Datasets:

codeparrot

/

github-jupyter

like 5

mprat/learningjulia

notebooks/06-image-stitching-part-2.ipynb

1

5758533

mit

wagnerf42/ws-simulator

result/strategy/simulation_ipynb/simulation.ipynb

1

604524

{ "cells": [ { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from collections import defaultdict\n", "from math import log2\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def load_file(filename):\n", " a = np.loadtxt(filename, dtype='str', comments='#')\n", " rsp = a[:,0]\n", " latencies = a[:,1]\n", " times = a[:,2]\n", " processors = a[:,3]\n", " work = a[:,4]\n", " i_steals = a[:,16]\n", " e_steals = a[:,17]\n", "\n", " return rsp, latencies, times, processors, work\n", "\n", "directory = \"/home/khatiri/these/projet/ws-simulator/Simulation/simulation/simulation/\"\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def compute_average(values, latence):\n", " average = defaultdict(int)\n", " run_number = defaultdict(int)\n", " \n", " for i in range(len(rsp)):\n", " if int(latencies[i]) == latence:\n", " run_number[float(rsp[i])] += 1\n", " average[float(rsp[i])] += int(values[i])\n", " \n", " for cle in average:\n", " average[cle] /= run_number[cle]\n", " return average\n", "\n", "def compute_overhead_for_latence(data, latence):\n", " rsp, latencies, times, processors, work = data\n", " average = defaultdict(int)\n", " run_number = defaultdict(int)\n", " \n", " for i in range(len(rsp)):\n", " if int(latencies[i]) == latence:\n", " run_number[float(rsp[i])] += 1\n", " average[float(rsp[i])] += float(int(times[i]) - int(work[i])/int(processors[i]))\n", " \n", " for cle in average:\n", " average[cle] /= run_number[cle]\n", "\n", " return average, min(average.keys(), key=lambda x : average[x])\n", "\n", "def compute_overhead(data, latence, variable):\n", " rsp, latencies, times, processors, work = data\n", " average = defaultdict(int)\n", " run_number = defaultdict(int)\n", " average = 0\n", " run_number = 0\n", " \n", " for i in range(len(rsp)):\n", " if float(rsp[i]) == variable and float(latencies[i]) == latence:\n", " run_number += 1\n", " average += float(int(times[i]) - int(work[i])/int(processors[i]))\n", " \n", " return average/run_number" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "def plot_for_best(filename):\n", " best = dict()\n", " data = load_file(filename)\n", " latencies = data[1]\n", " for latence in sorted(set(latencies), key=lambda x: int(x)):\n", " avg_overhead, minimum = compute_overhead_for_latence(data, int(latence))\n", " best_avg_overhead = compute_overhead(data, int(latence), minimum)\n", " best[latence] = best_avg_overhead\n", " return best" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "def latence_for_best_param(filename):\n", " data = load_file(filename)\n", " latencies = data[1]\n", " best = dict()\n", "\n", " for latence in sorted(set(latencies), key=lambda x: int(x)):\n", " overhead, minimum = compute_overhead_for_latence(data, int(latence))\n", " #plt.subplot(223)\n", " #plt.plot(overhead.keys(), overhead.values())\n", " best[latence] = minimum\n", " return best\n", "\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f1f54fc8400>" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1188x612 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "fig.set_size_inches(16.5, 8.5, forward=True)\n", "\n", "best = latence_for_best_param(directory + \"vss_proba_32_500000000\")\n", "plt.subplot(221)\n", "plt.plot(best.keys(), best.values(), \"o-\", label=\"32_500000000\")\n", "\n", "best = latence_for_best_param(directory + \"vss_proba_32_100000000\")\n", "plt.subplot(221)\n", "plt.plot(best.keys(), best.values(), \"x-\", label=\"32_100000000\")\n", "\n", "best = latence_for_best_param(directory + \"vss_proba_32_50000000\")\n", "plt.subplot(221)\n", "plt.plot(best.keys(), best.values(), \"x-\", label=\"32_50000000\")\n", "\n", "best = latence_for_best_param(directory + \"vss_proba_32_10000000\")\n", "plt.subplot(221)\n", "plt.plot(best.keys(), best.values(), \"x-\", label=\"32_10000000\")\n", "plt.legend()\n", "\n", "best = latence_for_best_param(directory + \"vss_proba_16_500000000\")\n", "plt.subplot(222)\n", "plt.plot(best.keys(), best.values(), \"o-\", label=\"16_500000000\")\n", "\n", "best = latence_for_best_param(directory + \"vss_proba_16_100000000\")\n", "plt.subplot(222)\n", "plt.plot(best.keys(), best.values(), \"x-\", label=\"16_100000000\")\n", "\n", "best = latence_for_best_param(directory + \"vss_proba_16_50000000\")\n", "plt.subplot(222)\n", "plt.plot(best.keys(), best.values(), \"x-\", label=\"16_50000000\")\n", "\n", "best = latence_for_best_param(directory + \"vss_proba_16_10000000\")\n", "plt.subplot(222)\n", "plt.plot(best.keys(), best.values(), \"x-\", label=\"16_10000000\")\n", "plt.legend()\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A faire :\n", "\n", "Tourner des résultats pour rsp entre 0.001 et 0.02 pour bien tracer les courbes\n", "\n", "Afficher l'interval de confiance\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## overhead en fonction proba " ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "min for vss_proba_16_10000000 : 0.09999999999999999\n", "min for vss_proba_16_500000000 : 0.1\n" ] }, { "data": { "image/png": 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cGPOuMWan83uYc39jjHnAGLPLGLPRGDO2OwIdFOwHwMEaLfUk0p/0hXkQZGAZ6L9zfSWvn4ojwFuzWIu4gYH+t1T6ju7+XTxjgWyt/cJam2WtzQLGAXXA68A9wPvW2mHA+87nAJcCw5xfi4BHuiPQoy3IBw9rqSeR/sLPz4/y8nIlWek11lrKy8vx8/NzdSgu01fy+oke/SiflfllOPy/LJBX5pfx6Ef5PXE5ETkHyt/SV/REXu9qF+vZQL61ttAYMxeY4dy+BPgQ+BEwF3jGObX258aYUGNMjLV237kE6uftSYifl1qQRfqR+Ph4iouLOXTokKtDkQHEz8+P+Ph4V4fRV7gsr58oI97B4hfWMyYuhOr6Zlbml7H4hfU8uCC7Oy8jIt1A+Vv6ku7O610tkK8BXnQ+HnRMctwPDHI+jgOKjjmm2LntnBPpi4smER08cO/6i/Q33t7eJCUluToMkYHMpXn9WFNSInlwQTbfeW4dF6ZFdxTHU1Iiu/MyItINlL+lP+v0Mk/GGB9gDvDKia857yp3qY+FMWaRMSbXGJPb2btPo2MdRAX7duUyIiIichJ9Ia+faEpKJAsnD+HVtSVcNzFRxbGIiPS6rqyDfCmwzlp7wPn8gDEmBsD5/aBzewmQcMxx8c5tx7HWPm6tzbHW5kRFRXUqgDUFFTzzWUEXQhYREZFTcHleP9HK/DKe/byQi9IG8deVBazMLzur84iIiJytrhTI1/JlNyyAN4GFzscLgTeO2X6Dc9bLSUB1d41Tem/rAe7/xzZNCCAiInLuXJ7Xj3V0zPGfrsnmgy8OMnNEezdrFckiItKbOlUgG2MCgQuBvx2z+TfAhcaYncAFzucAbwO7gV3AE8B3uyvY6BA/mlrbtD6iiIjIOegref1YG4ureXBBNtOGR5EaHUxVfTMPLshmY3F1T1xORETkpDo1SZe1thaIOGFbOe2zX564rwVu75boThAd/OVST2GBPj1xCRERkX6vr+T1Y902PaXjcVpMCB/vPMSUlEiNQxYRkV7VlS7WLjcopH0G64OHtdSTiIhIf5UWG8Khw43K9yIi0uvcqkA+2oJ8oKbRxZGIiIhIT0mLCQFg+77DLo5EREQGmq6ug+xS8WH+fPbjWUQGaaknERGR/io7MZRVP5ndcWNcRESkt7hVgezl6UGMw9/VYYiIiEgP8vP2xM/b09VhiIjIAORWXawBXli1l6Wr97o6DBEREelB/96yn/v/sdXVYYiIyADjdgXyW3mlvLK22NVhiIiISA/auq+GJ1fsoa6pxdWhiIjIAOJ2BXJ0iK9mtRQREenn0mJCsBa279dEXSIi0nvcrkAeFOLHwZpG2pdlFBERkf4oLbZ9JuutpTUujkRERAYStyuQo4N9aWxpo6ZeXa5ERET6q7hQf0L8vNi6TwWyiIj0HvcrkEP88DBQVqu1kEVERPorYwyZCaHUN7W6OhQRERlA3GqZJ4BL0wdz2a++hqeHcXUoIiIi0oOeuXkCxijfi4hI73G7Atnb0+0avUVEROQsqDgWEZHe5nbVZmub5Sevb2LZ5n2uDkVERER60MHDDVz7+Of8e8t+V4ciIiIDhNsVyJ4ehjfWl7BqT4WrQxEREZEeFOrvw9rCStbtrXJ1KCIiMkC4XYEMzqWeDmuSLhERkf7Mx8uDYYOC2FJa7epQRERkgHDLAjkq2JeDNQ2uDkNERER6WFpMCFtLa7DWujoUEREZANyyQI5WC7KIiMiAMDo2hPLaJg4p74uISC9wywI5NtQPT81sKSIi0u9lJYYxfXgURxpbXB2KiIgMAJ0qkI0xocaYV40x240x24wxk40x4caYd40xO53fw5z7GmPMA8aYXcaYjcaYsd0d9I8vHcXyH8zo7tOKiIgMCH0tr59OVkIoS26eQHJUUG9eVkREBqjOtiD/CVhmrR0JZALbgHuA9621w4D3nc8BLgWGOb8WAY90a8QiIiJyrtwur7e0trnisiIiMsCcsUA2xjiAacCTANbaJmttFTAXWOLcbQlwhfPxXOAZ2+5zINQYE9OdQecfOsLNf11DXpGWfRAREemKvpjXz+S/39jMxf/v4968pIiIDFCdaUFOAg4BTxtj1htj/mKMCQQGWWv3OffZDwxyPo4Dio45vti57TjGmEXGmFxjTO6hQ4e6HPjy7QfZU1bb5eNEREQGuD6Z108nMsiX3WW11GocsoiI9LDOFMhewFjgEWttNlDLl92uALDtay90af0Fa+3j1toca21OVFRUVw4lOtgXgIOHtdSTiIhIF/W5vH4maTEhWAvb99d063lFRERO1JkCuRgottaucj5/lfbEeuBoFyvn94PO10uAhGOOj3du6zZBvl74e3tyoEZLPoiIiHRRn8vrZ5IWGwLA1lIVyCIi0rPOWCBba/cDRcaYEc5Ns4GtwJvAQue2hcAbzsdvAjc4Z72cBFQf02WrWxhjGBTiq7WQRUREuqgv5vUziXH4ERrgzdZ9KpBFRKRneXVyvzuA540xPsBu4Cbai+uXjTHfAgqBq537vg18DdgF1Dn37XbpcQ4c/p0NX0RERI7R5/L66RhjuG16CnGh/r19aRERGWBM+zAj18rJybG5ubmuDkNERAYwY8xaa22Oq+PoD5TXRUTE1c42r3d2HWQRERERl7HWUlRRR01Ds6tDERGRfsxtC+S3N+3j0j99whEt+SAiItLv7ThwhPN/9wHLtx08884iIiJnyW0L5MaWVrbtq+FgjZZ6EhER6e+SowLx8fLQRF0iItKj3LZAjg72A9BSTyIiIgOAt6cHIwYFa6knERHpUW5bIA8K8QXg4GG1IIuIiAwEaTEhbN1XQ1+YYFRERPonty2Qo5wtyIe0FrKIiMiAMDouhIraJvUeExGRHuO2CwmH+HkxOTmCsAAfV4ciIiIivWDmiGgeWuBLkJ/bfnwREZE+zm0zjDGGFxdNcnUYIiIi0ksSwgNICA9wdRgiItJHPfpRPhnxDqakRJ71Ody2i7WIiIgMPJtLqvnwCy31JCIiX5UR72DxC+tZmV921udw6wL5V//cyjceXuHqMERERKQXPPpRPr/8xxbufXNLx7aV+WU8+lG+C6MSEZG+YkhEIPPHJ/DtJbl4BkfGns053LaLNUBzq2XHgSOuDkNERER6QUa8gz+9t5P65lYONzSzqaSaxS+s58EF2a4OTUREepm1ltY2i5enB5tLqvnO82spqqgHIDLIB8/A0JizOa9bF8iDQvw40thCbWMLgb5u/VZERETkDKakRLJ4Vgq//9cO7ntzCx98cYgHF2Sf01gzERFxD9ZaCsrrWLW7nFV7Kli1u5zrJg/huzNSiQ/zJy0mhJumJOHn7cEf/rWD1tqqfWdzHbfuYv3lWsha7kFERGQguGZ8IgZ4bV0J101MVHEsItJPWWupqmsCoLXNMv33HzLzDx9yz9828fGOQ2QlhjJiUDAAoQE+PHZ9DiNjgvnDv3fw4DezaT1cVno213XrZtdo51rIB2saSIoMdHE0IiIi0tO+OHAYL09DoI8Xz63ay6SUCBXJIiL9QFubZefBI6zaU86q3RWs2lNBYrg/f/vuVDw9DN8YG0dkkC+TksNJiQrCGPOVc2wsrj7nnkVuXSAPiQjg8owYda8WEREZAFbml7H4hfX8cX4Ws0cOYn1RZccYZBXJIiLupa3NsrvsCKnR7a3Ad720gbfy2ht9Yx1+nD8skqmpX/5t/88Lhp/xnLdNTznnuNy6skwID+DBBWNdHYaIiIj0ghNbBqakRPLggmw2FlerQBYR6eNa2yzb9tXwuXMM8ZqCCqrqmln7swuICPLlyrFxnD8sksnJEcSH+Z+0hbg3uG2BfOwi0G1tFg8Pw8r8MjYWV3fLnQMRERHpW47N77kFFfz09c08ddN4FcciIn1QS2sbm0trSAwPIDzQh1fXFvGj1zYBMDQigIvTBjMxORxfb08AZoyIdmW4HTpVIBtjCoDDQCvQYq3NMcaEAy8BQ4EC4GprbaVpL/X/BHwNqANutNau6+7Ajy4CHeTrxdjEUK4en6ClHkRERDqhL+b1rhoU4seOg4d5JbeoU93uRESkZ7W0tpFXXM2qPeV8vruCtQUV1Da18rsrM7h6fALTh0fzp2uymJgUwWCHn6vDPaWuzGI901qbZa3NcT6/B3jfWjsMeN/5HOBSYJjzaxHwSHcFe6yj3apKq+pZXVChMUgiIiJd06fyelclhAdwXmokr+QW09pmXR2OiMiA09jSyuo9FazfWwlARV0TVz6ykt8t+4J9VfX8x9g4/nxtNrNHtbcMD3b4MTcrrk8Xx3BuXaznAjOcj5cAHwI/cm5/xlprgc+NMaHGmBhr7VmtQ3U6U1IiSY91sKG4iu/OSFFxLCIicvZcnte7ar6z99inu8qYPjzK1eGIiPR7q3aXszK/nFV7ylm/t4rGljYuTBvEEzfkEB3sx9M3jicj3kFEkK+rQz1rnW1BtsC/jTFrjTGLnNsGHZMc9wODnI/jgKJjji12but2K/PLyC87AsCznxWyMr+sJy4jIiLS3/TJvN5VF6YNIizAm5fW7HV1KCIi/U5dUwuf7ixj6eov/8b+33e288DynRxpbOG6SUN4/Ppx/H5eRsfrM0dGu3VxDJ1vQT7PWltijIkG3jXGbD/2RWutNcZ0qX+TMyEvAkhMTOzKocCXSz38+dpsbn12LeelRqqbtYiISOf0ubx+Nny9PPnhJSMJ9ffuleuJiPR3eUVVLNuyn1W7y9lYXE1LmyXQx5Mrx8Xj7enB/1yVSVSwL45+/He3UwWytbbE+f2gMeZ1YAJw4GgXK2NMDHDQuXsJkHDM4fHObSee83HgcYCcnJwuDx46dqmHH186kuGDg7l+yhAt9SAiInIGfTGvn61rJ/ROMS4i0t9U1zeTW1DBqj0V3D4zFYe/N5/sPMQTH+8mI97Bt6clMzEpnJyh4Xh7tnc8To0OcnHUPe+MBbIxJhDwsNYedj6+CPgF8CawEPiN8/sbzkPeBBYbY5YCE4HqnhindOxSDzdOTep4rOJYRETk1PpqXj8XB2saeGfzfm6YPMRl62aKiLiDveV1LPmsgFV7ytlaWkObBR9PDy5MG8T4oeFcP3koN5+XRICP264GfM46884HAa87E44X8IK1dpkxZg3wsjHmW0AhcLVz/7dpXwpiF+3LQdzU7VGfwFrLppJqPIwhPc7R05cTERFxZ30+r3fVhzsOce+bW0iLDWH80HBXhyMi0ieUH2lk9Z72FuLpI6KYOSKauuYWnvu8kOzEUO6YNYyJyeGMTQzDz7kWcX/uOt1ZZyyQrbW7gcyTbC8HZp9kuwVu75bouuA7z60jPS6Ex67POfPOIiIiA5S75PWuuDwjhl+8tZWlq4tUIIvIgNbU0sYv/rGFVbsr2HmwfTJjP28P4kL9mTkimuHRwWy87yJ8vTxdHGnf1S/azo0xTBseyT/y9tHc2tbRR15ERET6vwAfL+ZkxfK3dcX899fT1AIiIgPC/uoGVu0p5/PdFQT5evLTy9Lw8fIgt6CSmFB/rsiOY1JyOGPiQvHxaq+PPDwMvh4qjk+nXxTIANOHR/Hi6iLW761iQpLuHouIiAwk14xP4IVVe3kzr5TrJw1xdTgiIj3mweU7eXVtMQXldQAE+3pxcfrgjtffuet8zcdwDvpNgTwlNRJPD8PHOw6pQBYRERlgxsQ5GBPnoLCs1tWhiIh02qMf5ZMR7zhuouGV+WXkFVVxeUYsn+0uZ9XuCjaXVPOPO8/D29ODplZLanQw100awqTkCEbFhODp8WVBrOL43PSbAjnEz5uxiaGszC8DRrg6HBEREelFxhhe+86Ujm6EIiLuICPeweIX1vPgtdlMSo7g8z3lLHpmLb5eht8u+wKA8EAfJgwNp6a+mYggX+6+cLiLo+7f+k2BDPC/V2cRGeTr6jBERETEBY4Wx4cbmgn20zhkEem7Wtss2/bVsGP/YYZFB/HNJ1cxJzOWT3aW8YOLhrOmoJKJyeFMSo4gNSoIDw+1CveWflUgJ4QHuDoEERERcZFHP8pnb3kt/9i4j1U/uQB/H09W5pexsbia26anuDo8ERnAGppbqWtqJTzQh8LyWi5/4FMON7YAEBfqz7DoIN4bZBFQAAAgAElEQVTYUMqds1K5cWoSN05NcnHEA1e/KpAB/vLJbuqaWrlz9jBXhyIiIiK9KCPewUMf7OJwQwtvb9pHTKhfe9fFBdmuDk1EBpjqumZyCytYU1DJmoIKNhVXc82EBH4xN534sAC+MTaOsUPCyBkaTmF5LYtfWM+ds1J5btVeJqVEHDcmWXpXvyuQN5VUs2JXGYtnpqorgoiIyAAyJSWSx64bx3VPruKX/9yKAR765lh90BSRHldaVc++6nrGDWmfLPjSP31MaXUD3p6GMXEObpo6lFkjowHw9DD8fG460D4h19EbeVNSIpmUEnHcc+l9/a5Anj48ijc2lLJ1Xw3pcQ5XhyMiIiK9aEpqJHOz4nh9fQmp0YFMTo5wdUgi0g8VlNWyIr+M3IJKVu+poKSqnrhQf1bcMwuA//56GqEBPmTGh+Lvc+p1hzcWVx9XDE9JieTBBdlsLK5Wgewi/a5APn9YFAAf7TikAllERGSAWZlfxkc7DjE1NYKVu8p5c0Mpc7PjXB2WiLixppY2NpdWs66wkpumJuHpYXjik908v2ovkUG+TEgK41vnJTEhKRxrLcYYLkmP6dS5TzY/wpSUSBXHLtTvCuSoYF9Gx4bw0Y5D3D4z1dXhiIiISC85tqvi5OQI/rVlPz95fTNRIb76sCkiXbL70BH+vr6ENQWVrC+qpKG5DYBpw6MYPiiYW6el8O3zkxkSEaB1h/uZflcgA1wyejCbS6tpa7MahywiIqf06Ef5ZMQ7VDz1Eyd2VbwkPYZgX2+e+Hg3QyICiQv1d3GEItIXHTrcSG5B+4RaV2THkhEfSmFFHQ9+sIvRsQ6unZDIhKHhjBsaRnSwHwCJEVo9p7/qlwXyHZrBWkREOiEj3qFZjvuRk3VVHBoVyJqCSr773FpeunUyft6nHgsoIgNHZW0Tv35nG7kFlewuqwXA18uDtNgQMuJDmZISwcb7LibIt1+WS3Ia/fpfvL6p9bSD4kVEZOCx1rK3oo4NRVXkFVUTGeTDt/6ai2dwZKyrY5PuFxfqz/9cncmtz67l529t4dffyHB1SCLSi1rbLNv21bCmoILcgkpGDg7mjtnDCPT14pOdZYyOdTB/fALjk8JJj3Xg4+UBgK+XJ6qNB6Z++8/+87e28N62A3z8XzM1LkBEZAA7WNNAXnE1Qb5eTE6JoKa+hem//xAAP28PRsc6mJTsz+7A0M7NqCJu5+LRg/nujBQe/jCfrIRQ5o9PdHVIItJDjh1i+b2XNvDu1gMcaWwB2m+YpUYHAeDj5cHKe2apTpCv6LcFcnJUEEUrCthTVktyVJCrwxERkV701Kd7WL2ngrziKvZVNwAwe2Q0k1MicAR486drshgWHczwQUGsLqhg8Qvraa2t2ufisKUHff+iEby9aR/3vrGFi9IGExboA7RP7LWxuPqk3bNFpO+rqmtibWElqwsqWLOngsMNLbx793QAIgJ9mJsVy4SkcHKGhn9lHgIVx3Iy/bZAnn7Mck8qkEVE+p/Glla27TtMXlEVecVVNLda/nxt+1jitzft49CRRsYPDScj3kFWQiijY79c+m9uVvuyP8fOejz1v8tKXfJGpFd4ehh+/LVR/NcreWzbX8OUlMjj/v1FxD2UVtUzOMQPDw/D//77Cx5YvgsAb0/DmDgHs0ZF09LahpenBz+7PM3F0Yo76nSBbIzxBHKBEmvt5caYJGApEAGsBa631jYZY3yBZ4BxQDkw31pb0O2Rn0FiRABJkYF8tOMQN01N6u3Li4hIN2ptsxSU15LivOH567e38dSKPTS3WoCOdSiPrj/5/Lcn4ut15jkoTpz1eCBxt7zeHS4ePZhgPy8Wv7CemSOiWL79IA99c+yA/PcXcQdtbZZdh46wek9FxyzTJVX1vHf3NFKjg5mQFMH3L/RgfFI4WQmhmoRPukVXWpDvArYBIc7nvwX+aK1daox5FPgW8Ijze6W1NtUYc41zv/ndGHOnTR8exdI1e2lobtV/GBERN1J2pLGji3ReURWbS2o40thC7s8uIDLIl9FxDm4+L4ms+FAyE0KJcfgd11WuM8UxnHzW4wHE7fJ6d5iSEslFaYNYuqaI0bEhTE6OcHVIIuLU1NLG5tJqBof4ERvqz/LtB7nlmVzgyxuht5yfRGhA+xCJ84ZFct4w3eCS7tWpAtkYEw9cBvwKuNu0fwqZBSxw7rIEuI/2RDrX+RjgVeBBY4yx1truC7tzrhwbz4jBwfT+lUVEpLMqa5vIK65iY3E1/5EdR0J4AMu3HeSHr23E29OQFhPCf2THkZkQiq9zdtE5mbHMydSk02fLXfN6d1iZX8a/tx4gZ0gYuYWV3PPaJn47TzNbi7hCc2sbn+WXk1tQweqCCjYUVdHQ3MaPLhnJd2akMH5oOL+bl8GEoeEMiQjQmGHpFZ1tQf5/wA+BYOfzCKDKWtvifF4MxDkfxwFFANbaFmNMtXP/sm6JuJMe/SifjPj2hb2P0kQcIiJ9Q1FFHb/71xfkFVWxt6IOAGMgJSqIhPAAZo+K5o3bpzIyJrjTrcHSJW6X17vDsWOOJyVFcN2Tq3gpt4ioYB9+cPFIV4cn0u8dOtxIbkEF3p4eXJA2iNY2yy1Lcmlpa2N0bPvn9glDw5mQFA6AI8Cbq3MSXBy1DDRnLJCNMZcDB621a40xM7rrwsaYRcAigMTE7l9uISPeweIX1vPLuaNpsxAR5KOJOEREelFzaxs7Dhwmr6iajcVVbCiq4htj41g0LQV/H0/WFlSQmRDKtRMSyUxwMCbOQbCfNwARQb5EBPm6+B30T+6a17vDiWPOn75pPHMfXMHDH+ZzdU4iiREBLo5QpP/5x8ZSPvriELmFlewpqwVgUnI4F6QNws/bk5duncSwQcEEadFh6SM685s4FZhjjPka4Ef7WKU/AaHGGC/n3eZ4oMS5fwmQABQbY7wAB+2TehzHWvs48DhATk5Ot3fTmpISyYMLsvnWklzqm1px+HvzyHWaiENEpCdYayksr+NIYwvpcQ5aWtsY98t3qWlob5B0+HuTmRDKYEf7EhuRQb6s/PFsV4Y8kLllXu8OJ/Yg8/Xy5KVFk3lv2wEVxyKddLSX5rGfqVfml7GhqIppw6JYU1DB3oo67v36aABeX1fC2r2V5AwJ55rxCYxPCif9mFUFshPDev09iJzOGQtka+2PgR8DOO80/8Ba+01jzCvAPNpnvFwIvOE85E3n88+cry931TilKSmRXDs+gadWFBAf5q/iWESkG326s4xVe8rZUNQ+fri6vpnxQ8N45bYpeHl6cMesYUSH+JIZH6qxY32IO+f1nuAI8ObKcfEAbC6pJjrEl+hgPxdHJdJ3He2lebQ3xp+X7+RP7+/Ey8Pwu2VfABAX6s8PLx6Jv48n/3t1FsF+Xnh4KAeIeziXvgw/ApYaY+4H1gNPOrc/CTxrjNkFVADXnFuIZ29lfhl/31DKmLgQNpXU8FZeKV/XpC4iIl1yuKGZTcXV5BVXU1pVzy+vSAfgqRV7+GjHIYYPCubS9MFkJoSSnRjacdy3pyW7KmQ5O30+r/ek+qZWbnx6DTEOP166dRIBPuruKXJUZW0TW0pr2Lqvmi2lNfh5e/Cd59axcPIQnvx0DzEhfkwfEcX4oeHkDA0nLtS/41hHgLcLIxfpOtMXbgLn5OTY3Nzcbj3nsRNxJIYHMO13H+Dt6cHTN41XS7KIyCk0trTi4+mBMYaX1uzliU/2kH/oSMdqAEMjAlj2n9Pw8/ZkX3U9of4++Pv0j0m0jDFrrbU5ro6jP+iJvN4b3t92gFuW5JKVEMort03Gy7N91nRN8ikDhbWWkqp6tpTWMGpwCIkRAXz4xUFufHpNxz6xDj/SYh1Eh/jywqq93DErle9fNMKFUYuc3Nnm9X57e/TEiTiuGpfAnrIj5BVVqUAWEQHa2iz5h46QV1xNXlEVG4ur2Lqvhn9/bzpJkYH4eXsyJDyAOZmxZCaEkhHnICzQp+P4GIf/ac4u4n5mjxrEjVOH8vSKAm57bi1P3JDDZ7vLNcmn9GtVdU08uHwXW/fVsKW0hur6ZgB+dtkobjk/mfQ4Bz++dCSjYx2kxYYQHujT0RB156xUnlu1l8kpEfp8Lf1Gv21BPlFLa1vHnWARkYHGWktpdQMbi6oYHesgMSKAZZv3c9tzawEI8vUiPS6EzIRQbpg89LjucQOFWpC7j7u2IB+1+IV1/GPjPuZmxfLJzrLjbriLuKOG5la27z/MltL2LtJbS2uYNjyKuy8cTkNzK2N/+S6p0UGMjg0hLdbB6NgQRg0OOWkPoWN7aU5JifzKc5G+Qi3IZ3C0ON5bXkeIvxehAT5nOEJEpO851eyhJ+v+WV3fzJKVBeQVVZFXXE3ZkUbgy1aBCUnh/OGqTDLjHSRHBeGpCVREAHjgmmwqa5t4Y0Mpd85KxdfLg8rapuN6UIj0VVV1TWwtraGxpY2ZI6MBOO+3H3TkgBA/L9JiQxgU0r6Unp+3J5vuu7jTOeDEXppHV47ZWFytAln6hQFTIAMcPNzA7P/9kFunpfCDizVWQkTcz4mzh67ML+P259dx1+xh/OWT3eQVV5MZ7+CW85Px8fTgz8t3MiQikGnDI8lKCCUjPpRRMcEAhAf6MM85e6+IfOnzPeVs23+YO2el8uznhTz7eSEtbZbbpqfQ1mYZNzSsUzepRHrLC6v28sEXB9laWkNJVT0Aw6KDOgrkH10ygmA/b0bHhhAf5v+VVQW6coP0ZL/nU1IiVRxLvzGgCuToYD8uTBvEkpUFfHtaMg5/zaonIu5lwtBwfjl3NItfWM91ExN5+MN82qzlvre2Au2Tp6REBQLg7+NJ3r0XaTZekS44sbvopJQIvvPcOoZFB/P7f31BqL83j3yUz2PXj+P8YVHH7S/SU1rbLHvKjrCltMb5VU1pVQPLvz8dYwxrCyvJP3iE7MRQrps0hNGxIYyODek4/qqcBBdGL+JeBtynpsUzh/H2pv0sWVnAnbOHuTocEZHTKqmqZ11hpbObdBWbSqoZEh7IdRMTeWD5LnKGhDE5JYLM+FAyEhxfWb9VxbFI15ys++gj141lY3E1P/7aSH77znZyCyv57vPruGnKUJ5btVdjL6VbNTS3suPAYbaU1nBFVhz+Pp784d9f8MiH+QD4eHowfHAQE4aG09Dchr+PJ7+fl6F1hkW6yYD75JQWG8IFowbx1Io93HxeEkG+A+5HICJ9VEVtE3nFVWzbV8N3pqdgjOH3y7bz9w2l+Hp5kB7n4NoJiQT6evHcqr0ds4fefdFwfTgX6SZn6j76ym2TWb2ngk93lfHA8l38R3Yc2QlhvR2m9BPWWowxbC6p5qlP97CltIZdh47Q2tY+ie7IwcFkJ4Zx2ZgYUqLaJ9FKjQ7C+4SJZ1Uci3SfAVkd3jErlXmPriS3oIIZI6JdHY6IDGCf7y7n+VV7ySuqYm9FHQDGwJVj4xkU4sdtM1K45fxkRgwOxtvT46TdPzV7qEjvMcbQai3Pr9rLt6YO5ckVBawpKOfpGycwbFCwq8OTPspay4Gaxo5ZpI9+/+/L07ho9GCONLawIr+MtJgQLkwb5Owi7SAhvH1FgfQ4B+lxDhe/C5GBYUAWyJkJoXz+49lEBPm6OhQRGQBaWtvYceAIecXtaw1vKKrmt1eOISM+lEOHG1lXWElmgoNvTkwkMyGU9DhHR++WkYNDjjuXZg8Vca0Tb1JFhfjx23e287UHPuFXV4zhqpz4r0yAJANLW5tlT3ktW0priA/zZ2xiGAXldcz8w4cd+yRFBpKZENqxqsrEpHBW/eQCF0UsIscakAUy0FEcV9Q2Ea5lG0Skm1hrKa6sx8fLg0Ehfmwqruaqx1bS0NwGgMPfm8yE0I7uc5dnxPD1zNhOn1+zh4q41ok3qW6bnkJimD+/XfYFP3xtI5/vLud/rs5UkTxAHO0i3dZm+flbW9hcWsO2fTXUNbUCcO2ERMYmhjEkPICfzxlNWmwIo2JCvjLET78vIn3HgC2QAX7zznb+tq6Yj384Ez/vry6ELiJyJi2tbXyyq6x9Ei3nesMVtU3cOXsYd184nKGRAVw7IZGshFAy40MZEhFw3AchfSgScS8nu0n1tYxYLk6P4eEPdtHSZnns492dXq9c3EdNQzNbj5lFemtpDUMiAnjs+hw8PAy5hZUE+nhxdU4Cac5ZpIdFt3e79/AwLJwy1LVvQEQ6ZUAXyNV1TRw83Mgra4u5ftIQQAlMRE6tvqmVzaXV5BVVEeznxfzxiRhjWPz8OuqaWxkWHcTskdFkJoQyJSUCgGA/b+79+mgXRy4iPc3Tw3CHc3WMlfll3PrsWq7IiuP/XJ5GbmGFloJyMwdrGthSWsPBww3MH58IwM1PryG3sBKAqGBfRseGkDMkvOOYf955vktiFZHuNaAL5K9nxvLK2mL+9O4OrhmfwJoCJTARaXe02xzA//z7C97bdpAdBw53dI2eOSKK+eMT8fQwvHTrZIZGBmpWfBEB2oc95AwJ49nPC1m6Zi8AN01JIjU6CIBHP8pXC3MvOt3Pe9H5yR0zQL+xoYTX1pWwtbSGsiONAPh6eXDl2Hi8PD24c/YwWq1ldGzIV5bUE5H+Y0B/mpuSGsndFw3nd8u+4Kan17B1X41mghUZgI6OG84rdnaTLqqmrLaR5d+fAUDZkUYig3y4YFTKSdcb1syiInKip24cz51L1/NW3j4CfTx5/JPdbCypYumiyWTEO/jOc+t4eMFYpg6LPG7iL+l+GfEOFr+wnv+9KpPIYF/e3FDKks8KGBIRwJ/f38lHP5xJZJAvB2oaOHS4kRkjojpmkR4ZE4yXc0mlacOjXPtGRKRXDOgCGeA701N4eU0Rn+4q47szUlQciwwAlc71hs9LjcTL04PfvLOdxz7eDYCPlwejY0OYNiyK5tY2vD09+PU3MlwcsYi4m892l7NiV3nHeuU/vWwUI2PaZ6VPiwnhcEMz1z25iuGDgymuqOOXV6QzObl9aMZAbWFua7McbmihrrmFuqZW6hpbqWtqISkykOgQPw7UNLBs835qm1qob2pt36eplesmJTI61sHawgru/+c26ptaO/apbWzlqRvH8+CCbL79TC61je2TZ/l5eRDi583kcREdPYMWTUth0bT++/MVkc4Z8AXyZ7vLqa5v5uapQ1m6pojzUiOZkqoiWaQ/Ka6sY9nm/eQVV7OxuIrC8vb1ht++83zSYtvXnIwPDyArPpQRg4Px8fJwccQi4s5Ot145tN+I+5+rM3n4g3y+2H8YgLtfzqO+uZVvThzCzoOH+fP7O3nihhympEZ2jGm+PCPGlW8LaC9i65tbjytSHf7exIb609jSyjub9h9XnNY1t3BeaiTnD4vi0OFG/uvVPGdh+2UR/J8XDOOaCYl8ceAwl/7pk69c83fzMrg6J4HiynrufXML0L5efIC3JwG+XlyYFs3oWAfenh4E+XoRFeRLgE/7awHenkSH+JISFcS8sfEs+ayQ6ycl8vM56R1dq0VEjjWgC+SjCeyhb45lSkoks0cO4sa/ruHyMTFaokHEDbW2WXYePExeUftaw1flxDM2MYz8Q7Xc/89txDr8yEwI5doJiWTGh5IcFQhAztBwcoaGn+HsIiKdc6b1ygN8vBgU4kd5bRN3zErlmc8KmTcunmnD2rvwxoT4UdvUyoK/rCItJoSiyvabep1dEs5aS2NLW3sB29yKh4EYhz8An+w8RFVdc0cra11TK0mRgXxtTHvx/YNX8qiqa+pona1rauHi0YP5/kUjaG2zpPzk7a9c75bzkvjZ5Wk0t1r+86UNx73m62ypPX9YFJ4ehsraJvx9PIkO9msvYn08iQ8LaH/fDj/+z+VpBPp44u/jSYCPF4E+nqQOah+7PSbOwdqfXUCAjxd+3h5f+ZyWER/Ks9+aeNKfycr8Mt7auK+jRf/SMTHqNSgiJ3XGAtkY4wd8DPg693/VWnuvMSYJWApEAGuB6621TcYYX+AZYBxQDsy31hb0UPzn5MQENiklgsnJ4fxtfQmRwb78+NKRKpJF+qijHwD9vD05WNPA4hfXs7mkumPtyWA/LyYmhTM2MYyJSeGs/slsokM0qYpIf87rfcWZ1is/sYV5srOFefaoaBLCA7hh8lCaWix/+XQ3W/fV4O1pWHLzBKakRPLIh/nsPHC4vXhtbqWusb0L8u+vygRg7kMr2FxS3dFtGNrHzj5z8wQA7nltEyVV9cfFdsnowR0F8s4Dh2lqtQT4eBLs58WgEF8GO9r/dnp6GO6+cDi+Xh4drbMBPp6kOCcfC/Tx5IMfzOgofP29PTvG7wKEB/rwxuLzTvlzCw3w4VvnJZ3ydR8vDyKCfE/9gz+F07Xoq0gWkRN1pgW5EZhlrT1ijPEGPjXGvAPcDfzRWrvUGPMo8C3gEef3SmttqjHmGuC3wPweiv+cnJjAPD0MT984gfve2sLjH++mrqmFX6gLjkifUFXXRF5x9THrDVdxeUYs980ZTVigD9Zars5JIDPBQWZ8KEMjAjv+7/p5e2qtc5Ev9du87i7O1MIcHeLHjJFRPL1yD22tlubWL4vddXsr2VpaQ6CvJ/4+7UVqkN+XH+cuHxPDeakRBPh4faWFFuAvC3Pw9DDO19r38T1mWMnpCliAO51LWZ2MMYakyMAu/zx62pl+3iIixzLW2jPvdXRnYwKAT4HvAP8EBltrW4wxk4H7rLUXG2P+5Xz8mTHGC9gPRNnTXCgnJ8fm5uae0xvpTtZafrNsO499tJsLRkXzl4XjO14bCJNkiLhaQ3MrW0qrqalvYebIaAAm/t/3OFDTiDGQGhVEZkIoF4waxCXpg10crfQXxpi11tocV8fRmwZKXnc3R8ccA1wzPoEXVxdhDDx2/TgVdCIinXS2eb1TY5CNMZ60d7dKBR4C8oEqa22Lc5diIM75OA4oAnAm2Wrau2uVdTU4VzHGcM8lI6msbeKdzftZmV/GlBQtwyByNjo7G+u7Ww/wwRcHySuqYvv+9vWGE8MDOgrk+74+GkeAN2PiHAT7eff6+xDpTwZaXnc3b+WVAl8WxDNHRnPrs2t5K69UBbKISA/rVIFsrW0FsowxocDrwMhzvbAxZhGwCCAxMfFcT9ftjDH8bl4mV2THsfiF9UxKjmDlrjIevm6skpNIFxxdf/LBBdlMTo7grY37+MnfNjFjRBQLn1rN0zeOx8PDsHz7Qf6xsZTM+FBum55MZnwomQmhHee5dIzrZ28V6S8GYl53J0MiAo9rLZ6SEslj149jY3G1iyMTEen/ujSLtbW2yhjzATAZCDXGeDnvNscDJc7dSoAEoNjZFctB+6QeJ57rceBxaO+KdfZvoWdNSYnk6xkxLPmsEG8Pw9bSGiYMDT9u0gkROV5DcytFFXUUltcxITncuf7k2o7lQQD+veUAo2JDqKhrIjLIl59dNopfXaEx/yK9aSDmdXdwpkm+RESk53RmFusooNmZRP2BC2mfoOMDYB7tM14uBN5wHvKm8/lnzteXn26cUl93dFmAhZOH8MLqvdz/z228tq6EX/1HOmMTw1wdnojLVNc1U1hRS3xYAOGBPqwtrOS3y7azt7yO/TUNHfu9tGgSU1IiuXBUNH/fUMqM4VHcfdFwRgwOxtfry4mzAn0H9KpzIr1moOd1ERGR0+nMJ9IYYIlzvJIH8LK19h/GmK3AUmPM/cB64Enn/k8CzxpjdgEVwDU9EHevOHFZgItHD2bRs2vZX13Pgic+57N7ZvNSblGnxleKuBtrLQcPN+Lt6UF4oA/FlXX8dtkX7C2vpaC8jur6ZgD+3/wsrsiOw9fLA2stU1MjGRIRwJCIABLDAxgxOJiV+WV8vLOsY/3JI40txxXHItKrBmxeFxEROZMzFsjW2o3AV2alstbuBiacZHsDcFW3ROdiX1kWIDWSx28YR25BJdmJoYQF+pAR5+DWZ9fy2HXjmJKqibzEvTS3ttHQ3Eqwnze1jS3877s7KCyvY29FLXsr6mhobuOHl4zguzNS8fLwIK+oiiERAVyeEcPQiEASIwLITmwfJ5we5+CV26Z85Rpaf1KkbxnIeV1ERORM1KfxNDozBqjNwuGGFm54ajWXZ8Ty8Y5DPPhNffCXvqO1zeLpYbDW8pdP9rCnvJa95XUUVtRSWtXAggmJ/PKKdHy9PHh5TRGxof4MiQhk2rAohkQEMCEpAoDBDj8+/uHMLl9f60+KiIiIiLtQgXyOpqRE8OtvjOHnb23h7xtKCPX3ZmtpDWMTw/DzVhdS6V3/2rKf7fsOU1heS6FzkqyshFD+sjAHYwxPrdhDQ3MriRGBZCeEcUVWABOSwgHw8vRg430XYUz3TpKlyWZERERExF2oQD5HHh6GIREB+Ht7Mm1YFMu3H+ShD3axcMpQAP7wry+YkhLBlFSNUZZzt7G4ii2lNR3doAvL6wjy9eKlWycD8NhH+awvqmJwiB9DIgKYPTKasUO+XCpp+fdn4O9z6hs33V0ci4iIiIi4ExXI5+jo+MqHvtm+PvLK/DK++/w61hRUMGFoOM+vKuSRj/JZOHkI37twOJtKqjVGWU6ppKqe7fuOFsB1FJbXUlXfzOvfnQrAYx/v5p8b9+HtaUgICyAxIoBRMSEdxz963ThC/L1P2XvhdMWxiIiIiPz/9u48SM66TOD495mZXAw5yGl2cqJZSEQ0ZCCsu1quLEWCLqi4KiICpQZ3ybJb6ioetVurtVXqbrFblAegIuCxeBcRsZCKCl4gA4vRgEAgYEKAXBqigZDJPPtHvxk6ZML0ZN6eTs98P1Wp7v793u5++kl3PfXM7z000tkgD1Jfx1d+5pwTWLNxB51zJ/Mvpx3LFbc8yFU/e5irf/4wrS3Bh05f2Lv95bc86FmwR5A/7e5m/dbKyu/DVccCf/H8kxg3upWrf7aez/1kPQBHjmljzuTK2aD37O1hVGsLHzjtWD64/FhmThxHax/XC54+YXtr8s0AAA5LSURBVOxQfyRJkiRp2LBBHqT+jq9869I5nH3SbN7/rTV8o2sj7aNamT+1HYAHntjJpj88xeU/fpDPvO3ZFWhXmJtXZrJl5+7e439/VxwL/P5lx9IxaRzX3bGBj91wT+/2U48cw9wpR7DjqT2MG93KW5fOZflLZjJ3cuXaws/d5XnOlCOG+iNJkiRJI4YN8hD4xUPbWH3v5t5rwI5qaQHgx/dt4dpfPALAuV/4JSfOPYp7HnuSz5yzZL8V5f5WmV2FLld/+eze28Ojf3iKR7ZVdoF+ZNsuzloyi4UzJ3DT2id495fv7H1eS8CfTRrHlp276Zg0jlOOnU7HpLHMmVy5RNKRY/b/Cc6f2s582ofss0qSJEl6lg1ynT3fNWDf9cqjOWXhdG5a+wRX/3w9t63fTltLsPToylmFb71/C2NHtbJw5vj9XuO5q8yPbPsTn/7ROq44d0nv/IVfupPXHj+zkR/9oDKTnqzcJpBZGR/dVvnDwa5nutnbUzWX0Noavc3k1j/upqdqPknGtrVyVPtoADZs31X1/Mrt+LFtTB8/lszkt4/v7H1eZuU1po4fzcyJ4+je28O40a28+0t38p5T/5yZk8Zxy/2bueFXj3H5uUtYu2kHZ3zqZ+ztyd7PM6athZfNmcTCmRN46eyJfPTMFxe7RrfTMWlc7+cCmDe1nXlTbYAlSZKkw1FkZv9b1VlnZ2d2dXU1Ooy6qGV1d1/D+4bFHXyta0Nvo/uay37C2k1PMqathaOntbN+6y6WH/cCbrl/y37HPe9riAEuePk8rrj1Ifbs7WHOlCNoH91GT1ZWMr938SsAuPQH93Hjbx6np9Il0pNJ+5i23vl/vf43rL53Mz1ZaSB7Mpk+YQw3/GNlfuVX7+Kn67YWzWVlm/nT2lm18q8AOOfzt3HHw7+HogntSTh+1sTeE00t+59b+e3jO/fL08tfOIWvvutkAF7xyR+yYftT+82fumgGn3t7JwBLPnYz2/70zH7zr1/cwX+/+WUAHPOR77O7u2e/+XOWzuE/Xv8Suvf28KIPf/+A/6cLX3k0Hzx9ITt27eGlH/3BAfNvXDKL//q7l/Lk03u48paHmDPlCOYWTfD08WNo6eN4YEnNJSLuzMzORscxHAznui5Jag6HWtddQa6z/o5Rfu4K86sXTu99/JV3LuWX67dz+/rt3L5+G0/v2ct3/u9RLn71i3j5C6dy6qW3MH5sG3OntHPqwhl8d80mLvvhOtpaguNnTWRy+xgqfVvQ+uwiJtMmjOWYGeMhoCWCAI6oOrvxgulHsuuZvQTFfMDEcaN655fOn1w5PpbKZYEiYNr4Mb3zy46byXEdEwmCloAIeMHEcb3z5/7FXLbufIYIKu/REsw66tn5i171InY+3c2+w29bonIprX0uWX4su7t7iudX3mPulGdXZT9x1vH0ZPbOR8C8Yr61JfjsOScUr70vvmBe8fpHjGnlixecSEsE375rI9ffvYkVr5jPh16zCIAJY0fxvtOO6f8/XpIkSVLTcQW5wWo9fvjnD27loq/cxRsWd/Cduzdx2VsWc8OaTb1nQt604+nebceOauGq80/c7zU1MPv+cPG2pXP48u2/22/FXtLw5ApyeUZyXZckHR5cQW5S/a0ww4HXWj5l0YwDjkm+8No76cnk7JPm8LWuDVz4pTt7d9XWwDzfcePmU5IkSRq+WvrfRI3W17WWP/XWxazZuAOA7/5qEwR87rxOPvLaRVxx7pJnxzVg/eVbkiRJ0vDkLtbDgJd5kqTBcxfr8ljXJUmN5i7WI1gtu2lLkiRJkp6fu1hLkiRJkoQNsiRJkiRJgA2yJEmSJElADQ1yRMyOiB9FxD0RsTYi/qkYnxwRN0fEA8XtUcV4RMRlEbEuItZExAn1/hCSJKk21nVJkg6ulhXkbuC9mbkIOBm4KCIWAZcAqzNzAbC6eAywHFhQ/FsBfLb0qCVJ0qGyrkuSdBD9NsiZ+Vhm3lXc3wncC3QAZwLXFJtdA7yuuH8mcG1W3AZMioiZpUcuSZIGzLouSdLBDegY5IiYBywGbgdmZOZjxdTjwIzifgewoeppG4ux577WiojoioiuLVu2DDBsSZI0WNZ1SZL2V3ODHBFHAt8C/jkzn6yey8wEciBvnJlXZmZnZnZOmzZtIE+VJEmDZF2XJOlANTXIETGKShH9SmZ+uxh+Yt8uVsXt5mL8UWB21dNnFWOSJOkwYF2XJKlvtZzFOoAvAPdm5qVVU6uA84r75wHXV42/vTjr5cnAjqpdtiRJUgNZ1yVJOri2Grb5S+Bc4NcRcXcx9iHg48DXI+IdwCPAm4q5G4HTgXXALuCCUiOWJEmDYV2XJOkg+m2QM/OnQBxk+pQ+tk/gokHGJUmS6sC6LknSwQ3oLNaSJEmSJA1XNsiSJEmSJGGDLEmSJEkSYIMsSZIkSRJggyxJkiRJEmCDLEmSJEkSYIMsSZIkSRJggyxJkiRJEmCDLEmSJEkSYIMsSZIkSRJggyxJkiRJEmCDLEmSJEkSYIMsSZIkSRJggyxJkiRJEmCDLEmSJEkSYIMsSZIkSRJggyxJkiRJElBDgxwRV0XE5oj4TdXY5Ii4OSIeKG6PKsYjIi6LiHURsSYiTqhn8JIkaeCs7ZIk9a2WFeSrgWXPGbsEWJ2ZC4DVxWOA5cCC4t8K4LPlhClJkkp0NdZ2SZIO0G+DnJm3AtufM3wmcE1x/xrgdVXj12bFbcCkiJhZVrCSJGnwrO2SJPXtUI9BnpGZjxX3HwdmFPc7gA1V220sxiRJ0uHN2i5JGvEGfZKuzEwgB/q8iFgREV0R0bVly5bBhiFJkkpyKLXdui5JGg4OtUF+Yt/uVcXt5mL8UWB21XazirEDZOaVmdmZmZ3Tpk07xDAkSVJJBlXbreuSpOHgUBvkVcB5xf3zgOurxt9enPHyZGBH1e5akiTp8GVtlySNeG39bRAR/wu8CpgaERuBfwM+Dnw9It4BPAK8qdj8RuB0YB2wC7igDjFLkqRBsLZLktS3fhvkzDz7IFOn9LFtAhcNNihJklQ/1nZJkvo26JN0SZIkSZI0HNggS5IkSZKEDbIkSZIkSYANsiRJkiRJgA2yJEmSJEmADbIkSZIkSYANsiRJkiRJgA2yJEmSJEmADbIkSZIkSYANsiRJkiRJgA2yJEmSJEmADbIkSZIkSYANsiRJkiRJgA2yJEmSJEmADbIkSZIkSYANsiRJkiRJgA2yJEmSJElAnRrkiFgWEfdFxLqIuKQe7yFJkoaOtV2SNBKU3iBHRCvwaWA5sAg4OyIWlf0+kiRpaFjbJUkjRT1WkE8C1mXmQ5n5DHAdcGYd3keSJA0Na7skaUSoR4PcAWyoeryxGJMkSc3J2i5JGhHaGvXGEbECWFE8/GNE3NeoWIaZqcDWRgcxjJjPcpnPcpnPch3T6ACamXW9bvydl8t8lst8lst8luuQ6no9GuRHgdlVj2cVY/vJzCuBK+vw/iNaRHRlZmej4xguzGe5zGe5zGe5IqKr0TEcxvqt7db1+vB3Xi7zWS7zWS7zWa5Drev12MX6DmBBRMyPiNHAW4BVdXgfSZI0NKztkqQRofQV5MzsjoiVwE1AK3BVZq4t+30kSdLQsLZLkkaKuhyDnJk3AjfW47XVL3dvK5f5LJf5LJf5LJf5fB7W9obxe1ku81ku81ku81muQ8pnZGbZgUiSJEmS1HTqcQyyJEmSJElNxwa5SUXEsoi4LyLWRcQlfcy/MiLuiojuiHhjI2JsJjXk8z0RcU9ErImI1RExtxFxNosa8vnuiPh1RNwdET+NiEWNiLMZ9JfLqu3OioiMCM9++Txq+G6eHxFbiu/m3RHxzkbEqZHHul4u63q5rOvlsraXpx513V2sm1BEtAL3A6cCG6mcXfTszLynapt5wATgfcCqzPzm0EfaHGrM518Dt2fmroj4e+BVmfnmhgR8mKsxnxMy88ni/hnAP2TmskbEezirJZfFduOB7wGjgZWZ6eWK+lDjd/N8oDMzVzYkSI1I1vVyWdfLZV0vl7W9PPWq664gN6eTgHWZ+VBmPgNcB5xZvUFmPpyZa4CeRgTYZGrJ548yc1fx8DYq1wBV32rJ55NVD9sB/1LXt35zWfgY8Ang6aEMrgnVmk9pqFnXy2VdL5d1vVzW9vLUpa7bIDenDmBD1eONxZgOzUDz+Q7g+3WNqLnVlM+IuCgiHgQ+CVw8RLE1m35zGREnALMz83tDGViTqvW3flax2+U3I2L20ISmEc66Xi7rerms6+WytpenLnXdBlkagIh4G9AJ/GejY2l2mfnpzHwh8AHgI42OpxlFRAtwKfDeRscyjHwXmJeZxwM3A9c0OB5JdWRdL491vRzW9tINuK7bIDenR4Hqv37MKsZ0aGrKZ0T8DfBh4IzM3D1EsTWjgX4/rwNeV9eImld/uRwPHAf8OCIeBk4GVnkyj4Pq97uZmduqft+fB5YMUWwa2azr5bKul8u6Xi5re3nqUtdtkJvTHcCCiJgfEaOBtwCrGhxTM+s3nxGxGLiCShHd3IAYm0kt+VxQ9fA1wANDGF8zed5cZuaOzJyamfMycx6V4+jO8EQeB1XLd3Nm1cMzgHuHMD6NXNb1clnXy2VdL5e1vTx1qettpYaoIZGZ3RGxErgJaAWuysy1EfFRoCszV0XEicB3gKOAv42If8/MFzcw7MNWLfmksuvVkcA3IgLgd5l5RsOCPozVmM+VxV/u9wC/B85rXMSHrxpzqRrVmM+LizOwdgPbgfMbFrBGDOt6uazr5bKul8vaXp561XUv8yRJkiRJEu5iLUmSJEkSYIMsSZIkSRJggyxJkiRJEmCDLEmSJEkSYIMsSZIkSRJggyxJkiRJEmCDLEmSJEkSYIMsSZIkSRIA/w/UPvSvHXpU3AAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 1188x612 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "fig.set_size_inches(16.5, 8.5, forward=True)\n", "\n", "data = load_file(directory + \"vss_proba_16_10000000\")\n", "overhead, minimum = compute_overhead_for_latence(data, 16)\n", "plt.subplot(221)\n", "plt.xlim(0.001, 0.5)\n", "plt.ylim(0, 800)\n", "print(\"min for vss_proba_16_10000000 : \", minimum)\n", "plt.plot(overhead.keys(), overhead.values(), \"x--\", label=\"vss_proba_16_10000000\")\n", "plt.legend()\n", "\n", "data = load_file(directory + \"vss_proba_16_500000000\")\n", "overhead, minimum = compute_overhead_for_latence(data, 16)\n", "plt.subplot(222)\n", "plt.xlim(0.001, 0.5)\n", "plt.ylim(0, 800)\n", "\n", "plt.plot(overhead.keys(), overhead.values(), \"x--\", label=\"vss_proba_16_500000000\")\n", "plt.legend()\n", "print(\"min for vss_proba_16_500000000 : \", minimum)\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f1f853fc518>" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WFhwczIcffuigiJRSLUnBkiVU5ufTcfFbdV7Qsc2ttxK48E94DBrUwNHVQ/kFyPwcYidBe5Ojo7HSge8NmjBhAhMmTHB0GEop1SycLzPzWsphort4MzKqs6PDUS1MQkICCQkJjg5DKdUCWUpLKf7gA7weGEnrPn1uqK5nfDwAYjZjuDTC4VyrNjBlJ1jMtZ9rRzrVWSmllMP85bMMTp0t48X7dfsipZRSLYeTuzshyR/h/9xzN1W/9PBhMhKGU7J3r40jq6e8I1BRAq7u4Obh6Giq0YGvUkoph8guLmHJ58d4ILozt3b1cXQ4SimllF2YCwsREVzat8elffubaqNVly5YysvInTMXey5WfF3mMvjHGHj/Z46OpEY68FVKKeUQ8z45jGHAjOE9HB2KUkopZRdisXDy54+T/fQz9WrHqW1b/J9+hpJ9+zizdp2NoqunXUugKBMGPOHoSGrUtAa+X/yx6kHpy2V+XlWulFKqyUg7UcjH+77n8YEhdPZu7ehwlKNoXldKtTCnP/6Y0gMHaHfXsHq35TV6FG69enLqD3/AUlJig+jq4Xw+fPYahN0NprscG8s1NK2Bb+Ct8K9H/5ckMz+veh94a72aLS0tJTY2lqioKCIiInjppZcAGDduHOHh4fTu3ZuJEydSUVFxzTYOHz7MbbfdhpubG/Pnz692LCUlhfDwcEwmE3PnzrWWZ2ZmEhcXh8lkYuzYsZSXlwNQVlbG2LFjMZlMxMXFcfz4cWudOXPmYDKZCA8PZ/369XbtQymlbMFiEV5Zc4iOnm5MujPU0eEoR2qgvD5x4kT8/f3p3bt3tfKFCxfSo0cPIiIimD59+jXrFxQUMGTIEDw8PHjyySerHUtLS6NPnz6YTCamTZtmnWJYWFhIfHw8YWFhxMfHU1RUBICIMG3aNEwmE5GRkezZs8fa1ooVKwgLCyMsLIwVK1bYtQ+llP1Zzp8nb8EbuEdG4nnvvfVuz3ByotOvf405J4fT//mPDSKshy2vQvn5RrV90VVExG6vfv36yZUOHjz4vzfrZogsH3H911s/EpnlK7IgournWz+6/vnrZlzV55UsFoucPXtWRETKy8slNjZWtm/fLmvXrhWLxSIWi0WSkpLkrbfeumYbubm5smvXLnn++efl9ddft5abzWYJCQmRjIwMKSsrk8jISDlw4ICIiDz00EOycuVKERGZNGmStf1FixbJpEmTRERk5cqVMmbMGBEROXDggERGRkppaakcO3ZMQkJCxGw226WP6143pZS6AR/uyZJuM9bI6t0n7d43sFvsmPea+6vWvC5Sc27euaTqWNn5mvP6nr9VHT+Xf3XdOvjss88kLS1NIiIirGWbN2+WYcOGSWlpqYhU5e1rOXfunGzbtk0WL14sU6dOrXYsJiZGtm/fLhaLRYYPHy7r1q0TEZHnnntO5syZIyIic+bMkenTp4uIyNq1a2X48OFisVhk+/btEhsbKyIiBQUFEhwcLAUFBVJYWCjBwcFSWFhotz6upHldqYZ36s035WB4Dzm/Z49N2z2fmioWi8Wmbd4Qc4XIsuEia59zSPd1ze1N644vgLs3tAuA0yerfrp717tJwzDw8KhadayiooKKigoMw2DEiBEYhoFhGMTGxpKVlXXNNvz9/YmJicHV1bVa+a5duzCZTISEhNCqVSuSkpJITk5GRNi8eTOJiYkAPPLII3z00UcAJCcn88gjjwCQmJjIpk2bEBGSk5NJSkrCzc2N4OBgTCYTu3btsksfSillCxfKzcz95DCRQV6M7hvo6HBUY9AAeX3QoEH4+vpWK1u8eDEzZ87Ezc0NqMrb19K2bVvuuOMO3N3dq5Xn5ORw5swZBgwYgGEYjB8/vsa8emW+HT9+PIZhMGDAAIqLi8nJyWH9+vXEx8fj6+uLj48P8fHxpKSk2KUPpZT9idnMmQ0b8BwxgjZ9+9q07Tb9+2MYBpXnztu03TpzdoEJ6yD+Fcf0X0eNa+One+bWfs6laVCDpsPuZTB4BgTXfwPnyspK+vXrx9GjR5k6dSpxcXHWYxUVFbz33nu8+eabN9xudnY2Xbp0sb4PCgpi586dFBQU4O3tjcvFvbeCgoLIzs6+qo6LiwteXl4UFBSQnZ3NgAEDqrV1qY49+lBKqfpa8vkxfjhTysKH++LkpNsXtQgT1l77WKs2VXn8Wnm9rd/169+AI0eOsG3bNl544QXc3d2ZP38+MTExN9RGdnY2QUFB1veX58jc3FwCAgIA6NSpE7m5udY6V+bo7Ozs65Y3dB9KKfszXFwI/uADLBcuNEj753fuImvqVLq+8w6t+/SuvYKt5Oyr+tLSw79qC6NGrGnd8b006H3orzD0haqflz8bVA/Ozs7s3buXrKwsdu3axf79+63HpkyZwqBBgxg4cGC9+1FKqZYq53QJf/ksg3sjA4jp7lt7BdX8NWBev5LZbKawsJAdO3bw+uuvM2bMmAbbAuTSbLGGZI8+lFK2UZGdjaW0FCc3N1x8Gmb7PveIXhhubuTOmWO/7Y3M5bB6IvxjLNirz3poWgPf7D1VSfHSN8HBg6reZ++5Xq0b4u3tzZAhQ6xTgWbNmkVeXh4LFiy4qfYCAwM5efKk9X1WVhaBgYH4+flRXFyM2WyuVn5lHbPZzOnTp/Hz87tmW/boQyml6uv1lG+xCMzU7YvUJXbI65cEBQXx4IMPWh9fcnJyIj8//4baCAwMrPbY0+U5smPHjuTk5ABVU6IvTaW+0dxtjz6UUvYjFgtZv/wl3z06oUEHpM4eHnR4ahole/Zw1l6PNKQuhYKjMHgmNIEv4prWwPeOp6+e1hw8qKq8HvLy8iguLgagpKSEjRs30qNHD5YuXcr69etZuXIlTk43958qJiaG9PR0MjMzKS8vZ9WqVYwcORLDMBgyZAirV68GqlZdfOCBBwAYOXKkdeXF1atXM3ToUAzDYOTIkaxatYqysjIyMzNJT08nNjbWLn0opVR97D1ZzL+/yubnA4Pp4tvG0eGoxqKB8npNRo0axZYtW4Cqac/l5eW0b9/+htoICAjA09OTHTt2ICK8++67NebVK/Ptu+++i4iwY8cOvLy8CAgIICEhgQ0bNlBUVERRUREbNmwgISHBLn0opeznzNq1lO77Gu8xYxp8lob3j3+MW3g4p16fj6WsrEH74kIhfDYXQodWbWHUFNRlBSxbveq0+qMD7Nu3T6Kjo6VPnz4SEREhs2bNEhERZ2dnCQkJkaioKImKirKW1yQnJ0cCAwOlXbt24uXlJYGBgXL69GkRqVptMSwsTEJCQmT27NnWOhkZGRITEyOhoaGSmJhoXWmypKREEhMTJTQ0VGJiYiQjI8NaZ/bs2RISEiK33HKLdZVHe/VxucZw3ZRSTYPFYpHRi76Q/rM3ytnSCofGgq7q3CLyelJSknTq1ElcXFwkMDBQli5dKmVlZTJu3DiJiIiQvn37yqZNm67bRrdu3cTHx0fatm0rgYGB1t0SUlNTJSIiQkJCQmTq1KnWlVTz8/Nl6NChYjKZZNiwYVJQUCAiVf/7nzJlioSEhEjv3r0lNTXV2seyZcskNDRUQkNDZfny5dZye/RxpcZw3ZRqbiovXJAjdw6WY6MfFEtlpV36PLd9hxwM7yFnPv20YTta+6zIy94iuY7/t6Ouud2QWm65G4bhDnwOuFG1GNZqEXnJMIy/AncCpy+e+qiI7L1eW/3795fdu3dXKzt06BA9e/a8mTG7ciC9bkqpuvp43/dMW/kVr/04kjExXWqv0IAMw0gTkf4ODcLBNK+rmuh1U8r28hYtIn/hn+n2t/do099+qafsWCZuIcEN14EIfDQFWrWFe+c3XD91VNfcXpdVncuAoSJyzjAMV+ALwzA+uXjsORFZXZ9AlVJKNV+lFZXM++QwEZ09+XG/oNorKHvQvK6UUg1MRCjZ8xXthg+366AXsA56K3Jzce3Y0fYdGAaMXgwWi+3bbkC1Dnwv3j4+d/Gt68VX41+2q4G88847V21rdPvtt7No0SIHRaSUUo3X0m3HyC4u4Q9jonDW7YsaBc3r/7N+/XpmzJhRrSw4OJgPP/zQQREppZoLwzDosvRtpIG2L6rNmZT1ZD/7LN3fX0XriAjbNXwyFVxbQ6fecJNrIDlKnfbxNQzDGUgDTMAiEdlpGMZk4FXDMF4ENgEzReSqp6gNw3gceByga9euNgvcUSZMmMCECRMcHYZSSjV6uWdKeWtrBsMjOjEgxM/R4ajLaF6vkpCQoIs9KaVsriwzE2cPD1w6dMBo29YhMbT90W04t2vHqbnz6PruCtssrFVZAclTQSwwdVeTG/jWKVoRqRSRaCAIiDUMozfwa6AHEAP4AjOuUXeJiPQXkf4dOnSwUdhKKaUau9fXf4u5Uvj1CN2+qLHRvK6UUg1DRMh5/gWO//SniAOnAjt7etLhqWlcSE3l7MaNtml09zuQ/y3Ev9LkBr1wg9sZiUgxsAUYLiI5FxfSKgPeAXTPG6WUUgB8k3WaD/ZkMeGO7nTzc8y33ap2mteVUsq2zn7yCSVffUX7n/8cw8GDQ+/ERNzCwjj12utYysvr11hJEWz9fdWWc+H32CZAO6v1ahiG0cEwDO+Lv7cG4oHDhmEEXCwzgFHA/oYMVCmlVNMgIvxuzUH82rbiySEmR4ejrqB5XSmlGoaltJTc+fNx69kTr9GjHR0OhosL/jNnYM7Lo/Trr+vX2GevQelpSJhTtbhVE1SXryECgC2GYXwNpAIbRWQN8HfDML4BvgHaA7MbLsyrvbX3LZu1VVpaSmxsLFFRUURERPDSSy8BMG7cOMLDw+nduzcTJ06koqLimm1s3boVLy8voqOjiY6O5pVXXrEeS0lJITw8HJPJxNy5c63lmZmZxMXFYTKZGDt2LOUXv4kpKytj7NixmEwm4uLiOH78uLXOnDlzMJlMhIeHs379+gbpQyml6uOT/T+w63ghv4wPp527q6PDUVdr9nl94sSJ+Pv707t372rlCxcupEePHkRERDB9+vRr1j9+/DitW7e25vQnnnjCeiwtLY0+ffpgMpmYNm0al7aFLCwsJD4+nrCwMOLj4ykqKgKqvgiaNm0aJpOJyMhI9uzZY21rxYoVhIWFERYWxooVKxqkD6WU/RT+9a+Yv8+h48yZGM7Ojg4HAI/bb8e0eVP9V5Z294LYx6sWtWqq6rLZr61ettzovvdfe99UvZpYLBY5e/asiIiUl5dLbGysbN++XdauXSsWi0UsFoskJSXJW2+9dc02tmzZIvfee+9V5WazWUJCQiQjI0PKysokMjJSDhw4ICIiDz30kKxcuVJERCZNmmRtf9GiRTJp0iQREVm5cqWMGTNGREQOHDggkZGRUlpaKseOHZOQkBAxm8027aOudKN7pVRNSsrNcvvcTZLwxmdirrQ4OpyrUMdN7vXVtPP6Z599JmlpaRIREWEt27x5swwbNkxKS0tFRCQ3N/ea9TMzM6vVvVxMTIxs375dLBaLDB8+XNatWyciIs8995zMmTNHRETmzJkj06dPFxGRtWvXyvDhw8Viscj27dslNjZWREQKCgokODhYCgoKpLCwUIKDg6WwsNCmfdwIzetK1d/3L70kJ5/8haPDqJHFYpGSw986OowGUdfcXqdVne1l3q55HC48XOfzJ6TUvrpyD98ezIitcX0OK8Mw8PDwAKCiooKKigoMw2DEiBHWc2JjY8nKyqpzbJfs2rULk8lESEgIAElJSSQnJ9OzZ082b97MP/7xDwAeeeQRXn75ZSZPnkxycjIvv/wyAImJiTz55JOICMnJySQlJeHm5kZwcDAmk4ldu3YB2KwPm6z4ppRqsd758jhZRSX84//idPsiBdScqxO6J5DUI4kScwlTPp1y1bkPmB5glGkURaVF/HLrL6vVfWf4O7X2OWjQoKtmMi1evJiZM2fi5uYGgL+//41+FHJycjhz5gwDBgwAYPz48Xz00Ufcc889JCcns3XrVqAq3w4ePJh58+aRnJzM+PHjMQyDAQMGUFxcTE5ODlu3biU+Ph5fX18A4uPjSUlJYfDgwTbrIyAg4IY/o1Lq5gW8/DJiNjs6jBoVrVxJ7uxXCf73B7j3uIFFJ09sh7IzEHZ3k53ifEmTWo4r+1xDVxa2AAAgAElEQVQ2u3N3szt3N4D19+xz2fVuu7KykujoaPz9/YmPjycuLs56rKKigvfee4/hw4dft43t27cTFRXFPffcw4EDB6pizs6mS5cu1nOCgoLIzs6moKAAb29vXFxcqpVfWcfFxQUvLy8KCgqu2ZYt+1BKqZt16mwpi7YcJb5XR35kau/ocFQTsOTrJTXm9c3fbbZ5X0eOHGHbtm3ExcVx5513kpqaet3zMzMz6du3L3feeSfbtm0DqnJnUFCQ9ZzL82pubq51oNmpUydyc3OtdW4kd9uyD6WUfZSlp1P67bdA1XO1jZHXiBE4t2tH7tx51scnalVphrW/hHXPVW1l1MQ1qitT253Zy/VZ0YdvHvnGZn07Ozuzd+9eiouLGT16NPv377c+GzRlyhQGDRrEwIEDr1n/1ltv5cSJE3h4eLBu3TpGjRpFenq6zeJTSqnGbsGGI5SZK3l+RE9Hh6IakevdoX3q1qd46tangJrzuo+7T53u8NaF2WymsLCQHTt2kJqaypgxYzh27FiNM50CAgL47rvv8PPzIy0tjVGjRlm/0K4LwzAafAaVPfpQStVORMiZNYvyEycwbdqEU6tWjg6pRs7e3rT/xS/InT2bc1u20G7o0Nor7VkBpw7CmPfApXF+rhvRpO742oO3tzdDhgwhJSUFgFmzZpGXl8eCBQuuW8/T09M6XXrEiBFUVFSQn59PYGAgJ0+etJ6XlZVFYGAgfn5+FBcXY744HeJSOVCtjtls5vTp0/j5+V2zLVv2oZRSN+PA96d5f/dJHrmtO8Htdfsi1fgEBQXx4IMPYhgGsbGxODk5kZ+fX+O5bm5u1pzYr18/QkNDOXLkCIGBgdUee7o8r3bs2JGcnBygakr0panUN5q7bdmHUqrhnd2wkZLdaXSY+mSjHfRe4jN2DK1CQ8mdNw+pbXujkmLY8ip0uwN63m+fABtYkx34To6abLO28vLyKC4uBqCkpISNGzfSo0cPli5dyvr161m5ciVOtezD9cMPP1inDezatQuLxYKfnx8xMTGkp6eTmZlJeXk5q1atYuTIkRiGwZAhQ1i9ejVQtbLjAw88AMDIkSOtqzuuXr2aoUOHYhgGI0eOZNWqVZSVlZGZmUl6ejqxsbE27UMppW6USNX2Rd6tXfnFsDBHh6OaKFvm9ZqMGjWKLVu2AFXTnsvLy2nfvuYp+Xl5eVRWVgJw7Ngx0tPTCQkJISAgAE9PT3bs2IGI8O6779aYV6/Mt++++y4iwo4dO/Dy8iIgIICEhAQ2bNhAUVERRUVFbNiwgYSEBJv2oZRqWJayMk69/jput9yCd+KPHR1OrQxXVzrOmI7l/AXKMo9f/+TPX4cLhZDwapN/tteqLitg2eply9UfbWnfvn0SHR0tffr0kYiICJk1a5aIiDg7O0tISIhERUVJVFSUtbwmCxculF69eklkZKTExcXJl19+aT22du1aCQsLk5CQEJk9e7a1PCMjQ2JiYiQ0NFQSExOtK02WlJRIYmKihIaGSkxMjGRkZFjrzJ49W0JCQuSWW26xrvJo6z7qojFcN6VU45CyP0e6zVgj724/7uhQaoWu6twi8npSUpJ06tRJXFxcJDAwUJYuXSplZWUybtw4iYiIkL59+8qmTZuuWX/16tXSq1cviYqKkr59+8rHH39sPZaamioRERESEhIiU6dOFYulavXy/Px8GTp0qJhMJhk2bJgUFBSISNVKqlOmTJGQkBDp3bu3pKamWttatmyZhIaGSmhoqCxfvrxB+qirxnDdlGpq8pYskYPhPeTcZX/3NwWVFy7UftJXfxfZ8NuGD8YG6prbDZE6PtxsA/3795fdu3dXKzt06BA9e+rzYE2NXjelFECZuZK73/gcNxcn1k0biItz455IZBhGmojUczNDdYnm9eZDr5tSNy5/yduUHjpI0BtvODqUGyYVFVzY8xVt42IdHUq91TW3N+6/UJRSSjVqK/57nBMFF/jNvb0a/aBXKaWUsqX2j/+cwFrWAWqs8pcs4bsJEyg9cqT6geNfws4lzWIV5yvpXyk36J133iE6Orraa+rUqY4OSyml7K7gXBkLNx1laA9/Bt3SwdHhKHXD1q9ff1VOHz16tKPDUko1cqXfHuHsli1V02eb6POvPg8/jJOHB6cu397IUgmfTIf/LgRL49yPuD4a1XZGTcGECROYMGGCo8NQSimHW7DxCCUVun2RaroSEhJISEhwdBhKqSZERMh99VXKvv2W0E2f4nxxV5emxsXHhw5Tp5A7Zy7nPvuMdoMHw1d/g9z9kPgOuLZ2dIg2p3d8lVJK3bDDP5xh5a7v+OmAbpj8m2bSV0oppW7UuU2buLBrF+2n/aLJDnov8fnJT2jVvTun5r2GnC2Ezb+DLgMgonnOfNGBr1JKqRsiIsxec4h27q48fZduX6SUUqplsJSXk/va67QyheIzdqyjw6k3o1Ur/GdMx3Bzw7zu93A+D4bPaT7bF11BpzorpZS6IZsPn+KLo/m8fH8vvNu0cnQ4SimllF0U/e3vVHz3HV3efhvDpXkMozwGD8Zj0CCM776ETv4QeKujQ2owzeOKKaWUsotys4VX1x4itENbxg3o5uhwlFJKKbtx6dABrwcfxGPgHY4OxWYMwwBnZyp9ozh/tAxPRwfUgJrkVOeKU6c4/tOfYc7Ls0l7paWlxMbGEhUVRUREBC+99BIA48aNIzw8nN69ezNx4kQqKq69rPfhw4e57bbbcHNzY/78+dWOpaSkEB4ejslkYu7cudbyzMxM4uLiMJlMjB07lvLycgDKysoYO3YsJpOJuLg4jh8/bq0zZ84cTCYT4eHhrF+/vkH6UEqpa3lvxwmO5Z/nN/f2wlW3L1I2Yuu8PnHiRPz9/endu3e18oULF9KjRw8iIiKYPn36NesXFBQwZMgQPDw8ePLJJ6sdS0tLo0+fPphMJqZNm2ZdDbWwsJD4+HjCwsKIj4+nqKgIqHo0YNq0aZhMJiIjI9mzZ4+1rRUrVhAWFkZYWBgrVqxokD6UUrbjdf99dP79q44Ow7aOfwkpz5O/8E2yn36GsowMR0fUYGr9q8UwDHfDMHYZhrHPMIwDhmHMulgebBjGTsMwjhqG8b5hGHab75b/1mJK0tLIW/SWTdpzc3Nj8+bN7Nu3j71795KSksKOHTsYN24chw8f5ptvvqGkpISlS5desw1fX1/+9Kc/8eyzz1Yrr6ysZOrUqXzyySccPHiQlStXcvDgQQBmzJjBM888w9GjR/Hx8WHZsmUALFu2DB8fH44ePcozzzzDjBkzADh48CCrVq3iwIEDpKSkMGXKFCorK23ah1JKXUvR+XLe/PQIg27pwOBw3b6oqWoJef3RRx8lJSWlWtmWLVtITk5m3759HDhw4Kp8fTl3d3d+97vfXfVFNsDkyZN5++23SU9PJz093drP3LlzGTZsGOnp6QwbNsz6JfQnn3xiPXfJkiVMnjwZqBrEzpo1i507d7Jr1y5mzZplHcjaqg+llG2UHjlC4bvvIde5CdYkWSyw/tdwMBm//3sMp9atyX3tNUdH1WDqMtW5DBgqIucMw3AFvjAM4xPgl8AbIrLKMIy/AI8Bi+sTzA+//z1lhw5f8/iF3bvh0j5TQPGqVRSvWgWGQZv+/Wus49azB52ef/66/RqGgcfFVdkqKiqoqKjAMAxGjBhhPSc2NpasrKxrtuHv74+/vz9r166tVr5r1y5MJhMhISEAJCUlkZycTM+ePdm8eTP/+Mc/AHjkkUd4+eWXmTx5MsnJybz88ssAJCYm8uSTTyIiJCcnk5SUhJubG8HBwZhMJnbt2gVgsz6a6l5kSqmG98dPj3C+vJLf3NtT/61o2uyW1wFO/Gz8VWXt7hmO78MPczgyCrk4Ewkuy+suLvTc/w3moiKypz1VrW63996ttc9BgwZdNZNp8eLFzJw5Ezc3N6Aqb19L27ZtueOOOzh69Gi18pycHM6cOcOAAQMAGD9+PB999BH33HMPycnJbN26FajKt4MHD2bevHkkJyczfvx4DMNgwIABFBcXk5OTw9atW4mPj8fX1xeA+Ph4UlJSGDx4sM36CAgIqPW/lVLq+kSEU3PnUnLgIJ7334eLj4+jQ7KdfSshZx88uBSXToG0nzKFU6+9xrlt2/AYONDR0dlcrXd8pcq5i29dL74EGAqsvli+AhjVIBFepnVkJM6+vuB0MWwnJ5x9fWkdFVXvtisrK4mOjsbf35/4+Hji4uKsxyoqKnjvvfcYPnz4DbebnZ1Nly5drO+DgoLIzs6moKAAb29vXC4+GH+p/Mo6Li4ueHl5UVBQcM22bNmHUkrVJD33LH/b+R3j4rpyS8d2jg5H1UNjyusha/5TY173f+45m/d15MgRtm3bRlxcHHfeeSepqak33EZ2djZBQUHW95fn1dzcXOtAs1OnTuTm5lrr3EjutmUfSqn6O7d1K+f/u50OU6c2r0Fv2TnYNAuCYqBPIgA+Px2Ha9eu5M6dh5jNDg7Q9uq0uJVhGM5AGmACFgEZQLGIXPovkgUEXqPu48DjAF27dr1uP7XdmQXIefllit//J4abG1JeTru77ybg5Zfq8jGuy9nZmb1791JcXMzo0aPZv3+/9dmgKVOmMGjQIAY2w28+lFKqLmavPUSbVs48fdctjg5F2YC98jpc/w5tq65daXd3/FV53e+RqrvELj4+dbrDWxdms5nCwkJ27NhBamoqY8aM4dixYw0ye8EwjAafFWGPPpRq6aS8nFPzXqNVcDA+P0lydDi29eUf4VwujP27dfsip1at6Dj9OYo/+DeVZ882r4E+dVzcSkQqRSQaCAJigR517UBElohIfxHp36FD/Z8JM+cX4J2URPf3V+GdlIQ5P7/ebV7O29ubIUOGWJ+nmTVrFnl5eSxYsOCm2gsMDOTkyZPW91lZWQQGBuLn50dxcTHmi9+mXCq/so7ZbOb06dP4+fldsy1b9qGUUlfa8u0pPjuSx1PDwvBtq9sXNQctKa9fEhQUxIMPPohhGMTGxuLk5ET+DfYVGBhY7bGny/Nqx44dycnJAaqmRF+aSn2juduWfSil6qdo5UrKjx+v2uvW1dXR4dhWZBIkzIEuMdWK2911F10Wv9XsBr1wg6s6i0gxsAW4DfA2DOPSHeMgwC5zarr8eSEBL72Ie48eBLz0Il3+vLDebebl5VFcXAxASUkJGzdupEePHixdupT169ezcuVKnJxubvXSmJgY0tPTyczMpLy8nFWrVjFy5EgMw2DIkCGsXl01q2zFihU88MADAIwcOdK6uuPq1asZOnQohmEwcuRIVq1aRVlZGZmZmaSnpxMbG2vTPpRS6nIVlVXbFwW3b8v427o7OhxlY801r9dk1KhRbNmyBaia9lxeXk779u1vqI2AgAA8PT3ZsWMHIsK7775bY169Mt++++67iAg7duzAy8uLgIAAEhIS2LBhA0VFRRQVFbFhwwYSEhJs2odSqn7ce/XC52c/w+POOx0diu21N8FtU655uPzECYo//MiOAdmBiFz3BXQAvC/+3hrYBtwH/AtIulj+F2BKbW3169dPrnTw4MGryuxt3759Eh0dLX369JGIiAiZNWuWiIg4OztLSEiIREVFSVRUlLW8Jjk5ORIYGCjt2rUTLy8vCQwMlNOnT4uIyNq1ayUsLExCQkJk9uzZ1joZGRkSExMjoaGhkpiYKKWlpSIiUlJSIomJiRIaGioxMTGSkZFhrTN79mwJCQmRW265RdatW2ctt2UfddEYrptSquH99ctM6TZjjWw88IOjQ7EJYLfUkqua+6sl5PWkpCTp1KmTuLi4SGBgoCxdulTKyspk3LhxEhERIX379pVNmzZdt41u3bqJj4+PtG3bVgIDA+XAgQMiIpKamioRERESEhIiU6dOFYvFIiIi+fn5MnToUDGZTDJs2DApKCgQERGLxSJTpkyRkJAQ6d27t6Smplr7WLZsmYSGhkpoaKgsX77cWm7LPuqqMVw3pZQdnNghsvJhkbO51z3t+9++KAcjekvpsWN2Cuzm1TW3G3LZKsk1MQwjkqpFLpypukP8TxF5xTCMEGAV4At8BfxURMqu11b//v1l9+7d1coOHTpEz549axufq0ZGr5tSzV/xhXIGz99KRGdP/vZYXLOYFWIYRpqI1LwNQAuheV3VRK+bUv9TlpFB8T//Rfsnp+Lcrhkt6GixwLK74Mz38Is0aNX2mqea8/PJSBhOm7g4ury1yI5B3ri65vZaF7cSka+BvjWUH6PquSCllFLN0Jub0jlTUsFv7u3VLAa9qormdaWUur7cefMo+WovfpMed3QotvXNvyA7DUb/v+sOegFc2rfH74lJ5P1hAef/+1/a/uhHdgqy4dzcg6st2DvvvEN0dHS119SpUx0dllJK2VRG3jne236CpNiu9AzwdHQ4SjWI9evXX5XTR48e7eiwlFIOdO7zzzn/+TbaT5mCy8V9tpuF8vPw6cvQuS/0GVOnKr7jx+MaGEjunLnNYnujOm1n1NBEpMncTZgwYQITJkxwdBgOVdv0eKVU0/f7tYdo7erML+N1+yJ145pKXk9ISCAhIcHRYTic5nWlqkhFBbnzXsO1W1d8xz3s6HBsa8dbcPZ7eOid/+2dXgsnNzf8Z0znQupupLwcw6VRDB1vmsOjd3d3p6CgAD8/vyaRJFs6EaGgoAB3d3dHh6KUaiCfH8lj0+FT/PqeHrT3cHN0OKqJ0bzetGheV+p/it7/J+UZGQQt+jNGq2a2fV//x6BdAHQdcEPVPO++G8+7726goOzL4QPfoKAgsrKyyMvLc3Qoqo7c3d0JCgpydBhKqQZgrrQwe+1Buvq24dHbuzs6HNUEaV5vejSvK1Wl7e0/ov2UyXgMHeroUGxLBNr4Qt+f3nQTF1JTKU1Px/fhpnsn3OEDX1dXV4KDgx0dhlJKKWBV6kmO5J7jLz/th5uLs6PDUU2Q5nWlVFPlFhxMh2nTHB2GbWWlwdpfQuJy8Au96WaK//0hp9esweP222nVrZsNA7QfXdxKKaUUAKdLKliw8QgDQnxJiOjo6HCUUkopuyg7lknWtKeo+OEHR4diWyKQMhPO5oCHf72a6vD00xiurpyaP99GwdmfDnyVUkoB8OfN6RRdKOe39+n2RUoppVqOU6+9xvkvv2zyizddZf8HkLULhr0IbvXbj9i1oz/tH/85Zzd+yvkdO20UoH3pwFcppRSZ+ef563+PM6ZfFyI6ezk6HKWUUsouzn35Jee2bqX95Cdwad/e0eHYTkUJbHwJOkVClG2ey/V99FFcOgeQO28eUllpkzbtqZl9raGUUupmzFl3iFbOTvwqQbcvUkop1TKI2cypufNw7dIFn/HjHR2ObaWtgDNZ8OCSOm9fVBsnd3c6Pf88FdnZVdOomxgd+CqlVAv336P5bDiYy3MJ4fi30y1NlFJKtQzFH/ybsvR0Av/0Jk7NbvuiieDdFbrfbtNm2911l03bsycd+CqlVAtWaRFeWXOQQO/WPHaHrsSrlFKq5fBMuBspK6VdfLyjQ7GtygpwaQU9RjRYF8UffURlQSF+j01ssD5sTZ/xVUqpFuyfu09y+IezPD+iJ+6uun2RUkqplsPZ2xvf8eOb14KO338Ff4yE7D0N2s2F7dvJe/NNyrOyGrQfW9KBr1JKtVBnSyv4w4Zvienuw4g+nRwdjlJKKWUX5SdOkDl2LKVHjjg6FNsSgZRfg6UC/EwN2lWHZ54BZ2dOzf9Dg/ZjSzrwVUqpFmrRlgzyz+n2RUoppVqW3Ndfpzz9KM7e3o4OxbYOfgTfbYehvwF3zwbtyrVTJ/wee4yzKSlcSEtr0L5sRQe+SinVAn1XcIHlX2Ty41uDiAxqZolfKaWUuobzO3Zw7tNN+E2ahKu/v6PDsZ2KUtj4InTsDX1/Zpcu/R6biEunTuT+fg5isdilz/qodeBrGEYXwzC2GIZx0DCMA4ZhPHWx/GXDMLINw9h78dVwT08rpZSyqTmfHMLZyWD68HBHh6LsTPO6UqqlkspKcufMxbVzZ3wffcTR4djWwY+g+DsYPgec7LNmh1Pr1nR68UXaT50CTWDmWF1WdTYDvxKRPYZhtAPSDMPYePHYGyIyv+HCU0opZWs7jxXwyf4f+FX8LXT01O2LWiDN60qpFunMunWUffstgW8swMnNzdHh2FbkWPANgS6xdu223dAhdu2vPmod+IpIDpBz8fezhmEcAgIbOjCllFK2d2n7os5e7vx8UIijw1EOoHldKdVSeSYkAAbthg93dCi2VXoa3L3sPui9RETIX/QWhosL7Z+Y5JAY6uKGnvE1DKM70BfYebHoScMwvjYMY7lhGD7XqPO4YRi7DcPYnZeXV69glVJK1c8He7I48P0ZZtzTQ7cvUprXlVIthlRWYrRqhdf99zWvBR1zvoYFveDopw4LwTAMyjMzyV+8mIrvv3dYHLWp88DXMAwP4APgaRE5AywGQoFoqr45rnEtaxFZIiL9RaR/hw4dbBCyUkqpm3GuzMzr67+lb1dvRkZ1dnQ4ysE0ryulWorykyfJSBjOhd27HR2KbV3avsi5FQT2d2go/r/6JQCnFrzh0Diup04DX8MwXKlKjn8XkX8DiEiuiFSKiAV4G3DMvXWllFJ18petGeSdLeNF3b6oxdO8rpRqSU7N/wPmggJcu3RxdCi2dXgNnPgChjwPrR27Q4Nr5874PTaRM2vWcOGrrxway7XUZVVnA1gGHBKRBZeVB1x22mhgv+3DU0opZQtZRRdYsu0Yo6I707drjTNYVQuheV0p1ZJcSE3l7Pr1+P38/3Dt2NHR4diOuQw2/AY69IB+ExwdDQB+jz2GS4cOnJr3GiLi6HCuUpdVnW8HfgZ8YxjG3otlzwM/MQwjGhDgONB4n2RWSqkWbu4nh3EyYPrwHo4ORTme5nWlVIsgFgu5c+bi0qkTfhMax+DQZjK3QdEJ+OkH4FyXIV3Dc2rbloDf/x4XP99GObOsLqs6fwHUFPk624ejlFLK1nYfL2TN1zk8NSyMzt6tHR2OcjDN60qpluLc559TevAgnV9/HafWzSz/hd0Fv0gDv1BHR1KNx8A7rL+LSKMaAN/Qqs5KKaWaFotF+N2ag3TydGfSnbp9kVJKqZbD48476frOcjzvu9fRodjW6ayqn41s0HuJmM18P2Mm+YsXOzqUanTgq5RSzdhHe7PZl3Wa6cPDadOqcUyFUkoppRqapaQEwzBoe9ttjequY73lHoA3o+Drfzo6kmsyXFywlJVR8PZSKn74wdHhWOnAVymlmqkL5WbmpRwmKsiLUdGBjg5HKaWUsouK7GyODh7CmQ0bHB2KbV3avqiVB5jucnQ01+X/7K+gspK8NxrP9kY68FVKqWbqL58dI/dMGS/e3wsnp2b0bbdSSil1Haf+sABLWRmt+/RxdCi2dSQFMj+r2r6oja+jo7muVkFB+D76KKeTP6bkm28cHQ6gA1+llGqWvi8uYcnnGdwf1Zl+3Rp3clRKKaVs5cKerzizbh1+EyfiGhBQe4WmwlwO61+A9rdA/4mOjqZO/B5/HOf27cl744+ODgWo23ZGSimlmpjXUg4jAjOGhzs6FKWUUsouxGIhd+5cXPz98fu/xxwdjm398A2c/QEe+is4uzo6mjpx9mhL0BsLcO3WzdGhADrwVUqpZmfPd0V8tPd7nhxiIsinjaPDUUoppeyi9OuvKf3mGzrPnYNTm2aW/4L6wdPfNPopzldqExMDVG1tRGUlhovjhp861VkppZoRkartizq0c2Py4Ma5zYFSSinVEFpHRxPycTKe99/v6FBsK+frqoWt2vpBE1yh2lJayomf/YyCpUsdGocOfJVSqhn5eN/3fPVdMdMTwmnrppN6lFJKtQzm/HwA3MLCMJya0RDn1CFYMhi2L3J0JDfNyd0dFx9f8pe8TcWpU46Lw2E9K6WUsqmS8krmfXKY3oGe/PjWIEeHo5RSStlFxQ8/cPTuBIpWrnR0KLYlAuufr9q+KOonjo6mXvyfexYqKsj745sOi0FvByilrP70ylS2l3bj8IUuPL93Ib+P/gU92pzkNvcTTHux6X7TCM33s135uUqif0G7syf58+xVTfpz8cUfIfBWKtr2YPfjPyHm7VW4nDsE2XvgjqcdHZ1SSjUdzfTf08vz31tb59CmzMKvdx6iV/rUJp3/Ln2u7ZYIbunwHhvObuZd810U/+HFJv25WnXtypqwQdzz7w+ZmuPHw5mr+X30Lyhy96S9Ryt2/ya+wWPQO75KKavtpd34s+ufeObb9+mVW8Qz377Pn13/xPbSxrEaX30018/WXD8XgbfCvx4lf95v8fz2e/Lm/gb+9WhVuVJKqbprpv+eXsp/v923Au/SCvI9vXjVa3mTz3+XPtcdTl+T0/4A31t8uM95Z5P/XADLgwdzplUbpn79b3rlFvHw4Y0A5J8rt0v/hojYpSOA/v37y+7du+3Wn1Lqxuzt1Qc3i/mqcgEqmvj8EFcz1LQcRFP/bC3tcxlubvTYt/em2zUMI01E+t98ZOpymteVavwOR0UjZWVXlRtubrT/9zKyH/jpVcdyhndn+PxPOJm2lsJHnr3qeN7ontz1u3/z7afLKH16/lXHC38aw5CZ77Lvg9fgpXeuOn7uiSHc/uRb7Fo+A7cFH191vPy5B4l55FW+eHMS7d7+/KrjTr/7Pypf+GuNf7OUO8OZ9+Yx8NaRzPn7BD4oT73qnOW3LyHylh/x0oqxrLUcuOr4h8M/pEtAGDOW388mI/Oq45//ZCdt3Nvy9NJ4vnDOqXbMVYTtE6vanLJkELtcC6sd97QImx+rOv7YktvY53q22vH2ZvDJHM8i1ze5M8SXVhahAhcsF7OimwSSNnENAHHvPMh5qsfXlhB2TvgAgFuXj6DcqB6fj1Nvtj3yHgBRy4ZS6VRU7Xgnlxg+/dkSAPosvx0xLlQ73s1tIMZIi7wAACAASURBVGsf/hMAvd/pD1RWOx7eJoEPxs7ldOkFbl95e7Vjf59fSqvqpwNQ5uRC9MFvrj5QR3XN7U34zyKllK1NSrqDn3+1lR8dqhp0mJ0gxwcyA13xdm/t6PDqpbi0hODsCgKKwMXSfD7b9T5XkJeno8O7aYUlF+iYWWL9XGUusDPcwPkX4+jh6OCUUqoJ+eKP4+jx3HJ8z/0vtx8KgjPPjyOpnR9Zt/ldVadTdNUWNB7tu/J1DceDomIB8O4czu4ajnfvXVXft3tEjfXDelQd9wvry+HbvrzqeK+wvgC079mfjNuuHphGd+/Ng0l38NT2rfQ5UT1PrP9RW+b6dQWgZ+cBDD72w1X1fTw7VR0PGMD5k2euOu7RxhuAiE63/f/27jw8qur+4/j7ZIOEsK9BAkFAVgkVRFlEFEUFC1qt4m6L1SpoRWxFRcQNrVZ/7guK1VbrUlcqKCiiqKAYkH3fdwj7kpD1/P6YCxIIJGRucjI3n9fzzJM799yZ+ZxnJvOdM/fOPeRtPHJwHRMVGkK1q9eFmPSCA+soE31wuV2d00nYUXBAVymq8sHlk2ucSs09Swu0z8rZzbyW/+ZMQtMWZUcZII+8jCacXCeVOpXrHNz29Ppns3FfwYFto8SGv7bXPZet+7cWaD+xesrB5U61z2NPTsH+t6nd8uByh+rnk5W/v0B7h3rtfu1f4gXkk1+g/fSkjgDERkfTJrFPgbab/zCD66euofMSS6XcX5+zMandmU7pK3KPrzEmGfgXUJ/QToTR1tpnjDG1gPeAFGAVcJm1dsfR7gf0zbBIeZcybBz//GEYDdJzyYmC2HzLz01bMSL1T6x6rK/reGFJGTaOh2a/SqeVi8iOMcTlBqNvQe0XwMY7bmLn+G8P9qtG3zNJevKVsO5Te3xV10UqmmXfvk32TQ8Dlhwf309dC2r9Sxk2ji5R83k+9ll6nliTb1bsYHDObUzLbxvR/YJQ3wbN+pA+q34kN8YSk2sYn3I6L3S4JKy+Fbe2F+c3vrnAUGttG+B0YJAxpg0wDJhkrW0BTPKui0gE+23eBOpuzWVnVbjn+mh+btqKbtnz6BJ15LetkaZL1Hy6Zs/j56atuPfa4PQtqP1i5RRy50+mRt+e3HttNDX69iR33mRYeeQhb3LcVNdFKpDmjZMxSXlUO69boN5Pg1r/Dgx6B+fcBsDgnNt4PvbZiO/XATWz9jAupQv3XhvNuJQu1MzaU/SNfFLkoc7W2o3ARm95jzFmIXAC0B/o6W32JvANcFeppBSRMtFz+VdYA092v4QlUXsZkXouXaLm06XyatfRwtal8mqGn3Yj0/LbEhf1ZWD6FtR+sX4myS+/Bk170GfWiyR1uCX0IW39TGjaw3W6iKa6LlJx7Fg7n5rrZ9L69WC9n27evZ/2ZkUg61+XyqsZvD+0hzcuvRfT8tsyOOe2iO8XQJ3EOB4+7XoA4qKqMq/DuQfXl4XjOrmVMSYFmAK0A9ZYa2t46w2w48D1o9EhUSLl1zfvPU3d+19hXsdELn/7yBNBiASBDnUuSHVdJLh2rlvI4n6/Y0fPxpz/1ATXcXx1539nM3bWBr6640wa105wHUcc8/NQ5wN3mAh8CNxurS3wK2gbGj0XOoI2xtxojEkzxqSlp6cX9+FEpAzl5eaS8eqr7IuHLiNfcx1HRMqA6rpIsP0w8gYSM6Bxr36uo/hq7rpdfDBjHX/onqJBrxyXYg18jTGxhIrj29baj7zVm40xSV57ErClsNtaa0dbaztZazvVrVvXj8wi4rNPH7+JpuvyWdGzKY1bpLqOIyKlTHVdJNhWTfuIxlO3s7JDAm36DnIdxzfWWh78bD51EuMYfFZz13EkwhQ58PUOdxoDLLTWPnVI01jgOm/5OuBT/+OJSGnbvWMLdcZOZVNt6P/wu67jiEgpU10XCb4Fj44kNxpOHfGM6yi+2rR7P+t2ZDK0d0uqVo51HUciTHHm8e0GXAPMNcbM8tbdAzwGvG+MGQisBi4rnYgiUprG33sFqTth6U3nEl8lcud9FZFiU10XCbANcybRYFUOm3qdwG/adHcdx1dJ1eP5emhP4mKK/WtNkYOKc1bn7wnNd12YXv7GEZGytHT2D7T4fgPLmkbTb8izruOISBlQXRcJtobtexH97qu0SgrWocC/rNlBm4bViI+Ldh1FIpS+LhGpwGaNupVKOVB78FDXUURERCRM62Z+QX5eHvXbdCehZgPXcXyzZc9+rn7tJ0aOXeA6ikQwDXxFKqjvPnqBNnMyWdChCl37/sF1HBEREQnD7o3LWH/DEMYP7OY6iu+emriE7Lx8buxxousoEsE08BWpgPJyc9n98otkVoLOI152HUdERETC9N39f6BaBqRcPMB1FF/N37CL99LWcl2XFJrWqeI6jkQwDXxFKqCxTw3mxDX5LOvRmJTWRc73LSIiIuXYmp//R/IPW1nePp52/W93Hcc31loe/N8CasTHcmuvFq7jSITTwFekgtm7azu1Pv6WzbXgt4+87zqOiIiIhGnuI8PJj4KOI54qeuMIsjszl/25+dzRuyXV4zV9kYSnONMZiUiAfDZ8AKk7YPHAM6lStbrrOCIiIhKGnesWUmNDNht6NqRDu56u4/iqekIsH9/cFes6iASCBr4iFciqhWk0n7KW5U2i+O2Q513HERERkTDVaNSa30yYjIkK1oGc3y1Np01SNWonVnIdRQIiWP8hInJM0x/4M/HZUOOWvxAdo++9REREItnir8aQuWsLCTUbEF+9nus4vtm6N4tb3prJiLHzXUeRANHAV6SCmDr2VdrM3seC9gl073+j6zgiIiIShr1bVrH9rn/w9R96u47iu6e+XEJmTh5DzjnJdRQJEA18RSqIbS8+TVYcdLz/JddRREREJEzf3n8dNfZB8tVXu47iq0WbdvPu9DVcfXoTmtdLdB1HAkQDX5EK4NMnB9F8VT5LzjiBE9t0dh1HREREwrBu5hc0+m4Ly9tVpv3v7nQdxzfWWh76bAFVK8dy+zmavkj8pYGvSMDt27OLah9+TXoNTV8kIiISBLMfHoYFTrnvCddRfJWVm0+NhDiGnNOCGglxruNIwOjsNiIB97/7Lid1Oyy6/gwSq9dyHUdERETCkLFjE3E7stnQoz4dUs9xHcdXlWOjeeHKU7BWExiJ/zTwFQmwNYtn0uyb1axIjqLfnS+6jiMiIiJhSqjZgLMnziIvK8N1FF9NnL+JlDpVOKl+VYwxruNIAOlQZ5EA+/GBm0jIgqo33azpi0RERCLcnI/+QfrS6UTHxhGXWMN1HN9s35fNnf+dzaPjF7qOIgFW5MDXGPO6MWaLMWbeIetGGmPWG2NmeZc+pRtTRI7Xj5+/SetZe1nQLp4elw52HUdEyhHVdpHIs3fbWvaOGsOMW//oOorvnv5qCfuy87i7T2vXUSTAirPH9w3g/ELW/5+1toN3Ge9vLBEJ15ZnnyAnBjoMf851FBEpf95AtV0koky5/1pq7oUGf7zKdRRfLd28h7d/WsOVnRtzUv2qruNIgBU58LXWTgG2l0EWEfHJ/565nRYr81jSLYkWqd1cxxGRcka1XSSybJgziYbfbGJFm0p0uOxu13F89fC4hSTERTPk3JNcR5GAC+c3voONMXO8w6VqHm0jY8yNxpg0Y0xaenp6GA8nIsWRuW83Vf47ga3Voc+od13HEZHIUmRtV10XKXszH7oTYyF1+GOuo/gqNy+f1knVuLN3S2pV0fRFUrpKOvB9CWgGdAA2Ak8ebUNr7WhrbSdrbae6deuW8OFEpLg+ve8KkrbCln6nUa1mPddxRCRyFKu2q66LlK3crExMnmXdGXVpdEphv1CIXDHRUQy7oBXXdU1xHUUqgBKd5tVau/nAsjHmVeAz3xKJSImtXT6PEyevYGUjw0V3veY6johEENV2kfIpplI8fT+aRV5Otusovvrf7A1Ui4/lzJP0BZqUjRLt8TXGJB1y9WJg3tG2FZGyM/X+P1IlE+JvuEHTF4nIcVFtFyl/Zrw1gqVfvwlAdGxwDgXemZHNfZ/OY/SU5VhrXceRCqLIT8bGmHeAnkAdY8w64H6gpzGmA2CBVcBNpZhRRIrh54nv0GbmHha2rcSlA+5wHUdEyjHVdpHyL2PHJvY/8192JBqanXk1UdHRriP55plJS9mdmcPwvm0wxriOIxVEkQNfa+0VhaweUwpZRCQMG54ZRXIMtLvnKddRRKScU20XKf++vf8qUvZAxpDLAzXoXbZlL/+etpoBnRvTOqma6zhSgYRzVmcRKSc+e/5OTlqey5Iu9WnV8WzXcURERCQMm+ZPocHkDaxoFUfHK+93HcdXo8YvJD42mjs0fZGUMf0IUCTCZWVmEP/eOLZVg/NHveM6joiIiIQp7cEhNM6Hdvc86DqKr6y1nNO6Pr1a16NOYiXXcaSC0cBXJMJ9MuIK2qfD/AEd6V47qegbiIiISLmVn5eHqVmFdWdWpX3n/q7j+MoYw5WnNXYdQyooDXxFItjG1YtImbSE1Q0NFw9/w3UcERERCVNUdDR9X57iOobvPpyxjoycPK7q3JioKJ3QSsqefuMrEsGm3Hc91TIgduD1mr5IREQkwk0f8zd+fHWo6xi+25WZw8PjFjBuzgZ0EmdxRQNfkQg1Y9J/aT1jFwvaVKLXVX9zHUdERETCkLlrC9mj/0fGvz4nLyfbdRxfPTdpKTszc7jvQk1fJO5o4CsSodY+/SD5UdDqrsddRxEREZEwfTPyamrvgmo3/o7o2DjXcXyzcus+3py2iss6JtO2YXXXcaQC08BXJAKNf/keWi7NZdFpdWh7Wm/XcURERCQMWxZNpf6ktaw8KZZO1zzsOo6vRo1fSFx0FEPP0/RF4pZ+FCgSYbIyM4j7z8dsrwq9H/mP6zgiIiISpukP3EaTXGg9bITrKL675vQm9G5Tn3pVK7uOIhWcBr4iEeaTB66i/RaY+/tUutVPdh1HREREwhTf6kTW1d3GBV0vdR3Fdz1Oqus6ggigga9IRNmyfjlNvlzEmiTDJfe/5TqOiIiI+KDX/e+7juC7/6atZdmWvQzt3ZK4GP26UtzTq1Akgnw9/Bqq7wNz3RWavkhERCTC/Tj6Dr4cfhE5mXtdR/HV7v05PPb5Imau2UFstM7iLOWDBr4iEWLWd5/SevoOFraKo/f197mOIyIiImHI2rONnNc/J3byYtdRfPfC5GVs25et6YukXNHAVyRCrPzHfVgDzf82ynUUERERCdPkB66mzk5IGNiP2PhE13F8s2ZbBv/8fhWXnNKI9o1quI4jcpAGviIRYMKYkbRanMOizrVo37Wv6zgiIiIShvSl06k7cRWrmsXQ+Y9/dx3HV3//YhHRUYa/nd/SdRSRAooc+BpjXjfGbDHGzDtkXS1jzJfGmKXe35qlG1Ok4srJzsL86312JsI5j+iEViISPtV2Ebd+GjmISjnQ4q67XUfx3e3ntODxS9tTv5qmL5LypTh7fN8Azj9s3TBgkrW2BTDJuy4ipeDjB64mebNl7XntqJPU1HUcEQmGN1BtF3Gm3jm9WH9hCs17XOk6iu9a1K/Kb1Mbuo4hcoQiB77W2inA9sNW9wfe9JbfBC7yOZeIAFs3rqTRhHmsq2+4WNMXiYhPVNtF3Or8h8c474nPXcfw1ftpaxn09kz2ZuW6jiJSqJL+xre+tXajt7wJqH+0DY0xNxpj0owxaenp6SV8OJGK6avh11BzL+Rf83ti4yq5jiMiwVas2q66LlJyP74yhHE3dGPftg2uo/hqb1YuT0xYzIZdmVSJi3YdR6RQYZ/cylprAXuM9tHW2k7W2k5169YN9+FEKow5U8fR6qdtLGoZy3k3POA6johUIMeq7arrIiWTvXcn2W98QeKC7cTGV3Edx1cvfbOM9D1ZjND0RVKOlXTgu9kYkwTg/d3iXyQRAVj2+D0YC03vfMh1FBGpGFTbRUrR5Ieuou4OqHT9BcQlVHcdxzdrt2fw6ncrufg3J/CbxjonnpRfJR34jgWu85avAz71J46IAEx842FaL8pm4ak16HBGf9dxRKRiUG0XKSXbVsyk9hcrWNU0ms4Dn3Adx1dPf7WUKIOmL5JyL6aoDYwx7wA9gTrGmHXA/cBjwPvGmIHAauCy0gwpUpHk5eZi3/wPu6pAzwf/5TqOiASQartI2Zo28mZSsqH5X/9KVHSwfgN7T59W9G3fgKTq8a6jiBxTkQNfa+0VR2nq5XMWEQE+evAa2m20zLmoFac3buE6jogEkGq7SNlqNfA2VjX9mHPOvq7ojSNEfn7oNAC1EytxdqujnudWpNwI++RWIuKf7ZvX0vDzWayvB7978F3XcURERMQHzc+8inMe+MB1DF99/Mt6LnrxB7buzXIdRaRYNPAVKUcmDr+KWnsg+8qLNX2RiIhIhPvx1aGMuyiV7atmu47iq31ZuTw+YREGqJUQ5zqOSLEUeaiziJSNhT9/Rasf01ncPIaL/jzKdRwREREJQ3bGLrL+OZ6qFhLrNHEdx1evfLuczbuzePGqU4iK0vRFEhm0x1eknFjw6J1E5UPykBGuo4iIiEiYvnnoGupth5hrziEusYbrOL5ZvzOTV6as4LepDenYpJbrOCLFpoGvSDkw6e3HabMgi4Udq9Ox1+9dxxEREZEw7Fg9l1qfL2V1SjSn3/S06zi+GvPdSgDu0vRFEmF0qLOIY3m5ueS8/ga7E6DHQ2+4jiMiIiJhmvrQn0nJgpQ7/hK46YvuuqAlF5zcgEY1E1xHETku2uMr4tjHo/5Ik/WWVWe3IKlJK9dxREREJEyn3/Mc2wZ2oVXvP7mO4htrLZnZeVSKiebUFB3iLJFHA18Rh3Zu20iDz35mQ1246CFNXyQiIhLp8vPyqH3iKfS483XXUXw1dvYGzn7yG9Zsy3AdRaRENPAVcejze6+g9m7IHHAhleJ1yJCIiEgkm/76XXzd62TWzfzCdRRfZWbn8djni6idGEejmvGu44iUiAa+Io4s/uUbWk7dzJJm0Vw46AnXcURERCQMOZl7yXh9LJX2W+q2ONV1HF+NnrKCjbv2M+LCtpq+SCKWBr4ijsx9ZAixuZD0l2Guo4iIiEiYvhl1DfW3grniTCpVre06jm827drPy98up8/JDejcVL/tlcilga+IA5PffYrW8/az4JSqdO59tes4IiIiEoad6xZS47NFrEmOouvgF1zH8dUHM9aSl2+5+4LWrqOIhEXTGYmUsbzcXDJfe4198dD1gWCd+EJERKQimvr3wTTJhMQ7BgVu+qJBZzXn3DYNSK6lc5FIZNMeX5Ey9snfb6TpOsuKs04kuVk713FEREQkTL1Gvc/eOy+k9QW3uI7iG2stW/dmYYyhZYOqruOIhE0DX5EytHvHFuqNncam2tD/oXdcxxEREZEwZe3ZRqWqtTnthmCdqHL83E30eHwy8zfsch1FxBdhDXyNMauMMXONMbOMMWl+hRIJqvH3DqDOLth72XnEV6nmOo6IyBFU20WKL+3fw5lxdneWfPVP11F8tT8nj1HjF9KkdhVaNdDnFQkGP37je5a1dqsP9yMSaEtn/0CL7zeytGk0/f7ytOs4IiLHotouUoTcrEx2j/6ISgaST+3rOo6vxny/kvU7M3ni9+2J1vRFEhA61FmkjMx65FbicqHO4KGuo4iIiEiYvn30WpLSLXlXdCe+ej3XcXyzZfd+Xpy8jPPa1qdrszqu44j4JtyBrwUmGmNmGGNuLGwDY8yNxpg0Y0xaenp6mA8nEpmmfPA8beZmsrBDIl37/sF1HBGRYzlmbVddF4FdGxaTOHYeaxtF0f22l13H8dU3i9PJ0fRFEkDhHurc3Vq73hhTD/jSGLPIWjvl0A2staOB0QCdOnWyYT6eSMTJy81lz+iXiK8Ep9//ius4IiJFOWZtV10Xgekv3kXDDEh88M+Bm77oslOT6dmyLvWqVXYdRcRXYe3xtdau9/5uAT4GOvsRSiRIxj45iBPX5LPszCY0bnmK6zgiIsek2i5StF4PfAhP3EzbC291HcU31lqWbdkLoEGvBFKJB77GmCrGmKoHloHewDy/gokEwd5d26n1yRQ214J+D7/nOo6IyDGptosUbcfa+URFR9P2t7e5juKrCfM3ce7/fcvU5TqvnQRTOHt86wPfG2NmA9OBcdbaL/yJJRIMn917OfV2wK5LzqZK1equ44iIFEW1XeQYZvznAVb1uZRf3n3YdRRfZeXmMWr8Ik6qV5XOKbVcxxEpFSX+ja+1dgWQ6mMWkUBZsWA6zb9bx7KUKPoPfcF1HBGRIqm2ixxdblYmO195j8qV4Tfn3eA6jq/e+GEVa7Zn8O+BnYmJ1qQvEkx6ZYuUkrQHb6ZyNtS+5XbXUURERCRMUx6/noabLbmXnU5CzQau4/gmfU8Wz329jHNa1+OMFnVdxxEpNRr4ipSC7z8dTZvZGSxITaBrvz+5jiMiIiJh2LN5BQmfzGFdQ8MZd7zmOo6v5qzbiTFwTx9NXyTBFu50RiJSiJ0vPkPlOOg04iXXUURERCRMs955jFoZUGX4wMBNX9SrdX1+vLsXVSppWCDBpj2+Ij779MlBNFudz7IeyZzYRrOAiIiIRLozbh9Ntbf+wckXD3UdxTfWWn5csQ1rrQa9UiFo4Cvio317dlH9w6/ZUhMufPhd13FEREQkTGtnjAMguWNfx0n89dXCLQwY/SOfz9vkOopImdDAV8RHY4dfTv3tsP2iHiRW13QAIiIikWzW+4+y++o7+eH5W1xH8VV2bj6PjFtA83qJ9G5T33UckTKhga+IT9YsnkmLb1ezonEU/TR9kYiISETLy8lm64v/ZlcVSL3ibtdxfPWvaatYtS2D4X1ba/oiqTD0ShfxyY8P3ER8FlT78y1Ex+i3MiIiIpFsyhN/5IRNluxLO5FYO9l1HN9s35fNM5OW0rNlXXq2rOc6jkiZ0cBXxAdTx/2T1rP2suDkeM743SDXcURERCQMe7etpfJHM1ifZDhj6BjXcXy1cus+qlWOZXhfTV8kFYt2S4n4YOvzT1IpBjrc+5zrKCIiIhKmJV+8RnwWJAy7lujYONdxfNWxSU2+/WtPHeIsFU5EDXyffXAQ0/Y3YVFGMvfMeo5RHW6lVcJaulRezW0jIvc3lUHtFwS3b4f26/Hpj9Fiex5TO1dl36f/oUVqN9fxREQiQlBrBAS3bxWpX6decCHtFqwl1XW4MHV6+Eu27s0+Yn2dxDjShp/rIJGIGxH1Vc+0/U14PvZZhix+jzabdzBk8Xs8H/ss0/Y3cR0tLEHtFwS3bwf6dceidzhhezaZcdCn8YaI75eISFkKao2A4PatQvRr0w6GLH6XR+Pfifh+AYUOeo+1XiSojLW2zB6sU6dONi0trcS3n9XmZCrl5x6xPs/AvK51wonmVLupW4ku5GmI9H5BcPt2tH5lRcXQYcHcsg8kIsVijJlhre3kOkdQlHZdj2qQxGWPvA/Au7f2hozMAtvFJDfh0pFvhdpvPhuycwq0xzVvye/ufi3UfsMZcNj7dnyb9vQf+gKZ+3bz6W1HztGa2KEzF976JNs3r2XiPVce0V79tB5ccOMjrFk6m6mPDS7QVpz616D3JfS8/HbmTB3HgjGPHbFt4/7X0rXfn/h54jssf+/FI9qbXzmITr0GMHXsq6z59F9HtLcZOIz2Xfsy+d2n2Pzlx0e0d7ztMVqkdmPiGw+z/bsJR7R3u+clkpu14/PR97LrpynF7tcBFz8/gUrxCXzyj1vYv/Cw2hhlGPBq6D4/GjWQ7OVLCrbHxTLgpa8B+GDk1eSuXV2wPSGeAc9NBOD9e39P/qbD5qKtXo0BT4Xm333vbxdht20r0Gxq1+byxz8B4N0hfWj3xcqj1vUZo8eyZ3/B11bzeolc2L4hAC9/u5z9OXkF2tskVaN32wYAPDtpKfmHfd5OTa7BWS3rkZuXz/OTlx3xuKem1KJb8zpkZufxypTlR7R3bVaHzk1rsSsjh39OXXlEe8+W9eiQXIP0PVmc+shXR3bMs+qxYM1NLBVTcWt7RB3qfNOA7gyc9S3dFtgCu6qjLaT+sNVZrtIS1H5B8PqWFQM/tTSMSe3OdNdhREQixIG63nmJpdIh498DNWJ5k+0H1yX/tJZauwvefnHznQeXm03bSOL+gu0Ldu45uNx26lZi8gu2z933EwA52fsLrUmzc6cCsHPbxsLbY34AYNu6ZYW2L2sAyVsptG8Ac+t9B5ffzsaFPxd6+wUp30O/P7Fx/rRC25eePA16DWDjrO8KbV/TbQbtu/Zly1HaN104hxap3dj2y/d0KKR924blJDdrx860wtshVP8214DGW4+s6znZ+6kUn0B22k+kzsoo2Bb967JNm0HqgqwC7Xvif12OSZtN22UFvyDZVu3X5YSfF9BsTcEnd2OdX7PU+HkJjTcWHHiuSfp1IFx/+pGD3kPreuz3K1m/s+CXLhe0a1Bg4Lszo+DA+NKOjQ4OfJ/7eik5eQUf4LouTTirZT3yrOXpr5ZyuFt6NgsNfHPyCm2PjY6ic9Na7N6fU2h7zYQ4OiTXYNu+rCPaRCqqiNrjmzJsHA/NfpVOKxeRHW2Iy7P83LQlI9oPZPkjfXxMWraa3TueB2eP4dRViwPVLwhu3wrvVytGpP5J356KlGPa4+uvsqjrB6aHy8s9cs+wX+1Hawunvbj1r7Qev7Taj7euH+v+y+K5LW77ser6ykcL/7xijAHgaJ+li9N+rM/hfrU3vXv8UbfRZxYJgjLZ42uMOR94BogGXrPWHnmcjo+6RM2na/Y8fmjajvfPWsZlk5vTLXseXaIXER3TrzQfulR1iV5Et5z5gesXBLdvR+1X1HxARUREIldZ1vbjqetFzY8eTntp3Pfx1L/S7Jvf7SWp6+Up/9Haj1XXjTl2XT8wwC1Jezi39aNdpCIp8cDXGBMNvACcC6wDfjbGfQXbVAAACXtJREFUjLXWLvAr3OG6VF7N8NNuZFp+W+KivmRE6rl0iZpPl8qri75xORbUfkFw+xbUfolIxVbWtT3I76VB7Zv6FXnqJMYd9azOIhVJiQ91NsZ0AUZaa8/zrt8NYK199Gi3CfeQKBERkXDoUOdjO97arrouIiKuFbe2hzOd0QnA2kOur/PWHR7kRmNMmjEmLT09PYyHExERkVJWZG1XXRcRkUhU6vP4WmtHW2s7WWs71a1bt7QfTkREREqR6rqIiESicAa+64HkQ6438taJiIhIZFJtFxGRQApn4Psz0MIY09QYEwcMAMb6E0tEREQcUG0XEZFAKvFZna21ucaYwcAEQlMevG6tne9bMhERESlTqu0iIhJUJT6rc4kezJh0wK/zwtcBtvp0X+VJUPsFwe2b+hV5gto39atoTay1+mGqT1TXiy2ofVO/Ik9Q+6Z+RZ4yr+1lOvD1kzEmLYhTUgS1XxDcvqlfkSeofVO/JJIF+XkOat/Ur8gT1L6pX5HHRd9K/azOIiIiIiIiIi5p4CsiIiIiIiKBFskD39GuA5SSoPYLgts39SvyBLVv6pdEsiA/z0Htm/oVeYLaN/Ur8pR53yL2N74iIiIiIiIixRHJe3xFREREREREiqSBr4iIiIiIiARaxA18jTFDjDHzjTHzjDHvGGMqu85UUsaY140xW4wx8w5bf6sxZpHXz8dd5SspY0xlY8x0Y8xsrw8PeOvfNsYs9p67140xsa6zHi9jTA1jzAfe87PQGNPlkLahxhhrjKnjMmNxFfb6M8Y84fVtjjHmY2NMDW99rDHmTWPMXK/fd7tLfmzGmGRjzGRjzALv9fcXb/1IY8x6Y8ws79LnkNu0N8ZM87afW17fV4wxq7x8s4wxad6633u5840xnQ7Z9lxjzAxv+xnGmLPdJT/SUV5/tYwxXxpjlnp/a3rrr/Jek3ONMVONMamH3Ve0MeYXY8xnZd0P8UdQarvqeuTVdQhObVddj7y6DsGp7RFR1621EXMBTgBWAvHe9feB613nCqM/PYBTgHmHrDsL+Aqo5F2v5zpnCfplgERvORb4CTgd6OO1GeAd4GbXWUvQtzeBG7zlOKCGt5wMTABWA3Vc5yxmXwp7/fUGYrzlvwN/95avBN71lhOAVUCK6z4cpV9JwCneclVgCdAGGAncWcj2McAcINW7XhuIdt2Po/Rt1eGvL6A10BL4Buh0yPrfAA295XbAetf5i/H6exwY5i0PO+T11xWo6S1fAPx02H3dAfwH+Mx1v3Qp0WshMLVddT3y6rrXp0DUdtX1g9tHTF338gWitkdCXY+4Pb6EXszxxpgYQv+oGxznKTFr7RRg+2GrbwYes9ZmedtsKfNgYbIhe72rsd7FWmvHe20WmA40chayBIwx1Qn9U48BsNZmW2t3es3/B/wNiJizxRX2+rPWTrTW5npXf+TX58gCVbz/u3ggG9hdVlmPh7V2o7V2pre8B1hI6IP10fQG5lhrZ3u32WatzSv9pP6w1i601i4uZP0v1toD74/zCb1vVirbdEd3lPe//oQ+gOL9vcjbdqq1doe3/tDXJcaYRkBf4LVSDSylLRC1XXU9suo6BKu2q64fFNF1HSKztkdCXY+oga+1dj3wD2ANsBHYZa2d6DaV704CzjDG/GSM+dYYc6rrQCXhHaIwC9gCfGmt/emQtljgGuALV/lKqCmQDvzTO/ziNWNMFWNMf0LfuM12nM9vfwQ+95Y/APYR+r9bA/zDWnv4m1u5Y4xJIfTt6IHX32Dv0JrXDxxuQ+h/zhpjJhhjZhpj/uYganFZYKJ3eNONx3G7S4CZBz54l2P1rbUbveVNQP1CthnIr69LgKcJfTDNL+VsUkoqQG1XXS/fKlJtV10vn4Jc28tVXY+oga/3gu5P6E2qIaFvqq52m8p3MUAtQocQ/RV43xhj3EY6ftbaPGttB0Lf4HQ2xrQ7pPlFYIq19js36UoshtAhHC9Za39DqGCMBO4BRjjM5TtjzL1ALvC2t6ozkEfo/64pMNQYc6KjeMVijEkEPgRut9buBl4CmgEdCBX6J71NY4DuwFXe34uNMb3KPnGxdLfWnkLosKBBxpgeRd3AGNOW0OFtN5V2OD95e5AK7GUxxpxFqEDe5V2/ENhirZ1R9gnFLxWgtquul28Vorarrpfbug4VpLaXh7oeUQNf4BxgpbU23VqbA3xE6BjxIFkHfOQdOTSd0Lcd5f6ECkfjHS40GTgfwBhzP1CX0LH7kWYdsO6Qb7k/IFQsmwKzjTGrCH0gmGmMaeAmYviMMdcDFwJXeW9SEPot0BfW2hzvML0fgE5HuQvnvL0PHwJvW2s/ArDWbvY+uOUDrxIq+hB6XqdYa7daazOA8YSe13LH2zN24FDJj/m1D4XyDhf6GLjWWru89BOGbbMxJgnA+3vwkFBjTHtChz31t9Zu81Z3A/p5/3vvAmcbY94q28jig6DXdtX18i3wtV11vfzWdQh8bS9XdT3SBr5rgNONMQnet6W9CB3nHySfEDoRBsaYkwidZGGr00THyRhT1/x61sB44FxgkTHmBuA84ArvTSqiWGs3AWuNMS29Vb0IHWJSz1qbYq1NIfRme4q3bcQxxpxP6PCSfl6xOGANcLa3TRVCey4WlX3ConnvDWOAhdbapw5Zn3TIZhcDB846OAE42XtfiQHOBBaUVd7i8g69q3pgmdBvmOYdY/sawDhCJ5X4oWxShm0scJ23fB3wKYAxpjGhwdA11tolBza21t5trW3k/e8NAL621gZpT2FFEfTarrpejgW9tquul9+6DhWitpevum7LwVnAjucCPEDoH3Me8G+8syRG4oXQGRA3AjmE3lQHEiqIb3n9mwmc7TpnCfrVHviF0Bn15gEjvPW5wHJglncZ4TprCfrWAUjz+vYJ3hnpDmlfRQSc+dHLWtjrbxmw9pDn6GVv20Tgv4ROpLAA+Kvr/MfoV3dCh9LMOaQffbz3i7ne+rFA0iG3udrr2zzgcdd9OEq/TgRme5f5wL3e+ou95y8L2AxM8NYPJ3TI3qxDLuXmbLJHef3VBiYBSwmdBbeWt+1rwI5D+pFWyP31RGd1jthLUGq76nrk1XWvH4Go7arrkVXXvZyBqe2RUNeNd8ciIiIiIiIigRRphzqLiIiIiIiIHBcNfEVERERERCTQNPAVERERERGRQNPAV0RERERERAJNA18REREREREJNA18RUREREREJNA08BUREREREZFA+3+WfX8ZqnJZXwAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 1188x612 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "fig.set_size_inches(16.5, 8.5, forward=True)\n", "\n", "plt.subplot(221)\n", "best = latence_for_best_param(directory + \"vss_static_32_500000000\")\n", "plt.plot(best.keys(), best.values(), \"s-\", label=\"32_500000000\")\n", "\n", "best = latence_for_best_param(directory + \"vss_static_32_100000000\")\n", "plt.plot(best.keys(), best.values(), \"x-\", label=\"32_100000000\")\n", "\n", "best = latence_for_best_param(directory + \"vss_static_32_50000000\")\n", "plt.plot(best.keys(), best.values(), \"+-\", label=\"32_50000000\")\n", "\n", "best = latence_for_best_param(directory + \"vss_static_32_10000000\")\n", "plt.plot(best.keys(), best.values(), \"*-\", label=\"32_10000000\")\n", "plt.legend()\n", "plt.subplot(222)\n", "\n", "best = latence_for_best_param(directory + \"vss_static_16_500000000\")\n", "plt.plot(best.keys(), best.values(), \"s--\", label=\"16_500000000\")\n", "\n", "best = latence_for_best_param(directory + \"vss_static_16_100000000\")\n", "plt.plot(best.keys(), best.values(), \"x--\", label=\"16_100000000\")\n", "\n", "best = latence_for_best_param(directory + \"vss_static_16_50000000\")\n", "plt.plot(best.keys(), best.values(), \"+--\", label=\"16_50000000\")\n", "\n", "best = latence_for_best_param(directory + \"vss_static_16_10000000\")\n", "plt.plot(best.keys(), best.values(), \"*--\", label=\"16_10000000\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A faire :\n", "\n", "tourner des simulations entre avec max_internal_steal entre {0 40} pour savoir une courbe bien precis\n", "\n", "Afficher l'interface de confiance" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "min for vss_static_16_100000000 : 41.0\n", "min for vss_static_16_500000000 : 21.0\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1188x612 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "fig.set_size_inches(16.5, 8.5, forward=True)\n", "\n", "data = load_file(directory + \"vss_static_16_10000000\")\n", "overhead, minimum = compute_overhead_for_latence(data, 512)\n", "plt.subplot(221)\n", "plt.ylim(0,6500)\n", "\n", "print(\"min for vss_static_16_100000000 : \", minimum)\n", "plt.plot(overhead.keys(), overhead.values(), \"x--\", label=\"vss_static_16_100000000\")\n", "plt.legend()\n", "\n", "data = load_file(directory + \"vss_static_16_50000000\")\n", "overhead, minimum = compute_overhead_for_latence(data, 512)\n", "plt.subplot(222)\n", "plt.ylim(0,8000)\n", "plt.plot(overhead.keys(), overhead.values(), \"x--\", label=\"vss_static_16_500000000\")\n", "plt.legend()\n", "print(\"min for vss_static_16_500000000 : \", minimum)\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f1f539a84e0>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GDg4Ovmu8s2fPZq1/DQkJIT09nVKlSlG/fn2OHj1KeHg4ycnJzJ8/n06dOmW9LiUlJWtNa0JCQlbBJy0tjeTk5Ht6zyIiedHGQ+cZvzKMNrXKMvIfNXO6O5LL2SKXOX36NEWKFKF3796MGDEia8nttTnDlStXKFq0KC4uLpw7d47Vq1dnvf7a+67VqlUrFi1axKVLlwBuuXTlQfrUsGFDfv/9d44dOwZY9vU4cuRIVlGjdOnSxMXF3fKUlRudOXMm6/clS5bg4+MDQKdOnZg/fz5JSUmEh4dz9OhRGjT4ewLz0aNHiYiIoEWLFsTHx2NnZ4dhGCQk3FyMEhHJrz5cc5ilu08zom11uvp75HR3bilPz+i4m8yNTz5ac5jT0Qm4uzozom31B94QpXr16kydOpX+/ftTq1YthgwZcsv7goODGTx4MPHx8VSuXJlZs2ZlPefk5ISfnx8pKSlZUyXfeOMNAgMDCQoKokOHDreNf6s9OmJjY+ncuTOJiYmYpsmkSZMA6NGjBwMHDmTy5MksXryY77//niFDhhAUFERKSgo9evSgTp06jBs3ju7du+Pm5karVq0IDw+/42ewePFipk2bhoODA87OzsyfPx/DMHBwcGDKlCm0bduWtLQ0+vfvj7f336NVU6dOJTAwkCJFiuDr60t8fDy1a9emffv2NtuxXUQktzh09gqvzttFzUdL8FmPutjfeAabyA1skcvs27ePESNGYGdnh6OjI9OmTQNg0KBBtGvXDnd3dzZu3Iifnx81atS4bsnure7L5O3tzahRo2jevDn29vb4+fndNHBzuz06stun2bNn07Nnz6ylt0FBQVSrVo2BAwfi4+NDuXLlqF+//l0/gzfeeIPdu3djGAaenp58+eWXWe/hueeeo1atWjg4ODB16lTs7f8uNI0aNYp337Wswu7ZsyddunTh/fffZ/z48dn9+EVE8rQ5W08wbdNxXni8IkNbVMnp7tyWcb+7Uj8MAQEBZmho6HXXDh48SM2aOTcCduLECTp27Mj+/ftzrA8FSU7/vUVErOV8bCLPTN1Cano6y15uSjkXp4feB8MwdpimGfDQAxdguTGXkYdHf2sRyU/WHjjL4O920KrGI0zvXQ8H+4e/QCS7uUy+ntEhIiKSGySmpDHw2x1cvprMosGNcqTIISIiInK/dp6MYtj8XdT2cGVyT78cKXLcCxU67pGnp6dmc4iISLalp5sMX7iHvRHRTO9dD5/yLjndJREREZFsC794lZeCQylbwomZgQEUKZT7ywi5uwwjIiKSx0363xF+2neG//6jBm29y+V0d0RERESy7WJcEoHfhAAQ/GIDShcrnMM9yh4VOkRERGzkhx0RTNl4jB71KzCwWeWc7o6IiIhItsUnpzJg9nbOxyYyMzAAz9JFc7pL2aZCh4iIiA2EhF9m5I97aVylFBO6+GQdpy0iIiKS26WmpfPq3F3si4zh857++FV0y+ku3ZPcv7hGREQkjzlx8Sr/nBNKhZJFmNarHo65fMMuERERkUymaTJ62QHWHzpPUBcf2tQqm9NdumfKvHJAixYtuPGoOVtZunQpYWFh9/362bNn88orr9x0PSYmhqeffpo6derg7e3NrFmzsp4LDg6matWqVK1aleDgYACSkpJo164dPj4+fPHFF1n3Dho0iJ07d953/0REcpuY+BT6z94OwDeB9XEp4pjDPRLJnk8//ZT4+Ph7vq99+/ZER0fnaJ9up1+/fixevPim67t376Zhw4bUrVuXgIAAQkIs689N02TYsGF4eXnh6+ublaMcPnyYevXq4evry9atWwFITU3lySeffKD+iYjkRlM3HmNeyEmGtqhC74aP5XR37kv+L3TsXQif+MA4V8vPvQsfSti0tLSHEuduHrTQcTtTp06lVq1a7Nmzh02bNjF8+HCSk5O5fPky77zzDtu2bSMkJIR33nmHqKgo1qxZQ9OmTdm7dy9z5swBYM+ePaSlpeHv72/1/omI5ISUtHSGfL+DU1HxfNknb61llVzsIeUy91voWLVqFa6urjnap3v1xhtvMHbsWHbv3s348eN54403AFi9ejVHjx7l6NGjzJgxgyFDhgDw5Zdf8tlnn7Fq1SomTpwIwLRp0+jduzdFihSxev9ERHLK4h0RTFx7hK5+5RnRtnpOd+e+5e9Cx96FsGIYxJwCTMvPFcMeKEE4ceIENWrUoFevXtSsWZNu3bplfQF7enry5ptv4u/vz6JFi7JGC3x9fXnmmWeIiorKamfOnDnUrVsXHx+frFGEkJAQGjVqhJ+fH40bN+bw4cP31LeRI0dSq1YtfH19ef3119myZQvLly9nxIgR1K1bl+PHj3P8+HHatWtHvXr1aNasGYcOHQJgxYoVPP744/j5+fHkk09y7ty5O8YyDIPY2FhM0yQuLo6SJUvi4ODAmjVraNOmDSVLlsTNzY02bdrw888/4+joSHx8PCkpKZimCcDo0aOZMGHCPb1HEZHcyjRNRi/dz5bjl3i/qy8NKpXM6S5JfmCDXObq1at06NCBOnXq4OPjw4IFC5g8eTKnT5+mZcuWtGzZEoAhQ4YQEBCAt7c3Y8eOBbjlfZ6enly8eBGAb7/9Fl9fX+rUqUOfPn2s3qe1a9fSqFEj/P396d69O3FxcQCMHz+e+vXr4+Pjw6BBg7JyjdsxDIMrV64Allmq7u7uACxbtoy+fftiGAYNGzYkOjqaM2fOZOUx8fHxODo6Eh0dzYoVK+jbt2+236OISG63+egFRv6wlyZepXj/Wd88vb9Y3t+jY1aHm695d4EGA2HdO5CScP1zKQmw+k3wfQ6uXoKFN3xBvfjTXUMePnyYmTNn0qRJE/r3788XX3zB66+/DkCpUqWypjn6+vry+eef07x5c8aMGcM777zDp59+CkB8fDy7d+/m119/pX///uzfv58aNWqwefNmHBwcWLduHW+99RY//PDDTfHr1q3L7t27r7t26dIllixZwqFDhzAMg+joaFxdXenUqRMdO3akW7duALRu3Zrp06dTtWpVtm3bxtChQ9mwYQNNmzbljz/+wDAMvv76az788EM+/vjj234Gr7zyCp06dcLd3Z3Y2FgWLFiAnZ0dkZGRVKhQIes+Dw8PIiMj6d69O3PmzKFhw4aMGDGC5cuX4+/vn5VYiIjkdV9t/pP520/xSksvnq3nkdPdkbzkIecyP//8M+7u7vz0k+W+mJgYXFxcmDRpEhs3bqR06dIAvPvuu5QsWZK0tDRat27N3r17GTZs2E33ZTpw4ABBQUFs2bKF0qVLc/ny5Ztih4aGMn36dL7++ut77tPFixcJCgpi3bp1FC1alA8++IBJkyYxZswYXnnlFcaMGQNAnz59WLlyJU8//fRtP4NPP/2Utm3b8vrrr5Oens6WLVsAbpvHvPzyy/Tt25ekpCS+/PJLJkyYwFtvvYWdXf4eMxSRguPA6RiGfLcTr0eKMa13PQo55O3/f8v7hY47uRJ56+sJN3/x3osKFSrQpEkTAHr37s3kyZOzCh3PP/88YPmCjo6Opnnz5gAEBgbSvXv3rDZ69uwJwBNPPMGVK1eIjo4mNjaWwMBAjh49imEYpKSk3DL+jUUOABcXF5ycnBgwYAAdO3akY8eON90TFxfHli1brutHUlISABERETz//POcOXOG5ORkKlWqdMfPYM2aNdStW5cNGzZw/Phx2rRpQ7NmzW57v4ODA3PnzgUgJSWFtm3bsmzZMl577TVOnjxJ37596dSp0x1jiojkVmsOnOW91YfoUPtRXmtTLae7I/mJDXKZ2rVrM3z4cN588006dux42+/vhQsXMmPGDFJTUzlz5gxhYWH4+vrett0NGzbQvXv3rAJIyZI3z2oKCAi4qciR3T798ccfhIWFZeVgycnJNGrUCICNGzfy4YcfEh8fz+XLl/H29r5joWPatGl88sknPPvssyxcuJABAwawbt26295fsWJFNm3aBMCxY8eIiIigZs2a9OnTh+TkZCZMmEC1avrfvojkTRFR8bw4azslnByY/WIDSjjl/f3F8n6h406jFi4eGVM9b7yeUakvWipbMzhudOMUnmsfFy2avfXYt2pj9OjRtGzZkiVLlnDixAlatGiR7T45ODgQEhLC+vXrWbx4MVOmTGHDhg3X3ZOeno6rq+stCyWvvvoqr732Gp06dWLTpk2MGzfujvFmzZrFyJEjMQwDLy8vKlWqxKFDhyhfvnxWIgCWAsqN7+OLL76gb9++/PHHH7i4uLBgwQJatWqlQoeI5En7I2P49/zd1PFw5ePn6mBnl3eneUoOeci5TLVq1di5cyerVq3i7bffpnXr1lmzITKFh4czceJEtm/fjpubG/369SMxMfGe4li7T6Zp0qZNG+bNm3fd9cTERIYOHUpoaCgVKlRg3Lhxd+1rcHAwn332GQDdu3fnpZdeAqB8+fKcOvX35x0REUH58uWve+2oUaMICgpi8uTJvPTSS3h6evLWW2/x/fff3/f7FxHJKTHxKfSbtZ2ElDR+GNKYci5OOd0lq8jb81HupvUYcHS+/pqjs+X6Azh58mTWjttz586ladOmN93j4uKCm5sbmzdvBix7cmTO7gBYsGABAL/99hsuLi64uLgQExOT9WU6e/bse+pTXFwcMTExtG/fnk8++YQ9e/YAULx4cWJjYwEoUaIElSpVYtGiRYAlYci879rYmSel3EnFihVZv349AOfOnePw4cNUrlyZtm3bsnbtWqKiooiKimLt2rW0bds263VRUVGsXLmSvn37Eh8fj52dHYZhkJCQcLtQIiK51tmYRAYEb6dk0UJ81TcAJ0f7nO6S5Dc2yGVOnz5NkSJF6N27NyNGjMhacnttznDlyhWKFi2Ki4sL586dY/Xq1Vmvv/a+a7Vq1YpFixZx6dIlgFsuXXmQPjVs2JDff/+dY8eOAZZ9PY4cOZJV1ChdujRxcXG3PGXlRu7u7vzyyy+AZSZK1apVAejUqRPffvstpmlmDcg8+uijWa/75ZdfcHd3p2rVqll5jJ2dnU5eEZE8KTEljYFzQjl5KZ4ZfQKoVrZ4TnfJavL+jI478X3O8nP9eIiJsIyKtB7z9/X7VL16daZOnUr//v2pVatW1o7cNwoODmbw4MHEx8dTuXLl645gdXJyws/Pj5SUFL755hvAsgN4YGAgQUFBdOhwi/W6GW61R0dsbCydO3cmMTER0zSZNGkSAD169GDgwIFMnjyZxYsX8/333zNkyBCCgoJISUmhR48e1KlTh3HjxtG9e3fc3Nxo1aoV4eHhd/wMRo8eTb9+/ahduzamafLBBx9kTVUdPXo09evXB2DMmDHXTV0dP348o0aNws7OjrZt2zJ16lRq167N4MGD7xhPRCS3uZqUyoDg7VxNSmPxkAaUKV44p7sk+ZENcpl9+/YxYsQI7OzscHR0ZNq0aYDlyPd27drh7u7Oxo0b8fPzo0aNGtct2b3VfZm8vb0ZNWoUzZs3x97eHj8/v5sGbm63R0d2+zR79mx69uyZtfQ2KCiIatWqMXDgQHx8fChXrlxWDnInX331Ff/6179ITU3FycmJGTNmAJajcletWoWXlxdFihS5LnczTZOgoKCswapBgwbRq1cvUlNTs/orIpJXpKebDF+0h5Dwy0zu6UejKqVyuktWZdxtV+qcFBAQYIaGhl537eDBg9SsWTOHemQ5daVjx47s378/x/pQkOT031tE5FbS0k3+OWcHGw6dY2a/+rSs/khOdylbDMPYYZpmQE73oyDJjbmMPDz6W4tIbhW0MoyvfwvnrfY1GPRElZzuTrZlN5fJ30tXREREbOD91QdZd/AcY5/2zjNFDhERERGAmb+F8/Vv4fRr7MnAZpVzujs2oULHPfL09NRsDhGRAmxeyEm+2hxOYKPHCGzsmdPdEREREcm2VfvOEPRTGO28yzG6Y62bDsnIL/JkoSM3L7cR69HfWURym9+PXWT00v20qF6G0R1r5XR3JA/Td1z+p7+xiOQ2209c5t8LduNf0Y1Pe9TFPh+fFJfnCh1OTk5cunRJXx75nGmaXLp0CSen/HG8kYjkfcfOxzH4ux1UKVOMz3v64WBvg6/QvQvhEx8Y52r5uXeh9WNIjlMuk/8pjxFCrZnTAAAgAElEQVSR3ObY+VheCg7Fw82Zr215UlwuyWXy3KkrHh4eREREcOHChZzuitiYk5MTHh4eOd0NEREuX02m/+ztFHawY2a/AIo7OVo/yN6FsGIYpGQctx1zyvIYHvi0MMldlMsUDMpjRCS3OH8lkcBvtuNob0fwiw1wK1rINoFyUS6T5wodjo6OVKpUKae7ISIiBURSahr/nBPKuSuJzBvUEA+3IrYJtH7834lBppQEy3UVOvIV5TIiIvKwxCWl8uLs7UTFJ7NgUCMqlLRRHgO5KpfJc0tXREREHhbTNBn5wz62n4ji4+fq4F/RzXbBYiLu7brcE8MwXA3DWGwYxiHDMA4ahtHIMIyShmH8zzCMoxk/3TLuNQzDmGwYxjHDMPYahuF/TTuBGfcfNQwjMOfekYiIyJ2lpKUz9PudHDoby9Re/tT2cLFtwFyUy6jQISIichtTNhxjya5IhrepRkdfd9sFij4JdrdZK+uiqe9W8hnws2maNYA6wEFgJLDeNM2qwPqMxwD/AKpm/DMImAZgGEZJYCzwONAAGJtZHBEREclNTNPkvz/u49cjF3jvmdq0rP6I7YMWu02MHMhlVOgQERG5hZV7T/Px/47Q1a88r7Tysl2gi8fgm3+A4Qj2ha9/ztEZWo+xXewCwjAMF+AJYCaAaZrJpmlGA52B4IzbgoEuGb93Br41Lf4AXA3DeBRoC/zPNM3LpmlGAf8D2j3EtyIiIpItn6w7yuIdEfz7yao8V7+C7QOe+A0SrgA3nOSSQ7mMCh0iIiI32HkyitcW7qG+pxvvPVvbtmfMb5kMqYnw0lroPAVcKgCG5efTk7U/h3VUAi4AswzD2GUYxteGYRQFypqmeSbjnrNA2YzfywOnrnl9RMa1212/jmEYgwzDCDUMI1QbjoqIyMM2P+Qkk9cf5bkAD/7VuurDCZoQDaUqQbv3ckUuk+c2IxUREbGlU5fjGfRtKI+6OPFlnwAKO9jo+DXTBMOA9h9B0/9AyUrwqK8KG7bhAPgDr5qmuc0wjM/4e5kKAKZpmoZhWOW8V9M0ZwAzAAICAnSGrIiIPDQbD51n1NL9NK9WhnefsfFgDcDFo1C6KtTsCNXagb0DNBxi25jZkK0ZHdrAS0RECoLYxBReCg4lOTWdmYH1KWmr49fCN8PMNhB/GRwKW4ocYksRQIRpmtsyHi/GUvg4l7EkhYyf5zOejwSunefrkXHtdtdFRERy3N6IaIZ+v5Oajxbni17+ONrbcAGHacL6CTD1cYgItVyzzz3zKLL7zrWBl4iI5Gupaem8MncXxy/EMa13PbweKWabQEfWwPfdICkW0pJtE0OuY5rmWeCUYRjVMy61BsKA5UDmwEsgsCzj9+VA34zBm4ZATMYSlzXAU4ZhuGXkME9lXBMREclRJy/F03/2dkoVK8Q3/epTtLANiw5pqbD8Vdg8Eeq+AI/WtV2s+3TXd3/NBl79wLKBF5BsGEZnoEXGbcHAJuBNrtnAC/gjYzbIoxn3/s80zcsZ7WZu4DXPem9HRETk/kxYGcYvRy7wXtfaNPEqbZsg+3+EHwdCWR/o/SMULWWbOHIrrwLfG4ZRCPgTeBHLgM9CwzAGAH8BmeuGVgHtgWNAfMa9mKZ52TCMCcD2jPvGZ+Y1IiIiOeXy1WQCZ4WQmm4S3L8BjxR3sl2w5HhY3B+OrIYnRkDLUZaluLlMdso8127gVQfYAfwLG27ghWUmCBUrVsz2GxEREblfs38PJ3jrXwx6ojI9G9jou+fAUvhhAFRoCC8sAKcStokjt2Sa5m4g4BZPtb7FvSbw8m3a+Qb4xrq9ExERuT+JKWm8FLydyOgE5r70OFXK2GhGaqb9i+HIz9B+IjQYaNtYDyA7S1cyN/CaZpqmH3CVW2zgBVhtAy/TNANM0wwoU6aMNZoUERG5rY2HzjN+ZRhtapXlzXY1bBeoYkPw7wu9f1CRQ0RERB5YWrrJv+bvYtepaCb3qEuAZ0nbBTMz/nPfrw8M2pirixyQvUKHNvASEZF86dDZK7w6bxc1Hy3BZz3qYm9n5amXpgkHlljWshYvB09/BoWKWDeGiIiIFDimaTJ+xQHWHDjH2I61aOfzqO2CnQuD6c3gwhHLMhV3P9vFspK7Fjq0gZeIiORH52MTGTA7lKKF7ZkZWJ8ihay8aZdpwv9Gw6J+sGeuddsWERGRAm3Gr39mLbvt18SGp7f9tQVmtYOrF/LUJurZzeq0gZeIiOQbiSlpDPx2B5evJrNocCPKuVh50670NPhpOOyYBfUHQt3e1m1fRERECqxluyN5b/UhOvo+ykhbLrs9uNKy8ahrRejzo+VnHpGtQoc28BIRkfwiPd1k+MI97I2I5sve9fAp72LdAGkpsHQI7FsETf8Drcfmyt3IRUREJO/Zcvwiry/aw+OVSvLxc3Wws/ay20xH18HCPlC+HrywEIrYcP8PG7Dh4boiIiK5z6T/HeGnfWd4q30NnvIuZ/0Al47BoVXQegw0G2799kVERKRAOnT2Cv+cs4NKpYsyo28AhR3sbRfMswk0fQ2avQaFitoujo2o0CEiIgXGDzsimLLxGD3qV2Bgs8rWbTwtFewd4JGa8GoolHC3bvsiIiJSYJ2JSeDFWdspUsieWS82wMXZ0fpB0tPgt0mWZbfOrtB6tPVjPCTZOXVFREQkzwsJv8zIH/fSuEopJnTxwbDmcpKEaJjdAbbNsDxWkUNERESs5EpiCi/O2k5sYiqz+jWgvKuz9YOkJMDCvrAhCA4ut377D5kKHSIiku+duHiVf84JpULJIkzrVQ9Heyt+/V29CMEdIXIHFC9rvXZFRESkwEtOTWfwnB0cOx/H9N71qOVewvpBEqJgTlc49BP840Pw72v9GA+Zlq6IiEi+FhOfQv/ZlgO/ZvWrj0sRK071jImEOV0g+hS8MB+8nrRe2yIiIlKgmabJG4v3sOX4JSY9V4emVUtbP8iV0/Dds5Y9xrp9Az5drR8jB6jQISIi+VZKWjpDvt/Bqah4vn+pIY+VsuJmWslXYdY/IP6y5ci1xxpbr20REREp8D5cc5ilu08zom11uvp72CaIaVp+9loMlZvbJkYOUKFDRETyJdM0Gb10P1uOX+Lj7nVoUMnKx6IVKgpNhoG7P5T3t27bIiIiUqDN2XqCaZuO0+vxigxtUcX6Ac4fhNLVwKU8DP4N7Gx4gksO0B4dIiKSL321+U/mbz/Fq628eLaeFUdBTu+CE79bfq//koocIiIiYlVrD5xl7PIDPFmzLOM7W3kDdYBDq2BGC/h1ouVxPitygAodIiKSD605cJb3Vh+ig++j/OfJatZr+K+tENwJVo2A9HTrtSsiIiIC7DwZxbD5u/D1cOXznn7Y21m5yLHzW1jQCx6pBfUHWLftXESFDhERyVf2R8bw7/m7qePhysfd62BnrQTh2HqY8wwUKwu9FoGdvkJFRETEesIvXuWl4FDKlXBiZmAAzoWsONPCNOHXj2D5q1C5JQSugKI22Nw0l1CWJiIi+cbZmEQGBG+nZNFCfNU3ACdHKyUIB1fAvB5QygteXG1ZzyoiIiJiJRfjkgj8JgQDmP1iA0oVK2zdAFEn4NePwbcHvLAAChezbvu5jDYjFRGRfOFqUioDgrdzNSmNxUMaUKa4FROEQz/Bo3UsMzmc3azXroiIiBR48cmpDJi9nfOxicwf1AjP0lY8JS49zbIHR8lKMGgjlK5eIGalqtAhIiJ5Xlq6yb/m7+bgmSt8068+NcqVsE7DyfFQqAh0+hxSk/L96IeIiIg8XKlp6bw6dxf7ImOY0SeAuhVcrdd4YgzM7wW1u0G9fvBITeu1ncvl/1KOiIjke++vPsi6g+cY+7Q3Lao/Yp1Gf/sEvmwGVy+BvaOKHCIiImJVpmkyetkB1h86z4QuPjxZq6z1Gr9yBma1h5N/QKGCl8NoRoeIiORp80JO8tXmcAIbPUZgY88Hb9A0YcME2Pwx+HQDJyvNDhERERG5xtSNx5gXcpKXW1ah1+OPWa/hC0fgu66QEAW9FkKVVtZrO49QoUNERPKs349dZPTS/bSoXobRHWs9eIPp6fDzSAj5EvwDoeMn+fJseREREclZi3dEMHHtEbr6l+f1p6pbr+H4y/BNW0v+0u8ncK9rvbbzEBU6REQkTzp2Po7B3+2gSplifN7TDwd7K6zG3PyxpcjR6BV4KggMK59dLyIiIgXe5qMXGPnDXpp6leb9rr4Y1sw3ipSEVqMsszhKVrZeu3mMCh0iIpLnXL6aTP/Z2ynsYM/MfgEUd3K0TsMBL4KTCzQYqCKHiIiIWN2B0zEM+W4nXo8UY1pvfwo5WGnbzN3zLCerVGwI9V+yTpt5mDYjFRGRPCUpNY1/zgnl3JVEvupbDw+3Ig/WYHI8/PIhpCZD0dLw+CAVOURERMTqIqLieXHWdko4ORDcv4F1BmpMEzZPgqWDYdv0B28vn9CMDhERyTNM02TkD/vYfiKKKS/44VfR7cEaTLwC83rAX1vAoz5UaWmdjoqIiIhcIyY+hX6ztpOYksbiIY0pW8LpwRu9dm8xn27QZdqDt5lPqNAhIiJ5xpQNx1iyK5LXn6pGR1/3B2ss/jJ89yyc3QvPfq0ih4iIiFjV0l2RfLTmMKejE3C0tyMtPZ3vBzakWtniD954ajIsGQQHlkDDly17i9lpwUYmFTpERCRPWLn3NB//z7I7+cstvR6ssdhzMKcLXDoOz38H1f9hnU6KiIiIYCly/PfHfSSkpAGQnJaOo73B2ZhE6wSws7csW2kzAZoMs06b+YgKHSIikitdOwpSulhhLl9NooFnSd7rWvvBdyePO5dxtvwiqNzcOh0WERERyfDRmsNZRY5MKWkmH605TBe/8vffcOxZSE8Dl/LQfbb2FbsNFTpERCTXuXEU5EJcEgbQqa47hR3s77/h+MuWY9ce9YVhu8HRCutjRURERK6RmpZOZHTCLZ87fZvr2XLxGHz3DBQtAy+tV5HjDrSIR0REcp1bjYKYwLRNx++/0bP7YGoD2Pal5bGKHCIiImJl+yJi6DTl99s+7+7qfH8NR+6Ab56ynBbX/iMVOe5ChQ4REcl1bjfacd+jIKe2w+wOYF8IqrR6gJ6JiIiI3Cw+OZV3fwqj89TfuBiXxItNHsPZ8fpZqM6O9oxoW/3eGz+2DmY/DYWKwYC1UL6elXqdf2npioiI5Cr7I2NwtLcjOS39pufuaxTkz19gXk8o9ggELgfXilbopYiIiIjFr0cuMGrpPk5dTuCFxyvyZrsauDg7UsfDLWu/MXdXZ0a0rX7v+3Okp8OGIChVGXr9AMXL2uZN5DMqdIiISK4QdTWZiWsPMy/kJM6O9pgYpKSZWc/f1yhI7DmY+zy4eULfpVC8nHU7LXmKYRj2QCgQaZpmR8MwKgHzgVLADqCPaZrJhmEUBr4F6gGXgOdN0zyR0cZ/gQFAGjDMNM01D/+diIhIbnD5ajJBK8P4cVcklcsUZcGghjxeuVTW8138yt//xqOmCempYO8IPReAozM4lbBSz/M/FTpERCRHpaalMy/kJBPXHiEuKZW+jTz5T5tqbDx0/sFHQYqXhWemQ6UnLJuQSkH3L+AgkJkpfgB8YprmfMMwpmMpYEzL+BllmqaXYRg9Mu573jCMWkAPwBtwB9YZhlHNNM20GwOJiEj+ZZomy3afZvzKMK4kpPBqKy9ebumFk+MDbJh+rfR0WPs2RIXDc3M0i+M+ZLvQoVEQERGxtm1/XmLcijAOnrlCo8qlGNfJm+rligMPOAqycw6UcAev1uDdxYo9lrzKMAwPoAPwLvCaYTmjuBXwQsYtwcA4LIWOzhm/AywGpmTc3xmYb5pmEhBuGMYxoAGw9SG9DRERyWGnLsczaul+fj1yAb+Krrzf1Tcrd7GK1GRYOgT2L4bHB4OhbTXvx73M6NAoiIiIWMWZmATeW3WI5XtOU97VmS96+fMPn3IY1thBfOsXsOa/UPNpS6FDxOJT4A0gMxstBUSbppma8TgCyKyslQdOAZimmWoYRkzG/eWBP65p89rXZDEMYxAwCKBiRe0JIyKSH6Slm8z6PZyP1x7BzoB3OnnTu+Fj2NtZ8fSTpFhY0Bv+3AStx0LT/+h0lfuUrUKHRkFERMQaklLT+HpzOFM3HiM13WRY66oMaV4F50JWmOppmvDLh7Dp/6BmJ3h25oO3KfmCYRgdgfOmae4wDKOFreOZpjkDmAEQEBBg3uV2ERHJ5cJOX2Hkj3vZGxFD6xqPMKGLz/0fE3snCwMhfDN0/gL8elm//QIkuzM6HtooiIiI5E/rD55j/Mow/roUz1O1yjK6Yy0qlCxincZN07KWdesUqPMCdPoc7LUNlWRpAnQyDKM94IRldupngKthGA4Z+YwHEJlxfyRQAYgwDMMBcMGyHDfzeqZrXyMiIvlMYkoan60/yoxf/8StiCOf9/Sjo++j1pmBeistR1mWq1R7yjbtFyB3zQIf9iiIpnuKiOQvf16IY8LKMDYevkCVMkX5tn8DnqhWxrpBTBMSo6HBIGj3AdhpPav8zTTN/wL/BcjIZV43TbOXYRiLgG5Y9hwLBJZlvGR5xuOtGc9vME3TNAxjOTDXMIxJWJbhVgVCHuZ7ERGRh2PL8Yu89eM+TlyK57kAD95qXxPXIoWsH+j0bgj/FZoMA4961m+/gMrOcNdDHQXRdE8RkfwhLimVKRuOMfO3PynsYM/bHWoS2NgTR3srFiHSUuDqBcvGo09PtmzYpbWskn1vAvMNwwgCdgGZ651mAnMyltlexrLHGKZpHjAMYyEQBqQCL2uvMRGR/CUmPoV3V4WxMDSCx0oVYe5Lj9PYq7Rtgh3faNmTw7kk1AsEJxfbxCmA7lro0CiIiIjci8wj1/5v1UHOxybRrZ4Hb7SrziPFnawbKCURFr8I5w7A0K1QqKh125d8yTTNTcCmjN//xLJf2I33JALdb/P6d7HsWSYiIvmIaZr8tO8M45aHERWfzODmVfj3k1Wtd2TsjfYuspyuUroa9P5BRQ4re5AFzBoFERGR6+yPjGHc8gOE/hWFr4cL0/vUw7+im/UDJcXB/Bcg/BdoP1FFDhEREblvp6MTGL10P+sPnad2eReC+9fH292GhYc/psHPI+GxptDje3B2tV2sAuqeCh0aBRERkVuJuprMxLWHmRtykpJFCvHBs7XpXq8CdtY8ci1TQjR83x0iQ6HLdKjb0/oxREREJN9LSzf57o+/+PDnQ6Sb8HaHmvRr7ImDNZfZ3oqTK9TqDM/MAEcrz3gV4MFmdIiISAGXmpbOvJCTTFx7hLikVPo19uTfT1bDxdnRdkHXvg2nd0H3YKjVyXZxREREJN86ci6WN3/Yy66T0TSrWpr/e6a29U6Dy7R3IawfDzER4FIe/PpAi5GWQZo6PbSvmA2p0CEiIvdl25+XGLv8AIfOxtKocinGdfKmernid3/hg3pqAvg+D5Wa2T6WiIiI5CtJqWlM3XCMab8cp1hhBz55vg5d6pa3/pGxexfCimGQkmB5HBMBm96zzOZoOFhFDhtToUNERO7JmZgE/m/VIVbsOU15V2e+6OXPP3zK2SZByBwFKV4O3CpDnx/B2U1FDhEREbln209cZuQPezl+4SrP+JXn7Q41KVWssG2CrR//d5HjWlunWAodYlMqdIiISLYkpabx9eZwpmw4RpppMqx1VYY0r4JzIRvsRn7jKEjsGcs/IV9Ck39ZP56IiIjkW1cSU/hg9SG+33YSDzdngvs3oHm1MrYNGhNxb9fFqlToEBGROzJNkw2HzjN+ZRh/XYqnrXdZ3u5Qy/rrWK91u1GQkK9U6BAREZFs+3n/WcYu38+F2CRealqJ156qRpFCNvzP4Cunofij4OIBMaduft7Fw3axJYsKHSIiclt/Xohj/MowNh2+QJUyRZkzoAHNqtp4BAQ0CiIiIiIP5NyVRMYuO8DPB85S89ESfNU3AF8PGx7jGn0KNn8Mu76DHnOh9ZjrZ6cCODpbrovNqdAhIiI3iUtK5fMNR/nmt3AKO9jzdoeaBDb2xNGWx62lp8Phn6Csj0ZBRERE5L6kp5vM336K91YfJDk1nTfb1eClZpVsl8PERFgKHDvnWB7794Wy3pZTVuCaU1c8LEUO3+ds0w+5jgodIiKSxTRNlu6O5L1Vhzgfm0S3eh680a46jxS34Rnv6elwcDn8+hGc229ZmqJREBEREblHx87H8daP+wg5cZlGlUvxXtfaeJYuaruA6ekwuwPERIJ/H2j6GrhW+Pt53+dU2MghKnSIiAgA+yNjGLv8ADv+iqKOhwtf9qmHX0U32wYNW245au18GJSqCs/MAJ9nwT7j60mjICIiInIXyanpTP/lOFM2HMO5kD0fdvOlez0P658IB5Y9OLZ/Dc1HgkMh6DQF3B4D14rWjyX3TYUOEZEC7vLVZCauPcy8kJOULFKID56tTfd6FbCzs9H57unpYJcxffTPjZCeCl2/Bp+uYHfNCS4aBREREZG72HkyipE/7OXIuTg6+j7K2Ke9KVPcBkfGXjkNv30CO2aDmQ5VWoFnUx15n0up0CEiUkClpqUzN+QkH689QlxSKv0ae/LvJ6vh4uxom4DpabD/R/j1Q+j0OVRsCG0mWJak2NngiFoRERHJt+KSUpm45jDBW09QroQTMwMDaF2zrPUDpSTA/8ZmFDjSoO4L0Ox1yywOybVU6BARKYD++PMS45Yf4NDZWBpXKcW4Tt5UK1vcNsHSUmH/D5Y9OC4dhUdqWWZxABQuZpuYIiIikm+tP3iO0Uv3c+ZKIn0bPsaIdjUoVtjK/2mbkgiOTuDgBJGhllmmT7wObp7WjSM2oUKHiEgBciYmgf9bdYgVe05T3tWZab38aedTzjZrWAFME2a1g4jtltNUnvsWajz999IVERERkWy6EJvEOysOsHLvGaqVLcbiFxpT7zEr7ycWexZ++xT2L4aXQ6BISei/BuxtNONVbEKFDhGRAiAxJY2Zv4UzZcMx0k2Tf7WuyuDmVXAuZIMlI2kpELYMvJ+xLEkJ6G85SaV6BxU4RERE5J6ZpsmiHRG8+9NBEpLTeK1NNQY3r0IhByvmFbHn4PfPIHSmJZep09PyE1TkyINU6BARycdM02T9wfNM+CmMvy7F0867HKM61KRCySLWD5aWArvnWs6Sj/4LChWD6u0sa1lFRERE7sOJi1d5a8k+thy/RH1PN97r6ovXI1Ze+hp7Fib7QWoS1OkBzYZDqSrWjSEPlQodIiL51J8X4nhnRRi/HLmA1yPFmDOgAc2qlrF+oLRU2DUHNk+CmJPg7gf/+BCqtbV+LBERESkQUtLS+XpzOJ+uO0IhezvefcaHnvUrWu9UuLgLcOJXy7H2xctBq9GW3EUFjnxBhQ4RkXwmLimVzzcc5ZvfwnFysOftDjUJbOyJo72Vl42YJmTu7bHlcyj2CHScBF5P/n1dRERE5B7ti4jhzR/2EnbmCu28y/FOZ2/KlnCyTuNxF2DLZNj+tWVz9ErNoWhpaDTUOu1LrqBCh4hIPmGaJkt3R/LeqkOcj02iez0PRrSrziPFrZQYZEpJtMzg2DUHXlwNhYpC/5+haBkVOEREROS+xSenMmntEb75PZzSxQozvXc92vmUs1Ljl+H3TyHkK0hNhNrd4Yk3LEUOyXdU6BARyQf2R8YwdvkBdvwVRR0PF77sUw+/ilbehTwlEXYGW3Yijz0NFR6HuPNQspJlNoeIiIjIffrlyAVGLdlHRFQCLzxekTfb1cDF2QqbgGbOQE2Jh21fQs1O0PwNKF31wduWXEuFDhGRPOzy1WQ+WnOY+dtPUrJIIT581pdu9Tyst341U+w5+PIJiDsLFRvDM9MsUz01g0NEREQewOWryUxYGcaSXZFULlOUhf9sRINKJR+84auXYOsUuHgEenwPLh7wnzAoWurB25ZcT4UOEZE8YOmuSD5ac5jT0Qm4uzozvE1V4pLT+HjtEeKSUnmxcSX+9WRV64x8ZEqOh8hQqPQEFC8LtbtZNunybKYCh4iIiNyTG3OZ15+qBgaMXxFGXFIqw1p5MbSlF06O9g8WKP6yZe+wkBmQfBV8ulpOU3EorCJHAaJCh4hILrd0VyT//XEfCSlpAERGJzB80V5MoHGVUozr5E21ssWtFzD5KoR+A79PhsQYeO2gJTFo+671YoiIiEiBcetcZg/pJvhVdOX9rr5UL2eFXObEbzD3eUsu4/2MZYnKIzUfvF3Jc1ToEBHJ5T5aczgrMchkAiWLOPL9S49jWGt2RVKcZQfyLZ9D/EWo3AKav6nRDxEREXkgt8pl0k1wcXZk8eDG2D/Iktv4y3AlEsrVhkfrQK3O0OgVKFvrAXsteZkKHSIiudix83FERifc8rmo+BTrFTkArpyG9e9kFDhGQsXHrde2iIiIFEhXk1Jvm8tcSUi5/yJHQhRs/QK2TYcS7jD0DyhcHLp88QC9lfxChQ4RkVwmIiqeFXvOsHzPaQ6euXLb+9xdnR8sUOIVy/rV6JPQaTKUqQavhEKpKg/W7v+zd9/xVVfnA8c/587sBMhOWJENYYnKUIaLIQJaB1VRaq3WVq39tbiLrdr+tNqfbR1t3aO4qoJoBVRUVFCRmYAM2WQBCWTn3txxfn+cbxZJlHGTQPK8X6/7ujffeb43N8mT5/ucc4QQQgjRoZV6fCzdtI/3swv4bOuBZrc7plimqhi+ehK++gd4S00Fx7jbZfww0YAkOoQQ4gSwv8zD+1kmubFmTzEAQ7vGMXfqAOw2eHBRw5LPcKedORP7HtvJPCXw9VNmJHJPMfSZBAEf2J2S5BBCCCHEMTlUUc2H3+5j0YZ8vthWiC+gSYkN48endyM6zMEzn++gyhes3f6YY5mtS2DZQ9D/QlOBmjwohFch2gtJdAghRBspqfSxeKNJbny5vYighn7J0cyZ2JdpQ1Lp2jmidtvYcFeDkcrnTOzLjGFpR5dI7VAAACAASURBVH/SHZ/CG1ebZEefyWaQrrThobsoIYQQQnQYB8q8fPBtAYs3FLBiexGBoCa9Uzg/GdOTSYOSGZoeVzvl/SkJUccWy3hK4Kt/mjHDTrsOBv3IJDeSBrbw1YmTmSQ6hBCiFVV4/Xy0aR/vrs9j2dYD+AKa7l0i+OWEXlw4JLXZ2VNmDEs7tsQGmD6s5QdM15SkTDjlHDjzVjNglxBCCCHEUSgo8bBkYwHvZ+fzza6DBDX0jI/khrEZTMlMYWBqTJNjiB11LOMpNeNvfPm4SXYMv9ostzskySF+kCQ6hBCihXn9AT7dcoB31+exdNN+qnwBkmPCuGZUD6YNTSUzLTa0g4rWqDwIXz4BX/8LEvrCz5aauyGXPh/6cwkhhBCi3co5VMniDQUs2lDA6t2HAOiTFMVNZ/dmSmYyfZOiQxvLZP0H3v+t6WLbdwqMv0Nu0IijIokOIYRoAf5AkBXbi3h3fR6LNxZQ5vHTKcLJxcPTmDYkldN6dK4t5Qy5iiJz92PlU1BdbgbpGntby5xLCCGEEO3SrsIKFm0oYNGGfLJySgAYkBLDb8/vw6RBKfRKjArtCb1lZsywiM4QnQzdRsH42yF1WGjPIzqEH0x0KKW6Ai8BSYAGntJa/00p1Rl4HegB7AIu01ofUiaV9zdgClAJzNZar7GOdQ1wj3XoB7TWL4b2coQQou0Eg5rVew7x7vo83s/Op7C8mmi3g/MHJnPhkBTG9IrHabe1fEM2vg1fPAoDZ5gEh8wjLzo4iWWEEOLIbNtfxqLsAt7fUFA789uQ9FjumNyPyYOS6d4lMvQn9ZaZmzMrHoPMy2DKn6HnWeYhxDE6kooOP/AbrfUapVQ0sFop9SEwG1iqtX5QKXUHcAdwOzAZ6G09zgD+AZxhBRP3AiMwQcZqpdRCrfWhUF+UEEK0Fq01G/NKWbg+j/fW55FX4sHtsHFu/yQuHJLK+L4JhDntLduI8gOw4u+Q2B+GXgHDZkGPsyCxX8ueV4iTh8QyQgjRBK01mwvKWJSdz6INBXy3vxyAEd07cc8F/Zk0KJn0ThE/cJQjlPUGLL0PSnIgNt0MiF5RaBIcVQeh9/kw5PLQnEt0eD+Y6NBa5wP51usypdQmIA2YDoy3NnsR+BQTHEwHXtJaa+ArpVScUirF2vZDrfVBACvAmAS8GsLrEUKIVrFtf3ltcmNHYQUOm2JsnwRum9SPcwckEeUOcc/Aw4ODc+ZCxnhY/jdY9Rz4PTDqJrOtM0ySHELUI7GMEELU0VqTnVtiuqVk57OrqBKbgtN7dmbWqIFMHJhMUkxYaE+a9Qa8ewv4qszXJXvh3VtBB6DXeTD+Tkg/NbTnFB3aUUXiSqkewDDgayDJChwACjDloGACh731dsuxljW3/PBzXA9cD9CtW7ejaZ4QQrSovQcreS/LTAe7Kb8UpWBkzy78bGwGkwclExfhapkTNxUcLPiFuZ9MwJR5jv0txPdumfML0Y5ILCOE6IiCQc3avcW1lRu5xVXYbYrRp3Th+rGncP7AJOKj3C3XgI/urYtjaugARCbCVW+23HlFh3XEiQ6lVBTwFnCr1rq0/qi6WmutlNKhaJDW+ingKYARI0aE5JhCCHGs9pd5eN9KbqzZUwzAsG5xzJ06gKmDU0gM9R2Ppiy9r3FwEPSBMwJ+/jV0OaXl2yBEOyCxjBCiIwkENat2HWTRhgIWbyigoNSD0644q3cCvzq3N+f1T6JTZAvdpAHwlsOyh2DHJ1Ca1/Q2FQda7vyiQzuiRIdSyokJDOZprd+2Fu9TSqVorfOtcs791vJcoGu93dOtZbnUlYfWLP/02JsuhBAto6TSx6IN+byblceX24sIauiXHM2ciX2ZNiSVrp1D1Ff1+2gNB7bAzs9MBUdTfFWS5BDiCEksI4ToCPyBIF/tOMiiDfks2VhAYXk1boeNcX0SuCOzH2f3TyQmzBn6EwcDUJAF2z8BVySccQM4w01VanxvcMeCt6TxfrHpoW+LEBzZrCsKeBbYpLX+v3qrFgLXAA9az+/UW36TUuo1zABeJVYAsQT4k1Kqk7Xd+cCdobkMIYQ4PhVePx9t2se76/NYtvUAvoCmR5cIbprQiwuHpNI7KbrlG1G81wzGlTLEjLnxr7MgUA3Kbso7DyfBgRBHRGIZIUR7Vu0Psnx7IYuy8/nw230cqvQR7rRzdr9EJmcmM6FvIpGhHjusxoa34Nt3zI2ZKmtc5r5TTKLDZodfbwC7s3E3XDCJkHPmtky7RId3JJ/4McAsIFsptc5adhcmKHhDKfVTYDdwmbXufcx0bNswU7L9BEBrfVApdT/wjbXdfTWDeQkhRFvw+gN8uuUAC9fnsXTTPjy+IMkxYcwe3YNpQ9IYlBZD/dL2kKsogp3LTHCwcxkc3AHpp8N1H5o//pe9ZGZS2btSggMhjo/EMkKIdsXjC/DZ1gMs3lDAh5v2UebxE+V2cG7/RCYNSmFcnwTCXSGe9a3yoIlZcr6B8x8ApeC7j2DvNya5kTEeeo6D6KS6fexW9chg69fr4QOr1ywXIsSUGVD8xDRixAi9atWqtm6GEKId8QeCrNhexML1eSzZWECZx0/nSBdTMpOZNiSNEd07YbO1UHLDWwZ56+rmhX/lcti6GNwx0H0MZIwzQUJi/8b7NjXrigQH4igppVZrrUe0dTs6EollhBChUlnt59MtB3g/O59PNu+nojpAbLiT8wYkMSUzmTG94nE7QpzcOLAF1r8GOz6FvLWABlc03LQSYlKhusKMGdaSN4aEqOdIY5kWqmESQogTRzCoWb3nEAvX5fF+dj5FFdVEux2cPzCZaUNTGXNKFxx2W+hP7Peaaoyaqo3c1RD0w2+3QVQCjL0NzvotpA4D+w/8Oh58mSQ2hBBCiA6mzOPj4837WZRdwKdb9+PxBekS6WLa0FQmD0ph1CldcIYqhgkGYf9Gk9ToM8mMrXFgs5nKPv00GH+HuSGTdmpdpYYrMjTnFiLEJNEhhGiXtNZszCtl4fo83lufR16JhzCnjXP6J3Hh4FTG900gzBniux7BAOSvh049IKIzrH8V3v0VKBukDofRt5iqjbBYs73MFy+EEEJ0SAvW5vLwki3kFVeRGhfOnIl9mTHMzFZdUunjw037WLwhn8+2FlIdCJIQ7eayEV2ZNCiZ03t0Dt0NmupK2Pi2GUR057K6WVBckSbR0Xsi3L4LwmJCcz4hWokkOoQQJ53vCw627S9j4fp83lufx47CChw2xbg+Cdw2qR/nDkgiKpSDcWkNhVthxzITHOz6HDwlMO0xGH419JkMMxOhx5i65IYQQgghOrQFa3O58+1sqnxmoPHc4irueDuLL7cXkl/qZcW2QvxBTWpsGFeN7M6UzGSGdwtR11pPCez6AmxO6HM+oOG9X0N4JzjlbFOxkTHedEsBcIaZhxAnGUl0CCFOKk0FB7e/lcVHm/ax/UAFm/JLUQpGZXTh+rEZTBqUTFxECOeIL8kx/VET+kL5fnjidLM8rhv0n1YXIIAZjKvflNCdWwghhBAnvYeXbKmNY2p4fEFeX5VDt84R/PTMnkzOTGFIemxoBkXfuxK++9B0ScldbWZy63GWSXS4IuGXK001qoyzIdoRSXQIIU4qTQUHXn+Q97LyGdYtjnsvHMAFmSkkxoTo7kNFoanUqKnaOLjDVGpc8ZpJZFzynOmW0rlnaM4nhBBCiHYlENRs219Odm4J2TnF5BZXNbmdApbNGX98yQ2tzbgaeetg6I/Nss8ehm0fmXjlrP8xN2TST6/bR2IY0Q5JokMIcULz+AJsyi9lY14pG/NKvjc4mP+LMcd/Qm8Z7N8MXU8zX79yOeSuMiOM9xgDp/0MTplQt/2gHx3/OYUQQgjRLgSDmh2FFWTnFpOVU0J2Tgkb80prb9JEuuy47DaqA8FG+6bGhR9bkqP8AGxfasbZ2PEplBeY5X0mmjHDJj8E4Z0hPO44rkyIk4skOoQQJ4xyr59v80rZkFvChrwSNuaWsu1AOYGgmQY7LsKJ22HD6286ODgmfq+ZD76mYiN3NdgccPtu0yf13HvBEX5kM6MIIYQQosMIBjW7D1aSlVNMdk4JWbklbMwtoaLaJDXCnXYGpsYw8/SuDE6PJTMtjoz4SBauz2vQDbdm2zkT+x7Zib1lsHuFqdCISoDN75pxNiK6QM9x5oZMxniT5ADonBHaCxfiJCBRuxCiTRRXVrOxNqlRysbcEnYWVaBNToPEaDeD0mKZODCJgWmxDEyNIS0unHfWHWdwUDMzSnwfcEfBl4/D0vusmVGGmZlReo4FmzUjS8+xIb5yIYQQQpxstNbsPVhFVq6V1MgxN2XKPH4A3A4bA1JjuOTUdDLT48hMi+WUhMgmZ0epGUC9uYHVGwn4IW9NXcVGzkozXf20x2H4LOg/3Uz5mpQJthDNxiLESU4SHUKIFre/zMPGXJPU2JhXyoa8EnIO1XVBSYsLZ1BaDBcNS2OQldRoboyNow4OmpsZZearZqDQgRdBQj/oPkZKOoUQQgiB1prc4iqyc0rMuBq5JrFRUuUDwGW30T8lmulDU8lMM5UavZOicB7FlK8z7MuZ4b4PwnLAnQ72ucBlNQ2Aom0QqIakgWbK12fPAxSkDIHRN5uKja4jzfaRXcxDCFFLEh1CiJDRWpNX4jEJDatSY0NuCfvLvLXb9IyPZGjXOK4a2Z1BqSap0Sny6GZF+d7gAMzMKAGfGVzrwGZ40goEYrtB/wuh53joZi3rnCElnUIIIUQHpbWmoNRjKjSshEZ2bgkHK6oBcNgUfZOjmZKZTGZaHIPTY+mTFI3LcRyVE1lvwLu3gM+66VOyFxbeDHu/Bp/HVG2U5kDfC+DHr0BMClzxH0gfUdcdRQjxvSTRIYQ4JsGgZs/BSjbklbAh1wwUuiG3hEOV5m6HTUHvxGjO7B3PoNRYBqXF0j8lmugw5/GduMng4BYzzkbAVzczyrBZMP1xU60x7XEzkGinnjJ1mhBCCNGB7S/11FZo1DwXlpsbMnabondiFOf2TyQzPY7BabH0TY4mzGkPXQMCfvhwbl0cU8PvgW+egbA402127G8go97g533OD10bhOgAJNEhhPhB/kCQHYUVZjyNXNP15Nu8Usq9pl+q027udkwcmMzAtFgGpcbQLzmGcFcIAwMwpZwf3ttEcFAFK5+qNzPKddDrXLNOKdN/VQghhBAdSmG515rStSaxUcy+UpPUsCnolRjFuD4JZqDQ9FgGpMQcf1JDazM1/aFdULzbzM6mFHzxKKx63lSd6kAzOyu4bUfdOGFCiGMmiQ4hRAPV/iBb95VZFRomqbEpvxSPz8x0Eua0MSAlhouHp5muJ2kx9E48zhLO+gJ+U65ZkmuSFgDLHoaNb8PBnSap0SQFt+8E+3FWjAghhBDipHOoorreeBrFbMgtrZ2SXinIiI9k9CnxZkwNK6kR6T7Gf4W85SaJcWg3ZIwDVySsfx2W/9UkOHyVddtmjIfIeIiIh66nQ+alsOpZqDrU+Lix6ZLkECJEJNEhRAdWVR1gU0EpG61ZTzbklbCloAxfwEx9Eu12MCA1hivP6M6gtBgGpcaSkRCF3Xac3T98HhMgdOll/qBnvwnrXzWJjOLdZiRxZYO794HDZaZ17dTDlHCumwee4sbHjE2XJIcQQgjRAZRU+WrH09iQW0JWbjF7D9bdCOkZH8mp3Tsxe3QPMtPNeGBH1XU24DOVF8W7zUwmkV3MjCcf32+SG5WFddtevwxSh5qZ3Dr1NImNTj0grjt06m66ooCpLq2pME3o27AbLoAzHM6Ze6xviRDiMJLoEOIkt2Bt7hHNQFLm8fFtXmntVK4b8krYtr+coDWda6cIJ4PSYvnpmRm1SY1unSOwHWtSw1sGdrdJVOz5Gtb92yQyDu6E0lxAw63ZENfN3NWoOADJmTBguhlEtHOGSXYAnPnruuOmDpXgQAghhGhnmotnyjw+U2GaW0JWbgnZOcXsKqqrmOjaOZzBaXFceUZ3BqfFMjAtltjwH0hqaG3ijkO7zI2SmFTYtxEW3W6SGyW5dd1LZr4C/S4wsYY7GvpPrUtixPUwSQsw2/S74MgudrA1gPrS+0xCJTbdxDGDL/v+/YQQR0xprdu6Dc0aMWKEXrVqVVs3Q4gT1oK1udz5djZVvrq+nuFOO/dc0J/uXSKtgULNlK47Cytqt0mKcVvdTsx4GoPSYkmJDUMdzUCdWpuHzWaSF1mvW4mMHXBopwkgfrIYuo8yFRuL7zB3OjpnmERGp57QdxKExR79hWe9IcGBEMdAKbVaaz2irdvRkUgsI8QPayqesStF5ygnB8qqa5elxYXXdj0ZnB7LoNTY5mdu85aZ6gt3tElKlO0zN0oO7TbJjJruJZMfhjOuh6LtMP/nphqjU/e6ZEZyJoR3asGrF0IcjSONZSTRIcRJSGtNaZWf8x5d1mDq1qakdwq3Zj2JYWCaKd9MjA47uhNWFcOmd+uSGAd3wMFdMPX/IPMS2PMVPDfJJB069airyBh4sQkStJbZToQ4AUiio/VJLCOEUeH1k1/ioaDEQ35JFftKPbVfL9t6gCl8zm2ON0hVheTpeP7sv4wPbGP55YReZKbHkpkWS5cod90BAz4z8xqYmMNfDfNvsMbO2AWVRWbdmF/BefeZcTWem9iwW0lcd0gZYqZvFUKcFI40lpGuK0KcYCqr/ewr9bKv1MO+Ug/7S70U1Hu9r8y8rhkctDmvXHcGA1JjiIto5k5HfbXTslpdS2qSGafOhpE3grcUFt4ENocJCjr3hPTTTbAAkDYC7i4AZzMJFElyCCGEEO2S1pqSKh8F9RIX5rmK/BJPbUKjzONvtG/nSBdJMWFM4XMedD5DhDLVG+mqkAedzxDjq+TmYb+Czolmh0W3my4mh3abgct1EAZdApc8a7rKHtxuqi/6X1iXzEgZavZ1R8GNy1vrbRFCtDFJdAjRSqr9QfaXedhX6mW/lbgoqHltLd9X2nQgEO60kxwbRmK0myHpcSTFuEmKCeOJT7ZxlueTRndAVsecx+he8Q0Psn/zYRUZO6HbKBg3x1RczLvUBAyOcKsi4xSItu5wxKTDr9abZ3sTvzbsjqaXCyGEEOKkFQxqiiqqa6swCkpNIqM2mVFqlh9+80UpSIhykxIbRk9rtpOkmDBSYsNIibaT6qoi0V6Ou7oYgn4KXv4PEVQ3OEaEquYB1wuwYAtcu9gs3L8J/F7oNtIkMTr1gKRBdTvd8FnLviFCiJOG/GcixHEKBDVF5d66KowyD/tKrMRFvcRGUUV1o32ddkVidBhJMW56J0ZxZq94EmPcJMeEkRRjlifGhBHtdjQ5fsaQQx8yaPUzhNe7A/KI8yn2dd4B77xlEhVn32M2fvkiKMszr8NiTZlnTXLC4YJrPzBdT6KTG1dg2Gx11RtCCCGEOOn5A0EOlHu/twpjX6mndia2Gg6bIikmjOTYMAakRDO5dySdO3chJTacDN9Wkj3biQmWYvcchIoiM6jntH+anf/zEzNdfH2x3UiikKZoQI2/s27BNQtD+A4IIdozSXQI0QytNcWVvgbVFvtKPA2SF/tKvRwo9xIINgwCbArio0zVRVpcGMO6xZEUHUZyrElcJFnJjU4RrqZnNfF7Td9SbxGEWaN5b3oPCrLNcutx2t6VoBomUFzKT9e8xVCSCL3OqVsx40lwx5hqjYjOjc/Z9bTjfcuEEEIIEQJHOqNac7z+APtKvA2qMGoTGqUmoXGgzMth4QtRjgD9o71kRHk5O8FDWloFSY4KSgZeRXKnGHrsXUD0ljdRlUUmiXGgCIIBmFtkpot/9yFY/bw5mN0FEfHmBkqNvpMhoZ+JQyK6QGQ8RCag5l1aN95GPSq2K2SMO5a3UAjRwUmiQ7Q7RxIclHv9tWNg7CutS2Tst54LSj3sL/NS7W88DkanCCdJMWEkxoTRJynaVF7EhpEU7baqMMKIj3LhsNsa7liaZ7qLVBZBXhFsKwJPCZx/v1n/8R8h6zWoPAjV5WZZWBzcsdu83vAmbJxv+p5GdDEPfxVNUzDnu4aLTplwlO+kEEIIIVrbgrW5fDH/SV7nNVLdheRVxvPX+TOBXzBjWBoVXv9hyYuGVRgFJaaKVBEkjnI6qzJydTxOdyQjo/ZxpWMNydEVxMeWEUcZUYFS9k16ivi0U4hb+yTqo3vh8PBiymyIiYP8AAT9pio0fYQVj8SbZIfNDuNugzNvNctckY0rRJubIe2cuTJ1vBAipCTRIdoFXyBIhdfP22ty2LjkWV5XdcHBX966nJe/vACXw16b2KioDjQ6RpTbQWKMm6ToMEZ071SbtKjpQpIUE0ZClJOwQLlJRlQWQWUe9BwHrgjYthRWv92g4oLKIjO2RXgcfP0vWP7Xhid1RcGEu80gnjGpZsyMiPiGdzpqTH8CLn6m4VgYjw5q8g4IsekhemeFEEII0VK01lRWByjz+Cnz+Cj1+Plm4T+5Tz3VYGDO+/RT3P2W5k/vnEGSdw9dVCmdKKOzKqOTKmOTczxVcb2Z4NrMbMdjREeV4PaXYsPcsKm88l0ieo+F9a/D/OfBGWklKTpDbCIx8W6IdJlK0LDYuhik5sZKuFUJetpPzaM5ManH9kbUJEBk6nghRIjI9LKizWitqfIFKPf4KfP6Kff4qfDWvS73mkeZtbzmdbnXR7nXT4U3UPt1zSBY02xfNBi1G6BSu7gr8DNy0qZalRjWGBjRbpIjgiTERZHUKYYozz7Y9UXjRMU590J8L1j3Crxzk+lrWt8vvoLE/vDNM/DZX+oCh5ogYcJdpgrjwFYzRkb9oKG5WUqOVNYbTd8BufDvEhwIIRqR6WVbn8Qy7VcwqKmo9ltJCpOoKPP4KbWe6y8r85jYpbzKS2RlDnhLsXnLcPjKiKKC7GAGm3U3EjnEJ+7/IVI1njo+JxjP4r73c913NzZYrpUdddmLZqaR/PXw+V/qKi1qYo6McRCVaLrG6qCJFYQQ4iQk08sK4Pj7eDbFFwg2SESUW4mJppMV1h92b4Dymj/y1rYVXn+jvqFNcdoVUW4HUWEOotxOot0OEqPDiIp3EO2CaLeDiPAwYh0BJn38iwZJDjCjdv/W9jrpF10BH9wNuUXwnVWR4ffAj1+H5EmwKwvmX292Ura64MBbYpYlDoAzf123vCahUTNI52nXmUdzEvqYRyjJHRAhhBDtXEvEMoGgprxBUsJ69vqs5fUSFVU+Kj0eir1Q5vHTtXIjtuoynP4yoqgimkq26nQ+Cw7BTTV/cz5BPJVEq0piVBUxqpK3nFOZH3UF6c5Sniq9oa4hTvO0ts+v2DtwOvGBfUQsbJzkAEi1FXHdxVNgz2sN4hDljjWDhgOkDIHLXmr+wh3u43rfhBDiZCGJjnasqT6ej749k32l13JGRpeGyQorCVGTgKhZV1Zvmwrra28T41Y0puns1sS7AoSHuSAslmiXnVGObcTE+Yixm0ekzYcnrhfelBHE2P302/R3XNqLW3txBj04glXYMy+FIZdDWQE8NxHKq+BQFfgqTT/RSQ/CyBvN9KmfFDfZmlSbNUhWdaWZIjV5SF3VRXxvs1GPM+Gm1WZ5WFxd0FB7kKHmcaIZfJkkNoQQooNoiX/6T2RNxjLzZ1LuvZ6zesc3WTlRZt1QMUmKaqqryvB6qsirjqTM46efZx3R/oNEK5OkiFaV7NGJvBEwY1k97fwLw1UhMaqKaFVJFFV87hrL0wl30CM+gsd23ItLeWuTFAB7elzKrtE/I9ptY8A7D6HCorGHp2ILj0W5o/lZn4n8rO9Z4K+GjU9BWIwZINx6HhYZzzBXJJBK5YcpRFTlN3ovPOHJRITHmQE9hRBCfK8Olej4ZuG/6LrmYRL1AfarBPYOn8Np02744R1DRGuNP6jx+oN4fQG8/iAe67n+MvMI4PGZZ6+vbpnZLoindnnj/T2+ANX+IIMPfcCfHE836ON5v36KO5cEeDR4OuF4icCLRpGHGQtitGMLKa5Kujj89HT4iHH48bgT2Nz1HCLdDqbt+wdxwYOEUY1be3FrD+VJp1N0xm1EhznoMW8M9qpC8FWhdBB8QObVMO0x0Br+MAkzWVg9Z9wIQ6eb7hcLXzXllM4I6zncJDTAvE4/vW69K6JuGUBsGl5XJ9zVhxq9957wZCIS+8N1Hzb/DXJHm4cQQghxAvqhQSpbitaa6kCQan8QX0BT7Tev65aZ1z5/EK/1XB2wltduq+u2tZ699b6uv732e83ffn81PQsW84D9NcKUDzCxzJ/0P3nnvSxe1ylEq0qiqaSESB72zwTg787HONX2HTGqkkiqsKHZ4hrIX7s9RnSYgznbXibBs7Pu+rBxsOs5XDVxDFFuB6kf/QdnMAF7eKxJRrijmZA6lAkDRpodtr9u4o/aREU03VzRdKu5QXLLV82/mQ6XuXnzPSIm34f/nZtxBDy1y/z2MCIm33cM3z0hhOiYOswYHd8s/BeDVt9DeL1uDVXaxVeD7qXruGu+N6nQIOnQRFKiLlkRwFftI+j3EvB7CfqrCfqqCfqr2envgsevSdJFJKuDOPHjUAFc+LETYGnwVABOU5vpZcvFiR8nAZz48WPnmcAFAFzu+Iwh9l2E2QK4bQHcyk+VLZJnY2/G7bAxq+J5eldvwoWftKotuFTjQTcP500+lYpZi4l023E/dSbs/7bhBhnj4ep3zOunz4aKwnqJiAjoORbG327Wf/A7q+9nvfVJA8w2ANs/AUdYw2RGeFzoEgxZbzQZHDimPyZVD0KIDk/G6Gh9oYxlfv/Avdzme7LROFT3q58zYOJPrcSBbpB8qE0iHJaYqEky+H0+CFSj/dWUBNxUBRTh/hI6BQpRAS+2YDW2oA83PlYEB+LFxUC1iyG27bgwy134cCk/j/kvohonF9i+4mz7WrMcPy58OPFzle8uQPFr51tMty3Hpfy16/04uDzmZVwOG3dU/Jmzg1UrPgAAIABJREFUvJ8d0XsSVE4Crmi8nftRdMmbRIc5if3yz9jLchpUTNCpOwyYbnba963polqzrqnZQdpa1hvSLVUIIZpwpLFMh0l0FPy+F8kcaLS8XIexMDCax/0zyCOes2xZXGX/yCQiCOBSfpz4uan6FvLpwkz7J/zCsdBaHqjd7oZOT1Pl6sIVlS9zcdkrjc7z8KmfYHNHcs7uRxma+2qDdRrFp5dvwe2003vlXSRsfb3helcU3jl7cNlt2BbeBFv+a+Ymt7vA5jAjXP/kfbPxB/dA3jqwO9HbP6apP9saUOf+oS7REJ0Cvc81K/dtPCxREW5G5na4juVtbxsSHAghRJMk0dH6QhnL5Mw9hXRbYaPl5TqMRYHTeSowle90OqerTfzKOR+38uFWftz4cSs/f3D/lr2uU5gU+IRfVPwDF9XYqeuO+sceL1AU0ZOzD/2HqfmPNzrPK6PfxxeVxrBdTzN4a8P1WtlYfdlq7JGdSM16gs6bXzHjQTjcYHejHG58s97F5XJhX/si7PwM7G4TX9jdpkrz3N+bg239AA7uAIcL/d6vm4xlgoDt7gJz8+RES1IIIYRoMSdsokMpNQn4G2AHntFaP9jctqEMDoL3xmJr4u+g1uANi2ft2GfwJQwiJXcx6VmPg92JcrhQdhfK4cI39TFcnbvi2P4hasNbYHOCvebhMvOGh8XC7hWwd6WViKi3ftAl5o/5/s1mOtCa5TXJipQh5g915UEzInb9fW3OhlOKHqHKh/o12cezMjyFiNs3H8vbKIQQ4iQmiY7WF9JY5vdx2A7v/om5gRGMTqNq6pPYe56FK2c59k8eMDGEw22SAXaXmQUsvjfkrIaNb9cmIWqTDYMvh8guULgN9m9suM4RBsmDzD6eEjPmlcNtnSPsmOKUIyGxjBBCiPpOyESHUsoObAXOA3KAb4Afa62/bWr71qjoKCCB5N9vC8k5TjjSjUMIIUQ9kuhofaGMZTrkP/0SywghhKjnSGMZ2w9tEGKnA9u01ju01tXAa8D01jjx3uFzqNINu19UaRd7h89pjdO3jcGXmUAgtiugILarBAZCCCHESSpi8n347WENlrX7QSollhFCCHEMWnvWlTRgb72vc4AzWuPEp027gW/AmnWlkP0qnr2ntu6sK21Cph4VQgghQupouuGG1ODLTOBWbxwqR0cYh0piGSGEEEfphJteVil1PXA9QLdu3UJ67NOm3QBWYiPZegghhBBCHCmrG+4T1OuGq5Ra2Fw33JCTf/qFEEKIH9TaXVdyga71vk63ltXSWj+ltR6htR6RkJDQqo0TQgghhPgBbdYNVwghhBBHprUTHd8AvZVSPZVSLmAmsLCV2yCEEEIIcaya6oabVn8DpdT1SqlVSqlVBw40HghdCCGEEC2rVRMdWms/cBOwBNgEvKG13tiabRBCCCGEaElSnSqEEEK0rVadXvZoKaUOALtb4NDxQGELHPdEJtfc/nW06wW55o5Crjl0umut5T/v46CUGgX8Xms90fr6TgCt9f82s73EMqEj19wxdLRr7mjXC3LNHUWbxjIndKKjpSilVh3J3LvtiVxz+9fRrhfkmjsKuWZxIlFKOYCtwDmYcca+Aa5o7QrVjvgZkWvuGDraNXe06wW55o6ira/5hJt1RQghhBDiRKW19iularrh2oHnpBuuEEIIcWKRRIcQQgghxFHQWr8PvN/W7RBCCCFE01p71pUTxVNt3YA2INfc/nW06wW55o5CrlmIxjriZ0SuuWPoaNfc0a4X5Jo7ija95g45RocQQgghhBBCCCHap45a0SGEEEIIIYQQQoh2SBIdQgghhBBCCCGEaDc6VKJDKfVrpdRGpdQGpdSrSqmwtm5TqCmlnlNK7VdKbThs+c1Kqc3W9f+5rdrXEpRSYUqplUqp9db1/cFaPk8ptcX6fj+nlHK2dVtDSSkVp5R60/q+blJKjaq37jdKKa2Uim/LNh6vpj7PSqmHrWvOUkrNV0rFWcudSqkXlVLZ1vtxZ9u1/NgppboqpT5RSn1rfZ5/ZS3/vVIqVym1znpMqbfPYKXUl9b22Sfj7zal1C6r7euUUqusZZda1xRUSo2ot+15SqnV1varlVJnt13Lj1wzn+fOSqkPlVLfWc+drOVXWp/xbKXUCqXUkMOOZVdKrVVKvdfa1yHalsQyEsu0dVtDSWIZiWXq7SOxzEngZIplOkyiQymVBtwCjNBaD8JMCTezbVvVIl4AJtVfoJSaAEwHhmitBwKPtEG7WpIXOFtrPQQYCkxSSo0E5gH9gEwgHLiu7ZrYIv4GLNZa9wOGAJvA/HEBzgf2tGHbQuUFDvs8Ax8Cg7TWg4GtQE0QcCng1lpnAqcCNyilerROM0PKD/xGaz0AGAn8Uik1wFr3qNZ6qPV4H0Ap5QD+Dfzc+vkeD/jaoN2hMMG6tppAYANwMfDZYdsVAhda3+trgJdbsY3H4wUaf57vAJZqrXsDS62vAXYC46xrvJ/GA3r9CutnXnQcEstILIPEMiejF5BYRmIZiWVaPZbpMIkOiwMIt36YIoC8Nm5PyGmtPwMOHrb4RuBBrbXX2mZ/qzesBWmj3PrSaT201vp9a50GVgLpbdbIEFNKxQJjgWcBtNbVWutia/WjwG3AST/ScFOfZ631B1prv/XlV9R9XzUQaf18hwPVQGlrtTVUtNb5Wus11usyzB+AtO/Z5XwgS2u93tqnSGsdaPmWtjyt9Sat9ZYmlq/VWtf8/t6I+b3ubt3WHb1mfj9PB160Xr8IzLC2XaG1PmQtr/85RymVDlwAPNOiDRYnKollkFimPZBYRmKZeiSWkVgm5DpMokNrnYvJ/u8B8oESrfUHbduqVtMHOEsp9bVSaplS6rS2blCoWaVP64D9wIda66/rrXMCs4DFbdW+FtATOAA8b5V8PaOUilRKTQdya/5QdADXAous128CFZif7z3AI1rrw38Rn1SsuzjDgJrP801WCeBzNWWBmJ9vrZRaopRao5S6rQ2aGgoa+MAq37z+KPb7EbCm5p+fk1CS1jrfel0AJDWxzU+p+5wD/BXzD0CwhdsmTjASy0gsg8Qy7ZHEMhLLSCzTAjpMosP6QZqO+aWaismWXtW2rWo1DqAzpnRsDvCGUkq1bZNCS2sd0FoPxWQKT1dKDaq3+kngM631523TuhbhAIYD/9BaD8P8Ufw9cBcwtw3b1WqUUndjSiPnWYtOBwKYn++ewG+UUhlt1LzjppSKAt4CbtValwL/AE7BlDTnA3+xNnUAZwJXWs8XKaXOaf0WH7cztdbDgcmYEtexP7SDUmog8BBwQ0s3rjVYd2wb3L20yvV/CtxufT0V2K+1Xt36LRRtTWIZiWUklmlfJJaRWEZimZbTYRIdwLnATq31Aa21D3gbGN3GbWotOcDbVuXjSkzm7KQe2Kk5VsnjJ1h9x5RS9wIJwP+0ZbtaQA6QU+9uz5uYYKEnsF4ptQsTKK1RSiW3TRNbjlJqNjAVuNL6hQpwBaafr88qaV4OjGjmECc0687dW8A8rfXbAFrrfVYQHASexgRDYD4Ln2mtC7XWlcD7mM/CScW6U11Tjj6fuutrklXyOB+4Wmu9veVb2GL2KaVSAKzn2nJ8pdRgTEnndK11kbV4DDDN+hl/DThbKfXv1m2yaEMSy0gs055ILCOxjMQyEsu0WCzTkRIde4CRSqkI6w7AOXScgdwWABMAlFJ9ABdmAJx2QSmVoOpGqw4HzgM2K6WuAyYCP7Z+obYbWusCYK9Sqq+16BxMyVui1rqH1roH5o/GcGvbdkMpNQlT6jbN+mNYYw9wtrVNJOau3+bWb+HxsX4/PQts0lr/X73lKfU2uwgzuBXAEiDT+t3mAMYB37ZWe0PBKlWOrnmN6au74Xu2jwP+C9yhtV7eOq1sMQsxg5BhPb8DoJTqhvkndpbWemvNxlrrO7XW6dbP+EzgY611R7mjLySWkVimHZFYRmIZJJaRWKYFYxlHqA94otJaf62UehNYgykRW0vjkV9PekqpVzEjFccrpXKAe4HngOeUmQaoGrimXua4PUgBXlRK2THJuze01u8ppfzAbuBLq7r1ba31fW3YzlC7GZinlHIBO4CftHF7Qq6Zz/OdgBv40Pq+fqW1/jnwBKaf70ZAAc9rrbPapOHHZwymH3a21VcbTBnvj5VSQzHlgLuwShy11oeUUv8HfGOte19r/d9Wb/XxSQLmW99PB/CK1nqxUuoi4DHMncz/KqXWaa0nAjcBvYC5Sqma8ubz9Qk+OGEzn+cHMSX4P8X8vrrM2nwu0AV40npf/LpuBHfRQUksI7EMEsucdCSWkVgGiWXaJJZR7etvhBBCCCGEEEIIITqyjtR1RQghhBBCCCGEEO2cJDqEEEIIIYQQQgjRbkiiQwghhBBCCCGEEO2GJDqEEEIIIYQQQgjRbkiiQwghhBBCCCGEEO2GJDqEEEIIIYQQQgjRbkiiQwghhBBCCCGEEO2GJDqEEEIIIYQQQgjRbkiiQwghhBBCCCGEEO2GJDqEEEIIIYQQQgjRbkiiQwghhBBCCCGEEO2GJDqEEEIIIYQQQgjRbkiiQwghhBBCCCGEEO3GUSU6lFLPKaX2K6U2NLN+ulIqSym1Tim1Sil1Zr11AWv5OqXUwuNtuBBCCCGEEEIIIcThlNb6yDdWaixQDryktR7UxPoooEJrrZVSg4E3tNb9rHXlWuuoELVbCCGEEEIIIYQQopGjqujQWn8GHPye9eW6LnMSCRx5FkUIIYQQQgghhBDiODlCfUCl1EXA/wKJwAX1VoUppVYBfuBBrfWCZva/HrgeIDIy8tR+/fqFuolCCCGEEEIIIYQ4yaxevbpQa53wQ9sdVdcVAKVUD+C9prquHLbdWGCu1vpc6+s0rXWuUioD+Bg4R2u9/fuOMWLECL1q1aqjap8QQgghhBBCCCHaH6XUaq31iB/arsVmXbG6uWQopeKtr3Ot5x3Ap8Cwljq3EEIIIYQQQgghOqaQJjqUUr2UUsp6PRxwA0VKqU5KKbe1PB4YA3wbynMLIYQQQgghhBBCHNUYHUqpV4HxQLxSKge4F3ACaK3/CfwIuFop5QOqgMutGVj6A/9SSgUxyZUHtdaS6BBCCCGEEEIIIURIHfUYHa1JxugQQgghhDAWrM3l4SVbyCuuIjUunDkT+zJjWFpbN0sIESI+n4+cnBw8Hk9bN0WINhcWFkZ6ejpOp7PB8iMdoyPks64IIYQQQojQWrA2lzvfzqbKFwAgt7iKO9/OBpBkhxDtRE5ODtHR0fTo0QNrNAAhOiStNUVFReTk5NCzZ89jOoYkOoQQQgghTmClHh8PLtpcm+SoUeULcPf8bNbtLWb0KV04f2Ay1f4g//h0Ow67wmFT2G0Kp93GkK5xDO0ah8cXYPGGAmu5wm6z4bAreiVE0bVzBB5fgE35pTjttgbbxEe5iA5z4gsEKfP4a4/vsNlw2BQ2m/xT1lak0qf98Hg8kuQQAlBK0aVLFw4cOHDMx5BEhxBCCCHECWT17oOs21tCdk4xWTkl7CisaHbbiuoAb63JIdJt5/yByXj9AR79aGuj7W49tzdDu8ZRXOnj1tfXNVp/zwX9ue6sDHKLq7joyRWN1v/pokyuOKMbm/JLmfb48kbr/zZzKNOHprFy50Gue/EbHHablQhROOw2Hrw4k9G94vlqRxF//O+mekkUk4i5a0p/+qfE8M2ug7y4YtdhiRbFjeN7kRYXzto9h/ho077aBEvNeS4b0ZXYCCebC0rJzimxEjF125zVO54wp529ByvJL/E0On+vhChsNkVJlQ+vL4C93rEddoXbYT/K72LrkEqf9keSHEIYx/uzIIkOIYQQQog2UFM9kZ1bQmV1gJ+POwWAu+dvYHNBGSmxYWSmxXLx8DReWLGLwvLqRsdIiwtn+R1n134dHeZkx5+m4AsGCQQ1voAmENS4HWaivfgoFx//ZlyDdb5gkLS4cACSY8J4fvZp+IMafyBonoNBhnbtBEBKbDi/v3CAtdzaPxCkX3JM7fEvHp6O/7Dzx0aYPtYuh42EaDe+gFnvD2gq/H6C1phxxZU+vs0rPez8mqtGdgfC+Ta/lH8u20Eg2HCMufMHJhEb4eSTzQd4aPHmRu/TqnvOJcxp541Ve3ns422N1m+6bxLhLjt//Wgrzy/f1WCdTcGO/70AgLvmZ/P2mhycNht2K5kSF+Hko/8ZB8AD733LF9sKcViVME6bIjHGzZNXngrA3z76jm/zS0wSxkq0pMaG89uJfQF4ccUu8oqrGiRaUuPCueTUdADeXZ9Hqcdnzm9TPPDfb5us9Hl4yRZJdAghOjRJdAghhBBCtDB/IIjDbpINz32xk7fW5LCloAy/9Q97RkJkbaLj/y4bSny0i8TosNr90ztFNLhzDxDutDPH+ge5PptN4bY1XYHgsNvISIhqtp2RbgcT+iU2uz4h2s3sMc33l85IiOL30wY2u354t048N/u0ZtefNyCJ8wYkNbv+yjO6c+UZ3dG6YaIlwmVC2qtGdmPq4BRrXbAu0RJuEi2XntqVkRldahMtNetdViJo6uAUTkmIqj1uIKipn1M5s1c8UW4H/oBJAPmDGpf1fQVIjHHTrXNEbYLGHwiiqLsrWVjuZXdRZYPzd+8SUbv+g28LWLXrkEkCWSce1i2uNtHx+Mfb2LKvrNn3p0ZucRUeX4Aw54lZiSLar/Hjx/PII48wYsQPjhXZIhYsWECfPn0YMGAAAHPnzmXs2LGce+65R7S/1po//vGPvPjiiyilSEtL4/HHH2fgwOZ/rwFUVVUxadIkPv74Y+x2O3a7nczMTAC6devGwoULAdi5cyczZ86kqKiIU089lZdffhmXy8Vjjz3Gv/71L7p168aCBQtwuVx88cUXvPXWWzz66KMAHDhwgFmzZrF48eJm2zF58mSefvpp0tPTQ35Nt912G//9738JBoOcd955/O1vf0MpxerVq5k9ezZVVVVMmTKldvntt9/OokWLGDp0KC+99BIA//73vyksLOTWW28FIDs7m7/85S+88MIL3/+NOQaS6BBCCCGECKFAULNtfzlZOcVk55awPqeErQVlrP7duUS4HFT5AnSOdHHDuAwy0+IYnB5LSmxdUmNAakyjY9bcnZexGAylTNcTp50G/8xHhzmJDnM2u1+3LhF0q5dYONyp3TtzavfOza6fkpnClMyUZtdfP/YUrh/bfLvvnzGo+ZXAvOtG1r7W2iRhAvVmSHz9hpF4/cHaJMql//yS/WXeJo81+A8fcEbPzvxkTA/O7td88kicvNpqfJZAIIDd3nZJtO87/4IFC5g6dWptouO+++47qmM/8cQTrFixgvXr1xMREcEHH3zAtGnT2LhxI2FhYc3u99xzz3HxxRfXtis8PJx16xp3E7z99tv59a9/zcyZM/n5z3/Os88+y4033si8efPIysriT3/6E0uWLGHq1Kncf//9vPrqq7X7JiQkkJKSwvLlyxkzZkyjY1dVVVFUVNQgyRGqa1qxYgXLly8nKysLgDPPPJNly5Yxfvx4brzxRp5++mnOOOMMpkyZwuLFixk9ejRr1qwhKyuL6667juzsbHr16sXzzz/fIFGTmZlJTk4Oe/bsoVu3bs225VjYfngTIYQQQgjRlGBQs/1AOQvW5lJUbv7hfOnLXUz862fMeTOLt9fkEu60MWtUd7y+IAC/nNCLl396BnMm9mPSoGRS48KPqC/yjGFpLL/jbHY+eAHL7zi7wyY5OgqlTPeV+uODxEW4SIoJIy0unO5dIrlrSn/CD6vaCHPauH5sT2aN7M6+Ug9FVpenvQcrufW1tby5Ood9pTJ96cmuZnyW3OIqNHXjsyxYm3vMx9y1axf9+vXjyiuvpH///lxyySVUVlYC0KNHD26//XaGDx/Of/7zH9atW8fIkSMZPHgwF110EYcOHao9zssvv8zQoUMZNGgQK1euBGDlypWMGjWKYcOGMXr0aLZs2XJUbTv8/E8//TSnnXYaQ4YM4Uc/+hGVlZWsWLGChQsXMmfOHIYOHcr27duZPXs2b775JgBLly5l2LBhZGZmcu211+L1Nk4SPvTQQzz++ONERJiE6Pnnn8/o0aOZN28e//znP5kzZ07tti+88AI33XQTAPPmzWP69Onfew1aaz7++GMuueQSAK655hoWLFhQu87n81FZWYnT6eTf//43kydPpnPnhonXGTNmMG/evCaP/+mnnzJ+/PgWuSalFB6Ph+rqarxeLz6fj6SkJPLz8yktLWXkyJEopbj66qtZsGABNpsNn8+H1rr2mh555BFuvvnmRtPFXnjhhbz22mvf+94dC6noEEIIIYQ4CnnFVby4YhdZOSVsyC2hzOsH4B9XDmdyZgoT+iYSG+5kcHocGfGRMiOJaDFHUumjrYqQ3UWVfP5dIQvW5QHQJymKs3oncMPYDBJjmr+rK9rO5f/6stGyqYNTmDWqB39e3PRMTL9/dyMzhqVxsKKaG/+9usH6128Y9YPn3LJlC88++yxjxozh2muv5cknn+S3v/0tAF26dGHNmjUADB48mMcee4xx48Yxd+5c/vCHP/DXv/4VgMrKStatW8dnn33Gtddey4YNG+jXrx+ff/45DoeDjz76iLvuuou33nqr0fmHDh3aZCXE4ecvKiriZz/7GQD33HMPzz77LDfffDPTpk1j6tSptcmEGh6Ph9mzZ7N06VL69OnD1VdfzT/+8Y/aLhQApaWlVFRUkJGR0WDfESNGsHHjRu68805GjRrFww8/bN7P11/n7rvvprq6mh07dtCjR48G5xsxYgQOh4M77riDGTNmUFRURFxcHA6H+Rc8PT2d3FyTmLrpppsYOXIkAwcOZMyYMUyfPp0lS5Y0eg9GjBjBPffc0+T7s2jRImbMmNFgWaiuadSoUUyYMIGUlBS01tx0003079+fVatWNaggqbmm6OhopkyZwrBhwzjnnHOIjY3l66+/5ne/+12T1/Tggw9y2223NXldx0oSHUIIIYQQh9FaU1DqISunhOycErJyS5gxNJWLh6dT7Q/y/PJd9E+JZvqwVAanxZGZHkvvRDP2RY/4SHrER7bxFYiOYsawtO+t7qmpFjqzdzzf3H0umwpK+fy7Qr74rpB5X+/mlxN6AbB4Qz47Cys5q3c8A1JiJEF3gssvaboqp7jSd1zH7dq1a223iKuuuoq///3vtYmOyy+/HICSkhKKi4sZN84MwnvNNddw6aWX1h7jxz/+MQBjx46ltLSU4uJiysrKuOaaa/juu+9QSuHzNd3O5pIc9c8PsGHDBu655x6Ki4spLy9n4sSJ33tdW7ZsoWfPnvTp06e2zU888USDRMcPSUhIICMjg6+++orevXuzefNmxowZQ35+PnFxcQ223b17N2lpaezYsYOzzz6bzMxMYmNjmz32rFmzmDVrFmC629xyyy0sWrSIl156ia5du/KXv/wFm81GYmIieXl5TR5j+fLlPPLII0d8PUdzTdu2bWPTpk3k5OQAcN555/H5558THh7e7LFvu+222uTFddddx3333cczzzzDBx98wODBg2sTNt93TcdDEh1CCCGE6PAKy72Ue/z0iI/E4wsw9s+f1I59YLcp+iRF18700b1LBNl/OP+EnXJUiObYbIqBqbEMTI3l5+NOwesP1H6Ov9hWyL+/2sNDi6FzpIsxveIZ3yeBH52a/gNHFS3l+yowUuPCyS2uarS8ZgalzpGuI6rgONzh3ejqfx0ZeWQJ3KaO8bvf/Y4JEyYwf/58du3a1WQXix9S//yzZ89mwYIFDBkyhBdeeIFPP/30qI93uJiYGCIjI9mxY0eDCojVq1fXJnVmzpzJG2+8Qb9+/bjoootQShEeHo7H0zDxlJZmko8ZGRmMHz+etWvX8qMf/Yji4mL8fj8Oh4OcnJza7Wrk5eWxcuVK5s6dy7hx4/j444954IEHWLp0Keeddx4ej6fJ5MKOHTvo2rUrLperRa5p/vz5jBw5kqgok9CfPHkyX375JbNmzapNfgBNXtPatWvRWtO3b1/uvPNOlixZwk9+8hO+++47evfu3ew1HS8Zo0MIIYQQHc6X24t44pNt3PDyKkb/71JGPPAR9733LWAGt7xoWBp/mDaQt38xmo1/mMiiX53FpSO6AiZolySHaA/qf44fmJHJyrvP4dHLhzC+TwJfbi/i1ZV7atc/v3wnH2/eR4XVVUu0rTkT+zYan6W5mZiOxp49e/jyS9Nl5pVXXuHMM89stE1sbCydOnXi888/B8yYHDX/NIPp/gDwxRdfEBsbS2xsLCUlJbX/AIdiho2ysjJSUlLw+XwNxqyIjo6mrKzxzER9+/Zl165dbNu2rck215gzZw633HILVVUmifTRRx/xxRdfcMUVVwBw0UUX8c477/Dqq68yc+ZMADp16kQgEKhNDBw6dKh2/I/CwkKWL1/OgAEDUEoxYcKE2jFDXnzxxUbjevzud7+rHUC1qqoKpRQ2m612rJStW7cyaFDjQY0XLVrEpEmTmnyvQnFN3bp1Y9myZfj9fnw+H8uWLaN///6kpKQQExPDV199hdaal156qclruv/++/H5fAQCprvVkVzT8ZKKDiGEEEK0W2UeHxtyS8nOLeZQpY/bJ/UD4NEPt7Jy10H+n737Do+qzB44/r1T0ntvJJSEENJICASlSwnFXrGACPa17E8X2yoq4tp7W10VWXct6KrsqhQpCoiI9F5CERIIaaSXae/vjxsmiQkISJgQzud58iRz33vnnjskYe7J+57TJcSbrM5BpMX40zsu0Hncg2OSXBWyEC4T5uvBJRkxXJIRg1LKuQzCYnPw4nc7qKyzYTZqZMYGMqh7KDnJ4cSH+bo46rNTW3ViSkxM5I033mDSpEn07NmT2267rdX9Zs6cya233kpNTQ1du3ZlxowZzjEPDw8yMjKwWq28//77gL6M4frrr2f69OmMHTv2qOeFekrwAAAgAElEQVQ/Vo2Opp544gmys7MJDQ0lOzvbmdwYN24cN910E6+++qozoXAkphkzZnDFFVdgs9no06cPt956a4vnvfPOOzl8+DCpqakYjUYiIiKYPXu2c8ZBYGAgSUlJbNmyhb59+zqPGzlyJMuWLWP48OFs3bqVW265BYPBgMPh4IEHHnB2gXnmmWcYN24cDz/8MBkZGUyePNn5HGvXrgUgMzMTgGuuuYbU1FQ6derkXAKyePHiVl+/uXPn8tprr7X6Wp2Ka7r88stZtGgRqampaJrGqFGjuOCCCwB48803ne1lR48ezejRo53P8dVXX5GVlUVUVBSg//umpqaSlpZGenr6Ma/pj9JUk5ZV7U1WVpZatWqVq8MQQgghxBmgzmp3thqduXwvM3/ay+6iaud4lxBvFt4zGINBY29xNYHebvh7Hr0VqRCiUZ3VzupfD7NkZxHLdhaz+UAFD4zuwa2Du1Fea2XOxoMMSAghJvDo7XvFsW3dupWkJNclWffu3cv555/Ppk2bXBbDmWrNmjW89NJLfPjhh216nkGDBjF79mwCAxsT8/X19fTv359Tfd98Oq6pvr6ewYMHs2zZMmeR1qZa+5nQNG21Uirr955bZnQIIYQQ4oxTb7Oz7WAlG/LL2bC/jI355ew4VMmKh4YR5uuB2Wiga4gPF/eKJjXGn7Rof4J93J3HS7FQIU6Mh9lI//gQ+seHwGi9ro2xoRbDz7tLeOCLjQB0DfFmQEIIAxNCGRAfgqebLPMSHV9mZiZDhw7FbrdjNLbN93xRURH33HNPsyQHgLu7+ylPcsDpuaZ9+/bx9NNPt5rk+KNkRocQQggh2jWr3cHOQ1VszC9jQEIo0QGefL46j798th7Qi+6lRvuTFuPPhHM6E+rr/jvPKIQ4lZRS5BZWsXRnMUt3FrFidym1VjsL7hlMfJgP2woqqLXYSYsJwCjdXI7K1TM6hGhvZEaHEEIIITqUQxV1vPX9LtbnlbHlQAX1NgcAz16expVZnRiYEMKb12aSFuNPdIBniyr/QojTR9M0EsJ9SQj3ZdKALtTb7KzbV0a3UH3m1DtLdvPFmnz8PEz0jw/RZ3zEhxIbLMtchBBtQxIdQgghhHAJpRR7S2rYkFfGxrxyNuSXMyo5gkkDumA2Gvhs1X6So/0Z3y9OX34SE0BckH5jFO7nwZjUSBdfgRCiNe4mI9ldg52PHx7bk6GJYSxrmPExZ1MBnYO9+H7KUADW7S+jS4i31MwRQpwykugQQgghRJtTSpFfVktFrY2eUX44HIrspxZSVKm34HM3GegZ5Ye3u74OOMjbjY2P5WCQae5CnPGCvN24ID2KC9KjUEqxq6ja+bNvdygmzlhJZZ2N9Bh/BiaEMqh7COkxAZiMBhdHLoQ4U0miQwghhBBtYtnOYn7eU8KGvHI25pdTWm2hd1wg/7ntXAwGjRv6dybY243U6AASwn0w/+amRpIcQnQ8mqYRH+ZDfJiP/hh4+7reLMstZsnOYl5btJNXFu7klsFdeXB0Ela7g/zDtcQFe8kSNSHEcTuhRIemae8D5wOFSqmUVsYvAp4AHIAN+LNSalnD2PXAww27TldKzfwjgQshhBCifSipqmdDfjkb88o5WF7LU5emAfDB8r0s2naI7uG+DE8KIzUmgIxOAc7jbh8S76qQhRDthMGgkd01mOyuwdw7MpGyGgvLd5XQLVRPhKzdV8aVb/9ETKAnAxNCGZgQwrndggnwcnNx5EKI9uxE54N9AIw6xvhCIF0p1QuYBLwLoGlaEPAokA30BR7VNC3wqM8ihBBCiHapvNaKw6F3bPv3z7/S/+lF9J6+gBtm/MJLC3awck8pdVY7ANMvTmHz46OY++dBPHt5OuP7xZES7e/K8IUQ7VyAlxtjUiNJjPAFoEuIN09clEzPSD++Xn+A2/+9hswnvmNjXjmg/06yNBQrPutsmAUvpcBjAfrnDbPa5DQvv/wyNTU1J7zfmDFjKCsra5OY2uL85eXlTJgwgfj4eLp168aECRMoLy//3ePWrl3L5MmTAfj+++/x9/enV69e9OrVi2nTpjn3mzt3LomJicTHx/P00087t1977bWkpaXx0EMPObdNnz6dr776yvn466+/ZurUqUeNwWq1kpmZ2SbXVF5ezgUXXEB6ejrJycnMmDHDud/MmTNJSEggISGBmTP1eQz19fWMGjWKlJQU3nzzTee+N998M2vWrHE+fv3113n//fd/N5aTppQ6oQ+gM7DpOPY7B9ja8PXVwNtNxt4Grv695+jdu7cSQgghhGvU1NvUil3F6h9Ldqk7P1qjhjy3WMXd/7XKLaxUSin19foD6k//Xq3e/iFXLc8tVhW1FhdHLIToyCw2u/plT4l66bvtqt5qV0op9eQ3W1TPR+aoSTNWqhnLdqvcwkrlcDhcHOnJ2bJly/HvvP5TpaaHK/WoX+PH9HB9+ykWFxenioqKTtl+J8vhcCi73d5m57/sssvUo48+6nw8depUdfnll//ucZdffrlat26dUkqpxYsXq7Fjx7bYx2azqa5du6pdu3ap+vp6lZaWpjZv3qzWr1+vJk+erJRSavjw4aqsrEwdOHBAnX/++c2OdzgcqlevXqq6urrVGBYtWqTuuOOONrmmJ598Ut13331KKaUKCwtVYGCgqq+vVyUlJapLly6qpKRElZaWqi5duqjS0lI1e/Zs9cQTTyi73a769eunlFJq3bp1atKkSc3OUV1drXr16nXMOFr7mQBWqePIW5zyCj+apl2iado24Bv0WR0A0cD+JrvlNWxr7fibNU1bpWnaqqKiolMdnhBCCCFaUWe1s2bfYWYu30tuYSUAS3cWcdU7K5j+zVZW7S2le7gPU3IS8XHXV76OTYvk9WsyuXlQN87pFoyvh3RMEEK0HbPRQFbnIP48vDtuJv02Zkj3UC7OiCa3qIrH/reFYS/8wNhXlx354yr1NrsrQ/5jZoxt+bHyH/rYgsfBWtt8f2stzLlf/7q6pOWxv6O6upqxY8eSnp5OSkoKn376Ka+++ioHDhxg6NChDB2qd8m57bbbyMrKIjk5mUcffRSg1f06d+5McXExAP/85z9JS0sjPT2d8ePHH/dLsHfvXhITE5kwYQIpKSns37//pM7/4osvkpKSQkpKCi+//HKL8+Tm5rJ69WoeeeQR57apU6eyatUqdu3axbhx4/jmm2+cYxMnTuTzzz+nsrKSDRs2kJ6efszrWLlyJfHx8XTt2hU3NzfGjRvH7NmzMZvN1NbW4nA4sFqtGI1Gpk6dyuOPP97seE3TGDJkCF9//XWrzz937lxGjx7dJtekaRqVlZUopaiqqiIoKAiTycS8efMYMWIEQUFBBAYGMmLECObOnYvZbKampgar1er8OXzkkUd44oknmsXn5eVF586dWbly5TFfu5N1youRKqW+BL7UNG0Qer2O4Sd4/DvAOwBZWVnqVMcnhBBCCF1JVT3Pzt3OhvxydhyqxN6wJOXxC5OJD/Mlu0swMyb2ISXan1BfdxdHK4QQLZ0bH8K58SEA/FpSzdKdxdRa7M7CpWNeWYq3u4kB8SEMTAild1ygM0lyRqvIb317belJP+XcuXOJiopy3vyWl5fj7+/Piy++yOLFiwkJ0V/nJ598kqCgIOx2O8OGDWPDhg3cddddLfY7YvPmzUyfPp3ly5cTEhJCaWnLGFetWsXf//533n333RZjO3fuZObMmfTr1++kzr969WpmzJjBzz//jFKK7OxsBg8eTEZGhnOfLVu20KtXL4xGo3Ob0WikV69ebN68mauuuopZs2YxduxYLBYLCxcu5K233mLFihWkpDQvXfnTTz+Rnp5OVFQUzz//PMnJyeTn59OpUyfnPjExMfz8888kJSURGhpKZmYm48ePJzc3F4fD0eoylKysLJYuXcqVV17ZYmzx4sXOpM+pvqY77riDCy+8kKioKCorK/n0008xGAytXlN+fj5XXHEFH374If369WPKlCn897//JTMzk6ioqKNeU9++fVuM/VFt1nVFKbVE07SumqaFAPnAkCbDMcD3bXVuIYQQQuhsdgc7C6vYmFfOhvwyNuaVM6h7KPeOTMTb3cSi7YUkRfoxrEcYqTH+pMcEEO6nJzX8vcwM7RHm4isQQojjExfsTVywt/Ox3aG4MD2aZblFvL1kN29+vwsvNyP/N7w7Nw3q6vxrc7vt5nLDN0cf84+B8v2tbG+48fQOPvbxrUhNTeXee+/l/vvv5/zzz2fgwIGt7jdr1izeeecdbDYbBw8eZMuWLaSlpR31eRctWsQVV1zhTEAEBQW12CcrK6vVJAdAXFycM8lxMudftmwZl1xyCd7e+vfGpZdeytKlS5slOn7P6NGjufvuu6mvr2fu3LkMGjQIT09PDh48SGhoqHO/zMxMfv31V3x8fPj222+5+OKL2blz5zGfu+kMkwsuuIC3336bJ598kvXr1zNixAhuuukmAMLCwjhw4ECL4/Pz8wkKCsLLy+u4r+dErmnevHn06tWLRYsWsWvXLkaMGHHU7w0Ak8nERx99BOi1Q3Jycpg9ezb33HMP+/btY8KECVx44YXOa9q2bdsJxX28TmmiQ9O0eGCXUkppmpYJuAMlwDzgb00KkI4EHjyV5xZCCCHOdg6HYndxNWU1FrI6628kBz27mAPldQD4uptIifYnKsATAA+zkZUPDWu/b/KFEOIPMBo07h6ewN3DE6iss7JidylLdxbRNVS/4d1dXM21//iZAQkhDEwIYUB8CME+Z8jstWFT4X93NV++YvbUt5+k7t27s2bNGr799lsefvhhhg0b1qIA5p49e3j++ef55ZdfCAwMZOLEidTV1Z30OY/HkQRFW56/Z8+erFu3DofDgcGgz/hxOBysW7eOnj174uHhwZAhQ5g3bx6ffvop48aNA8DT07PZ+f38/Jxfjxkzhttvv53i4mKio6PZv78xMZWXl0d0dPNKDrNnz6Z3795UVVWxa9cuZs2aRU5ODtdeey1eXl7U1dXh6enZIva5c+eSk5PTZtc0Y8YMHnjgAb01dHw8Xbp0Ydu2bURHR/P99983u6YhQ4Y0i+HNN99kwoQJrFixAn9/fz799FPOO+88Z6LjaNd0KpzQvC1N0z4GfgISNU3L0zRtsqZpt2qadmvDLpcBmzRNWwe8AVzVUDOkFH0Zyy8NH9MatgkhhBCiia/W5tP/6UV0eeAb+j+9iK/WHmV6coMfdhTxt2+3ctXbP5H2+HyGv/gDD36x0Tl++9B4Xr6qFwvvHcz6R0fy8c39uLpvrHNckhxnkNPUYUGIjsjXw8yInuFMuyiFYUnhgD7jo3dcIAu2HuLuT9bRe/oCxryylO0FlS6O9jikXQkXvNowg0PTP1/wqr79JB04cAAvLy+uu+46pkyZ4uyQ4evrS2Wl/ppUVFTg7e2Nv78/hw4dYs6cOc7jm+7X1Hnnncdnn31GSUkJQKtLV47XyZx/4MCBfPXVV9TU1FBdXc2XX37ZYkZCfHw8GRkZTJ8+3blt+vTpZGZmEh+vt0K/6qqrmDFjBkuXLmXUKL0RaVJSErm5uc5jCgoKnDOFVq5cicPhIDg4mD59+rBz50727NmDxWLhk08+cd7sgz7z4eWXX+a+++6jtrbW+X+z3W7HYrEAsGPHjhbLZKD1+hyn8ppiY2NZuHAhAIcOHWL79u107dqVnJwc5s+fz+HDhzl8+DDz589vlnA5fPgwX3/9NRMmTKCmpgaDwYCmadTWNibnjnZNp8IJzehQSl39O+PPAM8cZex9oA37xwghhBBntq/W5vPgFxupbWjPml9Wy4NfbNTXFHcNZkNeORvzy9hVWM1b12WiaRqz1+Xz9fqDJEX5cUlGNGkx/qTFBDif87p+ca66HHEqbZjV/K+35fv1x/CHbmyEOJt1D/fljWszsTsUm/LLWbqziKU7i4nw8wDggx/3sGh7EQPjQxjYPYTEcN/2lRxOu/KU/vxv3LiRKVOmYDAYMJvNvPXWW4DeFnTUqFFERUWxePFiMjIy6NGjB506daJ///7O43+73xHJycn89a9/ZfDgwRiNRjIyMvjggw+anftYNTqaSk9PP+HzZ2ZmMnHiRGcdiBtvvLHVZSvvvfced955J926dQPgnHPO4b333nOOjxw5kvHjx3PRRRfh5uYGQI8ePSgvL6eyshJfX18+//xz3nrrLUwmE56ennzyySdomobJZOL1118nJycHu93OpEmTSE5Odj73G2+8wfXXX4+XlxdpaWnU1NSQmprKmDFjCAjQ/09fvHgxTz31VLOY7XY7ubm59OjRo9XX61Rc0yOPPMLEiRNJTU1FKcUzzzzjXIb0yCOP0KdPH0AvdNp0WdK0adP461//isFgICcnhzfeeIPU1FRuvfVW5z4//vgjjz32WKux/1HakYxTe5SVlaVWrVrl6jCEEEKINqOUotZqp7rezvmvLeVQRX2LfQwaNNQJxWTQSIzw5aMb++HvZeZwtQVvd1PHKK4nju6llNbX4/uEwVUfgX80+ISDwdhyHyHESfnXil/5YPlecgurAAj1dee8xDCeviy1TRIeW7duJSkp6ZQ/r2hbL730Er6+vtx4441tdo5Dhw5xzTXXOGdWHLFs2TL+9a9/8fe///2Unu90XNPatWt58cUX+fDDD4+6T2s/E5qmrVZKZf3e87dZMVIhhBCio7HYHNRa7FRbbPh6mPD1MFNeY2XVr6VUW+zU1Nucn0elRJAQ7svmA+W8viiXaoudWouN6no7NRYbz12RTp/OQXyz8SB3fLT2mOd1KJh2UTKp0f4kRfrhYW68mQ30dmvryxautvfH1pMcAFWF8F5DgzuDCXyjoHN/uKThTe+2b0Az6okQ/xjwCID29BdpIdqx6/rFcV2/OA6U1bJsZzFLc4upqLM6kxz3fb4ef08zAxNC6dslqNnvZnH2uO222/jss8/a9Bz79u3jhRdeaLF9wIABDBgw4JSf73RcU3FxcYuWs6eSJDqEEEJ0OA6Hwq4UZqMBu0OxvaCSGoutWTIiMdyX1Bh/ymutvLJgJzUWGzUWPQlRXW/nmuxYLkiPYndRFZe8uZwaiw2rvXEW5DOXpXJVn1j2llQzeWbL2YexwV4khPtSb3OQW1iFl7sJbzcjUQFueLsb8XbT/wvuGenHA6N74O1m5MXvdnC4xtriuaIDPJlwTuc2e71EO7Z9Lnx8lZ6sUPaW4z4RcMErUJEH5XlQng/eTVorznsIDu9tfGz2hpRL4aLX9ccr3gJ3Xz0J4t8J/KL0goZCCKeoAE+u7NOJK/s0ttJ0OBQHy+v4au0B/rF0D24mA307B3FtdiyjUyNP+lxKqfa1PEb8Lg8PD8aPH9+m5ziyPOR0OR3XNGLEiGOO/9GVJ5LoEEII4XLlNVaqLLZmMyL8PM2kRPsDMHP5XspqrNRYbdTU6zMqMmMDua5fHEopxr66jOomsyVqLHYm9e/C1At6YrE5GPPq0hbnvGNoPKkx/tgdilmr9uPlZsTb3aR/djNx5L/XAC83Lu4V5UxUeLrpn490NUkI9+G/d/THy82Et7sRL7MJTzejcylJZmwg390z+KjX3jXUh1sH+wB6sbymNToAPM1GpuQknoqXWZwpKg9BSa4+MyN+GIx5Xk8+fPuXlh0WRj4BiaOO/lyT5jUkQJp8hOhF6FAKvnsU7L9ZLpV9K4x+BhwOPVHiF6knQvxiGpbIRIBR3kKKs5vBoPHh5GxqLDZ+3lOqz/jYWUR+mf4zWlpt4fH/bWZgQigDE0IIb6j7cSweHh6UlJQQHBwsyQ5xVlNKUVJSgofH7//cHI3U6BBCCHFcHA5FjbVxRoTdoYgP02/Qf8wtJr+s1rmso6beTpC3G5MGdAFg6uxN7DhUSY3FTnW9nohIi/Hn7fH6EsuBzy5if2lts/ON6BnOPybo41nTF1BcVY+7yeBMRoxNjeTBMfq6zT99tAazQXMmIbzcTfSOC2Rw91CUUszbfEhPQrg1JjICvc34ephP18t33L5am89z87ZzoKyWqABPpuQkcnFG9O8fKM589VWw/DX9wzMQ7l7fPKGwYRYsnKYnK/xj9DaSf7QQoa0eKvL1mSAV+foSmfBUPXlSUwqv9IL68ubHDH4Ahj4I1SUw5z49+eEX0zArJBqCuuqzRIQ4Cx2ZkbH618Pc8uEqiqv0jhndw30YEB/KpAGdiQn0avVYq9VKXl5em7drFeJM4OHhQUxMDGZz8/dqx1ujQxIdQgjhIm11Q6uUot7moMZiJ9DLjKZp/FpSzf7SWudsh2qLDavNwcT+eiLi01/28fPuUj1J0ZCMcDcZ+fjmfgDc9q/VzNlU0Ow8ccFe/DBlKADX/GMFy3eVOMcMGvTqFMAXt+vV0O+dtZ79pTV4NSzZ8HIzkhjhy40DuzpfC4vNgZe7ES83PSER6utOt1A9kVJVb8PDZMBklIKbogOy22DtP2HxU1BdCD0vgmGPQnA3V0emqytvngiJytA/infCv6/Qt9stjftf+BpkToBDW2Deg02SIA2JkKgMPZEjRAfncCi2FVQ6u7ms3FvKd/83iLhgb37YUcTmA+UMjA8lOcoPg0FmcAhxPCTRIYQQ7dhv24gCuJsM3HFePH07BzXUirAzJDEUb3cTq/aWsnh7oXNpxpHlHa9cnYGfh5l3l+7m3aV7nIkKe0OLjm1PjMLDbOSx/27mg+V7m8Vg0GDX38agaRpPfL2FeZsL9CREQzIiyNuNV6/OcMa7t6S62XiAl5khiWEA5B2uQSmcsy3cTQaZdivE8dr9PfzzIujUD0ZOh06ndy32H+ZwQE2xngQpz4eoXhAQC3mr9Rkf5XlQdQiOLAi77gt9Sc7OBbDwscYlMUeWxyQMl0SI6JDqrHZnwdKn5mzl7R92AxDk7ca53YIZlBDKFVkx8v+nEMcgiQ4hhGhnrHYHu4uq2Xqwgoe/2kRVve13j1lwzyDiw3x5b9ke/vbtVueyiyPJhvcn9iHU1515mwtYtLWwccZEw+dxfTvhbjKyu6iK4ipLw2yJxloUPu4meUMlhCvkr9ZnPGSO12tl/PojxPXvuB1RbBaoPKAnQsKTwTMA9iyBn95oqB2yX585AvCnlRCaCCv/AUtfbJwJciQRknEduPvoy26Mbh33NRMdXmFlHT/mFrN0p/4R4Gl21nT69Jd9hPi4069rMN7uUhNHiCMk0SGEEC5UWm1h68EK4oK9iAn0YvmuYia+/wsWu+N3j/33jdnOZERcsBfuJiMOh0LTkKSEEGe60j2w6AnY9B/wj4U7V4NJWgQDUF+pJ0KCuuqvSe4C2PRF40yR8jy9cOqD+XqiY/4jsPIdvVOMX7TeNcY/GoY8BAYDVBeDyV3qhYgzglKKkmoLIT7uKKXI/ttCCivrMRs1MmMDGZgQwoieESRGyPezOLsdb6JD0oNCCHEKlNda+fsPu9h6sIKtBys4VKF3MXh4bBI3DuxKt1AfbujfmaRIP5Ii/bjhg5UcKGtZbCw6wJP+8SEttsvaXSHOcDWlsOQ5fZaC0QyD7oP+d0mSoyl3Xwjr0fg4frj+cYRSUFOiJzkAugwGVGNb3d3fg60OzntYH59zP2z6HDz8G+uEhCbqnWoAinbor79vlPw7CJfTNI0QH3fn10vuG8rqXw+zZGcRy3YW8/z8HVRb7Nw/qgf1NjtfrMlnYELIUQubCnG2kxkdQghxnMpqLGw9WMnWgxVsK6hg68FKhiSGcu/IROqsdtIfn0+XEG96NiQzekT6khrtT4BXyzfQrdXo8DQbeerSVOmwIURHdGgLvD0Iel2tzzjwi3R1RB2Tww4GvQYCuxbDwfWNbXUr8sDNFybN0cffy4H9KwANfML1REjcOXqdFIA9S8HNS0+SeIfqs0SEcJHiqnocShHm68GK3SWMe2cFAF1CvBmYEMKA+BD6x4c4l7lIBy/RUcnSFSGEOEl2h2JviV5LA+D8tCiUUvSevoDSar2zQLC3G0mRfoxNi+TqvrGAXoPDfAJdQeRNiBAdmMMBGz+Dgg2Q86S+rbIAfCNcG5do9OtPUJLbmAQpz4PALnDBy/r4Syn6shnQa4H4RekdcUZM07et/xS8ghqWzcSAh59rrkOcdZRS5BZWNdT2KGLF7lJqrXb+c9u59I4L5J0lu9j+3fv8n/YJUVoxB1QILzOOAZfcLu8zxBlPEh1CCHEcmlZAf23hThZsPcT2Q5XUWfVaGj0ifJn750EA/Hf9Afw9zSRF+hLq4y71MoQQrdu1GL6bqic5ojLghjlg9nR1VOJEFWyEsv2NbXXL8yEyXV9y5LDDE6GgGmfl4e4H/W6DoQ/p48tebNJaN1pPiJjcXXc9osOqt9lZ82sZfToHYjIamPLoX3mcd/DSGts+1yg3njXfzmMPP86/VvzK2n1leLoZ8DQb8TQb8fdyY/IAveX8L3tLKamy4GFuGG8oXt61oeV7ndWOyaBJy3fhElKjQwghfuNAWS3r95ex9WAFWw5Wsq2ggup6G2seGYGmaZRUW/ByM3FN3ziSIn1JivQjPszHefyF6VEujF4I0e4d/hW+uUcvoukfC5e+CymXyZKHM1VEqv7RGs0Af97YPAlSkQ9hSfp4dREsmt7yuJHT4dw79ZotPzzbpKNMJz0R4hN+4t8vG2bBwmn6jBT/GBg2FdKuPLHnEC0pBXYr2C3gsILBrNeHcTjg8B5w2PRxhxXsNvAN19sq2yywa1HDdmvjfpFp+vdTXTmsntl4nKNhn+6jITZb/1764enGsSPP0fdm6DpYXwb3zT3Nzu3usHLOqKfBOAz2LOFp3sCoNf9jtpdm4UbLv2DnQMYsvouBFrAqAzZlwKIMPO12B5MH3Ai5C/D+fDo11XZsGCnHQDFGZvrcyKz7r4Ld37P887c4UGHFoZnAaEIzmFgZfT2vTR4G+1bw3Zz/cLjWgcFkxmg0YTCZqUi8kvGDkuDgBn7+5RiyXhcAACAASURBVCcsDgNmsxmTyYzZ7IY5cQQ9owOgdA+FBXmYTGbc3N1wN7thMruhhXbXL6T2sH7tBpO+TM1g0j8kiXh8zqLfF5LoEEJ0OFX1NrY1FAXdcrCSqef3xNPNyMyf9vL2D7sxaNA11IeM2ECSIn2xORRmo8ZjFya7OnQhxJlIKb3FqdkLirbrN7N9bgKzh6sjE21F0xoSFNHQqW/Lcd8I+GsBVBxongiJadi3Ih/W/BOs1c2Pu+hNyLgWCrfB0heat9X1j4Hgbs1nB22YBf+7C6y1+uPy/fpjOP03L0d+DgAs1WCta3KjbtW3B3XVPxft0AvLOsft+s9LF30GJTu/g8qDzRMF3qGQfpU+vuLv+nKjpsmA4Hh9tg3A1/+nv/bOZIQNOmXD8Ef18fdy9GRU02RDz4tg7Av6+JORYK1pfn19btTHlR1ey2x5/f3/DCMeB0sVfHxVy/GhDzckOirgu0eajxnMeqIrNls/7475etFig0n/bHTTuxKBnmQzmPTvA4O5cT/3hqVTPuEYtNZn7EcZSsAzkKDEAQQ5bPrr4rCj7FY+GNZf38nhID7ITGc/UHb99VEOGyGDGv7tyvZzjmMNytOK5rBjUDY0Zacu/EZ9fO9SRhx8p8W57zIO0hMdm/5D9pqXW4zfsn8+b1+fDT+9Qdgv/2g2Vq/MPJyykOeuSIc5D8CGT5qNVxr8+Xb0Mq7qE4uadT22rXNQBqOeiDEYqfaKoeDKb0iO8sfx5e3U7/0ZzWhGM5rQjCYITkC79G19+fGcB6B0d/NESmgiDHlAP9n3z+jfO03HQ7rrP7cAv7yr/zweScAYTBDUBboO0ce3fg3K0fhvazDq//ahifp4/prmCRyDCTwCwDu4oSBzactxg/H42my3p98Xp4EkOoQQZyyHQ5F3uJYgHzd83E0s3HqIx/+3hX2ljW9O/DxMTB7QmfgwX67pG8vY1Ei6h/s6l6sIIcRJq6uAH1+BvJUw4b/gEwp3rQOjvL0S6Deiwd30j9+KSIWH8qGurLF1bkUexPbTx6uL9EKpmxtu1o+YMFu/Ydq1GJa/Cvt+brxpOcJaC/P/2nij33TmwaApenebbd/A9m9bzhq4fIaecPj5Hb0F8pExu1W/ObtjpX6OuQ/B+o+bP7fZGx7cp4/P/hNs/rJ5XL5RcO9W/et5D0Hud83HgxPgzoYl60tfhH3Lm49HZTQmOjZ8oieDmiYD4s5t3Lc8T6+J47yZNDcWqQUISdCTSAaz/vNqMENUk+RF/z8DqvnxESn6mMEEl7zdfMxo1uu7gJ5wuGlRkyREwzk8A/Vxv2h4MK/J+G/ej4QkwF+2c1RhPWDi10cfD02k1jMSr9qDLYbqPCPwiukNMc0TERrgLJvefSRu3Ufy2zLq2Ue+yByPZ+b4Fs9905EvBtwL597d+L3XkEx51StYHz/3Lqp6XkV9vYV6i4V6qwWrxcpfohtmQ/W9iZ9MWdTVW7DZrNisFqw2O4O6h+rjva7hk4IIbFYrDrsVh91GrcOIraHbnTU+h5kb9eSLEQcm7JTXeFO3sYDkKH/qfDqxuHQnJhwYsWPCQX5eCWUhu/nT0HiqK0v5dedOzJoDk+bAhIN9ucVUhRaQkxxB/Y4F2A5tx4Ado9ITPflB2ViiLiQh3BfHD89jqGr+2ld2uwBjzAC83Eyor25Dq69o/uJlXAcXvaF//e4w/Wet2Yt/G4x+Wv/Zfq5ri9eegX+BYY9AVRG82qtlImTgvdBnsr6ksrXfFwunSaJDCCFcqbTawpxNBxtauFayvaCSqnobb4/vTU5yBME+7qRG+3NlVgw9IvxIivIjyt/DWUsjLtjbxVcghOgQ7FZYNUOfXl5TAqlX6H/BdveRJIc4fpqm3/x6BjbeRB/RZaC+NMZhh6rCxiUyEWn6uK1On8L/2xkhR1QVwoJHm5zLoN9YZ9+mJzpKdkHuosab/CM35EeSKpqmt9w1eDdPJhyZtRHVC+z1zRMF5iZtTtOvgdhzmicD3BuXgnLeX+Gc25vPSHBr8n/05e/rsTRNFBib3Hrf/P2xX9trPzv2+EWvH3t8yP1HH9M0SB939HGjCaJ7H33cYND/DdqQ1+hp2Gbficne2MbeZvTAa/S0Nj0voF+fwQ1apEoaeAfj4x2MT+ujEJrIOTmJR3/+roMZd/vgow67ZV7DTZnXYLM7qLM5qLXYqbPa8XLTE0qGIffhHTuZOquDKqudWqudeoudAXF6Iqpm9Gt85LaDWouDOqt+bK3Vzg0N7yV3X/AfbpjxC3U2O7UWO/U2B+TDKwcrSAj3ZeUFC5g8YwUm7JjQky3WzUaeyy1heM9wVpz3CdO+Wo+nUeFlAi+TonZ7IPfsO0xGbCBbh7zDgs35eBiV/mFQVFbEMbq8lkhvE4UDniCvpBI3gwOz5sBNc1DnmUEXqx0PkzuWXhOw26zOJI7BYdOXVAGqsoDW5n2o8rxWt5/ppBipEKJdUUqfpaG3cNVbuY5OjeTC9Ch2FVUx7IUf8HU3Odu3JkX6Mbh7KFEBUuhPCHEaFO+Ej66C0l3QeaDegSO6lWnsQpwOTTvDNOUfA39a2fAXXbPUiTkbnUW1GFzJ4VDU2eyYDAbcTAaq623sKa6m1mp3JllqrXayuwQT4e9BbmEV/1t/gDqbnTqLPlZrdXD3sATiw3xYtO0QL8zfQa1VHz+SrPnyT+fSI8KPmcv38uh/N7eIY+l9Q+kU5MWb3+fy7NzGGUFuRgPuZgPf/2UI1ueTiaCoxbEFhBLxWG6bvk6nUpsUI9U07X3gfKBQKZXSyvi1wP3oM6AqgduUUusbxvY2bLMDtuMJTgjRsdVa7Gw/VIlBg7SYAOqsdvo+uYCKusZpup2DvegfH9LwtTdL7xtKTKCndDwRQpxetWXgGaAXjQzqCqOegoSRx7cuWoi2Mmxq8zX3oC+ZGfZo8xkS4uyTdqUkNk4Dg0HDy63xltrb3URKtP9R948P8+H/RnQ/6vh5PcI5r0f4UcevzOrEyOTwhiSKQ0+IWO2E+urFWAclhOLrbmpItDSOe7ubuN9yBU+Z323Rjecp6xW8ciIXfYY40fmVHwCvA/88yvgeYLBS6rCmaaOBd2iypAsYqpQqPuEohRAdxnvL9rBm32G2Hqxgb3E1DgXDeoTx3sQ+eJiNjD8njqgAT3pE+NEjwhdv98ZfU0aDRqcgr2M8uxBCnGIlu/QlAAfWwx2/6PULrvvc1VEJoTtyIyt/uRfirODpZsTT7eizmFOi/Y+aaFnlN4IHKuA+0yyitBIOqGCetV3Jar8RbRWuS51QokMptUTTtM7HGG9aNWgFEHNyYQkhzlR1Vjs7D1U1dDypYFtBBWajgQ8n6znPuZsOUlBRR1KEHxekRZEU6UtyVOMv5Ck5PVwVuhBCNKoqgh+egdUzwOQB/e8G2u9yX3EWk7/cCyGOw5ScRB78wsJ/LQOc2zzNRp46Vk2UM1hbVsyaDMxp8lgB8zVNU8DbSqmWfYeEEGcMpRSHKurZerCC3cXVTB6gVxu/97P1fLNBrzbt5WYkMcKXHhF+zuM+uqmf3r5LCCHaq+JceGeI3uax90S9raBPmKujEkIIIU7axRnRADw3bzsHymqJCvBkSk6ic3tH0yaJDk3ThqInOgY02TxAKZWvaVoY8J2maduUUktaOfZm4GaA2NjYtghPCHGC6huKLBkNGvM2FzBz+V62HqzgcI3Vuc+lGdEEersxvl8cY1MjSYr0Iy7IC4Oh+fp1SXIIIdolhx0Kt+htP4O7Qd8bode1eqtHIYQQogO4OCO6wyY2fuuUJzo0TUsD3gVGK6VKjmxXSuU3fC7UNO1LoC/QItHRMNPjHdC7rpzq+IQQx1ZRZ2XtvjK2HaxwtnHdVVTF/+4cQFKkH3VWO9X1NnKSI0iK9CMp0o/ECF/8Pc0A9Osa7OIrEEKIE6AU5C6A76ZC2T64ewN4B8Pwx1wdmRBCCCFO0ilNdGiaFgt8AYxXSu1ost0bMCilKhu+HgmchkbOQoijsdgc7CqqakhmVHB+WhTpnQJYu6+M699fCUCUvwc9Iv0Y3jMMn4aioBf1iuaiXmdHJlgI0cEdWAffPQJ7lkBgF7joDfAKcnVUQgghhPiDTrS97MfAECBE07Q84FHADKCU+jswFQgG3mxo/XikjWw48GXDNhPwkVJq7im6BiHE7yipqsfuUIT5eVBQXsfEGSvZVVSF1a5PmnIzGUgI8yW9UwCZsQF8fFM/kiJ9CfByc3HkQgjRRg7vhX8MBY8AGPUMZE0Ck/zOE0IIIToCTan2uzokKytLrVq1ytVhCHFGUUoxe90Bthboy062HqygqLKeied25rELk7HYHNz6r9V0D/clKdKXnpF+dAnxxiS1M4QQHV1tGez+HpIv1h9v/Bzih4NngEvDEkIIIcTx0TRtdcNkimNqy64rQog2dLja4mzhuvVgJaG+7jwwugeapvHUnK0crrYSH+bDoIRQkiJ9nbUz3EwG3p/Yx8XRCyHEaWSrh1/ehSXPQX0ldMoGv0hIvdzVkQkhhBCiDUiiQ4h2zmZ3sKe4moPldQzqHgrADTNWsnh7kXOfEB93hic1tj78z23nEu7nIR1OhBBnN4cDNn8BC6dB2a/Q7TwYMU1PcgghhBCiw5JEhxCnwVdr80+oZ/XCrYeYt7mArQcr2XGoknqbA283Ixsfy8Fg0BiVEsE53YJJivSjR4Qfob7uzY6PCfRq60sSQoj2r+oQzP4TBMfDdV9A/DBXRySEEEKI00ASHUK0sa/W5vPgFxuptdoByC+r5cEvNnCoso7oAE+2Hqxg28FKthVUMufPA/HzMLNufxkLtxaSFOnHhHPi6BGht3E94qo+sa66HCGEaN8Kt8Gm/8DQh/SZG5PnQ3gKGIyujkwIIYQQp4kUIxWijfV/ehH5ZbVHHTcaNLqFepMU6ceDo5OI8PfAandgMmg0dCoSQgjxeyoLYPHfYO2H4OYDty6DwDhXRyWEEEKIU0iKkQrhYoerLSzYeuiYSY6v7xxAfJgPHubmf2mU2hpCCHGcLDXw4yuw/DWw10Pfm2HQfeAd7OrIhBBCCOEikugQ4hSy2R2YjAYKK+s456lF2B0Kowb2ViZORQd4khLtf/qDFEKIjkQ5YPUHkDAChk2F4G6ujkgIIYQQLiaJDiH+oL3F1czdXMDcTQVEB3jyxrWZhPl68NCYJPp2DiK3sJKHvtzkrNEB4Gk2MiUn0YVRCyHEGUop2P4trPkQrvoQ3H3gTyvAM9DVkQkhhBCinZBEhxAn6d8//8qHP/3KtoJKANJi/MmMa3yjPXlAFwBSY/zRNO2Euq4IIYRoRd4qmP8I7FsOwQlQngdBXSTJIYQQQohmJNEhxHFwOBRr9x/muy2F/N+IBNxNRgrK6/DzNDP1/J6MTA4/ZkvXizOiJbEhhBAnq64c/nc3bP4SvENh7AuQeT0Yza6OTAghhBDtkCQ6hDgKq93Bit0lzNtcwPzNhyisrMds1BidEkF6pwDuGdFduqIIIURbctj1trBuPlBxQC8y2v8ucPd1dWRCCCGEaMck0SFEE7UWOzUWG8E+7mzIK2P8eyvxcjMyJDGUnOQIhvYIw89D/wuiJDmEEKKNWGvh57dh9Qy4+Xt9acoNc8EgHamEEEII8fsk0SHOehV1VhZvK2TupgK+317EFVkxTLsohYxOgbx3fRb940NatH8VQgjRBhwO2DgLFj4BFXnQfRRYqvVEhyQ5hBBCCHGcJNEhzmr/9+k6vt5wAKtdEebrzuW9Y7gwPQoAg0FjWFK4iyMUQoizRH0lzBgDBRsgshdc8hZ0GeTqqIQQQghxBpJEhzhr7C+tYd7mAtbuL+P1qzPQNI1OgZ5M6t+FnJQIesUEYDDIchQhhDitqgrBJ0yvu9EpG/rfDcmXygwOIYQQQpw0SXSIDi3vcA1frc1n7uYCNuVXANAjwpfSagvBPu7cMzLRxREKIcRZqjwfFj8JGz+H23+C4G4w9nlXRyWEEEKIDkASHaJDUUqxIa+ccD8PIvw92JRfzvPzd5AZG8BDY3qQkxxBXLC3q8MUQoizV105LHsZVrwJygHZt4BXkKujEkIIIUQHIokOccaz2R38svdwQxvYAg6U1/GXkd2547wEhiSG8fNDwwj383B1mEIIIay18EY2VB6E1CvgvEcgMM7VUQkhhBCig5FEhzgjKaXQNA2r3cGgZxdzsLwON5OBQQmh3DMykWE9wgDwMBulY4oQQriSUrBvBcSdA2ZPGDQFojMhKsPVkQkhhBCigzqhRIemae8D5wOFSqmUVsavBe4HNKASuE0ptb5hbBTwCmAE3lVKPf0HYxdnmep6G99vL2Lu5gLKa638c1JfzEYDE8/tTKcgLwZ3D8XbXXJ3QgjRbuxbAfMfgbyVMGk+xGZDn8mujkoIIYQQHdyJ3hV+ALwO/PMo43uAwUqpw5qmjQbeAbI1TTMCbwAjgDzgF03T/quU2nJyYYuzydKdRcxc/itLdhZhsTkI8nYjJzkcu0NhNGjcMribq0MUQgjRVPFOWPAYbPsafCLgwtcgureroxJCCCHEWeKEEh1KqSWapnU+xvjyJg9XADENX/cFcpVSuwE0TfsEuAiQRIdo4VBFHfM3FzAmNZJgH3f2Flez5UA512bHkpMcQZ/OQRilDawQQrRPNgvMGAPWGhj6MJxzO7hJEWghhBBCnD5tOc9/MjCn4etoYH+TsTwgu7WDNE27GbgZIDY2tg3DE+3J3uJq5m0uYO7mAtbuKwPAz9PMRb2iGdc3luv6xaFpktwQQoh2yVID6/4NWZPA5AaXvQthSeAT5urIhBBCCHEWapNEh6ZpQ9ETHQNO9Fil1DvoS17IyspSpzg00U4opai22PFxN1FQXseQ578HIDXan7+M7M6olAjiw3wBMBsNLoxUCCHEUTnseoJj8d/0TirB8dBtKHQd7OrIhBBCCHEWO+WJDk3T0oB3gdFKqZKGzflApya7xTRsE2cRh0Oxdn+ZPnNjUwFJkb68PT6LCH8PXrgineyuQcQEerk6TCGEEL9HKchdAN9NhcItENMHLp+hd1YRQgghhHCxU5ro0DQtFvgCGK+U2tFk6BcgQdO0LugJjnHANafy3KJ9e/P7XD74cS+FlfWYjRrndgthZM8I5/hlvWOOcbQQQoh2xWGHuQ+CssMVM6HnRSDLC4UQQgjRTpxoe9mPgSFAiKZpecCjgBlAKfV3YCoQDLzZUE/BppTKUkrZNE27A5iH3l72faXU5lN2FaJdqbPaWbKjiEXbCnnswmQ8zEYAescFMiolgqE9wvDzMLs4SiGEECfk8K/w48sw4glw94FrZ4FfjF6TQwghhBCiHdGUar9lMLKystSqVatcHYY4DlX1NhZuPcS8zQUs3lZErdWOn4eJj2/uR3KUv6vDE0IIcbJqD8PSF+Dnt0EzwNWf6HU4hBBCCCFOM03TViulsn5vv7bsuiI6uOKqeqx2B5H+nuw8VMndn6wj1Nedy3pHk5McQb+uwVJIVAghzlQOB6x4E5Y8B3Xl0OsaGPpX8I92dWRCCCGEEMckiQ5xQvLLapm3SW8Du2pvKddkxzL94lTSYwL4z23nktEpAINB1mkLIcQZT9Ng53yI7g0jHoeIVFdHJIQQQghxXCTRIY7bDTNWsnh7EQA9Iny547wExqZGAmAwaPSOC3RleEIIIf6oPUv1VrGXvgMBnWDcR3o9DiGEEEKIM4gkOkQLSik25Vcwd/NBNuSV889JfdE0jeyuwWR3DSYnOYIuId6uDlMIIcSpUrgVFjwGO+bqBUbL8/REhyQ5hBBCCHEGkkSHcNp5qJKPVu5j/uZD5JfVYjRoZHcJorzWSoCXG7cO7ubqEIUQQpxKSsHX/wdrZoKbLwx/HLJvAbOnqyMTQgghhDhpkug4i9Xb7CzfVUJ8qA+dgrzYU1zNv3/ex6CEEP48PIHhSeEEekvbQCGE6HBs9WBy1+twaAboewsMmgLewa6OTAghhBDiD5P2smeZ6nobP+woYt7mAhZtLaSy3sa9I7pz57AELDYHFrsDH3fJfwkhRIdkt+mzN354BsZ9DDG99VkdmhSRFkIIIUT7J+1lhZPdoTAaNCw2B+c8tZCKOhtB3m6MTYskJzmCc+P1v+C5mQy4maQdrBBCdDhKwfZv4btHoWQnxJ4DpoYZe5LkEEIIIUQHI4mODqqwoo55Ww4xb1MBdVY7n992Lm4mAw+MTqJrqDdZcYGYjJLUEEKIDk8p+OhKvVVscILeSSVxjCQ4hBBCCNFhSaKjg5mz8SD/WLqbNfvKAOga6s3olAgcDoXBoHFNdqyLIxRCCHFalO0D/056QiNhJCSOhowJYJT/+oUQQgjRscm7nTOYUoptBZXM3VTAdf3iCPV1p7TGgsXu4C8juzMqJYL4MF9XhymEEOJ0qi6BJc/CL+/Bpe9AyqXQ9yZXRyWEEEIIcdpIouMM43Ao1u4vY97mAuZtLuDXkho0DZIi/RiVEsE1fWO5NjvO1WEKIYQ43ay1sOItWPYSWKogYzzEnevqqIQQQgghTjtJdJwBrHYHZTVWQn3dOVhRx2VvLcds1Di3Wwi3Du7G8KRwQn3dAdBkzbUQQpydPrwU9i2H7qNg+OMQ1sPVEQkhhBBCuIQkOtqpOqudJTuKmLf5EAu2HqJP5yDevT6L6ABP3rs+i6zOQfh7ml0dphBCCFfa/QN0ygazBwy8F0wPQZeBro5KCCGEEMKlJNHRDj09Zxv//GkvNRY7fh4mhieFc356pHN8WFK464ITQgjhegWb4LupsGshjH4Osm+GhOGujkoIIYQQol2QRIeLFVfVs2DLIRZtK+TVqzPwMBsJ93PnkoxoRqVE0K9rMGZpAyuEEAKgPB8WPwnrPgIPf8j5G/S+3tVRCSGEEEK0K5LocIGSqnpmrzvAvM0F/LK3FIeCTkGe7C+tISHclxv6d3F1iEIIIdqjL2+B/Svh3Dth4D3gGejqiIQQQggh2h1JdJxiX63N57l52zlQVktUgCdTchK5OCOa3MIqTAaNziHe5JfVMu3rLSSG+3LHeQnkJIfTM9JPCokKIYSADbNg4TQozwP/aIgbCCOngU8YjHkO3LwhINbVUQohhBBCtFuaUsrVMRxVVlaWWrVqlavDOG5frc3nwS82Umu1O7eZDBpB3m4UVtZzdd9Ynro0FaUUe0tq6BLi7cJohRBCtDsbZsH/7tJbxTaVegVc9q5rYhJCCCGEaCc0TVutlMr6vf1OqPiDpmnva5pWqGnapqOM99A07SdN0+o1TfvLb8b2apq2UdO0dZqmnTnZixPw3LztzZIcADaHorzWyuMXJnPXsHhAbwErSQ4hhBAtLJzWMskBsG/F6Y9FCCGEEOIMdaJVLj8ARh1jvBS4C3j+KONDlVK9jicDcyY6UNbKm1PAYnNw/bmdifT3PM0RCSGEaPdsFti1WP+6PK/1fY62XQghhBBCtHBCiQ6l1BL0ZMbRxguVUr8A1j8a2JkoKqD1RMbRtgshhDiLFe2A+Q/Di0nw4cVQuBX8Y1rf92jbhRBCCCFEC6ezb6kC5muatlrTtJuPtpOmaTdrmrZK07RVRUVFpzG8P25KTiKeZmOzbZ5mI1NyEl0UkRBCiHanOBfey4E3+sCKtyC2H1zzGYR0h2FTwfyb5LjZU98uhBBCCCGOy+nsujJAKZWvaVoY8J2madsaZog0o5R6B3gH9GKkpzG+P+zijGiAVruuCCGEOEspBflrwFoDXQbq3VNstTBiGqRfrT8+Iu1K/bOz60qMnuQ4sl0IIYQQQvyu05boUErlN3wu1DTtS6Av0CLRcaa7OCNaEhtCCCGgphQ2fAprPoTCzdApGybPBw8/uOUY//2lXSmJDSGEEEKIP+C0JDo0TfMGDEqpyoavRwLTTse5hRBCiNPuh+dgybNgt0BUJpz/MqRc5uqohBBCCCHOCieU6NA07WNgCBCiaVoe8ChgBlBK/V3TtAhgFeAHODRN+zPQEwgBvtQ07cg5P1JKzT1VF9GubJglU46FEOJsU54H6z6CrEngHQLB3fSvM8ZDRMr/s3ef4VFV69/HvysFEjAkNGmhRwmQTgClSBcQBQ8WUDSgoMeCWI4Cco4cC/rwt6EotoOCIgoIWECKCFIUEQIJVapGCKDUhBIgIVnPiz2ZJCShSMhA+H2ua18zs3a7Z7Izmbmz1r08HZ2IiIjIZeWcEh3W2jvOsP5PoKDS8IeAyHM51yVpzRSYMQgyXNPMpu5wHoOSHSIiJc3JdNg8B1Z9Atvmg81yEhxht0BYT2cRERERkWJXnMVIS775z+ckObJlHINZTzm9O6pGQOkrPBObiIgUneOH4K0mcHQPBFSH1v+CqD5Qoa6nIxMRERG57CnRUZRSkwtuP54C47pC3eug7wynbe1UKF/X6dLsU7r4YhQRkXOXfhTWfwUHfoMOzzgFRWPvhRpNIKQDeHmf+RgiIiIiUiyU6ChKgcHOcJVTlavuFKLzLuU8zjgG0+8HmwlevlClMVSPdro7121dvDGLiEjBsqeFTfgE1k6D9MNQuSG0GQI+paDd056OUEREREQKoERHUeowPG+NDgBff+j4HFzdOafNxw8eXQ27VjkfonclwLrpUDHESXQc2gVf9HMq9deIcW4r1AMvr2J/SiIil60VY2HWk+DjD43/ATFxUOsacApri4iIiMhFSomOopRdcPRMs64YA0E1naVRD6ctKwuyMpz7xw4CBlaOh1/eddpKB8LtH0P9dpB2ANKPQGBNfeAWESkKWVnw+yJImOD0rgvtBg1ucIakhN0CfoGejlBEREREzpISHUUt4va/N8OKlxd4uWp1VGkM/edC5knYt8nV62NVTpG7DV/BzMehTCVXj49op9dHvTZODxIRETk7qTshcaKT4EjZDn5BUMc1hDCwhlOHQ0REREQuKcZa6+kYChUbG2vj4+M9HcbF58Bv9bnpWgAAIABJREFUsHW+M+Rl5yonGWKzYMgf4B8E67+E/dtykiD+5T0dsYjIxcNapzectTCmufMeWreNMzQl9Ebw9fN0hCIiIiJSAGPMSmtt7Jm2U4+OS1GFetCsXs7jE0dg70YnyQGw7QdY9XHe7eu0gu5vOY+zMjVDgIhcfvZudgqLbp4L/1zs9IC76Q0IqKZpYUVERERKECU6SoLSV0BwrqRW99HQ6Tmnx0d2r4/jqTnrP+rsTJVYPQaqRzk9P6pomlsRKYGyp4VNmADbfwYvH7i6i1MLydcfarfwdIQiIiIiUsSU6Cip/MtD/fbOcqqru8D2ZbB5NiR+6rQ17A69Jjj3106FKxtCpQbgrUtERC4x1sLJ404iY8+v8PVDzqxWHZ+DyDsgoIqnIxQRERGRC0jfYi9H1z3p3FoLqTucHh9lKjhtR/fBtP7Ofd8yUDXC6fERdisEN/FMvCIiZyPtAKyZ4vTeqNHE6d1WowkMmO/capYqERERkcuCEh2XM2MgqJazZPOvAAPjc4a87FoF8eOcmWCCm8DeTTDrSWfYS40Y5zYwWF8gRMRz/lgKKz6EX2dA5gmnCHOta5x1xuQd2iciIiIiJZ4SHZKXlxdUuspZsqfJzTwJNtO5fzwVjh+Cn8dAVobTVrYy3DnZ+Y/p0f3ODDBXVPZM/CJyeTi0GwKqOomMddNh6zxo0g9i7oaq4Z6OTkREREQ8SIkOOTNvH9yXSs1m8M9FkHEc/lrv9PjYlQCBrl4hCZ/A989CuWCoEe0qeBrtzPri7eupZyAiJUFmBmyeA6smOImNft86xUTbDYPrX3BqcoiIiIjIZU+JDvl7fP2coSyn1u24uoszq0H20JdfZ4B3KXg62Vm/Zgoc3eskQKpFQKmyxR+7iFxajqfCktcg8TPn/SOgGrR6HIJqO+uzawyJiIiIiKBEhxS1Kxs6S7a0A7B/a87UtRu+ho0znfvGCyqHQkgHuH6E05aV5QyfEZHLW/pROJjk1Afy8YPVk6Bmc4i+G0I6akYoERERESmUPinKhVWmApRplvO490Q4/GeuYqcJTl2PbO+1dJIiuYudVm4AXt7FH7uIFC9rnfeEVZ/AumnONNmDEp33hEdXa2iKiIiIiJwVJTqk+AVUhQZdnSW3rCy46nrYudIZ4hL/odPepB/c9KbzJWjdNKfmR4V6mulFpCTZPBfmvwB/rQUff2h8s9N7I/v3XEkOERERETlL55ToMMZ8BNwI7LHWhhWwPhQYB8QA/7bWvpprXRfgTcAbGGutHXk+gUsJ5OUFnZ5z7mdlOUNedq3KGYd/4DeY1t+57xfoJDyqR0P47VClkWdiFpG/JysLkpZAxRAIrAFZJ52eW91eh/Bbnd9xEREREZG/wVhrz35jY64DjgCfFJLouBKoDdwMHMxOdBhjvIHNQCcgGVgB3GGt3XC688XGxtr4+Pizjk9KuMyTsPdX15AX17CXv9bDbeOh4U1OT5CF/5cz5KVGDJSt5OmoRSS3Q7sgcSIkfOrU4GgzFNo97fTYUi8tERERETkNY8xKa23smbY7px4d1trFxpg6p1m/B9hjjOl2yqpmwFZr7W+u4CYBPYDTJjpE8vD2garhztKkr9OWcTzny9Gxg5DyB2z5DnAl8AJrwt1fQqWr4Og+Z4pb/adYpPhZC5Pvgk2zwGZBndbQ7t9OkhKU5BARERGRIlNcNTpqADtyPU4GmhfTuaUk8/XLuR/S0VlOHIbdq3MKnpar4axf+hb89IbTVb56jDPspUYMBDfTTC8iF8K+rfDbD9DsPieREVANWj4G0XdBxfqejk5ERERESqiLrhipMeZ+4H6AWrVqeTgauSSVDoA6rZwlt0Y9oPQVsDPBqQ2wdgr4BcGQJGd9wqdw8oST/LiyMfiUKvbQRS556WnONNIJE+CPn8DLB0JvhHLVoNurZ95fREREROQ8FVeiYydQM9fjYFdbPtbaD4APwKnRceFDk8tGDVfdjmyHdkPK9pwu8wkTYftS5753KagSBqE3wHVPOW2qISByer8vgUl3wolDzsxIHZ+FyDshoIqnIxMRERGRy0hxJTpWAFcZY+riJDh6A3cW07lFClaumrNku2eWk/jYtcpV8DTBKZwITpLjzUhnGEyNmJwZXzTNrVzOjh2ENV9AuerQ8EaoGub03oi+C2q30O+GiIiIiHjEuU4v+znQFqhkjEkG/gv4Alhr3zPGVAXigXJAljHmMaCRtfaQMWYgMBdnetmPrLXri+5piBQBY6B8bWdp/I+8606egKu7OEmQFWPh5HGnvdXjzn+tT55wiqBWj3G+9J36BW/NFJj/PKQmQ2AwdBgOEbcXx7MSKVpZWfDHj7DqE9jwDWSegKi7nESHf3n4x7uejlBERERELnPnNL1scdP0snJRysyAPb86SY8q4RDcBJJXwtj2zvorqrh6fMRAxG2QHA8zBkHGsZxj+PrDTaOV7JBLz5Q4pwaHXyBE9ILou6FahKejEhEREZHLwNlOL6tEh0hROHkCdq9xkh/Zs73s2wx9v4GvHoLUHfn3CagGj68HL+/ij1fkbGRmwOa5kPgZ3DzG6bGx+Ts4nur04PD193SEIiIiInIZOdtEx0U364rIJcmnNNRs6izZThwG79LOcJWCHHYVQ61QF9ZOhY0znRoggTUhsIYzxKVqhBIhUvz2bYWETyDxczi6B66oCvu2QM1mcPX1no5OREREROS0lOgQuVBKBzi3gcEF9+jwL+8kNgDSDjg9QjbNzqn/AfCfPU6iY/Gr8PsiKBfsHC/QlRAJ6XDhn4dcXlJ2wNtNwHg7dWli7oaQTuCtPxciIiIicmnQJ1eRC63D8IJrdHR9GXxKOY+b3+8s1kLafqcXyJE9Tk8RcG4zjjvJjsO7wWY5tUCe3Oys/+oh2L3a1SPElQipdDU0vMlZn5UFXl7F95zl0mAt7E6EVRMgMx16vA1BNeHm96B+Owio6ukIRURERETOmRIdIhdadsHRs5l1xRgoW8lZcmvxiLMAZJ50kh3HDuasr9zA6RVyKBmSlzvrqkfnJDo+7OhMletOhAQ768NvddYfOwh+QZoO9HKRPS1swifw51rw8XOuBWudayDqDk9HKCIiIiLyt6kYqUhJlH4Ujh+CctWcx0vfdmaKOZTsJFtSdzrDXnpPdNa/cpVTYLJc9ZxESEjHnETI3s3OsbKH48ilJyvLufXygh9egkX/B9UinVlTwm8D/yDPxiciIiIicgYqRipyOStV1lmytRiYd721ObVArIXrnnLqiBza6SRCfl8C/hWcRMfJEzDGVWS1dGDO0JjI3hB2izMzx45fnPaA6jnDceTicGg3JE6EhE+h80sQegPE3guh3ZxEh4iIiIhICaNEh8jlyJicqUGNceqDnM4tHzoJkOxESGpyztCZQzthfLfsAzu1QwJrQOt/OV+mj6U4tUUCg51iqmUrq17IhZaVBZtnO7U3tsx1arrUaZ3TIyegqupviIiIiEiJpUSHiJyeT+mcISwFKVsZ7v7SGQ5zaKfTMyR1J3i7enb8tR6mxOVs713KGSJz05tQry0cTIJtC5xZZLJriPiVu4BPqAQ7lpIzBGXO006vnZaPQfRdULG+Z2MTERERESkmSnSIyPkpVRbqty98ffVo+Ofi/ImQMq6CqzuWw8zH8+5Tuhz0nQHVoyB5Jfx2SiKkXA0NkcmWnga/fgOrPoG9m+CJX53X5u4vIai2poUVERERkcuOPgGLyIVVqoxTC6KwehCNe0LtFq5ESK5iqeWqO+t3LIMFI/Lv9/h6J+nx60z446ecBEhgTWfozBVVSvYsMvu3wc9jYO1UOJEK5evCtQ9DVgZQSj04REREROSypUSHiHiWt0/OTC80z7/+2oehyT3O9Li5EyFXVHHW/7UOVo6HjLRcOxl4Zi94+8Ky92DXqryJkKCacGXDYnhyRezYQTiZDgFV4MhfTpHRRj2cmVPqtCrZiR0RERERkbOkRIeIXPxKlYFKIc5yqrZDoc0QJwmQXSw1bb+T5AA4vBv+WOokSmym0xZQDf610bk/ewjs35pTLDUw2OkNUbNZ8Ty3M7EWkn50hqb8+o2T1Oj2KtS6Fv61SdPCioiIiIicQokOEbn0GQNlKjhL1fC86zo95yxZmU4viNRkSD+as97LB47uhV2JkLbPaasRC/fNd+5P+IeTRMmdCKkaDvXaOOutvXA9KZa9B7+8Bwd/d6b2jeoDMXE5z1lJDhERERGRfJToEJHLg5e3U/cju/ZHts4v5tzPOOb0/Dh5IqftykawZwPs3QxbF0DGUWe4SHai4/WGTu+R3MVS67SCkA7O+uOpTnHVgpIha6bA/Oed5EtgMLT7DwRcCfXaOdvv/dU5Ztuh0LC707NFREREREROS4kOEZFsvv75i3jmToRYC8dTchIh1kJk75y6ITuWwfpdzrSuIR2cxMnIWlDqilw1QoKdREnafpgxyNkGnNlovvqnc3/AfAiOhRte06wpIiIiIiLnSJ+gRUTOljHgXz7v447P5t0mKzNXIiQLrh/hSoQkOzVE/lwDlRvAsndzkhy5lamUM0ONkhwiIiIiIudMn6JFRIqSl3fOEJNSZaHFI/m3sRbm/rvg/XMXUhURERERkXPm5ekAREQuO8a4ptMtQGHtIiIiIiJyVs4p0WGM+cgYs8cYs66Q9cYYM9oYs9UYs8YYE5NrXaYxJtG1fHO+gYuIXNI6DHdqguTm6++0i4iIiIjI33auPTrGA11Os74rcJVruR94N9e6Y9baKNfS/RzPKyJSskTcDjeNdmZrwTi3N4122kVERERE5G87pxod1trFxpg6p9mkB/CJtdYCy4wxQcaYatba3ecRo4hIyRRxuxIbIiIiIiJFrKhrdNQAduR6nOxqA/AzxsQbY5YZY24u7ADGmPtd28Xv3bu3iMMTERERERERkZKsOIuR1rbWxgJ3Am8YY+oXtJG19gNrbay1NrZy5crFGJ6IiIiIiIiIXOqKenrZnUDNXI+DXW1Ya7NvfzPGLASigW2nO9jKlSv3GWP+KOIYi0slYJ+ng5CLjq4LKYiuCymIrgspjK4NKYiuCymIrgspyKV8XdQ+m42KOtHxDTDQGDMJaA6kWmt3G2PKA2nW2hPGmEpAS+DlMx3MWnvJdukwxsS7erCIuOm6kILoupCC6LqQwujakILoupCC6LqQglwO18U5JTqMMZ8DbYFKxphk4L+AL4C19j1gFnADsBVIA+5x7doQeN8Yk4UzXGaktXZDUTwBEREREREREZFs5zrryh1nWG+BhwtoXwqEn1toIiIiIiIiIiLnpjiLkV5uPvB0AHJR0nUhBdF1IQXRdSGF0bUhBdF1IQXRdSEFKfHXhXE6YYiIiIiIiIiIXPrUo0NERERERERESgwlOkRERERERESkxFCi4wIwxjxujFlvjFlnjPncGOPn6Zik+BljPjLG7DHGrDul/RFjzEbXNXLGaZalZDHG+BljlhtjVruugedc7RONMZtc7xsfGWN8PR2rFC9jTJAxZqrr/eFXY8y1udb9yxhjXVO0SwlW0N8OY8wrrutijTHmS2NMkKvd1xjzsTFmreuaedpzkcuFZIypaYz5wRizwfW341FX+7PGmJ3GmETXckOufSKMMT+7tl+rz6MlkzEmyfXzTTTGxLvabnP93LOMMbG5tu1kjFnp2n6lMaa95yKXolTI344Kxph5xpgtrtvyrvY+rr8na40xS40xkaccy9sYk2CMmVncz6MoKdFRxIwxNYBBQKy1NgzwBnp7NirxkPFAl9wNxph2QA8g0lrbGHjVA3GJZ50A2ltrI4EooIsx5hpgIhCKM0OVPzDAcyGKh7wJzLHWhgKRwK/gfMEBrge2ezA2KT7jOeVvBzAPCLPWRgCbgeyExm1AaWttONAE+Kcxpk7xhCnF7CTwL2ttI+Aa4GFjTCPXulHW2ijXMgvAGOMDfAo84Pq80RbI8EDcUjzauX7+2UmNdUBPYPEp2+0DbnK9Z/QFJhRjjHJhjSf/346hwHxr7VXAfNdjgN+BNq7r4AXyFyZ9FNdnkEuZEh0Xhg/g7/ojUwbY5eF4xAOstYuBA6c0PwiMtNaecG2zp9gDE4+yjiOuh76uxVprZ7nWWWA5EOyxIKXYGWMCgeuADwGstenW2hTX6lHAYEDVwy8DBf3tsNZ+Z6096Xq4jJz3BwuUdX3e8AfSgUPFFasUH2vtbmvtKtf9wzhfQmqcZpfrgTXW2tWuffZbazMvfKRyMbDW/mqt3VRAe4K1Nvt7yXqc7yulizc6uRAK+d7RA/jYdf9j4GbXtkuttQdd7bn/pmCMCQa6AWMvaMDFQImOImat3YnzX/rtwG4g1Vr7nWejkovI1UBrY8wvxphFxpimng5Iip+rS2AisAeYZ639Jdc6X+BuYI6n4hOPqAvsBca5uouONcaUNcb0AHZmf1kRAe4FZrvuTwWO4nze2A68aq099YOulDCuXjvRQPbfjoGubugfZXdNx/m8YY0xc40xq4wxgz0QqhQPC3znGopy/znsdwuwKvufb1IiVbHW7nbd/xOoUsA2/cn5mwLwBs4/V7IucGwXnBIdRcz1B6YHzofW6jj/abnLs1HJRcQHqIDT7fQpYIoxxng2JClu1tpMa20UTga9mTEmLNfqd4DF1tolnolOPMQHiAHetdZG43x5fRYYBgz3YFxyETHG/BtnCMNEV1MzIBPn80Zd4F/GmHoeCk+KgTHmCmAa8Ji19hDwLlAfZyjkbuA116Y+QCugj+v2H8aYDsUfsRSDVtbaGKArzpCm6860gzGmMfB/wD8vdHBycXD1GM7TM9Q1pL4/MMT1+EZgj7V2ZfFHWPSU6Ch6HYHfrbV7rbUZwHSghYdjkotHMjDdNUJhOU62VMUFL1OuoQk/4BpTaYz5L1AZeMKTcYlHJAPJuXr3TMVJfNQFVhtjknASY6uMMVU9E6J4kjGmH3Aj0Mf1gRXgTpy6LhmuoZA/AbGFHEIuca4ef9OAidba6QDW2r9cyfMs4H84yS9w3lMWW2v3WWvTgFk47ylSwrh6k2cPh/6SnGugQK6hCV8CcdbabRc+QvGgv4wx1QBct+4h88aYCJzhKT2stftdzS2B7q7PHJOA9saYT4s35KKjREfR2w5cY4wp4/pPfQdKQDEXKTJfAe0AjDFXA6VwCkPJZcIYUznXjAn+QCdgozFmANAZuMP1gVUuI9baP4EdxpgGrqYOOF2Kr7TW1rHW1sH54hLj2lYuI8aYLjhdibu7vrRm2w60d21TFqe34Mbij1AuNNdnyg+BX621r+dqr5Zrs3/gFKEEmAuEuz6P+gBtgA3FFa8UD9cQx4Ds+zi1WdadZvsg4FtgqLX2p+KJUjzoG5yis7huvwYwxtTC+Wf83dbazdkbW2ufttYGuz5z9AYWWGsv2ZEJPp4OoKSx1v5ijJkKrMLpXppA/kq2chkwxnyOU+W8kjEmGfgv8BHwkWvqp3Sgb67/zMnloRrwsTHGGyfZPMVaO9MYcxL4A/jZNZppurX2eQ/GKcXvEWCiMaYU8Btwj4fjEQ8o5G/H00BpYJ7r/WGZtfYBYAxOXZf1gAHGWWvXeCRwudBa4tRvWuuq8QTO0LY7jDFROF3Sk3ANRbDWHjTGvA6scK2bZa39ttijlgutCvCl633BB/jMWjvHGPMP4C2cXqLfGmMSrbWdgYFACDDcGJM9LPJ6Fce/9BXyt2MkzjD5/jifMW93bT4cqAi847p2TuaasafEMPqOJSIiIiIiIiIlhYauiIiIiIiIiEiJoUSHiIiIiIiIiJQYSnSIiIiIiIiISImhRIeIiIiIiIiIlBhKdIiIiIiIiIhIiaFEh4iIiIiIiIiUGEp0iIiIiIiIiEiJoUSHiIiIiIiIiJQYSnSIiIiIiIiISImhRIeIiIiIiIiIlBhKdIiIiIiIiIhIiaFEh4iIiIiIiIiUGEp0iIiIiIiIiEiJoUSHiIiIiIiIiJQYSnSIiIiIiIiISImhRIeIiIiIiIiIlBhKdIiIiIiIiIhIieHj6QBOp1KlSrZOnTqeDkNEREREREREPGzlypX7rLWVz7TdRZ3oqFOnDvHx8Z4OQ0REREREREQ8zBjzx9lsp6ErIiIiIiIiIlJiKNEhIiIiIiIiIiWGEh0iIiIiIiIiUmJc1DU6LkVfJezklbmb2JVyjOpB/jzVuQE3R9fwdFgiIiIiIuKSkZFBcnIyx48f93QoIlIAPz8/goOD8fX1/Vv7K9FRhL5K2MnT09dyLCMTgJ0px3h6+loAJTtERERERC4SycnJBAQEUKdOHYwxng5HRHKx1rJ//36Sk5OpW7fu3zqGhq4UoVfmbnInObIdy8jkP1+tY96Gv9h7+ISHIhMRERERkWzHjx+nYsWKSnKIXISMMVSsWPG8elwVSaLDGPORMWaPMWbdabZpa4xJNMasN8YsKorzXmx2pRwrsP3IiZPc90k8//pitbttSvwO4pMOcPyUxIiIiIiIiFx4SnKIXLzO9/ezqIaujAfeBj4paKUxJgh4B+hird1ujLmyiM57Uake5M/OApId1QP9GH1HNNk/q6MnTjJk2hqsBR8vQ2i1ACKDg+gRVYNmdSsUc9QiIiIiIiIiJUeRJDqstYuNMXVOs8mdwHRr7XbX9nuK4rwXm6c6N+DHL9/hMSZR3exjl63EG/SmVZeHiK2Tk8AoW9qHX4Z1YPWOVBJ3HCRxRwrfJO7i6ioBNKtbgZ0pxxgydQ1RNYOIqhlEZM0gKgeU9uAzExERERG5fGnCAZFLS3HV6LgaKG+MWWiMWWmMiStsQ2PM/caYeGNM/N69e4spvKJxs/dPjPQdS7DXPrwMBHvtY6TvWG72/inftlcG+NGpURWe6hzKxAHXsPq/19O7WU0ADhxJ52BaOu8u2saAT+Jp+uL3tBy5gJV/HACcHiEa8iIiIiIicuFlTziwM+UYlpwJB75K2Onp0NwSExOZNWuW+/E333zDyJEjz+uYSUlJfPbZZ+e8XXx8PIMGDTrn86WmphIXF0dISAj169cnLi6O1NTUM+6XkJBA//793ce46aabiIyMpHHjxowbN8693ccff8xVV13FVVddxccffwzAiRMn6NKlC2FhYbzzzjvube+//35WrVrlfvz222/z0UcfFRrD7t27uf7668/5OZ9J27ZtiY+PP+/j/PjjjzRr1ozQ0FBCQ0P54IMPzmq/xx57jMWLFwMwf/58YmJiiIqKolWrVmzduhVwXsNevXoREhJC8+bNSUpKAuCnn34iIiKC2NhYtmzZAkBKSgrXX389WVlZ7nN07NiRgwcPnvdzPFVxJTp8gCZAN6Az8Iwx5uqCNrTWfmCtjbXWxlauXLmYwisi85/HJzNvwRSfzOMw//kz7urlZSjt4w1AeHAg3w5qzbpnO/PFA9fy7xsaElUziKqB/gBMXZlM2H/ncuNbS/j3l2v5In4HW/ccJivLFv1zEhEREREp4Xq9/3O+ZcLPSQC8PGdjgRMOPDtjPQAHjqbn2/dCOHnyZKHrTk10dO/enaFDh57X+f5uoiM2NpbRo0ef8/n69+9PvXr12Lp1K9u2baNu3boMGDDgjPu99NJL7sTKmDFjaNSoEatXr2bhwoX861//Ij09nQMHDvDcc8/xyy+/sHz5cp577jkOHjzI3LlzadWqFWvWrGHChAkArF69mszMTGJiYtznuPfee3nrrbcKjWHOnDl07tz5nJ8zQGbmhf0H9p9//smdd97Je++9x8aNG/nxxx95//33+fbbb0+73/79+1m2bBnXXXcdAA8++CATJ04kMTGRO++8kxEjRgDw4YcfUr58ebZu3crjjz/OkCFDAHjttdeYNWsWb7zxBu+99x4AI0aMYNiwYXh55aQh7r777jxJpqJSXImOZGCutfaotXYfsBiILKZzF5/U5ELad0DyynM+nH8pb5rWqcB919VjTJ8YagQ5iY6YWuW5/7p6lPPz5evEXTw1dQ2dRi0mzfUG/NPWfXy/4S/2HdEsLyIiIiIi52N3asEzP6SkZVzwc/fr148HHniA5s2bM3jwYJYvX861115LdHQ0LVq0YNOmTaSnpzN8+HAmT55MVFQUkydPZvz48QwcOBBwEhHt27cnIiKCDh06sH379nznWbRoEVFRUURFRREdHc3hw4cZOnQoS5YsISoqilGjRpGUlETr1q2JiYkhJiaGpUuXAuTbbuHChdx4440AHDlyhHvuuYfw8HAiIiKYNm1agc9z69atrFy5kmeeecbdNnz4cOLj49m2bRu9e/fO88W8X79+TJ06lcOHD7NmzRoiI52vlsYYDh8+jLWWI0eOUKFCBXx8fJg7dy6dOnWiQoUKlC9fnk6dOjFnzhx8fX1JS0sjIyMDa51/Gj/zzDO88MILeeIrU6YMderUYfny5QXGP2fOHLp27ZqnLSkpidDQUPr06UPDhg259dZbSUtLA6BOnToMGTKEmJgYvvjiCxITE7nmmmuIiIjgH//4R54eDhMmTCAqKoqwsDD3+Qu6DgozZswY+vXr507cVKpUiZdffpmRI0eSmppK7dq13T0sjh49Ss2aNcnIyGDatGl06dLFfRxjDIcOHQKcnjPVq1cH4Ouvv6Zv374A3HrrrcyfPx9rrfu1TUtLw9fXl23btrFjxw7atm2bJ77u3bvz+eefFxr/31VUxUjP5GvgbWOMD1AKaA6MKqZzF5/AYCepkY+Bse3h5nch6s7zPk14cCDhwYEAZGZZtu09wtY9R7iitPPj/GDxbyza7Az7CS7vT1TNIK6tX5E+zWuf97lFREREREqayf+8ttB1hU04kP1PyAplS512/7MxYMAAHnjgAWJjY/OtS05OZunSpXh7e3Po0CGWLFmCj48P33//PcOGDWPatGk8//zzxMfH8/bbbwMwfvx49/6PPPIIffv2pW/fvnz00UcMGjSIr776Ks85Xn31VcaMGUNY31zMAAAgAElEQVTLli05cuQIfn5+jBw5kldffZWZM2cCkJaWxrx58/Dz82PLli3ccccdxMfH59tu4cKF7uO+8MILBAYGsnbtWoBChyhs2LCBqKgovL293W3e3t5ERUWxfv16evXqxZQpU+jWrRvp6enMnz+fd999l2XLlhEWFubeZ+DAgXTv3p3q1atz+PBhJk+ejJeXFzt37qRmzZru7YKDg9m5cye33XYbEyZM4JprruGpp57im2++ISYmxv0lPrfY2FiWLFlCs2bN8rRnZmayadMmGjVqlG+fTZs28eGHH9KyZUvuvfde3nnnHZ588kkAKlas6B4eExERwVtvvUWbNm0YPnw4zz33HG+88Yb7dU9MTGTx4sXce++9rFu3jtDQ0AKvg4KsX7/enYjI/VzWr19PYGAgUVFRLFq0iHbt2jFz5kw6d+6Mr68vP/30E7feeqt7n7Fjx3LDDTfg7+9PuXLlWLZsGUCe19bHx4fAwED279/P008/TVxcHP7+/kyYMIEnn3zS3Qskt/Lly3PixAn2799PxYoVC3wOf0eRJDqMMZ8DbYFKxphk4L+AL4C19j1r7a/GmDnAGiALGGutLXQq2ktWh+EwYxBk5Hoj9PWHri/D8UPQwJXl+20RnDgEDbqB1/l1qvH2MlxdJYCrqwS42967qwnrdqWSuD2FxB0pJGxPYf+RdHei4/HJiZQt7U1UzfJE1QyiXqWyeHlpei0RERERkVM91bkBT09fm2f4ir+vN091blBk5xg7dmyh62677TZ3AiA1NZW+ffuyZcsWjDFkZJy5V8nPP//M9OnTAWeYwODBg/Nt07JlS5544gn69OlDz549CQ4OzrdNRkYGAwcOJDExEW9vbzZv3nzGc3///fdMmjTJ/bh8+fJn3KcgXbt25dFHH+XEiRPMmTOH6667Dn9/f3bv3k3ucgdz584lKiqKBQsWsG3bNjp16kTr1q0LPa6Pj4972E1GRgadO3fm66+/5oknnmD79u3ExcXRvXt3AK688ko2btyY7xi//PILzZs3L/D4NWvWpGXLlgDcddddjB492p3o6NWrF+D8TFNSUmjTpg0Affv25bbbbnMf44477gDguuuu49ChQ6SkpHD48OFzvg4K06tXLyZPnky7du2YNGkSDz30EEC+13bUqFHMmjWL5s2b88orr/DEE0+c9rqNiopyJ0MWL15MtWrVsNbSq1cvfH19ee2116hSpQrgvLa7du26+BId1to7zmKbV4BXiuJ8F62I253b+c87w1gCg53kR3Z7tuUfwMaZUKkBtHocwm8Fb98iCyN7yEvTXDO9ZBcvzcqy7D+azrwNB/l0mdNtLcDPh/tb1+ORDlcBsP/ICSpeoVleRERERESyZ1fx1KwrZcuWdd9/5plnaNeuHV9++SVJSUn5hgH8XUOHDqVbt27MmjWLli1bMnfu3HzbjBo1iipVqrB69WqysrLw8/MrknMDNGrUiMTERLKystz1G7KyskhMTKRRo0b4+fnRtm1b5s6dy+TJk+nduzcA/v7+HD+eM7Ro3LhxDB06FGMMISEh1K1bl40bN1KjRo08PU2Sk5PzvXbvvPMOcXFxLFu2jMDAQCZPnkz79u3diY7jx4/j7++fL/bZs2fnGeKRmzGm0Me5f66nU9AxzuU6aNSoEStXrqRHjx7utpUrV9K4cWPAGToybNgwDhw4wMqVK2nfvj2Q97Xdu3cvq1evdid0evXq5X7ONWrUYMeOHQQHB3Py5ElSU1PzJCystYwYMYJJkybxyCOP8PLLL5OUlMTo0aN58cUXgcJf2/NRXDU6Lh8Rt8Pj6+DZFOf21CQHwG0fwy0fgpcPfPUAjI6BtVMvaFh+vk4W2MvL8Mm9zVj93+v57vHrePmWCG6KrE6timUA+DP1OE1GfE+r/1vAwM9WMXbJb6z844BmeRERERGRy9bN0TX4aWh7fh/ZjZ+GtvfY1LKpqanUqOGcO/fwlICAAA4fPlzgPi1atHD3qpg4cWKBPRy2bdtGeHg4Q4YMoWnTpmzcuDHfMVNTU6lWrRpeXl5MmDDBXUTzdOfu1KkTY8aMcT8ubOhKSEgI0dHReYY2jBgxgpiYGEJCQgDny/W4ceNYsmSJ+0t2w4YN3bN/ANSqVYv58+cD8Ndff7Fp0ybq1atH586d+e677zh48CAHDx7ku+++y1M89ODBg8ycOZO4uDjS0tLw8vLCGMOxYzk99Tdv3pxnmEy2+fPn07FjxwKf1/bt2/n5Z6c47WeffUarVq3ybRMYGEj58uVZsmQJ4NTkyO7dATB58mTAmTklMDCQwMDAQq+Dgjz88MOMHz+exMREwCkyOmTIEHfPniuuuIKmTZvy6KOPcuONN7p7D+V+bcuXL09qaqq7F8+8efNo2LAh4CRKsmexmTp1Ku3bt8+TnPnkk0+44YYbqFChgvu19fLyctcrsdby559/UqdOndM+j3Nmrb1olyZNmtgSLSvL2o2zrR3bydr4cU5bepq1x1I8FtL+Iyfs+4u22gc/jbfXvvS9rT1kpq09ZKadvmqHtdbaHQeO2qnxO+yWvw7bzMwsj8UpIiIiIvJ3bdiwwdMh5NG/f3+7YsWKfO19+/a1X3zxhfvx0qVL7VVXXWWjoqLsv//9b1u7dm1rrbX79++3sbGxNjIy0k6aNMmOGzfOPvzww9Zaa5OSkmy7du1seHi4bd++vf3jjz/ynWfgwIG2cePGNjw83Pbu3dseP37cpqen23bt2tmIiAj7+uuv282bN9vw8HAbERFhBw8ebMuWLWuttfm2++GHH2y3bt2stdYePnzYxsXF2caNG9uIiAg7bdq0Ql+DAwcO2D59+th69erZevXq2T59+tiDBw+616enp9vy5cvbfv365dkvLCzMHjp0yFpr7c6dO22nTp1sWFiYbdy4sZ0wYYJ7uw8//NDWr1/f1q9f33700Ud5jvHYY4/ZH374wVpr7bFjx2ynTp1so0aN7OjRo93bREdH23379uXZb8+ePbZdu3YFPp/ff//dNmjQwPbp08eGhobanj172qNHj1prra1du7bdu3eve9uEhATbvHlzGx4ebnv06GEPHDhgrbW2TZs29tFHH7VRUVG2cePG9pdffrHWFn4dFGbRokU2NjbWNmjQwF599dX2nXfeybP+iy++sIBduHChu23x4sW2T58+7sfTp0+3YWFhNiIiwrZp08Zu27bN/Xrdeuuttn79+rZp06budmutPXr0qG3btq1NT093HzMsLMzGxMTYjRs3WmutXbFihe3Zs2eBcRf0ewrE27PIJRhrL94pSWNjY21RzBt80bPWWby8YNl78MOL0HQAXPMQXOHZKXb3HDpOwo4UYmqVp3JAaT5fvp2npzvFhMr5+RBZM4iomkH0a1FHw11ERERE5JLw66+/uv8jLZe2UaNGERAQcFZT0f5dCQkJvP766+4paLN9+umnJCcnFziVb1JSEjfeeCPr1l26pSlbtWrFzJkzCQoKumDnePTRR+nevTsdOnTIt66g31NjzEprbf6qvacorllX5HSMcRaAOi2hfnv4cRQsewdi4qDFIxBUyyOhXVnOj86Nq7of3x5bkya1y5O4PYWEHSms3pHCuwu3MaBVPQA+XfYHv/x+gKiaQUTVDKRx9UD3sBkREREREZGi9OCDD/LFF19c0HPs27cv35Sz4BQYLclee+01tm/ffkETHWFhYQUmOc6XenRcrPZtgZ/egNWToGZzuGeWpyMq1PGMTHcy492F25jwcxK7XPON+3gZomsFMeWf12KM4cDRdIL8fTXLi4iIiIh4jHp0eE7z5s05ceJEnrYJEyYQHh7uoYhKhrlz5zJkyJA8bXXr1uXLL7/0UETn73x6dCjRcbFLTYbjqVClMRzZA7MHQ4tBUCPG05GdVvaQl8QdKRxLz+TZ7k5V3x5jfuL3vUfcQ16yFw17EREREZHiokSHyMVPQ1dKssBgZwH4ax1sWwDrv4R67aD1E1Cndc6wl4tI9pCX3MNeAPpeW5sVSQdI2J7CmB+2kmWhc+MqvH+3c61OWr6dq6oE0Lh6OQ15ERERERERkXOmRMelpH57eGwdxH8EP4+Bj29yhrX0nQk+pTwd3VnpGRNMzxgncZOWfpK1yamU8nFmOT5wNJ2hrkKnvt6GhtXKEVUziJujaxBTq7zHYhYREREREZFLhxIdlxq/ctDqMWj+ACR+Cvu35SQ5kn6EmteA96XxYy1Tyofm9Sq6H1coW4rlwzq4h7wkbk9h2spkwqoHElOrPFv3HOG5GeuJrhlEVK0gIoM15EVERERERETy8vJ0API3+fo5U9B2+X/O431bYfyN8HYTWPEhZBz3bHx/U/aQlyFdQvn8/mtY82xnekRXByAlLZ29h0/w9g9buXd8PE1GfE/rlxewbmcqAEdOnOR4RqYnwxcRERGRkmjNFBgVBs8GObdrpng6ojwSExOZNStn8oJvvvmGkSNHntcxk5KS+Oyzz855u/j4eAYNGnTO50tNTSUuLo6QkBDq169PXFwcqampZ9wvISGB/v37A2CtZdCgQYSEhBAREcGqVasA2LRpE02aNCEiIoKff/4ZgJMnT9KxY0fS0tLcx+rduzdbtmwp9FyTJk3ixRdfPOfndiZXXHFFkRzngw8+IDQ0lNDQUJo1a8aPP/54xn2stbRv355Dhw4BMGfOHBo0aEBISEiea6hPnz5EREQwbNgwd9uIESP46quv3I9nzpzJ8OHDi+S5nC8lOkqKCvWg90QoUxG+fQLejIAf34ATRzwd2Xnx9jKU9nFqdcTWqcCcx65j3XOdmXz/NTzdNZSw6oFUDfQDYMLPfxD+7Fy6v/0jw79ex/RVyWzbe4SLueCuiIiIiFzk1kyBGYMgdQdgndsZg4o92XHy5MlC152a6OjevTtDhw49r/P93URHbGwso0ePPufz9e/fn3r16rF161a2bdtG3bp1GTBgwBn3e+mll9yJldmzZ7Nlyxa2bNnCBx98wIMPPgjA+++/z5tvvsmsWbN49dVXAXj33Xe56667KFOmjPtYDz74IC+//HKh55o9ezZdunQ55+dmrSUrK+uc9zsXM2fO5P333+fHH39k48aNvPfee9x55538+eefp91v1qxZREZGUq5cOTIzM3n44YeZPXs2GzZs4PPPP2fDhg2sWbMGf39/1qxZw4oVK0hNTWX37t388ssv3Hzzze5jdevWjRkzZuRJHnmKEh0lhZcXhHaDAfMh7hu4siEsHAkZx5z1JejLfvaQl3+2qc+7dzWhkmv4SvN6Fejfqh5lSnkzdWUyT0xZTdc3lpCe6bypLNq8lwUb/+LA0XRPhi8iIiIiF5tx3fIvy//nrPv+uZzP1NkyjsFs11SeR/fn37eI9OvXjwceeIDmzZszePBgli9fzrXXXkt0dDQtWrRg06ZNpKenM3z4cCZPnkxUVBSTJ09m/PjxDBw4EHASEe3btyciIoIOHTqwffv2fOdZtGgRUVFRREVFER0dzeHDhxk6dChLliwhKiqKUaNGkZSUROvWrYmJiSEmJoalS5cC5Ntu4cKF3HjjjQAcOXKEe+65h/DwcCIiIpg2bVqBz3Pr1q2sXLmSZ555xt02fPhw4uPj2bZtG7179+bbb7/N87pMnTqVw4cPs2bNGiIjIwH4+uuviYuLwxjDNddcQ0pKCrt378bX15e0tDTS0tLw9fUlJSWFGTNmEBcXlyeO1q1b8/333xeYVLLWkpiYSExM3tkvx48fT48ePWjbti1XXXUVzz33nPt1b9CgAXFxcYSFhbFjxw4+//xzwsPDCQsLyzcV7OOPP07jxo3p0KEDe/fuBeB///sfTZs2JTIykltuueW0CYT/+7//45VXXqFSpUoAxMTE0LdvX8aMGcOcOXO47bbb3Nvm/hlNnDiRHj16ALB8+XJCQkKoV68epUqVonfv3nz99df4+vpy7NgxsrKyyMjIwNvbm+HDh7ufazZjDG3btmXmzJmFxllclOgoaYyBem0g7msYtAquqOwkOSbcDLOHOtPVllAxtcoztGsok+6/lrXPdmbOY60ZfUe0u0fIGNeQl5gX5nHdyz8w6PMEpqzY4eGoRUREROSidmhnwe3HDhTZKQYMGEB8fHyB65KTk1m6dCmvv/46oaGhLFmyhISEBJ5//nmGDRtGqVKleP755+nVqxeJiYn06tUrz/6PPPIIffv2Zc2aNfTp06fAYSWvvvoqY8aMITExkSVLluDv78/IkSNp3bo1iYmJPP7441x55ZXMmzePVatWMXnyZPdxTt0utxdeeIHAwEDWrl3LmjVraN++fYHPccOGDURFReHtnTProre3N1FRUaxfv55evXoxZYrTgyY9PZ358+fTrVs34uPjCQsLc++zc+dOatas6X4cHBzMzp07efjhh3nppZfo27cvw4YN44UXXmDYsGF4eeX9Ouzl5UVISAirV6/OF2NCQgKRkZGYAma8XL58OdOmTWPNmjV88cUX7p/lli1beOihh1i/fj2+vr4MGTKEBQsWkJiYyIoVK9zDPo4ePUpsbCzr16+nTZs27gRCz549WbFiBatXr6Zhw4Z8+OGHBb5+AOvXr6dJkyZ52rKP2bFjR3755ReOHj0KwOTJk+nduzcAP/30k3u/wl6/hg0bUrlyZWJiYrjpppvYunUrWVlZ+ZI+2edcsmRJoXEWl0ujaqX8PeWc2hacPA4B1WH5B7BiLET2gpaPQ6UQz8Z3AXl7GUKrliO0ajl327h+TVm7M5XVrmKnK5IOcOTESW5v6vwyPzxxFZWuKEVUrSCiapanTsUyBb6RiYiIiEgJc8+3ha8LDHYNWzm13fWFsGzF0+9/FsaOHVvouttuu82dAEhNTaVv375s2bIFYwwZGRlnPPbPP//M9OnTAbj77rsZPHhwvm1atmzJE088QZ8+fejZsyfBwcH5tsnIyGDgwIEkJibi7e3N5s2bz3ju77//nkmTJrkfly//92ZS7Nq1K48++ignTpxgzpw5XHfddfj7+7N7924qV658xv1r1arFwoULAaf3SHJyMg0bNuTuu+8mPT2dF154gauvvhqAK6+8kl27duVLGsyZM4euXbsWePxOnTpRsaIzyULPnj358ccfufnmm6lduzbXXHMNACtWrKBt27buePv06cPixYu5+eab8fLycieo7rrrLnr27AnAunXr+M9//kNKSgpHjhyhc+fO5/jKOXx8fOjSpQszZszg1ltv5dtvv3UP0Tlw4AABAQFnPMYbb7zhvn/TTTfx/vvv8+KLL7J69Wo6derEfffdB+S8fp5WJD06jDEfGWP2GGPWFbK+rTEm1RiT6Foujgollwtff/jHu/BoIsTeA2unwtuxsPk7T0dWrMqW9uGaXENefn66A+/0cbKQGZlZHDiazhcrk3l88mravbqQ6BfmMXbJb4DTVU1DXkREREQuQx2GO5+nc/P1d9qLQdmyZd33n3nmGdq1a8e6deuYMWMGx48XzQQEQ4cOZezYsRw7doyWLVuycePGfNuMGjWKKlWqsHr1auLj40lPL7rPxo0aNSIxMTFPHYusrCwSExNp1KgRfn5+tG3blrlz5zJ58mR3UsDf3z/Pa1CjRg127MhJSiUnJ1OjRo085/r3v//NiBEjGD16NAMGDODll1/OMwTj+PHj+Puf8vMGvvvuO66//voC4z/1n6PZj3P/7M5F9v79+vXj7bffZu3atfz3v/897c+7UaNGrFy5Mk/bypUrady4MeAUWp0yZQoLFiwgNjbWndzw8fFxv+5n8/p9/fXXNGnShCNHjrBt2zamTJnC1KlT3cNqCnv9iltRDV0ZD5ypKssSa22Ua3m+iM4r5yKoFtzwCjy2FtoOhTqtnPYt30PSTyWqjsfZ8vN1suO+3l58fv817iEvI3uG06VxVWoEOb+kSfvT8gx5+ejH31m1/SAnTmqWFxEREZESLeJ2uGm0qweHcW5vGu20F7PU1FT3F8/x48e72wMCAjh8+HCB+7Ro0cLdq2LixIm0bt063zbbtm0jPDycIUOG0LRpUzZu3JjvmKmpqVSrVg0vLy8mTJhAZmbmGc/dqVMnxowZ43588ODBArcLCQkhOjqaESNGuNtGjBhBTEwMISFOL/RevXoxbtw4lixZ4i4I2rBhQ7Zu3erep3v37nzyySdYa1m2bBmBgYFUq1bNvX7RokVUr16dq666irS0NLy8vPDy8spT+2Lz5s15hsNkP/eTJ0+6e22cat68eRw4cIBjx47x1Vdf0bJly3zbNGvWjEWLFrFv3z4yMzP5/PPPadOmDeAkdaZOnQrAZ599RqtWzve0w4cPU61aNTIyMpg4cWKB5842ePBghgwZwv79+wGnQO348eN56KGHAGjTpg2rVq3if//7n3vYCkCDBg347Tfnn7tNmzZly5Yt/P7776SnpzNp0iS6d+/u3jYjI4M33niDwYMHc+zYMXdCJjMz0534Kuj184QiSXRYaxcDRTdITS6sK650Eh2lXBWGF78M42+AjzrD5rmXZcIjW/aQl97NajHylgi6hjtvjGVLezO0ayiNqpVj+e8HeH7mBnq+s5QfNu4B4I/9R/kyIZnf9x0tcJaXrxJ20nLkAuoO/ZaWIxfwVUIhYz1FRERE5OITcTs8vg6eTXFuizjJcboaHbkNHjyYp59+mujo6DwFM9u1a+euczF58uQ8+7z11luMGzeOiIgIJkyYwJtvvpnvuG+88QZhYWFERETg6+tL165diYiIwNvbm8jISEaNGsVDDz3Exx9/TGRkJBs3bnT3Vjh1u9z+85//cPDgQcLCwoiMjOSHH34o9Ll9+OGHbN68mfr161O/fn02b96cpybF9ddfz6JFi+jYsSOlSpUCIDQ0lNTUVHei5YYbbqBevXqEhIRw33338c4777j3t9YyYsQId8HT+++/n0cffZRu3brx5JNPAvDXX3/h7+9P1apV88Q2b948OnbsWGjszZo145ZbbiEiIoJbbrmF2NjYfNtUq1aNkSNH0q5dOyIjI2nSpIm7CGjZsmVZvnw5YWFhLFiwwD1F6wsvvEDz5s1p2bIloaGhhZ4fnCTPvffeS4sWLQgNDeW+++7j008/dSd6vL29ufHGG5k9e7a7ECk4M6VkD+vx8fHh7bffpnPnzjRs2JDbb7/d3SMEYMyYMfTt25cyZcoQERFBWloa4eHhNGnShKCgIAB++OEHunUruoK8f5cpqqk3jTF1gJnW2nzpG2NMW2AakAzsAp601q4v5Dj3A/cD1KpVq8kff/xRJPHJaaSnQcKnsHS0M/6wShh0eh5COng6sovWn6nHSdxxkOZ1K1K+bCk++vF3np+5AYCgMr5EBgcRWTOI/q3q8sPGPTw9fS3HMnJ6f/j7evP/eoZzc3SNwk4hIiIiIhfIr7/+SsOGDT0dhhSBUaNGERAQcFZT0Z7NscqVK0f//v3ztA8YMIABAwa4623kNn78eOLj43n77bfP+/yesHv3buLi4pg3b955H+uvv/7izjvvZP78+UUQWcG/p8aYldba/JmkUxTXrCurgNrW2kjgLeCrwja01n5grY211saeTWEZKQKlykDz+2FQAtz8HmRmQLpTkZf0o3DyhGfjuwhVDfSjS1g1ypd1ssl9W9TJM+Tlr0PHeW/hNkp5e/HK3E15khwAxzIyGfHthpzH6ZlkZl2+PWlERERERP6OBx98kNKlSxfJsYKCgujbt2++9rFjxxaY5CgJqlWrxn333cehQ4fO+1jbt2/ntddeK4Kozl+x9OgoYNskINZau+9028XGxtqz6cIlRSy7CJCXFywcCSvHw7UPQ5N7oPQVHg3tUnI8IxM/X2/qDv2Wwn7LkkY63bruHb+CBRv3EFDah3L+vgT4+dCgagBv9o4G4JOfk9h3JJ1yfs76cn6+/5+9O4+Psjz3P/55Zp+sk42shC2AyA4CsogoKm64tWptT9tfN+uxrVZb27ocbXvUtqeni3ZRu9hW67HFXazaqq1aFlEB2WQxQQIJZN+XSWZ5fn/cSSYRUMSQScL3/XrNKzPP88zkHhmBfLnu6yI31cf0kaZErLUjjN/txOHQlBgRERGRD6KKjviZN28eHR19/yH1wQcfZOrUqXFa0dByxx138Mgjj/Q5dumll3LzzTfHaUXHzkep6BiQ8bKWZeUAlbZt25ZlzcVUktQOxPeWo9B7nvSohbBnFfzjFvj3T2Dul2HelyEhPX7rGyK6G53mBfyUN7QfdD4rKZY8XzIrn6n5qTQFQzS1h2kKhkjwxOaIP7PpAK/v6dsGZ87oNB65agEAF/5qNSXVLSR5XaT43KT63Swan8lN55rfGO568R2itt0VkrhI9bspzEjoGb/bHAyR6HEpKBERERGRY2rdunXxXsKQdvPNNw/LUKO/9UvQYVnWw8ASINOyrDLgNsANYNv2vcDHgf+0LCsMtAOfsPurlESOrTGnmNu+N2DVT+GVH0LV23D5g/Fe2ZBxw7KJh+zRcfN5sXTy/Gl57/saK66aTyRq09IRpqk9RFMwhLNXKPGFRWM40BjsOdfUHu4JWgBWvLnvoLDlkpn5/PTyGQCcdPuLdEaiJHlNCJLic3PxzHy+tHgs0ajN7X/bTorfhCjdYcmE7GRGZyYSjdq0dIZJUlAiIiIiQ4ht2weNBRWRweGjxgX9EnTYtn3FB5z/JTA0u7OIMXIOXPEwVL4NVlfFR91uWPVzWHgtZIyL7/oGse6Goz/++072N7STF/Bzw7KJH7oRqdNhkeo31RrvdcXcwvd97urvnG6CkqCpFmlsD5HoNf/727bNDcsmdoUk4Z6gJMFrgpLWzjAr3txHS0e4z2teu3Q81505gZqWDube+RIOC5J97p5A5MrFY7lwRj41LR3c83JJV0gSC0um5KeQm+onFIkSDEVI8rr0lw0REREZED6fj9raWjIyMvT3D5FBxrZtamtr8fl8R/0a/daj4xJgOCEAACAASURBVFhQj45Bbsuj8OTVEA3B5Ith0XWQo711w1U4Eu2qKDFhSHqih7yAn6ZgiBVv7IsFJV1VJZfPKeTME7PZUdHEx+9Ze1BQ8pNLp/Ox2QWsL63jY/esPSgoufm8SSwYl8k7lc389Y19PZUk3T1KZo9KIy3RQzAUIRy1SfQ49RcVEREROSKhUIiysjKCwWC8lyIih+Dz+SgoKMDt7vuPvEfao0NBh3w0zRXw2q/hjd9DZwuccD5c/mfQD5zyHu8NSnJTfWQkednf0M7fNh/oqiSJhSVfWzqeGSMD/HNHJV/7v420dvadXPPIVfOZMzqdJzaWcd1fN+Gw6AlBUvwu7v7ETMZmJbFudy0vbq80IUqvoGRhUSZ+j5O2zjC2DQkKSkREREREBjUFHTKw2uvh9d+ZsOPM75lj+16HgjkKPaRfhCLRnq03Te1hxmQlkuR18U5lMy/vrD4oKLn94inkpvp5cO0efvDcDtreE5Ssu2kp2Sk+7n7pHX76wi6cDqtPEPLwlSeT5HXx/NYDrC+tj/Un6ao4Of2EEViWZfqlWNZRBSVPbiz/yFuaRERERESOF4Nq6oocB/xpcOoNscdlb8Lvz4ScaWZLy4kXgsN5+OeLfAC300Faooe0RE+f4+OzkxmfnXzY5316/mg+PX80oUiU5q4QpLE9REbX6ywan4nP7eipNOk+73OZXjSbyhp5aN3ePkGJx+lg5+1nA/D9lW/z6PqyPkFJbqqPv1w5H4D/W7eXPbWtfUKUrGQv1c0dfZrUlje0c+PjWwAUdoiIiIiIfASq6JBjI9wJW1bAqp9BbTGkj4NFX4dpnwCX54OfLzLIdAclje0hWjvCTMlPBeDVXdVsP9DU0+S1qT2My2nx08vMRJtrHt7IP96uIBiK9rzWuKxEgqHoIccO56b6WHvj0oF5UyIiIiIiQ4i2rsjgEI3A9pXw759Aw164bit4k8G2taVFjisd4UhPRUk4arPsZ69yuN99uyfaiIiIiIhIjLauyODgcMLki8zWlfo9JuSIRuFP58PYJTD3S2bbi8gw53U58SY5yUzyApAX8B+yoiPR46QwPQGAxrYQ33/mbc6fnsuiokzcTseArllEREREZCjS35plYFgWpI8x94MNJvD41x3wsynwj/8y01tEjiM3LJuI3923b43f7eSOi6fysdkFAOysbOYfb1fwuT+8wdw7XuSmJ7bw2u5aotHBW4knIiIiIhJv2roi8VOx1fTw2PY4ONzwueegYHa8VyUyYI5k6kpHOMKru2pYuWk/L7xdSXsowr++uYQxmYk0B0MkeV0aiysiIiIixwX16JCho7YENjwAp/8XOF2w83kIjITsyfFemcig0tYZZt3uOk47YQQAVz+0nq3lTSyfnssF0/OZmHP46TMiIiIiIkOdgg4ZmqJR+MUsqH8XJpwDp3wDRs6J96pEBqWn3irn0fVlrC6uIWrDxOxk/t/C0VwxtzDeSxMRERER6XdHGnSoR4cMLg4HfOmfsOQm2Pca/P4M+OP5UL4+3isTGXQunJHPg1+Yx+s3n8H3L5xMss/F/q4Gp53hKPevepeKxmCcVykiIiIiMrBU0SGDV0cLbPgTrPklXHwvjD0VOlvB5TeBiIgcJBq1cTgs1pbUcsVvX8OyYM7odC6Ynse5U3NJT/TEe4kiIiIiIkdFW1dk+Ah3gtNtJrc8920o+Scs/DpMu8wcF5FD2l3dwspNB3h6Uzkl1a04HRbPXnOKenmIiIiIyJA04FtXLMu637KsKsuytn7AdXMsywpblvXx/vreMsy5PCbkABi9CJxeeOpquGsGrLsPOtviuz6RQWpsVhLXnjGeF68/lWevOYVrl45n/IgkAH7w7HauenA9z245QDAUifNKRURERET6j6sfX+uPwC+BBw53gWVZTuBHwD/68fvK8WTScjjhfCh+Ef79E3juW1C9A87/WbxXJjJoWZbFiXkpnJiX0nMsweNi/d56nt9WQaLHyVmTc/jYrAIWjc+M40pFRERERD66fgs6bNt+1bKs0R9w2deAxwCN0ZCjZ1kw/kxzK10LSWbUJgc2w9bHYP5XYsdE5JCuPWM8Xz29iHW7a1m5eT/PbqnA43SwaHwmtm3zZmk9swrTcDqseC9VRERERORD6c+KjvdlWVY+cDFwGu8TdFiWdSVwJUBhoUYkygcYNT92v3QNrLkb1t0LM/8DFlwDaaPitzaRQc7psFhQlMmCoky+d8EUWjrCAGwtb+LSe9cyItnLedNyWT49j5kjA1iWQg8RERERGfz6tRlpV0XHM7ZtTznEuUeAn9i2/ZplWX/suu7R93s9NSOVD622BFb/HN56GOwonPR5OO9/470qkSGlvTPCSzsqWblpP//aWU1nOEpBmp/ff3aOGpmKiIiISNwcaTPSAavoAE4C/tL1L4KZwLmWZYVt235yANcgw13GOLjgF3Dqd2Dtr8CXao7bNlRsgdxp8V2fyBDg9zg5f1oe50/LoykY4h/bKvn7tgoK0xMAWPHGPiqagiyfnseYzMQ4r1ZEREREpK8Bq+h4z3V/RBUdMpDeeQEe+jiMWQynfAPGnBqb5CIiH8p3HtvMX97YB8DU/FSWT8/l/Gl55AX8cV6ZiIiIiAxn8Rgv+zCwFphoWVaZZVlfsCzrKsuyruqv7yFy1ApPhrNuh+pd8MCF8NvTYftKiEbjvTKRIeeHH5vG2htP55bzJmFZcOezO7j5iS095xvbQ3FcnYiIiIgc7/q1oqO/qaJD+l0oCJseNn08IiG45i1weeK9KpEhbU9NK+2hCJNyUzjQ2M4pP/oX88dlsHx6Hssm55Dqd8d7iSIiIiIyDBxpRYeCDjk+RcJQvwcyiyDcCQ9cAFM+Zqa1uFV+L3K0qpqD/GnNHlZuOsDeujY8TgenTszixnNOYGxWUryXJyIiIiJD2IBvXREZUpwuE3IAtFRCNALPfhN+PhX+/VMINsZ3fSJD1IhkHzcsO4FXbljCk19ZyKfnj2JbeSNJPtP7em1JLS+8XUlHOBLnlYqIiIjIcKWKDhEwU1lKV5uQo+Ql8KbClf8yU1xE5COxbZuuiVtc9eB6nt9WQYrPxdlTcrhgej4nj03H5VTuLiIiIiLvT1tXRI7W/o2w9TE487/NZJYdz0LOFAgUxntlIkNeKBJlVXENKzft5x/bKmnpCHPK+Ewe/MK8eC9NRERERAa5Iw06XAOxGJEhJW+muQGEO+Cpq6GjGaZeBou+DlkT47s+kSHM7XRw2sQRnDZxBMFQhJd3VuF0mGqO1o4wF/xyFWdMymb59Dwm56X0VIKIiIiIiBwpVXSIfJDGMljzS1j/RwgHYdL5cPqtkDUh3isTGVb21bVx61Nb+fc7NYSjNmOzElk+LY9PziskO8UX7+WJiIiISJypGalIf0ktgHN+CNdthcXfhHdfNYEHQKjd9PcQkY9sZHoCf/jcXN64+QzuvHgq2ck+7v7nOzS0hQAzxrasvi3OqxQRERGRwU4VHSIfVqg9NoL28S9DXQksuh4mnA0OZYci/am6uYOsZC8A1//1LR7fWM7sUWksn5bLedPyes6JiIiIyPCnig6RY6U75AAYNd+Mp/3LFXDPAti8AiLh+K1NZJjpHWRcd+YEblg2kdaOMN9d+Tbz7nyR61e8FcfViYiIiMhgpIoOkY8qEjZTWlb9DKq3w6nfhtNuiveqRIa1dyqbWblpP8k+N19aPJZI1Oabj2xiycQszpiUTaJXvbZFREREhhuNlxUZaNEo7HoOcmdAaj6UroV96+Ckz8Ou5+Gl75vGpqkFsPRWmHZZvFcsMmyU1rbyid+8xoHGID63g6WTsrlgeh5LJmbhdTnjvTwRERER6QcKOkTi7cXvwaqfgssP0RBEe21pcfth+d0KO0T6UTRq82ZpPU9vKufZLRXUtXbywOfnsnhCFs3BEH63E5dTOzZFREREhioFHSKDQfl6+MN5EG4/+FxqAVy3beDXJHIcCEeirCmpZcG4DFxOBz94djuPrC/j3Kk5XDA9n5NGpeFwWPFepoiIiIh8CEcadGgTs8ixlD87Nor2vRrL4L5TIWcK5EyDnKnm5k0e2DWKDEMup4PFE7J6Hp8yPouyhnYeXV/Gn1/bS26qj0tPGsn1Z06I4ypFRERE5Fjot6DDsqz7gfOBKtu2pxzi/IXAfwNRIAx83bbtVf31/UUGrdQCaNx38HFvMvjTYOfzsPHP5tjZP4KTr4KmA7DhgVj4kVoAlv71WeRoLRqfyaLxmbR2hHlxeyVPv7Wf/Q2xSqs/rH6XU8ZnUjRCQaOIiIjIUNdvW1csy1oMtAAPHCboSAJabdu2LcuaBqywbfuE93tNbV2RYWHzClh5DYR6bV/p3aPDtqG5Aiq3QtZECBRC8Yvw548DXf9/+gIm8Dj7B+ZrZxs4XODyxOUtiQwHtm1jWRZl9W0s/p9/EbXhhJxkLpiRx/JpeYxMT4j3EkVERESkl7j06LAsazTwzKGCjvdcNx+437btSe93nYIOGTY2r/jwU1c6WqDqbajYDBVboWILXPIbyBgHr/8Wnr8Rsk6IVX3kTIWR8xR+iByFquYgz24+wMrNB1hfWg/A7z97EksnZfcEIiIiIiISX4My6LAs62LgB8AI4Dzbttce4porgSsBCgsLZ5eWlvbb+kSGjbL1sP1pE35UboWWSnP8xjKzJWbzI1D7TiwACYzS1heRI7Svro1nNh/gP04uJNnn5v5V7/LC25VcMCOPsyfnkJaoMFFEREQkHgZl0NHrusXArbZtn/F+16miQ+QINVdCzS4Yc4p5vPLrsP6P9Gx98abAqAXwyb+ax41lkJgFLm88VisypPz1jb3c+8pu3q1pxeWwWDwhiwtn5HHhjPx4L01ERETkuDKop67Ytv2qZVljLcvKtG27Jh5rEBlWkrPNrdvyn8OyO6Fqe9fWly19r//LJ6FyG2RO7LvtZeScgV23yBBw+ZxCLjtpJNv2N7Fy035WbtpPMBTpCTpef7eOaQWp+NzOOK9URERERGAAKzosyyoCSrqakc4CVgIF9vssQBUdIsfI9pWwf6MJQCq2QPMBmHwxXPpHc/7xK01T1J6tL6PB4YjnikUGjWjUpqE9RHqih5qWDubd+RJ+t5OzJmezfHoei4oycTv1/4uIiIhIfxvwig7Lsh4GlgCZlmWVAbcBbgDbtu8FPgZ8xrKsENAOXP5+IYeIHEOTlptbt5ZqCLWZ+x0tJvzY8ijYEXPMkwSn3Qzzr4ZIyJwfMclMjxE5zjgcFuldfToCfjd/+txcnt5UznNbK3h8QzlpCW7u+sRMFk/IivNKRURERI5P/VrR0d9U0SESR6EgVG+PVX2MPwvGn2nu37sILCdkTuiq+pgCJ14IaaPjvWqRuOkIR3h1Vw0rN+3nW2dPpCAtgZe2V7KmpJbl0/OYXpCq6S0iIiIiH0FcmpH2NwUdIoNQewO8+2osAKnYAk1l8B+PQdEZsGcVrL4rtu0leyqkj9XWFzku/epfxdz14jt0RqIUpiewfHouy6fncUJOSryXJiIiIjLkKOgQkYHTVgfuBHD7YOfz8M//huodEA2b8+5EuOrfkDEOat6BYCOMOBE8CfFdt8gAaGwP8fdtFazctJ/VxTWMzUrihesWY1kW9a2dGlcrIiIicoQG9dQVERlmEtJj9yeebW7hDhN2VGyBiq2muSnA67+F1+8DywEZRZA9xVR+LPy6qj5kWEr1u7nspJFcdtJIalo62N/QjmVZBEMRFv/Pvxiblcjy6XmcPy2PnFQfT24s58d/38n+hnbyAn5uWDaRi2ZqlK2IiIjIkVJFh4gMrKb9UL6h79YXOwLXv23Or/w6NOw1fT9yppkQJKMIHBrdKcNLa0eYh9aV8vSm/Wwtb8KyYExGIuUN7XSEoz3X+d1OfnDJVIUdIiIictzT1hURGTo622LbWF78HhS/AFU7IBoyx0aeDF/4u7n/9lOQlAPZk8GbFJ/1ivSz3dUtPLP5AHe/9A7h6MF/LucHfKz+ztI4rExERERk8FDQISJDW7gTanaabS8uL0y5BKJR+EEBhFoByzQ5zZkKky+GyRfFe8UiH9mY7/yNw/2pvHx6HgvHZfCx2QW4ndrmJSIiIscf9egQkaHN5YlNbulmWfCVdb22vWyGA29B1gkm6GhvgF/M7tr2MrXX1pfx4NRvdzL45QX8lDe0H3Tc73by2u5a1pbUcPmckQA8vqEMt9PBgnEZZCR5B3qpIiIiIoOW/uYvIkOHZUFgpLmdcG7seHdlWjhoGqFWbIF190Gk0xw/76cw5wumP8j2Z7rG3k4Gn0Z8yuByw7KJ3Pj4FtpDkZ5j3T06LpyRR1VzB5ZlAfCbV3ezo6IZgEm5KSwcl8GZJ2Yzb2xGXNYuIiIiMlgo6BCRoa/rBz+Sc+DCX5n7kZAZZVuxBQrnmWP71sFzN8SelzbGhB5Lb4PMIvMchyv2eiIDrLvh6OGmrmSn+HqufeZri9hS3siaklpWF9fwwGulNAfDzBubQTRqc88rJcwZnc6MkQE8Lm11ERERkeOHenSIyPHDtqH5QN+JLxVb4DNPmSqRtb+GV/+n77aXnKlma4ymvsggFwxFaOkIk5nk5d2aVpb+5GWiNiR4nMwZnc7CogzOnZpLQVpCvJcqIiIiclTUjFRE5MPa/Qpse9yEH5XbzFYYywE37Qe3H7Y9Ac2VXQHIFPClxnvFIofV2BZi7e5a1pTUsLq4hpLqVn73mZM448RsiqtaeP3dOhYWZVCYntCzHUZERERkMFMzUhGRD2vsqeYGEAlDXQnU7TYhB5ig4+2nYtcHRsGYU2LbZdrqwJ+mrS8yKKQmuDl7Sg5nT8kBoKIxSCDBDcC/dlRxx7PbAcgP+FlYlMHCokyWTc7B51b1koiIiAxtqugQEfkwmitjE18qtoAnIRZ0/Ho+NJbHtrzkTIGCOZA18dCvtXkFvPR9aCyD1AJYeitMu2zg3osct2zbpqS6tafaY21JLR3hKJtuOwuf28lL2yuxbZg3Np1knzveyxUREREBtHVFRGTgrf+TGXfbvfUl1AbTPgGX3Gf6gzxzHWSONyFIbTH8/SYI9Rol6vbD8rsVdsiAi0Rt9tS2Mi4rCYBL713DG3vqcTosphWksqgokyUTs5g9Kj3OKxUREZHj2YAGHZZl3Q+cD1TZtj3lEOc/BXwbsIBm4D9t2970Qa+roENEhqxoxGx7ARNutNWZio+Wivd/Xko+XP/2sV+fyPsIhiJs2FvP6uIaVhfXsrmsgaWTsvntZ8zfK/5v3V6m5qdyYl4KToe2aomIiMjAGOigYzHQAjxwmKBjAbDdtu16y7LOAb5r2/a8D3pdBR0iMuy0VJmKjz9fcvhrfAHIGAcZRXDy1ZA3w1R+RELgSxm4tYp0aQqGaGwLMTI9gdqWDmbf/iIAqX4388dmsLAog6WTsskL+OO8UhERERnOBrQZqW3br1qWNfp9zq/p9fA1oKA/vq+IyJCTNAKKlkLqSGjcd/B5XwCmXAK1JbBnNcz8tDn+zguw4tOQmAXp40wQkj7WnE/ONltj1ARVjpEUn5uUrl4dGUleXr9pKWtKallVXMOa4hqe31aB1+XksjkjqWwKsrq4hoVFmWSn+OK8chERETkexWPqyheA5w530rKsK4ErAQoLCwdqTSIiA2vprbDymoN7dJz740P36BgxCc74rglA6nZD8UvQ8hBMvhjIhnX3weqfx0KQjHHm/vgzweUdoDclx4sRKT4umpnPRTPzsW2bPbVtpCd4AHh5ZxXffmwLAEUjklg4LoMFRZmcOiFLE11ERERkQPRbM9Kuio5nDrV1pdc1pwG/BhbZtl37Qa+prSsiMqx91KkrHc3gTgSHA955EbY+2hWElEBb12+xNx0wk2FW/Qx2v9yrGmRcbHuMKkGkH0WjNm8faDL9PUpqeePdOtpDEdbfcgYZSV7e3FNHRzjK7FFpCj5ERETkQxnQrStHwrKsacDvgHOOJOQQERn2pl320SaseJNj98efYW7d2uuhYa8JOQAcbgg2mTAk2GiO+dPh2++a+6t+Bs0VXQHIWPM1UAgO/SAqH47DYTElP5Up+al8+dRxdIajbD/QREaSqSy65+USXtpRhdfl4KTRaSwYl8kp4zOZVhCI88pFRERkuBiQig7LsgqBfwKfeU+/jvelig4RkX5m22YCTF2JCUMmLDPHH/si7HgWQq2xa/NmwpUvm/vr7gOHK1YFkpxnKklEPqTmYIjX361jdXEta0pq2FHRzKzCAI9fvRCA57YcYHx2EuOykrBUbSQiIiK9DGhFh2VZDwNLgEzLssqA2wA3gG3b9wK3AhnAr7v+0hI+ksWJiEg/syxIzDC33j72OxOCtFTGtr+4ejWSXPOLvs1TXT6Y9RnTUwRg8yOQkmeCkKRsbYeRw0r2uVk6KZulk7IBqGnpoLalEzBjba/9y1t0RqJkp3hZOC6TBUWZLB6fyQg1NhUREZEj1G8VHceCKjpERAaJaBSa98dCkNoSyDoBZn3aNFS9Iyd2rScJ0sfAnC/B7M9CJAzl600lSEK6QhB5X3tr21hdUsPq4hrWltRS29rJt86eyNVLimgKhlhTXMP8sZmkJrjjvVQREREZYIOuR4eIiAxhDodpmJpaAGNP7XvO6YVrN5nwo3cQ4uz6QbRxL9x/lrnvS401Qj3p8zBqAYQ7INQG/rSBfU8yKBVmJFCYUcgVcwuJRm12VjaTnmgmuqwpruWqP2/AsmBKXioLizJZWJTBnNHpamwqIiIiPVTRISIix1ZHC5Su7hWCFEPtblh2B5x4Abz7b/jT+ZCQ0RWCFJmGqNMuNw1RbVtVIAJAKBJl074GVhXXsKa4lo376glFbF68fjFFI5LZUdFEW2eEafmpuJzqISMiIjLcHGlFh4IOERGJr/pSePspE4DU7TaBSPN++OJLUHASbPorvPBfvUbido3HHXc6eJPivXqJo7bOMG/uqeeU8ZlYlsU3VmzisQ1lJHtdzBub3lXxkcmE7OQPfjEREREZ9BR0iIjI0NXZCk6P2f5SugY2PhTbEtNaZa65fgek5MIbv4e3n+wVhBTFKkM0Gea4UtfaydqS2p4eH6W1bYzOSODlG04DYE1xDaMyE8kP+OO8UhERETka6tEhIiJDlycxdn/UAnPrFmwyoUdyVwNUhxM620zY0V7fdcwFN1cCDnj9t1C9s2tLzDhIHwuBUeDUH4HDTXqih/Om5XLetFwAyurbqGgMAhCN2nzl/zZQ3xZidEYCC4oyzVSXcRmkdfUAERERkeFBFR0iIjJ8tNWZ7S/NB2DScnPsb9+AzSugoyl2XdoYuPYtc3/9nyDSGdsSk1pgwhMZVmzbZldlC6uLa1hTUsNru+to6QjzxUVjuOX8EwlFoqwurmHumHQSPArBREREBiNtXREREelm29Ba09UHpASiETP6FuDeRVCxJXat0wuTL4JLfmMev/0U+NNNEJKcq8aow0Q4EmVTWSPpiR7GZCbyxp46Lr13LW6nxcyRaSwoymBhUSYzRgZwq7GpiIjIoKCgQ0RE5EjYtqkA6T0aN7UA5n3ZnLszH0Kt5lp3gtn6MuNTMP9qc2zvOkgfA4lZCkGGsGAowuvv1rG6xEx02bq/EduGv1x5MiePzWBfXRvNwTAn5CTjcOjXWUREJB7Uo0NERORIWBak5JnbmFMOPn/12lgA0j0VpntrS1sd3H+Wue9NMSFIxjgThBQthUjYbJlJSB+49yNHxed2snhCFosnZAHQ0GYam84sDADw59dKue/V3WQkepg/zlR7LByXSWFGQjyXLSIiIoegig4REZGjFQrCnn/3qgYpNvcXfxNmfQYqtsK9C8GfFpsKkz4OplwCmeNNxch7q0A2r4CXvg+NZaayZOmtMO2y+Lw/6VHRGOTf71SzpqSW1cU1VDV3kOp3s+G/zsTpsNhS1khuwEdmkjfeSxURERm2tHVFREQk3porYMsjvYKQ3dBUBp96FMafCbv+Dk99NRaAdLbCzr+Z5qjd3H5YfrfCjkHEtm1KqlvYW9fG6SdkA3D6/77M7ppWTshJNtUeRRnMHZNBklfFsyIiIv1FQYeIiMhgFGoHywkuD5RvgDfvjwUhLZWHfo7LB9OvgJR8s8Vm8kVmBG80Cg41yhwM3trXwOriGlYX1/BmaT2d4SiXzMznp5fPwLZt1pfWM60ggMelXy8REZGjpaBDRERkqPluADjMn8sJmdBWY+5/Zy/4UuHF78Kbf4gFICl55v7ib5o+Iq014PSAL2Wg3oFgGpuuL60nxedmakEqJdUtLP3JK/jdTuaMSWdRUQYLxmVyYm6KGpuKiIh8CGpGKiIiMtSkFkDjvkMcHwnXbTU9QZr3m8anACNPho4WaNoPTeVwYBOEg7Dk2+b832+CzX8113cHIZkT4JwfmfMVW8FyQGq+uUZTY/qFz+1kYVFmz+PcVB/3fXo2a4prWFVcw53PVgNwz6dmcc7UXGpaOmgJhhmVkYClXwMREZGPrF+CDsuy7gfOB6ps255yiPMnAH8AZgE327b9v/3xfUVERIaVpbfCymvM9pZubr85DuD2mcku3SaebW69RUKx+zM+CdmTobHcBCFN+0240e25b0PpKnPfk2SCkMKT4YJfmGM7ngWHyxxPzQdfQGHIUUjwuFg2OYdlk3MA09h0TUkNC8aZMOSx9WX84Lkd5Af8LBiXwaLxmcwfl8GIZF+f13lyYzk//vtO9je0kxfwc8OyiVw0M3/A34+IiMhg1y9bVyzLWgy0AA8cJugYAYwCLgLqjzTo0NYVERE57gzk1JX9G83I3Kb9saqQ5Dw454fm/F3ToX5P7Hp3gpkYc+GvzOPVd5teIT1bZ/LNKF2FIR/Kvro2Xt5ZxeriWtburqWxPYTH6WDTbWfh9zjZXd3Cundr+f7K7bSHIj3P87ud/OCSqQo7RETkuDHgPTosRyfbbwAAIABJREFUyxoNPHOooKPXNd8FWhR0iIiIDAE9lSDlsTAkfSzM+YIZjXtnPoRa+z5n1mfhgrtNo9SnrobknL5BSNpo8Afi8naGgkjU5u39TRRXN3PxzAIALr13DW/sqT/k9fkBP6u+fZq2vIiIyHFhyPbosCzrSuBKgMLCwjivRkRE5DiWmm9uh2JZcOM+aKmKVYM0lZseIAAdTVC6GpoOQLTXdpolN8KS70BrLaz4dN8mqil5kDfr8N/zOOB0WEwtSGVqQWrPsRuWncBl96095PX7G9pZ9KN/EbVtRqT4yE72kp3iY/64DM6dmgvAO5XNZCZ5CSS4FYiIiMhxYdAFHbZt/wb4DZiKjjgvR0RERA7H4YSUXHNjdt9z/gB8fYup7GitjlWFZIwz5ztbzNeyN8zxSKd5fMEvYNZnTGPVv/xHLAhJzTdhyMRzIW0URMImbHE4B+ztxsvcMenkB/yUN7QfdC4v4OeSWfkcaAxS2RSktLaN1/fUYVlw7tRcQpEoZ/38VWwbPE4HWcleslO8XD5nJJfPKSQUifL0W/vJTvGRneJlRIqPFJ9LgYiIiAxpgy7oEBERkWHE4YDkbHPLnxU7njYKPvesuW/bZhRuU7kJMwBcPhg134QgB96Cnc+aiTKZE8xzdz0PKz4Dybm9wpACmHcVBEZCez10tkJSDjiH/l93blg2kRsf33JQj47DNSSNRs2/Fdk2/PKKWVQ2BalsDlLd1EFlc5DuncuVTUG+8cimPs/1uR381/kn8ql5o6hqDnLvy7vJTjGVIiO6vuYH/Pjcwz9kEhGRoWno/8kvIiIiQ5tlQVKWuXXLmgiX/Cb22Lahrc40PwXTK2TRdbEtM5VbYdffTTUIwOZH4LkbzPjcpJxYGHLeT833qS0xlSYpeSYscboH7v0ehe4w40inrjgcpiLD43Jw3rTcw75uToqPl7+5pCsI6aCqyVSGTMhOBqCysYO/vLGXts5In+fd9YkZXDgjn81lDdz57PauihAfI5JNVciCcRlkJnmxbVvVISIiMuD6a+rKw8ASIBOoBG4D3AC2bd9rWVYO8CaQAkQxE1pOtG276f1eV81IRURE5Ih1/53GsqB6V1ePkF4TZZr2w5deAm8yvHArrL6r64kWJGWb0OPzz4PLC3tWQXNFLCBJzjXHj1MtHWEThjQFqWrqYE7Xdpr1pXX88LkdVDZ1UNkUpCMcBWDFl+czd0w6T71Vzi1Pbu3ZGpOd7GNEio/PLxzNiBQfda2dtHaEyUr2qkJEREQ+0IBPXTkWFHSIiIjIMdGwD6p39pooU262u3ziIXP+0S/A1kf7PiejCL623tzf+BC0VJrtMj1hSB64fQP7PgYR27ZpCoapagqSn+YnwePirX0NPLmxvCckqWzqoKo5yD+/sYSR6Qnc+0oJP3xuBwCBBHdXEOLlF1fMJJDgYWt5I2X17T1bZ7KSvbidjji/UxERiZchO3VFRERE5JgLjDS3w1l+F5z6rVgQ0lgOdq/tG9uegOIX+j5nxIlwddd0lBduhY7mrmkyXRNl0kab/iJHa/MKeOn70FhmApalt8K0y47+9fqZZVmk+t2k+mPbgGaMDDBjZN9xwr3/ke2MSSNIT/R0bZnp6NlCk+Axf0V9dH0Zf1yzp8/zM5O8rLtpKU6HxZMby9ld3WImznRXjXTdFxGR45eCDhEREZH38iaZPiFZEw99/j8eNc1Oe2+LcXpi56u2Q9mb0F4XO1Z0pnkewJ8/ZvqHpORBSldVSM4UyJ1+6O+3eQWsvAZCXZNXGveZxzCowo4j0btnR9GIZIpGJB/22q+fMZ6Pzy6gqrmrGqSpg+ZgCGdXD5JVxTU8vqGMaK8C5RHJXl6/+QwA7vjb2+yubu0KQkwIMio9gQVFmQDqISIiMkwp6BARERE5Gp5EyBxvbu/1qUfM1842aD5gwhBXryoDtx/qS6F8A7TVmGOzPgsX3A3RCPzvBEgaEdsWs+3JWMjRLdRuKjyGWNDxYQQSPAQSPEDqIc//76XT+eElU6lt7ezZGtPZ1ScEIBy1OdAYZFNZAzUtZoTx9IJUnvrqIgAu+vUaqpqCJghJ9jIixcv0ggCXnmSqfUqqWwj43aQleHoavIqIyOCnHh0iIiIi8RQKQvN+cLggUGjCkRdv69oyU2a+tlYd/vnTr4DAKDjhPMidBtGuH/Qd6mXRW2c4Sk1LB8FQhLFZSQDc+0oJxVUtPU1WK5uDnDwmg3s/PRuA2f/9ArWtnbidFiO6+oecMyWHKxePA+Cpt8pJS/D0bJtJ9btVISIicgypR4eIiIjIUOD2mXG53TwJcO6P+17zs8km9HgvpxfefdWEIWmjTNBxYCPcfzakjjTHAqPM1xMvgvQxJgixLHM7jnhcDvIC/j7Hrjp13EHXRXvtg7nj4ilUNJq+IZWNQSqbg4S7zneEI1z7l7cO+h5fPa2Ia5aOJxiK8KPnd7xn4oyXvIC/pweJiIgcG/pdVkRERGSwW3pb3x4dYLa/LL/bbF0Jd8TG6/rT4OSroX4PNJTC/rdMr5C8WSbo2LESnvyKCT/SRseCkCkfg8RM8zrHWQjSW+8tKmdPyT3sdW6Hg1duWEJVc0dsokxTkCn5KQDUtnbyyJtltHSE+zzvlvMm8cVTxrKvro1vPLLJBCHJpn/IiBQvc0ankxfwH3H/kCc3lvPjv+9kf0M7eQE/NyybyEUz84/y3YuIDA8KOkREREQGu+4+HIebuuLyxq5NHwtnfq/v84NNsWsChTDjkyYEqS2Bkn9CqA3GLTVBx7p74d8/7VsNEhgFUy811SYCmEBkVEYiozISD3k+P+Bn6/eW0dIR7pkqU9UcZHKeCUI6wmaKz5ayBl5oChIMmS1Hv/zkTPICftburuXLD6xnRIqXEcmxZqpXzC1kdGYije0hHn1zHz/+x86e55Y3tHPj41sAFHaIyHFNPTpEREREjme2Da01kJAODqcJPrY9YZql1u8xwYodgZsrTBXJC7fBtsd7hSCjTWXI1I8f15UgH4Vt2zR3BSJZyT5S/W7eqWzmoXV7qe6uGOmaPPPwl05m9qg0Ht9QxvUrNh3y9fIDfn7+iRm8vLOKVL+bgN9Dit9NIMHNjJEBfG4nkaiNw0I9RURkSFGPDhERERH5YJYFSVmxx+NON7dukbCZHOPu6m+RM9VMkakvhXdegJZKSMiAaZea80/8J5Sv71URMhoyJ8CEswbsLQ01lmWR4nOT4nP3HBufncx3L5jc5zrbtnt2KJ00Kv2wr7e/oZ3NZY3c83JJn9G7AK/ftBSf28ndL73Dr18uJtXvIdXvIpDgIdXv5lefnIXf42R1cQ27KptNUJLgJtVvbuOykhSOiMigp6BDRERERA7P6YLAyNjjqR83t26hdhN2dMudDh1NZmvM3tfM/ZypsaDjzx+H1uq+W2Oyp0LhvIF5P0OYZVk9RTOFGQnkB/yUN7QfdF1ewM8XFo3h8wtH09IRpqEtRGN7iKb2EOmJHgDmjE7nC4vG0tjeSWO7OV/VHMTrMtN6ntl8gIdf39vndb0uBztvPweAm57Ywr92VPUEIKl+N3kBf0848+quahraQwR6nU9L8JCa4EZE5FhT0CEiIiIiR8/tN1Ub3U6+ytzAbItpr4dgY+x87jQ4sAkqt8HO5yDSCRPOgU/+xZz/zWng9JjX7A5D8mZAdt/qBoEblk3kxse30B6K9Bzzu53csGwiYIKRZJ+bZJ+bke957qLxmSwan3nY1/7vCyfzrWUTaegKQRrbQ7R3xr7P9IJUOsPRnnOltW3UtHT0nP/Nq7tZVVzT5zXHj0jihetPBeCr/7eB0tq2WFCS4GZidjKfXTAagDXFNWDRcz6Q4CHR41Q1iYgcEQUdIiIiInJsWJbp/ZHQa5vF0ltj96NRaKkwYQeYYCT7RKh7F/asgs1/BWyY80U47ydmG82vTzbNWHtXhHRPlDnOdDccPRZTV1xOB2mJHtK6KkDe6/I5hVw+p/Cwz7/rEzOoa41VizS0hfC6HT3nR6Yn0NoRprE9xP7GdpraQ5TXt/cEHTc+sYXS2rY+r3nGpBH87rNzAPjin94gHLW7epCYMGT6yABLJ2UDsGFvPUleV09Q4nM7P8p/DhEZYtSMVEREREQGp3AnNO4Dp9tMi2lvgGe+bvqDNJRCW625bultcMr10FgOD13atz9I2ijInw1JI+L6VuTD2VXZTG1Ld1BivuYHEjhvmhn5+8U/vUlVczC2LScY4vKTRvLDj00jGrUpuvnZPv1JvC4HXzplLN9cNpGOcISvPLTxoP4js0elMSU/lXAkyt66NgIJHlJ8LlxOx2FWKSIDTc1IRURERGRoc3kgY1zssT8Al/4x9rijGRr2gj/NPI50mECkfg/sfgVCreb4Jb81o3jLN8Cz3+wbggRGQf4s8KUO0JuSIzEhOxmyD3/+d5/t+3NONGrTGYn2PP7T5+f2hCDdt2kF5tc42BmlvKGdt/c30tgeorVrS84NyyYyJT+VA41BTv/JKz2v1V0Z8s1lE7h4ZgEHGtu568V3SO0VkgT8HmaNCpCb6qczHCUYjpDsdWmrjUic9FvQYVnW/cD5QJVt21MOcd4C7gLOBdqA/2fb9ob++v4iIiIicpzxJvft3ZE+Ntbro3tsbkNprIdINAKeJNi/AbY/DdGwOf7/noXRC2HX32H13X1DkLRRkDsD3L6BfGfyITkcFj6Hs+f+KeOzDnttaoKb5649pedxKGJ6jXi6GrEGEtz87PLpNLaFYj1K2kKMSDafgdqWTl7aUUVjW6hPuPKLK2ayfLqfN0vr+ORv1+Ho1WMkNcHDreefyOxRaeysaGblpv09vUm6t99Mzk8lyesiGrWxPuLo3zeevo+RG37MCLuaKiuLfbNuYM4FXz7q1xMZavqzouOPwC+BBw5z/hxgfNdtHnBP11cRERERkf7VPTa39+jckXPgs0+b+5EwNO8322Byp5ljdhSiISh+0fQO6XbtJhN+bHwItj3RNwQJjDJTZRzqATFUuZ0OMpO8PY+TfW4unllw2Oun5Kfyxs1nYNs2wZAJSRraO8lNMSOYC9MTuOW8ST0VJd1hSfdEm+KqFn79cvFBo3+f/MpCZowM8Mj6ffzXk9v6hCCpfje3XzyF3FQ/m/Y1sGFvfa9tN2Y08OiMBFxOB288fR9T1t+C3+oEC3KoJnX9LbwBCjvkuNFvQYdt269aljX6fS65EHjANk1BXrMsK2BZVq5t2wf6aw0iIiIiIkfE6TLbXAK9GmpOPMfcwIzNbdhntsGkdP3QG+k0o3T3vQ4d3ZNkLLil0gQda34B+9b12hrTdcscP2BvSwaOZVn4PU78Hic5qbGKn4K0BL54ytjDPu+8abmcM+VcWjrDNPbaXlM0IgmAiSMSuXJ+Ls3tQao73TS0haB+D55aF7TCzjdL+Mcbu2mwk9hujwLgfMdafrR8DC5nlAkbvmdCjl78VidjNtwJpyzjnRYvjWEP6UleMhK9pPi1xUaGn35tRtoVdDxzmK0rzwA/tG17Vdfjl4Bv27b95nuuuxK4EqCwsHB2aWlpv61PRERERKRftNebapDmCph4tjn28g9h62PmeKRr1GpSNnxzl7n/0n+b5qq9q0HSx0LqR5+SIkfBtk3lD5h+Lx0tJsyKdEK4A+wI5E435/dvhMYyc7z7vMsLMz5pzr/1MNTsNA10Ix3mfGIWnHGbOf/8jVCxJfb8SCdkTYz1nPn9WbHzdtcY37FL4DNPmfs/mwqNe/ssv3XsOexacg+N7SHmPzYHb2fDEb/1TtvJ7yPn8qPwFfidEe7x3cOSGRMhIYM3q2Bv0E971jSsrBPISHSR548wdWxB7L+XSJwM2Waktm3/BvgNmKkrcV6OiIiIiMjB/GmxJqjdlnzH3KJRU/nRUGp+gO7WWg2la2DLI2abDJjRuFf+y9z/2zcgEuq1NWaMGZvbezzve21eAS993/wQnlpgxvdOu6x/32t/sO1YQBAJxcKA5FzT/6S5EmqLu453xr5OWAa+FNNIds+/u16j1/nTbjLntz0BWx8/+Ht8diW4/fDKj2H9H/oGFdEw3Fprfnh//kbY+GDfNXuS4aYyc3/1XeZ79JaUEws6tj0BJf804YfTY772ruQJB02PGLffNNV1esyvb7cTzoORc8Hp7XoNd6y3DMA5PzLvx+k1TXqdXhKTRjAzq+sz+J+vgMMFTi+V/zuPbGoP+iWoJ4W0C39AfU0FzfWVzEyayi3Jk2hprGHK1nLYvgva6zjJjnIS8OPiy/hV5CLyqGGN7xpwuCEhnX0dfqrCiTyTcAnF6adQ6G3jPF5lwdSJ4E9nW4OLaEIGKZkFpAVS1JRV4mIgg45yYGSvxwVdx0REREREhg+HA1Jyza23C+42X8Od0FRmKj96/wBYvwf2vwVtNbFjk5bD5X829x//svkhuXtrTPUOeOV/INxuzjfug5XXmFDhxAvMD/ROj/nhOtwJdSV9f9CPdEDmBLN9p60OdvzNHIuEYtdNPBdypkBtCay5u2/FQiQEi79pfkAvXQPPfqvXua7v8YmHYNQCU+ny2BcO/m/1pX+a8b+7noOV1x58/iuvmyCjdA28cGvXQasrDPDCwmvM+dYaqNll3m930OBJMuECmMBo7Gk9IUHP1+6qjmmXQd7M2Ou6PODyx9ax9FZYdH0syHB6wNWrQe0n//r+1Q7n/+zw5wAWHuK993bCue9/vlcosnfWt0np7tHRpd32UDz7FubM/A/SgDSgEDgZgLFw/mZzYTRqtmW11fE1ZyKfIpXG2irKdtxMgbcN2mqJ7CsjrbWGjAQXG4Nhaqt2sqDjp1BsXqK7PfA1nV/l6egC5rqKuSvhfnJz8yEhnfXVDtpdqZQWXowrq4hsZyvj3NWMzC8Afzq2NwXLoZG+8tEM5NaV84CvYqauzAPutm177vu93kknnWS/+eab73eJiIiIiMjw0tFiqkHqS02wMWqBCRXuO9WEId1jc4/E0tvglOuh7l24e8bB58/5Mcy7Eiq2wr0LDz5/0T2maqFsPTz8iff8oO+Bs26HMYtNQPPKj2IhQ/fXuV+GrAlQtQN2PPOe53th/FmQmAlN+7uCil4hhNNjqltcXggFTQWGy2sqF1Qh8L5iU1dqqLIyj+3UlV7hCG11lJbvo62+ij1JM9lnZ+Kq2syZ1X9mpK8d2uqoqzlASrSJT3bezOv2JC5wrOZuz696Xi5kO2mykvlu6vdpTJ3EyWxluWMNIwsKiPrT2d7gxpeahadoCelpARKcUSyHywSMMuwd6daVfgs6LMt6GFgCZAKVwG2AG8C27Xu7xsv+EjgbM172c+/tz/FeCjpERERERHqxbWirNSHI704//HVLbzVhwaj5pmKisw12Pd+3YsHpNZUAydmmUqOlMhZgdG+h0DQZORZsm/bOMLVtIZqqy0hr3E6uu41way3rtu7CEaxnRcIV7O5IYW7Dc3zd8TCJ4cbYSGhgfvAXHCCDa91Pca3rURwJ6YS9aZQGfYQ8aayaegcpqQHGtm1hvLuK1PRsbH86VkKG2Q7mT1NgNgQNeNBxLCjoEBERERE5jJ9NMdtV3it1JFy3deDXI3Is2TadbY0U79lLa0MFpe4iatptkg68xhLXNgq8bbQ0VLHr3VISI02c23EHEZx83/UHPuN6oc9LhXFwScbTZCR5uaL1zyx0vk1iYATt7gBVkQRcqflE536ZzCQv/uY9JmBMSAdfQJUjcTZkm5GKiIiIiMgRWHqr6ckRao8dc/vNcZHhxrLwJAY4cXIAmMacnhPjeu4lAbMA27bZ3BmhrrWTuvoZ1HpbybBaqKzcz2tbdtHZ3kyax0tNSye7GmzmZTqhfg9WUzU57XXUkMopL08A4AHv/7DYegsA23LQ5kimMukEXpx9DxmJXqbve4BCXxuepEzshHSshEzTGDh3mllU7+k+MmBU0SEiIiIiMlQNlakrIkNAUzBESWUz9Y3NVHdY1LZ24qvYwMWjO0mjhZ3v7mHrO7upCvv5Uaf5/+xB950s9LyDo3ukNLDRMZnvZvyYjEQPdx74IiMcTTgS0ml3BWh1pRDKX4C94KukJ3rw7XjCNLZNyIjd/AFtGzsMbV0RERERERER6We2bdPSEaautZPa1k4m5ybjtTtYv72YDdtLaAiG2RwZRW1LJ+c0reA/p7txddSze+9ego3VvB6dyHfD/w+Abd7Pk2gF+7z+lqzzWDftdjIS3Zz+2udJDaRDQkZXj5F0KJxv+u9Eo1D7Dvi7eo4433/DRqxJbTVVVtaxbVJ7jCjoEBERERERERkkqpqC7Ktvo7bFBCR1rZ1Yjfu4em4atNfx1Jot7NpTyrbgCF6OTMVDiAf9P2FeNtBWR2dzNR67gwfdH+eJtM9T6Avy872X9Lx+yJNK2BOgbuZXcJ70WdIdrXjW/BQS0tmzayu5e5/Ga8UaurbbHrbOvn1IhR0KOkRERERERESGGNu2aQqGqW3poK0zwpT8VAAeW1/Gjn2V1LcGqWh30dzcyFmujXxlbhq01fLMuq1EW2t5OrKAF6OzGWeV8zffLfjsjsN+rwqyyPlu8UC9tY9MQYeIiIiIiIjIcWJvbRuVzUFqWzpMxUhLJ+lJHj41awTR23NwHKInatS2cHyvYeAXe5Q0dUVERERERETkOFGYkUBhRsIhz1VZWeRQfYjjmeQc64XFgYYAi4iIiIiIiAxj+2bdQLvt6XOs3fawb9YNcVrRsaWgQ0RERERERGQYm3PBl9k6+3YqyCJqW1SQNeQakX4Y6tEhIiIiIiIiIoPekfboUEWHiIiIiIiIiAwbCjpEREREREREZNhQ0CEiIiIiIiIiw8ag7tFhWVY1UBrvdRylTKAm3ouQQUefCzkUfS7kUPS5kMPRZ0MORZ8LORR9LuRQhvLnYpRt21kfdNGgDjqGMsuy3jySJilyfNHnQg5Fnws5FH0u5HD02ZBD0edCDkWfCzmU4+Fzoa0rIiIiIiIiIjJsKOgQERERERERkWFDQcex85t4L0AGJX0u5FD0uZBD0edCDkefDTkUfS7kUPS5kEMZ9p8L9egQERERERERkWFDFR0iIiIiIiIiMmwo6DgGLMu6zrKsbZZlbbUs62HLsnzxXpMMPMuy7rcsq8qyrK3vOf41y7J2dH1G/ide65P4sCzLZ1nW65Zlber6DHyv6/hDlmXt7Pp9437LstzxXqsMLOv/t3f/MVuVdRzH35/xIENqkWlEUoN+mFN+SeQoTQMSqUyiZtPIrHBpy7LFMqkNXf1jy7TW1D9ShCXVHEI1IYSZi+YPLB4QVMxWEkEoapb92OTXpz/OdccdP2xtz33Ow/18Xtu9+9zXuc6z77P77FzX+Z7rum5puKSl5fqwRdI72/bNk2RJxzcZY3Te4doOSd8q58UmScslDS/lgyUtlrS5nDPzm4s8OknSGyTdJ+nx0nZcWcqvlbRD0sbyen/bMeMlPVjqb05/tDtJ2lq+342SflPKLijf+35Jk9vqniNpfam/XtK05iKPvnSEtuM4SWsk/a68v7qUzyntyWZJD0iacNDfGiRpg6S76/4/+lISHX1M0onAF4DJtscCg4ALm40qGrIImNleIGkqMAuYYPtU4PoG4opmvQRMsz0BmAjMlDQFWAKcDIwDhgKXNhdiNOS7wCrbJwMTgC1Q3eAAM4BtDcYW9VnEQW0HsAYYa3s88CTQSmhcAAyxPQ54O3CZpNH1hBk12wvMs30KMAX4nKRTyr4bbU8sr5UAknqAO4DLS3/jPcCeBuKOekwt338rqfEo8GFg7UH1ngM+WK4ZlwA/qDHG6KxFHNp2XA3ca/utwL3lM8BTwNnlPPgGh67XcSWlD3I0S6KjM3qAoaWRORb4c8PxRANsrwX+clDxZ4HrbL9U6uyqPbBolCv/KB8Hl5dtryz7DDwMjGosyKidpFcBZwG3AdjebfuvZfeNwFVAFtUaAA7XdthebXtv+fgQB64PBoaV/sZQYDfwYl2xRn1s77TdW7b/TnUTcuLLHDID2GT7kXLM87b3dT7S6A9sb7H928OUb7Ddui95jOp+ZUi90UUnHOG+YxawuGwvBj5U6j5g+4VS3t6mIGkU8AHg1o4GXIMkOvqY7R1UT+m3ATuBv9le3WxU0Y+cBLxb0jpJv5T0jqYDivqVIYEbgV3AGtvr2vYNBi4GVjUVXzRiDPAscHsZLnqrpGGSZgE7WjcrEcCngZ+X7aXAP6n6G9uA620f3NGNLlNG7ZwGtNqOK8ow9IWtoelU/Q1LukdSr6SrGgg16mFgdZmK8pn/47iPAL2th2/RlUbY3lm2nwZGHKbOXA60KQDfoXq4sr/DsXVcEh19rDQws6g6ra+netLy8Wajin6kBziOatjpl4E7JanZkKJutvfZnkiVQT9d0ti23TcDa23/qpnooiE9wCTgFtunUd28Xgt8FVjQYFzRj0j6GtUUhiWl6HRgH1V/YwwwT9KbGgovaiDpFcBdwBdtvwjcAryZairkTuDbpWoPcCYwp7zPljS9/oijBmfangS8j2pK01n/6wBJpwLfBC7rdHDRP5QRw/81MrRMqZ8LfKV8Pg/YZXt9/RH2vSQ6+t57gadsP2t7D7AMeFfDMUX/sR1YVmYoPEyVLc3iggNUmZpwH2VOpaRrgBOALzUZVzRiO7C9bXTPUqrExxjgEUlbqRJjvZJe10yI0SRJnwTOA+aUDivAx6jWddlTpkLeD0w+wp+Io1wZ8XcXsMT2MgDbz5Tk+X7g+1TJL6iuKWttP2f7X8BKqmtKdJkymrw1HXo5B86BwypTE5YDn7D9+85HGA16RtJIgPL+nynzksZTTU+ZZfv5UnwGcH7pc/wYmCbpjnpD7jtJdPS9bcAUSceWJ/XT6YLFXKLP/ASYCiDpJOAYqoWhYoCQdELbLyYMBc4BnpB0KXAucFHpsMYAYvtp4E+S3laKplMNKX6t7dG2R1OYhmbbAAAB0UlEQVTduEwqdWMAkTSTaijx+eWmtWUbMK3UGUY1WvCJ+iOMTit9ytuALbZvaCsf2VZtNtUilAD3AONKf7QHOBt4vK54ox5liuMrW9tUa7M8+jL1hwMrgKtt319PlNGgn1EtOkt5/ymApDdSPYy/2PaTrcq259seVfocFwK/sH3UzkzoaTqAbmN7naSlQC/V8NINHLqSbQwAkn5Etcr58ZK2A9cAC4GF5aefdgOXtD2Zi4FhJLBY0iCqZPOdtu+WtBf4I/Bgmc20zPbXG4wz6vd5YImkY4A/AJ9qOJ5owBHajvnAEGBNuT48ZPty4CaqdV0eAwTcbntTI4FHp51BtX7T5rLGE1RT2y6SNJFqSPpWylQE2y9IugH4ddm30vaK2qOOThsBLC/XhR7gh7ZXSZoNfI9qlOgKSRttnwtcAbwFWCCpNS1yRhbHP/odoe24jmqa/FyqPuZHS/UFwGuAm8u5s7ftF3u6hnKPFRERERERERHdIlNXIiIiIiIiIqJrJNEREREREREREV0jiY6IiIiIiIiI6BpJdERERERERERE10iiIyIiIiIiIiK6RhIdEREREREREdE1kuiIiIiIiIiIiK6RREdEREREREREdI1/A2t7V881RVtQAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 1332x756 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "fig.set_size_inches(18.5, 10.5, forward=True)\n", "\n", "#filename = \"/home/khatiri/these/projet/ws-simulator/Simulation/vss_proba_32_100000000\"\n", "filename = directory + \"vss_proba_32_100000000\"\n", "best_proba_50 = plot_for_best(filename)\n", "\n", "filename = directory + \"vss_proba_32_100000000_0.8\"\n", "best_proba_80 = plot_for_best(filename)\n", "\n", "plt.subplot(321)\n", "plt.plot(best_proba_50.keys(), best_proba_50.values(), 'o-', label=\"proba : steal 50%\")\n", "plt.plot(best_proba_80.keys(), best_proba_80.values(), 'o--', label=\"proba : steal 80%\")\n", "plt.legend()\n", "\n", "\n", "plt.legend()\n", "\n", "\n", "#************************************ \n", "\n", "filename = directory + \"vss_static_32_100000000\"\n", "best_static_50 = plot_for_best(filename)\n", "plt.subplot(322)\n", "plt.plot(best_static_50.keys(), best_static_50.values(), 'o-', label=\"static : steal 50%\")\n", "\n", "filename = directory + \"vss_static_32_100000000_0.8\"\n", "best_static_80 = plot_for_best(filename)\n", "plt.plot(best_static_80.keys(), best_static_80.values(), 'o--', label=\"static : steal 80%\")\n", "plt.legend()\n", "\n", "plt.subplot(312)\n", "\n", "plt.plot(best_proba_80.keys(), [bp5/bp8 for (bp5, bp8) in zip(best_proba_50.values(), best_proba_80.values())],\\\n", " 'o--', label=\"proba : ratio Ov(50%) / Ov(80%)\")\n", "\n", "plt.plot(best_static_80.keys(), [bp5/bp8 for (bp5, bp8) in zip(best_static_50.values(), best_static_80.values())],\\\n", " 'o--', label=\"static : ratio Ov(50%) / Ov(80%)\")\n", "\n", "plt.legend()\n", "plt.subplot(313)\n", "\n", "plt.plot(best_proba_80.keys(), [bs8/bp8 for (bp8, bs8) in zip(best_proba_80.values(), best_static_80.values())],\\\n", " 'o--', label=\" : ratio static_Ov(80%) / proba_Ov(80%)\")\n", "\n", "plt.plot(best_proba_80.keys(), [bs5/bp5 for (bp5, bs5) in zip(best_proba_50.values(), best_static_50.values())],\\\n", " 'o--', label=\" : ratio static_Ov(0%) / proba_Ov(0%)\")\n", "\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# a faire : \n", "\n", "- Separer les courbes, les decrires,\n", "\n", "- afficher les intervals de confiance\n", "\n", "- mettre a jour les courbes avec les nouveaux dimulation\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## amount of work steal (80% vs 50%)\n", "we plot the overhead according to the latency for best parametre \"$rsp$\" or \"$max\\ internal\\ steal$\" " ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f1f515eaf98>" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1332x756 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "fig.set_size_inches(18.5, 10.5, forward=True)\n", "\n", "#filename = \"/home/khatiri/these/projet/ws-simulator/Simulation/vss_proba_32_100000000\"\n", "filename = directory + \"vss_proba_32_10000000\"\n", "best_proba_50 = plot_for_best(filename)\n", "\n", "filename = directory + \"vss_proba_32_10000000_0.8\"\n", "best_proba_80 = plot_for_best(filename)\n", "\n", "plt.subplot(321)\n", "plt.plot(best_proba_50.keys(), best_proba_50.values(), 'o-', label=\"proba : steal 50%\")\n", "plt.plot(best_proba_80.keys(), best_proba_80.values(), 'o--', label=\"proba : steal 80%\")\n", "plt.legend()\n", "\n", "\n", "plt.legend()\n", "\n", "\n", "#************************************ \n", "\n", "filename = directory + \"vss_static_32_10000000\"\n", "best_static_50 = plot_for_best(filename)\n", "plt.subplot(322)\n", "plt.plot(best_static_50.keys(), best_static_50.values(), 'o-', label=\"static : steal 50%\")\n", "\n", "filename = directory + \"vss_static_32_10000000_0.8\"\n", "best_static_80 = plot_for_best(filename)\n", "plt.plot(best_static_80.keys(), best_static_80.values(), 'o--', label=\"static : steal 80%\")\n", "plt.legend()\n", "\n", "plt.subplot(312)\n", "\n", "plt.plot(best_proba_80.keys(), [bp5/bp8 for (bp5, bp8) in zip(best_proba_50.values(), best_proba_80.values())],\\\n", " 'o--', label=\"proba : ratio Ov(50%) / Ov(80%)\")\n", "\n", "plt.plot(best_static_80.keys(), [bp5/bp8 for (bp5, bp8) in zip(best_static_50.values(), best_static_80.values())],\\\n", " 'o--', label=\"static : ratio Ov(50%) / Ov(80%)\")\n", "\n", "plt.legend()\n", "plt.subplot(313)\n", "\n", "plt.plot(best_proba_80.keys(), [bs8/bp8 for (bp8, bs8) in zip(best_proba_80.values(), best_static_80.values())],\\\n", " 'o--', label=\" : ratio static_Ov(80%) / proba_Ov(80%)\")\n", "\n", "plt.plot(best_proba_50.keys(), [bs5/bp5 for (bp5, bs5) in zip(best_proba_50.values(), best_static_50.values())],\\\n", " 'o--', label=\" : ratio static_Ov(50%) / proba_Ov(50%)\")\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f1f52e01400>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1332x756 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "fig.set_size_inches(18.5, 10.5, forward=True)\n", "\n", "#filename = \"/home/khatiri/these/projet/ws-simulator/Simulation/vss_proba_32_100000000\"\n", "filename = directory + \"vss_proba_16_100000000\"\n", "best_proba_50 = plot_for_best(filename)\n", "\n", "filename = directory + \"vss_proba_16_100000000_0.8\"\n", "best_proba_80 = plot_for_best(filename)\n", "\n", "plt.subplot(321)\n", "plt.plot(best_proba_50.keys(), best_proba_50.values(), 'o-', label=\"proba : steal 50%\")\n", "plt.plot(best_proba_80.keys(), best_proba_80.values(), 'o--', label=\"proba : steal 80%\")\n", "plt.legend()\n", "\n", "\n", "plt.legend()\n", "\n", "\n", "#************************************ \n", "\n", "filename = directory + \"vss_static_16_100000000\"\n", "best_static_50 = plot_for_best(filename)\n", "plt.subplot(322)\n", "plt.plot(best_static_50.keys(), best_static_50.values(), 'o-', label=\"static : steal 50%\")\n", "\n", "filename = directory + \"vss_static_16_100000000_0.8\"\n", "best_static_80 = plot_for_best(filename)\n", "plt.plot(best_static_80.keys(), best_static_80.values(), 'o--', label=\"static : steal 80%\")\n", "plt.legend()\n", "\n", "plt.subplot(312)\n", "\n", "plt.plot(best_proba_80.keys(), [bp5/bp8 for (bp5, bp8) in zip(best_proba_50.values(), best_proba_80.values())],\\\n", " 'o--', label=\"proba : ratio Ov(50%) / Ov(80%)\")\n", "\n", "plt.plot(best_static_80.keys(), [bp5/bp8 for (bp5, bp8) in zip(best_static_50.values(), best_static_80.values())],\\\n", " 'o--', label=\"static : ratio Ov(50%) / Ov(80%)\")\n", "\n", "plt.legend()\n", "plt.subplot(313)\n", "\n", "plt.plot(best_proba_80.keys(), [bs8/bp8 for (bp8, bs8) in zip(best_proba_80.values(), best_static_80.values())],\\\n", " 'o--', label=\" : ratio static_Ov(80%) / proba_Ov(80%)\")\n", "\n", "plt.plot(best_proba_50.keys(), [bs5/bp5 for (bp5, bs5) in zip(best_proba_50.values(), best_static_50.values())],\\\n", " 'o--', label=\" : ratio static_Ov(50%) / proba_Ov(50%)\")\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f1f53e97d30>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1332x756 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "fig.set_size_inches(18.5, 10.5, forward=True)\n", "\n", "#filename = \"/home/khatiri/these/projet/ws-simulator/Simulation/vss_proba_32_100000000\"\n", "filename = directory + \"vss_proba_16_10000000\"\n", "best_proba_50 = plot_for_best(filename)\n", "\n", "filename = directory + \"vss_proba_16_10000000_0.8\"\n", "best_proba_80 = plot_for_best(filename)\n", "\n", "plt.subplot(321)\n", "plt.plot(best_proba_50.keys(), best_proba_50.values(), 'o-', label=\"proba : steal 50%\")\n", "plt.plot(best_proba_80.keys(), best_proba_80.values(), 'o--', label=\"proba : steal 80%\")\n", "plt.legend()\n", "\n", "\n", "plt.legend()\n", "\n", "\n", "#************************************ \n", "\n", "filename = directory + \"vss_static_16_10000000\"\n", "best_static_50 = plot_for_best(filename)\n", "plt.subplot(322)\n", "plt.plot(best_static_50.keys(), best_static_50.values(), 'o-', label=\"static : steal 50%\")\n", "\n", "filename = directory + \"vss_static_16_10000000_0.8\"\n", "best_static_80 = plot_for_best(filename)\n", "plt.plot(best_static_80.keys(), best_static_80.values(), 'o--', label=\"static : steal 80%\")\n", "plt.legend()\n", "\n", "plt.subplot(312)\n", "\n", "plt.plot(best_proba_80.keys(), [bp5/bp8 for (bp5, bp8) in zip(best_proba_50.values(), best_proba_80.values())],\\\n", " 'o--', label=\"proba : ratio Ov(50%) / Ov(80%)\")\n", "\n", "plt.plot(best_static_80.keys(), [bp5/bp8 for (bp5, bp8) in zip(best_static_50.values(), best_static_80.values())],\\\n", " 'o--', label=\"static : ratio Ov(50%) / Ov(80%)\")\n", "\n", "plt.legend()\n", "plt.subplot(313)\n", "\n", "plt.plot(best_proba_80.keys(), [bs8/bp8 for (bp8, bs8) in zip(best_proba_80.values(), best_static_80.values())],\\\n", " 'o--', label=\" : ratio static_Ov(80%) / proba_Ov(80%)\")\n", "\n", "plt.plot(best_proba_50.keys(), [bs5/bp5 for (bp5, bs5) in zip(best_proba_50.values(), best_static_50.values())],\\\n", " 'o--', label=\" : ratio static_Ov(50%) / proba_Ov(50%)\")\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A faire :\n", "\n", "- tourner plus de resultats pour clarifier les choses\n", "- tourner des resultats pour les autres strategies" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

chrinide/optunity

notebooks/basic-cross-validation.ipynb

3

27663

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic: cross-validation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook explores the main elements of Optunity's cross-validation facilities, including:\n", "\n", "* standard cross-validation\n", "* using strata and clusters while constructing folds\n", "* using different aggregators\n", "\n", "We recommend perusing the <a href=\"http://docs.optunity.net/user/cross_validation.html\">related documentation</a> for more details.\n", "\n", "Nested cross-validation is available as a separate notebook." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import optunity\n", "import optunity.cross_validation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start by generating some toy data containing 6 instances which we will partition into folds." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = list(range(6))\n", "labels = [True] * 3 + [False] * 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Standard cross-validation <a id=standard></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each function to be decorated with cross-validation functionality must accept the following arguments:\n", "- x_train: training data\n", "- x_test: test data\n", "- y_train: training labels (required only when y is specified in the cross-validation decorator)\n", "- y_test: test labels (required only when y is specified in the cross-validation decorator)\n", "\n", "These arguments will be set implicitly by the cross-validation decorator to match the right folds. Any remaining arguments to the decorated function remain as free parameters that must be set later on." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets start with the basics and look at Optunity's cross-validation in action. We use an objective function that simply prints out the train and test data in every split to see what's going on." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f(x_train, y_train, x_test, y_test):\n", " print(\"\")\n", " print(\"train data:\\t\" + str(x_train) + \"\\t train labels:\\t\" + str(y_train))\n", " print(\"test data:\\t\" + str(x_test) + \"\\t test labels:\\t\" + str(y_test))\n", " return 0.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start with 2 folds, which leads to equally sized train and test partitions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "using 2 folds\n", "\n", "train data:\t[4, 2, 0]\t train labels:\t[False, True, True]\n", "test data:\t[3, 1, 5]\t test labels:\t[False, True, False]\n", "\n", "train data:\t[3, 1, 5]\t train labels:\t[False, True, False]\n", "test data:\t[4, 2, 0]\t test labels:\t[False, True, True]\n" ] }, { "data": { "text/plain": [ "0.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f_2folds = optunity.cross_validated(x=data, y=labels, num_folds=2)(f)\n", "print(\"using 2 folds\")\n", "f_2folds()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# f_2folds as defined above would typically be written using decorator syntax as follows\n", "# we don't do that in these examples so we can reuse the toy objective function\n", "\n", "@optunity.cross_validated(x=data, y=labels, num_folds=2)\n", "def f_2folds(x_train, y_train, x_test, y_test):\n", " print(\"\")\n", " print(\"train data:\\t\" + str(x_train) + \"\\t train labels:\\t\" + str(y_train))\n", " print(\"test data:\\t\" + str(x_test) + \"\\t test labels:\\t\" + str(y_test))\n", " return 0.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we use three folds instead of 2, we get 3 iterations in which the training set is twice the size of the test set." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "using 3 folds\n", "\n", "train data:\t[2, 1, 3, 0]\t train labels:\t[True, True, False, True]\n", "test data:\t[5, 4]\t test labels:\t[False, False]\n", "\n", "train data:\t[5, 4, 3, 0]\t train labels:\t[False, False, False, True]\n", "test data:\t[2, 1]\t test labels:\t[True, True]\n", "\n", "train data:\t[5, 4, 2, 1]\t train labels:\t[False, False, True, True]\n", "test data:\t[3, 0]\t test labels:\t[False, True]\n" ] }, { "data": { "text/plain": [ "0.0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f_3folds = optunity.cross_validated(x=data, y=labels, num_folds=3)(f)\n", "print(\"using 3 folds\")\n", "f_3folds()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we do two iterations of 3-fold cross-validation (denoted by 2x3 fold), two sets of folds are generated and evaluated." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "using 2x3 folds\n", "\n", "train data:\t[4, 1, 5, 3]\t train labels:\t[False, True, False, False]\n", "test data:\t[0, 2]\t test labels:\t[True, True]\n", "\n", "train data:\t[0, 2, 5, 3]\t train labels:\t[True, True, False, False]\n", "test data:\t[4, 1]\t test labels:\t[False, True]\n", "\n", "train data:\t[0, 2, 4, 1]\t train labels:\t[True, True, False, True]\n", "test data:\t[5, 3]\t test labels:\t[False, False]\n", "\n", "train data:\t[0, 2, 1, 4]\t train labels:\t[True, True, True, False]\n", "test data:\t[5, 3]\t test labels:\t[False, False]\n", "\n", "train data:\t[5, 3, 1, 4]\t train labels:\t[False, False, True, False]\n", "test data:\t[0, 2]\t test labels:\t[True, True]\n", "\n", "train data:\t[5, 3, 0, 2]\t train labels:\t[False, False, True, True]\n", "test data:\t[1, 4]\t test labels:\t[True, False]\n" ] }, { "data": { "text/plain": [ "0.0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f_2x3folds = optunity.cross_validated(x=data, y=labels, num_folds=3, num_iter=2)(f)\n", "print(\"using 2x3 folds\")\n", "f_2x3folds()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using strata and clusters<a id=strata-clusters></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Strata are defined as sets of instances that should be spread out across folds as much as possible (e.g. stratify patients by age). Clusters are sets of instances that must be put in a single fold (e.g. cluster measurements of the same patient).\n", "\n", "Optunity allows you to specify strata and/or clusters that must be accounted for while construct cross-validation folds. Not all instances have to belong to a stratum or clusters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Strata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start by illustrating strata. Strata are specified as a list of lists of instances indices. Each list defines one stratum. We will reuse the toy data and objective function specified above. We will create 2 strata with 2 instances each. These instances will be spread across folds. We create two strata: $\\{0, 1\\}$ and $\\{2, 3\\}$." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "train data:\t[0, 4, 2, 5]\t train labels:\t[True, False, True, False]\n", "test data:\t[1, 3]\t test labels:\t[True, False]\n", "\n", "train data:\t[1, 3, 2, 5]\t train labels:\t[True, False, True, False]\n", "test data:\t[0, 4]\t test labels:\t[True, False]\n", "\n", "train data:\t[1, 3, 0, 4]\t train labels:\t[True, False, True, False]\n", "test data:\t[2, 5]\t test labels:\t[True, False]\n" ] }, { "data": { "text/plain": [ "0.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "strata = [[0, 1], [2, 3]]\n", "f_stratified = optunity.cross_validated(x=data, y=labels, strata=strata, num_folds=3)(f)\n", "f_stratified()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Clusters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clusters work similarly, except that now instances within a cluster are guaranteed to be placed within a single fold. The way to specify clusters is identical to strata. We create two clusters: $\\{0, 1\\}$ and $\\{2, 3\\}$. These pairs will always occur in a single fold." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "train data:\t[0, 1, 2, 3]\t train labels:\t[True, True, True, False]\n", "test data:\t[4, 5]\t test labels:\t[False, False]\n", "\n", "train data:\t[4, 5, 2, 3]\t train labels:\t[False, False, True, False]\n", "test data:\t[0, 1]\t test labels:\t[True, True]\n", "\n", "train data:\t[4, 5, 0, 1]\t train labels:\t[False, False, True, True]\n", "test data:\t[2, 3]\t test labels:\t[True, False]\n" ] }, { "data": { "text/plain": [ "0.0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clusters = [[0, 1], [2, 3]]\n", "f_clustered = optunity.cross_validated(x=data, y=labels, clusters=clusters, num_folds=3)(f)\n", "f_clustered()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Strata and clusters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Strata and clusters can be used together. Lets say we have the following configuration:\n", " \n", "- 1 stratum: $\\{0, 1, 2\\}$\n", "- 2 clusters: $\\{0, 3\\}$, $\\{4, 5\\}$\n", "\n", "In this particular example, instances 1 and 2 will inevitably end up in a single fold, even though they are part of one stratum. This happens because the total data set has size 6, and 4 instances are already in clusters." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "train data:\t[4, 5, 0, 3]\t train labels:\t[False, False, True, False]\n", "test data:\t[1, 2]\t test labels:\t[True, True]\n", "\n", "train data:\t[1, 2, 0, 3]\t train labels:\t[True, True, True, False]\n", "test data:\t[4, 5]\t test labels:\t[False, False]\n", "\n", "train data:\t[1, 2, 4, 5]\t train labels:\t[True, True, False, False]\n", "test data:\t[0, 3]\t test labels:\t[True, False]\n" ] }, { "data": { "text/plain": [ "0.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "strata = [[0, 1, 2]]\n", "clusters = [[0, 3], [4, 5]]\n", "f_strata_clustered = optunity.cross_validated(x=data, y=labels, clusters=clusters, strata=strata, num_folds=3)(f)\n", "f_strata_clustered()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aggregators <a id=aggregators></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aggregators are used to combine the scores per fold into a single result. The default approach used in cross-validation is to take the mean of all scores. In some cases, we might be interested in worst-case or best-case performance, the spread, ...\n", "\n", "Opunity allows passing a custom callable to be used as aggregator. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default aggregation in Optunity is to compute the mean across folds." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n", "1\n", "2\n" ] }, { "data": { "text/plain": [ "2.3333333333333335" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "@optunity.cross_validated(x=data, num_folds=3)\n", "def f(x_train, x_test):\n", " result = x_test[0]\n", " print(result)\n", " return result\n", "\n", "f(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This can be replaced by any function, e.g. min or max." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "5\n", "1\n" ] }, { "data": { "text/plain": [ "5" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "@optunity.cross_validated(x=data, num_folds=3, aggregator=max)\n", "def fmax(x_train, x_test):\n", " result = x_test[0]\n", " print(result)\n", " return result\n", "\n", "fmax(1)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "4\n", "5\n" ] }, { "data": { "text/plain": [ "3" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "@optunity.cross_validated(x=data, num_folds=3, aggregator=min)\n", "def fmin(x_train, x_test):\n", " result = x_test[0]\n", " print(result)\n", " return result\n", "\n", "fmin(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Retaining intermediate results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Often, it may be useful to retain all intermediate results, not just the final aggregated data. This is made possible via `optunity.cross_validation.mean_and_list` aggregator. This aggregator computes the mean for internal use in cross-validation, but also returns a list of lists containing the full evaluation results." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.33333333333\n", "[0.0, 2.0, 5.0]\n" ] } ], "source": [ "@optunity.cross_validated(x=data, num_folds=3,\n", " aggregator=optunity.cross_validation.mean_and_list)\n", "def f_full(x_train, x_test, coeff):\n", " return x_test[0] * coeff\n", "\n", "# evaluate f\n", "mean_score, all_scores = f_full(1.0)\n", "print(mean_score)\n", "print(all_scores)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that a cross-validation based on the `mean_and_list` aggregator essentially returns a tuple of results. If the result is iterable, all solvers in Optunity use the first element as the objective function value. You can let the cross-validation procedure return other useful statistics too, which you can access from the solver trace." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'coeff': 0.15771484375}\n", "call log\n", "0.34521484375\t\t(0.8055013020833334, [0.0, 0.6904296875, 1.72607421875])\n", "0.47021484375\t\t(1.09716796875, [0.0, 0.9404296875, 2.35107421875])\n", "0.97021484375\t\t(2.2638346354166665, [0.0, 1.9404296875, 4.85107421875])\n", "0.72021484375\t\t(1.6805013020833333, [0.0, 1.4404296875, 3.60107421875])\n", "0.22021484375\t\t(0.5138346354166666, [0.0, 0.4404296875, 1.10107421875])\n", "0.15771484375\t\t(0.3680013020833333, [0.0, 0.3154296875, 0.78857421875])\n", "0.65771484375\t\t(1.53466796875, [0.0, 1.3154296875, 3.28857421875])\n", "0.90771484375\t\t(2.1180013020833335, [0.0, 1.8154296875, 4.53857421875])\n", "0.40771484375\t\t(0.9513346354166666, [0.0, 0.8154296875, 2.03857421875])\n", "0.28271484375\t\t(0.65966796875, [0.0, 0.5654296875, 1.41357421875])\n" ] } ], "source": [ "opt_coeff, info, _ = optunity.minimize(f_full, coeff=[0, 1], num_evals=10)\n", "print(opt_coeff)\n", "print(\"call log\")\n", "for args, val in zip(info.call_log['args']['coeff'], info.call_log['values']):\n", " print(str(args) + '\\t\\t' + str(val))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cross-validation with scikit-learn <a id=cv-sklearn></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we will show how to use cross-validation methods that are provided by scikit-learn in conjunction with Optunity. To do this we provide Optunity with the folds that scikit-learn produces in a specific format.\n", "\n", "In supervised learning datasets often have unbalanced labels. When performing cross-validation with unbalanced data it is good practice to preserve the percentage of samples for each class across folds. To achieve this label balance we will use <a href=\"http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.StratifiedKFold.html\">StratifiedKFold</a>." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "train data:\t[16, 6, 4, 14, 0, 11, 19, 5, 9, 2, 12, 8, 7, 10, 18, 3]\n", "train labels:\t[1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0]\n", "\n", "test data:\t[15, 1, 13, 17]\n", "test labels:\t[0, 0, 0, 0]\n", "\n", "\n", "train data:\t[15, 1, 13, 17, 0, 11, 19, 5, 9, 2, 12, 8, 7, 10, 18, 3]\n", "train labels:\t[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0]\n", "\n", "test data:\t[16, 6, 4, 14]\n", "test labels:\t[1, 0, 1, 0]\n", "\n", "\n", "train data:\t[15, 1, 13, 17, 16, 6, 4, 14, 9, 2, 12, 8, 7, 10, 18, 3]\n", "train labels:\t[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0]\n", "\n", "test data:\t[0, 11, 19, 5]\n", "test labels:\t[1, 0, 0, 0]\n", "\n", "\n", "train data:\t[15, 1, 13, 17, 16, 6, 4, 14, 0, 11, 19, 5, 7, 10, 18, 3]\n", "train labels:\t[0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0]\n", "\n", "test data:\t[9, 2, 12, 8]\n", "test labels:\t[0, 0, 1, 1]\n", "\n", "\n", "train data:\t[15, 1, 13, 17, 16, 6, 4, 14, 0, 11, 19, 5, 9, 2, 12, 8]\n", "train labels:\t[0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1]\n", "\n", "test data:\t[7, 10, 18, 3]\n", "test labels:\t[0, 0, 0, 0]\n", "\n" ] }, { "data": { "text/plain": [ "0.0" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = list(range(20))\n", "labels = [1 if i%4==0 else 0 for i in range(20)]\n", "\n", "@optunity.cross_validated(x=data, y=labels, num_folds=5)\n", "def unbalanced_folds(x_train, y_train, x_test, y_test):\n", " print(\"\")\n", " print(\"train data:\\t\" + str(x_train) + \"\\ntrain labels:\\t\" + str(y_train)) + '\\n'\n", " print(\"test data:\\t\" + str(x_test) + \"\\ntest labels:\\t\" + str(y_test)) + '\\n'\n", " return 0.0\n", "\n", "unbalanced_folds()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice above how the test label sets have a varying number of postive samples, some have none, some have one, and some have two. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "train data:\t[4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\n", "train labels:\t[1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0]\n", "\n", "test data:\t[0, 1, 2, 3]\n", "test labels:\t[1, 0, 0, 0]\n", "\n", "\n", "train data:\t[0, 1, 2, 3, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\n", "train labels:\t[1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0]\n", "\n", "test data:\t[4, 5, 6, 7]\n", "test labels:\t[1, 0, 0, 0]\n", "\n", "\n", "train data:\t[0, 1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15, 16, 17, 18, 19]\n", "train labels:\t[1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0]\n", "\n", "test data:\t[8, 9, 10, 11]\n", "test labels:\t[1, 0, 0, 0]\n", "\n", "\n", "train data:\t[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 17, 18, 19]\n", "train labels:\t[1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0]\n", "\n", "test data:\t[12, 13, 14, 15]\n", "test labels:\t[1, 0, 0, 0]\n", "\n", "\n", "train data:\t[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n", "train labels:\t[1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0]\n", "\n", "test data:\t[16, 17, 18, 19]\n", "test labels:\t[1, 0, 0, 0]\n", "\n" ] }, { "data": { "text/plain": [ "0.0" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.cross_validation import StratifiedKFold\n", "\n", "stratified_5folds = StratifiedKFold(labels, n_folds=5)\n", "folds = [[list(test) for train, test in stratified_5folds]]\n", "\n", "@optunity.cross_validated(x=data, y=labels, folds=folds, num_folds=5)\n", "def balanced_folds(x_train, y_train, x_test, y_test):\n", " print(\"\")\n", " print(\"train data:\\t\" + str(x_train) + \"\\ntrain labels:\\t\" + str(y_train)) + '\\n'\n", " print(\"test data:\\t\" + str(x_test) + \"\\ntest labels:\\t\" + str(y_test)) + '\\n'\n", " return 0.0\n", "\n", "balanced_folds()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now all of our train sets have four positive samples and our test sets have one positive sample.\n", "\n", "To use predetermined folds, place a list of the test sample idices into a list. And then insert that list into another list. Why so many nested lists? Because you can perform multiple cross-validation runs by setting num_iter appropriately and then append num_iter lists of test samples to the outer most list. Note that the test samples for a given fold are the idicies that you provide and then the train samples for that fold are all of the indices from all other test sets joined together. If not done carefully this may lead to duplicated samples in a train set and also samples that fall in both train and test sets of a fold if a datapoint is in multiple folds' test sets. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "train data:\t[1, 4, 2, 5]\t train labels:\t[True, False, True, False]\n", "test data:\t[0, 3]\t\t test labels:\t[True, False]\n", "\n", "train data:\t[0, 3, 2, 5]\t train labels:\t[True, False, True, False]\n", "test data:\t[1, 4]\t\t test labels:\t[True, False]\n", "\n", "train data:\t[0, 3, 1, 4]\t train labels:\t[True, False, True, False]\n", "test data:\t[2, 5]\t\t test labels:\t[True, False]\n", "\n", "train data:\t[1, 4, 0, 3]\t train labels:\t[True, False, True, False]\n", "test data:\t[0, 5]\t\t test labels:\t[True, False]\n", "\n", "train data:\t[0, 5, 0, 3]\t train labels:\t[True, False, True, False]\n", "test data:\t[1, 4]\t\t test labels:\t[True, False]\n", "\n", "train data:\t[0, 5, 1, 4]\t train labels:\t[True, False, True, False]\n", "test data:\t[0, 3]\t\t test labels:\t[True, False]\n" ] }, { "data": { "text/plain": [ "0.0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = list(range(6))\n", "labels = [True] * 3 + [False] * 3\n", "\n", "fold1 = [[0, 3], [1, 4], [2, 5]]\n", "fold2 = [[0, 5], [1, 4], [0, 3]] # notice what happens when the indices are not unique\n", "folds = [fold1, fold2]\n", "\n", "@optunity.cross_validated(x=data, y=labels, folds=folds, num_folds=3, num_iter=2)\n", "def multiple_iters(x_train, y_train, x_test, y_test):\n", " print(\"\")\n", " print(\"train data:\\t\" + str(x_train) + \"\\t train labels:\\t\" + str(y_train))\n", " print(\"test data:\\t\" + str(x_test) + \"\\t\\t test labels:\\t\" + str(y_test))\n", " return 0.0\n", "\n", "multiple_iters()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.5" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

ConradScott/IJuliaSamples

GLM/Example 2.2.1 Chronic medical conditions.ipynb

1

475845

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using DataFrames\n", "using Distributions\n", "using Gadfly\n", "using Optim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 2.2.1 (pp. 19–23)\n", "\n", "The numbers of chronic medical conditions reported by samples of women living in large country towns or in more rural areas in New South Wales, Australia." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "T = [0, 1, 1, 0, 2, 3, 0, 1, 1, 1, 1, 2, 0, 1, 3, 0, 1, 2, 1, 3, 3, 4, 1, 3, 2, 0];\n", "C = [2, 0, 3, 0, 0, 1, 1, 1, 1, 0, 0, 2, 2, 0, 1, 2, 0, 0, 1, 1, 1, 0, 2];" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Under the null hypothesis, $H_0$, $\\theta_{\\text{T}} = \\theta_{\\text{C}} = \\theta$, while under the alternative hypothesis, $H_1$, $\\theta_{\\text{T}} \\neq \\theta_{\\text{C}}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If $H_0$ is true, the log-likelihood function for the counts is\n", "\n", "$$\\ell_0 = \\ell(\\theta; \\mathbf{y}) = \\sum_i (\\text{T}_i \\log \\theta - \\theta - \\log \\text{T}_i !) + \\sum_i (\\text{C}_i \\log \\theta - \\theta - \\log \\text{C}_i !).$$\n", "\n", "The maximum likelihood estimate is\n", "\n", "$$\\hat{\\theta}_0 = \\frac{\\sum_i \\text{T}_i + \\sum_i \\text{C}_i}{\\lvert \\text{T}\\rvert + \\lvert \\text{C}\\rvert}.$$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.183673469387755" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "θ̂₀ = (sum(T) + sum(C)) / (size(T, 1) + size(C, 1))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ℓ(θ, Y) = mapreduce((y) -> y * log(θ) - θ - log(factorial(y)), +, 0, Y);" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ℓ₀(θ) = ℓ(θ, T) + ℓ(θ, C);" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-68.3868179494798" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ℓ̂₀ = ℓ₀(θ̂₀)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If $H_1$ is true, the log-likelihood for the counts is\n", "\n", "$$\\ell_1 = \\ell(\\theta_\\text{T}, \\theta_\\text{C}; \\mathbf{y}) = \\sum_i (\\text{T}_i \\log \\theta_\\text{T} - \\theta_\\text{T} - \\log \\text{T}_i !) + \\sum_i (\\text{C}_i \\log \\theta_\\text{C} - \\theta_\\text{C} - \\log \\text{C}_i !) .$$\n", "\n", "The maximum likelihood estimates are\n", "\n", "$$\\hat{\\theta}_{1\\text{T}} = \\frac{\\sum_i \\text{T}_i}{\\lvert \\text{T}\\rvert}$$\n", "\n", "and\n", "\n", "$$\\hat{\\theta}_{1\\text{C}} = \\frac{\\sum_i \\text{C}_i}{\\lvert \\text{C}\\rvert}.$$" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.4230769230769231" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "θ̂₁t = sum(T) / size(T, 1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.9130434782608695" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "θ̂₁c = sum(C) / size(C, 1)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ℓ₁(θt, θc) = ℓ(θt, T) + ℓ(θc, C);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-67.02295175203457" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ℓ̂₁ = ℓ₁(θ̂₁t, θ̂₁c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The maximum value of the log-likelihood function $\\ell_1$ will always be greater than or equal to that of $\\ell_0$ as one more parameter has been fitted. To decide whether the difference is statistically significant, we need to know the sampling distribution of the log-likelihood function.\n", "\n", "[Discussed later.]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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{\n", " return factory(Snap);\n", " });\n", " } else {\n", " // Browser globals (glob is window)\n", " // Snap adds itself to window\n", " glob.Gadfly = factory(glob.Snap);\n", " }\n", "}(this, function (Snap) {\n", "\n", "var Gadfly = {};\n", "\n", "// Get an x/y coordinate value in pixels\n", "var xPX = function(fig, x) {\n", " var client_box = fig.node.getBoundingClientRect();\n", " return x * fig.node.viewBox.baseVal.width / client_box.width;\n", "};\n", "\n", "var yPX = function(fig, y) {\n", " var client_box = fig.node.getBoundingClientRect();\n", " return y * fig.node.viewBox.baseVal.height / client_box.height;\n", "};\n", "\n", "\n", "Snap.plugin(function (Snap, Element, Paper, global) {\n", " // Traverse upwards from a snap element to find and return the first\n", " // note with the \"plotroot\" class.\n", " Element.prototype.plotroot = function () {\n", " var element = this;\n", " while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n", " element = element.parent();\n", " }\n", " return element;\n", " };\n", "\n", " Element.prototype.svgroot = function () {\n", " var element = this;\n", " while (element.node.nodeName != \"svg\" && element.parent() != null) {\n", " element = element.parent();\n", " }\n", " return element;\n", " };\n", "\n", " Element.prototype.plotbounds = function () {\n", " var root = this.plotroot()\n", " var bbox = root.select(\".guide.background\").node.getBBox();\n", " return {\n", " x0: bbox.x,\n", " x1: bbox.x + bbox.width,\n", " y0: bbox.y,\n", " y1: bbox.y + bbox.height\n", " };\n", " };\n", "\n", " Element.prototype.plotcenter = function () {\n", " var root = this.plotroot()\n", " var bbox = root.select(\".guide.background\").node.getBBox();\n", " return {\n", " x: bbox.x + bbox.width / 2,\n", " y: bbox.y + bbox.height / 2\n", " };\n", " };\n", "\n", " // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n", " // does everything right.\n", " // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n", " var events = [\"mouseenter\", \"mouseleave\"];\n", "\n", " for (i in events) {\n", " (function (event_name) {\n", " var event_name = events[i];\n", " Element.prototype[event_name] = function (fn, scope) {\n", " if (Snap.is(fn, \"function\")) {\n", " var fn2 = function (event) {\n", " if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n", " return;\n", " }\n", "\n", " var reltg = event.relatedTarget ? event.relatedTarget :\n", " event.type == \"mouseout\" ? event.toElement : event.fromElement;\n", " while (reltg && reltg != this.node) reltg = reltg.parentNode;\n", "\n", " if (reltg != this.node) {\n", " return fn.apply(this, event);\n", " }\n", " };\n", "\n", " if (event_name == \"mouseenter\") {\n", " this.mouseover(fn2, scope);\n", " } else {\n", " this.mouseout(fn2, scope);\n", " }\n", " }\n", " return this;\n", " };\n", " })(events[i]);\n", " }\n", "\n", "\n", " Element.prototype.mousewheel = function (fn, scope) {\n", " if (Snap.is(fn, \"function\")) {\n", " var el = this;\n", " var fn2 = function (event) {\n", " fn.apply(el, [event]);\n", " };\n", " }\n", "\n", " this.node.addEventListener(\n", " /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n", " fn2);\n", "\n", " return this;\n", " };\n", "\n", "\n", " // Snap's attr function can be too slow for things like panning/zooming.\n", " // This is a function to directly update element attributes without going\n", " // through eve.\n", " Element.prototype.attribute = function(key, val) {\n", " if (val === undefined) {\n", " return this.node.getAttribute(key);\n", " } else {\n", " this.node.setAttribute(key, val);\n", " return this;\n", " }\n", " };\n", "\n", " Element.prototype.init_gadfly = function() {\n", " this.mouseenter(Gadfly.plot_mouseover)\n", " .mouseleave(Gadfly.plot_mouseout)\n", " .dblclick(Gadfly.plot_dblclick)\n", " .mousewheel(Gadfly.guide_background_scroll)\n", " .drag(Gadfly.guide_background_drag_onmove,\n", " Gadfly.guide_background_drag_onstart,\n", " Gadfly.guide_background_drag_onend);\n", " this.mouseenter(function (event) {\n", " init_pan_zoom(this.plotroot());\n", " });\n", " return this;\n", " };\n", "});\n", "\n", "\n", "// When the plot is moused over, emphasize the grid lines.\n", "Gadfly.plot_mouseover = function(event) {\n", " var root = this.plotroot();\n", "\n", " var keyboard_zoom = function(event) {\n", " if (event.which == 187) { // plus\n", " increase_zoom_by_position(root, 0.1, true);\n", " } else if (event.which == 189) { // minus\n", " increase_zoom_by_position(root, -0.1, true);\n", " }\n", " };\n", " root.data(\"keyboard_zoom\", keyboard_zoom);\n", " window.addEventListener(\"keyup\", keyboard_zoom);\n", "\n", " var xgridlines = root.select(\".xgridlines\"),\n", " ygridlines = root.select(\".ygridlines\");\n", "\n", " xgridlines.data(\"unfocused_strokedash\",\n", " xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n", " ygridlines.data(\"unfocused_strokedash\",\n", " ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n", "\n", " // emphasize grid lines\n", " var destcolor = root.data(\"focused_xgrid_color\");\n", " xgridlines.attribute(\"stroke-dasharray\", \"none\")\n", " .selectAll(\"path\")\n", " .animate({stroke: destcolor}, 250);\n", "\n", " destcolor = root.data(\"focused_ygrid_color\");\n", " ygridlines.attribute(\"stroke-dasharray\", \"none\")\n", " .selectAll(\"path\")\n", " .animate({stroke: destcolor}, 250);\n", "\n", " // reveal zoom slider\n", " root.select(\".zoomslider\")\n", " .animate({opacity: 1.0}, 250);\n", "};\n", "\n", "// Reset pan and zoom on double click\n", "Gadfly.plot_dblclick = function(event) {\n", " set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n", "};\n", "\n", "// Unemphasize grid lines on mouse out.\n", "Gadfly.plot_mouseout = function(event) {\n", " var root = this.plotroot();\n", "\n", " window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n", " root.data(\"keyboard_zoom\", undefined);\n", "\n", " var xgridlines = root.select(\".xgridlines\"),\n", " ygridlines = root.select(\".ygridlines\");\n", "\n", " var destcolor = root.data(\"unfocused_xgrid_color\");\n", "\n", " xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n", " .selectAll(\"path\")\n", " .animate({stroke: destcolor}, 250);\n", "\n", " destcolor = root.data(\"unfocused_ygrid_color\");\n", " ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n", " .selectAll(\"path\")\n", " .animate({stroke: destcolor}, 250);\n", "\n", " // hide zoom slider\n", " root.select(\".zoomslider\")\n", " .animate({opacity: 0.0}, 250);\n", "};\n", "\n", "\n", "var set_geometry_transform = function(root, tx, ty, scale) {\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", "\n", " var old_scale = root.data(\"scale\");\n", "\n", " var xscale = xscalable ? scale : 1.0,\n", " yscale = yscalable ? scale : 1.0;\n", "\n", " tx = xscalable ? tx : 0.0;\n", " ty = yscalable ? ty : 0.0;\n", "\n", " var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n", "\n", " root.selectAll(\".geometry, image\")\n", " .forEach(function (element, i) {\n", " element.transform(t);\n", " });\n", "\n", " bounds = root.plotbounds();\n", "\n", " if (yscalable) {\n", " var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n", " root.selectAll(\".xfixed\")\n", " .forEach(function (element, i) {\n", " element.transform(xfixed_t);\n", " });\n", "\n", " root.select(\".ylabels\")\n", " .transform(xfixed_t)\n", " .selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var cx = element.asPX(\"x\"),\n", " cy = element.asPX(\"y\");\n", " var st = element.data(\"static_transform\");\n", " unscale_t = new Snap.Matrix();\n", " unscale_t.scale(1, 1/scale, cx, cy).add(st);\n", " element.transform(unscale_t);\n", "\n", " var y = cy * scale + ty;\n", " element.attr(\"visibility\",\n", " bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n", " }\n", " });\n", " }\n", "\n", " if (xscalable) {\n", " var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n", " var xtrans = new Snap.Matrix().translate(tx, 0);\n", " root.selectAll(\".yfixed\")\n", " .forEach(function (element, i) {\n", " element.transform(yfixed_t);\n", " });\n", "\n", " root.select(\".xlabels\")\n", " .transform(yfixed_t)\n", " .selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var cx = element.asPX(\"x\"),\n", " cy = element.asPX(\"y\");\n", " var st = element.data(\"static_transform\");\n", " unscale_t = new Snap.Matrix();\n", " unscale_t.scale(1/scale, 1, cx, cy).add(st);\n", "\n", " element.transform(unscale_t);\n", "\n", " var x = cx * scale + tx;\n", " element.attr(\"visibility\",\n", " bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n", " }\n", " });\n", " }\n", "\n", " // we must unscale anything that is scale invariance: widths, raiduses, etc.\n", " var size_attribs = [\"font-size\"];\n", " var unscaled_selection = \".geometry, .geometry *\";\n", " if (xscalable) {\n", " size_attribs.push(\"rx\");\n", " unscaled_selection += \", .xgridlines\";\n", " }\n", " if (yscalable) {\n", " size_attribs.push(\"ry\");\n", " unscaled_selection += \", .ygridlines\";\n", " }\n", "\n", " root.selectAll(unscaled_selection)\n", " .forEach(function (element, i) {\n", " // circle need special help\n", " if (element.node.nodeName == \"circle\") {\n", " var cx = element.attribute(\"cx\"),\n", " cy = element.attribute(\"cy\");\n", " unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n", " cx, cy);\n", " element.transform(unscale_t);\n", " return;\n", " }\n", "\n", " for (i in size_attribs) {\n", " var key = size_attribs[i];\n", " var val = parseFloat(element.attribute(key));\n", " if (val !== undefined && val != 0 && !isNaN(val)) {\n", " element.attribute(key, val * old_scale / scale);\n", " }\n", " }\n", " });\n", "};\n", "\n", "\n", "// Find the most appropriate tick scale and update label visibility.\n", "var update_tickscale = function(root, scale, axis) {\n", " if (!root.hasClass(axis + \"scalable\")) return;\n", "\n", " var tickscales = root.data(axis + \"tickscales\");\n", " var best_tickscale = 1.0;\n", " var best_tickscale_dist = Infinity;\n", " for (tickscale in tickscales) {\n", " var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n", " if (dist < best_tickscale_dist) {\n", " best_tickscale_dist = dist;\n", " best_tickscale = tickscale;\n", " }\n", " }\n", "\n", " if (best_tickscale != root.data(axis + \"tickscale\")) {\n", " root.data(axis + \"tickscale\", best_tickscale);\n", " var mark_inscale_gridlines = function (element, i) {\n", " var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n", " element.attribute(\"gadfly:inscale\", inscale);\n", " element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n", " };\n", "\n", " var mark_inscale_labels = function (element, i) {\n", " var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n", " element.attribute(\"gadfly:inscale\", inscale);\n", " element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n", " };\n", "\n", " root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n", " root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n", " }\n", "};\n", "\n", "\n", "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n", " var old_scale = root.data(\"scale\");\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " // compute the viewport derived from tx, ty, and scale\n", " var x_min = -width * scale - (scale * width - width),\n", " x_max = width * scale,\n", " y_min = -height * scale - (scale * height - height),\n", " y_max = height * scale;\n", "\n", " var x0 = bounds.x0 - scale * bounds.x0,\n", " y0 = bounds.y0 - scale * bounds.y0;\n", "\n", " var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n", " ty = Math.max(Math.min(ty - y0, y_max), y_min);\n", "\n", " tx += x0;\n", " ty += y0;\n", "\n", " // when the scale change, we may need to alter which set of\n", " // ticks is being displayed\n", " if (scale != old_scale) {\n", " update_tickscale(root, scale, \"x\");\n", " update_tickscale(root, scale, \"y\");\n", " }\n", "\n", " set_geometry_transform(root, tx, ty, scale);\n", "\n", " root.data(\"scale\", scale);\n", " root.data(\"tx\", tx);\n", " root.data(\"ty\", ty);\n", "};\n", "\n", "\n", "var scale_centered_translation = function(root, scale) {\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var scale0 = root.data(\"scale\");\n", "\n", " // how off from center the current view is\n", " var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n", " yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n", "\n", " // rescale offsets\n", " xoff = xoff * scale / scale0;\n", " yoff = yoff * scale / scale0;\n", "\n", " // adjust for the panel position being scaled\n", " var x_edge_adjust = bounds.x0 * (1 - scale),\n", " y_edge_adjust = bounds.y0 * (1 - scale);\n", "\n", " return {\n", " x: xoff + x_edge_adjust + (width - width * scale) / 2,\n", " y: yoff + y_edge_adjust + (height - height * scale) / 2\n", " };\n", "};\n", "\n", "\n", "// Initialize data for panning zooming if it isn't already.\n", "var init_pan_zoom = function(root) {\n", " if (root.data(\"zoompan-ready\")) {\n", " return;\n", " }\n", "\n", " // The non-scaling-stroke trick. Rather than try to correct for the\n", " // stroke-width when zooming, we force it to a fixed value.\n", " var px_per_mm = root.node.getCTM().a;\n", "\n", " // Drag events report deltas in pixels, which we'd like to convert to\n", " // millimeters.\n", " root.data(\"px_per_mm\", px_per_mm);\n", "\n", " root.selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " sw = element.asPX(\"stroke-width\") * px_per_mm;\n", " if (sw > 0) {\n", " element.attribute(\"stroke-width\", sw);\n", " element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n", " }\n", " });\n", "\n", " // Store ticks labels original tranformation\n", " root.selectAll(\".xlabels > text, .ylabels > text\")\n", " .forEach(function (element, i) {\n", " var lm = element.transform().localMatrix;\n", " element.data(\"static_transform\",\n", " new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n", " });\n", "\n", " var xgridlines = root.select(\".xgridlines\");\n", " var ygridlines = root.select(\".ygridlines\");\n", " var xlabels = root.select(\".xlabels\");\n", " var ylabels = root.select(\".ylabels\");\n", "\n", " if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n", " if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n", " if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n", " if (root.data(\"xtickscales\") === undefined) {\n", "\n", " // index all the tick scales that are listed\n", " var xtickscales = {};\n", " var ytickscales = {};\n", " var add_x_tick_scales = function (element, i) {\n", " xtickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", " var add_y_tick_scales = function (element, i) {\n", " ytickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n", "\n", " root.data(\"xtickscales\", xtickscales);\n", " root.data(\"ytickscales\", ytickscales);\n", " root.data(\"xtickscale\", 1.0);\n", " }\n", "\n", " var min_scale = 1.0, max_scale = 1.0;\n", " for (scale in xtickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " for (scale in ytickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " root.data(\"min_scale\", min_scale);\n", " root.data(\"max_scale\", max_scale);\n", "\n", " // store the original positions of labels\n", " if (xlabels) {\n", " xlabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"x\", element.asPX(\"x\"));\n", " });\n", " }\n", "\n", " if (ylabels) {\n", " ylabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"y\", element.asPX(\"y\"));\n", " });\n", " }\n", "\n", " // mark grid lines and ticks as in or out of scale.\n", " var mark_inscale = function (element, i) {\n", " element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n", "\n", " // figure out the upper ond lower bounds on panning using the maximum\n", " // and minum grid lines\n", " var bounds = root.plotbounds();\n", " var pan_bounds = {\n", " x0: 0.0,\n", " y0: 0.0,\n", " x1: 0.0,\n", " y1: 0.0\n", " };\n", "\n", " if (xgridlines) {\n", " xgridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.x1 - bbox.x < pan_bounds.x0) {\n", " pan_bounds.x0 = bounds.x1 - bbox.x;\n", " }\n", " if (bounds.x0 - bbox.x > pan_bounds.x1) {\n", " pan_bounds.x1 = bounds.x0 - bbox.x;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " if (ygridlines) {\n", " ygridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.y1 - bbox.y < pan_bounds.y0) {\n", " pan_bounds.y0 = bounds.y1 - bbox.y;\n", " }\n", " if (bounds.y0 - bbox.y > pan_bounds.y1) {\n", " pan_bounds.y1 = bounds.y0 - bbox.y;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " // nudge these values a little\n", " pan_bounds.x0 -= 5;\n", " pan_bounds.x1 += 5;\n", " pan_bounds.y0 -= 5;\n", " pan_bounds.y1 += 5;\n", " root.data(\"pan_bounds\", pan_bounds);\n", "\n", " root.data(\"zoompan-ready\", true)\n", "};\n", "\n", "\n", "// drag actions, i.e. zooming and panning\n", "var pan_action = {\n", " start: function(root, x, y, event) {\n", " root.data(\"dx\", 0);\n", " root.data(\"dy\", 0);\n", " root.data(\"tx0\", root.data(\"tx\"));\n", " root.data(\"ty0\", root.data(\"ty\"));\n", " },\n", " update: function(root, dx, dy, x, y, event) {\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var dx0 = root.data(\"dx\"),\n", " dy0 = root.data(\"dy\");\n", "\n", " root.data(\"dx\", dx);\n", " root.data(\"dy\", dy);\n", "\n", " dx = dx - dx0;\n", " dy = dy - dy0;\n", "\n", " var tx = tx0 + dx,\n", " ty = ty0 + dy;\n", "\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n", " },\n", " end: function(root, event) {\n", "\n", " },\n", " cancel: function(root) {\n", " set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n", " }\n", "};\n", "\n", "var zoom_box;\n", "var zoom_action = {\n", " start: function(root, x, y, event) {\n", " var bounds = root.plotbounds();\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", " var ratio = width / height;\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " x = xscalable ? x / px_per_mm : bounds.x0;\n", " y = yscalable ? y / px_per_mm : bounds.y0;\n", " var w = xscalable ? 0 : width;\n", " var h = yscalable ? 0 : height;\n", " zoom_box = root.rect(x, y, w, h).attr({\n", " \"fill\": \"#000\",\n", " \"opacity\": 0.25\n", " });\n", " zoom_box.data(\"ratio\", ratio);\n", " },\n", " update: function(root, dx, dy, x, y, event) {\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " var bounds = root.plotbounds();\n", " if (yscalable) {\n", " y /= px_per_mm;\n", " y = Math.max(bounds.y0, y);\n", " y = Math.min(bounds.y1, y);\n", " } else {\n", " y = bounds.y1;\n", " }\n", " if (xscalable) {\n", " x /= px_per_mm;\n", " x = Math.max(bounds.x0, x);\n", " x = Math.min(bounds.x1, x);\n", " } else {\n", " x = bounds.x1;\n", " }\n", "\n", " dx = x - zoom_box.attr(\"x\");\n", " dy = y - zoom_box.attr(\"y\");\n", " if (xscalable && yscalable) {\n", " var ratio = zoom_box.data(\"ratio\");\n", " var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n", " var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n", " dx = width * dx / Math.abs(dx);\n", " dy = height * dy / Math.abs(dy);\n", " }\n", " var xoffset = 0,\n", " yoffset = 0;\n", " if (dx < 0) {\n", " xoffset = dx;\n", " dx = -1 * dx;\n", " }\n", " if (dy < 0) {\n", " yoffset = dy;\n", " dy = -1 * dy;\n", " }\n", " if (isNaN(dy)) {\n", " dy = 0.0;\n", " }\n", " if (isNaN(dx)) {\n", " dx = 0.0;\n", " }\n", " zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n", " zoom_box.attr(\"width\", dx);\n", " zoom_box.attr(\"height\", dy);\n", " },\n", " end: function(root, event) {\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", " var zoom_bounds = zoom_box.getBBox();\n", " if (zoom_bounds.width * zoom_bounds.height <= 0) {\n", " return;\n", " }\n", " var plot_bounds = root.plotbounds();\n", " var zoom_factor = 1.0;\n", " if (yscalable) {\n", " zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n", " } else {\n", " zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n", " }\n", " var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n", " ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n", " zoom_box.remove();\n", " },\n", " cancel: function(root) {\n", " zoom_box.remove();\n", " }\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n", " var root = this.plotroot();\n", " var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n", " var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n", " var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n", " var drag_action = zoomable ? zoom_action :\n", " panable ? pan_action :\n", " undefined;\n", " root.data(\"drag_action\", drag_action);\n", " if (drag_action) {\n", " var cancel_drag_action = function(event) {\n", " if (event.which == 27) { // esc key\n", " drag_action.cancel(root);\n", " root.data(\"drag_action\", undefined);\n", " }\n", " };\n", " window.addEventListener(\"keyup\", cancel_drag_action);\n", " root.data(\"cancel_drag_action\", cancel_drag_action);\n", " drag_action.start(root, x, y, event);\n", " }\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n", " var root = this.plotroot();\n", " var drag_action = root.data(\"drag_action\");\n", " if (drag_action) {\n", " drag_action.update(root, dx, dy, x, y, event);\n", " }\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onend = function(event) {\n", " var root = this.plotroot();\n", " window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n", " root.data(\"cancel_drag_action\", undefined);\n", " var drag_action = root.data(\"drag_action\");\n", " if (drag_action) {\n", " drag_action.end(root, event);\n", " }\n", " root.data(\"drag_action\", undefined);\n", "};\n", "\n", "\n", "Gadfly.guide_background_scroll = function(event) {\n", " if (event.shiftKey) {\n", " increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n", " event.preventDefault();\n", " }\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseover = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseout = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomout_click = function(event) {\n", " increase_zoom_by_position(this.plotroot(), -0.1, true);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomin_click = function(event) {\n", " increase_zoom_by_position(this.plotroot(), 0.1, true);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_track_click = function(event) {\n", " // TODO\n", "};\n", "\n", "\n", "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n", "// The constants a, b, and c are solved using the constraint that the function\n", "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n", "var scale_from_slider_position = function(position, min_scale, max_scale) {\n", " var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n", " b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n", " c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n", " return a * Math.exp(b * position) + c;\n", "}\n", "\n", "// inverse of scale_from_slider_position\n", "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n", " var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n", " b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n", " c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n", " return 1 / b * Math.log((scale - c) / a);\n", "}\n", "\n", "var increase_zoom_by_position = function(root, delta_position, animate) {\n", " var scale = root.data(\"scale\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var position = slider_position_from_scale(scale, min_scale, max_scale);\n", " position += delta_position;\n", " scale = scale_from_slider_position(position, min_scale, max_scale);\n", " set_zoom(root, scale, animate);\n", "}\n", "\n", "var set_zoom = function(root, scale, animate) {\n", " var min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\"),\n", " old_scale = root.data(\"scale\");\n", " var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n", " if (animate) {\n", " Snap.animate(\n", " old_scale,\n", " new_scale,\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", " } else {\n", " update_plot_scale(root, new_scale);\n", " }\n", "}\n", "\n", "\n", "var update_plot_scale = function(root, new_scale) {\n", " var trans = scale_centered_translation(root, new_scale);\n", " set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n", "\n", " root.selectAll(\".zoomslider_thumb\")\n", " .forEach(function (element, i) {\n", " var min_pos = element.data(\"min_pos\"),\n", " max_pos = element.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n", " element.transform(new Snap.Matrix().translate(\n", " Math.max(min_pos, Math.min(\n", " max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n", " });\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n", " var root = this.plotroot();\n", " var min_pos = this.data(\"min_pos\"),\n", " max_pos = this.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\"),\n", " old_scale = root.data(\"old_scale\");\n", "\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n", " dx / (max_pos - min_pos);\n", "\n", " // compute the new scale\n", " var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n", " new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n", "\n", " update_plot_scale(root, new_scale);\n", " event.stopPropagation();\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n", " this.animate({fill: this.data(\"mouseover_color\")}, 100);\n", " var root = this.plotroot();\n", "\n", " // keep track of what the scale was when we started dragging\n", " root.data(\"old_scale\", root.data(\"scale\"));\n", " event.stopPropagation();\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragend = function(event) {\n", " this.animate({fill: this.data(\"mouseout_color\")}, 100);\n", " event.stopPropagation();\n", "};\n", "\n", "\n", "var toggle_color_class = function(root, color_class, ison) {\n", " var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n", " var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n", " if (ison) {\n", " guides.animate({opacity: 0.5}, 250);\n", " geoms.animate({opacity: 0.0}, 250);\n", " } else {\n", " guides.animate({opacity: 1.0}, 250);\n", " geoms.animate({opacity: 1.0}, 250);\n", " }\n", "};\n", "\n", "\n", "Gadfly.colorkey_swatch_click = function(event) {\n", " var root = this.plotroot();\n", " var color_class = this.data(\"color_class\");\n", "\n", " if (event.shiftKey) {\n", " root.selectAll(\".colorkey text\")\n", " .forEach(function (element) {\n", " var other_color_class = element.data(\"color_class\");\n", " if (other_color_class != color_class) {\n", " toggle_color_class(root, other_color_class,\n", " element.attr(\"opacity\") == 1.0);\n", " }\n", " });\n", " } else {\n", " toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n", " }\n", "};\n", "\n", "\n", "return Gadfly;\n", "\n", "}));\n", "\n", "\n", "//@ sourceURL=gadfly.js\n", "\n", "(function (glob, factory) {\n", " // AMD support\n", " if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n", " require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n", " factory(Snap, Gadfly);\n", " });\n", " } else {\n", " factory(glob.Snap, glob.Gadfly);\n", " }\n", "})(window, function (Snap, Gadfly) {\n", " var fig = Snap(\"#img-99e3cc4c\");\n", "fig.select(\"#img-99e3cc4c-5\")\n", " .init_gadfly();\n", "fig.select(\"#img-99e3cc4c-7\")\n", " .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#img-99e3cc4c-7\")\n", " .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#img-99e3cc4c-8\")\n", " .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#img-99e3cc4c-8\")\n", " .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#img-99e3cc4c-12\")\n", " .data(\"mouseover_color\", \"#CD5C5C\")\n", ";\n", "fig.select(\"#img-99e3cc4c-12\")\n", " .data(\"mouseout_color\", \"#6A6A6A\")\n", ";\n", "fig.select(\"#img-99e3cc4c-12\")\n", " .click(Gadfly.zoomslider_zoomin_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", "fig.select(\"#img-99e3cc4c-14\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#img-99e3cc4c-14\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#img-99e3cc4c-14\")\n", " .click(Gadfly.zoomslider_track_click);\n", "fig.select(\"#img-99e3cc4c-15\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#img-99e3cc4c-15\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#img-99e3cc4c-15\")\n", " .data(\"mouseover_color\", \"#CD5C5C\")\n", ";\n", "fig.select(\"#img-99e3cc4c-15\")\n", " .data(\"mouseout_color\", \"#6A6A6A\")\n", ";\n", "fig.select(\"#img-99e3cc4c-15\")\n", " .drag(Gadfly.zoomslider_thumb_dragmove,\n", " Gadfly.zoomslider_thumb_dragstart,\n", " Gadfly.zoomslider_thumb_dragend)\n", ";\n", "fig.select(\"#img-99e3cc4c-16\")\n", " .data(\"mouseover_color\", \"#CD5C5C\")\n", ";\n", "fig.select(\"#img-99e3cc4c-16\")\n", " .data(\"mouseout_color\", \"#6A6A6A\")\n", ";\n", "fig.select(\"#img-99e3cc4c-16\")\n", " .click(Gadfly.zoomslider_zoomout_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", " });\n", "]]> </script>\n", "</svg>\n" ], "text/plain": [ "Plot(...)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xs = linspace(0, 2.5)[2:end]\n", "\n", "ys = Array{Float64}(size(xs))\n", "\n", "for i in eachindex(xs)\n", " ys[i] = ℓ₀(xs[i])\n", "end\n", "\n", "plot(x = xs, y = ys, Geom.line)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.1836734533008915" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "θ̃₀ = optimize((θ) -> -ℓ₀(θ), 0, 2).minimum" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.183673469387755" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "θ̃₀ = θ̂₀" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d₀ = Poisson(θ̂₀)\n", "d₁t = Poisson(θ̂₁t)\n", "d₁c = Poisson(θ̂₁c);" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: tty_size is deprecated. use `displaysize(io)` as a replacement\n", " in depwarn at deprecated.jl:73\n", " in tty_size at deprecated.jl:924\n", " in show at /home/scottc/.julia/v0.5/DataFrames/src/abstractdataframe/show.jl:440\n", " [inlined code] from expr.jl:8\n", " in showcompact at show.jl:1425\n", " in writemime at replutil.jl:4\n", " [inlined code] from expr.jl:8\n", " in writemime at multimedia.jl:43\n", " in sprint at strings/io.jl:38\n", " in display_dict at /home/scottc/.julia/v0.4/IJulia/src/execute_request.jl:26\n", " in execute_request_0x535c5df2 at /home/scottc/.julia/v0.4/IJulia/src/execute_request.jl:212\n", " [inlined code] from dict.jl:733\n", " in eventloop at /home/scottc/.julia/v0.4/IJulia/src/IJulia.jl:141\n", " in anonymous at task.jl:435\n", "while loading /home/scottc/.julia/v0.4/IJulia/src/kernel.jl, in expression starting on line 31\n", "WARNING: tty_size is deprecated. use `displaysize(io)` as a replacement\n" ] }, { "data": { "text/html": [ "<table class=\"data-frame\"><tr><th></th><th>count</th><th>T</th><th>C</th><th>T̂₀</th><th>C_hat_0</th><th>T̂₁</th><th>Ĉ₁</th></tr><tr><th>1</th><td>0</td><td>6</td><td>9</td><td>7.959952787608398</td><td>7.959952787608398</td><td>6.265257036899882</td><td>10.4338263291891</td></tr><tr><th>2</th><td>1</td><td>10</td><td>8</td><td>9.421984932271165</td><td>9.421984932271165</td><td>8.915942706357525</td><td>9.526537083172656</td></tr><tr><th>3</th><td>2</td><td>4</td><td>5</td><td>5.576276796650282</td><td>5.576276796650282</td><td>6.344036156446701</td><td>4.349071277100561</td></tr><tr><th>4</th><td>3</td><td>5</td><td>1</td><td>2.200163634052492</td><td>2.200163634052492</td><td>3.0093504844683068</td><td>1.3236303886827792</td></tr><tr><th>5</th><td>4</td><td>1</td><td>0</td><td>0.6510688304849211</td><td>0.6510688304849211</td><td>1.0706343069743014</td><td>0.3021330235036778</td></tr><tr><th>6</th><td>5</td><td>0</td><td>0</td><td>0.15413058027806295</td><td>0.15413058027806295</td><td>0.3047189950619166</td><td>0.055172117335454206</td></tr><tr><th>7</th><td>6</td><td>0</td><td>0</td><td>0.030406713116080446</td><td>0.030406713116080446</td><td>0.07227309498263407</td><td>0.008395756985829988</td></tr><tr><th>8</th><td>7</td><td>0</td><td>0</td><td>0.005141659943827015</td><td>0.005141659943827015</td><td>0.014692881947019015</td><td>0.0010950987372821722</td></tr><tr><th>9</th><td>8</td><td>0</td><td>0</td><td>0.0007607558080152215</td><td>0.0007607558080152215</td><td>0.0026136376540370366</td><td>0.0001249840950159001</td></tr><tr><th>10</th><td>9</td><td>0</td><td>0</td><td>0.00010005405184780689</td><td>0.00010005405184780689</td><td>0.0004132674923050015</td><td>1.2679545871178271e-5</td></tr><tr><th>11</th><td>10</td><td>0</td><td>0</td><td>1.184313266769959e-5</td><td>1.184313266769959e-5</td><td>5.881114313571176e-5</td><td>1.1576976664988854e-6</td></tr></table>" ], "text/plain": [ "11x7 DataFrames.DataFrame\n", "| Row | count | T | C | T̂₀ | C_hat_0 | T̂₁ | Ĉ₁ |\n", "|-----|-------|----|---|-------------|-------------|-------------|-------------|\n", "| 1 | 0 | 6 | 9 | 7.95995 | 7.95995 | 6.26526 | 10.4338 |\n", "| 2 | 1 | 10 | 8 | 9.42198 | 9.42198 | 8.91594 | 9.52654 |\n", "| 3 | 2 | 4 | 5 | 5.57628 | 5.57628 | 6.34404 | 4.34907 |\n", "| 4 | 3 | 5 | 1 | 2.20016 | 2.20016 | 3.00935 | 1.32363 |\n", "| 5 | 4 | 1 | 0 | 0.651069 | 0.651069 | 1.07063 | 0.302133 |\n", "| 6 | 5 | 0 | 0 | 0.154131 | 0.154131 | 0.304719 | 0.0551721 |\n", "| 7 | 6 | 0 | 0 | 0.0304067 | 0.0304067 | 0.0722731 | 0.00839576 |\n", "| 8 | 7 | 0 | 0 | 0.00514166 | 0.00514166 | 0.0146929 | 0.0010951 |\n", "| 9 | 8 | 0 | 0 | 0.000760756 | 0.000760756 | 0.00261364 | 0.000124984 |\n", "| 10 | 9 | 0 | 0 | 0.000100054 | 0.000100054 | 0.000413267 | 1.26795e-5 |\n", "| 11 | 10 | 0 | 0 | 1.18431e-5 | 1.18431e-5 | 5.88111e-5 | 1.1577e-6 |" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = 10\n", "xs = 0:x\n", "\n", "Ht = hist(T, -1:x)[2]\n", "Hc = hist(C, -1:x)[2]\n", "Htc = DataFrame(\n", " count = xs,\n", " T = Ht, C = Hc,\n", " T̂₀ = pdf(d₀, xs) .* length(T), C_hat_0 = pdf(d₀, xs) .* length(T),\n", " T̂₁ = pdf(d₁t, xs) .* length(T), Ĉ₁ = pdf(d₁c, xs) .* length(T)\n", ")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"data-frame\"><tr><th></th><th>variable</th><th>value</th><th>count</th></tr><tr><th>1</th><td>T</td><td>6.0</td><td>0</td></tr><tr><th>2</th><td>T</td><td>10.0</td><td>1</td></tr><tr><th>3</th><td>T</td><td>4.0</td><td>2</td></tr><tr><th>4</th><td>T</td><td>5.0</td><td>3</td></tr><tr><th>5</th><td>T</td><td>1.0</td><td>4</td></tr><tr><th>6</th><td>T</td><td>0.0</td><td>5</td></tr><tr><th>7</th><td>T</td><td>0.0</td><td>6</td></tr><tr><th>8</th><td>T</td><td>0.0</td><td>7</td></tr><tr><th>9</th><td>T</td><td>0.0</td><td>8</td></tr><tr><th>10</th><td>T</td><td>0.0</td><td>9</td></tr><tr><th>11</th><td>T</td><td>0.0</td><td>10</td></tr><tr><th>12</th><td>C</td><td>9.0</td><td>0</td></tr><tr><th>13</th><td>C</td><td>8.0</td><td>1</td></tr><tr><th>14</th><td>C</td><td>5.0</td><td>2</td></tr><tr><th>15</th><td>C</td><td>1.0</td><td>3</td></tr><tr><th>16</th><td>C</td><td>0.0</td><td>4</td></tr><tr><th>17</th><td>C</td><td>0.0</td><td>5</td></tr><tr><th>18</th><td>C</td><td>0.0</td><td>6</td></tr><tr><th>19</th><td>C</td><td>0.0</td><td>7</td></tr><tr><th>20</th><td>C</td><td>0.0</td><td>8</td></tr><tr><th>21</th><td>C</td><td>0.0</td><td>9</td></tr><tr><th>22</th><td>C</td><td>0.0</td><td>10</td></tr><tr><th>23</th><td>T̂₀</td><td>7.959952787608398</td><td>0</td></tr><tr><th>24</th><td>T̂₀</td><td>9.421984932271165</td><td>1</td></tr><tr><th>25</th><td>T̂₀</td><td>5.576276796650282</td><td>2</td></tr><tr><th>26</th><td>T̂₀</td><td>2.200163634052492</td><td>3</td></tr><tr><th>27</th><td>T̂₀</td><td>0.6510688304849211</td><td>4</td></tr><tr><th>28</th><td>T̂₀</td><td>0.15413058027806295</td><td>5</td></tr><tr><th>29</th><td>T̂₀</td><td>0.030406713116080446</td><td>6</td></tr><tr><th>30</th><td>T̂₀</td><td>0.005141659943827015</td><td>7</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></table>" ], "text/plain": [ "66x3 DataFrames.DataFrame\n", "| Row | variable | value | count |\n", "|-----|----------|-------------|-------|\n", "| 1 | T | 6.0 | 0 |\n", "| 2 | T | 10.0 | 1 |\n", "| 3 | T | 4.0 | 2 |\n", "| 4 | T | 5.0 | 3 |\n", "| 5 | T | 1.0 | 4 |\n", "| 6 | T | 0.0 | 5 |\n", "| 7 | T | 0.0 | 6 |\n", "| 8 | T | 0.0 | 7 |\n", "| 9 | T | 0.0 | 8 |\n", "| 10 | T | 0.0 | 9 |\n", "| 11 | T | 0.0 | 10 |\n", "⋮\n", "| 55 | T̂₁ | 5.88111e-5 | 10 |\n", "| 56 | Ĉ₁ | 10.4338 | 0 |\n", "| 57 | Ĉ₁ | 9.52654 | 1 |\n", "| 58 | Ĉ₁ | 4.34907 | 2 |\n", "| 59 | Ĉ₁ | 1.32363 | 3 |\n", "| 60 | Ĉ₁ | 0.302133 | 4 |\n", "| 61 | Ĉ₁ | 0.0551721 | 5 |\n", "| 62 | Ĉ₁ | 0.00839576 | 6 |\n", "| 63 | Ĉ₁ | 0.0010951 | 7 |\n", "| 64 | Ĉ₁ | 0.000124984 | 8 |\n", "| 65 | Ĉ₁ | 1.26795e-5 | 9 |\n", "| 66 | Ĉ₁ | 1.1577e-6 | 10 |" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ " in depwarn at deprecated.jl:73\n", " in tty_size at deprecated.jl:924\n", " in getchunkbounds at /home/scottc/.julia/v0.5/DataFrames/src/abstractdataframe/show.jl:199\n", " in showrows at /home/scottc/.julia/v0.5/DataFrames/src/abstractdataframe/show.jl:338\n", " in show at /home/scottc/.julia/v0.5/DataFrames/src/abstractdataframe/show.jl:456\n", " [inlined code] from expr.jl:8\n", " in showcompact at show.jl:1425\n", " in writemime at replutil.jl:4\n", " [inlined code] from expr.jl:8\n", " in writemime at multimedia.jl:43\n", " in sprint at strings/io.jl:38\n", " in display_dict at /home/scottc/.julia/v0.4/IJulia/src/execute_request.jl:26\n", " in execute_request_0x535c5df2 at /home/scottc/.julia/v0.4/IJulia/src/execute_request.jl:212\n", " [inlined code] from dict.jl:733\n", " in eventloop at /home/scottc/.julia/v0.4/IJulia/src/IJulia.jl:141\n", " in anonymous at task.jl:435\n", "while loading /home/scottc/.julia/v0.4/IJulia/src/kernel.jl, in expression starting on line 31\n", "WARNING: tty_size is deprecated. use `displaysize(io)` as a replacement\n", " in depwarn at deprecated.jl:73\n", " in tty_size at deprecated.jl:924\n", " in writemime at /home/scottc/.julia/v0.5/DataFrames/src/abstractdataframe/io.jl:144\n", " in sprint at strings/io.jl:38\n", " in display_dict at /home/scottc/.julia/v0.4/IJulia/src/execute_request.jl:39\n", " in execute_request_0x535c5df2 at /home/scottc/.julia/v0.4/IJulia/src/execute_request.jl:212\n", " [inlined code] from dict.jl:733\n", " in eventloop at /home/scottc/.julia/v0.4/IJulia/src/IJulia.jl:141\n", " in anonymous at task.jl:435\n", "while loading /home/scottc/.julia/v0.4/IJulia/src/kernel.jl, in expression starting on line 31\n" ] } ], "source": [ "Htcₗ = stack(Htc, collect(2:7))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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root.hasClass(\"yscalable\");\n", "\n", " var old_scale = root.data(\"scale\");\n", "\n", " var xscale = xscalable ? scale : 1.0,\n", " yscale = yscalable ? scale : 1.0;\n", "\n", " tx = xscalable ? tx : 0.0;\n", " ty = yscalable ? ty : 0.0;\n", "\n", " var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n", "\n", " root.selectAll(\".geometry, image\")\n", " .forEach(function (element, i) {\n", " element.transform(t);\n", " });\n", "\n", " bounds = root.plotbounds();\n", "\n", " if (yscalable) {\n", " var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n", " root.selectAll(\".xfixed\")\n", " .forEach(function (element, i) {\n", " element.transform(xfixed_t);\n", " });\n", "\n", " root.select(\".ylabels\")\n", " .transform(xfixed_t)\n", " .selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var cx = element.asPX(\"x\"),\n", " cy = element.asPX(\"y\");\n", " var st = element.data(\"static_transform\");\n", " unscale_t = new Snap.Matrix();\n", " unscale_t.scale(1, 1/scale, cx, cy).add(st);\n", " element.transform(unscale_t);\n", "\n", " var y = cy * scale + ty;\n", " element.attr(\"visibility\",\n", " bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n", " }\n", " });\n", " }\n", "\n", " if (xscalable) {\n", " var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n", " var xtrans = new Snap.Matrix().translate(tx, 0);\n", " root.selectAll(\".yfixed\")\n", " .forEach(function (element, i) {\n", " element.transform(yfixed_t);\n", " });\n", "\n", " root.select(\".xlabels\")\n", " .transform(yfixed_t)\n", " .selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var cx = element.asPX(\"x\"),\n", " cy = element.asPX(\"y\");\n", " var st = element.data(\"static_transform\");\n", " unscale_t = new Snap.Matrix();\n", " unscale_t.scale(1/scale, 1, cx, cy).add(st);\n", "\n", " element.transform(unscale_t);\n", "\n", " var x = cx * scale + tx;\n", " element.attr(\"visibility\",\n", " bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n", " }\n", " });\n", " }\n", "\n", " // we must unscale anything that is scale invariance: widths, raiduses, etc.\n", " var size_attribs = [\"font-size\"];\n", " var unscaled_selection = \".geometry, .geometry *\";\n", " if (xscalable) {\n", " size_attribs.push(\"rx\");\n", " unscaled_selection += \", .xgridlines\";\n", " }\n", " if (yscalable) {\n", " size_attribs.push(\"ry\");\n", " unscaled_selection += \", .ygridlines\";\n", " }\n", "\n", " root.selectAll(unscaled_selection)\n", " .forEach(function (element, i) {\n", " // circle need special help\n", " if (element.node.nodeName == \"circle\") {\n", " var cx = element.attribute(\"cx\"),\n", " cy = element.attribute(\"cy\");\n", " unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n", " cx, cy);\n", " element.transform(unscale_t);\n", " return;\n", " }\n", "\n", " for (i in size_attribs) {\n", " var key = size_attribs[i];\n", " var val = parseFloat(element.attribute(key));\n", " if (val !== undefined && val != 0 && !isNaN(val)) {\n", " element.attribute(key, val * old_scale / scale);\n", " }\n", " }\n", " });\n", "};\n", "\n", "\n", "// Find the most appropriate tick scale and update label visibility.\n", "var update_tickscale = function(root, scale, axis) {\n", " if (!root.hasClass(axis + \"scalable\")) return;\n", "\n", " var tickscales = root.data(axis + \"tickscales\");\n", " var best_tickscale = 1.0;\n", " var best_tickscale_dist = Infinity;\n", " for (tickscale in tickscales) {\n", " var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n", " if (dist < best_tickscale_dist) {\n", " best_tickscale_dist = dist;\n", " best_tickscale = tickscale;\n", " }\n", " }\n", "\n", " if (best_tickscale != root.data(axis + \"tickscale\")) {\n", " root.data(axis + \"tickscale\", best_tickscale);\n", " var mark_inscale_gridlines = function (element, i) {\n", " var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n", " element.attribute(\"gadfly:inscale\", inscale);\n", " element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n", " };\n", "\n", " var mark_inscale_labels = function (element, i) {\n", " var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n", " element.attribute(\"gadfly:inscale\", inscale);\n", " element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n", " };\n", "\n", " root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n", " root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n", " }\n", "};\n", "\n", "\n", "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n", " var old_scale = root.data(\"scale\");\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " // compute the viewport derived from tx, ty, and scale\n", " var x_min = -width * scale - (scale * width - width),\n", " x_max = width * scale,\n", " y_min = -height * scale - (scale * height - height),\n", " y_max = height * scale;\n", "\n", " var x0 = bounds.x0 - scale * bounds.x0,\n", " y0 = bounds.y0 - scale * bounds.y0;\n", "\n", " var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n", " ty = Math.max(Math.min(ty - y0, y_max), y_min);\n", "\n", " tx += x0;\n", " ty += y0;\n", "\n", " // when the scale change, we may need to alter which set of\n", " // ticks is being displayed\n", " if (scale != old_scale) {\n", " update_tickscale(root, scale, \"x\");\n", " update_tickscale(root, scale, \"y\");\n", " }\n", "\n", " set_geometry_transform(root, tx, ty, scale);\n", "\n", " root.data(\"scale\", scale);\n", " root.data(\"tx\", tx);\n", " root.data(\"ty\", ty);\n", "};\n", "\n", "\n", "var scale_centered_translation = function(root, scale) {\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var scale0 = root.data(\"scale\");\n", "\n", " // how off from center the current view is\n", " var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n", " yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n", "\n", " // rescale offsets\n", " xoff = xoff * scale / scale0;\n", " yoff = yoff * scale / scale0;\n", "\n", " // adjust for the panel position being scaled\n", " var x_edge_adjust = bounds.x0 * (1 - scale),\n", " y_edge_adjust = bounds.y0 * (1 - scale);\n", "\n", " return {\n", " x: xoff + x_edge_adjust + (width - width * scale) / 2,\n", " y: yoff + y_edge_adjust + (height - height * scale) / 2\n", " };\n", "};\n", "\n", "\n", "// Initialize data for panning zooming if it isn't already.\n", "var init_pan_zoom = function(root) {\n", " if (root.data(\"zoompan-ready\")) {\n", " return;\n", " }\n", "\n", " // The non-scaling-stroke trick. Rather than try to correct for the\n", " // stroke-width when zooming, we force it to a fixed value.\n", " var px_per_mm = root.node.getCTM().a;\n", "\n", " // Drag events report deltas in pixels, which we'd like to convert to\n", " // millimeters.\n", " root.data(\"px_per_mm\", px_per_mm);\n", "\n", " root.selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " sw = element.asPX(\"stroke-width\") * px_per_mm;\n", " if (sw > 0) {\n", " element.attribute(\"stroke-width\", sw);\n", " element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n", " }\n", " });\n", "\n", " // Store ticks labels original tranformation\n", " root.selectAll(\".xlabels > text, .ylabels > text\")\n", " .forEach(function (element, i) {\n", " var lm = element.transform().localMatrix;\n", " element.data(\"static_transform\",\n", " new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n", " });\n", "\n", " var xgridlines = root.select(\".xgridlines\");\n", " var ygridlines = root.select(\".ygridlines\");\n", " var xlabels = root.select(\".xlabels\");\n", " var ylabels = root.select(\".ylabels\");\n", "\n", " if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n", " if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n", " if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n", " if (root.data(\"xtickscales\") === undefined) {\n", "\n", " // index all the tick scales that are listed\n", " var xtickscales = {};\n", " var ytickscales = {};\n", " var add_x_tick_scales = function (element, i) {\n", " xtickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", " var add_y_tick_scales = function (element, i) {\n", " ytickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n", "\n", " root.data(\"xtickscales\", xtickscales);\n", " root.data(\"ytickscales\", ytickscales);\n", " root.data(\"xtickscale\", 1.0);\n", " }\n", "\n", " var min_scale = 1.0, max_scale = 1.0;\n", " for (scale in xtickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " for (scale in ytickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " root.data(\"min_scale\", min_scale);\n", " root.data(\"max_scale\", max_scale);\n", "\n", " // store the original positions of labels\n", " if (xlabels) {\n", " xlabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"x\", element.asPX(\"x\"));\n", " });\n", " }\n", "\n", " if (ylabels) {\n", " ylabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"y\", element.asPX(\"y\"));\n", " });\n", " }\n", "\n", " // mark grid lines and ticks as in or out of scale.\n", " var mark_inscale = function (element, i) {\n", " element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n", "\n", " // figure out the upper ond lower bounds on panning using the maximum\n", " // and minum grid lines\n", " var bounds = root.plotbounds();\n", " var pan_bounds = {\n", " x0: 0.0,\n", " y0: 0.0,\n", " x1: 0.0,\n", " y1: 0.0\n", " };\n", "\n", " if (xgridlines) {\n", " xgridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.x1 - bbox.x < pan_bounds.x0) {\n", " pan_bounds.x0 = bounds.x1 - bbox.x;\n", " }\n", " if (bounds.x0 - bbox.x > pan_bounds.x1) {\n", " pan_bounds.x1 = bounds.x0 - bbox.x;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " if (ygridlines) {\n", " ygridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.y1 - bbox.y < pan_bounds.y0) {\n", " pan_bounds.y0 = bounds.y1 - bbox.y;\n", " }\n", " if (bounds.y0 - bbox.y > pan_bounds.y1) {\n", " pan_bounds.y1 = bounds.y0 - bbox.y;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " // nudge these values a little\n", " pan_bounds.x0 -= 5;\n", " pan_bounds.x1 += 5;\n", " pan_bounds.y0 -= 5;\n", " pan_bounds.y1 += 5;\n", " root.data(\"pan_bounds\", pan_bounds);\n", "\n", " root.data(\"zoompan-ready\", true)\n", "};\n", "\n", "\n", "// drag actions, i.e. zooming and panning\n", "var pan_action = {\n", " start: function(root, x, y, event) {\n", " root.data(\"dx\", 0);\n", " root.data(\"dy\", 0);\n", " root.data(\"tx0\", root.data(\"tx\"));\n", " root.data(\"ty0\", root.data(\"ty\"));\n", " },\n", " update: function(root, dx, dy, x, y, event) {\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var dx0 = root.data(\"dx\"),\n", " dy0 = root.data(\"dy\");\n", "\n", " root.data(\"dx\", dx);\n", " root.data(\"dy\", dy);\n", "\n", " dx = dx - dx0;\n", " dy = dy - dy0;\n", "\n", " var tx = tx0 + dx,\n", " ty = ty0 + dy;\n", "\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n", " },\n", " end: function(root, event) {\n", "\n", " },\n", " cancel: function(root) {\n", " set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n", " }\n", "};\n", "\n", "var zoom_box;\n", "var zoom_action = {\n", " start: function(root, x, y, event) {\n", " var bounds = root.plotbounds();\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", " var ratio = width / height;\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " x = xscalable ? x / px_per_mm : bounds.x0;\n", " y = yscalable ? y / px_per_mm : bounds.y0;\n", " var w = xscalable ? 0 : width;\n", " var h = yscalable ? 0 : height;\n", " zoom_box = root.rect(x, y, w, h).attr({\n", " \"fill\": \"#000\",\n", " \"opacity\": 0.25\n", " });\n", " zoom_box.data(\"ratio\", ratio);\n", " },\n", " update: function(root, dx, dy, x, y, event) {\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " var bounds = root.plotbounds();\n", " if (yscalable) {\n", " y /= px_per_mm;\n", " y = Math.max(bounds.y0, y);\n", " y = Math.min(bounds.y1, y);\n", " } else {\n", " y = bounds.y1;\n", " }\n", " if (xscalable) {\n", " x /= px_per_mm;\n", " x = Math.max(bounds.x0, x);\n", " x = Math.min(bounds.x1, x);\n", " } else {\n", " x = bounds.x1;\n", " }\n", "\n", " dx = x - zoom_box.attr(\"x\");\n", " dy = y - zoom_box.attr(\"y\");\n", " if (xscalable && yscalable) {\n", " var ratio = zoom_box.data(\"ratio\");\n", " var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n", " var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n", " dx = width * dx / Math.abs(dx);\n", " dy = height * dy / Math.abs(dy);\n", " }\n", " var xoffset = 0,\n", " yoffset = 0;\n", " if (dx < 0) {\n", " xoffset = dx;\n", " dx = -1 * dx;\n", " }\n", " if (dy < 0) {\n", " yoffset = dy;\n", " dy = -1 * dy;\n", " }\n", " if (isNaN(dy)) {\n", " dy = 0.0;\n", " }\n", " if (isNaN(dx)) {\n", " dx = 0.0;\n", " }\n", " zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n", " zoom_box.attr(\"width\", dx);\n", " zoom_box.attr(\"height\", dy);\n", " },\n", " end: function(root, event) {\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", " var zoom_bounds = zoom_box.getBBox();\n", " if (zoom_bounds.width * zoom_bounds.height <= 0) {\n", " return;\n", " }\n", " var plot_bounds = root.plotbounds();\n", " var zoom_factor = 1.0;\n", " if (yscalable) {\n", " zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n", " } else {\n", " zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n", " }\n", " var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n", " ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n", " zoom_box.remove();\n", " },\n", " cancel: function(root) {\n", " zoom_box.remove();\n", " }\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n", " var root = this.plotroot();\n", " var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n", " var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n", " var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n", " var drag_action = zoomable ? zoom_action :\n", " panable ? pan_action :\n", " undefined;\n", " root.data(\"drag_action\", drag_action);\n", " if (drag_action) {\n", " var cancel_drag_action = function(event) {\n", " if (event.which == 27) { // esc key\n", " drag_action.cancel(root);\n", " root.data(\"drag_action\", undefined);\n", " }\n", " };\n", " window.addEventListener(\"keyup\", cancel_drag_action);\n", " root.data(\"cancel_drag_action\", cancel_drag_action);\n", " drag_action.start(root, x, y, event);\n", " }\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n", " var root = this.plotroot();\n", " var drag_action = root.data(\"drag_action\");\n", " if (drag_action) {\n", " drag_action.update(root, dx, dy, x, y, event);\n", " }\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onend = function(event) {\n", " var root = this.plotroot();\n", " window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n", " root.data(\"cancel_drag_action\", undefined);\n", " var drag_action = root.data(\"drag_action\");\n", " if (drag_action) {\n", " drag_action.end(root, event);\n", " }\n", " root.data(\"drag_action\", undefined);\n", "};\n", "\n", "\n", "Gadfly.guide_background_scroll = function(event) {\n", " if (event.shiftKey) {\n", " increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n", " event.preventDefault();\n", " }\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseover = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseout = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomout_click = function(event) {\n", " increase_zoom_by_position(this.plotroot(), -0.1, true);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomin_click = function(event) {\n", " increase_zoom_by_position(this.plotroot(), 0.1, true);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_track_click = function(event) {\n", " // TODO\n", "};\n", "\n", "\n", "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n", "// The constants a, b, and c are solved using the constraint that the function\n", "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n", "var scale_from_slider_position = function(position, min_scale, max_scale) {\n", " var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n", " b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n", " c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n", " return a * Math.exp(b * position) + c;\n", "}\n", "\n", "// inverse of scale_from_slider_position\n", "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n", " var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n", " b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n", " c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n", " return 1 / b * Math.log((scale - c) / a);\n", "}\n", "\n", "var increase_zoom_by_position = function(root, delta_position, animate) {\n", " var scale = root.data(\"scale\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var position = slider_position_from_scale(scale, min_scale, max_scale);\n", " position += delta_position;\n", " scale = scale_from_slider_position(position, min_scale, max_scale);\n", " set_zoom(root, scale, animate);\n", "}\n", "\n", "var set_zoom = function(root, scale, animate) {\n", " var min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\"),\n", " old_scale = root.data(\"scale\");\n", " var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n", " if (animate) {\n", " Snap.animate(\n", " old_scale,\n", " new_scale,\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", " } else {\n", " update_plot_scale(root, new_scale);\n", " }\n", "}\n", "\n", "\n", "var update_plot_scale = function(root, new_scale) {\n", " var trans = scale_centered_translation(root, new_scale);\n", " set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n", "\n", " root.selectAll(\".zoomslider_thumb\")\n", " .forEach(function (element, i) {\n", " var min_pos = element.data(\"min_pos\"),\n", " max_pos = element.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(new_scale, min_scale, 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apache-2.0

Kaggle/learntools

notebooks/game_ai/raw/tut2.ipynb

1

14794

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction\n", "\n", "Even if you're new to Connect Four, you've likely developed several game-playing strategies. In this tutorial, you'll learn to use a **heuristic** to share your knowledge with the agent. \n", "\n", "# Game trees\n", "\n", "As a human player, how do you think about how to play the game? How do you weigh alternative moves?\n", "\n", "You likely do a bit of forecasting. For each potential move, you predict what your opponent is likely to do in response, along with how you'd then respond, and what the opponent is likely to do then, and so on. Then, you choose the move that you think is most likely to result in a win.\n", "\n", "We can formalize this idea and represent all possible outcomes in a **(complete) game tree**. \n", "\n", "<center>\n", "<img src=\"https://i.imgur.com/EZKHxyy.png\"><br/>\n", "</center>\n", "\n", "The game tree represents each possible move (by agent and opponent), starting with an empty board. The first row shows all possible moves the agent (red player) can make. Next, we record each move the opponent (yellow player) can make in response, and so on, until each branch reaches the end of the game. (_The game tree for Connect Four is quite large, so we show only a small preview in the image above_.)\n", "\n", "Once we can see every way the game can possibly end, it can help us to pick the move where we are most likely to win.\n", "\n", "# Heuristics\n", "\n", "The complete game tree for Connect Four has over [4 trillion](https://oeis.org/A212693) different boards! So in practice, our agent only works with a small subset when planning a move. \n", "\n", "To make sure the incomplete tree is still useful to the agent, we will use a **heuristic** (or **heuristic function**). The heuristic assigns scores to different game boards, where we estimate that boards with higher scores are more likely to result in the agent winning the game. You will design the heuristic based on your knowledge of the game.\n", "\n", "For instance, one heuristic that might work reasonably well for Connect Four looks at each group of four adjacent locations in a (horizontal, vertical, or diagonal) line and assigns:\n", "- **1000000 (`1e6`) points** if the agent has four discs in a row (the agent won), \n", "- **1 point** if the agent filled three spots, and the remaining spot is empty (the agent wins if it fills in the empty spot), and\n", "- **-100 points** if the opponent filled three spots, and the remaining spot is empty (the opponent wins by filling in the empty spot).\n", "\n", "This is also represented in the image below.\n", "\n", "<center>\n", "<img src=\"https://i.imgur.com/vzQa4ML.png\" width=70%><br/>\n", "</center>\n", "\n", "And how exactly will the agent use the heuristic? Consider it's the agent's turn, and it's trying to plan a move for the game board shown at the top of the figure below. There are seven possible moves (one for each column). For each move, we record the resulting game board.\n", "\n", "<center>\n", "<img src=\"https://i.imgur.com/PtnLOHt.png\" width=100%><br/>\n", "</center>\n", "\n", "Then we use the heuristic to assign a score to each board. To do this, we search the grid and look for all occurrences of the pattern in the heuristic, similar to a [word search](https://en.wikipedia.org/wiki/Word_search) puzzle. Each occurrence modifies the score. For instance,\n", "- The first board (where the agent plays in column 0) gets a score of 2. This is because the board contains two distinct patterns that each add one point to the score (where both are circled in the image above). \n", "- The second board is assigned a score of 1.\n", "- The third board (where the agent plays in column 2) gets a score of 0. This is because none of the patterns from the heuristic appear in the board.\n", "\n", "The first board receives the highest score, and so the agent will select this move. It's also the best outcome for the agent, since it has a guaranteed win in just one more move. Check this in figure now, to make sure it makes sense to you! \n", "\n", "The heuristic works really well for this specific example, since it matches the best move with the highest score. It is just one of many heuristics that works reasonably well for creating a Connect Four agent, and you may find that you can design a heuristic that works much better!\n", "\n", "In general, if you're not sure how to design your heuristic (i.e., how to score different game states, or which scores to assign to different conditions), often the best thing to do is to simply take an initial guess and then play against your agent. This will let you identify specific cases when your agent makes bad moves, which you can then fix by modifying the heuristic.\n", "\n", "# Code\n", "\n", "Our **one-step lookahead** agent will:\n", "- use the heuristic to assign a score to each possible valid move, and\n", "- select the move that gets the highest score. (_If multiple moves get the high score, we select one at random._)\n", "\n", "\"One-step lookahead\" refers to the fact that the agent looks only one step (or move) into the future, instead of deeper in the game tree. \n", "\n", "To define this agent, we will use the functions in the code cell below. These functions will make more sense when we use them to specify the agent." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#$HIDE_INPUT$\n", "import random\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Calculates score if agent drops piece in selected column\n", "def score_move(grid, col, mark, config):\n", " next_grid = drop_piece(grid, col, mark, config)\n", " score = get_heuristic(next_grid, mark, config)\n", " return score\n", "\n", "# Helper function for score_move: gets board at next step if agent drops piece in selected column\n", "def drop_piece(grid, col, mark, config):\n", " next_grid = grid.copy()\n", " for row in range(config.rows-1, -1, -1):\n", " if next_grid[row][col] == 0:\n", " break\n", " next_grid[row][col] = mark\n", " return next_grid\n", "\n", "# Helper function for score_move: calculates value of heuristic for grid\n", "def get_heuristic(grid, mark, config):\n", " num_threes = count_windows(grid, 3, mark, config)\n", " num_fours = count_windows(grid, 4, mark, config)\n", " num_threes_opp = count_windows(grid, 3, mark%2+1, config)\n", " score = num_threes - 1e2*num_threes_opp + 1e6*num_fours\n", " return score\n", "\n", "# Helper function for get_heuristic: checks if window satisfies heuristic conditions\n", "def check_window(window, num_discs, piece, config):\n", " return (window.count(piece) == num_discs and window.count(0) == config.inarow-num_discs)\n", " \n", "# Helper function for get_heuristic: counts number of windows satisfying specified heuristic conditions\n", "def count_windows(grid, num_discs, piece, config):\n", " num_windows = 0\n", " # horizontal\n", " for row in range(config.rows):\n", " for col in range(config.columns-(config.inarow-1)):\n", " window = list(grid[row, col:col+config.inarow])\n", " if check_window(window, num_discs, piece, config):\n", " num_windows += 1\n", " # vertical\n", " for row in range(config.rows-(config.inarow-1)):\n", " for col in range(config.columns):\n", " window = list(grid[row:row+config.inarow, col])\n", " if check_window(window, num_discs, piece, config):\n", " num_windows += 1\n", " # positive diagonal\n", " for row in range(config.rows-(config.inarow-1)):\n", " for col in range(config.columns-(config.inarow-1)):\n", " window = list(grid[range(row, row+config.inarow), range(col, col+config.inarow)])\n", " if check_window(window, num_discs, piece, config):\n", " num_windows += 1\n", " # negative diagonal\n", " for row in range(config.inarow-1, config.rows):\n", " for col in range(config.columns-(config.inarow-1)):\n", " window = list(grid[range(row, row-config.inarow, -1), range(col, col+config.inarow)])\n", " if check_window(window, num_discs, piece, config):\n", " num_windows += 1\n", " return num_windows" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The one-step lookahead agent is defined in the next code cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# The agent is always implemented as a Python function that accepts two arguments: obs and config\n", "def agent(obs, config):\n", " # Get list of valid moves\n", " valid_moves = [c for c in range(config.columns) if obs.board[c] == 0]\n", " # Convert the board to a 2D grid\n", " grid = np.asarray(obs.board).reshape(config.rows, config.columns)\n", " # Use the heuristic to assign a score to each possible board in the next turn\n", " scores = dict(zip(valid_moves, [score_move(grid, col, obs.mark, config) for col in valid_moves]))\n", " # Get a list of columns (moves) that maximize the heuristic\n", " max_cols = [key for key in scores.keys() if scores[key] == max(scores.values())]\n", " # Select at random from the maximizing columns\n", " return random.choice(max_cols)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the code for the agent, we begin by getting a list of valid moves. _This is the same line of code we used in the previous tutorial!_\n", "\n", "Next, we convert the game board to a 2D numpy array. For Connect Four, `grid` is an array with 6 rows and 7 columns.\n", "\n", "Then, the `score_move()` function calculates the value of the heuristic for each valid move. It uses a couple of helper functions:\n", "- `drop_piece()` returns the grid that results when the player drops its disc in the selected column.\n", "- `get_heuristic()` calculates the value of the heuristic for the supplied board (`grid`), where `mark` is the mark of the agent. This function uses the `count_windows()` function, which counts the number of windows (of four adjacent locations in a row, column, or diagonal) that satisfy specific conditions from the heuristic. Specifically, `count_windows(grid, num_discs, piece, config)` yields the number of windows in the game board (`grid`) that contain `num_discs` pieces from the player (agent or opponent) with mark `piece`, and where the remaining locations in the window are empty. For instance, \n", " - setting `num_discs=4` and `piece=obs.mark` counts the number of times the agent got four discs in a row.\n", " - setting `num_discs=3` and `piece=obs.mark%2+1` counts the number of windows where the opponent has three discs, and the remaining location is empty (the opponent wins by filling in the empty spot).\n", "\n", "Finally, we get the list of columns that maximize the heuristic and select one (uniformly) at random. \n", "\n", "(**Note**: For this course, we decided to provide relatively slower code that was easier to follow. After you've taken the time to understand the code above, can you see how to re-write it, to make it run much faster? As a hint, note that the `count_windows()` function is used several times to loop over the locations in the game board.)\n", "\n", "In the next code cell, we see the outcome of one game round against a random agent." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from kaggle_environments import make, evaluate\n", "\n", "# Create the game environment\n", "env = make(\"connectx\")\n", "\n", "# Two random agents play one game round\n", "env.run([agent, \"random\"])\n", "\n", "# Show the game\n", "env.render(mode=\"ipython\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the `get_win_percentage()` function from the previous tutorial to check how we can expect it to perform on average." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#$HIDE_INPUT$\n", "def get_win_percentages(agent1, agent2, n_rounds=100):\n", " # Use default Connect Four setup\n", " config = {'rows': 6, 'columns': 7, 'inarow': 4}\n", " # Agent 1 goes first (roughly) half the time \n", " outcomes = evaluate(\"connectx\", [agent1, agent2], config, [], n_rounds//2)\n", " # Agent 2 goes first (roughly) half the time \n", " outcomes += [[b,a] for [a,b] in evaluate(\"connectx\", [agent2, agent1], config, [], n_rounds-n_rounds//2)]\n", " print(\"Agent 1 Win Percentage:\", np.round(outcomes.count([1,-1])/len(outcomes), 2))\n", " print(\"Agent 2 Win Percentage:\", np.round(outcomes.count([-1,1])/len(outcomes), 2))\n", " print(\"Number of Invalid Plays by Agent 1:\", outcomes.count([None, 0]))\n", " print(\"Number of Invalid Plays by Agent 2:\", outcomes.count([0, None]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "get_win_percentages(agent1=agent, agent2=\"random\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This agent performs much better than the random agent!\n", "\n", "# Your turn\n", "\n", "Continue to the exercise to **[improve the heuristic](#$NEXT_NOTEBOOK_URL$)**." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

birdsarah/bokeh-miscellany

old/Clean Teeth.ipynb

1

93987

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Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] } ], "source": [ "df = pd.read_table('UKDA-6884-tab/tab/adult_dental_health_survey_2009_end_user_licence_270712.tab')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>ageband5</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " </tr>\n", " <tr>\n", " <th>nummiss</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>69</td>\n", " <td>137</td>\n", " <td>128</td>\n", " <td>38</td>\n", " <td>16</td>\n", " <td>3</td>\n", " 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98 58 25 4 1\n", "4 272 214 288 227 124 46 9 1" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "missing = df[df.nummiss != -1].copy()\n", "counts = missing.groupby(['ageband5', 'nummiss']).size()\n", "missing_table = counts.unstack('ageband5')\n", "missing_table.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>ageband5</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " </tr>\n", " <tr>\n", " <th>nummiss</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>10.697674</td>\n", " <td>15.054945</td>\n", " <td>9.984399</td>\n", " <td>3.169308</td>\n", " <td>1.384083</td>\n", " <td>0.372671</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>7.596899</td>\n", " <td>10.329670</td>\n", " <td>5.928237</td>\n", " <td>4.587156</td>\n", " <td>1.730104</td>\n", " <td>0.745342</td>\n", " <td>0.761421</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>10.542636</td>\n", " <td>13.516484</td>\n", " <td>9.204368</td>\n", " <td>6.255213</td>\n", " <td>3.979239</td>\n", " <td>1.614907</td>\n", " <td>0.507614</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>11.317829</td>\n", " <td>11.648352</td>\n", " <td>10.686427</td>\n", " <td>8.173478</td>\n", " <td>5.017301</td>\n", " <td>3.105590</td>\n", " <td>1.015228</td>\n", " <td>1.282051</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>42.170543</td>\n", " <td>23.516484</td>\n", " <td>22.464899</td>\n", " <td>18.932444</td>\n", " <td>10.726644</td>\n", " <td>5.714286</td>\n", " <td>2.284264</td>\n", " <td>1.282051</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "ageband5 1 2 3 4 5 6 \\\n", "nummiss \n", "0 10.697674 15.054945 9.984399 3.169308 1.384083 0.372671 \n", "1 7.596899 10.329670 5.928237 4.587156 1.730104 0.745342 \n", "2 10.542636 13.516484 9.204368 6.255213 3.979239 1.614907 \n", "3 11.317829 11.648352 10.686427 8.173478 5.017301 3.105590 \n", "4 42.170543 23.516484 22.464899 18.932444 10.726644 5.714286 \n", "\n", "ageband5 7 8 \n", "nummiss \n", "0 NaN NaN \n", "1 0.761421 NaN \n", "2 0.507614 NaN \n", "3 1.015228 1.282051 \n", "4 2.284264 1.282051 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def normalize(df):\n", " result = df.copy()\n", " for feature_name in df.columns:\n", " result[feature_name] = df[feature_name] / df[feature_name].sum() * 100\n", " return result\n", "normalized_missing = normalize(missing_table)\n", "normalized_missing.head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ageband5_labels = {\n", " 1: \"16 - 24\",\n", " 2: \"25 - 34\",\n", " 3: \"35 - 44\",\n", " 4: \"45 - 54\",\n", " 5: \"55 - 64\",\n", " 6: \"65 - 74\",\n", " 7: \"75 - 84\",\n", " 8: \"85+\", \n", "}" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from bokeh.palettes import Spectral8\n", "from bokeh.plotting import figure\n", "Spectral8.reverse()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " <div class=\"plotdiv\" id=\"db9d8d08-6ae9-45bf-9793-f3ac84b1be4c\"></div>\n", "<script type=\"text/javascript\">\n", " \n", " (function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", " \n", " if (typeof (window._bokeh_onload_callbacks) === 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Source: Adult Dental Health Survey 2009\",\n", " min_border_left=0, responsive=True, width=900, outline_line_color=None,\n", ")\n", "p.xaxis.axis_label = 'Number of teeth'\n", "p.xaxis.axis_label_text_font_size = '10pt'\n", "p.yaxis.axis_label = '%age of group'\n", "p.yaxis.axis_label_text_font_size = '10pt'\n", "p.title_text_align = 'left'\n", "p.title_text_font_size = '12pt'\n", "for count in range(1, 9):\n", " data = counts[count]\n", " p.line(\n", " x=normalized_missing.index,\n", " y=normalized_missing[count],\n", " color=Spectral8[count-1],\n", " line_width=5,\n", " line_cap='round',\n", " line_join='round',\n", " line_alpha=0.8,\n", " legend=ageband5_labels[count]\n", " )\n", "p.legend.border_line_color = None \n", "show(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.4.4" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0

UWPRG/Python

tutorials/Widgets InteractiveGaussian.ipynb

1

43227

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Jim's example of creating an interactive plot to adjust dimensions of a single Gaussian" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt \n", "import numpy as np\n", "\n", "from IPython.html import widgets\n", "from IPython.html.widgets import interact\n", "from IPython.display import display" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x108e525d0>]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x108e9cc90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mean=0\n", "sigma=1\n", "x=np.linspace(-6,6,500)\n", "y=(1/sigma/np.sqrt(2*np.pi))*np.exp(-(x-mean)**2/2/sigma**2)\n", "\n", "%matplotlib inline\n", "fig = plt.figure(figsize=(4,4))\n", "axes = fig.add_subplot(111)\n", "axes.plot(x,y)\n", "\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plt_arrays(x, y, title=\"title\", color=\"blue\", linestyle=\"solid\", linewidth=2):\n", " fig = plt.figure()\n", " axes = fig.add_subplot(111)\n", " axes.plot(x,y, color=color, linestyle=linestyle, linewidth=linewidth)\n", " axes.set_title(title)\n", " axes.grid()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f(sigma, mu, **kwargs): \n", " x=np.linspace(-6*sigma+mu,6*sigma+mu,500)\n", " y=(1/sigma/np.sqrt(2*np.pi))*np.exp(-(x-mu)**2/2/sigma**2)\n", " plt_arrays(x,y,title=\"title\", **kwargs)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x108f483d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "i = interact(f, sigma=(0.001,5),mu=(-10,10),color = [\"red\",\"blue\",\"green\"],linestyle=[\"solid\",\"dashed\"],linewidth=(1,5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

charliememory/AutonomousDriving

CarND-Advanced-Lane-Lines/src/.ipynb_checkpoints/camera_calibration-checkpoint.ipynb

1

875732

{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style> code {background-color : pink !important;} </style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%HTML\n", "<style> code {background-color : pink !important;} </style>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Camera Calibration with OpenCV\n", "===\n", "\n", "### Run the code in the cell below to extract object points and image points for camera calibration. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import cv2\n", "import glob\n", "import matplotlib.pyplot as plt\n", "%matplotlib qt\n", "\n", "# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)\n", "objp = np.zeros((6*8,3), np.float32)\n", "objp[:,:2] = np.mgrid[0:8, 0:6].T.reshape(-1,2)\n", "\n", "# Arrays to store object points and image points from all the images.\n", "objpoints = [] # 3d points in real world space\n", "imgpoints = [] # 2d points in image plane.\n", "\n", "# Make a list of calibration images\n", "images = glob.glob('../camera_cal/calibration*.jpg')\n", "\n", "# Step through the list and search for chessboard corners\n", "for idx, fname in enumerate(images):\n", " img = cv2.imread(fname)\n", " gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", "\n", " # Find the chessboard corners\n", " ret, corners = cv2.findChessboardCorners(gray, (8,6), None)\n", "\n", " # If found, add object points, image points\n", " if ret == True:\n", " objpoints.append(objp)\n", " imgpoints.append(corners)\n", "\n", " # Draw and display the corners\n", " cv2.drawChessboardCorners(img, (8,6), corners, ret)\n", " #write_name = 'corners_found'+str(idx)+'.jpg'\n", " #cv2.imwrite(write_name, img)\n", " cv2.imshow('img', img)\n", " cv2.waitKey(500)\n", "\n", "cv2.destroyAllWindows()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### If the above cell ran sucessfully, you should now have `objpoints` and `imgpoints` needed for camera calibration. Run the cell below to calibrate, calculate distortion coefficients, and test undistortion on an image!" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1180908d0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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odYzNN1UdisivAf9AXSfPAd4pIjfiFMccp0jHO4q+jTNs3j8pjiiuG/xC17t8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\nbvh9a+Szwnom6F7uhXnpIxMQcntvUS7aUZR9CsCHPz7qfWPz0olONHu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BIZxQT/hvnar3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74WaOAVPBS8buR4u1dA5my7lt9b/B4ouK19tHupp+2b+0KRvdp3/tRLX5fotte0vZXlLfx+be6t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SIEAEwwFy+OA+cGsFKqQXxLcOXcP+TxAIuAWpOBLxNutyse010El5AQCh6CJWe3CrQxmsWg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Qof8JfecQEg+2QhUw4oIz2pjpbsW4OM7MQOHAp23ejmPMaG4r06qvBa6QXRhOhWTjQ3te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5jHWIihmwv6345veJxMjDIiW5C3f8d2xXvG7HeedwdPUaf1t71c+SW9JEkRp1qpkGOJxou3sNTg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\nc/SHjU1d+zxhFWnB5IlhpnXPjLqMVP4c0yQyFkOYpuwP2j02Qv1gvPr+1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4pz1u0KmHZJ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bOp/YWp7vK+fOKroxfFEPhaw6jUayV9z/VJemurJ9zd9/Nsl4GaqKz58/4/Pnz9smOq43vPsO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EKBPMhYReZjbk55sroGX1pbzPGwQNh+uaDc3U0pAr9cX66f9LrmIGfMsNI1BgIE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dj+ia4qYOHA1kqVhYdl0aD/IO/D1wsf4Z8BtyNjpoyneMjewgiFCi93yE3tkkVikAtFkR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\nkiZdh9Dnp1NcVlIe+bBxn2wzEQ3lDeMs0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DoZ/i+i7HtM6nIIwRaa5RueNbimETS2rWDf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cccejgPEu6V/KidJ0nYwViM6qtWLfdzDv+PjhI969e9eO9zbNq/gRli/yLegjQ4sE7b+1zhjR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hvdZIT4OrF+ad9uJJvRxNqLGRllXC/GBxGagMW/6vhfXl25cE/Ywd3acmqjblUOFBpP10Re\njaGGY71WrvaQw0asEFzoZqcz1e9Am0AfYa86qtXT/kbnO5sDsM9nY+Wk/gop1j0rf3KsUPX3srS1\neeVuGzCyTXmZTBGBFucTLHP6uVIgElTs/SpqeoSaOB+R8YDVDxzGa83X8FE6ATc3N3jy6LFGJLJ+\nctaW4hpoEfMxPJvYMSp5LC2yerMU9dXZMueMdNuFkC8C+DcB/A6AfwrAfw7grxPRv9TefwYV57dD\nubfbOwD4NIDrZuzP8qSJFgALYDtUdzCyDVlij5blOxpsigSOA23DVi8cJQaK1JtMfiYOc4ezd5CG\nJyoFh4sL/OzP/iywbVU4GhbKhEwchPbdrBwon7y0eZziHa5BHyfCIm6jEWknb/onc+6zmI4WrwzX\nmVER+5CZ9Qjvejzi/XffbcKuT6KI0eAGvP0MA5011JUoi0xYOGeCKCjQyQQQzCmMdqsqoU+qEtUQ\nbJWenb5jPUJbuRjJ0nhfcc/6YcOc57L2bV9k8aHlu9ZDBFl0maXerhf8pwSfHsdNcDw1NphZw4kd\nDge88eabAFDjNzNjacdlPXynDeGYpI0sNE1GO6v0vEwMk3DAUI/QMDoGlq6S4sp8Rh+b19KTmdUA\nnzmmpZRh8j4a23v4z9p2BNB6fdkYj1eNmKnD058tFOVef90NmjHElMhYMXYAb6RnTm0md2Pew2Sx\n89B2+19eXqKUgsPlxWBMRRp0vgpOVqh7E6cmpD1ed3Kx7ZIbwzl63PfCPVo8rJ4c8oS+lzHmcLY8\nN8Ej65cMvyjGOpxQHmuV1B2Rps5Bv8Hzv+cluCWUGX1quUrri4sLHI9HvHzxAo8ePVKaxUXICkf9\nxHr3jtFnsssAVO/MsDIiw2ui47Nn2l/Yl/P+QW7kS7iuiEOE09JB9PqPff7zng9RJyDUKTaOQBx/\nVs4NNs+OTnyVfqDTR+ozAeKbwMmjuSXYnkd7FaOTb+1nKRNT1BmDvA2bqWy+TOcP7ycTOF3v5nKD\n2twBlT4JlNmte3o3CxWDJJ+jRfi9od7HdNxWvR/Myq733ntPw/jae1RUVrddnNmkBcIzmyejr9x7\n5esqasfM6Bz7pedJypl8Xqb2T9193e/o1MU51PujVu7nKrtNVzc19LsS5/La4mxtCz9lM9Jiz16a\npUzGz/0sTxt5yCdG62iPjLaCbTvTawMsE9wG3OH7UssYfXznTj0R4kNTmROwQIukVLKrOn17wSeI\ncweZPRJ9nEyfktjOBj5HH6ObC5WBJzL6xPGV0dz2x+C/MLt6U17EKIu0HYwyx7539Ah8YO1lmIly\n3XSV5Cdzoi2Ti6z3rnTbd6hDvodJ4oijyEoG/GJ9Qitph6jfhSmbm1xJ0z/WNxDbl5ldWOw61Mbx\nEWWeoy152sb8e7LEkzOOW0M58SlnZZq8bMYpuJ0gl7sPlTbM0FCUdgErqbfLyo6jtWcz+uzpkfq+\n/h0n9WdWy/47gXWEJ58Gn7U5jBfe0RNElfdlvIuOSzZbzuRCzd3nClRPktkAlqDtx3aQP9sGXle8\nfPECzIzlsGi7CHbNrA+jDhUZb+Vo1A/ZXG9m+1n709o72XiJ/TG1zXbSbUNjFQC/xsx/pf3+LSL6\nSQD/BoBfumVdt0601tAg27ph0cWOAmA1g44gWtRe9Cyph0LwYSVqXWb1nMIELYxikV2i3EMZqKmS\nKMY4oKwAp2XB57/wj+CrX/1qq8sqoHFwREW/N0GkbSpF+gp8HZ9mEsEZjuMEBRG1cBkjng6mFhtS\ny7bLdqpiN4AY3LKwMZkQihNzccU8wrtuG5ZSsG2r9t0733kbP/7lL2PbGEWODTdBAep9Lztp4wB1\nuKuDYIzyALO366xT5UNwSf5S2oXCqOLSiYzhh59ktf1ReWU0gOtvQJgsCmFnTAWFXlv1IUgif9qx\nZdNgkEgczELVmWt1bO1wcubAqPEBnKg/cX7UA3PFnHKITovlT9ktwMx48NpDbATQUmm/GuFeiFo4\nmF6nyIc9oxnw4cWk96vy6DBmEw9q3DQ+ptJ38Al/u3HcyCELKJRcEm/xju1pPYnRdsoRmPYPMj41\n+SY8JZOQw9iE6WpbrAlDzUc1RNVi2u0GY+tToTmP/lkt06ehCaXJkTHOq+IuWy7UNhZ5wpjJ/oye\nNtkwYhZG5rqId3E44N69e1i3DWVrS5qcXxxovyvZqC8OOV2rBp4YZT1sQcY3NW+DrfFW1n80kY/y\nPfKOrcPiI0nC29kFLq6V6SkVdb0aTxBRjVudyIYUL6unWsxkZq5hLWAnWxQopy/Y6OFoyAmfASE0\nBkHpVJJxbGHbtg3E9QTDi+uXIADPnj7D06dPcXW4qvnQ6xU7QEOvqX3V65w5O5F3LR5Kf/LhYAYe\nMONf+jfqOznJkvXDbkpOHVWMoYtJg22Y2D7ye1kWvP/+B/jMZz5TYSqktoQ2WZEZ8I06SNqNPP0q\nfSzTR+ozAei+s/xU2UwqA3Obae4MSz3x3dQuQJd8t+XpvfxiIUedo+9Lr4PNGVl2NgDXiZ4wHmd2\nirxTXNmcfq0FhrwABkd+OLnJq+qNjRl3793D0ydPcH1zjUu+o32oC6+l+weMehJ9ZseeJRNrocRw\n4kFHK02Mn9CfiQ/r24x3SnT4xMZrFFlQL3uA749apvvbFGydEZXcrj2ll+LzUKnfJkjQOyliezmu\nXnep36N2th9z3P5uvLbwWPPQH/l4NPQ1efbwFB04T+zHT6iHuW/sWErBvXv3AADH4xFMdfe9+Dam\nRqcvZzZFhNeN+zYOt20Dh7uLJJ+zH9q41/rlBAyRToTbMV77Au2S+zyMkZSRMRppHnHw9oU/NWU3\n0tl5q04zqF8Z5Z+lXmazWvgi/JD6AOjEPYe6bB6LF1VJP44nct+KbQ/VxhR3wl/9kfC6biI1WsWb\npgqT+LC2XyItnNxvNCzNB1AYWjj1B/fv9/kA4zvZPoh+rpeX4i2Otm7UX7fRk1JM9Q+1SBTU/Ev9\ne0KXBfr5fEX7Kyb1f0XghO6R+qiNLTC78PRscOh1astSQeO7kI95KDtLVucA9RSsaGOiTeWt1T2Z\nnHN9C7gFItePLPA3WdPok8nXbFNgnO8VuSSwgagaEoG/oy9Rx0CfL2UwjjdHPHr0GGIPCV2Iep/Y\nlMlked7Hv/CAIE9TWZTVufc89enDmLF8fJvxc9uFkG8D+Fp49jUA/3z7/h3Uvvo0/A6nTwP4TZPn\nkoheY7/D6dPt3TT917/0X+Hu/ftNiNaG/szP/xP4uT/9c2l+5m2qsIHAzNwHcY3JSek9EuIQb7Fe\nqyCCkrF1uLapKt0f/ZEfwXfffgef+uxn0k7s7ZzXsTGXnWyJ8AhOwzFYXlF3A+WTQJJ3JkRt6zku\n/ndm5JxjvFv4tmaAibAtpbRn9QQHLQXf++7bOB6PuFgWMBdn+Gu78E7bdLIHVQCqrLN4kD3ZgaGN\nPcO7GzOJsAy/B0FJozO4p+A6raPBhOlRc/t4SpvQ52K42Ha60Wxp0S4rw0irXqfn4z0HBEC7D+GW\nY8gYM7YfNtTQWOLULsuC47oqrRa0ezLAPRZ3EUU5Cuw9p8Ya4/Z5hFF/Nwcljfd51lj17Vs4s/Zn\nz1x+NXK8DHE4wdM7S9n7TBHauju39EnMbuTkDpevg5CdjiOiMIlRQMTOiCm0uPp7O+NYtMaJ0CPi\nkv2OtGRmc38WGdwJV5eXNfxisaEuLE1yGri+RMgf+iP2z15/+mQMWeI2GS8tjg6Dk5E7vDzAgxZv\nO+TbmIEhhJRWkjo6kmbjadRh8W9Pkud4POJwqCZZXPTXojwae3EMZDHK3djnOi43Zrz9ne+Y+0Fk\n8TXHJSdPLvvtJE/GB26MmpjQ8pBqprS8lXv6Vz7cnXNK+ESMdNuXbuf4znib0qDmxOXVFe7du+fs\nEQ1H0WzLrN4+ngqoAF/9jd/Ab/7mV10bL54/PwnHq/QDmT5SnwkA3v7uu4M/8sZrr+H1115zzwaZ\nEp7HFG2u3bFCZgJEH+UT9/bdqcQ8TqBYmMpiFxNzP6brwZPN9fJVYLiTfG7cJ3IkyiJrF8nEqp2Y\nPd7c4L333sPx5giioovGWrczKXPc5JnX48FmavKWNCSaqZe4z50RwCBdQ64iuzjRdp6+j6k3KH4H\nIfd/tA2daNmpdUcvW1ijLtnDIe68tbbQbWGxm4wkT+WTeh+Ig3Pr9Dh/bHhdbm3oU/7Hfr2ovMDj\n4oVuwjN673Bxgev1iMPFod5Ltq066CwvS12dphjMpb2+sT6T/WSn2gw2MhAHW3HwWRR/VvxhnmV2\nz6k0w8f6vzG/9NsSYBwuWa9AKCxSNrOfnXxCp6WOsW6KuzYG6NmP296/3r/RfiCBb20nbUyTqb/K\nWl+ta+4bA7kt7MA1fbbITnaK8wnQOyoPF/U0/XJxqBuXaF//DPBHOu7wiC2b6RP3rjMnZBMTlezk\nTdKRGGmj/ijbXmbX4ZkfksGatRVtgWleosoionsEZxBoabhY8ibj3D5L6agbhhVAh2dWzj1LkPBl\nhOZNHif+jESkKWj+6LaBlmVYkLNldPxw7GO4331sUz/RtW548vQpnjx5WsM0qr854pD6Kkl7/QW6\n3EHOu7Z8ppOtrIq4K2zm96NHj/D48WOXJ26Q20u3XQj5VQBfCc++gnb5HzP/LhF9B8CfA/D/AgDV\ni/7+FIC/0fL/BoBjy/PftTxfAfA5AH97r/E//+f+HH765/4MLkrBQjYcjaz4nojvDkwVVabUtP4w\nsVjCgI/CTnPLc+Nwi2A+mtW5w+UFPvuZz7opt6ljQcFA4mrAKg4J87aXzjiQegYFKu0SQGoBmx0g\nwbCSOtzEhgU3CEsg7nwfDcIIizWwMtrEARlP/YjKLlTwtb/3NfyFv/jP1PBk2LAsbZLUeFR79afC\nFNATABsJ3k0RKx2kn4S3On207rZqTCS/2F2Y7RynIDy4OQae1nOFFC9us0lwLKGNo/SBEXTWWJmd\nHpD2EJRyY1c/dlgmkvcmGMexCiJ3soSZWwixMNaNjrP8q7tv7Bjp2Ls4ybLDCaiTl9aIHmA2/Tzk\n4T7hW+XD0scoHJpaXsK2qLPBrLCLDMycm0G+QeRY2wlFts96f9q2I13i88zwO9dAmso705b8HXmf\nUxjV9DO7QvfaB8bFFe+YmUUxB4enVSkF25o4AwEfrVb4sBRvjE3gPFcOijNzKAuurq5waCHcbtZ1\nGsc8Okz+eXE8ae/q2Iwhda5zneWTU3DcMw3wzYyjWRtDm+gNbNvmTkRZHdUXtSrevFXD0967keFJ\n5E8U5rwdF+f86ZyZ41N5WYzq7hRkxqT9vq6r4c2jnqT41re+hYvDAXL1iXJ0GFOtoeAYjcn2T9Qv\nmWMiC19SfYWhOjfSftzVF+0C5urErqYPMxtIy6Uw66+prLF1WPoSgHU94qd+6qfqfSG8AU0OL43O\ncpq208DV2p4V8Mb46Z/5GfzMn/gTru3f//1v4hd/4RdmZH+VfnDTR+ozAcCnP/VDuHunn/iC9ReQ\nTyDZdEqepw5tLwxxWona5jKT79TEoboc9p3M+xC5Da5Z+dmmEP8bINqc/zi159CnNezmMRcmF0jl\nj05oDDUKrj00MnO9rPf58+d4/uI5XlvrInkpi8b9F30gft8IZZ/0spM21Gxj+S35ZRFY1FvH3Uzo\n6FdL+9qO8MmyxFPquc5w/iMIoAKmbXQiQxmZ3svku9SX2UIVhm23b8cy83yq55qfFv2KvXqdz+Pu\nFql6Qv0NoN5zaWBZwWCqh2YyXV/zjc8s3I720WafwL3nBzva241jzDgcDnpvluAluloCn4EIG699\nETGz8dHHl2z66tP1DOaao+Jk8Dc6Xy5x7/MVffNY1n8ytjOcY7J0nMnUGc+m32k8JRDrnW0Gy+qU\n33WMLgOscQ6nks6O5T6O41hzfh7sCehajG076LSsm1U3jwu6H+R5VZs3sJC+O9U/NmU2nh3vlmrM\nG6pJV3D3zh0AwHFdQcsCObku9Ext3Cxx95V6O9YXOM1nZPhjCzymPhn7e1hkETxdrFI47A7/bqtm\nm7MsLLEOoNIMIsdYNgZFex+ydgO7wGjHcf1t4WXERTfb7m6yOPHa1hGKLrj0vqtAMeAWS1SLBxkb\nx68f96Ver5CC13iw8b9sTGOJNNIHQ5t6s0YPhr6M/pHCzR2um+MRjz54hGePn+D+/fsmZFXX5f7U\nVsdzxgedIE02KYBzmWdhzvJkc0eiF0spOlfw8OFDPHz40OV7/vw5vvnNb2YEH9JtF0L+MwC/SkT/\nIYBfRjXW/xKAf83k+UUA/xER/QMA/xDAXwPw+wD++4bEIyL6LwH8AhG9D+AxgL8O4FeZ+df2Gv//\n/v7fx5/6s/8kAOgucuk8ESqORxojxzSbmOrl/KTeEHYDlUmpCT8qYuBDO8jWZS9ekrZquIyW93DA\nz//8z+N//T/+N8cUdgKglhsFd1RCAPyRMzY7JKlPKiCUzZjTKtlqUADWoB0uozV1qqKjGqLJKmuZ\nlHH0SZLUsxc6y+EtZdBOiwF6CW5XssDTp0/x+uUlNt6wyHHsJonJOASS4jHSCEv2G2IKiAzb6jPv\nXHCgA0E2AhFR25o10sS2M3NSu+F/WvBYhZPF8FOlKrgQenQf0/9xnIwGmneIrMGhih11Fz6b+iO+\nCGVc7VYxRz8uSVK/65WgxCI+d+/exfF49AaGUVZSrzU2Z8kZX4PyrH1oaRQNHjbfM2NEntkxVKjH\n6K9lC9bt2O4mme94z5wfZwAnfZT1V+SZKGdO0SxTzMuypBOv4lSx/V07GXZ8rOh3flg4Bl6S9h2O\nxU0eWDxtv9S287EooddKKXphoq1PaBXLRprE5yLzHzx4gIuLC6AZWqf6OJMvsf9jf4kxNIMlwtnz\nDI/kDVRGTnhiz+EYdGWsGhguE7c8JfII7PWU7NbM+F1+27CETuYza9v2bonUzjB9kfXBrizkqFt6\neKvq3B7B24b3338fhQhHuZidkJab2QiR9nv9Ic/XbXNhH6jRxe7us2FUcrnYv0e5GGkh+bqMznHo\nOrO36/ONMKic2Bhf+cpXen8JruuqDtaMh22d67qhLNlu4d2ir9IPbvpIfSYALbZ3+0F7/O3H1qmw\nu5kNDuTmViaHo++Q5bcTai5PGI+2nB2DszwOXuobiWZ5bPvWzozymptvlSWJMpDdmUaopoFMPhwK\nwNsKXL/Ak8eP8UOf+pSG+K07RUub2ZBFBIO/0mcOR5bWppv0rbFpufl+PSq5WPPUVFqjN/WQqwPd\nOPe9lQCWLnoyNO8TMs+ibtrTPd4+G22YLL/aBM2OnJ525I6vfS/PZn7rbHwJD3f+tM/8fVMR7gwX\n97v9rXqTVQcz2X7llIPIwmxoymLbyN1bVDX65eUl7ty5g5ubG92lO7NdtY3pCd2GfFWwhkelrm6f\n2zGqLVCCF/kxEW1/B5/hHbHlLS8N/uCJNMs7e2Z9JSvrZjw847sMzugLVxLHhWTvw+vprVC/O4nA\nvWSksyyCAD104HmbyCJOfvFrpldmKcpij0tNh2Wp9900G7kQyWCYwJjAIPybzUvCwtBmsCjXw7Hf\n1F80OEjknLF8kAWGF/Z4yeIm5eJvp+eZQfbEpCGCnHRQXWVcvKF9XUDw+NdNs3PfJMI55QmSxf8+\nnyxgqbtGgt9oK3nbhqvNQtKfvu+nsqH1S/TbhG7cPtYRIKKml/d941rMj+Pj8QaPPnzk/E9mbaXR\nt89Z2zB/OX+wQ3WL7/Ym4TD2ycnxKzBL7Ul/3ybdaiGEmX+diP45AP8JgL8C4HcB/GVm/m9Mnv+U\niO4B+C8AvAHg/wTwF5j52lT17wJYAfxNAFcA/kcA/9ap9t/5/T/A1nbAXJJliLqTh2pobkO8ghIu\nSy/oxiJR3eUOeILHOPzRKV3NQgIgjsamK1SDEORx8lHgEMP2C1/+MpZf/b+qscsbDsuCDSv0Qhxm\naAxV6WiuE3BWmcAa2gaf8bLqBYVEiJa6ixFdrtedE+2ivFJqPO+VsREPwy7Szk/gbLqLlblefLO0\nuOsA+g4xw8jZJb1DvOx2qxoDWFdvbJZmgElcx7ZPpK7qbowPPvgAD157HYUW3PCGpakdCWkE1DUI\nazzJTnygLq4ITGghcXTxxArSirYKIVVSiaHMzMBmJjq5x/y1ZaIg8s5YLjSsgutOsGnKCDyb374j\nqkdxRZhLHUSyy8fviLdJJvzshUorM9p1CsYAa7utuehquMCxiqDlPnZsYmYUPSLojeHCdRcVAe6E\njRunIcaj7RspIZNtD+7eqzvqGOBS6sSelF0WNfbjKaYqd+RybN//ArjUw9Qm04zBsAJ6SkfkFzen\nY1trQNBi+i5LgnO856XQojGI491KM8Vv64tOQ/xuy1oetuVmxrkYS2TK20WobLJWQiBVOSCwmn17\nIvuA7tzWUd4MDpHd/hREAVDaoou0eTwe9YIxsSWW0p2tiJviCw4GRivD7GRFNDyisWTl5rCoXAro\n4oCLqyu17DS2NncHTpBUw2hrE+ZbN/4iDL0JE7fb/jayaWoAhjuxAPgL+VqlbGTqgQqOW18srXRq\nO24ENq70jDRb11UXggSefgcQql5vrUlfiAGuMrjtWFNdEXblMXOHpQksbv8AgOQkWFnajUiVD27W\n1dUT9XbFv2iYPW5WOhmbJ8Kicr2dZqoG7g1WrHj54inWl0fw0ThBXCf1Zcytqhfajmny8jqOX+Lx\nQrtMFtixu4rcl75Dv7el2wKlewPqUNRn8Z42OX14DOOhCKsVycna73KSbuXgNDLX/mkzRARqO4Bb\nHlQdsIHx8I3XwYWwSLiY3mnV/lvjnTje2GdeURYzthgn4+C/Sj/Y6aP2mQDh5Xmo3qjP7bPZ73NT\nZqfZqZhML3jbKNd7/eG8XQaafMzrjxXV595/sbaPswelTqGPaTOzwbw+kaJGRqLUfdQqb6sMfPbs\nGR49+rD7HlTvyCyLXezpqZRSwx5OfQGBdkyFve7ViXbd5eE8mw6/oUu1WXse37a36+SZEXfNGOsg\nUqyHqMKp5W24sHFhqutBn9+0mPJ/lM2taTAw9ZWUBsEWjW3E5OuQCV1S/hcbzKZuTeQpa0/hIvJ6\nvPlUG9TcOZm6LeR9UNkhT6SGCS4uL3E4HPDy5cvBFojj4fQJsRE4Oy6r/0jWVNCT8yS2tuHf5iZW\nuy/4nIPPYuEi9IU6CnAktrl9N7s/5By/LeKc/Z7xmm03+k5aFhn/2g1kvs2ZvAvQe7gcfb09GHH2\nfcGON2O5GQi30V3Rf602HvXQWHoKpJMi7pyP8DlYayPqz0ol+hyWf+B+C3wzPMZx0xeHfLm+4dTC\n6sv25zpnsLMRL+Kr7wNMajNvW2clZieTon/f6+7zEpuZ2M/mDSJNIo6+bzR38zXtncdiyI+bACNe\nOlcB49NGuZ3yYved7XMblUDhNTSNuiria/Fc1zq3IaGxbm6u8fjJI9y9e9ctdsR7hccx5u2iTA4N\n8ifIjXMSUQ8/7vg/VEcYq7dj99x02xMhYOa/BeBvncjzVwH81Z33LwH8O+1zdtpuVjx5/wPcffgA\nTEubdKmk6MarZ9LIYJnhmCmObBV2T6DGUyBZsowVJ0nffOMNrOsRF4cD1gLcHI/g0hzhRMmJQo31\nC+xRmcfyzKwCfF1XvWC5vutKytKj0nK+s2WGs7ZLG2Ql3w7hQn0HpZSJOI+4mgpMW1EoCp4AsG71\nmO7vfv3r+JE/9rmaj6sY2gRxqTyBITpFbndGhyKlgZSPlxnZlBnQFqfphYVJezFZo6bmiUrNn7rJ\nDMKx/mAooY8DO4EXBenWdgTbxSVHA2OoxnEbL5i0sFoHNsJLbGvpaW/cZjQmAi7uXGl/0OaVUwaD\n6zsGrPHn5Eyrw/MiNG5kCfBmvG/x2DNuIk9lxmg0mhXOlC5zOTBTztlbB50BAAAgAElEQVT7mKS9\nGCdzbxyIsRV3Pu7BncGg9IQf1WuYsF4OB6zNwGDmdrS1GVWbhzvKzlMwZfy8lze2BdSxeHV1peEJ\nGPP+mukahDIRH9W11N9nsGXwE4X+NEeIM/27YTwdA4g5aXXX2KcZveNv0Ud7SZx9Z2skcju23Qr7\nuqgu9OrF4d1DcHJN8be7dIw3NdNXQD0CviwHbGsN5VdKwfvvvYebm5s6TqQPXR0dxrr3w7ZDg+Ee\nU+oYJX8Fpz0+s8f85Xmt38hPS1NDq1KK47FMxtnY/EMeIqzbisKkddk8VBZcXh5w7969tsi/aV/O\nHLH6fD4JDFQeYx1PeJU+pumj9JlqOp95Rn4NslJqI0K0P1oFu/aGy4tT8sM4vJNxIv6IPLN+hM1L\nrTJu9pdppfk71Qae2VD2GTc85b39xHbV/wg6hUwd8oALgbjrw1IKnj97hufPnvcJdtO+P3fRYG3H\n0GO4R5Wt2EkEWCna5dt40ntGo1rJ3P7MLisW85ncJruJvbCjmKMJ1+1XDDLb4jjX4aHtto2p817N\ntJqQwdG2s+3MfGabl0gmv9jzm0n1dH6/y9TWv3dBt2uXpa/72LCjvfsqI/wgahslMSRrK8sGrsvD\nAXcvr/Dy+Qus24oNddMhUHnL3q034Il63x6h7wBXulXA3Oaxre5qUFjrTacNd7GjqE0Et92OsnES\nJ+xxHYNSnutmjArbOvQrTP4sekB2AbqUsf1oTw7bPLGNCK9+b3ltnbENW9+i5fvfbANZN/b7cy1D\nPbSbZFkC3uvaTrhp2Ox6crGYhVBrx+mdF1XIuwHKvIHXuti1Gchtms9j9LYGX0V9sEqry6srlKV0\nXuLeWvTBM9+l1wvvh4X3Oh5o30e1sI94ce8XcYka/boEm+tki8uevz2Dp/LJ6CdUFezt9a0BJRzE\nJm/k+whrvN94OidA45yp+lYSMtH1nxR2kPuNfmaR0BS3LQDo8MQxp88NPSwees+xsxXIQJP3RakD\nC1vhunGiEI7rEUQMIq52xYsXvhBV3W3HgcU1u9NKcMzCRbtx1C96OZuHsiS8oX+NbI53Gt+mjTwm\n0Q9sYvz2V3/Txf6Pg0I6zBrKQGbkdKG8Z6SkUHCcMBiFnw4U26Zp2yojKgXLYcEXPv8FVRBlWSDd\nszcZtpkJuD0csompUoo7GmUQSid6TtGhPqzmIqGgevHR6Tg9uTfDwTUjOAd40rwCJ9eTO//w61/H\n9csXuNlW57iJfuUNbRd5dZIsnvWyt7pnmEFYGfU3738iPDJZ123L3JCJPHUqRQG7Q8EmTxkwfT0z\ntiwd+xjTVh2MWXitrL4M371n2XvLy3vtEZ03ttPyxKAC2CuI7t69W8Mx8dYNbQNbJiMyGeQNhNoX\n/pPDPevjrM9OpRnPxGfZLg2LRzUIqFqy7RRZzD9beM5gsnCsRsZltMyeR0PuHHpRMNCYeViEGeQX\n18mOhQgXy4LDJNSfa2MiA7Pn8Zl3nPeNe3n24MGDKf/v9YebJJCpF+q8meU/JftOwVsCfWoZhrCX\njA3G5mCKfWfpHOmV0y3QmOWJxVng9/yZ0XDALNICnoolfOxikG3LyjueyFrJK6mGjlsBAtbjEcfr\na3z7W9/Gtq6tTjmpBv3EthUvMo6xaWfYvTtJ0ZmJ4zV+b0/y56bJuDPRypt13ab9JPUyMxYS+jOI\nNxC6UzBzsLZ1xY9/8Ys4HA56wniP74Wf63e4v9Mxmj59lV6l26XzbS0GdKq9fag+q6FLz7+IMoXj\nzFypnhQbvfvWApX7V18WEC2o55nFgCPE+yTtZFbW5imbZWZ7NHCrDNHW8/IAgGJs2WZLPX78GNfX\n10YPqiI0NZKdrkzhkDrn/pf3A8Q/jHSZ2X2yq9fZDLsyHa4e/Z7m6tgOUAcbc/RFwqnKW/oBFvbY\nh0M/J2VSPAyNZz4Xg7HyFnirTjrZsFjWf11R76nMb39sbVteNEAT9YUeZh42nekHcHeiuP4bEcVF\nu2Cat63aVBP/NvLlIiHgTDuOTs538nqVYMZcrDvplnP4Y2b/27E2G1v2BHlmi9rxRkRJBI/vL2UX\nLcvvwUZv4/icvNH/GXideWqzOLybfVbnsXhgIFdv697oT1b6FSylNLt59LHPoWFuHxKYCuiw4HBx\ngeVwqP2oPl5TQG7JZ473OV15m7mTU/VkdrR74nTJXrnTbaXPzSdNRBrFR/0agWeia7Jxk43H822d\n8f1etpltvzfPcAqGc3zwrIwbHgaOuniyYT32+0CXsuCwFBAYL148x3o84ubmRueQ163dl1JII/+c\nmhMCRPY2WDFuDtQyM5vkRLrNfMK5fRDTx2ohhAD89v/z6zXunCE20IW1JFE6THMUN4zKdS8u7rkC\ndXb5t1UY0bFeDgf85E/+JB49eoTLiyuFZTbRJeUk1JQ8z3ZWrNuaMoxdRHHhqMhPdFhh043L2aQk\nhY+lHRB3CykNvg9BUBHx9cjHrhKLMbC1kB+/+/V/UB0LbGAVAttQPk7YrG3SiHmr8fOMcVrLmHKb\n+UyEQKejnRAZdyVkEzCRPtEB2BMeth2l0ZZPFM3KV74Tw83nz2Dz/CGZ+4RjQWWN+pk4UEHoz5ys\nDF6t+/tQ+kR9N1/FZcPl5WW9myKEgpvB0o3N/l1CaPWLEUnzRI8556H5uLB9GmGJsM7gtR8xzrOy\nrjz64izQnBlzSiiTT1GubqgG/Ga+y/NTRkj2PKPNrM8jLBm+qa6BPw0lqRRvqMVxZ/skm8i141nb\nsnlMvhjSUT7H4xH37t3rhuYZRoLkiO2pGU3ya0v5ddYfe0ag6pdMBsnuJjfR1WGyOmQ22QTk8b1r\nWWlrA0RuS6Ew5ySTAbuyI8BiJ06iY6DOwhkyW+jUk5evM/61xigR4XA44Bvf+IZOUqy6IELOGben\nT2YTFgqblVeGD20SWLou5aGM0snoYrEfZnJWaD7rD9aZ0xGWRkbVxdIOALUPxC7K+ub5s2f44he/\nqCfFRE6lekhht+DkDpvonbjz+1V6lf6wKRvHg93YZK4VYDIe7T0QOtbThvL2Iz9LncOOzY0ALiC0\nxQwuYLPBamtw7svifXvR4m7x2UvRJ9qT2xbHaP9bfOXU/Yauly4uLvDB++/j+bNnbddjl0VkJk6J\n6vYz+13C/R5KwUKEYekn0RWxZzJsVH/aT+sSO3li5Xf0q7vs9XQXvR69SELf2R95ttfpebg/93ZR\n/Dvz8/fsXEshAumJ867nLRa9vahHhT5A7f91PTaabHWCsFDbgFUdGNYNbPmmD/0d+mrA0eSJ9djd\nyTO67Ok23TjW/H1aFlzdudP9nDBxmcEnsOvYBg8Tni0XxCqsA93XN9heO/duap493EKeyGtRjkR+\ny8KqiZ+zcr1Enmnc2JfZShGmDM5t2zTWf+qv2Wftk/mAezQDEl0i5ZMybuyhj+2llL7wRVJLju9M\nRmfjeW+xMdY1lLd6j1kX5yp4IqcF4XFxkKgKxRrSFjrnMcPBlaO+cLKnk/bf9c+Yn6aG5Sldtpci\n/dh8bP198wIGIOO4suPG+qTZeJv157ZukI3atS/6d+9TmPq4n2rB1iLIbKx3VEd4UxsKuXwRG0I2\nw/f24b5Xcc/+YzbnzWSD/GNjWx2PK25ubvD40SM8e/Zc52eUj00dUWZMfTpV/M1W44KCpendaonY\nkPDn8tUs10yGRf/uNunWobE+0nRY8PzJEzx/8hT3X39dO8spIXilBHTlDvRFCjupNnPW4195lwlV\n+Rvvt2gvm1CrA8tOsnNTfLyu+NznPocHDx5UIwjA4XAAr34RoywEXmu8blbF7xWuw4s2YGVYc4eb\nQHdYN3hsvMMtxMseLmq2zEd9csjiv8lR7WYUlWIHQ3O0GDrJEHGQvIMRRPCS1aEyhpyQ0ELVGNrw\n3rvv4u7Dh+CyVWHIjOPGOrHPzCgMDeNU8QeAqhBHxTI6VlbhEEmcWmq/64W1Ec44wGdG5wzvqCRm\nSnLPuMnb68aZGOI2b1RWeTudHsysl0diG53QjC6FhVW8ottzYCNPZYpG3me0zmhChwWvf+JNPH78\nGMx5mB4bVmo8At0m3EqTX6gLBtjkTpsdhagPu7EU+SQq24hPpMmMnrFc1sfegPD9l+Pe88bj4iKX\nK4UMPA13MZxncO6dCtrjjY5Ml6cRRwbqTrpA5zixbidtgTpxUWhx+PZ6AbRo3oLfOWPd6aJmhFDx\nO+fEAC9UF9xkIcTWkfW/fWcdS2sc6vsmJ3WBAF4Gxb6NiaiGJ+AgO8m0V7ujLRJmY9HU7caqUS29\nTsWiqeR+t5HIaNps673gML7cnstmcEJIJuEmjCw+ayq7QlzbadAP4wuq96mQGvRSrpTSDHaG2AWb\nmWwSKLbjEe+//z4WLI1nfYi8Os5J23FjX9vz+FcaeDsopV3g8UyGWEPckQdG11AcqT3WucsHTk9p\neVut87xr0sDGkEseN70nCADeeP11fOITn0BZFg31Ye0Wm/bG92A78Xlc8yq9SucmnQgMA8fzZbPN\nYfWE5GvuTJN3YiuLjvS1SHiPAhd2agbbRC7EdzN7ItoxfXE7bzHTt9F/u43tbesc9cXpsgoD6kSL\n7I59/uw5nj19Wjfe9EKAkbfEZpHEIZm3MXtWQ2oBsngUKoGcAzKuje9zqjdzevtiB3moe9x/g91d\nfv1557XM3/VaXp7n9qngIPzr4TH+qeadLMCYBQkyCmnbBE4oLBqa1OhA+R79KOFf7d/AT3Gj4ixl\nujUbS5kfEJn2lD862tNdtx4u6lQTlQKwDws+88msvwQm3dBpfbUCwixemt6v5XzKUU7Z9iJt4mbU\njF63tdkjL8a7AFoBB9NsnqDblyGk8gTHWN7iJWVm3DT3pcSqNhsG0f2mEnCOPnUh0pMhbiaFEJb7\nOqyc8Lwdo3HhdeYz7NUR0+FwwHI4NLkx64/+O8NVqJv5LQ6/pmgZEv5/rveyVMWXN5CbS1LD2tn2\njMdVQRL/pcvmPRkjsMXfqc9dnUQdg9rfYeyJr9zD41VJK/chi+y18vGcZO/JObsc+xBv4ufu6RZX\n3IyKWb/vwSH+ZPSFRN/YxV3Vi+ZzPK7VdlgKjusNrq+v8eTJEzx79hR3Lu+4sSJtrOvaFiXJ17tH\nr+YA74nDQcdI0Z1CM10x5LN17YzjLH2sFkKO24Y3HzzA73396/iJn/lph+i2bS0uWv1tCbYx9UvT\n0QeepIwho6GSCdQ9RvZMS4NgjkywAXj42mu4/+ABnj59iotlwfXx6GKAVjzXNjCMlApJlXWTZNFo\niiFuAPQ44T1nykjOCEW4RwBeKIhiA3X1pu0yTDztxU18TtuzChVFy2SKJBqcAMBEuFlXPLz/AH/w\nzd/DZz/3I2BeQKUaWox+sRq4hgshmDjjivVoCMXfDgulDVz+/vo8Z8vmd23tGbMAZFJQJy5PpFHo\njTuBxFGIeGTCLROilg/q5cM9MKgaSEZhFfBQT3Q6M1jONdpPOYokXhMqP7z22kNDA+hzZnY7BS2c\nFi5RpsvSd19bMzTiJA6X/BIjgUodBxonl+tiIxKeyGTUbIxntCCiFmJnHv7FPhE89+qfwTDrD8E1\n8nwmN6yMzxya6V1KxsiRd4qb4c1MMdtxqbIRdSfKMB6EWs34lKoyfon1a742abAFXrP51nXF3bt3\nlU7LsmgInwxu1y41Qy50kdOJFp5JvbFcTC4kgDFuu8xt5esLZ/TIZwy75vlP5PJwFwcbY7XxzeDq\nxX62RrhtqxmoYihzJw6qg9FLuDqbvqkqnaSRcfOBLhAx3GSVcVbqZL11ClfonVBcw8w9f/YcSymg\ndu+FbAKwMkzsEt4APVjrzBrrZZ2WqdL3cezExYLZqdw6BjegXcgeJ0C0fuobXfaM4VE/5GPZprXF\nEhbZUUrBw9dfw73791Ue27KDHA/yZjbOASger9Kr9EeSePaj2TEQOzGMubaZqb7yJ4BtPkk6rlSM\n5WMw1dmhjtn4zca/PO9jK7dBsw07EZdssmIGdxzf2Xg/lVhxIshGOHDddHV9/QLX19dqe9T3dSOc\nbOJykwDarrenZ3idglWks2GDvggmrSgAvr2aRPdltqa0a/zFpN/cvViWbkNbFpjcLpz5BJ7/ej0Z\n/c61WSsMXt5Hu3HqvyFqpDnMzKyX1PeQnjlcmY9mU4H3q2Zt2jr6M7F7+rOyFNy7f1/fR79N27Wx\n9+14knmMxtN1cxFhWQqwbdiCrW/xcuOCe19weDfT0Vko3z25Zdu27yO+zgaKNoGxf6yPkvk6gsvg\nq3oAfP1h7Gfh0S0tsnfev933JZ2mCXxuxzczt003ufzP/GnbjjNHg/0VYXdlzQJm5mtJHXfu3MHF\n4YBt474gHVLEVVoY8sndItpug8TiC8+jmZ/r67R0lfpGORyT9zehcw2EekrJklp8QtOo8Vsj3lB/\nKrYn0Nj3FrdxYVA2mvXNfwy4uyls2ykvtju47HuF1bn8FP4mibxOsj6ordf6Z1Cf0MNY/RoGStss\n3fJ1f8/PRwyguP4TfLiFmjN3AbVeffH8OZ49eaplNTIQUDcytChDs43mrh0YeaA+qcA1J1+sYw+3\nWz3X/wGFz/eePlahsY7binVd8bW/+3exriuOxxsANST9zMGU7+1eLO2daIzYsrNkhbgoSfkeP4Yf\nAe5KFYCeTNDh1t5tYPz4l78M3jYcj8de2H06LBlsFhfmFn5CAGltZhdE+2PegEysOMfC0i4oaPk7\nTl4ILXLja0+BRzr7tG98ZP3bMMWdqyt88/e+geuXL7Gua4+RZ07fsKmDOYSOYt+GPjOfFayfI/dw\nL5JfwmhksNrf0XCwbXLjD1s2hvSqfNakUwv3QoG28lmWRT+Hw8HFLJW67DF3S97ZvSAKQ+AVMpOR\nGe6DQxkWCGz7wl/DRJLBc8/5OSls2xFKtHtvNgCvv/46ZNIzwurxzHlaxpfr3x049Do5w39Ap7ud\nCJdLBUuheoLM0jEYnjM5mNHELoBExdtpZSaR2Ts9mVHqeDdpP4PPtjt7ljk1pxxAYJS2DEMrU19m\nzNt2xMDIjJ6YMnpHWGMbcUKggIY2FqE5gPv377v31sGLfGj7ysIb44ULvQLgTrZkuMa/oi+rrPLP\nT+lki+/sXcav8r3WH3iNCAjHheMJB6sfo67Ixj4R9UshnVzrd1usbMLBYaRtrM/1V9L/ygfLojgw\n6iTCu9/9rjv9afNnNO9ko9kLxzcVJpFXvV6re/J2eKgr04EyCWrf9YvPbahPb+Rmtpq2fXLaqcMo\nsL18+RI/9mOfr0evSr/41dad4RrxzGwcZu73xbxKr9IfNqnxVy9b7iF32vO24G3ZsPKmfObh8TL/\nw/5NwQnyzKY9md7Ryeu29nqsb+Z72N979qCVSza0cNR3s/ZsGbEpov2+bf1d3Sm94dmTJzgej+qv\ndF3JKNJ3ZmNFLT/aTjEcV26fjM+qzI22mdFRzb9o29LBzQa1pIw6sn667kdDI+oyq+eydI5Ntadv\nxPSmQMeMh2d2p/fX0HwnmuJv4ToFdykFheE+lPCX+lITnb7nB2W4zcZP7A/XVwlNqBTcvXdXMNIy\nmX0caaF6vFReB6GGZF5Ku3jd4+dwBpmTRRX3dV2xic9v+4PQ74GsI2dYfIs0yODN5GNGe7fAEWgs\nIe9s2/I92uUSotv202yMZ/0ZcYv9YOWVvB9tXDtp25PYtZkdG+mn9FKfXmAOG6MafIV4+GTja2+8\nKg0E3nSeSVQj4/LyUjfEnuOXZO1He1N4T/SvTHxnm88G3ZGM8w7z/kbmqMtUtveMGqLNfsTXMBXu\nymZCHq5fYNH7IAEQ97opfCyOHfa5nZD34+i7dxoSwAG3UE/0R+xmqOzqAA8rwZMtzAewed7y1rCI\n3SDL7KVs3KrtQFW3EtVQyDc3N1jXI148f44PP/wQFxcXWiYLcZ+Np4w2jtbUFzJnfo3DG3N5anHJ\nbYcZjJO5iZ30sToRshKBlgW/8/e+hufPnuHe/Qcg1KM/zMCBJqtxABgEtJ02kWlmRrhVFtYAyI4S\njckshuD/Z+/NmiVLjjOxz8/JvLeW7upuDEAsBEGAQ3KwUZAZZ7GRXjQ/eUzPYzI+DzUaCkOMBG5o\nEI1Go9FbdVXdqnvzhOvBwyPcPTxOZjUpk9rsRlnWzTxLhIeHh/vnHpvOskgMioySyOHozPjjP/5j\n/G//6T/hrbeeoCxAwQYPKH2wQbZbGoVKl9NxRat2n1Qu+ewaw4HJdWArci5C5FWjLnTQBtijEuBR\nIVnFbAN1cTlbLDcDqpZH9h4tC25ubvDLd9/Fi8+f4cH1A9nzkSTAvbDUfyFC2cYzESRzF/+Bhsnt\n4FBxPBTAn9mKDLBm1/v3HrybGfkhPzbObf0bZ67O3ldgn/EyysmeA6l52KRKUg+yjQ5IpKnTsz9T\n0N5fILMabD57hjtLIhK93HVd8cabb2JdV2y8NcUf9YSlydKm963M68DYDMS1e4aPS10N0gxlpVO3\nofAAxAPYvVnY0bDEPulWgUVDZO4XltnnM5mOedXCpoax8Q1Aqfya8dQ+b/lvHZB0Ky3LsdAeWjaF\n+3m7+dnvmNiZ2WyszHnSMl2dUFc4UN8CqTsV1OT1cBhNfdRtM+Bs6zqASCSrY8isUEnyG2wDk3M6\nl2V1tpKZsdLSdKqWG+sQ0x646nUI9wzNdo1WZk96ncaaRhlmncJk6HK8R25199qk8S+UC8M3ZtmH\nF0YWT9uGX/7yl+2dUnWGDeDrDCFHQ7ADlLQxM7utEFbta6HdgXEGaHROoqy3Z0up9Qng3+S7Ll0G\nbRs2vgW5tnTN8ETUN4fDATc3N/j2H/yBO2/uuK5TXGTzjLPeBqwiX4T+10H19+k+mTTa837d/o22\nsj8vOjrTcdNUFVqGEzVdsjKj0zvTkKLu1BqqO9SDAGN+ERecrUtIgz+QvD/o+EB91Hmenq7XFyI8\nffoUH330Ud3j+4TlSoLAy9L1q5l9Uv0tu3oQ7a/qFVf3TlSnmdR2ICj4wIvBfhb3wqV8fR3+K42Z\nvrb5zXD4ubIv/a1+WGsC13e8vM5kfcTZ2gzej7W4f+YzR2yqfyOvBl4bDJthnMwXyNqr5U2EhRbw\nAmCTAYc33nijT8gkPyA3a6MBvy/UtoGx/kiWiKjhE9nlIeBY+WGwndliHdx2yZ3VM0uWL5ZPGbZ3\nfK0rSZdlabEZNs9GvbVX55lOUtnIZCj+jng3w0hD3XOGpH1nhtmt/usX0TDQ3rvx3h6P7Pfm24La\n2Uz2nqaFCFdXVzgeDr1tqaPvPezYlGRXtD0OY+tCAHM9/5iwm3dWlzHl/G/WMspWnaclLJ8PXjTP\nk9m0VB7fAswZqLN2w+h3T8t27VsngDVyel3ibg/S5/swU+HiB9wtBXXrX5D6bRpT7b76QgtolXdn\n24Xnlegr27jqwtZGRkdlOjjr21Y27BZ7SuNhXXF3OrXY5LKsePXqFs+fP5fdPba+dTnRgiWRS0bd\ndrPxUq9OfO+m+y7vh9k91dOcTLK2aDT2k0715elLNdmM1gV32wlvPHqEX/ziF41hOlsJeD1ApSt/\nrJDFv3aGX5xFq3+j8s06hj0gTDuqfEc7MIgBfOtb38LXv/ENeXYbgYJbmVAzOFfnGFCwtM4MUgTL\njU9JvvGZ9uxE+cWyspR1+syIXeLMtOcWwu3dHa6OR9zd3uKjj37XbJQ1itu2DTNbvIwEWtFHiPUA\n7LO0hGdmfLT1zECoLWbGi7jqQ1d72Pyy2QZ7M3YiHbN2zAK9WT1rLmleudKM5e2PXuu7TsleoCvk\nHd26SwzJtm14860n1RldUE7bkP8liagvS5yNnANe4dMSZOACvrpB0NDeUZ/sOTqtbwRgbstTY3w6\nnVLZmc4Q0E8i95ZuAG51lZ2ROQCfoMPjrP7XaatIQ6xXTAMNHAemztuqPWelXTMzaOx7iwGmjx49\nwvF43JexoDvOBaky+i9xerJ81ba2AbSyDc8g8H0mG1YGYv62TZrsJgBLgB+3C9YBbWVZ3MzeTkQ6\niGjYx9W1w1aaDqkP1Pu5zvX9z1Le83dlc4X/Qii20wnvvvtu68+EPkOxOxHtiEBTpqcnWy1oac3a\nwvbxTM9kti7qHVo8PsmcBP0s1M+H03vxr9NdqH0V+4eUH49HMDO++tWv4p2vvAMGnO3P6hBlIzvE\n09ajFDbnvdyn+/TPlF7P7FXcfx5fDe9MMMRATsC4vU8w2sGgtTey9kriGvJfsdAKohU1cvHFKnmG\n/oiVs3pELNVtBTc7k2H/rEyrM9Zlxc3NDW7v7vrWpJqP+fhac/tfZ42zEAAQtcOZS71mgwmOtr2Y\nzg7+mbf/PK+oz+NM20vTJT7lJTRndcjt1dIwtvoJl2CokZ7E3m8F9XBKh+NjomrndGVwxBhn/fAL\nn7socc2xzrBe1hXX19dYD+uwqvgchh5wly0mtFW81vIOPr6NxXQ6wuSuxPJnOiqjIb6jKdthoeWL\nHvCLvkpW59SH2pFvojqB7IIBuZkPFus0xATgdRGHe68jWw4vTSfsnpejGU+y5+0VJ0dAm/C11HN+\nADhceQktLbBO+3ITIyKpTbkguXyCbr1Et03ztfbMfGYp+uZ7xBK9vuWOfT7D1f0BtHYoKOC6IpbD\nv4YldEsuooGfcRKalpXqoC9Qp+gzRJ2VvTNcK9wOSycAKAVPP/tMng1ZDDlWXklR3GSXlu7HZT7p\nF5HV10mDP/nPkOeXakUIMaMsC65pwXt/93f4wY9/hAULDhvjhhZcoWBh2QaCuc9maUoAauzqjcK1\nle2BvgSxFQJo9F27lZGeZiANjWZE+4Gjq8kPKCwrVqygHeiADZubKXk4HEDX1/j2t7+Nd3/5S7x6\n8QILL60cAFjoAJAYs3ZgKJlAh3zDVjQgprRsUIAmB6qaVSUR8FceEff9wksp7lAr5bFrH6ssmuI1\nB6wyQGoICGCuM0cwzkixijvbj9/SpWVHwMwV9NOyiCNQGA+ur/5WnYYAACAASURBVPHqtOHBgwf4\n1T/8At/94z/FQgDXgJCCpFK2vmrCACnmeoi8lSsWN033K/T7iNcMWCRHgt5dRmPqiq+2pNXhDXTL\nSiBR+l7hH/VQOguC2Tg6hcFU2h7qAFAIQ7BF5DoA6bL1fGTanVOI9cn6/AIrk3v2wLcdtfd6YNS2\nu+1v+SwmlTmuh4Wt5GecA5B1VsbhykBBB9IM5oKFxKk8Lgc8OF5jPRxxur2re+xvLv9Ik8pDZrTb\n84XbyDSzzJQopGGAaoQwDojZQ7wFpKDOCuxMK4WdE3IygU9wnwW0bZs7B0T4P4JnXWVDtc7qaGuR\nrc9iBCq2LVz/Ne/ZdkDNRw88JvOezdeWu8jF6XNRboQXsiKsABh0ieFxabu6FWCR/m4P95Q8/eyt\nJgvETd8tNJ6VkMlz5tTq7wJZ5aYrb1SmJEwkjtexHvK3kcC/A61glDqolg/ORB3s+TRuRSSxKQGH\nmudCBM2+2cZQDqOAsNQ8lA9dr2g5RflfuPYBNGdV+e15Lb1GyiDI9ESvM5QuHYjs10x90Vez9XMz\n/Hkz0p4mb4XSi8wubCCU1UZ2+0lE4HpA7bLoNTS73No5yLEOXGx20IjruVtlkx1vVP4q31APG312\nc4Onz29wWFfQsorsLj2QD8gMIHVepU52kEP4UlCcM6Ayo9tOSFsuOG1bW712STDDfu921MsfKGwv\nYRHxwvVwRbj9tjNg3vp0kbPKDtW2MilWqtuKscwSXao9eXV7wno84Kvf+CbWq2uACAcaZ6dlZcXv\nuuWo1f2AyI8d9L1P9+mLpMFpVj9/0hfsO/0ZeUn14V4ZqgPlu97P35kFZeSBbBUyDK5R3Rtt76Te\nNWX+xCxZu68pm8ke37H3LQdZHnBYqZWDjkOkHAaJ9cKnn36C21evwEXOXODC2JaCDYTVaOFqCXcB\nt56D6No4PK/YTXlsc4/1bA/uYHzLautbRGxo8UWjI2D07F37e/QHkln4yXuXJB+MGicYKQ7prkOY\neJGU3es9oSXI+azeUZ7Un8r6uXtXsTeN9mYWdJul5sHZOjPj8ZtvVFwj4QvFFojPhuux79nrERs5\nWTJ/aSFsp3G7SpNhpTnKusU/vY/GusYU+aV+37quKFwnZ1QfhtED6ns+jc3L5a11JPKDDYZmW0+b\nR4bpuZSmG+IuAcq/rE0SJrTyLE2ZLzPivhADKlLLrE+n9gFehiJ+jPVu/A3YHIAbWLy+vhZfal1h\na+95keC7hK6ZLGo7Rr7ZtKdH+n0CSj1Px6zIINK4ACmQN/qDWt+d5zsmRvX/dtRE5MfgW5IEOjwt\nXHUfZMCiliWxjXqjR+pTWRjkNAUi6udFPaP39axPKX+pcrJtpSmAjEfez8xld6bjMj5F/kUf3U7Y\nB0u/KdUBJ4jdf/78BT799FMZLFIWwsodu/LSeIx5rjEQI6/3+mesny3zEpvT/fOBae3P67hMX6qB\nEAAASyDib/7m5/j3z57j8aPHYFplifCk4jMQMktRMVFVHNbw6hMSiPbOuk/z/Vj1mhXywowf/OAH\n+OlPf4rj1dUuYI/OfSZozKUeEOSBvLxL5nsOPvV5DUZF+mPaB3xoitgqgFh+5FdWZjTOthNZAEZE\nLcCj4INIDlp771e/wt3dHY6HI1YsOBwObnuQxlsOARmKMgJEwNlBsT7bQXJs07H+OgBiPD74Z+yo\ndwaQbRmLWfglCnOTqJs+B69QFEDHvNbYPqE9LJiPCv6czPgk/Ip7rfd7O4bO0mMGVTIexf6zm4ja\noOWyLLh+8ADH4xHlJMHFBfm+1DMgkRrGRWS2gwMD6BsoHLemsvXqPO99nhltcIOoB1idQTXvZwM2\nM7oj54b2DmBub39qvR+vS1silSdb/2i0M5mb6Sb3zPBEexLaF51D4HyNDhRSp4FJ9iJOwE/rr2YW\nZCYnjSdkDjqLAMLYw8dvvOEGwdThzXhmy5qBkuhINJ41cGb1TeTLmA8Xbk5cz8fLYtN79tRuo6a6\nno3l5f3e/o1Lt9UZsLK25wyMeMFPLhBgOtdXe0GemYxkZfdrssS5865v1Vi44PPPnsoSfwBb2bAu\nK/yggqDIdCaeqhXa6ye9aXQCiR1siry1tiLKvOoLawus3PXrSlxXOTOnJN4beajOlu+jZASulILT\ny1f47ne/K1uFDrI3Ypmov5y8TfjYdcxroPr7dJ9McrKvMYcJBs2wUr+Wy3VMZO7NbEd8N8Nn51KW\nl31/tvVvpmf2Uo5DR/wiJlcxQv3O4mf1B+qAB7q9621TdWH7XvXzuuKTTz7GRx9/jLff+YrB/oSF\ntcxKayNOCmH0AIfYyBErMjNAS7XBfcBadHYepIkHcvc6o+lCix8iRok4OdONmY3Q5zPZzdrGto/S\nrQp3RK/J84nd7/T2cqL/b+XxEpm2edWSnV3ruDivV+ShvTaWdZ4e957B+bP8GnbViY3qv9bpww+u\nHwAkW9eVUtC7RN7vVWbiNSezLDZ5CRizzrlMJ4+1vJD7f61+FV5afKurpxCuZzgd4XorK9SHzTuN\nF/A6NPLAXtdBlIXCwerykPit+u7/CxPIlAnE5Oomz8j5Ro0f9X8rQZmGnvVnUXkeLc30QJQVq79t\nX9GgcdNT6L6k6OfOX40fbduGZTk0Pe354yfmZjIW6TubqPu/DWeee0/lQ+thylI9MtNt7XtybSym\nY2UdEFc7Y+kH1+tyYdDnBNEb7Rxb0v7RBynDaH/r20CuY6e+ktITnnPtmQyM6ENt8HGZt8W8besQ\nT5vc6/WN61ZR10zwSuY7Eno91mWRM6cB3Ny8wNOnT1Hq5LRzaeaXWtJ94+TvZynDVLHMS/xdoOtq\navS8XvpSDYRw+zDuXt3i/V/9Cv/yBz/AVhiFT1h3XfPRYF0KhNGgq3+vN5QPUFvF18okcoY1c303\nluDD17/5TTx/9gxvf+Urg5Msq0A4fT8D6l2Zi1EahWuujEvdr1AM+DjoEA2M0pDxWA2ZOgvUFOd+\nHfTanuMya9ODrmTRPIqcuAJmLMsBn3zyCT7/7FM8fvAQIG07IIJNBVZdUfVZW73T5kDWPiOgYDx4\nd3TirONp6zQuj8sAsB3cAmTGeiurDt23HGs5xbG2ynsw4KWWbZ2aXGmNQNDXz5atPN03ZCN/5/c1\nEFsoPiMB/wjqL3YMTDsdDwccDgfcLoRyms8Q3DNe8Z5skddnU1pj3PraQijbvmHshaDlpiBc67Ea\nEI5Qxp7hbf2buYFwC3SdeU9A2zkdPDWMQY4yGnPwfJmOdwAaURq5rlKY5SdAwIHIokCJWjvaPn0R\nADbJO6ZK87yOogPkrIInT57U7fAk4P2qFJnVFNplr+zM4bgUpNgyGsANNgQ1+LwQYat6Mg4IqfPb\nf6PPDpqUpY6OHcTPZNDLU86LVuesXwC9f8nDu7wA8oMqnV0193QQ1g5OZG3Q9UWnpVS7f9o23J3u\n8NHvfifnG92dpquQGAC7LfuMDMhyk8p/kX1n55cFlAxotrqEGWrZlnHRSctkLe6H29tlHIi3ec3y\nkzlofgYVmTzbu0Q4Hg949uwZvvH1r7s8Zv1ocDrZrCy2QcSkzuu67m4PcZ/u025i/31m30cciuE5\n6R/LsNWf/a56a88eRJ0zBmVyPWuocvlk9Ykzn+2zlwQBbJ1s2j+boOM3DW609xJ+Rxs84CEWRXG6\nO+GTjz7Cy29/Gw+WBSvgMJziLcF69XfFf1jUfw143uD4bOVZt0eNwnmbkq7S7Cu4rd8SfcU0eDPR\n9VEuox6f4Zg4Yai5GWrGuhPU8p0FZ2L+fpX6F8d0imOydzsZ2T1K+4+9Z69H31Suqa2rtnwZV88z\nc1sdmeVvUz8PT338aptBeHB1jXU94Ba39WzFstt2ml+6GtTUa5jsZpqu1b34SZ2xvJ6v6SMsfqTF\nEZHHC3kfeaBt4ufEgVUVSQrP7aVM1w1tssM3S/MXnUDWkvYnU6a0raGpAmQ9h6K9yj1o22kNk4qp\nDrCxbotI7p09XTLrF/Y3N7kxPmB9rx3mvSy4vr6WrbHaIdYdu3VSqeO5YJsynB/1HpvyXXs23Z5P\nLhifR+MRl0qnXqNM9n3SK9l5oHq9yRC0v1R/N4gK6+4lrc/K/263GNL3F+GgwylVnxib3+nc91sd\nHRHXZC+1OpGT5fhe669crwQytB3H9mI4XwpG3owMWpozuxdlqFHG3Nu6ZrmVGlnigttXd7i7vas6\nTt9VPVS/E1x51nanNo6Ub+d11zS+U1PmE2dbLk8x7FkK8vSlGgjRIMW2bXhwvMJ//+lP8S9/+AOc\nwHJQ+g4QEYMcndvsuVINYC9zMHQszn4sbVxdkQM6Wx9riE6nE0qRwNWPfvQjfPTRR7g93YERD1kH\nbPBnz/nuqwG8gckUqgerjMJbU5xbOWFdjkP+WeeMSyobfSxBeValx2OwIgtgxDpq57B1UdBUapBv\ndhCbOmqn0wmFC3717rv4+u99A+txrWUrr3xZtp1moD0GTTKAbPOdKWoi/4wH/v5Zy2/LvyzZ2Qo2\nH9Hlht9M+aFJIe/sN+C3PHpd58AmqXucLZ3LrAWxU4UtmeiLQ1vu0WuNDjNjqUtk0WRqnHWhbXfu\nrJVY5/h7lKPLjI6t81bpOZmZAJlsa/lr2MomA0CgMKMx0KtpYxl4OeesZfxoz2IOCmbv2LrNZCLq\nqNGwiptC1GdxZHkNzk3Srtrv5F42EAa3MscBZOU/1W31Su/PsQygbpF02nA8HPDo0aP2zLquA915\n6qsylIaMHi3XXtdFG+Lk+IATA267IucgxsiRyV+AW3+n2EMzdeYZet/XA9fbeRdTXTvOThrKhXfK\nM8zX7I3qnwLQTqxNZgX1X8IbbwezgJuVa+vsufqUGs6PAwxE4HLCr3/1XtsCYVlWOUgvOBnWqe1l\nq75cQeiHokeenUpx/UFp1dUhMxykfLY8mF3X93zi5uSpXbQ2tK88gsvLO6J10keVN3XQrOUppeB4\ndYVvf/vbeOPRY1lifkYdDzq84oyqEORaNbpugCeZxXmf7tNrJbJ9xa++ao/YfhLujUm22dXEXPtU\ndYhbKIHGAMEsDTgMaAd86n2L7XTr1WKCubYeg47sFQ1Ej1hi17adxYps/ni8OsMfu/nJXlgAGJ98\n/DGYGXenO6zXcrCyBlwsdnBlAjLLtvGhB8JK0/tjOEl9wEhS44HhX72hSCn4OwyisNpwUtf43pw/\nPY8ORWdyYuQikNwCPkuOD+Y0zpS90pPjiYxWfW/isrX3bN/s71j77/Fg3JpYy9vD4NY/bPQFPlq6\nM9+93VsWmXTGjGVdcTgesa5yRkjZ5vZsbtvhrgttVY7VZrdAoK+vnTy2N4gpL7k/3dMiwoogtxNd\nEevTrpNMxHMrY0NZdqt07LTdjC+h8Klu29M758qaPTtGvGo5VGVTMQ+M7BDaRLHed9GuA107aV7u\n/UCL3sv6f9ThdvtmWxfrlxSuE1aY5Vw4QLYYZtliGOC2xbDiNfDI0xkNZ/lt1I19N4sLRCzbUavB\nwks9+4/G+MyCOunX5Lexl+9LpGKmN7PYhrp8To9Umo2mmpfLKh/Bp6h110Ga/rjBJWQGbbjLWayD\nkzWL9YXwsU47v2M57hb3eszkIbOR+l184X7cgAyIbDIRegH4tOH25Q3K3Qbaent21BCMI41tmcVt\n9vTpzO/eq1f8ne2YsFuW/l0ukdb66MVP/v8gieJkYBWyP/j1r7Hd3mJjxu3dnRwAB8YGPVLPHKYL\nyMgkA6R/uQtQpmB8Q3tw2Z1TSoPu/cmeoiKMiqwdar0e8Gc/+UkFqyxBSUOn5NXPT5CgGEFmaoXg\nEtuDguOZGp2vlhalzx6yPVPiewDJBiKWZal7po8BsKydo0GKh4zF93X7qxQAGxqWuo0FrQuOhyP+\n8d13UbYNGxcXsI7J1t8GZRyvnfM1ylULeIW21GR5rQBOl98pP/Xv/oFq/mNq0d6f8r54mex5+gPV\nY90jP84ZhATrpe3d5VLprz2bC2RgT0BWesByrBtz2xJoRpet+6AX6v3DQcaP33jjDeFJMKANRNVl\ntDM+zGRN5SDqh/Y+jY5f1odnPI7bKVm6m64MoDn2Wa7C1Ubwk3pk+sJezwYomq6YAPhLk27FE2cY\nZDSmW180YOFXJdAiof2lPoPKr2XJbUcsy/0uAGGRwQ3Wsz1y8AH41YadnqXdi2mpgx5vv/02qB5U\naWcyxvzbe+lB2SHvJHgwS04n8Ni/bB/TM0+iXMiBr/3gV4K3B8NWgc5fzcFjvObl1QcZyiZ2VlBw\n798ZX2ydI/+knv1AVU+I0L3QCi5A2fxggqVVr+WOPXlQb2zR3d0dPvzgA6D0wQKZDaz5ymdZ4hAW\nACzQVUaks/MmvNSgh6XV6hG9poeg783QVtpVb22Gp3avbWbPH4H2dQoiC2aigNeinmt/ibAaIK3f\ntN8fDgf8yZ/8SbMF4HxrRFvWdIJIzf+sE3Wf7tMXSDO8Pnsuw36DjqGuK2ipzhTsxzx4AV1DuQmu\nGWnaKg0MoMAGgpdlER1kgzcJjsnqu5fO06QBzq6Brb7LsNqQh/mt23C8evkSTz/7DLevbrGsaw3+\n9ln17UD0Hq+pH/F1eSvAJoczUAEWJqxq58vMLuZtrzTqpwglbjvIrK6X8DW+a30dNjanP5/Zbp+v\npugHrutaV8p6LBF9hJiPTSRDP/UDLODBblosrm9ZX/CStIdfI0639T1Lf/UnZr034+nrpm3bcHWU\nSZQyEXX+rK3DXh9rv93LIyKJcqJlXJL03MONGXFb8Eyu1YZn9AsmZWSVz9qJmdtZhVmK8Y2pfMD3\np1my/SfDRHsp4hhXerMTecyHS8eA2jelIannaL9fQNMlch/pH+Sq1b+33fF4bM609ht9ttO+n/Yw\nHhF1e8UM6AIYJudryt/uR4hP5etTvwHotLW4HG8TvlygqyftqOVerM92BgNY+4kpS5ogkWOKMjcU\nZInHQnqilio975POUlrn5JLnfUbOaI9a3ns6elqOfaYhjtQuvHp1i+fPn+P58+e4urqCTt51MUeg\nxsa5nllLbUt8r88ikZfRfUmKOMD6VfHebnqN4r9UK0JQBzi0ye9ubvDLv/t7/NEPf4ADLXBhifpQ\n60hcxUSBpjIxHDat5VA6nbMOHgSwmik3mT05OhfnhEPz/c53voPf/OY3+MpXv4qyFRzWFXenU9vS\npGyyRZY6/D5A0kd77UypwTADUKh9DkBJJzkP3ixwin8XWrCswBb2UTwn0DaIsgvoAu3ZNZvWdcX7\nv3oPr169Ah0OWK6uxVkIs7iyYPG5suMzeri5DWqO7yqva8CzHnblg0cjGPMBWE+Tfu+OxBiU8XXp\ngW0Fdpq3yl7Gg6igYvmq0GwZ2klnQNDen+3TPPvteZLQC7GBaxKQjM87x0weABHhYZ1pnwW5lFZ1\n4pSuOFPalqnBe/tcpKfNFq6HTGdBNc8X0UWlhIC/aafIQzkMrNc9A8dyEHYfjLNB/KxNtDUtbSoT\nNpg7G7jInDwOddhzhmfOlQWwri+GtmnveQzmflTT4PIa+MDc8khltJY9k/fICw6HWmsqRQ5/ZiI8\nevgQRPZAdd2UZ6RN7Ynalkyu43ua+n1vM2y7yBZjoY2NfMx0PMEDYzXovfyuN9oKELarl7S8XiYR\n3BYvsU3UflKwB3FVqR7gLvfRHDedfWuTtn/s+75/mXoFXG1nx2X9vutNAlOd8AGqh58zPvn4E9y+\nfInj1YNenyIel9dL3uaFQqSK4bmmH+FBfuzbccWD0hCftzq0yTu0n/UB2yZzxvY57BNwS9anmmy0\nKslg24K6OoS6fB4PB3z68cf43ve+h0Jo++j3Q083LMs6yPKgZ9DrZDHpOYx4n+7T66TBeaQ8eJoN\n7Ka6WHWP/rZKjsxv0v9yG7ZbDpE5D8q/E/Fuh4rSk9joV1sKm3wUk2Tlz2zuayXWA3jnj1jsNOMH\nVbvFW8GzZ8/w4vkzvPHWm9hOjPVwrLpH8aNs4anBZmZ2q9Ukn821OcHf17o6e9SIMvSZkI61q4RR\nXvbwV7w2C3DI9XJRsHFdFyiU0voQyQoFZ8sLo5AOonOrIgNmdY2Wr3klqzT1rxhusYEBL0c7GX3K\nyIf+nlAl7TluIenl9rwca36R+oK8bMErcx2Q0W1pUxtf6qScw+GAu7u7wdZHfaSrRy9J2WSQYftp\n6m2fnX/h3hdD3/Bl872Yp92Zue84YumKOyMwc5u8JNuc7+vBiO0zuYnX7buuI8DLxKzdzj1j7w3y\nZuwCg5ta5oahAq8XAgUM7vyQQLNAtJwmhw2DfGk+rf8z1S2jVLx9nuu6YqsTZGkRDPro0SPQIhPX\nmOWwbL2nE2LiWUFjncZ+6f05358BtVW2XfqKeHlu9OekRpUj2ocbDvcrP4QvsvqlCXip5zGa1f2x\nLqn/aupkn488aP6MtSnJ+60/NRsXd14QRkWOc29Yl1/uLwGosd1z9knboifxPWw+gOjTmf6ihdrk\nA883lY+x3HMTOomox4aNnShyKJPsgLNtePb559hOJxzM1thWV8d6N2+eogxILVU+kTwDVJxQ+5eT\nieprZbs1zJLll7WjssrJP7fHqyx96QZC9P+CAmLg5//tr/GnP/whStlA69pk33XyBmLmYMwmeU5W\nUNj3ABlkIHctHzSZORCzAAYg+3PqIdaP33gTb7/zTi1nwbad3PYcRHLw2NL2f5Ukh5qbgQZuHrw+\nAdWbNkBRuK6oQFW6IBB64HsM1uwDWwl6+cBH0ZkzLcCxb2Qz5yDbQ07LiNu+pEEcoO15um0btm3D\n008/wfHBg+Y4NP+t8iOCxIxWTaqsN2YzS1wOZ4/nx0Qat63U9vT59o7vncH4jKbMGAtGt23YvxfI\neCCBmmFXM2qbt5RcbueBJf897g2/n/p9Cyatc5PR4ZyyCQAERuAfabbg2gaDl2VBOZ1ARHjrrbeG\nbckUQLnn2+oLNMc1OjWONrNaI8q5d1BGAJIBWULVLZYfYSss6/SygtlwTw2t3l+MAeJSRI52lp77\nIUa0+qR0BHBYMMrUOX0+XM/kbgAjPbU9veusEdl90Tuyrk8ZABMd9rH9+rMqNzroiaTNbRm0dNAm\n7Rvam3oQ+u7uDg8ePnTBlrIVP9udYSYEdKBqwXcGeq2eL4U7L12wXpbEa71KBUXKI3net3vuTIwr\ndmz+rvWMs2TttD9LxM/SbPW0AwBY0E9XHeWptTF1p1l/6pZcmYMSy+ztWCkrxfRDiz0k/BRXi2U6\nQAmR7WUYp23Dtp3w0Ue/w/X1lThxROA5ebXU7sSCpW2XynBFY8y+/SxeUP2oumMzgTitfzv/ROuh\n+ifWJ+Fm5jzNMIqTX4yDpszcVmCqvFIdFGztQ4RDdS4fPnwos7N3dPV0YBd6JhRGpQik+d2n+/RF\nU8d9HR+9TkptETSw1fGlriNbKl5tOuKCfN11uTi17RHbWftrrzedek7Rhfde5/khJfvk7fmD8T5X\nhorulWDQ8XjE5599gs8+/Rhvf/UdPHwgk3BKZbL6B8M2ufBYI6tb1EHtPpstI7yxlK1WMddNe/5J\nxhdvP2Jx1sftfmNcma+4YVm8rPZael1f9AwDa4cB3bK+8ybQa+VfzxCwto7Nsw5/mL/RD7pU3iI+\n8rzxsYrIg9GPt8G/USYu6Xu2jIgPNYipeOXRo0d48eJFL8Pkn612HuT0DI+mfsdrTh6jKt/aprGd\nevyit4M//2D0T1RW9VBzLSc7+8u9Cwz9bM+3dvWRQlJdaesReT7r0xnWavWw8lhKXdW9DPRl+oQW\niTbFyaJ5qjE46gPABNVh1Lbd1RQHo/pKCmMTaewnfYIe2mDU1dVVyu8oC3tymsn11N5Q90e87wNn\nG5s8GVEa/EwYLcESh7CcsjaAiLrPBt/mUV/2shhUJ6eJNmHYVfs+htMHdyZMGiebwfeDzm/T/p51\njkezvt7bk4IcTPpbvVPdWCeHQ/km+T5bGo8x+CCo/B3ptv3V1mGUn+6vqA6TLbMInz19isPh0Nqh\nlAKqW2+6M31MLZSa0c+EsR++rs23JOVaYAxXVMijH7bX/6OOYlRZwKgnXyd9yQZCJDGAu+2ER8sR\nv373XZzu7rC0BsmBtV6LCp8xbisyG9WtubTGFCFjAOdnqsQOLMZxVCgHItmBd1nwv/yH/4C/+Iu/\n6A1vjChQt9Dg4mZODYZMQWW7rgMdVqA33zkhPF7ILxmOnXKmXGxyz6jwG2WzZ3hn+WYBjHh9D5wx\ngLUOUpTTCe/+4hf46je/hW3bcL0cRMFNFOE5OhswWgAd7ewzDzwg1YB5p5sHMFDKWKdIU5zhksmz\nvW5lEDBqqgIJdZ7iMr5YzzjrJqbc+fMyNXt+D5TNZDK6L/K+dwCJSA6GN87hjNZztDx58sSt4hgN\nBbu27YasNPm3KYKrjE/D2SvB6Z7RGnnmaTPtN6HfOnowlLf6k99KJ7p/EXDbOmdbxkzBITyY0n50\niR5hHlfykfk+5A+0PVLldw28JwNtNs+odzK6Iy8c/XWASR2oqN/s77htoc5Mkr1qZSDk4cOHAPpg\nsR7y6ICl0mPsW1a3rF1mq8viNox2tpw+u21bcwYFXO63o9ZTV9BHfitSdSuVxAC2ILfNp/0Wpa+K\n2ji0aANCTbebv6Vuz6dMi2088jLXkwpc3ZkQxumIz0fHK7aLAFxRD3puSSkFH7z/G2lj2U8TQN2S\njZOZVpEGHl0Ood3IhmkPXQ2nExTsAK/rA8oVZnFkJ/p96LvUZ45aPWxpi89fasutPBF5XVFKwQ++\n/31cX19r5u7d5kAn+s4Q1O2w5hH0oKZSyuDg36f7dGmy/boN7DZHVIMWfQWG3fZX7UE/c6Bur1Qn\nSamDXN3QHvxkv7WLBiaAGEDZSQ6Hk79eixzyYq76eml9NuoBKA4ImNba1YjjUl7G1AbNPaa6ROcM\nNKIHH0phXB0PuH35Ch+8/z6+9Z0/wNXxugb1CLpdI7PMFYeo2AAAIABJREFUNl/tyjMe/Y5Mr2YU\nNj/A0rZQC144+tXwVlvrbELAQzk/fIBlxhtbj4hdBQv5d2zZC/S++MDFTDNotFU6ir0hDdEIG3BD\n9aZLQqd9noiGwOy5JI94u2vrb/1o5blt0V628mJcaRx9hsGvIp0WOvanLLnBOHP9wYMHrbzZNpHW\nN7l0b/iM5unznPtp9vlS+uSx5r8orSZvV4bml+Bz/a78WE29tgS3JCTvJlsfHzsow0H35/ykyJtu\nN8ZZ95kMAWgyQkq8V90z9yLna7jHzAD34LMexN2knggU8LH9a1ekaH/ULQZjv2i4GtJmDx8+rJPQ\nqJ4B6J/N+JJVWuV/1t+6LUPTRxo/cH5Oyjvfh4jUn6uT1upzJzuh2vlpfXJUa8NAS6aPVf8Yj3/Q\n+/ZdpU0nbfvzd41pNqov83+lvvs+q032XX8dLQ9uF8b+0nBM9e22ZEeBrDy913ilvDQNKSvORKbt\noFRr2+LbXt1OW6c+cAtwEf2zsuwG8ez5M3zy6SfjVpBm+/ac/jFusqdHOiY5z5NL07SvoO9gQuH+\n6xTz5TojxHxZDgcULrh99RK/ee899OFQH+iz7ypwb39t3vr8BKT5YEvWmdh9MjAdR0UzgWAAd6cT\nDscjfvSjH2Gte5nqoj47w9ymTNiYWQ4ILbrZYFIzp6jsxyvU2KFnHdwDik5n63zVeF0CcDKAZHmZ\nAXy9Piq6bgjkeZnFfHV1hZ///Oc4rGudtRr4l9QzS3afvWVZcFgP4pBYpWVot2dHOKMV6kfUD9/S\nPeMz+iydnQe6LZPc37ZtGASxbeDz9O1j625luScPCnT1CBslqjRlcpUZTBEXMh+4dh6WP3Pe9i7f\n9l+eUr6Ee3LejQCjeK4LC0Kb8LjPZjsHxiMgi6AiBXmTci3PGv1EA90O+IRylmVpIIqZB6ev8UY/\nATg78BlkScHmFvSapdnW0+13Gupk++A558mWo+BLbUPjwU7gOtNDdpB8VlZMvc24HWJNgNOTzDwA\nGF+/ia0BsB4OIBIwL+DTwms45w3aY6kHUlDN4tRJavWyvd3Xrc/a8PK6VP3WVmLYd4M90vesHFq6\n7fsReCtg7HLu87X5zT5jnfTe3Fb2CtWyqj2Odk1/t72o6+Ciq0OgNfvrH+rlMxfc3Nzgo48+7vgF\nvd00jybPxilgZliBYf246lGTI1sflVm1d3HgTuvccJn5rrx2H0SJ6PwYbEuzYb0JZOZXdFT7c7Ff\na55VauVaKfjhj35UCSHzPrn+ZXVJt1+92Mzppq5AYmvep/v0hVLDvssCwiofWrFgBWEBsYyYEhb0\nE7DqSDPqKg8R6AF72N/aZ0opsCej0UQ3pPqyv2Xo6LRE/8S9EezvcH+HP3ElxZBvpC671vjnaZhh\ngVl57TqAAsJWCu5OJxCA9957Dy9f3KAUwfLNv6C6pTyTw8Y2OOD12rlPQeEiZycS+kcHEFp7FqN3\n4/c5zt/HR9E7l2Rxazy/UttEbbEto+llyOG/W90mkoxCVpuG0nFXt+2eDo+15/cyGclwvve/DC3I\nn/O88pM7s9Qx8R4upvQdHQTxZY5+0sz3gGmbN+vEsVnSttXzxWQrNF3hk8uE810mPFd6Synp+Qgz\n3mW+hv04PimFEx2XlaX5B0Lbe5b2jN+ZT2Cfz+Wllxv1XizXXQfAJKsIONH/uV9i7Ae8hA1yPwzc\nXe67WflXXzL2CZcf6Tmctu+PGFi/E5EMMB8ObqJPG2wPNtHVQ3lgbGQ20dqeZ5nVEZz3Pc+H/TNc\n9RynTD/6siwvjA5MJl1aOYr6NkuRT/Y81I7x9RlAnMMeC7L5t35Z9/cYPlXPW3sE8vV1+dj4w0T8\nWBgjrZq5XBfY+4wP3cdA83OSlxwCWkgQBzHaRKm4SrLV67Th6aef4dXLlzhtJ5Qi22XFuElmu5jF\nJhZjF2fvyGceexnsQ7ge2ybjW0YnJffPjiCb9KVaEcJ6FFl1aE8oWJnw07/8S3zlG9/E1bJiXQ/D\nFkR2P7x+GJOq3iKju9RHlXsD2ECrocN04hRwUx5k8c8XpwCYWZwHZhwPB2ynEx6/8SZuPn+GN956\ngldbX7WxrqtRrNG49vbvW4LJDzlcUJ9dQ8CrA79mVIxeHpYYGobMZnRbfvR6q3Eksw1T3x6j18Er\nvZmCSZWO1gVjZ1uWRYSeJdhUmPHhB7/BqxfPcP34TSxUV4RMOmSmKORrcM5quyz9lpPBOGPBG26b\nvwkuBjm2POkK1ebX5TZrE03C+30QEsGd8sCuWNlYDgtU+gBgq31oQQ9klaGOY5md7jLQ7p+LK1Mq\no0weDuhWg6PyNpNVW2/Zx7SXxZWxjx4/xrquOJ1O/Vl0YznS13pmo09pyGY/xfYdZN3MhNB7uj3V\nDARH3gF1VcdK6EFOBXHo95el7ZtMRFiVl0pX5auCUH0utlrmzmwqT7Vf2K1ydPVClL0IupyBtu0Q\n6jtAzbp7UdtXc+krJgBZOdZYx3VpNy2tT2YrNlTwtO3dtlRhcLi1MelAtxl8Wj1YypKCxQ6AZJst\nYnUwgKvr65a/luv5QwCr46stVGc9udWGI4B37VHNXuOXaSdm2VJJVinU8uumtK29KylKkxVZqvzX\nvJR6pzOhs5ZV95ntAphcG8hy/F5en53PTQ4l/6XJuT6j20hKs/kt5uRvkHI2OlPpbvUw/b0rrGab\nVT4i+LTnNVnAS1WXr1U1FBK9vG0nPPv8KT777DNcr8dAq65u8XpP/R5qwZaqY4yvwKCGIeKKn8FB\nDfqs1Rt9RVJVioMOVIzS+FbzW2gFQ9qzkgLmityo61ihVHQMGg6Bz9tip9YW8ue0bbg+yNYIr+5e\n4evf+hZ4XVHMXubDejlzfXHtrd8NiK/9Q/qBdKRma0xe9+k+fZHkbNTk/hbOj/ABk2YEYbe8yPIh\neNuvdsjmmW/fermMs5kVDEAObNdyW8Gl2RDZCqTqAZ1cdGFZVu/uBzn26c+CANlED5sED9VtZYjw\n9LOneP78Od555x1glSJ1JY/WXVatVtxJVPEqXOCm4fNtQxnO23KVT+pMcOdQmvd8QNP7bDEfj+Xm\nAwSdLz34G/2Rnh/cO/nuDtbP9XfM8FHFtcaWxVxMuREDZP5qhlntvU7/fkDf1LDTHVaZEAUb62xv\nx4qdFz1/Z7N94SlNts62vNiGjx8/Tq/busXJbXt9bsD9Vg4TuSNr4OH7XLaCIMs7lmHlQtFU5tdq\nvQozDsb/5/q7+U3kJypp0m2I3XUyvmWit60OnvFmtoVvfC5+J+oz15tfbHhlz0vRvlvKBj17cF+P\njtjYlqvfI/61bUQYZbD5KABArNJ/ts8BwOF4lEnJdYKS1n/2itBQHaKJpcnkNqOBqv5uPo+Jjdl6\n2y3xM5ytObDJY9DfLPeiX91okZZ151AAMQAPaGwixi8ymx/tYo4/xn6l/WWhRJ4EhBgfr+dZwH01\nD1WfvPoH1vcA9Tq06HD15ZouJGp5t/Iap/cxTdSVUL5VwgzZw3u2v7W8DI9lVVun83S6w83NDY6H\nA+62fkaT9YViinqziWCr7tgXm73cRVfd/kZe6Pdz+iFL/xQ8+aVaEbKYip1O9ZDYZcF//9nPQCd1\nSLmB+oyZsWMrSCdzbw/ADIohMerxfXXKYyAgdnStI1GfGfGv/+2/68vN6wBIJkAtL3Rl2J5d5jMe\nZoDkdYQoyzfmN+Np++2eVT3mQUSkbZYnJ/c0yayQPruDmXE8HPH06VOAGUycbfHb2iTOQpJypdRZ\nmTpTKgN2tp1UOewZV1uPS8BEtnJC753jY7w29h1TTiLLgDe41iHWqmSyERXsuXo6Wd+pw6yOmUOS\n8SQanuvraxwOh3Qmhx1p36N7Rm8GZMa+3/OxM5diPSKQuJSfQ/3Dc3tyY0Fe/LRniFqgLw6mtmeg\nMx3Pt6c+YyFa0yHwemTWf+w+mXuyZ+XaliPfJw4TjQezu5eSOtl6ZLPpMvJEx8mA0jvvvIP1eEjp\nsb+7bPiMZ/I74028ZutL7XycPuhu83J5TuQ42s++jVSQP/Tn7Sy+aHMznRYdFA82JzIfys74JCAc\nLs/43RJyCY8tDwaauW+1uK4LPvroI6erHF0R4+wA2VZOBe+ZTct06l5+M32b1TvTm9kcqqVOdLA8\nT2lJdGyms7Ztw7Is+Mq/+Bc4Xl21QZBLQLt9otfD/46rWRXHXJL/fbpPe8nqz7ia0z2HPZk7rxPa\nx5RbTNkXYzTm9lFfwIQcwrtzvHaJXsmwib2u3+31eG1Wpz0fa5YkkFCfq6+uy1rPnzvh+Wef4dVp\nk3MMDC4g3kDoW2OgzgDW0WGr7/V8xP00w64zP8fa6fN17bYr94eibxRtdlzd4mlAW309nm1oZwCP\neSg9GhB0HAm/7ZmO5+q7FyvoGH70L+f+4Jh/9j3WfZLb2D+x1+OzvP21VrfqM19fX+eTtMx7Y7v3\nCZSxqOiP2esDDYlPqZ+Nz8c7pvqhrjAt8H5How9oW5MSkWy3xX1ijU6Q2J1RX7FcxBAMAMsifpSl\nC3UNnerPxH+MbQ2gnSNgY2FaPkmHArGe81GApeoWPVg1ym39B9sGEac7+ibMjzwPcj60d6in/WvL\ntXnYLcwsv9alnzt7OB6HPIkZC6OuBSRhxY6+n9ma6NtQxdZRJ8kDvt76zLqsSWyK24C4rV+UBS2B\nIQNY+pFifd8B/Mqypu9ZyxttYFbP7P65fqip6f4L4mB7ZTmeGxqbXLfhstJVJ/XVkQ2UtFnn0ABA\nWn60U/H6LAmO8oPdXmZ8vLTpB6p+0GnD06dPmw9j5WCmI62MLE5myNlGT0+IrSRG5JLmynTWuZT5\nh5emL9WKEE3SwIRTKTjQhgeHI373/q/xzT/8btsK6LCssgSWx4NrbcoCiCAPCqzS0cOQo+LS1Jzm\nplyMwkXcCy8YIowG6M9+8hP8l7/6P3B1fY27012doZx3IKrLD/z2R6K4wGZ5uXZ0k2YGxpYzAx2R\nT5Fn9h0FALEsUkq1c0nmLQ/bTrYDa17ruvpAIY3ALtKhhhnM+Pu//Vv83te/icKMlXxHdweoVuo6\nb+cdb2Z8s/YTWtNczHVrvAG0mUt5/Wz9s7awhnQ0zB3kW2dE5YnIt70GHpfWLjVItPR6WpnwssQp\nreeUoFXoMc2u5TPETL2JmvOpeoYNoLS0XV1d4erqahh0VSAZaYn8t0bJHrAe3521EUhnnZhLyTtZ\n+UQ0zACN5ce+sgADYJ/Vr8lW4AGoHoRmaHbnItRnmtAbXah5FRYwnukYbWMNELj6A+6ayp6tv+w2\nMQ52W/lVkKp9wNWvog9bRgQ9DbDw1s5X0YPSdfa4ZqWydK6NHEoDQCT5PnnyZHp2QtbflpUa6Gr7\n5+4EPmZ56jUbYC+ALOFdvH79IqlsqockL3Y6RGWmrgyxvGHfnvGQSdkK39Sr/qfgmM/oD2sTvE7r\nT8x0U+w/s2ezd1vpFpTWgrfThu10wj+++0scj0fw3YYSRvs5gmxLNAuvnTMAO3i5tndnuk6/Z1sx\nZH0t1sf+9n0NqAeh1L9U29ysMgFQqOKeCQrXvpalUooMeDPh1atX+PM//3OxB+uyiy8Hh+/MfV5U\nL/AFcnaf7tOFib2sWac261+DzZZfcEvMk27kcHnSz2Y4pObe+iZVWx8xVXx3qKNbjSDXOmK3di6n\nPdbBprhyY5Zeu89qXYGGBSytx7ryb2MGGHj57Dk++/TTvq83d82lZxMpplHM3nyoyoiWPxTjj9jf\n8inaJYudmP2Zgx3v7Ad3Bt1oZFLOIfHPcfg9+it63efJ7K8JbcXdt8Ks1q9ytM/nrvyyfh8AbNv+\nVk/6d29LKM/f8freczN57XWNfLbXtS17zWPKLNsMGwNiw4hpxFBEuH7wIMcAMOg15ZWV79FXjHol\nJvJCMfhMDK+LMqyelcXgtpuC8mHYutz4LbN8YlqIhkGVUCEA5nzGmgoLPa3MHWyVYX/1q6qQuGcy\njFN7RA9809rlAb7fdd2+uMm6jgck+FvPNsjKtn7ywBZmLNVMKdzzgwnz9iTqA6a2TG3j4/HY+Omq\nBbgBesDizv1JrdH3jnQSyGTc/RrhcYEOiKD2ZTY2MPq9Snf0d1xitDK7v65xyUD7EuISzZ+oeJsL\nzq0cjfzIrseJVlFu0WjL8UbULQDaAOHou8/9Dr0mz5LbtaIRiu5/qn3N6I302PK8btK4aENkvV6u\n/nViALZ0kjYzYysFNy9e4NWrV3h4/RDbZidIz3VRKp+KITbvD+YmxK5yr48Ttb8zG3dJirRl9u7S\n9KUaCLGCu1SB3MqGI4D/83//L/jGd75TnfLqSC4MYH7glgZr1XCpg8DuWVE6+lsUse/0SpsqUGYG\ndP/BdiaAP4hw3TN2SgcRvv6tb+L6+gHWdcG2naqCROiEnkca4Cm8NRKzg7Kd4xKChhm/bD1tWfo9\nW146C/rEvAaLURFspMeWbfPamvEdAZIty15blwWFZJbGX/3X/4p/8+/+PY6rdAl7/pIF+Bmptgyl\nicjuiy6yqrJm+T4YPxqXvWVt3QE5DXyJec+cBjayMauj57eCPQF/pdTv9QWCbJti9/DXLcKiMVNn\nSe4t7bely/IlJssr/RsHNX1ZfjBtthWVfB3l0/Yf/Xs8HvHw4UPc3Nw43jI6D2Jb27a0RisO5EUa\nx7bjBo2i3JXAxEwOYv+P9DPkwFOn15L2yHjr6ju0XH2vvlvAONQ9JZseDgMjBPQDtSuIsW1hUzs4\n3ayea65rA41GLornDSH8nsjTABht3bjP+LLvqazarfGajCmdzG2VTNa2mV7I9LbK8ptvvglSBxJi\nlhgMwrilk+hwYVPURcA4YBX79Kyf23rqYYMxONf1mbY5TfOI7dPfby87/rj71mld/cBoNkDPpcj2\nS9rfWLcw6jqytB/cBmSUGpFXcjwEIIGspH87eSLvtEfbF5+X9lLHRMsjbCdGOW34/OlnYpcqjwij\ng6EziD2/O+aItJyzsdlzVl4tXzIgG58TujRzfWgsi4iwyd5u6QBEtjJGD91z5de2LVUO1nXFH/7h\nH+LEBQda22q1TA5dOwlhjWirD2JdY9A18uQ+3ad/jpStiizGpvjgV8V/6D5Npodi3x8c81IaTs/0\nRPxuk3tGdn9CDz70700PCUUuj9an4O2nzT9ey/TajM5z2Ht4vtJi8WWGp9TgbNuG337wAW5uXuDh\nw0eiQ0BAsRgyn71uLaz92+uRD0Jb/vbrXgbs347jRxww2oH+vb2/5fYtPhfp9OXP7GrEEt53KhCM\nRDaI2bBsLu8zm2efi/amPWPsU6ej/57V38cerO/iZdDyfbaF8Jh3z9jiuYyebLcGi9FVjzCAY109\nH7fxSstOUpSnLM35VX8j6d+6hVzo57GciH+XiQzY/KMesdcyzJ7VFVDtC7dVpl1t3XwIlSf2vhfX\nazOa7HN2Ekbz04Is6Ba+EZvH7VGzpBKf8oPRJp5aGY5nzUWeEdWAjTRw9fdCuTN6SG2VZGCfW+tg\n6HI44Hh9LfKsxSyLTsGqPlUdINj8CoxmoxhNh9gtxTMapQ/1SnTk6PFhzXaQHe9z9a2QLY6f82Ov\nL9b4QKnfmd2kNoKJY5B5h9leGPRl3qc9Xm6+SPNPlzbhCQnN0TZF+2Me3Knv2E8tzbMy965pX7T1\n7v5XWKXZzET3zwgjTSpnPGyPJpNsX97c4MWLFzgeDq0ZiPp2xlE/6fvNR4l1sLik0ZlgjoFPZMUi\n5dVMPiO2yvyuS3Bklr5UW2MBaI3DzNiKBI2ICH/zf/9fePXypoIZma1X35gwrjjgEAG7bKWhwdoV\nsictoR/c11PWqeO9GBJshiPZE5OIgGUBlgXLuuJ73/tenT2+pg0ttEspmg8zy1ZaYeluPBxHaYiH\nZzH35dPWIMWyLe02oB4HVmYzySNomJURn3HPJXzNeG3zsamUgo9+9zu8fHnTnyGgUA9sxfxsXl1h\nIPymOmgw458YXwEVnRbJewR+42x2rT3X50v7yLUF9kCvvSXXlr96jk6mXJyBtQCobj1DdWBEyzqs\na1fcRAIeWh2E/sgfy8dYpu3/+Uw03zbWsbT3ZsGmwRQnMrosC47HI9Z1xePHj53cRprtDJZYlk22\nToA4tRkd9rtthyjXSztwcBx8sXnagZ0GtJibobbP7emA2fZ10clzdR6cUrSzD2K9bburftTD+/Sj\nbaO8y8AVB97p4X/tM1DpafBt4fU+N3uRASc5h0W2J6/ftTyidkCkdW6WWses7fUT+e50OIC33npr\n0Pdxey4tq+1DHOyl3pttwRRlIgO2/vvo7BOJE9F0VOgrMwfL1ncGTDto69cjDzJgG+tRSsHBLJWP\n2w5yKS3Q50Akxv5PS1xaHiSPxAngdjxtaTPp8u0ZRf50P3leSLZOgdDx6uVLvLx52Zzc2J8aLYZN\nXk/HayZQSv18AaXF8jbTe7HdLD2ZrujyLg6iqICuy2zgsNSD/YzLCrTBq/GgUC1zK+Egy8qb47pi\nrTrn4ePHeOsrX2nbbhFRPbTQH+2MUrAStY+tfynFnStlV+No/7SYYFmW3mHu0336JySrW/W3+6D3\nGDC772AAxeOC+BnKqilisz08ZPVLNrmlfWXjVqdBr5EeqaN37PdwTYadZzrKvmvfmeG0holJ6OFS\nGs/VSG7b5vTigwcP8MGHH+LZ55+buN/YntbGWtpK0HFZvWP9uu+WD155Xnt5iP6mfgSjjX6ULV/p\n3ba5Xci2x4q8sPbF84gbDa0urW5I8x2xWOdFC4gafMjcz9RS20S0YF0P4IXqIkZqdoTIB/9ifeJv\ntR8c9uzP0qyf7qUBP1t5QcULJPXghdw2eEDHSAsRHjx86Pp0Rk/sa1nZmU7It43N5Hn0x/d01+vw\ni01ZfcvWXHYyjJThIOuX6qQL2e4zrE4xddXtQFmY1fyadlLlpL+IDI8+sLUX1kdhDX5WPWxpmP1l\nAWGpznKBf6P/I796vXVVBNLtzON7rl1g9U7un1rdfX11NdhOAKjrLCCno0qMUAL0GvcTGrlUfkmH\nafyytmFYuV//gZI2AZys2BTPpHSTvRI71Oub+1Zc40ttC7RESmb+hN8yigFiWXWfxNbyPt+7EKGe\n7VX0w04GIy9a+wU6cyGf02AqkMrKnn5IccIEa2hes89MDvp7VX6MHwqIn/Ls2TM8/fxzLOva7KnK\nAsBDuzk9XzwPvfzs60aLZeZ0+zJjv8/0ldWzmX1+HRsHfMlWhJDRdltdlrNxQWHCsqz47fu/wR/+\n0R8By4qXL29wPB5r5+5M7Q77OBpn+4fcm888kMtJHtpourxXgQ5MQ6Nf83mGTrEQeFnwg+//AD/7\nbz/F9cOH6fPiPJsZxYWNfM4DPWoUsmWP54Qp21ORmXE4HFretj5RsO2saBvgb8HbpI6RvlY29f02\n90BNTIVlf8d1Jay04Dfv/xpvPnnbv0Oivy2v9pwgZgwy0cvvs3Y085kDJu/ZAFDcH3B0IGx5/lrO\nw0i7B/m18juJSGY2bOAWfI5KycoBK2gJs/CboSO4dyNfrMzk9Pigpb2WBSXPbZPl2jGAbpXVR48e\njcacyG2XZXlraYz37XMWIMXfnkdz2Sbm1KDEOqTGWF4agY0tI6zCifxMV7TAGHPjIMby23XjCLTX\ndvpf5JcCb8u7DAzP8s143tqigV1ubRH75DlaNZ1Op2GQqr0baMzAo9WrQJeKt956y9EpDJAnbD5N\nBy+jXYpl7dVroLP2nd73Ftf/XT3Z8JIZg2xXuXTvBLqspEQnytFet1LK9G/GAYYAy2zVI0MHEs1s\nQdeWXX1Ee5mDf3U89mebjm1gZwahbvMlz73/61/jdHsrTuuanBkDDdj0OilAhr3C3mnT7f3s4GPs\nf9YWXVKXaPPidw1kbryh/nJtR7RgAw+zzwkjLbE9AGPrAUd/KQW///u/j+PxgNPQar4OcYWL2mGd\nCehlc1yZo+UR6SzI/f52n+7TLFk3VnXrvkOd9dEun7pvemov6yfNY1d3Gfom7/d3EguVYKlGD6te\nMLpN+ZDoHZ/tzM7kdGf6Ze993WJD8ESCTYnajPVt23B1dYWbmxt8+vHH+Pbvf7tNoMjqPis3oyvT\nP9n7Vqd3fumAwL7PGPW9xaD+epxdP8dTuRxF3iuW8PvXyzOJ7Unz9bq82QQdgKiC37cnk4vdFnpf\nW/GjCwYaoKBf9+TnEtk8hzX2UsTTNs/GC8X4FuSQDOrEGMBVDSYP/g/8+a+2fEu3DfBGv8a+s1ef\nVqCpC5FMGiFDV8av2L/Ex+r37La2zSeSm00nzmi1eQ+SZ5+rKNMG0md1jxiHmWVFSZ30Zmtng9iL\nadPWg5xsyoQUzYPqc4pZyZSfyk19cY598/ccVmv2qPMF1LddXhpNlOY7+ttoHJHvXZaZWQbxamwr\n6nrA471OZ1ytOI+zZXTZMti/5GwpGG1LMx1YFT4T1uXQtrHSytHgl3v9Hv1dfSSTObD3FUadwR2z\nw/dXV5bq+0APTFuojRnsSOBXL7uXC8sXI6Oqf7M2bS8OKsX7myqLg58TeLWY8mOKOnHmJ+m2kWqj\nrC4qqDZVnD7pD4Vxe3fCJ58/w8tXd7Wv1lU8IFCVHV6StoXG3Hjgl+OG1ROJ3qTw176n72TSOORp\neJdhmoxfl6Yv3YoQTeuyYFkPwLpgI+BwXPHXP/0ploVw2jYcH1y3Zy3PZrMN+rO92TIQAIyAPBWg\nKmBU/LM2j3MNRiAcDwd873vfxZMnT3B1OKS0l7KhcJ+daoGcP4purOsQZAgKxQPSSUCO2TkiWb0y\nRXOpwO6Bc9tZmHx72E8vVFWAzBYlIpSt4I1Hj/DeP/4j7k63OJW4Cqav3Jhxk+pnAWOFzmrZd55i\nfbJZvvq3OwxqtHupmQMX8zhPhxot/e5pmzqJ1EHAuCRy/q7Pp0vpjGd7oH9uxMY8Zg6V0riw6bus\nuwT7Z+xqjSdPnqTBUWCcmaFlZoMH2r56T2dyZDNIwtEwAAAgAElEQVQ+Gj8vkC0O/bJv15Yc2s3s\ntuvL+lBWvwEAINcDMT890M/mWWrwUmdWqy4bZlwnNNnVHlpedoBhdmA1dq7FethVKGzawdUDAkw2\nFDllI2mqi5w29N5IRFhoha70soGHqFe5BoJlaywvL9zcKUaf5dNe7nlM6Htdve36f6TT5nVGnKM+\nTHUK+ed9f2/WoufTNLfO5PKrCCzwts56r47YvnXp/dcOCpyrj37iLMDUdmEMALQ+gJG2Vkdm/OO7\nv+zbryTy7IDoRKbides0C04d+/sUM4S0bx+C48XqCej9vne0Oq7StraSjCkQyuipNOkKtWVZcHt7\niz/90z/FXdyneqfN9mxn56Ovv5OtePE+3acvmKJtntn3S+34XmL0IMXrpBk+G8ojBNs1sQVk75F7\nLg34vGY6h20zXG/9yoyPVm8uyyKrB1eAlkX0HANPP/0Ud7d3Fc8UmWjc7MhIx+V+wDw1n5IrnqG+\nipbhVyrazzm7treqg5nBhaeyO9LY+VCvtL/Z87H9PBQacXekwc/sGftGYR/kmdWx3YNv/xyPRvsx\nm+E92p5L7e9MTs5hZsGovlzFHYd1bQemax0jxpjFInz9x9WjWVwnjV/weK/9NoOKtl5z/5IdoiRM\n2iv+3sHVRH2yz1mP3eST6hh4WW39kfoKkbiSfjYxDkDbYqmU0jGjye9SWmuu7h6bfjKLMwz0VPnZ\nMn42X2fEtlHmsjIbtq3PHA4HLMsoB9Feub9WX6jPVQ/gjp7pTKfRhLdWt9rfPUNDJ6MFvd07GgdT\nJimbua8EFVw/X5E3TLIIdyO/5nEq9UfhPLAFaCuuz+Whu5/44mtOXONmbcl7xwK7unMnxivv2XfP\nx6EyvT7DANk9ayNcvqh8qnhB/SDwhtPdHT777DOz64OUvbHGnubDANLX9aTQOMihsmwEaKzw1D55\n/3X82HhPvG45P7TCF8A4X6oVIQCkrywELoytnLAspSrzFf/wd3+Hzz//HFdvvonb0wlXbSVIH6sX\ngzBfBqRJFEZSvLso3vheRyeiOmugN/gsGd8eoL4U8smbT/D1r34NH/7uwyFvoO4rx17BweRlDXQG\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2z2wSP3Cf8vRnI/tmcg3pFv9X/22LsOPc5q82osqNyGkQBTbAM61q6/tX+tTr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obcY6e+i8uNTGc8lRTjIM/zjO12i4P1IW7fuo3XXns9nvS4+wpu3byF9Xqd8QC/G+kDxtUBAkJc\nSADiPVqIcZb1XOzd/9UzSjTP+ZLzqn+YZ0k2DsOAeS47mynjD6HPAvDTD3+KG0c3MG93OUyXlGEy\nbBUAuHwKqx0Huv/TDxnHSR7IfNbudIlV5mvENH+ZXqZ9qTtGU+oZl1onVvpPlGnVZaXrGKxRVqFy\nPGv5UCFa45RoRsjOLc9P2Dihhzvqk4jJluvwtLYDlUfGaFfzvqYFiCf7ifLdeBG/IC+M73a7HCIx\n2ir26RerrdZvUqdqXkSaASlXW0ynNxbFc27xWY2jqp4VsCHLzY7dsWT7yPL5sttqXIt2y01Cla0Y\nH+S2WlhA1s86Q34vOLM+sehiA5uy7PLbNqknzTfFrW49/FzPl31jp0errj2WBRwfH6c6gDAF8Zuu\nR46PGqfyhhsHed+ZkBeuzH2WDfqkaK/thHJvKD/PjlyFD7jcXEb6m3W2czkSB3Mk+xqge0twTuCE\nnr0h71b04j4c51z0i4m2SvnUzB8Un4NFT7Y7UOdhDKjLb/gpZIt8h/0rlO5QXZrHcSREOyfOX3Hf\nIK6nU3L9Et8NA0Ak7tMobeNNPSTuSF2yNWvIV9MT5hlQ2LYaRxmflpQCyLTyJYcaQmUjcHlO2GeZ\np3Q930r1jqRF9a3E8vvsd1lPje8p22wEynfT6sT55VjTA7WyVSHGel5gtkMnS9sgqrmil5KaBW96\ntkKdaxqr8W/46GTddV5kZ3+tW4pPOdZTyqYwZ+wRphlXV1eZVsDob6IySFPi63kphHx3VNG30n5i\nvrvWvy3q0vhQQIPyLJSyuHypgyk/FZigi0vaZMm4pfTZWgiRzvP0TwLL7qXB4nNIjiU3DBidwze+\n+U38X//1v2K9PsB2nuJvQI6lBwDzRBj9AO/THSMogi4rNLHyXk0IpV1yB4u8OhwTf/fOpYuT02Qm\ngkvTISt0EMIu4HyzwQ/+8R/xT9//AdYHR/gP/8t/wINHD/DpLz/G2ZOnOH38GNurDVYHaxweHGI3\nT6nO+v4T74rzzTuHVbr3IDWy1pi+dVjkGP+CBzlPckiz8JF8kO3vHVnnPHLCyXtiCsiJdWWAubCj\nqQH/BvhjUDFPMzbbLZ4/v8B6fYghOXhmKmGCLGOiTGTmXQGHHP+vFSr81zf8SZQ34KIxUFXqGYIm\nsHbWO3aZer7peVgbgvVH0r9D8q5nfLXQXhpq8lkGyiKkTVWeAiWSBj025RgDCvgNRICPd/fcvn0b\nNAcWEPnYtixrrkB7GZ2BxLJYI8Rto4uVs2V8y3j1Wanzb+J9PWaW5lvmDRS3eYyjnTu6XD1eJTjk\nsr0aV81C3Z5Ugy1DJyhamOalY+VcbhPKQ7VBl1vxr9rZUvjgeLxA6Q+jDQHxWLou//LyEq+99mqU\nh749TWID24AqlJJLe0YM2jnEQb3AwWXF/3IISF2nY6gDLO3uSVkzuFvMtzBmTeOhU4Yz5ih5g1eI\n4DvMenEIeQMAi3cuJ4cuI2Ec8dzwtR4bMo02iIs01GN3s9ngk48/hguJZ07elBI5PvgBM81wbsi7\nML0bsZt28d6OcYXbt+/g1q1buHfvHl577TXcunMbB4eHOFgdwA988auLjUj8lTyoDA6ln2teGwt4\nok36d6mjpAw2mFNpeeccpolxTe0I8b6Us7m8xP2PP4FLu3PZiAghRCeDMLzyhpWEl8z5rWjQPLKS\nZWCayvZlepl+haR1IT/ryhiFL3r5AI24XjxZMlrKgBgL3kf8RpSdEsWGqHXoPj3P5fewcg+LSrtT\n5983t3X9UNjSsnWkLYSM4USZuS0eRGmzFgUcrFZ4+vgUz5+f4ebtO4U+8Ka+IS/8l/LqunO7+LPp\nxEnMKDEEzXJ0u8x6LDgFghfPHaKjhoga3Zx5YqQe5slmWPoj8aYOZ4T0OxPq4yUrRjtEoUnP8yYQ\nxk1WIrYTqK6z0C83nbU4fw9Mik7fRFOxOmT5ihaKecocr0dCHsuducb4EEh3ELITLdUcKGB1uK6x\nkOA/v89tM+1I5+DFRiI+fV/tOG/ZZWIQnWJ57cIDh9bjoi15k8tXPLmu/NVJY1xpnzjnqrsVa+Pa\nPh0n69Y2bs5r2UCUQseJ/Iv4W7Wz6l85l1z0N7m0kKvt7ZoOyk5VthHi3AqqznaeSToavJbHC+UF\nBF6gWa/Xeew6orhosi8lHRUXn1lfxYWqWd3RUTbQlvntMpat/R9FX8RK2B6TseGi7LTnJv9m2anS\n37NkN+nvcq4v2fry/Wwv5L72mW+dVbj8Q4X9icvkc24s3Wxdb9FvpjQ+iQg0Iy1iJvs3IG1grG0Q\nbdO0cgG5DRbuqPRjytmbo1o/xQ3gDmdnZ3jy+HG8ezT5oDRNS3Knh3Hye0musU4K4rceV53Sk7rt\n9ffiBwGSTHMuyTZX9FAHt/Xk3XXSZ2ohBLBBljamNXAE0pxxLjus2en4xX/1Jfzn//SfsD44yM6K\n/IKrnfJdMAcXVzLzYC/AJU9hBRxiVBRngmL5jmNhGKUYwhzShaXxancPgBxlR4B3wPOr54AHvvV7\nv4fh9z12l1d4fv4cJ6en+MUvf4n7n36Ks+fnuLi4QJjmbGwcHKwwTROcA6ZpwjDUIV68GFxEKbYv\nN5snuQJZFSiR/UXluJsWlPpzb1BL50TTN11QCbM/m/ITSHOIK/kecSf36IDT0xPcvXs3N0qeOGJa\nPRsbyiGp2yfkY5Vq4ZA/AtQqH0sAWM6ifUquR4PLILP0pWMdrRQMG1wamOT5oAQ+YBibFUDRSkdY\nDAtt4BBLtSKoxyckKIatgy2DWYLiAt7jBYyv3r2LaJiuQGnRVLbbkksulemE000nErvW5a6mpePK\npqyUZe5RiPK7M/JX8yn3f/6xbQPz2bnmd70zyjIypXNAO1i5bK5bgokXSZYjZa9zpAMuKrqdy4sT\ncgZETFEscstQ4x1oVpJ1zPOMYRwSjajArW4bvxMolN0fCGae8q5lNLPMq/PUhgea3XU9eS7pvW5q\n5Aq1jgxOuux5njEo46DZpUeRNz16CRzGLWt/BCXvdJMi7/sOsdgfrZzjXWtEhDBP2F5e4dGDh7H/\nU1xY4s0BiDJlnuc8X8ZxxNF6jVdeeQVvvPEGXn/9dbz66qs4Oj6uTnowPtEyWY5P5zjuvu1Y1Ian\n1Q/W2LSM1vpIfXGC5HdUn7BBqetkxxSH5froo49weXGBIZ1ebFxFQrZZdMvnvXlmtZvKYEG27mHz\nQrbvZXqZXjRZoeKWcLFMPWye9a0Yn/rtvUa3mlsJaFblEgGg5ISotGcrl3ptsNK+XayLekPZHSGF\noNC6WzthqvJl2ApDluyjg0/zhhDgB48wB/gh8u/xk8d49uwM997UDkJpF9R1DMlJV+8kZt1mYCBQ\n6hdb3vV4K/XhUldpp43UtfvwwxINRJT1Y+q06h1OWZ+UF5t67HZZ86zV7zKvSwvw/CyXEWqdwDpX\nJ8mnyNc4GyVNeTEE6OgSOY8o/yUqyEe6lx3ViEg73OQzpp3t9WEYMI4jdrtdDDuEOYmCGh9a/Mo2\nj7z3JA7CSKNy4od5qV/alPmcWMLZqn5ZkJOO5Zf1nHmg57viW69s2TYZZsgBzd2KPXloPffC+U9U\nwnHqfEs847/WjnlJk7T9pM7gd5fwFdMYy4kLwH5gm7m0RcoH2d6AEo7coR1veXNL8uU55/JdNkPa\nQHRtG4XKhrlqg64TsoX1RuaGleIg1LYAO5jtPrG0sW1f6/FwHflt5dHjV5crdU9TdjHcixGlaSFd\npsDbORQzb6xs26k/SxujbrsTi0VSxogFnFDqWNLVdRMF38T/1nuEYudJej1c3qBPeWwBIUzYbbd4\n/PhxHGtD8g272k9o2WgWXzSPAI5eoGWHOCUmwnETEYbq/h0eN6I9VScBcNKHUM/PlkftwtZ1cV8v\nfSYXQkwQjXKkiDtDxvgmJOXNg947wDusj47wh3/4h/jRD38In8JKxBVmZGDunI+7vAG4FOAITihs\nR8h7rDtGbO44L4SJ8/HCUFc7HDwrtyAvEmQFWC7HJQAzUjgqAhxmzEQYRo+/+Mu/wP/6pf8NDg43\n7tzB4a1bePWNe/jN3/oKQgi4vLjAxfMLPLh/H7/4xS/wzx9+iOeXl1itVpjnGQcHB2BANE1Txf/8\nl1pnBQsk2X6rD6u+RD2Q9aBeio8tBWy9KFImrlW3pXC104HYaEi7eh08Bufwwx/9AO+//z7GYYT3\nQ3XMsgalRWFJDVhPcHvBogJO6bLIkI5naieRfE8aYbrNjcAVisC59n3eaRG7WoWRIi4jNSvPBQna\n7RNaFs2Snn355LOlVLePgUhdzkxFmFsjRip+a1xyv89hBoFw+/YtHKwPME8sL9AYWxqApS9VeLUG\nQFAZr3pXkMWTOhZpwRosi6RB2eOtZdxYv1f84t/FaZSGp+pzlmFoT4Jw0o4Hq93asYzOuLOAgLWg\nas0h51zc5UYlhub1ABbSrjW5X7/Idko7fHRdeq7nslL75PNxHHHjxo1cIi+acvuYd+1u9jT2OgBS\nP5P8Ksa8A8g66F8Wv3pyq5VXhTc90M2/LQE7PYZ77Wqea5wuwHUce+qSRdRynIhyPODChX6ZGWAb\npOktHdxf8xzvMzs5OcFms4H3A8ZxhXkOWK2GXPfheo3bd+7gzbfewltvvYU7d+7ERY9xrOQIL4Jk\nvgGYKeTxwuEhmd4AgBINZQGvhGbgcvSdbSDUYVqo3bSiw0xWPIMte0xzcEGGsQz58Cc/wfpgjd12\ni1no57jZJC2+pHbkxSQfT93wJX5avsv6KiygZG18jcAbQnRYAIf64t6X6WX6VZIl85aMYa0L9R16\nGqszxlnCY5YcyRkfkwAAIABJREFUl9jARuv1POrhPxNPXaOdPd1t0ZBersrJeZMOkO97X+sIIqrC\nXPboM9vB76N1ELD+iUooAD467R4+fIh33/8Aq4ODSItPDj7KZq1ojsvOiEIDMi42+1UCStWeBbhv\npriozTrU5QOjWn5qnantTq3j9/Un26qW7Vk5wjrtW7IlY7s8rJ3DFpYjgXeyk4jyfzkvX9VZt0ue\nao58dE4tfoq6LBtHJ7YJAWRszu/lMbeApZge6bAnoti35DC4GLaSVah8t2dr6Psu9v0e/9rONAtX\nVM8dpYUo5HnhnMsh2K2k5YTGvV07WOKuDo1LcgwAe6VK/xrylpO2n3qnduWz/hhZthMt+iVuynPH\nyLdEQ5SHhLjoZ29WlJ+D5quwHbS+IwCDj7659dERyEVHsPcRA/NJj5iXT9BHzpd6y9irQsOKxhKK\nvSsPydsWFDJ+hisn/KRtV/Mb6nvmsHlPCdTiiaVLZVlS9nLqReSRZVploJqnqW9ai2lRr8j2yEUH\nWZ+UC9o2985Dcr6S/OTy9yIfbL+dVV/dBh73Fp9cNQazX4rku6h4z/6czWaD87MzHK7XCPOMwXtM\nKRSbbKfm2VKS9nUceqL/dHtzC2PiOV7bPzFHIILP4dBS23guZyHmsq+92IrxP+7nJjLHr5E+cwsh\nAJpBDMSO4Hs08m8ajCIJRSD3mB8G/M7Xvoa/+au/wuGNGxFEiKO3WsjnwQhD2IrwC5IuppJA9c7u\nNFgY3EiRBCA5vpMBjbLiNs8zyLOTASDnEMIMn8YRHOHjTz/BFKKLcRzGePTQ++zEG4YBx0fHuHfv\nHn7rt34Lw7/7d5imCY9OT3F6eoqf//zn+PCfP8TJo0e4ceNGjDOL2knB9xxICWUB4fY7D2bun1A5\n9nIfCv7rXRQx/vcc43BSPLIYFy60cBcdYCSBraNi8q0DYw4BnlKMR+/x0w8/xNUfb7A+PEoOkQCv\nADUUYLIAcC8vpxJvfy4CkJTQF+VqYdQDYRbY4KOgfcCVjlY6V/HTeWEYpPy8m2SeQ1bIXZklQFH1\nrHzJNDE/JMgoc7uWBQS9gJac//okQXqewb7oC+3EknIETsQ5pZDDyx2ly4OvLjdg41ceh5Uqvwqn\npoGtXOxA3DFmKXMNRPm3+sLuVuFq0GPKOcFfdohq2ZvHWurDnlGpx38DTBNJLKMBO0SBdnp0lbkC\ncpou2c5qF5GglYe65DUDZ7lDnfNIncDv8JyaqRiWbEjysNfvFJ6IHVRUL14xiJY0yfsbeKBVu1eS\nbGSndmm7oEvwRcsC/o3fd85l506M+RnSXROljUEcW5egk3JnF9kjOg+04KAIgeDQjlEi3n1iG44W\nsM9xlcVClE78WwypkdrpfRVKiU9uMu+43byTOcz1AhQB9S4+MuYeEI0eKrJ3niY4AjabDQbv8OGH\nH2IcR2w2W7iVx9tvvY333v0c3njjjXiZ+dFRNaecc/F4f763JTKMZeUcQgbp3MaKb8R0JeOQL0FP\nPOA5kP8J574X+gui70jNgZ5BpceRbBPPj0rGKMdIHLcz5hDzXl1d4enTJ5hzmNACsPXmCr3TUM5F\nOFedeMrtUvTXzyLv62c1ntR48GV6mX6VZDmIlsJLWuOW01KM/aVnFg37xrWlf6zP+wz6Ho089yoM\nbdAr66gcdgb2surJZcSCahvnGjKO9YW2J0AFfzvnMIUJKzdi2mxx9uQZPOKdIbQaQcHBBQ7rVHQk\ny+JAVN0JKDFBh5OCzg6vjP4qvG3xq35f655gLCRxvus807RwuNzFfETXkr9szxZ5XngYsbsI7Qph\ne2jcj3q3d8V/wbeldlmJx9Bg5LFs9up39bmaA6J8aw5JPeqcQ0ibKodhwOHhIfg+SrZve2OCfSR5\nw6F3QGg3dMn6gXauMy3ajoEuxyWLUbdR8VTzQfIpz/f0XW7yqtpHVEUD6MkA2Q7dZqi6ev2ik72J\ntMXLuk+uI+/4eYOL2KbR/FLtstqdT6+kzcq8MOF54Y9qJ3MuD/ZMt3jMY/Vqs8ljgVM8SabxMGmT\nOpVdn4DQZVV6IPPAoskBrmBNrqfp/6puLrWlyXkHR8kuSWHdXSyw4pnkzxLf9DP9XfarWZ60C4BK\nFuSyhJ2V9QciFik+u7iw2vR2km9si1k801s2s40j7m9p32vt06XUlc9sb+l7vBJFSQ3FFje4zSHM\nMy4vLnB+fo7dbofB+yjr02Ie161lj7QzezKdIuOSnLXb0WAoQb/MIxsWw3ur+tI8yvqZ5jJnQuxT\nhzLHa9VoY5Drps/kQgiABoRynH4Sv/NvlWMB5Ying8OEgNuv3sXde/ewubwEsZMGzHgkZ1LNaDYI\nokMJVQdKAFB1BwHOxfjtceGixO6Eq3f+ZaWZFDK7Z2ciYIiDY6I53SNCCeA4zBRPlBx7jx9/5zv4\n6re/jdnFUwWeUly35KwaxjHuLB3iIslqfYC3jo9w7+238KUv/yb+OPwJdrsdHj9+jJOHD3H/lx/j\n448/xqOTGIpjtVphNY4YhgGbzSaLn8EVR9ngx+xE5UucJgpVfE/HvGMeuLTs0wMpACYJiiXvKILO\neEpHFJGIY2GYBXMoYwVUwHHVl7FDMCMqkKePTnF6+gg3bt+JIbPIAfBwrg1TpJVKCyDi3RLZKY5Q\n3RnjnLggSl0QW8pw+ZKjZsyJPEwDMyTHVuTmV/VKoygO8ICQGFpWiR2vCorKue85FnuN+coX3tlU\ngbdMg/wqFmFQC/FKkJZuzPJBgg9nzGOXxg5JORHKTibrIrjY3Q4Txdi0fhiwogO4ecY4HmD0I4iu\nYhu9z5eU64uPnSCYKIH7SrkiXyoV6UxHgcN+gFvmlsvjKBq9Hi66mdNccdmYkGU1itM4hTIpp4gH\nEATfWyOvvv+Ik1yYzrQo0N0DWTJPnkc9wCXychkN2I4ZygJP+s7vyYXgih64fOmYByp+AWyAitNH\nFMe0PJZegwIh89JfD4B8uQMig6cQ+frKvdcw+KjSvfPZee+dj7vbiDDy6UZXAH+stjZ67DaGFN4r\nwHnASdkNAE4sZmS5gTy+o7Tw+TMAUAgYdExflxZ/0e5GzswB70RO5bqWfp0YBLOjgWUTOYc5TFVb\npMHJ4ZTy3JDzlOIxZFDs4zLffEUvnxDJc2gY44nPNGaJCLt5V8sb7xDmKfXziO1mi91uh91mg0cP\nT/DowQO8fvd1/M5Xfhs3bt/G4dERZhBWfhB0uOouLxco7g52GXZEncwA26X5KHjIYQCHxDefeLjy\nKxAR0q1pBYelpJ2mlPogCAPReQ+EOswgka0rLRlAROUeltIrQl+UvPM8YQ5xM0MA4Uc//D52mw3g\nHEIaZtJw4Pe8q+OD83PnXKU3dLIcCWy8mfl9yecozo05z9GOEfUyvUzXTPucG/o3yyiWckWeEHaw\ny7X09Iukgj9/9TIsWvJ83IMr5HOrXo3BrNOqmXdGuRJ/9HCcdDBIbORS6Ith8FGfE8dK9zh7eoar\niwusDg+z3K7dFIIGlrfXcPm3/Kkx69I7Wo71djK7vKNP1eQictX2ii7nuk48pr/Xt1b9Vip2Qqjm\nSbZVAOFT6NfjXN1ucxwtllDeY/2n9WKvr5Z4lm00a567mn/WHMu0C/kxDANu3ryJs7OzHMY42oz1\nSdqMxcUYKmVlKFjolPNJ2KaWDNEn8KtTXJInsHuuq8ddmW+yHGlrZTlayK/kkZa9S/VqOnWqbFdt\nk3UWDK9TvyxPLy71ZJlFv0zZb2TcxaHHWMFUCX+iyODmxLpT0s2VC9NlGwS12G63GA9WRW5lBpca\nRXboHujpUosnXGZ8ZuVMI0XJrh5fZHlNSc6J4uJOfE1e0QeurtPVefivxOBVPSqvxA+Vf0G+mujS\ng7k3fup6eazrvkG1fnWd1LVBXKHXtDuFzNVjYElmAL07V4o845BwPPdCWvzZbrfYbDYI04zhwBe/\nheSxrs8VXyihnCqLdYmaeViLIiiakeZY7fUPzyHtVbDy6nKii8DlV+K1EK7bpy9qM32mFkKWgFrP\nWWY59vR7zjn8yZ/8Cf7sz/4MboidxcIi4s46zhocIQQhYF0tFKw6606RO1hbpaYVltWu6OhKjnvv\nC2coOY0J+L//23/Db33jd4FhTIM9ctA7QmBjhnc8MpBHDP0wrFYAEQ5DwM2bN/Huu+8C3/gGrq6u\nsL26wuPHpzg5PcXDhw9xcnKC3elpXgw5ODjIi1LTPGOVFlzGdIrDw8FRmdjDIBdF6l6W4IgDr9Rg\nU/LE5VVFUH3hvQRFTioQV+rhvoEqV3+/efMGfv4v/4K3P/ce3EggN8K5kEPl6LGmFbekmx0s9Vhp\nd2pwHku5SEFWt6Ud99LRptN1DM7rChdKEpQBea8cPUdKm+L7KXc1x6zk0mTlyMtNnQu2mgSsHG9S\ng2rzPQYgIWCaJozeY7Va4ejoCOfPn8cxXTJXY9duu95tqcFDPRZ6tGnjHJC8S8fm2XGZnvYuCc/P\nBF3meLYAAcr8YSMAROlivAJaNQq7/hgjxa/y7DrA3KS3Kj8pYKrnnq43jh2BFwQAqerB8q6qstuJ\n8niRxFB8qaKDQzUNw4BXX30V4zg2AKrRQakPzN8M3izpVjbBdHPluMkfLSeMc/kUlqxHhvLS48k5\nHxcRmecLwMuaAyXOqtQFvjEGLANDz1nmQdOydPdKfD/kHWzjMGKaJnhQdXcHA+xpmhBCwGazweXl\nBS6vLvDwwQlOk67dXF7G9ynGL/7Tf/+nWK8PEQAMB6t8Ms274gyRzgA/RINxRh0ubYln9UK8mm+K\n/9ZnvYtJy34tr5hubWhbielpHCiyK4Qc4JBe827Cj378YwzDkBaB2jK4fj2Ts24w2ix5Zr3jMMQF\nbaM9ec5QogWLautlepleKPWwB/9m5Zdz08YTKLppoazeb1yihS106tZvtMc51yzE6mQ74muZr9sJ\nLsvAWJo2Lf9y2UAVJiuGFEw/uBqb6404XI4TepVpk/bcJ59+gqfPnuHm3btZHulT9USUQ7JE2UR5\nq0YPLtW8XO6PpdTKfPE+w0ldZrpjzXqnhwH2JW2Xx2fxM/EOnWYMtWOt9925GNs9nnCVcyp6hFxu\ncENYHhJM2T4cW9rOfxV/1Nzv2w/l/ThGJNYT9aTKenKE6ZUYZ05YYxgG3L59G5fnzzFjjqc7vO3r\nKJaLCH9OlDctyHoL7+0xom0CiS/22VRsv8n3rfrzX5FnyRZZCrdltSHzRfRJsUNsG0PbtL2xpOW8\nrr/XFh2ezkqV/LP44NowVUC7yJJxrOPFD5emU5GBVhs0VrWwa+afiyGtantKjW9QOpGXxlua1ro8\nmb8xlIzUs8VzncKOk4KjGmuutbd6fRPpshZD8pv1D64ur5KgHZm8NKYU2xJNqMe2cynATxnH8ndp\nkwsXYNugzvDk+aPlhtbd0p7S7ci4n1Jdvq0s0mm3VNs6eczm+vlETBwf0zQhzDMCBVxcXODJkydY\nr9egEJKN60w+VH0h9AJpezcqLD6M0WKOFE1b3x0d3y8YqbJ1nIubMh3KRmDetN5wq5Oy+qzHv8Zs\n18UAwGdsIQSolTh/Xrr4TjOFQ0MBkfHBxVARn//gA6zX67hrkOYiqJ3hEIGDd0NVNrs8uXP68fJa\np5FWJhb9/Nkneuc0QQexCEJp4NIUsF4dYDcF3P/4Y7z7wQcZWDlEEC4dCImI+Ecq2AT4JRAaVyvg\n1k3cee0uPh++CO98ctpc4vT0FE9PTvDg4QM8fPgQ5+fnCJeXmLZbjOMIEGG9WmFGfYcBCNHJgihE\nWACUy6sC4uUq5bq3eMWLy0CLjRMpsKoLejP3BVggqnYjR+dGRwFJYRuAD//pJ/jdb/0+xnGF7AlL\nr1qXQlnKVI9j7gdpBOjxY11+lMsQ7ZZAQwMWO1nzh8S/uh2tQJfg0i5Tt7cHPGtQ2QLr+FuhUeaX\nC15VWUkHOlUd7+YW2+yzsdu7N0K2IxBhDgKcOOD4xg24kxOmroBUbk0GMsvC2sFV4QoA5PtCWsVT\n6JN0ypS/U/rPAK49I4mIQGlxs3ucGnWPe9nWUkH1Do/bpaQBvDY6+LPmicUjzifzm++CMULtAJb1\nZvDvfby1DHJe2CAIrrRHzgPnyqKUBD9sUBXdU88DNkYB4I033mhik8s6dB80tBk87+nVSr5SmaUy\nfIGXJzTNFA0JzCSATZ+uSvYU7764xLCludcuCom/DD5ZzGUcW48xPfZUoTnkJY8dcsjOLgaLgQJ2\n0xZhDpjnKYfU21xeZiB7chIXPZ4/f47z83Nst9tqvA7OY95NwDDgK1/+Mo4Oj+CGeE8VOYdxtcr6\nk0+SDk6GdSpwXwJvXkDpGcqMPZq50sEzS/K9TX0wK59rLMQyugmJqt7Ln52Ddx7TNOHq8gK7qw0g\ncEhvQUi2Q8q6RQPD+K0YdL4b7zzWEV0+L2QcvEwvUyfJuWPpTvndmkfW+LfK0L/3aJHvW/pfz4se\nTb201Jal8rryvfOeaau54ljQ78mT3hl7pbjYxfZbponf5c9RjrO+Zbegiyf5Tx/jnffeTfTUm5Ja\n/c64J5Yh79dkHAQwnuCwmn1ssMQnWX/DPxScTMZ7eeuFwne67t5z1oFyPHOb4u+KFqXveu21fqv0\nR2qUd67spM26hCpMVvR326alVGjo0CPLp/oOVX7HpzEo8Y5pI6DGoppf8n39D4iY4+bNm7jP71Q4\nLtHJ/9SzChvr37kMKrRJJ73U8ZLeWvcjD8JiZtJif2jc3fZJyVfJ0oTb4NqoJrKWbDdWNkG5k68n\nK/bJTMbx2naT+LCXrPkr+7+X8iXhC+O6sg/VXOeTs4XQ8l4IAX6o38lhTRWdgep7HvX9iYeHh1it\nVlmc8wXUkr6WD4WT9Rhd3qTTGzO5HbqDUuN7cieGm5f2sd2f2SawzBpQ3lCV8zq5kay/kfu6qbS/\njjgRl+XrE1usH3NI/fijaIegGzWuWKJrSfdb70kfk7RDgKTHk9zgfPL9+n7XWFqxKfiUW+xsljh5\nA3igGJVF0MZ0PDs7i6dD1wN2mxkOrmyo7MhJAND3bFR60Ts4dU6kusNV4A+rbF7YlnqJZXeUfSzS\nfLk3SPKZ8RSXCyMJufzrpM/UQohssgmo9w1kMTAqZ7V3GA9W+N1v/i7+6i//EquDFaZ5zooqhqio\nL8Ou6mSlKX8zhRSl1Vf1VAgUbXToEwFElE8R5Nh+qr3eeczTjNUw4jt//3d49/PvR9GQLnRip4kE\nMfmzE5PDJQUfCnAaxjhkBvJp8cLhcBhweHyMu6+9BnzxiyAQdtsYxuP87AwnJyf49NNP8cnHH+Pk\n9DSHsGHgw5exZoUkwAwTEk+RJMDmHJwIMaGdFvkttcvj2kaaa8G8VJKggPsff4rHJyc4OFhjGHm3\nvqtOZVjCwernhjYqK6RLBmHst6Lkolxt+WAp2xYk9fPq+mtamlxgIL20466qWIFpm+72NxOIId1x\np7BJJYBl9fw12O22Fo7Y+SYvAeQ7dJz3eOXuXXz00UdZUehyZdxJaRiwgi+LhL6VK67sLLOSBP+5\njZpPmBOfivLRgJOf8fdZKDSL73EnhA0OszGSd5bXIcfM3dGo+1/eaaEvYNYLyLItlmFij2OlTyB0\nsJgbzFd5dDvTmd4hCEe9qDMq/PoEmATrk+ozBnpyDJFS/IP3eTzcuXMnyn9fn27Q/HVOOWXQjid+\nvjSOesZItUCm5JFpKEp5h1pWs/4JaN8jQpEfNQmNzK/4neZUvmAyj22CC/UJj2hQUAW6YziyUNkm\nZS8t5TZxWLsQZoQwYzdNODuPsVxPT0/x5MkTPEl/N5t4p9BqtRKGm4OnqOfnKV58N2GGcx7b7RZf\n/OCLcU5ForLuXDpt4Lwz+37pgmTOm4N9CrzA4LY2VlSdC0Dc6iM9LziPJVN68b7b74QQUk/OAX//\nt/89ykDn0v0tfb1p6RqgnkOyjURUNpQY417KJs0fHtfW6diX6WX6dZOl+3o6QOe/znMrj6ljZT6g\ncozJTVJLF7D2cO0+euR3XWePdqA4XuRczvdCJr08iFB2lk616LPq7dGb8zNGSbDLIToOwhzlxjiO\nODt7hplmDCHeNUlVCEqXd2byd2m3eiBviJPOjK7NYtEo8i23Wzi3uJ4mt3yvj0nY9ojlvLj0bOWy\nyzZWn566/7Q9nzIB7PlBOy6W26vHeSxD6ktRmtmmVJJ6jjSOxOY1vq8TSq8t0GfXVWiX9HnvsEvh\np28cHYLvPmAbrzdvKh6osaXroFC/r08X98Ztb0Ntlk9YwALiXQur7PNB9J5b+XJZadzEMOK2T0HK\noiXbPv1Q3u/Q02LwghelfbQP6/Bv0m7syUvZjoh3xRgFQBzlIOkLHX50aYNxADAoevnvrdt3cHBw\nUPw+nbFTtTMvP7RYdkm/lDx1eRYP99m04IVVySSDXsifxSCX8oLEaqCUC1xGTVvt6Lbkom6/pKdp\nBznInav5dxc3TVu2QjXOBc2sM0OYGz2c6VKiWY9Za4yY+D6RrV30shxrPvLSR/m9XK/g0+1RZdt7\nDFXvncflxQVOT07SRv655iP1FwqKrrXlgtbEzTBKPPV+gNS7lexj+yyajZV9H4M6pL7jNlf1EyA2\noaoRXvEybyJt9OH10mdqIUSCUAlEczz/lC3/pkCJNqoj4HOYw4zRe3zt61/H3/3t3wIUMHiP4EuH\ncbnWZOCypWHufBrITg7K0hRL8coyZDt06gn1/DvKzu3vfucf8Ef/5t/g9t1Xwbepe+/T5b31xJwT\noq6ELgA3ePgQj0hNvOLpXLkoWwF/B2B9Y8QYAm7evo03334bX/nqV2M8u0CYtlMO9fHzn/8cH3/y\nMU5PT0BEODw8whArqEDO6BxmIBsAIS2KyP6Xx8o1AGAlqQ0e2f7cXvEb86pxyBDw0c9+hrfffjs9\nDzHcGPnctzIFUApHU3gEsoFfK4LqvtZKj1J/xAuIWkHQM056glznlb/XjtHWSNW7KqAUguyfOTkT\nB6ccrvCwxHcPXEqwxEYcIHa1Cdqs8nonHGSengHoXIlnSnOMln/j1k3sdjsEZ89hfjaHGcMY79MJ\nNFfO8wL4Zdt44aUupwdOTSXN8lPwRfPRuheF5xMrWuZXVkRC/lb1KBlj0d0Di9bpBpkscK0B8FI9\nsn3c7kync9UY7OmQ9CXuXvBx178fhngZnaWUyWWgLnUIUVnIzDQinWAUOqEyeV10ag8+hhy8detW\n1ldRZ6U2UU1vSPdEVDyCr/vA1bVZ41jrwLjQIwAp6rnlnEsXZlMpm2pQo/sypFCKngDMoTZqq00C\nHGkaJTwUxZZFGkQoKKKIr1Ooxkhf1GXee+x2O4wpnCPfDwISC26O4l0Tgg+U5gVRPLJ8eRlPdDx7\n9gz379/HgwcP8PjxY2w2W8APWK/XRU6FeFk8hejIypsukkRgGoniPR0BcTPE0Y0bwOCTRyOCTof6\n3hkt561wMZaRQkB1ojKgXLSqMZV1f4ZlBMn3LFmu57mlg/Iz74GkmyvDR8im+v24x2yaZ8y7Hf7p\nRz/G4XoNAFV8Y1m3nOty51R2EnZku3NxkU2H7shlZrsrlc/3QPn4LIYva8t9mV6mXzVZOlLLb05L\nhuS+cah/l3MSrOOselzr9FpKFo0aa2gZtw/LafotWwLO5Q1WDaZyZcekhSGXEttTVtbe+7wjPDsJ\nKDqunHOYpwmPHj7A5eUlVgdrEA2Vk8uJUCi98vkOPxuLx1jlS7hA0l/bF5I/pPS4A1HEUU69Hz9f\nz8kR63LGM2vsFBuKEmYodWZ00pRnlWvpkVxfwj6B1MlDIO9gJoE7dW29efqr6AYPYK7KTItejr2B\nhQ5foFpp80LZss3ankgCAcMw4Pj4uMrPe8ArW1yNHbbPzXmbbE3n6o1SVR0Ghsk8qTZ/yEY77h1T\nflkyg/Gvpj/wBpq0AMDcrugV44WIyl2yst3OVdENKP7Y0KXbLdvew38weCPbpXWJtBt7NrZMg9wV\nr2jR9PDf7BeLRlJjB8WeD3key7rZXiD1XPOjbR/hIN0R0ksN7xbSXtsU7T2cvURtRGlVFyAs/Ppd\nrffT3IkLDLYPiHnM9pqpo9mWI7bH4nxnWwKdtmXbV9VZhVtzdX52fcvxV9oDnlSSGQClTWwdfvSk\nWsOHLCPLM63ziSjF8jPoM3jAySe6eeNfWbwtNmEco4g2ZwjYXlxhutpgnqZ4/7MfsvzR9Gt6Y8SR\nvl9F89Wh9uk4wVs9zlxa+HfOlTCT3M4kA+Y0LrKuR5HxomDRlTXGk7Zf73TLddJnaiFE7piO32tg\nYwlQiGeBKBvzMg3DgCk5Fr7whQ/w05/+NCkkwXAxF+0BXTO93UlO6aiRrYi14e1cdLDqnQY1P2xD\nfAoBI6KT7ObxMT78p5/gm7//+5imHY6OjkHTbL7n0+RpYCIrbKY5+oIqQcy0zIMAEz6e3oAfosIL\nAY4Ih+MKnzt+B+++/x6+/rvfyDHRr66ucPLoBKePT/Hxx/Fi9tPTUwDAeHiEcVhlp8UuxEtk8w4s\nHy9I5zRNE8ZxTDjGFUPFWDUWDa2PXYq2aVB+6+ZN/PgHP8Tv/d7vRfqGqDADRacYBB9yXWp3tYcx\nWRksQgmFBKJt8FI+z1VR9u4VdPJcR3D0AJL8vTZw+2UUhaqM044D9kUEm5SmSxzgMVHRZQBFq37p\ncMwyyXvcvH0bwSHP3x64kQufeud9VmSKl04YSj0g2zoElEEGVAuhOjUKHcVZe53+l87vAOTwQ1Za\nkuE6nwXGe4Yny9N9hjqXJfsy16kgU4+2+KwYb3MIGF172R+/q520ub+AyhAEEaAX5zQATY/9OOLw\n8DAvylXzRrWVca9sUzNG5fxRxlw/xZfM8lL95Th6zRMA+dLMqg8SHbruYRiqvM61dDbGHigfWybE\ny7O9j+N1TjuFpjkuqM9hAhywm7aAK0e0wxyAOc7zabfD1dUVnj59ipOTEzx8+BCPHj3Cg0/vw3vC\n0dFRaUsx7qpZAAAgAElEQVTqx8PDIwARi0wppJOjtCOmaqGWOWXH7mo14jd/48u4efMm5o6ck+1m\nnlvz2kpERbnv6/dabqNatOvJpd6clUaN1Q5JS1D9DNUevfuT72kBAQ8efJqxQcThcozUZfBzSetM\nJUzYEh9ln+bFcqphvi5ftrmKFfwiuu9lepk6aVmH2em6Rrwup+gSu75Kpxk07Nc3/WTpgqUksYV1\nknlpDrLcuC69xfZ4MT5queil/ATbcADIYRwHfPzJx3h+do7bt+8ATm70WKbPsn9iPfwkOrl67b0e\nH4RMDSEhLSfos2nQ1mmvT4QKq/LatJVFoR5W136FFxlPvaT1irS9LVprPVc74+I7aJ4VndqebGcd\nGnexJwefgcs4NMt1elXqco13I86KHtxhGHC4PkyOtTSHQlwkajouESH9m0QAH3AqmCDETT4k8J5n\nu7kuzupfiUHiZryylY41+YtgI6sdjMe4LTkis+YjCs8dkO8Rkff8tB6m6/dRM7ZYnjh3LYei5Ff1\nTJaZqOrxXstUqzxA4KBYWpXHpU1K12t5h4b4oZqP4zhiNY4Ate3S72v6icoYFITm9/T8yBnElNY8\nj3i2bqZt61CZz6pumSobjUcO1b6XXtKYfOn0shxLrKMkv8tvLP9r+rJubalA5ZQVdMn5rqWkHj86\nWf0qeZxlqUPTh828MuROry4eM8h9UvLI8cfP80nUEHBxeYntdpval3I5saki65a04A/KoRpByOHX\ndVqyybIdVNkoqOZR6WsWhX1Mlue6tMGuMa0tfbzPLrPSZ2ohhNROCg02msYbQKKKh8gDEclpN034\n1rd/Dz/44Q+xXq+xm3eRwS7t1uldUY96B8QSAF/afT6p3ZpEVDkRtQCNi2xlNS0rSecQ4DAAOD5a\n43v/8A/47a99DQfHx9hsNhid7SDUDgUCsuMIKDtCXXYRkrhMGPk3+T4l3oEIfoiAiHeYBIq7gVYA\nxoMDHN+4gVdefRVfCAHfnGfM84zLqys8Pz/H+ZOnePDgAU4fP8bjx4/x+MljbDYbDMMQldZqlUNq\nxYvZV3miJzYlBZFWhZ3oK5TxkHfjSOHHBaQ+GIcBm80Gm+0WD+/fxzvvvZcVApB2anTAQ/29yYJ0\npq7OlzKzkRDHQf4l0dkqKKvOJZqWBIhU4GV82kfUyxxoQ7RkR2yUyOnqFw2AZJn76cvPAhuE5Tev\n3tPGh6Rdf5fzUH9vlVjJc/PmzQbk6bJkfWXHd+2UBwjVKW/HR0/L+5XxlP6NPKBJ/lro0LLJaqNM\nvFCjL8TrtbEaI/ybyKN5bQFg/X3psmSmQ5bZyy/LN8NuEAk+x/HHfUCx8ObkYfwtD9qqDskP5h/v\nSrFksJflpr+azmocwuWwWtnZigLEy4kPpHYVdNEzPGK/1XdC6L7m1LRBX/bqRJ6EXbV8k2NCAmfO\nqucdf5aGYAgEKW5CFX4x7hTzQ9xRFhcnJb+jIRjCjCk58ufdBD8M2FxdIcwzLp4/x8npKU4ePcLZ\n2TlOTh7h/Pw5hiHeObHb7WI84XnG0dEB5nnGNE2ZFrgh6hcqAHZMO3eg263kFf9ERJgdgBDwm1/+\nMuB9PNjTAaqav77T15xn6bJxa55Wv6HGrDp/bwwRUYlCm/Vma47N1D7TbZWyvm5f0s1EcBTwo+//\nIF+SPvqy2SQA+b4ROSat73I89gyrxggzdEdlNCzo7BcF9i/Ty2SlffhOP9v3rh67lUFqlLM0jvfp\nGMuJZBnqvbKvk2R5+8rNuAPRlvHO5QVvq+7GoaAcVqynZJv5fS0zZP0Vf5JTYxw8trsdnj17htfv\nvYEx6aRhGCI2uIbjkJBOsqtF+YyNlDPI4pnNP3kapbWDXFYIdurVobGl2aZritEQAhw89KWCpVsk\nsjVAjaBVVp79CZbNQbXms/StZFjbztoZJMuxcrHjW3qeTEyf7LUyZqhii4xiUelHB2DwmCjp3mHA\nCIDIIYQJgx/qMFbewZPPYbshcD2HhMmcNoavNndy88TppyX51+dZbRMwSupdap2fWeWl/tN928MQ\nSNiKiPJJNIAxpT02dLlLdpqkLdtFqtzrzKv8e0NT8heJ+iycGcgl27X0s/P1CYlYBgCSDtjSdN1/\n0l6VMrJpN9rhNKfoDuv1QeENShmZbrUpO9ePAO/G/NmSjRVd6TSIvPPOsq1yLAdX162jyxBxuKGW\nvkaudMSXrj/bEVyOuOuF/RKNLO+NcUvP88ImUbwDWNyPmnUQhP/DdWQP0xJE//I8UP49QI+t+Exu\nmG7mSspoyvBSaFcr9HFFbfsCKSJE+oVbQ4Htmdj+588vcHl1FeUpubTgEXMPIuoB053L4gUQpU+K\nnneZEGnD83fpE8rtci6f/nGQPvN27si6Kv4URol88a+1OY1TDnUN5BDq102fsYWQ+FcyQF48uQ/l\n8CQBlcWQEALcOGC3m+AB3HvzDbzz7rs4efQo312xSyEyKFAOCwXUAn1cjTlsjJ6cDQ09g1gYxnpV\nXhVSYJhSfrnsdORps93g6dk5Hnz6Kd770hfjjglD53K4B2Z0Pl6q+M2OBZfpoIIFAfhQpj8LY4lc\nKAGr+CUU5QIA3mMAMBDFi2YBHB0f45VXXoF75x18+be/immesbm6wubqElfpgnbejXv+PMZf32y2\nCCFeGD/4odqRwkLVpUoD5mZC6cR84MQ7eUGEH3z/+/jc594GDYDHkIWBHIvakCv9hCpPEai2cI3P\nLPBS07rPoC3v2QDnOoBHK3T7PWksqLJdWk5TvHVSGwml1TN2shIDHzGXuJ7r6gPdUFeeF7EswMSf\nl+4+IYqXrB0fH2O73VYr9xawYIXSBYoVzaEaV977GF5M919RmYL1iUcCBEseWvFzm/aL9/L8N8aR\nbFcGJqiTBQY0oNP0We8tGRN64aaXrykj1x8Jr4CUUXe8q0m0UwxhXXYshx8UGqKxJ/Ir3SBBnrUg\ndfv27fhZlZk/pwZFkNDqoBYoo2lrz+hrxokTv5EC3sT/teXF/JSdOdaCCZcVwgy5KkhEiNOj1MWn\nPNhoCPOMfMEkOVAImCkuWEzThKurK1xs4gkPDt14eXmJ3dUGl5eX2UAhonzaa5qKTJi224gXdrto\nuCcwPLh4MipQvKfCOx8Nn87J1toyqU/MjeOIzdUWr9+7h8C85VOMCfjPVM+hbOAa40JjEE68SWFJ\nF8j5yrtLvZiri04CFGN18PXpqbxZRY5xw3jWwF3KSmu8hhDw/Nkz/PKXv4xAefB5ET47G1QZvTtK\n+PelRFTcWibgV/2ecaArO+Q1H16ml+lXSXKMLW0qkHquN2alHOYyTWdApw5+p9a1dp37dHUPW79o\nsu4a6tluTeK2o5UTWt7Vn2uHSrBCTXXaGzdSAEQuywjOExVhdAQ8ffoUhLjIP/gxYrjkLMmyxrWh\n/Lg9ngEQIKIppCj9Srf3MJ3FS8mH0ubaru/bEy0eWxp3dj9KvFsM2KoNzrY5jBoK3hP/S4dU3T7j\nFHzWFXU7LL1T2lFfZNtQJedooTTZ2hLHcxvsuhyEbwCAtZvF5k0Z36xf4wJT5On68JCN3fjb4uoX\n8niLX2sasg0odzdnHhvOb4WBunOWfR3E9pRtT5jykihffi43JFVOVtE80cy2+YZN1Mu3NNdkWbps\nK1mOR8seZ52iZYisQ8v++nuLgUDGM2411X9lmO5GNwl95eS7QB6LeexT3CjrvMM4jCjGTI0z83uo\ndapls8K1Y67Vn6HC0zp/5ru4y1TfH8wNkguW0haz+qRwoU37MKtlP0YdWIqVJbMMgNGu3D4i1Yaq\nGfFd7npXbGPv+LQjj5v4L8u7ps11XxW7PJYvbRjdZu99ljN6TFc8c0XOm+PC4O2Aeg5JPoQUUjve\ng0N5gfH8/BzzNEd7bS79wgsgPL5r0mw+VPomQYk6lc71fsgRFYpPwsP54mtNygMS50heNj4GyPEQ\n66HUn7SANyQutbm7nD5TCyERN/eFhNPPWZCiVTRSgM9zVM7jOILmGd/+1rfx53/2nzGMQwYL8b0y\nyPRCRaCiJAcx2Dm0R15pdDGEk+V4zOFZ0mo078iVKStPPQFTexng8qrc4D2OD9b4zt//Pd75wgdA\nWuBgYZOBgVA8zCczCWVgaeLBiSOlprKODOGjflk5QQpClyf8PM9YpXBYIQSsxxHjwUGMhx8C3nv/\nfcxznIjTPGO7iU6rR48e4eH9Bzg5OcmX0U7TlC+l5UWuwQ3J4RMwz7yAlVZN0+WqOZ5n4vngPeZ5\nwjCO+NlPf4Jp90fwzmMYR/BubCt+buOYYUDJCiNLHTHBNesNtkujygLO+ZkxdmX+fZfJNfPLaJcE\nLaXsWgiXzLn0RmHUWNveSVwZv5a7naiMd9hKXCpu58RxQiCfMJKgx1IgcZG0nPQaU5ii3W6XQ0rJ\nuzVk0k5DCXDlwgUJWmSqlAHTl2RE1kO4njHQe5YVN4MYCeAlAErf846AOMFzfh4/moeyjT35nuUu\n70Ix2h5Ij305Krhv6zFASPdXJPpzG9M8o8C7UajQH485FJDCspSdC94BCZDAlR1WmZ+pfA5FmARh\nAomFdtn2ap5SHeIxhIB7b9yLpxYTbVUfyvmpeBs/ujxWgHhiJWk7sAFoGkuUqqICUqDaSsnA1uNK\n7noqIRqQF3KI0imJVG7I4TNikos5DLJCCElmp1BujnXNjHmasd1tMc1bXF1ucHb2HOfPnuHJk8d4\n8uQJzs7OcHZ+hu00AeTgfbxw1rl4UpR1RgGZBW5F3hGcHzCHuInAORHWkpKTy3sRUqm8z6BRngSL\n5ZagFLneacJbb7+Nw8NDZqbSFeUkT5YDqu8sGS/rlxtL9Jw18RWVsc7xzy3DVwNuTrPeVScwgT52\nL6muQLDxHFH8Y54Tfx3ws5/9DKuDFYgoyeQybqXe0mVp2uV9Qlq/CEoqxxgUD7VDOmKGodK1Fb57\nmV6mXzNZc991xj7/BqCZ03qse0M+aIxgltuhb8nOs57/KkljEY21LNq1Ec4Yx6Ogz3205TJJ2nfK\nwOfynKuwZZG1ZbsL0m7iAMTQhC5ihM1uh8dPn2C72eDw4CjjHMZB7LggAsinnb5qDNQnVq7Xrp79\nsJRYj0cHSxuqlv/2xlKv/NbJA5iGq8ovdX18P5UhTzAQO1b5O8oH5/KAaMezQKWdOWfyusL/mica\n0ylbBYAcMb0TQb0+rm302tXU6jF7cavCPEQ4WK8xjiOmaap6xDHuyA8ckBbvGoIEzTL0eGVXkW23\n7Wu3tC0zHjFsMJnkWOT49/yc/1Z4XuFzmTfTwPpf20qdBW095nuybMn+0wuRPTks2+ycy76rkqc+\nmdGlM7Uv/2a2rNhZ9dxUukhis245yDiTxxoRwSPJw5lweHScx1M1xgPF0wpU9J6mK9bf8mupLyS2\nlHlz/mSkWTKZfRdB9F/vtEqbos0SC19eXNbjSeMD/Z4eQzzml3FzabOW/XocMc/jGPPgHg0I4CMm\nlP5jvWe1icdqPAVhb4KtaKL6fYtmUGsPA/VYauaYq+8Zk2Xlz97lDYbTdovzszOwWpH8quYw6wiD\n7t5YXEpxU27c1F76hLJ87IUX1Sdt7Lq4DYWn3ASiemxZMkzO5+umz9RCCNA2TjIyXokK8P5Cb0xS\noFZKMURFvHhzSg6Pd955F8fHx6AQMNGcQ1DFo8/IDhoug6jE+2b6gvPl8mBWiK6IXe0YAMoU9Xmy\ntTsC850CHB/P1QaHcM/FgRPi4sQvfvYRzs/OceP2LcT7OtXkVfztGiMM0jOw5NMWyVFDfPmsL20X\nKe/c54kihIXkp0s7KfwgLv7x8WLYAQksjSOICIMneCIMCVzdun0br7/+Or761a/GuKRz2fV7enKK\nJ48f45NPP8Wjhw/x9NkzbDcbAMBqXCGkXcTOe1DaaTvPcxZQcZWVcDCuABCeP32Kk/v38ca772Oe\nJrhhwEwBB8NQOWYpncTRQhwOmQ9sTFX8Zv4Q5WCtctd3tqkU+JHjiqiOEZ/HqHEvxaIAZFBmCFDr\nWf0ZdV6eT9kxzcVH4SpPM7DZIOmzlImOXOeqsimOe63EJbBVSomSU5lmdfzPELAM5Lz3ODw4wJ07\nd3B2dmbylRV3f7dx5IusJdYpd6u5cvmb4GmmRbSfDQGtfEwZJMZMxStZLudR7SeoMZF4Ugz/2pki\n0z7FKw1THg+qgCw3Wa6D7QxQGX/aKEIKa8HGN//L7xUlDCAeT0/jKP+egXx6l8pY5CHOi84Mdrh3\nKuM0y9aWL9XunyQbmU7ngTt3bmMYfMUYfndIoJxSA4u+kWC9UFLmQ9FD8C0w9fDgJSQODUJenggI\n+WSEHuNVbFGPfPm45A+I0r1S5SJ05lOgOe9EIaJ0WpOw2+2w3W6x2Wyw3cQTg0+fPsXpyWM8fnKC\ni8sLXF1u4OBxcHBQ3d0wwONgOAAzi3YB+VI+ONCcxhbaOVv8I7G9wQHkhV7NyxRi95twqli6lue8\nc/I+IuA3f+M34m9hhhuHLN/y/HNpcYlp5HmPIveseZ7rTA6zwItVQDX/CziPXSV3MAVQOnHNcqbd\nKaf/yrEv6WnkTwXq7VT1iyorTDt89LOfgeCwnXa5vxi8WzJQ9o80VDXPtPHKbWEDQS5GWXqTLwLV\nu8X4nqWX6WX6VVNP58rfe0YlUM+p4ghQOAq27SCfpxfyvKzkp3Da9E6sMG7S9DV1XCNZbV16dh2n\nDWDg914+1smMqePDWFfJXNlHFcZP+poxdC27QtRHBDx5dIppt4Mb42K8H+LdSD7jIJf1kcT2jQ2R\nfudd+5YjvWeDZNymnsXvWnbGf/H32r7RvNfyWtKg8+i0NB9iXflbepaeo0AsbQvU38sYLxEJtE1d\nb87JelacmuDnhXcvPtZzGZ225rqtDI6nOpXxSOVdbUdIPkQYkDMDqf2D9wjzjPV6jdVqhd1uVxGo\n5YLEzrJOST8SiWxPRxyUsGKgNq/BAwujaMzhxDu9MV21H2hor7Cv4JG2nTW9Db8NOUuoadHvsW2U\nbSRVl65X31PWmzfStik2FPeByqPaqO0xpz7V/OaXa5lQfeZ3DHsDgs5ERMWvgBgSzzmH9Xpd604S\npaedmmzfgAgkcLpzDuWayL6ukviv2x45PhamvtX3VtJ59tneS+XIzxyuifVRYbG9oMHfm3HlXGMb\n6bmFNLaqhWmwfVWkdM3rOmd5Ljamg8AbFJywlVtZt0/HUzeLRTOnIN7T87JsXE+8DAGXl5cpWkHA\nOJToN9M0CXpZt/D8UYTVU031RXzPtBcd62hRjzrZxWXk9lEtc8xTTTBsUpR6lsa4yzrqxXTkZ24h\nBOgrCf5N/tVKjgGd7Ijqcl8irI8O8a1vfxt/89d/XXar8kRE6tf0H0+eRoG0RJuKzlK6lpLXwiI6\n4wCXd7zLCZMmd6JjDgHbecIPvvc9/E9/8AeYiFIcPrmPaX+y+K7pk0KuZ7RI3i8BV600LYHtvC8t\nEP1J44gwTRiGAavVKj+/efMm3v/8+/g6vgHnHKZpwjzPOD8/x8XFBe7fv4+HDx/gk08+wenpKS4v\nL+Gcw40bNwAAx8fHmLZbeD8AYcZ6dYB//O7/h3tvvYNx5bHbbuHHEWGOgkgeZQyELDgS9bVy5Wei\n7cGlHWHOwXnjgjSh0yEcm3rs8GIeOZdUPVVj0BqLGhxxzH1Z7tKYeJFUhY4KBAdKCyKFK0sGT0nl\njZ6hJIXpInAIlOMoEmsTq0YqMeh5XN+6dSsuso5jBPqCDgbD8gROTaNtePNB+Agw+f6DYsjI9vG7\nSwakpL9v0O0vpyd7ZbJ2jL5oyuWj7HyxLmLP4NLVCwPchdUCYM/pgrp9Ufb7NNcMEEkoQIUEIFZh\nnpxzoHTxUl4oZeUdM1Rj3uJBTXHMdffu3XiCQVgeRefFkSPjfQPIBmKmi+cDeBwBlnVc+jAgBAY3\naZeKMFrlqb/Y3pD7jueA9+l0SHqHQgDSQn2YJnjyCK6cDuRyN9MGFxcXODs7w+PHj3F6corTR4/w\n8OHDDASPkiEzDAOmKV6M7uCxGg9SqCxkvmddNXjkBS0FwKv2EzVjRPLRCoUHqueanqvyL3+WNAzD\ngGEc8IUvfCGW4Z0ZXovlwmDMw54xq3fqaLyicRTXOac+hYsnb3inXAbrRlstebGEA+SzbOQa9Gha\neWGBjZqHDx/iwYMHODg4KPeUocUWFj1VCFZVD79vLWKUuS3mtepXLjPKDFfNUSJqseTL9DK9YNJj\nVT6z9D//XbJJ5Lv61GP+HcjyocovTzcYtO7DKLpdL5K0U8WS7xlD7IkzLeVRlwdUNr7UmJkXQJbp\nZZ1f9VHczQaJc7muHRwGP2A9ejw/P8PFxQXuhoBhEPnAOo4J6Mhf9um4VvbLOjnJjQ+lJuVpyfWW\nxlsyWNIkx9+SjuRnPTyr+zz+9U35lHTY0vAiSpthePMDLxakZtFc6+ZhiPK9HbN9fajbol+19WQp\nV747OFeFAq7qKSqzJY3LULZGlY1aW1DyWvMcANZHh1gfrvH8+fOsJ3u+gkBp4xgBvBvZoKLiT8+u\nscLg8e91w+vn1lCwcIPGPD37KWVqMKO+G8OqywrFJOvpye563Nv2MdOyVH89Jgs+9bBO5tdju7lX\ngOoFRko7PjW3uO8AO4ygtF/0nXv5rxHyqC4jDkXvPW7futUdE2b/xAddfSrbq3+TF3ADyfYKZftU\nXgxUNlzWo3IDG9qxJ5/bbW9l9Iskibmz47wzDyQfemMQir+aXrlgkOtlUzvwNoHlxbv8qlMO9mQA\nRvsulUeaRn6vtUVYfzgg2pJ6LqbxXuuBWAc5J+ZBbFdhrcuCmsf4ZrvBdrspbe/gK6rY5Up56R2G\nAQ0mIsrz2Z4vaiwvsLpqr0MM6UzlFSUxQGrRpPDQvnbi10mfrYWQBYAAlAmXd6d2GCUnh3RGEhH8\nMCAA+MpXvoL/47/8F7z26qvgw36WgI2DqB4AUdgznWkQCkFR/WA201accoDHQSfb6PJIlyuM5OK/\n9bDCP/ztf8e//oN/jTB4DFPLE67bokfTpsGNBqR8WU3PeLmOgOrxJX2pjCt+NlPZnezTiRE4F3cH\neA8/jvFkR4iX6a5XK4QQcHh8DBDh/c9/HtvtNp7wIcLV1RU2m028oP30MZ49fRqdb6ePcPb0AiFM\n+MH3vo9/82//La62G4zrNXzcPo3dtMMwxFAXvOPZubgQEYWMy00p3UdVPwYeMFkQW2NGgyJU38Fv\nCWkoBYsMzWIJ9Zz/hXqrJF1WLdikwaQE6wtUGMdT9cRQtGX3mwSeewpOxgOBHZmDIe29jyfAJiLM\nAG7cuJH5yvcJcL37BLjsZQkUuR4AeVd6BSpRG5TWPLvunNOg2lzUZDkrX1TyMeh3YAMHXbf8fSlx\n8CAGT0M6QVZ2AMnya2cs77augEouC/VqNhW56p0IlUcu7/RzzondEzwJC9CViyIMLgIQF3Nc2Zku\nedDwRrU/1uRx584rOb8O20hIF74L0CbBtBN9WHQLMqiF+E3znkEj1xOnSpStHL6q0FkueeS5Mc8z\nEOa0E4cwh4A5zJimHTZXV7i8uMCzp89w9uwcDx89xJOnT3F5eYntbo4hwtKJjmm3w8oDY5Jh48EB\nKMRNAGGOiz6zSxSTw+BX1f09Wf5xXwk52YxHGTqqdGmVv9GNaa6A4m5clqfW3KxBXyxrHEcMfsDh\n4RFu3boVTx4aElnLact4tWLUyvtPGFfIsahPDwICC0kmUAGwPDC0IXadZMnnpTdbTVKmL4fN/OEP\nf4ibx8fYpg0SZQdba6xZfaDp1/mlzJzmOQJ9MC99t3/5vWjc+8zvSo69TC/T/6C0bw5qGaaTpZ8r\n55f6XbtYrLJ7Tq3e7702vQi+6ZXzorbJdWGq5GumI79fO1U48b1hFFTIQaNSLptx/CqFen7+/DzK\nurGVXbqgWt6mDVSuLt+ql3GfA5rNUjGPzC+cO5ofHVuzZ5e+qDPE0kVRX3mjDvk96U7Vd3lDS1NR\ni8WX6HkxW7jut+uUkXlFyOFMMk2iL/bRuuQMs/wxsv58+nYWUSPYNt/Tj6wHrQXUMk5EY4Di2FO2\npW5ni5Ha9vSoW+IZ29zaiaj1v1VeCYpq17PPKanL1L8t2WT75J9lv2UMw3kgbCmV+pEQ4ltE1Gwg\n07KT+9rin8Xf3Beq/RIvSz+Wcw4H63UTeaJ7WtF8upwk38z+dPHECdtgrhoRiVuyL0Sb9vWfxq5w\n1hm//jjpP5M0tHUu0VR9Fu3S80daL9o+iDK5vK/z5XG0oPequkJty0haiShuajTaQgr1VD6GGLuy\n2Pgo47n8L/5KPqb3+K6li+fPcXb2DOPgMU0lqoI530m3WtJc9FnTTnaGZPM3NP1ZPhfdtE8vOccj\nutW7zhX+ORc3KxS7LC2UGnJNY6vrps/WQghqYdWmcr8D5+UkHTKEupPkwJmTsDw8voFvfutb+PlH\nH+WO4gvFtSNBhsriujzEIEda7fXx25wGHbn2AlMpGHs05u9psuV2Eq9cljKJ4g6hg2FA2Gzx0Yc/\nxdtf+gK8X1V4ygKgmndEVIUSkc46STeHo5K/y2RN0h6Yku/vA7684zk79J3LO74pFgAQxQWStLvY\nOYdhHMtxURBW/hCrJIwPj4/hAbz11lsgio67OQRMuy22V1eYNlucPj7B//nnf46j42O8du8ebt65\njeMbN3Dz1q24i3cYMK4Ocl9FQQxxOX0cI6MfG6dyDttDaVeZ840wC3Frc8OvfCQbBMfHUVgYiv4r\nIJIFbNzdjaGN/UpoHUA6Ty/1+l2OGxaOuR6VV7/P5dpjg9KCJBuOAXBqMVGWl98S343mWOPVuRgf\nlXf+3blzp3IuWsBdfq/ne1GO2SGmeG7JuF6f8Gkg3eeWoaaf8XMzFFkH5Gu6NCjRvFz6Lsutfktx\nMisZIxS0ll/XMSSAMu5ABAoEPzBor/NxHFoOz8QLnpJnDJxdlkNZ2MY+dr44OCyaDBpd9XMMBzaO\nI/+uho4AACAASURBVG7dvJXpIXWRW3uKLDldxQKqS6cw4juUdYm+VDSyub4EkOkKRHBJ9gPIOhCK\nJ5vNFtM04eLiIl5Q/vwcp6eneP78OU5PTrCbdthuN5imGRSolCeNEBd3n827XepfjzDzePRVvxDJ\nHf2xH+Z5Ts7wArhCoAzCnSunUPgdy5AueqaMuRBCWSyNYCMzqQmhlMFdGSuyf3MozJmwubrE17/+\nDUwpnKfz9QJl1F/I9PP7sjz+axm+RV9EjniVT8uOcpxc0SvGmZV6RsdSvmh0LDtqZCIKCOIemcvL\nC5w8fJTpm0OIfa9wVo9f8q+WLdpAzjipumy3GLREZYc4f4/x+x14wbWJE73Ap5fpZdqXXsQJsQ+v\nywuPZR4tIyybpvf7EqaU71v0aCxlzWGZb5/MsX7vberq0aHrscple6M4Dur3pGsBhixq+knVGULA\n1WbC2dkZdrsJ61UpVMrsLq4WNIgfow1F9bPqfqlsDvd3ksbX+IStfBhfLpEObH2xr197GL22r9ty\nerK+sGj/GJLvlc9xw4z3Ld0WruiXU/NBGyimXSN1nFVW+mttkMgYcuE0JOuvng0vx0bBjg7jwQHc\nMGQ/xNJJDZYL+hLjSqY4dYLHJUebNEVQ7u7gmVcOSpW5KNte22b1/GpoEHRpGWfxTn637oZYSlZf\n66Rlq1WGpsly9uv5pNtc319U5IfeINyjnTdCJaid70G0bLlYbHFeWfitmsfys+wf1LPHuRqjcb5y\nV++C3oCws1VawsF7dVqiOdof7fuS5xS4qQ5ptTNdOyvqcEa0mkQ3y2zNT0v+aj1d9H+Rd5pdPVlm\n88UiMfUJtNQTNKCcmOmNeSCOn2rDuIE7yo92G+L3mEHLCuecaa8Qy520WZBHcjUmnXzHlTGVhiD7\nhKbdDk+ePEGYJsAPKHdKKn4ZfLC/s1/CKiOL6QqfSPuPnzVcpJK/kus5eoCVxBhwsU+5Hfki9k57\nris/ZfpsLYRQfQoB0ErKg3cjUsrPeeLXIlx0GbnziaIBHYA/+uM/xv/+H/9jDG+TLtwkxA7kC4H5\nnRJmtRakSPl9mnjcRVmp8PuKRktIOufKxcvxSf17ysNx2Lwc0CAgBPzNX/81/vRLXyx8o1rg9YCx\nBjs9xdgoONFfUnBrwKl/syZjzxjKtKRJ6dVqoXzP890d/v9n712fLUmO+7BfVvc55z7msTOLBQyS\n2F0s8SBILQCbDNqWSQYVtvxBn+QI2/+ePipsBsNfFFbIthAQSJAWH+ZDS0IUSBAABYrAYud5Z+69\n53RX+kM+Kqu6+tw7oP1hI6YQ2Lmnu7oeWVn5qqxMMULaNdQYIocG8itbBM3LogR4GEfsc8a42+Ls\n/A7AGW9+6i0wT/ja176Gf/fBB5g5Y9hucXp6iu12i7OzM9y5cwd3797Fvfv3ca5/j5sddrstxo0c\nLmWaiuGUUsAXE6QzQMX45AyHgXEcMWe/P1IJjTJ1VmPlrDHg2z0gxFkIHiv9rRN5I8C+L5TXaxXf\n93C5fV/FC0Rdjglxq4JqYAwuIFCNk9V+gwgzMVSe4EY9n57CEAWwOWecn59j1ji4UZiwvtdomPej\nUiRDbxLw0nhd7ZcOPGKbzFwZKCOcoqISx3CT8t8a/4xGxDZaQRMrv9fwpVuf2T0jqvF2BLa27QXO\n9IQEbUtCJRaPkB5tt5tvx+bArAcdhkcOm/UbN/ZNDmscD/AIGud7SBg3IzbbsewjlH6YWUIoQUWu\niPfELrjYrZasOZBcUFRBPM8TCM1tExLvDBv7PM+YpwnT/oDD4YCrqyu8ePECl5eXcpPu2TNc7/e4\nePHCD0KmaQLPM8ZxDHlaZB7zPEsWkkwA5QqnZ/WeL/ulxnUAYEqYctlPbDSdksAkJLWvjPourEb+\n0VdUfZ21febloaXhia1du88qfAw4IIaT5OMGgPfeE95N4+D8q/D9Ja9tS0/g73lZMoAp0LmM+mAE\nzEBKC3nGlM1eaeffOxSKdb3vRjY5puD46AnIc/Y8J9/9znckzKXN09qCKIZ2k8zyzPlcmvG3Y7Ox\nRMcPfx6MZnJdv1HQQ92c7cZYn0+Or2+GvC5/j9KTj+x33MM3yWrgjvd720+njfZva7c3npZ3HzPQ\ntn8fows9mWKtvKoy3WvZ8gIyQ4wBoZKMxXh0f3yEJjdYpc8VWDcDBwCMw4DMGdf7a7y4eFG6tm7d\njW6pHzsvXAFXa4SMxg+Xi6BSxFobzh/jei/141dZs8I3l8mZy3pGvKm/b/XLZd9L3fzYrXKZYz22\nY+P2Xog8v1fFc2zBOuNr+Uor51byhbFwUlmrY41q2zb+eJv16MkyPZ0xpYTNWExQCxmk4bdrel7R\nNeIeK04tVt90s4W8z9zFBfmuDmfXk/3iMw/NzcHhsIHNjP46eR2sl9vAv4Vhu25rOnivjWN62CKc\nqo1d4WIqd6bGPz7ApLf3TTYrjlmlbpxPoqV+SkTSP4dw4K5/pWrTR/rpbQMYdF7b7VbqkUQaMfQi\nCk5esd8gqPf4Xwu/nizezjWrLAsuNDYd/ZbiFli22e0Nsk55htkD1/CFLflUM7caT47R0OWYYinr\nW82kfp/6bUbuSEfWwXAr0nP7txf5Yg0Pvc8V+aOnL5Q2S9bL1n7BwTmvusOh5CxncZ+4ur7C06dP\nMIwj5mmCoXNLl9ruj+GkbdRjMpglpbeDypmzf8hho5teHHqpx+XXx9ZTKMiQPCh89TzOc40237Z8\nvA5CsL5x9BeMangCzObbNSU3tpVSQk4D3njwAJ/69Kfx0YcfYkgJs3qYmgd+bXRohXUzNsmvHFC6\n2pxEyzF2NpN9s9/vy5VSNg/XahalvradmTFnxulmg+/+5V/h8uIC53fvgyAK9jzPXS+AvsCwzjyP\nMdsWeXt1en/3iGlPAJFnqDZeD1d6m6hqOxjOIzOHiovEjGEoRhTkATwT3nn3ZwH+Ok42O0wQb9TL\ny0vs93u8uLjAD//u71wIyzljmiac37mLk9MT3L1zB3fv3Sv/3r2L7XaL87MzbDXOfUqDh0eZuMRM\nldsihDxPKC4BljjaDG4BHnLcjHlWT+M867+F4RLMUOSQc+jEdeuV1ktmQaSozhfR9eBoiwkCK0Lo\nQhhzvGlvMXCX0dv7qBgulJDQrw9phfgSkYfGWtsjrWAdBTzDZWYGU/K4puYZbqU6qGj2SWXcVJpm\nAtTaAUe7f6MCYELCmsBs35unswvLNobOXlsrvT3f0sSWZkbBOuLDMYZo/CDOrV5fwEPa0HrOo7VS\nrTXYDSNEVMUBBgurb/lVCuNr55ZgY5qx2Yx+kDDlCUMa5dDFDOk69uQ8K8nfeULW/ZDVQ8XycFR7\nlCcwM+aD0K/D4YDr62tcvHiOy6tLXFy8wMXFc1xeXuLp4yce0uqgtzXGcfSbhCYcWTLvMQ2i+Bt+\nsAlUhDwxkNTHx4UqcoLW0hXnuY4nAZaKk/4NCQ0cdH1tfCXxN+kNEcDkCpcVG7zq8RXrM+KMK8jV\nPu8bDbwtJK/31ltv4e79+/6N3VgBFe+2Coeb9lrcbJXkql6Hbq3t35uUvbWypsz1lNuZudCSXruL\n3yWnUp4O+M63/1IU3Jz1sLumMZXxoiPHAOt5Qtr9ae0Z+5F5yqCsfkuDTYZMVONpzhkn2y1eXF6u\nwvF1eV2OlajXLN4BhTeL0FHTH8VtP7xFkZFiG3H3Rd6xpmdZ+8ee3VRnrZ21fo/RsLa929Avb1cq\nwjh4JTvm4zQz6oT+/1Jp4UTjr+ybzhyjDDOkAY8fP8bV5RXOTs/hOiNr8nQqBu413dParee67C/C\nQ8a3nG9dt5mX3zClakxtiXnh3AQT6vW+MR3nJh5W407E7CVulL7WZNWig1m9KFPHuvYss9kEWuNT\n43m7Ih+v7vOVedteXtPXnSfKw0InmrG07Uc9o9XN4ve7k5MK99bogJV40ND2G2PH+7hNnkS7SpGO\nMfxwME6vQ3va9erNu5JluXaAWPL8QAesbaCSiQnLftG02du/vbFHutHb68vcIzaigvs9hzn/vTan\niE/Mi1vD4jy05C/HaPnqntc2B51A7Se/TisE1jrXYcDJyUmpQ63MD9e5yxgLlsWQSlX4aKd1fTl3\n9TcFnbThyQ7/FkidolMRfG/AGtdxjdc6DUOZR2w7iBHVPNZ05zV8bWm6lXYtfRBc3IYdPnPf/uLr\n3FmDlnaWvQWVjXRP2HKj2GpepazxdAAa2avcnGPVG+xQjJlFj8kZ+/0eh8PBQ1HXPJWalo/wgTge\nxgLfa9qi9oG45SKvtERU1FurwOsUJ4bUO/Jalwl7deLvNdp8rHzsDkJ6ZSlcHRd2UBGvBhn1+ioT\ncH3Y49d+/dfxG//8n4shxRThlJACx5SNMVRXdmLoODsEOSasRQH+mKAffzeitCZQrxm21aBEOMwz\n3nz4EH/xwZ/hq//lf4XNZoM5EAtq4NKFT4chx9+tMtFD1Pi+bX9N8WlhMaMQPm+Pm43noSlsg6BC\nDWNw1e9wnmWMPVMwtJoHjQkTiYGcsNmd4P79B3j0+BEyAfN8wDCMIXTLAGICQxIZbk9PwDPj6uVL\nXL18iQ9/9CNXeq6urhyXTk5OcOfOHdy7dw+f+MQncPf8HHfv38fp6SlOT09xcnLi4beidzoAbDYb\nAOUEWTx4E3JWQzkkMXHWRF0yP8PrAkyHP5kR0QBUr2ErtHQZapZDopYLLwRMhCqMRV6OltD1/o7x\njVW6dI/gguvqhe4Ka7zeWXCg7hveqIXNsRdm5Nput9hutwuBPY47eh8vpIcObKReFFANp0W5iJ4i\nFk7IhN6knuMWn7ctx8JJtPTgNkaGNeWqx5zmiDcmxDRwK9eTw5JSnUS0hXWv35a5m0LgtxEC/rUK\nyLF4/T2YVLCTExBpO8QwJciSyo3B1ohNqEQEln0Z9wIBuHfvLjbbEXOekIYkt75mO5Q0fUxpAGdA\nk4dLH2JMn3jCdL2Xv6cJ11fXePb8GR4/foyLiwu51fHkCZ4+fYqrqyvnh2mQA1rAwmKwK6tGk0xg\n2wwDKI1gkCegyzkjDSVxu9NoZoyj5MGQNQgxSpUEmWKgwBb5K/D4EqNjafT35LWLg1iDar8YzXCF\nTkOetV7Lx/Z97C/Wb/tlZlAqyuZnPvOZCqamLDstSMvx/aSlt5cY7RxRjdn7M/qmY4wlerq2skqU\na3Ln3dqczFhQ9pTsL84Z8zTh2ZPH2BtPtX6o7O81ehb7bSbh3/mBBlP1Xcw/Is/7ClbsL6VB8tqE\nkIobzWG2U+/E1+V1edVieNrK28eoQ8/IZmWhl3T0lLV91ZMT7duePtB+1ys3GZTWnh1ro/2upy/2\nDIprZa17u2muDbslqTenWu/rjxFUjJVEjM1mxJPHj3F1eek8ahztsFV009SZF4AS/qoIXHCP0U7/\nZRoid+QOXix/lxsxkl8qGFS8vSV/OEa7274K7qytU1A0VubUK4VH1uOM7ysjsQ58DV+zyp0JWOBa\nlA+oUWQXfLrRhQiiG89oDnmCarO2xysdSioKXqzI1hEu8W9fB7vFqr8HlR0X9XryR0d+at1d7Vtm\nkzVCZAWiore38pq0qnIEYA5zzCGJeTO+ntE00qmk/bUltuHyiI1Pu6+edXSwtr21EscsMtz63q3p\niG139c4nwjzVN3PZ6jL7moKNnhiPKXp1ABQomdxa5G4CSd47k2k7W7bmE/ZwiXMDyO0HQzPHCI8F\nHAPeuf4uUPC2mdURrbPPOLRlNCfn0G/5orsGbWn3AUAevt9uaVk9NPNazjEc2HT0jThu14+aOcoI\n4p3AOIdyKBDXNs5hITt0CimfWdDT0iSWL8LPG2QPN+Jbgxyc1FEOHtrxyA92GmJ4V3TX/pzWnscD\n3XrNUN12KipuFvqp47+8eomXL17IjXYiZCQVIbLjHtC23Zct4t83yUJhQNZDxdfi2vfKUvdbP8Ql\nJ8gd3nQD3F+lfKwOQnrr00f6dpvevoiXpSrMw4C33noLJ7sdDvMMsIanyWVpROgUpOsybiXw7caK\nJ5uReMZ59ebnsehtlr6R+vMxpjppfPTr62v87jd+C1/8yldARNiMmxJKpenbxlQE61dDuJ/EEHPb\nb3or3DL49qDkGOG3kpQZ+q0t5Xe+FUk8DYzxgUiMj5sT/MI/eB/f+Ma/UV1m0Jj1gygFCr9RbxYd\nDgeJ887JPa6ThmQ73W0xTRImKw0JLy+e4/LFC/zw7/5OBA31BM7MmCfJen9+fo579+7h7OwMD958\nE3fv3sX5+bmE5Lp3T/KUDAPSMADDgDnXQjpB8Ioi/lIx9DgeNsJggW1/HZf703Ap1Ube+K3Cvfvu\nFsqP1WMNR2DVo8JX4VlUZGRT+b6phRebQb9E+Gw2G7z11lsSwzHExu0pS/btwhCg6+F5YlDjq+Ng\nGFPPYJCGckj7qgFWesJiO+ZXNVzc1EdLf7oMEstYpzcpCy3umPDY67sVE03xWRMmbppLpWgy9NCj\nqHHR+ODzALlzhbWRmo0xzTPu37+Pq6sr7HYb7PcTRs15ZAKt3eC4fnmJKw1H9fLlSzx69AjPnz/H\ns2fPcPHyZdnnQdHzkEE5i0KhHupEpAcSA3hiVxWM5srtpRnzJJAcaYTcXMs4eH4ODcGRW6OCHhEm\nAs+NUkyi4Cxy1JHSaocVPHxau472MfOMROGmiq6Ny8mLNa65zkIha0pLH3u4cROu2oHSxcUFPvvZ\n9/ywm22gnIU/paCoBwFxbR/2jBdrddpxRXhEiCSUHGukvGNu2mBpeAks1Htvvc/+N9lpW7gNBsHD\nP//gA+GTSktZ6fSsOL3gYyv8pWuIYTW6rI4wNtlXQupnBSfGccS032McR3zuc5/DX3/nOzf08rq8\nLstCQRmuXzS8HX16EHlX78AXqGnbTXx/LQb92revIkscK7dp4yeJ03/sWYHLKpPoyiA9uLa0wvin\n8WhmlpwLbijImGbJ2fXy5aXz9OR8Vm+ppqV+Wv02KYWxoN1r8k4xmtn/iz5m4wDMw7ZDg1eK0XWr\nm7Fcgx5fPbaeRMfft+uxDNV2Ox7VYxKO90Clcxi+1PvK3i9ZaLv/enwzodZl1iC9wN/KyFUf/mU7\nZjhCByrbQhhfAvDwjTfwbb2FvEZL4nq2cztq6engVcXj/5405VX1m76hb11nAVTH6egux/pa68fX\nI+j5xX5UbvP3StmzzfrEMZFTCkcuUkOzYR6BVP6yOuUAIaOsaQp5+Sz6hYyxkEu3rwV92cenYr7s\nq+V+YJbDG7uBFecASGhBdzpiXiBZ1gMi1k+K69y6XH9s/Y+tb7U3EPDXeHJLczu0oPpdfh3tqz8m\ngxdBXWmPjrudZ92S6Wf9Q85jekLcN71qLc1oZiA2vNCH4Wxu6G6ExUCFDzIHZ8mVcfX6jmUdbjJK\namhUAiGDMOcZeZ5x8eIlnl9clJBvLCMSp6tcjd36I+aaB6zon2UsK7jg7XC17yJcbiqVDtWtoO2X\nH1U/cYw30dGbysfqIMQFFmKYN3oUTCINl3qaMyQuTmAqfSSVE8NEhCkzckr4R//4H+M3f+M3cH7n\nDsbNBqxGaEBilTNyZZgRQwUZXsrYEf41A5AtIAkjME8QMxTnpp4RKTMIVaNmRiYJcTKmOqG5GXPn\nnJFUaP7h3/wA7/3cFzAjg5jQIpp9vyagHjPixG+jErVmfCEihSP8dNHVBxZGZ0qabzQqieadBihz\nMzhnE75z8CgPTNQEzspgVE2JlYm3cwPMw9O8eEDAZz/3s/jm7/4OUmYc8kHqKueOeRhs/HJVe/Y2\nZzXcTTkDnJBnY1yCTxrHBtNBPEYTJaRRDGOH/YSPPvoIH330Eb73ve+qUDPIdTlKGEbJIbDZbHB2\nfo57dyVPydn5Oc7unOL05AwnJ6fY6U2G7W7rHt92tXOegWFUpSsXJiYJpQlDIszIlaJATCAk33fM\njDQQMs8uRJjXjkCcNX+LzDUr/GS95APOGVk/JuVgBE08GXNHiMVZYO1ry6U9VvWDta7FnmT/j81C\naEgrjDgOM5Im6mMwmIA0Dnjj4UM8evwY4qqdNceHtMDM4JRdmLKrhYlqD7LMKiSyJdQtgo/gX0iW\njKQCpcBm6Owzw9m4lxDw3q4o+3UVlj3QJv4SJbSsWVIwyRoZQ5Z2CEVgzswYUvKDYNsLkeEbwwbK\ntwNKvgyPGOlGCzukLTxBXlAl7KQkRgLDB/lCjAfzPDuNMfhEHhEPBAe9/UdJ9mBWD0gbrytg1o7N\nK9LMlBSnshsaEpEK34Ue2+HddDjI4WfOSCR0ZBxHbE5GzPMBf/WX/wEf/fjHuN7vcXV9javLS1xe\nXuL6+hqHw1xg2cRIlnmG3BysqglZjoXJE70fZltXMrRA3CdCBxOmyV7WApLhrYShKu8sYbnpG4SE\nlIQeWgI4Rtb+CMTkIf2q2w9ksXsZeRZvhTTUyoKtkuGc3Tgx5QkBr+LYZU2U/8o1pMaTtsaZbNjN\nEnOYktBv89ZhDnvE16FAUuii5HvZDAPuvPkmHj58iDQMgVfK3ogGJqMbytn8eavgLgyaNo+ewmJ4\nzOQeScKXhF7NqnGKfCH9zDw7fygw0r6B6iZXkXmKAhRHYfJLNRebq/FXqFjI0P0oBx2Hq2t873vf\nk7aHcOgGNSDK9UjvN8459l/BoQlrV5SCUleMkyU8QlQOzHhnDhVFNpBxWULdnDOGccTzly/x6U9/\nerEur8vrcqvSbOm4n+IzdwIKThtr4W1uUjjXjD49veumttbe9xT227TXG1/P8GHPe0aVruLNqL6p\n2q5sCP2xVgbeBsYR/mjqVW2xeN2PKSFTwoYSnj9/jhcvL4Qv5YxMdR8LQ1Hsqzzszr8PRwLl7PxR\n2GDtCLQWjrmFQ/xdGT8QdcQlLNZ0/DW9f22d1+rW3/Xeqz7Q+X7ZHlRWBEB2sNDrj73dVSPfLfor\nlY3v5wU+F55fnArMqO2yAkzmK3p0HPPaGpgMmlLCvXv3ME2ThyZdwuYYnknpeQa7BO26YPHAjnXa\nNnu4dJtQY71vu/sIJWRn02BsrOhF4V2Lny2dW9vLXo/KIZatYSuHLekPL9qq9PvO4QgAt+PEsRXd\ngeHRJaSyy4oOs+D57gf5YsqAGaLt4zElTGbnq3TJIlNXtKOBUQ6yPCA08lRzvFoY+i5PcXgF+0Sz\nb+Netb7NBnJbm1ppi2G2T9Exwlyols3tG4p13NYBlbGpSswd19bxTO1VvgdcR0OFT25DCePtzaGe\nsP3T0PpsMgpLdAMIDoiz3RJmtgaCINJWzpJPtce3CUYzljz6GE2I9OyYLOTfsAErboS6Xtu+gSWu\nW5lD+fv66hofffgRpmnGGHJWM7fRAGqa4TprM8+o+5X/BrxBBnOxE7TfRv1MeAR1YdPOO9IYGXIY\nGZt9NcMPUjty1xpdvI0caOVjdRBiyOvADochq4JteMZoEogzLzyAaoAmZMz4mXfewac+9SlcXV3h\nOs9ubJEP5D8t4TDiHvsy5sxA92TON4+Ok1JaJS5EtDzNZq5udyyYIomxYtxu8ad//Ed493M/Cwxy\n+h2F+C4cO0JNj8m3BKKnOHSFJGNg1GzKRBrTMHIYFd7MyGK3G8LY55w1qVLNDBcKRrPB7JkxbmDp\neQ5qcCYJEz+7cwcPHjzE448+cqZOvoVrgkFEHuOF7MBOJ1cJEYHxlOTAKnyzKahRkJU2UhpcuMw5\nYzocACLs93tcXFzgox/9UJIbs3hc2E2jzWaD7XaLYRgxjhucnp7i/PwcJycnuKe5S87OzjwsV0oJ\n43br4bkyiid5zhnbcYc5T+CpwHlShu3eFghhnIg0J4YZWwUAlAZY0noQa2LZYvhzgZxM6VJjbjbm\nkCFhwfR6rx60VAnhO0JL+7comwEVAp1RFQYZcmB07949OVA9yK2deMghDDkZx9OVj+0XBEhpVESo\n93sRlA1P5L0ZNGJfQ0ou/LYKA8oQxHgatyTgTNb6rPY9hQNJ+6+/lsMxoJyrmGcPBRyYg2DtY++M\nMV41j0KgGerbeNxDGv0gzeFkVFj3YqIByMCYNi5AjGb8yYzNuMOsh5opPB/HEWZYANmhN7vQvAle\nmq0n1UEThA9EmCcxCFiIu3GUfB8nJyfy79mZ77nz83PsdjucnO4kf9BmgzlP+Bf/+7/AB3/2gfZD\nSo9sXZVPRh7FRYA0QSSlJd674MUAc6TZNe9sE6mVf4sBLeJdEUJrRSHSx+xrGQihUnyiBBrg8De6\nkZX/5cwY0lDRhh7thwv+chjkB3otnQY0p1JxKiBvs9Bc68PikVubmdkPBFtRu6d4x992MPnuu+9i\ne7LTm3JUaIXt0Ri+wRZPjSb1mvb38WyKJy9zCEWFvHj42Xpl+NGGKi8RR5Yav64vSlLK2EeUgar6\nDd1Kut7OG5XTZoKGXRPc+I8/+I/uMX2YZ1T+cPp9Gx6tLa28Qnr1yHChB095VueoomQyAarvPK8S\nljgwbjb4ype/jM121wfk6/K63FAyh5yJirOt7gFg9TnQl/Wrb1fe31Tvpj5qhbn/bq29WG4a31qd\ntfa7emdUCztyWqtrdGnfK44ZzFWYHwlPCeR5Bg1CGa8uX+LZs2e43l9hs7lb0bLWyNOD6WIeDT9v\njTmr86D6d09PbY1MXV2WakrZW/M1XOomw13Br/bb9m+X/8oblQVc43N9zWSE1XFSQJ5Gv6jgU1q2\nBrrjjd+0vLPtQ6ryAj6+vrhhj0cZQwWiZc5U+M34OBbzum/XIMqNLT7cxKd9/E1b9vcxG0cP99o1\nOGbcax1M1nAzylht2xzqtV/39tdPWir5Jco3QV+qQnpSY3BUnbJ65mMEUNS1hdwDppIz0fErjImL\nvojQhxnEy5igyZqXc+rNt6pn8IdpF7qOCaAEjJsBSOIfueCLzOp4U+uqNlerZ9t6QVcZwc60/LYd\nd01vi04TS8/ZKToL9/pIALj5zvvh4NzZ2Z9gqINjcKpEnPv63Cqcaeg8mX0LgN87CXgS27J/PH6A\nDAAAIABJREFUky5ShAtDHIpbvmZrvRgbA2lIrk8tdMZV+sfVPvDHqMOyVfoQifN3lxYRxDZoz8Jn\nnDOgdPTFixcYxw14nlzfi+30aF6kKdVsIg0M43WSzo7IRfds5l/akqotPW3n6bB1na/0V8GDGz2K\nRF+XT5cH8N4+bl8+VgchYmSQS02CqPW7tW/iZuslu+wVZnYiwWnA+1/5Cn73t38bm80Gh4N6+8f6\npcNqo/UEaWOYrWJPIWZ5ZvW6VxxokxAv5mwI3gpODWZlEsL2F//+W/jxhx/iE5/8JCZIUr0oxB9T\nAFo49er12ukJ2q3A0RZWhtMrPWKq2wmZCmMz42pXqG7bjAZDf8aN0AAnionEmG1G+i988Yv45je+\nISHtmRfC/2q/KzBvFYwIw8U6M4AUDY6FmGeNOw4jPJQwDCOYZ4w0+l7KM+P6ag/mawDAs6dPkXN2\nD65JQ3EdDgdsNxsMarQ9OzvDuNlgt9thd3KCO+fnGMYRZ2dncgvl7Ay73Q7jOGK322Gz2eh+FuOt\nGBk1dixYcx1kDIMcAkyzrAirwagIL6poAGA1AstaJGGCFi+RLVOPeUIUg522rH8U6c2xkcstBOSM\npYiP0A4BemD1QBMbW9Mp4Ggi8cYg6A0OZqQ0hEMwYQolhrQqBZn1lg1cYCQyjwDF99Cm3Taw/Dl2\nJyUpnFmZ0Ix2f4Z9oIAYaCn0wtYh0pigTHmCSltrmw/Z33KrgpjDu46iYcKGvQ8eQzbeMcRMBYA8\nTZIXx4WlQj89xCDJfrZwcxsNK4VQdxxCrhcwxu2Aw34vcxqGIrATu7EcALabjR9s7LZbnJ2e4kT3\nwb1797Db7XDnzh1st1s/DLH6lhsg6U2uQflDIsKsXjJ5nvH48UdqpB98oaZZYuR28dvg6fTcPEhs\nJeF/L+hL+Hf5t3yTUqRNNe2K/dp6tO9IG6sVD6gwpDQwBcnQ6tjB59xRwK399gWzHBZzULgbngIU\nOir0kivcL2MstFq8kYqQx1wbIuMeamHS/p7nGddXV/jse+85LpPl+yE1ODT7kmGH96qIdYR5G0ut\ntIU2OvxqOedygM/Qw2n79gi7qxTQDl51DQ3hbz98lMraTvD2JFnzw36PP/2TPy3zYbjy2cPJWtms\n6RkbrqyMec3QYvlcYhlSAgWv+xYfrIzDgOvLS/z8z/88nj55tgTk6/K63KL09JQezpkssGYYfJXS\nk2ePjvGIHrb2/Dbtr9GVm8Z37H0Lnwo2LpjVYyDqw7wd55ohrPqNWjb1PxGSDjO7b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FUs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\nejzyBRHBdcF5CuMmaKtG1eN/9EcY0yDBBalEexd698a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P6dqHEU8PMBxwQ9JKa7ItvD1r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XRhn2eJtFGb1LjQz3S7M2dKHcUhHChwMmV6hiGVFMXKlcvjI4DZ6f9aXlXKubamzDQRSYic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lk5WJnM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\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knBbazMMSy0wBjqbR1/kiJnIxSOR1gziA/365ia997Wv4yz//c3Qe+PTqVVy7cgXf+/bf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\nuHAhPN9sQINLfKuEwthwIKWTlzLF4kDjyo6x5Yxs4aCUXZSnkIMbsUsmZ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"text/plain": [ "<matplotlib.figure.Figure at 0x11053dd30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pickle\n", "%matplotlib inline\n", "\n", "# Test undistortion on an image\n", "img = cv2.imread('calibration_wide/test_image.jpg')\n", "img_size = (img.shape[1], img.shape[0])\n", "\n", "# Do camera calibration given object points and image points\n", "ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)\n", "\n", "\n", "dst = cv2.undistort(img, mtx, dist, None, mtx)\n", "cv2.imwrite('calibration_wide/test_undist.jpg',dst)\n", "\n", "# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)\n", "dist_pickle = {}\n", "dist_pickle[\"mtx\"] = mtx\n", "dist_pickle[\"dist\"] = dist\n", "pickle.dump( dist_pickle, open( \"calibration_wide/wide_dist_pickle.p\", \"wb\" ) )\n", "#dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)\n", "# Visualize undistortion\n", "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))\n", "ax1.imshow(img)\n", "ax1.set_title('Original Image', fontsize=30)\n", "ax2.imshow(dst)\n", "ax2.set_title('Undistorted Image', fontsize=30)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

garibaldu/boundary-seekers

Boundary Hunter Ideas/Local Only Boundary Hunter (Hoard).ipynb

1

38757

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib\n", "import autograd.numpy as np\n", "import matplotlib.pyplot as plt\n", "import random\n", "import math\n", "from autograd import grad\n", "\n", "def generateChevronData():\n", " xBounds = [-50, 50]\n", " yBounds = [-50, 50]\n", " totalPoints = 100\n", " \n", " points = []\n", " targets = []\n", " \n", " for i in range(0, totalPoints):\n", " x = random.randint(xBounds[0], xBounds[1])\n", " y = random.randint(yBounds[0], yBounds[1])\n", " \n", " if x >= y and x <= -y:\n", " points.append([1, x/50.0,y/50.0])\n", " targets.append(0)\n", " else:\n", " points.append([1, x/50.0,y/50.0])\n", " targets.append(1)\n", " \n", " return np.array(points), np.array(targets)\n", "\n", "def plotLine(points):\n", " xs = [x[1] for x in points]\n", " ys = [y[2] for y in points]\n", " \n", " plt.plot(xs, ys, color='r',linestyle='-')\n", "\n", "def plotScatter(points):\n", " xs = [x[1] for x in points]\n", " ys = [y[2] for y in points]\n", " \n", " plt.scatter(xs, ys)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sigmoid(phi):\n", " return 1.0/(1.0 + np.exp(-phi))\n", "\n", "def MSE(weights):\n", " localPoints = np.array(list(map(lambda x: (x[1] - weights[1])**2 + (x[2] - weights[2])**2 <= radius**2, points)))\n", " predictions = logisticPrediction(weights, points)\n", " \n", " s = 0\n", " for i in range(0, len(points)):\n", " if localPoints[i]:\n", " s += (targets[i] - predictions[i])**2\n", " \n", " return 1.0/2.0 * s\n", "# return 1.0/2.0 * np.sum(np.power((targets - predictions), 2))\n", "\n", "# def localLogLoss(weights):\n", "# predictions = logisticPrediction(weights, points)\n", "# print(targets)\n", "# print(predictions)\n", "# probs = predictions * targets + (1 - predictions) * (1 - targets)\n", "# print(predictions)\n", "# print(targets)\n", "# print(np.log(probs))\n", "# return -(1/len(points)) * np.sum(np.log(probs))\n", "\n", "# def localLogLoss(weights, example):\n", "# return (predict(weights, example))\n", "\n", "def logisticPrediction(weights, p):\n", " ins = np.array(list(map(lambda x: predict(weights, x), p)))\n", " return ins\n", " \n", "def predict(weights, i):\n", " return sigmoid((i[2] - weights[2]) - weights[0] * (i[1] - weights[1]))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def computeGradient(weights, example, target):\n", " prediction = predict(weights, example)\n", " E = -np.log(target * prediction + (1-target) * (1-prediction))\n", " dE_dO = computeErrorDifferential(prediction, target)\n", " \n", " dO_dZ = prediction * (1-prediction)\n", " \n", " dZ_dy = -1\n", " dZ_dm = weights[1] - example[1]\n", " dZ_dx = weights[0]\n", " \n", " dE_dZ = dE_dO * dO_dZ\n", " \n", " grad = np.zeros(3)#[0.0, 0.0, 0.0]\n", " grad[0] = dZ_dm * dE_dZ\n", " grad[1] = dZ_dx * dE_dZ\n", " grad[2] = dZ_dy * dE_dZ\n", " \n", " return grad, E\n", "\n", "def computeErrorDifferential(prediction, target):\n", " return -(target - prediction)\n", "# print(prediction, target)\n", "# if target == 1:\n", "# return -1/np.log(prediction)\n", " \n", "# return 1/np.log(1-prediction)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def trainBoundaryHunter(weights):\n", " iPoints = []\n", "# weights = np.array([0.0, 0.5, -1.0])\n", "# trainingGradient = grad(MSE)\n", " \n", " print(\"Initial Loss: \", MSE(weights))\n", " for i in range(0, 10000):\n", "# g = trainingGradient(weights) * 0.01\n", " if i % 1000 == 0:\n", " print()\n", " iPoints.append(np.copy(weights))\n", " print(\"Loss Before: \" + str(MSE(weights)))\n", "\n", " weights, error = computeStep(weights)\n", "# weights -= g\n", " \n", " if i % 1000 == 0:\n", " print(\"Loss After [i = \" + str(i) + \"]: \" + str(MSE(weights)))\n", " print(weights)\n", " \n", " print(\"Trained Loss: \", MSE(weights)) \n", " print(\"Weights: \", weights)\n", " return weights, iPoints\n", "\n", "def computeStep(weights):\n", " totalG = np.zeros(3)\n", " totalE = 0\n", " \n", " localPoints = np.array(list(map(lambda x: (x[1] - weights[1])**2 + (x[2] - weights[2])**2 <= radius**2, points)))\n", "\n", " \n", " for i in range(0, len(points)):\n", " if not localPoints[i]:\n", " continue\n", " \n", " g, error = computeGradient(weights, points[i], targets[i])\n", " totalE += error\n", " totalG += g \n", " \n", " totalG = totalG * (1/len(points))\n", " totalE = totalE * (1/len(points))\n", " \n", " weights -= totalG * 0.01\n", " return weights, totalE" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Type 0: 35\n", "Type 1: 65\n", "\n", "\n", "Boundary Hunter: 0\n", "\n", "Initial Loss: 1.63204891482\n", "\n", "Loss Before: 1.63204891482\n", "Loss After [i = 0]: 1.63177092365\n", "[-0.39671528 -0.20921633 0.43178954]\n", "\n", "Loss Before: 1.47661871083\n", "Loss After [i = 1000]: 1.47637251409\n", "[-0.38363837 -0.26778192 0.28139062]\n", "\n", "Loss Before: 1.92949262018\n", "Loss After [i = 2000]: 1.92917428007\n", "[-0.36813708 -0.32515023 0.12942068]\n", "\n", "Loss Before: 1.9262833571\n", "Loss After [i = 3000]: 1.92609961192\n", "[-0.33676569 -0.37688884 -0.01733495]\n", "\n", "Loss Before: 1.62486207501\n", "Loss After [i = 4000]: 1.62475450205\n", "[-0.30745253 -0.41262915 -0.12790452]\n", "\n", "Loss Before: 1.62910912867\n", "Loss After [i = 5000]: 1.62906299569\n", "[-0.2773988 -0.4341265 -0.20129866]\n", "\n", "Loss Before: 1.62270669344\n", "Loss After [i = 6000]: 1.62269168936\n", "[-0.24650195 -0.44421684 -0.23925384]\n", "\n", "Loss Before: 1.69421514509\n", "Loss After [i = 7000]: 1.69420127119\n", "[-0.21159601 -0.44737463 -0.25282676]\n", "\n", "Loss Before: 1.68046762413\n", "Loss After [i = 8000]: 1.68045399952\n", "[-0.1750584 -0.44857226 -0.25901199]\n", "\n", "Loss Before: 1.66695923437\n", "Loss After [i = 9000]: 1.66694583962\n", "[-0.13874726 -0.44945462 -0.26462529]\n", "Trained Loss: 1.65367297772\n", "Weights: [-0.10269841 -0.45007203 -0.26972778]\n", "\n", "\n", "Boundary Hunter: 1\n", "\n", "Initial Loss: 2.53941709941\n", "\n", "Loss Before: 2.53941709941\n", "Loss After [i = 0]: 2.53930519247\n", "[ 0.22902056 0.48837608 -0.21189279]\n", "\n", "Loss Before: 2.3962357145\n", "Loss After [i = 1000]: 2.39618832264\n", "[ 0.1932579 0.50465662 -0.2884653 ]\n", "\n", "Loss Before: 2.15242586398\n", "Loss After [i = 2000]: 2.15240624956\n", "[ 0.15690714 0.51163133 -0.32777545]\n", "\n", "Loss Before: 2.13372580379\n", "Loss After [i = 3000]: 2.13370793959\n", "[ 0.12094239 0.51494136 -0.35153468]\n", "\n", "Loss Before: 2.2640451613\n", "Loss After [i = 4000]: 2.26402931765\n", "[ 0.08265031 0.51717256 -0.37287914]\n", "\n", "Loss 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/kDR92FCSfJ0Kmiym5raUAVJK1v+8hWcajmbNnI3c/0Jvfjk3i1GfPVW0cACbt2UybUUz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\ncdDQsH0IHQe2IXRYR/zrFJ4bmZWZxZi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KhUUo8VAoFBbx/yoxL5gRWhe7AAAAAElFTkSu\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x20106d52a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "random.seed(1234)\n", "points, targets = generateChevronData()\n", "\n", "plt.axis([-1.5, 1.5, -1.5, 1.5])\n", "\n", "# Plot points on graph\n", "c1 = []\n", "c2 = []\n", "\n", "for i in range(0, len(points)):\n", " if targets[i] == 0:\n", " c1.append(points[i])\n", " else:\n", " c2.append(points[i])\n", "\n", "print(\"Type 0: \", len(c1))\n", "print(\"Type 1: \", len(c2))\n", " \n", "plotScatter(c1)\n", "plotScatter(c2)\n", "\n", "radius = 0.5\n", "\n", "boundaryHunters = []\n", "\n", "for i in range(0, 3):\n", " print(\"\\n\\nBoundary Hunter: \" + str(i) + \"\\n\")\n", " weights = np.random.uniform(-0.5, 0.5, 3)\n", " weights, iPoints = trainBoundaryHunter(weights)\n", " boundaryHunters.append(weights)\n", " \n", " plotLine(iPoints)\n", " plt.scatter(weights[1], weights[2])\n", " plt.plot([-1.0, 1.0], [weights[2] + weights[0]*((-1) - weights[1]), weights[2] + weights[0]*(1 - weights[1])], 'k-')\n", "\n", " x = np.linspace(-1.5, 1.5, 500)\n", " y = np.linspace(-1.5, 1.5, 500)\n", " X, Y = np.meshgrid(x,y)\n", " F = ((X - weights[1]))**2 + ((Y - weights[2]))**2 - radius**2\n", " plt.contour(X,Y,F,[0])\n", "\n", "plt.gca().set_aspect('equal')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

biosustain/cameo-notebooks

other/11-multiprocess.ipynb

1

1230

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "In order to take advantage of multicore processors and high performance computing clusters, many functions and methods in cameo where implemented in a parallel mode.\n", "Methods that can run in parallel mode have a \\emph{view} keyword argument that enables the user to select different parallelization strategies based on a simple API (see documentation ...).\n", "This way computations can easily be scaled depending on the available infrastructures.\n", "\n", "\\begin{minted}{python}\n", "\tfrom cameo.parallel import MultiprocessingView\n", " # ...\n", "\\end{minted}\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

computational-class/cjc2016

code/tba/powerlaw_fit_intro_Code.ipynb

5

268614

{ "metadata": { "name": "", "signature": "sha256:7d28a26cdf8dcc7dff4df2bbf55b997dd5a7f551e135c7144a649ca564393e89" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import powerlaw\n", "print(powerlaw.__version__)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.3.1\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "# Set up " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pylab\n", "pylab.rcParams['xtick.major.pad']='8'\n", "pylab.rcParams['ytick.major.pad']='8'\n", "#pylab.rcParams['font.sans-serif']='Arial'\n", "\n", "from matplotlib import rc\n", "rc('font', family='sans-serif')\n", "rc('font', size=10.0)\n", "rc('text', usetex=False)\n", "\n", "\n", "from matplotlib.font_manager import FontProperties\n", "\n", "panel_label_font = FontProperties().copy()\n", "panel_label_font.set_weight(\"bold\")\n", "panel_label_font.set_size(12.0)\n", "panel_label_font.set_family(\"sans-serif\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "from os import listdir\n", "files = listdir('.')\n", "if 'blackouts.txt' not in files:\n", " import urllib\n", " urllib.urlretrieve('https://raw.github.com/jeffalstott/powerlaw/master/manuscript/blackouts.txt', 'blackouts.txt')\n", "if 'words.txt' not in files:\n", " import urllib\n", " urllib.urlretrieve('https://raw.github.com/jeffalstott/powerlaw/master/manuscript/words.txt', 'words.txt')\n", "if 'worm.txt' not in files:\n", " import urllib\n", " urllib.urlretrieve('https://raw.github.com/jeffalstott/powerlaw/master/manuscript/worm.txt', 'worm.txt')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "from numpy import genfromtxt\n", "blackouts = genfromtxt('blackouts.txt')#/10**3\n", "words = genfromtxt('words.txt')\n", "worm = genfromtxt('worm.txt')\n", "worm = worm[worm>0]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "### Figure 1" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_basics(data, data_inst, fig, units):\n", " from powerlaw import plot_pdf, Fit, pdf\n", " annotate_coord = (-.4, .95)\n", " ax1 = fig.add_subplot(n_graphs,n_data,data_inst)\n", " x, y = pdf(data, linear_bins=True)\n", " ind = y>0\n", " y = y[ind]\n", " x = x[:-1]\n", " x = x[ind]\n", " ax1.scatter(x, y, color='r', s=.5)\n", " plot_pdf(data[data>0], ax=ax1, color='b', linewidth=2)\n", " from pylab import setp\n", " setp( ax1.get_xticklabels(), visible=False)\n", "\n", " if data_inst==1:\n", " ax1.annotate(\"A\", annotate_coord, xycoords=\"axes fraction\", fontproperties=panel_label_font)\n", "\n", " \n", " from mpl_toolkits.axes_grid.inset_locator import inset_axes\n", " ax1in = inset_axes(ax1, width = \"30%\", height = \"30%\", loc=3)\n", " ax1in.hist(data, normed=True, color='b')\n", " ax1in.set_xticks([])\n", " ax1in.set_yticks([])\n", "\n", " \n", " ax2 = fig.add_subplot(n_graphs,n_data,n_data+data_inst, sharex=ax1)\n", " plot_pdf(data, ax=ax2, color='b', linewidth=2)\n", " fit = Fit(data, xmin=1, discrete=True)\n", " fit.power_law.plot_pdf(ax=ax2, linestyle=':', color='g')\n", " p = fit.power_law.pdf()\n", "\n", " ax2.set_xlim(ax1.get_xlim())\n", " \n", " fit = Fit(data, discrete=True)\n", " fit.power_law.plot_pdf(ax=ax2, linestyle='--', color='g')\n", " from pylab import setp\n", " setp( ax2.get_xticklabels(), visible=False)\n", "\n", " if data_inst==1:\n", " ax2.annotate(\"B\", annotate_coord, xycoords=\"axes fraction\", fontproperties=panel_label_font) \n", " ax2.set_ylabel(u\"p(X)\")# (10^n)\")\n", " \n", " ax3 = fig.add_subplot(n_graphs,n_data,n_data*2+data_inst)#, sharex=ax1)#, sharey=ax2)\n", " fit.power_law.plot_pdf(ax=ax3, linestyle='--', color='g')\n", " fit.exponential.plot_pdf(ax=ax3, linestyle='--', color='r')\n", " fit.plot_pdf(ax=ax3, color='b', linewidth=2)\n", " \n", " ax3.set_ylim(ax2.get_ylim())\n", " ax3.set_xlim(ax1.get_xlim())\n", " \n", " if data_inst==1:\n", " ax3.annotate(\"C\", annotate_coord, xycoords=\"axes fraction\", fontproperties=panel_label_font)\n", "\n", " ax3.set_xlabel(units)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "n_data = 3\n", "n_graphs = 4\n", "f = figure(figsize=(8,11))\n", "\n", "data = words\n", "data_inst = 1\n", "units = 'Word Frequency'\n", "plot_basics(data, data_inst, f, units)\n", "\n", "data_inst = 2\n", "#data = city\n", "#units = 'City Population'\n", "data = worm\n", "units = 'Neuron Connections'\n", "plot_basics(data, data_inst, f, units)\n", "\n", "data = blackouts\n", "data_inst = 3\n", "units = 'Population Affected\\nby Blackouts'\n", "plot_basics(data, data_inst, f, units)\n", "\n", "f.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=.3, hspace=.2)\n", "figname = 'FigWorkflow'\n", "f.savefig(figname+'.eps', bbox_inches='tight')\n", "#f.savefig(figname+'.tiff', bbox_inches='tight', dpi=300)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/Users/jeff/Desktop/powerlaw/powerlaw.py:687: RuntimeWarning: invalid value encountered in true_divide\n", " )[1:]\n", "/Users/jeff/Desktop/powerlaw/powerlaw.py:687: RuntimeWarning: divide by zero encountered in true_divide\n", " )[1:]\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n", "Calculating best minimal value for power law fit" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Calculating best minimal value for power law fit" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/jeff/anaconda3/lib/python3.4/site-packages/matplotlib/scale.py:100: RuntimeWarning: invalid value encountered in less_equal\n", " a[a <= 0.0] = 1e-300\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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HOOUU04MwcSLUrm0i0o0c6bZUlrJItNUKRYjAvfea908+efC69XgiUdOGi3jD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\npIMlzZT0sqTb4nzXS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"text": [ "<matplotlib.figure.Figure at 0x10664b240>" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "blackouts = blackouts/10**3" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "# Introduction" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "####\n", "import powerlaw\n", "fit = powerlaw.Fit(data)\n", "fit.power_law.alpha\n", "fit.power_law.sigma\n", "fit.distribution_compare('power_law', 'exponential')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "(12.754562675882063, 0.1522925560442657)" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "# Basic Methods" ] }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "## Visualization" ] }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "### PDF Linear vs Logarithmic Bins" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = words\n", "####\n", "figPDF = powerlaw.plot_pdf(data, color='b')\n", "powerlaw.plot_pdf(data, linear_bins=True, color='r', ax=figPDF)\n", "####\n", "figPDF.set_ylabel(\"p(X)\")\n", "figPDF.set_xlabel(r\"Word Frequency\")\n", "figname = 'FigPDF'\n", "savefig(figname+'.eps', bbox_inches='tight')\n", "#savefig(figname+'.tiff', bbox_inches='tight', dpi=300)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x11c96a080>" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "### Figure 2" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = words\n", "fit = powerlaw.Fit(data, discrete=True)\n", "####\n", "figCCDF = fit.plot_pdf(color='b', linewidth=2)\n", "fit.power_law.plot_pdf(color='b', linestyle='--', ax=figCCDF)\n", "fit.plot_ccdf(color='r', linewidth=2, ax=figCCDF)\n", "fit.power_law.plot_ccdf(color='r', linestyle='--', ax=figCCDF)\n", "####\n", "figCCDF.set_ylabel(u\"p(X), p(X\u2265x)\")\n", "figCCDF.set_xlabel(r\"Word Frequency\")\n", "\n", "figname = 'FigCCDF'\n", "savefig(figname+'.eps', bbox_inches='tight')\n", "#savefig(figname+'.tiff', bbox_inches='tight', dpi=300)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n" ] }, { "metadata": {}, "output_type": 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"text": [ "<matplotlib.figure.Figure at 0x11d2595f8>" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "fit = powerlaw.Fit(data)\n", "###\n", "x, y = fit.cdf()\n", "bin_edges, probability = fit.pdf()\n", "y = fit.lognormal.cdf(data=[300,350])\n", "y = fit.lognormal.pdf()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "## Identifying the Scaling Range" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "####\n", "import powerlaw\n", "fit = powerlaw.Fit(data)\n", "fit.xmin\n", "fit.fixed_xmin\n", "fit.alpha\n", "fit.D\n", "fit = powerlaw.Fit(data, xmin=1.0)\n", "fit.xmin\n", "fit.fixed_xmin\n", "fit.alpha\n", "fit.D" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "0.37601504850371725" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "####\n", "fit = powerlaw.Fit(data, xmin=(250.0, 300.0))\n", "fit.fixed_xmin\n", "fit.given_xmin\n", "fit.xmin" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "272.0" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "fit = powerlaw.Fit(data)\n", "####\n", "fit = powerlaw.Fit(data, xmax=10000.0)\n", "fit.xmax\n", "fit.fixed_xmax" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n", "Calculating best minimal value for power law fit" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "True" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "### Figure 3" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = words\n", "#FigCCDFmax = powerlaw.plot_ccdf(data, linewidth=3)\n", "fit = powerlaw.Fit(data, discrete=True, xmax=None)\n", "FigCCDFmax = fit.plot_ccdf(color='b', label=r\"Empirical, no $x_{max}$\")\n", "fit.power_law.plot_ccdf(color='b', linestyle='--', ax=FigCCDFmax, label=r\"Fit, no $x_{max}$\")\n", "fit = powerlaw.Fit(data, discrete=True, xmax=1000)\n", "fit.plot_ccdf(color='r', label=r\"Empirical, $x_{max}=1000$\")\n", "fit.power_law.plot_ccdf(color='r', linestyle='--', ax=FigCCDFmax, label=r\"Fit, $x_{max}=1000$\")\n", "#x, y = powerlaw.ccdf(data, xmax=max(data))\n", "#fig1.plot(x,y)\n", "####\n", "#FigCCDFmax.set_ylabel(r\"$p(X\\geq x)$\")\n", "FigCCDFmax.set_ylabel(u\"p(X\u2265x)\")\n", "FigCCDFmax.set_xlabel(r\"Word Frequency\")\n", "handles, labels = FigCCDFmax.get_legend_handles_labels()\n", "leg = FigCCDFmax.legend(handles, labels, loc=3)\n", "leg.draw_frame(False)\n", "\n", "figname = 'FigCCDFmax'\n", "savefig(figname+'.eps', bbox_inches='tight')\n", "#savefig(figname+'.tiff', bbox_inches='tight', dpi=300)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n", "Calculating best minimal value for power law fit" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/jeff/Desktop/powerlaw/powerlaw.py:1165: RuntimeWarning: divide by zero encountered in double_scalars\n", " C = 1.0/C\n", "/Users/jeff/anaconda3/lib/python3.4/site-packages/scipy/optimize/optimize.py:461: RuntimeWarning: invalid value encountered in subtract\n", " numpy.max(numpy.abs(fsim[0] - fsim[1:])) <= ftol):\n", "/Users/jeff/anaconda3/lib/python3.4/site-packages/scipy/optimize/optimize.py:461: RuntimeWarning: invalid value encountered in absolute\n", " numpy.max(numpy.abs(fsim[0] - fsim[1:])) <= ftol):\n", "/Users/jeff/Desktop/powerlaw/powerlaw.py:808: RuntimeWarning: invalid value encountered in multiply\n", " likelihoods = f*C\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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BePFFiIuzgxrd5p9/6SXba7h/f5g+3ZlwlSoITSRuNJGoQhEfD3feaQcwXnMNpKfb6YJP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\nAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x11d8f10f0>" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "## Continuous vs. Discrete Data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "fit = powerlaw.Fit(data)\n", "####\n", "fit = powerlaw.Fit(data, xmin=230.0)\n", "fit.discrete\n", "fit = powerlaw.Fit(data, xmin=230.0, discrete=True)\n", "fit.discrete " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "True" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "# Comparing Candidate Distributions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "fit = powerlaw.Fit(data)\n", "####\n", "fit.power_law\n", "fit.power_law.alpha\n", "fit.power_law.parameter1\n", "fit.power_law.parameter1_name\n", "fit.lognormal.mu\n", "fit.lognormal.parameter1_name\n", "fit.lognormal.parameter2_name\n", "fit.lognormal.parameter3_name == None" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "True" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "####\n", "fit = powerlaw.Fit(data)\n", "R, p = fit.distribution_compare('power_law', 'exponential', normalized_ratio=True)\n", "print(R, p)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n", "1.43148048496" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 0.152292556044\n" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "## Generative Mechanisms" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = worm\n", "fit = powerlaw.Fit(data, discrete=True)\n", "####\n", "fit.distribution_compare('power_law', 'exponential')\n", "fit.distribution_compare('power_law', 'truncated_power_law')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n", "Assuming nested distributions\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "(-0.070651754953475088, 0.70698857057674624)" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "data = worm\n", "fit = powerlaw.Fit(data, discrete=True)\n", "####\n", "fit.distribution_compare('power_law', 'exponential')\n", "fit.distribution_compare('power_law', 'truncated_power_law')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n", "Assuming nested distributions\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "(-0.070651754953475088, 0.70698857057674624)" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "data = words\n", "fit = powerlaw.Fit(data, discrete=True)\n", "####\n", "print(fit.distribution_compare('power_law', 'exponential', normalized_ratio=True))\n", "print(fit.distribution_compare('power_law', 'truncated_power_law'))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n", "(9.1359147187769985, 6.485614241379581e-20)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Assuming nested distributions\n", "(-0.91712308337398118, 0.17562683168695525)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "print(fit.distribution_compare('power_law', 'truncated_power_law'))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Assuming nested distributions\n", "(-0.91712308337398118, 0.17562683168695525)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "### Figure 4" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = words\n", "fit = powerlaw.Fit(data, discrete=True)\n", "####\n", "fit.distribution_compare('power_law', 'lognormal')\n", "fig = fit.plot_ccdf(linewidth=3, label='Empirical Data')\n", "fit.power_law.plot_ccdf(ax=fig, color='r', linestyle='--', label='Power law fit')\n", "fit.lognormal.plot_ccdf(ax=fig, color='g', linestyle='--', label='Lognormal fit')\n", "####\n", "fig.set_ylabel(u\"p(X\u2265x)\")\n", "fig.set_xlabel(\"Word Frequency\")\n", "handles, labels = fig.get_legend_handles_labels()\n", "fig.legend(handles, labels, loc=3)\n", "\n", "figname = 'FigLognormal'\n", "savefig(figname+'.eps', bbox_inches='tight')\n", "#savefig(figname+'.tiff', bbox_inches='tight', dpi=300)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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aejN3rvWk49WrtsaokqaJRCnlEaJvRnMs79e4XmkJCYHfrt8GW7ZYNU3++MOu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\nAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x11e59f0b8>" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "# Creating Simulated Data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "empirical_data = blackouts\n", "####\n", "fit = powerlaw.Fit(empirical_data)\n", "simulated_data = fit.power_law.generate_random(10000)\n", "\n", "theoretical_distribution = powerlaw.Power_Law(xmin=5.0, parameters=[2.5])\n", "simulated_data = theoretical_distribution.generate_random(10000)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "theoretical_distribution = powerlaw.Power_Law(xmin=5.0, parameters=[2.5])\n", "simulated_data = theoretical_distribution.generate_random(10000)\n", "####\n", "fit = powerlaw.Fit(simulated_data)\n", "fit.power_law.xmin, fit.power_law.alpha" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "(5.0643668837801314, 2.5102907771973468)" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "powerlaw.plot_pdf(simulated_data,linewidth=3)\n", "fit.power_law.plot_pdf(simulated_data,linestyle='--',color='r')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11e70bb70>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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T5QE5wRVmlgoMK6qvD3Qzs7PN7DozG2xmNYD1wA9FL9kfoZhFJAEEdxVNmuQd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\nICIiwP8HMPOfDwN5UZMAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x11e6b5048>" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "#Advanced Considerations" ] }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "## Discrete Distribution Calculation and Estimation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "####\n", "fit = powerlaw.Fit(data, discrete=True, estimate_discrete=True)\n", "fit.power_law.alpha\n", "fit.power_law.estimate_discrete\n", "fit = powerlaw.Fit(data, discrete=True, estimate_discrete=False)\n", "fit.power_law.alpha\n", "fit.power_law.estimate_discrete" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n", "Calculating best minimal value for power law fit" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ "False" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "####\n", "fit = powerlaw.Fit(data, discrete=True, xmin=230.0, xmax=9000, discrete_approximation='xmax')\n", "fit.lognormal.mu\n", "fit = powerlaw.Fit(data, discrete_approximation=100000, xmin=230.0, discrete=True)\n", "fit.lognormal.mu\n", "fit = powerlaw.Fit(data, discrete_approximation='round', xmin=230.0, discrete=True)\n", "fit.lognormal.mu" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "0.39905257607692346" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "theoretical_distribution = powerlaw.Power_Law(xmin=5.0, parameters=[2.5], discrete=True)\n", "simulated_data = theoretical_distribution.generate_random(10000, estimate_discrete=True)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "empirical_data = blackouts\n", "####\n", "theoretical_distributionibution = powerlaw.Power_Law(xmin=5.0, parameters=[2.5], discrete=True, estimate_discrete=False)\n", "simulated_data = theoretical_distribution.generate_random(10000)\n", "\n", "fit = powerlaw.Fit(empirical_data, discrete=True, estimate_discrete=True)\n", "simulated_data = fit.power_law.generate_random(10000)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "## Nested Distributions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "fit = powerlaw.Fit(data)\n", "####\n", "fit.distribution_compare('power_law', 'truncated_power_law')\n", "fit.distribution_compare('exponential', 'stretched_exponential')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n", "Assuming nested distributions" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Assuming nested distributions" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "(-13.018342321459192, 3.3499122220614908e-07)" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "## Restricted Parameter Range" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "####\n", "fit = powerlaw.Fit(data)\n", "fit.power_law.alpha, fit.power_law.sigma, fit.xmin\n", "\n", "fit = powerlaw.Fit(data, sigma_threshold=.1)\n", "fit.power_law.alpha, fit.power_law.sigma, fit.xmin" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n", "Calculating best minimal value for power law fit" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "(1.7831398653341153, 0.063521030949327095, 50.0)" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "parameter_range = {'alpha': [2.3, None], 'sigma': [None, .2]}\n", "fit = powerlaw.Fit(data, parameter_range=parameter_range)\n", "fit.power_law.alpha, fit.power_law.sigma, fit.xmin" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/jeff/Desktop/powerlaw/powerlaw.py:687: RuntimeWarning: overflow encountered in double_scalars\n", " )[1:]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ "(2.3000107113942141, 0.17069976919260452, 234.0)" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "parameter_range = lambda self: self.sigma/self.alpha < .05\n", "fit = powerlaw.Fit(data, parameter_range=parameter_range)\n", "fit.power_law.alpha, fit.power_law.sigma, fit.xmin" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "(1.8833765811180314, 0.094168259953067143, 124.0)" ] } ], "prompt_number": 36 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "## Multiple Possible Fits" ] }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "### Figure 5" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "fit = powerlaw.Fit(data, sigma_threshold=.1)\n", "print(fit.xmin, fit.D, fit.alpha)\n", "fit = powerlaw.Fit(data)\n", "print(fit.xmin, fit.D, fit.alpha)\n", "####\n", "from matplotlib.pylab import plot\n", "plot(fit.xmins, fit.Ds, label=r'$D$')\n", "plot(fit.xmins, fit.sigmas, label=r'$\\sigma$', linestyle='--')\n", "plot(fit.xmins, fit.sigmas/fit.alphas, label=r'$\\sigma /\\alpha$', linestyle='--')\n", "####\n", "ylim(0, .4)\n", "legend(loc=4)\n", "xlabel(r'$x_{min}$')\n", "ylabel(r'$D,\\sigma,\\alpha$')\n", "\n", "figname = 'FigD'\n", "savefig(figname+'.eps', bbox_inches='tight')\n", "#savefig(figname+'.tiff', bbox_inches='tight', dpi=300)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n", "50.0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 0.0998297854528 1.78313986533\n", "Calculating best minimal value for power law fit\n", "230.0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 0.0606737962944 2.27263721983\n" ] }, { 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mpJaMqk4UkXnA5biO+TdCODZHRG4GpuBGiw1X1YUi0j/w/KvAA4HXfSVw6yhb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\n6itOO63wAJ68vDxSU1NJSUk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Cee9rA9sPKbI9Lq6Jqv6hAcAb7Ls1GvfXQkQq4hLMu6r6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\n", "text": [ "<matplotlib.figure.Figure at 0x11cf64ac8>" ] } ], "prompt_number": 37 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "## No Possible Fits" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "####\n", "fit = powerlaw.Fit(data, sigma_threshold=.001)\n", "fit.power_law.alpha, fit.power_law.sigma, fit.xmin, fit.noise_flag" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n", "No valid fits found." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 38, "text": [ "(2.2726372198302882, 0.16568325372336856, 230.0, True)" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "fit.lognormal.mu, fit.lognormal.sigma\n", "range_dict = {'mu': [10.5, None]}\n", "fit.lognormal.parameter_range(range_dict)\n", "fit.lognormal.mu, fit.lognormal.sigma, fit.lognormal.noise_flag\n", "\n", "initial_parameters = (12, .7)\n", "fit.lognormal.parameter_range(range_dict, initial_parameters)\n", "fit.lognormal.mu, fit.lognormal.sigma, fit.lognormal.noise_flag" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "No valid fits found.\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "(10.500000000422041, 5.1423189016918585, False)" ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "## Selecting x_min with Other Distance Metrics" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = blackouts\n", "####\n", "fit = powerlaw.Fit(data, xmin_distance='D')\n", "print(fit.xmin, fit.power_law.alpha, fit.D)\n", "fit = powerlaw.Fit(data, xmin_distance='V')\n", "print(fit.xmin, fit.power_law.alpha, fit.V)\n", "fit = powerlaw.Fit(data, xmin_distance='Asquare')\n", "print(fit.xmin, fit.power_law.alpha, fit.Asquare)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Calculating best minimal value for power law fit\n", "230.0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 2.27263721983 0.0606737962944\n", "Calculating best minimal value for power law fit\n", "219.0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 2.21822784881 0.0921084614746\n", "Calculating best minimal value for power law fit\n", "230.0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 2.27263721983 0.265613512591\n" ] } ], "prompt_number": 40 }, { "cell_type": "markdown", "metadata": { "variables": {} }, "source": [ "# Supporting Information" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from numpy import logspace\n", "from scipy.stats import variation\n", "import pandas as pd\n", "\n", "def validate(xmin, alpha, discrete='continuous', n_data=10000, n_trials=1):\n", " \n", " if n_trials>1:\n", " return array([validate(xmin, alpha, discrete=discrete, n_data=n_data, n_trials=1) for trial in arange(n_trials)]).T\n", " \n", " if discrete=='continuous':\n", " discrete = False\n", " estimate_discrete = False\n", " elif discrete == 'discrete':\n", " discrete = True\n", " estimate_discrete = False\n", " elif discrete == 'discrete_estimate':\n", " discrete = True\n", " estimate_discrete = True\n", "\n", " theoretical_distribution = powerlaw.Power_Law(xmin=xmin, parameters=[alpha], discrete=discrete)\n", " simulated_data = theoretical_distribution.generate_random(n_data, estimate_discrete=estimate_discrete)\n", " fit = powerlaw.Fit(simulated_data, discrete=discrete, estimate_discrete=estimate_discrete)\n", " return fit.xmin, fit.alpha" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "n_trials = 10\n", "n_data = 10000\n", "theoretical_xmins = unique(floor(logspace(0.0,2.0,num=20)))\n", "theoretical_alphas = array([1.5,2.0,2.5,3.0,3.5])\n", "distribution_types = ['continuous','discrete']\n", "\n", "filename = 'powerlaw_validation_%itrials_%idata.csv'%(int(n_trials),int(n_data))\n", "\n", "from os import listdir\n", "files = listdir('.')\n", "if filename in files:\n", " print(\"Reading previously calculated data from file %s\"%filename)\n", " df = pd.read_csv(filename)\n", " df.set_index(['type', 'alpha', 'xmin'], inplace=True)\n", "else:\n", "\n", " ind = [(d,a,x) for d in distribution_types for a in theoretical_alphas for x in theoretical_xmins]\n", " \n", " print(\"Calculating validation fits on %i parameter conditions, with %i trials for each conditions, with %i data points each. \"\n", " \"Could take a long time.\"%(len(ind), n_trials, n_data))\n", "\n", " ind = pd.MultiIndex.from_tuples(ind, names=['type', 'alpha','xmin'])\n", " df = pd.DataFrame(columns=['alpha_mean', 'alpha_sd', 'xmin_mean', 'xmin_sd'], index=ind)\n", " \n", " i = 0\n", " for dt, alpha, xmin in ind:\n", " i += 1\n", " print(\"Parameter condition number %i\"%i)\n", " data = validate(xmin, alpha, discrete=dt, n_data=n_data, n_trials=n_trials)\n", " df.ix[dt,alpha,xmin] = (mean(data[1]), std(data[1]), mean(data[0]), std(data[0]))\n", "\n", " df.to_csv(filename)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Reading previously calculated data from file powerlaw_validation_10trials_10000data.csv\n" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "subplot(2,2,1)\n", "for a in theoretical_alphas:\n", " y_vals = df.ix['continuous', a]['alpha_mean'].astype('float')\n", " error = df.ix['continuous', a]['alpha_sd'].astype('float')\n", "\n", " plot(theoretical_xmins, y_vals, label=a)\n", " fill_between(theoretical_xmins, y_vals-error, y_vals+error, alpha=.1)\n", "\n", "xscale('log')\n", "#xlabel(r\"$x_{min}$\")\n", "ylabel(r\"Fitted $\\alpha$\")\n", "yticks(theoretical_alphas)\n", "setp(gca().get_xticklabels(), visible=False)\n", "title(\"Continuous\")\n", "\n", "#########\n", "subplot(2,2,2)\n", "for a in theoretical_alphas:\n", " y_vals = df.ix['discrete', a]['alpha_mean'].astype('float')\n", " error = df.ix['discrete', a]['alpha_sd'].astype('float')\n", "\n", " plot(theoretical_xmins, y_vals, label=a)\n", " fill_between(theoretical_xmins, y_vals-error, y_vals+error, alpha=.1)\n", "\n", "xscale('log')\n", "#xlabel(r\"$x_{min}$\")\n", "#ylabel(r\"Fitted $\\alpha$\")\n", "setp(gca().get_xticklabels(), visible=False)\n", "setp(gca().get_yticklabels(), visible=False)\n", "title(\"Discrete\")\n", "\n", "########\n", "subplot(2,2,3)\n", "for a in theoretical_alphas:\n", " y_vals = df.ix['continuous', a]['xmin_mean'].astype('float').values\n", " error = df.ix['continuous', a]['xmin_sd'].astype('float').values\n", " up = y_vals+error\n", " down = y_vals-error\n", " ind = down<theoretical_xmins\n", " down[ind] = theoretical_xmins[ind]\n", " \n", " plot(theoretical_xmins, y_vals, label=a)\n", " fill_between(theoretical_xmins, down, up, alpha=.1)\n", "\n", "xlim(xmin=1)\n", "ylim(ymin=1)\n", "plot(xlim(),xlim(),linestyle='--', color='k')\n", "xscale('log')\n", "yscale('log')\n", "xlabel(r\"$x_{min}$ of Data\")\n", "ylabel(r\"Fitted $x_{min}$\")\n", "\n", "\n", "########\n", "legend_refs = []\n", "########\n", "subplot(2,2,4,sharey=gca())\n", "for a in theoretical_alphas:\n", " y_vals = df.ix['discrete', a]['xmin_mean'].astype('float').values\n", " error = df.ix['discrete', a]['xmin_sd'].astype('float').values\n", " up = y_vals+error\n", " down = y_vals-error\n", " ind = down<theoretical_xmins\n", " down[ind] = theoretical_xmins[ind]\n", "\n", " line = plot(theoretical_xmins, y_vals, label=a)\n", " legend_refs += line\n", " fill_between(theoretical_xmins, down, up, alpha=.1)\n", "\n", "xlim(xmin=1)\n", "ylim(ymin=1)\n", "plot(xlim(),xlim(),linestyle='--', color='k')\n", "xscale('log')\n", "yscale('log')\n", "xlabel(r\"$x_{min}$ of Data\")\n", "#ylabel(r\"Fitted $x_{min}$\")\n", "setp(gca().get_yticklabels(), visible=False)\n", "\n", "\n", "#######\n", "#figlegend(legend_refs[::-1], theoretical_alphas[::-1],'center right', title=r'$\\alpha$ of Data')\n", "subplots_adjust(wspace=.15, hspace=.1)\n", "legend( legend_refs[::-1], theoretical_alphas[::-1], loc = 'center right', bbox_to_anchor = (.1,0,1,1),\n", " bbox_transform = plt.gcf().transFigure, title=r'$\\alpha$ of Data' )\n", "savefig('Fig_powerlaw_validation_%itrials_%idata.pdf'%(int(n_trials),int(n_data)), bbox_inches='tight')\n", "savefig('Fig_powerlaw_validation_%itrials_%idata.tiff'%(int(n_trials),int(n_data)), bbox_inches='tight', dpi=300)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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GWzEax/4eksT7gxAtFpFyKZMjf+1cloiizDay4FKHjozC7t2wewgZ2o0MDyND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\n", "text": [ "<matplotlib.figure.Figure at 0x108384a58>" ] } ], "prompt_number": 45 } ], "metadata": {} } ] }

mit

johnpfay/environ859

06_WebGIS/Notebooks/GeocodingWithOSM.ipynb

1

8652

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Geocoding using the Open Street Map API\n", "\n", "Here we explore an example of using an Application Programming Interface, or API. Briefly, an API is a set of commands we can send over the internet to a remote server, spurring the server to process these commands and return a response. In this example, we'll explore how we can use the Open Street Map's geocoding API to get the coordinates responding to a particular address.\n", "\n", "This is not an in-depth exploration of this particular API, but rather an introduction on how to use an API within Python, specfically using the handy `requests` and `json` libraries. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "First we import the `requests` and `json` modules.<br>Usfeful documentation on these modules are found here: <br>\n", "* `requests`: http://docs.python-requests.org\n", "* `json`: https://pymotw.com/2/json/" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Import modules\n", "import requests\n", "import json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "Now we will form the request to invoke the Open Street Map API. Documentation on this API is found here: \n", "http://wiki.openstreetmap.org/wiki/Nominatim\n", "\n", "First, we'll generate an example address to geocode. Why not use Environment Hall? But feel free to use your own address!" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Get the address\n", "address = '9 Circuit Drive, Durham, NC, 27708' " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An API request consists two components: the **service endpoint** and a set of **parameters** associated with the service. \n", "\n", "When using the `requests` module to create and send our request, we supply the service endpoint is a string containing the server address (as a URL) and the service name (here, it's `search`). And the parameters are supplied in the form of a Python dictionary. Here, the two paramters we'll pass are the `format` and `address` parameters. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Form the request\n", "osmURL = 'http://nominatim.openstreetmap.org/search'\n", "params = {'format':'json','q':address} " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can use `requests` to send our command off to the OSM server. The server's response is saved as the `response` variable." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Send the request\n", "response = requests.get(osmURL, params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `response` object below contains a lot of information. You are encouraged to explore this object further. Here we'll explore one property which is the full URL created. Copy and paste the result in your favorite browser, and you'll see the result of our request in raw form. When you try this, try changing 'json' to 'html' in the URL..." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "u'http://nominatim.openstreetmap.org/search?q=9+Circuit+Drive%2C+Durham%2C+NC%2C+27708&format=json'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response.url" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Opens the URL as an html response (vs JSON) in a web browser...\n", "import webbrowser\n", "webbrowser.open_new(response.url.replace('json','html'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What we really want from the response, however, is the data returned by the service. The `json` function of the `response` object converts the response to an object in JavaScript Object Notation, or JSON. JSON is esentially a list of dictionaries that we can easily manipulate in Python." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "#Read in the response as a JSON encoded object\n", "jsonObj = response.json()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`pprint` or \"pretty print\" allows us to display JSON objects in a readable format. Let's make a pretty print of our JSON repsonse. " ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{u'boundingbox': [u'36.0052912',\n", " u'36.0061023',\n", " u'-78.9434647',\n", " u'-78.9409989'],\n", " u'class': u'highway',\n", " u'display_name': u'Circuit Drive, Crest Street, Durham, Durham County, North Carolina, 27705, United States of America',\n", " u'importance': 0.335,\n", " u'lat': u'36.0055806',\n", " u'licence': u'Data \\xa9 OpenStreetMap contributors, ODbL 1.0. http://www.openstreetmap.org/copyright',\n", " u'lon': u'-78.942014',\n", " u'osm_id': u'16544972',\n", " u'osm_type': u'way',\n", " u'place_id': u'71321894',\n", " u'type': u'unclassified'}]\n" ] } ], "source": [ "from pprint import pprint\n", "pprint(jsonObj)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our response contains only one item in the JSON list. We'll extract to a dictionary and print it's items." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[u'display_name', u'importance', u'place_id', u'lon', u'lat', u'osm_type', u'licence', u'osm_id', u'boundingbox', u'type', u'class']\n" ] } ], "source": [ "dataDict = jsonObj[0]\n", "print dataDict.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can easily grab the lat and lon objects from our response" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36.0055806 -78.942014\n" ] } ], "source": [ "lat = float(dataDict['lat'])\n", "lng = float(dataDict['lon'])\n", "print \"The lat,lng" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(u'-78.942014', u'36.0055806')" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = jsonObj[0]\n", "d['lon'],d['lat']" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Now let's inform the user of the result of the whole process..." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The address 9 Circuit Drive, Durham, NC, 27708 is located at\n", "36.0055806° Lat, -78.942014° Lon\n" ] } ], "source": [ "print \"The address {0} is located at\\n{1}° Lat, {2}° Lon\".format(address,lat,lng)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

mjabri/topographica

doc/Tutorials/som_retinotopy.ipynb

2

31900

{ "metadata": { "name": "", "signature": "sha256:060033d7e8fba6d7cea17c2181c0d5c800db86ae8ee6c3dca89f470937af6c04" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SOM Retinotopy\n", "\n", "This IPython notebook defines and explores the Kohonen SOM (self-organizing map) model of retinotopy described in pages 53-59 of:\n", "<blockquote>\n", " Miikkulainen, Bednar, Choe, and Sirosh (2005),\n", " <a href=\"http://computationalmaps.org\">Computational Maps in the Visual Cortex</a>, Springer.\n", "</blockquote>\n", "\n", "If you can double-click on this text and edit it, you are in a live [IPython Notebook](http://ipython.org/notebook) environment where you can run the code and explore the model. Otherwise, you are viewing a static (e.g. HTML) copy of the notebook, which allows you to see the precomputed results only. To switch to the live notebook, see the [notebook installation instructions](https://github.com/ioam/topographica).\n", "\n", "This IPython notebook constructs the definition of the SOM retinotopy model in the [Topographica](http://www.topographica.org) simulator, and shows how it organizes.\n", "\n", "A static version of this notebook may be viewed [online](http://ioam.github.io/media/som_retinotopy.html). To run the live notebook and explore the model interactively, you will need both IPython Notebook and Topographica.\n", "\n", "If you prefer the older Tk GUI interface or a command line, you may use the som_retinotopy.ty script distributed with Topographica, without IPython Notebook, as follows:\n", "\n", "```bash\n", "./topographica -g examples/som_retinotopy.ty\n", "```\n", "\n", "To run this Notebook version, you can run all cells in order automatically:\n", "* Selecting ``Kernel -> Restart`` from the menu above.\n", "* Selecting ``Cell -> Run All``. \n", "\n", "Alternatively, you may run each cell in sequence, starting at the top of the notebook and pressing ``Shift + Enter``." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%reload_ext topo.misc.ipython\n", "%opts GridSpace [tick_format=\"%.1f\" figure_size=70]\n", "%timer start" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model definition\n", "The next three cells define the SOM model in its entirety (copied from examples/som_retinotopy.ty). First, we import required libraries and we declare various parameters to allow the modeller to control the behavior:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Basic example of a fully connected SOM retinotopic map with ConnectionFields.\n", "\n", "Contains a Retina (2D Gaussian generator) fully connected to a V1\n", "(SOM) sheet, with no initial ordering for topography.\n", "\n", "Constructed to match the retinotopic simulation from page 53-59 of\n", "Miikkulainen, Bednar, Choe, and Sirosh (2005), Computational Maps in\n", "the Visual Cortex, Springer. Known differences include:\n", "\n", " - The cortex_density and retina_density are smaller for\n", " speed (compared to 40 and 24 in the book).\n", " - The original simulation used a radius_0 of 13.3/40, which does work\n", " for some random seeds, but a much larger radius is used here so that\n", " it converges more reliably.\n", "\"\"\"\n", "import topo\n", "import imagen\n", "\n", "from math import exp, sqrt\n", "\n", "import param\n", "\n", "from topo import learningfn,numbergen,transferfn,pattern,projection,responsefn,sheet\n", "\n", "import topo.learningfn.projfn\n", "import topo.pattern.random\n", "import topo.responsefn.optimized\n", "import topo.transferfn.misc\n", "\n", "# Disable the measurement progress bar for this notebook\n", "from topo.analysis.featureresponses import pattern_response\n", "pattern_response.progress_bar = False\n", "\n", "# Parameters that can be passed on the command line using -p\n", "from topo.misc.commandline import global_params as p\n", "p.add(\n", "\n", " retina_density=param.Number(default=10.0,bounds=(0,None),\n", " inclusive_bounds=(False,True),doc=\"\"\"\n", " The nominal_density to use for the retina.\"\"\"),\n", "\n", " cortex_density=param.Number(default=10.0,bounds=(0,None),\n", " inclusive_bounds=(False,True),doc=\"\"\"\n", " The nominal_density to use for V1.\"\"\"),\n", "\n", " input_seed=param.Number(default=0,bounds=(0,None),doc=\"\"\"\n", " Seed for the pseudorandom number generator controlling the\n", " input patterns.\"\"\"),\n", "\n", " weight_seed=param.Number(default=0,bounds=(0,None),doc=\"\"\"\n", " Seed for the pseudorandom number generator controlling the\n", " initial weight values.\"\"\"),\n", "\n", " radius_0=param.Number(default=1.0,bounds=(0,None),doc=\"\"\"\n", " Starting radius for the neighborhood function.\"\"\"),\n", "\n", " alpha_0=param.Number(default=0.42,bounds=(0,None),doc=\"\"\"\n", " Starting value for the learning rate.\"\"\"))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Customizing the model parameters\n", "\n", "The cell below may be used to modify any of the parameters defined above, allowing you to explore the parameter space of the model. To illustrate, the default retina and cortex densities are set to 10 (their default values), but you may change the value of any parameter in this way:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "p.retina_density=10\n", "p.cortex_density=10" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The <a href=\"http://ioam.github.io/media/som_retinotopy.html\">online version of this notebook</a> normally uses the full retinal and cortical densities of 24 and 40, respectively, but densities of 10 are the default because they should be sufficient for most purposes. Using the parameters and definitions above, the following code defines the SOM model of retinotopy, by defining the input patterns, the Sheets, and the connections between Sheets:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Using time dependent random streams.\n", "# Corresponding numbergen objects should be given suitable names.\n", "param.Dynamic.time_dependent=True\n", "numbergen.TimeAwareRandomState.time_dependent = True\n", "\n", "# input pattern\n", "sheet.GeneratorSheet.period = 1.0\n", "sheet.GeneratorSheet.phase = 0.05\n", "sheet.GeneratorSheet.nominal_density = p.retina_density\n", "\n", "input_pattern = pattern.Gaussian(scale=1.0,size=2*sqrt(2.0*0.1*24.0)/24.0,\n", " aspect_ratio=1.0,orientation=0,\n", " x=numbergen.UniformRandom(name='xgen', lbound=-0.5,ubound=0.5, seed=p.input_seed),\n", " y=numbergen.UniformRandom(name='ygen', lbound=-0.5,ubound=0.5, seed=p.input_seed))\n", "\n", "topo.sim['Retina'] = sheet.GeneratorSheet(input_generator=input_pattern)\n", "\n", "topo.sim['V1'] = sheet.CFSheet(\n", " nominal_density = p.cortex_density,\n", " # Original CMVC simulation used an initial radius of 13.3/40.0 (~0.33)\n", " output_fns=[transferfn.misc.KernelMax(density=p.cortex_density,\n", " kernel_radius=numbergen.BoundedNumber(\n", " bounds=(0.5/40,None),\n", " generator=numbergen.ExponentialDecay(\n", " starting_value=p.radius_0,\n", " time_constant=40000/5.0)))])\n", "\n", "topo.sim.connect('Retina','V1',name='Afferent', delay=0.05,\n", " seed = p.weight_seed,\n", " connection_type=projection.CFProjection,\n", " weights_generator = pattern.random.UniformRandom(name='Weights'),\n", " nominal_bounds_template=sheet.BoundingBox(radius=1.0), # fully connected network.\n", " learning_rate=numbergen.ExponentialDecay(\n", " starting_value = p.alpha_0,\n", " time_constant=40000/6.0),\n", " response_fn = responsefn.optimized.CFPRF_EuclideanDistance_opt(),\n", " learning_fn = learningfn.projfn.CFPLF_EuclideanHebbian())\n", "\n", "'Loaded the self-organizing map model (som_retinotopy)'" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploring the model" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from holoviews import Dimension, Image" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the model has been defined, we can explore it. The structure of the loaded model is shown in this screenshot taken from the Tk GUI's Model Editor:\n", "\n", "### **Model structure**\n", "\n", "<center>\n", "<img src='http://topographica.org/_images/som_network_diagram.png'/>\n", "</center>\n", "\n", "\n", "The large circle indicates that the Retina Sheet is fully connected to the V1 Sheet.\n", "\n", "### **Initial weights**\n", "\n", "The plot below shows the initial set of weights from a 10x10 subset of the V1 neurons (i.e., every neuron with the reduced cortex_density, or every fourth neuron for cortex_density=40):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "topo.sim.V1.Afferent.grid()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, the initial weights are uniform random. Each neuron receives a full set of connections from all input units, and thus each has a 24x24 or 10x10 array of weights (depending on the retina_density).\n", "\n", "### **Initial Center-of-Gravity (CoG) plots**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can visualize the center of gravity (CoG) of the V1 Afferent weights using the following measurement command:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from topo.command.analysis import measure_cog\n", "cog_data = measure_cog()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The center of gravity (a.k.a. centroid or center of mass) is computed for each neuron using its set of incoming weights. The plot below shows each neuron's CoG represented by a point, with a line segment drawn from each neuron to each of its four immediate neighbors so that neighborhood relationships (if any) will be visible." ] }, { "cell_type": "code", "collapsed": false, "input": [ "cog_data.CoG.Afferent" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this plot is is clear that all of the neurons have a CoG near the center of the retina, which is to be expected because the weights are fully connected and evenly distributed (and thus all have an average (X,Y) value near the center of the retina). The same data is also visualized below:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "xcog=cog_data.XCoG.Afferent.last\n", "ycog=cog_data.YCoG.Afferent.last\n", "xcog + ycog + xcog*ycog" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The V1 X CoG plot shows the X location preferred by each neuron in V1, and the V1 Y CoG plot shows the preferred Y locations. The monochrome values are scaled so that a preference of -0.5 is black and a preference of +0.5 is white, and since they all start out around 0.0 they are all shades of medium gray. The actual preferences can be accessed directly as a Numpy array if you prefer:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "xcog.data.min(),xcog.data.max(),ycog.data.min(),ycog.data.max()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The colorful XYCoG plot above right shows a false-color visualization of the CoG values in both dimensions at once, where the amount of red in the plot is proportional to the X CoG, and the amount of green in the plot is proportional to the Y CoG. Where both X and Y are low, the plot is black or very dark, and where both are high the plot is yellow (because red and green light together appears yellow). All of the units should be an intermediate color of yellow/gray at this stage, indicating preference for 0.5 in both X and Y. As you will see later, once neurons have developed strong preferences for one location over the others, some neurons will show up as bright black (X and Y both low), red (X high, Y low), red (X low, Y high), or yellow (X and Y both high) in this plot." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training stimuli\n", "\n", "We can have a look at what the training patterns that will be used to train the model without having to run it. In the cell labelled ``In [4]`` (in the model definition), we see where the training patterns are defined in a variable called ``input_pattern``. We see that the circular Gaussian stimulus has ``x`` and ``y`` values that are drawn from a random distribution by two independent ``numbergen.UniformRandom`` objects. We can now view what 100 frames of training patterns will look like:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "input_pattern.anim(30, offset=0.05)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Collecting data for animated plots\n", "\n", "At the end of the notebook, we will generate a set of nice animations showing the plots we have already shown evolve over development. We now create a ``Collector`` object that collects all information needed for plotting and animation. We will collect the information we have just examined and advance the simulation one iteration:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from topo.analysis import Collector\n", "from topo.command.analysis import measure_cog\n", "\n", "c = Collector()\n", "c.collect(measure_cog)\n", "\n", "c.Activity.Retina = c.collect(topo.sim.Retina)\n", "c.Activity.V1 = c.collect(topo.sim.V1)\n", "c.Activity.V1Afferent = c.collect(topo.sim.V1.Afferent)\n", "c.CFs.Afferent = c.collect(topo.sim.V1.Afferent, grid=True, rows=10, cols=10)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## **Activity after a single training iteration**\n", "\n", "The initial activities would be blank, but after running the model a single iteration, the sheet activities now look as follows:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = c(times=[1])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "data.Activity.Retina + data.Activity.V1" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the Retina plot, each photoreceptor is represented as a pixel whose shade of grey codes the response level, increasing from black to white. As expected, this particular example matches the first frame we visualized in the training stimulus animation. The V1 plot shows the response to that input, which for a SOM is initially a large Gaussian-shaped blob centered around the maximally responding unit." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## **Projection Activity**\n", "\n", "To see what the responses were before SOM\u2019s neighborhood function forced them into a Gaussian shape, you can look at the Projection Activity plot, which shows the feedforward activity in V1:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data.Activity.V1Afferent" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here these responses are best thought of as Euclidean proximity, not distance. This formulation of the SOM response function actually subtracts the distances from the max distance, to ensure that the response will be larger for smaller Euclidean distances (as one intuitively expects for a neural response). The V1 feedforward activity appears random here because the Euclidean distance from the input vector to the initial random weight vector is random.\n", "\n", "\n", "## **Learning after a few iterations**\n", "\n", "If you look at the weights now we have run a single training iteration, you'll see that most of the neurons have learned new weight patterns based on this input:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data.CFs.Afferent" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of the weights to each neuron have now changed due to learning. In the SOM algorithm, the unit with the maximum response (i.e., the minimum Euclidean distance between its weight vector and the input pattern) is chosen, and the weights of units within a circular area defined by a Gaussian-shaped neighborhood function around this neuron are updated.\n", "\n", "\n", "This effect is visible in this plot \u2013 a few neurons around the winning unit at the top right have changed their weights. Let us run a few more iterations before having another look:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "topo.sim.run(4)\n", "topo.sim.V1.Afferent.grid()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The weights have been updated again - it is clear after these five iterations that the input patterns are becoming represented in the weight patterns, though not very cleanly yet. We also see that the projection activity patterns are becoming smoother, since the weight vectors are now similar between neighboring neurons:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "topo.sim.V1.Afferent.projection_view()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now advance to simulation time 250 because we will want to make animations regularly sampled every 250 steps:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "topo.sim.run(245)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## At 5000 iterations\n", "\n", "Let us use our ``Collector`` object ``c`` to collect more measurements. We will start collecting measurements every 250 steps until we complete 5000 iterations, which should take a few seconds at the default densities, and may be a minute or two at the higher densities:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "times = [topo.sim.time()*i for i in range(1,21)]\n", "print(\"Running %d measurements between iteration %s and iteration %s\" % \n", " (len(times), min(times), max(times)))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "data = c(data, times=times)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the topographic grid plot evolves over this period as follows:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data.CoG.Afferent" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The X and Y CoG plots are now smooth, but not yet the axis-aligned gradients (e.g. left to right and bottom to top) that an optimal topographic mapping would have:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%opts Image {-axiswise}\n", "(data.XCoG.Afferent + data.YCoG.Afferent + \n", " data.XCoG.Afferent * data.YCoG.Afferent).select(Time=5000)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The weight patterns are still quite broad, not very selective for typical input patterns, and not very distinct from one another:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data.CFs.Afferent.last" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<center><h3 class='alert-success'>In the live notebook you can remove the ``.last`` to view an animation to 5000 iterations</h3></center> " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## At 10000 iterations\n", "\n", "Additional training up to 10000 iterations (which becomes faster due to a smaller neighborhood radius) leads to a nearly flat, square map:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "times = [5000+(250*i) for i in range(1,21)]\n", "print(\"Running %d measurements between iteration %s and iteration %s\" % \n", " (len(times), min(times), max(times)))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "data = c(data, times=times)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "data.CoG.Afferent.last" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "<center><h4 class='alert-success'>Note: In the live notebook you can remove ``.last`` to view an animation to 10000 iterations</h4></center> " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Yet the weight patterns are still quite broad and not very selective for typical input patterns, because the neighborhood radius (initially 1.0 in Sheet coordinates, i.e. larger than the entire V1) is still large enough to force most cells to respond to the same patterns:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "topo.sim.V1.output_fns[0].kernel_radius" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "data.CFs.Afferent.last" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## At 30000 iterations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By 30000 iterations the map has good coverage of the available portion of the input space:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "times = [10000+(500*i) for i in range(1,41)]\n", "print(\"Running %d measurements between iteration %s and iteration %s\\n\\n\" % \n", " (len(times), min(times), max(times)))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "data = c(data, times=times)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "data.CoG.Afferent.last" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final projection plot at 30000 now shows that each neuron has developed weights concentrated in a small part of the input space, matching a prototypical input at one location, and that neurons each have distinct preferences:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "topo.sim.V1.output_fns[0].kernel_radius" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "data.CFs.Afferent.last" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final topographic mapping may have any one of a number of possible arrangements, flipped or rotated by 90 degrees along any axis or both axes. E.g. a vertically flipped map will have blobs in each CF that are at the bottom for the top row of neurons, but the top for the bottom row of neurons. Nothing in the network reliably drives it to have any particular overall orientation or mapping apart from aligning to the square shape, and each of these possible organizations is equivalent functionally." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Animations over 30000 iterations\n", "\n", "We can watch how the topographic mapping unfolds over all 30000 iterations we have run:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%opts Gravity_Contours [rescale_individually=False]\n", "data.CoG.Afferent" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And how the XCoG and YCoG components unfold:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data.XCoG.Afferent + data.YCoG.Afferent + data.XCoG.Afferent * data.YCoG.Afferent" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the false color CoG plot, a perfectly organized mapping will have black in one corner and yellow in the opposite, with green and red in the two other corners, and smooth colors between each corner. Next, the weights (though by default this animation is disabled to save time, so you would need to remove \".last\" and re-run this cell, which may take several minutes):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data.CFs.Afferent.last" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Snapshots of the retinal and V1 activity over development. Notice how the activity in V1 becomes more focused over time as the kernel radius decreases:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data.Activity.Retina + data.Activity.V1" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploring the parameters of the SOM \n", "\n", "Now, you can re-run the basic simulation by restarting the IPython Notebook Kernel (<code>Kernel -> Restart</code>) or using the keyboard shortcut ``Ctrl+M .`` (see <code>Help -> Keyboard Shortcuts</code> for the full list). Then you can change one of the parameter values, either by editing the model definition above before running those cells, or (usually clearer) by setting the parameters in the cell labelled ``In [3]``.\n", "\n", "For instance, the starting value of the neighborhood radius (from which all future values are calculated according to exponential decay) is 1.0. You can change this value as you see fit, e.g. to 0.1, by setting ``p.radius_0=0.1``. With such a small learning radius, global ordering is unlikely to happen, and one can expect the topographic grid not to flatten out (despite local order in patches).\n", "\n", "Similarly, consider changing the initial learning rate from 0.42 to e.g. 1.0 (e.g. by setting ``p.alpha_0=1.0`` in cell 3). The retina and V1 densities cannot be changed after the simulation has started, but again, they can be changed by providing their values above and restarting the notebook.\n", "\n", "You can also try changing the input_seed (``p.input_seed=XX``), to get a different stream of inputs, or weight_seed (``p.weight_seed=XX``), to get a different set of initial weights. \n", "\n", "Note that with some of these values, you may encounter cases where the SOM fails to converge even though it seems to be working properly otherwise. For instance, some seed values result in topological defects like a kink:\n", "<br>\n", "<br>\n", "<center><img src=\"https://ioam.github.io/topographica/_images/som_grid_kink.png\"></img></center>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%timer" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

bsd-3-clause

raphaelshirley/regphot

examples/Display.ipynb

1

1739771

mit

statsmodels/statsmodels.github.io

v0.13.0/examples/notebooks/generated/variance_components.ipynb

2

18614

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Variance Component Analysis\n", "\n", "This notebook illustrates variance components analysis for two-level\n", "nested and crossed designs." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:21.406691Z", "iopub.status.busy": "2021-10-06T09:59:21.406248Z", "iopub.status.idle": "2021-10-06T09:59:25.235474Z", "shell.execute_reply": "2021-10-06T09:59:25.234705Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import statsmodels.api as sm\n", "from statsmodels.regression.mixed_linear_model import VCSpec\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make the notebook reproducible" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:25.239238Z", "iopub.status.busy": "2021-10-06T09:59:25.238891Z", "iopub.status.idle": "2021-10-06T09:59:25.241343Z", "shell.execute_reply": "2021-10-06T09:59:25.241035Z" }, "lines_to_next_cell": 1 }, "outputs": [], "source": [ "np.random.seed(3123)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Nested analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In our discussion below, \"Group 2\" is nested within \"Group 1\". As a\n", "concrete example, \"Group 1\" might be school districts, with \"Group\n", "2\" being individual schools. The function below generates data from\n", "such a population. In a nested analysis, the group 2 labels that\n", "are nested within different group 1 labels are treated as\n", "independent groups, even if they have the same label. For example,\n", "two schools labeled \"school 1\" that are in two different school\n", "districts are treated as independent schools, even though they have\n", "the same label." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:25.244671Z", "iopub.status.busy": "2021-10-06T09:59:25.242975Z", "iopub.status.idle": "2021-10-06T09:59:25.249974Z", "shell.execute_reply": "2021-10-06T09:59:25.250431Z" }, "lines_to_end_of_cell_marker": 0, "lines_to_next_cell": 1 }, "outputs": [], "source": [ "def generate_nested(\n", " n_group1=200, n_group2=20, n_rep=10, group1_sd=2, group2_sd=3, unexplained_sd=4\n", "):\n", "\n", " # Group 1 indicators\n", " group1 = np.kron(np.arange(n_group1), np.ones(n_group2 * n_rep))\n", "\n", " # Group 1 effects\n", " u = group1_sd * np.random.normal(size=n_group1)\n", " effects1 = np.kron(u, np.ones(n_group2 * n_rep))\n", "\n", " # Group 2 indicators\n", " group2 = np.kron(np.ones(n_group1), np.kron(np.arange(n_group2), np.ones(n_rep)))\n", "\n", " # Group 2 effects\n", " u = group2_sd * np.random.normal(size=n_group1 * n_group2)\n", " effects2 = np.kron(u, np.ones(n_rep))\n", "\n", " e = unexplained_sd * np.random.normal(size=n_group1 * n_group2 * n_rep)\n", " y = effects1 + effects2 + e\n", "\n", " df = pd.DataFrame({\"y\": y, \"group1\": group1, \"group2\": group2})\n", "\n", " return df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate a data set to analyze." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:25.253002Z", "iopub.status.busy": "2021-10-06T09:59:25.252299Z", "iopub.status.idle": "2021-10-06T09:59:25.288002Z", "shell.execute_reply": "2021-10-06T09:59:25.288940Z" } }, "outputs": [], "source": [ "df = generate_nested()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using all the default arguments for `generate_nested`, the population\n", "values of \"group 1 Var\" and \"group 2 Var\" are 2^2=4 and 3^2=9,\n", "respectively. The unexplained variance, listed as \"scale\" at the\n", "top of the summary table, has population value 4^2=16." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:25.294802Z", "iopub.status.busy": "2021-10-06T09:59:25.292276Z", "iopub.status.idle": "2021-10-06T10:01:13.384507Z", "shell.execute_reply": "2021-10-06T10:01:13.385139Z" }, "lines_to_end_of_cell_marker": 0, "lines_to_next_cell": 1 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Mixed Linear Model Regression Results\n", "==========================================================\n", "Model: MixedLM Dependent Variable: y \n", "No. Observations: 40000 Method: REML \n", "No. Groups: 200 Scale: 15.8825 \n", "Min. group size: 200 Log-Likelihood: -116022.3805\n", "Max. group size: 200 Converged: Yes \n", "Mean group size: 200.0 \n", "-----------------------------------------------------------\n", " Coef. Std.Err. z P>|z| [0.025 0.975]\n", "-----------------------------------------------------------\n", "Intercept -0.035 0.149 -0.232 0.817 -0.326 0.257\n", "group1 Var 3.917 0.112 \n", "group2 Var 8.742 0.063 \n", "==========================================================\n", "\n" ] } ], "source": [ "model1 = sm.MixedLM.from_formula(\n", " \"y ~ 1\",\n", " re_formula=\"1\",\n", " vc_formula={\"group2\": \"0 + C(group2)\"},\n", " groups=\"group1\",\n", " data=df,\n", ")\n", "result1 = model1.fit()\n", "print(result1.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we wish to avoid the formula interface, we can fit the same model\n", "by building the design matrices manually." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T10:01:13.393290Z", "iopub.status.busy": "2021-10-06T10:01:13.388253Z", "iopub.status.idle": "2021-10-06T10:01:13.408331Z", "shell.execute_reply": "2021-10-06T10:01:13.407981Z" }, "lines_to_end_of_cell_marker": 0, "lines_to_next_cell": 1 }, "outputs": [], "source": [ "def f(x):\n", " n = x.shape[0]\n", " g2 = x.group2\n", " u = g2.unique()\n", " u.sort()\n", " uv = {v: k for k, v in enumerate(u)}\n", " mat = np.zeros((n, len(u)))\n", " for i in range(n):\n", " mat[i, uv[g2.iloc[i]]] = 1\n", " colnames = [\"%d\" % z for z in u]\n", " return mat, colnames" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we set up the variance components using the VCSpec class." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T10:01:13.414116Z", "iopub.status.busy": "2021-10-06T10:01:13.413051Z", "iopub.status.idle": "2021-10-06T10:01:14.385817Z", "shell.execute_reply": "2021-10-06T10:01:14.386253Z" } }, "outputs": [], "source": [ "vcm = df.groupby(\"group1\").apply(f).to_list()\n", "mats = [x[0] for x in vcm]\n", "colnames = [x[1] for x in vcm]\n", "names = [\"group2\"]\n", "vcs = VCSpec(names, [colnames], [mats])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we fit the model. It can be seen that the results of the\n", "two fits are identical." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T10:01:14.391848Z", "iopub.status.busy": "2021-10-06T10:01:14.388110Z", "iopub.status.idle": "2021-10-06T10:02:06.190622Z", "shell.execute_reply": "2021-10-06T10:02:06.190941Z" }, "lines_to_end_of_cell_marker": 0, "lines_to_next_cell": 1 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Mixed Linear Model Regression Results\n", "==========================================================\n", "Model: MixedLM Dependent Variable: y \n", "No. Observations: 40000 Method: REML \n", "No. Groups: 200 Scale: 15.8825 \n", "Min. group size: 200 Log-Likelihood: -116022.3805\n", "Max. group size: 200 Converged: Yes \n", "Mean group size: 200.0 \n", "-----------------------------------------------------------\n", " Coef. Std.Err. z P>|z| [0.025 0.975]\n", "-----------------------------------------------------------\n", "const -0.035 0.149 -0.232 0.817 -0.326 0.257\n", "x_re1 Var 3.917 0.112 \n", "group2 Var 8.742 0.063 \n", "==========================================================\n", "\n" ] } ], "source": [ "oo = np.ones(df.shape[0])\n", "model2 = sm.MixedLM(df.y, oo, exog_re=oo, groups=df.group1, exog_vc=vcs)\n", "result2 = model2.fit()\n", "print(result2.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Crossed analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a crossed analysis, the levels of one group can occur in any\n", "combination with the levels of the another group. The groups in\n", "Statsmodels MixedLM are always nested, but it is possible to fit a\n", "crossed model by having only one group, and specifying all random\n", "effects as variance components. Many, but not all crossed models\n", "can be fit in this way. The function below generates a crossed data\n", "set with two levels of random structure." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T10:02:06.197520Z", "iopub.status.busy": "2021-10-06T10:02:06.193404Z", "iopub.status.idle": "2021-10-06T10:02:06.206945Z", "shell.execute_reply": "2021-10-06T10:02:06.207413Z" }, "lines_to_end_of_cell_marker": 0, "lines_to_next_cell": 1 }, "outputs": [], "source": [ "def generate_crossed(\n", " n_group1=100, n_group2=100, n_rep=4, group1_sd=2, group2_sd=3, unexplained_sd=4\n", "):\n", "\n", " # Group 1 indicators\n", " group1 = np.kron(\n", " np.arange(n_group1, dtype=int), np.ones(n_group2 * n_rep, dtype=int)\n", " )\n", " group1 = group1[np.random.permutation(len(group1))]\n", "\n", " # Group 1 effects\n", " u = group1_sd * np.random.normal(size=n_group1)\n", " effects1 = u[group1]\n", "\n", " # Group 2 indicators\n", " group2 = np.kron(\n", " np.arange(n_group2, dtype=int), np.ones(n_group2 * n_rep, dtype=int)\n", " )\n", " group2 = group2[np.random.permutation(len(group2))]\n", "\n", " # Group 2 effects\n", " u = group2_sd * np.random.normal(size=n_group2)\n", " effects2 = u[group2]\n", "\n", " e = unexplained_sd * np.random.normal(size=n_group1 * n_group2 * n_rep)\n", " y = effects1 + effects2 + e\n", "\n", " df = pd.DataFrame({\"y\": y, \"group1\": group1, \"group2\": group2})\n", "\n", " return df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate a data set to analyze." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T10:02:06.210826Z", "iopub.status.busy": "2021-10-06T10:02:06.209142Z", "iopub.status.idle": "2021-10-06T10:02:06.232268Z", "shell.execute_reply": "2021-10-06T10:02:06.231567Z" } }, "outputs": [], "source": [ "df = generate_crossed()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we fit the model, note that the `groups` vector is constant.\n", "Using the default parameters for `generate_crossed`, the level 1\n", "variance should be 2^2=4, the level 2 variance should be 3^2=9, and\n", "the unexplained variance should be 4^2=16." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T10:02:06.235792Z", "iopub.status.busy": "2021-10-06T10:02:06.233956Z", "iopub.status.idle": "2021-10-06T10:02:44.803938Z", "shell.execute_reply": "2021-10-06T10:02:44.804470Z" }, "lines_to_end_of_cell_marker": 0, "lines_to_next_cell": 1 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Mixed Linear Model Regression Results\n", "==========================================================\n", "Model: MixedLM Dependent Variable: y \n", "No. Observations: 40000 Method: REML \n", "No. Groups: 1 Scale: 15.9824 \n", "Min. group size: 40000 Log-Likelihood: -112684.9688\n", "Max. group size: 40000 Converged: Yes \n", "Mean group size: 40000.0 \n", "-----------------------------------------------------------\n", " Coef. Std.Err. z P>|z| [0.025 0.975]\n", "-----------------------------------------------------------\n", "Intercept -0.251 0.353 -0.710 0.478 -0.943 0.442\n", "g1 Var 4.282 0.154 \n", "g2 Var 8.150 0.291 \n", "==========================================================\n", "\n" ] } ], "source": [ "vc = {\"g1\": \"0 + C(group1)\", \"g2\": \"0 + C(group2)\"}\n", "oo = np.ones(df.shape[0])\n", "model3 = sm.MixedLM.from_formula(\"y ~ 1\", groups=oo, vc_formula=vc, data=df)\n", "result3 = model3.fit()\n", "print(result3.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we wish to avoid the formula interface, we can fit the same model\n", "by building the design matrices manually." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T10:02:44.809462Z", "iopub.status.busy": "2021-10-06T10:02:44.806580Z", "iopub.status.idle": "2021-10-06T10:02:45.190061Z", "shell.execute_reply": "2021-10-06T10:02:45.190349Z" } }, "outputs": [], "source": [ "def f(g):\n", " n = len(g)\n", " u = g.unique()\n", " u.sort()\n", " uv = {v: k for k, v in enumerate(u)}\n", " mat = np.zeros((n, len(u)))\n", " for i in range(n):\n", " mat[i, uv[g[i]]] = 1\n", " colnames = [\"%d\" % z for z in u]\n", " return [mat], [colnames]\n", "\n", "\n", "vcm = [f(df.group1), f(df.group2)]\n", "mats = [x[0] for x in vcm]\n", "colnames = [x[1] for x in vcm]\n", "names = [\"group1\", \"group2\"]\n", "vcs = VCSpec(names, colnames, mats)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we fit the model without using formulas, it is simple to check\n", "that the results for models 3 and 4 are identical." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T10:02:45.199287Z", "iopub.status.busy": "2021-10-06T10:02:45.198528Z", "iopub.status.idle": "2021-10-06T10:03:20.452278Z", "shell.execute_reply": "2021-10-06T10:03:20.452938Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Mixed Linear Model Regression Results\n", "==========================================================\n", "Model: MixedLM Dependent Variable: y \n", "No. Observations: 40000 Method: REML \n", "No. Groups: 1 Scale: 15.9824 \n", "Min. group size: 40000 Log-Likelihood: -112684.9688\n", "Max. group size: 40000 Converged: Yes \n", "Mean group size: 40000.0 \n", "-----------------------------------------------------------\n", " Coef. Std.Err. z P>|z| [0.025 0.975]\n", "-----------------------------------------------------------\n", "const -0.251 0.353 -0.710 0.478 -0.943 0.442\n", "group1 Var 4.282 0.154 \n", "group2 Var 8.150 0.291 \n", "==========================================================\n", "\n" ] } ], "source": [ "oo = np.ones(df.shape[0])\n", "model4 = sm.MixedLM(df.y, oo[:, None], exog_re=None, groups=oo, exog_vc=vcs)\n", "result4 = model4.fit()\n", "print(result4.summary())" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "main_language": "python", "notebook_metadata_filter": "-all" }, "kernelspec": { "display_name": "Python 3", "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": "3.8.11" } }, "nbformat": 4, "nbformat_minor": 4 }

bsd-3-clause

mne-tools/mne-tools.github.io

0.14/_downloads/plot_mne_crosstalk_function.ipynb

1

3672

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n===================================================================\nCompute cross-talk functions (CTFs) for labels for MNE/dSPM/sLORETA\n===================================================================\n\nCTFs are computed for four labels in the MNE sample data set\nfor linear inverse operators (MNE, dSPM, sLORETA).\nCTFs describe the sensitivity of a linear estimator (e.g. for\none label) to sources across the cortical surface. Sensitivity\nto sources outside the label is undesirable, and referred to as\n\"leakage\" or \"cross-talk\".\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Author: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk>\n#\n# License: BSD (3-clause)\n\nfrom mayavi import mlab\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.minimum_norm import cross_talk_function, read_inverse_operator\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nsubjects_dir = data_path + '/subjects/'\nfname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'\nfname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'\nfname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'\nfname_label = [data_path + '/MEG/sample/labels/Aud-rh.label',\n data_path + '/MEG/sample/labels/Aud-lh.label',\n data_path + '/MEG/sample/labels/Vis-rh.label',\n data_path + '/MEG/sample/labels/Vis-lh.label']\n\n# read forward solution\nforward = mne.read_forward_solution(fname_fwd)\n\n# read label(s)\nlabels = [mne.read_label(ss) for ss in fname_label]\n\ninverse_operator = read_inverse_operator(fname_inv)\n\n# regularisation parameter\nsnr = 3.0\nlambda2 = 1.0 / snr ** 2\nmode = 'svd'\nn_svd_comp = 1\n\nmethod = 'MNE' # can be 'MNE', 'dSPM', or 'sLORETA'\nstc_ctf_mne = cross_talk_function(\n inverse_operator, forward, labels, method=method, lambda2=lambda2,\n signed=False, mode=mode, n_svd_comp=n_svd_comp)\n\nmethod = 'dSPM'\nstc_ctf_dspm = cross_talk_function(\n inverse_operator, forward, labels, method=method, lambda2=lambda2,\n signed=False, mode=mode, n_svd_comp=n_svd_comp)\n\ntime_label = \"MNE %d\"\nbrain_mne = stc_ctf_mne.plot(hemi='rh', subjects_dir=subjects_dir,\n time_label=time_label,\n figure=mlab.figure(size=(500, 500)))\n\ntime_label = \"dSPM %d\"\nbrain_dspm = stc_ctf_dspm.plot(hemi='rh', subjects_dir=subjects_dir,\n time_label=time_label,\n figure=mlab.figure(size=(500, 500)))\n\n# Cross-talk functions for MNE and dSPM (and sLORETA) have the same shapes\n# (they may still differ in overall amplitude).\n# Point-spread functions (PSfs) usually differ significantly." ], "outputs": [], "metadata": { "collapsed": false } } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.13", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }

bsd-3-clause

mil3na/behavior-study-stackexchange

visualization/table-q3.ipynb

2

17608

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division\n", "import pymongo, pandas, random\n", "import numpy as np\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "from scipy import stats\n", "\n", "%matplotlib inline\n", "\n", "client = pymongo.MongoClient('localhost', 27017)\n", "\n", "results_db = client['results']['question_3']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cursor = results_db.find({'lifetime_pvalue': {'$lt': 0.05}}, \n", " {u'_id': False, u'community':True, 'lifetime_pvalue':True, \n", " 'lifetime_difference_median':True, 'category': True})\n", "\n", "df = pandas.DataFrame(list(cursor))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>category</th>\n", " <th>community</th>\n", " <th>lifetime_difference_median</th>\n", " <th>lifetime_pvalue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>technology</td>\n", " <td>apple</td>\n", " <td>-14.504535</td>\n", " <td>3.285098e-04</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>professional</td>\n", " <td>aviation</td>\n", " <td>-6.876311</td>\n", " <td>1.824813e-02</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>life-arts</td>\n", " <td>diy</td>\n", " <td>-12.619026</td>\n", " <td>9.250952e-03</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>technology</td>\n", " <td>dsp</td>\n", " <td>-17.150387</td>\n", " <td>1.705494e-03</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>culture-recreation</td>\n", " <td>english</td>\n", " <td>-9.046545</td>\n", " <td>3.144978e-03</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>culture-recreation</td>\n", " <td>homebrew</td>\n", " <td>-22.549983</td>\n", " <td>3.586600e-03</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>science</td>\n", " <td>mathoverflow</td>\n", " <td>-327.759879</td>\n", " <td>9.222933e-07</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>culture-recreation</td>\n", " <td>mechanics</td>\n", " <td>-6.417729</td>\n", " <td>1.797765e-02</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>technology</td>\n", " <td>networkengineering</td>\n", " <td>-7.260233</td>\n", " <td>3.092324e-02</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>science</td>\n", " <td>philosophy</td>\n", " <td>-1.663282</td>\n", " <td>2.130040e-02</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>life-arts</td>\n", " <td>photo</td>\n", " <td>-4.983799</td>\n", " <td>3.948523e-03</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>culture-recreation</td>\n", " <td>poker</td>\n", " <td>-3.829913</td>\n", " <td>6.463297e-03</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>life-arts</td>\n", " <td>scifi</td>\n", " <td>-8.804869</td>\n", " <td>1.758364e-05</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>technology</td>\n", " <td>serverfault</td>\n", " <td>-35.290989</td>\n", " <td>4.191207e-02</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>technology</td>\n", " <td>stackoverflow</td>\n", " <td>-346.874280</td>\n", " <td>2.501768e-69</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>technology</td>\n", " <td>superuser</td>\n", " <td>-70.928687</td>\n", " <td>2.551841e-03</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>culture-recreation</td>\n", " <td>travel</td>\n", " <td>-1.919741</td>\n", " <td>5.696090e-03</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>technology</td>\n", " <td>tridion</td>\n", " <td>-215.047721</td>\n", " <td>1.714240e-03</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>technology</td>\n", " <td>wordpress</td>\n", " <td>74.715491</td>\n", " <td>1.650433e-02</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " category community lifetime_difference_median \\\n", "0 technology apple -14.504535 \n", "1 professional aviation -6.876311 \n", "2 life-arts diy -12.619026 \n", "3 technology dsp -17.150387 \n", "4 culture-recreation english -9.046545 \n", "5 culture-recreation homebrew -22.549983 \n", "6 science mathoverflow -327.759879 \n", "7 culture-recreation mechanics -6.417729 \n", "8 technology networkengineering -7.260233 \n", "9 science philosophy -1.663282 \n", "10 life-arts photo -4.983799 \n", "11 culture-recreation poker -3.829913 \n", "12 life-arts scifi -8.804869 \n", "13 technology serverfault -35.290989 \n", "14 technology stackoverflow -346.874280 \n", "15 technology superuser -70.928687 \n", "16 culture-recreation travel -1.919741 \n", "17 technology tridion -215.047721 \n", "18 technology wordpress 74.715491 \n", "\n", " lifetime_pvalue \n", "0 3.285098e-04 \n", "1 1.824813e-02 \n", "2 9.250952e-03 \n", "3 1.705494e-03 \n", "4 3.144978e-03 \n", "5 3.586600e-03 \n", "6 9.222933e-07 \n", "7 1.797765e-02 \n", "8 3.092324e-02 \n", "9 2.130040e-02 \n", "10 3.948523e-03 \n", "11 6.463297e-03 \n", "12 1.758364e-05 \n", "13 4.191207e-02 \n", "14 2.501768e-69 \n", "15 2.551841e-03 \n", "16 5.696090e-03 \n", "17 1.714240e-03 \n", "18 1.650433e-02 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cursor = results_db.find({'frequency_pvalue': {'$lt': 0.05}}, \n", " {u'_id': False, u'community':True, 'frequency_pvalue':True, \n", " 'frequency_difference_median':True, 'category': True,\n", " 'frequency_difference_mean':True, 'frequency_pvalue_greater':True})\n", "\n", "df = pandas.DataFrame(list(cursor))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>category</th>\n", " <th>community</th>\n", " <th>frequency_difference_mean</th>\n", " <th>frequency_difference_median</th>\n", " <th>frequency_pvalue</th>\n", " <th>frequency_pvalue_greater</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>technology</td>\n", " <td>askubuntu</td>\n", " <td>0.109569</td>\n", " <td>0.233333</td>\n", " <td>1.148150e-03</td>\n", " <td>3.318362e-04</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>technology</td>\n", " <td>blender</td>\n", " <td>0.578160</td>\n", " <td>0.833333</td>\n", " <td>5.225765e-03</td>\n", " <td>2.612883e-03</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>culture-recreation</td>\n", " <td>boardgames</td>\n", " <td>0.241009</td>\n", " <td>0.443478</td>\n", " <td>4.388575e-02</td>\n", " <td>2.194288e-02</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>culture-recreation</td>\n", " <td>chess</td>\n", " <td>-0.396123</td>\n", " <td>-0.333333</td>\n", " <td>1.635500e-02</td>\n", " <td>9.919184e-01</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>life-arts</td>\n", " <td>cooking</td>\n", " <td>0.110438</td>\n", " <td>0.300000</td>\n", " <td>2.232337e-03</td>\n", " <td>1.116169e-03</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>culture-recreation</td>\n", " <td>english</td>\n", " <td>0.293234</td>\n", " <td>0.038462</td>\n", " <td>5.897432e-03</td>\n", " <td>2.185860e-03</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>technology</td>\n", " <td>gis</td>\n", " <td>0.097155</td>\n", " <td>0.166667</td>\n", " <td>4.886036e-02</td>\n", " <td>2.443018e-02</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>science</td>\n", " <td>math</td>\n", " <td>0.189950</td>\n", " <td>0.100000</td>\n", " <td>1.680208e-07</td>\n", " <td>7.121923e-08</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>life-arts</td>\n", " <td>movies</td>\n", " <td>-0.268306</td>\n", " <td>-0.078788</td>\n", " <td>3.227311e-02</td>\n", " <td>9.838916e-01</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>life-arts</td>\n", " <td>parenting</td>\n", " <td>0.277329</td>\n", " <td>0.352941</td>\n", " <td>9.921431e-03</td>\n", " <td>4.960716e-03</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>technology</td>\n", " <td>sharepoint</td>\n", " <td>0.305315</td>\n", " <td>0.173333</td>\n", " <td>8.171926e-03</td>\n", " <td>4.085963e-03</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>technology</td>\n", " <td>stackoverflow</td>\n", " <td>0.143532</td>\n", " <td>0.145274</td>\n", " <td>7.142909e-35</td>\n", " <td>2.872172e-35</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>science</td>\n", " <td>stats</td>\n", " <td>0.151898</td>\n", " <td>0.172840</td>\n", " <td>1.712220e-02</td>\n", " <td>8.561101e-03</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>technology</td>\n", " <td>wordpress</td>\n", " <td>0.131091</td>\n", " <td>0.229710</td>\n", " <td>1.671671e-02</td>\n", " <td>8.358355e-03</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " category community frequency_difference_mean \\\n", "0 technology askubuntu 0.109569 \n", "1 technology blender 0.578160 \n", "2 culture-recreation boardgames 0.241009 \n", "3 culture-recreation chess -0.396123 \n", "4 life-arts cooking 0.110438 \n", "5 culture-recreation english 0.293234 \n", "6 technology gis 0.097155 \n", "7 science math 0.189950 \n", "8 life-arts movies -0.268306 \n", "9 life-arts parenting 0.277329 \n", "10 technology sharepoint 0.305315 \n", "11 technology stackoverflow 0.143532 \n", "12 science stats 0.151898 \n", "13 technology wordpress 0.131091 \n", "\n", " frequency_difference_median frequency_pvalue frequency_pvalue_greater \n", "0 0.233333 1.148150e-03 3.318362e-04 \n", "1 0.833333 5.225765e-03 2.612883e-03 \n", "2 0.443478 4.388575e-02 2.194288e-02 \n", "3 -0.333333 1.635500e-02 9.919184e-01 \n", "4 0.300000 2.232337e-03 1.116169e-03 \n", "5 0.038462 5.897432e-03 2.185860e-03 \n", "6 0.166667 4.886036e-02 2.443018e-02 \n", "7 0.100000 1.680208e-07 7.121923e-08 \n", "8 -0.078788 3.227311e-02 9.838916e-01 \n", "9 0.352941 9.921431e-03 4.960716e-03 \n", "10 0.173333 8.171926e-03 4.085963e-03 \n", "11 0.145274 7.142909e-35 2.872172e-35 \n", "12 0.172840 1.712220e-02 8.561101e-03 \n", "13 0.229710 1.671671e-02 8.358355e-03 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

brunez/dl_workshop_upm

notebooks/ann_xor.ipynb

1

4687

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Learning XOR\n", "This notebook demonstrates a fundamental motivation of representation learning for machine learning: the XOR function.\n", "\n", "Learning the XOR function is impossible for a separating-hyperplane based classifier, unless an alternative \n", "representation is employed. Neural networks can be viewed as representation learners, and can therefore learn the\n", "XOR function (if the architecture is adequate).\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%matplotlib notebook\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Build data set (XOR truth table)\n", "X = np.array([[0,0],[0,1],[1,0],[1,1]])\n", "y = np.array([0,1,1,0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Build a network with w layers: the first one with two neurons, the second with one\n", "# Observe that each of these neurons individually looks just like a logistic regression model\n", "from keras.models import Sequential\n", "from keras.layers import Dense, Activation\n", "\n", "model = Sequential()\n", "\n", "model.add(Dense(units=2, input_dim=2,kernel_initializer='random_uniform'))\n", "model.add(Activation('sigmoid'))\n", "model.add(Dense(units=1))\n", "model.add(Activation('sigmoid'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Keras models need to be compiled once they have been defined\n", "# Here we determine the loss function, the optimization algorithm and the\n", "# metrics to be computed during optimization\n", "model.compile(loss='binary_crossentropy',\n", " optimizer='adam',\n", " metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# This call runs the optimization algorithm, as many times as specified by the \"epochs\" arguments\n", "# Generally, this network will have a hard time learning the XOR function, even though it can. This illustrates\n", "# a common problem with neural networks: they are difficult to train\n", "model.fit(X, y, epochs=10, verbose=1, batch_size=4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# This call applies the function learned by the network to the input data\n", "model.predict(X)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# We can print the parameters learned by the network\n", "weights = model.get_weights()\n", "weights" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# We can also modify them by hand. \n", "# By formalizing the problem we can derive a set of inequalities that lead to suitable weights (try it at home!)\n", "def sigmoid(x):\n", " return 1/(1+np.exp(-x))\n", "\n", "# Given a set of weights for the first layer and one weight for the second, \n", "# this gives us a range in which we can look for the remaining one\n", "for a in range(3):\n", " print sigmoid(a)/sigmoid(2*a)\n", "\n", "# These ones are correct for the XOR problem\n", "weights[0][0][0]=1\n", "weights[0][0][1]=2\n", "weights[0][1][0]=1\n", "weights[0][1][1]=2\n", "weights[1][0]=0\n", "weights[1][1]=0\n", "weights[2][0]=-1\n", "weights[2][1]=0.85\n", "weights[3] = [0]\n", "model.set_weights(weights)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

unlicense

neurohackweek/nhw2017

code/.ipynb_checkpoints/github-users-checkpoint.ipynb

1

1596

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import github\n", "\n", "user = \"arokem\"\n", "password = \"$Cis3SH2$\"\n", "\n", "from github import Github\n", "g = Github(user, password)\n", "\n", "org = g.get_organization('neurohackweek')\n", "\n", "user_names = [m.html_url.split('/')[-1] for m in list(org.get_members())]\n", "\n", "user_names\n", "\n", "import pandas as pd\n", "\n", "participants = pd.read_csv('/Users/arokem/Downloads/Participants - Sheet1.csv')\n", "\n", "for t in org.get_teams():\n", " if t.name == \"NHW2016\":\n", " for member_name in participants['GitHub username']:\n", " if not pd.isnull(member_name) and not t.has_in_members(g.get_user(member_name)):\n", " print(\"Adding %s\"%member_name)\n", " t.add_membership(g.get_user(member_name))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

agrc/Presentations

UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb

2

79952

{ "cells": [ { "cell_type": "markdown", "id": "3f8106d9", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Ditch the Cursor\n", "\n", "\n", "\n", "## Editing Feature Classes with Spatialy-Enabled DataFrames" ] }, { "cell_type": "markdown", "id": "8e86d377", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# ArcPy Is Great, But...\n", "\n", "" ] }, { "cell_type": "markdown", "id": "b2a3f3ff", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Problem one: `row[0]`" ] }, { "cell_type": "markdown", "id": "50f7b5d1", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "```python\n", "def _update_year_built(layer, year_fields):\n", " with arcpy.da.UpdateCursor(layer, year_fields) as cursor:\n", " for row in cursor:\n", " if row[0] is None or row[0] < 1 or row[0] == '':\n", " row[0] = f'{row[1]}_{row[2]}' \n", "\n", " cursor.updateRow(row)\n", "```" ] }, { "cell_type": "markdown", "id": "3f088219", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "```python\n", "# If new parcels' owner/owner addr changed, or if PID is new, add to appropriate lists\n", "with arcpy.da.SearchCursor(tville_parcels_fc, parcel_check_fields) as new_cursor:\n", " for row in new_cursor:\n", " if row[0] in old_parcels:\n", " old_name = old_parcels[row[0]][0]\n", " old_addr = old_parcels[row[0]][1]\n", " if row[1] != old_name or row[2] != old_addr:\n", " own_addr_changed.append(row[0])\n", " else:\n", " new_parcels.append(row[0])\n", "```" ] }, { "cell_type": "markdown", "id": "88b36a2b", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Problem 2: Nested cursors to transfer data between feature classes" ] }, { "cell_type": "markdown", "id": "895b0177", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "```python\n", "with arcpy.da.SearchCursor(new_data_fc, fields) as new_data_cursor, \\\n", " arcpy.da.InsertCursor(current_data_fc, fields) as current_data_cursor:\n", " for row in new_data_cursor:\n", " current_data_cursor.insertRow(row)\n", " copied_records += 1\n", "```" ] }, { "cell_type": "markdown", "id": "b1550ac6", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Problem 3: Renaming/reordering fields" ] }, { "cell_type": "markdown", "id": "b0c63f81", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "```python\n", "fieldmappings = arcpy.FieldMappings()\n", "\n", "fieldmappings.addTable(energov_parcels_fc)\n", "fieldmappings.addTable(tville_parcels_fc)\n", "\n", "fields_list = [\n", " ('PIN', 'parcel_id'),\n", " ('own_cityst', 'own_citystate'),\n", " ('own_zip_fo', 'own_zip_four'),\n", " ('prop_locat', 'prop_location'),\n", " ('property_t', 'property_type'),\n", " ('neighborho', 'neighborhood_code'),\n", " ('adjusted_p', 'adjusted_prcl_total'),\n", " #: ...\n", "]\n", "\n", "for field_map in fields_list:\n", " field_to_map_index = fieldmappings.findFieldMapIndex(field_map[0])\n", " field_to_map = fieldmappings.getFieldMap(field_to_map_index)\n", " field_to_map.addInputField(tville_parcels_fc, field_map[1])\n", " fieldmappings.replaceFieldMap(field_to_map_index, field_to_map)\n", "```" ] }, { "cell_type": "markdown", "id": "0ef28e46", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Problem 4: Intermediate feature classes" ] }, { "cell_type": "markdown", "id": "99f85b9b", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "```python\n", "ssa_summarized_roads = fr'{output_gdb}\\ssa_bike_lanes_roads'\n", "ssa_summarized_paths = fr'{output_gdb}\\ssa_bike_lanes_paths'\n", "ssa_summarized_lengths = fr'{output_gdb}b\\SmallStatisticalAreas_2018_bike_lane_lengths'\n", "tract_summarized_roads = fr'{output_gdb}\\tract_bike_lanes_roads'\n", "tract_summarized_paths = fr'{output_gdb}\\tract_bike_lanes_paths'\n", "tract_summarized_lengths = fr'{output_gdb}\\census_tracts_2020_bike_lane_lengths'\n", "buffered_tracts = fr'{output_gdb}\\census_tracts_2020_buffered_30ft'\n", "buffered_areas = fr'{output_gdb}\\small_areas_buffered_200ft'\n", "bike_lanes = fr'{output_gdb}\\bike_lanes_20220111'\n", "major_paths = fr'{output_gdb}\\major_paths'\n", "```" ] }, { "cell_type": "markdown", "id": "db3694ab", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Enter the Pandas!\n", "\n", "" ] }, { "cell_type": "markdown", "id": "7ed2ef5b", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "pandas gives you the tools to work with tables of data defined by rows and columns, called a DataFrame" ] }, { "cell_type": "code", "execution_count": 17, "id": "f3b6cd7c", "metadata": { "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>County</th>\n", " <th>Median_age</th>\n", " <th>educationHighSchoolGraduate</th>\n", " <th>educationBachelorOrGreater</th>\n", " <th>Median HH income (dollars)</th>\n", " <th>Mean HH income (dollars)</th>\n", " <th>Mean Per Capita income</th>\n", " <th>Avg_MonthlyIncome</th>\n", " <th>TI_1</th>\n", " <th>TI_2</th>\n", " <th>TI_3</th>\n", " <th>TI_4</th>\n", " <th>TI_5</th>\n", " <th>TI_6</th>\n", " <th>TI_7</th>\n", " <th>TI_8</th>\n", " <th>TI_9</th>\n", " <th>TI_10</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>BEAVER</td>\n", " <td>33.4</td>\n", " <td>90.3</td>\n", " <td>20.4</td>\n", " <td>54,212</td>\n", " <td>64,003</td>\n", " <td>22,558</td>\n", " <td>2805</td>\n", " <td>Gasoline stations</td>\n", " <td>Elementary and secondary schools</td>\n", " <td>Traveler accommodation</td>\n", " <td>Executive, legislative and general government</td>\n", " <td>Cattle ranching and farming</td>\n", " <td>Building equipment contractors</td>\n", " <td>Offices of physicians</td>\n", " <td>Residential building construction</td>\n", " <td>Depository credit intermediation</td>\n", " <td>Other crop farming</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>BOX ELDER</td>\n", " <td>32.5</td>\n", " <td>93.2</td>\n", " <td>23.3</td>\n", " <td>59,937</td>\n", " <td>73,085</td>\n", " <td>23,998</td>\n", " <td>3344</td>\n", " <td>Restaurants and other eating places</td>\n", " <td>Architectural and structural metals mfg.</td>\n", " <td>General freight trucking</td>\n", " <td>Building foundation and exterior contractors</td>\n", " <td>Utility system construction</td>\n", " <td>Justice, public order, and safety activities</td>\n", " <td>Grain and oilseed milling</td>\n", " <td>Gasoline stations</td>\n", " <td>Automobile dealers</td>\n", " <td>Offices of physicians</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>CACHE</td>\n", " <td>25.1</td>\n", " <td>93.1</td>\n", " <td>37.8</td>\n", " <td>56,840</td>\n", " <td>72,148</td>\n", " <td>22,666</td>\n", " <td>3049</td>\n", " <td>Restaurants and other eating places</td>\n", " <td>Elementary and secondary schools</td>\n", " <td>Dairy product manufacturing</td>\n", " <td>Accounting and bookkeeping services</td>\n", " <td>Other miscellaneous manufacturing</td>\n", " <td>General merchandise stores, including warehous...</td>\n", " <td>Offices of physicians</td>\n", " <td>Scientific research and development services</td>\n", " <td>Grocery stores</td>\n", " <td>Electronic instrument manufacturing</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>CARBON</td>\n", " <td>37.2</td>\n", " <td>90.8</td>\n", " <td>16.4</td>\n", " <td>50,278</td>\n", " <td>60,125</td>\n", " <td>23,473</td>\n", " <td>3436</td>\n", " <td>Justice, public order, and safety activities</td>\n", " <td>Machinery and supply merchant wholesalers</td>\n", " <td>Executive, legislative and general government</td>\n", " <td>Commercial machinery repair and maintenance</td>\n", " <td>Offices of physicians</td>\n", " <td>Gasoline stations</td>\n", " <td>Building equipment contractors</td>\n", " <td>Traveler accommodation</td>\n", " <td>Offices of dentists</td>\n", " <td>Home health care services</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>DAGGET</td>\n", " <td>46.1</td>\n", " <td>96.2</td>\n", " <td>12.4</td>\n", " <td>81,250</td>\n", " <td>78,210</td>\n", " <td>27,698</td>\n", " <td>2252</td>\n", " <td>Elementary and secondary schools</td>\n", " <td>Administration of environmental programs</td>\n", " <td>Postal service</td>\n", " <td>Support activities for mining</td>\n", " <td>Offices of real estate agents and brokers</td>\n", " <td>Justice, public order, and safety activities</td>\n", " <td>Executive, legislative and general government</td>\n", " <td>Personal care services</td>\n", " <td>Commercial machinery repair and maintenance</td>\n", " <td>Special food services</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " County Median_age educationHighSchoolGraduate \\\n", "0 BEAVER 33.4 90.3 \n", "1 BOX ELDER 32.5 93.2 \n", "2 CACHE 25.1 93.1 \n", "3 CARBON 37.2 90.8 \n", "4 DAGGET 46.1 96.2 \n", "\n", " educationBachelorOrGreater Median HH income (dollars) \\\n", "0 20.4 54,212 \n", "1 23.3 59,937 \n", "2 37.8 56,840 \n", "3 16.4 50,278 \n", "4 12.4 81,250 \n", "\n", " Mean HH income (dollars) Mean Per Capita income Avg_MonthlyIncome \\\n", "0 64,003 22,558 2805 \n", "1 73,085 23,998 3344 \n", "2 72,148 22,666 3049 \n", "3 60,125 23,473 3436 \n", "4 78,210 27,698 2252 \n", "\n", " TI_1 \\\n", "0 Gasoline stations \n", "1 Restaurants and other eating places \n", "2 Restaurants and other eating places \n", "3 Justice, public order, and safety activities \n", "4 Elementary and secondary schools \n", "\n", " TI_2 \\\n", "0 Elementary and secondary schools \n", "1 Architectural and structural metals mfg. \n", "2 Elementary and secondary schools \n", "3 Machinery and supply merchant wholesalers \n", "4 Administration of environmental programs \n", "\n", " TI_3 \\\n", "0 Traveler accommodation \n", "1 General freight trucking \n", "2 Dairy product manufacturing \n", "3 Executive, legislative and general government \n", "4 Postal service \n", "\n", " TI_4 \\\n", "0 Executive, legislative and general government \n", "1 Building foundation and exterior contractors \n", "2 Accounting and bookkeeping services \n", "3 Commercial machinery repair and maintenance \n", "4 Support activities for mining \n", "\n", " TI_5 \\\n", "0 Cattle ranching and farming \n", "1 Utility system construction \n", "2 Other miscellaneous manufacturing \n", "3 Offices of physicians \n", "4 Offices of real estate agents and brokers \n", "\n", " TI_6 \\\n", "0 Building equipment contractors \n", "1 Justice, public order, and safety activities \n", "2 General merchandise stores, including warehous... \n", "3 Gasoline stations \n", "4 Justice, public order, and safety activities \n", "\n", " TI_7 \\\n", "0 Offices of physicians \n", "1 Grain and oilseed milling \n", "2 Offices of physicians \n", "3 Building equipment contractors \n", "4 Executive, legislative and general government \n", "\n", " TI_8 \\\n", "0 Residential building construction \n", "1 Gasoline stations \n", "2 Scientific research and development services \n", "3 Traveler accommodation \n", "4 Personal care services \n", "\n", " TI_9 \\\n", "0 Depository credit intermediation \n", "1 Automobile dealers \n", "2 Grocery stores \n", "3 Offices of dentists \n", "4 Commercial machinery repair and maintenance \n", "\n", " TI_10 \n", "0 Other crop farming \n", "1 Offices of physicians \n", "2 Electronic instrument manufacturing \n", "3 Home health care services \n", "4 Special food services " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "medians_df = pd.read_csv('assets/median_age.csv')\n", "medians_df.head()" ] }, { "cell_type": "markdown", "id": "105dcc5e", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We can access individual rows and columns using `.loc` (with index labels) or `.iloc` (with indices)" ] }, { "cell_type": "markdown", "id": "14a21d61", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "```python\n", "medians_df.loc[row labels, column labels]\n", "medians_df.iloc[row indices, column indices]\n", "```" ] }, { "cell_type": "code", "execution_count": 18, "id": "3d95836f", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "0 BEAVER\n", "1 BOX ELDER\n", "2 CACHE\n", "5 DAVIS\n", "Name: County, dtype: object" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "medians_df.loc[[0, 1, 2, 5], 'County']" ] }, { "cell_type": "code", "execution_count": 19, "id": "7aaeb3cc", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>County</th>\n", " <th>Median_age</th>\n", " <th>educationHighSchoolGraduate</th>\n", " <th>educationBachelorOrGreater</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>10</th>\n", " <td>IRON</td>\n", " <td>29.1</td>\n", " <td>92.8</td>\n", " <td>29.1</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>JUAB</td>\n", " <td>30.4</td>\n", " <td>92.2</td>\n", " <td>17.1</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>KANE</td>\n", " <td>41.7</td>\n", " <td>92.8</td>\n", " <td>27.3</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>MILLARD</td>\n", " <td>35.8</td>\n", " <td>88.7</td>\n", " <td>22.4</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>MORGAN</td>\n", " <td>32.4</td>\n", " <td>97.6</td>\n", " <td>39.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " County Median_age educationHighSchoolGraduate \\\n", "10 IRON 29.1 92.8 \n", "11 JUAB 30.4 92.2 \n", "12 KANE 41.7 92.8 \n", "13 MILLARD 35.8 88.7 \n", "14 MORGAN 32.4 97.6 \n", "\n", " educationBachelorOrGreater \n", "10 29.1 \n", "11 17.1 \n", "12 27.3 \n", "13 22.4 \n", "14 39.5 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "medians_df.iloc[10:15, :4]" ] }, { "cell_type": "markdown", "id": "08bf06cf", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We can also get just a few columns from all rows" ] }, { "cell_type": "code", "execution_count": 20, "id": "aed3cb72", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Median_age</th>\n", " <th>Avg_MonthlyIncome</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>33.4</td>\n", " <td>2805</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>32.5</td>\n", " <td>3344</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>25.1</td>\n", " <td>3049</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>37.2</td>\n", " <td>3436</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>46.1</td>\n", " <td>2252</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Median_age Avg_MonthlyIncome\n", "0 33.4 2805\n", "1 32.5 3344\n", "2 25.1 3049\n", "3 37.2 3436\n", "4 46.1 2252" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "medians_df[['Median_age', 'Avg_MonthlyIncome']].head()" ] }, { "cell_type": "markdown", "id": "1df6333e", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Extending pandas Spatially\n", "\n", "The ArcGIS API for Python provides Spatially Enabled DataFrames, which include geometry information.\n", "\n", "" ] }, { "cell_type": "code", "execution_count": null, "id": "a979f723", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 21, "id": "f5f680c2", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>xid</th>\n", " <th>countynbr</th>\n", " <th>entitynbr</th>\n", " <th>entityyr</th>\n", " <th>name</th>\n", " <th>fips</th>\n", " <th>stateplane</th>\n", " <th>pop_lastcensus</th>\n", " <th>pop_currestimate</th>\n", " <th>globalid</th>\n", " <th>fips_str</th>\n", " <th>color4</th>\n", " <th>SHAPE</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>20</td>\n", " <td>2.010201e+09</td>\n", " <td>2010.0</td>\n", " <td>SANPETE</td>\n", " <td>39.0</td>\n", " <td>Central</td>\n", " <td>28437</td>\n", " <td>None</td>\n", " <td>02C0C074-657F-44AE-A886-44ADB97263BD</td>\n", " <td>49039</td>\n", " <td>2</td>\n", " <td>{\"rings\": [[[448347.6200000001, 4407163.6], [4...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>11</td>\n", " <td>2.010111e+09</td>\n", " <td>2010.0</td>\n", " <td>IRON</td>\n", " <td>21.0</td>\n", " <td>South</td>\n", " <td>57289</td>\n", " <td>None</td>\n", " <td>2ACA2EB9-31B5-4D6A-A858-A0F6B16C64E2</td>\n", " <td>49021</td>\n", " <td>3</td>\n", " <td>{\"rings\": [[[292688.5800000001, 4224956.960000...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>13</td>\n", " <td>2.010131e+09</td>\n", " <td>2010.0</td>\n", " <td>KANE</td>\n", " <td>25.0</td>\n", " <td>South</td>\n", " <td>7667</td>\n", " <td>None</td>\n", " <td>A250C849-8914-4E00-A80F-C1B45CD8F0A7</td>\n", " <td>49025</td>\n", " <td>4</td>\n", " <td>{\"rings\": [[[425313.88999999966, 4154648.07000...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>29</td>\n", " <td>2.010291e+09</td>\n", " <td>2010.0</td>\n", " <td>WEBER</td>\n", " <td>57.0</td>\n", " <td>North</td>\n", " <td>262223</td>\n", " <td>None</td>\n", " <td>1757A80B-1895-4975-81DA-3AD2EBBA64C9</td>\n", " <td>49057</td>\n", " <td>1</td>\n", " <td>{\"rings\": [[[422712.16000000015, 4554559.60999...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>19</td>\n", " <td>2.006191e+09</td>\n", " <td>2006.0</td>\n", " <td>SAN JUAN</td>\n", " <td>37.0</td>\n", " <td>South</td>\n", " <td>14518</td>\n", " <td>None</td>\n", " <td>EC858EAC-D7E7-4748-B7A5-B2B744668178</td>\n", " <td>49037</td>\n", " <td>3</td>\n", " <td>{\"rings\": [[[609343.4100000001, 4095382.08], [...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " xid countynbr entitynbr entityyr name fips stateplane \\\n", "0 1 20 2.010201e+09 2010.0 SANPETE 39.0 Central \n", "1 2 11 2.010111e+09 2010.0 IRON 21.0 South \n", "2 3 13 2.010131e+09 2010.0 KANE 25.0 South \n", "3 4 29 2.010291e+09 2010.0 WEBER 57.0 North \n", "4 5 19 2.006191e+09 2006.0 SAN JUAN 37.0 South \n", "\n", " pop_lastcensus pop_currestimate globalid \\\n", "0 28437 None 02C0C074-657F-44AE-A886-44ADB97263BD \n", "1 57289 None 2ACA2EB9-31B5-4D6A-A858-A0F6B16C64E2 \n", "2 7667 None A250C849-8914-4E00-A80F-C1B45CD8F0A7 \n", "3 262223 None 1757A80B-1895-4975-81DA-3AD2EBBA64C9 \n", "4 14518 None EC858EAC-D7E7-4748-B7A5-B2B744668178 \n", "\n", " fips_str color4 SHAPE \n", "0 49039 2 {\"rings\": [[[448347.6200000001, 4407163.6], [4... \n", "1 49021 3 {\"rings\": [[[292688.5800000001, 4224956.960000... \n", "2 49025 4 {\"rings\": [[[425313.88999999966, 4154648.07000... \n", "3 49057 1 {\"rings\": [[[422712.16000000015, 4554559.60999... \n", "4 49037 3 {\"rings\": [[[609343.4100000001, 4095382.08], [... " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from arcgis.features import GeoAccessor, GeoSeriesAccessor\n", "\n", "counties_fc_path = r'C:\\Users\\jdadams\\AppData\\Roaming\\Esri\\ArcGISPro\\Favorites\\opensgid.agrc.utah.gov.sde\\opensgid.boundaries.county_boundaries'\n", "counties_df = pd.DataFrame.spatial.from_featureclass(counties_fc_path)\n", "counties_df.head()" ] }, { "cell_type": "markdown", "id": "c8a679d6", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "pandas lets you work on rows that meet a certain condition" ] }, { "cell_type": "code", "execution_count": 22, "id": "dacfef17", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>stateplane</th>\n", " <th>fips_str</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>SANPETE</td>\n", " <td>Central</td>\n", " <td>49039</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>TOOELE</td>\n", " <td>Central</td>\n", " <td>49045</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>UINTAH</td>\n", " <td>Central</td>\n", " <td>49047</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>GRAND</td>\n", " <td>Central</td>\n", " <td>49019</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>MILLARD</td>\n", " <td>Central</td>\n", " <td>49027</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>WASATCH</td>\n", " <td>Central</td>\n", " <td>49051</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>JUAB</td>\n", " <td>Central</td>\n", " <td>49023</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>UTAH</td>\n", " <td>Central</td>\n", " <td>49049</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>DUCHESNE</td>\n", " <td>Central</td>\n", " <td>49013</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>EMERY</td>\n", " <td>Central</td>\n", " <td>49015</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>SEVIER</td>\n", " <td>Central</td>\n", " <td>49041</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>CARBON</td>\n", " <td>Central</td>\n", " <td>49007</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>SALT LAKE</td>\n", " <td>Central</td>\n", " <td>49035</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name stateplane fips_str\n", "0 SANPETE Central 49039\n", "8 TOOELE Central 49045\n", "12 UINTAH Central 49047\n", "13 GRAND Central 49019\n", "15 MILLARD Central 49027\n", "16 WASATCH Central 49051\n", "17 JUAB Central 49023\n", "18 UTAH Central 49049\n", "19 DUCHESNE Central 49013\n", "25 EMERY Central 49015\n", "26 SEVIER Central 49041\n", "27 CARBON Central 49007\n", "28 SALT LAKE Central 49035" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counties_df.loc[counties_df['stateplane'] == 'Central', ['name', 'stateplane', 'fips_str']]" ] }, { "cell_type": "markdown", "id": "75d427ff", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "You can easily add new columns" ] }, { "cell_type": "code", "execution_count": 23, "id": "8e23f233", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>xid</th>\n", " <th>countynbr</th>\n", " <th>entitynbr</th>\n", " <th>entityyr</th>\n", " <th>name</th>\n", " <th>fips</th>\n", " <th>stateplane</th>\n", " <th>pop_lastcensus</th>\n", " <th>pop_currestimate</th>\n", " <th>globalid</th>\n", " <th>fips_str</th>\n", " <th>color4</th>\n", " <th>SHAPE</th>\n", " <th>emperor</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>20</td>\n", " <td>2.010201e+09</td>\n", " <td>2010.0</td>\n", " <td>SANPETE</td>\n", " <td>39.0</td>\n", " <td>Central</td>\n", " <td>28437</td>\n", " <td>None</td>\n", " <td>02C0C074-657F-44AE-A886-44ADB97263BD</td>\n", " <td>49039</td>\n", " <td>2</td>\n", " <td>{\"rings\": [[[448347.6200000001, 4407163.6], [4...</td>\n", " <td>Jake</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>11</td>\n", " <td>2.010111e+09</td>\n", " <td>2010.0</td>\n", " <td>IRON</td>\n", " <td>21.0</td>\n", " <td>South</td>\n", " <td>57289</td>\n", " <td>None</td>\n", " <td>2ACA2EB9-31B5-4D6A-A858-A0F6B16C64E2</td>\n", " <td>49021</td>\n", " <td>3</td>\n", " <td>{\"rings\": [[[292688.5800000001, 4224956.960000...</td>\n", " <td>Jake</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>13</td>\n", " <td>2.010131e+09</td>\n", " <td>2010.0</td>\n", " <td>KANE</td>\n", " <td>25.0</td>\n", " <td>South</td>\n", " <td>7667</td>\n", " <td>None</td>\n", " <td>A250C849-8914-4E00-A80F-C1B45CD8F0A7</td>\n", " <td>49025</td>\n", " <td>4</td>\n", " <td>{\"rings\": [[[425313.88999999966, 4154648.07000...</td>\n", " <td>Jake</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>29</td>\n", " <td>2.010291e+09</td>\n", " <td>2010.0</td>\n", " <td>WEBER</td>\n", " <td>57.0</td>\n", " <td>North</td>\n", " <td>262223</td>\n", " <td>None</td>\n", " <td>1757A80B-1895-4975-81DA-3AD2EBBA64C9</td>\n", " <td>49057</td>\n", " <td>1</td>\n", " <td>{\"rings\": [[[422712.16000000015, 4554559.60999...</td>\n", " <td>Jake</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>19</td>\n", " <td>2.006191e+09</td>\n", " <td>2006.0</td>\n", " <td>SAN JUAN</td>\n", " <td>37.0</td>\n", " <td>South</td>\n", " <td>14518</td>\n", " <td>None</td>\n", " <td>EC858EAC-D7E7-4748-B7A5-B2B744668178</td>\n", " <td>49037</td>\n", " <td>3</td>\n", " <td>{\"rings\": [[[609343.4100000001, 4095382.08], [...</td>\n", " <td>Jake</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " xid countynbr entitynbr entityyr name fips stateplane \\\n", "0 1 20 2.010201e+09 2010.0 SANPETE 39.0 Central \n", "1 2 11 2.010111e+09 2010.0 IRON 21.0 South \n", "2 3 13 2.010131e+09 2010.0 KANE 25.0 South \n", "3 4 29 2.010291e+09 2010.0 WEBER 57.0 North \n", "4 5 19 2.006191e+09 2006.0 SAN JUAN 37.0 South \n", "\n", " pop_lastcensus pop_currestimate globalid \\\n", "0 28437 None 02C0C074-657F-44AE-A886-44ADB97263BD \n", "1 57289 None 2ACA2EB9-31B5-4D6A-A858-A0F6B16C64E2 \n", "2 7667 None A250C849-8914-4E00-A80F-C1B45CD8F0A7 \n", "3 262223 None 1757A80B-1895-4975-81DA-3AD2EBBA64C9 \n", "4 14518 None EC858EAC-D7E7-4748-B7A5-B2B744668178 \n", "\n", " fips_str color4 SHAPE emperor \n", "0 49039 2 {\"rings\": [[[448347.6200000001, 4407163.6], [4... Jake \n", "1 49021 3 {\"rings\": [[[292688.5800000001, 4224956.960000... Jake \n", "2 49025 4 {\"rings\": [[[425313.88999999966, 4154648.07000... Jake \n", "3 49057 1 {\"rings\": [[[422712.16000000015, 4554559.60999... Jake \n", "4 49037 3 {\"rings\": [[[609343.4100000001, 4095382.08], [... Jake " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counties_df['emperor'] = 'Jake'\n", "counties_df.head()" ] }, { "cell_type": "markdown", "id": "2c9e843d", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "pandas provides powerful built in grouping and aggregation tools, along with Spatially Enabled DataFrames' geometry operations" ] }, { "cell_type": "code", "execution_count": 24, "id": "fd25463d", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>xid</th>\n", " <th>countynbr</th>\n", " <th>entitynbr</th>\n", " <th>entityyr</th>\n", " <th>name</th>\n", " <th>fips</th>\n", " <th>pop_lastcensus</th>\n", " <th>pop_currestimate</th>\n", " <th>globalid</th>\n", " <th>fips_str</th>\n", " <th>color4</th>\n", " <th>SHAPE</th>\n", " <th>emperor</th>\n", " </tr>\n", " <tr>\n", " <th>stateplane</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Central</th>\n", " <td>13</td>\n", " <td>13</td>\n", " <td>13</td>\n", " <td>13</td>\n", " <td>13</td>\n", " <td>13</td>\n", " <td>13</td>\n", " <td>0</td>\n", " <td>13</td>\n", " <td>13</td>\n", " <td>13</td>\n", " <td>13</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>North</th>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>South</th>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>8</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " xid countynbr entitynbr entityyr name fips pop_lastcensus \\\n", "stateplane \n", "Central 13 13 13 13 13 13 13 \n", "North 8 8 8 8 8 8 8 \n", "South 8 8 8 8 8 8 8 \n", "\n", " pop_currestimate globalid fips_str color4 SHAPE emperor \n", "stateplane \n", "Central 0 13 13 13 13 13 \n", "North 0 8 8 8 8 8 \n", "South 0 8 8 8 8 8 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counties_df.groupby('stateplane').count()" ] }, { "cell_type": "code", "execution_count": 25, "id": "55e46ec6", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "stateplane\n", "Central 2.725686e+07\n", "North 8.633955e+06\n", "South 1.842508e+07\n", "Name: acres, dtype: float64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counties_df['acres'] = counties_df['SHAPE'].apply(lambda shape: shape.area / 4046.8564)\n", "counties_df.groupby('stateplane')['acres'].sum()" ] }, { "cell_type": "markdown", "id": "9529975c", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# pandas Solutions to our Arcpy Problems\n", "\n", "" ] }, { "cell_type": "markdown", "id": "877eab2b", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## `row[0]` Solution: Field Names" ] }, { "cell_type": "markdown", "id": "253dd74d", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "```python\n", "def update_unit_count(parcels_df):\n", " \"\"\"Update unit counts in-place for single family, duplex, and tri/quad\n", "\n", " Args:\n", " parcels_df (pd.DataFrame): The evaluated parcel dataset with UNIT_COUNT, HOUSE_CNT, SUBTYPE, and NOTE columns\n", " \"\"\"\n", "\n", " # fix single family (non-pud)\n", " zero_or_null_unit_counts = (parcels_df['UNIT_COUNT'] == 0) | (parcels_df['UNIT_COUNT'].isna())\n", " parcels_df.loc[(zero_or_null_unit_counts) & (parcels_df['SUBTYPE'] == 'single_family'), 'UNIT_COUNT'] = 1\n", "\n", " # fix duplex\n", " parcels_df.loc[(parcels_df['SUBTYPE'] == 'duplex'), 'UNIT_COUNT'] = 2\n", "\n", " # fix triplex-quadplex\n", " parcels_df.loc[(parcels_df['UNIT_COUNT'] < parcels_df['HOUSE_CNT']) & (parcels_df['NOTE'] == 'triplex-quadplex'),\n", " 'UNIT_COUNT'] = parcels_df['HOUSE_CNT']\n", "```" ] }, { "cell_type": "markdown", "id": "afa5b153", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Let's make Erik the emperor of the small counties that use State Plane North" ] }, { "cell_type": "code", "execution_count": 26, "id": "c30be000", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>pop_lastcensus</th>\n", " <th>stateplane</th>\n", " <th>emperor</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>9</th>\n", " <td>BEAVER</td>\n", " <td>7072</td>\n", " <td>South</td>\n", " <td>Jake</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>BOX ELDER</td>\n", " <td>57666</td>\n", " <td>North</td>\n", " <td>Erik</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>CACHE</td>\n", " <td>133154</td>\n", " <td>North</td>\n", " <td>Jake</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>CARBON</td>\n", " <td>20412</td>\n", " <td>Central</td>\n", " <td>Jake</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>DAGGETT</td>\n", " <td>935</td>\n", " <td>North</td>\n", " <td>Erik</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name pop_lastcensus stateplane emperor\n", "9 BEAVER 7072 South Jake\n", "10 BOX ELDER 57666 North Erik\n", "11 CACHE 133154 North Jake\n", "27 CARBON 20412 Central Jake\n", "20 DAGGETT 935 North Erik" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counties_df.loc[(counties_df['pop_lastcensus'] < 100000) & (counties_df['stateplane'] == 'North'), 'emperor'] = 'Erik'\n", "counties_df[['name', 'pop_lastcensus', 'stateplane', 'emperor']].sort_values('name').head()" ] }, { "cell_type": "markdown", "id": "a17431f5", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Nested Cursors Solution: Merged DataFrames" ] }, { "cell_type": "markdown", "id": "0f1294d9", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "```python\n", "def _get_current_attachment_info_by_oid(self, live_data_subset_df):\n", "\n", " #: Join live attachment table to feature layer info\n", " live_attachments_df = pd.DataFrame(self.feature_layer.attachments.search())\n", " live_attachments_subset_df = live_attachments_df.reindex(columns=['PARENTOBJECTID', 'NAME', 'ID'])\n", " merged_df = live_data_subset_df.merge(\n", " live_attachments_subset_df, left_on='OBJECTID', right_on='PARENTOBJECTID', how='left'\n", " )\n", "\n", " return merged_df\n", "```" ] }, { "cell_type": "markdown", "id": "dcff9435", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Let's add census data to our counties" ] }, { "cell_type": "code", "execution_count": null, "id": "c267ff2e", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 27, "id": "4fff8a4e", "metadata": { "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>xid</th>\n", " <th>countynbr</th>\n", " <th>entitynbr</th>\n", " <th>entityyr</th>\n", " <th>name</th>\n", " <th>fips</th>\n", " <th>stateplane</th>\n", " <th>pop_lastcensus</th>\n", " <th>pop_currestimate</th>\n", " <th>globalid</th>\n", " <th>fips_str</th>\n", " <th>color4</th>\n", " <th>SHAPE</th>\n", " <th>emperor</th>\n", " <th>acres</th>\n", " <th>geoid20</th>\n", " <th>aland20</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>20</td>\n", " <td>2.010201e+09</td>\n", " <td>2010.0</td>\n", " <td>SANPETE</td>\n", " <td>39.0</td>\n", " <td>Central</td>\n", " <td>28437</td>\n", " <td>None</td>\n", " <td>02C0C074-657F-44AE-A886-44ADB97263BD</td>\n", " <td>49039</td>\n", " <td>2</td>\n", " <td>{'rings': [[[448347.6200000001, 4407163.6], [4...</td>\n", " <td>Jake</td>\n", " <td>1.024680e+06</td>\n", " <td>49039</td>\n", " <td>4.117901e+09</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>11</td>\n", " <td>2.010111e+09</td>\n", " <td>2010.0</td>\n", " <td>IRON</td>\n", " <td>21.0</td>\n", " <td>South</td>\n", " <td>57289</td>\n", " <td>None</td>\n", " <td>2ACA2EB9-31B5-4D6A-A858-A0F6B16C64E2</td>\n", " <td>49021</td>\n", " <td>3</td>\n", " <td>{'rings': [[[292688.5800000001, 4224956.960000...</td>\n", " <td>Jake</td>\n", " <td>2.112861e+06</td>\n", " <td>49021</td>\n", " <td>8.537474e+09</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>13</td>\n", " <td>2.010131e+09</td>\n", " <td>2010.0</td>\n", " <td>KANE</td>\n", " <td>25.0</td>\n", " <td>South</td>\n", " <td>7667</td>\n", " <td>None</td>\n", " <td>A250C849-8914-4E00-A80F-C1B45CD8F0A7</td>\n", " <td>49025</td>\n", " <td>4</td>\n", " <td>{'rings': [[[425313.88999999966, 4154648.07000...</td>\n", " <td>Jake</td>\n", " <td>2.627117e+06</td>\n", " <td>49025</td>\n", " <td>1.033391e+10</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>29</td>\n", " <td>2.010291e+09</td>\n", " <td>2010.0</td>\n", " <td>WEBER</td>\n", " <td>57.0</td>\n", " <td>North</td>\n", " <td>262223</td>\n", " <td>None</td>\n", " <td>1757A80B-1895-4975-81DA-3AD2EBBA64C9</td>\n", " <td>49057</td>\n", " <td>1</td>\n", " <td>{'rings': [[[422712.16000000015, 4554559.60999...</td>\n", " <td>Jake</td>\n", " <td>4.220563e+05</td>\n", " <td>49057</td>\n", " <td>1.492537e+09</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>19</td>\n", " <td>2.006191e+09</td>\n", " <td>2006.0</td>\n", " <td>SAN JUAN</td>\n", " <td>37.0</td>\n", " <td>South</td>\n", " <td>14518</td>\n", " <td>None</td>\n", " <td>EC858EAC-D7E7-4748-B7A5-B2B744668178</td>\n", " <td>49037</td>\n", " <td>3</td>\n", " <td>{'rings': [[[609343.4100000001, 4095382.08], [...</td>\n", " <td>Jake</td>\n", " <td>5.075154e+06</td>\n", " <td>49037</td>\n", " <td>2.025317e+10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " xid countynbr entitynbr entityyr name fips stateplane \\\n", "0 1 20 2.010201e+09 2010.0 SANPETE 39.0 Central \n", "1 2 11 2.010111e+09 2010.0 IRON 21.0 South \n", "2 3 13 2.010131e+09 2010.0 KANE 25.0 South \n", "3 4 29 2.010291e+09 2010.0 WEBER 57.0 North \n", "4 5 19 2.006191e+09 2006.0 SAN JUAN 37.0 South \n", "\n", " pop_lastcensus pop_currestimate globalid \\\n", "0 28437 None 02C0C074-657F-44AE-A886-44ADB97263BD \n", "1 57289 None 2ACA2EB9-31B5-4D6A-A858-A0F6B16C64E2 \n", "2 7667 None A250C849-8914-4E00-A80F-C1B45CD8F0A7 \n", "3 262223 None 1757A80B-1895-4975-81DA-3AD2EBBA64C9 \n", "4 14518 None EC858EAC-D7E7-4748-B7A5-B2B744668178 \n", "\n", " fips_str color4 SHAPE emperor \\\n", "0 49039 2 {'rings': [[[448347.6200000001, 4407163.6], [4... Jake \n", "1 49021 3 {'rings': [[[292688.5800000001, 4224956.960000... Jake \n", "2 49025 4 {'rings': [[[425313.88999999966, 4154648.07000... Jake \n", "3 49057 1 {'rings': [[[422712.16000000015, 4554559.60999... Jake \n", "4 49037 3 {'rings': [[[609343.4100000001, 4095382.08], [... Jake \n", "\n", " acres geoid20 aland20 \n", "0 1.024680e+06 49039 4.117901e+09 \n", "1 2.112861e+06 49021 8.537474e+09 \n", "2 2.627117e+06 49025 1.033391e+10 \n", "3 4.220563e+05 49057 1.492537e+09 \n", "4 5.075154e+06 49037 2.025317e+10 " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "census_fc_path = r'C:\\Users\\jdadams\\AppData\\Roaming\\Esri\\ArcGISPro\\Favorites\\opensgid.agrc.utah.gov.sde\\opensgid.demographic.census_counties_2020'\n", "census_df = pd.DataFrame.spatial.from_featureclass(census_fc_path)\n", "counties_with_census_df = counties_df.merge(census_df[['geoid20', 'aland20']], left_on='fips_str', right_on='geoid20')\n", "counties_with_census_df.head()" ] }, { "cell_type": "markdown", "id": "8a845a8e", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Renaming/Reordering Fields Solution: `df.rename()` and `df.reindex()`" ] }, { "cell_type": "markdown", "id": "35ac5083", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "```python\n", "final_parcels_df.rename(\n", " columns={\n", " 'name': 'CITY', #: from cities\n", " 'NewSA': 'SUBCOUNTY', #: From subcounties/regions\n", " 'BUILT_YR': 'APX_BLT_YR',\n", " 'BLDG_SQFT': 'TOT_BD_FT2',\n", " 'TOTAL_MKT_VALUE': 'TOT_VALUE',\n", " 'PARCEL_ACRES': 'ACRES',\n", " },\n", " inplace=True\n", ")\n", "```" ] }, { "cell_type": "markdown", "id": "e365048c", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "```python\n", "final_fields = [\n", " 'SHAPE', 'UNIT_ID', 'TYPE', 'SUBTYPE', 'IS_OUG', 'UNIT_COUNT', 'DUA', 'ACRES', 'TOT_BD_FT2', 'TOT_VALUE',\n", " 'APX_BLT_YR', 'BLT_DECADE', 'CITY', 'COUNTY', 'SUBCOUNTY', 'PARCEL_ID'\n", "]\n", "\n", "logging.info('Writing final data out to disk...')\n", "output_df = final_parcels_df.reindex(columns=final_fields)\n", "output_df.spatial.to_featureclass(output_fc, sanitize_columns=False)\n", "```" ] }, { "cell_type": "markdown", "id": "4a73ab58", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "\"Emperor\" is too bold; let's use \"Benevolent Dictator for Life\" instead." ] }, { "cell_type": "code", "execution_count": 28, "id": "40599137", "metadata": { "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>xid</th>\n", " <th>countynbr</th>\n", " <th>entitynbr</th>\n", " <th>entityyr</th>\n", " <th>County Name</th>\n", " <th>fips</th>\n", " <th>stateplane</th>\n", " <th>Last Census Population</th>\n", " <th>pop_currestimate</th>\n", " <th>globalid</th>\n", " <th>fips_str</th>\n", " <th>color4</th>\n", " <th>SHAPE</th>\n", " <th>Benevolent Dictator for Life</th>\n", " <th>Acres</th>\n", " <th>geoid20</th>\n", " <th>Land Area</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>20</td>\n", " <td>2.010201e+09</td>\n", " <td>2010.0</td>\n", " <td>SANPETE</td>\n", " <td>39.0</td>\n", " <td>Central</td>\n", " <td>28437</td>\n", " <td>None</td>\n", " <td>02C0C074-657F-44AE-A886-44ADB97263BD</td>\n", " <td>49039</td>\n", " <td>2</td>\n", " <td>{'rings': [[[448347.6200000001, 4407163.6], [4...</td>\n", " <td>Jake</td>\n", " <td>1.024680e+06</td>\n", " <td>49039</td>\n", " <td>4.117901e+09</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>11</td>\n", " <td>2.010111e+09</td>\n", " <td>2010.0</td>\n", " <td>IRON</td>\n", " <td>21.0</td>\n", " <td>South</td>\n", " <td>57289</td>\n", " <td>None</td>\n", " <td>2ACA2EB9-31B5-4D6A-A858-A0F6B16C64E2</td>\n", " <td>49021</td>\n", " <td>3</td>\n", " <td>{'rings': [[[292688.5800000001, 4224956.960000...</td>\n", " <td>Jake</td>\n", " <td>2.112861e+06</td>\n", " <td>49021</td>\n", " <td>8.537474e+09</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>13</td>\n", " <td>2.010131e+09</td>\n", " <td>2010.0</td>\n", " <td>KANE</td>\n", " <td>25.0</td>\n", " <td>South</td>\n", " <td>7667</td>\n", " <td>None</td>\n", " <td>A250C849-8914-4E00-A80F-C1B45CD8F0A7</td>\n", " <td>49025</td>\n", " <td>4</td>\n", " <td>{'rings': [[[425313.88999999966, 4154648.07000...</td>\n", " <td>Jake</td>\n", " <td>2.627117e+06</td>\n", " <td>49025</td>\n", " <td>1.033391e+10</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>29</td>\n", " <td>2.010291e+09</td>\n", " <td>2010.0</td>\n", " <td>WEBER</td>\n", " <td>57.0</td>\n", " <td>North</td>\n", " <td>262223</td>\n", " <td>None</td>\n", " <td>1757A80B-1895-4975-81DA-3AD2EBBA64C9</td>\n", " <td>49057</td>\n", " <td>1</td>\n", " <td>{'rings': [[[422712.16000000015, 4554559.60999...</td>\n", " <td>Jake</td>\n", " <td>4.220563e+05</td>\n", " <td>49057</td>\n", " <td>1.492537e+09</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>19</td>\n", " <td>2.006191e+09</td>\n", " <td>2006.0</td>\n", " <td>SAN JUAN</td>\n", " <td>37.0</td>\n", " <td>South</td>\n", " <td>14518</td>\n", " <td>None</td>\n", " <td>EC858EAC-D7E7-4748-B7A5-B2B744668178</td>\n", " <td>49037</td>\n", " <td>3</td>\n", " <td>{'rings': [[[609343.4100000001, 4095382.08], [...</td>\n", " <td>Jake</td>\n", " <td>5.075154e+06</td>\n", " <td>49037</td>\n", " <td>2.025317e+10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " xid countynbr entitynbr entityyr County Name fips stateplane \\\n", "0 1 20 2.010201e+09 2010.0 SANPETE 39.0 Central \n", "1 2 11 2.010111e+09 2010.0 IRON 21.0 South \n", "2 3 13 2.010131e+09 2010.0 KANE 25.0 South \n", "3 4 29 2.010291e+09 2010.0 WEBER 57.0 North \n", "4 5 19 2.006191e+09 2006.0 SAN JUAN 37.0 South \n", "\n", " Last Census Population pop_currestimate \\\n", "0 28437 None \n", "1 57289 None \n", "2 7667 None \n", "3 262223 None \n", "4 14518 None \n", "\n", " globalid fips_str color4 \\\n", "0 02C0C074-657F-44AE-A886-44ADB97263BD 49039 2 \n", "1 2ACA2EB9-31B5-4D6A-A858-A0F6B16C64E2 49021 3 \n", "2 A250C849-8914-4E00-A80F-C1B45CD8F0A7 49025 4 \n", "3 1757A80B-1895-4975-81DA-3AD2EBBA64C9 49057 1 \n", "4 EC858EAC-D7E7-4748-B7A5-B2B744668178 49037 3 \n", "\n", " SHAPE \\\n", "0 {'rings': [[[448347.6200000001, 4407163.6], [4... \n", "1 {'rings': [[[292688.5800000001, 4224956.960000... \n", "2 {'rings': [[[425313.88999999966, 4154648.07000... \n", "3 {'rings': [[[422712.16000000015, 4554559.60999... \n", "4 {'rings': [[[609343.4100000001, 4095382.08], [... \n", "\n", " Benevolent Dictator for Life Acres geoid20 Land Area \n", "0 Jake 1.024680e+06 49039 4.117901e+09 \n", "1 Jake 2.112861e+06 49021 8.537474e+09 \n", "2 Jake 2.627117e+06 49025 1.033391e+10 \n", "3 Jake 4.220563e+05 49057 1.492537e+09 \n", "4 Jake 5.075154e+06 49037 2.025317e+10 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "renames = {\n", " 'name': 'County Name',\n", " 'pop_lastcensus': 'Last Census Population',\n", " 'emperor': 'Benevolent Dictator for Life',\n", " 'acres': 'Acres',\n", " 'aland20': 'Land Area',\n", "}\n", "counties_with_census_df.rename(columns=renames, inplace=True)\n", "counties_with_census_df.head()" ] }, { "cell_type": "markdown", "id": "c56a4c2f", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Now that we've got it all looking good, let's reorder the fields and get rid of the ones we don't want" ] }, { "cell_type": "code", "execution_count": 29, "id": "3cc5ad0d", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>County Name</th>\n", " <th>Benevolent Dictator for Life</th>\n", " <th>Acres</th>\n", " <th>Land Area</th>\n", " <th>Last Census Population</th>\n", " <th>SHAPE</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>SANPETE</td>\n", " <td>Jake</td>\n", " <td>1.024680e+06</td>\n", " <td>4.117901e+09</td>\n", " <td>28437</td>\n", " <td>{'rings': [[[448347.6200000001, 4407163.6], [4...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>IRON</td>\n", " <td>Jake</td>\n", " <td>2.112861e+06</td>\n", " <td>8.537474e+09</td>\n", " <td>57289</td>\n", " <td>{'rings': [[[292688.5800000001, 4224956.960000...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>KANE</td>\n", " <td>Jake</td>\n", " <td>2.627117e+06</td>\n", " <td>1.033391e+10</td>\n", " <td>7667</td>\n", " <td>{'rings': [[[425313.88999999966, 4154648.07000...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>WEBER</td>\n", " <td>Jake</td>\n", " <td>4.220563e+05</td>\n", " <td>1.492537e+09</td>\n", " <td>262223</td>\n", " <td>{'rings': [[[422712.16000000015, 4554559.60999...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>SAN JUAN</td>\n", " <td>Jake</td>\n", " <td>5.075154e+06</td>\n", " <td>2.025317e+10</td>\n", " <td>14518</td>\n", " <td>{'rings': [[[609343.4100000001, 4095382.08], [...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " County Name Benevolent Dictator for Life Acres Land Area \\\n", "0 SANPETE Jake 1.024680e+06 4.117901e+09 \n", "1 IRON Jake 2.112861e+06 8.537474e+09 \n", "2 KANE Jake 2.627117e+06 1.033391e+10 \n", "3 WEBER Jake 4.220563e+05 1.492537e+09 \n", "4 SAN JUAN Jake 5.075154e+06 2.025317e+10 \n", "\n", " Last Census Population SHAPE \n", "0 28437 {'rings': [[[448347.6200000001, 4407163.6], [4... \n", "1 57289 {'rings': [[[292688.5800000001, 4224956.960000... \n", "2 7667 {'rings': [[[425313.88999999966, 4154648.07000... \n", "3 262223 {'rings': [[[422712.16000000015, 4554559.60999... \n", "4 14518 {'rings': [[[609343.4100000001, 4095382.08], [... " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "field_order = [\n", " 'County Name',\n", " 'Benevolent Dictator for Life',\n", " 'Acres',\n", " 'Land Area',\n", " 'Last Census Population',\n", " 'SHAPE'\n", "]\n", "final_counties_df = counties_with_census_df.reindex(columns=field_order)\n", "final_counties_df.head()" ] }, { "cell_type": "markdown", "id": "0169caae", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Intermediate Feature Classes: New DataFrame Variables" ] }, { "cell_type": "markdown", "id": "1fdc9175", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "With everything we've done, we've not written a single feature class to either disk or `in_memory`" ] }, { "cell_type": "markdown", "id": "bb06f467", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "```python\n", "counties_df\n", "counties_with_census_df\n", "final_counties_df\n", "```" ] }, { "cell_type": "markdown", "id": "e08dddcd", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Finally, Write It All To Disk" ] }, { "cell_type": "code", "execution_count": 30, "id": "bbc4cd86", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "'C:\\\\gis\\\\Projects\\\\HousingInventory\\\\HousingInventory.gdb\\\\counties_ugic'" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_counties_df.spatial.to_featureclass(r'C:\\gis\\Projects\\HousingInventory\\HousingInventory.gdb\\counties_ugic')" ] }, { "cell_type": "markdown", "id": "861c1ce1", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "" ] }, { "cell_type": "markdown", "id": "945913f6", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "" ] }, { "cell_type": "markdown", "id": "f3814e32", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Spatial Joins" ] }, { "cell_type": "markdown", "id": "52e43dbe", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "```python\n", "centroids_df = pd.DataFrame.spatial.from_featureclass(centroids_fc)\n", "walksheds_df = pd.DataFrame.spatial.from_featureclass(walksheds_fc)\n", "\n", "walk_centroids_df = centroids_df.spatial.join(walksheds_df, 'left', 'within')\n", "```" ] }, { "cell_type": "markdown", "id": "aadb52c3", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Update an AGOL Hosted Feature Layer" ] }, { "cell_type": "markdown", "id": "85c89a90", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "```python\n", "feature_layer_item = gis.content.get(feature_layer_itemid)\n", "feature_layer = arcgis.features.FeatureLayer.fromitem(feature_layer_item)\n", "live_dataframe = pd.DataFrame.spatial.from_layer(feature_layer)\n", "\n", "#: maniuplate/transform the existing data\n", "cleaned_dataframe = do_stuff(live_dataframe)\n", "\n", "feature_layer.edit_features(updates=cleaned_dataframe.spatial.to_featureset())\n", "```" ] }, { "cell_type": "markdown", "id": "d89bfc40", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Resources?\n", "\n", "\n" ] }, { "cell_type": "markdown", "id": "5c832572", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Official Docs\n", "\n", "- Pandas docs: https://pandas.pydata.org/docs/user_guide/index.html\n", "- ArcGIS API for Python Reference: https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor\n", "\n", "## Example Code\n", "\n", "- Erik's 2020 UGIC Intro to Pandas presentation: https://agrc.github.io/Presentations/UGIC/2020/pandas.pdf\n", "- Updating AGOL with dataframe: https://github.com/agrc/palletjack/blob/main/src/palletjack/updaters.py#L175\n", "- So much dataframe craziness: https://github.com/agrc/housing-unit-inventory/tree/first-dev/src/housing_unit_inventory" ] }, { "cell_type": "markdown", "id": "9f4fcc45", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "\n", "### [jdadams@utah.gov](mailto:jdadams@utah.gov)\n", "\n", "### [gis.utah.gov/presentations](https://gis.utah.gov/presentations)" ] }, { "cell_type": "code", "execution_count": null, "id": "38172f87", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "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": "3.7.11" } }, "nbformat": 4, "nbformat_minor": 5 }

mit

luwei0917/awsemmd_script

notebook/Optimization/disulfide_analysis.ipynb

1

2159336

mit

encima/Comp_Thinking_In_Python

Session_7b/7b_Negative_Numbers_Binary Arithmetic.ipynb

2

8374

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Negative Numbers\n", "\n", "This works for all positive numbers but how do we show negative numbers?\n", "\n", "1001\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "## Sign and Magnitude\n", "\n", "The simpler method is to reserve the leftmost bit to indicate the sign: `0` for positive and `1` for negative.\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "source": [ "0010 = 2\n", "\n", "1010 = -2\n", "\n", "1111 = -7" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "## One's Complement\n", "\n", "This method involves creating the inverse of the binary representation after it has been converted.\n", "\n", "### Example\n", "\n", "Represent the decimal value -14 in binary using One's Complement:\n", "\n", "#### Step 1 - Convert 14 to binary\n", "1110" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "source": [ "#### Step 2 - Flip all the bits (only do this if the number is negative)\n", "0001" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "### Exercise\n", "\n", "Using sign and magnitude, what are the decimal representations of these numbers?\n", "* 0010\n", "* 1110\n", "* 1100\n", "* 10001\n", "\n", "Using one's complement, convert these values between decimal or binary, based on their original format:\n", "\n", "* 8\n", "* -18\n", "* 4\n", "* 1011" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "skip" } }, "source": [ "* 2\n", "* -6\n", "* -4\n", "* -1\n", "\n", "\n", "* 1000\n", "* 10010 = binary\n", " * 101101 = one's complement\n", "* 0100\n", "* -4\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## 2's Complement\n", "\n", "Two's complement is designed to make arithmetic with binary much easier, and is another way of storing signed binary numbers. The conversion process builds on from one's complement. \n", "\n", "The negative number will have a one at the leftmost bit, so be sure to add bits to allow this\n", "\n", "### Example: -3 in 2's complement\n", "\n", "#### Step 1: Convert to decimal (stop here if the number is positive)\n", "0011" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Step 3: Perform one's complement\n", "1100" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Step 4: Add 1 to the result and show the binary representation:\n", "1101 is -3 in 2's complement" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Exercise\n", "\n", "Show the two's complement representation of these numbers:\n", "* 3\n", "* -10\n", "* -8\n", "\n", "Convert these two's complement numbers back to their decimal representation:\n", "* 1001\n", "* 0011\n", "* 11011\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "* 0011\n", "* 01010\n", " * 10101 - one's\n", " * 10110 - two's\n", "* 01000\n", " * 10111 - one's\n", " * 11000 - two's\n", "\n", "* -7\n", "* 3\n", "* -5\n", "\n", "[Conversion](http://planetcalc.com/747/)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Binary Arithmetic\n", "\n", "Binary arithmetic is much like standard arithmetic and you will probably recognise the methods from how you performed arithmetic in school. The below example is addition of unsigned binary numbers:\n", "\n", "| Decimal | Binary |\n", "|---------|--------|\n", "| 4 | 0100 |\n", "| + 2 | 0010 |\n", "| --- | --- |\n", "| 6 | 0110 |" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Binary Arithmetic\n", "\n", "In this example, we see that we need to carry the bit across.\n", "\n", "So, where 1 appears in both columns, the result becomes 0 and the 1 is carried over to the next column to the left\n", "\n", "| Decimal | Binary |\n", "|---------|-------------|\n", "| 4 | 0100 |\n", "| + 6 | 0110 |\n", "| --- | --- (carry) |\n", "| 10 | 1010 |" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Subtraction \n", "## Or: Adding Negative Numbers\n", "\n", "How do I calculate `32 - 12` in binary? \n", "\n", "Sure, I could convert to decimal and then convert back, but this is less than ideal.\n", "\n", "This is where Two's Complement is vital. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "`32 - 12` can become `32 + (-12)`\n", "\n", "### Step 1: 32 in binary\n", "\n", "0010 0000\n", "\n", "### Step 2: 12 in binary\n", "\n", "0000 1100" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Step 3: Two's complement of 12\n", "\n", "1111 0011\n", "\n", "Add one:\n", "\n", "1111 0100" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Step 4: Subtract\n", "\n", "| Decimal | Binary |\n", "|---------|-------------|\n", "| 32 | 0010 0000 |\n", "| + -12 | 1111 0100 |\n", "| --- | --- (carry) |\n", "| 20 | 001 0100 |\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Another Way\n", "\n", "| Decimal | Binary |\n", "|---------|-------------|\n", "| 13 | 0000 1101 |\n", "| -6 | 0000 0110 |\n", "| --- | --- |\n", "| 20 | 0000 0111 |\n", "| | 0000 00B0 |\n", " \n", "B - Borrow \n", "\n", "[Animated example](http://courses.cs.vt.edu/~cs1104/BuildingBlocks/arithmetic.030.html)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "\n", "### Exercise\n", "\n", "Do exercise sheet on LC" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" }, "nav_menu": {}, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 0 }

mit

WNoxchi/Kaukasos

FAI_old/Lesson6/Theano_RNN_solved.ipynb

1

17886

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Wayne Nixalo - 1 Jul 2017\n", "\n", "RNN Theano\n", "\n", "I've been having a problem getting a RNN built in Theano to work. A corpus of\n", "Nietzsche is the training data. Done correctly, the model should start with\n", "a loss of ~25 and ends at ~14.4, and reasonably predict the next character.\n", "Done wrong, the model starts with a loss ~30~29, and ends at ~25, and\n", "predicts only empty spaces (obvious easy local minima).\n", "\n", "I've narrowed down the relevant parts of code, going on a goose-hunt pursuing\n", "red herrings, until finally discovering the model works as advertised when\n", "copied, but not when I rewrite it. So this is to see where I made errors and\n", "how they're responsible.\n", "\n", "**NOTE:** ohhh my holy fuck. The culprit was the:\n", " from __future__ import division, print_function\n", "line. Specifically `import division`. That single import is responsible for\n", "the last 2 weeks of tracking down this issue. So why? Well w/o looking into\n", "it, it seems like somewhere integer division was supposed to be done where\n", "floating-point div was instead done or vice-versa.\n", "\n", "**NOTE:** okay got it. importing division from __future__ gives you Python3\n", "divison: floating-point. My poor RNN's SGD optimizer was forced to use\n", "integer division everywhere instead of floating-point. Oof. Well, that's done." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Unsuccessful Theano RNN run" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "('corpus length:', 600901)\n", "('total chars:', 86)\n" ] } ], "source": [ "import theano\n", "# %matplotlib inline\n", "import sys, os\n", "sys.path.insert(1, os.path.join('../utils'))\n", "# import utils; reload(utils)\n", "from utils import *\n", "# from __future__ import division#, print_function\n", "\n", "path = get_file('nietzsche.txt', origin=\"https://s3.amazonaws.com/text-datasets/nietzsche.txt\")\n", "text = open(path).read()\n", "print('corpus length:', len(text))\n", "\n", "chars = sorted(list(set(text)))\n", "vocab_size = len(chars) + 1\n", "print('total chars:', vocab_size)\n", "\n", "chars.insert(0, \"\\0\")\n", "\n", "char_indices = dict((c, i) for i, c in enumerate(chars))\n", "indices_char = dict((i, c) for i, c in enumerate(chars))\n", "\n", "idx = [char_indices[c] for c in text]" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((75110, 8, 86), (75110, 8, 86))" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cs = 8\n", "\n", "c_in_dat = [[idx[i+n] for i in xrange(0, len(idx)-1-cs, cs)] for n in xrange(cs)]\n", "xs = [np.stack(c[:-2]) for c in c_in_dat]\n", "\n", "c_out_dat = [[idx[i+n] for i in xrange(1, len(idx)-cs, cs)] for n in xrange(cs)]\n", "ys = [np.stack(c[:-2]) for c in c_out_dat]\n", "\n", "oh_ys = [to_categorical(o, vocab_size) for o in ys]\n", "oh_y_rnn = np.stack(oh_ys, axis=1)\n", "\n", "oh_xs = [to_categorical(o, vocab_size) for o in xs]\n", "oh_x_rnn = np.stack(oh_xs, axis=1)\n", "\n", "oh_x_rnn.shape, oh_y_rnn.shape" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/WayNoxchi/Miniconda3/Theano/theano/tensor/basic.py:5130: UserWarning: flatten outdim parameter is deprecated, use ndim instead.\n", " \"flatten outdim parameter is deprecated, use ndim instead.\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Error: 28.986\n", "Error: 25.977\n", "Error: 25.761\n", "Error: 25.518\n", "Error: 25.341\n", "Error: 25.490\n", "Error: 25.177\n", "Error: 25.101\n", "Error: 25.151\n", "Error: 25.495\n", "Error: 24.801\n", "Error: 25.009\n", "Error: 26.446\n", "Error: 25.014\n", "Error: 24.888\n", "Error: 26.046\n", "Error: 25.932\n", "Error: 25.714\n", "Error: 25.041\n", "Error: 24.938\n", "Error: 24.872\n", "Error: 25.092\n", "Error: 25.351\n", "Error: 24.953\n", "Error: 25.128\n", "Error: 25.144\n", "Error: 25.317\n", "Error: 24.996\n", "Error: 25.114\n", "Error: 25.300\n", "Error: 25.438\n", "Error: 25.180\n", "Error: 25.548\n", "Error: 25.050\n", "Error: 25.253\n", "Error: 25.591\n", "Error: 25.060\n", "Error: 25.380\n", "Error: 25.277\n", "Error: 25.589\n", "Error: 24.948\n", "Error: 24.953\n", "Error: 25.200\n", "Error: 25.384\n", "Error: 25.643\n", "Error: 25.679\n", "Error: 25.149\n", "Error: 24.073\n", "Error: 24.635\n", "Error: 24.867\n", "Error: 24.429\n", "Error: 24.570\n", "Error: 24.357\n", "Error: 24.355\n", "Error: 24.773\n", "Error: 24.590\n", "Error: 24.631\n", "Error: 24.551\n", "Error: 24.523\n", "Error: 24.688\n", "Error: 24.431\n", "Error: 24.619\n", "Error: 24.658\n", "Error: 24.774\n", "Error: 24.477\n", "Error: 24.342\n", "Error: 24.341\n", "Error: 24.398\n", "Error: 24.153\n", "Error: 24.214\n", "Error: 24.885\n", "Error: 24.407\n", "Error: 24.177\n", "Error: 24.022\n", "Error: 23.951\n" ] } ], "source": [ "n_hidden = 256; n_fac = 42; cs = 8\n", "\n", "n_input = vocab_size\n", "n_output = vocab_size\n", "\n", "def init_wgts(rows, cols):\n", " scale = math.sqrt(2/rows)\n", " return shared(normal(scale=scale, size=(rows,cols)).astype(np.float32))\n", "def init_bias(rows):\n", " return shared(np.zeros(rows, dtype=np.float32))\n", "def wgts_and_bias(n_in, n_out):\n", " return init_wgts(n_in, n_out), init_bias(n_out)\n", "def id_and_bias(n):\n", " return shared(np.eye(n, dtype=np.float32)), init_bias(n)\n", "\n", "# Theano Variables\n", "t_inp = T.matrix('inp')\n", "t_outp = T.matrix('outp')\n", "t_h0 = T.vector('h0')\n", "lr = T.scalar('lr')\n", "\n", "all_args = [t_h0, t_inp, t_outp, lr]\n", "\n", "W_h = id_and_bias(n_hidden)\n", "W_x = wgts_and_bias(n_input, n_hidden)\n", "W_y = wgts_and_bias(n_hidden, n_output)\n", "w_all = list(chain.from_iterable([W_h, W_x, W_y]))\n", "\n", "def step(x, h, W_h, b_h, W_x, b_x, W_y, b_y):\n", " # Calculate hidden activations\n", " h = nnet.relu(T.dot(x, W_x) + b_x + T.dot(h, W_h) + b_h)\n", " # Calculate output activations\n", " y = nnet.softmax(T.dot(h, W_y) + b_y)\n", " # Return both -- `flatten()` is Theano bug workaround\n", " return h, T.flatten(y, 1)\n", "\n", "[v_h, v_y], _ = theano.scan(step, sequences=t_inp,\n", " outputs_info=[t_h0, None], non_sequences=w_all)\n", "error = nnet.categorical_crossentropy(v_y, t_outp).sum()\n", "g_all = T.grad(error, w_all)\n", "\n", "def upd_dict(wgts, grads, lr):\n", " return OrderedDict({w: w - g * lr for (w, g) in zip(wgts, grads)})\n", "\n", "upd = upd_dict(w_all, g_all, lr)\n", "\n", "# ready to compile the function:\n", "fn = theano.function(all_args, error, updates=upd, allow_input_downcast=True)\n", "\n", "X = oh_x_rnn\n", "Y = oh_y_rnn\n", "# X.shape, Y.shape\n", "\n", "# semi-auto SGD loop:\n", "err=0.0; l_rate=0.01\n", "for i in xrange(len(X)):\n", " err += fn(np.zeros(n_hidden), X[i], Y[i], l_rate)\n", " if i % 1000 == 999:\n", " print (\"Error: {:.3f}\".format(err/1000))\n", " err=0.0" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['t', 'h', 'e', 'n', '?', ' ', 'I', 's']\n", "[' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ']\n" ] } ], "source": [ "f_y = theano.function([t_h0, t_inp], v_y, allow_input_downcast=True)\n", "pred = np.argmax(f_y(np.zeros(n_hidden), X[6]), axis=1)\n", "act = np.argmax(X[6], axis=1)\n", "\n", "actual = [indices_char[o] for o in act]\n", "prediction = [indices_char[o] for o in pred]\n", "\n", "print(actual)\n", "print(prediction)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "\n", "Restarting Jupyter Kernel between this and bottom runs for full isolation.\n", "\n", "-----\n", "\n", "## Successful Theano RNN run" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "corpus length: 600901\n", "total chars: 86\n" ] } ], "source": [ "import theano\n", "# %matplotlib inline # this line doesn't affect result\n", "import sys, os\n", "sys.path.insert(1, os.path.join('../utils'))\n", "# import utils; reload(utils) # this line doesn't affect result\n", "from utils import *\n", "from __future__ import division, print_function # <<<--- here's the magical line\n", "\n", "\n", "path = get_file('nietzsche.txt', origin=\"https://s3.amazonaws.com/text-datasets/nietzsche.txt\")\n", "text = open(path).read()\n", "print('corpus length:', len(text))\n", "\n", "\n", "chars = sorted(list(set(text)))\n", "vocab_size = len(chars) + 1\n", "print('total chars:', vocab_size)\n", "\n", "\n", "chars.insert(0, \"\\0\")\n", "# ''.join(chars[1:-6])\n", "\n", "\n", "char_indices = dict((c, i) for i, c in enumerate(chars))\n", "indices_char = dict((i, c) for i, c in enumerate(chars))\n", "\n", "\n", "\n", "idx = [char_indices[c] for c in text]\n", "# the 1st 10 characters:\n", "# idx[:10]" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((75110, 8, 86), (75110, 8, 86))" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cs = 8 # use 8 characters to predict the 9th\n", "\n", "c_in_dat = [[idx[i+n] for i in xrange(0, len(idx)-1-cs, cs)] for n in range(cs)]\n", "xs = [np.stack(c[:-2]) for c in c_in_dat]\n", "\n", "c_out_dat = [[idx[i+n] for i in xrange(1, len(idx)-cs, cs)] for n in range(cs)]\n", "ys = [np.stack(c[:-2]) for c in c_out_dat]\n", "\n", "oh_ys = [to_categorical(o, vocab_size) for o in ys]\n", "oh_y_rnn = np.stack(oh_ys, axis=1)\n", "\n", "oh_xs = [to_categorical(o, vocab_size) for o in xs]\n", "oh_x_rnn = np.stack(oh_xs, axis=1)\n", "\n", "oh_x_rnn.shape, oh_y_rnn.shape" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/WayNoxchi/Miniconda3/Theano/theano/tensor/basic.py:5130: UserWarning: flatten outdim parameter is deprecated, use ndim instead.\n", " \"flatten outdim parameter is deprecated, use ndim instead.\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Error:25.178\n", "Error:21.430\n", "Error:20.898\n", "Error:19.878\n", "Error:18.802\n", "Error:19.265\n", "Error:19.050\n", "Error:18.418\n", "Error:17.950\n", "Error:18.213\n", "Error:17.478\n", "Error:17.620\n", "Error:18.383\n", "Error:17.275\n", "Error:16.774\n", "Error:17.742\n", "Error:17.418\n", "Error:17.178\n", "Error:16.817\n", "Error:16.673\n", "Error:16.525\n", "Error:16.417\n", "Error:16.705\n", "Error:16.162\n", "Error:16.724\n", "Error:16.547\n", "Error:16.068\n", "Error:16.220\n", "Error:16.217\n", "Error:16.437\n", "Error:16.679\n", "Error:16.384\n", "Error:16.601\n", "Error:16.303\n", "Error:15.988\n", "Error:16.744\n", "Error:15.991\n", "Error:16.337\n", "Error:16.034\n", "Error:16.263\n", "Error:15.312\n", "Error:15.717\n", "Error:15.735\n", "Error:16.018\n", "Error:15.998\n", "Error:15.845\n", "Error:15.624\n", "Error:16.066\n", "Error:15.917\n", "Error:16.007\n", "Error:15.228\n", "Error:15.536\n", "Error:14.973\n", "Error:14.809\n", "Error:15.601\n", "Error:15.318\n", "Error:14.675\n", "Error:15.446\n", "Error:15.087\n", "Error:14.910\n", "Error:15.058\n", "Error:15.369\n", "Error:15.296\n", "Error:15.039\n", "Error:14.748\n", "Error:14.846\n", "Error:14.307\n", "Error:14.751\n", "Error:15.189\n", "Error:14.778\n", "Error:15.092\n", "Error:14.675\n", "Error:14.383\n", "Error:14.463\n", "Error:14.454\n" ] } ], "source": [ "n_hidden = 256; n_fac = 42; cs = 8\n", "\n", "n_input = vocab_size\n", "n_output = vocab_size\n", "\n", "def init_wgts(rows, cols): \n", " scale = math.sqrt(2/rows) # 1st calc Glorot number to scale weights\n", " return shared(normal(scale=scale, size=(rows, cols)).astype(np.float32))\n", "def init_bias(rows): \n", " return shared(np.zeros(rows, dtype=np.float32))\n", "def wgts_and_bias(n_in, n_out): \n", " return init_wgts(n_in, n_out), init_bias(n_out)\n", "def id_and_bias(n): \n", " return shared(np.eye(n, dtype=np.float32)), init_bias(n)\n", "\n", "# Theano variables\n", "t_inp = T.matrix('inp')\n", "t_outp = T.matrix('outp')\n", "t_h0 = T.vector('h0')\n", "lr = T.scalar('lr')\n", "\n", "all_args = [t_h0, t_inp, t_outp, lr]\n", "\n", "W_h = id_and_bias(n_hidden)\n", "W_x = wgts_and_bias(n_input, n_hidden)\n", "W_y = wgts_and_bias(n_hidden, n_output)\n", "w_all = list(chain.from_iterable([W_h, W_x, W_y]))\n", "\n", "def step(x, h, W_h, b_h, W_x, b_x, W_y, b_y):\n", " # Calculate the hidden activations\n", " h = nnet.relu(T.dot(x, W_x) + b_x + T.dot(h, W_h) + b_h)\n", " # Calculate the output activations\n", " y = nnet.softmax(T.dot(h, W_y) + b_y)\n", " # Return both (the 'Flatten()' is to work around a theano bug)\n", " return h, T.flatten(y, 1)\n", "\n", "[v_h, v_y], _ = theano.scan(step, sequences=t_inp,\n", " outputs_info=[t_h0, None], non_sequences=w_all)\n", "\n", "error = nnet.categorical_crossentropy(v_y, t_outp).sum()\n", "g_all = T.grad(error, w_all)\n", "\n", "def upd_dict(wgts, grads, lr):\n", " return OrderedDict({w: w-g*lr for (w,g) in zip(wgts,grads)})\n", "\n", "upd = upd_dict(w_all, g_all, lr)\n", "\n", "# we're finally ready to compile the function!:\n", "fn = theano.function(all_args, error, updates=upd, allow_input_downcast=True)\n", "\n", "X = oh_x_rnn\n", "Y = oh_y_rnn\n", "X.shape, Y.shape\n", "\n", "err=0.0; l_rate=0.01\n", "for i in xrange(len(X)):\n", " err += fn(np.zeros(n_hidden), X[i], Y[i], l_rate)\n", " if i % 1000 == 999:\n", " print (\"Error:{:.3f}\".format(err/1000))\n", " err=0.0" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['t', 'h', 'e', 'n', '?', ' ', 'I', 's']\n", "['h', 'e', ' ', ' ', ' ', 'I', 't', ' ']\n" ] } ], "source": [ "f_y = theano.function([t_h0, t_inp], v_y, allow_input_downcast=True)\n", "pred = np.argmax(f_y(np.zeros(n_hidden), X[6]), axis=1)\n", "act = np.argmax(X[6], axis=1)\n", "\n", "actual = [indices_char[o] for o in act]\n", "prediction = [indices_char[o] for o in pred]\n", "\n", "print(actual)\n", "print(prediction)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Q E D" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

0rC0/DBS-election

iteration2/2_development.ipynb

2

6974

{ "cells": [ { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#!/usr/bin/python\n", "\n", "import psycopg2 as pg2\n", "import csv\n", "import re\n", "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cfg = { 'host' : '192.168.0.20',\n", " 'user' : 'testuser',\n", " 'pw' : 'testpass',\n", " 'db' : 'dbs'}\n", "\n", "def extract_hashtags(s):\n", " # Extract Hashtags from a string with regular expression and\n", " # return a list of those\n", " #source :https://stackoverflow.com/questions/2527892/parsing-a-tweet-to-extract-hashtags-into-an-array-in-python\n", " return re.findall(r\"#(\\w+)\", s)\n", "\n", "def string_to_bool(s):\n", " #Convert True and False from string to Boolean\n", " return s == 'True'\n", "\n", "def make_timestamp(s):\n", " #Convert a string in a datetime object:\n", " # https://www.postgresql.org/docs/8.0/static/datatype-datetime.html\n", " return datetime.strptime(s, '%Y-%m-%dT%H:%M:%S')\n", "\n", "def rm_non_ascii_chars(s):\n", " # Source: https://stackoverflow.com/questions/36598136/remove-all-hex-characters-from-string-in-python\n", " # interesting: http://farmdev.com/talks/unicode/\n", " return s.encode('ascii', errors='ignore')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "csv_file_name = 'american-election-tweets2.csv'\n", "\n", "data=[]\n", "with open(csv_file_name, 'r', newline='') as f:\n", " #dialect = csv.Sniffer().sniff(f.read(1024))\n", " datareader = csv.reader(f, delimiter=';') #quotechar=''\n", " #['handle', 'text', 'is_retweet', 'original_author', 'time', \n", " #'in_reply_to_screen_name', 'is_quote_status', 'retweet_count', \n", " #'favorite_count', 'source_url', 'truncated']\n", " next(datareader) # skip the header\n", " for row in datareader:\n", " # Format the data and put everything in a nice-looking JSON format\n", " d = dict()\n", " d['handle'] = row[0]\n", " d['text'] = rm_non_ascii_chars(row[1])\n", " d['hashtags'] = extract_hashtags(row[1])\n", " d['is_retweet'] = string_to_bool(row[2])\n", " d['original_author'] = row[3]\n", " d['time'] = make_timestamp(row[4])\n", " d['in_reply_to_screen_name'] = row[5]\n", " d['is_quote_status'] = string_to_bool(row[6])\n", " d['retweet_count'] = int(row[7])\n", " d['favorite_count'] = int(row[8])\n", " d['source_url'] = row[9]\n", " d['truncated'] = string_to_bool(row[10])\n", " data.append(d)\n", "#print(data)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#make hashtags set\n", "hashtags = set()\n", "for tweet in data:\n", " for h in tweet['hashtags']:\n", " hashtags.add(h)" ] }, { "cell_type": "raw", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "ename": "UnicodeDecodeError", "evalue": "'utf-8' codec can't decode byte 0x85 in position 1392: invalid start byte", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-45-7590dea24ee5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchardet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcsv_file_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/lib/python3.5/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0;31m# decode input (taking the buffer into account)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 321\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 322\u001b[0m \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconsumed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0x85 in position 1392: invalid start byte" ] } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

garth-wells/IA-maths-Jupyter

Overview.ipynb

2

3513

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Supporting notebooks for IA Paper 4 (mathematics) teaching \n", "\n", "This collection of Jupyter/Python notebooks is being produced as an experiment in supporting the teaching of mathematical methods to Part IA students at the Department of Engineering at University of Cambridge in the second half of Michaelmas Term. These notebooks are produced by Garth N. Wells (<http://www.eng.cam.ac.uk/~gnw20/>).\n", "\n", "\n", "## What these notebooks are \n", "\n", "The intention of these notebooks is to support the learning of and understanding of the mathematics that is taught in the course.\n", "\n", "\n", "## What these notebooks are not\n", "\n", "These notebooks are not complete learning material. They supplement the lecture handouts on the Moodle site <https://www.vle.cam.ac.uk/course/view.php?id=69781>.\n", "\n", "The notebooks are not intended to teach students how to use Python. It is hoped that students will modify and experiment with the notebooks.\n", "\n", "\n", "## This is an experiment\n", "\n", "Feedback is welcome. Send feedback to Garth N. Wells at <gnw20@cam.ac.uk>, or report error and typos on Github at <https://github.com/garth-wells/IA-maths-Jupyter/issues>.\n", "\n", "\n", "## Computer algebra systems (CAS)\n", "\n", "Some of these notebooks use a computer algebra systems. Computer algebra systems can perform symbolic mathematical operations and manipulations. Well known proprietary computer algebra systems are Maple and Mathematica (some Mathematica code can be executed in Wolfram Alpha). Open computer algebra systems include Maxima (<http://maxima.sourceforge.net/>) and SymPy (<http://sympy.org/>). Sage (<http://www.sagemath.org/>) is an interesting synthesis of a number of open packages.\n", "\n", "[SymPy](http://www.sympy.org/) is the CAS used in these notebooks.\n", "\n", "\n", "## List of lectures\n", "\n", "Each notebook corresponds to one lecture in the course.\n", "\n", "- [Lecture 1 (first-order ordinary differential equations)](Lecture01.ipynb)\n", "- [Lecture 2 (second-order ordinary differential equations)](Lecture02.ipynb)\n", "- [Lecture 3 (non-homegeneous second-order ordinary differential equations)](Lecture03.ipynb)\n", "- [Lecture 4 (non-homegeneous second-order ordinary differential equations (extension), balance laws)](Lecture04.ipynb)\n", "- [Lecture 5 (difference equations)](Lecture05.ipynb)\n", "- [Lecture 6 (partial derivatives and gradients)](Lecture06.ipynb) \n", "- [Lecture 7 (matrices and vectors)](Lecture07.ipynb)\n", "- [Lecture 8 (orthogonal matrices)](Lecture08.ipynb)\n", "- [Lecture 9 (coordinate transformations)](Lecture09.ipynb)\n", "- [Lecture 10 (eigenvalue problems)](Lecture10.ipynb)\n", "- [Lecture 11 (matrix diagonalisation)](Lecture11.ipynb)\n", "- [Lecture 12 (eigenproblem applications)](Lecture12.ipynb)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

blink1073/oct2py

example/octavemagic_extension.ipynb

1

994954

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# octavemagic: Octave inside IPython" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Installation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `octavemagic` extension provides the ability to interact with Octave. It is provided by the `oct2py` package,\n", "which may be installed using `pip` or `easy_install`.\n", "\n", "To enable the extension, load it as follows:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%load_ext oct2py.ipython" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loading the extension enables three magic functions: `%octave`, `%octave_push`, and `%octave_pull`.\n", "\n", "The first is for executing one or more lines of Octave, while the latter allow moving variables between the Octave and Python workspace.\n", "Here you see an example of how to execute a single line of Octave, and how to transfer the generated value back to Python:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 2.],\n", " [ 3., 4.]])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = %octave [1 2; 3 4];\n", "x" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 2., 4., 6.]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = [1, 2, 3]\n", "\n", "%octave_push a\n", "%octave a = a * 2;\n", "%octave_pull a\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When using the cell magic, `%%octave` (note the double `%`), multiple lines of Octave can be executed together. Unlike\n", "with the single cell magic, no value is returned, so we use the `-i` and `-o` flags to specify input and output variables. Also note the use of the semicolon to suppress the Octave output." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "%%octave -i x -o U,S,V\n", "[U, S, V] = svd(x);" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-0.40455358 -0.9145143 ]\n", " [-0.9145143 0.40455358]] [[ 5.4649857 0. ]\n", " [ 0. 0.36596619]] [[-0.57604844 0.81741556]\n", " [-0.81741556 -0.57604844]]\n" ] } ], "source": [ "print(U, S, V)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot output is automatically captured and displayed, and using the `-f` flag you may choose its format (currently, `png` and `svg` are supported)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "12*x^4 - 2.5*x^3 - 8*x^2 - 0.1*x^1 + 8" ] }, "metadata": {}, 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v5g1gOSOOrLpWq5Uxpqqq5XK5Wq2+9rWvHR0dHR8fF0Vxfn6+boeNwY9JoV9E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\ngaSRufOIFovFo0ePHj16tFq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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%octave -f png -w 600\n", "\n", "% butterworth filter, order 2, cutoff pi/2 radians\n", "b = [0.292893218813452 0.585786437626905 0.292893218813452];\n", "a = [1 0 0.171572875253810];\n", "freqz(b, a, 32);" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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MnDlzml9SnCjw10dxl0MbYgzW4uIohwMAwzARwNaPPhLqyDUgYISP/1MIdbf3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\nAJsOOsvTq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A1JC0cANu13VpxvHbrzDp55U/b7+3QoPG+WuvdADGiTj2NfFaCWP9DHKVzy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\n3ZzImAXovUKJCnkA7hLp4HqA1+crxWllb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33iNdrkOemJ58uKmpp0jn3et3099faFdxuv\n/8JprWfwVPtI3dGj8b/5XCKRyIc/q/f+4mNtCM+cOl5Yu5qhEX37S4AxdgbbagFr1BwacuZHUvoN\nnubvZ8EY4jH+nBlvz7mDuU37dbq15B7831a6lg/cXkgdQ7AE6F4l2MdkHdTd91jFX/MZ7bnPR1cn\nff2vIcESGNL7cf2j6VG5qKPY6xpK/hDg27GUk2q0VxdrWza6/3GpI3muJGp5/uZNh0vHhFSNeId3\n1q0nJQ6mR0TDQLrbDoQVoHhsnoXcXG8ARxrMQVX9BYJbV8UyhtsT6w+E2p4ETDh3+PIlCeCtVelh\n1bl7pZSph+uteMwtCU98+1f+YHDcL/wrWLJkyaJFixYtWvTupwsfhCOYxXduexBRF8SQXjxGFCyv\ns02OdzpX/N4LlSppR15wrjnlXPP//dTWAlb0Sq9xE4E8NdMaGDTe6IoTjiIPzcR2E6pR1QNpI5hs\n/7bTbePFst0Hfb5KQJYnAZ6XTRlmJ7V5sBsQBE0qfdnrSbF7MUDVGTl10N0rSKcAUi34K3nqnlzt\nIFB4Uk4EOBXz1D7lqZHsrqPgTJpW5kayM9TORoAR0wFPUQF60l68zas8l5HTYQzgugCet6eXKaMB\nimJDSNPess07VNHQ3PnZ3hSGHVasVwLGCidpi65I5nAqMMvQSux4myfl6cE8beeTieGn+ds32dc+\npjdvo73BrprBkW2+kdPE026mabOgqq4W+uzXf/LXn8tHQz7078LH2hCeNzNqTrvGt+AmNZsmnFqj\nNTwPEI74u3KFE8aAToGO1E8WNYpmUN+bCDRjXjrW5w4ChhMk1QCQrE+0+5ErNeEY9W1BmexPrho4\noh1+VBGPoZsqVidSONHShhkDtL2PMGRu/1p1kPanW7LLQae3eWHDKrMwAlC/Ol5ZDSiCSSgslRTa\nv17sepbR0qrguLH2wpHh/b/f23HEqZxetnfVkdEz8oEXFu498/qcCW/fa3/uVzMe+m4H8NQ9Hefd\nMKTv1OuWJc7670l/qs11F9y4qqeiOgRMn1e56Q0XePJRJtVkMzF84upxry43/7jEvnzuzX/hObzP\nyBZUHPcL3xH19fU333zztdde+y7nCu8XNfTPsWlLfTrdI0gScjAVb7R7LLvqMkVV1VW3GWferLTu\ntOd83/+nB/CH3Ylnkl8uJFpVyfQE1ciYUuQa9izQ84KCWk3PCj0YtLwqhB/LYr5mfta2tgIejYAo\nJgBJqiLnCzbDOF3fDqTTkhfyCBQyeArFETrqaey9J3oZIMT2A/jycsL6+1fQdDC3zb7XSfSq2w+a\nwJo/kH++5/bObrOUFK03d7D+KcacCQiOiOMSjgi7XoFBgKLkA46jKMpBwLL85AoK0bRSQJZsI/4l\nK5nSW2e6iQ1isid56DrT+rQarNDM5WS2G2bGFYpMLWzlDbMdpMXXpQZN9L200K8q8uM3qPWb5dUP\nJASfKQe8VKsaCHuuu3vf0b/5dD5uJUkfa0M4uzo6tKUuPbQm3bg3OyLm52oD+oTTjPIZHMgZvGQo\nSqIht/PgGuVwzmv01d3k5N9Gd79hswLVHF4KBA4vIvRDgFRj31oOLsxIF8jOMb8zTQmnUtNp7X99\nO1o1YObdrByoBTTfgDdRqsHMqzac8exfAbi9kVh19zPmifMA7c1HOHkuYBSUsGmVsXunNniE/82X\nBUFwkz2qTwkPLnBcD1U57fqxdb/ac9b1I4D2vWbWL9y5qm3QeB049bpRS+7ryi/JD4b76+hdTx43\nq6Sxt9Tq2UeT0+cOyy43NYpAwdC8rFsJZNOERmzk+/4m/Ss4ni/8S+h7Cu8Y5IzFYisGYM2aNe8X\nKebPcelnFyBpntUh0iMreYrikmpOn3itIJh0NXnFoxkWdVxbvPtTVl6ptHKBmFec8XTUoN12wMx0\nSfGtkLZ8M5WW//WcRuQaVTmQytQYyW/aRruu/dHzpgCu6wPS6SzbMw9WAqo6OBh8UhDqXbOVWJzO\n3RRFMHpQ/VREScUYdzngDb0YQNSYcnnuokO9C6j9YdLuFLu34K9kaC99prkOYHJvZX1XM/4wh1dR\nPIlUkrpf4M+yso+mUiMAz0uIYh0sN4wpQCbjAo7jAzyvHJrN1AzXHGMln1CUIdniQlcK6nnD/c4W\nOdEiSqbfanZaduI6ztyfyasfd/et7/zUbfqMuckzviUFCjVXllfPlwdNtOyg7R++Yc3Kd2PkPlZZ\nho+1IQSG+OHkea6/hO2rAWlILvgjhHNvdipq9KO9WbriKLsf7dtXC4QB9WBt2qzBH1Vbnuk/bijq\nT23BitndbdkBMyX0rQz0vI4aPaYxRaYhbVXhm+fvs6Zbb80oMwDUSr/VTqLB0voDU+r+e6zB8xh+\no1b3S7bUpnqThbrqZasJtfwwwNLbjE9eKa/5tTThDMrKMjt2yJ6p5/sdFzOVmfX92UVDg0BhiQ9o\na0iV9cqqvfarw6fMGwpUzSzds8vx9XeaYuvqruHVQcAUvWx01KGfClQ01v/myrR17M8q3oNE+V96\nBB8QjovO/APIqpn34ROf+MQHdKJNW+r3721A9Gll051ks1J2IqHBeeZedetjRmHEt3pheuYN8sr5\nxidvUP/jBl/LLnHqHOy0g+127hIURev8vRwYnortxx/1nPJ09x5A0U2IwMu2eRneH9Op30KXYQwF\nLGsE/AHGKcp2aEomdyYSumnOwNUZeqYQ2wtQNZu9K/GF2fESQOMKLAOgpbfHWeMbhOex+1WA9oOU\nfjo37ivLlVK4dk4WQ+rtuLL+qWwnJoCjbyIIKMWMnI6owRFYBiMBRSk2jGmy/AJUw3LTPA2aTbMM\n6hKJMfA6TFLVMGDbIb9/I+6hhBC1HdPOtKqSpuWHjbZGqXKmVh5Rt9Tac+8UTr3C9/L9icnnai/d\naZz+34bjSIURVw7Zg8+Q47tskXsefFcF7x8f4szH3hD6AJxP3SH/5h4SMaO3cDBREuVwzibJgV5X\nJhQJuU39O8s6ZkzYv5z8eUhhRRzQnh5kxac01xrCd7MfTT5BLPeT8qQxgMGcvhGl4T5LnweImdxM\nLWB14M/N2VMdXWz7Wbqwv/ZAVTyUMGAUXydvfpKRuWm7WVwF0Nlg+MJAwGghFBZaYubgianScqm8\nnJYWxzALywOu4218ZEv1VZHnbn1j9rVD6KXJZI+TX+Lvc+nihnJgc6rv1M/eezTr/51+w7gXa7uO\nNJgjqvuVdM6/I7rwB8lZV40YeCvyRvlPm35M5+F/Do6Lzvw5+u7Gv3aKcM6nvokgSrJqtK7FV5Fu\n25byV6Rb9tDdTVNbumia9ujFYutuKqMZ2W9uWym0HVL9umx2C7pP8/uddFIJVWAlSW/xF1QgjMVt\nkGQForq+FfIzGclx5qnq5zxvNXwDumGXojwNSVV9WNMKYRSU4JjEdnijalixAH9YSHYB5PV2DM1G\nRAtmkP1XpnoAYi0AYpCUkfP8plyOVAiQSWPEACLn547Q1UxRhJ4W/GF8Pq9rP/mzkEzPTKihR/3+\nDigDHMeDURAGVHULVMNKy5oEb8B4v38vVEiSD36WMWZIckaVn0HUkk7Y9kY7CKnuVvwFHFlnHNri\nJVqpjLrbPZRoOAAAIABJREFUXrE2vRJ67MtOxmTL80Gv0xh2mtiyUz26TAhWqMHyhx/747t8Uh8T\nW/hxN4SFiktbPSURPTwi8OsfWFXVrF8IMKqG5pW5jfL7mS/CAPVtwxfx1d1kFD6e/Sjrx7AiXSHo\nS+xArsx99s/LJQX33JaSrzpmBEK+IGIYMJMCVgxA6A8kWr4b5OSRPsUZwO2jj4ZnSp6u7agFWLcw\nUzkD4I1HjRlXAq4kBB/9vls5TX79KX3vejGcL9pmwYSK1h0tFdOHeCmzOBIi7pVGAgygyTx929bZ\n1/Z3Dw6VhBxVO1qfa1kcCOfYQyWV/gM7hCfvi2d9xz6EKwqyB+yD6A46fdZ7bb30j+G46MzbEIlE\n7rrrrkWLFr2//M+/CytX1bXFDM9JuKIh5o8LDh6vW23ygeXWid8VRvyHYhxm5s2B4ZPNU74s33Oh\n0rTVueEZIdmR7kkSLPFSsUy8Q8yfbBgJWzyfnt94bqOd/KpqfMulApCVUXCqqh6FKtOcaNtfleUW\n2KAoOyyrStcd07zINLMeW4mglFIymc59iCJrn/CqzgIQZIBMjGGXA7RvoakOQC8EGDQNoG0PaTNH\nnGmuozNbudsrKN99EMAfJsst37eaYVEkmfKT0c8V6n7LoKl2KCVJ2anzUsMYCQhCpywvzvas1/U3\nYVwo1AFDZFkHMhlb1zdDjeNFFN1UM3+SO1a4tuvYnZ5j6ppgpzPy4AnW8NOl/z3LPOla+/wFhlZo\nn/N9Zc/L6ZbD5A0WFRElJOkh2zRS7qB3/6f4ONjCD7Uh/ODeX311wT+66ToO1AGi5CUHfUVb8ah+\n6CUAPZyn9KYJxWBfatAewGcRYtvTyX4mZCZzjMB0gki87ZjbK3s9QNDrRM6ZtFyaMNOQTORKEUzv\nYrW5lsbaJAPeU2qlZw1o9rRvYaq4tx9hqsHNO9Pb8qq6/oFAfEeW/B20OymJ8Eatsf6VRMLvTJqt\nj67yDh4SjLTgWqnNewojBWWTCifMGfrgRSspz1/5rPCj/9riKrmrteJynyX79W07pl876oKfzly+\nqANYubj1jOv7pwVpxxPUft8xiwPbj+lFDAixQv51OC46MxBz58699tpr586d+8El//4mbrjhSddK\ny3qBIASk4PBU91EnNFMvHqFt+rFReoo84hO+9Qs7p17B/tek6CVa0x7AqL7EHjzZix/xzC70AtPo\n9o6+DuhWl+CfAlHXbs0kVwOCcADQ9RAM0fUNgKblw7l+/2hAECoA285WGYKkY3Rhpdi7kc7HqXuO\njnqatgLZ7DuAWp0zeEc2A+x7BUAeTllvJ8LmOsFwAIpn5XzHWG8lhigDCAog7PotRVUAygQsww3m\nW1YxoGkHspoyMALqHAdAVbOxmSyNfCg8bxjTZaUIVqWcWbJ/OHanLA/y6z2CkJFl0vGYFAwZJ10e\nbFrp3Pi09qfvUxRRRI+iiBoIOnN+LL/4LTEQNivPdTv24Cs30ocXPfl32LaPvC388BrCd8PzvqUX\nf9dDyioF93HhRqfryfam9w83xFM9obc8zsn5QFZpNYeWZpdTRbPY/xTg3/xdM3Oqnnql77CGNoPG\nH/d99PW8hl008Lx2qodMg2WX9O+S6AHUg3cZaq9Smj5LTdfrsQ2E+wmidNY6SR/JHJtZ61pFQc5M\nZpOF5pgHpQ7X7GziUB1gg/6n+8TXat3P/zHgdZBJZETBLRkmCK7iU3zDiodMKlu3cP22lW2yrp72\nnVMqLxzu06Qzvjn1vsu3rH36cFlVf8VIJq4WR0JAxhETMXvTy8ksmyaLaZcNj8ePKflYtrA+8h9V\n21b09zFurU9+Inr6X30gHziOi84MRDgc/mcSl96GBx9asXN3oxyI2Ol9niC4xiG3q9GSgoniaqF4\ngm/HfelJVwotGyiK+Fq2GFPn2YNOkBacr25aJnXXS3llgqxJqiinGxDR5A2e48UzHjToerUk6rBI\nEKYAjtMMyPIYQMqKxaAC6XSW81UJBvwCfQiKn64m9h0ESE5j2Q8ZcjJA6bTcFbsWO5YDDJ4DkDeS\n3SuwRkBvt6b2Voo/mdv4wIsABb2TxU3PAtn+MF7W0Ty8BLkYZNIxx3EAXc9KzPzJskbY9tmG0Uiv\nALfrlkFzPD5UVTfCbPCp6jNI0WTnWlKFaqBQlBzP0Swzo4QHCVZKfuHWxLyf5OS2l30nGRjMyoV2\nSRWCqJaPM2IxguWu0y2LtqgEl6/6+6ojPtq28MNrCN8lz3v+/Pnz589/99PbLB18YFxoiB8gE72Q\nPbWMnoc0Unv+C2RiZp+uUlnUn9ySWy6Oqh3rtE3fTdmfI3+eGux3jwjVaKleBYrYinQiGlD61WeA\nlDlZ3ntLv83rTRPqotTnIwJ2qkcyWgfu6E+voPAJ3/67sh+V/P6NdTWXLEyLk6zSz7DmNe2B862W\n1ox6nj9cSWGlFAwpW5aKR5sUxVIxALs7KQSV4vElk68YW3HqEGDNwremXVlZHAldfP/01Utap5yT\n/Weyc3V7UVXOA572pclL72sOlR4jq/36sx2homNabTTusM64/dRdq/qLdrf9oeutlTv4l+I4ifTD\ng29+4yGJkGMdEP0TNb+IIQaLJ6tdL9C6MVN5IUYbz30hpeX7n/1Kuubb7F8ttu9zLrpXFRXn/Fud\n9t2eY3iOLQTL5VCFZ3RqgUlK8iXkpaKYMJJflcWVWcUWQRgPiGIKcN1SwDA6AMsqg+XgD4XiVA4i\nvp+yqbil+M4DEEtpV2nbDhDblbvins2UTAXY8xT01tQHLwLobgI4soOuN3MbZysoBucK8AUrw+4V\nxA4CjDoTLYzu87q2UR4VJF1RYvRqguv6LqiE9a6ri+IyyyqD5mRy7NuYMopu4twvuoM9YZzthVJH\nVqPIghGznbSrKfZn7lcWXhj45RcJjdDadjP8ZGXXi/aRferauwiWWjPvlt/8Ud7ImbatiPmjXl/Z\nP4l/l6ipqfmoFhd+eA3hX+d5922zaNGibM7jHTeoq6sbyAXPaiS+bTqc5cswoUbtXAMYpdWGfpn+\nwi2W6GN3rnBC1nqdpPY6t3OPYV6DNBxAqRh4KD2YSxP6255AvFHgmLpA9PNFBg20edk0oelUDdwq\nkyw35GO614pSGPCcPKwY7asS/v5+hHavTpt25FFGzKXqRtvNc858GjBKx3JgdXxUtZpsdWOdXkO9\naJtKQC8s9e17Ye+Mr5301kNbq6+fAnRsaB1bMwRId5vFE8p/c3dWm5g/3Nsw8/qciltRZXDXptSJ\nF5QOvDDP9R9tPKY+Ui8NAYlYv1yAfbhw7ty5n//85/mX4rjozIcBV1210LbDKJ2CGJb1/EzbRrQK\nQy1TysZoPfX69ofT4Zma5VJyntnS5H/+W4HNi1Nz7kQQjWRMffwGUdaFgkFO8qiQ6cRMeFJ52goK\nzhhNf911fTBMlrtdtx4eyloX181qqhlAJnMy7O71BREEhZ59nuhQ/wKpT2LVA9gHER7AkgESRwCs\nGPnn0bASQBsN0HmI9b9FrQSEYbMBtOFeunfmOuSc3ML6p3jzKboi/N9GDu2jo550N2VRRJnQVLY/\niaukUhMVpdY0CwBVLQE0rRnOk6SVlhWG1X/OlBGkfDm9xuwZb9ltqUyTEBhFT6On+PH55dOv8r10\nvzj9EjlcYs24Xhg1k5JRYnGlM+v7TuxoqixK44tq4ej43lftsvOcts2upN7yP0/9Aw/xI2kLP7yG\n8N0g6zLefPPNfykJFI1GB3LB31Ea4+JZUbavAFS/B1AWpfOlTOXDjjdN3b8suPFH+pvfNyxL3fJ9\n3yuXUN+hq5OyfwMgbo8g8WdtKGIrUj0nArZ9rCG0D3pmhmNht7yZkY6RolYznbLQr21GYpUlRIGx\nFcbJXH2SfvvgvtL2AclCvTgnkO8vKATY9ag1bo62/hFOnZveul6bOFUIyJLoqkG55WDPqNMrW7a1\nVcwYTK87mN3393duO+XG6KSrJv76tn3AoAkDyiZA8ue17Om//p2r2kJVocmfm7puSa4t1Gu1h8dc\nMBLo6ckRaJMxa3zkhNmzZ0+dOvWOO+7gX43jfuG/ECtX1i1ZYriea5k+D8uybTk4GetwxpPN2EFP\nGy6JApVnixWnqQd+Y3/ybqF0sicWqIu/EHzrfuuyBYyaZp36JS8dFwOFttlt9nS6+lTLkc30KM+V\nEsmT4JCqFktiD3RnO04kEmPhaCYzAVZBGTTnfEFw3UIElYKR4EcaihAA8M3GbiBb1zT0HIBYHcke\nwifTXIc4CqD8PIK97LCDqwASCQK9DZiOrskt7FnJ2l2ePYT8b9A1Xqi9hc79AIefhQCyjudAvig2\nWtZEem12NkyqKI6qbpKkN/uYMqmUoeubIZyOb/L7KzV9labXK6IYLK5y3KRndQmZpPPig27pSKN6\nXvfo03h1YeakK9VX7jGqr6buEXXU6f6dL2v7nnT0fCl8Mj0b8EzdF/jVUy/9vQ/xoyo68+E1hO/I\n847FYu878eGCOTWB7S8ClFQA5EfyghZA2aUq4YTwzYx6u9Ez3IzPToefJnip5Q4wb4Eo8f4fk0kF\nmYasOwhkvHPp7m8ApsV/pcpvV1ZzLfEYHxF02ZONvX0ffYmlhSXif0SnThr5+K/u/cOTD6wd0vSZ\nEdYCwJ/e0pcsTGSpOomGTN4YICh1UhQJVIzklzeKkYmZI/VuPGF1xFVVKBzkO+WGE/7cHexoSIQq\nCnxhbUi0LOnoD1696eSr+rrP0NGQGDJjyKblbX0ja56NTZ43bvjMYQfeyFnHNUvahkTLgOFzRmbT\nhOuXtF4990bgS1/6kt/vv/fee9/tU/lgcDxG+i/EF79YaxhbHLtJDfgFRZNFVdE0fygod7wuUGqO\n/oLnK1U23ZcefaXq1whH7M7G1Oz/wUy68Rj+sDlorLzxV0L5GMfqRnJRRcc6rNov6fqbop0t13nZ\ndYeY5vWStA26IQl5sAYGgwVlqrqDrC8IhpHCEWmvE5oPokSQSulegBgmuYS4gRFj/+LcdQfmcvBl\n0jHsYoBYStjydHaNl2pn/wrMETi9tYbJZgAtTBycO7HrAZSxnrOAnsOQrbk6hFbspTsIb/e8JLTB\n7kRiKGDbFiDLmmmeJUm2qt6dSmWg1rICtt0qi992rM86Tp7mGywrDg497VsE2S/kl4vFQ6wLv+sF\nCnllEY3blZ3LWfeU1L6HYdE8ryM9dEbKN9L1ioyiKYZSpsR36cXjMj09tj79H3iOH8ny3A+vIXxH\nnvcll1xyyy239H285ZZbspya96iCGDiyh+0rDC2Y09r2cjQZ5F6BzSFfJLkyu2hoM2jta0MRyfP3\nk0WtwBwO3JN1BwGkqG73m0nBHZyMl+X+G70QncEkBxS32g1GqiTRUYiZ22zS6GduufHrjz195O5H\n9Ntvk0ZEmP8j5z9Hffc0+Vyxtzc9u25zRl1FVp60ah5g4dHZkPQ8pbNVHzZCTluq4gUrS4IVhWWj\nQn/JHZx+Q67LzJk/OLW7x8vSZPrWTpw31kJIxnq/r+z3hTUg2ZPzX/Vwrgpz8rzx2TRhqj7UF4i+\n6aabmpubn3rqH4nGvL/4SP6TP+SYMeObDQ0J0CR5um252I7ruaZckWirl4MjVcVhzyOpygt8ikvH\nNmtotfr7LxozbmbdQvPkz6amXy//oIZgkX3pfOKHkGQ8S5IExdzrON3IAkJ3Xmi1rm/NRkEDgUF+\n/580bS1Uq+pRGJSlj6rqSMC2CwDLqsLJoJeQ9JAjOAcRfEhhrBCd53FwBdlCqaZlyJUUXsju5RRe\nDqBGvaJernjRdOHgWvxfJ7Y+NxIaCdC0jqNTAdQpAOYbOMOxIhgxKi9h6EWCJVI0Fl+Pqhb6/WtV\ntQ4mQHsymTWHPkAUw6Y51bZTkvQ6eLo+VZZVGJ5MneQSzMS3e26LoBR4fl3QZWHIKIJFav0Gue4F\nhp5qnXw1clF68Bm+hTWGDYOjurmDomna5l/pzg4xWJFpb3TyLu5qWnvXwn+klfz729H+w4APryF8\nR573008/PX/+/L6P8+fPj0ajc+fOnTt37jsd490iesIp/jd/Z5VXcXApA/RFjT4xFzncz3wJ1WhW\nfxuKfqsJqBEpuSPrDuYGetW3ta7bMpmrsK9WzH71bbXji6b57YDQ/1JWu+8yzCsx79BTy4Ah/oX/\n9d++/b0Bycu/4Lv/Pk6dScaQv3vdi1NLfpU7eGY7oQhZ7owWZnetMelC3nrUlRQpr9TtafOO7Jd1\nWTAyHGkfdFLpyh++0fR6y++/vLJp7dG3uYPZA76+sE7N9+1d3a9JGCjJ84W1U78/a03tYWDn6o7S\n6l76a5F+oC62c3XHiDn9GdNsmrAqMnbgfV6wYEFtbW1DQ8NfeRb/BBwXnfkn48orH1yz5lzP61CU\ngCsc8exmyTdYEDNubLMT+nSm9AI7rypg7cNOZUJV2tZ7jfHzdE0jHPF3bGBMjbb6Pvu/FvnX1FIS\nEfKLET3PS+JJCHn+/MmO0ZQxLox3JzxPykZEXXdQKvUZ15U1bbmuhwBNqwJEMQOYZhrQtJ1CsAoj\nmBPU9s3G3IUaxVwFVwi7nqMkKx2sA7T3cLA35hmcRTyZW+7aQbbVrW9UbkQrAmjaLpibAcQCQBB0\ngLjNpl/StgagpxXTJtVuGKFUKg+S4IdXoRLWplKjYXcmUwWvwX/KMjDWMHyyHIbfgpFIZjxxNJLn\neRl8frFisnd4p/zrWxKzb7UvvEfbtpQT5uodG5h2rRsaYpsh+ZnPy/6QNeIyWwxYsTYjr1ogqDob\nnfSu3yzt/V5/Jz5iJNIPryHknXjefz7yvsgBzzoxkiq6WN+1yp/aQlZftKkWsPKraco5fwP7Lumh\n/tp50+z3CNWeWtU5pqjOtnPXpjhxiEClRr9HqHhxiAhuP99EFSSIAHJmT0nwj1dd89Anzg1nRN+W\nOhuonimvWSt3xfjp/dYvHhFPOiE5SbwNkMOjc6dTw4C/fQVjagLxPeLB3ZnSEZkdm+U8nyo7pVOH\ndjV2vvHQ1rP+59SLnjrb6DHO/OFpj1/z1t7VRwe6g0Drho6LHpmz6qHcpT5761snXDsBCFfmJdol\n4Pl7GyfNzfFoan54+sZlbW881903AvT0mOuWHLq45pq33eply5Z97Wtf+zDYwmyt/b/2Mj4OmDnz\nK08+aSjKC4bxFcdpwLHVYIUkJ11byy+a6ndf5+jSzKBzvfAYdfvPC+3GgnBB+Yrr4q7OuoWpk65k\n/2pxWJSiiOUrle76lD3jSrF4CJruaBqSz7R7PMeACJQ6bhNUQHPWL/T7BxnGkVRqE6zM1k6kUg6k\nbFvz+e4zvDbwSKc9cWguKGofBHAVwEv25HRkRB9A/jcI9GouOjEh3VsX23XIaz8MoE3IVTc1vUx3\nPS22Rz6A+QbgBc4HUKpofBOjAzXsuZUYMUEssqzxcJLrpoC8PAlKVPUAVGZ7SIVCKSjzPBXWWNYk\nGBIKdSDul90mVdMwO1AsNFVQVGHsTPv0z/HWryiKSLGDgCx56OFAyXDntLtlxEzjTjq3+IL5FJ9B\n11Y5WOBmjviLZ2zdcgxB/e/CR8kWfqgN4T8Nc8+Kkm4TnDIny42uqNFjywAKon47Vzhh0G9ubbtf\nTS3jv5DOWgAnJrQuF7xpDEDKnExmFZnVZiZHDfXs3npzu8F1qoFEV2FfvNQ2c5v5fc7Jo79+7mfy\ngavvHHrPD3NSpQ/UBm/4igB8/gvepMnkty+k8ZFkSQ3AzoWpygsAMS8MZJr2C6Kudu4WhkdEz1YE\nO7XvyCm3nTH8pLIh0bJNtTur5owojOSf/4uztr3c5UrSQHdw0pVjgHFzRnc0JAAjoRRG8rNrW9vS\nyZjVFwXNIt7mOZ46cGT4nJFvPNlRFZnw53f72Wef/exnP/uOD+Kfiaz62vFa+w8OmzbtP+WUr732\n2qWy3ASdsvwd1x1kU552S83uetudkHaL5UA4T2rR9z0wvqjnjcXfO7Ji/tFXHz3yykOvzJ87s/Bo\nUWudtuah9MwbWF8rDJ3snHNHcNPvnc/8lPwwTsyN77A6nrOtO1Tl16HQUUUep6qbNG1vJjMVVqXT\nMTjB7x8DXYnEG7L8U0GQZPlhkBxHQw5htpLy4R1G8KFEBH+WszYFwJ5I4TSAnu25L9PSu5Cq89Te\nCV94JhkTINmTM4Sexa7n6VmENApA6iW2dS9AO4WumfgKKIiiR5DyMNOgQ6Mg+BTlFTABRQH8mhYC\nvyD4Ac+rkOX1MM4wAoKYp2lbRQRZdVzJ7+mCpOje3nXGyFOYUKN17mDfqpRp8urPEkOraVyddCVA\n1X32tJ/KW/7H1cLO0MvFoy/IbrOoj0x1t2WY8PVbf/4PP+KPjC08bggBIpHIaKU+PfrLrqdn1QLl\nUM7s9RVOGIEZxHLVFKZQgdmQ2zlUoxprAF/LTUb88VR6FuaANJhSo6af8ScfM63rswN9aUIldl86\nfSXQHy9tuy2TyTFIC8sPFoSTfYe56Kul9y+wge6YlzR9t9+hvrmt4A9/cK+4wj5ZvYnBNYA/uYWy\nKPEGozDCyoXC8NPUkM9t3C5lOlXZET138v+L7nzozdNvnw40rGiaOC9ndDv2dOWVBWMNueK/1g0d\no2qGA5FzK5bduXXv6qPhMf3Fgif/9ym//Oq26PWTB97A9iPJsugxlRWT543Xlby/dMMfe+yxCy+8\n8C+t/afhuOjMB4Qrr/zplCm3z5hx47p1I0HQdcOyLhSEC/LyBvtDhxRrvywPVoXdadOKUxKUutY+\ncf1bv/vJ1HH9mf7Zp0Rfffx/9z547aRxVbzxmHZgpTl1HoGiTNqSH/iMlF/ilRR7UkbQxyGucd0N\nguhLp6cKwgpJ8sN46DTNk2BtPB6GPMeZYduDLWuSbYehSlVF3B56dmCfiHYK5i7kiJd+CcB5C6Dt\nMEYbQKCXoBDqbYJt1JPsLRcWi3JSiHJOpJdhF1C/CiDzJwBf715IAIkUzWsAUuswZLQqQFH2WNZM\nWd6fyWSnktkXjgmYpg1/sKxxfn8F1FrWVNvKc+2Mqg5LdR+gwE+gwD7na9Z1v9Ke+GrwsS+7vsGs\nfpRJ10gblgb21KlrHzIqZrHuNkMJE4pIgaFWIkXrcr3kxFSPYAYvlgQ0cf/GzZ3v5Vl/NGzhcUOY\nw8ghYcCquk578SqAvFy6y5V7XZ9wjW7k2lBYgTl09zeOVxVP7alNt2VzmVHVXdd/XCGsKmHswf0j\nvWbPJ7vkvMxK1XkDCAi7snHRcPkzF9+etjVtV13OFk6embenXrz7DvObXxPn/mhKV3BoOlDwjdop\nTz8rS1JyYuunATEQBuStd1slVcretfYJX0kPqSKQJ3qGJtpDppS3vnngE1+fCiy9ZsUn7851T3zl\n++tO+foJp3z9xDV3b2CAOwj4wlpeRXjFPbuqvzS17/LDlXnxtJxlh/Z/p4JST/YGjjTVtdz+rbv+\n0t2urKy89957Pwy28LjozAeBESOKtmw5K5V6wO/fq2l3JRI3BYPPWpaTSDSbqRZFHiMpmqLEdWfV\nteeEmzYsnjr+nclu4XD4rV/e/p1PBIoGVwL+5bfbF98vjpsjD5nA4BH4RYmAotdJUn4mHYMKwxhk\nWe0QCoViMBI6oAS2wzBVbYSiUCibyJBRJnj6cBiHvVmwDwEoEwCE0wAYTftbgBB7OXslQvxg7pqS\nuxF63wnmUOJDAQKX5ySCRR+JGQBC79yxewG+GgQ/UhjZh12EFaPgRMxKz25BetPnE0BPp32C0A7t\nplkA7YZRCG9kMifr+jaogkG6vh4aTPMQbjhjxhECntitCDav1/of/6pRdY5pOdYnb88rCDGyxjd0\ncvLk+aovrO5ZpXZsM4afy86FcrDMGnSZcvRZQQ874WvkWK0o9rhWZvv+95pa+gjYwuOGMIciKQZQ\nFnUp0dd915BznXUTvmhfo0G5j6WpRvzOxr59HVeWOt7CzTZYCKvHmgQj2ZRKXzVgoFKjnsyqTKq/\njt6zZcC2cvSTE855ZGJN2YV3nvDoD7v6thlzSuGm3YEr7p1YVqlf8PXh9XudXXXJHz4/rmhEoda6\njAOLE0XVgJQ4oK/7P61gqLrhHqtxq5dJ+XQrVCCGhucffK2h4a3WF7792sia4dlAaOxgHBgSLfOF\ntTEXjHjr/i197mAW0284wfP1d1kCYg3xnpidjh2jrCaGC4/WHeNa1S9pmvVXldUqKyuvuuqqiy++\nGIjFYnfddde/JGN3vKDig8Bpp5VWVS2G5zyv1XWrNe162w5L0irHbRG8wSmzIMlMN9PwyMLbH55/\n7d882p3Xzf39bZcW1s5LXfgTQLd77K640LLfKyvwvIBoF2ia33X3wiRd3w0VmrYcNAj4/TGo1PV6\n8CmKBwiCDzCMPKxteEMggv8sslUEWW1FN/tLaCHdA3i+XCMqT87VDgqCLog5Qygm1ud4oSAcfRGg\nbQuJKQByb2UhEiBkVqJGETpIXMzBp8AUPAO5BN0yjHxA1+OGUSXLT1vWRNhoWSNUtQEmq+og2ByP\nD1bVIYqyxnXjluVHaWaQwxmfl4pHyoadOvlzzLhWtlKA4S+hq8EaHOXwKqNgvDn2M2JomLb2Lq1j\nVTIvip0QRV+ypwtBIbMTMeRpM+KHn//O995ZkOTd499ddOa4Iczhc3NraFyBFtaKBmUS46XOXbQv\nAyitoXtlbiNfPytSEnpLy52YmexKx24YcLBj5GbczMa33WfP7vGll5pWf2eiVOJs2r5qGFcDQ8Z+\n74Jv5Rg3l/546oPfbgN21SV375WueerUhdcdyK664s5RT93b1VxvXPO9IYWD9BrfZxheo2x9wCM/\nU/oZoyCiOJ1ye7PX3eo3e9JdZuum5ov/9DmtJL+wKH/fioMNqw8DL926JhspBUbVDG/ceLTi9MED\nL3XpTetE/ZgI58v3bK9acM2W2t19I/tXH9GrR6YTzsDNiijhb+G888778pe/fPbZZy9atOjmm29+\njzU1H/lqAAAgAElEQVQw/zCO28L3EVk5+5qamu997wJNey2dvsuyCjzvItftcpzrJWmqFszX1A0j\nSnf+8bkHLv/0u+2AMXVsZN/zD1TvXaT88Y54pMb+j+8pgVJvwhnOeMl1g4bhwiT4kaZNsKwTHWdp\nOu0AsuwHVLWU3rbvtq0ClpVGLKCnHX6DGPaMDQBOtnV2JSDQgx2kdQVuAUB6hZh6K3slAaVDc7Zl\nl/2BoGr1ducOFAI+u3c6mPktkPMFgay8vrsaxnD0ZdL7EUKk1uM5hjEEkCQ/DJVlVPWA398Mlaqa\nFT0ug40w3rJcSKhqjyR1i0HFczLCmsXpU75sX/STwNqHgNS4c9m/2jjpSnXtXUZkjtK41KqYw9ZH\nnfAYY8StTsqjZJp25FEpWKF7ITX5lBYotjOmLQ51hdKf//yFv/9RvwP+fW3hcUOYw+xPRIt66gC8\nFMXzMsIsNfYagBIOiLnCCcML9qUGnWwVfKrOf/gmp+srsLTvUEYmiJvbTM180XFugmMmXMl4mWD1\nxUWzh7tUyuyDKMTGz3qur/lD0fBAWvTtqks+eHvrhXdOBi65a8Ij38zZwsvviMy//tDdNxzuSunx\nLm/2vgpr/z5z1H+rR56xJs3LyAp2prRSVXTphHknnv3QBQd/s103jeqvT7rgFzUtGzueOP93Z9zZ\nN28l1hB3Zf3Ipq6BI3pFack54+tX94umulKoYOrwWH1//nLlvduHzZ2eEpQ+N7GpruWM6LtSf92z\nZ8+nP/3ppqamv73pB4zjBRXvEdnu9tnSFGDevDNvumlWKPSKpm02zWmimKdpS0x7mmGqY0YPq3v1\nlpkzpvxdxw+Hwy/Mv7Y8tZ/iCPtXS3mlQuNez4i5g5ptq8m2T5Pl/ZaVhjGCEIYjkHQcHbBtDTBN\nE0ilKmErehizC05ADaFEUCdhNxC4hMwqOAVAKCSdxIrh5cSVXPFTWWPmOW7GyikgiuZBVc3FS/Sy\n6YCbsmTpDUAkV1wrZFYCnl4DIA8DyKSE4tM932wBWXB6YBBsSyazJwqaZqdlZbP1JbA9Hi/Ny5Og\nxTRbNC2iqkM0TbWG6IyY6l7yfd/a+4HktKtYfB1NO5Tnv8NrTwiOS6bbJ5vkRwJ2i1Uxh5bfCmVn\n6a9/WQuEUvmzMuJo1+gytWon72Yl/oAvb1gmecZ7T5b/qyay7wuOG8J+jCoGMPIiZBrI+7TshH2b\nr8GKyXm5UKEVqCbe24ZCnUXbz7SjP0h1/gKift+WvuNY9hx6s4mCm4BTA/5j1LexJxiZY9TLAMEL\nAsOm3RZrTQ4cv+jOiQ/f0XbtE6dmP5ZU+if959Cff+PQL7996IkF7Vcvvzg0YXjZlMETziwvKLDP\nO2sJRVHd77F2AR3NQb0jGPBOuOLEcddMeeWLv7Na2ifOyxVaxNszwUhZ41v9bSJe/uGGmfPPFvX+\nKpE/3rlx3A2zI3NP3PZczlBtWLw/dO4JQDzWLwKnhAuAwotP27eiITsSq+v5zNyB0eB3QCwWq6ur\nu/baa7/whS9UVFQcF535t0a2qdnbtO//53++eMUVZklJhaYtyGTOdz2Ki5+887Zpm9+Y/4+VPIXD\n4c1LfhJZcZO26depOXd65/8vaoFbUSCETdjmeQHDcCDo8xXY9kxV/XG2M5plhYFMZgLUwTBVbcFu\nFLyoQFxwXkaO4HTmRC0yuaeva9uJnyi2/JHA5YDPfpaUTaoOsFIydh49KwDPTEtqTn3JOPg7mpYY\nsSs0tRFwxVz+26d1AbgdANJEqKG7zDv4M8DzRMgAfv8hGA/bMpmhUC6Kfk37WTrdAGshkUrtlOVf\nOs4sw/DbdkG6sg0v7Z13M5VRR/PrT16p71zlt1xOukEdfgqTb3dtXXvz8XR3D3ULBS1EfiQgtVjF\nczLKuFRbD6U1mvO6pI8SeupI/1bSBqU6t8d7Zt7yrX+ksv5tiEQiS5a8D8f55+O4IezHkNIw2aZL\nXUsB1OK0+wt96y2Jht64QSjq97YAmPVa5nUt/orR/dvsGkkaKCIa8csbATXzRSPzBUDAN2AtPq3W\n53vb/OvHHiLUR2bVn3n3mbXf3tO34oX79o28bHrtd/tHxswsOdSQaW6TP/XDauCC/42ecv2EzZvk\nsZOUzv0d0cOlhm1rsc0jhh2prDTGzypZN//FFVc+ZXUmRn2qMnuEl35QN+LcqbPvPWffy7mS+dUL\nN1R9eooW9o2/9sSlX1lFrzuohv1AsjNn9rY8d6SsZiIQOvuEproWYMPi/UOvrwFKZo3rSxMGYvl/\n5T5nXYdYLNanGXTTTTc5jnNcdObfEdlYKH9BHP+BBz6/ZcuXrr9+zpw5Sx5/7LNtR2tvvuk9aV+E\nw+H1tXdPHV0BaLXXeNWfJZVyhkmSvE7T/KK4C1aABeNFURGEhKY9bFlBWN3Ll/FJkiKICt4Bj8Ge\nMhhADCKEUCLYfqgB0ulz4BL3wC+yJ5XUYXAddoxUnZkZgRUGcGI98XI32dvDwbMDPXVYp8jKSQCO\nQXoFIOonA6KzB8DJ0uiacBx8NYI0BGUorBRFH6Bpu2GEpm0xjBM9T3aciZrWAfWSNEjTArDTslKp\nkU2cdpn39aXCktu1RV/w/INlf35m9p1u6UT0cLIkyu5aa+z/M+QCa/INtG7PtB2h/gXBFyIQCahx\nu/RK9fWLtWDI8J3hOpLONk+pFLlIVu5e+cq+9/Jc+vBvKjpz3BD2ozJPZvtiyqJ+tgCpwCxStRnh\nYUee69txeWjv5XktP7IybfmxO9QDC4zDtyvO+L59HeeYsIAshci5g9lGoAO7FzV43om23T1w+4Dv\nVceeXz7+htm3VIUr8zKCdqAuBiRjVlO9M/HSsdO+cerD127Nbvzcgv3RG6Z/4tYZi7/21ubnW4DD\nW2Jqafi1dUplpWhnrOiw348s3FQ5RrZiie793Vc+e9HcJ865dPGnNj6y/a37t7x61/Yhc8YVnVwO\nnDr/7N9cuzLWEI8fcYd+MgLkRwotU6DXHcyesXTO2E21u2MN8YLqHKG0/NKZu353ANjzcmc4mlV6\nJJsm7KzvPilyTPeMgcj2A6mpqXlbIOWmm276kIjORKPRf99Uxz8Z2VdetrXnX9omHA7/+MeXvfDC\nT+bNm/WXtvm7kI2RFjz1X8a8XzCmxvvkt0mn3SmlrtttWSfAvqyAtSzn2fa54KjqJlWtD4V+A62a\ntthxMp7ged4EgQQksBvwzRYyK5EjPp7Lfi1IAtqQy7Jn9MztQMBcgRPDmgiXYNSTqsPURCX349dK\nzxEyPYDj7AZwzIHX7A9PB0SlHIDJ+GZiN2Ad9rxuUXMTiVJA04oBRTGgQFU1GCnL0v9n77zjo6jW\nPv492zebNqEmEAhLR8DAoqCCQYyAgt2gEbh2ileKShV7A2yAFYNwLWjAcAURrBGBgIgQSugQllAT\nAsmSZHub94/ZbEJHDdfy5vfHfs7OnDkze3ZmnvO03wPC4+kghCkqqkLVercwCrk4X7x7n7ZtX9nv\n8V31sL9eK8Ddsj/fP0H7NKNtHQ0sESfyaGCJ0Ab8yS9o1r/jsR3DWSB0UUipoHEU7iIqVa3xyKom\nHl+kN7hDhdFqdcz9LaV6z4HU1NSMjIyNGzeGK//89R+oWkFYhSdH3qpZ8zygVrkBpFQdPwMYRwZd\nmgr7p+VHxumD7coKnvY63wPAGT7W6UqBzPBXvy8qrA4CbncvCNXsNepfc7tvdbuaQpXm4fM3Bxq0\nL1KCOa95MWXBSwXAFy/ts4y+AohpGhN/TfOVnxxZv/ioUy017ZEoJUXf+mG/Q3tcH96bs+Yza/s7\nWza9vUtxuT5C68fn6dSnvquofMSSPtGR4si6kGHTftx5ZEvx8QPHG1hCeb56yZh4Tcvvnl1nGd89\nfDFdxvf48em1YXUQMKd13p1dvPzN3QmDrwp3qzjuA7yaqjx6xU2Yn3Xg9tQBp0+vYguVJOlsfHh/\nEdIZoJZ05kKgLGj+lBq/kiT9NOfluDXTWZ8pfp0r3zwVbZS7rQxerbbcbm8Iu6AOoNebvN5UgyGq\noqKfEDEeT2+vN04EXcjItEAIHF8ARr2SzB52WyYDsit0/wtVIqCJtBg8q+EqwBTI1fp3E3ggEAiF\niHuLl3ltSmCq4gcJ1a8X/r2ALKKBqLhLAbVGTcX1lL8r625FNsqyz2A4BAfLy5X4MgkIBiNgr8PR\nIiLiOER6PPVcTTVy2gTMV4jiYrlOS29yuiYuEXC366/7ZjiS2VSRD2g1QUBllAA1bqLM+thGqvh0\n3c7nPXoz7gJDVN1AvfHawyP0xiiP6iqtc40pYq8sd/L7zRMmfFhTf9CQIUM6deoUrvzz13cf1grC\nKkiS1LRdP/1X9wQqK9+GCjOBPiJEP213WyCUTeHxVP93U3WaKtY+pyuFQHG18NEOEYZwukUpxMA9\net1XlVue8Hpurd/23T4vVbHS9J7Wa84jW8ocspQUCtrsmN5u/wGxdmlxt8c6h7udKHK3fO7urvNG\n78jzrVtcWHTIj0p11W3xZftO9B/d4s3rlqqjond+VfDfe77+6blfuo/tcvP7qTFxuvKCKsNs2QG3\nL6jVS1XG2xhzXP6m0rA6GJqKhLiKMlkrmcJbXBr9wtErGt9ftcxX3IQGW+TpL8ewLfTcj8TChQtH\njhx5jg7/GyjXX5trfzYoxu0Lr4Zd48jLy5v3YUYvqcy4Y7F8yyskWmRjXWGQaH9Ary/T63dDkcej\no5LA2uPRQpnH0xF+BIHQgB3RVmgaISLQW7RRKUAwUAEY9CFpF3SGXPsicAQIlv+kqWTh10R2MarK\ngaArFE3qd9i8pTcCKlXoKTYEVwNBVTwgvBsBv3MdEBFxGF9X4TpKYJMIBGR/wO3uoNFsgHbwo92e\nAOucznY6XR601mhMsMWX7AgktSDrGbleR/mu98ThLYDzsvt0WcOpY9ZqZECu0xpwxbaivMATaaa8\nIBBlBoQuyhPXVwS94sA3FM3xqySiUmXPPld5EaY0taGZx6nx+a8yGI+cONH1/fc/u/j/3l8RtYLw\nJLRqVkc29XU6XRzPBIiolGSayoY2VadboDR9/q5QVWVJp69KHzTqF8hyFQcpoNEokuYNl2ugskWv\nDwWq6DT7oUnDjnlhGjMgpmlM8VG504PJ1Qc5uMMl9+z12aBlSnxm7id7ors2j2haF6hzeVtNq+aJ\nNyY3vzRy649HA/7Avo1lbbvVaz2gxR1zb2jdt4XUKkYZ/9oXrvhlYsjruei2+bGprZr9q+v2zM3h\ns2zPzDO2alqytbD6qR0+OeKqkxi0mzx+W8kxVdguCtRLaVvwU2Fn86m+ojPaQs+GN99886+QaG82\nm2uVwtORm5urTMufKAWHDh362Wef3X333VkfTH9swDUczBVLnuHyQfLVI0XjZMfVV3gMOo1ms8/X\nDHKcziTY5vG0hw0QbzLZoVD2R0MdkPS6YxpPtsk92e/4BZCDFYBaXUmjH6zAmwt4XDpAVjUTrtBj\n4i/LkZ2bAae7i7Il4GumiE+/f4eyRQQOARpjG8AdaAJ45CaASnUNxIrgLqFtRaAtchvwq1RGvT7H\nZLJDvMFQBo0NBjUYPR6jql0h3lI8Qn7wv2LzIkC+6gGWz6COWeNztt2RoXEc7VX+Wef2LVILpzcJ\n7I3cNs0X3Yotc5zxKezL9KujAG1UQ2/CxzrHXqcpBfuKiNgWalN/bON0ET6/f6RWPUWWDzoc96xa\nVfZ39PD9cdQKwpOQ0s3sjehqPGHTuX9GyZfwFwDlnmb4VwAISRcRVndOChatpv+t8PvrGk9Ov/N4\nooDoqG3QoXKLEiRdAA2M8T8FouxK+ImCUmuZv27DnNmHwhvzs/cbLmvVKL1727mPz3t4zY/Przli\ndTe+/XKgwnp8y1vZ+oQ6e9Yc69o7xmP3PTClVe7C/cUHnKsm/Qi0T29VkF2VotB9TOd1z2V/N/Tr\nK2cPrmtp0ii1zd4l+8J787OPdnht4IHFO6tfv7PQe3TJZk6GLrHBKVtsh5z3p1UVo1fMob/ppamQ\nztxwww3n73qR8XesaH8hWuzv1nSzs7MVd+CfYg4FbDZbv3792rRpM2XKlI4dOwIvPjZE89Wrcsse\nJFow1eXoPtmaS692clJdna7YYLBCK73+AMRGR/uBQKCxEHYhnxBiF0h+5yGTKtFR1M/j9hO0aTQ9\nAI1GMf+c8Htb4rPizfW6EwHZuVmlC2l7QkQJ0RhAjlWEJf4iWA4EQ/mIaE2XA37HOsCv6Yq/wK/p\nCgV+/15Aq26tEXsJuJBP6PXrvd7eKlW8z6cC1GoAv98A33mSjsmmeqJOa7l9P4wSRonSAqDRibyB\nB6Zu+eSZ7W8PObFuoWf1u5+MSvnh7dH5X71esXrGlGttLSKKAH3xt+6E/lhn+NVRqCWtTjIc/kjn\nWOAJSm7dvRrPOqfDAXV0Or/f19tguHfRouUWi+VvGvn5R1ArCE/CkEGpJke2K/E12VkE+GL74poD\nYEwluDzUSVS5BqsHi3o8kVAAGPVv+3xpQjSsPrLH0wuGu5xVWpHHfTtk6TRTvd7bYpN/aT/r3z+8\nVCVpfngh95L3hrZ+98Hlb21T9L/VM7c1G3Wjsjc589HdWwOFR8XyUd9+c+t/cqf86FFH+Hv0LDE2\nWZTlvf7eelmvFrS7XEp5tENQqH4cuwzo81qP/977LWArKF//4c7CvKMthl8d9gLWu8p8KKcAWPrA\nN21fuxsQsjp8MZtnrIgb3CvK3Ihq2PXmcu/JmR6Apf+NylsyrDqcMZjw3EhKSho3btygQYN+64E1\njtTU1L9REKlSmzMjI+Mc8nvq1KnZ2dkZGRm/9WX3J3oEFUybNm3QoEFz58599NFHq2/PzXxCLH8X\nEF9OkB/I4vL7qCgJRmq9SdsDAT/s1etNQCCg1+tz1OoTUB86G4wxOs3TfkdKMLgd0AddOucMv3cd\n4Pcr/NqbIC5SvZ6AjUBrwOHq4DkRYlxDJbnLywB85aH8Qu8hZY/LFRKWQc8uwBeodDqEyo5m+Xxu\nwKCvF3RvleXeQvyqrJKF2Of1ttfrsxyOlrDJ2SxRdFARGS8PeEu+7XXx05sAGq1qwbhPUjn0w+y5\nU6s4KFatWlXdvz5+aNqeHzM+Sad+jB6dFOHJc9ftT+kMv6jnNr4p2w94IvpTMcMQbdHLXTW6sf5A\n/UBggBAau33goEGv/U0jP/8IagXhSZAkqXl9GxpJaJoYD7+NwRxlPAygksK5gB5/lTCrHizq8/eF\nJUb9MJfrPqCsrCFUIx2lg1rs9fluqraliylivTEiGmjYG6DNtHu/nbQGcNk8HnXIK9nuw8cXDM35\ndtLa1q8/GD6y4ONVDUfc0vLdEUkzhhmu6mKaNDLxs5eL3vw85oarjh0XPfrH5q8rleLVG2bvumdu\niqPE/v24VUuGL9Po1B/fumTTl4Vdn+/T54PbDnxcdXltHr7aunjftvnb4lI7KY5AY9ek/MxQudFj\nG47HpXbSmBscW7EjfEhQZfA3buiwVpVxcViL21KPmlAdevbs+eCDDz700EPn73qR8TdKLrRarePH\njx8yZMg5Llip33nuPtWhZEecniP4v0ReXt5NN90ELF269PQ7qmPHjlgLxTvXy3e9C3AsX7j98r3v\nMOYLf/tmJLmdTlt09C8+nzMQcDkcBojW6/2BQHEgUAQWWW4BOByXaTz7HM5UQKMJu+p7qWSnxr8W\nugF4Zbcn5Krwlf3qdcYD0A/Ale339geFjEJS4uBEoBDwakM0+ggdagm1zuvrD/j9h9W6ZuCAVkIc\n1+lWOZ3toRkY9fptqrYHMdjknqNJHcuy6YDctIuYPYj1y0f1uWbQTWf4LxYuXDho0KDq6v6gW1MP\nrMp49JJs4T1GrCVC5Hki+uP4WK1vFXH83QhNnhezw54qAvqA3wsv+P3doNnKlW02bbL+A+hDfxNq\nBeGpiIsC8EZ28x/fT1GmMIQEkqiUTD66IkKuQacrBcLZb2adZnkgYAZFc+qm0y2pNvBBIZxwUoKd\nWlXkdETX6fp5q9E9AX3TBuUistRatuK1jc2frPKTNX5iQEGezR8IVWLy2RyF2dtje3QA/Db7iY17\ntU3jHbk7faUVRzceKTB1WJZV2uFyac/mgCrgAm54slPA5U6bd/3NGdc17ZCQPKqbXjLqJWPQ4ap+\nMUGTfv8vpfHpIca1xLRu+7LyqFQHgXrpKYcWhN6eO2f8oO3bTbr/1uJvq4zDtqwNw9LuUWyhf1x1\n6NmzZ0pKyl9BL7RYLHv27Pmzr+L8CM/5ObRwRZ5doL26qKjIarWeOzviYuP5559/5JFHFi9efIoi\nWB2CSOwakbeEvTnCECPfNZNF08XHY+UGHUmQ/THNgkHZ6+2r0bQWogQcOl1T5KDJ1AgIBI7C5xDl\nLGui1+0FvJ7vAKNxCSQEgya9OlSVhUALKrU9v68DAaXWZqLOt8TgX0iwt16/EgAH2ACV/nJA418b\ncWJAlO9DjWcVGjNqhYlmhkZzheyLRZUvhBviZfkAxMNaj7Glp1lUsMU16KOp14JEC04bHz7ApuVY\n9+PQvvHK8LPNw1dffXXffacSWbzx9JBVi6a3sU1Qa2QiLBGaPDeXO903++xuryFdZ3xdrbnc7x+s\n1e7y+fbr9a/a7Tfff/8U/v70ob8JtYLwVHRqI+G3EZfqE0Z98SqvN0Sh6ZdDghC1JcIUfvtbdJqw\nXmUNBn1eb5/Kr1E6XTA8rF7/WjDYBg5WP5fLVe7zJcddXhHe0vLFfy1+fHXxAXdEUhVX58GsXOmt\nZze/mr17Rjaw5blFzZ6/R9m18/HZzT59Fjj80seuO+91H6uw7zz80YwTNz0QHVUnKIzaBUNW1DVH\nGfUhO6ehoeZwTsgd2GX8VetGzFPa9oISm7U8IET1y4tp1ZhKdRDQSJEaWaPscuwpi069TJcU77JW\n1XDRF7urp8n/cQwaNCg5OflPJ50B4uPjs6vhTw8otVqtE6rhN72wFKv1GQWhEg4ahtPprMF/87ci\nLy+vV69e8fHxK1euPHfPBwZeRoWNXxeLle/K1z4GUHJI9uno/wJ9J9Eq1tEqCuloMLgDghERSXZ7\nvhAyOGGzy3UluKA1ODTqrVTaNnW69oDfuUdUkqVptcVGY2hpq0UHq5S2Wji1KgFotUrx3luU7X73\ndry5VGxwljaoOP6Q1n9YXzpYp1oIEpi83q0+9yCNpkKjaQMnAoFo+EV1qYZEOwmX0mcSt77Cty+L\n7FdF8UFRohEOIApfdeKOUyFJ0vTp00+PNUu+xPzzgvGN6xtx5moMURhTdaof1NrLDcWD9cZjbk9/\n+Eaj6QF9AgGtVvv0xo2dhg79RDn2/4ksrBWEp+KR+1MpzUYjmSKOeqLeCTr1ON4C3Jr+eEOKoKaq\nWr1UGSy6IsLwilrduPpQshzudtDniw4GU+Gk6GSVipjmc1qP7lx9o7ZdS6eqegI+ZQXluqT4hPee\n8HW2rE6b6bI55aAMlOfmq+LrWR96deedz7s7dQvWayA73HJUXEx8o/eetXmLfQNf71hR4iwpsHdO\nT/x5xjqg68Mdt88MSe4Yc5xBrQbsBSWrRy1qP2NwZOxJAT710i5bPjxLUQcV6MwNAWfBMXWrEO2c\n0xbymDqsxbd161PjVsS/COlMZGRkajX8ia4yBWazeUo1KBpbWDxX/xdOKT6sSMGzpXJKklT9Z/6J\niuDgwYMnT568bNmyCzGPZ7z/Mr56lHhkWQ/w0ww63YflAeaP4qcZ3Pa63OEWkXjQ1yiIOc7hOKpS\n5Xs81waD9WEPRBkMeyDSaPzF4dBCBkQAwWAO4Ha28dpDk6nTHdHpQoWmVapt4bNrjZaApwxQqxWR\n2VSj/gWQvfsj7G/43SOV9Hy1uo2nfKzsiwUz2Fyu7pAg5O16fbkQstwmTrRXBxM6MfQrkWhhfSZO\nGz6/nLdKOCLx2wlG4iwxJ8afeyqSkpJGjRo1ZsyYU7ZLkrQ9553H7rB67fmoJINJdgdvdHs7u8q0\n8GtkVLnLdafB8L3f/4xKdRz6zZtXd+jQqX/9/L+aQq0gPBVms7llnBUQ+ijAG/maunyR0f42OotB\n/aPSxx+o/h40wKd67ftO53CPp0d1UedwtFbchGr1lGBwAMSbTNV5jGZ5PDcYLy3VVUvOA47vKvX3\n7bfv4zXK190zsuu/FErMN/Xo5Ipu5Lx/xPav963t/0r+a4tK1PXKZn1WQWzgqWcC78+W6ydx9Pjg\n3gONDUzlpc5jBc4r7mr8zeTtm788XLIh5Mxr17d5OI+wSf9mG575auv0NV2/fFwrmUTdiOpeQMnS\nrOJEQFEHFRhT2u+c8d2ud1ZFDe4b2tSxlS13H+DIyvt32r0Xw80+ZsyYhQsXhgMBMjL+aMmYfyrM\nZvPUqVMzMjKqq3EDBgyYMGGC0s7NzZ0wYYLyOXTo0D/pMs8Dq9XatWvX0aNHZ2Zmnr93JUTAjl8W\nxwPi10+xrqfVDSRYKM7n0tswSqi1ckyi3C1NHvAEnXR+c2OI8vuLdLpCaKXXdwHU6jZwr0ado2TT\nq1TJAIHuiFBotFrtVqQjoNE01Wg2KW1/xWKnozkQDIYCrXUK3ai3rfPEECAyUinVUg4YjSLCODky\nch3EwkfB+Pr2JI/cpa0c3UhOe4tj+YB82UC2fcv8scIh0+A+WWhxCfw2NHH5e+addyp69uzZqVOn\nV199VfmqcDkp7defTxs+ZFBd9XR/UEJlNpl2+gOj1SLT7XbCNiHKIVaWe8F0n+/jjIzoTp1G/z8p\n2FkrCM8Ak3ADHq1ZyZ0wxbSV7S0Nx4ZqdSFFzRmocg263YejTOs9nvEAtDUYdlcbqZta/SUcVKvr\ng1LDrCqUNDp6tyommhv7rXuk6oE/mr1Vl9Y/Iv3GI8c1+z5aBRzN2aNLCi0DPdYjPp1ak3KF6pGH\nxejhZd36+J9/Wc5eprrSEvzkM+olYN2t1ZgeeWhM/boNTAl1fnh9R7e0RM+xCvPNbTxB1n+wGcaK\nL9QAACAASURBVGjev8n6F34Eygtsu74qKN5a3GZ6yA/XKP2K4i83hC9m42NZ6pi46jMTbWnh2F7s\nLveqpZChuO6ou4u/2gjE2UJm1YsRablgwYJHH300JydnwoQJf2LUxl8cShRMWlpa9Sn6/PPPp0yZ\norQtFssPP/ygKJHvv//+n3SZ58LYsWPHjRu3du3a32qSNTfW4zyOo4RfvuSK0QBfPUL/t/jxLUr3\ncyiPO9/j+HHxc5aIayEu7UGHr9ymrgaDCVACR4U4DAh2V6YDKvbYBNkdyg8OBo0qVUel7XKV6nQh\nlkSvuwxfV8KyE7Tay8AaCBwARYS0BmTZCQQC5U5H0Oc30Hkxl2wNDBwiR9YnMp7GydQxc8X9/PAq\nHw6myQB8akpLhG297PPI6jgCKgKV3srzYeDAgW63e/LkyUqZz+r3wxsv3vXDgpsMmoP4MlGbwWIw\n6Py+R1WqDDDodLcZjTa9/rDLNRW66/WxipHgHy8LawXhGdClndGw86Fw7oQfye3u43ZMcZXpIgNj\nDJ4B+NZoNAtMEYMiDMP8vmsqKqoSKgyG6lbNKKNR6PWveb23Kd8dDjMsBOAXl6uxrs2q6Htvkvv3\nDet/e97/WX/njUDEY0MO73FtfOzTuFF3hYc79MKnhvdCCz3X/O8YNRIIzvoPUr3gjJnBlBspc+5a\n9CEQtEVdN7JZaZEXiJZ0jSwN+r2Zmpe157tX9n7z1JbDO0q/e2T5xsXHmj2bXr9XR2fBMWVMrWRS\nq0LexOKc3cEWzQ3XdrHlVFmBAFmvjR5yc/UtgRNeW+6+u1KrAmIvBkXZ66+//uSTT/6JZQv/FpAk\n6RSz7elb/pqYN29er169LBbLggULfsfhe/YsQR1BwEuZSvz8LoDPhWSm9xTmP8pNLwHC6cDhl299\nXU4ZgUpD0q8Vlwq6vOWJUQMqVWdAiFglCcrhaAvADlHpOPf7t/v9ika43OeLU6lCa0QhXwEJVApU\nIBDYZTS84PP1rYwjLQc0mksBEZPEJbu9V8Yz7FVS7hNfv81t0+Xb3hCF2wEiJFwB4YsS+5cjmsuq\naJwHhNMgvCX4G+rUlVXvLwADBw78+eefL7nkktPN4Mkdzfl507p1LnC4QOT4A/UhKTIyweWaIkST\nsrI0j+dJg2FScvLsX355VjkkNTV16tSpF372vx1qBeEZ8MqLDwUr9uA+bIo4CjhFCqpMkPz+f9nL\nmrkdr+HqrBZ6h32s0zkcuppMVTX8vN7I6kPJskGW4yrVQaCnXr8M0Oszfb5eurYuICL9xgPf7fHZ\nHA5rsb9ulQZmenFcmdPg3FeifA3YKryGkBE1aN0fbNEckHM3YjIFj3hkc3sxd7bwapKSkoB25k4m\nSdcqpf6PH+xPfbjxtvnbjJK+6eWNW41L7fR2Ws+594u4iKYje2slU+LdV+ZP/7bqpF2TjuXsBPZ+\nuL7Ow7fFpfUq/OCkxeDRzUeC9pPCTZ0Vbl/23jtS+4e31KysUsL3JUn66KOP7r///hocuRZ/Edxx\nxx25ubnLli276667zt/7bAgEcKkRGtmXSNZgrpkEiOWvCW0DSgsoLUAryR3SxQ+v8d1k+k6iWQ+5\nYx9iGvnaW2g+qbzRBpJXUN+t1WbDclCKYx/SahXH+QG32+JwtAK02ny4Q4iQEyHoCzU8nsLKC9kZ\n8AegleIdlGUnzKpoupPWLzqu7crwxUQ2EfOeE8eOy7e9IX54BZB7j+eDO8XXbwhfhFz3ErmilMhr\nadxPeMpE0I/7KHLJr79MvsCZUEwyX3311Zdffrl8+fLTO0iStGb5xCnPW6KjPvR4b4UXysv7wEKP\nZ3Rk5DtwoFWryGXLnql+yPjx4//BemGtIDwDJElq0/5648GZipsQY6ohFC2WqtMpOe8dNJo64f5C\nVGmBTqcFquSKx1Pg9baoPrjBkABoNNHapjl1ngrR20d/Om3DY5/tnb0q5r2Xqo7N3Xai7WWHtY33\nDHwhYKs49NK8sDpof+Edxj8GBN96L9Cpj1ywHzlAfsHI20Ixq7em/mt9VvFVA5ssm7XXVNdYuOwA\n0Om+tpue/gKIMtf17A0R1mglU9BWJdgS07rtm/btL/fMqf/aCGWLXqpah+6dsVT9yJBjWTknzVfH\nVrr9p2bWm83mGnlsqqckKqQz6enpf3zYWvxFkJub26tXr1deeSXs0/rdiNT7kDXYrVQ48biRzIAc\nUMvdX+a/z4slU+TUF2ieKhdsoF1vEi30HCW+m83Vo+T+L3DHDFmlpZ7T2+cRf9sV1JnFpWW0e9Fg\nyAkElEjvA2AAFSw3GisAlSpEESVEBVQAXu81igoY8Md4vT1p/YrqqgC9v3C0jyLZFWxzLQ9+LltX\nA3KRVS4+IqeMoI5ZCXwj+w2iO8rthlC6g+ISccJNyQ+ibGvQf0XQswWPF1+FQqZzbiirxnDGy6xZ\ns6ZMmXI2IvvxY9IKrK89OsqakFBUp85mo3FpRMSXsbHFU6aUb9783umGhNTU1H8q6UytIDwzEura\nXIYXXEV5iptQYwzdE7rKPASHo324fMTJVZa66vVh9u1vhGip062rPrLb7YWpDsf1eotba66KMvUm\nd7EddFbvWfrGZ/KoUXJ6uv3TL3aPmGnP3eLLXhm07g9a9we8bv89w/0D7g/aZa69k307KPeCetpT\noVwrs9ksbJJJ0ra4LOGr90qUojBx5hhdaegU9bs2DVtEY7uaFS0wBLUqou/VYS9gUIp0F4Skpi23\nUJ92oyq0WA5BldBw3LAz5Hj9cdfC6czOSUlJ48eP/yuQkdbij2PYsGFLly5dtmxZjZgQVq16D5yy\nzyycu1RONXuzWT2DljejlzA0oEll4RTZxLpFAPMekXtP5cunAFa8S5eBxMTTKU0WKtE8hn9PZXSG\np4XX3UrLXfNFjx/ofIRWJ7hysb3ZJtjr0S6n/USS7vFfkkCL12k3ia47uOUDrnvG19JGHyt3vyUH\n4fI7UJloeAnt+2OUuOQG3rmR3q9w9Xh+mgHQNlXMSafTC+LEIVG2H6cT7ZVyvf54iylei/E6oa6D\nSqVWB847A2cksvj222/PUdRFkqQ33hhy+PAnx48/NW/eIIfjqYMH/zN+/FnXmv9U0plaQXhmDLwr\nlaBNE3kDzjmArArZNmU5nGDQXacL1eR0u08KFtXrIysbOT5fX4OhKpsQ8Hiu02oPqGKEscdJcSg+\nDOXRCZ7ckEMuaCv3R1d1cHS80v7ufFuwnm3xz7Z7xvradQtOnh2Mayo//xFfzkWloagwSnVS+d9I\nGgJRkvraKVeV2lXZT/wERNRT2wtKgGaDuxS8uVTpmfRw6rHFoRC4/Zm/uA0xPqquOXrw9Qfm/ADs\nnbE0+K87ATnypCyL5tayXh0v40z4fUtIhc3kbEnfycnJo0aN+iuQztTiwnGKz3jJkiW9evWaPHny\n008/XVOn6NixIwGHCBZhV8v2Erat5vB2mqQCnNiHdTkgFj3M5ROxe9mbg0YiwSLqd2bxJFIepks6\nZYV8NIhBc+S0t/jocT4aI/d7keYp1GksN7FQtx6GMh7+IHjnc7T+1N0rnWGf0nMIsoMOV3LfJ+hj\naX8djnLaXU+7PtQxy+ZefPU2A+fQ/3l+mEppAduWE9UIg0SChcN7cNnYkyvcBrQSjkL27Zb9TUXp\nchqkC8cWOdBO2BfijkcOvPTSuZwC5yb1VUhnzlvgTKHvOS/+kbEztYLwzBiUnlrPlOsxjNZ4tgEO\nOVR9yeHsUkmcFqPVhqVFW71+V/hYvz8SMBie8ngGAh6P/uSxi1WqYlqvVEsneROdq/Kcr7xf9PSs\noK0cOPHBosD4qmQgec06mibRPSUw4F+BhObcNxpQFR0CxPIv5TZ9cTnLtv1QfcBrut6QPeeA5Za6\n2+Zvu/G93rYDTltBebeRnXa9/h2gkyL0war0eV+pHdj3340uT3STT57xb6giUtGbE9zbDwMnNhRq\nU68GPJZLK1aEqkp5rEc6SwnnmMnfuoRUyngqBXLP1qdnz54333zzX4F0phYXiIyMjHA6Y3p6+oYN\nG5YtW1bjUTw6TSzCgCzhNVGmJaIBQHmBkFrKwojNisdLjJnLJ/DddFJfAOTGl1OwjUQLgN1GUB3i\ntra7kJqTaOHaMSJnAbEtuGkK1z8j3rmHb99nxI/szQFE/kqaXE5SN4wSCcl89SZ3z5ZTRpA7n18z\n8emIrfSMxDXjq5e4fjoNOnCiAKBeO5FxBy2GyPaD4ovbZLk1cn0S7pe9JaLwE9l0HUKPJw8g6Bg3\n7oEz/uQwB96542xP52j9I/jnkc7UCsKz4pLmNkBnTIw4/gjGVJ1RCWZL1elCr3UhqiSZXl+lJDmd\nFpgqy23ABHg8XaFKK4qLW6NWNw4m6cuzNygyD/BZD3mjYgFP1teFj80I2sqda7eQFEpal7OXBW8P\n0VXwWSaPPQUw+93gHQ+KxXPliIZia47Kpznl+vv2vGXZ+wcj6+h3zt9plPSxTaTvZxfnflpQr15I\ncYxsFRfOGozr237zuAUV3kjDvTcAat9Jdhhj22Z7ZywNDL5T+apLvbpkwWqlrc7KeWnIeSoIXvgS\nUrHtWCyW874i+/fv/xchnanFhWDKlCkZGRkvvfTSoEGDJk6cWIOKYHX8+utrBOzI5bLTLI5toPQA\nwOqn5C6P0fNtsh6Quz4BcHwr3kqv9s8zie+hRNMIfX2UWqTr59P6Jk6UKG25QWexaTGAqS4HtjHg\nHYDUMcx9SL75Zfo+Sc5MSvdTsFnEtQ0NG53EjlV0TKfLA8wbAbBnY+iNcendfD8JYN9KfHqxKF2O\nTEV7DfHDcVkxJImAjcMfox8nvJvwynBMpTqJ8ikM5bFSHplzz4ziX69Zn8I/SRbWCsKzolFDAKe4\nymnvrT/2gM6kpBNJYTeh09kIKpm4PVHVDk3Uavd7POFi7s0jIsJ3zGaXq6lTiuDRR5xT5x599HVl\na8nkOb635yhtxzufHOh1n7937/Bwgc++4raQEGLjdhKTADZvFmtz+OK/3PU8RdaXnzzDcq/L1Zdn\nTT4Yb44C2t3ePLp9Q0/L9js2lRxYuhVo/XCP0iXrgU1PfF684pDNejw6/TrlQNGqsbegqh6hPqXT\n8RV7FHUQEFIMlbUpmtr8FzCX57eRWq3W31S2kL8M6UwtLgQ2m23lypV2u33u3LkXEvTx+9CxY0eC\nTuQSIcqoMFLu5kC2QKCXANQmDBJA3gdcNom171KQQ8JVdLhffPEs2e/I3R/jypFkjhBbvqdjOg07\ns2E+1o2kTMJpB1j0tHzX53z2MMCPbwqPCyV6oFVPst+n/zty53vJfJiS/RwrQVlMSmaCav7zML3e\nlYNqAIOEHGTZVFzxcvOvkZsQP0F2WTEkEaigIhdPsey9Ek+uHPQgG5BtL798hqf7t9YDSUpKeuaZ\nZ2qKSOEflsVUKwjPijGj0nBkYUzVGX/wuCa5y0OkmmE3YTDYCxYr7WqcMkcMhg+NxrrVh1KpQmkP\ncXFrXK62XNqQJmbA1b2//csVgNd+kjjxtrrCv2KjnL0MwLpPDit7P2XTtiXAllxxYJesaiYMkeKd\nR/BEjHvg9tN/QpO6Tdrc1q74cPm+FYcaWRpUfL8xrk/7zrOHb8vcsmrC8lWjv7cdda576nvt7TfV\neXtMZFJi+MDIvt1ss6sYw22r872VtblDkxAZDVRkfPXs+dTBMM5mI1XcG/yuWq9jxoz55JNPzuv8\nqMWfi08//VSpoDR58uSL7V4yGo3IPlmNHGyCa6z4dbrceRTAoRwapIq8OQCRiST0oHArq9/l0ofR\nS2jqEt0Ug4RBotAqX/8aQPJAfp7H1eMB+YpRfHA31zwTEmzrMzH3lVvfTH4OIHb8SkUJgGRGRJD1\nFN0nE9sqdE1uN3WS0UtEN6NgBYDGRMwQnEcAZACVUAPCX86hDFn9HxX7hfM74W+D7ADtuHGnegFO\njyO7ECQnJ7dt2/aPB+gqqKnI8L8CagXhWZHc0dwkZjsqyRAlg4Vgf72uDye5CZuYTGGuh7YREfmQ\nGxHxidt9t893SjHCulAEm+32xkSdIDVELuq/YeCJBT/aZnzqfaAao3yBFZ0uOOkD//LdgTGTAm+8\nLU9/J7Rr4dfcP4qcbN58Vf7XR9hscv1E9u3pfkkrzoTbU9OC6qCpcYPdC7YB0ZIG0Eqm6NioxlPu\nbTx9iHTjlYbUy0yWNoAwx4c9f3pzQuBYKDnStf9YBVGG+ifV4FXchE1s/g7mk5JDzo3TSWcURdBi\nsfzuBea5g+Jq8eciLy9vwoQJW7duDVdQutglHrdt+4iAQLiRGwrvImHXKVqg2DiLS0bLhev4cQQd\nlcCTCBJ6KEfJR7dUvQxVEus/BijIoWQv7jIArxOvUFIyaHszm7PpmE77NH6eS/Zb8iXD0FXmUx0/\nQEJ3APONfDeJvPnEp4gDKwDa3c3aOeTNR66PSkJlBOSAC8BTiC07KAyi9CCBQFDXBN8ugkEo1Grl\n8K9T3IE2m+138yuNHj1aq9U+9dRTv+/wU/CPCZypFYRnhdVq1bGX8vl+JMDvv97vi9eq08BwRjch\nyHr9IqfzTsDlag1VGpXP10Onmx8Xt8brbU+L3dxZFZ3sfGRK+dc5wStTqob58CMmvg3Id48M9Pl3\n4EgJt90lnnmOl19k12bV8PtYY4VGSE3Fwc3y7s0I/YevjzjjT+hmubI819X9icttR92AZDYpKROm\nViGpVie1k+PLUOxrbPp1J7KWVR2sDt0bBU985HvmKd/JFhhd6tVHZ3/X19z+gqczhDDpjFLoADgb\nAfSFY+HChWPHjv3TK0LU4hRMnDjxs88+u/vuuydPPikT/GIQD4WRlJQEduE/LsR2OXA86GjCzxkA\nARkg8WYC/pAj8MRxinIBygtodAO2wwBf/JvOz7NnFcDP73HnClZOB/h5Jg27hoJc9qzGV1m22hBH\n4WEaWGh5O3nz2ZxJ81s5/DNAjJmjO9i3njbpRDcB0EsYE8SOn4kfwolcojrhthLRDFt2sF5/ji7A\n2x85AX8uwQO4kDkC6l275iqnUhYQf6TMp4LRo0d7vd4zJtr/DvwzkgtrBeFZIUnSVVc21lZ85NTc\ngnoGSCaTwed7XavOCwaPwSw46PE0gQ063YuRkS94vYc8nnBkV3OD4Ui1wUxqtdvlagpwxUnKInLQ\n443m5WrGioJqB67/mSuGM2Ke3PMZjkRx3+zgkP/g8tN7BMesFO0QbkN9Kf4cNgqJOkZJH9e87n+H\n53RMb33w9S+AmKta7H1iDqCRIg3ekFVWLUUZqt0PkbdeXZL5fcGzn7oeGwO4U1I8mYvCe4UUI4Ru\nVNpvjttUND8lNLQG6xu8+uqrDz/8cI0MVYs/DpvN1qtXr2uvvXbKlCmnewQvtnvJYulEsBj5sJBV\naLriiiBvjtxhNICxMcFKN0RApVRYEr+8Tst/Ybdhs+LyE2Wm3lVs+Vw0vBK9hMcdciW2G8zKaZwo\nwB+FplIUHT+KrANoYBEbP2NnNm3SUVf6Edweuj4ByGpdaIvLLTuCRCRxfA2NRoljS0m4X5QuF6XL\nRfE+xJ2y0Av/Krw+Wb5U4BVCKFxR4RzBGpmiqVOnfvrppzUlC/8ByYW1gvCskCRp2htj9bpm6uPv\n6AwbAK+vLeDzDdbrE6AJZGs02zWaTK/3Ort9sN9/F8wNH67RnJTV5/Ptd7na0vgXXCcz5775Gi8u\nQjKjBH0szKJrVdkj1q2hXch6w/ZVNEgC2L2Nukn8MFNuPRyvrWjz55zdRhFFNGC+tnFFQLdtWZGq\ntAyQLM00JaEiiLqW1QRztYyO6NTL7Et+ccYlCUsnQKT2Cny7IrxXtpWN6P87I9AUbaBmC90lJSVN\nnjy5lnTmr4CsrKxBgwYtW7bsHOa7i+peWrduNkTIQS/BQ6jqicISdn1JHQtA/iLkCDw2ts/H/G8S\n7mXj23ic6CTajOO/Q+kwHqDJTayfK3d6BCChOytnhFyJqgi+fpaW9xHXhc2Z7Msh1kLZgdCJ1bF0\nnQRQtz3lBeTOoN4VbP0Y4JJ7WPUEOU/R/Gn8FQDluwHZVYArH3ehXDRRyHH4s9F0l4MFwhsHLtDF\nx9dVzKE1Xvxr1qxZzz33XE35FFJTU//WZWFqBeG58OGHH+r1RSJ4rcADuD3d4SNCtDJe6OlyDdHr\njaBYGpubTFXUMD5fMwhlx+t0M/3+y+FzmkRibExOtVeAQtjd7Xa+yxGrVpG1iOsr3+aHrCF7DrAp\nm6tvA8jPpUkrvntHdWCPKNqmk6uSFM8oC29PTcvP3t8itanW4zq8X+/1+EI7/KF1cWT3dsUzQuUv\nIm7pUTI/NIK3oPBEUYVn5CPhoXRRVZGxTbK+fjrtN9NCWq3WrKwspdZdjb8HFdKZfv36hbfUGkv/\nx8jLyxs0aNCJEyeWLl163s4X1b2k1/uFiJMD5cL7LbKDYP3QjoCg6aNszxRb5hFrIa4He7+X2z0M\nEJlEwESUGUCoqKi8eaKaIiqpLVQRaBqgk2iWhnUDK2fS9t8YGwGUF8hOP0fWArQbyLrXKdxC8pMU\n5gHEmCk/yJGdbJuCHKA4W/hKxe5xuA9h3SZKoyAYVLfEmy3w4VfJsk2I9VDx5Zcv1qAieAp++umn\nGvSvDxky5O+rF9YKwjMjLy+vX79+O3funDPnCb+/jc9lgAKw6A3rAein14ez16syCNXqKq41j6eT\nTqfE1GxTq6Mh2RBfxg19uGEUWQsoswHigze5odKa+uhMeeHXSNXCTRd8xJC3Q+2fvyYlHWBxhjDG\nse67YPvHOJ733ksnBW2e/nJR3IRATJSuyWPXq1o2t76xGIjre4ltxRYg2tJCtb1A6WyytHF/vw7w\nFhTun/6DuD2Ngv3hocJuQtlWdpN0UuzMeaGsaqnmEbwYQRPJycljx46dMGGCzWabOnXqRQ3KqMUp\nGDly5OTJk+fOnXvhpD8XL3Bm7dpZsAuiCZQiO/AOoiSXohzqdiUiiQOriese6hrUhYRf0QqCKopy\nANZOIqZLqMPGD/F4Qu3ivXgqF7suB+Z/AcRfw45M8dOzJL/N7m8B9BJlB7n8VYBA5dLTUYEui/Iu\nBB5k60r5uFN2vKIqk+HGYDAacjHej1yK+3OC1wixH46ZTJqaNZycjoULFw4dOrQG9cK/qSysFYRn\ngFIde+nSpe+///5NN1oaN8oOBqepVYMBrTYUJGkyNVEaDkeLsOZXXm4Ot8Gk1doBo/Fbl+tKwNc4\nkeRUgBsmMGMKwPbtdOwRPi8OLcUyr0wMfT1Smclnt6FwntlteO0c0whdInu+UpeV3zfo+lMu/nTf\ntUQdwCipgLaT7zqyfE/+W8sT07odn1NJDu73hTvrpWjX/mO7Z/9inz4t0Ld3YM6H4V1hN+FvVQfD\nTv5TFrYWi6XGH5uePXv27ds3NTV1yJAhtZUL/zewWq29evXq3bv3byqlq+Bi3ANAx44dtVq/EGWy\nzyn7Twj7Sja/Rf4iGqUBuAJyzKUAzgLkeuRnAliX0O0L9iwA0Eg0H8GKpykvQJuI1JXyAgBNwyoj\njW0fSk5RQioHVqNNRCshJIDyAsqL0EkA9S6nvICfp+KqA6jcqzGkoOuAbhT+7KDuVvy5BK8VYjWq\nJIL7ZH9DaA5q0G3Z8p8an5nT8f7779eSztQKwpOgOPlPqY7doQMgGfSdTcYRHk9zOAg4HOHkvp56\nfdh5dqlWW0WxLcsNjMYMl6sngKkikFyZYt8gSWga8GmGHFWf6jhymLvfpNNQ/p3GtAmkDA5t//Yj\nbh9Nfi7vjCC6uxzXVfaXi8JNGdPGn/FXnOK79hz2u2yepinxhzNXAVKzBGfbS34d+llEdEh/jerb\nKZw4Ue4J7Hv9a98LzwOYm0UcLQ6Po7gJf6s6eO7siJrVCRS6qSZNmqSnp/8DItn+Fhg7dux77723\nbNmy/v37n7/3mXCRwg6/+OJl0AgRJeRE2bVeeFyiUjkTvkhs2wC2T6XpWCqOA7hKAeE6xt75xN+C\nySxshax+nRajSExn9at88zBJQ9BKAEU5xHUnv/KyK0rllo8Bob3LX6Pdi+zNBEi8gbWvUhoh/HqA\nYBmgcq1GkyJ8y9GlqwLbIAXKhOMh/C1VqhIhFoNRp1MpYTIXG7WkM9QKwup49913U1NTly1bdoo5\nYtiwVMgNymaHo6Ngl073EeDxXAshLhi9Puw8MxmNVXTVTqchEADqADQ+wM1V/jb5+sdY+AW3V1uI\n/ZRF93sA6ibxYBZb81WLZ/PGv8V7j7JtGTMnkpNPSQxth4vct4RfpfIGTlcHw6guYAbeNHjr1L0t\nUpue+PpXQBNniE69rM6UESfUph0TF20aMcd3vOzEO1n7Hvtg95jPbB26O2OrfPIiqjpjDipjxIWr\ng2En/7m7WSyWGnkPVi9AM2bMGJvNtnjx4j8+bC3OhpycnF69eg0fPvyPJ2inpaXVuCzs16+fVmuD\nnbJcgBwlH7HIusoFnK4tZXsA3OUYzLhPsGsOiekAcVeQn0X9VECWNTidaCW0EhVHketiMpNwC3vn\ns+Nj2kzC5wWwF4iSzSERGN+f7McwtaN+qihYAhBlxuHG11A23gnIgSJADlYAcvAYIAeUWJsDsteG\nfKMsV8jyVjj+66//C3VQQVJS0ujRo4cNG1Yjoymr3o0bN2ZX4q8vF2sFIcC8efPS09PDFCen4KYb\nLaaIMS73jTrdz7JcX6VWqvd1MJm8SgePJybc2esNB2GuiIgQKpUdABf13CcNarehaciUKtHImhW0\nrDSTHrOibhjs+yG93pHr9yN+IFdnYmxBw1YAJVup8M94aTjnRPVsrTFpE7bP3xsbowfq908+PuMz\ntRQVO7hvRcOGwedfPNIypYLIE0+95HjtVdWQB6prgT5zs+puQrle3cvc5y8Ho+QIXriT/4+HX2dn\nZ5/C0z1+/Ph33nmnNtH+ImHs2LEfffRRTVVQ4uKE4O/a9V/wCKGDhqDCXgqQ97Qs3Qpx1HvZZwAA\nIABJREFUHF9OdFeABiPZMz8k/Or1wFRJT6GODElHQBNP3asBYi3sXUBUB6jU/9a8ILeawe4Zob0l\nRTR/GJAVM+mRbPTpVPyCIQVvrhzRF5D9itfDhyIa5VdkkgUaOAh2Icrj4yMuHhHdGZGSktKmTZsa\nJJ3p1KlTaiX++nxstYKQBx98MDc3NzMzc9KkSWfr07fv9Vr1c1qd2+cb7PcJg2EOoFaHwmQ8nm7w\ntdJ2u1vDEqNxiV5f5nReGqpN39hKTDs2VXvOv/0P1z3LtS/x9kSAI9ZqhY/g+1lcXxkms3UpLe8E\n2DiTTqPY8LasTRa+0n8PuePcv0u5+TZt2lRUVFRfaigVNBTCB0iWZuTtBkyWNvq8rUKK0aZerWvd\ngsoCvNW1wFPchPUjIt8b+di5X1i5ubm/g/nCYrH8PhvpOYLLv/vuu4kTJ9bKwprFrFmzhg0bZrFY\nPvjgg5oduWbt5Lm5uTExMY0aGWT5qBDlsFJl34/PJsoK0Zmp/zzbXyNhMIAhSegrK6jseh93qE4n\ntgMcXhhqlxeHrKlAUEfTwQD1r2NvJpomSKmUbgI4lkP5oVA3RUxu/1g4CwlUALjXED0Kby6aPgSt\nqJoSyAWdIJfAv4RwwQ8QLwSHD1elKv3PULOkM38v/L8WhIqT/5577jnvOmhWxkNqdb7fFwsH/f47\n3e61RuPQ8vJGoBSsb2UyFVX2ba7RrA0EKjyejoDb3Q5+pVkjrphA9gLslTHZO38lLom4JBrfIN4a\nx3/fZ9DbVec7Xhkm47aFos48NjQmcWg1hdtEQD91wp1cAGw225o1axo2bGg2m6ePf8t+0KG4CSOi\nQgm/xspGMOUKOXO+0j5JC6zmJozI+mJa776c3alzSnXs3wRJkn4H4ch59c7MzMxhw4bV5lHUFPr1\n6xcbGztz5sy77vrNyTMXgpoinVFuDEmSDh5cqVKVwWEhgsHS9uz9BG3l3aJpEsqL3/OE7HbiswEE\n40Nxoc4CjF0JiFA74iqO5YUOLMvHVwZQP5Xtc0gcCSBiALa/ReS1oW4NUljxEEXPyoU6vEV4c/Hu\nAbAvQjcKfy7BUuF8mcBQIYxwOBhUCbEPDj7zzJ9GDTF69OiioqKaSrT/G+FvLwh/9zsu7OTv0aPH\neTtLktSt21VqtUen+wA6mEwql2uCTleg0XwD86FICBNs0+unGY3TNJpor/eKykMbaxqXkzISoMsE\nMX8KwKZsLrk5tD+phyxZRGGV+ZGcT+lcmVORMx3LGIC8j4huJP/yAb5EYVs95vF7z3vNyrtg+PDh\nGzeGAmGmPTXDtmQdoJYqsw+lULyMNvVq/behINJTtMCQgmg7cZW14MZK2+PphqwzVsf+TfhN4jNc\nreK8PWfOnHnPPff8vkv62+Hiifzc3NxevXrNnTv3j1PinQM1YkM7pSxDZuZbYAU/tBEH5svRvQG8\nBTgP47YCuI4hPUxxNs4C1HUx9edwFlunEnsLngAOK1unUWcwRAHsmkH9MeSH4gPwEZKmGoljORh7\nENOfXTMA6qdyIh+fGV8L7D0oXIb/CIV3CH8J9seEexqevvgFJMhysRAroUiWY+LjY/r1u/xPXLrN\nmjWrOulMVlbWX9/D98fx9xaEWVlZGRkZGRkZF2KsC3/Nycm56aabfquTf9q0YU5nbDBYDPj9dQGv\n9x6TKQEaQI7HU6jVfu3x3Opy3ep2t4SqW0dOujRU/CU2iQoV+blieRadqnGgHD8mR1wtZtzFMSvA\nhm9IrJTNRduITgI4+KM4aiMQKyq2fjB93LkvNUxgprwLOnXqpMxPiuWa228ZbMvdp7gJAVP/K30z\n3lOOMkZVJkGam0Wd5iZMnPr6kvETqp8lnDOkeARrJFfhQghHlNBQLrhaRVJS0ptvvlmzQXF/TVzg\n42Cz2QYMGHDuPqfsnTBhwvTp0y9GKd3TYTabf5+B1GazKffGKTfGgAF9mjdvKct2IT7Aqw3JrcMv\nI30oDv8HQH8JkSkUrRVbXiMundh0cewXAvHozNQbT+FSPAK1hM6Ms4Cju5DSqDgKsOUJNM1Dp6nf\nX2yZQv0RRFhE4XcA+RnY4wHBQhhK4E6cJlxvCGcR3nsINIAEWfbCWllOgZ1QR6OxHz78o8ViUZi1\nf/cc/kEopDNLly6dMGHCxUvn/0vh7y0IrVbr+PHjhwwZcu4nx2KxZGRk2O12QAmsX7x48W/9d5OT\nzV271oH28KPHM0AJGbXboyESLvP57tVowvxkyZGRlQXrGx4K9Hw8PIjcczKZU/HKJw1dkMclD8tX\nz+PzaWLhC6hDNZvYmkXnUezOYukQonrI9R4Uzq0aR/F99541WBTIyMg43TsdFlrPpf07Lmt/pLl+\ndTeh0icoVTEDBKu97wJ9e4uJT85OG3D6uSwWy/Llyy8kNPTCcW5H0dlSEs8NpRJbr169zt/174wL\nfBwyMjKGDBly3tEUxqyJEycOGzbs7rvv/uSTT2rsQs+H35FcqJjlJUk6442xZ89io9Eny2V4/Rz7\nEMBrQyXJFXvIfxrpJgCHFVcJagmQ7QWo6wLoksSBz4m8CiC2P3lPYVCI5k0AJYeR7uBoJoC2DoHK\nlCp1AsCxUnxdAVCWlcvhBjgQDDaAClnWww5oK8Qa2CnLbiHyfb611WfgT1TFZs+ePWnSJMUZ/Gdd\nw/8Sf29BGF6fnu3fys3NVeJ3LRbLuHHjBgwYMHHixDfffPP3na5DB63f30Wt/gxiTabjQCCQqtGs\nVPaqVK5qfRVR50K3HdfJKzuXQaZaTsLBXKRLQu0r35L3l1JcxpcTVQuGsGk+a2dztCHlBuqNEwff\nJ6B78dmzBosq1sKzvePCsjBzynsiY5NWDgXnnNFN6LR0YkVO6JfYTlzfslXKaTOsvG1jY2NrXEs4\nW+CM8j/+vtMlJyc//fTTEyZMOH/Xvy3O+zgAubm559irKPfKfZKfn//444+3b99+5syZ/+MIRn5j\n4MzpMcOnw+lcq9FoZFktHJvxFqBpB0BdHAfQmQGMXeSoG0O9vVHE9lWaslePlAagM+OwUycdQJvE\noc+J6ktUqihaArDjBeTKJawqjl1vU1qKPAqQgy5AiDXQDZbBTbAEegvxI9wtywVQJIR96tQnq19w\nWlqaouNe4CTUFMIWl02bNo0ZM+b/SazZ31sQnhcWi0WJ383NzX3iiScCgcAfeaRnzRoXGztXp2sY\nEfGqLCsmkcjo6NDbx+drBnuVtt2eAFbq7eSypXxTLQrLZoUoGo8Un1aKq+w36Tgq1PbYVGWldJpH\nk8nBhhNw1KXZh2gbIYIAji0an3bcmDMU4FVEIOezFoaDXGYNee7Ymp2hrWdyE6pSr1Ut+AKIyt34\ngHX/wudfOGWosExKTk4+55z9TpwiC2vE+tqzZ88rr7zygf9r79zDo6rO/f/dk3sCgYFiPRQ1nWOr\n/BB+nI5WJXqkbbwrqBhNmqgBk0CIRIKQSQXkUoOZgCEiJGawJC1BMDmK8dqeTGsQsWJnBLWP1FMz\nE7AcbLUzgHLLbZ8/VrKZzmXPvs1MMvN+Hh6enZ09a6/sWWu/a73Xhx8Ofumwx+FwVHogfesg/hj1\nej2bLwDGjx9/3XXXXX755dr0WD5S9oWyElJ3dj6v033NnzuOozVIfwgA0pYibjDeifv2a5wZ8gvt\n4/HVYBgf138WrqH0Guf+wbaMGHsH/voSE4o88wzHJJ4b9Lvm02/C39rR9wkAwA5MA8DzLDebE5jI\ncd2AkeNOA8s4bgzHfb1x47xly7xruYRfR8oeuKBx2bBhg1bBhcOckS0IhSEivnisrKwsLi4uKCh4\n/vnnpSdC9MvSpff19LhOn/5+X99RYC+AM2fGAl8D6Om5Oinpw6ELpyemH8Ll+QBw4QLuj88Onv7D\nOly1BSmX8Gk/4T7YjjNu6Dx2hx81DVy6ZvC463n88DkA+OJXuHwzd2g+To9/cs0D8IGpsCQG6zAn\nF71ef+C9D8dU/uaU/S/+zYT6sYn8QGrbyybHkQYfpajXyzRElQSYLBSSlGqifZ01a5Zer9/BCn2M\nZAwGQ7UH7Kv3Ox28dhVMurAHG6hx9m2yQMwNGzZE0Fglvi+UW5koM3PKO++06vr6uJ6vEG8AgBPb\n0D8Ut9Tfi95/AMC3e5F6B3r/DgB/e4K/4Dc48SEA/P0ZJF6Dnm4AgI7rGzJwxE3An0sx6iGk3TMo\nMk/+Ccdr0dfH4VHwbwLzgeNDL9uTADjuDLBrYOAIx50FRmdn3/boo/P89tloNIbt+Xs5GQHIyMiI\nEV+zkS0IDQaD2Wy2WCy+WhHP+V9dXc2+Xb1e/4tf/CI/X3YVPYHly/MmTkwfNepoT095QkIHgDNn\nbhyq05vKccKFZ/onTGYhutBfzx/+H7gdcDvQP+SueUkuur9CUz6mLz3f+v9+gNQMAOh1684cZed0\nvX/jDs2H6/PUxFO+20GmC5VlMGPJAC/Sj/9d9ZY8BwfrAT9mQvdxfmDgccQt83ARZDoTvxbBECXb\ndTgcH3/8sfpKpJ5s2LDhs88+i8qkM36nw3333eepEGYLJmF54QuLARW+4p07d65atSqCstDvvlCx\nhiAzc0pX1w4MDCbO5s79L85+iX43vt3LJ/8n+uPQ78bR9UjLRn8PAJz6HPEGru8UAHzzKcaU4h/b\nAODo0+CH6qyNmYnTxxFvQEoWXL8FgG9TgCNAFj9wC4d9wGKgGbgVYElkfjMwcJLj/gTcxHEnli+f\n8eKLaxAYvV4f6vTxLJeI3+eZkZFRXl6u5p05IuB4ng9+1TCGTVGvFyVTALrdbr1e7+vtbbfb29vb\n165dq/iOEyb8pL9/SULCezqd49y5a9LT/3zy5GwAiYlv9/SkA/+O9N9ixhaknZdP3P88wMcn4ke/\nOt9QjxvvlmDc9zHzKQA4YsUXX+F7uQDw+WaMugPJGfhiM068j9OXcef2D5x73bMb0uPWfV8Z7CXI\nxOcX7n/eNn9eUtZP/4b+U+7jZ7/65ooJF1wBblPxfM+nyqaiuBlG3P4kC/amZtpXFpuoSbMCOTk5\nlZWV0vW6WrnFhhrf6eB3gjDa2trY60/405ivPHM58fx7586dW1tbGwaX0UAIQ0tY4KoZEitWW6qa\nshBvwJe1iDdi3BHupJ0fW4e+bvBv4PgfMaEFp3ci+Ry+PY30hZxrJX/Rw/iiCePWcP8s5C99Hp8/\nhoTv49/uQGIGvnwGxw/iu00A8PUCjJmGL6ZxXAPPVwDguKd4vgSoAK4APgT+H/A3IIfjfqPTpT/5\n5J2VlY8E6a7ol6gSu92uH0Lksrq6ut7e3mXLlklveaRMmUH4qMZkMvk9b7PZysvLFTd7+eVz4+Pv\nBfYmJt6TlLQsLm46sAFoBupTUm5GWhEm27hJeZjDn//3Hxu4K4rwMH/+3/9fiUwnMp0w3Id8G6Ys\nPH/xxPvxMx6ZXZi4AOO7ucRbZt+1XLh7V1dXR0eHy+WS2NuOjg7fk11dXb4nX+noePGdTr8tSLyd\n33vJxWazeXbP5XL57a1KHnroIfZHtba2NjY2il+syd81DOno6PD806qrq70OGC6Xa9GiRWHt2b/C\nxoCsYS/e2vh/r8aYlRjbhXE8xpdhTCkMPAw8Rt2KSbbB4+SrkeGCgcckG0Zns2NuzCP4/i78Wwcu\n7sKkZ2HkkVrGJRexj3BjyjC6HDis090HHATWA88CncAc4BXgGqCe434GzEtPn+F0OqX32WazafK3\neyLreW7cuLGpqUlW40r6FCFGtmpUBKvVWllZGSj412g0XnvttYr1Y4cObevv/xvw64SEtHPnMvv7\nl8XFNY8a9Vp6+svnEsfg4vlINfJJt3OHPTzO3YdxcjwOexQsPX4EyRlIzsD3X8T+38B1GPYS/MOK\no22I+w738Tx8/EucuBxnX4vjBh4pnck+xDa76stV+43Wmp2Vdd/1N3iekVsdW2WWLFa516tahV6v\nD4X7XHNzc25ubnl5ucFgkBJOEAsE8jvV6/Vr1qwxmfxXOwkDer3+vffeU+wz7Nva1MswKsEFnQEA\n19uD5KFhf+4cEof+9v7R0OkBINHI8RewYz7hh9xXLyElC/EG7uR+fLsXumsGoykAPu47OH0MODIw\nwGr5fghMBTqAK4H3gP8Afhsf33vLLcknTuyTVVxCc8cZX4ugOIsXL7ZardGadCZqBWFWVpbJZBIx\nXGVnZ3/xxReKZeGuXWaO+/TUqdPAW0BaSsoPvz01/eSZCXzaFUg1AsDYXP5ox2DepsM7EHctP+Ep\nrtuGf9oB4NA2TFp9vrlvTiHldega0JWAz3fjm6V8/zac60XSo1zvW52/fyIrK2vLli1s7GqlJxT3\nypObOFtisyK3Yx4cftcuijORimC1WleuXNnV1RUjkVIq0ev1ubm5q1evDv+t2VDMz8/XUAxkXqXv\nGxhMrh3f87VwnutLhPsZADi7F33p6OsGgG938qeHHEqTpgKT2CGPMThSh8RcPn4Gzu4BgDN/Rv9P\nOW41wJKX/i8AjtsHTOa4D+PiPh871tXTs+ett7Yo6LNWslBiWRhfWlpaPJPORBNRKwghQaVeWlr6\n1ltvKRhYDodj3Lie8eO/x3E6TpcK7oNvzyVg9DUY/QJ/6jD6hxqc+Bvu4GPodePY2xibC4Afswrv\nr0WPG38/iOSMwcvOOtA75ETD9+Ls1dBloM+KhKu4U2VTJ2dkZmZardYrrrhCW7cRBMsaqljFL9d3\nhtk7vexSvm1qWKmH2cYyMzNjJOmMRMTdsKdPn/6jH/0onIUeBe9WNjAUJ53xpbDgJk43OOnidePS\n4/YDwNeP89yT3Le/B4CvLeDW4+tqAHD/HpgzKBS//hV/7sRgK1wadNcDQEIWTrbhjBXnMoELeP4s\n8E+OKwEmAa3AiaSk+ilTUvr6/tvt3qum2yplocPhsNvtapLFbN26taamZs+eCOQEDynRKQgtFgtL\nNxX0+25oaKioqJA1sJh5OSsr66uvWnU6DvxhcFUY+A5TsyDuee6L846gfNLN+OBhXOiRp/977fj9\nAxh7z/kzXeswZmiFeLIZiY8CwNlmrve9+IH9nW8/yd7aN9xwA0JQ8dIra6jgT6hynyR9D8e8HKXI\n+OzsbPXvQba9EP46VpU06pPO+IVNEzZT2BkRN2zGrFmzenp66urqwtA9v9lrtdINZGRkXDWdA4Ce\nF8+cLe458w2AUbqTgDEp8d8BoO97gIHr7QWA/onAbXBvA4C+eAwMhTyddYPnAIDTo+80TvwKA5dw\n3BvATGASzyfodF9eeqljYODDs2d/98kn2jgqM1mo4D0gxd9NCm+++WZdXV20BdpH2kgZKrq6uqTb\ngXNycqRc7HK5vLwqli2r47glnK4GOp5LfADjeIzjkV7HXfoqjDyMPC7ephtXhsm2wR+NPKa7MCoP\nYxbih88OesSMXzhonP+3Doyqwzge6R1c/EyOm/3RRx/ZbDbPO3r9KAUpVmt2jbb27aBfge/zlILi\nTnZ1dQXq0ttvvx3Ir0rlTUccLpcr6FxYuXKlgnEoC/EHronnSPHCRuhdaaOzoeORUIcLdyenPgMd\nn5T8AlLzoLNBx+vilnGpRdB1Qccj4SHo66CzQdeKiZ0w8JxuHhefPzjrkxdw8dcBBcA1HJel013z\nox/drbKHIsh6AiIjXzEPPviguLPPyJoyUSsIZeFyuQoLC8Wvsdlsra2tvud/+MP7OG42dF2Ie4dL\n28RmhW5MAaa7kLEd4/4L43hu1B2Y7hoUhPoHB2eO/h1c8CAunDcoBQ080n6OsV1cynokzOK4X111\n1UK/Y1fuCJN4/cqVK2U1K4VA3p4ul8tmsyl+mSr4oJcnqi/t7e0ijpEja1aHgerq6rfffjsULbO3\ndtDL1Etil8uVNqExNW0FdDx0fFziVYMCT+eKT3iQnYSuIym1iB3r4hfqEh5mF3CpJdzopdB16eKK\n2HROHF3qdDqdTueuXbtUdkwiEmVhiIau0+m8+eabw3/fEEGCcBARWcjeoSJj7rvfzee4HOh4LmHh\noEP2WCfSZ3P6lkGZN47HuIWDonHsrvMnx9iQmoekPC7tIaQvRuIcLi4f+CXw1KhRt4v0VtYgC3qx\ny+UKxY4w0N1bW1vVh0No+wQYjz32WEtLi/rbxQg5OTmyAgCCwgIkpF+vfl94wcScIeHHJyTeOyT8\nnPHxM4aO9yQmzhs63pKcso4dp4yenzrqQej4pORVGMdjbFf5Utm6DZWw1aSaC1TidDoffPDBQL8d\nWVOGBOF5urq6Zs6c6XVSou4uKelWjrsX3EouaQFSNnAJ90L3ONK2n5d5Y50Yk4MxD3uIRh4J9wIn\nhv49BBwGPua4OQkJWUHvKH2ciVzpK+NDEbHnORsbGxu1UtFIeQJyNUIrV65sb29Xdq8YpLCwUCtZ\nGHTL7heV30v+AzXQuZjA0+lmQbcHOj4lpSwxcSETeKmpD6Wl/ZwdJyblJySWDJ4f9Sh0rdDx0NmQ\nWpdxmZhePXQEEnUs7DLU6mue551OZ15ent9fjawpQ4LwX7DZbJ4aQlkWrLi4a4F14AqBCibbuLjq\nf5GFCQ8g4R6MsbEfubRN4H47JAXXA/8FHAZmx8XdJ/GOEodaoMv8LqglKqbkwu6l+cwU6aqwzZXL\n/ffff+DAAa+TI2tWh5OCggKVslClBUvNcHW5XOO/U80EHnAiJaUMOj4l5UHgI+iWQ8cnJj2ekPA0\ndE7o+OTkR0ePHlSTxsffCF0nO05KzQv/dtDzT/B6dFqlHZDIxo0ba2pqfM+PrCkTnV6jijEajbNn\nz164cCHzM5YVZ/3hh8/pdL8D/zPge8CLAPj+Epw9hH47AJxahN756N3IndqF0yvBu/lz74FnheyP\nA/uBqziuKS7uVF/fixLvqCbDZ6CSRqEowilUTA1FxJ5f9znmdq8s/GPXrl2rV6+OYHbNkUVtbe2a\nNWKpMsXx6xoqCzWpOPV6/dSp7PC7AHS6r4G6M2ceBjLSUg4lxpf2nLu/t/fexPinEuKKz56d19/P\nOrmH465JiHuZfTJ91PiVj/tP3BEGWA5CYbjKDZNXz+LFixMSErZsURIZOXwgQeiN2+0+fPjw/v37\n5b61p02b1t/fGRf3HHAp8HvgAwDoN+FUHU6VoCcPuAgA37cI527HN/eh7/sc9xxwkON+wXHguOKE\nhPa+vg5ZN5UrC0USZwtoGK0Fj5DEUATFGwwGL4nFbmcwGNQI3VdeeWXevHkkC6Wg1+tra2sVJJ3R\npLQW6wATBso+fssthsT4hadP5wHo7TUmJewBpgPo7780OZkDMoCxcfEDScmJQMbp07cDu1KTm3p7\nl6akngMAOPLzpkS2oLwQXBip9J6LFy/+wx/+MKID7UkQ/guVlZV6vf6NN95wuVzKks709XUmJj7P\ncUkc9yLQyXG/1A3E63pPgt9//iJ+O/qWAvfz/M3AJp5P4/mbUlOTzp37MHDDAZGe1UwQEkG3fcqy\nw3ghVPgUZJImzXrh2ab67YXAtm3bcnNzVfcuJmBJZ0pKApaM9kLQEGj11mbrIWWiyFSRnZjoAjIA\n9PRczfODBVjOnk04efIH7JgfiPv2m5sAANOTE1/q6/8egL7eMQAyLrHU1haHv3CgJyxP+kcffRTB\nHEkvvfTSyE46E2ndbFiRpTqvqKhQbH74z/9cyHEzgBnAYuAgcBCoBuYAH3PcfOC3QyfXAU9yXNmY\nMbOV3YghbtgTPELlWg7UmPRELAShsEE2NTVpbpNwuVy33XYbOx5ZBo+I0N7e7je+yIvQWbAUO5He\nemsVM9UnJT0walQeOx4zZmlSUgE7Tky8B1jCjhMS7gAOAyeAPdDVlZeftw6GIi92UDydjCLSAU8y\nMzM7Owez9o+sKRNDO0Kz2Wy1Wlk2jUDXCCW/rVar2Wx+6qmnlK3y9uzZsnfvrxIS+oF/AGuAb4Bb\ngJuAhUAXx20F9nPcTo777/j4N6qrjcePv6LiLwti2Pvyyy9lJc4WULyBC6qi0TA/DtsKTJ06VfP1\nuF6vb2lpue6667RtNlphSWeam5tFrgmpBYttyxR8sKLiZuAV4DDPT/r225uA94D3zpyZzHETAADv\n8XxmWpoTAHB4YOCfAMs7Oj058fXa2vNuBBEpKO+p4FGcdEYr3n333ZGadCbSkjh8CCsUkRwivr8q\nLCxUs8i6/fZHRo2aodPdwHFZwK1AI/AKUKTTTdPrf/zmm28qbtkXv+txm82mcmkmZZkvINFXU6ul\nK2tHaCoUi1CbzWYymVjL6h9m1FNeXh5IkRCeR6dsaF199ZMpKaVsq5ecnJ+UlD2450MzO05NXca2\njMBHSUk/B04AH5WXP+fblCZhskERUQJFfF/Iks6wr9urntewJYYEIUP8XVZdXd3Y2Mj+Z2dcLldO\nTo4mt66trZ0xo2j06MuLiqr40GgIPf80DcPkpcjCoGkHvFDfK79KtlDETrW3t+fk5FRXV8taE8Qs\nXklnQpHfSxwFQ6u4uCEtbTFTfqam5iYkPD2kLBWO9wDrExN/AZxIS/s5cOLqq58M1FpIRZGUtaaU\nPHmhgyWdqampMZlMkRXJ0oktQRgoTZovngsZKQnYguI7dkMUsccWpB0dHULjWhXLFfmtMsOPmo6J\nfFbznUd7e/tVV121Zs2ajiFGyvSOFIWFhSwWU1mYvHrkiqJ33nln9OiVQ1bA2cBT7DguboaQ8iI+\n/mdsy5iY+DTwxPbtIc+G6rdZKUu9UOeUEcfpdC5evPjKK68U5ktExoAsYkgQ+pWCgZZOvhW677rr\nLgU3Ze0HejWHaFO4fft2rzOatByot2ralztdWcqMoM9NQ1mYl5e3efNmnudbW1sj+HIZceTm5r72\n2msRXDFIXK8IG9b8/HrgBPAUsCM9fS5wIiXlKeCpxMQi4ATwZlzcz4aE4uGJE4O/DbSd3XI31pGS\nhfX19atWrXI6nS6XSzyR/bAiVgShzWZjpXpNJlNxcbFwPisry/NHk8nU2tra2NgN/iXNAAAaRUlE\nQVToKzK9ks5IvGlQHYW22jZBo+spCTQUhJ5/i0SZFBTp3ZOVp1t9x7Zv3+6VTpMMhBJpbGxsbGxc\ntGiRtslI5SJrwXTgQFda2qakpDzgcELCk8Cb7Dg9vQg4kZJyN7CXCcUJEzYdOBB8dGkoipQNPDZD\nNemAxNsVFhbu3r1bOBNxa6V0YkUQBsJXUIksu2TJQukKNK1Ul17tCD9q+PoW5pW2mQylNCVXIalG\nSLMp7bWxJiTS2NjInrzL5SooKIhgT8RFke+8yMi4H9gLHAYOx8dfC3wMHAZ2xMf/HHh9SCj695EJ\n1AH1kkClxiU8oqi1tVWlX2FkiXVBKJeqqiq/SZm9CFGlJL8wmSRSsEnbfQzblmm+0hSPO1R2O2Wi\nevv27UGndGSdEUYQTqdTpLhVGPD7TQWyVhw48PmECTXA4dTU5xIS7gb2AYeBfQkJ9zDpCNRPm1Yq\nqwOKRRHrufrlZqhlodPpXLBggZS3Ii8zkjuccDzPRzqCY4SxZcuWiy66aNasWb6/EtJbKMjYqSw9\nUltbGyvvHugCu93udru1SuHBQpTcbncoclj4PgG3222329V0XtZT7e7urq2tveiii5YtWyZyGYtD\ndbvder0+OztiSSaHD5WVlewgKyvL92l3d3evWbOmqakp7P0axG63C8mG2DGAQLGMc+b88uWXE5OT\nz509W5CU9Oi5c0tTU3fy/DVnznwOzJs0aeveveUZGeMUd0AiLBWwSD/ldkCv14cijbDZbD569Oim\nTZt8f9XW1sYmrzAk2traWBIcg8EQkVRwYkRaEo9I/CadUVNpliFr68ZcQ4OusLq6urRS8Xl2L0R2\neM+nqpX2VXpdYmbkl97yCPIFCClBn4PEpDOhg+2KJGrXL7ggj+3/EhNXJiXdzY5TU++95JL1b7/9\nJ8UdkK7VCEUkoub7QhZXJl6cmbmMCj8KHojDMLiQBKFCvLRnWrnUS3xryxrW6gWhX1VSKDxHBNc4\nbWNLxLvKprQsodvR0WEymciJlOEbfetLS0tLU1NTGDvlTU1NjcQpc+DA55MmrQDWpKYuTklZCrwH\nfPyd75S89tof1HRAypwNqaunhnOqqqqqoqIi6GVegtCvK98wgQShcpgs1DwcUHyUdHV1yaqSyPsM\nR1mIV/gMxYCur68PhSEhUFeZkV9Bgy6XaxgubCOL+AMpLy8X30CEAs8ZKn35+Pbbf9LrbwM+Aj5K\nSbln5szVmoxJkXcFW2uG2slT/b7wwIEDOTk5EvtJgjAmOHDgwJQpU0LRst9xxlaLCsaQYpcZKdNG\nW5UXk7ghmidezcoy8vuFBKEXQR+IV9KZUONrrZA1tJY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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%octave -s 600,200 -f png\n", "\n", "% Note: On Windows, this will not show the plots unless Ghostscript is installed.\n", "\n", "subplot(121);\n", "[x, y] = meshgrid(0:0.1:3);\n", "r = sin(x - 0.5).^2 + cos(y - 0.5).^2;\n", "surf(x, y, r);\n", "\n", "subplot(122);\n", "sombrero()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Multiple figures can be drawn. 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1KmQvMZW8t91+hdeJZUvCfOfLFCsnAPlzV5IGln1OyyjHq2XtUi6sOhGlPGG5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\nmDHCtQLPGTjEUxgsFCg4ZefyAkrBTc8WjJsEQwDbLx/CFmM0jHLT/v1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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%octave -f svg -h 300\n", "sombrero\n", "figure\n", "imshow(rand(200,200))" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.0" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

broundy/udacity

nanodegrees/deep_learning_foundations/unit_2/project_2/dlnd_image_classification.ipynb

2

116917

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# Image Classification\n", "In this project, you'll classify images from the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html). The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers. At the end, you'll get to see your neural network's predictions on the sample images.\n", "## Get the Data\n", "Run the following cell to download the [CIFAR-10 dataset for python](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All files found!\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "from urllib.request import urlretrieve\n", "from os.path import isfile, isdir\n", "from tqdm import tqdm\n", "import problem_unittests as tests\n", "import tarfile\n", "\n", "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n", "\n", "class DLProgress(tqdm):\n", " last_block = 0\n", "\n", " def hook(self, block_num=1, block_size=1, total_size=None):\n", " self.total = total_size\n", " self.update((block_num - self.last_block) * block_size)\n", " self.last_block = block_num\n", "\n", "if not isfile('cifar-10-python.tar.gz'):\n", " with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n", " urlretrieve(\n", " 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n", " 'cifar-10-python.tar.gz',\n", " pbar.hook)\n", "\n", "if not isdir(cifar10_dataset_folder_path):\n", " with tarfile.open('cifar-10-python.tar.gz') as tar:\n", " tar.extractall()\n", " tar.close()\n", "\n", "\n", "tests.test_folder_path(cifar10_dataset_folder_path)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Explore the Data\n", "The dataset is broken into batches to prevent your machine from running out of memory. The CIFAR-10 dataset consists of 5 batches, named `data_batch_1`, `data_batch_2`, etc.. Each batch contains the labels and images that are one of the following:\n", "* airplane\n", "* automobile\n", "* bird\n", "* cat\n", "* deer\n", "* dog\n", "* frog\n", "* horse\n", "* ship\n", "* truck\n", "\n", "Understanding a dataset is part of making predictions on the data. Play around with the code cell below by changing the `batch_id` and `sample_id`. The `batch_id` is the id for a batch (1-5). The `sample_id` is the id for a image and label pair in the batch.\n", "\n", "Ask yourself \"What are all possible labels?\", \"What is the range of values for the image data?\", \"Are the labels in order or random?\". Answers to questions like these will help you preprocess the data and end up with better predictions." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Stats of batch 2:\n", "Samples: 10000\n", "Label Counts: {0: 984, 1: 1007, 2: 1010, 3: 995, 4: 1010, 5: 988, 6: 1008, 7: 1026, 8: 987, 9: 985}\n", "First 20 Labels: [1, 6, 6, 8, 8, 3, 4, 6, 0, 6, 0, 3, 6, 6, 5, 4, 8, 3, 2, 6]\n", "\n", "Example of Image 3:\n", "Image - Min Value: 4 Max Value: 255\n", "Image - Shape: (32, 32, 3)\n", "Label - Label Id: 8 Name: ship\n" ] }, { "data": { "image/png": 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p1uzKC+nMZz/yTOnWK6/dKOXOb91KZw73a2+zmxeeS2e+H7VBkN3vfzedmZ/Unqk/+GZt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\nmWwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8ac424f630>" ] }, "metadata": { "image/png": { "height": 250, "width": 253 } }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import helper\n", "import numpy as np\n", "\n", "# Explore the dataset\n", "batch_id = 2\n", "sample_id = 3\n", "helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Implement Preprocess Functions\n", "### Normalize\n", "In the cell below, implement the `normalize` function to take in image data, `x`, and return it as a normalized Numpy array. The values should be in the range of 0 to 1, inclusive. The return object should be the same shape as `x`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def normalize(x):\n", " \"\"\"\n", " Normalize a list of sample image data in the range of 0 to 1\n", " : x: List of image data. The image shape is (32, 32, 3)\n", " : return: Numpy array of normalize data\n", " \"\"\"\n", " return x / np.max(x)\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_normalize(normalize)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### One-hot encode\n", "Just like the previous code cell, you'll be implementing a function for preprocessing. This time, you'll implement the `one_hot_encode` function. The input, `x`, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to `one_hot_encode`. Make sure to save the map of encodings outside the function.\n", "\n", "Hint: Don't reinvent the wheel." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def one_hot_encode(x):\n", " \"\"\"\n", " One hot encode a list of sample labels. Return a one-hot encoded vector for each label.\n", " : x: List of sample Labels\n", " : return: Numpy array of one-hot encoded labels\n", " \"\"\"\n", " return np.eye(10)[x]\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "\n", "tests.test_one_hot_encode(one_hot_encode)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Randomize Data\n", "As you saw from exploring the data above, the order of the samples are randomized. It doesn't hurt to randomize it again, but you don't need to for this dataset." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Preprocess all the data and save it\n", "Running the code cell below will preprocess all the CIFAR-10 data and save it to file. The code below also uses 10% of the training data for validation." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Preprocess Training, Validation, and Testing Data\n", "helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Check Point\n", "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import pickle\n", "import problem_unittests as tests\n", "import helper\n", "\n", "# Load the Preprocessed Validation data\n", "valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Build the network\n", "For the neural network, you'll build each layer into a function. Most of the code you've seen has been outside of functions. To test your code more thoroughly, we require that you put each layer in a function. This allows us to give you better feedback and test for simple mistakes using our unittests before you submit your project.\n", "\n", ">**Note:** If you're finding it hard to dedicate enough time for this course each week, we've provided a small shortcut to this part of the project. In the next couple of problems, you'll have the option to use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages to build each layer, except the layers you build in the \"Convolutional and Max Pooling Layer\" section. TF Layers is similar to Keras's and TFLearn's abstraction to layers, so it's easy to pickup.\n", "\n", ">However, if you would like to get the most out of this course, try to solve all the problems _without_ using anything from the TF Layers packages. You **can** still use classes from other packages that happen to have the same name as ones you find in TF Layers! For example, instead of using the TF Layers version of the `conv2d` class, [tf.layers.conv2d](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d), you would want to use the TF Neural Network version of `conv2d`, [tf.nn.conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d). \n", "\n", "Let's begin!\n", "\n", "### Input\n", "The neural network needs to read the image data, one-hot encoded labels, and dropout keep probability. Implement the following functions\n", "* Implement `neural_net_image_input`\n", " * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n", " * Set the shape using `image_shape` with batch size set to `None`.\n", " * Name the TensorFlow placeholder \"x\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n", "* Implement `neural_net_label_input`\n", " * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n", " * Set the shape using `n_classes` with batch size set to `None`.\n", " * Name the TensorFlow placeholder \"y\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n", "* Implement `neural_net_keep_prob_input`\n", " * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) for dropout keep probability.\n", " * Name the TensorFlow placeholder \"keep_prob\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n", "\n", "These names will be used at the end of the project to load your saved model.\n", "\n", "Note: `None` for shapes in TensorFlow allow for a dynamic size." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Image Input Tests Passed.\n", "Label Input Tests Passed.\n", "Keep Prob Tests Passed.\n" ] } ], "source": [ "import tensorflow as tf\n", "\n", "def neural_net_image_input(image_shape):\n", " \"\"\"\n", " Return a Tensor for a bach of image input\n", " : image_shape: Shape of the images\n", " : return: Tensor for image input.\n", " \"\"\"\n", " return tf.placeholder(tf.float32, shape=[None, image_shape[0], image_shape[1], image_shape[2]], name='x')\n", "\n", "\n", "def neural_net_label_input(n_classes):\n", " \"\"\"\n", " Return a Tensor for a batch of label input\n", " : n_classes: Number of classes\n", " : return: Tensor for label input.\n", " \"\"\"\n", " return tf.placeholder(tf.float32, shape=[None, n_classes], name='y')\n", "\n", "\n", "def neural_net_keep_prob_input():\n", " \"\"\"\n", " Return a Tensor for keep probability\n", " : return: Tensor for keep probability.\n", " \"\"\"\n", " return tf.placeholder(tf.float32, name='keep_prob')\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tf.reset_default_graph()\n", "tests.test_nn_image_inputs(neural_net_image_input)\n", "tests.test_nn_label_inputs(neural_net_label_input)\n", "tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Convolution and Max Pooling Layer\n", "Convolution layers have a lot of success with images. For this code cell, you should implement the function `conv2d_maxpool` to apply convolution then max pooling:\n", "* Create the weight and bias using `conv_ksize`, `conv_num_outputs` and the shape of `x_tensor`.\n", "* Apply a convolution to `x_tensor` using weight and `conv_strides`.\n", " * We recommend you use same padding, but you're welcome to use any padding.\n", "* Add bias\n", "* Add a nonlinear activation to the convolution.\n", "* Apply Max Pooling using `pool_ksize` and `pool_strides`.\n", " * We recommend you use same padding, but you're welcome to use any padding.\n", "\n", "**Note:** You **can't** use [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) for **this** layer, but you can still use TensorFlow's [Neural Network](https://www.tensorflow.org/api_docs/python/tf/nn) package. You may still use the shortcut option for all the **other** layers." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n", " \"\"\"\n", " Apply convolution then max pooling to x_tensor\n", " :param x_tensor: TensorFlow Tensor\n", " :param conv_num_outputs: Number of outputs for the convolutional layer\n", " :param conv_ksize: kernal size 2-D Tuple for the convolutional layer\n", " :param conv_strides: Stride 2-D Tuple for convolution\n", " :param pool_ksize: kernal size 2-D Tuple for pool\n", " :param pool_strides: Stride 2-D Tuple for pool\n", " : return: A tensor that represents convolution and max pooling of x_tensor\n", " \"\"\"\n", " \n", " F_W = tf.Variable(tf.truncated_normal([conv_ksize[0], conv_ksize[1], x_tensor.shape.as_list()[3], conv_num_outputs], stddev=0.05))\n", " F_b = tf.Variable(tf.zeros(conv_num_outputs))\n", " \n", " output = tf.nn.conv2d(x_tensor, F_W, strides=[1, conv_strides[0], conv_strides[1], 1], padding='SAME')\n", " output = tf.nn.bias_add(output, F_b)\n", " output = tf.nn.relu(output)\n", " output = tf.nn.max_pool(output, ksize=[1, pool_ksize[0], pool_ksize[1], 1], strides=[1, pool_strides[0], pool_strides[1], 1], padding='SAME')\n", " \n", " return output \n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_con_pool(conv2d_maxpool)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Flatten Layer\n", "Implement the `flatten` function to change the dimension of `x_tensor` from a 4-D tensor to a 2-D tensor. The output should be the shape (*Batch Size*, *Flattened Image Size*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def flatten(x_tensor):\n", " \"\"\"\n", " Flatten x_tensor to (Batch Size, Flattened Image Size)\n", " : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.\n", " : return: A tensor of size (Batch Size, Flattened Image Size).\n", " \"\"\"\n", " return tf.contrib.layers.flatten(x_tensor)\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_flatten(flatten)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Fully-Connected Layer\n", "Implement the `fully_conn` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def fully_conn(x_tensor, num_outputs):\n", " \"\"\"\n", " Apply a fully connected layer to x_tensor using weight and bias\n", " : x_tensor: A 2-D tensor where the first dimension is batch size.\n", " : num_outputs: The number of output that the new tensor should be.\n", " : return: A 2-D tensor where the second dimension is num_outputs.\n", " \"\"\"\n", " return tf.contrib.layers.fully_connected(inputs=x_tensor, num_outputs=num_outputs, activation_fn=tf.nn.relu)\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_fully_conn(fully_conn)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Output Layer\n", "Implement the `output` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages.\n", "\n", "**Note:** Activation, softmax, or cross entropy should **not** be applied to this." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def output(x_tensor, num_outputs):\n", " \"\"\"\n", " Apply a output layer to x_tensor using weight and bias\n", " : x_tensor: A 2-D tensor where the first dimension is batch size.\n", " : num_outputs: The number of output that the new tensor should be.\n", " : return: A 2-D tensor where the second dimension is num_outputs.\n", " \"\"\"\n", " return tf.contrib.layers.fully_connected(inputs=x_tensor, num_outputs=num_outputs, activation_fn=None)\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_output(output)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Create Convolutional Model\n", "Implement the function `conv_net` to create a convolutional neural network model. The function takes in a batch of images, `x`, and outputs logits. Use the layers you created above to create this model:\n", "\n", "* Apply 1, 2, or 3 Convolution and Max Pool layers\n", "* Apply a Flatten Layer\n", "* Apply 1, 2, or 3 Fully Connected Layers\n", "* Apply an Output Layer\n", "* Return the output\n", "* Apply [TensorFlow's Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout) to one or more layers in the model using `keep_prob`. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Neural Network Built!\n" ] } ], "source": [ "def conv_net(x, keep_prob):\n", " \"\"\"\n", " Create a convolutional neural network model\n", " : x: Placeholder tensor that holds image data.\n", " : keep_prob: Placeholder tensor that hold dropout keep probability.\n", " : return: Tensor that represents logits\n", " \"\"\"\n", " # Function Definition from Above:\n", " # conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides) \n", " c_layer = conv2d_maxpool(x, 32, (8, 8), (1, 1), (4, 4), (2, 2)) \n", " c_layer = conv2d_maxpool(c_layer, 128, (4,4), (1,1), (4,4), (2,2))\n", " c_layer = conv2d_maxpool(c_layer, 512, (2,2), (1,1), (4,4), (2,2))\n", " c_layer = tf.nn.dropout(c_layer, keep_prob)\n", "\n", " # Function Definition from Above:\n", " # flatten(x_tensor)\n", " flat = flatten(c_layer)\n", "\n", " # Function Definition from Above:\n", " # fully_conn(x_tensor, num_outputs)\n", " fc_layer = fully_conn(flat, 512)\n", " fc_layer = tf.nn.dropout(fc_layer, keep_prob)\n", " fc_layer = fully_conn(flat, 128)\n", " fc_layer = tf.nn.dropout(fc_layer, keep_prob)\n", " fc_layer = fully_conn(flat, 32)\n", " \n", " # Function Definition from Above:\n", " # output(x_tensor, num_outputs)\n", " o_layer = output(fc_layer, 10)\n", " return o_layer\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "\n", "##############################\n", "## Build the Neural Network ##\n", "##############################\n", "\n", "# Remove previous weights, bias, inputs, etc..\n", "tf.reset_default_graph()\n", "\n", "# Inputs\n", "x = neural_net_image_input((32, 32, 3))\n", "y = neural_net_label_input(10)\n", "keep_prob = neural_net_keep_prob_input()\n", "\n", "# Model\n", "logits = conv_net(x, keep_prob)\n", "\n", "# Name logits Tensor, so that is can be loaded from disk after training\n", "logits = tf.identity(logits, name='logits')\n", "\n", "# Loss and Optimizer\n", "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n", "optimizer = tf.train.AdamOptimizer().minimize(cost)\n", "\n", "# Accuracy\n", "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n", "\n", "tests.test_conv_net(conv_net)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Train the Neural Network\n", "### Single Optimization\n", "Implement the function `train_neural_network` to do a single optimization. The optimization should use `optimizer` to optimize in `session` with a `feed_dict` of the following:\n", "* `x` for image input\n", "* `y` for labels\n", "* `keep_prob` for keep probability for dropout\n", "\n", "This function will be called for each batch, so `tf.global_variables_initializer()` has already been called.\n", "\n", "Note: Nothing needs to be returned. This function is only optimizing the neural network." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n", " \"\"\"\n", " Optimize the session on a batch of images and labels\n", " : session: Current TensorFlow session\n", " : optimizer: TensorFlow optimizer function\n", " : keep_probability: keep probability\n", " : feature_batch: Batch of Numpy image data\n", " : label_batch: Batch of Numpy label data\n", " \"\"\"\n", " session.run(optimizer, feed_dict={\n", " x: feature_batch,\n", " y: label_batch,\n", " keep_prob: keep_probability})\n", " pass\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_train_nn(train_neural_network)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Show Stats\n", "Implement the function `print_stats` to print loss and validation accuracy. Use the global variables `valid_features` and `valid_labels` to calculate validation accuracy. Use a keep probability of `1.0` to calculate the loss and validation accuracy." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def print_stats(session, feature_batch, label_batch, cost, accuracy):\n", " \"\"\"\n", " Print information about loss and validation accuracy\n", " : session: Current TensorFlow session\n", " : feature_batch: Batch of Numpy image data\n", " : label_batch: Batch of Numpy label data\n", " : cost: TensorFlow cost function\n", " : accuracy: TensorFlow accuracy function\n", " \"\"\"\n", " loss = session.run(cost, feed_dict={ x: feature_batch, y: label_batch, keep_prob: 1.0})\n", " \n", " valid_acc = session.run(accuracy, feed_dict={x: valid_features, y: valid_labels, keep_prob: 1.0})\n", " \n", " print('Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(loss, valid_acc))\n", " pass" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Hyperparameters\n", "Tune the following parameters:\n", "* Set `epochs` to the number of iterations until the network stops learning or start overfitting\n", "* Set `batch_size` to the highest number that your machine has memory for. Most people set them to common sizes of memory:\n", " * 64\n", " * 128\n", " * 256\n", " * ...\n", "* Set `keep_probability` to the probability of keeping a node using dropout" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# TODO: Tune Parameters\n", "epochs = 15\n", "batch_size = 512\n", "keep_probability = .7" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Train on a Single CIFAR-10 Batch\n", "Instead of training the neural network on all the CIFAR-10 batches of data, let's use a single batch. This should save time while you iterate on the model to get a better accuracy. Once the final validation accuracy is 50% or greater, run the model on all the data in the next section." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking the Training on a Single Batch...\n", "Epoch 1, CIFAR-10 Batch 1: Loss: 2.2590 Validation Accuracy: 0.142000\n", "Epoch 2, CIFAR-10 Batch 1: Loss: 2.1837 Validation Accuracy: 0.173000\n", "Epoch 3, CIFAR-10 Batch 1: Loss: 2.1638 Validation Accuracy: 0.175400\n", "Epoch 4, CIFAR-10 Batch 1: Loss: 2.1070 Validation Accuracy: 0.197200\n", "Epoch 5, CIFAR-10 Batch 1: Loss: 2.0471 Validation Accuracy: 0.217600\n", "Epoch 6, CIFAR-10 Batch 1: Loss: 2.0097 Validation Accuracy: 0.221800\n", "Epoch 7, CIFAR-10 Batch 1: Loss: 1.9981 Validation Accuracy: 0.212200\n", "Epoch 8, CIFAR-10 Batch 1: Loss: 1.9701 Validation Accuracy: 0.212800\n", "Epoch 9, CIFAR-10 Batch 1: Loss: 1.9218 Validation Accuracy: 0.230200\n", "Epoch 10, CIFAR-10 Batch 1: Loss: 1.8726 Validation Accuracy: 0.243000\n", "Epoch 11, CIFAR-10 Batch 1: Loss: 1.8403 Validation Accuracy: 0.251800\n", "Epoch 12, CIFAR-10 Batch 1: Loss: 1.7896 Validation Accuracy: 0.269600\n", "Epoch 13, CIFAR-10 Batch 1: Loss: 1.7881 Validation Accuracy: 0.272200\n", "Epoch 14, CIFAR-10 Batch 1: Loss: 1.7382 Validation Accuracy: 0.296200\n", "Epoch 15, CIFAR-10 Batch 1: Loss: 1.7133 Validation Accuracy: 0.291600\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "print('Checking the Training on a Single Batch...')\n", "with tf.Session() as sess:\n", " # Initializing the variables\n", " sess.run(tf.global_variables_initializer())\n", " \n", " # Training cycle\n", " for epoch in range(epochs):\n", " batch_i = 1\n", " for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n", " train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n", " print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')\n", " print_stats(sess, batch_features, batch_labels, cost, accuracy)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Fully Train the Model\n", "Now that you got a good accuracy with a single CIFAR-10 batch, try it with all five batches." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training...\n", "Epoch 1, CIFAR-10 Batch 1: Loss: 2.1911 Validation Accuracy: 0.169400\n", "Epoch 1, CIFAR-10 Batch 2: Loss: 2.0260 Validation Accuracy: 0.226800\n", "Epoch 1, CIFAR-10 Batch 3: Loss: 1.9500 Validation Accuracy: 0.297400\n", "Epoch 1, CIFAR-10 Batch 4: Loss: 1.8198 Validation Accuracy: 0.350000\n", "Epoch 1, CIFAR-10 Batch 5: Loss: 1.8053 Validation Accuracy: 0.340000\n", "Epoch 2, CIFAR-10 Batch 1: Loss: 1.8489 Validation Accuracy: 0.368000\n", "Epoch 2, CIFAR-10 Batch 2: Loss: 1.6825 Validation Accuracy: 0.365800\n", "Epoch 2, CIFAR-10 Batch 3: Loss: 1.5295 Validation Accuracy: 0.387400\n", "Epoch 2, CIFAR-10 Batch 4: Loss: 1.4395 Validation Accuracy: 0.419200\n", "Epoch 2, CIFAR-10 Batch 5: Loss: 1.5192 Validation Accuracy: 0.433600\n", "Epoch 3, CIFAR-10 Batch 1: Loss: 1.6033 Validation Accuracy: 0.439600\n", "Epoch 3, CIFAR-10 Batch 2: Loss: 1.4155 Validation Accuracy: 0.455600\n", "Epoch 3, CIFAR-10 Batch 3: Loss: 1.2954 Validation Accuracy: 0.472600\n", "Epoch 3, CIFAR-10 Batch 4: Loss: 1.2760 Validation Accuracy: 0.487800\n", "Epoch 3, CIFAR-10 Batch 5: Loss: 1.3470 Validation Accuracy: 0.500400\n", "Epoch 4, CIFAR-10 Batch 1: Loss: 1.3762 Validation Accuracy: 0.505800\n", "Epoch 4, CIFAR-10 Batch 2: Loss: 1.2458 Validation Accuracy: 0.501400\n", "Epoch 4, CIFAR-10 Batch 3: Loss: 1.1065 Validation Accuracy: 0.531000\n", "Epoch 4, CIFAR-10 Batch 4: Loss: 1.1038 Validation Accuracy: 0.547600\n", "Epoch 4, CIFAR-10 Batch 5: Loss: 1.2476 Validation Accuracy: 0.507200\n", "Epoch 5, CIFAR-10 Batch 1: Loss: 1.2824 Validation Accuracy: 0.513600\n", "Epoch 5, CIFAR-10 Batch 2: Loss: 1.1195 Validation Accuracy: 0.549000\n", "Epoch 5, CIFAR-10 Batch 3: Loss: 0.9804 Validation Accuracy: 0.564000\n", "Epoch 5, CIFAR-10 Batch 4: Loss: 0.9671 Validation Accuracy: 0.570000\n", "Epoch 5, CIFAR-10 Batch 5: Loss: 1.0993 Validation Accuracy: 0.540800\n", "Epoch 6, CIFAR-10 Batch 1: Loss: 1.1113 Validation Accuracy: 0.568000\n", "Epoch 6, CIFAR-10 Batch 2: Loss: 1.0527 Validation Accuracy: 0.565400\n", "Epoch 6, CIFAR-10 Batch 3: Loss: 0.9382 Validation Accuracy: 0.581800\n", "Epoch 6, CIFAR-10 Batch 4: Loss: 0.8995 Validation Accuracy: 0.593200\n", "Epoch 6, CIFAR-10 Batch 5: Loss: 0.9372 Validation Accuracy: 0.600400\n", "Epoch 7, CIFAR-10 Batch 1: Loss: 1.0405 Validation Accuracy: 0.575800\n", "Epoch 7, CIFAR-10 Batch 2: Loss: 0.9476 Validation Accuracy: 0.577600\n", "Epoch 7, CIFAR-10 Batch 3: Loss: 0.8561 Validation Accuracy: 0.595400\n", "Epoch 7, CIFAR-10 Batch 4: Loss: 0.8039 Validation Accuracy: 0.607000\n", "Epoch 7, CIFAR-10 Batch 5: Loss: 0.8472 Validation Accuracy: 0.621800\n", "Epoch 8, CIFAR-10 Batch 1: Loss: 0.9194 Validation Accuracy: 0.604800\n", "Epoch 8, CIFAR-10 Batch 2: Loss: 0.8610 Validation Accuracy: 0.603800\n", "Epoch 8, CIFAR-10 Batch 3: Loss: 0.7812 Validation Accuracy: 0.622200\n", "Epoch 8, CIFAR-10 Batch 4: Loss: 0.7162 Validation Accuracy: 0.623000\n", "Epoch 8, CIFAR-10 Batch 5: Loss: 0.7847 Validation Accuracy: 0.634000\n", "Epoch 9, CIFAR-10 Batch 1: Loss: 0.8537 Validation Accuracy: 0.629800\n", "Epoch 9, CIFAR-10 Batch 2: Loss: 0.8275 Validation Accuracy: 0.603600\n", "Epoch 9, CIFAR-10 Batch 3: Loss: 0.7214 Validation Accuracy: 0.622600\n", "Epoch 9, CIFAR-10 Batch 4: Loss: 0.6524 Validation Accuracy: 0.646000\n", "Epoch 9, CIFAR-10 Batch 5: Loss: 0.7479 Validation Accuracy: 0.632000\n", "Epoch 10, CIFAR-10 Batch 1: Loss: 0.7899 Validation Accuracy: 0.647400\n", "Epoch 10, CIFAR-10 Batch 2: Loss: 0.7719 Validation Accuracy: 0.630600\n", "Epoch 10, CIFAR-10 Batch 3: Loss: 0.6664 Validation Accuracy: 0.634600\n", "Epoch 10, CIFAR-10 Batch 4: Loss: 0.6224 Validation Accuracy: 0.639400\n", "Epoch 10, CIFAR-10 Batch 5: Loss: 0.6688 Validation Accuracy: 0.658200\n", "Epoch 11, CIFAR-10 Batch 1: Loss: 0.7202 Validation Accuracy: 0.663200\n", "Epoch 11, CIFAR-10 Batch 2: Loss: 0.6876 Validation Accuracy: 0.656800\n", "Epoch 11, CIFAR-10 Batch 3: Loss: 0.6314 Validation Accuracy: 0.645800\n", "Epoch 11, CIFAR-10 Batch 4: Loss: 0.5938 Validation Accuracy: 0.638800\n", "Epoch 11, CIFAR-10 Batch 5: Loss: 0.6366 Validation Accuracy: 0.663200\n", "Epoch 12, CIFAR-10 Batch 1: Loss: 0.6726 Validation Accuracy: 0.667200\n", "Epoch 12, CIFAR-10 Batch 2: Loss: 0.6565 Validation Accuracy: 0.661000\n", "Epoch 12, CIFAR-10 Batch 3: Loss: 0.5775 Validation Accuracy: 0.668400\n", "Epoch 12, CIFAR-10 Batch 4: Loss: 0.5416 Validation Accuracy: 0.659800\n", "Epoch 12, CIFAR-10 Batch 5: Loss: 0.5864 Validation Accuracy: 0.681200\n", "Epoch 13, CIFAR-10 Batch 1: Loss: 0.5823 Validation Accuracy: 0.675000\n", "Epoch 13, CIFAR-10 Batch 2: Loss: 0.6041 Validation Accuracy: 0.675600\n", "Epoch 13, CIFAR-10 Batch 3: Loss: 0.5529 Validation Accuracy: 0.669800\n", "Epoch 13, CIFAR-10 Batch 4: Loss: 0.5157 Validation Accuracy: 0.659600\n", "Epoch 13, CIFAR-10 Batch 5: Loss: 0.5296 Validation Accuracy: 0.687200\n", "Epoch 14, CIFAR-10 Batch 1: Loss: 0.5507 Validation Accuracy: 0.680800\n", "Epoch 14, CIFAR-10 Batch 2: Loss: 0.5905 Validation Accuracy: 0.678400\n", "Epoch 14, CIFAR-10 Batch 3: Loss: 0.4997 Validation Accuracy: 0.679000\n", "Epoch 14, CIFAR-10 Batch 4: Loss: 0.4807 Validation Accuracy: 0.664600\n", "Epoch 14, CIFAR-10 Batch 5: Loss: 0.5168 Validation Accuracy: 0.679600\n", "Epoch 15, CIFAR-10 Batch 1: Loss: 0.5295 Validation Accuracy: 0.695600\n", "Epoch 15, CIFAR-10 Batch 2: Loss: 0.5550 Validation Accuracy: 0.685600\n", "Epoch 15, CIFAR-10 Batch 3: Loss: 0.5182 Validation Accuracy: 0.670600\n", "Epoch 15, CIFAR-10 Batch 4: Loss: 0.5003 Validation Accuracy: 0.676800\n", "Epoch 15, CIFAR-10 Batch 5: Loss: 0.4605 Validation Accuracy: 0.696400\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "save_model_path = './image_classification'\n", "\n", "print('Training...')\n", "with tf.Session() as sess:\n", " # Initializing the variables\n", " sess.run(tf.global_variables_initializer())\n", " \n", " # Training cycle\n", " for epoch in range(epochs):\n", " # Loop over all batches\n", " n_batches = 5\n", " for batch_i in range(1, n_batches + 1):\n", " for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n", " train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n", " print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')\n", " print_stats(sess, batch_features, batch_labels, cost, accuracy)\n", " \n", " # Save Model\n", " saver = tf.train.Saver()\n", " save_path = saver.save(sess, save_model_path)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Checkpoint\n", "The model has been saved to disk.\n", "## Test Model\n", "Test your model against the test dataset. This will be your final accuracy. You should have an accuracy greater than 50%. If you don't, keep tweaking the model architecture and parameters." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing Accuracy: 0.683835020661354\n", "\n" ] }, { "data": { "image/png": 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C3mYa2k/1dqe32lXPx7n0PDrZ3a2Gy7w61i0iIiJSN301rOIEomf4YeAMd7/b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\n383sPuBP8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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8a69f0b2e8>" ] }, "metadata": { "image/png": { "height": 319, "width": 355 } }, "output_type": "display_data" } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import tensorflow as tf\n", "import pickle\n", "import helper\n", "import random\n", "\n", "# Set batch size if not already set\n", "try:\n", " if batch_size:\n", " pass\n", "except NameError:\n", " batch_size = 64\n", "\n", "save_model_path = './image_classification'\n", "n_samples = 4\n", "top_n_predictions = 3\n", "\n", "def test_model():\n", " \"\"\"\n", " Test the saved model against the test dataset\n", " \"\"\"\n", "\n", " test_features, test_labels = pickle.load(open('preprocess_training.p', mode='rb'))\n", " loaded_graph = tf.Graph()\n", "\n", " with tf.Session(graph=loaded_graph) as sess:\n", " # Load model\n", " loader = tf.train.import_meta_graph(save_model_path + '.meta')\n", " loader.restore(sess, save_model_path)\n", "\n", " # Get Tensors from loaded model\n", " loaded_x = loaded_graph.get_tensor_by_name('x:0')\n", " loaded_y = loaded_graph.get_tensor_by_name('y:0')\n", " loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n", " loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n", " loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n", " \n", " # Get accuracy in batches for memory limitations\n", " test_batch_acc_total = 0\n", " test_batch_count = 0\n", " \n", " for train_feature_batch, train_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):\n", " test_batch_acc_total += sess.run(\n", " loaded_acc,\n", " feed_dict={loaded_x: train_feature_batch, loaded_y: train_label_batch, loaded_keep_prob: 1.0})\n", " test_batch_count += 1\n", "\n", " print('Testing Accuracy: {}\\n'.format(test_batch_acc_total/test_batch_count))\n", "\n", " # Print Random Samples\n", " random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\n", " random_test_predictions = sess.run(\n", " tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),\n", " feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})\n", " helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\n", "\n", "\n", "test_model()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Why 50-80% Accuracy?\n", "You might be wondering why you can't get an accuracy any higher. First things first, 50% isn't bad for a simple CNN. Pure guessing would get you 10% accuracy. However, you might notice people are getting scores [well above 80%](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130). That's because we haven't taught you all there is to know about neural networks. We still need to cover a few more techniques.\n", "## Submitting This Project\n", "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_image_classification.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.0" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }

unlicense

sods/ods

notebooks/index.ipynb

1

18345

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebooks for Open Data Science\n", "\n", "[Pandas, Python and Jupyter]($host/Pandas and Python.ipynb) This notebook provides an introduction to the notebook and to the `pandas` library for 'data analysis'.\n", "* [An interface to Google Spreadsheets]($host/Google Docs Interface.ipynb) This notebook demonstrates `pods.google` which provides a simple interface between `pandas` and Google spreadsheets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Neil Lawrence](http://inverseprobability.com)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Pandas, Python and Jupyter](./Pandas and Python.ipynb)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.insert(0, '/Users/neil/lawrennd/ods')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accessing Google trends to acquire the data. Note that repeated accesses will result in a block due to a google terms of service violation. Failure at this point may be due to such blocks.\n", "Query terms: big data, internet of things\n", "Fetching query:\n" ] }, { "ename": "HTTPError", "evalue": "HTTP Error 404: Not Found", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mHTTPError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-3-3609c9adbeea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpods\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpods\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgoogle_trends\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'big data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'internet of things'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrefresh_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/lawrennd/ods/pods/datasets.py\u001b[0m in \u001b[0;36mgoogle_trends\u001b[0;34m(query_terms, data_set, refresh_data)\u001b[0m\n\u001b[1;32m 843\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'http://www.google.com/trends/fetchComponent?q=%s&cid=TIMESERIES_GRAPH_0&export=3'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m\",\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquoted_terms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 844\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 845\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 846\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Done.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 847\u001b[0m \u001b[0;31m# In the notebook they did some data cleaning: remove Javascript header+footer, and translate new Date(....,..,..) into YYYY-MM-DD.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 221\u001b[0m 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"ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 1 }

bsd-3-clause

maciejkula/triplet_recommendations_keras

triplet_keras.ipynb

1

10338

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Recommendations in Keras using triplet loss\n", "Along the lines of BPR [1]. \n", "\n", "[1] Rendle, Steffen, et al. \"BPR: Bayesian personalized ranking from implicit feedback.\" Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press, 2009.\n", "\n", "This is implemented (more efficiently) in LightFM (https://github.com/lyst/lightfm). See the MovieLens example (https://github.com/lyst/lightfm/blob/master/examples/movielens/example.ipynb) for results comparable to this notebook.\n", "\n", "## Set up the architecture\n", "A simple dense layer for both users and items: this is exactly equivalent to latent factor matrix when multiplied by binary user and item indices. There are three inputs: users, positive items, and negative items. In the triplet objective we try to make the positive item rank higher than the negative item for that user.\n", "\n", "Because we want just one single embedding for the items, we use shared weights for the positive and negative item inputs (a siamese architecture).\n", "\n", "This is all very simple but could be made arbitrarily complex, with more layers, conv layers and so on. I expect we'll be seeing a lot of papers doing just that.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n" ] } ], "source": [ "\"\"\"\n", "Triplet loss network example for recommenders\n", "\"\"\"\n", "\n", "from __future__ import print_function\n", "\n", "import numpy as np\n", "\n", "from keras import backend as K\n", "from keras.models import Model\n", "from keras.layers import Embedding, Flatten, Input, merge\n", "from keras.optimizers import Adam\n", "\n", "import data\n", "import metrics\n", "\n", "\n", "def identity_loss(y_true, y_pred):\n", "\n", " return K.mean(y_pred - 0 * y_true)\n", "\n", "\n", "def bpr_triplet_loss(X):\n", "\n", " positive_item_latent, negative_item_latent, user_latent = X\n", "\n", " # BPR loss\n", " loss = 1.0 - K.sigmoid(\n", " K.sum(user_latent * positive_item_latent, axis=-1, keepdims=True) -\n", " K.sum(user_latent * negative_item_latent, axis=-1, keepdims=True))\n", "\n", " return loss\n", "\n", "\n", "def build_model(num_users, num_items, latent_dim):\n", "\n", " positive_item_input = Input((1, ), name='positive_item_input')\n", " negative_item_input = Input((1, ), name='negative_item_input')\n", "\n", " # Shared embedding layer for positive and negative items\n", " item_embedding_layer = Embedding(\n", " num_items, latent_dim, name='item_embedding', input_length=1)\n", "\n", " user_input = Input((1, ), name='user_input')\n", "\n", " positive_item_embedding = Flatten()(item_embedding_layer(\n", " positive_item_input))\n", " negative_item_embedding = Flatten()(item_embedding_layer(\n", " negative_item_input))\n", " user_embedding = Flatten()(Embedding(\n", " num_users, latent_dim, name='user_embedding', input_length=1)(\n", " user_input))\n", "\n", " loss = merge(\n", " [positive_item_embedding, negative_item_embedding, user_embedding],\n", " mode=bpr_triplet_loss,\n", " name='loss',\n", " output_shape=(1, ))\n", "\n", " model = Model(\n", " input=[positive_item_input, negative_item_input, user_input],\n", " output=loss)\n", " model.compile(loss=identity_loss, optimizer=Adam())\n", "\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load and transform data\n", "We're going to load the Movielens 100k dataset and create triplets of (user, known positive item, randomly sampled negative item).\n", "\n", "The success metric is AUC: in this case, the probability that a randomly chosen known positive item from the test set is ranked higher for a given user than a ranomly chosen negative item." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "positive_item_input (InputLayer) (None, 1) 0 \n", "____________________________________________________________________________________________________\n", "negative_item_input (InputLayer) (None, 1) 0 \n", "____________________________________________________________________________________________________\n", "user_input (InputLayer) (None, 1) 0 \n", "____________________________________________________________________________________________________\n", "item_embedding (Embedding) (None, 1, 100) 168300 positive_item_input[0][0] \n", " negative_item_input[0][0] \n", "____________________________________________________________________________________________________\n", "user_embedding (Embedding) (None, 1, 100) 94400 user_input[0][0] \n", "____________________________________________________________________________________________________\n", "flatten_7 (Flatten) (None, 100) 0 item_embedding[0][0] \n", "____________________________________________________________________________________________________\n", "flatten_8 (Flatten) (None, 100) 0 item_embedding[1][0] \n", "____________________________________________________________________________________________________\n", "flatten_9 (Flatten) (None, 100) 0 user_embedding[0][0] \n", "____________________________________________________________________________________________________\n", "loss (Merge) (None, 1) 0 flatten_7[0][0] \n", " flatten_8[0][0] \n", " flatten_9[0][0] \n", "====================================================================================================\n", "Total params: 262700\n", "____________________________________________________________________________________________________\n", "None\n", "AUC before training 0.50247407966\n" ] } ], "source": [ "latent_dim = 100\n", "num_epochs = 10\n", "\n", "# Read data\n", "train, test = data.get_movielens_data()\n", "num_users, num_items = train.shape\n", "\n", "# Prepare the test triplets\n", "test_uid, test_pid, test_nid = data.get_triplets(test)\n", "\n", "model = build_model(num_users, num_items, latent_dim)\n", "\n", "# Print the model structure\n", "print(model.summary())\n", "\n", "# Sanity check, should be around 0.5\n", "print('AUC before training %s' % metrics.full_auc(model, test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the model\n", "Run for a couple of epochs, checking the AUC after every epoch." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0\n", "AUC 0.905896400776\n", "Epoch 1\n", "AUC 0.908241780938\n", "Epoch 2\n", "AUC 0.909650205748\n", "Epoch 3\n", "AUC 0.910820451523\n", "Epoch 4\n", "AUC 0.912184845152\n", "Epoch 5\n", "AUC 0.912632057958\n", "Epoch 6\n", "AUC 0.91326604222\n", "Epoch 7\n", "AUC 0.913786881853\n", "Epoch 8\n", "AUC 0.914638438854\n", "Epoch 9\n", "AUC 0.915375014253\n" ] } ], "source": [ "for epoch in range(num_epochs):\n", "\n", " print('Epoch %s' % epoch)\n", "\n", " # Sample triplets from the training data\n", " uid, pid, nid = data.get_triplets(train)\n", "\n", " X = {\n", " 'user_input': uid,\n", " 'positive_item_input': pid,\n", " 'negative_item_input': nid\n", " }\n", "\n", " model.fit(X,\n", " np.ones(len(uid)),\n", " batch_size=64,\n", " nb_epoch=1,\n", " verbose=0,\n", " shuffle=True)\n", "\n", " print('AUC %s' % metrics.full_auc(model, test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The AUC is in the low-90s. At some point we start overfitting, so it would be a good idea to stop early or add some regularization." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

aoool/vehicle-detection-and-tracking

Vehicle_Detection_and_Tracking.ipynb

1

56728

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Vehicle Detection and Tracking" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "**Author:** Sergey Morozov \n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### NOTICE\n", "\n", "Some of the functions below were provided by [Udacity](https://www.udacity.com/). Author modified some of them and added original code." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Project Steps\n", "- Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier.\n", "- Apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector.\n", "- Implement a sliding-window technique and use your trained classifier to search for vehicles in images.\n", "- Run pipeline on a video stream and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles.\n", "- Estimate a bounding box for vehicles detected." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training Data Extraction\n", "\n", "Archive with vehicle and non-vehicle images should be manually downloaded and placed in the root of this repository.\n", "- [vehicles.zip](https://yadi.sk/d/z55uKF-J3KNDgg)\n", "- [non-vehicles.zip](https://yadi.sk/d/-blY05xV3KNDnV)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Extract Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import zipfile\n", "import shutil\n", "\n", "# Extract vehicle images\n", "if not (os.path.isdir(\"vehicle_images\")):\n", " zip_ref = zipfile.ZipFile(\"vehicles.zip\", 'r')\n", " zip_ref.extractall(\".\")\n", " zip_ref.close()\n", " shutil.move(\"vehicles\", \"vehicle_images\")\n", " shutil.rmtree(\"__MACOSX\")\n", " \n", "# Extract non-vehicle images\n", "if not (os.path.isdir(\"non-vehicle_images\")):\n", " zip_ref = zipfile.ZipFile(\"non-vehicles.zip\", 'r')\n", " zip_ref.extractall(\".\")\n", " zip_ref.close()\n", " shutil.move(\"non-vehicles\", \"non-vehicle_images\")\n", " shutil.rmtree(\"__MACOSX\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Explore Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import cv2\n", "import glob\n", "import numpy as np\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "def data_look(car_list, notcar_list):\n", " \"\"\"Extract useful information about provided data.\"\"\"\n", " data_dict = {}\n", " # Define a key in data_dict \"n_cars\" and store the number of car images\n", " data_dict[\"n_cars\"] = len(car_list)\n", " # Define a key \"n_notcars\" and store the number of notcar images\n", " data_dict[\"n_notcars\"] = len(notcar_list)\n", " # Read in a test image, either car or notcar\n", " example_img = cv2.imread(car_list[0])\n", " # Define a key \"image_shape\" and store the test image shape 3-tuple\n", " data_dict[\"image_shape\"] = example_img.shape\n", " # Define a key \"data_type\" and store the data type of the test image.\n", " data_dict[\"data_type\"] = example_img.dtype\n", " # Return data_dict\n", " return data_dict\n", "\n", "# List containing paths to training images\n", "vehicle_paths = glob.glob(\"vehicle_images/*/*.png\")\n", "nonvehicle_paths = glob.glob(\"non-vehicle_images/*/*.png\")\n", "\n", "# Extract useful information from data\n", "data_info = data_look(vehicle_paths, nonvehicle_paths)\n", "\n", "# Define useful constants\n", "cars_cnt = data_info['n_cars']\n", "notcars_cnt = data_info['n_notcars']\n", "image_shape = data_info['image_shape']\n", "data_type = data_info['data_type']\n", "\n", "# Print extracted information\n", "print(\"Vehicle image count:\", cars_cnt)\n", "print(\"Non-vehicle image count:\", notcars_cnt)\n", "print(\"Image shape:\", image_shape)\n", "print(\"Image type:\", data_type)\n", "\n", "# Show example images from each category\n", "fig, axs = plt.subplots(1,8, figsize=(16, 2))\n", "\n", "for i in range(4):\n", " img = mpimg.imread(vehicle_paths[i * i * 3])\n", " axs[i].axis('off')\n", " axs[i].set_title('Vehicle')\n", " axs[i].imshow(img)\n", " \n", "for i in range(4,8):\n", " img = mpimg.imread(nonvehicle_paths[i * i * 3])\n", " axs[i].axis('off')\n", " axs[i].set_title('Non-Vehicle')\n", " axs[i].imshow(img)\n", "\n", "# Create directory where to save output images\n", "if not os.path.isdir(\"output_images\"):\n", " os.mkdir(\"output_images\")\n", "\n", "plt.savefig('output_images/data_examples.png', bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature Extraction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Histogram of Color" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import cv2\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "def color_hist(img, nbins=32, bins_range=(0, 256), features_only=True):\n", " \"\"\"Function to compute color histogram features.\"\"\"\n", " # Compute the histogram of the color channels separately\n", " channel1_hist = np.histogram(img[:,:,0], bins=nbins, range=bins_range)\n", " channel2_hist = np.histogram(img[:,:,1], bins=nbins, range=bins_range)\n", " channel3_hist = np.histogram(img[:,:,2], bins=nbins, range=bins_range)\n", " # Concatenate the histograms into a single feature vector\n", " hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))\n", " # Return the individual histograms, bin_centers and feature vector\n", " if features_only:\n", " return hist_features\n", " else:\n", " # Generating bin centers\n", " bin_edges = channel1_hist[1]\n", " bin_centers = (bin_edges[1:] + bin_edges[0:len(bin_edges)-1])/2\n", " return channel1_hist, channel2_hist, channel3_hist, bin_centers, hist_features\n", "\n", "# Draw historgam for each color channel\n", "# As an example consider BGR color space \n", "fig, axs = plt.subplots(2,4, figsize=(16, 6))\n", "\n", "# Draw histograms for vehicle\n", "vehicle_img = cv2.imread(vehicle_paths[0])\n", "nonvehicle_img = cv2.imread(nonvehicle_paths[0])\n", "imgs_for_hist = [(vehicle_paths[0], \"Vehicle\"), (nonvehicle_paths[0], \"Non-Vehicle\")]\n", "\n", "for i in range(2):\n", " image = cv2.imread(imgs_for_hist[i][0])\n", " ch1_hist, ch2_hist, ch3_hist, bincen, feature_vec = \\\n", " color_hist(image, nbins=32, bins_range=(0, 256), features_only=False)\n", " axs[i][0].axis('off')\n", " axs[i][0].set_title(imgs_for_hist[i][1])\n", " axs[i][0].imshow(image)\n", " axs[i][1].bar(bincen, ch1_hist[0])\n", " axs[i][1].set_xlim([0, 256])\n", " axs[i][1].set_title('B Histogram')\n", " axs[i][2].bar(bincen, ch2_hist[0])\n", " axs[i][2].set_xlim([0, 256])\n", " axs[i][2].set_title('G Histogram')\n", " axs[i][3].bar(bincen, ch3_hist[0])\n", " axs[i][3].set_xlim([0, 256])\n", " axs[i][3].set_title('R Histogram')\n", "plt.tight_layout()\n", "\n", "# Create directory where to save output images\n", "if not os.path.isdir(\"output_images\"):\n", " os.mkdir(\"output_images\")\n", "\n", "plt.savefig('output_images/histogram_of_color.png', bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Color Distribution" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import cv2\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "%matplotlib inline\n", "\n", "def plot3d(pixels, colors_rgb, fig, pos,\n", " axis_labels=list(\"RGB\"), \n", " axis_limits=[(0, 255), (0, 255), (0, 255)]):\n", " \"\"\"Plot pixels in 3D.\"\"\"\n", "\n", " # 3D axes\n", " ax = fig.add_subplot(pos, projection='3d')\n", "\n", " # Set axis limits\n", " ax.set_xlim(*axis_limits[0])\n", " ax.set_ylim(*axis_limits[1])\n", " ax.set_zlim(*axis_limits[2])\n", "\n", " # Set axis labels and sizes\n", " ax.tick_params(axis='both', which='major', labelsize=7, pad=1)\n", " ax.set_xlabel(axis_labels[0], fontsize=10, labelpad=1)\n", " ax.set_ylabel(axis_labels[1], fontsize=10, labelpad=1)\n", " ax.set_zlabel(axis_labels[2], fontsize=10, labelpad=1)\n", "\n", " # Plot pixel values with colors given in colors_rgb\n", " ax.scatter(\n", " pixels[:, :, 0].ravel(),\n", " pixels[:, :, 1].ravel(),\n", " pixels[:, :, 2].ravel(),\n", " c=colors_rgb.reshape((-1, 3)), edgecolors='none')\n", "\n", " return ax # return Axes3D object for further manipulation\n", "\n", "\n", "# Read a color image\n", "img = cv2.imread(\"test_images/test4.jpg\")\n", "\n", "# Select a small fraction of pixels to plot by subsampling it\n", "scale = max(img.shape[0], img.shape[1], 64) / 64 # at most 64 rows and columns\n", "img_small = cv2.resize(img, (np.int(img.shape[1] / scale), np.int(img.shape[0] / scale)), interpolation=cv2.INTER_NEAREST)\n", "\n", "# Convert subsampled image to desired color space(s)\n", "img_small_RGB = cv2.cvtColor(img_small, cv2.COLOR_BGR2RGB) # OpenCV uses BGR, matplotlib likes RGB\n", "img_small_HSV = cv2.cvtColor(img_small, cv2.COLOR_BGR2HSV)\n", "img_small_HLS = cv2.cvtColor(img_small, cv2.COLOR_BGR2HLS)\n", "img_small_LUV = cv2.cvtColor(img_small, cv2.COLOR_BGR2LUV)\n", "img_small_YUV = cv2.cvtColor(img_small, cv2.COLOR_BGR2YUV)\n", "img_small_YCrCb = cv2.cvtColor(img_small, cv2.COLOR_BGR2YCrCb)\n", "img_small_rgb = img_small_RGB / 255. # scaled to [0, 1], only for plotting\n", "\n", "fig = plt.figure(figsize=(15,10))\n", "# Plot and show\n", "ax = fig.add_subplot(332)\n", "ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n", "ax.axis(\"off\")\n", "plot3d(img_small_RGB, img_small_rgb, fig, 334)\n", "plot3d(img_small_HSV, img_small_rgb, fig, 335, axis_labels=list(\"HSV\"))\n", "plot3d(img_small_HLS, img_small_rgb, fig, 336, axis_labels=list(\"HLS\"))\n", "plot3d(img_small_LUV, img_small_rgb, fig, 337, axis_labels=list(\"LUV\"))\n", "plot3d(img_small_YUV, img_small_rgb, fig, 338, axis_labels=list(\"YUV\"))\n", "plot3d(img_small_YCrCb, img_small_rgb, fig, 339, axis_labels=['Y', 'Cr', 'Cb'])\n", "plt.tight_layout()\n", "\n", "# Create directory where to save output images\n", "if not os.path.isdir(\"output_images\"):\n", " os.mkdir(\"output_images\")\n", " \n", "plt.savefig('output_images/color_distribution.png', bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Spatial Binning of Color" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import cv2\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "def bin_spatial(img, size=(32, 32)):\n", " \"\"\"Function to compute binned color features.\"\"\"\n", " # Use cv2.resize().ravel() to create the feature vector\n", " features = cv2.resize(img, size).ravel() \n", " # Return the feature vector\n", " return features\n", "\n", "# Read test image\n", "img = cv2.imread(\"test_images/test4.jpg\")\n", "\n", "# Convert subsampled image to desired color space(s)\n", "img_RGB = cv2.cvtColor(img_small, cv2.COLOR_BGR2RGB) # OpenCV uses BGR, matplotlib likes RGB\n", "img_HSV = cv2.cvtColor(img_small, cv2.COLOR_BGR2HSV)\n", "img_HLS = cv2.cvtColor(img_small, cv2.COLOR_BGR2HLS)\n", "img_LUV = cv2.cvtColor(img_small, cv2.COLOR_BGR2LUV)\n", "img_YUV = cv2.cvtColor(img_small, cv2.COLOR_BGR2YUV)\n", "img_YCrCb = cv2.cvtColor(img_small, cv2.COLOR_BGR2YCrCb)\n", " \n", "fig, axs = plt.subplots(2,3, figsize=(16, 6))\n", "\n", "axs[0][0].plot(bin_spatial(img_RGB))\n", "axs[0][0].set_title('RGB')\n", "\n", "axs[0][1].plot(bin_spatial(img_HSV))\n", "axs[0][1].set_title('HSV')\n", "\n", "axs[0][2].plot(bin_spatial(img_HLS))\n", "axs[0][2].set_title('HLS')\n", "\n", "axs[1][0].plot(bin_spatial(img_LUV))\n", "axs[1][0].set_title('LUV')\n", "\n", "axs[1][1].plot(bin_spatial(img_YUV))\n", "axs[1][1].set_title('YUV')\n", "\n", "axs[1][2].plot(bin_spatial(img_YCrCb))\n", "axs[1][2].set_title('YCrCb')\n", "\n", "plt.tight_layout()\n", "\n", "# Create directory where to save output images\n", "if not os.path.isdir(\"output_images\"):\n", " os.mkdir(\"output_images\")\n", " \n", "plt.savefig('output_images/spatial_binning.png', bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Histogram of Oriented Gradients" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import cv2\n", "import numpy as np\n", "from skimage.feature import hog\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "def get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):\n", " \"\"\"Function to return HOG features and visualization.\"\"\"\n", " if vis == True:\n", " features, hog_image = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),\n", " cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=False, \n", " visualise=True, feature_vector=False)\n", " return features, hog_image\n", " else: \n", " features = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),\n", " cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=False, \n", " visualise=False, feature_vector=feature_vec)\n", " return features\n", " \n", "# Read in the image\n", "image = cv2.imread(vehicle_paths[123])\n", "img_RGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # OpenCV uses BGR, matplotlib likes RGB\n", "img_HSV = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n", "img_HLS = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)\n", "img_LUV = cv2.cvtColor(image, cv2.COLOR_BGR2LUV)\n", "img_YUV = cv2.cvtColor(image, cv2.COLOR_BGR2YUV)\n", "img_YCrCb = cv2.cvtColor(image, cv2.COLOR_BGR2YCrCb)\n", "\n", "imgs = [(img_RGB, list(\"RGB\")), \n", " (img_HSV, list(\"HSV\")), \n", " (img_HLS, list(\"HSL\")), \n", " (img_LUV, list(\"LUV\")), \n", " (img_YUV, list(\"YUV\")), \n", " (img_YCrCb, ['Y', 'Cr', 'Cb'])]\n", "\n", "# Define HOG parameters\n", "orient = 12\n", "pix_per_cell = 8\n", "cell_per_block = 1\n", "\n", "fig = plt.figure(figsize=(32,32))\n", "\n", "ax = fig.add_subplot(732)\n", "ax.axis(\"off\")\n", "ax.imshow(image)\n", "\n", "for i in range(len(imgs)):\n", " pos = (i + 1) * 3 + 1\n", " features, hog_image = get_hog_features(imgs[i][0][:,:,0], orient, \n", " pix_per_cell, cell_per_block, \n", " vis=True, feature_vec=False)\n", " ax = fig.add_subplot(7, 3, pos)\n", " ax.axis(\"off\")\n", " ax.imshow(hog_image, cmap='gray')\n", " ax.set_title(imgs[i][1][0])\n", " \n", " pos += 1\n", " features, hog_image = get_hog_features(imgs[i][0][:,:,1], orient, \n", " pix_per_cell, cell_per_block, \n", " vis=True, feature_vec=False)\n", " ax = fig.add_subplot(7, 3, pos)\n", " ax.axis(\"off\")\n", " ax.imshow(hog_image, cmap='gray')\n", " ax.set_title(imgs[i][1][1])\n", " \n", " pos += 1\n", " features, hog_image = get_hog_features(imgs[i][0][:,:,2], orient, \n", " pix_per_cell, cell_per_block, \n", " vis=True, feature_vec=False)\n", " ax = fig.add_subplot(7, 3, pos)\n", " ax.axis(\"off\")\n", " ax.imshow(hog_image, cmap='gray')\n", " ax.set_title(imgs[i][1][2])\n", " \n", "# Create directory where to save output images\n", "if not os.path.isdir(\"output_images\"):\n", " os.mkdir(\"output_images\")\n", " \n", "plt.savefig('output_images/hog.png', bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train Linear Support Vector Machine Classifier\n", "\n", "#### Define Parameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Parameters to be tweaked\n", "color_space = 'HLS' # Can be BGR, HSV, LUV, HLS, YUV, YCrCb\n", "orient = 12\n", "pix_per_cell = 8 # HOG pixels per cell\n", "cell_per_block = 2 # HOG cells per block\n", "hog_channel = \"ALL\" # Can be 0, 1, 2, or \"ALL\"\n", "spatial_size = (32, 32) # Spatial binning dimensions\n", "hist_bins = 64 # Number of histogram bins\n", "spatial_feat = True # Spatial features on or off\n", "hist_feat = True # Histogram features on or off\n", "hog_feat = True # HOG features on or off\n", "bins_range=(0,256)\n", "\n", "print('Parameters were defined.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Combine Features and Normalize" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#####\n", "# The next step is quite long. You load scaler from the file system.\n", "#####\n", "\n", "import pickle\n", "\n", "# Load the scaler\n", "with open('scaler.pkl', 'rb') as file:\n", " X_scaler = pickle.load(file) \n", " print('Scaler was loaded from file', file.name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import cv2\n", "import pickle\n", "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "from sklearn.preprocessing import StandardScaler\n", "%matplotlib inline\n", "\n", "def extract_features(imgs, color_space='BGR', spatial_size=(32, 32),\n", " hist_bins=32, orient=9, \n", " pix_per_cell=8, cell_per_block=2, hog_channel=0,\n", " spatial_feat=True, hist_feat=True, hog_feat=True):\n", " \"\"\"Function to extract features from a list of images.\n", " \n", " This function calls bin_spatial(), color_hist(), and get_hog_features().\n", " \"\"\"\n", " # Create a list to append feature vectors to\n", " features = []\n", " # Iterate through the list of images\n", " for file in imgs:\n", " file_features = []\n", " # Read in each one by one\n", " image = cv2.imread(file)\n", " # apply color conversion if other than 'BGR'\n", " if color_space != 'BGR':\n", " if color_space == 'RGB':\n", " feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", " if color_space == 'HSV':\n", " feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n", " elif color_space == 'LUV':\n", " feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2LUV)\n", " elif color_space == 'HLS':\n", " feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)\n", " elif color_space == 'YUV':\n", " feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2YUV)\n", " elif color_space == 'YCrCb':\n", " feature_image = cv2.cvtColor(image, cv2.COLOR_BGR2YCrCb)\n", " else: feature_image = np.copy(image) \n", "\n", " if spatial_feat == True:\n", " spatial_features = bin_spatial(feature_image, size=spatial_size)\n", " file_features.append(spatial_features)\n", " if hist_feat == True:\n", " # Apply color_hist()\n", " hist_features = color_hist(feature_image, nbins=hist_bins)\n", " file_features.append(hist_features)\n", " if hog_feat == True:\n", " # Call get_hog_features() with vis=False, feature_vec=True\n", " if hog_channel == 'ALL':\n", " hog_features = []\n", " for channel in range(feature_image.shape[2]):\n", " hog_features.append(get_hog_features(feature_image[:,:,channel], \n", " orient, pix_per_cell, cell_per_block, \n", " vis=False, feature_vec=True))\n", " hog_features = np.ravel(hog_features) \n", " else:\n", " hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, \n", " pix_per_cell, cell_per_block, vis=False, feature_vec=True)\n", " # Append the new feature vector to the features list\n", " file_features.append(hog_features)\n", " features.append(np.concatenate(file_features))\n", " # Return list of feature vectors\n", " return features\n", "\n", "# Extract features\n", "car_features = extract_features(vehicle_paths, color_space=color_space, \n", " spatial_size=spatial_size, hist_bins=hist_bins, \n", " orient=orient, pix_per_cell=pix_per_cell, \n", " cell_per_block=cell_per_block, \n", " hog_channel=hog_channel, spatial_feat=spatial_feat, \n", " hist_feat=hist_feat, hog_feat=hog_feat)\n", "notcar_features = extract_features(nonvehicle_paths, color_space=color_space, \n", " spatial_size=spatial_size, hist_bins=hist_bins, \n", " orient=orient, pix_per_cell=pix_per_cell, \n", " cell_per_block=cell_per_block, \n", " hog_channel=hog_channel, spatial_feat=spatial_feat, \n", " hist_feat=hist_feat, hog_feat=hog_feat)\n", "\n", "# Normalize data\n", "X = np.vstack((car_features, notcar_features)).astype(np.float64) \n", "# Fit a per-column scaler\n", "X_scaler = StandardScaler().fit(X)\n", "# Apply the scaler to X\n", "scaled_X = X_scaler.transform(X)\n", "\n", "# save the scaler\n", "with open('scaler.pkl', 'wb') as file:\n", " pickle.dump(X_scaler, file) \n", " print('Scaler was saved to', file.name)\n", "\n", "# Visualize normalization\n", "car_ind = 28\n", "# Plot an example of raw and scaled features\n", "fig = plt.figure(figsize=(12,4))\n", "plt.subplot(131)\n", "plt.imshow(mpimg.imread(vehicle_paths[car_ind]))\n", "plt.title('Original Image')\n", "plt.subplot(132)\n", "plt.plot(X[car_ind])\n", "plt.title('Raw Features')\n", "plt.subplot(133)\n", "plt.plot(scaled_X[car_ind])\n", "plt.title('Normalized Features')\n", "fig.tight_layout()\n", "\n", "# Create directory where to save output images\n", "if not os.path.isdir(\"output_images\"):\n", " os.mkdir(\"output_images\")\n", " \n", "plt.savefig('output_images/feature_normalization.png', bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Train Classifier" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#####\n", "# The next step is quite long. You load support vector classifier from the file system.\n", "#####\n", "\n", "import pickle\n", "\n", "# Load the classifier\n", "with open('svc.pkl', 'rb') as file:\n", " svc = pickle.load(file) \n", " print('Classifier was loaded from file', file.name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import time\n", "import pickle\n", "import numpy as np\n", "from sklearn.svm import LinearSVC\n", "from sklearn.cross_validation import train_test_split\n", "\n", "# Define the labels vector\n", "y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))\n", "\n", "# Split up data into randomized training and test sets\n", "rand_state = np.random.randint(0, 100)\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " scaled_X, y, test_size=0.2, random_state=rand_state)\n", "\n", "print('Using:',orient,'orientations',pix_per_cell,\n", " 'pixels per cell and', cell_per_block,'cells per block')\n", "print('Feature vector length:', len(X_train[0]))\n", "# Use a linear SVC \n", "svc = LinearSVC()\n", "# Check the training time for the SVC\n", "t=time.time()\n", "svc.fit(X_train, y_train)\n", "t2 = time.time()\n", "print(round(t2-t, 2), 'Seconds to train SVC...')\n", "# Check the score of the SVC\n", "print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))\n", "# Check the prediction time for a single sample\n", "n_predict = 10\n", "print('My SVC predicts: ', svc.predict(X_test[0:n_predict]))\n", "print('For these',n_predict, 'labels: ', y_test[0:n_predict])\n", "t2 = time.time()\n", "print(round(t2-t, 5), 'Seconds to predict', n_predict,'labels with SVC')\n", "\n", "# save the trained classifier\n", "with open('svc.pkl', 'wb') as file:\n", " pickle.dump(svc, file) \n", " print('Classifier was saved to', file.name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Detect Vehicles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Sliding Window" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None], \n", " xy_window=(64, 64), xy_overlap=(0.5, 0.5)):\n", " \"\"\"Function that returns a list of bounding boxes for the search windows.\"\"\"\n", " # If x and/or y start/stop positions not defined, set to image size\n", " if x_start_stop[0] == None:\n", " x_start_stop[0] = 0\n", " if x_start_stop[1] == None:\n", " x_start_stop[1] = img.shape[1]\n", " if y_start_stop[0] == None:\n", " y_start_stop[0] = 0\n", " if y_start_stop[1] == None:\n", " y_start_stop[1] = img.shape[0]\n", " # Compute the span of the region to be searched \n", " xspan = x_start_stop[1] - x_start_stop[0]\n", " yspan = y_start_stop[1] - y_start_stop[0]\n", " # Compute the number of pixels per step in x/y\n", " nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))\n", " ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))\n", " # Compute the number of windows in x/y\n", " nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))\n", " ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))\n", " nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step) \n", " ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step) \n", " # Initialize a list to append window positions to\n", " window_list = []\n", " # Loop through finding x and y window positions\n", " # Note: you could vectorize this step, but in practice\n", " # you'll be considering windows one by one with your\n", " # classifier, so looping makes sense\n", " for ys in range(ny_windows):\n", " for xs in range(nx_windows):\n", " # Calculate window position\n", " startx = xs*nx_pix_per_step + x_start_stop[0]\n", " endx = startx + xy_window[0]\n", " starty = ys*ny_pix_per_step + y_start_stop[0]\n", " endy = starty + xy_window[1]\n", " # Append window position to list\n", " window_list.append(((startx, starty), (endx, endy)))\n", " # Return the list of windows\n", " return window_list\n", "\n", "def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):\n", " \"\"\"Function that draws color boxes on the output.\"\"\"\n", " # Make a copy of the image\n", " imcopy = np.copy(img)\n", " # Iterate through the bounding boxes\n", " for bbox in bboxes:\n", " # Draw a rectangle given bbox coordinates\n", " cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)\n", " # Return the image copy with boxes drawn\n", " return imcopy\n", "\n", "# Visualize the result\n", "fig = plt.figure(figsize=(20, 15))\n", "\n", "img_cnt = 1\n", "for name in os.listdir(\"test_images\"):\n", " # Read test image\n", " image = cv2.imread(os.path.join(\"test_images\", name))\n", "\n", " windows = slide_window(image, x_start_stop=[None, None], y_start_stop=[400, 700], \n", " xy_window=(128, 128), xy_overlap=(0.5, 0.5))\n", " \n", " window_img = draw_boxes(image, windows, color=(0, 0, 255), thick=6) \n", " \n", " ax = fig.add_subplot(3, 2, img_cnt)\n", " ax.imshow(cv2.cvtColor(window_img, cv2.COLOR_BGR2RGB))\n", " img_cnt += 1\n", "\n", "fig.tight_layout()\n", "\n", "# Create directory where to save output images\n", "if not os.path.isdir(\"output_images\"):\n", " os.mkdir(\"output_images\")\n", " \n", "plt.savefig('output_images/sliding_window.png', bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Search and Classify" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import cv2\n", "import time\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "def single_img_features(img, color_space='BGR', spatial_size=(32, 32),\n", " hist_bins=32, orient=9, \n", " pix_per_cell=8, cell_per_block=2, hog_channel=0,\n", " spatial_feat=True, hist_feat=True, hog_feat=True): \n", " \"\"\"Function to extract features from a single image window.\"\"\"\n", " #1) Define an empty list to receive features\n", " img_features = []\n", " #2) Apply color conversion if other than 'BGR'\n", " if color_space != 'BGR':\n", " if color_space == 'RGB':\n", " feature_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", " if color_space == 'HSV':\n", " feature_image = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n", " elif color_space == 'LUV':\n", " feature_image = cv2.cvtColor(img, cv2.COLOR_BGR2LUV)\n", " elif color_space == 'HLS':\n", " feature_image = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)\n", " elif color_space == 'YUV':\n", " feature_image = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)\n", " elif color_space == 'YCrCb':\n", " feature_image = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)\n", " else: feature_image = np.copy(img) \n", " #3) Compute spatial features if flag is set\n", " if spatial_feat == True:\n", " spatial_features = bin_spatial(feature_image, size=spatial_size)\n", " #4) Append features to list\n", " img_features.append(spatial_features)\n", " #5) Compute histogram features if flag is set\n", " if hist_feat == True:\n", " hist_features = color_hist(feature_image, nbins=hist_bins)\n", " #6) Append features to list\n", " img_features.append(hist_features)\n", " #7) Compute HOG features if flag is set\n", " if hog_feat == True:\n", " if hog_channel == 'ALL':\n", " hog_features = []\n", " for channel in range(feature_image.shape[2]):\n", " hog_features.extend(get_hog_features(feature_image[:,:,channel], \n", " orient, pix_per_cell, cell_per_block, \n", " vis=False, feature_vec=True)) \n", " else:\n", " hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, \n", " pix_per_cell, cell_per_block, vis=False, feature_vec=True)\n", " #8) Append features to list\n", " img_features.append(hog_features)\n", "\n", " #9) Return concatenated array of features\n", " return np.concatenate(img_features)\n", "\n", "def search_windows(img, windows, clf, scaler, color_space='BGR', \n", " spatial_size=(32, 32), hist_bins=32, \n", " hist_range=(0, 256), orient=9, \n", " pix_per_cell=8, cell_per_block=2, \n", " hog_channel=0, spatial_feat=True, \n", " hist_feat=True, hog_feat=True):\n", " \"\"\"Search cars in the given image within list of windows to be searched.\"\"\"\n", " #1) Create an empty list to receive positive detection windows\n", " on_windows = []\n", " #2) Iterate over all windows in the list\n", " for window in windows:\n", " #3) Extract the test window from original image\n", " test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64)) \n", " #4) Extract features for that window using single_img_features()\n", " features = single_img_features(test_img, color_space=color_space, \n", " spatial_size=spatial_size, hist_bins=hist_bins, \n", " orient=orient, pix_per_cell=pix_per_cell, \n", " cell_per_block=cell_per_block, \n", " hog_channel=hog_channel, spatial_feat=spatial_feat, \n", " hist_feat=hist_feat, hog_feat=hog_feat)\n", " #5) Scale extracted features to be fed to classifier\n", " test_features = scaler.transform(np.array(features).reshape(1, -1))\n", " #6) Predict using your classifier\n", " prediction = clf.predict(test_features)\n", " #7) If positive (prediction == 1) then save the window\n", " if prediction == 1:\n", " on_windows.append(window)\n", " #8) Return windows for positive detections\n", " return on_windows\n", "\n", "y_start_stop = [400, 700] # Min and max in y to search in slide_window()\n", "\n", "# Visualize the result\n", "fig = plt.figure(figsize=(16, 16))\n", "times = []\n", "\n", "img_cnt = 1\n", "for name in os.listdir(\"test_images\"):\n", " # Read test image\n", " image = cv2.imread(os.path.join(\"test_images\", name))\n", "\n", " draw_image = np.copy(image)\n", " \n", " tm_start = time.time()\n", "\n", " windows = slide_window(image, x_start_stop=[None, None], y_start_stop=y_start_stop, \n", " xy_window=(115, 115), xy_overlap=(0.8, 0.8))\n", "\n", " hot_windows = search_windows(image, windows, svc, X_scaler, color_space=color_space, \n", " spatial_size=spatial_size, hist_bins=hist_bins, \n", " orient=orient, pix_per_cell=pix_per_cell, \n", " cell_per_block=cell_per_block, \n", " hog_channel=hog_channel, spatial_feat=spatial_feat, \n", " hist_feat=hist_feat, hog_feat=hog_feat) \n", "\n", " window_img = draw_boxes(draw_image, hot_windows, color=(0, 0, 255), thick=6) \n", " \n", " tm_stop = time.time()\n", " \n", " times.append(round(tm_stop-tm_start, 2))\n", " \n", " ax = fig.add_subplot(6, 2, img_cnt)\n", " ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n", " img_cnt += 1\n", " \n", " ax = fig.add_subplot(6, 2, img_cnt)\n", " ax.imshow(cv2.cvtColor(window_img, cv2.COLOR_BGR2RGB))\n", " img_cnt += 1\n", "\n", "fig.tight_layout()\n", "\n", "print(\"Average time per image:\", sum(times)/len(times), \"sec\")\n", "\n", "# Create directory where to save output images\n", "if not os.path.isdir(\"output_images\"):\n", " os.mkdir(\"output_images\")\n", " \n", "plt.savefig('output_images/search_and_classify.png', bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Detection is quite good, but very slow. It can not be used in real time systems." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Faster Search (HOG Sub-Sampling)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pickle\n", "import cv2\n", "%matplotlib inline\n", "\n", "def find_cars(img, ystart, ystop, scale, clf, X_scaler, \n", " orient, pix_per_cell, cell_per_block, spatial_size, \n", " hist_bins, color_space, bins_range):\n", " \n", " draw_img = np.copy(img)\n", " \n", " img_tosearch = img[ystart:ystop,:,:]\n", " \n", " if color_space != 'BGR':\n", " if color_space == 'RGB':\n", " img_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_BGR2RGB)\n", " if color_space == 'HSV':\n", " img_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_BGR2HSV)\n", " elif color_space == 'LUV':\n", " img_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_BGR2LUV)\n", " elif color_space == 'HLS':\n", " img_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_BGR2HLS)\n", " elif color_space == 'YUV':\n", " img_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_BGR2YUV)\n", " elif color_space == 'YCrCb':\n", " img_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_BGR2YCrCb)\n", " else: img_tosearch = np.copy(img_tosearch) \n", " \n", " if scale != 1:\n", " imshape = img_tosearch.shape\n", " img_tosearch = cv2.resize(img_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))\n", " \n", " ch1 = img_tosearch[:,:,0]\n", " ch2 = img_tosearch[:,:,1]\n", " ch3 = img_tosearch[:,:,2]\n", "\n", " # Define blocks and steps as above\n", " nxblocks = (ch1.shape[1] // pix_per_cell)-1\n", " nyblocks = (ch1.shape[0] // pix_per_cell)-1 \n", " nfeat_per_block = orient*cell_per_block**2\n", " # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell\n", " window = image_shape[0]\n", " nblocks_per_window = (window // pix_per_cell)-1 \n", " cells_per_step = 2 # Instead of overlap, define how many cells to step\n", " nxsteps = (nxblocks - nblocks_per_window) // cells_per_step\n", " nysteps = (nyblocks - nblocks_per_window) // cells_per_step\n", " \n", " # Compute individual channel HOG features for the entire image\n", " hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\n", " hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)\n", " hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)\n", " \n", " bbox_list=[]\n", " for xb in range(nxsteps):\n", " for yb in range(nysteps):\n", " ypos = yb*cells_per_step\n", " xpos = xb*cells_per_step\n", " # Extract HOG for this patch\n", " hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() \n", " hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() \n", " hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() \n", " hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))\n", "\n", " xleft = xpos*pix_per_cell\n", " ytop = ypos*pix_per_cell\n", "\n", " # Extract the image patch\n", " subimg = cv2.resize(img_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))\n", " \n", " # Get color features\n", " spatial_features = bin_spatial(subimg, size=spatial_size)\n", " hist_features = color_hist(subimg, nbins=hist_bins, bins_range=bins_range)\n", "\n", " # Scale features and make a prediction\n", " test_features = X_scaler.transform(np.hstack((spatial_features, hist_features,\n", " hog_features)).reshape(1, -1)) \n", " test_prediction = clf.predict(test_features)\n", " \n", " if test_prediction == 1:\n", " xbox_left = np.int(xleft*scale)\n", " ytop_draw = np.int(ytop*scale)\n", " win_draw = np.int(window*scale)\n", " cv2.rectangle(draw_img,(xbox_left, ytop_draw+ystart),\n", " (xbox_left+win_draw,ytop_draw+win_draw+ystart),\n", " (0,0,1),6)\n", " bbox_list.append(((xbox_left, ytop_draw+ystart),\n", " (xbox_left+win_draw,ytop_draw+win_draw+ystart)))\n", " return bbox_list\n", "\n", "y_start_stop = [400, 700]\n", "\n", "# Visualize the result\n", "fig = plt.figure(figsize=(16, 16))\n", "times = []\n", "\n", "img_cnt = 1\n", "for name in os.listdir(\"test_images\"):\n", " # Read test image\n", " image = cv2.imread(os.path.join(\"test_images\", name))\n", "\n", " draw_image = np.copy(image)\n", " \n", " tm_start = time.time()\n", "\n", " bbox_list = find_cars(image, y_start_stop[0], y_start_stop[1], 1, svc, X_scaler, \n", " orient, pix_per_cell, cell_per_block, spatial_size,\n", " hist_bins, color_space, bins_range)\n", " draw_image = draw_boxes(draw_image, bbox_list)\n", " \n", " tm_stop = time.time()\n", " \n", " times.append(round(tm_stop-tm_start, 2))\n", " \n", " ax = fig.add_subplot(6, 2, img_cnt)\n", " ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n", " img_cnt += 1\n", " \n", " ax = fig.add_subplot(6, 2, img_cnt)\n", " ax.imshow(cv2.cvtColor(draw_image, cv2.COLOR_BGR2RGB))\n", " img_cnt += 1\n", "\n", "fig.tight_layout()\n", "\n", "print(\"Average time per image:\", sum(times)/len(times), \"sec\")\n", "\n", "# Create directory where to save output images\n", "if not os.path.isdir(\"output_images\"):\n", " os.mkdir(\"output_images\")\n", " \n", "plt.savefig('output_images/fast_search_and_classify.png', bbox_inches=\"tight\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Detection is quite good and fast enough. On a very-very good laptop it can process video in near real time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Filter False Positives" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pickle\n", "import cv2\n", "from scipy.ndimage.measurements import label\n", "\n", "def add_heat(heatmap, bbox_list):\n", " # Iterate through list of bboxes\n", " for box in bbox_list:\n", " # Add += 1 for all pixels inside each bbox\n", " # Assuming each \"box\" takes the form ((x1, y1), (x2, y2))\n", " heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1\n", "\n", " # Return updated heatmap\n", " return heatmap# Iterate through list of bboxes\n", " \n", "def apply_threshold(heatmap, threshold):\n", " # Zero out pixels below the threshold\n", " heatmap[heatmap <= threshold] = 0\n", " # Return thresholded map\n", " return heatmap\n", "\n", "def draw_labeled_bboxes(img, labels):\n", " # Iterate through all detected cars\n", " for car_number in range(1, labels[1]+1):\n", " # Find pixels with each car_number label value\n", " nonzero = (labels[0] == car_number).nonzero()\n", " # Identify x and y values of those pixels\n", " nonzeroy = np.array(nonzero[0])\n", " nonzerox = np.array(nonzero[1])\n", " # Define a bounding box based on min/max x and y\n", " bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))\n", " # Draw the box on the image\n", " cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)\n", " # Return the image\n", " return img\n", "\n", "y_start_stop = [400, 700] # Min and max in y to search in slide_window()\n", "\n", "# Visualize the result\n", "fig = plt.figure(figsize=(16, 16))\n", "\n", "img_cnt = 1\n", "for name in os.listdir(\"test_images\"):\n", " # Read test image\n", " image = cv2.imread(os.path.join(\"test_images\", name))\n", " heat = np.zeros_like(image[:,:,0]).astype(np.float)\n", "\n", " draw_image = np.copy(image)\n", " \n", " bbox_list = find_cars(image, y_start_stop[0], y_start_stop[1], 1, svc, X_scaler, \n", " orient, pix_per_cell, cell_per_block, spatial_size,\n", " hist_bins, color_space, bins_range)\n", " draw_image = draw_boxes(draw_image, bbox_list)\n", " \n", " # Add heat to each box in box list\n", " heat = add_heat(heat,bbox_list)\n", " \n", " # Apply threshold to help remove false positives\n", " heat = apply_threshold(heat,1)\n", "\n", " # Visualize the heatmap when displaying \n", " heatmap = np.clip(heat, 0, 255)\n", "\n", " # Find final boxes from heatmap using label function\n", " labels = label(heatmap)\n", " draw_img = draw_labeled_bboxes(np.copy(image), labels)\n", " \n", " ax = fig.add_subplot(6, 3, img_cnt)\n", " ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n", " img_cnt += 1\n", " \n", " ax = fig.add_subplot(6, 3, img_cnt)\n", " ax.imshow(heatmap, cmap='hot')\n", " img_cnt += 1\n", " \n", " ax = fig.add_subplot(6, 3, img_cnt)\n", " ax.imshow(cv2.cvtColor(draw_img, cv2.COLOR_BGR2RGB))\n", " img_cnt += 1\n", "\n", "fig.tight_layout()\n", "\n", "# Create directory where to save output images\n", "if not os.path.isdir(\"output_images\"):\n", " os.mkdir(\"output_images\")\n", " \n", "plt.savefig('output_images/heat_map.png', bbox_inches=\"tight\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Vehicle Detection Pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*NOTICE:* ideas related to vehicle tracking, such as remembering bboxes identified in previous N frames, were borrowed from Prerit Jaiswal's [repository](https://github.com/preritj/Vechicle-Detection-Tracking)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "from moviepy.editor import VideoFileClip\n", "from IPython.display import HTML\n", "from IPython.display import display\n", "\n", "class VehicleDetector:\n", " def __init__(self, n_iter=25, n_update=2, threshold=70, \n", " scale_yrange_map={0.8 : (380,500),\n", " 1.0 : (380,550), \n", " 1.5 : (380,580), \n", " 2.0 : (380,650), \n", " 2.5 : (380,700)}):\n", " # Number of processed frames\n", " self.count = 0\n", " \n", " # Labeled bboxes \n", " self.out_img_labeled = None\n", " \n", " # Vehicle labels \n", " self.labels = [None,0]\n", " \n", " # List of bbox lists from last n iterations\n", " self.bbox_list_n = [] \n", " \n", " # Number of frames to smooth over\n", " self.n_iter = n_iter\n", " \n", " # Number of frames after which to update detection\n", " self.n_update = n_update\n", " \n", " # Threshold for heat map\n", " self.threshold = threshold\n", " \n", " # Heat map\n", " self.heat = np.zeros((720, 1280))\n", " self.heatmap = np.copy(self.heat)\n", " \n", " # Map scale to y range\n", " self.scale_yrange_map = scale_yrange_map\n", " \n", " def pipeline(self, image) :\n", " # Increment number of processed frames\n", " self.count += 1\n", " \n", " # Image to be processed\n", " self.image = image\n", "\n", " # Frames are RGB, while the rest of the code works with BGR\n", " img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n", " \n", " # Find cars of the processed frame\n", " for scale, y_start_stop in self.scale_yrange_map.items():\n", " ystart = y_start_stop[0]\n", " ystop = y_start_stop[1]\n", " bbox_list = find_cars(img, ystart, ystop, scale, svc, X_scaler, \n", " orient, pix_per_cell, cell_per_block, spatial_size,\n", " hist_bins, color_space, bins_range)\n", " \n", " # Add found car boxes to the list of boxes captured in N previous frames\n", " self.bbox_list_n.append(bbox_list)\n", " \n", " # Update heat map each self.n_update frame (smoothing)\n", " if self.count % self.n_update == 0 :\n", " for bbox_list in self.bbox_list_n :\n", " self.heat = add_heat(self.heat,bbox_list)\n", " self.heat = apply_threshold(self.heat, self.threshold)\n", " self.heatmap = np.clip(self.heat, 0, 255)\n", " self.labels = label(self.heatmap)\n", " self.heat = np.clip(self.labels[0], 0, 1) * 2\n", " \n", " # Remove very old cars boxes\n", " if len(self.bbox_list_n) > self.n_iter :\n", " self.bbox_list_n.pop(0)\n", " \n", " # Draw labeled boxes on the image being processed\n", " self.out_img_labeled = draw_labeled_bboxes(np.copy(image), self.labels)\n", " return self.out_img_labeled\n", " \n", "# Create output directory for vidoes, if does not exist\n", "if not os.path.isdir(\"output_videos\"):\n", " os.mkdir(\"output_videos\")\n", "\n", "# Define paths to source and destination videos\n", "vid_src = \"test_videos/test_video.mp4\"\n", "vid_dst = \"output_videos/test_video.mp4\"\n", "\n", "# Process video frame by frame\n", "video = VideoFileClip(vid_src)\n", "video_clip = video.fl_image(VehicleDetector().pipeline)\n", "%time video_clip.write_videofile(vid_dst, audio = False)\n", "display(HTML(\n", " \"\"\"\n", " <video width=\"960\" height=\"540\" controls>\n", " <source src=\"{0}\">\n", " </video>\n", " \"\"\".format(vid_dst)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Apply Pipeline to Long Video" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "from moviepy.editor import VideoFileClip\n", "from IPython.display import HTML\n", "from IPython.display import display\n", "\n", "# Create output directory for vidoes, if does not exist\n", "if not os.path.isdir(\"output_videos\"):\n", " os.mkdir(\"output_videos\")\n", "\n", "# Define paths to source and destination videos\n", "vid_src = \"test_videos/project_video.mp4\"\n", "vid_dst = \"output_videos/project_video.mp4\"\n", "\n", "# Process video frame by frame\n", "video = VideoFileClip(vid_src)\n", "video_clip = video.fl_image(VehicleDetector().pipeline)\n", "%time video_clip.write_videofile(vid_dst, audio = False)\n", "display(HTML(\n", " \"\"\"\n", " <video width=\"960\" height=\"540\" controls>\n", " <source src=\"{0}\">\n", " </video>\n", " \"\"\".format(vid_dst)))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.5.2" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

aksp/vrview

examples/orientations/face-tracking-files-to-single-json.ipynb

1

26001

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import subprocess, os, json\n", "\n", "VIDEO_ROOT = \"videos/\"\n", "VIDEO_FRAMES_ROOT = \"video-frames/\"\n", "\n", "def get_immediate_subdirectories(a_dir):\n", " return [name for name in os.listdir(a_dir)\n", " if os.path.isdir(os.path.join(a_dir, name))]\n", "\n", "def many_jsons_to_one_json(analysis_path):\n", " # many jsons to one json\n", "\n", " def get_frame_from_fn(fn_hi): \n", " k = fn_hi.split(\"_\")[0].split(\"/\")[-1]\n", " return int(k)\n", "\n", " face_tracking_dir = analysis_path\n", " fns = [face_tracking_dir + f for f in os.listdir(face_tracking_dir) if \"landmarks\" in f]\n", " out_json = {}\n", "\n", " for fn in fns:\n", "\n", " with open(fn) as f: \n", " j = json.load(f)\n", " res = j[\"result\"]\n", "\n", " frame_number = str(get_frame_from_fn(fn))\n", " out_json[frame_number] = []\n", "\n", " for result in res:\n", " out_json[frame_number].append({\n", " \"face_minBBox\": result[\"face_minBBox\"],\n", " \"face_maxBBox\": result[\"face_maxBBox\"]\n", " })\n", " with open(face_tracking_dir + \"out.json\", 'w') as f_out:\n", " json.dump(out_json, f_out)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "many_jsons_to_one_json(\"analysis/face-tracking/equation/\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "all_videos = [v for v in os.listdir(VIDEO_ROOT) if \"small\" not in v and v[0] !='.' if \"arctic\" not in v and \"congo\" not in v and \"equation\" not in v]\n", "\n", "for video in all_videos:\n", " base = video.lower().split(\".mp4\")[0]\n", " video_path = VIDEO_ROOT + video\n", " video_frames_path = VIDEO_FRAMES_ROOT + base + \"/\"\n", " \n", " # delete any existing video frames\n", " sub_directory_paths = [VIDEO_FRAMES_ROOT + p + \"/\" for p in get_immediate_subdirectories(VIDEO_FRAMES_ROOT)]\n", " for d in sub_directory_paths: \n", " for frame in os.listdir(d):\n", " os.remove(d + frame)\n", " \n", " # make a new directory\n", " if not os.path.exists(video_frames_path):\n", " os.mkdir(video_frames_path)\n", " \n", " analysis_path = \"analysis/face-tracking/\" + base + \"/\"\n", " if not os.path.exists(analysis_path): \n", " os.mkdir(analysis_path)\n", " \n", " # split the video into frames\n", " # subprocess.call([\"ffmpeg\", \"-i\", video_path, video_frames_path + \"%05d.png\"])\n", " subprocess.call([\"ffmpeg\", \"-i\", video_path, \"-vf\", \"fps=2\", video_frames_path + \"%05d.png\"])\n", "\n", " # run facetracking analysis\n", " subprocess.call([\"../../../../../resources/OpenFace/bin/FaceLandmarkImg\", '-q', '-multi-view', '1',\n", " '-wild', '-gaze', '-fdir', video_frames_path, \"-ofdir\", analysis_path])\n", " # pts to json\n", " subprocess.call([\"node\", \"analysis/of2json.js\", analysis_path])\n", "\n", " # many jsons to one json\n", " many_jsons_to_one_json(analysis_path)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['344',\n", " '345',\n", " '346',\n", " '347',\n", " '340',\n", " '341',\n", " '342',\n", " '343',\n", " '348',\n", " '349',\n", " '298',\n", " '299',\n", " '296',\n", " '297',\n", " '294',\n", " '295',\n", " '292',\n", " '293',\n", " '290',\n", " '291',\n", " '270',\n", " '271',\n", " '272',\n", " '273',\n", " '274',\n", " '275',\n", " '276',\n", " '277',\n", " '278',\n", " '279',\n", " '108',\n", " '109',\n", " '102',\n", " '103',\n", " '100',\n", " '101',\n", " '106',\n", " '107',\n", " '104',\n", " '105',\n", " '1001',\n", " '1210',\n", " '99',\n", " '98',\n", " '91',\n", " '90',\n", " '93',\n", " '92',\n", " '95',\n", " '94',\n", " '97',\n", " '96',\n", " '559',\n", " '558',\n", " 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apache-2.0

utensil/julia-playground

dl/hello_scikit_learn.ipynb

1

419396

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "I ran the following notebook in a docker container with the following commands:\n", "\n", "```\n", "docker run -it -p 8888:8888 -p 6006:6006 -v `pwd`:/space/ -w /space/ --rm --name md waleedka/modern-deep-learning jupyter notebook --ip=0.0.0.0 --allow-root\n", "```\n", "\n", "The following code is adapted from http://scikit-learn.org/stable/user_guide.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Argmax and Argmin\n", "\n", "$$ \\underset{x \\in D}{\\operatorname{arg\\,max}} f(x) := \\{ x \\mid \\forall y \\in D : f(y) \\le f(x)\\} $$\n", "\n", "$$ \\underset{x \\in D}{\\operatorname{arg\\,min}} \\, f(x) := \\{x \\mid \\forall y \\in D : f(y) \\ge f(x)\\} $$\n", "\n", "See [Argmax and Max Calculus](https://www.cs.ubc.ca/~schmidtm/Documents/2016_540_Argmax.pdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supervised learning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generalized Linear Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ordinary Least Squares\n", "\n", "$$ \\underset{w}{min\\,} {|| X w - y||_2}^2 $$\n", "\n", "where $$ w = (w_1, ..., w_p) $$\n", "\n", "If $ X $ is a matrix of size $ (n, p) $ this method has a\n", "cost of $ O(n p^2) $, assuming that $ n \\geq p $." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ridge Regression\n", "\n", "$$ \\underset{w}{min\\,} {{|| X w - y||_2}^2 + \\alpha {||w||_2}^2} $$\n", "\n", "where \n", "\n", "$$ \\alpha \\gt 0 $$\n", "\n", "$$ ||w||_2 = ? $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lasso\n", "\n", "$$ \\underset{w}{min\\,} { \\frac{1}{2n_{samples}} ||X w - y||_2 ^ 2 + \\alpha ||w||_1} $$\n", "\n", "where\n", "\n", "$$ ||w||_1 = ? $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Elastic Net\n", "\n", "$$ \\underset{w}{min\\,} { \\frac{1}{2n_{samples}} ||X w - y||_2 ^ 2 + \\alpha \\rho ||w||_1 +\n", " \\frac{\\alpha(1-\\rho)}{2} ||w||_2 ^ 2} $$\n", " \n", "where $ \\rho $ is to control the convex combination of L1 and L2, a.k.a ``l1_ratio``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Least Angle Regression (LARS)\n", "\n", "Least-angle regression (LARS) is a regression algorithm for\n", "high-dimensional data. LARS is similar to forward stepwise\n", "regression. At each step, it finds the predictor most correlated with the\n", "response. When there are multiple predictors having equal correlation, instead\n", "of continuing along the same predictor, it proceeds in a direction equiangular\n", "between the predictors.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import linear_model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "reg = linear_model.LinearRegression()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg.fit ([[0, 0], [1, 1], [2, 2]], [0, 1, 2])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.5, 0.5])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg.coef_" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# based on http://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py\n", "# but added Ridge and Lasso\n", "import matplotlib.pyplot as plt\n", "\n", "# From http://matplotlib.org/users/usetex.html\n", "# Requires a working TexLive installation in PATH\n", "from matplotlib import rc\n", "# rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n", "# rc('text', usetex=True)\n", "\n", "import numpy as np\n", "from sklearn import datasets, linear_model\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "import math\n", "\n", "def fit_and_plot(data_X, data_y, test_ratio=0.0):\n", " \n", " if test_ratio == 0:\n", " data_X_train = data_X\n", " data_X_test = data_X\n", " data_y_train = data_y\n", " data_y_test = data_y\n", " else:\n", " print(data_X.shape)\n", " sample_size, _ = data_X.shape\n", " test_set_size = math.floor(sample_size * test_ratio)\n", " \n", " # Split the data into training/testing sets\n", " data_X_train = data_X[:-test_set_size]\n", " data_X_test = data_X[-test_set_size:]\n", "\n", " # Split the targets into training/testing sets\n", " data_y_train = data_y[:-test_set_size]\n", " data_y_test = data_y[-test_set_size:]\n", " \n", " # Create linear regression object\n", " lr = linear_model.LinearRegression()\n", " rg = linear_model.RidgeCV(alphas = [.1, .3, .5, .7, .9])\n", " ls = linear_model.LassoCV(alphas = [.1, .3, .5, .7, .9])\n", " en = linear_model.ElasticNetCV(alphas = [.1, .3, .5, .7, .9], l1_ratio = [.1, .3, .5, .7, .9, .99, .997])\n", " la = linear_model.LarsCV()\n", "\n", " # Train the model using the training sets\n", " lr.fit(data_X_train, data_y_train)\n", " rg.fit(data_X_train, data_y_train)\n", " ls.fit(data_X_train, data_y_train)\n", " en.fit(data_X_train, data_y_train)\n", " la.fit(data_X_train, data_y_train)\n", "\n", " # Make predictions using the testing set\n", " data_y_pred_lr = lr.predict(data_X_test)\n", " data_y_pred_rg = rg.predict(data_X_test)\n", " data_y_pred_ls = ls.predict(data_X_test)\n", " data_y_pred_en = en.predict(data_X_test)\n", " data_y_pred_la = la.predict(data_X_test)\n", "\n", "\n", " # The coefficients\n", " print('Coefficients: \\n', lr.coef_, rg.coef_, ls.coef_, en.coef_, la.coef_)\n", "\n", " print('Super parameters: \\n', (), (rg.alpha_,), (ls.alpha_,), (en.alpha_, en.l1_ratio_), (la.alpha_,))\n", " # The mean squared error\n", " print(\"Mean squared error: \\n %.2f %.2f %.2f %.2f %.2f\"\n", " % (mean_squared_error(data_y_test, data_y_pred_lr),\n", " mean_squared_error(data_y_test, data_y_pred_rg),\n", " mean_squared_error(data_y_test, data_y_pred_ls),\n", " mean_squared_error(data_y_test, data_y_pred_en),\n", " mean_squared_error(data_y_test, data_y_pred_la)\n", " ))\n", " # Explained variance score: 1 is perfect prediction\n", " print('Variance score: \\n %.2f %.2f %.2f %.2f %.2f' % \n", " (r2_score(data_y_test, data_y_pred_lr),\n", " r2_score(data_y_test, data_y_pred_rg),\n", " r2_score(data_y_test, data_y_pred_ls),\n", " r2_score(data_y_test, data_y_pred_en),\n", " r2_score(data_y_test, data_y_pred_la)\n", " ))\n", "\n", " # Plot outputs\n", " plt.rcParams[\"figure.figsize\"] = [15.0, 10.0]\n", " plt.scatter(data_X_test, data_y_test, color='black')\n", " # blue\n", " plot_lr, = plt.plot(data_X_test, data_y_pred_lr, color='#4572a7', label='LinearRegression', linewidth=3, linestyle='solid')\n", " # green\n", " plot_rg, = plt.plot(data_X_test, data_y_pred_rg, color='#1a9850', label='RidgeCV', linewidth=1, linestyle='solid')\n", " # orange\n", " plot_ls, = plt.plot(data_X_test, data_y_pred_ls, color='#ff7f0e', label='LassoCV', linewidth=1, linestyle='solid')\n", " # red\n", " plot_en, = plt.plot(data_X_test, data_y_pred_en, color='#aa4643', label='ElasticNetCV', linewidth=1, linestyle='solid')\n", " # purple\n", " plot_la, = plt.plot(data_X_test, data_y_pred_la, color='#886fa8', label='LarsCV', linewidth=1, linestyle='solid')\n", " \n", " plt.legend(handles=[plot_lr, plot_rg, plot_ls, plot_en, plot_la])\n", "\n", " plt.xticks(())\n", " plt.yticks(())\n", "\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All features are ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n", "Using bmi\n", "(442, 10)\n", "(442, 1)\n", "(442,)\n", "(442, 1)\n", "Coefficients: \n", " [ 976.05247196] [ 848.21749059] [ 929.33232687] [ 815.21201628] [ 976.05247196]\n", "Super parameters: \n", " () (0.10000000000000001,) (0.10000000000000001,) (0.10000000000000001, 0.997) (0.0,)\n", "Mean squared error: \n", " 3700.73 3692.15 3687.88 3703.55 3700.73\n", "Variance score: \n", " 0.35 0.35 0.35 0.35 0.35\n" ] }, { "data": { "image/png": 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1CkWZ16tRytsErVZY+AIc/N427rYcSjdwUuTOdTL2HP6hvezjHzpOSsdoHqws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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6d5cf57780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn import datasets\n", "\n", "# Load the diabetes dataset\n", "diabetes = datasets.load_diabetes()\n", "\n", "print(\"All features are %s\" % diabetes.feature_names)\n", "\n", "FEATURE_TO_USE = 2\n", "\n", "print(\"Using %s\" % diabetes.feature_names[FEATURE_TO_USE])\n", "\n", "print(diabetes.data.shape)\n", "\n", "# Use only one feature\n", "diabetes_X = diabetes.data[:, np.newaxis, FEATURE_TO_USE]\n", "diabetes_y = diabetes.target\n", "\n", "print(diabetes_X.data.shape)\n", "print(diabetes_y.data.shape)\n", "\n", "fit_and_plot(diabetes_X, diabetes_y, test_ratio=0.3)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(500, 1)\n", "Coefficients: \n", " [ 5.17019582] [ 5.15808425] [ 5.07888176] [ 4.97012335] [ 5.17019582]\n", "Super parameters: \n", " () (0.90000000000000002,) (0.10000000000000001,) (0.10000000000000001, 0.69999999999999996) (0.0,)\n", "Mean squared error: \n", " 106.42 106.40 106.33 106.26 106.42\n", "Variance score: \n", " 0.20 0.20 0.20 0.20 0.20\n" ] }, { "data": { "image/png": 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SbNadd96JyWSq1L6LiIiIiIhj2X/o4Ngadr8kY3Mv+5Ts7GwGDx7MoUOHKCoqIiwsDIAu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\nMQEAqD9mtNBgxStXaM7N+Spdvy4yNurOe9Qh/JX01lWRsXfCAzWzcqI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AgM2SOmiZpqEW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\nBQAAAAAxRtACAAAAgBgjaAEAAABAjP0/oKnlGJWaulsAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6d5ceef320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "SAMPLE_SIZE = 500\n", "\n", "t_dis = np.random.standard_t(100, size=[2, SAMPLE_SIZE])\n", "t_dis = np.random.normal(0, 1, size=[2, SAMPLE_SIZE])\n", "t_dis\n", "\n", "\n", "t_dis[1] = t_dis[0] * 5 + t_dis[1] * 10\n", "\n", "data_X = t_dis[0].reshape(-1, 1)\n", "data_y = t_dis[1]\n", "\n", "fit_and_plot(data_X, data_y, test_ratio=0.3)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(500, 1)\n", "Coefficients: \n", " [ 97.12339861] [ 97.09920249] [ 97.03618222] [ 96.95249591] [ 97.12339861]\n", "Super parameters: \n", " () (0.10000000000000001,) (0.10000000000000001,) (0.10000000000000001, 0.98999999999999999) (0.0,)\n", "Mean squared error: \n", " 914.87 914.84 914.75 914.64 914.87\n", "Variance score: \n", " 0.90 0.90 0.90 0.90 0.90\n" ] }, { "data": { "image/png": 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IgAVEgCXaEwAAAEDt5eXlKS0tTRaLRWlpacrLy6vyeofLq4m3LVCW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\nXpK0uccWvW731W1TT9Xpnx/p8AwBAACAzmFFq4hVVVUpFosplUopFos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AAAXD1UFLag5b\nFmUXAAAAAAoIy0AAAAAAkGUELQAAAADIMoIWAAAAAGQZQQsAAAAAsoygBQAAAABZRtACAAAAgCwj\naAEAAABAlhG0AAAAACDLCFoAAAAAkGUELQAAAADIMoIWAAAAAGQZQQsAAAAAsoygBQAAAABZRtAC\nAAAAgCwjaAEAAABAlhG0AAAAACDLCFoAAAAAkGUELQAAAADIMoIWAAAAAGQZQQsAAAAAsoygBQAA\nAABZZti23fEHG8ZaSfHcTQcAAAAACprPtu2D9vegTgUtAAAAAMD+sXUQAAAAALKMoAUAAAAAWUbQ\nAgAAAIAsI2gBAAAAQJYRtAAAAAAgywhaAAAAAJBlBC0AAAAAyDKCFgAAAABkGUELAAAAALLs/wMp\n4FqdMYgHiQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6d621528d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "SAMPLE_SIZE = 500\n", "\n", "data_X, data_y = datasets.make_regression(n_samples=SAMPLE_SIZE, n_features=1, n_informative=1, \n", " n_targets=1, bias=15.0, effective_rank=None, \n", " tail_strength=0.5, noise=30.0, shuffle=True, \n", " coef=False, random_state=None)\n", "fit_and_plot(data_X, data_y, test_ratio=0.3)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coefficients: \n", " [ 0.50009091] [ 0.49603246] [ 0.49009091] [ 0.49463916] [ 0.50009091]\n", "Super parameters: \n", " () (0.90000000000000002,) (0.10000000000000001,) (0.10000000000000001, 0.10000000000000001) (0.0,)\n", "Mean squared error: \n", " 1.25 1.25 1.25 1.25 1.25\n", "Variance score: \n", " 0.67 0.67 0.67 0.67 0.67\n" ] }, { "data": { "image/png": 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OHQvA7bffTv/+/Zk8eTKnnnoqU6dO5YwzzmDWrFlMmjSJyy67jJtvvplatQoPxrZt27Jw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\nk1orSZIk1WuGVkM3/z54/JuEAIvW92P52tpPsPa0G8i2HmM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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6d5cda1e48>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Coefficients: \n", " [ 0.5] [ 0.49594229] [ 0.49] [ 0.49454906] [ 0.5]\n", "Super parameters: \n", " () (0.90000000000000002,) (0.10000000000000001,) (0.10000000000000001, 0.10000000000000001) (0.0,)\n", "Mean squared error: \n", " 1.25 1.25 1.25 1.25 1.25\n", "Variance score: \n", " 0.67 0.67 0.67 0.67 0.67\n" ] }, { "data": { "image/png": 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1q3JyclSmzLUfgc1mU+/evfXYY48pNjZWkrRp0yYdPnxYAwcOVGxsrF577TVJ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\nA8nBhLPQmmPJAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6d5cd3ce80>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Coefficients: \n", " [ 0.49972727] [ 0.49567178] [ 0.40972727] [ 0.45395677] [ 0.49972727]\n", "Super parameters: \n", " () (0.90000000000000002,) (0.90000000000000002,) (0.90000000000000002, 0.10000000000000001) (0.0,)\n", "Mean squared error: \n", " 1.25 1.25 1.33 1.27 1.25\n", "Variance score: \n", " 0.67 0.67 0.64 0.66 0.67\n" ] }, { "data": { "image/png": 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SpFyxcOWP3PyfD4kIwQn7HQ+ccPxHfB+zEYD/nH4LXeudnF/LzBUGLUmSJEmB\nSUtPZ+Q7Cxg3YxklQtA2HaJ/Ox64IXozk46fwZ6ie4ktcTwLer7EsSXK5vOKc4dBS5IkSVKObdyy\nk5uf/ZCNW3ZSJT3z7tXMSnOZX345RMC1DXsxqG0vIiIiDjLakc+gJUmSJOmwTU9azcOvf0FkCBqm\nQ6Pfdq9SItIYG/s+m6O3UYxivHf+EzSqVCufV5t3DFqSJEmSsmXP3lQeef0LPvk6mdL7XW6xtPRq\nplf5irQi6ZxRqQ0vnHcDxQrB5RbZZdCSJEmSdEiWf7+Fa556H0JQa7/LLSZX+4SVpZMB+E+7W+la\nv01+LbNAMGhJkiRJOqBQKMTo6Yt5eerXFAtBq3SI+e144JZi25lQ4yN2Ru2mZnQNFl74MhVKlMnn\nFRcMBi1JkiRJWfz8yx4G/3cG3677mYr7HQ/88thFfHnsIoiAf8X15vZTLyz0l1tkl0FLkiRJUoYv\nFq/j7pGziAhB3H7HA8fGfsAPJTZTnGje7fYkjSvH5t9CCziDliRJknSUS0lNY/j4JN79ahWlQtA+\nHSJ/Ox74Xcw63q36OSmRqZxesQ2fdz06L7fILoOWJEmSdJRau3E71w//gB27Ujh+v92raVW+ZHHZ\n7wAYfuotdG9wcp6sKT0lhd2bN1PquOPyZL7cYtCSJEmSjjJvf7qCp99MomgImqZD2d92r3ZE7mZ8\n7HS2FdtBzWI1WNjrZSqUzJvLLTZ89SWf3DU44+ue703Lk3lzi0FLkiRJOgrs2L2X+17+hPnf/ki5\n/S63WFBuOTMrzyMUEeLqer258/ReebKmPVu28Pmw+/lp0dcZtVa3D6ZG+9PzZP7cZNCSJEmSCrGF\nK3/k5v98SEQITtjveODE4z9mbcwPFA+VYHLXJ2haJTbX1xNKT2fp2EQWvfRiRq3mmWdx0rXXUTS6\nRK7Pn1cMWpIkSVIhk5aezsjJCxn38VKiQ9A2HaJ/Ox64vsQm3qk+iz1F99Ku/MnM7D6cqMjcjwVb\nli5lxu23krprFwDFy5TltAcfomydurk+d34waEmSJEmFxMYtO7nl2Q/5YctOqqRn3r2aVWke88ov\ngwj4d9tbuaBRm1xfT8rOncx56gmSZ87IqDUdcDUnnN+90PfdMmhJkiRJR7gP567moVFfEBmChunQ\n8Lfdq5SIVMbGfsDm6G0cX7QmC3q/zLGlcv9yi9Xvv8fsxx/N+Lpy8+a0GnQHxcvkzcUaBYFBS5Ik\nSToC7dk81oJvAAAgAElEQVSbyiOjv+CThcmU3u9yi6WlVzO9ylekFUnnytq9GHJW71xfzy/rkvnk\nrsHsWLcuo3baQ49QudlJuT53QWTQkiRJko4gy7/fwjVPvQ8hqLXf5RaTq33CytLJFE8vyVvnPUqz\narVzdS3pKSkseOF5vp04IaN24oW9aNTvMopERubq3AWdQUuSJEkq4EKhEKOnL+blqV9TLASt0iHm\nt+OBm4ttY2KNj9kZtZtTSrdh+oVP5vrlFvv3vCpTqzZth9xLqeOq5Oq8RxKDliRJklRA/fzLHga/\nMINvk3+m4n7HA7+q8A1fVPwaIuDJVrdwYfzJubqWwtzzKjcYtCRJkqQC5ovF67h75CwiQhC33/HA\nsTU/4IeSm6keUZN5fV6m0jG5d8HE0dLzKjcYtCRJkqQCICU1jaffTGLql6soFYJ26VD0t+OB38Ws\n492qn5MSmcqlNS9k6N/65OpajraeV7nBoCVJkiTlo7Ubt3PD8Gn8smsvx++3ezWtylcsLruK4mml\nGNflYRJq5N7lFkdzz6vcYNCSJEmS8sHbn67g6TeTKBqCpulQ9rfdqx1FdzO+5nS2FdtB61Kt+a73\nQxQrGpVr6yiQPa9S90LRYvk3fwAMWpIkSVIe2bl7L/e98inzVmyk3H6XWywot5yZlecRigjx8Ek3\ncXGLU3JtHQWy59WqGTDm7/Drtn1fD9mWf2sJgEFLkiRJymULV/7Izf/5kIgQnLDf8cCJx3/M2pgf\nqBqqyezeL1KlbNlcWUN6SgoLX/gvKya+mVHL955X65JgbD/YtvaP2ul3wik35M96AmTQkiRJknJB\nalo6nW8dC0B0CNqmQ/RvxwPXl9jEO9VnsafoXi6pdiEPdsm9yy02zP6KT+68I+PrMrVq0XbIffnX\n82rTMhh3OWz845p42lwDHe6CqOj8WVMuMGhJkiRJAfrmu03c8PR0AJqlQXn+2L2aVWke88ovo3hq\nDKM7P0CrWnVyZQ17tmzh8weG8tPXCzNqrW67gxqnd8iV+f7S1rUw4WpY88kftWZ/h04PQHTp/FlT\nLjNoSZIkSQG4a+RMvly8nmL7ffYK4N2qn7O8zBpaFG/Nqr5jKR4V/OUWofR0lr0xhq9fHJlRy9ee\nVzs2waSBsGzyH7W486DLk1Dq2LxfTx4zaEmSJEmHafvOX+lx9wQAqqdn/uwVwIh649kbmcLpJc7g\no0ueypU1bFm2lBm3Ze55deoDD1Ku7gm5Mt9B7dkGU2+DBa//Uat1GnR7FspUz/v15CODliRJkpRN\nU79cyZNjZ0OY3atlpdfwXrXPAZhw1pO0rB0b+Pwpu3Yx58nH9+t5dRUnnH9B3ve8StkN0+6FL5/9\no3ZcE7hgJFSsl7drKUAMWpIkSdIhCIVCdBs8nt2/phITJmAlxr7PjyW2cMzOKnx/5ZsUKRJ84Fn9\nwfvMfuyRjK8rn9ScVoNup3gu3VR4QGkpMPMxmPHQH7WyNaHny1AtH6+IL0AMWpIkSdJBrP5hG/0f\nnQpAXDpU3e944PD6YwhFhLi19r8YeNaZgc//y7pkPrn7TnYkJ2fUTnvwYSqf1DzwuQ4qPR2+HAHv\n3f5HLbos9PrfvuOBysSgJUmSJIUxfPwcJn32LZFhdq9mVZrHvArLAFjYZxQVSpcMdO6wPa969qJR\nv0spUjQP/4QPhWD+6/DWP/9UjIDeo6D+OXm3jiOQQUuSJEn6ze5fU+l6xzgAKoW53GJk3YnsjNpD\nC9qxbsBD4YbIkQLT82rpZEi8KHOt2who2hvy+jNgRyiDliRJko56ny9axz0vzYIQnJwOJf7U+2p9\niU2Mi93XF2v0qY9yWoO6gc4dtufVoNup0eGMQOf5S9/NhDEX77s58Hd/exha9ociRfJ2LYWAQUuS\nJElHrasee5dVG7ZSIszxwInHf8zamB84Zkc1Vl8+jqiikYHNW2B6Xq2bC2/8Y19D4d+1vwNOvREi\ng+/1dTQxaEmSJOmo8uPPO7l46CQAaoc5HvjMiWNJK5LOP4/rz+CuZwc6d4HoebVpOYy/HH74YweN\n1v+CM+6CqHxobFxIGbQkSZJ0VBg97Rtemvo1EWF2r5LKL+HTygsgBF/2eIXqx5YObN7dm3/ik7vv\nZOu332bU8rzn1dbvYeLVsHrWH7X4i+FvD0B0mbxZw1HGoCVJkqRCKzUtnc63jgWgXJiA9WrtyWwt\n/gsNdrYluf+bgQafGYNu4cf58zK+zvOeVzt/gkkDYek7f9Tqd4EuT0FMxbxZw1HMoCVJkqRC55vv\nNnHD0/susGiWBuX/dLnFjqK7eLHu2xAB/014kM7N6wc27/63BgKceGEvmlx+ZWBzHNSe7fDu7TD/\ntT9qsadCt/9A2Rp5swYBBi1JkiQVIve8OIvPv1lHVJjdq/eqfs6yMmsovf14VvQbS8noYC57SN2z\nmwldz81S7/72ZCKLFw9kjoNK2Q3T74cvnvmjdlwTuOAFqHhi7s+vsAxakiRJOqJt3/krPe6eAED1\nMJdbjKg3nr2RKVwccykf9j0vsHnnP/ufTA2FAdo9/CiV4psFNscBpaXCrMfh4wf+qJWpARe+DNWa\n5/78+ksGLUmSJB2Rpn65kifHzoYQdEiHiD8dD1xeeg3vVvuciPRIPjr3OU6oXiGQObeuWsUHV/fP\nVKvSqjWn3Dc0kPEPKj0dvnoe3h30R614Gej1P6jdLvfnV7YYtCRJknTECIVCnH/nm+zak0JMmOOB\nY2LfZ2OJLVT/sSXfX/kmRYrk/HKLUFoab13Yg5Qdv2SqnzvmDaLLlsvx+AefPAQLEmHiVZnrvV6D\nuKzHFVVwGLQkSZJU4K3+YRv9H50KQFw6VN3veODw+mMIRYR4PO5eep/WJJA5v530FvOeHp6plnDj\nTdTqFGxvrbCWToHEi4DQH7Wu/4H4iyCvroRXjhi0JEmSVGA9/WYSb3+6gsgwu1ezKs1jXoVllN0W\ny9d9X6fcMdE5nm/35s28c1GvTLUSFStyzqujiChSJMfjH9TqT2DMxbD75z9qnR6EVldBbs+twBm0\nJEmSVKDs2ZvKebePA6BSmIA1su5EdkbtoQsX8c6AhwKZc/+eVwCd/juS0jVqBjL+Aa2fD2/0g5+/\n+6PW7jY47WaIDOZWROUPg5YkSZIKhM+/Wcc9L86CELRJh5J/utxiQ4mfeCN2GpFpxXjrrP+j2QnH\n5Xi+fOt59dMKGH8FbJj/R63V1XDG3VCsZO7OrTxj0JIkSVK+uvrxd1m5fivRYXav3jr+Y9bE/EC1\nDa1Yffk4oopG5miufOt5tS0ZJv4TvpvxR63pRXD2QxBdJvfmVb4xaEmSJCnP/fjzTi4eOgmA2mF6\nXz1z4ljSiqRz9/F3MKBzixzPF67n1WkPPULlZifleOwD2rQcntlv7SeeA+c+BTGVcm9eFQgGLUmS\nJOWZ0dO+4aWpXxMRZvdqbvmlfFJ5PuW21ubLC1+hSoWYHM2VLz2vtq+HJ+Ky1vtNgdi2uTevChyD\nliRJknJVWlo6Z986FoByYQLWq7Uns7X4L7TddgHJ/e8mIgfXl+dLz6vdW+HhMJdmXPgqNOiaO3Oq\nwDNoSZIkKVd8891P3PD0NADi06DCny632Bm5m5EnvEXR1BK8esqDtIvP2e1+Kye9zdyn/52plnDD\nTdT6Wy71vErZAw/VgLRfM9c7PwYtc/kyDR0RDFqSJEkK1JCXZvHZonVEhdm9er/qFywts5rq69uw\not9YSkYf/hXmYXteHVuRc/6XSz2v0tPgmVaweUXm+ik3wpn3BD+fjmgGLUmSJOXY9l2/0uOuCQBU\nC3O5xYh649kbmcI1ZW5keu9TczRXnva8CoXgtQtg5fTM9Sa9oPvzwc+nQsOgJUmSpMP27pereGLs\nVxCCDukQ8afjgcuPWcu71T+j/M91mdb1OepUO/zPSP0w+ytm5WXPq0nXQ9JLmWs1ToZLp0AOPkOm\no4dBS5IkSdkSCoXofueb7NyTQkyY44FjYt9nY4kt1FvTme+vfJMiRQ4vmOR5z6sZj8JH+91IeExV\nuP5riPTPZmWP/4+RJEnSIVm48kdu/s+HAMSlQ9X9jgcOrz+GqJRSPNXsLrqeUu+w58nTnldz/wdv\nX5O1fscGKFYy+Pl01DBoSZIk6aD6PfgO63/aQdEwu1ezKs1jXoVlHL++LV/3fZ1yx0Qf1hzbvlvF\n+1ft1/OqZStOuX/YYa/7gJa9C6N7Za3fsgpKVQh+Ph2VDFqSJEnKYueeFM4fPB6A2DCXW/ze+6p3\n6GreGfDQYc0RSkvj7d492bt9e6Z6rvS8+n42jDwza33gQiiXC5do6Khn0JIkSVKG8TOW8dzb8yDM\n7hXAv+MSKf5rGf7Tagh/a1X7sOZY+c4k5g7/v0y1XOl59dMKeDoha/2qT+C4xsHOJe3HoCVJkiQ6\n3pQIEPZyi48rJ7Gw/ApqrzmTVZe9QfGo7P8JmWc9r375AR4/MWv9H5Og1mnBzSP9BYOWJEnSUWr1\nD9vo/+hUAE5Kg3JkDljP1htHSmQqPff+i6kDHjmsOcL2vHp+JKVrBnhcb882eKhG1nqPl6BR9+Dm\nkbLBoCVJknSUuWvkTL5cvJ4iYXavNhfbxqg6U6m8qSnvn/sc9Y4vn+3xf5gzm1mDb89UC7znVeqv\n8HAtSNmZuf63h6H1VcHNIx0mg5YkSdJRICU1jXMGvQHAcWEut3ij5jQ2lPyJ+iu6k9z/TSKy2ZQ3\nT3pepafDsyfDpiWZ6ydfBx3vD2YOKSAGLUmSpEJs5oK1DH31MwDOSAtzuUX9RErsKc89cTfw906N\nsj1+2J5XDz5M5ZOaH96C9xcKweu9YMV7meuNLoAeLwYzh5QLDFqSJEmF0O+XW0SHOR44t/xSPqk8\nn9prOrL44kTKxGRvx2nj3CRm3j4oU+24li059f4HcrboP5t8M8z+b+Za9ZZw+fuQzd02HTlGjRrF\n4MGDWbt2LTVq1GDYsGH07ds3v5d1WAxakiRJhcTmbbvpc99bAMSlQ9X9jge+UHciu6L20Pr7v7Nu\nwD3ZGjs9LY3xnTtlqZ+b+AbR5QLqeTXrCZh+b+ZaqUpw42KIjApmDhVYo0aNon///uzatQuANWvW\n0L//vibWR2LYigiFQof8cEJCQmjOnDm5uBxJkiRl17NvzWXCzOUH7X113MaTeLbn5bSMq5qtsec8\n+TjfvTs1U63Ouedx0jXX5WjNGea/DhOvzlq/fR0UjwlmDh0RYmNjWbNmTZZ6zZo1Wb16dd4v6AAi\nIiKSQqFQmAZtmbmjJUmSdAQKhUJ0unkMAOXDBKzJ1T5hZelk4lZcwNorxxOZjV5Vv6xL5t3L+mWp\n95jyHhGRkTlaNwArPoBRPbLWb1kJpY7N+fg6Iq1duzZb9YLOoCVJknQEWbjyR27+z4cAnJoGxfbr\nffV0/TFE767IlZUv5daLWmdr7Dc6nZml1u6Rx6jUNP7wF/y75CR4oUPW+nXzoXytnI+vI16NGjXC\n7mjVqBGmR9oRwKAlSZJ0BLjsockkb/qFomF2r1bGJDP5+E+os7oTX/V8lSoVDv3I3bdvv8W8Z4Zn\nqpWtW5eznhmR80VvXgnDT8paHzATqjTN+fgqVIYNG5bpM1oAJUuWZNiwYfm4qsNn0JIkSSqgdu5J\n4fzB4wGIDdP76n+1p/Bz8e00XN6LdQMmHPK4KTt3MLF7tyz1bm++RVSpUjlb9C8b4fF6WeuXvAW1\n2+dsbBVqv194UVhuHfQyDEmSpALmzZnLGPHWvINeblH1hwTu+1tvzm5V55DHfa//FWxfszpTrfnA\n66nduUvOFrxnOzx0fNb6BSOhcZjPYklHMC/DkCRJOsL83vsqJkzA+rhyEgvLryBuRQ9WXvoG0cUO\n7c+4jfPmMvO2W7PUe743LWeLTd0Lj9aFX7dlrnd6ANr8K2djS4WAQUuSJCkfrflhG1c+uu/69JPS\noNx+l1s8W28cxXdX5G9R5zN1wCOHNGYoLY1xYXpenf3yq8RUyd717pmkp8N9YXpmtbkGOh2Zn6OR\ncotBS5IkKR/cPXImXyxeT5Ewu1dbim3jtTpTqfvd2bx/7nPUO778IY0556kn+G7qlEy1Ol3O5aRr\nB+ZssUPKZK0VLw23f5+zcaVCzKAlSZKUR1JS0zhn0BsAHBfmcos3ak5jQ8mfaLi8F8n93yQiIuvn\ns/a3Y906pl72jyz1HPe8Gp4Am1dkrd/9M2SjJ5d0tDJoSZIk5bKZC75n6KufAnBGWpjLLeonUu2H\nVlzX4Dou6dT4kMbMlZ5XiX1h6TtZ64M3QlT04Y8rHYUMWpIkSbnk98stosMcD5xXfimzKs8nbnkP\nFl+cSJmY4n853reT3mLe0/v1vKpTh7P+89zhL/KDu+HT/8tav/U7KHloRxYlZWXQkiRJCtDmbbvp\nc99bANRPh2r7HQ98oe5Eiu45lhN/bs+6Aff85Xi50vNq9gsw+aas9YELoFzs4Y0pKRODliRJUgCe\nfWsuE2YuP2jvqxO+O4dRZzxOy7i/vvkv8J5Xy6bC6N5Z61d+CNWaH96Ykg7IoCVJknSYQqEQnW4e\nA0D5MAFrcrVPWFk6mYbLe7H2yvFE/sUlEj/On8eMQbdkqR92z6t1SfDfDlnrfRLhxLMPb0xJh8Sg\nJUmSlE1fr/qRm575EIBT0qD4fr2vnq4/hqobWtO92kUMuqjNQccKvOfVz6vh/5pmrZ/zOLS4Ivvj\nSTosBi1JkqRDdNlDk0ne9AtFw+xerYxJZvLxn9BgeU++6vkqVSrEHHSsQHte7doCj9TKWj/5Ouh4\nf/bHk5RjBi1JkqSD2LUnhW6DxwNQM0zvq//VnkL6r+Wo/kNr1g2YcNCxAu15lbIHhlXOWq/fBXqP\nyt5YkgJn0JIkSQpjwsxlPPvWPOAAva/iEjlhVRcebXk3Z7euc9CxAut5lZ4O95XLWq9wAlw7J3tj\nScpVBi1JkqQ/+b33VUyY44EzKs9lQfnlNFzei5WXvkF0sQP/KbVy0tvMffrfmWqH3fNqSJkD1Ldl\nfyxJecKgJUmSjnprN27jikemAnBSGpTb73KLEfXGUfmHVrQseSZTBjx8wHEC7Xn1TGvYtCRr/e4t\nUCSbxwwl5TmDliRJOmrd8+IsPv9mHUXC7F5tKbad1+pMocHynrzbZQQn1qhwwHHeG3AF21evzlQ7\n6brrqXNONntejf0HLJ6YtT74B4gqkb2xJOUrg5YkSTqqpKal0/nWsQAcF+Zyi3E1p7N77zFU/bEF\nyf3fJCIi6+ezIMCeV9Pvg1mPZ63fsgpKHTjcSSrYDFqSJOmoMHPB9wx99VPgAJdb1E+k3nfnck3c\ntfzjb43DjnHAnlcvvUpM1Wz0vEp6GSaFucb92rlQ4eAXa0g6Mhi0JElSofb75RbFwxwPnFd+GbMq\nzafhigv55uLRlI2JDjtGuJ5XtTt3ofnA6w99Icvfh9d7Zq1fMR2qJxz6OJKOCAYtSZJU6Gzevps+\n974FQP10qLbf8cCRdSdS4ccWVN7TgHVX3R12jEB6Xq2fD8+3y1rvNQrisvn5LUlHFIOWJEkqNJ57\nex7jZyyDMLtXsK/3VYPlF/K/Do/TqkH4o35he149/BiV4g+x59XWtfBUmKOHZz8CrQYc2hiSjngG\nLUmSdEQLhUJ0unkMAOXDBKwp1T5lS1o0VTadxNorxxNZpEiWMVa+M4m5w/8vU61Mrdp0HPH8oS1i\n98/wcGzWeut/wd8eOLQxJBUqBi1JknREWrRqEzc+Mx2AU9Kg+H69r56uP5a6q7rQuVofBl3UJsvr\nc9zzKvVXGFopa/2ETtB37KG9CUmFlkFLkiQdUa58ZAprNm6naJjdq+9i1jG56hfErezOlz1epuqx\nx2R5fdieV9cOpE6Xc/968vR0uK9c1nrZmnD9wuy8DUmFnEFLkiQVeLv2pNBt8HgAaobpffVa7Skc\ns6kZMbuq8v0/s+4m/Th/PjMG3Zylfsg9r+6rAOmpWev3bIUD9NmSdHQzaEmSpAJrwqzlPDtxLnCA\n3le/XW7xUIu7OKdN3Uzfy3HPqxGnwg9hdqnu3gJFDvHWQUlHLYOWJEkqcH7vfRUT5njgzMpzSaYI\nlX9qyspL3yC6WOY/Z5L+70lWTZmcqXbIPa/GXQ6LxmWt37EBipXM3puQdFQzaEmSpAJh7cbtXPHI\nvqbAzdKg/H6XW4yoN45aq8+hQYkzmHx9x0zf27F+PVMvvSTLmIfU8+qjB2DGw1nrN38LMRWz9yYk\n6TcGLUmSlK+GvDSLzxato0iY3aufi21ndM2POHHVebzbZQQn1qiQ6fuH3fNq7v/g7Wuy1q9JgmPr\nZq1LUjYZtCRJUp5LTUun8637Lq04LszlFuNqTqfY5kaU2l2J1f8cRcSfLpxYOfkd5v77qUzPH1LP\nq2+nwWsXZK1f9j7UaHV4b0SSDsCgJUmS8syshd9z/yufAge43KJ+Ig1WXMi/6l9Dv7ObZNRTdu5k\nYveuWZ7v9uZEokrFHHjCDQvhuVOz1i98FRpkHU+SgmLQkiRJua7TzYmEQlA8zPHAeeWWsTRqL5U2\nN+Kbi0dTNiY643vvX9Wfbd+tyvT8X/a82pYMTzYMs4gHoc0/c/Q+JOlQGbQkSVKu2Lx9N33ufQuA\nE9Oh+n7HA0fWncjxa86m1J4GzL+nR0b9sHpe7d4KD9fMWm85ADo/cnhvQJJywKAlSZIC9fzb8xg3\nYxmE2b0CGFHnHU5YfQ6vdniM1g2qAYfZ8yp1LwwNcytgnTPg72/m6D1IUk4ZtCRJUo6FQiE63TwG\ngHJhAtbUap+SurUeJfccy7f/fIXIyCIAJP37KVZNfifTswfteRUKwb1ls9ZLV4MbF+f8jUhSQAxa\nkiTpsH3z3SZueHo6AG3TIHq/3ldP1x9L/RU96FSlN7ffcjJwmD2vhh4Hqbuz1u/ZChFZd80kKb8Z\ntCRJUrZd+ehU1vywjaJhdq++i1nHl9FbqfhzA77o8TLVjj0GOFDPq0epFN8s/CTPnw7r52at37UZ\nIv0TJr+MGjWKwYMHs3btWmrUqMGwYcPo27dvfi9LKnD8r5QkSToku/ak0G3weABqhul99VrtKVT6\n/gwid1dl3tAbgX09r97Yr+dV6dhYOj33QvhJJlwFC0Znrd+xHoqVyvmbUI6MGjWK/v37s2vXLgDW\nrFlD//79AQxb0n4iQqHQIT+ckJAQmjNnTi4uR5IkFTQTZy3nPxP37SyF6331XO2p1F3zNwb2SOCc\nNnWz3/Pq44fh4wey1m9eATGVcrx+BSc2NpY1a9ZkqdesWZPVq1fn/YKkfBAREZEUCoUS/uo5d7Qk\nSVJYHW9KBCAmzPHAWZXmsW1HDUr8Wp5vBrxAieJF+eCfA3hjyMpMzx2w59X80TDxqqz1f82GivUC\new8K1tq1a7NVl45mBi1JkpTh+x+3c/nDUwCIT4MK+11uMaLeOE5YeQEnlDidZ+7oxI/z5/POeX/L\nMk7YnlcrP4L/dctav3Qq1Dw5kPUrd9WoUSPsjlaNGjXyYTVSwWbQkiRJ3PfyJ3zydTIRYXavtkb9\nwnul13Hs1hOZcs4I6lcvy7jOnXjj3UczPXf2i68QU61a5oF/WAQj2madsMdL0Kh70G9DuWzYsGGZ\nPqMFULJkSYYNG5aPq5IKJoOWJElHqdS0dDrfOhaAymEutxhfYzrHrD+VyPQYku68m7n/3959x0dR\n538cf80kgRACSJHOJlQpggqRDhaKgIA0wTNgN/ZYz4YNMBZ+eoqeBTtqThRsSC8qIkgJIB0hQLKA\n1EAIIQlJduf3R8guYwKEsJuQ5P38bz8zO/OZu3tcfPud/X7ensD6qBGsP+mcRv2upd2DD9svfGQ3\nvNEy7w17jYMu0T5+CilKuRteaNdBkTPTZhgiIiJlzO9rdzJ20mIg/80tPgybR6NdPflXz5Zcf0mN\ngs28ykiBVxrkvVnE7dD/Pz7rXUSkuGkzDBEREbHp89jXuC2L8vm8Hvhn1S3szqhBcGZV4u78L/OG\n9of1MOukc/LMvHJlwbgaeW/UsDvc/JN/HkJEpIRQ0BIRESnFDqWkc8OYHwG4yA31//F64MdNfiR8\nx0DKZzTn064VWPXWm8wb6p1xlWfmlWXBmAvy3ii0Fjy2xS/PICJSEiloiYiIlELjv1rK/LgEyGf1\nCmBKtS1UO9KUTzuN5e+fcrZZX7XKezzPzKuXHXD8SN4bPZ8MRt7ri4iUdQpaIiIipYRlWVzz2NcA\n1MgnYM2qt5jAfe0xrUBe2rOLI9sX8vfT3tWqtvdH03jAQO8XPu4NO5flvdGzSRCgf4QQETkd/b+k\niIhICbd6y16emPgrkP/mFp86fsGx+0quowP1130KwMlrU7aZVz/eB6u/zHuTp3ZD+dC8dRERyZeC\nloiISAk14MkpHM9yEZjP6tXB8sksDXARknEB4+bEA/GcvC+7bebVb6/Bz+Py3uDRv6BSbf89gIhI\nKaagJSIiUoIcy8hi8OhvAWjshvB/bG7xVfgcau7sTc/ty4lOSbQda9i3HxEPPZLzYe038OGdeW9w\n7zKo2dwvvYuIlCUKWiIiIiXAl/PW8/nsnCWp/F4P/LrGJhrvrc3oWU7gI9sxz8yrHb/BC1XyXvyW\nGRDe1R9ti4iUWQpaIiIi57Hej04GoFI+rweuqL6R5CNNuWvdZzz1j+91GTOOuh07wf5NMK5a3gsP\n/RhaD/NT1yIioqAlIiJyntmxJ5m7XpsNQAeXRSim7fhnYfPpv7oGIzatABZ76ma5cgz9aSYk74Q3\nL4bZ/7hwj+eh2yN+7l5EREBBS0RE5LwRPWEem51JmLbVK+8q1rygfdy1dhpjNwHs8NSvm/Id5cpZ\n8Ioj76uBbW+CgW/7vXcREbFT0BIRESlG2S43/R7/BoC6boseln316scGC7l3fgLl3ZncdVL94ptv\npcWI4TCuBvyngf2ilevDIxv83LmIiJyOYVlWgU+OiIiw4uLi/NiOiIhI2TA/bgfjv8oZBpzf5hZx\n/PRFBVYAACAASURBVMHwTevz1K+fPQ/GXJD/RV84kn9dRER8xjCMlZZlRZzpPK1oiYiIFKHczS2C\n89ncYmslJ9csnwNAo5Pq13zwMZU/bZPz4Z8h6/lkMPIGNSm42NhYRo8ejdPpxOFwEBMTQ2RkZHG3\nJSIlnIKWiIiInx08ksaNY6cB0MpyUdtt//ObnvwDrfYcsIWrOu070LX6NEh2Qm7IyvXMAQgs5+eu\ny4bY2FiioqJIS0sDIDExkaioKACFLRE5J3p1UERExE/+76ulzItLgHxWrwAabfowT23oqOqY8f/c\nLhB4IhEqnOKVQSm08PBwEhMT89TDwsJISEgo+oZE5LynVwdFRESKgWVZXPPY1wBUt9z0cAfYjtfY\ns4jKyZtttS6DLqLuvi9zPsSfdOChdXCBw5/tlnlOp/Os6iIiBaWgJSIi4gOrt+7jifd/AU7e3MIb\nssI3f4JpuTyfzUCToZfnbIbBvhXeC935C9Rr6+925QSHw5HvipbDoYArIudGQUtEROQcDHxqKhmZ\n2QTm83pgUMYhGuz41la7LmIV5YJcthr/mgwX9fV3q5KPmJgY22+0AEJCQoiJiSnGrkSkNFDQEhER\nOUvHMrIYPDonQDUkk0au8rbj9XZ8T/mMg57PrRrsomX9PfaL9P0/6BDl917l9HI3vNCugyLia9oM\nQ0REpIBi521g0ux1QP6zr/65ucX1nVbYT+hwD/R9xW/9iYiI/2kzDBERER/JnX0Vms/mFhcc/JNq\nB7yBqvcl66gSkuE9odFVcNMPRdKniIicPxS0RERE8rFjTzJ3vZazzfrlVhaV3eU4eXOLsL8+J8B9\nHIA6FyTTtcVW75cr1oR/b0VERMouBS0REZGTPPTWPDYmJmHaNrfwDgc++fXAoR1XYJ78BuELR4qm\nSREROe8paImISJmX7XLT7/FvAKhpZNDDVcF2vNbOOVRMzZmr1OWirdStluw9+HwyGHl/ryUiImWb\ngpaIiJRZ81cmMP5/S4GTN7fwhqyGmz7EAEzDzdBOK71ffGY/BNp3GhQRETmZgpaIiJQ5uZtblLNc\n9HDb/xSGpOyg9u75wD9mXj2RABWqFmWbIiJSgiloiYhImXDwSBo3jp0GwEXmMepnhXLyn8EG8ZMJ\nyjqaM/Oq04mZVw+ugarhRd+siIiUeApaIiJSqr02eRlzV+yAkze3cIV6judubuGZeXXHz1C/XVG3\nKSIipYyCloiIlDqWZXHNY18DUDngGD1OClYANfb8TuXkTTkzrzplwIgvocWA4mhVRERKKQUtEREp\nNVZv3ccT7/8CnLS5xUkhK3zzJ9StkkS3Flvhmpeh0/ziaFNERMoABS0RESnxBo3+lrSMLExc9HDZ\n/7QFHT9Mg+1TGdpxBam3jaLy8DnF1KWIiJQlCloiIlIipWVkMWj0twDUDTpEJ1d1Tv6zVm/H91wd\n9gf7q9fk0vdyfn9VuTgaFRGRMklBS0RESpSv5m/g01nrgJNfD6zuOd5k80R6ddjE9AfmU7dnK+oW\nR5MiIlLmKWiJiEiJkDv7KtRIooerhu1YlYN/clvYRPpnvMv9P8yhYnAQNxZHkyIiIicoaImIyHlr\n76FUboqZDsDQ1M0kV2gBeEPWNamv8ESVfxPsaMfdMYeYW0x9ioiI/JNZ3A2IiIj808Rpq+n96GSW\njh1BD5dBD5dxImTl+C0gkwUBFtYTs5nz+o38+NKwYuz2/BMbG0t4eDimaRIeHk5sbGxxtyQiUuZo\nRUtERM4LLpebvo9/w4igWQRvMejh6Mtf3OQ5vsHcyV6jPgBzXhuJYRjF1ep5LTY2lqioKNLS0gBI\nTEwkKioKgMjIyOJsTUSkTDEsyyrwyREREVZcXJwf2xERkbJm9dZ9zP3oZQYmzWZmuUfJLmffG3C+\n6cYwDKIGXsqwK5oXU5clR3h4OImJiXnqYWFhJCQkFH1DIiKljGEYKy3LijjTeVrREhGRYjHh1fFE\nJY9n85pO0Gwk00I7eY6tDz7IvqycnQS/HTuEyhXLF1ebJY7T6TyruoiI+IeCloiIFJnU7XGEft6D\nGavaUCG0A2/VfAeaeY//GpiJywqi0YWNmfton+JrtARzOBz5rmg5HI5i6EZEpOxS0BIREf9K3glv\nXsyupKos2dKEHS0+gEbew0cDMlhOzorVq1G9uKxZ7WJqtHSIiYmx/UYLICQkhJiYmGLsSkSk7FHQ\nEhER30s7BOMbku0y+X55OzKC+/J3w8Hg3TiQ5eWPcjQ7FCjPrPHDCQjQRri+kLvhxejRo3E6nTgc\nDmJiYrQRhohIEdNmGCIi4htZGRBTC4A/tjRmV1I19jToQ3poA9tpC0w3GAaDuzfjnuvaFkenIiIi\nhabNMERExP/cLhhbDYDDqSHMX3c5bjOIhItugZre07aXP8KO7JzdBD8fPYDa1UKLoVkREZGio6Al\nIiJnx7JgQhtIdmJZMHXp5QAcrdKUAy2utJ36e0AmxwmiHFWZ+/r1xdCsiIhI8VDQEhGRgvliCGxb\nAMCmXXVYvzMnYG1vcWeeUxcE5LyW/lRkd65qG1Z0PYqIiJwnFLREROTUZjwGKz4EIP14ENNX5YSr\nrKDK7Gwxwnbq2qBjHHCHADDt5WEEl9OfGBERKbv0V1BEROwWT4B5z3k+zlzVmmPHgwE4ULsrR6u2\nsJ3+q2nhMqBLq2bE3tK1SFsVERE5XyloiYgIrJ0C393h+bgrqSp/bGkCgIXBjhZ32E7fH5jGOqsC\nAO89cg2N61Utul5FRERKAAUtEZGyavtC+Hyg52PuzKtcaRXrs9fR1/aVZQHZpBIAVgXmvDYCwzCK\nrF0REZGSREFLRKQs2bcB3utsKy3d0oidSdU9n52Nh5NdrortnAWmBQZEDWjHsCubF0mrIiIiJZmC\nlohIaXdkF7zRylbKmXnlrbkCgklsNsp2zpbADHZa5QGYOnYwlSuW93+vIiIipYSClohIaZSeDK/a\nt1U/eeZVrsPVL+VwTXttkWmRaUDD2jWZ+5j91UEREREpGAUtEZHSIisDYmrlKW8yBrF+yW7PZwvY\n8Y/ZV2lGNn+YAQC8cteVtG1W26+tioiIlHYKWiIiJZnbDWPz7viXXq8306cePvEpJ2RlBNfg74aD\nbeetNl0cMkwggFnjhxMQYPq5YRERkbJBQUtEpCR6qy0c2mavVW/KzLjmHNu7FzjsKS+95FpqZta1\nnfqzaWEZMLh7c+65rm0RNCwiIlK2KGiJiJQU/xsBW2bnKe/q8RN/vDj2xKe9AGytfiEBNQcBUDMz\n58jOgEy2EATApKf7U6d6qN9bFhERKasUtEREzmeznoBl7+cpZz/m5PuhQ3M+/DHWU//4itZctb8j\nASedu8S0SDcgKKA8c8cP93PD/hEbG8vo0aNxOp04HA5iYmKIjIws7rZEREROSUFLROR8s+S/MHd0\n3voTiSx947/sXPgr5IYsYHbDCJoFXwbAVfu9py8IsAB4MrIjV7cN92PD/hUbG0tUVBRpaWkAJCYm\nEhUVBaCwJSIi5y3DsqwCnxwREWHFxcX5sR0RkTJq/bcw9ba89Yc3cnh/GvPvv8dWzggozxtXNWLE\n7q62+kbTxR4jZ0OLaS8PI7hcyf/3aeHh4SQmJuaph4WFkZCQUPQNiYhImWYYxkrLsiLOdF7J/wss\nIlJS7d8M73bIW7/nD6wLmzO1b2/442bboS+bDya06mHaJDdlhHfHdn41LVwGdGrVgEm3dfNz40XL\n6XSeVV1EROR8oKAlIlKUUvbAhDbgyrTXb/4JGnZn0+T/sf6maNuhVRdeytyLKjI8qSWdAZJrAJBk\nZPPnidlX7z1yDY3r5d3mvTRwOBz5rmg5HI5i6EZERKRgFLRERPwt/TC83w2O7LTX7/gZ6rcjPekg\n02+8Ic/XPmx1K6n1fue6nZczPMlbX2FapBgAAcx5bQSGYfi1/eIWExNj+40WQEhICDExMcXYlYiI\nyOkpaImI+ENWOnx2Lexeaa+P/A6a9ABg9TtvEz/tCdvhHxsNIKGaSdesYHpkBsLOKz3HFpgWGHBn\n/0u5/qrm/n6C80buhhfadVBEREoSbYYhIuIrrmz4ZhT8NdNeH/oxtB4GwOH4rcy/z76xxcELG/N9\nravYXe9XbnJeZTu2zXCTYOasWE0ZM5gqoeX917+IiIickTbDEBEpCpYF0x+GlZ/a631ehY53A+DO\nymLBfXeTHB/vORzawMFbla7meFA2oVW20OOAASeFrEWmRaYBYbUvYO6/+xbJo4iIiIjvKGiJiBTG\nLy/DwlfstW6PQY9nPR+3z5zByglv2E6p/9gYnp+9k6Mhf9Mx6wi1M2rAgUsASMfNkoCc1atX7rqS\nts1q+/cZRERExG8UtERECmrFRzDjUXvtslEw4C0wc2ZXpe3fx4xR9t8ONR08lOkhbVi4xknSn98w\nwtUZjtbzHF9jWhw0AAxmjR9OQIDp5wcRERERf1PQEhE5nQ0/wBT7LCuaXgM3xEJAEACWZbH0xbHs\nWvSb5xQjIIAuH0zi5jd+5XhCCiFVfqSnqx3s7gzAEcPFSsPEMmBQt2bcO6htkT2SiIiI+J+ClojI\nP21fCJ8PtNfqXAq3zoRyFT2lvSuWs+iZp22ndX5+DGvM2rw5ZQUvTXqHa41GhGZUgYx2AGww3Ow1\nDcDU5hYiIiKlmIKWiAjA33/CB1fYa5Xqwj2LIaSap5SZmsqMUTeSfdJMpzodO9HhmeeJem0O73+/\njeS6nzLc1QsOXOw5J3dzi/6dmxI99IwbFYmIiEgJp6AlImVX0jZ4+x+v7BkB8NA6qFLPVl736Sds\nnvw/W63vp5/ztyuY+9+cS8q4CTQ1j9M7qzEk9gJgt+Fi84nfbk2I7kWLsOr+exYRERE5ryhoiUjZ\ncnQvTLgUstPt9ftWwIXNbKXkbfHMu/duW+3Se++j6XWDefeHVQx+fSF76yxipOsKSKnvOSfOtDhi\nQK1qlZjxRD+CAgP89jgiIiJyflLQEpHSLz0ZJnaH5ER7/fb50OByW8mdlcWChx6wzbyq5HDQ67/v\nccwFw579noylEwmouok+rk6wK+d1w+O4WWIauA24f3A7BnZt6vfHEhERkfOXgpaIlE5Z6TBpIOxa\nbq9HfgtNe+Y5fcfsWcS98bqtdvWEt6nevAWL1u6k79Pfs7/aWq4Oqk+t9OqQ3gmALYabnaYBGHz5\nzABqVq2Y59oiIiJS9ihoiUjp4crO2Yp983R7fciH0GZ4ntPzn3k1hEvvvhe32+Kx935mzccr2N9g\nASNd/TyDhQEWmxYZBnRp3YCPbu6CYRh+eSQREREpmRS0RKRks6ycIcJxH9vr17wEne7L53SLZS/H\nsHPhr56aERDAgK++oXyVKuw6kELvRydzJHQn9QOP0jurFWzvB8BBw80awwADXoq6goiL6vjzyURE\nRKQEU9ASkZJp4Xj4JcZe6/ow9Hge8lld2hu3gkWjn7LVOj8/hnqduwDw1fwNfDJrBrvq/cpNrqsw\njzg8560xLQ4aEBQYwI9jB1OhfJDvn0dERERKFQUtESk54j6B6Q/ba5dGwsD/wolt1E92qplXXZ4f\ng2GapB/P5rqnp3LYOkh2jTVc57oSnD085/5qWrgMGNX7YkZdc3Ge64uIiIicioKWiJzfNk6Db0bZ\na016wg1fQWC5fL+y/rNP2PRV3plXoXXrAvBn/D4ef+8X9lb/k85mTTqn1oOdVwKQYFhsO5HZPnq8\nL45aVXz6OLliY2MZPXo0TqcTh8NBTEwMkZGRZ/6iiIiIlAgKWiJy/tmxCCb1t9dqt4bb5kC5/Hf1\nO93MK8j5bVbM54tZsC6ev8PmcqtrIOy/zHPuMtMi1YAWYdWZdX8PAvJZIfOV2NhYoqKiSDux0paY\nmEhUVBSAwpaIiEgpYViWVeCTIyIirLi4OD+2IyJl1p61MLGbvRZaC+5dCiHV8v1KvjOvGjjo9c57\nBJQvD8DBI2ncOHYaRyolUq18Elfua+c59yhuVpgGlgFPj+zMlZc58tzDH8LDw0lMTMxTDwsLIyEh\noUh6EBERkcIxDGOlZVkRZzpPK1oiUnwObYe3Lstbf2g9XNDglF873cyrXDOXbuONKctIaDCfG4xu\nhCaHA+EAbDAs9poABlPGDKZKaPlzfpSz4XQ6z6ouIiIiJY+ClogUraP74O22kJlqr9+7DGo2P+XX\nTjfzKle2y82d42cRf9RJWs2VDHf1goRrPMcXmRaZBvTv3ITooWf8F1F+43A48l3RcjiKZkVNRERE\n/E9BS0T8L+MIfHBlzgrWyW6fBw3an/JrZ5p5lWvLzkPc/+Zc9ly4ikuOV6F/WhNI6AXA34bFphM/\nt5oQ3YsWYdV99VSFFhMTY/uNFkBISAgxMTGn+ZaIiIiUJApaIuIfWRnwxSBw/mGv3zgFmvU+7VfP\nNPMq13s/rmLK4rU4w2Zzp2sw7PX+/mqlaZFsQK2qIUx/8lrKBQac2/P4UO6GF9p1UEREpPTSZhgi\n4jtuF0y9FTb+aK8PngiX3HDar+Y786pDR7q8MBbjpB0AU9KOM+zZ70mulEDFkD30+buz9xpYLDbB\nbcD9g9sxsGtT3zyXiIiIyAnaDENEioZlwazHYfkH9nrvF6HzA2f8+plmXuX6fe1OxkxaxHbHHK4L\n6kCt5IaQ3BCArYaF80QW+/KZAdSsmv8W8CIiIiJFRUFLRArnt/+Dn1+01zpHQ6+xYBin/eqZZl7l\ncrst/v3ezyzf/RfJtZcx0tUPdvTzHF9sWmQY0Pnienx4S1eMM9xXREREpKgoaIlIwa2cBD9F22tt\nboBB78EZBvzmzLyKJjl+q6f2z5lXuXYdSOG2V2byd804mmVVoH/axbA9J2AdxGKNCRjwUtQVRFxU\nxyePJiIiIuJLCloicnqbfoKvR9prja6CG7+BwHJn/PqOObOI+8/pZ17l+mrBRj6cs5zt4TO4yzUE\nc8/lnmNrTIuDBgQGmPwwdjAhwUGFex4RERGRIqCgJSJ5JSyGz/rZazVbwe1zoXzoGb9ekJlXuTIy\ns7nhhR/YXX4LgaE7GZR5JWwZ5jm+0LTINmBk71bcdE3rQj2OiIiISFFT0BKRHHvXwftd7bWQGnDf\ncqh45tlTlmWx7JWX2PnrL56aYZoMmDzFNvMq15/x+3jsvflsC5tNb/MyOh1uDIcbA5BoWMSfeBPx\nw3/3Jax23u+LiIiInM8UtETKssMJMOGSvPWH1sEFjgJdIt+ZV8+9QL0uXfOca1kWL3/5BzM3r+Jg\nnT+41TWQXtv7e44vMy1SDWjuqM6sB3oQcIbffYmIiIicrxS0RMqa1APwdjs4fsRev+cPqNWyQJfI\nOpbKjFGRZB075qnlN/MqV9KRdP419kd211qOwxXIgGPtIH4gAEexWGGCZcBTIztx1WVhhX82ERER\nkfOEgpZIWZCRAh9eDUlb7fXb5oKjQ4Evs37SZ2z635e2Wt9PJhFar16+589auo3x3/3GtobTucno\nT6W/vffaaFjsOZHJvhkziAtCgwvch4iIiMj5TkFLpLTKPg5fDIHE3+31f30NF/Up8GXynXl1z300\nHTQ43/OzXW7uHD+LdVmrsSonMDyzF/w13HN8kWmRacC1nRrz4LDL872GiIiISEmnoCVSmrhd8O0d\nsOE7e33Qe3DpjQW/TFYWPz/8IIe3bvHUKtVvQK93388z8yrX1l2HuOfNWcSHz6Tr8ZZcn9wUDjUF\nYI9hsfHE6tWE6F60CDvz5hoiIiIiJZmClkhJZ1kw+ylY9p693mssdHnwrC6V78yrN9+ieotT/3br\n/R9X88Xy39lXdzF3Zg+m14nfXgGsNC2SDahZNYTpT15LucCAs+pHREREpKRS0BIpqRb9BxaMsdc6\n3Q+9XwTDKPBlzmbmVa6jaZkMefZbdtdeRi3LYuCxzrA151XCTCwWm+A24L7Bbbmua7OCP5OIiIhI\nKaGgJVKSrPoCpt1vr7UeDoPfB7Pgq0VnO/Mq1+/rdvHsl3PZ2nA61wf1pOfuTp5jWw0L54nXA798\nZgA1q1YscD8iIiIipY2Clsj5bvNMmPwve61hd4icCoH5/17qVM5m5lUut9vi8fd/5pekJWRWiWfk\n8X702TzCc3yJaZFuQIMqFls+eQqn00n7bx3ExMQQGRl5yuuKiIiIlGYKWiLno8Ql8Glfe+3C5nDH\nfChf6awudbYzr3LtPnCUm1+dxtaG02l3uAnDky6GpJzXAJOw+NMEDHjpziv4a+XPREVFkZaWltN+\nYiJRUVEAClsiIiJSJhmWZRX45IiICCsuLs6P7YiUYfs2wHud7bUKVeG+FRB64Vlf7mxnXuWavGAj\nb/8yn911FxG1ZQgBeMPYGtPioAGBASZTxw4mJDgIgPDwcBITE/NcKywsjISEhLPuXUREROR8ZRjG\nSsuyIs50nla0RIrT4USY0CZv/cG1UDXsrC+XvG0b8+69y1Y73cyrXBmZ2YwY8wNbqv7GBUYWg1Kv\nhC3DPMcXmhbZBozs1Yqb+rTO832n05nvdU9VFxERESntFLREitqxg/B2O8hIttfvWQK1Wp315dxZ\nWfz8yIMc3uKdeRVavz693514yplXuf6M38dDH05na8Pp9De60nlXF8+xRMMi/sRi1of/7ktY7VNv\nkuFwOPJd0XI4HGf5NCIiIiKlg4KWSFE4fhQ+6gkHNtvrt86CsM75f+cMCjPzCnJ2HHz5yz+YmjCP\nY1W3cNvxgfTdfIPn+DLTItWA5o7qzHqgBwGn+R1XrpiYGNtvtABCQkKIiYk5y6cSERERKR0UtET8\nJfs4xA6DHb/Z6zd8Bc37FeqSafv3M2PUjbZa00FDuPSeU8+8ypV0JJ0R475lS6OfaHXEwYikCEhq\nDkAqFstNsAx4KrITV7U9u9cWcze8GD16NE6nE4dDuw6KiIhI2abNMER8ye2G76Ng3RR7/bp34LKR\nhbpkfjOvME0GnmHmVa5Zy7Yx7qfp7Kr3Gzdtu5ZK2d75VhsNiz0nFqy+GTOIC0KDC9WjiIiISFmh\nzTBEioplwdxn4I//2us9X4CuDxf6soWZeZUr2+Xmzv+byR9BswkJSGdEam/4a7jn+CLTItOAazs1\n5sFhlxe6RxERERHJn4KWSGEtngDznrPXOt4L17wEhlGoS2YdS2XGTSPJSk311Aoy8yrX1l2HuPPt\n79jaaAZXZrTj1r3dPMf2GBYbT1xiQnRPWoTVKFSPIiIiInJmCloiZ2N1LPz4j99DtRoCQz8CM6DQ\nly3szKtc7/+4monrfiCl6maijg+h7ybv5hYrTYtkAy68IITpT11LucDC9ykiIiIiBaOgJXImf82G\nr0bYa2FdYdR3EHj67dNPp7Azr3IdTctk8HPfsLnxDzRJrcsNB7vAwZwdBzOxWGyC24B7B7VlULdm\nhe5TRERERM6egpZIfpxL4ZNr7LXqTeHOnyG4cqEvm+/Mq3r16P3eB2eceZXr93W7+Pc3U9hZbxHX\nB/ak11/Xe45tNSycJ14P/GL0AGpVq3iKq4iIiIiIPyloieTatxHe62Svla8CD8RBaM1zunRhZ17l\ncrstHn//Z37KmEq5wDRGHu0Hm72rbEtMi3QDOrWqx4e3dsUo5G/ERERERMQ3FLSkbEt2wput89aj\n/4RqDc/p0vnNvGoyaDCX3XNfga+x+8BRbnx9MvENZ9Lh0MXcdvBKz7EkLP40AQNi7ryCy5vXOad+\nRURERMR3FLSk7Dl2EP4bAemH7fW7f4fa+YSus5DvzCvDyJl5dcEFBb7ONz9v4uU/vuJwtU1EZQym\n30mbW6wxLQ4aEGAa/DBuCCHBQefUs4iIiIj4noKWlA3HU+HjXrB/o71+y0wI73LOl9+7Mo5FTz9p\nqxV05lWujMxsho+dyqr6U2hwrCY3HLgSDrTyHF9oWmQbENmrFTf3ObdAKCIiIiL+paAlpVd2Jvxv\nOGz/xV4fEQst+p/z5fOdedW+A13GjCvQzKtca+L3cffnsTjrLuJauvLAZu9g4UTDIv7EpT74d1/C\na1c5575FRERExP8UtKR0cbvhh3tg7WR7fcBb0O5mn9ziXGdeQc4rhi9/uYTPDn6OEZTKbSkDIcX7\neuAy0yLVgIsaVGNWdE8CziK4iYiIiEjxU9CSks+yYN5zsOQte/3qZ6H7Yz65xbnOvMqVdCSdIS9/\nQXzDWbQ+1ITb913tOZaKxXITLAOejOzI1W3DfdG6iIiIiBQDBS0puZa8DXOfsdc63AN9XgYfbG+e\nM/PqIQ5v+ctTO9uZV7lmL9vOkws+4VDVTYxyXWvb3GKjYbHnxILVN2MGcUFo8Dn3LiIiIiLFS0FL\nSpY1k+F7+8oSLQfBsE/ADPDJLXbMmU3cf16z1c5m5lWubJeb216bxoILJlErvRr/2t8b9ns3sVhk\nWmQa0LdjIyZd394nvYuIiIjI+UFBS85/W+bC/66318K6wMjvIMg3qz9pBw4wY+S/bDVX8xY8+eNP\nOJ1OHH37ERMTQ2Rk5BmvFb/rMCM/+ISd9X7nyvR2RO/xrl7tMSw2GoABE6J70iKshk/6FxEREZHz\ni4KWnJ92Ls/Zjv1k1RpD1C8Q7Jud9yzLYvmrL+P85Wdv8cTMq6kzZhAVFUVaWhoAiYmJREVFAZwy\nbL3/4ype3/Ee7qAUolKG2Da3WGlaJBtQo0oFpj/dn3KBvll9ExEREZHzk2FZVoFPjoiIsOLi4vzY\njpRp+zfDux3stXKh8MAqqFTLZ7fZt3Ilvz39hK3W6dnnqd+1m+dzeHg4iYmJeb4bFhZGQkKC5/PR\ntEyuHfcp28Jn0zSlAX13e2dyZWHxuwluA+4d1JZB3Zr57BlEREREpHgYhrHSsqyIM52nFS0pXsk7\n4c2L89ajV0O1Rj67TX4zr2q3b0/XF8ZhBORdXXI6nfleJ7e+eN0u7v3pXZKqbeZ6qwfXnrS5xVbD\nwnlic4svRg+gVrWKPnsOERERESkZFLSk6B1Lylm5OnbAXr/rN6hziU9vteHzSWyM/cJWK8jM2AHY\nrgAAFL9JREFUK4fDkc+KlkGHm56n3sTBVD1emVH7+8F+b79LTIt0Azq2rMuHt3XD8MHOhyIiIiJS\nMiloSdHIPAYf94Z96+31m6dDw275f6eQkrdvZ949UbbapXffS9PBQwp8jZiYGM9vtCpcUIvW0Y+y\nq+4Sah/IYtBJq1eHsFhtAga8eEd32reo66vHEBEREZESTEFL/Cc7E766AbYtsNeHfwEtB/r0Vu7s\nbH555CEO/bXZUyvszCvI2fBi7V74NuBnssuncN2WegQc8QasNabFQQNM0+D7cUOoGBzkk+cQERER\nkdJBQUt8y+2GH++DNf+z1/u/CRG3+vx2CXPnsOL1/7PVrn7jLaq3PLuZV7kyMrMZ9PJnrKs9Awe1\nuTOht+34QtMi24Abe7bklr5tCt23iIiIiJRuClpy7iwL5r8Ai9+0168aDVc87vPb5Tfzqsl1g7js\n3vsLfc212/Yz8pvXSaq6hWuzunLVSa8HJhoW8Sc2t/jg330Jr+2b7eVFREREpPRS0JLC++NdmPOU\nvdY+CvqOBx9vBJHvzCtg4NdTKX/BBYW+5vNf/sLHaW8TmhXCbfsGwr62nuPLTItUA5rWr8qsB3sR\nYJrn9AwiIiIiUnYoaMnZWfsNfHenvdbyOhj6CQT4/n9O+c68euY56nfrXuhrJqWk0+8/b7GrzlJa\nH2pC9D7v6lUqFstNsAx4MrIjV7cNL/R9RERERKTsUtCSM9s6H2KH2muOTjDqBwgK9vntcmZejSIr\n9ainVvvy9nQdk//Mq4KasXQr0cteIrN8Cjcdu5bKJ70euNGw2HNiwerrFwZRtZLvn0tEREREyg4F\nLcnfrjj4qIe9VjU8Z9ZVsH9+o1TYmVenk+1y8683vmRJpe+plV6NqIQ+tuOLTItMA/p2bMSk69sX\n+j4iIiIiIidT0BKvA3/BO/8IG4EV4ME/oVJtv9wyv5lXl9x1D82GDD3FNwomftdhBn/5MoeqbuXK\n1HZE7/KuXu0xLDYagAFvPtCTluE1zuleIiIiIiL/pKBV1h3ZBW+0ylt/YBVUb+yXW55q5lWvdycS\nGHxur+y98f3vvLb/dYKzyxG1dwjsbec5ttK0SDagRpUKTH+qP+WCCv8aooiIiIjI6SholUVph+Dd\njpC6z16PWgh1L/XbbX098ypXanomV7/6OrvrLKdpSgOid3tXr7Kw+N0EtwH3DmrLoG7NzuleIiIi\nIiIFoaBVVmQfh59fhCVv2es3TYNGV/jttv6YeZXrt7WJ3PLrc2SWO8qw4z2oc9LmFlsNC+eJzS0+\nH92f2tVCz/l+IiIiIiIFpaBVmrldsHgCLBhjr1//GbQa7LfbWpbF8vGv4Px5ga1+LjOvcrndFndO\nnMxs8xuqHq/MXdv72Y4vMS3SDejYsi4f3tYNw8fzvERERERECkJBq7SxLIj7BGY84q0FVYQRX0CT\nHqf+ng/sW7WS357y7cyrXH8fPErvj8dw+IJttE9qRfRB7+rVISxWm4ABL97RnfYt6p7z/URERERE\nzoWCVmmxbip8e7u95ueVK4CsY8eYefNIMo/6duZVro/nLue5HS8T5A7kzr2DCdxzuefYGtPioAGG\nAd+/OJSKwUHnfD8REREREV9Q0CrJts6DyZHgOu6tDXgL2t6Ukz78aMOXn7Pxi89ttT6ffEalevXP\n+doZmdn0fP11dlRbSoPUWkTvvMF2fKFpkW3AjT1bckvfNud8PxERERERX1PQKmmcS+HrUXBsv7fW\naxx0ug9M/25X7q+ZV7lWbv2bYbOfIDMolX5pXRiwzxuwEg2L+BObW0x8rA8N65zbb71ERERERPxJ\nQask2LsOptwKSVu9tW6PwhVPQmC5Al8mNjaW0aNH43Q6cTgcxMTEEBkZedrv+HPmFeRsnPHwl98x\nJe1LQrNCuHvbQNvxZaZFqgHNGlRjVnRPAkzznO8pIiIiIuJvClrnq6Rt8F0U7I7z1i6/A3qNhXIV\nz/pysbGxREVFkZaWBkBiYiJRUTmrU/mFLX/NvMqVlJJO9/dHk1xlB60PNyF6r3f16hgWy0ywDHji\nxo70aBfuk3uKiIiIiBQVw7KsAp8cERFhxcXFnflEKZyUPfDjfbDtpG3RW18PfcdDSLVzunR4eDiJ\niYl56mFhYSQkJACQfvAg0yPtv4fy1cyrXJMXr+bR9WMxLZNR2/pRJcs732qjYbHnxILV1y8Momql\nc18xExERERHxJcMwVlqWFXGm87SiVdzSDsGMR2HDd95ak14w8G2oXMdnt3E6naesLxv/Cs4F8211\nX8y8yuVyuen39husr/A7tdKrEZ1gD3OLTItMA/p2aMSk4e19ck8RERERkeKkoFUcjqfC3Gdg5afe\nWv32MGQiVGvkl1s6HA7bilbralV5NqItgCdk+WrmVa5Nzv30mfYQ2UHpXHGkrS1g7TEsNhqAAW/c\n35NWDWv47L4iIiIiIsVNQauoZB+HX16CxW96azUugmGfQO2L/X77mJgY7r/7bp5t04oGod7X9dx1\n6zH8o098MvMq1zNTp/Fp0qcEZ5fj3vghtmMrTYtkA6pXrsD0p/tTLsi/OyWKiIiIiBQHBS1/crtg\n8QRYMMZbC60Nwz8HR4cia2Pnbwsp9/mnfNDZe89Xtyfy8AsvnHHXwYI6mnacDu88zpHKTpqkNCB6\nt3f1KguL301wG3DPoLYM7tbMJ/cUERERETlfKWj5mmXBys9g+kPeWmAFGPElNO1ZZG0c27ePJWOe\nI3nbNk+t69gXqdOhIwDX++g+P61cx91xz4EF1x/qSZ3dnT3H4g2LxBObW3w+uj+1q4We4ioiIiIi\nIqWLgpavrP8Opt5qrw37BC72zTDfgnC7XGyY9Bmbv/7KU2ty3SDa3BFFQLmCz9s6433cFkM+mMAK\nYyFVj1ciert9c4slpkW6AR1a1uWD27phGIbP7i0iIiIiUhIoaJ2L+PkweSRkp3tr/d+EdrdAEYaL\n/Wv+ZOHjj3k+h9atR9exL1KpQQOf3mf7viSunHofrsDjtD/YiuiD3oB1CIvVJmDAi3d0p32Luj69\nt4iIiIhISaKgdbacy+CbmyB1r7fWcwx0fgDMotvY4XjKEZaPf5W9K5Z7ahGPPEbDa/r4/F4vzZjB\nO7s+IsgdyF3xgwm0vM+5xrQ4aOTkyu9fHErF4CCf319EREREpKRR0CqIfRtgyq1w8C9vresjcOWT\nEFi+yNqwLIv4aT/w57vveGr1u3Un4uFHCapY0af3ysjM4vJ3HuNQiJMGx2oR7bS/HrjQtMg24F89\nWnJrvzY+vbeIiIiISEmnoHUqh7bDd3fBLu+KERG3Q6+xUL5oN3VI3r6dRaOfJOPQIQACgoO54pXx\nVG/R0uf3mrd+E7csfhos6HeoC00SvZtbJBoW8Sc2t5j4WB8a1vHNQGMRERERkdJGQetkR/fCj/dD\n/Dxv7eKh0O81CKlWpK1kZ2Sw+p23SZg7x9vKLbfRfMQNGKbp8/vd8NnbLDr+M6FZIUTH21evlpkW\nqQY0qV+VWQ/2IsAP9xcRERERKU0UtNIOwcx/w/qp3lrjHnDdf6Fy0W/osPO3hSyNGef5XKPVxXQc\n/SwVqlf3+b12HUqm0+Qo3AFZtD7chOi93oB1DItlJlgGPHFjR3q0C/f5/UVERERESquyGbQyj8Hc\nZyHuY2+tXgQM+QCqNy7ydtL272PxmOdJjo/31E6eeeVrb8yfzWvbJmJaJqN29KNKlvdVyI2GxZ4T\nC1ZfvzCIqpWC/dKDiIiIiEhpVnaCVnYm/Poy/P4fb616U7j+U6jdusjbcbtcbPh8Epsn/89T88fM\nq1zZLhfXvDOezeWXUzO9GtEJ9tcDF5kWmQb0ad+ISSPa+/z+IiIiIiJlSekOWm4XLHkb5j/vrVWs\nCcM/h7BOxdJSUc28yrXh790M/e55jgYl0TK9kW248F7DYoMBGPDG/T1p1bCGX3oQERERESlrSl/Q\nsixY9Tn8FO2tBZSHEV9Cs97F0lJmSgrLxr9SJDOvcr06fxpvbfuUIFcgffZ0puEx7+/NVpoWyQZU\nqxzM9KcHUC6o6OZ/iYiIiIiUBaUnaG34AabcbK8N/RhaDyuWdizLYtu0H1n97n89NX/NvMqVfCyN\n6/73IvHuTdROq050onf16m/DYrORs7nF3dddxpDuF/mlBxERERERKQ1Ba8dvMGmA9/O1/4GI28Aw\niqWdIzu289vTT5FxKAnw78yrXDPWr+au31/Ewk3HA63pl+QNWGtMi4MGNGtQjcm3d9fmFiIiIiIi\nRaDkB63qTXPmXEXcBmbxvAKXnZHB6nf/S8Kc2Z6aP2deAbjdbu6Y+i5zDi+gYlYwkc5rqJZZBYCj\nWPxpQqYBt/Vrw4irW2AUU/AUERERESmLSnTQio2NZfTo0TidThyOGGJiYoiMjCyy+xflzKtcf+37\nm0HfPktKwCEap9Qnerd39Wq7YbHDANM0ePuhXjStX7RDlkVEREREJEeJDVqxsbFERUWRlpYGQGJi\nIlFRUQB+DVtFPfMq1xsLp/Pa5o8JcJv02Nue5inhnmMrTIsUA7pf0oAJN3QguFyJ/a9VRERERKRU\nMCzLKvDJERERVlxcnB/bKbjw8HASExPz1MPCwkhISPDpvYp65lWuoxk5m1v8lbWJ6hlVGJHQi0Ar\nJ0QdwGKDCS4DnorsxFVtw/zWh4iIiIiI5DAMY6VlWRFnOq/ELn04nc6zqhfGP2deVaxTl27jYvw2\n8yrX3L/WcOsvLwBw2aGLiN7vfT1wg2Gx14S6NUKZdPdV1Kzqnx0MRURERESk8Eps0HI4HPmuaDkc\njnO6bv4zrx6l4TV9z+m6Z2JZFnd9/y4zDswnOLscw3b1oG76hQBkYLHShAwDhl/VnFv7tSHAT5ts\niIiIiIjIuSuxQSsmJsb2Gy2AkJAQYmJizvpap5559QhBFUN90u+pxB/cy8CpozliHKJBai2id3pX\nr5yGRfyJ2Vev3Xs1bRrX9GsvIiIiIiLiGyU2aOVueOHdddBx1rsOFsfMq1xvLZ7Bq+s/wrAMuu+7\njEsO9/YcW2VaHDagbbNafHdTFypW8N/vwERERERExPdK7GYYhVUcM69ypWamM+ircWzK2ESVzFCG\nJvYgNLsCAIexWGtCtgEPDG3HgM5N/dqLiIiIiIicvVK/GcbZ+ufMq+qtWtFp9HN+nXmVa378Gm5e\n8AIALZIbEr3H+3rgFsNipwGVK5Zj4gM9aVCzst/7ERERERER/yrVQSu/mVddxrxI3Y7+nXkF4Lbc\n3PfT+0zbM48gVyADdnen4bG6AGRjEWfCMQP6dWzM+4PbEhQY4PeeRERERESkaJS6oGW5XGz4YhKb\nvjpp5tXA62hz511+nXmVa9uhvQyY+jRHrMPUTqtOdKJ39epvw2Lzic0txt3enQ4t6/q9HxERERER\nKXqlJmgdWLuGXx9/DE785qyoZl7lenfpTGLWfAgWdDzQmvZJ13iOrTUtDhjQrEE1Jt/enaqVgouk\nJxERERERKR4lPmgd2ryZBQ/e7/lcFDOvcqVmpjHk6xfZkLaJilnBRDr7Uj2zCgBHsfjThEwDbu3X\nhhuuboFhGEXSl4iIiIiIFK8SH7SCQkNp2Kcvl0Td5feZV7l+2b6WkfOeB6BxSn2id3tfD9xuWOww\nwDQN3n6oF03rVyuSnkRERERE5PxR4oNWpfr1iXj4Ub/fx225iZ75Pt/vmkeA26T3no40Twn3HF9h\nWqQY0P2SBky4oQPB5Ur8f7QiIiIiIlJISgNnsOPwXq6d+hRH3MlUz6jCvQnXE2jl7BB4AIsNJrgM\neDKyI1e3DS/eZkVERERE5LygoHUK76+YybhVOZtbXHboIrrt7+M5ttGw2GNC3eqhTLrnKmpWrViM\nnYqIiIiIyPlGQeskqZnpDJ0ylvWpmwnOLsewnT2pm1EDgAwsVpqQYcDwq5pza782BJhmMXcsIiIi\nIiLnIwUt4NeEtUTOydncokFqLaJ3eje3cBoW8SdmX71279W0aVyzuNoUEREREZESoswGLZfbxcNz\nJvKtcx6GZXDFvrZccriZ5/gq0+KwAZc1rcV3N3ehYgX/DzsWEREREZHSocwFrR3Je+g35UlS3ClU\nyQzl9oRBVHTlDBA+jMVaE7INeGBoOwZ0blrM3YqIiIiISElUZoLWxJUzGRv3IQAtkhtyy55+nmNb\nDIudBlSqWI737++Jo1bl4mpTRERERERKgVIdtFKOH2P4dy+yLmUzQe5ABu66gvBjdQDIxiLOhGMG\n9O3YiPcHtyMoMKCYOxYRERERkdKgVAathc413DjrBQBqp1UnOtG7ucUew2LTic0txt3enQ4t6xZT\nlyIiIiIiUlqVmqCV7Xbx6LyJTE2YBxZ0OHgxHQ5e7Dm+1rQ4YECzBtWYfHt3qlYKLsZuRURERESk\nNCvxQSsp/QhdYx8gxXWUilnBjErsT9WsUACOYvGnCZkG3NqvDTdc3QLDMIq5YxERERERKe1KdNBK\nTc+k/8sfc2GlKtyy+1pPfbthscMA0zR4+6FeNK1frRi7FBERERGRsqZEB60l63fT9GhdWqXUA2CF\naZFiQLc2DZjwrw4ElyvRjyciIiIiIiVUiU4irRtdiKtqBX5OTscy4MnIjlzdNry42xIRERERkTKu\nRAetOtVDiX3uuuJuQ0RERERExMYs7gZERERERERKGwUtERERERERH1PQEhERERER8TEFLRERERER\nER9T0BIREREREfExBS0REREREREfU9ASERERERHxMQUtERERERERH1PQEhERERER8TEFLRERERER\nER9T0BIREREREfExBS0REREREREfU9ASERERERHxMQUtERERERERH1PQEhERERER8TEFLRERERER\nER9T0BIREREREfExBS0REREREREfU9ASERERERHxMQUtERERERERH1PQEhERERER8TEFLRERERER\nER8zLMsq+MmGcQBI9F87IiIiIiIi57Uwy7IuPNNJZxW0RERERERE5Mz06qCIiIiIiIiPKWiJiIiI\niIj4mIKWiIiIiIiIjyloiYiIiIiI+JiCloiIiIiIiI8paImIiIiIiPiYgpaIiIiIiIiPKWiJiIiI\niIj4mIKWiIiIiIiIj/0/aYcKGmpj73oAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6d5cf50ac8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Coefficients: \n", " [ 0.49990909] [ 0.49585212] [ 0.46990909] [ 0.46369623] [ 0.49990909]\n", "Super parameters: \n", " () (0.90000000000000002,) (0.29999999999999999,) (0.69999999999999996, 0.10000000000000001) (0.0,)\n", "Mean squared error: \n", " 1.25 1.25 1.26 1.26 1.25\n", "Variance score: \n", " 0.67 0.67 0.66 0.66 0.67\n" ] }, { "data": { "image/png": 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L1+k9eRVOBtSxgmdGwTpmMjjllEbc41uI4zIAA2q8wKBanbK/YFmtsHo4bJ9u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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6d5cde8d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# The data is from https://gist.github.com/endolith/3299951 , might change to use\n", "# the dataset in sns instead: https://seaborn.pydata.org/examples/anscombes_quartet.html\n", "x1 = np.array([10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0]).reshape(-1, 1)\n", "y1 = np.array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])\n", "\n", "x2 = np.array([10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0]).reshape(-1, 1)\n", "y2 = np.array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])\n", "\n", "x3 = np.array([10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0]).reshape(-1, 1)\n", "y3 = np.array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])\n", "\n", "x4 = np.array([8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 19.0, 8.0, 8.0, 8.0]).reshape(-1, 1)\n", "y4 = np.array([6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.50, 5.56, 7.91, 6.89])\n", "\n", "fit_and_plot(x1, y1)\n", "fit_and_plot(x2, y2)\n", "fit_and_plot(x3, y3)\n", "fit_and_plot(x4, y4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L1-norm:\n", "\n", "$$ \\left\\| \\boldsymbol{x} \\right\\| _1 := \\sum_{i=1}^{n} \\left| x_i \\right| $$" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " <th>sex</th>\n", " <th>bmi</th>\n", " <th>bp</th>\n", " <th>s1</th>\n", " <th>s2</th>\n", " <th>s3</th>\n", " <th>s4</th>\n", " <th>s5</th>\n", " <th>s6</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>151.0</th>\n", " <td>0.038076</td>\n", " <td>0.050680</td>\n", " <td>0.061696</td>\n", " <td>0.021872</td>\n", " <td>-0.044223</td>\n", " <td>-0.034821</td>\n", " <td>-0.043401</td>\n", " <td>-0.002592</td>\n", " <td>0.019908</td>\n", " <td>-0.017646</td>\n", " </tr>\n", " <tr>\n", " <th>75.0</th>\n", " <td>-0.001882</td>\n", " <td>-0.044642</td>\n", " <td>-0.051474</td>\n", " <td>-0.026328</td>\n", " <td>-0.008449</td>\n", " <td>-0.019163</td>\n", " <td>0.074412</td>\n", " <td>-0.039493</td>\n", " <td>-0.068330</td>\n", " <td>-0.092204</td>\n", " </tr>\n", " <tr>\n", " <th>141.0</th>\n", " <td>0.085299</td>\n", " <td>0.050680</td>\n", " <td>0.044451</td>\n", " <td>-0.005671</td>\n", " <td>-0.045599</td>\n", " <td>-0.034194</td>\n", " <td>-0.032356</td>\n", " <td>-0.002592</td>\n", " <td>0.002864</td>\n", " <td>-0.025930</td>\n", " </tr>\n", " <tr>\n", " <th>206.0</th>\n", " <td>-0.089063</td>\n", " <td>-0.044642</td>\n", " <td>-0.011595</td>\n", " <td>-0.036656</td>\n", " <td>0.012191</td>\n", " <td>0.024991</td>\n", " <td>-0.036038</td>\n", " <td>0.034309</td>\n", " <td>0.022692</td>\n", " <td>-0.009362</td>\n", " </tr>\n", " <tr>\n", " <th>135.0</th>\n", " <td>0.005383</td>\n", " <td>-0.044642</td>\n", " <td>-0.036385</td>\n", " <td>0.021872</td>\n", " <td>0.003935</td>\n", " <td>0.015596</td>\n", " <td>0.008142</td>\n", " <td>-0.002592</td>\n", " <td>-0.031991</td>\n", " <td>-0.046641</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " age sex bmi bp s1 s2 s3 \\\n", "151.0 0.038076 0.050680 0.061696 0.021872 -0.044223 -0.034821 -0.043401 \n", "75.0 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163 0.074412 \n", "141.0 0.085299 0.050680 0.044451 -0.005671 -0.045599 -0.034194 -0.032356 \n", "206.0 -0.089063 -0.044642 -0.011595 -0.036656 0.012191 0.024991 -0.036038 \n", "135.0 0.005383 -0.044642 -0.036385 0.021872 0.003935 0.015596 0.008142 \n", "\n", " s4 s5 s6 \n", "151.0 -0.002592 0.019908 -0.017646 \n", "75.0 -0.039493 -0.068330 -0.092204 \n", "141.0 -0.002592 0.002864 -0.025930 \n", "206.0 0.034309 0.022692 -0.009362 \n", "135.0 -0.002592 -0.031991 -0.046641 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " <th>sex</th>\n", " <th>bmi</th>\n", " <th>bp</th>\n", " <th>s1</th>\n", " <th>s2</th>\n", " <th>s3</th>\n", " <th>s4</th>\n", " <th>s5</th>\n", " <th>s6</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>4.420000e+02</td>\n", " <td>4.420000e+02</td>\n", " <td>4.420000e+02</td>\n", " <td>4.420000e+02</td>\n", " <td>4.420000e+02</td>\n", " <td>4.420000e+02</td>\n", " <td>4.420000e+02</td>\n", " <td>4.420000e+02</td>\n", " <td>4.420000e+02</td>\n", " <td>4.420000e+02</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>-3.639623e-16</td>\n", " <td>1.309912e-16</td>\n", " <td>-8.013951e-16</td>\n", " <td>1.289818e-16</td>\n", " <td>-9.042540e-17</td>\n", " <td>1.301121e-16</td>\n", " <td>-4.563971e-16</td>\n", " <td>3.863174e-16</td>\n", " <td>-3.848103e-16</td>\n", " <td>-3.398488e-16</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>4.761905e-02</td>\n", " <td>4.761905e-02</td>\n", " <td>4.761905e-02</td>\n", " <td>4.761905e-02</td>\n", " <td>4.761905e-02</td>\n", " <td>4.761905e-02</td>\n", " <td>4.761905e-02</td>\n", " <td>4.761905e-02</td>\n", " <td>4.761905e-02</td>\n", " <td>4.761905e-02</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>-1.072256e-01</td>\n", " <td>-4.464164e-02</td>\n", " <td>-9.027530e-02</td>\n", " <td>-1.123996e-01</td>\n", " <td>-1.267807e-01</td>\n", " <td>-1.156131e-01</td>\n", " <td>-1.023071e-01</td>\n", " <td>-7.639450e-02</td>\n", " <td>-1.260974e-01</td>\n", " <td>-1.377672e-01</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>-3.729927e-02</td>\n", " <td>-4.464164e-02</td>\n", " <td>-3.422907e-02</td>\n", " <td>-3.665645e-02</td>\n", " <td>-3.424784e-02</td>\n", " <td>-3.035840e-02</td>\n", " <td>-3.511716e-02</td>\n", " <td>-3.949338e-02</td>\n", " <td>-3.324879e-02</td>\n", " <td>-3.317903e-02</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>5.383060e-03</td>\n", " <td>-4.464164e-02</td>\n", " <td>-7.283766e-03</td>\n", " <td>-5.670611e-03</td>\n", " <td>-4.320866e-03</td>\n", " <td>-3.819065e-03</td>\n", " <td>-6.584468e-03</td>\n", " <td>-2.592262e-03</td>\n", " <td>-1.947634e-03</td>\n", " <td>-1.077698e-03</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>3.807591e-02</td>\n", " <td>5.068012e-02</td>\n", " <td>3.124802e-02</td>\n", " <td>3.564384e-02</td>\n", " <td>2.835801e-02</td>\n", " <td>2.984439e-02</td>\n", " <td>2.931150e-02</td>\n", " <td>3.430886e-02</td>\n", " <td>3.243323e-02</td>\n", " <td>2.791705e-02</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>1.107267e-01</td>\n", " <td>5.068012e-02</td>\n", " <td>1.705552e-01</td>\n", " <td>1.320442e-01</td>\n", " <td>1.539137e-01</td>\n", " <td>1.987880e-01</td>\n", " <td>1.811791e-01</td>\n", " <td>1.852344e-01</td>\n", " <td>1.335990e-01</td>\n", " <td>1.356118e-01</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " age sex bmi bp s1 \\\n", "count 4.420000e+02 4.420000e+02 4.420000e+02 4.420000e+02 4.420000e+02 \n", "mean -3.639623e-16 1.309912e-16 -8.013951e-16 1.289818e-16 -9.042540e-17 \n", "std 4.761905e-02 4.761905e-02 4.761905e-02 4.761905e-02 4.761905e-02 \n", "min -1.072256e-01 -4.464164e-02 -9.027530e-02 -1.123996e-01 -1.267807e-01 \n", "25% -3.729927e-02 -4.464164e-02 -3.422907e-02 -3.665645e-02 -3.424784e-02 \n", "50% 5.383060e-03 -4.464164e-02 -7.283766e-03 -5.670611e-03 -4.320866e-03 \n", "75% 3.807591e-02 5.068012e-02 3.124802e-02 3.564384e-02 2.835801e-02 \n", "max 1.107267e-01 5.068012e-02 1.705552e-01 1.320442e-01 1.539137e-01 \n", "\n", " s2 s3 s4 s5 s6 \n", "count 4.420000e+02 4.420000e+02 4.420000e+02 4.420000e+02 4.420000e+02 \n", "mean 1.301121e-16 -4.563971e-16 3.863174e-16 -3.848103e-16 -3.398488e-16 \n", "std 4.761905e-02 4.761905e-02 4.761905e-02 4.761905e-02 4.761905e-02 \n", "min -1.156131e-01 -1.023071e-01 -7.639450e-02 -1.260974e-01 -1.377672e-01 \n", "25% -3.035840e-02 -3.511716e-02 -3.949338e-02 -3.324879e-02 -3.317903e-02 \n", "50% -3.819065e-03 -6.584468e-03 -2.592262e-03 -1.947634e-03 -1.077698e-03 \n", "75% 2.984439e-02 2.931150e-02 3.430886e-02 3.243323e-02 2.791705e-02 \n", "max 1.987880e-01 1.811791e-01 1.852344e-01 1.335990e-01 1.356118e-01 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Adapted from http://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-py\n", "# Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>\n", "# Alexandre Gramfort <alexandre.gramfort@inria.fr>\n", "# License: BSD 3 clause\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "from sklearn import linear_model\n", "from sklearn import datasets\n", "\n", "diabetes = datasets.load_diabetes()\n", "X = diabetes.data\n", "y = diabetes.target\n", "\n", "df = pd.DataFrame(X, index=y, columns=diabetes.feature_names)\n", "display(df.head())\n", "display(df.describe())" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Computing regularization path using the LARS ...\n", "." ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: 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0.000000 0.000000 -5.718948 -7.011245 -10.012198 \n", "1 -197.756501 -226.133662 -227.175798 -234.397622 -237.100786 -239.819089 \n", "2 522.264847 526.885467 526.390594 522.648786 521.075130 519.839787 \n", "3 297.159737 314.389272 314.950467 320.342554 321.549027 324.390428 \n", "4 -103.946249 -195.105830 -237.340973 -554.266328 -580.438600 -792.184162 \n", "5 0.000000 0.000000 33.628274 286.736168 313.862132 476.745838 \n", "6 -223.926033 -152.477259 -134.599352 0.000000 0.000000 101.044570 \n", "7 0.000000 106.342806 111.384129 148.900445 139.857868 177.064176 \n", "8 514.749481 529.916031 545.482597 663.033287 674.936617 751.279321 \n", "9 54.767681 64.487418 64.606670 66.330955 67.179400 67.625386 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>10</th>\n", " <th>11</th>\n", " <th>12</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>10.0</td>\n", " <td>10.000000</td>\n", " <td>10.000000</td>\n", " <td>10.000000</td>\n", " <td>10.000000</td>\n", " <td>10.000000</td>\n", " <td>10.000000</td>\n", " <td>10.000000</td>\n", " <td>10.000000</td>\n", " <td>10.000000</td>\n", " <td>10.000000</td>\n", " <td>10.000000</td>\n", " <td>10.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>0.0</td>\n", " <td>6.011927</td>\n", " <td>66.366996</td>\n", " <td>88.891024</td>\n", " <td>102.249340</td>\n", " <td>95.154223</td>\n", " <td>92.101799</td>\n", " 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</tr>\n", " <tr>\n", " <th>max</th>\n", " <td>0.0</td>\n", " <td>60.119270</td>\n", " <td>361.894612</td>\n", " <td>434.757960</td>\n", " <td>505.659558</td>\n", " <td>511.348071</td>\n", " <td>512.044089</td>\n", " <td>522.264847</td>\n", " <td>529.916031</td>\n", " <td>545.482597</td>\n", " <td>663.033287</td>\n", " <td>674.936617</td>\n", " <td>751.279321</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 \\\n", "count 10.0 10.000000 10.000000 10.000000 10.000000 10.000000 \n", "mean 0.0 6.011927 66.366996 88.891024 102.249340 95.154223 \n", "std 0.0 19.011382 140.629650 168.930834 209.244879 226.881654 \n", "min 0.0 0.000000 0.000000 0.000000 -114.100980 -169.711394 \n", "25% 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "50% 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "75% 0.0 0.000000 0.000000 59.427335 143.452413 175.615962 \n", "max 0.0 60.119270 361.894612 434.757960 505.659558 511.348071 \n", "\n", " 6 7 8 9 10 11 \\\n", "count 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 \n", "mean 92.101799 86.331296 96.830424 99.732661 121.360930 121.390954 \n", "std 234.972742 269.681995 277.171838 282.475949 355.759653 364.973745 \n", "min -196.045443 -223.926033 -226.133662 -237.340973 -554.266328 -580.438600 \n", "25% 0.000000 -77.959687 -114.357945 -100.949514 -4.289211 -5.258434 \n", "50% 0.000000 0.000000 32.243709 49.117472 107.615700 103.518634 \n", "75% 192.414800 236.561723 262.377655 264.058883 311.940958 319.627303 \n", "max 512.044089 522.264847 529.916031 545.482597 663.033287 674.936617 \n", "\n", " 12 \n", "count 10.000000 \n", "mean 137.597406 \n", "std 435.780706 \n", "min -792.184162 \n", "25% 9.397198 \n", "50% 139.054373 \n", "75% 438.656985 \n", "max 751.279321 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th 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" <td>0.256910</td>\n", " <td>0.361472</td>\n", " <td>0.416415</td>\n", " <td>0.444238</td>\n", " <td>0.553343</td>\n", " <td>0.611484</td>\n", " <td>0.634554</td>\n", " <td>0.809934</td>\n", " <td>0.827459</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 \\\n", "|coef| 0.0 60.119270 663.669955 888.910243 1250.695364 \n", "|coef|/max(|coef|) 0.0 0.017375 0.191812 0.256910 0.361472 \n", "\n", " 5 6 7 8 \\\n", "|coef| 1440.798043 1537.065983 1914.570529 2115.737744 \n", "|coef|/max(|coef|) 0.416415 0.444238 0.553343 0.611484 \n", "\n", " 9 10 11 12 \n", "|coef| 2195.558855 2802.375093 2863.010804 3460.004955 \n", "|coef|/max(|coef|) 0.634554 0.809934 0.827459 1.000000 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>10</th>\n", " <th>11</th>\n", " <th>12</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Alphas</th>\n", " <td>2.148044</td>\n", " <td>2.012027</td>\n", " <td>1.024663</td>\n", " <td>0.7151</td>\n", " <td>0.294414</td>\n", " <td>0.200865</td>\n", " <td>0.15603</td>\n", " <td>0.045206</td>\n", " <td>0.012392</td>\n", " <td>0.011514</td>\n", " <td>0.004937</td>\n", " <td>0.002965</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 \\\n", "Alphas 2.148044 2.012027 1.024663 0.7151 0.294414 0.200865 0.15603 \n", "\n", " 7 8 9 10 11 12 \n", "Alphas 0.045206 0.012392 0.011514 0.004937 0.002965 0.0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"Computing regularization path using the LARS ...\")\n", "alphas, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True)\n", "\n", "df = pd.DataFrame(coefs)\n", "display(df)\n", "display(df.describe())\n", "\n", "# xx are the L1-norms\n", "xx = np.sum(np.abs(coefs.T), axis=1)\n", "\n", "# the last of xx is the maximum of L1-norms\n", "normalized_xx = xx / xx[-1]\n", "\n", "df = pd.DataFrame(np.array([xx, normalized_xx]), index=[\"|coef|\", \"|coef|/max(|coef|)\"])\n", "display(df)\n", "\n", "df = pd.DataFrame(alphas.reshape(1, -1), index=[\"Alphas\"])\n", "display(df)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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kSZLJJHEbP/M7IOsPMkP9IWfgjzGxZIyB+2k78H9Zrwdy2IFj2bGfXZdzng9R\n/uzPSr84p/yQx84un5UHXM61L1Z+0PEhrj1U+Qu1Uc7a+CLlBx0bqvwF2vhvbvkbbhx/I4VG+xwW\nsJ6eHgBKSkryWka8zQt9wgt1cJIb2q+zu5NoIkq4KHzmr/9D3MAN9XP26Ej2T09fD9FEFBuw54yW\nDTmCdpGRuIHnlzsSEfQFByVnA0lZ2B+mLFTGGP+YVLKWlbhlErihkroh3st+vNwpbQMrfLa3b6O9\nYwsdLdvo6tpOt40POm8g6QuH6igpmUw4vJBwqI5QdtIXHksoGBnxpC8X/d4N/9uRixsY3cnVFE/d\nn8lwDfQHN1Ny6IAPfvCDwHvbA+Vyyoi3eaFPeKEOThpu+8WTcXrjvanRrngfvYkzz/sSffTEe868\nHuJ4b6z3nPf64n30xnsz140mozms8aUL+ALnTbZKA6VUF1VT7C/OjKYNHD97VG3g2NnJ2dnJm99X\nmKNi8Xgn7R1v0tH+Bu0d2+joeDMzAuj3l1JRcS1rVhfT0hLg0Uf/hHB4LKFwHaFgNaZAv1OVi98b\n+t0jQ9H9mQyXF/qD48mhMWY/0AkkgLi1doExphp4EZgC7Afusda2mtSk3r8EPgj0AA9aa193Im4R\nESc1dzfTP7WfZFGSH73zo0wylkn2EllJXfysJC6d6MWT8Yt/0FlCvlBmumJxoDiTOBUHiqkqqqLY\nX5w5XhQo4oV/fAETN3zxC18cvChG1uqMZy+Wcc55Q6z8+HsP/x4k4Id/+8NBSdpAcleoyVo+WZug\nu3s37ZlEcBvd3bsZmC9VWjqdmpr3U1kxl8rKeZSWXokxfv7HV1cAUFOz0rngRUSkIDieHKattNae\nynr9CPDf1trvGmMeSb/+GnAHMD39sxj42/SjiIintfS1sKl5E5uObWJT8yYOdByAFaljf771zwEy\niVp24lYcKKYsVEaNvybzfnGgODOaljk3/XrgeXbil/36vSZdLz38EgAPXP1ALpuD4LEgALPGzMrp\ndd0kGj111qjgWyQSqW0fAoEqKivnMrbuTioq51FRfi3BYIXDEYuISKErlOTwbB8hc9vD08AqUsnh\nR4BnbOpbohuMMVXGmAZr7TFHohQRyZOuaBdbj29lY/NGNh7byM7WnQCUBktZMHYB98y4h+8/9n18\n3T5++a+/1NLqHpdMRunq2n5mVLB9G719B4HU9wPLyq6ivv5j6VHBuRQXT9G+dyIi8p4VQnJogf80\nxljg762goGtsAAAgAElEQVS13wfGZiV8zcDY9PPxwKGssofT7w1KDo0xnwM+BzBp0qQ8hi4ikhv9\niX62ndjGxmMb2di8kXdPvUvCJgj5Qsyrm8fvzPsdFjUs4uoxVxPwpX51/6jlRwAUB4qdDF1yzFpL\nf/+xTBLY3vEGnZ3vkEx/fzMcGktF5TzGT7iPioq5VJTPwe9XHxARkeErhORwmbX2iDGmDvgvY8z2\n7IPWWptOHC9ZOsH8PqS2sshdqLnx4IMPjkgZ8TYv9Akv1OFyxZNx3j39LhuPbWTTsU28ceINosko\nfuNnTs0cHprzEEsalnBd3XWE/eEhr+GG9stXjG6o+6VKJHrp6HznzPTQ9m30R48D4POFKC+/hgnj\nf52KynlUVsylqKghp5/vtrbMRbxuq7OMDN2fyXB5oT8U1D6HxpjHgS7gN4EV1tpjxpgGYJW1dqYx\n5u/Tz59Pn79j4LzzXVP7HIpIIUjaJLtad2VGBrce30p3LPX9sJmRmSxuWMzihsVcX3c9ZaEyh6OV\nfBm8lcQ2OjreoKtrO9YmACgunkRlxTwqKudSWTGXsrKr8PlCDkctIiJu54p9Do0xpYDPWtuZfn4b\n8A3gJeAB4Lvpx5+ni7wEfNkY8wKphWja3fh9w1OnUmvv1NTU5LWMeJsX+oQX6nA+1loOdh5MJYPH\nNrK5eTOt/aktBKZUTOHOqXeyqGERi+oXESmKXNZnuKH98hWjG+oO524l0d6+jXi8DQC/v4yKimuZ\nPOm3qKycR0XFdYRCY0Y8Rre05YBcxOu2OsvI0P2ZDJcX+oOjI4fGmGnA/02/DADPWWu/ZYwZA/wT\nMAk4QGori5b0VhZ/DdxOaiuLz1prLzgsWIgjhytWrADe2x4ol1NGvM0LfcILdcjW3N3MpuZNmYTw\neE9qamBdSR1LGpawuGExi+oXUV9an5PPc0P75SvGQqy7tQm6unelE8E3z9pKwlBaeuWgUcGBrSSc\nVohteSG5iNdtdZaRofszGa5C7g+uGDm01u4Frhvi/dPALUO8b4EvjUBoIiIX1drXyubmzanvDTZv\nYn/HfgCqwlUsql+UmSo6qXySVo70oGj01Jnpoe1v0NH5dmYriWAwQkXFXMbW3ZUeFbyWQKDc4YhF\nRCSf/AYShfONvctSCAvSiIi4QnesO7W9RDoZ3N6SWj+rJFDCgvoFfHLGJ1nSsITpkenaVsJjksko\nnV1Ng6aH9vWlFs82JkBZ2VU01H88MypYXDxZfxAQERkFkokE+996nabVq/hAbSnrW3udDmlYlByK\niJxHf6KfN0+8mdlr8J1T72S2l5hbN5ffnvfbLKpfxNU1VxP0BZ0OV3Iks5VEZvXQN+jsevfMVhLh\neioq5jJhwqeprJhHefnV2kpCRGQUsdZyfO9uGle/wo51q+lpb6OorJxDvXGiSXcPHSo5FBFJiyfj\nNJ5uzKwouu3ENvoT/fiNn6trruahOQ+xuGEx19VeR1GgyOlwJUcSiR46Ot6ho+PMqGA0egIAny+c\n2kpiwv2p7wtWXJfzrSRERMQd2k8cp2nNKhpXv0rr0cP4g0GuuH4Rs5avZOq8+dzy/ludDnHYlBw6\n4Atf+MKIlBFv80KfcLoOA9tLDCwis+X4lsz2EjMiM7hn5j0srl/M/LHzC3J7Cafb71LkK8bLve6Z\nrSTO7CnY1Z29lcRkqiM3nLWVhLdHhd3Qj7LlIl631VlGhu7PZCh9XV3s3LCGxtWvcGR7IwATZs1h\nwV0fY8aSGykqPXN/4IX+UFD7HOZDIa5WKiLOsNZyqPNQZpro5ubNtPS1ADCpfFJqNdH09hLVRdUO\nRyu5EIt10NHxZmZPwfb2NwdtJVFZcV06EUwtGuPEVhIiIlJY4rEY+97YTNPqVex9fROJeJzq8ROZ\nvXwls5atoKK2zukQ3zNXrFY6Wh06lFrEYOLEiXktI97mhT4xEnU43n08MzK4qXkTx7pTW6PWFddx\n47gbM9tLNJS5b6qgG/pAvmIc6rqDt5JITQ/t6dmdPmooLZ1OXe1t6dVD51JaekVBbCXhNDf0o2y5\niNdtdZaRofuz0c1ay9EdTTSufoWd69fQ191FSWUV1912J7OXr6Ru6hUXXWjMC/1BI4cO0D46kgte\n6BP5qENbXxubj2/O7DWYvb3EwvqFLK5PjQ5OqZji+tUk3dAH8rnPYWlpgqee+npmemhH51skEj0A\nBIPVVFbMzUwP1VYS5+eGfpRN+xxKvuj+bHRqOXqEpjWv0rT6VdpPHCcQDjN94VJmLV/J5Gvm4vNf\n+h8RC7k/aORQREaFnlgPW45vYdOxTZntJSyWkkAJ88fO55MzPsnihsXMiMzQ9hIjwNoEsVhb+qeV\nGTN6KS5OcPjIc9hklKSNYZMxkslY+nn2e1nPs44lkzHsoMcoX/nqEcrLk7z19ufTW0nMoqHhE+np\noXMpLtbekiIiMrSe9ja2r1tN05pXad69E2N8TLrmOm64+9NcuWgpoaLRuwK1kkMRcZVoIsqbJ9/M\njAy+c+od4jZO0Bdkbt1cvjT3SyxuWKztJXIgmYwTj6cSvWislXislWislVi0lVg8/RhL/URjrcRi\nbcTj7cCZGSn3fir1uGPHY+dc35ggPl/wnEefL3zue6YE4wvhM0GML8j6dac4eTLAI498n/LyOfj9\nWj1WRETOL9bfx54tG2las4p927Zik0lqp0zj5l//f7jqhpsoq9Z3zkHJoYgUuHgyTtPppswiMm+c\neIP+RD8+42POmDk8OOdBFjcsZm7tXG0vcQHJZIxYvD2VzGUlddk/AwleLNaSlegNzecLEwxGCAar\nCQarKC8aRzAYIRSMpN9P/Xzpy1+jr9fHiy/+FJ8vNCjpG87I3pe/tAKAqqqLzpAREZFRKplMcLjx\nHRpfe5Vdm9YS7e2lbEwNCz70cWYvW0HNpClOh1hwlByKSEGx1rKrbRebjm1iY/NGtjRvoSvWBcD0\nyHTunnE3ixtS20uUh0bvd8istVRXxygrT3DixH+cleAN/Awkeq3E453nvZbPV0wwWEUonegVF40n\nGIoQDETSj1UEQ9WDEr9L3fS9+VgIgHDYfSu7iYiIO508uJ/G115h+9pf0dVymlBxCTOWLGPWspVM\nnD0H49PXTM5HyaEDHn744REpI97mhT7x8MMPp7aX6DizvcSm5k2Z7SUmlk/k9qm3s7h+MQvrFzKm\nePRO+UgmY3R2vktb+xba2jbT3r6VL325FYC33/lS5jy/vySdzKVG9YqLJw4ayRs8slf1nhK9y5Gv\nfuqF/l8o3NaWuYjXbXWWkaH7M3frbDnF9rWv0fTaK5w8uB+f38+UufNZcf9vMG3+IoKhcN5j8EJ/\n0GqlIuKIgx0HeWHHC7x84OXM9hK1xbUsblic2V5iXNk4h6N0TjzeTUfHNtraNtPWvoX29m0kk71A\naqP2qsoFVFUtoKhofGpqZ3qkz+/P/3/8RERECkG0t4ddm9bTuPpVDr7zJlhLw5UzmXXTSmYuXU5J\nRaXTIRYMrVZawHbs2AHAzJkz81pGvM2NfcJay/qj63l2+7OsPrwan/FxfdX1PDTnIRY1LGJqxdRR\nu8JkNHqKtvattLWlRga7uhqxNgH4KC+bxbhx91BVtYCqygWZKZo7duygs7Ow+0C++qkb+3+hcltb\n5iJet9VZRobuz9whmUiw/63XaVq9it2bNxCP9lM5tp4lH/8Us5evINIw3rHYvNAfNHLoAO2jI7ng\npj7RHevmpT0v8fz259nXvo/qomrumXkPL/7Bi/h6fa6oQy5Za+nrO5QaFWzbQlv7Fnp69gLg84Wo\nqJibHhlcSGXlvPPuz+eGPpDPfQ7zcd3RyG1tqX0OJV90f1a4rLUc37ubxtWvsGPdanra2ygqK2fm\n0uXMvmklDdOvKog/Lhdyf9DIoYg47mDHQZ7f/jw/2/0zumJdzBkzh28v+zYfmPIBQv4Q/9z7z06H\nOCKsTdDVtePM9wXbttIfPQ5AIFBBVeUCxjV8ksqqBVSUz8Hn09RQERGR9hPHaVqzisbVr9J69DD+\nYJArrl/ErOUrmTpvPv6AtqzKNSWHIpJTSZtMTR1tepY1R9bgN35um3Ibn571aa6tvdbp8EZEItFP\nR+dbtKe/L9jWtpVEIrXiajhcT1VkEVWVC6mqWkBp6XSM0appIiIiAH1dXezcsIbG1a9wZHsjABNm\nzWHBXR9jxpIbKSotczhCb1NyKCI50R3r5ue7f87z259nf8d+xhSN4fPXfZ67Z9xNbUmt0+HlVSzW\nQXv71szIYEfH21gbBaC0dDr1Yz9EZdUCqioXUlzs3HchREREClE8FmPfG5tpWr2Kva9vIhGPUz1+\nIss+dT+zlq2golbbIY0UJYciMiwHOg5kpo52x7q5puYavrP8O9w2+TZC/pDT4eVFX39z5vuC7e1b\n6OraAViMCVBefg0TJ95PVeVCKiuvJxSqdjpcERGRgmOt5eiOJhpXv8LO9Wvo6+6ipLKK6267k9nL\nV1I39YqC+B7haKPk0AGPPvroiJQRb3OyTyRtknVH1/Fc03OsPrKagC/AB6Z8gPuuuu89TR11Q7+2\n1tLTsye9cEwqIezrOwyk9hSsrLieaVNvp7JqAZUVc/O6Z+DZ3NB++YrRDXV3C7e1ZS7idVudZWTo\n/mxktBw9QtOaV2la/SrtJ44TCIeZvnAps5avZPI1c/H5/U6HeNm80B+0WqmIXLKuaBc/3/NzXtj+\nQmbq6L0z7+XumXdTU1zjdHg5kUzG6OxqTC8cs4W29q3EYi0ABINjUttJVC2kqnIBZWWz8Pn0NzYR\nEZEL6WlvY/u61TSteZXm3Tsxxseka65j9vKVXLlwCaHiEqdD9DytVlrAtm3bBsDcuXPzWka8bST7\nxNlTR6+tuZbvLv8ut02+jaD/8lcKK4R+fWaz+dTI4ODN5idRM2ZlJiEsLp5SUFNcCqH9LiZfMbqh\n7m7htrbMRbxuq7OMDN2f5Vasv489WzbStGYV+7ZtxSaT1E6Zxs2feYirbryZsuoxToeYc17oDxo5\ndID20ZFcyHefSNoka4+s5bntz7HmyBoCvgC3T7md+666j2tqr8nJZzjRr6PR0+mFY7bQ3raFzq53\n05vNG8rKZmWNDM4nHB47YnFdDjf8XtA+h4XPbW2pfQ4lX3R/NnzJZILDje/Q+Nqr7Nq0lmhvL2Vj\napi1bAWzl62gZtIUp0PMq0LuDxo5FJHLMjB19Pntz3Og4wA1xTV8ce4XuXuG+6aOntlsPrWK6FCb\nzU+e9FtUVS2gsvL68242LyIiIud38uB+Gl97he1rf0VXy2lCxSXMWLKMWctWMnH2HIxPWza5hZJD\nEQFgf/v+zNTRnngP19bmZuroSEptNr8zs3BMe9uWQZvNV1bOp6Hhk1Rps3kREZFh6Ww5xfa1r9H0\n2iucPLgfn9/PlLnzWXH/bzBt/iKCIf031o2UHIqMYgNTR5/d/ixrj6wl4Atwx5Q7uG/WfcypmeN0\neBd1ZrP5ge8Lvk483glkbzafmiaqzeZFRESGJ9rbw65N62lc/SoH33kTrKXhypm876HPM3Ppckoq\nKp0OUYZJyaHIKHT21NHa4lpXTB0dvNn8Fjo63spsNl9SciV1dXdmVhItKhpfUIvHiIiIuFEykWD/\nW6/TtHoVuzdvIB7tp3JsPUs+/ilmL19BpGG80yFKDik5dMC3v/3tESkj3nY5fWJf+z6e3/48P9/9\nc3riPVxXex1fXP5Fbp18qyNTRy9Wh77+5tSoYHpkcPBm83OYOOHXqapaSGXl/FG52bwbfi/kK0Y3\n1N0t3NaWuYjXbXWWkaH7szOstRzfu5vG1a+wY91qetrbKCor5+qbb2H2TStpmH6V/gA7BC/0B61W\nKuJxSZtkzZE1PLf9OdYeWUvQF0ytOlpgU0dTm83vTS8cs5m2tq309R0CBjabn0dl1cLU4jEV1+H3\na08kERGRXGo/cZymNatoXP0qrUcP4w8GueL6RcxavpKp8+bjD7hjDQI5l1YrLWDr1q0D4IYbbshr\nGfG2i/WJzmgnP9+dmjp6sPMgtcW1fGnul/jkjE8WzNTRNWt+SiL5BpGqk2dtNl9NVdVCJk64n6qq\nBZSVzdZm80Nww++FfMXohrq7hdvaMhfxuq3OMjJG6/1ZX1cXOzesoXH1KxzZ3gjAhFlzWHDXx5ix\n5EaKSsscjtA9vNAfNHLoAO2jI7lwvj6xt30vzzc9z0t7XqIn3sPc2rncN+s+3j/p/QWx6qi1SVpa\nVnPo8NOcPv0rAIqLJmX2F6ysXEBJyVRNV7kEbvi9oH0OC5/b2lL7HEq+jKb7s3gsxr43NtO0ehV7\nX99EIh6netwEZt/0PmYtW0FFbZ3TIbpSIfcHjRyKjCKZqaNNz7H2aGrq6B1T7+C+q+7j6pqrnQ4P\ngHi8i2PNP+Xw4Wfo6dlHKFTDr1ZVsG1bKS+99KrT4YmIiHiatZajO5poXP0KO9evoa+7i5LKKq67\n7U5mL19J3dQr9IdZUXIo4mbJYJJ/bPxHXtj+Agc7D1JXXMeX536ZT874JGOKxzgdHgA9Pfs5fPgf\nOXrsX0gkuqgov5arZ/8ZdXV38NijtzkdnoiIiKe1HD1C05pXaVr9Ku0njhMIh5m+cCmzlq9k8jVz\n8fn9TocoBUTJoYgLtfW10b24m77pffzx5j9mbu1cfnveb3PL5FsI+gph6qilpXUthw49xenTqzDG\nT13dB5k44QEqK+c6HZ6IiIin9bS3sX3daprWvErz7p0Y42PSNddxw92f5sqFSwgVa1E3GZqSQxGX\n+a8D/8U3N3yTvqv6CO8J8/RXn+bqMYUydbSb5uafcejwM/T07CYYHMPUKV9m/PhfIxwe63R4IiIi\nnhXr72PPlo00rVnFvm1bsckktVOmcfNnHuKqG2+mrLowZhRJYdOCNA7Ytm0bAHPnXvoIyuWUEW9p\n6Wvh2xu/zS/3/5JZ1bP4TM1nmFQ8qSD6RG/vQQ4f/jFHj/0T8Xhneg/CBxk79oP4fOHzllO/Hh43\ntF++YnRD3d3CbW2Zi3jdVmcZGW68P0smExxufIfG115l16a1RHt7KRtTw6xlK5i9bAU1k6Y4Etdo\n5XR/uJBLXZBGyaFIgbPW8ssDv+TbG75NV6yLL1z3BR6c86Dj00ettbS2ruPQ4Wc4deq/U1NHa29n\n4sQHqKiYpy+1i4iI5MnJg/tpfO0Vtq/9FV0tpwkVlzBjyY3MWraSibPnYHw+p0OUAqPVSgvYyy+/\nDMD73//+vJYR9zvVe4pvbfgWLx98mTlj5vDEjU9wZeRKwLk+kUj0cKz5Zxw+/Azd3bsIBquZMuWL\njB9/H0Xh+vd0LfXr4XFD++UrRjfU3S3c1pa5iNdtdZaRUej3Z50tp9i+9jWaXnuFkwf34/P7mTJ3\nPivu/w2mzV9EMHT+mToyMrzwu0Ujhw4YTfvoyOWx1vJv+/6N72z6Dr2xXr4070vcP/t+AlkbwY90\nn+jtPczhI//I0aP/RDzeQXnZ1Uyc+AB1dXfh91/ef5DUr4fHDe2nfQ4Ln9vaUvscSr4U4v1ZtLeH\nXZvW07j6VQ6+8yZYS8OVM5l100pmLl1OSUVlXj5XLk8h/27RyKGIS53sOck3NnyDVYdWcW3ttTxx\n4xNMq5zmSCzWWlrbNnD40NOcPPXfGGOorf1AetXR+Zo6KiIikmPJRIL9b71O0+pV7N68gXi0n8qx\n9Sz5+KeYvXwFkYbxTocoHqbkUKRAWGv5xd5f8N1N3yWaiPI/F/xPPjPrM/h9I7//UCLRS3Pzzzl0\n+Gm6u3cSDEaYMvm3GD/+0xQVNYx4PCIiIl5mreX43t00rn6FHetW09PeRlFZOVfffAuzb1pJw/Sr\n9AdZGRFKDkUKwPHu43xjwzd47fBrzKubxzdu+AZTKqeMeBy9vUc4cuTHHDn6IvF4O2Vls5h11f/L\n2LF34fcXjXg8IiIiXtZ+4jhNa1bRuPpVWo8exh8McsX1i5i1fCVT583HH3B+72IZXZQcijjIWsvP\ndv+MP9n8J8SSMb628Gv82lW/NqKjhdZa2to2cejw05w8+V8A1NV+gAkTH6CqcoH+UikiIpJDfV1d\n7NywhsbVr3BkeyMAE2bNYcFdH2PGkhspKi1zOEIZzbQgjQN27NgBwMyZM/NaRgpbc3czj697nLVH\n1zJ/7Hy+ccM3mFQx6ZLLD7dPJBJ9HD/+EocOP01X13YCgSrGj/8UE8Z/mqKicZd1zfdK/Xp43NB+\n+YrRDXV3C7e1ZS7idVudZWTk8/4sHoux743NNK1exd7XN5GIx6keN4HZN72PWctWUFFbd/mBS8Eo\n5N8t2ucwrRCTQxndrLX8ZNdP+N6W75G0Sb46/6vcO/NefGZk9iTq6zvK4SPPcuTIC8TjbZSVzmTi\nxAcZO/bDmjoqIiKSI9Zaju5oonH1K+xcv4a+7i5KKqu46sabmb18JXVTr8jJ7BxrLVgLySQkk1hI\nPU+/N+i4tanXA88HzrvgccAmh75WMv367OMDn519PHN+1rWTFsiK8+zjZ3/2eY5n6pH1eXbQ8/S5\nySSQ9dkDxwfKZh8fOP8ix60d/NnVn/0sRTNnDPvfNde0WmkB+8UvfgHAhz70obyWkcJzpOsIj697\nnA3HNrC4fjGP3/A4E8onXNa13kufsNbS1r4lverof2Ktpbb2ViZOuJ+qqsWOTR1Vvx4eN7RfvmJ0\nQ93dwm1tmYt43VZnL7LJJCQS539MJCGZ9RhPDH6deUykbtAv9HhOmaEf333rbUgmuOqKK7CxGDYa\nw0ajqZ9Y1vOs1y0nTkAiQWVFRSYh6iTBIT8cClh6fAa/tTRELROiCWpPNePb/QIdTz1PRyYZy0pU\nhkrQzpf8DSREMnw+X+rHmNQ9kTHg8w16js+HGTh3qOPG0NvXx7v1Y7m1AJPDS6WRQwcU4j46kl9J\nm+Sfd/wzf7b1zwB4eMHD3D3j7mElZZfSJxKJfo4f/0V66mgjgUAl48fdy/jxn6G42PmlsNWvh8cN\n7ad9Dguf29rSiX0O33MiM2SCkttEZsjPTCSxyUt7PO+1LlTPcx4v8hkXKFvw/H5MMIgJhdI/QUww\niC8UwgTT7wWDbHnzTRIG5i1ZwqFYLwf6u2mJ92OAulAJU0ormFBcTtAfAN9A4jGQiIDx+VKvjRn6\nuMlKRHzpRGTQ+VnPB5KW9Hup8y9w7Yt9dtb1Bn12+vmZ4+f5bJ8PyLp25vPS177Y8bM/O33cGM4k\naOccz76WGXz+kMdNTv9AXsi/TzVyKFIgDnUe4o/W/RGbmzeztGEpj9/wOOPK8vudvr7+Zo4cfpYj\nR18gFmuhtHQ6V838JvX1H8XvL87rZ4uI5IJNJIifPEns2DHizc3EjjUTaz7Gb7a2EbaWgw89lJo2\nlkwnI5nnySHeSz9PJLDW8sTJUxhr2XXzCm8kMtl8vlRik8NHEwhgwiHw+cHvw7ynx6xr+X3gu/jj\nkNfy+1MJ0tmPgcDQ7/v95/+M89Tzzg9/mIQx/Ocrr6TKX4Kv3vo+riwNcbjjGDaZpHbKNG5etoKr\nbryZsuoxef7HFsk9JYcieZK0SZ7f/jx/+fpf4jd+Hl/6OB+f/vG8TeG01tLevpVDh5/h5Mn/wNok\nNTW3MHHCA0QiS7XqqIgUDGstidOnMwlf/FgzseZm4s3H0u81E09P18tmSkpoiMfp8/lI9vSmR1PS\nCUTQlxpF8flSyYQZuOlPj2Zkvbf7xAksMG3ZjSObyGQnLH7/sBKZoR4z09zksvT5Ut/9v5TEsPP0\nKX714x9xY3UJvYkkCz70cWYvW0HNpCl5jlIkv5QciuTBwY6DfH3d19l6fCvLxi/jj5b+EfWl9Xn5\nrGSyn+PH/5VDh5+ms/MdAoFyJk54kAkTfp3i4ol5+UwRkfOx1pJsbyfW3HzOqN+ZJLAZG4sNKmdC\nIQL19QTr6yldtJBAfQPBhvrUew0NBOvr8VVU8PmVKwFY9cLzlx3j0+mpXw9961uXfQ0ZneKxGFv/\n9Wds/OmL2GSSHV1RdndHefS+B50OTSQnlByK5FAimeDZpmf5qzf+iqAvyBM3PsFHrvhIXv6SW1aW\nYP6CLtasXU4sdprS0unMnPkEDfUfxe8vyfnniYgAJLu704lf9kjfmcQv1tyM7ekZXMjvJzC2jmB9\nA8XXXEPgtlsJZpK/1KO/ulqjXlLQ9r2xhVef/j6tx45y5cKlrLj/N/jIPfc6HZZITmlBGgccOnQI\ngIkTL31U53LKyMja176Pr6/9OttObuPmCTfz9aVfp64k9/sWJZMxDhz4O/bu+2sgQU3N+9JTR29w\n3Y2V+vXwuKH98hWjG+ruFtltmezvHzzSN8SoX7KjY/AFjCFQU0MgPbqXnfAF6+sJNDQQqKm55O9w\nvZd4nbyGeM/5+kXb8WZWPfMD9mzZSKRhPO978HNMmTv/gmVkdCrk/qB9DtMKMTkUb0kkEzzT+Ax/\ns+1vCPvDPLLoEe6adldeErXOzkYam75GV1cjdXV3csW0hykpmZzzzxER70p0dhLdu5f+vfuI7t1L\ndP8+YkeOEmtuJtHScs75/kiEQEN9aqSvvv7M84EksK4WEwo5UBOR/Ir197Hp5//C5pd+gs/nZ8kn\nPsX8Oz+CPxB0OjSR90yrlRawF198EYB77730qQiXU0byb0/bHr6+9uu8deot3jfxfTy65FFqS2pz\n/jnJZJT9+/+W/QeeJBis4pprnuTVV9pofHcD997r3uRQ/Xp43NB++YrRDXV3kk0miTc3pxPAPfTv\n3Ut07z769+0lcfLUmRMDAfqqqohWVTHpllvOSvxSI3++4sJa4TgX//bqPzKUgX5xzz33sGvTOlY9\n80M6T53kqhtv5qbPfJby6przllFfEvBGf9DIoQO0z6H7xZNxnnr3KZ7c9iSlwVL+cPEfcvuU2/My\nWtjR+Q5NTV+jq2s79WM/yowZjxIMRjzRJ7xQBye5of20z2F+Jfv6iB44kB4JPJMARvftx/b2Zs7z\nVcJzc9YAACAASURBVFQQnjaN0LRphKdNJTRtGqGpUwlNmMDKW28F3NOWTuxzKKPDihUrKPMbHlhx\nAwffeZPaSVN432c/z4TZcy5YBtSXJKWQ+4NGDkXyZFfrLh5b+xjvnn6XWyffyh8u/kNqis/9a+Jw\nJZP97Nv31xw4+PcEg2O49pq/p7b2/Tn/HBEpbNZaEq2tRPfsyUwF7d+XSgRjR47AwB95jSE4bhyh\nadMoXbiQ0NRphKZNJTxtGv4xY1z3nWSRkdTb1cnsshBTS4Ic37eb9332t7ju1g/iy9F3ZUXcQsmh\nyCWKJWP86O0f8Xdv/R0VoQq+d/P3+MCUD+Tlszo63qKx6ffp7t5FQ/3HmT79UYLByrx8logUBhuP\nEzt8OJUA7jszEhjdu5dEe3vmPFNURGjqVIqvvZbKj370zEjg5MkFNwVUpNDFYzG2/ccv2Ph//4lp\nJUEO9sb57g+/T0mF/psro5OSQ5FLsKNlB4+tfYymlibumHIHjyx+hOqi6px/TiLRz759f8mBgz8g\nHK7jumt/SE3Nypx/jog4J9HVRXTfvkGLwvTv20v0wEHI2vvPX1NDeOpUym+/PZMAhqdNI9DQkNr0\nXEQum7WWHeteY80Lz9B+4jhTrruep15ZQ2c8qcRQRjUlhyIXEEvE+MHbP+AHb/2AynAlf7HiL7hl\n8i15+az29jdobPoaPT17GNdwD9On/yGBQHlePktE8staS/z48XMTwD17iZ84ceZEv5/QpEmEpk2j\nfOVKQlPT3wmcOhV/pW5QRfLhcOM7/OrH/4fmPbuonTSFT/yvJ5hy7Tz+6j9XOB2aiOO0II0DTp1K\nrRRXU3Pp31O7nDIyPI2nG3ls7WPsbN3JndPu5JGFj1BVVJXzz0kk+ti79884eOgfCIfHMuuqbzNm\nzE0XLeeFPuGFOjjJDe2XrxgLpe7JaJTo/v2p6Z/7shPBfYM2gveVlRG6YhrhqWctCjNhguPbQBRK\nW16qXMTrtjpLbrQcPcxrzz7Fni0bKKsew433/jqzb1qJz5f6XqHuz2S4Crk/aJ/DtEJMDqWwRRNR\n/u7Nv+NH7/yI6qJqHlvyGCsn5WdqZ1vbFpq2P0JPzz7Gj/s1rrzyaxotFClA8dbWwVNB9+yhf98+\nYocPQzKZOS8wrmFwApheFCZQW6sFYUQc0tPexrp/eZ63Xv53guEwiz5yN9d/8MMEw0VOhyYyYrRa\naQF76qmnAHjwwQfzWkbeu3dPvcujax9ld9tuPnzFh/n9hb9PZTj3U7sSiR727PlTDh1+mqKiccyb\n+wzV1Te+p2t4oU94oQ5OckP75SvGfFz3/2fvvsPjKs7Fj3/PFm1Vl9UsyZLcC8bGHXdKwPRiwGAS\nQgnJj5sQciGAHTCmhwuEhNwQLj3YYBswOBAMJIAb7gYbjG3JvarY6tL23TO/P1aSJVmyVXa1RfN5\nHj2r3T3lnaPR7nnPzJkRPh+eY8dODgTTpCXQV1nZuJwSE0NMXh7GoUOIv+yyk4lgbi4aszlg8XSX\nSKhHTQUi3kgrs9Q5HpeT75Z/zKZ/vo/H5WL4BTM4d+aNmONb7wUkz8+kroqG+iBbDkNAznMYflw+\nF3/f9nfe2vEWyaZkHpnwCFOyzty1szMqKzeyq+BBHI7D9O59M/36/h6dztrh7URDnYiGMoRSJBy/\ncJznULXZcB082GxuQPf+/bgPHUK43Y3LaZOS/FNBNG0J7NsXfUYGShQNbx8J9agpOc+hdCaq6mPn\n6hWsXbKAuopy+o4ez+SbbiG5d/Zp15PnZ1JXhXN9CPuWQ0VRsoG3gTRAAK8IIf6iKMp84BfAifpF\n5wohltevMwe4HfABdwshvuj2wKWo8/2J75m3dh77q/dzTf9ruG/0fcTGBL5rp9drY9/+Zzl6dAEm\nYw7njHyHxMTxAd+PJEn1A8IcP3HKlBCuAwfwFhefXFCjISY72z834JTJ/oni8/KJyctFl5gYsvgl\nSeqcgz9sZfXCNzhx6ADpfftz6W9+f9pJ7CVJai6U3Uq9wL1CiO8URYkFvlUU5T/1770ghHiu6cKK\nogwBZgFDgUzgS0VRBgghfN0atRQ1nF4nf9v2N97e+Tap5lRevuBlJvbuWNfO9qqoWMeugrk4nUfJ\nyrqFfn3vQ6uNvO5nkhRuhNuN+/DhVruCqjZb43Iai8WfAI5tPjm8PicHTYgHhJEkqetOHDrA6nfe\n5OD33xHXK41L7/49AydMltO+SFIHhSw5FEIUA8X1v9cqirIL6H2aVa4EFgshXMABRVH2AmOB9UEP\nVoo6W49vZd7aeRysOcjMATO5d9S9WGM63rXzTLzeOvbue4Zjx97FZMpl1DmLSUg4Y4u+JEmtEKqK\nq6AA28ZN/LKyigyvl4KR54Dv5DVCXXo6hvw84q+6qjEBjMnPR5eaKgeEkaQoVFtRxrr33uHHlV9i\nMJuZ+tPbGXHRZej0+lCHJkkRKSwGpFEUJRcYCWwEJgK/VhTlZ8AW/K2LlfgTxw1NVjvK6ZNJSTqF\nw+vgxe9e5J1d75BhyeDVn7zK+IzgdO0sr/iGgl1zcLqKycm+nfz836HVmoKyL0mKRkJVce3ejX3T\nJmwbN2HfsgW1uhqATK2WIzodQ2679WRX0NxctFZLiKOWJKk7uB12Nn+8lC3/WoZQfYy65ErGXXMD\nJqsc8VuSuiLkA9IoimIFVgFPCiE+VBQlDSjDfx/i40CGEOI2RVH+F9gghFhYv97rwGdCiA9a2ead\nwJ0AOTk5ow4dOtRNpWkfe/3cV+YOjGrXmXWk5raUbGHeunkcqT3CDQNv4HejfodFH/gTSa+3lj17\nnqKo+D3M5nwGD/4jCfGjAr6faKgT0VCGUIqE49eRGIUQuPbswb5xE/ZNm7Bv3oyvqgoAfXY25rFj\nsIwbh3nsWDxxce3ernR6kVCPmgpEvJFWZukk1edj+9dfsO79d7FXVzFwwmQm3XgLCWnpXd62PD+T\nuiqc60PYD0gDoCiKHlgKvCOE+BBACFHa5P1XgX/VPz0GNB1mKqv+tVMIIV4BXgH/aKWBj7xrOlNh\nwrGSRQq7x85fvvsL7xa8S5Y1izcueoMx6WOCsq+y8pUUFPwBl+s4fXLuJC/vt2i1wZlHKRrqRDSU\nIZQi4fidLkYhBO79+7Ft3Ih902bsmzbhq6gAQJ+ZiXX6dMzjxmIZOxZ9ZmazdWWHscCJhHrUVCDi\njbQyS/7Pi/3fbWL1wjepKDpK70FDuer+h8noNzBg+5DnZ1JXRUN9COVopQrwOrBLCPGnJq9n1N+P\nCHA18GP97x8D7yqK8if8A9L0BzZ1Y8gB89JLLwFw1113BXUdCTYVb2LeunkU1RUxe/Bs7h55N2Z9\n4P9xPZ4a9ux9kuLiD7BY+nPWsL8RHz8i4PtpKhrqRDSUIZQi4fg1jVEIgfvAQX+r4KaN2DZtxldW\nBvjvFbROnoR57DjM48YRk3X6uwYioeyRItKOZSDijbQy93Ql+/awauHrHN35I4kZvbnyvofoO3pc\nwO8jludnUldFQ30IWbdSRVEmAWuA7YBa//Jc4EZgBP5upQeBXzYki4qi/AG4Df9Ip/cIIT47037k\nPIc9k81j44VvX2BJ4RJyYnN4bOJjjEoLfNdOgLKyrykoeAi3p6y+tfA3aDSGoOyrqWioE9FQhlCK\nhON31eTJDHW5uGvKVOybNuE9fhwAXWoq5nHjGruK6rOzO3SiFwlljxSRdizlPIc9R/XxUr5Z/DYF\na1dhiovn3Jk3cdb5F6HVBadtQ56fSV0VzvUh7LuVCiG+AVo7E1h+mnWeBJ4MWlBSVFhftJ756+ZT\nbCvmZ0N+xq9H/hqTLvADwXg8Veze8zglJcuwWAYwfPj/ERd3VsD3I0mRRKgqzh07qFuxgtqVK3n6\nhL9l0LZxA5ax/vsFLePGou/TR44eKklSq5x1dWxc9h5bP/sYRdEw7urrGXPFTAxR0GVPksJdWIxW\nKkmBUOuu5fktz7N0z1Jy43J5e8bbjEgNTtfOEyf+TUHhPDyeSvJyf0Nu7l1oNHKuNKlnUm02bOvX\nU7tyJXWrVuE7UQYaDaaRI/nQauVHg4F3V6+WyaAkSafl83rY9sVyNny4GKetjqFTzmPiDT8lNjkl\n1KFJUo8hk0MpKnxz7Bvmr5vPCccJbh12K3edfRdGXeAHgnG7K9i95zFKSz/Bah3MiLNfJzZ2aMD3\nI0nhznPsmD8ZXLkK+8aNCLcbTWws1smTsE6fjmXSJHSJifynvouNTAwlSWqLEILdG9ayZtFbVJeW\nkHPWCKbefBupufmhDk2SehyZHEoRrcZdw7Obn2XZ3mX0je/Ln6b9ieG9hgdlX8ePf05B4Ty83mry\n8u4ht88vZWuh1GMInw/HDz9Qt3IVdStW4Nq9G4CYPn1IvOkmrNOmYR51DoqceFqSpA44VrCTVQtf\np3hPISnZfbhmzqPknn2OvKAkSSES8nkOgy0cB6SRAmP10dU8uu5Ryp3l3DrsVn519q8waAM/EIzb\nXUbh7kc5fnw5sbFDGTz4f4i1Dgr4fiQp3Pjq6rB9s5a6lSupW73aP82EVot51Cis06ZhnT4NQ15e\nqMOUJCkCVRYfY827/2DPpnVYE5M494abGTr1fDQabahDk6SoFPYD0khSZ1W7qnlm0zN8sv8T+iX0\n48XzXmRoSuC7dgohOH78Uwp3P4rXW0ff/HvJyfkFGo1sGZGil/vIEepWrKRu5Qpsm7eAx4MmPh7r\nlClYp03FOmkS2vj4UIcpSVKEstdUs/6DRfzw5WdodXrOvX42oy+9Gr0xOHMCS5LUMTI5DIHnnnsO\ngPvuuy+o60Sjrw9/zeMbHqfKWcUvh/+SO4ffSYw28F07Xe4yCgvnceLEF8TFDmfw4GewWgcEfD9d\nEQ11IhrKEEqBOH7C68Xx/feNo4u69+4DIKZvX5J+9lNip0/HNGIESieHjg/W31jWncCJtGMZiHgj\nrczRwON28d3yj9m07H08LidnnfcTzr1uNpaExFCH1kien0ldFQ31QXYrDQE5j07HVToreXrT03x2\n4DMGJg7k8YmPMzh5cMD3I4SgtPRjCnc/hqrayc+7h+zs29Fowu86SjTUiWgoQyh19vj5amqwffMN\ntStWYlu9Gl91Neh0mMeMJnb6dKzTphGTkxPSGEO13Z4o0o6lnOcwsghVZeeaFaxdspDa8hPkjxrL\nlJtuJTkrO9ShnUKen0ldFc71QXYrlaLGfw79hyc2PEGNq4a7RtzFHcPuQK8NfNdOl+s4BYUPU1b2\nJXFxIxky+Bkslr4B348khYL74EFqV6ykbuVK7N9+C14v2sTExnsHLRMnoo2NDXWYkiRFkUPbt7F6\n4ZscP7iPtPx+zPiv35E9NDiDxkmSFBgyOZTCVoWzgqc2PsUXB79gcNJgXrnwFQYmDQz4foQQlJR8\nxO49j6OqLvr3m0t29s9RFHlTvBS5hMeD/but1K1YQd3KlbgPHgTA0L8/ybfdhnXaNExnD0fRynou\nSVJglR05xOp33uTA1i3EpvTikt/cx6Bzp6BoNKEOTZKkM5DJoRR2hBB8cegLntrwFLWeWu4eeTc/\nH/Zz9EEYCMbpKqGg4A+Ul68kPn4UQwY/g9ksR1+UIpOvqoq6NWv8A8p88w1qTQ2KXo953DgSb77Z\n3100q3eow5QkKUrVVVaw7r2F/LjiS2JMJqbMvpWRF1+OLkZO+yRJkUImhyFgMpm6ZZ1IVOYo48kN\nT/Ll4S8ZmjyUxyc+Tv/E/gHfjxCC4uIP2LP3SVTVQ//+D5Gd9bOIai2MhjoRDWUIJZPRSKrHQ/nr\nr1O7YgWO77aCqqJNTib2wguwTpuGZcK5aK2W0MUYpL+xrDuBE2nHMhDxRlqZw53b6WDLJx+y+ZMP\nUb0+Rs64nPHX3IApNi7UoXWIPD+Tuioa6oMckEYKC0IIlh9YztObnsbhcXDXiLu4Zegt6IIwEIzT\nWcSugrlUVKwhIWEsgwc9jdmcG/D9SFKweCsqqP7wQ6re/wD3oUMAGAYPxjptKrHTp2McNkx235Ik\nKehUn48fV/6Hde+9g62qkgHjJzH5xltISM8IdWiSJLUgB6SRIsYJ+wke2/AYK4+sZHiv4Tx+7uPk\nJ+QHfD9CCIqKFrNn7x8BlQED5pPVezaKIk+ipfAnhMDx3XdULlpM7RdfIDwezKNHk3Trz7FOnYo+\nQ56MSZLUPYQQHNi6hdXvvEn50cNkDhjMFffOJXNA4EcRlySpe8nkMAQef/xxAB5++OGgrhPuhBB8\nsv8T/rjpj7h9bu4bfR83D74ZrSbwXTsdjqPsKphDZeU6EhMnMHjQ05hM4TeMdkdEQ52IhjIEm6+u\njuqPP6Zq8RJcu3ejsVpJuOEGEmfdwP8sWgR79vDwrFmhDrNNwfoby7oTOJF2LAMRb6SVOZyU7t/L\nqoVvcGTHDySkZ3DFf8+l39gJKIoS6tC6TJ6fSV0VDfVBdisNATmPDpTaSnl0/aOsObaGkakjeezc\nx8iNzw34foRQOXZsEXv3/RFQ6NfvQXpn3hgVX2LRUCeioQzB4iwooHLRYmo++QTVbsc4ZAgJN84i\n/tJL0ZjNQGQcPznPYfiLtGMp5zkMjZqy43yzeAG71qzAGBvHhGtv5OwLL0arC/xgcaEiz8+krgrn\n+iC7lUphSQjBsr3LeHbzs3hUDw+MeYAbB90YpNbCw+zc9SBVVRtJSpzEoEFPYTLJkRql8KW6XNR+\n/jmVixbj2LYNxWAg7pJLSLxxFsazzoqKixqSJEUWl93GxmXv893yfwIw5sqZjLvqOgzm0A10JUmh\noKoqdrsdu92OzWZrfGz6+9lnn82+fftCHWqXyORQ6jYlthLmr5vP2qK1jEobxWPnPkZOXE7A9yOE\nytGjC9i771kURcugQU+RmXG9PLGWwpb70CEql7xH9Ycf4quqIiY3l9QHHyDhqqvQJiSEOjxJknog\nn9fD9//5jPVLF+OsrWHI5OlMnPVT4lJSQx2aJAWEz+c7JdFr69Fms+FwONrcltFoxGKxoNFo0ET4\ngHAyOZSCTgjB0j1LeW7Lc6hCZc7YOcwaNAtNEAaCsdsPsGvXHKqqN5OcPJVBA5/AaMwM+H4kqauE\n10vtihVULVqMbd060GqJPf98Em+chXn8eHkxQ5KkkBBCsGfTOta8+xZVJcXkDBvOlNm3kZbfL9Sh\nSdJpeb3e07bqtXx0Op1tbstsNmM2m7FYLPTq1Yvc3NzG5xaLpfH3huW0Wn8PuIZupZFMJochkJyc\n3C3rhINjdceYv24+G4o3MDZ9LI+e+yhZsVkB348QPo4c+Qf79j+PRqNn8OBnyEi/NqpPsCO1TjQV\nDWXoKE/pcaref5+q99/HW1qKLi2NlN/8moSZ16FP69gV+Ug4fsGKMRLKHiki7VgGIt5IK3N3Kdq9\ni1UL3qBo9y6Ss3K4+sFHyBsxOqq/S5vqSednkcDj8ZyxNa/pay6Xq9XtKIrSLJlLT09v9rzpo8Vi\nwWQydbr1LxrqgxyQRgoKVai8X/g+f/r2TwDcO/peZg6YGZTWQpttP7t23U91zVZSks9j0KAnMBjS\nAr4fSeosoarYN2zwT0Px9dfg82GZNInEG2dhnToVRSev00mSFDqVJUV88+4/2L1xLZaERM69fjbD\npl2IRhv48QCknsvtdre7Vc9ut+N2u1vdjkajOW1y1/I1o9EY8V09A0EOSCOFzJHaIzyy7hE2l2xm\nQsYE5p87n0xr4Lt2CuHj8OHX2X/gz2g0RoYMeZ70tCt7zBVOKfz5qqqo+mgZVYsX4z50CG1CAkk/\nv4XEG24gJifw99tKkiR1hKO2hg1LF7Pt38vR6LRMmHkToy+/mhijKdShSWFOCIHL5Wp3q57NZsPr\n9ba6La1W2yyZS0pKOm3iZzQa5bleEMnkMATmzJkDwNNPPx3UdbqbKlQWFSziL9/9BY2iYf6E+VzT\n/5qg/APX2fawa9eD1NRso1fKhQwc+BgGQ8+6ST4S6sSZREMZWhJC4PzhB/80FJ99hnC5MI0cSeZ/\n3UXsRRehMRgCtq9IOH7BijESyh4pIu1YBiLeSCtzoHndbrZ+/gkbP3oPt8PBsPMu5NzrZmNNTAp1\naCEVredn7SGEwOl0tqtVr+F3n8/X6rZ0Ol2zpC41NbXNlj6z2YzBYIiaZC8a6oNMDkNg/fr13bJO\ndzpcc5h56+bxbem3TOw9kfkT5pNuSQ/4flTVy+HDr7L/wIvodBaGDv0zaamXRc2HSkeEe51oj2go\nQwPVbqf6X/+icvFiXDt3oTGbib/6KhJvvBHjwIFB2WckHL9gxRgJZY8UkXYsAxFvpJU5UISqUrB2\nFd8sWUDNiePkjRzNlNm3kpLdJ9ShhYVoOj9TVRWn09nuVj273Y6qqq1uKyYmpjGZi42NbXbPXmsJ\nX0xMTDeXNnyEa33oCJkcSl3iU328s+sd/rr1r+g1eh6f+DhX9g1O1866ukJ27nqA2trtpPaawYCB\n8zHEpAR8P5LUEa49e6hctJjqjz9GravDMGAA6Y/MI+7yK9Ba5TxgkiSFh8M//sDqd96gdP9eUnP7\nctGvfkvOsLNDHVZIeL3eVhOkvLw8PB4PBw8eJD09HaPRGOpQG7U1x15biZ/dbqetcUUMBkNjIpeQ\nkEBmZmabrXoWiwW9Xt/NpZVCSSaHUqcdqD7AvLXz2HZiG1OzpvLw+IdJswR+IBhV9XDo0MscOPg3\ndLpYhg37K2mplwR8P5LUXqrbTe2//0Pl4kU4tnyLotcTO+NiEmfNwjRyZI9syZYkKTyVHz3C6nfe\nYP93m4lN7sWM//pvBk+ahhIlA3QIIXC73a0mS20lUG0NdJKTk4OiKLz11lsAJCUlkZGRQWZmJhkZ\nGWRkZGAyBeZ+zIY59trbqme329vcVsMce2azmeTkZLKzs9ts1TObzejkIGjSacjaIXWYT/Xx9s63\n+du2v2HQGnhq0lNclh+crp21tbvYtesBaut2kJZ6GQMGzCMmJvKHCZYik/voMaqWLKFq6VJ8FRXo\ns7NJ/f19xF9zDbrExFCHJ0mS1MhWVcm6999h+9f/Rm8wMvmmnzNyxuXoYwJ333MwtLz3rT2Ppxvo\npGlSlJSUdNp73y655BL0ej0vvfQSxcXFFBUVcfToUXbs2NG4zYaWtoZkUa/X4/F4Wp1j73SJX0fm\n2Dvd4CxN59iTpECQyWEIZGV1fJ6/zqwTDPuq9jFv7Tx+KPuB87LP46HxD9HL3Cvg+1FVNwcP/p2D\nh15Cr0/grLNeIrXXRQHfTyQLlzrRFZFQBuHzUbd6NZWLF2NbvQYUBev06STOmoVl4rkhvfoeCccv\nWDFGQtkjRaQdy0DEG2ll7giP08mWf33E5o+X4vN6GPGTSxl/7SzMcfEhiUdVVRwOxxkTvPbc+6bX\n64M60ElDvejfvz/9+/dvfN1ms1FSUkJRURHFxcUUFxezc+dOACZOnIiqqjzxxBOtbrNhjr2GuNLS\n0k7bqmc2m+W0CxEsGj5b5DyHUrt4VS9v7XiLl7a9hEVvYe64uVyce3FQWgtran9k164HqKsrID3t\nKgYMeAi9XrbKSN3LW1ZG1QdLqXrvPTxFRWh7pZB43XUkXHcd+oyMUIcnSZLUjKr62LHyK9a+txBb\nZQX9x57L5JtuITGjd0D309Adsr2teg6H47T3vp3uXreWSVM4DXTicDgaE8WamppmCaCcY08KR3Ke\nQylg9lTu4eG1D7OjfAcX9rmQuePmkmIK/EAwquriwIH/5dDh/0OvT2b4Wf9Hr14XBHw/ktQWIQT2\nTZupWrKYmv98CR4P5vHjSb3/fmLPPw9F3pQvSVKYEUJw8PvvWL3wDcqOHCKj/0Auv+dBeg8a0q71\nPR5PuxM9m82Gy+Vqc1ttdYdsK9mL5HvfTCYT+fn55OfnhzoUSQqoyP2vjGD33HMPAH/+85+Duk5X\neVQPb2x/g5d/eJlYfSzPTX2Oi3KD07WzpuYHdu66H5ttDxnp19C//0Po9aHpAhMpQlEnAi1cyuCr\nqaF62T+pXLIE9759aOLiSLrpRhJumIUhPy+ksZ1OuBy/0wlWjJFQ9kgRaccyEPFGWpnbcvzgflYt\nfIPD27cRn5bOZfc8QJ+RY7Db7Rw5cqRdo1t6PJ5Wt63RaJoldhkZGadt3TOZTBHfQhYp52dS+IqG\n+iCTwxDYtm1bt6zTFYUVhTy89mF2Vezi4tyLmTNuDknGwE+O6/O5OHDwRQ4degWDIZWzh79GSsr0\ngO8nGnV3nQiGUJfB8eMOKhcvoubT5QiHA+Pw4WQ8+SRxl8xAE6AR6YIp1MevPYIVYySUPVJE2rEM\nRLyRUuaGuepaJnSVZWXs+/47ykqKUQxGTKMmU63V8d6XK/F98VWr29LpdM0SupSUlFNa8lp2h+xp\nIy9HwvmZFN6ioT7I5FBqxuPz8Or2V3n1h1eJM8TxwrQXuKBPcLp2VldvZeeuB7Hb95KZcT39+89F\np4sNyr4kqYHqcFCz/DMqFy/GuX07islE/GWXknDDLEzDhoY6PEmSopjP5zvt4Cytvdbm2BCqD2Ny\nKom9emG1xp7xnr2YmJgel+xJktRxMjmUGu0s38nDax9md+VuLs2/lAfHPEiCMSHg+/H5nOw/8AKH\nD7+BwZDGiLPfJDl5SsD3I0lNufYfoGrJYqo+WoZaU0NM376k/eEPxF95Bdq4uFCHJ0lSBGrP9AUt\nB2dpS9O56pKSksjKymp8bjIaKd29k4JVX+GurmTwuHOZcuMtxPVK7cbSSpLUE8jkUMLtc/Py9y/z\nxo9vkGhM5MXpLzI9JzhdO6uqtrCr4EHs9gP0zryRfv0ekK2FUtAIj4far76mcvFi7Bs2gF5P3IUX\nkDBrFuYxY+RVdEmSmnG73e1O9Ox2e5uDszSdvsBsNpOWlnbGkThbm6tOCMHeLRtY84+XqSw+RvaQ\ns5hy7xzS+/ZvZa+SJEldJ5PDEBgwYEC3rNMeO8p28NDah9hbtZcr+l7B/WPuJ94Q+IFgfD4HDIKU\nfgAAIABJREFU+/Y/z5Ejb2E0ZjJyxNskJU0M+H56kmDVie4UrDJ4ioupfO89qj74AN+JMnSZGfS6\n5x4SZl6LLiXwI+2GSiTUgWDFGAlljxSRdizbG68Q4pRkr+H30aNHo9FoWLhwYbP325pMXaPRNEvo\nEhMTT9uNMxDTFxTvKWTVwtc5VrCTpMwsrrr/YfLPGSsvagVROJ2fSZEpGuqDnOewh3L5XPx92995\na8dbJJuSeWTCI0zJCk7XzsrKTewqeACH4zC9e99Mv76/R6ezBmVfUs8lVBXb2rVULlpM3cqVIASW\nKZNJnDUL65QpKK1clZckKfI4nU7KysqaJXytJYCnS/Z0Ol2z+ejONM9eRydT74qq0hK+WfQPCtev\nwRyfwLnXzeas836CRn6GSZLUBXKeQ6lN35/4nnlr57G/ej9X97ua+8bcR1xM4O+58npt7Nv/LEeP\nLsBkzOGcke+QmDg+4PuRejZvZSXVS5dSueQ9PEeOoE1OJvmOO0i4/npisgI7+bMkSaG1fft2Pv30\nU5xOZ7PXmyZ7FouF1NTUZsldy9/DaTL1Bo66WjZ+uJitn3+KRqtl/LWzGHP5NcSYzKEOTZKkHkQm\nhyFw5513AvDKK68EdZ2WnF4nf9v2N97e+Tap5lRevuBlJvYOTtfOisr17No1B6fzKFlZt9Cv731o\ntfILLpACUSdCrbNlEELg2LqVykWLqf38c4THg3n0aHrd81viLrwQJQxP/IIhEupAsGKMhLJHikg4\nlg6Hg+XLl7N9+3bcbje1tbU88MADnR6JM5zK7PV42Pb5J2z4aAkuu51h0y7g3OtnE5sUPV3gI0Wo\nzs+kyCZUgafEhvtwLauWfMFqx3aeefn5UIfVaTI5DIHdu3d3yzpNbT2+lXlr53Gw5iAzB8zk3lH3\nYo0JfNdOr7eOvfv+h2PH3sFkymXUOYtJSDhjC7bUCV2tE+Ggo2Xw1dmo+eRjKhctxrV7NxqrlYTr\nrydx1g0Y+ve8ARoioQ4EK8ZIKHukCPdjeeDAAT766CNqa2uZPn068+fPRwhBVlZWp7cZDmUWqkrB\n+jV8s+htak6UkjtiFFNm30qvnNxQh9ZjheL8TIo8qt2D63At7sM1uA/X4j5ci3D7AMhV0lhetDrE\nEXaNTA6jnMPr4MXvXuSdXe+QYcnglQtfYULmhKDsq6JiLbsK5uB0FpGTfTv5+b9Dqw3/icSl8Ocs\nLKRy0SJqPv4E1W7HMGQw6Y89Svyll6KxWEIdniRJQeD1evn6669Zt24dycnJ3HHHHfTu3bvtef8i\nyNGdP7Jq4euU7NtDrz55XPuHx8kdPjLUYUmS1IJQBd4TdtyHanEdrsF9qAbvifopaTSgT7dgPicV\nQ584YnJiufnaGaENOABkchjFtpRsYd66eRypPcINA2/gd6N+h0Uf+BNpr7eWPXufpqhoCWZzPqNG\nLSEhflTA9yP1LKrLRe3nn1O5aDGObdtQDAbiZswg8cZZGIcPlyP2SVIUKy0t5cMPP6S0tJTRo0fz\nk5/8JCzvE+yoiqKjrH7nLfZt2YA1KZmL7/odgydPQ6ORg81IUjhQnV7cR2pxH6ppbB0UTn+roMas\nIyYnDvM5qcTkxBGTFYvGEH3/uzI5jEJ2j50/f/dnFhUsore1N6//5HXGZowNyr7Ky1exq2AuLtdx\n+uTcSV7eb9FqjUHZl9QzuA8fpnLxEqo//BBfVRUxffqQ+sADJFx9FdqEhFCHJ0lSEKmqyoYNG/jq\nq68wGo3cdNNNUTE0vL26inXvv8sPX32O3mBg0qyfcc4lV6A3yO9LSQoVIQTeMgfuQ/4k0HWoBu9x\nOwhAAX2aGfPwXv5EsE8suhRTj7gwLZPDEBgxYkTQ1tlUvIl56+ZxrO4YswfP5u6Rd2PWB34gGI+n\nhj17n6S4+AMslv6cNexvxMd3vFxS53WmHoWbhjIIr5e6lSupXLQY29q1oNUSe/75JM66AfP48Shd\nnC8sWkVCHQhWjJFQ9kgRLseyurqaZcuWceDAAQYOHMgVV1yBpZVu44GIt7vK7HE5+fbTf7L54w/w\nuFycfeEMJlx7I+Z4eaErHAXz/EwKPdXlw33U3yrorm8VVO3+6W4Uo9bfKnhWCjF94ojJjkVj7Hia\nFA31Qc5zGCVsHhsvfPsCSwqXkBObw2MTH2NUWnC6dpaVraCg4A+4PWX1rYW/QaMxBGVfUnTznjjh\nn6z+/Q/wlpSgS0sj4frrSJh5Hfq01FCHJ0lSN2mYosLn8zFjxgxGjhwZ0VfoVdXHztUrWLtkAXUV\n5fQbM57JN/2cpMzOD6IjSVL7CSHwVTj9XUMP1eA+XIOn2OZvFQR0qSZicuIwNLQK9jKjaCL3M6c9\n5DyHPcj6ovXMXzefYlsxPxvyM3498teYdIEfCMbjqWL3nscpKVmGxTKA4cP/j7i4swK+Hyn6uQ8e\npPz1N6hetgzh8WCZOJH0h/6Addo0FJ38WJKknsLhcPDpp5/y448/kpWVxTXXXENSUlKow+qSg99/\nx+qFb3Di8EHS+w3g0rt/T9bgYaEOS5KimvD4cB+tq+8eWt8qWOcBQInREpMTS+z0bGL6xGHIjkVj\n1oc44vAlz8JC4OabbwZg4cKFXVqn1l3L81ueZ+mepeTG5fL2jLcZkRqc5uwTJ/5DQeHDeDyV5Ob+\nmrzc/0KjifzBASJZZ+pRqDm2/0j5a69R++9/o+j1fJsQz4a0NP76+muhDi0iRUIdCFaMkVD2SBGq\nY9lyiopJkyah1Z55cIdAxBuMMp84dIBVC9/g0A9biU9N49Lf3s/ACZMjugW0pwnU+ZkUXEIIfNUu\nf4tg/SiiniIbqP5mQV2yEeOARP+9gjmx6NMt3dYqGA31QSaHIXD06NEur/PNsW+Yv24+JxwnuHXo\nrdw14i6MusDf2O52V7B7z2OUln6C1TqYEWe/Tmzs0IDvR+q4ztSjUBBCYFu7jvLXXsO+YQOa2FiS\nf/ELkn56M7+67jooKwt1iBErEupAsGKMhLJHiu4+ll6vl6+++or169c3m6KivQIRbyDLXFtRxtol\nC9mx6iuMZgtTf3o7Iy66DJ1etkxEmkCcn0mBJ7wq7mN1jfMKug7VoNa4AVD0GvRZscRO6d2YDGqt\noWu8iIb6IJPDCFPjruHZzc+ybO8y8uPz+dO0PzG81/Cg7Ov48S8oKHwYr7eavLx7yO3zS9laKLWb\n8Hqp/fe/KXvtNVw7d6FLTSX1978n4Ybr0VqtoQ5PkqQQKC0tZenSpRw/frzbp6hoGGNBAAIFFPCq\nov55/aNoeN//Gs2e+7fR8LvH6eC7zz5h67+XI4TK0Mtncs4lV2IwWyhXQbg8/u22sY3GWASnjaFh\naIiG12gRR8ttnC7mk8s2iaGV/bUVL61soz0xny4GUb9gWzHQyjZouY12HePW4j25PsCJ6ZcgNBoe\n3XsMrxB4hL+OeIWof97k9/rXD//svxAaDVd+t6fZMWqq5fAeosUSpyx/xvU7tr2WL5xxfy231+H1\nWzw/Q/lbUn0C4VURHv+P6lUb31MSFUg1oujNKDoNik6p35oDahzwY2knjt/p4zvT8CxN3z5+zyNk\nfhS5rYYgk8OI4s5yc/Wyqyl3lnPHWXfwq7N/hUEb+IFg3O5yCnfP5/jx5cTGDmXw4LeJtQ4K+H6k\n6KQ6nVR/9BHlb7yJ58gRYvLyyHjiceKuuAJNFMxTJklSx7WcomLWjTfSu28/Sr0+auocVHt8VHu9\nVHt9jT81Xh9VHv9jjddHVf1jyYN/RNXpyFq5DWieLDR7frqAHnkBgKxV33etYL2GwOwhJ59vO9i1\n7UkhowBi0gUgVN46Vo5OAb1GQaco6BUFbf2jTqOgU2h8XSgKiupD36L7cMvexC3eRTnllRbLn6EX\n5CnLt9zeafd/5v2dcftnWL+lNrcnBKrTi7B7UW0eVLsH4a5PBjWgNenRmGPQWvRoLHoUnaaN/Z8h\nvtOHF7DyL9+wBq3ddoa9hTeZHEYAIQR1E+pwDXKRZkjjxfNeZGhK4Lt2CiE4fnw5hbvn4/XW0Tf/\nXnJyfoFGI7vGSGfmq66mctFiKhYswFdejnH4cFLv/z2x558vp6KQpCjk9KnNkrmTCZ2XmiavlTuc\n7C0updrrQ4y/CK/ByAtFDtSi7afdfpxOQ7xOR7xOS5xOS77JQJxOyxcr/4PG62H27NmNJ4QNJ2pt\nnbCdfN//21tvvgnAbbfdilL/voKCojTflqIoje8DVBw9zP5vN+KoriI+NZ1+Y8YT3yv15DaaLK/U\n778hDVDqN9ra/lpbn1bWPyW+M8TcWgxNj8mZYjgZs9Iipo7H3PhaJ+OllW20J+bTxdCwboNp06YB\nsHLlStpr2r13AvDBbe1fpyfz1bkb7xN0H6rBc6wO4fEng9r4mPo5Bf3dQ2MyrY3JYKTY+qvFoQ6h\ny2RyGAITJkzo0PILdi7ANchFfkU+S25eQow28K0vLncZhYXzOHHiC+JihzN48DNYrZE/8XA062g9\nChZPaSkVb/2DqiVLUO12LJMnk3zHHZjHjjnjQAzhUoZIFQnHL1gxRkLZI4FPCIZNPx9XjIHVFbXN\nkz2P92TC5z21Bc+lnr6vlUmjYBYCYavF4PWQl5RIdlIiCfqTCV+8Xku87uRPXP1jrE6Lto3Pjzlv\nlwIwt29mp8td7qkB4L9z09u1fMm+Paxa+Dr2nT8yMTOLKTf9nL6jx8nBZqJMZz5X5GdR24RP4Cmx\nNbtX0Ffh9L+pVYjJtGIZm16fDMahS4j8adGioT7IeQ7D3Pcnvufnn/2cqdlTeWHaCwH/IhJCUFr6\nCYW7H0VV7eTn3UN29u1oNPK6gXR6rn37/NNRfPIJqCpxM2aQfMftGAfJLsiSFE6EEJR7fOyzO9nn\ncLHf7mJf/c9Bhwt3G+cBWoUmidvJhC5BfzKJa/kTV5/s6T1uvvzsM3788Ueys7O5+uqrI3KKiurj\npXyz+G0K1q7CFBfPuTNv4qzzL0Irp9yRpFP4bB7cR+rnFTxUg/tobWMXUU2s3j+vYEOrYO9YFH1k\ntQpGOjnPYRSoclZx36r7SLOk8djExwKeGLpcxykofJiysi+JixvJkMHPYLH0Deg+pOjj2LaNstde\no+7Lr1CMRhKvv56kW39OTJac3FmSQsnm83HQ4Wav3dksAdzvcFHt9TUup1cUck0x9DUbuDAljnyT\ngV4xulMSPrNW06nvnf3797Ns2TLq6uo6NEVFOHHW1bFx2Xts/exjFEXDuKtvYMwV12Iwm0MdmiSF\nBaEKvMftuA75WwXdh2vwnnD439SAPsOKeVRafTIYhzbRIFvaI4RMDkPg2muvBWDp0qVtLqMKlbnf\nzKXcUc6CGQu49cZbz7hOewkhKClZxu49j6GqLvr1m0NO9q0oSmR9efd07alHgSKEwLZ6NeWvvoZ9\nyxY08fGk3PX/SLz5ZnRdaA3ozjJEo0g4fsGKMRLKHgxeVXDU5Wav3cV+u7Mx+dtnd1Hk8jRbtrdB\nT77ZwNVpifQ1Gcg3G+hrNpBliEHXZM6vQB1Lj8fD119/3ThFxe23396hKSraKxDxtrUNn9fDti+W\ns2HpIpx2G0OnnM/EG24mNjml8wFLEaMzdaunfBapDi/uI7X1yaA/IRQu/0UnjUVHTE6cPxnMiUWf\nFYsmpmeeU0ZDfZDJYQiUl5efcZk3f3yTNcfWMHfcXIamDG3XOu3hdJVQUPAQ5eUriI8fxZDBz2A2\n5wVk21L3ClSdOB3h9VLz2WeUv/Y6rsJCdBkZpM15kISZM9FYLF3efneUIZpFwvELVoyRUPbOEkJQ\n5vE2tvz5E0BnfTdQN54m3UDjdVr6mg2cm2Cln9lAvtlIX7OBPJMBs7Z9XbYCcSxLSkr48MMPOX78\nOGPGjOHCCy8M2hQVgYi35TaEEOze8A1rFv2D6tIS+gwfyZTZt5Kam9/lfUmRozN1Kxo/i4Qq8JY5\n/Elg/eAx3uN2/xDACujTLJhH9GrsJqpNNspWwXrRUB9kchiGvi39lr9u/SsX5V7ErIGzArJNIQTF\nxUvZs/cJVNVD//4PkZ31M9laKLVKdTio+mApFW++iaeoiJh+fcn449PEX3opipzYWZICwub1Nbb6\nNW0B3Gd3Uus7Oa9XjKKQZzbQ32zkopR4+poN9S2BRpL12pCelKmqyvr16/n6668xmUzMnj2b/v37\nhyyezjhasIPVC96geG8hKTm5XDvnUXJHjAp1WJLUbVSXt/5eQX/3UNfhWoTDC4Bi0mHIicU8vBcx\nfWKJyY5FY5DpQzSTf90wU+4o5/5V95MVm8X8CfMD8qXvdBaxq2AuFRVrSEgYy+BBT2M253Y9WCnq\neCsrqXznXSoXLsRXVYXpnHNIe+ghrNOmyukoJKkTPKrgiNPtHwymWQLoosR9ajfQfmYjM9OTmiSA\nBrKMMW2O3BlKVVVVLFu2jIMHDzJo0CAuv/xyLAHoUdBdLFqFfz73JHs3r8eamMRPfnU3Q6eej0Yj\nL5pK0UsIga/c2TiVhPtwLZ4SW+PEoLpUM+ZhKf5BY/rEoUsxoWjC7/NHCh6ZHIYRn+pj7jdzqXJV\n8dIFL2GNsXZpe0IIioqWsGfv04DKgAHzyeo9G0WRJ/lSc56iIsrfeouq9z9AOBxYp00j+Rd3YB4l\nr55L0pkIITju9jYmfw0Dwux3+EcD9TYZDDSxvhvolCQrfU3+LqB9zQZyTQZM7ewGGg5++OEHPv30\nU4QQXHnllYwYMSJiupXZa6oZGhtDrknPoe3bmHj9zYy69Cr0RmOoQ5OkgFPdPjxH65olg6rNf2FK\nMWiJyYkl9rwc/8Ax2bFoTDI16OlkDQiB888/v9XXX93+KuuK1vHIhEcYmDSwXeu0xeE4RkHBHCoq\n15KYMJ7Bg/+IyZTd6Zil8NPROtEa5+7dVLz+OtWfLgcg/tJLSbr9NowDumeOy0CUoSeLhOMXrBhD\nUfY6r69xKojGEUHrn9c16QZq0CjkmQwMtBi5JCWefLOBfmYj+WYDSfrw+9rtyLF0OBx8+umnIZ2i\norN/e6/bzdbPP2HjR++RZ47Bm5jK7X98HktCYoAjlCJVZ+pWOH0OCyHwVbqazSvoKbZB/ZykuhQT\nxkFJxOTEYugThy7VLFsFAyyc6kNnyXkOw8TG4o3c+Z87mZE3g6cnPd3pK7BCqBw7toi9+54BoF+/\nB+mdOUu2FkrN2L/9lvJXX6Nu5UoUs5nE62aSdMst6DM7P6m0JEUDjyo45Gw+F+A+hz8RLHV7G5dT\ngCxjDH1N/pa/hpFA+5qN9Dbo0URIK1pHNJ2iYtq0aUyaNAlNBHQ3F0JQuH4Na979BzUnSskbOZqp\nN99GclZOqEOTpC4RHhV3UV3jvIKuw7WotW4AFL2GmOzY+gnmY/3TSVjkmAE9mZznMIKUOcp4YPUD\n9Inrw7zx8zqdGDoch9m1aw6VVRtISpzEoEFPYTIFfhhxKTIJVaVu5UrKX30Nx9ataBMTSfnNr0m8\n6SZ0ifLKudRzCCEocXv83UAbE0D/74ecLnxNrpkm6bX0NRmZlhRXPxqogXyTfzRQYwR1A+0Kj8fD\nV199xYYNG0hJSeGOO+4gM0IuJB0r3MWqt1+jeG8hvXJymfmHJ+gzfESow5KkTvFWuxq7hroP1eAu\nqqPhA0ubZMTYN74+GYxDn25B0UbfRSop+GRyGAIzZswA4LPPPsOn+rh/9f3YPDZe/cmrmPWtT7Db\ndJ2WhFA5enQBe/c9i6JoGTToKTIzro+Y+z+kzjldnWhKuN1Uf7qc8tdfw713H/revUl76CESrr0G\njcnUHaG2qb1lkFoXCccvWDG2Z7s1Xl99AuhkX/1AMA1dQe1NuoGa6ruBDrEauTw1odlgMIlh2A00\n0E53LLtzior2as/fvqqkmDXvvsXujWuxJCZx0a9+y5Cp5zUONhMJ/ztS9+tMvQhWXRJeFU+x7eS8\ngodq8VW7/G/qNMRkWbFO6o2hoVUwNrT/l5JfNHy2RP+3XhhyOByNv7/0/UtsLtnMExOfoH9i28N/\nN12nKbv9ILsK5lBVtYnk5KkMGvgERmNkXNGVuqatOtFAtdmofP99Kt76B96SEgwDB5L57LPEzbgY\nRRce//pnKoN0epFw/IIVY8N23arKQYe7Menb1+RewBNNuoFqgGxjDPlmA+MSLPQ1GxsTwMwo7Qba\nXq39jcJ5iorT1SlnXR0bPlzM1s//hUanZcLMmxhz+TWnDDYTCf87UvfrTL0IVF3y1bobu4a6D9fg\nPloHXv9FLG2CwT+NRJ/eGHLi0GdYUHQ9o+dCpImGz5Z2nSEqivI/wBOAA/gcGA78TgixMIixRb11\nx9bx6g+vclW/q7iy35UdWlcIH0eO/IN9+59Ho9EzePAzZKRfK1sLJbwVFVQsWEDlu4tQq6sxjxlD\nxmOPYpk8WdYPKWLVeX3stjsptDnZbXNy5MZf4E5JJXfVD6hNlkvW6+hnNnBBchz5JkPjxPC5phgM\nEXB/XDhoOkXF4MGDueyyy8J+igqf18O2L5azYekinHYbw6ZdwMTrb8aalBzq0CTpFMIn8JTY/HMK\n1ncT9VU4/W9qFWJ6W7GOzyCmTyyGnDi08YbQBiz1KO1tPviJEOJ+RVGuBg4C1wCrgW5PDhVFuRj4\nC6AFXhNC/LG7YwgEn9nHg2sepG9CX+aOm9uhdW22/ewqeIDq6u9IST6PgYMex2hID1KkUqRwHz1K\nxRtvUrV0KcLtJvaC80m+4w5MZ58d6tAkqd3qvD5225wU1ieCDcngMdfJOQFjFAUlLgFj0VHuGDms\ncTCYfJOBhB7QDTRYhBBs3749oqaoEEKwd9N6Vr/7JlUlxfQZPpKpN99Grz55oQ5Nkhr5bJ6T9woe\nrsF9pBbh8V/W0sTF+LuGjs/w3y/Y2ypbBaWQau+3aMPwRpcC7wshqkPxZaEoihb4G3AhcBTYrCjK\nx0KInd0eTBcIRVA3tQ69T8/z057HpGvffV+KIjh0+FX2738BjcbIkCHPk552ZVh/cUvB5ywooPzV\n16j5/HPQaIi/4nKSb78dQ35+qEOTpDbVtkgCd7eSBBo0Cv3MBsYlWBlg9k8NMcBipI/RwAXn/RaA\nB35za6iKEFV0Oh0ffPABO3bsCNkUFR1VvLeQVQte51jBTpKzcrjmwfnkjhglvxOlkBKqwFNqr79P\n0J8QesvquxpqFPSZFixj0v3dRHPi0CYYZJ2Vwkq7prJQFOWPwFX4u5WOBRKAfwkhxgU3vFPimADM\nF0JcVP98DoAQ4um21gm3qSyOHdzH3z96FYfWRZo+GauufV11ausqsOYWEhNfha86C1/xOPCGdjAR\nKbSqjp/AXGcjxukGjQKpKZCWihITOUNVl5aW4sZDRnZGqEOJSIeOHMGBiX79u2deys7Ys2cPPo2G\n2EGDKNbp6n/0VGm1jcvohSDd6yHd6yXd6yXD6yXD6yHF56Ot6+ff//ADXq2HiZMmdk9Boti6tWvR\naXxoNAr5/QbSJ69/WJ+sfvavTzGXHcZUVUyMNY6+F19L5thpaJrUqTNZsMDf8emWn/0UrUZBq1HQ\nKP5HraKg1fofNRr8zzVKq8tp5BxxUeVvz7yIUdXz05/e3O51FixYiFHouGz0hf6WwSO1CJcPAI1V\nT0xOXOO8gvreVjQx7a+nUuR57rnnALjvvvtCHMmp2juVxWmTQ0VRrhNCvK8oSh5QDVQLIXyKoliA\nWCFESeBCPjNFUWYCFwsh7qh//lNgnBDi122tE27J4Ztv/5U52ZNDHYYkSVK30/m8JNjrSLTXkmSr\nIdFeS6Kthlinvc0kUOoeGpcDY9EBtE57qENpF6+iZWvc2XybMBKPJrSjNOo0/iSxIYnUKKDTauqT\nSBqTyFOXU9BpWyaboNNo6pejeTKqaZ6UNk1iTy4HWo2m2X5bX66V7Wnwx1S/fsNyDbGfXK4+9pbJ\ncsO+G5LqJuu0WnZN6JNr4RO4D9fgLKjAUVCBt7QL9V8BfbrF3zW0TxyGnFi0ScawvtAi9SyBmudw\nDvA+sFQIcU7Di0IIG2DrWojBoyjKncCdADk54TXJ7eRRU7lz3b/QoaUjHxcCUG0JCJ8cqliqpygQ\no/c/RioBejUGi8+MVmjwKF7sejt2rR2hnLlXQ4+n+jA4itF5HXh1JlzmLDyGeOjQp0vwaYQg2e0m\nwe1ukQSawWSGTnSCEKrAVeHEU+VG+EBr0mJIMWJIMaCVV+Y7RFEULNY4NJrzQx1Ku5kzchgdl8gv\nurANIUAVAlUIvKrAp/p/96mgqvWvCYHa5L3G5Zq813w56rfhf73pcj7R5LXGfdW/Jhq2Cw6fr9Xl\nfPXbaLp/nwo+Va1fjmbL+NTI+Aw9JTGuT4pbJpGnJJtNkldtk3VOaQXWNF1OwaJCfq2P/Bovfaq9\nGH0CVYGSOB3H8szYLDp0Wg16rX8/eq0GnVbxv6bx/97wqNPWv2/UIdJMuAw6NIr/AoGCQKl0oNEo\nKHDydUVBUZo8R0GpT8Yblmt4v9lykfxdL0WUMyWH5Yqi/BvIUxTl45ZvCiGuCE5YbToGZDd5nlX/\nWjNCiFeAV8Dfctg9obVPv6HDWf1fdwOwcuXKdq83bdq0Dq8jRbdoqBMNZVjx5dfYt5dhW1+E+3At\nil6DeWQqlgmZxGSE9yiJoTRt2jQUBCv++htY8SSU7ofeo+H8eZA/NdThAcGrpw3b/eKzL9n33XEK\nN5RQtKcK5yHI7J/AwHHp9B2VisEkB6g5k0j7LAlEvJFW5s5SWySLTZPdpq+rKs2Xa5qUNkmCm25P\nFQKv72RC3b7l2toebS53SmLcIk6foNlyHp/afDmfINMtOMsFZ7sV+nlBg0KVIlirVdls8PGt4qPO\nKajba0coWtCE5wWmxqSR1pNIRfFfGtTUJ8j+hln/4ynLNSaeTdZr+lxpvp7SmMyeXA6FxmXaWu5k\nMtxyvVOX0zSJq+V6Cg3b7vhySpP4NE3Xo/65pvl6Dcs1Pgc09Qk8LY5Ty+Xmzp2Doa5ZvpvOAAAg\nAElEQVSYNV9G7zyHlwLnAAuA54MfzhltBvrXd3M9BswCbgptSJIkdZWi02AZmYplZCruY3XUrS/C\n9t1xbJtKiOkTh3VCBqZhKXIEt1YIFDhrJgy5Era9C6uegbevgLyp/iQx64w9SCKawaRjyMRMhkzM\npKbMwe5NpRRuLGHFwgJWL95N3tkpDByXTvbQJLRaWX+knkWjUdCgoA/PXCdoVLcP194qnAUVOAsr\n8FW7AdD3tmIclIRpUBK9e1sZplG4pcl6DRcNvv56BW6fisur4vaquH3+R5fX539e/+Pyqdz/4B9A\no+HheY8ghGjSIk2z54LWX29rOVGf9DZdj/rHxvU4dTlRv72my4H/AkDL7TddTtRvr+lyLd9vbTl/\ni7XafDtN4mvcXsPz+uVoUQ5VbbHeGY7T6ZYLqUHXkrbr/RAH0TWnTQ6FEG5gg6Io5wohTiiKYhZC\nhOyGBCGEV1GUXwNf4J/K4g0hxI5QxSNJUuDF9LaSNHMACZfkYfu2lLoNxVQsLkRj3Y9lbDqWcRno\n5JxPp9LqYdQtMPwG+PZNWP0cvHY+DJgB5z0E6cNCHWHQxaWYGH1JLqNm9OH4oVoKN5SwZ0spe789\njilWT//RaQwcn06vnFjZRUuSooy3wtl476BrfxV4BUqMFmP/BIwXJGEcmIQ2rn235mg0CkaNFmM7\nsmpL5R4Arjg7s0vxS4HTWhIpWiS5bSWbLZdrbT2aJLmq2ny9X/7yV+idFaE+BF3S3v42/RRFWQVY\ngRxFUc4GfimEuCt4obVOCLEcWN7d+5UkqXtpzHpiJ2dhndgb155K6tYXU7viCLUrj2AanIxlQgaG\nvgnyJL8lvRHG/z8Y+VPY+DKsfRFengTDroFpcyGlX6gjDDpFUUjLjSMtN46J1/Xj8I4KCjeUsGNN\nET+sOEpiupkB49IZOC6d2CRjqMOVJKkThE/FfagGR0EFzoIKvMf900XoUkxYx2VgHJSEIS9e9jjp\ngRq7mobg/nuDrVvH6gyK9iaHfwYuAj4GEEJ8ryjKlKBFJUmSVE/RKBgH+q/6eiuc1G0sxr65BMeO\ncnS9TFjHZ2AelYbGKO8ta8ZghSn3wZjbYd1fYcPfYccyGDkbpj4A8VmhjrBbaLUa8oankDc8BZfd\nw95vj1O4sYSN/9zPxn/up/eABAaMS6ffOanEyPsTJSms+ercOAsrcRZW4NxdiXD6QKtgyIvHMtaf\nEOpT5DRfktQV7f4mFEIcaXGF3hf4cHqG66+/vlvWkaJbNNSJjpZBl2QkYUYe8Rf0wf7DCeo2FFP1\nyX6qvziIeWQq1gmZ6NN7zgA27Tp+pkT/vYfjfgVrnoctb8D3i2H07TD5v8GaGvoYu2m7BrOeoZN7\nM3Ryb2rKHBRuLPHfn7jAf39i/tkpDBiXTs6QJDQ96P7ESPssCUS8kVbmnkoIgafI5r93sKAC99Fa\nEKCJ1WMaloJpUBKG/gloDIG5sCPPz6Suiob6cNp5DhsXUpQPgD8B/wuMA34LjBZCzApueF0XbvMc\nSpIUWO6jtdStL8b+/QnwqsTkxWEdn4lpWDJKDzrBb7eqI/5Ba7a9CzqDvwvqub/xJ5E9kBCC0gM1\nFG7035/osnkxxcUwoP7+xJRsq+y6LEndSHX5cO2txFlQiaOwArWmfjCZLCumQUn+1sFMK0qI50iU\npEjT3nkO25scpgB/AS4ANPgHhPmtEKK8q4EGWzgmh3a7f0wfs9kc1HWk6BYNdSKQZfDZPNjrB7Dx\nVTjRxOqxjM3AOjYdbZQOYNOl41e2F1Y+BT8uBWM8nHu3v3XRYA2fGLt5uz6vyqEfyyncWMLB7WWo\nXkFihoVB49MZMDYNa2J03p8YaZ8lgYg30soc7bzljsZ7B137q8EnUAxajAMS628rSEQbG/x5nuX5\nmdRV4VwfApocRrJwTA47M79ST5mTSWq/aKgTwSiDUAXO3ZXY1hfh3F0JCpiGpmAZn4EhPz6qWoEC\ncvxKtsPXT8Luz8DSCybfC6Nu9Q9sEy4xduN2Gzht9fcnbiihZH81KNB7QKJ//sRzehETRfe4Rtpn\niZznMPIJr4rrYE3jVBPeE/WDyfQy+ZPBwUkY+sR1+2Ay8vxM6qpwrg/tTQ7b9e2mKEoW8FdgYv1L\na/C3HB7tfIiSJEmBp2gUTPXzWHnLHf4BbLaU4thehi7N7B/A5pzUgN2jEvHSz4KbFsORzfD1Y/D5\ng7Duf2Hq/TBiNmh75nEyWvQMm9KbYVN6U33CTuHGUgo3FPP127tYvaiQvBG9GDg+nexBiT3q/kRJ\n6ixfbYvBZFz1g8nkx2MZn4FpUBK6ZDmYjCSFWnu/9d8E3gWuq39+c/1rFwYjKEmSpEDQJZtIuCSf\n+Av7YP/+BHXri6n65z6qPzuI+ZxUrBMy0Kf1nAFsTit7DNzyCexfCV89Dp/cDWv/AtPnwtBrQNNz\nE6D4XmbGXpbHmEtzKdnvvz9x75ZS9mwuxRwXQ/+xaQwcl05Klrw/UZIaCFXgKaprnHvQc7QOAE1c\nDObhvfxTTfRLQGM481yCkiR1n/Ymh72EEG82ef6Woij3BCMgSZKkQFP0Wiyj07GMTsd9pJa69UXY\ntpRg21Dsv2o9IQPTEDmADQD50yBvKuz+3J8kLr0dvnkBpv8BBs6AHpz8KIpCRt94MvrGM/m6/hz8\nsYzCDSVsX3GU7788QlLm/2fvzsOjOs+7j3/PjGa07zvaEIskdhA7tjEYbEy8xEuc2I2T2mniJE3a\npHFaN3sbZ2uapm3apnmzNI4b19hObMdOwmqMjcO+iB2xIwQSCATa15nz/jHDYhuDQDM6i36f65oL\nZqQzc9+PHuNz6zznuRMpn5FH2dQ8ktLdeZ+ryJUEu3rp2n8udP9gdSPBlh4wwF+UTMqtJeHNZBL1\nSxQRG+trcXjGMIyHgWfDzx8CbL8ZjYjIO/mLkskoKif1jmG0bQwViI3P7MWT4idpWh6J0/LxpkR/\n4wNbM4xQIThyAex6EV7/Dix6CAqmwLyvhQrIQc7r8zB8Ug7DJ+XQ2drD/k0nqV5fz9oXD7L2pYMU\nlqdTMSOP0onuuj9R5J16Gtrp3BtaLtp1OLyZTNw7NpNJGuT/poo4SF//j/UxQvcc/itgAmuAR6IU\nk+s98sgjA3KMuJsb5oSVOXgTfaTMKSJ5diGdextpXVdH84oamlceI35sJkkzhuAvTbH1b7ijPn4e\nD4z7AIy+B7b9H6z6J3j6/VA6G275emgpqkUx2mn+xyX5GDenkHFzCjl3sp3q9fXs21DPiqf2EOOv\nZtikbCqm51NQkY7Hhtvv22ks+yIS8TotZzsxe4N0HW4KbyZzlt7T4c1kchJIuqGA+Ip0/CUpjlyJ\nofMz6S83zIe+trL4FfB50zTPhp9nAD8wTfNjUY6v3+y4W6mI2FPv6Q5a19XRtukkZmcvvrwEEmcM\nIWFSju6LAejphM2/hNX/Am0NULYQbvkq5I21OjLbMU2TuoNNVK+r58DmU3R39JKY6mfktLwL9yeK\nOEWguZvO6tC9g137z2F2ByDGIHZY2oXegzEZ7mz1IuIWke5zuNU0zUlXe82O7Fgcnj59GoCsrKyo\nHiPu5oY5Ydccgt0BOrY10Lr2BD0n2jBivSROziVxRj6+HPv0LrJs/LpaYf1PYM2PoLMJxt4Pc74M\nWSMGLEa7zp3L6e0JcGR7qH9izc4zBIMmmYVJlE8P9U9MtLgPp5PGEiITr9NyHmhm0KTneOuF3oM9\nx0ObyXhT/cRVZBBXHt5Mxu+uX5rp/Ez6y87zIdLF4TZgzjuuHL5hmua4fkcaZXYsDtVHRyLBDXPC\n7jmYpkl3TQtta0/QvuM0BExih6eSNHMIcaMyMbzWLhG0fPw6zsKa/4B1P4HeTpj4Z3DzE5BWFPUY\nLc/9OnW0dLN/0ymq19dz6kgzhgFFozIom57HsInZ+Cy4Qu20sVSfw+gIdvbSue9saLnovrMEW8Ob\nyRSnEFcRun/Ql+/uzWR0fib9Zef5ENE+h8C/AGsNw3gh/PwB4NvXG5yIiBMYhkFsSQqxJSmk3tlN\n28aTtK2v48yv9+BN9ZM4LZ/EaXl4kwfpZgvx6TDv6zD9U7D6h7DpF7D9OZjyMbjpcUjKsTpC24lP\n9jN+biHj5xZytr4tdH/i+pOs+OVufLFehk0K9U8sKLPn/YniHqZp0tvQESoG9zbSdaQZgiZGXAxx\n5enEV2QQW5aON9FndagiMoD6VByapvm0YRibgFvCL91nmubu6IUlImIv3iQ/KXOLSL65kM49jbSu\nO0Hz8qM0r6whfmwWSTPzQ5swuPi36u8pKQcWfg9mfgbe/D5s+BlseRqmf4qkmF5ae7Vb5+Wk5yUy\n4/3DmX7XME4cOEf1+noObj5F9bp6EtNiKQv3T8ws0P2JEhlmb5CuQ00Xeg8GGjsBiMlNIPmmAuIq\nMvAXp1i+KkJErNPn/2OHi0EVhCIyqBkeg/gxmcSPyaSnoZ22dXW0bT5Jx7YGfHmJJM7MD21g47J7\ncfokrQju/g+44fOh9hdv/ZBFM7wsqsmBrhaITbY6QlsyPAYFZekUlKUz+0NlHN5+mur19VStOMbW\nZTVkFYXuTxw51fr7E8V5Ak1ddFQ30rn3LF0HzmJ2ByHGQ9yINJJnFxBXnkFMujaTEZEQ/TpXROQ6\n+bITSLtrOCkLhtJedYq2tXWce+kATYsPX9zAJts+G9gMmMzh8IFfwI1/Q9W33sfHh9XBv0+AGz4H\nUz8B/kE4Jn0U4/cyckouI6fk0t7cHeqfuK6eP/3mAGtePEjRqAzKZ+RSOiEb32D8BYRclRk06a5t\noXNPeDOZujYAvGmxJFTmEleRQeyw1MH5CywRuSoVhxb49Kc/PSDHiLu5YU64IQcAj99L0rR8Eqfm\n0X20mda1dbSuq6P1TyeIHZlG0owhxFVkRHyplu3HL28sx2/6Z5Z3HOZW7wZY/nVY859w49/AlEfB\nF3/db2373CMgIcXPhFuKmHBLEY115+9PrGf5L87gi/MyvDKH8ul5FIxMw+jH/YlOG8tIxOu0nK8m\n2HHpZjKNBNt6Q5vJlKSQcvtQ4isyiMlNGJzL3q+Bzs+kv9wwH/q0W6mT2XG3UhFxv0BLN20b6mnb\nUEegqRtvWiyJ0/NInJqHN2mQbmBTsy603PTwG5CcH9q0pvKjEKOlkn1lBk2O7794f2JPV4Ck9FjK\npof6J2bkJ1odogwA0zTpPdVO596zdOw9Q/fRZgiCJyGGuLL0ULuJsnQ8CdpMRkRCItrKwsnsWBwe\nO3YMgKKioqt8Z/+OEXdzw5xwQw5XYwZMOvecoXVdHV0HzoHXIGFcFokzh+AvTu7Xb/KdMH6XjfHI\nW7Dy21CzBlIKYfYXYeKHIabvRbMTco+2nu4Ah7c1UL3uJMd2n8E0Ibs4mfIZeYyckktCSt/G02lj\nGYl4nZYzgNkToDO8mUzn3kYCZ7sA8OUlhorBURn4i5L7dRV5sNP5mfSXneeDisMwOxaH6qMjkeCG\nOeGGHK5Fz6mLG9iYXQF8QxJJmjGE+InZ13X/jxPG7z1jNE04tApe/zbUboS0Ypj9dzDhIfBe/Y4H\nJ+Q+kNqauti/8STV6+s5fawVw2NQPCaD8ul5lI7PIuYK88tpYzmY+hz2nuu62Gri4DnMniCGz0Ps\niLQLzehj0nTlPVJ0fib9Zef5EOk+hyIi0k++nATS7g5vYLP1FK1rT3D2xf2c++NhEqeEN7DJuv77\n8BzFMGD4XBg2Bw6sCBWJr3wW3voh3PwEjHsAPNowo68SU2OZOL+YifOLOXO8NXR/4oaTHN2xC3+c\nl+GTQ/cnDhnRv/sTJbrMoEl3TTOde0P3D/bUhzeTSY8lYUpuqPfgsDQMn8fiSEXErVQciogMME+s\nl6QZ+SROz6P7SDOta0/QuuYErW8dJ7YsnaQZ+aENbAbDSbxhwMhbYcR8qF4cuifxpU/Cmz+AOX8P\nY+4Dj06Er0VmQRKz7hvBjHuGc3zfWarX1bN/0yn2/KmO5Iw4yqaH+iem5+n+RDsItvfQue8sHXsb\n6dp3lmB7L3jAX5JK6sJS4irSicnRZjIiMjBUHIqIWMQwDGJLU4ktTSXQ3E3bhjpaN9Rz5undoQ1s\nZuSTOCV3cGxgYxhQ8T4oux32vgqvfxd++xehInHul6DiLhWJ18jjMSiqyKCoIoObHwpwqKqB6vX1\nbFlylM2Lj5JTkkz5jHz83ni6Ax1WhztomKZJ78l2OsLLRbuPNoMJnsQY4sozLm4mE69TNBEZePqX\nR0TEBrwpflLml5A8t4iO3WdoW1tH85IjNK84SsK4bBJn5oc2m3D71QOPB0a/P1QM7n4JVn0Pnv8o\n5I6DuV+G8oWhQlKuiS/WS3l4R9O2pi72bQjdn7j6uX3cNeILHDy3ma72HmK1u2VUBLsDdB1qonPP\nGTqrzxI4F95MZkgiyXOLiKvIwF+ozWRExHoqDi3w+OOPD8gx4m5umBNuyCHSDK+HhHHZJIzLpudk\nG63r6mjffIr2rafwFSSRNDOfhAnZGD6vI8bvumP0eGDs/TD6HtjxG3jje7DoIRgyCeZ+hce/8AUV\nidcpMTWWSbcWM+nWYk7XtvKHp//ESGMqz3xjHbPuG0H59DxbFymRmPcD8d9O79nOC5vJdB5sgt4g\nht9D7Ih0km8pIr48A2+qNpOxE52fSX+5YT5ot1IREZsLdvXSvuUUrWvr6D3VjhEfQ+KUXJJm5BOT\nOUg2sAn0wvZF8MY/wbkaKJwKkz4SWoaanGt1dI7XUNPCG89Wc/JwM3nDUpn9UBnZRclWh+UoZiC0\nmcz55aK9J9sB8GbEEV8RWi4aOywVI0bLo0Vk4KmVRZgdi8Pq6moAysvLo3qMuJsb5oQbchhIpmnS\ndaiJtnV1dOw6jRmE4BAf2TcPJ35Mpi1POiP+M+7thqpn6H79+/jbToReG1IZWm5adjvkjdMVxWt0\n/mdUNrKMvevqWPPiQbraehh7cyHT7y613VLTSMypSM3LQFtoM5nOvY107juL2dELHoPYoSmhewcr\nMojJjnf/cnCX0PmZ9Jed54OKwzA7FofqoyOR4IY54YYcrBJo7uI/P/09bs6pJCs2DU9CDAkTc0iY\nmoc/3z67UEbrZzxnzs0MT+zgF088ANVL4PhmwISUQihbECoWh94EvriIfq4bvfNn1NnWw4ZXDrHz\nzePEJflst9TUyj6HpmnSU9dGZ3UjnXsa6T7WEtpMJslHXFk6caMyiBuZjidOd+04kc7PpL/sPB/U\n51BExMW8KbG8dPx1Xj6+isU/fZG2TfW0rq+jdc0JfIVJJE7JI2FitotPUg0OtiXA7L8NPVpPwb6l\nsG8JbHsWNv0CfImhXoplt4cKxqQcq4N2hLhEH7MfKmfUDUN4c1E1r/1qD7tWnxi0S02D3QG6DpwL\nXR2sbiTQ1A2AryCJ5FuKia/IwFeQZJviWUSkP9x61iAiMiiYmKErFmXpBNp6aN96ivZN9Zx7+QBN\nfzhE/LgsEqfk4S9NcffStqQcqPxI6NHTCUdWh/om7lsCe38PGFAwGcpvh7KFkDtGy0+vIrs4mfu+\nOJm96+pY+9JBXvjORtsuNY203sbQZjIdexvpOnQOek0Mv5fYkWmkzM8grjwDb8ogaDEjIoOOikMR\nEZfwJvpIvrGApBuG0FPbStvGetq3NdC+5RQxWfEkTMklsTLX/Se1vjgYeWvoYf4L1O8IFYnVi2Hl\nt0KP1OLw8tPbQ8tPY7Rr5OUYHoNRs4ZQOiE7tNT0jVoObD7JzHtHUDHDPktN+8sMBOk60hxaLrq3\nkd5Tob6PMVnxJE3PD20mU6rNZETE/VQcioi4jGEY+IuS8Rclk3rnMDp2nKZtY32ob+KyI8SVZ5A4\nNY+48gwMrztO7t+TYUD++NDj5r+DlvqLy0+3/ho2/gz8SeHlpwtDBWNiltVR2847l5qufHoPu986\nzuwHy8kuduZS0+SYBNo2nwwVhPvOYnYGwGsQW5pK4tR84irS8WUnWB2miMiA0oY0FlixYgUA8+fP\nj+ox4m5umBNuyMFK1zp+PQ3ttG86SduWkwRbevAk+0iszCVhSm7UToKj9TOOyPv2dMDhNy8uP22p\nA4xQm4zzy09zRrl++em1jqUZNNm7rp61Lx2gs3XgdzW93p/9hc1k9jZycv1h4poMDAw8yT7iyjOI\nr8ggdkSai+/TlavR+Zn0l53ng3YrDbNjcSgiYiUzEKSz+ixtG+vprG6EIPiHppA4NY/4cVl4/F6r\nQxx4pgl12y4uP62rCr2eVhLa0Kb8dii5EWJcviT3GnS29bDh1cPsfKOWuCSfLZeamj0BOg820bnn\nDJ17L9lMpjDpQu9B3xBtJiMi7qfiMMyOxWFVVeikY+LEiVE9RtzNDXPCDTlYKRLjF2jupm3LSdo3\nnaT3dAdGrJeECdkkTs3DV5jU701sovUzjvrcaT5xcfnpoVXQ2wn+ZBhxS+iK4sjbIDEzOp89wPo7\nlg3HWnjz2WrqDzWTNywl6ktNrxZvb1NXaGfRPY10HTyH2RPE8HuIHZl+oSDccXD3Fd9DBiedn0l/\n2Xk+qDgMs2NxqD46EglumBNuyMFKkRw/0zTpPtxM26Z6OnacxuwJ4stLIGFKHgmTcvAmXt+Swej1\nOYzO+15WdzscfiO8/HQptNaD4YHCaReXn2aXO3b5aSTG8l1LTWcXMO3uYcRd57y5knfGawZNumtb\n6NwT2kymp64NAG9G3IViMHbY2zeT0b89cjk6P5P+svN8UJ9DERHpM8MwiB2WSuywVIJ3D6d9WwNt\nG+tp+v0hmhYfJn5MJolT8ogdkTb4luD5E6B8YegRDIaWnJ5ffrriH0KP9KGhIrH8dii5AbzubvXw\nTqFdTfMpnZB1YanpgS2nmHnvcCpm5Ed8zsR7Y2nf0RAqCKvPEmzrAQ/4S1JIXVhK3KgMYrLj3d2+\nRUQkClQciojI23jiYkiank/S9Hy669po31hPe9UpOrafxpsWS8LkXBKn5BKTHmd1qAPP44GCytBj\n7peh6XioUNy3BDb9D6z/b4hNgRHzwstPb4WEDKujHjBxiT5mP1jGqBvyefPZfax8ei+73zoRkaWm\n569uf6H8w4xPHUHjM3sx4mOIKw8vFy1Lx+Py/osiItGm4lBERN6TPz8R/93DSV1YSsfuM7Rtqqdl\nZQ0tK2uIHZFG4pQ84sdkDt7+b6kFMPUvQo/uttD9ieeXn+56KbT8tGjGxeWnWSMdu/z0WmQXJXPf\nFysvLDV94bsbr3upqRk06dzbSMuqY3TXtDA8qZAl9Wt59B8/g784xf3tWEREBpCKQxERuSrD5yFh\nQjYJE7LpbeykbXNoE5vGZ/fiSYghYVJOaBObvESrQ7WOPxEq7gg9gkE4sRX2LYbqJbD866FHxrCL\ny0+LZ7p6+en5pabDJmax/pVrX2pqBoK0b2ug5Y1aek+2402LJe39w3n0yx+lx+zlU6VfHqBMREQG\nD21IY4E1a9YAMGvWrKgeI+7mhjnhhhysZPX4mUGTrgPnaNtYT8fuMxAw8RUmkTg1j4QJ2XjiYqIW\no9W5X7Nzxy4uPz38JgS6IS4VRswPLz+dD/HploQ2UGMZ2tV0H/WHmq64q2mwO0D7ppO0vFlL4FwX\nMbkJpMwpIn58FobXE5F4HTd/ZEDo/Ez6y87zQbuVhtmxOBQRcZtAWw/tW0/RtrGe3pPtGD4P8eOy\nSJyah39oijYGuVRXKxx6PXRFcf9SaGsAwxu6knhh+ekIq6OMCjNoUr2+njUvvntX02B7D61r62hd\nc5xgWy/+khSS5xQSV54x+DZBEhGJMBWHYXYsDvWbKYkEN8wJN+RgJTuOn2ma9NS20raxnvZtDZhd\nAXoSIXNWCQmTcojJiMwmNnbM/boEg3B888Xlp6d2hV7PHAFlt4d2SC2aAd7o3QVixVh2tfew/tXD\n7FxVS2qSj5llqcQea8XsDhBXnk7ynCJiS1OjFq9r5o9ElM7PpL/sPB9UHIbZsThUHx2JBDfMCTfk\nYCW7j1+wO8A3PvxFZmdPYlRKKQD+oSkkTMohYVxWv3aWtHvu1+1cTahI3LcEjqwOLz9NC+16WnZ7\naBlqfFpEP9Kqsew53UHDHw/Tu/sMhmlyJjaG/HtGkFuZc8XjIhGva+eP9IvOz6S/7Dwf1OdQREQs\n5fF7Wd2wldUNW1nx4hLat52ifcspzr10gHOvHCS+IoOEypzQssHButvpO6UVw/THQo+uFjj4evhe\nxaWw4wXwxISXny4MFYuZw62O+Jp1H2+lZdUxOnaeBq9B8vQ8TiXHsmXpUTp/tvO6dzUVEZH+U3Eo\nIiJRF5MRR8rcYpLnFNFzvJX2rado39ZAx64zeBJiiB+XRUJlLv7iZN2feF5sMoy+O/QIBkLLT6sX\nh4rFpV8OPbLKLi4/LZwW1eWn/WGaJl2HmmhZdYyu/ecwYr0k31xI0g0FeJP9pANDZ+VfWGp6Lbua\niohI5Njz/yIiIuJKhmHgL0zGX5hM6vtK6dx/LlQobjlF2/p6vBlxoWWnk3LwZcVbHa59eLxQNC30\nmP8NOHskdDWxejGs+29Y86PQbqcjbwsvP50X2g3VYmbQpHPPGVpW1dJ9rAVPko+U24eSNCMfT9zb\nT0FiE3zM/lAZo2bls3rRPlY+vZddq09w80OX39VUREQiT8WhiIhYwvB6iK/IIL4ig2BnLx07z9Be\ndYqWlTW0vFaDvziZhEk5xI/Pxqslhm+XPhSmfzL06GyGgysvLj/d/lxo+WnJDReXn2aUDmh4ZiBI\ne1UDLW8co/dUB96MONLuGUHi5BwMn/eKx2YXJXPvFytDu5r+9gAvfHcjY2YXMF1LTUVEok4b0lig\nqqoKgIkTJ0b1GHE3N8wJN+RgJSeM3/XE2NvURUfVKdq2nKL3ZDt4DOLK00mozCG+IhPD53FE7pYI\nBqB248Xlpw17Q69nV1yy/HRq6EpkWCTHMtgdoG1DPa2rjxNo6sKXl0jynELix/22SeIAACAASURB\nVGVjeK99eWhXew8bXj3MjlW1xCb6mHnvcLoSTmIYRr/i1fyRy9H5mfSXneeDdisNs2NxKCIifdN9\nInx/YlUDwZZujDgvCeOyiZ+YjT8/ESM+RvcoXknjoYvLT4/+CYK9kJB5cfnp8FsgLqXfHxNs76F1\nzQla15wg2N6Lf2gKyXOLiCtLj8jP53RtC28+u4+6g03klqYw58PlZBVqqamISF+pOAyzY3G4YsUK\nAObPnx/VY8Td3DAn3JCDlZwwfpGK0QyadB08R/uWU3TsOo3ZHQx9IcaDN9mHN9mPN9mPJ8X/7r+n\n+PEk+LSxSWcTHHgtdEVx/zLoOAseH2dSRtOUXM6wWe+HvHGhHVP7WND1NnXRuvo4bRvqMLuDxFVk\nkDynkNihkb/f0TRNqtfXs+rZ3QS6YeY9I5h4azGe6/i5OuG/HRl4Oj+T/rLzfFBxGGbH4lB9dCQS\n3DAn3JCDlZwwftGIMdgd4O8+9Fdkx6bxyY9+nGBzN4GW8KO5B7Oz990HeQy8Sb63F4/hwvFtxWSS\nD8M7CNpqBHqhdgNUL+bQsp9SktjJhVWfsamQNzZUKOaNg9yxkDMKYmIvHN7T0E7LG7W0bz0FpknC\nhBySby7El5cY9dBvu2UhlXl3UJQymiEj05j3yChSMq9t8yIn/LcjA0/nZ9Jfdp4P6nMoIiKu5PF7\n2XR2NwBPvO877/q62RMg0NITLha7CTZ3XXze0k3gbBfdNS0E23re/eYGeBJ877oK6U1+e2HpTfFf\ndWMVW/PGQMksKJnFx76zmlhPkKVP/yvUb4f6HXByJ2z5X+hpC32/JwayyulOupmWs7PoqEuFGA+J\n0/JIvqmQmIy4AQu9O9jBuhO/4ZG/fpY3F+3juSc3MPvBMsqm52mJsYhIP6k4FBERVzF8XmIyvFct\nWMxAkEBrzzuuPHYTbLn4vLe+jUBrNwQv8zlx3ssuZ/WmhK9Kni8iY722L1q6gh4onBx6nBcMwtnD\nmHXb6dpdS0t1Bl01xRi0kux9jiTvq3gPJkHbJVcY88ZBeil4on/1tWJGPkNGpLHiqd2seGoPh7ef\nYc6Hy7WjqYhIP6g4FBGRQcnweohJjYXU2Ct+nxk0Cbb3vKtwvPi8h+6aFgLN3dD77irS8HneVixe\nWNJ6/nm4mPQk2GtzHRODzrpUmlcNpac2C0+yj9SFhSSO8+E5Gwf15RevMu5fDmYgdKA/KVwoXrI0\nNWc0+CLftzIlK557vlDJ1mVH2fDKYeoPnmPeI6MpGpUR8c8SERkMVByKiIhcgeEx8Cb58Sb5r/h9\npmlidgbeXji+46pkT10bnfvOYnYF3v0G3tDnXLwKeZnlrSl+PIn+62oL0Vdmb5D2radoebOW3oYO\nvJlxpN07gsTKXAxf+IpgRi4Mn3vxoJ5OaNgTKhbrd0D9Ttj2HGz8eejrhgcyR14sFs8/knL6Ha/H\nYzD59qEUj85k+f/s4pV/r2L8LYXMvGc4MX4HL/0VEbGANqSxQHV1NQDl5eVRPUbczQ1zwg05WMkJ\n4xetGJ2Q+5UEuwNXXM56/nmw/TKb6xjgSfRddgnr265KJvsvFnNXcH4sRw4dEe5RWEuguRtffiLJ\nc4qIH5d1fTu9BoNw7ujFgvHkztCfTccufk9S7jsKxvGQMextfRjfK97L/ex7uwOseekgO16vJWNI\nIrd+bPRlW144ff5IdOj8TPrLzvNBu5WG2bE4FBER6QuzN0ig9R3FY3M3wUs32GnuJtjaDZf537kR\nH/Mey1l9F54bPi9tG+tpWxvqURg7LJXkOUXEjkyLzjLX9saLhWJ9+M+GPaEejAC+hNAy1EuLxpzR\nEJvU54+o2XWG157eQ2drD9PvHnbdLS9ERNxCxWGYHYvDV199FYC77rorqseIu7lhTrghBys5Yfyi\nFaMTch9IZtAk2NZzYRnr25a0vuOqJIHL/38/bnRmqEdhccoARw/0dkFD9TuuMm4P9WYEwIDM4ZA3\njj1nY+j0pTOpckpo11XP+YcvdMXR6wNPDJ2dMby+uItDe3sYMtTPvPuzSMmMBU8My1a+jomXBQvv\nuOT4mNDxNrrvUwaWzs+kv+w8H1QchtmxOFQfHYkEN8wJN+RgJSeMX7RidELudmSaJsH23rddhfyP\nf/43djQd5JnFz1sd3tuZZmgJ6oX7GMOPc0ev6S32dsxldcvHMTCZnfIzyuLeuHL9Z3jfXTBG6rnX\ndx3HX89nvtf3+K5yzCDo8XkFOj+T/rLzfFCfQxEREXkbwzDwJvrwJvouNKxfUr/W4qjeg2FAWnHo\nUXHHhZfvmHcTyb5eFj3z69BS1EBP6M/zjwvPAxjBHkYFeyk428uKZfGsqP88RzIfY1fVP+I12vnc\nZz9zybGBt7/PZV/rw/PeLgi2QbDn2o83L9MzZUAZ0SuMPTHvuNIb6cK3L8dcqTjW5kUioOJQRERE\nHKQt4KUt4A0tM+2jFOCeGeaFlheB+K+wru53fG7mX0Yv0OsRDIZaglxTQXqZ1wJXK3Kvs/Dty3v0\ndl7/e1rstZuhM+CBH46G2BSIS3mPP1MvPB+f2kpHwAN120O78hpG6E+Mtz83jHe/dsXnffkezyXv\nLRIZKg5FRETE9c63vCgalcEv/mExNxd/hNXP77NXywuPB/CElp8ONqYZunLa36L2XYVx34vU/3v6\nl8R5gjwwZS50NYXueW1rgMaD0NkMXc0Q6H5b2D+aFP7L/7tp4MfsbfpSUHrAoI9FZ7SK2b6+bxQ+\newDe967806xrtOC+7QiypDg0DOOfgbuAbuAg8KhpmucMwxgK7AGqw9+6zjTNT4WPmQw8BcQDfwQ+\nZ7r9hkkRERGJqJySFFYc+RnjsufBSji2u5H5j44mp8TZJ3SOZxiE7vf0ArGWhPCLf1wKwAP3/Nd7\nf1NPZ6hI7GyGriYe/+wniPME+fa3nrxY4BL+0zTf47X3et6X7xmA973wvC/ve8n39iXeYCTfl3c8\n70tOV3nffnq8HP52W99XNdiRJRvSGIZxG7DSNM1ewzD+CcA0zSfCxeHvTdMce5ljNgB/DawnVBz+\nyDTNxVf7LDtuSHPsWKjHU1FRUVSPEXdzw5xwQw5WcsL4RStGJ+TuFE4by0jEe/49aEnktaf30NHc\nzZQ7hjL59hI83sG9KctgpvMzuXzhfaVilrc9P368lmBsKkWlI6zM4rIcs1upYRj3Ah8wTfPD71Uc\nGoaRD7xummZF+PlDwBzTND95tfe3Y3EoIiIi9tDZ1sObi/axf+NJcktTmP/IaNJyE6wOS0Qkovpa\nHNrh12MfAy69AlhqGMZWwzDeMAzj/ALuAqD2ku+pDb92WYZhPGYYxibDMDY1NDREPuJ+eu6553ju\nueeifoy4mxvmhBtysJITxi9aMTohd6dw2lhGIt5L3yMu0cdtfzGG2z4+hnMn23nuWxvYsaoWq395\nLgNP52fSX26YD1G7cmgYxgog7zJf+oppmr8Lf89XgCnAfaZpmoZhxAJJpmmeCd9j+DIwBigDvmea\n5vzwcTcBT5imeefV4rDjlUP10ZFIcMOccEMOVnLC+KnPof05bSwjEe97vUfbuS5WPr2Hmt2NFI/O\nYO5HRpGUbs39bzLwdH4m/WXn+WB5n8Pzhdx7MQzjEeBOYN75jWVM0+wCusJ/32wYxkFCheFxoPCS\nwwvDr4mIiIhERGJaLHf+1QR2vXmcP/32AIueXM/Nf1bOyCm5VocmIjIgLFlWahjG7cDfAXebptl+\nyevZhmF4w38fBowEDpmmWQc0G4YxwzAMA/go8DsLQhcREREXMwyDsTcX8qGvTCMtN4FlP9/Fsl/s\norOtx+rQRESizqp7Dv8TSAaWG4ZRZRjGT8Kvzwa2G4ZRBfwG+JRpmo3hr/0l8HPgAKH2F1fdqVRE\nRETkeqTlJnDfFyuZfncpBzefYtGTGzi2u/HqB4qIOJglfQ5N07zs/q6maf4W+O17fG0T8K4WFyIi\nIiLR4PF6mPK+UorHZLLil7t55UdVjLu5gJn3j8Dn91odnohIxFneyiLa7LghzenTpwHIysqK6jHi\nbm6YE27IwUpOGL9oxeiE3J3CaWMZiXiv5z16uwOse/kQ21YeIy03gfmPjiZ3aMp1xyD2o/Mz6S87\nzwfH9DmMNjsWhyIiIuJMtXsbee1Xe2hr6mbKwhImv28oXq8dOoOJiLw3J/U5HHSeeuopnnrqqagf\nI+7mhjnhhhys5ITxi1aMTsjdKZw2lpGItz/vUViRwYNfm0bZ1Fw2/uEIL35/M2fr2/oVj9iDzs+k\nv9wwH3Tl0ALqoyOR4IY54YYcrOSE8VOfQ/tz2lhGs8/htTq45RSrnqmmpzvAzHuHM35OIYbH6Nd7\ninV0fib9Zef5YHmfQxERERE3G16ZQ97wVF7/37289fx+jmw/zS0fHUVyRpzVoYmIXBctKxURERG5\nTompsdzxmfHM+XA59YebWfTkBqrX1+P2lVki4k4qDkVERET6wTAMxtxUwINfnUpGfiIrfrmbpT/b\nRWdrj9WhiYhcExWHIiIiIhGQmp3AvV+sZMY9wzi8rYFnn1zP0Z1nrA5LRKTPtCGNBdrb2wFISEiI\n6jHibm6YE27IwUpOGL9oxeiE3J3CaWMZiXgHIueGYy2s+OVuGk+0MWZ2ATfcPwJfrDdqnyf9p/Mz\n6S87zwf1OQyzY3EoIiIi7tfbE2D9K4epWlFDalY88x8dTd6wVKvDEpFBSH0ObezHP/4xP/7xj6N+\njLibG+aEG3KwkhPGL1oxOiF3p3DaWEYi3oHKOcbn5Yb7R3DP30wiGDB58Z83s+53Bwn0BqP+2XLt\ndH4m/eWG+aArhxZQHx2JBDfMCTfkYCUnjJ/6HNqf08bSTn0Or0V3Ry+rX9jP3jV1ZBUlceujY8gY\nkjhgny9Xp/Mz6S87zwddORQRERGxCX98DPM+OoqFnxpH27kunv/ORqpW1GAG3f1LehFxlhirAxAR\nEREZLIZNzCZvWCqv/3ovf/rNAY7sOM28Px9Nckac1aGJiOjKoYiIiMhASkjx875Pj2PuRyo4daSF\nRd9cz951dbj9Vh8RsT8VhyIiIiIDzDAMRt8whAe/No3MwiRee2oPS366k46WbqtDE5FBTBvSiIiI\niFgoGDSpWlHD+lcOEZvg45aHKxg6PsvqsETERbQhjYiIiIgDeDwGlbeV8MDfTyUh2c8ffryd1/93\nD92dvVaHJiKDjIpDC/zgBz/gBz/4QdSPEXdzw5xwQw5WcsL4RStGJ+TuFE4by0jEa9ecswqTeODv\np1C5oJjda+p47lsbOHHgnNVhDRo6P5P+csN80LJSC6iPjkSCG+aEG3KwkhPGT30O7c9pY+nUPofX\n6sSBc7z21G6az3RSeVsx0+4chten3+lHk87PpL/sPB+0rFRERETEoYaMSONDX53G6BuGsGVpDS98\nbxNnjrdaHZaIuJyKQxEREREb8sfFMPfhCu74y/G0t3Tz/Hc3smXZUYJBd6/6EhHrqDgUERERsbGh\n47N46GvTGDo2i7UvHuTlH26h+XSH1WGJiAvFWB3AYBQfHz8gx4i7uWFOuCEHKzlh/KIVoxNydwqn\njWUk4nVazgDxyX5u/+RYqtfXs3rRPhY9uYEbPziSUbPyMQzD6vBcQedn0l9umA/akEZERETEQZrP\ndLDyV3s4vu8cpROymPPhChJS/FaHJSI2pg1pRERERFwoJTOe939+Ejd8YAQ1uxpZ9OR6DlU1WB2W\niLiAikMLPPnkkzz55JNRP0bczQ1zwg05WMkJ4xetGJ2Qu1M4bSwjEa/Tcr4cw2MwcX4xD3x5Colp\nsSz+yQ5ee3oP3R29VofmWDo/k/5yw3zQslILqI+ORIIb5oQbcrCSE8ZPfQ7tz2ljOVj6HF6LQG+Q\njX84zJYlR0nKiGP+I6MYMjLd6rAcR+dn0l92ng9aVioiIiIyCHhjPMx4/3Du+9vJeDwGL/1wK3/6\n7QF6ewJWhyYiDqPiUERERMQF8oal8sGvTGXMTQVULa/hhe9u4nRti9VhiYiDqDgUERERcQl/XAxz\n/qycOz87gc7WHl747iY2LzlCMOju24hEJDLU59ACmZmZA3KMuJsb5oQbcrCSE8YvWjE6IXencNpY\nRiJep+V8PUrGZvLQ16ez6v+qWffyIY5sP8P8R0eRmp1gdWi2pfMz6S83zAdtSCMiIiLiUqZpsm/D\nSd5ctI9g0OTGD4xg9I1DMAzD6tBEZABpQxoRERGRQc4wDMqn5/Hg16aRV5rCqmeq+cOPt9PW1GV1\naCJiQyoOLfClL32JL33pS1E/RtzNDXPCDTlYyQnjF60YnZC7UzhtLCMRr9NyjoTkjDju/uuJ3PjB\nkdTuPcuib27g4JZTVodlKzo/k/5yw3zQPYcWWLt27YAcI+7mhjnhhhys5ITxi1aMTsjdKZw2lpGI\n12k5R4rhMZhwSxFFozJY8cvdLPnpTspn5HHTh8qIjdcpoc7PpL/cMB905VBERERkEMnIT+T+JyYz\n5Y6h7NtwkkXfXE/t3karwxIRG1BxKCIiIjLIeL0ept81jPv/djIxfi+/+7cq3np+P73dAatDExEL\nqTgUERERGaRyS1P44FemMm5OIdtWHuP5726ioabF6rBExCJaYG6BwsLCATlG3M0Nc8INOVjJCeMX\nrRidkLtTOG0sIxGv03KONp/fy+wHyxg6PpOVv9rDb763ial3DqVyQQke7+C5jqDzM+kvN8wH9TkU\nEREREQA623p4c9E+9m88SW5pCvMfGU1aboLVYYlIP6nPoYiIiIhck7hEH7f9xRhu+/gYzp1s57lv\nb2DnG7W4/WKCiIRoWakFPv/5zwPwb//2b1E9RtzNDXPCDTlYyQnjF60YnZC7UzhtLCMRr9NytsLI\nKbnkD09j5f/u4Y1n93F4+2lu+cgoEtNirQ4tanR+Jv3lhvmg4tACVVVVA3KMuJsb5oQbcrCSE8Yv\nWjE6IXencNpYRiJep+VslaT0WO76qwnsfOM4a357gGefXM/ND5Uzckqu1aFFhc7PpL/cMB+0rFRE\nRERELsswDMbNKeRDX51GanYCy36+i2W/2EVnW4/VoYlIFKg4FBEREZErSstN4P6/rWTaXaUc3HyK\nRU9u4NieRqvDEpEIU3EoIiIiIlfl8XqYekcp9z8xGX+cl1f+vYo3n9tHT3fA6tBEJEJ0z6EFysrK\nBuQYcTc3zAk35GAlJ4xftGJ0Qu5O4bSxjES8TsvZbnJKUvjgl6ey9uWDbF9Zy7Hdjcx/dDS5Q1Os\nDq1fdH4m/eWG+aA+hyIiIiJyXY7tbWTlr/bQ1tTNlPcNZfLCErxeLUwTsRv1ORQRERGRqCqqyODB\nr01j5NQcNv7+MC9+fzNn69usDktErpOKQws89thjPPbYY1E/RtzNDXPCDTlYyQnjF60YnZC7Uzht\nLCMRr9NytrvYBB+3PjqGBZ8YS9PpDp7/9ka2v16LGXTW6jSdn0l/uWE+6J5DC+zbt29AjhF3c8Oc\ncEMOVnLC+EUrRifk7hROG8tIxOu0nJ1ixOQc8keksvLpvax+bh9Htjdwy0dHkZQeZ3VofaLzM+kv\nN8wHXTkUERERkYhITI3lzs+O5+Y/K6fuYBOLntzAvo31VoclIn2k4lBEREREIsYwDMbOLuBDX51G\nel4Cy3+xm6U/30lnW4/VoYnIVag4FBEREZGIS8tJ4N7HK5n+/mEc2tLAs99cz9FdZ6wOS0SuQPcc\nWmDixIkDcoy4mxvmhBtysJITxi9aMTohd6dw2lhGIl6n5exkHq+HKQuHUjImkxVP7eb3/7GNsbML\nmHX/CHyxXqvDexudn0l/uWE+qM+hiIiIiERdb0+A9b87RNVrx0jNjmf+o6PJK021OiyRQUF9DkVE\nRETENmJ8Xm74wEju+fwkAr1BXvz+Zta/cohAIGh1aCISpuLQAg8//DAPP/xw1I8Rd3PDnHBDDlZy\nwvhFK0Yn5O4UThvLSMTrtJzdpqA8nQe/Np3yGXls+uMRfvtPm2k80WZ1WDo/k35zw3zQPYcWqK2t\nHZBjxN3cMCfckIOVnDB+0YrRCbk7hdPGMhLxOi1nN4qNj2Hen4+mdHw2rz+zl+e/s5GZ9w5n/NxC\nDI9hSUw6P5P+csN8UHEoIiIiIpYYNimbvOGpvP7rvbz1wn4Obz/NvD8fRXJGnNWhiQxKWlYqIiIi\nIpZJSPHzvk+PY+5HKjh1pJlF31xP9bo63L5poogdqTgUEREREUsZhsHoG4bwoa9OI7MwiRVP7WHp\nT3fS0dptdWgig4qWlVpg5syZA3KMuJsb5oQbcrCSE8YvWjE6IXencNpYRiJep+U8mKRmx3PPFyqp\nWl7D+lcOUXewibkfqWDouKyof7bOz6S/3DAfLOlzaBjGPwCfABrCL33ZNM0/hr/2JeAvgADw16Zp\nLg2/fjvw74AX+Llpmt/ry2epz6GIiIiI85yubWXFL3dx5ngbo28awg33j8Afp+saItejr30Orfwv\n7F9N0/zBpS8YhjEaeBAYAwwBVhiGURb+8n8BtwK1wEbDMF4xTXP3QAYsIiIiIgMjqzCJB/5+Kutf\nPcTW5TXU7j3L/EdGkz881erQRFzLbvccvh9YZJpml2mah4EDwLTw44BpmodM0+wGFoW/15Huv/9+\n7r///qgfI+7mhjnhhhys5ITxi1aMTsjdKZw2lpGI12k5D2Zen4dZ943g3i9UYgZNXvrBZta+fJBA\nbzDin6XzM+kvN8wHK68cftYwjI8Cm4DHTdM8CxQA6y75ntrwawDH3vH69Pd6Y8MwHgMeAyguLo5k\nzBFx5syZATlG3M0Nc8INOVjJCeMXrRidkLtTOG0sIxGv03IWGDIyjQe/No23XtjPliVHqdl1hvmP\njCazIClin6HzM+kvN8yHqF05NAxjhWEYOy/zeD/w38BwYCJQB/xLJD/bNM2fmqY5xTTNKdnZ2ZF8\naxERERGxgD8uhls+Mor3fXocbee6eP67G9m6vAYzqJYXIpEStSuHpmnO78v3GYbxM+D34afHgaJL\nvlwYfo0rvC4iIiIig0TphGzyhqXy+q/3sua3Bziy/TTz/nwUKVnxVocm4niW3HNoGEb+JU/vBXaG\n//4K8KBhGLGGYZQCI4ENwEZgpGEYpYZh+AltWvPKQMYsIiIiIvYQn+xn4afGMe/PR9FwrIVF39rA\nnjV1WLELv4ibWHXP4fcNw5gImMAR4JMApmnuMgzjeWA30At8xjTNAIBhGJ8FlhJqZfE/pmnusiLw\nSJg3b96AHCPu5oY54YYcrOSE8YtWjE7I3SmcNpaRiNdpOcvlGYZBxcx8hoxM47Vf7WHl03s4vK2B\nuQ9XEJ/sv+b30/mZ9Jcb5oMlfQ4HkvocioiIiLibGTTZtvIY614+hD/ey9yHKyidoH0nRM7ra59D\nu7WyEBERERG5JobHYOL8Yh740hQS02L543/vYOXTe+ju7LU6NBFHUXFogYULF7Jw4cKoHyPu5oY5\n4YYcrOSE8YtWjE7I3SmcNpaRiNdpOUvfZRYk8YEnpjD59hL2rq3juW9t4MT+c306Vudn0l9umA9W\n9jkctDo6OgbkGHE3N8wJN+RgJSeMX7RidELuTuG0sYxEvE7LWa6NN8bDjHuGUzIuixVP7ealH25h\n0q3FTL9rGF7fe18X0fmZ9Jcb5oOuHIqIiIiI6+QPT+VDX5nKmBuHsHVZDS98byOna1utDkvE1lQc\nioiIiIgr+eNimPPhCu74zHg6Wnp44bsb2bL0KMGguzdkFLleKg5FRERExNWGjsviwa9Po3RCFmtf\nOsjLP9xCU4PzlwCKRJruObTAnXfeOSDHiLu5YU64IQcrOWH8ohWjE3J3CqeNZSTidVrOEhnxSX4W\nfGIs+zac5M1F+3juWxu48YGRjLohH8MwdH4m/eaG+aA+hyIiIiIyqLQ0dvLar/ZwvPosQ8dnMffh\nChJS/FaHJRI16nMoIiIiInIZyRlxvP9zE7nxgZEc29PIs99cz6GtDVaHJWI5FYcWmDNnDnPmzIn6\nMeJubpgTbsjBSk4Yv2jF6ITcncJpYxmJeJ2Ws0SH4TGYMK+ID355KskZcSz+fzt44s9+REtjZ5/f\nQ3NJLuWG+aDiUEREREQGrYz8RO5/YjI7G1ZRkFTO/31jHetfPURPV8Dq0EQGnIpDERERERnUvF4P\ne868yeJD/0XphCw2/eEIz3x9LdXr6jDV9kIGERWHIiIiIiJAR28zt318LPf97WQS02JZ8dQefvNP\nm6g72GR1aCIDQsWhiIiIiMgl8oen8oEnpjD/kVG0nevixX/ezLJf7Lqm+xFFnEh9Di3wwQ9+cECO\nEXdzw5xwQw5WcsL4RStGJ+TuFE4by0jE67ScZWC8c14YHoPyGfmUTsxm67Iati6v4VBVA5NuLaZy\nQQm+WK/mkryNG+aD+hyKiIiIiFxFS2Mna186yP6NJ0lM9TPz3uGUTcvD8BhWhyZyVX3tc6ji0ALt\n7e0AJCQkRPUYcTc3zAk35GAlJ4xftGJ0Qu5O4bSxjES8TstZBkZf50XdwSbeen4fp462kFWUyLR7\nhlI6JncgQhSbs/O/LSoOw+xYHJ7vf7Jq1aqoHiPu5oY54YYcrOSE8YtWjE7I3SmcNpaRiNdpOcvA\nuJZ5YQZN9m2o59WfrSfel0JBeRqVC0ooGpWBYehK4mBl539b+locakMaEREREZFrcP5+xMWH/ouq\nk8s4V9/Oqz/axvPf2cj+TScJqv2FOJSKQxERERGR6xAwe9h/dh0f+dYs5n6kgt7uIMt+vov/+8Y6\ndq0+TqAnaHWIItdEu5WKiIiIiPSD1+dh9A1DqJiZz+FtDWxZcpRVz1Sz4feHmTCviLE3FeCP12m3\n2J9mqYiIiIhIBHg8BsMn5TBsYja11WfZsuQoa188yObFRxl3cwHjbykiIcVvdZgi70nFoQUeeeSR\nATlG3M0Nc8INOVjJCeMXrRidkLtTOG0sIxGv03KWgRHJ8zPDMCiqyKCoipB0CAAAGJlJREFUIoNT\nR5vZsvQom5cepeq1Y4yalc+kW4tJyYrvX8BiO274t0W7lYqIiIiIRNm5k+1sWXaU6nX1mCaMnJJD\n5YISMguSrA5NBgG1sgizY3F4+vRpALKysqJ6jLibG+aEG3KwkhPGL1oxOiF3p3DaWEYiXqflLANj\noM7PWs92se21GnauPkFvV4CScZlULihhyIi0awtYbMfO/7aoOAyzY3GoPocSCW6YE27IwUpOGD/1\nObQ/p42l+hxKtAz0+VlnWw8736hl28paOlt7yB+eSuXtJZSMzVSvRIey878tfS0Odc+hiIiIiMgA\ni0v0MeV9pUyYX8yeP51g6/Ia/vBf28ksSGTSbSWMnJKDx6uuczKwVByKiIiIiFjE5/cyfm4RY2YX\ncGDjSTYvrWHFL3ez/pVDTLq1mFGz8onxe60OUwYJFYciIiIiIhbzej2Uz8inbFoeR3aeYcuSI7y5\naB8b/3CY8bcUMe7mAmITfFaHKS6n4lBERERExCYMj0Hp+CyGjsuk7sA5Ni+pYf3vDrFl6VHG3lTA\nhPlFJKbGWh2muJSKQwt8+tOfHpBjxN3cMCfckIOVnDB+0YrRCbk7hdPGMhLxOi1nGRh2Oz8zDIMh\nI9MZMjKd07UtbFlaQ9WKGra9foyKmaFeiWk5CVH7fLl2bvi3RbuVioiIiIg4QFNDO1XLj7FnTR3B\nQJDhlaFeidnFyVaHJjanVhZhdiwOjx07BkBRUVFUjxF3c8OccEMOVnLC+EUrRifk7hROG8tIxOu0\nnGVgOOn8rK2pi+0ra9n5Ri3dnQGKRmdQuaCEgrI0tcGwkJ3/bVFxGGbH4lB9DiUS3DAn3JCDlZww\nfupzaH9OG0v1OZRoceL5WVdHL7vePE7Va8foaO4mtzSFygUllI7PwvCoSBxoVs+HK1GfQxERERER\nF4uNj6FyQQnj5xayd109W5cdZfFPdpCel8Ck20oom5aLN0a9EqXvVByKiIiIiDhYjN/L2NkFjL4h\nn4NbGti89Cgrn97DhlcPMXF+MaNvHIIvVr0S5epUHIqIiIiIuIDH62Hk1FxGTMmhZncjW5Yc5a0X\n9rPxj4cZP7eI8XMKiUtSr0R5byoORURERERcxDAMSsZkUjImk7qDTWxZepSNvz/M1mVHGXNjqFdi\nckac1WGKDak4tMDjjz8+IMeIu7lhTrghBys5YfyiFaMTcncKp41lJOJ1Ws4yMNx6fpY/PJU7/nI8\nZ060snVZDdtX1bLjjVrKpudReVsx6XmJVofoGk6YD1ej3UpFRERERAaJ5jMdVK04xp63TtDbG2TY\nhGwqF5SQW5pidWgSRWplEWbH4rC6uhqA8vLyqB4j7uaGOeGGHKzkhPGLVoxOyN0pnDaWkYjXaTnL\nwBhs52cdLd1sf72WHatq6WrvpaA8nckLSigcla5eidfJzvNBxWGYHYtDJ/bREftxw5xwQw5WcsL4\nqc+h/TltLNXnUKJlsJ6fdXf2svutE1Qtr6GtqZvs4mQqF5QwbFI2HvVKvCZ2ng/qcygiIiIiIlfk\nj4th4vxixt1cSPWGerYuq2Hpz3aSmh3PpNuKqZiRj9enXomDhYpDEREREZFBzuvzMPqGIVTMzOdw\nVQOblxxl1TPVbPj9YSbOK2bM7CH441Q6uJ1+wiIiIiIiAoDHYzC8Modhk7KprT7LliVHWfPiATYv\nOcLYmwsYP7eIhBS/1WFKlKg4FBERERGRtzEMg6KKDIoqMjh5pJmtS4+yeclRqlYcY/SsfCbeWkxK\nVrzVYUqEqTi0wFe/+tUBOUbczQ1zwg05WMkJ4xetGJ2Qu1M4bSwjEa/TcpaBofOz95Y7NIXbPzmO\ns/VtbF1ew663TrBz9QlGTs2h8rYSMguSrA7RFtwwH7RbqYiIiIiI9Fnr2S6qXqth1+oT9HYFGDou\nk8oFJeSPSLM6NHkPamURZsfisKqqCoCJEydG9RhxNzfMCTfkYCUnjF+0YnRC7k7htLGMRLxOy1kG\nhs7Prl1nWw87VtWyfWUtnW095I9IpXJBCSVjMwdlr0Q7zwcVh2F2LA4Hax8diSw3zAk35GAlJ4yf\n+hzan9PGUn0OJVp0fnb9eroC7Flzgq3La2ht7CKzIJHKBSWMmJyDxzt42mDYeT6oz6GIiIiIiESd\nL9bL+LlFjJldwP6NJ9mytIbl/7Ob9a8cYuL8YkbNyifG77U6TOkDFYciIiIiItJvXq+Hihn5lE/L\n48iO02xecpQ3F+1j4x8OM2FeEWNnFxCb4LM6TLkCFYciIiIiIhIxhsegdEI2Q8dnUXfgHJuX1LDu\n5UNsXnKUsbMLmDCviMTUWKvDlMtQcSgiIiIiIhFnGAZDRqYzZGQ6Dcda2Lr0KFXLa9i+spaKmXlM\nvLWYtJwEq8OUS2hDGgusWbMGgFmzZkX1GHE3N8wJN+RgJSeMX7RidELuTuG0sYxEvE7LWQaGzs8G\nRlNDO1uXH2PvmjqCgSDDJ4d6JWYXJ1sdWr/ZeT5ot9IwOxaHIiIiIiKDWVtTF9tX1rLzjVq6OwMU\nj86gckEJQ8rSBmUbjGhTcRhmx+JQv5mSSHDDnHBDDlZywvjpyqH9OW0sdeVQokXnZ9bo6uhl5xu1\nbHvtGB0tPeSWplC5oITS8VkYHmcViXaeDyoOw+xYHKqPjkSCG+aEG3KwkhPGT30O7c9pY6k+hxIt\nOj+zVm93gL3r6tm67CjNpztJz0ugckEJI6fl4nVIr0Q7zwf1ORQREREREUeI8XsZO7uA0Tfkc2DL\nKbYsqeG1X+250Ctx9I1D8MWqV2K0qTgUERERERFb8Hg9lE3NY+SUXGp2NbJl6VHeemE/m/54hPG3\nFDJuTiFxieqVGC0qDkVERERExFYMw6BkbCYlYzOpO9jElqVH2fDqYbYsq2HMjUOYOL+IpPQ4q8N0\nHRWHIiIiIiJiW/nDU7njL8dz5ngrW5fVsP31WnasqqVseh6VtxWTnpdodYiuoQ1pLFBVVQXAxIkT\no3qMuJsb5oQbcrCSE8YvWjE6IXencNpYRiJep+UsA0PnZ87RfLqDqteOseetE/T2Bhk2MZvKBSXk\nDk2xNC47zwdb71ZqGMZzQHn4aRpwzjTNiYZhDAX2ANXhr60zTfNT4WMmA08B8cAfgc+ZfQjejsWh\niIiIiIj0T0dL94WriF3tvRSUpzP59hIKK9LVK/EdbL1bqWmaHzr/d8Mw/gVouuTLB03TvFy5/d/A\nJ4D1hIrD24HF0YwzWlasWAHA/Pnzo3qMuJsb5oQbcrCSE8YvWjE6IXencNpYRiJep+UsA0PnZ84T\nn+xn+t3DmHRbMbtWn2Dbihpe+fcqsouTqVxQwrBJ2XgGsFeiG+aDpctKjVBJXwPcYprm/vCVw9+b\npjn2Hd+XD7xummZF+PlDwBzTND95tc+w45VD9dGRSHDDnHBDDlZywvipz6H9OW0s1edQokXnZ84X\n6AlSvaGeLUuP0nSqg9SceCpvK6F8eh5eX/R7Jdp5Ptj6yuElbgJOmqa5/5LXSg3D2Ao0A181TXM1\nUADUXvI9teHXRERERERE8Po8jL5hCBUz8zm0tYEtS4/y+q/3suHVQ0yYX8yYm4bgj7O6/LG3qI2O\nYRgrgLzLfOkrpmn+Lvz3h4BnL/laHVBsmuaZ8D2GLxuGMeY6Pvsx4DGA4uLiaz1cREREREQcyuMx\nGDE5h+GV2dTuPcuWpUdZ89sDbF58hHFzChk/t5D4ZL/VYdpS1IpD0zSvuNjWMIwY4D5g8iXHdAFd\n4b9vNgzjIFAGHAcKLzm8MPzae332T4GfQmhZ6XWmICIiIiIiDmUYBkWjMigalcHJI81sWXqUTYuP\nULW8hlE3DGHirUWkZMZbHaatWHlddT6w1zTNC8tFDcPIBhpN0wwYhjEMGAkcMk2z0TCMZsMwZhDa\nkOajwH9YErWIiIiIiDhK7tAUFn5yHGfr29i6rIZdq4+z883jlE3NZdJtxWQWJFkdoi1YtiGNYRhP\nEWpV8ZNLXrsf+CbQAwSBb5im+Wr4a1O42MpiMfBXTm1lUV0d6tRRXl5+le/s3zHibm6YE27IwUpO\nGL9oxeiE3J3CaWMZiXidlrMMDJ2fDS6tZzupeu0Yu1afoLcrwNDxWVQuKCF/eOp1v6ed54Ot+xwO\nJDsWhyIiIiIiYr3O1h52vFHL9pW1dLb1kD8ilcm3D6V4TIareiWqOAyzY3H46quvAnDXXXdF9Rhx\nNzfMCTfkYCUnjF+0YnRC7k7htLGMRLxOy1kGhs7PBreergC7/3SCquU1tJ7tIrMgicrbixlRmYPH\n27c2GHaeDyoOw+xYHKqPjkSCG+aEG3KwkhPGT30O7c9pY6k+hxItOj8TgMD/b+/ug+2qzjqOf3+9\ngdCUSFNvwExIGyhURJQEbC3aoUgFYmcs1UGLM1Z8G61tfZvxhfqPbdWx6qgz/hGpjvRSByFMNS1T\n2wY0FDpTS6ghgQC2pEBIUiCNNEjNFCFd/nFW2kuae3Nvcs7ZL/l+Zs6cfffZ58yz9rPWvvs5e5+9\nD3yDhzc9xeYNO/jqk/v5jsmTWH3ZKznnomUsOHFi1ve2uT905T6HkiRJktQKExMv4ZyLlvHdP/hd\nPHrfXjZv2MGdN32RTf/6GOdfejrnvfF0Fr60vyVUf1smSZIkSUchLwlnrlrKGedP8uWH97F5ww4+\n99FH2PypHZz3xuV8/6UreNkpC5sOc+gsDiVJkiTpMJKw/DVLWP6aJXzl8WfZfNsO7r3tcbb++y7O\n+aFlrL5sBacsXdR0mENjcShJkiRJR7D0lYu54pfPY99b9rPl9sd56LNf5sHP7OasC09l9RWvajq8\nofCCNA3YuXMnACtWrBjpe9RvfegTfWhDk7qw/kYVYxfa3hVdW5fDiLdrbdZ4uH+m+frfZ57jvo07\nuf/O3Tz/9QOceuYizn3TJN974VlNh/ZtvFpp1cbiUJIkSVI/PLf/ebbdtZutG3fx479+PktXLG46\npG8z1+Jwbjft0FCtW7eOdevWjfw96rc+9Ik+tKFJXVh/o4qxC23viq6ty2HE27U2azzcP9PRWrjo\nBC5cs5JFq3ez8bOfaDqcY+KRwwZ4Hx0NQx/6RB/a0KQurD/vc9h+XVuX3udQo+L+mY5Vm/uDRw4l\nSZIkSXNmcShJkiRJsjiUJEmSJFkcSpIkSZLwgjSN2Lt3LwCTk5MjfY/6rQ99og9taFIX1t+oYuxC\n27uia+tyGPF2rc0aD/fPdKza3B+8z2HVxuJQkiRJksbFq5W22NTUFFNTUyN/j/qtD32iD21oUhfW\n36hi7ELbu6Jr63IY8XatzRoP9890rPrQHzxy2ADvo6Nh6EOf6EMbmtSF9ed9Dtuva+vS+xxqVNw/\n07Fqc3/wyKEkSZIkac4sDiVJkiRJFoeSJEmSJItDSZIkSRJekKYR+/fvB2DRokUjfY/6rQ99og9t\naFIX1t+oYuxC27uia+tyGPF2rc0aD/fPdKza3B/mekGaBeMIRi92NB2mjZ1MzepDn+hDG5rUhfU3\nqhi70Pau6Nq6HEa8XWuzxsP9Mx2rPvQHTyttwNq1a1m7du3I36N+60Of6EMbmtSF9TeqGLvQ9q7o\n2rocRrxda7PGw/0zHas+9AdPK22A99HRMPShT/ShDU3qwvrzPoft17V16X0ONSrun+lYtbk/eJ9D\nSZIkSdKcWRxKkiRJkiwOJUmSJEkWh5IkSZIkvCCNJEmSJPWaF6SRJEmSJM2ZxaEkSZIkyeJQkiRJ\nkmRxKEmSJEnC4lCSJEmShMWhJEmSJAmLQ0mSJEkSFoeSJEmSJCwOJUmSJElYHEqSJEmSsDiUJEmS\nJGFxKEmSJEnC4lCSJEmShMWhJEmSJAmLQ0mSJEkSFoeSJEmSJCwOJUmSJElYHEqSJEmSsDiUJEmS\nJGFxKEmSJEnC4lCSJEmShMWhJEmSJAmLQ0mSJEkSkFJK0zGMVJKvADuajuMwJoG9TQeheTNv3WTe\nusecdZN56ybz1k3mrZuayturSilLj7RQ74vDtkry+VLKDzQdh+bHvHWTeesec9ZN5q2bzFs3mbdu\nanvePK1UkiRJkmRxKEmSJEmyOGzS3zUdgI6Keesm89Y95qybzFs3mbduMm/d1Oq8+ZtDSZIkSZJH\nDiVJkiRJFoeSJEmSJCwOxy7JmiRfSLI9ybVNx6MXS/JYkvuTbEny+TrvFUluT/JwfV5S5yfJ39Rc\n3pfkgmajP34kuT7JniTbps2bd56SXFOXfzjJNU205XgyQ97em2R3HXNbkrx52mvvqXn7QpIrps13\nOzpGSVYkuSPJg0keSPKbdb5jrsVmyZtjrsWSnJRkU5KtNW/vq/PPSHJ3zcG6JCfW+Qvr39vr6yun\nfdZh86nhmiVnU0kenTbWVtX57d5GllJ8jOkBTABfAs4ETgS2Auc2HZePF+XoMWDykHl/Dlxbp68F\n/qxOvxn4JBDg9cDdTcd/vDyAi4ELgG1HmyfgFcAj9XlJnV7SdNv6/Jghb+8Ffucwy55bt5ELgTPq\ntnPC7WgjeVsGXFCnFwNfrPlxzLX4MUveHHMtftRxc3KdPgG4u46jW4Cr6/zrgF+r0+8ErqvTVwPr\nZstn0+3r42OWnE0BVx1m+VZvIz1yOF6vA7aXUh4ppfwfcDNwZcMx6ciuBG6o0zcAb502/8Nl4HPA\ny5MsayLA400p5S7g6UNmzzdPVwC3l1KeLqV8FbgdWDP66I9fM+RtJlcCN5dSniulPApsZ7ANdTs6\nZqWUJ0opm+v0s8BDwHIcc602S95m4phrgTpuvlb/PKE+CnAp8JE6/9DxdnAcfgR4U5Iwcz41ZLPk\nbCat3kZaHI7XcmDntL93MfuGWuNXgNuS/GeSX6nzTiulPFGnnwROq9Pms13mmyfz1x7vrqfWXH/w\n1ETMWyvVU9ZWM/hm3DHXEYfkDRxzrZZkIskWYA+DAuFLwL5Sygt1kek5+GZ+6uvPAN+JeRurQ3NW\nSjk41v6kjrW/TrKwzmv1WLM4lF7sDaWUC4AfA96V5OLpL5bBcX/v/9Jy5qlT/hZ4NbAKeAL4y2bD\n0UySnAz8M/BbpZT/mf6aY669DpM3x1zLlVIOlFJWAaczONp3TsMh6QgOzVmS84D3MMjdaxmcKvr7\nDYY4ZxaH47UbWDHt79PrPLVEKWV3fd4DrGewUX7q4Omi9XlPXdx8tst882T+WqCU8lT9p/oN4O/5\n1mlP5q1FkpzAoMC4sZTyL3W2Y67lDpc3x1x3lFL2AXcAFzE49XBBfWl6Dr6Zn/r6KcB/Y94aMS1n\na+qp3aWU8hzwIToy1iwOx+se4Ox6xakTGfxw+NaGY1KV5GVJFh+cBi4HtjHI0cErRl0DfKxO3wr8\nXL3q1OuBZ6adYqXxm2+eNgCXJ1lST6u6vM7TGB3yO92fYDDmYJC3q+uV+M4AzgY24XZ07Orvl/4B\neKiU8lfTXnLMtdhMeXPMtVuSpUleXqdfClzG4PeidwBX1cUOHW8Hx+FVwMZ6JH+mfGrIZsjZf037\n8iwMfiM6fay1dhu54MiLaFhKKS8keTeDRE8A15dSHmg4LH3LacD6wRhmAfBPpZRPJbkHuCXJLwE7\ngJ+uy3+CwRWntgP7gV8Yf8jHpyQ3AZcAk0l2AX8IfIB55KmU8nSSP2Kw4wPw/lLKXC+WoqMwQ94u\nqZf3LgyuFvyrAKWUB5LcAjwIvAC8q5RyoH6O29Hx+mHg7cD99Tc1AH+AY67tZsrbzzjmWm0ZcEOS\nCQYHcW4ppXw8yYPAzUn+GLiXQeFPff7HJNsZXPDrapg9nxq6mXK2MclSBlcl3QK8oy7f6m1kBl8u\nSJIkSZKOZ55WKkmSJEmyOJQkSZIkWRxKkiRJkrA4lCRJkiRhcShJkiRJwuJQkqQ5S/LpJCuH+Hm/\nkeShJDcmWZnk08P6bEmS5sv7HEqS1Jx3Aj9aStk1zKJTkqSj4ZFDSZLmKclZSf4tydYkm5O8OgN/\nkWRbkvuTvG3a8r+b5J4k9yV5X513HXAm8Mkkv91UWyRJOsgjh5Ikzd+NwAdKKeuTnMTgy9afBFYB\n5wOTwD1J7gK+DzgbeB0Q4NYkF5dS3pFkDfAjpZS9HjmUJDXN4lCSpPlZDCwvpawHKKV8HSDJG4Cb\nSikHgKeS3Am8FrgYuBy4t77/ZAbF4l3jDlySpNlYHEqSNFoB/rSU8sGmA5EkaTb+5lCSpPl5FtiV\n5K0ASRYmWQR8BnhbkokkSxkcMdwEbAB+McnJdfnlSU5tKHZJkmbkkUNJkubv7cAHk7wfeB74KWA9\ncBGwFSjA75VSngSeTPI9wH8kAfga8LPAniYClyRpJimlNB2DJEmdUO9D+POllMdG8NkrgalSyiXD\n/mxJkubC00olSZIkSRaHkiTNwxSwb0Sfva9+viRJjfC0UkmSJEmSRw4lSZIkSRaHkiRJkiQsDiVJ\nkiRJWBxKkiRJkrA4lCRJkiQB/w+/CbtzbCRCFAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6d58aa7208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(xx, coefs.T)\n", "ymin, ymax = plt.ylim()\n", "plt.vlines(xx, ymin, ymax, linestyle='dashed')\n", "plt.xlabel('|coef|')\n", "plt.ylabel('coefs')\n", "plt.title('LASSO Path')\n", "plt.axis('tight')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From https://stats.stackexchange.com/a/4938 :\n", "\n", "> In scikit-learn the implementation of Lasso with coordinate descent tends to be faster than our implementation of LARS although for small p (such as in your case) they are roughly equivalent (LARS might even be a bit faster with the latest optimizations available in the master repo). Furthermore coordinate descent allows for efficient implementation of elastic net regularized problems. This is not the case for LARS (that solves only Lasso, aka L1 penalized problems).\n", "\n", "> Elastic Net penalization tends to yield a better generalization than Lasso (closer to the solution of ridge regression) while keeping the nice sparsity inducing features of Lasso (supervised feature selection).\n", "\n", "> For large N (and large p, sparse or not) you might also give a stochastic gradient descent (with L1 or elastic net penalty) a try (also implemented in scikit-learn)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

mdeff/ntds_2017

projects/reports/terrorist_attacks/project/report.ipynb

1

1877374

mit

utexas-ghosh-group/Experiments

Untitled.ipynb

1

1491

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import re\n", "import os\n", "import time\n", "import datetime\n", "import gc\n", "#from input_helpers import InputHelper\n", "from siamese_network import SiameseLSTM\n", "\n", "\n", "from tensorflow.contrib import learn\n", "import gzip\n", "from random import random" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#(X_train, y_train), (X_test, y_test) = imdb.load_data()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

biof-309-python/BIOF309-2016-Fall

Week_06/Week 06 - 02 - Conditionals.ipynb

1

22885

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Conditions\n", "\n", "Source: This material adapted from the [Python for Biologists](http://pythonforbiologists.com/index.php/introduction-to-python-for-biologists/conditions/) website." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conditions, True and False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A condition is simply a bit of code that can produce a true or false answer. The easiest way to understand how conditions work in Python is try out a few examples. The following example prints out the result of testing (or evaluating) a bunch of different conditions – some mathematical examples, some using string methods, and one for testing if a value is included in a list:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(3 == 5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(3 > 5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(3 <= 5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(len(\"ATGC\") > 5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"GAATTC\".count(\"T\") > 1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"ATGCTT\".startswith(\"ATG\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"ATGCTT\".endswith(\"TTT\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"ATGCTT\".isupper())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"ATGCTT\".islower())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"V\" in [\"V\", \"W\", \"L\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But what’s actually being printed here? At first glance, it looks like we’re printing the strings “True” and “False”, but those strings don’t appear anywhere in our code. What is actually being printed is the special built-in values that Python uses to represent true and false – they are capitalized so that we know they’re these special values.\n", "\n", "We can show that these values are special by trying to print them. The following code runs without errors (note the absence of quotation marks):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There’s a wide range of things that we can include in conditions, and it would be impossible to give an exhaustive list here. The __basic building blocks__ are:\n", "\n", "- __equals__ (represented by __==__)\n", "- __greater__ and __less than__ (represented by __>__ and __<__)\n", "- __greater__ and __less than or equal to__ (represented by __>=__ and __<=__)\n", "- __not equal__ (represented by __!=__)\n", "- is a value __in__ a list (represented by __in__)\n", "\n", "are two objects the same (represented by is)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the test for equality is two equals signs, not one. Forgetting the second equals sign will cause an error.\n", "\n", "Now that we know how to express tests as conditions, let’s see what we can do with them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## if statements" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simplest kind of conditional statement is an if statement. Hopefully the syntax is fairly simple to understand:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "expression_level = 125\n", "if expression_level > 100:\n", " print(\"gene is highly expressed\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We write the word if, followed by a condition, and end the first line with a colon. There follows a block of indented lines of code (the body of the if statement), which will only be executed if the condition is true. This colon-plus-block pattern should be familiar to you from the sections on loops and functions.\n", "\n", "Most of the time, we want to use an if statement to test a property of some variable whose value we don’t know at the time when we are writing the program. The example above is obviously useless, as the value of the expression_level variable is not going to change!\n", "\n", "Here’s a slightly more interesting example: we’ll define a list of gene accession names and print out just the ones that start with “a”:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "accs = ['ab56', 'bh84', 'hv76', 'ay93', 'ap97', 'bd72']\n", "for accession in accs:\n", " if accession.startswith('a'):\n", " print(accession)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you take a close look at the code above, you’ll see something interesting – the lines of code inside the loop are indented (just as we’ve seen before), but the line of code inside the if statement is indented twice – once for the loop, and once for the if. This is the first time we’ve seen multiple levels of indentation, but it’s very common once we start working with larger programs – whenever we have one loop or if statement nested inside another, we’ll have this type of indentation.\n", "\n", "Python is quite happy to have as many levels of indentation as needed, but you’ll need to keep careful track of which lines of code belong at which level. If you find yourself writing a piece of code that requires more than three levels of indentation, it’s generally an indication that that piece of code should be turned into a function." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## else statements" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Closely related to the if statement is the else statement. The examples above use a yes/no type of decision-making: should we print the gene accession number or not? Often we need an either/or type of decision, where we have two possible actions to take. To do this, we can add on an else clause after the end of the body of an if statement:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "expression_level = 125\n", "if expression_level > 100:\n", " print(\"gene is highly expressed\")\n", "else:\n", " print(\"gene is lowly expressed\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The else statement doesn’t have any condition of its own – rather, the else statement body is execute when the if statement to which it’s attached is not executed.\n", "\n", "Here’s an example which uses if and else to split up a list of accession names into two different files – accessions that start with “a” go into the first file, and all other accessions go into the second file:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "file1 = open(\"a_accessions.txt\", \"w\")\n", "file2 = open(\"other_accessions.txt\", \"w\")\n", "accs = ['ab56', 'bh84', 'hv76', 'ay93', 'ap97', 'bd72']\n", "for accession in accs:\n", " if accession.startswith('a'):\n", " file1.write(accession + \"\\n\")\n", " else:\n", " file2.write(accession + \"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how there are multiple indentation levels as before, but that the if and else statements are at the same level." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## elif statements" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What if we have more than two possible branches? For example, say we want three files of accession names: ones that start with “a”, ones that start with “b”, and all others. We could have a second if statement nested inside the else clause of the first if statement:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "file1 = open(\"a_accessions.txt\", \"w\")\n", "file2 = open(\"b_accessions.txt\", \"w\")\n", "file3 = open(\"other_accessions.txt\", \"w\")\n", "\n", "accs = ['ab56', 'bh84', 'hv76', 'ay93', 'ap97', 'bd72']\n", "\n", "for accession in accs:\n", " if accession.startswith('a'):\n", " file1.write(accession + \"\\n\")\n", " else:\n", " if accession.startswith('b'):\n", " file2.write(accession + \"\\n\")\n", " else:\n", " file3.write(accession + \"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This works, but is difficult to read – we can quickly see that we need an extra level of indentation for every additional choice we want to include. To get round this, Python has an elif statement, which merges together else and if and allows us to rewrite the above example in a much more elegant way:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "file1 = open(\"a_accessions.txt\", \"w\")\n", "file2 = open(\"b_accessions.txt\", \"w\")\n", "file3 = open(\"other_accessions.txt\", \"w\")\n", "\n", "accs = ['ab56', 'bh84', 'hv76', 'ay93', 'ap97', 'bd72']\n", "\n", "for accession in accs:\n", " if accession.startswith('a'):\n", " file1.write(accession + \"\\n\")\n", " elif accession.startswith('b'):\n", " file2.write(accession + \"\\n\")\n", " else:\n", " file3.write(accession + \"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how this version of the code only needs two levels of indention. In fact, using elif we can have any number of branches and still only require a single extra level of indentation:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for accession in accs:\n", " if accession.startswith('a'):\n", " file1.write(accession + \"\\n\")\n", " elif accession.startswith('b'):\n", " file2.write(accession + \"\\n\")\n", " elif accession.startswith('c'):\n", " file3.write(accession + \"\\n\")\n", " elif accession.startswith('d'):\n", " file4.write(accession + \"\\n\")\n", " elif accession.startswith('e'):\n", " file5.write(accession + \"\\n\")\n", " else:\n", " file6.write(accession + \"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another way of handling complex decision branches like this – especially useful when dealing with validation and errors – is using exceptions, which have their own chapter in Advanced Python for Biologists." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## while loops" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here’s one final thing we can do with conditions: use them to determine when to exit a loop. In section 4 we learned about loops that iterate over a collection of items (like a list, a string or a file). Python has another type of loop called a while loop. Rather than running a set number of times, a while loop runs until some condition is met. For example, here’s a bit of code that increments a count variable by one each time round the loop, stopping when the count variable reaches ten:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "count = 0\n", "while count<10:\n", " print(count)\n", " count = count + 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because normal loops in Python are so powerful2 , while loops are used much less frequently than in other languages, so we won’t discuss them further." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building up complex conditions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What if we wanted to express a condition that was made up of several parts? Imagine we want to go through our list of accessions and print out only the ones that start with “a” and end with “3”. We could use two nested if statements:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "accs = ['ab56', 'bh84', 'hv76', 'ay93', 'ap97', 'bd72']\n", "\n", "for accession in accs:\n", " if accession.startswith('a'):\n", " if accession.endswith('3'):\n", " print(accession)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "but this brings in an extra, unneeded level of indention. A better way is to join up the two condition with and to make a complex expression:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "accs = ['ab56', 'bh84', 'hv76', 'ay93', 'ap97', 'bd72']\n", "\n", "for accession in accs:\n", " if accession.startswith('a') and accession.endswith('3'):\n", " print(accession)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This version is nicer in two ways: it doesn’t require the extra level of indentation, and the condition reads in a very natural way. We can also use or to join up two conditions, to produce a complex condition that will be true if either of the two simple conditions are true:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "accs = ['ab56', 'bh84', 'hv76', 'ay93', 'ap97', 'bd72']\n", "\n", "for accession in accs:\n", " if accession.startswith('a') or accession.startswith('b'):\n", " print(accession)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can even join up complex conditions to make more complex conditions – here’s an example which prints accessions if they start with either “a” or “b” and end with “4”:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "accs = ['ab56', 'bh84', 'hv76', 'ay93', 'ap97', 'bd72']\n", "\n", "for acc in accs:\n", " if (acc.startswith('a') or acc.startswith('b')) and acc.endswith('4'):\n", " print(acc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how we have to include parentheses in the above example to avoid ambiguity. Finally, we can negate any type of condition by prefixing it with the word not. This example will print out accessions that start with “a” and don’t end with 6:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "accs = ['ab56', 'bh84', 'hv76', 'ay93', 'ap97', 'bd72']\n", "\n", "for acc in accs:\n", " if acc.startswith('a') and not acc.endswith('6'):\n", " print(acc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By using a combination of and, or and not (along with parentheses where necessary) we can build up arbitrarily complex conditions. This kind of use for conditions – identifying elements in a list – can often be done better using either the filter function, or a list comprehension.\n", "\n", "These three words are collectively known as boolean operators and crop up in a lot of places. For example, if you wanted to search for information on using Python in biology, but didn’t want to see pages that talked about biology of snakes, you might do a search for “biology python -snake“. This is actually a complex condition just like the ones above – Google automatically adds and between words, and uses the hyphen to mean not. So you’re asking for pages that mention python and biology but not snakes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writing true/false functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes we want to write a function that can be used in a condition. This is very easy to do – we just make sure that our function always returns either True or False. Remember that True and False are built-in values in Python, so they can be passed around, stored in variables, and returned, just like numbers or strings.\n", "\n", "Here’s a function that determines whether or not a DNA sequence is AT-rich (we’ll say that a sequence is AT-rich if it has an AT content of more than 0.65):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def is_at_rich(dna):\n", " length = len(dna)\n", " a_count = dna.upper().count('A')\n", " t_count = dna.upper().count('T')\n", " at_content = (a_count + t_count) / length\n", " if at_content > 0.65:\n", " return True\n", " else:\n", " return False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We’ll test this function on a few sequences to see if it works:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(is_at_rich(\"ATTATCTACTA\"))\n", "print(is_at_rich(\"CGGCAGCGCT\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output shows that the function returns True or False just like the other conditions we’ve been looking at:\n", "\n", " True\n", " False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Therefore we can use our function in an if statement:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "if is_at_rich(my_dna):\n", " # do something with the sequence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because the last four lines of our function are devoted to evaluating a condition and returning True or False, we can write a slightly more compact version. In this example we evaluate the condition, and then return the result right away:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def is_at_rich(dna):\n", " length = len(dna)\n", " a_count = dna.upper().count('A')\n", " t_count = dna.upper().count('T')\n", " at_content = (a_count + t_count) / length\n", " return at_content > 0.65" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a little more concise, and also easier to read once you’re familiar with the idiom." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Recap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this short section, we’ve dealt with two things: conditions, and the statements that use them.\n", "\n", "We’ve seen how simple conditions can be joined together to make more complex ones, and how the concepts of truth and falsehood are built in to Python on a fundamental level. We’ve also seen how we can incorporate True and False in our own functions in a way that allows them to be used as part of conditions.\n", "\n", "We’ve been introduced to four different tools that use conditions – if, else, elif, and while – in approximate order of usefulness. You’ll probably find, in the programs that you write and in your solutions to the exercises in this book, that you use if and else very frequently, elif occasionally, and while almost never." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

S-Suren/PythonPlayground

Probability_Dice_Analysis.ipynb

1

79855

{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#Analysis-of-dice-rolls.\" data-toc-modified-id=\"Analysis-of-dice-rolls.-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Analysis of dice rolls.</a></div><div class=\"lev1 toc-item\"><a href=\"#Playing-with-Widgets\" data-toc-modified-id=\"Playing-with-Widgets-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Playing with Widgets</a></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis of dice rolls." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-01-16T03:00:47.574671", "start_time": "2017-01-16T03:00:46.244791" }, "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "from ipywidgets import widgets, interact" ] }, { "cell_type": "raw", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Create a dice.\n", " - Default: 6 side, but can be n > 2 sides.\n", " - Output: An `Int`\n", "- Create a Trial\n", " - Default: 100 rolls, but can be n.\n", " - Output:\n", " - rollsList: Array with all of the roll results.\n", " - cumltList: Array with the cumulative probability." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-01-16T03:00:48.223997", "start_time": "2017-01-16T03:00:48.177967" }, "collapsed": false }, "outputs": [], "source": [ "def dice(num_of_sides = 6, loaded_num = 1, loaded_weight = 1):\n", " '''\n", " Virtual dice with output of a side. Default is an equally weighted dice. \n", " Input:\n", " num_of_sides: Number of sides for the dice. (Default = 6)\n", " loaded_num : The number that is biased or \"loaded\". (Default = 1)\n", " loaded_wght : Number used to get loaded weight. (Default = 1)\n", " For equal weight distribution, the loaded number has\n", " the same probability as any other dice number. (1/6, 1/3,..., 1/n) \n", " \n", " Output: Int value corresponding to a side of the dice.\n", " \n", " Example:\n", " [dice(10, 7, 9) for output in range(20)] -> [7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 7, 8, 7, 7, 7, 7, 1, 7, 7, 7]\n", " [dice(6, 5, 3) for output in range(20)] -> [5, 5, 6, 5, 6, 5, 2, 6, 5, 2, 4, 6, 6, 5, 2, 3, 5, 5, 6, 5]\n", " '''\n", " \n", " if loaded_num > num_of_sides:\n", " raise ValueError('The loaded number chosen is greater than the total number of sides on the dice!')\n", " elif loaded_weight > num_of_sides:\n", " raise ValueError('The loaded weight chosen is greater than the total number of sides on the dice!')\n", " elif num_of_sides < 1:\n", " raise ValueError('Total number of sides is less than one!')\n", " \n", " sides = [num for num in range(1, num_of_sides+1)]\n", " return np.random.choice(sides, p=wght(num_of_sides, loaded_num, loaded_weight))\n", "\n", "\n", "def wght(num_of_sides = 6, loaded_num = 1, loaded_weight = 1):\n", " '''\n", " Weight function for the probabilities of a side on a roll.\n", " Input:\n", " num_of_sides: Number of sides for the dice. (Default = 6)\n", " loaded_num : The number that is biased or \"loaded\". (Default = 1)\n", " loaded_wght : Number used to get loaded weight. (Default = 1)\n", " For equal weight distribution, the loaded number has\n", " the same probability as any other dice number. (1/6, 1/3,..., 1/n)\n", " \n", " Output: List of corresponding weights for each side.\n", " \n", " Example:\n", " Dice with 10 sides, loaded number to be 4, and the loaded weight to be \n", " the chance of 3/10(rather than 1/10): \n", " \n", " wght(10, 4, 3) -> [0.07777777777777778, 0.07777777777777778, 0.07777777777777778,\n", " 0.3,0.07777777777777778, 0.07777777777777778, 0.07777777777777778, 0.07777777777777778,\n", " 0.07777777777777778, 0.07777777777777778]\n", " '''\n", " \n", " if loaded_weight == 1:\n", " return [1/num_of_sides]*len(range(num_of_sides))\n", " \n", " ld_wght = loaded_weight/num_of_sides\n", " wght_list = [(1-ld_wght)/(num_of_sides-1)]*len(range(num_of_sides))\n", " wght_list[loaded_num-1] = ld_wght\n", " \n", " return wght_list\n", "\n", "\n", "def trials(num_of_trials=100, num_of_sides=6, loaded_num=1, loaded_weight=1):\n", " '''\n", " Trials consisting of a specified number of time the dice will be rolled. Output gives\n", " a list of all the rolled values and a list of cumulative proportion per roll.\n", " \n", " Input:\n", " num_of_trials: Number of rolls. (Default = 100)\n", " num_of_sides : Number of sides for the dice. (Default = 6)\n", " loaded_num : The number that is biased or \"loaded\". (Default = 1)\n", " loaded_wght : Number used to get loaded weight. (Default = 1)\n", " For equal weight distribution, the loaded number has\n", " the same probability as any other dice number. (1/6, 1/3,..., 1/n)\n", " Output: (In this order.)\n", " rollsList: List of rolled values. Length equal to the num_of_trials input.\n", " cumltList: List of cumulative proportion per roll. \n", " '''\n", "\n", " sides = list(range(1, num_of_sides+1))\n", " rollsList = []\n", " \n", " cumltList = [[] for side in range(num_of_sides)]\n", " cumlt = [0 for side in range(num_of_sides)]\n", " \n", " for trial in range(num_of_trials):\n", " roll = dice(num_of_sides, loaded_num, loaded_weight)\n", " rollsList.append(roll)\n", " \n", " for side in sides:\n", " if roll == side:\n", " cumlt[side-1] += 1\n", " \n", " cumltList[side-1].append(cumlt[side-1]/(trial+1))\n", " \n", " return rollsList, cumltList\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-01-16T03:00:48.925070", "start_time": "2017-01-16T03:00:48.897551" }, "code_folding": [], "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "def simu_dice(num_of_trials = 100, num_of_sides = 6, loaded_num = 1, loaded_wght = 1, \\\n", " plt_style = 'seaborn-ticks', cuml_tot = True, ret = False):\n", " rolls, cuml = trials(num_of_trials, num_of_sides, loaded_num, loaded_wght)\n", "\n", " plt.style.use(style=plt_style) \n", " \n", " if cuml_tot is True:\n", " for side in cuml:\n", " ax = plt.plot(np.array([num+1 for num in range(len(rolls))]), np.array(side), \\\n", " label = 'Side {}'.format(cuml.index(side) + 1)) \n", " else:\n", " ax = plt.plot(np.array([num+1 for num in range(len(rolls))]), np.array(cuml[loaded_num-1]), \\\n", " label = 'Side {}'.format(cuml.index(cuml[loaded_num-1]) + 1))\n", " \n", " \n", " ax = plt.plot([num+1 for num in range(len(rolls))], np.array([1/num_of_sides]*len(rolls)), \\\n", " color = 'k', linewidth = 2, label = 'Theoretical \\n Probability')\n", " ax = plt.title(plt_style)\n", " ax = plt.legend()\n", " ax = plt.ylim(0.0, 1.0)\n", " ax = plt.xlabel('# of Trials')\n", " ax = plt.ylabel('Cumulative Proportions')\n", " \n", " plt.show()\n", " \n", " if ret is True:\n", " return rolls, cuml" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-01-16T03:01:28.320852", "start_time": "2017-01-16T03:01:27.826025" }, "collapsed": false }, "outputs": [ { "data": { "image/png": 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FkiVLqKqqqrf/r776iqeeeooFCxa0/sVdpyTdQgghhBDXtHKdgZP5pe3aZzcfF5wdNY2u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s2N2G6OINgIsYuJUpgwhSm56JJLXHCJ+xV2KsMJ6S6WvfgopVSK6a07CB2XYipDyctQcDLkPUUx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\nGUilUnsFbfBxJpM5E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"text/plain": [ "<matplotlib.figure.Figure at 0x18c6e6c7a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_of_sides = widgets.IntSlider(6, 1, 25, 1)\n", "\n", "interact(simu_dice, \\\n", " num_of_sides = widgets.IntSlider(6, 1, 25, 1) ,\\\n", " num_of_trials = widgets.IntSlider(100, 0, 100000, 100) ,\\\n", " loaded_num = widgets.IntSlider(1, 1, num_of_sides.max, 1) ,\\\n", " loaded_wght = widgets.IntSlider(1, 1, num_of_sides.max, 1) ,\\\n", " plt_style = tuple(style for style in plt.style.available) ,\\\n", " cuml_tot = True ,\\\n", " ret = widgets.fixed(False)\n", " );" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Playing with Widgets" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T16:41:58.538197", "start_time": "2017-01-15T16:41:58.534694" }, "collapsed": false }, "outputs": [], "source": [ "from ipywidgets import widgets\n", "from ipywidgets import interact\n", "from IPython.display import display" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T16:32:59.274103", "start_time": "2017-01-15T16:32:59.261093" }, "collapsed": false }, "outputs": [], "source": [ "text = widgets.Text()\n", "display(text)\n", "\n", "def handle_submit(sender):\n", " print('Butters')\n", " \n", "text.on_submit(handle_submit)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T16:38:05.685877", "start_time": "2017-01-15T16:38:05.673369" }, "collapsed": false }, "outputs": [], "source": [ "button = widgets.Button(description = 'Click me!')\n", "display(button)\n", "\n", "def on_button_click(b):\n", " print('Button clicked!')\n", " \n", "button.on_click(on_button_click)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T17:57:19.287223", "start_time": "2017-01-15T17:57:19.163140" }, "collapsed": false }, "outputs": [], "source": [ "def f(x, y):\n", " print(x)\n", " print(y)\n", " \n", "sld = interact(f, x=100, y=50)\n", "chk = interact(f, x=True, y=False)\n", "txt = interact(f, x='Suren', y='Butters') " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T17:20:16.493538", "start_time": "2017-01-15T17:20:16.482031" }, "collapsed": true }, "outputs": [], "source": [ "outputText = widgets.Text()\n", "outputText" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T17:21:48.104109", "start_time": "2017-01-15T17:21:48.089600" }, "collapsed": true }, "outputs": [], "source": [ "inputText = widgets.Text()\n", "\n", "def makeUpperCase(sender):\n", " outputText.value = inputText.value.upper()\n", " \n", "inputText.on_submit(makeUpperCase)\n", "inputText" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-01-15T21:11:07.347061", "start_time": "2017-01-15T21:11:07.082386" }, "collapsed": false }, "outputs": [], "source": [ "t = np.arange(0.0, 1.1, .01)\n", "\n", "def pltSin(A, f):\n", " plt.plot(t, A*np.sin(2*np.pi*t*f))\n", " plt.axis([0, 1, -10, 10])\n", " plt.autoscale(False)\n", " plt.show()\n", "\n", "\n", "interact(pltSin, A = widgets.IntSlider(1, 0, 10, 1), f = widgets.FloatSlider(1, min=0, max=10, step=1))" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "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": "3.5.2" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "227px", "width": "237px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": true, "toc_position": { "height": "420px", "left": "0px", "right": "951px", "top": "70px", "width": "62px" }, "toc_section_display": "block", "toc_window_display": true }, "widgets": { "state": { "019d944cd7ee4bef83bd832c33e4e211": { "views": [ { "cell_index": 12 } ] }, "07be34f5f5c942519bd8df2396ed896a": { "views": [ { "cell_index": 15 } ] }, "17780dc4856c48429bb2c4622233c355": { "views": [ { "cell_index": 17 } ] }, "3e71c829c45a47bdb786a74da573f952": { "views": [ { "cell_index": 15 } ] }, "42b366fe3a2949dcb5dda4a08d512092": { "views": [ { "cell_index": 12 } ] }, "4985f5105d43408d85cb978e1afe7693": { "views": [ { "cell_index": 15 } ] }, "4f6ab9dc81ff4f9698774ea5ba39c4a1": { "views": [ { "cell_index": 14 } ] }, "5841ed5259414e04aa307ac9735f85cb": { "views": [ { "cell_index": 11 } ] }, "594d0c7ab1af4fd490949f00861ccc01": { "views": [ { "cell_index": 13 } ] }, "5c5a06ea0c2c49a5adcbb8375df9396d": { "views": [ { "cell_index": 9 } ] }, 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gpl-3.0

cmgerber/Simple_Gif_Creator

Making_Gifs_from_YouTube.ipynb

1

2826

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Need to intall**\n", "\n", "- moviepy (dependent on ffmpeg and ImageMagick - http://zulko.github.io/moviepy/install.html\n", "- pafy (Python YouTube API) - http://github.com/np1/pafy\n", "\n", "A couple options for viewing your gifs:\n", "\n", "- Upload to http://gfycat.com/\n", "- Upload to http://imgur.com/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from moviepy.editor import *\n", "import pafy" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Imbed video in notebook to make it easy to find and adjust the time range you want to use for the gif." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#the video id is the string that comes after the \"https://www.youtube.com/watch?v=\"\n", "\n", "from IPython.display import YouTubeVideo\n", "YouTubeVideo(#put your video id here)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Access the video through the YouTube API and download it." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#this find the youtube video and downloads it.\n", "\n", "#add the video url here\n", "url = 'https://www.youtube.com/watch?v=your_video_id'\n", "video = pafy.new(url)\n", "\n", "#get the best download option\n", "best = video.getbest(preftype=\"mp4\")\n", "\n", "#create a filename for the video\n", "myfilename = 'Your_Video_Name.' + best.extension\n", "title = best.download(filepath=myfilename ,quiet=False)\n", "print title" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#subclip = start and stop time\n", "#resize = downsample\n", "#name your gif file\n", "#fps = frames per second\n", "VideoFileClip(title).\\\n", " subclip((4,02.40),(4,08.00)).\\\n", " resize(0.25).\\\n", " to_gif(\"Your_Gif_Name.gif\", fps=7)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

agpl-3.0

erikdejonge/github-stars-syncer

Untitled1.ipynb

1

6634

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[{'archive_url': 'https://api.github.com/repos/pouchdb/pouchdb/{archive_format}{/ref}',\n", " 'assignees_url': 'https://api.github.com/repos/pouchdb/pouchdb/assignees{/user}',\n", " 'blobs_url': 'https://api.github.com/repos/pouchdb/pouchdb/git/blobs{/sha}',\n", " 'branches_url': 'https://api.github.com/repos/pouchdb/pouchdb/branches{/branch}',\n", " 'clone_url': 'https://github.com/pouchdb/pouchdb.git',\n", " 'collaborators_url': 'https://api.github.com/repos/pouchdb/pouchdb/collaborators{/collaborator}',\n", " 'comments_url': 'https://api.github.com/repos/pouchdb/pouchdb/comments{/number}',\n", " 'commits_url': 'https://api.github.com/repos/pouchdb/pouchdb/commits{/sha}',\n", " 'compare_url': 'https://api.github.com/repos/pouchdb/pouchdb/compare/{base}...{head}',\n", " 'contents_url': 'https://api.github.com/repos/pouchdb/pouchdb/contents/{+path}',\n", " 'contributors_url': 'https://api.github.com/repos/pouchdb/pouchdb/contributors',\n", " 'created_at': '2010-06-10T18:34:24Z',\n", " 'default_branch': 'master',\n", " 'description': ':koala: - PouchDB is a pocket-sized database.',\n", " 'downloads_url': 'https://api.github.com/repos/pouchdb/pouchdb/downloads',\n", " 'events_url': 'https://api.github.com/repos/pouchdb/pouchdb/events',\n", " 'fork': False,\n", " 'forks': 612,\n", " 'forks_count': 612,\n", " 'forks_url': 'https://api.github.com/repos/pouchdb/pouchdb/forks',\n", " 'full_name': 'pouchdb/pouchdb',\n", " 'git_commits_url': 'https://api.github.com/repos/pouchdb/pouchdb/git/commits{/sha}',\n", " 'git_refs_url': 'https://api.github.com/repos/pouchdb/pouchdb/git/refs{/sha}',\n", " 'git_tags_url': 'https://api.github.com/repos/pouchdb/pouchdb/git/tags{/sha}',\n", " 'git_url': 'git://github.com/pouchdb/pouchdb.git',\n", " 'has_downloads': True,\n", " 'has_issues': True,\n", " 'has_pages': True,\n", " 'has_wiki': True,\n", " 'homepage': 'http://pouchdb.com/',\n", " 'hooks_url': 'https://api.github.com/repos/pouchdb/pouchdb/hooks',\n", " 'html_url': 'https://github.com/pouchdb/pouchdb',\n", " 'id': 714074,\n", " 'issue_comment_url': 'https://api.github.com/repos/pouchdb/pouchdb/issues/comments{/number}',\n", " 'issue_events_url': 'https://api.github.com/repos/pouchdb/pouchdb/issues/events{/number}',\n", " 'issues_url': 'https://api.github.com/repos/pouchdb/pouchdb/issues{/number}',\n", " 'keys_url': 'https://api.github.com/repos/pouchdb/pouchdb/keys{/key_id}',\n", " 'labels_url': 'https://api.github.com/repos/pouchdb/pouchdb/labels{/name}',\n", " 'language': 'JavaScript',\n", " 'languages_url': 'https://api.github.com/repos/pouchdb/pouchdb/languages',\n", " 'merges_url': 'https://api.github.com/repos/pouchdb/pouchdb/merges',\n", " 'milestones_url': 'https://api.github.com/repos/pouchdb/pouchdb/milestones{/number}',\n", " 'mirror_url': None,\n", " 'name': 'pouchdb',\n", " 'notifications_url': 'https://api.github.com/repos/pouchdb/pouchdb/notifications{?since,all,participating}',\n", " 'open_issues': 137,\n", " 'open_issues_count': 137,\n", " 'owner': {'avatar_url': 'https://avatars.githubusercontent.com/u/3406112?v=3',\n", " 'events_url': 'https://api.github.com/users/pouchdb/events{/privacy}',\n", " 'followers_url': 'https://api.github.com/users/pouchdb/followers',\n", " 'following_url': 'https://api.github.com/users/pouchdb/following{/other_user}',\n", " 'gists_url': 'https://api.github.com/users/pouchdb/gists{/gist_id}',\n", " 'gravatar_id': '',\n", " 'html_url': 'https://github.com/pouchdb',\n", " 'id': 3406112,\n", " 'login': 'pouchdb',\n", " 'organizations_url': 'https://api.github.com/users/pouchdb/orgs',\n", " 'received_events_url': 'https://api.github.com/users/pouchdb/received_events',\n", " 'repos_url': 'https://api.github.com/users/pouchdb/repos',\n", " 'site_admin': False,\n", " 'starred_url': 'https://api.github.com/users/pouchdb/starred{/owner}{/repo}',\n", " 'subscriptions_url': 'https://api.github.com/users/pouchdb/subscriptions',\n", " 'type': 'Organization',\n", " 'url': 'https://api.github.com/users/pouchdb'},\n", " 'private': False,\n", " 'pulls_url': 'https://api.github.com/repos/pouchdb/pouchdb/pulls{/number}',\n", " 'pushed_at': '2016-01-17T22:07:22Z',\n", " 'releases_url': 'https://api.github.com/repos/pouchdb/pouchdb/releases{/id}',\n", " 'size': 143761,\n", " 'ssh_url': 'git@github.com:pouchdb/pouchdb.git',\n", " 'stargazers_count': 5582,\n", " 'stargazers_url': 'https://api.github.com/repos/pouchdb/pouchdb/stargazers',\n", " 'statuses_url': 'https://api.github.com/repos/pouchdb/pouchdb/statuses/{sha}',\n", " 'subscribers_url': 'https://api.github.com/repos/pouchdb/pouchdb/subscribers',\n", " 'subscription_url': 'https://api.github.com/repos/pouchdb/pouchdb/subscription',\n", " 'svn_url': 'https://github.com/pouchdb/pouchdb',\n", " 'tags_url': 'https://api.github.com/repos/pouchdb/pouchdb/tags',\n", " 'teams_url': 'https://api.github.com/repos/pouchdb/pouchdb/teams',\n", " 'trees_url': 'https://api.github.com/repos/pouchdb/pouchdb/git/trees{/sha}',\n", " 'updated_at': '2016-01-17T22:16:07Z',\n", " 'url': 'https://api.github.com/repos/pouchdb/pouchdb',\n", " 'watchers': 5582,\n", " 'watchers_count': 5582}]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pickle\n", "d = pickle.load(open('starlist.pickle', 'rb'))\n", "d" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0

MingChen0919/learning-apache-spark

notebooks/ipynb/RandomForest.ipynb

1

19262

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pyspark Random Forest Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Set up spark context and SparkSession" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pyspark.sql import SparkSession\n", "\n", "spark = SparkSession \\\n", " .builder \\\n", " .appName(\"Python Spark Random Forest Regression\") \\\n", " .config(\"spark.some.config.option\", \"some-value\") \\\n", " .getOrCreate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. load dataset" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = spark.read.format('com.databricks.spark.csv').\\\n", " options(header='true', \\\n", " inferschema='true').load(\"./data/WineData.csv\",header=True);" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "root\n", " |-- fixed acidity: double (nullable = true)\n", " |-- volatile acidity: double (nullable = true)\n", " |-- citric acid: double (nullable = true)\n", " |-- residual sugar: double (nullable = true)\n", " |-- chlorides: double (nullable = true)\n", " |-- free sulfur dioxide: double (nullable = true)\n", " |-- total sulfur dioxide: double (nullable = true)\n", " |-- density: double (nullable = true)\n", " |-- pH: double (nullable = true)\n", " |-- sulphates: double (nullable = true)\n", " |-- alcohol: double (nullable = true)\n", " |-- quality: integer (nullable = true)\n", "\n" ] } ], "source": [ "df.printSchema()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# convert the data to dense vector\n", "#def transData(row):\n", "# return Row(label=row[\"quality\"],\n", "# features=Vectors.dense([row[\"fixed acidity\"],\n", "# row[\"volatile acidity\"],\n", "# row[\"citric acid\"],\n", "# row[\"residual sugar\"],\n", "# row[\"chlorides\"], \n", "# row[\"free sulfur dioxide\"],\n", "# row[\"total sulfur dioxide\"],\n", "# row[\"residual sugar\"],\n", "# row[\"density\"], \n", "# row[\"pH\"],\n", "# row[\"sulphates\"],\n", "# row[\"alcohol\"]\n", "# ]))\n", "def transData(data):\n", " return data.rdd.map(lambda r: [Vectors.dense(r[:-1]),r[-1]]).toDF(['features','label'])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pyspark.ml import Pipeline\n", "from pyspark.ml.regression import RandomForestRegressor\n", "from pyspark.ml.feature import VectorIndexer\n", "from pyspark.ml.evaluation import RegressionEvaluator" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+-----+\n", "| features|label|\n", "+--------------------+-----+\n", "|[7.4,0.7,0.0,1.9,...| 5|\n", "|[7.8,0.88,0.0,2.6...| 5|\n", "|[7.8,0.76,0.04,2....| 5|\n", "|[11.2,0.28,0.56,1...| 6|\n", "|[7.4,0.7,0.0,1.9,...| 5|\n", "|[7.4,0.66,0.0,1.8...| 5|\n", "+--------------------+-----+\n", "only showing top 6 rows\n", "\n" ] } ], "source": [ "from pyspark.sql import Row\n", "from pyspark.ml.linalg import Vectors\n", "\n", "#transformed = df.rdd.map(transData).toDF() \n", "transformed= transData(df)\n", "transformed.show(6)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Split the data into training and test sets (30% held out for testing)\n", "(trainingData, testData) = transformed.randomSplit([0.7, 0.3])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Train a RandomForest model.\n", "rf = RandomForestRegressor()\n", "model = rf.fit(trainingData)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.getNumTrees" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Make predictions.\n", "predictions = model.transform(testData)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+-----+------------------+\n", "| features|label| prediction|\n", "+--------------------+-----+------------------+\n", "|[4.9,0.42,0.0,2.1...| 7| 6.489667556875804|\n", "|[5.1,0.42,0.0,1.8...| 7| 6.267301910170284|\n", "|[5.1,0.585,0.0,1....| 7|6.0526786505470245|\n", "|[5.2,0.32,0.25,1....| 5| 5.257985010985523|\n", "|[5.2,0.48,0.04,1....| 7| 5.943264423589821|\n", "|[5.2,0.645,0.0,2....| 6| 5.909094836353475|\n", "|[5.3,0.47,0.11,2....| 7| 6.41491729478567|\n", "|[5.4,0.74,0.0,1.2...| 6| 5.855394156577842|\n", "|[5.4,0.835,0.08,1...| 7| 5.989289110209313|\n", "|[5.5,0.49,0.03,1....| 8| 6.245232161181737|\n", "+--------------------+-----+------------------+\n", "only showing top 10 rows\n", "\n" ] } ], "source": [ "predictions.show(10)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------------+-----+--------------------+\n", "| prediction|label| features|\n", "+------------------+-----+--------------------+\n", "| 6.489667556875804| 7|[4.9,0.42,0.0,2.1...|\n", "| 6.267301910170284| 7|[5.1,0.42,0.0,1.8...|\n", "|6.0526786505470245| 7|[5.1,0.585,0.0,1....|\n", "| 5.257985010985523| 5|[5.2,0.32,0.25,1....|\n", "| 5.943264423589821| 7|[5.2,0.48,0.04,1....|\n", "+------------------+-----+--------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "# Select example rows to display.\n", "predictions.select(\"prediction\", \"label\", \"features\").show(5)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Root Mean Squared Error (RMSE) on test data = 0.659148\n" ] } ], "source": [ "# Select (prediction, true label) and compute test error\n", "evaluator = RegressionEvaluator(\n", " labelCol=\"label\", predictionCol=\"prediction\", metricName=\"rmse\")\n", "rmse = evaluator.evaluate(predictions)\n", "print(\"Root Mean Squared Error (RMSE) on test data = %g\" % rmse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pyspark Random Forest Classifier" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pyspark.ml import Pipeline\n", "from pyspark.ml.classification import RandomForestClassifier\n", "from pyspark.ml.feature import IndexToString, StringIndexer, VectorIndexer\n", "from pyspark.ml.evaluation import MulticlassClassificationEvaluator" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = spark.read.format('com.databricks.spark.csv').\\\n", " options(header='true', \\\n", " inferschema='true').load(\"./data/WineData.csv\",header=True);" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "root\n", " |-- fixed acidity: double (nullable = true)\n", " |-- volatile acidity: double (nullable = true)\n", " |-- citric acid: double (nullable = true)\n", " |-- residual sugar: double (nullable = true)\n", " |-- chlorides: double (nullable = true)\n", " |-- free sulfur dioxide: double (nullable = true)\n", " |-- total sulfur dioxide: double (nullable = true)\n", " |-- density: double (nullable = true)\n", " |-- pH: double (nullable = true)\n", " |-- sulphates: double (nullable = true)\n", " |-- alcohol: double (nullable = true)\n", " |-- quality: integer (nullable = true)\n", "\n" ] } ], "source": [ "df.printSchema()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# convert the data to dense vector\n", "def transData(data):\n", " return data.rdd.map(lambda r: [Vectors.dense(r[:-1]),r[-1]]).toDF(['features','label'])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+-----+\n", "| features|label|\n", "+--------------------+-----+\n", "|[7.4,0.7,0.0,1.9,...| 5|\n", "|[7.8,0.88,0.0,2.6...| 5|\n", "|[7.8,0.76,0.04,2....| 5|\n", "|[11.2,0.28,0.56,1...| 6|\n", "|[7.4,0.7,0.0,1.9,...| 5|\n", "|[7.4,0.66,0.0,1.8...| 5|\n", "+--------------------+-----+\n", "only showing top 6 rows\n", "\n" ] } ], "source": [ "from pyspark.sql import Row\n", "from pyspark.ml.linalg import Vectors\n", "\n", "data= transData(df)\n", "data.show(6)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+-----+------------+\n", "| features|label|indexedLabel|\n", "+--------------------+-----+------------+\n", "|[7.4,0.7,0.0,1.9,...| 5| 0.0|\n", "|[7.8,0.88,0.0,2.6...| 5| 0.0|\n", "|[7.8,0.76,0.04,2....| 5| 0.0|\n", "|[11.2,0.28,0.56,1...| 6| 1.0|\n", "|[7.4,0.7,0.0,1.9,...| 5| 0.0|\n", "|[7.4,0.66,0.0,1.8...| 5| 0.0|\n", "+--------------------+-----+------------+\n", "only showing top 6 rows\n", "\n" ] } ], "source": [ "# Index labels, adding metadata to the label column.\n", "# Fit on whole dataset to include all labels in index.\n", "labelIndexer = StringIndexer(inputCol=\"label\", outputCol=\"indexedLabel\").fit(data)\n", "labelIndexer.transform(data).show(6)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+-----+--------------------+\n", "| features|label| indexedFeatures|\n", "+--------------------+-----+--------------------+\n", "|[7.4,0.7,0.0,1.9,...| 5|[7.4,0.7,0.0,1.9,...|\n", "|[7.8,0.88,0.0,2.6...| 5|[7.8,0.88,0.0,2.6...|\n", "|[7.8,0.76,0.04,2....| 5|[7.8,0.76,0.04,2....|\n", "|[11.2,0.28,0.56,1...| 6|[11.2,0.28,0.56,1...|\n", "|[7.4,0.7,0.0,1.9,...| 5|[7.4,0.7,0.0,1.9,...|\n", "|[7.4,0.66,0.0,1.8...| 5|[7.4,0.66,0.0,1.8...|\n", "+--------------------+-----+--------------------+\n", "only showing top 6 rows\n", "\n" ] } ], "source": [ "# Automatically identify categorical features, and index them.\n", "# Set maxCategories so features with > 4 distinct values are treated as continuous.\n", "featureIndexer =VectorIndexer(inputCol=\"features\", \\\n", " outputCol=\"indexedFeatures\", \\\n", " maxCategories=4).fit(data)\n", "\n", "featureIndexer.transform(data).show(6) " ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Split the data into training and test sets (30% held out for testing)\n", "(trainingData, testData) = transformed.randomSplit([0.7, 0.3])" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Train a RandomForest model.\n", "rf = RandomForestClassifier(labelCol=\"indexedLabel\", featuresCol=\"indexedFeatures\", numTrees=10)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Convert indexed labels back to original labels.\n", "labelConverter = IndexToString(inputCol=\"prediction\", outputCol=\"predictedLabel\",\n", " labels=labelIndexer.labels)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Chain indexers and forest in a Pipeline\n", "pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf, labelConverter])" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Train model. This also runs the indexers.\n", "model = pipeline.fit(trainingData)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Make predictions.\n", "predictions = model.transform(testData)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+-----+--------------+\n", "| features|label|predictedLabel|\n", "+--------------------+-----+--------------+\n", "|[4.7,0.6,0.17,2.3...| 6| 5|\n", "|[5.0,1.02,0.04,1....| 4| 5|\n", "|[5.1,0.42,0.0,1.8...| 7| 6|\n", "|[5.1,0.47,0.02,1....| 6| 6|\n", "|[5.2,0.32,0.25,1....| 5| 5|\n", "+--------------------+-----+--------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "# Select example rows to display.\n", "predictions.select(\"features\",\"label\",\"predictedLabel\").show(5)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test Error = 0.402083\n" ] } ], "source": [ "# Select (prediction, true label) and compute test error\n", "evaluator = MulticlassClassificationEvaluator(\n", " labelCol=\"indexedLabel\", predictionCol=\"prediction\", metricName=\"accuracy\")\n", "accuracy = evaluator.evaluate(predictions)\n", "print(\"Test Error = %g\" % (1.0 - accuracy))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RandomForestClassificationModel (uid=rfc_d3482d5f110a) with 10 trees" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rfModel = model.stages[2]\n", "rfModel" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[DecisionTreeClassificationModel (uid=dtc_83a486d1ea3f) of depth 5 with 59 nodes,\n", " DecisionTreeClassificationModel (uid=dtc_ecc86f56fd85) of depth 5 with 57 nodes,\n", " DecisionTreeClassificationModel (uid=dtc_c808860908b2) of depth 5 with 53 nodes,\n", " DecisionTreeClassificationModel (uid=dtc_9cc34e2a2a6f) of depth 5 with 61 nodes,\n", " DecisionTreeClassificationModel (uid=dtc_67e03b4e5ce4) of depth 5 with 61 nodes,\n", " DecisionTreeClassificationModel (uid=dtc_1d972cb63e4e) of depth 5 with 55 nodes,\n", " DecisionTreeClassificationModel (uid=dtc_8d0e0c19f6fa) of depth 5 with 59 nodes,\n", " DecisionTreeClassificationModel (uid=dtc_650db857e494) of depth 5 with 61 nodes,\n", " DecisionTreeClassificationModel (uid=dtc_cc2e16db50a1) of depth 5 with 61 nodes,\n", " DecisionTreeClassificationModel (uid=dtc_14ac6d89880a) of depth 5 with 59 nodes]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rfModel.trees" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

siddhartha-gadgil/ProvingGround

notes/HoTTCoreExperiments.ipynb

1

7287

{ "cells": [ { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[32mimport \u001b[39m\u001b[36m$ivy.$ \u001b[39m" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import $ivy.`in.ac.iisc::provingground-core:0.1.0`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[32mimport \u001b[39m\u001b[36mprovingground._, functionfinder._\u001b[39m" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import provingground._, functionfinder._" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[32mimport \u001b[39m\u001b[36mspire.math._\u001b[39m" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import spire.math._" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mres20\u001b[39m: \u001b[32mSafeLong\u001b[39m = 1" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1 : SafeLong" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[32mimport \u001b[39m\u001b[36mNatRing._\u001b[39m" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import NatRing._" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mres22\u001b[39m: \u001b[32mNat\u001b[39m = ScalaSymbol(1) : (Nat.Typ)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1 : Nat" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[32mimport \u001b[39m\u001b[36mHoTT._\n", "\u001b[39m\n", "\u001b[36mA\u001b[39m: \u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m] with \u001b[32mSubs\u001b[39m[\u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m]] = A : 𝒰 _0\n", "\u001b[36mB\u001b[39m: \u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m] with \u001b[32mSubs\u001b[39m[\u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m]] = B : 𝒰 _0" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import HoTT._\n", "val A = \"A\" :: Type\n", "val B = \"B\" :: Type" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36ma\u001b[39m: \u001b[32mTerm\u001b[39m with \u001b[32mSubs\u001b[39m[\u001b[32mTerm\u001b[39m] = a : (A : 𝒰 _0)\n", "\u001b[36mf\u001b[39m: \u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m, \u001b[32mTerm\u001b[39m] with \u001b[32mSubs\u001b[39m[\u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m, \u001b[32mTerm\u001b[39m]] = f : ((A : 𝒰 _0) → (B : 𝒰 _0))\n", "\u001b[36mmp\u001b[39m: \u001b[32mFuncLike\u001b[39m[\u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m] with \u001b[32mSubs\u001b[39m[\u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m]], \u001b[32mFuncLike\u001b[39m[\u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m] with \u001b[32mSubs\u001b[39m[\u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m]], \u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m with \u001b[32mSubs\u001b[39m[\u001b[32mTerm\u001b[39m], \u001b[32mFunc\u001b[39m[\u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m, \u001b[32mTerm\u001b[39m] with \u001b[32mSubs\u001b[39m[\u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m, \u001b[32mTerm\u001b[39m]], \u001b[32mTerm\u001b[39m]]]] = (A : 𝒰 _0) ↦ ((B : 𝒰 _0) ↦ ((a : (A : 𝒰 _0)) ↦ ((f : ((A : 𝒰 _0) → (B : 𝒰 _0))) ↦ ((f : ((A : 𝒰 _0) → (B : 𝒰 _0))) (a : (A : 𝒰 _0)) : (B : 𝒰 _0)))))" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val a =\"a\" :: A\n", "val f =\"f\" :: A ->: B\n", "val mp = lambda(A)(lambda(B)(lmbda(a)(lmbda(f)(f(a)))))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mmpPf\u001b[39m: \u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m with \u001b[32mSubs\u001b[39m[\u001b[32mTerm\u001b[39m], \u001b[32mFunc\u001b[39m[\u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m, \u001b[32mTerm\u001b[39m] with \u001b[32mSubs\u001b[39m[\u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m, \u001b[32mTerm\u001b[39m]], \u001b[32mTerm\u001b[39m]] = (a : (B : 𝒰 _0)) ↦ ((f : ((B : 𝒰 _0) → (A : 𝒰 _0))) ↦ ((f : ((B : 𝒰 _0) → (A : 𝒰 _0))) (a : (B : 𝒰 _0)) : (A : 𝒰 _0)))" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val mpPf = mp(B)(A)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mres26\u001b[39m: \u001b[32mTyp\u001b[39m[\u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m with \u001b[32mSubs\u001b[39m[\u001b[32mTerm\u001b[39m], \u001b[32mFunc\u001b[39m[\u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m, \u001b[32mTerm\u001b[39m] with \u001b[32mSubs\u001b[39m[\u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m, \u001b[32mTerm\u001b[39m]], \u001b[32mTerm\u001b[39m]]] = (B : 𝒰 _0) → (((B : 𝒰 _0) → (A : 𝒰 _0)) → (A : 𝒰 _0))" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mpPf.typ" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Scala", "language": "scala", "name": "scala" }, "language_info": { "codemirror_mode": "text/x-scala", "file_extension": ".scala", "mimetype": "text/x-scala", "name": "scala", "nbconvert_exporter": "script", "version": "2.12.8" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

necromuralist/machine_learning_studies

machine_learning/udacity/project_1/linear_regression.ipynb

1

5425

{ "cells": [ { "cell_type": "markdown", "metadata": { "ein.tags": [ "worksheet-0" ] }, "source": [ "# Linear Regression" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "autoscroll": "json-false", "collapsed": false, "ein.tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "# third-party\n", "import matplotlib.pyplot as plot\n", "import numpy\n", "import seaborn" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "autoscroll": "json-false", "collapsed": false, "ein.tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": [ "worksheet-0" ] }, "source": [ "## Problem 1" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": [ "worksheet-0" ] }, "source": [ "First, an example where the values are perfectly linear." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "autoscroll": "json-false", "collapsed": false, "ein.tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "x_1 = numpy.array([3,6,4,5], dtype=float)\n", "y_1 = numpy.array([0, -3, -1, -2], dtype=float)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "autoscroll": "json-false", "collapsed": false, "ein.tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "expected_w_0 = 3\n", "expected_w_1 = -1\n", "def f(x, w_0, w_1):\n", " return w_0 + w_1 * x\n", "assert all(f(x_1, expected_w_0, expected_w_1) == y_1)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "autoscroll": "json-false", "collapsed": false, "ein.tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "def weight_1(x, y):\n", " m = float(len(x))\n", " return (m * (x * y).sum() - (x.sum() * y.sum()))/(m * (x**2).sum() - x.sum()**2)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "autoscroll": "json-false", "collapsed": false, "ein.tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "w_1 = weight_1(x_1, y_1)\n", "assert w_1 == expected_w_1" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "autoscroll": "json-false", "collapsed": false, "ein.tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "def weight_0(x, y, w_1):\n", " m = float(len(x))\n", " return y.sum()/m - (w_1/m) * x.sum()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "autoscroll": "json-false", "collapsed": false, "ein.tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "w_0 = weight_0(x_1, y_1, w_1)\n", "assert expected_w_0 == w_0, \"Expected: {0} Actual: {1}\".format(expected_w_0,\n", " w_0)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "autoscroll": "json-false", "collapsed": false, "ein.tags": [ "worksheet-0" ] }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7f7faf90e510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = plot.figure()\n", "axe = figure.gca()\n", "exes = numpy.arange(x_1.min(), x_1.max() + 1)\n", "line = axe.plot(exes, f(exes, w_0, w_1), color='firebrick')\n", "line = axe.scatter(x_1, y_1)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": [ "worksheet-0" ] }, "source": [ "Given x, y, calculate the weights for a linear regression." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "autoscroll": "json-false", "collapsed": false, "ein.tags": [ "worksheet-0" ] }, "outputs": [], "source": [ "x_2 = numpy.array([2, 4, 6, 8])\n", "y_2 = numpy.array([2, 5, 5, 8])\n", "\n", "w1_2 = weight_1(x_2, y_2)\n", "w0_2 = weight_0(x_2, y_2, w1_2)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "autoscroll": "json-false", "collapsed": false, "ein.tags": [ "worksheet-0" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5\n", "0.9\n" ] } ], "source": [ "print(w0_2)\n", "print(w1_2)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "autoscroll": "json-false", "collapsed": false, "ein.tags": [ "worksheet-0" ] }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7f7fafb14310>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = plot.figure()\n", "axe = figure.gca()\n", "exes = numpy.arange(0, x_2.max() + 1)\n", "line = axe.plot(exes, f(exes, w0_2, w1_2), color='firebrick')\n", "line = axe.scatter(x_2, y_2)" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }

mit

jinzekid/codehub

python/Python源代码剖析.ipynb

1

1527

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "object" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = int(10)\n", "a\n", "type(a)\n", "int.__base__" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] }, { "data": { "text/plain": [ "type" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class MyInt(int):\n", " def __getitem__(self, key):\n", " return key + str(self)\n", " \n", "a = MyInt(1)\n", "b = MyInt(2)\n", "print(a+b)\n", "a['key']\n", "\n", "MyInt.__class__" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

ericjang/julia-cuda-samples

simplegpu.ipynb

1

7555

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# GPU Matrix Addition\n", "\n", "This demonstrates the exposed API functions of CUDA.jl (Julia interface for CUDA driver API)\n", "\n", "Julia v0.3.11" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CUDA Driver Initialized\n" ] } ], "source": [ "using CUDA" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CUDA driver version: 7000\n" ] } ], "source": [ "println(\"CUDA driver version: $(CUDA.DriverVersion)\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Devices\n", "device[0]: GeForce GTX 970, capability 5.2, total mem = 4095 MB\n", "\n" ] } ], "source": [ "println(\"Devices\")\n", "list_devices()\n", "println()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "create context\n" ] }, { "data": { "text/plain": [ "CuContext(Ptr{Void} @0x0000000008102ab0)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dev = CuDevice(0)\n", "# create context \n", "ctx = create_context(dev)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Device memory (GB): 4.2942464\n", "Device capability: CuCapability(5,2)\n" ] } ], "source": [ "# device API functions\n", "println(\"Device memory (GB): $(totalmem(dev)/1e9)\")\n", "println(\"Device capability: $(capability(dev))\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "// filename: vadd.cu\n", "// simple CUDA kernel to add 2 vectors\n", "\n", "extern \"C\"\n", "{\n", " __global__ void vadd(const float *a, const float *b, float *c)\n", " {\n", " int i= threadIdx.x + blockIdx.x * blockDim.x;\n", " c[i]=a[i]+b[i];\n", " }\n", "}\n" ] } ], "source": [ "# compile kernel\n", "run(`cat kernels/vadd.cu`)\n", "run(`nvcc -ptx kernels/vadd.cu`)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "load module from vadd.ptx\n" ] }, { "data": { "text/plain": [ "CuModule(Ptr{Void} @0x000000000a4c5970)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "println(\"load module from vadd.ptx\")\n", "md = CuModule(\"vadd.ptx\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "get function vadd\n" ] }, { "data": { "text/plain": [ "CuFunction(Ptr{Void} @0x000000000b0adc70)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "println(\"get function vadd\")\n", "f = CuFunction(md, \"vadd\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "12" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "siz = (3, 4)\n", "len = prod(siz)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "CuArray{Float32,2}(CuPtr(0x0000000503e40400),(3,4),12)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load array a to GPU\n", "a = round(rand(Float32, siz) * 100)\n", "ga = CuArray(a)\n", "# load array b to GPU\n", "b = round(rand(Float32, siz) * 100)\n", "gb = CuArray(b)\n", "# create array c on GPU\n", "gc = CuArray(Float32, siz)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# launch kernel\n", "launch(f, len, 1, (ga, gb, gc))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3x4 Array{Float32,2}:\n", " 107.0 91.0 57.0 31.0\n", " 49.0 100.0 86.0 121.0\n", " 120.0 88.0 98.0 68.0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# fetch results from GPU\n", "c = to_host(gc)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "CuPtr(0x0000000000000000)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# free GPU memory\n", "free(ga)\n", "free(gb)\n", "free(gc)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Results:\n", "a = \n", "Float32[7.0 47.0 3.0 3.0\n", " 29.0 59.0 80.0 66.0\n", " 47.0 6.0 62.0 13.0]\n", "b = \n", "Float32[100.0 44.0 54.0 28.0\n", " 20.0 41.0 6.0 55.0\n", " 73.0 82.0 36.0 55.0]\n", "c = \n", "Float32[107.0 91.0 57.0 31.0\n", " 49.0 100.0 86.0 121.0\n", " 120.0 88.0 98.0 68.0]\n" ] } ], "source": [ "println(\"Results:\")\n", "println(\"a = \\n$a\")\n", "println(\"b = \\n$b\")\n", "println(\"c = \\n$c\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# unload module - this is sensitive, will crash kernel if unload twice\n", "unload(md)\n", "# destroy context\n", "destroy(ctx)" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.3.11", "language": "julia", "name": "julia-0.3" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.3.11" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

georgetown-analytics/machine-learning

examples/melissabphd/Reading & Wrangling Diff Dataset for Drug Use Predictor.ipynb

1

17335

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Reading in and wrangling a completely different dataset to use for the Drug Use Predictor\n", "\n", "Notebook Author: Melissa Burn\\\n", "Georgetown University School of Continuing Studies, Certificate in Data Science, Cohort 11 (Spring 2018)\n", "\n", "Data Source: \n", "- Johnson's IPIP-NEO data repository\n", "- Contributors: John Anthony Johnson\n", "- Date created: 2015-09-22 04:21 PM | Last Updated: 2015-11-04 06:25 PM\n", "- Description: This project makes available information about International Personality Item Pool (IPIP) versions of the NEO Personality Inventory.\n", "- URL: https://osf.io/sxeq5/ \n", "\n", "Specific dataset used: Data from the Johnson (2005) JRP study and documentation for those files. File ipip20993.dat contains 20,993 cases of item responses to the IPIP-NEO-300 in ASCII format. The file also contains facet and domain scale scores and two measures of intra-individual reliability described in the publication. Variables are listed at the top of the file. ipip20993.doc is a Word.doc description of the dataset\n", "\n", "Note that, prior to reading into this Notebook, I opened the ASCII file in Excel, took the top 3K some instances and discarded the rest. I deleted 300+ columns I didn't need, added an ID column, and adopted the IMMODERA and EXCITE columns as stand-ins for \"Impulsiveness\" and \"Sensation Seeking\". The columns will be renamed below." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from numpy import random\n", "from random import randint\n", "\n", "pd.options.mode.chained_assignment = None # get rid of this pesky warning; default='warn'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Project Workflow\n", "\n", "This Notebook moves through the following steps to ingest, sort, and wrangle the dataset so it fits into the Drug Use Predictor model:\n", "1. Ingest the required xlsx data into a dataframe\n", "2. Wrangle the data to provide the right format and column structure, keeping the age, gender and personality test scores\n", "3. Use a random number generator to any features needed for the Drug Use Predictor that don't exist in the Johnson dataset\n", "\n", "### Data Ingestion\n", "\n", "Grab the dataset from the data subdirectory" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ID</th>\n", " <th>AGE</th>\n", " <th>GENDER</th>\n", " <th>NS</th>\n", " <th>ES</th>\n", " <th>OS</th>\n", " <th>AS</th>\n", " <th>CS</th>\n", " <th>IMMODERA</th>\n", " <th>EXCITE</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>37</td>\n", " <td>2</td>\n", " <td>122</td>\n", " <td>158</td>\n", " <td>251</td>\n", " <td>204</td>\n", " <td>266</td>\n", " <td>33</td>\n", " <td>15</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>31</td>\n", " <td>2</td>\n", " <td>130</td>\n", " <td>162</td>\n", " <td>256</td>\n", " <td>241</td>\n", " <td>218</td>\n", " <td>13</td>\n", " <td>28</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>24</td>\n", " <td>2</td>\n", " <td>127</td>\n", " <td>161</td>\n", " <td>229</td>\n", " <td>221</td>\n", " <td>244</td>\n", " <td>26</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>23</td>\n", " <td>2</td>\n", " <td>158</td>\n", " <td>241</td>\n", " <td>225</td>\n", " <td>244</td>\n", " <td>168</td>\n", " <td>39</td>\n", " <td>40</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>33</td>\n", " <td>2</td>\n", " <td>186</td>\n", " <td>189</td>\n", " <td>232</td>\n", " <td>227</td>\n", " <td>230</td>\n", " <td>30</td>\n", " <td>24</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ID AGE GENDER NS ES OS AS CS IMMODERA EXCITE\n", "0 1 37 2 122 158 251 204 266 33 15\n", "1 2 31 2 130 162 256 241 218 13 28\n", "2 3 24 2 127 161 229 221 244 26 16\n", "3 4 23 2 158 241 225 244 168 39 40\n", "4 5 33 2 186 189 232 227 230 30 24" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_excel('data/Johnson_ipip3K_partial.xlsx') \n", "data.head()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# There's an order of magnitude difference in the scale of the numbers and df needs normalizing\n", "import sklearn\n", "from sklearn import preprocessing" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ID AGE GENDER NS ES \\\n", "count 3.167000e+03 3167.000000 3167.000000 3167.000000 3167.000000 \n", "mean 1.158250e-16 -1.754436 0.707610 -0.070051 0.396142 \n", "std 1.732871e+00 0.800656 2.915814 1.024715 0.946197 \n", "min -3.000000e+00 -3.000000 -3.000000 -3.000000 -3.000000 \n", "25% -1.500000e+00 -2.400000 -3.000000 -0.773756 -0.270142 \n", "50% 0.000000e+00 -2.025000 3.000000 -0.149321 0.440758 \n", "75% 1.500000e+00 -1.350000 3.000000 0.610860 1.094787 \n", "max 3.000000e+00 3.000000 3.000000 3.000000 3.000000 \n", "\n", " OS AS CS IMMODERA EXCITE \n", "count 3167.000000 3167.000000 3167.000000 3167.000000 3167.000000 \n", "mean 0.497913 0.904180 0.388287 0.317141 0.318330 \n", "std 0.964612 0.884711 0.965160 1.088756 1.215007 \n", "min -3.000000 -3.000000 -3.000000 -3.000000 -3.000000 \n", "25% -0.127273 0.350254 -0.264249 -0.384615 -0.600000 \n", "50% 0.527273 0.959391 0.419689 0.230769 0.300000 \n", "75% 1.181818 1.507614 1.072539 1.153846 1.200000 \n", "max 3.000000 3.000000 3.000000 3.000000 3.000000 \n" ] } ], "source": [ "# I have learned that preprocessing strips the column headings, so create a working array\n", "X = np.array(data)\n", "X = X.astype(np.float64)\n", "\n", "# Scale the data in the range of the UCI dataset\n", "X = preprocessing.minmax_scale(X, feature_range=(-3,3)) \n", "\n", "# Make a df again and restore the headings\n", "df = pd.DataFrame(X, columns = data.columns)\n", "print(df.describe())" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3167 instances with 10 features\n", "\n" ] } ], "source": [ "# Aaack! How do I avoid scaling the index? I couldn't find the answer through much googling\n", "\n", "# Below is the features list I need. So, I'll have to invent data for the missing columns\n", "# Note, this isn't the same order as in the UCI database but that shouldn't matter\n", "\n", "FEATURES = [\n", " \"ID\", # May not be used to identify respondents\n", " \"Age\", # 18-24, 25-34, 35-44, 45-54, 55-64, 65+\n", " \"Gender\", # Female, Male\n", " \"NS\", # Neuroticism Score\n", " \"ES\", # Extroversion Score\n", " \"OS\", # Openness to experience Score\n", " \"AS\", # Agreeableness Score\n", " \"CS\", # Conscientiousness Score\n", " \"Imp\", # Impulsivity, Lickert scale with -3 = least impulsive, +3 = most impulsive\n", " \"SS\", # Sensation seeking, part of the Impulsiveness assessment, -3 < score > +3\n", " \"Cntry\", # Country: AUS, CAN, NZ, Other, IRE, UK, USA\n", " \"Educ\", # Left before age 16, left @ 16, @ 17, @ 18, some college, prof cert, univ degree, masters, doctorate\n", " \"Ethn\", # Ethnicity: Asian, Black, Mixed Bla/As, Mixed Whi/As, Mixed Whi/Bla, Other\n", " \"Alcohol\", # Class of alcohol consumption\n", " \"Caffeine\", # Class of caffeine consumption\n", " \"Choco\", # Class of chocolate consumption\n", " \"Nicotine\", # Class of nicotine consumption\n", "]\n", "\n", "print(\"{} instances with {} features\\n\".format(*df.shape))" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ID Age Gender NS ES \\\n", "count 3.167000e+03 3167.000000 3167.000000 3167.000000 3167.000000 \n", "mean 1.158250e-16 -1.754436 0.707610 -0.070051 0.396142 \n", "std 1.732871e+00 0.800656 2.915814 1.024715 0.946197 \n", "min -3.000000e+00 -3.000000 -3.000000 -3.000000 -3.000000 \n", "25% -1.500000e+00 -2.400000 -3.000000 -0.773756 -0.270142 \n", "50% 0.000000e+00 -2.025000 3.000000 -0.149321 0.440758 \n", "75% 1.500000e+00 -1.350000 3.000000 0.610860 1.094787 \n", "max 3.000000e+00 3.000000 3.000000 3.000000 3.000000 \n", "\n", " OS AS CS Imp SS \n", "count 3167.000000 3167.000000 3167.000000 3167.000000 3167.000000 \n", "mean 0.497913 0.904180 0.388287 0.317141 0.318330 \n", "std 0.964612 0.884711 0.965160 1.088756 1.215007 \n", "min -3.000000 -3.000000 -3.000000 -3.000000 -3.000000 \n", "25% -0.127273 0.350254 -0.264249 -0.384615 -0.600000 \n", "50% 0.527273 0.959391 0.419689 0.230769 0.300000 \n", "75% 1.181818 1.507614 1.072539 1.153846 1.200000 \n", "max 3.000000 3.000000 3.000000 3.000000 3.000000 \n" ] } ], "source": [ "# Rename the two columns I'm adopting to match the Drug Use Predictor format, and correct upper/lower of others\n", "df.rename(columns={'IMMODERA': 'Imp', 'EXCITE': 'SS', 'AGE':'Age', 'GENDER':'Gender'}, inplace=True)\n", "\n", "# Take a look at the data again\n", "print(df.describe())" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ID Age Gender NS ES \\\n", "count 3.167000e+03 3167.000000 3167.000000 3167.000000 3167.000000 \n", "mean 1.158250e-16 -1.754436 0.707610 -0.070051 0.396142 \n", "std 1.732871e+00 0.800656 2.915814 1.024715 0.946197 \n", "min -3.000000e+00 -3.000000 -3.000000 -3.000000 -3.000000 \n", "25% -1.500000e+00 -2.400000 -3.000000 -0.773756 -0.270142 \n", "50% 0.000000e+00 -2.025000 3.000000 -0.149321 0.440758 \n", "75% 1.500000e+00 -1.350000 3.000000 0.610860 1.094787 \n", "max 3.000000e+00 3.000000 3.000000 3.000000 3.000000 \n", "\n", " OS AS CS Imp SS \\\n", "count 3167.000000 3167.000000 3167.000000 3167.000000 3167.000000 \n", "mean 0.497913 0.904180 0.388287 0.317141 0.318330 \n", "std 0.964612 0.884711 0.965160 1.088756 1.215007 \n", "min -3.000000 -3.000000 -3.000000 -3.000000 -3.000000 \n", "25% -0.127273 0.350254 -0.264249 -0.384615 -0.600000 \n", "50% 0.527273 0.959391 0.419689 0.230769 0.300000 \n", "75% 1.181818 1.507614 1.072539 1.153846 1.200000 \n", "max 3.000000 3.000000 3.000000 3.000000 3.000000 \n", "\n", " Cntry Educ Ethn Alcohol Caffeine \\\n", "count 3167.0 3167.000000 3167.000000 3167.000000 3167.000000 \n", "mean 3.0 -3.053803 -2.924421 -3.033622 -2.971994 \n", "std 0.0 2.983656 2.990705 2.931054 2.983613 \n", "min 3.0 -12.396163 -13.860629 -12.442075 -11.750012 \n", "25% 3.0 -5.097133 -4.971756 -5.036436 -5.084885 \n", "50% 3.0 -3.098763 -2.904049 -3.066110 -2.887685 \n", "75% 3.0 -1.010245 -0.928653 -1.061128 -0.970281 \n", "max 3.0 6.315914 8.225119 6.344689 6.682071 \n", "\n", " Choco Nicotine \n", "count 3167.000000 3167.000000 \n", "mean -2.990313 -2.941755 \n", "std 3.004880 3.022518 \n", "min -14.382956 -13.050001 \n", "25% -4.981528 -4.993154 \n", "50% -3.072716 -2.957549 \n", "75% -0.972593 -0.845246 \n", "max 8.767262 6.877888 \n" ] } ], "source": [ "# I'll make all these people Americans for Cntry = 3\n", "df['Cntry'] = 3\n", "\n", "# Perhaps because I'm using .loc, it needs me to establish the other feature columns in advance\n", "df['Educ'] = 0\n", "df['Ethn'] = 0\n", "df['Alcohol'] = 0\n", "df['Caffeine'] = 0\n", "df['Choco'] = 0\n", "df['Nicotine'] = 0\n", "\n", "# Now I need to generate data for the Educ, Ethn, Alcohol, Caffeine, Choco, and Nicotine features\n", "# HOWEVER, it will help to ensure they're the same scale as the other data in the df\n", "for i in df.index.values:\n", " df.loc[[i],['Educ']] = np.random.normal(-3, 3)\n", " df.loc[[i],['Ethn']] = np.random.normal(-3, 3)\n", " df.loc[[i],['Alcohol']] = np.random.normal(-3, 3)\n", " df.loc[[i],['Caffeine']] = np.random.normal(-3, 3)\n", " df.loc[[i],['Choco']] = np.random.normal(-3, 3)\n", " df.loc[[i],['Nicotine']] = np.random.normal(-3, 3)\n", "\n", "print(df.describe())" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# Now, save this df in a file that can be read by the Drug Use Predictor\n", "df.to_csv('data/Johnny_data_out.csv', index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

DiCarloLab-Delft/PycQED_py3

examples/QWG_examples/iPython/3 - SSB vector to big, higher than 1.ipynb

1

3466

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pycqed.instrument_drivers.physical_instruments.QuTech_AWG_Module \\\n", " import QuTech_AWG_Module\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy import signal\n", "%matplotlib inline\n", "\n", "#qwgDevice = \"QWG1\"\n", "qwgDevice = \"QWG2\"\n", "\n", "ip = None;\n", "\n", "if qwgDevice == \"QWG1\":\n", " ip = \"192.168.0.10\"\n", "elif qwgDevice == \"QWG2\":\n", " ip = \"192.168.0.11\"\n", "else:\n", " raise RuntimeError('Did not select support device')\n", " exit()\n", "\n", "qwg1 = QuTech_AWG_Module(\n", " 'QWG', address=ip,\n", " port=5025)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qwg1.reset()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qwg1.stop()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fs = 1e9\n", "\n", "# For continuous mode this value should be a multiple of 4e-9\n", "time = 52e-9\n", "\n", "length = int(time*fs)\n", "halflength = int(time*fs/2)\n", "\n", "waveformSine = np.sin(np.arange(length)*2*np.pi/length)\n", "waveformCosine = np.cos(np.arange(length)*2*np.pi/length)\n", "\n", "qwg1.createWaveformReal('sin', waveformSine)\n", "plt.plot(waveformSine)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set for continuous\n", "qwg1.set('ch1_default_waveform', 'sin')\n", "qwg1.set('ch2_default_waveform', 'sin')\n", "qwg1.set('ch3_default_waveform', 'sin')\n", "qwg1.set('ch4_default_waveform', 'sin')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qwg1.ch_pair1_transform_matrix(np.array([[1, 0],[0, 1]]))\n", "qwg1.ch_pair3_transform_matrix(np.array([[1, 0],[0, 1]]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qwg1.ch_pair1_sideband_frequency.set(20e6)\n", "qwg1.ch_pair3_sideband_frequency.set(0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qwg1.ch1_offset(0)\n", "qwg1.ch2_offset(0)\n", "qwg1.ch3_offset(0)\n", "qwg1.ch4_offset(0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qwg1.ch1_amp(1.6)\n", "qwg1.ch2_amp(1.6)\n", "qwg1.ch3_amp(1.6)\n", "qwg1.ch4_amp(1.6)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qwg1.ch1_state(True)\n", "qwg1.ch2_state(True)\n", "qwg1.ch3_state(True)\n", "qwg1.ch4_state(True)\n", "\n", "qwg1.run_mode('CONt')\n", "qwg1.start()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

tommytwoeyes/continuity

Strategy_Series.ipynb

2

1355

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Strategy for Testing Series Convergence & Divergence\n", "\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<img src=\"http://localhost:8888/files/11_Infinite_Seq_and_Series/serieschart.png\"/>" ], "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(url='http://localhost:8888/files/11_Infinite_Seq_and_Series/serieschart.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

CalPolyPat/phys202-2015-work

assignments/assignment03/NumpyEx04.ipynb

1

51172

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Numpy Exercise 4" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [], "source": [ "import numpy as np\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Complete graph Laplacian" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "In discrete mathematics a [Graph](http://en.wikipedia.org/wiki/Graph_%28mathematics%29) is a set of *vertices* or *nodes* that are connected to each other by *edges* or *lines*. If those *edges* don't have directionality, the graph is said to be *undirected*. Graphs are used to model social and communications networks (Twitter, Facebook, Internet) as well as natural systems such as molecules.\n", "\n", "A [Complete Graph](http://en.wikipedia.org/wiki/Complete_graph), $K_n$ on $n$ nodes has an edge that connects each node to every other node.\n", "\n", "Here is $K_5$:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [ { "data": { "image/png": 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5eQgMDIREIsHjx48xY8YMiMVi9O7du1bXKSwoQMzKlWgRE4OJmZnQeePPywCc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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7effc50baa10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import networkx as nx\n", "K_5=nx.complete_graph(5)\n", "nx.draw(K_5)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "nbgrader": {} }, "source": [ "The [Laplacian Matrix](http://en.wikipedia.org/wiki/Laplacian_matrix) is a matrix that is extremely important in graph theory and numerical analysis. It is defined as $L=D-A$. Where $D$ is the degree matrix and $A$ is the adjecency matrix. For the purpose of this problem you don't need to understand the details of these matrices, although their definitions are relatively simple.\n", "\n", "The degree matrix for $K_n$ is an $n \\times n$ diagonal matrix with the value $n-1$ along the diagonal and zeros everywhere else. Write a function to compute the degree matrix for $K_n$ using NumPy." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true, "nbgrader": { "checksum": "00d28c9ea423c0f2985eda865ec5ccee", "solution": true } }, "outputs": [], "source": [ "def complete_deg(n):\n", " return (n-1)*np.identity(n, dtype=int)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "7f2a5f03b1a59c05f397ce1e4d9ae4a1", "grade": true, "grade_id": "numpyex04a", "points": 4 } }, "outputs": [], "source": [ "D = complete_deg(5)\n", "assert D.shape==(5, 5)\n", "assert D.dtype==np.dtype(int)\n", "assert np.all(D.diagonal()==4*np.ones(5))\n", "assert np.all(D-np.diag(D.diagonal())==np.zeros((5,5),dtype=int))" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "The adjacency matrix for $K_n$ is an $n \\times n$ matrix with zeros along the diagonal and ones everywhere else. Write a function to compute the adjacency matrix for $K_n$ using NumPy." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true, "nbgrader": { "checksum": "5285cd3c10582e2d30d4a93530092306", "solution": true } }, "outputs": [], "source": [ "def complete_adj(n):\n", " return np.ones((n,n), dtype=int)-np.identity(n, dtype=int)\n", " " ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "658e2e7db6ac6b06f7349682477e75ce", "grade": true, "grade_id": "numpyex04b", "points": 4 } }, "outputs": [], "source": [ "A = complete_adj(5)\n", "assert A.shape==(5,5)\n", "assert A.dtype==np.dtype(int)\n", "assert np.all(A+np.eye(5,dtype=int)==np.ones((5,5),dtype=int))" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Use NumPy to explore the eigenvalues or *spectrum* of the Laplacian *L* of $K_n$. What patterns do you notice as $n$ changes? Create a *conjecture* about the general Laplace *spectrum* of $K_n$." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7effc4305410>]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7effc43f9610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def L(n): return complete_deg(n)-complete_adj(n)\n", "smalleig = np.empty((100,))\n", "for n in np.arange(2,100):\n", " lap = L(n) \n", " eig = np.linalg.eigvals(lap)\n", " np.append(smalleig, np.min(eig))\n", "plt.plot(np.arange(100), smalleig)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "662bdfcc6fa217197b1ba6a46fc50211", "grade": true, "grade_id": "numpyex04c", "points": 2, "solution": true } }, "source": [ "The smallest eigenvalues of all the laplacians of $K_n$ are equal to n, the order of the graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

survey-methods/samplics

docs/source/tutorial/replicate_weights.ipynb

4

79912

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Replicate weights\n", "Replicate weights are usually created for the purpose of variance (uncertainty) estimation. One common use case for replication-based methods is the estimation of non-linear parameters fow which Taylor-based approximation may not be accurate enough. Another use case is when the number of primary sampling units selected per stratum is small (low degree of freedom). Replicate weights are usually created for the purpose of variance (uncertainty)estimation. One common use case for replication-based methods is the estimation of non-linear parameters fow which Taylor-based approximation may not be accurate enough. Another use case is when the number of primary sampling units selected per stratum is small (low degree of freedom). \n", "\n", "In this tutorial, we will explore creating replicate weights using the class *ReplicateWeight*. Three replication methods have been implemented: balanced repeated replication (BRR) including the Fay-BRR, bootstrap and jackknife. The replicate method of interest is specified when initializing the class by using the parameter *method*. The parameter *method* takes the values \"bootstrap\", \"brr\", or \"jackknife\". In this tutorial, we show how the API works for producing replicate weights. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "import samplics\n", "from samplics.datasets import PSUSample, SSUSample\n", "from samplics.weighting import ReplicateWeight" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We import the sample data..." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>cluster</th>\n", " <th>region</th>\n", " <th>psu_prob</th>\n", " <th>household</th>\n", " <th>ssu_prob</th>\n", " <th>inclusion_prob</th>\n", " <th>design_weight</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>72</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>73</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>75</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>715</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>722</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>724</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>755</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>761</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>764</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>782</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>795</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>7111</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>7112</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>7117</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>7</td>\n", " <td>North</td>\n", " <td>0.187726</td>\n", " <td>7123</td>\n", " <td>0.115385</td>\n", " <td>0.021661</td>\n", " <td>46.166667</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cluster region psu_prob household ssu_prob inclusion_prob \\\n", "0 7 North 0.187726 72 0.115385 0.021661 \n", "1 7 North 0.187726 73 0.115385 0.021661 \n", "2 7 North 0.187726 75 0.115385 0.021661 \n", "3 7 North 0.187726 715 0.115385 0.021661 \n", "4 7 North 0.187726 722 0.115385 0.021661 \n", "5 7 North 0.187726 724 0.115385 0.021661 \n", "6 7 North 0.187726 755 0.115385 0.021661 \n", "7 7 North 0.187726 761 0.115385 0.021661 \n", "8 7 North 0.187726 764 0.115385 0.021661 \n", "9 7 North 0.187726 782 0.115385 0.021661 \n", "10 7 North 0.187726 795 0.115385 0.021661 \n", "11 7 North 0.187726 7111 0.115385 0.021661 \n", "12 7 North 0.187726 7112 0.115385 0.021661 \n", "13 7 North 0.187726 7117 0.115385 0.021661 \n", "14 7 North 0.187726 7123 0.115385 0.021661 \n", "\n", " design_weight \n", "0 46.166667 \n", "1 46.166667 \n", "2 46.166667 \n", "3 46.166667 \n", "4 46.166667 \n", "5 46.166667 \n", "6 46.166667 \n", "7 46.166667 \n", "8 46.166667 \n", "9 46.166667 \n", "10 46.166667 \n", "11 46.166667 \n", "12 46.166667 \n", "13 46.166667 \n", "14 46.166667 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load PSU sample data\n", "psu_sample_cls = PSUSample()\n", "psu_sample_cls.load_data()\n", "psu_sample = psu_sample_cls.data\n", "\n", "# Load PSU sample data\n", "ssu_sample_cls = SSUSample()\n", "ssu_sample_cls.load_data()\n", "ssu_sample = ssu_sample_cls.data\n", "\n", "full_sample = pd.merge(\n", " psu_sample[[\"cluster\", \"region\", \"psu_prob\"]], \n", " ssu_sample[[\"cluster\", \"household\", \"ssu_prob\"]], \n", " on=\"cluster\")\n", "\n", "full_sample[\"inclusion_prob\"] = full_sample[\"psu_prob\"] * full_sample[\"ssu_prob\"] \n", "full_sample[\"design_weight\"] = 1 / full_sample[\"inclusion_prob\"] \n", "\n", "full_sample.head(15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Balanced Repeated Replication (BRR) <a name=\"section1\"></a>\n", "\n", "The basic idea of BRR is to slip the sample in independent random groups. The groups are then threated as independent replicates of the the sample design. A special case is when the sample is split into two half samples in each stratum. This design is suitable to many survey designs where only two psus are selected by stratum. In practice, one of the psu is asigned to the first random group and the other psu is assign to the second group. The sample weights are double for one group (say the first one) and the sample weights in the other group are set to zero. To ensure that the replicates are independent, we use hadamard matrices to assign the random groups. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 1, 1, 1, 1, 1, 1, 1],\n", " [ 1, -1, 1, -1, 1, -1, 1, -1],\n", " [ 1, 1, -1, -1, 1, 1, -1, -1],\n", " [ 1, -1, -1, 1, 1, -1, -1, 1],\n", " [ 1, 1, 1, 1, -1, -1, -1, -1],\n", " [ 1, -1, 1, -1, -1, 1, -1, 1],\n", " [ 1, 1, -1, -1, -1, -1, 1, 1],\n", " [ 1, -1, -1, 1, -1, 1, 1, -1]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import scipy\n", "scipy.linalg.hadamard(8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In our example, we have 10 psus. If we do not have explicit stratification then *replicate()* will group the clusters into 5 strata (2 per stratum). In this case, the smallest number of replicates possible using the hadamard matrix is 8. \n", "\n", "The result below shows that *replicate()* created 5 strata by grouping clusters 7 and 10 in the first stratum, clusters 16 and 24 in the second stratum, and so on. We can achieve the same result by providing setting *stratification=True* and providing the stratum variable to *replicate()*. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>_stratum</th>\n", " <th>_psu</th>\n", " <th>_samp_weight</th>\n", " <th>_brr_wgt_1</th>\n", " <th>_brr_wgt_2</th>\n", " <th>_brr_wgt_3</th>\n", " <th>_brr_wgt_4</th>\n", " <th>_brr_wgt_5</th>\n", " <th>_brr_wgt_6</th>\n", " <th>_brr_wgt_7</th>\n", " <th>_brr_wgt_8</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>46.166667</td>\n", " <td>0.000000</td>\n", " <td>92.333333</td>\n", " <td>0.000000</td>\n", " <td>92.333333</td>\n", " <td>0.000000</td>\n", " <td>92.333333</td>\n", " <td>0.000000</td>\n", " <td>92.333333</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>50.783333</td>\n", " <td>101.566667</td>\n", " <td>0.000000</td>\n", " <td>101.566667</td>\n", " <td>0.000000</td>\n", " <td>101.566667</td>\n", " <td>0.000000</td>\n", " <td>101.566667</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2</td>\n", " <td>16</td>\n", " <td>62.149123</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>124.298246</td>\n", " <td>124.298246</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>124.298246</td>\n", " <td>124.298246</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>2</td>\n", " <td>24</td>\n", " <td>58.940741</td>\n", " <td>117.881481</td>\n", " <td>117.881481</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>117.881481</td>\n", " <td>117.881481</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>3</td>\n", " <td>29</td>\n", " <td>65.702778</td>\n", " <td>0.000000</td>\n", " <td>131.405556</td>\n", " <td>131.405556</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>131.405556</td>\n", " <td>131.405556</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>3</td>\n", " <td>34</td>\n", " <td>75.661566</td>\n", " <td>151.323133</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>151.323133</td>\n", " <td>151.323133</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>151.323133</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>4</td>\n", " <td>45</td>\n", " <td>85.398025</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>170.796049</td>\n", " <td>170.796049</td>\n", " <td>170.796049</td>\n", " <td>170.796049</td>\n", " </tr>\n", " <tr>\n", " <th>105</th>\n", " <td>4</td>\n", " <td>52</td>\n", " <td>85.520635</td>\n", " <td>171.041270</td>\n", " <td>171.041270</td>\n", " <td>171.041270</td>\n", " <td>171.041270</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>120</th>\n", " <td>5</td>\n", " <td>64</td>\n", " <td>218.893889</td>\n", " <td>0.000000</td>\n", " <td>437.787778</td>\n", " <td>0.000000</td>\n", " <td>437.787778</td>\n", " <td>437.787778</td>\n", " <td>0.000000</td>\n", " <td>437.787778</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>135</th>\n", " <td>5</td>\n", " <td>86</td>\n", " <td>213.491667</td>\n", " <td>426.983333</td>\n", " <td>0.000000</td>\n", " <td>426.983333</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>426.983333</td>\n", " <td>0.000000</td>\n", " <td>426.983333</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " _stratum _psu _samp_weight _brr_wgt_1 _brr_wgt_2 _brr_wgt_3 \\\n", "0 1 7 46.166667 0.000000 92.333333 0.000000 \n", "15 1 10 50.783333 101.566667 0.000000 101.566667 \n", "30 2 16 62.149123 0.000000 0.000000 124.298246 \n", "45 2 24 58.940741 117.881481 117.881481 0.000000 \n", "60 3 29 65.702778 0.000000 131.405556 131.405556 \n", "75 3 34 75.661566 151.323133 0.000000 0.000000 \n", "90 4 45 85.398025 0.000000 0.000000 0.000000 \n", "105 4 52 85.520635 171.041270 171.041270 171.041270 \n", "120 5 64 218.893889 0.000000 437.787778 0.000000 \n", "135 5 86 213.491667 426.983333 0.000000 426.983333 \n", "\n", " _brr_wgt_4 _brr_wgt_5 _brr_wgt_6 _brr_wgt_7 _brr_wgt_8 \n", "0 92.333333 0.000000 92.333333 0.000000 92.333333 \n", "15 0.000000 101.566667 0.000000 101.566667 0.000000 \n", "30 124.298246 0.000000 0.000000 124.298246 124.298246 \n", "45 0.000000 117.881481 117.881481 0.000000 0.000000 \n", "60 0.000000 0.000000 131.405556 131.405556 0.000000 \n", "75 151.323133 151.323133 0.000000 0.000000 151.323133 \n", "90 0.000000 170.796049 170.796049 170.796049 170.796049 \n", "105 171.041270 0.000000 0.000000 0.000000 0.000000 \n", "120 437.787778 437.787778 0.000000 437.787778 0.000000 \n", "135 0.000000 0.000000 426.983333 0.000000 426.983333 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "brr = ReplicateWeight(method=\"brr\", stratification=False)\n", "brr_wgt = brr.replicate(full_sample[\"design_weight\"], full_sample[\"cluster\"])\n", "\n", "brr_wgt.drop_duplicates().head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An extension of BRR is the Fay's method. In the Fay's approach, instead of multiplying one half-sample by zero, we multiple the sampel weights by a factor $\\alpha$ and the other halh-sample by $2-\\alpha$. We refer to $\\alpha$ as the fay coefficient. Note that when $\\alpha=0$ then teh Fay's method reduces to BRR. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>stratum</th>\n", " <th>cluster</th>\n", " <th>_samp_weight</th>\n", " <th>fay_weight_1</th>\n", " <th>fay_weight_2</th>\n", " <th>fay_weight_3</th>\n", " <th>fay_weight_4</th>\n", " <th>fay_weight_5</th>\n", " <th>fay_weight_6</th>\n", " <th>fay_weight_7</th>\n", " <th>fay_weight_8</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>46.166667</td>\n", " <td>13.850000</td>\n", " <td>78.483333</td>\n", " <td>13.850000</td>\n", " <td>78.483333</td>\n", " <td>13.850000</td>\n", " <td>78.483333</td>\n", " <td>13.850000</td>\n", " <td>78.483333</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>50.783333</td>\n", " <td>86.331667</td>\n", " <td>15.235000</td>\n", " <td>86.331667</td>\n", " <td>15.235000</td>\n", " <td>86.331667</td>\n", " <td>15.235000</td>\n", " <td>86.331667</td>\n", " <td>15.235000</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2</td>\n", " <td>16</td>\n", " <td>62.149123</td>\n", " <td>18.644737</td>\n", " <td>18.644737</td>\n", " <td>105.653509</td>\n", " <td>105.653509</td>\n", " <td>18.644737</td>\n", " <td>18.644737</td>\n", " <td>105.653509</td>\n", " <td>105.653509</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>2</td>\n", " <td>24</td>\n", " <td>58.940741</td>\n", " <td>100.199259</td>\n", " <td>100.199259</td>\n", " <td>17.682222</td>\n", " <td>17.682222</td>\n", " <td>100.199259</td>\n", " <td>100.199259</td>\n", " <td>17.682222</td>\n", " <td>17.682222</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>3</td>\n", " <td>29</td>\n", " <td>65.702778</td>\n", " <td>19.710833</td>\n", " <td>111.694722</td>\n", " <td>111.694722</td>\n", " <td>19.710833</td>\n", " <td>19.710833</td>\n", " <td>111.694722</td>\n", " <td>111.694722</td>\n", " <td>19.710833</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>3</td>\n", " <td>34</td>\n", " <td>75.661566</td>\n", " <td>128.624663</td>\n", " <td>22.698470</td>\n", " <td>22.698470</td>\n", " <td>128.624663</td>\n", " <td>128.624663</td>\n", " <td>22.698470</td>\n", " <td>22.698470</td>\n", " <td>128.624663</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>4</td>\n", " <td>45</td>\n", " <td>85.398025</td>\n", " <td>25.619407</td>\n", " <td>25.619407</td>\n", " <td>25.619407</td>\n", " <td>25.619407</td>\n", " <td>145.176642</td>\n", " <td>145.176642</td>\n", " <td>145.176642</td>\n", " <td>145.176642</td>\n", " </tr>\n", " <tr>\n", " <th>105</th>\n", " <td>4</td>\n", " <td>52</td>\n", " <td>85.520635</td>\n", " <td>145.385079</td>\n", " <td>145.385079</td>\n", " <td>145.385079</td>\n", " <td>145.385079</td>\n", " <td>25.656190</td>\n", " <td>25.656190</td>\n", " <td>25.656190</td>\n", " <td>25.656190</td>\n", " </tr>\n", " <tr>\n", " <th>120</th>\n", " <td>5</td>\n", " <td>64</td>\n", " <td>218.893889</td>\n", " <td>65.668167</td>\n", " <td>372.119611</td>\n", " <td>65.668167</td>\n", " <td>372.119611</td>\n", " <td>372.119611</td>\n", " <td>65.668167</td>\n", " <td>372.119611</td>\n", " <td>65.668167</td>\n", " </tr>\n", " <tr>\n", " <th>135</th>\n", " <td>5</td>\n", " <td>86</td>\n", " <td>213.491667</td>\n", " <td>362.935833</td>\n", " <td>64.047500</td>\n", " <td>362.935833</td>\n", " <td>64.047500</td>\n", " <td>64.047500</td>\n", " <td>362.935833</td>\n", " <td>64.047500</td>\n", " <td>362.935833</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " stratum cluster _samp_weight fay_weight_1 fay_weight_2 fay_weight_3 \\\n", "0 1 7 46.166667 13.850000 78.483333 13.850000 \n", "15 1 10 50.783333 86.331667 15.235000 86.331667 \n", "30 2 16 62.149123 18.644737 18.644737 105.653509 \n", "45 2 24 58.940741 100.199259 100.199259 17.682222 \n", "60 3 29 65.702778 19.710833 111.694722 111.694722 \n", "75 3 34 75.661566 128.624663 22.698470 22.698470 \n", "90 4 45 85.398025 25.619407 25.619407 25.619407 \n", "105 4 52 85.520635 145.385079 145.385079 145.385079 \n", "120 5 64 218.893889 65.668167 372.119611 65.668167 \n", "135 5 86 213.491667 362.935833 64.047500 362.935833 \n", "\n", " fay_weight_4 fay_weight_5 fay_weight_6 fay_weight_7 fay_weight_8 \n", "0 78.483333 13.850000 78.483333 13.850000 78.483333 \n", "15 15.235000 86.331667 15.235000 86.331667 15.235000 \n", "30 105.653509 18.644737 18.644737 105.653509 105.653509 \n", "45 17.682222 100.199259 100.199259 17.682222 17.682222 \n", "60 19.710833 19.710833 111.694722 111.694722 19.710833 \n", "75 128.624663 128.624663 22.698470 22.698470 128.624663 \n", "90 25.619407 145.176642 145.176642 145.176642 145.176642 \n", "105 145.385079 25.656190 25.656190 25.656190 25.656190 \n", "120 372.119611 372.119611 65.668167 372.119611 65.668167 \n", "135 64.047500 64.047500 362.935833 64.047500 362.935833 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fay = ReplicateWeight(method=\"brr\", stratification=False, fay_coef=0.3)\n", "fay_wgt = fay.replicate(\n", " full_sample[\"design_weight\"], \n", " full_sample[\"cluster\"], \n", " rep_prefix=\"fay_weight_\",\n", " psu_varname=\"cluster\", \n", " str_varname=\"stratum\"\n", ")\n", "\n", "fay_wgt.drop_duplicates().head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bootstrap <a name=\"section2\"></a>\n", "\n", "For the bootstrap replicates, we need to provide the number of replicates. When the number of replicates is not provided, *ReplicateWeight* will default to 500. The bootstrap consists of selecting the same number of psus as in the sample but with replacement. The selection is independently repeated for each replicate. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>_psu</th>\n", " <th>_samp_weight</th>\n", " <th>_boot_wgt_1</th>\n", " <th>_boot_wgt_2</th>\n", " <th>_boot_wgt_3</th>\n", " <th>_boot_wgt_4</th>\n", " <th>_boot_wgt_5</th>\n", " <th>_boot_wgt_6</th>\n", " <th>_boot_wgt_7</th>\n", " <th>_boot_wgt_8</th>\n", " <th>...</th>\n", " <th>_boot_wgt_41</th>\n", " <th>_boot_wgt_42</th>\n", " <th>_boot_wgt_43</th>\n", " <th>_boot_wgt_44</th>\n", " <th>_boot_wgt_45</th>\n", " <th>_boot_wgt_46</th>\n", " <th>_boot_wgt_47</th>\n", " <th>_boot_wgt_48</th>\n", " <th>_boot_wgt_49</th>\n", " <th>_boot_wgt_50</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>7</td>\n", " <td>46.166667</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>51.296296</td>\n", " <td>102.592593</td>\n", " <td>0.000000</td>\n", " <td>102.592593</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>51.296296</td>\n", " <td>51.296296</td>\n", " <td>51.296296</td>\n", " <td>51.296296</td>\n", " <td>51.296296</td>\n", " <td>0.000000</td>\n", " <td>51.296296</td>\n", " <td>102.592593</td>\n", " <td>51.296296</td>\n", " <td>102.592593</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>10</td>\n", " <td>50.783333</td>\n", " <td>56.425926</td>\n", " <td>112.851852</td>\n", " <td>56.425926</td>\n", " <td>56.425926</td>\n", " <td>56.425926</td>\n", " <td>169.277778</td>\n", " <td>112.851852</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>56.425926</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>112.851852</td>\n", " <td>56.425926</td>\n", " <td>56.425926</td>\n", " <td>56.425926</td>\n", " <td>169.277778</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>16</td>\n", " <td>62.149123</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>69.054581</td>\n", " <td>69.054581</td>\n", " <td>0.000000</td>\n", " <td>69.054581</td>\n", " <td>138.109162</td>\n", " <td>...</td>\n", " <td>69.054581</td>\n", " <td>69.054581</td>\n", " <td>0.000000</td>\n", " <td>69.054581</td>\n", " <td>0.000000</td>\n", " <td>69.054581</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>69.054581</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>24</td>\n", " <td>58.940741</td>\n", " <td>65.489712</td>\n", " <td>65.489712</td>\n", " <td>65.489712</td>\n", " <td>0.000000</td>\n", " <td>65.489712</td>\n", " <td>65.489712</td>\n", " <td>130.979424</td>\n", " <td>130.979424</td>\n", " <td>...</td>\n", " <td>130.979424</td>\n", " <td>130.979424</td>\n", " <td>65.489712</td>\n", " <td>0.000000</td>\n", " <td>65.489712</td>\n", " <td>65.489712</td>\n", " <td>65.489712</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>130.979424</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>29</td>\n", " <td>65.702778</td>\n", " <td>146.006173</td>\n", " <td>73.003086</td>\n", " <td>0.000000</td>\n", " <td>73.003086</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>146.006173</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>73.003086</td>\n", " <td>73.003086</td>\n", " <td>73.003086</td>\n", " <td>146.006173</td>\n", " <td>0.000000</td>\n", " <td>146.006173</td>\n", " <td>0.000000</td>\n", " <td>73.003086</td>\n", " <td>73.003086</td>\n", " <td>73.003086</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>34</td>\n", " <td>75.661566</td>\n", " <td>252.205222</td>\n", " <td>84.068407</td>\n", " <td>84.068407</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>84.068407</td>\n", " <td>84.068407</td>\n", " <td>...</td>\n", " <td>168.136814</td>\n", " <td>168.136814</td>\n", " <td>168.136814</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>84.068407</td>\n", " <td>84.068407</td>\n", " <td>84.068407</td>\n", " <td>0.000000</td>\n", " <td>84.068407</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>45</td>\n", " <td>85.398025</td>\n", " <td>0.000000</td>\n", " <td>94.886694</td>\n", " <td>189.773388</td>\n", " <td>94.886694</td>\n", " <td>0.000000</td>\n", " <td>94.886694</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>94.886694</td>\n", " <td>189.773388</td>\n", " <td>94.886694</td>\n", " <td>0.000000</td>\n", " <td>284.660082</td>\n", " <td>189.773388</td>\n", " <td>94.886694</td>\n", " <td>94.886694</td>\n", " </tr>\n", " <tr>\n", " <th>105</th>\n", " <td>52</td>\n", " <td>85.520635</td>\n", " <td>190.045855</td>\n", " <td>285.068783</td>\n", " <td>95.022928</td>\n", " <td>95.022928</td>\n", " <td>380.091711</td>\n", " <td>95.022928</td>\n", " <td>0.000000</td>\n", " <td>190.045855</td>\n", " <td>...</td>\n", " <td>0.000000</td>\n", " <td>95.022928</td>\n", " <td>95.022928</td>\n", " <td>95.022928</td>\n", " <td>95.022928</td>\n", " <td>190.045855</td>\n", " <td>190.045855</td>\n", " <td>95.022928</td>\n", " <td>95.022928</td>\n", " <td>95.022928</td>\n", " </tr>\n", " <tr>\n", " <th>120</th>\n", " <td>64</td>\n", " <td>218.893889</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>243.215432</td>\n", " <td>0.000000</td>\n", " <td>243.215432</td>\n", " <td>0.000000</td>\n", " <td>243.215432</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>243.215432</td>\n", " <td>243.215432</td>\n", " <td>243.215432</td>\n", " <td>0.000000</td>\n", " <td>243.215432</td>\n", " <td>243.215432</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>243.215432</td>\n", " </tr>\n", " <tr>\n", " <th>135</th>\n", " <td>86</td>\n", " <td>213.491667</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>237.212963</td>\n", " <td>474.425926</td>\n", " <td>237.212963</td>\n", " <td>237.212963</td>\n", " <td>0.000000</td>\n", " <td>474.425926</td>\n", " <td>...</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>237.212963</td>\n", " <td>474.425926</td>\n", " <td>474.425926</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>237.212963</td>\n", " <td>237.212963</td>\n", " <td>0.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>10 rows × 52 columns</p>\n", "</div>" ], "text/plain": [ " _psu _samp_weight _boot_wgt_1 _boot_wgt_2 _boot_wgt_3 _boot_wgt_4 \\\n", "0 7 46.166667 0.000000 0.000000 51.296296 102.592593 \n", "15 10 50.783333 56.425926 112.851852 56.425926 56.425926 \n", "30 16 62.149123 0.000000 0.000000 0.000000 69.054581 \n", "45 24 58.940741 65.489712 65.489712 65.489712 0.000000 \n", "60 29 65.702778 146.006173 73.003086 0.000000 73.003086 \n", "75 34 75.661566 252.205222 84.068407 84.068407 0.000000 \n", "90 45 85.398025 0.000000 94.886694 189.773388 94.886694 \n", "105 52 85.520635 190.045855 285.068783 95.022928 95.022928 \n", "120 64 218.893889 0.000000 0.000000 243.215432 0.000000 \n", "135 86 213.491667 0.000000 0.000000 237.212963 474.425926 \n", "\n", " _boot_wgt_5 _boot_wgt_6 _boot_wgt_7 _boot_wgt_8 ... _boot_wgt_41 \\\n", "0 0.000000 102.592593 0.000000 0.000000 ... 51.296296 \n", "15 56.425926 169.277778 112.851852 0.000000 ... 56.425926 \n", "30 69.054581 0.000000 69.054581 138.109162 ... 69.054581 \n", "45 65.489712 65.489712 130.979424 130.979424 ... 130.979424 \n", "60 0.000000 0.000000 146.006173 0.000000 ... 73.003086 \n", "75 0.000000 0.000000 84.068407 84.068407 ... 168.136814 \n", "90 0.000000 94.886694 0.000000 0.000000 ... 0.000000 \n", "105 380.091711 95.022928 0.000000 190.045855 ... 0.000000 \n", "120 243.215432 0.000000 243.215432 0.000000 ... 243.215432 \n", "135 237.212963 237.212963 0.000000 474.425926 ... 0.000000 \n", "\n", " _boot_wgt_42 _boot_wgt_43 _boot_wgt_44 _boot_wgt_45 _boot_wgt_46 \\\n", "0 51.296296 51.296296 51.296296 51.296296 0.000000 \n", "15 0.000000 0.000000 0.000000 112.851852 56.425926 \n", "30 69.054581 0.000000 69.054581 0.000000 69.054581 \n", "45 130.979424 65.489712 0.000000 65.489712 65.489712 \n", "60 73.003086 73.003086 146.006173 0.000000 146.006173 \n", "75 168.136814 168.136814 0.000000 0.000000 84.068407 \n", "90 0.000000 94.886694 189.773388 94.886694 0.000000 \n", "105 95.022928 95.022928 95.022928 95.022928 190.045855 \n", "120 243.215432 243.215432 0.000000 243.215432 243.215432 \n", "135 0.000000 237.212963 474.425926 474.425926 0.000000 \n", "\n", " _boot_wgt_47 _boot_wgt_48 _boot_wgt_49 _boot_wgt_50 \n", "0 51.296296 102.592593 51.296296 102.592593 \n", "15 56.425926 56.425926 169.277778 0.000000 \n", "30 0.000000 0.000000 69.054581 0.000000 \n", "45 65.489712 0.000000 0.000000 130.979424 \n", "60 0.000000 73.003086 73.003086 73.003086 \n", "75 84.068407 84.068407 0.000000 84.068407 \n", "90 284.660082 189.773388 94.886694 94.886694 \n", "105 190.045855 95.022928 95.022928 95.022928 \n", "120 0.000000 0.000000 0.000000 243.215432 \n", "135 0.000000 237.212963 237.212963 0.000000 \n", "\n", "[10 rows x 52 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bootstrap = ReplicateWeight(method=\"bootstrap\", stratification=False, number_reps=50)\n", "boot_wgt = bootstrap.replicate(full_sample[\"design_weight\"], full_sample[\"cluster\"])\n", "\n", "boot_wgt.drop_duplicates().head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Jackknife <a name=\"section3\"></a>\n", "\n", "Below, we illustrate the API for creating replicate weights using the jackknife method. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>_psu</th>\n", " <th>_samp_weight</th>\n", " <th>_jk_wgt_1</th>\n", " <th>_jk_wgt_2</th>\n", " <th>_jk_wgt_3</th>\n", " <th>_jk_wgt_4</th>\n", " <th>_jk_wgt_5</th>\n", " <th>_jk_wgt_6</th>\n", " <th>_jk_wgt_7</th>\n", " <th>_jk_wgt_8</th>\n", " <th>_jk_wgt_9</th>\n", " <th>_jk_wgt_10</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>7</td>\n", " <td>46.166667</td>\n", " <td>0.000000</td>\n", " <td>51.296296</td>\n", " <td>51.296296</td>\n", " <td>51.296296</td>\n", " <td>51.296296</td>\n", " <td>51.296296</td>\n", " <td>51.296296</td>\n", " <td>51.296296</td>\n", " <td>51.296296</td>\n", " <td>51.296296</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>10</td>\n", " <td>50.783333</td>\n", " <td>56.425926</td>\n", " <td>0.000000</td>\n", " <td>56.425926</td>\n", " <td>56.425926</td>\n", " <td>56.425926</td>\n", " <td>56.425926</td>\n", " <td>56.425926</td>\n", " <td>56.425926</td>\n", " <td>56.425926</td>\n", " <td>56.425926</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>16</td>\n", " <td>62.149123</td>\n", " <td>69.054581</td>\n", " <td>69.054581</td>\n", " <td>0.000000</td>\n", " <td>69.054581</td>\n", " <td>69.054581</td>\n", " <td>69.054581</td>\n", " <td>69.054581</td>\n", " <td>69.054581</td>\n", " <td>69.054581</td>\n", " <td>69.054581</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>24</td>\n", " <td>58.940741</td>\n", " <td>65.489712</td>\n", " <td>65.489712</td>\n", " <td>65.489712</td>\n", " <td>0.000000</td>\n", " <td>65.489712</td>\n", " <td>65.489712</td>\n", " <td>65.489712</td>\n", " <td>65.489712</td>\n", " <td>65.489712</td>\n", " <td>65.489712</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>29</td>\n", " <td>65.702778</td>\n", " <td>73.003086</td>\n", " <td>73.003086</td>\n", " <td>73.003086</td>\n", " <td>73.003086</td>\n", " <td>0.000000</td>\n", " <td>73.003086</td>\n", " <td>73.003086</td>\n", " <td>73.003086</td>\n", " <td>73.003086</td>\n", " <td>73.003086</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>34</td>\n", " <td>75.661566</td>\n", " <td>84.068407</td>\n", " <td>84.068407</td>\n", " <td>84.068407</td>\n", " <td>84.068407</td>\n", " <td>84.068407</td>\n", " <td>0.000000</td>\n", " <td>84.068407</td>\n", " <td>84.068407</td>\n", " <td>84.068407</td>\n", " <td>84.068407</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>45</td>\n", " <td>85.398025</td>\n", " <td>94.886694</td>\n", " <td>94.886694</td>\n", " <td>94.886694</td>\n", " <td>94.886694</td>\n", " <td>94.886694</td>\n", " <td>94.886694</td>\n", " <td>0.000000</td>\n", " <td>94.886694</td>\n", " <td>94.886694</td>\n", " <td>94.886694</td>\n", " </tr>\n", " <tr>\n", " <th>105</th>\n", " <td>52</td>\n", " <td>85.520635</td>\n", " <td>95.022928</td>\n", " <td>95.022928</td>\n", " <td>95.022928</td>\n", " <td>95.022928</td>\n", " <td>95.022928</td>\n", " <td>95.022928</td>\n", " <td>95.022928</td>\n", " <td>0.000000</td>\n", " <td>95.022928</td>\n", " <td>95.022928</td>\n", " </tr>\n", " <tr>\n", " <th>120</th>\n", " <td>64</td>\n", " <td>218.893889</td>\n", " <td>243.215432</td>\n", " <td>243.215432</td>\n", " <td>243.215432</td>\n", " <td>243.215432</td>\n", " <td>243.215432</td>\n", " <td>243.215432</td>\n", " <td>243.215432</td>\n", " <td>243.215432</td>\n", " <td>0.000000</td>\n", " <td>243.215432</td>\n", " </tr>\n", " <tr>\n", " <th>135</th>\n", " <td>86</td>\n", " <td>213.491667</td>\n", " <td>237.212963</td>\n", " <td>237.212963</td>\n", " <td>237.212963</td>\n", " <td>237.212963</td>\n", " <td>237.212963</td>\n", " <td>237.212963</td>\n", " <td>237.212963</td>\n", " <td>237.212963</td>\n", " <td>237.212963</td>\n", " <td>0.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " _psu _samp_weight _jk_wgt_1 _jk_wgt_2 _jk_wgt_3 _jk_wgt_4 \\\n", "0 7 46.166667 0.000000 51.296296 51.296296 51.296296 \n", "15 10 50.783333 56.425926 0.000000 56.425926 56.425926 \n", "30 16 62.149123 69.054581 69.054581 0.000000 69.054581 \n", "45 24 58.940741 65.489712 65.489712 65.489712 0.000000 \n", "60 29 65.702778 73.003086 73.003086 73.003086 73.003086 \n", "75 34 75.661566 84.068407 84.068407 84.068407 84.068407 \n", "90 45 85.398025 94.886694 94.886694 94.886694 94.886694 \n", "105 52 85.520635 95.022928 95.022928 95.022928 95.022928 \n", "120 64 218.893889 243.215432 243.215432 243.215432 243.215432 \n", "135 86 213.491667 237.212963 237.212963 237.212963 237.212963 \n", "\n", " _jk_wgt_5 _jk_wgt_6 _jk_wgt_7 _jk_wgt_8 _jk_wgt_9 _jk_wgt_10 \n", "0 51.296296 51.296296 51.296296 51.296296 51.296296 51.296296 \n", "15 56.425926 56.425926 56.425926 56.425926 56.425926 56.425926 \n", "30 69.054581 69.054581 69.054581 69.054581 69.054581 69.054581 \n", "45 65.489712 65.489712 65.489712 65.489712 65.489712 65.489712 \n", "60 0.000000 73.003086 73.003086 73.003086 73.003086 73.003086 \n", "75 84.068407 0.000000 84.068407 84.068407 84.068407 84.068407 \n", "90 94.886694 94.886694 0.000000 94.886694 94.886694 94.886694 \n", "105 95.022928 95.022928 95.022928 0.000000 95.022928 95.022928 \n", "120 243.215432 243.215432 243.215432 243.215432 0.000000 243.215432 \n", "135 237.212963 237.212963 237.212963 237.212963 237.212963 0.000000 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jackknife = ReplicateWeight(method=\"jackknife\", stratification=False)\n", "jkn_wgt = jackknife.replicate(full_sample[\"design_weight\"], full_sample[\"cluster\"])\n", "\n", "jkn_wgt.drop_duplicates().head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With stratification..." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>_stratum</th>\n", " <th>_psu</th>\n", " <th>_samp_weight</th>\n", " <th>_jk_wgt_1</th>\n", " <th>_jk_wgt_2</th>\n", " <th>_jk_wgt_3</th>\n", " <th>_jk_wgt_4</th>\n", " <th>_jk_wgt_5</th>\n", " <th>_jk_wgt_6</th>\n", " <th>_jk_wgt_7</th>\n", " <th>_jk_wgt_8</th>\n", " <th>_jk_wgt_9</th>\n", " <th>_jk_wgt_10</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>North</td>\n", " <td>7</td>\n", " <td>46.166667</td>\n", " <td>46.166667</td>\n", " <td>46.166667</td>\n", " <td>46.166667</td>\n", " <td>0.000000</td>\n", " <td>92.333333</td>\n", " <td>46.166667</td>\n", " <td>46.166667</td>\n", " <td>46.166667</td>\n", " <td>46.166667</td>\n", " <td>46.166667</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>North</td>\n", " <td>10</td>\n", " <td>50.783333</td>\n", " <td>50.783333</td>\n", " <td>50.783333</td>\n", " <td>50.783333</td>\n", " <td>101.566667</td>\n", " <td>0.000000</td>\n", " <td>50.783333</td>\n", " <td>50.783333</td>\n", " <td>50.783333</td>\n", " <td>50.783333</td>\n", " <td>50.783333</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>South</td>\n", " <td>16</td>\n", " <td>62.149123</td>\n", " <td>62.149123</td>\n", " <td>62.149123</td>\n", " <td>62.149123</td>\n", " <td>62.149123</td>\n", " <td>62.149123</td>\n", " <td>93.223684</td>\n", " <td>93.223684</td>\n", " <td>0.000000</td>\n", " <td>62.149123</td>\n", " <td>62.149123</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>South</td>\n", " <td>24</td>\n", " <td>58.940741</td>\n", " <td>58.940741</td>\n", " <td>58.940741</td>\n", " <td>58.940741</td>\n", " <td>58.940741</td>\n", " <td>58.940741</td>\n", " <td>88.411111</td>\n", " <td>0.000000</td>\n", " <td>88.411111</td>\n", " <td>58.940741</td>\n", " <td>58.940741</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>South</td>\n", " <td>29</td>\n", " <td>65.702778</td>\n", " <td>65.702778</td>\n", " <td>65.702778</td>\n", " <td>65.702778</td>\n", " <td>65.702778</td>\n", " <td>65.702778</td>\n", " <td>0.000000</td>\n", " <td>98.554167</td>\n", " <td>98.554167</td>\n", " <td>65.702778</td>\n", " <td>65.702778</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>East</td>\n", " <td>34</td>\n", " <td>75.661566</td>\n", " <td>113.492350</td>\n", " <td>113.492350</td>\n", " <td>0.000000</td>\n", " <td>75.661566</td>\n", " <td>75.661566</td>\n", " <td>75.661566</td>\n", " <td>75.661566</td>\n", " <td>75.661566</td>\n", " <td>75.661566</td>\n", " <td>75.661566</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>East</td>\n", " <td>45</td>\n", " <td>85.398025</td>\n", " <td>128.097037</td>\n", " <td>0.000000</td>\n", " <td>128.097037</td>\n", " <td>85.398025</td>\n", " <td>85.398025</td>\n", " <td>85.398025</td>\n", " <td>85.398025</td>\n", " <td>85.398025</td>\n", " <td>85.398025</td>\n", " <td>85.398025</td>\n", " </tr>\n", " <tr>\n", " <th>105</th>\n", " <td>East</td>\n", " <td>52</td>\n", " <td>85.520635</td>\n", " <td>0.000000</td>\n", " <td>128.280952</td>\n", " <td>128.280952</td>\n", " <td>85.520635</td>\n", " <td>85.520635</td>\n", " <td>85.520635</td>\n", " <td>85.520635</td>\n", " <td>85.520635</td>\n", " <td>85.520635</td>\n", " <td>85.520635</td>\n", " </tr>\n", " <tr>\n", " <th>120</th>\n", " <td>West</td>\n", " <td>64</td>\n", " <td>218.893889</td>\n", " <td>218.893889</td>\n", " <td>218.893889</td>\n", " <td>218.893889</td>\n", " <td>218.893889</td>\n", " <td>218.893889</td>\n", " <td>218.893889</td>\n", " <td>218.893889</td>\n", " <td>218.893889</td>\n", " <td>437.787778</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>135</th>\n", " <td>West</td>\n", " <td>86</td>\n", " <td>213.491667</td>\n", " <td>213.491667</td>\n", " <td>213.491667</td>\n", " <td>213.491667</td>\n", " <td>213.491667</td>\n", " <td>213.491667</td>\n", " <td>213.491667</td>\n", " <td>213.491667</td>\n", " <td>213.491667</td>\n", " <td>0.000000</td>\n", " <td>426.983333</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " _stratum _psu _samp_weight _jk_wgt_1 _jk_wgt_2 _jk_wgt_3 \\\n", "0 North 7 46.166667 46.166667 46.166667 46.166667 \n", "15 North 10 50.783333 50.783333 50.783333 50.783333 \n", "30 South 16 62.149123 62.149123 62.149123 62.149123 \n", "45 South 24 58.940741 58.940741 58.940741 58.940741 \n", "60 South 29 65.702778 65.702778 65.702778 65.702778 \n", "75 East 34 75.661566 113.492350 113.492350 0.000000 \n", "90 East 45 85.398025 128.097037 0.000000 128.097037 \n", "105 East 52 85.520635 0.000000 128.280952 128.280952 \n", "120 West 64 218.893889 218.893889 218.893889 218.893889 \n", "135 West 86 213.491667 213.491667 213.491667 213.491667 \n", "\n", " _jk_wgt_4 _jk_wgt_5 _jk_wgt_6 _jk_wgt_7 _jk_wgt_8 _jk_wgt_9 \\\n", "0 0.000000 92.333333 46.166667 46.166667 46.166667 46.166667 \n", "15 101.566667 0.000000 50.783333 50.783333 50.783333 50.783333 \n", "30 62.149123 62.149123 93.223684 93.223684 0.000000 62.149123 \n", "45 58.940741 58.940741 88.411111 0.000000 88.411111 58.940741 \n", "60 65.702778 65.702778 0.000000 98.554167 98.554167 65.702778 \n", "75 75.661566 75.661566 75.661566 75.661566 75.661566 75.661566 \n", "90 85.398025 85.398025 85.398025 85.398025 85.398025 85.398025 \n", "105 85.520635 85.520635 85.520635 85.520635 85.520635 85.520635 \n", "120 218.893889 218.893889 218.893889 218.893889 218.893889 437.787778 \n", "135 213.491667 213.491667 213.491667 213.491667 213.491667 0.000000 \n", "\n", " _jk_wgt_10 \n", "0 46.166667 \n", "15 50.783333 \n", "30 62.149123 \n", "45 58.940741 \n", "60 65.702778 \n", "75 75.661566 \n", "90 85.398025 \n", "105 85.520635 \n", "120 0.000000 \n", "135 426.983333 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jackknife = ReplicateWeight(method=\"jackknife\", stratification=True)\n", "jkn_wgt = jackknife.replicate(full_sample[\"design_weight\"], full_sample[\"cluster\"], full_sample[\"region\"])\n", "\n", "jkn_wgt.drop_duplicates().head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Important.** For any of the three methods, we can request the replicate coefficient instead of the replicate weights by *rep_coefs=True*. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>_stratum</th>\n", " <th>_psu</th>\n", " <th>_samp_weight</th>\n", " <th>_jk_wgt_1</th>\n", " <th>_jk_wgt_2</th>\n", " <th>_jk_wgt_3</th>\n", " <th>_jk_wgt_4</th>\n", " <th>_jk_wgt_5</th>\n", " <th>_jk_wgt_6</th>\n", " <th>_jk_wgt_7</th>\n", " <th>_jk_wgt_8</th>\n", " <th>_jk_wgt_9</th>\n", " <th>_jk_wgt_10</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>75</th>\n", " <td>East</td>\n", " <td>34</td>\n", " <td>75.661566</td>\n", " <td>1.5</td>\n", " <td>1.5</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>East</td>\n", " <td>45</td>\n", " <td>85.398025</td>\n", " <td>1.5</td>\n", " <td>0.0</td>\n", " <td>1.5</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>105</th>\n", " <td>East</td>\n", " <td>52</td>\n", " <td>85.520635</td>\n", " <td>0.0</td>\n", " <td>1.5</td>\n", " <td>1.5</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>North</td>\n", " <td>7</td>\n", " <td>46.166667</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>North</td>\n", " <td>10</td>\n", " <td>50.783333</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>South</td>\n", " <td>16</td>\n", " <td>62.149123</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.5</td>\n", " <td>1.5</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>South</td>\n", " <td>24</td>\n", " <td>58.940741</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.5</td>\n", " <td>0.0</td>\n", " <td>1.5</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>South</td>\n", " <td>29</td>\n", " <td>65.702778</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.5</td>\n", " <td>1.5</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>120</th>\n", " <td>West</td>\n", " <td>64</td>\n", " <td>218.893889</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>135</th>\n", " <td>West</td>\n", " <td>86</td>\n", " <td>213.491667</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " _stratum _psu _samp_weight _jk_wgt_1 _jk_wgt_2 _jk_wgt_3 _jk_wgt_4 \\\n", "75 East 34 75.661566 1.5 1.5 0.0 1.0 \n", "90 East 45 85.398025 1.5 0.0 1.5 1.0 \n", "105 East 52 85.520635 0.0 1.5 1.5 1.0 \n", "0 North 7 46.166667 1.0 1.0 1.0 0.0 \n", "15 North 10 50.783333 1.0 1.0 1.0 2.0 \n", "30 South 16 62.149123 1.0 1.0 1.0 1.0 \n", "45 South 24 58.940741 1.0 1.0 1.0 1.0 \n", "60 South 29 65.702778 1.0 1.0 1.0 1.0 \n", "120 West 64 218.893889 1.0 1.0 1.0 1.0 \n", "135 West 86 213.491667 1.0 1.0 1.0 1.0 \n", "\n", " _jk_wgt_5 _jk_wgt_6 _jk_wgt_7 _jk_wgt_8 _jk_wgt_9 _jk_wgt_10 \n", "75 1.0 1.0 1.0 1.0 1.0 1.0 \n", "90 1.0 1.0 1.0 1.0 1.0 1.0 \n", "105 1.0 1.0 1.0 1.0 1.0 1.0 \n", "0 2.0 1.0 1.0 1.0 1.0 1.0 \n", "15 0.0 1.0 1.0 1.0 1.0 1.0 \n", "30 1.0 1.5 1.5 0.0 1.0 1.0 \n", "45 1.0 1.5 0.0 1.5 1.0 1.0 \n", "60 1.0 0.0 1.5 1.5 1.0 1.0 \n", "120 1.0 1.0 1.0 1.0 2.0 0.0 \n", "135 1.0 1.0 1.0 1.0 0.0 2.0 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#jackknife = ReplicateWeight(method=\"jackknife\", stratification=True)\n", "jkn_wgt = jackknife.replicate(\n", " full_sample[\"design_weight\"], full_sample[\"cluster\"], full_sample[\"region\"], rep_coefs=True\n", ")\n", "\n", "jkn_wgt.drop_duplicates().sort_values(by=\"_stratum\").head(15)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>stratum</th>\n", " <th>cluster</th>\n", " <th>_samp_weight</th>\n", " <th>fay_weight_1</th>\n", " <th>fay_weight_2</th>\n", " <th>fay_weight_3</th>\n", " <th>fay_weight_4</th>\n", " <th>fay_weight_5</th>\n", " <th>fay_weight_6</th>\n", " <th>fay_weight_7</th>\n", " <th>fay_weight_8</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>46.166667</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>50.783333</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2</td>\n", " <td>16</td>\n", " <td>62.149123</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>2</td>\n", " <td>24</td>\n", " <td>58.940741</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>3</td>\n", " <td>29</td>\n", " <td>65.702778</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>3</td>\n", " <td>34</td>\n", " <td>75.661566</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>4</td>\n", " <td>45</td>\n", " <td>85.398025</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " </tr>\n", " <tr>\n", " <th>105</th>\n", " <td>4</td>\n", " <td>52</td>\n", " <td>85.520635</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " </tr>\n", " <tr>\n", " <th>120</th>\n", " <td>5</td>\n", " <td>64</td>\n", " <td>218.893889</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " </tr>\n", " <tr>\n", " <th>135</th>\n", " <td>5</td>\n", " <td>86</td>\n", " <td>213.491667</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " <td>0.3</td>\n", " <td>1.7</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " stratum cluster _samp_weight fay_weight_1 fay_weight_2 fay_weight_3 \\\n", "0 1 7 46.166667 0.3 1.7 0.3 \n", "15 1 10 50.783333 1.7 0.3 1.7 \n", "30 2 16 62.149123 0.3 0.3 1.7 \n", "45 2 24 58.940741 1.7 1.7 0.3 \n", "60 3 29 65.702778 0.3 1.7 1.7 \n", "75 3 34 75.661566 1.7 0.3 0.3 \n", "90 4 45 85.398025 0.3 0.3 0.3 \n", "105 4 52 85.520635 1.7 1.7 1.7 \n", "120 5 64 218.893889 0.3 1.7 0.3 \n", "135 5 86 213.491667 1.7 0.3 1.7 \n", "\n", " fay_weight_4 fay_weight_5 fay_weight_6 fay_weight_7 fay_weight_8 \n", "0 1.7 0.3 1.7 0.3 1.7 \n", "15 0.3 1.7 0.3 1.7 0.3 \n", "30 1.7 0.3 0.3 1.7 1.7 \n", "45 0.3 1.7 1.7 0.3 0.3 \n", "60 0.3 0.3 1.7 1.7 0.3 \n", "75 1.7 1.7 0.3 0.3 1.7 \n", "90 0.3 1.7 1.7 1.7 1.7 \n", "105 1.7 0.3 0.3 0.3 0.3 \n", "120 1.7 1.7 0.3 1.7 0.3 \n", "135 0.3 0.3 1.7 0.3 1.7 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#fay = ReplicateWeight(method=\"brr\", stratification=False, fay_coef=0.3)\n", "fay_wgt = fay.replicate(\n", " full_sample[\"design_weight\"], \n", " full_sample[\"cluster\"], \n", " rep_prefix=\"fay_weight_\",\n", " psu_varname=\"cluster\", \n", " str_varname=\"stratum\",\n", " rep_coefs=True\n", ")\n", "\n", "fay_wgt.drop_duplicates().head(10)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.9.4" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

brumar/WPsolving

make a different version of a problem.ipynb

1

17298

{ "metadata": { "name": "", "signature": "sha256:85b9904b38689dfb012beac7930b9c904651dbdbc769358c02618ee8f6bf501e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## create a new problem starting with a old one" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###imports" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import lib.operations as operations\n", "from lib.schemas import *\n", "from lib.subjectRepresentations import *\n", "from lib.textRepresentations import *\n", "from lib.paths import *\n", "from lib.dataManager import *\n", "from lib.optionsFactory import *" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### create the problem1" ] }, { "cell_type": "code", "collapsed": false, "input": [ "schema1=Schema(\"PoissonEF\",\"PoissonEI\",operations.addition,\"PoissonGAIN\",\"change\")\n", "schema2=Schema(\"ViandeEF\",\"ViandeEI\",operations.addition,\"ViandeGAIN\",\"change\")\n", "struct=ProblemStructure()\n", "struct.addSchema(schema1)\n", "struct.addSchema(schema2)\n", "struct.addBridgingSchemas(schema1,schema2)\n", "struct.updateObjectSet()\n", "\n", "text=Text()\n", "text.addTextInformation(TextInformation(Representation(Quantity(\"PoissonGAIN\",\"P1\"),'Au supermarch\u00e9, le kilo de poisson a augment\u00e9 de 5 euros cette ann\u00e9e')))\n", "text.addTextInformation(TextInformation(Representation(Quantity(\"PoissonEF\",\"T1\"),'Un kilo de poisson coute maintenant 12 euros.')))\n", "text.addTextInformation(TextInformation(Representation(Quantity(\"PoissonEIminusViandeEI\",\"dEI\"),'Au d\u00e9but de l\\'ann\u00e9e, le kilo de viande coutait le m\u00eame prix que le kilo de poisson.')))\n", "text.addTextInformation(TextInformation(Representation(Quantity(\"PoissonGAINminusViandeGAIN\",\"d\"),'Le kilo de viande a augment\u00e9 de 3 euros de moins que le kilo de poisson')))\n", "text.setGoal(TextGoal(Goal('ViandeEF','Combien coute le kilo de viande maintenant?')))\n", "\n", "text.getTextInformation(0).addAlternativeRepresentation(Representation(Quantity(\"PoissonEI\",\"P1\"),'Au supermarch\u00e9, le kilo de poisson \u00e9tait de 5 euros'))\n", "text.getTextInformation(0).addAlternativeRepresentation(Representation(Quantity(\"PoissonEF\",\"P1\"),'Au supermarch\u00e9, le kilo de poisson coute 5 euros'))\n", "text.getTextInformation(1).addAlternativeRepresentation(Representation(Quantity(\"PoissonEI\",\"T1\"),'Un kilo de poisson \u00e9tait de 12 euros.'))\n", "text.getTextInformation(2).addAlternativeRepresentation(Representation(Quantity(\"PoissonEFminusViandeEF\",\"dEI\"),'Au la fin de l\\'ann\u00e9e, le kilo de viande coute le m\u00eame prix que le kilo de poisson.'))\n", "text.getTextInformation(2).addAlternativeRepresentation(Representation(Quantity(\"PoissonGAINminusViandeGAIN\",\"dEI\"),'Le kilo de viande a augment\u00e9 du m\u00eame prix que le kilo de poisson.'))\n", "text.getTextInformation(3).addAlternativeRepresentation(Representation(Quantity(\"ViandeGAIN\",\"d\"),'Le kilo de viande a augment\u00e9 de 3 euros'))\n", "text.getTextInformation(3).addAlternativeRepresentation(Representation(Quantity(\"ViandeGAIN\",\"-d\"),'Le kilo de viande a diminu\u00e9 de 3 euros'))\n", "text.getTextInformation(3).addAlternativeRepresentation(Representation(Quantity(\"PoissonGAINminusViandeGAIN\",\"-d\"),'Le kilo de viande a augment\u00e9 de 3 euros de plus que le kilo de poisson'))\n", "text.getTextInformation(3).addAlternativeRepresentation(Representation(Quantity(\"PoissonEFminusViandeEF\",\"d\"),'Le kilo de viande vaut 3 euros de moins que le kilo de poisson'))\n", "text.getTextInformation(3).addAlternativeRepresentation(Representation(Quantity(\"PoissonEFminusViandeEF\",\"-d\"),'Le kilo de viande vaut 3 euros de plus que le kilo de poisson'))\n", "text.getTextInformation(3).addAlternativeRepresentation(Representation(Quantity(\"ViandeEF\",\"d\"),'Le kilo coute 3 euros \u00e0 la fin'))\n", "probleme1=Problem(struct,text)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Deep copy of problem1" ] }, { "cell_type": "code", "collapsed": false, "input": [ "problem2=copy.deepcopy(probleme1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **The third text information differs, we create a new one**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "info3_prime=TextInformation(Representation(Quantity(\"PoissonEFminusViandeEF\",\"d\"),'Le kilo de viande vaut 3 euros de moins que le kilo de poisson'))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "info3_prime.addAlternativeRepresentation(Representation(Quantity(\"ViandeGAIN\",\"d\"),'Le kilo de viande a augment\u00e9 de 3 euros'))\n", "info3_prime.addAlternativeRepresentation(Representation(Quantity(\"ViandeGAIN\",\"-d\"),'Le kilo de viande a diminu\u00e9 de 3 euros'))\n", "info3_prime.addAlternativeRepresentation(Representation(Quantity(\"PoissonGAINminusViandeGAIN\",\"-d\"),'Le kilo de viande a augment\u00e9 de 3 euros de plus que le kilo de poisson'))\n", "info3_prime.addAlternativeRepresentation(Representation(Quantity(\"PoissonGAINminusViandeGAIN\",\"d\"),'Le kilo de viande a augment\u00e9 de 3 euros de moins que le kilo de poisson'))\n", "info3_prime.addAlternativeRepresentation(Representation(Quantity(\"PoissonEFminusViandeEF\",\"-d\"),'Le kilo de viande vaut 3 euros de plus que le kilo de poisson'))\n", "info3_prime.addAlternativeRepresentation(Representation(Quantity(\"ViandeEF\",\"d\"),'Le kilo coute 3 euros \u00e0 la fin'))\n", "\n", "problem2.text.textInformations[3]=info3_prime" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **The goal is not the same, we change it**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "problem2.text.setGoal(TextGoal(Goal('ViandeGAIN','De combien le kilo de viande a t-il augment\u00e9 ?')))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Last steps : set initial values,and name it" ] }, { "cell_type": "code", "collapsed": false, "input": [ "problem2.setInitialValues({\"P1\":5,\"T1\":12,\"dEI\":0,\"d\":3,\"-d\":-3})\n", "problem2.name=\"Tc4p\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "upD=Updater(problem2) \n", "upD.startAsUnderstood() \n", "c1=IntervalConstraint(['EF','EI'],operations.superiorOrEqualTo0) \n", "c2=BehavioralConstraint(breakTheOldOne=True)\n", "constraints=[c1,c2]\n", "autoSolver=Solver(upD,constraints)\n", "l=[autoSolver.SOLVER]\n", "autoSolver.generalSequentialSolver(listOfActions=l)\n", "autoSolver.TreePaths.scanTree()\n", "print(autoSolver.TreePaths.treeOutput)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 12 - 5 = 7 (PoissonEI)\r\n", "\t 7 - 0 = 7 (ViandeEI)\r\n", "\t\t 12 - 3 = 9 (ViandeEF)\r\n", "\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t 3 - 0 = 3 (PoissonGAINminusViandeGAIN)\r\n", "\t\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t 3 - 0 = 3 (PoissonGAINminusViandeGAIN)\r\n", "\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t 12 - 3 = 9 (ViandeEF)\r\n", "\t\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t 12 - 3 = 9 (ViandeEF)\r\n", "\t\t 7 - 0 = 7 (ViandeEI)\r\n", "\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t 3 - 0 = 3 (PoissonGAINminusViandeGAIN)\r\n", "\t\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t 3 - 0 = 3 (PoissonGAINminusViandeGAIN)\r\n", "\t\t\t 7 - 0 = 7 (ViandeEI)\r\n", "\t\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t 3 - 0 = 3 (PoissonGAINminusViandeGAIN)\r\n", "\t\t 7 - 0 = 7 (ViandeEI)\r\n", "\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t 12 - 3 = 9 (ViandeEF)\r\n", "\t\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t 12 - 3 = 9 (ViandeEF)\r\n", "\t\t\t 7 - 0 = 7 (ViandeEI)\r\n", "\t\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", " 12 - 3 = 9 (ViandeEF)\r\n", "\t 12 - 5 = 7 (PoissonEI)\r\n", "\t\t 7 - 0 = 7 (ViandeEI)\r\n", "\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t 3 - 0 = 3 (PoissonGAINminusViandeGAIN)\r\n", "\t\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t 3 - 0 = 3 (PoissonGAINminusViandeGAIN)\r\n", "\t\t\t 7 - 0 = 7 (ViandeEI)\r\n", "\t\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t 3 - 0 = 3 (PoissonGAINminusViandeGAIN)\r\n", "\t\t 12 - 5 = 7 (PoissonEI)\r\n", "\t\t\t 7 - 0 = 7 (ViandeEI)\r\n", "\t\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", " 3 - 0 = 3 (PoissonGAINminusViandeGAIN)\r\n", "\t 12 - 5 = 7 (PoissonEI)\r\n", "\t\t 7 - 0 = 7 (ViandeEI)\r\n", "\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t 12 - 3 = 9 (ViandeEF)\r\n", "\t\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t 12 - 3 = 9 (ViandeEF)\r\n", "\t\t\t 7 - 0 = 7 (ViandeEI)\r\n", "\t\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t 12 - 3 = 9 (ViandeEF)\r\n", "\t\t 12 - 5 = 7 (PoissonEI)\r\n", "\t\t\t 7 - 0 = 7 (ViandeEI)\r\n", "\t\t\t\t 9 - 7 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\t(T1-d)-(T1-P1) : interpretation -> (PoissonEF-PoissonEFminusViandeEF)-((PoissonEF-PoissonGAIN)-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\t\t 5 - 3 = 2 (ViandeGAIN)\r\n", "\t\t\tP1-d : interpretation -> PoissonGAIN-(PoissonEFminusViandeEF-PoissonEIminusViandeEI)=ViandeGAIN\r\n", "\n" ] } ], "prompt_number": 25 } ], "metadata": {} } ] }

mit

RuiShu/cvae

notebooks/GMM_gradient_checker.ipynb

1

20796

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "require 'nn'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = nn.Sequential()\n", "cat = nn.ConcatTable()\n", "inner1 = nn.Sequential()\n", "inner1:add(nn.Linear(1,2))\n", "inner2 = nn.Sequential()\n", "inner2:add(nn.Linear(1,5))\n", "inner2:add(nn.ReLU(true))\n", "inner2:add(nn.Linear(5,1))\n", "inner2:add(nn.SoftMax())\n", "cat:add(inner1)\n", "cat:add(inner2)\n", "model:add(cat)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = nn.Sequential()\n", "cat = nn.ConcatTable()\n", "cat:add(nn.Linear(1,3))\n", "cat:add(nn.Linear(1,4))\n", "model:add(cat)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "k = model:forward(torch.rand(10,1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "k[2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = torch.rand(10,1)\n", "v = torch.rand(10,3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "x:cmul(v[{{},1}])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "torch.zeros(3,1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "require 'criteria/GMMKLDCriterion'\n", "gkld = nn.GMMKLDCriterion\n", "pmu = torch.zeros(3)\n", "plogv = torch.zeros(3)\n", "mu = torch.randn(3)\n", "logv = torch.randn(3):pow(2):log()\n", "pi = torch.rand(3)\n", "input = {pmu, plogv, pi}\n", "target = {mu, logv}\n", "gkld:forward({pmu, plogv, pi}, {mu, logv})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "require 'criteria/GMMKLDCriterion'\n", "require 'criteria/KLDCriterion'\n", "gkld = nn.GMMKLDCriterion()\n", "kld = nn.KLDCriterion()\n", "pmu = torch.zeros(3,2)\n", "plogv = torch.zeros(3,2)\n", "pi = torch.ones(3,1)\n", "\n", "mu = torch.randn(3,2)\n", "logv = torch.randn(3,2):pow(2):log()\n", "input = {pmu, plogv, pi}\n", "target = {mu, logv}\n", "print(gkld:forward({pmu, plogv, pi}, {mu, logv}))\n", "print(kld:forward({pmu, plogv}, {mu, logv}))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.7855699232485\t\n", "1.0924227426886\t\n" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "require 'criteria/GMMKLDCriterion'\n", "require 'criteria/KLDCriterion'\n", "gkld = nn.GMMKLDCriterion()\n", "kld = nn.KLDCriterion()\n", "\n", "N = 1\n", "D = 3\n", "K = 2\n", "\n", "pmu1 = torch.zeros(N,D)\n", "pmu2 = torch.ones(N,D)*40\n", "\n", "plogv1 = torch.zeros(N,D)\n", "plogv2 = torch.ones(N,D)\n", "\n", "pi = torch.ones(N,K):div(K)\n", "\n", "mu = torch.ones(N,D)*.1\n", "logv = torch.ones(N,D)\n", "\n", "input = {pmu, plogv, pi}\n", "target = {mu, logv}\n", "print(gkld:forward({pmu1, pmu2, plogv1, plogv2, pi}, {mu, logv}))\n", "print(kld:forward({pmu1, plogv1},{mu, logv}))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "-- compare with direct KLD:\n", "print(kld:forward({pmu1, plogv1}, {mu, logv}))\n", "print(kld:forward({pmu2, plogv2}, {mu, logv}))\n", "print(kld:forward({pmu3, plogv3}, {mu, logv}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gradient Check" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "require 'criteria/GMMKLDCriterion'\n", "require 'criteria/KLDCriterion'\n", "gkld = nn.GMMKLDCriterion()\n", "kld = nn.KLDCriterion()\n", "\n", "N = 5\n", "D = 3\n", "K = 1\n", "\n", "pmu1 = torch.randn(N,D)\n", "pi = torch.Tensor({{1}}):expand(N,1)\n", "plogv1 = torch.randn(N,D)\n", "\n", "mu = torch.randn(N,D)\n", "logv = torch.randn(N,D)\n", "\n", "h = 1e-4" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "gkld:forward({pmu1, plogv1, pi}, {mu, logv})\n", "dpmu1, dplogv1, dpi, dmu, dlogv = unpack(gkld:backward({pmu1, plogv1, pi}, {mu, logv}))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " 0.4285 0.3246 -5.9314\n", " 0.0432 0.3800 0.1303\n", " -1.3696 -6.0556 0.2066\n", " -2.2196 0.3846 -0.1272\n", " -4.6368 -17.9626 0.2407\n", "[torch.DoubleTensor of size 5x3]\n", "\n" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a, b, c, d = unpack(kld:backward({pmu1, plogv1}, {mu, logv}))" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "require 'criteria/GMMKLDCriterion'\n", "require 'criteria/KLDCriterion'\n", "gkld = nn.GMMKLDCriterion()\n", "kld = nn.KLDCriterion()\n", "\n", "N = 5\n", "D = 3\n", "K = 3\n", "\n", "pmu1 = torch.zeros(N,D)\n", "pmu2 = torch.randn(N,D)\n", "pmu3 = torch.randn(N,D)\n", "pi = torch.Tensor({{1,2,3}}):expand(5,3)/6\n", "plogv1 = torch.zeros(N,D)\n", "plogv2 = torch.randn(N,D):pow(2):log()\n", "plogv3 = torch.randn(N,D):pow(2):log()\n", "\n", "mu = torch.zeros(N,D)\n", "logv = torch.zeros(N,D)\n", "\n", "h = 1e-4" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gkld:forward({pmu1, pmu2, pmu3, plogv1, plogv2, plogv3, pi}, {mu, logv})\n", "dpmu1, dpmu2, dpmu3, dplogv1, dplogv2, dplogv3, dpi, dmu, dlogv = unpack(gkld:backward({pmu1, pmu2, pmu3, plogv1, plogv2, plogv3, pi}, {mu, logv}))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dmu" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "x = mu\n", "y = dmu\n", "print(\"Error:\")\n", "for i=1,x:size(1) do\n", " for j=1,x:size(2) do\n", " x[{i,j}] = x[{i,j}] + h\n", " fph = gkld:forward({pmu1, pmu2, pmu3, plogv1, plogv2, plogv3, pi}, {mu, logv})\n", " x[{i,j}] = x[{i,j}] - h - h\n", " fmh = gkld:forward({pmu1, pmu2, pmu3, plogv1, plogv2, plogv3, pi}, {mu, logv})\n", " x[{i,j}] = x[{i,j}] + h\n", " print((fph - fmh)/2/h - y[{i,j}])\n", " end\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gradient" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dpi = unpack(gkld:backward({pmu1, pmu2, pmu3, plogv1, plogv2, plogv3, pi}, {mu, logv}))\n", "print(dpi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Math" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{\n", " VolumetricMaxUnpooling : table: 0x41ce8e50\n", " ConcatTable : table: 0x41d16708\n", " SpatialAveragePooling : table: 0x41bdb718\n", " BCECriterion : table: 0x40933360\n", " Reshape : table: 0x41cebc90\n", " Jacobian : \n", " {\n", " forward : function: 0x401f9240\n", " testAllUpdate : function: 0x401eff80\n", " testDiagHessianInput : function: 0x41308cf8\n", " testDiagHessianWeight : function: 0x41308d18\n", " testDiagHessianBias : function: 0x41308d38\n", " testDiagHessian : function: 0x41e349d0\n", " testJacobian : function: 0x401f0048\n", " testIO : function: 0x401eff60\n", " testJacobianUpdateParameters : function: 0x41e349b0\n", " backwardDiagHessian : function: 0x41308d60\n", " testJacobianParameters : function: 0x4092d8f0\n", " backwardUpdate : function: 0x401f9220\n", " forwardUpdate : function: 0x401f0028\n", " backward : function: 0x401f9200\n", " linearModuleDiagHessian : function: 0x41308d80\n", " }\n", " SparseLinear : table: 0x41ce9400\n", " SpatialCrossMapLRN : table: 0x401f1310\n", " CAddTable : table: 0x41e3d838\n", " TemporalConvolution : table: 0x41ce8970\n", " PairwiseDistance : table: 0x41e4d138\n", " WeightedMSECriterion : table: 0x41306850\n", " SmoothL1Criterion : table: 0x40923b40\n", " SpatialLPPooling : table: 0x41d8aa00\n", " TanhShrink : table: 0x40935860\n", " MixtureTable : table: 0x413098f0\n", " MSECriterion : table: 0x40933b28\n", " LogSoftMax : table: 0x4092da40\n", " Identity : table: 0x40cf4e88\n", " Exp : table: 0x40927b30\n", " Add : table: 0x41e36320\n", " SpatialConvolutionLocal : table: 0x40cf06d0\n", " BatchNormalization : table: 0x401f7da0\n", " AbsCriterion : table: 0x402097a8\n", " MultiCriterion : table: 0x40205350\n", " Max : table: 0x40200950\n", " MulConstant : table: 0x41e34368\n", " NarrowTable : table: 0x401ec140\n", " View : table: 0x401eba20\n", " VolumetricConvolution : table: 0x40942b38\n", " SpatialSubSampling : table: 0x41d2ab68\n", " HardTanh : table: 0x4092a1b8\n", " DistKLDivCriterion : table: 0x40923d98\n", " SplitTable : table: 0x41cd6020\n", " DotProduct : table: 0x41e4e748\n", " HingeEmbeddingCriterion : table: 0x401ed980\n", " SpatialBatchNormalization : table: 0x41d7cb00\n", " DepthConcat : table: 0x41cdf778\n", " CMulTable : table: 0x41e410f8\n", " SpatialAdaptiveMaxPooling : table: 0x4092f350\n", " Parallel : table: 0x41cdac20\n", " SoftShrink : table: 0x4093ccd8\n", " SpatialSubtractiveNormalization : table: 0x41dd2a78\n", " Log : table: 0x40928eb0\n", " SpatialDropout : table: 0x41e3b4f8\n", " LeakyReLU : table: 0x40942ae8\n", " VolumetricMaxPooling : table: 0x41663678\n", " hessian : \n", " {\n", " enable : function: 0x41cd69e8\n", " }\n", " Linear : table: 0x41ce22d0\n", " Euclidean : table: 0x41e43390\n", " CriterionTable : table: 0x40c52160\n", " SpatialMaxPooling : table: 0x412fcf28\n", " MultiMarginCriterion : table: 0x409373a8\n", " ELU : table: 0x40ce5b90\n", " CSubTable : table: 0x41e42890\n", " MultiLabelMarginCriterion : table: 0x41e3e840\n", " " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ " Copy : table: 0x401fc1f0\n", " L1HingeEmbeddingCriterion : table: 0x40925448\n", " VolumetricAveragePooling : table: 0x40849f60\n", " StochasticGradient : table: 0x40ceaba8\n", " SpatialContrastiveNormalization : table: 0x41bc9d70\n", " Bilinear : table: 0x41ce5d48\n", " CosineEmbeddingCriterion : table: 0x41e477f0\n", " Padding : table: 0x401f92f0\n", " Container : table: 0x41cd3d48\n", " MarginRankingCriterion : table: 0x401f49e8\n", " Module : table: 0x41ccebf8\n", " VolumetricFullConvolution : table: 0x416627c8\n", " Concat : table: 0x41cd77e0\n", " CrossEntropyCriterion : table: 0x41dcdb18\n", " LookupTable : table: 0x40ce7728\n", " MarginCriterion : table: 0x41e34b90\n", " HardShrink : table: 0x4093b7a8\n", " Abs : table: 0x40936cb8\n", " SparseJacobian : \n", " {\n", " forward : function: 0x41bc6738\n", " testJacobian : function: 0x41bd1db8\n", " testIO : function: 0x41bc6778\n", " testAllUpdate : function: 0x41bd1d30\n", " testJacobianParameters : function: 0x41dcfe68\n", " testJacobianUpdateParameters : function: 0x41bc6758\n", " forwardUpdate : function: 0x41bd1d98\n", " backward : function: 0x41dcfe48\n", " backwardUpdate : function: 0x41bc6718\n", " }\n", " SoftMin : table: 0x40930e20\n", " WeightedEuclidean : table: 0x41e47010\n", " Contiguous : table: 0x401ee170\n", " L1Cost : table: 0x40c405f0\n", " PReLU : table: 0x409412c8\n", " utils : \n", " {\n", " recursiveType : function: 0x41668b30\n", " recursiveResizeAs : function: 0x41668ba8\n", " recursiveAdd : function: 0x41668b70\n", " addSingletonDimension : function: 0x41665ed8\n", " recursiveFill : function: 0x41665cb0\n", " }\n", " JoinTable : table: 0x41d255f0\n", " ClassNLLCriterion : table: 0x41d0dc40\n", " CMul : table: 0x40206df0\n", " CosineDistance : table: 0x41e4e898\n", " Index : table: 0x401f1f98\n", " Mean : table: 0x40202950\n", " Dropout : table: 0x41e38ff0\n", " SoftPlus : table: 0x40932070\n", " SpatialDivisiveNormalization : table: 0x41bdaf98\n", " L1Penalty : table: 0x41e401d8\n", " Power : table: 0x409381b8\n", " Sqrt : table: 0x4093a618\n", " Sequential : table: 0x41cddfb0\n", " Square : table: 0x409393b0\n", " AddConstant : table: 0x41e38760\n", " GMMKLDCriterion : table: 0x41141ec8\n", " test : function: 0x41144818\n", " MM : table: 0x41ccd3b8\n", " SoftMax : table: 0x4092faa0\n", " ParallelCriterion : table: 0x412fb480\n", " Cosine : table: 0x40925da0\n", " Clamp : table: 0x4092b410\n", " SpatialConvolutionMM : table: 0x40cf7bf8\n", " Sigmoid : table: 0x4092ea38\n", " LogSigmoid : table: 0x4092c548\n", " TemporalMaxPooling : table: 0x41d833a8\n", " Threshold : table: 0x4093db28\n", " Sum : table: 0x40205690\n", " SoftSign : table: 0x40933508\n", " ParallelTable : table: 0x401f6330\n", " Min : table: 0x401fe450\n", " KLDCriterion : table: 0x40af9350\n", " Replicate : table: 0x401f3ae0\n", " Tanh : table: 0x409344b0\n", " CDivTable : table: 0x41e3f4b8\n", " Mul : table: 0x402098e0\n", " Select : table: 0x401ef570\n", " ReLU : table: 0x4093f5c0\n", " SpatialFullConvolutionMap : table: 0x40cf6238\n", " GradientReversal : table: 0x401fb648\n", " SpatialConvolution : table: 0x40cea750\n", " Criterion : table: 0x41e4bab0\n", " SpatialConvolutionMap : table: 0x40cfa878\n", " tables : \n", " {\n", " full : function: 0x40cfa8f0\n", " oneToOne : function: 0x40cfd2e8\n", " random : function: 0x40cfd6f0\n", " }\n", " SpatialMaxUnpooling : table: 0x41dc9690\n", " TemporalSubSampling : table: 0x412f7f00\n", " Transpose : table: 0x401f4d80\n", " SpatialFullConvolution : table: 0x40cf1f68\n", " SpatialUpSamplingNearest : table: 0x4093ae30\n", " RReLU : table: 0x40ce3a70\n", " SpatialZeroPadding : table: 0x41e3ea50\n", " FlattenTable : table: 0x41cd8228\n", " Narrow : table: 0x401f0338\n", " Normalize : table: 0x41e53598\n", " SpatialSoftMax : table: 0x40ce2b88\n", " SelectTable : table: 0x4092dea8\n", " SpatialFractionalMaxPooling : table: 0x40850558\n", "}\n" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "require 'nn'" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = nn.Sequential()\n", "model:add(nn.Linear(2,3))\n", "model:add(nn.ReLU(true))\n", "model:add(nn.Linear(3,4))\n", "model:add(nn.SoftMax())\n", "model:add(nn.Linear(4,1))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = torch.randn(10,2)\n", "out = model:forward(x)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dout = torch.randn(10, 1)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0.01 *\n", " -0.0470 -0.0789\n", " -0.1024 1.5457\n", " 3.3797 5.5302\n", " -0.0277 0.4179\n", " -0.0052 0.0786\n", " -2.3183 -3.8346\n", " 0.0474 -0.7152\n", " 0.1209 -1.8247\n", " 0.4824 0.7931\n", " -0.2984 -0.5001\n", "[torch.DoubleTensor of size 10x2]\n", "\n" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model:backward(x, dout)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# GMM Sampler" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " 0.1667 0.3333 0.5000\n", " 0.1667 0.3333 0.5000\n", " 0.1667 0.3333 0.5000\n", " 0.1667 0.3333 0.5000\n", " 0.1667 0.3333 0.5000\n", "[torch.DoubleTensor of size 5x3]\n", "\n" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pi" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " 3\n", " 1\n", " 1\n", " 3\n", " 3\n", "[torch.LongTensor of size 5x1]\n", "\n" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = torch.multinomial(pi, 1)\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " 2\n", "[torch.LongTensor of size 1]\n", "\n" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] } ], "metadata": { "kernelspec": { "display_name": "iTorch", "language": "lua", "name": "itorch" }, "language_info": { "name": "lua", "version": "20100" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

Danghor/Formal-Languages

Python/Rewrite.ipynb

1

5999

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%HTML\n", "<style>\n", ".container { width: 100% }\n", "</style>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Term Simplification via Rewriting" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import string" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def is_variable(s):\n", " return isinstance(s, str) and s != '' and s[0] in string.ascii_uppercase" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def match(pattern, term, Substitution={}):\n", " if is_variable(pattern):\n", " V = pattern\n", " if V in Substitution:\n", " return match(Substitution[V], term, Substitution)\n", " else:\n", " Substitution[V] = term\n", " return True\n", " if isinstance(pattern, str) or isinstance(pattern, int):\n", " return pattern == term\n", " if isinstance(term, str) or isinstance(term, int):\n", " return False\n", " if len(pattern) != len(term):\n", " return False\n", " if pattern[0] != term[0]:\n", " return False\n", " n = len(pattern)\n", " for i in range(n):\n", " if not match(pattern[i], term[i], Substitution):\n", " return False\n", " return True" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def apply(term, Substitution):\n", " if is_variable(term):\n", " V = term\n", " if V in Substitution:\n", " return Substitution[V]\n", " else:\n", " return V\n", " if isinstance(term, str) or isinstance(term, int):\n", " return term\n", " return tuple(apply(arg, Substitution) for arg in term)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def rewrite(term, rule):\n", " lhs, rhs = rule\n", " Substitution = {}\n", " if match(lhs, term, Substitution):\n", " return True, apply(rhs, Substitution)\n", " else:\n", " return False, term" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def simplify_once(term, Rules):\n", " if isinstance(term, str) or isinstance(term, int):\n", " return term\n", " for rule in Rules:\n", " flag, simple = rewrite(term, rule)\n", " if flag:\n", " return simple\n", " return tuple(simplify_once(arg, Rules) for arg in term)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def simplify(t, Rules):\n", " while True:\n", " old_t = t\n", " t = simplify_once(t, Rules)\n", " if t == old_t:\n", " return t" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Rules = {\n", " (('+', 'R', 0), 'R'),\n", " (('+', 0, 'R'), 'R'),\n", " (('+', 'R', 'R'), 'R'),\n", " (('+', '', ('*', 'R')), ('*', 'R')),\n", " (('+', ('*', 'R'), ''), ('*', 'R')),\n", " (('+', '', ('&', 'R', ('*', 'R'))), ('*', 'R')),\n", " (('+', '', ('&', ('*', 'R'), 'R')), ('*', 'R')),\n", " (('+', ('&', 'R', ('*', 'R')), ''), ('*', 'R')),\n", " (('+', ('&', ('*', 'R'), 'R'), ''), ('*', 'R')),\n", " (('+', 'S', ('&', 'S', 'T')), ('&', 'S', ('+', '', 'T'))),\n", " (('+', 'S', ('&', 'T', 'S')), ('&', ('+', '', 'T'), 'S')),\n", " (('&', 0, 'R'), 0),\n", " (('&', 'R', 0), 0),\n", " (('&', '', 'R'), 'R'),\n", " (('&', '', 'R'), 'R'),\n", " (('&', ('+', '', 'R'), ('*', 'R')), ('*', 'R')),\n", " (('&', ('+', 'R', ''), ('*', 'R')), ('*', 'R')),\n", " (('&', ('*', 'R'), ('+', 'R', '')), ('*', 'R')),\n", " (('&', ('*', 'R'), ('+', '', 'R')), ('*', 'R')),\n", " (('*', 0), ''),\n", " (('*', ''), ''),\n", " (('*', ('+', '', 'R')), ('*', 'R')),\n", " (('*', ('+', 'R', '')), ('*', 'R')),\n", " (('+', 'R', ('+', 'S', 'T')), ('+', ('+', 'R', 'S'), 'T')),\n", " (('&', 'R', ('&', 'S', 'T')), ('&', ('&', 'R', 'S'), 'T')),\n", " (('&', ('&', 'R', ('*', 'S')), ('+', '', 'S')), ('&', 'R', ('*', 'S')))\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.3" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }

gpl-2.0

performlabrit/ECEM-2017-Eyetracking-in-VR

attic/4. Dispersion.ipynb

1

4733938

gpl-3.0

pcmagic/stokes_flow

HelicodsParticles/helicoid_dumb/compare_Darci2020.ipynb

1

4557127

mit

kl456123/machine_learning

workspace/PCA and SVD.ipynb

1

2961613

mit

basnijholt/orbitalfield

Phase-diagrams.ipynb

1

10630

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Phase diagram for multiple angles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start a `ipcluster` from the Cluster tab in Jupyter or use the command:\n", "\n", "```ipcluster start``` \n", "\n", "in a terminal." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from ipyparallel import Client\n", "cluster = Client()\n", "dview = cluster[:]\n", "dview.use_dill()\n", "lview = cluster.load_balanced_view()\n", "len(dview)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This next cell is for internal use with our cluster at the department, a local ipcluster will work: use the cell above. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# import os\n", "# from scripts.hpc05 import HPC05Client\n", "# os.environ['SSH_AUTH_SOCK'] = os.path.join(os.path.expanduser('~'), 'ssh-agent.socket')\n", "# cluster = HPC05Client()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Make sure to add the correct path like:\n", "\n", " sys.path.append(\"/path/where/to/ipynb/runs\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "%%px --local\n", "import sys\n", "import os\n", "# CHANGE THE LINE BELOW INTO THE CORRECT FOLDER!\n", "sys.path.append(os.path.join(os.path.expanduser('~'), 'orbitalfield'))\n", "import kwant\n", "import numpy as np\n", "from fun import *\n", "\n", "def gap_and_decay(lead, p, val, tol=1e-4):\n", " gap = find_gap(lead, p, val, tol)\n", " decay_length = find_decay_length(lead, p, val)\n", " return gap, decay_length" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import holoviews as hv\n", "import holoviews_rc\n", "hv.notebook_extension()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uncomment the lines for the wire that you want to use." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%px --local\n", "# angle = 0 # WIRE WITH SC ON TOP\n", "\n", "angle = 45 # WIRE WITH SC ON SIDE\n", "p = make_params(t_interface=7/8*constants.t, Delta=68.4, r1=50, r2=70, \n", " orbital=True, angle=angle, A_correction=True, alpha=100) #r2=70\n", "\n", "p.V = lambda x, y, z: 2 / 50 * z\n", "lead = make_3d_wire_external_sc(a=constants.a, r1=p.r1, r2=p.r2, angle=p.angle)\n", "\n", "# WIRE WITH CONSTANT GAP\n", "# lead = make_3d_wire()\n", "# p = make_params(V=lambda x, y, z: 0, orbital=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can specify the angles that you want to calculate in `thetas` and `phis`.\n", "\n", "Also specify the range of magnetic field and chemical potential in `Bs` and `mu_mesh`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# give an array of angles that you want to use\n", "\n", "# thetas = np.array([0, np.tan(1/10), 0.5 * np.pi - np.tan(1/10), 0.5 * np.pi])\n", "# phis = np.array([0, np.tan(1/10), 0.5 * np.pi - np.tan(1/10), 0.5 * np.pi])\n", "\n", "thetas = np.array([0.5 * np.pi])\n", "phis = np.array([0])\n", "\n", "# the range of magnetic field and chemical potential\n", "Bs = np.linspace(0, 2, 400)\n", "mu_mesh = np.linspace(0, 35, 400)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "# creates a 3D array with all values of magnetic field for all specified angles\n", "pos = spherical_coords(Bs.reshape(-1, 1, 1), thetas.reshape(1, -1, 1), phis.reshape(1, 1, -1))\n", "pos_vec = pos.reshape(-1, 3)\n", "\n", "mus_output = lview.map_sync(lambda B: find_phase_bounds(lead, p, B, num_bands=40), pos_vec)\n", "mus, vals, mask = create_mask(Bs, thetas, phis, mu_mesh, mus_output)\n", "\n", "N = len(vals)\n", "step = N // (len(phis) * len(thetas))\n", "print(N, step)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Check whether the correct angles were used and see the phase boundaries" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import holoviews_rc\n", "from itertools import product\n", "from math import pi\n", "\n", "kwargs = {'kdims': [dimensions.B, dimensions.mu],\n", " 'extents': bnds(Bs, mu_mesh),\n", " 'label': 'Topological boundaries',\n", " 'group': 'Lines'}\n", "\n", "angles = list(product(enumerate(phis), enumerate(thetas)))\n", "\n", "boundaries = {(theta / pi, phi / pi): hv.Path((Bs, mus[i, j, :, ::2]), **kwargs)\n", " for (i, phi), (j, theta) in angles}\n", "\n", "BlochSpherePlot.bgcolor = 'white'\n", "\n", "sphere = {(theta / pi, phi / pi): BlochSphere([[1, 0, 0], spherical_coords(1, theta, phi)], group='Sphere')\n", " for (i, phi), (j, theta) in angles}\n", "\n", "hv.HoloMap(boundaries, **dimensions.angles) + hv.HoloMap(sphere, **dimensions.angles)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculate full phase diagram" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure `tempdata` exists in the current folder. \n", "\n", "Set `full_phase_diagram` to `False` if you only want the band gap in the non-trivial region or `True` if you want it in the whole `Bs, mu_mesh` range." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "full_phase_diagram = False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next cell calculates the gaps and decay lengths.\n", "\n", "You can stop and rerun the code, it will skip over the files that already exist.\n", "\n", "Make sure the folder `tempdata/` exists." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "import os.path\n", "import sys\n", "\n", "fname_list = []\n", "for i, n in enumerate(range(0, N, step)):\n", " fname = \"tempdata/\" + str(n)+\"-\"+str((i+1)*step)+\".dat\"\n", " fname_list.append(fname)\n", " \n", " if not os.path.isfile(fname): # check if file already exists\n", " lview.results.clear()\n", " cluster.results.clear()\n", " cluster.metadata.clear()\n", " print(fname)\n", " sys.stdout.flush()\n", " if full_phase_diagram:\n", " gaps_and_decays_output = lview.map_async(lambda val: gap_and_decay(lead, p, val[:-1] + (True,)), vals[n:(i+1) * step])\n", " else:\n", " gaps_and_decays_output = lview.map_async(lambda val: gap_and_decay(lead, p, val), vals[n:(i+1) * step])\n", " gaps_and_decays_output.wait_interactive()\n", " np.savetxt(fname, gaps_and_decays_output.result())\n", " print(n, (i+1) * step)\n", "cluster.shutdown(hub=True)\n", "\n", "gaps_and_decay_output = np.vstack([np.loadtxt(fname) for fname in fname_list])\n", "gaps_output, decay_length_output = np.array(gaps_and_decay_output).T\n", "\n", "gaps = np.array(gaps_output).reshape(mask.shape)\n", "gaps[1:, 0] = gaps[0, 0]\n", "\n", "decay_lengths = np.array(decay_length_output).reshape(mask.shape)\n", "decay_lengths[1:, 0] = decay_lengths[0, 0]\n", "\n", "if full_phase_diagram:\n", " gaps = gaps*(mask*2 - 1)\n", " decay_lengths = decay_lengths*(mask*2 - 1)\n", " gaps_output = gaps.reshape(-1)\n", " decay_length_output = decay_lengths.reshape(-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Save\n", "Run this function to save the data to `hdf5` format, it will include all data and parameters that are used in the simulation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fname = 'data/test.h5'\n", "save_data(fname, Bs, thetas, phis, mu_mesh, mus_output, gaps_output, decay_length_output, p, constants)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Check how the phase diagram looks\n", "This will show all data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%output size=200\n", "%%opts Image [colorbar=False] {+axiswise} (clims=(0, 0.1))\n", "phase_diagram = create_holoviews(fname)\n", "\n", "(phase_diagram.Phase_diagram.Band_gap.hist()\n", " + phase_diagram.Phase_diagram.Inverse_decay_length \n", " + phase_diagram.Sphere.I).cols(2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%opts Image [colorbar=True]\n", "phase_diagram.Phase_diagram.Band_gap" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "phase_diagram.cdims" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-2-clause

jeffalstott/network_clustering_growth

MRC_Notebook/SBM_rewiring_example.ipynb

1

123400

{ "metadata": { "name": "", "signature": "sha256:bdc18ea7e5b7439585474431f509381fbb1295c5d56e18e5d2801972989b2d16" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import networkx as nx\n", "import community \n", "import random\n", "from math import floor\n", "import clusterrewire\n", "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "WARNING: pylab import has clobbered these variables: ['random', 'floor']\n", "`%matplotlib` prevents importing * from pylab and numpy\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "def sbm(cmtysize, pin, pout):\n", " graphs = []\n", " for i in range(0, len(cmtysize)):\n", " graphs.append(nx.gnp_random_graph(cmtysize[i], pin))\n", " G=nx.disjoint_union_all(graphs)\n", "\n", " s=[]\n", " s.append(0)\n", " for i in range(0, len(cmtysize)):\n", " s.append(s[i-1]+cmtysize[i])\n", "\n", " for i in range(0, len(cmtysize)):\n", " for n in range(s[i], s[i+1]):\n", " for m in range(s[i+1], G.number_of_nodes()):\n", " if rand()<pout:\n", " G.add_edge(n, m)\n", " return G;" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "g=sbm([20, 20, 20], .64, .5)\n", "partition_before_rewire = community.best_partition(g)\n", "sets_before_rewire = [[]]\n", "for i in range(0, len(partition_before_rewire)):\n", " s = partition_before_rewire[i]\n", " if s>len(sets_before_rewire)-1:\n", " sets_before_rewire.append([])\n", " sets_before_rewire[s].append(i)\n", "\n", "print(sets_before_rewire)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[0, 1, 5, 7, 8, 10, 11, 13, 17, 18, 24, 25, 28, 33, 36, 38, 40, 43, 44, 46, 52], [2, 6, 9, 21, 23, 26, 29, 32, 34], [3, 4, 14, 16, 19, 30, 39, 41, 45, 48, 50, 51, 53, 54, 55, 56, 57], [12, 15, 20, 22, 27, 31, 35, 37, 42, 47, 49, 58, 59]]\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "imshow(nx.to_numpy_matrix(g))\n", "title(\"Network before rewiring\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "<matplotlib.text.Text at 0x10788c320>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Y5pyhFFKsyCsJ811iLSHEFDn3mOaAyUofBrhknk2m2eM+L1r61xe+qXPPbjOX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ymv4Lumk+pH4vydM9p2/fUref/iOhA74P/kUR/nRwzD+8hH3fSqb9JQXQzygiUlqCa+4X6ymZbZ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\nm/6LfJEfM/l/AW8uAmjqfopWAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x1076ee128>" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "A = nx.to_numpy_matrix(g)\n", "A = clusterrewire.cluster_rewire_graph(A,\n", " percent_of_edges_to_rewire=1,\n", " verbose=False,\n", " property_functions=None)\n", "rewired_graph = nx.Graph(A)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "partition_after_rewire = community.best_partition(rewired_graph)\n", "sets_after_rewire = [[]]\n", "for i in range(0, len(partition_after_rewire)):\n", " s = partition_after_rewire[i]\n", " if s>len(sets_after_rewire)-1:\n", " sets_after_rewire.append([])\n", " sets_after_rewire[s].append(i)\n", "\n", "print(sets_after_rewire)\n", "\n", "imshow(nx.to_numpy_matrix(rewired_graph))\n", "title(\"Adjacency after rewire\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[0, 4, 6, 7, 12, 14, 15, 17, 18, 22, 24, 25, 26, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 43, 45, 49, 50, 52, 53, 54, 57, 58], [1, 2, 3, 5, 8, 9, 10, 11, 13, 16, 19, 20, 21, 23, 27, 30, 39, 40, 41, 42, 44, 46, 47, 48, 51, 55, 56, 59]]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "<matplotlib.text.Text at 0x1079b8cf8>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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M9rXgyeoucIwiwvyECrKRpNE69xI/rDhM5iHbR7eEHI4oBl+VZ0KeG7lV1QeX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rZfMVJPb/Sm/G8lqhwxxCTbdraLvyvpMNVHABLJR/kOFhJQzwWM332dnTS7LWKTfJOuzWvHhIDk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\n9pHZ06R/sif7yOz/A3dV+y1a8z9ZAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x1076a6da0>" ] } ], "prompt_number": 19 } ], "metadata": {} } ] }

mit

wheeler-microfluidics/mr-box-peripheral-board.py

mr_box_peripheral_board/notebooks/Streaming plot demo.ipynb

1

7435

{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#Embedded-in-Jupyter-notebook\" data-toc-modified-id=\"Embedded-in-Jupyter-notebook-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Embedded in Jupyter notebook</a></div><div class=\"lev1 toc-item\"><a href=\"#Using-GTK\" data-toc-modified-id=\"Using-GTK-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Using GTK</a></div><div class=\"lev2 toc-item\"><a href=\"#Example-of-how-to-compress-bytes-(e.g.,-JSON)-to-bzip2\" data-toc-modified-id=\"Example-of-how-to-compress-bytes-(e.g.,-JSON)-to-bzip2-21\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Example of how to compress bytes (e.g., JSON) to bzip2</a></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Embedded in Jupyter notebook" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-08-04T05:49:33.077000Z", "start_time": "2017-08-04T05:49:31.785000Z" }, "collapsed": true }, "outputs": [], "source": [ "%matplotlib notebook\n", "\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "\n", "import time\n", "import threading\n", "\n", "import ipywidgets as ipw\n", "import numpy as np\n", "import pandas as pd\n", "\n", "\n", "fig, axis = plt.subplots()\n", "\n", "axis._get_lines()\n", "stop_event = threading.Event()\n", "np.random.seed(0)\n", "data = []\n", "\n", "\n", "def _draw():\n", " while True:\n", " if data:\n", " pd.Series(np.concatenate(data)).plot(ax=axis)\n", " fig.canvas.show()\n", " data.append(np.random.rand(10))\n", " if stop_event.wait(.5):\n", " break\n", " stop_event.clear()\n", " start.disabled = False\n", " stop.disabled = True\n", " \n", " \n", "def _start(*args):\n", " start.disabled = True\n", " stop.disabled = False\n", " thread = threading.Thread(target=_draw)\n", " thread.daemon = True\n", " thread.start()\n", "\n", "start = ipw.Button(description='Start')\n", "start.on_click(_start)\n", "stop = ipw.Button(description='Stop')\n", "stop.on_click(lambda *args: stop_event.set())\n", "clear = ipw.Button(description='Clear')\n", "def _clear(*args):\n", " axis.cla()\n", " for i in xrange(len(data)):\n", " data.pop()\n", "clear.on_click(_clear)\n", "\n", "widget = ipw.HBox([start, stop, clear])\n", "widget" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "------------------------------------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using GTK" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-08-04T08:46:58.496000Z", "start_time": "2017-08-04T08:46:53.215000Z" }, "collapsed": true }, "outputs": [], "source": [ "import gtk\n", "import gobject\n", "import threading\n", "import datetime as dt\n", "\n", "import matplotlib as mpl\n", "import matplotlib.style\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from mr_box_peripheral_board.ui.gtk.streaming_plot import StreamingPlot\n", "\n", "\n", "def _generate_data(stop_event, data_ready, data):\n", " delta_t = dt.timedelta(seconds=.1)\n", " samples_per_plot = 5\n", "\n", " while True:\n", " time_0 = dt.datetime.now()\n", " values_i = np.random.rand(samples_per_plot)\n", " absolute_times_i = pd.Series([time_0 + i * delta_t\n", " for i in xrange(len(values_i))])\n", " data_i = pd.Series(values_i, index=absolute_times_i)\n", " data.append(data_i)\n", " data_ready.set()\n", " if stop_event.wait(samples_per_plot *\n", " delta_t.total_seconds()):\n", " break\n", " \n", "with mpl.style.context('seaborn',\n", " {'image.cmap': 'gray',\n", " 'image.interpolation' : 'none'}):\n", " win = gtk.Window()\n", " win.set_default_size(800, 600)\n", " view = StreamingPlot(data_func=_generate_data)\n", " win.add(view.widget)\n", " win.connect('check-resize', lambda *args: view.on_resize())\n", " win.set_position(gtk.WIN_POS_MOUSE)\n", " win.show_all()\n", " view.fig.tight_layout()\n", " win.connect('destroy', gtk.main_quit)\n", " gobject.idle_add(view.start)\n", " \n", " def auto_close(*args):\n", " if not view.stop_event.is_set():\n", " # User did not explicitly pause the measurement. Automatically\n", " # close the measurement and continue.\n", " win.destroy()\n", " gobject.timeout_add(5000, auto_close)\n", " \n", " measurement_complete = threading.Event()\n", " \n", " view.widget.connect('destroy', lambda *args: measurement_complete.set())\n", "\n", " gtk.gdk.threads_init()\n", " gtk.gdk.threads_enter()\n", " gtk.main()\n", " gtk.gdk.threads_leave()\n", " \n", " print measurement_complete.wait()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example of how to compress bytes (e.g., JSON) to bzip2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-08-04T07:04:14.111000Z", "start_time": "2017-08-04T07:04:14.080000Z" }, "collapsed": true }, "outputs": [], "source": [ "from IPython.display import display\n", "import bz2\n", "\n", "\n", "data = pd.concat(view.data)\n", "data_json = data.to_json()\n", "data_json_bz2 = bz2.compress(data_json)\n", "data_from_json = pd.read_json(bz2.decompress(data_json_bz2), typ='series')\n", "len(data_json), len(data_json_bz2)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" }, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "12px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": true, "toc_section_display": "block", "toc_window_display": true, "widenNotebook": false } }, "nbformat": 4, "nbformat_minor": 2 }

mit

gileno/curso-citi

notebooks/fizzbuzz.ipynb

1

1807

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Digite num: 10\n" ] } ], "source": [ "num = input(\"Digite num: \")\n", "num = int(num)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Valor: 10\n", "Buzz\n", "Valor: 5\n", "Buzz\n", "Valor: 3\n", "Fizz\n", "Valor: 6\n", "Fizz\n", "Valor: 15\n", "FizzBuzz\n", "Valor: 20\n", "Buzz\n", "Valor: 12\n", "Fizz\n", "Valor: 54\n", "Fizz\n", "Valor: 65\n", "Buzz\n", "Valor: 23\n" ] } ], "source": [ "for i in range(num):\n", " valor = input(\"Valor: \")\n", " valor = int(valor)\n", " if valor % 3 == 0 and valor % 5 == 0:\n", " print(\"FizzBuzz\")\n", " elif valor % 3 == 0:\n", " print(\"Fizz\")\n", " elif valor % 5 == 0:\n", " print(\"Buzz\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

cc0-1.0

edjuaro/cuzcatlan

tests/OC_hierarchical_clustering_of_samples.ipynb

1

68929

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", "Note: if this notebook is taking too long to run, consider using the module HierarchicalClustering v7.3.4 available on gp-beta-ami.genepattern.org" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook can be used to find genes which are differentially expressed in two phenotypes. \n", "\n", "A note on some of the parameters we are using:\n", "\n", "- **gene expression**: We use a compilation of 19 RNASeq samples taken from TCGA, 9 of those are Breast Cancer primary tumors (BRCA) and 10 are matched normal tissue. Here is the GCT file which contains those HTSeq counts: \n", "https://datasets.genepattern.org/data/test_data/BRCA_minimal_60x19.gct" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "genepattern": { "param_values": { "clustering_type": "Single", "clusters_to_highlight": "3", "distance_metric": "manhattan", "file_basename": "HC_out", "input_gene_expression": "https://datasets.genepattern.org/data/test_data/BRCA_minimal_60x19.gct" }, "show_code": false, "type": "uibuilder" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "29ee3d24e6c046dc995495b703d3097b", "version_major": 2, "version_minor": 0 }, "text/html": [ "<p>Failed to display Jupyter Widget of type <code>GPUIBuilder</code>.</p>\n", "<p>\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n", " Widgets Documentation</a> for setup instructions.\n", "</p>\n", "<p>\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n", " it may mean that your frontend doesn't currently support widgets.\n", "</p>\n" ], "text/plain": [ "GPUIBuilder(description='This function performs hierarchical clustering to group samples (columns) with similar phenotypes..', function_import='hc_samples', name='Hierarchical Clustering of Samples (Columns).', params=[{'name': 'input_gene_expression', 'label': 'input_gene_expression', 'optional': False, 'default': '', 'description': 'gene expression data filename (.gct file) where rows are genes and columns are samples', 'hide': False, 'type': 'text', 'kinds': None, 'choices': []}, {'name': 'clustering_type', 'label': 'clustering_type', 'optional': False, 'default': '', 'description': 'single or consensus -- Only single is suported at the moment', 'hide': False, 'type': 'text', 'kinds': None, 'choices': []}, {'name': 'distance_metric', 'label': 'distance_metric', 'optional': False, 'default': 'pearson', 'description': 'the function to be used when comparing the distance/similarity of the columns in the input_gene_expression dataset', 'hide': False, 'type': 'text', 'kinds': None, 'choices': {'Information Coefficient': 'information_coefficient', 'City Block (Manhattan or L1-norm)': 'manhattan', 'Euclidean (L2-norm)': 'euclidean', 'Pearson Correlation': 'pearson', 'Uncentered Pearson Correlation': 'uncentered_pearson', 'Uncentered Pearson Correlation, absolute value': 'absolute_uncentered_pearson', 'Spearman Correlation': 'spearman', \"Kendall's Tau\": 'kendall', 'Cosine distance': 'cosine'}}, {'name': 'file_basename', 'label': 'file_basename', 'optional': True, 'default': 'HC_out', 'description': 'the name to use when naming output files', 'hide': False, 'type': 'text', 'kinds': None, 'choices': []}, {'name': 'clusters_to_highlight', 'label': 'clusters_to_highlight', 'optional': True, 'default': 'None', 'description': 'how many clusters to highlight in the dendrogram', 'hide': False, 'type': 'text', 'kinds': None, 'choices': []}])" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import genepattern\n", "# import cuzcatlan as cusca\n", "# import pandas as pd \n", "from cuzcatlan import hc_samples\n", "\n", "genepattern.GPUIBuilder(hc_samples, name=\"Hierarchical Clustering of Samples (Columns).\", \n", " description=\"This function performs hierarchical clustering to group samples (columns) with similar phenotypes..\",\n", " parameters={\n", " \"distance_metric\":{\n", " \"default\": \"pearson\",\n", " \"choices\":{'Information Coefficient':\"information_coefficient\",\n", " 'City Block (Manhattan or L1-norm)':'manhattan',\n", " 'Euclidean (L2-norm)':\"euclidean\",\n", " 'Pearson Correlation':\"pearson\",\n", " 'Uncentered Pearson Correlation':'uncentered_pearson',\n", " 'Uncentered Pearson Correlation, absolute value':'absolute_uncentered_pearson',\n", " 'Spearman Correlation':'spearman',\n", " \"Kendall's Tau\": 'kendall',\n", " 'Cosine distance':'cosine',\n", " }\n", " }\n", " })" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Currenty clustering_type is being ignored, only 'single' is supported.\n", "Now we will start performing hierarchical clustering, this may take a little while.\n", "----------------------------------------------------------------------\n", "The PDF of this heatmap can be downloaded here:\n" ] }, { "data": { "text/html": [ "<a href=\"HC_out.pdf\" target=\"_blank\">PDF of the heatmap</a>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----------------------------------------------------------------------\n", "The CDF which is compatible with HierarchicalClusteringViewer is here:\n" ] }, { "data": { "text/html": [ "<a href=\"HC_out.cdt\" target=\"_blank\">TXT containing the output data</a>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----------------------------------------------------------------------\n", "The ATR which is compatible with HierarchicalClusteringViewer is here:\n" ] }, { "data": { "text/html": [ "<a href=\"HC_out.atr\" target=\"_blank\">TXT containing the output data</a>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "----------------------------------------------------------------------\n" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7f2fd817ada0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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IETme+9hjj6lt27Y6efKkYmNjFRcXp8qVK0uSRo8erfr162vWrFlKSkrSxIkT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HLkCJYsWQKVSoXY2FgyTddWrFiBpqYmxMfHIyUlBXfu3GGt1GUoP6saNMfvZGRk4MiR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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2f9d6d2a58>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Done with Hierarchical Clustering!\n" ] }, { "data": { "text/plain": [ "AgglomerativeClustering(affinity=<function my_affinity_p at 0x7f2f9ddb9158>,\n", " compute_full_tree='auto', connectivity=None, linkage='average',\n", " memory=Memory(cachedir=None), n_clusters=2,\n", " pooling_func=<function mean at 0x7f2fd8135b70>)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hc_samples(input_gene_expression=\"https://datasets.genepattern.org/data/test_data/BRCA_minimal_60x19.gct\", clustering_type=\"Single\", distance_metric=\"pearson\", file_basename=\"HC_out\", clusters_to_highlight=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.6", "language": "python", "name": "python3.6" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

hmgaudecker/econ-project-templates

{{cookiecutter.project_slug}}/src_python/sandbox/few_agents.ipynb

4

3572

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "The ipython notebook is great to interactively play around with some things. Feedback is immediate and directly in front of you.\n", "\n", "Once settled on a model / specification, you can just export the code and use it in the \"normal\" workflow." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Do not delete this cell. It ensures that you can do the imports,\n", "# load datasets etc. in the same fashion as in any Python script\n", "# in the project template.\n", "\n", "%matplotlib inline\n", "\n", "import sys\n", "sys.path.insert(0, '../..')\n", "from bld.project_paths import project_paths_join as ppj" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from src.analysis.schelling import setup_agents" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "agents = setup_agents({\n", " 'n_types': 2,\n", " 'n_agents_by_type': [20, 10],\n", " 'n_neighbours': 5,\n", " 'require_same_type': 2,\n", " 'max_moves': 50\n", "})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def sim_one_round():\n", " someone_moved = False\n", " for agent in agents:\n", " old_location = agent.location\n", " # If necessary, move around until happy\n", " agent.move_until_happy(agents)\n", " if not (agent.location == old_location).all():\n", " someone_moved = True\n", " return someone_moved" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "locs_0 = pd.DataFrame(\n", " [a.location for a in agents if a.type == 0],\n", " columns=['x', 'y']\n", ")\n", "locs_1 = pd.DataFrame(\n", " [a.location for a in agents if a.type == 1],\n", " columns=['x', 'y']\n", ")\n", "ax = locs_0.plot(x='x', y='y', kind='scatter')\n", "ax.plot(locs_1['x'], locs_1['y'], \"o\", markerfacecolor=\"orange\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sim_one_round()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

ml-ieor/ml-ieor.github.io

notebooks/OutlineOct6th.ipynb

2

3976

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plan for the October 6th, 2016 Columbia Advanced Machine Learning seminar series ###\n", "\n", "\n", "\n", "** Outline **\n", "* Generative Adversarial Networks [1]\n", " * Introduction: Goal, learn (and sample) from a distribution. \n", " * Previous attempts (e.g. Restricted Boltzman machine)\n", " * Relation to noise contrastive estimation [7]\n", " * Algorithm \n", " \n", "* f Divergences as a more general framework [2]. \n", " * What is a f divergence [9]\n", " * Fenchel conjugate [10], derivation of a lower bound [11,12]\n", " * De-mystification of GANs\n", "\n", "* Alternative methods: Maximum Mean Discrepancy Optimization [6,7] based in the Kernel Two sample test from [13]\n", "\n", "* Further discussion [3,4,5]\n", "\n", " \n", " \n", "** References **\n", "\n", "Main papers: \n", "* [1][Generative Adversarial Nets](https://arxiv.org/pdf/1406.2661v1.pdf) Goodfellow et al, NIPS 2014\n", "\n", "* [2][f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization](https://arxiv.org/pdf/1606.00709v1.pdf) Nowozin et al, NIPS 2016\n", "\n", "Additional papers\n", "\n", "* [3][Improved Techniques for Training GANs](https://arxiv.org/pdf/1606.03498v1.pdf) Salimans et al, 2016, ArXiv\n", "* [4][On Distinguishability Criteria for Estimating Generative Models](https://arxiv.org/pdf/1412.6515.pdf) Goodfellow, 2015, ICLR Workshop\n", "* [5][Adversarially Learned Inference](https://arxiv.org/pdf/1606.00704v1.pdf) Dumolin et al, 2016, ArXiv\n", "* [6][Training generative neural networks via Maximum Mean Discrepancy optimization](https://arxiv.org/pdf/1505.03906v1.pdf) Dziugaite et al, UAI 2015\n", "* [7][Generative moment matching networks](https://arxiv.org/pdf/1502.02761v1.pdf) Li et al, ICML 2015\n", "\n", "\n", "Related ideas from the 'oldies'\n", "* [8][Noise-contrastive estimation: A new estimation principle for unnormalized statistical models](http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf) Gutmann and Hyvärinen, ICML 2010\n", "* [9][A general class of coefficients of divergence of one distribution from another.\n", "JRS](http://www.zabaras.com/Courses/BayesianComputing/Papers/fetch.pdf) Ali and Silvey, Journal of the Royal Statistical Society. Series B (Methodological) (1966): 131-142.\n", "* [10] [Convex Analysis](http://www.convexoptimization.com/TOOLS/ConvexAnalysis.pdf) R. Tyrell Rockafellar Princeton University Press.\n", "* [11] [Estimating divergence functionals and the likelihood ratio\n", "by convex risk minimization](http://papers.nips.cc/paper/3193-estimating-divergence-functionals-and-the-likelihood-ratio-by-penalized-convex-risk-minimization.pdf) Nguyen et al, 2008 NIPS\n", "* [12] [Random Variables, Monotone Relations and Convex Analysis](http://www.math.washington.edu/~rtr/papers/rtr226-Relations.pdf) Rockafellar and Royset, 2013\n", "* [13] [A Kernel Two-Sample Test](http://www.jmlr.org/papers/volume13/gretton12a/gretton12a.pdf) Gretton et al, Journal of Machine Learning Research (2012)\n", "\n", "Upcoming NIPS workshop\n", "* [Here](https://sites.google.com/site/nips2016adversarial/)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

tbreloff/ExamplePlots.jl

notebooks/scratch/contours.ipynb

2

42821

{ "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Subplot{Plots.PyPlotPackage() p=2 n=2}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using Plots, Distributions\n", "pyplot(leg=false, size=(600,200))\n", "xx=linspace(-1,1,100)\n", "yy=xx\n", "ff(x,y)=pdf(MvNormal([0.,0],[3. 2;2 3]),[x,y])\n", "gg=[ff(x,y)::Float64 for x=xx,y=yy]\n", "qlevels=[0,.25,.5,.75,0.8,0.9,0.95,0.975,0.99,1]\n", "levels=quantile(vec(gg),qlevels)\n", "\n", "p1 = contour(xx,yy,gg,fill=true,levels=levels,c=:reds)\n", "p2 = contour(xx,yy,gg,fill=true,levels=levels,c=ColorGradient(:reds,qlevels))\n", "subplot(p1, p2, link=true)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "contour(xx,yy,gg,fill=true,levels=levels,c=ColorGradient(:heat,[0,0.1,0.8,1]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "contour(xx,yy,gg,fill=true,levels=levels,c=ColorGradient(:heat,[0,0.8,0.95,1]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ColorGradient(:heat,[0,0.01,0.95,1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Plots; pyplot()\n", "surface()\n", "x = y = 0:.1:1.\n", "z = exp(x) * cos(y)'\n", "surface!(x, y, z)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Plots; gadfly()\n", "default(size=(500,300))\n", "x = linspace(0,10,100)\n", "y = x / 2\n", "plot(y, x, (x,y) -> sin(y)+cos(x), levels=-1:.1:1, fill=true, fc=:heat)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot(x, y, (x,y) -> sin(x)+cos(y), nlevels=10, fill=true, c=ColorGradient(:heat,alpha=0.5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Vector[zeros(3), ones(3)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Plots; qwt()\n", "subplot(Vector[zeros(3), ones(3)],n=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sp = current()\n", "p = sp.plts[2]\n", "Plots.convertSeriesIndex(p, 2)\n", "sp.initargs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "push!(current().plts[1], rand(2));" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Plots\n", "x = [\"x1\", \"x2\"]\n", "y = [0.2, 0.7]\n", "bar(x, y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Plots; using Compose\n", "p1 = plot(rand(10))\n", "p2 = scatter(rand(100))\n", "compose(context(),\n", " (context(0.6,0,0.4,0.4), Gadfly.render(p2.o[2])),\n", " (context(0,0,1,1), Gadfly.render(p1.o[2])))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Plots\n", "import Contour\n", "default(size=(500,300))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n = 100\n", "x = sort(randn(n)); y = sort(randn(n))\n", "cs = Contour.contours(x, y, x * y', 5)\n", "@show typeof(cs) length(cs)\n", "#xys = [Contour.coordinates(c.lines) for c in cs]\n", "for clevel in cs\n", " @show length(clevel.lines)\n", " #for (x,y) in Contour.coordinates(clevel.lines[1])\n", " # @show x y\n", " #end\n", "end" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x, y = Contour.coordinates(cs[2].lines[2])\n", "@show x y\n", "plot(x,y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Pkg.add(\"GeometricalPredicates\")\n", "Pkg.clone(\"https://github.com/JuliaGeometry/VoronoiDelaunay.jl\")\n", "Pkg.add(\"Contour\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Plots; gadfly()\n", "default(size=(500,300))\n", "n = 100\n", "srand(123)\n", "x = randn(n)*3\n", "y = randn(n)*3\n", "z = Float64[sin(x[i]) + cos(y[i]) for i in 1:n]\n", "scatter(x,y,z=z,c=:heat)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using VoronoiDelaunay\n", "tess = DelaunayTessellation(n)\n", "tmin, tmax = min_coord, max_coord\n", "twidth = tmax - tmin\n", "function squash(a)\n", " amin, amax = extrema(a)\n", " v = similar(a)\n", " for i in eachindex(a)\n", " v[i] = tmin + twidth * (a[i] - amin) / (amax - amin)\n", " end\n", " v\n", "end\n", "function zippoints(x, y)\n", " x, y = squash(x), squash(y)\n", " Point2D[Point(x[i], y[i]) for i in eachindex(x)]\n", "end\n", "function zippoints(x, y, z)\n", " x, y, z = squash(x), squash(y), squash(z)\n", " Point3D[Point(x[i], y[i], z[i]) for i in eachindex(x)]\n", "end" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = zippoints(x, y)\n", "push!(tess, a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for tri in tess\n", " println(tri)\n", "end" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tess" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.2", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

quantumlib/ReCirq

docs/qaoa/binary_paintshop.ipynb

1

21172

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "SzKwuqYESWwm" }, "source": [ "##### Copyright 2021 The Cirq Developers" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "id": "4yPUsdJxSXFq" }, "outputs": [], "source": [ "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "J3Ov8gwSTnHB" }, "source": [ "# Binary Paintshop Problem with Quantum Approximate Optimization Algorithm" ] }, { "cell_type": "markdown", "metadata": { "id": "zC1qlUJoSXhm" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://quantumai.google/cirq/experiments/qaoa/binary_paintshop>\"><img src=\"https://quantumai.google/site-assets/images/buttons/quantumai_logo_1x.png\" />View on QuantumAI</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/quantumlib/ReCirq/blob/master/docs/qaoa/binary_paintshop.ipynb\"><img src=\"https://quantumai.google/site-assets/images/buttons/colab_logo_1x.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/quantumlib/ReCirq/blob/master/docs/qaoa/binary_paintshop\"><img src=\"https://quantumai.google/site-assets/images/buttons/github_logo_1x.png\" />View source on GitHub</a>\n", " </td>\n", " <td>\n", " <a href=\"https://storage.googleapis.com/tensorflow_docs/ReCirq/docs/qaoa/binary_paintshop\"><img src=\"https://quantumai.google/site-assets/images/buttons/download_icon_1x.png\" />Download notebook</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6lnV5PlLnjLk" }, "outputs": [], "source": [ "from typing import Sequence, Tuple\n", "import numpy as np\n", "\n", "try:\n", " import cirq\n", "except ImportError:\n", " print(\"installing cirq...\")\n", " !pip install --quiet cirq\n", " print(\"installed cirq.\")\n", " import cirq\n", "\n", "import cirq_ionq as ionq" ] }, { "cell_type": "markdown", "metadata": { "id": "LlhpXxx8HtqX" }, "source": [ "## Binary Paintshop Problem\n", "\n", "\n", "Assume an automotive paint shop and a random, but fixed sequence of 2*n cars. Each car has a identical partner that only differs in the color it has to be painted." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kvMfI5pPoJ-N" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4 2 9 0 0 4 3 7 5 6 5 3 8 9 8 7 1 2 6 1]\n" ] } ], "source": [ "CAR_PAIR_COUNT = 10\n", "car_sequence = np.random.permutation([x for x in range(CAR_PAIR_COUNT)] * 2)\n", "print(car_sequence)" ] }, { "cell_type": "markdown", "metadata": { "id": "YfL2r-WWOXrD" }, "source": [ " The task is to paint the cars such that in the end for every pair of cars one is painted in red and the other in blue. The objective of the following minimization procedure is to minimize the number of color changes in the paintshop. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Q3UfXJND3qF1" }, "outputs": [], "source": [ "def color_changes(paint_bitstring: Sequence[int], car_sequence: Sequence[int]) -> int:\n", " \"\"\"Count the number of times the color changes if the robots\n", " paint each car in car_sequence according to paint_bitstring,\n", " which notes the color for the first car in each pair.\n", "\n", " Args:\n", " paint_bitstring: A sequence that determines the color to\n", " paint the first car in pair i. For example, 0 for blue\n", " and nonzero for red.\n", " car_sequence: A sequence that determines which cars are\n", " paired together\n", "\n", " Returns:\n", " Count of the number of times the robots change the color\n", " \"\"\"\n", " color_sequence = []\n", " painted_once = set()\n", " for car in car_sequence:\n", " if car in painted_once:\n", " # paint the other color for the second car in the pair\n", " color_sequence.append(not paint_bitstring[car])\n", " else:\n", " # paint the noted color for the first car in the pair\n", " color_sequence.append(paint_bitstring[car])\n", " painted_once.add(car)\n", " paint_change_counter = 0\n", " # count the number of times two adjacent cars differ in color\n", " for color0, color1 in zip(color_sequence, color_sequence[1:]):\n", " if color0 != color1:\n", " paint_change_counter += 1\n", " return paint_change_counter" ] }, { "cell_type": "markdown", "metadata": { "id": "xF4t6p5NOhCE" }, "source": [ " If two consecutive cars in the sequence are painted in different colors the robots have to rinse the old color, clean the nozzles and flush in the new color. This color change procedure costs time, paint, water and ultimately costs money, which is why we want to minimize the number of color changes. However, a rearrangement of the car sequence is not at our disposal (because of restrictions that are posed by the remainig manufacturing processes), but we can decide once we reach the first car of each car pair which color to paint the pair first. When we have chosen the color for the first car the other car has to be painted in the other respective color. Obvious generalizations exist, for example more than two colors and groups of cars with more than 2 cars where it is permissible to exchange colors, however for demonstration purposes it suffices to consider the here presented binary version of the paintshop problem. It is NP-hard to solve the binary paintshop problem exactly as well as approximately with an arbitrary performance guarantee. A performance guarantee in this context would be a proof that an approximation algorithm never gives us a solution with a number of color changes that is more than some factor times the optimal number of color changes. This is the situation where substantial quantum speedup can be assumed (c.f. [Quantum Computing in the NISQ era and beyond](https://arxiv.org/abs/1801.00862)). The quantum algorithm presented here can deliver, on average, better solutions than all polynomial runtime heuristics specifically developed for the paintshop problem in constant time (constant query complexity) (c.f. [Beating classical heuristics for the binary paint shop problem with the quantum approximate optimization algorithm](https://arxiv.org/abs/2011.03403))." ] }, { "cell_type": "markdown", "metadata": { "id": "5c6HZHMsUBAc" }, "source": [ "## Spin Glass \n", "To be able to solve the binary paintshop problem with the Quantum Approximate Optimization Algorithm (QAOA) we need to translate the problem to a spin glass problem. Interestingly, that is possible with no spatial overhead, i.e. the spin glass has as many spins as the sequence has car pairs. The state of every spin represents the color we paint the respective first car in the seqence of every car pair. Every second car is painted with the repsective other color. The interactions of the spin glass can be deduced proceeding through the fixed car sequence: If two cars are adjacent to each other and both of them are either the first or the second car in their respective car pairs we can add a ferromagnetic interaction to the spin glass in order to penalize the color change between these two cars. If two cars are next to each other and one of the cars is the first and the other the second in their respective car pairs we have to add a antiferromagnetic interaction to the spin glass in order to penalize the color change because in this case the color for the car that is the second car in its car pair is exactly the opposite. All color changes in the car sequence are equivalent which is why we have equal magnitude ferromagnetic and antiferromagnetic interactions and additionally we choose unit magnitude interactions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hr7TT_nq5aOP" }, "outputs": [], "source": [ "def spin_glass(car_sequence: Sequence[int]) -> Sequence[Tuple[int, int, int]]:\n", " \"\"\"Assign interactions between adjacent cars.\n", "\n", " Assign a ferromagnetic(1) interaction if both elements of the pair are\n", " the first/second in their respective pairs. Otheriwse, assign an antiferromagnetic(-1)\n", " interaction. Yield a tuple with the two paired cars followed by the\n", " chosen interaction.\n", " \"\"\"\n", " ferromagnetic = -1\n", " antiferromagnetic = 1\n", " appeared_already = set()\n", " for car0, car1 in zip(car_sequence, car_sequence[1:]):\n", " if car0 == car1:\n", " continue\n", " if car0 in appeared_already:\n", " appeared_already.add(car0)\n", " if car1 in appeared_already:\n", " yield car0, car1, ferromagnetic\n", " else:\n", " yield car0, car1, antiferromagnetic\n", " else:\n", " appeared_already.add(car0)\n", " if car1 in appeared_already:\n", " yield car0, car1, antiferromagnetic\n", " else:\n", " yield car0, car1, ferromagnetic" ] }, { "cell_type": "markdown", "metadata": { "id": "6x3QEHTrYGyM" }, "source": [ "## Quantum Approximate Optimization Algorithm\n", "We want to execute a one block version of the QAOA circuit for the binary\n", "paintshop instance with p = 1 on a trapped-ion\n", "quantum computer of IonQ. This device is composed of 11 fully connected qubits with average single- and two-qubit fidelities of 99.5% and 97.5% respectively ([Benchmarking an 11-qubit quantum computer](https://www.nature.com/articles/s41467-019-13534-2)).\n", "As most available quantum hardware, trapped ion\n", "quantum computers only allow the application of gates\n", "from a restricted native gate set predetermined by the\n", "physics of the quantum processor. To execute an arbitrary gate, compilation of the desired gate into available gates is required. For trapped ions, a generic native\n", "gate set consists of a parameterized two-qubit rotation, the Molmer Sorensen gate,\n", "$R_\\mathrm{XX}(\\alpha)=\\mathrm{exp}[-\\mathrm{i}\\alpha \\sigma_\\mathrm{x}^{(i)}\\sigma_\\mathrm{x}^{(j)}/2]$ and a parametrized single qubit rotation:\n", "\n", "$R(\\theta,\\phi)=\\begin{pmatrix}\n", "\\cos{(\\theta/2)} & -\\mathrm{i}\\mathrm{e}^{-\\mathrm{i}\\phi}\\sin{(\\theta/2)} \\\\-\\mathrm{i}\\mathrm{e}^{\\mathrm{i}\\phi}\\sin{(\\theta/2)} & \\cos{(\\theta/2)} \n", "\\end{pmatrix}$\n", "\n", "QAOA circuits employ parametrized two body $\\sigma_z$ rotations, $R_\\mathrm{ZZ}(\\gamma)=\\mathrm{exp}[-i\\gamma \\sigma_\\mathrm{z}^{(i)}\\sigma_\\mathrm{z}^{(j)}]$. To circumvent a compilation overhead and optimally leverage the Ion Trap, we inject pairs of Hadamard gates $H H^{\\dagger} = 1$ for every qubit in between the two body $\\sigma_z$ rotations. This means we are able to formulate the phase separator entirely with Molmer Sorensen gates. To support this, the QAOA circuit starts in the state where all qubits are in the groundstate $\\left| 0\\right\\rangle$ instead of the superposition of all computational basis states $\\left| + \\right\\rangle$," ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uensYoJ1tUpB" }, "outputs": [], "source": [ "def phase_separator(\n", " gamma: float, qubit_register: Sequence[cirq.Qid], car_sequence: Sequence[int]\n", ") -> Sequence[cirq.Operation]:\n", " \"\"\"Yield a sequence of Molmer Sorensen gates to implement a\n", " phase separator over the ferromagnetic/antiferromagnetic\n", " interactions between adjacent cars, as defined by spin_glass\n", " \"\"\"\n", " for car_pair0, car_pair1, interaction in spin_glass(car_sequence):\n", " yield cirq.ms(interaction * gamma).on(\n", " qubit_register[car_pair0], qubit_register[car_pair1]\n", " )\n", "\n", "\n", "qubit_register = cirq.LineQubit.range(CAR_PAIR_COUNT)\n", "circuit = cirq.Circuit([phase_separator(0.1, qubit_register, car_sequence)])" ] }, { "cell_type": "markdown", "metadata": { "id": "5dnVhwjUk3GT" }, "source": [ "Because we replaced the two body $\\sigma_z$ rotations with Molmer Sorensen gates we also have to adjust the mixer slightly to account for the injected Hadamard gates." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WSo8TGlOwgko" }, "outputs": [], "source": [ "def mixer(beta: float, qubit_register: Sequence[cirq.Qid]) -> Iterator[cirq.Operation]:\n", " \"\"\"Yield a QAOA mixer of RX gates, modified by adding RY gates first,\n", " to account for the additional Hadamard gates.\n", " \"\"\"\n", " yield cirq.ry(np.pi / 2).on_each(qubit_register)\n", " yield cirq.rx(beta - np.pi).on_each(qubit_register)" ] }, { "cell_type": "markdown", "metadata": { "id": "pQps4f3qLJ94" }, "source": [ "To find the right parameters for the QAOA circuit, we have to assess the quality of the solutions for a given set of parameters. To this end, we execute the QAOA circuit with fixed parameters 100 times and calculate the average number of color changes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3BkHML3qxcC4" }, "outputs": [], "source": [ "def average_color_changes(\n", " parameters: Tuple[float, float],\n", " qubit_register: Sequence[cirq.Qid],\n", " car_sequence: Sequence[int],\n", ") -> float:\n", " \"\"\"Calculate the average number of color changes over all measurements of\n", " the QAOA circuit, aross `repetitions` many runs, for provided parameters\n", " beta and gamma.\n", "\n", " Args:\n", " parameters: tuple of (`beta`, `gamma`), the two parameters for the QAOA circuit\n", " qubit_register: A sequence of qubits for the circuit to use.\n", " car_sequence: A sequence that determines which cars are paired together.\n", "\n", " Returns:\n", " A float average number of color changes over all measurements.\n", " \"\"\"\n", " beta, gamma = parameters\n", " repetitions = 100\n", " circuit = cirq.Circuit()\n", " circuit.append(phase_separator(gamma, qubit_register, car_sequence))\n", " circuit.append(mixer(beta, qubit_register))\n", " circuit.append(cirq.measure(*qubit_register, key=\"z\"))\n", " results = service.run(circuit, repetitions=repetitions)\n", " avg_cc = 0\n", " for paint_bitstring in results.measurements[\"z\"]:\n", " avg_cc += color_changes(paint_bitstring, car_sequence) / repetitions\n", " return avg_cc" ] }, { "cell_type": "markdown", "metadata": { "id": "d2ydfRBrLrnl" }, "source": [ "We optimize the average number of color changes by adjusting the parameters with scipy.optimzes function minimize. The results of these optimsation runs strongly depend on the random starting values we choose for the parameters, which is why we restart the optimization procedure for different starting parameters 10 times and take the best performing optimized parameters." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xXPCgWMaSPqJ" }, "outputs": [ { "data": { "text/plain": [ "7.840000000000001" ] }, "execution_count": 17, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "from scipy.optimize import minimize\n", "\n", "service = cirq.Simulator()\n", "beta, gamma = np.random.rand(2)\n", "average_cc = average_color_changes([beta, gamma], qubit_register, car_sequence)\n", "optimization_function = lambda x: average_color_changes(x, qubit_register, car_sequence)\n", "for _ in range(10):\n", " initial_guess = np.random.rand(2)\n", " optimization_result = minimize(\n", " optimization_function, initial_guess, method=\"SLSQP\", options={\"eps\": 0.1}\n", " )\n", " average_cc_temp = average_color_changes(\n", " optimization_result.x, qubit_register, car_sequence\n", " )\n", " if average_cc > average_cc_temp:\n", " beta, gamma = optimization_result.x\n", " average_cc = average_cc_temp\n", "average_cc" ] }, { "cell_type": "markdown", "metadata": { "id": "y0tJ2GErNa7w" }, "source": [ "Note here that the structure of the problem graphs of the binary paintshop problem allow for an alternative technique to come up with good parameters independent of the specifics of the respective instance of the problem: [Training the quantum approximate optimization algorithm without access to a quantum processing unit](https://iopscience.iop.org/article/10.1088/2058-9565/ab8c2b)" ] }, { "cell_type": "markdown", "metadata": { "id": "WoBQG2f2L8HC" }, "source": [ "Once the parameters are optimised, we execute the optimised QAOA circuit 100 times and output the solution with the least color changes.\n", "Please replace `<your key>` with your IonQ API key and `<remote host>` with the API endpoint." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lfUmwcxdo79w" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The minimal number of color changes found by level-1 QAOA is: 6\n", "The first cars of the car pairs have to be painted with [0 1 0 0 0 0 0 0 0 0], with index i representing the paint of the first car on pair i.\n", "The other car in pair i is painted with the second color.\n" ] } ], "source": [ "repetitions = 100\n", "circuit = cirq.Circuit()\n", "circuit.append(phase_separator(gamma, qubit_register, car_sequence))\n", "circuit.append(mixer(beta, qubit_register))\n", "circuit.append(cirq.measure(*qubit_register, key=\"z\"))\n", "service = ionq.Service(\n", " remote_host=\"<remote host>\", api_key=\"<your key>\", default_target=\"qpu\"\n", ")\n", "results = service.run(circuit, repetitions=repetitions)\n", "best_result = CAR_PAIR_COUNT\n", "for paint_bitstring in results.measurements[\"z\"]:\n", " result = color_changes(paint_bitstring, car_sequence)\n", " if result < best_result:\n", " best_result = result\n", " best_paint_bitstring = paint_bitstring\n", "print(f\"The minimal number of color changes found by level-1 QAOA is: {best_result}\")\n", "print(\n", " f\"The car pairs have to be painted according to {best_paint_bitstring}, with index i representing the paint of the first car of pair i.\"\n", ")\n", "print(f\" The other car in pair i is painted the second color.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ngLJ66wRPuh3" }, "source": [ "Note here, that in a future production environment the optimization and execution phase of the QAOA should be merged, i.e. we output in the end the best performing sample gathered during the training phase of the QAOA circuit. For educational purposes, we separated here the training and the evaluation phase of the QAOA." ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "binary_paintshop.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

caspar/KentLab

File Selector.ipynb

1

22748

{ "cells": [ { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Graham's Computer or Lab: G/L? g\n", "X coordinate of sample 21\n", "Y coordinate of sample 2\n", "Current through sample (mA) 1\n", "enter: 1 for single graph, 2 for two comparative trial graphs, 3 for range of trials \n", "4 for separated single graph, 5 for Quad Split\n", "Which option listed above? 5\n", "Apply filter, Y/N? y\n", "trial # 5\n", "do a fit of the data, Y/N? y\n", "Logrithmic Fit, Y/N? (default is Linear) n\n", "[ 0.101 17.682]\n", "Increasing Field, Increasing Resistance equation: Resistance = 0.101(Ohms/mTesla)*Magnetic Field + 17.682 Ohms\n", "[ -0.141 17.692]\n", "Increasing Field, Decreasing Resistance equation: Resistance = -0.141(Ohms/mTesla)*Magnetic Field + 17.692 Ohms\n", "[ 0.097 17.683]\n", "Decreasing Field, Decreasing Resistance equation: Resistance = 0.097(Ohms/mTesla)*Magnetic Field + 17.683 Ohms\n", "[ -0.157 17.693]\n", "Decreasing Field, Decreasing Resistance equation: Resistance = -0.157(Ohms/mTesla)*Magnetic Field + 17.693 Ohms\n", "Continue with this sample, Y/N? \n", "Change sample, Y/N? \n", "Good bye\n" ] } ], "source": [ "#Graham Jordan Prog with parts by Caspar Lant\n", "import sys\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import math\n", "from datetime import datetime\n", "import os\n", " #naming conventions\n", "np.set_printoptions(precision=3)\n", "Dict = {\"y\":1,\"Y\":1,\"n\":0,\"N\":0,\"\":0,\"g\":1,\"G\":1,\"l\":0,\"L\":0,'1':1}\n", "Term = {1:'Increasing Field, Increasing Resistance',2:'Increasing Field, Decreasing Resistance',3:'Decreasing Field, Decreasing Resistance',4:'Decreasing Field, Decreasing Resistance'}\n", "color = {1:'red',2:'green',3:'blue',4:'orange',5:'black'}\n", "color2 = {1:'red',2:'blue'}\n", "evens = {\"1\":1,\"2\":1,\"3\":2,\"4\":2}\n", "direction = {1:'Up',2:'Down'}\n", "Local = Dict[raw_input(\"Graham's Computer or Lab: G/L? \")]\n", "ResTitle = {1:\"Log of Resistance (ln[ohms])\",0:\"Resistance (Ohms)\"}\n", "if Local == 1:\n", " Location = \"C:\\Users\\Graham\\OneDrive\\LabData\\KentLab\\data\"\n", "elif Local == 0:\n", " Location = \"C:\\Users\\KentLab\\Desktop\\caspar\\data\"\n", "sample = 1\n", "while sample == 1:\n", " title = \"Magnetoresistance in F5 Nanopillar Sample \"\n", " cont = 1\n", " DyeX = raw_input(\"X coordinate of sample \")\n", " DyeY = raw_input(\"Y coordinate of sample \")\n", " Current = raw_input(\"Current through sample (mA) \")\n", " while cont == 1:\n", " compare = 0\n", " print(\"enter: 1 for single graph, 2 for two comparative trial graphs, 3 for range of trials \")\n", " print(\"4 for separated single graph, 5 for Quad Split\")\n", " compare = int(raw_input(\"Which option listed above? \")) #compare is the variable used to determine the process for the data\n", " #if compare == 5 or compare == 4:\n", " \n", "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", " #option 1: Single Graph Print\n", " if compare == 1:\n", " #Data Grab\n", " j = raw_input(\"trial # \")\n", " field, resistance = np.loadtxt((Location+\"\\Sample_F5_\"+DyeX+\"_\"+DyeY+\"_\"+Current+\"mA_trial\"+j+\".txt\"), skiprows=0 , unpack=True, delimiter='\t')\n", " #separator and plotter (written by Caspar)\n", " color = []\n", " array = []\n", " i = 0\n", " while (i < len(field) - 1):\n", " if (float(field[i]) >= float(field[i+1])):\n", " color = 'blue'\n", " else:\n", " color = 'red'\n", " \n", " fig = plt.plot(field[i], (resistance[i] + math.fabs(float(i)/75000.00)), '.', color = color)\n", " i = i+1\n", " #plot title\n", " plt.ylabel(\"Resistance (Ohms)\");\n", " plt.xlabel(\"Magnetic Field Strength (Tesla)\");\n", " plt.title((title+DyeX+' by '+DyeY+', Trial '+str(j)+', '+str(Current)+'mA'))\n", " plt.show ()\n", "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", " #option 2: Comparative Graph Print For 2 Trials \n", " elif compare == 2 :\n", " #data grab\n", " j = int(raw_input(\"first trial # \"))\n", " a = int(raw_input(\"second trial # \"))\n", " field, resistance = np.loadtxt((Location+\"\\Sample_F5_\"+DyeX+\"_\"+DyeY+\"_\"+Current+\"mA_trial\"+str(j)+\".txt\"), skiprows=0 , unpack=True, delimiter='\t')\n", " plt.subplot(2,1,1)\n", " #separator and plotter for 1st trial (written by Caspar)\n", " color = []\n", " array = []\n", " i = 0\n", " \n", " while (i < len(field) - 1):\n", " if (float(field[i]) >= float(field[i+1])):\n", " color = 'blue'\n", " else:\n", " color = 'red'\n", " fig = plt.plot(field[i], (resistance[i] + math.fabs(float(i)/75000.00)), '.', color = color)\n", " i = i+1\n", " #Plot titles\n", " plt.ylabel(\"Resistance (Ohms)\");\n", " plt.xlabel(\"Magnetic Field Strength (Tesla)\");\n", " plt.title((title+DyeX+' by '+DyeY+', Trial '+str(j)+', '+str(Current)+'mA'))\n", " \n", " field, resistance = np.loadtxt((Location+\"\\Sample_F5_\"+DyeX+\"_\"+DyeY+\"_\"+Current+\"mA_trial\"+str(a)+\".txt\"), skiprows=0 , unpack=True, delimiter='\t')\n", " plt.subplot(2,1,2)\n", " #Separator and plotter for 2nd trial (written by Caspar)\n", " color = []\n", " array = []\n", " i = 0\n", " \n", " while (i < len(field) - 1):\n", " if (float(field[i]) >= float(field[i+1])):\n", " color = 'blue'\n", " else:\n", " color = 'red'\n", " fig = plt.plot(field[i], (resistance[i] + math.fabs(float(i)/75000.00)), '.', color = color)\n", " i = i+1\n", " #Plot titles\n", " plt.ylabel(\"Resistance (Ohms)\");\n", " plt.xlabel(\"Magnetic Field Strength (Tesla)\");\n", " plt.title((title+DyeX+' by '+DyeY+', Trial '+str(a)+', '+str(Current)+'mA'))\n", " plt.show ()\n", "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", " #option 3: Print Range of Trials (from _ to _)\n", " elif compare == 3:\n", " j = int(raw_input(\"starting trial #\"))\n", " first = j\n", " last = int(raw_input(\"Last Trial #\"))\n", " \n", " while j <= last:\n", " \n", " # Data grab\n", " Trial = str(j);\n", " field, resistance = np.loadtxt((Location+\"\\Sample_F5_\"+DyeX+\"_\"+DyeY+\"_\"+Current+\"mA_trial\"+str(j)+\".txt\"), skiprows=0 , unpack=True, delimiter='\t');\n", " PlotNum = 2*(j-first)+1\n", " PlotH = 2*(last-first)+1\n", " plt.subplot(PlotH,1,PlotNum)\n", " #Separator and plotter (writen by Caspar)\n", " color = []\n", " array = []\n", " i = 0\n", " \n", " while (i < len(field) - 1):\n", " if (float(field[i]) >= float(field[i+1])):\n", " color = 'blue'\n", " else:\n", " color = 'red'\n", " fig = plt.plot(field[i], (resistance[i] + math.fabs(float(i)/75000.00)), '.', color = color)\n", " i = i+1\n", " \n", " #Plot titles\n", " plt.ylabel(\"Resistance (Ohms)\");\n", " plt.xlabel(\"Magnetic Field Strength (Tesla)\");\n", " plt.title((title+DyeX+' by '+DyeY+', Trial '+str(j)+', '+str(Current)+'mA'));\n", " j = j+1\n", " \n", " plt.show()\n", "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", " #option 4: Separate Plot into Increasing Field and Decreasing Field\n", " elif compare == 4 or compare == 5:\n", " philter = Dict[raw_input(\"Apply filter, Y/N? \")]\n", " #Data Grab\n", " j = raw_input(\"trial # \")\n", " field, resistance = np.loadtxt((Location+\"\\Sample_F5_\"+DyeX+\"_\"+DyeY+\"_\"+Current+\"mA_trial\"+j+\".txt\"), skiprows=0 , unpack=True, delimiter='\t')\n", " i = 0\n", " UpField = []\n", " DownField = []\n", " UpRes = []\n", " DownRes = []\n", " while (i < len(field)-1):\n", " #Seperator and filter (written by Graham)\n", " sig = np.std(resistance[(i-10):(i+10)])\n", " if philter == 1 and float(Current) < 1 and (abs(resistance[i]-resistance[i+1]) > (0.5*sig)):#Due to the lower currents having more variable data points (i.e. more noise) a stricter filter is applied of half a standard deviation\n", " i=i+1\n", " elif philter == 1 and float(Current) < 1 and (abs(resistance[i]-resistance[i-1]) > (0.5*sig)):#Due to the lower currents having more variable data points (i.e. more noise) a stricter filter is applied of half a standard deviation\n", " i=i+1\n", " elif philter == 1 and (abs(resistance[i]-resistance[i+1]) > (sig)): #should exclude any data point that is of a distance larger than the standard deviation from the next point\n", " i=i+1\n", " elif philter == 1 and (abs(resistance[i]-resistance[i-1]) > (sig)): #should exclude any data point that is of a distance larger than the standard deviation from the next point\n", " i=i+1\n", " elif (field[i] < field[i+1] ): # While the magnetic field is increasing the prog will place the values in the separate arrays\n", " UpField.extend([field[i]])\n", " UpRes.extend([resistance[i]])\n", " i = i + 1\n", " else : #Will place all remaining data points (the decreasing mag field side) in a separate array\n", " DownField.extend([field[i]]) \n", " DownRes.extend([resistance[i]])\n", " i = i +1\n", " #Plots and plot titles\n", " if compare == 4:\n", " #Does an nth degree polynomial fit of the data (Still under construction, has trouble with very large n and with some samples that were not filtered by kiethly)\n", " Poly = Dict[raw_input(\"do a polynomial fit, Y/N? \")]\n", " if Poly == 1:\n", " n = int(raw_input(\"What degree polynomial? \"))\n", " a=1\n", " UpCoeff = np.polyfit(UpField,UpRes,n)\n", " DownCoeff = np.polyfit(DownField,DownRes,n)\n", " while a <= 2:\n", " x = eval(direction[a]+'Field')\n", " y = eval(direction[a]+'Res')\n", " firstx = x[0]\n", " lastx = x[-1]\n", " step = 0.001\n", " if firstx > lastx:\n", " xfit = np.arange(lastx,firstx,step)\n", " else:\n", " xfit = np.arange(firstx,lastx,step)\n", " i = 0\n", " yfit = 0\n", " while i <= n:\n", " yfit = yfit+(xfit**(n-i))*eval(direction[a]+'Coeff')[i]\n", " i = i+1\n", " plt.subplot(2,1,a)\n", " if a == 1:\n", " plt.title((title+DyeX+' by '+DyeY+', Trial '+str(j)+', '+str(Current)+'mA increasing field'))\n", " else:\n", " plt.title((title+DyeX+' by '+DyeY+', Trial '+str(j)+', '+str(Current)+'mA decreasing field'))\n", " plt.plot(xfit,yfit,'b-',color = color[5], label=('Polynomial fit '+str(a)+ ' degree '+str(n)))\n", " plt.plot(x,y, '.', color = color2[a])\n", " a = a+1\n", " plt.legend()\n", " print('Note: coefficients go in the order of C1x^n + C2x^(n-1) + ...')\n", " print('Increasing Field Polynomial fit Coefficients: '+str(UpCoeff))\n", " print('Decreasing Field Polynomial fit Coefficients: '+str(DownCoeff))\n", " plt.show()\n", " else:\n", " plt.subplot(2,1,1)\n", " plt.plot(UpField, UpRes, '.', color='red')\n", " plt.ylabel(\"Resistance (Ohms)\");\n", " plt.xlabel(\"Magnetic Field Strength (Tesla)\");\n", " plt.title((title+DyeX+' by '+DyeY+', Trial '+str(j)+', '+str(Current)+'mA increasing field'));\n", " plt.subplot(2,1,2)\n", " plt.plot(DownField, DownRes, '.', color='blue')\n", " plt.ylabel(\"Resistance (Ohms)\");\n", " plt.xlabel(\"Magnetic Field Strength (Tesla)\");\n", " plt.title((title+DyeX+' by '+DyeY+', Trial '+str(j)+', '+str(Current)+'mA decreasing field'));\n", " plt.show()\n", "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", " #Option 5: Splits data into 4 quadrants as listed below\n", " elif compare == 5:\n", " s = max(UpRes)\n", " s = UpRes.index(s)\n", " Quad1 = [] #increasing field part 1\n", " Res1 = []\n", " Quad2 = [] #increasing field part 2\n", " Res2 = []\n", " Quad3 = [] #decreasing field part 1\n", " Res3 = []\n", " Quad4 = [] #decreasing field part 2\n", " Res4 = []\n", " #divides the increasing magnetic field plot into increasing resistance and decreasing resistance\n", " i = 0\n", " while (i < s ): \n", " Quad1.extend([UpField[i]])\n", " Res1.extend([UpRes[i]])\n", " i = i + 1\n", " while (i < len(UpRes)-1): \n", " Quad2.extend([UpField[i]])\n", " Res2.extend([UpRes[i]])\n", " i = i +1\n", " g = max(DownRes)\n", " g = DownRes.index(g)\n", " #divides decreasing magnetic field into increasing resistance and decreasing resistance\n", " i = 0\n", " while (i < g ): \n", " Quad4.extend([DownField[i]])\n", " Res4.extend([DownRes[i]])\n", " i = i + 1\n", " while (i < len(DownRes)-1): \n", " Quad3.extend([DownField[i]])\n", " Res3.extend([DownRes[i]])\n", " i = i +1\n", " a = 1\n", " STOP = 1\n", " #Horizontal filter (under construction)\n", " while a<= 4:\n", " i=0\n", " X,Y = 0,0\n", " X,Y = [],[]\n", " while i < (len(eval(\"Quad\"+str(a)))-1):\n", " sig = np.std(resistance)\n", " if abs(eval(\"Quad\"+str(a))[i]-eval(\"Quad\"+str(a))[i+1]) > (0.5*sig) and philter == 1 and STOP == 0:\n", " i = i+1\n", " elif abs(eval(\"Quad\"+str(a))[i]-eval(\"Quad\"+str(a))[i-1]) > (0.5*sig) and philter == 1 and STOP == 0:\n", " i = i+1\n", " else:\n", " X.append(eval(\"Quad\"+str(a))[i])\n", " Y.append(eval(\"Res\"+str(a))[i])\n", " i = i+1\n", " if a ==1:\n", " Quad1 = np.array(X)\n", " Res1 = np.array(Y)\n", " elif a ==2:\n", " Quad2 = np.array(X)\n", " Res2 = np.array(Y)\n", " elif a ==3:\n", " Quad3 = np.array(X)\n", " Res3 = np.array(Y)\n", " elif a ==4:\n", " Quad4 = np.array(X)\n", " Res4 = np.array(Y)\n", " a = a+1\n", " a = 1\n", " x = []\n", " y = []\n", "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", " #Fitter (under construction-Linear works)\n", " Fit = Dict[raw_input(\"do a fit of the data, Y/N? \")]\n", " Log = 0\n", " if Fit == 1:\n", " Log = Dict[raw_input(\"Logrithmic Fit, Y/N? (default is Linear) \")] \n", " if Log == 1:\n", " Res1 = np.log(Res1)\n", " Res2 = np.log(Res2)\n", " Res3 = np.log(Res3)\n", " Res4 = np.log(Res4)\n", " def LineFit(x, y):\n", " #Returns slope and y-intercept of linear fit to (x,y) data set\n", " xavg = x.mean()\n", " B = np.array((y*(x-xavg)).sum()/(x*(x-xavg)).sum()) # slope\n", " A = np.array(y.mean()-B*xavg) # intercept\n", " return B, A\n", " while a <= 4:\n", " x = eval(\"Quad\"+str(a))\n", " y = eval(\"Res\"+str(a))\n", " B,A = LineFit(x,y)\n", " Coeff = np.around([B, A], decimals=3)\n", " firstx = x[0]\n", " lastx = x[-1]\n", " step = 0.001\n", " if firstx > lastx:\n", " xfit = np.arange(lastx,firstx,step)\n", " else:\n", " xfit = np.arange(firstx,lastx,step)\n", " yfit = A + B*xfit\n", " Loc = evens[str(a)]\n", " plt.subplot(2,1,Loc) \n", " plt.plot(xfit,yfit,'b-',color = color[a], label=('Linear fit '+str(a)))\n", " print(Term[a]+\" equation: Resistance = \"+str(Coeff[0])+\"(Ohms/mTesla)*Magnetic Field + \"+str(Coeff[1])+\" Ohms\")\n", " plt.plot(x,y, '.', color = color[a])\n", " a = a+1\n", " plt.legend()\n", " \n", " plt.subplot(2,1,1) \n", " plt.plot(Quad1,Res1, '.', color = 'red', label='increasing resistance')\n", " plt.ylabel(ResTitle[Log]);\n", " plt.xlabel(\"Magnetic Field Strength (Tesla)\");\n", " plt.title((title+DyeX+' by '+DyeY+', Trial '+str(j)+', '+str(Current)+'mA increasing field'));\n", " plt.plot(Quad2,Res2, '.', color = 'green', label='decreasing resistance')\n", " plt.legend()\n", " plt.subplot(2,1,2)\n", " plt.plot(Quad3,Res3, '.', color = 'blue', label='decreasing resistance')\n", " plt.ylabel(ResTitle[Log]);\n", " plt.xlabel(\"Magnetic Field Strength (Tesla)\");\n", " plt.title((title+DyeX+' by '+DyeY+', Trial '+str(j)+', '+str(Current)+'mA decreasing field'));\n", " plt.plot(Quad4,Res4, '.', color = 'orange', label='increasing resistance')\n", " plt.legend()\n", " plt.show()\n", " cont = Dict[raw_input(\"Continue with this sample, Y/N? \")]\n", " sample = Dict[raw_input(\"Change sample, Y/N? \")]\n", "print(\"Good bye\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

parksurk/dl_with_tf

2-2. logistic regression mnist.ipynb

1

6293

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Logistic Regression with MNIST" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Packages are loaded!!!\n" ] } ], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "\n", "print (\"Packages are loaded!!!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Download and Extract MNIST DataSet " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting data/train-images-idx3-ubyte.gz\n", "Extracting data/train-labels-idx1-ubyte.gz\n", "Extracting data/t10k-images-idx3-ubyte.gz\n", "Extracting data/t10k-labels-idx1-ubyte.gz\n", "MNIST loaded!!!\n" ] } ], "source": [ "mnist = input_data.read_data_sets('data/', one_hot=True)\n", "trainimg = mnist.train.images\n", "trainlabel = mnist.train.labels\n", "testimg = mnist.test.images\n", "testlabel = mnist.test.labels\n", "\n", "print (\"MNIST loaded!!!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create Tensor Graph for Logistic Regression" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = tf.placeholder(\"float\", [None, 784])\n", "y = tf.placeholder(\"float\", [None, 10])\n", "W = tf.Variable(tf.zeros([784, 10]))\n", "b = tf.Variable(tf.zeros([10]))\n", "# Logistic Regression Model\n", "actvation = tf.nn.softmax(tf.matmul(x,W) + b)\n", "# Cost Fuction\n", "cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(actvation), reduction_indices=1))\n", "# Optimizer\n", "learning_rate = 0.01\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prediction and Accuracy " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Prediction\n", "pred = tf.equal(tf.argmax(actvation, 1), tf.argmax(y, 1))\n", "# Accuracy\n", "accr = tf.reduce_mean(tf.cast(pred, \"float\"))\n", "# Initializer\n", "init = tf.initialize_all_variables()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train Model " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch : 000/050 cost: 1.177067931 train_acc: 0.840 test_acc: 0.854\n", "Epoch : 005/050 cost: 0.441086413 train_acc: 0.870 test_acc: 0.896\n", "Epoch : 010/050 cost: 0.383993804 train_acc: 0.890 test_acc: 0.904\n", "Epoch : 015/050 cost: 0.356193866 train_acc: 0.990 test_acc: 0.909\n", "Epoch : 020/050 cost: 0.341433665 train_acc: 0.890 test_acc: 0.912\n", "Epoch : 025/050 cost: 0.330003295 train_acc: 0.890 test_acc: 0.915\n", "Epoch : 030/050 cost: 0.322628716 train_acc: 0.930 test_acc: 0.916\n", "Epoch : 035/050 cost: 0.317337896 train_acc: 0.880 test_acc: 0.917\n", "Epoch : 040/050 cost: 0.309249855 train_acc: 0.890 test_acc: 0.918\n", "Epoch : 045/050 cost: 0.306366050 train_acc: 0.890 test_acc: 0.918\n", "Done!!!\n" ] } ], "source": [ "training_epochs = 50\n", "batch_size = 100\n", "display_step = 5\n", "# Session\n", "sess = tf.Session()\n", "sess.run(init)\n", "# Mini-Batch Learning\n", "for epoch in range(training_epochs):\n", " avg_cost = 0.\n", " num_batch = int(mnist.train.num_examples/batch_size)\n", " for i in range(num_batch):\n", " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", " sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})\n", " feeds = {x: batch_xs, y: batch_ys}\n", " avg_cost += sess.run(cost, feed_dict=feeds)/num_batch\n", " # Display\n", " if epoch % display_step == 0:\n", " feeds_train = {x: batch_xs, y: batch_ys}\n", " feeds_test = {x: mnist.test.images, y: mnist.test.labels}\n", " train_acc = sess.run(accr, feeds_train)\n", " test_acc = sess.run(accr, feeds_test)\n", " print (\"Epoch : %03d/%03d cost: %.9f train_acc: %.3f test_acc: %.3f\" \n", " % (epoch, training_epochs, avg_cost, train_acc, test_acc))\n", "print (\"Done!!!\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

tensorflow/docs-l10n

site/zh-cn/hub/tutorials/bangla_article_classifier.ipynb

1

23089

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "IDdZSPcLtKx4" }, "source": [ "##### Copyright 2019 The TensorFlow Hub Authors.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-g5By3P4tavy" }, "outputs": [], "source": [ "# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# http://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS, \n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License.\n", "# ==============================================================================" ] }, { "cell_type": "markdown", "metadata": { "id": "vpaLrN0mteAS" }, "source": [ "# 使用 TF-Hub 对孟加拉语文章进行分类" ] }, { "cell_type": "markdown", "metadata": { "id": "MfBg1C5NB3X0" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td><a target=\"_blank\" href=\"https://tensorflow.google.cn/hub/tutorials/bangla_article_classifier\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 TensorFlow.org 上查看 </a></td>\n", " <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/hub/tutorials/bangla_article_classifier.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 中运行 </a></td>\n", " <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/hub/tutorials/bangla_article_classifier.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 GitHub 中查看源代码</a></td>\n", " <td><a href=\"https://storage.googleapis.com/tensorflow_docs/hub/examples/colab/bangla_article_classifier.ipynb\">{img1下载笔记本</a></td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "GhN2WtIrBQ4y" }, "source": [ "小心：除了使用 pip 安装 Python 软件包外，此笔记本还使用 `sudo apt install` 安装系统软件包：`unzip`。\n", "\n", "此 Colab 演示了如何使用 [Tensorflow Hub](https://tensorflow.google.cn/hub/) 对非英语/本地语言进行文本分类。在这里，我们选择[孟加拉语](https://en.wikipedia.org/wiki/Bengali_language)作为本地语言并使用预训练的单词嵌入向量解决多类分类任务，在这个任务中我们将孟加拉语的新闻文章分为 5 类。针对孟加拉语进行预训练的嵌入向量来自 [FastText](https://fasttext.cc/docs/en/crawl-vectors.html)，这是一个由 Facebook 创建的库，其中包含 157 种语言的预训练单词向量。\n", "\n", "我们将使用 TF-Hub 的预训练嵌入向量导出程序先将单词嵌入向量转换为文本嵌入向量模块，然后使用该模块通过 [tf.keras](https://tensorflow.google.cn/api_docs/python/tf/keras)（Tensorflow 的高级用户友好 API）训练分类器来构建深度学习模型。即使我们在这里使用 fastText 嵌入向量，您也可以导出任何通过其他任务预训练的其他嵌入向量，并使用 Tensorflow Hub 快速获得结果。 " ] }, { "cell_type": "markdown", "metadata": { "id": "Q4DN769E2O_R" }, "source": [ "## 设置" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9Vt-StAAZguA" }, "outputs": [], "source": [ "%%bash\n", "# https://github.com/pypa/setuptools/issues/1694#issuecomment-466010982\n", "pip install gdown --no-use-pep517" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WcBA19FlDPZO" }, "outputs": [], "source": [ "%%bash\n", "sudo apt-get install -y unzip" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zSeyZMq-BYsu" }, "outputs": [], "source": [ "import os\n", "\n", "import tensorflow as tf\n", "import tensorflow_hub as hub\n", "\n", "import gdown\n", "import numpy as np\n", "from sklearn.metrics import classification_report\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": { "id": "9FB7gLU4F54l" }, "source": [ "# 数据集\n", "\n", "我们将使用 [BARD](https://www.researchgate.net/publication/328214545_BARD_Bangla_Article_Classification_Using_a_New_Comprehensive_Dataset)（孟加拉语文章数据集），内含从不同孟加拉语新闻门户收集的约 3,76,226 篇文章，并标记为 5 个类别：经济、国内、国际、体育和娱乐。我们从 Google 云端硬盘下载这个文件，此 ([bit.ly/BARD_DATASET](bit.ly/BARD_DATASET)) 链接指向[此](https://github.com/tanvirfahim15/BARD-Bangla-Article-Classifier) GitHub 仓库。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zdQrL_rwa-1K" }, "outputs": [], "source": [ "gdown.download(\n", " url='https://drive.google.com/uc?id=1Ag0jd21oRwJhVFIBohmX_ogeojVtapLy',\n", " output='bard.zip',\n", " quiet=True\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "P2YW4GGa9Y5o" }, "outputs": [], "source": [ "%%bash\n", "unzip -qo bard.zip" ] }, { "cell_type": "markdown", "metadata": { "id": "js75OARBF_B8" }, "source": [ "# 将预训练的单词向量导出到 TF-Hub 模块" ] }, { "cell_type": "markdown", "metadata": { "id": "-uAicYA6vLsf" }, "source": [ "TF-Hub 提供了一些方便的脚本将单词嵌入向量转换为 TF-Hub 文本嵌入向量模块，详见[这里](https://github.com/tensorflow/hub/tree/master/examples/text_embeddings_v2)。要使模块适用于孟加拉语或其他语言，我们只需将单词嵌入向量 .txt 或 .vec 文件下载到与 export_v2.py 相同的目录中，然后运行脚本。\n", "\n", "导出程序会读取嵌入向量，并将其导出到 Tensorflow [SavedModel](https://tensorflow.google.cn/beta/guide/saved_model)。SavedModel 包含完整的 TensorFlow 程序，其中包括权重和计算图。TF-Hub 可以将 SavedModel 作为[模块](https://tensorflow.google.cn/hub/api_docs/python/hub/Module)进行加载，我们将用它来构建文本分类模型。由于我们使用 tf.keras 来构建模型，因此我们将使用 [hub.KerasLayer](https://tensorflow.google.cn/hub/api_docs/python/hub/KerasLayer)，它为 Hub 模块提供用作 Keras 层的封装容器。\n", "\n", "首先，我们从 fastText 获得单词嵌入向量，并从 TF-Hub [仓库](https://github.com/tensorflow/hub)获得嵌入向量导出程序。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5DY5Ze6pO1G5" }, "outputs": [], "source": [ "%%bash\n", "curl -O https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.bn.300.vec.gz\n", "curl -O https://raw.githubusercontent.com/tensorflow/hub/master/examples/text_embeddings_v2/export_v2.py\n", "gunzip -qf cc.bn.300.vec.gz --k" ] }, { "cell_type": "markdown", "metadata": { "id": "PAzdNZaHmdl1" }, "source": [ "然后，我们在嵌入向量文件上运行导出程序脚本。由于 fastText 嵌入向量具有标题行并且相当大（转换为模块后，孟加拉语大约为 3.3 GB），因此我们忽略第一行，仅将前 100, 000 个词例导入文本嵌入向量模块。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Tkv5acr_Q9UU" }, "outputs": [], "source": [ "%%bash\n", "python export_v2.py --embedding_file=cc.bn.300.vec --export_path=text_module --num_lines_to_ignore=1 --num_lines_to_use=100000" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "k9WEpmedF_3_" }, "outputs": [], "source": [ "module_path = \"text_module\"\n", "embedding_layer = hub.KerasLayer(module_path, trainable=False)" ] }, { "cell_type": "markdown", "metadata": { "id": "fQHbmS_D4YIo" }, "source": [ "文本嵌入向量模块以一维字符串张量中的句子批次作为输入，并输出与句子相对应的形状 (batch_size, embedding_dim) 的嵌入向量。它通过按空格拆分来对输入进行预处理。我们使用 `sqrtn` 组合程序（请参阅[此处](https://tensorflow.google.cn/api_docs/python/tf/nn/embedding_lookup_sparse)）将单词嵌入向量组合到句子嵌入向量。为了演示，我们传递一个孟加拉语单词的列表作为输入，并获得相应的嵌入向量。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Z1MBnaBUihWn" }, "outputs": [], "source": [ "embedding_layer(['বাস', 'বসবাস', 'ট্রেন', 'যাত্রী', 'ট্রাক']) " ] }, { "cell_type": "markdown", "metadata": { "id": "4KY8LiFOHmcd" }, "source": [ "# 转换为 TensorFlow 数据集\n" ] }, { "cell_type": "markdown", "metadata": { "id": "pNguCDNe6bvz" }, "source": [ "由于数据集确实很大，因此我们使用生成器通过 [Tensorflow 数据集](https://tensorflow.google.cn/api_docs/python/tf/data/Dataset)的功能在运行时批量生成样本，而不是将整个数据集加载到内存中。同时，数据集还非常不平衡，因此在使用生成器之前，我们将打乱数据集的顺序。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bYv6LqlEChO1" }, "outputs": [], "source": [ "dir_names = ['economy', 'sports', 'entertainment', 'state', 'international']\n", "\n", "file_paths = []\n", "labels = []\n", "for i, dir in enumerate(dir_names):\n", " file_names = [\"/\".join([dir, name]) for name in os.listdir(dir)]\n", " file_paths += file_names\n", " labels += [i] * len(os.listdir(dir))\n", " \n", "np.random.seed(42)\n", "permutation = np.random.permutation(len(file_paths))\n", "\n", "file_paths = np.array(file_paths)[permutation]\n", "labels = np.array(labels)[permutation]" ] }, { "cell_type": "markdown", "metadata": { "id": "8b-UtAP5TL-W" }, "source": [ "打乱顺序后，我们可以查看标签在训练和验证样本中的分布。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mimhWVSzzAmS" }, "outputs": [], "source": [ "train_frac = 0.8\n", "train_size = int(len(file_paths) * train_frac)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4BNXFrkotAYu" }, "outputs": [], "source": [ "# plot training vs validation distribution\n", "plt.subplot(1, 2, 1)\n", "plt.hist(labels[0:train_size])\n", "plt.title(\"Train labels\")\n", "plt.subplot(1, 2, 2)\n", "plt.hist(labels[train_size:])\n", "plt.title(\"Validation labels\")\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": { "id": "RVbHb2I3TUNA" }, "source": [ "要使用生成器创建[数据集](https://tensorflow.google.cn/api_docs/python/tf/data/Dataset)，我们首先编写一个生成器函数，该函数从 file_paths 读取文章，从标签数组中读取标签，并在每个步骤生成一个训练样本。我们将此生成器函数传递到 [tf.data.Dataset.from_generator](https://tensorflow.google.cn/api_docs/python/tf/data/Dataset#from_generator) 方法，并指定输出类型。每个训练样本都是一个元组，其中包含 tf.string 数据类型的文章和独热编码标签。我们使用 [`skip`](https://tensorflow.google.cn/api_docs/python/tf/data/Dataset#skip) 和 [`take`](https://tensorflow.google.cn/api_docs/python/tf/data/Dataset#take) 方法以 80-20 的比例将数据集拆分为训练集和验证集。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eZRGTzEhUi7Q" }, "outputs": [], "source": [ "def load_file(path, label):\n", " return tf.io.read_file(path), label" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2g4nRflB7fbF" }, "outputs": [], "source": [ "def make_datasets(train_size):\n", " batch_size = 256\n", "\n", " train_files = file_paths[:train_size]\n", " train_labels = labels[:train_size]\n", " train_ds = tf.data.Dataset.from_tensor_slices((train_files, train_labels))\n", " train_ds = train_ds.map(load_file).shuffle(5000)\n", " train_ds = train_ds.batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)\n", "\n", " test_files = file_paths[train_size:]\n", " test_labels = labels[train_size:]\n", " test_ds = tf.data.Dataset.from_tensor_slices((test_files, test_labels))\n", " test_ds = test_ds.map(load_file)\n", " test_ds = test_ds.batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)\n", "\n", "\n", " return train_ds, test_ds" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8PuuN6el8tv9" }, "outputs": [], "source": [ "train_data, validation_data = make_datasets(train_size)" ] }, { "cell_type": "markdown", "metadata": { "id": "MrdZI6FqPJNP" }, "source": [ "# 模型训练和评估" ] }, { "cell_type": "markdown", "metadata": { "id": "jgr7YScGVS58" }, "source": [ "由于我们已经在模块周围添加了封装容器，使其可以像 Keras 中的任何其他层一样使用，因此我们可以创建一个小的[序贯](https://tensorflow.google.cn/api_docs/python/tf/keras/Sequential)模型，此模型是层的线性堆叠。我们可以像使用任何其他层一样，使用 `model.add` 添加文本嵌入向量模块。我们通过指定损失和优化器来编译模型，并对其进行 10 个周期的训练。`tf.keras` API 可以将 TensorFlow 数据集作为输入进行处理，因此我们可以将数据实例传递给用于模型训练的拟合方法。由于我们使用的是生成器函数，tf.data 将负责生成样本、对其进行批处理，并将其馈送给模型。" ] }, { "cell_type": "markdown", "metadata": { "id": "WhCqbDK2uUV5" }, "source": [ "## 模型" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nHUw807XPPM9" }, "outputs": [], "source": [ "def create_model():\n", " model = tf.keras.Sequential([\n", " tf.keras.layers.Input(shape=[], dtype=tf.string),\n", " embedding_layer,\n", " tf.keras.layers.Dense(64, activation=\"relu\"),\n", " tf.keras.layers.Dense(16, activation=\"relu\"),\n", " tf.keras.layers.Dense(5),\n", " ])\n", " model.compile(loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " optimizer=\"adam\", metrics=['accuracy'])\n", " return model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5J4EXJUmPVNG" }, "outputs": [], "source": [ "model = create_model()\n", "# Create earlystopping callback\n", "early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=3)" ] }, { "cell_type": "markdown", "metadata": { "id": "ZZ7XJLg2u2No" }, "source": [ "## 训练" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OoBkN2tAaXWD" }, "outputs": [], "source": [ "history = model.fit(train_data, \n", " validation_data=validation_data, \n", " epochs=5, \n", " callbacks=[early_stopping_callback])" ] }, { "cell_type": "markdown", "metadata": { "id": "XoDk8otmMoT7" }, "source": [ "## 评估" ] }, { "cell_type": "markdown", "metadata": { "id": "G5ZRKGOsXEh4" }, "source": [ "我们可以使用由 `fit` 方法返回的 `history` 对象（包含每个周期的损失和准确率值）来可视化训练和验证数据的准确率和损失曲线。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "V6tOnByIOeGn" }, "outputs": [], "source": [ "# Plot training &amp; validation accuracy values\n", "plt.plot(history.history['accuracy'])\n", "plt.plot(history.history['val_accuracy'])\n", "plt.title('Model accuracy')\n", "plt.ylabel('Accuracy')\n", "plt.xlabel('Epoch')\n", "plt.legend(['Train', 'Test'], loc='upper left')\n", "plt.show()\n", "\n", "# Plot training &amp; validation loss values\n", "plt.plot(history.history['loss'])\n", "plt.plot(history.history['val_loss'])\n", "plt.title('Model loss')\n", "plt.ylabel('Loss')\n", "plt.xlabel('Epoch')\n", "plt.legend(['Train', 'Test'], loc='upper left')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "D54IXLqcG8Cq" }, "source": [ "## 预测\n", "\n", "我们可以获得验证数据的预测并检查混淆矩阵，以查看模型在 5 个类中的性能。`predict` 方法返回每个类的概率的 N 维数组后，我们使用 `np.argmax` 将其转换为类标签。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dptEywzZJk4l" }, "outputs": [], "source": [ "y_pred = model.predict(validation_data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7Dzeml6Pk0ub" }, "outputs": [], "source": [ "y_pred = np.argmax(y_pred, axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "T4M3Lzg8jHcB" }, "outputs": [], "source": [ "samples = file_paths[0:3]\n", "for i, sample in enumerate(samples):\n", " f = open(sample)\n", " text = f.read()\n", " print(text[0:100])\n", " print(\"True Class: \", sample.split(\"/\")[0])\n", " print(\"Predicted Class: \", dir_names[y_pred[i]])\n", " f.close() " ] }, { "cell_type": "markdown", "metadata": { "id": "PlDTIpMBu6h-" }, "source": [ "## 比较性能\n", "\n", "现在，我们可以从 `labels` 获得验证数据的正确标签，并与我们的预测进行比较，以获得 [classification_report](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html)。 " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mqrERUCS1Xn7" }, "outputs": [], "source": [ "y_true = np.array(labels[train_size:])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NX5w-NuTKuVP" }, "outputs": [], "source": [ "print(classification_report(y_true, y_pred, target_names=dir_names))" ] }, { "cell_type": "markdown", "metadata": { "id": "p5e9m3bV6oXK" }, "source": [ "我们还可以将模型的性能与原始[论文](https://www.researchgate.net/publication/328214545_BARD_Bangla_Article_Classification_Using_a_New_Comprehensive_Dataset)中报告的精度为 0.96 的发布结果进行比较。原作者描述了在数据集上完成的许多预处理步骤，例如删除标点和数字、去除前 25 个最常见的停用词等。正如我们在 classification_report 中所见，在仅训练了 5 个周期而没有进行任何预处理的情况下，我们也获得了 0.96 的精度和准确率！\n", "\n", "在此示例中，当我们从嵌入向量模块创建 Keras 层时，我们设置了 `trainable=False`，这意味着训练期间不会更新嵌入向量权重。请尝试将此设置为 True，使用此数据集仅用 2 个周期即可达到 97% 的准确率。 " ] } ], "metadata": { "colab": { "collapsed_sections": [ "IDdZSPcLtKx4" ], "name": "bangla_article_classifier.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

buruzaemon/stats-110

Lecture_32.ipynb

1

7787

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture 32: Markov chains (cont.), irreducibility, recurrence, transience, reversibility, random walk on an undirected network\n", "\n", "\n", "## Stat 110, Prof. Joe Blitzstein, Harvard University\n", "\n", "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examples of Markov Chains\n", "\n", "Markov chains are memoryless, in a way, since the past doesn't really inform the future; only the present counts. Recall that the future is conditionally independent of the past, given the present.\n", "\n", "### Some key concepts\n", "\n", "* A chain is **irreducible** if it is possible to get from any state to another.\n", "* A state is **recurrent** if, when starting there, the chain has probability of 1.0 for returning to that state. Note that if there is probability 1.0 for returning to a certain state, then it follows that in a Markov chain, you can return to that state _infinitely_ many times with probability 1.0.\n", "* Otherwise, the state is **transient**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 1\n", "\n", "\n", "\n", "* This Markov chain is _irreducible_, as it is indeed possible to go from any one state to another.\n", "* All of the states in this Markov chain are _recurrent_." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 2\n", "\n", "\n", "\n", "* In this example, the chain is _reducible_; notice how there are actually two chains (1-2-3 and 4-5-6).\n", "* However, note that all of the states are _recurrent_.\n", "\n", "And if we connected states 3 and 6...\n", "\n", "\n", "\n", "* This example is still not _irreducible_.\n", "* But states 1, 2 and 3 are now _transient_, since there is no way to return to any of those states once that edge from 3 to 6 is traversed.\n", "* The chain would become _irreducible_ and all states _recurrent_ if we added yet another edge from 4 to 1." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 3\n", "\n", "\n", "\n", "* The Markov chain in this example is _reducible_.\n", "* States 1 and 2 are _transient_.\n", "* States 0 and 3 are _recurrent_, but once you reach states 0 or 3, you cannot leave; these states are called _absorbing states_.\n", "* In case you didn't notice, the Markov chain in this example is the Gambler's Ruin, where a player either loses all her money (say state 0) or wins all the money (state 3)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 4\n", "\n", "\n", "\n", "* This is a _periodic_ Markov chain.\n", "* It is _irreducible_.\n", "* All states are _recurrent_.\n", "\n", "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stationary Distributions\n", "\n", "Recall the definition of a stationary distribution from the last lecture.\n", "\n", "$\\vec{s}$, a probability row vector (PMF), is _stationary_ for a Markov chain with transition matrix $Q$ if $\\vec{s} \\, Q = \\vec{s}$." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Theorems of Stationary Distributions\n", "\n", "For any _irreducible_ Markov chain with finitely many states:\n", "\n", "\n", "1. A stationary distribution $\\vec{s}$ exists.\n", "1. It is unique.\n", "1. $\\vec{s}_i = \\frac{1}{r_i}$, where $r_i$ is the average return time for returning back to $i$.\n", "1. If we also assume there is no _periodicity_ in the chain, where $Q^m$ is strictly positive for some $m$, then $P(X_n = i) \\rightarrow s_i$ as $n \\rightarrow \\infty$\n", "\n", "Regarding 4, if we any probability vector $\\vec{t}$, then $\\vec{t} \\, Q \\rightarrow \\vec{s}$.\n", "\n", "So the above theories of stationary distributions are worthy of study, since\n", "\n", "* they assure existence and uniqueness of stationary distribution under certain assumptions\n", "* they capture long-run behavior\n", "* show relation to average number of step for return to a state\n", "\n", "_But how would we compute the stationary distribution?_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reversible Markov Chains\n", "\n", "**Definition** Markov chains with transition matrix $Q = \\left[ q_{ij} \\right]$ is _reversible_ if there is a probability vector $\\vec{s}$ such that $s_i \\, q_{ij} = s_j \\, q_{ji}$ for all states $i,j$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Theorem: Reversible transition matrices and Stationary distribution\n", "\n", "If a transition matrix is _reversible_ with respect to $\\vec{s}$, then that $\\vec{s}$ is _stationary_. This reversibility is with reference to time, so it is also called _time reversible_.\n", "\n", "For intuition, imagine a video tape of some particle changing states. If you ran that video backwards and show that to someone, and that person could not tell if the action was moving forwards or backwards, then that would be an example of _time reversiblity_.\n", "\n", "\n", "**Proof**\n", "\n", "Let $s_i \\, q_{ij} = s_j \\, q_{ji}$ for all $i,j$; show that $\\vec{s} \\, Q = \\vec{s}$.\n", "\n", "\\begin{align}\n", " \\sum_i s_i \\, q_{ij} &= \\sum_i s_j \\, q_{ji} \\\\\n", " &= s_j \\sum_i q_{ji} \\\\\n", " &= s_j &\\text{ but this is just the definition of matrix multiplication} \\\\\n", " \\\\\\\\\n", " \\Rightarrow \\vec{s} \\, Q &= \\vec{s}\n", "\\end{align}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example of reversible Markov chain\n", "\n", "A random walk on a undirected network is an example of a reversible Markov chain.\n", "\n", "\n", "\n", "In the diagram above, the nodes 1 through 4 are joined in an undirected graph. The degree of each node $d_i$ is the number of edges emanating from said node, so $d_1=2, d_2=2, d_3=3, d_4=1$.\n", "\n", "With transition matrix $Q$ for the graph above, then $d_i \\, q_{ij} = d_j \\, q_{ji}$.\n", "\n", "**Proof**\n", "\n", "Let $i \\ne j$. \n", "\n", "Then $q_{ij}, q_{ji}$ are either both 0 or both non-zero. _The key is that we are talking about an undirected graph, and all edges are two-way streets._\n", "\n", "If there is an edge joining $i,j$), then $q_{ij} = \\frac{1}{d_i}$. \n", "\n", "So in a graph with $M$ nodes $1, 2, \\dots , M$, where each node has degree $d_i$, then $\\vec{s}$ with $s_i = \\frac{d_i}{\\sum_{j} d_j}$ is stationary.\n", "\n", "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "View [Lecture 32: Markov Chains Continued | Statistics 110](http://bit.ly/2McjRIq) on YouTube." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }

bsd-3-clause

ocefpaf/system-test

Theme_1_Baseline/Scenario_1D_Dissolved_Oxygen/Scenario_1D_Dissolved_Oxygen.ipynb

2

129820

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This test searches dissolved oxygen data in real-time sensors and models, and tries to ask if all the values are in the same units for comparison.\n", "\n", "## Guiding Questions\n", "1. Can we discover, access, and overlay dissolved oxygen information in sensors?\n", "2. Can we discover, access, and overlay dissolved oxygen information from models?\n", "3. Is data from different sensors and satellite data (or models) directly comparable? Same units? Same scales?\n", "4. If not, how much work is necessary to aggregate these streams?\n", "5. Is metadata for these data intelligable?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Q1 - Can we discover, access, and overlay salinity information?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pylab import *\n", "from IPython.display import HTML" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Query MMI for CF standard names related to the IOOS Core Variables" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "pd.set_option('display.max_columns', 20)\n", "pd.set_option('display.max_rows', 500)\n", "from SPARQLWrapper import SPARQLWrapper, JSON\n", "sparql = SPARQLWrapper(\"http://mmisw.org/sparql\")\n", "\n", "query = \"\"\"\n", "PREFIX ioos: <http://mmisw.org/ont/ioos/parameter/>\n", "SELECT DISTINCT ?cat ?parameter ?property ?value \n", "WHERE {?parameter a ioos:Parameter .\n", " ?parameter ?property ?value .\n", " ?cat skos:narrowMatch ?parameter .\n", " FILTER (regex(str(?property), \"Match\", \"i\") && regex(str(?value), \"cf\", \"i\") )\n", " } \n", "ORDER BY ?cat ?parameter\n", "\"\"\"\n", " \n", "sparql.setQuery(query)\n", "sparql.setReturnFormat(JSON)\n", "j = sparql.query().convert()\n", "\n", "cf_standard_uris = list(set([ x[\"value\"][\"value\"] for x in j.get(\"results\").get(\"bindings\") ]))\n", "cf_standard_names = map(lambda x: x.split(\"/\")[-1], cf_standard_uris)\n", "pd.DataFrame.from_records(zip(cf_standard_names, cf_standard_uris), columns=(\"CF Name\", \"CF URI\",))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/home/will/anaconda/lib/python2.7/site-packages/SPARQLWrapper/Wrapper.py:88: RuntimeWarning: JSON-LD disabled because no suitable support has been found\n", " warnings.warn(\"JSON-LD disabled because no suitable support has been found\", RuntimeWarning)\n" ] }, { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>CF Name</th>\n", " <th>CF URI</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0 </th>\n", " <td> sea_surface_height_correction_due_to_air_press...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>1 </th>\n", " <td> lagrangian_tendency_of_air_pressure</td>\n", " <td> http://mmisw.org/ont/cf/parameter/lagrangian_t...</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> radiation_wavelength</td>\n", " <td> http://mmisw.org/ont/cf/parameter/radiation_wa...</td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> mole_concentration_of_phosphate_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mole_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>4 </th>\n", " <td> platform_course</td>\n", " <td> http://mmisw.org/ont/cf/parameter/platform_course</td>\n", " </tr>\n", " <tr>\n", " <th>5 </th>\n", " <td> sea_surface_swell_wave_mean_period_from_varian...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> wind_to_direction</td>\n", " <td> http://mmisw.org/ont/cf/parameter/wind_to_dire...</td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> air_pressure_at_convective_cloud_base</td>\n", " <td> http://mmisw.org/ont/cf/parameter/air_pressure...</td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <td> surface_partial_pressure_of_carbon_dioxide_in_...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_part...</td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> tendency_of_air_pressure</td>\n", " <td> http://mmisw.org/ont/cf/parameter/tendency_of_...</td>\n", " </tr>\n", " <tr>\n", " <th>10 </th>\n", " <td> sea_surface_swell_wave_significant_height</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>11 </th>\n", " <td> moles_of_nitrate_per_unit_mass_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/moles_of_nit...</td>\n", " </tr>\n", " <tr>\n", " <th>12 </th>\n", " <td> sea_surface_height_correction_due_to_air_press...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>13 </th>\n", " <td> sea_surface_height_amplitude_due_to_earth_tide</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>14 </th>\n", " <td> mole_concentration_of_dissolved_organic_carbon...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mole_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>15 </th>\n", " <td> eastward_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/eastward_sea...</td>\n", " </tr>\n", " <tr>\n", " <th>16 </th>\n", " <td> water_surface_height_above_reference_datum</td>\n", " <td> http://mmisw.org/ont/cf/parameter/water_surfac...</td>\n", " </tr>\n", " <tr>\n", " <th>17 </th>\n", " <td> sound_intensity_level_in_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sound_intens...</td>\n", " </tr>\n", " <tr>\n", " <th>18 </th>\n", " <td> sea_surface_wave_mean_period_from_variance_spe...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>19 </th>\n", " <td> sea_surface_wave_mean_period_from_variance_spe...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>20 </th>\n", " <td> mole_concentration_of_nitrate_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mole_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>21 </th>\n", " <td> northward_wind</td>\n", " <td> http://mmisw.org/ont/cf/parameter/northward_wind</td>\n", " </tr>\n", " <tr>\n", " <th>22 </th>\n", " <td> freezing_level_altitude</td>\n", " <td> http://mmisw.org/ont/cf/parameter/freezing_lev...</td>\n", " </tr>\n", " <tr>\n", " <th>23 </th>\n", " <td> mass_concentration_of_phosphate_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mass_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>24 </th>\n", " <td> height_above_sea_floor</td>\n", " <td> http://mmisw.org/ont/cf/parameter/height_above...</td>\n", " </tr>\n", " <tr>\n", " <th>25 </th>\n", " <td> eastward_wind</td>\n", " <td> http://mmisw.org/ont/cf/parameter/eastward_wind</td>\n", " </tr>\n", " <tr>\n", " <th>26 </th>\n", " <td> sound_intensity_level_in_air</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sound_intens...</td>\n", " </tr>\n", " <tr>\n", " <th>27 </th>\n", " <td> air_temperature</td>\n", " <td> http://mmisw.org/ont/cf/parameter/air_temperature</td>\n", " </tr>\n", " <tr>\n", " <th>28 </th>\n", " <td> depth_below_geoid</td>\n", " <td> http://mmisw.org/ont/cf/parameter/depth_below_...</td>\n", " </tr>\n", " <tr>\n", " <th>29 </th>\n", " <td> altitude</td>\n", " <td> http://mmisw.org/ont/cf/parameter/altitude</td>\n", " </tr>\n", " <tr>\n", " <th>30 </th>\n", " <td> sea_surface_wind_wave_mean_period_from_varianc...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>31 </th>\n", " <td> product_of_eastward_sea_water_velocity_and_sal...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/product_of_e...</td>\n", " </tr>\n", " <tr>\n", " <th>32 </th>\n", " <td> surface_geostrophic_northward_sea_water_veloci...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_geos...</td>\n", " </tr>\n", " <tr>\n", " <th>33 </th>\n", " <td> height_at_cloud_top</td>\n", " <td> http://mmisw.org/ont/cf/parameter/height_at_cl...</td>\n", " </tr>\n", " <tr>\n", " <th>34 </th>\n", " <td> sea_surface_wave_variance_spectral_density</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>35 </th>\n", " <td> sea_surface_wind_wave_significant_height</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>36 </th>\n", " <td> sea_floor_depth_below_sea_level</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_floor_de...</td>\n", " </tr>\n", " <tr>\n", " <th>37 </th>\n", " <td> sea_surface_wind_wave_mean_period_from_varianc...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>38 </th>\n", " <td> northward_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/northward_se...</td>\n", " </tr>\n", " <tr>\n", " <th>39 </th>\n", " <td> sea_water_alkalinity_expressed_as_mole_equivalent</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_water_al...</td>\n", " </tr>\n", " <tr>\n", " <th>40 </th>\n", " <td> depth</td>\n", " <td> http://mmisw.org/ont/cf/parameter/depth</td>\n", " </tr>\n", " <tr>\n", " <th>41 </th>\n", " <td> sea_surface_subskin_temperature</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>42 </th>\n", " <td> surface_altitude</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_alti...</td>\n", " </tr>\n", " <tr>\n", " <th>43 </th>\n", " <td> air_pressure_at_convective_cloud_top</td>\n", " <td> http://mmisw.org/ont/cf/parameter/air_pressure...</td>\n", " </tr>\n", " <tr>\n", " <th>44 </th>\n", " <td> sea_water_ph_reported_on_total_scale</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_water_ph...</td>\n", " </tr>\n", " <tr>\n", " <th>45 </th>\n", " <td> sea_surface_wave_from_direction</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>46 </th>\n", " <td> upward_latent_heat_flux_in_air</td>\n", " <td> http://mmisw.org/ont/cf/parameter/upward_laten...</td>\n", " </tr>\n", " <tr>\n", " <th>47 </th>\n", " <td> surface_downward_northward_stress</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_down...</td>\n", " </tr>\n", " <tr>\n", " <th>48 </th>\n", " <td> sea_surface_wind_wave_to_direction</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>49 </th>\n", " <td> sea_surface_wind_wave_mean_period_from_varianc...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>50 </th>\n", " <td> air_pressure_at_sea_level</td>\n", " <td> http://mmisw.org/ont/cf/parameter/air_pressure...</td>\n", " </tr>\n", " <tr>\n", " <th>51 </th>\n", " <td> wind_speed</td>\n", " <td> http://mmisw.org/ont/cf/parameter/wind_speed</td>\n", " </tr>\n", " <tr>\n", " <th>52 </th>\n", " <td> platform_pitch_angle</td>\n", " <td> http://mmisw.org/ont/cf/parameter/platform_pit...</td>\n", " </tr>\n", " <tr>\n", " <th>53 </th>\n", " <td> mass_fraction_of_chlorophyll_a_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mass_fractio...</td>\n", " </tr>\n", " <tr>\n", " <th>54 </th>\n", " <td> radial_sea_water_velocity_away_from_instrument</td>\n", " <td> http://mmisw.org/ont/cf/parameter/radial_sea_w...</td>\n", " </tr>\n", " <tr>\n", " <th>55 </th>\n", " <td> cloud_top_altitude</td>\n", " <td> http://mmisw.org/ont/cf/parameter/cloud_top_al...</td>\n", " </tr>\n", " <tr>\n", " <th>56 </th>\n", " <td> sea_water_pressure_at_sea_floor</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_water_pr...</td>\n", " </tr>\n", " <tr>\n", " <th>57 </th>\n", " <td> moles_of_nitrate_and_nitrite_per_unit_mass_in_...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/moles_of_nit...</td>\n", " </tr>\n", " <tr>\n", " <th>58 </th>\n", " <td> volume_absorption_coefficient_of_radiative_flu...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/volume_absor...</td>\n", " </tr>\n", " <tr>\n", " <th>59 </th>\n", " <td> direction_of_radial_vector_away_from_instrument</td>\n", " <td> http://mmisw.org/ont/cf/parameter/direction_of...</td>\n", " </tr>\n", " <tr>\n", " <th>60 </th>\n", " <td> upward_sensible_heat_flux_in_air</td>\n", " <td> http://mmisw.org/ont/cf/parameter/upward_sensi...</td>\n", " </tr>\n", " <tr>\n", " <th>61 </th>\n", " <td> sea_surface_wave_period_at_variance_spectral_d...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>62 </th>\n", " <td> cloud_base_altitude</td>\n", " <td> http://mmisw.org/ont/cf/parameter/cloud_base_a...</td>\n", " </tr>\n", " <tr>\n", " <th>63 </th>\n", " <td> air_temperature_at_cloud_top</td>\n", " <td> http://mmisw.org/ont/cf/parameter/air_temperat...</td>\n", " </tr>\n", " <tr>\n", " <th>64 </th>\n", " <td> upward_heat_flux_in_sea_water_due_to_convection</td>\n", " <td> http://mmisw.org/ont/cf/parameter/upward_heat_...</td>\n", " </tr>\n", " <tr>\n", " <th>65 </th>\n", " <td> altitude_at_top_of_dry_convection</td>\n", " <td> http://mmisw.org/ont/cf/parameter/altitude_at_...</td>\n", " </tr>\n", " <tr>\n", " <th>66 </th>\n", " <td> equilibrium_line_altitude</td>\n", " <td> http://mmisw.org/ont/cf/parameter/equilibrium_...</td>\n", " </tr>\n", " <tr>\n", " <th>67 </th>\n", " <td> upward_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/upward_sea_w...</td>\n", " </tr>\n", " <tr>\n", " <th>68 </th>\n", " <td> sea_surface_salinity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>69 </th>\n", " <td> sea_surface_swell_wave_to_direction</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>70 </th>\n", " <td> surface_air_pressure</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_air_...</td>\n", " </tr>\n", " <tr>\n", " <th>71 </th>\n", " <td> sea_water_electrical_conductivity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_water_el...</td>\n", " </tr>\n", " <tr>\n", " <th>72 </th>\n", " <td> sea_water_practical_salinity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_water_pr...</td>\n", " </tr>\n", " <tr>\n", " <th>73 </th>\n", " <td> sea_surface_height_amplitude_due_to_equilibriu...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>74 </th>\n", " <td> surface_geostrophic_eastward_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_geos...</td>\n", " </tr>\n", " <tr>\n", " <th>75 </th>\n", " <td> secchi_depth_of_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/secchi_depth...</td>\n", " </tr>\n", " <tr>\n", " <th>76 </th>\n", " <td> mass_concentration_of_inorganic_nitrogen_in_se...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mass_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>77 </th>\n", " <td> sea_water_temperature</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_water_te...</td>\n", " </tr>\n", " <tr>\n", " <th>78 </th>\n", " <td> sea_water_density</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_water_de...</td>\n", " </tr>\n", " <tr>\n", " <th>79 </th>\n", " <td> sea_water_pressure_due_to_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_water_pr...</td>\n", " </tr>\n", " <tr>\n", " <th>80 </th>\n", " <td> surface_carbon_dioxide_partial_pressure_differ...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_carb...</td>\n", " </tr>\n", " <tr>\n", " <th>81 </th>\n", " <td> thickness_of_rainfall_amount</td>\n", " <td> http://mmisw.org/ont/cf/parameter/thickness_of...</td>\n", " </tr>\n", " <tr>\n", " <th>82 </th>\n", " <td> temperature_of_sensor_for_oxygen_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/temperature_...</td>\n", " </tr>\n", " <tr>\n", " <th>83 </th>\n", " <td> air_temperature_anomaly</td>\n", " <td> http://mmisw.org/ont/cf/parameter/air_temperat...</td>\n", " </tr>\n", " <tr>\n", " <th>84 </th>\n", " <td> sea_water_pressure_at_sea_water_surface</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_water_pr...</td>\n", " </tr>\n", " <tr>\n", " <th>85 </th>\n", " <td> upward_air_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/upward_air_v...</td>\n", " </tr>\n", " <tr>\n", " <th>86 </th>\n", " <td> wind_speed_of_gust</td>\n", " <td> http://mmisw.org/ont/cf/parameter/wind_speed_o...</td>\n", " </tr>\n", " <tr>\n", " <th>87 </th>\n", " <td> sea_water_pressure</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_water_pr...</td>\n", " </tr>\n", " <tr>\n", " <th>88 </th>\n", " <td> bolus_eastward_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/bolus_eastwa...</td>\n", " </tr>\n", " <tr>\n", " <th>89 </th>\n", " <td> platform_yaw_angle</td>\n", " <td> http://mmisw.org/ont/cf/parameter/platform_yaw...</td>\n", " </tr>\n", " <tr>\n", " <th>90 </th>\n", " <td> sea_surface_wave_significant_height</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>91 </th>\n", " <td> sea_floor_depth_below_geoid</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_floor_de...</td>\n", " </tr>\n", " <tr>\n", " <th>92 </th>\n", " <td> sea_surface_height_above_sea_level</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>93 </th>\n", " <td> difference_of_air_pressure_from_model_reference</td>\n", " <td> http://mmisw.org/ont/cf/parameter/difference_o...</td>\n", " </tr>\n", " <tr>\n", " <th>94 </th>\n", " <td> sea_surface_wave_to_direction</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>95 </th>\n", " <td> fractional_saturation_of_oxygen_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/fractional_s...</td>\n", " </tr>\n", " <tr>\n", " <th>96 </th>\n", " <td> geostrophic_northward_wind</td>\n", " <td> http://mmisw.org/ont/cf/parameter/geostrophic_...</td>\n", " </tr>\n", " <tr>\n", " <th>97 </th>\n", " <td> mole_concentration_of_ammonium_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mole_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>98 </th>\n", " <td> bolus_northward_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/bolus_northw...</td>\n", " </tr>\n", " <tr>\n", " <th>99 </th>\n", " <td> model_level_number_at_sea_floor</td>\n", " <td> http://mmisw.org/ont/cf/parameter/model_level_...</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td> sea_surface_swell_wave_zero_upcrossing_period</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>101</th>\n", " <td> depth_of_isosurface_of_sea_water_potential_tem...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/depth_of_iso...</td>\n", " </tr>\n", " <tr>\n", " <th>102</th>\n", " <td> baroclinic_eastward_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/baroclinic_e...</td>\n", " </tr>\n", " <tr>\n", " <th>103</th>\n", " <td> geostrophic_eastward_wind</td>\n", " <td> http://mmisw.org/ont/cf/parameter/geostrophic_...</td>\n", " </tr>\n", " <tr>\n", " <th>104</th>\n", " <td> platform_azimuth_angle</td>\n", " <td> http://mmisw.org/ont/cf/parameter/platform_azi...</td>\n", " </tr>\n", " <tr>\n", " <th>105</th>\n", " <td> sea_water_turbidity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_water_tu...</td>\n", " </tr>\n", " <tr>\n", " <th>106</th>\n", " <td> barotropic_eastward_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/barotropic_e...</td>\n", " </tr>\n", " <tr>\n", " <th>107</th>\n", " <td> air_pressure_anomaly</td>\n", " <td> http://mmisw.org/ont/cf/parameter/air_pressure...</td>\n", " </tr>\n", " <tr>\n", " <th>108</th>\n", " <td> mole_concentration_of_nitrite_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mole_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>109</th>\n", " <td> height</td>\n", " <td> http://mmisw.org/ont/cf/parameter/height</td>\n", " </tr>\n", " <tr>\n", " <th>110</th>\n", " <td> sea_surface_height_above_reference_ellipsoid</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>111</th>\n", " <td> air_temperature_lapse_rate</td>\n", " <td> http://mmisw.org/ont/cf/parameter/air_temperat...</td>\n", " </tr>\n", " <tr>\n", " <th>112</th>\n", " <td> moles_of_nitrite_per_unit_mass_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/moles_of_nit...</td>\n", " </tr>\n", " <tr>\n", " <th>113</th>\n", " <td> tropopause_air_temperature</td>\n", " <td> http://mmisw.org/ont/cf/parameter/tropopause_a...</td>\n", " </tr>\n", " <tr>\n", " <th>114</th>\n", " <td> mass_concentration_of_chlorophyll_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mass_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>115</th>\n", " <td> platform_roll_angle</td>\n", " <td> http://mmisw.org/ont/cf/parameter/platform_rol...</td>\n", " </tr>\n", " <tr>\n", " <th>116</th>\n", " <td> sea_surface_height_bias_due_to_sea_surface_rou...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>117</th>\n", " <td> surface_partial_pressure_of_carbon_dioxide_in_air</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_part...</td>\n", " </tr>\n", " <tr>\n", " <th>118</th>\n", " <td> mole_concentration_of_bacteria_expressed_as_ca...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mole_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>119</th>\n", " <td> sea_surface_wave_zero_upcrossing_period</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>120</th>\n", " <td> surface_geostrophic_eastward_sea_water_velocit...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_geos...</td>\n", " </tr>\n", " <tr>\n", " <th>121</th>\n", " <td> latitude</td>\n", " <td> http://mmisw.org/ont/cf/parameter/latitude</td>\n", " </tr>\n", " <tr>\n", " <th>122</th>\n", " <td> mole_concentration_of_nitrate_and_nitrite_in_s...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mole_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>123</th>\n", " <td> magnitude_of_surface_downward_stress</td>\n", " <td> http://mmisw.org/ont/cf/parameter/magnitude_of...</td>\n", " </tr>\n", " <tr>\n", " <th>124</th>\n", " <td> sea_surface_wave_mean_period_from_variance_spe...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>125</th>\n", " <td> sea_surface_swell_wave_mean_period_from_varian...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>126</th>\n", " <td> sea_floor_depth_below_sea_surface</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_floor_de...</td>\n", " </tr>\n", " <tr>\n", " <th>127</th>\n", " <td> air_pressure_at_cloud_base</td>\n", " <td> http://mmisw.org/ont/cf/parameter/air_pressure...</td>\n", " </tr>\n", " <tr>\n", " <th>128</th>\n", " <td> thickness_of_snowfall_amount</td>\n", " <td> http://mmisw.org/ont/cf/parameter/thickness_of...</td>\n", " </tr>\n", " <tr>\n", " <th>129</th>\n", " <td> sea_surface_skin_temperature</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>130</th>\n", " <td> sea_surface_height_amplitude_due_to_pole_tide</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>131</th>\n", " <td> air_pressure_at_cloud_top</td>\n", " <td> http://mmisw.org/ont/cf/parameter/air_pressure...</td>\n", " </tr>\n", " <tr>\n", " <th>132</th>\n", " <td> mass_concentration_of_oxygen_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mass_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>133</th>\n", " <td> mole_ratio_of_nitrate_to_phosphate_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mole_ratio_o...</td>\n", " </tr>\n", " <tr>\n", " <th>134</th>\n", " <td> sea_surface_temperature</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>135</th>\n", " <td> surface_eastward_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_east...</td>\n", " </tr>\n", " <tr>\n", " <th>136</th>\n", " <td> barotropic_northward_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/barotropic_n...</td>\n", " </tr>\n", " <tr>\n", " <th>137</th>\n", " <td> longitude</td>\n", " <td> http://mmisw.org/ont/cf/parameter/longitude</td>\n", " </tr>\n", " <tr>\n", " <th>138</th>\n", " <td> sea_water_salinity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_water_sa...</td>\n", " </tr>\n", " <tr>\n", " <th>139</th>\n", " <td> air_pressure</td>\n", " <td> http://mmisw.org/ont/cf/parameter/air_pressure</td>\n", " </tr>\n", " <tr>\n", " <th>140</th>\n", " <td> surface_downward_eastward_stress</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_down...</td>\n", " </tr>\n", " <tr>\n", " <th>141</th>\n", " <td> sea_surface_wave_directional_variance_spectral...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>142</th>\n", " <td> wave_frequency</td>\n", " <td> http://mmisw.org/ont/cf/parameter/wave_frequency</td>\n", " </tr>\n", " <tr>\n", " <th>143</th>\n", " <td> wind_from_direction</td>\n", " <td> http://mmisw.org/ont/cf/parameter/wind_from_di...</td>\n", " </tr>\n", " <tr>\n", " <th>144</th>\n", " <td> tropopause_air_pressure</td>\n", " <td> http://mmisw.org/ont/cf/parameter/tropopause_a...</td>\n", " </tr>\n", " <tr>\n", " <th>145</th>\n", " <td> sea_surface_swell_wave_mean_period_from_varian...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>146</th>\n", " <td> sea_surface_height_above_geoid</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>147</th>\n", " <td> direction_of_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/direction_of...</td>\n", " </tr>\n", " <tr>\n", " <th>148</th>\n", " <td> sea_water_speed</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_water_speed</td>\n", " </tr>\n", " <tr>\n", " <th>149</th>\n", " <td> sea_surface_height_amplitude_due_to_geocentric...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>150</th>\n", " <td> change_over_time_in_sea_water_density</td>\n", " <td> http://mmisw.org/ont/cf/parameter/change_over_...</td>\n", " </tr>\n", " <tr>\n", " <th>151</th>\n", " <td> mole_concentration_of_particulate_organic_matt...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mole_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>152</th>\n", " <td> mass_concentration_of_chlorophyll_a_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mass_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>153</th>\n", " <td> surface_geostrophic_northward_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_geos...</td>\n", " </tr>\n", " <tr>\n", " <th>154</th>\n", " <td> moles_of_phosphate_per_unit_mass_in_sea_water</td>\n", " <td> http://mmisw.org/ont/cf/parameter/moles_of_pho...</td>\n", " </tr>\n", " <tr>\n", " <th>155</th>\n", " <td> height_above_reference_ellipsoid</td>\n", " <td> http://mmisw.org/ont/cf/parameter/height_above...</td>\n", " </tr>\n", " <tr>\n", " <th>156</th>\n", " <td> eastward_sea_water_velocity_assuming_no_tide</td>\n", " <td> http://mmisw.org/ont/cf/parameter/eastward_sea...</td>\n", " </tr>\n", " <tr>\n", " <th>157</th>\n", " <td> northward_sea_water_velocity_assuming_no_tide</td>\n", " <td> http://mmisw.org/ont/cf/parameter/northward_se...</td>\n", " </tr>\n", " <tr>\n", " <th>158</th>\n", " <td> air_pressure_at_freezing_level</td>\n", " <td> http://mmisw.org/ont/cf/parameter/air_pressure...</td>\n", " </tr>\n", " <tr>\n", " <th>159</th>\n", " <td> mole_concentration_of_dissolved_inorganic_carb...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/mole_concent...</td>\n", " </tr>\n", " <tr>\n", " <th>160</th>\n", " <td> platform_speed_wrt_ground</td>\n", " <td> http://mmisw.org/ont/cf/parameter/platform_spe...</td>\n", " </tr>\n", " <tr>\n", " <th>161</th>\n", " <td> surface_carbon_dioxide_partial_pressure_differ...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_carb...</td>\n", " </tr>\n", " <tr>\n", " <th>162</th>\n", " <td> surface_northward_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/surface_nort...</td>\n", " </tr>\n", " <tr>\n", " <th>163</th>\n", " <td> convective_cloud_base_altitude</td>\n", " <td> http://mmisw.org/ont/cf/parameter/convective_c...</td>\n", " </tr>\n", " <tr>\n", " <th>164</th>\n", " <td> sea_surface_wind_wave_period</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>165</th>\n", " <td> sea_surface_swell_wave_period</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>166</th>\n", " <td> baroclinic_northward_sea_water_velocity</td>\n", " <td> http://mmisw.org/ont/cf/parameter/baroclinic_n...</td>\n", " </tr>\n", " <tr>\n", " <th>167</th>\n", " <td> sea_surface_height_amplitude_due_to_non_equili...</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>168</th>\n", " <td> sea_surface_wind_wave_zero_upcrossing_period</td>\n", " <td> http://mmisw.org/ont/cf/parameter/sea_surface_...</td>\n", " </tr>\n", " <tr>\n", " <th>169</th>\n", " <td> air_temperature_threshold</td>\n", " <td> http://mmisw.org/ont/cf/parameter/air_temperat...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ " CF Name \\\n", "0 sea_surface_height_correction_due_to_air_press... \n", "1 lagrangian_tendency_of_air_pressure \n", "2 radiation_wavelength \n", "3 mole_concentration_of_phosphate_in_sea_water \n", "4 platform_course \n", "5 sea_surface_swell_wave_mean_period_from_varian... \n", "6 wind_to_direction \n", "7 air_pressure_at_convective_cloud_base \n", "8 surface_partial_pressure_of_carbon_dioxide_in_... \n", "9 tendency_of_air_pressure \n", "10 sea_surface_swell_wave_significant_height \n", "11 moles_of_nitrate_per_unit_mass_in_sea_water \n", "12 sea_surface_height_correction_due_to_air_press... \n", "13 sea_surface_height_amplitude_due_to_earth_tide \n", "14 mole_concentration_of_dissolved_organic_carbon... \n", "15 eastward_sea_water_velocity \n", "16 water_surface_height_above_reference_datum \n", "17 sound_intensity_level_in_water \n", "18 sea_surface_wave_mean_period_from_variance_spe... \n", "19 sea_surface_wave_mean_period_from_variance_spe... \n", "20 mole_concentration_of_nitrate_in_sea_water \n", "21 northward_wind \n", "22 freezing_level_altitude \n", "23 mass_concentration_of_phosphate_in_sea_water \n", "24 height_above_sea_floor \n", "25 eastward_wind \n", "26 sound_intensity_level_in_air \n", "27 air_temperature \n", "28 depth_below_geoid \n", "29 altitude \n", "30 sea_surface_wind_wave_mean_period_from_varianc... \n", "31 product_of_eastward_sea_water_velocity_and_sal... \n", "32 surface_geostrophic_northward_sea_water_veloci... \n", "33 height_at_cloud_top \n", "34 sea_surface_wave_variance_spectral_density \n", "35 sea_surface_wind_wave_significant_height \n", "36 sea_floor_depth_below_sea_level \n", "37 sea_surface_wind_wave_mean_period_from_varianc... \n", "38 northward_sea_water_velocity \n", "39 sea_water_alkalinity_expressed_as_mole_equivalent \n", "40 depth \n", "41 sea_surface_subskin_temperature \n", "42 surface_altitude \n", "43 air_pressure_at_convective_cloud_top \n", "44 sea_water_ph_reported_on_total_scale \n", "45 sea_surface_wave_from_direction \n", "46 upward_latent_heat_flux_in_air \n", "47 surface_downward_northward_stress \n", "48 sea_surface_wind_wave_to_direction \n", "49 sea_surface_wind_wave_mean_period_from_varianc... \n", "50 air_pressure_at_sea_level \n", "51 wind_speed \n", "52 platform_pitch_angle \n", "53 mass_fraction_of_chlorophyll_a_in_sea_water \n", "54 radial_sea_water_velocity_away_from_instrument \n", "55 cloud_top_altitude \n", "56 sea_water_pressure_at_sea_floor \n", "57 moles_of_nitrate_and_nitrite_per_unit_mass_in_... \n", "58 volume_absorption_coefficient_of_radiative_flu... \n", "59 direction_of_radial_vector_away_from_instrument \n", "60 upward_sensible_heat_flux_in_air \n", "61 sea_surface_wave_period_at_variance_spectral_d... \n", "62 cloud_base_altitude \n", "63 air_temperature_at_cloud_top \n", "64 upward_heat_flux_in_sea_water_due_to_convection \n", "65 altitude_at_top_of_dry_convection \n", "66 equilibrium_line_altitude \n", "67 upward_sea_water_velocity \n", "68 sea_surface_salinity \n", "69 sea_surface_swell_wave_to_direction \n", "70 surface_air_pressure \n", "71 sea_water_electrical_conductivity \n", "72 sea_water_practical_salinity \n", "73 sea_surface_height_amplitude_due_to_equilibriu... \n", "74 surface_geostrophic_eastward_sea_water_velocity \n", "75 secchi_depth_of_sea_water \n", "76 mass_concentration_of_inorganic_nitrogen_in_se... \n", "77 sea_water_temperature \n", "78 sea_water_density \n", "79 sea_water_pressure_due_to_sea_water \n", "80 surface_carbon_dioxide_partial_pressure_differ... \n", "81 thickness_of_rainfall_amount \n", "82 temperature_of_sensor_for_oxygen_in_sea_water \n", "83 air_temperature_anomaly \n", "84 sea_water_pressure_at_sea_water_surface \n", "85 upward_air_velocity \n", "86 wind_speed_of_gust \n", "87 sea_water_pressure \n", "88 bolus_eastward_sea_water_velocity \n", "89 platform_yaw_angle \n", "90 sea_surface_wave_significant_height \n", "91 sea_floor_depth_below_geoid \n", "92 sea_surface_height_above_sea_level \n", "93 difference_of_air_pressure_from_model_reference \n", "94 sea_surface_wave_to_direction \n", "95 fractional_saturation_of_oxygen_in_sea_water \n", "96 geostrophic_northward_wind \n", "97 mole_concentration_of_ammonium_in_sea_water \n", "98 bolus_northward_sea_water_velocity \n", "99 model_level_number_at_sea_floor \n", "100 sea_surface_swell_wave_zero_upcrossing_period \n", "101 depth_of_isosurface_of_sea_water_potential_tem... \n", "102 baroclinic_eastward_sea_water_velocity \n", "103 geostrophic_eastward_wind \n", "104 platform_azimuth_angle \n", "105 sea_water_turbidity \n", "106 barotropic_eastward_sea_water_velocity \n", "107 air_pressure_anomaly \n", "108 mole_concentration_of_nitrite_in_sea_water \n", "109 height \n", "110 sea_surface_height_above_reference_ellipsoid \n", "111 air_temperature_lapse_rate \n", "112 moles_of_nitrite_per_unit_mass_in_sea_water \n", "113 tropopause_air_temperature \n", "114 mass_concentration_of_chlorophyll_in_sea_water \n", "115 platform_roll_angle \n", "116 sea_surface_height_bias_due_to_sea_surface_rou... \n", "117 surface_partial_pressure_of_carbon_dioxide_in_air \n", "118 mole_concentration_of_bacteria_expressed_as_ca... \n", "119 sea_surface_wave_zero_upcrossing_period \n", "120 surface_geostrophic_eastward_sea_water_velocit... \n", "121 latitude \n", "122 mole_concentration_of_nitrate_and_nitrite_in_s... \n", "123 magnitude_of_surface_downward_stress \n", "124 sea_surface_wave_mean_period_from_variance_spe... \n", "125 sea_surface_swell_wave_mean_period_from_varian... \n", "126 sea_floor_depth_below_sea_surface \n", "127 air_pressure_at_cloud_base \n", "128 thickness_of_snowfall_amount \n", "129 sea_surface_skin_temperature \n", "130 sea_surface_height_amplitude_due_to_pole_tide \n", "131 air_pressure_at_cloud_top \n", "132 mass_concentration_of_oxygen_in_sea_water \n", "133 mole_ratio_of_nitrate_to_phosphate_in_sea_water \n", "134 sea_surface_temperature \n", "135 surface_eastward_sea_water_velocity \n", "136 barotropic_northward_sea_water_velocity \n", "137 longitude \n", "138 sea_water_salinity \n", "139 air_pressure \n", "140 surface_downward_eastward_stress \n", "141 sea_surface_wave_directional_variance_spectral... \n", "142 wave_frequency \n", "143 wind_from_direction \n", "144 tropopause_air_pressure \n", "145 sea_surface_swell_wave_mean_period_from_varian... \n", "146 sea_surface_height_above_geoid \n", "147 direction_of_sea_water_velocity \n", "148 sea_water_speed \n", "149 sea_surface_height_amplitude_due_to_geocentric... \n", "150 change_over_time_in_sea_water_density \n", "151 mole_concentration_of_particulate_organic_matt... \n", "152 mass_concentration_of_chlorophyll_a_in_sea_water \n", "153 surface_geostrophic_northward_sea_water_velocity \n", "154 moles_of_phosphate_per_unit_mass_in_sea_water \n", "155 height_above_reference_ellipsoid \n", "156 eastward_sea_water_velocity_assuming_no_tide \n", "157 northward_sea_water_velocity_assuming_no_tide \n", "158 air_pressure_at_freezing_level \n", "159 mole_concentration_of_dissolved_inorganic_carb... \n", "160 platform_speed_wrt_ground \n", "161 surface_carbon_dioxide_partial_pressure_differ... \n", "162 surface_northward_sea_water_velocity \n", "163 convective_cloud_base_altitude \n", "164 sea_surface_wind_wave_period \n", "165 sea_surface_swell_wave_period \n", "166 baroclinic_northward_sea_water_velocity \n", "167 sea_surface_height_amplitude_due_to_non_equili... \n", "168 sea_surface_wind_wave_zero_upcrossing_period \n", "169 air_temperature_threshold \n", "\n", " CF URI \n", "0 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "1 http://mmisw.org/ont/cf/parameter/lagrangian_t... \n", "2 http://mmisw.org/ont/cf/parameter/radiation_wa... \n", "3 http://mmisw.org/ont/cf/parameter/mole_concent... \n", "4 http://mmisw.org/ont/cf/parameter/platform_course \n", "5 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "6 http://mmisw.org/ont/cf/parameter/wind_to_dire... \n", "7 http://mmisw.org/ont/cf/parameter/air_pressure... \n", "8 http://mmisw.org/ont/cf/parameter/surface_part... \n", "9 http://mmisw.org/ont/cf/parameter/tendency_of_... \n", "10 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "11 http://mmisw.org/ont/cf/parameter/moles_of_nit... \n", "12 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "13 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "14 http://mmisw.org/ont/cf/parameter/mole_concent... \n", "15 http://mmisw.org/ont/cf/parameter/eastward_sea... \n", "16 http://mmisw.org/ont/cf/parameter/water_surfac... \n", "17 http://mmisw.org/ont/cf/parameter/sound_intens... \n", "18 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "19 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "20 http://mmisw.org/ont/cf/parameter/mole_concent... \n", "21 http://mmisw.org/ont/cf/parameter/northward_wind \n", "22 http://mmisw.org/ont/cf/parameter/freezing_lev... \n", "23 http://mmisw.org/ont/cf/parameter/mass_concent... \n", "24 http://mmisw.org/ont/cf/parameter/height_above... \n", "25 http://mmisw.org/ont/cf/parameter/eastward_wind \n", "26 http://mmisw.org/ont/cf/parameter/sound_intens... \n", "27 http://mmisw.org/ont/cf/parameter/air_temperature \n", "28 http://mmisw.org/ont/cf/parameter/depth_below_... \n", "29 http://mmisw.org/ont/cf/parameter/altitude \n", "30 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "31 http://mmisw.org/ont/cf/parameter/product_of_e... \n", "32 http://mmisw.org/ont/cf/parameter/surface_geos... \n", "33 http://mmisw.org/ont/cf/parameter/height_at_cl... \n", "34 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "35 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "36 http://mmisw.org/ont/cf/parameter/sea_floor_de... \n", "37 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "38 http://mmisw.org/ont/cf/parameter/northward_se... \n", "39 http://mmisw.org/ont/cf/parameter/sea_water_al... \n", "40 http://mmisw.org/ont/cf/parameter/depth \n", "41 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "42 http://mmisw.org/ont/cf/parameter/surface_alti... \n", "43 http://mmisw.org/ont/cf/parameter/air_pressure... \n", "44 http://mmisw.org/ont/cf/parameter/sea_water_ph... \n", "45 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "46 http://mmisw.org/ont/cf/parameter/upward_laten... \n", "47 http://mmisw.org/ont/cf/parameter/surface_down... \n", "48 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "49 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "50 http://mmisw.org/ont/cf/parameter/air_pressure... \n", "51 http://mmisw.org/ont/cf/parameter/wind_speed \n", "52 http://mmisw.org/ont/cf/parameter/platform_pit... \n", "53 http://mmisw.org/ont/cf/parameter/mass_fractio... \n", "54 http://mmisw.org/ont/cf/parameter/radial_sea_w... \n", "55 http://mmisw.org/ont/cf/parameter/cloud_top_al... \n", "56 http://mmisw.org/ont/cf/parameter/sea_water_pr... \n", "57 http://mmisw.org/ont/cf/parameter/moles_of_nit... \n", "58 http://mmisw.org/ont/cf/parameter/volume_absor... \n", "59 http://mmisw.org/ont/cf/parameter/direction_of... \n", "60 http://mmisw.org/ont/cf/parameter/upward_sensi... \n", "61 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "62 http://mmisw.org/ont/cf/parameter/cloud_base_a... \n", "63 http://mmisw.org/ont/cf/parameter/air_temperat... \n", "64 http://mmisw.org/ont/cf/parameter/upward_heat_... \n", "65 http://mmisw.org/ont/cf/parameter/altitude_at_... \n", "66 http://mmisw.org/ont/cf/parameter/equilibrium_... \n", "67 http://mmisw.org/ont/cf/parameter/upward_sea_w... \n", "68 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "69 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "70 http://mmisw.org/ont/cf/parameter/surface_air_... \n", "71 http://mmisw.org/ont/cf/parameter/sea_water_el... \n", "72 http://mmisw.org/ont/cf/parameter/sea_water_pr... \n", "73 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "74 http://mmisw.org/ont/cf/parameter/surface_geos... \n", "75 http://mmisw.org/ont/cf/parameter/secchi_depth... \n", "76 http://mmisw.org/ont/cf/parameter/mass_concent... \n", "77 http://mmisw.org/ont/cf/parameter/sea_water_te... \n", "78 http://mmisw.org/ont/cf/parameter/sea_water_de... \n", "79 http://mmisw.org/ont/cf/parameter/sea_water_pr... \n", "80 http://mmisw.org/ont/cf/parameter/surface_carb... \n", "81 http://mmisw.org/ont/cf/parameter/thickness_of... \n", "82 http://mmisw.org/ont/cf/parameter/temperature_... \n", "83 http://mmisw.org/ont/cf/parameter/air_temperat... \n", "84 http://mmisw.org/ont/cf/parameter/sea_water_pr... \n", "85 http://mmisw.org/ont/cf/parameter/upward_air_v... \n", "86 http://mmisw.org/ont/cf/parameter/wind_speed_o... \n", "87 http://mmisw.org/ont/cf/parameter/sea_water_pr... \n", "88 http://mmisw.org/ont/cf/parameter/bolus_eastwa... \n", "89 http://mmisw.org/ont/cf/parameter/platform_yaw... \n", "90 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "91 http://mmisw.org/ont/cf/parameter/sea_floor_de... \n", "92 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "93 http://mmisw.org/ont/cf/parameter/difference_o... \n", "94 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "95 http://mmisw.org/ont/cf/parameter/fractional_s... \n", "96 http://mmisw.org/ont/cf/parameter/geostrophic_... \n", "97 http://mmisw.org/ont/cf/parameter/mole_concent... \n", "98 http://mmisw.org/ont/cf/parameter/bolus_northw... \n", "99 http://mmisw.org/ont/cf/parameter/model_level_... \n", "100 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "101 http://mmisw.org/ont/cf/parameter/depth_of_iso... \n", "102 http://mmisw.org/ont/cf/parameter/baroclinic_e... \n", "103 http://mmisw.org/ont/cf/parameter/geostrophic_... \n", "104 http://mmisw.org/ont/cf/parameter/platform_azi... \n", "105 http://mmisw.org/ont/cf/parameter/sea_water_tu... \n", "106 http://mmisw.org/ont/cf/parameter/barotropic_e... \n", "107 http://mmisw.org/ont/cf/parameter/air_pressure... \n", "108 http://mmisw.org/ont/cf/parameter/mole_concent... \n", "109 http://mmisw.org/ont/cf/parameter/height \n", "110 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "111 http://mmisw.org/ont/cf/parameter/air_temperat... \n", "112 http://mmisw.org/ont/cf/parameter/moles_of_nit... \n", "113 http://mmisw.org/ont/cf/parameter/tropopause_a... \n", "114 http://mmisw.org/ont/cf/parameter/mass_concent... \n", "115 http://mmisw.org/ont/cf/parameter/platform_rol... \n", "116 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "117 http://mmisw.org/ont/cf/parameter/surface_part... \n", "118 http://mmisw.org/ont/cf/parameter/mole_concent... \n", "119 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "120 http://mmisw.org/ont/cf/parameter/surface_geos... \n", "121 http://mmisw.org/ont/cf/parameter/latitude \n", "122 http://mmisw.org/ont/cf/parameter/mole_concent... \n", "123 http://mmisw.org/ont/cf/parameter/magnitude_of... \n", "124 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "125 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "126 http://mmisw.org/ont/cf/parameter/sea_floor_de... \n", "127 http://mmisw.org/ont/cf/parameter/air_pressure... \n", "128 http://mmisw.org/ont/cf/parameter/thickness_of... \n", "129 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "130 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "131 http://mmisw.org/ont/cf/parameter/air_pressure... \n", "132 http://mmisw.org/ont/cf/parameter/mass_concent... \n", "133 http://mmisw.org/ont/cf/parameter/mole_ratio_o... \n", "134 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "135 http://mmisw.org/ont/cf/parameter/surface_east... \n", "136 http://mmisw.org/ont/cf/parameter/barotropic_n... \n", "137 http://mmisw.org/ont/cf/parameter/longitude \n", "138 http://mmisw.org/ont/cf/parameter/sea_water_sa... \n", "139 http://mmisw.org/ont/cf/parameter/air_pressure \n", "140 http://mmisw.org/ont/cf/parameter/surface_down... \n", "141 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "142 http://mmisw.org/ont/cf/parameter/wave_frequency \n", "143 http://mmisw.org/ont/cf/parameter/wind_from_di... \n", "144 http://mmisw.org/ont/cf/parameter/tropopause_a... \n", "145 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "146 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "147 http://mmisw.org/ont/cf/parameter/direction_of... \n", "148 http://mmisw.org/ont/cf/parameter/sea_water_speed \n", "149 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "150 http://mmisw.org/ont/cf/parameter/change_over_... \n", "151 http://mmisw.org/ont/cf/parameter/mole_concent... \n", "152 http://mmisw.org/ont/cf/parameter/mass_concent... \n", "153 http://mmisw.org/ont/cf/parameter/surface_geos... \n", "154 http://mmisw.org/ont/cf/parameter/moles_of_pho... \n", "155 http://mmisw.org/ont/cf/parameter/height_above... \n", "156 http://mmisw.org/ont/cf/parameter/eastward_sea... \n", "157 http://mmisw.org/ont/cf/parameter/northward_se... \n", "158 http://mmisw.org/ont/cf/parameter/air_pressure... \n", "159 http://mmisw.org/ont/cf/parameter/mole_concent... \n", "160 http://mmisw.org/ont/cf/parameter/platform_spe... \n", "161 http://mmisw.org/ont/cf/parameter/surface_carb... \n", "162 http://mmisw.org/ont/cf/parameter/surface_nort... \n", "163 http://mmisw.org/ont/cf/parameter/convective_c... \n", "164 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "165 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "166 http://mmisw.org/ont/cf/parameter/baroclinic_n... \n", "167 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "168 http://mmisw.org/ont/cf/parameter/sea_surface_... \n", "169 http://mmisw.org/ont/cf/parameter/air_temperat... " ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Geographic subset\n", "This test looks at the East Coast of the United States." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bounding_box = [ -81.03, 27.59, -66.14, 44.92] # East Coast\n", "\n", "\"Geographic subset: {!s}\".format(bounding_box)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "'Geographic subset: [-81.03, 27.59, -66.14, 44.92]'" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Temporal subset\n", "January 1, 2014 to August 1, 2014" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from datetime import datetime\n", "start_date = datetime(2014,1,1)\n", "start_date_string = start_date.strftime('%Y-%m-%d %H:00')\n", "\n", "end_date = datetime(2014,8,1)\n", "end_date_string = end_date.strftime('%Y-%m-%d %H:00')\n", "\n", "\"Temporal subset: ( {!s} to {!s} )\".format(start_date_string, end_date_string)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "'Temporal subset: ( 2014-01-01 00:00 to 2014-08-01 00:00 )'" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Set variable subset\n", "\n", "Define the variables. It turns out that dissolved oxygen is not an IOOS Core Variable in MMI, so we need to append it to the list of other standard names that are standard." ] }, { "cell_type": "code", "collapsed": false, "input": [ "variables_to_query = [ x for x in cf_standard_names if \"oxygen\" in x ]\n", "custom_variables = ['dissolved_oxygen', 'oxygen'] # Do we need any, or are they all extractable from MMI?\n", "\n", "variables_to_query += custom_variables\n", "\"Variable subset: {!s}\".format(\" , \".join(variables_to_query))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "'Variable subset: temperature_of_sensor_for_oxygen_in_sea_water , fractional_saturation_of_oxygen_in_sea_water , mass_concentration_of_oxygen_in_sea_water , dissolved_oxygen , oxygen'" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Define all known the CSW endpoints" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# https://github.com/ioos/system-test/wiki/Service-Registries-and-Data-Catalogs\n", "known_csw_servers = ['http://data.nodc.noaa.gov/geoportal/csw',\n", " 'http://www.nodc.noaa.gov/geoportal/csw',\n", " 'http://www.ngdc.noaa.gov/geoportal/csw',\n", " 'http://cwic.csiss.gmu.edu/cwicv1/discovery',\n", " 'http://geoport.whoi.edu/geoportal/csw',\n", " 'https://edg.epa.gov/metadata/csw',\n", " 'http://cmgds.marine.usgs.gov/geonetwork/srv/en/csw',\n", " 'http://cida.usgs.gov/gdp/geonetwork/srv/en/csw',\n", " 'http://geodiscover.cgdi.ca/wes/serviceManagerCSW/csw',\n", " 'http://geoport.whoi.edu/gi-cat/services/cswiso',\n", " 'https://data.noaa.gov/csw',\n", " ]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Construct CSW Filters" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from owslib import fes\n", "def fes_date_filter(start_date='1900-01-01',stop_date='2100-01-01',constraint='overlaps'):\n", " if constraint == 'overlaps':\n", " start = fes.PropertyIsGreaterThanOrEqualTo(propertyname='apiso:TempExtent_end', literal=start_date)\n", " stop = fes.PropertyIsLessThanOrEqualTo(propertyname='apiso:TempExtent_begin', literal=stop_date)\n", " elif constraint == 'within':\n", " start = fes.PropertyIsGreaterThanOrEqualTo(propertyname='apiso:TempExtent_begin', literal=start_date)\n", " stop = fes.PropertyIsLessThanOrEqualTo(propertyname='apiso:TempExtent_end', literal=stop_date)\n", " return fes.And([start, stop])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# Standard Name filters\n", "cf_name_filters = []\n", "for cf_name in variables_to_query:\n", " text_filter = fes.PropertyIsLike(propertyname='apiso:AnyText', literal=\"*%s*\" % cf_name, wildCard='*')\n", " cf_name_filters.append(text_filter)\n", "cf_name_filters = fes.Or(cf_name_filters)\n", "\n", "# Geographic filters\n", "geographic_filter = fes.BBox(bbox=bounding_box)\n", "\n", "# Temporal filters\n", "temporal_filter = fes_date_filter(start_date_string, end_date_string)\n", "\n", "filters = fes.And([cf_name_filters, geographic_filter, temporal_filter])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### The actual CSW filter POST envelope looks like this" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from owslib.etree import etree\n", "print etree.tostring(filters.toXML(), pretty_print=True)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<ogc:And xmlns:ogc=\"http://www.opengis.net/ogc\">\n", " <ogc:Or>\n", " <ogc:PropertyIsLike wildCard=\"*\" singleChar=\"_\" escapeChar=\"\\\">\n", " <ogc:PropertyName>apiso:AnyText</ogc:PropertyName>\n", " <ogc:Literal>*temperature_of_sensor_for_oxygen_in_sea_water*</ogc:Literal>\n", " </ogc:PropertyIsLike>\n", " <ogc:PropertyIsLike wildCard=\"*\" singleChar=\"_\" escapeChar=\"\\\">\n", " <ogc:PropertyName>apiso:AnyText</ogc:PropertyName>\n", " <ogc:Literal>*fractional_saturation_of_oxygen_in_sea_water*</ogc:Literal>\n", " </ogc:PropertyIsLike>\n", " <ogc:PropertyIsLike wildCard=\"*\" singleChar=\"_\" escapeChar=\"\\\">\n", " <ogc:PropertyName>apiso:AnyText</ogc:PropertyName>\n", " <ogc:Literal>*mass_concentration_of_oxygen_in_sea_water*</ogc:Literal>\n", " </ogc:PropertyIsLike>\n", " <ogc:PropertyIsLike wildCard=\"*\" singleChar=\"_\" escapeChar=\"\\\">\n", " <ogc:PropertyName>apiso:AnyText</ogc:PropertyName>\n", " <ogc:Literal>*dissolved_oxygen*</ogc:Literal>\n", " </ogc:PropertyIsLike>\n", " <ogc:PropertyIsLike wildCard=\"*\" singleChar=\"_\" escapeChar=\"\\\">\n", " <ogc:PropertyName>apiso:AnyText</ogc:PropertyName>\n", " <ogc:Literal>*oxygen*</ogc:Literal>\n", " </ogc:PropertyIsLike>\n", " </ogc:Or>\n", " <ogc:BBOX>\n", " <ogc:PropertyName>ows:BoundingBox</ogc:PropertyName>\n", " <gml311:Envelope xmlns:gml311=\"http://www.opengis.net/gml\">\n", " <gml311:lowerCorner>-81.03 27.59</gml311:lowerCorner>\n", " <gml311:upperCorner>-66.14 44.92</gml311:upperCorner>\n", " </gml311:Envelope>\n", " </ogc:BBOX>\n", " <ogc:And>\n", " <ogc:PropertyIsGreaterThanOrEqualTo>\n", " <ogc:PropertyName>apiso:TempExtent_end</ogc:PropertyName>\n", " <ogc:Literal>2014-01-01 00:00</ogc:Literal>\n", " </ogc:PropertyIsGreaterThanOrEqualTo>\n", " <ogc:PropertyIsLessThanOrEqualTo>\n", " <ogc:PropertyName>apiso:TempExtent_begin</ogc:PropertyName>\n", " <ogc:Literal>2014-08-01 00:00</ogc:Literal>\n", " </ogc:PropertyIsLessThanOrEqualTo>\n", " </ogc:And>\n", "</ogc:And>\n", "\n" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Filter out CSW servers that do not support a BBOX query" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from owslib.csw import CatalogueServiceWeb\n", "bbox_endpoints = []\n", "for url in known_csw_servers:\n", " queryables = []\n", " try:\n", " csw = CatalogueServiceWeb(url, timeout=20)\n", " except BaseException:\n", " print \"Failure - %s - Timed out\" % url\n", " if \"BBOX\" in csw.filters.spatial_operators:\n", " print \"Success - %s - BBOX Query supported\" % url\n", " bbox_endpoints.append(url) \n", " else:\n", " print \"Failure - %s - BBOX Query NOT supported\" % url" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Success - http://data.nodc.noaa.gov/geoportal/csw - BBOX Query supported\n", "Success - http://www.nodc.noaa.gov/geoportal/csw - BBOX Query supported" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Success - http://www.ngdc.noaa.gov/geoportal/csw - BBOX Query supported" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Success - http://cwic.csiss.gmu.edu/cwicv1/discovery - BBOX Query supported" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Success - http://geoport.whoi.edu/geoportal/csw - BBOX Query supported" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Success - https://edg.epa.gov/metadata/csw - BBOX Query supported" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Success - http://cmgds.marine.usgs.gov/geonetwork/srv/en/csw - BBOX Query supported" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Success - http://cida.usgs.gov/gdp/geonetwork/srv/en/csw - BBOX Query supported" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Success - http://geodiscover.cgdi.ca/wes/serviceManagerCSW/csw - BBOX Query supported" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Failure - http://geoport.whoi.edu/gi-cat/services/cswiso - Timed out" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Success - http://geoport.whoi.edu/gi-cat/services/cswiso - BBOX Query supported\n", "Success - https://data.noaa.gov/csw - BBOX Query supported" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Query CSW Servers using filters" ] }, { "cell_type": "code", "collapsed": false, "input": [ "urls = []\n", "service_types = []\n", "servers = []\n", "for url in bbox_endpoints:\n", " print \"*\", url\n", " try:\n", " csw = CatalogueServiceWeb(url, timeout=20)\n", " csw.getrecords2(constraints=[filters], maxrecords=200, esn='full')\n", " for record, item in csw.records.items():\n", " # Get URLs\n", " service_url, scheme = next(((d['url'], d['scheme']) for d in item.references), None)\n", " if service_url:\n", " if len(item.title) > 100:\n", " title = \"{!s}...{!s}\".format(item.title[0:50], item.title[-50:])\n", " else:\n", " title = item.title \n", " print \" [x] {!s}\".format(title)\n", " \n", " urls.append(service_url)\n", " service_types.append(scheme)\n", " servers.append(url)\n", " except BaseException as e:\n", " print \" [-] FAILED: {!s}\".format(e)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "* http://data.nodc.noaa.gov/geoportal/csw\n", " [x] Temperature, salinity, oxygen, chlorophyll, and pr...from 1985 through present (NODC Accession 0068584)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " [x] OC334L01: WHOI cruise 334 leg 01 aboard the R/V Oc...m 1998-11-30 - 2020-12-01 (NODC Accession 0078935)\n", " [x] Temperature, salinity, and other profile data coll...rom 07/24/1972 to present (NODC Accession 0038589)\n", " [x] Temperature, salinity and dissolved oxygen profile... 1995-09-07 to 2014-10-31 (NODC Accession 0042682)\n", " [x] Oceanographic and surface meteorological data coll... 2014-02-13 to 2014-10-31 (NODC Accession 0118793)\n", " [x] Oceanographic and surface meteorological data coll... 2014-02-13 to 2014-10-31 (NODC Accession 0118794)\n", " [x] Oceanographic and surface meteorological data coll... 2014-02-13 to 2014-10-31 (NODC Accession 0118795)\n", "* http://www.nodc.noaa.gov/geoportal/csw\n", " [-] FAILED: timed out" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "* http://www.ngdc.noaa.gov/geoportal/csw\n", " [x] HRECOS Aggregated Station HRALBPH Data" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " [x] HRECOS Aggregated Station HRLCK8H Data\n", " [x] HRECOS Aggregated Station HRMARPH Data\n", " [x] HRECOS Aggregated Station HRPIER84 Data\n", " [x] HRECOS Aggregated Station HRWSTPTH Data\n", " [x] lbhmc.2ndave.pier\n", " [x] lbhmc.apachepier.pier\n", " [x] lbhmc.cherrygrove.pier\n", " [x] HRECOS Aggregated Station HRPMNTH Data\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...Massachusetts Bay: A01 OPTODE51m Massachusetts Bay\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...1 Massachusetts Bay: A01 DOPPLER Massachusetts Bay\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...stern Maine Shelf: B01 DOPPLER Western Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...ntral Maine Shelf: E01 DOPPLER Central Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...ions: F01 Penobscot Bay: F01 DOPPLER Penobscot Bay\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...stern Maine Shelf: I01 DOPPLER Eastern Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...ations: M01 Jordan Basin: M01 DOPPLER Jordan Basin\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...rvations: F01 Penobscot Bay: F01 MET Penobscot Bay\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...1 Eastern Maine Shelf: I01 MET Eastern Maine Shelf\n", " [x] B01 SBE16 - CTD Observations\n", " [x] E01 SBE16 - CTD Observations\n", " [x] I01 SBE16 - CTD Observations\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...achusetts Bay: A01 ACCELEROMETER Massachusetts Bay\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...A01 Massachusetts Bay: A01 CTD1m Massachusetts Bay\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...01 Massachusetts Bay: A01 CTD20m Massachusetts Bay\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...: A01 Massachusetts Bay: A01 MET Massachusetts Bay\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ... Massachusetts Bay: A01 OPTICS3m Massachusetts Bay\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...tern Maine Shelf: B01 AANDERAA Western Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...Maine Shelf: B01 ACCELEROMETER Western Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...Western Maine Shelf: B01 CTD1m Western Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...estern Maine Shelf: B01 CTD20m Western Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...estern Maine Shelf: B01 CTD50m Western Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...tern Maine Shelf: B0126 CTD52m Central Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...1 Western Maine Shelf: B01 MET Western Maine Shelf\n", " [x] B01 Aanderaa - Historic Surface Currents\n", " [x] B01 SBE16 - CTD Transmissivity\n", " [x] TEMPESTS MA101 Water Level - CINAR\n", " [x] TEMPESTS MA101 Met - CINAR\n", " [x] TEMPESTS MA101 SBE37 1m CTD - CINAR\n", " [x] TEMPESTS MA101 Waves - CINAR\n", " [x] TEMPESTS MD101 Water Level - CINAR\n", " [x] TEMPESTS MD101 Met - CINAR\n", " [x] TEMPESTS MD101 SBE37 1m CTD - CINAR\n", " [x] TEMPESTS MD101 Waves - CINAR\n", " [x] TEMPESTS ME101 Water Level - CINAR\n", " [x] TEMPESTS ME101 Met - CINAR\n", " [x] TEMPESTS ME101 SBE37 1m CTD - CINAR\n", " [x] TEMPESTS ME101 Waves - CINAR\n", " [x] TEMPESTS NJ101 Water Level - CINAR\n", " [x] TEMPESTS NJ101 Met - CINAR\n", " [x] TEMPESTS NJ101 SBE37 1m CTD - CINAR\n", " [x] TEMPESTS NJ101 Waves - CINAR\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...tral Maine Shelf: E01 AANDERAA Central Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...Maine Shelf: E01 ACCELEROMETER Central Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...Central Maine Shelf: E01 CTD1m Central Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...entral Maine Shelf: E01 CTD20m Central Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...entral Maine Shelf: E01 CTD50m Central Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...entral Maine Shelf: E01 CTD87m Central Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...1 Central Maine Shelf: E01 MET Central Maine Shelf\n", " [x] E01 Aanderaa - Historic Surface Currents\n", " [x] E01 SBE16 - CTD Transmissivity\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...ons: F01 Penobscot Bay: F01 AANDERAA Penobscot Bay\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...F01 Penobscot Bay: F01 ACCELEROMETER Penobscot Bay\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...ations: F01 Penobscot Bay: F01 CTD1m Penobscot Bay\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...tions: F01 Penobscot Bay: F01 CTD20m Penobscot Bay\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...tions: F01 Penobscot Bay: F01 CTD50m Penobscot Bay\n", " [x] F01 Aanderaa - Historic Surface Currents\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...tern Maine Shelf: I01 AANDERAA Eastern Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...Maine Shelf: I01 ACCELEROMETER Eastern Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...Eastern Maine Shelf: I01 CTD1m Eastern Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...astern Maine Shelf: I01 CTD20m Eastern Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...astern Maine Shelf: I01 CTD50m Eastern Maine Shelf\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...tern Maine Shelf: I0129 CTD87m Central Maine Shelf\n", " [x] I01 Aanderaa - Historic Surface Currents\n", " [x] I01 SBE16 - CTD Transmissivity\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...tions: M01 Jordan Basin: M01 AANDERAA Jordan Basin\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...: M01 Jordan Basin: M01 ACCELEROMETER Jordan Basin\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...rvations: M01 Jordan Basin: M01 CTD1m Jordan Basin\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...vations: M01 Jordan Basin: M01 CTD20m Jordan Basin\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...vations: M01 Jordan Basin: M01 CTD50m Jordan Basin\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...ations: M01 Jordan Basin: M01 CTD100m Jordan Basin\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...ations: M01 Jordan Basin: M01 CTD150m Jordan Basin\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...ations: M01 Jordan Basin: M01 CTD200m Jordan Basin\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...ations: M01 Jordan Basin: M01 CTD250m Jordan Basin\n", " [x] NERACOOS Gulf of Maine Ocean Array: Realtime Buoy ...servations: M01 Jordan Basin: M01 MET Jordan Basin\n", " [x] M01 Aanderaa - Historic Surface Currents\n", "* http://cwic.csiss.gmu.edu/cwicv1/discovery\n", " [-] FAILED: 'REQUEST_EXCEPTION: MISSING_CRS - Required srsName attribute was not found in <Envelope>.'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "* http://geoport.whoi.edu/geoportal/csw\n", " [-] FAILED: 'Invalid parameter value: locator=PropertyName'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "* https://edg.epa.gov/metadata/csw\n", "*" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " http://cmgds.marine.usgs.gov/geonetwork/srv/en/csw\n", "*" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " http://cida.usgs.gov/gdp/geonetwork/srv/en/csw\n", "*" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " http://geodiscover.cgdi.ca/wes/serviceManagerCSW/csw\n", " [-] FAILED: 'ORA-00907: missing right parenthesis'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "* http://geoport.whoi.edu/gi-cat/services/cswiso\n", " [-] FAILED: timed out" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "* https://data.noaa.gov/csw\n" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### What service are available?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "srvs = pd.DataFrame(zip(urls, service_types, servers), columns=(\"URL\", \"Service Type\", \"Server\"))\n", "srvs = srvs.drop_duplicates()\n", "pd.set_option('display.max_rows', 10)\n", "srvs" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<pre>\n", "&lt;class 'pandas.core.frame.DataFrame'&gt;\n", "Int64Index: 86 entries, 0 to 91\n", "Data columns (total 3 columns):\n", "URL 86 non-null values\n", "Service Type 86 non-null values\n", "Server 86 non-null values\n", "dtypes: object(3)\n", "</pre>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 86 entries, 0 to 91\n", "Data columns (total 3 columns):\n", "URL 86 non-null values\n", "Service Type 86 non-null values\n", "Server 86 non-null values\n", "dtypes: object(3)" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### What types of service are available" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pd.DataFrame(srvs.groupby(\"Service Type\").size(), columns=(\"Number of services\",))" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Number of services</th>\n", " </tr>\n", " <tr>\n", " <th>Service Type</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>urn:x-esri:specification:ServiceType:ArcIMS:Metadata:Onlink</th>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>urn:x-esri:specification:ServiceType:distribution:url</th>\n", " <td> 85</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ " Number of services\n", "Service Type \n", "urn:x-esri:specification:ServiceType:ArcIMS:Metadata:Onlink 1\n", "urn:x-esri:specification:ServiceType:distribution:url 85" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"error\" style=\"text-align: center\"><strong>SOS and DAP Servers are not properly identified</strong><br />One can not tell (programatically) what the \"urn:x-esri:specification:ServiceType:distribution:url\" scheme actually is.</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SOS" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def find_sos(x):\n", " d = x.lower()\n", " if \"sos\" in d and \"dods\" not in d:\n", " return x\n", " return None" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "sos_servers = filter(None, srvs[\"URL\"].map(find_sos))\n", "sos_servers" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "[]" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"error\" style=\"text-align: center\"><strong>No SOS Servers Found</strong><br />There are no SOS servers that are found using the CSW filters</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# DAP" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def find_dap(x):\n", " d = x.lower()\n", " if (\"dap\" in d or \"dods\" in d) and \"tabledap\" not in d:\n", " return x\n", " return None" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "dap_servers = filter(None, srvs[\"URL\"].map(find_dap))\n", "dap_servers = map(lambda x: os.path.splitext(x)[0], dap_servers)\n", "dap_servers" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 55, "text": [ "['http://sos.maracoos.org/stable/dodsC/stationHRALBPH-agg.ncml',\n", " 'http://sos.maracoos.org/stable/dodsC/stationHRLCK8H-agg.ncml',\n", " 'http://sos.maracoos.org/stable/dodsC/stationHRMARPH-agg.ncml',\n", " 'http://sos.maracoos.org/stable/dodsC/stationHRPIER84-agg.ncml',\n", " 'http://sos.maracoos.org/stable/dodsC/stationHRWSTPTH-agg.ncml',\n", " 'http://tds.secoora.org/thredds/dodsC/lbhmc.2ndave.pier.nc',\n", " 'http://tds.secoora.org/thredds/dodsC/lbhmc.apachepier.pier.nc',\n", " 'http://tds.secoora.org/thredds/dodsC/lbhmc.cherrygrove.pier.nc',\n", " 'http://sos.maracoos.org/stable/dodsC/stationHRPMNTH-agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/Doppler/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/Doppler/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/Doppler/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/Doppler/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/Doppler/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/Doppler/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/Met/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/Met/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/Accelerometer/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/CTD1m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/CTD20m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/Met/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/OPTICS_S3m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/OPTODE51m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/Aanderaa/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/Accelerometer/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/CTD1m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/CTD20m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/CTD50m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/CTD52m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/Met/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/Aanderaa/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/Accelerometer/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/CTD1m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/CTD20m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/CTD50m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/CTD87m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/Met/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/Aanderaa/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/Accelerometer/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/CTD1m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/CTD20m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/CTD50m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/Aanderaa/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/Accelerometer/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/CTD1m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/CTD20m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/CTD50m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/CTD87m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/Aanderaa/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/Accelerometer/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD1m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD20m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD50m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD100m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD150m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD200m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD250m/HistoricRealtime/Agg.ncml',\n", " 'http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/Met/HistoricRealtime/Agg.ncml']" ] } ], "prompt_number": 55 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Try to extract dissolved oxygen data from all of the DAP endpoints" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import iris\n", "import iris.plot as iplt\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "variables = lambda cube: cube.standard_name in variables_to_query\n", "constraint = iris.Constraint(cube_func=variables)\n", "\n", "\n", "def iris_grid_plot(cube_slice, name=None):\n", " plt.figure(figsize=(12, 8))\n", " lat = cube_slice.coord(axis='Y').points\n", " lon = cube_slice.coord(axis='X').points\n", " time = cube_slice.coord('time')[0]\n", " plt.subplot(111, aspect=(1.0 / cos(mean(lat) * pi / 180.0)))\n", " plt.pcolormesh(lon, lat, ma.masked_invalid(cube_slice.data));\n", " plt.colorbar()\n", " plt.grid()\n", " date = time.units.num2date(time.points)\n", " date_str = date[0].strftime('%Y-%m-%d %H:%M:%S %Z')\n", " plt.title('%s: %s: %s' % (name, cube_slice.long_name, date_str));\n", " plt.show()\n", "\n", "for dap in dap_servers:\n", " print \"[*] {!s}\".format(dap)\n", " try:\n", " cube = iris.load_cube(dap, constraint)\n", " except BaseException as e:\n", " print \" [-] Could not load: {!s}\".format(e)\n", " continue\n", " \n", " print \" [-] Identified as a Grid\"\n", " print \" [-] {!s}\".format(cube.attributes[\"title\"])\n", " try:\n", " try:\n", " cube.coord(axis='T').rename('time')\n", " except:\n", " pass\n", " if len(cube.shape) == 4:\n", " cube = cube[0, -1, ::1, ::1]\n", " elif len(cube.shape) == 3:\n", " cube = cube[0, ::1, ::1]\n", " elif len(cube.shape) == 2:\n", " cube = cube[::1, ::1]\n", " else:\n", " raise ValueError(\"Dimensions do not adhere to plotting requirements\")\n", " iris_grid_plot(cube, cube.attributes[\"title\"])\n", " \n", " except ValueError as e:\n", " print \" [-] Could not plot: {!s}\".format(e)\n", " continue \n", " " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[*] http://sos.maracoos.org/stable/dodsC/stationHRALBPH-agg.ncml\n", " [-] Could not load: NetCDF: Malformed or inaccessible DAP DDS\n", "[*] http://sos.maracoos.org/stable/dodsC/stationHRLCK8H-agg.ncml\n", " [-] Could not load: NetCDF: Malformed or inaccessible DAP DDS" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "Exception AttributeError: \"'CFReader' object has no attribute '_dataset'\" in <bound method CFReader.__del__ of CFReader('http://sos.maracoos.org/stable/dodsC/stationHRALBPH-agg.ncml')> ignored\n", "Exception AttributeError: \"'CFReader' object has no attribute '_dataset'\" in <bound method CFReader.__del__ of CFReader('http://sos.maracoos.org/stable/dodsC/stationHRLCK8H-agg.ncml')> ignored\n", "Exception " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://sos.maracoos.org/stable/dodsC/stationHRMARPH-agg.ncml\n", " [-] Could not load: NetCDF: Malformed or inaccessible DAP DDS\n", "[*] http://sos.maracoos.org/stable/dodsC/stationHRPIER84-agg.ncml\n", " [-] Could not load: NetCDF: Malformed or inaccessible DAP DDS" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "AttributeError: \"'CFReader' object has no attribute '_dataset'\" in <bound method CFReader.__del__ of CFReader('http://sos.maracoos.org/stable/dodsC/stationHRMARPH-agg.ncml')> ignored\n", "Exception AttributeError: \"'CFReader' object has no attribute '_dataset'\" in <bound method CFReader.__del__ of CFReader('http://sos.maracoos.org/stable/dodsC/stationHRPIER84-agg.ncml')> ignored\n", "Exception " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://sos.maracoos.org/stable/dodsC/stationHRWSTPTH-agg.ncml\n", " [-] Could not load: NetCDF: Malformed or inaccessible DAP DDS\n", "[*] http://tds.secoora.org/thredds/dodsC/lbhmc.2ndave.pier.nc\n", " [-] Could not load: failed to merge into a single cube.\n", " cube.var_name differs: u'dissolved_oxygen' != u'dissolved_oxygen_2'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://tds.secoora.org/thredds/dodsC/lbhmc.apachepier.pier.nc\n", " [-] Could not load: failed to merge into a single cube.\n", " cube.var_name differs: u'dissolved_oxygen' != u'dissolved_oxygen_2'" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "AttributeError: \"'CFReader' object has no attribute '_dataset'\" in <bound method CFReader.__del__ of CFReader('http://sos.maracoos.org/stable/dodsC/stationHRWSTPTH-agg.ncml')> ignored\n", "/home/kwilcox/.virtualenvs/system-test/lib/python2.7/site-packages/Iris-1.7.0_dev-py2.7.egg/iris/fileformats/_pyke_rules/compiled_krb/fc_rules_cf_fc.py:1357: UserWarning: Failed to create 'time' dimension coordinate: The points array must be strictly monotonic.\n", "Gracefully creating 'time' auxiliary coordinate instead.\n", " error=e_msg))\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://tds.secoora.org/thredds/dodsC/lbhmc.cherrygrove.pier.nc\n", " [-] Could not load: failed to merge into a single cube.\n", " cube.var_name differs: u'dissolved_oxygen' != u'dissolved_oxygen_2'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://sos.maracoos.org/stable/dodsC/stationHRPMNTH-agg.ncml\n", " [-] Could not load: NetCDF: Malformed or inaccessible DAP DDS\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/Doppler/HistoricRealtime/Agg.ncml\n", " [-] Could not load: NetCDF: Malformed or inaccessible DAP DDS" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "Exception AttributeError: \"'CFReader' object has no attribute '_dataset'\" in <bound method CFReader.__del__ of CFReader('http://sos.maracoos.org/stable/dodsC/stationHRPMNTH-agg.ncml')> ignored\n", "Exception " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/Doppler/HistoricRealtime/Agg.ncml\n", " [-] Could not load: NetCDF: Malformed or inaccessible DAP DDS" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "AttributeError: \"'CFReader' object has no attribute '_dataset'\" in <bound method CFReader.__del__ of CFReader('http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/Doppler/HistoricRealtime/Agg.ncml')> ignored\n", "Exception " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/Doppler/HistoricRealtime/Agg.ncml\n", " [-] Could not load: NetCDF: Malformed or inaccessible DAP DDS" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "AttributeError: \"'CFReader' object has no attribute '_dataset'\" in <bound method CFReader.__del__ of CFReader('http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/Doppler/HistoricRealtime/Agg.ncml')> ignored\n", "Exception AttributeError: \"'CFReader' object has no attribute '_dataset'\" in <bound method CFReader.__del__ of CFReader('http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/Doppler/HistoricRealtime/Agg.ncml')> ignored\n", "Exception " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/Doppler/HistoricRealtime/Agg.ncml\n", " [-] Could not load: NetCDF: Malformed or inaccessible DAP DDS\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/Doppler/HistoricRealtime/Agg.ncml\n", " [-] Could not load: NetCDF: Malformed or inaccessible DAP DDS" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "AttributeError: \"'CFReader' object has no attribute '_dataset'\" in <bound method CFReader.__del__ of CFReader('http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/Doppler/HistoricRealtime/Agg.ncml')> ignored\n", "Exception " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/Doppler/HistoricRealtime/Agg.ncml\n", " [-] Could not load: NetCDF: Malformed or inaccessible DAP DDS" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "AttributeError: \"'CFReader' object has no attribute '_dataset'\" in <bound method CFReader.__del__ of CFReader('http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/Doppler/HistoricRealtime/Agg.ncml')> ignored\n", "Exception " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/Met/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/Met/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/Accelerometer/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/CTD1m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/CTD20m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/Met/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/OPTICS_S3m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/A01/OPTODE51m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/Aanderaa/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/Accelerometer/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/CTD1m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/CTD20m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/CTD50m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/CTD52m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/B01/Met/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/Aanderaa/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/Accelerometer/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/CTD1m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/CTD20m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/CTD50m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/CTD87m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/E01/Met/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/Aanderaa/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/Accelerometer/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/CTD1m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/CTD20m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/F01/CTD50m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/Aanderaa/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/Accelerometer/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/CTD1m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/CTD20m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/CTD50m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/I01/CTD87m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/Aanderaa/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/Accelerometer/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD1m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD20m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD50m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD100m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD150m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD200m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/CTD250m/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[*] http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/Met/HistoricRealtime/Agg.ncml\n", " [-] Could not load: 'long_name' is not a permitted attribute" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "AttributeError: \"'CFReader' object has no attribute '_dataset'\" in <bound method CFReader.__del__ of CFReader('http://www.neracoos.org/thredds/dodsC/UMO/DSG/SOS/M01/Doppler/HistoricRealtime/Agg.ncml')> ignored\n" ] } ], "prompt_number": 59 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"error\"><strong>Miscellaneous Errors</strong> - Many DAP endpoints for dissolved oxygens said data was available, but returned errors related to \"malformed or inaccessible DAP DDS\" (a catchall issue that can be difficult to resolve for users), variable names such as \"dissolved_oxygen_2\", and missing attributes in netCDF files.</div>" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

unlicense

jairideout/scikit-bio-cookbook

Index.ipynb

1

1541

{ "metadata": { "name": "", "signature": "sha256:a6ca15047262c22bbb790e374ab37a15aefba952493abe27d54f96ed773bcf13" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "The [scikit-bio](http://scikit-bio.org) cookbook: delicious and nutritious bioinformatics recipes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This series of IPython Notebooks contains executable \"recipes\" for bioinformatics workflows using [scikit-bio](http://scikit-bio.org).\n", "\n", "Disclaimer: **This is not the main scikit-bio documentation.** You can find the latest scikit-bio API documentation [here](http://scikit-bio.org/docs/latest/). **This is an experimental project**, and while we'll make an effort to keep these notebooks up-to-date, at this point we are not guaranteeing that they will work. If these notebooks seem to be useful, we'll hook up automated testing to ensure that they work. " ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Table of Contents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " * [Finding PCR primers in sequencing products](Finding PCR primers in sequencing products.ipynb)\n", " * [Progressive multiple sequence alignment](Progressive multiple sequence alignment.ipynb)" ] } ], "metadata": {} } ] }

bsd-3-clause

cleuton/datascience

nlp/sentimentAnalysis/SentimentTweets.ipynb

2

3370

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sentimentos de tweets quando ao lançamento do Falcon Heavy" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "library(twitteR)\n", "library(ROAuth)\n", "library(httr)\n", "library(plyr)\n", "library(stringr)\n", "library(tidytext)\n", "library(readr)\n", "library(dplyr)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# parameters: api_key,api_secret,access_token,access_token_secret\n", "setup_twitter_oauth('1', '2', \n", " '3', \n", " '4')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tweets_falcon <- searchTwitter('#falconheavy', n=3000, lang='pt')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "head(tweets_falcon)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df <- twListToDF(tweets_falcon)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "head(df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oplexicon <- read_csv('oplexicon_v3.0/lexico_v3.0.txt', col_names = c('word', 'type', 'weight', 'other'), col_types = \n", " cols(\n", " word = col_character(),\n", " type = col_character(),\n", " weight = col_integer(), \n", " other = col_character()\n", " ))\n", "head(oplexicon)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "stopwords <- read_csv('portuguese-stopwords.txt', col_names = 'word')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tweets <- df %>%\n", " unnest_tokens(word,text) %>%\n", " anti_join(stopwords,by=\"word\") \n", "head(tweets)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sentimentoTweets <- tweets %>%\n", " inner_join(oplexicon) %>%\n", " group_by(screenName) %>%\n", " summarize(peso = sum(weight, na.rm = TRUE))\n", "head(sentimentoTweets)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sum(sentimentoTweets$peso)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mean(sentimentoTweets$peso)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

lkmsk/OpenDataToNeo4J

DB_OpenData/Explore_With_py2neo_NetworkX.ipynb

1

197574

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Explore DB-OpenData with py2neo and NetworkX\n", "\n", "In this notebook we explore the carsharing data from Deutsche Bahn, they're migrated to a Neo4J graph database. For this we use two libraries: [py2neo](http://py2neo.org/v3/index.html) and [NetworkX](https://networkx.github.io)." ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "seperatingLine = \"\\n########################################################################################################\\n\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Access the object informations over the py2neo-API\n", "\n", "A single record in a cursor as result of a cypher-query execution contains the informations about a single (or a list of) node(s)/relationship(s), depending on which type the result objects of the query have.\n", "\n", "For example the following query:\n", "\n", "```\n", "MATCH (v:VEHICLE:MITSUBISHI) RETURN *\n", "```\n", "\n", "In this case ist the result data (Cursor) a set of node objects. Each row in this cursor has records. A record contains in the top level a key/value map with a key, that called with the same variable-name, such that used in the query for the object (here \"v\" as variable-name of a vehicle-object). The value of this item represents the node informations and is from Type Node:\n", "\n", "```\n", "('v': (ac742de:AUTO:MITSUBISHI:STROM:VEHICLE {fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8009\",vehicleID:148221,vin:\"JMBLDHA3WBU000341\"}))\n", "```\n", "\n", "By an access to the value of the top level map, we access to an object of type Node:\n", "\n", "```python\n", "sub = record[\"v\"]\n", "```\n", "\n", "```\n", "(ac742de:AUTO:MITSUBISHI:STROM:VEHICLE {fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8009\",vehicleID:148221,vin:\"JMBLDHA3WBU000341\"})\n", "```\n", "\n", "Because of this object type we can also access to the labels of the node:\n", "\n", "```python\n", "record[\"v\"].labels()\n", "```\n", "```\n", "SetView({'AUTO', 'MITSUBISHI', 'VEHICLE', 'STROM'})\n", "```\n", "\n", "With a conversion of the node to a dictionary it is possible to select the attributes of the node;\n", "\n", "```python\n", "dict(record[\"v\"])\n", "```\n", "\n", "```\n", "{'ownershipType': 'Langzeitmiete', 'modelName': 'i-Miev', 'modelDetails': 'ELEKTRO 35kW Automatik 4-Sitzer', 'fuelType': 'Strom', 'vin': 'JMBLDHA3WBU000341', 'registrationPlate': 'F-R 8009', 'vehicleID': 148221, 'kw': 35}\n", "```\n", "\n", "Alternatively we can access to the attributes with the following way:\n", "\n", "```python\n", "print(record[\"v\"][\"modelName\"])\n", "```\n", "\n", "```\n", "i-Miev\n", "```\n", "\n", "For the query `MATCH (v:VEHICLE:MITSUBISHI)-[r:WAS_BOOKED_IN]->(s:STATION) RETURN v, r, s` it will be returned three objects and one from type Relationship (variable r).\n", "\n", "By a relationship its possible to access following informations over the API:\n", "\n", "- Type of relationship\n", "\n", " `relationship.type()`\n", " `-> WAS_BOOKED_IN`\n", " \n", "- All nodes\n", "\n", " `relationship.nodes()`\n", " ```\n", " ((a34e56f:AUTO:MITSUBISHI:STROM:VEHICLE {bordComputerType:\"Invers BCSA 2006 GPRS\",fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW NAVI Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8011\",vehicleID:148261,vin:\"JMBLDHA3WBU000344\"}), (f55f604:INACTIVE:STATION:STATIONBASED {city:\"Stuttgart\",code:\"STG\",latitude:48.780357360839844,longtitude:9.186469078063965,name:\"Parkgarage Staatsgalerie\",poiAirport:\"Nein\",poiLongDistanceTrains:\"Nein\",poiSuburbanTrains:\"Nein\",poiUnderground:\"Nein\",rentalZoneID:401727,type:\"stationbased\"}))\n", " ```\n", " \n", "- Start or end node\n", "\n", " `relationship.start_node()`\n", " ```\n", " (a34e56f:AUTO:MITSUBISHI:STROM:VEHICLE {bordComputerType:\"Invers BCSA 2006 GPRS\",fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW NAVI Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8011\",vehicleID:148261,vin:\"JMBLDHA3WBU000344\"})\n", " ```\n", " \n", " `relationship.end_node()`\n", " ```\n", "(f55f604:INACTIVE:STATION:STATIONBASED {city:\"Stuttgart\",code:\"STG\",latitude:48.780357360839844,longtitude:9.186469078063965,name:\"Parkgarage Staatsgalerie\",poiAirport:\"Nein\",poiLongDistanceTrains:\"Nein\",poiSuburbanTrains:\"Nein\",poiUnderground:\"Nein\",rentalZoneID:401727,type:\"stationbased\"})\n", " ```\n", " \n", "- Attributes of the relationship\n", " \n", " `relationship[times]`\n", " \n", " `28`" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def printOutNodeInformations(node, singleAttributeName):\n", " print('keys of the node')\n", " print(node.keys())\n", " print('labels of the node')\n", " print(node.labels())\n", " print('single attribute access')\n", " print(node[singleAttributeName])" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def printOutRelationshipInformations(relationship, singleAttributeName):\n", " print('type of the relationship')\n", " print(relationship.type())\n", " print('single attribute access')\n", " print(relationship[singleAttributeName])\n", " print('all nodes of relationship')\n", " print(relationship.nodes())\n", " print('start node of relationship')\n", " print(relationship.start_node())\n", " print('end node of relationship')\n", " print(relationship.end_node())" ] }, { "cell_type": "code", "execution_count": 388, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "raw view of a record:\n", "('v': (ac742de:AUTO:MITSUBISHI:STROM:VEHICLE {fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8009\",vehicleID:148221,vin:\"JMBLDHA3WBU000341\"}))\n", "value in the records top level map:\n", "(ac742de:AUTO:MITSUBISHI:STROM:VEHICLE {fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8009\",vehicleID:148221,vin:\"JMBLDHA3WBU000341\"})\n", "dictionary representation of the node attributes\n", "{'ownershipType': 'Langzeitmiete', 'modelName': 'i-Miev', 'modelDetails': 'ELEKTRO 35kW Automatik 4-Sitzer', 'fuelType': 'Strom', 'vin': 'JMBLDHA3WBU000341', 'registrationPlate': 'F-R 8009', 'vehicleID': 148221, 'kw': 35}\n", "dictionary keys (node attribute names)\n", "\n", "########################################################################################################\n", " Node informations (VEHICLE) \n", "########################################################################################################\n", "\n", "keys of the node\n", "dict_keys(['ownershipType', 'modelName', 'modelDetails', 'fuelType', 'vin', 'registrationPlate', 'vehicleID', 'kw'])\n", "labels of the node\n", "SetView({'AUTO', 'MITSUBISHI', 'VEHICLE', 'STROM'})\n", "single attribute access\n", "i-Miev\n", "raw view of a record:\n", "('v': (a34e56f:AUTO:MITSUBISHI:STROM:VEHICLE {bordComputerType:\"Invers BCSA 2006 GPRS\",fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW NAVI Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8011\",vehicleID:148261,vin:\"JMBLDHA3WBU000344\"}))\n", "value in the records top level map:\n", "(a34e56f:AUTO:MITSUBISHI:STROM:VEHICLE {bordComputerType:\"Invers BCSA 2006 GPRS\",fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW NAVI Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8011\",vehicleID:148261,vin:\"JMBLDHA3WBU000344\"})\n", "dictionary representation of the node attributes\n", "{'ownershipType': 'Langzeitmiete', 'modelName': 'i-Miev', 'modelDetails': 'ELEKTRO 35kW NAVI Automatik 4-Sitzer', 'fuelType': 'Strom', 'vin': 'JMBLDHA3WBU000344', 'registrationPlate': 'F-R 8011', 'bordComputerType': 'Invers BCSA 2006 GPRS', 'vehicleID': 148261, 'kw': 35}\n", "dictionary keys (node attribute names)\n", "\n", "########################################################################################################\n", " Node informations (VEHICLE) \n", "########################################################################################################\n", "\n", "keys of the node\n", "dict_keys(['ownershipType', 'modelName', 'modelDetails', 'fuelType', 'vin', 'registrationPlate', 'bordComputerType', 'vehicleID', 'kw'])\n", "labels of the node\n", "SetView({'AUTO', 'MITSUBISHI', 'VEHICLE', 'STROM'})\n", "single attribute access\n", "i-Miev\n" ] } ], "source": [ "from py2neo import Graph, Path, Subgraph, Node, PropertyDict, Relationship, Walkable, walk\n", "\n", "graph = Graph(\"http://neo4j:neo4jj@localhost:7474/db/data\")\n", "\n", "query = \"\"\"\n", "MATCH (v:VEHICLE:MITSUBISHI) RETURN *\n", "\"\"\"\n", "\n", "cursor = graph.run(query)\n", "\n", "for record in cursor:\n", " print('raw view of a record:')\n", " print(record)\n", " print('value in the records top level map:')\n", " print(record[\"v\"])\n", " print('dictionary representation of the node attributes')\n", " print(dict(record[\"v\"]))\n", " print('dictionary keys (node attribute names)')\n", " print('%s Node informations (VEHICLE) %s' %(seperatingLine, seperatingLine)) \n", " \n", " node = record[\"v\"]\n", " \n", " printOutNodeInformations(node, \"modelName\")" ] }, { "cell_type": "code", "execution_count": 389, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "########################################################################################################\n", " Node informations (VEHICLE) \n", "########################################################################################################\n", "\n", "keys of the node\n", "dict_keys(['ownershipType', 'modelName', 'modelDetails', 'fuelType', 'vin', 'registrationPlate', 'vehicleID', 'kw'])\n", "labels of the node\n", "SetView({'AUTO', 'MITSUBISHI', 'VEHICLE', 'STROM'})\n", "single attribute access\n", "i-Miev\n", "\n", "########################################################################################################\n", " Node informations (STATION) \n", "########################################################################################################\n", "\n", "keys of the node\n", "dict_keys(['code', 'poiAirport', 'city', 'rentalZoneID', 'poiSuburbanTrains', 'latitude', 'name', 'longtitude', 'type', 'poiLongDistanceTrains', 'poiUnderground'])\n", "labels of the node\n", "SetView({'ACTIVE', 'STATION', 'STATIONBASED'})\n", "single attribute access\n", "Wilhelmstraße-ELEKTRO\n", "\n", "########################################################################################################\n", " Relationship informations (WAS_BOOKED_IN) \n", "########################################################################################################\n", "\n", "type of the relationship\n", "WAS_BOOKED_IN\n", "single attribute access\n", "791\n", "all nodes of relationship\n", "((ac742de:AUTO:MITSUBISHI:STROM:VEHICLE {fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8009\",vehicleID:148221,vin:\"JMBLDHA3WBU000341\"}), (b227752:ACTIVE:STATION:STATIONBASED {city:\"Ludwigsburg\",code:\"WIL-ELEKTRO\",latitude:48.8958625793457,longtitude:9.191786766052246,name:\"Wilhelmstraße-ELEKTRO\",poiAirport:\"Nein\",poiLongDistanceTrains:\"Nein\",poiSuburbanTrains:\"Nein\",poiUnderground:\"Nein\",rentalZoneID:403352,type:\"stationbased\"}))\n", "start node of relationship\n", "(ac742de:AUTO:MITSUBISHI:STROM:VEHICLE {fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8009\",vehicleID:148221,vin:\"JMBLDHA3WBU000341\"})\n", "end node of relationship\n", "(b227752:ACTIVE:STATION:STATIONBASED {city:\"Ludwigsburg\",code:\"WIL-ELEKTRO\",latitude:48.8958625793457,longtitude:9.191786766052246,name:\"Wilhelmstraße-ELEKTRO\",poiAirport:\"Nein\",poiLongDistanceTrains:\"Nein\",poiSuburbanTrains:\"Nein\",poiUnderground:\"Nein\",rentalZoneID:403352,type:\"stationbased\"})\n", "\n", "########################################################################################################\n", " Node informations (VEHICLE) \n", "########################################################################################################\n", "\n", "keys of the node\n", "dict_keys(['ownershipType', 'modelName', 'modelDetails', 'fuelType', 'vin', 'registrationPlate', 'bordComputerType', 'vehicleID', 'kw'])\n", "labels of the node\n", "SetView({'AUTO', 'MITSUBISHI', 'VEHICLE', 'STROM'})\n", "single attribute access\n", "i-Miev\n", "\n", "########################################################################################################\n", " Node informations (STATION) \n", "########################################################################################################\n", "\n", "keys of the node\n", "dict_keys(['code', 'poiAirport', 'city', 'rentalZoneID', 'poiSuburbanTrains', 'latitude', 'name', 'longtitude', 'type', 'poiLongDistanceTrains', 'poiUnderground'])\n", "labels of the node\n", "SetView({'STATIONBASED', 'STATION', 'INACTIVE'})\n", "single attribute access\n", "Bahnhof-ELEKTRO\n", "\n", "########################################################################################################\n", " Relationship informations (WAS_BOOKED_IN) \n", "########################################################################################################\n", "\n", "type of the relationship\n", "WAS_BOOKED_IN\n", "single attribute access\n", "245\n", "all nodes of relationship\n", "((a34e56f:AUTO:MITSUBISHI:STROM:VEHICLE {bordComputerType:\"Invers BCSA 2006 GPRS\",fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW NAVI Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8011\",vehicleID:148261,vin:\"JMBLDHA3WBU000344\"}), (dfa6823:INACTIVE:STATION:STATIONBASED {city:\"Ludwigsburg\",code:\"BF-Elektro\",latitude:48.891685485839844,longtitude:9.183795928955078,name:\"Bahnhof-ELEKTRO\",poiAirport:\"Nein\",poiLongDistanceTrains:\"Nein\",poiSuburbanTrains:\"Nein\",poiUnderground:\"Nein\",rentalZoneID:404993,type:\"stationbased\"}))\n", "start node of relationship\n", "(a34e56f:AUTO:MITSUBISHI:STROM:VEHICLE {bordComputerType:\"Invers BCSA 2006 GPRS\",fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW NAVI Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8011\",vehicleID:148261,vin:\"JMBLDHA3WBU000344\"})\n", "end node of relationship\n", "(dfa6823:INACTIVE:STATION:STATIONBASED {city:\"Ludwigsburg\",code:\"BF-Elektro\",latitude:48.891685485839844,longtitude:9.183795928955078,name:\"Bahnhof-ELEKTRO\",poiAirport:\"Nein\",poiLongDistanceTrains:\"Nein\",poiSuburbanTrains:\"Nein\",poiUnderground:\"Nein\",rentalZoneID:404993,type:\"stationbased\"})\n", "\n", "########################################################################################################\n", " Node informations (VEHICLE) \n", "########################################################################################################\n", "\n", "keys of the node\n", "dict_keys(['ownershipType', 'modelName', 'modelDetails', 'fuelType', 'vin', 'registrationPlate', 'bordComputerType', 'vehicleID', 'kw'])\n", "labels of the node\n", "SetView({'AUTO', 'MITSUBISHI', 'VEHICLE', 'STROM'})\n", "single attribute access\n", "i-Miev\n", "\n", "########################################################################################################\n", " Node informations (STATION) \n", "########################################################################################################\n", "\n", "keys of the node\n", "dict_keys(['code', 'poiAirport', 'city', 'rentalZoneID', 'poiSuburbanTrains', 'latitude', 'name', 'longtitude', 'type', 'poiLongDistanceTrains', 'poiUnderground'])\n", "labels of the node\n", "SetView({'STATIONBASED', 'STATION', 'INACTIVE'})\n", "single attribute access\n", "Parkgarage Staatsgalerie\n", "\n", "########################################################################################################\n", " Relationship informations (WAS_BOOKED_IN) \n", "########################################################################################################\n", "\n", "type of the relationship\n", "WAS_BOOKED_IN\n", "single attribute access\n", "28\n", "all nodes of relationship\n", "((a34e56f:AUTO:MITSUBISHI:STROM:VEHICLE {bordComputerType:\"Invers BCSA 2006 GPRS\",fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW NAVI Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8011\",vehicleID:148261,vin:\"JMBLDHA3WBU000344\"}), (f55f604:INACTIVE:STATION:STATIONBASED {city:\"Stuttgart\",code:\"STG\",latitude:48.780357360839844,longtitude:9.186469078063965,name:\"Parkgarage Staatsgalerie\",poiAirport:\"Nein\",poiLongDistanceTrains:\"Nein\",poiSuburbanTrains:\"Nein\",poiUnderground:\"Nein\",rentalZoneID:401727,type:\"stationbased\"}))\n", "start node of relationship\n", "(a34e56f:AUTO:MITSUBISHI:STROM:VEHICLE {bordComputerType:\"Invers BCSA 2006 GPRS\",fuelType:\"Strom\",kw:35,modelDetails:\"ELEKTRO 35kW NAVI Automatik 4-Sitzer\",modelName:\"i-Miev\",ownershipType:\"Langzeitmiete\",registrationPlate:\"F-R 8011\",vehicleID:148261,vin:\"JMBLDHA3WBU000344\"})\n", "end node of relationship\n", "(f55f604:INACTIVE:STATION:STATIONBASED {city:\"Stuttgart\",code:\"STG\",latitude:48.780357360839844,longtitude:9.186469078063965,name:\"Parkgarage Staatsgalerie\",poiAirport:\"Nein\",poiLongDistanceTrains:\"Nein\",poiSuburbanTrains:\"Nein\",poiUnderground:\"Nein\",rentalZoneID:401727,type:\"stationbased\"})\n" ] } ], "source": [ "query = \"\"\"\n", "MATCH (v:VEHICLE:MITSUBISHI)-[r:WAS_BOOKED_IN]->(s:STATION) RETURN v, r, s\n", "\"\"\"\n", "\n", "cursor = graph.run(query)\n", "\n", "# print(cursor.data())\n", "\n", "for record in cursor:\n", " print('%s Node informations (VEHICLE) %s' %(seperatingLine, seperatingLine)) \n", " vehicle = record[\"v\"]\n", " printOutNodeInformations(vehicle, \"modelName\")\n", " \n", " print('%s Node informations (STATION) %s' %(seperatingLine, seperatingLine)) \n", " station = record[\"s\"]\n", " printOutNodeInformations(station, \"name\")\n", " \n", " print('%s Relationship informations (WAS_BOOKED_IN) %s' %(seperatingLine, seperatingLine)) \n", " station = record[\"r\"]\n", " printOutRelationshipInformations(station, \"times\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Draw informations as graph over NetworkX\n", "\n", "In the library networkX is every graph a set of edges, where each one of them connects two nodes. Every edge or node can also have attributes. In the following we prepare the data in the dataframe - as result of a trivial query - for this view about the rental-zone/vehicle data:\n", "\n", "```python\n", "dfn['VEHICLE_ID'] = dfn.apply({'v' : lambda x: x[\"vehicleID\"]})\n", "dfn['RENTALZONE_ID'] = dfn.apply({'s' : lambda x: x[\"rentalZoneID\"]})\n", "dfn['vModelName'] = dfn.apply({'v' : lambda x: x[\"modelName\"]})\n", "dfn['sName'] = dfn.apply({'s' : lambda x: x[\"name\"]})\n", "dfn['TIMES'] = dfn.apply({'r' : lambda x: x[\"times\"]})\n", "```\n", "\n", "Alternatively we can also access to the needed informations in this way:\n", "\n", "```python\n", "dfn[\"v\"][\"vehicleID\"]\n", "dfn[\"s\"][\"rentalZoneID\"]\n", "dfn[\"r\"][\"times\"]\n", "```\n", "\n", "NetworkX provides an easy way to import the data in a dataframe as edges and nodes:\n", "\n", "```python\n", "G2=nx.from_pandas_dataframe(dfn, 'VEHICLE_ID', 'RENTALZONE_ID', ['TIMES'])\n", "```\n", "\n", "The disadvantage of this approach is, that we can't import node attributes over this interface. This will also don't work, if we try to import the nodes over a explicit function, \n", "\n", "```python\n", "def addVRZNodesToGraph(row, graph):\n", " graph.add_node(row[\"RENTALZONE_ID\"],code=str(row[\"s\"][\"code\"]))\n", " graph.add_node(row[\"VEHICLE_ID\"],vin=str(row[\"v\"][\"vin\"]))\n", " return graph\n", "...\n", "```\n", "\n", "... and import on this basis the data to the graph:\n", "\n", "```python\n", "G2=nx.Graph()\n", "\n", "dfn.apply(addVRZNodesToGraph, axis=1, graph=G2)\n", "\n", "print (G2.nodes(data=True))\n", "\n", "G2=nx.from_pandas_dataframe(dfn, 'VEHICLE_ID', 'RENTALZONE_ID', ['TIMES'])\n", "\n", "print (G.edges(data=True))\n", "\n", "print (G.nodes(data=True))\n", "\n", "```\n", "\n", "If we look at the result of the above code, we find out, that the nodes were overridden within the import operation:\n", "\n", "```\n", "########################################################################################################\n", " Node informations in the graph before import \n", "########################################################################################################\n", "\n", "[(403352, {'code': 'WIL-ELEKTRO'}), (148221, {'vin': 'JMBLDHA3WBU000341'}), (404993, {'code': 'BF-Elektro'}), (148261, {'vin': 'JMBLDHA3WBU000344'}), (401727, {'code': 'STG'})]\n", "\n", "########################################################################################################\n", " Node informations in the graph after import \n", "########################################################################################################\n", "\n", "[(148221, {}), (403352, {}), (148261, {}), (404993, {}), (401727, {})]\n", "\n", "########################################################################################################\n", " Edge informations in the graph after import \n", "########################################################################################################\n", "\n", "[(148221, 403352, {'TIMES': '791'}), (148261, 404993, {'TIMES': '245'}), (148261, 401727, {'TIMES': '28'})]\n", "\n", "```\n", "\n", "Because of this side effect we use only the way to add nodes and edges manually to the graph:\n", "\n", "```python\n", "def addVRZEdgesToGraph(row, graph, relationshipType):\n", " graph.add_edge(row[\"VEHICLE_ID\"], row[\"RENTALZONE_ID\"],{'type': relationshipType, 'times': row[\"TIMES\"]})\n", " return graph\n", "...\n", "\n", "\n", "G=nx.Graph()\n", "\n", "dfn.apply(addVRZNodesToGraph, axis=1, graph=G)\n", "\n", "dfn.apply(addVRZEdgesToGraph, axis=1, graph=G, relationshipType='WAS_BOOKED_IN')\n", "\n", "...\n", "\n", "print('%s Node informations in the graph after import %s' %(seperatingLine, seperatingLine)) \n", "\n", "print (G.nodes(data=True))\n", "\n", "print('%s Edge informations in the graph after import %s' %(seperatingLine, seperatingLine)) \n", "\n", "print (G.edges(data=True))\n", "```\n", "\n", "The output of the code above is:\n", "\n", "```\n", "########################################################################################################\n", " Node informations in the graph after manual import \n", "########################################################################################################\n", "\n", "[(403352, {'code': 'WIL-ELEKTRO'}), (148221, {'vin': 'JMBLDHA3WBU000341'}), (404993, {'code': 'BF-Elektro'}), (148261, {'vin': 'JMBLDHA3WBU000344'}), (401727, {'code': 'STG'})]\n", "\n", "########################################################################################################\n", " Edge informations in the graph after manual import \n", "########################################################################################################\n", "\n", "[(403352, 148221, {'type': 'WAS_BOOKED_IN', 'times': '791'}), (404993, 148261, {'type': 'WAS_BOOKED_IN', 'times': '245'}), (148261, 401727, {'type': 'WAS_BOOKED_IN', 'times': '28'})]\n", "```\n", "\n", "In our case we'll draw also labels for the nodes and edges. NetworkX provides in the functionality a way to pass the labels of nodes and edges as parameter. For the node labels most them exist as dict and for edges as simple list.\n", "\n", "Therefore we prepare the labels on the basis of the existing data as follows.\n", "\n", "We've two different types of nodes: Vehicle and rental zone. For this reason we must collect different informations as values for keys (IDs) from both entities. The id-namespace of both entities aren't overlapping in our case. We bring the IDs of both data in the first step together: \n", "\n", "```python\n", "dfnnl = dfn.drop(['r', 's', 'v', 'TIMES', 'vModelName', 'sName'], axis=1).copy(True)\n", "\n", "dfnnl = dfnnl.reset_index()\n", "dfnnl = dfnnl.drop(\"index\", axis=1)\n", "\n", "dfnnl = dfnnl.stack()\n", "\n", "dfnnl = dfnnl.reset_index()\n", "\n", "dfnnl = dfnnl.rename_axis({\"level_0\": \"levelName\", \"level_1\": \"columnName\", 0: \"ID\"}, axis=\"columns\")\n", "\n", "dfnnl[\"columnName\"] = dfnnl[\"columnName\"].astype(str)\n", "```\n", "\n", "In the second step we append a label column to the data frame with the specific label-information for each entity (vehicle or rental zone):\n", "\n", "```python\n", "\n", "def produceLabelInformation(row, orgData):\n", " label = \" \"\n", " if str(row[\"columnName\"]) == 'VEHICLE_ID':\n", " label = orgData.loc[(orgData['VEHICLE_ID'] == row[\"ID\"])]\n", " .drop_duplicates(subset=[\"vModelName\"],keep=\"first\")[\"vModelName\"].values[0]\n", " else:\n", " label = orgData.loc[(orgData['RENTALZONE_ID'] == row[\"ID\"])]\n", " .drop_duplicates(subset=[\"sName\"],keep=\"first\")[\"sName\"].values[0]\n", " return label\n", "\n", "...\n", "\n", "dfnnl[\"LABEL\"] = dfnnl.apply(produceLabelInformation, axis=1, orgData=dfn)\n", "\n", "print('%s IDs from vehicles and rental zones with label informations %s' %(seperatingLine, seperatingLine)) \n", "\n", "print(dfnnl)\n", "\n", "```\n", "\n", "The code above produces following output:\n", "\n", "```\n", "########################################################################################################\n", " IDs from vehicles and rental zones with label informations \n", "########################################################################################################\n", "\n", " levelName columnName ID LABEL\n", "0 0 VEHICLE_ID 148221 i-Miev\n", "1 0 RENTALZONE_ID 403352 Wilhelmstraße-ELEKTRO\n", "2 1 VEHICLE_ID 148261 i-Miev\n", "3 1 RENTALZONE_ID 404993 Bahnhof-ELEKTRO\n", "5 2 RENTALZONE_ID 401727 Parkgarage Staatsgalerie\n", "\n", "```\n", "\n", "Because of the uniformity in the relationships between the nodes, we need only a constant label for each edge. Therefore it's enough to produce a list containing string members for a single label:\n", "\n", "```python\n", "def prepareEdgeLabelsForGraph(dfn):\n", " dfnel = dfn.apply({'r' : lambda x: 'WAS_BOOKED_IN'})\n", "\n", " print('%s Edge labels %s' %(seperatingLine, seperatingLine)) \n", "\n", " dfnel = dfnel.reset_index()\n", "\n", " dfnel = dfnel.drop(\"index\", axis=1)\n", "\n", " dfnel = dfnel.rename_axis({\"r\": \"edgeLabel\"}, axis=\"columns\")\n", "\n", " print(dfnel)\n", " \n", " return dfnel\n", " \n", "...\n", "\n", "dfnel = prepareEdgeLabelsForGraph(dfn)\n", "```\n", "\n", "The code above produces following output:\n", "\n", "```\n", "########################################################################################################\n", " Edge labels \n", "########################################################################################################\n", "\n", " edgeLabel\n", "0 WAS_BOOKED_IN\n", "1 WAS_BOOKED_IN\n", "2 WAS_BOOKED_IN\n", "\n", "```\n", "\n", "We can now pass all informations together (graph and labels) to a simple function, that draws a graph as image:\n", "\n", "```python\n", "import matplotlib.pyplot as plt\n", "import networkx as nx\n", "import pandas as pd\n", "\n", "%matplotlib inline\n", "\n", "def draw_graph(graph, layout, edgeLabels, nodeLabels, name):\n", " \n", " edge_labels = dict(zip(graph.edges(), edgeLabels))\n", " \n", " G = graph\n", " \n", " graph_pos = layout\n", " \n", " \n", " plt.figure(3,figsize=(30,30)) \n", "\n", " # draw nodes, edges and labels\n", " nx.draw_networkx_nodes(G, graph_pos, node_size=15000, node_color='blue', alpha=0.3)\n", " # we can now added edge thickness and edge color\n", " nx.draw_networkx_edges(G, graph_pos, width=5, alpha=0.3, edge_color='green')\n", " nx.draw_networkx_labels(G, graph_pos, nodeLabels, font_size=16, font_family='sans-serif')\n", " nx.draw_networkx_edge_labels(G, graph_pos, font_size=16, edge_labels=edge_labels)\n", " \n", " plt.savefig(\"graph_\" + name + \".png\", \n", " dpi=100, \n", " facecolor='w', \n", " edgecolor='w',\n", " orientation='portrait', \n", " papertype=None, \n", " format=None,\n", " transparent=False, \n", " bbox_inches=None, \n", " pad_inches=0.1) \n", " \n", " plt.show()\n", "\n", "...\n", "\n", "draw_graph(G, nx.spring_layout(G, 2, 1), edgeLabels, nodeLabels, \"spring\")\n", "```" ] }, { "cell_type": "code", "execution_count": 390, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import networkx as nx\n", "import pandas as pd\n", "\n", "%matplotlib inline\n", "\n", "def draw_graph(graph, layout, edgeLabels, nodeLabels, name):\n", " \n", " edge_labels = dict(zip(graph.edges(), edgeLabels))\n", " \n", " G = graph\n", " \n", " graph_pos = layout\n", " \n", " \n", " plt.figure(3,figsize=(30,30)) \n", "\n", " # draw nodes, edges and labels\n", " nx.draw_networkx_nodes(G, graph_pos, node_size=15000, node_color='blue', alpha=0.3)\n", " # we can now added edge thickness and edge color\n", " nx.draw_networkx_edges(G, graph_pos, width=5, alpha=0.3, edge_color='green')\n", " nx.draw_networkx_labels(G, graph_pos, nodeLabels, font_size=16, font_family='sans-serif')\n", " nx.draw_networkx_edge_labels(G, graph_pos, font_size=16, edge_labels=edge_labels)\n", " \n", " plt.savefig(\"graph_\" + name + \".png\", \n", " dpi=100, \n", " facecolor='w', \n", " edgecolor='w',\n", " orientation='portrait', \n", " papertype=None, \n", " format=None,\n", " transparent=False, \n", " bbox_inches=None, \n", " pad_inches=0.1) \n", " \n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 391, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def addVRZNodesToGraph(row, graph):\n", " graph.add_node(row[\"RENTALZONE_ID\"],code=str(row[\"s\"][\"code\"]))\n", " graph.add_node(row[\"VEHICLE_ID\"],vin=str(row[\"v\"][\"vin\"]))\n", " return graph" ] }, { "cell_type": "code", "execution_count": 392, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def addVRZEdgesToGraph(row, graph, relationshipType):\n", " graph.add_edge(row[\"VEHICLE_ID\"], row[\"RENTALZONE_ID\"],{'type': relationshipType, 'times': row[\"TIMES\"]})\n", " return graph" ] }, { "cell_type": "code", "execution_count": 401, "metadata": {}, "outputs": [], "source": [ "def produceLabelInformation(row, orgData):\n", " label = \" \"\n", " if str(row[\"columnName\"]) == 'VEHICLE_ID':\n", " label = orgData.loc[(orgData['VEHICLE_ID'] == row[\"ID\"])]\\\n", " .drop_duplicates(subset=[\"vModelName\"],keep=\"first\")[\"vModelName\"].values[0]\n", " else:\n", " label = orgData.loc[(orgData['RENTALZONE_ID'] == row[\"ID\"])]\\\n", " .drop_duplicates(subset=[\"sName\"],keep=\"first\")[\"sName\"].values[0]\n", " return label" ] }, { "cell_type": "code", "execution_count": 402, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def prepareDataForGraph(df):\n", " dfn = df.reset_index()\n", " dfn = dfn.drop(\"index\", axis=1)\n", "\n", " print('%s Original dataframe without index %s' %(seperatingLine, seperatingLine)) \n", "\n", " print(dfn.head())\n", "\n", " dfn['VEHICLE_ID'] = dfn.apply({'v' : lambda x: x[\"vehicleID\"]})\n", " dfn['RENTALZONE_ID'] = dfn.apply({'s' : lambda x: x[\"rentalZoneID\"]})\n", " dfn['vModelName'] = dfn.apply({'v' : lambda x: x[\"modelName\"]})\n", " dfn['sName'] = dfn.apply({'s' : lambda x: x[\"name\"]})\n", " dfn['TIMES'] = dfn.apply({'r' : lambda x: x[\"times\"]})\n", "\n", " print('%s Extended dataframe %s' %(seperatingLine, seperatingLine)) \n", "\n", " print(dfn)\n", " \n", " return dfn" ] }, { "cell_type": "code", "execution_count": 403, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def prepareNodeLabelsForGraph(dfn):\n", " dfnnl = dfn.drop(['r', 's', 'v', 'TIMES', 'vModelName', 'sName'], axis=1).copy(True)\n", "\n", " dfnnl = dfnnl.reset_index()\n", " dfnnl = dfnnl.drop(\"index\", axis=1)\n", "\n", " dfnnl = dfnnl.stack()\n", "\n", " dfnnl = dfnnl.reset_index()\n", "\n", " dfnnl = dfnnl.rename_axis({\"level_0\": \"levelName\", \"level_1\": \"columnName\", 0: \"ID\"}, axis=\"columns\")\n", "\n", " dfnnl[\"columnName\"] = dfnnl[\"columnName\"].astype(str)\n", "\n", " dfnnl = dfnnl.drop_duplicates(subset=[\"ID\"],keep=\"first\")\n", "\n", " # dfnnl.append(dfn.get(['RENTALZONE_ID']).copy(True), ignore_index=True)\n", "\n", " print('%s Stacked ids from vehicles and rental zones %s' %(seperatingLine, seperatingLine)) \n", "\n", " print(dfnnl)\n", "\n", " dfnnl[\"LABEL\"] = dfnnl.apply(produceLabelInformation, axis=1, orgData=dfn)\n", "\n", " print('%s IDs from vehicles and rental zones with label informations %s' %(seperatingLine, seperatingLine)) \n", "\n", " print(dfnnl)\n", " \n", " return dfnnl" ] }, { "cell_type": "code", "execution_count": 404, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def prepareEdgeLabelsForGraph(dfn):\n", " dfnel = dfn.apply({'r' : lambda x: 'WAS_BOOKED_IN'})\n", "\n", " print('%s Edge labels %s' %(seperatingLine, seperatingLine)) \n", "\n", " dfnel = dfnel.reset_index()\n", "\n", " dfnel = dfnel.drop(\"index\", axis=1)\n", "\n", " dfnel = dfnel.rename_axis({\"r\": \"edgeLabel\"}, axis=\"columns\")\n", "\n", " print(dfnel)\n", " \n", " return dfnel" ] }, { "cell_type": "code", "execution_count": 405, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#\n", "# Not recommend way to hold data to a graph\n", "#\n", "\n", "def importDataToGraph(dfn):\n", " G2=nx.Graph()\n", "\n", " dfn.apply(addVRZNodesToGraph, axis=1, graph=G2)\n", "\n", " print('%s Node informations in the graph before import %s' %(seperatingLine, seperatingLine)) \n", "\n", " print (G2.nodes(data=True))\n", "\n", " G2=nx.from_pandas_dataframe(dfn, 'VEHICLE_ID', 'RENTALZONE_ID', ['TIMES'])\n", "\n", " print('%s Node informations in the graph after import %s' %(seperatingLine, seperatingLine)) \n", "\n", " print (G2.nodes(data=True))\n", "\n", " print('%s Edge informations in the graph after import %s' %(seperatingLine, seperatingLine)) \n", "\n", " print (G2.edges(data=True))\n", " \n", " return G2" ] }, { "cell_type": "code", "execution_count": 406, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#\n", "# Recommend way to hold data to a graph\n", "#\n", "\n", "def addDataToGraph(dfn):\n", " G=nx.Graph()\n", "\n", " dfn.apply(addVRZNodesToGraph, axis=1, graph=G)\n", "\n", " dfn.apply(addVRZEdgesToGraph, axis=1, graph=G, relationshipType='WAS_BOOKED_IN')\n", "\n", " print('%s Node informations in the graph after manual import %s' %(seperatingLine, seperatingLine)) \n", "\n", " print (G.nodes(data=True))\n", "\n", " print('%s Edge informations in the graph after manual import %s' %(seperatingLine, seperatingLine)) \n", "\n", " print (G.edges(data=True))\n", " \n", " return G" ] }, { "cell_type": "code", "execution_count": 407, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "########################################################################################################\n", " Original dataframe without index \n", "########################################################################################################\n", "\n", " r s \\\n", "0 {'times': '791'} {'code': 'WIL-ELEKTRO', 'poiAirport': 'Nein', ... \n", "1 {'times': '245'} {'code': 'BF-Elektro', 'poiAirport': 'Nein', '... \n", "2 {'times': '28'} {'code': 'STG', 'poiAirport': 'Nein', 'city': ... \n", "\n", " v \n", "0 {'ownershipType': 'Langzeitmiete', 'modelName'... \n", "1 {'ownershipType': 'Langzeitmiete', 'modelName'... \n", "2 {'ownershipType': 'Langzeitmiete', 'modelName'... \n", "\n", "########################################################################################################\n", " Extended dataframe \n", "########################################################################################################\n", "\n", " r s \\\n", "0 {'times': '791'} {'code': 'WIL-ELEKTRO', 'poiAirport': 'Nein', ... \n", "1 {'times': '245'} {'code': 'BF-Elektro', 'poiAirport': 'Nein', '... \n", "2 {'times': '28'} {'code': 'STG', 'poiAirport': 'Nein', 'city': ... \n", "\n", " v VEHICLE_ID \\\n", "0 {'ownershipType': 'Langzeitmiete', 'modelName'... 148221 \n", "1 {'ownershipType': 'Langzeitmiete', 'modelName'... 148261 \n", "2 {'ownershipType': 'Langzeitmiete', 'modelName'... 148261 \n", "\n", " RENTALZONE_ID vModelName sName TIMES \n", "0 403352 i-Miev Wilhelmstraße-ELEKTRO 791 \n", "1 404993 i-Miev Bahnhof-ELEKTRO 245 \n", "2 401727 i-Miev Parkgarage Staatsgalerie 28 \n", "\n", "########################################################################################################\n", " Stacked ids from vehicles and rental zones \n", "########################################################################################################\n", "\n", " levelName columnName ID\n", "0 0 VEHICLE_ID 148221\n", "1 0 RENTALZONE_ID 403352\n", "2 1 VEHICLE_ID 148261\n", "3 1 RENTALZONE_ID 404993\n", "5 2 RENTALZONE_ID 401727\n", "\n", "########################################################################################################\n", " IDs from vehicles and rental zones with label informations \n", "########################################################################################################\n", "\n", " levelName columnName ID LABEL\n", "0 0 VEHICLE_ID 148221 i-Miev\n", "1 0 RENTALZONE_ID 403352 Wilhelmstraße-ELEKTRO\n", "2 1 VEHICLE_ID 148261 i-Miev\n", "3 1 RENTALZONE_ID 404993 Bahnhof-ELEKTRO\n", "5 2 RENTALZONE_ID 401727 Parkgarage Staatsgalerie\n", "\n", "########################################################################################################\n", " Edge labels \n", "########################################################################################################\n", "\n", " edgeLabel\n", "0 WAS_BOOKED_IN\n", "1 WAS_BOOKED_IN\n", "2 WAS_BOOKED_IN\n", "\n", "########################################################################################################\n", " Node informations in the graph after manual import \n", "########################################################################################################\n", "\n", "[(403352, {'code': 'WIL-ELEKTRO'}), (148221, {'vin': 'JMBLDHA3WBU000341'}), (404993, {'code': 'BF-Elektro'}), (148261, {'vin': 'JMBLDHA3WBU000344'}), (401727, {'code': 'STG'})]\n", "\n", "########################################################################################################\n", " Edge informations in the graph after manual import \n", "########################################################################################################\n", "\n", "[(403352, 148221, {'type': 'WAS_BOOKED_IN', 'times': '791'}), (404993, 148261, {'type': 'WAS_BOOKED_IN', 'times': '245'}), (148261, 401727, {'type': 'WAS_BOOKED_IN', 'times': '28'})]\n", "\n", "########################################################################################################\n", " Node informations in the graph before import \n", "########################################################################################################\n", "\n", "[(403352, {'code': 'WIL-ELEKTRO'}), (148221, {'vin': 'JMBLDHA3WBU000341'}), (404993, {'code': 'BF-Elektro'}), (148261, {'vin': 'JMBLDHA3WBU000344'}), (401727, {'code': 'STG'})]\n", "\n", "########################################################################################################\n", " Node informations in the graph after import \n", "########################################################################################################\n", "\n", "[(148221, {}), (403352, {}), (148261, {}), (404993, {}), (401727, {})]\n", "\n", "########################################################################################################\n", " Edge informations in the graph after import \n", "########################################################################################################\n", "\n", "[(148221, 403352, {'TIMES': '791'}), (148261, 404993, {'TIMES': '245'}), (148261, 401727, {'TIMES': '28'})]\n" ] } ], "source": [ "data = graph.data(query)\n", "\n", "df = pd.DataFrame(data)\n", "\n", "dfn = prepareDataForGraph(df)\n", "\n", "dfnnl = prepareNodeLabelsForGraph(dfn)\n", "\n", "dfnel = prepareEdgeLabelsForGraph(dfn)\n", "\n", "#\n", "# Recommend way to hold data to a graph\n", "#\n", "\n", "G = addDataToGraph(dfn)\n", "\n", "#\n", "# Not recommend way to hold data to a graph\n", "#\n", "\n", "G2 = importDataToGraph(dfn)" ] }, { "cell_type": "code", "execution_count": 408, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{148221: 'i-Miev', 403352: 'Wilhelmstraße-ELEKTRO', 148261: 'i-Miev', 404993: 'Bahnhof-ELEKTRO', 401727: 'Parkgarage Staatsgalerie'}\n", "0 WAS_BOOKED_IN\n", "1 WAS_BOOKED_IN\n", "2 WAS_BOOKED_IN\n", "Name: edgeLabel, dtype: object\n" ] }, { "data": { "image/png": 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1qlflL//yL3PWWWdl1KhRmTRpUm655ZZcfPHFufLKK/PZz342I0eOzGGHHZZ/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\nrpJTupRWtpLtK7izb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AAADkTF5ens4//3z95S9/0VtvvSVJqq6u1rJly/Tss8+qvb1da9eu1UknnaSLLrpI3//+9yVJ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\nYQ0NDYx8rLHxzZEgazzXbU8+X4kqK2fr3nuvViTSove/f5mqqw8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"text/plain": [ "<matplotlib.figure.Figure at 0x1194c87f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nodeLabels = dict(zip(dfnnl[\"ID\"], dfnnl[\"LABEL\"]))\n", "\n", "edgeLabels = dfnel[\"edgeLabel\"].astype(str)\n", "\n", "print(nodeLabels)\n", "\n", "print(edgeLabels)\n", "\n", "draw_graph(G, nx.spring_layout(G, 2, 1), edgeLabels, nodeLabels, \"spring\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

usantamaria/iwi131

ipynb/23-ProcesamientoDeTexto/Texto.ipynb

2

36101

{ "cells": [ { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "text/html": [ "<style>\n", "\n", ".reveal {\n", "overflow: visible;\n", "}\n", "\n", "/*********************************************\n", " * CHANGE CURSIVE FOR RED\n", " *********************************************/\n", "em {font-style: normal !important;\n", " color: #800000;}\n", "span.good {color: #008000;}\n", "span.warning {color: #808000;}\n", "span.bad {color: #800000;}\n", "\n", "/*********************************************\n", " * GLOBAL STYLES\n", " *********************************************/\n", ".reveal h1 {color: #000000; text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2);}\n", ".reveal h2 {color: #222222; text-shadow: 0px 0px 5px rgba(0, 0, 0, 0.2);}\n", ".reveal h3 {color: #444444; text-shadow: 0px 0px 4px rgba(0, 0, 0, 0.2);}\n", ".reveal h4 {color: #666666; text-shadow: 0px 0px 3px rgba(0, 0, 0, 0.2);}\n", ".reveal h5 {color: #888888; text-shadow: 0px 0px 2px rgba(0, 0, 0, 0.2);}\n", ".reveal h6 {color: #AAAAAA; text-shadow: 0px 0px 1px rgba(0, 0, 0, 0.2);}\n", "\n", "/*********************************************\n", " * IMAGES\n", " *********************************************/\n", ".reveal section img { margin-left:auto; margin-right:auto;}\n", "\n", "</style>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "IPython Notebook v4.0 para python 2.7\n", "Librerías adicionales: Ninguna.\n", "Contenido bajo licencia CC-BY 4.0. Código bajo licencia MIT. (c) Sebastian Flores.\n", "\"\"\"\n", "\n", "# Configuracion para recargar módulos y librerías \n", "%reload_ext autoreload\n", "%autoreload 2\n", "\n", "from IPython.core.display import HTML\n", "\n", "HTML(open(\"style/iwi131.css\", \"r\").read())" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<header class=\"w3-container w3-teal\">\n", "<img src=\"images/utfsm.png\" alt=\"\" align=\"left\"/>\n", "<img src=\"images/inf.png\" alt=\"\" align=\"right\"/>\n", "</header>\n", "<br/><br/><br/><br/><br/>\n", "# IWI131\n", "## Programación de Computadores\n", "\n", "### Sebastián Flores\n", "\n", "http://progra.usm.cl/ \n", "\n", "https://www.github.com/usantamaria/iwi131\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Fechas\n", "\n", "* Actividad 05: ***Miércoles*** 6 Enero 2016 (8:00).\n", "* Certamen 3: ***Viernes*** 8 Enero 2016 (15:30).\n", "* Certamen Recuperativo: Lunes 18 Enero 2016 (8:00)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Clases\n", "* Mie 23 Dic 2016: Procesamiento de Texto.\n", "* Lun 28 Dic 2016: Escribir y leer archivos.\n", "* Mie 30 Dic 2016: Ejercicios tipo certamen.\n", "* Lun 04 Ene 2016: Ejercicios tipo certamen.\n", "* Mie 06 Ene 2016: Actividad 5.\n", "\n", "Consejo: Baje el libro del curso, lea, aprenda y practique.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## ¿Qué contenido aprenderemos?\n", "\n", "* Procesamiento de texto" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## ¿Porqué aprenderemos ese contenido?\n", "\n", "* Procesamiento de texto\n", "\n", "Habilidad crucial para resolver una gran variedad de problemas." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Motivación\n", "\n", "Queremos conocer cuales son las palabras más comunes en un idioma. Para eso, necesitamos saber cuantas veces aparece cada palabra en una frase. Desarrolle una función contar_palabras que al ser aplicada sobre un string, entregue un diccionario con las palabras y la cantidad de veces que aparece en la frase. Omita espacios y signos de puntuación y exclamación.\n", "\n", " t = 'El sobre, en el aula, esta sobre el pupitre.'\n", "\n", " contar_palabras(t)\n", " {'el': 3, 'en': 1, 'esta': 1, 'aula': 1, \n", " 'sobre': 2, 'pupitre': 1}\n", " \n", "¿Cómo realizaría usted esta difícil tarea?" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## Consejos \n", "\n", "El procesamiento de texto utiliza:\n", "\n", "* **Reconocimiento de patrones**: usted debe reconocer que patrones se repiten y puede explotar para procesar el texto.\n", "* **Utilización de funciones específicas**: el tipo de dato `string` posee una rica colección de métodos que debe manejar para simplificar la tarea de procesamiento de texto.\n", "* Recuerde que todo string es inmutable, por lo que al aplicar diversas funciones se obtiene siempre un ***nuevo*** string." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Salto de línea\n", "\n", "El string ***`\\n`*** corresponde a un único carácter, que representa el salto de línea." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "casa\n", "arbol\n", "patio\n", "16\n", "casa\n", "arbol\n", "patio\n", "16\n", "True\n", "a\n", "b\n", "c\n", "5\n" ] } ], "source": [ "print len(\"\\n\")\n", "\n", "a1 = 'casa\\narbol\\npatio'\n", "print a1\n", "print len(a1)\n", "\n", "a2 = '''casa\n", "arbol\n", "patio'''\n", "print a2\n", "print len(a2)\n", "\n", "print a1==a2\n", "\n", "b = 'a\\nb\\nc'\n", "print b\n", "print len(b)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Tabulación\n", "\n", "El string ***`\\t`*** corresponde a un único carácter, que representa una tabulación." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "casa\n", "\tarbol\n", "\tpatio\n", "a\tb\tc\n", "5\n" ] } ], "source": [ "print len(\"\\t\")\n", "\n", "a = 'casa\\n\\tarbol\\n\\tpatio'\n", "print a\n", "\n", "b = 'a\\tb\\tc'\n", "print b\n", "print len(b)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "\n", "**Importante**: `\\n` y `\\t` aparecen frecuentemente cuando analicemos archivos leídos del disco duro." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Reemplazar secciones de un string\n", "* La función ***mi_string.replace(s1, s2)*** busca cada ocurrencia del substring s1 en mi_string, y lo reemplaza por s2. \n", "* La función ***mi_string.replace(s1, s2,n)*** busca las primeras n ocurrencias del substring s1 en mi_string, y lo reemplaza por s2. \n", "* La función ***mi_string.replace(s1, s2)*** regresa un nuevo string, el string original no es modificado. " ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cara\n", "casa\n", "pasa\n", "cesa\n", "oso\n", "cara\n" ] } ], "source": [ "palabra = 'cara'\n", "palabra2 = palabra.replace('r', 's')\n", "print palabra\n", "print palabra2\n", "print palabra2.replace('ca', 'pa')\n", "print palabra2.replace('a', 'e', 1)\n", "print palabra2.replace('c', '').replace('a', 'o') # Encadenamiento de metodos\n", "print palabra" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Separar un string\n", "\n", "Para separar un string tenemos 2 opciones:\n", "* Separar en caracteres, utilizando `list(mi_string)`, que genera una lista con los carácteres de `mi_string` en orden.\n", "* Separar en palabras, utilizando `mi_string.split(s)`, que generar una lista de \"palabras\" que han sido separadas por el string `s`. El string `s` no estará en ninguno de los substrings de la lista. Por defecto, `s` es el caracter espacio \" \"." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['t', 'a', 'c', 'a', ' ', 't', 'a', 'c', 'a']\n", "set(['a', ' ', 'c', 't'])\n", "['taca', 'taca']\n", "['t', 'c', ' t', 'c', '']\n", "['', 'aca ', 'aca']\n", "['t', 'a t', 'a']\n" ] } ], "source": [ "oracion = 'taca taca'\n", "print list(oracion)\n", "print set(oracion)\n", "print oracion.split()\n", "print oracion.split(\"a\")\n", "print oracion.split(\"t\")\n", "print oracion.split(\"ac\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Unir una lista de strings\n", "Para unir una lista de strings es necesario utilizar el método `join`:\n", "\n", "```Python\n", " s.join(lista_de_strings)\n", "```\n", "\n", "Regresa un único string donde los elementos del string han sido \"pegados\" utilizando el string s." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ex umbra in solem\n", "Exumbrainsolem\n", "Ex -> umbra -> in -> solem\n" ] } ], "source": [ "mi_lista = ['Ex', 'umbra', 'in', 'solem']\n", "print ' '.join(mi_lista)\n", "print ''.join(mi_lista)\n", "print ' -> '.join(mi_lista)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "set(['Ex', 'solem', 'umbra', 'in'])\n", "Ex solem umbra in\n", "Exsolemumbrain\n", "Ex -> solem -> umbra -> in\n" ] } ], "source": [ "mi_conjunto = {'Ex', 'umbra', 'in', 'solem'}\n", "print mi_conjunto\n", "print ' '.join(mi_conjunto)\n", "print ''.join(mi_conjunto)\n", "print ' -> '.join(mi_conjunto)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Unir una lista de strings\n", "\n", "Observación: `join` funciona sólo sobre una lista de strings. Si quiere pegar números, debe convertirlos a strings ***antes***." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1, 2, 3\n" ] } ], "source": [ "lista_de_strings = [\"1\", \"2\", \"3\"]\n", "print \", \".join(lista_de_strings)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "sequence item 0: expected string, int found", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-36-ac88427f974d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mlista_de_ints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[1;34m\", \"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlista_de_ints\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: sequence item 0: expected string, int found" ] } ], "source": [ "lista_de_ints = [1, 2, 3]\n", "print \", \".join(lista_de_ints)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0, 1, 2, 3, 4, 5, 6, 7, 8, 9\n" ] } ], "source": [ "lista_de_ints = range(10)\n", "lista_de_strings = []\n", "for x in lista_de_ints:\n", " lista_de_strings.append(str(x))\n", "print \", \".join(lista_de_strings)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Unir una secuencia de valores (no strings) v2\n", "\n", "También es posible utilizar `map` que aplica genera una nueva lista aplicando a cada elemento de la lista original la función pasada como argumento." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n", "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n", "[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]\n", "[1.0, 2.5, 4.333333333333333, 6.25, 8.2, 10.166666666666666, 12.142857142857142, 14.125, 16.11111111111111, 18.1]\n", "0, 1, 2, 3, 4, 5, 6, 7, 8, 9\n", "1-2-3-4\n" ] } ], "source": [ "numeros = range(10)\n", "print numeros\n", "def f(x):\n", " return 2.*x + 1./(x+1)\n", "\n", "print map(str, numeros)\n", "print map(float, numeros)\n", "print map(f, numeros)\n", "\n", "print ', '.join(map(str, numeros))\n", "\n", "# \n", "print \"-\"join(\"1,2,3,4\".split(\",\"))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Interpolación de valores por posición\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "ename": "IndexError", "evalue": "tuple index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-47-36380a8ee462>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Soy {0} y vivo en {1} {2}'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Perico'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Valparaiso'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Erika'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Berlin'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Wang Dawei'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Beijing'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mIndexError\u001b[0m: tuple index out of range" ] } ], "source": [ "s = 'Soy {0} y vivo en {1} {2}'\n", "print s.format('Perico', 'Valparaiso')\n", "print s.format('Erika', 'Berlin')\n", "print s.format('Wang Dawei', 'Beijing')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Interpolación de valores por nombre\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Perico estudia en la UTFSM\n", "Fulana estudia en la PUCV\n", "Yayita estudia en la UPLA\n", "Mago Merlin estudia en la Camelot University\n" ] } ], "source": [ "s = '{nombre} estudia en la {u}'\n", "# Datos pueden pasarse ordenados\n", "print s.format(nombre='Perico', u='UTFSM')\n", "print s.format(nombre='Fulana', u='PUCV')\n", "# También es posible cambiar el orden\n", "print s.format(u='UPLA', nombre='Yayita')\n", "# O con magia (conocimiento avanzado)\n", "d = {\"nombre\":\"Mago Merlin\", \"u\":\"Camelot University\"}\n", "print s.format(**d)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Mayusculas y Minúsculas\n", "\n", "Para cambiar la capitalización de un string, es posible utilizar los siguientes métodos:\n", "\n", "* `.upper()`: TODO EN MAYUSCULA.\n", "* `.lower()`: todo en minuscula\n", "* `.swapcase()`: cambia el order que tenia la capitalización.\n", "* `.capitalize()`: Coloca únicamente mayuscula en la primera letra del string." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1. RAMO DE PROGRA\n", "1. ramo de progra\n", "1. RAmO DE pROgRA\n", "1. ramo de progra\n" ] } ], "source": [ "palabra = '1. raMo de ProGra'\n", "print palabra.upper()\n", "print palabra.lower()\n", "print palabra.swapcase()\n", "print palabra.capitalize()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Ejemplo de Motivación\n", "\n", "Queremos conocer cuales son las palabras más comunes en un idioma. Para eso, necesitamos saber cuantas veces aparece cada palabra en una frase. Desarrolle una función contar_palabras que al ser aplicada sobre un string, entregue un diccionario con las palabras y la cantidad de veces que aparece en la frase. Omita espacios y signos de puntuación y exclamación.\n", "\n", " t = 'El sobre, en el aula, esta sobre el pupitre.'\n", "\n", " contar_palabras(t)\n", " {'el': 3, 'en': 1, 'esta': 1, 'aula': 1, 'sobre': 2, 'pupitre': 1}\n", " \n", "¿Cómo realizaría ***ahora*** usted esta difícil tarea?" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Consejos\n", "\n", "Subdividir en tareas menores:\n", "* ¿Cómo sacar los simbolos indeseados?\n", "* ¿Cómo separar las palabras?\n", "* ¿Cómo contar las palabras?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def contar_palabras(s):\n", " return s\n", " \n", "t = 'El sobre, en el aula, esta sobre el pupitre.'\n", "contar_palabras(t)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Motivación: Solución\n", "\n", "INPUT:\n", "\n", " t = 'El sobre, en el aula, esta sobre el pupitre.'\n", " contar_palabras(t)\n", "\n", "OUTPUT: \n", "\n", " {'el': 3, 'en': 1, 'esta': 1, 'aula': 1, \n", " 'sobre': 2, 'pupitre': 1}" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "{'aula': 1, 'el': 3, 'en': 1, 'esta': 1, 'pupitre': 1, 'sobre': 2}" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def contar_palabras(s):\n", " s = s.lower()\n", " for signo in [\",\",\".\",\";\",\"!\",\"?\",\"'\",'\"']:\n", " s = s.replace(signo,\"\")\n", " palabras = s.split()\n", " contador = {}\n", " for palabra_sucia in palabras:\n", " palabra = palabra_sucia\n", " if palabra in contador:\n", " contador[palabra] += 1 # Aumentamos\n", " else:\n", " contador[palabra] = 1 # Inicializamos\n", " return contador\n", " \n", "t = 'El sobre, en el aula, !! Esta sobre el pupitre.'\n", "contar_palabras(t)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Ejercicio 2\n", "Escriba un programa que tenga el siguiente comportamiento:\n", "\n", "INPUT:\n", "\n", " Numero de alumnos: 3\n", " Nombre alumno 1: Isaac Newton\n", " Ingrese las notas de Isaac: 98 94 77\n", " Nombre alumno 2: Nikola Tesla\n", " Ingrese las notas de Nikola: 100 68 94 88\n", " Nombre alumno 3: Albert Einstein\n", " Ingrese las notas de Albert: 83 85\n", "\n", "OUTPUT:\n", "\n", " El promedio de Isaac es 89.67\n", " El promedio de Nikola es 87.50\n", " El promedio de Albert es 84.00" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Ejercicio 2: Análisis\n", "¿Cuáles son las tareas necesarias?" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Ejercicio 1: Solución\n", "Las tareas a realizar son:\n", "* Leer número de alumnos\n", "* Para cada alumno, leer nombre y notas.\n", "* Procesar notas para obtener el promedio.\n", "* Almacenar nombre y notas.\n", "* Separar nombre de apellido.\n", "* Imprimir resultados apropiadamente." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Solución Alumnos\n" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numero de alumnos: 2\n", "Nombre alumno 1:Felipe Lopez Correa\n", "Ingrese las notas de Felipe: 90 90 90 90 90 0\n", "Nombre alumno 2:Pablo Alvarado Seguel\n", "Ingrese las notas de Pablo: 100 0 100 0 100 0 100\n", "El promedio de Felipe Lopez Correa es 75.00\n", "El promedio de Pablo Alvarado Seguel es 57.14\n" ] } ], "source": [ "# Solución\n", "# Guardar datos\n", "N = int(raw_input(\"Numero de alumnos: \"))\n", "notas_alumnos = []\n", "for i in range(N):\n", " nombre = raw_input(\"Nombre alumno {0}:\".format(i+1))\n", " nombre_pila = nombre.split(\" \")[0]\n", " notas_str = raw_input(\"Ingrese las notas de {0}: \".format(nombre_pila))\n", " notas_int = []\n", " for nota in notas_str.split(\" \"):\n", " notas_int.append(int(nota))\n", " promedio = sum(notas_int)/float(len(notas_int))\n", " notas_alumnos.append( (nombre_pila, promedio) )\n", "\n", "# Imprimir promedios\n", "for nombre, promedio in notas_alumnos:\n", " print \"El promedio de {0} es {1:.2f}\".format(nombre, promedio)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Procesamiento de ADN\n", "Una cadena de ADN es una secuencia de bases nitrogenadas llamadas adenina, citosina, timina y guanina.\n", "En un programa, una cadena se representa como un string de caracteres 'a', 'c', 't' y 'g'.\n", "A cada cadena, le corresponde una cadena complementaria, que se obtiene intercambiando las adeninas con las timinas, y las citosinas con las guaninas:\n", "\n", " cadena = 'cagcccatgaggcagggtg'\n", " complemento = 'gtcgggtactccgtcccac'" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de ADN\n", "## 1.1 Procesamiento de ADN: Secuencia aleatoria\n", "\n", "Escriba la función ***cadena_al_azar(n)*** que genere una cadena aleatoria de ADN de largo n:\n", "\n", "Ejemplo de uso:\n", "\n", " cadena_al_azar(10) \n", " puede regresar 'acgtccgcct', 'tgttcgcatt', etc.\n", "\n", "Pista:\n", "\n", " from random import choice\n", "\n", "choice('atcg') regresa al azar una de las letras de \"atcg\"" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de ADN\n", "## 1.1 Secuencia aleatoria: Análisis\n", "\n", "¿Que tareas son necesarias?" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "g\n", "g\n", "g\n", "t\n", "tgaccggacg\n", "tacccacact\n", "aagcatccga\n", "tccattcgga\n" ] } ], "source": [ "# Definicion de funcion\n", "from random import choice\n", "def cadena_al_azar(n):\n", " bases_n=''\n", " for i in range(n):\n", " base=choice('atgc')\n", " bases_n+=base\n", " return bases_n\n", "\n", "# Casos de uso\n", "print cadena_al_azar(1)\n", "print cadena_al_azar(1)\n", "print cadena_al_azar(1)\n", "print cadena_al_azar(1)\n", "print cadena_al_azar(10)\n", "print cadena_al_azar(10)\n", "print cadena_al_azar(10)\n", "print cadena_al_azar(10)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de ADN\n", "## 1.1 Solución Secuencia aleatoria v1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from random import choice\n", "\n", "# Definicion de funcion\n", "def cadena_al_azar(n):\n", " adn = \"\"\n", " for i in range(n):\n", " adn += choice(\"acgt\")\n", " return adn\n", "\n", "# Casos de uso\n", "print cadena_al_azar(1)\n", "print cadena_al_azar(1)\n", "print cadena_al_azar(1)\n", "print cadena_al_azar(1)\n", "print cadena_al_azar(10)\n", "print cadena_al_azar(10)\n", "print cadena_al_azar(10)\n", "print cadena_al_azar(10)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de ADN\n", "## 1.1 Solución Secuencia aleatoria v2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from random import choice\n", "\n", "# Definicion de funcion\n", "def cadena_al_azar(n):\n", " bases = []\n", " for i in range(n):\n", " bases.append(choice(\"acgt\"))\n", " adn = \"\".join(bases)\n", " return adn\n", "\n", "# Casos de uso\n", "print cadena_al_azar(1)\n", "print cadena_al_azar(1)\n", "print cadena_al_azar(1)\n", "print cadena_al_azar(1)\n", "print cadena_al_azar(10)\n", "print cadena_al_azar(10)\n", "print cadena_al_azar(10)\n", "print cadena_al_azar(10)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "#### Procesamiento de texto\n", "## Procesamiento de ADN: Secuencia complementaria\n", "\n", "Escriba la `función complementaria(s)` que regrese la cadena complementaria de c: el complementario de \"a\" es \"t\" (y viceversa), y el complementario de \"c\" es \"g\" (y viceversa).\n", "\n", "```Python\n", " cadena = 'cagcccatgaggcagggtg'\n", " print complementaria(cadena)\n", " 'gtcgggtactccgtcccac'\n", "``` " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "#### Procesamiento de texto\n", "## Procesamiento de ADN: Secuencia complementaria\n", "\n", "¿Tareas?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Solucion estudiantes\n", "def cadena_(n):\n", " adn = \"\"\n", " for i in range(n):\n", " adn += choice(\"acgt\")\n", " return adn" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "#### Procesamiento de texto\n", "## Solución Secuencia complementaria v1" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ttttagtgcttcagaatgtc\n", "aaaatcacgaagtcttacag\n" ] } ], "source": [ "def complementaria(adn):\n", " rna = \"\"\n", " for base in adn:\n", " if base==\"a\":\n", " rna += \"t\"\n", " elif base==\"t\":\n", " rna += \"a\"\n", " elif base==\"c\":\n", " rna += \"g\"\n", " else:\n", " rna += \"c\"\n", " return rna\n", "\n", "adn = cadena_al_azar(20)\n", "print adn\n", "print complementaria(adn)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "#### Procesamiento de texto\n", "## Solución Secuencia complementaria v2" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "atccttcaaaaactcctagc\n", "taggaagtttttgaggatcg\n" ] } ], "source": [ "def complementaria(adn):\n", " pares = {\"a\":\"t\", \"t\":\"a\", \"c\":\"g\", \"g\":\"c\"}\n", " rna = \"\"\n", " for base in adn:\n", " rna += pares[base]\n", " return rna\n", "\n", "adn = cadena_al_azar(20)\n", "print adn\n", "print complementaria(adn)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "#### Procesamiento de texto\n", "## Solución Secuencia complementaria v3" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "caactcatagcttgtcatgg\n", "gttgagtatcgaacagtacc\n" ] } ], "source": [ "def complementaria(adn):\n", " rna = adn.replace(\"a\",\"T\").replace(\"t\",\"A\").replace(\"c\",\"G\").replace(\"g\",\"C\")\n", " return rna.lower()\n", "\n", "adn = cadena_al_azar(20)\n", "print adn\n", "print complementaria(adn)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#### Procesamiento de texto\n", "## Digitos no presentes\n", "\n", "Dado un string con dígitos, indique que digitos no estan presentes (en orden).\n", "\n", "INPUT:\n", " \n", " 13579\n", " 3210\n", "\n", "OUTPUT:\n", "\n", " 02468\n", " 456789" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

cc0-1.0

kdheepak/psst

docs/notebooks/interactive_visuals/Demo.ipynb

1

34560

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How `NetworkModel` Works" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "from psst.network.graph import (\n", " NetworkModel, NetworkViewBase, NetworkView\n", ")\n", "\n", "from psst.case import read_matpower\n", "case = read_matpower('../cases/case118.m')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## I. Creating a `NetworkModel`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Create the model from a PSSTCase, optionally passing a sel_bus\n", "m = NetworkModel(case, sel_bus='Bus1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### In the `__init__`, the `NetworkModel`..." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<psst.case.PSSTCase(name=case118, Generators=54, Buses=118, Branches=186)>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<psst.network.PSSTNetwork(nodes=290, edges=351)>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<networkx.classes.graph.Graph at 0x7f223c0ec630>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<psst.model.PSSTModel(status=solved)>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(m.case) # saves the case\n", "display(m.network) # creates a PSSTNetwork\n", "display(m.G) # stores the networkX graph (an attribute of the PSSTNetwork)\n", "display(m.model) # builds/solves the model" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>x</th>\n", " <th>y</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Bus1</th>\n", " <td>1256.40</td>\n", " <td>309.10</td>\n", " </tr>\n", " <tr>\n", " <th>Bus10</th>\n", " <td>1160.10</td>\n", " <td>125.97</td>\n", " </tr>\n", " <tr>\n", " <th>Bus100</th>\n", " <td>302.49</td>\n", " <td>229.74</td>\n", " </tr>\n", " <tr>\n", " <th>Bus101</th>\n", " <td>264.54</td>\n", " <td>249.43</td>\n", " </tr>\n", " <tr>\n", " <th>Bus102</th>\n", " <td>244.91</td>\n", " <td>278.67</td>\n", " </tr>\n", " <tr>\n", " <th>Bus103</th>\n", " <td>223.94</td>\n", " <td>180.12</td>\n", " </tr>\n", " <tr>\n", " <th>Bus104</th>\n", " <td>254.56</td>\n", " <td>190.97</td>\n", " </tr>\n", " <tr>\n", " <th>Bus105</th>\n", " <td>239.27</td>\n", " <td>146.20</td>\n", " </tr>\n", " <tr>\n", " <th>Bus106</th>\n", " <td>278.94</td>\n", " <td>166.79</td>\n", " </tr>\n", " <tr>\n", " <th>Bus107</th>\n", " <td>266.64</td>\n", " <td>122.28</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " x y\n", "Bus1 1256.40 309.10\n", "Bus10 1160.10 125.97\n", "Bus100 302.49 229.74\n", "Bus101 264.54 249.43\n", "Bus102 244.91 278.67\n", "Bus103 223.94 180.12\n", "Bus104 254.56 190.97\n", "Bus105 239.27 146.20\n", "Bus106 278.94 166.79\n", "Bus107 266.64 122.28" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Creates df of x,y positions for each node (bus, load, gen), based off self.network.positions\n", "m.all_pos.head(n=10)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>start_x</th>\n", " <th>end_x</th>\n", " <th>start_y</th>\n", " <th>end_y</th>\n", " </tr>\n", " <tr>\n", " <th>start</th>\n", " <th>end</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">Bus1</th>\n", " <th>GenCo0</th>\n", " <td>1256.4</td>\n", " <td>1293.9</td>\n", " <td>309.10</td>\n", " <td>304.33</td>\n", " </tr>\n", " <tr>\n", " <th>Bus2</th>\n", " <td>1256.4</td>\n", " <td>1224.4</td>\n", " <td>309.10</td>\n", " <td>320.97</td>\n", " </tr>\n", " <tr>\n", " <th>Bus3</th>\n", " <td>1256.4</td>\n", " <td>1208.9</td>\n", " <td>309.10</td>\n", " <td>284.35</td>\n", " </tr>\n", " <tr>\n", " <th>Load_Bus1</th>\n", " <td>1256.4</td>\n", " <td>1288.0</td>\n", " <td>309.10</td>\n", " <td>329.57</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Bus2</th>\n", " <th>Bus12</th>\n", " <td>1224.4</td>\n", " <td>1171.4</td>\n", " <td>320.97</td>\n", " <td>301.64</td>\n", " </tr>\n", " <tr>\n", " <th>Load_Bus2</th>\n", " <td>1224.4</td>\n", " <td>1251.0</td>\n", " <td>320.97</td>\n", " <td>346.28</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">Bus3</th>\n", " <th>Bus5</th>\n", " <td>1208.9</td>\n", " <td>1164.1</td>\n", " <td>284.35</td>\n", " <td>239.66</td>\n", " </tr>\n", " <tr>\n", " <th>Bus12</th>\n", " <td>1208.9</td>\n", " <td>1171.4</td>\n", " <td>284.35</td>\n", " <td>301.64</td>\n", " </tr>\n", " <tr>\n", " <th>Load_Bus3</th>\n", " <td>1208.9</td>\n", " <td>1242.1</td>\n", " <td>284.35</td>\n", " <td>286.85</td>\n", " </tr>\n", " <tr>\n", " <th>Bus4</th>\n", " <th>GenCo1</th>\n", " <td>1187.6</td>\n", " <td>1200.6</td>\n", " <td>226.31</td>\n", " <td>190.94</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " start_x end_x start_y end_y\n", "start end \n", "Bus1 GenCo0 1256.4 1293.9 309.10 304.33\n", " Bus2 1256.4 1224.4 309.10 320.97\n", " Bus3 1256.4 1208.9 309.10 284.35\n", " Load_Bus1 1256.4 1288.0 309.10 329.57\n", "Bus2 Bus12 1224.4 1171.4 320.97 301.64\n", " Load_Bus2 1224.4 1251.0 320.97 346.28\n", "Bus3 Bus5 1208.9 1164.1 284.35 239.66\n", " Bus12 1208.9 1171.4 284.35 301.64\n", " Load_Bus3 1208.9 1242.1 284.35 286.85\n", "Bus4 GenCo1 1187.6 1200.6 226.31 190.94" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Creates a df of start and end x,y positions for each edge, based off self.G.edges()\n", "m.all_edges.head(n=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The `sel_bus` and `view_buses` attributes" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Bus1'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# `sel_bus` is a single bus, upon which the visualization is initially centered.\n", "# It can be changed programatically, or via the dropdown menu.\n", "\n", "m.sel_bus" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Bus1']" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# At first, it is the only bus in view_buses.\n", "# More buses get added to view_buses as they are clicked.\n", "\n", "m.view_buses" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## II. Creating a `NetworkView` from the model" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create the view from the model\n", "# (It can, alternatively, be created from a case.)\n", "\n", "v = NetworkView(model=m)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8565c2b76bea412a9133bdcf81a4837b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "A Jupyter Widget" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "v" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## III. Generating the x,y data for the view" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Whenever the `view_buses` list get changed, it triggers the callback `_callback_view_change`\n", " * This function first calls `subset_positions` and `subset_edges`\n", " * Then, the subsetted DataFrames get segregated into seperate ones for `bus`, `gen`, and `load`\n", " * Finally, the x,y coordinates are extracted into a format the `NetworkView` can use." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Bus1']" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The subsetting that occurs is all based on `view_buses`\n", "m.view_buses" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The `subset_positions()` call" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>x</th>\n", " <th>y</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>GenCo0</th>\n", " <td>1293.9</td>\n", " <td>304.33</td>\n", " </tr>\n", " <tr>\n", " <th>Bus2</th>\n", " <td>1224.4</td>\n", " <td>320.97</td>\n", " </tr>\n", " <tr>\n", " <th>Bus3</th>\n", " <td>1208.9</td>\n", " <td>284.35</td>\n", " </tr>\n", " <tr>\n", " <th>Bus1</th>\n", " <td>1256.4</td>\n", " <td>309.10</td>\n", " </tr>\n", " <tr>\n", " <th>Load_Bus1</th>\n", " <td>1288.0</td>\n", " <td>329.57</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " x y\n", "GenCo0 1293.9 304.33\n", "Bus2 1224.4 320.97\n", "Bus3 1208.9 284.35\n", "Bus1 1256.4 309.10\n", "Load_Bus1 1288.0 329.57" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Subset positions creates self.pos\n", "m.pos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function looks like this:\n", "\n", "```python\n", "def subset_positions(self):\n", " \"\"\"Subset self.all_pos to include only nodes adjacent to those in view_buses list.\"\"\"\n", " nodes = [list(self.G.adj[item].keys()) for item in self.view_buses] # get list of nodes adj to selected buses\n", " nodes = set(itertools.chain.from_iterable(nodes)) # chain lists together, eliminate duplicates w/ set\n", " nodes.update(self.view_buses) # Add the view_buses themselves to the set\n", " return self.all_pos.loc[nodes] # Subset df of all positions to include only desired nodes.\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The `subset_edges()` call" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>start_x</th>\n", " <th>end_x</th>\n", " <th>start_y</th>\n", " <th>end_y</th>\n", " </tr>\n", " <tr>\n", " <th>start</th>\n", " <th>end</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">Bus1</th>\n", " <th>GenCo0</th>\n", " <td>1256.4</td>\n", " <td>1293.9</td>\n", " <td>309.1</td>\n", " <td>304.33</td>\n", " </tr>\n", " <tr>\n", " <th>Bus2</th>\n", " <td>1256.4</td>\n", " <td>1224.4</td>\n", " <td>309.1</td>\n", " <td>320.97</td>\n", " </tr>\n", " <tr>\n", " <th>Bus3</th>\n", " <td>1256.4</td>\n", " <td>1208.9</td>\n", " <td>309.1</td>\n", " <td>284.35</td>\n", " </tr>\n", " <tr>\n", " <th>Load_Bus1</th>\n", " <td>1256.4</td>\n", " <td>1288.0</td>\n", " <td>309.1</td>\n", " <td>329.57</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " start_x end_x start_y end_y\n", "start end \n", "Bus1 GenCo0 1256.4 1293.9 309.1 304.33\n", " Bus2 1256.4 1224.4 309.1 320.97\n", " Bus3 1256.4 1208.9 309.1 284.35\n", " Load_Bus1 1256.4 1288.0 309.1 329.57" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Subset edges creates self.edges\n", "m.edges" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function looks like this:\n", "\n", "```python\n", "def subset_edges(self):\n", " \"\"\"Subset all_edges, with G.edges() info, based on view_buses list.\"\"\"\n", " edge_list = self.G.edges(nbunch=self.view_buses) # get edges of view_buses as list of tuples\n", " edges_fwd = self.all_edges.loc[edge_list] # query all_pos with edge_list\n", " edge_list_rev = [tuple(reversed(tup)) for tup in edge_list] # reverse order of each tuple\n", " edges_rev = self.all_edges.loc[edge_list_rev] # query all_pos again, with reversed edge_list\n", " edges = edges_fwd.append(edges_rev).dropna(subset=['start_x']) # combine results, dropping false hits\n", " return edges\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** If you want a closer look...**" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m.view_buses = ['Bus2','Bus3']" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('Bus2', 'Bus1'),\n", " ('Bus2', 'Bus12'),\n", " ('Bus2', 'Load_Bus2'),\n", " ('Bus3', 'Bus1'),\n", " ('Bus3', 'Bus5'),\n", " ('Bus3', 'Bus12'),\n", " ('Bus3', 'Load_Bus3')]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "edge_list = m.G.edges(nbunch=m.view_buses) # get edges of view_buses as list of tuples\n", "edge_list" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>start_x</th>\n", " <th>end_x</th>\n", " <th>start_y</th>\n", " <th>end_y</th>\n", " </tr>\n", " <tr>\n", " <th>start</th>\n", " <th>end</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">Bus2</th>\n", " <th>Bus1</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Bus12</th>\n", " <td>1224.4</td>\n", " <td>1171.4</td>\n", " <td>320.97</td>\n", " <td>301.64</td>\n", " </tr>\n", " <tr>\n", " <th>Load_Bus2</th>\n", " <td>1224.4</td>\n", " <td>1251.0</td>\n", " <td>320.97</td>\n", " <td>346.28</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">Bus3</th>\n", " <th>Bus1</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Bus5</th>\n", " <td>1208.9</td>\n", " <td>1164.1</td>\n", " <td>284.35</td>\n", " <td>239.66</td>\n", " </tr>\n", " <tr>\n", " <th>Bus12</th>\n", " <td>1208.9</td>\n", " <td>1171.4</td>\n", " <td>284.35</td>\n", " <td>301.64</td>\n", " </tr>\n", " <tr>\n", " <th>Load_Bus3</th>\n", " <td>1208.9</td>\n", " <td>1242.1</td>\n", " <td>284.35</td>\n", " <td>286.85</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " start_x end_x start_y end_y\n", "start end \n", "Bus2 Bus1 NaN NaN NaN NaN\n", " Bus12 1224.4 1171.4 320.97 301.64\n", " Load_Bus2 1224.4 1251.0 320.97 346.28\n", "Bus3 Bus1 NaN NaN NaN NaN\n", " Bus5 1208.9 1164.1 284.35 239.66\n", " Bus12 1208.9 1171.4 284.35 301.64\n", " Load_Bus3 1208.9 1242.1 284.35 286.85" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "edges_fwd = m.all_edges.loc[edge_list] # query all_pos with edge_list\n", "edges_fwd" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('Bus1', 'Bus2'),\n", " ('Bus12', 'Bus2'),\n", " ('Load_Bus2', 'Bus2'),\n", " ('Bus1', 'Bus3'),\n", " ('Bus5', 'Bus3'),\n", " ('Bus12', 'Bus3'),\n", " ('Load_Bus3', 'Bus3')]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "edge_list_rev = [tuple(reversed(tup)) for tup in edge_list] # reverse order of each tuple\n", "edge_list_rev" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>start_x</th>\n", " <th>end_x</th>\n", " <th>start_y</th>\n", " <th>end_y</th>\n", " </tr>\n", " <tr>\n", " <th>start</th>\n", " <th>end</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Bus1</th>\n", " <th>Bus2</th>\n", " <td>1256.4</td>\n", " <td>1224.4</td>\n", " <td>309.1</td>\n", " <td>320.97</td>\n", " </tr>\n", " <tr>\n", " <th>Bus12</th>\n", " <th>Bus2</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Load_Bus2</th>\n", " <th>Bus2</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Bus1</th>\n", " <th>Bus3</th>\n", " <td>1256.4</td>\n", " <td>1208.9</td>\n", " <td>309.1</td>\n", " <td>284.35</td>\n", " </tr>\n", " <tr>\n", " <th>Bus5</th>\n", " <th>Bus3</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Bus12</th>\n", " <th>Bus3</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Load_Bus3</th>\n", " <th>Bus3</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " start_x end_x start_y end_y\n", "start end \n", "Bus1 Bus2 1256.4 1224.4 309.1 320.97\n", "Bus12 Bus2 NaN NaN NaN NaN\n", "Load_Bus2 Bus2 NaN NaN NaN NaN\n", "Bus1 Bus3 1256.4 1208.9 309.1 284.35\n", "Bus5 Bus3 NaN NaN NaN NaN\n", "Bus12 Bus3 NaN NaN NaN NaN\n", "Load_Bus3 Bus3 NaN NaN NaN NaN" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "edges_rev = m.all_edges.loc[edge_list_rev] # query all_pos again, with reversed edge_list\n", "edges_rev" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>start_x</th>\n", " <th>end_x</th>\n", " <th>start_y</th>\n", " <th>end_y</th>\n", " </tr>\n", " <tr>\n", " <th>start</th>\n", " <th>end</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Bus2</th>\n", " <th>Bus12</th>\n", " <td>1224.4</td>\n", " <td>1171.4</td>\n", " <td>320.97</td>\n", " <td>301.64</td>\n", " </tr>\n", " <tr>\n", " <th>Load_Bus2</th>\n", " <td>1224.4</td>\n", " <td>1251.0</td>\n", " <td>320.97</td>\n", " <td>346.28</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">Bus3</th>\n", " <th>Bus5</th>\n", " <td>1208.9</td>\n", " <td>1164.1</td>\n", " <td>284.35</td>\n", " <td>239.66</td>\n", " </tr>\n", " <tr>\n", " <th>Bus12</th>\n", " <td>1208.9</td>\n", " <td>1171.4</td>\n", " <td>284.35</td>\n", " <td>301.64</td>\n", " </tr>\n", " <tr>\n", " <th>Load_Bus3</th>\n", " <td>1208.9</td>\n", " <td>1242.1</td>\n", " <td>284.35</td>\n", " <td>286.85</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Bus1</th>\n", " <th>Bus2</th>\n", " <td>1256.4</td>\n", " <td>1224.4</td>\n", " <td>309.10</td>\n", " <td>320.97</td>\n", " </tr>\n", " <tr>\n", " <th>Bus3</th>\n", " <td>1256.4</td>\n", " <td>1208.9</td>\n", " <td>309.10</td>\n", " <td>284.35</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " start_x end_x start_y end_y\n", "start end \n", "Bus2 Bus12 1224.4 1171.4 320.97 301.64\n", " Load_Bus2 1224.4 1251.0 320.97 346.28\n", "Bus3 Bus5 1208.9 1164.1 284.35 239.66\n", " Bus12 1208.9 1171.4 284.35 301.64\n", " Load_Bus3 1208.9 1242.1 284.35 286.85\n", "Bus1 Bus2 1256.4 1224.4 309.10 320.97\n", " Bus3 1256.4 1208.9 309.10 284.35" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "edges = edges_fwd.append(edges_rev).dropna(subset=['start_x']) # combine results, dropping false hits\n", "edges" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Segregating DataFrames and extracting data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The DataFrames are segregated into `bus`, `case`, and `load`, using the names in `case.bus`, `case.gen`, and `case.load`\n", "* x,y data is extracted, ready to be plotted by `NetworkView`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "Extracting bus data looks like this:\n", "\n", "```python\n", "bus_pos = self.pos[self.pos.index.isin(self.case.bus_name)]\n", "self.bus_x_vals = bus_pos['x']\n", "self.bus_y_vals = bus_pos['y']\n", "self.bus_names = list(bus_pos.index)\n", "```\n", "\n", "(Similar for the other nodes)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x_vals: [ 1171.4 1224.4 1208.9 1164.1 1256.4]\n", "y_vals: [ 301.64 320.97 284.35 239.66 309.1 ]\n", "names: ['Bus12', 'Bus2', 'Bus3', 'Bus5', 'Bus1']\n" ] } ], "source": [ "print(\"x_vals: \", m.bus_x_vals)\n", "print(\"y_vals: \", m.bus_y_vals)\n", "print(\"names: \", m.bus_names)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "Extracting branch data looks like this:\n", "\n", "```python\n", "edges = self.edges.reset_index()\n", "\n", "_df = edges.loc[edges.start.isin(self.case.bus_name) & edges.end.isin(self.case.bus_name)]\n", "self.bus_x_edges = [tuple(edge) for edge in _df[['start_x', 'end_x']].values]\n", "self.bus_y_edges = [tuple(edge) for edge in _df[['start_y', 'end_y']].values]\n", "```\n", "\n", "(Similar for the other edges)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bus_x_edges:\n", "[[ 1224.4 1171.4]\n", " [ 1208.9 1164.1]\n", " [ 1208.9 1171.4]\n", " [ 1256.4 1224.4]\n", " [ 1256.4 1208.9]]\n", "\n", "bus_y_edges:\n", "[[ 320.97 301.64]\n", " [ 284.35 239.66]\n", " [ 284.35 301.64]\n", " [ 309.1 320.97]\n", " [ 309.1 284.35]]\n" ] } ], "source": [ "print(\"bus_x_edges:\")\n", "print(m.bus_x_edges)\n", "\n", "print(\"\\nbus_y_edges:\")\n", "print(m.bus_y_edges)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

JAmarel/LiquidCrystals

ElectroOptics/MinimizeAttempt.ipynb

1

39845

{ "cells": [ { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from scipy.integrate import quad, dblquad\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import minimize" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "thetamin = 25.6*np.pi/180\n", "thetamax = 33.7*np.pi/180\n", "t = 1*10**-6 #Cell Thickness" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tempsC = np.array([26, 27, 29, 31, 33, 35, 37])\n", "voltages = np.array([2,3,6,7,9,11,12.5,14,16,18,20,22,23.5,26,27.5,29,31,32.5,34,36])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "voltages = np.array([1.826,3.5652,5.3995,7.2368,9.0761,10.8711,12.7109,14.5508,16.3461,18.1414,19.9816,21.822,23.6174,25.4577,27.253,29.0935,30.889,32.7924,34.5699,35.8716])\n", "measured_psi1 = np.array([[11.4056,20.4615,25.4056,27.9021,29.028,29.6154,30.2517,30.8392,31.1329,31.5245,31.8671,32.014,32.3077,32.5034,32.7972,32.9929,33.1399,33.3357,33.4336,33.6783]])" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#This Block just converts units\n", "\n", "fields = np.array([entry/t for entry in voltages])\n", "\n", "KC = 273.15\n", "tempsK = np.array([entry+KC for entry in tempsC]) #Celsius to Kelvin" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# measured_psi1 = np.array([[11,20.5,25.5,27.5,29,30,30.5,31,31.25,31.5,31.75,32,32.25,32.5,32.75,33,33.25,33.5,33.75,34]])\n", "# measured_psi2 = np.array([[7.6, 11.5, 22.3, 24.7, 27.8, 29.4, 30.1, 30.7, 31.2, 31.6, 31.9, 32.2, 32.4, 32.6, 32.7, 32.8, 32.9, 32.9, 33.0, 33.1]])\n", "# measured_psi3 = np.array([[4.7, 7.3, 15.5, 18.1, 22.7, 25.9, 27.5, 28.6, 29.6, 30.3, 30.8, 31.2, 31.5, 31.8, 32.0, 32.1, 32.3, 32.4, 32.5, 32.6]])\n", "# measured_psi4 = np.array([[3.5, 5.4, 11.5, 13.8, 18.1, 21.9, 24.1, 25.9, 27.5, 28.7, 29.5, 30.1,30.5, 31.0, 31.3, 31.5, 31.7, 31.9, 32.0, 32.2]])\n", "# measured_psi5 = np.array([[2.5, 3.7, 8.0, 9.6, 12.9, 16.3, 18.7, 20.9, 23.4, 25.3, 26.8, 27.9, 28.5, 29.4, 29.8, 30.2, 30.6, 30.8, 31.1, 31.3]])\n", "# measured_psi6 = np.array([[1.9, 2.9, 6.1, 7.3, 9.8, 12.6, 14.7, 16.8, 19.4, 21.7, 23.6, 25.2, 26.1, 27.4, 28.0, 28.6, 29.2, 29.5, 29.9, 30.3]])\n", "# measured_psi7 = np.array([[1.5, 2.3, 4.7, 5.6, 7.5, 9.6, 11.2, 12.9, 15.2, 17.5, 19.6, 21.4, 22.7, 24.4, 25.37, 26.1, 27.02, 27.5, 28.0, 28.6]])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# AllPsi = np.concatenate((measured_psi1,measured_psi2,measured_psi3,measured_psi4,measured_psi5,measured_psi6,measured_psi7),axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Calculate the Boltzmann Factor and the Partition Function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ {Boltz() \\:returns:}\\:\\: e^{\\frac{-U}{k_bT}}\\:sin\\:{\\theta}\\ $$" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def Boltz(theta,phi,T,p0k,alpha,E):\n", " \"\"\"Compute the integrand for the Boltzmann factor.\n", " Returns\n", " -------\n", " A function of theta,phi,T,p0k,alpha,E to be used within dblquad\n", " \"\"\"\n", " return np.exp((1/T)*p0k*E*np.sin(theta)*np.cos(phi)*(1+alpha*E*np.cos(phi)))*np.sin(theta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Calculate the Tilt Angle $\\psi$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ numerator() \\:returns: {sin\\:{2\\theta}\\:cos\\:{\\phi}}\\:e^{\\frac{-U}{k_bT}}\\:sin\\:{\\theta} $$" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def numerator(theta,phi,T,p0k,alpha,E):\n", " boltz = Boltz(theta,phi,T,p0k,alpha,E)\n", " return np.sin(2*theta)*np.cos(phi)*boltz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ denominator()\\: returns: {({cos}^2{\\theta} - {sin}^2{\\theta}\\:{cos}^2{\\phi}})\\:e^{\\frac{-U}{k_bT}}\\:sin\\:{\\theta} $$" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def denominator(theta,phi,T,p0k,alpha,E):\n", " boltz = Boltz(theta,phi,T,p0k,alpha,E)\n", " return ((np.cos(theta)**2) - ((np.sin(theta)**2) * (np.cos(phi)**2)))*boltz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ tan(2\\psi) = \\frac{\\int_{\\theta_{min}}^{\\theta_{max}} \\int_0^{2\\pi} {sin\\:{2\\theta}\\:cos\\:{\\phi}}\\:e^{\\frac{-U}{k_bT}}\\:sin\\:{\\theta}\\: d\\theta d\\phi}{\\int_{\\theta_{min}}^{\\theta_{max}} \\int_0^{2\\pi} ({{cos}^2{\\theta} - {sin}^2{\\theta}\\:{cos}^2{\\phi}})\\:e^{\\frac{-U}{k_bT}}\\:sin\\:{\\theta}\\: d\\theta d\\phi} $$" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def compute_psi(T,p0k,alpha,E,thetamin,thetamax):\n", " \"\"\"Computes the tilt angle(psi) by use of our tan(2psi) equation\n", " Returns\n", " -------\n", " Float:\n", " The statistical tilt angle with conditions T,p0k,alpha,E\n", " \"\"\"\n", " \n", " avg_numerator, avg_numerator_error = dblquad(numerator, 0, 2*np.pi, lambda theta: thetamin, lambda theta: thetamax,args=(T,p0k,alpha,E))\n", " \n", " avg_denominator, avg_denominator_error = dblquad(denominator, 0, 2*np.pi, lambda theta: thetamin, lambda theta: thetamax,args=(T,p0k,alpha,E))\n", " \n", " psi = (1/2)*np.arctan(avg_numerator / (avg_denominator)) * (180 /(np.pi)) #Converting to degrees from radians and divide by two\n", " \n", " return psi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Least Square Fitting $\\alpha$ and $\\rho_0$" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def compute_error(xo,fields,T,thetamin,thetamax,measured_psi):\n", " \"\"\"Computes the squared error for a pair of parameters by comparing it to all measured tilt angles\n", " at one temperature.\n", " This will be used with the minimization function, xo is a point that the minimization checks.\n", " \n", " Parameters/Conditions\n", " ----------\n", " x0: \n", " An array of the form [alpha^13,p0^33].\n", " \n", " Returns\n", " -------\n", " Float: Error\n", " \"\"\"\n", " \n", " alpha = xo[0]/(1e10)\n", " p0 = xo[1]/(1e30)\n", " \n", " p0k = p0/1.3806488e-23\n", " \n", " computed_psi = np.array([compute_psi(T,p0k,alpha,E,thetamin,thetamax) for E in fields])\n", " \n", " Err = computed_psi - measured_psi\n", " ErrSqr = np.array([i**2 for i in Err]) \n", " return np.sum(ErrSqr)*1e8 #Scaling the Squared Error up here seems to help with minimization precision." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It might be better to use the minimization function individually for each temperature range. The minimization function returns a minimization object, which gives extra information about the results. The two important entries are fun and x. \n", "\n", "fun is the scalar value of the function that is being minimized. In our case fun is the squared error. \n", "\n", "x is the solution array of the form [alpha^10,p0^30]\n", "\n", "The reason it might be better to just minimze the squared error function, instead of using the minimize_func that I wrote below is because the minimize function is very picky about the initial guess. Also the minimization function tends to stop when the result of the function is one the order of 10^-3." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Final Result for $\\alpha$ and $\\rho_0$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Right now everything below this might not work as well as manually guessing and checking. The idea for this section was to automate that process and just return our entire solution arrays at the end of the notebook." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def minimize_func(guess,fields,T,thetamin,thetamax,measured_psi,bnds):\n", " \"\"\"A utility function that is will help me construct alpha and p0 arrays later.\n", " Uses the imported minimize function and compute_error to best fit our parameters\n", " at a temperature.\n", " \n", " Parameters/Conditions\n", " ----------\n", " guess: \n", " The initial guess for minimize().\n", " \n", " Returns\n", " -------\n", " Array: [alpha,p0]\n", " \"\"\"\n", " \n", " results = minimize(compute_error,guess,args=(fields,T,thetamin,thetamax,measured_psi),method = 'SLSQP',bounds = bnds)\n", " xres = np.array(dict(results.items())['x']) \n", " \n", " \"\"\"Minimize returns a special minimization object. That is similar to a dictionary but not quite.\n", " xres is grabbing just the x result of the minimization object, which is the [alpha,p0] array that\n", " we care about\"\"\"\n", " \n", " alpha_results = xres[0]\n", " p0_results = xres[1]\n", " \n", " return np.array([alpha_results,p0_results])\n", " " ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "guess = (2575,2168)\n", "bnds = ((1000,2600),(200,2400))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " fun: 780556183.17644727\n", " nit: 6\n", " message: 'Max. number of function evaluations reach'\n", " x: array([ 1927.02701419, 2400. ])\n", " status: 3\n", " nfev: 100\n", " success: False\n", " jac: array([ 9.53674316e+01, -2.57666111e+06])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results = minimize(compute_error,guess,args=(fields,tempsK[0],thetamin,thetamax,measured_psi1),method = 'TNC',bounds = bnds)\n", "results" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "alpha micro: 0.192702701419\n", "p0 debye: 719.502104544\n" ] } ], "source": [ "res = np.array(dict(results.items())['x'])\n", "alpha = res[0]\n", "p0 = res[1]\n", "alpha = alpha*1e-4\n", "p0 = p0/3.33564\n", "print(\"alpha micro: \" + str(alpha))\n", "print('p0 debye: ' + str(p0))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Minimization claims that it did not succeed. But the results were pretty good. I think it believes that it did not succeed because I have the squared error scaled up very high." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def solution(initial_guess,fields,tempsK,thetamin,thetamax,AllPsi,initial_bnds):\n", " \n", " \"\"\"Constructs Alpha and p0 arrays where each entry is the value of alpha,p0 at the corresponding temperature in\n", " tempsK. Initial guess and initial bounds are changed each iteration of the loop to the previous values of alpha and p0.\n", " Alpha and p0 decrease so this helps to cut down on the range.\n", " \n", " Parameters/Conditions\n", " ----------\n", " initial_guess: \n", " The initial guess for minimize().\n", " initial_bnds:\n", " The initial bounds for minimize().\n", " \n", " \n", " Returns\n", " -------\n", " Array,Array: Alpha Array in micro meters, p0 Array in debye\n", " \"\"\"\n", " \n", " alpha = np.array([])\n", " p0 = np.array([])\n", " \n", " guess = initial_guess\n", " bnds = initial_bnds\n", " \n", " for i in range(len(tempsK)):\n", " res = minimize_func(guess,fields,tempsK[i],thetamin,thetamax,AllPsi[i],bnds)\n", " \n", " alpha = np.append(alpha,res[0])\n", " p0 = np.append(p0,res[1])\n", " \n", " guess = (res[0]-10,res[1]-10)\n", " bnds = ((initial_bnds[0][0],res[0]),(initial_bnds[1][0],res[1]))\n", " \n", " alpha = alpha*1e-4\n", " \n", " p0 = p0/(3.33564)\n", " \n", " return alpha,p0" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "initial_guess = (2575,2168)\n", "initial_bnds = ((1000,2600),(200,2300))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-21-93f2319eabd0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0malpha_micro\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mp0Debye\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msolution\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minitial_guess\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfields\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtempsK\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mthetamin\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mthetamax\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mAllPsi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minitial_bnds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-19-9d3110bc6299>\u001b[0m in \u001b[0;36msolution\u001b[1;34m(initial_guess, fields, tempsK, thetamin, thetamax, AllPsi, initial_bnds)\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtempsK\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminimize_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mguess\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfields\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtempsK\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mthetamin\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mthetamax\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mAllPsi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbnds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m<ipython-input-18-945be5dd5160>\u001b[0m in \u001b[0;36mminimize_func\u001b[1;34m(guess, fields, T, thetamin, thetamax, measured_psi, bnds)\u001b[0m\n\u001b[0;32m 14\u001b[0m \"\"\"\n\u001b[0;32m 15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcompute_error\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mguess\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfields\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mthetamin\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mthetamax\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmeasured_psi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'SLSQP'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbounds\u001b[0m \u001b[1;33m=\u001b[0m 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\u001b[1;34m'slsqp'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 434\u001b[0m return _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[1;32m--> 435\u001b[1;33m constraints, callback=callback, **options)\n\u001b[0m\u001b[0;32m 436\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'dogleg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 437\u001b[0m return _minimize_dogleg(fun, x0, args, jac, hess,\n", "\u001b[1;32m/usr/local/lib/python3.4/dist-packages/scipy/optimize/slsqp.py\u001b[0m in \u001b[0;36m_minimize_slsqp\u001b[1;34m(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, **unknown_options)\u001b[0m\n\u001b[0;32m 382\u001b[0m \u001b[1;31m# Compute the derivatives of the objective function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 383\u001b[0m \u001b[1;31m# For some reason SLSQP wants g dimensioned to 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solution(initial_guess,fields,tempsK,thetamin,thetamax,AllPsi,initial_bnds)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

bgruening/EDeN

examples/Sequence_example.ipynb

2

25214

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Example\n", "\n", "Consider sequences that are increasingly different. EDeN allows to turn them into vectors, whose similarity is decreasing." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build an artificial dataset: starting from the string 'abcdefghijklmnopqrstuvwxyz', generate iteratively strings by swapping two characters at random. In this way instances are progressively more dissimilar" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import random\n", "\n", "def make_data(size):\n", " text = ''.join([str(unichr(97+i)) for i in range(26)])\n", " seqs = []\n", "\n", " def swap_two_characters(seq):\n", " '''define a function that swaps two characters at random positions in a string '''\n", " line = list(seq)\n", " id_i = random.randint(0,len(line)-1)\n", " id_j = random.randint(0,len(line)-1)\n", " line[id_i], line[id_j] = line[id_j], line[id_i]\n", " return ''.join(line)\n", "\n", " for i in range(size):\n", " text = swap_two_characters( text )\n", " seqs.append( text )\n", " print text\n", " \n", " return seqs" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcdefghijkmlnopqrstuvwxyz\n", "abcdxfghijkmlnopqrstuvweyz\n", "abcdxfgkijhmlnopqrstuvweyz\n", "obcdxfgkijhmlnapqrstuvweyz\n", "obcdxfgkijhvlnapqrstumweyz\n", "obcdxfgkijhvlnapqrstumweyz\n", "obcdxfgkujhvlnapqrstimweyz\n", "otcdxfgkujhvlnapqrsbimweyz\n", "otcdxfgkujhvlnapbrsqimweyz\n", "ytcdxfgkujhvlnapbrsqimweoz\n", "atcdxfgkujhvlnypbrsqimweoz\n", "atcdxfikujhvlnypbrsqgmweoz\n", "atbdxfikujhvlnypcrsqgmweoz\n", "atbdjfikuxhvlnypcrsqgmweoz\n", "atbdjhikuxfvlnypcrsqgmweoz\n", "atbdjhzkuxfvlnypcrsqgmweoi\n", "atbdjhzkuxfvlnyicrsqgmweop\n", "awbdjhzkuxfvlnyicrsqgmteop\n", "awbdjhzkuxfvlsyicrnqgmteop\n", "awbdjhzkuxfvlsyicrnqtmgeop\n", "awbsjhzkuxfvldyicrnqtmgeop\n", "agbsjhzkuxfvldyicrnqtmweop\n", "agbsjdzkuxfvlhyicrnqtmweop\n", "agbsjdqkuxfvlhyicrnztmweop\n", "agbsjdqkuxfvlhyicwnztmreop\n" ] } ], "source": [ "seqs = make_data(25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "define a function that builds a graph from a string, i.e. the path graph with the characters as node labels" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import networkx as nx\n", "\n", "def sequence_to_graph(seq):\n", " '''convert a sequence into a EDeN 'compatible' graph\n", " i.e. a graph with the attribute 'label' for every node and edge'''\n", " G = nx.Graph()\n", " for id,character in enumerate(seq):\n", " G.add_node(id, label = character )\n", " if id > 0:\n", " G.add_edge(id-1, id, label = '-')\n", " return G" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "make a generator that yields graphs: generators are 'good' as they allow functional composition" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def pre_process(iterable):\n", " for seq in iterable:\n", " yield sequence_to_graph(seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "initialize the vectorizer object with the desired 'resolution'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 139 ms, sys: 110 ms, total: 249 ms\n", "Wall time: 331 ms\n" ] } ], "source": [ "%%time\n", "from eden.graph import Vectorizer\n", "vectorizer = Vectorizer( complexity = 4 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "obtain an iterator over the sequences processed into graphs" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3 µs, sys: 2 µs, total: 5 µs\n", "Wall time: 6.2 µs\n" ] } ], "source": [ "%%time\n", "graphs = pre_process( seqs )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "compute the vector encoding of each instance in a sparse data matrix" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Instances: 25 ; Features: 1048577 with an avg of 599 features per instance\n", "CPU times: user 460 ms, sys: 15.8 ms, total: 476 ms\n", "Wall time: 473 ms\n" ] } ], "source": [ "%%time\n", "X = vectorizer.transform( graphs )\n", "print 'Instances: %d ; Features: %d with an avg of %d features per instance' % (X.shape[0], X.shape[1], X.getnnz()/X.shape[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "compute the pairwise similarity as the dot product between the vector representations of each sequence" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 0.56228675 0.33769148 0.19905347 0.14222445 0.14222445\n", " 0.11383999 0.09230998 0.07920908 0.07614308 0.08296049 0.07606405\n", " 0.07120395 0.06749513 0.06568933 0.0623502 0.05895679 0.05887773\n", " 0.05385955 0.05030224 0.04856311 0.05030224 0.05197472 0.05204137\n", " 0.05552622]\n", " [ 0.56228675 1. 0.60896138 0.39792015 0.19601458 0.19601458\n", " 0.16382356 0.13059198 0.11749108 0.09866611 0.1073017 0.092395\n", " 0.08272978 0.07079764 0.06888933 0.06388353 0.06049012 0.05881107\n", " 0.05379289 0.05023557 0.04849644 0.05183557 0.05350806 0.0535747\n", " 0.05545955]\n", " [ 0.33769148 0.60896138 1. 0.5938797 0.29512062 0.29512062\n", " 0.1921417 0.15388681 0.14078591 0.12196094 0.12892405 0.10251645\n", " 0.09452371 0.07758577 0.07230751 0.06730171 0.0639083 0.06222925\n", " 0.05898928 0.05365375 0.05009644 0.05525375 0.05350224 0.05694698\n", " 0.06046482]\n", " [ 0.19905347 0.39792015 0.5938797 1. 0.48318995 0.48318995\n", " 0.33217904 0.20345757 0.16263458 0.13514089 0.12196094 0.09555334\n", " 0.08922726 0.07410751 0.06882925 0.06382345 0.05883004 0.05715099\n", " 0.05213281 0.04857549 0.04675731 0.05191462 0.05016311 0.05190224\n", " 0.05378709]\n", " [ 0.14222445 0.19601458 0.29512062 0.48318995 1. 1.\n", " 0.45516702 0.3094078 0.23179593 0.18978087 0.15341082 0.12518504\n", " 0.11885896 0.09572896 0.08571223 0.07888825 0.07025848 0.06714348\n", " 0.05547852 0.05192121 0.05010303 0.06006692 0.0583154 0.05838642\n", " 0.05709284]\n", " [ 0.14222445 0.19601458 0.29512062 0.48318995 1. 1.\n", " 0.45516702 0.3094078 0.23179593 0.18978087 0.15341082 0.12518504\n", " 0.11885896 0.09572896 0.08571223 0.07888825 0.07025848 0.06714348\n", " 0.05547852 0.05192121 0.05010303 0.06006692 0.0583154 0.05838642\n", " 0.05709284]\n", " [ 0.11383999 0.16382356 0.1921417 0.33217904 0.45516702 0.45516702\n", " 1. 0.56602361 0.45194732 0.38840225 0.30952144 0.16177326\n", " 0.15544719 0.10439458 0.08926954 0.08404557 0.07723397 0.07241646\n", " 0.06412069 0.06056337 0.05707852 0.0653743 0.06195612 0.06187706\n", " 0.06047194]\n", " [ 0.09230998 0.13059198 0.15388681 0.20345757 0.3094078 0.3094078\n", " 0.56602361 1. 0.61263436 0.43219334 0.35331252 0.17667996\n", " 0.16067117 0.10787943 0.09093621 0.08571223 0.07890064 0.07067733\n", " 0.06238155 0.06056337 0.05867852 0.0669743 0.06355612 0.06347706\n", " 0.05866614]\n", " [ 0.07920908 0.11749108 0.14078591 0.16263458 0.23179593 0.23179593\n", " 0.45194732 0.61263436 1. 0.71893216 0.44912766 0.21006965\n", " 0.17782149 0.12680796 0.10808653 0.10104437 0.09059641 0.07881579\n", " 0.06738155 0.06556337 0.06207852 0.07211343 0.06869525 0.06354373\n", " 0.05873281]\n", " [ 0.07614308 0.09866611 0.12196094 0.13514089 0.18978087 0.18978087\n", " 0.38840225 0.43219334 0.71893216 1. 0.5938797 0.24686073\n", " 0.21627923 0.16348749 0.14654428 0.1108553 0.10040734 0.08382159\n", " 0.07238735 0.07056917 0.06708432 0.08192435 0.07850617 0.07335466\n", " 0.0637386 ]\n", " [ 0.08296049 0.1073017 0.12892405 0.12196094 0.15341082 0.15341082\n", " 0.30952144 0.35331252 0.44912766 0.5938797 1. 0.46054252\n", " 0.27637296 0.19492565 0.16955188 0.1338629 0.11365317 0.09392896\n", " 0.07593808 0.07411989 0.0707141 0.08373595 0.08031777 0.07516625\n", " 0.06381107]\n", " [ 0.07606405 0.092395 0.10251645 0.09555334 0.12518504 0.12518504\n", " 0.16177326 0.17667996 0.21006965 0.24686073 0.46054252 1.\n", " 0.52707015 0.32898489 0.29693881 0.18929535 0.17072134 0.11339166\n", " 0.08248813 0.07405323 0.0723141 0.0818453 0.0802453 0.07509379\n", " 0.06207194]\n", " [ 0.07120395 0.08272978 0.09452371 0.08922726 0.11885896 0.11885896\n", " 0.15544719 0.16067117 0.17782149 0.21627923 0.27637296 0.52707015\n", " 1. 0.5786609 0.51929046 0.38702988 0.22668159 0.14020718\n", " 0.09439696 0.08414387 0.08080474 0.09033595 0.08873595 0.0818453\n", " 0.06722345]\n", " [ 0.06749513 0.07079764 0.07758577 0.07410751 0.09572896 0.09572896\n", " 0.10439458 0.10787943 0.12680796 0.16348749 0.19492565 0.32898489\n", " 0.5786609 0.99673333 0.56120377 0.42211921 0.2601352 0.15903236\n", " 0.1112215 0.10096841 0.09267264 0.10402203 0.10562203 0.09873138\n", " 0.08410953]\n", " [ 0.06568933 0.06888933 0.07230751 0.06882925 0.08571223 0.08571223\n", " 0.08926954 0.09093621 0.10808653 0.14654428 0.16955188 0.29693881\n", " 0.51929046 0.56120377 0.99833333 0.59940671 0.35240116 0.22983642\n", " 0.15403034 0.14377726 0.12885817 0.14020756 0.13506263 0.12817199\n", " 0.11355014]\n", " [ 0.0623502 0.06388353 0.06730171 0.06382345 0.07888825 0.07888825\n", " 0.08404557 0.08571223 0.10104437 0.1108553 0.1338629 0.18929535\n", " 0.38702988 0.42211921 0.59940671 0.99833333 0.60463069 0.38143914\n", " 0.27330291 0.26304982 0.19658666 0.20793605 0.16007749 0.12643286\n", " 0.11355014]\n", " [ 0.05895679 0.06049012 0.0639083 0.05883004 0.07025848 0.07025848\n", " 0.07723397 0.07890064 0.09059641 0.10040734 0.11365317 0.17072134\n", " 0.22668159 0.2601352 0.35240116 0.60463069 0.99833333 0.61983243\n", " 0.30497677 0.29108732 0.22462416 0.25854412 0.21068556 0.17522274\n", " 0.13496433]\n", " [ 0.05887773 0.05881107 0.06222925 0.05715099 0.06714348 0.06714348\n", " 0.07241646 0.07067733 0.07881579 0.08382159 0.09392896 0.11339166\n", " 0.14020718 0.15903236 0.22983642 0.38143914 0.61983243 1.\n", " 0.54519437 0.45993613 0.25113412 0.22629083 0.17843227 0.14470858\n", " 0.13838251]\n", " [ 0.05385955 0.05379289 0.05898928 0.05213281 0.05547852 0.05547852\n", " 0.06412069 0.06238155 0.06738155 0.07238735 0.07593808 0.08248813\n", " 0.09439696 0.1112215 0.15403034 0.27330291 0.30497677 0.54519437\n", " 1. 0.71985284 0.28577596 0.25081358 0.20295502 0.15965422\n", " 0.14356637]\n", " [ 0.05030224 0.05023557 0.05365375 0.04857549 0.05192121 0.05192121\n", " 0.06056337 0.06056337 0.06556337 0.07056917 0.07411989 0.07405323\n", " 0.08414387 0.10096841 0.14377726 0.26304982 0.29108732 0.45993613\n", " 0.71985284 1. 0.50897584 0.29576424 0.24790569 0.16843551\n", " 0.15060853]\n", " [ 0.04856311 0.04849644 0.05009644 0.04675731 0.05010303 0.05010303\n", " 0.05707852 0.05867852 0.06207852 0.06708432 0.0707141 0.0723141\n", " 0.08080474 0.09267264 0.12885817 0.19658666 0.22462416 0.25113412\n", " 0.28577596 0.50897584 1. 0.61983243 0.26151542 0.17524386\n", " 0.15741688]\n", " [ 0.05030224 0.05183557 0.05525375 0.05191462 0.06006692 0.06006692\n", " 0.0653743 0.0669743 0.07211343 0.08192435 0.08373595 0.0818453\n", " 0.09033595 0.10402203 0.14020756 0.20793605 0.25854412 0.22629083\n", " 0.25081358 0.29576424 0.61983243 1. 0.49672721 0.27230242\n", " 0.19238978]\n", " [ 0.05197472 0.05350806 0.05350224 0.05016311 0.0583154 0.0583154\n", " 0.06195612 0.06355612 0.06869525 0.07850617 0.08031777 0.0802453\n", " 0.08873595 0.10562203 0.13506263 0.16007749 0.21068556 0.17843227\n", " 0.20295502 0.24790569 0.26151542 0.49672721 1. 0.44994304\n", " 0.31617024]\n", " [ 0.05204137 0.0535747 0.05694698 0.05190224 0.05838642 0.05838642\n", " 0.06187706 0.06347706 0.06354373 0.07335466 0.07516625 0.07509379\n", " 0.0818453 0.09873138 0.12817199 0.12643286 0.17522274 0.14470858\n", " 0.15965422 0.16843551 0.17524386 0.27230242 0.44994304 1.\n", " 0.60437868]\n", " [ 0.05552622 0.05545955 0.06046482 0.05378709 0.05709284 0.05709284\n", " 0.06047194 0.05866614 0.05873281 0.0637386 0.06381107 0.06207194\n", " 0.06722345 0.08410953 0.11355014 0.11355014 0.13496433 0.13838251\n", " 0.14356637 0.15060853 0.15741688 0.19238978 0.31617024 0.60437868\n", " 1. ]]\n" ] } ], "source": [ "from sklearn import metrics\n", "\n", "K=metrics.pairwise.pairwise_kernels(X, metric='linear')\n", "print K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "visualize it as a picture is worth thousand words..." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x112f8c3d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pylab as plt\n", "plt.figure( figsize=(8,8) )\n", "img = plt.imshow( K, interpolation='none', cmap=plt.get_cmap( 'YlOrRd' ) )\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

enakai00/jupyter_ml4se_commentary

Solutions/Titanic Example.ipynb

1

234907

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. データを取り込んで特徴を確認" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from pandas import Series, DataFrame" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.csv')\n", "data['pclass'] = data['pclass'].astype(str) # pclassの型を文字列型に変換" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Index([u'pclass', u'survived', u'name', u'sex', u'age', u'sibsp', u'parch',\n", " u'ticket', u'fare', u'cabin', u'embarked', u'boat', u'body',\n", " u'home.dest'],\n", " dtype='object')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib64/python2.7/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n", " RuntimeWarning)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>survived</th>\n", " <th>age</th>\n", " <th>sibsp</th>\n", " <th>parch</th>\n", " <th>fare</th>\n", " <th>body</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>1309.000000</td>\n", " <td>1046.000000</td>\n", " <td>1309.000000</td>\n", " <td>1309.000000</td>\n", " <td>1308.000000</td>\n", " <td>121.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>0.381971</td>\n", " <td>29.881138</td>\n", " <td>0.498854</td>\n", " <td>0.385027</td>\n", " <td>33.295479</td>\n", " <td>160.809917</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.486055</td>\n", " <td>14.413493</td>\n", " <td>1.041658</td>\n", " <td>0.865560</td>\n", " <td>51.758668</td>\n", " <td>97.696922</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.000000</td>\n", " <td>0.170000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>0.000000</td>\n", " <td>NaN</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>0.000000</td>\n", " <td>NaN</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>1.000000</td>\n", " <td>80.000000</td>\n", " <td>8.000000</td>\n", " <td>9.000000</td>\n", " <td>512.329200</td>\n", " <td>328.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " survived age sibsp parch fare \\\n", "count 1309.000000 1046.000000 1309.000000 1309.000000 1308.000000 \n", "mean 0.381971 29.881138 0.498854 0.385027 33.295479 \n", "std 0.486055 14.413493 1.041658 0.865560 51.758668 \n", "min 0.000000 0.170000 0.000000 0.000000 0.000000 \n", "25% 0.000000 NaN 0.000000 0.000000 NaN \n", "50% 0.000000 NaN 0.000000 0.000000 NaN \n", "75% 1.000000 NaN 1.000000 0.000000 NaN \n", "max 1.000000 80.000000 8.000000 9.000000 512.329200 \n", "\n", " body \n", "count 121.000000 \n", "mean 160.809917 \n", "std 97.696922 \n", "min 1.000000 \n", "25% NaN \n", "50% NaN \n", "75% NaN \n", "max 328.000000 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>1046.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>29.881138</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>14.413493</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.170000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>21.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>28.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>39.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>80.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " age\n", "count 1046.000000\n", "mean 29.881138\n", "std 14.413493\n", "min 0.170000\n", "25% 21.000000\n", "50% 28.000000\n", "75% 39.000000\n", "max 80.000000" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[['age']].dropna().describe()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>fare</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>1308.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>33.295479</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>51.758668</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>7.895800</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>14.454200</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>31.275000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>512.329200</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " fare\n", "count 1308.000000\n", "mean 33.295479\n", "std 51.758668\n", "min 0.000000\n", "25% 7.895800\n", "50% 14.454200\n", "75% 31.275000\n", "max 512.329200" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[['fare']].dropna().describe()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>body</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>121.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>160.809917</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>97.696922</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>72.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>155.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>256.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>328.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " body\n", "count 121.000000\n", "mean 160.809917\n", "std 97.696922\n", "min 1.000000\n", "25% 72.000000\n", "50% 155.000000\n", "75% 256.000000\n", "max 328.000000" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[['body']].dropna().describe()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x45f2ad0>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1f97610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data[['age']].dropna().plot(kind='hist', bins=16)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x47bc3d0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x46848d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data[['fare']].dropna().plot(kind='hist', bins=20)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x4a7c210>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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L4GfAqallnwCe61ZSZaqpVoyiV/9U37iHW9uX2pT0X++Comq2R0c7e4IUMU31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\nFQck6XUWm9msziRTREREsvotKDkGeDyz7PHUZ/W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"text/plain": [ "<matplotlib.figure.Figure at 0x468e0d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = data[['age','fare']].dropna()\n", "df.plot(kind='scatter', x='age', y='fare')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x4b4c710>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JiFkRsaFm/7MRcU5EHBoRB0TEuyLi8ZHojA2u9lycWZGcf5aS868YvpdHSSxd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\niwUvbFUMT8osiY6OjtQhWKk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"text/plain": [ "<matplotlib.figure.Figure at 0x4b65390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = data[['fare','pclass']].dropna()\n", "df.boxplot(column='fare', by='pclass')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x5108dd0>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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DDA0NlWx74YUXajrWmVkWZUo+oXP3Ak8AVwP7gBPN7Huh/fcDj5nZJufcSmAH0BMehXDO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\naJlqKWe4THHHJ5UpmrbWespRtE0IPnFD/HF5Vau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"text/plain": [ "<matplotlib.figure.Figure at 0x4c98a50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df1 = data[data.pclass=='1'][['age','fare']].dropna()\n", "df2 = data[data.pclass=='2'][['age','fare']].dropna()\n", "df3 = data[data.pclass=='3'][['age','fare']].dropna()\n", "plt.scatter(df1.age, df1.fare, facecolor='blue')\n", "plt.scatter(df2.age, df2.fare, facecolor='green')\n", "plt.scatter(df3.age, df3.fare, facecolor='red')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>survived</th>\n", " <th>0</th>\n", " <th>1</th>\n", " </tr>\n", " <tr>\n", " <th>sex</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>female</th>\n", " <td>127</td>\n", " <td>339</td>\n", " </tr>\n", " <tr>\n", " <th>male</th>\n", " <td>682</td>\n", " <td>161</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "survived 0 1\n", "sex \n", "female 127 339\n", "male 682 161" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = data[['sex','survived']].dropna()\n", "pd.crosstab(df.sex, df.survived)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x5072210>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7FCwBgupsQO1swQjgoZoxgyNiWJfZghFF3wZNmzaNsWO35u1/JUnqve7+oTx/\n/nzGjRu3Scf36+WDzHyK6g/2iWvbioWFBwG/LZoeBFZ3GTMaaAbu6896JEnSpuvxTEFEvAkYRXVG\nAGDviNgf+J/MfJbq7YbnR8QTwNPAFOAPwC1QXXgYEVcBUyPiBWAFcAUw1zsPJEkqT28uHxwI/Jrq\ngsIELi/arwFOz8zLIqIBuBLYBbgHODIzV9V8RiuwBpgFDKF6i+NZvToDSZLUL3qzT8HdvMFlh8y8\nELhwI/2vAucUL0mSNAD47ANJkgQYCiRJUsFQIEmSAEOBJEkqGAokSRJgKJAkSQVDgSRJAgwFkiSp\nYCiQJEmAoUCSJBUMBZIkCTAUSJKkgqFAkiQBhgJJklQwFEiSJMBQIEmSCoYCSZIEGAokSVLBUCBJ\nkgBDgSRJKhgKJEkSYCiQJEkFQ4EkSQIMBZIkqWAokCRJgKFAkiQVDAWSJAkwFEiSpIKhQJIkAYYC\nSZJUMBRIkiTAUCBJkgqGAkmSBJQcCiLirIh4KiJeiYj7I+J9ZdYjSdK2rLRQEBGfBC4Hvg68F3gE\nuCMimsqqSZKkbVmZMwWtwJWZ+a+ZuRA4E1gJnF5iTZIkbbNKCQURsQMwDpizti0zE/h3YEIZNUmS\ntK0ra6agCdgOWNqlfSmwa/3LkSRJ25ddwCYaCrBgwYKy69is/nJ+twFb87nOrf7n90Cl1EI2vxeq\n/9naf+9uK/wzupXZRv581pzf0DcaG9VZ+/oqLh+sBI7PzJ/VtF8NNGbmsV3Gfwr4SV2LlCRp6zI5\nM6/b2IBSZgoy87WIeBCYCPwMICKieH9FN4fcAUwGngY66lSmJElbg6HAnlR/lm5UKTMFABFxAnA1\n1bsO5lG9G+HjwLsz80+lFCVJ0jastDUFmXljsSfBRcAI4GFgkoFAkqRylDZTIEmSBhaffSBJkgBD\ngSRJKhgKJEkSYChQnUXEqIiYFBE7Fu+j7JokSVWGAtVFRLwlIv4d+C+q28HtVnRdFRGXl1eZpLUi\n4tCImBkR90XE7kXbyRFxSNm1qT4MBaqXacBqoJnqbpZr3QD8dSkVSVonIo6nurnNK1QfZz+k6GoE\nvlpWXaovQ4Hq5QjgvMz8Q5f23wNvL6EeSZ2dD5yZmZ8GXqtpnwuMLack1ZuhQPXyJjrPEKz1ZuDV\nOtciaX2jgd90074M2KXOtagkhgLVyz3AKTXvMyIGAecCvy6nJEk1lgCjumk/BHiyzrWoJFvKo5O1\n5TsXmBOmtco/AAAGy0lEQVQRBwKDgcuAfajOFBxcZmGSAPgX4NsRcTqQwMiImAB8C5hSamWqG7c5\nVt1ERCNwNrA/sBMwH5iRmYtLLUzS2tuDvwp8BWgoml8FvpWZF5RWmOrKUCBJWiciBlO9jLAT8Hhm\nvlRySaojQ4E2m4jYb1PHZuajm7MWSdIbMxRos4mI16lem3yjXQszM7erQ0mSakTEzZs6NjOP25y1\naGBwoaE2p73KLkDSRi0ruwANLM4USJIkwJkC1VlEvIfqVseDa9sz82flVCRJWstQoLqIiL2B2cBf\n0XmdwdqpKtcUSCWLiI8DJ9B9cHer422AOxqqXr4NPAUMp7rd8T7AYcADwP8uryxJABHxeeDHwFKq\nD0SaB/wZ2Bv4ZYmlqY5cU6C6iIgK8OHMfDQilgHjM3NRRHwYuDwz31tyidI2LSIWAv+UmW0RsQLY\nPzOfjIiLgDdn5tkll6g6cKZA9bIdsKL4ugKMLL5+huqDWCSVqxn4bfH1K8DOxdfXAi2lVKS6MxSo\nXh6jur0xwO+AcyPiYOAf8WEr0kCwhOqzSADagfcXX+/FG+81oq2EoUD1cjF/+f32j1T/orkHOAr4\nfFlFSVrnP4CPFl//GJgWEXcCN1BdJKxtgGsKVJqIeDPwQvqbUCpd8SjzQZm5unj/SapPMP098IPM\nfK3M+lQfhgJJEgARMRTYj+pdQrUzyZmZPy+nKtWT+xSoLoq/bM4BPsT6f+F4D7RUsoj4a6qLCt/S\nTXfiXiLbBEOB6uUq4AhgFtX7n52ikgaW7wA3Ahdl5tKyi1E5vHyguij2JjgqM+eWXYuk9UXEcuC9\nmfnfZdei8nj3gerlOf6yT4Gkgedm3F10m+dMgeoiIv4PcDbw2cx8pux6JHUWEW8CbqK6X8F/Ap3u\nNsjMK8qoS/VlKFBdREQT1fUEh1J99kHXv3De3N1xkuojIk4Dfgh0UH3mQe0Ph8zMvUspTHVlKFBd\nRMSvgD2pLjhcSpeFhpl5TQllSSpExBLgCuCbmfl62fWoHIYC1UVErAQmZOYjZdciaX0R8T/A+1xo\nuG1zoaHqZSGwY9lFSNqga4BPll2EyuU+BaqXLwOXR8TX6H4R0/JSqpK01nZUH1Q2CXiU9f+MfrGU\nqlRXXj5QXUTE2muUXX/DBdVFTO6WJpUoIn69ke7MzA/XrRiVxpkC1cuHyi5A0oZlpn9G5UyBJEmq\ncqGh6iYiDo2ImRHx24jYvWg7OSIOKbs2SZKhQHUSEccDdwCvAGOBIUVXI/DVsuqSJP2FoUD1cj5w\nZmZ+ms6rmudSDQmSpJIZClQvo4HfdNO+DNilzrVIkrphKFC9LAFGddN+CPBknWuRJHXDUKB6+Rfg\n2xFxENW9CkZGxGTgW8D3S61MkgS4T4E2o4jYD3gsM1/PzEsiYhAwB2igeinhVeBbmfmdMuuUJFW5\nT4E2m4hYA+yWmX+MiCeB9wErqF5G2Al4PDNfKrNGSdJfOFOgzelFYC/gj1QfmzwoM1cBj5dZlCSp\ne4YCbU43AXdHxGKq6wgeKGYP1pOZe9e1MknSegwF2mwy8zMRcTPVywVXUF1suKLcqiRJG+KaAtVF\nRPwY+HxmGgokaYAyFEiSJMB9CiRJUsFQIEmSAEOBJEkqGAokSRJgKJAkSQVDgSRJAgwFkiSpYCiQ\nJEmAoUDSRkTExyPi0YhYGRGViPhVROxY9J0REY9HxCvFfz9Xc9zJEbEiIt5R0/a9YtzQMs5F0htz\nR0NJ3YqIXYF24EvAT4GdgUOBfwWOBS4FzgIeBt5L9dkWX8zMa4vjr6f6lMwJwJFUH5D1/sx8uL5n\nImlTGQokdSsi3gs8AOyZmc926fs9cH5m3lDT9jXgqMw8uHi/C/AIcCtwHDA9My+tV/2Ses5QIKlb\nETEIuB04CLgD+BUwC1gFvASspPpI7LW2A17MzJE1n3F4cezczDy0TqVL6iUfnSypW5n5OnBEREwA\njgDOAS4GPloMOQOY1+WwNV3efxBYDewWEW/KzJc3Y8mS+siFhpI2KjPvy8x/orpu4DXgYOA54B2Z\n+WSX1zNrj4uIDwD/F/gbqjMLM0ooX1IPOFMgqVsRMR6YSPWywR+B9wNNwOPAhcC3I2I51UsMQ4AD\ngf+VmdMiYmeqCxK/nZl3RMRzwLyI+Hlm3lT/s5G0KQwFkjZkOXAY8AVgGPAM1bsL7gCIiJeBc4HL\ngJeB/wSmF8dOB1YAXwPIzMeKhYg/iIjfZubiep6IpE3jQkNJkgS4pkCSJBUMBZIkCTAUSJKkgqFA\nkiQBhgJJklQwFEiSJMBQIEmSCoYCSZIEGAokSVLBUCBJkgBDgSRJKvx/5taVRFEMlVIAAAAASUVO\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x4b34790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.crosstab(data.sex ,data.survived).plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "survived 0.381971\n", "dtype: float64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.mean()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.7274678111587983" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "339.0/(127+339)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>survived</th>\n", " <th>0</th>\n", " <th>1</th>\n", " </tr>\n", " <tr>\n", " <th>pclass</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>123</td>\n", " <td>200</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>158</td>\n", " <td>119</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>528</td>\n", " <td>181</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "survived 0 1\n", "pclass \n", "1 123 200\n", "2 158 119\n", "3 528 181" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = data[['pclass','survived']].dropna()\n", "pd.crosstab(df.pclass, df.survived)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x533f050>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x4b34990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.crosstab(data.pclass ,data.survived).plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x55f3450>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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phhFJkpQrw4gkScqVYUSSJOXKMCJJknJlGJEkSblyaa80TKXSJtra2jLtczztzCip/hlG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\nIexqGPnmoNfX16o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"text/plain": [ "<matplotlib.figure.Figure at 0x5072110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_, bins = np.histogram(data.age.dropna(), bins=20)\n", "data.reset_index().pivot('index','survived','age').plot(kind='hist', bins=16, alpha=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. 年齢と性別を特徴量としてロジスティック回帰を適用" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from PIL import Image\n", "from sklearn.model_selection import train_test_split, cross_val_score\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.tree import DecisionTreeClassifier, export_graphviz" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " <th>sex</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>29.00</td>\n", " <td>female</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.92</td>\n", " <td>male</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.00</td>\n", " <td>female</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>30.00</td>\n", " <td>male</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>25.00</td>\n", " <td>female</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " age sex\n", "0 29.00 female\n", "1 0.92 male\n", "2 2.00 female\n", "3 30.00 male\n", "4 25.00 female" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp = data[['age', 'sex', 'survived']].dropna()\n", "X_ = tmp[['age', 'sex']]\n", "y = tmp['survived']\n", "X_.head()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " <th>sex_female</th>\n", " <th>sex_male</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>29.00</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.92</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.00</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>30.00</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>25.00</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " age sex_female sex_male\n", "0 29.00 1.0 0.0\n", "1 0.92 0.0 1.0\n", "2 2.00 1.0 0.0\n", "3 30.00 0.0 1.0\n", "4 25.00 1.0 0.0" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = pd.get_dummies(X_)\n", "X.head()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on Training Set: 0.775\n", "Accuracy on Validation Set: 0.795\n" ] } ], "source": [ "X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, random_state=1)\n", "clf = LogisticRegression()\n", "clf.fit(X_train, y_train)\n", "\n", "y_train_pred = clf.predict(X_train)\n", "y_val_pred = clf.predict(X_val)\n", "print 'Accuracy on Training Set: {:.3f}'.format(accuracy_score(y_train, y_train_pred))\n", "print 'Accuracy on Validation Set: {:.3f}'.format(accuracy_score(y_val, y_val_pred))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scores: [ 0.84761905 0.83333333 0.78947368 0.74641148 0.67788462]\n", "Mean Score: 0.778944 ± 0.0617\n" ] } ], "source": [ "clf = LogisticRegression()\n", "scores = cross_val_score(clf, X, y, cv=5)\n", "\n", "print 'Scores:', scores\n", "print 'Mean Score: {:f} ± {:.3}'.format(scores.mean(), scores.std())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 年齢と性別を特徴量として決定木を適用" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on Training Set: 0.780\n", "Accuracy on Validation Set: 0.810\n" ] } ], "source": [ "X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, random_state=1)\n", "clf = DecisionTreeClassifier(criterion='entropy', max_depth=2, min_samples_leaf=2)\n", "clf.fit(X_train, y_train)\n", "\n", "y_train_pred = clf.predict(X_train)\n", "y_val_pred = clf.predict(X_val)\n", "print 'Accuracy on Training Set: {:.3f}'.format(accuracy_score(y_train, y_train_pred))\n", "print 'Accuracy on Validation Set: {:.3f}'.format(accuracy_score(y_val, y_val_pred))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scores: [ 0.85714286 0.83809524 0.79425837 0.74641148 0.64423077]\n", "Mean Score: 0.776028 ± 0.0762\n" ] } ], "source": [ "clf = DecisionTreeClassifier(criterion='entropy', max_depth=2, min_samples_leaf=2)\n", "scores = cross_val_score(clf, X, y, cv=5)\n", "\n", "print 'Scores:', scores\n", "print 'Mean Score: {:f} ± {:.3}'.format(scores.mean(), scores.std())" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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7ciofUoKs1d/v8K4A3mx9/4t34t9/f3//CUt//nx3b5YC3ZyVfw5aP//80cz+/n+wAbjF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\nGQOYKj87K2SJfxx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"text/plain": [ "<PIL.PngImagePlugin.PngImageFile image mode=P size=690x373 at 0x6C3B9D0>" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.fit(X, y)\n", "export_graphviz(clf, out_file='tree.dot',\n", " feature_names=X.columns, \n", " class_names=['not survived', 'survived'],\n", " impurity=False, filled=True)\n", "!dot -Tpng tree.dot -o tree.png\n", "Image.open(\"tree.png\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. 年齢、性別、社会的地位を特徴量として決定木を適用" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " <th>sex</th>\n", " <th>pclass</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>29.00</td>\n", " <td>female</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.92</td>\n", " <td>male</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.00</td>\n", " <td>female</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>30.00</td>\n", " <td>male</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>25.00</td>\n", " <td>female</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " age sex pclass\n", "0 29.00 female 1\n", "1 0.92 male 1\n", "2 2.00 female 1\n", "3 30.00 male 1\n", "4 25.00 female 1" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp = data[['age', 'sex', 'pclass', 'survived']].dropna()\n", "X_ = tmp[['age', 'sex', 'pclass']]\n", "y = tmp['survived']\n", "X_.head()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " <th>sex_female</th>\n", " <th>sex_male</th>\n", " <th>pclass_1</th>\n", " <th>pclass_2</th>\n", " <th>pclass_3</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>29.00</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.92</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.00</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>30.00</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>25.00</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " age sex_female sex_male pclass_1 pclass_2 pclass_3\n", "0 29.00 1.0 0.0 1.0 0.0 0.0\n", "1 0.92 0.0 1.0 1.0 0.0 0.0\n", "2 2.00 1.0 0.0 1.0 0.0 0.0\n", "3 30.00 0.0 1.0 1.0 0.0 0.0\n", "4 25.00 1.0 0.0 1.0 0.0 0.0" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = pd.get_dummies(X_)\n", "X.head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scores: [ 0.5047619 0.83809524 0.8277512 0.75598086 0.59134615]\n", "Mean Score: 0.703587 ± 0.133\n" ] } ], "source": [ "clf = DecisionTreeClassifier(criterion='entropy', max_depth=3, min_samples_leaf=2)\n", "scores = cross_val_score(clf, X, y, cv=5)\n", "\n", "print 'Scores:', scores\n", "print 'Mean Score: {:f} ± {:.3}'.format(scores.mean(), scores.std())" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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zwiEI69baQrP1Z1UJfbFmBVk6pcLWojWZ8M5Z7y5R80w4aC55LIV1am2h2Xqz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\ndJd5aMLKMgGNB/3/jP/xly8FrUVXtNlMHsntnRdmsMwKaDx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"text/plain": [ "<PIL.PngImagePlugin.PngImageFile image mode=P size=1434x517 at 0x6D04D50>" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.fit(X, y)\n", "export_graphviz(clf, out_file='tree.dot',\n", " feature_names=X.columns, \n", " class_names=['not survived', 'survived'],\n", " impurity=False, filled=True)\n", "!dot -Tpng tree.dot -o tree.png\n", "Image.open(\"tree.png\") " ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.5" } }, "nbformat": 4, "nbformat_minor": 1 }

apache-2.0

pcmagic/stokes_flow

head_Force/do_calculate_table_loc/jump_branch-Copy4.ipynb

2

9528178

mit

jdhaines/ProcessTechnology

test.ipynb

2

1234

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Testy Test Test Notebook\n", "Header markdown above..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is a print statement made by Jim\n" ] } ], "source": [ "print(\"This is a print statement made by Jim\")" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is a print statement.\n" ] } ], "source": [ "print(\"This is a print statement.\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

JoyMonteiro/CliMT

lib/examples/CliMT -- Tropical Cyclone.ipynb

1

4553

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DCMIP test case 4 demo - 3\n", "-------\n", "\n", "Here, the ability of the dynamical core to simulate a tropical cyclone is tested.\n", "\n", "The main aim here is to simulate a tropical cyclone using a simple physics package \n", "(Reed and Jablonowski (2012)).\n", "\n", "Jablonowski's group provides a fortran file which generates the initial conditions\n", "to test any dynamical core. We have written a Cython wrapper around it, and use it to\n", "start the simulation. We also have a wrapper around the simple physics package, which\n", "provides the tendencies during each time step." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#%%file testSimple.py\n", "%matplotlib notebook\n", "\n", "from climt.dynamics import dynamics\n", "from climt.dcmip import getTropicalCycloneICs\n", "from climt.simple_physics import simple_physics\n", "from climt.federation import federation\n", "\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.set_style('whitegrid',rc={'grid.linestyle':'dotted', 'grid.color':'0.0'})\n", "# Dynamical core parameters\n", "import matplotlib as mpl\n", "\n", "mpl.rc('text', color='w')\n", "\n", "kwargs = {}\n", "kwargs['dt'] = 1200\n", "kwargs['nlon'] = 192\n", "kwargs['nlat'] = 94\n", "\n", "#Init the dynamics Component\n", "dycore = dynamics(scheme='gfs', **kwargs)\n", "\n", "#Get the pressure and lat/lon values; this is needed\n", "#to generate the initial conditions\n", "pressure = dycore['p']\n", "ps = dycore['ps']\n", "\n", "full_latitudes = dycore.Extension.latitudes\n", "full_longitudes = dycore.Extension.longitudes\n", "\n", "#Get new initial conditions\n", "u,v,t,q,phis,ps = getTropicalCycloneICs(pressure, full_longitudes, full_latitudes)\n", "#Dynamical core expects virtual temperature\n", "theta = t*(1+0.608*q)\n", "\n", "#Initialise model topography\n", "dycore.Extension.set_topography(phis)\n", "\n", "#Initialise winds, surface pressure and temperature\n", "dycore.Extension.initial_conditions(u,v,theta,ps,q)\n", "dycore_grid = dycore.Grid\n", "#Setup simple physics\n", "\n", "kwargs['grid'] = dycore_grid\n", "kwargs['dt'] = 1200\n", "kwargs['U'] = u\n", "kwargs['V'] = v\n", "kwargs['T'] = t\n", "kwargs['ps'] = ps\n", "kwargs['pint'] = dycore['pint']\n", "\n", "phys = simple_physics(**kwargs)\n", "\n", "#Setup federation\n", "kwargs = {}\n", "kwargs['U'] = u\n", "kwargs['V'] = v\n", "kwargs['T'] = t\n", "kwargs['ps'] = ps\n", "kwargs['pint'] = dycore['pint']\n", "kwargs['MonitorFields'] = ['U','ps'] # Display zonal velocity during simulation\n", "kwargs['MonitorFreq'] = 1200.*3 #6 hourly update\n", "kwargs['grid'] = dycore_grid\n", "\n", "fed = federation(dycore, phys, **kwargs)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import time\n", "#Run the code for 10 days. each time step is 1200 seconds = 1/3 hour\n", "num_steps = 10*24*3\n", "\n", "p_min = np.zeros(num_steps)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "for i in range(num_steps):\n", " #Go ahead one time step\n", " fed.step()\n", " p_min[i] = np.min(fed['ps'])\n", " \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "del(dycore)\n", "del(fed)\n", "plt.figure()\n", "plt.ioff()\n", "\n", "plt.plot(p_min)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

RinaldoB/course-neuro-datasim

Day3_2_solutions.ipynb

1

119891

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "#bigger plots\n", "figsize(14,5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 60 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "2.Inter-spike intervaldistribution and spike train autocorrelation" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "1. Load dataset A1 or PP1. For the exercises you need to access the array of\n", "spike\n", "times" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import scipy.io\n", "\n", "a1 = scipy.io.loadmat('data/A1_SCV_SPK_040528_cell1_001_sec333_end_nostruct.mat',squeeze_me=True)\n", "pp1 = scipy.io.loadmat('data/PP1_Gamma_course_day2_nostruct.mat',squeeze_me=True)\n", "pp1_select = 0 #0-2 select one of the 3 spiketrains from the data" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 61 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "2. ISI distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you detected nspike in your observation interval, how many inter-spike intervals do you have?\n", "\n", "Compute the inter-spike intervals by simply subtracting from each spike time the previous spike time.\n", "\n", "What is the average ISI length?\n", "\n", "To estimate the distribution of ISIs construct a histogram of ISIs.Define a reasonable width of your histogram classes (bin width) and construct a vector of histogram edges (bin vector)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Load the Data\n", "spikes_rat_cortex = a1['TotalOriginalSpikeTrainMS']\n", "#select one of the three spike trains (pp1_select) and convert to ms\n", "spikes_gamma_renewal = pp1['Spikes'][pp1_select] * 1e3\n", "\n", "# Compute the ISIs\n", "ISIs_rat = diff(spikes_rat_cortex)\n", "ISIs_gamma = diff(spikes_gamma_renewal) " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "print 'mean ISI cortical cell:', ISIs_rat.mean(),'\u00b1',ISIs_rat.std(), 'ms'\n", "print 'mean ISI renewal gamma:', ISIs_gamma.mean(),'\u00b1',ISIs_gamma.std(), 'ms'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean ISI cortical cell: 91.3319838057 \u00b1 44.4900606344 ms\n", "mean ISI renewal gamma: 125.43326017 \u00b1 44.1728793504 ms\n" ] } ], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "# Construct the bin vector\n", "# min value: 0, max value: longest ISI (rat or gamma), number of bins: nbin\n", "nbin = 20\n", "bins = linspace(0, max(ISIs_rat.max(),ISIs_gamma.max()), nbin)\n", "\n", "# Plot\n", "subplot(1,2,1)\n", "cr,binr,_ = hist(ISIs_rat, bins, normed=True)\n", "xlabel('time[ms]')\n", "title('ISI distribution cortical cell')\n", "\n", "subplot(1,2,2)\n", "cg,bing,_ = hist(ISIs_gamma, bins, normed=True)\n", "title('ISI distribution gamma point process')\n", "xlabel('time[ms]')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "<matplotlib.text.Text at 0x7fbf5f3e1710>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Oh3JycuRwOFRcXKyFCxequLjYbd2rr75aK1eu1M0336wtW7ZoyZIlrT5Rzmaz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\n05EMAQACWlVVleLj413Ldrt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dp2PHjkmS9u3bp1tvvVXjxo3TTTfdpI8//tjV9vnnG+69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\n", "text": [ "<matplotlib.figure.Figure at 0x7fbf5e4ae550>" ] } ], "prompt_number": 64 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "5. Inter-Spike-Interval Coefficient of Variation (ISI CV)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ISI CV is a measure of Interval Variability" ] }, { "cell_type": "code", "collapsed": false, "input": [ "CV_rat = ISIs_rat.std()/ISIs_rat.mean()\n", "CV_gamma = ISIs_gamma.std()/ISIs_gamma.mean()\n", "\n", "print 'ISI cortical cell:', CV_rat\n", "print 'ISI renewal gamma:', CV_gamma\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "ISI cortical cell: 0.487124649883\n", "ISI renewal gamma: 0.352162411232\n" ] } ], "prompt_number": 65 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "2.2 Spike train auto-structure" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "6. Biological Mechanism" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Can you think of biophysical mechanisms that explain why the output train of action potentials of a biological neuron cannot be memoryless as in the case of the Poisson process?\n", "Briefly describe them in your protocol." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Refraction Time (relative & absolute)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 66 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "7. Autocorrelogram\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The autocorrelogram measures for any spike the (average) probability of finding another spike in temporal distance dt. In other words, we ask for any given spike $s_i$ at time $t_i=0$, how likely is it to find a second spike $s_j$ at time $t_j \\neq 0$. \n", "\n", "\n", "Optional: Program your own function that performs a cross - correlation of discrete event times. Numerically you may follow three steps:\n", "\n", "1. loop across all spikes $s_j = s_1,s_2,...,s_3$\n", "2. from each the spike time $t_j$ subtract all other spike times $t_i$ where $i \\neq j$ and pool theses differences for all spikes in one array.\n", "3. compute a histogram from the pooled differences.\n", "4. View your code with your tutor." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#shorten variable names (optional)\n", "spikes_r = spikes_rat_cortex \n", "spikes_g = spikes_gamma_renewal" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 67 }, { "cell_type": "code", "collapsed": false, "input": [ "#select the correlation time and resolution [in ms]\n", "tcorr = 200 #ms\n", "dtcorr = 20 #ms\n", "\n", "#construct bin vector for histogram\n", "nbin = tcorr/dtcorr\n", "bins = linspace(1,tcorr,nbin+1)\n", "\n", "#calculate pairwise spike-time differences\n", "spikes_pdiff_r = subtract(spikes_r[:,newaxis],spikes_r[newaxis,:])\n", "spikes_pdiff_g = subtract(spikes_g[:,newaxis],spikes_g[newaxis,:])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot\n", "subplot(1,2,1)\n", "cr,bins,_ = hist(spikes_pdiff_r.flatten(), bins, normed=True)\n", "xlim([0,tcorr])\n", "xlabel('time[ms]')\n", "title('Autocorrelation cortical cell')\n", "\n", "subplot(1,2,2)\n", "cg,bins,_ = hist(spikes_pdiff_g.flatten(), bins, normed=True)\n", "title('Autocorrelation gamma point process')\n", "xlim([0,tcorr])\n", "xlabel('time[ms]')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 69, "text": [ "<matplotlib.text.Text at 0x584cbd0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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UpC1btkg6Phidc845Gjx4sA4ePKhVq1a1K35Juummm7Ry5UpVVFTIGKPt27fr4MGDuuaa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\nhg0bJEklJSUaMmSIwsPDfdY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"text": [ "<matplotlib.figure.Figure at 0x5075c50>" ] } ], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "# Alternatively, use numpys correlate function on PSTH\n", "#first, calculate the psth using a bin vector with desired resolution\n", "bins_r = arange(0,spikes_rat_cortex.max(), dtcorr)\n", "bins_g = arange(0,spikes_gamma_renewal.max(), dtcorr)\n", "\n", "PSTH_r,bins_r = histogram(spikes_rat_cortex,bins_r)\n", "PSTH_g,bins_g = histogram(spikes_gamma_renewal,bins_g)\n", "\n", "# the autocorrelation at time t is the correlation of the signal with signal shifted by t \n", "# roll(PSTH,i) shifts the time of the psth by i*dt\n", "autoc_r = [correlate(PSTH_r,roll(PSTH_r,i)) for i in arange(1,nbin)]\n", "autoc_g = [correlate(PSTH_g,roll(PSTH_g,i)) for i in arange(1,nbin)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot\n", "subplot(1,2,1)\n", "plot(linspace(dtcorr,tcorr,nbin-1),autoc_r) \n", "xlim([0,tcorr])\n", "xlabel('time[ms]')\n", "title('Autocorrelation cortical cell')\n", "\n", "subplot(1,2,2)\n", "plot(linspace(dtcorr,tcorr,nbin-1),autoc_g,)\n", "title('Autocorrelation gamma point process')\n", "xlim([0,tcorr])\n", "xlabel('time[ms]')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 71, "text": [ "<matplotlib.text.Text at 0x7fbf5eeacf90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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HlSvA99/LpGf/fvk32KuXnA2qV69oz8W+WD+2DdGjjh+X/d+vv8oDvMeNAypX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\nAAAAwHVodAAAAAC4Do0OAAAAANf5P7XSGZN8/WfPAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x3c77810>" ] } ], "prompt_number": 71 }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Side Note_: The naive way of calculating the PSTH autocorrelation would be:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ac_full = correlate(PSTH_r,PSTH_r,mode='full')\n", "ac_full[ac_full==ac_full.max()]=0 #optional, remove the peak at 0\n", "\n", "time = linspace(-len(PSTH_r)*dtcorr,len(PSTH_r)*dtcorr,len(ac_full))\n", "plot(time,ac_full)\n", "xlabel('correlation time [ms]')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 72, "text": [ "<matplotlib.text.Text at 0x7fbf5eebcc10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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58wZfFyvYudO9LJSf7/PPA3v2BP96IopePgOiPn36YPHixaoyh8OBIUOGYNOm\nTdi0aRPatm0LAEhNTcWsWbOQmpqKxYsXY9CgQcjJyTGm5kRkOVo3M3v2iHEDShcuaN+4nD5tTL3C\npUULsSxf3r/jp00zrCpkcWPHArffri7zFRzExxtXHzO88IK8/s8/Yqm8ZcjKAvbtc3+dNAaJiEgv\nPgOiZs2aoXjx4m7lTo1P6nnz5qF79+6IiYlBlSpVUKNGDaxbt06fmhKRLcXHiwlElYYPF+VHj6rL\n58wxr15m0rpxVX6ESi0DPXvqf+0BA4DXXtP/vHqzS/ew118H3n9f//MOGCBaAZU8/UyaN/e+347q\n1hVL6T1dvCgeKFSv7n5svXrA4cPa57FLSyMRWUvQY4g+/PBD3HrrrejXrx/Onj0LADh69Cji4uJu\nHBMXF4cjegwqICJb8HSD5joB49atYlmhgrH1CTfp5uyZZ9z3jRrl+3Wu68GoXdv7tSgwd9whumbp\nLSfH/e+nUiX34x58UF6PpIBIIr2nLl2ADRs8H3f1qjn1IaLoENTErAMHDsRr1x85vvrqq3jhhRfw\nxRdfaB7r8PJtPnLkyBvrLVq0QAupfwkRRbSsrHDXwFydO6u3q1QRLQJamjYFHnjA8CpZTrQ/2S9Q\nwL2saFHgww/VAfXcuYG1ELVuDSxbpk8dzSC9J1/zF3l679H+e0QUCVJSUpCSkmLqNYMKiMqUKXNj\nvX///njg+rd3hQoVkJaWdmPf4cOHUcHLI2BlQERE9ud0ioHStWppdwlTHicpUMB9jFGkUL7v8uXl\nLoL793t+zZo1vs8ViSKxtcOVp/9D6b1r/QyefhrYsUOko/fn+Ejw7rvArl3ej5Hee48eonVtxgzj\n60VE5nBtJBllQjeHoLrMHVPMxDZnzpwbGejat2+PmTNn4sqVK9i/fz92796Nxo0b61NTIrKcMWPk\nFMGAuEnRutn3djN/6VLk3uz72/XNW5Y56fkTP0rtr1QpsWzSRHt/zZqBnS8SA6K77wZefNH3cdJ7\nnz4dmDlTLnc4RIIGfxOZEBEBfrQQde/eHb/++itOnTqFihUrYtSoUUhJScHmzZvhcDhQtWpVfPLJ\nJwCAhIQEdO3aFQkJCciTJw8mT57stcscEdnbxIlAejowZYr3406dAlq2BFasEE9z1683p3528dpr\nwLBh2vsaNgT++0+7SxXZS5063v8vX3oJeO453+cJpIXIbl/BWpkmf/4ZuP9+YNIk9/fjcLj/HDZs\nABTPbYmP4ohwAAAgAElEQVSIfPIZEM3QaIfu27evx+OHDx+O4cOHh1YrIrIFf2+2du8GNm8WN/7R\n8PEwbBiQnOx/C1GuXED+/J73FymiX90ovLz9X3r6PShUSPt4fwIib79XdvHuu8BNN4nugxLpvWsF\nREREgQo6yxwRRb7du9VjWnbuBFatUs8sD7hvS6TnKdJN2ZtvqucZsTNpYHvp0uryIkWAypXdj5cC\nIq3JKIm8ef11dWY7ZTAAiDFGnnhK3gEAdhnGm5MD9O4t1qX3PHeuaG1zfdAwe7aY50yyYweQkgL8\n+KNcdv585Kb4J6LgBJVUgYiiQ/v2wL//yjdgtWrJ+5xOuVtKly5ymfJp7SOPiKXypsUuT3PvvBNY\nvdrz/kaNgF9/FU/1lRo0AHr1AqpW1X59oONEiAoWVI8zk/6Gli4VQYHWXD2SmBjP+556yh5BkdMp\nf4ZILUIvvihSb7sGRCNGAElJYj0zE7jnHtGtFwAyMoDixYEvvxRdE+3yWURExmNAREQeBXrD4Ol4\n5WzzvtLpWoWv7oBSIKQ8bsIEMZlkgQJAmzZissmyZQ2rIkUR5d/WpEni76hKFXnfiBHA228Hdk67\njC86dkwEN4B7C7O39/DGG+r9Tz4JzJpln/dNROZhlzki0lV2tlgqJ048flxeT0w0tz56+Ogj9zKt\nm6quXYF69eTtChWAQYPE+vjxImAiCkabNsCjj4r1+vXlVllA/C6+9ZZ/58mfX7RiSq+zg717tZMt\nAPLnjZbLl9Xv8dIlsbTL+yYi8zAgIiLdOJ3yJJLX526OCE895V4m3VQNHepfZrDOnYHBg/WtF0WP\nSpWAb77xfVzDhsD1mTA0/fwz0L+/WGdgQEQkMCAiIl0dOiSWe/eGtx6h8tVdULqZHDxYtP4oy4jC\npXZtYOtW78e4/m5L8yNFCm+TQnsqI6LoxoCIiDw6elQsA51sFQC+/17/+oRb0aLyOm+qyI7KlZPH\n4dg9qcDLL3vfP3EicOSIe7nUik1EJGFARESaEhJEeloAqFZNzCPky/U5miOC682i0wmcPet+nDIw\nypvX2DoR+VK1qna5lAEyIUEERYAcGCnHvdlV7dr+HafMyFevnsiiSUTEgIiINLnObbJqVXjqYaQz\nZ4AnnnAvA4DYWLG8917t17qm2z5zRqT0JQqXc+fEnEW+dOokllIwv3Ch+zFSVje7UAY23lq+lBkv\nt21Tz7NGRNGLARER+cXu3Wu0FCvmnvmtWDHRVVAawF6ihPZrlfOiSK8jCqfChYHcuX0fJ/3O5s8P\n7NoF5MsHNG4s7581C7jpJv+uafdEIez6SkQA5yEiIoV160TLh9ZNguv8H3Y0aBAwebK6LF8+8b6V\nN4RSl6KtW4G4OO1z8UaK7Oy330TQEx8vtqX5spYsAVq39v88N9+sf93MlJoKrFwpWsTatw93bYgo\nXNhCREQ3NGkC3HYb0KiR+z67tRC99557mXL8gNJtt2mX33KLdjc4h4MBEdmDp9/TZs20j0tK0n5N\n3bruZXFxwN13h1a/cBs3DmjZEnjwwch46ENEwWFARER+sduYgiFD3Mv0CGLmzRP/GBDpy24Bt100\naQI8/3zo53EdMwcA06dbM5HIuXPa5VoZ55T4N00UvRgQEZFf3ngj3DXwX7duYuk6rkePG5727YEH\nHtC+QSSymiJFgPff931cz57e92uNFVJmrLOSdeu0yzdu9P46BuVE0Ytf6UQUcR55RCxd50LyFcT4\nm7rXn3NRYPh0Xl8NGgR2fNOm3ve3bateAkDJkkDBgoFdh4jIiviVTkQRQTkQPCZG+xhv863ceado\n+fFXw4b+H0u+8em8vu67T3ssoCdFinjfLwWsnuY5igT8HSSKXswyR0QRQdnCkEfjk83Xzc4ffwR2\nvbZteQNF1vXGG4F1cy1SxPfvs9MpZ2lkix4RRRK2EBFFsAcfBP7+W6w7nUDFivK+iRNFhqVIUaOG\nvC4FREWLhqcuFDjeYFub9P8jBU2BdC+1i82b5fXDh9XdCJ95Bpg7V95OTAROnTKvbkRkLAZERDb2\n1VfA4sXa+375BZg/H+jaFXj8cXEjc/iwfEPz4ovASy+J9b59gQsXTKmyIfLmFROsnjwptqVxDY0b\nA4UKha9eRJHCNWD96y/1dnIycPo0sGaNeXXS28KFwOuvAzt3Ahs2qN/jRx8BkyYBM2YA334rgqc2\nbbTPc+kS0KuXOXUmIn0wICKysT59gIEDtff17SuWe/cCn38uB0IffijWpRucJUuAqVPFjPV2FRsr\nWoVKlQK2bFHPKxQbq/2aLVvMqRv5h90PrefgQXmeIenzQloqHzSkpoo09yVKBNYq60/2OzM5HKKb\n4dixwMMPy+ULF4ql0ykStjz6qNiWWt9dpaUB33xjbF2JSF8MiIgiQFqa74BGmnTw2WeBK1fkG9B7\n7xVL14xs4ZaQ4P+xypvpevV8d7+qVs17ggUiAipVAmbN8n1c7dpyIhPpb9GfYKdHj+DrZoQ5c8Ty\nyy/FZ6TkvvvE0lfQnp0N/PYbu38S2REDIiIbSE31vv+uu4Cbb/Z+/LFj8vrKle77x4wJrm5mU84t\nNGJEcOfwlWKYzMebSGsqXVq97U/iBUCeDFaapyguTt96GUGrxSctTV7X+lxNSwPOnxfrs2cDzZt7\nv8aBA/bunkwUqRgQEdlAnTpAZqb2PodD9FmXXLokjndVubK83q6dvvUzgqcbL9cb588+E337PZk4\nEZgyRd5+4w3guedCrx/pi13mrKtTJ6B4cbHevj0waJDnY6tWVXc3k1qBtP5/7RAEV6okr6ena++X\nutBdvSqW3t5X1ar8/CGyIqbdJrKga9eA3LnVZd5uGK9d8+84pezswOtlRf37e9/vGvy9+qpxdSGK\nRD/+KK9XrOj9AUTBgsDMme7lkRzwSslc/JWRIa9rfdYTkfnYQkRkQXnyAP/+6//xp0+7l/l6+mr1\nGxSr14+IfIuGv2NpfKbk5Ze9H6/8meTJY73xm0TRiAERkUX5+9TR0w2HVW9EQk1mYIduNkQkSJ9D\nWp9HUlnNmt7PUb26vnXSm+t785R9zpOdO/WrCxEFhwERkUX5e+Ov/DL+6Sd5+8QJ/eukB2/prn/6\nSV53OtWJHqRxQA0bAvHxxtSNiPRVtqznfdJnVf367skblJKSgFtu0bdeepJaiB57TCwDbZ236sMr\nomjCgIgogsycae0v1yFDvO/v0EHO2ASIiWOzskRZ/vyi7JdfgG3bjKsjEemnRQvx9yt9LmVliZTW\nWVlyIPHtt8ChQ97Ps369odUMSaifuVb+zCaKFkyqQGRR/rYQHTwor+fkuPdntxLlZI5aHA71Mbly\niUHa0j5Anu8EAAoU0Ld+RKSv3LnVf9PS33NMDHDmjLyu/Lt25XQC+fIZV8dQuX7m7tnj/XjXAMjK\nn9lE0YItREQ24U+AlJMDLF9ufF0CIc3yHihfT003bwZeeCG4cxOR8dauBRITxbq3MUSBatAg+DoZ\n4e+/tYOac+f8ez1biIjCjwERkUUFkzzg2DExZ4iVtG3r33He5jYB3H8et94qd6MjIutp0kT+u/Un\nINq8Wfs8rn/7zZqFXje9TZvmXvbhh9rHcgwRkfUwICKyiatX1f3s9+93P2btWvPq448RI/w/tmNH\n9bbrTcL99wNvvRV6nYjIfN9/D8ybpy5z/Ru/9Vbt12oFDL/8AhQrpk/d9NC7t3vZwYPqsVPHj2u/\nlgERUfgxICIykTJhgNa2Nx98AFSuLG9Xq6ZPnYzkTwDjadZ215uEEiUCC7CIyDqaNwfat1eXeRo7\n07Wr7/O1a+ffceH02WfAggVivXdvOeOeP2OILl5UT559+bJIRkFExmBARGSS1FSgSBF5+/Rp9bYn\n0pfnqFHG1MsoDz3k33HjxrmXPfkk8Mwz+taHiKylVCntbq8NGgADBvh+vdUDIkAeRzR7tudjtFqI\nKlQAevaUtxs0AFq21LduRCRjQERkEimjkuTiRfdjHA55Lh6HAzh1SmRaUxoxAqhY0Zg66umHH3wf\n43SKmdoBoGpVuXzKFGDwYGPqRUTWUKiQ++dg7txirrFPPpHnHvrf/9THSGOK7BAgzJrlPgZq/nzg\niSfkbadTHLN7t1x25gywdau8nZoa+ISvROQ/BkREBrh40XNShA0bxNJTv3HlHDsdOrjvHz0aOHw4\ntPrpRTlxqj9q1RLLd95Rlzud1p+NnoiMd/Uq0KqVuqx798DPc999+tQnVD//rF3+6afyutRlznWM\nket3xIUL6u22bYGxY0OrHxEJDIiIDOD6xaW0aZNYSi0oa9ao90tfgg4HsHq1/nXTU6CZ8Lp1E8ty\n5fSvCxFFllq11PMTxcf7/9oaNfSvj9l27vS+f/FiMRk3EYWOARGRCbp0ATIzxboUREhz6Lg+QZSe\nFvqa3M8qTp3y/1jpvffsKcZQERF5Mm2a+nNCepBStKhcptVtLiVFe2yi1XzwgXpb6wHT77+rt196\nCdi+Xd5mhjoifTAgIjKA6xfb7NnAvn3yPuWX2N69IiWtRNqnLLMqhwMoWVLefv557e4t27ern3Y6\nHCJrHJGEN3bkKl8+oHBheXvePOCPP4CXX5bLChUSyx075LIiRdQtSwCQkGBcPYP1/PPqbYcDOHAA\n+O47ucy1W/K4ccDUqfI2/26I9JEn3BUgikTKgGjVKrFUdoVbvFjeP2uW+FevntiWWoj27jW+noGq\nWBFIS5O3XQO/mjVFelhXdeoYWy8iinzFigF33qm9Txqf6IlrgGRVd90FHDkiby9f7p54YulS4No1\nsc6AiEgfbCEiMtg996i3HQ4xh4ar2rXFUkrTmppqbL2CUbOmelsKiH78USydTn5BU3ACHY9GpKVd\nO6Bu3XDXIjBS6/msWepgCBBzEX3xhbps+3b3B21EFBoGREQG0Lq5U7YQefPRR/rXRy+5c2uXd+ok\n+rY/+KC59SEikjz7LPDNN9qtQVYOHObPF8uJE7X3a9VdKrPy+yKyEwZERCbRmnfIbooVU28rg7t3\n3gHKlze3PhQ5eGNHofrgA/WYRqVhw8yti1n4d0OkDwZERAZSflm9+KJY2rFrUFycWObLB6xbJ5fb\n8b0QUeTw5zOoWLHg5jKyMul9MyAi0gcDIiKdpaQAxYuL9YIF3ffbMYgYMUK7XJpJXolf0BQMO/5d\nEJlB62+jdWuxTE0ViRiIKDQMiIh0tm2bvH7pkvv+Xr3Mq4sRlH3XpS9lpZtvNrc+FBkYSFMwvCVQ\niJRxNs88432/1SfwJrIDBkREHqxbB/ToEfjr7P7lqyRlvgskZe2zz2oHgkREehs1SjvVfzBatFBv\n33STPue1ol9+AZ57Lty1ILIOnwFR3759ERsbi1sUfWMyMjLQunVr1KxZE0lJSTh79uyNfWPGjEF8\nfDxq1aqFpUuXGlNrIhPMmgVMn+77uF27RJeyQYOMr5PeDh/2vj9fPrGsUEEu8xXwORzy64j8xS5z\nFIxcuYC8eb0f4/qZ1aGD53NJjh4N7oFYOM2aBYwcCUyZ4vvYiROBCRMMrxKRbfgMiPr06YPFylkk\nASQnJ6N169bYtWsXWrZsieTkZABAamoqZs2ahdTUVCxevBiDBg1CjjTLJFGE+vRTYPRo8SWk15NK\nsygDHaW0NODLL+V5h5KS5H28cSUjRFLLKlmLcrqAtWuBMmXE+ooVnifALldOHSDZQbduosXMjg/n\niMLN5597s2bNUFwaIX7d/Pnz0ev6QIhevXph7ty5AIB58+ahe/fuiImJQZUqVVCjRg2sU6akIrIR\nf2L5X39Vb3/4obVv7IoW9e+4uDigTx/g6lWxrbwxaNRIdLcgIrK61FRg/Xp5u3x5+aHOPfcA1arJ\n++z8sEfRUQcAsGEDkJnp+fj9+42tD5HdBPX84/jx44iNjQUAxMbG4vjx4wCAo0ePIk7KzwsgLi4O\nR1ynXSayCU83/WlpwKZNIvBp0QL46y95n5Ra26ry5NEuL1wY+P579/IxY4CZM9VluXKJ2eCJiKyu\ndm2genV12bBhwFdfyds//iiWQ4YA779vWtV09X//p96+7TbgrbeAPXuAHTvcj9+925x6EdmFh9sj\n/zkcDji8PFbxtm/kyJE31lu0aIEWriMaicJI6wvjzBmgUiWxLmX2+eMP92OsSqsLyLhxQL16cre4\nIUPkfTVryt3miIjszuEAqlQR/ySdOollpUric3DZMvVr2rcH5s83q4bBOX/evezSJSA+XqyfOCEm\nrbVbN0CKTikpKUhJSTH1mkEFRLGxsUhPT0fZsmVx7NgxlLneIbdChQpIS0u7cdzhw4dRwdMgBagD\nIiI7KFFCXveUSW3UKHPqEgzp+YTyC971ySIRUTRyOEQr+sKF6vJu3awfEGm18Cu7fZcpA0yaxPFF\nZA+ujSSjTLixCupZQfv27fH1118DAL7++mt0uJ6ypX379pg5cyauXLmC/fv3Y/fu3WjcuLF+tSUy\nwCefeO873r699sR3niYrtTJ/+shbeQwUEVEoghknZNfPRGkMqCQtTYwt8vYzePNN31n7iCKRz4Co\ne/fuuOOOO7Bz505UrFgRU6dOxbBhw7Bs2TLUrFkTK1euxLBhwwAACQkJ6Nq1KxISEtC2bVtMnjzZ\na5c5IivYssX7/hUrtCe+W7vWmPoYSfpib9oUKFQovHUhIrIS19uVBg3E0lOCHSv3BgCAK1fU25cv\ni6DImw0bgOxs4+pEZFU+A6IZM2bg6NGjuHLlCtLS0tCnTx+UKFECy5cvx65du7B06VIUK1bsxvHD\nhw/Hnj178O+//6JNmzaGVp7ICJUqAQcPhrsWoWnSRCwLFpTLnE6gfn2gcmXg5Ze1+5xLxxERRaJA\nntE+/rj4PLyeQ8rjZ2OrVqHXywiuLUSepoVITBSJgoiiGYfXUdTZvBl4+mnP+9PSgO3bzauPEaQv\n7pMnRaYhyYIF2hmHlEqWNK5eRERWlT+/dnnr1sB//4n1jh3FUpnm2qoPkVwDIk/B4ObN7lNISFas\nAF5/Xd96EVkRAyKKOtOmicGl3ixaZE5dgvXzz/4dV7Ag8NJLcotX3rxAgQKejz94UBxPRBSJPAUF\ne/ao5yRyVaSIWH77LXD0qP9zupUpAzz8cGB11Mt33/k+5qmnxFLqXuf68xk7FnjjDX3rRWRFDIgo\n6klP95xO+UtBK2DKyjKvTr7cd5+YA8kT5ZdaTIycKtyXSpW8D6jlkEAiikSucxV5UrAgUK6cusxb\nC9G2bUCNGsHXS085OaKXgNLkyWK5dKlYrlljbp2IrIIBEUWErCzgn38871+3zr1MOXu5dA5vqVU/\n/ji4ugXK0+Sprh57TL39yy/Avn0iZawRXTiWLwfeeUf/8xIRmWH6dKBsWf3P+9VXwO+/a6e+LlNG\n/SApnNMc5OQAU6dq71uxQixPnJDL0tKAY8fUx+3dC2RkaJ/jv/+AnTtDrydRODAgoojw+utA3bra\n+zIz5SQD6elyK1DjxuILwtcTsWvXxHLjRn3q6kutWv4dd//9wKuvytvt2gFVqwJt2xpTr5YtjbmZ\nICIyQ/fu+rZyS+eqWFFMzdCli7wvORn44Qf1cUB4uyTv2uX/sQcPih4D27aJ7UOHxLJGDfFz1PL0\n0/5/fxFZDQMiigj+dmcrVw746CN5++WX5bTbTqf2l6WUmWfGjNDq6C9/+5uXLs2+3UREVtKunVgO\nHQo89FB46+Jq1Sr/j61SRb1duTJw+rRYz8zUfo2nciI7YEBEES/X9d9yrW5kY8dqHxtOt93mXta2\nLdC5s/bxhQu7l/Xr5/kpHhERha5lS+CWW9Rlffq4H6f8XlF+Xlvh+yYQ0vxEHEtKkchmf45EwfP1\n5eN0Ap06mVMXb3LnFkvlmKUnn/R8fPfucipYyYABor88EREZ4447gK1b1WXx8e7HKQMIZWpvqTu2\nXUjJJBgQUSRiQEQRQWr9CaVVxN80qkaT5gFSfulIk6pq+eQT4KefjK8XkZGsOpcLUSBuvdX9d9nf\nAMK1m5rZ/K2n1sPFDRuAuXP1rQ+RmRgQUUSZOdO9rF8/8+sRrOxsOUV26dJyudMJjBgBdO0annoR\nEVFwpJYV14lSla5eBR5/3Jz6hOq339xbt1yzthLZDQMiinhaQZJVKVNud+ggz4YuJXz45hv1DOlE\nkYLdcChS9esHnDwpd4cG1AlxGjVS77MDb8EdkR0xIKKIpswoZ1XDhqm3pRtDh0Puxid1wciXzzpd\n+4iIyLdcuYBSpdRlsbHyurfJsK1q+nQxjQVRpGBARBFr1y7gmWfCXQt3NWuqt4sXd9+WZg2X5OQY\nWyeicOMYIoom0oOvrVuB2bO9H5uQYHx9AtW3r5gLjyhSMCAiW9u+Xbt83Djg5pvNrYu/mjcHPvhA\n3nYdoOpwAK1bq8t4s0hEFDmkgOiWW+QxRlqf8x07AnPmmFevQGzcCHz4IXDihPu+tDTgv//MrxNR\nsBgQkW2dP+8+BwQgBneGczZwX/LkAZ59Vt52DX5cDRwItGhhaJWIwo5jiChaDB4sT+Cq5euv5fV2\n7dx7FVjJ4MHq7n+SSpWAbt3Mrw9RsBgQkW0pB3Uq1xs3Nr8ugXBtESpbVnsyVsnkyeqMc0SRiK2g\nFC0mTADKl/e8/7HHzKuLXjIy3MtOnTK/HkTBYkBEtqUcV/PFF+Grh5K3YExKhuCaTcjhAB55BHjg\nAePqRURE1tasGVChQrhrEZw//5TXpUyoHPtKdsKAiGzLNWuP1UlPwLUmtXvuOWD+fHPrQ0RE1vG/\n/wGHD/t3bNWqxtYlUAsXyusXLojl338DU6aEpz5EgWJARGG1ciXQsGHgr1u2TF4fN06/+oTKn3EQ\nNWqot7UCJCIiIk8GDgx3DTxTzpU3aFDgr586FejcWb/6EPmDt2IUVkuWiKdI/rjnHrlP8qJFcrmV\n5hqqWFG9ffGivC61aLl+QXAwORERBeKFF4CSJcNdC22e5ifq3t1zZlilb74BfvpJ3zoR+cKAiGxj\n1Spg2zaRRe6rr+TyQ4fCViU3U6eKGcnffFNs588v7+vQQSwZABERUaCUvQty5QIKFBDrGRn+d7Uz\ng2uClLFjxXLmTGDuXPPrQ+QPBkRkK199JRIXnDkT7prIxoyR1wsWFC1BcXH+v54BEhER+VKsmHZ5\n8eLqZAxSoBQurVqpt4cOBT7+WKwrM8J6woyTFA4MiMjy/vhD/ANEU7rVVKsGtGkj1r0FN54+5BkQ\nERGRFuX3hr+Bwr//GlOXUEhjnlJTgcWL5cQLRFbBgIjC6uBB38c0ayb+Wc3994ulwyGn/fYW3PTp\no13OgIiIiHxxOkVriy+VKomlFb83f/gBaNtW3bOCyAoYEFFYzZrlXvbff/LSn+Z1M7RpA5QooS6r\nXl1eL15cvU/5JO/tt0USiFq1RGIIVwyIiIjIm+RkYNQo8X3SooXn4+64Q17XmuD1+eeB11/XvXoB\nO3ECyMoCsrOBS5eAy5fdj9mxw/x6UfRiQESWsnWr6CednS2WzzwT7hoJixcDr76qvc/h8N6VYfhw\n4N57gZgYYMUK7dcTERF5MnSo6JWQO7dIMBSM0qWB998HbrtN37oF49NPgUKFgKeeEvVp3tz9mEuX\nzK8XRS8GRGSaCxfEzf8LL8hN+q6kZAnXromllZ4QPfecCHy0gp9QBoHmyxf8a4mIKPLUrSuW9eqF\nfq4dO8R31IkTYttKD+F27BCpuDdt0t7/44+ivpUrA0OGmFs3ii55wl0Bih5Sk/gffwBpaaK5XEn5\nIZ2TI5ZWyDaj1c0N8D7Y1Z96W+G9ERGR9WzbFvo5PH3HBJIF1WhSHa9cEfcAP/4o73v7bTkgPHQI\nWL3a/PpR9GALEZlu3TqxzMyUy77+Wn2M1EL022/m1MkbrW5uSqVLA3n4aIGIiMKsZk15PTZWLF1b\nhPRoddKL9PBT0rmzvP7jj2K+JSIz8DaOdPXZZ0CRIsDDD/s+NndueT05Wb2vSBF962UEp1NMiCcl\nVMjICG99iIgoep0/L3fBzsjwPG8RIL5/pQeP4bRmjff9/nTvO3cO6NULmDNHnzpRdGLsTboaMAB4\n8kl1WVYWMG6c+7EzZ8rrWhlmzLZvn0jqEAhldjnXTHNERERmKVRIJO8BxPeRFExoBRWuLS/DhwM7\nd1qrOx3g3u3v11/dk0rs3AnMnWtenSgysYWIDPfrr8BLLwH9+6vLlRnk9u83t05aqlYN7HhvY4A4\nPogoMPybIQqf//1PdLezQsKFX3+V1//+W73vnntENzt+XpDe2EJEpklPD3cN/Felivf9CxYAr7xi\nSlWIiIiCMnWqes48yeLF2sdbISBSUnaDczrdxxwR6YUBEfnkdAJHjng/5soVOaWnUk4OcPSoWE9I\n0L9uwZCyxg0dCnTsqH2M9PQpJUV7//33ywNWiSh0VrsRI4oEvXurx+tK/vc/7eOVXemWLLFWF7r1\n6+V1rTG72dna9yFK58/Lk78TKTEgIp9WrvT9oThsmBwgKJuyp04FHn/cuLr5orzJ6tZNLKWMcMnJ\nYtZub7Qmi/PH3XcDbdsG91qiaMQuMESBeftt8S8YefIA8fHu5dJ35sCBQFIS8NBDwdfPSCVLyuvS\nZ8eoUb4fVDZrBtSpY1y9yL4YEJFPZ8/6PkZqBQLUT19OndK/PoHIyZFbeV56SSx79JCDlWbNgMmT\n3V93++2eJ4/1R7VqwMKFwb+eiIjIm8ceE8kQgvXEE8Cjj6rLpIBI+l68916gYUOgXTvgq6/k45xO\nYOPG4K9thE8/9X3M7t2+e7xQdGJSBfLJrk9upSdI0gd8YqL8Xh57TD5Oq09yo0bAjBnG1o+IZOwy\nR2SuF14Qy2nT5DLXv8M2bcQ/Se/ewE03GV61oJw8Ge4akJ2xhYh0MWuWdvmwYebWQ0nqC+1rYje7\nBnxERER68ufBhFUeXkhzGLnW5/vvPb/GKnUn62FARCEZMUK7JaVhQ3M/eLTmOZJm7PZVj1Kl3MtK\nl/6MoLQAACAASURBVA69TkTkPz6YIAofaTL0+Hggb17vx0rfqYUKae9v3Fi/enlzxx2iLhs2qMtn\nz5a//13xc4Y8YZc5Csno0WK8jCvXuQOMJk1Gp7RkiVj6CogeflgkQZCkpwNlyuhXNyIiIqs6dgwo\nW1as//ijyBrrjfSdqgw6SpSQM7+Z3Qrz00/q7TNnxFghokCwhYj8du4c8O237uWnT5tflz/+EMkR\nJK5pRWNj5X7Ovj6cHQ51ZprYWDarE5mNf3NE4SEFQwBQoABQtKj345V/q3v2iGW5ctrHpqaGVjd/\nuF5j1Sr3Y7ZvFxO+8nOGPGFARH777jt1Rhopw0w4cvonJABTpsjb/fqp9996q7zOD0Ai62NXFiL7\nqVpVLKV5jbZulffVrw/Urm18HZRZbgHg2jWxXLFC/lx58EGgRQvj60L2xYCI/LZzp1hKHz6NGhl7\nvc6dPe/LlQsoXFjeLlBATqU9Zw4wYYK8LzEReO89Y+pIREQULVauBBYvlrcdDmDMGHl+v1tukR9C\nKh9MhkOrViLF9rVrwL59osxXd0CKXgyIyM2pUyLoOX9eXS4FGRUqAJcuGV+PsWM979OaeVvSoQNQ\nq5a8nS8fMGSIfvUiIiKKRnffDTRtKm87HJ6zyT79tPdzmdF7IycHeOsteTs7W153OoFdu0S3f7ZQ\nEwMiclO6tAh6pKwzWjp1Mr4e1aq5T466YoVYSqm0R440vh5EREQUGGUvkqQk9/3SpOlGOnLE833C\n/PnAzTeLTLOzZxtfF7I2BkQUlF27zLnOwYPqp0jNm4ulFBDddZecEIFPeIiIiMKra1egdWv3cqmX\nSd68IhOtNO7ISM8+63lferq8fuKE8XUhawspIKpSpQrq1auHxMRENL6eeD4jIwOtW7dGzZo1kZSU\nhLNnz+pSUQofrUBj717zrp+TI5IoAHJwJAVELVuqP9SIiIgofJ5/Hli61PP+y5fF2F4zrF9vznXI\n/kIKiBwOB1JSUrBp0yasW7cOAJCcnIzWrVtj165daNmyJZKTk3WpKIVHfLw1ricFZVJA5G0MERHZ\nD7NBEpFZ3nsPeOaZcNeCrCTkLnNOl+aD+fPno1evXgCAXr16Ye7cuaFegsJImmPALM8+K/r1unIN\niLRunjzNg0BE1scur0T25Wsyc2/fz7lyuSdxMtr776sTLBCF3ELUqlUrNGrUCJ999hkA4Pjx44i9\nPqgjNjYWx48fD72WFFbTphlz3ttucy97/HGR0MGVMsHD/v1ylzmlSZPEjNtERERknqFDgcOHtfcd\nOybmDcyXT3t/hQpAoULG1U2L6z0EH8hQSAHR6tWrsWnTJixatAiTJk3C77//rtrvcDjgYD8Iy5s9\nW3wY/Pmn+wRnALBggTHX1cr8kju36Fu8dq17HaTWqipVtM9XoIB6xm0iIiIyXkyM9sNMQHwvFygA\n9O0LbNig3nfggPv3vVLFirpVUUUrePN1L0SRLU8oLy53vQ20dOnS6NixI9atW4fY2Fikp6ejbNmy\nOHbsGMp4aUcdqbgjbtGiBVpwGuGw6NIFWL0auPNOoEYN867r6WmRwwE0aaIuK1PGd5M8ERERWVNM\nDNCwobqscmXvr2nSBEhLM65OkqlTRea7P/8U90Jt2wILFxp/XdKWkpKCFDPysis4nK6DgPx04cIF\nXLt2DYULF0ZWVhaSkpLw+uuvY/ny5ShZsiSGDh2K5ORknD17VjOxgsPhcBt/RMY7dEg8xcnOFn/4\ndeqEr1VlxQqRJQ4QgdiePWJGaa3ucEQU2Xr1Ar75hl1XiKKVVoeiLl2AH35wP87oz4m2bUW2vIQE\ncc904IDn3ilkPDNihqBvPY8fP45mzZqhfv36aNKkCe6//34kJSVh2LBhWLZsGWrWrImVK1dimKcp\njCksKlcGPv5YzCDdsqWxwdAdd4hljx5yWb162sfedZdYsoclERFR9Bo8WF5XPiDdv18sJ08Wy9de\nM64OixaJyWTj4sR21aru3f0osgTdZa5q1arYvHmzW3mJEiWwfPnykCpFxjpzBvjiC+Ovs3q1CHAK\nFBDbDz8MtGgBjBolAiMGP0RERKSkbAjo1g1YuVJkqZNaaLp0AQYOBB57DKhdG+je3dj6XL0qlpmZ\nxl6Hwoudk6KQGYGIMnW28npPPikyzixZot13mEESERFR9JICoh49gA4dgBMngC1btI/t1s28euXk\nmHctMh8Doij0yivGX+OBB+R1T0FOtWrG14OIiIis7+GHxVIKiK7P5uLG9Z7ivvuMqxMgki0AYowz\nRS4GRBHk88+BF1+Uty9eBMqXl7dnzjS+Dv37u5dJLUHeWn84kJoouvEzgCi6SfcI/n4WSN3xpYy1\nzZrpXycA2LpVLJOS5LIhQ4Avv5S3U1NFdjqyLwZEESQ5GXj3XXk7I0M9UemkScZePybGPUNcRoaY\nsA0ASpTw/NrixY2rFxEREVmbdI9QtKhYenqImjs3cOqU+oEvILriG+Hxx93Lxo8H3ntP3k5JEZl7\nyb4YEEUQ5VOVrCxg4kSx3r69yCj3xx/6XWv0aPeyH390LyteXHx47d8PjBvnvj81VfwbPVrOIENE\n0YfjB4mi27hx4j7gtdeAX34B8ud3PyY1VQRMJUvKZdK9j9RipNSggb51fO45kXFOqstvv4l1fn7Z\nX0gTs5J1bNkCHDki1tevB77/Xm4tWrBA/+vddpt7WeHCnj8UPOXvr13b9zFEREQU2QoWlO8D2rXT\nPkZ5z+CPZs3kMUB6mDBBvd28OXDyJPDXX2J70SIxhxHZDwOiCFG/vrzeuLHx18udW709YQJw++3A\njBnGX5uIiIjI1cyZwNGjYowPYM7YxDvuAHbvFuvt2nE8pF0xIKKgNGwor/foIU+k9swz2um0iYiI\niPQ2YoQ8ubuUqc7MgIjZ5yIDxxCRT8rMKU2aiGWRIkDduu7H1q0LDB9uTr2IiIgoujVsKAdASs2a\nyQGSKz2n/bhwQb9zUfiwhYi8mjYN6NlTHhv0009AhQpifds2Uc7mYSIKFT9HiEhP06cDcXHAoEHi\nIe4XX4jxPtJnjV6JENLT9TkPhRdbiGzu2jVjs5v07CmWUoKG8uV540JERETWJt0bTZoEjBkDlClj\n3nXHjDHnWqQfBkQ2l8ekNj5vGeAYIBFRqJi2loj05DovYoMG5gVFw4cDc+eacy3SBwMiG2jTRjT3\nXrkitr/+GujUCXj0UfPq0KkTcPGiedcjoujCBytEpCfXhyxTpwJpaeZdf8AAoGNHdRrugQOBvHmB\nAwfMqwf5hwGRRW3dCnzwgVhfuhQ4fx44d05sDx8OzJkDfPut/tft3Vu73OHQniSNiIiIyOpy5xbB\niKRpU2Ovd/KkaCVavFgu+/hjIDsb+P13YPt2YPx4Y+tA/mNAZFFjxwLPP68uW7pULF2bgUO1caNY\npqQA7dvL5Vu26HsdIiIiIjP4uldasAAYPVre3rpVLJs2BWJj9a+Pa/KFsWO1s+NReDDLnEVJTb3K\n/PY9egBduwKHD+t7rQYNgPnzgf/9D7h6VQxAbN4cqFNH3+sQERERmcHXuMRSpYAXXhBDEu65B6hd\nG1izRqTkzsgQ23rZtw/o0EFd9uef+p2fQseAyGKcTjHjsfRkY/Zs9X5lC44emjUTywceEMuYGJGi\nMhDs+09ERERW4k+ilrx5gaeekrelbnSFCulbl+rV1dt79gB794r1U6fEPV+JEvpekwLDLnMWs3Ej\ncPPNckDUrZt6/6JF+lzn++/FMtTMTq+8AgweHHp9iIiIiPTQti1QtGjwr5fujSZPBm6/XZ86Kb3x\nhryekAA0bqz/NSgwDIgsJitLLPVKQascNFi4sFjWrg106aLPdd5805gPCyIiIqJgLFwoerwES3oo\n/cQT6qQIRmT3PXnS3Ox3pI0BURg5HMDq1eoyqfuZXokTevcGGjUS582d2/3cnPuDiIiISCbdJzkc\n6mEB33wD/PQTUKmSOp12qLSGHjgcwG+/6XcN8o4BUZjt36/elv4o9ApUnngCWL9erEt/4FJgpOd1\niIiIiCKBdG/kcAA5Oep9HTsCBw/qO37a07n27dPvGuQdAyKLkf7wPv9c/3MnJorlLbfIZTffrP91\niIiIiOxK+bA4jwnpx1yDLgmTVpmHWebCzOkUaR7nzQNatVIPtAtW06bA2rXu5b/8AmRmihSTgFjP\nly/06xERERFFity5gbNnxXrhwvK6UlycWG7fDtStG9r1cnKAp58GevYUXfLGjg3tfBQ4BkRh9vHH\nQOXKwIwZwDvv6HPOJUuAM2eAixfV5fnyqQOgm27S53pEREREkUSZpU4rY92UKSITcJ06QK1awL//\nhna9SZNE4PXdd3LyBrYQmYdd5kxy7ZqYFdnVn3/q/wtfpIgIsmrV0ve8RERERCS60rVsKdZr1NDn\nnN99J5b/93/u+1JTgV279LkOuWNAZJK1a9WTqipTLGp1byMiIiIi6/OUoEo5ZjsQS5eKpZQUCxAt\nUfXrB3c+8o0BkUmkPxYpEFLOWnzgQODnq1dPPEEoXjzkqhERERFRkJQBkZTAauhQ4OefQzvvxx+L\npTSGiV3ojMMxRAY7e1aM1ZFSXleqJAbPZWeHdt4BA4CnngLGjRPbTJ9NREREZL5SpeT1RYuA2Fh9\nzy8lcJDu9U6fFg/E9ZqzkthCZLjixYHbb1f/0k6cGNo58+QBuncP7RxERFbCJ59EZFcFCsjrrg+o\nGzcGkpJCO39Wlry+f78IwD74ILRzkhoDIhNs3Ag0aSJvP/dcaOfLzgZKlFCXlS8v+qrqNbCPiIiI\niHy78055ShPXDL5//QXcfXfw51YGWBcvAtu2ifXdu4M/J7ljl7kIceRIuGtARBQ8dvslIrvq3t17\nzx3luPFQnTyp37lIxhYindWqBZw7p/95pTmFpk/X/9xEREREZIyHHhLLVauA1q1DO1f//trnX706\ntPNGOwZEOtu5U84k98or+p03f37g+HExCRgRUaThGCIiilQOh7iHa9FC3M/pQcpABwA//gjMmaPP\neaMVu8zpJDsb+PRTsb5kiVi+/ba+1yhTRt/zEREREZHxpHu4fPn0O2dODvDRR2L9zz+BTZvktN8U\nGLYQ6SQ1FXj6abH+wgtAw4bBn2vUKODDD8V6ejqwZk3o9SMisjKOISKiaDBliki2JZEmYVVau9a/\ncw0bBjz7rFhfswZo1y70+kUrBkQ6OHAAyMxUl12+7N9rt2xRZyT58EPgtdeA5s3Fdmws0LSpLtUk\nIrIsdpkjomhQqhTQoIFYL1tWjCnaskV9jDIzsTfSXJQSpxPYvj30OkYjdpnTQdWqQP36wb22Rg35\nRqBHD7mVqXp1sU1EREREkWXqVKBkSbFerx7w+eeiZejzz0M77y23ABcuqOdGIt8cTmd4nss5HA6E\n6dK6cziAypWBgwcDf+2FC8Bnn4kWpuHD9a8bEZEdPPYYMG0aW4qIKHqlpQGdOgHr14fWjfjsWaBo\nUf3qFW5mxAxsIdJJIMFQwYIiEAKAPHmAwYONqRMRkV0wECKiaFexogiGAOCuu8T49K5d1Rnl/HHl\niv51i3QcQxQkhwPIyADy5g38tePGiS9/pxOIidG/bkRERERkX7//Dpw+LZIwBKpMGeDQIbGcNk3/\nukUithAF6MoVOWWi1PczUHwSSkRERERGqVxZLB97DLh4ERgwILz1sTq2EAVo0aLQz1G8eOjnICKK\nJEy7TURkjCeeCHcNrI8BkZ9WrRKZOzp0CO71gwaJ5aFDQPfu+tWLiCgSsOWciEjbwYNyNmNpnspA\n9ewpZzImd+wy50N6OvDFF8ArrwT3+ldfBfr3B8qXB/r1EwPmiIiIiIj8UakSsGSJSMhVsSJw++1A\no0aBneO778QyPh6oWxdo2VL/etoZ02574HQCkycDn3wCbNsW+OtjYoC+fYH33xdZ5YiIyLNHHwW+\n/ZYtRURE/hg5UgRH/fsH9/rp00UmOzs8qDcjZmCXOQ9+/lk0LQYSDE2eLK8vWiTSJDIYIiIiIiI9\njRwJdOsmJ08AgLff9v/1jzwiWp5IYJc5DTk5QPv2/h371lvAli1A48Yig8egQcDVq0Du3MbWkYiI\niIii1003AQcOiKQ0SUlizqJ168T23Ln+nSMzEyhUyNBq2oJhLUSLF/9/e3cfFFXVxwH8ewFFm1EJ\ns0XhmUF2F0FglwUFYvIV8WUM0BTELIFoGnCiZvKtGVNxHCxHtClq7BlHBQxFtEwtQRySMU1BAZXA\ncRyBBAWneCklbUXP8wdxH97jZXdh4/v5hz3n3HvOueu93PvjXM/JgouLC9RqNbZv326sZoxi586e\nb7thA5CRAaxZA1j8/W1ytiQiIiIiMhVJAlSq5kDo2DFg8+ae7TdqlHH7ZS6MEhA9ffoU77zzDrKy\nslBaWopDhw7hxo0bxmjKKG7f7rps0aLmd9z9/DqWtQRCDIioN3Jzcwe6C0SDRO5Ad4BoUOB9gXpD\npwMWLuy8TAigvNy0/TFHRgmI8vPzoVKp4OjoiGHDhiE8PBzHjx83RlMGVVLSHMz897+dlwvRHHUD\nzTN8KBSdb8OAiHqDNz6iFrkD3QGiQYH3BeqNwkIgLq5t3uTJ///s6Nj8fFpY2Pn+kgRMnGi07pkF\nowREd+/exX9aTVvh4OCAu3fvGqMpg+rN/Oy7djVPyU1ERERENJiEhXWctXPMmK63r6gwancGPaME\nRJIZDpE8eQJ09geZpUubfz56ZNLuEBENKWPHDnQPiIj+3ZycgJEjuy4/fdp0fRlsjDLLnL29PSor\nK+V0ZWUlHBwc2myjVCrNInA6erT5Z3cnEFF/bdmyZaC7QDQoSBKvBSKA9wUyvfnzB7oHnVMqlUZv\nwygLszY1NWHSpEnIycnBhAkT4OPjg0OHDsHV1dXQTREREREREfWZUUaIrKys8Pnnn2PevHl4+vQp\noqOjGQwREREREdGgY5QRIiIiIiIiInPQ50kVjhw5Ajc3N1haWqKw3Tx+H330EdRqNVxcXJCdnS3n\nFxQUwMPDA2q1Gu+9956c/9dff2HZsmVQq9Xw8/PDL7/8IpelpKTA2dkZzs7OSE1NlfPLy8vh6+sL\ntVqN8PBwPHnypK+HQmRQ8fHxcHBwgE6ng06nQ2ZmplxmimuDyByZ82LeRF1xdHSERqOBTqeDj48P\nAKCurg6BgYFwdnbG3Llz0dDQIG9vyHsE0UB78803oVAo4OHhIeeZ6vzv9TOS6KMbN26Imzdvipkz\nZ4qCggI5v6SkRGi1WqHX60V5eblQKpXi2bNnQgghpk6dKvLy8oQQQixYsEBkZmYKIYT44osvRGxs\nrBBCiPT0dLFs2TIhhBC1tbXCyclJ1NfXi/r6euHk5CQaGhqEEEKEhoaKw4cPCyGEiImJEbt37+7r\noRAZVHx8vNi5c2eHfGNfG/X19aY4PCKDa2pqEkqlUpSXlwu9Xi+0Wq0oLS0d6G4R9Zujo6Oora1t\nk7d27Vqxfft2IYQQH3/8sVi/fr0QwrD3CKLB4Ny5c6KwsFC4u7vLeaY4//vyjNTnESIXFxc4Ozt3\nyD9+/DiWL1+OYcOGwdHRESqVCnl5eaiursaDBw/kv5CsXLkS3377LQDgxIkTiIiIAAAsWbIEOTk5\nAIDTp09j7ty5sLGxgY2NDQIDA5GZmQkhBM6ePYulf8+JHRERIddFNBiITt5ENfa1kZWVZaKjIzIs\nc13Mm6gn2t8PWv9eb/38Ysh7BNFgMG3aNDz//PNt8kxx/vflGcng6xDdu3evzRTbLYuyts+3t7eX\nF2ttvZCrlZUVxowZg9ra2i7rqqurg42NDSwsLDrURTQYJCUlQavVIjo6Wh4ONsW1QWSOzHUxb6J/\nIkkS5syZgylTpmDPnj0AgPv370OhUAAAFAoF7t+/D8Bw94i6ujqTHBtRXxj7/O/rM1K3s8wFBgai\npqamQ/62bdsQFBTUbcXGYg5rF9G/X1fXRkJCAmJjY7Fp0yYAwMaNG7F69Wrs3bvX1F0kMhv8vU7/\nVhcuXMD48ePx66+/IjAwEC4uLm3KJUni+U9D1mA6/7sNiM6cOdPrCtsvylpVVQUHBwfY29ujqqqq\nQ37LPnfu3MGECRPQ1NSE33//HWPHjoW9vT1yc3PlfSorKzF79mzY2tqioaEBz549g4WFBaqqqmBv\nb9/rvhL1VU+vjbfeekv+44Eprg0ic9STxbyJzNH48eMBAOPGjcPixYuRn58PhUKBmpoa2NnZobq6\nGi+++CIAw90jbG1tTXiERL1j7PO/r89IBnllrvX7scHBwUhPT4der0d5eTlu3boFHx8f2NnZYfTo\n0cjLy4MQAgcOHEBISIi8T0pKCgDg6NGjCAgIAADMnTsX2dnZaGhoQH19Pc6cOYN58+ZBkiTMmjUL\nR44cAdA8k8SiRYsMcShE/VZdXS1/PnbsmDy7iimuDSJzNGXKFNy6dQsVFRXQ6/U4fPgwgoODB7pb\nRP3y559/4sGDBwCAxsZGZGdnw8PDo83v9dbPL4a8RxANVqY4//v0jNTXmSO++eYb4eDgIEaMGCEU\nCoWYP3++XJaQkCCUSqWYNGmSyMrKkvOvXLki3N3dhVKpFHFxcXL+48ePRWhoqFCpVMLX11eUl5fL\nZfv27RMqlUqoVCqRnJws55eVlQkfHx+hUqlEWFiY0Ov1fT0UIoN64403hIeHh9BoNCIkJETU1NTI\nZaa4NojM0alTp4Szs7NQKpVi27ZtA90don4rKysTWq1WaLVa4ebmJp/XtbW1IiAgQKjVahEYGNhm\n9itD3iOIBlp4eLgYP368GDZsmHBwcBD79u0z2fnf22ckLsxKRERERERDlsFnmSMiIiIiIjIXDIiI\niIiIiGjIYkBERERERERDFgMiIiIiIiIashgQERERERHRkMWAiIiIiIiIhiwGRERERERENGQxICIi\nom7Fx8dj586d3W5z/Phx3LhxQ05v3rwZOTk5/W772rVryMzMlNMnT57E9u3b+11vezNnzoSLiwu+\n++67fte1YsUKjB07Fl9//bUBekZERMbGgIiIaAh6+vRpt+nWJEn6x/qOHTuG0tJSOb1lyxYEBAT0\nvYN/KyoqwqlTp+R0UFAQ1q9f3+9625MkCQcPHsQrr7zS77rS0tIQHBzco++NiIgGHgMiIiIzlpqa\nCq1WC09PT6xcuRIAUFFRgdmzZ0Or1WLOnDmorKwEAERGRiImJgZ+fn5Yt24doqKi5PT69etx+/Zt\nLFiwAFOmTMH06dNx8+bNDu3t2bMHPj4+8PT0xNKlS/Ho0SP89NNPOHnyJNauXQsvLy+UlZUhMjJS\nHiHJycmBl5cXNBoNoqOjodfrAQCOjo6Ij4+Ht7c3NBpNh/b0ej02bdqEw4cPQ6fTISMjA8nJyYiL\ni5OPZ9WqVXjppZegVCqRm5uLiIgITJ48GVFRUXI92dnZ8Pf3h7e3N8LCwtDY2NjpdymEkD/PnDkT\n77//PqZOnQpXV1dcvnwZixcvhrOzMzZu3AgAaGxsxMKFC+Hp6QkPDw9kZGR0WR8REQ1eDIiIiMxU\nSUkJEhIScPbsWVy9ehWfffYZACAuLg5RUVG4du0aVqxYgXfffVfe5969e7h48aL8ClxLOjExEW+/\n/TaSkpJw5coV7NixA6tWrerQ5pIlS5Cfn4+rV6/C1dUVe/fuhb+/P4KDg5GYmIjCwkI4OTlBkiRI\nkoTHjx8jKioKGRkZuH79OpqamrB7924AzaMy48aNQ0FBAWJjY5GYmNimreHDh2Pr1q0IDw9HUVER\nwsLCOoy6NDQ04OLFi/jkk08QHByMdevWoaSkBMXFxbh27Rp+++03JCQkICcnBwUFBfD29sauXbs6\n/T5b1y1JEqytrXH58mXExsYiJCQEX375JX7++WckJyejrq4OWVlZsLe3x9WrV1FcXIz58+f34V+R\niIgGGgMiIiIz9cMPPyAsLAy2trYAABsbGwDApUuX8NprrwEAXn/9dZw/fx5A80N+aGhomwf/lvTD\nhw9x8eJFhIaGQqfTISYmBjU1NR3aLC4uxrRp06DRaJCWltbmNbn2IyJCCNy8eRMTJ06ESqUCAERE\nRODcuXPyNq+++ioAwMvLCxUVFR3aE0J0OdIiSRKCgoIAAO7u7rCzs4ObmxskSYKbmxsqKipw6dIl\nlJaWwt/fHzqdDqmpqbhz50433+r/BQcHy3W7u7tDoVBg+PDhcHJyQlVVFTQaDc6cOYMPPvgA58+f\nx+jRo3tULxERDS5WA90BIiLqG0mSugwWusp/7rnnOk0/e/YMNjY2KCoq6rItoPk1tRMnTsDDwwMp\nKSnIzc3tsE1n+7XuV+s8a2trAIClpSWampr+cf/2hg8fDgCwsLCQ62pJNzU1wdLSEoGBgTh48GC3\n9XSmpb6u6lar1SgqKsL333+PDz/8EAEBAfLrdEREZD44QkREZKZmz56NI0eOoK6uDgBQX18PAPD3\n90d6ejqA5v/gP3369H+sa/To0Zg4cSKOHj0KoDlwuX79ulzeEmA9fPgQdnZ2ePLkCb766is5YBk1\nahT++OOPNnVKkoRJkyahoqICt2/fBgAcOHAAM2bM6PExjho1Cg8ePOjQj56QJAl+fn64cOGC3H5j\nYyNu3brV4zq6IoRAdXU1RowYgRUrVmDNmjUoLCzsd71ERGR6DIiIiMzU5MmTsWHDBsyYMQOenp5Y\nvXo1ACApKQn79++HVqtFWloaPv30U3mf9iMurdNpaWnYu3cvPD094e7ujhMnTnTYbuvWrfD19cXL\nL78MV1dXuTw8PBw7duyAt7c3ysrK5Hxra2vs378foaGh0Gg0sLKyQkxMTIe2W/7PUXuzZs1CaWmp\nPKlC++26+tzihRdeQHJyMpYvXw6tVgt/f/9OJ4voTmd9kyQJxcXF8PX1hU6nw9atWzk6RERkpiTB\naXCIiGiImzVrFhITE+Ht7W2Q+iIjIxEUFIQlS5YYpD4iIjIejhAREdGQZ2tri8jISIMtzPrjrIni\n9wAAAEtJREFUjz9i5MiRBugZEREZG0eIiIiIiIhoyOIIERERERERDVkMiIiIiIiIaMhiQERERERE\nREMWAyIiIiIiIhqyGBAREREREdGQ9T8pFzinhyNcGwAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x7fbf5cdd4990>" ] } ], "prompt_number": 72 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the full autocorrelation up to times of 100000ms (=10s). The triangular shape is actually an artefact due to finate data. However, we are only intersted in the biophysical time on the order of 100 ms. If we zoom in into this time scale we should see the same autocorrelation shape as above." ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Acknowlegdments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thanks to Katharina and Diana for the code base, thanks to Christian for hinting towards \u00b4np.subtract\u00b4 for pairwise differences " ] } ], "metadata": {} } ] }

mit

DawesLab/LabNotebooks

Dashboard widgets.ipynb

1

17240

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": {} } } } }, "outputs": [], "source": [ "import numpy" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": {} } } } }, "outputs": [], "source": [ "import ipywidgets as widgets\n", "from IPython.display import display" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": {} } } } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "col": 4, "height": 11, "hidden": false, "row": 0, "width": 4 }, "report_default": {} } } } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = [1,2,3]\n", "y = numpy.array([2,5,4])\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "line1, = ax.plot(x, y, 'r-')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": {} } } } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9da3a60cca994a7790a0e17acdc9c90c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FloatSlider(value=5.0, description='Value:', max=10.0)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "slider = widgets.FloatSlider(min=0.0,max=10.0,value=5.0,description=\"Value:\")\n", "display(slider)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": {} } } } }, "outputs": [], "source": [ "def on_value_change(change):\n", " line1.set_ydata(y*(10/change['new']))\n", " fig.canvas.draw()\n", "\n", "slider.observe(on_value_change, names='value')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": {} } } } }, "outputs": [], "source": [ "line1.set_ydata(numpy.array([2,1,3]))\n", "fig.canvas.draw()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": {} } } } }, "outputs": [ { "data": { "text/plain": [ "array([2.7027027 , 6.75675676, 5.40540541])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "line1.get_ydata()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": {} } } } }, "outputs": [], "source": [] } ], "metadata": { "extensions": { "jupyter_dashboards": { "activeView": "grid_default", "version": 1, "views": { "grid_default": { "cellMargin": 10, "defaultCellHeight": 20, "maxColumns": 12, "name": "grid", "type": "grid" }, "report_default": { "name": "report", "type": "report" } } } }, "kernelspec": { "display_name": "Python 3", "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

Naereen/notebooks

agreg/public2018_D1.ipynb

1

56114

{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#Texte-d'oral-de-modélisation---Agrégation-Option-Informatique\" data-toc-modified-id=\"Texte-d'oral-de-modélisation---Agrégation-Option-Informatique-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Texte d'oral de modélisation - Agrégation Option Informatique</a></div><div class=\"lev2 toc-item\"><a href=\"#Préparation-à-l'agrégation---ENS-de-Rennes,-2017-18\" data-toc-modified-id=\"Préparation-à-l'agrégation---ENS-de-Rennes,-2017-18-11\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Préparation à l'agrégation - ENS de Rennes, 2017-18</a></div><div class=\"lev2 toc-item\"><a href=\"#À-propos-de-ce-document\" data-toc-modified-id=\"À-propos-de-ce-document-12\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>À propos de ce document</a></div><div class=\"lev2 toc-item\"><a href=\"#Question-de-programmation\" data-toc-modified-id=\"Question-de-programmation-13\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Question de programmation</a></div><div class=\"lev3 toc-item\"><a href=\"#Modélisation\" data-toc-modified-id=\"Modélisation-131\"><span class=\"toc-item-num\">1.3.1&nbsp;&nbsp;</span>Modélisation</a></div><div class=\"lev3 toc-item\"><a href=\"#Exercice\" data-toc-modified-id=\"Exercice-132\"><span class=\"toc-item-num\">1.3.2&nbsp;&nbsp;</span>Exercice</a></div><div class=\"lev2 toc-item\"><a href=\"#Solution\" data-toc-modified-id=\"Solution-14\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Solution</a></div><div class=\"lev3 toc-item\"><a href=\"#Types-et-représentations\" data-toc-modified-id=\"Types-et-représentations-141\"><span class=\"toc-item-num\">1.4.1&nbsp;&nbsp;</span>Types et représentations</a></div><div class=\"lev3 toc-item\"><a href=\"#Calcul-récursif-du-nombre-$\\rho$\" data-toc-modified-id=\"Calcul-récursif-du-nombre-$\\rho$-142\"><span class=\"toc-item-num\">1.4.2&nbsp;&nbsp;</span>Calcul récursif du nombre <span class=\"MathJax_Preview\" style=\"color: inherit;\"></span><span class=\"MathJax\" id=\"MathJax-Element-441-Frame\" tabindex=\"0\" style=\"position: relative;\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mi>&amp;#x03C1;</mi></math>\" role=\"presentation\"><nobr aria-hidden=\"true\"><span class=\"math\" id=\"MathJax-Span-3069\" role=\"math\" style=\"width: 0.681em; display: inline-block;\"><span style=\"display: inline-block; position: relative; width: 0.525em; height: 0px; font-size: 127%;\"><span style=\"position: absolute; clip: rect(1.877em, 1000.49em, 2.786em, -1000em); top: -2.45em; left: 0em;\"><span class=\"mrow\" id=\"MathJax-Span-3070\"><span class=\"mi\" id=\"MathJax-Span-3071\" style=\"font-family: STIXMathJax_Main; font-style: italic;\">ρ</span></span><span style=\"display: inline-block; width: 0px; height: 2.45em;\"></span></span></span><span style=\"display: inline-block; overflow: hidden; vertical-align: -0.316em; border-left: 0px solid; width: 0px; height: 0.932em;\"></span></span></nobr><span class=\"MJX_Assistive_MathML\" role=\"presentation\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mi>ρ</mi></math></span></span><script type=\"math/tex\" id=\"MathJax-Element-441\">\\rho</script></a></div><div class=\"lev3 toc-item\"><a href=\"#Algorithme-demandé-pour-décorer-l'arbre\" data-toc-modified-id=\"Algorithme-demandé-pour-décorer-l'arbre-143\"><span class=\"toc-item-num\">1.4.3&nbsp;&nbsp;</span>Algorithme demandé pour décorer l'arbre</a></div><div class=\"lev2 toc-item\"><a href=\"#Complexités\" data-toc-modified-id=\"Complexités-15\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Complexités</a></div><div class=\"lev3 toc-item\"><a href=\"#En-espace\" data-toc-modified-id=\"En-espace-151\"><span class=\"toc-item-num\">1.5.1&nbsp;&nbsp;</span>En espace</a></div><div class=\"lev3 toc-item\"><a href=\"#En-temps\" data-toc-modified-id=\"En-temps-152\"><span class=\"toc-item-num\">1.5.2&nbsp;&nbsp;</span>En temps</a></div><div class=\"lev2 toc-item\"><a href=\"#Implémentations-supplémentaires\" data-toc-modified-id=\"Implémentations-supplémentaires-16\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>Implémentations supplémentaires</a></div><div class=\"lev3 toc-item\"><a href=\"#Évaluation-des-expressions\" data-toc-modified-id=\"Évaluation-des-expressions-161\"><span class=\"toc-item-num\">1.6.1&nbsp;&nbsp;</span>Évaluation des expressions</a></div><div class=\"lev3 toc-item\"><a href=\"#Evaluation-par-lecture-postfix-et-pile\" data-toc-modified-id=\"Evaluation-par-lecture-postfix-et-pile-162\"><span class=\"toc-item-num\">1.6.2&nbsp;&nbsp;</span>Evaluation par lecture postfix et pile</a></div><div class=\"lev3 toc-item\"><a href=\"#Affichage-dans-ce-langage-de-manipulation-de-registre\" data-toc-modified-id=\"Affichage-dans-ce-langage-de-manipulation-de-registre-163\"><span class=\"toc-item-num\">1.6.3&nbsp;&nbsp;</span>Affichage dans ce langage de manipulation de registre</a></div><div class=\"lev3 toc-item\"><a href=\"#Méthode-d'Ershov\" data-toc-modified-id=\"Méthode-d'Ershov-164\"><span class=\"toc-item-num\">1.6.4&nbsp;&nbsp;</span>Méthode d'Ershov</a></div><div class=\"lev2 toc-item\"><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-17\"><span class=\"toc-item-num\">1.7&nbsp;&nbsp;</span>Conclusion</a></div><div class=\"lev3 toc-item\"><a href=\"#Qualités\" data-toc-modified-id=\"Qualités-171\"><span class=\"toc-item-num\">1.7.1&nbsp;&nbsp;</span>Qualités</a></div><div class=\"lev3 toc-item\"><a href=\"#Bonus\" data-toc-modified-id=\"Bonus-172\"><span class=\"toc-item-num\">1.7.2&nbsp;&nbsp;</span>Bonus</a></div><div class=\"lev3 toc-item\"><a href=\"#Défauts\" data-toc-modified-id=\"Défauts-173\"><span class=\"toc-item-num\">1.7.3&nbsp;&nbsp;</span>Défauts</a></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Texte d'oral de modélisation - Agrégation Option Informatique\n", "## Préparation à l'agrégation - ENS de Rennes, 2017-18\n", "- *Date* : 6 décembre 2017\n", "- *Auteur* : [Lilian Besson](https://GitHub.com/Naereen/notebooks/)\n", "- *Texte*: Annale 2018, [\"Expressions arithmétiques\" (public2018-D1)](http://agreg.org/Textes/public2018-D1.pdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## À propos de ce document\n", "- Ceci est une *proposition* de correction, partielle et probablement non-optimale, pour la partie implémentation d'un [texte d'annale de l'agrégation de mathématiques, option informatique](http://Agreg.org/Textes/).\n", "- Ce document est un [notebook Jupyter](https://www.Jupyter.org/), et [est open-source sous Licence MIT sur GitHub](https://github.com/Naereen/notebooks/tree/master/agreg/), comme les autres solutions de textes de modélisation que [j](https://GitHub.com/Naereen)'ai écrite cette année.\n", "- L'implémentation sera faite en OCaml, version 4+ :" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The OCaml toplevel, version 4.04.2\n" ] }, { "data": { "text/plain": [ "- : int = 0\n" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "4.04.2\n" ] }, { "data": { "text/plain": [ "- : unit = ()\n" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Sys.command \"ocaml -version\";;\n", "print_endline Sys.ocaml_version;;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "## Question de programmation\n", "La question de programmation pour ce texte était donnée en fin de page 6 :\n", "\n", "### Modélisation\n", "On est libre de choisir l'implémentation qui nous convient pour les expressions arithmétiques sous forme arborescente.\n", "\n", "Je choisis de ne considérer que les variables et pas les valeurs (on aura des expressions en OCaml comme `F(\"x\")` pour la variable $x$), et uniquement des arbres binaires.\n", "Les noeuds `N(t1, op, t2)` sont étiquetées par un opérateur binaire `op`, dont on fournit à l'avance une liste fixée et finie, et les feuilles `F(s)` sont étiquetées par une variable `s`.\n", "\n", "### Exercice\n", "> « Écrire une fonction qui reçoit en argument une expression algébrique donnée sous forme\n", "arborescente et décore cette expression en calculant pour chaque nœud interne quelle est\n", "la valeur du paramètre ρ et quelle branche doit être évaluée en premier selon l’algorithme\n", "d’Ershov. »" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "## Solution\n", "\n", "On va essayer d'être rapide et de faire simple, donc on choisit une algèbre de termes particulière, mais l'algorithme d'Ershov sera implémenté de façon générique (polymorphique, peu importe la valeur de `op`)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Types et représentations" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "type operateur =\n", " Plus\n", " | Moins\n", " | MoinsDroite\n", " | Mul\n", " | Div\n", " | DivDroite\n", " | Modulo\n", " | Expo\n" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type operateur = Plus | Moins | MoinsDroite | Mul | Div | DivDroite | Modulo | Expo ;;\n", "(* On utilisera MoinsDroite et DivDroite pour la compilation avec la méthode d'Ershov *)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "type ('a, 'b) arbre_binaire =\n", " N of ('a, 'b) arbre_binaire * 'b * ('a, 'b) arbre_binaire\n", " | F of 'a\n" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type ('a, 'b) arbre_binaire = N of (('a,'b) arbre_binaire) * 'b * (('a,'b) arbre_binaire) | F of 'a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Par exemple pour l'expression $\\frac{x - yz}{u - vw}$, c'est-à-dire `(x - y*z)/(u - v*w)` :" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val exp1 : (string, operateur) arbre_binaire =\n", " N (F \"x\", Moins, N (F \"y\", Mul, F \"z\"))\n" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(* exp1 = (x - y*z) *)\n", "let exp1 =\n", "N(\n", " F(\"x\"),\n", " Moins,\n", " N(\n", " F(\"y\"),\n", " Mul,\n", " F(\"z\")\n", " )\n", ")\n", ";;" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val exp2 : (string, operateur) arbre_binaire =\n", " N (F \"u\", Moins, N (F \"v\", Mul, F \"w\"))\n" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(* exp2 = (u - v*w) *)\n", "let exp2 =\n", "N(\n", " F(\"u\"),\n", " Moins,\n", " N(\n", " F(\"v\"),\n", " Mul,\n", " F(\"w\")\n", " )\n", ")\n", ";;" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "val exp3 : (string, operateur) arbre_binaire =\n", " N (N (F \"x\", Moins, N (F \"y\", Mul, F \"z\")), Div,\n", " N (F \"u\", Moins, N (F \"v\", Mul, F \"w\")))\n" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(* exp3 = (x - y*z)/(u - v*w) *)\n", "let exp3 =\n", "N(\n", " exp1,\n", " Div,\n", " exp2\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calcul récursif du nombre $\\rho$\n", "\n", "C'est assez immédiat, en suivant la définition récursive :\n", "$\\rho(F) = 0$ et $\\rho(N(t_1, t_2)) = \\rho(t_1) + 1$ si $\\rho(t_1) = \\rho(t_2)$ et $\\max(\\rho(t_1), \\rho(t_2))$ si $\\rho(t_1) \\neq \\rho(t_2)$. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val nombre_rho : ('a, 'b) arbre_binaire -> int = <fun>\n" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let rec nombre_rho (expr : ('a, 'b) arbre_binaire) : int =\n", " match expr with\n", " | F _ -> 0\n", " | N(t1, _, t2) ->\n", " let d1, d2 = nombre_rho t1, nombre_rho t2 in\n", " if d1 = d2 then\n", " d1 + 1\n", " else\n", " max d1 d2\n", ";;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour comparer avec le calcul, plus simple, de la hauteur de l'arbre :" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val hauteur : ('a, 'b) arbre_binaire -> int = <fun>\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let rec hauteur (expr : ('a, 'b) arbre_binaire) : int =\n", " match expr with\n", " | F _ -> 0\n", " | N(t1, _, t2) ->\n", " let d1, d2 = hauteur t1, hauteur t2 in\n", " 1 + (max d1 d2)\n", ";;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exemples qui concordent avec le texte :" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : int = 2\n" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "- : int = 1\n" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = hauteur exp1;;\n", "let _ = nombre_rho exp1;;" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : int = 2\n" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "- : int = 1\n" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = hauteur exp2;;\n", "let _ = nombre_rho exp2;;" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : int = 3\n" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "- : int = 2\n" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = hauteur exp3;;\n", "let _ = nombre_rho exp3;;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Algorithme demandé pour décorer l'arbre" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On choisit d'ajouter une *décoration* de type `'c` :" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "type ('a, 'b, 'c) arbre_binaire_decore =\n", " N2 of\n", " ('c * ('a, 'b, 'c) arbre_binaire_decore * 'b *\n", " ('a, 'b, 'c) arbre_binaire_decore)\n", " | F2 of 'a\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type ('a, 'b, 'c) arbre_binaire_decore = N2 of ('c * (('a, 'b, 'c) arbre_binaire_decore) * 'b * (('a, 'b, 'c) arbre_binaire_decore)) | F2 of 'a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On a besoin d'attacher à chaque noeud son paramètre $\\rho$ et un drapeau binaire permettant de savoir si l'algorithme d'Ershov indique d'évaluer en premier le sous-arbre gauche (`premier_gauche = true`) ou droite (`= false`)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "type decoration = { rho : int; premier_gauche : bool; }\n" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type decoration = {\n", " rho : int;\n", " premier_gauche : bool;\n", "};;" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val decore :\n", " ('a, 'b) arbre_binaire -> ('a, 'b, decoration) arbre_binaire_decore = <fun>\n" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let rec decore (expr : (('a, 'b) arbre_binaire)) : (('a, 'b, decoration) arbre_binaire_decore) =\n", " match expr with\n", " | F v -> F2 v\n", " | N (t1, o, t2) ->\n", " let d1, d2 = nombre_rho t1, nombre_rho t2 in\n", " let d = if d1 = d2 then d1 + 1 else max d1 d2 in\n", " N2({rho = d; premier_gauche = (d2<= d1)}, (decore t1), o, (decore t2))\n", ";;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dans nos exemples, on voit que l'évaluation favorise en premier (avec des `premier_gauche = false`) les expressions les plus profondes (à droite) au sens du paramètre $\\rho$ :" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur, decoration) arbre_binaire_decore =\n", "N2\n", " ({rho = 1; premier_gauche = false}, F2 \"x\", Moins,\n", " N2 ({rho = 1; premier_gauche = true}, F2 \"y\", Mul, F2 \"z\"))\n" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "decore exp1;;" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur, decoration) arbre_binaire_decore =\n", "N2\n", " ({rho = 1; premier_gauche = false}, F2 \"u\", Moins,\n", " N2 ({rho = 1; premier_gauche = true}, F2 \"v\", Mul, F2 \"w\"))\n" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "decore exp2;;" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur, decoration) arbre_binaire_decore =\n", "N2\n", " ({rho = 2; premier_gauche = true},\n", " N2\n", " ({rho = 1; premier_gauche = false}, F2 \"x\", Moins,\n", " N2 ({rho = 1; premier_gauche = true}, F2 \"y\", Mul, F2 \"z\")),\n", " Div,\n", " N2\n", " ({rho = 1; premier_gauche = false}, F2 \"u\", Moins,\n", " N2 ({rho = 1; premier_gauche = true}, F2 \"v\", Mul, F2 \"w\")))\n" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "decore exp3;;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Complexités" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### En espace\n", "Les deux fonctions présentées ci-dessus n'utilisent pas d'autre espace que l'arbre décoré, et la pile d'appel récursif.\n", "\n", "- Le calcul récursif de $\\rho(t)$ prend donc un espace proportionnel au nombre d'appel récursif imbriqué, qui est borné par la taille du terme $t$ (définie comme le nombre de noeuds et de feuilles), donc est **linéaire**,\n", "- Le calcul de la méthode d'Ershov est elle aussi **linéaire** puisque l'arbre décoré est de même taille que l'arbre initial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### En temps\n", "Les deux fonctions présentées ci-dessus sont **linéaires** dans la taille de l'arbre." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implémentations supplémentaires\n", "\n", "On peut essayer d'implémenter une fonction qui afficherait ceci pour la méthode d'évaluation naturelle :\n", "\n", "<img width=\"70%\" alt=\"images/public2018-D1_ex1.png\" src=\"images/public2018-D1_ex1.png\">\n", "\n", "Et ceci pour la méthode d'Ershov :\n", "\n", "<img width=\"70%\" alt=\"images/public2018-D1_ex2.png\" src=\"images/public2018-D1_ex2.png\">\n", "\n", "Ce n'est pas trop difficile, mais ça prend un peu de temps :\n", "\n", "- on va d'abord montrer comment évaluer les expressions, en lisant l'arbre et en effectuant des appels récursifs,\n", "- puis on fera un parcours postfix de l'arbre afin d'utiliser l'évaluation avec une pile, avec la stratégie naïve,\n", "- et enfin la méthode d'Ershov permettra de réduire la hauteur maximale de la pile, en passant de $h(t)$ (hauteur de l'arbre) à $\\rho(t)$,\n", "- en bonus, on affichera les instructions style \"compilateur à registre\", pour visualiser le gain apporté par la méthode d'Ershov.\n", "\n", "> Bien sûr, tout cela fait beaucoup trop pour être envisagé le jour de l'oral ! Mais un des points aurait pû être implémenté rapidement." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Évaluation des expressions\n", "\n", "Un premier objectif plus simple est d'évaluer les expressions, en fournissant un *contexte* (table associant une valeur à chaque variable)." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "type ('a, 'b) contexte = ('a * 'b) list\n" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "val valeur : ('a, 'b) contexte -> 'a -> 'b = <fun>\n" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type ('a, 'b) contexte = ('a * 'b) list;;\n", "let valeur (ctx : ('a, 'b) contexte) (var : 'a) = List.assoc var ctx;;\n", "(* une Hashtbl peut etre utilisee si besoin de bonnes performances *)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val contexte1 : (string, int) contexte =\n", " [(\"x\", 1); (\"y\", 2); (\"z\", 3); (\"u\", 4); (\"v\", 5); (\"w\", 6)]\n" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let contexte1 : (string, int) contexte = [\n", " (\"x\", 1); (\"y\", 2); (\"z\", 3);\n", " (\"u\", 4); (\"v\", 5); (\"w\", 6)\n", "];;" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val intop_of_op : operateur -> int -> int -> int = <fun>\n" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let intop_of_op (op : operateur) : (int -> int -> int) =\n", " match op with\n", " | Plus -> ( + )\n", " | Moins -> ( - )\n", " | MoinsDroite -> (fun v1 -> fun v2 -> v2 - v1)\n", " | Mul -> ( * )\n", " | Div -> ( / )\n", " | DivDroite -> (fun v1 -> fun v2 -> v2 / v1)\n", " | Modulo -> ( mod )\n", " | Expo ->\n", " (fun v1 -> fun v2 -> int_of_float ((float_of_int v1) ** (float_of_int v2)))\n", ";;" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val eval_int :\n", " (string, int) contexte -> (string, operateur) arbre_binaire -> int = <fun>\n" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let rec eval_int (ctx : (string, int) contexte) (expr : (string, operateur) arbre_binaire) : int =\n", " match expr with\n", " | F(s) -> valeur ctx s\n", " | N(t1, op, t2) ->\n", " let v1, v2 = eval_int ctx t1, eval_int ctx t2 in\n", " (intop_of_op op) v1 v2\n", ";;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Par exemple, $x$ vaut $1$ dans le contexte d'exemple, et $x + y$ vaut $1 + 2 = 3$ :" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "- : int = 1\n" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = eval_int contexte1 (F(\"x\"));;" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : int = 3\n" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = eval_int contexte1 (N(F(\"x\"), Plus, F(\"y\")));;" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : int = -5\n" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = eval_int contexte1 exp1;;" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : int = -26\n" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = eval_int contexte1 exp2;;" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : int = 0\n" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = eval_int contexte1 exp3;;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On voit la faiblesse de l'interprétation avec des entiers, la division `/` est une division entière !\n", "\n", "On peut aussi interpréter avec des flottants :" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val contexte2 : (string, float) contexte =\n", " [(\"x\", 1.); (\"y\", 2.); (\"z\", 3.); (\"u\", 4.); (\"v\", 5.); (\"w\", 6.)]\n" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let contexte2 : (string, float) contexte = [\n", " (\"x\", 1.); (\"y\", 2.); (\"z\", 3.);\n", " (\"u\", 4.); (\"v\", 5.); (\"w\", 6.)\n", "];;" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val floatop_of_op : operateur -> float -> float -> float = <fun>\n" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let floatop_of_op (op : operateur) : (float -> float -> float) =\n", " match op with\n", " | Plus -> ( +. )\n", " | Moins -> ( -. )\n", " | MoinsDroite -> (fun v1 -> fun v2 -> v2 -. v1)\n", " | Mul -> ( *. )\n", " | Div -> ( /. )\n", " | DivDroite -> (fun v1 -> fun v2 -> v2 /. v1)\n", " | Modulo ->\n", " (fun v1 -> fun v2 -> float_of_int ((int_of_float v1) mod (int_of_float v2)))\n", " | Expo -> ( ** )\n", ";;" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val eval_float :\n", " (string, float) contexte -> (string, operateur) arbre_binaire -> float =\n", " <fun>\n" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let rec eval_float (ctx : (string, float) contexte) (expr : (string, operateur) arbre_binaire) : float =\n", " match expr with\n", " | F(s) -> valeur ctx s\n", " | N(t1, op, t2) ->\n", " let v1, v2 = eval_float ctx t1, eval_float ctx t2 in\n", " (floatop_of_op op) v1 v2\n", ";;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Par exemple, $x$ vaut $1$ dans le contexte d'exemple, et $x + y$ vaut $1 + 2 = 3$ :" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : float = 1.\n" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = eval_float contexte2 (F(\"x\"));;" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : float = 3.\n" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = eval_float contexte2 (N(F(\"x\"), Plus, F(\"y\")));;" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : float = -5.\n" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = eval_float contexte2 exp1;;" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : float = -26.\n" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = eval_float contexte2 exp2;;" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : float = 0.192307692307692318\n" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = eval_float contexte2 exp3;;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluation par lecture postfix et pile\n", "On va commencer par lire l'arbre en parcours postfix (cf. [TP2 @ ENS Rennes 2017/18](https://nbviewer.jupyter.org/github/Naereen/notebooks/tree/master/agreg/TP_Programmation_2017-18/TP2__OCaml.ipynb)) et ensuite l'évaluer grâce à une pile." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "type ('a, 'b) lexem = O of 'b | V of 'a\n" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "type ('a, 'b) parcours = ('a, 'b) lexem list\n" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type ('a, 'b) lexem = O of 'b | V of 'a;;\n", "type ('a, 'b) parcours = (('a, 'b) lexem) list;;" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val parcours_postfix : ('a, 'b) arbre_binaire -> ('a, 'b) parcours = <fun>\n" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let parcours_postfix (expr : ('a, 'b) arbre_binaire) : (('a, 'b) parcours) =\n", " let rec parcours vus expr =\n", " match expr with\n", " | F(s) -> V(s) :: vus\n", " | N(t1, op, t2) -> O(op) :: (parcours (parcours vus t1) t2)\n", " in\n", " List.rev (parcours [] expr)\n", ";;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On le teste :" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur) parcours = [V \"x\"; V \"y\"; V \"z\"; O Mul; O Moins]\n" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parcours_postfix exp1;;" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur) parcours =\n", "[V \"x\"; V \"y\"; V \"z\"; O Mul; O Moins; V \"u\"; V \"v\"; V \"w\"; O Mul; O Moins;\n", " O Div]\n" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parcours_postfix exp3;;" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val eval_int_2 :\n", " (string, int) contexte -> (string, operateur) arbre_binaire -> int = <fun>\n" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let eval_int_2 (ctx : (string, int) contexte) (expr : (string, operateur) arbre_binaire) : int =\n", " let vus = parcours_postfix expr in\n", " let pile = Stack.create () in\n", " let aux lex =\n", " match lex with\n", " | V(s) -> Stack.push (valeur ctx s) pile;\n", " | O(op) ->\n", " let v1, v2 = Stack.pop pile, Stack.pop pile in\n", " Stack.push ((intop_of_op op) v1 v2) pile;\n", " in\n", " List.iter aux vus;\n", " Stack.pop pile\n", ";;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Par exemple, $x - y*z$ avec $x = 1, y = 2, z = 3$ vaut $1 - 2 * 3 = -5$ :" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur) arbre_binaire =\n", "N (F \"x\", Moins, N (F \"y\", Mul, F \"z\"))\n" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "- : int = -5\n" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = exp1 ;;\n", "let _ = eval_int_2 contexte1 exp1;;" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur) arbre_binaire =\n", "N (F \"u\", Moins, N (F \"v\", Mul, F \"w\"))\n" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "- : int = -26\n" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = exp2;;\n", "let _ = eval_int_2 contexte1 exp2;;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut maintenant faire la même fonction mais qui va en plus afficher l'état successif de la pile (avec des valeurs uniquement)." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val print : ('a, out_channel, unit) format -> 'a = <fun>\n" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let print f =\n", " let r = Printf.printf f in\n", " flush_all();\n", " r\n", ";;" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val print_pile : int Stack.t -> unit = <fun>\n" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let print_pile pile =\n", " print \"\\nPile : \";\n", " Stack.iter (print \"%i; \") pile;\n", " print \".\"\n", ";;" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val eval_int_3 :\n", " (string, int) contexte -> (string, operateur) arbre_binaire -> int = <fun>\n" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let eval_int_3 (ctx : (string, int) contexte) (expr : (string, operateur) arbre_binaire) : int =\n", " let vus = parcours_postfix expr in\n", " let pile = Stack.create () in\n", " let aux lex =\n", " print_pile pile;\n", " match lex with\n", " | V(s) -> Stack.push (valeur ctx s) pile;\n", " | O(op) ->\n", " let v1, v2 = Stack.pop pile, Stack.pop pile in\n", " Stack.push ((intop_of_op op) v1 v2) pile;\n", " in\n", " List.iter aux vus;\n", " Stack.pop pile\n", ";;" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur) arbre_binaire =\n", "N (F \"x\", Moins, N (F \"y\", Mul, F \"z\"))\n" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Pile : .\n", "Pile : 1; .\n", "Pile : 2; 1; .\n", "Pile : 3; 2; 1; .\n" ] }, { "data": { "text/plain": [ "- : int = -5\n" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = exp1 ;;\n", "let _ = eval_int_3 contexte1 exp1;;" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur) arbre_binaire =\n", "N (N (F \"x\", Moins, N (F \"y\", Mul, F \"z\")), Div,\n", " N (F \"u\", Moins, N (F \"v\", Mul, F \"w\")))\n" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Pile : 6; 1; .\n", "Pile : .\n", "Pile : 1; .\n", "Pile : 2; 1; .\n", "Pile : 3; 2; 1; .\n", "Pile : 6; 1; .\n", "Pile : -5; .\n", "Pile : 4; -5; .\n", "Pile : 5; 4; -5; .\n", "Pile : 6; 5; 4; -5; .\n", "Pile : 30; 4; -5; .\n" ] }, { "data": { "text/plain": [ "- : int = 0\n" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = exp3;;\n", "let _ = eval_int_3 contexte1 exp3;;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Il y a un soucis dans l'ordre d'affichage des lignes, dû à Jupyter et pas à notre fonction." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On vérifie qu'on utilise au plus 4 valeurs sur la pile, comme représenté dans la figure de l'énoncé :\n", "\n", "<img width=\"70%\" alt=\"images/public2018-D1_ex3.png\" src=\"images/public2018-D1_ex3.png\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Affichage dans ce langage de manipulation de registre\n", "On ne va pas trop formaliser ça, mais juste les afficher..." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val print_aff : int -> int -> string -> unit = <fun>\n" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let print_aff (line : int) (i : int) (s : string) : unit =\n", " print \"\\n%02i: R[%d] := %s ;\" line i s;\n", ";;" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val string_of_op : operateur -> string = <fun>\n" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let string_of_op (op : operateur) : string =\n", " match op with\n", " | Plus -> \"+\"\n", " | Moins | MoinsDroite -> \"-\"\n", " | Mul -> \"*\"\n", " | Div | DivDroite -> \"/\"\n", " | Modulo -> \"%\"\n", " | Expo -> \"^\"\n", ";;" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val print_op : int -> int -> int -> int -> operateur -> unit = <fun>\n" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let print_op (line : int) (i : int) (j : int) (k : int) (op : operateur) : unit =\n", " match op with\n", " | MoinsDroite | DivDroite -> (* on gère ici les opérateurs \"inverses\" *)\n", " print \"\\n%02i: R[%d] := R[%d] %s R[%d] ;\" line i k (string_of_op op) j;\n", " | _ ->\n", " print \"\\n%02i: R[%d] := R[%d] %s R[%d] ;\" line i j (string_of_op op) k;\n", ";;" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val eval_int_4 :\n", " (string, int) contexte -> (string, operateur) arbre_binaire -> int = <fun>\n" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let eval_int_4 (ctx : (string, int) contexte) (expr : (string, operateur) arbre_binaire) : int =\n", " let vus = parcours_postfix expr in\n", " let pile = Stack.create () in\n", " let ligne = ref 0 in\n", " let aux lex =\n", " incr ligne;\n", " match lex with\n", " | V(s) ->\n", " Stack.push (valeur ctx s) pile;\n", " print_aff !ligne ((Stack.length pile) - 1) s;\n", " | O(op) ->\n", " let v1, v2 = Stack.pop pile, Stack.pop pile in\n", " Stack.push ((intop_of_op op) v1 v2) pile;\n", " print_op !ligne ((Stack.length pile) - 1) ((Stack.length pile) - 1) (Stack.length pile) op;\n", " in\n", " List.iter aux vus;\n", " Stack.pop pile\n", ";;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Essayons ça :" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur) arbre_binaire =\n", "N (F \"x\", Moins, N (F \"y\", Mul, F \"z\"))\n" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "04: R[1] := R[1] * R[2] ;\n", "05: R[0] := R[0] - R[1] ;\n", "01: R[0] := x ;\n", "02: R[1] := y ;\n", "03: R[2] := z ;\n" ] }, { "data": { "text/plain": [ "- : int = -5\n" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = exp1 ;;\n", "let _ = eval_int_4 contexte1 exp1;;" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur) arbre_binaire =\n", "N (N (F \"x\", Moins, N (F \"y\", Mul, F \"z\")), Div,\n", " N (F \"u\", Moins, N (F \"v\", Mul, F \"w\")))\n" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "10: R[1] := R[1] - R[2] ;\n", "11: R[0] := R[0] / R[1] ;\n", "01: R[0] := x ;\n", "02: R[1] := y ;\n", "03: R[2] := z ;\n", "04: R[1] := R[1] * R[2] ;\n", "05: R[0] := R[0] - R[1] ;\n", "06: R[1] := u ;\n", "07: R[2] := v ;\n", "08: R[3] := w ;\n", "09: R[2] := R[2] * R[3] ;\n" ] }, { "data": { "text/plain": [ "- : int = 0\n" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = exp3;;\n", "let _ = eval_int_4 contexte1 exp3;;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Méthode d'Ershov\n", "Enfin, on va générer, en plus de l'évaluation, un affichage comme celui qu'on voulait, qui montre les affectations dans les registres, et permettra de visualiser que la méthode d'Ershov utilise un registre de moins sur le terme exemple qui calcule $(x - y*z)/(u - v*w)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On rappelle qu'on a obtenu un arbre binaire décoré, représenté comme tel :" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur, decoration) arbre_binaire_decore =\n", "N2\n", " ({rho = 1; premier_gauche = false}, F2 \"x\", Moins,\n", " N2 ({rho = 1; premier_gauche = true}, F2 \"y\", Mul, F2 \"z\"))\n" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "decore exp1;;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On modifie notre parcours postfix pour prendre en compte la décoration et savoir si on calcul d'abord le sous-arbre gauche ou droit." ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val parcours_postfix_decore :\n", " ('a, operateur, decoration) arbre_binaire_decore ->\n", " ('a, operateur) parcours = <fun>\n" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let parcours_postfix_decore (expr : ('a, 'b, decoration) arbre_binaire_decore) : (('a, 'b) parcours) =\n", " let rec parcours vus expr =\n", " match expr with\n", " | F2(s) -> V(s) :: vus\n", " | N2(dec, t1, Moins, t2) when dec.premier_gauche = false ->\n", " O(MoinsDroite) :: (parcours (parcours vus t2) t1)\n", " | N2(dec, t1, MoinsDroite, t2) when dec.premier_gauche = false ->\n", " O(Moins) :: (parcours (parcours vus t2) t1)\n", " | N2(dec, t1, Div, t2) when dec.premier_gauche = false ->\n", " O(DivDroite) :: (parcours (parcours vus t2) t1)\n", " | N2(dec, t1, DivDroite, t2) when dec.premier_gauche = false ->\n", " O(Div) :: (parcours (parcours vus t2) t1)\n", " | N2(dec, t1, op, t2) when dec.premier_gauche = false ->\n", " O(op) :: (parcours (parcours vus t2) t1)\n", " | N2(_, t1, op, t2) ->\n", " O(op) :: (parcours (parcours vus t1) t2)\n", " in\n", " List.rev (parcours [] expr)\n", ";;" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "val eval_int_ershov :\n", " (string, int) contexte -> (string, operateur) arbre_binaire -> int = <fun>\n" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let eval_int_ershov (ctx : (string, int) contexte) (expr : (string, operateur) arbre_binaire) : int =\n", " let vus = parcours_postfix_decore (decore expr) in\n", " let pile = Stack.create () in\n", " let ligne = ref 0 in\n", " let aux lex =\n", " incr ligne;\n", " match lex with\n", " | V(s) ->\n", " Stack.push (valeur ctx s) pile;\n", " print_aff !ligne ((Stack.length pile) - 1) s;\n", " | O(op) ->\n", " let v1, v2 = Stack.pop pile, Stack.pop pile in\n", " Stack.push ((intop_of_op op) v1 v2) pile;\n", " print_op !ligne ((Stack.length pile) - 1) ((Stack.length pile) - 1) (Stack.length pile) op;\n", " in\n", " List.iter aux vus;\n", " Stack.pop pile\n", ";;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Essayons ça :" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur) arbre_binaire =\n", "N (F \"x\", Moins, N (F \"y\", Mul, F \"z\"))\n" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "10: R[1] := R[1] - R[2] ;\n", "11: R[0] := R[0] / R[1] ;\n", "01: R[0] := y ;\n", "02: R[1] := z ;\n", "03: R[0] := R[0] * R[1] ;\n" ] }, { "data": { "text/plain": [ "- : int = -5\n" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = exp1 ;;\n", "let _ = eval_int_ershov contexte1 exp1;;" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "- : (string, operateur) arbre_binaire =\n", "N (N (F \"x\", Moins, N (F \"y\", Mul, F \"z\")), Div,\n", " N (F \"u\", Moins, N (F \"v\", Mul, F \"w\")))\n" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "10: R[1] := R[2] - R[1] ;\n", "11: R[0] := R[0] / R[1] ;\n", "01: R[0] := y ;\n", "02: R[1] := z ;\n", "03: R[0] := R[0] * R[1] ;\n", "04: R[1] := x ;\n", "05: R[0] := R[1] - R[0] ;\n", "06: R[1] := v ;\n", "07: R[2] := w ;\n", "08: R[1] := R[1] * R[2] ;\n", "09: R[2] := u ;\n" ] }, { "data": { "text/plain": [ "- : int = 0\n" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let _ = exp3;;\n", "let _ = eval_int_ershov contexte1 exp3;;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Et voilà, ce n'était pas trop dur !" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "## Conclusion\n", "\n", "Voilà pour la question obligatoire de programmation, et un petit bonus.\n", "\n", "### Qualités\n", "- On a décomposé le problème en sous-fonctions (d'abord le calcul de $\\rho$ puis la méthode d'Ershov),\n", "- On a fait des exemples et *on les garde* dans ce qu'on présente au jury,\n", "- On a testé la fonction exigée sur de petits exemples et sur un exemple de taille réelle (venant du texte).\n", "\n", "### Bonus\n", "On a fait pas mal de bonus, en interprétant les termes, d'abord via l'arbre et des appels récursifs, ensuite par une lecture postfix et une pile, qui nous a permis de vérifier l'évolution de la pile et de sa hauteur (avec le même exemple que dans le texte), et ensuite avec une espèce de \"compilation\" en visualisant les affectations dans ces registres. La \"compilation\" n'est pas réelle, on a uniquement affiché des choses, mais elle permet de vérifier que la méthode de Ershov aide effectivement à réduire le nombre de registre requis.\n", "\n", "### Défauts\n", "- ?\n", "\n", "\n", "> Bien-sûr, ce petit notebook ne se prétend pas être une solution optimale, ni exhaustive.\n", "\n", "> Vous auriez pu choisir de modéliser le problème avec une autre approche, n'hésitez pas [à me contacter svp](http://perso.crans.org/besson/callme.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> C'est tout pour aujourd'hui les amis, allez voir [ici pour d'autres corrections](https://github.com/Naereen/notebooks/tree/master/agreg), et que la force soit avec vous !" ] } ], "metadata": { "kernelspec": { "display_name": "OCaml 4.04.2", "language": "OCaml", "name": "ocaml-jupyter" }, "language_info": { "codemirror_mode": "text/x-ocaml", "file_extension": ".ml", "mimetype": "text/x-ocaml", "name": "OCaml", "nbconverter_exporter": null, "pygments_lexer": "OCaml", "version": "4.04.2" }, "notify_time": "10", "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "429px", "width": "251px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": true, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

mit

barjacks/pythonrecherche

Kursteilnehmer/Sven Millischer/06 /01 Rückblick For-Loop-Übungen.ipynb

2

8689

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 10 For-Loop-Rückblick-Übungen" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In den Teilen der folgenden Übungen habe ich den Code mit \"XXX\" ausgewechselt. Es gilt in allen Übungen, den korrekten Code auszuführen und die Zelle dann auszuführen. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1.Drucke alle diese Prim-Zahlen aus:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "primes = [2, 3, 5, 7]\n", "for prime in primes:\n", " print(prime)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2.Drucke alle die Zahlen von 0 bis 4 aus:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for x in range(5):\n", " print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3.Drucke die Zahlen 3,4,5 aus:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for x in range(3, 6):\n", " print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4.Baue einen For-Loop, indem Du alle geraden Zahlen ausdruckst, die tiefer sind als 237." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "numbers = [\n", " 951, 402, 984, 651, 360, 69, 408, 319, 601, 485, 980, 507, 725, 547, 544,\n", " 615, 83, 165, 141, 501, 263, 617, 865, 575, 219, 390, 984, 592, 236, 105, 942, 941,\n", " 386, 462, 47, 418, 907, 344, 236, 375, 823, 566, 597, 978, 328, 615, 953, 345,\n", " 399, 162, 758, 219, 918, 237, 412, 566, 826, 248, 866, 950, 626, 949, 687, 217,\n", " 815, 67, 104, 58, 512, 24, 892, 894, 767, 553, 81, 379, 843, 831, 445, 742, 717,\n", " 958, 609, 842, 451, 688, 753, 854, 685, 93, 857, 440, 380, 126, 721, 328, 753, 470,\n", " 743, 527\n", "]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for x in numbers:\n", " if x in range(237):\n", " if (x % 2 == 0):\n", " print(x)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Lösung:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5.Addiere alle Zahlen in der Liste" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sum(numbers)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Lösung:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6.Addiere nur die Zahlen, die gerade sind" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "numbers = [\n", " 951, 402, 984, 651, 360, 69, 408, 319, 601, 485, 980, 507, 725, 547, 544,\n", " 615, 83, 165, 141, 501, 263, 617, 865, 575, 219, 390, 984, 592, 236, 105, 942, 941,\n", " 386, 462, 47, 418, 907, 344, 236, 375, 823, 566, 597, 978, 328, 615, 953, 345,\n", " 399, 162, 758, 219, 918, 237, 412, 566, 826, 248, 866, 950, 626, 949, 687, 217,\n", " 815, 67, 104, 58, 512, 24, 892, 894, 767, 553, 81, 379, 843, 831, 445, 742, 717,\n", " 958, 609, 842, 451, 688, 753, 854, 685, 93, 857, 440, 380, 126, 721, 328, 753, 470,\n", " 743, 527\n", "]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "new_list=[]\n", "for elem in numbers:\n", " if elem % 2 == 0:\n", " new_list.append(elem)\n", "sum(new_list)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "7.Drucke mit einem For Loop 5 Mal hintereinander Hello World aus" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for x in range(5): \n", " print (\"Hello World\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Lösung" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "8.Entwickle ein Programm, das alle Nummern zwischen 2000 und 3200 findet, die durch 7, aber nicht durch 5 teilbar sind. Das Ergebnis sollte auf einer Zeile ausgedruckt werden. Tipp: Schaue Dir [hier](https://www.tutorialspoint.com/python/comparison_operators_example.htm) die Vergleichsoperanden von Python an." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "l=[]\n", "for i in range(2000, 3200):\n", " if (i%7==0) and (i%5>=0):\n", " l.append(str(i))\n", "\n", "print(','.join(l))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "9.Schreibe einen For Loop, der die Nummern in der folgenden Liste von int in str verwandelt." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "lst = range(45,99)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98']\n" ] } ], "source": [ "new_list=[]\n", "for elem in lst:\n", " str(elem)\n", " new_list.append(str(elem))\n", "print(new_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "10.Schreibe nun ein Programm, das alle Ziffern 4 mit dem Buchstaben A ersetzte, alle Ziffern 5 mit dem Buchtaben B." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "newnewlist = []\n", "for elem in new_list:\n", " if '4' in elem:\n", " elem = elem.replace('4', 'A')\n", " if '5' in elem:\n", " elem = elem.replace('5', 'B')\n", " newnewlist.append(elem)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['AB',\n", " 'A6',\n", " 'A7',\n", " 'A8',\n", " 'A9',\n", " 'B0',\n", " 'B1',\n", " 'B2',\n", " 'B3',\n", " 'BA',\n", " 'BB',\n", " 'B6',\n", " 'B7',\n", " 'B8',\n", " 'B9',\n", " '60',\n", " '61',\n", " '62',\n", " '63',\n", " '6A',\n", " '6B',\n", " '66',\n", " '67',\n", " '68',\n", " '69',\n", " '70',\n", " '71',\n", " '72',\n", " '73',\n", " '7A',\n", " '7B',\n", " '76',\n", " '77',\n", " '78',\n", " '79',\n", " '80',\n", " '81',\n", " '82',\n", " '83',\n", " '8A',\n", " '8B',\n", " '86',\n", " '87',\n", " '88',\n", " '89',\n", " '90',\n", " '91',\n", " '92',\n", " '93',\n", " '9A',\n", " '9B',\n", " '96',\n", " '97',\n", " '98']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "newnewlist" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

NelisW/ComputationalRadiometry

03-Introduction-to-Radiometry.ipynb

1

699014

{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "%This notebook demonstrates the use of the workpackage template, replace with your own.\n", "\n", "\\documentclass[english]{workpackage}[1996/06/02]\n", "\n", "% input the common preamble content (required by the ipnb2latex converter)\n", "\\input{header.tex}\n", "\n", "% the following three lines are required to support the tikz examples\n", "\\usepackage{tikz}\n", "\\usepackage{sansmath}\n", "\\usetikzlibrary{shadings,intersections}\n", "\n", "% then follows the rest of the preamble to be placed before the begin document\n", "% this preamble content is special to the documentclass you defined above.\n", "\\WPproject{Computational Radiometry} % project name\n", "\\WPequipment{} % equipment name\n", "\\WPsubject{03-Introduction-to-Radiometry} % main heading \n", "\\WPconclusions{} \n", "\\WPclassification{} \n", "\\WPdocauthor{CJ Willers}\n", "\\WPcurrentpackdate{\\today}\n", "\\WPcurrentpacknumber{} % work package number\n", "\\WPdocnumber{} % this doc number hosts all the work packages\n", "\\WPprevpackdate{} % work package which this one supersedes\n", "\\WPprevpacknumber{} % work package which this one supersedes\n", "\\WPsuperpackdate{} % work package which comes after this one\n", "\\WPsuperpacknumber{} % work package which comes after this one\n", "\\WPdocontractdetails{false}\n", "\\WPcontractname{} % contract name \n", "\\WPorderno{} % contract order number\n", "\\WPmilestonenumber{} % contract milestone number\n", "\\WPmilestonetitle{} % contract milestone title\n", "\\WPcontractline{} % contract milestone line number \n", "\\WPdocECPnumber{} % ecp/ecr number\n", "\\WPdistribution{}\n", "\n", "% bibfile added in this notebook\n", "%\\addbibresource{./analyseRio.bib} \n", "\n", "% this is entered just before the end{document}\n", "\\newcommand{\\atendofdoc}{\n", "\\bibliographystyle{IEEEtran}\n", "\\bibliography{03-Introduction-to-Radiometry}\n", "}\n", "\n", "%and finally the document begin.\n", "\\begin{document}\n", "\\WPlayout\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3 Brief Introduction to Radiometry" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook forms part of a series on [computational optical radiometry](https://github.com/NelisW/ComputationalRadiometry#computational-optical-radiometry-with-pyradi) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The date of this document and module versions used in this document are given at the end of the file. \n", "Feedback is appreciated: neliswillers at gmail dot com." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "from IPython.display import display\n", "from IPython.display import Image\n", "from IPython.display import HTML\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [`pyradi`](http://nelisw.github.io/pyradi-docs/_build/html/index.html) toolkit is a Python toolkit to perform optical and infrared computational radiometry (flux flow) calculations. \n", "Radiometry is the measurement and calculation of electromagnetic flux transfer for systems operating in the spectral region ranging from ultraviolet to microwaves. Indeed, these principles can be applied to electromagnetic radiation of any wavelength. This book only considers ray-based radiometry for incoherent radiation fields.\n", "\n", "The briefly summarised information in this notebook is taken from [my book](http://spie.org/Publications/Book/2021423?origin_id=x646), see the book for more details.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/jpeg": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Image(filename='images/PM236.jpg')) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Electromagnetic radiation can be modeled as a number of different phenomena: rays, electromagnetic waves, wavefronts, or particles. All of these models are mathematically related. The appropriate model to use depends on the task at hand. Either the electromagnetic wave model(developed by Maxwell) or the particle model (developed by Einstein) are used when most appropriate. The part of the electromagnetic spectrum normally considered in optical radiometry is as follows:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Image(filename='images/radiometry03.png')) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The photon is a massless elementary particle and acts as the energy carrier for the electromagnetic wave.\n", "Photon particles have discrete energy quanta proportional to the frequency of the electromagnetic energy, $Q = h\\nu = hc/\\lambda$, where $h$ is Planck's constant." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Definitions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following figure (expanded from Pinson) and table defines the key radiometry units. The difference operator '$d$' is used to denote 'a small quantity of ...'. This 'small quantity' of one variable is almost always related to a 'small quantity' of another variable in some physical dependency. For example, irradiance is defined as $E=d\\Phi/dA$, which means that a small amount of flux $d\\Phi$ impinges on a small area $dA$, resulting in an irradiance of $E$. 'Small' is defined as the extent or domain over which the quantity, or any of its dependent quantities, does not vary significantly. Because any finite-sized quantity varies over a finite-sized domain, the $d$ operation is only valid over an infinitely small domain $dA=\\lim_{\\Delta A \\to 0}\\Delta A$. The difference operator, written in the form of a differential such as $E=d\\Phi/dA$, is not primarily meant to mean differentiation in the mathematical sense. Rather, it is used to indicate something that can be integrated (or summed).\n", "\n", "In practice, it is impossible to consider infinitely many, infinitely small domains. Following the reductionist approach, any real system can, however, be assembled as the sum of a set of these small domains, by integration over the physical domain as in $A=\\int dA$. Hence, the 'small-quantity' approach proves very useful to describe and understand the problem, whereas the real-world solution can be obtained as the sum of a set of such small quantities. In almost all of the cases in this notebook, it is implied that such 'small-quantity' domains will be integrated (or summed) over the (larger) problem domain.\n", "\n", "Photon rates are measured in quanta per second.\n", "The 'second' is an SI unit, whereas quanta is a unitless count: the number of photons. Photon rate therefore has units of [1/s] or [s$^{-1}$]. This form tends to lose track of the fact that the number of quanta per second is described. The notebook may occasionally contain units of the form [q/s] to emphasize the photon count. In this case, the 'q' is not a formal unit, it is merely a reminder of 'counts.' In dimensional analysis the 'q' is handled the same as any other unit. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Radiometric quantities can be defined in terms of three different but related units: radiant power (watts), photon rates (quanta per second), or photometric luminosity (lumen). Photometry is radiometry applied to human visual perception.\n", "The conversion from radiometric to photometric quantities is\n", "covered in more detail in my book. It is important to realize\n", "that the underlying concepts are the same, irrespective of the nature of\n", "the quantity. All of the derivations and examples presented in this book are equally valid for radiant, photon, or photometric quantities.\n", "\n", "__Flux__ is the amount of optical power, a photon rate, or photometric luminous flux, flowing between two surfaces. There is always a source area and a receiving area, with the flux flowing between them. All quantities of flux are denoted by the symbol $\\Phi$. The units are [W], [q/s], or [lm], depending on the nature of the quantity.\n", "\n", "__Irradiance (areance)__ is the areal density of flux on the receiving surface area. The flux flows inward onto the surface with no regard to incoming angular density. All quantities of irradiance are denoted by the symbol $E$. The units are [W/m$^2$], [q/(s$\\cdot$m$^2$)], or [lm/m$^2$], depending on the nature of the quantity.\n", "\n", "__Exitance (areance)__\n", "is the areal density of flux on the source surface\n", "area. The flux flows outward from the surface with no regard to angular density. The exitance leaving a surface\n", "can be due to reflected light, transmitted light, emitted light, or any combination thereof. All quantities of exitance are denoted by the\n", "symbol $M$. The units are [W/m$^2$], [q/(s$\\cdot$m$^2$)], or [lm/m$^2$], depending on the\n", "nature of the quantity.\n", "\n", "__Intensity (pointance)__ is the density of flux over solid angle. The flux flows outward from the source with no regard for surface area. Intensity is denoted by the symbol $I$. The human perception of a point source (e.g., a star at long range) 'brightness' is an intensity measurement. The units are [W/sr], [q/(s$\\cdot$sr)], or [lm/sr], depending on the nature of the quantity.\n", "\n", "__Radiance (sterance)__ is the density of flux per unit source surface area and unit solid angle. \n", "Radiance is a property of the electromagnetic field irrespective of spatial location (in a lossless medium). For a radiating surface, the radiance may comprise transmitted light, reflected light, emitted light, or any combination thereof. The radiance in a field created by a Lambertian source is conserved: the radiance is constant anywhere in space, also on the receiving surface. All radiance quantities are denoted by the symbol $L$. The human perception of 'brightness' of a large surface can be likened to a radiance experience (beware of the nonlinear response in the eye, however). The units are\n", "[W/(m$^2$ $\\cdot$sr)], [q/(s$\\cdot$m$^2$ $\\cdot$sr)], or [lm/(m$^2$ $\\cdot$sr)], depending on the nature of the\n", "quantity.\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Image(filename='images/radiometry02.png')) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spectral quantities" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See notebook 4 in this series, [__Introduction to computational radiometry with pyradi__](http://nbviewer.ipython.org/github/NelisW/ComputationalRadiometry/blob/master/04-IntroductionToComputationalRadiometryWithPyradi.ipynb), for a detailed description of spectral quantities.\n", "\n", "Three spectral domains are commonly used: wavelength $\\lambda$ in [m], frequency $\\nu$ in [Hz], and wavenumber $\\tilde{\\nu}$ in [cm$^{-1}$] (the number of waves that will fit into a 1-cm length). \n", "Spectral quantities indicate an amount of the quantity within a small spectral width $d\\lambda$ around the value of $\\lambda$: it is a spectral density. Spectral density quantity symbols are subscripted with a $\\lambda$ or $\\nu$, i.e., $L_\\lambda$ or $L_\\nu$. The dimensional units of a spectral density quantity are indicated as [$\\mu$m$^{-1}$] or [(cm$^{-1})^{-1}$], i.e., [W/(m$^2$ $\\cdot$sr$\\cdot$ $\\mu$m)].\n", "\n", "The relationship between the wavelength and wavenumber spectral domains is $\\tilde{\\nu}=10^4/\\lambda$ , where $\\lambda$ is in units of $\\mu$m. The conversion of a spectral density quantity such as [W/(m$^2$ $\\cdot$sr$\\cdot$cm$^{-1}$)] requires the derivative, %$d{\\tilde{\\nu}}=-\\frac{10^4}{\\lambda^2}d\\lambda=-\\frac{{\\tilde{\\nu}}^2}{10^4}d\\lambda$.\n", "$d{\\tilde{\\nu}}=-10^4d\\lambda /\\lambda^2=-\\tilde{\\nu}^2d\\lambda/10^4$.\n", "The derivative relationship converts between the spectral _widths_, and hence the spectral densities, in the two respective domains.\n", "The conversion from a wavelength spectral density quantity to a wavenumber spectral density quantity is \n", "$d{}L_{\\tilde{\\nu}}=d{}L_\\lambda \\lambda^2/10^4=d{} L_\\lambda 10^4/\\tilde{\\nu}^2$.\n", "\n", "Spectral quantities denote the amount in a small spectral width $d\\lambda$ around a wavelength $\\lambda$. It follows that the total quantity over a spectral range can be determined by integration (summation) over the spectral range of interest:\n", "$$\n", "L=\\int_{\\lambda_1}^{\\lambda_2}L_\\lambda d\\lambda.\n", "$$\n", "The above integral satisfies the requirements of dimensional analysis (see my book) because the units of $L_\\lambda$ are [W/(m$^2$ $\\cdot$sr$\\cdot$ $\\mu$m)], whereas $d\\lambda$ has the units of [$\\mu$m], and $L$ has units of [W/(m$^2$ $\\cdot$sr)].\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solid Angle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The _geometric_ solid angle $\\omega$ of any arbitrary surface $P$ from the _reference point_ is given by\n", "$$\n", "\\omega=\\int\\!\\!\\!\\!\\int^{P} \\frac{d^2 P \\cos\\theta_1}{R^2},\n", "$$\n", "where $d^2 P \\cos\\theta_1$ is the projected surface area of the surface $P$ in the direction of the reference point, and $R$ is the distance from $d^2 P$ to the reference point. The integral is independent of the viewing direction $(\\theta_0, \\alpha_0)$ from the reference point. Hence, a given area at a given distance will always have the same geometric solid angle irrespective of the direction of the area.\n", "\n", "The geometric solid angle of a cone is $\\omega=4\\pi\\sin^2\\left(\\frac{\\Theta}{2}\\right)$, where $\\Theta$ is the cone half-apex angle.\n", "\n", "The _projected_ solid angle $\\Omega$ of any arbitrary surface $P$ from the _reference area_ $dA_0$ is given by\n", "$$\n", "\\Omega=\\int\\!\\!\\!\\!\\int^{P} \\frac{d^2 P \\cos\\theta_0 \\cos\\theta_1}{R^2},\n", "$$\n", "where $d^2 P \\cos\\theta_1$ is the projected surface area of the surface $P$ in the direction of the reference area, and $R$ is the distance from $d^2 P$ to the reference area. The integral depends on the viewing direction $(\\theta_0, \\alpha_0)$ from the reference area, by the projected area ($dA_0\\cos\\theta_0$) of $dA_0$ in the direction of $d^2 P$.\n", "Hence, a given area at a given distance will always have a different projected solid angle in different directions. \n", "\n", "The _projected_ solid angle of a cone is $\\omega=\\pi\\sin^2\\left(\\Theta\\right)$, where $\\Theta$ is the cone half-apex angle.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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M8GTrshFo2aQf5m6K7YR7LumKvBky4C+FBmK1cZvXhrnoXy2r9hoZ8H6F4Vgts6ZxxAXEppx3pre+fPnSeFYLQvrDbk67s+VTshQVp1zpvDMPn9392KGVUXjL1IpHjx6p1tdsBlWsWDH1LyP2eq9rrbYs6IeG5Qogk2bMM/y1KAavNu3bimUjWqJJv7nYZDLwG6ehbaWSKJTzfbz317/ivX98gI+y5UC2TP8P7/3tH/ggRyHU6jkdHjIgxNGiRYvU78VcWKYuSTRHcDXo5NAupXr1lHSndRhV9h/IYKiGCRuitnl5TEXP1r10o+YbJ9dW9jxr45nYbL5v7XCU+btm5/+/0hi5zmy7KJbYW4DVZhho4Uy5pDgK6RG7OO3Onk+5YsUKlRPdsmVLle7CAZBRdObG04Dws9HxmzBhgm0rFWyZh7Y5NWOesy3mb+E2L3hM7YnWvfQccE8s6ZoXGTL8BYUGmqZqvbBhbn9Uy8op3PdRYbhjpCY5opi2xfJwjOZwxkUi74KrwPOb1VQsz3lRCmW0z4ycr9L+9to4FwM79MSUtTEOu9fWzdhqtNWr+n+FP2f4G4r9YNGBdfMMNKaN/nsZcdqtEANqrBDH8r5sFiYI6Qm7OO0HDx5UC4b0LkBnFHPV2SCCKRasUpMa5SuV1o1C2X9kgKHaBGzQHPCNcweiQ88pWBvtsHth6+atxhJjGzG5dibNOc+KxjM3x7yGprXDy+DvGTLg/ys9MtZ2UVzReWc0h2XmWLVCct4FZ4cLNtmNUu98FyVNXhvmY0SXpqhbuz6atqiDT/+aATlaz8WWrcswrH4RfPHFF8hfoAAKFMiv/v/FF5UxeAVTGzfCvW5mzT5nQ/PZFoGddaNRXrPzGfJ1wiJPs+2ieMUxl2VATX0HJOoupBfs4rSzC53kUyZdW+a1RU4VOV+FrcuGoX4RDgL5tQFBGxTYcY+DQuXBWKEM/Sr0/+rPyPC3YvhhbezX2TyjMbJqTvvfy4jTbq3Y+IOzKZxZ4d/JbfAiCGnJ/fv3VeoX1+xYnuOipMgTqye0QeVva6Jpuy7o3LYxKhfMqGYwK4/boPZv2rgRm7d4xtRp37pZ27YZnvzbayX6FviTSoEZ4WH+ul5YPbw8/pnh/1Csz9Kox4qsFmfBuVaOVcHu3btnPOsFwXVJdafdlE/JnG+9i05kLi9smD8CXZrWRe36TdGizqf4a4YcaD13C7Z7bsJGbQDY4mmaemWUXdu22bhAdaM76mbOgAzZmmO2SqWJ0brR5fEPzWnP12lRrO2ixMX6z6wLzYHh/PnzxrNaEJwDrtlgl1G9c1tkvTZOa4Z8mUtjoIqaR21bPaQY3vtLQfRdaUXVl42TUZuVZnK1xwKzaPqWxf1RMdOf8H7JnlhkYbdF1olRdzZVZGoj/QyJuguuTKo77bt27VK5Z3oXm8hMnqsxoU1lfFuzKdp16Yy2jSujYEbNyL9fGeOMi5wSktfKvijwJ6bAjICH+T6v1Rhe/p/I8H/F0GepPWsYu5bYDKt8+fKqCodEdARngQvp58yZo3tOi6zUlgXo/MV7yFh1HDZEb9+ACdX+hQzZmmLWZrPHxiOvZT3xeYYM+GfFkZjeswZKla6ACqXz46P3P0bRBoOw0IrXECUsVgGrWbOmSgdjI0dBcEVS3WlnCcUff/xR9yITmbQR05rlQ+bSA42pLtRqDCn2Hv5SsC9WWlH1xVSZIFf7BWZTrFuwuH9FZPrT+yjZU5p8pFRMMejXr5+K6MyaNUstsBYER2Xv3r2qJ4HeuSyyXptnNsHHGQyoNsFsttg4s/mP8qOxzuyx8WnDuCr4l2af83ZdotlnL2xavQjzF63CRslht6nYi4OBFd6s3rlzx3glCILrkKpO+5MnT1RlFXak1LvARFHasqAzvngvI6qq3Ejj9g0TUO1fGZCt6azY5cF05YVlPT9Hhgz/RMWR09GzRimUrlABpfN/hPc/LooGgxZa8Roia8XSY6wkVEH7jg8dOmQ82wXBcaDtZWUribKnVFswv10uZHivOIZGrxXyxMrBZVTK4Wc9rMlD98Siznk0+/wvVBkvaaL20PDhw1Wp5kuXLhmvCEFwDVLVaWf1DU5V6V1UIpM2Y2aTj2PV+qU2utdF5gz/QPnR68weG582YFyVf2mDQl50XeKJ7V6bsHrRfCxatVEWNqWiZsyYgcqVK6tqM1J6THAUWPGIKYm9e/fWPW9FSdFaDCn211i56J4rR6FZtZLI9KdMqDPFmtr3xnrumn3uRvus+xiRrcXSvawuwxRdQXAVUtVpZy1VOjZ6F5TIqC3z0S5XBrxXfCjWmrZ5rsTgMjTyn6GHNXnonovQOU8GZPhXFYy3Iv9dZDt5enpi0KBBanDgICElIoW0hucjq2nona/OLDaHYhWyevXqqVmEr776SlXSKly4sKqQw21MB+rSpYvqmcEZMb3XSZK2zEGLbBnw568GKfvs5eGOTnWaY0jPonjvLwWiFqF6xVSM0dWW2WiuvUaGf1bCWLHPdhWbHJYrV05VmREEVyDVnHZ/f391saRaDXM7i42h5s6dqxYkUu7u7iqvecGCBSmrjLN2CIr91TwX3RMrRzVDtZKZ8KdMdTBlo8Xj9WSs554hbzcskRzJNBHPDzawYZUZds0VhLSAfQbouNq0yZsDiGk+dMw5s8UbEjrmnEno27cvevXqpdrcszlamzZtULduXVSqVEk581x/4ubmpjpys5KO3msnqM2z0IwOd7aGGNi3OSqUqIkBi9fBvV4WZPhbIbTv3xENmw1MsPKL18r+KPQX7TVytsU8qRBjd/HmjUUENm3aZLxKBMF5SRWnnTWtGzZs6PS12XnDMWTIEFSsWBGFChVSkRwOGlWqVFH5zLwpYd4cIz78l04bO6eyYxsjsHqvaaktc1ogW4Y/46tB7JLnBQ/3TqjTfAh6Fn0PfykQtQjVK7rMo762zG6uvUYG/LPSWLPqBqK0EHMpGXVfv3698WoQBPvw66+/KieVUUW9c9NZRWeraNGiaNWqFQYMGJAkdevWTXWuZlURrq/65ptvlJNv/Xe0FsNK/x0ZNPv6fqFmGL2CN0PrMPqbfyDDnz9EyTYTsDreQMkmzO5WF99+yXrumtOe4V/4tHgF1O8zX9YY2Vn8vcuUKYM9e/YYrxZBcE5SxWlnt1BGO5w5ys5V6OziSiPPvHxGavQGBYr7GPlhMx5GuejAf/nll8q5ZzQooQFi86xmyuHO1nAg+javgBI1B2DxOnfUy5IBfyvUHv07NkSzgQlVfvHCyv6F8BftNXK2nScVYhxAy5Yti56mf/r0qfGqEITUpXPnzqq6kd456czirCYjpXq2N6lizXo68EWKFEG1atVUZbPEGk95eszD1DkrY9nWzSvmYtG6xAIzXtiybi3WrtuIzZ6e8NyyERs8tL83SF57WoipMux0LYtTBWfG5k77/v37VQSarf71Lhxn0aRJk9SdOQdBPeOfmPr06aOcfdMAwQg9c03jpNKsHYbSf2cU5n0UajYaKzh9um40vvlHBvz5w5JoM2F1/ItJN81Gt7rf4kvWc2ck51+foniF+ugzX6r1pLU408IoH2/cpPSYkNpwITTtlbUzfM6khQsXqtkrPTubXNGut2jRQs2YMorP1BppAOj6mjx5supyHRYWZrxyBMG5sKnTHhAQoIwrjazeBeNMolGvVauWrsFPqvhazLVk7Vim2XC6lp02o97LEx7zpmLOSvMc1M1YMXcR1iWWn+61BevWrsU6tsrWBustGzfAQ/t7g+S1O4yYLkNn6sqVK8arRBBsDxdpumqlLto2pv20b99e176mVLy5rlGjhnLe2VnTFW98RDHiDChruQuCM2Izp/3Nmzcqqjhu3DjdC8XZxDtyzhjoGfmUiPmUXBjFmxvmykt1HdfXlClT1O8t07JCasG8bzq1euefK2jMmDEqtYEzmHp21RaiM0ebzPcZO3as7nGInF9MhzI1HRMEZ8NmTjsjx4wgW14gzipe2MxNZ/RKz8DbQqyCwIoIzP9fsmSJ7nGIXENTp05VEffbt28brxhBsB28IaTD6cpRYi5EZWpDclMWrRVtPm0/g1AxM6IiVxJ/V6ZGSYlewdmwidN+//59NX1pk7q4DiTmtbPiQI8ePXSNuy3EAYgLXpn33rp1a1U6UO9YRM6vkSNHqlJ0kk8ppAZcZMkSiHrnniuIxQE4S0nHnQv89eyprcTiAgxCMWVGmlS5pljhTqp8Cc6GTZx25lMyZ1vvwnB2cRDkdCnTWvSMu63EgYHNqFhbmI68q9S3F8VWx44dMXr0aOOVIwi247ffflNlaYcOHap77rmC6LizSg5nKPmvni21FFNqmJ5GJ43567Tpeo/TEwM2/E753M2bZYG/K4mlmVkd7r///a/xChIEx8cmTvuoUaNUZRS9C8MVxEUrjLhbO0hQjNSwIoHevoTE96IhYfSdeap6xyNyXrFCBW8Cg4KCjFePINgOVipiJJopHomVMnRmsVQjZyfZG0PPjpqL0fkvvvhCKU+ePMqW6z0uPtHJZw401zixeZXe8YicU3Xq1MHJkyeNV48gOD42cdqZj00HVe+icBVxYRIHCU6Z6hl2S7GcGAcJDqBMiUhKig0j7axmQOedNb/1jkfkvGIDLnZwFITU4OLFi2DDN+bsurL9YJUyfkbaSaYGxWdLabdNTrsp2q732MRUv3595bhLaUjXEX9XrikTBGfBJk779evX1cId5ofpXRiuIrbBZpSUUZeePXvGMerm4uNMA8WHH36o2tvrPS4hNW7cWOVULliwQPd4RM4pDvr8XVlxSRBSg99//101D2K6HdNDXHWBKtMIOdPLNVXshWHpkDPIYrLDDKCY70uOmMLIUsB6x+KM4mwM16KtWrUKK1euVDMJ7LHCVCCmIuk9x5XEMZ03dYLgLNjEaScTJkxQU5WuXuOWDhfzkjkY1qtXTzc/kqXXTAMFcylTUqasZcuWajrX1VqTp3ex6Zafn5/x6hEE2/Pu3Tv88MMPKv+blWXmzJmjey66grZs2aLsLB0wOtWmNUh05k22WK/OO9diWW5LSEx7ZIDKmUpC0iGfOHGi+k5YgYczBpz9LVasmOrczc/DvH3OIvBfni/sJ5I/f34ULFhQPY43PEwl4U0Qxz+uwWIPChZrmDdvnnL2nXXs52e8evWq8aoRBMfGZk77kydP1FQlnZH0MH1IJ5pTrTRoTZo0UcbcZNhp4EwDBcs6mhv95Ijvw/Jjeschck7xnGGESxBSG0ZMaafowNJp4wI8vXPSFcQocbt27VRQxdwO0zG1tKu8Bv/85z8naa0Sxe+Qsxh675+WotPMWVlWqeJn4uwuf3d+dqZrckZi6dKl6nE//fSTChqwky7L0Manmzdvwt/fX+V979q1S9kslkvkYnreBDCoxJsklrM1Ofi8QWTKCb8nrtHiwmj2qmBUm0Ur9I49LcWg2KxZs4xXiyA4NjZz2kl4eLhy3Dk48ALVu0BcTYxesdMpDSONFBt0mAYKRn0sawoz9zKp07S8IWAEhIuv9I5B5HxitIvrHATBHtDxKl++vHLeGFFlcIWOl6MsVuWNBR06OoXMw6fogCe3itbatWuVzTTZYjrZehXAkhppp3jd0rbrva89xe+LzjDXx9BB540KgztcXzZt2jT1nbITs54znhriDQBvBI4fP66Oj78hI/ycAWHwisfGY+R3x0g/Fwhz9oNRe95QsNEgg2GcNbH8rKkpprLyBk4QnAGbOu0muNCOC6HobKaHvDhq/PjxKsLA6UbTQEGjZGnwGYHIkSNHklNmaFSYfqT33iLnE6NTHMQEwV5wNpTOEwMHTGdgqgPT9zp16qTSG+0xQ0qn3N3dXY0RTLPgzSsXkjJKS2eOwQ/aUQY2GAE1LailLaWzbO0sARepmuwwP2OjRo1Uygdfm3aUjq6enbVGvPFh/rze+6am+N2xMyx/P5MDzM/FbYyec22ZnjPtaLp27RqOHTum8ub5e/LmkWsR+BvzxpJjKCP2dOxZRY03Vvy9mOrFmwCm4/C7sIVvwQg7zzWuMWLQURAcnVRx2snOnTujp8pceTrWXIzuFChQIHqwoDHgwGjpoJun0lgrNl5iRRm993UGsWkUB1JGhkaMGKGiK5zC5cDNQYiOA6dSucCXsxMc1Pk4U84k8zJdZb0EzxNeG8wdffTokfGKEYTUhzWp6STRlpw+fVqlPbBbLx1ROs2cNeR1SeeIkW6989caMUK+fPlyjBs3Tl3XdL44A0uZ0glnzpypoqpnzpxBSEiIroP3888/q8gtHTxTczJG0GkvGJmNz3HjzYnJDpvq1vM16ODyWOik8Rqk88sbB0Z86RzydfXsL0XbxMfyuXxvy/e0pXgDxe+HdpApKLyxob1gKU9G0Q8cOIDg4GDd78wVxBxzOva0+YsXL1bnI8cFjoMMeDA6bkrH4Y0ex1qeu9zHtWa8meE5zd+TMyx0+Hku8nebPHmyOid4nvM75djC7/jo0aPGq0QQHJdUc9rJr7/+qpx2DgZMG3H1uuMcnEwDBT8v01k4bckIDwcH5qbTSbVMmUlMdPo5pc3n6b2vI4nT7bxJo8PNz8pqCxzkaBw5Rc3BlIMfvxsOSsyPnDt3rop4MAJHw8rnsu4/H2fKmaRhtsyZpFGm4eX3wgGdz+eNgSNXMaIzw0jfkCFDosukCoK9oQPLPGReryZHiY7z/v371Y01gw281hjt5jVMZ5F2iA4/HSjeTNP5oUw34XR86SzxOuc1T5vF5zGKT9t/4cKFWI5ZcuXt7a3en8dFu0I7w1xt0zVGJ8wUPOHxx5duwVQMU+SaNwP/7//9P2TJkkVFe2ljaLP5Wfh/2h/acd5wmL9XSsVjY8lkHgejyRwj+LtwzKTzyZsbppry5krvu0jP4vnKtC+eV/x+jhw5gr179yoby9QhVsPhuMLvlmMEz09+vxw3WECC5xBvGPladOY5JgmCo5OqTjv5448/lPPF6AgHAUsD6yrioERDy4GCziWjqaZ9dCLpVDJKYJr+Y+SEeaWfffaZivxw6pcDn8mh53fG74rOKQcmRoEcLdLMKBcXPjEaxHx+ThlzOpsRD053Tp8+HTt27FCG1dLgJkfmOZM0zDTKNLasYsD3ozPM96bDwAGW0+qMjNFxYLSFvwGPibmWaZHLy+gZvyf+3lzgxeuAg4cgpAWXL19W9ohOut71RvExXIBoWuDIYARtEc9j3lDTAaKzSYeHM2I8xw8fPmyzaz4xnT9/XlVyYeSV1zpvIhhdNQVPeN3rXYt6orPPNUoMJvA7Ycojc635mnSc6WDTBjO1iIEJPobfCW02gwf8LmivGWRgpRX+n8fEtA/ezNDh57+mHhy0Uab0HzqUfD9+f2fPnk10gajItuKNIG/cBMHRSXWn3QQdJUYr6Iwyx5DGjHe6aZXzznQNGmdTlIhOHyMudPBMU2qMKvFO3dwBj08DBw6MHijonOs9xiRGuehsMnL1ySefqKgQI0I04vx//vz51Q0Ap/x4TI5Sqo2DFr8zfl+mKWY66vwcnMLkYM1On3pG0d4KCAhQzj2dc0bf6VRwJoSRFn6vvHHiVD1r7nMw5W/OwZe/OX8bOtT8nfS+h6SKaQL8zuhY8IbMlHt66dIldS1EREQYrxJBsC8PHz5U5z9nfZiKYnkdOYt4E8zrjIEQ2k/aYV7jyU3xMaVlcOaPARV+R3Su+Zocx+h4c9aQNyx03Gkz6PTzufv27cPu3btVwILjG+0mI788Fv7L2QxGeGmj9D6LKG3EcSE0NNR4ZQiCY2I3p52cOnVKRdzphNLAMhJBx4+RCE4DMlJjayeeRpKOJp1qOtN00kzpGpx+pEGmQ8fUCqZpMKJCR53bGFXiMdKh5lQpHXm9qjg08ObNlDjdafmYpCitbmQsxagPb6w4oDN6xJsKpvsMGzZM7WPUW8/4OYMYyaLTfPDgQXUDx9+cEXt+Vjr2HKD5eXmucHqc5wrPHzr+nFngQM0BnVE4Ru8p5gYz+sbvh5E3ntdMEeC5wXQfTt9aHgcjcoxmCkJa8fbtWxW84DXOKK/lOepM4jVossN0sHnd8lpldFzPxtF2014zyk37TkecgQg+l7OgDOIwks+FjwwCuHIeeXoXAyo8TwTBkbGr007u3r2rHBVGNRkd4YITRiDo1JhSGziFyIg8jSWdZGtSGeh4cQqXTiYjv8zJpMNkaoLEC5KRE0Y5kjp1y7vvPXv2qMUrfE1OKdO5o5PP96aDbxoo6KhZHpuji6kmzO9krioHLkaV+BkZ7WfKCXNSGTVy5khccsVKB6Z0HEbfeX7xXGWEjbMgnBKnk8/utfyuTDn7dB54w8gbVZ4/eq9NMbLPm1hBSGuYxsd8ap7reueqo4vjianMI2csT5w4ocYFpp5wVo03z6a0FUbKeWPO9BTelNM5px1nyh0XJDJSzvx2/l/vvUSuJwaiaNMFwZGxu9NOWFqJ6QJ0dhjtNL9wWFeW6Sg0oDS2jHDTsNLg0tBy8RGjvTS8/D9zAxkVYeScUXQ6UYx00vjSaCfkMCVXjJhycQuNPW80zKPsjNjqOcbWiJFb3rQkZcEuB1gOTKYZAjqAnLHgDQxvUuhoclaD6Ulc9EnHnN8Pv39+v6aFohzsmKdKJ5JRJX538VV0ENlOzBfmdSAIjgDtMQMTrFCid746smjrTHaYQRXL/T4+PtFpK6y+QhuXUDpfegxSpGcxXYnr7rgOTxAclTRx2k3QqeQAwRxAvYvIJDreXCFOQ8uINx0dGl4aYOYGplUeNVMsmA5hGiiYn0yHV8+5tkbMvWauuPmCU85CMF/SVG2EKRp8jOlmgYteecPCv7lOgDc0nObmDQxveHhjwVw9ivv4fDrsdM45U8DSnPZaNCaKK94Y8fd7+vSp8aoQhLTlwYMH6kaSs0nO4rjSFtPmmWzxoUOHdB8nEiUkztDzZk4QHJU0ddoJUw84JUsHXu8icnSxgoJpoGCkmvnvHPAYiY+v1JiemErBKDnzqrkokik9vAngnT+dcaYT8QaB0SSmC3HR58WLFyXH0gXE/HfeiAqCo/DmzRu1doM3+qzQonfeOpK4aNxkh5mmpveYlIiLTDlbqbdP5DpiWiNnvAXBUUlzp53wYmF0mLnCzuSEsvmDaaDg4iXOCAQGBqp0FFOdY6brcBEso+VMWWGKCqee6ZzTWaNDzmoEzL1nviXzobmokdF2Z18UJrJOTG3iOSIIjgZnDpk6xxk5vXPXUcR1OCZbzGCJ3mNSIhYlYDBFb5/IdcQxnbPWguCoOITTTl6+fKmmY7mgj9U09C4oRxMrz5gGCjrklvt9fX1VZJyLYJnSwnJkzMXnAEPnnM47BxhGsizr8vIGgM487/rNt4tcT0z94m8dGRlpvBoEwXE4d+6cWjvENTN6529aixWgTHaY655So8Z5arymyDHFc/2XX34xnv2C4Fg4jNNO/ve//6lFlMy/piHWu6AcRXS0TF33mFueGnn1LFXJO3+9fSLXEm/ouLZAEByRf//732pWkNWlHKUXg0mMgpucdgZJ9B4jElkrNrzjWjJBcEQcymk3wTJbjDw6cg4h01tMAwUrsug9RiSyVqyTzdkaQXBUWFWDs4Zcb8Oghd55bG8xhdAUPGEaD8s+6j3OlmKFM5b2tax8JnINsXodU1cFwRFxSKedMD2E1QBYG93RSg+yoyXLJHKgYC46a83rPc7Wstf7iOwvLkbmAjpBcHTY04EpBKzepXcu21NcLGsKnnARod5jbC2mPbLlPauM6O0XObfYm4MFJdh0TBAcDYd12smLFy/U1CcXdTpS903zrnscNPQeY2ux7nymTJnUgKG3X+TcYoSQC5fDwsKMZ78gOC6nT59WkW2WjdU7n+0hBjEYNKEdZhCFwRS9x4lESRXX1vEcFwRHw6GddsLFeVwAxXrjem3g7S3LrntcpKX3OFvrxo0buotdRa4jduNjzwJBcAbu3bunSkKyHG1a5LkzLdEUPGG6ot5j4ig0AGd2rcLcWcuw40wgQvUeI0r3mjx5spIgOBoO77Sb4MJU5rkz4qx3kdlLiXXdE4mSKy7C/uGHH4xnvCA4Pq9evVIL91gdy5453gximDpRs8GcNTn2IVe2Y0LHlug6aBh61f0Mhuy14H485TcbbFTXsWNH3X0i5xT7oLA4gCA4Gk7jtBNGuVlDlVGVtMhzd6SuexykpM22a8lUWo9VlATBmbC2u7WtNH/+/Gg7zPK5eo+JpcADGFW1GNqsuoQQ7e9Q3+Vo8LEBn3bahgDLxyZR7MsxYcIE3X0i5xTHejZ9ZNUkQXAknMppJ8+fP1dRDXYitffCzNTuupcUMYdTmn24nqpVq6YadAmCs8GFmXR0Fi5cqHtu20osUsCFoCZbnHhZXH94diuIj78Zj+M3jduCjmJkUQMMJcbBO9j8sSJRlFjelJ3KBcGRcDqnnTDPnY2JmOduzzrmKeq6Z+NcSqYLSTUZ1xOrJTFqKQjOCJvS1K5dW5WGTK3u1qyhbbLDrVq10n2MuW56T0T5zB+h5gKfGLsbchbTyxpg+LwHdgXGfnxK5UxdvUXxa9WqVejevbvxzBYEx8ApnXYTnIplnjtbbetddLZUSrrupVYupcj15OnpiRYtWhjPcEFwPtjdmikrjRo1SpVqV8yfN9lilkrVe0yMArCts2ZzP6yNxZdCY7aH+mBWOYPNnXZ2t86TJw927Nihu1/kPGJQTEo/Co6GUzvthAuSKleujFGjRqlpU72LzxZKdte9VMylNBedPb3tIucSq3Cw9CPLnQqCs8J1Gcw7t3V36z179kTb4Tp16ug+JpYCtqHTpwYYys/A2RCz7Te9Mba4tr3YKBwzpczYQAzmcDE5uxvr7Rc5l9hEi2uNBMFRcHqnnTx79gzt2rVDmzZtUsVYJr/rnn1yKTds2ID33ntP1ZXV2y9yLrVt21YtchYEZ4fpi8xzT1KgIwG1b98+2mln+oLeY8wVdHQEihoMMOT4GpVq1UeTlq3Rskl91KpSCnm17VkbrMZVneeJRBQXGLMviyA4Ci7htJOIiAhVV7VKlSo4fvy47gWYXCW36549cym9vb11t4ucT7Nnz1YVkgTBFeAMKFMKhwwZkqKqXydPnoy2w4zgW/NaV9e4IavmnBdw645Bw8di8owZcB87AgM7VEJ2Q0ZUmHlOzYDqPdcWYuSdHTb19okcXwcOHFAzOoLgKLiM025i165dKs+di5X0LsKkKvld9+ybSylyHXHGpHz58sYzWhCcH+a5d+vWTS3mv3Lliu55n5jYxMnktLPhnt5jYisUPjPLwWDIh07bAmLtu7KqvubMF8ePx6ydNU2eevfujZIlS+ruEzm+eMPJfgAPHjwwnsmCkLa4nNNOAgICVEmwsWPHpjjPPVld9yg751Kaa/fu3XatqiOyvbhOg/ntguAqMM99zpw5KkrO5jV653184oLWr776Stnh4sWLW9mjIgSnp5TWnPavMPDADbPtV7GueU5kqeCOU8YUxdDrJ7Bxak/Uq9gGHldNj0u5eAO+adMm3X0i59D333+PrVu3Gs9iQUhbXNJpJ0+fPkXr1q1VDqT10fHYSk7XPZPSMpeSKUL87Hr7RM4hphKwaYsguBp02DkbyvNb79zX05gxY6KDJ/y/3mP0dGl+Bc1pL4A+ewKjt4VeWo6GOXOi0UpflbYYdHwOenQcAvcx3yFH9vpYbUOnXeT84nnKmu2C4Ai4rNNO3r17h/Hjx6suqsyH1LsgE1KSu+6ZKS1zKXmTkhYdY0W2Ext5cWG1ILgirGXO7tLDhg1L1Fax2Rij67TDjLYnpYzk1bW0wznQbIOpQEEQjo4ugzxVp8DbYqbz+ta2yJe7Kdb7x95uS506dUp3u8hxxXQunn/0JwQhrXFpp93Etm3bVGSH3c30Lko9Jb3rnrnSPpdS5Nzi9D9LP4aHhxvPYkFwLf7zn/+gc+fOaN68eYKN4pi/brLDzGvXe0x8Cj45EWUNBhTstweB2t83z8xD01JumO4dt08GnfZP87XG5uuxt9tKly9fxj//+U8sWrRId7/IcVWvXj1cvHjReOYKQtqRLpx2wlKQFSpUUCWcrGmMlNSue7GV9rmUJjHFh102pUuf84nn3dGjR41nsCC4HuxuPWPGDBUg4blueQ0wCs8ceJMtTvKMaagftvYqioyGvChXzw1uTftg4bGYVBlzKaf90zbYkkynneMKZwVYInj//v0ql53djadMmaJsMHOjWYmkVq1aqpv3Tz/9pPs6IscT18fNnDnTeNYKQtqRbpx28uTJE9VtsmPHjmqxqt7FaVLSuu7FlaPkUrJrbE7tPdeuXQsvL68EI1oixxKdmdGjRxvPXkFwXVhaj7Ohq1evjnUNsBa7yQ5zfZL5PusVAv9TB7DvJ18E6e6PUnKddjbfYSS2UKFC0ceamFauXKn7WiLH1N69e9VvLAhpTbpy2sl///tf/PjjjyqfMr5mREnuuqej1M6l5KJYlrekMz537lyMGzcOAwcORKdOnVT7cObxm/JATerQoUOqdo0V2VYnTpxQETlBSA+wWhIX0Y8cOTI6z53212S/aJctrxFbyn9zK+TN2xKbkpHTfv78edV4z9zexifm8fM5jMozIm/5WiLHE8/HEiVK4PHjx8azVRDShnTntJtgCSd26tuyZUucCzSpXff0lNq5lHTaeedvPhgkJDrxya2iI0o7MaWLN1qCkB4ICwtTwQWmhrHTs8l+ceZT7/qwpfw9GiJHzuQvRGW6C9ehmNtdSzVr1iz6hmTAgAHaTULeRBfiihxDXbt2VbPugpCWpFunnXBVOJvYsJOq6cJMTtc9Xdkhl5JOOPMjzQcFPXEgMeWCzps3T9VKtnwtkWOKsydMGRCE9AK7WzMP3NTUjrJVszxdhfrj8LLJGOSWDwZDfjQbOg1rTpqvRbJezGE3t73m4nji5+cX/dibN2/GO9srcjwtXbpULYQWhLQkXTvt5NGjR2jatCm6dOmiKnYkveteQkrdXErq0qVLKhprPjhYiuUDTY9nhz7K/DVEjitGGznzIwjpCS6gN9kvzoimblpfKG4GBiIwKBghocEI4v+NhQKsEccN1vLmOMLup3ppMsx311toK3Iecazl78ubSkFIK9K9007evn2LESNGoFq1avjyyy+VkbW+617KlZJcSuY9s2ya5SBhEju66j1P5BzigmnOlPzxxx/Gs1UQXJ8ffvgh2obRUeI6JEdaj3P8+HE1Y8lUHo4Vbdu2VbMBjJ5z8b+5DaasSbOk459YgQRR2oprLDhDLwhphTjtZpg7v0npupdSJTWXkkaDjZ+Y0/7NN9+oCiP169ePNUhQrJIjC0+dX8yD9fb2Np6lguDaPHjwIDp4whQZDw8PNGnSRDUbu3btmu41kpriAlnmq7O+erdu3VSVGy4Q56wsnXHaY/PHs/QjZwdMdpgBIfP9IucVx1rerAlCWiFOuxE2sWFEx2Ro7eK0W5lLyYGKaRJsbV+jRg0V2eHg4enpGe2Uc4GM6dgpVsdJLGrDxaxcoMrKB3r7RY6hqVOnqupAgpAecHd3j7ZjDKT06dNHOckFCxZUHVG5SJXXAx3mw4cPKztGx1rv2rFWtJUs3cjSk8uWLcOoUaPQrl07VT+e70l72qNHD5XXbI29pH3m8bPEsCw0dR2xYhurswlCWiFOuxGWTjQNFIzu0IGvXbu22s7FQqljeOPmUp6/5Ifdu3dj4cKFqiHHd999p9IjOFBNmzZNDVJ60XMOWhxcePx8vCxwch0xysebK0FwdV68eIGvv/5a2bEiRYrg2bNnxj3Aq1evsGDBAmXfWMmDYroCZxtp+2i3y5Urp+w2nWWmrsQn5p/XrFlT5Z/zuXwvLhTl7CWdcxYnWL9+vVrAnxzbz2gs1xol1BcjNOAMdq2ai1nLduBMoMyIOoN4LjBo9vTpU+NZKQj2RZx2jXfv3qFy5crRTvuZM2ewbds2NSBwgOBAQMPOnHemnDAKM336dBV1Ydc75jCeOnVKdV1lHnx8YkToyJEj6rVZEYT11cePH4/+/fujcePG6kaBBsHNzU1NvbK5Dps6WNvNlAMRj1+vjKXIucVz8M6dO8YzVhBcE+Z1m+wwbaMenHlk5J37zbtb08b6+PiowAZtLGcn6Xhbitt37NiBY8eOqdb0KY3S64mL//n6wRe2YOKAoVh43HwGNQRXtk9Ax5ZdMWhYL9T9zIDstdxxPCj2a9hDN8+uwci+Y7He56buflFccfaHEXdBSAvEadegY2waKBhlMYeRdkZjWNedEU/mV3L6dvjw4ejVq5dagEQnm04/HW5WCdATo0CMCLFEY/PmzVWUaNCgQWqal/npO3fuxOXLl3WNhLViZN68fGWCCg3AmV2rMHfWMuw4E6g6tOo+TuQQ4k0czz1BcFVYEIDRbtrhAgUK4N69e8Y9cfn999/RsmVLVVnJEftP8GYi8Ig7mtXojtUXYkfqAw+MQtVibbDqEreHwnd5A3xs+BSdtqXNItRgn5XoVrUhxu27LuOAFeLaBpbiFYS0QJx2DTrdJqedZZ0sYfSGDvfMmTN1L2JH0cGDB2NFnvSjPLcRcmU7JnRsia6DhqFX3c9gyF4L7sdtH21KVDfPYs3Ivhi73gc39faLokWHnREeQXB2GCTRc8jN1+VYUw+b3a3Hjh2r8s1NfSgcRSEXVqBVoZIYuMeiN4e/J7oV/BjfjD8ebfOCjo5EUYMBJcZ5x36sHRW49weULuCGuacl4p6YOGPOxciRkZHGM1EQ7Ee6cdr/97//Gf8Xm7Nnz0YPFKzSER+//fab6srXs2fPVJlOtbXii/LcDjyAUVWLoc2qSwjR/g71XY4GHxvwaadtCDB/nL0U7IOV3aqi4bh9uB6qs1+kxGgic33fvHljPCMFwTlhOcf8+fOrtECmuhDaZ67fMdliphpaCxfk04ky70eRpgq5gGWNcyNH3cW4EMum3YT3xPLI/FFNLPCJyWEPOTtddc/+vMcus8faWaGXsKJhTmSvPh0nb+rsF8US10Mk5RwVBFuRbpx2pr0wxcXS6TEv88hcyIR4/fq1SmnhYiXebetdzI6geKM8t/3h2a0gPv5mPI6bDHPQUYwsaoChxDh4J6GhiE0VuBc/lC4At7mnJeKegLjugWsnBMGZYbdTk82lmGLIBaamv1naMalwwSdTayZNmhRrtjEtFLinH74yfIhaiy/FTjcJ2IbOnxnwYe3FuGTmzIf6zEK5lDrtQWfgMbwlalZrirF7jOlCN89hx/azCTb2M5ffajd8bMiLDpv9dfeLYsQynjxnBcHepBunnVF0DghMc2Gr6bCwsFhd91hK0drprpUrV6rX4WIUvQs6TRVvlOc2bnpPRPnMH6HmAp+YwSTkLKaXNcDweQ/sCoz9ePspFJdWNETO7NUx/aRMz8YnrldgsyxBcGYSavVPsXNocnj8+LGy8wzEBAZaBizspevY2j4PDIZSmHgydgGBgG2d8KnmnJefcVbNcpq23/Qei+La9mKjjsV6vNUKPIyJdQujeMVSyKm9Tq7Wm3Fd2x50aCjKVZuGMyE6z9FRqM9sfKs9P2u95fCVWc8ExVQuFn4QBHuTbpx2U91ck4oWLapKg5n+ZhWYpMAKM1ygytJeehd1WineKM/tAGzr/BkMH9bG4ktm5cVCfTCrnCFlTrsNojy3/VbD7WMD8nbYDH+9/SI1E8SbS0FwZmhrzW2xpRgxZxUZln9MKlzMyupeTF+gjda7jlJVgTvQ/TMDDDmaYUOsZnlBODqiqObMG5Dj60qoVb8JWrZuiSb1a6FKqbza9qxosDr+8pDxKwC7B5RCsW6e8A+9glX1s8JQdqrmqAdiz6Ca6LDG1/rFpQFe6JjXAEP2RvBIRnfu9CR2vmVlOQb/BMGepBun3bwttqVYztHPz8/4SOvhYqq6deuq5h+Okecef5SHU7OdPjXAUH4GzppHXm56Y2xxbXuxUTiWnFxGG0V5ePMw+1sDDFnrYbmv1CzWk6nT4q+//mo8AwXB+WADIz07bCmu4WClrvv37xufaT28MeC1wnx3vWsptRRydoZKdTF8ORAHbpjvu4o1bppDbSgAt+6DMHzsZMyY4Y6xIwaiQ6XsMGSsgJnnkl4PPvTCYtTNUwIjj3KGMgiHfigMQ76O2HpkDtp1WQqfpKQ8BhvHAkNhDD3s+Ou20lqs989yz4JgT9KN026ZR6knXoTMGY5v0aoef/zxh6p0wEWqrPmrd3HbTfFGeVihYISqUGDI8TUq1aqPJi1bo2WT+qhVpRTyckq0wWpcNXu8dbJhlEd7La+OjDhlRyMPyamMT71798bGjRuNZ58gOB/sPKpnf+MTmxQ9fPjQ+Gzr8fX1Vf0N6PjrXUupoRsHB6MQ7ezXY2LWDVGhPpjJGc18nbAtwGz7baPdLP5jMoImITgz/VtkLTQIB403CL6LqyKToRCatesftwhBYuIxfmPQbHAetPN0vDKajiaWamYwUBDsSbpx2hPLozRX/fr1Vd10lhSzFjZaYp47y5npXeD2UPxRntu4usYNWbV9Bdy6Y9DwsZg8Ywbcx47AwA6VkN2QERVmnouVZ2mNbBrluR0M77HFtQHDgMJDD+vsF1FsysVUL0FwVszXEiUm1mF/9OiR8ZlJh85+kyZN1DXD5kt615QtFbi3Hwoqp300fjJ3wkNOY0ppbftXFrb56jo0z5kFFdxPIVhtC8X1ExsxtWc9VGzjkXAgJeQcZlfKghxN10WnFAZs74LPDFnx3aILcex56PUT2Di1J+pVbAOPq7H3RekSFlQwaDY4J1puksBJYuLNJ8f8pAT5BCGlpBunPbE8SksxV52dUpMCawVzSpZ34HoXeWor3iiPNhD4zCynGeN8cRp4XFlVX3Pmi+PHY0ldAGrjKI86xm+U056nnX2ntJ1JLDPGtAHm7gqCM/LgwQNdm2suloRcuHAhIiIijM9KPrxWhg0bptYw0dHSu65speAT41GCNtjSOdcc4vl0iAv0wZ7otUOhuLS8IXLmbISVKiUwCMfn9EDHIe4Y810OZK+fyOzn1bVomC1qAWtU1a1g+CxtilyGD1B53oVYs5xBx+egR8chcB/zHXJkr4/Vek57yBm4lzFoNrgA+sSpPCbSE7ukc9GzINiLdOO0W5tHycWlXMCUXO7evYs6deqoGsRcrKJ3oaeW4o3yaA726SmlNWP8FQYeMG+0dBXrmudElgruOGWMiicejTHK5lGe27i0oIJy2nO23KS7XxSlhg0bqv4CguCMsHSunu01iQtRWVLX1qxbt04FVbZt26Z7XdlE/hvQLIcBhk9aYmOsFMWrWMucdvPUxaCjGF0mD6pO8bYodXsdW9vmQ+6m6+Msyr/pexges6dj3dmbCDoyHEU0e1lh/iVtXwgubRqGzv0GoVZ2Az5puUF3Qf/1rW2RL3dTrNdbaHrjIIYUNsCQuRoW++rsF8URu5BzFl8Q7EW6cdqtyaPkVCzLhqWU8PBw9OrVSzlX7LCqd7GnhuKP8mgO8Xw6xLEjKKGXlqNhzpxotDIq99yqaIxJto7yMHLvXkY57QX67NHZLzKJZR+ZpysIzkqRIkV0bXCXLl3w9OlT46NsD9cdMc99+vTputdWihV6EYtrfajZsTJwP2M+2xiMkxPLatsLop+ywTdxZl5TlHKbDu8gs+cr0Wn/FPmMi/pN24NPu+Mb2vc8bbDeL9QYJDGgYJfpmDaoFVoO3QzfoDOYVj4jDFmrYuiiRZjQ63u4H4mx+XTaP83XGpuvx7xutK6uQYOsBhjKTsSJtOrZ4WTiQucWLVoYzy5BSH3SjdOeUB6lLadiTTDPja9Zvnx57N+/X/eCt7nijfLcxtW1zGnPgWYbTLmKQTg6ugzyVJ0Cb4sFUPFFY1I1ynP7Bg4OKawNaplRbbGvzn6RSQcPHlRT/YLgrFSsWDGWDS5QoIAq82iP1vCsRuPm5qYa7nFc0LvGkq9Q+K5siByGj+G2yi/WvlC/rehVVHOo85ZDPe39m/ZZiGO6ZXajnPZP22yJ5bT7b2yJnIYPUG7icRUoCT41BeUyGjSbmR8NRm+Dr6rUFQKfRW7a+2vb89bGCE/fWLOeymn/tA226Djtgfv6o6BmfyvPOGPMrxclJlaNY+nH5JQnFYTkkG6c9vjyKFNrKtbE8ePH1ZQsp9D0LnqbKt4oj2bgT05UrbIL9tuDQO3vm2fmoWkpN0z3jlvaSy8ak+pRHpZEa8CSaGUx8YRFuUpRLIWGhqq27TynBcEZYbUtkw2uVKkSrly5YtxjH9gZe/Dgwfjuu+9w/vx53ess2bpxEEOLZ0Ku5h7ws9wX4o9TB/bhJ9+ESirqOe1XsaFNHhjyd4VndNAjCJeOHYNPoGWJ3Bu4+NNxXIoTwU/IaQ/CkWFFkOmzLmavL7JG7Oh76NAh45klCKlLunHa9fIoU3sq1gQvbDb7GDRoEIKDU9MhjT/KczvUD1t7FUVGQ16Uq+cGt6Z9sPCY/mIjPcOe2lGe24H70L+gAZkrz8AZmZpNVIwSbtmyxXiGCYJzwfK6tME9e/ZM0wY1a9asUeuYWC1M7zpLrgL2DEapXJUwJVkdnv2xuVVe5G25KXrG8uZJd1TJWRS9t/knoYxuXPlvboW8eVtik4VjHuq3Hm2/KIAOHkkp0yuiZs+ejeHDhxvPKEFIXdKN004KFy6sBgp7TsWaePnyJbp3767Kj12+fFn34reJEoryaE61/6kD2PeTb4KdSuM616kd5bmNoCPDUCTTZ+jiKaXGrNGKFSuUwyMIzgjrW69du9YhyuWZSvfNmjVL91pLnm7izMLWKFNrHA4nudO0Pzwa5kBO00LUUF94tCuF6iP3ISDOY5Mmf4+GyJHTIkUx5CLWdK2AumMPpPj106NOnz6t0mAFwR6kK6edTTrSYirWBAeoefPmqZQcTqfpGQBbKGVRnrjRmNSO8nAWYH1b7Waqgwd8Qy32iXTFc7h48eJJLksqCI4AF+s7EuwyzP4cbF5mu+7WwbiwYTjadhgLT1/rSuCG+h/GssmD4JbPAEP+Zhg6bQ1O3riN4BspnKEN9cfhZZMxyC2fmh1tNnQa1py8gdDrRzC/Xwf0XXBUpU3qPleUqCpXrqzOG0FIbdKV006HOS2nYk0cPXpU5SQvW7ZM1wCkXCmJ8lhEY1I7ynM7BBfXdEWFumNxIFanQFFiopPBahiCIKQcdrdmqd569eqpdU5611xyFHr9NI5esjKAEnpT1f0OCg5BaHCQ+r9tFoWG4qb2WoFBwQgJDUYQ/x+s3Qxc+QnHr8gaopRqyJAhavZeEFKbdOW0OxJcTFi9enU1TRwSktRGRNYo6VGe+KIxqRXluR16HUfm90OHvgtwNBk3F+ld48aNw4wZM4xnlCAItoDOF9Nldu/erXvdiUSW2rx5M9q0aWM8gwQh9RCnPQ1hmSguhm3WrBn8/CwWjtpISYryxBON0X9sUhTP6wZfwU/Hr0h5sWRq3759KiooCIJtOXXqlFqgytlZvWtPJDLXzz//rEo/Olral+B6iNOexnAxLBdAsW7xkSNHdA2CSKQnztCUKFECjx49Mp5NgiDYinv37qmSkP369bNhnrvIVdWqVSuV+ioIqYk47Q4CG+Ywz33VqlW6BkEk0lO3bt1UW3ZBEGzPq1ev0KdPH9WMyZ7drUXOJ6Yqjh492njmCELqIE67A3Hz5k1UrVpV1XxNnTx3katp6dKl6Nu3r/EMEgQhNWBzvHLlyqmUNL3r0Fl0+PBh3e2ilOvEiRNqxlwQUhNx2h2M58+fq8YjLVu2hL+/1CwXJSxfX1+ULFkSERERxjNIEITUgE4Zu1svWrRI91p0dK1fvx5/+9vfVF1xvf2ilItlpVlkQhBSC3HaHRA6YNOmTVM15Y8dO6ZrHJxFx48fx6hRo3T3Wa8A7BzSABW+zoP33/sb3vu/9/HRJ3nxad7syPKv95Hpk6KoO2A5Tt3Qe67ri3m3bNglCELqcufOHdSuXRsDBw5M5e7WqaM9e/bobhfZRjwvVq9ebTxbBMH2iNPuwHAqlnnubLWtZyCcQQsWLFCRYFss5Ao5NQ5fZsiAv5ZbgEvGbaHXj2Jus+zIoG3/rNeudNkg5Mcff1SttAVBSH3Y3bpHjx5o1KiRmunSuyZF6VMbNmxA+/btjWeKINgecdodnBs3bqhua1zgwmk3PUPh6LLVcQd4tUNWzTnP3XsfbphtD/WZiZJ/zoAMeQfgYDqMtrOedMOGDY1njCAIqQ27W8+fP191t2YRAb3r0tHFSieSKmNbBQQEqNKPbNQlCKmBOO1OwLNnz9CuXTvVvCE957lfmFMGf8nwf6iy9ErsfRfnovRfNKe96GScDjHbnk7ERcvFixfH77//bjxjBEGwB0xfZJ47F4TrXZuOLN5wNG/eXHefVQrYiSENKuDrPO/jvb+9h/97/yN8kvdT5M2eBf96PxM+KVoXA5afihVgSQ/id+rt7W08QwTBtojT7iQwz33SpEmoUqWKyhPXMxaOLjqXHTt2xK5du3T3J6wAbO+YDRky5MOgw+apNkE4Maks/i/DB6i32AchsZ6TftS5c2fs3LnTeLYIgmAvOJNYo0YN1cremap+MbWHM7l6+6xXCE6N+1Kzy39FuQWmkpihuH50Lpplz6Bt/wy9dgWaPd71NXXqVIwdO9Z4dgiCbRGn3cmgw8s893Xr1ukaDEcWB7T69evDw8NDd3+CCvXBzJJ/QoZ/1sbqq6btN3BqWVcUef8DlOm3GZfSYZTdJFa0GDBggPEsEQTBnjDPnT0TmjZtiitXLGYCXVoB8GqXVXPOc6P3PvMbgFD4zCyJP2fIgLwDDqaraPtPP/2kSjcLQmogTrsTwrw5Vpbh3fytW7d0DYfLKcAL7bJmQIYCw7F5fnc0rFkBJYuXR43mfTDN6wKC9Z6TjnThwgWUKlVKddgVBMH+MM+dC8JZ9s/Z6qGzEs6UKVOSMZ5cwJwyf0GG/6uCpVdi77s4tzT+ojntRSefTnczoKzpz0pDgmBrxGl3Up4+fYrWrVurlerXr1/XNRyupNAzU1BUGwAMDdfD7/xC1M2oOfDvlcUEb2kvblKtWrXUmgdBENIOOuycDV2+fLnudeqIYqWyTJkyJb3EcMB2dMym2eJ8g3A4yGx70AlMKvt/yPBBPSz2SX+NAvv166dmlAXB1ojT7sS8e/cO48aNQ7Vq1XDy5Eld4+HI2rx5s8rRtyav8trGpsikOe0Fx5xA8O0Q+Cyqi4za33+v4I6TwfrPSW8aOXKkqmghCELawsg17fKwYcOcJs+dM7h62xOSqtz1pwz4Z+3VuGrafuMUlnUtgvc/KIN+my+ly3VGdNi5zkgQbI047S7Atm3bVAWDTZs26RoQRxWjOlyYmniTkmCcGFMQGTIY0HD91ahtIT5YVDejtu2fqDLzdLpPj6F27NiBJk2aGM8KQRDSkv/85z/o1KkTWrRogatXjXbLxWQqw1tg+GbM794QNSuURPHyNdC8zzR4XXC+5lO2Eme/v/76a7x588Z4NgiCbRCn3UVgWgRzKSdMmOCCee5XsbrO+5qDXhBjTsQMBCE+C/GdIQMyvF8ds8+k3wHCpJs3b6qBIiwszHhWCIKQlnCNyfTp052uuzUb+q1cuVJ3X4xCcWZKUWMwxQ/nF0bNfr5XdgK8zVNl0qkaN26MU6dOGc8EQbAN4rS7EE+ePFE1Yhm9Ts5UZ1or3mnkGwfQL6/mnBvcsC66cgwVgnNza+B9psmUG4dj6bCxkqU6dOig8lMFQXAc9u/fr/Lc2eJe77p1NHGtFG2J3r4YXcPGppliginRs59/RwX3k+l+9nPy5MmYOHGi8QxwDH7++WfVnVxwXsRpdzH++9//qrb21atXd6pud6x1XLZsWVWLPs6+8zNR+q+a067X8TTIG+NLv6cNFH9CgT47cM18XzoUc9pZL9qR4NoLQUjvcO0O1/Bw7Ymjd7e2arY2+ATGFGQwpSHWG4MpIT6LoooE/LMKZp5O37OfXJDM+v2OAqsb8Uasa9euxi2CMyJOu4uyZcsWlefOf/UMiiOKJcfYWjtmWyAOTOiAuiWzaE65NhBkyIhi37VE79nHYtX9vbF/EL78E/f/AwUaTsKRdBxxP3funLr5oYF2FNhshA6LIKR3mLrGKHarVq2cv7v11dWo875mdwuOwYnoYgAh8Fn4HQyavX6/+mycScdFAnjjwzH4119/Nf76aQtTdb744guls2fPGrcKzoY47S4Mm3yUL19eTdPpGRXHVyhuXL2MK1cDcCM4BME3AnDNT/v7umUEJwSBfn64FhCAq1evp/tpWc6yMD3KEbh37x6++uortSBPEISo7tYMUFSuXNkpulv7abaV+dmXL1+Otf3GgX7IqznnBrd1MZVjqJBzmFuDa5D+jnLjYgdY0pt69eqFjRs3Gn/5tIPnXL169aKddjc3N+np4aSI0+7iPHr0SHXp69Kli8pn0zMsjqpr167pbhclLJaZY4dUR4BdWk0DBdO1BEGIYvfu3U7R3frixYv49ttvceLECbPtoTg/szT+qjnteh1Pg7zHo/R7GZDhTwXQZ0f6teNcw8BOuWkNK8yZ7LBJ7K4uOB/itKcD3r59ixEjRqjmO5wW0zMujiZWLsiSJUuc6I4ocXl5eakFyWkNp//NB4n69euriI8gCFEEBgaqyjJjxoxx+Dz3aAUewIQOdVEyC1MSNWUshu9a9sbsY+b9Nm5g/6Av8Sfu/0cBNJx0xGxf+hFtICt6cQxOK/744w9102Vuiymed1KS0vkQpz0dsWHDBpVjR6dOz8A4kjgrMHfuXN19ooTF6gDFihVTdaLTCubUs2Ov5UCxfft24yMEQSDsbt2mTRu0bdvWObpbh97A1ctXcDXgBoJDgnEj4Br8tL+vW+avhwTCz+8aAgKu4mqclMb0o4YNG6ZpDvnixYvj2GGT2LVXcC7EaU9nXLp0CeXKlcO0adN0DYzINdSuXTscPHjQ+KvbH9ak1hskGPFh5EcQhBhYYYnlAatWrWqRhuJ4Gjt2rKpAordPFFf8Xd3d3Y2/tH35/fffVaRfzxZTxYsXx7Nnz4yPFpwBcdrTIQ8ePECjRo3QvXt3p8lzv3DhgqpAordPFFdz5sxRue1pAR2Q2rVr6w4S1NKlS42PFATBHHY1Zp47Z0X1rmtHENfL0HHX2yeKKwZPaA/TgvHjx+vaYHOxUIXgPIjTnk5hLtsPP/yAOnXqqDKBesbGkVSkSBG1mFZvnyiuzpw5oyoHpQWbN2/WHRxMYuSHKQGCIMSFC/ArVqyoHC7X626d/sS1CrwRY7DMnvC9CxYsqGuDzfXll1/i7t27xmcJjo447emctWvXqrrejPBYGhtHEjsKsvSY3j6RvlhSzt710V++fIlvvvlGd3AwFx0SQRD0YVpDy5YtVTMcR+9u7TQLaNNQPXr0UD1T7AnLTerZXj3179/f+CzB0RGnXYCPj49ytGbOnKlrcETOKXZGtfdCo3nz5ukOCpZiBIjHKAiCPuxuzaoy7LvAxjiW17cj6Pz58/j0009V+UC9/aIorVixAj179jT+sqkP167p2d2EdPXqVeOzBUdGnHZB8e9//xsNGjRQhoXVR/QMjyPJ0WcGHEGM7LCCi714+PChSmPSGxD01Lt3b+MzBUGID09PT1X1i2lnetd5WopR9kGDBimHT2+/KEpsdMhFn7wRS21YuatZs2a6NjchsUuvI3XSFvQRp12I5vXr1xg4cKDqnMaFn3rGxxHEZiTvvfee09ScTytxkTFLPzJlxR6MGjVKdzBISL6+vsZnC4IQH0wNZOUlLhqUPHfnFPtUcFxNbWjv2bjLUiNHjoy2u6NHj9Z9THh4uPFVBEdFnHYhDmxsxHQZXsR6xscRxJKCettFscVI+5EjR4y/bOoRHByM/Pnzx3LIrRGbQEl0RxASh92tGUHt3Lmzasqkd707ghw9Bz+txIo7M2bMMP6a9mfr1q3Rdlf6ZTgv4rQLurD6CBeosnSgngESOYe4ToER8LRiwoQJ0QMFG8cIgpB82FmT13PNmjWVjda75tNSTHkrVaqU7r70rn379qlZ7LRCnHbXQJx2IV7u3buHunXrok+fPg6d575nzx789NNPuvvSu06ePKnKx6UV4rQLgu3ZtGmTynNnvrvedZ9W8vb2duga82mpkJAQlChRQs2YpAXitLsG4rQLCcLulX379lWLVC9evKhrjNJadEqZBqK3TxT1/XDASAvEaReE1IHrQdjdmt029a57keOpW7du2LZtm/EXtC/itLsG4rQLVsEulsxz37t3r64xSkv5+/urnGq9faLbanHxqlWrjL+kfRGnXRBSD1Zsaty4sXIGHbG7tSOm8KSlOI4yCJYWiNPuGojTLlgNUy04JTt//nxdgyRyTHG6un379sZf0b6I0y4IqQvz3IcNG6Za5TtSd+vLly/jn//8J5YsWaK7Pz2K9dNLliyJiIgI469nP8Rpdw3EaReSxC+//II6depgwIABuHnzpq5hSkuxAyhLW0mXvhix0gRLP7569cr4K9oPcdoFwT6wFC6LB9Ah07MDaSHm3jM1T29fehXHT97Q2Btx2l0DcdqFJMNarmyR3KhRIxU50DNMaaX9+/cje/bsOH78uO7+9CqWVuR3Yj2RePnLRezb4IGd5+8iPNK4OYmI0y4I9oN1wJnnztKCenZAlPb68ccfMXv2bOMvZj/EaXcNxGkXkgVray9cuBDly5dXjrKecUorOUNHV3tr2rRpqk6wVUQ8xbmF/dB14ARMHdEUBQyfwG15MN4YdycFcdoFwb7cv38fbm5u6NGjh5p51LMHaSHm3B88eFB3X3oS+580bNjQ+GvZD3HaXQNx2oUUwVKLzHNfvHixroESOYYYZa9SpYrxV0uIcFyd9R1Kfb8Pj5h2GfkYu1pmh+GL/jibjMaq4rQLgv158+YNBg8ejO+++w4+Pj66NsHe6tevH/Lly5fuUxeZLlS8eHH8/vvvxl8rIWwz40nEaXcNxGkXUgwNEZt9cJBwtCounA2g9PalN3FWhP8mRNjpwSiSvRIWBL81bnmDGzNKwmD4BvND3hm3WY847YKQdqxZs0ZV/dq5c2cce2BvMerPYgZ6+9Kb2NWWv0mC2HDGk4jT7hqI0y7YhJcvX6J79+5o0qQJrly5omuo0kLff/+9yr/X25fexEjX2rVrjb+YDm9DsLhqZmR124L70RGdCNxbXUlz2gti6MVw4zbrEaddENIWVpSh4848aj27ILK/Fi1apIo5xI9tZzyJOO2ugTjtgs1gnvu8efPw7bff4tChQ7rGSpR2YnWJTp06GX+tuLw82x/5DR+h0bZHiJmFjcRvHpXFaRcEJ+bXX39F/fr10bt3b4dZ88PeEaxspbfP1cUFw6VKlUJkpH6+i61nPIk47a6BOO2CzTl69ChKly6N5cuX6xosUdqITjNLPzLfNS4vcbb/F9qgUBVr7pnXEH6LkHllte2lMCvINIBYjzjtguAYsLt1//79lfNOp1HPRthLtAVZsmTBrFmzdPenBzGllI0B45AKM55EnHbXQJx2IVXgYqPq1avjhx9+cJg6vRyoaCjTesBKSzF9iXmlcXjzM6aXNGiDQi6UqdUQLdp3Rfeu7dGiYR2U/0zbnq0l9ocZH5sExGkXBMeCwRSmy+zZsyeWbbC3fH19dbenF7GfCBsVWpIaM55EnHbXQJx2IdV48eIFunTpomqE+/n56Roue4rHwMYWZ8+e1d2fHjRlyhRMnDjR+AuZEXYArbIZYCjcCkPGT8PcpWuwZvk8TB/XB7U+MSBj9bX4NRlN/MRpFwTH49SpU6oRk3S3TjvRcWYQJTapM+NJxGl3DcRpF1IV5uzNnDkTFStWxJEjR3SNl8h+4m/AGRBLIn9bi8oGAz7vf1YbNsx4uhctNGe+7JwgbdhIOuK0C4JjcvfuXRXE4AL1tO5uzcWy6a0hFL9zpiuGhZlNYabSjCcRp901EKddsAsHDhxQee5cfKRnwET20a1bt1SE7d69e8ZfJoqIO8tQQXPai467itfGbSTsSCfk/rA6VtyJWvwU+cwHS0ePwo9DuqKpWw8s83tuNoUbF3HaBcFxefXqFfr06aOa/Thad+v0oA4dOmDfvn3GX0MjlWY8iTjtroE47YLdYGShatWqGD58uMPkuadHcZDesGGD8Vcx8mgzqmtOe6FRvnhl3ITIR9jVJjdyt92Dx/TMIx7Aq1VpdD/0RHPUI/Dblkb4OG9fnHkR9XA9xGkXBMeHzfHKlSunHEg9myFKHTE9aciQIcZfIfVmPIk47a6BOO2CXXn+/LmKLrRs2VKtnNczZKLUFRuudO3a1fiLGAk7qCI8uToew3Pjpjc3ZqNivu+wLCRqmIi4uwoVPnHD7mfqT+DJdtTJmBeDL0a7+XEQp10QnANvb2/V3Zo1xPXshsj2YloQZz5ZLpmk1ownEafdNRCnXbA7ERERmDp1KipVqoRjx47pGjNR6ok3S19//TXevjWL2by7jcWVDDAUGYPL9MHf/oJNHb5F69Uh0R34Xp7pjdy5uuGkKbL+ygcD8mRCg2gvPi7itAuC83Dnzh3Url0bAwcOdLju1q6qatWqISAgIOoHSKUZTyJOu2sgTruQZuzdu1flubNLp54xSyuFBpzBrlVzMWvZDpwJDNV9jLOLOaysohNDJJ6dHIGSGQ34rEpztG7VAaO2BsG8uFjYoZbI9vkQXDKNJm+uY0IBA6pte2rcEBdx2gXBuWB36x49eqBx48a4fPmyrv1IfYUi4MwurJo7C8t2nEFgqN5jXENDhw5VsxuKVJrxJOK0uwbitAtpyo0bN1C5cmWMHj1a1XbXM2qpoeALWzBxwFAsPH7DbHsIrmyfgI4tu2LQsF6o+5kB2Wu543hQ7OfaQzfPrsHIvmOx3id1qjqw7CPLP1oSEXYHV/1u4rFO/6WwQy3wcd4B8Il22v0xtmBWNE6gnIE47YLgfJh3tz548KCuDUm5gnFhy0QMGLoQx2+YbQ+5gu0TOqJl10EY1qsuPjNkRy334wiK9Vx76CbOrhmJvmPXw+em3n7byNPTU5VFVqTSjCcRp901EKddSHOePXuGtm3bok2bNrh27Voco2ZrBR5xR7Ma3bH6QuzFsIEHRqFqsTZYdYnbQ+G7vAE+NnyKTtsCYj3OXgr2WYluVRti3L7rCNXZnxJxIK5Vq5bxF7COVxcHI1+WxjElx16cxvf5CmGsv16H1SjEaRcE54XdrZnnvnTpUl07knwF4oh7M9TovhoXQmJvPzCqKoq1WYVL3B7qi+UNPobh007YFmD+OHspGD4ru6Fqw3HYdz11gkpBQUGq9ON//vMf7RtPnRlPIk67ayBOu+AQMM990qRJqFKlCo4fP65r3GyhkAsr0KpQSQzcExh7n78nuhX8GN+MP46bxm1BR0eiqMGAEuO8Yz/Wjgrc+wNKF3DD3NO2jbiz9CNTk+7fv2/8Bazg2T40+/hzDPWNGi0i7ixGxWI/IKFZWXHaBcG54QxojRo1bNjdOgQXVrRCoZIDsScw9j5/z24o+PE3GH/cZO+CcHRkURgMJTDOO61y7AOx94fSKOA2F6dTKeLOoBUDKSZsPeNJxGl3DcRpFxyKXbt2KWdy/fr1usYtRQq5gGWNcyNH3cW4ECtH8ia8J5ZH5o9qYoFPTDQl5Ox0lGX5rR67zB5rZ4VewoqGOZG9+nSctPGAwbzVzZs3G795a3iLWx7tULxSf6zavgZjO7bFlNNPE6xaIE67IDg/zHPv1q0bmjZtiitXrujaE2sVcmEZGufOgbqLL8SeQbzpjYnlM+OjmgvgE22fQ3B2OjuBfo4euywCLXZU6KUVaJgzO6pPPxkd1LGl5syZg2HDhhm/7YRJzownEafdNRCnXXA46NyxsszYsWNVRFjPyCVHgXv64SvDh6i1+FLswSJgGzp/ZsCHtRfjkpkzH+ozC+VS6rQHnYHH8JaoWa0pxu65HrXt5jns2H7W6hxNv9Vu+NiQFx0227ZE5sqVK9GzZ0/jt249754G4fzJK7j7MrEiY+K0C4KrwDz32bNno0KFCjh8+LCuTUlcgdjT7ysYPqyFxZdip5sEbOuMzzT7XDuWfQ6Fz6xyKXTag3DGYzha1qyGpmP34LradhPndmzHWavXK/lhtdvHMOTtgM3+evtTptOnT6N8+fLGbzoRkjHjScRpdw3EaRcckqdPn6J169Zo3769cvb0DF3SdB1b2+fRjH8pTDwZe5o1YFsnfKo55+VnnEWI2fab3mNRXNtebFQyy1IGHsbEuoVRvGIp5NReJ1frzWrACDo0FOWqTcOZWLmc8SvUZza+1Z6ftd5y+NqwioKfnx+KFy+O//73v8Zv3faI0y4IrsWhQ4fUbChv+vXsSoK6vhXt8xhgKDURJ4PN9wVgW6dPNftcHjPOmqfg3IT32OLa9mIYdSw5KYKBODyxLgoXr4hSOQ0w5GqNzde17UGHMLRcNUw7Y226j3bzMPtb7Tiyot5yX5uvMaJYkIGFGRIn6TOeRJx210CcdsFheffuHcaNG6fq2J48eVLX0FmtwB3o/pkBhhzNsCFWpCQIR0cwZ9KAHF9XQq36TdCydUs0qV8LVUrlVUa6weqrZo+3VgHYPaAUinXzhH/oFayqnxWGslM1Rz0QewbVRIc1STD8AV7omNcAQ/ZG8LBxlKd+/fq4cOGC8Ru3PeK0C4LrwRrutMtJ7W4duKM7PqOtbbYB/ub7go5iRFGDZm9z4OtKtVC/SUu0btkE9WtVQSnavqwNsPqq2eOtVMDuAShVrBs8/UNxZVV9ZDWUxVTNUQ/cMwg1O6xJUhAkwKsj8mrHnr2RR+xjt5HYGXX58uXGbzhxkjLjScRpdw3EaRccHi8vLxXZ2bRpk66xs0YhZ2eoVBfDlwNxwLy82O2rWOOmOdSGAnDrPgjDx07GjBnuGDtiIDpUyg5DxgqYeS7pi69CLyxG3TwlMPIoo0NBOPRDYRjydcTWI3PQrstS+MSKMiWiYG+MLW7QjrEwhh4Oirs/BeJN0fTp043ftO0Rp10QXBNWO+nUqRNatGiBq1etCWyE4OwMproY8OXAA7hhvu/qGrhlNcBQwA3dBw3H2MkzMMN9LEYM7IBK2Q3IWGEmzlk5Mxmt0AtYXDcPSow8qvLQgw79gMKGfOi49QjmtOuCpT5JW9gabJx5NRQeisOpUAaY64s4u5xaiNPuGojTLjgF7OLJXErWF09OnvuNg4NRiAb36zE4br6gM9QHM8tp2/NZlhQzRseL/4hjSV4AGoIz079F1kKDcNB4g+C7uCoyGQqhWbv+cUpNJioe4zcGbbDLg3aetkgVitG+fftQt25d47dse8RpFwTXJTIyUt30W9fd+gYODi6knPavx8RU6aJCfWaqoEq+TtsQYLb99pVVqK8588V/PJbkBaAhZ6bj26yFMOigsReH72JUzWRAoWbt0H/1hVipkNaIx/gNx5A87eDJFBudx6REP//8syr9yEW/qYE47a6BOO2C0/DkyRPVhKJjx46q7bOe4YtPgXv7oaBy2kfjJ3MnPOQ0ppTWtn9lEYG/ug7Nc2ZBBfdTCObfoX7YMbkv+vTtjpZ1a6HtlH3wj29qNeQcZlfKghxN10VPowZs74LPDFnx3SLLwSIUfjsmo2+fvujesi5qtZ2Cff6W9YAvYUEFgzbY5UTLTbZdjMpybiVLlsTDhw+N37JtEaddEFyf/fv3q9nQNWvW6NqZKAVib7+CUU776J9iOeEhp6egtLb9K4sI/NV1zZEzSwW4n4qKiof67cDkvn3Qt3tL1K3VFlP2+ceTZhiCc7MrIUuOplhnSikM2I4unxmQ9btFFrXhrXzdSwtQgWNIzpbYlAqLUalWrVrhyJEjxm/VtojT7hqI0y44FVw0ye6prBvMFfd6hk9PwSfGowQNrqVzrjnE8+kQF+hjVjM4FJeWN0TOnI2w0pcOdAguLHLD1609VA5kiM8C1M6aGx294ol6X12LhtmiFrBGDUzB8FnaFLkMH6DyvNhlzkIuLILb163hwfcJ8cGC2lmRu6OXscKBUSFn4F7GoA12BdDHsr68DdS9e3fVlS81EKddENIHXETJPhujRo2Kp7t1ME6ML6Gcdkvn/Pal+cohLtBnDwJN20IvYXnDnMjZaGVU7nnIBSxy+xqtPbgeKAQ+C2oja+6O8NKNel/F2obZYCg2KnqmNNhnKZrmMuCDyvNil/y18nVDzrijDMeQWGOFbTVjxgz1/aUG4rS7BuK0C07Jli1bVKc+/qtn/OLIfwOa5TDA8ElLbIwVJdGMO3PazReoBh3F6DJ5UHWKd5TTrTnN00pnR80Vfsbn+GJJlYzI3S2mFORN38PwmD0d687eRNCR4SiiGfcK8y9p+0JwadMwdO43CLWyG/BJS/MFWCE4M600stdcAT/jNt8lVZAxdzfsMh8UbhzEkMIGGDJXw2Jfs+020rJly9CnTx/jN2tbxGkXhPRDWFiYqvjF3GymNFraGv8NzZBDs42ftNwYezHn1bUqp918gWrQ0dEok6cqpnhHVY0JOTMNpbPXxAo/43N8l6BKxtzoZioFedMXhz1mY/q6s5oNP4LhRQwwVJiPS3zupU0Y1rkfBtXKrsYA82IEib6uUTcODkFh7dgzV1sMX7PtttSJEydQsWJF47dpW8Rpdw3EaRecFjb5YG3byZMn6xrAWAq9iMW1PoTBUAbuscp8BePkRDbvKIh+Kop9E2fmNUUpt+nwNi02CvBC+5w50GqrKbIeiB1dciFTjRXq7+DT7sZcxzZY7xdqTIUxoGCX6Zg2qBVaDt0M3yDN8S+fEYasVTF00SJM6PU93I9cglf7nMjRamt0ZD1wRxfkylQjZgChrq5BAy7SKjsRJ5KygNVK+fr6okSJEqorra0Rp10Q0he0I1OmTNHtbh16cTFqfWiAoYx77JK3wScxsay2vWC/qCj2zTOY17QU3KZ7R/ezCPBqj5w5WmGrKQIeuANdcmVCDQZTgk/D3bjup8167W9jKoyhYBdMnzYIrVoOxWbfIJyZVh4ZDVlRdegiLJrQC9+7H8GlhF7X+N7U1TUNkFWz62UnnohKmUwlce0WK/JYw9u3b9UMB1NqWKhh/vz5qrhA37591Y1T7dq11etxnGS+vDjtzo847YJTw1xsdunr0qWLWsijZwSjFArflQ2Rw/Ax3FbFNsahflvRq6jmUOcth3pubmjaZyGOmUe6/T00pzkfuu82RV6CcGjQZzB8u1j97b+xJXIaPkC5iVGLq4JPTUG5jAZtAMmPBqO3wVcNTiHwWeSmokyGvLUxwtMXIbf94dEgK/J13x09JRx0aJDm8H+LxVeM760pcF9/FDRkRuUZZ1JtsOBiVDrvKeGPP/7AvXv3VISNZSTPnj2LXr16idMuCOmQ3bt3qzz3devWxdiaUF+sbJgDho/dsMo8MMG1PVt7oahmN/OWqwc3t6bos/BYTKqMJn8PzWnO1x27TbY56BAGaY75t4uvaDZ6I1rmNOCDchOjCg0En8KUcppN1+xt/gajsc03KlAT4rMIbpxxNeRF7RGeyjYn+LrG92agZl//gjBkrowZZ5JWdSapGjhwIFatWmX8FqPggt+goCDs3btXNbiiXa1ZsyYKFy6MOnXqqCZ5P/74I+bOnau6iXONgY+Pj0pTun//vhon+ZritDs/4rQLTg+jDSNGjECtWrWUo6hnCJVuHMTQ4pmQq7lHdDpKtEL8cerAPvzkq1NSUXPa62fNjS47Ypz2gwM+x0d1Vmv/v4oNbfLAkL8rPKOnXINw6dgx+ARa5nXewMWfjuNSdLkwzWmvnxW5u+yIcdoPDsDnH9Uxq0kchCPDiiDTZ13MXt/2osGfNWuW8RvV5/nz57h06ZIqTcYqPj169FA3TFWrVkXRokVRqFAhVUWicePG0Y2xWMtZnHZBSJ8EBgZGd7c25bnfODgUxTPlQnOP2METKsT/FA7s+wm+OiUV/T3qI2vuLtgR7VwfxIDPP0Kd1VdxdUMb5DHkR1fPmJScoEvHcMwnMM6C0hsXf8LxSzF2PqHXNT2G6TbDimTCZ108Y6f1pII2bNiAdu3aqeZ3TF3s2rWrmgll1Lxfv34qmn7gwAEVjU9KYzxJj3ENxGkXXAYaO+a5s667njGkAvYMRqlclTDlZBK66wXuQrc8mc2M+HV4ao56gQEHcfOkO6rkLIre2+KrYpCQArGrWx5k1pz/q8Zt1z21wafAABw0DlqhfuvR9osC6KAWSZk/17ZiVMzNzc34TQIPHjxQ25jeQue7XLly+Prrr9GsWTN1g8SoDadkmaJ09+7deMuUSXqMIKRvTN2t6YjSBrDx3J7BpZCr0hScTEI53cBd3ZAns1lA47on2uQpgAF7j8G9Sk4U7b0t/opeCSje1z1ocuxD4be+Lb4o0CGqYIDZc20pphIx1ZOBENrLevXqYdKkScrO8jtMKeK0uwbitAsuBSPBdDCnTZumaxhVzvrC1ihTaxwOm6fAJCg/rKqbFfl6GCsbhJzCxDKF8P2OM/BoVwrVR+6LXVs4CfJbVRdZ8/UwViMIwamJZVDo+13G97mINV0roO7YA8l+fWt16tQpFClSBAMGDFCR87Jly6rFqXTOWaWHTnxyEKddEAR2t+bsHG0LF1syZ31h6zKoNe5wrBSYBOW3CnWz5kMPYwWtkFMTUaZQd6xb3g6lqo/Evlh9NpIg3df9ProYQMjFNehaoS7GHkhamWFrdPToUYwZM0Z9L8w7Hzp0qJrJ5GwlnXhbIk67ayBOu+By0MFs2LChKmWon+cejAsbhqNth7HwNOY6JqabJ2ahceGy6DxtCWYOaIZGQz1xJfQ2gm+kML/x5gnMalwYZTtPw5KZA9Cs0VB4XglF6PUjmN+vA/ouOGr9oJYEcWp1x44dKn+SNzl00jloDBs2zOpFUNYgTrsgCCboLDLPnbOit4MvYMPwtugwNiq3XM9OxdZNnJjVGIXLdsa0JTMxoFkjDPW8YoMZyPheNxTXj8xHvw59seCoKTUy5WITKpZ1ZNoQK8WMHDkSBw8ejPWYqVOnKmfelojT7hqI0y64JG/evMEPP/ygFumcO3culkE0KfT6aRy9lIQ0meArOLZjK/aeCbBxqkowrhzbga17zyDAOL0bfOUnHL9i2wVPdMaZOsS8yFKlSqnFpxwcGGXn/sWLF6N///7Gb9A2iNMuCII5165dU84qbcOtWyG4fvooLiUhTSb4yjHs2LoXZwJsm6oS93U1u/zTcVyxQcWu4OBgNWvJFMRvv/1WOeRMe9F7LPXTTz+p6ju2RJx210CcdsGlYYc+RpEZVdYzjulBhw4dUiXAuJipQYMGqoGH3o3MxYsXlTPPSgW2Qpx2QRAs+f3339GyZctkdbd2JjFdkyUYudaqRYsWaoZBv/FUXHEGlP/aCnHaXQNx2gWXh6WvvvnmG8ycOTOOYXRV3bx5E8uXL1eLmVinlzn+LMOo91hzsULB1atXjd9cyhGnXRAEPVj5hBHn6tWrR8/2uYqYAsOqL8WLF8fgwYNx8uRJ3cclJM6Irl271vhtpRxx2l0DcdqFdMG///1vFWVmPVvWu9UzkraStZGU1BAdcw6EzBtlJIsNN5JyPMyvnDdvnvFbSznitAuCkBCenp5J626dTNGRji9VMjExkKG33VJ8/e+//17N7rKEbkpmETw8PNCpUyfjt5RyxGl3DcRpF9INr1+/VgsvGX22JursTDp//ry6IWEKDHP5kxu52rlzp6pcYCtS22l/9eqV8X+CIDgrrEnOXG+WPLx165aubUqpmG7CFBW9fQmJzeIYNdfbZxJL3w4ZMgQlS5ZUVXISbvRnnWgv2cWU67NsgTjtroE47UK6Y+XKlSpdhnXI9YylrcQIN409u9jp7U9IXLi0b98+3X3m4mDHMmF01sePH5/i/FC+L+uxP3v2zPhtpQxbOu3/+9//1GJaDj78zDVq1FBrFgRBcH4ePXqkapR37txZNWXSs08pEdfssOW/3r6ExBuJAgUK4MyZM3H20TmnjaP9pU2iPbZ8TErEAApTa2yBOO2ugTjtQrqEBphTmGz7rGcsbSE6wKxew7xEvf0JacWKFapkpd4+ioMFIzqM7DDCw0iP3uOSIy4O442GLUiJ0/7HH3+o9QisasObH35W02tR3333nar/LAiCa8Du1kzRY3drPSfZ3qKTz8X5tDeW9phBFZZt5Pbkpt0kJt4w0M7bAnHaXQNx2oV0y71791TZQzYRSu0896SI08OMIjO6w8ZGlvvZ2po3HBwszp49G2d/SsU22Vw8ZQuS4rQ/fPgQ+/fvV10AGWEqWLBg9HP1RIdeEATXY+PGjSrPnfnuejYqpeKMHato6e0zF9f3mNucAwcOqEX+rLPO41P15nWeZysdPnxYLdS1BeK0uwbitAvpGkZzWQ6Ri1Q5fapnOO0tdsQzGddu3bpFb798+TLat2+vKrzQmJs/x5ZifjxvCpiOklLic9ojIiLUFPj69evVOoPKlStHP84a8UZLEATXxdfXV+Whs5eEnp1Kifbs2QODwaAWp+rtp+jYs/KWud1hNRh2L+VspC1nN+MTAzi8OWCAKaWkhtPOMYJpoHxtNubjGCqkLuK0C4LGkiVL1ACRnPxza8XqCIygJ5ZXySiz+UDBCjDr1q1TxpuVYZh2o/c8W4rHmdIcdGLptHMQYs4q8+bNP2NSlD9/fhQpUkRFoFgWbenSpSrv8+nTp8Z3FQTBFeDsG+0hgxe2WNxpLjZ50ttu0urVq3XtDytzcZZP7zmpod69e6uIfkqxhdPOYg4s4sDxkr+JKXWIYrUyWwR6hIQRp10QjNDxo2O8YMECXeOZUjE6zkh5Qk438yRNRtAkpsmUL19eTc3qPSc1xKjJwoULjd9M8tGLtPPzt27dOtZnTIropDNSzwgPI2aMxPF75WIwVgZyd3dX1XM4wAiC4Nwwz52LPBPqbm1rMbjAvHo9+0Oxqgsd19SqdGMu3jzQQU4pyXHauTj44MGDKre+SZMm+PLLL2N9DyZxjOJNlZD6iNMuCGb88ssvanAYMGCAyl3UM6KpKTqfekaR07JMm9F7Tmpo27ZtaN68ufFbST7xpcdwAemPP/4Y6zNao5o1a6pBXA868qynvGjRInVTwGh+hw4dVH7s8+fPjY8SBMEZ4Wwj0/bocOrZrJSINdEp09/MpdezP5ZitZtdu3bFei1biyUnacvis3vWkpjTzk7YdLwZ1R80aBCqVKkS67MmJHZ9FeyDOO2CYEF4eLiqed6oUSOVV6lnSG0hy6ZH3t7eugbRXJwqTWxa1xbiwlxGk/7zn/8Yv5XkkdhCVA7EjNKYf8aExAi6tfB3ZM4qb8AYhefahZ9++kkqzgiCk8LUDKYxWrOINClq27atCpiY/qYzrmd/TCpUqBC6dOmiHH1bp+3oqWHDhqroQErQc9oZ5GDhAeboJzdlkemKDI6wmRSj8nfv3pU0mVREnHZB0IFGh+khqZWWwkVOHHyYymHaRodczyhaihEQOp/mr5caateunfrsKSExp50cOXIk1ueLT7yRSi4vXrxQg1arVq3Ub8opZ0mfEQTn47fffovubp2cuut6Mg+g0PHUsz9fffWV6lDK3hCpUUc+IbHs45QpU4zfQPLQc9q5Dmjs2LFJCpxYavny5So4Quefv0nFihXVzDAdeVY6Y+8QceJthzjtgpAAdI6Z585a4XrGNCWiITZVgWEEKSHDyQU/rLLCjqV0+C1fKzXEGvbMbU8JiTntYWFhytCbf1Y9MbL166+/Gp+VMjiLwOozdN45ANuq46AgCPaB1yzL0rJkr60XhdIxN9kd5nDT+Vy1alWKG9elRLyRYI59SkgoPYY18TkbadpvrZjnzrQaS9ic7/jx48r+s9oZx1DOePJ9GUARko847YKQCFxsxFxqDhKpVbmFzrGlQaQR7d+/vzJ09nLUzUVDztmAlJCQ084bFcuSavGJURxbwyhdr1691MzFpUuXjFsFQXAWOGPG7ta2yitnMQI66mzkxhrttFl6j7O3OBPAqjX37983fvKkk1hOO8c5VuQyt7uJifn21sDj9vLyUsESRuE5rp04cUKtQxKShjjtgmAFL1++VM2MGFmwdX1eGr7ChQsrI0iDxtxrGjh7lHZMTHRo6dwmFz2nnYaaUXzmQpr2ceDlZzZ9D+ZiXeTUjIZzLQGj7nPmzJF8d0FwMpjrTfvB61fPhiVFfC3T7Crtgt5j0ko9evRQxQiSS2JOO+HMJ0s3mtvf+DRixAjjs5IG10nxc7DQAYNCM2fOVJF5wTrEaRcEK2FeHp3Nb7/9FocOHdI1rMkRX5O1iHPkyKGiz3qPSSv98MMPKi8xuVg67cxHZV65ufHndPSTJ0/U4/VmHJjzntrw/bt27aoWpEmuuyA4F0ydY7lXR+tubUutWLEiRet6rHHaGRxheU1z+6snzgLboi8GPxdz6jmLQOedNw1CwojTLghJhE4kjQwX4Fga1uSI05LMl+Tqe739aSkaejrZycXcaeegw2ln09+chuY280VKrGZg2k+xQoO94HEMGTJEDYwybSsIzgW7WzPtwpG6W9tSnOHlTOx///tf4ydOGok57UzB5I2Puf2NT6z6ZUsYzGEJYI6rHBP08uSFKMRpF4RkQAPH/D9GotMi39xeYmoMSz8yPSg5mDvt5qpWrVq8+ZAcdPkYVmvgMdgTDojs2Dp+/HjjFkEQnAnODDJdxlbdrVmDnGtf9PbZW/Xr11ezsckhIaed3brNUxOLFi0aa0GuuXgMqRXU4IwJZzupBw8eGLcK5ojTLgjJhLl5jAQzN8/Pz0/XyCZXXBjpKNO8bFSU3BQVPaedjTsSuglgcw8+jtOlaQGPjTmttqpWIwiCfWE/BzZi4gJ2PZuWFPE1GAXW22dv8QZi+vTpxk+ZNPScdo5hXENl2k4xaMLZX85cMLJvvo9K7UX7jLKz6zVvvJLSlyO9IE67IKQAGhg6l6xNS8dWz9AmR0WKFFE51nr77C2m7YwcOdL4iZOGudPOSM6OHTuMe+KHnWhZVebVq1fGLfaHAyNbdwuC4JywyQ+7W/fr1y9Nulunhvbt26dSWJKDpdN++fJlVK5cOXobxTLE5p1XOeNovp8BF3vBdCDeePE4hRjEaRcEG8AmRMzHYz1fPWObVO3evVsZK7199hbLoNGJTg7mTjvr9loDp15ZwSEtuXPnTrI/syAIjgFv/Nm0jh1FGSHWs29JlWUna3uKqZhcBPro0SPjJ7Qec6ed34l5XxDOLOrZZ97smB7DjqnJed+UcPr0aRVx5+cWohCnXRBsBNNZWJ6QpbBcLc+9UqVKqgRlUjF32vWaKzkqHOw52yG4Gq9xZ/809GnXAaN33jVuE1wdNsdjWdf9+/fr2jdrde7cOWXL9uzZo7vfHurWrRu2bdtm/GTWY+60m4udrx8+fGh8VFxMJSBXrlxp3GJfNm7cqBpcCVGI0y6kGZGvHuC6tycWTxyCUetvwhX6Uj5//lwZGBo6LrTUM7rJESPvetvtJU6LJsdoO6vTznx2lvYUUp9X1xeje8OaKPv5x8iSJQsyZ/oAmbPnw1dfF0ehTz9B1o9zIX/xWug57zjupchIhCNweRuUKlYWZb/KjeKjfI3bhfQA666ntLs1gzEsK8kqV3r77SHme/MYkoq5087odcGCBdV3kVilliVLlqiupsmtWpNS+L60xSnpF+JKiNNuK94Ew6NPE9QsnhsfZTYgY243LA+Ob4R5jZ+X90bjKoWR86OPkadELTQfuBm3Y1LJnIKIBwcwrO43qDVgB+4ltSfN65+xokcDVPryAxgMBnw5/BJSlsEcgQcHhqHuN7UwYMc9pGWLHKZ3TJ06VUWnjx07pmt4kyIPDw+89957qvGH3n57iNEOruhPKs7qtPPzsvyjYC8i8dvaysoWGCqtwr3o4hTv8PTyanQuqG3X9hX/8QKS1wQ9Avd390D5phvwK187MhyPn0k9/vQGbVmtWrVUEMIRmtclR76+vqp0blIruJg77QsWLLA6V5ydsTn2pCW8uZCKXlGI025j3t1Zim858GjK+M14XExohHm2D81zFUD/s8krp5fWhPsMRgF+1rw9cTJ5Iyme7qyPzNprFJvor93KWMfbhxewZfUR3It1kxMOn8EF1Peet+fJZA7stoUlx5jnvnbt2lhGNzk6fPhwnG1cqGOrPM3EFBgYqEo/JnVxqDM67eHh4WphcWpXSRBi82xXA2ULMjfYBcv+iGHevfApbU3urvB+btyYFMK80T0v7cw1q+2M4JqwOtT333+vGtrZYt0QbaPe9tTUd999p5z3pGC5ENVaHKFnBdc48TcTxGm3PU88USdfcVT81KAcyAK99uNhfOf8yzPoW7gUJvo76TAS+Qq/nDuI07deIrmtEMKOtEdO7XsqPiXAuvSYxzvQ8CPtuy38Iy5b+I+Rr37BuYOnceul4zRm+Pnnn1XEnSXDbL2AiYMFpzk5ddi9e3fVxpudWlMrn75FixZJXiDqjE77lClTVGdWwb6E7WuKjzRb8GGjPXGc9nch8/ENnfZcHXA0GU77a7+xKKI9v9SsIDjZhKaQCrCR2rx585TtPHjwYBxbZ624oJMVTvT2paY4nsyePdv4aawjuU67I8BxLSXdYF0Jcdptjea01y3aA4fOzUK1LAbNcf8ETdbc1h8owi9iWImKmBWUfoeR595dkVsbTEtM+9k6p137fmtw8C4zB8FO8rU9e/ZMpZa0adMG165di2V8kyOW/WJFF/5/9erV0YbYJJZWZBRp9OjRKs2DEXnL10iOWAZxzJgxxk9lHc7ktHMgnzt3rlpMbIsW3ULSCNvfAh9r13bWpvsQu5l5JJ7sb49c2r5cbfficZx78ki8vHUaO1bPweSJ07DIywf3LYzJy7P98XkCTnvky1s4vWM15kyeiGmLvOBj+QIab54E4czuLThwW9v36g68PeZj/rpTuG+Wi5fY60S+foAA753Yduq+dhxv8fDiTqyYOxuLNx3G1YcJWMDXv+LMpnmYPHoExkyei3XHgvHC/HuIfIlbp3dg9ZzJmDhtEbx87lu9Rujds5va5/LAgtnzsHLHBfyqF0N68wRBZ3Zjy4Hb2uu+wh1vD8yfvw6nYn94q47BqvezE0ePHlV57swT17N5iYlBDKYv6u1LTXGNk5ubm/FTWIczO+0sRcnfSBCn3fYYnfaTL97hnlcH5KODmakiJvvqJGy8uoIfy1bBwlt6Gdjv8PT6IWxaNhvu7nOxZvtxXHuciJf69iGunT8Hn0uX4XflMi76nMU5v19jcsXf/oZzJ2/H/K0Z2TtXzqvHX7l8CT7nr+LXVzEjQaIDWWQ4fvM/ju3bzuJBnI+gHX/AYWxaOhOTJs7Ash2X8Ejn8F+c+B55te+o5IwbmoF/gwdqEJuDJZuPwF/vCU93ou4H2ndadQMeWAzekeG/wf/4dmw7+yAmpz3yNR4EeGPnNm1w1V7u7cOL2LliLmYv3oTDVx/qDiqKd08RcHgTls6chIkzlmHHpUcpitBxipGGp0qVKmpBlJ4htlaMDrHhkenvZs2aRRvj+MRoPyMVbBRC59n89awVS4Kxrm9ScBan/c2bNxgwYICaTRCHPW0IO2B02pvtN3PaIxF+cxM6f2GAoVgf7PrNwtCEB2HToHqo2mYi1h88iePb56BT8Uz4oOj32HqHV6zm0AfvhXvLgmrm05CvBMqVK4tS5dtjs0qcD0fQpkGoV7UNJq4/iJOa/ZjTqTgyfVAU32+9o675d/d2oF/1L/Ehn2/4CI23emN69Y+iXi9XZxxXkf9EXufNTazuUhGf0XZpz/ui7z4cnFQHX+TKi7zZorYZMpdFv+13Y9uZiGe4vGYg3CpXgVuHPujdvj7KqVncz9DndFRaZXjQJgyqVxVtJq7HwZOaPZ7TCcUzfYCi32+F+griQxsPjs7shJqVqsOteTPU+yZv1HFo49d20yKld/ewo191fPlh1DF+1HgrvKdXVzMiBkMudI768NYdgzXvlwZwBrRGjRpO1d2ax8nGR7///rvxUySOszrttMecUebnFsRptz3RTrv2/8gwnBtTJsowFeiBvQ8s8mRMTvvt2AYr4tk5zGpWAqVaTIDHfm8c2boAQxt8DsNHZdFzjT+ex5f9EX4D22f0QeWPtffT3rNoxwlYuM0fYcbHvzjTHwXztMaeJ8YNEU9wceNsDHXLrT0+D9x+3AA/9eDEBrLXCFzcHuXzRb2PocBg+IRHvSR5c2cnRtQvg8pdZ2LT3t1YN6lFVO7752VRuUplzYHUjPv8APXYFyd7RDntU0/jwJiqyJ01F3Ibj9+QpRwG7DItKtUG71sHMbd3ZWTnvg+LoVLVKqhUoRp6eJ7Covblo26QNBUY7KN9gje4uboLKn4WtdDV8EVf7Ds4CXW+yIW8ebNFbTNkRtl+23E31sD2Bnd2jkD9MpXRdeYm7N29DpNaROXKf162MqpUrqS9bxvMD0heeGjXrl0qz339+vWwNMTWipULzBdR8SaAlQBMBjk+cRqXhtv8tZIq3jCwW561OIPTziYsvPHh4jTzxiKCfYlx2nfCb9tk/NCvO9q41dDsRX10n7ITgZaG791dbGql2a7CI3HR3P4ETEMZ7XWyNfHE/Yh3eHhxP7bOcEMObVvB3h44eOQwDh2/gkfv3uHuplbIbSiMkbFfANPKGGDI1gSe9002+yXO9NNssCEjClVviN7TV2HFpNYoVno0fF9Z/zrP9jZFVtqez6ui95oreKY2v8btrd3xJbcXGIILppeIeISD/YriwyJDcOJZzGePfHEJE76tpGZo393dhFa5DSg88qJm80y8QcA0jjvZ0MTzPixGnWgiH6zHd2VHmH13L3F58jdRtq7fWe2vGF6e6admKjIWqo6Gvadj1YpJaF2sNEb7vrL6GJLyfvbmxYsXqpQi7UByZybXrVunGh/ZeoYzPnXu3Bk7d+40foLEcVanfejQoXB3dzf+JYjTbmvMnXbyJgQrG+ZQhunjeotxw9zX03Pa39zA/Bof4cOay3DL3H94E4QF1TNrr5MHHXY+iNcQcwC4Or6oer/Kq+/GPC7yEXa25HF8CLct5oY8HBeHFUG+1jvxUI0LSRjInuxEQ0ZgiozBFVP4Xjv+OZU0R7n0TNyIPn7tmCYWV8f0RdfVOHDsNAKjRqtop/2DApXw/YrLCFObX+PW5q5Rjv6Xw4xGPgJP/A5g+5pBKMrtlaZin+asep84h5+Nr/VkZ0MVDSsy5krMbMKzvWiaVXu84XNU7b0GV4yPfX17K7p/ye0FMCR6lOThz0GlDwwoPfNGTMTr9VVMLM7HfoGuqw/g2OlA42CbPOi8cqHj2LFjlQOsZ5CTKtaGNxlkPXHh0sWLF3WfmxT1798fa9asMX6SxHFkp52NQpjuw+lxNsUS0pYYp30fHgYtQT11854dDVff0p3leu0/EcW1xxca5Ru78tSryxhdWHtu1mbYbwzZvzjdRy1kjZUe89o/6rouNEpzvI3bFK9weXRh7b2zopnpBTSbdG3S19o2A0pMDTTO0L3F03uP8ToJr/PiZC8VXMjT3RuxUvMj7mJlJe01MtfDTuNEz+trk1FCe+w384ItPv9L+C6aD5/nr+Gv7GohjIr9xtpXMBqFtefGnrWw4N1j3Ah+Fmssibi7EpW05xmqrI81k/n62iR8ze0lpiLQOD359uk9PH6dhGNIwvulBUyPY544m6rpLfxPTFwYmjt37lh213yGk5XEbBnJX7RokbLH1uKMTjuLOHB2l8UBhCjEabc1lk67RsTjw+hXyBBl8EecQnTQRMdpf7qvDT4xZETNjfdhacPCjnVDHhq40tPxc7x5HTSwE5WB/bjpLjwxbnsbshj1SlVCQW17xqrLcMf0lm8CMa1iOYzzMxrcpAxkL7zRPY/22OLuMYY8ZJ5aMJar0/FYg9Ir35EopG3P3Tn2dpPTnrvLkdiDy7s7WPqt9tpmgxiJvO+BytrjP2y8N85itRfe3dX3U9zdNKhqvDiJXpwRyNPdoupEBO6urKR9nsyoF/0GbxEyj5GfXOhknPaN4hV8RxbStueOng5OKZzWbNWqFdq3b5/sdBWKDZ3oePI1OIVoPmCYxIHDVjXjGU3q2LGj8VMkjiM67f/5z38wc+ZMNeMxbdo0hIXF69YIdiR2ekwEHuzujvza34bMlTDD33LQfoc7Syoom5qzdh+MmeSO6bPmYO68OZg9dSjq59KeZ6iKDUZPUM9pf3dnCSrw9XPWRp8xk+A+fRbmzJ2HObOnYmj9XOq1q254YLTDbxE0q5SyDR0sVsIm5XVenh+oPlPRcVe12wBznmFXAwZlKsPjPh9per9saHUwnvNTs5FLKvBz5kTtPmMwyX06Zs2Zi3lzZmPq0PpqDYBeGqEl78JCcH7fBiyYOBjft64S9bxqm2Debudt0CyU0rbn6nA09s1GMo7BmvdLS7jokbaBfSn0bGBCmjFjRqxOo5YqWrQomjdvrgI2dKJTYpcvXLiAUqVKJVpn3YQzOe3v3r3DuHHjULduXfz73/82bhWIOO22RsdpJ+H+poWpedBm892olI84TvsLnOzBPL/c6KpX1+zpTrjxNT6oA6/Hxm16vPbD2KLa43J1xFFl78NxYWQ1tN92HV5NmRpSEu4BUW4tozmV6y6F6RCSNJC9OI0+zK8sNQumtbRvb8xUxj1jDY+oeshGwi8O1W4YMqP2GrPov4bJaY/laCueYsd3TG0xDWJRmJz2rM3jRpB0o2kvz2Ngfu0Yi47DVYuMFlOJucoephukt7gxkwNlRtTw+NXsOMNxcWhBzXmojTV3zY8+ZdAw0XhXq1YtemFpUsVFqVmzZlX55nqLUjlINGnSRNX1HTx4sBqQ9F7HWtHxZunH16+tSw9yFKedAxtrDQ8fPlwNdKNGjUqwC6Bgf6IXojYzLkSNfI4L479BRm2b4evhOGXK81O8wpUfGUQwIH+3Jdi2c6dKFdi5cxf2HjgC7/P+CHnwInpti55teHXlRxUJNuTvhiXbTM/fiV17D+CI93n4hzzAC9MLRDvRn6LP6djGPSmvE+20j7cscRuG/S0+NrN3L3F+YH7t7w/RaLdleMIIxw/OKBjyo9uSbdHvu3PXXhw44o3z/iF4EPMB4hD53A8r+tZG0S8rocP41dh3IQRPH+5EvUzaa9baplngGExO+6d9Tscup5uEY0jK+6U1TD+kXaa9SGp0nLnx5jY4PtGOsuJXSmZbWXOe6ZLW4CxOO1OVOnXqhK5du6rynEJsxGm3NfE47ZqLFrMwNXNlTPfTTkYavDKVsSB6Ieoz7HZjtCUH2hzWia68PIf+XJBlqIL1Zo5sXF7BdxQjw5+j35mXiHy0Ex2qj1K5kqZofcEfLmiu6CtcHlsdbbc/jI7qJ2kg03Ha8ToA0ytk1N67KAYffqAGyMjwIKxpng+5683DdYuAWXROu1qIas4z7Gn0ofY6+k579tYHk+i0j4dlZU2TkxDjtPPwp6NCRj5+MA4/UEeP8KA1aJ4vN+rNu26Ws2k7vLy8VIrGpk2boGeYExM7xZn+b7kolY48t/v4+GDSpEkq6s5FV4wwczrX9LykqGnTpjhx4oTx6BMmrZ12fjdsdMVc/IYNG6rvg2kxguMRXfKx8d6Ya9ssvfDzrrsQnWKuXeHBc6LWC33e90yi+dBc8E67Z16l6m3wHJX7bvi8LzQzmQhvEDithPZ++dDTwrgn5XVMueFFxvrFcdoPKKe9ElaqwEBMOk6c9B8Tb4MxhymLmp3vm/gHsOAFzg76UntuCUwyzbKSp0Ynuvb2WE70m8BpKlUnn2UPDKuPIWnv5wiwuzVnFdndmo6xni3UE+0cI/XmdthSTPk4deqU7vOTopEjR6rUG2twdKed64mYeskZ48mTJztEfXhHRJx2W/NwI2p9ZaooYAEXpo4tqwyxoUA37Lx1UXPaK2FOdO3Clzjd5zO1v8KSO9FRomhenkG/z7Xn5vkeJ+LcFMTGFNH5cvg5XFvUAA0Wh0Q5suEXMfwr7TVyd8GR385hWK0+OGbm/SZpIHtxAt8zPabEtFjpOhGPz2BJv1qaM54FhWq2Qof2HfHDinN4pBP0MZV8LB6dJ2rC5LRXwqqY9oiI/C0Bp11nYI7+zoqMhZ+l026cjq+00jz6H4HHZ5agX628MGQphJqtOqB9xx+w4tyjuL+HDeE0KR1LVphJSeTFfFEqczP1XmvPnj3o27evqj7QqFEjZfi5MNZaJ56LguiMW4O9nXY65Jx94AwGW2+zWs+sWbNUhQjBsXm2uyGyaNejZXOliIf70IM33oZscFsZEn1tv7oyRtVe5yJz71hR+LiEHWqjFrDHSkt5dQVjivB1v0Bf77DoG3d9XuPqOK4VyokOUdOXMSThdZ57d1P2rsAQLpY3Jwx7G9PelYsO4pjy3w2f94nn873ClTFFtOewGo13dMEBqzAdc65OOGY2Vr26PhNl+Z4WTvTrq+PUWqKcHY5a2F0rjyGJ7+cocIaOpW4Z7EhKd+vly5dH2z1L1a9f32bpijt27FDlfa3BUZ12fsc8Ht7IsIHSzZs3jXsEPcRptzER91ajyuetEF8aIt7extpmUWkmmcu2RcPC32JmzIpNhB2LcmIz116DXyy8xIhf16J6RgM+73MijsMaB82h7pHXAMOn9dGoUgfsfGSypm/w83RGqLKi1oDWqDWKEXczkjKQhR1Cm+zaY4vGdojfPTqFGS0qotWywEQj02GH26iqDrEWjyqeYnsdRuy/xZLoBHztO7izTKXv6OW0Rw/M5lGs597olls7xgJDYlW4IWF7G6uFq+UW3DJzyN/h0akZaFGxFZYFpkZcPX4eP36s8h05NRgQEKCMclLFds/MladhZrRC7zEmMQpNo8/HsY58iRIllKPPRk1ckMWmI3qtvlnbmFPH1pCaTjsjMXfu3FEGn9PYPCbOWPTq1UtF1PkdcnGZ4Bw83FhN2UVDtY0W+c2RCDs7SkV6DRnLYPSpZ1F2SVWPibKluepNwuF7Jnc+Ei9DDmLlOr/oqPDTHXXxgfa4L/qbVymJWnSvcqpz1cOkw/eibwgiX4bg4Mp18IsOjrzE2f5faO+VGfV3xmn9ZPXrhO1rpqrH5O5qsRBVs3c762XSXr84phhTF/HmJhbVpiNvwCe1JuIYa9YqIvHijj9uhkVEVW5R+fu5UG/SYcR8BS8RcnAl1sV8gNi8vYGZpfm8gui79x5ev/oNl7dPRqfGtaJuFGpsgfl8FOvcf6Ftz1x/Zxy7a9UxJPH9HA0GAhg9ZyTY0h7GJ9ZRN9k+k7766itlZ5mqmJzFrpaig/v111+rXiCJ4WhOO0s5MljEIgkcs9idVkgccdptDHP/Sueog22mFaA6RD49gWGqGglVAtPMw9SaU7/ajXnnn6Przt9inMnIF7g4oQw+Kvg9dpk3tIiXMBzuEDWgfd43dh7iuzurUINTktp7T7lu9t6KJAxkT3dE1Uz/oj/ORo+E2uCmSqPlQsMpm7DP+zx8r/ji0oXz8PELwePXsW8Dnu6sh0zqGC2muCPvY10VHmNJTDf7fiIfbER1HluBPtge+CtCLnjj0sOo70N3YA7bh2asHqPT/tz03rG6sb48GxWZz9UQUzbtg/d5X1zxvYQL533gF/IYFodvc/773/+qkmFMYTl9+jT0DHVCYh4gxSnG8+fP6z4mIXHKdtmyZapmOfMl2aiJ0XgOMoywM4JEg88BjE5/YtjCaadx5ywA04gY9aJTzih6oUKFVCMkHitLrPEGQ5x05+N1kAeGdmqKSgwy8No25EWlxh0wZG2QWQrJK1yfXU2tQTFkKY5mYw+rTtORYVewvEspY93wzMhTuASKf1UAJer1x0rfZ4h4exd7p/RFyzJZol47V3V07DsUc048ippdiwzDleVdUIpdlrX9mfMURoniX6FAiXrov9JXVYmKfHYO8wZ1QHXlmBqQpVQTdOk3AV63zGxnYq/zxz3sn9oPbb7NGnUcn1RG+wFzcOKxcY4v4h5WsXqMti9ftXYYvv6m+uwRj09iahNjjXlNuQppn69QARStMxrH1XO1G5ory9GllLFufOY8KFyiOL4qUAL1+q+Eb7xlrt7hwaGhKJc56nWZO19uwDbcurclyr5mKYPWQ1ch4OUznJs3CB2qR40lhiyl0KRLP0zwuhVjM606BivfTzcPyDGgvePMHdfDWNPdmpF5y0Wpmzdvxrlz55RdZAWxmjVrqsWrdFj1XsMadejQAXv37jUeZfw4gtPOtVC8AWJ5TdPNC9caCdYjTrutiHiIo5O/R9NvcyqjVLBWG/RdeDXeSPPrmyvQ6BOD9tjimGoqvWIk8vkVLG5bVBugPkXtPpMxf/EsjOpQFZXazcZJo4NqDU92NUM2QzGMt1yBGfkQXo0/hqHUDP0qNIkOZG9xd+8U9GlRWk1n00Gv1mEQ5p1jBOwt7mztjXKMwKt9lsqGKuPPIuz1XezRBtNWZaMiSYZcVdFh4FycfmIcZFiVgNVjtH3mgxje/YptnaLqplOf1JmCU7/d0QbmPmhR2jQwV0OHQbOwa6s7+rX5NqousuETVG4/QBusHxtTYSJwbxWrx2j78lVDu+HrcZNv8PYOtvYuF1ULXk/ZqmD82cSm0lMOjTujxjS0eoY6MW3btk13e1LFaDUNPFNMmEbD6UvmtHMRFaNG/JcDDyP1NMDMlWeZLh43j4E5oaaBgtFvRukZYTpw4IAy3lwvwe1ckMWUFpYw4yDEKBWnpLmQlotomavPer3sisfnM8LEhkiCC/D2BZ4+eYbn4a/xNiISEe/eIPz5Mzx7aWnr3uLli3C85f7wt7GuwTcPr+Pkvu3w2nUUF4KfxQQ7tP+FP3uKsJevtNeOwNtXLxH27AmevbJwZt88xPWT+7DdaxeOXgjGM/O3jnyNsKdhePHqDd5ptu/1y+d49uQp4hweifd1IhAeph3HCx7HO7x59UK9ZkwQIBJvnofh5Zu3ePMyTHtczOwrefvsDq6e1K6Zw6fhd4e1dSx5g4fXT2Lfdi/sOnoBwbE+QPywQ2ugz3n4331hfM23ePFMO45wfk/Ptb8i8dp43G/ead/f65d4rn1/T/U/fKLHkPj7OTasNMWKX2xuZ02Ki/mi1OrVq8daf8TURXY27d27t0pVpA1lR2bat6QsfmVO+5AhQ4xHGD9p5bTfv39f9Sfhd0FHnfXl+ferVw58h+bAiNNuMyLxKkwbDJ6H4/W7d3gbHqb9P2ET9C7sNnw058VPNxoSgbAbJ7Bz/Wp47DiOK5qhts4MxxD56jdcv3obz3Ve/s3DQFy9/VzH+JuRwED2LvwZnoa9xKu3EXj3JhwvaNg5AkU8g9+GYWhUrTUmbDqMk6fPaHfSp3D88F54zu+FMozwZ6yO1Xffmg2m2iAWnoRBTBtEH928At8bD42PNx+YY14r/EWYdowvjMf4KuYYjUS+ea495w3evtEGDO1xfIeIZ37YMKwRqrWegE2HT+L0mbM4e+o4Du/1xPxeZVRkPmP11bBhEZl4YfSlfPnymDJlShxDndbiNHGXLl3U6n5GnRgtoQPOKD3z8hmNGjZsmIrWmwYKDnSsWcxBql+/fipCTkPO9BzWHN6wYYNy5PlagYGB+O2338SwC4LgUDAtjzaZUXdW7dKzjyaZL0rNli2bSjvUe9zPP/+sSkzSLnKW1VT1y5pGTYzcs3FeYrOM9nLaeUx8L9p25qnz2Pr06aNSYZ48SSAFQbAKcdoFGxKBexsa4CNDYYw4r5dLyUZOrCBQFOMtS7k4AhH3sKHBRzAUHgH9w7+IYWzIpFOJJrVgWUIab6a80LBbGuyExGZKtmqqZClGmZhLmVgHUXsvRDXx5u4hTOvqhnod5uKy8bd8++t5nItOuBUEQUg+pu7W7F2hZyNNYkohbSWda739eqKt5EwlAyDM92Yknuk0nOlkNJ5rkVgmmMENPp5RfM6KJoQtnXYuHr13756qIsYADnuFtGvXTqVl8jg567plyxYpAJAKiNMu2JAwHG77CQw52kCvYmV0pYGc7XAg8XUz9ifsMNp+YkCONof1F/oaF+nmbHcgzmKs1ISOMTueMmrNKLSlgY9PzAOnMT9z5ozu/pSKue587YRIC6c9PGARmhYvixrlc2s3iHnQ3Zte+xtcn1IZ9Vb9kvDskiAIgpXQaWYaH1P7EspzZ7ldve3Wiqk0dJCZHshoPFMUGennmiNG5enUc30P02S47sfDw0MVEmCEns41gz/s+myyxXSouX6KY8sff/yhZkzZdI7Vt5h6yMZNTNOho8/ZU6Y9cgzq0aMH6tSpo9YTMYrOognjx49XUXSOBQ8ePDB+M+QN7h6ahq5u9dBh7mXjurq3+PX8uZjFykKSEaddsCGvcW0yW1oXw9jLcbP5Ix4dQM/82VF1VoDZAjMH4vU1TOYC4WJjEffwI/DoQE/kz14VswLS5uhpGJnnbqt89ZSKAxGniRPC7k77y0sY+20pDD4Vhsine9Eim7GkZ7gvJrj1wYEnMelRgiAIKYUL5Zn6l1B3a8vSu+ybwXU85tuSK74n0wvpTDNFkY497S7TELkWiM48ywkz2m+yxV9++aUS1yUVKVJE7WO+ebly5dTsLD8PF/xzHRNvAjhbQAf+yJEjKi8/8eZ64QhY1BTFy9ZAeVZvY0dyFTu5jimV62HVLxI6SS7itAs2JTLsItyrZYUhfwtM3RuAR68iEPn6Ifz3zkHXSqXQcOJxVfXBMYlE2EV3VMtqQP4WU7E34BFeRUTi9UN/7J3TFZVKNcTE4w/TNFJ76dIlZVgZ+dAz4PYUu6sy+p8Q9nXaI/FgW1N8+s1MqCqq2gDBm7DP+51E4PpeGLDDrBqTIAiCjWB3a9o6a7tbM9jByjJBQUG6+5Mqvg6LAjBaHh/2ymknLy+NxbelBqsuxk/3tkA2Y9OwcN8JcOtzABI7ST7itAu25+1jXPacgaG9OqJ1i9bo0LUPRs3ZijO/hGtulePz9vFleM4Yil4dW6NF6w7o2mcU5mw9g1/CHePoOQXJ7p7Mb0xKnjsXBjEapLcvOWL0iDmdXDAaH3Z12iN+wepq2VAsukX8Y2yrmwmGYh3Ra+x+PJDgjiAIqQidYc6GcvGons00l3klGVuIOeWsyhUfdnPaIx9gW9NP8c3MG6q4w5vrk1Gc3dlPBmJ9rwHY8ZuETlKCOO2C4ISw3CHzFzklam09dk5xcqpTb19yxWowmzZtMh5VXOzptEf8ug61PsyFDkdMKxJe4tyA/DB83ARelh575DP4LB2NUT8OQdembuixzA/PJfojWEHkqwe47u2JxROHYNT6m2b1yu1NBF784otDG+ZizKAJOHA//rtS82MevuaGY6YnphqRePnLRezb4IGd5+8itWMvXKTPJnW0fXodqVNLTLdhxa74sJfTHvHLalTLViym2MTjbaibyYBiHXth7P4HsqYohYjTnta8voP90/qgXYfR2Hk3diWO13f2Y1qfdugweicsdrk4kXj14Dq8PRdj4pBRWH/TBVatRL7ELxf3YYPHTpy/a7sZB67cZ0ktVhPQM+SpLS5u4uKk+LCn0x52kN11S2FWkPFiefcbdnTIA8MHtbHpgfk3HoEHXq1QuvshNU0b8dsWNPo4L/qe0SsZJLgW2m9/YBjqflMLA3bcS3q61OufsaJHA1T68gPVt+HL4ZcsOjnbj5eX3NH2u3LIo3pIlMGc4HgGiTc3saaXGyp/FXXMBQb7JNqp2mWIeIpzC/uh68AJmDqiKQoYPoHb8uBUv9FiacOWLVuqPhXWdLdmtRU2Y9LbZ624EJSpk/FhH6c9Ar+uq4UPc3VATOzkHAbkN+DjJl5xZjsjn/lg6ehR+HFIVzR164Flfs+dYjY+LRGnPS0JD8TyNqVQTHO6vspdHKN8Y8x/eOBytClVTHPIvkLu4qNgtss5iXiAA8Pq4ptaA7DjXsJD5eufV6BHg0r4kt1WDV9i+CXn/vART89hYb+uGDhhKkY0LQDDJ25YHmy7YYORdpbaYgMkPWMen1glQG97UuTn56cWMbESgR72c9rfIHBaCe18qY7N7Ice8QjeU/tjzAQ3fGLIjS7HzOoBRdzFqgra4L3bVAPoCbbXyYi8gy8a/xZcl3D4DI5qzpa358lYnaKTwtOd9VV31mITTalYifEWDy9sweoj92zbQOhtMOaUMWifpzwW30nYrj7b2xgfasdcZMyVNLvRsC/huDrrO5T6fh8e0VmMfIxdLbNbdPBOPWgTWQqRFbwS6m7NfHTa78WLF+vuT4pY0SW+TtX2cdrDcLBNDhhKzUJM7GQHOuQx4IPamxA7dvIAXq1Ko/uhJ5qjHoHftjTCx3n7QmInCSNOe1oRcR+7e5RH0w2/qumiyPDHeGZs/BNxfzd6lG+KDb+qPQh//CwV2uen0iASH+E+GFzAoA0uedHzZOyr8u3DC9iy+gjuxTqQp9hZP7P2+GKY6Ig13a0l/CpmfVcK3++Lapse+XgXWmY34Iv+Z2HLcePf//43GjRooNJVrFncxKhMjhw5bBKh5/uyGoIe9nPajakwhiIYsHoVxnfrCnfvx3jzyypUzWhAtrru8PJaiBE/rEDg4zPonTsXukWfh6/gMyAPMjXYbfxbcGUiX/2CcwdP49bL5BvVsCPtkVNzgItPCbAqavt4R0N8pD2+8I+XbeswR/6KNZUM2nlfCavuWYQxLXh+rCNyacdQdPzVdJEeE3Z6MIpkr4QF0TMQb3BjRkntu/oG80Psl1dNZ5l57iyzqGc/KVul0TBlkiUa9bCL0/4mENNKGGCovhlRsRNvTO0/BhPcPoEhdxfEjp2sQoVP3BATO9mOOhnzYvDF9HFLmVzEaU8jwry7Iy8d0muW5jMM3t3zwlBsIuLssiGpNojESyRe/XIOB0/fQqyx8vEONPxIu8gL/4jLsQ4kDEfa59QMbHFMCXDW9JgwnB5cBNkrLUDMuHEDM0pqn/eb+bD1uMEyXAMHDkS9evVUnV09o24SBwnW3bVm6jYxsU4vq9noYT+n/R3uLKuCjNo5bfiqJWaffaJukhDxG7xa8Twy4LNG03HqsbY17BBaZvscQ6JncN7g+oQCMFTbZvxbEBLmuXdX5NbOqRLTfrbKaX/iWUOdg2XmBNs4SPIYW6tr57yhCtbHCmPG5cWpXsinHUOxiddc32l/G4LFVTMjq9sW3I/+WiJwb3Ul7bsqiKEX7ZsgxBlJll1k92c9G2or8caA5Rr1sIvTbkyFMRQZgNWrxqNbV3d4P36DX1ZV1WxzNtR194LXwhH4YUUgHp/pjdy5uiEmduKDAXkyoUG0Fy/oIU57mvAafmOLaMbDLP/WxGs/jC2infRm00upQeoNIknkiSdqaMdhKDMnxrFVPId3VzbHKYFpPzun0/42ZDGqZs4Kty33Y/L0Iu5hNSNjBYcitcaNFStWqOnW3bt3K0Oe2tq/f7+q7auHPXPa8eYRgoLux11o9vohbgY/inGuNKe9xcd5McAnxmn3H1sQWRvvN/4t2JO3D6/h/DkfXLrshyuXL8Ln7Dn4/RpzB//2t3M4eTvm78iXd3DlPB9/BZcv+eD81V/xKvo3j8TLW6exY/UcTJ44DYu8fHDfwnxEhv8G/+Pbse3sA52c9nd4GnAYm5bOxKSJM7BsxyU80jGQL058j7ya3So544Z29rzBg4s7sWLuHCzZfAT+Ok94urMuPtAeX3XDgxhbYEbky1s4vWM15kyeiGmLvOBjedDRaJ/vtjc2LZqN2Ys8sMf3Z2ypk1Gzk9Wi0sIS4OW5AcivHcPXk69rI5D2Oa8fwoYlczB7oQcOBDyLusnV4Pd7+dw5XLh0GX5XfHHh/DlcvvNSHXdE2E1cPH9Bffe+F3207dbkMrzGr2c2Yd7k0RgxZjLmrjuG4Bexv4V3T6/j0KZlmO3ujrlrtuP4tce649K7R77YsWwGxo0cjYmzV2D7mVt4rjPB8PJsf+2zfoRG2x6Zfd+R+M2jcpo47YSNi9gUqUuXLglW/aLdbt68OUJCQnT3JyS+Lks/slmSJXZx2t/dwbIqPB8N+KrlbJx9EvXjRPzmhVY5Ddr2z9Bo+ilExU5aItvnQxATO7mOCQUMqLbtqXGDoIc47alB5EvcOr0Dq+dMxsRpi+Dlc98iGvMSZ/t/rp3AOk77y7Po/7l2csfrtCc+KEXz+lec2TQPk0ePwJjJc7HuWDBMtjKxQcSE7QbUSIT/5o/j27fh7AOzofLpTtRl7nrVDbHz3fACJ77Pq31HJTHjhvYB3zzAxZ0rMHfOEmw+4q87kCZIAt9FFFGD2KZls+HuPhdrth/Htce6wwYe+e7AshnjMHL0RMxesR1nbj2PHvBi4G+cH4aPGmHbI7M3ivwNHpW1z5uKTjth+gsXqLLltZ5xtxS7+V29elV3X2Lic0uWLKm67lliV6fdWl5dxOB8WdB4v2mu9gVOf58Phcb6G/8W7En4je2Y0acyPtbskcFQFB0nLMQ2/zCjXXqBM/0LIk/rPdG1nSOeXMTG2UPhppq2uOHHDX4I477wIGwaVA9V20zE+oMnNVszB52KZ8IHRb/H1jvatfw6EIvbl1fRZjoVlgsy39zZiRH1y6By15nYtHc31k1qgQJ87OdlUblKZVSqVBVt5ke1in9xskeU0z71NA6MqYrcWXMh98dRr2vIUg4DdhkXuUaG49bBuehdObva92GxSqhapRIqVOuBXQ/VQSNo0yDUq9oGE9cfxEntZmJOp+LI9EFRfL/1TizH9e3DM1jSxw3fteiFYSP7o32dr43fGZW40x7uM1h9nq8neGP38ArIlikrPuEsp3p+VlQeewL0sSKe+GDNiHoqlcZgyI6aQxZg+5Wnysa9/fUIFo9oppz/zN/0wMKTca/5GCLw7PIaDHSrjCpuHdCnd3vUL/eper/P+pyOSg+MeIZzs5qhRKkWmOCxH95HtmLB0Ab4XHO4y/ZcA//okk4ReLB/AMoXrodRq3fjyEFP7TgaoaB23I33WkZmaXu/0N6nKtbEShl6i5B5ZbXtOuOunWAHUjYsYn8L2mg9e8qURc6WJrckJCPtbIJkiX1y2hk7CULQ/bjFFl4/vIngRzHOStihFvg47wDExE78Mbag9ntG22VBD3HabUx40CYMqqcZ94nrcfDkcWyf0wnFM32Aot9vBccNOvTBe93RsqBBGa98JcqhXNlSKN9+M355Hoy97i01Q8R9+VCiXDmULVUe7Tffi3IKExuUTGiG8PKagXCrXAVuHfqgd/v6KPcpX/Mz9Dn5KJFBJDa2GFAfXF+M9uXzqfczGApgsA+HSs2Jv3UQc3tXRnZu/7AYKlWtgkoVqqHHrofa3hc42SPKaZ96+gDGVM2NrLlyRw9SWcoNwK5EFrQqEvouTkdFIyKencOsZiVQqsUEeOz3xpGtCzC0gXZT9VFZ9FzjH1MKMOIB9g8oj8L1RmH17iM46LkYIxoVhCFrY8QdN7Sbry+096m6BrHHjRDMK6ttT+WZFML21XXr1kXfvn0TzXPv2rUrSpUqpbvPGnXv3h2enp7Gd47BIZ12PMO+Zh/j86G+UalhEXewuGIx/CC5lGnH66sYX9SgXZeVsfpuzAUT+WgnWubQtn/ohi3mJQ3DL2JYkXxovZO2QuPdXWxqlRuGwiNj3Qy/CZiGMpq9yNbEE6anP9nZMO6CzDc3MKfSBzCUNjbmUrzG1Yns8GzAF11X48Cx0wh8FvUiJqf9gwKV8P2KywhTm1/j1uauUY7+l8OijiPiCfwObMeaQUXV61Saug/e3t44ce5nPIt4h7ubWiG3oTBGxj5oTOPi0mxN4Gk86FcBC9Agdy7UW3rDLJ3xHR4cHY+a2fm9We+0Z/6yItrNOYH7ajwKx+29o1ApC1/jC218eB714Hd3sKo21xTlQJsDsY3b2+D5qJCrKhbcTMiAReDRwX4o+mERDDnxLMaBi3yBSxO+RaVZQZoL/QY35tfARx/WxLJb5q/1BkELqquFvnk67IyqOBJ2FJ1zZ0SFZXeixkLFa/hNqI0uhy2cvDc/YzpTEA25UKZWQ7Ro3xXdu7ZHi4Z1UP4zfq8tkdZ+Ieu4M8/dy8sLevY0JZo5cyZGjRplfKcY7OW0W8uri4ORL0vjmN/ixWl8n68QxvrHF4UUiDjtNuTd3U1opTmrhUdeNIvgvEHAtDKaAcmGJp73EfHuIS7u34oZbjm0bQXR2+Mgjhw+hONXHml3ohexf+sMuHGQKtgbHgeP4PCh47jySHNOrR2UIh7hYL+i+LDIEJx4Fm0qNVt5CRO+rYRZgb8lMIgYH2xJSgdUxRPsbPih9hpFMOYKh50IPPE7gO1rBqGodhyGSlOxTzsO7xPn8LM6EJPT/gEKVPoeKy6HRRnr17ewuWtU9Ycvh5l/zzok9l3Qa9YG6/k1PsKHNZch9rgRhAXVOWjlQYedUbVlw452Ru6MFbDsjtlnfe2HCbW7IO64MR0l+blylUGthi3Qvmt3dG3fAg3rlMdn/L1a7oc9xo1Xr14pp52LRS9evAg9I0+xusG2bdt091kj1oBn22xLHNNp1xyPWx5oV7wS+q/SroWxHdF2ymk8jTlFBLvzGtcmfq1dbx+j6a4nxm1vEbK4HkpV0m6MDRlRVXPYTLfpbwKnoWK5cfAzerCv/SeiuHZdFRplvBEz8eoyRhc2aDfWzaKdgxfe3VWZxOLugdEzoG9D5uEbbVuuTsdhdFsVr3xHopC2PXfn2NtNTnvuLkdiX8eas7v0W+39MtfDzuhZ/kjcV2kZH8aOCr/2x8Ti2mMLWVYHe4XLowtrj8+KZjxoOtC1NFuUfyDOxcl6eIZdDWinrHfac3U4YHGuv0Xw/AraaxiQt8cJzfJG8eywZu/4+PYHtXcx8QY/z6iAYn1Pxvo+4vD6GiZzQeI38yzSHoGXvosw30d79tN9aPOJARlrbjTLOzcSdgzd8mjPN5TG9J/faF/BUnzL77v9NvxqFqt5ffcC/Dg+mhN2AK2yac8t3ApDxk/D3KVrsGb5PEwf1we1+H7V10LVeEhjTN2tp06dCj2bmlyxI2vFihWN7xKDoznteLYPzT7+HEONJ3/EncWoWOwHSOwkYcRptxmv4a+iMoVilW4kry6PRmHN4GRtZnLUXuB0H04T6kzTaXebfRgJtojEWjsovb42GSW0x30zzzJX/SV8F80HbWW8g0i8pGxAjeIFvLvn0R5bHO6BMXfSkfc9UFk73g8b7zUbGIjJac+NLtEFX6MwGfDM9XYioew3a76Lp/va4BPt+GtuNMs7NxJ2rFtUDeTS0/Hzm3e4s/RbdTztt/0a/Vn53dy94Ie440YrZNOeW7jVEIyfNhdL16zB8nnTMa5PLfV+1ddGVQ2yF0uWLFF57vv27Ys27rbU5cuXUaJECdXO2xxHddoV754i6PxJXLkbla8rpC2v/caqG/hcHY9G2cnwCxhZrT22XfdCUzphJd0RtSZds0eTK6Pu0tvG61C7NpdEOZ05a/fBmEnumD5rDubOm4PZU4eifi7tuYaq2GDMv3txug8+1R5bSkV7o3h7YyZKadsy1vCI5dCFXxyKgobMqL3mbqzr1eS0mzv+UTzFju9YD70yPKI9UZO9zYrmZiHed3eWoIL2GoactdFnzCS4T5+FOXPnYc7sqRhaP5f6PExffPfbOtT8ID5798xYZSsJ6TEqpz02r/0nRAVPannBZOG1O5aosSV7S+x6bPws2rZR5WpgXoJRdu37DJqlvs9srQ7GG5yIvvHp6q1zA/AUO92yaJ/rA9Txeqy971VMVKUtNX1WHg27jcaC7edwR6cCUORva9WY8rllha6ne9FCO4/Kzon53dMaphQ2atQI3bp1081zZ147U2kWLFgQZ19CotPO55rjcE679ivc8miH4pX6Y9X2NRjbsS2mnH4qtjgRxGm3Fe/uYEkFg2ZUcqJ2nzGY5D4ds+bMxbw5szF1aP2o/MDovO2kOu3WDkqvETSrlPb/bGh1MD5TSfQHkYRI/oBqQv8zm5z2rM0tI88mpz22k694ugPfMQ++skfcCE00b634LmJuDLp668SNnu6EG6eNP6iDqHFjoprV4O/wWfmG6DZ6AbafuxO7Go4iEr+t5ff7OfpbFAR+ureF5syXxRzL390OMALDKVlrBoDjx4/rbk9ITMVh9Mgch3baBceCDmEhAwyf98MZ7aJ6tLMDqo+6gHDNMhzrxhv+gvjhQrj2uMsYW70ttken873ClR8ZmTYgf7cl2LZzJ3Yq7cLeA0fgfd4fIQ9eRNsjPacdrwMwvQIX0BXF4MMP1PbI8CCsaZ4PuevNw3WLKb3onHa1ENWcZ9jTiDOKek57drQ2s0WvrvyogjmG/N2wZJvpmHdi194DOOJ9Hv4hD/BCO2iT7c3abJ+OA/wEnjW110ih0/7u1kKU0/bl7GC074q3uDmHgYrMqLM26qYl7HgfVOy4ByYfPj5enh+o8t4/bLTbIhgTw7PdbioFJkebwzqf6yXOqbx0A6qsjwqovPllHya3q4yvON5o25Uyl0Qfz9i5/xF3lqmboaLjYpe2DDvSCbk/rI4Vxnr2jtLYh3nuQ4cOVd2tz507B0u7ysZLSS3NyypibHxnjuM57VG8exqE8yev4G4KSrCmJ8RptxWvruBHRiUM+dFtybZoA7xz114cOOKN8/4heEALrEiq027toPQS5weyVvWHaJRg2ST9QSRBkj2gmkjYac/e2jIiE5PTrhaimvNsDxp9qB1Lgk67Nd/FM+x2M+ZtWua3kJfnovLSWU5NvdEb/LJvMtpV/kpF0U0DR+aSfeBpvqZAG97uLONNVlGMuxpr2MCRTrnxYfUViBo3IvHMZylGj/oRQ7o2hVuPZfBL5V76v/zyC2rXro0BAwbE21yJjvff//53Ve9Xb398YiMR5lOaI067YD2ma/ZLDD93DYsaNMDikKjrKvzicHylXWtMR/nt3DDU6nMslnMZPIcpiAZ83vdM7OiqDqz8whk0y3KNEY/PYEm/WpozngWFarZCh/Yd8cOKc3Fm0Yip5GPxqZaRdpPTbl433VS1JLa9fRs8JyoI8HlfzaYaN+rw9uYclObjSs4wy7c3wkZhqk574k77y3P98YX2OnpOe/iFHzSHPjua7zBPadRe/t561GXgosx0BL56AK82NTA2dm1eXV5fm4SvecxxUn9ieKndPDFV0FBhidEemvMSZ/qxWEMefH/CokJNZDjuXtiJhYNqqd8gTrOkR5tRXdsea1Y68hF2tcmN3G2NNxwO2NjHw8PDZt2tN2zYgPbt2xtfOQpHddqFpCFOu62I7kz3OfomZIEVrIxCR1ennOGLE/ieuXwlpiFml7WD0mtcm8Q0Fp00mljoDyIJk9wB1YT+Z478LT6n3VTysTimWkbaTU57pVWxF3nGwprv4qV2I/GZesz/3955hkV1vGH/2/t1k5j6T2JDEQuiYG8IYkexN1SsaBR7L9GoWKNii0ZjL9gFFVFsYG+IFaWoCERpyiILsvvu7nu/M+ec7QvsKhjQ53ddcymzp8yZM/PMfWaemWm5yeDeo0dxFeP5Sj6VR8Gy3UjGrZANmNyep1HcLMmYjP1eLN7UVUqbEQrfSpUw8ESm0DBq0g6jfyN/nBH30seB7j+iCnu/pd1u5OXlCZsw8WHZ6OhowcibB95w2Lvhx4kTJwTfeWNItBP2kBsl9mA7dOkOd78Q6Bde4pMLuX39oT0mDmiPObyDwIj8mHmoy86TOY5DpLCUTOHIz/gKk99d5t8zEa/qjMv4s28r9N8cW/RcGYY8whc/s2tY7i76VthdVyZrgU16Jar7iDdzR8yPwTy+vK/MEeMidZP7raC4JtohZnunXDY9Tv1yB7yFFWAM7j+FkXtJXKfdvAeadyZEBTiiQuMllnuDaLNwciDf58AJE4JXwKfXNisC2wq5lzCmKk9XdQQU9mzy8xjBFyyo0AE7XppdVJOKnV4sH6sHIIo1DKrErfD/46ZZ28d33/wFsiqjDWt9c+SnBZ/2ikPO691ulE9Xo1XVTtgstVlldWMfvr8Gd2PknR/WbCwPtuxkHRsbKyz9yOc06SDR/nlAor3EyEfMPL72OhNw4yLFJcgKRY4zvnz1FvOeWIb8DHz5agAuf8D4J1sbJZ1h5sau8OMKaUSK4UMbVBHDM8+/Z3gw3VCmpU+7HBF8O2T9xFUj3nIDy+7XwloPjQFb8kJ+Xuwxq9BhByzbjZ3wYvepHhDFUqNC4lZ//HHT7JOJNRB8MlWV0ZekCBH5ae7TXhFDzuubDTxd3QpVO22G2G5okLytJX7xMQwfZx3tiK+qTPkkE3H+3//7f9iwYQOaN2+O8PBwWDP89gbuQ8n92rOy9F6xJNoJ+5BHwE9w96uOcVeMlZgaSdva4hten92W4JHZd7w4UV/0A6/oHYiIFN0BWigST2Pr7nv6j+G3x8Tlbk13JVbgmtCzWxHdlgQjLPIGomOicefWDdy8l4hMsy2p34Z4C2mx6ETRvsZuD57+BsIESikSafv4R7wMTgFHEZuaiFuRd5CuFlePEVwnK3ojMCJF32uvVSTi9NbduCckWol4aTUV2fetMGnXZcS9SsHDMxsxrmc/9BFGeJthw/Oi1bRuF1eZoy/WXRFdgPhqZrE7hqG5+wQcTTLPVJHcK+NRnZ9XwRF+Ybxn2hZYmv/qIKzSI/ulPRadf613YdHmJuFBPF9YQIUX232EUcvqI0LwSp98LXJvL0Tj72tiVOhroTNF+Wgp2nTbAaN1EESXpqY/wW3uTdOODvULbOSjD3XniRv2qV4i2K8FBmxP1Oevogxv7PPq1Sv97tbmyz7y3vgqVaogJibGJN5a6Nu3r+DmqINE++cBifYSRFg9RmhwKsI7MAKGdkOBxNNbsVu0wIy3ONaZT1ZytPB55v7awrrl5kN+tjZKynj81YEPz8rwS/tFOC+s68VhhjDpAeKFtckKa0TEIwvlQxtUAevPrE3bJwxlypwCcDQ2FYm3InFHSMhbhHh/I97LbORC+3o3PPg5DfgEUSnSGrbkheoFtvv8JNxnRMgrQ2+7Nhe3FzbG9zVHIfS10Gzg0dI26GY2Ia3g8Qo0/ckNc2+aNBus3dgId3bfuvPEHWdVL4Ph12IAtifqmw1cHVsJFUde0jc4+TcnovI3XQ29P5+ACxcuCH7ufKKqNcPPw8OHD4VedGu/mQe+cQjvpddBop2wjyyE9mb1sd4Ck04Ljjb9MHr8KEPDP63vQqqVx2DL8IbCTs/cD7uysxtcazvBzXsCtkZnQ6NKxsklAejbiE9wZMdUbAO/yWtxXVhZSoWkg2PRTFg+0Vr4CR4LrkFekIwTS8ahfxPRrsgqesJv0hpckTaREeY28dVj2G9V2wzCzD3xQq+2OvUIhjrprvULOi65LKyJzhKNmC3D0VBaL71CZWe4udaGk5s3JmyNNqzopcnEpWW9UId3VgjXkOF7999xLj0Nh9qKf1drPxQz/nlg1httIOtoV9Ro4g13oQechV8cUKVibXSdfxxGW21YolvsoNYMWO2PKQwhzT2lJYxZqFiHPVsdOLl0xNyLmaId1eYgZuNAuFSQwaFDABav24hVc/zg6T4Iqy+l6+0x3yxJWPmmZX+Mm78S64MW4Dfv5ug4YT8SzcoJuyiyL81CA5ZX1Tz6YEB/P8w5GGcyelLWN/bhu1tPnjxZmCd08+ZN6Owr31mVL+dobHMLC8uXLxdcFnWQaP88INFeomghj9mC4Q2/F41UhcpwdnNFbSc3eE/YimhmgVXJJ7FkXD80FtbFZUbIawjGTQ9CVMZ7JJ9cgnH9GuNbwchVhNeQcZgeFIUMyXAX2yiJhzFbeQnLevJVXaR71GHH1XGCS8e5uMi3ImMU2ogUyYc1qMIzB/RFI90zt/HD5LXXIbSV6lQcGSou4cjDLx2X4ML9UJZH/dGEu8Dw4z39MGnNFX36+KoLfPUYvpZ9m0EzsSfewmrrsSUvtDkx2DjQBRVkDugQsBjrNq7CHD9PuA9ajUv6Lxm+YQdPZ0W07D8O81euR9CC3+DdvCMm7E80G25maLNxaVYDfCWrBo8+A9Dfbw4Oxpk0GzjT7ydUn3pHP7yufLSQNUxt8KnbDe4G065dO2HCU0JCAsyNf0BAAKpXry5somT+m3ng4n/ChAnSlUm0E/aiRf6rR7j/wtqGZUqkx97HC2tbYBqhTH+ES2FHcTj0HG4lZBs+xNn/8rLfQq7Ih0qjhjIvF/K3cgid6Jps3Ns7A93bDMDC4AhcunIV165dxsWIkzi0bgwafyOD7CsvbE9WFX4NAS2UOXIolCooFXLIc3UdBeyXggzEx0TjaXoBO8oMZToeXQrD0cOhOHcrAdmGRJugyU3Bo5uXceNRKnKlbFDlZAvpyc+V461caXltCfXbJKTxne6UWYi/eQ7h528hPtNKWizIw81pjdBmbby+t9wuVNlIun8J4WERuHIvSVrT3hSN/CmiQvZg+65juBjDjrHy/Cp5Eu5fOYOQg8E4fOoyHqZabuBjjIYffy8emVa+8MrLxj7bt28X3GVCQ0MtbG1xgfeye3h4SFci0f65QKK9VGCNy6NLCDt6GKHnbiHB2AKr85DNjLwiXwWNRoV8Ztizs7KRzwyZOi+bGV0F8lUaaFT5UMizkZWdb9F4Fd4omaLKZkbrUjjCIq7gXpK0zrkRRTYiVvnABtX4mdVK5AmNi9E9tQXIiI9B9NN0qQEt7nglclg+KVVKlkdyGLWLhVJcXrCbQv40CiF7tmPXsYuIYcdY5qsK8qT7uHImBAeDD+PU5YdItdgz3xh2TXb8vfhMKz2DTLT3/RFVJt40iPYH81HzB6PNJj4hfNtrvkFSz549LYZeuQ/ljRs3TOIKC3wSK9+kSasV84VEO1H20SBlb1d8L3PGrBumI2Yiebg9oxb7YHfBggeFdxB8thQ8wOJOg3CkGJ/58kR52tjn2rVrgnAPCgqysLd8FJS7JZrH6wJ3f+T/cki0fx6QaCeI/4R83J5SFd/2MCx1mXtlFKrWmY//qt3gfu5r1qxBixYtcObMGYsGwNbAV6fhw7gcEu1E2UeOiIG/QPazr8UmaSLSfKVfB8Fsc9AvAC2yz49D+8mXi95MqbxRzjb2SU1Nhbe3tzDqaby7taurK8aMGWNif43D+PHjsXPnTuEaJNo/D0i0E8R/RHZYb/xYfbq0JJoGSRtbod602/qe9/+Ks2fPolGjRsIup9YaAj6BlQtwa7/xwH0uufjnkGgnyj58bwm+MV49zL9r6bStyQjHbzX+B89Vjy3d4D5TVPJMKDRMsufeQWA7b2woZjOl8kf529jn/fv3ggg33t16//79Vtd214Xdu3dj6NChwvkk2j8PSLQTxH+F6jl2DXKF+4RtOLpjPoYMXIIrZWQvfT7k6uXlhWnTppkMv/Lh2B9//LHIDZr4HgI9evQQrkOinSgPaOW3sbTND5DV6ItlJx8jI18DbUE6HpwMwgj3hui26CLSrXj+fZ5k47RfdVRp2gEezhXhNuNKMauhlV/K48Y+fP8M7i5z8uRJq/bXOHCby5d+VCqVJNo/E0i0E8R/ihpv427gUkyylZ1V/1vevXsnrAbTp08fwd1F1xA8ePDApGEwD3wya/369fH27VsS7UT5QZWJu4f+xPQxQzCg7wD4jQjAnKCDuPqy6AmPnx3qNFz8cwz6+47B/G1Xi19VjPjk8N2t+UZM69atM7G9kZGRJn/z0KtXL+F4Eu2fByTaCYIoFD6hlG/00apVK5w7d86iQSgs8CFZvkwkiXaCIIiSJzk5GR07dhRW6+KLBfA13P/P//k/wsRVY1u8ZMkSwQ6TaP88INFOEESx8A2YuJ/7tm3bTBoE3oOzaNEikzgeuPsMX2eYRDtBEETpwHe3Hjt2LLp16yas3HX48GELW8w7W7irI4n2zwMS7QRB2ARftcDT0xOzZs3S+7mvWrVK8Jk031qbbwjCN20i0U4QBFG6/PXXX8LyjqdOnTKxw7rAfeD5Hhok2ss/JNoJgrCZnJwc+Pn5oV+/fnrf9sLWCW7btq0wdEuinSAIonTh/uy8o2Tjxo2C/eWbK926dUv4P18qko98kmgv/5BoJwjCLjQaDZYtWwZ3d3ecP3/eRKgbh5kzZ6J3794k2gmCID4B3O62b99eEOh8omrfvn2FuB07dqBz584k2j8DSLQTBPFB8CXHuJ8737yDNwzPnj0TdlXl/u/87yNHjqBZs2Yk2gmCID4RfHfrUaNGCSJdNymVj4rWqVOHRPtnAIl2giA+mCdPngg97r///juePn0qTHjavn270FBwH/hatWqRaCcIgviE8N2t165dK+xuffr0acEecztNor38Q6KdIIiPIjs7GwMHDoSvr6+w+RJvIHi4ceOGsF67rqHgS4/xtd8JgiCI0oevHMNHQ/kkVA8PDxLtnwEk2gmC+Gi4nztf+pE3DHxC1Ny5c+Hq6mrSu8O33+YiPiwsTDqLIAiCKE242yIX7l9//TWJ9s8AEu0EQZQYoaGhgjDnYn3Pnj3CSjO6hoLv3seXJuMNyJkzZ6QzCIIgCB3cVtaoUQNOTk4lGvg1jYO1Yz428LTzdeOJ0oNEO0EQJUZaWpqwbvvu3buF3hxz0c7j+NrufIdVvtsqQRAEYYDbyg0bNghLN5a3MGnSJIwZM0Z6EqI0INFOEESJsWbNGgwbNkwQ5zxw8f7PP/8Ige/Ip4vny5JFRUVJZxEEQRAcLtq5D/rmzZvLXZg6dSqJ9lKGRDtBECUGHxqdN2+eXpwXFoYPHy6sMkMQBFFSvH37VpjwXp4DF+0+Pj7lMnC3SL5rtrXnKg+B+/+XdUi0EwRRYvAdUOfMmWNVqBuHIUOGCL3wBEEQJcW/qalo0bQp+vfpi23bthUahg0ZikaubsKyiNZ+52EpE3F1a9fBlMmTrf7Ow5YtW9ChXTt0bN9e+L+1Y3jg1+DXWrZ0qdXfeVi7Zg0asjTVqVVb2N+isODMfq9RrbqweZK133lo4OaGalWqwo1dz9rvPDRl5ztVd0SdmrXQlOWZtWN4qFe3LqpXdUDjRo2s/s4Dn6dUvWpVOLNnHDBggNXn46OtXTt7w8vDU+iVt3YMDzNnzEBd9oyBixZZ/Z2H9evXo2mjxhjk62v1d13wGzQITRo2Elwzrf3Ow+LAQOF+06dNQyorP2UdEu0EQZQYwcHB6NWrl1WhrguHDx8WGgm+xjtBEERJ8i7nHbp16YrAhQulGOv8tX4D2np6IjMjU4qxJO7pUzRp0BBhJ09KMZb83//7fzFqxEiM9vcX/l8YJ0+cEK4VHx8vxViSkZEhiNpNf22UYqyz8I8F6N7VB7lFLKEbfeeO8BFw7epVKcaS/Px89OvdB7NnzBTWdi+MXTt3wr15C+GjqDCSXrxAiyZNcejAQSnGEr7K2KTxE+A3cBCUBUop1pIL58+jkVsDPHzwQIqxhC813LlDR6xcsUKKsU7QqtXo2K493rx5I8VYwvcQ4feLOBMhxZRdSLQTBFFi8EbAzc1N8G23Jth5mDZtmiDsCYIgSgMS7iIk3EXsEe5lHRLtBEGUKBcuXEDDhg2xdOlSE7F+6NAhYaIS72UvD76DBEGUX0i4i9y5fZuEO8NW4V7WIdFOEESJc+nSJXh5eQmTkrj/+qBBg9C4cWP069cPz58/l44iCIIoPUi4i5BwF7FFuJd1SLQTBFFq3Lx5E3v37sX+/fuLbKAIgiBKAxLuIiTcRcq7cCfRThAEQRBEuSY6Olr6nyWfg3D/eyMJdxLuJNoJgiAIgijnNKzv+lkL9zatPUi4k3An0U4QXx4FSDq1HAGD/DA3JFmKIwiCKL9cvnSJhDuDhPvnLdxJtBNEWSD/ETb6d0O7JtXx47ff4tsK3+DrCv9D1dr14VrHAb/88CMq1nBF+9/W4mJK4QatePIQu8UXDes1QZPaleA6p/AGjvhYNEgLn4HOTdtj4rEUqKVYgiBKh/Iu3Js2bETCnYR7kZBoJ4gyhPbVTrSWySCTuWNbikaKBdRv72L7sJosnv3m+jtu5Uo/2IUGr4+PRvNee5HKL63NQ2Z2gfgTUQrk4eYUJ+GdVfntEj7olelQpePWge04m6KSIgiCsEbZEe6GEc3ZR57bJtyPU497mRXuqreIv3IUm1ZsQGSGoW0uHA0KchVQaaU/SwgS7QRRlsgORdcKTJhX6IrQbClOhzwSYxy4oK+EEZE5UqQdsPP9q8hQb9FD1pwQnwJt/ktcP30FzxUfY7kzcazb9+y9O+P3u/lSHEEQ9pL/aCP8u7VDk+o/4ttvv0WFb75Ghf9VRW0m8us4/IIffqyIGq7t8dvai/ioAU0a0fysUCYewDy/9qjJO82+64aQLOmHQtEi++I4OH1VDwsflGxrS6KdIMoS8jD0+p4bhu44YS7a1YlY15SL9orwO2e/aC+4Nx91mdFpuCoO1F9bnsjCobb8vTdGUAK9OYL4OLR4tbO1MAImc98Gw4CmGm/vbsewmryuyeD6+60PHB2jEc3PElU8ghqzstE4CMWaYflVTHPm5egn9AuXS5ElA4l2gihLyE+h74+ssv/QC2FmdV2bdQqDK7LfKg7EyUzznlstFM+v4Nj2ICxetBx/Hb6J12Y9RYprE1C9KNGuVeD5lWPYHrQYi5b/hcM3X8O8s0mZFYerxw8g/AX7JT8JkbvWYd3uy3ht5LCtVTzHlWPbEbR4EZb/dRg3zRKiLUjD48gQHLn8mqVDhfTbIfhnzWpsDI7A/fQiurcKUnE1eC0Wz52FeYvXYPf5BOQaZ4MN6bdEhbfx1xC6bTXWn0llzTZruB+cxI51K7Fqw06EXktCntVOcnbcozMI3rwaS5euwY6jF/Ew0zJXtXmv8ODiURy5lmbk065FQdpjRIYcweXX7BxVOm6H/IM1qzciOOI+LLPgLUI6f80aAE/sTSvhsVaC+ALJDu2KCswWVugaCssBzTFw4IK+0gh8yIAmjWh+psjPYvCvMlQbdxUKKco6Ctye1wINBvrDlZWjpusSjWz/x0OinSDKEvJwSbT3xikj0a7Ni0fwMEcm3OohIPSVqRHIi0PwZG94+i7CntOXcPFoEIa6foOvXUbhYBIXkkzQJ5zE0n6ST3xVNzRr1gQNmw/GfqmbKS8uGJO9PeG7aA9OX7qIo0FD4frN13AZdRD8EuqUYxjvVQvf8fNl36PHwUis8OIuG/wjYhguCo1bHuKCJ8Pb0xeL9pzGJSZWg4a64puvXTDqYBJUynhsH94K1b7m15DBcVwYTgd2hGPFKqjykxgnq9AE448mm35UaLJxd8ck+LT2gI9fAMYO7oJmgptQNQRcEc1ncem3Sv4jbBjsjtr/E+9db8E1XF/XG7V+rQzHqj+K6WGhus8yXMs2iGVN9nWs6u2Ghn0XYtepSJw9uB7Tu1aH7Psm+G3HA+TwQwtisXFwc1SVruE05SbLHfbRE78dw1tVw9dCvCPGhZ1GYEdHVKxSBT9Jx1ZoMh5Hk8VEa/Oe4/SasWgtpPE71HP3hId7S7QZHYp00u8E8UHIw3rhe1bXvut+wkK0qxPXoSmvixX98AEDmjSi+Zmiig9CY1kFdDmSIcVYJ//+Yri7BeBs0mn4/ixDldFRHzefyQwS7QRRljAS7SH3jmDxtPHw9/VBW3d3dPFfgpDYHCbBjVAnI7h/JcicZ+M2V4USysfLmYGR4aeeh/Bao0b67VM4+KcPfmZxNcfuwumzEThzMQYZTP2rk4PRv5IMzrNvC8JSRInHyxszofgTeh56DVHaK3B1PBOnsq9Qx6sbxq7Yhn8CB6Beo7mIzlcjObg/KsmcMds0IVjOhxR/6olDr8WrZJ/shR94o1jdE2N3xCBbiC7Ai4P+qMXjnabilu4SmgycHu+C7+pORZSRcNbm3sHCFu5YFaeyI/3WeRviLfS6fd+gCwI230Cm8EWkgfz+Loyqx9LDfqs+NhLCN5TyKda1/R7ftduM58YtsjIO670qsGMrwy8kTX+/rJBuwodO3XkxMHijZ+Nkrx/E63qOxY6YbPH4ghc46F9LiHeaekt4Fk3WPYQf3YHJLjwd7lgWFonIyChcfyKdQxCE3chP9cWPrJ790CtMrNd6tMg6NRgV2W8VB56ExYBmCYxoFjcSyW1XVtxVHD8QDnFAMxK71q3D7suvTUbrik6HvaN5RpTKiKaIOjseV4/vwvrVa7H12C2kWhuKUGYh7upxHAh/wa6bj6TIXVi3bjd7DqOuKhvTYNP9CoV3dp3G5sAZmDI7EMumuuNbWUOsfFrEp1hBLFZ71cOwE+nQFDzEIt5+eO7G6xLsYCHRThBlCSPRHpYeh03eYo/v/7ptNxWJEgUPFglDcHXmRBuJQkb+XczlPnVGPfa5VwKEYV/TxqQADxa5snvUwZxo00mO+Xfnwpkd/0PvU1LDVoCHgfWF9Lgti5WMpApvUzJRUPAAi1zZ/erMYQJe+EEiH3fnOrNzfkBvKSG5l8YIPdCV/SNh0pGlScZWd3aNCt4IeStGFTxcDDd2bNO1CWYNoALRf63DzRx70m8d+dnB+JUd5zTpmkWPiO7+sopDhdGEt2G++IV9tLTb95qZdFPk50eiMj+20Qo8kVqQ3Eh/Ic51qS6/OLm4NKYqS3Nl+JuNv2uSt8KdHV/BOwRSFrC24zV2tWbX/a4HTpp3CxIEYTfycEm0m9gGLfLigzHMkdW1egEIfWUynsmH8z5yRLOYkUgmyVOOjYdXre/E87/vgYORK+DF5zixvysOuyjay2LSYe9onp7SGtHkqF7h3MqhaOfuBZ8+veHdtIr4jC6jcTRFymd1Co6N90Kt78Q0ft/jICJXeAkjInwe1zBxONe2NNhyv6LQZOHK6tEYNnER1mzehKA/RqNDdXZ+ldGIKrTbXIWEjZ3gMuAgUoVbZOBQh68hqzUTd4z6sT4WEu0EUZYwc4/RpB2Hfw1utCrA/c8HRj3JHDWSNrUUjNGvHQIwL3ApVqwKwpq1QVi9bDq6cP93Iz9oq6JdnYRNLflxv6JDwDwELl2BVUFrsDZoNZZN7yL0OMk890K8hApxqxqyYy0nwqqTNqElP/bXDgiYF4ilK1YhaM1aBK1ehuldKgpp9NybJghdxY1JqMH+dvnjvpnPZzZCu/Le6tbYJXRN6O73E/qfLkR225V+6+RGjRKEdZO1iYZ80VFwH38IvdxtEJzOxPZobvwLWb3nbQh8vmXHft0Rh6UV4qx/KClwY1INdh0X/HHfrOtHt3pQ612G3hmdaP+hj4nLFEEQH4ZBtIfg3pHFmDbeH74+beHu3gX+S0IQK/i4GfHRI5q2j0RCcRXjuUD8qg68uo3Fim3/IHBAPTSaG418m9LBY2wfzRMo5RFNbdoedGoyyyjNCtxd3FRM3/hr7C8DiqvjhZGKr+p4odvYFdj2TyAG1GuEudH5NqfBnvtZkoeYxe6oNeCIlJecLIT0+AEVOgQX2paokrajm0t37EjSfRQocHVcNcgq+Og7oUoCEu0EUZbQT0TtLU1E1SLn1gI0/YrFyepj5mW5UQ9vPmJ+573YMtQYuQlHQkIQIoRQnAw/i8gbD5CYlqsfUrUqIPNj8Lswy70GRm46Ip3PQuhJhJ+NxI0HiUjL1V1BJ6IdEHDFtLshP+Z3oVdbVmMkNh3RpSMEoSfDcTbyBh4kpkF3Gb1oX/DATLTLcaovH1nQiXaduP0O3Y8X0sVsV/qtU6RoVyWIKwZUHILzOdk47sM/Kn6Gb4QV9ay4jgm8l07mgT2S4i5OtC8wXw5M9/6tifb/DUBh3y4EQdiOQbSHIT1uE7x5nZP9D922P7e0AYyPHtG0YyQSBQ8RWJ+nxw3LYqXxOdVbpGQW2JEO+0bzSn1EU52JpwmmLn26dMg89pgI4YKHgajP492WwfD4KcgssCMNdtzPHFXiJnj96Iixl4zauNwrCHCQoX5gIZOL1SkI7lMZ1fqswO7gYAQLYQ/WDuXz0FwN71GPFgVZyUh6nWM6P80GSLQTRFlCv+RjD5zUW0AlErd2E3pvZNVHIFT/+a9CQhDvYZCherEz2g3i1G35E4Orhk6Uyqpj3NXirqBE7HI3dmxV/GZs0BiqBD5Jh6dvHIq7jK4npe78exaiPVwQ7e7Ymsyf0eCOY9FI6bAr/dYpUrTnRGJEJRkqDYtANsvhKwHVhPS03JRkaWx1PWSVR+mHUK3mObuOODegLubfMxft0kiL+1YIWcDRviLRThAliKl7jAZpx/2FjgRZBXf8+cDQhyvy8SOa9oxEQhWHVQ3ZsRYTYe1Jhz2jeZ9mRJOjlifiRtherF80BaMGeIjntQlGuvQ7RxW3Cg1ZfEW/c6bukx+QBlvuZwr7MFhYD7JqAZA8ggTybs9ELVlFDDlvZYSVlZ/Xx/xQ23UQZs6fj/lGYfawBvha9gsGnTXkqzY3BhuHd8fgSfMw3a81nFuNxcEkc1FfOCTaCaIskX0c3biLhfnmSpp0hI3mRliGn3y2IlGq4/kx84SVCmSO4xApL9piys/44n/sWFO3lHzEzKsrXNdxXCSKvkQB7v/hIhhNv3Nmxj0/BvPqcoPqiHGRxqMBluREjkQldj+nqeKKKgbkONmD+3M2w/rnoiTW+b/LqgcU8nz2pN86hYt2DdKPD0alb1ti+SMxx+TnRwhpr9BhB16aqXZN6k54fcU+oAKi9D1O+jw3+UDJQeTISizNTph600wgyE+iB/fpbLYeUhawCydhM2+syKedIEoE/UTU3tJEVG0Obi1oiq9YnKz+TFw2MSQfP6Jpz0ikXrQ7MOFo0jdiTzrsGc0r/RFNbc49/DOuA1xqucNvwXaE3UrE2/QQeH/Drtn+iGH+DkMn2h0CrpjOMbIjDfbczwRVAtY0leHH3icMx2jSEDKQf1h5YIfRLuU6tJmn4O/cEHNuWXYaFdz/Ay7sWVpuTpJ6/XNxbUoDNF8sjTKzdj3EtyJ+9N4jrulvAyTaCaIskb4Pbbhxl7XBPrPuAK38Gua48d++QuO5lyG4Hgo+jmJPTUXvQETot/HTQpF4Glt339MbvrfHOguTkxwnmPr0CX6CQi9NRXgHRhh2AtQqkHh6K3bf011BgWsT+HBfBXQJMTfuos+m0JNR0RuBESn6nmWtIhGnt+6G7jLysN7C6jGVRphNRGVmMsT7G3Z9Vyx5LJ2tjMdfHcSJWb+0X4TzfCUEAS1ykx4gXq6xI/3W0Yl2WbUeWH4uCQrBeCqRenE5+rj3wB+nUgxiXvUC231+YveqjhEhRktvanNxe2FjfF9zFEKNVjmwnudyhPXm/qZWfOPfSg2L6xLosgDaNOzz4s/nhICjsUhNvIXIO+mGexMEYRf6JR97nNR/YEOZiK3dfhZsTfURoUb+zB8/omnPSCSUsVjO7XzV32A6oGlPOuwZzSvtEU0mVCdzP3o3BN4zurrO1nU4aiKilbHLBVedqr9dMhXtNqfBvvuZ8DYU3b8zXltdg/SzixHQzwlf1ZxmWNVMhzYb58fVhfPYc2J7bIY2fT/aGb+vnHPwq+KEidcNFxJWU/u2K44Vu8uqCIl2gigLFMRh1/Sh6OUuzXJnoQoTjH5TdyLOyObmP1qNNnxoU/YtXHvPR0S6hon5GGwZ3lCaZV8BlZ3d4FrbCW7eE7A1OhsaVTJOLhmHfo2/Fa9d0QtDxk1HUFSG9PWvhTxmC4Y3lNZdr1AZzm6uqO3kBu8JWxGdze6RfR1rJ/vBSxDHMnzbsCeGj1+Iw891zRJDK0fMluFoyN172DEVKjvDzbU2nNy8MWFrNLLfp+DUsvHwbSFOkJL90hqDJwYhKlNqHTUp2MZXj2G/VW0zCDP3xAu9EZrMS1jWU1qRgYWKddjz1XGCS8e5uCicW3z6i0Iv2r/n/urs328qomplR7iP3Y4YK1332pwYbBzoggoyB3QIWIx1G1dhjp8n3AetxqV0SUoLeR6Avo10ed4GfpPX4tKjMCwb74sWP4jP8kvrwZgYFAVDFmwT/S5lVdFm0Ezsiec5oEbqkaFwEuJZ+KUjllzOkt4dQRD2kn28G75ldcl8cyVNehhGCxP/f4LP1kS96P7oEU07RiL1k99/9YPlgKat6bBvNK9URzR1z15xKIy9S/IfrUQTfk8zEa3rnf7V75zhg0rAxjTYeT9jlLHL4Cr7Gp2P8SNYu3J7I6YuCcHObj+gQvv9wt4YWo3uxuz3y1Pg4tAXB18VYo1zozCqigxfSeeq4lehwTdu+NNo2cj8OzOZba+HpRZ+79Yh0U4QZQIVct9mITsnDwUqJpI1aijzcpCdrbDoUVUpcpGn4r/nQWVkuJTpj3Ap7CgOh57DrYRso/PUyMt+C7kiHyqNBqp8BeTZ7F755oZGifRHlxB29DBCz91CQrbRnbUFkL+VIzdfCbVGhQIFS1vWWyjME8dRpuPRpTAcPRyKc7cSYLiMBnlylo5cng6W/vxc4ZoF+mfQQpkjh0KpglIhZ8eZOquospNw/1I4wiKu4F4S90Q1p4j0F4HePWbNE6TH30Lk2Yu4lSi3yHdTNJA/jULInu3YdewiYlh6TI83znP+rqRn/b95Uj6qoFErkZ8rx1t5gaER1yqRI1dAqVJCIWfH6bNAi4KMeMREP0W6IcMIgvgA0ve1EcSfrM0+M/9mJsSuzRGXef2qMeZezhbr5kePaNo+EgnFNXFCe4UusBzQtDUddo7mleaIpuopVjbi59XEuJMpKMh/hbtHF2Noj/bih0LbAzDeroivc+/I4it0CTH5oOLYlAY772dMwaPFwiRY1/lHsX/JCPSZeBhJ8juYVVuG773X4eCG+Vh8hK8fz26Zcw2z6v2AJsseGbk+mqGbVOw4AddYYRAE+tctsEU/YYll/ZNl7J5OmHnH6hiHBSTaCYL4otGtpW51IipBEJ8NBXG7MH1oL7hX4aKOhypw7+GHqTvjjIRXPh6tbiNsuCb71hW950cgnWmsjx7RLG4kUqNF9vW1mOznJYp72bdo2HM4xi88DNMBzaLT8T7l1AeM5rH4UhvRVCPtzHQ0E0aIefgOzSYewfOUA/Dif3/bGAOmb8NjRTaur50MPy/xo0T2bUP0HD4eCw8/13/g2JYGG+9nTSO/DYPvL/ycKuiy+ByEbxf5OfixD4Wvnfth9VU+wqlGysmF8HP/Vbh+1bYDEbAwxGydeg0yIldh4tCuqCus/MbeZa+xWBjkh0pfuWPnK0PHizpxDRp9XQfzYgqV/iaQaCcI4otGHjEQvzDj2+DPp0aNA0EQnx2qXLzNykZOXgFUTCTz0a68nGxkWwwZqqDIzYOK/56nYlLRwEePaBY6EskkaQEfectFvlLNPgIKoGBpy3prOdrKKTQdmg8dzRMprRFNbUEaYm/ewIPkXOmaKuRmy6HI4/mUw/7SokAaiVWqWf4VKJDD8u+t9eHcYtNQ/P2soYE8PhqxJlvGFuD10xeQG2WEmn1gZOeIo6gFCpa/2XkW+aTJy2b5rkC+isl8ZR5y2bO9ujQJjrLGWJtoSK/y8RLUq9ACW15a5rQ1SLQTBPFFk33cR/BvtVyCkiAIgiBKBk3Kdrh/XQVjLhtciXIjR6Ka0/jiJyhLkGgnCOILRoOkzS2FYc7K/uar2RAEQRBECaFKwNqW38J5Xoy0So8Sj5c0g9sMo91pi4FEO0EQXybK59g/ww/9evVE77590KtnbwzwX45rZis2EARBEERJkHt3JbxrN8foDUcQsmMuBo/ciAcK2xcXINFOEARBEARBEJ8C1VvEXzuHK7EZds+jItFOEARBEARBEGUcEu0EQRAEQRAEUcYh0U4QBEEQBEEQZRwS7QRBEARBEARRxiHRThAEQRAEQRBlHBLtBEEQBEEQBFHGIdFOEARBEARBEGUcEu0EQRAEQRAEUcYh0U4QBEEQBEEQZRwS7QRBEARBEARRxiHRThAMbV4Wsgu00l+2oIY8QwGN9BdBEARBEERpQqKd+OLRZJzDbC8PzLuTJ8XYgDIO6308MCE0lcl3giAI4tOjRV5WNgrU+Uh7FIlDGxdh6swdeFog/VwYajkyFNTlQpQ/SLQTXzb5j7HOuwbcA6OhkKJsRRn/N7pUa4w513NY00EQBEF8OjTIODcbXh6zcfTvMfBpXRtfy2SQOU3BzWL6X5Rx6+HjMQGhqdTlQpQvSLQTXzB5iAlsih/rz0dMvhRlFyok/uWBn12m4bKcZDtBEMSnIv/xOnjXcEdgtNTdkn0SPb5jor3uPBvsuRLxf3dBtcZzcD2HbDdRfiDRTnyxKJ+sRqsKP6DLnpQP9k3Xvj2DYZVlcJlzm30CEARBEKVOXgwCm/6I+vNjoNfnOecxpCIT7S4LcL849xiOKhF/efwMl2mXQX0uRHmBRDvxhZKDqIBqkP06CKflUpQJamTHX8XxXeuxeu1WHLuVCuvtQB5uT68J2Y89cPA1+UgSBEGULko8Wd0KFX7ogj0pRjY39zLGVGWivd4iPLRFtEOLt2eGobLMBXNuU5cLUT4g0U58mcgjMPhXGSp0OYIMKUqH6tU5rBzaDu5ePujT2xtNq7CGQCaDy+ijSLHiApkb6c8MfwV4B78m33aCIIjSJCcKAdVk+HXQaZj0tyiuY2INZqvrL8YjJtrVbx/hzN5NCFq9AbvCHyPbWp9K3m1MrynDjz0OgvpciPIAiXbiiyTv9gzUYkK8fuBDsx50LdL2dEKTWUbuLoq7WNyUC/fqGH/NcrqqNnUnWrNr/TwwwrQRIQiCIEoUecRg/CqrgC5HzLpb8m5iihMX7QsReXwmWv70DX745Xuhw4WHH1rPR1SWuTLPRaR/ZcgqeCP4NXW5EGUfEu3EFwgT5nvbCIa83QHzfnZAnfkUCSbdMhokb3UXjvfYk2bZm55zDn7cl7LRasSrpDiCIAiihMnD7Rm1mC2uj0BzHxidaK9QC60GBSHqNTfGWuS9OIk57t8K9tsx4BJyxKMltEjd2Zr99jMGRlCXC1H2IdFOfIEo8fTPBsxQf4+eJwsz1GrIE28gbO96LJoyCgM8KgpGv01wuvS7Ebph2ZrTQa6RBEEQpYQ2DXvb8J7zdrDob9GJ9op+CH9r2rWiSliHlsx+y6qMRlSuFCmRc84PFdlvjVbHg/pciLIOiXbiC0SJ2GWuzPD/gF5h5qJdi5x7/2BcBxfUcvfDgu1huJX4Fukh3viGGfb2R95KxxmhE+2OE2DFe4YgCIIoCZRP8WcDZmu/7wmL/ha9e4zo025CwQMsdGG/ydrjcJYUJ6G4PhE1mG2vOZ1WACPKPiTaiS8QDV5uackM+NfodNTMgudew+RazLi7BeKe0Vq/byXR3uGoFdGeE4kRldg5ti41RhAEQdiPMhbLXJmt/aEXLPpbihLt6ufY0Iz99qsfzpmdpxPtjhOu2b3BHkF8aki0E18kfEj0V2aom6xNNBkSzY+Zh7osvuLQ80a+j/l4tLKJ4B5jVbRnHUGnr2X4ptMhi5VoCIIgiBJC8xJbWjLx/XUnmPe38BHPCY6FiPa8W5jGBP3/+hxDutmkpJzIEajEbLvLgvuFLOtLEGUHEu3EF4kmZSe8vpKh0rCLJhOTVE9XohEz4LKa43AypQD5r+7i6OKh6NG+qiDa21qZuKoT+m7Ln0ApxREEQRAlTQ7O+f3KbHETrE0080DPvSSu0+7yh8WIpzwqAI4VGmOJlQXcs450wteyb9DpEHW5EGUfEu3El4n6Bf5u/RUz8AvxwNiOq9NwZnozVODCnYfvmmHikedIOeAl/P1t4wGYvu2xYRc+7mqzja8+UA8LyDeGIAiiFNEgZacXvpJVwrCLpuvAQH5W2HtDJnOE77orSBM0vRaK2B0Y1twdE44mWelUyUfMvLrsHDcsf0JdLkTZh0Q78YWiwetDPfGTzBlzoo2c1wW0KEiLxc0bD5CcKy39qMpFtlyBPIUc2TlGPTx8K2z3b/CN+wYk0NIDBEEQpYr6xd9o/ZUMLgsfmLqzZB1F1xpN4O0ujorKZL/AoUpF1O46H8dfmNt4Cc1LbGvNjq1H85GI8gGJduLLJf8eFjb4ChUHnkTmB+6robg5HbVkjgi4KGdSnyAIgihVNK9xqOdPkDnPgUl/i/otktLymR1WIiv+Js6Fn8et+EwUFGGYVYl/wf2bb+C+IYGWeyTKBSTaiS+a3Fvz0fjnhph/y2zxXltQxmFD+19Rc/QpZNAW2ARBEJ+E/HsL0eCrihh4MvMjOksUuDm9FmSOAbgopy4XonxAop34wlHh5cHhcG06C1ftMtx8RZm2cO6xEY9pcV+CIIhPSC5uzW+MnxvOx4f0t3CUcRvQ/teaGH0qA9TnQpQXSLQTBAqQcHAptj0qxO/RGsrnOLD4H8TkUA8NQRDEJ0f1EgeHu6LprKuwu6M8/xFWtnVGj42PaUMlolxBop0gCIIgiPJHQQIOLt0Ge/pbOMrnB7D4nxhQnwtR3iDRThAEQRAEQRBlHBLtBEEQBEEQBFHGIdFOEARBEARBEGUcEu0EQRAEQRAEUcYh0U4QBEEQBEEQZRwS7QRBEARBEARRxiHRThAEQRAEQRBlHBLtBEEQBEEQBFHGIdFOEARBEARBEGUcEu0EQRAEQRAEUcYh0U4QBEEQBEEQZRwS7QRBEARBEARRxiHRThAEQRAEQRBlHBLtBEEQBEEQBFHGIdFOEARBEARBEGUcEu0EQRAEQRAEUcYh0U4QBEEQBEEQZRwS7QRBEARBEARRxiHRThAEQRAEQRBlHBLtBEEQBEEQBFHGIdFOEARBEARBEGUcEu0EQRAEQRAEUcYpOdGueYek2Md4/KiI8DgOr/K00BZkIvExj4vF8zcq6QLlGS1U75XQSH/ZjVaF98oPPtsU9TskP7qB88cP42hYJO48SYXigy+txJvEWDx59ob9j6PF+4xExMY+h1wtRBSKNu9fxD9+itQPv3mpocx6hiexz/BGfKhSQIu8f+MQ+/RD857le1IsHsan472WV60UxD2Ox6t89ofN2PautO8zWF2MxYviXmgZRJnxDImv8tmT2ooGipR4vMj+HGzOp8T2ev/pINtUdrHX/imR+SxRtG9qOV7ExiIxQ1l0vdYokBr/HGWtKqvelLf397FtlX2Yl2+b2x9tHl7FxSIuRSFFlCGUWXjGyuzzT/TSS060vwlF70qV4VBkcMHkq++gjF0JTymu87Zk6QLlD3XWHeyc7IMWtaXnq1YfbfovwNHH72wQ8KyyJBzDQt/WcJbywqW5N0avOI3kAukQu1AiKWwJ/JpKaTEKNdxHIuj8v7DbvqkSsbENu0bbzXghnPwej5a2YNfsggNpRUul3KsT4VqpJkadz5FiygoqJG5ow56hHbY8Ly2L/w7XJrjAwWkkLtj7+KpkHPZvKL67+lNxg9ko+ZnBqFmpIebczpMOsgUr70qjhCLnHfJUhnf3/uEyuLN7+exPk2LKCYpoLGnTHFMvZBfduJugQVrIMDTzXo+nH1THPi80SgXe5eTBqDgUgu31/pNBtqkMY5/9U0QHol2zyYjM1kKbfgDdmD3yWBErfYwVgiYNoUMbw2fdk6KP+6SokbytI3t/bbAxoTTenwZKxTu8y1PZYfOK4yPaKruxLN82tz/vrmJy3cqoM+K8FFF2UCX8hXbsGTpsfi7FlC4k2j8Qbc51/NHC/Pl0wQvLot9JR1pHmbADA52snVsZzaZFgtkvu8i9OV8o/A4NhmBVyE3E/5uJtGcPELVnPvo58+t6YeX9fOloG7FoGJV4tns0unWfjnNZ1DAWzocbQmX8Wnix99hkfDBuJ2YKPe2Km4swoMsIbHliT/Nk+a6Uj1fAg12724F0vdFXJu7BmC7dMfNslhRTHniPx6vao3737VK5tIP3j7G6jTN6//OsDDX2/wVKxK5oxepBVxxML87Y2F7vPxlkm8owdtg/Vh+D2tVF7+3P2dOzdtVW0c54/2gV2jv3wLbEslKTNUg/OQU9fcbjWEopDEkVxGJlS1bmuxxAsVXWZv5b0W5z+0OiXU/piHavdbjz/CVevjQPqXhTwEqbKgevkvnfyUh7V/6G5XnlfHWgFxyF562GrovP4fmbTMSfmI02Uh7U6H0Qrwrrbue9BH41xbyqOxTbbqchJz0GO4c4i3EOfRGSYU+tfIeL/rXZua2x6tF7Kc4A78nggr7++MvIleJswqJhtB0S7R9mCPOjf0ezSlUx4GS2FFNyWBPt5RFt1mmMdnbGyPCsD3gONVL3dketBtNxvejv6s8ce0R7GYRsUxnGVvunxZtwf9R3Ho7T0oeWPaId6lQEd6+BJtOu29eulVc+Q9FuMyTa9ZSOaO+8t3DBylAl7ceE7t7w6dwLM06lS7GcfCQcW4wxPT3RrHE7+E7agKhnV7GoXxd2bE9MOZYqHKW4vRSDvNn5XYdgzT1D77E69Rim9ODXNRyLgif4x78bi/PGgHnXkPZoL2b2bAX3EachyiIlUi9swpyRvdGxqSsaNvVC77ErcfxxThEuLu9wZZwksOv8hov6wp6JE32rifHO43GlEFGgeXUAvauIedVtVwqTESLa91lI5R83ya+QY0+Z1r7Cfm92vap9ccLaB+t7lgejesJ31lkYd0Jp3tzF4TXzMXnYQAz9bSaW/xOBF8ad8RYNowZZ13dh7doDiDVyLdMq4hC2cSGmDh+EERP+wIYj95AaZVvD+P7leWwNnIFxQ/rBd9BITP5jC84lKAxiTJuD+/vWsGsmIC/7Hg4tnYjh/fphyKiZWH8qAQpz41WQjIvblmGG/2D4T16MbRHPkJMcgS1rtuNKOs/pwg0HT8vO5bMQMKg/ho6agTXH7uNNsd+UWijiTuLvhZPhP3AYJs9fh6P3UnHJqiF8j5fnt+PPmWMwpN9gjJkehJD7WdL71+DNrd1YNdUbNVm5aDZqMYK2nEUqS6Ly2XFsXL0VF//l6dUi594+rFt3BAl52bh/YBkmC3k3GrPXnkKCPkOM35UW8rt7ETSjJ+qyazcctgCr/z6NZNYqajJvYHfQWhx8bOwryNISfQjr5k3ESN/BCJixjOXjc1Y7DWhz7mH/GvasCXnIvncQyycMhS9/phlrEW78/jjad4gP/wvzx4/EoH4DMXriAmyPSmW5YUT+UxxeuxJB/1zAqyLLvhovd3aDk/NvuCCXovTw/P0HS6ePxTBeRkZOxMLNZ5FoVkg0aYfh61gLQ0LSi6jjOpRIvrANy6f5Y8jwKVjK6sjznGREbF6DnZeNbZet5af4vDWU+XjkpERh1/LpGD1oGCb9sQkRz/KhVWfgxq6lmOk/CAOHjMeS4BjL+7x/iQvbl2POb4PhO3g0Zq0+hgdZ0kFaOWL2rsbsHvxD3w0jF6zClvBkKIX4NfjrWCIUGdexdcZwDJl2BMkq6/Wev4vMm8EImjsOwweOxNQF63HkTiarYUVgS1lglAvbZHNdlPjktsk6JWtz7bF/ZqhfYpdPddQffR66qmwQ7feReusA1swNwLCB/pgeuFko+6ZokHa4H2rVHIzQ9CJqsiIWh9auxaHYHGTdCcay8cxW+fpj5tJtuMiNoB7RTq5bf4zZjExc3zITIwdNw9Fk6V1o3uDuwTVYMGE4Bg8eg9lLt+LscxOrKJaHoL2IfmOaHtveX+H1SSuPQfCqmehdi5V51+FYtPJvnDFKu23X/9B3pUVufDg2zp2AUQP6YfDISVi0NQqplpW2mPyxLN9W2x+tAvEnNyJw0gj4DZ2IBax+3Wd20CbRzu3elsWYNWaI0B6NnrAA/5xl9sxQuO2rszbY/0JFe1E2+CP4T0S7dfcYBe6t6YnaumvoQqOOaCj9v92GROHInIghgrBxqOSM8VEGZazLPONjkXcbcxpI12ozEb6CqwgL3UPBpzAl7hyEetI5JqFKOyyMzCykUc9DzOpRGNR/AIZOO4yXevsqR8RgR/F8l8m4btLAGciNCpDu2QQLot8gNToCh3fuxtHz9/CvXZMNdShwfVJ94b7uAf/gSkrxk/PyHv+DYW48DdXQ0NMbnZvXEc6v0XY2zrySCpaVIegnf7qz4wx+o+pXpzDbs6pwbs2GnujkXg/V2P8btm+BGkU2jFq8iZwDr8o8DfXRtktP9OrYXHz/1brj76eSQVInY0cHFtdhPqZ6OKJuy27wG9wbbWrw82pi0J4kvUjQym9jTU8uRPj8gI7o0poZo0qO6DzEm123GRbf41bGWsOoRtrZP9ClOrtm5fpowz7wvOqLz9Rg6E7EWw5eSKjx6tRMtBU+wBzR1LMDPF2qsP+7oVNz9vFmbAjVaTg3v5NQbqvV82AfkZ5oKJxXHyN2xjPRosLL/b+hS3Mx/dUbecJ70FrwgRNTn3ad32RHLJjkiZq1WqHnwMHo29pJOK/OgL1IsnhXKrwKGQefFtKHZgMPdOr3J2LY5XR10eBTmIfYLUPQiB/n4Ia2nTuilTBnoxo6zD4NXdFQJ29DZ3ZM53mT4VW9Njy6DcLQXh7i+3PyxT4xEeylZOPq7zwdleHYoB16dO+M1oJbWE0M3mt4d8g8ip783Aazcaco131NKvZ4V0HNgWGs/hqhfYOoOZ5C2XOo54luPbrDu5mYJ07dNyLO2IedXWMfv0b/4yYfsRYwEXtndS/hQ8ehdgt07uTBxB4r5x2GwoeVvxaL7kkH2lp+bMtbfZlvPgS96tZAi64DMaR7c1afWJxzP0wd1Bg1XdphwOB+6FBHPL/3tmf6vFSnncWCTtwOVUGD1t7w8XBFdX5uvWHYxROj/heh47zRWrKFjVt3gO+fMchTPceWtuw9+SzDHF7v2W/OPpsQr7Ss97xhffj3YDTg163VHN5dOqB5NX5OXQzf+5yVPivYWBbKjW2yuS7yovSpbZM1Strm2mH/rKBJ2Q2fyo7wCzPUZJ1or+fdVShbTg0M783BoSMWXjBtkzWpe4VrDAzNLLTN013TfdRgNNVds3V9sU44dceqG7p5MSq82NwODlW7YcVMLyGPHWp2w+Z4li95sdg6pIEQV8PVEz4dmsOF/+7QDnPDX7Gc4Fjzabfx/RVTn/L+DcGEzi0l3dAAbdr3x6q7YntgW/n40HelRfaVeaJeq9oQHXy6o2sL0Uugjq+hfNuWP5bl26L9Ub9C+AxP8d1Ub4S27VvDjZdX1w5wdyhatGvfROF3D/5M7Nk9u6J3945oKZTZ6ui18alkk2yvs7baf2uivVgb/BGUmk97dYdqqGESasNnnZhx1kS7Mm49OunOd/PDqgNhOLZhnP44Hj5atPNQtQl8+g3B2BW3IP/3IHx5YWfxzn3XICrhXzy7ug2/NZSObTQbN4owOuaoUw9jaE3x3Jr9Dhfy4aJF+oGuUnqc0a13S9Eg6UJTf+x8aNZTaQPK5/vgX093nZrw7DUGf6w7gMjHGVCaX0wZhw3t2XGVvbDoYqZUofKRsM8fbuz8eqMj8IafU1zDqM1CxCguBGtj0Ob7eCfcR430s3MlN6EiGkbtKxzwYcc49mcCT9cIaiG/NgfN2Lkeyx+LlUzXgLA499nnkKFrs5ODMdCRxXfYhpdCXD5iFvKJaM4YuuMJ8oS0sK/kY+PQWEhL4Q2jNiMMo7n4aTkbEUJvNkOVjsgF3HAzQST5W5qjzYrAGC58avliy/134jtTp+PcXA8hvQZDqEVm2EjBiHnMjtD3JKvSL2IRz1+HHtgppUXvHhNmcI+xLtrZec1n47whQ7C/fw0W3xHbhQyxFDFWfdrNjKauHlZrswBRumvnJ2C/fz12LWeMjXgjnKsT7Q6VWmDuuQy9UU4OHoBaLL7z1pdCjDplJ3zY385+h5EqXU6bdRYTeVltvgyPdGI6Px6hf6/Hpt2XkSYdZw1t5gkMYnW2w98GkcrRvtqPHuw+tfoxo6svTnJcn9OYpbEV/nyki+S8x4PA5kwAByCqiPqdf3eh4FbmMngHnooFCsrkY5gg2ROd0ba1/Niat4YyzwTSbp3vfR7uLmRpZuc7uC/E9WzRuKiT96Ifb9x1HSXaTJwayetkK8w9o5t8rkL6hQVoz86toZ8HYMU9RhLt/B4NBm/CrQxdDluWpfePVqItO86532Y8knqnNBkXMJtPhHeZiKtWRhltKgvlyTbZXBc/vW2ySgnbXNvtnzW0yDruy+wae9ZnhifQCWwuLHuuvg259N4yIwPRuSqLN3dre38fS5uxcjE2CoUMbBtdszp8/rwBseqokRW1GN78ms0XIkboEJZEOz+23mBsvpkh5a0S8es6sPgqaL8gEplSfuQn7MNoF3as8284KxRKS9Fu6/uzqT5ZcY+x9fof/K7UKdjVmf1e0w9HdH76rG6dG887CVtg+UNeYmzNn+JEO/uoPDNa+DCpO2AzHuSID6lOP4t5rdl1WHzhol2LV/u7sWNqwJd9VOpLt/wafm/Ezm25HI+FtsbWOsvSb6P9txDtNtvgD+OTTkT1/FP0U7MU7awBEQwuj2uEOXzJDAFWibb5iF8oLHy0aK/eF7ue6V6nBv/u9RHjK7lj1WP9a0ZmKDcmPN4FkwvzcTFDmXIKs6WC5eDgg/UPCusuVOPlP7yAS8cygdai+zD49/WQ7skCMyLCR7SdqLPu4/ja6RjauYnhWjy4dsWkDVFIlwqKMnaVkP8Np1yGST1Vv8TOruwr3XEwTvPOj+Iaxqww+PGPns6bYWR3WRZm42IA70kqomHUyPHo1AEcijAdbtVmHBZ6XJvNjWZNHUPXgFQfgGPGQ6Asrdt54946CLwjBHk3MJP3zrXbgATdq+Sw8/d04z0PhTWMGqQd7MXKWH1MjjRLq+IG5vAPOO89SDW6tY43YYOEfPbZbCYgsy9gfF12ns4QatJwuCdLQ72JFiJRcWO20PvjsztV6EGyXbQ7YuBRY/cOVq628nLlgbVxQi37ANHOzlnZmp3jimmXTROqfrkD3auwj9HB4UIPt0601+x/FKavZZsgTNsExQl/K5+sFkRSg/FnkaU/ToU3cTGIfvASuVbytSjybs1kQocJ3Ium9VIjf4jwAwdx1sx9IeMQN+SNMT/adGj9zYm+7J0b13tz8nBzGu85ao+/TAsUknd1E2ySaLRtLT+2562+zNebYjJa9+78SGGlqW77/jW8d+VTrOF2R6qjmrRD6MOu1WBClGndhgI3ZzUSysPeFH52EaK9Sg/sNynw5mUpH3fnN2F/8zk0xnnDPtoOT0a/PlMQqlPlRthSFsqVbbK1Lv4HtskqJWxzbbZ/VsnD7RmsftX5zcQm6gV288W4b9IhmYc7c/kHeD1MvWY8hP0GJ/uyPGy1ErGFrAilv2aTBbhrYgZ012yEuYJt1Yn2qui9T7THAkwsr3Zn59efgiumhRIvd3B94oih4YJVNBPttr4/G+uThWi3vXx88LtSPkEQty/1JuBcpqEsqLLiEBP9AC/fsTib86c40Z6FUwN577Q3tiSapBLZFwKEXu7CRbsG8oencPBAhKmLizYDR7qxtDX6HWITYGv7aav9Z09lJtptt8EfRumI9sYTsXnPPuzfZxwO4PQjuZBJlqKdVbw+3HixOOdxJr7gmlfB0ldyCYj2zvuMer8VuD7VVYxnX6QtvDqicwcxdGwpuRGw4L09mb2molDi37OL0Yv7mvFzavXEqiuFudVweOGQvvRYaLPkrmRA3+Pphs5Sr3sDzLr5AardCE1eKmJO78GqKf3QykG8V/NpF4VeKnm4L2rwinTa3CmYfTEH8S/vlmLPZDENo1JarslrbTz7xZQ3Yb5wKqph1KFVIjspBlEhO7FufgAGtBLdiywakJYrpC9lCe4m0YnFSw2IOnk7vNl5vCKZ2HqWsqer+DMV1jAqcEMoB24YseBPrPpzpVFYgJG8sW04R6rwxiilJeY8sV74ajDmDU4NqG4whMyATq/PruM6DItMrs/CguGCu0TTOXeE57VdtLfCShMDzz5Cd3Vm8R8j2uU4PaAaE0Z+iLAoGnFYy413C7HHQifaPVboeh1FNKm70ZXF60Q7n0+xobMDS0dl1GjaCxMCN+HIxaf40O0ZssP6wZGJ8AWWL0RAq8xGUkwkju9cgwUBfeHJewatiHbRRc0Z4yIL+Shn5W5nR3Zu00Vm4oFlhSQ+RaNta/mxPW/1ZZ6VbeFVSryLHAOXSrUw+rxRms3qqOL6VMGdsOHQBWZp+ROLhvFGqBF+v8PzogjR3sJoBETArCzp5tA0mAmhSNqKDWWhXNkmG+vip7dNxVAiNtcO+2eVbJzqx8pCo/kmQlonsBtOu8FkkymKa5OF0Zauu4wENd7h0ljWXjuPhZEUMEF3TbdJ11iOmqK7ps++V8wm6kR7S6wQepAl5OEYxNrQmn5n9L73OpRxQYItaC2MUpiLdhvfn8LG+mQh2m0tHx/zrpguWectLrrh0Ax9xgVi86FIxJlWWhvzpxjRXvAQy5uz+3isFTvijHkThkHVinaPEWF1PzsJ9y6GYteaPzC+r7uoFS1EezHtp832n9VaM9Fuuw3+MMqGTztvBPgQDI9rbFqJmWKBn+Db9fGivVrvk00hMasAABKZSURBVNLkU847RI4SfQyFUNXcnUdy6fnL0ujrUWcgakUv0XeLhRrtZ+Pky0KPltD1/vFzWmN1rOF4VeJGdJCu1TvE9iX4tO/T8CT6Lp6kvdeLMWOUqScxRciDztiepEbWse7s/3Ux0WIUQYOUnZ3Yb1KhKqZhFAUmq3B7jI2oSG7kWCaKimoYlXgZMgs+fGiPP3OdVug2eDz+WDxJaGwtGhBdj7oOM9GufCoaB8tGWldJC2sY+co7tdjfVdHE3RNeHpahQ7+VMJrvLJGPaKGXxhv7LLq63iFqDGtIdIYw5wJGc7epKg3haeX6Xh5tBZ9ifgvbRbtOnOsoCdGeiZDuLJ3O4y3dGzQp2MXzWxIJOtHOxbnpazET7QxtXiJOrZ4MXw/Dx7BDrQ6YFhwniR/beSOUXWZwzXvIlS9xfKa3vi66tOyKoePnYelE3lhZiva8m7zH3gkjzhZSPnU92FYaEN2zi0bb1vJje94WVub1ov2C0QXM6ui7C/5Cb3z1Bq2tpsXLqx9WCb4ARYj2Nhtgusy0WVlSPcNmL3ac7iPDDoorC+XKNtlYFz+9bSqMkrS5dtg/q2SJ9YF9GBj3kOsEdqvF980+cJh8vLcILdlvXBwZimcebvEe+xrDcK6Qe+mu2eIP0cYao7/mJt4DrRPtbfCXcQXIPIZe7Jj6469YrFKjSdmJLuw3sdNF9z51ot3G9ye3sT5ZiHZby8dHvittHhLDgjC1n6fkU89DbXSaEox4nqE2508xoj0/GvO5KwvTj/9aJDMK45yLFu3Kl6GY01lnV5zh0WUwJv6+BFP4h4CFaC+m/bTZ/rNaaybabbfBH0YZmYgqR8Qg8WvfodoAHDda7tD4WEvRXtukAVM+Wi6IEuNjCxftrCD/zgsy+82hP04ZfrANTRai5rQUz2fBY+IhJNg4iZR/3QsTTio1x9IHBtOkfPyn9KzWepoKR/1yq+CS0GTWbYveCRFdpRV7KMVC5YB+x8wn70i+vuyLfB0vuMU0jKqEjYKPlmUPkhbpB3ugWhENoyZ1P/rzEYDWU7D7ynODm0R2GAZUsV+080lNXCw2suih0T1TYQ1jHu7M5kNW3tj/yrb3J8Ku8xf3G9Rd1whtOg53r2IwhKwMzuVDlXykp5hb/LeiXWoEqvaxXHJU8h118Fgj5Lc9ot2AFsrMp4javQCDeH5U6oQtpuqwWMS6bz4SxZ59f19homabKbtw9blhc7PsMO4GYynaxTpQ32yo3Qhd+XKbDvNBr/f3FwsNvWi0bS0/tuftx4j2vNuSu5XQc1gURYn2v2AyOm1elviQMxdcLpNgnn3qrIe4cuEKYotd2sR6WShXtsnGuvjpbZN1Stbm2mH/rJKDs0NYm99gBm4ZZYpOYLuMusBqjCk55/1Rt1I1+IYad2hJ9ape4Qs/6K7pPOKsRU+weM3K6H04g5UMg2g3TCRlSJ0ujr2PIdPsNehsgdcabgfNRbuN78/W+mQh2m0tHx/7rgzw3ezjLu7BogH8vpXR5e9EqGzOH/Pybdb+sDq9iddpK73b2vRD6F25CNHOyuaBPtXYtT0wbdcVvOBuOwLZOMXdp+wV7Tbbf5ZsM9Fuuw3+MMqIaFcjaSvPMDGuw5/R4sSh94nYp1u7nAWdEH938Td9j5p7YLQ45KXJRNRcg4guXrSz1xkhVljuux4QpvNvUiMtfDb6d+qIzt4jsc3kxRpQ3JiJJtK9HBpPwO7wczh/zhAuXn0iTHhRPjuIOSOHYviQkZgdLKZJmxWGocKs5spoOO4EhPkjqlScHOcmXo81YNukQm0T7Av1D/6FWmckTqRZyXhlPDbzAujQF0fY7+qUPejBKkCN3vv0E8IEWF790Zgdp/OlLaZhRN5NzOZ5yycHGVd4VRJ2+nB3p8IbRt2Hi88eI/9chjJujbC5kL2iHe/vYQkXPqySmUxUyr+HpfxLu9CGUYs3p4agdqXqGBCcyt6+AU1GOCa1bogOM69ZXQdY7K2tjMbTrpk0MKqkHejBGkG9IdRmIXyIE/sg7Yv9JptuaJAZPgFtGrTFnGviFf5b0a5Gyu5uTNBUQ799pnmRd3semrPjGkwWh5htFe3ZZyegQ/POmGPWvZx1rCe7jzMCCnNPKQT+Yd5a8JE0rsm61ZO8sc+ki0aJ+DWeLN5ctPPJ4Hy0qz3+eW78lMa8x31BUDXATOndiORL8TqjbWv5sT1vP0a0a7NOYRizLU59g03rNrOPp8d7oInXLKl+fIRo538Lbh2umGoySeM9Hi3jNtjMvUDClrJQrmwTe4s21cX/wDZZo6Rtrs32zypKPF7OyorjYJwxqso6ge1QayROGX/catJwbBBf4aMDNhsXTiY6D/HJte224IVxBhmhv6bTEJww9tHX75figSBh47pCRLs6BXt9mLB16GNmv5lo/p37ous+/s1Fu63vz8b6ZCHabS8fH/yuss9iklcLdJ111fQjKvOYIKJdfotCrs35U4xoZ8cLoyYWNleFpB2iH3mhol1xDVP5hHZz7amMwzoPFm+vaLfZ/rPUmYl2223wh1FGRDt7nvQTGC0tQcaDS4u28DT6mwedEFen7EM/3mMgxddr7glPV9FX0vzYokQ78mKwnL9Q4ZwG6DZ8HMb289IPAdXssw36easmSBsSSMdZDdIweH70PGGYlsfxnnCRPMQs5mJCOrZuM7QwelbX38JhNOfDBlRIOTBY/ABp4Y/V+yNw4+Ez/JuSgPtRh7ByqDii0HzGZXE2Pis8EWPEJce6LTqJ2AwFcl7ewI5RvIJVRsdVsazI8ssW0zDyv9eIk2rdBq7FhbhM5KQ9QOjcjnDiaSmiYVTGrRUKeu0+f+OenD2sJg8pV7cgQNpl1m3CZdFI2CraWR48+6e70NvabNQ23Ex+B0VaDA5O8ZDS0hxLhM93S8OBdzexQGg8PTAj+CaScpR49/w8gvrxpeaa6gW1BcyIruMTsyrVg9/a84jPzEHagxDM71CdxbF4I0PId6xtxeNaT8P+m0nIKXiHF+dXw5cv+ddktl5YfArRLg4Fi5gaTV40zmAcn5jk2BWLT8YiQ5GDl9e3YQwXTEzkBj0Wu0BsFe3KxysFQVDdazaO3k9DnlqJN09OYFGnaszI98BuaaY+3oRjVGNnNOy8HEVu3PvuKqa4VEbLQOMeVCbO1/J36oT+f8dALE4puLZ5DBP4PN31MPmy8TvkIr+e1R4dY1SJW9GL25lGo7DjRjLeKdJw78AUtJHc9VoG3hcPtLH82Jq3HyPauW26NU9sVNpMC8atpBwoc57jwqp+QkdHi1m6Rlsn2tsZRJDNop0ncZ+4kojbcGy5ysrz+xwkhi9CF5Zf1Tqtw1Mrw/w2lYXyZJtsroulb5tUz3fAt4EzGvfkS3QKURaUuM21w/5ZI/fqJLjxZzfq/dULbBZc+qzCWf7eXt3HiQVdhdF1lxHHYdIvxcUasweWIyoGjK/p3HMFIp7ysvAQJxf5MMErrjiVLJihQkQ7e59Zp8eKy/11WYSwxxlQ5LzEjW2jhY9th3arESvc3Fy0M2x8fzbVJ51ob7vJMLna1rbrQ98VO28V10hV2mLOkftIy1ND+eYJTi7oxMpzNfTe9ZI9ta35U5xo53+vEVcRdBmE9efjkJWThochv6OzZHMLFe1MnK/n9qBGX2yJ4XMnNchLuYp/fpM6cl0mSpNkba2zLLU22n9z0W6zDWb2dme/hqjXoJ9+9ThbKDOinWeaPHojhuiXLeShJnrNXYAufJ1O9rdeiLOvnSebB0kuJrrQAMMDZ9jhHiOiTInA0p5G/pVCcEDr4RsRLa43ZQkzXru58TI5xywUKdoZBUkImdZeMESG86rAc9xuPJKWOrIL7Ts82jURHYSJd+ahMQbMO4pEIzGkzbmPXb+JBcsQaqNnYAT+1ZXlYhtGHpWKM/PE9ccN12mFSX8tRvsiGkZhOb5A8/OaYMSq7ZjLDRP7u4H/OeTYLNoZqn8RwQxxHeNrNh+HjYHcUBh6UywaRsb7ZyGY014yYLpQuTlG73gi9apZR5lyWlqP1ei8lhOwKdDLzBC+x/OQ2egkVXhdqNbcHzufGO5QmqJdm3FcP8JTrfEUcHtubjTZUXh3fycChIbAKNTqhiVnUtlVRWx2j2HlMnpVbyNfSClU98K0Y8nsbUjYuk473uD0ECdU67wLxhPwtfLrWGr+HhoPQ9D22ZJNqI/ROqdX5VNh4qfbxKvF9FKq8OrMInQX1hLXhRaYsCFQMNJtVj2RjrO1/NiWtx8n2hnvnyF0VgdJEOpCFbTy34Gn+sRokRHqJ4gW/lvzySwv7BDtvBxmXVuLwSb2mpWrlgEITihEPtlYFsqNbbJDAJS2bVImrBeWDDT3ETehFGyu7fbPCuxDfXiNKvDZlcJyTEQnsH3+XIFB/APX6LqNhmxGjFnbKM4XqIcpFhNFDOiu2WR0IMa1Mr2mS+9VuKpb9o/lv3XRzmBl98HOMWLHi1Go2y0QEam6TLIi2hm22QYb6pM2Ayf8xPXEHSo3wVTpmW1tuz7sXTGbFb0afc06UPlHdfspx6Dbd8q2/CletLMYpIbPF9edN7qOx/iNWMrqe+E+7Xzp0sUW5zUfuho7Z/POCfZ3PX+cz7Gjztpo/y1FO8MWG6xMwAZPHu+JDSYr1BRNyYn2EkKjSMWDC0exL/gErj7JhDLtkNiYs9BxS5J0FEeDd4k3EH5gN/afuIqnGdYnYdqERoF/H13Fqf07sffIGdxI4Ob4U6CFIukGwth9dx+MwO2ELBMB9EEoM/Ek8jgO7tqGHXuO4HRkNJ7JC3karRKZ8TcRcYjlYWgU7ie/+8DnVkH+/A7OHd6Lg+G38CzbzOAVigpvHl9E6N5dOBB2FU/SlcI7VKbfR8T+vTh2LY0dYS9qyBN4uQjGiavxeKPMx/1FzVj56YbDOjeAwlDJ8SL6AkL2sPSciEJspo135+fdOYujew7g9K1nKOrxVfLnuHvhGPbt2o+TUbHIsv8BPwINchMu4vCObdgTcku//rI1uO9i/M0zOLJrH05E3kPyu4+pEVrkJd/G2ZB92Ll9L0LO3DTyObQf+cUJaFDNB7vF7jEDqizEXghBMMvbU1djkV4glCak3z+DA3uO4nqamNm8ofeqwjc2K/LrQI9anoibpw6wOnIV8VlK5N9bhBbMHvU8aLojqq3lp2TztjB4nbyLi8f2Yvf+E7j0ONOyLmneIeHCIezcuhuht3Rr7dsHX6Xq0eUT2M/Lc+RDZBRrwGwsC2SbTLGhbGkzDsG3yzpToW1BKTyXHfbPFDmixteHU9ddUk+3Kdr36Xh6hZUtpgWuPHoFyyljSsQFeaJ64/koqirrRHurxQ+Q/z4Nsby8MntwMSbZziVntVBmxOPW6cPYs+84ou6x803SbV20C9hoG4qrT5p3CYg8uAPbd4XgtrEBt/H6H/qutHnJuBMRgv07tiP4WARuGc0bMlBc/tgObyejzx7GvgPhuJ2YbXOZVGXFIjKE27wwXItNF/eoKUjHg9P7se/odUhNgF3YbP8tsMEGfwBlRLSr8HzHYHg2aoxmTTthZrjkX86/3tb5CMOKwprpJkPcBGEE3xK7pzPqeC0xXeM+7744vOe+Ck+KmplPlB/eP8SKVo7o/LfpmsO28R4Pl7REzU5/m67fbYEaL3f2gotTWyw1UQR5ePAnd20zXfmJIArlU9gmjQLPjo6Dz6RIs7WhyzbvHy6Dp2NHs3W5beT9Ayxr4YguxdgBY9FemAtNyaDCs01trYt2opxRdu1/melpVyXtwzBjv273TugsbenOg8ugPXhObSRRKGr8e9Rf8Kur5z0BKzZtx/Z1CzDGiw8HumB0iG4rZaL8o0HWmfFo1mQ6rtrpSqbhOwg2aIl513KEHsaiUKcexWg+RO/cBZOWbcLOreuwaFRbwb3A1T8Uuh31CaJoSt82qdMisGrhVtzQbUdZXuBzGMY3RotpV2BfVdYgM8wfjVvMxfViTvwkor3gGU6snQVf7opbtQ+OFjdyQpR5yqr9L1PuMcpX17FzzmD4tG4CNydH1HVtjvbdRiJwfzRs9VQgvmCE9WRXIcCnNRrXcYJLAw/4DJiAoPDnpdzDQnxyNOk4Pa0vJoe+sjJMWxgqJO3xx4A/LiPbpjZVi7yEMKz+rRs8Gzijdu0GaNPZF5NWheMFFSjCHsg2FYomPRwz+0zEiaImwpmjSsI+/75YeDm72I9vbcYR+NZyRIeVphvBlSjyCIysXxNuLXpjzoEEam8+C8qm/S9zPu0EQRAEQRAEQZhCop0gCIIgCIIgyjgk2gmCIAiCIAiijEOinSAIgiAIgiDKOCTaCYIgCIIgCKKMQ6KdIAiCIAiCIMo0wP8H9q+pA4WTTw0AAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Image(filename='images/radiometry04.png')) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lambertian radiators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A Lambertian source is, by definition, one whose radiance is completely independent of viewing angle. Many (but not all) rough and natural surfaces produce radiation whose radiance is approximately independent of the angle of observation. These surfaces generally have a rough texture at microscopic scales. Planck-law blackbody radiators are also Lambertian sources (see my book). Any Lambertian radiator is completely described by its scalar radiance magnitude only, with no angular dependence in radiance.\n", "\n", "The relationship between the exitance and radiance for such a Lambertian surface can be easily derived. If the flux radiated from a Lambertian surface $\\Phi$ [W] is known, it is a simple matter to calculate the exitance $M=\\Phi/A$ [W/m$^2$], where $A$ is the radiating surface area. The exitance of a Lambertian radiator is related to radiance by the projected solid angle of $\\pi$ sr, _not_ the geometric solid angle of $2\\pi$ sr as one might expect. The details are given in my book." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conservation of radiance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Radiance is conserved for flux from a Lambertian surface propagation through a lossless optical\n", "medium. Consider the construction below: two elemental areas $dA_0$ and $dA_1$ are separated by a distance $R_{01}$, with the angles between the normal vector of each surface and the line of sight given by $\\theta_0$ and $\\theta_1$. A total flux of $d^2\\Phi$ is flowing through both the surfaces. It can be shown (see my book) that _for a Lambertian radiator_ the radiance in an arbitrary $dA_n$ is the same as the radiance in $dA_1$.\n", "\n", "As light propagates through mediums with different refractive indices $n$ such as air, water, glass, etc., the entity called _basic radiance_, defined by $L/n^2$, is invariant. It can be shown that for light propagating from a medium with refractive index $n_1$ to a medium with refractive index $n_2$, the basic radiance is conserved: \n", "$$\n", "\\frac{L_1}{n_1^2}=\\frac{L_2}{n_2^2}.\n", "$$" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Image(filename='images/radiometry05.png')) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Flux transfer through lossless and lossy mediums" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A lossless medium is defined as a medium with no losses between the source and the receiver, such as a complete vacuum. This implies that no absorption, scattering, or any other attenuating mechanism is present in the medium. For a lossless medium the flux that flow between both $dA_0$ and $dA_1$ is given by \n", "$$\n", "d^2 \\Phi= \\frac{L_{01}\\,d A_0\\,\\cos\\theta_0\\, d A_1\\,\\cos\\theta_1}{R_{01}^2}.\n", "$$\n", "\n", "If the medium has loss, the loss effect is accounted for by including a 'transmittance' factor $\\tau_{01}=\\Phi_1/\\Phi_0=L_{10}/L_{01}$, i.e., the fraction of the flux from $A_0$ that arrives at $A_1$, then \n", "$$\n", "d^2 \\Phi= \\frac{L_{01}\\,d A_0\\,\\cos\\theta_0\\, d A_1\\,\\cos\\theta_1 \\tau_{01}}{R_{01}^2}.\n", "$$\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sources and receivers of arbitrary shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above equation calculates the flux flowing between two infinitely small areas. The flux flowing between two arbitrary shapes can be calculated by integrating the equation over the source surface and the receiving surface. In the general case, the radiance $L$ cannot be assumed constant over $A_0$, introducing the spatial radiance distribution $L(dA_{0})$ as a factor into the spatial integral.\n", "Likewise, the medium transmittance between any two areas $dA_{0}$ and $dA_{1}$ varies with the spatial locations of $dA_{0}$ and $dA_{1}$ --- hence $\\tau_{01}(dA_{0},dA_{1})$ should also be included in the spatial integral.\n", "\n", "The integral can be performed over any arbitrary shape, as shown in the following figure, supporting the solution with complex geometries. Clearly matters such as obscuration and occlusion should be considered when performing this integral:\n", "$$\n", " \\Phi=\\int_{A_0}\\int_{A_1}\n", "\\frac{L(dA_{0})\\,dA_0\\,\\cos\\theta_0\\, dA_1\\,\\cos\\theta_1\\,\\tau_{01}(dA_{0},dA_{1})}{R_{01}^2}.\n", "$$\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Image(filename='images/radiometry06.png')) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multi-spectral flux transfer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The optical power leaving a source undergoes a succession of scaling or 'spectral filtering' processes as the flux propagates through the system, as shown below. This filtering varies with wavelength.\n", "Examples of such filters are source emissivity, atmospheric transmittance, optical filter transmittance, and detector responsivity. The multi-spectral filter approach described here is conceptually simple but fundamental to the calculation of radiometric flux." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Image(filename='images/radiometry07.png')) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extend the above flux-transfer equation for multi-spectral calculations by noting that over a spectral width $d\\lambda$ the radiance is given by $L = L_\\lambda d\\lambda$:\n", "$$\n", "d^3 \\Phi_\\lambda=\n", "\\frac{L_{01\\lambda}\\,dA_0\\;\\cos\\theta_0\\,dA_1\\;\\cos\\theta_1\n", "\\;\\tau_{01}\\,d\\lambda}{R_{01}^2},\n", "$$\n", "where $d^3\\Phi_\\lambda$ is the total flux in [W] or [q/s] flowing in a spectral width $d\\lambda$ at wavelength $\\lambda$, from a radiator with radiance $L_{0\\lambda}$ with units [W/(m$^2$ $\\cdot$sr$\\cdot$ $\\mu$m)] and projected surface area $dA_0\\cos\\theta_0$, through a receiver with projected surface area $dA_1\\cos\\theta_1$ at a distance $R_{01}$, with a transmittance of $\\tau_{01}$ between the two surfaces. The transmittance $\\tau_{01}$ now includes all of the spectral variables in the path between the source and the receiver.\n", "\n", "To determine the _total flux_ flowing from elemental area $dA_0$ through $dA_1$ over a wide spectral width, divide the wide spectral band into a large number $N$ of narrow widths $\\Delta\\lambda$ at wavelengths $\\lambda_n$ and add the flux for all of these narrow bandwidths together as follows:\n", "$$\n", "d^2 \\Phi=\n", "\\sum_{n=0}^{N}\n", "\\left(\n", "\\frac{L_{01\\lambda_n}\n", "\\,dA_{0}\\,\\cos\\theta_0\\,\n", "\\,dA_{1}\\,\\cos\\theta_1\\,\n", "\\tau_{01\\lambda_n}\n", "\\Delta\\lambda}{R_{01}^2}\n", "\\right).\n", "$$\n", "\n", "By the Riemann--Stieltjes theorem in reverse, if now $\\Delta\\lambda\\rightarrow 0$ and $N\\rightarrow\\infty$, the summation becomes the integral\n", "$$\n", "d^2 \\Phi=\n", "\\int_{\\lambda_1}^{\\lambda_2}\n", "\\frac{L_{01\\lambda}\n", "\\,dA_{0}\\,\\cos\\theta_0\\,\n", "\\,dA_{1}\\,\\cos\\theta_1 \\,\\tau_{01\\lambda}d\\lambda}{R_{01}^2}\\ .\n", "$$\n", "\n", "This equation describes the total flux at all wavelengths in the spectral range $\\lambda_1$ to $\\lambda_2$ passing\n", "through the system. This equation is developed further in my book." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The flux transfer between any two arbitrary surfaces, over any spectral band can be calculated by\n", "$$\n", "\\Phi=\n", "\\int_{A_0}\n", "\\int_{A_1}\n", "\\int_{\\lambda_1}^{\\lambda_2}\n", "\\frac{L_{01\\lambda}\n", "\\,dA_{0}\\,\\cos\\theta_0\\,\n", "\\,dA_{1}\\,\\cos\\theta_1 \\,\\tau_{01\\lambda}d\\lambda}{R_{01}^2}\\ .\n", "$$\n", "\n", "In practice these integrals are performed by finite sums of small elemental areas and spectral widths. \n", "Any arbitrary problem can be solved using this approach. For a simple example see the [flame sensor](http://nbviewer.ipython.org/github/NelisW/ComputationalRadiometry/blob/master/12a-FlameSensorAnalysis.ipynb) and the other pages of this [notebook series](https://github.com/NelisW/ComputationalRadiometry#computational-optical-radiometry-with-pyradi)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Python and [module versions, and dates](https://github.com/jrjohansson/scientific-python-lectures/blob/master/Lecture-0-Scientific-Computing-with-Python.ipynb)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Software versions\n", "Python: 3.8.3 64bit [MSC v.1916 64 bit (AMD64)]\n", "IPython: 7.26.0\n", "OS: Windows 10 10.0.19041 SP0\n", "matplotlib: 3.4.3\n", "numpy: 1.20.3\n", "pyradi: 1.1.4\n", "scipy: 1.7.1\n", "pandas: 1.3.2\n", "Mon Aug 23 12:02:06 2021 South Africa Standard Time\n" ] } ], "source": [ "try:\n", " import pyradi.ryutils as ryutils\n", " print(ryutils.VersionInformation('matplotlib,numpy,pyradi,scipy,pandas'))\n", "except:\n", " print(\"pyradi.ryutils not found\")" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 }

mpl-2.0

getsmarter/bda

module_2/M2_NB1_SourcesOfData.ipynb

1

22557

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"right\">Python 3.6 Jupyter Notebook</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Sources of data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Your completion of the notebook exercises will be graded based on your ability to do the following: \n", "\n", "> **Apply**: Are you able to execute code (using the supplied examples) that performs the required functionality on supplied or generated data sets? \n", "\n", "> **Evaluate**: Are you able to interpret the results and justify your interpretation based on the observed data?\n", "\n", "> **Create**: Are you able to produce notebooks that serve as computational records of a session and can be used to share your insights with others? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Notebook objectives\n", "By the end of this notebook you will be expected to:\n", "> \n", " - Use \"trusted\" and \"untrusted\" data sources to enrich your analysis;\n", " and\n", " - Understand the implications of the five Rs on data quality from external sources.\n", " \n", "#### List of exercises\n", "> - **Exercise 1**: Enriching analysis with data from \"trusted\" sources.\n", " - **Exercise 2**: Pros and cons of using data from \"untrusted\" sources." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook introduction\n", "\n", "Data collection is expensive and time consuming, as Arek Stopczynski alluded to in this module's video content.\n", "In some cases, you will be lucky enough to have existing datasets available to support your analysis. You may have datasets from previous analyses, access to data providers, or curated datasets from your organization. In many cases, however, you will not have access to the data that you require to support your analysis, and you will have to find alternate mechanisms. \n", "The data quality requirements will differ based on the problem you are trying to solve. Taking the hypothetical case of geocoding a location, which was introduced in Module 1, the accuracy of the geocoded location does not need to be exact when you are simply trying to plot the locations of students on a map. Geocoding a location for an automated vehicle to turn off the highway, on the other hand, has an entirely different accuracy requirement.\n", "\n", "> **Note**:\n", "\n", "> Those of you who work in large organizations may be privileged enough to have company data governance and data quality initiatives. These efforts and teams can often add significant value both in terms of supplying company-standard curated data, and making you aware of the internal policies that need to be adhered to.\n", "\n", "As a data analyst or data scientist, it is important to be aware of the implications of your decisions. You need to choose the appropriate set of tools and methods to deal with sourcing and supplying data.\n", "\n", "Technology has matured in recent years, and allowed access to a host of sources of data that can be used in your analyses. In many cases you can access free resources, or obtain (at a cost) data that has been curated, is at a lower latency, or comes with a service-level agreement. Some governments have even made datasets publicly available.\n", "\n", "You have been introduced to [OpenPDS](http://openpds.media.mit.edu/), in the video content, where the focus shifts from supplying raw data -- where the provider needs to apply security principles before sharing datasets -- to supplying answers rather than data. OpenPDS allows users to collect, store, and control access to their data, while also allowing them to protect their privacy. In this way, users still have ownership of their data, as defined by the new deal on data. \n", "\n", "This notebook demonstrates another example of how to source external data to enrich your analyses. The Python ecosystem contains a rich set of tools and libraries that can help you to exploit the available resources.\n", "\n", "This course will not go into detail regarding the various options to source and interact with social data from sources such as Twitter, LinkedIn, Facebook, and Google Plus. However, you should be able to find libraries that will assist you in sourcing and manipulating these sources of data.\n", "\n", "Twitter data is a good example because, depending on the options selected by the Twitter user, every tweet contains not just the message or content that most users are aware of. It also contains a view of the network of the person, home location, location from which the message was sent, and a number of other features that can be very useful when studying networks around a topic of interest. Professor Alex Pentland pointed out the difference in what you share with the world (how you want to be seen) compared to what you actually do and believe (what you commit to). Be sure to keep these concepts in mind when you start exploring the additional sources of data. Those who are interested in the topic can start to explore the options by visiting the [Twitter library on PyPI](https://pypi.python.org/pypi/twitter). \n", "\n", "Start with the **five Rs** introduced in Module 1, and consider the following questions:\n", "- How accurate does my dataset need to be?\n", "- How often should the dataset be updated?\n", "- What happens if the data provider is no longer available?\n", "- Do I need to adhere to any organizational standards to ensure consistent reporting or integration with other applications?\n", "- Are there any implications to getting the values wrong?\n", "\n", "You may need to start with “untrusted” data sources as a means of validating that your analysis can be executed. Once this is done, you can replace the untrusted components with trusted and curated datasets, as your analysis matures." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-warning\">\n", "<b>Note</b>:<br>\n", "It is strongly recommended that you save and checkpoint after applying significant changes or completing exercises. This allows you to return the notebook to a previous state should you wish to do so. On the Jupyter menu, select \"File\", then \"Save and Checkpoint\" from the dropdown menu that appears.\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Load libraries and set options" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "from pandas_datareader import data, wb\n", "import numpy as np\n", "import matplotlib.pylab as plt\n", "import matplotlib\n", "import folium\n", "import geocoder\n", "import wikipedia\n", "\n", "#set plot options\n", "%matplotlib inline\n", "matplotlib.rcParams['figure.figsize'] = (10, 8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Source additional data from public sources \n", "This section will provide short examples to demonstrate the use of public data sources in your notebooks.\n", "\n", "## 1.1 World Bank\n", "\n", "This example demonstrates how to source data from an external source to enrich your existing analyses. You will need to combine the data sources and add additional features to the example of student locations plotted on the world map in Module 1's Notebook 3.\n", "\n", "The specific indicator chosen has little relevance other than to demonstrate the process that you will typically follow in completing your projects. Population counts, from an untrusted source, will be added to your map, and you will use scaling factors combined with the number of students, and population size of the country to demonstrate adding external data with minimal effort.\n", "\n", "This example makes use of the [pandas-datareader](https://pandas-datareader.readthedocs.io/en/latest/) module, which supports remote data access. This library has support for extracting data from various internet sources into a Pandas DataFrame. Currently, the supported sources are:\n", "\n", "- Google Finance\n", "- Enigma\n", "- Quandl\n", "- St.Louis FED (FRED)\n", "- Kenneth French’s data library\n", "- World Bank\n", "- OECD\n", "- Eurostat\n", "- Thrift Savings Plan\n", "- Nasdaq Trader symbol definitions.\n", "\n", "This example focuses on enriching your student dataset from Module 1, using the [World Bank's Development Indicators](https://pandas-datareader.readthedocs.io/en/latest/remote_data.html#world-bank). In the following sections, you will use the data you saved in a previous exercise, add corresponding indicators for each country in the data, and find the mean location for all observed coordinates per country." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Prepare the student data\n", "In the next code cell, you will load the data from disk, apply the `groupby` method to group the data by country and, for each group, find the total student count and the average of their GPS coordinates. The final dataset containing the country, student count, and averaged GPS coordinates is saved as a separate DataFrame variable. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load the grouped_geocoded dataset from Module 1.\n", "df1 = pd.read_csv('data/grouped_geocoded.csv',index_col=[0])\n", "\n", "# Prepare the student location dataset for use in this example.\n", "# We use the geometrical center by obtaining the mean location for all observed coordinates per country.\n", "df2 = df1.groupby('country').agg({'student_count': [np.sum], 'lat': [np.mean], \n", " 'long': [np.mean]}).reset_index()\n", "# Reset the index.\n", "df3 = df2.reset_index(level=1, drop=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Review the data\n", "df3.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The column label index has multiple levels. Although this is useful metadata, it would be better to drop multilevel labeling and, instead, rename the columns to capture this information." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df3.columns = df3.columns.droplevel(1)\n", "df3.rename(columns={'lat': \"lat_mean\", \n", " 'long': \"long_mean\"}, inplace=True)\n", "df3.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get and prepare the external dataset from the World Bank\n", "> Remember you can use \"``wb.download?``\" (without the quotation marks) in a separate code cell to get help on the pandas-datareader method for remote data access of the World Bank Indicators. Refer to the pandas-datareader [remote data access documentation](https://pandas-datareader.readthedocs.io/en/latest/remote_data.html#world-bank) for more detailed help. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# After running this cell you can close the help by clicking on close (`X`) button in the upper right corner\n", "wb.download?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The selected indicator is the world population, \"SP.POP.TOTL\", for the years from 2008 to 2016 \n", "wb_indicator = 'SP.POP.TOTL'\n", "start_year = 2008\n", "end_year = 2016\n", "\n", "df4 = wb.download(indicator = wb_indicator,\n", " country = ['all'],\n", " start = start_year,\n", " end = end_year)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Review the data\n", "df4.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data set contains entries for multiple years. The focus of this example is the entry corresponding to the latest year of data available for each country." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df5 = df4.reset_index()\n", "idx = df5.groupby(['country'])['year'].transform(max) == df5['year']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can now extract only the values that correspond to the most recent year available for each country." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create a new dataframe where entries corresponds to maximum year indexes in previous list.\n", "df6 = df5.loc[idx,:]\n", "\n", "# Review the data\n", "df6.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now merge your dataset with the World Bank data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Combine the student and population datasets.\n", "df7 = pd.merge(df3, df6, on='country', how='left')\n", "\n", "# Rename the columns of our merged dataset and assign to a new variable.\n", "df8 = df7.rename(index=str, columns={('SP.POP.TOTL'): \"PopulationTotal_Latest_WB\"})\n", "\n", "# Drop NAN values.\n", "df8 = df8[~df8.PopulationTotal_Latest_WB.isnull()]\n", "\n", "# Reset index.\n", "df8.reset_index(inplace = True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df8.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot the data.\n", "> **Note**:\n", "The visualization below does not have any meaning. The scaling factors selected are used to demonstrate the difference in population sizes, and number of students on this course, per country." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot the combined dataset\n", "\n", "# Set map center and zoom level\n", "mapc = [0, 30]\n", "zoom = 2\n", "\n", "# Create map object.\n", "map_osm = folium.Map(location=mapc,\n", " tiles='Stamen Toner',\n", " zoom_start=zoom)\n", "\n", "# Plot each of the locations that we geocoded.\n", "for j in range(len(df8)):\n", " # Plot a blue circle marker for country population.\n", " folium.CircleMarker([df8.lat_mean[j], df8.long_mean[j]],\n", " radius=df8.PopulationTotal_Latest_WB[j]/20000000,\n", " popup='Population',\n", " color='#3186cc',\n", " fill_color='#3186cc',\n", " ).add_to(map_osm)\n", " # Plot a red circle marker for students per country.\n", " folium.CircleMarker([df8.lat_mean[j], df8.long_mean[j]],\n", " radius=df8.student_count[j]/50,\n", " popup='Students',\n", " color='red',\n", " fill_color='red',\n", " ).add_to(map_osm)\n", "# Show the map.\n", "map_osm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<div class=\"alert alert-info\">\n", "<b>Exercise 1 Start.</b>\n", "</div>\n", "\n", "### Instructions\n", "\n", "> 1. Review the available indicators in the [World Bank](http://data.worldbank.org/indicator) dataset, and select an indicator of your choice (other than the population indicator). \n", "2. Using a **copy** of the code (from above) in the cells below, replace the population indicator with your selected indicator. Instead of returning the most recent value for your selected indicator, compute the mean and standard deviation for the years from 2006 to 2016. You will need to use the Pandas ``groupby().agg()`` chained methods, together with the following functions from NumPy: \n", " - ``np.mean``\n", " - ``np.std``.\n", " \n", "> You can review the data preparation section for the student data above for an example. \n", "\n", ">Add comments (lines starting with a \"#\") giving a brief description of your view on the observed results. Make sure to include, in one or two sentences in each case, the following:\n", " 1. A clear description why you selected the indicator.\n", " - What your expectation was before including the data.\n", " - What you think the results may indicate.\n", "\n", "> **Important**:\n", "- Only the external data needs to be prepared. You do not need to prepare the student dataset again. Just use the student data that you prepared above and join this to the new dataset you sourced. \n", "- Only plot the mean values for your selected indicator (**not** the standard deviation values).\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Your solution here\n", "# Note: Break your logic using separate cells to break code into units that can be executed \n", "# should you need to review individual steps.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<div class=\"alert alert-info\">\n", "<b>Exercise 1 End.</b>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise complete**:\n", " \n", "> This is a good time to \"Save and Checkpoint\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.2 Using Wikipedia as a data source\n", "\n", "To demonstrate how quickly data can be sourced from public, \"untrusted\" data sources, you have been supplied with a number of sample scripts below. While these sources contain distinctly rich datasets, which you can acquire with minimal effort, they can be amended by anyone, and may not be 100% accurate. In some cases, you will have to manually transform the datasets, while in others, you might be able to use pre-built libraries.\n", "\n", "Execute the code cells below before completing Exercise 2." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Display MIT page summary from Wikipedia \n", "print(wikipedia.summary(\"MIT\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Display a single sentence summary.\n", "wikipedia.summary(\"MIT\", sentences=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create variable page that contains the wikipedia information.\n", "page = wikipedia.page(\"List of countries and dependencies by population\")\n", "\n", "# Display the page title.\n", "page.title" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Display the page URL. This can be utilised to create links back to descriptions.\n", "page.url" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<div class=\"alert alert-info\">\n", "<b>Exercise 2 Start.</b>\n", "</div>\n", "\n", "### Instructions\n", "\n", "> After executing the cells for the Wikipedia example in Section 1.2, think about the potential implications of using this \"public\" and, in many cases, \"untrusted\" data source when doing analysis or creating data products.\n", "\n", "> **Please compile and submit for evaluation a list of *three pros* and *three cons*.**\n", "\n", ">> **Note**: Your submission can be a simple markdown list using the syntax provided below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add your answer in this markdown cell. The contents of this cell should be replaced with your answer.\n", "\n", "**Submit as a list:**\n", "- Pros: \n", " - Description 1\n", " - Description 2\n", " - Description 3\n", "- Cons:\n", " - Description 1\n", " - Description 2\n", " - Description 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br>\n", "<div class=\"alert alert-info\">\n", "<b>Exercise 2 End.</b>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **Exercise complete**:\n", " \n", "> This is a good time to \"Save and Checkpoint\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Submit your notebook\n", "\n", "Please make sure that you:\n", "- Perform a final \"Save and Checkpoint\";\n", "- Download a copy of the notebook in \".ipynb\" format to your local machine using \"File\", \"Download as\", and \"IPython Notebook (.ipynb)\"; and\n", "- Submit a copy of this file to the Online Campus." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

PMEAL/OpenPNM

examples/reference/data_management/data_exchange_between_objects.ipynb

1

2432

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Exchange Between Objects\n", "\n", "In OpenPNM there are different types of objects for storing different types of data, however, it is possible to access data from one object via another object. \n", "\n", "This is done for convenience, but also for flexibility, and generality, as will be demonstrated here.\n", "\n", "First lets summarize what sort of data is stored on each object:\n", "\n", "* Network objects store pore coordinate and throat connection data\n", "* Geometry objecs store geometrical data such as pore diameter and throat length\n", "* Phase objects store thermophysical properties of the phases such as viscosity\n", "* Physics objects store the values of transport conductance that are used by algorithms\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-06-24T11:24:57.394419Z", "iopub.status.busy": "2021-06-24T11:24:57.392731Z", "iopub.status.idle": "2021-06-24T11:24:57.968594Z", "shell.execute_reply": "2021-06-24T11:24:57.967988Z" } }, "outputs": [], "source": [ "import openpnm as op" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First let's create network, which has only 'pore.coords' and 'throat.conns' on it:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-06-24T11:24:57.974840Z", "iopub.status.busy": "2021-06-24T11:24:57.973063Z", "iopub.status.idle": "2021-06-24T11:24:57.976801Z", "shell.execute_reply": "2021-06-24T11:24:57.977152Z" } }, "outputs": [], "source": [ "pn = op.network.Cubic(shape=[5, 5, 1], spacing=1e-4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Sorry, this example is still in progress" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

ESMG/ESMG-configs

ocean_only/Rossby_soliton/open/Untitled.ipynb

1

14250

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import netCDF4\n", "import os\n", "import sys \n", "import subprocess\n", "import pyroms\n", "from pyroms_toolbox import jday2date\n", "from mpl_toolkits.basemap import Basemap\n", "import numpy as np\n", "import matplotlib.pyplot as plt \n", "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def readtext(filename):\n", " f = open(filename, 'r')\n", " # Eat first two lines\n", " f.readline()\n", " f.readline()\n", "\n", " ttime = []\n", " energy = []\n", " msl = []\n", " mass = []\n", " for line in f:\n", " a, b, c, d, e, f, g, h = re.split(',\\s+', line)\n", " print(a, b, c, d)\n", " print(e, f, g, h)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0 0.000 0 En 4.0021589354361712E-04\n", "CFL 0.10056 SL 4.4729E-03 Mass 7.98435E+05 Me 0.00E+00\n", "\n", " 50 10.000 0 En 3.9369949762223531E-04\n", "CFL 0.09720 SL 4.4731E-03 Mass 7.98436E+05 Me 1.65E-07\n", "\n", " 100 20.000 0 En 3.8809902764580485E-04\n", "CFL 0.09421 SL 4.3803E-03 Mass 7.98362E+05 Me -9.23E-05\n", "\n", " 150 30.000 0 En 3.8314900914778453E-04\n", "CFL 0.09020 SL 4.3774E-03 Mass 7.98360E+05 Me -2.94E-06\n", "\n", " 200 40.000 0 En 3.7877958019264505E-04\n", "CFL 0.08877 SL 4.3714E-03 Mass 7.98355E+05 Me -6.01E-06\n", "\n", " 250 50.000 0 En 3.7462890069380199E-04\n", "CFL 0.08700 SL 4.3659E-03 Mass 7.98350E+05 Me -5.46E-06\n", "\n", " 300 60.000 0 En 3.7076775501069110E-04\n", "CFL 0.08575 SL 4.3616E-03 Mass 7.98347E+05 Me -4.27E-06\n", "\n", " 350 70.000 0 En 3.6713172170675418E-04\n", "CFL 0.08448 SL 4.3351E-03 Mass 7.98326E+05 Me -2.64E-05\n", "\n", " 400 80.000 0 En 3.6389093860060274E-04\n", "CFL 0.08375 SL 4.0674E-03 Mass 7.98113E+05 Me -2.67E-04\n", "\n", " 450 90.000 0 En 3.3805515545456814E-04\n", "CFL 0.09962 SL 2.2727E-03 Mass 7.96687E+05 Me -1.79E-03\n", "\n", " 500 100.000 0 En 3.1226412117449186E-04\n", "CFL 0.14147 SL -1.0956E-03 Mass 7.94009E+05 Me -3.37E-03\n", "\n", " 550 110.000 0 En 3.1399898595970875E-04\n", "CFL 0.12356 SL -2.7594E-03 Mass 7.92687E+05 Me -1.67E-03\n", "\n", " 600 120.000 0 En 2.6440728043311573E-04\n", "CFL 0.10502 SL -3.3585E-03 Mass 7.92210E+05 Me -6.01E-04\n", "\n", " 650 130.000 0 En 2.2203818902851661E-04\n", "CFL 0.08741 SL -3.3730E-03 Mass 7.92199E+05 Me -1.45E-05\n", "\n", " 700 140.000 0 En 1.8827461262810444E-04\n", "CFL 0.06840 SL -2.8657E-03 Mass 7.92602E+05 Me 5.09E-04\n", "\n", " 750 150.000 0 En 1.4284027464004726E-04\n", "CFL 0.05744 SL -1.8848E-03 Mass 7.93382E+05 Me 9.83E-04\n", "\n", " 800 160.000 0 En 1.1392134223923268E-04\n", "CFL 0.05056 SL -1.3134E-03 Mass 7.93836E+05 Me 5.72E-04\n", "\n", " 850 170.000 0 En 9.8748082221344259E-05\n", "CFL 0.04077 SL -1.0410E-03 Mass 7.94053E+05 Me 2.73E-04\n", "\n", " 900 180.000 0 En 8.7986255009010525E-05\n", "CFL 0.03881 SL -9.3146E-04 Mass 7.94140E+05 Me 1.10E-04\n", "\n", " 950 190.000 0 En 7.9578793244685476E-05\n", "CFL 0.03214 SL -9.6554E-04 Mass 7.94113E+05 Me -3.41E-05\n", "\n", " 1000 200.000 0 En 7.2443298923439316E-05\n", "CFL 0.03055 SL -9.9291E-04 Mass 7.94091E+05 Me -2.74E-05\n", "\n", " 1050 210.000 0 En 6.5481482746467060E-05\n", "CFL 0.02678 SL -8.8813E-04 Mass 7.94174E+05 Me 1.05E-04\n", "\n", " 1100 220.000 0 En 5.9345105393542501E-05\n", "CFL 0.02419 SL -7.6736E-04 Mass 7.94270E+05 Me 1.21E-04\n", "\n", " 1150 230.000 0 En 5.4511281640941031E-05\n", "CFL 0.02294 SL -7.0918E-04 Mass 7.94316E+05 Me 5.82E-05\n", "\n", " 1200 240.000 0 En 5.0074496336000540E-05\n", "CFL 0.02441 SL -6.2274E-04 Mass 7.94385E+05 Me 8.65E-05\n", "\n", " 1250 250.000 0 En 4.5989073227757276E-05\n", "CFL 0.02022 SL -5.1711E-04 Mass 7.94469E+05 Me 1.06E-04\n", "\n", " 1300 260.000 0 En 4.2533425643503937E-05\n", "CFL 0.02017 SL -4.6668E-04 Mass 7.94509E+05 Me 5.05E-05\n", "\n", " 1350 270.000 0 En 3.9512203548550537E-05\n", "CFL 0.01732 SL -4.1026E-04 Mass 7.94554E+05 Me 5.64E-05\n", "\n", " 1400 280.000 0 En 3.6812167356549476E-05\n", "CFL 0.01789 SL -3.2087E-04 Mass 7.94625E+05 Me 8.94E-05\n", "\n", " 1450 290.000 0 En 3.4548710121953125E-05\n", "CFL 0.01610 SL -2.3054E-04 Mass 7.94697E+05 Me 9.04E-05\n", "\n", " 1500 300.000 0 En 3.2956639903005890E-05\n", "CFL 0.01501 SL -1.0576E-04 Mass 7.94796E+05 Me 1.25E-04\n", "\n", " 1550 310.000 0 En 3.1994060809326635E-05\n", "CFL 0.01393 SL 4.2790E-05 Mass 7.94914E+05 Me 1.49E-04\n", "\n", " 1600 320.000 0 En 3.1549446852363636E-05\n", "CFL 0.01349 SL 1.9492E-04 Mass 7.95035E+05 Me 1.52E-04\n", "\n", " 1650 330.000 0 En 3.1755170602671174E-05\n", "CFL 0.01436 SL 3.6952E-04 Mass 7.95174E+05 Me 1.75E-04\n", "\n", " 1700 340.000 0 En 3.2093510616961276E-05\n", "CFL 0.01589 SL 5.3071E-04 Mass 7.95302E+05 Me 1.61E-04\n", "\n", " 1750 350.000 0 En 3.2091547813122876E-05\n", "CFL 0.01489 SL 6.4655E-04 Mass 7.95394E+05 Me 1.16E-04\n", "\n", " 1800 360.000 0 En 3.1606558199208056E-05\n", "CFL 0.01404 SL 7.1507E-04 Mass 7.95448E+05 Me 6.85E-05\n", "\n", " 1850 370.000 0 En 3.0554766806447751E-05\n", "CFL 0.01402 SL 7.1300E-04 Mass 7.95447E+05 Me -2.08E-06\n", "\n", " 1900 380.000 0 En 2.9187735703138164E-05\n", "CFL 0.01350 SL 6.4836E-04 Mass 7.95395E+05 Me -6.46E-05\n", "\n", " 1950 390.000 0 En 2.7786813751464963E-05\n", "CFL 0.01333 SL 5.4203E-04 Mass 7.95311E+05 Me -1.06E-04\n", "\n", " 2000 400.000 0 En 2.6573654950403144E-05\n", "CFL 0.01294 SL 4.0288E-04 Mass 7.95200E+05 Me -1.39E-04\n", "\n", " 2050 410.000 0 En 2.5550294648119235E-05\n", "CFL 0.01199 SL 2.6782E-04 Mass 7.95093E+05 Me -1.35E-04\n", "\n", " 2100 420.000 0 En 2.4724190264563750E-05\n", "CFL 0.01282 SL 1.5926E-04 Mass 7.95007E+05 Me -1.09E-04\n", "\n", " 2150 430.000 0 En 2.4061665167000865E-05\n", "CFL 0.01146 SL 8.3814E-05 Mass 7.94947E+05 Me -7.54E-05\n", "\n", " 2200 440.000 0 En 2.3523574679794141E-05\n", "CFL 0.01008 SL 4.9214E-05 Mass 7.94919E+05 Me -3.46E-05\n", "\n", " 2250 450.000 0 En 2.3054393746725471E-05\n", "CFL 0.00963 SL 4.7057E-05 Mass 7.94917E+05 Me -2.16E-06\n", "\n", " 2300 460.000 0 En 2.2583815115840778E-05\n", "CFL 0.00923 SL 7.1449E-05 Mass 7.94937E+05 Me 2.44E-05\n", "\n", " 2350 470.000 0 En 2.2127483153947108E-05\n", "CFL 0.00946 SL 1.0987E-04 Mass 7.94967E+05 Me 3.84E-05\n", "\n", " 2400 480.000 0 En 2.1703752860769137E-05\n", "CFL 0.00957 SL 1.4276E-04 Mass 7.94993E+05 Me 3.29E-05\n", "\n", " 2450 490.000 0 En 2.1348748177147352E-05\n", "CFL 0.00929 SL 1.6429E-04 Mass 7.95011E+05 Me 2.15E-05\n", "\n", " 2500 500.000 0 En 2.1054086903714983E-05\n", "CFL 0.00902 SL 1.7046E-04 Mass 7.95015E+05 Me 6.17E-06\n", "\n", " 2550 510.000 0 En 2.0783449114589276E-05\n", "CFL 0.00848 SL 1.6507E-04 Mass 7.95011E+05 Me -5.39E-06\n", "\n", " 2600 520.000 0 En 2.0502232814295275E-05\n", "CFL 0.00798 SL 1.5905E-04 Mass 7.95006E+05 Me -6.02E-06\n", "\n", " 2650 530.000 0 En 2.0179147438002688E-05\n", "CFL 0.00834 SL 1.5313E-04 Mass 7.95002E+05 Me -5.91E-06\n", "\n", " 2700 540.000 0 En 1.9808846711272145E-05\n", "CFL 0.00817 SL 1.4794E-04 Mass 7.94998E+05 Me -5.19E-06\n", "\n", " 2750 550.000 0 En 1.9415582852438950E-05\n", "CFL 0.00821 SL 1.4293E-04 Mass 7.94994E+05 Me -5.00E-06\n", "\n", " 2800 560.000 0 En 1.9023794962423388E-05\n", "CFL 0.00804 SL 1.3281E-04 Mass 7.94986E+05 Me -1.01E-05\n", "\n", " 2850 570.000 0 En 1.8647330512327328E-05\n", "CFL 0.00777 SL 1.1682E-04 Mass 7.94973E+05 Me -1.60E-05\n", "\n", " 2900 580.000 0 En 1.8294049979688880E-05\n", "CFL 0.00732 SL 9.3697E-05 Mass 7.94954E+05 Me -2.31E-05\n", "\n", " 2950 590.000 0 En 1.7978340948454189E-05\n", "CFL 0.00686 SL 6.2790E-05 Mass 7.94930E+05 Me -3.09E-05\n", "\n", " 3000 600.000 0 En 1.7716230767388042E-05\n", "CFL 0.00712 SL 2.8566E-05 Mass 7.94903E+05 Me -3.42E-05\n", "\n", " 3050 610.000 0 En 1.7513256324977912E-05\n", "CFL 0.00725 SL -6.7701E-06 Mass 7.94875E+05 Me -3.53E-05\n", "\n", " 3100 620.000 0 En 1.7362038207597260E-05\n", "CFL 0.00727 SL -4.3220E-05 Mass 7.94846E+05 Me -3.65E-05\n", "\n", " 3150 630.000 0 En 1.7252656508364880E-05\n", "CFL 0.00709 SL -7.9624E-05 Mass 7.94817E+05 Me -3.64E-05\n", "\n", " 3200 640.000 0 En 1.7188392441757075E-05\n", "CFL 0.00689 SL -1.1478E-04 Mass 7.94789E+05 Me -3.52E-05\n", "\n", " 3250 650.000 0 En 1.7160098879821911E-05\n", "CFL 0.00709 SL -1.4486E-04 Mass 7.94765E+05 Me -3.01E-05\n", "\n", " 3300 660.000 0 En 1.7140318600849792E-05\n", "CFL 0.00724 SL -1.6627E-04 Mass 7.94748E+05 Me -2.14E-05\n", "\n", " 3350 670.000 0 En 1.7119458424626557E-05\n", "CFL 0.00732 SL -1.7767E-04 Mass 7.94739E+05 Me -1.14E-05\n", "\n", " 3400 680.000 0 En 1.7104154689616464E-05\n", "CFL 0.00730 SL -1.7817E-04 Mass 7.94738E+05 Me -5.06E-07\n", "\n", " 3450 690.000 0 En 1.7116549130004496E-05\n", "CFL 0.00721 SL -1.6679E-04 Mass 7.94747E+05 Me 1.14E-05\n", "\n", " 3500 700.000 0 En 1.7178900307569013E-05\n", "CFL 0.00707 SL -1.4167E-04 Mass 7.94767E+05 Me 2.51E-05\n", "\n", " 3550 710.000 0 En 1.7313694157527220E-05\n", "CFL 0.00692 SL -1.0203E-04 Mass 7.94799E+05 Me 3.96E-05\n", "\n", " 3600 720.000 0 En 1.7526099497961747E-05\n", "CFL 0.00680 SL -5.1057E-05 Mass 7.94839E+05 Me 5.10E-05\n", "\n", " 3650 730.000 0 En 1.7797037665893071E-05\n", "CFL 0.00673 SL 6.1512E-06 Mass 7.94885E+05 Me 5.72E-05\n", "\n", " 3700 740.000 0 En 1.8089813228149357E-05\n", "CFL 0.00672 SL 6.3360E-05 Mass 7.94930E+05 Me 5.72E-05\n", "\n", " 3750 750.000 0 En 1.8355278710816941E-05\n", "CFL 0.00676 SL 1.1375E-04 Mass 7.94970E+05 Me 5.04E-05\n", "\n", " 3800 760.000 0 En 1.8568204559769947E-05\n", "CFL 0.00682 SL 1.5234E-04 Mass 7.95001E+05 Me 3.86E-05\n", "\n", " 3850 770.000 0 En 1.8714060447264938E-05\n", "CFL 0.00688 SL 1.7533E-04 Mass 7.95019E+05 Me 2.30E-05\n", "\n", " 3900 780.000 0 En 1.8799083992331018E-05\n", "CFL 0.00691 SL 1.8229E-04 Mass 7.95025E+05 Me 6.96E-06\n", "\n", " 3950 790.000 0 En 1.8853197772388260E-05\n", "CFL 0.00690 SL 1.7667E-04 Mass 7.95020E+05 Me -5.61E-06\n", "\n", " 4000 800.000 0 En 1.8907208613653862E-05\n", "CFL 0.00688 SL 1.6237E-04 Mass 7.95009E+05 Me -1.43E-05\n", "\n", " 4050 810.000 0 En 1.8993339782527482E-05\n", "CFL 0.00685 SL 1.4526E-04 Mass 7.94995E+05 Me -1.71E-05\n", "\n", " 4100 820.000 0 En 1.9134036563804566E-05\n", "CFL 0.00683 SL 1.2980E-04 Mass 7.94983E+05 Me -1.55E-05\n", "\n", " 4150 830.000 0 En 1.9339627104464263E-05\n", "CFL 0.00682 SL 1.1743E-04 Mass 7.94973E+05 Me -1.24E-05\n", "\n", " 4200 840.000 0 En 1.9623178365458754E-05\n", "CFL 0.00684 SL 1.1312E-04 Mass 7.94970E+05 Me -4.31E-06\n", "\n", " 4250 850.000 0 En 1.9991008109530478E-05\n", "CFL 0.00688 SL 1.1791E-04 Mass 7.94974E+05 Me 4.79E-06\n", "\n", " 4300 860.000 0 En 2.0431473295828010E-05\n", "CFL 0.00693 SL 1.2360E-04 Mass 7.94978E+05 Me 5.69E-06\n", "\n", " 4350 870.000 0 En 2.0896530686557789E-05\n", "CFL 0.00698 SL 1.3411E-04 Mass 7.94987E+05 Me 1.05E-05\n", "\n", " 4400 880.000 0 En 2.1339041695633583E-05\n", "CFL 0.00703 SL 1.6213E-04 Mass 7.95009E+05 Me 2.80E-05\n", "\n", " 4450 890.000 0 En 2.1811940208091804E-05\n", "CFL 0.00707 SL 1.9056E-04 Mass 7.95031E+05 Me 2.84E-05\n", "\n", " 4500 900.000 0 En 2.2310186294927576E-05\n", "CFL 0.00729 SL 2.0353E-04 Mass 7.95042E+05 Me 1.30E-05\n", "\n" ] } ], "source": [ "readtext('ocean.stats.zero')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

sysid/nbs

cnn/tw_vgg16.ipynb

1

435247

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Convolutional Neural Networks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is running on theano!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Welcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to deep learning." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction to this week's task: 'Dogs vs Cats'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're going to try to create a model to enter the [Dogs vs Cats](https://www.kaggle.com/c/dogs-vs-cats) competition at Kaggle. There are 25,000 labelled dog and cat photos available for training, and 12,500 in the test set that we have to try to label for this competition. According to the Kaggle web-site, when this competition was launched (end of 2013): *\"**State of the art**: The current literature suggests machine classifiers can score above 80% accuracy on this task\"*. So if we can beat 80%, then we will be at the cutting edge as at 2013!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There isn't too much to do to get started - just a few simple configuration steps.\n", "\n", "This shows plots in the web page itself - we always wants to use this when using jupyter notebook:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define path to data: (It's a good idea to put it in a subdirectory of your notebooks folder, and then exclude that directory from git control by adding it to .gitignore.)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#path = \"data/dogscats/\"\n", "path = \"data/dogscats/sample/\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A few basic libraries that we'll need for the initial exercises:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import division,print_function\n", "\n", "import os, json\n", "from glob import glob\n", "import numpy as np\n", "np.set_printoptions(precision=4, linewidth=100)\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have created a file most imaginatively called 'utils.py' to store any little convenience functions we'll want to use. We will discuss these as we use them." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using gpu device 0: GeForce GTX 750 (CNMeM is disabled, cuDNN not available)\n", "Using Theano backend.\n" ] }, { "data": { "text/plain": [ "<module 'utils' from '/home/tw/nbs/utils.py'>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import utils\n", "import importlib\n", "importlib.reload(utils)\n", "from utils import plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Use a pretrained VGG model with our **Vgg16** class" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our first step is simply to use a model that has been fully created for us, which can recognise a wide variety (1,000 categories) of images. We will use 'VGG', which won the 2014 Imagenet competition, and is a very simple model to create and understand. The VGG Imagenet team created both a larger, slower, slightly more accurate model (*VGG 19*) and a smaller, faster model (*VGG 16*). We will be using VGG 16 since the much slower performance of VGG19 is generally not worth the very minor improvement in accuracy.\n", "\n", "We have created a python class, *Vgg16*, which makes using the VGG 16 model very straightforward. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The punchline: state of the art custom model in 7 lines of code\n", "\n", "Here's everything you need to do to get >97% accuracy on the Dogs vs Cats dataset - we won't analyze how it works behind the scenes yet, since at this stage we're just going to focus on the minimum necessary to actually do useful work." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# As large as you can, but no larger than 64 is recommended. \n", "# If you have an older or cheaper GPU, you'll run out of memory, so will have to decrease this.\n", "# batch_size=64\n", "batch_size=2" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import our class, and instantiate\n", "import vgg16\n", "from vgg16 import Vgg16" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['tench',\n", " 'goldfish',\n", " 'great_white_shark',\n", " 'tiger_shark',\n", " 'hammerhead',\n", " 'electric_ray',\n", " 'stingray',\n", " 'cock',\n", " 'hen',\n", " 'ostrich',\n", " 'brambling',\n", " 'goldfinch',\n", " 'house_finch',\n", " 'junco',\n", " 'indigo_bunting',\n", " 'robin',\n", " 'bulbul',\n", " 'jay',\n", " 'magpie',\n", " 'chickadee',\n", " 'water_ouzel',\n", " 'kite',\n", " 'bald_eagle',\n", " 'vulture',\n", " 'great_grey_owl',\n", " 'European_fire_salamander',\n", " 'common_newt',\n", " 'eft',\n", " 'spotted_salamander',\n", " 'axolotl',\n", " 'bullfrog',\n", " 'tree_frog',\n", " 'tailed_frog',\n", " 'loggerhead',\n", " 'leatherback_turtle',\n", " 'mud_turtle',\n", " 'terrapin',\n", " 'box_turtle',\n", " 'banded_gecko',\n", " 'common_iguana',\n", " 'American_chameleon',\n", " 'whiptail',\n", " 'agama',\n", " 'frilled_lizard',\n", " 'alligator_lizard',\n", " 'Gila_monster',\n", " 'green_lizard',\n", " 'African_chameleon',\n", " 'Komodo_dragon',\n", " 'African_crocodile',\n", " 'American_alligator',\n", " 'triceratops',\n", " 'thunder_snake',\n", " 'ringneck_snake',\n", " 'hognose_snake',\n", " 'green_snake',\n", " 'king_snake',\n", " 'garter_snake',\n", " 'water_snake',\n", " 'vine_snake',\n", " 'night_snake',\n", " 'boa_constrictor',\n", " 'rock_python',\n", " 'Indian_cobra',\n", " 'green_mamba',\n", " 'sea_snake',\n", " 'horned_viper',\n", " 'diamondback',\n", " 'sidewinder',\n", " 'trilobite',\n", " 'harvestman',\n", " 'scorpion',\n", " 'black_and_gold_garden_spider',\n", " 'barn_spider',\n", " 'garden_spider',\n", " 'black_widow',\n", " 'tarantula',\n", " 'wolf_spider',\n", " 'tick',\n", " 'centipede',\n", " 'black_grouse',\n", " 'ptarmigan',\n", " 'ruffed_grouse',\n", " 'prairie_chicken',\n", " 'peacock',\n", " 'quail',\n", " 'partridge',\n", " 'African_grey',\n", " 'macaw',\n", " 'sulphur-crested_cockatoo',\n", " 'lorikeet',\n", " 'coucal',\n", " 'bee_eater',\n", " 'hornbill',\n", " 'hummingbird',\n", " 'jacamar',\n", " 'toucan',\n", " 'drake',\n", " 'red-breasted_merganser',\n", " 'goose',\n", " 'black_swan',\n", " 'tusker',\n", " 'echidna',\n", " 'platypus',\n", " 'wallaby',\n", " 'koala',\n", " 'wombat',\n", " 'jellyfish',\n", " 'sea_anemone',\n", " 'brain_coral',\n", " 'flatworm',\n", " 'nematode',\n", " 'conch',\n", " 'snail',\n", " 'slug',\n", " 'sea_slug',\n", " 'chiton',\n", " 'chambered_nautilus',\n", " 'Dungeness_crab',\n", " 'rock_crab',\n", " 'fiddler_crab',\n", " 'king_crab',\n", " 'American_lobster',\n", " 'spiny_lobster',\n", " 'crayfish',\n", " 'hermit_crab',\n", " 'isopod',\n", " 'white_stork',\n", " 'black_stork',\n", " 'spoonbill',\n", " 'flamingo',\n", " 'little_blue_heron',\n", " 'American_egret',\n", " 'bittern',\n", " 'crane',\n", " 'limpkin',\n", " 'European_gallinule',\n", " 'American_coot',\n", " 'bustard',\n", " 'ruddy_turnstone',\n", " 'red-backed_sandpiper',\n", " 'redshank',\n", " 'dowitcher',\n", " 'oystercatcher',\n", " 'pelican',\n", " 'king_penguin',\n", " 'albatross',\n", " 'grey_whale',\n", " 'killer_whale',\n", " 'dugong',\n", " 'sea_lion',\n", " 'Chihuahua',\n", " 'Japanese_spaniel',\n", " 'Maltese_dog',\n", " 'Pekinese',\n", " 'Shih-Tzu',\n", " 'Blenheim_spaniel',\n", " 'papillon',\n", " 'toy_terrier',\n", " 'Rhodesian_ridgeback',\n", " 'Afghan_hound',\n", " 'basset',\n", " 'beagle',\n", " 'bloodhound',\n", " 'bluetick',\n", " 'black-and-tan_coonhound',\n", " 'Walker_hound',\n", " 'English_foxhound',\n", " 'redbone',\n", " 'borzoi',\n", " 'Irish_wolfhound',\n", " 'Italian_greyhound',\n", " 'whippet',\n", " 'Ibizan_hound',\n", " 'Norwegian_elkhound',\n", " 'otterhound',\n", " 'Saluki',\n", " 'Scottish_deerhound',\n", " 'Weimaraner',\n", " 'Staffordshire_bullterrier',\n", " 'American_Staffordshire_terrier',\n", " 'Bedlington_terrier',\n", " 'Border_terrier',\n", " 'Kerry_blue_terrier',\n", " 'Irish_terrier',\n", " 'Norfolk_terrier',\n", " 'Norwich_terrier',\n", " 'Yorkshire_terrier',\n", " 'wire-haired_fox_terrier',\n", " 'Lakeland_terrier',\n", " 'Sealyham_terrier',\n", " 'Airedale',\n", " 'cairn',\n", " 'Australian_terrier',\n", " 'Dandie_Dinmont',\n", " 'Boston_bull',\n", " 'miniature_schnauzer',\n", " 'giant_schnauzer',\n", " 'standard_schnauzer',\n", " 'Scotch_terrier',\n", " 'Tibetan_terrier',\n", " 'silky_terrier',\n", " 'soft-coated_wheaten_terrier',\n", " 'West_Highland_white_terrier',\n", " 'Lhasa',\n", " 'flat-coated_retriever',\n", " 'curly-coated_retriever',\n", " 'golden_retriever',\n", " 'Labrador_retriever',\n", " 'Chesapeake_Bay_retriever',\n", " 'German_short-haired_pointer',\n", " 'vizsla',\n", " 'English_setter',\n", " 'Irish_setter',\n", " 'Gordon_setter',\n", " 'Brittany_spaniel',\n", " 'clumber',\n", " 'English_springer',\n", " 'Welsh_springer_spaniel',\n", " 'cocker_spaniel',\n", " 'Sussex_spaniel',\n", " 'Irish_water_spaniel',\n", " 'kuvasz',\n", " 'schipperke',\n", " 'groenendael',\n", " 'malinois',\n", " 'briard',\n", " 'kelpie',\n", " 'komondor',\n", " 'Old_English_sheepdog',\n", " 'Shetland_sheepdog',\n", " 'collie',\n", " 'Border_collie',\n", " 'Bouvier_des_Flandres',\n", " 'Rottweiler',\n", " 'German_shepherd',\n", " 'Doberman',\n", " 'miniature_pinscher',\n", " 'Greater_Swiss_Mountain_dog',\n", " 'Bernese_mountain_dog',\n", " 'Appenzeller',\n", " 'EntleBucher',\n", " 'boxer',\n", " 'bull_mastiff',\n", " 'Tibetan_mastiff',\n", " 'French_bulldog',\n", " 'Great_Dane',\n", " 'Saint_Bernard',\n", " 'Eskimo_dog',\n", " 'malamute',\n", " 'Siberian_husky',\n", " 'dalmatian',\n", " 'affenpinscher',\n", " 'basenji',\n", " 'pug',\n", " 'Leonberg',\n", " 'Newfoundland',\n", " 'Great_Pyrenees',\n", " 'Samoyed',\n", " 'Pomeranian',\n", " 'chow',\n", " 'keeshond',\n", " 'Brabancon_griffon',\n", " 'Pembroke',\n", " 'Cardigan',\n", " 'toy_poodle',\n", " 'miniature_poodle',\n", " 'standard_poodle',\n", " 'Mexican_hairless',\n", " 'timber_wolf',\n", " 'white_wolf',\n", " 'red_wolf',\n", " 'coyote',\n", " 'dingo',\n", " 'dhole',\n", " 'African_hunting_dog',\n", " 'hyena',\n", " 'red_fox',\n", " 'kit_fox',\n", " 'Arctic_fox',\n", " 'grey_fox',\n", " 'tabby',\n", " 'tiger_cat',\n", " 'Persian_cat',\n", " 'Siamese_cat',\n", " 'Egyptian_cat',\n", " 'cougar',\n", " 'lynx',\n", " 'leopard',\n", " 'snow_leopard',\n", " 'jaguar',\n", " 'lion',\n", " 'tiger',\n", " 'cheetah',\n", " 'brown_bear',\n", " 'American_black_bear',\n", " 'ice_bear',\n", " 'sloth_bear',\n", " 'mongoose',\n", " 'meerkat',\n", " 'tiger_beetle',\n", " 'ladybug',\n", " 'ground_beetle',\n", " 'long-horned_beetle',\n", " 'leaf_beetle',\n", " 'dung_beetle',\n", " 'rhinoceros_beetle',\n", " 'weevil',\n", " 'fly',\n", " 'bee',\n", " 'ant',\n", " 'grasshopper',\n", " 'cricket',\n", " 'walking_stick',\n", " 'cockroach',\n", " 'mantis',\n", " 'cicada',\n", " 'leafhopper',\n", " 'lacewing',\n", " 'dragonfly',\n", " 'damselfly',\n", " 'admiral',\n", " 'ringlet',\n", " 'monarch',\n", " 'cabbage_butterfly',\n", " 'sulphur_butterfly',\n", " 'lycaenid',\n", " 'starfish',\n", " 'sea_urchin',\n", " 'sea_cucumber',\n", " 'wood_rabbit',\n", " 'hare',\n", " 'Angora',\n", " 'hamster',\n", " 'porcupine',\n", " 'fox_squirrel',\n", " 'marmot',\n", " 'beaver',\n", " 'guinea_pig',\n", " 'sorrel',\n", " 'zebra',\n", " 'hog',\n", " 'wild_boar',\n", " 'warthog',\n", " 'hippopotamus',\n", " 'ox',\n", " 'water_buffalo',\n", " 'bison',\n", " 'ram',\n", " 'bighorn',\n", " 'ibex',\n", " 'hartebeest',\n", " 'impala',\n", " 'gazelle',\n", " 'Arabian_camel',\n", " 'llama',\n", " 'weasel',\n", " 'mink',\n", " 'polecat',\n", " 'black-footed_ferret',\n", " 'otter',\n", " 'skunk',\n", " 'badger',\n", " 'armadillo',\n", " 'three-toed_sloth',\n", " 'orangutan',\n", " 'gorilla',\n", " 'chimpanzee',\n", " 'gibbon',\n", " 'siamang',\n", " 'guenon',\n", " 'patas',\n", " 'baboon',\n", " 'macaque',\n", " 'langur',\n", " 'colobus',\n", " 'proboscis_monkey',\n", " 'marmoset',\n", " 'capuchin',\n", " 'howler_monkey',\n", " 'titi',\n", " 'spider_monkey',\n", " 'squirrel_monkey',\n", " 'Madagascar_cat',\n", " 'indri',\n", " 'Indian_elephant',\n", " 'African_elephant',\n", " 'lesser_panda',\n", " 'giant_panda',\n", " 'barracouta',\n", " 'eel',\n", " 'coho',\n", " 'rock_beauty',\n", " 'anemone_fish',\n", " 'sturgeon',\n", " 'gar',\n", " 'lionfish',\n", " 'puffer',\n", " 'abacus',\n", " 'abaya',\n", " 'academic_gown',\n", " 'accordion',\n", " 'acoustic_guitar',\n", " 'aircraft_carrier',\n", " 'airliner',\n", " 'airship',\n", " 'altar',\n", " 'ambulance',\n", " 'amphibian',\n", " 'analog_clock',\n", " 'apiary',\n", " 'apron',\n", " 'ashcan',\n", " 'assault_rifle',\n", " 'backpack',\n", " 'bakery',\n", " 'balance_beam',\n", " 'balloon',\n", " 'ballpoint',\n", " 'Band_Aid',\n", " 'banjo',\n", " 'bannister',\n", " 'barbell',\n", " 'barber_chair',\n", " 'barbershop',\n", " 'barn',\n", " 'barometer',\n", " 'barrel',\n", " 'barrow',\n", " 'baseball',\n", " 'basketball',\n", " 'bassinet',\n", " 'bassoon',\n", " 'bathing_cap',\n", " 'bath_towel',\n", " 'bathtub',\n", " 'beach_wagon',\n", " 'beacon',\n", " 'beaker',\n", " 'bearskin',\n", " 'beer_bottle',\n", " 'beer_glass',\n", " 'bell_cote',\n", " 'bib',\n", " 'bicycle-built-for-two',\n", " 'bikini',\n", " 'binder',\n", " 'binoculars',\n", " 'birdhouse',\n", " 'boathouse',\n", " 'bobsled',\n", " 'bolo_tie',\n", " 'bonnet',\n", " 'bookcase',\n", " 'bookshop',\n", " 'bottlecap',\n", " 'bow',\n", " 'bow_tie',\n", " 'brass',\n", " 'brassiere',\n", " 'breakwater',\n", " 'breastplate',\n", " 'broom',\n", " 'bucket',\n", " 'buckle',\n", " 'bulletproof_vest',\n", " 'bullet_train',\n", " 'butcher_shop',\n", " 'cab',\n", " 'caldron',\n", " 'candle',\n", " 'cannon',\n", " 'canoe',\n", " 'can_opener',\n", " 'cardigan',\n", " 'car_mirror',\n", " 'carousel',\n", " \"carpenter's_kit\",\n", " 'carton',\n", " 'car_wheel',\n", " 'cash_machine',\n", " 'cassette',\n", " 'cassette_player',\n", " 'castle',\n", " 'catamaran',\n", " 'CD_player',\n", " 'cello',\n", " 'cellular_telephone',\n", " 'chain',\n", " 'chainlink_fence',\n", " 'chain_mail',\n", " 'chain_saw',\n", " 'chest',\n", " 'chiffonier',\n", " 'chime',\n", " 'china_cabinet',\n", " 'Christmas_stocking',\n", " 'church',\n", " 'cinema',\n", " 'cleaver',\n", " 'cliff_dwelling',\n", " 'cloak',\n", " 'clog',\n", " 'cocktail_shaker',\n", " 'coffee_mug',\n", " 'coffeepot',\n", " 'coil',\n", " 'combination_lock',\n", " 'computer_keyboard',\n", " 'confectionery',\n", " 'container_ship',\n", " 'convertible',\n", " 'corkscrew',\n", " 'cornet',\n", " 'cowboy_boot',\n", " 'cowboy_hat',\n", " 'cradle',\n", " 'crane',\n", " 'crash_helmet',\n", " 'crate',\n", " 'crib',\n", " 'Crock_Pot',\n", " 'croquet_ball',\n", " 'crutch',\n", " 'cuirass',\n", " 'dam',\n", " 'desk',\n", " 'desktop_computer',\n", " 'dial_telephone',\n", " 'diaper',\n", " 'digital_clock',\n", " 'digital_watch',\n", " 'dining_table',\n", " 'dishrag',\n", " 'dishwasher',\n", " 'disk_brake',\n", " 'dock',\n", " 'dogsled',\n", " 'dome',\n", " 'doormat',\n", " 'drilling_platform',\n", " 'drum',\n", " 'drumstick',\n", " 'dumbbell',\n", " 'Dutch_oven',\n", " 'electric_fan',\n", " 'electric_guitar',\n", " 'electric_locomotive',\n", " 'entertainment_center',\n", " 'envelope',\n", " 'espresso_maker',\n", " 'face_powder',\n", " 'feather_boa',\n", " 'file',\n", " 'fireboat',\n", " 'fire_engine',\n", " 'fire_screen',\n", " 'flagpole',\n", " 'flute',\n", " 'folding_chair',\n", " 'football_helmet',\n", " 'forklift',\n", " 'fountain',\n", " 'fountain_pen',\n", " 'four-poster',\n", " 'freight_car',\n", " 'French_horn',\n", " 'frying_pan',\n", " 'fur_coat',\n", " 'garbage_truck',\n", " 'gasmask',\n", " 'gas_pump',\n", " 'goblet',\n", " 'go-kart',\n", " 'golf_ball',\n", " 'golfcart',\n", " 'gondola',\n", " 'gong',\n", " 'gown',\n", " 'grand_piano',\n", " 'greenhouse',\n", " 'grille',\n", " 'grocery_store',\n", " 'guillotine',\n", " 'hair_slide',\n", " 'hair_spray',\n", " 'half_track',\n", " 'hammer',\n", " 'hamper',\n", " 'hand_blower',\n", " 'hand-held_computer',\n", " 'handkerchief',\n", " 'hard_disc',\n", " 'harmonica',\n", " 'harp',\n", " 'harvester',\n", " 'hatchet',\n", " 'holster',\n", " 'home_theater',\n", " 'honeycomb',\n", " 'hook',\n", " 'hoopskirt',\n", " 'horizontal_bar',\n", " 'horse_cart',\n", " 'hourglass',\n", " 'iPod',\n", " 'iron',\n", " \"jack-o'-lantern\",\n", " 'jean',\n", " 'jeep',\n", " 'jersey',\n", " 'jigsaw_puzzle',\n", " 'jinrikisha',\n", " 'joystick',\n", " 'kimono',\n", " 'knee_pad',\n", " 'knot',\n", " 'lab_coat',\n", " 'ladle',\n", " 'lampshade',\n", " 'laptop',\n", " 'lawn_mower',\n", " 'lens_cap',\n", " 'letter_opener',\n", " 'library',\n", " 'lifeboat',\n", " 'lighter',\n", " 'limousine',\n", " 'liner',\n", " 'lipstick',\n", " 'Loafer',\n", " 'lotion',\n", " 'loudspeaker',\n", " 'loupe',\n", " 'lumbermill',\n", " 'magnetic_compass',\n", " 'mailbag',\n", " 'mailbox',\n", " 'maillot',\n", " 'maillot',\n", " 'manhole_cover',\n", " 'maraca',\n", " 'marimba',\n", " 'mask',\n", " 'matchstick',\n", " 'maypole',\n", " 'maze',\n", " 'measuring_cup',\n", " 'medicine_chest',\n", " 'megalith',\n", " 'microphone',\n", " 'microwave',\n", " 'military_uniform',\n", " 'milk_can',\n", " 'minibus',\n", " 'miniskirt',\n", " 'minivan',\n", " 'missile',\n", " 'mitten',\n", " 'mixing_bowl',\n", " 'mobile_home',\n", " 'Model_T',\n", " 'modem',\n", " 'monastery',\n", " 'monitor',\n", " 'moped',\n", " 'mortar',\n", " 'mortarboard',\n", " 'mosque',\n", " 'mosquito_net',\n", " 'motor_scooter',\n", " 'mountain_bike',\n", " 'mountain_tent',\n", " 'mouse',\n", " 'mousetrap',\n", " 'moving_van',\n", " 'muzzle',\n", " 'nail',\n", " 'neck_brace',\n", " 'necklace',\n", " 'nipple',\n", " 'notebook',\n", " 'obelisk',\n", " 'oboe',\n", " 'ocarina',\n", " 'odometer',\n", " 'oil_filter',\n", " 'organ',\n", " 'oscilloscope',\n", " 'overskirt',\n", " 'oxcart',\n", " 'oxygen_mask',\n", " 'packet',\n", " 'paddle',\n", " 'paddlewheel',\n", " 'padlock',\n", " 'paintbrush',\n", " 'pajama',\n", " 'palace',\n", " 'panpipe',\n", " 'paper_towel',\n", " 'parachute',\n", " 'parallel_bars',\n", " 'park_bench',\n", " 'parking_meter',\n", " 'passenger_car',\n", " 'patio',\n", " 'pay-phone',\n", " 'pedestal',\n", " 'pencil_box',\n", " 'pencil_sharpener',\n", " 'perfume',\n", " 'Petri_dish',\n", " 'photocopier',\n", " 'pick',\n", " 'pickelhaube',\n", " 'picket_fence',\n", " 'pickup',\n", " 'pier',\n", " 'piggy_bank',\n", " 'pill_bottle',\n", " 'pillow',\n", " 'ping-pong_ball',\n", " 'pinwheel',\n", " 'pirate',\n", " 'pitcher',\n", " 'plane',\n", " 'planetarium',\n", " 'plastic_bag',\n", " 'plate_rack',\n", " 'plow',\n", " 'plunger',\n", " 'Polaroid_camera',\n", " 'pole',\n", " 'police_van',\n", " 'poncho',\n", " 'pool_table',\n", " 'pop_bottle',\n", " 'pot',\n", " \"potter's_wheel\",\n", " 'power_drill',\n", " 'prayer_rug',\n", " 'printer',\n", " 'prison',\n", " 'projectile',\n", " 'projector',\n", " 'puck',\n", " 'punching_bag',\n", " 'purse',\n", " 'quill',\n", " 'quilt',\n", " 'racer',\n", " 'racket',\n", " 'radiator',\n", " 'radio',\n", " 'radio_telescope',\n", " 'rain_barrel',\n", " 'recreational_vehicle',\n", " 'reel',\n", " 'reflex_camera',\n", " 'refrigerator',\n", " 'remote_control',\n", " 'restaurant',\n", " 'revolver',\n", " 'rifle',\n", " 'rocking_chair',\n", " 'rotisserie',\n", " 'rubber_eraser',\n", " 'rugby_ball',\n", " 'rule',\n", " 'running_shoe',\n", " 'safe',\n", " 'safety_pin',\n", " 'saltshaker',\n", " 'sandal',\n", " 'sarong',\n", " 'sax',\n", " 'scabbard',\n", " 'scale',\n", " 'school_bus',\n", " 'schooner',\n", " 'scoreboard',\n", " 'screen',\n", " 'screw',\n", " 'screwdriver',\n", " 'seat_belt',\n", " 'sewing_machine',\n", " 'shield',\n", " 'shoe_shop',\n", " 'shoji',\n", " 'shopping_basket',\n", " 'shopping_cart',\n", " 'shovel',\n", " 'shower_cap',\n", " 'shower_curtain',\n", " 'ski',\n", " 'ski_mask',\n", " 'sleeping_bag',\n", " 'slide_rule',\n", " 'sliding_door',\n", " 'slot',\n", " 'snorkel',\n", " 'snowmobile',\n", " 'snowplow',\n", " 'soap_dispenser',\n", " 'soccer_ball',\n", " 'sock',\n", " 'solar_dish',\n", " 'sombrero',\n", " 'soup_bowl',\n", " 'space_bar',\n", " 'space_heater',\n", " 'space_shuttle',\n", " 'spatula',\n", " 'speedboat',\n", " 'spider_web',\n", " 'spindle',\n", " 'sports_car',\n", " 'spotlight',\n", " 'stage',\n", " 'steam_locomotive',\n", " 'steel_arch_bridge',\n", " 'steel_drum',\n", " 'stethoscope',\n", " 'stole',\n", " 'stone_wall',\n", " 'stopwatch',\n", " 'stove',\n", " 'strainer',\n", " 'streetcar',\n", " 'stretcher',\n", " 'studio_couch',\n", " 'stupa',\n", " 'submarine',\n", " 'suit',\n", " 'sundial',\n", " 'sunglass',\n", " 'sunglasses',\n", " 'sunscreen',\n", " 'suspension_bridge',\n", " 'swab',\n", " 'sweatshirt',\n", " 'swimming_trunks',\n", " 'swing',\n", " 'switch',\n", " 'syringe',\n", " 'table_lamp',\n", " 'tank',\n", " 'tape_player',\n", " 'teapot',\n", " 'teddy',\n", " 'television',\n", " 'tennis_ball',\n", " 'thatch',\n", " 'theater_curtain',\n", " 'thimble',\n", " 'thresher',\n", " 'throne',\n", " 'tile_roof',\n", " 'toaster',\n", " 'tobacco_shop',\n", " 'toilet_seat',\n", " 'torch',\n", " 'totem_pole',\n", " 'tow_truck',\n", " 'toyshop',\n", " 'tractor',\n", " 'trailer_truck',\n", " 'tray',\n", " 'trench_coat',\n", " 'tricycle',\n", " 'trimaran',\n", " 'tripod',\n", " 'triumphal_arch',\n", " 'trolleybus',\n", " 'trombone',\n", " 'tub',\n", " 'turnstile',\n", " 'typewriter_keyboard',\n", " 'umbrella',\n", " 'unicycle',\n", " 'upright',\n", " 'vacuum',\n", " 'vase',\n", " 'vault',\n", " 'velvet',\n", " 'vending_machine',\n", " 'vestment',\n", " 'viaduct',\n", " 'violin',\n", " 'volleyball',\n", " 'waffle_iron',\n", " 'wall_clock',\n", " 'wallet',\n", " 'wardrobe',\n", " 'warplane',\n", " 'washbasin',\n", " 'washer',\n", " 'water_bottle',\n", " 'water_jug',\n", " 'water_tower',\n", " 'whiskey_jug',\n", " 'whistle',\n", " 'wig',\n", " 'window_screen',\n", " 'window_shade',\n", " 'Windsor_tie',\n", " 'wine_bottle',\n", " 'wing',\n", " 'wok',\n", " 'wooden_spoon',\n", " 'wool',\n", " 'worm_fence',\n", " 'wreck',\n", " 'yawl',\n", " 'yurt',\n", " 'web_site',\n", " 'comic_book',\n", " 'crossword_puzzle',\n", " 'street_sign',\n", " 'traffic_light',\n", " 'book_jacket',\n", " 'menu',\n", " 'plate',\n", " 'guacamole',\n", " 'consomme',\n", " 'hot_pot',\n", " 'trifle',\n", " 'ice_cream',\n", " 'ice_lolly',\n", " 'French_loaf',\n", " 'bagel',\n", " 'pretzel',\n", " 'cheeseburger',\n", " 'hotdog',\n", " 'mashed_potato',\n", " 'head_cabbage',\n", " 'broccoli',\n", " 'cauliflower',\n", " 'zucchini',\n", " 'spaghetti_squash',\n", " 'acorn_squash',\n", " 'butternut_squash',\n", " 'cucumber',\n", " 'artichoke',\n", " 'bell_pepper',\n", " 'cardoon',\n", " 'mushroom',\n", " 'Granny_Smith',\n", " 'strawberry',\n", " 'orange',\n", " 'lemon',\n", " 'fig',\n", " 'pineapple',\n", " 'banana',\n", " 'jackfruit',\n", " 'custard_apple',\n", " 'pomegranate',\n", " 'hay',\n", " 'carbonara',\n", " 'chocolate_sauce',\n", " 'dough',\n", " 'meat_loaf',\n", " 'pizza',\n", " 'potpie',\n", " 'burrito',\n", " 'red_wine',\n", " 'espresso',\n", " 'cup',\n", " 'eggnog',\n", " 'alp',\n", " 'bubble',\n", " 'cliff',\n", " 'coral_reef',\n", " 'geyser',\n", " 'lakeside',\n", " 'promontory',\n", " 'sandbar',\n", " 'seashore',\n", " 'valley',\n", " 'volcano',\n", " 'ballplayer',\n", " 'groom',\n", " 'scuba_diver',\n", " 'rapeseed',\n", " 'daisy',\n", " \"yellow_lady's_slipper\",\n", " 'corn',\n", " 'acorn',\n", " 'hip',\n", " 'buckeye',\n", " 'coral_fungus',\n", " 'agaric',\n", " 'gyromitra',\n", " 'stinkhorn',\n", " 'earthstar',\n", " 'hen-of-the-woods',\n", " 'bolete',\n", " 'ear',\n", " 'toilet_tissue']" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vgg.classes" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 160 images belonging to 2 classes.\n" ] } ], "source": [ "# %%capture x # ping bug: disconnect -> reconnect kernel workaround\n", "vgg = Vgg16()\n", "# Grab a few images at a time for training and validation.\n", "# NB: They must be in subdirectories named based on their category\n", "batches = vgg.get_batches(path+ 'train', batch_size=batch_size)\n", "batches.nb_class" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "val_batches = vgg.get_batches(path+'valid', batch_size=batch_size*2)\n", "vgg.finetune(batches)\n", "vgg.fit(batches, val_batches, nb_epoch=1, verbose=1)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#x.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code above will work for any image recognition task, with any number of categories! All you have to do is to put your images into one folder per category, and run the code above.\n", "\n", "Let's take a look at how this works, step by step..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Use Vgg16 for basic image recognition\n", "\n", "Let's start off by using the *Vgg16* class to recognise the main imagenet category for each image.\n", "\n", "We won't be able to enter the Cats vs Dogs competition with an Imagenet model alone, since 'cat' and 'dog' are not categories in Imagenet - instead each individual breed is a separate category. However, we can use it to see how well it can recognise the images, which is a good first step.\n", "\n", "First, create a Vgg16 object:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vgg = Vgg16()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vgg16 is built on top of *Keras* (which we will be learning much more about shortly!), a flexible, easy to use deep learning library that sits on top of Theano or Tensorflow. Keras reads groups of images and labels in *batches*, using a fixed directory structure, where images from each category for training must be placed in a separate folder.\n", "\n", "Let's grab batches of data from our training folder:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 160 images belonging to 2 classes.\n" ] } ], "source": [ "batches = vgg.get_batches(path+'train', batch_size=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(BTW, when Keras refers to 'classes', it doesn't mean python classes - but rather it refers to the categories of the labels, such as 'pug', or 'tabby'.)\n", "\n", "*Batches* is just a regular python iterator. Each iteration returns both the images themselves, as well as the labels." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "imgs,labels = next(batches)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(3, 224, 224)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "array([[ 1., 0.],\n", " [ 1., 0.],\n", " [ 1., 0.],\n", " [ 0., 1.]], dtype=float32)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "imgs[0].shape\n", "labels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the labels for each image are an array, containing a 1 in the first position if it's a cat, and in the second position if it's a dog. This approach to encoding categorical variables, where an array containing just a single 1 in the position corresponding to the category, is very common in deep learning. It is called *one hot encoding*. \n", "\n", "The arrays contain two elements, because we have two categories (cat, and dog). If we had three categories (e.g. cats, dogs, and kangaroos), then the arrays would each contain two 0's, and one 1." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GRGMpTMkkrlPOtWBHOLNBKAKhHbK2fpgHX3OIA3f0GazAxrGWk8c3eP6ZEaFp\nqStPjB61jtxllOK6OTIyZtQvYH7AyeE61hjy3DKZ1CAOVWE8HiGmG9B775GsxBgYVxNW5hyn6jFx\n7zLxzjtZf9azwD7sSFFXUZQ5Ei3Li3t5/uiYd3/Le/ilpz/HeLTK3IKhLOBMM8QyZLCYo65EXE7U\nSDVp2bO8gLEjkCEIOOuYz3tU6yfZHG8yX0BrlSCG1vewYUA1rtk4Hbn7oQJrG5YPwFf/8Cu0cYNJ\nvUxjR0yaIb1+TttURDEYk6PREaJS4ghtJLM5be1xNiN6hVCixjAZN/R7A6pRTRvazkFvDT6CimFt\nbY3bFvagMbBv3z6IgUm1Rq8/x6m1ETFGrM0Y9AZYWnpFydg4JusbTKqaEJQ8D9RVS1GUNE2DtZbM\n7s6A7OXqeLrMKzZifW1RXmRXnz9RPbFrbPf834hcjpany229v5q6falrxJVwseXPn3ZyOdsV6eZg\nhhC2jII8z7HWEkLYMhLatp3+xek1/9yI1G5zdkrDi3/3hfbDTvbXxZ1+l8+VnAsv+l1XoS83grYv\nR8diBO9bovdo9DRtRdu2iBiysk8WwPYMzeaYssjR0M2xNc6SFTkAdRghYojSGV7WZvSMI7rIpG0I\nw5r5vEeTCUWRIyL05wacPLbK+voGxhX0Bj2KfonNBGM7A2+mmdnc2hgCfmrkEZXcZRRlicu7edhN\n26UOEzrD0zaB2LTUk4p5YxhNxrh+N17LrcUgYMzUAOxqD/kQpsagIM6iashcDmaaqp1Z2mhweWc8\nNu2ki+JGS6uKUYMgGAVrc0KMVE1FVPBRacdV51BrYxe5boWgFoOjDZ0B3qUxB3yoESyqMk0Rtxhj\nMdrZNNYolkj0gcZ340GvinrfXXemjj4xFsTjQ6SdeIw4okZEDM4J1poufRw7zQTYnnrdbBnVfrre\n1rc0vsXZaVQYgxhHbsuuf2IBg2oXYfa+y7I83/nmXI61jrbxGLGESGeExzg9T9yWs7Jt2y2NOeco\neyWqSl1PjXlrMSbDGItzJdbYrt9iMBYyFwmNEr0SZZYF0WnfIBRZvmUraozEECCYbu7dDrlZqoID\n8MAdy3gxmMLiQ00G6Og0D921n88e2cBoQYyO4WaFLYV9CzBXOopWaOt2Ok/BYi2UC3tZbUaMYuQ2\ntItwzu1l89RT+HHF/MISZVkSTaRua0II9LJ5Dt13iKPNKnrG47OM9UKofYP6uOWRDkT+5PGvsidb\n4N7b93MrGGOnAAAgAElEQVRsvWB1OCCS8cRXvsrmqidng8NP/CmhHdF3Xd7/5//oj/Fti9SnWOlb\njp44zdLAEoLj6aePkRVj5veB2BET+3V8exRTHENWB1hnaGLk/lffSTX5GiaeweoZWplgswmbw5Oc\nOHmMQMaj3/gQoxdOMxzNc+yIsL4KJ0/AxgYMNwIPLizQsxmV8/i5gjN5xGpN8IY5DQQfQIXMGUxm\nMVmf1bWTiC2wWU7joTfIETyjk6epFf6fT3+Ch1/7KKa8n80//grZsCVfXEejYTxq6fcW6fcsv/Gx\n3yLiWVpeYHnJ4r3FqGHeZpRas1EZrOkxv1DQNg2rGzWqGe94TZ9euUwMhtFoyHBzTJZnONtjtWo4\nvbZOL7uDjfWA1IYjLwzBPs87v+U1vHDq99h7sKDIFzm0526ePfE55ueXmUwqrM3JnKDqUOnmq2hr\ncOIwahgU+ZaR2NbQhIrgAjqdr2KAUEfUBbQIRNt5H9vW8+43vYY2HAbAlQUaTSdssfTLPuoF3xpq\nAXF9sjJA3SNqQ9NassxSNZH1zXWyrAB3azwp40YYmJzP+z7wvt3uwksyS+GbhooBeP/3vv/c76e7\n9Ubcv9v5nm39vll4/w1+flwr3vzYg8C1M6yvBe/55jdcss2VGPTbnYbOua3BYAjTKWnT77Msm6a6\nznHixAkAmqbZinJv593vePiS272WXE4U/8+949L7+Ubj3Tdhn68Ws/m8XU2QiEjEIljrUNOlWJvc\n04YRGNtFMrUzRzAOYzpDTawHsWA6g9MAVrpngFkfsRjKaSGyoijIXUZd19RtzVxekhcZzhkwcTp3\nNtsyrOFskanQtDz8pq5IYpZlZFmGdY5IF3EOIRC8R2PsDEsfUN+lrvsYyTMHBqwIToWGeG6asnZF\nKHVaELBLNRaMsYhoVyzZZIgYQlCCN5jMAZYu712Q2GVUGGtptQsO3vfw/m5e9NRJINI5KDR2GZEy\njY6H0BUeDiHggweNU8O6M2KNASsRZwyFc2hsu0zL1m+LEr/YiO0KqkKILTKttSLdbNbpX2cgn035\ndtz72v0vuiZ2dYi6WkRtCGT5dL/QRdVj6GxRQ3de+Tbgp3bS9j7NIscAIehWQddZ30ERcZ1tFeK2\n6LWhm27fGcx5XtA009R9UR56+CBWLGb6W7qUeXDG4VyY9mOaLo9BiRg7LfYWu/Ms+IBq7I75tGbM\nTripDOu7DixShQYrHq2G9Pt9ynqTh191F/rJEyh9QhuQqFiF+++5HScNGipMNGjs0giMdTx3dI21\nkGPLHmoDWdugUlKWJeNJZ0hX1QQrkSzLKIqSMCl5zaMPk20eZWN0lCpAHZvOgxINGg1gEJfxwjBw\nuD4OUckmK3z0409waN+AO/aXPHTvApONIWNfMDy8yi9/6RcpexkubzHiefV9r+IzX/kqi4u3064c\nYLI+Qg89xBPhOU6cjOxbfpDXvvEQX3ziX7L29Jc5+Or3sLj/LoIrODM+xnxZYkKP2EwQM6KuK8w4\n8PSxDWyxj4/+609z5MRpDhxYZmlPn0ignsupTI7PbufgAdC6RpuIHxhMCKwEZT7mnIg19WSEsX2s\nswQNGKPUtqUNBrUledbHosSmZjMW3L7S47ZijfHjn2JPv2Tvw0tMNnKWJj28H2KM4fTac+SDeR59\n07184t88TywcG5MhsVKeevYItw1gpW959Rsf4LkXnufUmVOILWiD5dRpx94FzzPPDhnECcMzE7I9\nJTp3gKca4cx6gwkZcbiKqTcI+YTBgWXuuXsvo9Xn+foXv8I3vf0vcLQR7rrnDj7z+w/jTEGvbCid\nh9EqWkVoAYVRdozBoKsMnmUZRgwhBupylar1uNwiUiD1HOKXsbKGzQzSa1ACasHOr/DWR29ndGLI\naLTO+ugM1jqYRPLG04RVJvWA515YpfXQFFC4Ib1yH2qnhSIEXFEQTUNVTdD2xh7QbuelBmjbv3tx\n9OblGYXbB7nGGIxIN//gAt9fqF+z77/r/d99TvTmJVP+dNsbeXH06ezg5ew2zt322bjOxdIlxXTV\nsae5kNNMHNkq6hJj4C99zwe6r6eLGiOozCIF5/7Gc363np0DP/vNs3Sqc/pyXt/OLypm5fIKKX3v\n937PRQ2bSx2vC7U9f5mzc8P0Rcdxdmy7IP/OI4cf+MD7ztnOVr9mLztIEd2+7OWmt15vvuGND13U\n4No6f+zOByTncyVG+jn7c9v+f/c73nDeMVAwHrFn93qnUd16360H3DRtUBFi7OZydlVmZ20DiuJU\nCd5z5NRpBr0+Oh1MdgPxaUbOdL2Le5ZZmd9D2cs5s77KZDJGnMVaIcRAiJ73vPN1O7r07TSSvBNm\nc2YvxswI2Rq0Z9NihdO/d33LG15U8UP13Gv69qkR522dnQxPZbbBHbL9enCh7b7r7Y9Mdb+zwmnn\nyzRsXbjZ+k4vcA+4EVEVyrKgLAbEUOEHjjgpGGrLyEbmMAzyCVVTsDlaY6Gc66RjlDwvicYgYrFt\nRvShM668p585NHpAKGrDia89wz1vebgrUKawfnoVbODVr3sQY3KqugarBJppBNVuHavxeMxkMqGq\nKrIm8MgbH6A/N0fRG2CzDIxhMh5uRTVjiMSmxY8qxmvrrJ08zf6lfSysLBFsV+wqtAHvFV8q9aQm\n+s4gr9u2m6cbwfuIr1uaCZCbadVsZdJ0Ud4YFCiJMSN4T1XU2AimCtAqPo6YhIjScPDBFVoFiZ1t\nYY0ltC3G9oCuWnbnAIggFpsJWENTR4zNiFEJPnbXIxOxElEfib7bL95Y6s1ZxoDFWAfapUKreiRO\nEKP0BxlNo/hW8S3U7RlEuqcuxWDxLTjbw9iWg/cssL6+Ps3c7XV20njcBRPFEkOD9wbrlKataZox\nRnrTSu45GjsHjDEZeZ5hrd3K5pk5GLrjZWnbmhBajIFI6CLloT1Hsz5002asNYw3x9PidhaJXaTd\nOce9Dx2gGtVb96G2bbeuZ9HXBO9xpujmaU/bWBdA2EpDNwSaaoPC7esqy++Qm8qw9j5grEW0y+UX\nlEGZE6wht446GqoQyGyB+gm9XpfSHWPoyrzHLjVDBI4ePYoPc1gznesUAuO6xk7nReV53p0UJnYR\nSuewxRx5npPnGZkTMqucvQB3HpSIBTG0KngD46qmWBvS5ooNm+Tq2LuwxN237+PwC5ZJ5bGSEX2k\nDhUPv/5+Fpf24so+q5sVDWPe9rZ34rB8+dk/BRlShf1E32OhLAmScWr9GXp7F7BZV+ghYCE4fGtp\nfWBzc8wgQD1y9N0S9ahCZIG1TdisJrgyYjJH3ZR4nafWU+ShIhuULCz2ObPe0m+hDB5st++RiFOI\nIrhZEQbp5kN0Xi6LjwEvJZONMXtM5K6VBUabp9gYbuKznIVJwIcGX1e0MTBu1/j1j/0yJh7grgN7\nGR9bpxq2zPX3EvwGTRU5euQIJ08cZWMyYn5pL0ELglgaL6CWXpbTZhPKsscmlmEbqUNJFg3DjVPM\n9QxLewpMOWJzmOF9w+tf/XrGa55cFphUI1ZWHHtWSprJBqJDiGe6AnhZhhXL0Bhi9HjfoNql9ilK\nFbuyC0KYFrwzxOAwOjM4AlE8Hk/jIyG6rqqzCjHS+cyChxApSsPC8gJlmOCx+MFSV2Bjs0Tp0gyr\neszG5hjvO+dO4wdAvUvqvHm51OD9+kfgptHnl2Br0D9reXPYYbvG9ukF58/hv9jA/kJOlsStyvnn\ngHYeqWlF/+6RMEobt82RN2ZaTbZ7fIuIJUYhRBiPRhhj6Pfn2FjfoHDTiIyNLzKsfVMzGm2iGphf\nWqRYyKnbhvF4jBpQukfomcv3TyQSl6S7FnbRQ7URQ8C3rpuDbIAIWVagRUEzMtMCT9N7kJ6N9qpe\n2CEjdJHrpqrR0BmV1WhMHWoGgz6DwQDVruCXmoBBMK7LtJpde2fpwDGcLeDVpf52now4HX/O5lcb\nY2imj2UK00dviQjGGqKRrhIZ5zpDOwMvnOMAmhlnMUZEZasIaIxK5oppobHucz9bNiiECD52c6xR\n7LSIlvBiR3T3f5c2rejUsQWIIWpnt4goxlri9Jow61vwEd80nfGcma3smG6/y9a4XDXiRBHpxvFG\nhBCgbeO0cngXwQ0+EIPBmqlzIm6b773tWMz6bYxD5NwiZ0gXaJwlWRtjsbZLOT/f0TxLMd/+mYjZ\n5og79+J3NlW7KxQXYyQEmc6Dj4gExCtqZo5sobuYmnP6OIuez95bNwtYzOpgQBcxN1uBhJ1wUxnW\nz57c4BtfdxA3bHE+pw2wuWeDLJzmoazlyTawumcfp46PuHOk3Dfw5O0IHyxCIM8NG43HZDknGdIU\nI/oZaNVH2nk24gvcX/S2UiNyK5RlDzs3x8ryASItc+4BHrpjjT/68p9hiz7BZKgRvHTFAqJ0wlts\nFnG2ZByUoVfG6jk1blhr5vnq0yfJlybsXThEq2PWV1cJsaHIlLvveJT2xLN8w2sf4LNPHObPnj/G\n3uef573vfS9Lr76XtbU1Hn/8cRYqizdv48Dr38rx459m3Bryap3QNhht6TnHJ3/7E+zbv0A7zBk2\nA3p7n2KNltVqiZqctdUJKjlqHSYvKQc9IiOa6AhSs1IEll1g392LHDl5gok4ekeOo2WPcrmHsTWF\ny9gYBk6tHee2PQ+RhR42m2e0sM5wseLh031OFgucnlsh+uc4lG+wfHrEyZPK8/U8eW5YWFxhuD6B\nTBgP1ygO5vyV/+bH+Jkf/xAhRBYGL9Dr98EUSHs3zfAF9i2XLC80qIx46+vu5vGvwWa7DvMlcek2\nRtLn9KkGP3Hc1i8Zrzf058fcd6/hoYdy1s9s8MRzBZvjU7zu3kUef/yrvO6hv8hzL5ykWqjZe98h\nPvvJT3PbfIb1NWJKapsTTYbNWoIDxVGPIqUrUa9sFHeyx20wGK2zVtcMy4jrj5D1ZbI4xI9aBmae\n5QNv5sxkAemt0ETl9NqIGAb0Fw4RN76GWT7FwDh6eYGhT88GRBuiFHhTo1lEcthoW7IMap9hNePY\nht9tid50nD8n7vxL527Nmdtu1F00wnm9OnOLcKF9en6K642expy4HnSZJhr91qBPp08GkdDNJRWj\nXeEdQNV0Vm+MxGCJCpnrIjqqLUbcVlpnf7EHQFWNO8PBGE6dOsXS0hLrww1ijPTn5yiKkt7KgNFo\nxGQy6SodJ8UnriEOpcSS1Qbr5hiRYd2Ifswg9DBAk3uyg/uozhxm7zhSLS/Rtpay9mQRNGvwJlBM\nLJQZ+IK8tFTSFYYaacugsoyeOYnt9TlRTyjrCW964FFcVtBqJPqKoVeEEh+6wmcI+BBpRt0zs01U\nxGVkWU5WdM+4DqqYGImhQUKA6Tzmtm4YjyZs1hUTMQzmVqhp6E0rTwcnNAbKtun+FyVmlhC7aGxj\n687RYAqc5vgQqYLHN+3UCLQE3yLSzYEWIlJViDiQHJxlQEvWbIBz5BbK3FLVNdq2SFGQO9DYggac\nzFKlu0d3hRggWDLJcCbbSrEWA2ontBjWFIItaGyGqJmmdHeG6mQy2crSyrIcIxMEQz2R6RxoS9tM\naMNCV8xLQKnBgKfGGYOz5dbj6gSHbxUjOcaCsS3guxRthExyyOZxzmAtRG3oEgW77WkQWt9lXXbb\n6zK0YlA8AXGCakvAIMZMH4llCNFjrRJi25XYUyFExWtFbLr1xRCp6oqsychocdl0XaFLQ3e2RCRj\n4rv6AWq6dPBmeuwzWcCImaaGC8ZlSMhpgsfHm6R42ctlOB4zqWsKH8imnowsz5HacMcdt3PydMEZ\nP31Oq9BFlq3gjUEj27wrhqhgrOs8LTBNm5DuGclVVxXOmi5FodfrdSkV2nlVil4Pl+VISxfxFtmq\nxquAlbPelS7VwRDDdM5HiMSg1FXLOBvjbI/BYJ6yZ5nrOzbWNzFVxeLSAawBa5TTp47zmU9/iloD\ne/fuZb7MaaoRe5bmGa8e5+C+A/SynOXFZU6ePMkzTz3D3n6f8UbFsIA46TE/2NfNcQgBUaUejSny\nAjWCYmhCZLw5Iu/3u9QOL0SvHNizj+dOnmG+P8dzR07iij6VWBDbeXGMAxGUjP7cPJMo+DBhLutT\njzbYs/82jq1voqEr8pW5gsiEoldQ0DDXN/SyCYNBBllGtI5seZnP/MGn+fKXvshrDi2zZ2kJtYag\nGesbJzhwYB9eu5S5+fl52mCYm1/mxDMnaCQwv2eZUVUxHjcQStbPnMKPAss9Zc/KHLEdU2SGV+2/\ni8/9ySl0WFCd2WC5nOMPv/I4r3pLCWpZWtzLaPMouXpc4RBXgNGtIh8adVrNtbuoWmqqyZCs6Ryh\nGQ6tA+CJ6lFtIRacWT3Jyh2HOLI+ZDKqIMauAr0IGro5IrOCHWg3wIvBo7Ybv6l2nkeloWkblALr\nMnqDZFhfDjdaxHp7vPrCRvWsiuW2iHXikpwfIdjuJT+fbn5X2rNXwqVSiV8O2x0h29e/0ykl2z87\nPx1/9pz5WXPVSNR2q3iODy3QzSDxYRZRCRjXRa2FDNShsRtwNm2ApntkTDd4NGTO0E6j0DCtIDzt\nQq/I2VxfwxjLcGNECIrOKXnZw0pOWU7nS0rzov1xsf10tXip9V3OdfFqnBPnnwOztN/t58isGvHF\niq69dN+7FOHt29rpb93JlI8blW6ubjf32VmL9S1YgxVDV96ry+50+QAd9KhPVLgYp3OIBQld4Sqr\nGeoaXFaQZd10OddkOGkQo8S2YfXEKdzBFcaTEQcP7GN+fp668UhUFhYW0NGYUd3gp49nmqXyNk3T\njc2tJStKirIgL4qugrdvp5WfAxojRGgbTzVpqJuG8airQbO0tIQ1nVHdTc8QcIJW0/vCVvR9WsHe\nGIKCrz1NZahrj5eAkW6fRR+6cdvsOZeq5L0SiULbgHrfFen1dsv4jrGbG9z6Fudmj/ZqsXY6nVQU\nH1pCaPGhQci6c8p0UfKujekitnHq9FOHs90c7LBNZ7No7KyqdZE5Wu/ZWJvgnKfIe7QtZM7hjO2e\nsGO7AnCxjUQ15FmX1j2LFFdVvVVYzJhs2oc4nRvedv9jCVG2oufW5Whs8f6sZtu23UoJV5RxU289\nQWH2napOPQkRBJqmAnWEAL6N5M6goYvAd8/djqhvCQayaaaPD57WB0I0WAO+brrshcJuFcBDldAE\nTGbwIdA2Lc7lhLYltN0yO9bSVVXmNeboiQ2GlVKIAw2EJhB9oB5t8trX3su/+egfM5QBwQh7bp+n\ncBZCpCuoDk0bcS7HkBE99PJ5LI6ggpdIbhxVaBmOR/SKgrzIsSh7FhdYmeuxML9MqxXZch+3uES2\n3nD7Qs7x9QrfeEKMoIpaJfgWk5UUriTPe0wmHieg0SLGAJaTJ1dxtsSqcOrUhL0ri/zO73yS+w8s\nMOIFNjfOsG95gWef/ByPf+H3uPee/5+9N+217Drv/H5r2sOZ7lS3ZhUnWRQtWoM7drvjdhpxx+0g\ncDeS7k6ABIiBfJsk7wIEyJt8gQ6CRr8LOgliu92JB1mSbVkUSRWHIousqnvrTmfYwxrzYu1z7mWR\nRV5StCRKXkQVwFv7nLuntfd6nv/0LCenZ/z+7/8+yTa888ZrVDjqwlOPBfG04d1X7vL2j95A3XmW\n67t3cO4Ro7JkdTZne1IhZIlMnp3RDkjBwcEBW1f2qJWm6Szt4TFq9yrCSeYHc+4HwYPHR8hqxrXd\nfR5HCcHhlSEmSSkyLUXIK/QR5vGIqqh5Jn0Vc9ixfMFQlCNsu6DpGqIq6ESJ2dnh2a17aGFRylOU\nJaIsCVKiRr/Gt772Mr/ytecp2gOmoxLrBPNFw/5+ycHjx1y/toVzFculx/eJH7z6FjZKuhPLLVVw\ntlggYmA6hZtjQ2jgxWevcO0KtE1EhprHjw64mr7C8i78Z//wGzx+cJc9sc+LX36Wt3/0A5Sa0vnH\naJXorMX1PTZEtPYI6amKklrX6LKgKioih/g+4AGlJ8woqZ2mkSdIlpByV29xeo9f+fXf5GB+SLRQ\nKMm4kMjoiD34vmB8u0AXuVOnYvYFiAqE7IDctZuQqEYagUHJinb+k43a+rvxtzcug5R/sZZtP3vj\nSY01nFMN8+L8oxflfzcuP37a5+yTfv+GJjigV+uFdYiBGDw+eHzozgusvgAiiIAIkYikKqcobRBS\nYK3HuogKDNRHRUqRvnNoI9GmHIqEHi2zfKxrGiazGU3ToZRhOV/hXWR/v6aux3gXaEJDSP2H9OI/\n7fFZi+sfZ6wd1de6zLIsNz9fF+5933+ouF0XGpe5Jz4rU+lnxRX8swytVC6opcwWWgnchrEhhiAu\nTxc9clTjRYP2HiUyOJWkIEkBYTCAEoJAGnS7+V5XMtPBgw/YpiOFwGQyoaoqEA5FoiOiewu93QBS\ncW1GFsLmGkslN5FIIZ5vc04RzoCF954QEtZahBAUWg0E5TW9+oIgHjaxcZGUJY5IUgoEIXA+EFUc\nzL4yshl9giSGZndesxlpiAlidAQfCEESgySmQNKCwpBdvqMnBgZPjdzcSykSYk7picmzftOvj2/N\nUBcyR2FlqrQcEOEhkgqRlSyQa5KQi8cUAkFkXbVzAVLEDFVgjsAaHLLjQHcn5Rzu4vxdmc9PYN3W\nT1ERQjYgitFneae3CFkMzS2AgXIvsmZda71plED2cIkpEX0gyiHrOsbBn2BodMWQ47pT/r4UMguC\naDK4lxJKGqTIB5SkGIy8ExFJSHHwBBDDszllV/Roh0bLugmRNkZrKXmcDaSYi+1Lz6VLb/kzMBYN\n3Hv/mCvPXAXXkbylbxtGZcXNGzWV6Tk7WKFrw7Xb++gQcNZSSUUMgugSUhc0qx6jtijFCOk1USYa\n3yGsx+uIrgsm4xFGJcZlgQoO7T0zLXl4fJ+rW4rdL3+F+995hdK1aAkiO5oghCKERJNW1FUJSSAw\nSFHR24amT5gCRC8pdEXwadCLaB4fnhGjZ3V8yLx1qHLErTsjypnhq88+z/PPP8+rP3ydf/tv/je2\nxhXP3b7Otb0JB28f0i563njzTaZbM44ezDmeOqaTWxzPl0y3rnPv4TuItmKyd40bt7d4cH/FarlC\nSs3qbMnu3jbP3LrOaz96lX6+oBKBksi9H77Br/+DX2dr/xbvvPuAt5qe8aSEEEmxxZQCExpmxQ7j\n6YRF7Jh3HVfUiFG1z/uhQbqOXW+5WcwobU8sHDZFdlJBc7Zka1pRFyW2E0itUFbz3t23kPGM+dk9\n/ubRCWVZs72zy7373+d3fucf85d/+ddMxju0nefV1x/gk6aot3numa/wN6+9wnQm2Z5JJqOW3bEA\n03Fr13B2eoJII44eRZp6iRtPYW/CW6s3WMU9vvRrv83/82//FBlafuvvf40DYFy1JOVpbU9re5rF\nkpvXr6IELJdLpBG0oWFSJuLuNouVRnQJ2Z0xrSNOrdCyRRIQtFzfqbGLR6hwgm9W2PaQUgsKltTK\n0HqDKSKIfk02pO0avHCotELr/KAvSkk90tjeE/ySbjUCmp/uJL3kSEA6h4jyy5ABbXiqoQ2svQye\n+r2J3H28sP36b5HEoJP5pIXPE//+5L48sb8fUmcK2IQ/rl9uT2y1QaR5wjTrohkZT0HYyD56MuUX\nqBYCIwUiBkQiL3+EQopcNGTvg6zPQqV1DfGhYzr//nR+jB9HQ/8IrfIHEKkfw7PnyQXqk8hRIhE2\nL1mBHPZBMvzuJJBRgRCEMFD8gmUyHeN9R9f3uNhTFhO6rtss3gpTEFJAa0Mc3FWlzMVRpoflc4MQ\npAvmbOLCPj5xJB93lFn3NRxR9usYOAjp8i/xn+fxYVO59eLu8zWEEuKi3i8QvKO3Dd47un61QYWU\nGyNEQulIFJZANm4qzZjgBGenC3obqFVG0eq6pCxyAZCix4WIVAmlMm3Re0+hFYuzM4ypssZVl7je\nc/fuGxhdcuPGDbTS9E+ci1/EsTY92traOmd1cY7KrZHNtSPyp0Wcf5GHVjoj0zEhtMhU6hCxMeFC\nIApIybG0S8zeLv7BCWHVMJ7OaAn5PZMEIiowGhkTSheDZ1FFGSJOeZyz9L3j8bvvs/XsDaQp0Mag\nExAjhTYoIZExkXzMSLa4gHwqlV3A64qiKkkCrHWEQUcdY87ktp1jsVhhVz1907CYL/EuMqprpHQY\nJUgh5eVCisMTOA2F7bmu2EpBlBKlDaYwyCIhRMrFZBJDASayo3eu1HGdI0dMDTpv53OElvM4FxFC\nD/ekYl2vhdigk8Y5S9uuKKvMHlBak4LCOYv3PTGQPaME2X9hYDgKofIRpEQ/RLBeZIfk5xsEr/Eu\nomSBMTVKloxHFTGtPyNIhAGhDiglB/31OZNyjV5nt3aD8w0QKStFJSuaJqCUQOvz/PGEQyoF6ZzR\nm02iu40reNY156J6fc3LsiINpmTJR7RRSGEghYGJLIZoL0FZ53PmvSeQcrNGDSzlKFAy34tGifPm\nWxRok3OtYxAE74dmTCSG3Cwl5WbQpefSjzUTf8IjSjg6bXF3sjV6CIHoE7oSpH7BbKrhoCMlzWha\nYIpAsBYtwQ4ZwiRB3wVSKjAUaCFBxNzZCJHeOwpjGI1GTKsKTWQ2GlFoiRSB999/h6q4znjvKsjX\n0dEiSIiUhm6RJKw7OnFwRY0gVUlI/dBtkqSkUMogBVjfYUyB60OmVZApwMEGjh4dsliecfzwgFde\neQVtSuq65nf+k3/ByaP7PD465o0336Gsaw4eH1FOJiw7x6r1lLXidBnRuieVY7poKJkx3b3K0ekh\nB2fHJCVobYNYQD2rqauS3i5o7TGzLc0//73fZv/Gbb7716+xPDlG6RGFiGgSIlmqpKmSpZAOnQJK\nj/BC4UJP350QvaT0HWJ1ho8tq+aUqkzoqqBfTrKZgapJ3uCdRRYV/+1/9V/zl9/+A+7cvgZ7gaM3\nX6OqJkynM2S/4ux0ycnpivv3j2iaxLje57kXvsLRac+rd9/CFAWzqWY27qjLFW3XgbW0fabgtE2k\n6a6gTaIAACAASURBVEp2X/wqHTVbt67z+ntv8v3vv8p0PGKiPKMyOyOacow0EaEkWiSMCMhVfriL\nFBlNptkxMgSQBYsGrK7QY0EljoisECmQoh8W3xHbt1zZ3uK2DcxrSTnQyZXqqEtNqSRSudztDJbU\nO1LK5hD5QSmBwTQtDYYWUlHoMV+UwvriuEhnvswC6GPpnx+hRDz3+hV8stNX+kSTivX+bgqpjyi8\nN46wH9iZdVF4YRc+vLOfOMSFv5/y7ec/Eef7sC7iP/HXpM+ByvgpP/qZjcKGPskGLDj/gnWXZViA\ngNIS53q6rqVtW+rZiOV8QVmWCAnz5YpgfM5B9YFqMJyKMQz7lzYn8yOsrj5mB58y0jlS8cXEuM4R\n/ovj86J/w9Pn+mV/x9Puo4tmdgjwwmcEKkFwAdc7Qt8TnUV5i4iO4DqSmJOQRCfRcpx1j71kXGdH\n4jK0GAGjskRKKHRCpFzkBe8GGVo2w1nHxVgfCAm6tsWHkE2cNFy7eZW277j34C2m0ynTnW2sbTFl\nQoiMpqzRQGczkqSku9x55TKO/etmz+W2vOyk11xsSK0flBmciINkL8ZIKgNCakKURCEYjbaRMmvc\nHx0cEYOjNAWlKmiaBlNqytKga81oPMbZhhQ8tu9IyUOxdan9A0hpjW4P+/kRzVzBhWfD0BRNg6nW\nB+7a9MUo6pXMqF6hC/xa2yvyG1WqlHWm3hJiAqEZ39yF+6fEviVKgWUw8uoTUUiSgLIeYXTJaJRd\nqbsUqJPEtY40b6mEYb5YbRqyKQT6tkOEiIoQnWMtbY2DsfA6F76sK6TS9H2Pc26gZMeN3LJpmk1W\nfLPqWCyXjMcTCqNQySFSRA7vxziwTZ332WhsQKwDCZ8i1nu0HKGMwlQapQR9u8L3YbPuF0IOGcyB\nEAQx+BwxViqscwht0ENNcDETeq1/zih1wDlLiIHlsmdnZ4fRqGS1iAOVO8efARvae0bhs/Sh0Iau\n61BCsLEOG5Do1nu0lFTlCCl6SD7HnZky55VHMgqcEkpIjNLZHltEetvl56PM2eOmUDgXKUpN8CKb\nPRcCrQWmEEgZcS59KH98/TxfMwvWx765toCzbvjc0KhPicl0TNNAiD1FoXMdpTV1WSJifiZksCOh\npEIoiZFD04SAEJpRqTG6AjJabmSBEBmxz1FciRTyu0GLgvUrptRmuL8uL7X8YhXWwOPTwMpJqpRt\n/IkVXbOgKBb81q+9yN2z12hs5NaNLWz3HkpEgkv4VKBMjTBj3nn/Lfb3vsxpH3J3RlmSUEgXaNKK\n63ee4c6d2xy+f5+r+3uUOjGdlIwqzevvvc3B0QP+/te/RT3bxZ41FF4wG5WcrHqy4kRgColUubPl\nnWe6tcNZb+l9AFWilUMg0UYRncf7gFIFShp6m9BqlHP2XMSkmtXKs0wJpCWe9vyP//P/ikqBQgTG\nqmdra8p4VHM4P+M/+K3/CB8Up0EwuvYyLYb9r+4RTMc8Kexoi9svb/G13/x7GfGvDDH0NMtTXv7V\nL2PwjFRPf/aA48O3+Jv3fsT9dw5xaEb1FXYnFWOjsO0Z16Vgp+wRs5ZVO2d5AHpym+/d/za3Zz1X\nxRV2JhF/doI7OmUyrjibn+CWR5wdbtMve67uFYzGJUenHaZS/Be/98/55jdu8ei973JtF66oMZOw\nwvoVykz47nfe5HQeKOopL758mwcHD3nr/bdZNoImBHZnI/aulFTymELOaS3cvj6lCwuihk5uE+sr\n/Jv//QG//bu/yR/+4Wvcuvl7jNRdYmg5OnrEf/rf/EvuvfF9JIHTxZKQVli3RBnFctXiuikiRU5W\nK+6+Pefm7QlvPww05TaTa1tcKZa88FzNWdOS3q3QlcA12Sl2b3eHH73yCiedYzxuiaElRE/fPubK\n3g5tn6hHmsXyFMkKE1XWziiFSmNSjFTVhJh8zjQka2qK+otXVH/S+JC52CcW3uIj6txPV7b8pKl8\nn1RUfuQx/4R2cf3y+6Txt40IPWk8lhF4huJ0vRFPnJdcWOeCOFKUBc619H2L8z3GabSSrJYLuq6j\n6zrKsmQymWRtLWKj8dpovS6Mzxs1/XkYT6Kpn8dceppW+uL4rLRdyG2NGDPRNcSIdw7Xd3jnCK4n\nhrzgDSESY4BhyRqlySyQJAcX3URdjZBSUVfTjPDodSarp5xMyP4YKS/Z1/KDwEZL6mOgbXt0WXDv\n3ruoAZ1dLhqE0aQUsjeMWsfHhIElNzB5LjsNP/fnR7rc704Q03nxL9IFJ+AoBqOkrMc865aYQlFV\nFbqomS87lvMTrLXUxiCI9O2Sbpnfq1s7M6ztCAuPKRTGCLSEsixIyeA/1TF/sgzkXEZwMR7vfLvz\nn3wxCuswUJ/Xf2JKuJTj3iIy026HxnNE0hUCmQKSOHgNiMyikgYnNDEkkssAgAwZUdWD/tcg6EIk\nOb9BdtesgzQwD9YEq8h5o1dKuWEtKJXTaNaFWQgBBm209x7nAiHkZs06qcYYg7rQbGYAxRKRMGir\nY3qiTSQHl27FEHM56JoHHw6FHE7a8L6JMV/xmA9AKrWWB2cG64V76UmH6pTkRgu9iYYaXjNZVgIp\nZsp4jIkUElJJiLlxnDXjiniB3r3+s0b6lTIoFdH6g+urTB8fmGqD7libzNhyPn0gcnMTkzcAO0qr\nodmXhudk/s5wgT69vk7rky9lZvKst1lf3/V3r7X0620S6+MOAzAp0Uqjld6wU7z3+fqSCJF8HDC4\nxjNo0nNDIgaPkYqEyoxjBEblPPTg09D8AK0yzd7oy5fLX6jCWmvJfN5hLfgiYiPUyaB0QEnHZFSQ\nbEcpCkY6QJ87UoiU9SFC0rnAqvPEmkFHEgkpQ/8qKfZ2ZiwWZ2xvvQTdDpPpiLE2jMYVqtSszk6x\nC0NnJS985Wv86ZtvYnRJ1/j8IJAalxzW9xQpIAZkPURAKiI+azc2EF0acvAGqmGCEAuIDrTKTnlB\n4rzASglJERGoJIgEBJ5gEqnKGmW0YtE19E4hdUnTembTGWa2zS+//FWEGeHCiL3tmp2tGe+8+zbN\n/BQRW5aLE5r5KTQLVqcPWZ28z5WdGfPFYdYZ9x1TEdgvNd6uUKFnIiO72xOOaQjB8fy15zkVe5j2\nB8RiTv+4xbnAOEaK6YikDMoVyBRJI4V3Aqdh7hv0pEIaxde//nWEOOLFX/4ywp4yCjU7eyX1xGOb\nCUJpTL1FSI6QwIUVq76gcRJHQuhATA0hrFASUiEQKhtQJAEWzcrDza1rbOmOOzsT2oeBG2aLJr7P\ntZd+iWa1IKQAQnK2aNFFoB5NgcDe3gglNG3fkKJCKlh2gVbMCNUeViuE8UTTIyYSqSpIAiUDStUg\nJF3b5Qd0WOH8Cu/BeYG3BTEs0XqKUgIRM100uEBMAZLJ1NQokSJ36qSUoCTI+U9pZn628WTB/FG0\n6vW/XXqIn+xC5qJO98mfX2Y8jfL8Udt9YBuRqe2XpYWuPwNrfPRjUH8BDEY1lxlPpW1f6tMf/Myn\n3k4wLIwYAOVhUZJypz7GgHU9maY26LoGB9C+adnf3+fua6+zXC7Z29ujLkqi80iVDaO01ucxKz9j\n+tafhfHk/fdFpdxqoYCU39feZdpln6ngIrkPsD3yUn/Ag5JAa4PtPX0fMiVRalar1ea7Y8p6yaIo\nWC/XtdbZUVfKrLVM56ZJQgiWyyXee5q25fHjx9R1TRwo5D4o6rrCFNU5NX6IurnsuMxz5m9nXOQn\nsXlISCmROs85ay3tWU81mZGipllZVo+XHJ8sUUMOOD4zAlMMm6JhtVjivUUVAmMUk3GNKjQ5tudv\nZ77+PD0PrPfYFDBktNamQB8CPgYSARE9mozuWWmYzzS4FaOgoRqhVUHZa0pf4whIbehCxJgSO8Rr\nFUkhlcTZyLKxrI4XTK9dyRGsPiPDi8UC22Vqt4i5UFu/Z7XWjEYjRqMRwWjSIPcM3hOsGyzWFG3b\n06wabOewfY+1jrIs2draQQoB0SOVHLThoKLAhhyJtaaEZ8QeosqJP7oQOOUz/ToFpIoYpQg+4Z0d\n1u4Ca3uklHif2bJKC4LNa9VCC2KUKCVwLg7FZY6JcjZRFHldJ5TMBodCZ41vgrqu8T7QNn0usmMa\n/HU0SulNnBVAceG+jDFLTrRUGKU3Mgkp8/syJUfOzM5NonThfWe0QSowIW4Ka+89RZGp6Nb1wzGA\n99m0TMhE33uCz9sqpSiKghghEFEyfqBJIoQYGiEuR6cN1xkhKIzBaE3wDmIYGpkRJRRJZk15SmEw\nesvNyjUL2S+yF1IioLTOEgeZEIHc9IliYHwODQIJhak3Rngp5bupLEeYQtIuf06p4NN6l9XilIOT\nhqtf0jkkPI7Q0iLSnC9d2See9Yy3E1PdIdZ6wgBeCDyGLkA52aEqDEWhsLahSZYm9EyLKTokdne2\nuX/vHr/0/B0IPUJB0h5flpQ+EpPhT/7sNf7R3/tlysmIovFE75DDs1VKhXMLBD1KJyKRqqrw4wmp\ns3TBIkIkmYiUgqLM1PBgs+NfSHLIPs773SWBTZoka6xzRIZw9eSJIjAuC6rJNkWlUclT1UWmTvQC\nWYwpqoJv/83f8H/+yV+BVBR1wf604tr1qyxOHtO3c/rFIdNRiY6W3/j6l0Bp5qJCiYgPlhu3bhIo\nOT1dIOYnxHaOUQm1XFCNCtzJKd/49X/In791SkiOSdVS+CO2VhXdg/vU9ZRXHzak8Q10IZlqhZus\nmEw0N+/sUlUFr999gys3v8S/+Kf/Hf/qX/0vdHGBDyMIU27uFBTjOW+/cZ+USm4/+wJvvXOPx2dL\nvPD4NEXXBqka+rSg6xPCedQYtndHyBSpKIlSUlYlXvSM+CvmD/+Ea6N9Tk6WpPQWv/riAf/+zY6u\nf8TR8SN+6cu3UPWYybhgtjXG2pbYnpEcxGAQeoSpHagpvb6Cmt0gycdsj/PkFrt3BtTiFKUFhRnj\n+wYzqzg5O0EYCz53JVNU9KFBF7njXhiJiDWFqfG2JmlLpUqUkljbImREa5VRa62RaRd4/6c5RX+s\n8Xkt6p6GmF1Gb5fWFdonjCddaH+ccRkznSc11msK66cZm++55O5+Gjr4h47hx1xnfhRq/+HzPbRQ\nhlORBIiYm5YxJQoDMXjaZo4xBmcl3vXYrqOsDFVpuP/uPQ4ePciLPqMwWmL7lqIoqMoxSsj80k55\ngSCkzKYxH9+b+IUZPzd63ySIMWs0nW3o+zPwjhT9xgiIpPDJs6ZG5PswURY11kbapqUsJhhT0NFt\n5o8e9JQ5kcsjZS7Gtc5FRllUGRUb6Kfee6z3vPvuu1RVzf7+VVarFScnJ3if6biz2Yxr1/cxRlGW\nJcbooRH2GRtUw/hJXMuUug09OoqcLRsQdK3b6GmV0pwcd5yeHeJDQiiDKSvaNpsdeZlyL5mEkp4Q\nPbbvKY0Cm4hCID34ylCORwj5+R/XRzmAf5HnwrxrmDerDWW3DY6gMvCqAREzu4IgkEHxeBwpk6Pp\nVoRao4saJQy10YggB1PbTNvuvWNUjbGpRSmDST0GyeLklL2YdbZt27NYrXC9JcZcIGmRHbLX+t7x\neJxTerTCp2wq2zlLCmHgDkDf9/SdJZFN1IKPSCGZjGdMJhNyDo5gfUvIBEiQWiGsIInse+RiACkI\nWAIWdEbvkweloSwVhVCsVi0kl2nwCVIAHxxCZj+FRK5FjK5QyWGkwhiN7GMu8kUg4ajKEWWVwZIY\nA4i4QWPXMkCl2BSqzjqqoqAqq+x87TuSyM1BIc0GwV2bhCml6PseqcrcGJCZPeJC9iQw0QwadoGS\n2S07OId38QOsgBACbdueSzboKUKB956+7zc+B6Q1yytg+7jRUUd17oGwfq8bY2jblj5GiiLr8ssi\n087XTvAhuM15iDFgbU9KgkpXeGtp2xYAbw1SKfqF5cr+jBAdXddBkuiSbFimClQhQESMVljX59VE\ngGbV4FxuZmhVYKTGyOxnc9nxhSqsCZqd3S/xnb98lZdeeIHoBJ0dYWLLbNQyFi2//5//Fv/fn34H\n0zzAiayZkkgcglRUSFnxlW98g604xXjFew/e4fWDB3gJZSyYKkl3cspXv/UyPrRs78xAeEQlaUvN\nxGlwhtcPTjg++AN+9x//x/zJH/8Zy5XDRWhcQEhBUWlCbDBFyaJdogrDZDamFytcmDMuhny1aPMk\nV7mbE2NC6EgIC5I2RC3xg1Zg0kp6obE+oRTIJCmQ6GjQoWCcoIjgDw5xYkFdTXFuSTPv2SoiZ51H\nacXtm5oJiv7BA2S/ZOQ7xq5nOyYmpUJ3DdFa9md7vPbmfZS+ymz/BR6fdrx45zqvvvbXJCTXv3Qb\nhOLxScPXXviXfO/f/b+88NyYk9N3uLoaI/ttqt0R8abnpAk81or32xHlUrFjLQvTUSbH4uBdJJFq\nNKG8eYP//n/6H6hGjq5dEPqScHTKUhxx407Hr/7Gl3nttTP+/Dt/TllcoRxvkfSEILZobUtrW/b2\nKoySjKJnS5bcGq9ITWLba5yMxGLFYuL59v51fvCgg2XFczciuztT/nL+kGvXBSfz99i9MuKt+29z\nfHzMs7dvYd96yGw2BfeQF68/w+JoxZuHR/Sy5Eq9g9lRmLHgmkrcISLbm6zG/yGeP0YIT6EiLi5o\nmne5dl0SV/eBEQgDXiOZ0nUPuXL9KpDpp6UyjNQWy3k2rFquHtP1LUJAPZKMdIXISh6S6H668/Mz\njItI4GZRchkq9FNG1ip9tOZzjcBezin4k3/Px233aejrFw3KNrSsC/TrJz8vhp9t/rskYr1G3T4J\nsf7AZ56CyF/mGC7GDn7S7/k037/+jEgSMRQSIuUFRUx2Q2Wbny1ZLOZsbW1RFJKuOcMoxd3XX2E2\nm/Hv/uAP2dra4vnnn8e1LUcPHvCD732PGzducP36DY76Y5bLJfv7+2xtbSGMQZlsrBNTzA64n/I4\nfhHGZ27GwIfuoY8q3C/el2uN3o8zMoVVQbBY19M2p0S3yPdUTJlmjUSikXpAORAoZRjVW6SkODud\no2RN10a0MhQmMz6adkmMkaoq8S7SdXYosNuNxnJtUCSlBJkXkoFEVY1IArQuuHJlzOL+G7Rtz/HR\nCY8eHnN22jAa14xGFftXdygKTRoiQZ9m+rY+p5+nDn49nrwOF6/hhuoaI8L1eBdBaawLLFYdp2cr\n+pAYT7YxZUWKPcY55mdLOusISTCajNnb20GIRLs8ZVRVA4If0FpRFRqdBJVUNG1L41b0ZQGqQhUa\npT/7ffI09s6GEsuna0T+LI6jk7d4/2RGXY0pizHWhsHxO7t9C1USfE8g02n12YTT7S30fMnWacto\nMiLoEY8rie4E3gioFNXWCFkqmuUKxFXarRVWLJn4lu50TnCe9x4dMi4qhE9sjyb0KdAGRxpJ8I4Q\nsmt5PR0jhMR6z+psRdd1uM7ihyKz955u2eJtxOgKqyNBJOpxQUFkVDl8ahkVJUiNtfl4+mDpY6RN\nni4EApmXEhOM3Yh+JIizhI9zkvf4vsCZmqgcIUnG0x1MkYj0TGRB38is0Q6BSKJvTzBFRJhEkpJR\nWaG1wtmISBItNFWlM62bjJxLJRD43FgoJCFkpLooB0drqSiDRGIzTV9aus7hQkIUo2yO6CwyiWwI\nh8A2HRg3oM8SoiAGCA5sWIKAsixR0gyIeI61cj4RU5dlHCni+5LoVfaEkg2xT/gObK/wLmHUhKSK\nzACKPT0WOcrU7NNVhfMdpghUoyybVVJnnbOr8KFF1B6RCpyLxKTp3MNBHmDwi2yAJoTCOU9XBqx1\nuL5HkFAiF9hVMOxXM2zXcxoCXfBQwdwu8GJObSZMRtcQQtF2c5qmQVQd09mUWV3TLJZ0q4bGLhn1\nGuEuL/36QhXWZYgkmyjH2/gQGQWLcoeIUYEPW4zFKd/cX3G204B6Gdm9nW+qqLCAF44kQGpN7AJt\nbHGiR3mJRvFYPUbFm+xMptx/94hfefkrSJXwoaV3kVkrOXINx7alVxVxtMf/9RdvcPP2M1SnC2Rq\nePbKhKZzhMlNAo5VFxCCTLuQEqMrQt9Qj05xqcJTE2ONSIYoEyJCHTxJRAoc3i4ogqBAcyIMyQjU\nWJF8j0geER1STQiiYeES02qbZZNt5v1iQVWUtK2nsZLKKIIMCFMyHkHfecpCMj9eIcrESbL48YT9\n1KLGFV0Dtq0wqebFb32TGyliGsXhw/uksMToDlVpzrojfve//Cf833/8r/nK7a+wPHZMvxRZijnL\ng5bf+ge/wYOTOT+8/222OUOOKqzs2KtaQrOP8Hv03SlKdoxHx0ynp9y88RwHB9uMi11W6TtM0xbV\n2T7vx+f5zg//jHrvJh0BWLJTVjy2P2JrUhHvH3JNTZiJgq2JQVWSa8tEIwzfXY5ZFFf4o1cOsWLC\nTO6j44LdOwW+dryzlJT661gF1t/lWzfAja/x78s7HJcde0Fif3SKe7bnwdGz/OjNrxFvvYa+1nOk\nNOPpmIlb8ez2LsuU6KVG2xOaTuFDoioidVXQtJ4f/fAeq25BoCXFSF2Bjo958bmKuqzQbkE12kIm\nyeHxgqqcIGNFEjdZtY+QKjKaTCDJjRYlcTnjmp+lsV6wbLRAH7OIfhrt7kOLmSeK0ycXRR+nYV7T\noD9u+ycXjJ9+XJCCwIeO76N+5wc/zcY87Qli5Ye1yAnEkNO7KdoH6tNTvz99cJ/WnemPW4x/GLH5\nfHW2Fwup9cjd8sHkPKUMp6RAHDRbbbvEuZ66LtFas1zO6fue+XzOg/cfMD9bMh5NefTwkL7v+Ytv\nf5eXXnqJGODRwwOOTuc8//zzmZYGgz4t03YF5+ZXlzk/fzd+hkcCETMd03tPCpYYA1rEjHqhEJte\n1GA8hthoFoPPES1GK1xMpCgpypzFKjsN0m8oj8aYzX0kZH5uexex3tFbR9dnaqU0GqUMXd/z9lvv\n4L3nmV+6RUqC1bJjtWoRQjGb7nB8csjVa7sDChSyGd8FjaJSaoP6rOe0+ryB1bSWmFz40YCYwXlU\nUEqJMmWH3971nJwuOJ039D4y2d6nd7BolsznC3ZMydb2Lvt1hS6yA7S1HTFZ9vd3UTISvEfHKpsn\nhYSwgeXjs01urjElUhakbIv0OR/0z9f44Q9fByWpqhE3b9xma2ubQpc5Cile1MvGjPQpQzmaEM6W\nhBRxwSJVR6IAKc710KogmUhZliQUoR2cmqNES+iXKxYrTV1WVHVFoQ2lgFpC0IJ5s8L7c6mDtTYX\n1HbtAp4RVWst3jm883mOitzcquqaMkX0IMeIKQ6Rt+B9wAZPZ3MsV9Z7C1KKJCGIaShzpaDvLV56\nRCBruUNgZXukqDNd3g+O/0JRFAYfQ3aXjjlf2YdAETPNubc9Co02GokiBoZC2CNVpqFrlVHSxFp/\nLRCSzTvIeUepNCFFrG/pXJtlphFIJkeN9RajNFVZYF1AG4OUmrIoN9pzVeYiu20Xw3zNLt65yPdY\n32G9IMUeKRNFUeTQCpmp1YWpiA6kklRVgVea4BwkjzaKUtXEJCgLifeOopTowlCUmqqWmEKzXGTJ\n6Kwe0/S5YSKkpLcBIXJKAmTavLc+O9hrTRQim735bOxmTKZ8r5olo6KiD57FakXTdngJ/WpFKvL7\n3DnH6ekxxtTZRyAFVFAQEq7paVct0Uf6rkWaEu8u//z4QhXWWstsy6813oVBOyNIIht3JPLknc0g\nBE9Mau0lACKHlXtlQEiapkXLjAoqqXCD9bogDnbyucNWjwqquiYwdHkGHYLUiuVqxUh4YhxTFBXF\nNDvuIS2dEogkMEqjTYL1IkyQ9zUqkpCIlENchPBZ/C/yJC6MQEnHs1+6xpv3D7DBUamS1TJ3Y4zM\nLoTrF/Z6pZxfZpEkIYmE9yHrt4VCyLw4FAJc3+OtIzmHYHAPBIxUKKMJEZaLhhwCX+a8vZi1aFVV\n4Lr8kPPBMx3PePDgQY4V8A4hspW9UpIrN0a8+fb3WQVJkh2TehdRjGj7nLkspMwPIJFYdS1vvHOf\nt995QNMVaLPNannMjStX6fwBr959l+koPxC7tsOMzMb5ULoJLkYKCbPxBMESpXK0QhMKellw78ED\nDt0pRl1hPNrFBINWY2TsCJ0judxsKWvJ1f2CiMUFT1UUHB2/x5SK3fGEyc0dDn94hLcRLR2FNsh6\ngpEKLcUQcxSH65Hvl7UTpMCyXEYoK3rXoxRZ5CPIDoxSEUlIlXMQfQjEJIgp4WMaEIwSrfO9K5RC\nxIgUalg8fPHGhxDZH0cr+DkUcZ/GfOlp+/pZKYGXLtafssmHsOiPoML/OBzmy+yf+ITC/ccZH2qu\npLW+Os+5lIM9iTGwahZUVUXbtvnZZC3377/P8fEph4eHNE1D27YcHBzQNA137tyhaRru3r2LMYYb\nt+8wHo+RUtK27YA8VpsCuk9hY6LzRaaA/t041yKmMLiciziwIRLZjYUh2g3Wz/Y1PTXHwwyNsbRu\ntOTFsPd+0FivdYvqgzE4KS+0t3d3AHDBbzwzvPdY59jb2wPgr374HbQuuHnzNoeHh9y79y7vvfce\n3/zm1/mjP/ojikLz0ktfZTweb4qKoigoimJT1K6bU+pyZJJPfQ6f/P9crJzrPFOM9J3F9o5l23Bw\ncErTWSIGU1lkVBhT8vwLL1L0K9yQRdx7x3g6YrFsCdHSLjtOTx6jBEy2dihNwUhqShR72zssl0u6\nNpvFxqpE1yWfwnvoF3KMRzNGo8kmB7zrWigyTTfnRQ+NWpETcCQFRVUTpQIjgJgLLQW910iZEFKf\nN821Qrp4HpcVHEYKutWK3tUgBWVRYpQmCtBK4Em0piQlu5lb63kVnCcMztibxuagdNJabdbHyhhM\nWmfLr+cdmXWUhrV5TJsC/Zx5IDLqLECarPldE4IjkjCYsxVaEIbMeyF9ln9an5sNzuGH6M38DMhN\nCu8jiICSg0O+SPiYndjVoG1KiEHWNMSCxUBIEYEajMsiUSUECRds9lwSkjSwXqzNFPlCG1LKyTVV\nCwAAIABJREFUjZGyqkBkE7TMtlAX6NglayO5HNuVo7bOfarAKIUpsjdSTOvs64JIbpYILZAS2gBC\nplyzaUlEY0zOQR+N8/4UlaCsMmNnMW/yc1HFgaKeOQNZy56z0HPDQJCiACWHZkQAlb15lM6NS5nA\n9tmkre0svfeEBFEIQkhoqYkpa9T7vhuSCPJxB+fx1uGjpV81mXbuPFGZTwUQfKEeNUUp8aEDoWga\nwc5sggsdHoE0mhAcW7MRt29dJYYOW26hnQfbgypYOMlcj/FixvH8LbZn07wwEgIVJbOqotQOmTra\n5RntaoJWNePJNqUuiSkMXSjovaNrWnph2RqPSLKgqkvaJue5FdIhhcsdFW3wrkfqAlnUWLfC26vo\nQmWnZ9khRUNMDYmOyBbTbce4hpd/eZuyOuXV1w/xcRdZGULIOXRCl4MBStbEKJlfps46kpIk6SFm\nx1GiRGtFPa7BJ5bNKTKmjHiHgBK5QK2LgpA0nQs8enzKYr7kuZe+ius7kgTnVxQ64URAkbAucfPW\nLb7z3T/n+V96jpP5MdIImq7l6tUrjKanLE5O6YPmW998lqa4zcFjTyMii7MDdvd36TvozyJOGqzY\n4nf/2T/hrbfusVh0yLrmlXvvU6ZEpW+wePgAMx0TgweX9SC2cYy6F3DdIc9e20V0h0i95MqNmslk\nxf/xPYEsI1e/+jLv/fU9nrn+PIeHPVI7diclu1XJ/Xtv0rUNarZFGxY898wWfXNI5wPd4pirWxUz\nqxk5xSzVvHt6xM6OQE2XIGcYMQOjqAoHcp4pqXGKt4YYFMErTF0ghaaoamRR0rolZTmlbzukrFBS\n0DlHiAJT1Zw2AWcDZTGmDZIQBFJq6ukuUnockegiWmqcE4RUAouf8iz9bOPcoffpZd9lackXt/9p\nj6dqGT+BRvxJiDVPOw/pg2j4z8v48Plh0Gnm4kesQ7NjJEaXF+Ntx+72Tl58CUGzbHnjR3c5eHjI\nO++8Q6ENZ2cL3n33XUII3L59h4cPDzg7O2N/f59nnn2BN+++wc2bN7ly5UqOi2naDXKjJtPN/nym\nwvpvsQHxRRif5p6/OC42dz7eYC/r+z5xP0TC655uNadzS0giM8iyjowkZb7DRER3ILTOzU4hGE1n\nEBVS5wLbNi29kLiY80GUyfmpXkaEHhbmVV6kW59RZPpAe9SCynpUXZi821IitMC1WSP55ee/QlEU\nPHz4EJLDdysWbcP3v/c9Xnrplzk+OuSP/+wvmE6nCJG4c/tL7G7PkFIyKiqkFEQfKEzF1vUxb7zx\nBleuXGe1WlGVNWEwmPKhIwRHVethnsmBxaIQZDMwEITgUDobW8UYCK4kxoBU2azN+Z4Qe+yABjZN\ni/eeedexmLe0jSX6krqesj3dY2d7j+l0ilSgjYatHYoQOD095eTomIPDQ6b1BJEkha7Zn76AMYZq\nnHXXMuRnX5cSclpTDmZUdWEolMqePB+6lz7yDuOyE/Oj2FCfRgb0szS+8fKv8uLXvpzjq3yHc5bF\nMmtzq6raRF0BIBOSGlVPiWWZ9dhxSS0TphwhtSIJgTGRVkmkMmgVEcJRFJq6rqmTp4iWk/cesX1n\nn6IaMdveoVKGJIZ4LmexZEaJc56m7WmbjrZtsf0AViRIPhAJG58BKXPxWRRFLlaTQERPCAEtVbZe\nGpo+cmCf0DNoiLMtcAJsTHTKIasaFwUBiYqQoiSkRPKAzjnavV3hQ0+MHoJGDPNZKEldjfEx65qF\nyIwNlCClPiPWMeeFxxgJAzLrhuirGCPYmJMrWktR1LlhIDWNaNA6YYMjyuxSHhDEUKBlia4kdVWj\nhEQEicSgTQ0C6qoGhizpvsmmacKTGyQZta9qjdIFtJGYhlxqLSHG7G9gBESDKSWCiBaQkEgVcFGj\ntEBqUMJQ1BIUSC0JQWYjMZFNzCaTCUIopPdMpjWoHOElpBrunR261g7NwiqzIHJPhNIovAAldNbL\nJ8GonoBQHK3muVFR6OzFogQxBtQQnyfweNsgZYkWEu8SvrG5IdoPgoCQ6G0zFPuXG1+swrqQqCgY\nj0d0NpIoCMJiY9a7iTj0kmTunEVVkiIIYSFB4yJtqXGpyI5ySuGHLpURiuAjMXTEKAmupCoNEkHf\n9IxmZui8KYTMN3ldFQQXWSybHEcgFC4kRqUm4Ii+z0YPUeKto9AFUhl8UvRdQaFMRs1lh5ABJXqi\n7EnSIkTL7l5F3z7g9rWawwcwf3SGVCNSlKQgSGpAvWMuoOVaUy4lCfBpHZUTicmjRKagBefRPnej\n1NB5Uzo7jRdS0VvPorEsm4a2t9SmQKbBERJLwhJCpkUYpalMRRcct27d4u4P3mekFVJ6Uoq03ZKt\nrRnvvPqAK8+9xPXbL9J3B4ilZxkPqUpNWSoOj1uUTly9epsvf/ll6nqH9x4+5PadO/zru69y1kZU\nDDxTa5QpQJBNYZLAtZHQBqJNVLLCdoLt7RnjeotmecC9VcXN7V3uPTikmky5snMFt2pRUxDecn13\nSh0C33/tNXwXqPZGLPvH1BK8kFzZ3aGsThg3kme2buLNghRXTGcRX1mS1IhYkkJECQiiRxNJSRF8\nnqzOBYpCEtTgCKuzk2PbOKwNGCNQAVLQlNWIxrdDd1ZgVIkPEdBoIQZDjUhvs/ECSZJiwg95fl+k\ncZEC/fGmYonLobl5UfSzvqB5mi7vszYDviiRLp/3yCjiOfXfxbyob9u1Cc95Tubjx4+5d+9dHj54\nyNHjY6bTLZr2EadnSyaTCW+8eW9j8nLr9jMcHx9jjOH4+JimaVitVtn3oCzZ2tpie2t7s9D8yKbP\nJxl7XbjUw13Lx7eWfnHGZXwJPrcxoFc++HNTHc4bfUmIzaXayCPiWvogcT4v2JPKcznEQL/qs1mP\nyEWBUQapJMooRqMRq3ZJoTOanExGcwOZYbZqFqxWK86WC+q6Znt7G60LDh9nkz1rLcYYbj9zh8PD\nQzrneO/hQ+7cucN3Xr3L0WGD1prZ5BrLxRGjsmJvT+OdZWtrSpKK47OHnC2PMJVCIFg0LWVZ4foV\nphAUhcSnFSrMMpozFNgAa9cnkQSEweQoJGxocwRkyhpc6zradjXEBUmck1gr6JuC5XyFkmOuXb2O\nlAWFqXDOcXJyglRkSqmUzOdziILCjDJ6lgRKSowqcmyQUCQbh+dAvjZGZRZiEzti8IgokCjgw4X1\nRz9u02eagk/VWP+Mv4vWo9QjJqNt9ncNTbvAugYfJU3TZP2pEFRVBQwNhTrraBmNsMsFAkuFRiGJ\nYoyQkqQNxhTItVldjETr0L2k1Iai73HHpxTGUFUV9WiE7XvUsL2Iee1ke0vfOx49PODk5ITlooGo\nspO0kESf2UNSCJDZ8dk5nynLMTOZgvc4lzOf133Y5D1uMN0ChhSeRBCC8P+z92Y9lmXped6zxr33\nmWLMqTKrKmvuZg+UOIqgSEiyLJiWBAiweGVY9o0NX/jCgAH/Av8TG/SVr3wjWbZsUGbTZKsHsgd2\nTV1ZlXOMZ9zDGn2xTkRmV2VVVzfZ6qpWLyAyMjNOnGHvvdZe3/e93/MKQRCZdXaQNKt5SRQ1QmMr\njbAKpIUtRTplQc6SjKLvBoQqilqpFMZWiBQLGEypohYVYmsGGbf7twIzS7kUrIqndVlbjE8453G9\nx8i6QORUwmVHJRVRwGg82lqOZQyjcm9KBYAmESSli8Q6CiaTMV3XFfVvLNVtEFvb30LjRhTCtzaK\nygmQJVwcvMP5kpRQWhGDYVJbUnZb5WtCTSr67XQrwEZQxqJyAuGIvvTF5yxQcqBpxggkVS1BCCIC\nKRXaWBICQrHOizlibUVVNUhRfLt9XCDJyCwYjRp876nqCZvNgJKKCMWq0HusqdhsNqgKhEiMRoa+\nL+5KWhkaXRXFMpJmYvDDQOfaAp/+RZWCS5uJQ88Qau49aLk+3aNRgo0vmUkhM861iOSQciAljcgZ\nlSMyDmzWjtYc4HVGanCuJccBmSLCByphEWGFzopaC7JzRCGJJhM9oBNCSZSSZJE4ny85nBhO5msO\npztIkRlPJNmtGMvIaDbmeN7hfMANPUlbetfRhcSjQbE3mlLlEgBXJJTyICQyJ9r+jJ3pNfLwgP3Z\nAb/7t6a0f9px/blb/Nm33sPaG1ilSxb6IjmgNEoopCzySCXVpSeflIK6sVitCN5h0agEyfdUUmGl\ngJzRWfLgeM7DozPW5xumqkbkROrX6Fqh2NCMJV0ncK5nVO1SyxpRa3IYULZI9qWULOZzFqee289N\nsUHy4uGv0aarfPXll/nAfQ8xf5+rTc/kYMrxoyWjyS61yBzffUQlBYdTTewe4qJDqCl7B1eZjM9Z\nrObUTY1LiuAj0QnW8+/TVJq7D5esajg9qjh6JGjXNcPuy7z5wTG//hu32SzLojDemeGnHaoPLM9O\neeOlVzifr/nO0Ql1NWWZNCaNWLeCo8UDXrgFq/OWrz845dqX32C8+xzN1Z5VkxlNb9F1VyCcYHRG\nqLZkLdOYEA1tD21b7BmCFwhZo5VBacNy2TNqJoRYse579vb2cUjcEDBGg5SsQ0U/bK0IYiwWDykg\nBVgh6UNZhFr/+ZOCXwQmPwkZ+xMfK54EmR/3+E/qsf5ZjGdttj78ik9DuX5cZT4/4wmKDPXCgzLz\nM9F6/hzHRyX/5etCypeJICLr9ZIUPUePH1JVDYvFEoFisVjxr/7l/8kPfvAWR0cnaK05PVtfkkS1\nnvPenXtMp1MODg54860f8rWv/b9cu3aN11577fL71atXGY1GtG3Ler1mOp1ubZSecR39uOvqQxVr\nsW0zeuZz/Qcwfp6f+Wnibc55y3p4xry96FHe2tY4N6Dk5FLueiFV9anYv2hd+iiT38pC14GjoyOM\neULt1Rd2UEpetpsdHh5yeO0qZ2dnPHr0CKUUk8mMrutKNSZLTs7OqeoJ/WLB6dmSlB4SU43rO4IU\nfOcvfkjTVOScuXH9kBA9f/f3fov14NjMz3jv7kP2r9zi3XffLS11OdP3Pa++dpud2ZTBO3bt1m87\ny9JmsZW7sxXEhhAYXKlEp61n99npCW27viQST8cHuCFwdrbCe0/XG3Z3bpUWizZjbUaKhLUCqcpx\nd86xWHu0NlhjqIxFS0PqHUKIbVJdbrEKnpQzIguSlKjGFHeAEIkhQPrJLRH/A5x+KG1QUm6DK43J\nFdEHmqa57GGOsdibIQTBezrvMUqBKZagOQZy8kirySEiPEilSDohgry0mFNKorSgCppaakiCrhuI\nCOpmTHAeYsa5wNAPZZ8XAqvFktV8zWazwfvt/W5rvSa3c9PWBiUNSmn6fqBWRdru25aD3WulfcOF\nUuV+Sv4dY7pslbhoBFHK4FOmb1v6OiFyIqsCzUJIdNY/sm5AkSxrrS89ti9aRopXPSVBJHUhdEtJ\nvWUikDODc5dAvK7rEEKUYNkVz2otFbVtirdzyCRRqquj8Qitt8kLwdZqMiHZskgyl/Mx+G0boUjE\n5Mlb2XX57EXqb4wmZUrrxjCQoyKHskZln/HOUTfNk+SxzIQhIE0Be5YCZ6GCl3NdmjaVUnT9ZitF\n13gXIJfjV9cao4r0vXAsJEJp+rbFDVuHhctKdSbmgJCQwpNKcoyRuhptyeHlM6Xyg2KdGQLZB4QV\naCUxWpKNgKzJSaIE26p/JARPzgmlBCl5tPr0++vPVWA9GlU4Mj4k2jbhg6TWxQ6loPW3Fa2LCzp6\niA6ZApICF8jRI4IjJ4f3pQpLjBATe7s7ID1WAyLT9RuE0oxNRcqC4HzJnkWwtuLk6DGH00MQCqEM\nwfeoBAjJ/mxKXWs2rUf3mSFHYogMLhAy9EYRbUMyGudLascAUjhEagkBcgoYBUO7ZHfngL3ZKW+8\ndp1vf+ddsswomZGKsqhkUFKWDpCUkVqShdxmizNSgjGyTJxYqtvRB6JL1NoUCYgopN3zZcuydZCL\nFzY5Fl/tEImhK9knVbKEORVPOZETm/WywANkLJllMkNb064kMjWcHs+xB4e8/NIVpuEW/UPFjnbM\nmsDVmWCyU1FnR17PCQxMbDl//+wP/iEnRz3z8zXPX7/Cw3/3Z1STmk3b410mB42jpVYTlp1ntQjE\n3jGuAiPTkEQixEy/7hBC0/kOM55xGs+4dbjL+p07DNGjmgY1GnGyWNHsW9Yx03qDMZH1cs6eqWh2\nJyyHhlBPYJKJqiGqMS5abBQokUFFUpKkbErmNGzR/brC2IrzRYtOiXW/YugFWiU2XctoNELahvVq\ng6oNShiEUPRRMgRBQiNDgWrkbNBC00UPsWTpw6f0Hf6sjA+Tan/c+FhZ9TN6tD/usZ8FeTg8W8L6\nYSLyx41n/fQSuvYZ+Xz/PsbFWb9IJrjkGfqWGCPL5YKdqWSQlvl8zuPHx9y/d5/F+Zxu01NVFT4l\nwvam7H2grmuGwbFYLFmt1hzsjfjVr36VW7duYa1ltVwSvCelRNd13Hzt9Sfv5eOq05+ownjWQ39Z\ntf55jJwzbhi2vquaHMumCkocmSnWUEop4rY3UwCLxYLD/dll37TWGnLGKlsqH9qQRWa9LsFANaou\nN/NQNoJKV4AghoQbAllA2/YIXfqxb9y4WRLV58cE7/EhYesGrTbMN0uksjw8OidkzfXrz3Hv7n2a\npqFtexbLFeNJzfd+8D51Y6i++T2MlZwcn7NeD7g/ews3BL7//R+glGI0qjk9T7z44i0Or0zRcUFl\nK2KMOO/Y3d3F+SLvLm4UsNwsShBQjZgvFpzPz5hMJpyfL+nanoMvvcr58pghKrSpGYkxRjc0Tc3O\n7gwhYLNZsV5vQJS+8Ol0ymw2xnU9JFEqj1tNBykV5g0apRUWw+AGXAg0dU3XleqjVBprFQlBSMX2\n9MMKqWdXmH+6a+hj72GfkzXZak1laoQAISqCrkAOWwWHprKBruuIoUAju9SSRUJWFpVqki+BWyUl\nwgiEUCRtsbZGXni1x56qMoTQkKSk845JEqRNT2x7zk9OkaYEnf2mZblYcufoEWdnZywXK96/c3+r\nHurIoRxzLRVKlOA5eo+qFUZX7OzsMm4mDGTccoHfrHj1pRdQCLQqUnGFJFEqxaKAiUDIwm2Sgq7f\n4KpMnzPdEJA+UNWFRZQTeJfwKRDicMlYSLkErcW5Q5GzQGvLcrUpilcU0Sdc7zBGleowCUPZr6cY\nS1Drire9dx4jNY0dobVld7pbXielrTdzpFKGvLURU2SEZrsHLy2eKUaEjHT9mlUbaNs144ndgj0l\nKQcEAmPV1gYQ2KoV+n6gSTUxF6/z3he7rCg9TV0TSMSUSBS5eFk2BWLrE26NoarrbYsBeJ9JsUAg\nYxQMg0eIlqrWpaUkGUKCnDJZOPrekV1JVjS1xblY4rmcUSJSaUkUGVJRLdvKkCIEFwgioiuLcw5p\nLK4fEC6hphXWaowtmoEUi7I0ZYdIRUUgZESoSDPS6Frh3C+oFHw0qvCuxUdF1BPm60hjEkrIcmJT\n4vRsDtIUH7y0RKeMFJnoPcNizni2j0TjYs9IWzKB29cPiV4zBEM0Y3LMeO85PjnjiqowXY+KGWkL\nYKDvIifzjqoZcXI2RylDcIGRtRgih5MZh4eWxdkRldZkv0HVgrbfgBD4GGAmyLM9ghwhhhnBrYnu\nlOg27Kf7XD+cQMxoLTDC0K87vvQrUxpzzsE0smh7uuWaw6vTJ5ASbTDCMq4VLsdiPyMUq3aDMYbx\nqML7RM6aznsaXeEHz2Zw1FZgpARlOV8Heq8hRqKUSCI69cQhkMKGZmSYnyektLjNgHABHxZslnOG\nfjthpUbEzN7YkvslaVgwbU4wxvCDv/waxs05lAsOrOf0/Xd4bVdi6464eJeD3V2OTx5QjTMnpw9Q\n9Q12nOHdN7/Pvbs1q+WSTT/QbhxCVii9h87XOVmVC19EjdVT2iTIbWT21iPqZuCD7/XsPHeIPVD4\nJjFu9nEJHq9X3PvTP2H3+i32bt7g7aN7uNBhPLCqef5wRs5L1kGwWnv++X/933J02nJ/9cf0qxrn\nd3HGcmVkmEwUIQWyqxhVe5fS0nrUEGJgWLbY6gqmalh1A01d0Q2R8c4O9WTG6TqQpcVmvbUz0MSY\ncN4SsGUhdbnQIXNGZoXC0EfB6aL7+U7Qn2L8JAH1p6pYf87HTx/4/+Iek08cmUsacQj+sqrS9z05\nzkkR3n//Lvfu3WOzbun70qflQ6APhcJsrUUphU+R5AZQpWLw+qvP8/jxY46PjzHGMBqNUEqxt7fH\nyy+/fEl3/uX46cZPAgn8WY+cMynnbTB9AS178rcLQF5MiZgTyIxW6rI1IKcVXdfjvaeuRwhRNsWk\nsolvqhFNNeJ8ccYwDMxmM3SlySqjthLLQu8vgXWMkc26BcC7QhWPISNRLBYLNm3HZPeAgytj7t67\nj21mdEHy2kt7rFbnvPf+B+ztXiVLjY+apDSrzvGN77yNcwOuLdfzw8fHpUKpb5ByYt3CD96a88M7\na4RyvPFKzc7ODtevX2UybXjn3l0gbeFWDoHmgw8eMJ/PcaEGEqNxzfDBOePxmJdf/iJvv/uQqqqR\negZCUleW3b0Z1hpSHpACxhPD7t4EJSukLG1SdTNGyxKQkRJaCjaLBcEP+H5AiIEUBX30SCExlS7P\n34zY9B1JFLAt1hTA1jPGs+8lP/21+ItwbyrVQoXIsoBYRcIahTVgzYjNZsN6syYYx5A8UguMtqAN\noRtIBFKTMVKgtCFqi6aA+bTWKGuw3uNiYKQtB3XDvTffY3m6gMaSbNmzdutNqVCvS1tPVTW8cOsW\nKRYy+NB5pJQFHitlIVGnzJt33sI5x8nJCXO5QKbIWEmu7u4AhaqttSQncNtEmRYSL/K2EFWumywk\nLkTSRJCUICTB1EwwsiIG6OOAERU5eHzwW/WdIkWByYWSLdUTJYtzDqNHYEvFXmuPUrpUt3PEIFC2\ntK96HzGVKSwl0WGVxSiLFoY4hGJXR3E4IgtcG0hiS0hHY+qyF5YZBJlus6bbrBHyohrt0caiTEbI\nAAzUdUVVm5JEG3pihNOTJd5FrJL4JOmHgMsJnyOuX6GEZOfqLkNXeBE+RlJIaG2QQuJ9+Yw5bdUl\nSZNTec9SWIyCnDakHPG+K+fEJQYPZIlQBXonRMC7HiUv1HmhAMskaGvxQyQnhZaadtOTQqaSlnXf\nkaVCJEFyCekyJmsUGlKi2yxIUSJoSF7ihd+qCCxmUhLtIhRCeoyf3s72cxVYCwWITBaSmBRt7yjk\nuCLjIENIEiE1CU0tfQENZE0fBSE6Kt+hw5paZWxOKK3Qs4rBKR48miOUhJzp2p4URZlYZGKO1BRP\nyZDWXHbE5WJcfgEi0TmyPyvwhlFd04eAlKXKK5FkWYijWSSUtQg0xAbvB5KoiCohosKoCq0MIEhZ\nIpJE0KOkR5DYm83oh8CwXjEaafTWVoNUyHjRF4p02NIAm6ZCiozIoISk6z1mUpGVwWpNiC1NPWax\naumGhM/lOIRMkVJFBziIT6iOORRpShwiSQ34YUOKHluNGElD7AbGo54rO4aqASOOOT56gOtOWK8f\nM2sOcMszxpVkNB6z7nustTx+/7ts1nPs4YRxdoT2nK5NNKx5fDonhYjzHSRBVTeMpjPCpsKHDrKn\nmSak8sQ04IceNRgIgrOjDUeLluqWYnotYmc1SWQW/QaVI/N2hdq/yqrNZAxhHZBrQXIn1Drx3J4h\nVBPGhzeZ6BU7zQs8bjWD0ygTsBaUKLYIMVlikqQ8sFivMLpnd3dGvTvih3cWvPL686ANm2WHiImY\nIArJZnBoY9FaQSr97z6UXmsEhSK53bDFFJGUxYSkyrn63IwiDbrYhzy9r05Pb2xymWlCFEkZQmwx\n/2WIp/68/JWtN86F0vZH9uzPkHjG/KPV4x/ZG23luRcb6/L8H3mKj4x80cT1jH3W089VXuKjlXt1\n8ZqX7Y0flrOX41KOjSrvMVPkU4JLgrHg4uYqtvIrCTl9xBrnRw9AeZNPV3Y+TOMua99T0qhnBEaf\n+Bp/3SEEPhUbw+LVGTEqY5G89/5d2tMV8kBx/9E93nz3bT64d5+H52esY2CTKInSIFGywbtUNk4i\nY61gHRyz2Yx7d484OV6iVOktrCrDl778RUajBmsNjTUY8aQ7Ol5IZS8rz3z664SnD6G4qM39cvx7\nGk+rRT5JNVJOrbhsQ7gkbSu5pfhuLa0SKEoVO4VM3xWVxM50l7VaXcpAjTWIrfTzgkybBVhracZj\nYoys12u6ruX85IgQI72LOBe4d/cBSMW6G1C6IiMZ/IrrN/dZrM9wQ0eMim4IKGMxzZR+WNGHHttU\nzDcrKtsghGTTbUq/d45ApgstVSX45rfPqOsTdnaPeO65qyAi5+enpJQYBk9OF5wQyQsv3GC1WrJc\ndrz66htUleHP//wvee7GC5zPz7h+7Saz2QzpHYNbE5NkNKpAZLQxzGYTurZ40hYBp6DrWrSQWGuo\njcYawbhuMLvjS9J46Af6vi+b4FqVzbSAKDJZSzCapOWzWqx/OZ4aF/T6nHPpTU0Xa1BpNStSYUWM\nib4fSLnDhQEVAh6opAYG0pZpkbe/o5RCZIOUw9ZF4YltojEa4zXDpuXIDXQisZaRwTlySiQfkEJR\nVRXT6ZRRM9syM8BdtAVIhdpKpkWGG8MN2k2/9TYOyCQYjxpms6IsUUKWivIFrfxH1totQ4GLzyDI\nRiK0RGdJY0ZoFD4MZeXW6nKJz6n4XqeYUbn4UyupLq291DYRp3Vx2XGZSzu8GBNb8TMJyp5OKkT2\nOIrHc44QUmDoPd57pNTF6zorvBvwyZGRKJUQpewKXFDUhwIbVBqxDU5TikAk5YgQGW3BGFGI4znj\nXcQNiZQyzhe/7uS3oDdlcaEnR1C2FLAipWqc0rbHPWZ8zOAjQnpCKvEZufhQS2mQUjAMHdZqqtrA\nkJFSQ06k7e3UKAsqbyX3YetstLU93N5rc05ooTHG4PttLCbL+iszxT0nglUaI8HlzDAQOdr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1y9epXVao73ASEzg+tolwuapuHs7ITdvQNiCuiksZXF+0BKkeQlH3xwh/PzU2KMvPvDdzDG8MUv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\nF96h7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+udDSz2D7Z7fl3XeOdwg4VNFJJAspNSSiGVwQ0FM6RIhaxhDl5vtI7q/WGggywW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\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zCjLHtxkhl1pL6q4DKel7wWJZsNtveP+D58Cg1B8DJ6crTo6WmCLj5OSYstBkuaZaFFzf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Khz7HuL1tdRs2lwEumk8SfP82saJa2HAhwSYzvSTI9ItscGZ+Xejom1MWZsSRQTjjRNsQOi5oHX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\nwab/5B3xoo52vEtZ7v6uL5x5QfAgCaiq8wrnTD+GjTE4YxkMBoB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xxnA8f8y3vvMB1WZN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J9TeCViJVebgTRmLZByGFstN2/nHDQIL2JV57P+fkzTkssLE3tUJO52KMoKYyUIh6kq8iwl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\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"text/plain": [ "<matplotlib.figure.Figure at 0x7f67a37fef60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plots(imgs, titles=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now pass the images to Vgg16's predict() function to get back probabilities, category indexes, and category names for each image's VGG prediction." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 0.8745, 0.2226, 0.5382, 0.5448], dtype=float32),\n", " array([361, 261, 281, 215]),\n", " ['skunk', 'keeshond', 'tabby', 'Brittany_spaniel'])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vgg.predict(imgs, True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The category indexes are based on the ordering of categories used in the VGG model - e.g here are the first four:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['tench', 'goldfish', 'great_white_shark', 'tiger_shark']" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vgg.classes[:4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(Note that, other than creating the Vgg16 object, none of these steps are necessary to build a model; they are just showing how to use the class to view imagenet predictions.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Use our Vgg16 class to finetune a Dogs vs Cats model\n", "\n", "To change our model so that it outputs \"cat\" vs \"dog\", instead of one of 1,000 very specific categories, we need to use a process called \"finetuning\". Finetuning looks from the outside to be identical to normal machine learning training - we provide a training set with data and labels to learn from, and a validation set to test against. The model learns a set of parameters based on the data provided.\n", "\n", "However, the difference is that we start with a model that is already trained to solve a similar problem. The idea is that many of the parameters should be very similar, or the same, between the existing model, and the model we wish to create. Therefore, we only select a subset of parameters to train, and leave the rest untouched. This happens automatically when we call *fit()* after calling *finetune()*.\n", "\n", "We create our batches just like before, and making the validation set available as well. A 'batch' (or *mini-batch* as it is commonly known) is simply a subset of the training data - we use a subset at a time when training or predicting, in order to speed up training, and to avoid running out of memory." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "batch_size=64" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "batches = vgg.get_batches(path+'train', batch_size=batch_size)\n", "val_batches = vgg.get_batches(path+'valid', batch_size=batch_size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calling *finetune()* modifies the model such that it will be trained based on the data in the batches provided - in this case, to predict either 'dog' or 'cat'." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vgg.finetune(batches)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we *fit()* the parameters of the model using the training data, reporting the accuracy on the validation set after every epoch. (An *epoch* is one full pass through the training data.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vgg.fit(batches, val_batches, nb_epoch=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That shows all of the steps involved in using the Vgg16 class to create an image recognition model using whatever labels you are interested in. For instance, this process could classify paintings by style, or leaves by type of disease, or satellite photos by type of crop, and so forth.\n", "\n", "Next up, we'll dig one level deeper to see what's going on in the Vgg16 class." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "nav_menu": {}, "nbpresent": { "slides": { "28b43202-5690-4169-9aca-6b9dabfeb3ec": { "id": "28b43202-5690-4169-9aca-6b9dabfeb3ec", "prev": null, "regions": { "3bba644a-cf4d-4a49-9fbd-e2554428cf9f": { "attrs": { "height": 0.8, "width": 0.8, "x": 0.1, "y": 0.1 }, "content": { "cell": "f3d3a388-7e2a-4151-9b50-c20498fceacc", "part": "whole" }, "id": "3bba644a-cf4d-4a49-9fbd-e2554428cf9f" } } }, "8104def2-4b68-44a0-8f1b-b03bf3b2a079": { "id": "8104def2-4b68-44a0-8f1b-b03bf3b2a079", "prev": "28b43202-5690-4169-9aca-6b9dabfeb3ec", "regions": { "7dded777-1ddf-4100-99ae-25cf1c15b575": { "attrs": { "height": 0.8, "width": 0.8, "x": 0.1, "y": 0.1 }, "content": { "cell": "fe47bd48-3414-4657-92e7-8b8d6cb0df00", "part": "whole" }, "id": "7dded777-1ddf-4100-99ae-25cf1c15b575" } } } }, "themes": {} }, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

bbglab/adventofcode

2016/ferran/day6.ipynb

1

2950

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Chellenge 6\n", "\n", "## Challenge 6.1" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "myinput = '/home/fmuinos/projects/adventofcode/2016/ferran/inputs/input6.txt'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "598 inputs/input6.txt\r\n" ] } ], "source": [ "! wc -l inputs/input6.txt" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from collections import defaultdict\n", "\n", "def parse_freqs(path):\n", " freq = defaultdict(lambda: defaultdict(int))\n", " with open(path,'rt') as f:\n", " for line in f:\n", " message = line.rstrip()\n", " for i in range(len(message)):\n", " freq[i][message[i]] += 1\n", " return freq\n", "\n", "def most_freq(freq_dict, most=True):\n", " message = []\n", " for ind in freq_dict:\n", " freq_sort = iter(sorted(freq_dict[ind].items(), key=lambda i: i[1], reverse=most))\n", " message.append(next(freq_sort)[0])\n", " return ''.join(message)\n", "\n", "def unnoise(myinput, most=True):\n", " freq = parse_freqs(myinput)\n", " return most_freq(freq, most)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'asvcbhvg'" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "unnoise(myinput)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Challenge 6.2" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'odqnikqv'" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "unnoise(myinput, most=False)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:adventofcode]", "language": "python", "name": "conda-env-adventofcode-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

jdvelasq/machine-learning

regresion-R-medical-expenses.ipynb

1

5836

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Predicción de gastos médicos\n", "===\n", "\n", "**Juan David Velásquez Henao** \n", "jdvelasq@unal.edu.co \n", "Universidad Nacional de Colombia, Sede Medellín \n", "Facultad de Minas \n", "Medellín, Colombia\n", "\n", "---\n", "\n", "Haga click [aquí](https://github.com/jdvelasq/machine-learning/blob/master/01-archivos-y-directorios.ipynb) para acceder a la última versión online.\n", "\n", "Haga click [aquí](http://nbviewer.jupyter.org/github/jdvelasq/machine-learning/blob/master/01-archivos-y-directorios.ipynb) para ver la última versión online en `nbviewer`. \n", "\n", "---\n", "[Licencia](https://github.com/jdvelasq/machine-learning/blob/master/LICENCIA.txt) \n", "[Readme](https://github.com/jdvelasq/machine-learning/blob/master/readme.md)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Definición del problema real" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Definición del problema en términos de los datos" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Exploración" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# carga de los datos\n", "insurance <- read.csv(\"insurance.csv\", stringsAsFactors = TRUE)\n", "str(insurance)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "summary(insurance$expenses)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hist(insurance$expenses)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "table(insurance$region)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cor(insurance[c(\"age\", \"bmi\", \"children\", \"expenses\")])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pairs(insurance[c(\"age\", \"bmi\", \"children\", \"expenses\")])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pairs.panels(insurance[c(\"age\", \"bmi\", \"children\", \"expenses\")])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Metodología" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Entrenamiento del modelo" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ins_model <- lm(expenses ~ age + children + bmi + sex + smoker + region, \n", " data = insurance)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ins_model <- lm(expenses ~ ., data = insurance)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ins_model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluación del modelo" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "summary(ins_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Mejora del modelo" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "insurance$age2 <- insurance$age^2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "insurance$bmi30 <- ifelse(insurance$bmi >= 30, 1, 0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ins_model2 <- lm(expenses ~ age + age2 + children + bmi + sex + bmi30*smoker + region, \n", " data = insurance)\n", "summary(ins_model2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernel_info": { "name": "python3" }, "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.4.3" }, "nteract": { "version": "0.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

eabdullin/nlp_mthesis

yandex_translate.ipynb

1

496

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "url_base = 'https://translate.yandex.net/api/v1.5/tr.json/translate'\n", "api_key = \"trnsl.1.1.20160131T134129Z.551138fac8444143.d4d1893d45993c98d622f57494c87a805719cf86\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }

mit

katyhuff/berkeley

possible_topics/learn_and_teach.ipynb

5

28652

{ "cells": [ { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# need pandas\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# create a dataframe for the learners csv\n", "df_learn=pd.read_csv(\"learn.csv\")" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>topic</th>\n", " <th>popularity</th>\n", " <th>teacher</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>23</th>\n", " <td> advanced git</td>\n", " <td> 22.0</td>\n", " <td> Ross Barnowski </td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td> advanced git</td>\n", " <td> 22.0</td>\n", " <td> Aaron Culich</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td> advanced git</td>\n", " <td> 22.0</td>\n", " <td> Katy Huff</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td> advanced git</td>\n", " <td> 22.0</td>\n", " <td> Kyle Barbary</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td> spark/hadoop</td>\n", " <td> 22.0</td>\n", " <td> Spark Team (Zhao &amp; Jey?)</td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> advanced python</td>\n", " <td> 20.0</td>\n", " <td> Sven Chilton</td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> advanced python</td>\n", " <td> 20.0</td>\n", " <td> Matthias Boussonier?</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td> vizualization</td>\n", " <td> 16.0</td>\n", " <td> Jennifer Jones (matplotlib)</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td> vizualization</td>\n", " <td> 16.0</td>\n", " <td> John Naulty (highchart) </td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td> vizualization</td>\n", " <td> 16.0</td>\n", " <td> Biye (d3) </td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td> vizualization</td>\n", " <td> 16.0</td>\n", " <td> Ross Barnowski (pyqtgraph) </td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td> pandas</td>\n", " <td> 14.0</td>\n", " <td> Sven Chilton </td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td> pandas</td>\n", " <td> 14.0</td>\n", " <td> Jennifer Jones</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td> LaTeX and whatnot</td>\n", " <td> 14.0</td>\n", " <td> Jennifer Jones (LaTeX)</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td> pandas</td>\n", " <td> 14.0</td>\n", " <td> Sean Wahl </td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td> LaTeX and whatnot</td>\n", " <td> 14.0</td>\n", " <td> Chris Paciorek (r)</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td> pandas</td>\n", " <td> 14.0</td>\n", " <td> notes on databases from Josh Rehak</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td> workflows/pipelines</td>\n", " <td> 14.0</td>\n", " <td> Jess Hamrick</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td> LaTeX and whatnot</td>\n", " <td> 14.0</td>\n", " <td> Mike Pacer (latex &amp; rst &amp; fonts)</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td> Webscraping</td>\n", " <td> 14.0</td>\n", " <td> John Bohannon</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td> Webscraping</td>\n", " <td> 14.0</td>\n", " <td> Sven Chilton (twitter)</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td> computer vision &amp; image analysis</td>\n", " <td> 13.0</td>\n", " <td> Stefan van der Walt </td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td> gpus &amp; parallelization</td>\n", " <td> 13.0</td>\n", " <td> Aaron Culich</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td> gpus &amp; parallelization</td>\n", " <td> 13.0</td>\n", " <td> Biye</td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> scikit-learn</td>\n", " <td> 13.0</td>\n", " <td> Shannon McCurdy</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> scikit-learn</td>\n", " <td> 13.0</td>\n", " <td> Ross Barnowski</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td> beginner git</td>\n", " <td> 10.0</td>\n", " <td> Harrison Dekker</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td> high performance python</td>\n", " <td> 10.0</td>\n", " <td> Chick Markley</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td> beginner git</td>\n", " <td> 10.0</td>\n", " <td> John Naulty</td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> cython/wrapping/forthon/swig/f2py</td>\n", " <td> 9.0</td>\n", " <td> Kyle Barbary </td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <td> cython/wrapping/forthon/swig/f2py</td>\n", " <td> 9.0</td>\n", " <td> Ross Barnowski </td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td> cython/wrapping/forthon/swig/f2py</td>\n", " <td> 9.0</td>\n", " <td> Sven Chilton</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td> javascript</td>\n", " <td> 8.5</td>\n", " <td> John Naulty (johnny5library) </td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td> javascript</td>\n", " <td> 8.5</td>\n", " <td> Biye</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td> webservers</td>\n", " <td> 8.0</td>\n", " <td> Alex Goodell</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td> make</td>\n", " <td> 7.0</td>\n", " <td> Katy Huff</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td> make</td>\n", " <td> 7.0</td>\n", " <td> Chris Paciorek</td>\n", " </tr>\n", " <tr>\n", " <th>0 </th>\n", " <td> advanced julia</td>\n", " <td> 7.0</td>\n", " <td> Kyle Barbary</td>\n", " </tr>\n", " <tr>\n", " <th>37</th>\n", " <td> sampling techniques</td>\n", " <td> 7.0</td>\n", " <td> Daniel Turek</td>\n", " </tr>\n", " <tr>\n", " <th>38</th>\n", " <td> sampling techniques</td>\n", " <td> 7.0</td>\n", " <td> Shannon McCurdy</td>\n", " </tr>\n", " <tr>\n", " <th>1 </th>\n", " <td> advanced julia</td>\n", " <td> 7.0</td>\n", " <td> Chris' friend </td>\n", " </tr>\n", " <tr>\n", " <th>5 </th>\n", " <td> intro python</td>\n", " <td> 7.0</td>\n", " <td> John Bohannon</td>\n", " </tr>\n", " <tr>\n", " <th>4 </th>\n", " <td> intro python</td>\n", " <td> 7.0</td>\n", " <td> Alex Goodell</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td> R</td>\n", " <td> 6.0</td>\n", " <td> Daniel Turek?</td>\n", " </tr>\n", " <tr>\n", " <th>41</th>\n", " <td> hardware &amp; embedded systems</td>\n", " <td> 4.0</td>\n", " <td> John Bohannon (lightning talk)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " topic popularity \\\n", "23 advanced git 22.0 \n", "21 advanced git 22.0 \n", "24 advanced git 22.0 \n", "22 advanced git 22.0 \n", "16 spark/hadoop 22.0 \n", "6 advanced python 20.0 \n", "7 advanced python 20.0 \n", "30 vizualization 16.0 \n", "27 vizualization 16.0 \n", "29 vizualization 16.0 \n", "28 vizualization 16.0 \n", "14 pandas 14.0 \n", "13 pandas 14.0 \n", "44 LaTeX and whatnot 14.0 \n", "12 pandas 14.0 \n", "43 LaTeX and whatnot 14.0 \n", "15 pandas 14.0 \n", "18 workflows/pipelines 14.0 \n", "42 LaTeX and whatnot 14.0 \n", "25 Webscraping 14.0 \n", "26 Webscraping 14.0 \n", "40 computer vision & image analysis 13.0 \n", "36 gpus & parallelization 13.0 \n", "35 gpus & parallelization 13.0 \n", "3 scikit-learn 13.0 \n", "2 scikit-learn 13.0 \n", "19 beginner git 10.0 \n", "11 high performance python 10.0 \n", "20 beginner git 10.0 \n", "9 cython/wrapping/forthon/swig/f2py 9.0 \n", "8 cython/wrapping/forthon/swig/f2py 9.0 \n", "10 cython/wrapping/forthon/swig/f2py 9.0 \n", "31 javascript 8.5 \n", "32 javascript 8.5 \n", "39 webservers 8.0 \n", "33 make 7.0 \n", "34 make 7.0 \n", "0 advanced julia 7.0 \n", "37 sampling techniques 7.0 \n", "38 sampling techniques 7.0 \n", "1 advanced julia 7.0 \n", "5 intro python 7.0 \n", "4 intro python 7.0 \n", "17 R 6.0 \n", "41 hardware & embedded systems 4.0 \n", "\n", " teacher \n", "23 Ross Barnowski \n", "21 Aaron Culich \n", "24 Katy Huff \n", "22 Kyle Barbary \n", "16 Spark Team (Zhao & Jey?) \n", "6 Sven Chilton \n", "7 Matthias Boussonier? \n", "30 Jennifer Jones (matplotlib) \n", "27 John Naulty (highchart) \n", "29 Biye (d3) \n", "28 Ross Barnowski (pyqtgraph) \n", "14 Sven Chilton \n", "13 Jennifer Jones \n", "44 Jennifer Jones (LaTeX) \n", "12 Sean Wahl \n", "43 Chris Paciorek (r) \n", "15 notes on databases from Josh Rehak \n", "18 Jess Hamrick \n", "42 Mike Pacer (latex & rst & fonts) \n", "25 John Bohannon \n", "26 Sven Chilton (twitter) \n", "40 Stefan van der Walt \n", "36 Aaron Culich \n", "35 Biye \n", "3 Shannon McCurdy \n", "2 Ross Barnowski \n", "19 Harrison Dekker \n", "11 Chick Markley \n", "20 John Naulty \n", "9 Kyle Barbary \n", "8 Ross Barnowski \n", "10 Sven Chilton \n", "31 John Naulty (johnny5library) \n", "32 Biye \n", "39 Alex Goodell \n", "33 Katy Huff \n", "34 Chris Paciorek \n", "0 Kyle Barbary \n", "37 Daniel Turek \n", "38 Shannon McCurdy \n", "1 Chris' friend \n", "5 John Bohannon \n", "4 Alex Goodell \n", "17 Daniel Turek? \n", "41 John Bohannon (lightning talk) " ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# What were the most popular topics?\n", "df_learn.sort(\"topic\").sort(\"popularity\",ascending=False)" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# We only have time for 12 meetings this semester\n", "# So, we pick the 12 most popular topics\n", "fall_topics=df_learn.drop_duplicates(cols='topic', inplace=False).sort(\"popularity\", ascending=False)[:12][\"topic\"]" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>topic</th>\n", " <th>popularity</th>\n", " <th>teacher</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>23</th>\n", " <td> advanced git</td>\n", " <td> 22</td>\n", " <td> Ross Barnowski </td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td> advanced git</td>\n", " <td> 22</td>\n", " <td> Aaron Culich</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td> advanced git</td>\n", " <td> 22</td>\n", " <td> Kyle Barbary</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td> advanced git</td>\n", " <td> 22</td>\n", " <td> Katy Huff</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td> spark/hadoop</td>\n", " <td> 22</td>\n", " <td> Spark Team (Zhao &amp; Jey?)</td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> advanced python</td>\n", " <td> 20</td>\n", " <td> Sven Chilton</td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> advanced python</td>\n", " <td> 20</td>\n", " <td> Matthias Boussonier?</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td> vizualization</td>\n", " <td> 16</td>\n", " <td> John Naulty (highchart) </td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td> vizualization</td>\n", " <td> 16</td>\n", " <td> Ross Barnowski (pyqtgraph) </td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td> vizualization</td>\n", " <td> 16</td>\n", " <td> Biye (d3) </td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td> vizualization</td>\n", " <td> 16</td>\n", " <td> Jennifer Jones (matplotlib)</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td> LaTeX and whatnot</td>\n", " <td> 14</td>\n", " <td> Jennifer Jones (LaTeX)</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td> pandas</td>\n", " <td> 14</td>\n", " <td> Sven Chilton </td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td> pandas</td>\n", " <td> 14</td>\n", " <td> notes on databases from Josh Rehak</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td> pandas</td>\n", " <td> 14</td>\n", " <td> Jennifer Jones</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td> workflows/pipelines</td>\n", " <td> 14</td>\n", " <td> Jess Hamrick</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td> pandas</td>\n", " <td> 14</td>\n", " <td> Sean Wahl </td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td> LaTeX and whatnot</td>\n", " <td> 14</td>\n", " <td> Chris Paciorek (r)</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td> Webscraping</td>\n", " <td> 14</td>\n", " <td> Sven Chilton (twitter)</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td> Webscraping</td>\n", " <td> 14</td>\n", " <td> John Bohannon</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td> LaTeX and whatnot</td>\n", " <td> 14</td>\n", " <td> Mike Pacer (latex &amp; rst &amp; fonts)</td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> scikit-learn</td>\n", " <td> 13</td>\n", " <td> Shannon McCurdy</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> scikit-learn</td>\n", " <td> 13</td>\n", " <td> Ross Barnowski</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td> gpus &amp; parallelization</td>\n", " <td> 13</td>\n", " <td> Biye</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td> computer vision &amp; image analysis</td>\n", " <td> 13</td>\n", " <td> Stefan van der Walt </td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td> gpus &amp; parallelization</td>\n", " <td> 13</td>\n", " <td> Aaron Culich</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td> high performance python</td>\n", " <td> 10</td>\n", " <td> Chick Markley</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " topic popularity \\\n", "23 advanced git 22 \n", "21 advanced git 22 \n", "22 advanced git 22 \n", "24 advanced git 22 \n", "16 spark/hadoop 22 \n", "6 advanced python 20 \n", "7 advanced python 20 \n", "27 vizualization 16 \n", "28 vizualization 16 \n", "29 vizualization 16 \n", "30 vizualization 16 \n", "44 LaTeX and whatnot 14 \n", "14 pandas 14 \n", "15 pandas 14 \n", "13 pandas 14 \n", "18 workflows/pipelines 14 \n", "12 pandas 14 \n", "43 LaTeX and whatnot 14 \n", "26 Webscraping 14 \n", "25 Webscraping 14 \n", "42 LaTeX and whatnot 14 \n", "3 scikit-learn 13 \n", "2 scikit-learn 13 \n", "35 gpus & parallelization 13 \n", "40 computer vision & image analysis 13 \n", "36 gpus & parallelization 13 \n", "11 high performance python 10 \n", "\n", " teacher \n", "23 Ross Barnowski \n", "21 Aaron Culich \n", "22 Kyle Barbary \n", "24 Katy Huff \n", "16 Spark Team (Zhao & Jey?) \n", "6 Sven Chilton \n", "7 Matthias Boussonier? \n", "27 John Naulty (highchart) \n", "28 Ross Barnowski (pyqtgraph) \n", "29 Biye (d3) \n", "30 Jennifer Jones (matplotlib) \n", "44 Jennifer Jones (LaTeX) \n", "14 Sven Chilton \n", "15 notes on databases from Josh Rehak \n", "13 Jennifer Jones \n", "18 Jess Hamrick \n", "12 Sean Wahl \n", "43 Chris Paciorek (r) \n", "26 Sven Chilton (twitter) \n", "25 John Bohannon \n", "42 Mike Pacer (latex & rst & fonts) \n", "3 Shannon McCurdy \n", "2 Ross Barnowski \n", "35 Biye \n", "40 Stefan van der Walt \n", "36 Aaron Culich \n", "11 Chick Markley " ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Given this, list the topics and the teachers in order of topic popularity\n", "topic_mask = df_learn.isin(fall_topics.values)[\"topic\"]\n", "df_learn[topic_mask].sort(\"topic\").sort(\"popularity\",ascending=False)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

ekostat/ekostat_calculator

.ipynb_checkpoints/sharkdata_dwca_obis_env_data_test-checkpoint.ipynb

1

3246

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Reload when code changed:\n", "%load_ext autoreload\n", "%autoreload 2\n", "%pwd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sys\n", "path = \"../w_github_sharkdata/proj_sharkdata/proj_sharkdata/sharkdata_core/dwca_generator\"\n", "sys.path.append(path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "import dwca_generator" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dwca = dwca_generator.DarwinCoreArchiveGenerator('Bacterioplankton')\n", "dwca.add_data('test_data/shark_data.txt')\n", "dwca.add_metadata('test_data/shark_metadata.txt')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print('Length: ' + str(len(dwca._data_df)))\n", "dwca._data_df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dwca._data_df.info()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(len(dwca._data_df.shark_sample_id_md5.value_counts()))\n", "print(dwca._data_df.shark_sample_id_md5.value_counts().max())\n", "print(dwca._data_df.shark_sample_id_md5.value_counts().min())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dwca.create_dwca_file()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dwca._metadata_dict" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#dwca._data_df.value.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#dwca._data_df.value.max()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#dwca._data_df.parameter.unique()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

Diyago/Machine-Learning-scripts

general studies/learning python/learning pandas/ДЗ №3.ipynb

1

26081

{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "years=range(1880, 2017)\n", "\n", "pieces = []\n", "columns = [ 'name', 'sex', 'births' ]\n", "for year in years:\n", " path = 'C:/Users/User/Desktop/python/pandas/Pandas_Python3/names/yob%d.txt' %year\n", " frame = pd.read_csv(path, names=columns)\n", " frame['year'] = year\n", " pieces.append(frame)\n", "\n", "names = pd.concat(pieces, ignore_index = True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "names_uniq = names.name.unique()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "96174" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(names.name.unique())" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "group1 = difflib.get_close_matches('Mary', names_uniq, n=100, cutoff=0.8)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "81\n" ] }, { "data": { "text/plain": [ "['Mary',\n", " 'Mmary',\n", " 'Maury',\n", " 'Maryn',\n", " 'Maryl',\n", " 'Marye',\n", " 'Marya',\n", " 'Marty',\n", " 'Marry',\n", " 'Marny',\n", " 'Marly',\n", " 'Marky',\n", " 'Margy',\n", " 'Marey',\n", " 'Mardy',\n", " 'Marcy',\n", " 'Maray',\n", " 'Mabry',\n", " 'Mry',\n", " 'May',\n", " 'Mar',\n", " 'Merary',\n", " 'Mavryk',\n", " 'Mavery',\n", " 'Mauryn',\n", " 'Maurya',\n", " 'Maurey',\n", " 'Maudry',\n", " 'Maryum',\n", " 'Maryse',\n", " 'Marysa',\n", " 'Maryon',\n", " 'Maryna',\n", " 'Marylu',\n", " 'Maryln',\n", " 'Maryla',\n", " 'Maryke',\n", " 'Maryka',\n", " 'Maryjo',\n", " 'Maryia',\n", " 'Maryha',\n", " 'Maryem',\n", " 'Maryel',\n", " 'Maryan',\n", " 'Maryam',\n", " 'Maryah',\n", " 'Marvyn',\n", " 'Marvyl',\n", " 'Martyn',\n", " 'Marthy',\n", " 'Martay',\n", " 'Marryn',\n", " 'Marney',\n", " 'Marnay',\n", " 'Marlys',\n", " 'Marlyn',\n", " 'Marlye',\n", " 'Marley',\n", " 'Markya',\n", " 'Markey',\n", " 'Markay',\n", " 'Mariya',\n", " 'Marivy',\n", " 'Marily',\n", " 'Mariby',\n", " 'Margey',\n", " 'Mareya',\n", " 'Marely',\n", " 'Marcys',\n", " 'Marcey',\n", " 'Maraya',\n", " 'Malory',\n", " 'Malery',\n", " 'Malary',\n", " 'Mairyn',\n", " 'Maevry',\n", " 'Maeryn',\n", " 'Maebry',\n", " 'Macray',\n", " 'Mabrey',\n", " 'Mary']" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "group1.append('Mary')\n", "print (len(group1))\n", "group1" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " app.launch_new_instance()\n", "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:8: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n" ] } ], "source": [ "f_total = []\n", " \n", "f_names = names[names['name'].isin(group1)][names.sex == 'F']\n", "\n", "for year in years:\n", " #все имена для мальчиков и девочек в год year\n", " f_names_for_year = f_names[f_names.year == year] \n", " names_for_year = names[names['name'].isin(group1)][names.year == year]\n", " f_total.append((f_names_for_year['births'].sum() / names_for_year['births'].sum() )* 100) " ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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cE+405hngScG5fNGkiZ2sUp2QpkypfFxdUpgzx0ZK77ZbZuKrT1q2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\nXAVPCs455yr8f+sim5+8cOwHAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xdc482e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(years, f_total, color = 'red')\n", "plt.title('Доли популярных имен в разрезе полов')\n", "plt.xlabel('Года')\n", "plt.ylabel('Доля в %')\n", "plt.legend(['Ж'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f_total" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

luwei0917/awsemmd_script

notebook/Optimization/family_fold_may11.ipynb

1

3928674

mit

deepmind/dm_control

tutorial.ipynb

1

68918

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "MpkYHwCqk7W-" }, "source": [ "# **`dm_control` tutorial**\n", "\n", "[](https://colab.research.google.com/github/deepmind/dm_control/blob/master/tutorial.ipynb)\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "_UbO9uhtBSX5" }, "source": [ "\u003e \u003cp\u003e\u003csmall\u003e\u003csmall\u003eCopyright 2020 The dm_control Authors.\u003c/small\u003e\u003c/p\u003e\n", "\u003e \u003cp\u003e\u003csmall\u003e\u003csmall\u003eLicensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at \u003ca href=\"http://www.apache.org/licenses/LICENSE-2.0\"\u003ehttp://www.apache.org/licenses/LICENSE-2.0\u003c/a\u003e.\u003c/small\u003e\u003c/small\u003e\u003c/p\u003e\n", "\u003e \u003cp\u003e\u003csmall\u003e\u003csmall\u003eUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\u003c/small\u003e\u003c/small\u003e\u003c/p\u003e" ] }, { "cell_type": "markdown", "metadata": { "id": "aThGKGp0cD76" }, "source": [ "This notebook provides an overview tutorial of DeepMind's `dm_control` package, hosted at the [deepmind/dm_control](https://github.com/deepmind/dm_control) repository on GitHub.\n", "\n", "It is adjunct to this [tech report](http://arxiv.org/abs/2006.12983).\n", "\n", "**A Colab runtime with GPU acceleration is required.** If you're using a CPU-only runtime, you can switch using the menu \"Runtime \u003e Change runtime type\"." ] }, { "cell_type": "markdown", "metadata": { "id": "YkBQUjm6gbGF" }, "source": [ "<!-- Internal installation instructions. -->" ] }, { "cell_type": "markdown", "metadata": { "id": "YvyGCsgSCxHQ" }, "source": [ "### Installing `dm_control` on Colab" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IbZxYDxzoz5R" }, "outputs": [], "source": [ "#@title Run to install MuJoCo and `dm_control`\n", "import distutils.util\n", "import subprocess\n", "if subprocess.run('nvidia-smi').returncode:\n", " raise RuntimeError(\n", " 'Cannot communicate with GPU. '\n", " 'Make sure you are using a GPU Colab runtime. '\n", " 'Go to the Runtime menu and select Choose runtime type.')\n", "\n", "print('Installing dm_control...')\n", "!pip install -q dm_control\u003e=1.0.3.post1\n", "\n", "# Configure dm_control to use the EGL rendering backend (requires GPU)\n", "%env MUJOCO_GL=egl\n", "\n", "print('Checking that the dm_control installation succeeded...')\n", "try:\n", " from dm_control import suite\n", " env = suite.load('cartpole', 'swingup')\n", " pixels = env.physics.render()\n", "except Exception as e:\n", " raise e from RuntimeError(\n", " 'Something went wrong during installation. Check the shell output above '\n", " 'for more information.\\n'\n", " 'If using a hosted Colab runtime, make sure you enable GPU acceleration '\n", " 'by going to the Runtime menu and selecting \"Choose runtime type\".')\n", "else:\n", " del pixels, suite\n", "\n", "!echo Installed dm_control $(pip show dm_control | grep -Po \"(?\u003c=Version: ).+\")" ] }, { "cell_type": "markdown", "metadata": { "id": "wtDN43hIJh2C" }, "source": [ "# Imports\n", "\n", "Run both of these cells:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "T5f4w3Kq2X14" }, "outputs": [], "source": [ "#@title All `dm_control` imports required for this tutorial\n", "\n", "# The basic mujoco wrapper.\n", "from dm_control import mujoco\n", "\n", "# Access to enums and MuJoCo library functions.\n", "from dm_control.mujoco.wrapper.mjbindings import enums\n", "from dm_control.mujoco.wrapper.mjbindings import mjlib\n", "\n", "# PyMJCF\n", "from dm_control import mjcf\n", "\n", "# Composer high level imports\n", "from dm_control import composer\n", "from dm_control.composer.observation import observable\n", "from dm_control.composer import variation\n", "\n", "# Imports for Composer tutorial example\n", "from dm_control.composer.variation import distributions\n", "from dm_control.composer.variation import noises\n", "from dm_control.locomotion.arenas import floors\n", "\n", "# Control Suite\n", "from dm_control import suite\n", "\n", "# Run through corridor example\n", "from dm_control.locomotion.walkers import cmu_humanoid\n", "from dm_control.locomotion.arenas import corridors as corridor_arenas\n", "from dm_control.locomotion.tasks import corridors as corridor_tasks\n", "\n", "# Soccer\n", "from dm_control.locomotion import soccer\n", "\n", "# Manipulation\n", "from dm_control import manipulation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gKc1FNhKiVJX" }, "outputs": [], "source": [ "#@title Other imports and helper functions\n", "\n", "# General\n", "import copy\n", "import os\n", "import itertools\n", "from IPython.display import clear_output\n", "import numpy as np\n", "\n", "# Graphics-related\n", "import matplotlib\n", "import matplotlib.animation as animation\n", "import matplotlib.pyplot as plt\n", "from IPython.display import HTML\n", "import PIL.Image\n", "# Internal loading of video libraries.\n", "\n", "# Use svg backend for figure rendering\n", "%config InlineBackend.figure_format = 'svg'\n", "\n", "# Font sizes\n", "SMALL_SIZE = 8\n", "MEDIUM_SIZE = 10\n", "BIGGER_SIZE = 12\n", "plt.rc('font', size=SMALL_SIZE) # controls default text sizes\n", "plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title\n", "plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels\n", "plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n", "plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n", "plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize\n", "plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title\n", "\n", "# Inline video helper function\n", "if os.environ.get('COLAB_NOTEBOOK_TEST', False):\n", " # We skip video generation during tests, as it is quite expensive.\n", " display_video = lambda *args, **kwargs: None\n", "else:\n", " def display_video(frames, framerate=30):\n", " height, width, _ = frames[0].shape\n", " dpi = 70\n", " orig_backend = matplotlib.get_backend()\n", " matplotlib.use('Agg') # Switch to headless 'Agg' to inhibit figure rendering.\n", " fig, ax = plt.subplots(1, 1, figsize=(width / dpi, height / dpi), dpi=dpi)\n", " matplotlib.use(orig_backend) # Switch back to the original backend.\n", " ax.set_axis_off()\n", " ax.set_aspect('equal')\n", " ax.set_position([0, 0, 1, 1])\n", " im = ax.imshow(frames[0])\n", " def update(frame):\n", " im.set_data(frame)\n", " return [im]\n", " interval = 1000/framerate\n", " anim = animation.FuncAnimation(fig=fig, func=update, frames=frames,\n", " interval=interval, blit=True, repeat=False)\n", " return HTML(anim.to_html5_video())\n", "\n", "# Seed numpy's global RNG so that cell outputs are deterministic. We also try to\n", "# use RandomState instances that are local to a single cell wherever possible.\n", "np.random.seed(42)" ] }, { "cell_type": "markdown", "metadata": { "id": "jZXz9rPYGA-Y" }, "source": [ "# Model definition, compilation and rendering\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "MRBaZsf1d7Gb" }, "source": [ "We begin by describing some basic concepts of the [MuJoCo](http://mujoco.org/) physics simulation library, but recommend the [official documentation](http://mujoco.org/book/) for details.\n", "\n", "Let's define a simple model with two geoms and a light." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZS2utl59ZTsr" }, "outputs": [], "source": [ "#@title A static model {vertical-output: true}\n", "\n", "static_model = \"\"\"\n", "\u003cmujoco\u003e\n", " \u003cworldbody\u003e\n", " \u003clight name=\"top\" pos=\"0 0 1\"/\u003e\n", " \u003cgeom name=\"red_box\" type=\"box\" size=\".2 .2 .2\" rgba=\"1 0 0 1\"/\u003e\n", " \u003cgeom name=\"green_sphere\" pos=\".2 .2 .2\" size=\".1\" rgba=\"0 1 0 1\"/\u003e\n", " \u003c/worldbody\u003e\n", "\u003c/mujoco\u003e\n", "\"\"\"\n", "physics = mujoco.Physics.from_xml_string(static_model)\n", "pixels = physics.render()\n", "PIL.Image.fromarray(pixels)" ] }, { "cell_type": "markdown", "metadata": { "id": "p4vPllljTJh8" }, "source": [ "`static_model` is written in MuJoCo's XML-based [MJCF](http://www.mujoco.org/book/modeling.html) modeling language. The `from_xml_string()` method invokes the model compiler, which instantiates the library's internal data structures. These can be accessed via the `physics` object, see below." ] }, { "cell_type": "markdown", "metadata": { "id": "MdUF2UYmR4TA" }, "source": [ "## Adding DOFs and simulating, advanced rendering\n", "This is a perfectly legitimate model, but if we simulate it, nothing will happen except for time advancing. This is because this model has no degrees of freedom (DOFs). We add DOFs by adding **joints** to bodies, specifying how they can move with respect to their parents. Let us add a hinge joint and re-render, visualizing the joint axis." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "R7zokzd_yeEg" }, "outputs": [], "source": [ "#@title A child body with a joint { vertical-output: true }\n", "\n", "swinging_body = \"\"\"\n", "\u003cmujoco\u003e\n", " \u003cworldbody\u003e\n", " \u003clight name=\"top\" pos=\"0 0 1\"/\u003e\n", " \u003cbody name=\"box_and_sphere\" euler=\"0 0 -30\"\u003e \n", " \u003cjoint name=\"swing\" type=\"hinge\" axis=\"1 -1 0\" pos=\"-.2 -.2 -.2\"/\u003e\n", " \u003cgeom name=\"red_box\" type=\"box\" size=\".2 .2 .2\" rgba=\"1 0 0 1\"/\u003e\n", " \u003cgeom name=\"green_sphere\" pos=\".2 .2 .2\" size=\".1\" rgba=\"0 1 0 1\"/\u003e\n", " \u003c/body\u003e\n", " \u003c/worldbody\u003e\n", "\u003c/mujoco\u003e\n", "\"\"\"\n", "physics = mujoco.Physics.from_xml_string(swinging_body)\n", "# Visualize the joint axis.\n", "scene_option = mujoco.wrapper.core.MjvOption()\n", "scene_option.flags[enums.mjtVisFlag.mjVIS_JOINT] = True\n", "pixels = physics.render(scene_option=scene_option)\n", "PIL.Image.fromarray(pixels)" ] }, { "cell_type": "markdown", "metadata": { "id": "INOGGV0PTQus" }, "source": [ "The things that move (and which have inertia) are called *bodies*. The body's child `joint` specifies how that body can move with respect to its parent, in this case `box_and_sphere` w.r.t the `worldbody`. \n", "\n", "Note that the body's frame is **rotated** with an `euler` directive, and its children, the geoms and the joint, rotate with it. This is to emphasize the local-to-parent-frame nature of position and orientation directives in MJCF.\n", "\n", "Let's make a video, to get a sense of the dynamics and to see the body swinging under gravity." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Z_57VMUDpGrj" }, "outputs": [], "source": [ "#@title Making a video {vertical-output: true}\n", "\n", "duration = 2 # (seconds)\n", "framerate = 30 # (Hz)\n", "\n", "# Visualize the joint axis\n", "scene_option = mujoco.wrapper.core.MjvOption()\n", "scene_option.flags[enums.mjtVisFlag.mjVIS_JOINT] = True\n", "\n", "# Simulate and display video.\n", "frames = []\n", "physics.reset() # Reset state and time\n", "while physics.data.time \u003c duration:\n", " physics.step()\n", " if len(frames) \u003c physics.data.time * framerate:\n", " pixels = physics.render(scene_option=scene_option)\n", " frames.append(pixels)\n", "display_video(frames, framerate)" ] }, { "cell_type": "markdown", "metadata": { "id": "yYvS1UaciMX_" }, "source": [ "Note how we collect the video frames. Because physics simulation timesteps are generally much smaller than framerates (the default timestep is 2ms), we don't render after each step." ] }, { "cell_type": "markdown", "metadata": { "id": "nQ8XOnRQx7T1" }, "source": [ "## Rendering options\n", "\n", "Like joint visualisation, additional rendering options are exposed as parameters to the `render` method." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AQITZiIgx7T2" }, "outputs": [], "source": [ "#@title Enable transparency and frame visualization {vertical-output: true}\n", "\n", "scene_option = mujoco.wrapper.core.MjvOption()\n", "scene_option.frame = enums.mjtFrame.mjFRAME_GEOM\n", "scene_option.flags[enums.mjtVisFlag.mjVIS_TRANSPARENT] = True\n", "pixels = physics.render(scene_option=scene_option)\n", "PIL.Image.fromarray(pixels)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PDDgY48vx7T6" }, "outputs": [], "source": [ "#@title Depth rendering {vertical-output: true}\n", "\n", "# depth is a float array, in meters.\n", "depth = physics.render(depth=True)\n", "# Shift nearest values to the origin.\n", "depth -= depth.min()\n", "# Scale by 2 mean distances of near rays.\n", "depth /= 2*depth[depth \u003c= 1].mean()\n", "# Scale to [0, 255]\n", "pixels = 255*np.clip(depth, 0, 1)\n", "PIL.Image.fromarray(pixels.astype(np.uint8))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PNwiIrgpx7T8" }, "outputs": [], "source": [ "#@title Segmentation rendering {vertical-output: true}\n", "\n", "seg = physics.render(segmentation=True)\n", "# Display the contents of the first channel, which contains object\n", "# IDs. The second channel, seg[:, :, 1], contains object types.\n", "geom_ids = seg[:, :, 0]\n", "# Infinity is mapped to -1\n", "geom_ids = geom_ids.astype(np.float64) + 1\n", "# Scale to [0, 1]\n", "geom_ids = geom_ids / geom_ids.max()\n", "pixels = 255*geom_ids\n", "PIL.Image.fromarray(pixels.astype(np.uint8))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uCJQlv3cQcJQ" }, "outputs": [], "source": [ "#@title Projecting from world to camera coordinates {vertical-output: true}\n", "\n", "# Get the world coordinates of the box corners\n", "box_pos = physics.named.data.geom_xpos['red_box']\n", "box_mat = physics.named.data.geom_xmat['red_box'].reshape(3, 3)\n", "box_size = physics.named.model.geom_size['red_box']\n", "offsets = np.array([-1, 1]) * box_size[:, None]\n", "xyz_local = np.stack(itertools.product(*offsets)).T\n", "xyz_global = box_pos[:, None] + box_mat @ xyz_local\n", "\n", "# Camera matrices multiply homogenous [x, y, z, 1] vectors.\n", "corners_homogeneous = np.ones((4, xyz_global.shape[1]), dtype=float)\n", "corners_homogeneous[:3, :] = xyz_global\n", "\n", "# Get the camera matrix.\n", "camera = mujoco.Camera(physics)\n", "camera_matrix = camera.matrix\n", "\n", "# Project world coordinates into pixel space. See:\n", "# https://en.wikipedia.org/wiki/3D_projection#Mathematical_formula\n", "xs, ys, s = camera_matrix @ corners_homogeneous\n", "# x and y are in the pixel coordinate system.\n", "x = xs / s\n", "y = ys / s\n", "\n", "# Render the camera view and overlay the projected corner coordinates.\n", "pixels = camera.render()\n", "fig, ax = plt.subplots(1, 1)\n", "ax.imshow(pixels)\n", "ax.plot(x, y, '+', c='w')\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": { "id": "gf9h_wi9weet" }, "source": [ "# MuJoCo basics and named indexing" ] }, { "cell_type": "markdown", "metadata": { "id": "NCcZxrDDB1Cj" }, "source": [ "## `mjModel`\n", "MuJoCo's `mjModel`, encapsulated in `physics.model`, contains the *model description*, including the default initial state and other fixed quantities which are not a function of the state, e.g. the positions of geoms in the frame of their parent body. The (x, y, z) offsets of the `box` and `sphere` geoms, relative their parent body `box_and_sphere` are given by `model.geom_pos`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wx8NANvOF8g1" }, "outputs": [], "source": [ "physics.model.geom_pos" ] }, { "cell_type": "markdown", "metadata": { "id": "Wee5ATLtIQn_" }, "source": [ "The `model.opt` structure contains global quantities like" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BhzbZIfDIU2-" }, "outputs": [], "source": [ "print('timestep', physics.model.opt.timestep)\n", "print('gravity', physics.model.opt.gravity)" ] }, { "cell_type": "markdown", "metadata": { "id": "t5hY0fyXFLcf" }, "source": [ "## `mjData`\n", "`mjData`, encapsulated in `physics.data`, contains the *state* and quantities that depend on it. The state is made up of time, generalized positions and generalised velocities. These are respectively `data.time`, `data.qpos` and `data.qvel`. \n", "\n", "Let's print the state of the swinging body where we left it:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "acwZtDwp9mQU" }, "outputs": [], "source": [ "print(physics.data.time, physics.data.qpos, physics.data.qvel)" ] }, { "cell_type": "markdown", "metadata": { "id": "7YlmcLcA-WQu" }, "source": [ "`physics.data` also contains functions of the state, for example the cartesian positions of objects in the world frame. The (x, y, z) positions of our two geoms are in `data.geom_xpos`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CPwDcAQ0-uUE" }, "outputs": [], "source": [ "print(physics.data.geom_xpos)" ] }, { "cell_type": "markdown", "metadata": { "id": "Z0UodCxS_v49" }, "source": [ "## Named indexing\n", "\n", "The semantics of the above arrays are made clearer using the `named` wrapper, which assigns names to rows and type names to columns." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cLARcaK6-xCU" }, "outputs": [], "source": [ "print(physics.named.data.geom_xpos)" ] }, { "cell_type": "markdown", "metadata": { "id": "wgXOUZNZHIx6" }, "source": [ "Note how `model.geom_pos` and `data.geom_xpos` have similar semantics but very different meanings." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-cW61ClRHS8a" }, "outputs": [], "source": [ "print(physics.named.model.geom_pos)" ] }, { "cell_type": "markdown", "metadata": { "id": "-lQ0AChVASMv" }, "source": [ "Name strings can be used to index **into** the relevant quantities, making code much more readable and robust." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Rj4ad9fQAnFZ" }, "outputs": [], "source": [ "physics.named.data.geom_xpos['green_sphere', 'z']" ] }, { "cell_type": "markdown", "metadata": { "id": "axr_p6APAzFn" }, "source": [ "Joint names can be used to index into quantities in configuration space (beginning with the letter `q`):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hluF9aDG9O1W" }, "outputs": [], "source": [ "physics.named.data.qpos['swing']" ] }, { "cell_type": "markdown", "metadata": { "id": "3IhfyD2Q1pjv" }, "source": [ "We can mix NumPy slicing operations with named indexing. As an example, we can set the color of the box using its name (`\"red_box\"`) as an index into the rows of the `geom_rgba` array. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "f5vVUullUvWH" }, "outputs": [], "source": [ "#@title Changing colors using named indexing{vertical-output: true}\n", "\n", "random_rgb = np.random.rand(3)\n", "physics.named.model.geom_rgba['red_box', :3] = random_rgb\n", "pixels = physics.render()\n", "PIL.Image.fromarray(pixels)" ] }, { "cell_type": "markdown", "metadata": { "id": "elzPPdq-KhLI" }, "source": [ "Note that while `physics.model` quantities will not be changed by the engine, we can change them ourselves between steps. This however is generally not recommended, the preferred approach being to modify the model at the XML level using the PyMJCF library, see below." ] }, { "cell_type": "markdown", "metadata": { "id": "22ENjtVuhwsm" }, "source": [ "## Setting the state with `reset_context()`\n", "\n", "In order for `data` quantities that are functions of the state to be in sync with the state, MuJoCo's `mj_step1()` needs to be called. This is facilitated by the `reset_context()` context, please see in-depth discussion in Section 2.1 of the [tech report](https://arxiv.org/abs/2006.12983)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WBPprCtWgXFN" }, "outputs": [], "source": [ "physics.named.data.qpos['swing'] = np.pi\n", "print('Without reset_context, spatial positions are not updated:',\n", " physics.named.data.geom_xpos['green_sphere', ['z']])\n", "with physics.reset_context():\n", " physics.named.data.qpos['swing'] = np.pi\n", "print('After reset_context, positions are up-to-date:',\n", " physics.named.data.geom_xpos['green_sphere', ['z']])" ] }, { "cell_type": "markdown", "metadata": { "id": "SHppAOjvSupc" }, "source": [ "## Free bodies: the self-inverting \"tippe-top\"\n", "\n", "A free body is a body with a `free` joint, with 6 movement DOFs: 3 translations and 3 rotations. We could give our `box_and_sphere` body a free joint and watch it fall, but let's look at something more interesting. A \"tippe top\" is a spinning toy which flips itself on its head ([Wikipedia](https://en.wikipedia.org/wiki/Tippe_top)). We model it as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xasXQpVMjIwA" }, "outputs": [], "source": [ "#@title The \"tippe-top\" model{vertical-output: true}\n", "\n", "tippe_top = \"\"\"\n", "\u003cmujoco model=\"tippe top\"\u003e\n", " \u003coption integrator=\"RK4\"/\u003e\n", " \u003casset\u003e\n", " \u003ctexture name=\"grid\" type=\"2d\" builtin=\"checker\" rgb1=\".1 .2 .3\" \n", " rgb2=\".2 .3 .4\" width=\"300\" height=\"300\"/\u003e\n", " \u003cmaterial name=\"grid\" texture=\"grid\" texrepeat=\"8 8\" reflectance=\".2\"/\u003e\n", " \u003c/asset\u003e\n", " \u003cworldbody\u003e\n", " \u003cgeom size=\".2 .2 .01\" type=\"plane\" material=\"grid\"/\u003e\n", " \u003clight pos=\"0 0 .6\"/\u003e\n", " \u003ccamera name=\"closeup\" pos=\"0 -.1 .07\" xyaxes=\"1 0 0 0 1 2\"/\u003e\n", " \u003cbody name=\"top\" pos=\"0 0 .02\"\u003e\n", " \u003cfreejoint/\u003e\n", " \u003cgeom name=\"ball\" type=\"sphere\" size=\".02\" /\u003e\n", " \u003cgeom name=\"stem\" type=\"cylinder\" pos=\"0 0 .02\" size=\"0.004 .008\"/\u003e\n", " \u003cgeom name=\"ballast\" type=\"box\" size=\".023 .023 0.005\" pos=\"0 0 -.015\" \n", " contype=\"0\" conaffinity=\"0\" group=\"3\"/\u003e\n", " \u003c/body\u003e\n", " \u003c/worldbody\u003e\n", " \u003ckeyframe\u003e\n", " \u003ckey name=\"spinning\" qpos=\"0 0 0.02 1 0 0 0\" qvel=\"0 0 0 0 1 200\" /\u003e\n", " \u003c/keyframe\u003e\n", "\u003c/mujoco\u003e\n", "\"\"\"\n", "physics = mujoco.Physics.from_xml_string(tippe_top)\n", "PIL.Image.fromarray(physics.render(camera_id='closeup'))" ] }, { "cell_type": "markdown", "metadata": { "id": "bvHlr6maJYIG" }, "source": [ "Note several new features of this model definition:\n", "0. The free joint is added with the `\u003cfreejoint/\u003e` clause, which is similar to `\u003cjoint type=\"free\"/\u003e`, but prohibits unphysical attributes like friction or stiffness.\n", "1. We use the `\u003coption/\u003e` clause to set the integrator to the more accurate Runge Kutta 4th order.\n", "2. We define the floor's grid material inside the `\u003casset/\u003e` clause and reference it in the floor geom. \n", "3. We use an invisible and non-colliding box geom called `ballast` to move the top's center-of-mass lower. Having a low center of mass is (counter-intuitively) required for the flipping behaviour to occur.\n", "4. We save our initial spinning state as a keyframe. It has a high rotational velocity around the z-axis, but is not perfectly oriented with the world.\n", "5. We define a `\u003ccamera\u003e` in our model, and then render from it using the `camera_id` argument to `render()`.\n", "Let us examine the state:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "o4S9nYhHOKmb" }, "outputs": [], "source": [ "print('positions', physics.data.qpos)\n", "print('velocities', physics.data.qvel)" ] }, { "cell_type": "markdown", "metadata": { "id": "71UgzBAqWdtZ" }, "source": [ "The velocities are easy to interpret, 6 zeros, one for each DOF. What about the length-7 positions? We can see the initial 2cm height of the body; the subsequent four numbers are the 3D orientation, defined by a *unit quaternion*. These normalized four-vectors, which preserve the topology of the orientation group, are the reason that `data.qpos` can be bigger than `data.qvel`: 3D orientations are represented with **4** numbers while angular velocities are **3** numbers." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5P4HkhKNGQvs" }, "outputs": [], "source": [ "#@title Video of the tippe-top {vertical-output: true}\n", "\n", "duration = 7 # (seconds)\n", "framerate = 60 # (Hz)\n", "\n", "# Simulate and display video.\n", "frames = []\n", "physics.reset(0) # Reset to keyframe 0 (load a saved state).\n", "while physics.data.time \u003c duration:\n", " physics.step()\n", " if len(frames) \u003c (physics.data.time) * framerate:\n", " pixels = physics.render(camera_id='closeup')\n", " frames.append(pixels)\n", "\n", "display_video(frames, framerate)" ] }, { "cell_type": "markdown", "metadata": { "id": "rRuFKD2ubPgu" }, "source": [ "### Measuring values from `physics.data`\n", "The `physics.data` structure contains all of the dynamic variables and intermediate results produced by the simulation. These are expected to change on each timestep. \n", "\n", "Below we simulate for 2000 timesteps and plot the state and height of the sphere as a function of time." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1XXB6asJoZ2N" }, "outputs": [], "source": [ "#@title Measuring values {vertical-output: true}\n", "\n", "timevals = []\n", "angular_velocity = []\n", "stem_height = []\n", "\n", "# Simulate and save data\n", "physics.reset(0)\n", "while physics.data.time \u003c duration:\n", " physics.step()\n", " timevals.append(physics.data.time)\n", " angular_velocity.append(physics.data.qvel[3:6].copy())\n", " stem_height.append(physics.named.data.geom_xpos['stem', 'z'])\n", "\n", "dpi = 100\n", "width = 480\n", "height = 640\n", "figsize = (width / dpi, height / dpi)\n", "_, ax = plt.subplots(2, 1, figsize=figsize, dpi=dpi, sharex=True)\n", "\n", "ax[0].plot(timevals, angular_velocity)\n", "ax[0].set_title('angular velocity')\n", "ax[0].set_ylabel('radians / second')\n", "\n", "ax[1].plot(timevals, stem_height)\n", "ax[1].set_xlabel('time (seconds)')\n", "ax[1].set_ylabel('meters')\n", "_ = ax[1].set_title('stem height')" ] }, { "cell_type": "markdown", "metadata": { "id": "UAMItwu8e1WR" }, "source": [ "# PyMJCF tutorial\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "hPiY8m3MssKM" }, "source": [ "This library provides a Python object model for MuJoCo's XML-based\n", "[MJCF](http://www.mujoco.org/book/modeling.html) physics modeling language. The\n", "goal of the library is to allow users to easily interact with and modify MJCF\n", "models in Python, similarly to what the JavaScript DOM does for HTML.\n", "\n", "A key feature of this library is the ability to easily compose multiple separate\n", "MJCF models into a larger one. Disambiguation of duplicated names from different\n", "models, or multiple instances of the same model, is handled automatically.\n", "\n", "One typical use case is when we want robots with a variable number of joints. This is a fundamental change to the kinematics, requiring a new XML descriptor and new binary model to be compiled. \n", "\n", "The following snippets realise this scenario and provide a quick example of this library's use case." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gKny5EJ4uVzu" }, "outputs": [], "source": [ "class Leg(object):\n", " \"\"\"A 2-DoF leg with position actuators.\"\"\"\n", " def __init__(self, length, rgba):\n", " self.model = mjcf.RootElement()\n", "\n", " # Defaults:\n", " self.model.default.joint.damping = 2\n", " self.model.default.joint.type = 'hinge'\n", " self.model.default.geom.type = 'capsule'\n", " self.model.default.geom.rgba = rgba # Continued below...\n", "\n", " # Thigh:\n", " self.thigh = self.model.worldbody.add('body')\n", " self.hip = self.thigh.add('joint', axis=[0, 0, 1])\n", " self.thigh.add('geom', fromto=[0, 0, 0, length, 0, 0], size=[length/4])\n", "\n", " # Hip:\n", " self.shin = self.thigh.add('body', pos=[length, 0, 0])\n", " self.knee = self.shin.add('joint', axis=[0, 1, 0])\n", " self.shin.add('geom', fromto=[0, 0, 0, 0, 0, -length], size=[length/5])\n", "\n", " # Position actuators:\n", " self.model.actuator.add('position', joint=self.hip, kp=10)\n", " self.model.actuator.add('position', joint=self.knee, kp=10)" ] }, { "cell_type": "markdown", "metadata": { "id": "cFJerI--UtTy" }, "source": [ "The `Leg` class describes an abstract articulated leg, with two joints and corresponding proportional-derivative actuators. \n", "\n", "Note that:\n", "\n", "- MJCF attributes correspond directly to arguments of the `add()` method.\n", "- When referencing elements, e.g when specifying the joint to which an actuator is attached, the MJCF element itself is used, rather than the name string." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "both", "id": "SESlL_TidKHx" }, "outputs": [], "source": [ "BODY_RADIUS = 0.1\n", "BODY_SIZE = (BODY_RADIUS, BODY_RADIUS, BODY_RADIUS / 2)\n", "random_state = np.random.RandomState(42)\n", "\n", "def make_creature(num_legs):\n", " \"\"\"Constructs a creature with `num_legs` legs.\"\"\"\n", " rgba = random_state.uniform([0, 0, 0, 1], [1, 1, 1, 1])\n", " model = mjcf.RootElement()\n", " model.compiler.angle = 'radian' # Use radians.\n", "\n", " # Make the torso geom.\n", " model.worldbody.add(\n", " 'geom', name='torso', type='ellipsoid', size=BODY_SIZE, rgba=rgba)\n", "\n", " # Attach legs to equidistant sites on the circumference.\n", " for i in range(num_legs):\n", " theta = 2 * i * np.pi / num_legs\n", " hip_pos = BODY_RADIUS * np.array([np.cos(theta), np.sin(theta), 0])\n", " hip_site = model.worldbody.add('site', pos=hip_pos, euler=[0, 0, theta])\n", " leg = Leg(length=BODY_RADIUS, rgba=rgba)\n", " hip_site.attach(leg.model)\n", "\n", " return model" ] }, { "cell_type": "markdown", "metadata": { "id": "elyuJiI3U3kM" }, "source": [ "The `make_creature` function uses PyMJCF's `attach()` method to procedurally attach legs to the torso. Note that at this stage both the torso and hip attachment sites are children of the `worldbody`, since their parent body has yet to be instantiated. We'll now make an arena with a chequered floor and two lights, and place our creatures in a grid." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "F7_Tx9P9U_VJ" }, "outputs": [], "source": [ "#@title Six Creatures on a floor.{vertical-output: true}\n", "\n", "arena = mjcf.RootElement()\n", "chequered = arena.asset.add('texture', type='2d', builtin='checker', width=300,\n", " height=300, rgb1=[.2, .3, .4], rgb2=[.3, .4, .5])\n", "grid = arena.asset.add('material', name='grid', texture=chequered,\n", " texrepeat=[5, 5], reflectance=.2)\n", "arena.worldbody.add('geom', type='plane', size=[2, 2, .1], material=grid)\n", "for x in [-2, 2]:\n", " arena.worldbody.add('light', pos=[x, -1, 3], dir=[-x, 1, -2])\n", "\n", "# Instantiate 6 creatures with 3 to 8 legs.\n", "creatures = [make_creature(num_legs=num_legs) for num_legs in range(3, 9)]\n", "\n", "# Place them on a grid in the arena.\n", "height = .15\n", "grid = 5 * BODY_RADIUS\n", "xpos, ypos, zpos = np.meshgrid([-grid, 0, grid], [0, grid], [height])\n", "for i, model in enumerate(creatures):\n", " # Place spawn sites on a grid.\n", " spawn_pos = (xpos.flat[i], ypos.flat[i], zpos.flat[i])\n", " spawn_site = arena.worldbody.add('site', pos=spawn_pos, group=3)\n", " # Attach to the arena at the spawn sites, with a free joint.\n", " spawn_site.attach(model).add('freejoint')\n", "\n", "# Instantiate the physics and render.\n", "physics = mjcf.Physics.from_mjcf_model(arena)\n", "PIL.Image.fromarray(physics.render())" ] }, { "cell_type": "markdown", "metadata": { "id": "cMfDaD7PfuoI" }, "source": [ "Multi-legged creatures, ready to roam! Let's inject some controls and watch them move. We'll generate a sinusoidal open-loop control signal of fixed frequency and random phase, recording both video frames and the horizontal positions of the torso geoms, in order to plot the movement trajectories." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8Gx39DMEUZDt" }, "outputs": [], "source": [ "#@title Video of the movement{vertical-output: true}\n", "#@test {\"timeout\": 600}\n", "\n", "duration = 10 # (Seconds)\n", "framerate = 30 # (Hz)\n", "video = []\n", "pos_x = []\n", "pos_y = []\n", "torsos = [] # List of torso geom elements.\n", "actuators = [] # List of actuator elements.\n", "for creature in creatures:\n", " torsos.append(creature.find('geom', 'torso'))\n", " actuators.extend(creature.find_all('actuator'))\n", "\n", "# Control signal frequency, phase, amplitude.\n", "freq = 5\n", "phase = 2 * np.pi * random_state.rand(len(actuators))\n", "amp = 0.9\n", "\n", "# Simulate, saving video frames and torso locations.\n", "physics.reset()\n", "while physics.data.time \u003c duration:\n", " # Inject controls and step the physics.\n", " physics.bind(actuators).ctrl = amp * np.sin(freq * physics.data.time + phase)\n", " physics.step()\n", "\n", " # Save torso horizontal positions using bind().\n", " pos_x.append(physics.bind(torsos).xpos[:, 0].copy())\n", " pos_y.append(physics.bind(torsos).xpos[:, 1].copy())\n", "\n", " # Save video frames.\n", " if len(video) \u003c physics.data.time * framerate:\n", " pixels = physics.render()\n", " video.append(pixels.copy())\n", "\n", "display_video(video, framerate)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "u09JfenWYLZu" }, "outputs": [], "source": [ "#@title Movement trajectories{vertical-output: true}\n", "\n", "creature_colors = physics.bind(torsos).rgba[:, :3]\n", "fig, ax = plt.subplots(figsize=(4, 4))\n", "ax.set_prop_cycle(color=creature_colors)\n", "_ = ax.plot(pos_x, pos_y, linewidth=4)" ] }, { "cell_type": "markdown", "metadata": { "id": "kggQyvNpf_Y9" }, "source": [ "The plot above shows the corresponding movement trajectories of creature positions. Note how `physics.bind(torsos)` was used to access both `xpos` and `rgba` values. Once the `Physics` had been instantiated by `from_mjcf_model()`, the `bind()` method will expose both the associated `mjData` and `mjModel` fields of an `mjcf` element, providing unified access to all quantities in the simulation. " ] }, { "cell_type": "markdown", "metadata": { "id": "wcRX_wu_8q8u" }, "source": [ "# Composer tutorial" ] }, { "cell_type": "markdown", "metadata": { "id": "1DMhNPE5tSdw" }, "source": [ "In this tutorial we will create a task requiring our \"creature\" above to press a colour-changing button on the floor with a prescribed force. We begin by implementing our creature as a `composer.Entity`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WwfzIqgNuFKt" }, "outputs": [], "source": [ "#@title The `Creature` class\n", "\n", "\n", "class Creature(composer.Entity):\n", " \"\"\"A multi-legged creature derived from `composer.Entity`.\"\"\"\n", " def _build(self, num_legs):\n", " self._model = make_creature(num_legs)\n", "\n", " def _build_observables(self):\n", " return CreatureObservables(self)\n", "\n", " @property\n", " def mjcf_model(self):\n", " return self._model\n", "\n", " @property\n", " def actuators(self):\n", " return tuple(self._model.find_all('actuator'))\n", "\n", "\n", "# Add simple observable features for joint angles and velocities.\n", "class CreatureObservables(composer.Observables):\n", "\n", " @composer.observable\n", " def joint_positions(self):\n", " all_joints = self._entity.mjcf_model.find_all('joint')\n", " return observable.MJCFFeature('qpos', all_joints)\n", "\n", " @composer.observable\n", " def joint_velocities(self):\n", " all_joints = self._entity.mjcf_model.find_all('joint')\n", " return observable.MJCFFeature('qvel', all_joints)" ] }, { "cell_type": "markdown", "metadata": { "id": "CXZOBK6RkjxH" }, "source": [ "The `Creature` Entity includes generic Observables for joint angles and velocities. Because `find_all()` is called on the `Creature`'s MJCF model, it will only return the creature's leg joints, and not the \"free\" joint with which it will be attached to the world. Note that Composer Entities should override the `_build` and `_build_observables` methods rather than `__init__`. The implementation of `__init__` in the base class calls `_build` and `_build_observables`, in that order, to ensure that the entity's MJCF model is created before its observables. This was a design choice which allows the user to refer to an observable as an attribute (`entity.observables.foo`) while still making it clear which attributes are observables. The stateful `Button` class derives from `composer.Entity` and implements the `initialize_episode` and `after_substep` callbacks." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BE9VU2EOvR-u" }, "outputs": [], "source": [ "#@title The `Button` class\n", "\n", "NUM_SUBSTEPS = 25 # The number of physics substeps per control timestep.\n", "\n", "\n", "class Button(composer.Entity):\n", " \"\"\"A button Entity which changes colour when pressed with certain force.\"\"\"\n", " def _build(self, target_force_range=(5, 10)):\n", " self._min_force, self._max_force = target_force_range\n", " self._mjcf_model = mjcf.RootElement()\n", " self._geom = self._mjcf_model.worldbody.add(\n", " 'geom', type='cylinder', size=[0.25, 0.02], rgba=[1, 0, 0, 1])\n", " self._site = self._mjcf_model.worldbody.add(\n", " 'site', type='cylinder', size=self._geom.size*1.01, rgba=[1, 0, 0, 0])\n", " self._sensor = self._mjcf_model.sensor.add('touch', site=self._site)\n", " self._num_activated_steps = 0\n", "\n", " def _build_observables(self):\n", " return ButtonObservables(self)\n", "\n", " @property\n", " def mjcf_model(self):\n", " return self._mjcf_model\n", " # Update the activation (and colour) if the desired force is applied.\n", " def _update_activation(self, physics):\n", " current_force = physics.bind(self.touch_sensor).sensordata[0]\n", " self._is_activated = (current_force \u003e= self._min_force and\n", " current_force \u003c= self._max_force)\n", " physics.bind(self._geom).rgba = (\n", " [0, 1, 0, 1] if self._is_activated else [1, 0, 0, 1])\n", " self._num_activated_steps += int(self._is_activated)\n", "\n", " def initialize_episode(self, physics, random_state):\n", " self._reward = 0.0\n", " self._num_activated_steps = 0\n", " self._update_activation(physics)\n", "\n", " def after_substep(self, physics, random_state):\n", " self._update_activation(physics)\n", "\n", " @property\n", " def touch_sensor(self):\n", " return self._sensor\n", "\n", " @property\n", " def num_activated_steps(self):\n", " return self._num_activated_steps\n", "\n", "\n", "class ButtonObservables(composer.Observables):\n", " \"\"\"A touch sensor which averages contact force over physics substeps.\"\"\"\n", " @composer.observable\n", " def touch_force(self):\n", " return observable.MJCFFeature('sensordata', self._entity.touch_sensor,\n", " buffer_size=NUM_SUBSTEPS, aggregator='mean')" ] }, { "cell_type": "markdown", "metadata": { "id": "D9vB5nCwkyIW" }, "source": [ "Note how the Button counts the number of sub-steps during which it is pressed with the desired force. It also exposes an `Observable` of the force being applied to the button, whose value is an average of the readings over the physics time-steps.\n", "\n", "We import some `variation` modules and an arena factory:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aDTTQMHtVawM" }, "outputs": [], "source": [ "#@title Random initialiser using `composer.variation`\n", "\n", "\n", "class UniformCircle(variation.Variation):\n", " \"\"\"A uniformly sampled horizontal point on a circle of radius `distance`.\"\"\"\n", " def __init__(self, distance):\n", " self._distance = distance\n", " self._heading = distributions.Uniform(0, 2*np.pi)\n", "\n", " def __call__(self, initial_value=None, current_value=None, random_state=None):\n", " distance, heading = variation.evaluate(\n", " (self._distance, self._heading), random_state=random_state)\n", " return (distance*np.cos(heading), distance*np.sin(heading), 0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dgZwP-pvxJdt" }, "outputs": [], "source": [ "#@title The `PressWithSpecificForce` task\n", "\n", "\n", "class PressWithSpecificForce(composer.Task):\n", "\n", " def __init__(self, creature):\n", " self._creature = creature\n", " self._arena = floors.Floor()\n", " self._arena.add_free_entity(self._creature)\n", " self._arena.mjcf_model.worldbody.add('light', pos=(0, 0, 4))\n", " self._button = Button()\n", " self._arena.attach(self._button)\n", "\n", " # Configure initial poses\n", " self._creature_initial_pose = (0, 0, 0.15)\n", " button_distance = distributions.Uniform(0.5, .75)\n", " self._button_initial_pose = UniformCircle(button_distance)\n", "\n", " # Configure variators\n", " self._mjcf_variator = variation.MJCFVariator()\n", " self._physics_variator = variation.PhysicsVariator()\n", "\n", " # Configure and enable observables\n", " pos_corrptor = noises.Additive(distributions.Normal(scale=0.01))\n", " self._creature.observables.joint_positions.corruptor = pos_corrptor\n", " self._creature.observables.joint_positions.enabled = True\n", " vel_corruptor = noises.Multiplicative(distributions.LogNormal(sigma=0.01))\n", " self._creature.observables.joint_velocities.corruptor = vel_corruptor\n", " self._creature.observables.joint_velocities.enabled = True\n", " self._button.observables.touch_force.enabled = True\n", "\n", " def to_button(physics):\n", " button_pos, _ = self._button.get_pose(physics)\n", " return self._creature.global_vector_to_local_frame(physics, button_pos)\n", "\n", " self._task_observables = {}\n", " self._task_observables['button_position'] = observable.Generic(to_button)\n", "\n", " for obs in self._task_observables.values():\n", " obs.enabled = True\n", "\n", " self.control_timestep = NUM_SUBSTEPS * self.physics_timestep\n", "\n", " @property\n", " def root_entity(self):\n", " return self._arena\n", "\n", " @property\n", " def task_observables(self):\n", " return self._task_observables\n", "\n", " def initialize_episode_mjcf(self, random_state):\n", " self._mjcf_variator.apply_variations(random_state)\n", "\n", " def initialize_episode(self, physics, random_state):\n", " self._physics_variator.apply_variations(physics, random_state)\n", " creature_pose, button_pose = variation.evaluate(\n", " (self._creature_initial_pose, self._button_initial_pose),\n", " random_state=random_state)\n", " self._creature.set_pose(physics, position=creature_pose)\n", " self._button.set_pose(physics, position=button_pose)\n", "\n", " def get_reward(self, physics):\n", " return self._button.num_activated_steps / NUM_SUBSTEPS " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dRuuZdLpthbv" }, "outputs": [], "source": [ "#@title Instantiating an environment{vertical-output: true}\n", "\n", "creature = Creature(num_legs=4)\n", "task = PressWithSpecificForce(creature)\n", "env = composer.Environment(task, random_state=np.random.RandomState(42))\n", "\n", "env.reset()\n", "PIL.Image.fromarray(env.physics.render())" ] }, { "cell_type": "markdown", "metadata": { "id": "giTL_6euZFlw" }, "source": [ "# The *Control Suite*" ] }, { "cell_type": "markdown", "metadata": { "id": "zfIcrDECtdB2" }, "source": [ "The **Control Suite** is a set of stable, well-tested tasks designed to serve as a benchmark for continuous control learning agents. Tasks are written using the basic MuJoCo wrapper interface. Standardised action, observation and reward structures make suite-wide benchmarking simple and learning curves easy to interpret. Control Suite domains are not meant to be modified, in order to facilitate benchmarking. For full details regarding benchmarking, please refer to our original [publication](https://arxiv.org/abs/1801.00690).\n", "\n", "A video of solved benchmark tasks is [available here](https://www.youtube.com/watch?v=rAai4QzcYbs\u0026feature=youtu.be).\n", "\n", "The suite come with convenient module level tuples for iterating over tasks:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "a_whTJG8uTp1" }, "outputs": [], "source": [ "#@title Iterating over tasks{vertical-output: true}\n", "\n", "max_len = max(len(d) for d, _ in suite.BENCHMARKING)\n", "for domain, task in suite.BENCHMARKING:\n", " print(f'{domain:\u003c{max_len}} {task}')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qN8y3etfZFly" }, "outputs": [], "source": [ "#@title Loading and simulating a `suite` task{vertical-output: true}\n", "\n", "# Load the environment\n", "random_state = np.random.RandomState(42)\n", "env = suite.load('hopper', 'stand', task_kwargs={'random': random_state})\n", "\n", "# Simulate episode with random actions\n", "duration = 4 # Seconds\n", "frames = []\n", "ticks = []\n", "rewards = []\n", "observations = []\n", "\n", "spec = env.action_spec()\n", "time_step = env.reset()\n", "\n", "while env.physics.data.time \u003c duration:\n", "\n", " action = random_state.uniform(spec.minimum, spec.maximum, spec.shape)\n", " time_step = env.step(action)\n", "\n", " camera0 = env.physics.render(camera_id=0, height=200, width=200)\n", " camera1 = env.physics.render(camera_id=1, height=200, width=200)\n", " frames.append(np.hstack((camera0, camera1)))\n", " rewards.append(time_step.reward)\n", " observations.append(copy.deepcopy(time_step.observation))\n", " ticks.append(env.physics.data.time)\n", "\n", "html_video = display_video(frames, framerate=1./env.control_timestep())\n", "\n", "# Show video and plot reward and observations\n", "num_sensors = len(time_step.observation)\n", "\n", "_, ax = plt.subplots(1 + num_sensors, 1, sharex=True, figsize=(4, 8))\n", "ax[0].plot(ticks, rewards)\n", "ax[0].set_ylabel('reward')\n", "ax[-1].set_xlabel('time')\n", "\n", "for i, key in enumerate(time_step.observation):\n", " data = np.asarray([observations[j][key] for j in range(len(observations))])\n", " ax[i+1].plot(ticks, data, label=key)\n", " ax[i+1].set_ylabel(key)\n", "\n", "html_video" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ggVbQr5hZFl5" }, "outputs": [], "source": [ "#@title Visualizing an initial state of one task per domain in the Control Suite\n", "domains_tasks = {domain: task for domain, task in suite.ALL_TASKS}\n", "random_state = np.random.RandomState(42)\n", "num_domains = len(domains_tasks)\n", "n_col = num_domains // int(np.sqrt(num_domains))\n", "n_row = num_domains // n_col + int(0 \u003c num_domains % n_col)\n", "_, ax = plt.subplots(n_row, n_col, figsize=(12, 12))\n", "for a in ax.flat:\n", " a.axis('off')\n", " a.grid(False)\n", "\n", "print(f'Iterating over all {num_domains} domains in the Suite:')\n", "for j, [domain, task] in enumerate(domains_tasks.items()):\n", " print(domain, task)\n", "\n", " env = suite.load(domain, task, task_kwargs={'random': random_state})\n", " timestep = env.reset()\n", " pixels = env.physics.render(height=200, width=200, camera_id=0)\n", "\n", " ax.flat[j].imshow(pixels)\n", " ax.flat[j].set_title(domain + ': ' + task)\n", "\n", "clear_output()" ] }, { "cell_type": "markdown", "metadata": { "id": "JHSvxHiaopDb" }, "source": [ "# Locomotion" ] }, { "cell_type": "markdown", "metadata": { "id": "yTn3C03dpHzL" }, "source": [ "## Humanoid running along corridor with obstacles\n", "\n", "As an illustrative example of using the Locomotion infrastructure to build an RL environment, consider placing a humanoid in a corridor with walls, and a task specifying that the humanoid will be rewarded for running along this corridor, navigating around the wall obstacles using vision. We instantiate the environment as a composition of the Walker, Arena, and Task as follows. First, we build a position-controlled CMU humanoid walker. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gE8rrB7PpN9X" }, "outputs": [], "source": [ "#@title A position controlled `cmu_humanoid`\n", "\n", "walker = cmu_humanoid.CMUHumanoidPositionControlledV2020(\n", " observable_options={'egocentric_camera': dict(enabled=True)})" ] }, { "cell_type": "markdown", "metadata": { "id": "3fYbaDflBrgE" }, "source": [ "Next, we construct a corridor-shaped arena that is obstructed by walls." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "t-O17Fnm3E6R" }, "outputs": [], "source": [ "#@title A corridor arena with wall obstacles\n", "\n", "arena = corridor_arenas.WallsCorridor(\n", " wall_gap=3.,\n", " wall_width=distributions.Uniform(2., 3.),\n", " wall_height=distributions.Uniform(2.5, 3.5),\n", " corridor_width=4.,\n", " corridor_length=30.,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "970nN38eBx-R" }, "source": [ "The task constructor places the walker in the arena." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dz4Jy2UGpQ4Z" }, "outputs": [], "source": [ "#@title A task to navigate the arena\n", "\n", "task = corridor_tasks.RunThroughCorridor(\n", " walker=walker,\n", " arena=arena,\n", " walker_spawn_position=(0.5, 0, 0),\n", " target_velocity=3.0,\n", " physics_timestep=0.005,\n", " control_timestep=0.03,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "r-Oy-qTSB4HW" }, "source": [ "Finally, a task that rewards the agent for running down the corridor at a specific velocity is instantiated as a `composer.Environment`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "sQXlaEZk3ytl" }, "outputs": [], "source": [ "#@title The `RunThroughCorridor` environment\n", "\n", "env = composer.Environment(\n", " task=task,\n", " time_limit=10,\n", " random_state=np.random.RandomState(42),\n", " strip_singleton_obs_buffer_dim=True,\n", ")\n", "env.reset()\n", "pixels = []\n", "for camera_id in range(3):\n", " pixels.append(env.physics.render(camera_id=camera_id, width=240))\n", "PIL.Image.fromarray(np.hstack(pixels))" ] }, { "cell_type": "markdown", "metadata": { "id": "HuuQLm8YopDe" }, "source": [ "## Multi-Agent Soccer" ] }, { "cell_type": "markdown", "metadata": { "id": "OPNshDEEopDf" }, "source": [ "Building on Composer and Locomotion libraries, the Multi-agent soccer environments, introduced in [this paper](https://arxiv.org/abs/1902.07151), follow a consistent task structure of Walkers, Arena, and Task where instead of a single walker, we inject multiple walkers that can interact with each other physically in the same scene. The code snippet below shows how to instantiate a 2-vs-2 Multi-agent Soccer environment with the simple, 5 degree-of-freedom `BoxHead` walker type." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zAb3je0DAeQo" }, "outputs": [], "source": [ "#@title 2-v-2 `Boxhead` soccer\n", "\n", "random_state = np.random.RandomState(42)\n", "env = soccer.load(\n", " team_size=2,\n", " time_limit=45.,\n", " random_state=random_state,\n", " disable_walker_contacts=False,\n", " walker_type=soccer.WalkerType.BOXHEAD,\n", ")\n", "env.reset()\n", "pixels = []\n", "# Select a random subset of 6 cameras (soccer envs have lots of cameras)\n", "cameras = random_state.choice(env.physics.model.ncam, 6, replace=False)\n", "for camera_id in cameras:\n", " pixels.append(env.physics.render(camera_id=camera_id, width=240))\n", "image = np.vstack((np.hstack(pixels[:3]), np.hstack(pixels[3:])))\n", "PIL.Image.fromarray(image)" ] }, { "cell_type": "markdown", "metadata": { "id": "J_5C2k0NGvxE" }, "source": [ " It can trivially be replaced by e.g. the `WalkerType.ANT` walker:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WDIGodhBG-Mn" }, "outputs": [], "source": [ "#@title 3-v-3 `Ant` soccer\n", "\n", "random_state = np.random.RandomState(42)\n", "env = soccer.load(\n", " team_size=3,\n", " time_limit=45.,\n", " random_state=random_state,\n", " disable_walker_contacts=False,\n", " walker_type=soccer.WalkerType.ANT,\n", ")\n", "env.reset()\n", "\n", "pixels = []\n", "cameras = random_state.choice(env.physics.model.ncam, 6, replace=False)\n", "for camera_id in cameras:\n", " pixels.append(env.physics.render(camera_id=camera_id, width=240))\n", "image = np.vstack((np.hstack(pixels[:3]), np.hstack(pixels[3:])))\n", "PIL.Image.fromarray(image)" ] }, { "cell_type": "markdown", "metadata": { "id": "MvK9BW4A5c9p" }, "source": [ "# Manipulation" ] }, { "cell_type": "markdown", "metadata": { "id": "jPt27n2Dch_m" }, "source": [ "The `manipulation` module provides a robotic arm, a set of simple objects, and tools for building reward functions for manipulation tasks." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cZxmJoovahCA" }, "outputs": [], "source": [ "#@title Listing all `manipulation` tasks{vertical-output: true}\n", "\n", "# `ALL` is a tuple containing the names of all of the environments in the suite.\n", "print('\\n'.join(manipulation.ALL))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oj0cJFlR5nTS" }, "outputs": [], "source": [ "#@title Listing `manipulation` tasks that use vision{vertical-output: true}\n", "print('\\n'.join(manipulation.get_environments_by_tag('vision')))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "e_6q4FqFIKxy" }, "outputs": [], "source": [ "#@title Loading and simulating a `manipulation` task{vertical-output: true}\n", "\n", "env = manipulation.load('stack_2_of_3_bricks_random_order_vision', seed=42)\n", "action_spec = env.action_spec()\n", "\n", "def sample_random_action():\n", " return env.random_state.uniform(\n", " low=action_spec.minimum,\n", " high=action_spec.maximum,\n", " ).astype(action_spec.dtype, copy=False)\n", "\n", "# Step the environment through a full episode using random actions and record\n", "# the camera observations.\n", "frames = []\n", "timestep = env.reset()\n", "frames.append(timestep.observation['front_close'])\n", "while not timestep.last():\n", " timestep = env.step(sample_random_action())\n", " frames.append(timestep.observation['front_close'])\n", "all_frames = np.concatenate(frames, axis=0)\n", "display_video(all_frames, 30)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "YkBQUjm6gbGF", "YvyGCsgSCxHQ", "wtDN43hIJh2C", "jZXz9rPYGA-Y", "MdUF2UYmR4TA", "nQ8XOnRQx7T1", "gf9h_wi9weet", "NCcZxrDDB1Cj", "t5hY0fyXFLcf", "Z0UodCxS_v49", "22ENjtVuhwsm", "SHppAOjvSupc", "rRuFKD2ubPgu", "UAMItwu8e1WR", "wcRX_wu_8q8u", "giTL_6euZFlw", "JHSvxHiaopDb", "yTn3C03dpHzL", "HuuQLm8YopDe", "MvK9BW4A5c9p" ], "last_runtime": { "build_target": "", "kind": "local" }, "name": "dm_control", "private_outputs": true, "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

jmhsi/justin_tinker

data_science/courses/learning_dl_packages/tensorflow_tutorials.ipynb

1

1306

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apache-2.0

google/physics-math-tutorials

colabs/RiemannHypothesisColab.ipynb

1

36796

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"RiemannHypothesisColab.ipynb","provenance":[{"file_id":"1hRgC0_nh-J-f7iRpkTiMQDl7PaBwb9gV","timestamp":1636424929084},{"file_id":"1kXeIG7pikUVbh0gFbOhV3O58lA-nt0pz","timestamp":1540052134550},{"file_id":"1eQlldmZxxsazCTwZ9JKppGpgQb4wGLMd","timestamp":1539093865247},{"file_id":"1oyIyXXizrDkSjqCGmssKSOiRqNFZVtnk","timestamp":1538861980568}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"pK5-wELXZTmH"},"source":["Copyright 2021 Google LLC\n","\n","Licensed under the Apache License, Version 2.0 (the \"License\");\n","you may not use this file except in compliance with the License.\n","You may obtain a copy of the License at\n","\n"," https://www.apache.org/licenses/LICENSE-2.0\n","\n","Unless required by applicable law or agreed to in writing, software\n","distributed under the License is distributed on an \"AS IS\" BASIS,\n","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","See the License for the specific language governing permissions and\n","limitations under the License."]},{"cell_type":"markdown","metadata":{"id":"ltV6M4R6dX1d"},"source":["#Riemann Zeta, the Riemann Hypothesis and the Primes\n","*Draft Colab*\n","\n","In this colab we will explore the **Riemann Zeta function** (\"Z\" or \"zeta\" for short). The zeta function has many interesting properties and relations: \n","\n","1. In Riemann's paper \"On the Number of Primes Less Than a Given Quantity\" [Riemann 1859], he explored the connection between the zeta function and the prime numbers. If the Riemann Hypothesis (RH) about the zeroes of the Riemann zeta function is true then we will be able to minimize the error in the Prime Number Theorem (PNT) \n","\n",">While Hadamard and Poussin independently built on the ideas of Riemann to prove the PNT in 1896, that proof did not give us insight into the distribution of the prime numbers, only the number of primes equal or less than a given target. Given the principle of number theory (the fundamental law of arithmetic) that all numbers can be expressed as a unique factorization of primes, progress in understanding the RH goes to the very heart of mathematics. \n","\n","\n","\n","2. The Z function is equivalent to Euler's product which we will explore below.\n","\n","3. The Z function is a key link between number theory and real and complex analysis.\n","\n","Here is the Riemann zeta function: \n","\n","$$\\zeta(s)=\\sum_{n=1}^\\infty\\frac{1}{n^s}\\hspace{.3in}(1)$$\n","\n","\n","where $ s \\in \\mathbb {C} - \\{1\\} $. \n","\n","The zeta function is defined using infinity in an absolutely integral way; in other words, the usual concept that we may have of \"converges to\" does not apply here. The sum does converge to a number, in the sense of limits of sequences of complex numbers. The convergence, however, may not be obvious as it is for sequences of real numbers. \n","\n","We can see this in the following polar plot of Z(.5 + i $\\theta$)\n","\n","\n","\n","\n","For many sequences of real numbers, we have no trouble determining the limit from truncated subsequences. For example, it's not too difficult to see that the limit of the sequence of real numbers $ (1, \\frac {1}{2}, \\frac {1}{4}, \\frac {1}{8}, \\frac {1}{16}, ...)$ is 0. \n","\n","However, in the case of the zeta function, we can only obtain values for the zeta function outside the half-plane of Re s > 1 through analytic continuity, not through a numerical series as we do in Re s > 1. \n","\n","This has made developing numerical approaches to the Riemann zeta function a challenge. We will explore several methods of implementing the zeta function in python using different libraries. We will also explore its deeper relationship to the prime numbers. \n","\n","\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"bJvD0uqfZRcU"},"source":[""]},{"cell_type":"markdown","metadata":{"id":"5UUx2zoz4aY2"},"source":["Here are few properties that are known about the zeta function:\n","\n","1. If we restrict its domain to $ s \\in \\mathbb{R} $, then s must be greater than 1 as $s = 1$ is a singularity. $s=1$ is a singularity because it yields the harmonic series which we know to be divergent: $1+\\frac{1}{2}+\\frac{1}{3}+\\frac{1}{4}+...$\n","\n","2. We must use analytic continuation to extend the domain to s < 1. When we do so, we find that the zeta function converges for s < 0. We will explain more about analytic continuity below. \n","\n","3. We also find in this extended domain a series of trivial zeroes at each s = -2n. For example, s = -2, -4, -6... all converge to zeta (s) = 0. however, since these zeroes are well understood and do not relate to the prime number theorem we call them trivial zeroes. \n","\n","3. Now we come to the mystery of the RH: we find that the nontrivial zeroes of the function all lie within 0 < s < 1 -- **we call this the critical strip**. Riemann's Hypothesis takes this a step further and claims that **all the zeroes of zeta lie on the critical line s = .5**. \n","\n"," \n","\n","4. With numerical calculation, mathematicians have found billions of zeroes on the critical line and *no counterexamples to date that are not on the critical line*. [Here](http://www.dtc.umn.edu/~odlyzko/zeta_tables/index.html) are are the first 100 non-trivial zeroes of the zeta function. \n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"sNnx-gVL_MEo"},"source":["#Euler, Zeta and the link to the Primes\n","\n","The zeta function was not discovered by Riemann. Euler investigated the properties of the zeta function in 1737. Euler, though, limited his inquiries to the real values as input to the zeta function. As we shall see, Riemann built on Euler's work to analyze the zeta with complex inputs. \n","\n"," First, let's start with the zeta function itself: \n","\n","$$\\zeta(s)=\\sum_{n=1}^\\infty\\frac{1}{n^s}\\hspace{.3in}(1)$$\n","\n","\n","It was known at the time that the harmonic series did not converge. A number of researchers began to explore the convergence of the series that are similar to the harmonic series, including the zeta which takes each denominator to the same power. Unlike the harmonic series, the zeta function does converge to a real number for s greater than 1. \n","\n","In 1737, Euler further realized that: \n","\n","$$\\zeta(s)=\\sum_{n=1}^\\infty\\frac{1}{n^s}=\\prod_{p\\text{ prime}}\\frac{1}{1-p^{-s}} \\hspace{.3in}(2)$$\n","\n","Euler used the sieve of Erastosthenes to come up with this equality. This is interesting: *the values of the zeta function are related to the product of factors that are based on the primes. *\n","\n","Riemann extended the work of Euler in several significant ways: he used analytic continuation to explore the values of the zeta function to the left of the x = 1 line. He further realized that the non-trivial zeroes of the zeta function would all be in the critical strip between x = 0 and x =1. His strongest conjecture is that all the non-trivial zeroes in fact fall on the line x = .5. This line where x=.5 is known as the critical line.\n","\n","To date, billions of zeroes have indeed been found on that line, and no counterexample has yet emerged of a non-trivial zero that is not on that line, but we have yet to prove the RH. \n","\n","The RH is one of the [Clay Millenium Prizes](http://www.claymath.org/millennium-problems/riemann-hypothesis) and was noted by Hilbert in 1900 and others in their lists of important unsolved problems in mathematics. \n","\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"UPIgT8C9eSDG"},"source":["#The Analytic Continuation of the Zeta Function\n","\n","The formulation in (1) is for Re s > 1; for Re s <1 we must use the analytic continuation of the zeta function. \n","\n","$$\\zeta(s)=2^{s}\\pi^{s-1}\\sin\\Bigl(\\frac{\\pi s}{2}\\Bigr) \\hspace{.1in} \\Gamma(1-s) \\hspace{.1in} \\zeta(1-s)\\hspace{.3in}(3)$$ \n","\n","(3) is known as the functional equation for the Riemann zeta function.\n","\n","The $\\Gamma$ function can be expressed as:\n","\n","$$ \\Gamma (x) = (x-1)! \\hspace{.3in} (4) $$\n","\n","*for natural numbers 1 and greater* and (4) is equivalent to the more general extended version which continues the function into the complex plane: \n","\n","$$ \\Gamma \\left( x \\right) = \\int\\limits_0^\\infty {s^{x - 1} e^{ - s} ds}\\hspace{.3in}(5)$$\n","\n","\n","Here is a graph of the Gamma function. We call functions like this and the extended zeta function meromorphic in that they are smooth and differentiable across the complex plane except for certain points which are singularities or poles. You can see these poles spiking up in the graph. \n","\n","Gamma Function"]},{"cell_type":"markdown","metadata":{"id":"ndQcD5fXeVp9"},"source":["Analytic continuation is a technique in complex analysis that we can use to extend the domain of a function from the set of real numbers to an additional region of (or in some cases all of) the complex plane. For example, if we plug in natural numbers greater than 1 to this more extravagant Gamma function (5), we get the same values as (4); but we can also now input numbers outside of the natural number set. \n","\n","As a simple example of continuation (this example is not analytic continuation, but serves to illustrate the general concept of continuation), consider the function $f(x)=\\frac{x}{x}$ which evidently has domain equal to $\\mathbb{R}-\\{0\\}$. We can ask if there is a continuous \"extended\" function $f_{ext}$ defined instead on all of $\\mathbb{R}$ such that $f_{ext}=f$ where $f$ was already defined. Graphing the original $f$ and observing that the limit at $x=0$ of $f(x)$ is 1, makes us realize that we should define $f_{ext}$ like so:\n","\n","$$f_{ext}:=\\begin{cases}f(x)\\text{ if } x\\neq0\\\\ 1\\text { if } x=0\\\\\\end{cases}\\hspace{.3in}(6)$$\n","\n","So, more ambitiously, we can ask when we have a function $f$ defined on the reals a priori if there exists an extension function defined on all of the complex numbers that agrees with our original function $f$ as it was originally defined. This question, as it stands, seems to have an easy answer: just extend the function by defining it to be zero at numbers that are complex and not real! But that's not helpful because such a function will not be differentiable (think \"smooth\"), since it will suddenly drop to 0 at non-real numbers. \n","\n","So, the question becomes: can you find a differentiable extension function? This is the motivation of analytic continuation and equation (3) is the analytic continuation of (1) into the complex plane, allowing us to breach the s = 1 barrier and extend into the left half-plane. \n","\n","Here is a graph of the Riemann zeta function. You can see the singularity at s = 1. \n","\n","*Riemann Zeta *\n","(from Figueroa 2014)\n"," \n","\n","and here you can see a graph of the zeta function showing the zeroes of the critical line. Note the zero at .5 + 14.1347.. i and the other well-known zeroes\n","\n","*Riemann Zeta Zeroes* \n","\n","(From Figueroa 2014)"]},{"cell_type":"markdown","metadata":{"id":"jcIijUeo6H9k"},"source":["#The Riemann Zeta Function and the Primes\n","\n","We saw above that the zeta is connected to the primes via the Euler product. Let's now delve deeper into this connection. Let's define the number of prime numbers less than or equal to a given number x to be the prime counting function: \n","\n","$$\\pi (x) \\hspace {.3in} (8) $$ \n","\n","(Note: This $\\pi$ has no relationship to the number $\\pi$ -- we are just using it as a symbol.) \n","\n","So, for example, $ \\pi (x) $ where x = 100 is $ \\pi (100) = 25 $ that is there are 25 primes less than or equal to the number 100. These are primes such as: 2, 3, 5, 7 etc. \n","\n","How can we compute this function? There are several methods for doing so and this is a longstanding problem that has been worked on by many mathematicians. \n","\n","Gauss and others realized that a good approximation for this prime counting function $ \\pi (x) $ is the following:\n","\n","$$ \\pi (x) \\sim \\frac {x}{\\log{x}} \\hspace {.3in} (9) $$ \n","\n","that is the number of primes less than or equal to a given number is approximately equal to that given number divided by the log base $ e $ of that given number. In fact, the approximation formula gets closer and closer to the right number the greater x is. We can state this as:\n","\n","$$\\lim_{x\\to\\infty}\\frac{\\pi(x)}{\\frac{x}{\n","\\text{log}(x)}}=1 \\hspace {.3in} (10)$$\n","\n","That is, the ratio of the prime counting (the prime counting function $\\pi(x)$, i.e. the actual number of primes less than or equal to a given number $x$) and the approximation formula in the denominator tends to 1, so we can say their difference tends to 0.\n","\n","It was then discovered that a more precise approximation formula for the prime counting function is:\n","\n","$$\\pi(x)\\sim Li(x):=\\int_{t=2}^{t=x}\\frac{1}{\\text{log}(t)}dt. \\hspace {.3in} (11)$$\n","\n","$Li(x)$ is known as the off-set logarithmic integral. It is considered off-set because the original logarithmic integral is \n","\n","$$li(x):=\\int_{t=0}^{t=x}\\frac{1}{\\text{log}(t)}dt, \\hspace {.3in} (12)$$ \n","\n","so we see that $Li(x)$ is an integral of the same function $\\left(\\frac{1}{\\text{log}(t)}\\right)$, but on an off-set interval ($[2,\\infty]$, as opposed to $[0,\\infty]$). One justification for this off-set version is an avoidance of the singularities at $t=0$ and $t=1$ of the function $\\frac{1}{\\text{log}(t)}$. \n","\n","We can relate this off-set version to the original by recognizing \n","\n","$$Li(x)=\\int_{t=2}^{t=x}\\frac{1}{\\text{log}(t)}dt=\\left[\\int_{t=0}^{t=x}\\frac{1}{\\text{log}(t)}dt\\right]-\\left[\\int_{t=0}^{t=2}\\frac{1}{\\text{log}(t)}dt\\right]=li(x)-li(2), \\hspace {.3in} (13)$$ so we have $$Li(x)=li(x)-li(2). \\hspace {.3in} (14) $$\n","\n"," \n","\n","\n","\n","\n","Let's now look at the prime power function: \n","\n","$$ \\ J (x) = \\pi (x) + \\frac {1} {2} \\pi (\\sqrt {x}) + \\frac {1} {3} \\pi (\\sqrt[3] {x}) + \\frac {1} {4} \\pi (\\sqrt[4] {x}) + \\frac {1} {5} \\pi (\\sqrt[5] {x})\\hspace{.05in} + \\hspace{.05in}... \\hspace{.3in}(15)$$\n","\n","\n","This function has the following behavior:\n","\n","$$J(x)\\text{ jumps }\\begin{cases}1\\text{ when x is prime}\\\\\\frac{1}{2}\\text{ when x is exactly the square of prime}\\\\\\frac{1}{3}\\text{ when x is exactly the cube of a prime}\\\\etc...\\end{cases}\\hspace{.3in}(16)$$\n","\n","Now we can connect the zeta function with the prime number theorem. Specifically, we can say that the error or discrepancy between the actual number of prime numbers equal or below a given number x can be calculated as follows: we take (10) and subtract $Li(x)$ from it. Although it is not immediate from the previous discussion, a bit of fiddling yields:\n","\n","$$ J (x) - Li (x) = \\sum_{\\rho}Li(x^{\\rho})+\\text{log}(2)+\\int_{x}^\\infty\\frac{1}{t(t^2-1)\\text{log}(t)}dt \\hspace{.3in}(17)$$\n","\n"," where $\\rho$ is any zero of the Riemann zeta function; so, the sum is over all zeroes of the Riemann zeta function (Note: to convert from $ \\text L\\text i (x) $ to $ \\text l \\text i (x)$ referenced at the top of the colab, use Li(x) = li(x) - li(2)\n","\n","This is one of the reasons why it is so compelling to determine out whether all the nontrivial zeroes of the zeta function lie on the critical line. If RH is true, then we can calculate, as precisely as possible, this error term which would give us a more precise method of calculating the distribution of primes over any given interval. As Michel Lapidus wrote: \n","\n","> \"As is well known, ... the Riemann Hypothesis is equivalent to the statement that the prime numbers are asymptotically distributed as well as \n","'harmoniously' as possible, or more precisely, that the error term in the statement of the Prime Number Theorem is ... is the best possible.\" \n","\n",">(Lapidus 2008)"]},{"cell_type":"markdown","metadata":{"id":"l4rBMXvuI2Ym"},"source":["#Computing the Riemann Zeta Function\n","\n","First we will explore the zeta function with its implementation in the **mpmath** library. This library uses the Hurwitz formulation of the zeta function: \n","\n","\n","$$\\zeta(x,q):=\\sum_{k=0}^\\infty\\frac{1}{(k+q)^x}.\\hspace{.3in}(18)$$\n","\n","You'll notice that if $q=1$, we specialize to the original Riemann zeta function.\n"]},{"cell_type":"markdown","metadata":{"id":"K7c8ffa8pQTr"},"source":["There are several methods for approximating values of the Riemann zeta function. Since the analytic continuation of the zeta function (3) employs the Gamma function, we need to find methods of computation to arrive at our solution that are easier than directly computing the Gamma function. Let's consider these methods:\n","\n","1. Borwein - see his paper in references below. this is used my mpmath.zeta for positive integer s. will use Bernoulli numbers for even integers. when s is very large compared to precision, \n","\n","\n","2. The reflection formula (3) for s in the left half-plane. The gamma function of the reflection formula uses the Stirling asymptotic series. (which is also used by mpmath.gamma)\n","\n","3. Riemann-Seigel - see exposition by Berry in references below. Used by mpmath.zeta for complex s with large height (i.e. Im s is large)\n","\n","4. Euler-Maclaurin summation method in other cases where s is real or complex and low height. \n","\n",">The Euler-Maclaurin summation method, which does not attempt to approximate the global series given by the Riemann zeta function, but instead approximates remainders $\\sum_{n>N}n^{-s}$. This results in $$\\zeta(s)=\\sum_{n=1}^N\\frac{1}{n^s}+\\frac{N^{1-s}}{s-1}+\\frac{N^{-s}}{2}+\\sum_{r=1}^{q-1}\\frac{B_{2r}}{(2r)!}s(s+1)...(s+2r-2)N^{-s-2r+1}+E_{2q}(s) \\hspace{.3in} (19) $$ where $B_{2r}$ is the Bernoulli number corresponding to $2r$ and $E_{2q}$ is an error term. See paper by Menz below. \n","\n","\n","Here is the decision-making code that determines which of the four functions is being used depending on where s is in the complex plane\n","\n"]},{"cell_type":"code","metadata":{"id":"91IcXBlJB2-Y"},"source":["#not code for running \n","\n","\n","\n","\"\"\"\n","zeta(s):\n"," if s is an integer:\n"," if s < 0:\n"," use Bernoulli numbers\n"," else:\n"," use zeta_ui(n)\n"," else if |im(s)| >= 24 * prec^1.5 and |re(s)| <= 10 + 0.1 * prec:\n"," use Riemann-Siegel formula\n"," else if re(s) < 0:\n"," use reflection formula + Euler-Maclaurin\n"," else:\n"," use Euler-Maclaurin\n","\n","zeta_ui(n):\n"," if n == 0 or n == 1\n"," (special cases)\n"," else if n > 0.7 * prec:\n"," use Euler product\n"," else:\n"," if n is even:\n"," if (prec < 10000 and n < 40 + 0.11*prec) or (prec >= 10000 and log2(|B_n|) * 0.9 < prec)\n"," use Bernoulli numbers\n"," else\n"," use Euler product\n"," else:\n"," if n < prec * 0.0006:\n"," use Borwein algorithm\n"," else if n > euler_product_cutoff(prec))\n"," use Euler product\n"," else\n"," use Borwein algorithm (fallback)\n","\n","euler_product_cutoff(prec):\n"," if prec > 200 and prec < 15000:\n"," return 0.39 * prec^0.8\n"," else:\n"," return 7 + 0.535 * prec / log(prec)\n","\n"," \"\"\""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qmV-ZopSFe6U"},"source":["# code block 1\n","#uses mpmath functions for the zeta. see text in the colab for more detail \n","#on the 4 algos used in this calc depending on s\n","\n","import mpmath\n","\n","def zeta_func (s):\n"," zeta_result = mpmath.zeta (s)\n"," return(zeta_result)\n","\n","sR = float (input (\"Which s real part would you like to use for the zeta function? (use decimals) \" ))\n","sI = float (input (\"Which s imaginary part would you like to use for the zeta function? (use decimals) \" ))\n","s = complex (sR, sI)\n","#iterations = int (input (\"how many terms would you like to go to? \"))\n","zeta_prec = str(mpmath.mp.prec)\n","\n","print (\"The precision for this calculation of the zeta function is \" + zeta_prec)\n","\n","if s == 1:\n"," print(\"note: you have chosen s =1 which is a singularity of the zeta function. we will still give you a sum, but it is only due to the limited number of terms \")\n","\n","zeta_result = zeta_func(s)\n","print(\"The zeta value for s = \" + str(s) + \" is \" + str(zeta_result))\n","#print(str(s))\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"IxJrcS2OGufo"},"source":[""]},{"cell_type":"code","metadata":{"id":"0o4A6HkRdQyF"},"source":["#code block2\n","#let's check for some system settings\n","import sys\n","\n","print(\"The max float of this system is \" + str(sys.float_info.max))\n","\n","print(\"The version of Python is \" + str(sys.version_info))\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LLd36wG--5-1"},"source":["Next, we will run through some code that takes us step by step through the calculation. Note that this approach will not be as accurate as the numerical recipes described above - each of which is optimized to work in a certain region of the domain. The following code does allow us to think through the zeta function in a step-by-step manner. \n","\n","The Riemann zeta function as we have it in the following program only works well for real inputs greater than 1. For other inputs, we would need to use the analytic continuation to the entire complex plane. See earlier in this colab for other implementations. "]},{"cell_type":"markdown","metadata":{"id":"M2SUV62QemVv"},"source":["First, let's import some useful libraries. You may be familiar with **math**. **cmath** is a useful library for the handling of complex numbers. This is important since we will want to accept complex values for s to plug into Z(s). "]},{"cell_type":"markdown","metadata":{"id":"0aoQLYz0U7EN"},"source":["Now we can define a function that will raise the successive bases (base = 1, 2, 3....) to the power s as in (1):\n","\n","$$\\zeta(s)=\\sum_{n=1}^\\infty\\frac{1}{n^s}\\hspace{.3in}(1)$$\n","\n","We first negate s to give us the reciprocal value of the exponentiation. We then separate the real and imaginary parts of s since s may be a complex number (recall that even if s is just a real number we can think of it as the real part + 0i). \n","\n","We then convert the bases to polar coordinates. Recall that any complex number can be expressed either in rectangular (Cartesian) coordinates or as polar coordinates. With polar coordinates we use r for radius and t for theta (i.e., $\\theta $ is the angle measured counterclockwise from the x axis to the line through our point). We use the cmath python library to make all this happen.\n","\n","Now that we have the exponent s separated into real and imaginary parts and the base expressed in polar coordinates, we can proceed with the exponentiation. We know that a base raised to a power that is a sum is the same as that base to the first summand, multiplied by the same base to the second summand. \n","\n","Now we are ready to compute the real and imaginary parts of the output of the zeta function given the supplied s and the number of terms to compute. We first take the radius of the base and take it to the real part power of s. We then take this number and multiply it by e to the power of negative of the imaginary portion of s multiplied by t which is the theta angle of the base. \n","\n","Note that in this python code we use $ j $ instead of $i $(in electrical engineering $i $ is used for current, so to be clear we use $j$ for the square root of -1). \n","\n","We then recall Euler's identity that $e^{i\\theta}=\\text{cos}(\\theta)+i\\text{sin}(\\theta)$ . We then distribute the result so far $r^c e^{i\\theta(-d)}$ with the cosine of the log of the imaginary part of $s $ times the real part times $\\theta$. \n","\n","We then do a similar calculation to obtain the imaginary part of the answer applying the sine portion.\n","\n","\n","\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"u3J1Q8ZYDS6w"},"source":["#code block 4\n","# basic calculation of the zeta function - for illustrative purposes only\n","# see other code blocks for the various numerical methods \n","\n","\n","import math\n","import cmath\n","\n","\n","def power_func(base, s):\n"," s= -s\n"," c = s.real\n"," d = s.imag\n"," #print (c, d)\n"," r, t = cmath.polar(base)\n"," #print (r, t)\n"," R = math.pow(r,c)*cmath.exp(-d*t)*cmath.cos(d*cmath.log(r)+c*t)\n"," I = math.pow(r,c)*cmath.exp(-d*t)*cmath.sin(d*cmath.log(r)+c*t)\n"," #print(R.real)\n"," #print (I.real)\n"," newnum = R.real\n"," newnumimag = I.real\n"," #print(newnum, newnumimag)\n"," newcomplex = complex (R.real, I.real)\n"," #print(newcomplex)\n"," \n"," return newcomplex\n"," \n","\n"," \n","def new_func (s, max):\n"," zeta_list = []\n"," \n"," for i in range(1,max+1):\n"," #print(i)\n"," nextnum = power_func(i, s)\n"," zeta_list.append(nextnum)\n"," #print(zeta_list)\n"," \n"," newsum = sum(zeta_list) \n"," return(newsum)\n","\n","sR = float (input (\"which s real part would you like to use for the zeta function? \" ))\n","sI = float (input (\"which s imaginary part would you like to use for the zeta function? \" ))\n","s = complex (sR, sI)\n","iterations = int (input (\"how many terms would you like to go to? \"))\n","newsum1 = new_func (s, iterations)\n","\n","if s == 1:\n"," print(\"note: you have chosen s =1 which is a singularity of the zeta function. we will still give you a sum, but it is only due to the limited number of terms \")\n","print(newsum1)\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"TcOFFdNolkXh"},"source":["Next let's calculate the zeta with a method from scipy. This method only takes in real numbers so is much more limited than the methods in mpmath and arb. "]},{"cell_type":"code","metadata":{"id":"E1Ad1sNtusKv"},"source":["#code block 5\n","#this codeblock implements zeta with the method from scipy. \n","#this method only accepts real numbers\n","\n","import scipy\n","\n","def zeta_func (s):\n"," zeta_result = scipy.special.zeta (s)\n"," return(zeta_result)\n","\n","sR = float (input (\"This version of zeta only accepts real numbers. \\n Which real number would you like to use as s? (use decimals) \" ))\n","#sI = float (input (\"which s imaginary part would you like to use for the zeta function? (use decimals) \" ))\n","#s = complex (sR, sI)\n","#iterations = int (input (\"how many terms would you like to go to? \"))\n","zeta_result = zeta_func(sR)\n","\n","if sR == 1:\n"," print(\"note: you have chosen s =1 which is a singularity of the zeta function. we will still give you a sum, but it is only due to the limited number of terms \")\n","\n","print(zeta_result)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xm50quUgymVJ"},"source":["Now let's implement the zeta with a different library - the C arb library. This is in progress\n","\n","\n",". "]},{"cell_type":"code","metadata":{"id":"ng3f1BSMtl-E"},"source":["#codeblock 6\n","#experiment with arb, a C library\n","# install and build is working...\n","\"\"\"\n","!apt-get install libflint-dev\n","!git clone https://github.com/fredrik-johansson/arb/\n","%cd arb\n","!./configure --disable-static\n","!make -j2 #> /dev/null\n","!make install && ldconfig\n","\n","!make examples\n","!build/examples/hilbert_matrix 100 \n","\n","\"\"\"\n","\n","\n","\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"TT4pywBFD692"},"source":["#codeblock 6b\n","\n","\n","!apt-get install libflint-dev\n","!git clone https://github.com/fredrik-johansson/arb/\n","%cd arb\n","!./configure --disable-static\n","!make -j2 > /dev/nul\n","!make install && ldconfig\n","!pip3 install cython\n","!pip3 install python-flint\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZSVs3gY8GIjK"},"source":["!pip3 install cython\n","!pip3 install python-flint\n","!make -j2 #> /dev/null"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"iIJ0VH9aEkO5"},"source":["#codeblock 7\n","\n","import flint\n","\n","def zeta_func(s):\n"," return flint.acb(s).zeta()\n","\n","\"\"\"\n","def zeta_func (s):\n"," zeta_result = mpmat.zeta (s)\n"," return(zeta_result)\n","\"\"\"\n","\n","sR = float (input (\"Which s real part would you like to use for the zeta function? (use decimals) \" ))\n","sI = float (input (\"Which s imaginary part would you like to use for the zeta function? (use decimals) \" ))\n","s = complex (sR, sI)\n","#iterations = int (input (\"how many terms would you like to go to? \"))\n","zeta_prec = str(flint.ctx.prec)\n","\n","print (\"The precision for this calculation of the zeta function is \" + zeta_prec)\n","\n","\"\"\"\n","if s == 1:\n"," print(\"note: you have chosen s =1 which is a singularity of the zeta function. we will still give you a sum, but it is only due to the limited number of terms \")\n","\"\"\"\n","\n","zeta_result = zeta_func(s)\n","print(\"The zeta value for s = \" + str(s) + \" is \" + str(zeta_result))\n","#print(str(s))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Srs4GQ6zGx2n"},"source":["The zeta value for s = (2+0.5j) is (1.44627790564658 - 0.368771304604047j)\n"]},{"cell_type":"markdown","metadata":{"id":"MOEGiUz1PiRZ"},"source":["This next codeblock gets the C code for Arb implementation of the Zeta funntion\n"]},{"cell_type":"code","metadata":{"id":"tP_yxxv-IUs1"},"source":["#codeblock 8\n","!wget https://raw.githubusercontent.com/km-git-acc/dbn_upper_bound/master/dbn_upper_bound/arb/RH_LinecountthreadedV3.c\n","\n","!gcc RH_LinecountthreadedV3.c -larb -lflint -lpthread\n","\n","\n","!./a.out 0 1000000 1 0 100\n","!./a.out 0 1000000 2 0 100\n","!./a.out 0 1000000 3 0 100\n","!./a.out 0 1000000 4 0 100\n","!./a.out 0 1000000 5 0 100\n","!./a.out 0 1000000 6 0 100\n","!./a.out 0 1000000 7 0 100\n","!./a.out 0 1000000 8 0 100\n","!./a.out 0 1000000 9 0 100\n","!./a.out 0 1000000 10 0 100\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"l2SNwy8lJBPk"},"source":[" !gcc RH_LinecountthreadedV3.c\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"51hHJk0npcUB"},"source":["# an example plot of zeta in the complex plane\n","\n","import mpmath\n","mpmath.fp.cplot(mpmath.fp.zeta, [-10,10], [-5,50], points=30000)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"tZb1M6Sjpi5b"},"source":["# A plot of the absolute value (the modulus) of zeta on the critical line, and numerical values \n","# of the first few zeros\n","\n","mpmath.plot(lambda t: abs(mpmath.zeta(0.5+t*1j)), [0,50], points=1000)\n","\n","for i in range(1,11):\n"," print(mpmath.zetazero(i))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9iLpjv4vtZJZ"},"source":["Here is another graphical way to vizualize the zeroes of the z function. \n","\n","\n","\n","\n","\n","\n"," \n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"OToqU9SMMP1C"},"source":["#Summary\n","\n","In this colab we have explored a number of topics related to the zeta:\n","\n","1. The connection between the RH, the zeta and prime numbers\n","\n","2. The work of Euler and Riemann on the zeta\n","\n","3. Analytic continuation in general and as used for the zeta\n","\n","4. Various methods of calculating the zeta -- each having an advantage in different regions of the domain\n","\n","5. Code to implement these various methods\n","\n","6. Plotting the zeta\n","\n","*Suggestions for additional content and code blocks are welcome *\n","\n","\n","\n","---\n","\n","\n","\n","\n","\n","\n","\n","\n","This colab developed by Jack Hidary hidary@google.com \n","\n","\n","\n","Thanks to Michel L. Lapidus, James Myer and Fredrik Johansson for reviewing this colab and for their valuable input. \n","\n","\n","**References and Further Reading**\n","\n","\n","Berry, M.V. [The Riemann-Siegel expansion for the zeta function: high orders and remainders](http://rspa.royalsocietypublishing.org/content/450/1939/439) 1995\n","\n","Borwein, P [\"An Efficient Algorithm for the Riemann Zeta Function\"](http://www.cecm.sfu.ca/personal/pborwein/PAPERS/P155.pdf)\n","\n","Caldwell, C [Riemann Hypothesis on Prime Pages](https://primes.utm.edu/notes/rh.html) \n","\n","de Reyna, J. A HIGH PRECISION COMPUTATION OF RIEMANN’S ZETAFUNCTION BY THE RIEMANN-SIEGEL FORMULA\n","\n","de Reyna, J. A. [X-RAY OF RIEMANN’S ZETA-FUNCTION](https://arxiv.org/pdf/math/0309433.pdf)\n","\n","Figueroa, C. [Contributions of Euler, Gauss and Riemann to the Study of\n","Primes Numbers](http://ijrsset.org/pdfs/v1-i9/11.pdf) 2014\n","\n","Frenkel, Edward. [The Riemann Hypothesis: Numberphile](https://www.youtube.com/watch?v=d6c6uIyieoo)\n","\n","Gamma function graph: [By Geek3 - Own work, CC BY-SA 3.0](https://commons.wikimedia.org/w/index.php?curid=5156881)\n","\n","Gourdon, X., Sebah, P. “[Numerical evaluation of the Riemann Zeta-function”, Numbers, constants, and computation](http://numbers.computation.free.fr/Constants/Miscellaneous/zetaevaluations.pdf), 2003. \n","\n","Johansson, Fredrik. [ mpmath ](http://mpmath.org/doc/current/)\n","\n","Lapidus, Michel L., \"In Search of the Riemann Zeros\", Amer. Math. Soc. 2008.\n","\n","Lapidus, Michel L. \"The sound of fractal strings and the Riemann hypothesis\" in Analytic Number Theory: In Honor of Helmut Maier's 60th Birthday, Springer, 2015, pp. 201-252.] arxiv version: (https://arxiv.org/pdf/1505.01548.pdf ) \n","\n","Lapidus, Michel L. and van Frankenhuijsen, Machiel, Fractal Geometry, Complex Dimensions and Zeta Functions: Geometry and Spectra of Fractal Strings. Second edition, Springer, 2013. \n","\n","\n","LMFDB, [Riemann Zeta Function](http://www.lmfdb.org/L/Riemann/)\n","\n","\n","Margarete Menz, Petra. 1992 [An Algorithm for Computing the Riemann Zeta Function Based on an Analysis of Backlund’s Remainder Estimate](http://people.math.sfu.ca/~pmenz/thesis.pdf)\n"," \n","Montgomery, H. et al. Exploring the Riemann Zeta Function, Springer 2017 \n","\n","Odlyzko’s works: http://www.dtc.umn.edu/~odlyzko/doc/zeta.html\n","\n","Odlyzko, Andrew. [The first 100 zeroes of the Riemann Zeta function ]( http://www.dtc.umn.edu/~odlyzko/zeta_tables/index.html) \n","\n","Riemann, B. \"Ueber die Anzahl der Primzahlen unter einer gegebenen Grosse\" (On the Number of Primes Less Than a Given Quantity). https://www.claymath.org/sites/default/files/ezeta.pdf 1859\n","\n","Tao, Terence [The Euler-Maclaurin formula, Bernoulli numbers, the zeta function, and real-variable analytic continuation](https://terrytao.wordpress.com/2010/04/10/the-euler-maclaurin-formula-bernoulli-numbers-the-zeta-function-and-real-variable-analytic-continuation/)\n","\n","Tao, Terence, [Vaporizing and freezing the Riemann zeta function](https://www.youtube.com/watch?v=t908N5gUZA0)\n","\n","Titchmarsh, E. C. [The Theory of the Riemann Zeta-function 2nd edition](https://global.oup.com/academic/product/the-theory-of-the-riemann-zeta-function-9780198533696?cc=us&lang=en&). Oxford University Press. [online version](https://books.google.com/books?id=1CyfApMt8JYC&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false) 1986\n","\n","Wolfram Math http://mathworld.wolfram.com/RiemannZetaFunctionZeros.html\n","\n","\n","\n"]}]}

apache-2.0

huongttlan/statsmodels

examples/notebooks/statespace_sarimax_internet.ipynb

10

9122

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SARIMAX: Model selection, missing data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example mirrors Durbin and Koopman (2012), Chapter 8.4 in application of Box-Jenkins methodology to fit ARMA models. The novel feature is the ability of the model to work on datasets with missing values." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from scipy.stats import norm\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import requests\n", "from io import BytesIO\n", "from zipfile import ZipFile\n", "\n", "# Download the dataset\n", "dk = requests.get('http://www.ssfpack.com/files/DK-data.zip').content\n", "f = BytesIO(dk)\n", "zipped = ZipFile(f)\n", "df = pd.read_table(\n", " BytesIO(zipped.read('internet.dat')),\n", " skiprows=1, header=None, sep='\\s+', engine='python',\n", " names=['internet','dinternet']\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Selection\n", "\n", "As in Durbin and Koopman, we force a number of the values to be missing." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get the basic series\n", "dta_full = df.dinternet[1:].values\n", "dta_miss = dta_full.copy()\n", "\n", "# Remove datapoints\n", "missing = np.r_[6,16,26,36,46,56,66,72,73,74,75,76,86,96]-1\n", "dta_miss[missing] = np.nan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can consider model selection using the Akaike information criteria (AIC), but running the model for each variant and selecting the model with the lowest AIC value.\n", "\n", "There are a couple of things to note here:\n", "\n", "- When running such a large batch of models, particularly when the autoregressive and moving average orders become large, there is the possibility of poor maximum likelihood convergence. Below we ignore the warnings since this example is illustrative.\n", "- We use the option `enforce_invertibility=False`, which allows the moving average polynomial to be non-invertible, so that more of the models are estimable.\n", "- Several of the models do not produce good results, and their AIC value is set to NaN. This is not surprising, as Durbin and Koopman note numerical problems with the high order models." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import warnings\n", "\n", "aic_full = pd.DataFrame(np.zeros((6,6), dtype=float))\n", "aic_miss = pd.DataFrame(np.zeros((6,6), dtype=float))\n", "\n", "warnings.simplefilter('ignore')\n", "\n", "# Iterate over all ARMA(p,q) models with p,q in [0,6]\n", "for p in range(6):\n", " for q in range(6):\n", " if p == 0 and q == 0:\n", " continue\n", " \n", " # Estimate the model with no missing datapoints\n", " mod = sm.tsa.statespace.SARIMAX(dta_full, order=(p,0,q), enforce_invertibility=False)\n", " try:\n", " res = mod.fit()\n", " aic_full.iloc[p,q] = res.aic\n", " except:\n", " aic_full.iloc[p,q] = np.nan\n", " \n", " # Estimate the model with missing datapoints\n", " mod = sm.tsa.statespace.SARIMAX(dta_miss, order=(p,0,q), enforce_invertibility=False)\n", " try:\n", " res = mod.fit()\n", " aic_miss.iloc[p,q] = res.aic\n", " except:\n", " aic_miss.iloc[p,q] = np.nan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the models estimated over the full (non-missing) dataset, the AIC chooses ARMA(1,1) or ARMA(3,0). Durbin and Koopman suggest the ARMA(1,1) specification is better due to parsimony.\n", "\n", "$$\n", "\\text{Replication of:}\\\\\n", "\\textbf{Table 8.1} ~~ \\text{AIC for different ARMA models.}\\\\\n", "\\newcommand{\\r}[1]{{\\color{red}{#1}}}\n", "\\begin{array}{lrrrrrr}\n", "\\hline\n", "q & 0 & 1 & 2 & 3 & 4 & 5 \\\\\n", "\\hline\n", "p & {} & {} & {} & {} & {} & {} \\\\\n", "0 & 0.00 & 549.81 & 519.87 & 520.27 & 519.38 & 518.86 \\\\\n", "1 & 529.24 & \\r{514.30} & 516.25 & 514.58 & 515.10 & 516.28 \\\\\n", "2 & 522.18 & 516.29 & 517.16 & 515.77 & 513.24 & 514.73 \\\\\n", "3 & \\r{511.99} & 513.94 & 515.92 & 512.06 & 513.72 & 514.50 \\\\\n", "4 & 513.93 & 512.89 & nan & nan & 514.81 & 516.08 \\\\\n", "5 & 515.86 & 517.64 & nan & nan & nan & nan \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "For the models estimated over missing dataset, the AIC chooses ARMA(1,1)\n", "\n", "$$\n", "\\text{Replication of:}\\\\\n", "\\textbf{Table 8.2} ~~ \\text{AIC for different ARMA models with missing observations.}\\\\\n", "\\begin{array}{lrrrrrr}\n", "\\hline\n", "q & 0 & 1 & 2 & 3 & 4 & 5 \\\\\n", "\\hline\n", "p & {} & {} & {} & {} & {} & {} \\\\\n", "0 & 0.00 & 488.93 & 464.01 & 463.86 & 462.63 & 463.62 \\\\\n", "1 & 468.01 & \\r{457.54} & 459.35 & 458.66 & 459.15 & 461.01 \\\\\n", "2 & 469.68 & nan & 460.48 & 459.43 & 459.23 & 460.47 \\\\\n", "3 & 467.10 & 458.44 & 459.64 & 456.66 & 459.54 & 460.05 \\\\\n", "4 & 469.00 & 459.52 & nan & 463.04 & 459.35 & 460.96 \\\\\n", "5 & 471.32 & 461.26 & nan & nan & 461.00 & 462.97 \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "**Note**: the AIC values are calculated differently than in Durbin and Koopman, but show overall similar trends." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Postestimation\n", "\n", "Using the ARMA(1,1) specification selected above, we perform in-sample prediction and out-of-sample forecasting." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Statespace\n", "mod = sm.tsa.statespace.SARIMAX(dta_miss, order=(1,0,1))\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# In-sample one-step-ahead predictions\n", "predict_res = res.predict(full_results=True)\n", "\n", "predict = predict_res.forecasts\n", "cov = predict_res.forecasts_error_cov\n", "predict_idx = np.arange(len(predict[0]))\n", "\n", "# 95% confidence intervals\n", "critical_value = norm.ppf(1 - 0.05 / 2.)\n", "std_errors = np.sqrt(cov.diagonal().T)\n", "ci = np.c_[\n", " (predict - critical_value*std_errors)[:, :, None],\n", " (predict + critical_value*std_errors)[:, :, None],\n", "][0].T" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Out-of-sample forecasts and confidence intervals\n", "nforecast = 20\n", "forecast = res.forecast(nforecast)\n", "forcast_idx = len(dta_full) + np.arange(nforecast)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Graph\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "ax.xaxis.grid()\n", "ax.plot(predict_idx, dta_miss, 'k.')\n", "\n", "# Plot\n", "ax.plot(predict_idx, predict[0], 'gray');\n", "ax.fill_between(predict_idx, ci[0], ci[1], alpha=0.1)\n", "\n", "ax.plot(forcast_idx[-20:], forecast[0], 'k--', linestyle='--', linewidth=2)\n", "\n", "ax.set(title='Figure 8.9 - Internet series');" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

thalesians/tsa

src/jupyter/python/distrs.ipynb

1

118003

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os, sys\n", "sys.path.append(os.path.abspath('../../main/python'))\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import thalesians.tsa.distrs as distrs" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "normal_distr = distrs.NormalDistr([3., 7.], [[4., -3.], [-3., 9.]])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NormalDistr(mean=[[3.]\n", " [7.]], cov=[[ 4. -3.]\n", " [-3. 9.]])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "normal_distr" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[3.],\n", " [7.]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "normal_distr.mean" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 4., -3.],\n", " [-3., 9.]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "normal_distr.cov" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data = normal_distr.sample(1000)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1000, 2)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.shape(data)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data, 'x');" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "empirical_distr = distrs.EmpiricalDistr(data)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1000" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "empirical_distr.particle_count" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "empirical_distr.dim" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0000000000000004" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "empirical_distr.weight_sum" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[3.03771919],\n", " [7.13587533]])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "empirical_distr.mean" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[3.90916885],\n", " [8.37304867]])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "empirical_distr.var" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 3.90916885, -2.65787735],\n", " [-2.65787735, 8.37304867]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "empirical_distr.cov" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "empirical_distr.vol" ] }, { 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apache-2.0