datetime import date, datetime + +import matplotlib.pyplot as plt +import numpy as np +from pybats.analysis import analysis +from pybats.plot import * +from pybats.point_forecast import median + + +#################################### +# Plotting Functions +#################################### +def plot_forecast( + fig, + ax, + y, + f, + samples, + dates, + linewidth=1, + linecolor="b", + credible_interval=95, + **kwargs, +): + """ + Plot observations along with sequential forecasts and credible intervals. + """ + + ax.scatter(dates, y, color="k") + ax.plot(dates, f, color=linecolor, linewidth=linewidth) + alpha = (100 - credible_interval) / 2 + upper = np.percentile(samples, [100 - alpha], axis=0).reshape(-1) + lower = np.percentile(samples, [alpha], axis=0).reshape(-1) + ax.fill_between(dates, upper, lower, alpha=0.3, color=linecolor) + + if kwargs.get("xlim") is None: + kwargs.update({"xlim": [dates[0], dates[-1]]}) + + if kwargs.get("legend") is None: + legend = ["Observations", "Forecast", "Credible Interval"] + + ax = ax_style(ax, legend=legend, **kwargs) + + # If dates are actually dates, then format the dates on the x-axis + if isinstance(dates[0], (datetime, date)): + fig.autofmt_xdate() + + return ax + + +def forecast_ax_style( + ax, + ylim=None, + xlim=None, + xlabel=None, + ylabel=None, + title=None, + legend=None, + legend_inside_plot=True, + topborder=False, + rightborder=False, + **kwargs, +): + """ + A helper function to define many elements of axis style at once. + """ + + if legend is not None: + if legend_inside_plot: + ax.legend(legend) + else: + ax.legend( + legend, + bbox_to_anchor=(1.05, 1), + loc=2, + borderaxespad=0.5, + frameon=False, + ) + # Make room for the legend + plt.subplots_adjust(right=0.85) + + if ylim is not None: + ax.set_ylim(ylim) + if xlim is not None: + ax.set_xlim(xlim) + if xlabel is not None: + ax.set_xlabel(xlabel) + if ylabel is not None: + ax.set_ylabel(ylabel) + if title is not None: + ax.set_title(title) + + # remove the top and right borders + ax.spines["top"].set_visible(topborder) + ax.spines["right"].set_visible(rightborder) + + plt.tight_layout() + + return ax + + +#################################### +# Forecasting Functions +#################################### + + +def evaluate(epex_data, horizon=6, forecast_start_index=0, forecast_end_index=-1): + prices = epex_data.values[:, 0] + datetimes = epex_data.index + horizon + + forecast_start_date = datetimes[forecast_start_index] + forecast_end_date = datetimes[forecast_end_index] + + mod, samples = analysis( + prices, + family="poisson", + dates=datetimes, + forecast_start=forecast_start_date, # First time step to forecast on + forecast_end=forecast_end_date, # Final time step to forecast on + ntrend=1, # Intercept and slope in model + nsamps=500, # Number of samples taken in the Poisson process + seasPeriods=[ + 48 + ], # Length of the seasonal variations in the data - i.e. every 24hr here + seasHarmComponents=[ + [1, 2, 3, 4, 6] + ], # Variations to pick out from the seaonal period + k=horizon, # Forecast horizon. If k>1, default is to forecast 1:k steps ahead, marginally + prior_length=48, # How many data point to use in defining prior - 48=1 day + rho=0.3, # Random effect extension, which increases the forecast variance (see Berry and West, 2019) + deltrend=0.98, # Discount factor on the trend component (the intercept) + delregn=0.98, # Discount factor on the regression component + delSeas=0.98, + ) + + forecast = median(samples) + + return datetimes, prices, samples, forecast diff --git a/Notebooks/forecast.ipynb b/Notebooks/forecast.ipynb index a8e7f23..d45d111 100644 --- a/Notebooks/forecast.ipynb +++ b/Notebooks/forecast.ipynb @@ -1,103 +1,74 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Attempt to do some Bayesian forecasting\n", - "\n", - "* Used the work done on:\n", - " * https://towardsdatascience.com/forecasting-with-bayesian-dynamic-generalized-linear-models-in-python-865587fbaf90" - ] - }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "import sys\n", + "import sys # noqa: E402\n", "sys.path.append(\"../\")\n", - "from Hack import load\n", - "from Hack.rl import get_expected_price as get_ep\n", + "import pybats\n", + "import matplotlib.dates\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", - "import pybats\n", + "from Hack import load\n", + "from Hack.rl import get_expected_price as get_ep\n", "from pybats.loss_functions import MAPE\n", "from pybats.analysis import analysis\n", "from pybats.point_forecast import median\n", - "from pybats.plot import plot_data_forecast" + "from pybats.plot import *\n", + "from datetime import datetime,date\n", + "from Forecasting import Bayesian_Forecast\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "#Load the data\n", - "epex = load.epex().load()" + "# Load the data\n", + "epex = load.epex().load()\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "beginning forecasting\n", - "MAPE: 86.55\n" - ] - } - ], + "outputs": [], "source": [ - "forecast_start = 100\n", - "forecast_end = 1000\n", - "length=forecast_end-forecast_start\n", - "indexs=np.arange(forecast_start,forecast_end+1)\n", - "prices=epex.values[:,0]\n", - "date_indexs = np.arange(np.size(prices))\n", - "\n", - "mod, samples = analysis(Y = prices[1:], X=date_indexs[1:], family='poisson',\n", - " forecast_start=forecast_start, \n", - " forecast_end=forecast_end, \n", - " k=1,\n", - " ntrend=1, # Intercept and slope in model\n", - " nsamps=5000, # Number of samples taken in the Poisson process\n", - " seasPeriods=[48], # Length of the seasonal variations in the data - i.e. every 24hr here\n", - " seasHarmComponents=[[1,2]], # To pick out the half dayly and daily harmonics\n", - " prior_length=48, # How many data points to use in defining prior - i.e. 48 = one day\n", - " deltrend=0.94, # Discount factor on the intercept parameter\n", - " delregn=0.90, # Discount factor on the regression parameters\n", - " delVar=0.98,\n", - " delSeas=0.98,\n", - " rho=.6, # Random effect to increase variance\n", - " )\n", - "\n", - "forecast = median(samples)\n", + "#Obtain the foreacsted samples and data \n", + "datetimes,prices,samples,forecast = Bayesian_Forecast.evaluate(epex)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "plot_start = 45500 #Offset from the start date at which to begin the plot\n", + "plot_length = 500\n", + "horizon = 6\n", "\n", - "# set confidence interval for in-sample forecast\n", - "credible_interval=66\n", - "alpha = (100-credible_interval)/2\n", - "upper=np.percentile(samples, [100-alpha], axis=0).reshape(-1)\n", - "lower=np.percentile(samples, [alpha], axis=0).reshape(-1)\n", - "print(\"MAPE:\", MAPE(prices[-18:], forecast[-18:]).round(2))" + "plot_start_date = datetimes[0] + pd.DateOffset(hours=(horizon + plot_start)/2.)\n", + "plot_end_date = plot_start_date + pd.DateOffset(hours=(plot_length - 1)/2.)\n" ] }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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47rrrbD+/YMECnH/++X06XydjkyTIQohaIURl7HExgIMAfCOEGKEcdjyAL2OP5wA4SQhRKIQYD2BrAPMH8JQHDFICjY1ARIcfamhoaGho9AnFxcVYtGhR723cuHFpfS4cDmf1vPpCkJNh3333xaeffooFCxbg0UcfxcKFC03vh8NhTJkyBbfddlu//aZTsEkSZAAjALwthPgcwMcgH+SXAPxDCPFF7PUDAFwEAFLKrwA8BeBrAK8CONepGSw6OoClS4H29sE+Ew0NDQ0NDedg0aJF2HPPPTFx4kQcf/zx2LBhAwBg6tSpuOqqq7D//vvj1ltvxcKFC7H//vtj1113xaGHHoo1a9YAAJYsWYKDDjoIkyZNwi677IKlS5eiq6sLBx54IHbZZRdMmDABL7zwAgCgu7sbRx55JCZNmoSddtoJTz75JG677TasXr0aBxxwAA444AAAwGuvvYa99toLu+yyC0444YTeEtOvvvoqtttuO+yzzz547rnnUrattLQUu+66K5YuXYprr70W06dPxyGHHILTTjvNpEB3dXVh2rRpmDBhAiZOnIhnn3026Xk4GpyPMR9vu+66q8w1/PijlPX1Ui5dOthnoqGhoaGhkTm+/vrr3sdkF+3/Wyq4XC45adIkOWnSJHncccdJKaWcMGGCbGxslFJKec0118gLLrhASinl/vvvL2fMmCGllDIYDMq99tpLrl+/Xkop5RNPPCGnTZsmpZRy9913l88995yUUsqenh7Z3d0tQ6GQbG9vl1JK2dTUJLfccksZjUblM888I3/3u9/1nk9bW5uUUsqxY8fKpqam3uP33Xdf2dXVJaWU8oYbbpDXXXed7OnpkaNHj5bfffedjEaj8oQTTpBHHnlkXBvffvvt3tebm5vl2LFj5Zdffin/9Kc/yV122UX6fL644y677LLedkspZWtra8LzyEWofU+BLUfcVPMgayTAmjWA2033W2wx2GejoaGhoaGRe2AXC0Z7ezva2tqw//77AwBOP/10nHDCCb3vn3jiiQCAb7/9Fl9++SUOPvhgAEAkEsGIESPQ2dmJVatW4fjjjwcAFBUVAQBCoRCuuuoqzJ07Fy6XC6tWrcK6deswYcIEXHrppbj88stx1FFHYd999407x48++ghff/01fvaznwEAgsEg9tprL3zzzTcYP348tt56awDAKaecgnvvvde2ne+++y523nlnuFwuXHHFFdhxxx3x9NNP45hjjkFxcXHc8W+88QaeeOKJ3udDhw7FSy+9ZHseTocmyDmGn34Chg8HVq0a7DPR0NDQ0NDYOMhkFQ42IZSWlgIgq/uOO+6IDz/80PR+R0eH7ecaGhrQ1NSEhQsXwuv1Yty4cfD7/dhmm22wcOFCvPzyy7jyyitxyCGH4I9//KPps1JKHHzwwXj88cdNry9atAhC2CXvise+++6Ll156KWF7rJBSxn13ovNwOjZVH2QNGwSDQGsrMHQosGEDEAoN9hlpaGhoaGjkPoYMGYKhQ4fi3XffBQA88sgjvWqyim233RZNTU29BDkUCuGrr75CRUUFRo8ejdmzZwMAAoEAfD4f2tvbMWzYMHi9Xrz99ttYvnw5AGD16tUoKSnBKaecgksvvRSffPIJAKC8vBydnZ0AgD333BPvv/8+lixZAgDw+Xz47rvvsN122+HHH3/E0qVLAaBfieshhxyCO+64o/f5hg0bEp6H06EJcg4hNmbgctGuWwfqaWhoaGho9A8eeugh/OEPf8DEiROxaNGiOEUXAAoKCvDMM8/g8ssvx6RJkzB58mR88MEHAIhU33bbbZg4cSL23ntvrF27FnV1dViwYAGmTJmChoYGbLfddgCAL774ArvvvjsmT56M+vp6XH311QCA6dOn4/DDD8cBBxyA2tpaPPjgg/jNb36DiRMnYs8998Q333yDoqIi3HvvvTjyyCOxzz77YOzYsf32H1x99dXYsGEDdtppJ0yaNAlvv/12wvNwOoTMFftGFjBlyhS5YMGCwT6NtLFuHTB7NjByJLlYHHUUMHr0YJ+VhoaGhoZG+li8eDG23377wT4NjTxEgr5n66+iFeQcgjX3cb65WGQ5/aSGhoaGhoaGBgBNkHMKkYg5oCGfCGNPD/Dmm4N9FhoaGhoaGhr5AE2QcwiqgiwE4PcP3rkMNHp6gOZmIBod7DPR0NDQ0NDQcDo0Qc4hqATZ48kvghwIAF1dRJQ1NDQ0chkNDQ2oqamBEAJCCNTU1KChoWGwT0tDQ0OBzoOcQ1BdLNzu/CPI3d2AzwckSN+ooaGhscmjoaEB06ZNQ0gJImlpacEZZ5wBAKirqxusU9PQ0FCgFeQcQihEKd6A/CPIfj8RZK0ga2ho5DJmzpxpIseMYDCImTNnDsIZaWho2EEryDmEYDB/CXJXF6nnXV2DfSYaGhoafceKFSv69J6T8dxzlMa0vzB8OPCLXyQ/5tZbb8V9990HKSXOOussXHjhhQCAa6+9Fvfddx9qa2sBAH/9619xxBFH4P3338eMGTNQWFiIxx9/HFtttRXa2tpw4okn4tVXX7WtbBcKhXDNNdfg2WefRWFhIUpKSnDdddfh8MMPz7hNTU1NOOqooxAMBnHbbbfhb3/7Gx577DFUVlaajrv22mtRVlaGSy+9NOPf2Bg8+OCDWLBgganIiBXLli3DBx98gJNPPjnr51NWVoaujSQMmiDnEEIhIsZA/hHkzk5yrWhrG+wz0dDQ0Og7xowZ01tNze69fMS6df2b03/lyuTvf/nll7jvvvswf/58FBQU4LDDDsORRx6JrbfeGgBw0UUXxRHMm266Cc8++yyWLVuGu+66CzfddBP+8pe/4KqrrkpY9vmaa67BmjVr8OWXX6KwsBDr1q3DO++806c2vfnmm9huu+3w0EMPAaAS0rmGZcuW4bHHHsuIIEciEbiZ+AwwtItFDsHqYhEMDu75DCS6uoCyMiqxraGhoZGrqK+vh9frjXu9oKAA9fX1g3BG+YfFixdjzz33RElJCTweD/bff388//zzST/j9XrR09MDn88Hr9eLpUuXYtWqVbblqAEqx3zffffh9ttvR2FhIQBg+PDh+PWvfw2AykNPmDABO+20Ey6//PLez5WVlWHmzJmYNGkS9txzT6xbtw6LFi3CZZddhpdffhmTJ09GT08Pxo0bh+bmZgDUp7bddlscdNBB+Pbbb3u/a+nSpTjssMOw6667Yt999+2tfvfb3/4W559/Pvbee29sscUWeOaZZ3o/849//AMTJkzApEmTcMUVVyT9nkRI9P1XXHEF3n33XUyePBn/+te/EIlE8Ic//AG77bYbJk6ciHvuuQcA0NjYiAMOOAAnn3wyJkyYgMsvvxx33nln7/dfe+21uOmmm9DV1YUDDzwQu+yyCyZMmIAXXngh6XllCk2Qcwiqi0W+ZbHo7iYFWZfX1tDQyGXU1dXhgQceQHV1de9r1dXVmDVrlg7QGyDstNNOmDt3LlpaWuDz+fDyyy/jp59+6n3/jjvuwMSJE3HGGWdgQ0yVufLKKzF9+nTccsstOO+88zBz5kz85S9/SfgbS5YswZgxY1BRURH33urVq3H55ZfjrbfewqJFi/Dxxx9j9uzZAIDu7m7sueee+Oyzz7Dffvvhvvvuw+TJk/HnP/8ZJ554IhYtWoTi4uLe71q4cCGeeOIJfPrpp3juuefw8ccf9743ffp03H777Vi4cCFuvPFG/P73v+99b82aNXjvvffw0ksv9RLhV155BbNnz8a8efPw2Wef4bLLLkv5PYlg9/033HAD9t13XyxatAgXXXQR7r//fgwZMgQff/wxPv74Y9x333348ccfAQDz589HfX09vv76a5x00kl48skne7/7qaeewgknnICioiI8//zz+OSTT/D222/jkksuQX9Wh9YuFjkEVUF2uYgwS0k5kZ2O7m5g6FCgqYlyIbv01k5DQyNHUVdXh7q6OqxZQ/6y+TSfSTn4Ra623357XH755Tj44INRVlaGSZMmweMhOjRjxgxcc801EELgmmuuwSWXXIJZs2Zh8uTJ+OijjwAAc+fOxciRIyGlxIknngiv14ubbroJw4cPT+v3P/74Y0ydOrXXz7murg5z587Fcccdh4KCAhx11FEAgF133RWvv/560u969913cfzxx6OkpAQAcMwxxwAAurq68MEHH+CEE07oPTYQCPQ+Pu644+ByubDDDjtgXcwB/I033sC0adN6v6uqqirl9ySC3fdb8dprr+Hzzz/vVZjb29vx/fffo6CgALvvvjvGjx8PANh5552xfv16rF69Gk1NTRg6dCjGjBmDUCiEq666CnPnzoXL5cKqVauwbt06bLbZZinPLx1ogpxDCIUMMiwE3cJhwMZa5yiEw7QZ8HppcvX7gdj41dDQ0MhJSAk0NgIHHAD003qeE+jpobSdgy10nHnmmTjzzDMBAFdddRVGx5ygVZJ71lln9ZJVhpQS119/PZ588kmcd955uO6667Bs2TLcdtttJheZrbbaCitWrEBnZyfKy8vjviMRvF5vr0+z2+1GOI3dhJ0PdDQaRWVlJRYtWmT7GXb7UM9HShn3Xam+JxHsvt8KKSVuv/12HHrooabXGxsbUWrJ5/qrX/0KzzzzDNauXYuTTjoJAKVMbGpqwsKFC+H1ejFu3Dj4+9G0nkf71tyHqiCrrzkdgYB5Y5DG5lVDQ0Njk4bPR8FpS5cO9pkMLDo7Kaf/YKvI69evB0CZQ5577jn85je/AUCuAYznn38eO+20k+lzDz30EI488kgMHToUPp8PLpcLLpcLPp/PdFxJSQnOPPNMnH/++QjGAobWrFmDRx99FHvssQfeeecdNDc3IxKJ4PHHH0/oy5wK++23H55//nn09PSgs7MTL774IgCgoqIC48ePx9NPPw2AyOhnn32W9LsOOeQQzJo1q7ctra2tffqeRCgvL0dnZ2fv80MPPRR33XVXb9rD7777Dt3d3bafPemkk/DEE0/gmWeewa9+9SsApDgPGzYMXq8Xb7/9dsLg175CK8g5BCtBZgXZ6VAJspT5sSnQ0NBwNrq6KNj622+BPfc0MhQ5HV1dpB4Hg0BBAb02fHjqzBOZIB1Ph1/+8pdoaWmB1+vFv//9bwwdOhQAcNlll2HRokUQQmDcuHG9gWMABd499NBDeO211wAAF198MX75y1+ioKAAjz/+eNxvXH/99bj66quxww47oKioCKWlpfjzn/+MESNG4G9/+xsOOOAASClxxBFH4Nhjj+1TW3fZZReceOKJmDx5MsaOHWvKbtHQ0IAZM2bg+uuvRygUwkknnYRJkyYl/K7DDjsMixYtwpQpU1BQUIAjjjgCf/3rXzP+nkSYOHEiPB4PJk2ahN/+9re44IILsGzZMuyyyy6QUqK2trbXF9uKHXfcEZ2dnRg1ahRGjBgBgFxTjj76aEyZMgWTJ0/Gdtttl/E5JYPoT4fmXMOUKVPkggULBvs00sZjj9GEwpaLVauAE04AlFgPR2LtWuCFF4CRI6nNRx3VvymBNDQ0NAYaS5YA7F563HFAbM13PObNA4RYjK222h5VVYN9Nhr5hsWLF2P77be3vmwbyaVdLHIIdgpyPqipgYBRYhvIr/R2GhoazkRzM4kdQlAQcr5g/Xpax8JhcrXQ0NhUoQlyDiEcNhPkTSEaeCCgbgKkzC+C7Pf3r+lRQ0Nj00BTE1BURAS5p2ewz2bg0NxsBJlHo4N9NhoaiaEJcg4hX4P0gkHDB9njoeCWfEFzM/Dpp4N9FhoaGv2NpiaguJiy8+SLguz3G/n7o1GJPPbw1BgEZOpSrAlyjoB32moGlnxRkAMBY2Pg9eYXQe7uBlav1qZIDQ0nwe+nec3jya9Nf1cXzeWhUBG6uloQjWqGrDEwkFKipaUFRUVFaX9GZ7HIEdgRJJcrP6rp9fQYEd4eT/6oLQCV1u7qotRIlZWDfTYaGhr9Ab/fXBU1X+Y0v5+EnZaW0ejpWYnu7ibH5/HX2HRQVFTUm+86HWiCnCOwI8geT374rgUCBkF2u/OjzYy2NrISdHRogqyh4RSolj+vN3/mNHYJjEa9+P778aiqAuITCmhobBrQLhY5gkQKcr74IDNBzqfFBCCCXFICtLQM9ploaGj0F1SCnE8uFtZ259NcrpF70AQ5RxCJkP/xffcBt95KZiqXKz8yOuSrgiyloRyvXTvYZ6OhodFfiESM1JVuN7ke5ENGB7/fHHCdL3O5Rm5CE+QcQSRCavGLLwJvvgmsWZM/CrLfb/ZBDgTyYzHp6aF2lpZSSVoNDQ1nQFVShcifCqF+P83hAM3p+aKca+QmNEHOEUQipCYyvv02fwiyqiAz8qHdvHh4vbSw6EwWGhrOgDX7kBD5YQ20ih35EGSukbvQBDlHEIkA7e3G83wiyKoPMpA/i4lVXcmHa62hkQ+wG8v5MKdpgqyRS9AEOUcQiVDAFiNfCDJXzrMSZKe3G6D0biryIee1hkY+QM3tzsg3gqxdLDQ2deg0bzkCK0H+8UciTGrhECciURvzYTHx+Qx/PSE0QdbQcAqsbmMsBDgdVoIcidDN6kKnobEpYJNUkIUQRUKI+UKIz4QQXwkhrou9XiWEeF0I8X3sfqjymSuFEEuEEN8KIQ4dvLPPDqwEORolkux0JdWOFObLYhIMmlUmp19rjfzFF1+YYyycDru4inyZ06wbAz2vaWyq2CQJMoAAgJ9LKScBmAzgMCHEngCuAPCmlHJrAG/GnkMIsQOAkwDsCOAwAHcKIRy1J7X6IANAa6vzVcVQKF5BzpdJ1bqIOv1aa+QvvvsOaG4e7LMYOFhdLPKlmp6axQLIH3c5jdzEJkmQJYE9ML2xmwRwLICHYq8/BOC42ONjATwhpQxIKX8EsATA7gN3xtmHqiAPG0b3PT3On1wSkUKntxugNvIiKqUmyBrOBJUezq9c39bNbz4QZLb8qRsDTZA1NmVskgQZAIQQbiHEIgDrAbwupZwHYLiUcg0AxO5jVBGjAPykfHxl7DW7750uhFgghFjQ1NSUtfPvb4RChoI8Zgzdc4CDk3MCh8NGQn2G252f5ki9kGg4EX4/bfbXrBnsMxk4WAlyPhRA4ngSq0UwH+ZyjdzEJkuQpZQRKeVkAKMB7C6E2CnJ4XahatLmNUgp75VSTpFSTqmtre2HMx0YBIMGQR49mu5ZcXByflwmyLfcAjz1FL3mdtMC43SoaotWkDWciu5uoKAAaGpy9lymIhSKJ8hOn9PsNvj54i6nkZvY5LNYSCnbhBCNIN/idUKIEVLKNUKIESB1GSDFeHPlY6MBrB7YM80u2tpIYfB6geHD6TVWkCMRet2JCIeB1auBt96i57vtBpSX54fqEA4bBDkfFlCN/ITPZ6iKnZ1UWt3pCASAsjLjOVcIdTISZSTSBFljU8UmqSALIWqFEJWxx8UADgLwDYA5AE6PHXY6gBdij+cAOEkIUSiEGA9gawDzB/Sksww2P1ZVASUl9FglyE5FKGQO3nnssfx0scgHE6xGfqKryyBO1kBkp8Lqi+tyOX9OC4Xi3eUA528MNHIXmyRBBjACwNtCiM8BfAzyQX4JwA0ADhZCfA/g4NhzSCm/AvAUgK8BvArgXCmlo2gjB7BUVRnKQ3c3LSxOJ8gtLcbzefMoe4fTFxPAvIhqBVnDqWhpAQoLqY+3tg722WQf7C6Vjy4WVgXZ7dbV9DQ2XWySLhZSys8B7GzzeguAAxN8ph5AfZZPbdDAGSzKyw0FOR98kP3++EWzszM/CLLqYuFyOX8B1chPbNhABBlwfiYHwD6WIB8Icj4HXGvkJjZVBVnDAk6iX1ycXy4WgYBZQQbI1cDpk2o0SjcmyB6PVlo0nInWViLI+ZDqDLAnyC6XMeadCjtf43xwLdHIXWiCnCPo7KT7khKgtJQe54uCzASZ8z8HAvmhtqjIB4VJI/8QidAY93rplg8EORIhV4P77wdmzjQTRCdnqrFTkPPJMub3A19+OdhnoZEJNEHOEXTFyqaUlOSXguz3U/onANhiC7r3+Zwf+Wy9ppogazgR6jjONwX5hReoxPb778e/50T4/USIW1rIrQagec3pczmjrQ1YvHiwz0IjE2iCnCPghaO4mG4AuRpEIs4myBs20MRaXGykt/P7nW+Ws6ZE0gRZw4lQ+7nHQ3OaXaYDJyEcNrtLsaoohLMJMl/b888Hpk83KoXmy7zm89HmIF82BE6AJsg5AibIJSVEloqLabIJBJxNkFetovthw8zKuZ25zkmwU5CdvinQAJYsya+KcmrqL/bDdTJJBKh9ajq7+fON8e5k8hQKAevXk7tgIAB89ll+zWvt7bSO54OVxCnQBDkHEI0aOXCZJLIfss/nbIK8OlbuZfhwM0F2utoSiZg3AELoanr5gJ9+An74YbDPYuBgl/rL6YpiOGxkJQKIOH33nfGeUxEIGIIHALz7bn65WLB6rAly7kAT5BwAB7IABklUyaKTCTIXCampya/gxERVp5y8gGoQWVqxYrDPYuBg7c9COF9RtBJkAFi61HjPqQiFDMEDAD76iNrr9OvN2LCBSqprgpw70AQ5BxAOGwoy+x8zQQ4EnD3BqMGJqmoOOHsxsSrIgPNVcw0iyM3N+VM10a66Wr4pyACl8XS6hciqIPf00MYgHxRkKYkgl5fnRzEcp0AT5ByAnYLM1fQCAWdPMKrvtdXFwukKciava+Q+IhEiDR5P/iyi1rlLSmdv+AFqM+e1r6qie07j6eTxHQoBK1fS41Gj6L6z0/n5nwEjVqikJD6vv8amC02QcwBq1LPVxcLJCrKUhlpcXBzvYuHkxcROQQacvRnKd/AY93iAdesG91wGCsGg2ZUoHwiy328E6Y0dS/ddXdR2J4/v9nZK2enxAFttRa9xu508lwO0ZrlcQFFR/mx+Ga2tFJCZi9AEOQeQzAfZ73fupBqJGOZWVUHOF4Js9UHOh4Ukn9HTQ9e8pCR/CDLnxmV4PIZblVMRCBgEedw4uu/ocH61zGXL6H70aGDIEHrc3Z0frmM+H83fXAzH6e1VsXYt8OabRj2DXIImyDkA1QfZmsWip8fZBJkXDFVBzocCKaEQXduZM4G33jJez6eJNd/AeWLzgSQy2KWE4fUa49upCAYNH2RWkDs7nZ/ybO1auh850nARZILs5LkcMAgyix5O3ghZsXo1jfF33x3sM8kcmiDnAFgldrkoChYwK8hOnVStBFlVkJ2eLzUYpKpLX3wBzJlDr2kF2dngTXBBQf4Q5ECAiCEjHzYHfr89QXa5nEucpDT8rMvLDYKcLy4Wfr+xERTC+YGoKtaupU3R+vW5V7tAE+QcAE+mJSU0uEIhs5qaDwpySQmpSwUFRI6DQWdPqoGA4av200+Gy4WT25zvYDO7x0PX3+mBSwBtClSCzCZoJ6O1lebs4mIqgAQQUXS7nUuQOQAVoLWL16+urvyY16z9PF8Ist9PG6PCQnqea1xFE+QcAPurlZTQDuzLL2khAZydxcKqIAP5UyAlFDKinUMh2oXz5kjDmWhrMyxEQH4sooGA2cXCySSRwa4GVVU0nwlhbArywRpYUmJ2seD3nQyVIHMF3HxAR4c5libX1i9NkHMArCAXF9NOu6zMCGzJF4Jsl73Dqe0GaKFU0wEtX07XPF8m1nxEe7uhtAjhfKIIUBtVZc3tdl4fb2hoQE1NDYQQEELgyitvAECuBi6XQRZ7epzXdkY4bPiWl5WZFeR8cLGwbgTzJc+5mu87F4sAaYKcA1AV5HDYmFgBZxNFNb2dVUEOBJy7mAA0kahRvytW5Ie6ls/w+cyLqJP7N8OOIOfaIpoMDQ0NmDZtGlqU3a7fXxC7p9fKy+l1n8+519zqYqH6IPP7ToaqIHu9zvezZzQ3m61iuTa2NUHOAWzYQPclJYbvGi+kTk7zxjmehaD8kYA5ONGpiwlA7eMy24BBkJ3c5nyHShbzwQzLOY9VgiyEsxTFmTNnIhQ3QRMjXrPme3qmEORcIxDpQs3EZEeQnXK9E0FVkPOJIHd3G+3OxRznmiDnAKwKckmJYYp1cqEQrjZVXGz4MeVD9g6AIn7VRWP5ck2QnQzOyqISZKdbC7h/d3UBS5bYv5frWLFiheUVAYDYYSBAygcT5O5u585pqoJcUmIu+uT0jESAefObTwTZ7zfipYDcE/M0Qc4BMEFmH+Rhwwyzhd/v3FKd6saAkS8uFqtX0z2ngVq9Ojd34BrpIRw2B7PkwyLKpOjWW4GLLwY+/pieOymrwZgxYyyvuAGQv1hxMU3aTJC7umged6K7QSRi9kEuLKQ+Hg4bWYmcCrYG5SNBtmbvyLVNvybIOQBWUtnFoqoKqKyk17jDOXFSVRVkBpNlJwe0AMCqVXS/+ea0gLIC4+SFJJ9hVVa8XqP/OxXc5vnz6f6pp+Lfy3XU19fDq0po8ACgSWzXXbcDYBBkzhPslM2BCquLBZAfwYkAtV0tEuLxOD+VIUMtBOTx5F4RIE2QcwA8cTI5LCoi4gQYBNmJk2oyghwMOmcRtQOngho+HKiooMfd3c5eSPIZ+aggh0LmwgFLlxpj2inzWV1dHR544AFUV1fHXvHA4yFGPHHilgDygyCrCrKVIDvZTRCIv55uN73mxOtshepa4vHkXvYOTZBzALxQMlEsLARqaoy0KU714VJdSxj82MnBieGw0fbqamDIEHrc1eXshSSfYSWL+VAwIxQy+jlA/X7RIuOxU1BXV4fm5mZIKdHa2omJE/cCYGz2VYLsJPcSFVzQyuUy4meYKDtdQQ6FzJtfID+q6bFyzhm3tIKskRVwpyospIFVUGAO1AsGnelioVYQZDBBdrLqoKa3Ky01FGRNkJ0L6yKaDwpyJGLO1AIAn31Gi6qTN79WVwOrguzEtvNGiAujAGYXCyfPa4mup9MJsrXduehaoglyDoAnVE51VlhIj5kg+/3OVB1U32uGqiA7dVINhcwR30yQOzqcG5CZ72C1hcFmWCdufBnhsLkYDkBEyqkqKpDcF5fJgxPbzhshbrP6WBNkZ4JTtDI8Hh2kp5EFqAQ5GiUFuaDAnOrNiZOqnQ9yPrhYWAkyu1jw/+HEa53vSNSXnXytVQV53Di6502gU9ttrSgHGAKAz+esHNAqOJc/t1l9nA8EWd38AvmRkcjabu2DrJEV8K6LCXFBAZlgVQXZiUqTGpwYiVDgGhPknh4afE5st+piYVWQ+X0NZyERQXbqJhAwK8hbb033nZ2knufaQpouVAWZibFKkAFnXvPWVrq3S9mZDz7IdnD6PG51G+MqmblkAdUEeROHWlHL6yUV2eUyE2SnKshqcGJPj7F4AsZi4kSCnMjFgv34nHit8x1+vxHMwhDCmWSJEQgYBHmrrei+o4OUJqcSJjsXCyaNTnax4HgS1cWCFWQnVxAE7BVkp49tIHG7c+laa4K8iSMcNjqU221MMB5PfhHkQAAYMcJQVnmRcWK7QyFzkJ7qYuFk/8x8ht9v5AtV4eRrbUeQOztpo+BUgtzVRdfU4zGKPfGc7vPlHoFIF3YEWc1p78Q2M9RcwAy3O/f8cTNFMBhPkIHc2hhogryJIxIxFguPx3AxsLpYOHEhZUWFCfL48Uabe3qcSxZVBbm4WPsg5wPsFGQgtxaTTNHTY/imjhtnuFZEo851sVCD1dj8XFhI154JhRM3Bzx3JSLIkUhumd4zgZoLmMHuBk5GIqtYLrVbE+RNHJGI0aE8HmOCYXcLwKwyOwkqQZaSFOTRo+k1XkCd6GLR3m4EY3q92sUiH2CnIDs1YIvR2kr9vLSU+jmnO3OyTyor5ipRFMJ4Hgw6c3OgEuRoFPj+e3M8iVPFDsAot/zf/wLffEOvuVzOV5DtlHMgtzb9miBv4lDJr8tlkGLVRBcMOnNBUVVUIUhxGDqU/odQiMixEydVVplYYbGmeXNim/MdgUC8ygTk1mKSKaxmd7VipBM3/IChmHObeYPPYz0QcOZczgHXpaVGoJY1nsSp85rfD/z0E3DPPcDNN9Nr+eBi4fPFz2m5tunXBHkTRzhsdrHgiVR1sQgGnbmgqNk7pKTJtbjY2CQ4NXuHNeKb815zdotcmmA00oPfD3zxBXD++cCSJcbrThzXDGshIJUgO5U8WAny0qX0mtMJMlsDS0qoT1dVGf6p7Hvt1HktEKAMTADdr1uXPy4WdlaxXNr0b5IEWQixuRDibSHEYiHEV0KIC2KvXyuEWCWEWBS7HaF85kohxBIhxLdCiEMH7+z7F93d1Kk8HrrnidTtNoiiUxVklSCzeq4S5J4eZ06qdimRmDx0djpzU5DvCASARx8Fli0DLr6YxrrTVSYrQWYXC5/PmfMZYK4oF41SJofOTnOFUCdec2s8yZAhhrro5IBrgNbn9euN5198kR8E2efTLhbZQhjAJVLK7QHsCeBcIcQOsff+JaWcHLu9DACx904CsCOAwwDcKYSwMVjmHtg0VVREu2yv13iP0+Q4seyyGpzodhNBdLlognV6cCKrTHYE2cnm53yG32/OGbpoUW4m1s8E1sAttaS6XYooJ0AlyKEQzeETJhjzuhMrhEppdpcLBimehDcFTk7ZCdA6tm6d8ZwJshM3QirsFORcy3G+SRJkKeUaKeUnscedABYDGJXkI8cCeEJKGZBS/ghgCYDds3+m2YdKkAHD7xgwCLITXSysgYdDh9K9WmI7EHDmpMqLqEqQOZOFk9W1fAX7lfNYB4BXX3X+IqoWAgLMvvZOLQKktjkUItW8psa86XfaNVcLHzEpHjnSXCjEybEVwaCZIH/+ee5lc+gL/H6gqQk46yzgtdfotVzLcb5JEmQVQohxAHYGMC/20nlCiM+FELOEEDHahFEAflI+thIJCLUQYroQYoEQYkFTU1O2TrvfwLmACwvjFWQ2STpRQVZ9r4UwCHJBgdkHOZfMNekiWUqkYNB5C2i+IxSiG1sOAHKzyTW1JVOogVuAMZ/x604c26ovLhNkdh0DnKkgq3M5B1yXlhJJ5vgSp+byZ59b9kH2eCiTSUeH866zFcEgMH8+bQ5eeIFec7lya07bpAmyEKIMwLMALpRSdgC4C8CWACYDWAPgJj7U5uO2Bjop5b1SyilSyim1tbX9f9L9DKuCrBJkVlycGNjB5FcIcxokNb2dE9sNxCtrgLGAOtXfPJ8RDht+54zOztxTWzKBlGayCJgVZKcGbbE7QWEhjeXyctr0qynPwmFn5QRWCXJREV374mKygFrb7TRw5cT2drrOo2KyHWckctJ1VhGJ0HVevpye//QTqckeT24JPJssQRZCeEHkuEFK+RwASCnXSSkjUsoogPtguFGsBLC58vHRAFYP5PlmC0yWeKetuliw4uJEJVVttxCGCVIlyE50LZEyOUF2osKU7wiHjdR+48bRPZdVz6XFJBPYlVy2KshOI0xWX9xwmDYFKkF2YsozOxeL4mK67qo10EltZkQixtgeMcJc9CnXUp5lAm7XsmXGa59+mntWsU2SIAshBID7ASyWUt6svD5COex4AF/GHs8BcJIQolAIMR7A1gDmD9T5ZhNsbrdTkCsr6d6JLhbcbpUY872TCbK6mNgRZKeq5vmMcNgoILHllnTf1UUbQ6de60jEXE4dMCvIgPM2/dGo0Waewzimgsd3d7fz1PNQyGg3V4N1u+m6q/EkTpvLAbqO7Mm52Wbmok9Ou84qwmEa4z8pjq+ffELXP5eu8yZJkAH8DMCpAH5uSen2DyHEF0KIzwEcAOAiAJBSfgXgKQBfA3gVwLlSSkeEeKg+yFLaE2TefTvJXPPMM/8DALS3r8Xvfz8D48aNwrhx4/Dcc0+aXCxyabClA7XMtEqQ+bHf71zSlK9QFeQRI4g4SGlYC5yYzUFVkK1p3jo7namuWV0NhKB7Na7CiTmBu7tpbfJ46J5V1KIi57vLhULG2FYJspPdiABq17p1dM/jm7N35JKCbJOlbvAhpXwP9n7FLyf5TD2A+qyd1CBBdTVgdwMGB65xzfNIJL72eS6ioaEBf//70wAOBdAD8qgJYvny1Tj//BnYYYe9AWzuSIKcSkHOFxcLTmfHfdzJUH2Qhw8notjdTZtjJorqxtgJsHOx4Kw8TnQzAMypK3k8M0FWFeRcK6aQCpyVh1O8bbYZPVfb7dT8z2p2mqFDjevPVhKn9XFGKASsXEmPJ0wAFiwwcvjn0kbIAXTK2WAFWVUZGKwg9/Q4Ky3SzJkzEQyyszWPJppJenrasXjxp/ROHhDkaJTaqC4kTmuzHVavBv73P2dZRRJBVZCHDzeIopMX0UgkXkHmeyeSRMA+WK2oyHA3AOg/EcJZbVcJciRitFVd05wafByJGMGoZWVmFwvAmWMboHatWkWPx483rAZMknOFq2iCvImDB5cdQVbTvAHOGWwrVqwAEJtJwLICrxhR+HzEJpyoplpNz21tFOigulg4afFMhM5O4IcfzEEeTkU4bCyY1dXGuGY/ZCdebzsF2eOhOS4apTnNae2ORIz5qqCArH0cdM2WEiduDrhvFxVRu3izr65pTt34h8OGyFVRYQ7SA5x1nVWocRUjRxr9m6tn5kq7NUHexMEE2es158UFaHLxemlBCYWcQ5DHjBmDxATZUNic6I9rJQ49PUBVleFa4/PR9c6VHXhf0dlJi8mnnw72mWQfoZDhVlBebi4rDjhnXKuw80FWHzvR5K4qyB6PsRECDALB/SBXCEQ64PzexcWGag7E57R3KkG2G9vt7c6y+lqhju/ycsPavWEDbQxzpX9rgryJgwcXqysqPB5zFLBTFtL6+np4PDWxZ2aCXFJSggMPnELvOFBNtSMOm29utJPfc8q1ToSODlpMWHFwMjo66PryeGbixKmgnNbHAXsXC8AQAZxIkFX/S4/HLHhwSn6fj665k8ii6mIBGKq56oPsRLEDoHmaRS6VIDvZfQowbwxKSw2CrBVkjX4FLyJerzGZMJxKkOvq6nDQQcfHngUghAQgMXbsWNx777342c8m0jsONMvZBentuKOxOcoXgtzdTW0OBp2rsjDY/7i0lCwFbCHp7KTnTmx/Tw9dW5fLPK+pBDmXot3Tgaogu91mwaO2lq51T0/uVRtLBdXFAjACTjnlG+BcBTkYNBNkax5kJ7YZMFd8LSkxE+RcylCjCfImDlVBVpUWgCZZJshOS7Q+evT2AIAjjzwUDz10L6SUWLZsGerq6hyd3o6VFCHomhcUmNMDcRCPE0mTis5OWkidnAuYwb56TIz5WqtZLJwGVpK49DBDdZ9yGnnganEuV/zGoKzMmMvVVI9OAKulfK2ZIAthWEt4w+Q0+P3mID01laGT57ZAQCvIGgMA3oXZBek5VUEGkmfvUNPbOY0scrqv4mK6pqwscZvzQUEOheiaut3OXkQY7KPJ5NDpFeUA4Nln3wQAdHevwzHHHI26ujo0Njb2KshONLnz9eRsDipBLikxl5N3knuJSpABczVYVexwIkFubSUBp7iYNgYeD41zDkR1Wh9n+P3GWlVWpgmyRpbAE2VhIQ0uFU5WkHn3qVbOY1jJYq4MtnTAamJJCf0HI2K1I4cMoesdDjsrINMO1kXDqYsIQDm/r7jiBgDAt98uQF1dHX744XMAhhnWSRtAgNp8000Pxp6RvNbZ6cGttz6D5ubVAGhsO4kkAgZBLiqKJ8iFheaMDk5SkNWNgbXYlTqXOzH4mKvoqQGZbCHy+Zw7t3V2GnEVBQWaIGtkAWpQR0EBESQVqoIcDOZOp0sHano7q+91dTXds4LsJLLIamJJCbWrqoqel5ebI76d1GYr+LoCzvbTa2howLRp09DTwzvALnR2dmDOnEfpWReZ4p3W/pkzZyIarYw9i+2EUYRI5BMsXfodACJMTiMPTA44lR3P3YCzi2bwXM7tUwkyz28siDiNIHN8gR1B7upyXh9nqOuYagFta6PnuTKnaYK8CUMlyEVF9goym6ucZpZTFWQrQS4rM1LFRCLO2hjwIsr+5nx9S0rMgXpOJsj5oiDPnDkToVAIQCxyB+RXFImQn01npzMJMuU55xKJMfYEAWAl/H4yoTiRICfyxQXM85xTCTKX11bXMTV7B+CsuRwwLIIqQeZAPScryOwqyC5TqoKcS+WmNUHehBEOmxPLWxVkIQwiFQo5Z7CxfxZg72JRUGAQR6cVFGCCzBkNeDFRFWSn+ZtbEQiQcgxQn2d/dKeBiCJgJcgA2aQ7O6n9ThnXDMpzXhl75gNQCKAdQA9KSsh00NND49pJAbiqgpyMIPv9ziTIhYV0U4MymSA7sYIgYFxzOwW5u9t5Y5uhrmOAIWpx4HGutFsT5I1EJGL4WPU3QiFjouSSpFYwQXZSqU51Y6CmAmJ4vc71veaUSHxdeREtKjLa7HQFubvbWES9XucSZCKKgFEUhxvqAxDt9ct0yrhm1NfXQ4iYbR3dAIoANMPrFdh99wkAjCqCTurn3I+t6c6A+JRnTtkcqKnt7NzlVBcLJ+b8ZlcDlSBzMK4T/ewZvI4xQXa5DBW5q0sryHmD1auBV17JTn1xdXKxc7EAnEmQVTW8sBB45ZUXUFNTAyEEhBDYccdtISWNMKepqbzZshJka1lWp1xrO3CKN4DuWYFyGqggjhcAV4wgBuXxeFFURJOJ02ILAMpzPmXKAbFnPgBFqKgI4a67bsOECVsDMK65k8a2NZuDVUHmDfCmRBY3lqSred3tCHJJCbWdy3BvCm3uTzBRVAkyk0afz3nuUwwrQQYM15KurtzZGNhQLo1M0NEBfP898PnnwDffAMOGAXvtFZ+zuC9QSa/Xa68gcwd0EkFWNwbff/8lbr/9EoTDLb3vb9iwDkATgDGOc7HgRbSkxBzxrUa5O8mdxg6dnYYLjcfjXIJcV1eHSERg2rSKGBHpQnl5Bc466yw89pgXa9c60xcXACorxwIATj/919hjj1/jsMOAUaOA7yhGr9eK4KSxrboaWLM5eL3GmqG2XQ3kG2iEwyT+HHxwvJtbJt+RzF2OK8Ry7mcnXW/AniCzguxkH2QWelSCrObyz5V2awV5I9HcTGait9+mi/7DD8Djj9P9xoLTPLH/sZ2CzIMtGHTOblR1sXj33TcQDvssR4TAJaidtDEADDMsL5Z8zVUF2WlttqKry6wgO9XFAgB+/euTMX78jgCAv/71ajQ0NGDq1KmmfMBOGdeMSMRcRICrB6p+uN3dziuSwv3YLh+wy2WM+U0lYG3dOhJ/OBNDX6Bu5tVNAEMlzU6rjBqNmqvoMXhscx93WuYOa5lphupakivrlybIG4mWFkphssUWlH6Mq57997/Al19u3Hdba9jbKcjc6Zxkdlcn1fb2dQCsq2QIQKD32Fwx16RCNGpMLBzpzgSZA1wA5y0kVqguFh4PTagctOc0qIsJj2UAJoI82ESpv6FWiistpWtbVkb9XY3wd7KCDMQLHmrbgcFv+3ff0TmsWtX371CzK9llJLL6XjtlLgfov7MjyDzOecM02Ne5vxEOG+Nb3RDlonKuCfJGQEpKZ2INoCsqInNhY+PGKckqQZbSniDzwHOS0qQqyJWVJYgnyGGoBDlXBlsqRCLGAlFcTAoTB6up/ntOutZWRKPUPiYP3H6ntjccNpeiZTBBdmIJXpUg89zJKiLnS+3upr7gJAVZJcjq2Gao2Q2AwSVOoRCpx2PGAMuW9f171JR1dgHX7GLBv5krwVvpQCXIdmPbiX72gJkgq+1WCXIwmBuihybIGwH2f7Ujrl4vMHw48MYbBtHNFJxLkBcRl83V4knVSaqiSnqPPvpQeDyWlQQSQhguFk6ZVNWJRVWMAbr+TgzItILbpZIHJ5ebtirIUtLNyYE8VoLM1hKA5jOv16gY6SR1TR3bdj69HOW/KQTpLVhAG/ayMlqHfFYvtzShVgUsLk7sg2w91gmwWkoAw1oCODNTCwA89thTeO+9hQCAe++9ubeMPLc7l1xLNEHeCHAHT4SiIiK1n33Wt+/nXIIrV36Ps8/+LTweD4QQGDduHBoaGgAYZjm/nxQXJ6QG8vlo0nC7gb322h133/1vVHP5PADV1dXYaactADjLxUKN+LZbRHMxyCFTqFX0VDhtEWF0dlLbuCrm6tXA2rVmguwkkgiYiYO1n5eUmH1xc/W6NzQ0YNy4cRBC9M7bTBrsgtWAwXWxCIeBhQuBn34CPvmEHnOZe4DWsL4ofhs20JpUUEB93OpWov4XTit2pa5NJSV0Xb/5Jl5BdtL4bmhowLnnXohIhCNQqTrobbfdhlWrvqVXcsi1RBPkjUA60fU1NcDXX/ctEv/ttz8BAITDnQBCiMS2XMuXL8f06dPR0NBgIsh0bOa/s6mBlXMuU3nKKSehubkZUkpIKdHc3IytthoJwFlqqp2ypoIVJidZC6xIdC1zYTLtC7jSFgerAaSsqAQ5Gs0NtSVdJCPIRUUGQc7VPLENDQ2YPn06li9fDgCxeduFYJAu8I8/fhM3tgFzTmC3u++qbV+wdi3w7rvASy8BH39M5JgtoyNGEGH+4ovMv3f9erq3pq1kqO41weDAtjnbsCrI3d2U5Yrnbi6a4YQ1mzFz5kyEwxIAOx/TBQ2HQ5g//00ABkHOhXZrgrwR4LriycCTzPffZ/79L7/8fuyRHxSYZsDn82HmzJmOJMhMGvItOFH1QVazVjCYIDstmEVFIBBvBXHaIqKCCwmovnqqcscpv5zUfpU4WHPjlpQYz3PVfWrmzJnwxTE9L6hiIPDBB3NtFeSaGrrv7h749Ibff08K9ujRwMiR5gwbbjeR9xjfzwhNTXTPYodVQXa5zAGpTprXurqor7tctBEMBoHx4+m+oIDme6fl8afqoB4APKiNTtzRsRqAYXnPBdFDE+SNQHt7enkqKyrIdJUpjAp9fnBQmooVK1b0BulxqU4nDDZVQQbsCbLqe+2USVX1QS4oiO9bTJA5cMsJ7jRWcD+2Ihcm076A3aiYFEoJTJxoKG1MkpykILPbiMsVH7il+qnmqk+qUUJcRQGYIHd1tSRVkHt66H8ZqPSGoRCwZIkRIGmHoqK+pXvjzyRSkAFjc+g0gsybX9U6tPXW9FwNWHPS3EbVQb0wE+RiALWorKSLn0vKedYJshBimBDieCHEuUKIM4QQuwshHEHM1XRUyVBQYBSAyASlpZvFHgVhVZAB6oxMFJ2kIPPEUlxM/68dYeKNAbsb5EJEbCqoipld1Sm2FjCJdNLEyujqss/37cS2AvGZaoSgjZDqp+iUjS+DVcXSUiL+aj9XXYtydfNrlBBXYZCGiopiW4JcXk7jnmNJBoogr19vxHwkgtfbNwKrkkQp7ce2mokpEHDGXA4Y7iXWYhnl5eaiME4a21QdtBiGi0U3gEq4XGX45S8PBaAVZACAEOIAIcT/APwXwOEARgDYAcDVAL4QQlwnhKjI1u8PBLq70yfIXV2ZK37bb79H7JEfRJINlJSUoL6+vnd3GgrRYuOEwaaSBtXUp2JT2hj01//u89F3eTykriXyQWYSnQsTTKZQq+gxhMhNopQOWEH2+Zpx5plnY8aMOtTWVuDOO/8BAFi9mgaDE8Y1QzW7f/jhPBx11IG9wWzbbDMOX345D0Du+iDX19ejJK6UqkGQDzlkqmlsNzQ0oKamBttuOx7BYBsA4N13Pxow9by11T5DkhUuV+aknft3MgWZCfKmkv+5v8CugiUltPZzMZiyMvMm0CkuggBVB/3jH/8JoAhAFEAPAIFo1If7778dANDeHs6ZFI7ZVHKPAHCWlHI3KeV0KeXVUspLpZTHAJgE4FMAB2fx97MOu8XcDkLQrjjTyb6qipSI4mIPgCDcsS3+2LFjce+996Kurq43+h0YHH8mKWnB609zPxPkoqLE/6/qjwsMXLu7u+Ov4xdfAC+8sPHmYKtribXtqgk2V3bgmaKzk/rUiy8a/YCLhTgRTCB+/PErNDe3A+hCNNoDWliANWs6MH/+PEe5WHA/j0Y78eijDViz5kcAHMwWQiBAsuM33/yYk+Shrq4O9957L8aOpXLaNG8XgFW1vfaa3KukNjQ0YNq0aWhpaYFaIfTBBx/HW299NCDnu3Zt/GbcDtGo6vaX2fElJYkVZKdu/Lmfl5bSmlFVRSS5rMyY453mVgIAe+55BACgsDDSy1mAFgBuAFEEgx589NG8nLjOWSPIUso/SCntnLEgpQxLKWdLKZ/N1u9nG5ynM5lZSoUQmS3ykYhx/C9+cRTef38uwuEwpJRYtmwZ6urqAMBEkP3+gSXIoRDw5ptUWvv996ni0g8/bHxwCU+q7GJhBzU4cSDJ4vz5QEMDFYGZPZv+72+/pfRcr7++cd9t9dezLiZOXUhUdHYCb70F3HcfcOGF9Jrb7SyVRU0BVl9/GwAgGu0GqYwdoEI4NIikLMHs2S84kiC3ta1CKBSAuRBQEBz5vmjRdzm3MWI1+JRTTsHy5ctRXV2Nhx56CN98swKkqtHGn8f2zJkzEeodyGHwxiga9eL55+cMyBhfvz6+BLQdPB7DZSIdqC5jJSU0jpPFkzhNQVYFj54eoLaWnpeXG372uWolSQZudyjU1pt5C1gMsqDQvPbCC6/nRCambLpYHCyEqEzw3unZ+t2BQqYLtpSZEeRw2PgNu4wGDGui9YEkyD/+SORwzBjgq6+AOXOA//0PePLJzJQGK9hfO5mCPFjZO9asIdL+44+0IfjqK5oQxo6l533xNWdY09tZNwdcjjcYpA2UUxYSFd3dVOIWIBPl4sXOUpCtKcAArkHrA6mM3IGYiZSitbU1J8yR6YJV81CIKyipK2UITBJ7emRObYzMajChpaUFZ5xxBh577FUABlFkgmwO6AuBNwdAGTZsaM16+0MhmrPSsYQWFxt+telAzWucjjXQaeXFVfcSv9/IUqJmamG/ayeBhZ5otBMUmLoBQMyvCuSj097ekxPtzqaLxc1SyjZ+IoT4i/LeBVn83QFBooIGyZBJjke1mlxBQWIl1e02K8gDNblEo1RtqbqazmHUKEoRNHo0vfdRH62D0Wh8SVY7DIYPMi8mlZU02Q0dCnz6KfUDvvUlWwlDNckB8ddcTarf05MbPlyZIByON+M+9RT1L6eoLPEpwJgg94AUZG58O4AIgCIMHVrrqGvNxKGwkNUlddIyCHJhYVVO5YA2q8EGgsEg7rzzGQDx1iFzQF8IRlqsMgwdWpV1EtHZacxdqVBcbPiPp4NUVfQYXAPKaQoyiyUcoKi6zuV6ru9k4PHt9QZBbkXrAbQBEGCCXFExLCdEj2wSZLcQQl3ij8zibw04Mo229XiMjpMO1Co81rLD1u9VE60PVKdbu5bao0boMoYNo7yamUymDLWaXFFR4narCvJAlWXlCY8Xk/Jy8pPlcxkyhIrC9BWpAlrUpPp+v/OKhTAJXLXKeG3JEmcR5PgUYJwAmQcuN9QHVhMPPfS4nCGJycDuB/fe+xgAUpDJR9FKkKndm222Vey4gT1PO6SzQbFP70ZobqYgDZ4vmSDX19fD2zvQw+C2u1yVOO64YweEIKeLggISL9K9Hup6lExB5vRyPT2DX2K7P6ESZJfLaH9hoZkgO20e53VszJgauFylANbBCNijDeA++xyc9wrygwC+F0K8KoSYA2C8EGKOEOJFAOOz+LsDgkwX7MLCzAiy6mJRWJjY11nNIxoIDFwlouXLE5NXIeh8163L/Hut5ZYTKedq2WUpB2aS6eiI3xRtsYWR07KsjMxLfXUvYf++RAEtVneaXJhg0kVDQwNGjRqLGTMujI0TYiSdnbS4OKWt8SnAeIfZA0AC8MdIYzdcLhoI2223a86TBsP9oBW8KYhGOxCNAlVVNJiZLJeX06AvLKSo1MFuezBI8QZr19q/zz7lMoliMmQIkX2rglxXV4cHHngA1dXVoM0BKWx77HEgdt99j6z3+9bW9ONoGOlmsrAS5EQKslpBcKDm8mxDTdPH15zXS5Ug+3zO2fwzOLh67NganH766ais5IWsEx4PDebx43fIiXZnM0jvHwCmAPgTgJsBNAO4EcBNscc5jUQFDRKhoMDoOOlAVZDVoA4r3G5z5amB6nQrVpirf1lRXk5+upnCqpwnmlStCvJAEKimpvTS+rGrRKZQI76B+N9SCbJajSzXweSptbUdwPDYqysAdCIadZYZMj4FmEGQvd4C3Hvv7QiHw3j//bex+eYU1eOEzZDhfmAuIiBlFGVlhZBS9gYhX375DHq3e9PwSf3sM7JkLF4c/168T3k8CgoKsN9+xwAwzO3q2K6rq0NzczOi0RB+9aujAADV1aQhZfu6NzUlnmMTId0g7J4eY4564IG7cOSRB8Dj8UAIgXHjxqGhoQGAobCGQvTfDGQFwWzBWmYaMBNkXrO7u3N/bFuhKud77LEHvv32C0gp8b//PYf99tsNAG0McmH9ymrBDills5RynpSyEcDxUsq5scdXZPN3BwLt7emRJUZhob0CmQiqglxUlHiXL4S5VOdAdDq/n9TOZJHPpaWU2SHTxe3xx5/G559TlNbdd/8LP//5z3onUhWsLktpzviRTaxblzrau7AQWLmyb9/PE0s6CvJAboayDTN5YoK8EuSHS/9LJOKMyoHmFGAuMFkcMqQQp5xyCk499UQANH5Uy1AuLCbJYLgfqATZByCKn34y76RZUWSiNJgE+ccfgXnzqALad9/Fjzn7stIGXC4XZs2ahVGjdgKQOEMNQHM5b/w7O+mYjQl2TgfNzZkRZCHSD0RubwdaWkhG7excDyDYm9Vg+fLlmD59OhoaGkxFkcLhgbOCZhMqQeb5nF0s1PayguyU4ihSGso58xJud0VF7mXvyGYWC5O+KKX8Unn8rN0xuYSurswIsttNpCZdNwu1ilQyggwYSu5AVZ5KRyF1uWiwKAHdKdHQ0ID/+7+LEY3yHxtAe3sTzjjjjDiS7PEYi00oNDCTalNT6nyhZWWkrmcKVTnhAinW5P1qSr+BavNAwCBPHlA9IQBYBQrsMCwvg60k9hfq6uqwbNky+P0RjB+/PQDgyisvwm677dG7mKiR7gPpOpUtGK4lKkHuARCIczuxmtwHK0Bx7VrglVeA4cONCnfWsZ3M7xgApJSoq6uLC9hKNJ9z23l9yWY1vUiEyG4iVzk7FBamH1vS0QG0trIcHIThZ0/w+XyYOXOmyU0wGMz9vg6YBQxrXnuXK34T6BQVWd3gcF9XCTL3NdW6sCkjmwryC0KIm4QQ+wkhekO5hBBbCCHOjFXZO8zug0KIzYUQbwshFgshvhJCXBB7vUoI8boQ4vvY/VDlM1cKIZYIIb4VQhyaxXYBoIkrndQ4KoQgVTUdqKYXdrHgIBchBIQQqKmpQUNDgymjw0AQ5HXr0q+8lMhvzw4zZ85EOCxhVpjCCAaDmDlzpulY1bUkEMi+WS4UokkvkasLo7iYNkGZDv5w2PhMItcSa5udMqkaBMkDQ0FeBVaQnUaQGVY3KnVT5PWaU0HlutnZCEYrgDG+/XC7Q6ivrzcdO2SIISiEQgN73dUc1SNHnoBzz63DueeeicbGRpSXU/CxeqxI4Wc3ZswYkz9qcbFRKdMOTJw6O7NPkDmtWiaugsXFRhqvVOjoAKJRZt9BGAGoBlasWDFocTTZhKogs0uF+j9zyreuLmfFWKjrmFVBLikxl9gOhTZ9q2A2fZAPBPAmgLMBfCWEaBdCtAB4FMBmAE6XUj6T4ONhAJdIKbcHsCeAc4UQO4BcM96UUm4d++4rACD23kkAdgSR7juFEBmGHmSG7u7UZMmKIUMob3A6aG2lzuP10mLx3HNPJ8yxuWbNNwAGble2cmVy/2NGaWlmac9IjfGCE+rThBpW3jNg9b3O9qSa6cYjUz9kdUJNFNCiKshOcrEwyJMHQMzGjCYIQfbltrZNwxe1v6EGpKqEGDCngnKCiwUHo1VWDgMT5OJiD84/f3pv0SOGupD6fANz3RsaGlBWVhYr8DEMwC6QshZAB5qa1uOOO+7AJ580YuVKuh7sNx9NssKXlJSgvr7eFKxWXJxcsWXi1NlJx7W2Zs/83t2d+XcXFpJVMJ3PkXsI+6T5Yc5WQhgzZozJGuj3O48gFxTEr5fDhtG90xRk6zqm5vwuKjKuM2/8NvWAzGz7IL8spayTUo6TUg6RUlZLKfeWUtZLKRNqi1LKNVLKT2KPO0FlWEYBOBbAQ7HDHgJwXOzxsQCekFIGpJQ/AlgCYPcsNQsAdYJMCXJpKamv6ahBbMZiolRff13CHJvz5r0BwKikl+1dWWtren5rpaWkIKd7PqQkWglyRHkv/vuBgVHOMyEoLldmGUuA9NLbCeEs0sQwyFMtOC9waWkEe+yxHQBDQXZSLmDAnOtczXENxOdKzXUFGaDr/MknX2DYMApA+9OfrsShh06NO87qXpLtfs5kt7u7G0ANKLZ8N1B5XEIwGMCjjz6CaJTm8EQ5jxljx47Fvffei7q6OpOlIFk+YMCotsYWSp8vfcU2U/RFnXa7aT5PRWLDYe6zBSCRIwQrQeYNhKogbwoEubkZeOONjSNv6qZIHcuM6mpaJ3jNdgpBVhXk4mJzuwsLjTWb57PBJsiLFiV3GcoqQe4PCCHGAdgZwDwAw6WUawAi0QBi+zCMAqBqlStjr9l933QhxAIhxIKmviTqhWEacLkoWO3WW4E770wdvMDmrHTIEwvF7OD/00/LEh7b2UkR1APR6XgApON/7XaTn1u62Tvq6+vhcpWBCHIUAJWhLSgoiDPDAsaufCAm1UwW6cLCzCpOAfGKQ6JFlNvc0+OcSRUg8jR37nwUF9OQvueeGzFp0jgA+eFi4fWac4rbBfIMljkyEACWLu2f71ItH4n6eXHxwGbmMZPdrUHuAKtgVLUjNDU1obiY/otkvsdCCCxbtqxXGbemO0s2d6oEORIhEWbZsr60KjVaWzOLo1GRasOmihZFRRJDhw4FEI6l8jNvIFQFmee1ge7rGzYAn38OfPABpfT76ivgvff6rt6rGzs7BbmszGwlccpcrhLkoiKzVUx93tVlVIUdLEQiVOxswYLEx2zSBDkWxPcsgAullMnop50XlW3XllLeK6WcIqWcUsuzUYbgi9rcDJx3HvDmm8CrrwLnnAPccQdVV2tutu/0UqZH5lg14IE1ZsyIhMdWVdFlHIhSnX0hopzfNxXq6upw6aX/4F8CIDF06BDMmjUrzgwLmAlytifVTBS8TCtOAfHp7RIFA6ptjkadpap2d9Pk6nJROzmiv73dWQUEGF1ddA1VEyTD6zUvoIO5mKxYAbzwQv8QNdUNLFE/V02xA+E2Zg4S3R6JspDW1taivJz+DzuLFsP6ntUfNZmCXFFB/wmvE0OHElnLxtyWaQYLFanUZ/W6VVZ68be//Q1r167sTeWnbiBUBZlT+w1UXw+HiRQ//jjw4YfAN99QitLx4ymtXx81NLS10Xfz2LYSZHUT6IQYA4YaPF5QYN70qwrypuBi0dxMv//jj4AQqLQ7ZpMlyLEqfM8CaJBSPhd7eZ0QYkTs/RGgGoYAKcabKx8fDSDNcLjMEQzSIP7oI/Kz2morYMIEuuivvQb86U/AGWcAZ54Zryp7vakJo5r9gQfW9ddfq1RcMlBQUIBTTjkCwMCU6uQJLF0UFgJr1qR//E47HQQAqKkpw91334sffvjOlhwDRrGQgciX2t6eflBmURFd40wWNauCnIgg8wTDxzqJNHJhmfJyIsmVlfTcqQoyW5KKi0nNUM2Rqro62ObIxYtps/LSS8CsWZQTuK9oa6Pr6HKRhcnOlUj1xx6I+AKD0O4GwA1261JRUFCIU089FQUFNPb++McbEs7HVmuXauFKVh2U3+cxzn7Ifn920r21tPSNIHs8qWMs1JSjyVLbAdQPVNP7QBLkH34AFi4ERo6kW20tna8QNAYziaFRwXNZaWn82AbiYwyyGYw5kFCzWFhdS1wuo6w4iwMDPadHo8a6vGIFzTWxfjnO7vgBIchCiH2EENNij2uFEEkr6QkKDb4fwGIp5c3KW3MAnB57fDqAF5TXTxJCFMa+e2sA8/uzDSp48P7wA93//OfA9deTq8WJJ9Lus6iIyHFjo/mz6VTUC4eNAVNWRhPIqaeqFZcI1dXVmDVrFo49dn8AA0OQOfVSusg0UI+Vcx5YydLbscI4EAS5oyN9gswp7jJRBdJ1seA2O5Egs1sKt5E3QO3tdH2d4nPNUAlyOGxeTNxuo/2DaY7s7qbS37W1wOab0+bljTf6XgyHN/6lpbRQ2W0ErRk8sk0e6uvr4XaPB3ny2YfGBIMB3Hfffbjrrrtw6aWX4MwzL47zQeb52Lqh7+42W4eSEWTVcsCkWIj+3yRwZg07FwspgY8/TpzPPR0LmXrduB8nc+cop9CD3s8MxLwWjRI5rq21X2cqK0lR7oubBf8/LHBZ53PrJtApBFlNNav2ZcawYcZcHokM/Jz22WfAgw+SkLloEV3j2LW37Z1ZJ8hCiD8BuBzAlbGXvKBMFsnwMwCnAvi5EGJR7HYEgBsAHCyE+B7AwbHnkFJ+BeApAF8DeBXAuVLKeBmgnxAM0qBhgrzFFnTRx48H6uqIKF9wAb33+uvmAZYOQVZT3ZSWGhMqV1ySUkJKiebmZtTV1fXuypi8ZltJzaQ0aVGRoRqlA9X3GkgeCKkSZCC7gy0TBZmRierDbiIAXe9Ev6WW2B5sH67+Bi8q3EZWkNvaaLIdSDNkopSK/Qm2JBUX02JtXUQHO1eqlMDXXxuxEy6XoXC++mrfzkclyELYj291YR2IFI51dXX405/+jdJSD9gzr7CwCC6XeaLr7OzAK6+8jO7uTgCGUFFQUIBHH320dz62or09M4LMpIpJU7pueZmAraB21sA5c4C//AU4/3zgqafiCWJRUerAwc5O47oNGWJf+EjFQM7ljDVraAyqbgAqWOTKpAIuQyXILlf8NVdjDpykIG/YQNea3YSsBFn1vR6MlH7LllE/XLeOziWVBSXDPAx9wvGgrTlnpVgthChP9gEp5Xuw9ysGgAMTfKYeQHwkVxYQCpHis2KFQYyt2H132hUvX05BHVttRa8XFtLFkTKxq4KVIKe6iCppyrbZoi+VlwCaZDiFUSJEo2bSkO6kyv9Vtglypi7rmRBkTudUXEwTaiK1Rb3WgLMIMpMnbiMvLrxoDtRkylkNVIWQUyoCSOjykym4r7NJ13rNebxwOq7BUFs++oiUYxWVlUQu3nsPONB2Nk4MVp4TlVMHzC5GXHGLg6KzhX32ORxjxhzeuyk588wz0dSUKNK2GxQDTrWvOE97on7R3m62DqUiyKqLBUCCRF9IWjIwQVbx9ttkHfj6a3oeDgOPPkrX+5BDjOMKCogAhkKJ56n2dmO88jydzlze1TUwfd3vJ+su/24iuFy0fu+6a2bfz/2cNztWwcMamOgUgszulNw2a18vLaX3uM7DQIoe4TBZKTfbLP25ZCBcLIJSSonY1lwtGpKr8PupI4TD5LeUyEx4wAH0+I03jNddLprsk6kvwaDRcUpKUiuXauCH35/dyaWtLbPKS4x0ypOq/oZsckumVg+NlYnJ9qQaDNK1zkQ5z6TiFGAoMpxQPtHCw21ma4GTCDIvKrxouVzmDcFAEeREKbzsCtZsDFQXC/Z5VMEbMibIA6kgd3YSOR492r7fb7YZFc3I9Jy4zakUZDVoC8h+263uBskzHPlgVHwkJMtqwQoyj+tkAoOdi0U6im2msP6fn3wC3HIL8MUXZPr+xS8MK+g999i7WyQjda2txqagoiIzBXkg5rWPPqL5hOeXRBg2DJg/P/O0nXw8E2TrfK5eZ/bXdkK5aR42vMlLRJCBxApytqx3HOydyUZ7IAjyU0KIewBUCiHOAvAGgPsG4Hezhq4uoyLeFlskPu4gijfDO++YJ6RUPmXqzkrNCZoI1lKd2fLVlNJMkBsbG1FXV4djjjkaxxxzNOrq6tBodboGLfzpVNRTo3nZ9zpZZ7aaoLOlnHNmhUxQXJxZqjcmhzyxpCLIg0Gasg1WVNVFSw3EHCgf5GRkJ1Vp4UzAm0Yeu3YKsttN11iIgVVbPvvMnOTfClYf+0ocOH2lXT9XyQPHF2S7n3d3m88leYajMIBCAFW9ryTLasGqWmkp/afJNtp2CnI6bnmZgt0EAVrPbr6Znh93HLkI/va3ZB3Yf3+aV99+O/47EhHkYJCIkip2qFUi7aDO5R5P9jfDP/1k/GYycHGmhQvT/24u4Q0A8+a9iXPOORuVlaUQQmDcuHFoaGgwKcgDpZonQ2treiJWKqjBiXZCj0qQ1UBOBlvv7AqibSxJ7kvcRNYJspTyRgDPgDJSbAvgj1LK27P9u9lETw8FrgDAvHmPJySI48aRa0V3N+1YGal8ylSCXFycWkH2eAxVIptR38GgYepsbGzErbfeis5OY1R1dnbgtttuiyPJJSXpZbJQ211Wlrrd6qQqRPbypQYCme/uWfVJN5OFNTgxETFRVUW3e/CT6vcnmASsX/9978ZrxQoyY8+bt2jA2ppJCq+NAZvN2Z3I2t/Ly80b34EiyH4/cP/983DVVb/DMcccjeOOOw7HHHM0zjzzzLixnYmyGYkY/ZXdSuz6ucdjkEQ+Ptu5kK0E+dRTT4XbncwDMQSA/OYS5WkH6LrxdU4nWE1NjcWfY4LcnwqjuuGYPZvI0Y47EjFWXQbZhWbePPPnXa7EFjLe6PL5M0FOBp7Lu7ro+mezr3Ohk3RzQFdUGMQvHYRCwPffk+Tu968HIBCN0h++fPlyTJ8+Hc8++0RcarvBEjt8PvI7f+mljQ+K5fmALcDW665mtrAjyNm03q1cmXnWloEI0hsP4F0p5R+klJcCeC9W/CNn4fMB335L25Fg0Migb0cQVRWZkSrDQXe3OS1QqotqjYjN1mLCfmuNjY3417/+hUgkPglvOBzCzTffZNosFBfTwImkCJtUgxXU4MREUN0Nsjmp9mXiSrfiFIMXm2TKGkAbB6+XXD6yEbwzWIhEDMWssXGOsvGiDjF79v/wzjvzBsQMaZS+jsfy5cv7zeSXSkFWc6WGQgNHkO+//1k0NDSguZlYQTRKA5dLLvO4Li1NnOnADmqmltLS5P1czeCRbUsJR9Oryu7UqVNxAfsY2KIVwI4APAnztANmqxiThmSuBh6PMa+xiMZuef05r7PLR0cH8OKL9Nrpp8ervDvuSHPSihVmkaOyktL/2Y3Hpib6bibIZWXGXJ7IfM6B5rxR6Q81MxFY8Eg3XanXa/TDdBAMAosXs6WpG1T0ylj8fD4frr326riyy4NBkEMhquPAmbMefphyQvc1rSCrtOXl9pt+a4VQ6/qVLetdJEIBeuVJo9/iMRAuFk+DeggjEnstZ+HzAatWcW82by3D4RAeeeSR3ud77EH3X31lFHUoLk6+sHR2mss1poy0tCjI2VpIg0Fg3rx5uPXWWyFlcmlU3Sxw2rNUk55aUjcd32tVQc7mpNoXBZmRzjmpwYnJgpcAc3CHk6Kf1dSGUqqJwmmmjkZLMHv2CwNSGIVLX6spFVX0l8mP+wYrhtb+Xlg4OBHf9fV3IhSyX62DwUDv/FZaSpa0TIhDurlxra5E2VSQ2YXFiqlTp6K2dlj8GwDIzaIAo0btmjRoU53TKioSq+YMIYDhw+mxqtD2t4sN+1y/9x6d4847A9ttF3+c12sEqM1XEqcWFxMBtjNbL1tGpFjt30VFyc3n7733X3g8RNjc7swU20zBm4N0kekGJRgEAgGmVt2girBmrFixFMuXf0NHDFKWGp+PstGsXk19bsQIslBGo32rnPnQQ4/hiSdeAgC8+eZzuOSSS/DMM4+ZjlEJMlcIVeePZBY6IUSf59y1a6mfZ5qJaiAIskdK2etdE3uc4WluWujuBiIRdmCKH8lqgEd1NTBqFF0cTrBfWUkp4hIt9mvXZqYgqz7IoVD2iGIwCLzwwgu2yrFd0hF1s8D+y8nQ1WVeQFO1m/2UWdnOVrvZnQGgakvnn0+FYP72t+R+xlKmF32uVp1qb1+DSy65BEVFrl6VRVVbXnzxOZOJyikVmFSCDKh/Gr9Yhg0bWgfMT49TKo4dO9b2/Y01+akbxpISIiJW9U6tNBcIZKdYhBUNDQ1Ys8YFa5llFTy/eb2ZnZdqUk3la2/1Sc1mP09GTKg4iL0py+t14Zxzrkn63Xb5gJMRZIACvwGz+0qmedVTgf9XJkJTpiQ+lkWeuXPNr3s8lKVJRSRCSnNpqTH3sTUwmfn8zjtvjoujydamqC/fm0ke9mAQ8HorY898AOx/8M03nwUw8IWApCQu8sQTtBHh/gbQNa2qomDNTApdNTQ04He/OxehEFO7Tvh8nTjrrGkmUqsSZLuAzGTWu2g02mdh4quv4lPOpYOBIMhNQohj+IkQ4lgkquWZI6DdbSGADgDxo8Ya4DFhAt1//jndu91ECOx8uHp6yLTW2Um988orz8MOO2yZ1KyrKsisKmajNGkoBLSaJIMqANsAuBRkFDgV1nzbvJgWFqYO1OvoMCvIqVwsVNeSUKj/UyExWG15+WUixcuW0eL14YdUavzFF2nCsS606QbqqQT5yy/nw+drh12l9JaWFlxyyQWIRolZ2flw5SrMBFnd6TD7KkdVVdWAB7Jky+SnuhuorhQqrP56fn92UziywgeMhLExiYc6vwmRfvCL2l+Li+ODcFXz+5lnHg0A6OiIoKAg+yb3RJg6dSrOO++8XiWZcyPX1g7D7353MiZNOjLpd3d3G/06kdnZimHD6L/p7DTnRue8+/0B9vVVc/knwh57EMn9/ntzufGhQyklnKoAbthAYzkS4WsdxjnnnI7DDtsfy61sWsGaNSviAjOzdc2tqmU6kDIzglxZOSr2rAd2CjIARKO0NnIhoIGayxcsIOW4rMywVqgoKqK+l0kWppkzZyIcdgFgFtoNwB8nJKhzml272XrnShDR2Rdhwu+nfs5WqUwwEAT5HABXCSFWCCF+AhUNOXsAfjcrCIXUjhPPfjweL0499VTTaxMn0v0XXxiveb3myYbR3Ax88MHHiERcoIEVAhBOatYVwhzUki2TZCAADB3KyvnmoGQkNwHYH7Rh+DWAW0GxmAReTNkcmwxdXWaCnEpBdrvNvteBQGJVfmPABJn9yE8+GbjzTuBnP6P/+b77gIsvpuT6KjIhyIY61IFEigMAhELdaG1d1fs5p/ggq9WkXC61UUSQhajAscceO+AEOVsBe6FQaiuRqiAPxCJKCp8XQDHIhSAeXHLZeJ56XDPUjaA1+Dje/L4BQAThsBsffPB+Vl2JUrlQTZ06Fffffz/mzHkRs2fPxpw5L+L+++/HIYfsg7Vrk4sRbW3mdGdAagW5tNRQ0HmtGTqUCGp/bZCYhDJntcvlzygsBPbbjx6/+abxOhMp1TK4bh197//+92HslXZQ+e7kC9KoUcNNfT1d61tfwEWW+vK5dECl1MnZdciQItiJaARyJeP1ZaDc5ZYuJWKcLDuWx5Ne5ikGiQWFADiLr6Gcq0KClSAD8ZaRuro6yCQDMlNhgjdafcmjPhBZLJZKKfcEsAOAHaSUe0spl2T7d7OFYNAInth66wqUlxs5qcrLK3D++edj6tSpps+wgrx4sRGoVllpuFyoWLkSeOml12LPWD0LxX478e6J80h2dmanNClA33n88cfEortPBnnKrAPwLoC/AVgJIs71AMaaNguc1SEZge3sNAZNcXFqBVn1x+VBlg0CweSEzZFHHkn5YS+/HLjySgpk8XrJQqCO3aIiUtdSkXafT50cfQCSscAQQqG23s9FIqmDH3MB7e1G6eELLzxXGVf0x2y11WTsvvseA15aO5HJL1nmgnSgZptJpiBb8wFnc0NEC88Q2FkvAFJNzzvvPNP8Vl5uv9G3Q3u7WRFV2xxvfu8Bu3k888zzWSUPfRUT3G5zcKkdmpvj8wEncitpaGjAuHHjcNhh+6Cp6VsAwJVX3ojGxsbe30onG1A68PmIAIXD5HuayvzMwebvvmt+XQgj5SkA/Pgj9Ymnn3499ko7iGYk/pMLCgpw2WUXm+bywsLM0mRmgo6O9DNYMDye9FPtqUH21113FYYNSyRdEpHw+cwFkbKJUCi9Wgalpea1LBVILCgEEEv8DB94Y6AKCUIYFVITEWTrZzJ5zw6ZBFhakTWCLIQ4JXZ/sRDiYgDTAZylPM9JqAR5p52GoaGhARdffAlqa4ehs7MDt9xyS1xKpCFD6BYMGsFYhYVmn1vGDz8A7e0cpMSrgsGwEu2e2HzAybCzQRR7eoCf/WxPnHzyVQD2ARG5y1BefjcOP7wSZWVXA3gHQCGEuAozZlzYu5jy7i2RKsCBan6/UUkuHYJsJRDZUM67ukglCwbJn1yNhN1rL3K74KIwqsLCwYmplBC10pY6sdgjjMLCaO95Ac4oFsL+lqWlpNg98EADnnjiRfz5z5cDAIqLycQ90G21C9irrq5OmrkgHaguFmqhHxUul9HX2F8vmwoyLTwlsIsnqK0dhvvvvz9u88/leNM5r44OY3wWFZkV5Ph5rQdMqlpbu7JKHtgft69INr6bm81VURPlA25oaMD06dNjbgg9oCwZQHu7pzdzSEkJWSE3NpMLZ+3gjU0y9wrGVlsZG351szJkCLlZvPwysGgRzZOlpUBbG8vq7aCCvfYTM4+lX//6FyZlsaQkMwUzE3R0ZB6sVVCQPkHu6jJbh37/+9/abrLd7hC83giiUbomA6Egc19NpaCXldHGJ12LbH19PdzuchguFjR+7YQEnko7O+l/tXPR6k9hork58w0RI5sKMmvt5QluOYlAwFjMhw+nlGd33HFHb1nSRCmR2NdHjc4VwjzoWlupAw8ZwpHT8QSZd0/WdDkPP3wzAEPNyMaCwunU2tt3AwAceWQB5sx5CA0NDZgxYwYee+xBPP30/th8c0DKkSgs3M/0eSmNDYIVajAL++pZCTIrLEIIeDwelJcXYelS8lvhdvc3gWBSwmr/ttvaH3fwwXT/1lvxk0oqH822NmNCFcKPZAqy1yuw/fYUOMbKthOKhagEGSDTcmursZDx9c12Plw7cMDeo48+irFjx6KlpQWnn366KfF/plAVZKuaqkKtFulyZdcXlxa5arDFimHnNmaFkpggIVQibW1zvCoUBCvIlZUje12osoHOzuQL6Jw5wHXX2RNhtzsxkQsEzK5DnM3BDjNnzoSv1zzgA9AWe1zbmzlk6FAitRtbq4Y3mex/vOWWqT8jhBHMpWZgKi0lVXvNGlKXIxH6T8rLR8eO6ABZJIxJcezYsZBSQkqJ5uZm1NXVmayBnZ30P7W0ZCeWJtX1tkNhYfouHytW0P/Am6Fjjz3MdpN91VUXo7ycfNr9/oFxl0t3/mBxJ9F6bUVdXR2uuebvEIIV5B5UVBTYCgnDhhnnUlhoP3f0pzCxfn3qYmuJkDWCLKW8RwjhBtAhpbzOesvW72YbVoL8yCOPIBhMnRIpEUFWO8eSJURA99vviNgrZhcL3j3Zpcvx+2nW/Omn9ox2u5mA06l98gk932ef+GM+/LARra3PAABuvPF/JjW9uDixv6LPZy4SYk2HZFZYgEgkAiCCUIjY58KF3/d+T3+CF+XvvqN7u1RIALDNNuR20d5OEbOMoqLUuWLb2oy2/+IXh6O83F46r66uxgMP3IdttqHFx0kKsrVEKUCR9UyIOahosHyu7fufkfg/U5JsVZATTeA1NXTPaks2xjWjrq4Of/hDPUpLDXktkduYCo8nPXN4Z6fRz60KcrxiFAJbUg488NisBm1Zy0yrWL8eePBBqqR2443x7kzV1eRaZbdx4w2sNd2ZHcwKehDsnwpQB2hqaoIQ9HvvvrtxxJHnNPY/Hjcuvc+NisWdqXO4EOS/PGwYsPnmBvnZZZepsSPaYvfmNcwKj8fw0W5vN1KrZWO894UgFxSkR5ClpCp9AAk94TCNbd5kqxuDY489tHdTMFCFgNasyUw9z6QQ0D77HA6Xi3w977zzFrz11n9tySxvtFjJT0TC7f6zvljtmps3QYIMAFLKCIBjUh6YQ/D7DUVw+HBzSjc78PtMkNWFpLjY8N8Kh4Evv6QFccyYnQAAHk8AZO4Mm3ZP9ulyiHmvW+dHYWH6O79M0NND37tyJZ27VU1lNb27+4PYK+R8zWr6J5/MTUgW1UA1Niurk5hZYWGEwSr7vHlfZ4VAcI7Ub8klMKGCLISRDkmtOlVWlpogq9k79txzMl57bU7vpKDeeIJQyy8PdonS/oJKkHnx32knc8GIbKf7Sgb7/kfw+XxpRVarVp8tttgx1rekyVXICqvako1xrWLKlINw//13Yc6cFzFnzotoaGhISo6B1AG4VAiiFr/97e9jloAoPvvsQ1Ob4xWjMNxu6thbbTUJUmYvzZ21ih5ARPjTT4H//MewCH32GfDKK+bjvF46Vt0UM9j3kQlyspSdZgU9BHaxYIKsBjt3dW3cf8FzGl+z0aOTH8/g4xLNZ263YfUbOpSrDPLOIZxUAfR6jXmf5/BslFePRGgN74sPcjpWDJ/PuDYs9CT6raIiY9z7fPT92Y4nWbvWLEIkAxeISRfr19P5FxbSBieZVay42HD16erKXrsDgb5db8ZAZLH4QAhxhxBiXyHELnwbgN/NCrq7jQmvqio+pZsV/D4vdKqCzH4+AHVEvpC8CB533FTcddfdCAZ7TLsnez9krnpVmvZuN1P4fEYmjkmT4v32DDV9KchMOBI8wQeDATzxxEMJ/RXVdEh2CfUTR662xc7Nk5V2BwKk9q1bR4M+2WLCBHn+fMNPsLDQHLRhBafu4kk1ncIw1iAHJxBkNoaUltJ/NWwY/Rfl5fS/c+T5YLhYAIn6XxGAiiTvG4i3+tCEIGUXFiyYl3ACZwWZCXK2IvsZ7e2Z+2eyv6idqsntbm3thuF114mHHnoYb775X9OxqmK0bt0q7LEHpf9hApuJmpUJurrMbV69mgJw//Qn4KOPqP8xp3v11Xgf4NpasqpZxyGXh1YDjxMFw9XX16Ok901VQa6NyxySienbDuze09pK/2uKJawXdgpyInA3P/fcE3H33fdg/frVSRVAt9vYDDNB7u/cz8DGzR/p/O8dHcZcXxbzNkg0tlXL0UCUm5aSrku65ZbLy2kzlI61wqqcR6OJY4hKSozNUEdHdjZCjI3dVA8EQd4bVJPzz6CcYDcBuHEAfjcraGqiTsyqT7JE8urEZudiwaldVq6knLqc2ocVan5uJaL2UZzrQUUKy3rNev1ZlpdTx332GT3fxWaLY6jpUQBfxh5PUN5fj/nz59lOMm1txgLDE4va7sSRq/RlhYXDslJNj11qpCTSlmwnus02RF7Xr4+P7E+kbHd3U7vDYVqk3e7UE5jql+p2Z580DQS4T7BCNmYMkasLL/wdolFq4O9/fzFeeeWdJN+SPcT3v10BnAbgOADelJHV8VYfZiWdmD17dsJ+xfMGm4V7erKTyhCgjVowmHnAWrJsDka7i2BEuLcjEpH4979vSvidao7z7m5aVLNRXS0SoTHOhYCWLydy/N13NP/uvTfw+98Dv/gFBaStWEHp1qznGg4bBIHB84aURmntRKShrq4O9957b6wwTQgGQR6Oc881Zw4pLDRnjsgUgYDxX1ZUdOG00+pwzDFH45hjjkZdXV1v3IwVmRBk3szU1FC7U/Upj8cgTTwXuN39bzFJVDUxHbjdqV2JVGtgKgXZ4zHUXHbHyabY4fcTcU033RlXNkxnffH7jTWuvDx5u1n4AIzPZIsgb0wGC2BgCPIJUsoDLLefD8DvZgU8MVVWUidIlkheTYlkR5ABmkBefJE6IQ8WJsiVlUSarAPaPsLTD0rObRQK6c+oWB64LJTZuRqY1fQYk8aupmMeeeQhPPigWTlqaGjAIYf8Gg8++DgA4N13X8a8efNMk6pZYVFBUsWwYVv1Rln3ZyqwQMAw/2+2WfJjXS6jIhVvJIDkxRSshQSA1OYg3jh1dSUOcsg18ERZWkqk5YMPnse0adPQ3b0a3K99vjDuuGPWRpd47gvM/W9XAHsCWAugGIWFk1NGVscrzDFpGN3YsKE14TXnPscb3mzmQu5rfliG3SbQaHcxWG0nq4/EmjWJVXcrQS4uzqxwQbpQCVMoZATjTZ4M/PvfwBVXAIccQufDmWpefz3+eyoqzHnu/X6a63nerKigfp0snVpdXR2WLVuGjo4W3H33zbH1oBCTJ081HVdamtptKxm6u43/sqXlc3R2GqpCZ2cHbr75Jvz617+OI8pMkNesSW0S5zmpujp1eW2A3udUpdyPiov732qQKud1MpSWxlcOtGL9ekOl5vYkGttqH+c1IJsKcl/GN6vOqaDGF6QiyAUFxlqXzaxbwMZbHLOZ5u1oIUQTgM+FECuFEHtn67cGEhyxzOYgIHEieXXXX1trBOWpClBpKZFklXypBNlOcbCL8KysLENlJc1CrKL25+47GKSOzATfjiya1fSPY/e7ghLFEyKRVvzrXy/2PmcTbGenAC2iQCjUjIceegjPPvtk73FmhQVwxySfoUNppna5hvZG3qZb2Ssd9PQYi8mIEamP32YbulerXhUXJ85f2t5umOTSJchsdmeC3J/tTYRwmHwyVRLQn1DL0goB/POf18aUxw4YZY+LEYl4NqrEc18QjQI77VSHCy+cjREjDgCwByjndxTAegQCE3D++ZcmJe7xCjOP3S5UVVUnvOYVFbSghMPUF7O5mCT73lCI+nAik6XHY6/wGu0uhpG8qB2AwKhRw+I/EIOVILMPaH+33e83SMN77xEh23xz4Oqr4301eTrnIGUV5eX0/7S10bh86ik6byY8FRVGwFYqeL3U5xKJKsXFNOb7qjZ2dambjZ9sj/H7e3DzzTeZVOWiIpp7wuHkan40aibIyXI/q+CNP7umcCaL/oQdYQoG6drNng389rdUBMpO0SwrS705WL/eILvcnmREkfsYx5NkmyBniuLi9DZjapGvVK4lVoKcTSsoZ97qK7KpINcD2FdKORLAL0GVJHIebGLh3WG68HppsohG43fFRUXmzsSEp6IisbndGuH55ZeLUFtL8kRHR+aVcFIhGKROHA4Tcbeb6M1q+hoAK0Bm1R2Vo7rR3OzqJe+GCXYIVB/FaDSCv/zlj3FtXrZsGaSUeOihh1BdXY0NG74BAKxY0YbGxka4XGYyak0Nl2lqru5uY5JOhyBzTlGVIHMqJDs0NxuKN++8Uw1o3hdxQn1eULKJN94gN6DGRrM63h9QrR1sil65MlaVBV0wCHIpAC+WL98I+awPePddIk9jxx6Myy57C//3fyfC7WYpJgTAi9ZWd8JKl4Cd1YdWTyF6cOyxxyb0+/V4jAWHN779HdnPY2SzzcZhxowZcXnc33rrHZxwwnKcfTZQVxfEr399W5y6mEjVNNq9DaiQAAC0we124corL0l4Tl5vfBEgoH9dqBoaGrDVVjvgnHPOxjHHHI1bb/0RAHDssfZ+2OPG0Tk1NcUTNyFoof/mGyoI5feTiMDny2M7Hf9ur5eOtQvs5t+KRvuuInd1qQQ3tb9EZ2cHbruNrvnmm9NryYrDtLcTiSwvNwSedEjKkCF0PFeZ5BLj/RnAZVVRP/gAOPNM4OyzgVmzaO199VXgvPOoJLMKzqyRSHgKh2k+503k0KHx7iXqerTZZtV48cUnAADffrsq6wHXPT2ZZz8pL0+tmgPxBXGAxH3dSpCzmZ0nWcaS7m7KTpNsPcsmQQ5LKb8BACnlPORw7mNGJGImryrCYZqwki1ePOElq4YUiZh9eVIVy2CoUcDt7ellT8gEoZBB7JO5GrCaTiSZ0znsYTqmqqqiN68wmWBLQF1RNcECK1b8ADuYA55o9ZCyHDfffBPOPvsk7LvvaWhoaOiX1Fyq2pLKxQIAxo6liXTlSkMNKCigwWinXrS2Gsfx9Uu1mFRWGn6PoRBNetksINHRQRWyRo8mM+v8+f3rxqLmBGYyOGYM/9kqQS4DIDFmTBqJW/sJq1eTaj56NG2QttwSeOKJhxCJqI7AAQDjk1a6jLf6VAIAdtxxHHbffY+E19zrNVQmrpLZn65T5jFSCko8ZGSeueuuu3DrrZ8jGh0LamcB/P6zcOutz5lIcmkpkS7r/FdXV4d77nkYJSXbgwlyQYEfp512Gn7zm18mPC+Xy+yfyegvpYnnkA0b+IS3RDQ6HkA7XK65Cc9p663pMWe1UVFbSynfPvvMsPIwmUqlJqpwu+lmF9jNqKmhTWtf/o/ublU8SW+RCIdDeOSRR2wFACus/sfpbPoBut6qmwUT2f7cEKpV9D77DLjhBvoPq6qoGMq555IVsKUFuP56+zU0kavPhg1mAm295tb1KBo1qkV+8cUPWLDgo6xlagFo/shUTS0oMGeYSgSOzQKMOgbJghN5nmeCnE0F2e48PvyQYguuuw64/fbEn88mQR7GVfNilfOszwcdmfqnBALGZM2ZBAC6CKtXU8nhpiYj9daPP5qJC9e7twZ5qGhro0HGE0W6+fu8XrPSVFxMykN/EZlAwFAymOgnw6mnngqXa2HsmRHR5/F4ccopR2DePOCxx4BRo3YGMDb2LsvybQCAMWNG2X63OeCJTfBukMLYja6uYkybdjYuuOCCjU7N5fMZEyLnb0yGwkIiU9GoeectRPwkwP5dVoKcahFVo+GTlevsLyxdSuSA/cpSmVgzhbXssssF/PWv18WURz8MglwBl8uNq6/+c//9eAq89x4pQWpgS3xqxzYAWwMQSbNZqFaf44+fBgDYemvqVKraoqaDGzmyBqtWERvLRqo3c/q6SqhFaoLBAF599X1IeULslX8DaAS5ukzHww8/0nsskxm7zf+++56EG2+8CYcc8msAwO9+dwJ2333PlJt/DmlgwaCwsP/8kI05pBCUSnOH2Dvz8fjjDyX8HMde2BFkj4fGdDhsjGHV1QBIP91USYlBsu3GWnExfdd776X3fSpUBdnlSn8gNzU19RYVSUaQuc01NUbBjHRQUmKfyaI/+zuni5QSiJUowPHHk3p8883AoYcCf/87BWdGo8D775s/X1ZG67odmptpHKxYQSy3vv5izJhxNjbbrAYNDQ02qSKD4Fzf0WgRXnjhyazGk7S1ZZ6hRv0sw84qe9ppl2DJEtpNpCqprirIXPY6WwSZrzdjwwa6vn/7Gz0eNw6YODHx57NJkO+DuXKe9fmgI1N/H5Ug80Du6aGLfPzxwL77Aj/99CZ+85vL8Ktf/R4XXLAPpk27vFdpSTa5Mlihrq6mySXdlCxWgsz+uP1luvD701OQGVOnTsX//d/hoFzFIwEU9xYdOOig/TBqFDB37vtYv35HAFMANINVNaADbrfEX/9qH/hkJiE9MCoOsgItEApVmgqppP4ee3R2GotyOhsDwKhMtXSp+XWrrzBHFVuD9FLt8q2qopTZK6ARjZIqpri7o7jYKJzSH1B9SwsLqW2suA4dWgoumFNQUIPTTz8dxx9/Yv/9uIL4id+Nc86ZiU8+aTQdF5/aMQygAEBNymwWAF133tCUlJBayAQ8Ph1cqDeLx7x5i/vdHGkeA0NBKjFjb0h5O4DNQO5S7wC4C3Q9dkJzs3nHWFpqVJxU8d131Lf5vFOZYBk83njclJQkt75lAqPd5SCiwlHH3yTNbZ9qDh8xwryR5jkzU4LM/r5A4s1odTVtwjPZrHKMRiBAY+3CC89CYWF6i0xtbW1GCjKvYelaQcvK4rMbFBcn/61MwcUpFiygfjlkCPCb35g3wG63EZCp5rTnc1y50t7tY8UK4LPP3sf69SzetAMIorW1BWeccUavcmwgAqMYWBlaWlZlNZ6kLykcAfpv1q+nuamsrAynnHKKxSpbiNbWbnzxBb1WUUH/YaJ1TA3IbG83svNkIxeyqiB/8QW5zrz/Po2v6dOBW24Bzj8/8eezWUkvrnreplZJLxTKLGWSSpD5Are0APvvT5PiY4814Oabj4PP9ziApwG8D5/vS9x66yNobGw0Ta6JfEZ5XayqSj+oAzATZHU31l8+e08//TJee42csp588pak6YAYBx64P7bYwgPAhRtueMpUdOC99xrxn//ciGCwFKT8BsAKckFBBNOnn5YwZ6aZhARhEOQhymtjkQrpkBmeDKur05/o7RaRykoKcguFyEWhrY2IdzRq+Ky9+GIDzjnnbFRUFKGmpiahC4j1Wrtc2d2B+3zmtldWUh9Op3paOrCWXWbyX1dXh+XLv8WJJx4LAPj5z4/D7rvv0a/uHQx7d5wSbNjQbCoZD5B1xO22zv5ReDxjUmazAMwll4uLzWM8Ph2cUQzn3XcX9fqc9xfMY6AKpNgDwBkArgSR5i9BWTqjIDX/OQCAx3Oa6bsqKsg3VRUeolGyrpWVGX3ULs+5HTbbjI5razPEgv4qP2y0uxzkR84lMr9Nmtue5/AlS+zXDiHMPq6qmgpkpiCziT4RARaC/tf589P7TsCctmvoUBIynn76aVx88SUoL69I+DkuN77ZZkaQYCJl16ogbwxBHjKE/uv+Ik+cU/vNN+n5ccfZi1CTJxOZXLLE7G/udlP/sxJZ9gl/+ulHQEJPFDRuaSccDAZ7A8vN4C8qRU1NRa+bRjbQ3p7+tWA0NjbikkumY5ddjsYpp5yCbltTZSUAiWiUFqV0XEPZfUhdt/rbTZBT03o8tNbecAOttZMnk1vFUUelTnk3EGneNllEo5kRi2DQIJyctsflMkp1zpw5E+FwF8iviyPxvkQk4sUjjzyC4cNpwHd0JA6gU3MgR6N9V5CB/jNJNjQ04Nprb0QkUhl7ZY0pcCMZmCxef31DLNfmMTjmmKNx8803xfw4VwJYDTJ1FgMIoby8AFOn2tSxjsEc8BRCPEHeAGBrVFXVJEgNB5SUlKQkM9GosTjxgE4HiQL1Nmwgv8EPPgCefx7473/pOi9ZQp0hEOCLFUZLS0vCoC+v11yWNRvR3gxO5K6Ck/o//fTG5WNlxXbUqHFoaaGJ989/vgofffRm7zFFRfH9OhuBLPaV8opjv2eUjAeIVFxwwQUmQlFWJnHeeTelVQpVTYlUXGxeTOKtGiHwItvd7YLXS5/tr0XUSF/nBsUChAGMBhVADWOHHebC5fojuBAR4SUAPoTDW5vIm9tNi9LixcZrXV1EJNVI9fJyuq6JUk5xv/jFLw6GlG2IRoEzz7wEc+c29ltFPWMOKYu1ezgAH9zutaaiHFZUVJAffjBor5ZbYfXHzURBZje+5ubEBLGykohZuutYIGD8f6qb4NSpU9HQ0IA5c160kOUqlJUZ5cZdLsNVMJGyqyrIoZAxfq1QXYmEENh//93Q3k5meibfHg/1n/5Yx9jSJqWRiWTffe2PLSwEdt6ZHn/8cfz71vWbXeVaWvwgWtUeuzfmlEgkYrMe8cRdjtNOOyVr1sBIhAhoJj7IjY2NuPXWW9HVtQ5kRUpEFytj97T+lpXFC3tW69wVV5wOAGhuJjEgG+krOT5HCOCOO6jf77ILcO216VuD854gZ6LGWH2QN2ygoA0msfYme5otmprW43e/OxM1NfT8m2/sf0MlyMkSy1vhcplJE2D4IW8sZs6ciUikAEZxA5odOHAjGSIRcrju6mKGaSedR6H6H7e0bEi6MTAHPIVhmKl4Ug9DiBL4/aUm0uOKbRfHjh2Le++9NyWZ4cwdgFGcIx3wArJ8uXlhq6kh//Nx46jPVFezWZpZRifo/4nEft8+6EtNqs8EOVumudZWeyJTUbFx5k+zYsubI2DDhnW45ZbrezcGavApL+zZIMj2Y9dYzKxmdyYUF198CWprh6Grax1uueVxCFHQu+Db3WpqavDoo6/2xj+o5WYBO6uGoSAD5TjllDrMnz+v3xYTTp+4+ebbAZCxPO6/AeDGxInrccMN++HCC8+3qIt+AMQwzjrrHhx77LG9mS8WL56LBQuM+A51fuWxVFKSOB+wuV/0gOMRWlujuPnmm3DBBRdg1qwXNtrnnueQ0tLNANCO1u3+ARdcYC7KYYcJsdpHn3+e/Dek7LuLxdy5r+Lii88H0IpwGKiru8JWjODsGVZ3rkQIBIyNpkqQVey111Q8+GADzjrrSlRVbY6uLuCWW27pvcaFhURiE8XS8FCprqaxyuNXRbwrEdDWthaff/5e7LFxrMfTPy5dnKr0yy+pf44fn1z42DWWwn/RIvPr1dUw9XGACHdxMVBZGVNHsAGAB7y5BYx1x5yqtCn2eAimTp2atapyfK52c3kgQIr6yy+brQKPPPJITMSKgjbQVQm+fRRoI2BPkO2tc+sAROH3e3HMMcdixowZGDZsTEYZplIhECBe5PNRQKbLBVx0UfqFUoA8J8hSZubTZi2HHAhQ5CvD3mTvAwWSFaKpaT2WLfsfgMQDXvVBBjKrIc67Ip6UObn+xqYAI/JQDRoAapUnu4AlMz799JnYoy2SHmcQ5HZUVw9LuTHggKf161djwoTxlu8ApAzD5zPn4vN4PHj00UexbNmytJQ+dTHJhCCXldG1CAbNUdAlJTQpu93mSSQYVPPDmtmfHXGzZizhYiHZSPW2dm1iMsPmz778rlmxrYWhtrgRCDSZNgZs8Wbf+myUm7Yfu6XgDZ2d2b2xsRF33HEHmprWw9j4Vccdp6KlpQV/+cu9WL+eJpLSUvNiEp8OToL6BQBUoLOzAw8//BAefvgZ9Bfq6uowf/6XuOuue/Dgg7MhxH7weIALLiBnWnUzYLiWfBS73wNSkpzd1LQed999Gz74YB7+/Oc3UF1dg7Fjd8WMGb/DySf/tlfBUvO/WmHuFz1K2+l/DQa78Yc/vIbLL3+3X9r9r3/dh1/96nIAwPHH75SSHANGUE+qnOCcB5mtBJydIhUaGhrwl79cge7uHgDkP+HzHZ7QYldVRWQ9HTeERAoygzMxvfTSPDz44FNobX0VQAWiUSO7yRdfPAYgPg0aQKITp4AbM4ZUPLuUqPGuRAAQhJS0tqhEraYG+OqrjXcj45zX7Fe8xx7Jj1evs2qxKSqiuZ3bv349bVCqq4H99z8+dlQrAC94c1tQUID6+npTqtJwOIx33pkDjweIRFy9BD4bBNma3k5K4LXXKIXl9dcDt94K3H03cMklNM82NjbG5jVGGBSIbIeRoLFKm2jVTQ5IZJ3rhCFsVcbmkNKMMkylAv+fX39N12/rrTNPz5vNQiG3CyHi9o5CiO2EEG9k63czgctFZTPTXeC7u43JZcgQ6nBqR7CvcAcAP4A7TyRC0R2JlDc2WTJBzsQkMno03Tc10WTpdhs5JTcGRB44CXArVBU4mb8eALS1fQragY4F7agTgXquEB34xS9OSmiWs8LrNTIBCFGpvNMBYLzp2GRpuOygutQkIsiNjY2oq4sv1ZpOMAtDCP4PN8AcJGVP3Kw+yOwXlw3T3Lp1iQlyQYG5xGgmMBN/lnG6QRkF/Kb3OSiU0xRlYwGxr9RIQWtqyXgVjzzyCIJB9XpFYIyTxIhEhqK1lRxYrWqLXREgw0+xPPb5KOrr/5Xyd1JBNXGPGLElLrnkEjzzzBeQEth+e2NjwjAUJQBYCGrvTjDyl5M7ytNP34a//30OWltHg9w1utDVRUtNUVEAkUhigmzuFywuAIZ6tQ6RyEo89tirG11UIRSieZLN5ZzjNxV22onuFy9OniVIVY+j0cRttoIsdt0g1e4pkCixL8LhEbYWu8JCIkDpWJHsFGQew4EAjfVTTgHeeON3CIWeBfAVaP422FU4PB9AEN99F/+bq1fTPFRdbVhB7eYPe4tNAOxyoLowuN0059kR8kzAVfR4Y7Pbbonnb4ACLmtqaN6xxtcNH04xJR99RMor+9WPGkXmBa+3C6wgV1dXY9asWbaiTEmJuZqe250dayC7ljA++YTcDv75T1JXhwyhFKXNzcDVV7fglltutXxDMyjTi3UNLwKN/0JQf+3Ehx9+aOrr9te6G2wdoqqiQbCrRroZplKBq2SypYctP5kgmwryWgCLhBAnA4AQokQI8Q8Ac0D5ggYdLheRyTSSGQBQc9aG8ZvfHI1zzjkb22yzee9uhxe3eKwCRbkDANnCli2L9yOU0kghM25c8nKNdhgyxPCNVnfgG+uz95e/1EMIJjHGNp4DN5KhtrYc5GPsgZWwmkEEefvtR2LXXfeyNcvZQfXHlVI1AflAfoXmPzCdzBWMVC4W7KNlLdV62223weUiZpzK9Em73HIYpnRj1WfVwQqVIKvktD/z4wKGS1GqyOe+ZBYwE39mYkyQA6b32Qza0UELSDZyPttXaqxEdfUQU8l4FfHWkw0AJkCtHBmPYgBeRKPUwa1Benwuzc3NEL2SD5uhWf4IY9WqjfMziTdxD4HP144XXyTT1nbbxX/G3N4uAItB43oH03FdXR2IRn8CsDdozPeAFz+/fy3C4cRk0dwvAjAWUXVsR7FhQ8tGz2usKHL/TacQEEDEcuxYGrvJMhKpKd4yCbimOcoPEiKaALwJWqp/ltBi53Kll83C748nyK2ttEY0NwM77MB53L8CxYf4ASwH+aAyAgAWAYj3z2W3C84XLYR9HI29xSYAdjlYu9a8PtbWklvixhS/8vtpbeT/admyuQnn78bGRgiR2J3G7aYN1cKF5qwMvO4ef/xU3HXXvVi69Gs0NzcntFiWlppTdhYXZyeeRI0l6e4G7r6b2xwGEEYwWI9DDiFiu2xZNaJR62CIgMb6YQAmx869FC5XDaifVsaOa8MDDzyKt99+sfeT9te6G4YluhY0Rxh9LJN1OhFYQeYNUbJ0bomQzSwW9QAOAlAnhJgL4HPQ1ZgspXw+W7+bKWprgXfeSc+vcfZsNiu2xe4lWltXmYKp6urqehdZA02gTiQAdMDlakVPTzyxaGqiQTJkSPouFqoKdMwxB6Orixi26nu8sZksTjihDgcccFzsWRsA9KZsS2WSpHzI7Ky2re0xHo8X++xzNB2x7WaQMnFghxVer+pwr6ad4v/bbPJOJ3MFQ1Vbqqri3zcragbC4RC++GIOgNQKMk+GFRVhlJUNARPkZKqDNUgPyI5pzi5Az4qhQyltTqZBNGbFlv/cbgASxcUe08agqsootxwOZ6eSXE1NTW/6ourqajz00EN4+OGXcM89tyXs4/HWkwBITRmd5NcqYse4e8tqJyJORl/lwcy7tA5UV++esl3JEG/iJrVcSmI229oM1fj28ri2c58Kg8oYrwQpkJTGIRwmgpzIKmHuF4kIMlBVVdUvBFl1s0snzzmDVeSvv058jBqgFwqlryDTde+BYalbFLvfNqHFLll+XhUdHfG5/KUk62N3txE/YZ4n3wWRZIM0lZVRJOZHH8EEDlxUXQ/tCLK9tTUItzuCkpIgQiGzkupy0fk2NvY9o4XfTwQ2HAZcrlbcfvs/E87frNQnc6dxu8mNRDXbqzFEQGpxobDQTJCLivovO5CKlhb6rSeeoLR269ZVgAjqNADT0dPzER544J8YO5Z3IHb+J2sAVOC00+7B4sUSNTU1seI6IagukpFIGLff/o/eT9lb54IwNv61IEGrFiwuZLJOJ4LfT2vFDz/QJmb77TP/jmz7IPMI98R+a7GUMksZW/uGkhL6E63ln+3w7LPs99YOUoRpQbea7uM7RACkIpejoKAQ48bR1thKnlht3GILWjhTRT3Hq0A9iEaJqbzzzte97eOghL4iEABGjNgGAHDQQbtjzpwXTSnbkmHq1Kk44AAu+LEtVFMdYBDt6mpKHszEL93gRI9HDbQYbfl+s8k7kSKbCH5/cgXZUHPKYAQIEjo6PgVA1zhZxgG+dKNGFeHf/74Xr7/+X0gpTaqDNQLY5RK47z6q3M7n5/Wm14czQVdXavejkhJa+F9+ObOFy6zY8ibGh6qqKtxyy99NG4OSEmPD5PP1L0G2CxZqaWnBtGln4u235yUdf6eeeioKCqwddQOAXZP84hC4XNSZuE8lWkQNErEGRDiHgEyZPejs9OKBB55I1rSkiFdoqkGLFhHkbbaJ/0x8ejuewJLFF7BFhAhyUVF37N7+aHO/CMIgyMZG1+Px4vjjj+mz0sYbomHDNseMGZfEyhqH07ZaAYbCnix4zKogJ9oUWFFfXw+PJwRjeeaI7m1xyin2FruyMnJvSJUCsb0dWLOGdv033HA5jjnmaFx66SVYseI5TJ5sCDPmNcwH8jmnubWgoBCnnLIV3G5yM1CJLBNkVpCltL/Wdq5E1dVDMW1aHcaMoQFhFZAqKshi1lcXhK4u4P33idhHo6uSHstzOyvIX32V3vympmkVIvU6VlBgrHkbNtDxXV39byVraaHrYN7Q3A8aX9TWcDiElSufjb1nR5CjqK0twjHH7I633waWLw+D1vQWWIt8rV5t7Nas1jmyjAVhuI7VwhC0KtPKMJUOfD7abEhJFp9MU9wB2fVBvhrAGwAellLuDWBfAMcKId4RQuyQ/NMDj3T8KNvbebXsABFkw91AXXDszbXfoapqLM477zzsthstFlaC/NZbywBQYNtxxx2HGTPOwQ47bJPQYT1eBeoB78reeYcmgqFDiTj97399D+JKFdiRCsccQ6vtZptNxZw5czBnzou9NybaVrNfuuntXC7awZeUBEHkoUZ5tw3AjgC8SRXZRPD57H2Qw2HyXR86dAJo7zcU1to3tbUe1NTQdyQr+a2qTHZp/RKVy25vp0WTfVmzYZrjoLhUKC83VxxMFxywMm3apQCAAw7YDfX1f8Opp/7adFxpqaG+vf/+J5gx4yJTFaeNiXy2DxYCQiE3Zs9+IamCPnXqVJx33nmxsuoATfBdoAnffjYuL98Whx9OgTyVlcndqJhEFBVJGIsJ9e9gsAdnnz2zz+2OV2gqQJvJYrhc623HeXx6O57AtkzjF4kITZkyDlImV9a4X7S3N+Hoo6fGXiVJjjfUBxywZ5+qOZo3REVg//dgcDneeacx7e9JJ6c9n1+mCnJdXR1mzboTpaVMUFsgRCuAMmy99VTbz3BhqFRzwH//Oxfr1jH72gBAoLu7ExdffDLWrm3o7e/WNczl8gGIoKZmM5x33nk44oifYbfdaM56+236TCBgrGlbbmn8L4mIiVpZkkWBAw/cq1fwSOS61VchoL0dePfdL2PPkvuFsVI/bBjFQHR3pxdPwurvsGFI2c8Bep/VZp4/s5HXfsMGEpOMtehEUOEfMzo6GkHkdRsYbhMEdqlkUl9VRdluzFmo2gFIjB5tTg/CY/rRRx+Fx+OBmSAb8+eIETullWEqHXR3G5updN2nrMimglwLYGcp5ZMAIKVcJaX8FYAbADyb9JMDjJKS1PlcSQFgJZQV5Lbe960LjjVataPjO1x//V8xdepU2wCuxsZGzJ/PI39pb9TwihVLEkZ1xqtAPWC/Hp+vrPd7L720DscdNwMuV23SAhSJoAarsTmpu5tI4sqV8SW1rRgzhojf2rWJNyJqYnggfYLMx44bRzNRcbEhfZWXF2DatPPwwQdBfPRRM44/PrNB19ZGu3k1hR5A7Zg8Gbjwwp/B5doGRBSCUAMYOjs7UVVFs6WaF9YKVWWyS6pvHwEM0OQSRjDoQSBA/0F/K8itrZkp+XGFotJANGr0LVbwrItKQQEQidCK8eSTc3pzJvNmYWMinxP7ulVgQxo1bqdOnYr7778/tuGjzd9dd92Ndev8vQu/erv77jnYcssdARibwWQqdV1dHSori2GYI3kxCSEUGobTTz+9T5sFs4nbBTVGYOutE++k1Xy5zz//b5BCPBxqoJ4dPB5aoXbbbQsIkV5Fr4ICYJ99SMIrLx9v2lBzhp5MYd4QVcBQplenTFmpgnPat7cn9v1lf9wtt8xMQQaAU0+twz333IynnyYhYa+9iEUlSg8KGDE1yfDoo3Og+ovSJqEVoVAgLjBKXcMikRDmzPkPbr31vl7L4cEH03GvvUZj+MkniSRvvTXNl+x3nUlardJSJCXIpaXpxwxZQfnH2Z8pMUG2xtawm0U6af34/2dPmHQUZBZf+LNc7bC/EAjQBq2lhdZyl6sVan5mM/wAPgPNCUYtAqtLZUUF8MtfHg+Xqy12RGXsvh1utwd//OPltt9ujD+rggxUVZWioaGxX8gxQH2S18RNjiBLKS+QUsZ5iUkpXwF7eW8iSKeEaTAI7LAD+/21gxSiNgDpme45OX4oZJifvvnGMIk9/PAjkHJc7OgfQJcmDEAmjOqMV4GMKGCvd5QliIzMp8kKUFjBpv3NN98SjY0LAQBPPXUPGhsb0dYGTJkCHHkk8POfm8mZlSy73eY224F3zEyQMzGHFBYaA+DUU68wqdPHHLMHFi4EXngh84l11SrjnDg9E5do3WUX4Oqrj8DMmT9HSclXoOBLwxnN7+/BkiXkh5xsUVNzhtotookJnBHhz+VTOzoSV7fqC9jklw6GDk2upiVCMGj4RJaVEdG2psJ68cVnsXo127LLQUqtmWH1NfI5sa/bMFRVVWb8fQy7jSBvNNlXPJWCzFi79keYA1oADgiMRKjDZLpZMJu4CwBIFBTQf7Hzzull0adysmyqTuxmUVBQiOHDyWjIFvV0CXJpKd13dpqDULk8baZ+yObxVAHDt3lNypSVKoQwVGS78d3VReKB12sEXGdq4q2sNKoS8m99+WXCw1FSAvz0U+L3w2Fgw4YQaO0KgASVYrCPe6rAqFGjzHEOu+xCZHbNGuCss4DnnqPXzzqL7hPlQE6G0lKj6qDdmsylnvtSKKezEygo4PgA+wVfCFdcbE26ea+7u6lPqsWN+qIgl5Qktzpmiu5u6n8sYIwZI20qgap4K3Z/EACgtnaYrUvl0UfvjQsvPCdmUaK1r7AwgNNOOx2nnnqi7TcbfSyeILe2LsPPf35qb774jU311t5u/KebHEEWQjylPP675e0XsQmB/X6S+ekGg0B1NZmbCgr8oEU6eQoXBvu8nXHGsfjlL0/CBRfUoaamCz09hvN/c/MQkPm0HTR43TDKvtpPXvGBDiEwQS4r28ISRNYCijbfCsGgJyWZMJv2i0F+tkBn5wrccccdmD9/HsaOJXV4u+1oYluzhgZhU1O8nyg7yFuTrgM02XF08tChNGlkkt6uqMhIBWadWNxu8j8aNszIz5kumCDzDp/Pc8896RxdLuDPfz4UtbVu0KbGzG6j0a8AJCfIrCDX1hK5tAZsJSZwPnAeSSZjpaWU17K/8iFnUpq0sJCueaYEXS0zraY8UvH3v18PI90Xr7jxclxfIp8TpWZ0u7fCiScemfH3AYa1xAreBPL1qqxMr7rayJE1iCfI4dht77jj090ssIm7vb0Hd999D/bai1w/0q0yBQA77MCmnsRuFsFgAGvWkBLAFeXSIcguF/VpHgLWIDSvF5g7NzOyZB5PFTD+zzUpU1ZawX7aX30V/x77Jm+5JZ1nuqq5ispK4J13PsSZZ56JBx74PwBRvPVWFKeeerNtPuTSUrKEJvo//H6gooJz2bXF7ovAVRKTBUY1NDTgwAMnYvr0c3pToh1//NFYv/4suN1fo6eHfnfy5JX45z/PxDHHHI2TTz4dv/zlgSmtGmqg+YEH7oYHH7wZgD1B9nhIVMo08JyDmMvLOZdv/JcXFBTioosuiiOCTJBTpfVT1eNIxFgjEqGhoQGbbz4c//nPDQCA+fN/QGNjI8rK+pYZKBG6u6n9TJB33rkaF1xwAYRIdHLzQGvLlgC2SLpxZIvSnnseDgC44IJp2G23PRL2daOPhWK/EQSNw8LY41IAQzIS8uwgJXE6dnnZ5AgyzFmlD7a8l3ImEkLMEkKsF0J8qbx2rRBilRBiUex2hPLelUKIJUKIb4UQh2Z6skIk9/sJBg214qyzTsRdd92DNWt+TJrCBbD6vLUAKERnZwdaWl4BAMynPPAoKjo69om3QQ7rbqgpv+wmr/hAB2NXtmGD25LoOwLqlEcAmJySTJhN++Vgggy0IxgM4IUX5vSqAx4PcOihVHnoiCOAQw6JN/fvHhPf58+PJ3Dr1hFJGjqUJvl0M1gwioqMRZ1JrRXl5aSuZBJIxm43TJBXrQImTQIWLjSXSKVNxFoASwCMgTGsfgQQwKpVwMsvv2/7G6qLBRC/iNpHAAOAD0KQnMP9trqa2rgx5Z8ZoRCpV5mkGUzHB9Lud1iVKimxd61ZufIHGCZB9nWJ/0/6EvlsFyxUVTUMp512GQ491L7ceSRCG71EG+pEFikmxipBBlL/x5dddhGEsBJkgIJrxmFjNwucDoldBTIhyFOncuqHZGElAtFoJQCjn6e78SopoQ0uALz22hJTztqLL67D00/Px6efpn++5g1ROdhlxe1uSpmy0oopU+j+7bfjN4ac/k3NBpIpQX7vvRfx4IOPxebxZQAeB+BCe/s03H77PXEkmcsyJ1LV/X5gn30Ojz1TT7gzqRWU17C2Nrs0GWsRiVwOt/t3mDr1YXz11YW9646UbgAdSa0a8UGyXfD7SQZfuTJiS/aFSDzPJ0IgwP8NuQJVV5NwRJUjSSVNlM6xqopSuvn9iasHAmaCnMqlhtvd2roeHCQXjVbjtttuw/vvN6K7u2/55e3A/WHhQrouzz//L9xyyy29BX7iEQLQGHv887Q2jnyunNc90cbAGH8y9jtMFPg3JDgbVaZ1C1QEAmbhjQW0TJFNgpxMx0pH43oQlHTPin9JKSfHbi8DQCzo7yRQRNZhAO4UQqRRs8iMZOqXGqjGbgDp+JSZfd44MASQ8gMARBgpgTkXhX89dm8oyMmiOlkFIud3CSM1UiHiq3q1AlgBYGtsvrk1FZ0Z5gW2BAYxaQPgQWvrKhOZ2WwzIshbbEGqydChZnPcVlvRRNPcnDh7x5ZbEmHK1CxXUmL4fCUyMbrdRGzS9e0KhYz+MHQoEQiXC1i27DGceaY56wEhDLp2X8AIFIwAICnp3nvn2qo+apCenRnWPuATGDlyKLbYghZ3dWNXXJxeuqdU8PlSp3izoqQkczeWYNDoJ8XFiXKmbgaj3HIFyHpjHnwbE/lsDRb65pt12G23PWwneSmBW24B/vhH4Lzz7NN8FRVR37GqTd99R/+R2q+A1AT5xBOPw8EHc2YM6uhm9Sc+B2Emm4VgkK61GmCULlhdE2IiEi8nQwB4UV5OJM7rTa+iHED/Fzdl7tyf4nLWPvroP3DbbfPT7ne8IaqqqgGpVTRWp007MmFWnkiE+rXVKrbFFlSNLRgEnrEUNrQjyJm6WNx++98QCqn5R58CbbqHIhSabOszLWXidczvB0aNIv93j8dg0VVVBUmtoMYa1gUSYeIvXiSyDo2NTyMUUqu3eMGWn0RWjfgg2QBoM9yMUMhtS4Srq2ndzKTkPK/fwSD5zz7wwL8xZ86LmD17NubMeRH3339/0qxM3M+TVU9UCXKqoExzu9eBU6WFwy488sgjKChARhu/ZOjoABYu/BDffcc7+hW9MU6J8V7sfueUG0cpjQ1LTU1yXmQWJPyIJ8gdUNPBMg9RrQzpuGD09NCN3Q/t0rSmg2wS5BIhxM5CiF0BFMce78LPU31YSjkXhpNKKhwL4AkpZUBK+SNIyssoUSglyE78vqogV1QkTl9jhZlodsFIQ7YEQBOam4ErrwQCAQ8226wDtbXUiYUoBODvrd+eynF95syZCIe7Y9/PfgZ2paHCAAqxYkVXUtOXeYEdAlJbIiCzSElclS0VLhdVKbLmsWQVmUt9Mpgwb7FF3whyeTltWoqL6TeTWQLSjXxX/RuHDjUmvGuuuco264GBL2H2j+W0QlvHLWiBAO283W7D3G6nMlkDPqWU+OCDRowfT4u7+j9XVpLK0RcfPRV9SaXGKn0mLh5WgmznYnH99X+EEKqCHIIaYZ3uGEkXra2JNwevvEJ50wEilH/9a7y5Vwij5K7PR+Rq7VoycVZWGmpLRQWNi1TuRAUFwN57k4/SZptNxJw5L+Kiiy6KpZgLwVzEIfPNAlsLWlupL1Zb99VJMHw43aQshcdjkxsORoAe+9mnm80BoGO5QqiU8YWGIpEA/vvfh/Df/6avKtbV1WHlyibcccc9EKIaQgBHHLFX7/vNzcb4iUTIbWv77en1sCVt7skn0/1rrxn+wn6/EZjLBDldtxIVq1YtgVlLigL4X+zxwbam78LCxP9DT4/R9w45ZDfMnv0i7rrrHjQ1LUs6dsxr2FqkCsi0/GqC70n0GpM42mHYFWIpLKT/OFmRFiv8fmOezGQDyEgnUM+qICfr5+Z2d8MIwq1BU1MTamupD/VHTElHB/Dcc89AypGgPpTEUb0X34E2K2MwadLUpEe2tdFaWVpKa0Cq8c2CxLPPNmD//bl6B5utumKPSYUcM2ZMwlScyVww/H6ze0WmYg8jmwR5LYCbAdyoPL5Jed5XnCeE+DzmgsHJt0bBfNVXxl6LgxBiuhBigRBiQVeXMcGUl9NCZp0AGX6/ESTCZabTMT+biSblTTZ+k2SHJUtoYbr44oreiPhZsx7C3LmvY9my5JMXgwZcMPb9yQgyQINk86SmL7NpnztvB4AovN4hOP98eyd8xrhxRs16RjoEuS+BHSUldD3GjTN/nxVfffUu9tzzHAjhSbkLtVac4vNKbbreAFLpOWCPU1hsH7egsb/0yJGGopbuIur1GsEsKun3emmh3tiUb2pp0tdfB/785+S+1Pzbfn9mgVOqD3JRkf2m89RT63D44XvGnlUACKC8fCweffRRSCnTHiPpgktaW/H998B//kOPL7yQVKWODuDhh+OPHTaMSNNjj1GA6Jw5Rj9lklJenp6qqEa6M3njFHPV1UUAxvVaFvqyWQgGDfJQW5u+ustgdW3vvc/pTXmnmq6POIIUKE53lkk2h9JSNo9GQPNZ/ABpafkJVVW0eUlXVezpoY20lPTf8vX2+eg72E1pzRoKRjvgAGCvveJ9y8ePJ+tYIEAlewEqnNPTQ7EZqpCQibsSAGy+eTXil+h3QPP8ZFRV7Rj3mZKSxCJAZ6e58FEwSHNbqiwT5jVsJTIjyIYfkp1Vwz7QHEhGkAH6X9n6mtZZKASZ5007dHXZWxm5MMw33yTuY1YFOZmrYDwvMIpm1NbWwuWi/pIsA1K6MNrkAVEv+z9NHbMXX3wBJk+mySlZYChgWA3HjCH+lK6LZHGxMT6EGKm80wPgV/B4tkB9fX3CVJzJXDB6epL7Hzc2NuLMM8lX/qyzzgKw+7z4o7KbxWKqlPKARLc+fu1dIM/xySAv+5tir9vtD2x1LCnlvVLKKVLKKWVlxuzFJvhEqbK6ugyCXFpKFzedXYnZ582Q5TweL846a3v84hc0QZ11lrm8azicWaozGnDciZjEEUHmjm9gA4CdALgSmr7Mpn1WqNpRWzsMp5xyOurqDo/7jAqvF9h5Z3PaoYkTqU0//mh+XSXIkUjmPshMMuzS5zEaGxtx1103o7tbgNNZJduFqkRvyBCaFIcMSW66Hjt2bOz/WgJjEWFWuTVqasxKHwdNqIUX0zXDFhQYk4vV39XlSi9nZzK0t9OYCASA++8HFiwALr+cCGKyYNZMUxT5fKkVZADYf38iA5tvPhFPPvko/vOfZ/qVFKugaHd6HI0SWZo3D6ivp3F5xBGUuWXGDCJWr70WP29wZpXKStq4lZcTKYlGDQsHzyOpUFBgFBSgTAT0+tSpUzFr1r9x111Po7s73OfNgs9nXLNM/I8ZrK75fFv2bvBV0/VmmxGDrq5OTRysKCuj/ux2rwWZ9uNdw2pra1FSQv9Nuoko1CJAqmK+YQMR4eHDSYkdMYI29kJQO4cOjbcYqPEVAPDGG3R/ECUBQCSSmVsJo77+6tjaoS40XaDKdi54vVfGufEUFdG1tLPitLcb515dTSQiGVk0zkNdw1pgv9wmAinIiawa8UGyAQACbjdVHElEkAsLqS+lSyB7eoxxk8z6uWEDHWv9/yoqaDOU7DetBFlNDWqFud3dYJ9wl2tEr0tDdTW5cKUq/pIKnZ1AeTmTC3uBp7Z2WJy7SbIqgiqM7BiZE2Tuf1tuuY+SX70VZWUS06Y9gl/+si6pKJXovc5O43pb57TGxkbccccdiq98BIC07dTZzGKxtRBithDiSyHE40IIW0U3E0gp10kpI5K8y++D4UaxEma5dDSAlKFK1t2n2504wOnZZ9+KEYMQTjvt1zjvvGlpRViafW4CIBcHF8LhEG6++SY899zRiEZ/jbvvPro3+KSurg4ffDAv6QCzwhhwARhN3xwej9fG3ygACrqjS5Kok9XV1WHJkmX4v//7EwBg0qRxuP/++7H77nukpfKyossoKCA1BjBU5DVrSFErLaWO3Jd0SIWF9LlkBNnI6NECtUpQol2o6mIxZAgwd+5HOPnkw3sLdljBQS5UBUvNc9cFmpQKcPDBZ5s+Y51YUkU9A4YvVm1tBW6/nc77xx/NjLW2Fvjkk43LpdnWRv/r/7d33vFxVNce/91d9d7lKskdGxsMNhhIAAMhCcU2PZi1aX4xIQSTQAIEpz14JnmUEEI3DwzYwiEkAUwCJEAwvTlAEhw6WDausiSr1937/jh7NXdmZ7ZLq909389HH2lndldzptx77qmvvUYKlPKabNgA/PSnzoN2VpZ9BYdg/6e31wg1cFIYVZKFOq59++yVgEhj1ezo6DCsfQ89BHznO6QcNzcDM2YAy5bRvnHjjPv5nXcCvycz07iXCwoM67HXSwqz2x3eIlhVQVADvW4hVIv0e+/9U9Ry69VHonE/693G7DxwKrFp3LjIJlDAOD9Tpyrt0lwtQ69Zm5HhHANvvS/23/9gvPUWHZhSkH0+uqcmTwYWLgSWLAFOPtmwLmdkAEcfHRjCNc8/nLz5Jt0vmzfTdf+qP8cz0rASxdKlHnz722ehooLGaWXoKC9/AkVFPdi9uwi/szRTdLvp/9nVpbdakHt6giuLCvMctg+RKcjBwwQDk2R7kZeXj+9+95twu+1jvxWVlcA//uHs9dVpazOum9Oi4C9/eQ1XXeXBxRefgEWLlsLj8ZjyRmbPpt+bNgV+duPGjfjoI3qIrrlmGa644nL89a+POx6PWW5DQT7kkBMHY6EzM2mcjaXkm89H98KMGapOQuADkpWVbRtnHG55O/XM1dZG1hBHzx0aGKgcrK++YcOTePjh+zB37hHYtCm4Ucpp3759xr1uvd5r165FX194roehDLG4H8BfAJwO4B0At8X6hUII3Vh+KijgEwA2ADhbCJEthJgAqqDxVqjvs7qCi4uNVpk69fX1uPnmh/2v2gBkoKNjd9hlSPREOre7DSpRz8A8mrW3t+Ghh9bimWceC/nd+v9Ys2YN8vPdUCVshKjBihUrtE5fOq0ADgIQ/AbUY69VcqKU4U1yJSX0Pt3iqKwtL71E36uSWw45hCb7aBVkILiCbIQ3dIOsu8bqw26B0NZmeAw+/vhtrF27Hrt2fRzwPgAoKCgYTHLxeDy4++4bkZ9vnKDMTMpCzMmZY/qc1TUVyvVsjsVSFUsG0NGRg2effWnwfUrRfOUVp28KjaqBrKxhHg9w00002Hz0EVmV7cjLi0xBVpelsJCUZKcQk+pq2t/eTgqmzxdoyY4mVs2O9nYjVOWv/pDP2lrg3HOB//kfs6s8mIJsh14pIlwvkRB0PZXy+sILH5iqOVx22cVYseK3Ucvd3R3agqxicXfuDKwEU15ONXK7uwPHTykNC9QBB0Q2gQLGs3344bRCysw03GzW5gUlJZTwa1042d0X+/Z148UXKSZCTaDt7XSd8/LoGVJ1uXWqquj66+egro62t7Ya49k55xjPc6RhJToLFx6JX//6LpNVfs2a3+Kaa+jG2bAhME5VCHOCtKK52QjvUd6McDujqjnM5+vEmjV347LLfhiilq4LwACE8Ib0apiTZPtxyy0347jjjsKECXSMTpUjsrLoGVWu9GC0thqyWxUmrxfYsOEN3HvvQ+jo+BuAzQAK0N7eht/+9reDSrIeIqjfYxs3bsRvfrMeUpaCcnT2oqurEz/60aVBnz8l98cfv4fzzqMqVm53nek9RUX0/yJJSNRRRkAh6HsLC5WlOnT1jsmTadzZsSN4Eyp9Hotk/s7LM+6/nTsDn9vqalLOr7zyJttSnMEqr7S2Oi+IIql3PpQKcqGU8l4p5UdSyhtB9YjCRgixHsDrAKYJIb4UQiwDcIMQ4t9CiH8BOAbADwBASrkZlOL7HwDPALhEkt08KD095tWnatdrXX2vXLkSPp+KkWkFZed2RlyGZOXKlfB6GxGoIAfi9fbjppuuC/u7AXrg1q+/D/X19yA3F5CyCAcdNN/fHtJ617YBGI3c3OqgCT26glxSQoN9dnb4E/u0aeZyNXPnkpXjww9J4Xj+eVJ8vqWFNEeazJKdTQ9XTQ1NaNu3B1odzKVqXNArfNgtEFpbDQX5qaceRn9/D6wLGUV5eblpArjwwm/hlltuxmOP0Wr4sssooujvfzcPAvrAEo7iYI7FUvHmNDusW/c3yzHReYi0XqhCyf/vf9P1OOooGjCvvprO8VNP2VcMUR3OlJyhLLpKYVQeCac4zcJC4z2trXRvWa9xNLFqdnR2koxvvEF/T54M3HYbcMYZgQqTUpD/+c/wSgiqyby6mt4fTogFYO4w9re//dtUzaGvbyeknAxrdYFw5e7qMmLWnSzIO3dSHOa0afYxrk7Wpp07aXItLDRCicKVGQhc/E6YcBwuv/wKFBYWob29Db/+9c2Dlr7sbLOVVGF/XxRBSrqhlPGys5MU/WC43eTN0O89ISjc5ogjyML8q18Bp55q7I+0i55Oebl9nO2MGWRU6O01GnQofD5zUxUAWLNmPa64YiW2b6cDv/TSxbjmmmvw9NOPRnQ8QlDOxMEHH21pO24lG0BrVKUX8/NpPNzfH2IdzMWflRVefft9+4yFhFKYfD66P3fvBjZs+A18vnpQVYUdoHDIHAwM9A8mV++3H93Hu3aZx761a9fC51NlDt8H5fgA/f1tYT1/WVl0ToFAD0hREY13r70WXeJ1Tw9dM/W9q1ZdFHb1Drc79DXQ6yurSx3u/J2TQ89FYSHdx9aFnstF+yorz8Dq1Q+aSnGG6j+hh1hYk47NukARSJ+zZygV5BytcsXBMFeyODjUh6WUi6WUo6WUmVLKcVLK+6SUS6WUs6SUB0gpF0opd2rvXyWlnCSlnObv1hcSn88cT6TcldYEp4aGHTAUqlZQsDuNQJHUG6X3NoIGj9B8+aWNOTsEubk0IKvM723bjIQew5JsuMi6uwtw2WWXOa50e3vNbaZ7esKLW1Mo66iiqAi48kqqJywlXYPjjjMmJikjtyBnZdG1y8gwJmGr1WHp0qWaxaMLKpbRaRXa0kKTjBBAU5Oqm2YffGu9B4Sgh/LZZ1/BsmXLcNNNpwJoRkMDcO65N2Hjxo3o6iJFMjOT4hzDccOa/4+qI7nTf7zZWLZsmckl6HLR4NXfH54bUqGqGqhnY+ZMY4KfOpXibwHDuqyjYvk7OsKz6CrLhApBcFKQ1UAKGOEVSglQHR+dwl8A6jAXTthBfz8dv9sdGEtqx6hRNLl1doaXVa+Uy6oqoxVvOOjuSCmtZl4vaJAPLPYZzvikh1jYPdvNzSTn4YfTgkCFIug4xSuq17Nm0f0YaTUHtRBXCvJnn3nxm9/cFlDuTVn6hDDKRirsz0EBVCKtklnK8Cp4jBsXqIDOmUOLxyuuIOVVJ5rEY0VpqfOzqypoPPecOeQpI8OsbNTX1+OSS65CczNVIKIQuw60tDTj8ssvijgEqaaG7hnVJOLyy6+wsSZnwu3uiqr0olKQw6kcUVpK5RNDKY9tbYFJert20eL33HOBxsbfwygl6QXZ5uiNyuLodtOiBKDFs4L2q2oM6mAFgJ6wnr+sLJoDXC6y1lrXcqNGUdjOq69GriT39tL9t3MnfX+oBaCVUNdgzx4yKBYXh1/XXaHep5Lo7GrHq6o/LtdifPihUYozWP8Jn48UZKekTMNgmA96HpwVmuGoYnGz/8dayWJEYK2okJkZGPMzfvx+MDp4UYiFSriLZIVM721HeDFcAjU1kQcE5uUBr7zyOrZtI5f7ypVrsHDhAqxduxZLly71D2bK0tQJYGJQd6zVghypglxWFpjMePjhwHXXUbzez38OXGQOzY3YgiwEyd3fb2QbWyfq+fPnaxaPNgC1KCtzXoUqlw+tYNWsaW9BtrsH/vnPv+Lee9f6EwEGADwFAGhtPRq33XY77rqLFj/jxhlxg6GsTIH/pxuqCxYwGo2Ne3D77bcPKsklJZSB/NhjoePIdLq6aDDVXeM6SmH8+9+dJ++2tuAW3SVLlqC2tg4vv0xaZSgLsiohBBil8RobrR0fgxNO2IGy1vX307kTgqznwVANI8IJadFDLCKxIOfl6c+dXVuodgBHwlphIJzxSU9gsiqIAwN0Pxx9NClehYWkIFmbGOjdxvR7TXXONOolR95GHqAFVFUV4PW64fMFLgSUpa+ykv6nftvZn4MCAFQaRJdZhZEFo6oqMkWlv9+oQhIpwRbNkyZRA4vOTnM3P+XFUaxcuRLd3S4YlXWaQVO/F93dLRE3Y1A12xXmsVUddzl++tMVUSXSFhTQOZsxg8ahTz6xj6kGaK7Q7187+vv1RaAPy5YtwsKFC3DFFT/EZ589gtxcu3vkc9AzlWuyOH7lK/Rbr5hSUVEJs4IsQFbkvrCeP6p5/Cak3AmfDzj99Evh8Xhw1113wePx4JRTFuB731uIo466BG73/hBChOxOqOjpAZ599t9+Y9RWnH76goDY6mCEqv+sJ9gDkXWMVIYtpSA7heaNGkXj/dNPO98HOt3dNG6RYm1+9nbtAqZOnQ+P50coLZ0A4GkIsROAsC3qkKgqFscO1f+NlDfeMFtDiooCGy386Ee/gBDq4VcZGr1BY2DsoASucIKJ3HC7vbj++v8O+7sVGzf+BQ88sBY9PW8DAKQkjVEpT/fee6/WfpoURcDt6I7VLciq3FkkCnJmJg3YdjFURUVkeVEP1MAATYihEtXsCMfqoCweGzY8hjvvfAAff2y/CvX5jAmmuBhYsmSpPwYq0ILslJ19772/tFgCnvZ//hD09/8IL744GYAx4IZT/zkw47sTqguTUpr6+noHXYJ5eWShbWoiZS/c+sQqi1udQzVIKqZNo4m5tZWUZCtC0GQUynqydWsjNm6kjJdQCnJ2tpEVrpI6d+ywdnwMTaiwA6Ugb91KCuzYsaHj7ZVFfePG0LGCuoIcibckP18vdm+nILeCQrdOBC3giYaGhqCTqZRmC7JeUL+3l4wFhxxi3j5rVqAFtbiYYjT7+oCf/ITOxY4dwOuv0/M8Rwu/j0RBzsw0Yn4nDebnTbJ9b2NjI7KySCnQ3e727cQLoBqsVFTQ96u441BEquwKEV2SHhD6c9YKGgBZ3fWYUXoOy2DkXTSDPA6Re0EBkl8Ic0iRMbZSWNlNN/0WZ55p1+crNAUFhkdt8mT6P3YNeXSChZJ1dwMvvfSe/1UTSHnNRmfnTnzve+eivr7e5h7xAngDbne1KYFtzhygoqIDTU3AGWesxsKFC9DaOgbkYd4HSoKjEMxw9YPHHnsEDz74IKiFAwDUor29DU8//ZTmKZEgb+HhAPKClmjVeeSRJ/HnPysrICXvW2OrgzFhAl2PPXvsCxhYFWQpI7MgSxncgqwoKqLn+tVXQ3+vahCiSjgqe+COHTRvfetbwC23zMO2bb+ElF/io4/uB/DWgXbfNZRVLK7U/j7Tsu/6ofq/keB204W3rr737TOvVL7+9dMwY8YR/lc0k5SU5AWNgbGDSqfdgry84KNwQUE5vvvdpVGtvu+440b09/tguHr2h4pN7OvrNbkmaaAw4nHtutZMmTITH35IAVclJTQwRuourKgIXhpMEWkCj45SkMOxOgB07a2uWIVewULKFtx774Po7zdKQrj8Gnyw7OydO61Fg9sA/Nn/9zwA/fjBD4Azz1T/J3Rct0rENH+nmgmNOpJ6EkJdHVmpOzuDJ1roqJjUXbvovKrBTyEEcNpp9Pe99xoxaIrcXBrsQltPCuH1krZUWBh8cFVlzgB6PlW914aGyCZ3gO5zFZYhhEBGRsagVWb9+j+Z3PRW2e2YOJF+OjoCPVJW9BhkIPzJRLVwzcz0wtz6nXC53MjIaAU9y/ub9gWbTFVr4oEBc11mKekafu1rZuUWoPuptDRQSb7qKuN+vv12itv2+aiGsKpCEk3DjMJCUryNa7GfwzslFi5cgEsuOQ9Tpx4zuDCwayeelzcKLhe9Liuje15V0QmFCvcJx5oFkMzRjmvKq+S0uNUraKj3ZGWRgqDGXHoOK2F0n1QKcru2P3zcblJq7BIBdaKVubDQ8ACoxXmw5yojI3hb5q4u4K9/VSsINTYWA/h0cMFsd48AX8Drbcavf33nYELsKacswN69v/Hv9wD4Kvr7VwAA3O6N/u2ZKChA2PrBddf9BD6fD0aFCafr0Q9SlL8KQDiWaNX5zW/uhZRqrDDmBT22GnAOw1NNvwBqqW7FTkGO1oIcqlpHVRWF04Sax/QFv7qc6tk45hjSY4qLjftzyhRAStjayIcyxOJs7e8fW/ZFt7SMM+oEPfusebsQ5hVpSwtQUEAKyPe/fx7uvvsefPzx+1EpsBdcsBi33XYz/vSnJwdX29afu+66DyedFMKv68COHVtAD1EzqPpdHoDJIT5FlhT7rjWF8PnoRH3wwetRDfaVleEpyLEks6hBNVyrQ0UFWVXtBgVdQd6+/T/o6OiF4TkAMjIysG7duqDZ2TU1dquIx6BCcwoKfo9jjjFPyOFY1jwez2DbaTomlcI9YfA9lTa1mzIywktmAUh25UXZf3/7+q3HHkuDTW8vKck677zzIk455So0NIQqqlsIFRKgLHfBLMhqYdbSYoSljB8/PSyZdKSUWLJkyWBYhtdvCmtoaMCPfvRTvPHGmwEDfyhU2InqsmeH12t4Jqqqwm82BBielXHj6GLk5RnPdGFhEb7//e+jtLQUZGU6DIY7nVCTqTVpctSoGrz6Ks0NupW4vZ2sLdOnByqNLheFSVnd2pmZVBrtmGNIod28ma6TSsCVMnoFub/fCGWhUJJgFRQ6AVSZFgZ6pYTeXomf/vRm+HwuFBXR8XR22jcUcGLGjOBKmZVoxzW320hismPqVFqsNDYaeReqGpDqqEfW0bEwwgSboCzI0bZor60NXCDpCBFZHX9FfX09vvrVOfjOdy7CwoUL8PTTVwKQeO455xrXOTnB61/TeK7uF6VdZUI11FKGIY/Hg1tvvVWzJPsAvAE9oZt4E9SKOR/AVaDFxwfweh9EYWERli9fgQ0b6sPWD7Zu/cz/v5SlIdiCZQ/IgzLLdOxO7NrVBaPrqPkkKUOKlGRddWowo8a2558PTETWDQk+X3idQRXKgjxlCr3evDm4l9PlIiOBXZk9Hbuciu5uek4izW8aSgVZOPxt9zohqEn51VfNq2EpzbUum5rM9SOByAd5HVWD0gnVlCIaxo7V45aVFXn24JbCwiJLQkUXgLGD7qDAuNE8qIH1scceBBB5Iw/VICAUsViQVdwaQAmAAD3QTqiub3Zuna4uswWZkir3De4PpzrA9df/ApmZnTB3VW8DcB2A36C//wmTiyuS2C2jy2E7SEluBw2CZY41LcvKqNJCOAuV5mZjgHFK6hACWL6cBqx//ctQqKkI+2/Q1fUhgIOhl9MLpALKqqXKvDkNrtYQCwB46603sXdvGDdWBPT0SDzxxOMRK8jK1b15s3M1i6Ym2ldWZlzrcBVkq7Xl4ouvG1xQ19fXY/78+f4JzwtKwjoi4DsaGhoCkiabm9vw5z9T8LRuPGttDQyt0ampIYurXYmx736XakWfcgrw4x8b1mOVlBhpCFVRkWFBnjABAAqRk3NMkE/0gp4Ht62VTW8CpMbzgYHIQsdqa8OrWqLqm8cyX0yf7hxj63IZMfJ60mxBgdH58qyzPFiy5CLk5tKFEKIFQBZGjcqPukV7XZ2RZG1FbY+kjj9gJPXu22cEo3Z1fQAhXsHAAPDII/afs4aUWOnoAHJzlYdNfyN5BXULeuD89wVo7LdOTDcBeBakdD4NYBWAAbS3t+G++x7Cq68+5XxAFmpra0Dhd8qEah9CZLALNLYCQoigYRaVlZNgKMi67AIVFWTAaGyk59lJOd1/f3qG9+41cgoAGoebm+mZHj2anoecnPDbOquxb+xYUl5bW+0rI+mUl9NcEyzpvLnZ3BAHCK9CjR1DqSBLh7/tXieEHTsaALyPvj6gvt7op5iba15NNTcbCnNZWXSVFnScSvcoYlGQf/KTK5GZqUZjtdQ6DcA4ZGVl49vf/rYloaIT+fnTBt1BgSvSKtBquwtNTY3Iz498ggtXoVZu3mjIzzcG629+kxStV14JjCfXycqyV5A7O/Ua2a2g9rbNpveEWrl7PB78+MdLUVZWa9nzPoDn0dvbbYoDkzJ8K5Pqcjh2bAXoUSIhi4oOdqxpmZVF5zeczlOtrc5diHTy8w3rwpNP0m+jIYvP/xPsglZCiBIA5PYK9kxlZZmT9DZu3IiHHnoA3d32WW61tbW4+OKLLS7TcChES8uewfsmXAW5qorOVWencxdDvYKFIlwrm1KwlPNAtTXWMTwHe0EehXGm/W632yZpMgc+H40F6lQppW78eDiiFLOOjkBFMTsbWLQIuPBCY+GgvjcaS6pSkIUAjvf3O5g+fQVC21lo4GloaIDb7daSm/4wOJ6reOJIQ8dKS2mMDhX+3tsbfq1hJyZNonPnpMCoZ/Cll4x5pbDQaLKxcycwd+48zJnzdQDA5ZdfgLvu+j+89NJfou5GWVRElj+7ZkTt7aQwRVLOD9CVU3PsipT1AHx4/nn7RK7sbFKInBYs+/YBdXWz/a/2wkii6wmIEw4c1yWoooU18NwL4LcALgRwJ3QPo9cL3HXXDY5yWlm1ahVcrj6QdbgLZJEuC/KJfpDRJg8+ny9o4vFpp50HwwJuKMhu9wSceOK3sXcv3fvHHEPPf1sbeRr1c+lyGfeYXlJQWY/r6ug9kVTlAWiOVpVtwqlYAtCxulzBw3v0jpFq0dvbS/dkpI2khlJBPlAI0SaEaAdwgP9v9TqIbWK4eRcA8Je/bBtUVnJzjQdRudvVQBAPC3J5eWhLXrQuufPPPwMezxJ/Sbe3AbwEIA8ZGT/GJZeQ8mROqPgTbrrpDpx6Kg2UgfFoyu+4D+Xlo6Ky8IarIMdSUF9v/V1ZSUoyAKxf7/wZFcdqpaVFn/j2gQZU8xMZTtze+ed/A//zPzc6NGox4sDUxBfJ4OLxePDuu6/j7rvvwZw5NHi3tVWY6sJaqaqizlOhrPnKMgCE7rR18sl03l94gSZkcxF2CaurH8BgFZWysv1QVkbWkpKS4PJnZBjP3r59qvZoG6xKIEDK8ZYtW3DnnXdaXKbhUICSksno7SXZI7GChWrNqlzgSvEMJ+5coWp9H300vX7llcAYWHO98zbosch5eXmDoSRm8qAsTEpBbmwk63GoMa6ykhYQ1oZLTkS78NfHnKOOonvhvfeAsrKpIT5pXDyff/Xc0NCAyy//Md57j1YxSkEOt/mRQgiyrAWrngDQOF8WTNcJg+Jisn45JaLV1lKoRVeXUX7M5TIq0bz/PslmncNiMfIAdI/YxWF3dFAib6QYymkHzIuf7QBegNcLLF/+fMD4psZ9J6Vp3z4gJ4es5zk5nSCDR6ttLV37cX0HVG3j8JDYscNhlWyDx+PB5ZcvR35+CYCP/cc5GyeccGKQOtMS6v528mj6fMDs2UcgP1/JRApyfv4EfP/7p+AnPzkUbjctZgsL6Zpt3UrzhHXhc9JJdA/9+9/G4lzFJKtayZEqyIBRljbcrn0bN27ENddcg5KSMabcEV3JtWsKIwTw7LOPOpYdFUKcY/f/hrKKhVtKWSSlLJRSZvj/Vq8jmbGGGJrNpNx/MGhd9bNXcay9vTTQZWfTTzgtgYMRyqIQiTXRSk4OcPTR83D33fdhw4Yn8cgjR6GiAhgYqIHbPd/xc/ff/yebWrICRm3VNpx55jlRKcjKrRqqNJKUkT9gisJCs2vnjDPoOr3xhn13RHVcdrFrra3GYOtydYAGR8NUFG52cmkpyR2sc09j4x4sWnQaLr54CaqrI2uLnJsLvPnmm3jnHX/7LpC50ylLOTOTBqNgk7pqU6tclsEsyACtyr/+dWU1USWPFN0AjC+ora2FlBIDAwPo7JRYteoG9PSQVlJSElpZVMeyb586px2gBZzZkqhbgZxKzTmRlVWJQw45FUD41mNFKCuIXhPY6zUqNISDUmbGjKH4156ewLJy5nrnraCmotmDyaRG7LpOIdQiRnVXGxigpgjhUF0dfrJatPWA9fuiqAg47DAaK/bb75Ig3dwkDNeymZ4eH955h8xfpaW0YMzNjVxhrK0NPab19oZXWzkUc+YAzz33Ns45x+igqCuKhx1G79M9RNXVtCDeupWeL11BjjZGWKey0khw1PH5jMYXkWAop8qKrF/b9aBymfPR3j7OdnxzioluazPGvOuv/wHWrv0DnnnmEdtauvYVT/pByfnhT07jxkW2KjrllGNx2233YN48WrH19IzH009TmMbll1+BDRueRKDHpGTwLzuPpip31tWVDZcLeOyx+wcrjFxzzTGYMoVyBtT9WVdHC9Ajjww04OXnU8gUQB1V//xnCk11uYATTqDt0bRUV+Gmaux8/33nZ4rC925Hc3MTgHxT7ojKNZDSfL1vv/2/sXDhQlx88XJcfPE5jmVHAdgWjhhKC3KS8AnoYaxBYyMFtiizf0MDBYSrJKWyMprYolVeFaWlzu6y3l4arGMZVPVOT7m5wNn+dMn16wPdUBs3bsSKFd/DpZf+zKaWbD7UQzh5cjkOPfQrEccfA3Q+q6uDJ3Wo90Vr1cjPp0FfPVxlZcCJJ9LfDz1kjlnauHEjli1bhtNOW4D/+q+LIUSuaRXa0mJYa8444+vIzy+EUpBDdfDRKSoia1dFRWDdVjMU4xxpW+ScHODxxzdASr9pEoZGZ81SVggRPKGlq8v8nlAW5I0bN+LVV5cD2IfNm4HW1rGDbUx1Bdm6qPjySxrAVde63NzwFGQhyGJZUTEatHBxwxrnrFuBIilhVVtbi/PPX4HcXNIOI1WQlRVk8+bAEKqBAaNazqxZ4ZX101GLcp/PCDN46aXA982fPx/33XcfNmzYgDvuuAufftozmExqP/mXQrcgt7ZSnG+4ll6lVIdDf3904QbW+0LJ/9lnE7BihVM3N/PizEweurtJ+Sotpfsw1H1uR0kJnbNg7l6fL/qwMZ0XX6zHunU/QUeHcTLa29vwm9/8BmeeeSYeeuhnAIBnnvl4UHF0u2kMUudPGc6UBTlWBVkIUmz0BXdLC80/0XgKzPdnM8wdZ3cDeBz0vF+JgYG8gPHNzpPh9dJ2teCvqKBn06lUn301CwDYBhWyIwSpTYWFRdpYZ+ByZeDaa692lNOOkhLg5ZffxNtvK5n288tkGDvMyddd0CsX2Vm+e3roedbLnfl8dN3U9dENfcXFVHZ01CijrJrOwoU0drW2AqtX07k94gjj2RkYiPyeUgn81dVkue7ocA6LXLt2Lfr6ekELlhLTPpVr0NNDMu7YQQNwd3cD6D5qgpRBu2XZuoRZQcYAqEM1UFhoJLYIAVx33Zu44opLcNll5L7Ys+cDvPDCK1Enkilyc+kGtItDbmkxSpVFS3W1eVV/7LF002/fDqxbZ2zfuHEjbr31VvT3t8C+E3gB1KAweXJlVCtExX77hdf2ONrQFZcrMHTl9NPpXL/3HnW4amoyVqHUwAOQkuJk1Sp03bp6dHQYg/5xxx2EX//6Zvh8XSE7+NgdU00NcPrp5wexdAGkINM/jKQtMnXM2glK2ugCKQRGdQM7y3V+fmBZNp2uLrpOytoXzKKv7p+Ojp2grG6gv59CJrKzc0DJUsUoK6sKWFT861+GYqXq0IZafBUVGZP7ggUX+s+pgB4faFXEQ4XClJeXY926dZBS4rPPtuCgg44YPD+RKshlZRSX2dsbmGn96ad0b44dS/dppNZUIYxYXFV26YMPgtdd/te/XsVBB505GG+3ZMmSAAtKQUENysqoAkp5OSmLtoZmByJZMEfrGVPhJYoDDqCJdfduoLHRXH/XCGfqgl1nQaIImZlGibdoFWSAGhMFq2ahrlusrFy5El7vm7BW7/D5vOjt7QHwmf/1eNx6q9Es6N13N2LFCg8WLVrsD63qwptvbow5j0ZRV0e/W1roPHZ0AF/9anTfZVZOrQoyAKwDsBkUU3uOaXzLy7NXrDo76Rnp6CBvTXFx6IWaXvFE/Xzxxeu4806ywD7xxBODybHf//73TQu0goJinH/+Upx//tnO/8CGggIydvh8ygUwBao8qzJ2BHaDpfvbyaPZ0xMYKqdCfuwqEylcLnrGrGEWOTnU4OvSS2k+LyszyjrScUauH6ixUIjQTUmM692LwMoiZAzp6lKt1jNBBhRl+Q9iFfJ/3G4jK8gAKHEKaG8fO9iu9+OPN2Lt2l+hs3MrVMC817sHq1evwZtv/i3m/2jXrhSgm2zChMDtkVBebrbqZGQAK1bQjf/HPwLvvEPbjYSqdgBjEdiTvBDKOldSQkpMtAqyan0drIxLrIN2VZXZ3VtcDPzsZ7Rg+OIL4H//F3jooYf9q1Ad0lS6urpwzTXXYmDAGBwKCmiCCzcz10pdHcWBWTtNmcmGngQYidWzoiIbNGk+7d9iKKF25d4KCmih5GT1UzWQAXMymR3G/QMAqs/yNPh8XhQVFflLFt6NTz/dbVKOm5uNFqWAUeUk1L1VUGDElNXVzcNll12G/PxMKEuKnXXf3mpKk8q6detMCx7VICXSChY6Rx5Jv19+2bxdZX8rV2J/f+SKk6oHXFRE91Vfn3N7640bN2L16t+gvd1ZI83KysLixd9Bby+d+IqKyGNxI1Xyo7Faqs+oscPtpkoZAPDww+aERUOJ6IPuAdPJyhqF8vI6AEYr50gqWOgEKw2nyl5Fm3CtQ2NCKygXwm6QbAPFmObC663A2rVrBxewVPteufybcOutt+Htt9+IKY9GkZdH8alVVXR95s2LfrEBGMrpDTdchdJS68n1ArgDpPgcj7IyIw6ouJiSy6xWz44OYywvLzfG8UjnMdUcxYq1Qcr996/D8ccfFvF8kZsLtLQ0ga7jNtA1PnBwf2Njo6VjYR+AQpSVjXH0aOodBtX93dkZXvjLhAn2LeVdLvLg3HAD8MADZl0lGu+68vwCoUPUjPmsB3YKck1NDbq6VEEFFyh/aAC00NoT8H5FFj0I19jtYwUZgFp9AxMdOs6pi9EMr1fgvvtuifk/jh0bGLvX10cPSrSDtcJu4p05EzjHH4a+Zo3qFqdWVRJkibOObJVQ5W3UIB9LCESwRBP1IMYSvlJVFThA7r8/xUyVl1PZo717j7V8qh+63Nu2NaG9nc5PcbHxO1rUM60G0ssvv8Imac+cBBhJ4f4LLzwDLlcugD+BrApzAUxDRkambbk3t5sGMierl17BIpSCbLZQGwqyvk/KwMzzLVto0aa7PX2+8BRk5RrdvZvO6Zo1t+G++17Fnj321n07l6lTmIyaUNrbSfGL5jlUnRE3bTK8OL29wDPP0N+qlm9fX+TlEktKjATLUJMJJTH2gCYHe620r8+LRx/9Gzo7ybqm4vgjeQazspw7ZdoRTY6B221YehVz5gBnnUX3zU03GYs6sxIhoRZPenOfCy64YrByh1J8ogkdA+iaqGfKSlcX3UPBrHXhYowJn8K5dKJKDJuIxsZGywJWn8OAJ55YH/Wi38r48ZQU/a1vBTaViYb6+nr87GeX+xVGK9sAvAggE83NXxs0aCmPq6r9rGhvN8YzXXGP9D4sLKR7PVQ6Q7DwjWDk5gKlpWoRs9H/+/jB/Uo51BXyO++8G+++u93Ro9neHpis1tsbOq8EcG4pH4xI6ror9OdOD1Gze56MJOQ+kGfbUF9VPW89/pjqfSuM+Buh3fhqLpBSPmx3fKwgAzAU5AkAXDYd54zVN5CBXbscsr4iQMXutbQYymFrK5X0iXXgUhOd1Up4yin0oDQ0AK+9ZrUw9oOsyDpVg2W4VGJHLFaHKVOc45DDcf2EwskiV1wM/PCH6tWpMFuVOkDttumkjx071bTq7uuLbsDT/3d2Ng2s1vAOAwkV4xxp+/JFi47DsmXf8VvyqNaa230+VqxYYVvuTaHX+dbZt8/YF0pBNt8/O0EWrlIA1YP7Kiup7fC2bca1/+gjOi9KqVG6a6jFlx6br6qP5OTQPfnYY84yWV2mTmEyPT1GHc6JE6N7DisraVHW10fHBABPP01WrIkTDQXZ54tcKdPDssJ3RwLOClUe9u2jmUg939GEQdgtTO2IpGqHlfHjA2N9Fy+mmuetrWTRUnkGSol4+OF78Mc/boaUEl6vF1JKfPLJFhx44BGDE7/KB4nWM0bNW+zHtc5Ow3MWK4Yn5EtQFQY7DENPZWWl5R5Qc1gzgEw0NQWpf5lgKJY0WLu09SBr8nFobHTj9tsppKSgILCM5d69gSW/ornHXS4KfWyy09k1YlGQTzllkd/78TxIvnkAihyNHbm5zt1gAfNYri/2wzX4zJwZuluilUj1g7w8I7eiooKs293d1KLeijkJGXC5yH2ld7Tdu1c/5kAFOSsry185KvhcoGAFGYDunjLKmunog4vE2LGxpyWXllL2Z2kpZRn39tKkGkn8nxNuN32vddLKyiKrCwA8+igsMU2toLgng7KyKSgvp23KhRZLCESwckexxAEqioqCFzunmqzZcLnO0v8zyLoyDnl5eVi27OeDyR4qPiqWOqZCUGc/VZosMLwDIOW8Cy6XK+L25SUlwMEHH+5XCL6F/HzA652JsrL5jp9xu+3rlwK0YAvXgmy+fwDditzYuAcejwevv74RxcXAX/5CSaINDUaraN2CLEToeys72xjo92hrDGXpD9WqNBRdXcZ3RBNeoVBz2R//CNxyC7kiAcDjMZTuaMINCgqM+3v//Wli+egje7nNixcnBTkXhYU04KiwLNWtKhIqKympM1iOgddL9120C+zRowOt1G435RaUl5Ni9NBD5v35+bSQ0vMxOjuNsTYnx5A1loS1mhp7RWJgIDxrXTgoT0hZmQ+kPNk9LMqCPMm/CNcHQzVnNQHIQlVVjJnmQwiFk3TCuc71TpAS6QawGH19vVi7di0KC2mBq1/vxkYjeU/lOmRmRne9p0wJbUGONhE1IwM46qh5+N73vo/Cwn5Q+dlMZGd/w9HYUVxMCrJT/We7cmfqc+EwalR41acU0ViQXS6zrnLSSfT7jjvsexSoJOQ777wHb7+9D1JKU0fbpibjes+ePQ4FBWrs64wowX7w+CITJ5UxVt+BqLurCW63GytX/tDmPZEhBE3CJ59MMT27dtHkF6uSqBg92r780nHHkYX5iy+A8eN1d2QvKKZpLNatW4eODirD1d1NZi51XLFYkEtKnBVYlckaC3l59OPk7lUhJi7XiSgvr/H/7QbQgvLyBbj77tWYOvUbg+dNxWTGmu2tErfsy70JqKL1UsqIC/fn5xvntKCAaloCNMA4rf5zc80Kps6+fYaVJNS9aHZnA4aCTPVpVQb2P/6xEWPH0uD58stGAqpuQQ5n8ZWVZQz01vrVxcXO8bjhoheYD3Yv9vbSRLx9u/15nDGDCusPDFCtUCkpYdRolUxEel/pXaoKCij51usF7rkn8LkyFi+9oGY/gWRmluCgg+YDMJoXlZREbjmfOJFiT4O5Y1XVjmi9Y05WuZIS4MorSVl+4glqmatQLZd1xb2zEybrcX8/nddYPFfBnpNYm4ToeDweNDXtwJgxH4DCwqwPTLA5zDDyuN35WLEi0CI5UqBwkgFQ8rGTaf8R0ELhKAB5aGxstA2zaG42rndlZfQWXoCekcrK0NWYovVGUAnDo1FfX4+rrqLBYtSoc3H00fNt35+RQfevU1UivX9DZaXRZyDcOTw7mzwg4dY5j7ZMa0WFoSCffDKVLOzsBH71K+eGaqNGkZVZf96lNHfRO/DAcbj//nqsX38PpPRGlGCvSGsFWZhGayN+y/IuqGYE+fntOPfc87BkyRlxPAYq0D1nTnTdh5yorrZ3e2ZmAmox+uyz5pimu+66G5s3f+kfiGkl3tlpdDGL1YKs2gXbHZeUsSezCEHWHKcHeuJEsrwNDLixePEd2LDhSTz++OPYsGE9rr32Dsye7UFbmzGgqqSOWJNZqqtVTKudvzUfNBHIiGKPFdbWvaeeSglcO3cCd97p/Bm71qx9ffSj9ikLshqkBgYCLSjq/iG3l5qgJw3u18vNqTJiSmnQLchA6HsrJ8dZQVbWwkhdgjq6S1L3dvT303b1PDQ2Uueps84iWezO5fLlwEUXARdcAPz618B555mVw2gWXrm5ZkX4/PPpvvrnPwNLvqnFS0FBBuyqOZSXl+PnP/81yssnD8obbde3sjLg8MPtvVaKWLqDAiRnRoZ9i9np02lMk5I8FVb00Jv2dpi66EVabs+OsjJSPPRyZ8piHo8Sb1ZuuOFiuN1/AynJ+k3UCHIlF8Mw6gwepf93C84993ycffZJ8T+wOLFq1Srk5eWBxhMn78ce0ILcDWD/QY9JUZERZtHTYx7PKirIaBRLCdVp00IrjNHO4SUlhnHn0ENJloaG4GEUbrdZSVT4fDRWKSOEUkIjXRxMnRqeguz10vMZzeJAV5CFAC67jBTgL74Abr7Z3jOVkUHn5913jW09PfQ86zJ3dwP/+tdzEXXP00lrBbmsrFxLmDLcU2aKARQgPx94+OE7ceih82K2KNpx6KFGfc94EKzygmob+eKLgdZWdXN99pnhqqqspAcuI4N+YmHsWGclJh6TybhxweMh1Tl+7jnz9tJSSqwSItD1H6uCrOqFnnzyeVqnM0U+gC8HkwwixToYZ2cDV19Nv19+2d6qmpVFVhDrte/sJCVDWUWrqkhp+vxzev+OHfRj59Ij67geyy8s+4iaGuCttzbinHM8+OILMsX89KcX46233gx5ntUCKyODlBz9Oqt7/Z//DO0GdaK11VCe9El01y5DYe7ooFj+6dNp2/HH0/mw3nM5OeQuPPVUyiuwIxoLsk5RESneAHDffYGWrfnz52Pduv/DXXf9Ed3dRskqFXs3e/ZJg8+iKpEYS9e32lrnyVRV34gWl4viE53GjoUL6fdzz5nPQ06O2cqvFv4APfPRNi/RycgAFiwgZWX7dgzGQY4ZE1u5Tic8Hg8efPBaFBe/icDEaidDjxFiceih8+JmiBkKPB4PVq9ejXHjqIyjUWvYOqFRhqrLddBgjG5hIV2Dzk7jXlTXXxmNYvHSqrnQiVji2UtKDGOEbsi67z77RThAz5Rda/sHHvgdrrjiav856McllyzBxo2vR7w4CFalRUeFSEbjIbKGRubnAz/+MT27b7xBCrOd/EVFRjt1gK65y2W8t7wcePHFN3DDDVfads8LR0lOawU5Pz8f9913n19JVprEdJjLnVHiWmFhG3y+yLpfRYLbHf2DZUdxsXM4w4QJNGl3dhqtSQGygmzfThP+Z58Zk35lZXQ1Du1QQfg6qnh5tJnkOioswokjjiCl8sMPzSvvggI6rrw88woUiE+90NGjgXnz5lmSDNwA3Bg7NmswySBSrFZFgM7xySfT3w/b5ubSQGJVqFpbaVtPD52HggK6RyZNIot0bS15Ouxiw8iCsw8Up58PvUmDHg9r1E6WoEzkLnR17cRDD92L9euDD1iqWYb6Omt4w+jRpCA//XT4cXM6eta3UhT7+uhcqCz98883TxpFRdR9yilkxY5o3frZ2YExgV/7GtUk3bcP+PnPjSRDhctFz9Y//mHeLiVdR2WdKS+n5z6WhNSxY53Dm2JNdgXsE/UUEyYYrY/ffNPYbg0n0mMUVXnBeIRBlJSQR2HRInp+OjuNluJDgcfjQXPzO6ivvxO/+92TNoYeXUE2vKDl5f1x6aI31Hg8HjQ0vIv/+7978Oijj2PDhidx+eWXa3IKKAXZ59sf9957LzZu3DiooH35pVHvXa3PlUcsloVasCZfXV00bkQ7XxQXmz0kqr3z5s1GHX8reXkkn75Ar6+vx0UX/QhdXcrisBcdHa24//51eOWVJyM6poICeoZCGR26usjqGw12esWECZS/MWUKya0n4SrU4lPlYCgDjz5/P/74Bni9LbASbr+BtFaQFRSv1w4aXHIAzND2koK8Z8+7+PvfXxoSl9lQoFqnOt3YypL67LPGtoICmjR376bPWeOX4qEgV1XRhK0fV0sLTa7xsLYUFxstle3IyQEOOoj+3ryZFDaPh9q3XnLJAnzvex58+SVp8CpxKR71QtUkfPTRqtMZhXfcddc9+OijN6JSjgGyXuXkBF7nU0+le+DddwNr8gI0kFgV5O3bDeVBTSbd3WQtPfpoqvF7yCH0/6yxYUbMq3mCtmZgG6WnlJa5E0AmvN62kAOWEHQPqmOzKupuN3kQtm0DPvkk6FcF0N9Psra20n2orldLi5EQR/IEfnbyZFKawy2JFG0og1pE6tfa5aIa5xUVJPPPfhZ4baqrqQ6zPsG2t5NCrNeIBWIrsxjM+uzzxZ5jUFkZfPF7+OH0W9V5B+hebWqi/+/zkXVJj0mN1bKtk5lJSkJeXnySjkPhctGCtblZf/4Cw5woxCYPQDPOO29R1A1bhhuXi8LF1JikErQuv/wKuN1uAB+CSn5NRHu7HOw4V1xMLYtV58qBAXrelOIaiyEmJ8c5TLC9PbYeBtb4/9Gjgdtuo/G3pQW48cbAeU3F2evP9sqVKzEwkA+jgRKZVL1eL2677ZcRH5femdeJvr7o73ene3HsWBrPVInWBx8MfE9REfAf6vOGpiYjDhmg8YjaUtskYyG8fgOsIMOI16PMUQA4WNtLK2+frwEPP/yHpFGQAZqQ7BL1ALJ6ZWZSHVUVz5mZSUrTX/5CiojebjheCnJJCXVa2rGDJiyvlx6+I44I+dGwcLnIchMsbmoalerFxo07tEL6RHt7O9raSAtSE348PAaZmXQe9YFG1b2O9byqDmvWbcr9fvvtgfVBXS5zzCRguCYBcwWL0lKaiIuLSUGcMyfQ5aWeoawsVVJhEgoLiwIysI1wC7OCDHSENWCpetqAc0vS6mpaFLS3mxNVgqGUYymN2rYA3feqW5gTLheVGwuVvKOIJVFo9OhAK+q4cXSNJ0ygSULVXFa43XTd9AXFvn1ma0s82g+rzotW5aG3lybBWMI3gOBJvgBwsH/Yfu89IwzI5aLPdHbS/dLTE5iEGs9wAyHIoi9E7BbzcJgwgWQ86ih6/vLyVHD+RP/xuKCU5QkTJI46aj5crvh4xYaD8eMD5zBjkd0PQNV1238w36GgwKheosY4fXEWq6fSyZMxMBBeEw4nlIKse4jKyyncoKyMFEFLd20AgXHINI6OhuqCqxRkQGL79o8jPi67McdKLB0jVR6NXehecTFw1VVGEu5rr5n3FxaS7Hv2UMlLpVOUlZFhq6ysHE4Kcjg5P2mtIAthWFtoElemh0AFGdiOpqa2uFkbhoNgCWsFBZR5LiXw9tvG9qIiGthLS80K8sBAfEIgALLIzZ5Nlr4vv6T461gnT52JE4M/0EpB/s9/vFohfUUpSGFrw1lnnYpLLrkQbrdAXV1d2IH9Towfb1aiWlpiKyem0JM7dE44gay+3d3A735n3ldQYFYw+/rMGd+6gmxNrpoyhZ4dqzVj/vz5+MEPTgEAHHzwWaivrw8oT2SEWygFeReUghzOgFVQYLiu7WLvAJr83W7gr38F/vAH8/3thGqxDRj3oko8CSduT8Wrh0Nvb/T3u12DIYAU0CVL6O8//CFQSc3Pp3g9xd69dLxKgVAyxqIgC0EeBuviad8+euZire+ek0P3ulOOwejRpAi1t1Nrb4WUtPh7/XWSUx/XhiLcoKaGfoYjjCEri2Tu7FQJszf6/28V1qyhtsinn34VAOCQQ8rR30/PULyahAw1ds+JuRqQUpCNBkWqZKQeXlNdTWNcfn7sBg+7MEG1cIulyZfbTeOu1VqrKrW4XFRb/fXXyfO5bNkyLFy4ABdddDYOPfTswTmKxtHRUB1i9TbL48dHnqGoqgyFIlrdSC0mnSpW7LcfhbYBQH29+VhUjtBrr9EzoIwhKgxu4cJFyMgIdCeH228grRVkvRYrAFRUNIJWG3Uw3BOqecaXKCsbFTclcTgYOzb4jT1zJv3WE7l0V5Q+kcTTFSkEWYyPOIKSW6zlr2Klujr4BDBpkup+NRZU+1pHBVLtAJABKUmjbWhowPLly2NSkkeNMpTKvj5a7apwj1hwGlyEoIHF5QJeecXshlPhNGqgV4qS1WuQkxPoAsvNJUXI2iEPoKxngDLJ7cJ7DFew2YKckdEb1oBVUGBYaZwUZIAmqpYWOgfbt4ce4PW2rGpS7u6m7wkn9KeoKPyOclJGnxgWTLGeO5fOf2trYDUHa4vx7dvpXvT5jMTHcGpRh2LKFLpflDW+u5sU2lBW+HCxLjJ1hDCsyHqYRW4uJSSr2Hr9Hlf748moUVROc7iYMME4J243lRkEKMwAMLdOj+c4PhyoUCTdqmqu8a0mr/1M+6qqSLFT3lHVzCaWChYKpXzpx9TZSfdTrIuimhr7+3vGDENJvPnmAdx22x8GG05J2QmgBA0Ne7F8+XJ84xsL4HKVwhxi4YLb7cL11/8s4mMKVX2mq4vmoFhCEVUVHSdOOomOY9s2Q19R4ZHf/vYinHrqD3HVVRfhkUdeBABs3vw3LFy4CI8++jC+/e3zwuqkakdaK8g5OTT5KaXl3HPPgRDqgZsB6lhUDcCHzMxmLFx4alKFWJSU0ETsdOMpS6pT/Vi1+lar4njGrblcNJnFozGKlaIiktvJ0pSdrWLFXLA2RzErbhnQW0B3dXWFFdjvhErwUJUivvKV+Fjli4qcY64rKyk20+s1K00qdm3nThqQ33mHrolucensdM5injWLzrF1MK+sJGWou9u+y5sKxXC5lGdmB/LySnD99SvDGrCKiuh+zMqiYw0W1jBqlNHsJVSpora2QAtyd3f4iSdC0D0VTkmkSNs569gpDPr3qlP4pz+ZLVF6i3GvlxY3ar9KVlNdrWIhM5OUw/x8UsK7usiTEa+GGWPGBK9SoxacegvusjL6XHU1HU9HB90/qsFMvC29QgxNeTcnRo0y3w96G3IpAxXkWMtpDicZGaTc6hZbc4MiFTIwBW53dkDHOaUgqwoWsVh4Ffn5NMa1thphAS0t8TF2VFc7N/5YtIjG8r6+DPT3L7d5RxW6urrw1FOv4rzzzoXbrQavvSgoqMDFF5+DJUsiz3UpKKDr4HRcKk8jFkIpyBkZVPcdoEo1KtmbwiN9AD5BR8cObN6s4ul2AshGZ+d23Hfffbj11lvD7p6nk9YKsoodVErB/PnzcfjhavTYH0IcBMCNjIytuPTSi3DYYSO7PI4VVWPZGmuqqKsjZXHXrsA2vaqeoKoakAyZzzqqe50TanEghPXJVkFkyvVvjtUIJ07WicJCI5nL7aZjjAe5ucEt5qp5yF//ah6E8vIoiW/9elJmRo0yngUVL+2kIGZm0oDV3ByonM+bR7/1agI68+fPR2EhmZrvv/+XuOmmm3DWWQtDSGkcs0reAYJbkXVCJdDt22cot3oFi0gUu/Hjw2+5HK2CrBQGp6SZ2bPJ2tTeHhiLDNAzrSZ2ZUlV7ud4KXXjxlGS6MUXk9Vr0qT4ufRDlZKaOZP2f/SR/YSrPIaqXFcs3f1GCqpeu1KS9TbkamzPz6frHG2CaCKxNqswNyhqA3n7srF48dUBIV26ghyviiUA3Wd799L5/eILGjPi4SUJdnxCAJdeClDs9f4wN1HphIo7//LLLqxZcz+8XrIgL116Au644z6ccsqRUR2TEHQNnDplSkmW71goKXFWwBWqPO3LLwMPPfSwJTxSrRD10L0sAK1hV6ywI60VZMAYTJQ7+MQTKQ114sQFOP74nwAAzjqrDvPnz49LV7Xhpq7OuZKF200uUQD42BK7v3u30dlPxWwlk+xTp5LcTuW+1Ip33LgTtE5wQDALMhBeYH8wDjiAlNFp0+KXKKO3ILZj2jSj4PsLLxjbi4roOpeWGoqgXgNZyuAu/TFjyAq+c6e5lJZSkN96y/64urpo0qYkCkSUNKTep2K3gxXRV2Rk2IeD6LS0BCrIQGRKo1pMhKqTCsTm1g8VZnDaafT3s8+az39eHlW6aG42yryp4+7vTw7LYmEh/TglHxcU0L0xMGA0jNBRi4KKCpI5mcINnMjIoMW2CqGaOJEU4l27jNyDuXPp3pAyfrkkw0VdHS3g9IW43uBq+nRSY9atewsLFy6Ax+PBxo0bAZhDLID4lVIdM4YqQS1dSu7/+fPjU4UpPz+4tZasuV9ANUgxaAPVn88EdTJth+qg+fDDt+KFF96MKYFw1iz7Maejg85trM+R8uYEY/x4UtS7u4G9e0sc3qXP31mg0qPRG7bSXkHOy6MyKsqCOm0aKY6ffw74n7HBCR+If7zaUFNRQcqP0+rPKcxCTZ56vFUyKcilpXRdnQqsq4XR7t2lWLOGBtoNG55EdfVs/zsoBllXkKNt5qEzbhyttvfbL6avMaESK52sikJQrDcAPPmkoTS53TToKKWzvZ3CKvLyjAHPbuCrr69HXV0dhBCYMycDl1xSjquuugTPPPMKALIYlpfThG2nwG7ZQr9HjzYmlUgUZCEM67sq8ROMggJzgpoddk1CpIxs4M/NpWvr9KwBRjWYWFobB3PDAlRlpLSUFmIffmhsLymh2rCffkrPsv6M9/Ymh4IM0KLe6vHS0UMMrFjzKpIpZC4YBx1E3gtlFT/wQNquFsTK+hZLeE+iqKqihbhdx7iNGzfiww+f8L8ihVG1uH/22ZfQ3Gz2gsZr/na7aQ7Jz6fxLtoawFaECAwpsTJ3rnJ5HKBt9YGU5mmgvCk3qGRtO7zeVjzxxBMxJcKPHk3jh1VJ3rcv9vAKgIwS4bTxVvN2Xt5hNnsFjBwiVR2JBopoDVtpryADNKkp12h2ttH5qq+Pbta6OqOZRbKUx1GoxBWnCSWUgjxqFCkKyVQaSHHwwYGWB0VxMcU/9/UZslNtYJoxy8sHQF2cyPxeW1sbdTMPnawsUlb1KhHxYNq04ErDV75iJDlYm0kodIVJSvtYyvr6eixfvhwN/ir8Pp8XQDOamp7Gvfc+Olis/9BD6f12YRYqNlkNrJG0MFfv00t6BYtdA4xkXKcEOq9XWSXotYpbLiiI/J7fb7/gg3w8XNyhPu92m+P1FCru/MsvjSRNgK6315s8yuK4cc7PNWAOMbCiN42Ip8s90ZSV0RigrMjnnmsowtXVxjkBkk9BBmjRU1kZGOO/du1aSPlP/ytDSCr59oJ/3t6B009fgIsvvhjl5TlxqUg0lISqO7xgQR0AwO0+2LKnBcAcABLKegyQCb2lpSWmBbDLRUn13d00fuzZQ2OGEPHLIzrwwNChcGrxW15+jBaHrigFkA1SitUJ7A67YoUdrCAjsETTsccaHahOPNFobJFM5XF0ampopWsXH6mqDnz8sdkqpVzSo0cbVq9kk724mKz/27aR1dIai60eNjWRPvPMK/7ycF1oavoM+fkFuOWW/4WUElu2bIlZOVYEawMeLePHB7cqZmQYViU7xQEwL4pUQovVbbhy5Up02Y7e29Df346HHloPIHgcsrLs6ZN2uIpoTg4p1BUVZEXu7bW3FOqoc+0Ui9/VRd+p3LGjRpElPZrEsrFjA2uZ6uzbF/uEUlBgJN05cdRR9Puf/zRvLy+n85CTY37GgeTxEFVVUQzo7t3OGf8uF4WTWG9VPca+tzc1QiwU06YZi8UxY4DLLydl+PTTjec4WZqEWHG5gMMOC3yGqeTbNpByWAajLCuwbx+t+LzezwEISDkAoDcuFYmGEuXdcGLaNDK0eL3jAZRoe7oAFAHogNHJdA+AbJSXZ8X8fNfVUU7BqafSfPPJJ+TNiZdVvqbGCANyQvf8XnLJD0zhkbm5FC/qcql4P4nS0tywK1bYwQoyaMIpKDAGlxNPBB59lOqJqni+ZI5Xc7vJ4mbXqrKszHDpqJaNgNm6pKxpycisWWSpnDcvsDayUpDfe49cdffcs8G/h4Tv7OzAD3+4YsQOpDplZaHLjAVzPQPma+6UoOccyzUA4CPs3UsW91mz6Hi2bDHH//b1GW7/WbPICpiVFX7IQWamoRwGs1JbEcJQgK10dxvJhmVlpChGUsFCJzvbqJZgpa2Nvl+V4YoWl8tQ4p2oqTHqwOpKRU4OWbl7ekjmjAzDQJAsCnJGBnV2XLDA6J6lk5dHE7fPFxiCoxrmjB5N+5N1XLOjstLcivzQQykB95vfpNeqDnAs4T2JZPx48u5u22Z4y4ySb2pQ08MOlFt9Kyge1Yh9irUi0VBSVBQ8njkry/C+FRYeY9n7BciCqhTk3XC7i3HRRSfH5djU2HPccdTw64ADQn8mXHJyaHwMFl5SVGTEpFdWHjUYh75hw5NYsoRyxo49dgo2bHgSd921Gtu2fRyTYYsVZD8TJphjBzMzzQXFk93a4NTcATDCLPR4RaUsjRmTPAk8dqiyU6q7lc6sWTTZfvwx8OCDj8Hnm+DfYwS79feHboE8EnC56BoHc1Gp1ff779tbOHWLYm+vfRhI8FiunSgrI3NkZqYRBvH3vxvv+PBDup8mTKDnSXlmIqG8nBQ8ZaV+7bXQrVCDxSF3dBiWRWVNDZWgGAyncm+trdS4xa5ddaSMGxdcQdYTcO3KOKprXV1N75Uy+fIrxo6l59ouz8BuMej1GvdATQ09M8myKAiH7OzAhZM+5iVjBQsdIUjZP+00mqu9Xr3km3KLaW4pKFdNA8j1vs/0fbFUJBpKCgtD121XY19V1QKbUANAKcjZ2a1YunQ5zj33+Lgeo9tNJefi3U597NjQXfvUvKIaQKmmKffeS/Fkzz13Ny68cBneeuvNmMc0VpD91NQEj2VMZiURoIlg1ix7K7IKs1ATqddrLo+TCskshYVkWbKWOZs1i5TFpqYJAFR1f2WSFAC6RuxAamXChOAW5FGjyFrY3m4kyunoFmQh7BXXVatWIc/BR5ub24NFi4xybSedRL83bKD/2d8PrFtH21S4R39/5PdWRYXRfGLGDFJwN2wI/pn8fErysQtL2L3bsLLqVuNoF8Qqplenp4fOp1Nd6UiprAxdFilYnXNreEUyVugBKBHXbty2i0PevZuej/Jyo/JLsi0KQjF5snOSaF9fcivIilGjKBF1926j5JuhIB8ISlADzBbkQAU51opEQ0VWFl2nYIt+5T378stKfPe75lCDwsIi1NTQG6666nzMmzcvaTwlyjgTDN1z+MILG3H77bf7m6aotrSfY+/eFqxbdy/Wr4/N+8sKsp/qarN7ykoyJbE4MX06DZLW1amqqPDBB0YsptdLE0l2dnRKzEhDCCp/ZJ081Erc7T4LVB6nA8DrUO2PAe+IHUitVFYGLxEkhKE42FV/sFYusVOYPB4PVq9ejVp/IK3b76+tra3FPffciMMPnzf4/2fOJEW4qwtYtoxaIX/4ISm4Z5xB74nGgqwUZCGM9sqPP260GbXD7aZn2y4OeedOc4tSn4/GgmgnFVW6zuejwb6hgRTSAw6ITykogKz72dnBJxP1XNspyPq1Hhig74qHZXu4sYYVKKZPJ3k+/9yw5uvWY0UyLgqCESxuPpYW5yMNtcD2euGvfbwTwJeg9srTQR1Sq0E1g3eAQiyMhz8eFYmGktmzg49nFRUUbkJeAXOoQX19Pfr76UIrL2CyLARLSkKPkdOmkbFy927ggQf+jr6+XlDFqRpQNY8vAGShv78xZu8vK8h+srKCl2hKphg9J0pLyepmdUlOnEjWsu3bqXGEajCgLMvxLI+TSGpqyH2jr8yVguz1qiKRLwHoA5XIaYkpA3a4ycykQVOvSWzFSWnq7DRqE6uOf07X3OPxYMuWLZBSYmBgYDCJcelSD0aPNrvIzjuPvrOnh2LLiouBK680rLPRKMi6ZXfmTHK5dXUBN90U2qpqvfd9PqpsoLaPGRNZi2k7MjMpBKK1lb53+nRabKrqOPEgK4vuXVWVwQ71/H7ySWBolco3UDkGyeody8oit2xHh1lJzs4m+aU07nVdQVaLoGSrzBOKYO75ZKyB7ERODoUQqQUvxSK/4d97GAxr4pdwuQQACZeL3Gvxqkg0lEycSOOIUw8DADjkEPr93nvm7dYmQEDyzN9qbPd6ne9jt9uQvaVFdcKtBSnJOwD0QDUJidX7O2IVZCHE/UKIPUKI97VtZUKIZ4UQn/h/l2r7fiyE+FQI8ZEQ4hvR/M8pU5xLNKWKO+7wwym0QK8pmZlpJCPefz/w1FP091ln0e9UUZDHjwdOPpkGDzWZlpdTXFtJCVBW1on8/Of9785BUZE3pgzYRNDQsB5XXHEeFi68MKBoPhBeWT9VDiyaDmPWJhaTJwMPPwzU19PP/feba0B7vZFP2tb3X3YZKfXvv2+EcNiRlxdY4q6tjZ5tPeSgqyv21sizZ5Pl0uejwXzx4vh7YaZOJYvg9u32nq/iYlIee3up9rGOv0ofamuTqwayHZMm0Xj2xRfmSj3We12XOZmrEgUjO5vGajvFKhlrIAdj+nQjoWvp0qVwuTb598wD4J/Q8A58Pi/y8vJxxx03xr0i0VCRlUVjSLAFsFPS9d69tCAuKaHvSabytBkZNPbu2EFjtV0uB0CNbwAgM9P/hxZeQVCTkFi9vyNWQQbwAIBvWrZdDeB5KeUUAM/7X0MIMQPA2aBK4d8EcKcQIuJc3epq5wEzVVbfpaVU9qeszFw396ST6IHaupUsSocdZli8UmVxIATF6Y4fb7Zyfve7wEMPAQ88kI/162/2Z8Dej7feembED6Q69fX1uOii89DR8ah/Szba2wVuvfW2QSW5ttYo8aUn9H3xBQb39/cbLZ2t319RUQEhBIQQqKioCKjwUV0dqKxlZRkd0PTEV0Wkg7d6DpWFobQU+NGP6Hj/+EfgpZecP7dtm9ky0doa2FWOMqQjOyYrY8aQcjpu3NCU9QPoXJ56Ki04nCp02MXiSmksFGpqSN5kTkAeP55Kcx53nFmhsCrIypik6p8ns8zBqKy0rwQgZfw6yY0Eqqvpme7poTCLFSu+CSFaQc0iDgXQDeAxAEBXVydWrPivpKhIpFDVbpysyKrcW0ODeSxX4XOTJxsVqOIV2jUcHHsslZM7/XQyttiFkc2cSb99vmnIzCyA0VVQdabKQE7OQMze3xF72qSULwGwRuEsAvCg/+8HAZyibf+dlLJXSvkFgE9BT0hEFBTQDWd1R6qbLBqL2kgkM5PKJLW2GspMdjZw9dVU4m7RIuCii2h7PFrjjjSCJbLoJNuCaOXKlejv7wdNDC+BOiq54PUWY+3atQCc24t/7l94T5xoH3NeX1+PCy64AE1almdTUxMuvPBC06RTURE8DtpKNKFLGRl0fHpC4syZ1PYVoFCL228PVNQzM2mw1a/93r00CKtEVFXvO9ZrLwS1n/3qV2P7nlBkZRnuRrsKNXZWpsZGUqBKSshyrKxNyUpBASVtTZ9O108ph0pB/vhjCinZto3uf9VoJJmt5sGwU5BVOcVUGsddLrq/1ZB07LHz8bOfFUMIZXZ8ElTyzAXAlzQViRR5efRs79xJ49ZnnwVW2po+nf5+/31ju15nPhkXgkVFJPuoUVT1Z/duWgTpho2iIjJ2eb0unHjiNQC+AgAQgkpblJWV44Ybro3ZwDViFWQHqqWUOwHA/1sVohoLqhau+NK/LQAhxHIhxCYhxKZGi/9CCATEUAJGTGIqUVVFypBuRZ4xA/jOdyihSrXbHRigQTVZa2faoboDhiLZrC3meKvPATwC4FkA2dDvdRWbqpf10xVku0HVUL7N9PX1mSadjIzQ7ZatROP+q6gIVAJOOw244AJSBP72N6pjboeyFgMUeqAsFCqsIl6WNgrbif17QpGfT62G7WLPlaXlgw8MS5QeaqBIBcUpI4Pk1VuGV1RQyMzNN9OC6dhjSdbeXvI8pCIVFYGLpa4uo91yKjFpEj2vajyfMweQ8mIAqwCohXs2VIJeslQkUsycSUrw7t0UcmEdV5WHSF8AK2/RAQckp4KsM22a0QBHH7cBY/H/6quzAORgxgzgiSfuxIYNT+L661fB4zkt4PsiJdkUZCfsHntbFUhKuVpKOVdKObfSxo9qV4evuzv+rYFHAtOmha45mOzxiXaUlhrVOZxIRnekOd5KgtqM7gbQhooKo8OUKjKvkjt8PiPEQlmQrRbUYBOLdd/EiaHrEg8eZZThO7W1gfeuEBRy8OMf0+uHHw7sJFdRQSEY+/ZRfFtzsxHnpi+Cky1Wc8YMOpdWy31JiRFSoBZE1moOqZJjAASWv1Px7p99Rgq0yquQMvkr8zhhJ1dXV/xKDI4kCgvJI6DHqlZWZoMS9pQLyVCQk6UikSIrixZ1555L+UMul/n+njOHfr/2Go3bu3bRQrmgwCj7mczeIZcL+NrXqOqRy2Wes5WCrBKsj9dKPccrLDTZFOTdQojRAOD/rWwmXwIYr71vHPRuDxGgLKc6qsNWqjFmTPDSdkBquiJdLlqV29WEBughzM21j5cdyaxatQqZAQctkZHxH5x88nmDW2bNogXCp5/S4LJjh9FaurjYvuZ3sInFuq+6OjwLfW9v9KFLY8Y475szhxQhn4/CLfTrnJNDsj/7LFlShTDiVisrSfacnOS79vn5pAzalYZSNS0nsAAAKGlJREFUCS0qV1NZkNVlS5UcAyBQGTjuONqWn0/3hPISCJF8C+BwsQsPGhhIPS+ooq7OvFg2mocosgE0J1VFIiuFhTRuTZhg9vpOnEgL4LY2apyhjB4zZxpzeyosBHNyaBzTvWQHHEDe0Lw8+v0VirKA1xu/JkDJpiBvAKBm+vMAPKFtP1sIkS2EmABgCoC3ovkHJSWBk3s8YhJHIjk59HDpD5yVvr7UdEXOmEGThp0i190d/w5Bw4HH48GaNWtQrq3yysvLce+9V+KrX503GEqQnU0ueQB46y1zeIXCqjDZK9+wnXRULG+wpiVAbNUiioqCF9NfvJhqpba2UgKmTlkZKc1vvknPtVKQq6qSu9vYzJl0znftMi96j/P3v3nlFYq3Vkk8tbXG/Z/sJSwVhYXmGPg5c+j6r18PnH228b5k9BCFS24uLXCtHfWS2dUejPJy8/2umocYzTPcKClxJV1FIjumTzdXCRKCLKwA8NxzRtdSVb40lbxDqmyjGrOys8kA8rvf0W81hqn5Ox7hRCNWQRZCrAd1bJgmhPhSCLEMwK8AHC+E+ATA8f7XkFJuBvB7AP8B8AyAS6SUYaYJmcnPpxvKOrmnwirMjkmTgrvDvd7UHFiLi2k1bmdxi0eZr0Th8Xiwd+9eSCkhpcTevXtx/vmLceih5gx/NYC++KLRhW7yZPptVxbISfm2m3SEIKtOqDjk7m5z57pImTbNeXHndgOXXEJ/v/pqYDiGapBRWGi2ICdzM4WKCsr8rqszV7UYN84oifXLX5IVZswYStZMtRwDIQLbLVuJp4VppDJvnuE5UQpFqs5hdgva+fPnDzbPuPvue/DBB/9IeuUYoBDQykrz2HrMMbQo3LSJwqhycw1raip5h/LzaV4OFRba1RXcwxgJI1ZBllIullKOllJmSinHSSnvk1I2SSmPk1JO8f9u1t6/Sko5SUo5TUr5dCz/W09e6+qigSVVB9NQltJUWoFamT7dXDdVkYruyKlTSQlSlrW5c+me/uADyvKvqKAKJgqnLnpW5dtp0gnVuh2gwTsW78S4ccHDg0aNMhJVrKXfXC4jxChVFGSAZPjqV+la6+f/m/6CmSqBZ/Fi4z2pFkJll0eioxL0Ui1hTaeujiyrHR20MCorS76woXDJy7M3aimkTL6cAidcLopF1su6FRUB+jB85JHG+J1KCjJA81gwjzdA90G8csZGrIKcSFRSC0CrcNX7OxUpLqaHKVjCWio9YDpOypnLlXrWluxsc4xqcTFw7bWkZObmAlddZXgKpIx9QVhREV4ccizeCdUKPVgoh3I/Pv+883uUgqyOOdmvfW4ucMQRZo/B/PlUoSY3l+4DVX4uFas5VFcHLzPY22ufa5JKuFwUXrJvH/2ocmCpil31KcAYg1JFQQZozB41yqwonnYaWY0zMgxDh9dLr5OlSUg4jB0bel4RIn6L/ozQb0k/KivpIrS304nWYzNTDSFoQdDQEDhpqHifVBpcdAoLjbrXGdqTkApKkh3TppnrZe63H9UL7uszFGJVLzXWQbWggJ6jvXvtrfHKzR1LbL/LRRaFDz90thgcfjhZzj7+mNySVoW8u5ue88xMeta7ulLjfp8wgazmXi9ZioWgifPrX6f7W4VU9PQkZ7x9MKqqjDa9dlbTnp7UV5AB8uKoqgdJVrwhYsaNo0ol1sVeTw9ty0ghTUcIauT1xBOGIigEcOWVNJ6p8SsVE+xVwm1vr/0cpZTneIWFsgXZhtxcskK0tZEFKlXi85yoqbHvvNTZSZNNqjRIsSIErUj1pIe+PnoAU1HmqqrA5DlrLGZPT/xCDL7xDTqPdi6xzk56xmLt8FRXF9yCrCzngHlxoFAlgiorjWNJheStnByKMW5pMW/PyAhUGlNtMZiRQdfcKrsiVfMqrKh7v7Iy9RQlK2PGmBO4FKla3m7MGBo/9fJ21lbiyZxw7IQQNK45hVmocKJ4LYhYQXbgsMOo9l6yJmtFwtixdENZi8u3tRmJW6nKuHFm11xXV2rWvAZocKmpMS8IrMTT/VxYSM+RPogrOjqMhiWxUF1NC1q7WHKFXTF9hSobVFlJ8cyplLylEvOCkarlziZPdo6Bj9VzkUzMnj303RxHAiUl5DWxLop6emJLBB6pCEH17IMlQid7DWQnamqcjSLxTNADWEF2ZMyY1EvUciInh5QIS2NB+HypufrWKS83Wx3a2qiyR6oydmxwpSne7ufycvtkKJ8vPgNZRgZl7CtLsB2qoLxKUNPZvp1+V1cbXaditWqPFCoryVocLB7X50tNBbmy0rkZUKrUhg2H4uLUH8MVs2fbxyGnopIIkHEHcI7J7e9PTdmVt88uQbu3N773e4pMBUysTJ9urgusYlFTPVavspLcUr29NLgqS0SqEk74RDzdz8XFRpw3QJbj5mY6z/Fy+06ZQtfQyYo8ZQopS9u2BSrSH31Ev5XFMZUS1kK1/VZNUVIpiUfhdtM1tbpiVROgVPESMAbV1YELQilTN7ykoICs43YeOkUqLn6zssi44iR3PBcFrCAzAGgQ0WsMtrUZSR6pjNtNTTN27yYL+hFHpHbMuRo8gpVHi+egKoTRCra3l37n5lKjlniRkUFNQexqWgM0aapuco89Zt6nFORp01IzeWvSJOeSZ6lezWHixMBFU7I2AWJC43KZa2D39hod6FKVKVOcQ+ZSuUTrzJmBC381p8VzQZTi6g8TCXrjhe5uSoBKB5QF8sgjU9t6DJAyWVUVPMwi3laHmhqKDdy5k87xWWeRQhtPJk2iAdLJ3XjWWfT7mWcMK3JLC8Ug5+bSMQ4MpJ5LMlg8fSouCHRUEqi+GOzqSs2YVIYYP95QGJuaUn88Ly52HvNSuQLV+PFGNQtFTw+FxcazYgkryMwg1hqD6WJpyc0FzjyTYtjSgQkT7N3uyuUe7woeY8fSuZ03jxZhQ0FhISm5u3eTEtTYaL6XJ0ygZKX+fuD3v6dtyno8ZYrhNUg1l2RREf3YhZ8ke1OUUGRl0cJJT9waGEjtRUG6U1VFz31LC933ynOUqhQWOud4uFypaz3PyCDPr+oWCZDnYOzY+P4fVpCZQUpK6IHbt48erFSN3bIjXbLaAXI96/Hmiq6uoVkUFRYCRx9NDXeGMmTniCOoprdKwrKGXCxeTP//uedIkf7gA9qulHZrmaRUYfJkc+cthZSpW7FFMWsW3deKVGwCxBiUltLz391NZSZTVUFUFBTYl7fr6aH5PJW7RSqvofIQ9fXF3zvECjIziBDUcaulhcIrUvnhSmeKi2mlbU1yaG8fOgvvcFBaSo0wFi+m+7iryzxxjB8PHHUULQ5uvhn4y19ou6pykaouyXHjAks49veT8pBKSYl2VFXRoq+tzXDHsoKcuuTmkpJ03HGp7R1RuN00nltLGra0GPXfU5X8fNJTWlsNJTneFVtYQWZMjBtH4QYzZyb6SJihZNYsc5iFz2c0TkkFKivt66IuXkwD64cfksXh+OMpHlqFl9h1Xkt27MoitbWRJyHVk3CFoBa8PT3Arl2kOKViEyDGYMGC1K/fr1NZGZhTIiV501KdmTPJsNPaSspyvKvTpPjwyETD6NHpE3+crqgaxEppUlVLUinrecYMs3sdoHv7t7+leOQjjgCWL6ftqZywpsoi6dnu6ZSEO2YMcPbZ1PgpnRSndCUVF7nBsCrIHR2UrJYOIZJjxlCoxe7dQ+P9ZAWZYdKQnBzyFiilqb2damFbqa+vR11dHYQQyMjIgBACdXV1qK+vH94DjgLVBdNa0q6yErjySuDqq40YxZ6e1F4UTp1qeAxU2Emqxx/r5Odz9QomNSktNYdQtbbGt4zmSMbtBr72NQqtGwrvJyvIDJOmTJli1CfOy6MYXZ36+nosX74cDQ0NAACvvwJ/Q0MDli9fPuKVZOsiIBh9falrQQaMayslWZvKy1Mz3pph0o2qKnq+t241EvZSJVQuHFQd/KEInWIFmWFSkPr6elRUVEAIASEEKioqAhTaMWMoRnPbNuDggwPrR65cuRJd1hgFP11dXVi5cuVQHX7cmDqV6h5bO6rZkcrJW3l5tFhoa6OKFhxqwDCpQU4OcPLJ9Exv307l7dIhvGI4YAWZYVKM+vp6XHDBBWjSikQ2NTXhwgsvNCnJRUXAaadRCbapUwO/Z+vWrUH/T6j9I4HJk4FTT6UamdZKDlZSrQaylRkzKGnR6zVi0BmGSX5cLqr53N1N1Su4AlV8YAWZYVKMlStXor+/P2B7X19fgNW3spKsx3bZvzU1NUH/T6j9IwG3m5IPlXJoh6rgkeoK8sSJVFw/N5eSeBiGSR3Ky9OjG+xwwgoyw6QYwSy7kVh9V61ahTyHQNW8vDysWrUq4mNLFJMmBdYKVfT2UqJLOpQ8O+IIYNGi+LZjZRhmZHDIIelR/3m4SPEpgWHSj2CW3Uisvh6PB6tXr0atv6Cm29+Puba2FqtXr4bH44ntQIeR6mqqWGFjWEdPT/pYVF0unkAZhmHCgRVkhkkxVq1ahUybYqBZWVkRW309Hg+2bNkCKSUGBgYgpcSWLVuSSjkGKNRixozA9tMA1UpOp6xvhmEYJjSsIDNMiuHxeLBmzRqUa3XLysvLcf/99yedYhtPpkyhcm5WpOSkNYZhGMaMkKpqfBoyd+5cuWnTpkQfBsMww8Sjj1I1i4ICeq2q2H3rW4k7JoZhGCah2Nb9YAsywzBpwwEHUB1gRWvr0LQoZRiGYZIbVpAZhkkbamspUc3fFBADA1QGjmEYhmF0WEFmGCZtyMmhWOSWFmpBXVXFVR0YhmGYQFhBZhgmrZgxgzrrNTZSyAXDMAzDWGEFmWGYtGLUKGDBAgq34PAKhmEYxg7up8QwTNpRW0s/DMMwDGMHW5AZhmEYhmEYRoMVZIZhGIZhGIbRYAWZYRiGYRiGYTRYQWYYhmEYhmEYjaRM0hNCbAHQDsALYEBKOVcIUQbgEQB1ALYAOEtK2ZKoY2QYhmEYhmGSk2S2IB8jpZwtpZzrf301gOellFMAPO9/zTAMwzAMwzARkcwKspVFAB70//0ggFMSdygMwzAMwzBMspKsCrIE8DchxD+EEMv926qllDsBwP+7yu6DQojlQohNQohNjY2Nw3S4DJM46uvrUVFRASEEhBCoqKhAfX19og+LYRiGYUYsSRmDDOArUsodQogqAM8KIT4M94NSytUAVgPA3Llz5VAdIMOMBOrr63HBBRegv79/cFtTUxMuvPBCAIDH40nUoTEMwzDMiCUpLchSyh3+33sAPAbgUAC7hRCjAcD/e0/ijpBhRgYrV640KceKvr4+rFy5MgFHxDAMwzAjn6RTkIUQ+UKIQvU3gK8DeB/ABgDn+d92HoAnEnOEDDNy2Lp1a1T7GIZhGCadScYQi2oAjwkhADr+h6WUzwgh3gbweyHEMgBbAZyZwGNkmBFBTU0NGhoaHPcxDMMwDBNI0inIUsrPARxos70JwHHDf0QMM3JZtWpVQAwyAGRlZWHVqlUJOiqGYRiGGdkkXYgFwzDh4/F4sGbNGpSXlw9uKy8vx/33388JegzDMAzjACvIDJPieDwe7N27F1JKSCmxd+/ekMoxl4ZjGIZh0pmkC7FgGGZo4dJwDMMwTLojpEzfUsBz586VmzZtSvRhMMyIoq6uzjGxr7a2Flu2bBneA2IYhmGYoUPYbeQQC4ZhTHBpOIZhGCbdYQWZYRgTwcq/cWk4hmEYJh1gBZlhGBOrVq1CZmZmwHYuDccwDMOkC6wgMwxjgkvDMQzDMOkOJ+lxkh7DMAzDMEy6wkl6DMMwDMMwDBMKVpAZhmEYhmEYRoMVZIZhGIZhGIbRYAWZYRiGYRiGYTRYQWYYhmEYhmEYDVaQGYZhGIZhGEaDFWSGYRiGYRiG0WAFmWEYhmEYhmE0WEFmGIZhGIZhGA1WkBmGYRiGYRhGgxVkhmEYhmEYhtFgBZlhGIZhGIZhNFhBZhiGYRiGYRgNVpAZhmEYhmEYRoMVZIZJE+rr61FXVwchBDIyMiCEQF1dHerr6xN9aAzDMAwzoshI9AEwDDP01NfXY/ny5ejq6gIAeL1eAEBDQwOWL18OAPB4PAk7PoZhGIYZSQgpZaKPIWHMnTtXbtq0KdGHwTBDTl1dHRoaGhz319bWYsuWLcN3QAzDMAwzMhB2GznEgmHSgK1bt8a0n2EYhmHSCVaQGSYNqKmpiWk/wzAMw6QTrCAzTBqwatUq5OXl2e7Ly8vDqlWrUF9fj4qKCgghIIRARUUFJ/AxDMMwaQkryAyTBng8HqxevRq1tbUAALfbDYBij1evXg0AuOCCC9DU1DT4maamJlx44YWsJDMMwzBpByfpcZIewwRN4uMEPoZhGCaF4SQ9hmHsCZakxwl8DMMwTLrBCjLDMEGT9DiBj2EYhkk3UkpBFkJ8UwjxkRDiUyHE1Yk+HoYZqVgT8vbu3TsYl6yTlZWFVatWJeAIGYZhGCZxpIyCLIRwA7gDwAkAZgBYLISYkdijYpiRR319fUBCXmdnJwAgPz9/cFt5eTnuv/9+7rDHMAzDpB2p1Gr6UACfSik/BwAhxO8ALALwn4QeFcOMMFauXIn+/v6A7V6vFxUVFejo6EjAUTEMwzDMyCFlLMgAxgLYpr3+0r/NhBBiuRBikxBiU2Nj47AdHMOMFDghj2EYhmGCk0oKsl2ZjoAadlLK1VLKuVLKuZWVlcNwWAwzsgiWdCelRF1dHdc+ZhiGYdKaVFKQvwQwXns9DsCOBB0Lw4xYVq1ahczMTMf9DQ0NWL58OSvJDMMwTNqSSgry2wCmCCEmCCGyAJwNYEOCj4lhRhwejwdr1qyBy+X8+Hd1dWHlypXDeFQMwzAMM3JIGQVZSjkA4HsA/grgAwC/l1JuTuxRMczIxOPxIFQXTY5HZhiGYdKVVKpiASnlUwCeSvRxMEwyUFNT49heWu1nGIZhmHQkZSzIDMNExqpVq5CXl2e7Ly8vjxuEMAzDMGkLK8gMk6Z4PB6sXr0atbW1ADDYSa+2tharV6/mBiEMwzBM2iJCxSGmMnPnzpWbNm1K9GEwDMMwDMMwicGuTDBbkBmGYRiGYRhGhxVkhmEYhmEYhtFgBZlhGIZhGIZhNFhBZhiGYRiGYRgNVpAZhmEYhmEYRoMVZIZhGIZhGIbRYAWZYRiGYRiGYTRYQWYYhmEYhmEYDVaQGYZhGIZhGEaDFWSGYRiGYRiG0UjrVtNCiHYAHyX6OIaZCgB7E30QCYDlTi9Y7vSC5U4f0lFmgOUeSvZKKb9p3ZgxxP90pPORlHJuog9iOBFCbEo3mQGWO9HHMdyw3OkFy50+pKPMAMudiP/NIRYMwzAMwzAMo8EKMsMwDMMwDMNopLuCvDrRB5AA0lFmgOVON1ju9ILlTh/SUWaA5R520jpJj2EYhmEYhmGspLsFmWEYhmEYhmFMsILMMAzDMAzDMBqsIKcwQgiR6GNghh4hRGaij4FhGIZhUomUVpCFEKcJIcoTfRzDiSB+IIQYJ9MowNwv9yohxPxEH8tw4Zf5FwC+r14n9ICGCb/cJwkhqhJ9LMONEGKKECIn0ccx3Agh9hNC5CX6OIYbIYTb/zstnm2FEGKSECI30ccx3AghDhBCFCT6OIaTkaynpayCLIT4OYB7AXwr0ccyXAghzgXwAoCDALSly6AqhDgIwFsApgPYmg4WVSHEEtC1PhfAEgBIhwWREOIUAB8A+B6A+4QQ+yf2iIYHIcQiIcRnAK4F8H9CiLJEH9NwIIT4phBiF4D/BfCIEKIi0cc0HAghzhdCvAvgskQfy3AihPAIITYDuBHAH4UQadHMzC/3vwD8N+g+z0r0MQ0HI11PSzkFWQihZOoC8CCA/YQQcyz7Ug4hxFcAPADgh1LKc6WUbUphSlVFWZNrPwAPSSlPk1J+DsCbwMMaMvyW0wwhxDIA3wZwpZRyIoDt6aAo+q0MFwK4QEp5AoBMADP8+1LyHgcAvzL8XwDOkVIuBrAHwEohxNTEHtnQIoTIBnAqgCVSykUAdgD4vhBidkIPbIgRQuwH4LsA/gzgKCHERCmlTOX5CwCEEAsBXARgmZTyNAB5oHEu1efuE0ByXyylPBXAJAAL/PtSclxLFj1txBxILAghCtXfUkqf/083gA4AX8J/s2n7UgKL3K8CeBtkRYUQ4mohxAIhREGqWRaV3JpcJwDo9+/7LYCfCSEOSSVLshCiUBIDAP4gpTxaSvmWEGI6gHYAqTqQFmov+wAUABjlf+0DMFoIMXrYD2yIscgN0PVV49fvAJwO4MRUszSpkAIAkFL2ApgKQLlfbwTNWcf6leeUwSL3hyCv0C0A/gPylqTc/AWY5QbJulRK+Yb/9W8BnAKknuwWuTdKKY+SUr4qhCgG8Ln/PSKV5m6LzEquEa2nJb2CLIS4GsC7Qoj/FUKc59+WCaAIwMMAngZNorcIIb6ZwEONKxa5l/k3fxfAg0KI9wCUALgUwI1+i0RKYJH7Qv/mPwE4QQjxKICd/m3fBeBJxDHGG03mG4QQ50kpW4UQbv8A+gGAOgCz/e9N+mdaYZF7iZSyHcA6AOcIIfYA2ArgAADXAfhaAg81rljkPkdK2Qzg3wDOE0KUApgLYBNooTA2gYcaV4QQ14IWt5X+1y4AjwGYIoTIklJ+CuAdAGMATEvckcYXq9wAIKX82H/dHwMwSQhxlP+9qfR8K7lVLsE2KWWD9paJAF4b/iMbWmyud69/ezWApwDsAy2AU2butpHZ5beOj2g9LakfNiHEsQBOBHA8gGcA/EoIMVtK2e9/SxGAWQAWATgJwEcJOdA4YyP3/wghDpZS/gOkFP+XlPJqkII4GkBtwg42jtjIvcofWvAuyIqaL6X8JYBfAngTNLEktaXJIvPToHv8ACmlF7T6BoB6AF8FRtbqOxZs5L5RCDFLSnk/aECtl1J+B8APALSA3JJJj43cN/tDKe4GWdDXAfgKgJ8DOAyGJSZpEUJkCyF+DOA80ILnIGDwXm4AUAbgGP/bN4LG9KSeuwBnuf37lEfoA1CuwUUAnROLJS7psJF7NjDoMYAw4o5HwW9NTQVC3OeQUu4GcKKU8hyQ12AqaJGQtASR2eu3jkuMYD0t2QeZTADvSim/kFK+AOBWANf7LciZoNiW7wO4HhTPdXSiDjTO2MoNAFLKO6SUm/x/NwJoBk0wqYBV7t+C5N4OUpir/fF6vQCKAfSrQTeJsbvWvwQAf7gFQBaIVkEk+zOtsJP7Bv++ZgBeIUSp36osQPGKqYBV7tsA3CylbJBSXg6Kz/RIKTeD3JKp8Gz3g8bnGQDeAHCMEEIteF4GsBvA8YIq8+wFxWBPTsiRxhc7uScApvCxTgB/ANAhhLhOCHEDyGOUzDjKDZjGtVkAXhZCTBVC/EKM0EoHERBUbgCQUrb6fzeD7vPS4T7IOOMos6BqPJmg3KnvYwTqack+meYBKPefaEgpfwVyvx0Nck3dIKU8VEr5WwDvAdiSoOOMN3ZyVwkhzlRvEEKUCSFuBq3a3k7MYcYdq9y/BFnHF0gp1wF4AsDtQog7QVmxbybsSOOH3bUerV9rAB+CEtdkqliQYS/3KCHEAgC7AFSDrMo3g6ytbyXsSOOLVe7rAYwVQnzL/3qXEGK8EOIOUHjFiLG2RIv/nv1YStkJ4BEA4wDMFULkaGEGXgDrhBCrARwMCrVIahzkPlR5vYQQLr+i3ANSFi8G0Cil/CxRxxwPwpBbCCFqQIu//wbF3O+VUjYl6pjjQThy+3+XCSFuAlnWk3ruDiJzjpSyByTfqhGrp0kpR/wPABFk3+sAztBeewC8Esl3jNSfCOU+G8Dr/r8nAPgryMKan2g5hljuxUpu/+u5AJYnm9zRXmv/azdoBZ6RbPd5hHKfA+Bl/9+TQJPnfyfbtY7D9X4CFHKRl2g54im39p6LANwJ4CDL9lNAITUFiZZjiOU+0LL9PlAprFS/3gdq26aAQqduBJCbaDmGQ26QJ+wAAM+BPGZJNa5FKPPB0X7HsMqU6AMIcTIXgcIkZltPIoBs/99nA3gJQJ3/dQ2A20GWGFeiZRhmue8AkAUgB0BZouUYZrmTajCJk8xJN2HGQe471bUeaYPpMFzvAv/rpLvuIeR2+f92+38XgUJLzgFVczg90cefILnP8G9PRgUxWrmXguJQBYAxiZZjmOU+2b+9MtFyDJPMSwCcqt6baDnsfkZsEW4hxDGg7PR+AIcLIRqklC3+zH0JoFcIMRFktp8B4CeCCqsvALBFStmVsIOPgTjI3ef/qp5EHH+0xEHuzoQdfJTwPR7btfa/N2mIg9wdAJBs1z0MuaVf7mYA+6SUbUKIT0CLgiYAKxJ28DEQL7mllN0JEiEqYpS7GcCl/vftSJQM0RDH692YIBEiJo4yj8yxPNEautMPKBFhFIBjQUHcR2v7XACuBtAI4EhQQtZXQKuYHyX62FlulptlZrlZ7ojk3gmqZy5AjX8+B3BNoo+d5Wa5We70lXnEWJCFEN8FsFtK+Ud/sPo2SaWsdgkhvgHgaCHEp1LK7aAL0gpgqpSyxf8Vrwoh3vB/JmlgudNH7nSUGWC5We6w5J6h5BZCbAEwSyaZV4jlZrlTXe60kznRGjqAQlDCyW5QR5UMaaw+hP/vA0H1P0+1+bxbvS+Zflju9JE7HWVmuVnuKOTOSLQMLDfLzXKzzOon4WXeJNUxfVFKWQ2qgXeHf9dgm0Up5T9B5UBmCSGOFdRpSrViVAWnkwqWO33kTkeZAZab5Y5Y7gG77x3psNwst39XysqdjjIDSKwFGcbKQ2WljwLQBmCK/3WG9p4qUGvZRgA36p9Pth+WO33kTkeZWW6Wm+VmuVnu1JA7HWVWP0qoYUMI4ZY2MXWCiqL7hBC/AnC4lPJobV8+qBZkIaiN8s7hO+L4wHIHbE9ZudNRZoDlttnOcrPcah/LzXInDekosy3DtAI5HMC1disTGHXyXNr2rf7PVAM4RK1MEr2aYLlZbpaZ5Wa5WW6Wm+VONbnTUeaQ52QYTvp5AD4G4ANwln9bhuU9BQBKtdeX+N//MbSyIcn0w3Knj9zpKDPLzXKz3Cw3y50acqejzGGdl2E48V8D9d/+OoCt2nbVWeXnAJ4BcKT/9QkAPgRwE4DMRJ8glpvlZplZbpab5Wa5We5UlTsdZQ7rvAzBiT4FwDUw2ia61QkE8AqA67T3VgF4GMAkbdsMAOMTfWJYbpabZWa5WW6Wm+VmuVNN7nSUOarzFMcTXgngcQAvAfgOgD0w+mxn+X/vDyocXW3zeXeiTwbLzXKzzCw3y81ys9wsdyrKnY4yx3S+4njiD4PWEhXAUgCvWU8sgP8DsMb/9wmJPgEsN8vNMrPcLDfLzXKz3KkudzrKHNP5ivFknwtgPoA8AJnwrzhA5vo5AO7xvx7MgvS/9gFoAfXpdsVyDAk5aSx32sidjjKz3Cw3y81ys9ypIXc6yhyvnwxEiL//9ihQTIoPwGcAvg3gMinlblU/TwgxHUApAEg621IIUQvgFgAvA7hESvl+pP8/UbDc6SN3OsoMsNxguVlulpvlRvLLnY4yDwmRaNMwzO9TAazz/50B4DYAf7K85yEY5UIq/b9LABya6FVBpD8sd/rInY4ys9wsN8vNcrPcqSF3Oso8VD9hWZCFEBkArgXgFkI8BaAIgBcApJQDQogVAHYIIY6WUr7o/1gHgC+EENcCOE0IcaKUciuAt8L5nyMBljt95E5HmQGWGyw3y81ys9xEUsudjjIPNa5QbxBCHA3gHyAz/KcArgPQD+AYIcShwKBp/loAv/B/xg3gQgB/AF2kY/wnPWlgudNH7nSUGWC5wXKz3Cw3y43klzsdZR4WQpmYARwJYKn2+k4AFwM4H8A//NtcoHiX3wOoBTAJwG8AHJxoE3m0Pyx3+sidjjKz3Cw3y81ys9ypIXc6yjws5zWME58HIBtGzIoHwC/9f78H4FL/33MB/C7RAsXtxLDcaSN3OsrMcrPcLDfLzXKnhtzpKPNw/IQMsZBSdkkpe6WUXv+m4wE0+v++AMB0IcSfAawHmfhVBmVSw3Knj9zpKDPAcrPcLDdYbpYbyS93Oso8HIRd5s0fryIBVAPY4N/cDmpXOBPAF1LK7cBgrEtKwHKnj9zpKDPAcoPlBlhulhssd7KTjjIPJSEtyBo+UJHpvQAO8K9GfgrAJ6V8RZ30FITlTh+501FmgOVmuVluljv1SEe501HmIUNEsogQQhwG4DX/zxop5X1DdWAjCZY7feROR5kBlhssN8udwrDc6SN3Oso8VESqII8D9e7+tZSyd8iOaoTBcqeP3OkoM8Byg+VOC1huljvVSUeZh4qIFGSGYRiGYRiGSXUiiUFmGIZhGIZhmJSHFWSGYRiGYRiG0WAFmWEYhmEYhmE0WEFmGIZhGIZhGA1WkBmGYRiGYRhGgxVkhmGYOCGE8Aoh3hNCbBZC/FMIcbkQIug4K4SoE0KcE8X/yhVCvCiEcPu/QwohrtP2Vwgh+oUQt/tf/8L/nsnae37g3zbX5vvnCyFahRDvCiE+EkK8JIQ4Wdv/CyHEdr+8nwgh/iSEmOFwrA8IIc6wbOvQ5H/fsu8XQogf+v++SQhxbKTnh2EYJhZYQWYYhokf3VLK2VLK/QEcD+BEAD8P8Zk6ABEryAAuBPAnKaXX//pzACdr+88EsNnymX8DOFt7fQaA/wT5Hy9LKQ+SUk4DsALA7UKI47T9t/jlnQLgEQB/F0JURiFLMG4DcHWcv5NhGCYorCAzDMMMAVLKPQCWA/ieIOqEEC8LId7x/xzhf+uvABzpt8T+wG8RvlEI8bYQ4l9CiIsc/oUHwBPa624AH2jW4G8B+L3lM48DWAQAQoiJAFoBNIYpz3sArgXwPYf9jwD4G6JT9oP93wYA5UKIUfH8XoZhmGCwgswwDDNESCk/B42zVQD2ADheSnkwSHn9rf9tV4MstbOllLcAWAagVUp5CIBDAHxbCDFB/14hRBaAiVLKLZZ/+TsAZ/u7aXkB7LDsbwOwTQgxE8BikNU3Et4BsF+U+2/0LwLeE0K8Z9k3ybLvOzbf+5UIj5VhGCZqMhJ9AAzDMCmO8P/OBIUozAYpr1Md3v91AAdoMbvFAKYA+EJ7TwWAfTaffQbAdQB2w1n5/R0ozOIbAI4DcEE4QvgRMez/kZTyD4Nv9Mcg+/lMSjlb2/cLy2f3ABgT5jEyDMPEDCvIDMMwQ4Q/jMELUvB+DlJcDwRZlXucPgbgUinlX4N8dTeAHOtGKWWfEOIfAK4AsD+ABTaffRLAjQA2SSnbhBDqWE+FES/9Xw7/9yAAHwQ5roMAbAqyP1pyQDIzDMMMCxxiwTAMMwT4k9XuBnC7lFKCLME7pZQ+AEsBuP1vbQdQqH30rwAuFkJk+r9nqhAiX/9uKWULALcQIkBJBnAzgKuklE12xyWl7AZwFYBVlu2P+cM8ZkspA5RcIcQBAH4K4A4HeU8HWb/X2+2PkakA3g/5LoZhmDjBFmSGYZj4keuPoc0EMABgLYBf+/fdCeCPQogzAbwAoNO//V8ABoQQ/wTwAIBbQZUt3hFk3m0EcIrN//obgK8CeE7fKKXcjMDqFbC853dhynOkEOJdAHkgK/gKKeXz2v4fCCGWAMgHKbDHSinDSvoLF/9CYTKGxjLNMAxjiyDDBsMwDJNMCCEOAnC5lHJpoo9lKPGHfhwspfxpoo+FYZj0gUMsGIZhkhAp5bsAXhBCuEO+ObnJAIWNMAzDDBtsQWYYhmEYhmEYDbYgMwzDMAzDMIwGK8gMwzAMwzAMo8EKMsMwDMMwDMNosILMMAzDMAzDMBqsIDMMwzAMwzCMxv8DGZEhXyHC/E0AAAAASUVORK5CYII=", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -107,26 +78,22 @@ } ], "source": [ - "from matplotlib import lines\n", - "\n", - "# Plot the 1-quarter ahead forecast\n", - "h = 1\n", - "start = forecast_start + h\n", - "end = forecast_end + h + 1\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 6))\n", - "#line1 = ax.plot(date_indexs[start:end],prices[start:end],label='Data',color='r')\n", - "ax.plot(date_indexs[start:end],prices[start:end],label='Data',color='r',alpha=0.5)\n", - "plot_data_forecast(fig, ax, y = prices[start:end],\n", - " samples = samples[:,:,h-1],\n", - " f = forecast[:,h-1],\n", - " dates = indexs,\n", - " xlabel='Time index', ylabel='EPEX', title='0.5 hour ahead forecast',credible_interval=50)\n", - "##handles, labels = ax.get_legend_handles_labels()\n", - "#handles.append(line1)\n", - "#labels.append(\"Data\")\n", + "fig, ax = plt.subplots(figsize=(10,6))\n", + "ax = Bayesian_Forecast.plot_forecast(\n", + " fig, ax,\n", + " dates=epex.loc[plot_start_date:plot_end_date].index,\n", + " f=forecast[plot_start:plot_start+plot_length,horizon-1],\n", + " samples=samples[:,plot_start:plot_start+plot_length,horizon-1],\n", + " y=epex.loc[plot_start_date:plot_end_date].values[:,0],\n", + " linewidth = 2,\n", + " credible_interval=95)\n", "\n", - "plt.show()" + "ax = Bayesian_Forecast.forecast_ax_style(ax=ax,\n", + " ylabel='EPEX Price (£)',\n", + " xlabel='Date (MM-DD HH)',\n", + " title='3 Hour Ahead Forecast',\n", + " legend=['EPEX Price','Forecasted Price','95% Confidence Interval']\n", + " )" ] } ], From 1593cd070c2e1d6fdaed02aca1202f77ffee0d8e Mon Sep 17 00:00:00 2001 From: griffinfarrow Date: Sun, 20 Mar 2022 17:46:59 +0000 Subject: [PATCH 09/30] updating tuningT --- Notebooks/load_data.ipynb | 47 ++++++++----------------------- Tuning/hyperparam_tuning.ipynb | 51 ++++++++++++++++++++++++---------- 2 files changed, 47 insertions(+), 51 deletions(-) diff --git a/Notebooks/load_data.ipynb b/Notebooks/load_data.ipynb index 10732e4..514552a 100644 --- a/Notebooks/load_data.ipynb +++ b/Notebooks/load_data.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -23,16 +23,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "WindowsPath('C:/Users/Ronan/Projects/Hackathon')" + "WindowsPath('C:/Users/mgmf4/Documents/AI_Hack/Hackathon')" ] }, - "execution_count": 8, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -44,59 +44,34 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\Ronan\\Projects\\Hackathon\\Data\n", - "C:\\Users\\Ronan\\Projects\\Hackathon\\Data\n" - ] - } - ], + "outputs": [], "source": [ "df = load.systemprice().load()" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data\\epex_day_ahead_price.csv\n" - ] - } - ], + "outputs": [], "source": [ "df = load.epex().load()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data\\spot_intraday_price.csv\n" - ] - } - ], + "outputs": [], "source": [ "df = load.spot().load()" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ diff --git a/Tuning/hyperparam_tuning.ipynb b/Tuning/hyperparam_tuning.ipynb index 379a5d3..af08d21 100644 --- a/Tuning/hyperparam_tuning.ipynb +++ b/Tuning/hyperparam_tuning.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,9 @@ "from stable_baselines3.common.vec_env import DummyVecEnv\n", "from stable_baselines3.common.env_checker import check_env\n", "\n", - "%matplotlib qt5" + "%matplotlib qt5\n", + "%load_ext autoreload\n", + "%autoreload 2" ] }, { @@ -61,18 +63,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (1556052740.py, line 13)", - "output_type": "error", - "traceback": [ - "\u001b[1;36m Input \u001b[1;32mIn [10]\u001b[1;36m\u001b[0m\n\u001b[1;33m model =\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" - ] - } - ], + "outputs": [], "source": [ "def objective():\n", " \"\"\"\n", @@ -86,7 +79,7 @@ " power = 0.5\n", " env = rl.energy_price_env(obs_price_array, window_size=24*2, power=power)\n", " model = PPO(MlpPolicy, env, verbose=0) \n", - " model.learn(total_timesteps = 50000)\n", + " model.learn(total_timesteps = 1000)\n", "\n", " test_start_idx = start_of_2020\n", " test_end_idx = start_of_2021\n", @@ -94,10 +87,38 @@ " test_price_array = price_array[test_start_idx:test_end_idx]\n", "\n", " new_env = DummyVecEnv([lambda: rl.energy_price_env(test_price_array, power=power)])\n", - " mean_reward_after_train = rl.evaluate(model, new_env=new_env, num_episodes=100, index=epex.index[test_start_idx:test_end_idx])\n", + " mean_reward_after_train = rl.evaluate(model, new_env=new_env, num_episodes=10, index=epex.index[test_start_idx:test_end_idx])\n", "\n", " return mean_reward_after_train" ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mgmf4\\anaconda3\\envs\\ml\\lib\\site-packages\\gym\\logger.py:34: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n", + " warnings.warn(colorize(\"%s: %s\" % (\"WARN\", msg % args), \"yellow\"))\n" + ] + }, + { + "data": { + "text/plain": [ + "82.58175" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "objective()" + ] } ], "metadata": { From f4ca6d06ae17c3b90c46a3f7b601da73c1d98a17 Mon Sep 17 00:00:00 2001 From: griffinfarrow Date: Sun, 20 Mar 2022 19:19:06 +0000 Subject: [PATCH 10/30] final import, formatting not working --- Hack/rl.py | 17 ++++ Tuning/hyperparam_tuning.ipynb | 149 --------------------------------- Tuning/test_func.py | 42 ++++++++++ Tuning/tuningtodo.md | 4 + 4 files changed, 63 insertions(+), 149 deletions(-) delete mode 100644 Tuning/hyperparam_tuning.ipynb create mode 100644 Tuning/test_func.py create mode 100644 Tuning/tuningtodo.md diff --git a/Hack/rl.py b/Hack/rl.py index bc011a8..40c6fc9 100644 --- a/Hack/rl.py +++ b/Hack/rl.py @@ -244,3 +244,20 @@ def evaluate(model, new_env=None, num_episodes=100, index=None): ) return mean_episode_reward + + +def quick_eval(model): + """ + Evaluation func for the multiprocessing that we have designed to be as quick as possible! + """ + print("called") + env = model.get_env() + env.reset() + done = False + episode_rewards = [] + obs = env.reset() + while not done: + action, _states = model.predict(obs) + obs, reward, done, info = env.step(action) + episode_rewards.append(reward) + return sum(episode_rewards) diff --git a/Tuning/hyperparam_tuning.ipynb b/Tuning/hyperparam_tuning.ipynb deleted file mode 100644 index af08d21..0000000 --- a/Tuning/hyperparam_tuning.ipynb +++ /dev/null @@ -1,149 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Notebook for hyperparameter tuning" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a very simple notebook to test out some algorithms for hyperparameter tuning, to find one that works well and apply it to our model" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Necessary imports\n", - "\n", - "import sys\n", - "sys.path.append(\"../\")\n", - "\n", - "from Hack import load \n", - "from Hack import rl\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import optuna\n", - "from stable_baselines3 import PPO\n", - "from stable_baselines3.ppo.policies import MlpPolicy\n", - "from stable_baselines3.common.vec_env import DummyVecEnv\n", - "from stable_baselines3.common.env_checker import check_env\n", - "\n", - "%matplotlib qt5\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# load epex data\n", - "epex = load.epex().load()\n", - "price_array = epex['apx_da_hourly'].values\n", - "\n", - "start_of_2020 = None\n", - "start_of_2021 = None\n", - "\n", - "for idx, (i, row) in enumerate(epex.iterrows()):\n", - " if i.year > 2019 and start_of_2020 is None:\n", - " start_of_2020 = idx\n", - " if i.year > 2020 and start_of_2021 is None:\n", - " start_of_2021 = idx\n", - " break" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def objective():\n", - " \"\"\"\n", - " Function to take in hyperparameters, train a reinforcement model, and output the \"profit\" of the model as a metric\n", - " \"\"\"\n", - "\n", - " start_idx = 0\n", - " end_idx = start_of_2020 #4 * 2*24*7 #start_of_2020 # 2019->2020 # 2*24*7\n", - " obs_price_array = price_array[start_idx:end_idx]\n", - "\n", - " power = 0.5\n", - " env = rl.energy_price_env(obs_price_array, window_size=24*2, power=power)\n", - " model = PPO(MlpPolicy, env, verbose=0) \n", - " model.learn(total_timesteps = 1000)\n", - "\n", - " test_start_idx = start_of_2020\n", - " test_end_idx = start_of_2021\n", - "\n", - " test_price_array = price_array[test_start_idx:test_end_idx]\n", - "\n", - " new_env = DummyVecEnv([lambda: rl.energy_price_env(test_price_array, power=power)])\n", - " mean_reward_after_train = rl.evaluate(model, new_env=new_env, num_episodes=10, index=epex.index[test_start_idx:test_end_idx])\n", - "\n", - " return mean_reward_after_train" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\mgmf4\\anaconda3\\envs\\ml\\lib\\site-packages\\gym\\logger.py:34: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n", - " warnings.warn(colorize(\"%s: %s\" % (\"WARN\", msg % args), \"yellow\"))\n" - ] - }, - { - "data": { - "text/plain": [ - "82.58175" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "objective()" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "040d274fdfca6ecc88f65f18dafc70b49547e52dd567f9545727ec9f8e0b0ee0" - }, - "kernelspec": { - "display_name": "Python 3.9.10 ('ml')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.10" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Tuning/test_func.py b/Tuning/test_func.py new file mode 100644 index 0000000..ae9ac9f --- /dev/null +++ b/Tuning/test_func.py @@ -0,0 +1,42 @@ +import sys + +sys.path.append("../") +import multiprocessing as mp +import time as time + +import numpy as np +from stable_baselines3 import PPO +from stable_baselines3.ppo.policies import MlpPolicy + +from Hack import load, rl + + +def objective(idx): + """ + Function to take in hyperparameters, train a reinforcement model, and output the "profit" of the model as a metric + """ + epex = load.epex().load() + price_array = epex["apx_da_hourly"].values + + start_idx = 0 + end_idx = 4 * 2 * 24 * 7 # start_of_2020 # 2019->2020 # 2*24*7 + obs_price_array = price_array[start_idx:end_idx] + + power = 0.5 + env = rl.energy_price_env(obs_price_array, window_size=24 * 2, power=power) + model = PPO(MlpPolicy, env, verbose=0) + + # this part is the slow part that we want to split across processors: + + mean_reward_eval = rl.quick_eval(model) + return mean_reward_eval + + +if __name__ == "__main__": + time_start = time.time() + with mp.Pool(5) as p: + val_list = p.map(objective, range(10)) + print("mean = ", np.mean(np.array(val_list))) + time_stop = time.time() + print("time = ", time_stop - time_start) + print("val_list = ", val_list) diff --git a/Tuning/tuningtodo.md b/Tuning/tuningtodo.md new file mode 100644 index 0000000..e340ed8 --- /dev/null +++ b/Tuning/tuningtodo.md @@ -0,0 +1,4 @@ +What do we want to do for the hyperparam tuning? + +* Basically, algorithm should define a function that defines the model using the given parameters, trains it and evaluates it, spitting out the mean of the evaluations as the parameter to optimise over +* Could parallelise the training part, but seems far far far more useful to parallelise the evaluation part (that takes forever!) From 1739c604cb36375cd2c701c675c71a228d52a7af Mon Sep 17 00:00:00 2001 From: griffinfarrow Date: Wed, 23 Mar 2022 10:37:31 +0000 Subject: [PATCH 11/30] fixing for multiprocessing --- Tuning/test_func.py | 48 ++++++++++++++++++++++++++++++--------------- 1 file changed, 32 insertions(+), 16 deletions(-) diff --git a/Tuning/test_func.py b/Tuning/test_func.py index ae9ac9f..91ef8da 100644 --- a/Tuning/test_func.py +++ b/Tuning/test_func.py @@ -1,17 +1,33 @@ import sys - sys.path.append("../") import multiprocessing as mp import time as time - import numpy as np from stable_baselines3 import PPO from stable_baselines3.ppo.policies import MlpPolicy - from Hack import load, rl +import gym - -def objective(idx): +def quick_eval(idx, model): + """ + Evaluation func for the multiprocessing that we have designed to be as quick as possible! + """ + print("called") + env = model.get_env() + env.reset() + done = False + episode_rewards = [] + obs = env.reset() + while not done: + action, _states = model.predict(obs) + obs, reward, done, info = env.step(action) + episode_rewards.append(reward) + return sum(episode_rewards) + +def add_two(idx, a, b): + return (a+b) + +def objective(): """ Function to take in hyperparameters, train a reinforcement model, and output the "profit" of the model as a metric """ @@ -26,17 +42,17 @@ def objective(idx): env = rl.energy_price_env(obs_price_array, window_size=24 * 2, power=power) model = PPO(MlpPolicy, env, verbose=0) - # this part is the slow part that we want to split across processors: + starmap_obj2 = [(i, model) for i in range(10)] - mean_reward_eval = rl.quick_eval(model) - return mean_reward_eval + starmap_obj = [(0, i, j) for i, j in list(zip(range(10), range(10)))] + # # this part is the slow part that we want to split across processors: + with mp.Pool(2) as p: + val_list = p.starmap(add_two, starmap_obj) + # mean_reward_eval = np.mean(np.array(val_list)) + # return mean_reward_eval + return sum(val_list), starmap_obj2 -if __name__ == "__main__": - time_start = time.time() - with mp.Pool(5) as p: - val_list = p.map(objective, range(10)) - print("mean = ", np.mean(np.array(val_list))) - time_stop = time.time() - print("time = ", time_stop - time_start) - print("val_list = ", val_list) +if __name__=="__main__": + a = objective() + print(a) \ No newline at end of file From fa739ef74c1ed5e0f5e863192e7c68bf9ad39265 Mon Sep 17 00:00:00 2001 From: griffinfarrow Date: Wed, 23 Mar 2022 10:38:24 +0000 Subject: [PATCH 12/30] fixing for multiprocessing --- Tuning/test_func.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/Tuning/test_func.py b/Tuning/test_func.py index 91ef8da..c445a6f 100644 --- a/Tuning/test_func.py +++ b/Tuning/test_func.py @@ -1,4 +1,5 @@ import sys + sys.path.append("../") import multiprocessing as mp import time as time @@ -8,6 +9,7 @@ from Hack import load, rl import gym + def quick_eval(idx, model): """ Evaluation func for the multiprocessing that we have designed to be as quick as possible! @@ -24,8 +26,10 @@ def quick_eval(idx, model): episode_rewards.append(reward) return sum(episode_rewards) + def add_two(idx, a, b): - return (a+b) + return a + b + def objective(): """ @@ -44,7 +48,7 @@ def objective(): starmap_obj2 = [(i, model) for i in range(10)] - starmap_obj = [(0, i, j) for i, j in list(zip(range(10), range(10)))] + starmap_obj = [(0, i, j) for i, j in list(zip(range(10), range(10)))] # # this part is the slow part that we want to split across processors: with mp.Pool(2) as p: val_list = p.starmap(add_two, starmap_obj) @@ -53,6 +57,7 @@ def objective(): # return mean_reward_eval return sum(val_list), starmap_obj2 -if __name__=="__main__": + +if __name__ == "__main__": a = objective() - print(a) \ No newline at end of file + print(a) From fc3ac57617d14453277551e90418ee0aec177075 Mon Sep 17 00:00:00 2001 From: griffinfarrow Date: Wed, 23 Mar 2022 10:40:21 +0000 Subject: [PATCH 13/30] change to rl.py --- Hack/rl.py | 35 +++++++++++++++++------------------ 1 file changed, 17 insertions(+), 18 deletions(-) diff --git a/Hack/rl.py b/Hack/rl.py index 40c6fc9..d48e87e 100644 --- a/Hack/rl.py +++ b/Hack/rl.py @@ -41,9 +41,8 @@ def __init__(self, obs_price_array, start_energy=1, window_size=1000, power=1): self.action_space = gym.spaces.Discrete(3) # current_price, mean_price, current_energy, time self.observation_space = gym.spaces.Box( - low=np.array([-np.inf, -np.inf, 0, 0]), - high=np.array([np.inf, np.inf, 1, np.inf]), - dtype=np.float32, + low=np.float32(np.array([-np.inf, -np.inf, 0, 0])), + high=np.float32(np.array([np.inf, np.inf, 1, np.inf])) ) # our state is the charge self.start_energy = start_energy @@ -246,18 +245,18 @@ def evaluate(model, new_env=None, num_episodes=100, index=None): return mean_episode_reward -def quick_eval(model): - """ - Evaluation func for the multiprocessing that we have designed to be as quick as possible! - """ - print("called") - env = model.get_env() - env.reset() - done = False - episode_rewards = [] - obs = env.reset() - while not done: - action, _states = model.predict(obs) - obs, reward, done, info = env.step(action) - episode_rewards.append(reward) - return sum(episode_rewards) +# def quick_eval(idx, model): +# """ +# Evaluation func for the multiprocessing that we have designed to be as quick as possible! +# """ +# print("called") +# env = model.get_env() +# env.reset() +# done = False +# episode_rewards = [] +# obs = env.reset() +# while not done: +# action, _states = model.predict(obs) +# obs, reward, done, info = env.step(action) +# episode_rewards.append(reward) +# return sum(episode_rewards) From 705d9fa78c292da8cf61217105b046134ac70e75 Mon Sep 17 00:00:00 2001 From: griffinfarrow Date: Wed, 23 Mar 2022 10:46:23 +0000 Subject: [PATCH 14/30] fix to annoying warning message --- Hack/rl.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/Hack/rl.py b/Hack/rl.py index bc011a8..85b1c9c 100644 --- a/Hack/rl.py +++ b/Hack/rl.py @@ -41,9 +41,8 @@ def __init__(self, obs_price_array, start_energy=1, window_size=1000, power=1): self.action_space = gym.spaces.Discrete(3) # current_price, mean_price, current_energy, time self.observation_space = gym.spaces.Box( - low=np.array([-np.inf, -np.inf, 0, 0]), - high=np.array([np.inf, np.inf, 1, np.inf]), - dtype=np.float32, + low=np.array([-np.inf, -np.inf, 0, 0], dtype=np.float32), + high=np.array([np.inf, np.inf, 1, np.inf], dtype=np.float32), ) # our state is the charge self.start_energy = start_energy From 29d4477229491758c8e43f0a74fe53d2a58ef4e4 Mon Sep 17 00:00:00 2001 From: griffinfarrow Date: Thu, 24 Mar 2022 12:10:08 +0000 Subject: [PATCH 15/30] adding parallel evaluation --- Hack/rl.py | 33 ++++++++++----------- Tuning/pathos_tuning.py | 65 +++++++++++++++++++++++++++++++++++++++++ Tuning/test_func.py | 63 --------------------------------------- Tuning/test_serial.py | 40 +++++++++++++++++++++++++ Tuning/tuningtodo.md | 4 --- 5 files changed, 121 insertions(+), 84 deletions(-) create mode 100644 Tuning/pathos_tuning.py delete mode 100644 Tuning/test_func.py create mode 100644 Tuning/test_serial.py delete mode 100644 Tuning/tuningtodo.md diff --git a/Hack/rl.py b/Hack/rl.py index d48e87e..7ffbbba 100644 --- a/Hack/rl.py +++ b/Hack/rl.py @@ -41,8 +41,8 @@ def __init__(self, obs_price_array, start_energy=1, window_size=1000, power=1): self.action_space = gym.spaces.Discrete(3) # current_price, mean_price, current_energy, time self.observation_space = gym.spaces.Box( - low=np.float32(np.array([-np.inf, -np.inf, 0, 0])), - high=np.float32(np.array([np.inf, np.inf, 1, np.inf])) + low=np.array([-np.inf, -np.inf, 0, 0], dtype=np.float32), + high=np.array([np.inf, np.inf, 1, np.inf], dtype=np.float32), ) # our state is the charge self.start_energy = start_energy @@ -245,18 +245,17 @@ def evaluate(model, new_env=None, num_episodes=100, index=None): return mean_episode_reward -# def quick_eval(idx, model): -# """ -# Evaluation func for the multiprocessing that we have designed to be as quick as possible! -# """ -# print("called") -# env = model.get_env() -# env.reset() -# done = False -# episode_rewards = [] -# obs = env.reset() -# while not done: -# action, _states = model.predict(obs) -# obs, reward, done, info = env.step(action) -# episode_rewards.append(reward) -# return sum(episode_rewards) +def quick_eval(idx, model): + """ + Evaluation func for the multiprocessing that we have designed to be as quick as possible! + """ + env = model.get_env() + env.reset() + done = False + episode_rewards = [] + obs = env.reset() + while not done: + action, _states = model.predict(obs) + obs, reward, done, info = env.step(action) + episode_rewards.append(reward) + return sum(episode_rewards) diff --git a/Tuning/pathos_tuning.py b/Tuning/pathos_tuning.py new file mode 100644 index 0000000..168ea85 --- /dev/null +++ b/Tuning/pathos_tuning.py @@ -0,0 +1,65 @@ +import sys + +from pathos.multiprocessing import ProcessingPool + +sys.path.append("../") +import time as time + +from stable_baselines3 import PPO +from stable_baselines3.ppo.policies import MlpPolicy + +from Hack import load, rl + + +def create_train_model(price_array, learning_rate): + """ + Creates and trains the deep learning model + """ + start_idx = 0 + end_idx = 4 * 2 * 24 * 7 # start_of_2020 # 2019->2020 # 2*24*7 + obs_price_array = price_array[start_idx:end_idx] + power = 0.5 + env = rl.energy_price_env(obs_price_array, window_size=24 * 2, power=power) + model = PPO(MlpPolicy, env, verbose=0, learning_rate=learning_rate) + return model + + +def evaluate_model(iter_obj): + val_list = [] + for obj in iter_obj: + idx = obj[0] + model = obj[1] + val = rl.quick_eval(idx, model) + val_list.append(val) + return sum(val_list) + + +def objective(learning_rate=0.01): + """ + args: params of our model + """ + if ( + __name__ == "__main__" + ): # only do this calculation if we're on the main processor + data = load.epex().load() + price_array = data["apx_da_hourly"].values + model = create_train_model(price_array, learning_rate) + obj_to_iterate = [(i, model) for i in range(50)] + obj_to_iterate2 = [(i, model) for i in range(50)] + # obj_to_iterate3 = [(i, model) for i in range(100)] + time_start = time.time() + results = ProcessingPool(3).map( + evaluate_model, [obj_to_iterate, obj_to_iterate2] + ) + # results = ProcessingPool(4).map(evaluate_model, [obj_to_iterate3]) + # calculate the mean result + time_stop = time.time() + print("time = ", time_stop - time_start) + return results + # mean_result = (results[0] + results[1])/(float(len(obj_to_iterate) + len(obj_to_iterate2))) + # return mean_result[0] + + +a = objective() +if a is not None: + print(a) diff --git a/Tuning/test_func.py b/Tuning/test_func.py deleted file mode 100644 index c445a6f..0000000 --- a/Tuning/test_func.py +++ /dev/null @@ -1,63 +0,0 @@ -import sys - -sys.path.append("../") -import multiprocessing as mp -import time as time -import numpy as np -from stable_baselines3 import PPO -from stable_baselines3.ppo.policies import MlpPolicy -from Hack import load, rl -import gym - - -def quick_eval(idx, model): - """ - Evaluation func for the multiprocessing that we have designed to be as quick as possible! - """ - print("called") - env = model.get_env() - env.reset() - done = False - episode_rewards = [] - obs = env.reset() - while not done: - action, _states = model.predict(obs) - obs, reward, done, info = env.step(action) - episode_rewards.append(reward) - return sum(episode_rewards) - - -def add_two(idx, a, b): - return a + b - - -def objective(): - """ - Function to take in hyperparameters, train a reinforcement model, and output the "profit" of the model as a metric - """ - epex = load.epex().load() - price_array = epex["apx_da_hourly"].values - - start_idx = 0 - end_idx = 4 * 2 * 24 * 7 # start_of_2020 # 2019->2020 # 2*24*7 - obs_price_array = price_array[start_idx:end_idx] - - power = 0.5 - env = rl.energy_price_env(obs_price_array, window_size=24 * 2, power=power) - model = PPO(MlpPolicy, env, verbose=0) - - starmap_obj2 = [(i, model) for i in range(10)] - - starmap_obj = [(0, i, j) for i, j in list(zip(range(10), range(10)))] - # # this part is the slow part that we want to split across processors: - with mp.Pool(2) as p: - val_list = p.starmap(add_two, starmap_obj) - - # mean_reward_eval = np.mean(np.array(val_list)) - # return mean_reward_eval - return sum(val_list), starmap_obj2 - - -if __name__ == "__main__": - a = objective() - print(a) diff --git a/Tuning/test_serial.py b/Tuning/test_serial.py new file mode 100644 index 0000000..d7886c4 --- /dev/null +++ b/Tuning/test_serial.py @@ -0,0 +1,40 @@ +import sys + +sys.path.append("../") +import numpy as np +from stable_baselines3 import PPO +from stable_baselines3.ppo.policies import MlpPolicy + +from Hack import load, rl + +""" +This script is what we want to somehow parallelise for tuning +""" + + +def objective(num_episodes=100): + """ + Function to take in hyperparameters, train a reinforcement model, and output the "profit" of the model as a metric + """ + epex = load.epex().load() + price_array = epex["apx_da_hourly"].values + + start_idx = 0 + end_idx = 4 * 2 * 24 * 7 # start_of_2020 # 2019->2020 # 2*24*7 + obs_price_array = price_array[start_idx:end_idx] + + power = 0.5 + env = rl.energy_price_env(obs_price_array, window_size=24 * 2, power=power) + model = PPO(MlpPolicy, env, verbose=0) + val_list = [] + + for i in range(num_episodes): + val = rl.quick_eval(i, model) + val_list.append(val) + + return np.mean(val_list) + + +if __name__ == "__main__": + a = objective() + print(a) diff --git a/Tuning/tuningtodo.md b/Tuning/tuningtodo.md deleted file mode 100644 index e340ed8..0000000 --- a/Tuning/tuningtodo.md +++ /dev/null @@ -1,4 +0,0 @@ -What do we want to do for the hyperparam tuning? - -* Basically, algorithm should define a function that defines the model using the given parameters, trains it and evaluates it, spitting out the mean of the evaluations as the parameter to optimise over -* Could parallelise the training part, but seems far far far more useful to parallelise the evaluation part (that takes forever!) From 5bb550a98ab3e354ce369899c4a94ef40b833b8f Mon Sep 17 00:00:00 2001 From: griffinfarrow Date: Thu, 24 Mar 2022 18:15:24 +0000 Subject: [PATCH 16/30] opt --- Tuning/parallel_optimization.py | 62 ++++++++++++++++++ Tuning/pathos_tuning.py | 65 ------------------- ...{test_serial.py => serial_optimization.py} | 19 +++--- 3 files changed, 73 insertions(+), 73 deletions(-) create mode 100644 Tuning/parallel_optimization.py delete mode 100644 Tuning/pathos_tuning.py rename Tuning/{test_serial.py => serial_optimization.py} (66%) diff --git a/Tuning/parallel_optimization.py b/Tuning/parallel_optimization.py new file mode 100644 index 0000000..5b81371 --- /dev/null +++ b/Tuning/parallel_optimization.py @@ -0,0 +1,62 @@ +import sys + +sys.path.append("../") + +import optuna # noqa: E402 +from pathos.multiprocessing import ProcessingPool # noqa: E402 +from stable_baselines3 import PPO # noqa: E402 +from stable_baselines3.ppo.policies import MlpPolicy # noqa: E402 + +from Hack import load, rl # noqa: E402 + + +def create_train_model(price_array, learning_rate): + """ + Creates and trains the deep learning model + """ + start_idx = 0 + end_idx = 4 * 2 * 24 * 7 # start_of_2020 # 2019->2020 # 2*24*7 + obs_price_array = price_array[start_idx:end_idx] + power = 0.5 + env = rl.energy_price_env(obs_price_array, window_size=24 * 2, power=power) + model = PPO(MlpPolicy, env, verbose=0, learning_rate=learning_rate) + model.learn(100) + return model + + +def evaluate_model(iter_obj): + val_list = [] + for obj in iter_obj: + idx = obj[0] + model = obj[1] + val = rl.quick_eval(idx, model) + val_list.append(val) + return sum(val_list) + + +def objective(trial): + """ + args: params of our model + """ + data = load.epex().load() + price_array = data["apx_da_hourly"].values + learning_rate = trial.suggest_float("learning_rate", 1.0e-5, 1.0e-3) + model = create_train_model(price_array, learning_rate) + obj_to_iterate = [(i, model) for i in range(10)] + obj_to_iterate2 = [(i, model) for i in range(10)] + obj_to_iterate3 = [(i, model) for i in range(10)] + results = ProcessingPool(3).map( + evaluate_model, [obj_to_iterate, obj_to_iterate2, obj_to_iterate3] + ) + # calculate the mean result + mean_result = sum(results) / ( + float(len(obj_to_iterate) + len(obj_to_iterate2) + len(obj_to_iterate3)) + ) + return mean_result[0] + + +if __name__ == "__main__": + # carry out the optimisation part of the problem here + study = optuna.create_study(direction="maximize") + study.optimize(objective, n_trials=2) + print("Best value: {} (params: {})\n".format(study.best_value, study.best_params)) diff --git a/Tuning/pathos_tuning.py b/Tuning/pathos_tuning.py deleted file mode 100644 index 168ea85..0000000 --- a/Tuning/pathos_tuning.py +++ /dev/null @@ -1,65 +0,0 @@ -import sys - -from pathos.multiprocessing import ProcessingPool - -sys.path.append("../") -import time as time - -from stable_baselines3 import PPO -from stable_baselines3.ppo.policies import MlpPolicy - -from Hack import load, rl - - -def create_train_model(price_array, learning_rate): - """ - Creates and trains the deep learning model - """ - start_idx = 0 - end_idx = 4 * 2 * 24 * 7 # start_of_2020 # 2019->2020 # 2*24*7 - obs_price_array = price_array[start_idx:end_idx] - power = 0.5 - env = rl.energy_price_env(obs_price_array, window_size=24 * 2, power=power) - model = PPO(MlpPolicy, env, verbose=0, learning_rate=learning_rate) - return model - - -def evaluate_model(iter_obj): - val_list = [] - for obj in iter_obj: - idx = obj[0] - model = obj[1] - val = rl.quick_eval(idx, model) - val_list.append(val) - return sum(val_list) - - -def objective(learning_rate=0.01): - """ - args: params of our model - """ - if ( - __name__ == "__main__" - ): # only do this calculation if we're on the main processor - data = load.epex().load() - price_array = data["apx_da_hourly"].values - model = create_train_model(price_array, learning_rate) - obj_to_iterate = [(i, model) for i in range(50)] - obj_to_iterate2 = [(i, model) for i in range(50)] - # obj_to_iterate3 = [(i, model) for i in range(100)] - time_start = time.time() - results = ProcessingPool(3).map( - evaluate_model, [obj_to_iterate, obj_to_iterate2] - ) - # results = ProcessingPool(4).map(evaluate_model, [obj_to_iterate3]) - # calculate the mean result - time_stop = time.time() - print("time = ", time_stop - time_start) - return results - # mean_result = (results[0] + results[1])/(float(len(obj_to_iterate) + len(obj_to_iterate2))) - # return mean_result[0] - - -a = objective() -if a is not None: - print(a) diff --git a/Tuning/test_serial.py b/Tuning/serial_optimization.py similarity index 66% rename from Tuning/test_serial.py rename to Tuning/serial_optimization.py index d7886c4..42c64c3 100644 --- a/Tuning/test_serial.py +++ b/Tuning/serial_optimization.py @@ -1,15 +1,14 @@ import sys sys.path.append("../") -import numpy as np -from stable_baselines3 import PPO -from stable_baselines3.ppo.policies import MlpPolicy -from Hack import load, rl +import time as time # noqa: E402 -""" -This script is what we want to somehow parallelise for tuning -""" +import numpy as np # noqa: E402 +from stable_baselines3 import PPO # noqa: E402 +from stable_baselines3.ppo.policies import MlpPolicy # noqa: E402 + +from Hack import load, rl # noqa: E402 def objective(num_episodes=100): @@ -26,6 +25,7 @@ def objective(num_episodes=100): power = 0.5 env = rl.energy_price_env(obs_price_array, window_size=24 * 2, power=power) model = PPO(MlpPolicy, env, verbose=0) + model.learn(100) val_list = [] for i in range(num_episodes): @@ -36,5 +36,8 @@ def objective(num_episodes=100): if __name__ == "__main__": - a = objective() + time_start = time.time() + a = objective(num_episodes=150) + time_stop = time.time() print(a) + print("time = ", time_stop - time_start) From 7b955ec2aa6f5a47749c73e0c0907d69992189b6 Mon Sep 17 00:00:00 2001 From: Griffin Farrow <40544204+griffinfarrow@users.noreply.github.com> Date: Fri, 25 Mar 2022 11:40:49 +0000 Subject: [PATCH 17/30] Update Hack/rl.py Co-authored-by: Ronan Laker --- Hack/rl.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Hack/rl.py b/Hack/rl.py index 7ffbbba..804ad12 100644 --- a/Hack/rl.py +++ b/Hack/rl.py @@ -252,7 +252,7 @@ def quick_eval(idx, model): env = model.get_env() env.reset() done = False - episode_rewards = [] + total_reward = 0 obs = env.reset() while not done: action, _states = model.predict(obs) From afcc636e039d8caf84e36b8e3dfccc36d98289d0 Mon Sep 17 00:00:00 2001 From: Griffin Farrow <40544204+griffinfarrow@users.noreply.github.com> Date: Fri, 25 Mar 2022 11:40:57 +0000 Subject: [PATCH 18/30] Update Hack/rl.py Co-authored-by: Ronan Laker --- Hack/rl.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Hack/rl.py b/Hack/rl.py index 804ad12..62fe874 100644 --- a/Hack/rl.py +++ b/Hack/rl.py @@ -257,5 +257,5 @@ def quick_eval(idx, model): while not done: action, _states = model.predict(obs) obs, reward, done, info = env.step(action) - episode_rewards.append(reward) + total_reward += reward return sum(episode_rewards) From 4152f288851adbab0953a281a767b77d951edf46 Mon Sep 17 00:00:00 2001 From: Griffin Farrow <40544204+griffinfarrow@users.noreply.github.com> Date: Fri, 25 Mar 2022 11:41:03 +0000 Subject: [PATCH 19/30] Update Hack/rl.py Co-authored-by: Ronan Laker --- Hack/rl.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Hack/rl.py b/Hack/rl.py index 62fe874..9aa3c11 100644 --- a/Hack/rl.py +++ b/Hack/rl.py @@ -258,4 +258,4 @@ def quick_eval(idx, model): action, _states = model.predict(obs) obs, reward, done, info = env.step(action) total_reward += reward - return sum(episode_rewards) + return total_reward From ac733713756c5791921025f98c0162228c791aef Mon Sep 17 00:00:00 2001 From: Griffin Farrow <40544204+griffinfarrow@users.noreply.github.com> Date: Fri, 25 Mar 2022 11:41:26 +0000 Subject: [PATCH 20/30] Update Tuning/parallel_optimization.py Co-authored-by: Ronan Laker --- Tuning/parallel_optimization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Tuning/parallel_optimization.py b/Tuning/parallel_optimization.py index 5b81371..3fca97f 100644 --- a/Tuning/parallel_optimization.py +++ b/Tuning/parallel_optimization.py @@ -25,7 +25,7 @@ def create_train_model(price_array, learning_rate): def evaluate_model(iter_obj): - val_list = [] + total_val = 0 for obj in iter_obj: idx = obj[0] model = obj[1] From 1f5ec62d96e7f0a9eb3a2a5e44a6b95583105fd3 Mon Sep 17 00:00:00 2001 From: Griffin Farrow <40544204+griffinfarrow@users.noreply.github.com> Date: Fri, 25 Mar 2022 11:41:31 +0000 Subject: [PATCH 21/30] Update Tuning/parallel_optimization.py Co-authored-by: Ronan Laker --- Tuning/parallel_optimization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Tuning/parallel_optimization.py b/Tuning/parallel_optimization.py index 3fca97f..87c7c45 100644 --- a/Tuning/parallel_optimization.py +++ b/Tuning/parallel_optimization.py @@ -30,7 +30,7 @@ def evaluate_model(iter_obj): idx = obj[0] model = obj[1] val = rl.quick_eval(idx, model) - val_list.append(val) + total_val += val return sum(val_list) From a418694eb0d25cce37aed1989703fea2038faf51 Mon Sep 17 00:00:00 2001 From: Griffin Farrow <40544204+griffinfarrow@users.noreply.github.com> Date: Fri, 25 Mar 2022 11:41:36 +0000 Subject: [PATCH 22/30] Update Tuning/parallel_optimization.py Co-authored-by: Ronan Laker --- Tuning/parallel_optimization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Tuning/parallel_optimization.py b/Tuning/parallel_optimization.py index 87c7c45..062a67c 100644 --- a/Tuning/parallel_optimization.py +++ b/Tuning/parallel_optimization.py @@ -31,7 +31,7 @@ def evaluate_model(iter_obj): model = obj[1] val = rl.quick_eval(idx, model) total_val += val - return sum(val_list) + return total_val def objective(trial): From 86a2caa5b264903b8a88a58a6f53de167a68d447 Mon Sep 17 00:00:00 2001 From: Griffin Farrow <40544204+griffinfarrow@users.noreply.github.com> Date: Fri, 25 Mar 2022 11:42:33 +0000 Subject: [PATCH 23/30] Update Tuning/parallel_optimization.py Co-authored-by: Ronan Laker --- Tuning/parallel_optimization.py | 1 - 1 file changed, 1 deletion(-) diff --git a/Tuning/parallel_optimization.py b/Tuning/parallel_optimization.py index 062a67c..3bac9fd 100644 --- a/Tuning/parallel_optimization.py +++ b/Tuning/parallel_optimization.py @@ -17,7 +17,6 @@ def create_train_model(price_array, learning_rate): start_idx = 0 end_idx = 4 * 2 * 24 * 7 # start_of_2020 # 2019->2020 # 2*24*7 obs_price_array = price_array[start_idx:end_idx] - power = 0.5 env = rl.energy_price_env(obs_price_array, window_size=24 * 2, power=power) model = PPO(MlpPolicy, env, verbose=0, learning_rate=learning_rate) model.learn(100) From 5c92090349974df2c0f9a85ba662a41db7122d8a Mon Sep 17 00:00:00 2001 From: Ronan Laker Date: Fri, 25 Mar 2022 12:02:15 +0000 Subject: [PATCH 24/30] =?UTF-8?q?=F0=9F=90=9B=20removed=20E402=20to=20stop?= =?UTF-8?q?=20imports=20breaking?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 39ded37..d3e563b 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,7 +7,7 @@ repos: rev: 3.9.1 hooks: - id: flake8 - args: ['--count', '--select', 'E101,E11,E111,E112,E113,E121,E122,E123,E124,E125,E126,E127,E128,E129,E131,E133,E20,E211,E231,E241,E242,E251,E252,E26,E265,E266,E27,E301,E302,E303,E304,E305,E306,E401,E402,E471,E502,E701,E711,E712,E713,E714,E722,E731,E901,E902,F822,F823,W191,W291,W292,W293,W391,W601,W602,W603,W604,W605,W690', "--ignore=E203"] + args: ['--count', '--select', 'E101,E11,E111,E112,E113,E121,E122,E123,E124,E125,E126,E127,E128,E129,E131,E133,E20,E211,E231,E241,E242,E251,E252,E26,E265,E266,E27,E301,E302,E303,E304,E305,E306,E401,E471,E502,E701,E711,E712,E713,E714,E722,E731,E901,E902,F822,F823,W191,W291,W292,W293,W391,W601,W602,W603,W604,W605,W690', "--ignore=E203"] exclude: ".*(.fits|.fts|.fit|.txt|tca.*|extern.*|.rst|.md|.svg|versioneer.py)$" - repo: https://github.com/myint/autoflake rev: v1.4 From f098238dbec20f9f29eb4a78e345e3727172118f Mon Sep 17 00:00:00 2001 From: Ronan Laker Date: Fri, 25 Mar 2022 12:02:45 +0000 Subject: [PATCH 25/30] =?UTF-8?q?=F0=9F=8E=A8=20moved=20to=20notebooks=20f?= =?UTF-8?q?older?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../multiprocessing}/load_and_evaluate_model.ipynb | 13 +++++++------ .../multiprocessing}/train_model_multiprocess.ipynb | 7 ++++--- 2 files changed, 11 insertions(+), 9 deletions(-) rename {multiprocessing => Notebooks/multiprocessing}/load_and_evaluate_model.ipynb (99%) rename {multiprocessing => Notebooks/multiprocessing}/train_model_multiprocess.ipynb (95%) diff --git a/multiprocessing/load_and_evaluate_model.ipynb b/Notebooks/multiprocessing/load_and_evaluate_model.ipynb similarity index 99% rename from multiprocessing/load_and_evaluate_model.ipynb rename to Notebooks/multiprocessing/load_and_evaluate_model.ipynb index c12c9e3..8b2f752 100644 --- a/multiprocessing/load_and_evaluate_model.ipynb +++ b/Notebooks/multiprocessing/load_and_evaluate_model.ipynb @@ -12,8 +12,9 @@ "from stable_baselines3.common.env_checker import check_env\n", "import gym\n", "import numpy as np\n", - "from RL import helpers\n", - "from Hack import load\n", + "import sys\n", + "sys.path.append(\"../../\")\n", + "from Hack import load, rl\n", "%matplotlib qt5" ] }, @@ -45,7 +46,7 @@ "price_array = epex['apx_da_hourly'].values\n", "max_time = 30769 \n", "max_time = 30769#2*24*28\n", - "env = helpers.energy_price_env(price_array, max_time=max_time, window_size=24*2)\n", + "env = rl.energy_price_env(price_array, max_time=max_time, window_size=24*2)\n", "model = PPO(MlpPolicy, env, verbose=1)\n", "check_env(env, warn=True)" ] @@ -64,7 +65,7 @@ } ], "source": [ - "mean_reward_before_train = helpers.evaluate(model, num_episodes=10, index = epex.index)" + "mean_reward_before_train = rl.evaluate(model, num_episodes=10, index = epex.index)" ] }, { @@ -1084,8 +1085,8 @@ "source": [ "# Trained Agent, after training\n", "periods = 48*7\n", - "new_env = DummyVecEnv([lambda: helpers.energy_price_env(price_array, start_time=max_time, max_time = periods)])\n", - "mean_reward_after_train = helpers.evaluate(model, new_env=new_env, num_episodes=10, index=epex.index)" + "new_env = DummyVecEnv([lambda: rl.energy_price_env(price_array, start_time=max_time, max_time = periods)])\n", + "mean_reward_after_train = rl.evaluate(model, new_env=new_env, num_episodes=10, index=epex.index)" ] } ], diff --git a/multiprocessing/train_model_multiprocess.ipynb b/Notebooks/multiprocessing/train_model_multiprocess.ipynb similarity index 95% rename from multiprocessing/train_model_multiprocess.ipynb rename to Notebooks/multiprocessing/train_model_multiprocess.ipynb index d96cb23..ff2c910 100644 --- a/multiprocessing/train_model_multiprocess.ipynb +++ b/Notebooks/multiprocessing/train_model_multiprocess.ipynb @@ -12,8 +12,9 @@ "from stable_baselines3.common.env_checker import check_env\n", "import gym\n", "import numpy as np\n", - "from RL import helpers\n", - "from Hack import load\n", + "import sys\n", + "sys.path.append(\"../../\")\n", + "from Hack import load, rl\n", "%matplotlib qt5" ] }, @@ -76,7 +77,7 @@ " :return: (Callable)\n", " \"\"\"\n", " def _init() -> gym.Env:\n", - " env = helpers.energy_price_env(price_array, max_time=max_time, window_size=24*2)\n", + " env = rl.energy_price_env(price_array, max_time=max_time, window_size=24*2)\n", " env.seed(seed + rank)\n", " return env\n", " set_random_seed(seed)\n", From 0bc36a743c6d77048bf9cfe62a953fd1e69b9661 Mon Sep 17 00:00:00 2001 From: Ronan Laker Date: Fri, 25 Mar 2022 12:03:37 +0000 Subject: [PATCH 26/30] =?UTF-8?q?=F0=9F=8E=A8=20moved=20to=20notebooks=20s?= =?UTF-8?q?ince=20they=20are=20an=20output?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- {Tuning => Notebooks}/parallel_optimization.py | 14 +++++++------- {Tuning => Notebooks}/serial_optimization.py | 10 +++++----- 2 files changed, 12 insertions(+), 12 deletions(-) rename {Tuning => Notebooks}/parallel_optimization.py (80%) rename {Tuning => Notebooks}/serial_optimization.py (80%) diff --git a/Tuning/parallel_optimization.py b/Notebooks/parallel_optimization.py similarity index 80% rename from Tuning/parallel_optimization.py rename to Notebooks/parallel_optimization.py index 3bac9fd..58e7a07 100644 --- a/Tuning/parallel_optimization.py +++ b/Notebooks/parallel_optimization.py @@ -2,22 +2,22 @@ sys.path.append("../") -import optuna # noqa: E402 -from pathos.multiprocessing import ProcessingPool # noqa: E402 -from stable_baselines3 import PPO # noqa: E402 -from stable_baselines3.ppo.policies import MlpPolicy # noqa: E402 +import optuna +from pathos.multiprocessing import ProcessingPool +from stable_baselines3 import PPO +from stable_baselines3.ppo.policies import MlpPolicy -from Hack import load, rl # noqa: E402 +from Hack import load, rl -def create_train_model(price_array, learning_rate): +def create_train_model(price_array, learning_rate, power=0.5, window_size=2 * 24): """ Creates and trains the deep learning model """ start_idx = 0 end_idx = 4 * 2 * 24 * 7 # start_of_2020 # 2019->2020 # 2*24*7 obs_price_array = price_array[start_idx:end_idx] - env = rl.energy_price_env(obs_price_array, window_size=24 * 2, power=power) + env = rl.energy_price_env(obs_price_array, window_size=window_size, power=power) model = PPO(MlpPolicy, env, verbose=0, learning_rate=learning_rate) model.learn(100) return model diff --git a/Tuning/serial_optimization.py b/Notebooks/serial_optimization.py similarity index 80% rename from Tuning/serial_optimization.py rename to Notebooks/serial_optimization.py index 42c64c3..4508029 100644 --- a/Tuning/serial_optimization.py +++ b/Notebooks/serial_optimization.py @@ -2,13 +2,13 @@ sys.path.append("../") -import time as time # noqa: E402 +import time as time -import numpy as np # noqa: E402 -from stable_baselines3 import PPO # noqa: E402 -from stable_baselines3.ppo.policies import MlpPolicy # noqa: E402 +import numpy as np +from stable_baselines3 import PPO +from stable_baselines3.ppo.policies import MlpPolicy -from Hack import load, rl # noqa: E402 +from Hack import load, rl def objective(num_episodes=100): From 418a8b65af4de8040e7f8785a0cd5430fb3a34e6 Mon Sep 17 00:00:00 2001 From: Ronan Laker Date: Fri, 25 Mar 2022 12:04:18 +0000 Subject: [PATCH 27/30] =?UTF-8?q?=E2=9E=95=20added=20dependencies?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- environment.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/environment.yml b/environment.yml index 92b6fdb..830a7a7 100644 --- a/environment.yml +++ b/environment.yml @@ -28,3 +28,5 @@ dependencies: - pip - pip: - stable_baselines3 + - optuna + - pathos From 7e1c17ca86c8c286774e2a1ea5bb578bda9980d4 Mon Sep 17 00:00:00 2001 From: Ronan Laker Date: Fri, 25 Mar 2022 12:11:33 +0000 Subject: [PATCH 28/30] =?UTF-8?q?=F0=9F=92=9A=20trying=20to=20fix=20github?= =?UTF-8?q?=20actions?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- setup.cfg | 1 - setup.py | 3 --- 2 files changed, 4 deletions(-) delete mode 100644 setup.py diff --git a/setup.cfg b/setup.cfg index 693d2e7..008602b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,4 +1,3 @@ [options.extras_require] test = pytest - sklearn diff --git a/setup.py b/setup.py deleted file mode 100644 index 6068493..0000000 --- a/setup.py +++ /dev/null @@ -1,3 +0,0 @@ -from setuptools import setup - -setup() From 9ac854c3cdc4b94fa970ceee17e4966d193591c8 Mon Sep 17 00:00:00 2001 From: Ronan Laker Date: Fri, 25 Mar 2022 13:03:24 +0000 Subject: [PATCH 29/30] =?UTF-8?q?=F0=9F=92=9A=20trying=20to=20specify=20pa?= =?UTF-8?q?ckage=20in=20setup.cfg?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- setup.cfg | 3 +++ 1 file changed, 3 insertions(+) diff --git a/setup.cfg b/setup.cfg index 99cbae4..fa6e491 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,3 +1,6 @@ +[options] +packages = Hack + [options.extras_require] test = pytest From 7a724e649ab060aee87808d71d94131230938c1b Mon Sep 17 00:00:00 2001 From: Ronan Laker Date: Fri, 25 Mar 2022 13:04:03 +0000 Subject: [PATCH 30/30] =?UTF-8?q?=E2=9C=85=20fixed=20linting?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- environment.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/environment.yml b/environment.yml index d564e8f..d341bd4 100644 --- a/environment.yml +++ b/environment.yml @@ -31,4 +31,3 @@ dependencies: - optuna - pathos - pybats -