From f884ee9b8c1fe4e2b7feff49b74b682f94ee1925 Mon Sep 17 00:00:00 2001 From: aloctavodia Date: Tue, 6 Mar 2018 16:08:44 -0300 Subject: [PATCH 1/2] remove df_summary --- RELEASE-NOTES.md | 1 + docs/source/notebooks/BEST.ipynb | 158 ++++++++------ docs/source/notebooks/Bayes_factor.ipynb | 100 ++++----- .../GLM-negative-binomial-regression.ipynb | 60 +++-- .../notebooks/GLM-poisson-regression.ipynb | 205 ++++-------------- pymc3/stats.py | 8 +- 6 files changed, 215 insertions(+), 317 deletions(-) diff --git a/RELEASE-NOTES.md b/RELEASE-NOTES.md index a695c7af4f..574ca45f82 100644 --- a/RELEASE-NOTES.md +++ b/RELEASE-NOTES.md @@ -27,6 +27,7 @@ ### Deprecations - DIC and BPIC calculations have been removed +- df_summary have been removed, use summary instead - `njobs` and `nchains` kwarg are deprecated in favor of `cores` and `chains` for `sample` - `lag` kwarg in `pm.stats.autocorr` and `pm.stats.autocov` is deprecated. diff --git a/docs/source/notebooks/BEST.ipynb b/docs/source/notebooks/BEST.ipynb index 35de247974..1b08373a29 100644 --- a/docs/source/notebooks/BEST.ipynb +++ b/docs/source/notebooks/BEST.ipynb @@ -7,16 +7,35 @@ "# Bayesian Estimation Supersedes the T-Test" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Runing on PyMC3 v3.3\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import pymc3 as pm\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('seaborn-darkgrid')\n", + "print('Runing on PyMC3 v{}'.format(pm.__version__))" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "This model replicates the example used in:\n", - "Kruschke, John. (2012) **Bayesian estimation supersedes the t-test**. *Journal of Experimental Psychology*: General.\n", - "\n", - "The original pymc2 implementation was written by Andrew Straw and can be found here: https://github.com/strawlab/best\n", - "\n", - "Ported to PyMC3 by [Thomas Wiecki](https://twitter.com/twiecki) (c) 2015, updated by Chris Fonnesbeck." + "Kruschke, John. (2012) **Bayesian estimation supersedes the t-test**. *Journal of Experimental Psychology*: General." ] }, { @@ -43,22 +62,6 @@ "To illustrate how this Bayesian estimation approach works in practice, we will use a fictitious example from Kruschke (2012) concerning the evaluation of a clinical trial for drug evaluation. The trial aims to evaluate the efficacy of a \"smart drug\" that is supposed to increase intelligence by comparing IQ scores of individuals in a treatment arm (those receiving the drug) to those in a control arm (those recieving a placebo). There are 47 individuals and 42 individuals in the treatment and control arms, respectively." ] }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import pymc3 as pm\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "sns.set(color_codes=True)" - ] - }, { "cell_type": "code", "execution_count": 2, @@ -66,9 +69,9 @@ "outputs": [ { "data": { - "image/png": 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MoLQBADDC7XQARF/BnL+HbVp/nNI/bNMCADSMPW0AAIygtAEAMILSBgDACEobAAAjKG0A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\nAAAASUVORK5CYII=\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -170,9 +173,9 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -183,7 +186,7 @@ "with model:\n", " ν = pm.Exponential('ν_minus_one', 1/29.) + 1\n", "\n", - "sns.distplot(np.random.exponential(30, size=10000), kde=False);" + "pm.kdeplot(np.random.exponential(30, size=10000), shade=0.5);" ] }, { @@ -250,11 +253,10 @@ "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using ADVI...\n", - "Average Loss = 231.07: 8%|▊ | 15144/200000 [00:03<00:44, 4112.61it/s]\n", - "Convergence archived at 15400\n", - "Interrupted at 15,400 [7%]: Average Loss = 243.61\n", - "100%|██████████| 2500/2500 [00:10<00:00, 236.95it/s]\n" + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (2 chains in 2 jobs)\n", + "NUTS: [ν_minus_one_log__, group2_std_interval__, group1_std_interval__, group2_mean, group1_mean]\n", + "100%|██████████| 2500/2500 [00:03<00:00, 727.76it/s]\n" ] } ], @@ -277,9 +279,9 @@ "outputs": [ { "data": { - "image/png": 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+2RPAmz2RyTBa0uXsbAsLC+2L693cHIy1SZNVHPsM5GO/I6LzZz1kFBIALi72\nhd2MAlUcx3Rx7DMRmS7e7InMjdGSrpiYJK0yNzcHPH8eb6xNmqTi2Gcg//ptyIMnqXA4yqXYci9O\n72fVmPtRx+I4pt+0z0zYiIiouOP0QiJ6Y4YdcQR41JGIiIiKE969kIiIiIiIyIh4pouIiIqV7K45\nNlXmPj3T3NsP5NAHXnNsFkzluuMiPRbMSGH1gUkXEREVK7quOTZV5n4Nobm3H8i+D7zm2HxERycU\n+nXERXksmJPX+1CQCRinFxIRERERERkRz3QRERERUZFk6IOUAT5MmYyDSRcRERERFUmGPEgZMP/H\nmpDpYtJFREREREUWH2tCpoBJFxU4Qy4+NnRKABERERGRqWHSRQVKJpPgULRS79P8HnaWRm4RERER\nEZFxMemiAmfIaX4nuX7JGRERERGRqeIt44mIiIiIiIyISRcREREREZERMekiIiIiIiIyIiZdRERE\nRERERsSki4iIiIiIyIh490IiIiKi/+h6lqSuMj5HkogMwaSLiIiICNk8SzIiWmddPkeSiAzBpIvM\nXlpKEi5uWoqtfx2DIiUVbj7+COozFC7lq6jrRIXcxB/fDNJ6b93O76Pm+58BAP7dvw3Xdm2AUCnh\n17orAroNVNdTpikwp18X/G/kVNh618ixPeE3LmP/tM/x8fx1sC/vq7V835TPYGFtg1Zjf1S/jrh5\nRaOOTG4FxzKe8GnREdXadlOXr+3VSKOeVGYBaydnuFevi9pdB8CxjEeObSMiopzp+yzJ/HiOZGb8\nenD+GNJTU1DKp4be8at6+/dR78NX8Wvb7g1QKlXwbf2eVvza/kVPNBs+GaX9auXYnsz41fG7VXD1\nrqq1XN/45VrOE9Xe6YQKLbuqy3OKX479BkNWsmyObSMyd0y6yOwdnfsNIm9fR4s+g2Dv6Y2rx/Zj\n7+Sh6PjdKjiVLQ8AiA4LgYWVDdpMmK9+n7utDHAoCSWA2CcP8df6BWgw4EvI7exxetn3KO1bA2Vr\nBAEAbh7cidIVvFGuWi29H+xsCK9qNdGw7+eIT8tYd3pKMu6e2Ivz6+YBgEbiVbVtNzRq1RYJaQKx\nicmIf/YEV3dtwB/fDEL7qctQolyFfG8fERHlv8z4VbvbQLh4eSPkzEF1/IKvNwDd8QsAbJ1dAbyK\nX+0+HQVhbYcDC7/Til/OXpVyTbjyqpRvTQR9MEz9Oj0lGeFn9uPYyrmonya04lelRq0AAEpFqjp+\nrRgxAD0d5aoAAAAgAElEQVS+Xw5pKS+jtJHIFDDpIrMWdf8Wnl77C28NGo1GXbohTqGCU9Ug/Dnx\nE1zeugrNR0wDAMSE3YOzZ0WUquKvfq+ngwXiFCrEpKoQE3YP1o4l4Ne6CwDg331b8eLBHZStEYS0\nlCRc/30j+k/7yWj9sLZzQFlff42Ezr16Xby4fwu3Du7QCFp2JUvD068G4hQq2KSq4F69LjzqNMLu\nsf1wdtWPaPftYqO1Mz9IYPi1EEqlME5jiIgKSdb45ffOuwCAcrXqq+NXrYk/ANAdv7LKjF/12ndF\nnEKFC79v0YpfrcfNNVo/5Lb2Wm1r0KA+Ht39V2f8ylo3M379Oa4fDi+bidaTTDt+Eb0JJl30xnRd\nYJxdeX5fePwy/BEAoFzN+hrlpX1r4PaR39Wvo8NC4OxVOdv12Lu5IyUhDlEhNyG3c0Bc+CPYu7kD\nAG7s+RVlqgbA3dsXcYr8P8uVHYlUCmevynh06XSudW1LlIRvy864unMd4p6Gwams6R4tdJRLcSAq\nXfOaiRyUkMvQykXGxIuIipT8jl9P7t5EutxOZ/wqWdHHCD3InlQqhVuFKgj561SudW1LlERg2y44\n8esak49fRG+CSRe9EZ0XHQMFduGxXclSAIDEF88Ab091eXxkONKSE5Ga8BJW9o6ICbsPmYUcu8f2\nQ+zjB7BzLY13en+Eim//DwDgVrkaKjdpq5437xXYBOXrNUVqwkv8u28b2k9dZnDbhFIJlTIdKqVm\noiagf/LwMuIx7EvpN8+9rH8gru5ch8g7100+aOl7zcQrMqO1hYioMGSNXw6l3NXlmfErKT4OsHLQ\nGb9qvdcfVZq2A/Aqfq0Y0R9APsUvlQoqZbr6tVIJqJQqg+JXbPgjveNXpdpBOPHrGrOIX0R5xaSL\n3pghP6Dz48LjrFy9q8LR3RPBq2fD0/lbWLiWxa3jh/D472AAQFpKMpSKVKTGx+JlxCPUfX8IrOwc\ncP/MYfw2byraKgXcG7YFADQe8jUCug+CECrYu5YBAFzbvQFeQW/DrmRp/DZvKh7+ex2lqgWg3ofD\nYWFlnWPbVn71UbbLPAIaarwWQrxK0ASQFBuFW4d+Q/SDO6jXd7hen4W1kzMAIDkuRq/6RERUeLLG\nryaffgOHMh4IDT6SJX6lICExRWf8Or30O0gkElT+78Bh4yFfo+OAT/AyVQmlY0YylzV+nVr6HSLv\nXId79Tqo9+FwwME+x7b9OfHjbJe9Hr8A8SpB+y9+HdqxG5H39Y9fdoxfVAww6SKzJrOUo8WX3+PE\nwinqo3xuVfxRo2Mf/L1jDSysrGEht0Lr8XPh7OWtvvC4bI0giPgXCN6yBu/9l3QBr448AkBSTBTu\nHP0DnWeux+UtK5D8PBKdx8/E/mU/4sq2VQj64LMc2/beV9/Cukx5xL82JfHsqh+16t69eBbzuzXR\n7JvcCtXb9UTVNl216hMRkX6ymwKvS0E+eytr/MqcZZE1fllaWcNKYqkzfiXFROHv7WvUSRcAOLmW\nBv67Tvn1+JX4IhItR81E8JrZuLJtFSp/OiLHtjUZOhElypVXv3a3s0BCmgoHlszUqvv4SjDW92mq\nUWZpZYU6HXsxfhFlwaSLzJ6zZyW8O2s9nFNeIC4lDaoSZXBl+xpIJFLIbe0glVmgXK36Wu+rXLcB\n7l0KRlpKEiytbbWWX925DpXfbgt719J4cP442g0eDhePCvB7511c3LQ016TL1bMi7Mv7wuq1s4C6\ntuVVvRYa9/8iI0GTSGBpbQOHUuUgtdB/iCZFPwcA2Lm46f0eIqKiLNsp8Nko6GdvZcavhKhnECol\nHEqVVccvKzs7pCmlOuNXuVr18eTqOb3jV9AHw1CiXHl1/EIuSVeJcuU1bhlf7r8bT+naVmnfmq/O\naP0Xv/y9yyNBJdV7FszLF4xfVPQZLelydraFhYX2dRhubg7G2qTJKvJ9zub6rYKQnpqCB+ePo6x/\nXVQo764+yhcTdg8lPCtBKrNA3NMwhN+4hCrN2kNmKc/y3lRYyK1gYWWjtd74yKcIDT6CLnM2AQBS\nXsbAxt4RACC3c0By7It87Ye1rT3KVK6qlaAZIvzGZQBAKd+cnyNmjlxccp4KU9CK/JjWoTj2mYqG\nwpwCn5Os8cvetbS6PDN+yWQWiAl7gFtXLmjFL2VaKmQGxC8rI8YvS1t7rWd6ySwsAANuPBV67RIA\n04hfvMMuGYvRkq6YmCStMjc3Bzx/Hm+sTZqkot5nQ6ZtGINUZoHg1T8ioMdgVCv/AYCMgPP4SjCq\nd3gfAJAU8xzBq2fDxskF5etlTIEQQuBm8HGUq1YbEol2H65sWw2/Vl1g8988c2tHZyTEvIArgOTY\nF+rrp0xFyssY3Dn6O8rWCISDnhcum5Po6ASTCWpFfUzr8qZ9ZsJGpC1r/PJv3wuAdvyKj9Ydvx7+\ndQKl/WrpHb+SYzMOjppq/Lp8YDe8agWZRPziHXbJWDi9kMya1MICVZp3xLXf1sOzVEko5bY4vn4x\nrB2dUb1dTwBA6aq1Udq3Js6u/hGpifGwLVESt4/sxrPQe+jxvfZdnWIfh+Lx3+fQ7act6jKPgLcQ\nvGszhK0jbuzdCq/AJlrvKyiJL57h0a3r/z0cOQVxTx/inz2/QgiBBgO+KrR2EZmL7GZimCpzT1oL\nvf2FOBsjJ1njl42jMyxtbHFx01KN+OVRTXf8igkLQbvJS7TW+eKR7vj1z55fYeXgZBLxK/LuPwAA\npUKRJX6p0HLwqEJr1+sMvcOui4uTXvUKfSzkA/Yh75h0kdkL7P0pJBLg4JqFSFMoUKZ6HQT1GQZr\nh4w/glKpDC1Hz8SlX5fhyrZVSI2PQ8mKvug7fQFKVK6q9Yf18taV8O/wPuS2r6a01e31CS4un449\ncyaiTPW6qNNjcIH2Maub+7fj5v7tAAALK2vYurjBo3YD+HforTFFhYh00zUTw1SZ+5nVwm5/Yc/G\nyE1m/Lrwy2Io0xRwfz1+yXTHr9Zfz9Oa0gcAZzat0Bm/Ti6ehuM/TYK7v2nGr//16gs4uRn4KBHT\noc9sjMIeC/mhKPahIBMwiRDCKOdDde2UorCzDFXU+yyTSbAtQqH3H8oK/12Ma4z6xly3seubUlsM\nrW/stjhbSdG9jNxkpm4U9TGtS1GbXmhO+8/cv2+F3X5jxihT+jtsaH1Taoux65tKjCrssZAfimIf\nCjI+8UwXEZk0XtRMRERE5o5JFxGZNF7UTEREROaOSRcRmTxDL2oGzOcmCURERFT0MenK4vTpE5gy\nZSIOHTqpLhNC4Oef12D37p2Ii4tFjRq1MHLkGJQvX0FdR6FQYNmyhTh8+CCSk5NRv34DjBgxGq6u\nuh/yFx7+FN27d8q2HQsXLkdAQF3ExcWifft3tJY3a9YC06fPAgDcuvUvBg3qq1WnV68P8NlnOT/8\nMDuGXHhs6LQvImMKu3gKGxdNRfdjp9Rl+ozhly/jsGLFUgQHn0Z8fDwqVqyEwYM/RWBgvRy3t2/f\nn/j111/w5MkjuLq6oVWrtujbdyAsLTMesKrPGE5NTcHq1Stw5MhBJCTEw9e3Kj77bAR8fPzy4RMh\nylBQ8e11N27cQPfu3bF79wGUKFFCZ50TJ47ihx+mYv/+49muJyYmGn36dEePHu+jf/9Bem2byJQc\nOXIEX301qkDGYHp6OtatW4W9e/9AXFwsfHx8MXjwUNSpEwgAuHz5IoYPH5Lt+7dv/wNlyrgDAA4f\nPoCff16Dx48fwd3dHV26dEe3br3e8NMonph0/ef69auYOnUSAM0pSWvXrsTGjevx6aefw93dHevX\nr8YXX3yKjRu3wd4+4+5As2f/gNOnT+Kzz0bAxsYGy5cvxqhRX2D16g06t1WypCuWLVv7WqnA9OmT\nYWUlR7Vq1QEA9+7dBQDMnbsItrZ26ppOTq9uTXrv3l3Y2Nhg3jzNW8fqGxBfJ5NJcChaqfdULg87\nyzxthyi/Pbt9HScXT4WhY1gIgQkTxuLRozAMHvwpXF3dsGfP7/jyy8+wZMkq+PvX1Lm9vXv/wA8/\nTEWvXh+gXr0RCAm5i9WrlyMmJgajRo0DoN8YXrBgLg4e3IchQz6Hh4cnNm/egOHDP8XPP/+KUqV4\nN0p6c8aKbzJZzmeUw8IeYMSIoVAqs48nV6/+jenTJ0Mmk+a4rrlzZ+Hlyzh9uktUoPS57vjatasY\nPXo0AAGZTKKe/m6sMThnzkzs3fs7+vTph4CAOjh3LhhfffU55s5dhICAuvD19dP6HapQpGLixLHw\n8fFTx54jRw5iypQJ6NXrA3zxRUPcvHkV8+fPhp2dPf73vw5v9sEVQ8U+6VIoFNi2bTNWrVoGa2sb\npKe/msKUlJSIzZs3YuDAwejePSOrr1kzAN26dcSff+5Cr14f4MmTx9i/fw++/XY6WrZsDQCoXNkH\nvXt3xenTJ9CtW2etbcrlcvj7az51fevWTYiMjMDatZtgZWUNAAgJuQsXl5KoV69Btu0PCbmHihW9\ntdb3JgyZyuUk1y85IzIWZZoC/+7bhstbV8LCyhpCma5eps8YvnXrX1y+fBHz5y9Rn9kKDKyHBw/u\nY8uWTdkmXZs2bUDr1m0xbNgXAICgoPpQqVRYunQhhg4dDltb21zHsEqlwsGD+9CzZx907doDAFCj\nRk106NAKhw8fRO/eH+bb50TFj7HjW9OmLXRuV6VSYe/e37Fo0fxsfxQqFAps2fIL1qxZAWtrGwiR\nfcw5ceIYrly5qD6DzNkYZEpyuu44PU2BK39uxdlNK2BhbQ2VUOFQtBKtXGSIj08wyhiMiYnGnj27\n0bt3X3z88VAAQFBQA0RFPceSJT9h5cqfYWdnr/W78aef5kAikWDSpOmQSqUQQmDJkgXo0qWbOs61\nbdsCISEPcOHCeSZdeZDzoaVi4Ny5s9iwYR2GDv0C3br11Fh248Z1JCcnoXHjpuoyR0dH1K5dB+fP\nBwMALl26AABo2PDVwwY9Pb1QsWIlnDsXrFcbYmJisHr1cvTo0VvjlPK9e3fh7V05x/eGhNyFt3cV\nvbZDVBQ9/vscru3egKA+w1CtbTeNZfqMYYlEio4d30WNGrXUdaRSKcqV80R4+FOd21SpVKhfvwHa\ntm2vUe7lVR5CCEREhAPIfQyrVCqkpaVpnAWztraBpaUl4uNf6vkJEOlWWPEtJOQu5s79EV279sSo\nUbofeHvmzEls2rQBn302Al26dNNZBwBevnyJuXNnYvjwr2BhYQGpNGM2xrYIhV7/zsSZ53OfyLxk\nHqx+/d8/58/i/I6fEdhnGKq26QYhoE7OjDUGnzx5DJVKpXWwr2bN2rh581+dZ4xDQ+9j586tGDTo\nUzg7OwMAbt++iWfPItCp03sadb/9djomTZpm6EdEYNKFqlWrYdu239VHGbIKCwsDAJQr56FRXrZs\nOTx6lLHs0aMwuLiUhI2NjY46D/Vqw4YNayCTWaBv34Ea5SEh95CSkoIhQwaiRYuG6NKlHX75ZT2y\nPlrt/v17iIx8hv79e6NZswbo2fNd7Nv3p9Y2ZDKJXv94VJDMjat3VXRbsA3V/tcdGRM9Mo5uy2QS\nPH78CADg5eWp8T338MgYwzKZBH5+VTF27ARYWVmp15mYmICrVy+jfPnyOrcplUrx+edfIihIM6id\nOXMKcrkV3N3LAsh9DFtYWKBz5/ewY8cW3Lx5Ay9fvsTSpQuRmpqa7VkEIn0VVnwrXboMtm7dhcGD\nP4WFhe4JNdWr18C2bb+ja9eeOpdnWrRoHipX9kHr1v9Tl2X3A1fXv/g0zsagwqMrPmUy1hjMnBr4\n7FmERnnmQcTw8HCt96xYsQSenl7o1KmLuixzerxSqcRnn32MZs0aoGnTpti5c1uOfabsFfvphW5u\npbJdlpSUCLlcrp7SkMnW1haJiYkAMn6c2draar3X1tYOkZHPct1+UlIi/vwzIyhmXY9KpcKDB/dh\nY2ODYcNGoHTpMggOPoPlyxdDoVBgwIDBiIp6jtjYWDx+HIZPPvkMDg4OOHz4AL77bjIAqE/9GnKd\nFq/RInNj56J5/aIEUE/1+OtZHGSWcux6IQAo1HUeqqwRk5Cgnubx+u3l586diYSEBPTs2Ufvdpw/\nH4y9e/9At269YGNjo9cYBoABAz7GjRv/YPDgfhntl0jwzTeT4edXNW8fCNF/Ciu+OTo6Zbsskz7X\nK164cA7Hjh3Bhg1bcq1LZIpej09ZGWsMlipVGgEBdbFixRKULl0GPj5+uHjxPPbs+R0AkJKSrFH/\n6dMnOHPmJMaM+RpS6atzMbGxMZDJZBg37kt06dINAwYMxoULZzB37kw4OTmppzuS/op90pWTjKPR\n2md+hBDqM0JCZPxI0lVHIsn9ROKhQweQmpqivp4j6/tnzZqP0qXLwMPDEwBQp04gkpOT8Msv69G7\nd1/Y2ztgzpyF8PauAldXVwAZ15VERUVh7dqVGvNt9b1Oi9doUVGQ+X1PSsv4zr/+3U9OVwES6X8H\nIl5dcyKEwNy5s3DgwD6MGDFK7zsIXrp0ARMmjEG1av7qOfT6jGEhBIYMGYi0NAUmTJgCN7dSOH78\nKGbMmAY7Ozs0adLszT8MIh0KIr69iaSkJMyc+R0GD/5UfRc1oqLEmGNw4sSpmDp1ovoOhRUqVMSA\nAYOwYMFc9X0DMv3xxy44ODigdet2GuXp6elQKpXo1KmLeiZW27YtcP/+A6xdu5JJVx4w6cqBnZ09\n0tIUSE9P15gikZycDDu7jLvK2NvbIykpSeu9yclJ6jvP5OTUqeOoXbsuXFxKapTLZDLUrRukVb9+\n/bewa9cOPHnyCJUqVUb9+m/prHP+/FkkJSXpPEJCVFzIbe2hTE+DKj0d0ixjOC0lGXIbO426aWlp\nmDZtEo4ePYQhQz7T+5a4R44cxHffTYaPjx9mzZqvnqaozxi+e/cOHj8Ow8qV61G1asZdS+vWDcLL\nl7GYN+9HJl1kNAUR397E8uWL4OzsjC5duiE9/dXNcVQqFVRKJXT9WCUyB5l3O3RwcEBamgJCpMPC\n4tXZrtTUZNjb20Mmk8DBIW9jsFSp0li0aAWio18gISEBnp5e2L9/D4CM68ayOnXqOJo0aQa5XK5R\nnvn78fXfmYGB9bF48XykpaVpnaWjnBX7a7py4unpBSEEnj59olH+9OkTeHllXOvh4eGJ6OgXSE1N\n0arj6an7epBMCoUCly9fQtOmzbWWRUU9x+7dOxETE6NRnpqaCgBwciqBsLCH2LVrOxQKhVYdKysr\nrTnARMWNYxkPQAjER2reECMh8ikcy3qpX6empmDUqC9w/PgRjBo1Dh980F+v9e/atR2TJ3+DWrUC\nMG/eYjg4OKiX6TOGIyOfQSaTwc+vmkadmjVrIzLymc5gS5QfjB3f3tTJk8dx8+a/aN78LTRr1gDN\nmjVAcnIyVq1ajp96NM19BUQmKvNuh6H27hBCYM3Vhxo3f7l4/xEsS3thW4QCCS4eBo9BIQQOHdqP\nsLAHcHEpCS+v8pBIJLh37y7s7R00zhxHRETgwYNQnb9Dy5XLmKGRlpauUZ6env7fmTYe+DAUk64c\n+PvXhFxuhVOnjqvLXr58ib//voy6dV/dWlqpVOL06VcPY330KAyhofcRGKh9lDur+/fvQaFIRfXq\n2rd7VygU+PHH73Hw4F6N8uPHj8LT0wslS7oiKuo5Zs+egeDgM+rlQgicPHkUtWoFcEBQsVfKtwZk\nlnKEXXw1PlMTXiLi5hWU9Q9Ul02ZMhF//30J3377Hd59N/s7qWV18uRxzJkzE02bNsesWfO1DnLo\nM4Y9Pb2gVCpx48Y/GnX+/fcflCjhzAMnZDTGjm9v6scff8KqVT9r/LOyssJ773VD71mrjLptImOL\nVShhW7E6ZJZyXD97Qn3jl4gXsXh04wrcqtVFTKoKrtXqGDwGJRIJVq9eoXHDi5cvX+Lw4f1o2LCx\nxnVbN29mxJ5q1fy11lO7dgDkciscO3ZYozw4+DT8/Kple5Mcyh4/sRzY2tqiW7ceWLlyKSQSKby8\nvLB+/RrY2dmhY8d3AWTcdaZ583cwa9Z0JCYmwMHBAcuXL4a3dxWNqUF37tyCpaUcFStWUpfdvx8C\nAOqjilmVLVsO77zTBqtWLYNEIkWFChVx7NhhnDhxFD/8MBsAUKtWAGrWrI3Zs39AfPxLlCzpit9/\n34mQkHtYsoRBicjS2hZV23bD5S0rAIkETu6euPrbz7C0sYNPi46QADh58hhOnjyG//2vPcqWdVcH\nIQCwtrZG5coZj2S4ffsW5HI53NxqIjU1FbNn/4CSJV3RrVsv3L59S2O73t6V9RrDjRs3RZUqPpg0\naZz6wcxnzpzCgQP7MHLkaB44IaMxdnwzROb1K1mfv+Xr66OjnhRubqVg5+2n97MkiUxVbvEJAEq4\ne6BFC8PHYJcuXbF8+WJ4eVVAuXIeWLt2JVJTU9G//yCNNty/H4ISJUrAyamEVvvs7OzRt+8ArFmz\nAnZ2dqhduw7Onj2Ov/++jB9//MlYH0uRxqQrFx9/PAwSiRS//roRyclJ8PeviQkTpmjMpf3662+x\nYMFcLF26EEKoEBhYDyNGjNZ4KOTXX49GmTLuWLRohbosJiYaMpks26PZ48dPxLp1q7Ft22a8eBGF\n8uUrYPr0WepnOshkMsyYMQfLly/G6tXLERcXB19fX8ybt1hruhJRcVW31yeQSKS48edmpKUko5SP\nP5oMnQC5rT0c5VJsPngMALBv3x7s27dH470lPSuh34JfAACrRn8FlzJlcWD7Zty4cR3R0S8AAJ99\n9rHWNlet+hl+ftVyHcMWFhaYP38JlixZgMWL5yM1NRXly1fEtGkz0Lz5O8b8WIiMGt/0JZNJcD9Z\nhTQVsC1CkWPddAGEpajA+3pSUZFTfMo0YcJkzJs3x6Ax2L37+0hOTsbGjeuQkJCA6tX9sWDBMq2D\n/DExMbC3d0B2+vcfBDs7e+zYsQWbN29AhQoZMaxBg4b5/EkUDxKR9aFP+ej583itMjc3B53lRZmx\n+pz1iGBupFIJtjxN1evIYAUHC8QpVHofRTSl+qbUFkPrm1JbDK1vSm0xtL6h63a2kuKTai78O5aH\n95sSc9p/5h43c2u/TCbBtgiFSfw9MLS+KbXF0Pqm1BZj1zelthha39lKiu5l5FqPNSks5v73CNDu\nQ0HGJ57pMkOGPHcL4LO3iIiIiIgKE5MuM6Xvc7cAPnuLiIgKVtbZGDnNzMi8nouIqKhj0mUiDJ0u\nSEQFL3PkGTJeTWVaCFFB0ZiNERGdY13OxCDKXuYzvQzBmGO6mHSZAGNMF3x87S8cX/I9Yh6Hvmnz\niEySs0dFNBv6NTxq1iuwbTrKpdhyL07vsVpCLkMrFxmDIBU7+s7GyMtMjNC//8KfP01nfCOTlh8x\nKvOZXvrGHGe5DK1dLaBS6R9zGJ8KDpMuAxhydNuQ90mlknyfLnhs0TTEPg3Ta31E5ijmcSiOLZqG\nD1f8UaDbNWSsZhylNOzPLAMgFYS8xjN9GHs2xp75UxHz5KFRt0H0pvIrRhn2+9CwJI0HBgsWky49\nGXI2ysPOEglpKr2mVmTWJ6KipyCOUhqCgZWAvM2uUMc0PesTUeEw9oFBTq/PO5NOuox5JM5Qxj5y\nV0Iuy73SfxwsZZAg5/Z0HPkt9i38DlFh99+0aUQmydWrEv73+TdwtpJqlOszPvJSN6/1E9L0f4ir\nnaUU514KxKfp9+O2tI0FktL1q+9gKUMDRymnneSjgopR+b2dgrguWN+Ylpcx1XPUZGybN43xjUya\nOcSocnaWhsWclEQkpcNoMcdQ5hajjPacLl2OHz+OZs2aFdTmTEJx7DNQPPvNPhcP7DMVJHP/7M29\n/QD7YCrMvQ/m3n6AfXhT0tyr5J8TJ04U5OZMQnHsM1A8+80+Fw/sMxUkc//szb39APtgKsy9D+be\nfoB9eFMFmnQREREREREVN7LJkydPLsgNVqhQoSA3ZxKKY5+B4tlv9rl4YJ+pIJn7Z2/u7QfYB1Nh\n7n0w9/YD7MObKNBruoiIiIiIiIobTi8kIiIiIiIyIiZdRERERERERsSki4iIiIiIyIiYdBERERER\nERkRky4iIiIiIiIjYtJFRERERERkRAYnXUeOHEFAQIBGmRACS5cuRbNmzVCrVi0MGDAAISEhGnUU\nCgW+//57NGrUCAEBARg+fDiePXuW6/YOHz6Mjh07ombNmujUqROOHTtmaJPfWEH2WQiBOnXqwNfX\nV+Pfe++9l+/9ykle+5zV999/j08++USv7Znzfs5K3z6byn4G8t7v2NhYTJ48Gc2bN0edOnXQs2dP\nBAcH57o9c97XeemzqezrvPY5MjISX331FerXr4+goCCMGTMGL168yHV7prCfC0tiYiKmTp2Khg0b\nIiAgAB999BFu3bqlXv7ixQuMGjUKQUFBCAoKwvDhw/H48eMc1/n69yfrv99++w1A/n3XTDHOb926\nFa1bt0bNmjXRs2dPXLlyxWTa/9tvv6FTp06oXbs2WrdujYULF0KhUKiXx8TE6Nxvw4cPN4k+6Pu9\nMWQfFGQfHj9+nOP4+OuvvwAU7H7IKr9/CxXEWMhr+01pLOSlD8YaC1k3oLdLly6JgIAAUbt2bY3y\nhQsXiho1aoj169eLw4cPi65du4rGjRuLly9fquuMGzdO1KtXT+zYsUPs27dPtGrVSnTq1Emkp6dn\nu72zZ8+KqlWriqlTp4oTJ06IUaNGiWrVqokrV64Y0uw3UtB9DgsLEz4+PuK3334TV65cUf+7ffu2\n0fr4ujfpc6YNGzYIHx8f8fHHH+e6PXPfz5kM6bMp7Gch8t5vlUolPvzwQ9GkSROxY8cOcerUKTFy\n5Ejh5+cnLl++nO32zHlf57XPprCv89pnhUIhOnfuLNq0aSP2798vDh48KNq0aSM6d+4slEplttsz\nhf1cmAYOHChq164tVq1aJU6dOiXGjBkjAgICREhIiEhNTRUdOnQQ9evXF7/++qs4ceKEGDRokGjc\nuLq/TxEAACAASURBVLGIjo7Odp1ZvzuZ/95//33RuHFjERUVJYTIn++aKcb53377Tfj5+YmFCxeK\n48ePi48++kgEBASIsLCwQm//jh07hK+vr5gxY4Y4c+aMWLNmjahdu7b49ttvNfro4+MjTp06pbFf\nQkNDda7TFH93GLIPCroPqampWmPj8uXLonXr1qJDhw4iOTm5wPdDpvz+LVRQYyEv7Te1sZCXPhhj\nLGSlV9KVmpoqVqxYIapXry6CgoI0Oh8fHy9q164tli9fri6LjY0VAQEBYs2aNUIIIR4+fCj8/PzE\nnj171HVCQ0OFr6+vOHDgQLbb7dOnj/joo480ynr37i0++eQTfZr9Rgqrz4cOHRJ+fn4iKSnJCL3K\n2Zv2WQghoqKixLhx44Sfn5+oW7euXl9yc97PQuStz4W5n4V4835fvXpV+Pj4iLNnz6rrKJVK0aFD\nBzF8+PBst2vO+zqvfTbnMX38+HHh4+Mj/vnnH3Wd8+fPCx8fH3HhwoVst1uY+7mwXb9+Xfj4+IjN\nmzdrlPfs2VMMHz5c7N+/X/j4+IiTJ0+ql6WmpormzZuLmTNn6r2dQ4cOCR8fHxEcHKxRltfvmqnG\neZVKJZo3by4mTZqkXq5QKESLFi3EtGnTCr397dq1E6NGjdIoW7lypfD19RUJCQlCCCHWrl0rGjZs\nmO06CrsPuX1v9N0HhdmH161du1b4+/uLe/fuaZQVxH4Qwji/hQpqLOS1/aY0FvLah/wcC7roNb3w\n5MmTWLFiBcaMGYMPPvhAY9nVq1eRlJSEli1bqsucnJxQr149nDp1CgBw7tw5AECzZs3UdSpUqIAq\nVaqo67wuJSUFV65cQYsWLTTKW7ZsieDgYCiVSn2anmeF0WcAuHXrFry8vGBjY5OPvdHPm/YZAJYt\nW4ZLly5h9erVqFq1aq7bNPf9DBjeZ6Bw9zPw5v2WSqXo3r076tSpo64jlUpRvnz5bKdJmfu+zkuf\nAfMe04GBgdi8eTOqV6+urmNpaQkASEtL07nNwt7Phe3BgwcAgMaNG2uUBwQE4PTp03jw4AFkMhne\neust9TK5XA5/f/8cY0NWmdOv2rdvjwYNGqjL3+S7Zqpx/uHDh3jy5IlGHUtLSzRr1kxjvYXRfpVK\nhcaNG+Pdd9/VKK9YsSKEEHj69CkA4Pbt2/D19dW5jqxM9XeHvvugMPuQVXR0NBYuXIj+/fvD29tb\nXV5Q+wEwzm+hghoLeWm/qY2FvPQByN+xoIteSVeNGjVw5MgR9O3bFxKJRGNZZoDx9PTUKPfw8FAv\nCw0NhaurK2xtbbOt87pHjx4hPT0d5cuX1yj39PRESkoKwsPD9Wl6nhVGnwHgzp07kMvlGDhwIGrV\nqoUGDRpg1qxZ2f7AyU9v2mcAeP/997F37140bNhQr22a+34GDO8zULj7GXjzfvv7+2P69OmwsrJS\nL09ISMCFCxdQqVIlnds0932dlz4D5j2m7ezs1EmmQqHA9evXMX36dHh7eyMwMFDnNgt7Pxe2MmXK\nAIBWP588eYKEhASULl0aSqUSkZGRGssfP36MJ0+e6LWNzZs3IzIyEqNGjdIof5PvmqnG+cz36qoT\nFhamTuILo/1SqRTjx49Ho0aNNMqPHTsGKysrlCtXDkDGD83k5GT06tULNWrUwNtvv42VK1dCCKHx\nPlP93aHvPijMPmS1bNkyWFhYYMiQIRrlBbUfAOP8FiqosZCX9pvaWMhLH4D8HQu6WOjTiNKlS2e7\nLCEhAXK5HHK5XKPczs4OCQkJADIuKrazs9N6r52dHSIiIrJdb2ad19+TdbmxFEafgYwvZEREBHr2\n7IlPP/0UFy9exNKlSxHzf/buO6ypq48D+Dck7L1kuhUXKiiIC0er1jpat3Wvqq111a21VbRq66Zu\nxNXa1jp41Vats2q11lH3Fieg4gCVIQTIff+gpMQwEswNIfl+nqdP5d5zT845N7knv5tzzk1MxJw5\nc4pYG828bZ0BFPjlM798c/J5M9/c+8VSHHUGivc8A7qp95vCwsKQnJyMAQMG5JtvTj5v5pt7v1iK\no85Ayf9M5xg0aBBOnToFS0tLLF++XPmLV1755uTzZr659xurWrVqoVy5cggLC8OcOXNQtmxZ7N69\nG0eOHAEAhISEwNnZGRMmTEBYWBhcXFywceNG3Lp1C5mZmYXmr1Ao8MMPP+D999+Ht7e3yr63ea8Z\naj9fUBqFQoHXr1/Dzs6u2PrsN/3555+IiopC3759YWNjA4VCgdu3b8Pa2hoTJ06El5cXjhw5goUL\nFyI9PR3Dhw9XHmuo3zs0PQfFWYfcr7F161b07dtXJR99ngdAnO9C+vosFKX8eSnOz0JR66DLz0Je\nNAq6CiIIgloUmiNne35pCjo2J+p9c3/OdjOz4lvtXqw6A9krrNja2qJq1aoAgODgYEilUixcuBDD\nhw9X3i3QN03qXNR888qjpJznojLU8wxoX29BEDBjxgzs3LkTU6dORfXq1fPNN688SuK51rTOgOGe\na23rPGrUKMjlcmzbtg1Dhw7FypUrERoamme+eeVhCOdZHywsLLB06VKMHTsWXbp0AZA9tPDjjz/G\n0qVLYWVlhaVLl2LChAlo06YNAKB58+bo1q0boqKiCs3/r7/+QkxMDBYtWqS2T6z3WnH284Wl0eRa\nLGafnduJEycwcuRI1K5dG59//rny+JUrV8Lb21t5d7x+/fpITU1FZGQkBg8erPLreXHUobD3jS7O\ngdh1yLFr1y6kpaWpDUnT53koCkP6LOhCcX8Wikrsz8Jb93729vaQy+VqwxdSUlJgb28PALCzs0NK\nSorasampqco0eeWbk8+bx+TeXxzEqjMA1K1bV3myczRp0gSCIODmzZs6KH3RaFLnouabk09uJeU8\nF5WhnmdAu3rL5XJ8/vnn+OmnnzB27Fj06dOnwHxz8smtpJ1rbeoMGO651vb9HRQUhIYNG2L+/Pmo\nWrUqIiMj8803J5/cDOE860vlypWxc+dOHD58GAcOHMCmTZsgkUhgZmYGe3t7BAUF4eDBgzhw4ACO\nHj2KlStXIjU1FU5OToXmfeDAAZQpUwY1a9ZU2yfWe604+/mC0piZmakNQ9Nn+XPbvXs3hgwZgipV\nqmDVqlXKL4858/feHI4UGhqK169f4/79+4XmLXYdCnvf6OIciF2HHAcOHEC9evXg5uamsl2f56Eo\nDOmz8LYM4bNQVGJ/Ft466CpbtiwEQVCbSB4bG4vy5csDyJ4E+ezZM6SlpeWb5k2lS5eGmZkZYmJi\nVLbHxMTAxsYGpUqVetuiF5lYdU5KSsKWLVvw4MEDle05eTg7O+uqClrTpM5FUdLPc1EY8nkGNK93\nWloahgwZgr1792L69OkYMmRIgfkaw7nWts6GfK41qfONGzfw22+/qeyXSCSoWrVqvs/NMeTzrA+v\nX7/G9u3bER8fDy8vL+W8gxs3bqBy5cp49eoVoqKikJqaitKlSyuH0dy4cUOts8/Ln3/+iZYtW6pt\nF/O9Vpz9fM4XtLzSlC9fXqM722KVP8fPP/+MMWPGIDg4GGvXroWDg4NyX3x8PH755RckJCSoHJOe\nng5A8/NSnN87dHEOxKxDDrlcjpMnT+b5+dDneSgKQ/osvA1D+SwUhT4+C28ddAUGBsLS0hIHDhxQ\nbnv58iVOnTqlXJ2pQYMGyMrKwqFDh5Rp7t27h1u3bqms4JSblZUVAgMDVfIFsh+SFhISAqlU+rZF\nLzKx6mxubo4ZM2bg+++/V9m+d+9eODo6ws/PT4TaaEaTOhdFST/PRWHI5xnQvN7jxo3D6dOnsWDB\nAvTo0aPQfI3hXGtbZ0M+15rU+ezZsxg3bpxKJySXy3HmzJl8y27I51kfZDIZpk+fjt27dyu3xcTE\n4MiRI2jevDkyMjIwefJkHD9+XLn/3LlzuHLlitrKZW9KSEhAbGwsAgIC1PaJ+V4rzn6+XLly8PLy\nUkmTkZGBw4cPa3wdFqv8QPYvK2FhYWjVqhVWrlypdqdbLpfjq6++ws6dO1W27927F+XKlYO7u3ux\n1kGT940uzoGYdchx48YNpKen5/n50Od5KApD+iwUlSF9FopCH5+Ft57TZWtri969eyM8PBxmZmYo\nV64cVq5cCTs7O3Tt2hUAUKZMGbRu3RpffvklkpOT4eDggIULF6JKlSpo0aKFMq+rV6/CwsIClSpV\nAgAMHToUQ4YMwZdffokWLVrgt99+w/nz57Fx48a3LfZbEavOVlZWGDBgACIjI+Hk5IQ6derg+PHj\nWL9+Pb744guNf8IXgyZ11pQxnWdNlZTzDGhW7/3792P//v3o0KEDvL29cf78eeXxVlZWyjv2xnSu\ni1JnQz7XmtS5Xbt2WLNmDT777DOMGDECMpkM69evx5MnT/Ddd98p8yop51kfzM3N0aVLF6xcuRIu\nLi6ws7PD/Pnz4eLigv79+8PZ2RnvvPMOvvnmG0gkEmRkZGD27NmoWrUqPvzwQ2U+0dHRkMvlKvMF\nb926BQB53s0V871WnP28RCLB4MGDMXPmTDg6OqJOnTrYuHEjEhMT0b9//2Itf3p6OqZNmwZ3d3f0\n6dMHV69eVXldPz8/lC5dGu3atUN4eDgkEgkqVqyI33//Hfv27cOyZcuK/Rxo+r5523MgZh1yFPT5\n0Od50JShfhaKUn5D+ywUpQ56+SwU+iSvN3z33XdqT4bOyMgQ5s2bJzRs2FAICAgQBgwYoPJAOkEQ\nhJSUFGHq1KlCcHCwULduXWHEiBHC48ePVdI0b95c6N27t8q27du3C61atRL8/f2F9u3bC3/88Ye2\nRX5r+qxzZmamEBkZqazze++9J2zatEm8yuWjqHXOrXfv3nk+jM7YznNumtbZUM6zIBSt3hMnThT8\n/Pzy/K9t27bKdMZ0rotaZ0M510V9f8fFxQmjRo0SQkJChICAAGHgwIHC1atXVdIY6nkuLq9fvxa+\n/vproVGjRkJQUJAwfPhw4cGDB8r9iYmJwvjx44V69eoJISEhwqRJk4Tnz5+r5NG7d2+hefPmKtt2\n7dol+Pn5qfUjOXT1XjPEfn7NmjVC06ZNhVq1agndu3cXzp49W+zl//vvv/O9Jvj5+QkXL14UBCH7\n/bBgwQKhefPmgr+/v/Dhhx8K+/bty7f8+qyDIGj+vtHmHOi7DoIgCBEREUL16tXzLY8+z0Nuuv4u\npI/PgrblN8TPgrZ1EATxPgs5JILwxuL4REREREREpDPGvXYvERERERFRMWPQRUREREREJCIGXURE\nRERERCJi0EVERERERCQiBl1EREREREQiYtBFREREREQkIgZdREREREREImLQRUREREREJCIGXURE\nRERERCJi0EVERERERCQiBl1EREREREQiYtBFREREREQkIgZdREREREREImLQRUREREREJCIGXURE\nRERERCJi0EWkB48ePUJoaCgSEhKKnEdSUhL69u2b576EhARUqVKlyHkTEZHpSUtLw+TJk9GuXTu0\nbdsWkydPRlpaWpHzGzhwYL79XGBgIGJjY4ucN1FJx6CLSGTbt29Hr1698OTJk7fK5+XLl7h06ZKO\nSkVERKZuxYoVyMrKws6dO7Fz506kp6dj1apVRc7v+PHjOiwdkXGRFXcBiPQlIiICW7duha2tLYKC\ngnDw4EHUq1cPL168QExMDJo1a4ZPPvkEYWFhuH79OiQSCUJDQzFmzBjIZDJUqVIFJ06cgIuLCwAo\n/7516xbmz58Pb29v3LlzB1ZWVvjmm29QsWJFxMfH48CBA1izZg1at26tUTmfPn2KiRMnIjExEQDQ\ntGlTjB49WnkH8sMPP0RUVBQOHjyIRYsWwdraGv7+/qK1GxERia84+qjg4GD4+PjAzCz7Hny1atUQ\nHR1dYDlTUlIwefJk3L9/H2ZmZqhRowZmzJiBL774AgDQr18/REREIC4uDjNnzoREIkHNmjWhUCjE\nbUAiA8dfusgk/Pnnn4iKisLWrVsRFRWFlJQU5b60tDTs2rUL48ePx9dffw0nJyf8+uuv2LZtG27c\nuIG1a9cWmv/ly5fRp08f/Prrr+jUqRPGjx8PAPDw8MDSpUtRvnx5jcu6efNm+Pr64n//+x9+/PFH\n3L9/H0lJSZgzZw6srKywY8cOJCYmYsqUKViyZAmioqLg4+OjfaMQEZFBKK4+qnHjxsr+KS4uDhs2\nbCj0BuH+/fuRkpKCHTt2YOvWrQCAmJgYzJkzBwCwYcMGuLq6YtSoUZg0aRK2b9+OkJCQtxq2SGQM\nGHSRSThy5Ahat24NBwcHSCQS9OrVS7mvbt26yn8fPXoUvXv3hkQigYWFBT766CMcPXq00PyrVq2K\noKAgAEDnzp1x7do15S9V2goNDcW+ffswePBg/PLLLxg7dizs7e1V0vzzzz/w8/NDpUqVAADdu3cv\n0msREVHxK+4+6vLly+jVqxd69+6N5s2bF5hX3bp1ER0djT59+iAiIgL9+vVD2bJlVdLcvHkTMpkM\nDRo0AAC0a9cOtra2hTcEkRFj0EUmQSaTQRAE5d9SqVT5bxsbG+W/FQoFJBKJyt+ZmZlq+cnlcpW/\nc+dX0DZN1KpVCwcPHkT37t0RFxeHrl274vLly2rpctdHJuNIYSKikqo4+6hdu3Zh4MCBGDt2LD75\n5JNCy1q6dGns378fQ4YMQXJyMgYMGIBDhw6ppctdH4D9FBGDLjIJTZs2xb59+5CUlAQAyiERb2rc\nuDE2btwIQRAgl8uxefNmNGzYEADg4uKiXMjit99+Uznu+vXruH79OgDgl19+QWBgIBwcHIpU1vnz\n52P58uVo0aIFvvjiC1SqVAm3bt2CTCZDVlYWBEFAcHAwoqOjla8ZFRVVpNciIqLiV1x91KFDh/D1\n119jzZo1aN++vUZl/emnnzB58mQ0btwY48ePR+PGjXH16lUA2YFcZmYmqlSpAkEQcOTIEQDAwYMH\n8fLlSy1bhci48LYDmYQGDRqgW7du6N69O6ysrFC5cmVYW1urpZs6dSq+/vprtG/fHhkZGQgNDVXe\n+Zs6dSpmzJgBBwcHNGzYEO7u7srj3NzcsHjxYsTFxcHFxQVz584tcln79euHSZMmoV27drCwsECV\nKlXQtm1bSKVS1KpVC23btsWPP/6I+fPnY9y4cTA3N0dwcHCRX4+IiIpXcfVR3377LQRBwNSpU5Vp\n69Spg2nTpuVb1g4dOuDUqVNo06YNrK2t4eXlhT59+gAAWrdujT59+mDJkiVYtmwZpk+fjoULF6Ja\ntWpwdXXVSVsRlVQS4c3ff4mM0KVLl3Du3Dnlc67WrVuHCxcuYPHixW+d98mTJzFz5ky1O4tERESa\nYB9FZPz4SxeZhPLly2P16tXYvHkzJBIJvLy8MHPmzGIrT8+ePVVWp8rtxx9/hJ2dnZ5LRERExcXQ\n+qjRo0fj7t27ee5btGgRKlSooOcSEZV8/KWLiIiIiIhIRFxIg4iIiIiISEQMuoiIiIiIiEQk2pyu\nzMwsyGRFe04RERGRWJ4+TdJJPs7ONkhMTNVJXiUV24BtYOr1B9gGQMltA3d3e729lmhBV0ls+Lfh\n7m6vs468JGG9TYup1hsw3brrot767NT0iTcW2QYA28DU6w+wDQC2gSY4vJCIiIiIiEhEDLqIiIiI\niIhExKCLiIiIiIhIRHw4MumdVCrRKn1WFh8lR0QlhzbXOF7fiIhMA4Mu0iupVIL9CVl4Ic/SKL2T\nhRQtXaT8YkJEJYI21zhe34iITAeDLtK7F/IsJKYrtDiCK+IQUcmh3TWO1zciIlPAOV1EREREREQi\nYtBFREREREQkIgZdREREREREImLQRUREREREJCIupEFERFQMJADMzPgIDSIiU8Cgi96aNs+k0fYL\nBhGRrjk720Am082qge7u9uobHydodKyDhRn2PsvU6hEa3Ss5alM8vcizDUyMqbeBqdcfYBsAbIPC\nMOiit6bNc7d8bc0LTRN/4xLO/rIKCfduwdzSCldDQjBs2Ei4uLhq9BrffbcAsbExmDt3cb5pJk78\nHN7evhg1aqzK9nnzZmPHjii19D/+uBVly5bT6PWJyLAlJqbqJB93d3s8fZqksk3bh79r+wiNhIRk\ng/q1K682AIDU1BT06dMdw4ePRvPmLTTOL7/r9/Xr17B8eTguX74Ee3t7tGjxHoYM+RSWllbKNHfv\n3sGKFUtw9eolAEDNmrUxfPjn8PHxLWLtNJNfG5gKU68/wDYASm4b6DNQZNBFb02bLw2OFgUHZy/i\n7mHv1yPhXSsYTUdMh0yejIubV2PMmBGIjPweMlnBb9lt237B5s0/o2HDxnnuFwQBS5cuwvHjf6Jr\n1x5q+2/fjkaLFu+hS5ePVLZ7enoVUjMiIgKyA65Jk8YiPv6xVsfld/1+8OA+RowYCl9fX0yb9jUy\nMzMRGbkCd+/exsKFSwEAiYkJ/6YpjUmTvoRCIWD9+tX47LPB+OGHzbC35x14IipeDLpIjbZ3anXp\n2t5tsHZ2wzufz4aZTAZnSzN08a+AQYP64vTpv9GgQd7BVGJiAlasWILff98FOzu7PNPExcVi4cK5\nOH/+H1hYWKrtFwQBd+7cRtu2H8Dfv6ZO60VEZArOnfsH8+fPQUKCZkMsgcKv39u2/QIzMwkWLlwG\nZ2dnAEDlypXRs2cXnDhxDA0aNMaePb9BLpdj7tzFcHBwAADUqOGPTp3aYv/+39GpU1fdVJCIqIgY\ndBmRxo2DMGnSlzhx4hhOnjwBW1s79O//MRo3boK5c2fh7NkzcHf3wKhRY9GgQSPlcadP/42IiBW4\nfTsajo6OqP5uewR2GQAzafach6zMTJzcsg7X/9yPpKePIbO0Qmn/Omj+8eeoVjZ72MaW4Z1RtVUn\nJD15iLsnDkJQZKFMcBM0GDAG5ta2SHryCFtHdsm37AGdByKw6yA4+ZaHk085mOX6Rats2bIAgIcP\nH+Z7/Pffr8PFi+exYMESbNiwJs804eHz8fLlC6xcuRYTJnyutv/hwzikpqagYsVKBbQyEZHurfuo\nERoNnYzYc38h7sJJmNvYIqDjAJQOaoy/Vn+LR1fOwt61FHzHj0e9eg2Vx715/W7b9gMMGDAY0n+v\n35mZmdiwYQ3279+L+PhHsLS0Qp06QRg1aiw8PDwBAF26tEfHjl3w6NFDHDy4H1lZWWjSpBnGjJkA\nGxtbPHr0EF27fpBv2QcMGIxBg4YCACZPHofg4BB88UVvDBnSX6O6F3b9jol5gMqVqygDLgAoU6Yc\nnJyccPLkCTRo0BgeHp7o0aO3MuACAFdXN2X5iYiKG4MuI7NkyUJ06NAFnTp1Q1TUZixaNBdbt27C\ne++1QceOXbF2bQRmzPgS//vfblhZWeHMmVMYN24UmjV7B4MGDUVs7H0sXbEM8QmJaDAwe77TibWL\ncOf4fgT3Hg4HDx8kxt7FPz+vxL7Vi+A7ba7ytS9u/x4+tUPQbGQYXj58gNMbl8La0RXBvYbBxtkV\nbWeugpeNFMkZApIyVIcj2rqUAgBUa9VJrU7Hjh0FgALnVHXs2BmffTYKMpks36Drk09GoEKFivnm\ncft2NADg1193YOLEMUhOTkJgYBA+/3w8Spcuk+9xRES6cOr771C1ZUdUbdUZ1/dtw9/rFuLq71tQ\nMfQ9VG3ZCZei1mLatKn5Xr8fPLiPiIhlePnyJcaOnQgge47U/v178dlno+Dj44u7d+9g1aql+O67\nBZg1a57ytX/4YR1CQhogLGw27t+/h2XLFsPFxRXDho2Eq6sbVq5cl2eZnZ1tYG7+369Ty5evRoUK\nlbQKdAq7fpcq5YFz5/6BIAiQSLJHYiQlJSEpKUn5Ou++20rtuIsXzyMp6ZXyxh0RUXFi0GVk/P1r\n49NPRwAA3N3dceTIH6hRoyb69h0IALCwsMDo0cMQE3MflStXwerVK1C9uj/CwuYAAKTSRrgu2GLv\nd1/Dv11P2JfyQtqrFwjuPRx+zdsBADyrB+Llwwe4c3yfymvbuLij6cgwSCQS+NQOwaOrZxF7/gSC\new2D1NwCpSr7o7S9DC/lCo3ngCU9i8eGJYtRtWp11K0bnG+6MmXKFZpXQQEXANy+fQsAIJenISxs\nNhISnmPt2ggMHz4E33+/CY6OThqVmYioKEr51URQz08BALYubrh/6ghK+fmjdsd+AAAnW0tsnTYy\n3+t3/foN4eDggNmzw9CzZx94eXnjxYsX+OyzUWjX7kMAQGBgXTx4cB/79+9ReW1391KYPn02JBIJ\n6tWrj3Pn/sHffx/HsGEjYWFhke+Q6zcnz1eooP1IgcKu361avY/fftuBRYvmol+/QcjIyMCiRXMh\nlUqRlpaW5zHJycmYN282SpXyQIsWrbUuExGRrjHoMjLVq9dQ/tvZOXu1v6pVqyu3OTpmLzeclJSE\ntLQ0XLt2BYMHD0NmZiYAQBAkKBdYH4KgwOOrZ2Ffqi2aj54JAEhNeIoXDx/gZdw9PLlxEVkZcpXX\ndq9UXXkXEgBsXUsh4X608m9FViaysgBFlgKKLNWgSyIxg8RM9Vndyc/isX/WKEgUAsLCZqvkLYb3\n3muDatVqoH79/4buVK/uj549O2Pnzv+hT58Bor4+EZk290rVlP+2cnQBALhWqKrcZm2fff1OTU1G\nRkb29Xvo0GEQhP8WKGrYsBEUCgXOn/8Hvr4+mDEjOyB79uwp7t+/h3v37uLixfOQyzNUXrtatRoq\n19hSpUrh1q2byr9z+og3ZWZmQqFQwOyN67cu1akThHHjJmHZsnBERW2Bubk5unXriVevXqqsXpgj\nKSkJ48aNxKNHDxEevhJWVuppiIj0jUGXkbG2tlHbll+Hk5T0CgqFAqtWLcWqVUvV9qcmPgOQvYT7\niTXzkfggGhY2dnApVxlSC0vgjVWLpW8sTiGRSABFdnCl6ZyuHIkxd7BvzlhAkYlVS5aLvuQvAHh7\n+8Db20dtW5kyZREdfTOfo4iIdENmpX79luW6rtrKsgObwwmZOH/7ORQKBVasWIoVK9Sv34fvbBEy\nOQAAIABJREFUPYZlQhY8Yi9j7tw5uH37Fuzs7FC5chVYWlrizQv4m/2ERGIGQci+fmszp0ssHTp0\nQbt2HRAbGwNXVzfY29uja9cP4ONTWiXdkyfxyoDrm28WokYNf1HLRUSkKQZdJszW1hYA0K/fIISG\nNgWQ/fDiA88zkCRXwMbZDfLUZByYOx4eVWvhnTGz4OCZHfyc/nEZEu7d0vi1bFzc0H5WJLxsZUjO\nUCBJrvpLl42zm/LfT29dwb5vxsLC2hZdZ65EpUoV9fJcmuPH/4SZmZnKIiMAkJ6ezqGFRGQwkuQK\nWEqtAQC1O/ZDmaBQtTQ2zm6IT3yJeeNGo1at2pg1ay58fbMDlOXLw7W6keTm5o7IyO/z3OfkZAOZ\nzLYItdDc3bt3cOdONN59txXKlSsPIPvXrCdP4lG5sp8yXWxsDEaO/ARpaWlYtGg5V6ElIoPCoMuE\n2djYolIlP8TFxSqHIEqlEvzx91X883046nQbAgCQpySh+vvdlQGXoFDg4aXTUPupqwBSmTncKlaD\nz79zuizzmdOV9OQR9n0zFtaOLmg9NRwuXh4wM9N8WOHbBGcHDuzF+fNnsWnT//69EwzcvHkdDx/G\nISCgbpHzJSLSNXNrW7iUrYSk+Idwq/jfsMSE+9E4vXEJ6nQbArMkMyQlvUK3bj2VAZdCocDp0ych\nCJpfK83NzVWGqeemjwei3rp1E7NmTUOdOkFwds4edrl9+zYIgqC8SZaUlITPPx+OrKxMLFu2GuXL\nVxC1TERE2mLQZeI+/ngoJk8eBzs7OzRp0hyvXr3AzhXLoYAEzmUqQpGVCXNrG1yIWgdBkYUseTqu\n7YtCwv1oSCDRquPWxMkNi5HxOhUNBo5F8vN4vHz9DJF3JEjKUMDB3RN2Lm5IT03B85i7cPL0gY3j\nf0sIO1lI0dJFWuTX7tGjNw4fPojJk8ehW7ceSEh4jtWrV6BGjZpo3vxdXVSPiEhnArt+jIMLJsPc\nxhZlg5sgLeklzm5eDYkk+/rtIFXAxsYW69dHQqHIQnp6OqKitiA6+hYkEonKaoD6kpKSjLt378LH\nx1dlCfiCNG4cChcXV0yfPhW9e/fF7dvRiIxcgQ8+6KRchGPNmlV49CgOI0eORUpKCi5fvqQ83tXV\nFV5e3mJUh4hIYwy6TFzjxk0xZ84CrF8fid27f4WtrS28agajZrdPIPt3gnLzz2fhzI/LcHDeRFja\nO8GzWm00Hz0Tfyyaitgbl+FQoUYhr6IZRWYmYs+fgKDIwpEl09X2B/X6DDXb98Sj69fw+8wRaPzJ\nFFRu1vaNVEUPuvz8qmLx4hWIiFiGr76aDHNzGZo0eQeffjpC719MiIgKUyYoFO+O+wYXtq1D9JHd\nMLe2gXfNYNTt8SlkllawtDTDnDlzsWRJOCZOHAsnJyfUrh2ImTO/wdSpE3HlymW9D8G7ceM6Ro78\nBFOmTEObNu01OsbGxhYLFnyHRYvmYcqUCXBycsKAAYNVFjc6duwIgOwl8t/UqVNXjBkzUTcVICIq\nIomg658q/iX2cANDo48hFvoglUqw5bFc4yXdy2m5BLyY6Z0tzdDV00Iv87+M5Xxry1TrDZhu3XVR\nb3d3ex2VRjd0dR7zahttrqFiXz/1cU001c9FbqbeBqZef4BtAJTcNtBn/yTeGq9ERERERETEoIuI\niIiIiEhMnNNlIqRSzeYkabNSIBERERERFY5BlwmQSiXYn5CFF/KsQtP62prroURERERERKZDtKDL\n2dkGMlnRV5IriQxtsnhuLx4naDT52tGi8MDMkLm42OnttQz5fIvJVOsNmG7dTbXeREREuiJa0JWY\nmCpW1gbJkFdt0XRooTFISEjm6oUiMtV6A6Zbd2NcvZCIiEjfOLyQiIjICEmg3TxdfdywIiIyVQy6\niIiIjJCDhRn2PsvUaD6vk4UULV2kDLyIiETCoIuIiEyKLucc5zl08nGCTvLWhRfyLI0fpuzi4lik\n1+DwUbaBqdcfYBsAbIPCMOgio6HtUBqAw2mITJGu5hznNd+tJM+hLcqcWFOd65ibqbeBqdcfYBsA\nJbcN9BkoMugio6HNUBqAw2mIiIiISD8YdJFR0WYoTTbTeqwBEREREemfWXEXgIiIiIiIyJgx6CIi\nIiIiIhIRgy4iIiIiIiIRcU4XmSyudkhElI3XQyIicTHoIpPF1Q6JiLIV9XpIRESaYdBFJo2rHRIR\nZeP1kIhIPJzTRUREREREJCIGXURERERERCLi8MISSirVfMKztpOjiYiIiIhIdxh0lUBSqQT7E7I0\nnvDsa2sucomIiIiIiCg/DLpKKG0mPDtaaBacERERERGR7nFOFxERERERkYgYdBEREREREYmIQRcR\nEREREZGIOKfLQHA1QiIiIiIi48SgywBwNcKSQQLVgFeTQDkrSxCxRERExSP39ZDXQiKiwjHoMhBc\njdDwOViYYe+zzOzg+HFCoemdLKRo6SLllw0iMjrK6+FDXguJiDTBoItIC9oEx9mkopWFiKg4aXc9\n5LWQiEwbgy4iIjIpzs42kMl0EwS4u9urb9Tgl3BT4+JiV9xFEFWe7wMTYur1B9gGANugMAy6iIjI\npCQmpuokH3d3ezx9mqSyTZtFkUxJQkKy0Q4vzOt9YEpMvf4A2wAouW2gz0CRS8YTERERERGJiEEX\nERERERGRiBh0ERERERERiUi0OV26nKhcUrzVuFBOvDZKxjh53JQnyppq3U213kRERLoiWtClq4nK\nJcXbTCDkxGvjZWyTx0vqRFldMNW666LeDNpM25sPlteEMV03iYgArl5IREREIlJ5sLwG+DBlIjJG\nDLqIRKLt3V1+wSAiY8UHyxORqWPQRSQSbe7u8s4uERERkfFi0EUkIu3u7vLOLhEREZEx4pLxRERE\nREREImLQRUREREREJCIGXURERERERCJi0EVERERERCQiBl1EREREREQiYtBFREREREQkIi4ZLxKp\nVPOH4mrzAF0iIiJjxgfLE5ExYtAlAqlUgv0JWRo9FBcAfG3NRS4RERFRycAHyxORMWLQJRJtHorr\naKFZcEZERGQK+GB5IjI2nNNFREREREQkIgZdREREREREImLQRUREREREJCLO6SIiIpPi7GwDmUw3\n84Dc3e3VNz5O0EneVDgJABcXu+IuRt7vAxNi6vUH2AYA26AwDLqIDIC2SyQDXCaZqKgSE1N1ko+7\nuz2ePk1S2abN40Lo7TlYmOGX6JcarxYsxmqHeb0PTImp1x9gGwAltw30GSgy6CIyANoskQxwmWQi\nohzarXQIcLVDIioODLqIDAS/OBAREREZJy6kQUREREREJCL+0qWFwsbq5+zXdm4OEREREREZLwZd\nGpJKJdifkJX/nJtcq1X52prrqVRERESkKS5aRETFhUGXFjSdc+NoodliCERERKQ/XLSIiIoLgy4T\nkpGWijM/rcC9k38gMz0NpfxqIrjXMLiUraxME3frGiJG91c7tkbbHqjXZzgA4OrvW3Bx+w8QFFmo\n374r6nQZqEyXlSHH1lHd0WzkdHhUrV1geR5dOYvfZ45A+1mRcKtYTW3/nrDhcLCzQbsp85V/P752\nTiWN1MISDp6l4fdOe5Tr+pFy+7qPGqmkM5PKYOXoDK8adRHQeQAcPH0LLBsRkbHL3Sco5OnwqlIT\nAT0+VekTnt2+hl+/+Fjt2IYde6J+3xEAVPuEqq06I/CNPmFBv454//MZsKlYs8Dy5PQJQxavh13Z\nKmr794QNh8zKGi0nzlP+XVCfUL11F+X2t+sTuGgREb09Bl0m5NDCL/DkxiUEdBkIlzIVcfv4Puye\nPgztZ0XC0bssACD+7i2YW1mj1ReLVY61cXYDALyIu49TG75D/QFjYGFrh2Or5sC1sj8cqgYBAK7t\ni4JzmQqFBlxFVapKLQT3/kz5d2baa9w6shsn1y+Cq5UUVd/rrNxXrXUXVGjUEgCQJU9HUnwcLmz/\nAb9+8THazlgJVK0kShmJiEqC3H1Ctap++Ofg72p9QsKD25BZWuO9qap9gp+vJ4A8+oSVs+FRpSa8\nawYDyO4TPMpVhE/12lquzqqZMtVroWHfEUjKyM47d58AQCXwqta6Cxq1bI3kDAEvUl6r9QlOPuV0\nXj4iohwMukxE/O3reHjxFBp8PB5VW3QAAPjUDsFvXw7F2c2RaD56Zna6e9FwLVMBpSr755lP4oNo\nWDk4oWqrjgCA2/u24Mmdm3CoGoSMtFRc2rkRrSYtFK0eFjZ2amXzqlEXz+9cx6nftqgEXbauHipp\nvWrUhW+dRtgxsR/+ipyHgPmrRCsnEZEhe3ZHtU8oZy+Du389PJ0wWKVPSHwQDefS5dWuu072MryU\nK9T6hKt7NuP5vZvwrhms7BP6zwwXrR5WtvbwruKvEtDl9AnX921TCbpsXT1QumpNvJQrYJ2uUOsT\n2kxbJlo5iYi4ZLyJSHz4AADgUytEZbtHlZqIu3BS+Xf83Wi4l83/FyA7dy+kJb/Es9vX8OpxLJ7H\nxcCxlBcA4MquTfCsFgjX8n4i1CB/EjMzOJephBdPHhea1sbJFVXe/RDx187jWdwDPZSOiMjwvHoU\nA6DwPiHhwW04l9G8T3j5KAZ27qp9gldF9aGCYsrpE5KfatcnvHzIPoGIxMOgy0TYu3oAAFKex6ts\nT3ryCBmvU5Ce/AoAEH/vNpKexWPHxH7Y0Kspto7qhltHdivTu1eqjkqhrfHrFx9j2+juKOsfgEr1\nmyE9+RWu7tmCOt3Ux/4XRlAooMjKVPtPgOYTl189joWzh5dGab39s4dCxly9qHVZiYiMga1rKQCF\n9wmJD+4g5fkTjfsEz2oBKFuv6dv1CVlZOukT7Ep5a5Q2p094cvOS1mUlItKUaMMLnZ1tIJMZ2eTT\nXMvClzQelavBwas0TqyZj9BPv4C9py/unjiI2PMnAAAZaa+RJU9H6qsXSHwUg8CPPoGlrT3uHD+A\nYytmQSKRoFKT9wEAjT+ZgsCuH0MQFPAv74uXcgUu7vgBZYKbwNbVA3+umIUnNy/Bq0Yd1OszEjJL\nqwLL9tuXQ/Ld5xjc6I0tAhRZmTn/ROqLZ7i+/39IuHcTrQeP1qgtrBydAQDJL0ru+QQAFxe7Ynld\nd3f7YnldQ2CqdTfVehszt4qqfYJHpXK4+Mc+tT4hPekFXj2OQd0eqn2Cu7UM5UNbA1DtE+zcsud6\n5e4T/rdoBu5fvYRS1QM16hNWjx2U7z7fwIYqfwuC8G9QplDrE+r1HalRW+T0Ca9fJmqUnoioKEQL\nuhITU8XKulgU9mBkQyczt8A7Y2bjyJIw5UpU7pX9UbN9L5zfthYySyvILCzRZ0Y4rH0qIMPWBQDg\nXTMYqYnPcH7rWmXQBfx3lxQAkhOe4eahX/Hhtxtw9pcIpDx/gnfHfYsTa+fj3JZIBPceXmDZQod9\nCSefsmrb/4qcp7Yt9twJbOjVVGWb1MISNdp0R732XZFsQqv1JyQk630ZY3d3ezx9mqTX1zQUplp3\nXdSbQZvhkb7RJ/wKwKuKep/QavJCOJepqFxMKadPOPxTpDLoAlT7hNRE1T7h9dMn+HDyt/h95TyN\n+oROY6fByrMskuSqC2/k1SfcOvMXFncJVa3bv31CtVxzfImIihsX0jAhzqUroMPcDUh+Fg9BkQX7\nUt44t3UtJBIzWNjYwkwqQ7m69bMnR+ealOxTOwRxF/5GRloqzK1s1PI9uWUdKjVpDTs3D9w7eRjB\nvT+Dk09ZVG3RAWd+WlFoB+vkUzbPJePzei2PKrX+u3spkcDcyhr2pXxgJpNBKpUBWYWvjpWa8BQA\n4ODqXmhaIiJjlbtP8LGWwMzFE4d+jFTpE3xqh6gd51M7BKe+/xvy16mAmfqvVhei1qv0CW0Gj4SL\nbzmN+wS30uVhV7YKLN9Y7TCvPqFMjdpo3H9UdoD2Rp+gqZw+wdaFfQIRiYdBl4nISE9D9NFD8Pav\nCzs3D+X2xAfRcCpdAWZSGV4+fIDTt8+jQtM2yP3WyMpIh9TCEjJLa7V8Ex8/xI1jB9BhwU8AgLRX\nibC0cwAAWNja4/WL5zqth7mNXZ4BmjYeXTkLAChTXZxl7YmIDF1mehrunTys7BOcc61GmLtPeHTl\nH1Ru1hZScwvlsVkZ6TC3tIS5lTUgV/21PenJQ9w9cRAdc/UJ1iL2CVY2dvCsVE0tQNNGTp9Qqor6\nc8QkAMzMCh/pkjMahg9RJqL8MOgyEWZSGU6smYfAboPh3zb7IcJJTx4i9twJ1GjXAwCQmvgUvy/7\nFu3tnOAW2ARA9nj5+6eOwKNqbUgk6h3PHz9GoPb7nWD975h4KwdnvP53rtTrF8+VY+UNRdqrRNw8\ntBPeNYPg7OmNl3LdPzeGiMjQ5dUnvIxX7xNOrJkPa0cXlK2XPaw7p08oUyPg3z5BNcg4t2UNqrbs\nqNInJCc+hxsMv0+wz2PhDQcLM+x9lokX8gLGrv8739vJQoqWLlIGXkSUJwZdJkIqk6Fy8/a4+L8N\nsHZwhrm1Dc78tAJWDs6o0aY7AMCjWgDK1KiNgyvnIvCjV7BxcsWNgzuQ+OA22kxfrpbni9i7uHXm\nBAYs34KcGXy+gQ1wedcmWNo74sruzSgTFKp2nL6kPI/Hk1uXAQBZcjlePryPy7s2QRAE1B8wttjK\npQua3n3NjV8EiCiH2Rt9QpqLPfauXarWJ3hUqYW/1sxDekqSSp/QaV6EWp4vYu8i9vzf6BL+i3Kb\nb2ADnNj+MwQbB4PoE2KuX/r34chpGvcJL+RZGj3YOfu6rN3XKl6XiUwHgy4TEtTzU0gkwOkflyEr\nQw6vGnUQ3OszWNk7AgDMzKTo8eU87Fm7HOe2RCI96SVcy1dBqymL8hzSd3bzajTs1AuWtnZI/bdD\nqvvRUBxdNhOHw7+Cl39d1Ok2WK91zO3a71tx7fetAACZpRVsXNzhG1Af/u16qgyxLIk0uvuaC+/A\nEtGbcvcJpzLk8K1ZFwE9hqn0Ce+O/xb/bFqp1if4VK6mNlLg7ObV8G/XAxY2/62sWvejoTiz6mvs\nWvAlPGsYd5/A6zIRFUQiCIIon3ZjW+VLKpVgy2O5Rne7yuWMjddwjLkhpTeksmib3pDKom16scvi\nbGmGrp4Wb925m+oKfoDp1t0YVy/U1XnMq21Kal/BvPVfFl1dl4ubqV4bc2MblNw20Gf/xIcjExER\nERERicikhxdq8+wtbefPEBGRYXJ2toFMJtVJXnneJX1csh+8TvpTXA+51zVD+zW7OLAN2AaFMdmg\nSyqVYH9ClsZjr31tzUUuEZF4uPAG0X8SE1MLT6SB/IYXEmmqOB5yr2sldViZLrENSm4b6DNQ1GnQ\ndfDgQYwbNw7nzp0rMN2lSxcQEbEct27dgKWlFYKC6uGzz0bBxcVVmebYsaNYuzYCMTH34e5eCp07\nd0OnTt1Uli3fs+c3bNr0I+LiYuDm5o6WLVujb9+BMDfXLEDSdEUiAHC00Cw4IzJE2kzwVmRl4drv\nW3B9/w7Exz+Gp6cXOnbsgk6duuV7zP79v+P779ciLi4Wnp5e6NatJzp06KySZu/e3di4cT3i4uLg\n6+uLPn0GoGXL1nnmd+PGdQwZ0g87duyFk5OTdpUl0sCxY0cQFvYl9u8/WmC6O3eiER6+AFevXoa9\nvQM6deqKXr365fkIDSJDo+n7fMKE0fjrr2Nq2/ftOwobm+yHUl+4cA5Lly7GnTvRcHcvhd69+6Nd\nuw9V0v/22w5s2vQj4uMfwdvbF337DsS777bUXYWISjCdBV1nz57F+PHjC013795djBo1DMHB9TBt\n2iwkJSUhMnIFxowZgcjI7yGTyXDp0gVMmTIOLVu+h08/HYErVy4hPHwBAKBz5+ylbHfv/hVz5szA\nRx/1Rr16o3H79i2sWbMKiYmJGDdukq6qRWQ0NL3JcG7LWlzauRGDB32M6tVr4sKFc/juu4WQy9Mx\ncuRnanfy9+7dg7CwqXjnnZYYPvxzxMTcx/Ll3yEp6RX69BkAADh06ABmzvwKPXv2Rb169XHq1N8I\nC5sKCwsLNG36jkp+Dx7cw8SJnyMrizc6SByXLl3AjBlf4c1nTL0pMTEBo0d/hvLlK2LGjDm4ceM6\nVq9eATMzKXr27KOfwpLR0nYEgra/iGn6PgeA6Ohb6Nq1B959t5XKdisrKwDZ393Gjh2BRo1CMWjQ\nUJw+/Te++WYmbG1t0a1bRwDAgQN78c03M9G9e0/Ur98If/11DNOmTYaVlRUaNSq+RwUQGYq3Drrk\ncjk2bNiA8PBw2NjYICMjo8D027ZthqurG2bNmgeZLPvlS5cujcGD++H06b/RoEFj7N27G6VKeeCL\nL8JgZmaG4OAQ3Lt3F9u3b1MGXT/99ANatWqNzz4bBQAIDg6BQqHAihVLMGzYSOWdGSLSnKBQ4Mru\nTWjUuRe82/fHPXkWHEsHwD/uGdb+8AMsW/RQO2bjunXw96+FsLDZkEgkCAlpAJnMHEuWLMQHH3SE\no6MTfv75ezRqFIphw0YCAIKC6uHatSuIitqqDLoUCgV2796JpUsXQyLhGj+ke7n7Kysra2RmFnwT\nIipqC7KyMvHttwthZWWFBg0aIyMjAxs3rke3buqfBSJtaDMCQZvl5eVyObZs+RmRkSs1ep8nJSXh\nyZN4hIQ0gL9/zTzTbNy4Hp6e3pg+Pfs6X79+Q7x4kYj16yOVQdeePbtQu3YgRowYAyD7e9m1a1ew\nY8c2Bl1E0MHqhUePHkVERAQmTJiA3r17F5q+fPkK+OijXsqACwDKlCkLAHj48CGA7AuGtbU1zMz+\nK56joyNevXoFIPvLWUhIfbRu3VYl7zJlykIQBDx+/Ohtq0VkkuSpyagY2hrVGzZX/jKWmK6AlUcZ\nvH6ViCcvU5Tbcv57HvcAISH1VYZb1aoVgPT0dJw/fxYA8NVXX2PkSNWHj5qbmyMjQ678+/btW1i4\ncB46d+6OTz8doZ8Kk0nJ6a+GDRuFLl26F5r+zJmTqFu3nvJuPwCEhjbDq1cvce3aFTGLSiYi93W2\noP80nX8OAH///Rd++GG9xu/z27dvAQAqVaqcb5ozZ06hYcPGKtf50NBmuH07GvHx8QCAjAw5bG1t\nVY5zcPjvuxuRqXvroKtmzZo4ePAg+vbtq9EY906duqJzZ9W5IceP/wkAKFu2HACgffsOiI2NwZYt\nm5CcnIzTp09iz55daNHivexCm5lhxIgxCA6ur5aPhYUlvLy837ZaRCbJ0s4BDQaOhVfFKirbY84e\ng41LKZhbWasdY+dWCo8fP1bZ9ujRQ5X/ly5dBj4+vhAEAS9evMDPP2/EmTOn8OGHnZTHeHh4YvPm\n7Rg8+FOVmzJEupLTX3Xt+pFG6WNiHsDHx1dlm7e3j3IfkSGqVq06tmzZqfH7/PbtW7CwsEBExHK0\nafMu3n23EaZOnYjnz58BAF6/fo1nz57C17e0ynE5n4V79+4BADp27IJTp/7GoUMHkJycjIMH9+Hk\nyb/QooXqkEUiU/XW32w8PN7uKe7x8Y+xbNliVK1aHXXrBgMAatasjT59BiA8fD7Cw+cDAOrXb1jg\n3e+TJ09g9+5f0a3bR7CzK3xoIZeAJ9LMzUM78fDSGYT0H53n/mpNW+P3retRs2YtNG36LmJjH2DV\nqqWQSCRIS0tTSXv+/FmMGDEUANCwYWM0a/bffC4HB0fxKkGE//orTVfYSklJgY2N6p37nKHrKSkp\nui0ckY64u5fSKn10dDTkcjlsbGwxe/Y8PHwYh9WrV2DUqE+xdu2Pyvf6m9M2cj4bycnJALJ/+Xr/\n/Xb46qv/5tW3b98BXbpoFvwRGbtivZ0cH/8Yo0YNg0IhKOeDAEBExHJs3LgevXv3R7169fHgwX1E\nRq5AWNhUzJz5jVo+//xzGlOnToBPFX+4dxiMLY/lamnexCXgiQp3+9he/BU5H+VCmqPae13yTBPS\nuR8801/g229n4ZtvvoaDgyNGjx6HmTO/gqWllUpaX9/SWLJkFWJjYxARsRxjx47EkiWruBIcGSRB\nEJDfW5M37shYfPRRL7Rs+R7q1AkCAAQE1EHZsuUxdGh/HDq0H0FB9QBA7TotCNnzy3KmgsyeHYYj\nRw7hk0+Go0aNmrh27QrWrVsNOzt75fx7IlNWbEHXnTvRGDduFDIzM7Fo0TLlEI7MzExs2vQjPvyw\nMz75ZDgAoE6dIHh6emHcuJH455/Tyl/EAODgwX2YNWs6/PyqosmkeUgSzAENVmjjEvBEBftn5884\nun4JytRtjCYjpuUbGMnMzTF58hcYPnwUnjyJh4+PL54/fw5BEODk5Kiy2qGnpwc8PT0QGFgXzs4u\nmDRpDC5ePI/atQP1VS0ijdnZ2SE1VfWZXjl/29oax0NtqWQQ81mLZcuWU07vyFGjhj/s7OwRHX0L\nTZo0BwC1z8Lr19l/29vbIz7+Mfbt24Nhw0aiZ8++AIDAwLqwsbHBggXfokOHzmpDdYlMTbEEXVeu\nXMa4cSNha2uLZctWo3TpMsp9L168gFyejho1/FWOqVUrAABw794dZdC1fftWLFjwLYKC6uHbbxfg\nt5dSvNbwuVtElL8DG5bjz80bULFJazQeOhlm0vwvFc+uncXK62ZwrhoAWPviTAJw+8xVAMB9l/LY\nFJuKWyf+QKnyleHiW065CpefX/a8sadPn+ilTkTa8vUtg4cP41S25fydswAUkT5os9IhADhbSNHK\nTaYM1Ap6aPf+/Xvh4uKGgIA6ym2CICAjQw5HRyfY2NjA1dUt389CuXLlcOnSDQBAjRqqqx/WqhUA\nQRBw795dBl1k8vQedD169BDjxo2Ei4sLwsNXwM3NXWW/k5MT7OzscenSBZXVCa9evQwA8PLKnrh5\n9OhhLFjwLZo2bY5p02bBysoCeFn4sEIiKtiV3ZtxavMGBLbrhtq9RhY69O/ykX24f/0y2n/7PYDs\nzvrMrijYunlA6lUBr7LM8MfacHjXCkaTYV/+e5QUp079DQCoUKGSmNUhKrK6dYOxY0fGNDVLAAAg\nAElEQVQUXr9+DWvr7EVk/vzzMBwdHVG5cpVCjibSLU2ftQgAjv8GaVeSs5ApoMBpF1Gbt8BCnop1\n6zYqhwoeP34M6enpqFOnDqRSCYKD6+H48aMYOvRTSKXZS9f/+edhVKhQEW5ubvDxSYGZmRkuXryg\nMnLhv+9uXOCMSPSgKy4uFomJicpnP4SHz0dqagrGjJmAx48fq6x65unpBTc3N/TtOxArVy6Bra0d\n6tdviJiYB1izZhWqVauBkJAGSE9Px/z5c+Dq6oYuXT7CjRvXIZVK8PB5BpIyFHAuXTHPVdaIqGCp\nic/wz88r4FGuEqo0bomn0arLYrtVqIrkp4+RlvQCpSpn/xpd9/2OOLv/N5zcsBil64bizrF9iLt4\nEk1HTIeZmRQAUKtDX5zcsBi2LqXgFxiEjQeisXbtarRu3RYVKlTUez2J8vJmf9WxY1ds2/YLxo8f\nhR49+iA6+iY2blyPoUOHw9yc84LJsL2QZyEtU4AgQCVYe/U4VuUaHtqtPzZOG42Bk6bA/512SHz4\nAMd/jkDlBs1xo1Q13Hgsh3urj7DvwED0Hzse9dt0RNb109i7dw9mzMieZ+/s7IIOHTpj3brVALKH\nJ968eR1r165Gs2bv8DpPBD0EXevXR2LPnt9w7NgZZGZm4sSJ48jKykJY2FS1tMOGjULPnn3Qs2cf\n2NjYYMuWn7Fly89wdy+Fli1bY9CgIZBKpbhw4RwSEp4DAIYPH6KWT/tZkXCrWE3sqhEZnbgLJ5GV\nIUf8vWhsmjRYbX+PiF24ELUe0Uf3YMCm4wAAn8rV0G781/jzxwjcOLgTjl6l0WzUDJSv/9/KhNVb\nd4HMwhJXdv+CK7s2oZSbG3r37oe+fQfkOewl5xF9Umn2sBhN5yYQvY3c/RUAuLm5YfHi5QgPn48v\nv5wIZ2cXDB78KXr27FPMJSUqujev4ZXq1seHk+fi2C9rseObiTC3sUWlpu1Qp/tgZbAm866IFuPn\n4sxPy/Hz1xPg7emJKVOm4Z13WijzHTVqHFxd3bBzZxTWrYuAp6cXevXqq5zjRWTqJELO8jM6pumS\nvLoilUqw5bFc45/ey9nL8FKuECW9mHmLnd6QyqJtekMqi7bpDaks2qYvSt4SSDSem5AzB6y4Ay93\nd3u9X9cMgS7q7e5ur6PS6IauzmNebaNNX1TSP8fGnrchlcWQ8naxNEM3b0soFNnXZBcXOyQkJBd4\nTHFfv8Vmqv1DbiW1DfTZP/EJpESkd9rMTcgmFa0sRESkObVFPR4nFJg+Z1GPnCCtMMYeoJHpYtBF\nRAZNzKWSiYhIe0VZ1EOT0Q2GMrKBSAwGHXQVtMTpm/igSiLjVNSlknlXlYjIMGg3uoEjG8g4GWzQ\nJZVKsD8hS+MvWr622StJxV48hcPLZyMx9q6YxSMySs6+5dFs2BSUa9SwuIuiQqy7qtoGaFzUgwDg\n9Om/MXv2DNy9e6e4i0KkwlCv4WLS5ga9tni9J10y2KAL0PaLVvYXrD+WzsSLhw/ELBaR0UqMvYs/\nls5E40Z7irsob0XTa4c2ARoeJ2gdpAHstI3RzJnT8ODB/eIuBpGakn4N13Y4uZmZRONruK+tOZIz\nFCVuEScyHgYddBERiU2sX9EA7X9JKwp+ISAiY6HtcHJfW3MtbrJlabVKY3YAqPnXZDF/cdOWtv2C\nNmVnn1N0Bh10OVloPq7X3lwKCSRo//k07FkyC88ecNgHkbbcylTA+yO+UH6eNKFNWkNLX5S8kzM0\nX3XR1twMf78SkJSh2RcID2sZUjM1T29vLkV9BzPRgjp2rnn78sswzJkzE3fu3C7uohCpKOnXcG2v\nsYDm3xW1LbePrbnG12+PtBSkZkKUa73Y/YKZmUTjehaWt74Dz5LWR4n2nC5Tc/jwYTRr1qy4i6F3\nrLdpMdV6A6Zbd1OttybYNmwDgG1g6vUH2AYA20ATZsVdAGNx5MiR4i5CsWC9TYup1hsw3bqbar01\nwbZhGwBsA1OvP8A2ANgGmmDQRUREREREJCLp9OnTpxd3IYxFuXLlirsIxYL1Ni2mWm/AdOtuqvXW\nBNuGbQCwDUy9/gDbAGAbFIZzuoiIiIiIiETE4YVEREREREQiYtBFREREREQkIgZdREREREREImLQ\nRUREREREJCIGXURERERERCJi0EVERERERCQiBl1aOHjwIAIDAwtNN3ToUFSpUkXtv5SUFD2UUjey\nsrKwbt06vP/++wgICECbNm2wceNGFPSEgZs3b6Jfv34IDAxEs2bNEBERUWB6Q1WUuhvDOZfL5Vi0\naBGaN2+OgIAA9O3bF1euXCnwGGM450WptzGc79zkcjnef/99TJo0qcB0xnC+i0LTa7+xto+m9T97\n9iz69OmDoKAgNG7cGBMmTMCzZ8/0UELxadoGuS1ZsgRVqlQRqUT6p2kbJCQkYMKECahXrx6CgoLw\nySefICYmRg8lFJ82n4UePXogMDAQ7777LpYuXYqMjAw9lFD3TPn7oK7JirsAJcXZs2cxfvx4jdLe\nuHEDffv2Rdu2bVW2W1tbi1E0USxfvhwREREYNmwYAgICcObMGcyePRuvX7/G4MGD1dI/f/4cAwYM\nQOXKlbF48WJcuXIFixcvhlQqxaBBg4qhBkWnbd0B4zjnc+bMwY4dOzBu3DiUKVMGP/zwA/r27Yud\nO3fCx8dHLb2xnHNt6w0Yx/nObenSpbhz5w5q166dbxpjOd/a0vTab6zto2n9b9++jf79+6Nhw4ZY\nsGABXr16hfDwcAwaNAhbt26Fubm5HkorDm36/xw3b97EqlWrRCqR/mnaBhkZGRgwYADS09Mxc+ZM\nSKVSLFq0CB9//DF+/fVXWFhY6KG04tC0DR48eIBBgwahTp06WLJkCe7evYv58+cjJSUFEydO1ENJ\ndcuUvw/qnEAFSk9PFyIiIoQaNWoIwcHBQkBAQIHpX758Kfj5+QlHjhzRUwl1LysrSwgMDBQWLVqk\nsn369OlC/fr18zwmPDxcqFevnpCamqrctmjRIqFevXqCXC4Xtby6VJS6G8M5f/XqlVCjRg1h7dq1\nym2vX78WatWqJSxbtizPY4zhnBel3sZwvnO7cuWKEBAQIISEhAgTJ07MN50xnG9taHvtN7b20bb+\n06dPF9555x2Vul64cEHw8/MTDh8+LHZxRaFtG+TIzMwUunTpIoSGhgp+fn4il1Jc2rbB5s2bhVq1\naglxcXHKbVevXhUaNWokXLp0SeziikLbNli1apVQs2ZNISUlRbltwYIFQmBgoKBQKMQurk6Z8vdB\nMXB4YSGOHj2KiIgITJgwAb179y40/Y0bNwCgRA8pSEpKQocOHdCqVSuV7eXLl0dCQgJSU1PVjvnr\nr7/QoEEDlTv9LVq0wIsXL3Dp0iXRy6wrRam7MZxza2trbN68GZ06dVJuk8lkkEgkkMvleR5jDOe8\nKPU2hvOdIzMzE1OmTMGgQYPg4eFRYFpjON/a0Pbab2zto239K1WqhIEDB6r8olWhQgUAQGxsrGjl\nFJO2bZBj/fr1SE5O1uoYQ6VtGxw4cAChoaHw9vZWbqtWrRqOHTsGf39/MYsqGm3bQC6XQyaTwcrK\nSrnNyckJqamp+fYrhsqUvw+KgUFXIWrWrImDBw+ib9++kEgkhaa/ceMGLCwssHjxYoSEhKB27doY\nOXIknj59qofS6oajoyO++uorVK9eXWX7H3/8AU9PT9jY2Kgdc+/ePZQtW1ZlW+nSpZX7Soqi1N0Y\nzrlMJkP16tXh6OgIhUKBmJgYTJkyBRKJBB988EGexxjDOS9KvY3hfOdYvXo1MjIyMGTIkELTGsP5\n1oa2135jax9t69+rVy/06tXr/+3dd3wUZR7H8c9m00gvhB6qIBCQoqEIKiBIU0BA8CyIR7GAiB4q\nisIhUkUQiFGKEIon0gTrUcWCCCIoKiAiAoFQAkkgBbLZzd4fyJ4xAXYxk0023/fr5Ut29pmZ3zyz\nA/vdmXkmz7RNmzYB/w9fJY2rfQBw+PBh4uLiGDduXIm+lO6Sa/kOVLNmTeLi4mjVqhUNGjRg8ODB\nJCUlFUG1xnC1D7p164bZbOa1114jLS2N3bt3s3DhQjp06ICfn18RVFx4SvP3QSModF1F+fLlCQkJ\ncbr9L7/8gsViITAwkLi4OMaMGcP333/PQw89VOJ+4fiz5cuX8/XXXzNw4MAC38/IyCAwMDDPtEuv\nMzIyDK/PSFfbdk/b5/Hx8bRv3541a9YwcODAy35h8rR97ux2e8r+/u2333jrrbd45ZVXnPpy6Gn7\n+2pc/bvf0/rH1e3/q+PHjzNlyhQaNGhAixYtCrGyouNqH9jtdl588UW6devGTTfdZGBlRcfVPkhJ\nSWHVqlV8+eWXjB8/nilTpnDgwAEeeeQRrFargZUax9U+qFq1Ks8++yzz58+nefPm3HPPPURGRjJx\n4kQDqyw6pfn74N+lgTQKWf/+/enatavjH5nY2Fhq1apFnz59+OSTT+jRo4ebK3TdBx98wJgxY+jY\nseM1XS7h5VVys70z2+5p+7x9+/Y0a9aMbdu2ER8fT05ODsOHD3dpGSVxnzu73Z6wv3Nzcxk1ahS9\ne/d2eUS2gpTE/V2USlv/HD9+nP79+5Obm8v06dOdPktU0i1dupTDhw/z5ptvursUt7FareTk5DB3\n7lxHUImOjqZ3796sW7eOLl26uLlC4y1fvpwXX3yRvn370rlzZ06dOsXMmTMZPHgwCQkJJfoMaGn+\nPlgYFLoKWa1atahVq1aeaY0aNSIkJMRxL0hJkpCQwKRJk2jXrh1Tp0697D+eQUFB+YbLvvQ6KCjI\n8DqN4Oy2e9o+r1u3LgDNmjUjMzOTt99+myFDhuQbfczT9rmz2+0J+3vx4sUkJSUxe/bsPL8+2+12\nrFYr3t75/2nwtP1d2NQ/F+3fv59BgwZhtVqZP38+VatWdXdJReL48eO8+uqrTJw4EX9/f6xWq2OI\nbKvVipeXV6n4whkQEMANN9yQ58xQw4YNCQkJYf/+/aUidM2ZM4fbbruNl19+2TGtQYMGdOnShQ8+\n+IDevXu7sbprV5q/DxYWz/8boIh9/PHHfPvtt3mm2e12LBYL4eHhbqrq2kybNo2JEyfSvXt3Zs6c\necVfZ6pXr57vZulLz+Uoidfzu7LtnrDPk5OTWblyZb5T//Xq1cNisZCWlpZvHk/Y59ey3Z6wvzds\n2MDJkydp1qwZMTExxMTEsG/fPlavXk1MTEyBAx94wv42kvoHfvjhBx544AHMZjPvvPOO44eM0mDr\n1q1kZmYybNgwxzE1adIkAGJiYnjjjTfcXGHRqFq1aoHPo7JaraXmjOfx48fzPX6jVq1ahIWF8dtv\nv7mpqr+nNH8fLEw601XI3n33XTIyMli1apXjV63PP/+cCxculKhrvBcuXMjs2bPp16+fY2CBK2nR\nogXvvfceWVlZjhsrN2zYQFhYWIn7h9fVbfeEfX7u3DleeOEFAHr16uWYvmXLFiIjI4mMjMw3jyfs\n82vZbk/Y32PHjs33S+SIESOoUaMGQ4YMoVy5cvnm8YT9baTS3j9Hjx5l0KBBREZGkpCQcNXRMD1N\n27ZtWbFiRZ5pH3/8MQsWLGDFihUFHlOeqHXr1iQkJHDy5EnHZ2D79u1kZWUVyqXMJUGNGjXYuXNn\nnmmHDx8mLS2NKlWquKmqa1eavw8WNoWuv+nIkSOkpKTQuHFjAB555BEGDRrEM888Q8+ePTl06BAz\nZsygY8eONG3a1M3VOufUqVNMnTqVOnXq0LVrV3744Yc87zdo0ICkpKQ8233fffexZMkSBg8ezIAB\nA9i3bx9z5szhX//6V4m6fvlatt0T9nmtWrXo2LEjkydPJicnh+joaNatW8eaNWuYMGECXl5e+T7r\nnrDPr2W7PWF/F/Rro7+/P2FhYTRs2BDI/3ebJ+zvwlTa++ev2z9+/HgyMjIYPXo0x48f5/jx4462\nlSpV8sjQ8ec+CA8Pz3em+7vvvgNwHFOe6K+fg/79+7Ny5UoGDRrEsGHDOH/+PFOmTKFJkya0bt3a\nzdUa46998PjjjzN8+HBGjRrFnXfeSXJyMnFxcVSuXJnu3bu7uVrXlObvg4Zw50PCSpqZM2fmeyje\nc889l+/hh5s2bbL36tXL3qhRI3urVq3skyZNsp8/f74oS/1bVq5caa9Tp85l/ztz5kyB27179257\n37597Q0aNLC3adPGPnv2bDdtwbW71m0v6fvcbrfbs7Ky7FOmTLG3bdvWHhMTY+/evbv9008/dbzv\nqfv8WrbbE/b3X3Xr1i3Pw5E9dX9fC2f/7vfU/rna9lssFnv9+vUv+/fmvHnz3FF2oXL2M/BnCxYs\nKPEPR/4zZ/vg8OHD9scee8zeuHFje2xsrP25556znz17tihLNYyzfbB27Vp7jx497DExMfbbbrvN\n/vzzz9tPnz5dlKUWitL8fdAIJrv9jzs9RUREREREpNBpIA0REREREREDKXSJiIiIiIgYSKFLRERE\nRETEQApdIiIiIiIiBlLoEhERERERMZBCl4iIiIiIiIEUukRERERERAyk0CUiIiIiImIghS4RERER\nEREDKXSJiIiIiIgYSKFLRERERETEQApdIiIiIiIiBlLoEhERERERMZBCl4iIiIiIiIEUukRERERE\nRAyk0CXihBkzZrB69Wp3lyEiIiIiJZDJbrfb3V2EiIiIiIiIp/J2dwEihelf//oXMTEx/POf/wTg\nP//5D9u3b+f1118vsP3IkSPx9/dn//79nDlzhnbt2hEWFsZnn31GcnIyr7zyCi1btmTkyJHUrl2b\nAQMG0LBhQwYPHsyWLVs4deoUAwcO5L777mPVqlWsXbuW2bNnA+R5vWPHDiZNmkRubi4AjzzyCB07\ndrzituzYsYMpU6Zw/vx5fHx8GD58OLfeeiurVq1i/fr1eHl5cfjwYfz9/Zk8eTK1atUiPT2d8ePH\ns3//fnJycmjZsiXPPvss3t461EVERETcRZcXike55557eP/99x2v33//ffr06XPFefbs2cPChQtZ\nsmQJ8+fPJyAggKVLl9KvXz/mzp2br73FYiE8PJylS5cyc+ZMJk6cSHZ29hXXMWvWLB5++GFWrVrF\nhAkT+Oabb67YPjU1lWHDhjFq1Cg+/PBDJk+ezDPPPENiYiIA3377LS+99BIfffQRjRo1Ys6cOQBM\nmDCBmJgYVq1axerVq0lNTWXBggVXXJeIiIiIGEs/f4tHad68OdnZ2fz444+UKVOGlJQUWrZsecV5\n2rZti4+PD1FRUQQEBHDLLbcAULVqVdLS0gqc5/bbbwcgJiYGi8VCVlbWFdfRuXNnXn75ZTZt2sTN\nN9/M008/fcX2u3fvpmrVqjRq1AiA2rVr07RpU7Zv347JZCImJoYKFSoAUL9+fdavXw/A5s2b+fHH\nH1mxYgUAFy5cuOJ6RERERMR4Cl3iUUwmE71792bNmjX4+PjQu3dvTCbTFefx9fXN89qZS/H8/Pwc\n6wOw2+2YTCb+fItkTk6O48/33nsvbdu2ZcuWLXz55ZfExcXx3//+17Gcv7LZbPnqttvtWK1WfHx8\n8Pf3z7PNl9abm5vLjBkzqFWrFgDnzp276vaLiIiIiLF0eaF4nLvvvptNmzaxdu1aevbsWWTrjYiI\n4NdffyU7O5ucnBzWrl3reO/ee+9l79699OzZk3HjxnHu3DmSk5Mvu6zGjRtz8OBBdu/eDcCvv/7K\nt99+S7Nmza5YQ+vWrUlISMBut2OxWHjsscdYsmRJ4WygiIiIiFwTnekSjxMVFUX9+vWxWq2UL1++\nyNbbqlUrYmNj6dy5M1FRUTRv3pxffvkFgBEjRjBhwgRef/11TCYTQ4cOpUqVKpddVkREBDNmzGDc\nuHFcuHABk8nExIkTqVGjBrt27brsfKNGjWL8+PHcdddd5OTkcPPNNzNw4MBC31YRERERcZ6GjBcR\nERERETGQznSJRzt48CBPPfVUge/VqFHjskPJF4V58+bx4YcfFvjegAED6NatWxFXJCIiIiJG0Jku\nERERERERA2kgDREREREREQMpdImIiIiIiBjIsHu6kpPTnW4bHh5AauqVHy4rhUN9XXTU10VHfV20\nXO3vqKhgA6sREREp/orFmS5vb7O7Syg11NdFR31ddNTXRUv9LSIi4ppiEbpEREREREQ8lUKXiIiI\niIiIgRS6REREREREDKSHIxcTZrPJpfY2mx6vJiIiIiJSEih0FQNms4n1KTbSLDan2of5mukQYVbw\nEhEREREpARS6iok0i43U7FwX5tDoYSIiIiIiJYFCl0FcuVzQy8u1SwtFRERERKTkUOgygKuXC1YJ\n9DG4IhERERERcReFLoO4crlgqK9z4ezv+POZt6udhdO9YiIiIiIihUehqwQy4doliV5eJtaetl48\n83Yi5YptNUiHiIiIiEjhUugqgUJ8vf4fopxQJdDHxYE6NEiHiIiIiEhhUegqoQrr8sXUxINsS3id\n5AN78AsKoUmXXvR+7J9XXN7Ro4nExU3nu+924OfnS6tWt/L448MIDQ0DYPz4f/Pppx8VOG+TJjcy\na9ZsAPbs+Yn4+Jn88ss+goOD6dq1Gw89NABvb30sRURERMRz6NttKXb+bCprX3mSsOiatBn+Mmd+\n38+Wd2bzn1Af7r33wQLnOXs2jSFDBuHr68czzzxPQEAgCxe+zbBhjzJv3mJ8fHzo338g3bv3yjPf\nrl3fMXt2HHfd1QOAY8eO8tRTQ2jUqAnjx08hKeko8fGzSE9PZ/jwEYZvu4iIiIhIUVHoKsX2rVtJ\nbq6N9s9MxtvPn+gmN+Nrz2HRogR69/5HgWecPvnkI1JSzrB48TKqV68BQIMGN3DPPd348MPV9Ox5\nD5UrV6Fy5SqOeTIzMxg9eiSdOnXljjs6A7BmzUp8ff0YN24yfn5+AKSkpLBkSQJDhw7X2S4RERER\n8Rj6ZnuNWre+iZEjX2Lr1q/Ytm0rgYFB9O8/kNatb+XVV8ezfccOAiLK0fyhJ6nSpKVjvmO7t7Pz\nvbmkHjmAX3Aotdt0pWr/wVwcHgNyrVZ+eD+Bg1s2kHH6BN5+/lSo34TmDw0nqGx5AKY/3IOGnXpy\nKukYv2/diD3XRtXYW2n58NP4lAkk/dRxVgzrfdnaG/f6J03uGUDSjzuoGHMj3n7+jveua34b25Yn\nsHfvzzRs2CjfvImJhylfvoIjcAGEhYVRrVp1tm37mp4978k3z+LFCWRlZTJkyJOOaffe+wAdOnR2\nBC4AHx8fbDYbubmuPCRaRERERKR4U+j6G2bNmkaPHr3p2bMPq1YtY/r0KaxYsZROnbpSru3dfPXu\nPD6PG0vf+NV4+/mT9OMO1k8aQfXmbWhyzwDOJh1h53uz8c1O55aBFy+p27ZoBge3rCf2gaGElK9M\n6tHf+e7dt9i+aAbtnp7gWPf2FQup1Kg5bYaN5WzSEb5dEkeZ0Ehi73+cgPBIuo6b7WhbMcBMRo6d\n9JyLYSYwohwAZ48nUqF+kzzbFFq+EgCJiUcKDF3lypXn7Nk0srMv4PdHWLNarZw6dZKcHEu+9mfO\nnGbZsnd5+OGBhIdHOKZHREQSEREJQFZWFjt37mDp0iV06XIXvr6+ru8MEREREZFiSqHrb2jQoBGP\nPfYEAFFRUXz++WfExDSkf/9/svyEhfP4sHb8k5w9foTI6nXYuWwOUbVjaPPkywBUadwCv6AQvnpr\nPDd0ux9Cy3PhXBqxDwylTts7AahQvwlnk45wcMu6POsOiizHbcPGYjKZqNyoOcf37OTo91uJvf9x\nzD6+lKvdwNE2Otibs5bcfANv5JzPxKdMQJ5pvn+8zszMLHCb27Vrz6JF8xk3bjRDhz6Fj48P8+bN\nJiMjHX9//3ztV69eidlspkePgs+82Ww2Ondui81mo2LFyjz00IDL9reIiIiISEnk5e4CSrL69WMc\nfw4Pv3jWpm7d+o5pfsEhAFgyM7BmX+D0gb1EN72ZXJvV8V+Vxs2x5+aS+ON3ALQdPo46be8kKyWZ\npJ++Y+/alZz6ZTe2v5xFqlC7HibT/5/VFRhZDmv2BcfrP6/D9qc/59qs2C9dvme3c+myxr+63HPA\nqlatzpgx4/n++5307n0Xd9/dBZvNSqtWt+YLXXa7nY8+WkPnzl0JDg4ucHm5ublMnTqDCROmEhwc\nzODB/Tl9OrnAtiIiIiIiJZFhZ7rCwwPw9nb+eU9RUQV/KS/OoqLCHXX7+dn/mBZGRERQvocQZ2em\nY7fn8t27b/Hdu2/lW1Zm6mkATv7yI1vfnkrqkQP4BgQRUb02Zl8/+Muziv98HxZwMYD9EaacvafL\nJyCInAtZed6znL/4ukKFspfdJ717d+Puu7ty5MgRQkNDiYiI4MEHHyQyMiLPPLt37yY5+RS9evW4\n4v6tVKkDAG3btqJNmzZs2vQpQ4YMuWz7kqQkfq5LKvV10VJ/i4iIOM+w0JWamnX1Rn+IigomOTnd\nqFIMk5GR7ag7PT3jj/9fICUlI1/bS5ftNbr7IaredEue9yoGekNwBJlZGWyY8gzl695Au6fHE1Lh\n4giA377zBimHfnW6roCIstw1fl6e5Wfk5JJuuRjKAsLLAhBSoQoZJ5PyzHv2j9fh4eUL3CcnThxn\nx47t3Hlnd4KCymKzwcmTZ9m/fz/t23fKM89//7uBiIhIqlatk29Z3367DS8vL268MfZPU30oWzaK\nw4ePlsjPw1+V1M91SaS+Llqu9rcCmoiIlHa6p6uI+JQJJKLadaSfTKJsrXqO6SmHD7Ah4Q2a/2Mw\n6ZZcLJnp1O/c1xG47Lm5JP34LflOdV2B2dsnzzoq/3FPl99f7umq1OBGftm4hpwL5/HxLwPAb9s+\nJzQ0jLp162I257/E8MyZZCZNGkfdutdz/fUX17Fhw3rS0tJo1SpvmNy792fq1auf5zLISz76aDX7\n9u3lnXdWOIaHT0w8wokTx6lZ8zqnt1VEREREpLhT6CpCTe4ZyMbXnscnIJBqsUGF3C8AABzeSURB\nVLdyIf0sO5fNxdfsRdlqtfDKsuBTJoAfVi3AnmvDZslm77pVpBw+gAkTdru9wABzrere0ZO9a1ey\nfvIIGt55HylHDvD9ysW07z+E1WfsgIXsrEzOJP5OWIXKBISGkxtZh3I16/DM2LG0vv8xcs+dYd3c\nabRocTOxsc3zLP/gwd9o27Z9gev+xz/68dhj/2T06Ofp0aMXZ86cZv78OVSpEk3Xrt0KbRtFRERE\nRNxNA2kUoao33cLtIyZx5uA+Nk4dyfZFMyhXO4b+E+Px8fPHNyCItk+Nx5KZzsZXn2Pr/Gn4B4fS\ndvg47PZckg/8XKj1BISXpeOo17HbbHz2+ov8snEN7fo9SoO7/kFq9sXRDn/bt5elIwfx0zdfkZqd\ny1mriduenoh/RHk+eu0lNiyZTffuPXnllSn5lp+amkJQUFCB665btx6vv/4mZ8+mMWrUs8TFTadJ\nkxt54415BY6CKCIiIiJSUpnsdrvz1625wNXr/T3pfgyz2cTyE5Z8Q7RfTvXLDOnujvauLjvcz4t7\nKvhisxnyMSrRPO1zXZypr4uW7ukSERFxjc50iYiIiIiIGEihS0RERERExEAKXSIiIiIiIgZS6BIR\nERERETGQQpeIiIiIiIiBFLpEREREREQMpNAlIiIiIiJiIIUuERERERERAyl0iYiIiIiIGEihS0RE\nRERExEAKXSIiIiIiIgZS6BIRERERETGQQpeIiIiIiIiBFLpEREREREQM5O3uAkoSs9nkVDsvL+fa\niYiIiIiI51PocpLZbGJ9io00i+2qbasE+hRBRSIiIiIiUhIodLkgzWIjNTv3qu1Cfa8ezERERERE\npHTQPV0iIiIiIiIGUugSERERERExkEKXiIiIiIiIgQy7pys8PABvb7PT7aOigo0qpfCcSHF3BcWO\nCYiICHJ3GcVWifhcewj1ddFSf4uIiDjPsNCVmprldNuoqGCSk9ONKqVQODtcfGkT4uvFewfOOjWq\nI0CYr5kOEWZsNrvBlblfSfhcewr1ddFytb8V0EREpLTT6IXytzk7quP/OX8GVERERESkpNM9XSIi\nIiIiIgZS6BIRERERETGQQpeIiIiIiIiBFLpEREREREQMpNAlIiIiIiJiIIUuERERERERAyl0iYiI\niIiIGEihS0RERERExEAKXSIiIiIiIgZS6BIRERERETGQQpeIiIiIiIiBFLpEREREREQM5O3uAqR0\nMQFeXiaX5rHZ7MYUIyIiIiJSBBS6pEiF+Hqx9rSVNIvNqfZhvmY6RJgVvERERESkxFLokiKXZrGR\nmp3rwhxmw2oRERERETGa7ukSERERERExkEKXiIiIiIiIgRS6REREREREDKTQJSIiIiIiYiCFLhER\nEREREQMpdImIiIiIiBhIoUtERERERMRACl0iIiIiIiIGMuzhyOHhAXh7O/9Q26ioYKNKKTwnUtxd\nQakUERHk7hKuWYn4XHsI9XXRUn+LiIg4z7DQlZqa5XTbqKhgkpPTjSrlssxmk9NtvbycbyuFKyUl\nA5vN7u4yXOauz3VppL4uWq72twKaiIiUdoaFruLObDaxPsVGmsXmVPsqgT4GVyQiIiIiIp6o1IYu\ngDSLjdTsXKfahvo6F85ERERERET+TANpiIiIiIiIGEihS0RERERExEAKXSIiIiIiIgYq1fd0SfFn\nwrWRI0viKIciIiIi4tkUuqRYC/H1Yu1pq1OjTIb5mukQYVbwEhEREZFiRaFLij1XRpkE5x/ILSIi\nIiJSFHRPl4iIiIiIiIEUukRERERERAyk0CUiIiIiImIghS4REREREREDaSAN8RiuDi8PGmJeRERE\nRIyn0CUew5Xh5UFDzIuIiIhI0VDocrOcC1l8vGQ2P325EUv2BcrVaUjs/Y8TUa22o83p3/by4aiB\n+eaN6foPmj04FIA9/13O7tWLsefaqHtHL5r0/qejnTXHwpyBvbj1iX9Tvm6jK9bz++7vSHj+ce4a\nP4+yterle//TsUPx9i9Dh+deBWDByMc49OPOPG3Mvn6EVIimTru7qN+pt2P6gntb5WnnZfbGPzSc\nijE30rjXw4RUqHLF2pzh2vDyoCHmRURERMRoCl1utmnaKE7v/5EWfQfgX6kmv21Zxyf/vhh6QitV\nAyDlyG94+5Wh44uvA1AxwExGjh1bUAQAaccOs33hTFo8/DS+gUF89dYEyl/fkEoNYwHY/tEKylar\nddXAda0q1b2BJvcPcby2XjjPr59/wraE6QB5glfzu/pQo1UH0nNysVmyST95jB9WL+bDUQPp+vJb\nhFWubkiNIiIiIiLuotDlRqcP7iNp93buHPocdW7vQWp2LpUbNeejlx5h57J5tB0+DoDUIwcIj65B\nudoNAIgO9uasJddxRif1yAH8Q8Koe8fdAOz5dBlnDu2nUsNYci5k8dWKRdz90nTDtsMvMMhR2yUV\nY27kzMF97Fu3Mk/oCo0qT6XrGzhqrxhzI1WatmLNcw/x9bxX6TLmDcPqFBERERFxB41e6EbnjicC\ncF3TFnmml7++Icd+2OZ4nXLkN8KrXnfZ5QRFVeRCxllO/7aXcyeOcvZ4IkFRFQH4+eOlVG94I+Vq\nXm/AFlyeycuL8KrXkZF84qptA8Iiuf727pzc+z1nk44UQXUiIiIiIkVHZ7rcKDCyHABnk08SHl7B\nMT391HFyzmeSnXEOv6AQUo8cxOzty5rnHiLt6CHCylUgtnd/KrXqDEDUdfW57pZOjvu+qt50C9Wa\n3UZ2xjn2fLqcwa/Nc7k2e24uuTZr/uk4P+jEuRNHCSpXyam2lRrcxA+rEji1/0dCK1V1eh0iIiIi\nIsWdR4Uus9n54cJdHVrcCGVr1SOkYjQfx0+h/dAXIaISv2/dyNHvtwKQc+E8Nks22elpnDuRyI3/\neBS/wGBObd/I2lmvcIsNrrv1YvBq/egLNLlnIHZ7LkFlLwa43WsWUzX2VkLKlmf1rFdI3LubijFN\nafbgMLz9/K9Y20cvDb7se1Wa3JxvmiOg2SEr7TT71r9PyqH9NOs3zKm+8A8NB+D82VSn2ouIiIiI\nlBQeE7rMZhPrU2xODxdeJdDH4IquzuzjS7unJ7A1fiz/eebiaINRtRvQ8K77+X7lfLz9/PH29eOO\n56cRXrUWAeFlAbj55paknk7m+xXzHaEL/n/mDCAr9TT7N31I98kL2bT4LdJPn+T2EZPZOn8qu5bP\nI/aBoVes7ZbHXyKscrV807+e92q+ab9/9zW/339b3m3z9SOmS1/qdezlfIeIiIiIiHggjwld4Npw\n4aG+zoUzo4VH1+TxuHc4mnSc1PM5BJerxK4V8zGZvPANCMTL7E3lRs3zzVe9SQsO7fqGnAtZ+PgH\n5Hv/h1UJXHdrJ4LKlmfPlk20fmgoYZWrUbd9D3b8582rhq6wytUKHDK+oHVVrteIpg88cfGFyYSP\nfxmCy1XGy9v5j1dWSjIAgRFRTs8jIiIiIlISeFToKmms2Rc4tG0zES2aE1y2PNY/jUYYFl0TL7M3\nZ5OOcPzn76jdpitmH9//z2vJxuzrh7dfmXzLTT+VxO9bN3L3a/8BIDMtFf+gEAB8A4M5n3amULfD\nNyCwwIDmiuM/X3zWV7nrGxZGSSIiIiIixYZGL3QjL7M3W99+lZ++WO+Yln4qiaO7thJ948UHCWel\nJrP17akc3bXV0cZut/PrN5spX7cRJlP+e9N2LX+buh3upswf90kFhoWTmZoCwPm0M477p4qLC+dS\n2b/pAyo1vIlgJwfeEBEREREpKQw70xUeHoC3t9np9lFRwX9/pSdS/v4yipCXtze1297FF+8twBQY\nRo5PGXb85038Q8KJ6dIXgPL1GlP++hv4+u1Xyc5MJyAskq2ff8Dpw7/R5d/x+ZaZdvR3jn7/Db1n\nvOeYVju2FTs/fJdGZUL4+ZNlVL3pliLbxr86m3ySpF9++uPhyBbOJh3mp4+XYrfbafHwv4q0FhMQ\nERFk6DoK5XMtTlFfFy31t4iIiPMMC12pqVlOt42KCiY5Of1vrc+VkQuLk5vue4xQPy++XBRHjsVC\nxZimxN4/BP/gUAC8vMzc/sxkvlv6FruWzyM7/SyVrqtLr3/PILCAS/p2LptLgzv/gW/A/8PE7f0e\nZdmr/2bzjNFUbHAjTfsMKrLt+6ttHy5j24fLAPD28ycgIooqjVvQ4M77CCpbvkhrCfH14r0DZ50e\nfCXM10yHCDM2m3PD5hfG51qco74uWq72twKaiIiUdia73e78g5dc4Oo/yIURupafsDg9kEb1YG/O\nWnINaW/kso1uX5xqcbW90bWE+3lxTwVfha5iSH1dtBS6REREXKN7ukRERERERAyk0CUiIiIiImIg\nDRkv4iQT4OVVMu8dFBERERH3cTp0LVu2jHnz5nHixAnq1avHyJEjadKkyVXny8rK5MEH+zJ06HDa\ntm2f571jx44SFzed7777Fl9fX5o3b8nQoU8RHh7haLN//z7eeGMGP/20m6CgYNq0acejjz5BmTL5\nn08lYqQQXy/WnrY6P/BGylk6RJj5/PPNjB37EuvXf3HF9uvX/5dFi+Zz7NhRKlSoSJ8+99GjR688\nbS4dMzt2fIufX8HHzEcfrWHp0nc4efI4lSpVoV+/f3L77R1c32ARERERKRROXV64evVqxowZQ7du\n3Zg1axbBwcEMGDCAxMTEK86XlZXJyJH/4uTJE/neO3fuHEOGDCIlJYXp06czbNi/2LVrJ6NHP+9o\nc/RoIkOHPoKvry+TJr3Gww8PYu3aT5k+fYqLmylSONIsNlKzc536L81iY/fuH3j55dHAlQffWLfu\nv4wd+yI1atRi4sTX6NnzHuLjZ7J48QJHmz8fM2PHTijwmNmwYS2TJo2jefMWTJgwlaZNb2LMmOfZ\nsuVLo7pERERERK7iqme67HY7M2fOpE+fPgwdOhSAm2++mU6dOrFw4UJefPHFAufbtes7pk6dSEpK\nwc/Oeu+9d7Dbc5k+PY5q1SqQnJxOQEAg06ZN5syZ00RGlmXBgjlUrFiJiRNfw9vbm9hYsNlsrFix\nFKvVire3ro6U4smWY+HbT1Yw6905+PuXwWq98giJS5YsoEGDGxg7dgImk4nmzVvi7e3DrFnT6Nbt\nbkJDw/IcMwEBgQD5jplPP/2YRo2a8MQTTwMQG9ucvXt/Zs2albRq5b7ns4mIiIiUZlc903X48GGO\nHTtGu3btHNN8fHxo06YNX355+V/Pn39+BDVrXsdrr80s8P0vvviM9u07Or48ArRufSurVn1MZGRZ\ncnNz+fLLL7jzzu55wlWvXn14991VClxSrB39/hu2r1zE0KFP0rt336u2T0w8Qmxsc0ym/98zdsMN\njcnOzub773cCVz9mAHJyLAQGBuZZdkhIKOfOnSuMzRIRERGRa3DV0HXo0CEAqlWrlmd6dHQ0R44c\nwWYr+P6W+Pi5jBs3Kc+9Jpfk5ORw5MhhKlasxOuvv0psbCy3396Kf/97lOPL4fHjSWRlZRIREcm4\ncS/RocMtdOx4G9OmTcZisbi6nSJFqmytegyYvZI+ff7hVPty5crnuwz3+PEkx///esx06tQ23zED\ncPfdvdm+/Rs2bdpARkYGGzeuY9u2r2nf/o7C2zgRERERcclVQ1dGRgZAvl/PAwMDyc3N5fz58wXO\nV7PmdZddZnr6OWw2G4sXLyApKYnp06fz1FPPsmPHNsaOvXi5YlpaGgAzZ07FbPZm4sTXGDDgET75\n5ENmznzNua0TcZPAiCj8A51/IOwdd3Rm7dpP+Oij1aSnp7N378/Mnh2HyWTiwoUL+Y6Zl1+emO+Y\nAbjlljZ07nwno0ePpFOnNowZ8wJdutxF7973GrGZIiIiIuIEp+7pAvJc9nSl6c6wWq3AxftRJkx4\nlYoVw7n++kYEBgby0ksj2bPnJ0ebatVq8MILYwC46aZmWK023nprFg8/PMhxSZVIcXRpiPlLw8yb\nzZc/Vh5+eAApKWeYPHk8kya9QkhIKMOHj2DcuNH4+fnnO2YuXV7752Omfv0GTJgwls8/38Sjjw4l\nJqYhe/f+zIIFcwkKCmbIkCcN32YRERERye+qoSs4+OKv9ZmZmZQt+/+Qk5WVhZeXFwEBAS6vtEyZ\ni/PceGOzPPdmxca2AODgwQNcf309AFq2vDnPl9UWLVoQHz+Dw4d/p1y5KMd0PT9JiptLQ8z/nGHD\naoflJy5/WWyYr5mRI0cxZMiTnDx5ksqVq5CScga73U5ISIhTx0xkZFnWrfuUxx8fxn339QOgSZMb\nCQgI4LXXJtOjRy8qV65i4BaLiIiISEGuGrou3cuVmJiY576uxMREatSocU1nuoKDgwkLC8Nqzckz\n/dKv+WAiOjoak8nErtQL+P3py+qJkxcvZ/wi1cqhP02vEujjch0iRkuz2LhgtWO3Q2r25UcwPLJ7\nB5FlfWnU6EZq1KgJwIEDvwJQu3Ydp46Z5ORTAMTENMzT5oYbGmO32zl06HeFLhERERE3uOo9XdWr\nV6dixYps2LDBMS0nJ4fNmzfTsmXLa17xTTc1Z+vWLVy4cMExbevWrwBo2PDipYYV6sSw7+vPSDlv\ndTz7aM+2LZh9fPGLrpPnmUjpOc49sFakOPrlqw1Mn/4qZrMJs9mElxesXr2CChUqUKdOHcxmE7Gx\nzfnmmy3k5FxwnP398zFTuXIVvLy82L37hzzL3rPnJwAqVqxUtBslIiIiIoATZ7pMJhODBg1i3Lhx\nhIaG0rRpU5YsWUJqair9+/cH4MiRI6SkpNC4cWOnV9y//0C2bPmCESOG8fjjj7J//++89dYsbr/9\nDqpVqw5Aq/sfZeXYJ/ns9Ze4vn13Ug79yu7Vi4jp2hffgKBr2mCR4uDciaNcSE+jXO0GALTsejfz\nnv6Ix8dPplbsLez9Yi0/b9tK16dfZlWyDbBR6a7+bP7yCx4c+gRt+jxEpcxTxMfPzHPM9OjRiwUL\n5gIQE9OA/fv3MX/+XNq0aUfNmrXctLUiIiIipZtTD7u6//77yc7OZtGiRSQkJFCvXj3efvttoqOj\nAYiPj+f999/nl19+cXrF1avXYNasObz55kyGDRtGmTIBdO3ajUceGepoU7XhjXR4bio735vLxlef\nwy84jMa9HuaG7g+6uJkixcsPqxI48MWnPLx0CwCVa9ej6zOv8OU7c9i9bg2hFaNp8+TLlItt57gs\n0atcVTqNjmPHO/EsHf8cwYGB+Y6ZJ58cQWRkWT74YBULFsyhQoWK3H9/P8c9XiIiIiJS9Ez2S8MQ\nFrLk5HSn20ZFBedrbzabWH7CcsX7YP6serA3Zy25xaJ9carF1fbFqRZX2xenWlxt7+qyw/28uKeC\nLzabIYdviVPQ3yFiHFf7OyrK+ccniIiIeKKr3tMlIiIiIiIi106hS0RERERExEBO3dNVHB3dvZ3N\n8RNIPfq7u0sRcQivUoM2j79AlRuaGbqeSw9edoWrlyJe6WHOf3fZIiIiIqVJiQ1dn8WNIy3piLvL\nEMkj9ejvfBY3jgfnfGjoei49eDnN4tyjEsJ9zdxR1pvcXOfCkZeXyenlu7psUEgTERGR0qXEhi6R\n0i7NYnN64I1QF0NalUAfp5fv6rIV0kRERKS0KbGhq+3Ql9j85kRSEw+6uxQRh/DomrR57Hl3l1Eg\n10Kaaw8bNzIAOhvSXLkcsigpMIqIiEixDl1hvubLvhce24KGsWscr6sE+pCRk+vSL/lGtS9Otbja\nvjjV4mr74lQLQLCPGRPOBQFX2ha39tey7Iwc5wIaQKCPF9+cs5Oec4V+Tz3r+GP5Mt5kWa/S/hra\nXkv7YB8zLUK8XDqrV5woMIqIiBQOw57T5YrNmzfTpk0bd5dRKqivi476uuior4uW+ltERMQ1xWLI\n+M8//9zdJZQa6uuio74uOurroqX+FhERcU2xCF0iIiIiIiKeqliErttuu83dJZQa6uuio74uOurr\noqX+FhERcU2xuKdLRERERETEUxWLM10iIiIiIiKeSqFLRERERETEQApdIiIiIiIiBlLoEhERERER\nMZBCl4iIiIiIiIHcGrqWLVvGHXfcwQ033EDfvn3ZtWuXO8spFTZu3EiTJk3cXYbHstlsLFiwgM6d\nO9O4cWO6dOnCkiVL0CChxrBYLEyfPp22bdvSuHFj+vXrx88//+zusjyaxWKhc+fOjBw50t2liIiI\nlBhuC12rV69mzJgxdOvWjVmzZhEcHMyAAQNITEx0V0keb+fOnTzzzDPuLsOjxcfHM23aNLp168ab\nb75J586dmTBhAvPmzXN3aR5p4sSJLF68mEGDBhEXF0eZMmXo168fx44dc3dpHisuLo6DBw+6uwwR\nEZESxS2hy263M3PmTPr06cPQoUO57bbbePPNNwkPD2fhwoXuKMmjWSwW5s6dS79+/fD29nZ3OR4r\nNzeXBQsWMGDAAB577DFatmzJE088Qd++fZk/f767y/M46enpLF++nCeeeIL77ruP1q1bM2PGDKxW\nK2vWrHF3eR5pz549LF68mPDwcHeXIiIiUqK4JXQdPnyYY8eO0a5dO8c0Hx8f2rRpw5dffumOkjza\nF198wZw5c3j22Wd54IEH3F2Ox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ucDVZGena9vaLur5pa8eylxcs0MCBj+jOO+/UnDlz9N13X6hXr156441XtWHD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\nKK9Lir3fMCqIs2fPGgMGDDCee+45Y8mSJbmW33333W6Iqvh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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -307,9 +309,9 @@ "outputs": [ { "data": { - "image/png": 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TsCvlxrYvBpMYfvOB752amsKYMR8SGRlh9JrkghCPlpyhEg9No9EQFPQzS5Ys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crg4GAsWbIEBoMBjzzyiHTMmyl1Lqqrq7F48eIJz3dgbudCDiXWxUwopS7kUGJdEBEROROHvfTo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\nF1REREREREQycUFFREREREQk038B4a/ayfHDed0AAAAASUVORK5CYII=\n", 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TZlkuFxQR16QCS9xe3br1MZlMBAYGUatWbSwWCwEBgRw7dhS73c7o0UMBSExM4MKFcCpU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\ndmjnOy8r5LqWahM/TJmHvlbaVzt0Nv2EAiNitGb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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -336,9 +338,9 @@ "outputs": [ { "data": { - "image/png": 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7tmzZoo4dOyojI0NHjhxRt27dNG7cOM2aNUupqamy2Wzq3LmzJk+eLC8vL7Vu3VqJiYkK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\nRhEjAADAKGIEAAAY9f8wRy/zd1WGfQAAAABJRU5ErkJggg==\n", 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hPDxQGRmfSbo4cbA08PR2uOMqGKOufPldrVrS7t2efRwkz/9dkmiDJ/nh4Enl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\n", 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GqdkFmJVwkX05AXovOBs3bsSUKVPg7e2N6dOnw9LyYdW/dOkSTpw4gcaNG8PX1xfbt2+H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\nRAbBgiNo5MiheOk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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -381,18 +383,18 @@ "data": { "text/html": [ "
\n", - "\n", "\n", " \n", @@ -403,42 +405,55 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
mc_errorhpd_2.5hpd_97.5n_effRhat
difference of means1.0165740.4357220.0055700.1523921.8701921.0034880.4645560.0086730.1100081.9296362618.0069850.999933
difference of stds0.9229350.4410760.0078560.0795571.7828490.9443550.4505250.0069410.1524681.8854343567.9532580.999944
effect size0.6049150.2724880.0041620.0831811.1518700.5954060.2897380.0051660.0409751.1754112756.5963390.999857
\n", "
" ], "text/plain": [ - " mean sd mc_error hpd_2.5 hpd_97.5\n", - "difference of means 1.016574 0.435722 0.005570 0.152392 1.870192\n", - "difference of stds 0.922935 0.441076 0.007856 0.079557 1.782849\n", - "effect size 0.604915 0.272488 0.004162 0.083181 1.151870" + " mean sd mc_error hpd_2.5 hpd_97.5 \\\n", + "difference of means 1.003488 0.464556 0.008673 0.110008 1.929636 \n", + "difference of stds 0.944355 0.450525 0.006941 0.152468 1.885434 \n", + "effect size 0.595406 0.289738 0.005166 0.040975 1.175411 \n", + "\n", + " n_eff Rhat \n", + "difference of means 2618.006985 0.999933 \n", + "difference of stds 3567.953258 0.999944 \n", + "effect size 2756.596339 0.999857 " ] }, "execution_count": 13, @@ -447,7 +462,7 @@ } ], "source": [ - "pm.df_summary(trace,varnames=['difference of means', 'difference of stds', 'effect size'])" + "pm.summary(trace,varnames=['difference of means', 'difference of stds', 'effect size'])" ] }, { @@ -460,6 +475,15 @@ "2.\tJohnson D. The insignificance of statistical significance testing. Journal of Wildlife Management. 1999;63(3):763-772.\n", "3.\tKruschke JK. Bayesian estimation supersedes the t test. J Exp Psychol Gen. 2013;142(2):573-603. doi:10.1037/a0029146." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The original pymc2 implementation was written by Andrew Straw and can be found here: https://github.com/strawlab/best\n", + "\n", + "Ported to PyMC3 by [Thomas Wiecki](https://twitter.com/twiecki) (c) 2015, updated by Chris Fonnesbeck." + ] } ], "metadata": { @@ -479,7 +503,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.3" }, "latex_envs": { "bibliofile": "biblio.bib", diff --git a/docs/source/notebooks/Bayes_factor.ipynb b/docs/source/notebooks/Bayes_factor.ipynb index e005b746e0..7b9fd6e8d4 100644 --- a/docs/source/notebooks/Bayes_factor.ipynb +++ b/docs/source/notebooks/Bayes_factor.ipynb @@ -1,12 +1,25 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayes Factors and Marginal Likelihood" + ] + }, { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Runing on PyMC3 v3.3\n" + ] + } + ], "source": [ "%matplotlib inline\n", "import pymc3 as pm\n", @@ -16,16 +29,15 @@ "from pymc3.step_methods import smc\n", "from tempfile import mkdtemp\n", "from scipy.special import betaln\n", - "from scipy.stats import beta" + "from scipy.stats import beta\n", + "\n", + "print('Runing on PyMC3 v{}'.format(pm.__version__))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Bayes Factors and Marginal Likelihood\n", - "\n", - "\n", "The \"Bayesian way\" to compare models is to compute the _marginal likelihood_ of each model $p(y \\mid M_k)$, _i.e._ the probability of the observed data $y$ given the $M_k$ model. This quantity, the marginal likelihood, is just the normalizing constant of the Bayes' theorem. We can see this if we write Bayes' theorem and make explicit the fact that all inferences are model-dependant. \n", "\n", "$$p (\\theta \\mid y, M_k ) = \\frac{p(\\theta \\mid \\theta, M_k) p(\\theta \\mid M_k)}{p( y \\mid M_k)}$$\n", @@ -214,9 +226,9 @@ "outputs": [ { "data": { - "image/png": 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Auto-initialising step object using given/default parameters.\n", - "/home/osvaldo/Documentos/Proyectos/01_PyMC3/pymc3/pymc3/step_methods/smc.py:118: UserWarning: Warning: SMC is an experimental step method, and not yet recommended for use in PyMC3!\n", + "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/step_methods/smc.py:118: UserWarning: Warning: SMC is an experimental step method, and not yet recommended for use in PyMC3!\n", " warnings.warn(EXPERIMENTAL_WARNING)\n", "Adding model likelihood to RVs!\n", "Init new trace!\n", @@ -305,10 +317,10 @@ "Sample final stage\n", "Initializing chain traces ...\n", "Sampling ...\n", - "/home/osvaldo/Documentos/Proyectos/01_PyMC3/pymc3/pymc3/step_methods/smc.py:488: UserWarning: Warning: SMC is an experimental step method, and not yet recommended for use in PyMC3!\n", + "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/step_methods/smc.py:491: UserWarning: Warning: SMC is an experimental step method, and not yet recommended for use in PyMC3!\n", " warnings.warn(EXPERIMENTAL_WARNING)\n", "Argument `step` is None. Auto-initialising step object using given/default parameters.\n", - "/home/osvaldo/Documentos/Proyectos/01_PyMC3/pymc3/pymc3/step_methods/smc.py:118: UserWarning: Warning: SMC is an experimental step method, and not yet recommended for use in PyMC3!\n", + "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/step_methods/smc.py:118: UserWarning: Warning: SMC is an experimental step method, and not yet recommended for use in PyMC3!\n", " warnings.warn(EXPERIMENTAL_WARNING)\n", "Adding model likelihood to RVs!\n", "Init new trace!\n", @@ -386,30 +398,22 @@ "execution_count": 8, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/osvaldo/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: df_summary has been deprecated. In future, use summary instead.\n", - " \"\"\"Entry point for launching an IPython kernel.\n" - ] - }, { "data": { "text/html": [ "
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\n", - "\n", "
\n", " \n", @@ -514,7 +510,7 @@ } ], "source": [ - "pm.df_summary(traces[1], varnames='a').round(2)" + "pm.summary(traces[1], varnames='a').round(2)" ] }, { @@ -526,22 +522,22 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1000/1000 [00:02<00:00, 377.29it/s]\n", - "100%|██████████| 1000/1000 [00:02<00:00, 385.86it/s]\n" + "100%|██████████| 1000/1000 [00:02<00:00, 467.10it/s]\n", + "100%|██████████| 1000/1000 [00:01<00:00, 589.37it/s]\n" ] }, { "data": { - "image/png": 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Cir7nclPodmT7x+wLZxTw3SW7CbRnsG5TML5fKruq2mgKWYlzm+0pZdRWLNlZ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\noVAoUogSXYVCoUghSnQVCoUihSjRVSgUihSiRFehUChSiBJdhUKhSCF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d6AN8tM506GrdrrapmUdj55JFG1F8XGK+xX41mB1qBOcbO1AKHnWXJJ+t+KL1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\naeJMo0Q3QbBsybOf7sOhi1YGzZoj6OfycbNYGVX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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -585,7 +581,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.3" } }, "nbformat": 4, diff --git a/docs/source/notebooks/GLM-negative-binomial-regression.ipynb b/docs/source/notebooks/GLM-negative-binomial-regression.ipynb index a9b878faee..40aa8b4a2b 100644 --- a/docs/source/notebooks/GLM-negative-binomial-regression.ipynb +++ b/docs/source/notebooks/GLM-negative-binomial-regression.ipynb @@ -4,52 +4,42 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# GLM: Negative Binomial Regression\n", - "\n", - "This notebook demos negative binomial regression using the `glm` submodule. It closely follows the GLM Poisson regression example by [Jonathan Sedar](https://github.com/jonsedar) (which is in turn insipired by [a project by Ian Osvald](http://ianozsvald.com/2016/05/07/statistically-solving-sneezes-and-sniffles-a-work-in-progress-report-at-pydatalondon-2016/)) except the data here is negative binomially distributed instead of Poisson distributed.\n", - "\n", - "Negative binomial regression is used to model count data for which the variance is higher than the mean. The [negative binomial distribution](https://en.wikipedia.org/wiki/Negative_binomial_distribution) can be thought of as a Poisson distribution whose rate parameter is gamma distributed, so that rate parameter can be adjusted to account for the increased variance.\n", - "\n", - "#### Contents\n", - "\n", - "+ [Setup](#Setup)\n", - " + [Convenience Functions](#Convenience-Functions)\n", - " + [Generate Data](#Generate-Data)\n", - " + [Poisson Data](#Poisson-Data)\n", - " + [Negative Binomial Data](#Negative-Binomial-Data)\n", - " + [Visualize the Data](#Visualize-the-Data)\n", - "\n", - "\n", - "+ [Negative Binomial Regression](#Negative-Binomial-Regression)\n", - " + [Create GLM Model](#Create-GLM-Model)\n", - " + [View Results](#View-Results)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" + "# GLM: Negative Binomial Regression" ] }, { "cell_type": "code", "execution_count": 1, - "metadata": { - "scrolled": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Runing on PyMC3 v3.3\n" + ] + } + ], "source": [ + "%matplotlib inline\n", "import numpy as np\n", "import pandas as pd\n", "import pymc3 as pm\n", "from scipy import stats\n", - "from scipy import optimize\n", "import matplotlib.pyplot as plt\n", + "plt.style.use('seaborn-darkgrid')\n", "import seaborn as sns\n", "import re\n", + "print('Runing on PyMC3 v{}'.format(pm.__version__))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook demos negative binomial regression using the `glm` submodule. It closely follows the GLM Poisson regression example by [Jonathan Sedar](https://github.com/jonsedar) (which is in turn inspired by [a project by Ian Osvald](http://ianozsvald.com/2016/05/07/statistically-solving-sneezes-and-sniffles-a-work-in-progress-report-at-pydatalondon-2016/)) except the data here is negative binomially distributed instead of Poisson distributed.\n", "\n", - "%matplotlib inline" + "Negative binomial regression is used to model count data for which the variance is higher than the mean. The [negative binomial distribution](https://en.wikipedia.org/wiki/Negative_binomial_distribution) can be thought of as a Poisson distribution whose rate parameter is gamma distributed, so that rate parameter can be adjusted to account for the increased variance." ] }, { @@ -246,7 +236,9 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# Gamma shape parameter\n", @@ -670,7 +662,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.3" } }, "nbformat": 4, diff --git a/docs/source/notebooks/GLM-poisson-regression.ipynb b/docs/source/notebooks/GLM-poisson-regression.ipynb index 30f10d235b..86d790fa3a 100644 --- a/docs/source/notebooks/GLM-poisson-regression.ipynb +++ b/docs/source/notebooks/GLM-poisson-regression.ipynb @@ -4,73 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# GLM: Poisson Regression\n", - "\n", - "## A minimal reproducable example of poisson regression to predict counts using dummy data.\n", - "\n", - "This Notebook is basically an excuse to demo poisson regression using PyMC3, both manually and using the `glm` library to demo interactions using the `patsy` library. We will create some dummy data, poisson distributed according to a linear model, and try to recover the coefficients of that linear model through inference.\n", - "\n", - "For more statistical detail see:\n", - "\n", - "+ Basic info on [Wikipedia](https://en.wikipedia.org/wiki/Poisson_regression)\n", - "+ GLMs: Poisson regression, exposure, and overdispersion in Chapter 6.2 of [ARM, Gelmann & Hill 2006](http://www.stat.columbia.edu/%7Egelman/arm/)\n", - "+ This worked example from ARM 6.2 by [Clay Ford](http://www.clayford.net/statistics/poisson-regression-ch-6-of-gelman-and-hill/)\n", - "\n", - "This very basic model is insipired by [a project by Ian Osvald](http://ianozsvald.com/2016/05/07/statistically-solving-sneezes-and-sniffles-a-work-in-progress-report-at-pydatalondon-2016/), which is concerend with understanding the various effects of external environmental factors upon the allergic sneezing of a test subject.\n", - "\n", - "\n", - "## Contents\n", - "\n", - "+ [Setup](#Setup)\n", - " + [Local Functions](#Local-Functions)\n", - " + [Generate Data](#Generate-Data)\n", - "\n", - "\n", - "+ [Poisson Regression](#Poisson-Regression)\n", - " + [Create Design Matrices](#Create-Design-Matrices)\n", - " + [Create Model](#Create-Model)\n", - " + [Sample Model](#Sample-Model)\n", - " + [View Diagnostics and Outputs](#View-Diagnostics-and-Outputs)\n", - "\n", - "\n", - "\n", - "## Package Requirements (shown as a conda-env YAML):\n", - "```\n", - "$> less conda_env_pymc3_examples.yml\n", - "\n", - "name: pymc3_examples\n", - "channels:\n", - " - defaults\n", - "dependencies:\n", - " - python=3.5\n", - " - jupyter\n", - " - ipywidgets\n", - " - numpy\n", - " - scipy\n", - " - matplotlib\n", - " - pandas\n", - " - pytables\n", - " - scikit-learn\n", - " - statsmodels\n", - " - seaborn\n", - " - patsy\n", - " - requests\n", - " - pip\n", - " - pip:\n", - " - regex \n", - "\n", - "$> conda env create --file conda_env_pymc3_examples.yml\n", - "$> source activate pymc3_examples\n", - "$> pip install --process-dependency-links git+https://github.com/pymc-devs/pymc3\n", - "\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" + "# GLM: Poisson Regression" ] }, { @@ -82,39 +16,37 @@ "outputs": [], "source": [ "## Interactive magics\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ + "%matplotlib inline\n", "import sys\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", "import re\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", + "plt.style.use('seaborn-darkgrid')\n", "import seaborn as sns\n", "import patsy as pt\n", - "from scipy import optimize\n", - "\n", - "# pymc3 libraries\n", "import pymc3 as pm\n", - "import theano as thno\n", - "import theano.tensor as T \n", - "\n", "\n", - "sns.set(style=\"darkgrid\", palette=\"muted\")\n", - "pd.set_option('display.mpl_style', 'default')\n", "plt.rcParams['figure.figsize'] = 14, 6\n", - "np.random.seed(0)" + "np.random.seed(0)\n", + "print('Runing on PyMC3 v{}'.format(pm.__version__))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a minimal reproducible example of Poisson regression to predict counts using dummy data.\n", + "\n", + "This Notebook is basically an excuse to demo Poisson regression using PyMC3, both manually and using the `glm` library to demo interactions using the `patsy` library. We will create some dummy data, Poisson distributed according to a linear model, and try to recover the coefficients of that linear model through inference.\n", + "\n", + "For more statistical detail see:\n", + "\n", + "+ Basic info on [Wikipedia](https://en.wikipedia.org/wiki/Poisson_regression)\n", + "+ GLMs: Poisson regression, exposure, and overdispersion in Chapter 6.2 of [ARM, Gelmann & Hill 2006](http://www.stat.columbia.edu/%7Egelman/arm/)\n", + "+ This worked example from ARM 6.2 by [Clay Ford](http://www.clayford.net/statistics/poisson-regression-ch-6-of-gelman-and-hill/)\n", + "\n", + "This very basic model is inspired by [a project by Ian Osvald](http://ianozsvald.com/2016/05/07/statistically-solving-sneezes-and-sniffles-a-work-in-progress-report-at-pydatalondon-2016/), which is concerned with understanding the various effects of external environmental factors upon the allergic sneezing of a test subject.\n" ] }, { @@ -150,9 +82,9 @@ " \n", " ax = pm.traceplot(trcs, varnames=varnames, figsize=(12,nrows*1.4),\n", " lines={k: v['mean'] for k, v in \n", - " pm.df_summary(trcs,varnames=varnames).iterrows()})\n", + " pm.summary(trcs,varnames=varnames).iterrows()})\n", "\n", - " for i, mn in enumerate(pm.df_summary(trcs, varnames=varnames)['mean']):\n", + " for i, mn in enumerate(pm.summary(trcs, varnames=varnames)['mean']):\n", " ax[i,0].annotate('{:.2f}'.format(mn), xy=(mn,0), xycoords='data',\n", " xytext=(5,10), textcoords='offset points', rotation=90,\n", " va='bottom', fontsize='large', color='#AA0022') " @@ -178,22 +110,16 @@ "+ The subject may or may not drink alcohol during that day, recorded as `alcohol (boolean)`\n", "+ The subject may or may not take an antihistamine medication during that day, recorded as the negative action `nomeds (boolean)`\n", "+ I postulate (probably incorrectly) that sneezing occurs at some baseline rate, which increases if an antihistamine is not taken, and further increased after alcohol is consumed.\n", - "+ The data is aggegated per day, to yield a total count of sneezes on that day, with a boolean flag for alcohol and antihistamine usage, with the big assumption that nsneezes have a direct causal relationship." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Create 4000 days of data: daily counts of sneezes which are poisson distributed w.r.t alcohol consumption and antihistamine usage**" + "+ The data is aggregated per day, to yield a total count of sneezes on that day, with a boolean flag for alcohol and antihistamine usage, with the big assumption that nsneezes have a direct causal relationship.\n", + "\n", + "\n", + "Create 4000 days of data: daily counts of sneezes which are Poisson distributed w.r.t alcohol consumption and antihistamine usage" ] }, { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# decide poisson theta values\n", @@ -222,9 +148,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -296,15 +220,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##### View means of the various combinations (poisson mean values)" + "##### View means of the various combinations (Poisson mean values)" ] }, { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -369,9 +291,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -474,9 +394,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "(mx_en, mx_ex) = pt.dmatrices(fml, df, return_type='dataframe', NA_action='raise')" @@ -485,9 +403,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -621,9 +537,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -653,9 +567,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -692,9 +604,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -752,7 +662,7 @@ } ], "source": [ - "np.exp(pm.df_summary(trc_fish, varnames=rvs_fish)[['mean','hpd_2.5','hpd_97.5']])" + "np.exp(pm.summary(trc_fish, varnames=rvs_fish)[['mean','hpd_2.5','hpd_97.5']])" ] }, { @@ -807,13 +717,6 @@ " \n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -838,9 +741,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "with pm.Model() as mdl_fish_alt:\n", @@ -858,9 +759,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -890,9 +789,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -920,9 +817,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -987,7 +882,7 @@ } ], "source": [ - "np.exp(pm.df_summary(trc_fish_alt, varnames=rvs_fish_alt)[['mean','hpd_2.5','hpd_97.5']])" + "np.exp(pm.summary(trc_fish_alt, varnames=rvs_fish_alt)[['mean','hpd_2.5','hpd_97.5']])" ] }, { @@ -1004,9 +899,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1033,9 +926,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1078,7 +969,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.3" }, "latex_envs": { "bibliofile": "biblio.bib", @@ -1089,5 +980,5 @@ } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/pymc3/stats.py b/pymc3/stats.py index 585716de91..997b2988b6 100644 --- a/pymc3/stats.py +++ b/pymc3/stats.py @@ -18,7 +18,7 @@ __all__ = ['autocorr', 'autocov', 'waic', 'loo', 'hpd', 'quantiles', - 'mc_error', 'summary', 'df_summary', 'compare', 'bfmi', 'r2_score'] + 'mc_error', 'summary', 'compare', 'bfmi', 'r2_score'] def statfunc(f): @@ -971,12 +971,6 @@ def summary(trace, varnames=None, transform=lambda x: x, stat_funcs=None, axis=1, join_axes=[dforg.index]) -def df_summary(*args, **kwargs): - warnings.warn("df_summary has been deprecated. In future, use summary instead.", - DeprecationWarning, stacklevel=2) - return summary(*args, **kwargs) - - def _calculate_stats(sample, batches, alpha): means = sample.mean(0) sds = sample.std(0) From 7c8df99773c220003e2ad4eaed2d6d2ba6fd8abc Mon Sep 17 00:00:00 2001 From: aloctavodia Date: Tue, 6 Mar 2018 16:42:17 -0300 Subject: [PATCH 2/2] fix typo --- docs/source/notebooks/BEST.ipynb | 4 +-- docs/source/notebooks/Bayes_factor.ipynb | 4 +-- .../notebooks/GLM-model-selection.ipynb | 4 +-- .../GLM-negative-binomial-regression.ipynb | 12 ++++++-- .../notebooks/GLM-poisson-regression.ipynb | 28 +++++++++++++------ docs/source/notebooks/SMC2_gaussians.ipynb | 4 +-- docs/source/notebooks/model_averaging.ipynb | 4 +-- docs/source/notebooks/model_comparison.ipynb | 4 +-- 8 files changed, 42 insertions(+), 22 deletions(-) diff --git a/docs/source/notebooks/BEST.ipynb b/docs/source/notebooks/BEST.ipynb index 1b08373a29..b325236ae0 100644 --- a/docs/source/notebooks/BEST.ipynb +++ b/docs/source/notebooks/BEST.ipynb @@ -16,7 +16,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Runing on PyMC3 v3.3\n" + "Running on PyMC3 v3.3\n" ] } ], @@ -27,7 +27,7 @@ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "plt.style.use('seaborn-darkgrid')\n", - "print('Runing on PyMC3 v{}'.format(pm.__version__))" + "print('Running on PyMC3 v{}'.format(pm.__version__))" ] }, { diff --git a/docs/source/notebooks/Bayes_factor.ipynb b/docs/source/notebooks/Bayes_factor.ipynb index 7b9fd6e8d4..3128852b15 100644 --- a/docs/source/notebooks/Bayes_factor.ipynb +++ b/docs/source/notebooks/Bayes_factor.ipynb @@ -16,7 +16,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Runing on PyMC3 v3.3\n" + "Running on PyMC3 v3.3\n" ] } ], @@ -31,7 +31,7 @@ "from scipy.special import betaln\n", "from scipy.stats import beta\n", "\n", - "print('Runing on PyMC3 v{}'.format(pm.__version__))" + "print('Running on PyMC3 v{}'.format(pm.__version__))" ] }, { diff --git a/docs/source/notebooks/GLM-model-selection.ipynb b/docs/source/notebooks/GLM-model-selection.ipynb index 05f9e03f44..e1a2caa48c 100644 --- a/docs/source/notebooks/GLM-model-selection.ipynb +++ b/docs/source/notebooks/GLM-model-selection.ipynb @@ -16,7 +16,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Runing on PyMC3 v3.3\n" + "Running on PyMC3 v3.3\n" ] } ], @@ -31,7 +31,7 @@ "from ipywidgets import interactive, fixed\n", "\n", "plt.style.use('seaborn-darkgrid')\n", - "print('Runing on PyMC3 v{}'.format(pm.__version__))\n", + "print('Running on PyMC3 v{}'.format(pm.__version__))\n", "rndst = np.random.RandomState(0)" ] }, diff --git a/docs/source/notebooks/GLM-negative-binomial-regression.ipynb b/docs/source/notebooks/GLM-negative-binomial-regression.ipynb index 40aa8b4a2b..4ad5d905ca 100644 --- a/docs/source/notebooks/GLM-negative-binomial-regression.ipynb +++ b/docs/source/notebooks/GLM-negative-binomial-regression.ipynb @@ -12,11 +12,19 @@ "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/osvaldo/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Runing on PyMC3 v3.3\n" + "Running on PyMC3 v3.3\n" ] } ], @@ -30,7 +38,7 @@ "plt.style.use('seaborn-darkgrid')\n", "import seaborn as sns\n", "import re\n", - "print('Runing on PyMC3 v{}'.format(pm.__version__))" + "print('Running on PyMC3 v{}'.format(pm.__version__))" ] }, { diff --git a/docs/source/notebooks/GLM-poisson-regression.ipynb b/docs/source/notebooks/GLM-poisson-regression.ipynb index 86d790fa3a..50e611f2c1 100644 --- a/docs/source/notebooks/GLM-poisson-regression.ipynb +++ b/docs/source/notebooks/GLM-poisson-regression.ipynb @@ -10,10 +10,16 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on PyMC3 v3.3\n" + ] + } + ], "source": [ "## Interactive magics\n", "%matplotlib inline\n", @@ -29,7 +35,7 @@ "\n", "plt.rcParams['figure.figsize'] = 14, 6\n", "np.random.seed(0)\n", - "print('Runing on PyMC3 v{}'.format(pm.__version__))" + "print('Running on PyMC3 v{}'.format(pm.__version__))" ] }, { @@ -119,7 +125,9 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# decide poisson theta values\n", @@ -394,7 +402,9 @@ { "cell_type": "code", "execution_count": 10, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "(mx_en, mx_ex) = pt.dmatrices(fml, df, return_type='dataframe', NA_action='raise')" @@ -741,7 +751,9 @@ { "cell_type": "code", "execution_count": 16, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "with pm.Model() as mdl_fish_alt:\n", diff --git a/docs/source/notebooks/SMC2_gaussians.ipynb b/docs/source/notebooks/SMC2_gaussians.ipynb index d3ff078f5e..7d9d76e889 100644 --- a/docs/source/notebooks/SMC2_gaussians.ipynb +++ b/docs/source/notebooks/SMC2_gaussians.ipynb @@ -16,7 +16,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Runing on PyMC3 v3.3\n" + "Running on PyMC3 v3.3\n" ] } ], @@ -34,7 +34,7 @@ "test_folder = mkdtemp(prefix='SMC_TEST')\n", "\n", "plt.style.use('seaborn-darkgrid')\n", - "print('Runing on PyMC3 v{}'.format(pm.__version__))" + "print('Running on PyMC3 v{}'.format(pm.__version__))" ] }, { diff --git a/docs/source/notebooks/model_averaging.ipynb b/docs/source/notebooks/model_averaging.ipynb index 2a7da93734..ccc2f12868 100644 --- a/docs/source/notebooks/model_averaging.ipynb +++ b/docs/source/notebooks/model_averaging.ipynb @@ -16,7 +16,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Runing on PyMC3 v3.3\n" + "Running on PyMC3 v3.3\n" ] } ], @@ -28,7 +28,7 @@ "import matplotlib.pyplot as plt\n", "\n", "plt.style.use('seaborn-darkgrid')\n", - "print('Runing on PyMC3 v{}'.format(pm.__version__))" + "print('Running on PyMC3 v{}'.format(pm.__version__))" ] }, { diff --git a/docs/source/notebooks/model_comparison.ipynb b/docs/source/notebooks/model_comparison.ipynb index 094426b559..7b63264013 100644 --- a/docs/source/notebooks/model_comparison.ipynb +++ b/docs/source/notebooks/model_comparison.ipynb @@ -16,7 +16,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Runing on PyMC3 v3.3\n" + "Running on PyMC3 v3.3\n" ] } ], @@ -27,7 +27,7 @@ "import matplotlib.pyplot as plt\n", "\n", "plt.style.use('seaborn-darkgrid')\n", - "print('Runing on PyMC3 v{}'.format(pm.__version__))" + "print('Running on PyMC3 v{}'.format(pm.__version__))" ] }, {