{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning\n",
    "\n",
    "Matthew Graham, Dmitry Duev, Ashish Mahabal, Mauricio Cerda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction \n",
    "\n",
    "<img src='https://blogs.gartner.com/smarterwithgartner/files/2018/08/PR_490866_5_Trends_in_the_Emerging_Tech_Hype_Cycle_2018_Hype_Cycle.png'>\n",
    "<table>\n",
    "<tr><td><img src='img/img-mamc1.png'></td></tr>\n",
    "<tr><td><img src='img/img-mamc2.png'></td></tr>\n",
    "<tr><td><img src='img/fig-dl3.png'></td></tr>\n",
    "<!--<tr><td><img src='img/fig-dl2.png'></td></tr>-->\n",
    "<tr><td><img src='img/img-mamc3.png'></td></tr>\n",
    "<tr><td><img src='img/img-mamc4.png'></td></tr>\n",
    "<tr><td><img src='img/dl4.png'></td></tr>\n",
    "<tr><td><img src='img/img-mamc6.png'></td></tr>\n",
    "<tr><td><img src='img/img-mamc7.png'></td></tr>\n",
    "<tr><td><img src='img/img-mamc8.png'></td></tr>\n",
    "<tr><td><img src='img/img-mamc9.png'></td></tr>\n",
    "<tr><td><img src='img/img-mamc10.png'></td></tr>\n",
    "<tr><td><img src='img/img-mamc11.png'></td></tr>\n",
    "<tr><td><img src='img/img-ufa.png'></td></tr>\n",
    "<tr><td><img src='img/fig-dl.png'></td></tr>\n",
    "<tr><td><img src='img/dl5.png'></td></tr>\n",
    "<tr><td><img src='img/dl6.png'></td></tr>\n",
    "<tr><td><img src='img/dl7.png'></td></tr>\n",
    "<tr><td><img src='img/dl8.png'></td></tr>\n",
    "<tr><td><img src='img/dl9.png'></td></tr>\n",
    "<tr><td><img src='img/dl10.png'></td></tr>\n",
    "<tr><td><img src='img/dl11.png'></td></tr>\n",
    "<tr><td><img src='img/dl12.png'></td></tr>\n",
    "<tr><td><img src='img/dl13.png'></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Types of deep learning networks\n",
    "\n",
    "### Convolutional neural network (CNN)\n",
    "\n",
    "<img src=\"img/cnn.png\">\n",
    "\n",
    "### Autoencoder\n",
    "\n",
    "<img width=\"100%\" src=\"https://miro.medium.com/max/650/1*iDcAplzGhttLcYI56MIMxQ.png\">\n",
    "\n",
    "### Recurrent neural network (RNN)\n",
    "\n",
    "<img src=\"https://colah.github.io/posts/2015-08-Understanding-LSTMs/img/RNN-unrolled.png\">\n",
    "\n",
    "### Generative adversarial network (GAN)\n",
    "\n",
    "<img src=\"https://skymind.ai/images/wiki/GANs.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practical advice from one of DL godfathers Andrej Karpathy\n",
    "\n",
    "Highly recommended: [Andrej Karpathy's blog post](http://karpathy.github.io/2019/04/25/recipe/)\n",
    "\n",
    "- Neural net training is a leaky abstraction\n",
    "\n",
    "```bash\n",
    ">>> your_data = # plug your awesome dataset here\n",
    ">>> model = SuperCrossValidator(SuperDuper.fit, your_data, ResNet50, SGDOptimizer)\n",
    "# conquer world here\n",
    "```\n",
    "\n",
    "- Neural net training fails silently\n",
    "\n",
    "Lots of ways to screw things up -> many paths to pain and suffering\n",
    "\n",
    "#### The recipe\n",
    "\n",
    "- Become one with the data\n",
    "    - probably, the most important and time consuming step\n",
    "    - visualize as much as you can\n",
    "    - check normalizations\n",
    "    \n",
    "    \n",
    "The neural net is effectively a compressed/compiled version of your dataset, you'll be able to look at your network (mis)predictions and understand where they might be coming from. And if your network is giving you some prediction that doesn't seem consistent with what you've seen in the data, something is off.\n",
    "\n",
    "\n",
    "- Set up the end-to-end training/evaluation skeleton + get dumb baselines\n",
    "    - fix random seed\n",
    "    - simplify\n",
    "    - add significant digits to your eval\n",
    "    - init well\n",
    "    - fancy loss func? verify at init\n",
    "    - verify decreasing training loss\n",
    "    - visualize just before the net\n",
    "    - active learning: check model's misclassifications, both training and testing\n",
    "\n",
    "\n",
    "- Overfit: first get a model large enough that it can overfit (i.e. focus on training loss) and then regularize it appropriately (give up some training loss to improve the validation loss).\n",
    "    - picking the model: don't be a hero\n",
    "    - adam is safe\n",
    "    - complexify only one at a time\n",
    "\n",
    "\n",
    "- Regularize\n",
    "    - get more data\n",
    "    - data augment\n",
    "    - smaller model size\n",
    "    - batchnorm\n",
    "    - decrease the batch size\n",
    "    - use dropout\n",
    "    - early stopping\n",
    "\n",
    "\n",
    "- Tune\n",
    "    - first prefer random over grid search\n",
    "    - hyper-parameter optimization. Check out [https://github.com/keras-team/keras-tuner](keras-tuner)\n",
    "\n",
    "\n",
    "- Squeeze out the juice\n",
    "    - ensembles\n",
    "    - leave it training\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: a simple CNN\n",
    "\n",
    "First install keras: `pip install keras`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Convolution2D, MaxPooling2D, Activation\n",
    "from keras.models import Sequential\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2  # `pip install opencv-python` only used for loading the image, you can use anything that returns the image as a np.ndarray\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x134a24f28>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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89yXg3ytZVMM/qhPezzJPC1tUjt/XTl0Bbc0I6Ezr5TEkeV64CFkrKHJNEERsbGzw+GNXeOjCJVrdFQB6Kyc4efIk7W4XhSIrNLowaCytVt8lTitBHekGlSrSV1j0pGyPFYDAelVcCEy53Rlpcd8bMQP87jIsSIU1zvXT6HzOrEVX99YYN6hRHsP9BrJUk2Zuv0xpVFAeQ7r7JoUgyy1x3CGMYtLUeVyMRxMKbThx8gxnzp7jqSef4O233uT111/nRz/6AR988AHjSRlhLmw52CmMzx9U0/ClFG52Q3nN9iD/fWsKgJzb9+dp5Edr+AHGFAfOX6d0vFHyMA2//n399/W21KN75x3rsG0PGvDf94Avyr+q4za+r88CjjJu3oocBfofttyOhl//7bztdyIuJ5qdgq+dPX6n3WM0HGON4ML5yzz37Gd46qmnOHXqNCqICFsO8AtbepIEikAEoAwZObrIETLCWlt5nRRF6bmDnr68UIKa+zC9VnC9RGL9YGBtCdKzgI+wtJJ2xZdnmXP/q3v+jNOpV9FMjh3jPGisLVAyqABnnOdYDEopwjAgTkKCUDEap+TZhCxLKy+dVqtFJ+6BNmidgRQ89thjbGxs8Nxzz/DGG2/w4z93NYN+9hdvsHXjOlEUVYbQGQ2/AYDVddfkVvtDUztelA9vauDzwLUJ3osYbettbx67OSgsyt8/aHz+sqbtUpaylKU8IHLfa/gfldxvXjrePfOwZX0fYSxWioZLZ6kKHyJHXlsV4m+mGn3F7gjq/tfvvvcBjzz2OC88/1mefuoZHjp3nl6vRxwliCBmd9f5q4dxgpIBRVEwGk+w1hLFLfora+zvuXTD9UyLxjotPM802uSs9LrlOf2VldN/C8K6/PQCZ6jFOAOnwGKFRVRRqpZC5wjhuHdvrtHaa/ypN9+glESIEMrUz8ZotC7999GV5i+VQMqwMihmkwnZBOIkQgUR7SCoIrWVklgNWVagi4J2HJNpTZx0efzKJ3n0sU/w9DPPAfDa66/w53/+Y9742b/BhW7ZMkbDzWIkAbZG6dhS87dCOENzk8uv0XOzUbueJmtqxp4ea1qcpst5nLs4JCK4yeEfpeVXLTtkhnJU311q+bNyfwH+nAdnSu8KPxUxuAcvy2XdnHOU/7E7/PGArZsPvv75Vrwc5LQwR3NaeiudSwp3VUIwdwnTdAVCTF1YRbmUFrQry+E+ezc4KcqDTPO8C+EKVmRZhs7zat8kbpO0YnKTu9QDJcSm45Q017S7PQBOrJ/g5V//DTbOnuf8xUv0e6uMjWU8GNBpC7q9mLwcHGyagS2D3GSAAAqdsT/InBeNpXJJFAKCAMJQ0UpCEBE6c/SOkhKDrQq45EWBznOyouDcxgZpnjvQTVN0nqOtJSh92YWEkBCEQdfSGQhpkUikwrltAhaXlrnIJy7Vg8mr3PuV4bi8vz7QSAUCUX5XpDkeFLMybbMQpvydIlQt8sIgRIwQmsnEubP2eicA+OIXX+bzX/gy/+yf/SFv/vxnvPX2zymKCUEQIISlKKY2liRJ2N3dpcgyknabTruNMYYsT9Faz75mJbbXnSGMKAPn3BWVfVYD0qWFEBIwpbeTcfsIU+3r2uGiqH1tCyUEqp6SxA9AWqNEGQldDkwu/1FQ0Wq7+438/cblHaoPLAcjkO2B9Vl8mH0f/XfevdZvry+n9+zOB4ePaoC5bcAXQlwA/ldgA/fUv2Gt/ftCiHXgHwGXgZ8Df81au33nTX1wRRyxbOrtzX1c5/RaVKmlA3U2T1qXdsIBVUCn00HaKV/tPWPScc5Kfw2kZGdnD4vh9JkNLl28DMD6qdM8/MgVur1VklYPGcaERqCtJdOG/cEEWQYyVQbZmQt17QwCz92WVaRKIPBRmY6md3x8WpiZAdVal8ohjhRRHKACQRgIojioiqfUX7b9/X13D+ppj5FV4a5CT6NS/XmkAoRClgNS/UqkBCktAg1GVnd7yjc3I5UprxGSKMBYgcSgkCA0ssxpo42zDXz9V3+Dza1rfPd73+GVV77F5uYmrbbLEeSLlmzfvIlUgihJXGDb2NkNpFCIQGBnEm/59ujqs8vh4zX9OtiaBZfN9aNF1pSj6teNSNq6HGUsvh1ZaviLSQH8Z9ba14QQPeBVIcT/A/wHwD+31v5dIcTvAb8H/O07b+pSblekdRq9N4D7bbL0spEWwqAs5Wect4cSqtJ6dK7JTE6/t0aRGUaTMeiASxfO88STT/HoI48B0F1Z4+SJU6gwIghaKBKCKECooPLmsWXJO2EOJgarQ2d9aBJQaYAesKWdpkTwKRyA0lgaVpGqQakpJkkyzadfC+tv5k5qGgGLRqoND9z1rKqiBhZNzxEPJD49QNO4OjMQIFFSIEuPHDfAlF5CGCwuAnd1dZWNM+e4fOlhvvmn/5of/egHbO3dIGm5a+mvriOkJRtPGI5cmpJWOwYLk3RMK46rnjDP5VGU1b4+LKlr5/WcR0VRzDFGH+6+eZws4qXziy63DfjW2g+AD8r1fSHEj3HFy38H+Eq52z8E/pj7BPAflFG8LsK6KFBT5qL0KqXyVJEFISRJklDkZQlBbVHB9CVQMgQMOhPs7U8Iw5ArT1zhmU89x8XLD7O6ug446idutbHCpTtwrn0SoQVCBQRSMinGZcsOT6pVFS5hfoENvw2mHjUeNIIgIAzDqlqQHwC8+1+e51UumTzPOX36dC2XTkGe51VaBWNMta8HngMFUIRPsTDfI6aaFcjZ4jH1Y/p1N8iUACSlmwFUA4sB4Y61u7eDMZZPPvk0Z86c4cL5i3zzm9/krbffBODMxikQmkBFVapkUVIsUqgZ3t49IzOTv6c824cm/r56aSa6OwyMb8euNg/oHyRcuCscvhDiMvBp4M+AM+VggLX2AyHE6btxjqXcvjiW2EWfToOknCYvkGBB5waJJA7jKsCp0mKtwCIYjjIuPPQwn/zkJ3n6U89y9uxZVBhXNE0UJnR6fYQM0FYwHk0YDMdkaY6SLnrUmDLz4RE86Hg8ngGBJtCCq5WglAP3oGYIrQOACqRzjYyjKoAqz3PSNKjWgyCocucUhXJG11ygtax4b6D0eXemYWPLYudlPIJaIJVwnudzAb++VHUXS1OuV/YZV12rFXdRqyH7+/uMJ0PW107xxS98mZMnTvP9H3wXgNe/8woG5y4aJW2EsRQ6Q0hBt79KNhlVUz1RgqaxtqLZ3ITjwwPBuktlPSGcB35xgJ8/KIuA9mEa/oPigw93AfCFEF3gnwD/ibV27xamV78L/C7AxYsX77QZC8mD8EDnSWVQtLggJpyGb62z/AqhyLOCKEwIwwiBRBeGIp/6pSsV8dKLv8Qjjz/BJz7xSdbW1hAyQKkQWSYus9aSp4YwLF8iKxG2NsgIMS3ccIzhq679euOd/xNCoEvNPoqiisLx4oGi1XJpGHy6ZH9+T+Nordne3p7RJP0A0gzkqWuc9T9jDFromXbPW04mk2MBvx3FIF1WT5eeYUoXCQEYixaWJG7RbnUYDofs7G6DyXjs0Ss88sij7jidFm+++TPeeecdJpMJSRSVnkWWLM0BCVWpydKjSYhZemduoZp7I5668bOgJqff9NSZhzGLvttHafgPgrZ/R4AvnJ/aPwH+N2vt/1FuviaEOFtq92eB6/N+a639BvANgBdeeOEX+y5/xCJLV0pVBSOBRGAFSJTj7WVAKAOXz0ZrdGGrGgQbZ85y8tQZvvorv8bq+kn6fRcJK4UijlsV4BtjuLm979IGC4kxgiRxxUd8kFCg/Mt6+CNv9ZzXT+XqKCVKBjM0CkFYgYNC1DyVRJXhMpSKQCoUwu0jJEoJAjEFlFEwqNabxl+ASE2vbS7tYw316kP1WcjMjER7z5yD/H19YHMnr9s3fHvcf+12h/3BHkIIVtfWWFlZY3Nzk62t61Xe+F/7td/khz/8Pq+++m3eeust0jRFCJd6Op9kxEnItEiBcZ5a1rmbciB9wr2XoigOOAh4McZUNOTtcvfHyYMA9F7uxEtHAP8A+LG19r+tffV/An8T+Lvl8p/eUQuXckfiEx9MjbV+Lu9oHjcAOGrHIFzlJqNot2IeftgZYz/74md54qlPEoQxNlDIULpMllYwTicYPXUH7PZbZGlOmmsHriqkMIbhcMhoNKLbKY2kHnDmaJLdbuljXwfHRoxgGAbVtN9riEBF7yilZoDYWlvNDurbVldXKw+gZsUrYwz9vsvYqbWu+P86Hw8uErgJ5E2e31dbO9xoS5UN1A08coZTt9aCkGRZwerKGkEQoI2zt/R7K1gDN2463UoEAU8++RTddo/v9F/jJz/5CXv7uwQqJIoitC4qYzCi1PYtlUeXa8+Hx+L759Sk8LzUPZ1gCva3CtLHGW0fBNC/Ew3/C8DfAL4vhPhOue2/xAH9PxZC/C3gbeDfWfiI1oK1Fd8qhCvWYK09wsS3uMwLwGiu36rOcJh/fVNTnNeZmp3sdnz1jzJogXuPdWGIgoBca7LMGU2jMKHbSRAqJJ1kxElMoCLSSYYKIp588lO8/KtfB+Dy+UsM0gknTp9ES2cAzE2KQBLFquJ+c5vR6Sbs7N6g3epybWuTtdX1kk7JabVUlRsmCEtqpjCVcbXT6dBut5HCa8dTXt6aKehqrbGBQqArsK5SG9S43zAM3SDU7RKFHcLSuOsBxhhDGAiUCpDSDUR5njMejxmPxxQ6o2CaS0dgCBQQKQIVoSOXLG5/OJopwOKNyPW21bX+imKrDQhCCOIgrs41fX7T84PTgI2hzJ1v0dpRQO1Wh/jsBQAm+Yjrmx9w+vRZvva1X+XS5Uf45jf/hPfee4dAuus1ZUReoTMKXYApmObrOWiEntffFumj9X3mvW/+mup0nafK/CzKNqiveeseMw4zntel3l9gGjshhJgp9XnYu2Xs8Wh0vw4ed+Kl8684HB9fvt3jLmUpS1nKUu6N3F+Rth8zuV9H8aa0Wi0mkwlaG9qtsvZrq4NQIUZbZBASRy02t26wurLO57/4JV584SX6fRflOSks6ydPYoRFSlBKEIZxLaWw60ZSSnq9Pv3VDj/+0U+4eMFx/46bvsDbb79d1eZ0nLhFB05LD4OIVqvl/NVLH3tfg1hrTZFPaRYJjDM3U5g3k6prwy5FQla5ZtYNvE5bngZ3VceXcqbYuT/uvELnxhi8+bN+fhfRKg7QP03vozqlU6egpuL3NdU+woiKEXNtcIFblc3VSnrdFQSKNBvz+GNXaLVa/Nmf/mtee/0V+t1OVWhFydBV9gpjksTVACh0zng0Ob5j3SVpavYwpW2UUgei3w/T9JvycXk/P0xZAv5tyHHTxrttULqj35f/x3HiqJcSbNI8xxau4lMn6TIaT3jo/CWeevpZPv3C5zh34VIVLNRKOvRXe2gxQQSSKIqI46SswhQThT43jDu8Ctbp9dusrfcJAkOvFyOE5OzZk/ia1uPxmOFgRJZlbkoexERRjFKKLM3dlD51gJ+mKUVRBlihytzvBT5KtA6qHjw8b+7A1ZJlKY4CKSrjqJ/KOwrIYIyjSYLAZbqE2YGkHujll9aCCuIqs6ev8OWBq0mRNH3vZwC/dFmtb58+f7fu3TslUxuINZS2kOm2bm+NIGyxs3uTJG5z5bEOWmukUnz3u6+jyuIyYeDoEG0KBuORM3QHHy4s+Bqt9UC1+nP16Rmasii99KAFVx0lS8C/Q7kd3v3DFAMMRiM6nQ5RmFR5XPJMIxVoDWlecOr0WT79/As8+9wLrKyuY5B0emsArK+vE7cC4u4JhDRVsFMYKoLAAT1UzhRYa3n8ymMMBgOM1XR7CXu7Iy5cOFu1a3dnzI0bNxiNRlhTGuysc5tM07TUomeLVPt9hHCRwUJM+V8PUt5g6wG/rpVPJhOyLJsBfO+J5H974P7VNE5v+J0toi1ABFVbgyCoonnr7p5+VtEEnfpnY1zlLGGdz71EzezXHDT8tmm8hFsoEyCEJAhCWkmHdDJEioDHH7vChQsXKPKcq9feA2B/b4dAKqJQMRzlpNkIpUQ12H8Y4oPi/MwIpgN33Xh7FId/mK3sfnwnP0pZAv5tylFAfz9p+AC5LtgfjmklU28RKUNybUBJoiTh87/8ZTbOPcQU/pZfAAAgAElEQVTqiZOsrp2k1WrT6Tj3yHZ/hdV1QdwGhKMPigJsAZOxK1oO0+Lf77z9LoGUbG5usra2xo3QGSOzMGTtRFmKULYodA+EIUsLdOEonDwvGA6HDiTLsoUOfMopv3aZM4MAlBRVZK2nX7wfvTeIFkUBZaCUL09oPYgDWZpWx3CJyETlfnmAYimdCgRlWgopXbH2MgeRb6uno/yf1voAPTR72BKsqkyeLo+Q8Y2k5lQgcYNCYxYghHDpKsrjTfIJujDEceLy+k+GSOXovd/+7d/me993QVqvvfYKN7Y3SWTA6uoqw+GQ4WCfDxHvj5XDBsnjNPzmILDU8JeAf1typ541H7bEcavyHS9KEBtPCrSFS5cf49OfeYnLjzzKiROnOXlqg25/hUCFhInTflsdQRgBxs0YsgwmE0M6GjOZTKpC31prhsMRQRCwu7NHoEKUDNi8vsXTTz+OENNsOUJQAXWWFuRFxmTi2+gic1W5s6/0ZK0LPMKANXkVRFaXOgBmWTbNvVMOAPWoXKUUeZ7PRHrW+fS6uyfMtxMAREGAy6g21TbrMw1XxD048Nv6DMBag1ICP7BZS1Upyu0rZq4DO6U8DnifqYB20KUoCibpiALn565zQZqNuXjhclU6MQgk3/yzP2Hz6jVa7Zg4jsuB7sONtJ03E6pz+n7bvPV5cpR33IMsS8C/A/m4dCYVBEilMNqyPxoCEKqERx5+mBc/+zmef+GzgOLE6VN0V/rkhWacZrRLeB6NW0gFg1GBNRlpmjKZTBiPHeAXZb6ZoijYu7lHv99nMBgQhiGjoct1f2LtJHGSkLR89scCjEYhXMWnLMdqjbCWOAgRQlZRpqYonCucz7ApmHFnhIPaXpNOqYK0aikj/ADgKQWfN2fKzx/+fOvcehTHlKEIFUh5A3GdrmgGd9W3g0sfbDElDWXKnPulC6me/s5dRzhj7LSGqhA7SjpDvFIY6wZTKQKiKCaMFKPxpMp/9Oyzz5IVE15/5VW2blwnsIput8t4uH9LfexOpBlpW7+P82bLhwH/ks45XpaAf5tyHKVz10TUA47sbErkKoXtEWKdhqnCiMl4QpY7AHnyycf56te+zhOfeJos0zz+xBWGownXNjcJo5hAhezs7wEwzlI6nQ7Z2HHyOs+ZZBl5OibLssqbxhjDyto629vb6MIynricOJcvX+aNN37O2toaK6uOJgoCB1ZGC4yx6NL9WcmAIAlLDdu1NU3zKXjLwKX+jQJkSemoUpt2t6v0qZaSWMrqXlkA62ormLK9Fmi3WpiSO9bGFcH1vxFSliGubl8pxMxSlE8kiiLyEph1VpDrArRFSkEoI1CCyXCMkYDGzVIAhaDAIK07vlIKYw8aiqEs81i20w1as4Fk3lsHQImQyf4eUkq6vTa9Tpv9wR7D0T6WAjsxlb/56uo6L73wOdpxi29961tsbl5HEmKsQh5If1GeoFZAptnX6iJZLHzLX1Ods69z+vIQI/KtxAQswd/J/QX4jei3pkZ0nFReaQvQLEIIx8d6LdGf+5Ac3HU5KoDLf/ZueUftc6RLWelfkxc1yBIGiXWVrsrPQijGYxdMJXAcrSrdJB04hgxHGZNhRpZpHnv8EwD8+r/1l3jyk88QRQlJq0cYtemFLSwuf721tnKHy3RKtpcx3h05LdtqClOUmijokjs21jKYZFgVUhS50zKTFls7e1gjGH6wxfXrOwBsbGywsrKC0YYiD5AqQir3ol/fvFZp3+ConziKCcOAIHBac6fTAVzaCF/dC9y6sRZjNEkYuWIt2qCt810snCoMgMYy2U59CY9DlwBCKaIgQChFUBtIsBZt8opdkqFChRFWg0G7alvCEMchBo3VoMvCL0ZYhDVYXY0rbsBTIVFoZzJ7YtPqs5u15DO1d939cq2dZCOXIUdbdrZHbhCzGiUF1gZEQUQQOLrOGYdDLl/6BIP9gh/a7/Pe+++S54azG6dJEmd/2d65wWC4T5wEJHFYpphIy2cwnXm5topqNibltGiRpfbu1N8hKdHWYmqpqIUQ7l4rVRmQs8zNLo0xM9Scp7qOk3pEb3P/W8GaZpRuc33RfT4Kub8A/y7LR3mDjwP7xY8jp8AjXNZKIcrC5kIgMERxGZFqJUWuSXWGNRAEEUFgQAQYDI89/gm+9OWvAHD23EWMgTBq0+2vkhcGg6XQlsI6SqOoFSARBrJMI7TBWO00fePKDfoXyWK4ub1TJSyLkhgVBOS5IU0zsklKv0ybMJmkGLNDmqZYa4nChMFgwGC4V+Ph/T0AhKu0ZCmwdtptjXA4U5VytBZT/nCUTqqXuO5ZU1co/OB4WCkPP+ipcgD0SyGmA6Mpte8Dz866/0T5zKQVGGGq9Ba2XDfCefs06QyYphvw8QN5nlfX5I3C1fVQKxiDqR3D/T6K3TH29vYqo6wKBHHU4uKFywQqIs8LRsMJu3vbXLu+VWn5YeRSaGuTlUb1kmqpSl/OAuhh2v08m8NxMs83388IvBdWPXneYXK3jbZNz6m5feA2rvdeyi8U4B92Y+8XK/2tAb/npt3Uv/ow/QpQGK2rMnxY4QKsjMUagTAu5XGaF6ysneClz/0Sn//ClwAIVYy2gna3TxA6P3JjHeBn2hlPs1qhEKMh0oEz3FqN1kVZBq+mnlpLp+2Cp+IoqfhrrXMEligOabUd0oSRRJsMIQ0n1tedL/ZVzTgdIGRUvti1hFoiKFOgqfJUs5pTc3rvjaV1zW2WMy/3WzBnTD0oq8nHZ7WqWPO8Z+rPuu6TX9cymzSOr+5VT9lQN2T6JG51TyAf8u/z7Ph4ACkhjmOsdekmOp1Oddwsd66qURRx6dKlqvLZP/+j/3tm0LWm9PenTHNhC+d5VNPmj5JFwPEw8fSTL3QzkzW1dLldBPAXkYUwouZ+XB+kD/utv977AfyPf1JLWcpSlrKUXwj5hdLwvdwvvNmdcPhejVDCT41LO0OtRi04zwxZFuW2WIIgJFAxlG58SgYEkeTpp5/huedeYGXVpUsockMUt2i1u2XRcElhNHlhyApDlhWkuQuvLwqD1RalWmWZQY2xReUP7gs/i1J79fSD10TB2RbiJKRVRrC6aFlLr9Ph5KlVisKQ7Eb0+70yne/UgFf3rpFy6up43P2tP3t/vGbfMPpoDb++f13zrrxjrEXnxYwmP68q1iKaoP++XraxruF7rt67jGZZVv3V3UvjJEKbouL8tc5J07S6r6trK9NkY+jK6yoMQzY2NojikA+uvsu161fZ3b4BuGIyQSjICzDkZWK7ur4o5joQCOEqqzU13FsxuFaUoZ26pU5LQFq0OT6Z2aJyKxr+ItKc1XzULMMvFOAf5i3zUVA688DodigdWfc1FxzwkJBBiC33yTONNoI4jilyQ57miEDw5JOf4qXPfoGz5y6itXtJO90+nW4fCBA4n3Vd5mTR2rkFFmX92SLXGK0ZTkovFlvWVw0kQTg1rqpAsrOz4wpux75zW4RwbupSCvYHzmibmyFJktBPWlhhGKcD8iIlCCWI0NE31X2bFsbwpfq8Z9BR93R+bppZ0ccAfvPYc90/G/nwmzSS55rr22Z+P4d2al6PP3d9APEDgkt5PLUjVHl2rBsYPKCnpVfV5uZmZfRutVp0O32yfFLROSdOnOAv/dbv8Ef/4v/ltRs3AcjSnDBsuUzKRhKGCmvstD8e4y3WBLyPQhFb5P2/W4A/j766Hyidu1HxSgGvAO9Za39LCPEw8AfAOvAa8DestdlRx7gX0gT7j0KOCxRZqHNJF2qP8GH8M0cgDD0Igi4sxliK3DAcTQhlwNr6ab78lZd57ImniDvdKuf66toJIGA4nhC3Okz2x+Rec8w1WeYSlgGOyy8s+WQMpvQWUoJIBIRRQhA6LTSMFI8+9rBLY5y5VAa6cMBcFAVKWrKypq3ICvorXTrdFgiX5qHdjRkOvLHRVtyw9zM3WuNr4WqtZzxW5wG/T9TWBM26eKPtIjJNp8C0XdbOeJx4wPZA7//qoF5Pz+yBep6Nwe/j75/WuvLa8ftMPXSm15HlqZsRlaUXffK8ySQiyzK2t7cZMqyOH8fxTEI5gIceavFLn/tCNZi9+uor3Lh5g1bSotPulqUfS3/SI+wg3hvOr9+O8uXbVJ/5eC8456p7d/j7hdvV2OUozf2omc1xci+w625o+P8x8GOgX37+b4D/zlr7B0KI/wn4W8D/eBfOs5B8XOicWzmOqNM41U/dSqEt0huspMJ5IQqMVZzZOM/nv/DLPPX0syTtPlHUptvx1aQixpOCdFwwHG0zHk0oSgom044iSEs6pigKhDYoa1HCvXBhENBOYrrdNt2uc/GLk4hz584xHA7Z3dlDWF2WUrRlPEBZbQlodQLWT6zQ67eRUtHr9+l2O/zFX7zJcDh0lJSZTuWdZll7ceT8F6j+2Q9YhxlS3V1cXMM/DPBVow11Ld6L1/zrx6hn3Jw3SPh9/DM4SvOvS2CDskqYmjFyuijavEr/ADBJx+zs7FT1CJI4waLZ2dnhiSc+QbtMxVEUBa+//prLrkpIqNys0Ckic0yBVcUucQDwbvW9rLtfejrL39M67XWcLPLe3Urbmsc7ykvnftDu4c5LHJ4HfhP4r4H/VLg78DXgr5e7/EPgv+JDAvyjbuj9cLNvBfgrL0NEU6mvxFhBUeSEgdsYqAgtBAJFqxVw6fJjfPGLX6bTXSUtXArcMHYePfv7E3JtyLXh/feuknTaztWvTG2QFxm599IxDvA7rRiFLdMHh3R6LVZWevRX3CCSJAkffPBBlTHSFyDR2jIZp4zTMWVandJjJ0YFAixEMUSRovip456tllPnH4PLoVNmpwRX8Ypy8DnsntbD9Jt0iN++CO3jpa6l1z8LIQ/s09TWZzjnQzR8rfVMMQ6YBXytdVUN7Kj+HIStWaqoTE7n8wUlSVIdf3t7m8FgwGQyIYoiRw8Vrh5wkWtOnzoDwNe++itEYcK3vv2nbO/sceb0qWl9XFHOyI6QOwH9ejrpeQPj3fLQ8ec4fie3aF7DcXaJ+wH071TD/3vAfwH0ys8ngB1rrS8b8y7w0MJHK6fHR2kvR8kiwRcHTzk7hZ4XkFFf3qrM1Srn8JjN/fyy0HpGg7Q+EtEKlBT0+y0mkzKgJAgZDibovODzn/8Cv/07f5UzG+dRSZtYRmgj2d5xNVyzNGc4GDHYH5EVGjMcYyk1Tcqps39BzRTEup02YIjikF6vh5SSvT0XkZsXKXmREicRLRkTBi5id3d3l729vfLldO1fWe2zfmKNOAJdwN6eZvvmLtZal5vHqCmQWjeIKeVcMyuAsbJK3DZ9XvVKRs17e3BWoIL5M7HmM6pr7PUKTVJK0uFo7nOvn7vez+aV8rPWMhqNZipk1ftDFDlXVZ+3aHq9B/und+F0Bk5R2hAE2rpZQpbleBomiiIeOne+4vlv3LhBnuecPXvWBTnhXuXz5y7y5S99FV0YfviD75FNNIKAdjuhyCfkRUoUudmbNyIn7bYbvBvta75LHrDnUVnAzDXPy+JZv4/zzuHXfXnKoBaZ7c/VrEx2lDRnefPWfR3nevxHM/3zcbIITtyq3ElN298CrltrXxVCfMVvnrPrXKQUQvwu8LsAFy9evN1m3DO5myPxYQ/5OO6v+RshRHUz3XbJaJwSRo5S2dsdkqeGZ5/5NF/64ld56PwFpIqJwhhNgLWizPnuyh46Tcl5PYRKkWuwOqfQORZTAWYQRQQSOklMFLl8L1EckiSRS8JVTt9b7YRLly9SFAXj0aSkFVxU5mAwINeGfr/MwNnqVMcvCuh0FEXW5z3zQZnf3VY9RyCh0o4cPUSZVbJ5v477fNQzuBWj4nFG1sOO1VQwmt8f9TJbOxtRWqcKmtpl9dk6w3lFi1Xn8seZP8PZ2tqi2+0SBo7SGY2H9PurfPalzxGHIT/68Q/RxRBdOE+w3LpMp/4alFJMJpO7qn0vIsf1g+O2LyKHzVbq6/Oe88ddw/8C8NtCiN8AEhyH//eAVSFEUGr554H35/3YWvsN4BsAL7zwwkfPt5Qy74Ec98IeJ4eBSPNlnQ/yZQeCcjj1GqNvkKwqHenCcGbjLF/5yst89nNfpNVqk+WaKEwojCVLC4rUGf3yNMMUBcI4DtpViC21F2MQwhKWKX/jOCYOFe0wIFQObDtJi7WVFdbWV0laDjxaLadVF1oxDCOKwvGtgRJ02glB2Oahh06765Iu1TEK8tyiC8cJTyaTkvOvA7oHeq+yO8+fefe0+awOA8R5z2UR49o8fv6wZ3gchdGkh+Zpl83fzQvWmrc+e+za8Q6YLHyZe7+DqP52tndRZaBb0ororvdY6a+iUEih+PYr3yKdZEShII6SyiBvjEFJ6RLkfUhyXD+A+bMt//2tas1Hgf689vjtR9UL/jDkTmra/h3g7wCUGv5/bq3994UQ/zvwb+M8df4m8E/vQjs/MrldSmeR/Q8D+iZAzV+XWCQicMnFAE6d3uALX/gqzz73Au12hzw1BEEMCLR2fts+785k7Py3jXYKutYaYQ2BBIQiUIKgTNmQRAFBoFBKuPB9azG2IM3cVD7B2QWMhc3ro9ItVLuqVsMRg+EeYRhy4uQavZ6z7W9tbTIe5cSRy6h588Y2g8GQ4WCMneZJKO+HQSARlVY/HRAW0ewP037di7/YM/L7110qPZc877zzpNmXmoB/lBHQr4dheOwA5Y2aM+epa/fVDOlwCcOYvb1BlaMIwBpJq5Xw6CNXnDKRZfz4Jz9kb+8G7Xa9JGSAQJNN5lequheyCODX00nXU2J7uVUwPmpQP+q5fpSa/r2ItP3bOAPuGzhO/x/cg3MsZSlLWcpSblHuSuCVtfaPgT8u1/8CeOluHPejkuO0hUVH5+NG8nnaWZ2GABBS4PM2eg5fCIHA0TrpJCMuOfzPfPp5fvlLX2ZlZZXdvRFJ3HI1YrOMPMvI05S8NIDlkwxdFNgyYMbgtNUgCIiDiEC52QPg1oXBWum0beEMYFtbW6TZmF7PeY7EScTu7i79lS5Fbtjf36/q08ZxiBCC61dd5OZwNCJULUzR4fq1LTav3yBLcyajHCFkdX3u/kzpHYsGO6190ry9s5/FgW11OkyI4zXzeRp83QccQB5xjCav21zOo3QO44HnfZ6/LgBRW5r5Wv6MzFI7u7u7roBN5c2zQ5Frzj10lvWVVTbOnOPlr32dVjvhe999lZs7m5WrbK/fJgxC9FATqJAPQxbV8Oc9g7ohdBGvrcPsNEd56dT7i7X2lrzD7qb8QkXa3g05rOPcDUrnqOl/c2o49fTzHWXq9WFqL2sURTz22BUAXvzs57h48SJpapAqpNXulcE6o6riVTWVlf4FEAgLUkikgiBUKCUJA1FR5i5fp8XoHKUknW6LIFTOKyfPqra6YJ9ptanBYEAcJayurgKCwWDAjRsO8Hu9HoPBkKsfbLK9vV352RtjkQIspuaQKsqBpv7SzL/Xh3G39W0zz8GIms/49Dkdx7nP0GwsNmU/CvDhcECqL+uU0rxrPuo8HvSPF8lk7PpMIGa9Wa5dvU4+yWi1EjY2zvK1r77MxulT/Ms/+SPeev9NAHZ2djixvkKv1ytdN++9LHK//TOb5y1TL76yqCxC6dTPUT9X0w606DXdqSwB/xBZRDNf5BiHvfx1Y+JR/K3T+qzzOUfNeOkIq3jmU0/z0kufB+DKlStIERCGkk63j5SK8SSlyF0HV0ISh86tTQmFDjTWgLC2xg2X/LiZtilQEqmc9tNttzh5Yh0h4MbNTXSeV9GYwmqkhVYUU4QZwlqSKOLUiRPkWeE8PzqOw+91++zv7/Peu+9TFAXtVheti+n9srLm82VnEZ55mv3hAFjfNv8+2wPP47Dfe5lx0zwk0nPesebNEGef9ex+TdCua4aHKSPWirkAVB3vSFxzVVqCIGD75g6ToYtW7vf7rhLWeMy1q5ucOLmGRbN+cp3nn38epKb1mguyeO/9t5lMxiSd9ocG+F6O6geHzbYOKzB/mCzqpVM/5p26U94tWQL+HFlkerjQMRxnAMLpgMa/2MIV6ZDltnoFJf95Kg1NwAqEUBijMFby4ou/xGeecwzaSm+d0Tin11+nlXTYHwxJswkaDRJUJBBl1KIsFLooC3tbSxQlmMIFXGmTY4Cw9JmXKiIIFO12i06nQ6e7wngyZGdvwGg8YH/owvRb7YR2knDy5GmSpF29SHHkXEMHowkbZ9cAlwZi89pNxiNXRHw8drVxAxW61M4NuqUy484Mju6uOd97r7X7dVvt4x+FtMIVCi9vqZXTvO3CCKy01VJaWeauL9NWoJFWVktD+V0Vaeqfk6e9VLX0lJy1Gg+orq2+7e67RQzA83zADwKbOjiTOUz8bLG+tG5w77QTdm66XDp77+1w4cIF1tbWmIxGXL36Ae1Oi0KnRIniU898htMbGwD8f//yX/D9732H8WSfMImq658u7z6dsSh12vTSOcw54jhZxEtnnrF2Cfh1aXTQppYz3a022Z/jnXCYNN3e5j3sRSid+nnmHUMIQV5orBAutUBtCaCEq1qFBitdhneLIHA8S+V+CaCtJkkSV+JOCwIVMxoVnDh5is985nO88OkvkEQuERY6otvuoAvD3mAfKSXtXpthuo+xhixPWStrmcZxi73dfUTpEZNnBWk2RqMRgWR1rYcM3DUPdvdQUZut7ZuM0gk3d3cYj8dkWUEUdUhTt98kHfLe4BpXr96k0225HC1hxCTTdLuK8+dPkJRxM9e3IM0Luv0VV082zel0++zu7rugFWDqhw9YW1NMNWhbcvvOe8e7D0oFlReKd0GyEisMBlUCbBnoY9wpjLXkRe7sGFaAhEAGIN39dwcGiUSoclqupPssJLowldeLwGnQAuvsLxYQokw0Nl26BrhYCj+IzyRAE9Osm/W+uIjCMQU1XdbGnQ38qXsXAUjh3CyxAl249Bqhkvz052+wvb0NOOrwjTf2ObvxEOcvXqTdazOZuGhtPXaJ89ZPXADg5Zf/CucvfIKf/uRH/PSn36OwKcZalJQEKnT3rpYJ1VqXDE8ISxzHGOs8vKy1pcfX1L+/fo319Xr6hbrUP9cT7jW1+mZg1GHnccuD3zVlnj2mGbzXbOO8WV0YhjNZZ4uiqILPbscOcH8B/h3K7Y6ed2vU9aO6qT0wTam5i2mkp6/IZARgXYUmb5oV/jc+V4y17O7ukcRtwjBhNCpod1b45BPP8PQnnyVULWz5GI2xFFlGXliM0CgZIiPBcLxHt9dhrbvKyRMnAZClRVYKF8a/c2MHmQmslmTZiN09TRC6dg4n+2Q6B6uYpDmIso6tUAgVVh15PE5JswK9u894PKbVaoGVtFtdWq0V2i2qYt+DwZD9vUFVsi/LMsIwptvtVpGVgtogPPMi2toMwN1hK8rOb91IKhBTAy8O2W19eyWB8yfFPQCL0+6tnS6hnAEIe2BJmd+naqtPhiFcW2yZYMxdA7U2+vY31w+jae4+nwtl3zcl3cA0pbPzx7RMI5fdepaPyfIJSZIQt1skiSt049IvO0ANgzaPPfIU586ep7/S4c03f8LW1lZVeGV3b7vqM91epzYAObALgoB2GaXbLBVatXnO+sdVmvTbvXTbfGABv2lIuZPzHOBKKakF6V54IWTpv17/nacapl4jFo0QPqrUbZNCUuQGE0m0drlkLl68yIsvvshjjz+OlPWwclv+M6WGaQmkmzmosl6Ur3SdZiPS8RCsJM9zRsMhg/09tMkY7O+S5ylJy3Gy2uRIKVldO1VpoT7Lon/ZAUajEUJYlyUzd1rjaDhma/MGRlv6/T6bWy498tbmDay19Hq9KhLXld6LMLrk0mt0lvPa8Sq/wfsW+WdRQbi1VcK26XelJ8bcd2g2PXH1DA+ZXdarXjWf/YEjz5nWHwrkh3TDe/HiN6kF/7mew98KcWB2YYxlPJ4wmZSAH8f0+33iKGEymTAoqT2tNe12mxXd4eWXf4UffP8Er776Cpubm4RhyEp/reoz+/v7dLtuhloULsNqGLkSmSqQZGleJcGbdx0fV1nUtne35RcK8I+TRTSFRafM8/atQKK2LoSseNqpinfw9we4RU9RiIAkCTFakBaGM6fP8qmnn+X8pYtl9kOLKmcDYaSIlCI2BiPK1LmB5NzKGYwt0IWlyJwRTuem8oYZDve4fu0qO7vbFDrnxuY1hqOBS12MS1QWxwmtuEtQli601lZFSOoFKuLYZWJsJwn9ft8Z/7a32d7eJgoTrm/dKG+Wy9sfhmGVeyXLxi6qN0oOPi9b+1wCvhC2ejnqA7jxL0zzwdkmCLtn4gcwf++9xlmfhtdBsbmt/iyP4nAP6zPV9cmj7UW3QuksKnWg9/mBAhtgi2myNX/uonBUi087rbUrnkJpJO71etW+o9GI/f0RSkQ8/+mXOH/uEq+8+m2++73X2RvtE/s03f118sKlcy4KyPLMpV4WliRJiMqC6UCVBrx5jR9X4J/XV+6VZu/lFwrwb0fDv9Xfwaz7XBMoPOAjbDWdR9SW7owlIPlpvJ/6HxzVjXV1ZCfjlJOnT/LiCy/xiSeeRElfschURVKCUCGDwHHiReGMr4UlilpIBFKpyiBsTI4s25hNxuzvbbO/d5PhaMDV999ld2+HuCTc2+02J9ZPsrFxgShKiEP3Euo8R9Q6a6vVotvtYq17WVudLsYYxoMRw+GwpDXK82s3YOzt7ZFlhZ94VEms3MVNk4xN6345G4e1U9dVIVxlML+Ot5eX21QN/Gc1fTdw1Llt/3z9+jxwb3pe1Lc1B6Am8B9J1VjwvvCe4pndv26MPkq8cjFvX7/dt9vtq1SIUpogcDO4Qgja7TZJ4gb9yWQyraOb6SoaejweMxyMEELQ6TrA7/f7JEmLVitm++Z1wgAuXXyEdrtLr9vntddf5ebuljvuOCUrxrTaCUEQkGZO8/dU38xsxE+G7vt73BAAACAASURBVGB2fr/IYUZf//leyQML+M3f3M5g0ZzWe9AwGJea3goQujQiag4Gu0zlAEVQLrSGNLVEcYdPPvkpnn/hc6yurDnOezWk0FlVvLoocqTR5EajdekOpwQqaNNOurRb3artHwz22du5SZqmXH3vLdLJgGwyZm/7BjdvbjEY7KONO0av02F9dY0oVFX5wnp+dm9EiuO4Avx6KT4pJVHoimj7QJzhcEie52WqB1lVbZoF/IP3HA/0Aqw56NbquGhRtQGoNP5D7vxClE79HH5AqANSU8Ov/+Yo0J950RsAP0/bu1saYHPQqmv4xji7Tr/fZzBwNM14PJ4pCg8uHXYYhlhDlRIb3HqSJHQ7PTpJi92dG+zsbJNEbV568Zc4f/4i3/rWnwLwg59+l1C4Y6ggpNPuoE2BxZCmKUVRzJ3xfZzB3stRs8N7Jcsi5ktZylKW8oDIA6XhH+ZydavawrxpvF86SqecjuN9wgVYeXBERznjrBDOt0NKN1Wu8RF5Iei2V7hy5QrPPfcCqysnKo+PNE3Ji6zyOsl0hlTO5SyMYzqdDq12zMmTJ0kiRRiCTy3+gSnIJmOGgwFbW1e5uX2D4XDI9asfcGPrKgBp6rjaUMDqSpeVbg8VhK7wtzaEUtFJWoRJXF1XErec101RMCk1NIQiSlqOBvK0SRDRX+3SzXNGoxHj8ZjxJHOFNyobXS3BVTWdB8poXM95CyEqo23p6+HabacGXUFJdDS0bXmMht9MsuW33U7thaYcnCEe/O6wz3dL6n3Za/nGOBN/t9uj290HYHNzkyI3SBFUxVwCFdLr9onCmMlkwjhtcPvGgtWcOLlBp7vC1WvvM56MOHvuPM9++nkAwiTixs1N9vZ3GI72USHErRYW4zKn1ovVNyid5vrHVeZp+ksvnbss96Kj1AHfTmnSme/r554FH+eZ4z6L6Y+t4uSps3z6My9x+dKjjIZjlFK0Oh22t7cJopCirBsrAke5tDsJK/0OGxsbrKxFDPcNw+EEnTlwBRf+PklHZNmYvf1d3v75m6TZmGvXrrG7u8Nqv0+RT8qmdVlbW6PT6ZAXbsqutYsPSJIEVRa+8EWwgyAAKUnTzKVKkNJl60yLCijjOObMmTMURcG7777L1tYWWZZx9uxZBvsHC4r4++bu4Tx+u3afmX1pqvU5U2Z7i14687xzDqNr5tE5B85/zIt9r414Xur0lG9vkiQVXVcf6PwzTNPUpV9QLk9SVcy+7APWWqwO0YUhUBGnT51lOByws3eDk+suTfazz3yGN3/+Bv/mjZ+ws3uTPNdTw70VLj7gkCwEH2ewPw7U79W13V+Aby3UXrKZYJGaJtI0jE1/fvTLcdj+i0UvTn9b5zLrL7SvgGSFxaIdmFRKfu0lLyNJg0BWyc1GowFSSOfmFscUqTvmFz//Za5c+RRPPHGFKG6XxaOdFhXHsQsMKo/dTlpsbGywsbFB1EoYjYa8+bObvPvzd5FSMhgMaj7zrqzdcH+P7Rtb3Li5SZpOuH71A+IkJMsnDIeuOtZf/St/mcsXL7C21uf69ZsU+YQ4CisPm6hMLdDt9IijxBnyJmPyTGOMIM9LY19NW7NZxp//5KdVtaF2p0uSdNndG9QekAdow4zvurCQO7fTioOu5Rryf6EvzF2es2g8f+dqPjsI+/7l7QnNwdkv59WfbR6nLosVAplWfppnH6of96hBQxezhmYlA5Sc/rYZoOiN6VKoCrxXV9bY3rnJ+Yculse0vPrqqwghOHv2HK1Wi3a7i4/KjeOYOHFJ/Hyxda01pjDkvs1AGCesrZ+k23cpNrTNuPzoZc6cO8srr/4Z77z7NoNhSphpLJoiLwjL/tUs/1i/H81KYPNkkfw1i8zc5lXoOuBsUTvXUcb9+j7zziOlrALG/HGNMdV3typ3WtN2Ffifgadxz/M/BH4C/CPgMvBz4K9Za7fv5Dz3qxw1Snsvi9mNdb9mF/SUpTlShKyvdigKTZ5p+r02ly8+DMAjjz5Bu9tnfzD5/9l7z1jbkuy+71dVO5x847sv9Xv9XueZJjmBzUmWyDFJEJZImIBBEQ4gJEMyv8mG/UWEv8gfKdiAQViGDQIOFGBAlOXAL4RFaEhDoO1hGlDg5J6Z7pnume4Xbzxhh6ryhwo73HND9+sZ9hBdFxfnnH322aF21aq1/mut/6LWljRLSFJnUld1yXg6Is8dpHLt2jVu3bqFUnDv3kPu379PURTM5wuUTCmKEq3dZC+KglXhtPvjk0PyPCPLFePJECkly+WCj370o/G4d+7cofbOvDCYh3lOPhySZe78SZZR1zW1NejaomNdV2isltAn67TprmBtO237k0uIvqZ+9jM5a/E2gsj3fpGD9Z1qXOc5ai/6Xfv9eULlvO/WRX6sc0C3vw+vccH0DnqAnZ0d7t69y2QyiSUCg9CJxGM9eMgpO75Uo3QpaeH8RvpFyQoECc89+zxpmjL813/Gt7/9bWpduVKNmUL6QipB2IdggFAD+LKC773Smi+jIL5f25Nq+L8B/F/W2l8SQmTACPjPgc9Za39dCPFrwK/hOPL/SrWzhIPbDljZgWja8j/EE+vaIlBkWe40/UKTZ0Pu3H6Wj33skwBcu3ETJQckaYrBsioKZC2YTEfMphvcvHmN8dglrmxsbGCN4d79fd56620ODw/RxlIVBpNarHbJWwB17Y61f3jIwdGRC7vTJTJJODw6YDIc8/LLPwLAzVu3mc1mvP3WPnVVYrWJAmEwGKB8ZSyLg3XKoqautE8Us2gj0EZ4zppuARFjDNYI99+i8Y0HZI1gExZtnIYf9OZQM8Vag/RwT+3PFTR82V5MwnNs4cLrJu2659tuZ42Bde8vamfBSp17v+D9edex7lr62mcU1lKS53mEaa5duxYjrfJsQJbmUeALoRBCdQW+UKCayHkhLQjpmEP8P4DWFdtbOyyWA+4+nTCZTNjZ/gpf+tJfxNDg0LvKUzK0/StnWfvfz9anYFj3/r3w8cDpRWqdtfBO2pPUtJ0BPwn8HQBrbQmUQohfBD7rd/stHE/+XymBf55wiBPNCBBNRSXneAwTTiCEpKpqhoMxuoKjgyPybMAzd57jYx99hZdefBmAqsoxRiKQvki0RaUwm03Zu7rL9vY2GxtTv6/mtdde497bD7DWOguirkizEVmWOSx9HnhJHAfM4fERJycnnMyPWZwckWYJWmteeOklbt509eefeuop5icLHj58iK48dp/lTtuv67iIaANl6SprFZUL07OeKUhj0VYggjTvcLvYDnx3itCLniYlLEZYl03cg1XCAtuGXQLVxTqt19iuHbZun3DedX6BszTpdyLoQ+tTH58H7Zy1rX3N7b4575ragl5KCdYikoTMNgXDJ5MJk/GU5XKJtsQs6z5nTDhes52o4QshML3i7I5S22VpZ1nO07eeZTreJE0yvvjFL7J/8BAhQn6E2z8UdA8Jf1LKC3m02v3ypG3dc/p+tvdyMXsSDf8Z4AHwPwkhPgL8GfCfAFettW8BWGvfEkLsPfllvv/auodwyhwHz7EiwKpo0grhNFopEhYnK0pbk4mM5559kU9+4jM8e/cFlHSPRsuEsnR1aPN8wtbWJqNJzng8YjwZUpYlRx73ns/nHOwfURYVeZ4jpMKaiuF4iFQZGkvteWOq2lJUmoPDYxbLJauiYFWWWCF4/oWX+NiP/wQ3n3oagOlsi7e+d5/FYsFstunqmqYp2jiNPvDjGCtYrUpWZekctj48xkrHc+8478O+BqMd04M2Bm0MRvedpF2/h+95QhGWsDYI0U28CtsEUel0x+g9MiVAiPPT2NcJ+LD9fCvvbMz9rGZN+F3Iuo4uBuD0ccLb/qEduhKK5TR9EF5jJBggpEtqEH6khsIyMnUZ02HsWQODgVvkV6WDW6xQWNtkJNteBwshMEIjpHT/wmn8VhhUIJuzwo+rbcqy4OTkhNlsi0984jNMJpv8+Z//Kd9765sA1LVTCJIkiVnA6wjPzmqXEZzvJ2hmneXypNbMkwj8BPg48PettX8khPgNHHxzqSaE+FXgV8Fxw/wwtYvM/4jfhxfjpI+gpfUgyPMBB9URIPjwSz/KZz/7WZ577nmUUhwf+UgVCYPBkM2tDbZ3NplMxgzHGbPZlMl0xKNHD3j02GUtHh8uqOuawWDgJqKB6WSTWgsMFl0b5gsXeVOWJca45BYjnKNNJs5E/8hHPsK1a9fY2t1xx50v/KScce3aNXa2d9Fas394zMl8jq5dX2hrI4lWWdZoa3Gkk02h9agd+UimNpOjMQ0dRdOPrjUCH285OU2/r+G3NU4r5CmB3X5Oxlqk7LgK1j7PcLzzNPzzYJ3+vZzVBGdr+Gd9Xvd+HUzTvs52WGkbEokZ5FaiUB0HaciyFULECBxjTe/+25nR3Wtu94kUCTY4TwykSUaeZyTKFb4vCslklPLSCy+zd2WXf/F7/ycAy+VBTNjLssyHkJqYlXth/75HAj/007rF/PuxYLxfNPw3gTettX/kP/9znMC/J4S47rX768D9dT+21v4m8JsAr7zyyvtnWb1k6w/q/ndum4ombadZ4bU3QULK7Zt3+NSnPs2HPvRhsIL5fI4QLtRRKMXO3hWuX79GNlCYuopazmDgJl6IUFitViiZkIiEWmvSJGU0nvFg/xhwWHrl1XGhEvLBiMF4xOa2K2gyn8955u5dbty8xXA4JkmcSb9clQxGY7amm1zZvsJgNOb4+DgK6srT1xZV5XlxaupauzpZwsFR2gowjlIBnJCNVBSn/rvRFEEIxffCVeASLZg0xOEbY1CigXRcdztzQK2ZoMizF4W2gL0Mdt/e76zvz2vr4JuzBP47wfDbn9v93G8RirESjINPQkBAXdeUZRU166IoED5rOiL1ohsd5CAz7SAimoW1vziNx2MWC6esjEdTpEg4PNpnOBzx4t5LHB59CoA33vgG3/jGNzg4OKCqqpjNXVVVvM4fROuTyvXvad3nd9POEvR/KRq+tfZtIcQbQogXrbVfA34G+LL//9vAr/vX33mnxzbv3YL2g22eM8dPeU/H6zBsrIqmtzeeKZY1ezuODO25Z58nVQMePHqEMLC94wqFDMabbM+2GQ+HIDQmheEwR0rLYrFisVhEx9pkOqIqDMJIsjRBydTFxksw0pIoSEI5wjQjSadszDYpihWT4YS33v4uP/7xV5iMp2xv70aBnyYZ+VbOOBuTD0cYY5gvC8eHXtZxwZl7mgStNdpUWBTKWKxyjlSzhq5yncAXMQKnJbC8SJfgKBVcFtWp2B8ZNE5LzCOPkzI8pnO08XeKvcffiDDxvUPSCs9aKlue4YBBBSxGNNsAhIcnRIAEGwdDs90vhqJ1jaJ/vYGridg7zWd/HREh89anEFhjQBiUFVRGgFTkuafextVDTrIMJRKqckHqFY5mYAeYyNNuS2/RCePqLviqba5GQbgvBwtaa/2iYpBKMptuMp/PefzwkB/3AQxpmvLgwRFHx0ssAisUUp5Hm/H9aX3rKLxvt4u46s9y6fbL6ayDdJ6kPWmUzt8H/hfhInS+BfyHuOv9Z0KIvwt8B/hbT3iOD9oH7YP2QfugvQftiQS+tfbPgVfWfPUz7+qAXgsJlaGsFBgPhXf0FyHapAXt67n0qc7EPyMO2dpZ9o7b0uacRudJuozxEIFGKVdDFhtqkZZYf2yDQpBwbWuHX/6lf4/nX3yJ/ceH3Lu3z2xrm8lwxHjqElO2N69irKAua7JcoRLLcDBAYrn/9lucHB02MImBLMlJkwESR6lcaY1KDVJpjK7Y3p4ATqtarmqu712nXK0YZkNe/vCPcv3GbW7evMnVazfZ2HQYvtaak4MTsq0J37v3gOPjYxbzJVVVUdQVVVnH+xdCIJUgE4mLgBE1ykoXU61yfD4Zpa7RpabUNaaqO9WebODcD/flNSkpJYkPBRzk+Vrow/qIIWNtHEcRs+85vIIGfNa4OctpJluRJo1239ZyjVfzjdtqjYsU8jTZja4tIZZBdDQPUVsP476nxYuWNt/OEhatfYV3TAjhXbHeexsjY0KdZACjsdbE7GQlcOGUWlHUhlVR+uelUaTUBpI0YffqHoeHhzEeP9AtuPFSRTZVNy5wBXkSiRQKlUjSJFScSlgul6RJQpokEaIxtSFLMtR4g9Jfw0svfpzxaJc//pPP8/VXv8JiWZAlkiTPWJUlg0EeYbul91WNRi5vwF3PxclZaS+pqjM2/PvqEklenXHTsgSiJXqm48h/54MOtIeuQmtTkb8bbf/9lWnba20kd53TLGxvf/7LaQ12GUzuJEmoqsrzwggUGUpm/rsReTbip//Nn2V79wrFskYgyYcjJuMpk8mE6WQDcOntZVmSSMF0MnQROpOMqq7J544tMwyILB0wHk0YDWZYLZmfrCiXJ2SppKZCpTCeuGzI4dDVp33xxRc5PDzk4cOH7O3tceXKlciCmHjOcikMo4lgf3+fYlW6LNq6ik679nMK5rkOQlq4LE+VCIQSmGXjmDwPTw5OWf8pkkiH5d+2TObOwt8PSbmgnTdx1mH4fQekg23avzeXeBXrvwvj+yJIp7UoxP6wrX07dRfEqfeiVTSnnbHpFBNHfVwZB0eq1GHjuUpBmIibm5Vxi7jvB7dgu2fifDM6LoYuLDNcxuln3l5YrbUNL3+SQA11EHIatjb3eOmlH2G+OOHVV79EVWh29q5gjvbdvfhjOseyX+g9FbeINYgv39YFaLwXsmbdESzNQiBbSN972d6XAv88R0h0PPlt/Yl4XnuvFgVBcPS1NDV3FYBFCEM+HFBUFUoqxqMZ80XJytMN3929yic/8Wk+8rGPAa6832A8Ynt7m83NTUajEXnuhK1QNVaUWKEYTwds76QkGZzMIR8qphvDmEovhSQbSIbjFImboBUKkQxZVTJmJoLjuN/d3WU4HGKM4fHjxwyHQ5RSFEXBcrnk5MSFe2qtqSvNyeEJdeXol2F93c4QD21bGnC734OGoo3uReicnogdDb73XSyK0dK4wzW90+fcd9RexiHf/IbeWHj3re+UPcsK7e+zztnbvpf+ffVDGdvc/8a4cFm3v4z+IecD0JEKwxjDYJjHz25b3bkO0fIPrLvX0Nq0E+sUuczXZTiZu4pot27doqqW1HXJW/e+y3K58scQ8TG4senGZ8jKTcRZyPnpdp5y+V60tUrO6at4z84X2vtO4K8T8lHb8e2ssMgfZHOD4HTkhmsSJVOG+RhrXR3XQT7k6swVEP/oRz/KRz/+cbQ21BrG4wlXrlxhNt2IQrf2fPa1LtGmQqicJBUkmTuesTXDUcZTt65zfOQ4y0+OF5TVkqJ05FPZQDJhSG00RnSrOoXiyGERuH79uuPWmc9dck0LYgmp7EVRYLSN1bT6wk9r3fCMtARNXddoa7BCNMWYTcN939ekz3q2bQFXe56btqbafjbtKJ2zH2JT0Dwcf53Qbz/bvqXphuU70xrPav2U/bOE/lkCv3+c/oLbDrEMv+kX73YwkEImacdyCXOwfa4IlehmIQgtJmWJrtBsX394H55hX9MPxwmF40d25BZYUXPnzh3SLOGLX/rXfOObr6Jk4iq66YY3VcpWVJIBkYgLLb++A79vibjvzj3E+7q9rwT+OmF/1nfr2nuiwa8vfHrqPNZ6m6sN37bKGFalxpKwWC6RwvKhF1/mk5/6awDcvHGbo8MTlErY3tnj5s2nmM42nSAzBlPXxHqusmYwSslGCaSGUkOlNVZZZlsbjlAq80laWFaLkpVeYCpH2yCVoFy5MnSj4ZjBwBWTGAxchSEbEp+koViVLBcryqLqCIFgZod6s7VtiMOsbSiEtdYMh47mQfvtRltHpGUN2toYs1+bhg/FroloOKXt9r8PxcvjX9je0FO3eBOaV9Gd9AE6aiCaUB4x4OTtMSVaj9sv+FY0A+AJ23mC/DIafmjriLvaQj/AjeA4lcqyjILXQXluJLctrxBtA04Ih6Sn9oLdPkdf4Ift66z3oDyE34XxFvY5WThLczjKSdMEOXdP4trV6xTFEikl333jOyxX81j/VgiBSKR7XqZbn/cy7Txr7zJy5iJ5dRkN//sBUr+vBD6sN2XXTfi/bC2/MxDi/HKLgBVQVYbFaoki44UXP8zHf/yT3LzhEswkKbVZsbO7y81bt9m7ctWVAvRVhRyfvdM8kyxhMhmT5S7jdlU4QebqzCYYQ+TSsUayGhbUlXN41lVJ7WkLLG6ih3hlR5C2jJmUQKu2bMlisYi1S5MkYTgckmcD6rqOuQB9aCAIE7cYeg3Sm/tlXVHpEKaKWwACI+UlTee2gOtP4HWEVpdiqBRdAdSf6P37Ww8dvjfKyDpIpz8X+hDmOgHaZvlcJ/DbllEQzG1nYEjIazJ/vSau6GjjRVHE/dvPo31OIc8ns2vDTn1NP2xLfXhwXRkQlkE+QvqQz6t7NxgMBuiq4tGjhxybQ8AHECBj9raD+riUhn+WtfmBhv8DbH0Ty7IeZ/v+XkTwtLfYHC2EzNAQDWGty2rN0wnPP/cSn/rkp9nbu8bBoSsmMRpOefr2XabTGbPphoNLqhKVJlgBxmjSzNUSzXJXQrAoCopyRV1PGU9GpN6ZprWNJeDSzQHFoGKxWLFcrJDCkiQCXVsKHwkThHhd1zFr0SVxDaLDNcsyn1DltMC2YDHGRM089H+YqKmvdes05KatxfBtgwULcz5n/LrPbQed38G9aB3PHSIu+m7LdZFd50E44fs+rNO85z1plxH45+3b/66P44f7CDURQgvO1zA+jF5hECjZFDFPkiT6R4JmX5SrTmlEKdcvsMHPcdb1Gnwfxu/x1rOHckYu0OBkfkxRFGR5wjCfxCxbpRTP3n2BPBvwlk/iWa2WTcSP8RFzl3TYnif0w7aL2l+mMnpee18J/Ism+TrNq4+5/WBbv1i0z9M3El1rXv6Rl/n4x36Cza0rrFY1mRfMO9tXmG5somRCUZUcHh8hhGBvb49Ka+aLE8cUhVtMjIaT+YKiWCGFIs+HVFKjRIJAkCTN6YVNqQqoE8gSiVIZRWmotIk85dAUC8+yLGY6LhYLjDG+ePUgas0uwUojlIwp9qFWbZtAK0kSyrLyNQG6WppSCgPUIVTPtrD7SwjdNp4sgaSVIbtO073MhJN0jxvPdY6Tcd34e6/gxrMEYvu7896ft619rcfHx1GQp2kan1+oMVwWNTJJydJBFLZOqLs+D5ZZKHgTxoBSzT22ob6LWgjnbV9jvAfbkKQFTT8oVJKUPBuitebmjdsURcnc+7OstbFAizEG+Q5DXt4PTtu/8pBOKA4dOiPADlJKMpV0NBYpLsbY+uZs26nUMTtb+4SC4KKXCydEo3HM50tHToar5BR+m6iEsq6ZrxZ88hM/xd1nn2dv7wbIBDDMNlz27MbmDoPBhIcPH1IZmM1mKKV4+Pgx2lqGo0E0G48O5xwfn3i4JOPooKAqHzEY5o4/fDhs7he3ONS1piwsRbGgrk449DQIYaK2+8poOKyOvXYvwErmJ8tu5IyVWGx05obvgjaPaApUCCG8k9aH10lHdGVw/olQQQndOPvQJv5WSomxljzP475CCEyto2BIpAS0i1uHTvhiIOkSomHmDEI9UoRFWRKYNM8WTH2ooj12wvt1RTn6jtLzBH54FoEuICyQbaEZ5sU6euB1ju9w3LO+D+8DjBeEoxCO9nq2uYUUSVwY2nBLEPKrYhlhQsdY2SgTAecPoZEXrXedOWgM7rbds2nLg0SlaFNjtEXJhOlkg8lkwr54xPVrN8kTR/fw5nff4Bvf/LpjpB0OfPavjjkz/ecV2mq16ozFsMCF+zLGkA8aFtGzWj/T9pSVcIGfRQhB7YMg0jyPilqQkXVdx2fzTtr7SuBf1Npm6jpIZz3edjYe3N73osWjrWWG0MVEpWRZRlm4yJOTxYLhcMjdW89w8+YtsnzEqtDkecJsusVs5gR+rS1v33/gH6xEqQSVeUKpunbmsteEpUwdxm0tuhKUaKypqApI0oqqaGl7RsQQSqeJ1xijqWuN9gK2n7gR/tsQQl9AtL9r90v8batfwyTpa3cNzNDlIdFaY6peiGXvXFLKjsC3iSBrxYCvE7btZ3q+9Xe+FnrWsfvfvRcWZ5qmnXOs68PLtnWQUHt7m+K3rusYPaWUIk2c1i9FY725hah7jDYU1L738BsH+13sR+nDTt1wXSK9tfXBAlIo7y+3YMAay3g8ZXR8zHLg4KrNjS2u7F7l3v23sBbyLKOulp0x0T8/EKmXwz30rY/2HDqvXfTs14Wi0htbxWLpWUlFZ9EJ378bzv33hqX/g/ZB+6B90D5o7/v2Q6Phn9JULrF/W2toH+NSmleI3sC2aI0tQiiGg5FjtESRZ0NM7Yt924Kd7V1+7Mc+xtWrNxFJjlI5eT5mOt1iOHDRNGVRcXR0wtbWlsPqZUhwMb5QiI0O02E6Auu1HWvRtfbFwAuEEKyGdRMWWdtOXHS8b9XAOWdp+G0tvw0VhH2tddmtp2K2RdcNGjR8SWM5BW1Nax3x+uALKIoCXVbxXFJKlGgcg/GY2sRoEGt8vVpxsda9znLrWn+W80JxpWqmyDqzG5yGeWr4rNEiL2ptCCtCbmti89vHuwjDX2edtY9bVZXz0Xg8PoTfBjguaKJKKYJPNhwry7JoITiNv/HlhNyNsO28FqDBtoYfxrHWGm2d9dHAXTJSbmtjsUYyHs7Y2ioinUM47mq14uj4wEFB9SoGVZzVZ30LIFxLOJ4xhjS7WGxeGCFWnx+KDMQSk+s0+XdqPYb2QyPwozkfHkjv+z5E09633zlnCQFrm8m/Xkg4YZbnmTNXfYhhCHUcj8fcvfsMt+/cRSRjsmzEaDRmNJyQJhm19gMaST4ckg+HpInjhCnrGmOc2Qoujh3cIJNWRK74eJ/CZzWaxtyryjpOvoi1qka4tYVou2/aUpKQLgAAIABJREFUYXl9XDjsa63FtKoKdQRewMStRSnZWyhMFO5VVVGs3IRcVSWr1YrVaoUuq4hPC9EUHw9NKYX15RKVUkghnO/EY939uqbrhOHaxd7TLJ/XzsPmo4CyptUXp6HC/vazWhuTXVdG7yxhfx58s+77Ni5dliVlWUahFmLxXT/LCwV+cOC762/w8XAOpS6+7xDx1Rb4YcFwik7sFZdb4guqOKZMg8AghGRztoMuq3iMWlccHh7G8FEpZZNn0eub9vu+EtRv71bYtluapp3P1toudxIw8gVnREvoP+m5f2gEfvthOMF8Om3/tOa53oHW3r9/DPfWYFsFx0MYZtirLEsS5ZKQilXJZOJKDN66dYtbTz3thLhKSdIhw9GMwWDs8D9PgZAmGYPhmPFogkgkZV2D9slOUndCDotyiaBJTBFRsLmrcdE3vk6tNY5aWoCQLvFEKFwSl+XUvbb7JFIi2F65QboCXwiBlb3jtObEKStB26jJF0XB/r6Lky51HZN+8DVyozbjNfmYtWsd3XF4XsYIRyqVSKSwCHUq1sZde0yQ8H0XHbaiue4LFNDLCOr3ap/LtHei4fe1+3UafhCswWEcnts6y6kva5RSnfDcdVE5xhguIoWxPSZEo22sjuYSvsLzclq9BTeekSiZIJBUZcFoPGQ2cxxU8/mcyXjK3t4e88UJR8f7Dg+nGwXU91dBY0m0k8CAJtnMXozhX1RycTAYnnoe/WcaF2Hbdd6Ha3w37YkEvhDiPwX+Hu4Z/AWOHvk68E+BbeALwK9YV+/2idpZGn5byK9LKW9r8+3XtVq+5zEnxAW3v2ut9ovFyjl3cEJpY8MNsqtXrzKdTqlrzWwyBZXGSk1SKKzXdmSSMhgMUWmGxVBXNbUpXAWmxA8+5U3uoiSRbmAnSYJsPbJGsIb7bgo9uyQZx1xZVV3emna/hf4IVAodYb0G0nH9qnrCoHs97YggXbtY6dVqxXK1ivw8pa5jhEiI0kkSByEY1UT9xGffkmtSCMrSmfJhoWg/y3ANl4HzLhLEfYfkOgXiIll+lqbYb21+oL4A7VpU6x2x7c/97esshP7vQ6hlWZbkw1GHSsHt090/9H0YL20rIQhKG6NtLt86ChgSqRrhFsew12wSlYGCebFybLFpk0le1gN2dnY4PjmiKBdAwwfUFqBtBSdEBSZJEnMP2laOtRZtnrx+bsh5gWbe6HbmMhYRMpBbi0e7j99Ne5Ii5jeB/xj4sLV2KYT4Z8C/C/xN4L+21v5TIcR/D/xd4L97t+cJ7dQgvWCfddr7hecwtqNVQj9KxzUpJVVVMR5NuHbtOk/ddBm0s6krvpyPRkynU8raVXuyBirbDLJEOTMtaAu10TESQWtNpZuQQ7TGSoVNrGdmrDtCGSujqe0KQku3cAlfN7ZuyhmuC8sMMEpb4HeOT4tcK3DlnOrbrqYStMa6rtG1idr9arWK529j+NbjmUHDH7aoj8FHr9jWwmQkQkmwDTVv+zmFawhRJOsW9/azPa+tC+Xtv5dCdY65Dta5jMAPpfrC/7pM5nY7C8IJ79dp9v3PAdIL91qWJUquGE8dlUKXWqF3bEyEgBy00+DnYfGuq4vvu003HY7f1rAD1OUK64TsbBeiqVSCSiSFSp1vy0cFTacztC0xtmJ3d5f54oj5fB+L7Aj8mO3tn1PwX2RZRpZl8T46/X8J+pWLnne5WMb3McxZdxejJHc+EgOxyFCbofTdtCeFdBJgKISogBHwFvDTwL/vv/8t4L/gPRD4sM7hdjrmOLSzMNX2cU59F01PGd979Mh99q/5YMBqVTAYTLh16y43rj/lfiUUSg7Y2LhCkuYYKchUClJSlSWlxxdlkrCRbDjt1lcfclmKrgyhrioKT56WqwRtNcLXZw1mbsA3BYok9fTIWYYQKUJ4bcxoR61Q1e61rqmCxmotVgiUcBz+wuD4661zhIVXt6/zLQStWdFMgK4wERijnUleG3RtMJWhrgx1odFlN+XfWpdAo8s6HltK6ZJkrIiT1xhXMUsIR90gkAjrFgftwvibJEph/QJqHLd7eKT+fcBwwb9vfW3XvNpg5eCraflXhMsFAWLsf3+Mrvt8XmvH4cf6sq22zlI4D9Jpfz5r0QnCVgkB1o+RusB4euPQQ25sKPBjy9L4VMJCHAwSY2qUSlEqpapCQIOgU/ErVvlyobpCOugUYZFWxvNLJaLADQpSoOMIPqoklWR5ynI1J/M8+6PRiKIcUVYFk/GU6XRGUZw4SEdr6rBY+2tQ2kGi1/auINOEQZqRDQcM0hwZso19QuGqaIR1py/bVug6Dbx136r1fIOC4rT8ulnohaCuNEVdRWe0y/8RGG2R8nLjqt2epMThd4UQ/xWuqtUS+D3gz4ADa22wQd4Ebr7D43Y6Ig7ygMdaQvoMcNqp1j7OeRpR0Cj6DyYhwRpBrcvmOD7L0FhLVVYslnOuXb3Jjdt3GU22EakrKjKbbbO5scVwPGOxqsnHY0aDgeOQMTV54s3NYY6QUJQryrKImoVFRwdTljunjtGCUkPZMgGhGVyZksjgb9CgrcZUgeZWO63B0w04t7N3/uChCOssG+FMAucc9oI+kSGtXiEUCOm0jUYL9ZQKIRLDSg4Pj0iEyytIlaamwmgglyQqI/XZxkXtLKR65qKO5vM5xwdHHB3NkSJlsaw4OXZZk8PRgMl0TJ7naGqWhSXLps5/sViyLJqaAAHSEkKgkiBUuvBOW0OLceYWrBRI69AChSu+E6SYEaCs918g4vrfH1tnafjrWn/shoIthlZ0ThBMXui0oaqw8FdVReWjWoDIPR8ipaRf+cKYKYoCq33RmrpCYkm9s11XJYu64vBwnyRLY+RQiE8PVqXra4XUGqygLGvqus3r7xbHyXRKrUvKwi8k2j0PKQLtsnXCT4Qi9hprBUiLSpXPZdfxObp/i8UpMaKuMSTkwwShhhhvZQiZsLO9R6IyimXF1e2CajlnOM6oyhVF5eZ3nqWUdUldFQxGQxKpsAKUSEizjDzN4zOoy5qqKhl4WpNGVnUzxsEtiHfv3mV5MscYw2Aw4ODwMaulI6rT1jKfu/G9u73FcrnE+jvd2NpiPp87y0YkFHVFuXQLp6k0ZHgm0LXD6tz2JJDOFvCLwF3gAPhfgb+xZte1lyWE+FXgVwFu3779bi/j3HYZ7aozOZGOoMk6nBrc2JZSgFBI4cyselEwHI/Z2NxmNN1iOnWVoba29hgNJ1gr2NyeuUxO3MQYjUYdzHs+P2G1WsZJtFy2k0JgOXcDMktPE0oJr2HGezDNq0cFvSAHYYXTiOM9Nq29zeGizW+geQ28JkIY998KU22f3x3ACY6qrCmLitWqZLlcUqxKSl1h/YgTQjiTXCYkKkdKhSIhywZYq/2NyHj8QIKFxzeNkzNOUAMqhNEaixSuKEnd6pgu7t4I50DcpvHWje8+7XczkRvTLQACx0oqwj6AWqOJt/un/36dlXpZ2OdMkrIzILbuNZy2hBMpMVJShWvSGm0tB4f7XNnbYzIZx3OVZVA6Agzisr2Dnya0kWdMPTw8ZGtry41rGayBEBBh4jUJ6S3SUOu2/9q6Fzf2jReuAoT01kZjhYIbuxaLlIosHTIajZlMpiSpRQqLSpqEsiRVmDwlzRLG47HTsI0PEZZJ15elDSLtKZm+YhitxT1TKfffehuMKxSzXByzXHrLQCSUq4LJxCmK8/mCsiyYDDIG2RCJZTwcuEpy0pDY1lyNjpTOY750exJI52eB16y1D9y9iv8d+AywKYRIvJb/FPC9dT+21v4m8JsAr7zyyru49LNbH/o5y9nlr7vzu1q76lMibQaEtR6/lglpkrF3ZcSN6ze5dvU6Gxsbka1yOpsxyEcsVgWj0YjFahXJnUL6OcByuYy8NSGkc7VaRU0/fAbQdZePJvyzxj8R0+/fgQDp/7YthCJGLSVCCtJUYYSJLIoxVLQ1IUKkh9aasiooihVVVWC88B0OHSlcYmrQIfpHkEqFMO6c8/lxxOKhwVm11k7jszZy+TR4sjeVlcLahuLB3cvpnAPwkI48rZm331+mD21v5q3zH7U187X+oXMsgXa7jMAPzlbbOpfrp+Za2j6cEG1T1zV15VhNTZqwv78f+zDE3LsxmjuyvVIzn8/dPgIyr/mC80dEugbZhNx2cjI4TeW8DsYywY1iW6GJPX9C2+EZ+1Q6zHs0GmHMjOVqi7KakyQSi7vWuq5RiSRJGsqCEFkU+jSEUIYgDds7N5wOECnLiuVyxdbGDK01+/sHJIlbUNI8xxgb5cbRwQF1XZOmY7Is42R+3FCQ0Jyr336gGj4OyvmUEGKEg3R+BvhT4A+AX8JF6vxt4Hee4BzvqPUn7HmCvv8b98H9awxJwPBx2GpZa/JsyChLePH5D/HU7Tts7e4wGkxI8yZhRkrJYDBgtVrFAZ+maSw4AsSolSzLTjlL24VHwAn+gF13qg+1Jng/tt5e4r7Pam0h345OEEowHObUtsboEM6nO9drjIkCtC1QsiwjTQVGGAZjR8ZV1nX8fS0MZdksUv37inQRiUZZiUXHZJ3IO0Pzm4CBV1VYtJtn04fxOhZPxwrotrYQP0XNbLtCqz3+2v25TtCHz+/mWZ3V2kKvvZAbD9VFDiQ8zNfyGYRInVxKHj16xOGhC6Pd3d1lOp3G4yqlOJgfcXR0hFKK3d3dOD5PTk6oqorhaBgX6Xa+RD8xsN0H6xbBfmRZEPid73sCPxwnCHwlDcvlBodHIfy0SaZy9N85q9WKhYdgsJ7qO2uqfpkzEuxAdK43HDfwQR0fH5PnOdPp1C8qmsl4ymrpHLE727t+PhmsLTg6PEYlkulk5ixaazswZHQwvYv2rqkVrLV/BPxzXOjlX/hj/SbwD4D/TAjxDWAH+B/e7Tk+aB+0D9oH7YP23rUnitKx1v5D4B/2Nn8L+MSTHPdJW3+1PUujOqVZWZw6aBpc2mApi4pCa/LUZc7euH6TzY0tZtNNZtMtUh/7K6VE42CZ+48eIJQiTRvHVzvEL4R8BQqFUDik/RlgtTw5xVToErQaOKN93+6ae+b+JZZ1lTTHC1pshws9EeTZAGV1NPO1z34NiV/GGBaLhdMS6xpjNVIJpEpcxI2SMUoIq8Fox4VvHRWDcilmKBQWjfaaXXBMJqkiMHNWZR0dkg4mCNYISGXifYd7cf+mU0nJ4a9dP89l3gd4Jvyv0/D77/vQRbtd5Gdqt/Zx2jHknRyL4MnpxZgTn1crWsp2Q3DD9ZSrJUo5GA/AWo311lO5Krh/Mufx48cuPHk8Rdc1hXUYdVEsSVTKaOg0fAyerdL7noTF+EEpJMiY2OjZS21vWzs01Hp/lPDhxQYMvkqcbSCvWEULxSCHRDkIZbk6YrnUaO19N63yjHVdMxqOY/ZwmI9xvNcO8hmMBr5Pus7adh+GbOXDgyPqumYymWIN7D8+oLaG6XgT4SOKPvOZz/DVr32Fr33pi6xWC2azGZubmxwfH8fj9SHGd2sQ/tBk2l6mvRPsfu1268ajsbZxjmDRVgCKyWyDa9efYrqxxXRjk+2tHba2d2PEwapwiUaFx0FHec5kMnGOrZYZGzD9WOLPNkVEwud27G/fzxBe+4OgjeF36AYuIU/a+QcBJulwpif+OrzjL5j+RVE45xKOy+fk5MSZ2br7DIR00Mf8xA1ibRvHGAhUIsnyBK2daY111YoAtOgyOgoJpahRCKTtmv3GGKShs9g1/o8GXgl96970nbpN61M29OEcay1tS7+9EKyDp/rnWKeYnNdigZkWBBj+I+OlX7wDHBAx85bAj+f3nxt6BMfOuFwumW1txjHw4MEDHj545JIMreD4+BitNbPZjMFgwGKxiNef5znDwcj3Vw968S0c9zwY9lxIh2Y/q5t6yqlyeHuSNvPHcThlDAdjhoMxi7kr4wkuGKAqnXAvy7LjqwA31pRPdJFCRX9HZ+6tEfiVj5uvi5LBYMB8vuDo6JDpdMbGeEyejtg/cnDZV778NR7vP2Y0HHP79m2GwyFf+MKfsrOz00A6/SbawRKXb3+lBD6cLfTD59DWOohwoWTGNGFjKknIUhgkCdev3eT551/g1tN32Ltyg70r15FJymrhK0MJQ1GVrJYnHQdr0Mrb/CVBgIXBHJy27c/QaCoBfw73cZ7AD3fVd+ad19o8LkFABmdVEPhV5SJtgkNquVw6X8Wq4UFvcHURHW5R+7Q2hjmCD7OxLsxR+lBbKU8vaFprKqNJtHZEc7hCLFoIpJUoS+RsMYC0EiENeOGoEhdOaoWrKBZEdiJtTNMPvdbuKiFanE3BURfei/a2i5226yyxvpC7TGsnCVnbLIRBeQCQSkRB1NH+11kD/WSfJEEmCVo2BVHAjUelFMtVSpbmpFnCNJ8ymUxQSvnKac6SHQ6H5Hnm/VddXn/o+odCEuOpudian/G7mAXvFDDXB0746tp0xmyWZRjbKFlCCAaDIePRhMePH8eoIpUojLYsKrdgLZfLqNW7uaeQmbcakgRpBYtl2buuELzQXH9V1iQqBQRKJTx6+JC9q1f4m3/jFzhZLPjGq9/kF37x3wbg4b37LFe3eO3Vr3H3mTt8+OUPIaXktde+eWrMdPrp8sMmtr9yAh/OF/rr9mlvs8ZQW0PIaUiUIskkg9GY7d0r3Lj5NM8+8yJXrl5jkI959PiAuY+RrUpNsSo5PDlmY2MWJyQ0sAQQq0uFjD5rbcykS9M0ahsA08mwI/D7Qr/f3GL1zqN02ux+7dRyaCCd+fyEUlcURRkzZ0PIZbjH7a1df72+dq23BKpSU+uS4SiP23Xt4pGNtp3MW9m6R3dTwkcGuTVD+4UjJK+0I2CEaMLyTIB2tAspDQ7d0LQVLlt0TX+INX14VhTJum2d6++9v0wwwVmtD+m0NfxYGYpGe+78dxy4TfZ028pUSpEohRpkPNx/HK/x2rVrbG1t+UgeTZqmTMbTSI/hivE01bHaiXTtRaYNsYV9w7xY14QQ3ZrRorXdWtzy7Wsl0IUhjZFUsiFTy/Oc4XCEFCpG/rgFqaYqa9IsiclMwerRupmbeGsy8YvKKUinJfDH4zHWWpbLEiVdfYGf/Ouf5T/6e7/K5/7gDxgNp3z2p34agP/yH/0jtrY2ePrpu3z6U5/mxs3r/PZv/zbnuVh/6CGddQK4HRWxjoI2/C60y/JLtDXi9rksAm00VkiW/iGXlSZNM155+cf4yI99jB//+Cvcvfs8WT7i1a9/k++9fZ/lwgn8VVEznTr8bblcdCZXuyh4MD/DQApmZ/t++4RNQSAK4aIHslBjtFUPVggXQiZF1yIwxpAkTRWlsC3s32bu60ISnoWzMtjahZMuyyIuWGGiDgYO01RKsbHpIjlCJI+jddDUskYYSGSI97YgLXWpKVcVWZIxHgw5OTlx2bRFQVX60D1bYyWopNGIE9Hw6KhEkoT7sgqVeJ4XmyCERYS4eeNx7DBehMOMFaJ1741mL4SI2cZBs2wW32ANOIqBdv8FgdZ+LpctmhGeffh9W7MzxsW7h/d9bb3NPdQWRA281UBZQTBWuEQsrTWDwcCHHQowmtEgi8/2pz/7k/zEKz/KfG75zne+w1e/+lXu33/gBLYfxzMfV55IqKrCQZmhkA/NQqerqkNjnKYpsnXP4T7LsqSs63gNbcoOl0zYLPRKKebzOStfr3c4HDg4NclIk4pytUDJlMl4xvXrN6Jf7Vuvv4pKJHVdISScnBwxm83IW1WmQg1gXRum0ylZnrpw1EqT57kv7VlSVzo+g3w84+DggCu7e7z++uvs7u7y0osf4jvffpMrV67y6te/xT/+b/5bAN5847s89dRT/Py/9bMcHDzmd3/3d/nWt77F9vZ29DWEa2kqkF04nNa2943A/0G2s5xx4DJV25V+kjzjyu51nn3uJe7cfY6trV0sisWqYL4sKMoa4+ODDZaqRYAVBm7gHA8Cvm3ytydr+BwmfruFsLm2QA77nNIk21qnsBhTY0xySgitO1b7OJ3jCmfiB8HVJpZKPG7a5rQR0jhnbIvITSWCum4EmKsp65y3WlQund5Yas+tE+9PK+rKZeQKFCoRyDRpLB7bE4zaxdc7SgaJEaZ3v/HhI/E+m55FeB7cEiyF8H2bHjleQ8+auGzrZJfT4jHqOWDP+g/7Wm8FdbRrv0+b0ygwoIY8EbeorzDCcP3mTe7evQvAU089hZQwHApeeulpbt26xe///h9wcHDA0aHzywSFJkkSBoMBeZ5HsrzQL+vet0OT23PCLexJpyZZ934djOrGQNcCDlng7UW0qoynP3EcPIDPmzkmSRRYRzS4WCwi3Jrnw8aXlbgQ49Vq5bV/vwD7gItwL+AE9Gw24+TkhOl0yssf/lFe/fo3+fz/98dsbu9wsH/I/v4BAM8+8xw/93M/x2q55Otf/wb/zx/+v1jjc0p6DnX7boD7VntfCvy+UGpvi/uc87vLHv/0b0QcXEHDzgcjbj19hxdf+hC3b98lH0yoSs3h8ZzDgyOKVdXgkUL5BIo0mrUBplgulx02xACbBCdcIEVqf4Yu3nueBhjaKSjHhkXAuoiZSCXguWlSRZKqThKTExg9RkHbFLpoJ5K1cdPg0HJ94SwmqUAaT3NgJHjWDYF2yL1xLJzWNBET/UilsM1iMMBIDBBZD44LUVXC0/sagU7c8ZRVXtvtEscpIbGeG6bv7wivZyU6dcZPL5M3wCPh9fQ4W9/aC024ziYiyt+LUp3n1F8I3IFsR+C3awf3IZ3Qz2G8hs/pIGV7c4MrOy6LfDoeMxqGznZ997GP/Bhf+cpXKBau1nHQ2hcnJ1RpynI+j2PD2oa3qP0e3KJjhcC0+9Zax++jJLoOETkO2uv4hfCRPhZPI94Wju7fEbzlrJbHpFlKmmSRVXM23eDo+IDBYIC1dUyGMqZskv38M6x1ycncsVqGPtRljTVZLAZf+gAGKRRlUXFyvODpp5/mzp27PHr0iG9/+zteQZS8/tp3ALhz+ynmJwveeO1b/Mmf/AmvvvoqN5+64ZQlTsN4eKKUdwPrvG8E/nkTom2Oxm3v8Bjrvu9PXIsgzxKq2qBSZ/Jtb29z95lnuH7tBoPRmFVZUZUF9+4/5PH+EbVxSRQAKnVQgEpTEJa6rDoFosOEjELXO6wCtNLWbsJE6UM97f+2RRDaKe3SNph8WIja99yOxmkLj7YT0LEUGv/bBift47FSyg4PuHtu3kmXSEQlo2SWwmlhArDGa3i9sMG2Y0+XFmMqhEqaqkzCnhJg0sgmHLOUrbBNg7EiCk1w5TQSJaLAj9fcE96d+xGio927Pm/gmvZC3H4+70QZCb8PzwGcdWWMYTgcXijwjXdkhgicIPSFz4zujEWc7yVwOI0GA0bTMcNhzmKx4I033gDw1CDPMZ2mVCUcHS0ZjUIkjks2FL3436IoOuPtLA1/na+kmySnO/t1FjHc14k8XWGsYykkCcbgcf4sZrhvbW3x3be+42C32pKlOVVdxnHdhju11iyXS0QrZFlrjZGOPsEIZyGE5/XgwQOubF/hueeei0EZzz/3AlYo5icLXnzhJQCu7G7z+c9/ngdvfY+vf+1VxuMpw8EIbWpClnjXgtUImVwKJuy3943AD60/4b4fGn5/3+a9IEkzKltGjvvbt+/w4osfYmf7CmmSU2t4+OARjx8dOHOORmvOfFy9M8Vk1ND6HNZBQ28P9LNeq6o+pdV39+velzEG4R1lwfeR56nD/T31a/h9mKzBsggTKUQpdEq7WUuSpE7r8s1iOkKpLazBtrQR9y8k8feuyEtCqZoojrY1I6WM2bNtbLquHY9+4S2iMDEDUZq00lM04FhAtWdcNIbEJigjkCE8VggwqoMv97X4IK/CPbnn1hbiDjIL+4d+D6/vRNiHtk7ghwU4CKB1wj6OCR1pK6NgbGv4AXqBBlsP+SK5P35d1xyeHEX8uihWvPnmm8xmM/b29rh58ybFqgtHQjN2tTbUdVO6Mmxf9z7cT7jWsNinaYqQaq0gdz4iRzdmrQXPmS88vKqVbeai7c45pRS5J/FLUsl45PIIjDGs5ktqXSGEZTAYkKaq4wherVbUpkIqEJ4WO85NGuXn0aNHDAYjnn32OTY3t7h/74GLxx9t8ODxIzY3tvgPfuVXAJifHPGHf/ivuH/vAUXhaFn29/eZziaOH6snJ4J/pt+Hl2nvOtP2g/ZB+6B90D5oP1ztfafhr2tCiFMa/XsB6fS3CSGorSXPc27cuAHA3Wee4Zm7zzLb2qSuJWVV8nj/iKIqSbOc2piI+6dKMV8tyaqKypNHlWXZiU8P52lrw0J0o3TCZwCjyw60EbSUxlyVpzXjlvMuwBv9ew8aVdDy+xmHbQ0/QDpJknaOpf3+IYIgxC67a2lpYy0NNEYfCQlSkZXKa8QuWiZVikGWuXBNG5y3KobZOUd4xdzOOwliiWmsp6BxBbhM2a5/QgWeJCGQsim4fq5F2YJa2vsKAWhXW7X9bPv7XkYba+8fPvctvn5lsnUafoB02k7bkHgV+iiOGWsZDocx7HI5n/PW/bcxpvFFABwfH7O/f4BSiqtXr6K1ZjKexfEW4vTb19r3FfS/C60didZOAAMiq2X7eGHuaK0x3idktS+EHqKPqgqEidi6Es7hWpUrsCJG6cgkZXd3l++88TpSeYvXCJQ6fV9COD9HCJ0O4znMV6Pb5G2CF154gevXr3N0dBTv7fj4mLt3nuGv/Rs/yY0bro7G7/wf/1uM/59MJiyWjuCtuccuv5ab54J2GdbLtvedwD9LaF8G0nk3x+5s8w9uY3uTW56y+erVq0wmE6x1/NUPH+xTliVpkjEaTDhezCOsUhtHc2yqGl2XlCsXq+7CIrsFqtuma1vgt523AHVVxm0dR21wDqpuBZx1GH5d16hadnD5AI1EJ2trIWlikBtGQ2NdPLLwArZNttXFmXsFqW1GzuvLAAAgAElEQVQd/63VKG96JzIhSVKKxJFZCaldEpVSDAYJq6KioikvmGUZdRtyKoso7NM0JfO8y4G3Pdy88830Ix189I8QqIQQX7lW6Ldx4PX/gDEEZ2F4Pn2n7WVaYABt+0XCtbR9NpfF8M9y2objQROHPxwOuXr1KkcHB9x/9MCF65omJDJck9aaN998k8ViwdbmDkdHRyxXC6SUDIcOBg0CXCkVI67C2Gy/htbOXo2Fy0O0Eg4edb/rKhFBGLq+69aCdk/fHdOFfUqyNGe5OPHbHIafZpIru3t89WtfZjjKGAxztKmx1oVSR156YDJxuTWZSl0oa20jJcpyuUTXzb3duHGDzc1N6rrm8aN98myINYI0zfj5n/95nr59l3/5B78PwOc+9zmM1Ty69zZSQaJSxuMBRblCW3tq/AohQHQXw8u2953Ah9Yg9++lhUtVd2kXS76gDJkbTOGDH/xWUtcwHm+xvXMNgDQdU9VwdLzk0cN9Xv/2mwzyEXmeMx6PKVo4pdWacrmiEgVYTbkqYtm69iQO2kwQVkKIuF/QPMIxy8q4SpzSYhRY6ZzLDh03GCVRPf5z29Myq6pCKIGSSQTxqqJiWSzRlcZgGOZDDAarrSuiUhu0DfdlMViWReEc0ngtke4EboRjCItzDI3GRV6CEQgfDieU8oVllMNDtcBIH0nko52Cg1cIX+jFQGlqTK2p6hKrBMKkJMJiVciU0wijkNK6gi5COKxVunBNKw1JJLIHbUQM7XNh9S0t2zYUGy3XI/iyHBCsqcDf76OflPQVxXCZxVKAseuKPcVt3f47m23zPGHffg4d6yo6rQ3CWI6PG/rdsiw59rHnO7vbVMZzyNQl2lQxM7oyFYN0QD6dcnRw4JLwliuSPGOQJaAko6GnHDaGxXzuIqryIcYXXzG9cRpaUHqCAhGEva4tQuoW8GxBGCfIw78XiEZrEJbgxxRCRFpmpRRWOj9FqHch/ZxXUjGbbVCVmsFQsFysWCyXGKspyxXHx8cxcmdra4s8z6l0YwFLKdFoyqKirg2pz9C/du0aR/uHCC3AGCpf5jNPE1547lm+9OWv8+1vfxuAp59+mj/50z/mW9/8JrtXtrn19A2WS8faqa0+1V/v1CfUbu8LgW+spbbaJ9PAauEqwUgsk6F3rggZwwJlmjQZqh5OKFYlg2wYE3Ismso4LdXQaNPg6FulUEw2ZgzSnLJy1Xa0TZFa8KMf/jTj4R4AVZnw3e8+IBucUKyaRCNrNAf7jzE+wgFcVIKwFl3X1FUJwpDlIV7cRvNTSEGWOy2oqp2mmmZu0JfVyk/SkPFnKDEIZZDauIIIvmitkAmHRyckqnGEBq03Uwkqcfed5TmV1tQnC3SoHqShttplpAp3LCGMz3LVKAkyaNjGURtrazHCURtbH+nhrAy3CAyGKcVyydHJEVuzTZRSnCyXMRchy3K0j8Nf6oqqOma1dMXbS105QjRpqKsKJTV5oFcvaoqVy2WoqsoJorrCSIHRKdrUVMbTW+iMWqWgpAsd9cJEWYW2oMs6lo+0GLI8JfWQkVIKJV1Gqqmd4En9vkqECB1IROIWKVQs7BE0TbRB19ZVl1KSVDrSOLSJlbSsV14SIbHSEcY5rc2C1ehaU5+hDQctP2i40ew37vkAJGnmFlqf06Dryvdb7eoHK0HtrzcfDRjkCQfHh/zZn/+ZGz/SJZxZU7NauDj67b0dfvnf+VtURnPve2/x7Tff4PVvfotidcIwyxlOhqSJWxzyNGE62mZV1lTLGm1BYDsLUaxNYy2PH7mwyPFkA5Xk3mqogBSZZyyX87hvrWvXTy6y0cepO2Hv5rzvN2MROon9VUuB1YGyeMpieeLnt2FxUrC9tcvJ/AghLWmSUeuKLBuwtzeMcqOqS5JUcXh4ECN4rHX7P3ywj9CSV/7aTwJw+8ZtiuWS8WhKXVVsbGxw48YNPvGJT3C4v88XvvAFXnvtNQDu3bvH7u42j+9vsLk1w5iak/kxSaoQibP6tR+H48nIBQ3QJe27bLtQ4Ash/kfgF4D71tof8du2gd8G7gCvA79srd0Xbun5DVwh8wXwd6y1X7j4HHSSK0KkgBCCTPnIl0Qgk4SyrtFlRS1lJ+okRodYgZAutV8Ek8e/BIhiOp2i0gwpFMcnS7Q2qMQVzr525Taz2R557rm/STg6XpIsK9Iki9whMaqlrqh9/LGua6cVEvDYi+6829ZFMRgDJMpXhnIVoqyBSrskqHZRYylbBamxMQu3jFE0XdMQKz09iWOxbGuQXfzYoLGU5QpN6zvd1SwBytUq8prkeY61kGUVx4dHFMsVwpdNrLSreVv7eH+LRliDxbCYHzv4xvdrWVXUq5K6LLF1jTU1iZIkEqTVGFOhS9/Z2qCTKlo5UinS2nGQC59F2sGYgVLVZMb1pXL2FASIxBfhroWr+yolWKmRRiKlRhgnqNs5C9a6UnWhEL2kISzTQrjfhPdCuGMjnJaKw9VN69Udw/ME4aglhLXUxrix1vPdFEURrQMpJVoItDGsloXT3MsqavjjyZAsT9z8ko1VRl0zm0y4ds1Zunt7e+zsDUDC3u5zPPvcM9z70Evcv3+f1157jf39fczAPa/ZbIZUijxVrE4K8CyYwS6yws3R8AxGg5GzWH0fDvIhWZpHuCRqtMLVWxDSuvwOdzCMkZCcDn8NFoODt1w5x9FoTFGsYqTS1atXmS9OuHb1Bl/92r6rfuWF+DAd+izqpi7D/sEjhEg4OjoiTTPGoyknJwu2NrfZ2djl2tXrAHz3O9919WetZGtri0RIjvYP+Jf/4veojOaZF19ic3Pmntdqk2yuXHJbYhE+Z2Zds7ZVh/ldaPqX0fD/Z+AfA/+kte3XgM9Za39dCPFr/vM/wJU4fN7/fxJXvPyTF5/CmVhlVZKlWSfGVSkVq0a1BZzwiSJhX+dwKjBGIDROA0tCOOPpgZBlmcsQtZ6tUaacLGqefvppZrNZt5Yn0vHF5A3G7vBGHXllgA4ZmjuRq5blcggEjefB88IYB89Y4V6FEE5jMV0GxWCWBtO3rmtqrzVvb2/H00nRZWes/bVouolBsddbWHQ7tLLtHAvbjGic5w4t8YRkLdjDWgcXhBC/LMvAWFKVMD8+YblcxrDCWjtLyNQVxtQe89FIDEbXbvKHmH0sSgqyVGEEGCPJUgU4rTnAVuCzgf00LWuN9JnAKkuRwfcgAkBjsD4D2SQalVTeesJlAAORClNYkuA0FxYpLULWXoNsMi3b+LOUEqPWU3W338dtAdIJsI+HgcI2g3W0wf47g42v7nvXdFXH5xh8LFVdgjFIK1yd4+B3WVUUonBl9AYOkyZx1nSW+JquOAjwjdceIYRgNpsxmSQ8+9x1drZ2mB/POdw/jDWQMXB8ckyxqhz+bpv7FEKQyGabgUhNUJUlRmtSpcA7luuijHCuoRWG653+WgVM21u5oU9pktXcdsgSxWAw4OTkOC7Aw+GQsizZmG2yvb1DkiiO5wckqSIfJD4m3/er1o76I3WEaIlMsdpy/PiQD3/4R7iycxXbgpXrSvP22/ex1hW8H44GHD7ex0rBm/fu88UvfxmARw/vo01JriRW1GRZs9CFsdPxf3ihfzqU5eJ2ocC31v4rIcSd3uZfBD7r3/8W8H/jBP4vAv/Euqv7vBBiUwhx3Vr71kXnCRoMtDLLbBO5ELjjLY5+tagr5vM5ygv8zdkmySjxMeOeJTH1BGA+QUe2JmVZVNhEUlWeUjZPGY1GXL16NWoW4AbEZDx1zlhjWK1WpEkeo1NCJA502SJ9362d7O3tp7XprsYcrJjg0ArnCXHDnUQqTEdYS/9Z2MYx23cC9iOHQv/0r0lYF6XgBGH3WtslDlerVaz0E51Zrdj+jmCsa4x2zkSso1owwHQ8dJTUAT/WTuOuasf4aPGZt1ZT2xDd0USx1D6Ra1GfgJIolSJT7y9RqlVAW4DJkEo4AjCpqJVycftCOI0yyHsPuUjlyN1jjL3sVinrC/zw33ban9m8Mzlo8eE1bDO4BKNAHlcbE19Fe9xYE+k8AvdOliTk2YA0SzCjMbpV8Wk5n1MVBcOhiwEfZBllUcXoEnB8Mq+//jp1XbO7u8vOzg53797lmWfu8PLLL7Narbh37x4A9+/fj5Fu3379TVcaUAhsL8sbXEZ2tSoo66oJIrAiWjgagxo0pRPDs2hbNFLKuIDRm0dx4cMyHW/6+WHJM5c2vFwu4//tW3d49Pg+B4cWRI2qBKtVEamWjTEcHc7Z2R2yubHNal6QJgOeffYGP/nXf4qD/WMe3nsAQD4cUa0qyrLm8eMDlxshDHmacuOpp3j78SNWhWMiLcsVxmp2NnY4OHrEclmBNE7w04126s/5d9reLYZ/NQhxa+1bQog9v/0m8EZrvzf9tnMFflinsrTrsARfKKQd8eAdnkGDNLaZ6JPJmHJZsiyDwANhHbaptY4sd2maYrSJPCJVqSET7OzuMpvNnCb9/7P3ZjG2Jdl53hexxzPmfPMOVbequqonNrvJnihBTUqWRNg0YFuAbQHSgyzLAggDAvwqCH7wkwAbfvWTBBuGAUOmIEOwDGugaFnNoQeazeLQ7GZ3DV1ddeeb0xn3GBF+WBF775P3FlXdpIESUHGRyMyT556zz46IFWv961//8jhQHCUdzS+OhAESdHECNt02fWJFa+XL9XepeeHvsGtc38/gh72b5yOcUztsGKXk6sLB1L0uu8m6YOh7nZGevaO16x6TDSQFKuG9w5dcE4Cj9NWYATd2rdnxogTmEczfNMJyePz4qadS+kOx6fFz5w25VpZYg4sk2ZtPx9iBwe8NGygrmzccwI0/TGqviti2La4V+mZZN7hWgWpRjWioBKxe7oEm0n3DF6vEMEexxvnIMERJSkkkpkV/eXD/dsv8rxv8sHaicbLrpfHs4a51b+ifB+kY57BaFEadMdJ7tm0FshtcQ6zANi1t04KFOIrJkoRRnpOmCek07uajLEvqssC2hnJTYmpDkWhunt4gT9IOflJWsV2JxsyFu6Auar777e/ycz/3c9y9e5fPfeZzmE/Jenv48CFvvvkm773zLsqJ6HhIiGulUMOoUEPbNCjnJN9hLE3T0jpLoiN0ovt7iwXrdg7hyJMvrqPZQ1aSc44W6x2O0HxI1sBisWB/74AHDx6wtz9lOp2xXC6xNJLsF11tgK6ndRplJDplWW158c4pf+HP/Xm+8md+lq/+q1/lfiktvE3raBvDwf4hOlJURcnjx0/FoUwSHj58yHK5BCQqjVNNPkrJa5F1jn0EGyKX4R6TNfPjJW7/pJO2z7uK59JllFK/CPwiwF1PgZRnu44K9tJLdzne26dpmk6ECCQ5eXrrFrdeuNMt3IcPHoFxFJsNq80G6wxpKkqQjd2lGU4nM5qmZW9+wJ0XXpaqwqslUTrpRJ+GTYQ3m40P+8akacrDhw89rXG3krD7CFZuxQfx8J8Xsg0Nvvy9b+TdGZI46qRlrxv8YPSDwY900r3XdZrhYD7+SA8/PD5MFtprOuzhMFgulzx+/JjNZsOTJ0+6hHmWRLigS+tfXyNMmlhroUk6h7NiFFw4dBFjFynr9e7pewR3nvagijmW66wayakYK4eL1bLpk+Ahea0SsCgVSUI20DyThCge0iMh9u+VRJKs1SoeePjB4DtPTdVoLaG/UhDHCSDzam24X/JzN9fal/F76o4Tq9895rAUJrBSHMa2mNZibCuCcd7s1bYl1sr3Ek56ZVUUbWvI4rRb30kU0WaZHJq1MEmKrWF9tSDNkq7pfJZljMdj4jgWVcqy5OTkhDfffJPf+Z3fwTnHiy++CMDe3h4nJyfMxgVPn5xDSEr7yMSGn5GDJNGxh+q8w2JcR9FVsabwTkKrjEBYA3aSGqz36/Bi6OYV7vVqsWI6m5DEGXUjGH5VNrz4wl3uP7jHarng6PiQ9XrN5dU5y8WKbbFhMpF7MJvNuHFyStMYFDEH+0fcPL3FdDLjzTff5gdv/7BzFCMdY5RlPJpQee7/fD7n1Vdf5datm3wxVhzfFJ2iRw/us1hesd6s2ATblSVMJqOd+xL2YEAqfpzx4xr8xwGqUUrdAp74x+8BLw6e9wLw4Hkv4Jz7e0gPXL70pS+5sioZZzmmbTtc+vOf/zyxE1ZNpETRL05TRqMR+4eHzPb3Ovx8Ptuj3BRs12s2mw3OWWEg5DkOQ9UYzs/PAXj5pVcoygpFxE986rNY63jrzbcpakjTTE7xUJihtdDDfMOF7XbL2jc6dh5mfj/VSjGo/dfQiHc4vuPa38PPfvPWNZ6O8AwMY63ttbqh2/BdqOt/DwHg84z4+3mdwzCye1z7xdcKpNTBNAO+fl1VXJyfs16tcM5RF9Ks3WlDpJIukamwPgnqBcx0r3teVRW4vum2NZ4m6qMJKXbTPdyhdad+qJRGabn+OBbGE60kOK01kuz0krNKa9b1GrwYlda9xIBJU6JYDcJntwPR6Mi3bdFiNAMDyxok6etEJTQSqJliWwmN0Kqd51rXdjmbKE6xkoUVcCt8h+4QKLYlKCeG5dr32BubNMnJY90ZfJGp8BBa09KoakfmI89j0jghjRPquKY1DevlFXVhca1fU638PBqNUBk4bbn3w/eYTCZd1BvgjPF4zOHhIQcHB3K4uoFT47wEnveaW+eYjHOJlqsK4yyxz1WFyDAo0AZhPOcPDpzrIvwA6XRsbLfblkYp1eV5gmMS1mwURbz26sd5+wdvUZU1SZyRxBmlq4h11tUBmBZG+RRlSmajOac3bjIdzfj+d99guyk5P7vgxslpdw+2asu2rNlsNlTlltdee40XX7pLURd873vf5XIpapmLxSVtW7MpRGwuyyfko4y2rRnCp8PvP+74caUV/gnw1/3Pfx34PwaP/2dKxp8GFh8Ev/9ofDQ+Gh+Nj8b//+OD0DL/AZKgPVZK3UOalv+3wD9USv1N4F3gL/un/1OEkvkmQsv8Gx/0Qtq6hizHGcPB/j4An/r4J9iu15yfn/Py3btdReB4OmW9XvPee/c6cac8zzFNTV1X1HUlBTx471tB6hx3fCnzV/7Mz7JcrvmDP/guZ2fnImO6XjPdO9nRdAcJ8bM06RqQPH36VBTzlBQIXU/SXsdl3288z6O+Du8AVFXjNedVhwfL/7Nch5OGLB0Zu1S166Hh9WTQ8Hqus3QAyroWT7Fpu/vRtm3nXTnnaOqas7MzFosFo9GIPEn7qtymIU78klPPqlSinoXBwuvuwElWisLM4PqGyejIP5ZGMY3yzbcDTGUsVveFfSEvoZ2GCLK47bzZIBsNdEU8/ZeSaEKBzvPe0zROCtKcxTkFWr5Xru6gmMDZDz9bI358kvSY/ft5dKvVqoPIQleykJcIncL2ZjPizr/tJTSUkyI00zqs7udMW03kFS9HI+mwtjebUZfbbn9tt1sWiwXj8ZjJZEKWZWRZxmq1egYCNcZwdnbG40dPuXlyCoMWMdbnY0yXH+qppEE7PxuNO5JGUW5xqVeOHahmqsG+2YlGr229bl0AOo7BKeqq3qlzOXt6wYsv3MU5x5tvvcGNk1Nu3ryJdS1NU7PxtQjb7RZrLXk8Yjadc3p8ymaz5ezinFE+5sUX7nZwdFFUKBV1KqRFWTPf32exWPCb3/pNvv7Nr5OO5Z6N85T9gz1cWzIa5xjbstms5LV+BJvyQcYHYen81ff50198znMd8Ld+1ItwzjIN3XKSRJr3IlTLi7MzVosFTVVTVCXf+c53uFoupXT58LCDNM7OzkR/O4oYjzJ0HEvhjbKsNhtZrCPJAbzxxhu8+urHuXP7NutlwQt37rC4WjHKx2RpTlMbxiO5NXXVYox076mqmuVyxWa97dT8ogHrozNMrn1fHm0YIWQNB8xQzW+YBAxTpPVuKKpczw0OiyHyujkBh87TTIqftuUzG+N6hebw0Lp+GDjnQNlOm6YoChaLRcfnDv+3aRrOz87I85zpaNz9zRpHliQkScxkLBvCOtOzd5pev0eMt8BnQQfdtHI9URSRxBlozXpbeNhHoJoA/wyLkJwTzrt2igiNmGLVJ/2sI408dOcUVjlc69iaku22xDnTsWuyvJfUbdsWhyGJM9Ispi6qzqQF3F2hiROpn4hiTaTb98XlcUK1rOuayh+iYR7DaBopngryyCF3Yox0Xdrf3xenCWGWYa0vtnIoK3pEUSzNY6qiQHf5kQitIgjd3jxOOcpzgUG8ZW7MFothtSnYFFIsOJvNBIKoW58I7VUlZa5irq6uMG3LgYd4Cl+I5zz9dWjoR6NRty5D28S9bI9NLXvc+Hsg+8EXWAYygwmJ3X4E3r9SAnZWvpp9Op1ivRRHWZZsNluKbcWf+pk/TVVVXF5dcHi4L7CdUl0Xt7Isaasa0zSkSU5VFNRJwsnJCaa1nibuK5irtXR7c4btdsNrr73GwdERb7/1Bk/Ozrh55xajiTz36aPHwuuPNXVT8fGPfxyt4a233kJ5TZ1woBpjyKNY8hwfRH3g2vhQVNoqpWmrWnjOaYqpZVLvv/ceTx8/xjnH5eWln5yNNMxwjrauu0z32dkZP/mpT7HZbHzHmprVuu4MQBzHnbb37Vt3OD095eW7r/J7v/v7XF4sRRgMX4atemZA2FhDrjr0hlHw0WusC6cDLLzjfQ5HMGDXJY+7hJTfaHESUDe7Y4CVL6IaFl4xwPCDN3zdU35eZOFcr5H+fgbfOdcluoLhd87tNHZpmobZbCZVk/loRwMojWPiJOqSgNYZqaxF42zl73GEtabrPmSjgRE3YEyLtbV4h9cOR2P6A3B4mF2/5wo6T1g5TRPYQwO8vIuClOtazBkj/Yr7IiBHoy1VpcmSRGoJCNCy6ZLAUZR4HndfOtXr58tjwt22FHUliWTdC2eF9wutKO/du8d4PGY0GglLzVpJ9Nm+N69r6q4YtU/yaxLnsFYTJzmepNKxkoRwK8V61rVcLVboSDHKpU9tNhph6pZtVVBtS8q6huWaOEuYtJJXybyxa1NLnmToJKIutr4ATw6p09NTjk9vsFqKkf/2d75DrOJ+3hRSJeuvW0X9wZeQdHMe1l9grzm7G+0+b8h6Lbt6HkDyMESsVmvu33vI3RdfYjweM5tP0VoIG1UR6NCO8XhKsd5QlyVXlwuqqmE8mqC1pixrqkryGMdHJ9x/cI8nT55wcvOEF1++yxe++HlG4xwXWX7rd36T3EtRzPdmZHmCdi1VVQhN09OPFdc6ef0xBY4/FAYfIPWhEE3D2dkZQNfouy6FPWCt5WBvn2yUM9/f73jwgGeHSBIkSSKaxnH+9CmL9ZLZTDS8f/7nfx6AYlvyG7/6ayhivvHN36KuWl584S5HR35hmrYLZYOSo9YVddVQlXVvGI1UtDo9FIjqqY3YUEzF9bor8eAirz6JPMe6Z/uURnGAYMyOwQ4e/pCPrOg3AUjSVoeiLydfXUm79ZCCN4yB7TR8/yH7xmHR2I6SGhq71HXdbTatNdPRuAv3oyjqKlalKll1RU/WWYxTtBZaC3VraWo5XFsjTJ0+So9wytHalqZpaYwhShIsTqp1Bw3ipUbBfy7jdWS8QXBGboVV4XqBNtxT8QedkyIzeV8rFTtA3dTS3F5rdCyVqXITW6o47mCawJqRim/pfKS03Ovr4jnB2w/fW9fDLMCOlx886NOTEzabDavFQjzgvT1iral9gw0AV4sgXRLHxHFEBNhWDjClBf7sirSc8xxQKWySZKfGOqTVZyxwShxrXOwglog2amvKbUllRPYiqzKmY4nSx3qMVobWWrSzxDiuVkvOLi/YlAUHN0549eMfB+DGzZv8+q99TYrljBT0OSeHso4idBqRBU/WH1LG03lbn4Q2ddMbxMEhbwJU2u1N5/sxS89boNOy2qwLmuYR8/mMLM05Ojhhvjfj8vKSR48lDblabcA48iSjaFuMl1UJRrkse4fwvXvvMp/PaZ0Isb351hvceek2D5885OnFU84vnlK3cphWZYElxdYV67XUPlR12TlHYS2EPRaYez/O+FAY/B1PzEMGICHuZDRGOaF6PXz4kKKQTjtYy9XVFWvv4Zum4Z133mE2mzEej7FWdFL29ZxsJJ15QrPq7XbLOz94l5Pjm+zt7aFJuHnzNqN8QttYalN3BrBn1Diqsu7K1q214Hp54fDc0PXHWeVrv99/DOVkh4Z26J2GwpnwXgH+iQZibMP7GLwfgMYZzyjJCLIK4XWHGizhkBhGBuELerw/TxPKUgSlhoqaI39YB1juOnVzh07qP3OQTqiahrauKWvPILEG24inG+iTDo1WETpKiK38vi1KjDPPGPy2bTuDr7VUY3cKjGaXjhqpCOWiTgrDecE3HSiWSovgnH/dphGoLlYKrROsNaJVUxvR96FvtD002sMI73lRR/czLSrWJDqGSKCoOtBYjaWJNfff3RClMQfzfYgUm+WKzWZNrKKOttwg15gohdIROpLiw8ZaaBzGlp3+ksAvA+nnsBbRWNcLDDqEdZSkOVpFxG1GpGPW2y2b7ZairjvmjYpF/kMpxXSUY5wjjSLiNGW12fB7v/d7PPGMnr3DA9EW8nz64CyFQ1cp1Xd/w/RR9bX12avD9hFst48C5OklUYzp2xZq7XN2TtonPnjwkCSJmU23jMcC8SY+cnFGsV1XVGzI85Tj42NmZQNKpLiznM5IP3jwiKoqsFaqvVvX8sab3+fNN9/k97/9u5LPCbTpWHJCaPnZ2HZnrw6FFyUnqZ7JVXzQ8aEw+AGLU0JY7jbIgwcPGKcZ0+mUT7z2GqvFQiiaA9pWWAx5nrNdL2maiuVSWpSdnp5ydHTEZr3l/qOHfPWrXwXg9PQm52cXxDpnko+JdCoJoqJglO0Dw+biijgWeYegjic0uuECHOrVy3xcTzy+3xga+x0P3t+D0BoN18sny/sEjfgB3j/QzXZOyvBBEo7D94JdozVcVB0AACAASURBVDzcHOGx6y0OJelaU2wr2kY+c5bEkMB0LJ7KbDLzbe3ANtZDRWKMBdXQOCWhfCimaowRHNYIBuYQiQ0i10cjCrCWuhX9ncZY1pvNTkgfRNnatsX6642jxH/O3WrY3jiIEmkwVMbvIjtAgj0jD2scTS04vHVKkrPO+TZ0uotGTOs9VRvm0XP4gy7MtbnvfldScJSnKaM8hUhhG9OJnClE2G69WVEtarS1VG1Nsd4y3ZsxyccdJBVHCVGS0eSGLGu7XIpEk1JlHnuZ6sj3MBgafOcM1jQkNiHywnix7+bkLFgMFs14OsdFCVrHbMuSxUpEzirjmE+njEYZFlhtNlRVxd7BAUkc8+TsjEdPxOBPZzPpJOf1mqxgbhjnRGdpYJwjXwMRKYXx+7+NRHYl8tdvB3suHORhbcdRSqRFTmXoUEWRZjKZMJtNvbOpePjwEYvFgizLMJ6aOp3MMW3JxZMHZPE+eZ4TRRFFWfvIYcva53kOD/d5+4c/QGm4fecms/mUr33913n0+DGb7Zq9/UnfG8KTAKIk6SBRpZ3YOpIdYkhn8OlrdX6U8aEw+ADKh/9A57VfXl7SjsYsFgtef/11Hj58iI7jTpP7nXfeofKe3d7+jMwnK+taJiBJU+bzuSSByooXXhCWzksvvYwxb7Ner3EWjo/kJkuGXe14zgGeAFHkzLJMONUhXFSq29zDCk5ZULszcr3idmhkh4ybXVy/8Zz/AUNBKZJId8mtHtKxOxHH9TE8UIawzfXvQwYO9Aa/2IiWeJpKAjMJSbog2OY353Wvdvj+hYfgrKPTgXHGecka8UjRMdY6ypAbaCtM01I1NW3d0FpDVTWdPK58lmfvc5iHcACHv4VITympCB7i6kGbRy7HQYgcmv5+Na3tirKUUqRpX+rev+duH+L36+3a/a4ss1lGXVeUxZZ8NOJgf48kJOvalqIs2b9zm7KqePmll3jy9ClvnH+ftsqole5hyDRDR6kcHqMRWZ50kFCspIbAmMDDt0RRnytwVoTsYqWp2xa7ESOuylLkJnyxmlNShZyNRlIEt1x2+3bj4VenZ2KIrWG73VLWNbPZTLR/PBZdty2imOEdnlC74hxV06Bt22trec93qCvlfGLfdgn/wL4xXZXqcHTECB84VabqoghnpShTR4qrqytpSTjtRRDTNMVGMJlMaNuW5Xojwo466RK8ARl4evaE5WrBaJxxsbjERZb33ntX9JAiKMstbWjag8h4RM565dWWNEr9Ogz5oIHBlxn5t9vgAwKq2r4MP2yos7Mzyu2WPM+5decOWmtWqxVt2zdoiOOYqqwYjYVaVhQFjx8/FsGuWG7eYrEAhNp2dHREM7MoYm7cuIEi4uLyknwvx6qkWzhZlnmRNdV1dOqTm2LUh80vdoqwnOpcPOUjg/A858QbFIMvp3UHe+i4e5009Uam3oVg8OqOO6Gf2tVQ/6Moo9chpJAjCcyZ6wY/FMHkeU6WpiRBTnhwUFdVJZK/SmQKgseoOgOqxEsGWseOHkxjnChd4qiLisaYToahrNodCQHnHDpyXS4kQFzhc6pQCh8aa7S9hy9eboDgtGD0TnfevfHGN+ylxvYdmFJyakqJShpHlsUkSV/w5T8i0TW56mGepVsbg+GcQyvD0cEB6+2CuijJ8oT9+ZS9wwNZB1FMbVqqbcHB8RH//r/77/GDd3/IP/5HBVbBJB+xWPv+s62itnJob4qip3Ammlhrj/sPIR3JMzgnSVuHYTIeY5qe/dI6keXWSUwWJ6hYZBCINNpBlCZkHs4o6orles2m3DDNck6OD5lMJnIw4NgURbcO6rYlz8ddxXQw+Caszdp06rl4aZAoikgjObyCImy53fr7GJKb0vr8/ZycMFpTdw5OXUuzoelsQpqmzPemjMdjrq6u/D5oibTg/k29pSq2REnK6ekpWZaxKbadA/r1r38dpRTbskSvI26cHvO5n/4pqqrkD7//B+iBHE4cx8LscyFnuEseGO7jXXiQH3l8aAx+U1U4Y0jHY+aehx8SMAd7e2w2GzH+T57QNA1XyyXT8YTJTHDjqqqIkwSlZJONx1O22y11JeEpLiL0w1ktN4wnexTbNY8ePmRv/1DajcUjHl0uUXHuqWqQxClZKsmZoiiIPY3PuX4x7RhVq3DaEZqqhGytdTs5W/EuuzLx8DqiGiq4q7x/no2omwrbGtomeOI1zhiiqPWa3N69VaLtEnJcCkmwCQeE7hrDGBr82nPs20aaizQDCpwx0h4vSSOyTLTg66ZEG02ejTvegLWWxpruMwszw9AaKwqbWtH4C2mt2xFV6yAka4X509gdXD7gtHIgSnNxpfROe0L5zGI4gmqitcrjva47cLvNgybSKUGsq8tvBGhHuS6fhIrJTURRC8ymlGLkpQfK7abbfZr+IIojTRTpTgIj3P9hErFbDw6cbTk+OCQ/TWmMVAUHg/vCi7eFypdlvP3OO6yWV6wWVxwfHfDJT3+ayWjE7/6BV1+8XFE1bUdqqMq2y+10B6RfXzvecucUWKrGEMVKaLBAGicorTHGsqy2GGOYTac0Vd/RbbonezGrEs7Pz3ny9JKbR8cAlEXF/GCPaZR4SqHMwXQ6ZVMWvhFOKge3kkij9RLg3U7SAsuotKcxd+vXQ59d9cHg83bRC0YMfNXiPAPMtYaiLNiu1iyXCw7394W15OmbozTjrBUSiXYwmuYsmwacJskFbh5PRXHz0dOHPDmX516sL3nhhReYzSfsHexRtzVXV5fUTellQerOYGuthdnXCoxZVy2KxrP9tDcc1w3+swjCBxkfGoOfZAm2cdBUvPSyqDPM51Nee+UVjvYPeOutH3Dx9Iw8SVlsC+bjMbdv3+7CWFs3qDhjsdyilCZCk8ZjDg6O+OnP/RS/9a3fZuPD08vzJW+99R44xf7hMdkoZ7ldsS1BKZHTHeXS5cYaePL4TPSznahr1nWNoufPK08fUHg6odUELrmc1n2yFeiMQFUHMbTAXFE0LdLkAe8BlQXWNh5bt+CjgcYYWmVJdDLAhV1vPYzDKYdRtoMrNAP4BcTDroVx40yLsQ2msVhTS4/QkFRSDhc79venYoScIvLX3FQFQ0nYKE4wFkwVNHMilIp8nkmzXK7kczWWthHGj6nl81lnUODDa4vyxk5bRxop8jzzdMSEPNcSBg8kc6GPChWQpFK805QVVV34moreu5ObH6I09Qzrx7YWZYSK6pQiJmKeRcyziWeVOEy5YTrKng+VtTV1vUufjQfGdlg0pbTl5PAQp3wUgyKNY/JMvFtnW4rtih+88T2Obpzwp778Rf78X/hz/PKtE/7Vv/7XzKdTTg5FX32Sj5hMJqy2G374zntcXFyIwUazKQruvfeAiWfUxFkKThPFcngZ5yirknRbMZtNGI08S8lYokiRxgkGqOqGaFuRJNJoJo2D1ikQaQ4P5synE54+PaOoGoqiomgbqtoymY663NumlJqOo6NDrKmpK6k1UBhwDdPJWLpUIbUS29WGYrNhbzpDKUVViUxE7qmfQacoSeJOUC4cZGVZMhnlmKjtIjdiiW7K7ZZtsaFIYtIs4mBvj9XVgiZNOfZRVlMJiyZLxlR1SVmuOb+6x9nykulswqpe8eDpfQBuvXyDKAWXWB4+usejR49wzjEejYi0JonzLmlblhXOWVIfvSSJ9L3IUtDaF9f5XEpVVeRxhI48++5HHH88UudH46Px0fhofDT+rRkfCg/fWoOxRtgVqM4Td84xnU45Ojri9ultvvWtb/HmG28wGo85OTlhPp+zN/VUtNaisowkh8XlJa11nJ6c8NM/9QV+4tOfZnm15V1feHVwdMwLtx21kwIn5bFe40Moa9gpsgoMnS4EU3EXKmqtO/hnWCSh0Pjmij687NkfxonXGvrYAs+En0FkzFkPRTjh3ITkUPBqxbMMCV7AsxesEe8eFK3t+d3DIqkAp0gisufWS9+BvrlMmqUdI0hgtr5Ji7P0Hr5TmKYWWE379nJG3rOpWxpjOTsTPLR1XoTNikerlBIoSkNbCbwUopEsjcgTSUCORiPSPCLPYrTuW1d2zc10n0zFGaxNYJrh3OwZOENFEZt1IbLD3isv69bDIDLnZdVLdreN0EBrI4JxyouCtXUvYCfrQ6EjLTi3H3meE3Txg3cbhNqiSCpgR3mCjl03T9barnr24uKCstxy79498lFGPsrYm035zGc+w4OH95nP5x3b6NHDxzx+/JRiueT0eJ/5OOf+w8cslkuiOCEfpd06qBqDdQJdWqdEumQyY1MssZuC2jNUsiwj1oaSklhrYl/0VTfSsNjGqssLoBx5mkKiMAfHbDcFF1dXLNdrVssNBwcHjCeSe5OcmOXx48ddtFNWhfSObRoePXrEyaGor4/ycddnIcCAdV13XPow/8MR2HzOSU8A5Zx0x7JxN6+ptZgkJjMJral5eP8+i8tL7t69y3w26XKKFxcXLBaXpFFE6xqcU7RO6OHLYoXDcHJLICwiiTKMrdlUa1rTdNc2Go1omqrLN+gQ/Xn7YlqLaYUgUrctVVt38xXHsS8g2bVRH3R8KAy+1qJVHuUJOMd2I2F0XbdsthVXV0uyOOXOnRcwRrDcqqp48OgxBwc+qZWmpOMZVkcsFyuyJGWUT8jSnOViBU7xwp2XADg8PiaKYipjuLpakmdjoVyqfoEMC7qapm9YPKyOjXS8o2/TF0UI93fIax8yCMLzdrn+1zni+O9S4KOc8+qSYrCC5G1repZOgPSUp61pfy1VU3XXMjT4oXDKGENZlt11Cp7aG6Ysk3ZzbdNIhy6fsA5sBxe6QiGfSavY89c1bSOvXRbyXkW1q1joMJKsigIDRJRC40SL0UCKZKa+ujTLMuIk6Ng74lg/g0knUTh0+5oEuWdRV7HqJ4LtphQKYCN5jG1VCqOk8AdgSC7WNcW2lIYulce6lRy2xu2+T0iSDvHxocEfdlML7Jkoithulr7aW5yIYR7l6uqK5VLu2/17D/gX//yX2RaS16qrhi998ct85jOfAeDpoyf83re/wze+8Q3eeOst6roWthqwWK7ITdNJJlggSlKU8hCbsiR5QpbErDdLNgsvKT6dMhmPUVp68aZZIkQD7XAtRG2E81XhcRwRe5XOvb2EJE5RS1HUvLy8ZLvdMhpn3T04ODjw3dsOJCdSlsRRwsHBgVT6mp5AEfZmV/TnRE69zz/0ewC//ruczYA9NYTgxHGSg61pK5n/smQ8HpNm/WES+l9slkvwncyEFWhEAQDTtVWtK6GLNpW8Xth/ZVmSpMI0jOJwv2T+Y0cHJwbJjLBXhnTrP874UBj8neFcJ5dQFAVPnjxhtVgwn8xEu6KuOb5xg9OxCKiFaKCsDC++ept8POVgdsjh4SERGqU0T59cMJ3tDcrEJ7TGoRNHkbbS/UY16KbtemsGrFcmue1w2GDgJXkY7Rh8uXxJHDlrpZrU0xyHtEvoD5bw83V2TRdNRNpXuQbvtdfKiaKItmh23huCJrvtWEV1Xe9QLmHXww/0zjiOyfNcXjvumSUhuRprzwryuQLreeydtIT1zWScxbnGs4t6j7lp2644q3UWYxWu9fxvDZHWKOUYjUYkacx0JPM1mY6YjqakWeyxcGibCq2lGjQYWMB7+6JXP8qyrqoV8M3Ch0JxMJpMxGNsrPDVk6ADk3oPX+6vSDb7RjTW9VGCEs74v8ngBy78ToShVGcInHPSkyGWQyHUTgyLi1BCid1ut/zWb/0W5+fnUjXrDK+//nrHfJmOpnzp81/g05/4JF/99V/jV7/6azx++pTTkyNefPFFzp6esy0lGW0sxIlCqYaqLmnbhJHKSXNJzgdee1lvMUaM2ChP0DoTcqXWEGtpdO+pnkZZGt91zjjF3t4eR0dHbLdbLi7PWa/XnbOTZtKMRZwgus/86NEjzi9i0iQjiyUa0Crq1mri82Ahagv6Q13ho3tWhK/fQ3pnP4YoQymF3bbdAXR2dkZZFRwfi9c+Ho9J05jHDx7QOmH11DSEHNB2W7DyQmtlU3J4eIjxvTyUFZbfslySpDFpGnedtJx3QpTSOwdUVVWdJlhY323bEhvpz/yBuqhdGx8+g++LigAO9g85PDziYL7HZrnmvXsPyLKMF7IRr37s4zTWdJ3f909uko3nHBwdc3p0i6OjI6qttHiri5r9/eOuM5XSMVkWew+vQbkYZw1tY3A28oY6aOmYzsuKdPJMv9khza/j1HsTOCxgGiYLh2wJgR6uGX3HzobXyvVNuf27hefGSvfd6xU7nkAXYTRtb/AHDdedsZ3iQ+oXXZZl4gGrvk+s9YY7nYz6RLSynakPkI6n1tM20v6xrhqauvUGzcNQocLTQaQj8D1Jo1gqa6MoYm9vjySJGHvK7Wick8UpUaT9gWOlIEU7IqV9taQ3olpL5ab2FMsu+BFBNdvsahLFSTZQTjS+kUjPm48HWjrh/oS5UL7VYagtGM7tsL4iFMwNI6znjcJ7gvI52FFuVUqaq6+Wa8aTEavVhul0LsnITcm//OVf4Wu/8XUAyk3Jz3z5y/zCL/wCf/k//k/4xKuv8X/+0/+Lt956i1g5XrhzyuVSKMrr9ZqmbWibFmsNcSIMtaqumc9zIi0H9GazoSw2PkKZ4FxC4zVpYlJUHHlKpSTkK9N00KLej5hMJp0zkWWXXQQdkqlJknBxccn+/gGvvfYaT58+5fz8nP2TA1wb2EyiaW+tJR15cTfvDfeGfVB4NXCiwKtlPmfIAeDrXDBeF6dksVhweXnZGeAbN24wHo/Z39+nbAouLi64uLwALPFIqtBDz41NKU2TTNOwWq1IIw+BlbXXlMo6D7/20N446aO9JEmeu1a6+p/3XUV/9Pgg8sj/E/AfAE+ccz/pH/vvgf8QqIG3gL/hnLvyf/s7wN9EiCD/lXPuX/zIFxX61B4esre/z82bt3l7+yYvvvQKs/GEomx59OQpJ6enHB5Jw4F8MmWx3BLrjEhHrJcFbSUaG5qYumyJU48bJgmT8Zw4y7hcCHe2bhx1ZXCuL5zxn6endQ28MaU8/ZIBnc8b+NZKo4v2WjXrdX58J+Cknp2+TgjLe9T9e7iOXw7sSibb0F7v2YKvIe1u+HiIUuJkupMXCNo93fVoacjurJJKUiMsFSlI6sNj0wq+WlfiAVl/vXGcEkWKxrNetMLfg4QsF0M6SjPiJGJ/f7+DrWQ9RF7tMtBIFZPJpP8MalfeQisFylFUDbHuIbQhNVbul1B0h/entc82fgmfP0kSqYKMokF0Jgb/esg9lFfo79/zFUvDmhrnWYdLG2NE4nsQPVprKast260cvIdHAmfmeU5VVZSesXZ0eMxv//Zv893vfpdXXnmF0WhEhOLWjVPiLKaoK7QHjEd5TFFVXf9bi6FtCib7c5I0IhjQfJwwneaMc5Ew327XQnNWlsT6im/CwWkxtRxuxjZcXFxQVRWTifDbj49OqBsx+AFazEdpJyU8Ho8F5x+P+cIXvtgZ/MXVknfffZfz83PKsuzuaeyLMWU+dtf99e/Xi+DC34TeLEVR6/Watm1FosWZTs3TOcfe3oyqLInSqBOFW69X2K1jtV50Bp9IMP+mqthsNyRK1k7i2TZlWXYGP+SwWqV3EIQ0TanblrIsO+crjuPu4ApR0o8yPoiH/z8D/wPwvwwe+5fA33HOtUqp/w74O8DfVkr9BPBXgM8At4FfUUp9wvXygM8dIfHYeMnb+WwPgOPjE7abgkcPn3B4dIN62rBeLrFo0tGEKM2JM1k4F5cLjk/uUNctcZxI95zG0TS1x5NdVyRTbGu0qxhHKVolJHFEGjsiXVE3ruOzg4cIonRHSMy0omLnvCiWUr1SY9u2NEZK/dvBZoY+oRSKt/pF+iykE3QFAmSirMNZh/FejgawlixNegMSEslYgTqU7iCP8P7DmgGlROBLKUWcZB3cINV+fWRT1zVtYyiKGmN1f7C1vsVegB0MFGXVbf4oSYmTXrc9iSW6Aoi0VBWmadrpsI/znCSJGY/Hu96Zst4LdyJ3bGOSLCNw/fXgQHTKdmJuxss1RErqG4Tf7QK1fVCGL3GOvGe471FXLxHmLM0k8nGazrM0Jkhh71ZRX/+9aevu92GE0R3oXl7V2mHltcYGeQsbehC0OLclTVNWyzXGSvFhHMcCpyF4f4Ritbjkm7/5yBcTiZRxvW1ERmAicNk4z0QQry4pfK1JUVekicLYhshLMBwezjjY20NrzWa5xtiaclPinPV5F4fxxkwRoeOEJIqJTSxQxmJNUxtm84nkYtI+aRuS19Zazs7OeP3136EoCtI0YTKZ8srdV7r91TSNqIO2TYehy73cNfjX9xQMVFIHjw0PAa01WZx1EKhSCmPbrhZjuVxSFBu0c6SjpIORyrJksV2yXF2xreXQTTORa1HOoVBYJzIwk9GEpq2p63IHww/5nKFSaiis/JMcH0QP/1eVUi9fe+yXB79+A/hP/c9/CfjfnHMV8AOl1JvAzwBf/6PeI0xA6yyj8YSf/NznAPG+3n7zTVZXS0b5mMVixXKx5is/+7O8/LFX+cPvf5/WJ3RunN6hqeHs6SVHB4fEcYaLGhZPLr1OxlyKGICriyXnbsXh4TF11TCezEElbDYN22qLinaNs412oZKhZzasouz514amMZ1aX2dcry20ofztMxi+NzyphzHo3sPrx3iPdJRnz0QIAasMr5n4A+a6wYee9RNFklicTqeiuY7tFvp6vabYliwXBa1ROwZ/qGMTEmrDhHYcpd1miiPH0ZFwxZWWFocqisiTXMr/k5w40WR5shON2CBE53wtg+45yM45WtcXKNkgMudM195Pe1E0gVREswak6CbPR7v3z0djzvYa/N2c+LmPvOEJ96FLArNraIbz2RVwscskGb53SAoOYaAwQqvBAPPMZrMuElgul9JDwtuG7XbLKEvk+qqAu2foOKJcbhllkqsI71lXGyLlmIwzsjQiKTXLxSWj+YQbxzcBODw6QDnRd0kyzQsv3uF73/lDD8lIsXWayAVkyZgoSUmjGJwcigGeKMu+UCvci/l8LoVcXvwNJEFb1xWvv/463/rNbwFShGiMwIXaz2eIdqfTkb/nu/Owk1/rZ/mZfdfDrJqDgwPKsuTq6qrT5QeJ2oqiYD6Z+Fana1arFev1msVqQdn2cxwkTyajkagBGMkB5FlOvap25jfLpL4ki2Xdh/xamqboQZOb8LpR0xD5Q/JHHX8SGP5/AfyS//kOcgCEcc8/9m8eDkZjCdPfffceAO+88w7LxYqqahmNZmh9xSc/9Wn2Dg55+513Waw25JmUc5dVQ1M5ZtM5VVWLFw/sHUjytq4atluf4HTSnCFNc6YTxXK9kRuY5YwaoaqVhUQOYVMFIxYkhk1rqWyQbZaXHWbYkySTJKF+1qvAOKwxTCYTYb/4haSU6nqEVnWvPZ/GEZPpVJ63XZFPp8zn864hRjAmygn1LM+FurZZrVisVuAhjyjqcfHOgHkDOR4Li+TOzVskSSJ0ST/efvttnq4uJH/ROmIds1wtmUwmbIttlxuJ45hES6gbvE6txMPPsowsjxllvogmdqKGqUUdMkpjkjj1XaZ6jxkQmM0OvGenWW024LxRVa6DwMJ91miqxqAjQ6KjgcHucw4WMcTO9kVxKgoNbWphjHjKpSS3W7bbNZfLBZ/+9Ke5desWv/5rX8N5tUTYTdoCHUMjKLgGhhnQVY+DHKq2Ed2YqqrY29tjW2w69cXAElPadeumaSXZORqNqOuyUxedTifEWtbQ0fEhRVXxg3feliSitdy/f5/pTDx8rTVNXdI2DXGakiUxTueQRUznU6lSB4rNWjz86YS6auQ98ohyWzOZCXV6s5I1W9YVWT6ibGqwhtxTKVerFWZpmE6n7B/MuzUT1r50EFPdgRcOY+0C80W8+jRNqcuiw/7ruubiovAHtDxXophJ1zmrqqru9+uwWjjY8zynNcL8SRJhCdXNWIouwQsD9o5aFElHq4vLCxpaxtmY0kNVretlWMK6WK/XrBYrrDPMZpNujTgnMKiL4h0W13K5RCM0zkB2yPNc4L6dPhgffPyxDL5S6r9GSkL/1/DQc5723JhEKfWLwC8C3L17F2OsTJaju8HL9QaL5ujomIurBfsHJ5ydX3F2+dsc37gJxJyfS/IpH09oihZrhO0R6Rjr+d9ZnDGa9Op0Slua1rAtKgzSPFpFEdbIDXWq2dGHD6JgscdU7QDeke/s/A4813u/PkFFUfgya9uFh8FDrr2hSZM+SZymKXE0Yzabda3oNptN58UEelpZlh37Zjoes62KZxginafvm7MHjDVw3eum2tE0ipQi8dTMyBuZJPCHAxapHNPJhHycMxlNSNJUZB1c0Dix5JnH5RNFlo06LzzcPzPAz4fQx1C733i1yrC0lFKddIRWtvtdWel65bQl1oOketjsTtHWrYiBhSiMkFsQ2uvJyUk3V20r9MbD6pjDQ9GHufvSC6zWBZtN3z8hGPXg/XfiXz7xG+YgGLYAa1TbAmNbn1OJqBvRegExNqNxxnx+0DXOUJ61M52OMcZQeIbIo0dL0ixhko+5Wi1omoY8zzvOet1W1HWYhz4fEcgAsda0RcV6LRRZgPEkk6bucUpraharCz7xiY9zfn7B6nLN/fv3mE33/TrNPYQXY5q6i4bTNO2Mb8DFwyGodH/vdqqnne4IFMHTbZqmy6mMvAe92Sx9VCjP3W63ItXiYZIAlYT1dD3nEubB2EEtjO5bWkKAYoPzYHuNoiiWNT7ID3Sf65oNGCZch8hAsDPhOeH9El+nMbzeKI5+LGMPfwyDr5T660gy9y+63tLdA14cPO0F4MHz/r9z7u8Bfw/gS1/6UhAxwRjLxfklAA/uP0I7RRJlWAtatZyfL8RIJyOINEXp6V2jmZAXtQgvFcUW07YkceZPQ816JZtSRSKBUBSl97OPUQAAIABJREFUiHfpmDwTBUjXGnTTpxykoYYVfq1TvsgqNCMJei0DTNZCEFobGvrnTf6OQR0slOFIfPIy9V5ykk6ZzWZESmNbw3q17fi6KngvbYsZbArrzDOeZ5+IFiO6bWuyJCFPU7IkoSq3FJ7y2njec6Ri6qqg3LRy30yDVo79A8m5JFnGKB0RZzF5kkvPRSNdjBIdESUwn/oDJ3ZE2jfoiIThYYeqiU5kiAGsEXE1Y/qciGmFrCqfY0C97PoXimibdhqlLEb3SevuFlvLZDJFu74/QFX5/gOI15XmfUl7WZZefTLlanHJtth0yb0wB8HYl2XBZDLm+PhYcG/fZrNt+2YdaRrkm1uqSvqZtqZmPJpSNyVm3RB785CkETdOT/z/83OoJ76HrhWdfi/DMJmMefT4gScQyDqwtWF1vuLmzZtdwhMgaROMsT4hj8gmxxm3b93BYBlPJMKYT6cY05JmMTdOj4TuWxmJyFTM/fceMfUkhpaWohZ1zVRHHt6ANJV7VBQF61VvwMLhGvI9EKMT6USltcZ4OGg8HpMkwoZRrodT1+s1xrTeWPb7tqmqHi7Jsg5Weh4uPqylGcKfwz0je9OirMg2hxxUlmWYxh8A3qRaY3cODO2jF6Wls1qe5x0tM9RiDN8T6EgCwSkM15B6faMfZ/xY0gpKqV8A/jbwHznntoM//RPgryilMqXUK8DHgd/8sa/uo/HR+Gh8ND4af2Ljg9Ay/wHw7wDHSql7wH+DsHIy4F96b+Ubzrn/0jn3B0qpfwh8B4F6/ta/iaETRhc2ac18LnSzGyenRGgOD6U8+1uv/y7bTcGdOy9ydnEJKiYbiwdyuVjiWsdsPEUhXpYzAom0reH8/JxtIR710ckxiU4o61qqeiNpW5emGRfLCyqP24friqJdhksfdu02HB8yckJ4NkwcDf9/+D144eEU7xKt/vSfTDJGWdrp9Y8nwmipPRtmGKqGysPAXgjvPZ/Pn4kyArxhfCOPLMtI01SqYsuS84uzrgCuqiqUM2RpTpYklE1DliQ4Y4i1lgInxMM/PtyXVnoWatOgtSLLEuaTGeNJhsbr4du689jjOCbPMqlz0IrlcunZT70nFnBXYwzOggRh5pnQ1qpQFSHVyUb5fsTK827UQE7aKdarDWkcY23/WlmWMZ1KYs7T+5lMJiyXS/G4Rimr1UpacMaaoiy4dVNSVfv7+zs0vrZtefToEbdu3erw17BeQmIv4MdZFqMay2iS4rYtJzeOmc8Fuz0/P6eqCsH0PZRz58VbpGnKcrmUSs+VeLDnF+dsyq1Id/gIsaxrqqJgsxEN91B7kGXBE42wRkEcEekI2zqW22Xn4Z+enpKPElrTdDh7gOMO9g+lWnvrq5Kb1qtsaqpGZCjC/QhwWZiDsqjJcvfcPRK8XUPvmQ8ZLCH/td1u0Tro5Ot+L9q+21nYkx0zagDlyPvsRuDD/Tms4oYYZS2ta/rcVJZRO4mkYuU9fA8tDos0BRoVsba9vb2uJiX1hZS5x/A7GvHgHu8W4PV1Iz/q+CAsnb/6nIf/xz/i+X8X+Ls/ykUEEkrYu0Hjfu9gn2JTsFityNKcNE15cP8x6+1bHBwdcXRyg8ozLrabkkTHVPOS2XhKFEUUZcnTp09JvOLcaDzrXr+tLW3ldbDLFrSmdaKVbzwsA6IxksR0YZksFGF9hM0bGo7v8Le9SrDSkowS/U6/qNGo0ITDaZQLh4nBGksUR4x8eD6fT5mM8m6B52kmfTmqiqba1XBRVvBBF3BhD+Pkk743ZlgkYSOETZT7QparqyvquuZqcdk9dz6fo1XE44fnzKcT8jhivr+PctLQZBIak4OE2sqhlSaNZQOkecp0PGIyyWlqD39Zh4tErXEynpH6Q8Nay9MnZ1gjiqDhmqumL2AT/aME5xtlwCAp7qwUACnXGZqQ2I2eA62Nx2NGvmQ/SRIMlul0yo0bJ9KAx7cu3Nvb4+LynLZtycbSoH25vMKYlL39Q05PpR4kaOMcHh7SNA1HR0fM53Ourq46TfiQtO3016dTolixXi1wtIBlNpvwsY99jNt3bgHw3nvv8e57P+Tzn/88URTx+PFjVqsF2+2aw8ND/uyf/VmOjg8B+KVf+iUMhje/9yaPnjwRLnhdcjQ7YOk18wO7zTg54CKnpegogjSJ+PKXvsC3fuf/7bj9EYqjgwPKsmBdbmiN4fDwCGuumE72ODo+4Hvfe8ev75ipdrTGYMuaNEpQutd+GY3GGOO1fKqKuI0htt1ectfn1I+iKDpmVJ4Kfh8KG51rPbWxZ0xZD6sGZpO9xrAL7yF/HzYuovvbdYfNOd1h8CGBnCQJquoPCYDY621FIXfmO9TFOkZ7RlxQUH/eugzXFdZ/uHfPqxH5UcaHotLWOUfdGvIoom1tlwBbXC05f3rBZr3l9PQWX/7yn0LpnK/+2q9zvC0g7imJeTYim+Sst1uchVGWUVUVy+WSk8Nj7t69i/XZ/izLKIulbPjZFOv7cm7LiiiKvZHePe0DPi832e54nMHgh/G8ZO11T0Ep1S3W8PdAyYuiqMOEA1VSKdUZvLquu8VvXU9xSzytq/VJrtR3PArCPMH7Dz8P3zt4EYvFonufcPAeHh4ym5aM0ykH+0c8vH+fKElEu76qOnZI61rKzZp0lJPnKXE2IlYROomIlMO0ddfmVycJeTZiPp9LqzznWK3XbDcF241omzedV+Oo/b2yRpK2me9LcN0oKE/BFAquzFWXlPTeeldFq3R3aAdvu2wqoeJaK4k/tbvByrKkdUJZFaE5w97+Yfeci4sLtNaMx2O22y1HR0e8+uqrfPvb3+6kLMI17+3tdYfAcnXFjRvHbIsR2+2Wqi7ZFhuWKyElHB4d0LQlN24cM51OBcdXt7lz5w6vvPIKR0dH/OCdtwD4a//5X+s0dF762EsoIn7/93+f1XLL2cUFB/N9nGffaBURRxlETpwN3RKnKTdv3ubg4ICnZ0+6z5WPpbp5NMq4deuUoqgwrma9WaK1Qumw7mIury5QFvbzueRo/DyEBLXWvT6NFKlFgHjJulGd8R0WSUHPWQ8d34Kmzv7+bMfDV0qh07SjP2qtpcXmYN8O5xV2CxrD48NooF9ju4nVIcYftHTiyNNOB3s/SRLSOCWKg+aWjzzC9aieWBEOjlCM9ryq2yHl9IOOD4XBB79xNcRpf9KVZempUBE/eOcdjo9vCDskSVgtN9x7735HYbp58ybjPKdpJGFZV+JpnZyccHx0wmQ2Y3ElWuxhkeRjUQZMNylFUZPmI5JsRGv6GxyKi4ZwTV233QEQ2CPDzzGET4bsmKHBD48PYRyQRSbMi2n3mkkimvdt09AE2QKvP5LqqIsWggHPvdcRNDiKunyGJRQWoFIBijKdiNr161VK0VQVP/WTn+X27Rf45je+xtOzM9FeqSvqYJixZGnqva+MJEvQaJySPqmmcX3z7IEGTkh0rpbrvlIREdXzN4U4ElqsjSSS0Fpjne7StZEeemY97148Mn99Xse/hw8j8ixlNp5RN6UwWdqWo+NDXnrpJbTWvHvvh37Og2FyGNPgPM8/jlMeP37Mfmjaox3GNsSJpig3vPHm97DWcnx8TGalWCdJA1w34aWXXiLLMh4+ekBTb9ls1zx58oTxeMwnP/lJjr3X/sUvfpEkSfj7f//v87M/9xVefuUlmrbi6dOnPHh4j1u3T/nCl78IwINHj1gUa770Mz/DjRs3uHHjJt/85jf55X/+f/Pw4UOMc5jQ68A4WiNdwqw1RNqQtpZ//I/+dxabKxLPqtpsCoqiQEcw0hlJFDE/PUEpxXvvPYZIcfOWqFpORnN+8NZ73Dy9TU6KqaV6WDq5hTqOEH3pbi0bqztjbFrrKbqaPOkT4mG9GCMR6ng8lhoEjK9S7qPeyB8O4f+0O7Ud12FY1zlBYYTDYciQCV/BURjCLwqHHnRbc56dNrQD0nksFDf6NwpRh+ujjnBt5UAHq7sur6Xzb63B11qRJbojW2w2goHeu/8el+eXTMYzlosV/+yf/1Pq1jDfm7Deljx9ep/U8/CLcs16/4jpSPitdZZx59YdXrhzF2scb7/9dodbjqcTWmeYj0ZM92a4WDGZWZI0p24cZd12jVWKohCBJOO6RidhSEdW2zd+wBsb5byao4RrkXYiKTsw+E5JWBf422HRRFHEaDTqilCKYtG9toSupvNGkyRhnOeDay1J05TZZM7YNxav65qmrAcHjzd2aFHE1FJVW5al0PKsIooVyukOMlpeLnhw/xHz0T5HRyedd5skCXGSYLy3iFJkaUrsqYhCszSg+0MuUDiNtRRli9IFVW0pq4arqyWLxYrUV2FahtGVwuCkc5aCbVF1ekOBThl+9j8JrDPoDBRp+QoyvgHy0hHYylLXFauN4PJXV1c8efKkcyhk88rcRFFE0xhG6YgoTVguVrzy0ksA/PCHP+Sdd94hiiLm0ylZlnF5dcH52RPu3LnDyckL3ZyPRiM++9nP8vLLL1PXJdkk43vf/y5f+9rXSNOUGzdv8OjRIwC+/d3vcHV1xeHJMY+ePOH+w4fcvXuX45ObbLdbFssNG1+PsTff56c+99PszQ55/fXXKYuGl+6+zKc+9SlGozFPHj7aMXjSglIa7IRGNNuyIs1yUGJopPhun2wUsV43nJ0/5S995StcXS4p119lu1gyHwn8dHRwg7ao+bNf+Vn+4Hf/kOXlgrpxxEnUUY5D8ZtSdAbf2rifa2OIG89c8U1YrDO4TV9cdzDf47XXXuMnfuIn+I3f+FU2m80O9BGp3SraVutO78cEI25FtFwpWVfGGJzPA4UuaOaawW+VQf4J/OZ0WKMW5VGESKkOo1ZKoX0eMPLqrmmadg1QXMhLsOsgDtVsr1O+owEE9KOMD4XB7z6MkLY5v5Qw8lf+n3/Gpz72SdarC/CCVnEUURbnRFpBbIh9dd/V5QJbFjwxmtPTm9z97Ge5deuWL8q4oigqbt++DcByveaFu3fJJzlWGQ4O52zKLWmeEZWGJNNkqUzceJRQbEs2m4LNeithfCVwyna7pWkaZj6xdnh4CMqxXm1I0rE0cEYEw7TShANZIThzSHpaa2mbBqU0WZailO4SplFkubg8E08licFvBh2n1E2D21Y4v8j29w6ZzyWEDh5pGidkUd7psoRrsBZaYzBti7Etm+Wa2XzK8cER1om3tNlIRLTartibzXjy5AkPHv8KGCudkqxDRZo8VJpGmoP5Hq2z2EY0hbQLBTUREPH0TA6wLMuYTFLqylEWW1abgvW6xpiIZlPtFKAkSSJKiQowltYZWh+1OOeo66oThZPnx175sk+IK6XASbIvHIZSS5DhHIymY+z/x96b/FiW5fd9n3POHd4YQ841ZVWxq1psNsdmSxZt0JIWMkDDkP4Fe6OVl4Y3XvgP8MIwYMCwIAuCYMAGDBuwF4Yk0IQmWmyxu0Sy1XOxa8jKOTLijXc8gxe/c857kV0EuwoQXATqFgKRFfnyxX33nvs7v+E7eMfy7BQIPHr0KKojyjnsdjv6dqDrG+mvFgVaKVxvUT7wR995D4B+6AjO4iNOezap+epf/k2cc1xeXqKC45d+8auyDrdrPvjpj/nGb3yVfWfABMpJwW/+lW8SQuDs7Ix//7f/AwA++vBjiqrit//aX+Of/dN/wW7f8MGHn3D//pssT25S1Qtu3boJyDD4p+8/4E9/9FPeeOU+H3/4IU3T8Atvvs20mmB8yD6t280G5UY519mMup7w5NEnTG+csm87lidyrUbrefz0KXfv3ODsxpLNas0//X9+l9u37lBrRRg6iMH2yeYDvvrGq9x/5Q6//vVf5X/8u3+P0Q9MqgltK89M0wjX5ubNm6i+o+06KgLlpEYXJZOZ8Ey22+1BgqAUcAXeyfNUarbNlg8+/gDnPcM4Uk0OksJ9P1Bay3y5lEDdtihjmM7nqLhmN+srNus18+mUsxs3UNbggsd7B1pTlGVuWQYcAzC4lmJWsLla89HDj3h++RRtoIyYf4Ch6+JQtzqSwda4YPHWsd2FA0Q6VgzGFLmNmrg0k8nkWhtwOp2i4pziZQj3z3N8IQK+UqIYowCCZxaJJX/jt39b/s45FEZozPuWwBD71TaTMsZh4MF2zYm5yZ07d1nMpYWTmJVvvPlmftDr+ZzZcsFsOaOsKmbzKU3fsNntxE9SHfq9x314pQSHnwJIURSgDjdOZAo0VWHQXspkbVIfL/sWiKzsUfaaNEJGd5Br0DGzmkyk8knIBBUOg9eMs089amWEBRw8Qy/94uAcykdNniDCY+n34mXoqYLi/OxMgmffC0GmH9CxojHKE1QMvLFVor2/Vqqma+UV0mIyGu1T5hPow4hylhCXXNc7ul42tVzGO0dhSnrbR2G2ZJOoMCbk8rnUhls3bpAG513X0cfs1lorWZQGo1VsBclV10pMOpLXgArSEjhuHaY22DAMost+LLtcFJhR5iQqiJgcQdREt3GDHu0Q1zNY75nNat56800+/PAD7DgQvKNrpSIL3jGpKx58/IR6WvF8e8mTi6coNK+//jrvfOVdToXiwGg92/2ebrB8/Vd/hf2u4Y//+I/56Qcf8dWvfpWr1YYf/PDHANy6ec5qtSYE+OCDD2m2O4qiyCJmr7/+OqfxjZ89eSKIo90eVGA2neCiATqQ78FgHcvEWI7Iqq7r8NZyfnrKK3fvcPVcNvOnj57w4IMPefrkGWU149bNc/723/pPePDgAb/7u7/LpKoysmt99QJnpXIzqMi7McwnM+mpD4eNXNa7wcSkpx16Li8vryVfuW1aGDhCuBxLoShjqCfRk6CfUA0DLgSGscNUJX4cGF0gWIsFXBzc98PAYHs629EPDdv9BuvHLCV+/CwU5TS2TY/bSoe26jVeQHyWQgiC/IoZfuLpJAY/ROZ9VWa9n896fCECPiFgh4GyquHoZvyHf+23+fiDjyiU5s6de7z/05/y5MkT5osZd27fZfSOLrYdLi82FMy5dX6PmzducX52xtOnFxSmZLk84avv/iUePhS/ydlsjrMewpGmti5ZX65RSkS5Dj61GjiQroyGwkjZWZgq/tt4GYNIAhRFSRhFwEzF4K6ysDGoBD2L8KQU3Ow4EmwcpKoU1CvcCFrbXOEc9wPror6mnAmHAGqthaCoTKKTH5BQxyzVNGQcx5GyKrh9+3aUnT5sDkVVElzcII7hZi+xdzPkMX7GY4lo7z3eqmvneCwglwL6y4zYtNmlbN0UJhpohAzrzINja8WkoxAdl+MBmI6DueNS+Bixkc4j9W6Pf54DvjFZQyhdm+NyuyyqaNJicoDZbreA4u23f4GiMDx6JFzEmzdv8pe/+Vd48vQx3/m9bzPieeX1V3jnnbe5c+cOzW7P+iq2f+qaV+/d409//BPquubWrdu8/uqrPPzkEf/q938/a78AvPnWfU5PT1lMZ3z/u/9WBq6TCSoEXr13j6urq/zZ3nrrLU5OTnj88BE/+tGPeNh2VDOR6zBVkdt6XdOgzhd4HxgHx3RWs97uqMoXKAz1dEZZS9Y+Wy5AG0bn+OmPfsSd23dpdhs0HrzFxvsJMPQtBEmeZlORdlDBE5ylKAvm0ym9lXMwuowVWRBZk27k4uKS7XYv5Dk/5opM7leJ1ibCf11Ga2mjKHQy15nS9U3u/y8WC4ZB3OhGex0FJzMykYjYbK9Yr9dZ8ybd6/S5ilLngJ8Qdsfrrm3bvC5nk4kE+H1D27Ysl0tu3rwpayx2ADIwI1Ymn0dHB74gAT+EQKEOAwgTL0xlCpazOYv5nLu374jGyHJJUIo7t+/SjyNj1LHuGstbr/8lgivou5G6rjk/u8F0Ouf07Ab1bEo1kX5/NZngCKLYFxTrqx1ee/a7jsm0iEqJadpv8nBWYTBGAWOEkWq0KvJsYBxE74Sg0Fqy8RQY1cs4/tizP5bHTX8nGX5i7AVQR2JtsbKoI339aFaZg1tiL4L0qTUBrcO1BXf8eiJmeDabcev2Td58802eP39+hCf3LGYLLi9XOH528PXyvUzVUArcwzCI1MB4GD4ZfV03JMlLHPeWj98znW+6RjJc1odBXoKGei/a+mXJGPWIDuvqusZ8CIH1ep2vecqwjgN+glCmc8vILO+vfdZUiqcNcL6YsVwu0Vrz+PHjqP5YEYLOlebl5SW///u/z/OLZ+hC8cZb97n32r0o27DAucODfu+u+BQ4G/jBD37A1dWKq6srnj59yg9/+EOKosgtyxcvXrBarXjj1deyFehms6FvWz755BNOTk6yRs+NszO+9rWvsV1vKMuSBw8eMHjH0PfUGvo4XG1LxRDNbJTylJUI412tt3gn5i3Pry4BaIYBXVUErTi/ccbHH33A//G//2/M53PpjQdHE+GhlRGpBLyjUJpJJdj6/XaNCoG6NAjlR9aMIqGwojNdtKKsIiomDehhoIweBBKoxdTe+aifn16nPFoLYmYcR1arFc6Pgn4rhZGflE77oaPr92w2Gy6vLtntdhn8kAATB8SOzWvq5UoYoGvbfA+S/IaO7Ny6rvPzmOw305qVXn/5qaZKP8/xuZi2Xx5fHl8eXx5fHn/xji9Eho8CVYlmPYGctf7pT35CVVR0SvHRh4IrPl+KN6cfB/abNXW0LTxfiAVioafM53O6ruO1115jNlswmy9ZrzaR/SdZO94j+jp7nj1/ijGGZteh9SRCByNjz4VoKix+mQojKAIrfq4olecIXTscCE9axwzfXMvy5eNKltk6yR41kokrJaYH88mM2SypWg6ZsGW0mIPX1YTpRHRFhq5DJ7QK4oWqtKIsPIUpqauCse9Q6no2IIPUI4ZzIUQPrUw2Yz+IRh0xAINoe4sIlEL563BPZw/wsYRvH7qBoR+uebRWVYVWE1SRRLICBId3NppRHAtbBbQKKLww2rxicANlWeR5yjHkNdkElsXsWtsoZfjpc6UKJLWS0tdxJi/tmENr6jjDT/8uZXkgvAmRI56xWCwoioKmafDeZwGz5VKUIodhYL1e8+orr/HOV9+hXk4ZvUW8gD1XV1d88okoxw7du3RdJyQtY3jw4BO+973vsd3s0FrE7FJFVhrN1eYFN07P+KVf+iU2qxWffPIJ9+/fZ7vd8q1/9a9yJvrJZkvXtJycnHD37l26ruPH7/+E6dlpbDfGHn7f0zUt7b7EOUtRliwXFcPQ45ygrNposdg7C8HRtCOFg9OTJevVFevVFacnZxSmYLdNoIQCEJMRozWz2YSiEAcq70ZUdZAGJuhouCP+CEmJ1RRK3Ns0R31vTahBqwKtLVVVUlaGsekYhi63NsdxlKw/fnVdR1GaqFtV4Kyn28t86Nnzp1ytLhhdS7PfMw4Duq7RqhB9Lg6a++NgUSFqE0V4pjmq8k0UKkzPAohWUGqtPn/+nO12S1Aq84lAWkHLSX0N//9Zji9EwE8+lAmYmsqOBx9/zL3bd2nW8mGdczR1w+CEVbfebnPrxVowasmdu2/w9lu/IL21uhTJhOmE1W5P7LxI/0wZtps9V6sVVy9Eaa/veupqIFSaiNzDWc84iJG5d+KhSlD5S+tDS2cYRsoytVFEKOmagFLGzcZAgQw5c7kXe89lKcbPck1EsCm1LqqyFhnhpBx4FGxTK+JlevoxzvhwzQ8MRK01dVXT9Q3Pnj2LCJ39NTaiIAZmn9p7P/79iSiSXjeOY9Z5P+7Lp6HTMAy5DaKUyuXty+d6PBMIwVFUZb5WWuvr9H0jn62u6rzJeO8pjT66HvLa6XSWzy1JHIBIF/fRECSdw/F84lgSI+Dyw3vr1i3quma32/Hi4hITN7RHj55k3PixgN35+Tlf+cpXmM1mPL14TtN13Di/SQiBhw8f8t57gv4JXnH//n3+5E/+hKZpsklMUsIMIXBxcQHAtBY54tPTU772ta/xpz/5CW3b8hu/8RtUVcVHH3yQ79vlxQuRZYj37LXXXuPGrZt8/MknjHhsfDZLXWD7kb4b0Br2uxaFoR9H7Ohpuh6XYIhVSXAajcPageXpIsJ3HdVEPHltbMUqo0HLQHS733Dan7BcLqnMFF0IRl/Ffrvzh1aa3Iciz1WGsaHQRVYXdc5RRmVNsS0UY6B+aKMUw2HTH+1A2zWZbGjdSLtusHa4puy5Wq3YNevcDspr72itpvV9zGU5ZtGmoyxLTk5k459Pp+z3e5r1JicV1opUduKcHJIvA0cw5M96fCECfj68B+8Zeum9unFg6FrJgItKDIGHlSz46YKh73PguVw3zKcD5zfuCA7eyLCsGjzVZCrY7iDZ5W7fUlUF682Wvu1ZzBayCFRJ340oirzr+NHjBosbRMNFG01lSpy2YMI1s2/rRJ2xMKXs6JIDR0TO8VxAMLplKQ/mGLwocnovsMOjoaABtDFobTCmxOjyWpWhVRF1HMhwyhRIBSFjBbIYrhPEtCEG1gJjZEjlbGC0HY8fP44Ud3mt0ipvJMfBMuGDj5nFCTeckAUH6nvI3IF0HLN9Uz8/2dUdB/mXZwLWOuaTOqsJFkWR5z5AJrY4e5C4lg3uuoH1sbzE8ZHO1Xufpbqza1fckA76NzJETJaLN85vYa3lWfOM1WolFUPEja+uNvzWb/2WwHeBb3/72zx69IibN2/y+v03+NMPP+DZxQX3bt/DGMPp4pS/+pf/KgBvvH6fYRh48exC1nVVcbY8YWjFmrCeTXnjNenhp+s29j0aePPNN3nllVfAe548esT919/In8sOshm6ceTFxQUnJye8+cZ9CXTt/lAdVVpABg60Khl6y1BarPNsmz37pmOI2Pq262iaAeU8987OafcNqtCiH6M1pjJUU9lYX7x4gS4NN27fFC+IQrPvGiZ1IOhA31nqSZy5JDVV0lrUjEHWmI0InWvkwkLhg6VtpWdfVIGm3dP1XV6HWmusG2naPU2zY7lcst9Lnz6poR6vF5BkparLzMw9rhLi30l3AAAgAElEQVTT9To9FXh0WovXE5aQkWAg4I2u64TzMwwRsjzn3r17DNbKrObyRV6HeE8fuTif9fhiBXxjIIScVZVRoKuezdiut1mGttnvWV1d0Q9DZqSeLhes1lvs2AuJJWYSYwCz2VBNpqxiWfTickVd12y3WxRwfn5O34/stltOdYErfBbN8i7gYluHoPDKU5Y14+gIQcXy8zBoNLqgMpUEb45YtEpdC4wpwxYphIPgGoAZBsqIC1VlRPeotGGIBKxI2oZr2YN35CCbnIW8reQv8D8T8FOw897QdV22GuwjNDMNLIXYpLF2uJa1J+bpyxlO8mU9Jo2k8zzOblPVkjLlpBXf9/3PwD2PkTvOWZyzhFBlolodH+CyLKlqea9nTx9fq2LS1/F12G63ebMJce2lzweHh/yYHZ1+z2w2YzqdZiMakOv/5MkTnj17Rl3XzGYz1us1k+mEk5MT3nnnHd5//30APvjgA8qy5OLign/5L/8lTdex3W55+uQZH370AYv5kjcjoevp06f863/9r3nw4AGPHz/OcNLZbEaS5UjnnDb7p0+f8p3vfIfT01O+/vWvo0LglVdeodnt8zPWNI3oTRnDrVu3KMuSZrfj7OQ03ttY5RUKcBg0pS4Z3BDZsCXTyQxTTEhG8tvtDuuvCKOlG0ccgfNzGRQP/cjbb/wCRsv9+r3f+z1MWXL37l3Oz2+wWq14+vQpdd2xmC/pu5EzLZtD8CoDDHyUN3FjNIoPjnpywMGnhMZaGTQHHIX39EODdSNllRBzMmDtuobdfsuTp4+xbsj3XTaWGPDdmJ+v2XySxQaFQ3NoBwJZsDBZiB5XxqnSe/78udyD3Y66rjmdzbPPhTHyTA4xcTqutomD3c9zfIECvhcUYHlAMQgEbmQcB6qqZL/dsN/E9snYAYH9XoJ401mcn7Bav6DtGmYLMQ/QRvH02RMCJvFCMoPNu8Buu2Wz3jIpK5SDzXrLmTLoOraKhkGs8KpKfGy94sXzS+pJxWI6Q5tDoJ5OqhzA3DBQmRpdFodgFU9ARR34wohOuneOxWyGKeschFMQ3e9aprMJVVVnRNDQS3upKEVv/hg1M45SRh/65x11WUgf0Rw8QOuqjhhlMaRYLOYxUB9o3unh8c7Tux48Ik1QakpTZvRNFyuylM2P45ix7UkPRTJjQ1VE39M8d/B4N2Lj9xS8JpNJ1iMRXZs9u91ONhHvGSK7N1UNKcNvmoabt24wmYiDl7U2k4rcOLDZbK7JcVytdgcGZXy40vpILlMAFxcX3Lx5MwfgJEEhG2bg4kIysLYVaJ3C0Hcj47hGa82LFy/YbDb8/f/pH+T1sl6vs+PTfLGgqCo2+y3/+B//E27fvsXpyTn/5t/8m/i+LX/4h3/Im2++ya/8yq/w2muvsVqtePjwIdPplO9+97uHaiQShp49eyaVK/CH3/oWb775Jl/5ylf4R//oH2Vjl+16zSReD601F8+eMV8usdYyrSfZ+ayoDM8vnomOU9NxdvOc4GNP3Qm79+atW4AkI/umZzGbgXVcXlzknvPJ8pRf+7Vfy94UP/zpn3L//n2stWy3Oxo70gfPbrthCHC2PMvs2JRMKB2o6ohsiZo6dTnJRuQgEM3NZoP3MfvWgReXFzgvTmY2rp+nz56wb7a0/Z7V+pL9fn/waMDT9d21Nk09qTifLKQtXIpg4bSqI5xzyLErwVlTwNdaY7SmiPo+mQyIzH1msxmzqSC7QghZYiRVp+m5vbq64s7s4IL2WY+fRx757yNGJ89CCL/80t/9F8B/A9wOIVwo+QT/HfAfAw3wn4YQ3vusJ3UtY1SH3rD3nqqsqU39M5maVgOD02gVIIx47+j7Hc16xb6zzGYnmfTT2x4dNM5K60MHUFYw/UVVxwGJ9O3apmMchAWn0DgbrvXuEh1dLkgUe/LXCVtH1yt/T+Vfan9orfEcPpOISUGhhWWaMP4iGwxeBZwK9G2XpQ18zkCGnIkE5/CTmll0BkoZapZnqEzMUm0kZqWAb/PnOtDdD0YvKYtPgT29Z8ouvT9Iux7o7VJiAxhMbo/kjSVmQWVZXiObpHPKD7S1jNFRCiT4NkeSxErD8+fPGYcut4e6rsONQ958QbL7ruuvZf+pmkif5fihrI8UPRPZRzajNr/nOA7XHtKqllbQxcUFu93uWpXQDyIS98knn7BvGlwI/MK77/Dxxx9zdXXFkydPcwD5nd/5HX7xF3+R9957LwvqXV5eUkeRwBAC+2hYc+PsTEABEeut4ob28OFD8buNrmYg2eXl5SXjOHJ+fs5isUArxWAt5zdu8PiJcAaqaslb99/i/lv3+eTRI1k/LkigLWpKXVBGE/PlfMJ8Jkzjr7z5Fg8ePGC/a9jv91yuVnzvBz/g5ES0h772y1/He8/V5Yr1dkvTdaIT42G93bK62vLqDfHVrY0EdaVD3JBFprusJDjvousVCAlSo9BGYJW7F5co4wjKcXl1wWVskSRYrnOO0fZCsnJJMsUR8Cgj71mUZQYKpHUagrR167q+NgMqSkk2RntIHBLjtizLPBcAMtkvPVMizy0Df9e2uSIAct8f+Hc2tP0HwH8P/MPjHyql3gD+JvDx0Y9/BzE9eRf494D/IX7/cw5RNUyN86RQ6HA58F1evUADi/lSAhciV+ASgUjDYjanbbd88ugjJpMlnYOmd3RD4Ox8xEZp3raz4pPpPUM3UCiNLSq54M7StC1jZBh2bY/RJbPZHK3EvaeeTA5lWsT1giwyCZZHWvgBBB18xDNAo5TGe4t3jnFwKBzBR/ZcUKjY6tYT6dkTN6gYciUIk8TH4lXMqpguDyFVXGjCopWhMkBZHTat4z679y4HtJcFpry/PoxNLlDHWPXEWH25TSOBM+QgjgqUqogDVmFDOi+zi92+zxsWkMXklieiGmojuzZl4UqprCfUNA3D2NM0DXVV5E1uGAbceL1Ns15HjZfigPY5zAlsHijH9U7f93mona6PfPbDQDfNNIoioTZkQ1sul+x2h2oCEDc2remqgb63eGd5/uQ5p4tTmn0DDl5/RfryP/ze91kul9x/7Q2+//3v88nHH7Pdbjk9O2G73TKpS95+6z4A29WGKvaIg5MnpK5rtusNVy8uRfIj95qldbO+uiLEoWVRFJyfn/POO+9kQls39gQfwMPqxRWjt2zWO6aLuWjPGIMfZb288sorzKIK6NMnz6jKmjDTjKPj3r0T3n777exFrdD8wR/8AaurdTYNr8qaSS1M3r7pcvsplB4fYttGBbRWFAa0D3LP2z2TKK0wKcVO0USARNs1uNBTVprLyxd8/EBE8froiuW9Zxg7ZvNJbPN4xtEz2oFk6SHPnGdSVoLEU4q6LDGFpioKCi3fZR3uYkJnDzh651E+JktH1WNao24Yczt1Pp/TtjJgTgNcuK6Q+e+EaRtC+OdKqbc+5a/+W+C/BP7Po5/9beAfBlnRf6CUOlNKvRJCePxZTiqhadI24FXUkAghGw4YHZlzSQzMVASlWe92PH3yCbqcUs6W6GJGP1ievXhGu49DPKdQXrJMFRQTUxCMJWho2oGyqkh6+AHJdr136Ii4qCrx9XR2jAsiTzfx3uGcpkSRtNaOg0E6dDhk/MmoAWIJh75+YyMT1zmE6aoPwXda15gyDRwOTNs0ZDUx0wwxUB9LBWTW6Bgy1DK97zHTNAV8aw9SwsfZfeodp+HVp33JBghdL4E5DTJT9pMCbYJCpr5/um4pkJZlmTeA5y9e0LYtd+/eZR9fm7RXEoLl2FAiZXLHLTDnDu0CONDYD4PeWGkVYi6+Xq8zxDJleIlpeXytUpUQQsAM0v+fzWZiFhLbJNvtNvvenp2dQVDY4Dk/P+fevSIyakWB8tGjRyglfgUnJye8/QvCkB3twHe/+91okCIbb11V2eQ+ZYqvvfYa2/WG1WpFG4eSIIYtr776KlVRsFqt8nW/eXaOH22eJTWjY7fb8uRhiVEF8+Wc5y+uaNuObhgIeDZrkVZoNltu3rrFfL6gKiec3bhBXQjJqi4qtqsN33/wPQD++I/+hMePH2eyYWWqbBYyWZ5ilmfYNirXDjKbMoVmMqmZTCsCnnHs0RGoMV/IRlKUhtXVJaMVXabV5pJ9e8V0XrFZ7zKap6wM1ve0TSeGJCqJ7fnsTZB1602c2bkROJitp27DcdchVVdFYbKvLkQY6DjIgPpoHhRCiBBvISqmGVpVVcxmM7Zde1hfzmVC1mc9PlcPXyn1t4CHIYQ/fglu9Brw4Oj/P4k/+3MDvqiseEBldTqZrQsMb3lyIrjXosCFgA1OTAWS8mFl2O07FJbR9sIUDIrFaYUNlqHZ4VLLK2jCCNZbcaCxDqvEjKLZ7zk5P2M+k2GwLTzOiW6jUZqiMiglbjreOYI7aivFwZbzYCgI1uOTaYr3kD9XRKF4mQl471HOMUbtGa3D0QKR4bD3gkcntiyq2hNCgR2GLC5llEDMUvarVABjsNYxqWom04N/wDhYOtdnVmrXuaz6+WcO/50n2IAfPH7whDGABe2zSFBGq4Aob4ZRtHS00TIojhvb6Cy2Hz5lU1BMqzoHdoiDM+fZb7by2rLg5q1bGUWz3+9z/9r7g7l08IehsrTaTB40Q0QpceALHGPt08aeAn5d1xleWpYl8/k8I4pAEed6OH9A/ngfcqUyqWdUJ8KGTRtkCOLHmh7c2WzG2195lz/6oz/ixs0zLl9c8YMf/ACAd999l8ePn/Lxxx8yn89ZrcX3eTaf8tprr+G9z5j99eUqB5H9fs+0njD2ku2Psf2TrkEd22qTySQjf27dusWN83P+7fe+x+WlsGeNMUzKmnbXcLY8ZfSWV2/fY7PbUKgOa4cMn/7pdsezh4+YzhfcOL/F88dP0Vrn/v+zZ8949kwEEhN3ZToTbklGeo0WXRrKqqSs5R603tMPlrFz+DAS1ASCZNiTSuCxo5V10A2WF1fP2DdSVb24fIYyDrOGoR8PblN1yW7XMtqBxWTG5eUFRWmihIdmMq2z1Mt8tmAymdBtWmH9xgQkJRhppgNkvkSSVjhOamzwnJyciC80MieUdnWZ2499LxvVefRMSAFfa9EC+rzD18/875RSM+C/Av6jT/vrT/nZp4YPpdTfAf4OIOYk8YUKeHnf8gp0YVD2cNGOszCQBy0oiylhdANPn13SuSecnt8lmAllOWcyFclhrYwoTVpL0CXeijtSVZeSseNzEJXfF/DBEijRqojYbukhBg70d1NI2yJYEU7VQaHVoeedP3tK3oPGjzb3ipUyOTglg3RCl2GAUXlZ5IsTVNFZ6ohTLuKcIS2+ohD7wUlVUejrkLVrCzDDE8lGFal1k65BCKKhkoXexqNWVkJGaJ0x4ek9UpskBcCDrMEhE04PSWr9JGPo1DNPM5w0gA1acXV1lYPD48ePGY7gk1pr2rZlMZ/mz3n8e9L5FkWBjRl/+lzHn0kplfviCaWTrCCLiPEWvaIjwro6SGTId/nx48eP8d6z2WzyWlitrzKEVqH5tV/9Buc3b3J6esp+J2SoBOGsqooXL14wm82w1vL4iUg3z+bSj1+tVqxjhp2EzZxzrFYrbp7f4OLiAm8tq9WKvu/z0Pb27dusViuMMdy9e5e/8pvf5Bvf+Ab/4p//c8ajIeR0OqXrOi4vLzHGsNntpB3kAtOyQtcTNjv5/fv9Dtt17K7WPP7gE5YnZ2KNOQx4a/FoTip53+Vkju1GESX0AbRINljlRQq7H7JMttaKqippu4HtrmHfbphPJ0xnEy6e79juNsQ5P9oo9rtdnmft9huqiabbSk98Hl3glArReUyC+3a3EdnwqsgbfhqYJojlYrGgKkXDqq7r7OZVlmVuWRZlfshzApBkkSsj1V6XUFXxXhVxY2lj3/54/af3HYaBclKjjPlcGf7nkVb4CvA28MdKqQ+B14H3lFL3kIz+jaPXvg48+rQ3CSH83RDCN0MI37x1+9bnOI0vjy+PL48vjy+Pz3J85gw/hPBd4E76/xj0vxlROv8X8J8rpf5XZFi7/iz9e0fAIM5JIEiUwAF6BzKMKYqCepxc67HqvqeoS9q+Rw0OU0Cz3bFtBnqnmU5POT2TjaUqpgztgBphXs2EUOI9gSnayGA2U+qtwM7qeoJWIyOjmGgnO8JC5928KEwc5FppYwSFSZC/I/hkUUgVM4zSkmijNnjKhrURq0cAWx4Mm5UGjRLhqtJQlJpJXTONJJZpVeN8TVWXMUMQgxS8Z+wPA04gm1G07UjbthRlhBiG6/IBAN5LFpyyxuMMP/XW0/mnnnzKjNJnOkbagGRrn8ZEDCFkpMO1uUbM/I0xqMKw3+8xEc3Ttm2GZabWQdd1zKZ1PoemaXDjkKul9L6pxXPMPD6uCFLGf0weAzILN4SQhe7SNRDexc8Wtm3bcnV1+Fypx75YLDC64Ic//CHr1QZTCGnwL737W7zzzjsAfOe9b6N84MmjR3JvJxU3z88Zx4Efff8HLJdLvvkb3wAEB391dUW7bxj7gZs3b7JcLjGxYhn7PrfWUjb5yh3x5E0eAO+//35kK8u9KIuSNnQiheACs3rKfrNjHHvKSkTKQiRezcoJk/lUKtxZyWJ2igqBYd+jHJydnmB7yW4fPnnMyfwE11nGwWOKiklVURdiodl7x76P/WsO1eYwDAxjx26zxhSa/W7FMHQQbRZNoTBKZyBFXVeYAtQQqKqDaF8IIfM2lBIRvMm0jtaTJr9G1qO0Xs6mZ0wnB76KzBHGvE4AAgm4cYBlHtaQv9adSL8jtYTSWhyGgYuLC5lBRCXUVI0fk7w+y/HzwDL/F+CvA7eUUp8A/3UI4c8yMf+/EUjm+wgs8z/7+U5DyEQoYaaq2M7QQeWG8na7RflAuThY8ykFyQx5HAfOb51TtA1nxYTl2Sk3nm948PAJzx48YrV6zHYj+9SknhNGMRMPy3MxWVaKGkVZzRhdQx/V/MbBUpY1RaUYnWjqaK1xwRMIwn5NPpoGIOCs2NwppQhajKKtt4dBsHM4ZejHgaYfaLshD8ucF9MTF/viPmiM81TWH5mYaLFWdFF10hxIRwWHFsZ0NmExnbG+vGRSTlDVYZG0nUgHpEHuOIxHA2pZkNYfBdwg3AdnD33p46FVXCt0nbhupZ5wWuhCwvLZMq+sCqZ1TVFVFFpjI9tzdI5XX7nLZrdjHzfd3g4S0I3BO48de9ElkZNjNplQxwd4v98LszMObWcz2dD7SKg6OVlG/RaBc3o3CMpER+crZ3B+xI5yD5N14WRa0bUDPsimmR5o6e0fNNtViA5J3kmP1ntcCFRFgZtWLGaz3LtNJhk63q/N5RWmKCiV5ux0ifKeP4o4/Mtnz+ntyKt37nJxdUldFNRlxVtvvM6jJ0/w1vLgIxmfvfHGfZ49eUbTtGy3O6qqpihK6qLE6II3Xr+fA8jV1RW3b9/m3Xff5enTp3zrW9/ivffeExny+fyAfmr3tE3HbDalb3t0oTldLpnbCT7IoN3HFsXJyQk3b9ySjX4ItM0Wowtun8kg2boeHQPzzbOFeOlKFIAwMlov862IiglBEqLejbhxxGEpCmmFrrZr9s2Ws5Mlk1mVteuHoac/IlBVdUFV1wRmLGYz6ghLHboOrwRW2vQtbhzxVYVByXN/hATUUSE3wTV9cPFL5oYon0EJZYRwHj8Hx+zzY8ivQR3sDkHw+hH6ud/v5dlIXJ/pVPyk+XyetuoYOfL/1/Gb3/zN8J1vfzv////892Q/+b1/8k84mc2Z1jXPnz7OQk7Jr1VrzXDU25IeXCMZYNNTVjWmmPCjH7/Pw0fP8kXdtz3d0AOaSTFlMV8KCkhXvPnmuyL7eikPxHQq0/ShHzk5PWPoLdY66koYcXdfeTWLvZ2ci4nIo4eP2a62TCcihqSLSoJen/DyYsTtrKcbLG3TRZlXTwgq694DnJ+eSEVTltkmrS4LZvMpk0nFfDajqmUBLGbSzxRoWKxanMO3QnN3UXoBYIxwUh8cNsiAbBh7tvtNFpBKC6ttW9ZXV/S7FuWPbNuO8PPpe5J2tdZydnZGWZb0fc98PseUcHImvduikIDdDQPtfk/b9xilUFF8znOQySaijdL3oBT7VoaP06n06dPQNmGhQwi5n28ihFZQM44i6hTpUkdkkQR8FfMLa0esFUhd8tUVuGqI8gwm8jyGDENNhw5cs2JUPuAV+NFiKtHtT+bsY98zWEsZq6FJJN5557DOQQiEWCkYpUErvBuppxPaZs9f/xt/g7Io+INvfYvzszPGKFz36OkFV1erHGz6vuPGjRvcu3ePcRx5+vRpDkwJMppYo+1+j/OWWVWLLv0RoikEsEEMdRwerUw2S0mIpOO1obUkN6iQh9pwyNRBcqDdboeK2e2Y4MRK3K5erC7R1QHsEJxj9BY/HnksBJtnKmk+lvrfiaNQlQXTiUBVZ5MJRUwQhq5Dl5pJOaHpG8a+RxlDVRSYspT5V3xtVSSI7SJDkhPv5Bh0AJLpn5wuWSwWWRAtMc0fPnxIURTZxrQ0hs1qHUlfRSRhliJpPQ4oY7hYSzz6znvvUSZWt3dUpvxOCOGb/JzHF4JpK1JAKo93Q2xnuFHaB0bJKxJpQR4wKY2K8uAwU0UW26Secn7iqSayk5/Mp/zKL/VcrgRFcLXa8PT5C1brNV3Xs20u2OwVzmuGoWc6O2GaStmJglExuJGuT45IBhc6ri5XjGFgMRVkSjExUt4rj64U+37HereR4GUOBgh2CAzWURY1w2jp2pG+Fy0bTwz4Ktm6GeqixMZ2RlVG1yfrKV1gGA8wwzTcKYpCVMNDEI3tMYAb6ceBYRTo3hCtDcHjVaCe1XRDL0JxLrBvdxn/vO8ayaz6ERMMhRaruaoQlcCsAY5sxjZWCG2sIPpoEoHWObMsykOZO4yD+HsqLZ/bZNXz+LmcbNYeRoCgqKLAWvKl9Uc45fR3Kch3kXwlbab6gK1HpDIOJLm4HlUSnTtkUPLvdQ74EPK/qYpDwA8hoOPv1jFgayRA4j1eqSiIF18bv+M9Lg3PrcN5hwo+AxiUFj/W2bQWA5+h5/2f/IRJVVGVJWcnJ+w7ubelLijKCcEr2r5n6HouV2tAYaPNownRJm+0bPY7ytZIS7WQitHbEfC52na4+GdB0oHHDhaHkxIWnw28XbAob3DKY10v7S0XJFA7Qdj5mH15PE0r7VilNIMbCb0AG5q+ZfQtxFZR4OBc5YMn/RcImKJktD6bo6fVkwb9ppBWXd+2wj6PG0KS1fDeY3vLfLbMLclseqPSejncdx+csHVVoK6r3KpMmfhyshA12+EgR5KACMvlMivkgmzms9mM4ghMUBiJNTZWiilRs9aS1KiMPqy7n/f4QgT8a8cRomUYBjotD0gKZMk5ZhzlQurigGVdzOaRwlzgrWXougxxKsuSX//1Xweg7QbatmfftnRtz67Zs93s2O17pvWSq9WWIqbtwQ9U5YSA5sXFE0GA7Adu37uL1gFnW9brGBjbNeNg6buRO3fuErwT6GaI0SokeWSRFOi7HYN1DLknHghBEbzBxYC/2TmqoszZmg+VZCBesLhSIsqD1g8DTYI5xoVZAL4Xxq21NlPKnRMbZtFwBkzBdt/RtOLbu91tuIzB2XkrbZNCp5PHKcfghCyXzWuMpqhnODviOsfgB9ww0o0D5WBwqmTXyHuaQuV7eSArFQR1YDZezy4PaAdA7okHU3ich2FMrl9Q2GMsvMvElbIqxVUtHs4LwiYEYRKj5bsY22gwMPQp2HiRttDEfyMSRdY6qnn9ErbfxdmTJ/jIIYiuad4dZbtB5Z87D6R2Ws7wfeoC4r1k+Ottzxg3gsurK+qqQhsjLNXYFx/ciDLQDz3DOOKsZQxO7BcNFMZQHM1ShrGncx5lNIU2YAOzQmckFYALDh+rxOACHiEtBiUWmUEJ/Da9trd9DIq9tGWsyGHY0Ul1mWZUwTEOlsl8SmFKOtszDhaPx46OfuzwkTB3DPg7riZSa+3TDHTSuqrrmrHvhEFuXZb20GaC0kHmYdOK+WJ6IE0eQYbTtYLDnCptFMebwzECLKHFrrHp4xyr0EdChMjcqo4on0zMK0vMYK61dD6PnMLx8cUL+Ee72TiODFqh4iaQyjO5cEk06cgGTxvRMVGaejKhMiU2eBazKfV0Rhf7kaYouHl+yquvvkpZVPRxSLvfN/Rd4OL5i7xgPv7oAR89e5+yqLl96w7f+MY3eP2NNzk5OeEHP/oxDx484OEnj/P5lkXFyckZH3zwY6pJzbSeorSm73yWJiBoghLmrLUyKEWJ2FpQCi8RGBDvV+8F+pXs2Spf4uPO30X2Zrp2aXPRcdEapVBjHATpn4VFWkRS4XK1YrVdsdtsaPo2ZiayOZRlSaFGuqanjBlRVVVS8kbBNSDrvWutUVGPJA1Gm76jtwPbVmCOWh+gjll6ISoeFkN6wK8fhyGXph9E1mB0Mjjv2ugtYGTWEvDU1QRTFswmU8qqwGgJ2CnY+CB/Vhp82vyCkmLTg1Imkmwk1AQ8yUvBRxc02SAMcH2Alga+x8PqlzgrP6PumCqj4D3We9EZSjFOy3tu12uevrjg5vkZ59MJKMVmv+P58+eYyDLddy2WwGAHOhuZ5MHSj4FClzS7PbMoOlgV0ru2/YCppM8fcEzKee5PAyKDPAz044i3Vs41WvAVsfpQcR0O1uLGUcTG7ID3ByCAc47RHwAB1gtb2RdCYGt7SdIOG6jLs6Q0AP2zvo414lMV545aRAlGeUzqS9c/yRmk3vqxWN6xhacUaEK8S74bTdNkr4LEQ0kSzOnfJzmG9NpZfFbk1irpzUfhwPwMxfM+Pofj9uHnOb54Af8ICXGMB09m3YmQUEU99DEObfu+x1vH0HUED8WtgvlyITmBNgSleXEhJBK0piprpvM9s+kCXRTURsN0wnyimNYmB7Gb50veXL3KbLbg1q1b/PLXf6b1wqkAACAASURBVIXbt+8yDJYf/2ik3V3R7q/y6etQ0/eKfljjVY1mRBWihRNt2jGmwBQlF+tLrI9DXuejAXmIQ8E43CxmBOMxPoBzYB1mDJheiExJzwVkYJiyYBkwxYU+2twOOsi02ih81jDagUePH9L3LV3T4LxlPp9n3Q5jDE3TcHoyo6wOMgQuOAY7UPrYHsDTRkQFGnShCS5gvWXf7uXBjz1prwKMh1ZL5hWEg21gOtdjbXF5bWDsRpQPgEhLdN0QX6sIwRCCyHKc1AtOz25gjKJte5pmlzoqgsHuh6NAH+3ltXA1UAFTJi322I7wAaPBo1BG2h/HmeVBH8llnaUQAnVdHLFw0xzE5KzPe2lR2uAJwUmvPITc0kGJ6Ycyhma/ZdpPqNqWcRzYNnva3Z6T8ma8thYXwCtP0JKN+xAIEXRQVEWmDnRjz67dYfuBWk2pqgJTFHRjK1IXMShJa6ij6bo8XC+0/tSAnzcFNzCOByZ6Rj4dZepeSatndCPKqQO/JXpJKB0y4EkpHxMEFb8OffO23eUAKo948smwDIMFnJAnjcn6SnBkL6iFgJgC9XGgzTj84mBEXlbFNVTaQcn1ukdzWr9ARo+5KA2SmNFG6ZycpbmA1poqSrikCgWue9la99mz/S9ewD8afBw/+IWpMoQu3QRjDIwJ+SJ5sbBlW5rtDhyMzqFNQdN1Gc2CLnBo7DBiix4TorOPjcQbN2A7edTOT6bcvn2K0QXr9Zpv/b//jLKW3f3i4iFV5ZnPDlrXhJ5232OUY7/d8uK5VCWFOQx/tDb4oOi6geBF9tX541JfZ7JJNTvFhAneVxRDQdEVNN2Utt+ya2dMj1oUKrYhlA/ygMeWwnq3jtfyADMce9Ee2e/3jEPPrtlQxIBb1QUnJwtu35YAEkJgs4Wzm+eYQvSBml4GfNvG03VSOe3aKW4YwWiCdZjKEKyj6RtwUtKXszRwslmKIQX84wfmZSniYx36Qhn86MT1qBhRRow4AJQ36MLiQmDYbtFVxXQcCVaGgOvtNpNPyrJk7FsU11sEx3DRtPFLH3YgDAGtx0zoUoURoACHVsLxw5+CfGo5HEPpDpuvk0Gu8tjgCNYxBgmAPqJZCqelOsEznc9Ba9a7DYXWMgTVis1WiE9eK8nIg8X5kTZlkgZ861ksFthwUBtVCnSlCFj2/Q4zwmaQgJ/hm33P2Pd0wyDZu/eUxgjCiUiWjJve6FysgmTzShE7qWUd04WCAlUEPCODdVjf47WL7b7A6NOTcFzhcXTusmaTrPXLmXsy2gnOsphPcC7kFi+Q++rWWpqmuRZzUsXwcsCfTOscvK21eXaY4L9ARnftdru8zpNuT13XuduQjr7vZSAdWzrHiY85ehYypDj4a2vp5z2+kAH/eGdMfXvbt9dKrawtHQ7SpX50zOopbrAHLL3WnJ6cMa1r+pgFBuXQAcZBMxYaHx/EfhxZLmbcvXObtpHd9+rqiudPn6GVMOxCBVqP1EXg1bvn3L6xYBo3XdmVZ7FXHtg1e65WWwmqo8VHyJizPc4Ghq6JzIOIPg0iKxGCyllgvx/QpqQ1h5ZAWdTS86tEY91ouY1FdMLS0eA5BZjVZpMHmMmQ2fYD1gndHuepJqK3o1AUkwIKGJFrMJtOuDU942L1XCorrSWg2AG0Rkdt8Wpa8Wx9iQfcONJ5ef/VdoVRStoU8RqM8dwS+zBlumlWc8yxGOLPDhm+pghRt0T5iKLq0k0AJVDIse/p/Ujb7fFA1zR0w5CHZXVdE4YxV5Wp1E9rLrWkQCopuY9jzgaLogAVcBHSKvfx8F7HMwfVNBmSl+F4caMTVcUeVWhcEEE96y3BhUyNFJSOWA1OZzO6sccNVs49LqBdZAWrqmQbER9aa3Z70fyfTqe07Z6m3+fzEl0akZJu9q20U7xFuUGcnRIG3TnRY4otJx+C6EnFmUP6ORwY80qJdEHO7L3Ii7ij65ISuNHbDNLQWmOiz4TzFh3bZcEfuCrHPXogCwS+3MOX56LIa6w0GrzLQXw2qalLQe9oAsv5LGfV2fSmSME2SoCEgIvS47knXxxk0EFcrKy10qKO0Gc3jlCW1GXJoA+a9oUW7fsxro2knmnKksGOuKP3TYze4+fjsxxf+ICfgv76ck9dS6ATOrrIJauIgS9NkVs/yTygG/ZiJbY8pTQFswgJbI+GuX3TZiGsQimePH6A8nfY76PIl/ecn8xEkna+5OGTx6jg2Tcr7GA5WS7Rrwm+f7aQofGTpxdMF3OcPWfftlxeXvH8+XOuLiUD621L8AFjHGAlMw9KMPdp2BjL2N7uwBX0Mn5FHidDuRO53idPD9IOdT2hKici4xyx79ZaqumEIapbJoKIIlCXJdPoHGWUYrdrGYaewRnGccu+l37kK3fucfv2LZ483+FCeTCWGKW9NnhZuFMmtKO0mNq2ZfByj/btVlpygI0ks8HZfL+ckgdCevIjqoxmEtl4wjKGEe2i167XaKLsLzI0S4iipGOeiGLd2LPeyKA4BfRUFvvg0E6C0DH93YeEszY0ceaw2+1Yr9cZhpkkFowxEkTUIeCn4/jPXe8zfDGT3zIAYaS3A0UlMEfnDuql16pdLefZDy37ZperomEYmEwqmrjpNauO1WbDYiHaL/v9ntl8ykzX+OBoui7L8zrnmC8E4ZZMa4yCcWgwKlaNkKGrWitUhLp23YBzlnG0WDtm5I1S0rbUWlGbUohGzuFxWGd/Zrg6Xy5xfmAYZb2KXIEneIt3Ap/+845bt26JUmqqZo7kPqqqYjGbYruG2XyWobzyzNQ5eE6jhPjLWf7LVUMTN+90HK+dY0e3dKQ5VYizSO/lmU39fqMEoz8gVed0OpW1YUxEzg3X5prldCqJmfns4fvzSCt8eXx5fHl8eXx5/AU8vjAZfghxOKMONmG73Y6ha5lFw4ekjii7aTQUiMiE0hRYM9K1A4uTM8q6p2hbXn/9dW7cuMHjp09pGsnW2raNAl0lN29Kn/rRo0csFgvOT5ZcXV3kHbyMv6PQis36EqMDhVH0fYMdBi4v9zljbDtpM5WFo91dgi6oCsUrd865dfMka+wPw4AdHReXl3G4J9dAxZ2+6TraZCqiFvSjmEdLWyjg/YD1HbaDEHoYozRwa4ACw6HfjdZsrp6TOqc6zjGqsgRl2XUbxt3IYjojKEdRRsq38riYsa3Wj2naFzx4+CAPvbq+QyvNfD7n+UXMmI/o4tPpFFSVFQyvVhc4Am3ESRdHbZzUbgAy+/c4C+u6DqUUy6UQ5HDCoC9UQdsdDKnTOVytLjOS4rjMT+2jlIVVpmCIiInU1uja7tpsIWXCuUUWHM5Ky6Ufe3S0yEuQ6GNoXoLj5UomtiGPRelSC0lrxWq7J+hDO2gcR/qhy9dlHEcm0yr/Du891o3XoIEAl6sVu6ZhvVll279uaFhtXtA04siV5DjEKrGlaaTNYwolGPBJBUpkRYCYnUcinyMPkbURk55gPC5CWF2wKGUwpeZy8+IaEquoC+pZfS1rnta1GKl7xaSWCn6zvkRrzXwxQ8UJc9+0eW0lsl1qqew2a2F4Lw5qrWmgmvrmwSgKczAiSffLGMMktlfK4mDBmbL7VDXs92INeXKyZDabcbo8YRiGGKeEXHiyEDKVCtA1LZOqPgjvRQ9rvMAws4Vo4CCRHQ/nHF2MUxyt73SPA+HTVSn/nOMLE/D/vCOp9TknJg1nZ6fSvy7lIwTnuXj6gulSAkcyEVitVlxeXjKbzXJwT7hs0ePo2e/3rNdrgndUN06oy0JsAYGgFOPYYa3cnEKDHVu0CkyqEsyR84wBrwsKrTipF7kcDkj/MT3o3gsc7ex0Hmn2sTeI2PvtmoY2lucfPnzAndvnTKczCVxBxoNN07FarXj9tTcOzk/twL5tZGgd4V/OO6aLEym3g8YUcg6VMQQcWlkCIy7shbvgejyW7QDVPiKFTElhStqhpzQ1hTIERgiabtiR2uet7TBoUIrJvma9LVEedu2ewfeMOBTyoI3dDigoypLCTOnHvfgTGM9+sxaziuh72o2dGNZoix1lwy8SAUwVEmxDkqIQ+0cfLM7PpdUSpOWiVcFgVfZTLbSmb1vapso94NR2AfKsCLjWk78GB9SBYWgPuO5IvCNoUVj1Kp8Tygt5Rx3Bc5Hhc8DRdA1BH94/BfS0ZmXOVF6bL4y2zwE/rcOqLqi8BJmyErKQNrK5aBM4PVscKbyC96PMDEIAb/ABgj8MdV/+rrS09k9OF7lNocfrDkzS/zagDkNbCNKy8tfRJZqAtaOcpxZjo7KIXsQKqpikdKHJrRM4DGTT0PUYbnksuW2MwejAEBzOXVeuTTOVNGdIstJKqWsmPkCGTiYzdpCNIa2dNJgFedZT8jKbzZjP5zjnaNv2GsQcDpdHheshvO06CldeI17l2UWkq37W4y9MwL+4uADIOi3J7Df1btu2ZTKb8fVf/mUuLy/58Y9/TF3XlNGsfKZ17uUeO8gANPsdzo50fcvVlaMwB7NtHR8unYY42tC1DWVdUVe1MH3jgNUGJ38sBC1zIN0AKEx1YIMqpZhWBT7dZKOz9PKirdjHzzWdac5v3qAoCvG/9Z56OmUcRPp2uTzJVP2hj1DLtpfvkZw2my8jYkSgiHIOBd6LVWAfxbSstVhb4Ud5eI6Zf0aXFFtElnV0BNtjCShfUaahsfZMqwpTFRTK4LBopTg5mRBsRecsIQ7A2mHAoDGVxo0N232DUYZ6NgF6+n7Exo6jV4GyrAEhzHVdR2UmGCXB++WA72wg4FitL1FaxM2UDihMDrAgD1hpDJU5ZMxJ8z49jC+brh9XE6L14vB+yENb76L+ixe2aIJ52tGJ0J4psxY7QWEjGcl5y2wxi4iMGKy0gjzUF9SVwJJBm2NxO9DGoyNhrZ4WBCbZyNs5h9EeYxSl0ZzGQJ2ehX60OCsbqVYlo/K4cThIW3CEf49/RsnAU7TrhQ+TvCmOSUh1XRM4YPCDPRh5p8+lfcgDfBPnA+kqF1pnpJBRivliwb27d2WgO47s93tUEMayUSoP5BNy5nDv0jpWEVqZ7mGCxMqGniqdQyCf5IBfllKJLZfLLK2QKsmE8z+2+0yVaVmWuUPxMnRTIsN1xFE6+r4Xo8UQfibgf97jL0zABzLZIZVD+/0+auLIBT5d3pCWUAi0UVtlMo0DqXHExswtwa+SUmGasvsgGh29tZnIU9Ulk+kcY0KmSSulqEvRItdaH5TyBotP1Ot+ONzgEND6AO8yZYk2BcHoPLwUzJpFEaiMgoncmtdeeYtqMsFby4WXgF4ZxXxRM6luXDNuFrRHndEg1gobtzAVzvlrmQ3EsrEr6PsCg8oUcGslIz0YjUu2uDyZo5CBeLXVudo6kFwm0a0nOn/1PQ6Y1TPMpMJYhYrXYK5qFBrrRrabDhhQuqQsPGZRM1pxFAOY1CWLuWyuzb5ltA04ycxKXYgUw/Gmaz3u/2vvXGMkO646/jtVdW+/5rVrr5MQYmJHiURAUbCWKBIQ8RIQC8kg5YM/ESFLkULC4wOKHEWC8AEJkMgHBCIKwiJBkAchQCSEIECifIA4ccCJHaKQzUPCWce7633NTE9333ur+HCqbt/u6ZnpWa93dmbvXxp1z+3bt+tU1T331Klz/odkHQuZOMQZxGs4qI8l6yoqBqbHCAEMIcRKQvooAzxhEjdYMTiT45xB6xZUsU5CQb8/LfatEStKlGcQrHEYZ+hmke1Upv1KAHGCdWpVW6cPZDGaDFafR1rVCzZSQ1g7jWpS1lbB2Rj26xzeV6r8rCCTCowWh7OZWu9l7ANfxToSAiUVvoTSK2eNZ6qUmhZzOpYYQ9MmZjO2PbkxXKYxNvMhlTWChkmmwusdo66pjpvO6yL27WAwYGNjoy70PZlo5ag8z7l69epMCGWqMQtRSfqSrNvFGJlhTE0P8GThz5b79LXM6dyklEdx49sYU7ch1WxISCuvlD+UHnrzpGd78ZklN12I7WC/flwSx0bhb2xs1B03HA4JIfqLzfQpfunKZf7j8c/psi/PEGO4trXJeLhDMMJKX/2WGC3pJ1b9jZNyDCZQjkvO3HWXWrtFCjFTy3hnOIpKtGR1Y51U1FyYpkhXlWa/WtFlqRgTVYmGdGWxqEqeWYyzdDKlc04F2q3RZJ6uy/Ah0rcWJZiCjrXctbrGjtvRQhJFSSaens3qHM/MGHJrqRwUXvCVhorddfqemNU7rfZEMLUVMhqNEGdrFr/xeKx7BmUqCamTf2V1NT5gVtnZWal9pM1i4n5zjLGV1vr1ozqJzNicjEAlqsStMVRVQVmM6HSE1dVTWKuKqqo8rtT9CIA8z8iygPdjRAr6HYNPfnapZhS+1h8NECq6WY44Q2aaCl9XKGm8RFK+goZ4OtG2JjfQTnzwiwsYByYDIwJikDLgMfRWMlISte7JGEAtSWsd1hqc02iVqvL45BhHcCIY4xDJGuGhpYYuVtMkxOQGcpnF4PDBaFlKJrFojaEy9fTGSMAZIZigdAhBGU+twPbmdXy8nk15LpnB4vBFRVUW+pDH1I4DCQIeXXXE8MjhVgw1nZSUxXRuVUWMo7dCV/I4HgGLMp7ahsKTqPBz68it1oXN4moaiEp3Gkab5ZbNrWuEEOj1O5w6va6F3Cc76n7JY+5GPo2YKcsSX2gGemZNnS0Os3suaQ6nFYiNJGpp1aD6ADavXWNrc1NpxZ2jm+cEa+uVRmq3MwZrDJPRiMtxD0ZEcI2SmNoHu11noPsrLs+UYG+OITPUwa+HwzL0yI8BPw9cCCH8YOP4rwLvBErgH0MI74rH3w08guaa/1oI4Z8P3aoFSKF+yTrvdjuxtqzePONJSRDL5raGb6bqM3meM1hbZTgcshWTHapKrR9jTB1fPbWUDf3eCnZl6ifc2hlSFBVZpjzZVbQuAtHPGy2QZrxxICA+YEQ0a1CmVM+JJKuT97AidaaitVbrXFpIhX2vXbqC6XvywYCB6yCZjyUZBeeyuKk4dQup1UtdLk2wTLa2dlv4QZjEWOLxZMw999wTfcK92sop/CwXfIpP9oVahb4QTOYQrzdPp+fodpSEq5oUdHoOE8B1lN88GKGIE137dRtfaFWl9fUVqqri2rUhubVkjWQTY4SiGNaMmINBh2JSYSTF5U8nfhZ0peK81idIy/l0TzscPq9Tr+q9FVX+rl7t1Vm/iV89WtVJQZjoxkJynNP6yADOC95PrbFpiF9MxKoKqtBIvhKDMQExhkFf92nKQnlvtN/rMwFNZAoAonsBAU+QKsbbJQ4qr3UcophVVeh8sgZrAuNiXLNwirU4k5FnGQEoKZgEr7kdQWb6dkrLMUtMNhgM6jrH6V4VEQ0M8EogV7vEUn/Ea1o0ka8fQ6OtMbi4iZoZS+ErRjuRf6mv45189/1+v15NrK+v1+OUXtNDQwNCPJPxBCuuvk+bciXZ+nEMkj+/WeYyzZNUbD2t9lNNgWZsfLpf1E00rF2F3W6X1Syjm3dqnqTEnzHvwy9S/H7YTQURQph+/xBYxsL/C+CPgQ81Ougn0ILlrwshjEXknnj8tcDDwA8A3wP8q4i8JoTGDL9BpA5Nyrzb1SVbcumMJ7rEH6yu4ZxyUhggNwaX5/SNqSlhpapw1tBbUdrSCs/qYMDO9g5bm9uEnieLu/Y+qB/WWkseozkuX77MeFSA0QeFa1gLmQG8Z2dzS5/wMVLG+4CPrH8Fev+NzFiTUeJ3k1tlMplQRiplF4S+6eCCMB6OGA93NIbeZRgRBk0ysBh/XoUKP1Emv+Chl63gPVTiCA2/phXBeEGCYef6EIwQ0JVJ5ix541xE+eGRwMQXCCXWePLcaLEKoOszxpnRvQMndHoO8YEyeHIr2Dyr6xWPbIDQoerGUnLG46zh7rvWEdGs2CIyexpx5JkQfKQzlopux2JkysXTnCfeg/dSW9LBgw9TH3yyAo0xYByTcTGlubVS+9h95en2OtM+QP3yxgrORT+1FYpiKy00Y33T2WLyIXilaXCoKy/MFtbQS3u8V5I7X1b4ckLwvt4bSEop+aGtEc3GtmrpGwPOJsK9LaogOAGs0WIjFoLTh8bqSr/e98Gr+8gZQZzFifrNJajCX7Rpq+/Vau91tcRis2jQeDwmcxmdbpfmrZ/mHDCzP7DSG9CNdOehqrAYOi6nm+VMqrK2xrNsSiKYlH7yma+trc3E9zfdMNZa8JEl1GlkUXKXBSrdRwuz1rW1WmAoy6cGhdJTVDW9S6/XqyMHJ5NJnScE0032tApu+uiTp2Be4dfMqRHjoqAKnqKqdm/aSmQxPCQOVPghhM+KyCvnDr8d+L0QwjiecyEefwj4SDz+LRE5B7wB+M+lWpOYAeNrBVSidsvmSItPVFVgu9gmbG/VVgNoNI0RgykKMLB+agNjDOfPn2cymXDvvfeSo4lX28NNtkZjBrFwRRFgOCl4/soVTq2dZliUSDXt4PTEH+8M2RrtMJqMY4heoNvLWY1RBLZrcaJ7ARXqsDU2i8XNbU1EVZXqItkq1QeYdTq4zIFz+KKIHPH6cOpnfVy3i+3k7GxvsTXaidm0ntH2hJXVQeOmkugW0P5Qa1CUzKrSLMnmjVCFQElFEI/r9ut+TGNQL9F9FZk/C7LMkXVzVmWlDqmsJ+mkZO3UOi76dNNG6Hg8Vouv1+XixYuxqcLqmhZ6Tq6mU6c2OHPmDFeuXGE03qk32fv9PhsbGwxW+mxubjLaGdPtOvVLRzoJE5Iv1mMrZXrsmg5lKBGvNMiZcdjc0s30phSrBd4rX0AZM0mD0mCmMMx0AyeyOU+FkYBYg82U139SRVcHMazPKNd/UVVIWVJUWi8ZY9Q9kCJJ4jmhqpTQTUKkSQgxY3haTSsVvxmPxzgclqCb7mWF92pp+2g3p5DeLMs0o9wXGIzy84iwur5er5ZGkfsoGE3hN50ccerLD40CRMFPC7PX/u6qZCUzdAZdTGEZTuK1yjHBUhs7CaGhWJtbj3knx3U7iA9MvDKBirUQN0nvfkkssBdDWKuqguh+zDod3ePyHiLxGOiW2JSUVGsodPs9jIHM5rFYEYi4yLMfqZq3dhALnaxLMEbde3GVVxSecaF7G7aT08lyxsWEnc0tquB1fGO4qwdGZUE5Kcg6SmEtmdPQa1/x3UsXZ/uF3Qo/63SmrLiJHrkRpXMrwzJfA/yYiPwuMAJ+M4TwBeDlwOca5z0Tjx2I69evs76+ztb161y6rhmpz1x4jslkwpkzZxhub5PIrZBpBmITnXxa9jBt4DjnsN0e5y89rzQCAOiE/fZ3nt21YTLZVM78+es3/2/63yY7Y64PdfC8aFnDZjtNECUKi8f15/VaKysrkWN3yNXtWW4NYljotqk4d+l89K+P62zIsiwpg2d9tA77DL0X6OTR8m9YCkqzMN2gCsOtuDoIdSRBM5JCmSIPtiiabqNdfsnxuOZsT1EOiYtkfX2drNOF5y/z1FNP6YM2fu3KtetcfP7yzLVVwVH3b/Lh+0afW8zM/7vOhV3jP4/qyuzitNvp1pZcNlF3QuWn/uutqmJSKgdNrcxD0FKTRrn6mwo/sU56rwXOD4fkx00yzI7P9njUCImEK7HI+DPfXbLq6BI+g0tXn9/7w4vUxXn2w6teeR/DuF8kmaGkYnjt8nQTuBFRN+9iur69yfXtzQPHEaipMEwY1iR+83NjPBzF/6/PHK/PBXxZ0hmNWF9dJe926a2vszUc8tyVK3zzvJbw7ua5PrhCwA+HWhMh+vg9cDFGHcLe2dmnTp3SnKFelzrRw1i2t4cMBoMbypq9UYXvgFPAG4EfBj4mIvezeI2xcCRE5G3A2wDuvffe2t+2srbGux59FIBffuSRWnnvCmVaoHy2NzexTBMm6opH0c/bTIdetEkC1JWmGu3c9X6Z0KhllOMy51g39TnW/EGNVUfi49gLXjRqIzQUOTBzrRACp0+frn8nXX9G4Ydp/PN+2GvypvfpGmlZmzbHaiUaK2Q1/ayL5G6G1e6FZfr3IEXh5pTN/F9qX7pOM/SuWQc3y7Jdqfrp/CTbfPTGScAy41TTfSy412Dq/klBS/vtK+yHZfp3Gb75NP/yPK9XuCm2Pn2/ea/M3wde4gpmwXWbr4nLZzgc1oWDUpWsoigW0jgchBudYc8AnwiKz6MPrbvj8Vc0zvte4PyiC4QQPhBCOBtCOHvmzJkbbEaLFi1atFgWN2rh/z3wk8BnROQ1QA5cAj4J/LWIvA/dtH018PmlGuIco9FoZnf9pS996aEadfr06UOdvws34hR7Qb+3xA/GJJcX5+enVvSi2OCmhQ/LWWs3oz1Nzu9FbV3GmoObY+EvWtU0V0CL2pvauigSZJGV2WTVPGlYZgwOtLybffMC49BfFIRAJkKP1Rd4mdn5nebM2tparQuHw2G9srgRLBOW+WHgx4G7ReQZ4LeBx4DHRORpYAK8NWgrvyIiHwP+B91rfceyETrJNdGs5g7TijVNnpbYrqUEnMGcK2aRy8HI3ORr/s40Pe/wv70IS1ynLItZZSEy26ZlrlHNRkoset90S8wrq70eCovQvOZ+Lp29sJdro+lCOWosigxK2EupH3S9OxnVQfNzgQu1/uwQ9+Qy/PFLUQ4rgdKMSzTNiXosF7RrxgBY8Dvz7uoQAtvb2wwGg/q7/X7/4PbtA7kdLIuzZ8+GJ554oo4cSJERQG3134i/CtAOTzLO31hN2ev3c0rlqJXMTfj5xBfSVJpHpTyTj/OgeTff1mWzE+evcRAOuk6K+piNqZ/NOG1y3M/382FwO9yLNxvLKNkXe+V4EpDCTzuRaC6RGK6srHwxhHB22evcVj2dNimaGI1GM8p+P4U183RuKPp6iTT/hUU35a2855Z16Sz63l4PsQVI/bKfEkqKeF5xzTblYCXWJBubNrcRatYYy7SSmP+tZTbob9YD66DrzG+KN2VJlv3NUli3ywrmeGu04wAABNZJREFUZmIZi3l+o3TeyjeL+uUmrPAXn7IcV41pZvOl7zbyAGbCldn9MDdLzBmlKckju2mvPj4YDGZChg+D28LCF5GLwDa6D3AScTetbMcRrWzHE3eSbN8XQlg66uW2UPgAIvLEYZYmxwmtbMcTrWzHE61se+PO3i1q0aJFizsIrcJv0aJFizsEt5PC/8BRN+BFRCvb8UQr2/FEK9seuG18+C1atGjR4sXF7WTht2jRokWLFxGtwm/RokWLOwRHrvBF5OdE5Gsick5EHj3q9rxQiMi3ReQpEXlSRJ6Ix06LyKdE5Ovx9dRRt3NZiMhjInIh0mikYwvlEcUfxbH8sog8cHQtPxh7yPZeEflOHL8nReTBxmfvjrJ9TUR+9mhafTBE5BUi8mkR+aqIfEVEfj0eP/bjto9sx37cAESkKyKfF5EvRfl+Jx6/T0Qej2P3URHJ4/FO/P9c/PyV+/5Ak+jpVv+hxfy+AdyPErB9CXjtUbbpJsj0beDuuWN/ADwa3z8K/P5Rt/MQ8rwJeAB4+iB5gAeBf0KTmt8IPH7U7b8B2d6L1neYP/e1cX52gPvivLVHLcMecr0MeCC+XwX+N7b/2I/bPrId+3GL7RVgJb7PgMfjmHwMeDgefz/w9vj+V4D3x/cPAx/d7/pHbeG/ATgXQvhmCGECfAStmnXS8BDwwfj+g8AvHGFbDoUQwmeBy3OH95LnIeBDQfE5YENEXnZrWnp47CHbXqiruYUQvgWkam63HUIIz4YQ/iu+3wS+ihYiOvbjto9se+HYjBtAHIOt+G8W/wLKTvzxeHx+7NKYfhz4KdmHo+OoFf7Lgf9r/L90hazbGAH4FxH5omiRF4CXhBCeBZ2wwD1H1rqbg73kOSnj+c7o2nis4X47lrLFJf4PoZbiiRq3OdnghIybiFgReRK4AHwKXZVcDSEk0qGmDLV88fNrwF17XfuoFf7SFbKOEX4khPAA8GbgHSLypqNu0C3ESRjPPwVeBbweeBb4w3j82MkmIivA3wK/EULYr37iSZDtxIxbCKEKIbweLSD1BuD7F50WXw8l31Er/KUrZB0XhBDOx9cLwN+hA/ZcWiLH1wt7X+FYYC95jv14hhCeizecB/6M6fL/WMkmIhmqEP8qhPCJePhEjNsi2U7KuDURQrgKfAb14W+ISKLYbMpQyxc/X2cfN+VRK/wvAK+OO9A5uunwySNu0w1DRAYispreAz8DPI3K9NZ42luBfziaFt407CXPJ4FfilEfbwSuJRfCccGc7/oX0fEDle3hGBVxH4eo5narEX24fw58NYTwvsZHx37c9pLtJIwbgIicEZGN+L4H/DS6T/Fp4C3xtPmxS2P6FuDfQ9zBXYjbYFf6QXSn/RvAe466PS9QlvvRiIAvAV9J8qA+tX8Dvh5fTx91Ww8h04fRJXKBWhOP7CUPurz8kziWTwFnj7r9NyDbX8a2fzneTC9rnP+eKNvXgDcfdfv3ketH0WX9l4En49+DJ2Hc9pHt2I9bbOvrgP+OcjwN/FY8fj/6oDoH/A3Qice78f9z8fP797t+S63QokWLFncIjtql06JFixYtbhFahd+iRYsWdwhahd+iRYsWdwhahd+iRYsWdwhahd+iRYsWdwhahd+iRYsWdwhahd+iRYsWdwj+H/uaOxX3TCZGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = cv2.imread('img/blobfish.jpeg')\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(166, 304, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what does the image look like?\n",
    "image.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a model with 1 convolutional layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: matplot lib is pretty inconsistent with how it plots these weird cat arrays.\n",
    "# Try running them a couple of times if the output doesn't quite match the blog post results.\n",
    "def nice_image_printer(model, image):\n",
    "    '''prints the cat as a 2d array'''\n",
    "    image_batch = np.expand_dims(image,axis=0)\n",
    "    conv_image2 = model.predict(image_batch)\n",
    "    print(conv_image2.shape)\n",
    "    \n",
    "    conv_image2 = np.squeeze(conv_image2, axis=0)\n",
    "    print(conv_image2.shape)\n",
    "    conv_image2 = conv_image2.reshape(conv_image2.shape[:2])\n",
    "\n",
    "    print(conv_image2.shape)\n",
    "    plt.imshow(conv_image2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 164, 302, 1)\n",
      "(164, 302, 1)\n",
      "(164, 302)\n"
     ]
    },
    {
     "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": [
    "model = Sequential()\n",
    "model.add(Convolution2D(1,    # number of filter layers\n",
    "                        (3,    # y dimension of kernel (we're going for a 3x3 kernel)\n",
    "                        3),    # x dimension of kernel\n",
    "                        input_shape=image.shape))\n",
    "\n",
    "# Keras expects batches of images, so we have to add a dimension to trick it into being nice\n",
    "nice_image_printer(model, image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 164, 302, 1)\n",
      "(164, 302, 1)\n",
      "(164, 302)\n"
     ]
    },
    {
     "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": [
    "model = Sequential()\n",
    "model.add(Convolution2D(1,    # number of filter layers\n",
    "                        (3,    # y dimension of kernel (we're going for a 3x3 kernel)\n",
    "                        3),    # x dimension of kernel\n",
    "                        input_shape=image.shape))\n",
    "\n",
    "# Keras expects batches of images, so we have to add a dimension to trick it into being nice\n",
    "nice_image_printer(model, image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add a ReLU (rectified linear unit - a piecewise linear function that outputs the input if it positive or zero otherwise) activation: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 164, 302, 1)\n",
      "(164, 302, 1)\n",
      "(164, 302)\n"
     ]
    },
    {
     "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": [
    "model = Sequential()\n",
    "model.add(Convolution2D(1,    # number of filter layers\n",
    "                        (3,    # y dimension of kernel (we're going for a 3x3 kernel)\n",
    "                        3),    # x dimension of kernel\n",
    "                        input_shape=image.shape))\n",
    "# Lets add a new activation layer!\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "nice_image_printer(model, image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try max pooling - this is a sample-based discretization process that downsamples an input representation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 32, 60, 1)\n",
      "(32, 60, 1)\n",
      "(32, 60)\n"
     ]
    },
    {
     "data": {
      "image/png": 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bVrBjRMctFqP3Uzo+UuWwkgllUn4AAOYG+P3y2LtTZdXVf3aMT23sua0cABR6KCviS+BDeqHPSgN6QhdCiJwghy6EEDlBDl0IIXKCHLoQQuSECyIo6o006BeGjGjH7h66g/fQ8RsAvJIGk574jRE61kbnuj7uyTpP5//ObFq3/Noyrwvd8DS488gUD3RONbYltpkmD5RN1nmK/pnpamKLAmjtmfSaFIPgZ7sU1CInqf+sHECEt6K07dTeDuqh1+Z4rfYCCRAWgnroLL2+FJUaCNLrm830WjcbPLhXn2U1wPleFCtpQLI6wMsEsDlERBU2egkGR8doDaf7/ORv8TIBV96ZroX5GwAw4ht8LnhPByUBjN0CJf4+Y3hUaqBL9IQuhBA5QQ5dCCFyghy6EELkBDl0IYTICQs6dDO728yOm9nDZ9k+bGaHzWxP588bz+00hRBCLEQ3KpfPAPgTAH/xHPsn3f0Pl2ISPksiyVETCSOfQYFiwFjTijJXLUQ88fa1ia21kRfkZ6nRJ2cH6djTtVQxAgB/Pv2qrudWztJ119tciTBdTyPts3W+F1EaPGtO0Kzz8xUn0ltr+AA/bm01t8+tSW0edJ5nypVI2cHUE1GZgLCvRC+lKdgcgnX0pAIJ5uBNcoxovkTlEsHUOgC/X9qB+qlF5hauObKX0vdZLX2bAgBO/tyWxFY5zf1FYyi9l7Nmb7n4zUo659IsVz+1C+nY2bXnWOXi7t8HQAoiCCGEuJDo5zHj3Wb2UOcrmbT3mhBCiGVlsQ79UwCuBLAdwFEAfxQNNLPbzGy3me0+MR58jSKEEKJvFuXQ3f2Yu7fcvQ3g0wB2Ps/YO919h7vvWLu6h7KVQgghemJRqf9mttHdn81HfzOAh59v/LM4HC1SX9jrJD03CIoaq2XMOnsDAEnbj0IOR2+9nNqr10+kx6jzbWNB0bETK+jYKAhXqKZrGRni6ccsSFVv8rmxOtTrRnhN9kqB7+eh02k99HYQbDOSSh8FP6sngtR/EgBvzfJAbnOI3FetqDN7am8HKfco84CWk8AcWzPAr9PkDC+vEFWmyMixafAToAFiC8Y2jw8ktqlLgrr1Rb6+AhElZEEZBDqHaO97wAb5PXviJakPWPMgXx+P+QYp/sG91S6l4w/dFByDXJOhp+nQrllwJ83scwBuBrDGzA4B+BCAm81sO+bLsR8A8Dv9TUMIIUS/LOjQ3f1txHzXOZiLEEKIPlCmqBBC5AQ5dCGEyAly6EIIkROWtcGFwVBgqftEucKKzYdEZQLqaSF7D6rmv/ydD1L7QJYqcL762IvoWNrg4EywxYFioF1I92e2xpUdTFUzOjhLx45WUqVMFqRyF2mVfqBB1AglosoBgMZgek39DH9+GDgZqFHILdAYDhQDjfTYzaFI5ZKaPGqywY+ANmme0QgaajD1C3sbzP8imAc7NlkzABhpJFKY5WObq9Lr93c3/zEd+4XTL6X2zz6+Iz1u0AyDvUdKZX4PtWYDmTNRVmVV7gPaA+l+ZsF1Kk+mx4gaTkQql2dekY5/+Y376diJeqowOnRwGx3bLXpCF0KInCCHLoQQOUEOXQghcoIcuhBC5AQ5dCGEyAnLqnKJyFak9U4sakRBFC3eSNUsAGADaRT5yXdeRsdubD1E7Q+c2JxOYZZvW+1wer5CIJNoVQI1A1EuNLLoMpG6L+Wg+Qap5TJZ5/VExqaGqD1qcEAhjwrtYM3NaqA6OEMUCkHZHmP1S4J6OU4URqzZAAC0y8ExiCrG60HDCWYMHqUsUGCACI+KgXLFyB41VnEVyMDKVBX1w7m0KQQAXFM9Su0t0syiFahc2NiorlHU4KIwRc5XChQxRAl26lruWzb+Q7px1uKKr+mN/BgZURj9+tr76diP7vulxFZIRXU9oSd0IYTICXLoQgiRE+TQhRAiJ8ihCyFETljWoGjU4KK9bUNim948SI9RPZlGDQozPFJWW5UG/eoreZDje49fRe2sI3npeBAQqZPu5+UgvX6GB3wapfQzlnVVB3gj94MneXtXto7wuHUeYGo3Unt5iEdxMhKwK0RrDlL02R4NjPPrl5FgcnGaDkVzMD1ui8eH0SZd3AHAWZOM6N3ESg0EQ6OCF0ZimlktCBqSS+JFfk3nWiQAfgOfw3hrmNpffdmTie3b+66hY41Moz3F309RUw5mzyb55jtRJTRG+D3E0vmjFP9jP8+DzIWJdB6fePJ1fG7fXE2MdGjX6AldCCFyghy6EELkBDl0IYTICXLoQgiRExZ06Ga2xcy+a2Z7zewRM7u9Y19lZrvM7PHO3zwaJ4QQYlnoRuXSBPBed3/AzEYA3G9muwD8NoBvu/vHzOwOAHcAeN9iJvHUrWn0PIpwV8fS9Pp6Wjlg3n5dmtbcPh18hgXKjuJYGoGPFCpODs3UCfO/4ObSmXQezSEelW8107nNWlAygTXUiPpKBI0TSpOp3eqBQiG9TPDgbmsO8M3IGkxJwscOH0nLPxRm+ebXL0nnXBsNGkAEZQlo842hKIU9NUUlDKJrUqilv6it4mOZ2qYwG+xxLV33B775z+nYoS2T1F6vpye8essxOvYFI2OJ7X89wBvGRKn/zFqe4NevviJ977QG+PvpqbentuJRLn8qjVMzLenRaAXNPsg19aCCQbcs+ITu7kfd/YHOvycB7AWwCcAtAO7pDLsHwK39TUUIIUQ/9PQdupltA3AjgPsArHf3o8C80wewbqknJ4QQonu6duhmNgzgSwDe4+5nenjdbWa228x2jwVJIUIIIfqnK4duZiXMO/PPuvuXO+ZjZrax8/uNAI6z17r7ne6+w913rFktUY0QQpwrFgyKmpkBuAvAXnf/xFm/+hqAdwD4WOfvry52EiyIUwn+D1CZIIGyoGa1PcICqDzqZODRiMpJkjIfBS7YbkaBx6DudcZKu7NoK+Ka6ozidHq+KLhb4CXVqb3JS6fTlOvouKWpIPV/LrWXSGd2ACidSfPdrRbUAJ9Kxw4+FUWvA0j9dKsHkc42CWhuHqVDn3kZD8KxYPDQkaC+PAkyB7cQjAQes6CWefbUJXxuG9N5nBxOBQkAcH8t7S8QCQS8wv9H3yAB/sIMd2XVE+nCa2uC842QN19UiyGAiTlOPM0FgJvJNxZnLusvKtqNyuUmAG8H8GMz29OxfQDzjvyLZvYuAAcBvKWvmQghhOiLBR26u9+L+HPqF5Z2OkIIIRaLvtQWQoicIIcuhBA5QQ5dCCFywrI2uIhgzeRnN/AI9+z61NZewaQhoB0gqkeCJQcfbRnr3xAcgs24GKhZohRfpnKJyiAwdRDbSwAoEZVLdSyS4HAzU0qUeDY4SkSltOIgv06nr+DlA6ZXpBM5dU2wF+V0MyJlR0YELYOH+XFXP8qlOVkzvdo1UlIAAGqj6cWO5tYOKjds/bmDie3UHKmvAKA+mypl1n+6yseOpvtWGwn2OJjb8IF0/OlAStIaIE0kArWWs3IV4AqxBknxnz8IaVBDSlgAQK1c7ublnWPwX4wcTOdcPcmPQfezR1XNc9ETuhBC5AQ5dCGEyAly6EIIkRPk0IUQIifIoQshRE64IFQuzZE0Qj10KZdPTJ8mkf1ABZINp6qKVpXLS1Y8xefGmhYUmPIFQHWc1JkJlAFRo4YaKfvQHOTRfqaIiaLvrIbN7PVzdOzovVwRUZ4kNTQCgdGZy9JnhWdexjfDs0DN0CZr6eERJFL8GCm50kh7rAAAjr+U11YZfSKVyrCGBQAwMJ6eMGp8MvI0V2ucOrAltf0zfozyRLpvk1v53CoTRK2zim8yu78BwMlGDx7l92GLNAypvWyKjo1oHh5MjaEyK51bk4uDUBlLfUORl6TByv28bs/wY6fSORS4z5m6Kq3n00qFNj2hJ3QhhMgJcuhCCJET5NCFECInyKELIUROWPagaJt0fMhIR/K5x3gDgKxEOmVH6bKTJB08StsP7JVT6fmKQfCrVSITCeY2eQU/RmOUBNtm+Oduu0oCPiP8fKzhxOAAj+6e2hEEjh9JIzaz64J1rEmjpQMreYSp+TifdJHExTNSwgAAWiR2GV3T2pZ03ZVV03Ts3F7e1OHoq1jDEH6dWJo/C1wCwMCrx6j99GQayfu1a39Ex/7NX78ysbHgJwCMbU8nV1/NA35zN/L75ZJd6dzKQdOSekb2bQ+PSIflMYg9Eg4Ye0sGwXJ2nbKo2QtpvhIxu5Xf361Kn3n+BD2hCyFETpBDF0KInCCHLoQQOUEOXQghcsKCDt3MtpjZd81sr5k9Yma3d+wfNrPDZran8+eN5366QgghIrpRuTQBvNfdHzCzEQD3m9muzu8+6e5/2MsJMyL7YAXnB47xKPLktvT12TauUBgZSlPbJ6d5WvtUa4jaL9mX2mqjQQOAQvdRa5YuDYCqYiK1Bk2Zj6ZAPrqLBa58yKo8L336UtKcoPtgP5qk4QgAFGe6L1cQno8corGO1yXIyun6plhJCQCFqBHFilQJ4hv4+f7Ni76X2K6oHKNjV2f8Xv7EkdcntpcO8XoVX7nhRYntdIsrSYZJQ4aTgXLphs2HqX3Pz29Njad5mYeNf09Uahnf5BZ/q2LmRalayk/ynHnWDKN8mp/PgjIWvVBfnypa2kz9BsDI2y8qNdAtCzp0dz8K4Gjn35NmthfApv5OK4QQYqnp6Tt0M9sG4EYA93VM7zazh8zsbjMjZaWEEEIsF107dDMbBvAlAO9x9zMAPgXgSgDbMf8E/0fB624zs91mtntsPOj7J4QQom+6cuhmVsK8M/+su38ZANz9mLu33L0N4NMAdrLXuvud7r7D3XesWS1RjRBCnCsW/A7dzAzAXQD2uvsnzrJv7Hy/DgBvBvDwYifBUm5rK4MU7+E0oDUapLA3W2mOcGOCR1pIRQEAvJ55lLLLUpWjsgTl08H6BlK7V7r/nw2tIQ4A1TSIVynxFO+pE7wGOKvhHQUph/enG9cc5IGyVpkfpE3sA9t4nfxKKY1onRzjKdftufRC2SzPM29ewveoMEBqnAd13R+fXZfYoqDo/529ktr3jaXHeN++t9Cx79p5b2K7a+pVdGzxoTSYWDnCr9ODjcupvfpM6kbmtvAI44mXpGMH+FYAwW0/+KM0gB2VoGA3aBb0MyjOpLaRQ1wgUJrk66uPpntXH+EPsk3mR3oQGTC6UbncBODtAH5sZns6tg8AeJuZbe9M4QCA3+lvKkIIIfqhG5XLveBiuK8v/XSEEEIsFn2pLYQQOUEOXQghcoIcuhBC5IQLosHF8KHum1bMrU9/MfUYz2kqnUnHDgbd1ktB43GWXs+UL8DzNNogRJH2rJ4epB0cmK2vORJ0Zp9KL/XYmVV0bKHGz8eUOeXTdCgaK1IbS3UGgAJZMwC0STZ3vcZv2dkZMrgWdEhgKddTgRIhahjyTKqWagXr+5sz21Nb60Y69uqrjlD77EyqPBp8gqe7311KG1yApMADwMpfSdP5j/4DTwSvnOB7X7syLbGxYR2/McaG0g0d/BFXnjWJ4gsAGsPkff1MkF7fTO2jB7hyafBQWnahtoaXhJhdHyjB2qy0QfeOYXZDfzIXPaELIUROkEMXQoicIIcuhBA5QQ5dCCFyghy6EELkhGVXubAGF3OrSBQ4qoFyMv1FVBR++HD3NVCa1UBpQRQtrKYJAGSBnVGe5NHsrJF+xk5dztdRX0tOGClJZtPjFqf5mqOGE9XxdM6NoaAmDRFgRPvWHOJ7webXfnKQjvVL0oUzhQMAFGdTu3HhA8rjXClTSIUdIX4q3XsP3nn7s438F6RGj68OGpQcJwqMTfxNsn4wrY1zIng/FXnvDQwcS1UqU4NcubJyrHvVWHWCr2/4cHojFaeDmju1dGxziJ9wZkva5KYxGDTDCO7lxlA6PlofU30VyL3ZC3pCF0KInCCHLoQQOUEOXQghcoIcuhBC5IQLIvW/QNLgewkkNIJ097HtJJAUNCGIUvHblXT86H4+duTpNDBTnuAHLp7mUbWJG0gZA+Ofu+1S95/HGanHv+KnQWQniMvMko5TlVN8P0skgBY1BilPBg1DyPKag1HwOh0clRpggeOoUUdkZ0HNQhBMZMeIgmrVcX7jswYOWVCiYeAEST9/gqew7x24NrEVgwBNDwCjAAAG4UlEQVRxZYJvRqGR2ksH+eaXT6c3YlYLApoTpOMEAC+ne2QtvqGNNcOJbXYdL5lQG033s1Xie5w1+V6wsgSRAILd39F7r1v0hC6EEDlBDl0IIXKCHLoQQuQEOXQhhMgJCzp0M6ua2Q/M7Edm9oiZ/V7HfrmZ3Wdmj5vZF8yMRxqEEEIsC92oXGoAXuvuU2ZWAnCvmf1vAP8WwCfd/fNm9mcA3gXgU4uaBQmI11fwyHB9fRoRL47zZbB07iwo9B9Gl0nK9cxGPnhyG5mH8bkVp9I042geURMJloofNdkoT6djK6eI9AVARtKlAWDoqXRDW0EadfnIRDq3qNC/BY0MNoymxyjysc1qmqLfJGnY82NJSYFCoMCZ4nvBFApR6YeMqECI2Gv+fEQFMj+eqMOC65RN19KXl/l96GTvjZwLANrloAzCNJOpcZWLl9JjtEZ4mYCZq3gDFiMKk1Y1utapPVJbsWva4uIgtAM/wtR59RVRwxiyz91XK6Es+ITu8zzbz6fU+eMAXgvgrzv2ewDc2t9UhBBC9ENX36GbWcHM9gA4DmAXgCcATLj7s49shwDQvlVmdpuZ7Taz3SfGe6heJYQQoie6cuju3nL37QA2A9gJ4IVsWPDaO919h7vvWLs66PEohBCib3pSubj7BIDvAXg5gEvM/v8XxJsB8O62QgghloUFg6JmthZAw90nzGwAwOsAfBzAdwH8BoDPA3gHgK8udhIr35R+FkSd7ofLacCneg0PJLVJlKMdRD+zIEpVJJGuYsYjF2zO1UIQ5AqokPON13gNcLa+Js0nBmqt9FJHezzT5LdFi4yvNXjadqudRpM2jKS1twHg+FSang0AUySQG0UTnUSTCgV+nZr19H+KWYEfd2Awvd8AoNUi91ab732plO7R3FxQ2yKKarOhzv/HmxV4kLFb2NoAoFzm17rd7l7gViym12SgzFP8iwX+FW2F2BstvhfVqI4BISM1GkpBpLsVvM8ma6QWfcBMPb0HXrbhEB372O93d8xuVC4bAdxjZgXMP9F/0d3/1sweBfB5M/t9AA8CuKu7UwohhDgXLOjQ3f0hADcS+5OY/z5dCCHEBYAyRYUQIifIoQshRE6QQxdCiJxgHqT5npOTmZ0A8NPOj2sAjC3byZefPK8vz2sDtL6LnTyu7zJ3X7vQoGV16P/oxGa73X3HeTn5MpDn9eV5bYDWd7GT9/U9H/rKRQghcoIcuhBC5ITz6dDvPI/nXg7yvL48rw3Q+i528r6+kPP2HboQQoilRV+5CCFETlh2h25mbzCzx8zsJ2Z2x3Kff6kxs7vN7LiZPXyWbZWZ7eq059tlZivP5xz7wcy2mNl3zWxvpwXh7R17Ltb4T6HFYqefwYNm9redn/O0tgNm9mMz22Nmuzu2XNybi2FZHXqnwNefAvglANcBeJuZXbecczgHfAbAG55juwPAt939KgDf7vx8sdIE8F53fyHmyyb/buea5WWNz7ZYfDGA7QDeYGYvx3xF0U921ncK8y0WL1ZuB7D3rJ/ztDYAeI27bz9LqpiXe7NnlvsJfSeAn7j7k+5ex3zp3VuWeQ5Lirt/H8DJ55hvwXxbPuAib8/n7kfd/YHOvycx7xg2ISdrzHuLRTPbDOBNAP6887MhJ2t7HnJxby6G5XbomwA8fdbPYeu6i5z17n4UmHeIANad5/ksCWa2DfOVN+9DjtbYT4vFi4D/CuA/4Gfth1cjP2sD5j98v2Vm95vZbR1bbu7NXummHvpSwqr3S2ZzEWBmwwC+BOA97n7GSLf4ixV3bwHYbmaXAPgKemixeCFjZr8M4Li7329mNz9rJkMvurWdxU3ufsTM1gHYZWb7zveEzifL/YR+CMCWs37Oa+u6Y2a2EQA6fx8/z/PpCzMrYd6Zf9bdv9wx52qNQC5bLN4E4FfN7ADmv958Leaf2POwNgCAux/p/H0c8x/GO5HDe7Nbltuh/xDAVZ0oexnAWwF8bZnnsBx8DfNt+YA+2/Odbzrfud4FYK+7f+KsX+VijWa2tvNkjrNaLO7Fz1osAhfp+tz9/e6+2d23Yf699h13/xfIwdoAwMyGzGzk2X8DeD2Ah5GTe3MxLHtikZm9EfNPCQUAd7v7R5d1AkuMmX0OwM2Yr/B2DMCHAPxPAF8EsBXAQQBvcffnBk4vCszsVQD+D4Af42ffw34A89+jX/RrNLMXYT5wdnaLxY+Y2RWYf6pdhfkWi//S3XmD0YuAzlcu/87dfzkva+us4yudH4sA/tLdP2pmq5GDe3MxKFNUCCFygjJFhRAiJ8ihCyFETpBDF0KInCCHLoQQOUEOXQghcoIcuhBC5AQ5dCGEyAly6EIIkRP+H3ePRI2NaA3nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Convolution2D(1,    # number of filter layers\n",
    "                        (3,    # y dimension of kernel (we're going for a 3x3 kernel)\n",
    "                        3),    # x dimension of kernel\n",
    "                        input_shape=image.shape))\n",
    "# Lets add a new max pooling layer!\n",
    "model.add(MaxPooling2D(pool_size=(5,5)))\n",
    "\n",
    "nice_image_printer(model, image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and now activation then pooling: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 32, 60, 1)\n",
      "(32, 60, 1)\n",
      "(32, 60)\n"
     ]
    },
    {
     "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": [
    "model = Sequential()\n",
    "model.add(Convolution2D(1,    # number of filter layers\n",
    "                        (3,    # y dimension of kernel (we're going for a 3x3 kernel)\n",
    "                        3),    # x dimension of kernel\n",
    "                        input_shape=image.shape))\n",
    "# Lets activate then pool!\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(5,5)))\n",
    "\n",
    "nice_image_printer(model, image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and finally the result after the convolutional and pooling stages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 17, 32, 1)\n",
      "(17, 32, 1)\n",
      "(17, 32)\n"
     ]
    },
    {
     "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": [
    "# 1 filter in each conv layer for pretty printing\n",
    "model = Sequential()\n",
    "model.add(Convolution2D(1,    # number of filter layers\n",
    "                        (3,    # y dimension of kernel (we're going for a 3x3 kernel)\n",
    "                        3),    # x dimension of kernel\n",
    "                        input_shape=image.shape))\n",
    "# Lets activate then pool!\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(3,3)))\n",
    "model.add(Convolution2D(1,    # number of filter layers\n",
    "                        (3,    # y dimension of kernel (we're going for a 3x3 kernel)\n",
    "                        3),    # x dimension of kernel\n",
    "                        input_shape=image.shape))\n",
    "# Lets activate then pool!\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(3,3)))\n",
    "\n",
    "nice_image_printer(model, image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: real-bogus classification in ZTF (Duev et al. 2019)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Events observed by ZTF may have been triggered from a flux-transient, a reoccurring flux-variable, or a moving object. The metadata and contextual information including the cutouts are put into \"alert packets\" that are distributed via the ZTF Alert Distribution System (ZADS). On a typical night, the number of detected events ranges from $10^5 - 10^6$.\n",
    "\n",
    "The real/bogus ($rb$) machine learning (ML) classifiers are designed to separate genuine astrophysical events from bogus detections by scoring individual sources on a scale from 0.0 (bogus) to 1.0 (real). Currently, ZTF employs two $rb$ classifiers: a feature-based random forest classifier ($rfrb$), and `braai` a convolutional-neural-network, deep-learning classifier.\n",
    "\n",
    "In this example, we will build a deep $rb$ classifier based on `braai`. We will use a data set consisting of $11.5k$ labeled alerts from the [ZTF public alert stream](https://ztf.uw.edu/alerts/public/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup\n",
    "\n",
    "#### Imports\n",
    "\n",
    "First you may have to install tensorflow (`pip install tensorflow`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import HTML, display\n",
    "import tqdm\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import roc_curve, auc, confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "import datetime\n",
    "from astropy.time import Time\n",
    "\n",
    "import os\n",
    "import io\n",
    "import gzip\n",
    "from astropy.io import fits\n",
    "from bson.json_util import loads, dumps\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import register_matplotlib_converters, scatter_matrix\n",
    "register_matplotlib_converters()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data\n",
    "\n",
    "This may take a while as the data sets are large (1GB!):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!wget -O candidates.csv https://skipper.caltech.edu:5001/s/sCTdtkCxz5Nsy8a/download\n",
    "#!wget -O triplets.norm.npy https://skipper.caltech.edu:5001/s/jmbkK7PFnK86fTp/download\n",
    "#!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Candidates\n",
    "\n",
    "First load the CSV file containing the `candidate` block of the alerts (cutouts and previous detections are excluded). All alerts are labeled (0=bogus, 1=real)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Unnamed: 0</th>\n",
       "      <th>aimage</th>\n",
       "      <th>aimagerat</th>\n",
       "      <th>bimage</th>\n",
       "      <th>bimagerat</th>\n",
       "      <th>candid</th>\n",
       "      <th>chinr</th>\n",
       "      <th>chipsf</th>\n",
       "      <th>classtar</th>\n",
       "      <th>clrcoeff</th>\n",
       "      <th>...</th>\n",
       "      <th>szmag1</th>\n",
       "      <th>szmag2</th>\n",
       "      <th>szmag3</th>\n",
       "      <th>tblid</th>\n",
       "      <th>tooflag</th>\n",
       "      <th>xpos</th>\n",
       "      <th>ypos</th>\n",
       "      <th>zpclrcov</th>\n",
       "      <th>zpmed</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.051</td>\n",
       "      <td>0.308211</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.290616</td>\n",
       "      <td>737414800515015021</td>\n",
       "      <td>3.024</td>\n",
       "      <td>1.074084</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.090182</td>\n",
       "      <td>...</td>\n",
       "      <td>19.752300</td>\n",
       "      <td>18.349100</td>\n",
       "      <td>19.709101</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1102.627808</td>\n",
       "      <td>2502.003174</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>26.278999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.283801</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.273209</td>\n",
       "      <td>548211733715015007</td>\n",
       "      <td>0.895</td>\n",
       "      <td>1.868645</td>\n",
       "      <td>0.921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>18.470301</td>\n",
       "      <td>20.777000</td>\n",
       "      <td>20.725700</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2904.806396</td>\n",
       "      <td>280.736298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.586078</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.488886</td>\n",
       "      <td>527282584015015018</td>\n",
       "      <td>2.373</td>\n",
       "      <td>1.259607</td>\n",
       "      <td>0.876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>17.358500</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1045.247070</td>\n",
       "      <td>2085.618652</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.534324</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.507909</td>\n",
       "      <td>540383446315015002</td>\n",
       "      <td>0.217</td>\n",
       "      <td>5.922930</td>\n",
       "      <td>0.992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>14.093500</td>\n",
       "      <td>19.026699</td>\n",
       "      <td>20.882000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>279.002289</td>\n",
       "      <td>381.362091</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.317466</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.301712</td>\n",
       "      <td>806286202415015004</td>\n",
       "      <td>3.389</td>\n",
       "      <td>3.200528</td>\n",
       "      <td>0.983</td>\n",
       "      <td>-0.080435</td>\n",
       "      <td>...</td>\n",
       "      <td>20.331200</td>\n",
       "      <td>20.488501</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1296.672852</td>\n",
       "      <td>861.606018</td>\n",
       "      <td>-0.000052</td>\n",
       "      <td>26.166000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.281992</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.272797</td>\n",
       "      <td>669136891615015022</td>\n",
       "      <td>7.770</td>\n",
       "      <td>4.304990</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.078656</td>\n",
       "      <td>...</td>\n",
       "      <td>20.375401</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>16.879499</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2776.215820</td>\n",
       "      <td>1663.289185</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>26.191999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.298239</td>\n",
       "      <td>0.742</td>\n",
       "      <td>0.261268</td>\n",
       "      <td>540292995315015018</td>\n",
       "      <td>1.325</td>\n",
       "      <td>0.999686</td>\n",
       "      <td>0.978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>15.185000</td>\n",
       "      <td>19.643900</td>\n",
       "      <td>19.476601</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1236.580688</td>\n",
       "      <td>2640.000977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.327933</td>\n",
       "      <td>0.527</td>\n",
       "      <td>0.294413</td>\n",
       "      <td>675144872815015062</td>\n",
       "      <td>7.422</td>\n",
       "      <td>1.742821</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.095612</td>\n",
       "      <td>...</td>\n",
       "      <td>18.376801</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>564.192993</td>\n",
       "      <td>1361.725952</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>26.330999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.453448</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.413780</td>\n",
       "      <td>671461315015015045</td>\n",
       "      <td>3.011</td>\n",
       "      <td>2.007285</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.101529</td>\n",
       "      <td>...</td>\n",
       "      <td>16.375799</td>\n",
       "      <td>18.535400</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>397.519012</td>\n",
       "      <td>960.680725</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>26.287001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.282000</td>\n",
       "      <td>0.649</td>\n",
       "      <td>0.259600</td>\n",
       "      <td>713456381615015039</td>\n",
       "      <td>1.809</td>\n",
       "      <td>1.126633</td>\n",
       "      <td>0.876</td>\n",
       "      <td>-0.046420</td>\n",
       "      <td>...</td>\n",
       "      <td>18.462299</td>\n",
       "      <td>19.901100</td>\n",
       "      <td>18.954800</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>1343.400757</td>\n",
       "      <td>2941.009033</td>\n",
       "      <td>-0.000149</td>\n",
       "      <td>23.504000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.366516</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.335294</td>\n",
       "      <td>643113934915015008</td>\n",
       "      <td>1.007</td>\n",
       "      <td>7.479780</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.056851</td>\n",
       "      <td>...</td>\n",
       "      <td>20.523600</td>\n",
       "      <td>16.165600</td>\n",
       "      <td>18.843700</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1495.528931</td>\n",
       "      <td>906.415100</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>26.191000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.396685</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.334254</td>\n",
       "      <td>606166925615015007</td>\n",
       "      <td>2.880</td>\n",
       "      <td>1.123649</td>\n",
       "      <td>0.845</td>\n",
       "      <td>-0.044078</td>\n",
       "      <td>...</td>\n",
       "      <td>17.462700</td>\n",
       "      <td>21.091801</td>\n",
       "      <td>17.618099</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2275.073975</td>\n",
       "      <td>1323.382446</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>26.291000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.306250</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.292411</td>\n",
       "      <td>541251494615015004</td>\n",
       "      <td>0.434</td>\n",
       "      <td>7.556235</td>\n",
       "      <td>0.857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>15.868200</td>\n",
       "      <td>19.186800</td>\n",
       "      <td>20.595699</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>354.270599</td>\n",
       "      <td>863.774597</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.721</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>0.616</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>463203553015015008</td>\n",
       "      <td>1.861</td>\n",
       "      <td>1.230512</td>\n",
       "      <td>0.992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>16.039101</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2026.512451</td>\n",
       "      <td>1096.865723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.540779</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.519345</td>\n",
       "      <td>540454345315015016</td>\n",
       "      <td>0.714</td>\n",
       "      <td>4.039593</td>\n",
       "      <td>0.980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>11.641400</td>\n",
       "      <td>11.633000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>446.462402</td>\n",
       "      <td>2086.012939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.641649</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.616886</td>\n",
       "      <td>589165214915015018</td>\n",
       "      <td>1.113</td>\n",
       "      <td>0.991862</td>\n",
       "      <td>0.597</td>\n",
       "      <td>0.069088</td>\n",
       "      <td>...</td>\n",
       "      <td>19.412701</td>\n",
       "      <td>20.025999</td>\n",
       "      <td>20.939600</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1809.097656</td>\n",
       "      <td>1261.588257</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>26.146999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.329740</td>\n",
       "      <td>0.716</td>\n",
       "      <td>0.266171</td>\n",
       "      <td>548443560315015005</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.821699</td>\n",
       "      <td>0.868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>19.489599</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>18.663799</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2045.520996</td>\n",
       "      <td>2851.742676</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.368996</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.325764</td>\n",
       "      <td>721509665915015005</td>\n",
       "      <td>6.164</td>\n",
       "      <td>0.907599</td>\n",
       "      <td>0.975</td>\n",
       "      <td>-0.020890</td>\n",
       "      <td>...</td>\n",
       "      <td>17.931299</td>\n",
       "      <td>20.005699</td>\n",
       "      <td>19.206400</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1037.806519</td>\n",
       "      <td>2612.273926</td>\n",
       "      <td>-0.000028</td>\n",
       "      <td>26.312000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.859</td>\n",
       "      <td>0.249709</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.207849</td>\n",
       "      <td>718520154415015009</td>\n",
       "      <td>3.117</td>\n",
       "      <td>1.446563</td>\n",
       "      <td>0.911</td>\n",
       "      <td>-0.054953</td>\n",
       "      <td>...</td>\n",
       "      <td>18.396400</td>\n",
       "      <td>16.757799</td>\n",
       "      <td>19.865200</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>527.156006</td>\n",
       "      <td>544.836182</td>\n",
       "      <td>-0.000025</td>\n",
       "      <td>26.291000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.338835</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.322330</td>\n",
       "      <td>540458075815015028</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.089895</td>\n",
       "      <td>0.980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>16.524099</td>\n",
       "      <td>20.554800</td>\n",
       "      <td>20.409901</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1185.079468</td>\n",
       "      <td>2668.985596</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.733216</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.673104</td>\n",
       "      <td>540313932115015034</td>\n",
       "      <td>0.390</td>\n",
       "      <td>43.569832</td>\n",
       "      <td>0.047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>11.123000</td>\n",
       "      <td>14.061200</td>\n",
       "      <td>16.833200</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2426.998291</td>\n",
       "      <td>1134.494385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.511230</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.452941</td>\n",
       "      <td>733519053815015013</td>\n",
       "      <td>0.161</td>\n",
       "      <td>1.351089</td>\n",
       "      <td>0.936</td>\n",
       "      <td>-0.052905</td>\n",
       "      <td>...</td>\n",
       "      <td>18.105101</td>\n",
       "      <td>21.419201</td>\n",
       "      <td>20.964100</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>499.907013</td>\n",
       "      <td>943.767395</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>26.212000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.316170</td>\n",
       "      <td>540357892415015003</td>\n",
       "      <td>0.408</td>\n",
       "      <td>3.322113</td>\n",
       "      <td>0.997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>13.685000</td>\n",
       "      <td>19.652300</td>\n",
       "      <td>16.926600</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>588.096985</td>\n",
       "      <td>880.659424</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.335662</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.322059</td>\n",
       "      <td>748489661115015007</td>\n",
       "      <td>0.871</td>\n",
       "      <td>3.733582</td>\n",
       "      <td>0.984</td>\n",
       "      <td>-0.063086</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>19.263901</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2088.093994</td>\n",
       "      <td>414.101807</td>\n",
       "      <td>-0.000056</td>\n",
       "      <td>26.152000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.305532</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.268511</td>\n",
       "      <td>607270240615015009</td>\n",
       "      <td>1.236</td>\n",
       "      <td>10.746737</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.060617</td>\n",
       "      <td>...</td>\n",
       "      <td>20.790100</td>\n",
       "      <td>20.289700</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1971.924072</td>\n",
       "      <td>787.639282</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>26.301001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.313675</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.292735</td>\n",
       "      <td>472391270115010000</td>\n",
       "      <td>2.843</td>\n",
       "      <td>1.984797</td>\n",
       "      <td>0.894</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>16.710100</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2864.901367</td>\n",
       "      <td>206.689407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>1.003</td>\n",
       "      <td>0.404435</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.318952</td>\n",
       "      <td>512243981415015000</td>\n",
       "      <td>6.630</td>\n",
       "      <td>1.277926</td>\n",
       "      <td>0.976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>19.279200</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>781.290894</td>\n",
       "      <td>1145.643799</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>0.974</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>0.832</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>472499660415015028</td>\n",
       "      <td>0.822</td>\n",
       "      <td>1.559985</td>\n",
       "      <td>0.876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>17.132500</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>766.579224</td>\n",
       "      <td>2460.427979</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>1.281</td>\n",
       "      <td>0.213856</td>\n",
       "      <td>1.072</td>\n",
       "      <td>0.178965</td>\n",
       "      <td>671528463915015027</td>\n",
       "      <td>1.993</td>\n",
       "      <td>1.074707</td>\n",
       "      <td>0.690</td>\n",
       "      <td>-0.044614</td>\n",
       "      <td>...</td>\n",
       "      <td>18.696600</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>21.125401</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1068.085083</td>\n",
       "      <td>2810.945068</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>26.094000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.222967</td>\n",
       "      <td>0.723</td>\n",
       "      <td>0.172967</td>\n",
       "      <td>527264303315015010</td>\n",
       "      <td>1.193</td>\n",
       "      <td>1.103376</td>\n",
       "      <td>0.696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>19.625500</td>\n",
       "      <td>19.883900</td>\n",
       "      <td>19.965500</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2239.733887</td>\n",
       "      <td>2014.218140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11526</th>\n",
       "      <td>11526</td>\n",
       "      <td>1.405</td>\n",
       "      <td>0.282129</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.190964</td>\n",
       "      <td>786208592215015087</td>\n",
       "      <td>0.863</td>\n",
       "      <td>11.402366</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.118292</td>\n",
       "      <td>...</td>\n",
       "      <td>15.632100</td>\n",
       "      <td>12.362000</td>\n",
       "      <td>20.124300</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>1228.687256</td>\n",
       "      <td>1893.212402</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>26.416000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11527</th>\n",
       "      <td>11527</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.430891</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.320796</td>\n",
       "      <td>741222994915015055</td>\n",
       "      <td>0.313</td>\n",
       "      <td>93.579430</td>\n",
       "      <td>0.997</td>\n",
       "      <td>-0.063208</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>12.180000</td>\n",
       "      <td>21.392599</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>2861.200928</td>\n",
       "      <td>2029.687378</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>26.139000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11528</th>\n",
       "      <td>11528</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.398139</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.315032</td>\n",
       "      <td>842190841115015005</td>\n",
       "      <td>1.019</td>\n",
       "      <td>276.043854</td>\n",
       "      <td>0.997</td>\n",
       "      <td>-0.081304</td>\n",
       "      <td>...</td>\n",
       "      <td>12.118000</td>\n",
       "      <td>15.060500</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>713.289978</td>\n",
       "      <td>926.252014</td>\n",
       "      <td>-0.000023</td>\n",
       "      <td>26.219999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11529</th>\n",
       "      <td>11529</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.433582</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.327974</td>\n",
       "      <td>740466961615015043</td>\n",
       "      <td>0.321</td>\n",
       "      <td>279.181824</td>\n",
       "      <td>0.997</td>\n",
       "      <td>-0.100461</td>\n",
       "      <td>...</td>\n",
       "      <td>11.258000</td>\n",
       "      <td>20.523701</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>2010.190552</td>\n",
       "      <td>2875.514648</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>26.158001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11530</th>\n",
       "      <td>11530</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.154269</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.147438</td>\n",
       "      <td>760144470815015096</td>\n",
       "      <td>3.616</td>\n",
       "      <td>2.345076</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.074892</td>\n",
       "      <td>...</td>\n",
       "      <td>20.057100</td>\n",
       "      <td>12.343000</td>\n",
       "      <td>18.802299</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>1564.928345</td>\n",
       "      <td>1099.196899</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>26.261000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11531</th>\n",
       "      <td>11531</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.357728</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.264435</td>\n",
       "      <td>792300843015015009</td>\n",
       "      <td>0.643</td>\n",
       "      <td>56.792229</td>\n",
       "      <td>0.997</td>\n",
       "      <td>0.107531</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>12.376000</td>\n",
       "      <td>17.662399</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2885.297607</td>\n",
       "      <td>3036.010254</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>26.243999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11532</th>\n",
       "      <td>11532</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.514808</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.394995</td>\n",
       "      <td>739201431715015009</td>\n",
       "      <td>0.557</td>\n",
       "      <td>363.456329</td>\n",
       "      <td>0.997</td>\n",
       "      <td>-0.090926</td>\n",
       "      <td>...</td>\n",
       "      <td>12.149000</td>\n",
       "      <td>19.707399</td>\n",
       "      <td>20.848400</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>165.596695</td>\n",
       "      <td>1752.099121</td>\n",
       "      <td>-0.000021</td>\n",
       "      <td>26.156000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11533</th>\n",
       "      <td>11533</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.439749</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.339778</td>\n",
       "      <td>741222994915015064</td>\n",
       "      <td>0.968</td>\n",
       "      <td>84.353371</td>\n",
       "      <td>0.997</td>\n",
       "      <td>-0.063208</td>\n",
       "      <td>...</td>\n",
       "      <td>11.696000</td>\n",
       "      <td>18.039900</td>\n",
       "      <td>21.608801</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>2833.287598</td>\n",
       "      <td>1939.674561</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>26.139000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11534</th>\n",
       "      <td>11534</td>\n",
       "      <td>2.528</td>\n",
       "      <td>0.459636</td>\n",
       "      <td>1.655</td>\n",
       "      <td>0.300909</td>\n",
       "      <td>806469363515015035</td>\n",
       "      <td>0.746</td>\n",
       "      <td>6.540512</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.084826</td>\n",
       "      <td>...</td>\n",
       "      <td>16.664301</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>21.000401</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1559.899170</td>\n",
       "      <td>987.594910</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>26.294001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11535</th>\n",
       "      <td>11535</td>\n",
       "      <td>0.680</td>\n",
       "      <td>0.440981</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.315820</td>\n",
       "      <td>768171553415015016</td>\n",
       "      <td>0.950</td>\n",
       "      <td>250.648529</td>\n",
       "      <td>0.997</td>\n",
       "      <td>-0.029441</td>\n",
       "      <td>...</td>\n",
       "      <td>11.708000</td>\n",
       "      <td>14.736800</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>391.118713</td>\n",
       "      <td>2323.190430</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>26.237000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11536</th>\n",
       "      <td>11536</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0.392944</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.255590</td>\n",
       "      <td>755188836315015009</td>\n",
       "      <td>0.707</td>\n",
       "      <td>141.658356</td>\n",
       "      <td>0.996</td>\n",
       "      <td>-0.040612</td>\n",
       "      <td>...</td>\n",
       "      <td>13.392200</td>\n",
       "      <td>10.873000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>130.946793</td>\n",
       "      <td>2031.903076</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>26.188000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11537</th>\n",
       "      <td>11537</td>\n",
       "      <td>0.492</td>\n",
       "      <td>0.388074</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.282379</td>\n",
       "      <td>786152535215010011</td>\n",
       "      <td>0.364</td>\n",
       "      <td>152.949066</td>\n",
       "      <td>0.997</td>\n",
       "      <td>-0.086900</td>\n",
       "      <td>...</td>\n",
       "      <td>13.216600</td>\n",
       "      <td>9.803000</td>\n",
       "      <td>11.819100</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>378.983612</td>\n",
       "      <td>2884.036865</td>\n",
       "      <td>-0.000007</td>\n",
       "      <td>26.177999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11538</th>\n",
       "      <td>11538</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.372926</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.270050</td>\n",
       "      <td>793242020015015001</td>\n",
       "      <td>1.227</td>\n",
       "      <td>239.140793</td>\n",
       "      <td>0.997</td>\n",
       "      <td>-0.024018</td>\n",
       "      <td>...</td>\n",
       "      <td>11.876000</td>\n",
       "      <td>11.838100</td>\n",
       "      <td>16.358999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1830.754028</td>\n",
       "      <td>986.742676</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>26.163000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11539</th>\n",
       "      <td>11539</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.571999</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.367821</td>\n",
       "      <td>818485475815015089</td>\n",
       "      <td>0.713</td>\n",
       "      <td>169.053787</td>\n",
       "      <td>0.996</td>\n",
       "      <td>-0.045754</td>\n",
       "      <td>...</td>\n",
       "      <td>13.172100</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>2573.898682</td>\n",
       "      <td>2569.170410</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>26.271000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11540</th>\n",
       "      <td>11540</td>\n",
       "      <td>4.179</td>\n",
       "      <td>0.720517</td>\n",
       "      <td>2.833</td>\n",
       "      <td>0.488448</td>\n",
       "      <td>793148771415015031</td>\n",
       "      <td>0.453</td>\n",
       "      <td>10.503124</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.078190</td>\n",
       "      <td>...</td>\n",
       "      <td>12.628000</td>\n",
       "      <td>20.290100</td>\n",
       "      <td>16.239700</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>2220.919189</td>\n",
       "      <td>2042.546753</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>26.038000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11541</th>\n",
       "      <td>11541</td>\n",
       "      <td>2.682</td>\n",
       "      <td>0.525882</td>\n",
       "      <td>1.696</td>\n",
       "      <td>0.332549</td>\n",
       "      <td>768548070715015068</td>\n",
       "      <td>2.113</td>\n",
       "      <td>13.740769</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.101218</td>\n",
       "      <td>...</td>\n",
       "      <td>20.849600</td>\n",
       "      <td>12.163000</td>\n",
       "      <td>18.504900</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>97.573799</td>\n",
       "      <td>2726.532227</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>26.233000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11542</th>\n",
       "      <td>11542</td>\n",
       "      <td>0.567</td>\n",
       "      <td>0.406869</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.272681</td>\n",
       "      <td>854403175715010034</td>\n",
       "      <td>1.257</td>\n",
       "      <td>189.841843</td>\n",
       "      <td>0.997</td>\n",
       "      <td>-0.067524</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>19.938299</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2802.614258</td>\n",
       "      <td>2645.082520</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>26.157000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11543</th>\n",
       "      <td>11543</td>\n",
       "      <td>2.789</td>\n",
       "      <td>0.477569</td>\n",
       "      <td>1.793</td>\n",
       "      <td>0.307021</td>\n",
       "      <td>768227390515015080</td>\n",
       "      <td>0.684</td>\n",
       "      <td>85.339645</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.075182</td>\n",
       "      <td>...</td>\n",
       "      <td>19.907600</td>\n",
       "      <td>14.029000</td>\n",
       "      <td>17.732201</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>2639.854492</td>\n",
       "      <td>2456.470215</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>26.275000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11544</th>\n",
       "      <td>11544</td>\n",
       "      <td>5.326</td>\n",
       "      <td>0.786706</td>\n",
       "      <td>4.118</td>\n",
       "      <td>0.608272</td>\n",
       "      <td>846395602015015021</td>\n",
       "      <td>0.304</td>\n",
       "      <td>10.398323</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.081571</td>\n",
       "      <td>...</td>\n",
       "      <td>13.770300</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>21.148399</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>188.390701</td>\n",
       "      <td>2539.527832</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>26.200001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11545</th>\n",
       "      <td>11545</td>\n",
       "      <td>0.723</td>\n",
       "      <td>0.395552</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.299810</td>\n",
       "      <td>741263394215015022</td>\n",
       "      <td>1.424</td>\n",
       "      <td>76.838898</td>\n",
       "      <td>0.997</td>\n",
       "      <td>-0.037693</td>\n",
       "      <td>...</td>\n",
       "      <td>10.871000</td>\n",
       "      <td>10.167700</td>\n",
       "      <td>14.963800</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2607.035400</td>\n",
       "      <td>2662.958252</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>26.281000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11546</th>\n",
       "      <td>11546</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.426196</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.310288</td>\n",
       "      <td>740183801715010002</td>\n",
       "      <td>0.300</td>\n",
       "      <td>77.442604</td>\n",
       "      <td>0.019</td>\n",
       "      <td>-0.085127</td>\n",
       "      <td>...</td>\n",
       "      <td>11.089800</td>\n",
       "      <td>14.460300</td>\n",
       "      <td>13.960500</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>304.294312</td>\n",
       "      <td>739.737183</td>\n",
       "      <td>-0.000014</td>\n",
       "      <td>26.186001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11547</th>\n",
       "      <td>11547</td>\n",
       "      <td>2.776</td>\n",
       "      <td>0.630909</td>\n",
       "      <td>2.116</td>\n",
       "      <td>0.480909</td>\n",
       "      <td>813140151615015036</td>\n",
       "      <td>0.781</td>\n",
       "      <td>118.523621</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.094964</td>\n",
       "      <td>...</td>\n",
       "      <td>17.861799</td>\n",
       "      <td>12.913000</td>\n",
       "      <td>16.619200</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>2740.830811</td>\n",
       "      <td>1318.207764</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>26.200001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11548</th>\n",
       "      <td>11548</td>\n",
       "      <td>2.704</td>\n",
       "      <td>0.494333</td>\n",
       "      <td>1.695</td>\n",
       "      <td>0.309872</td>\n",
       "      <td>754469972515010004</td>\n",
       "      <td>0.517</td>\n",
       "      <td>3.779280</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.124624</td>\n",
       "      <td>...</td>\n",
       "      <td>14.930900</td>\n",
       "      <td>21.316799</td>\n",
       "      <td>18.669201</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2951.753662</td>\n",
       "      <td>2822.529297</td>\n",
       "      <td>-0.000029</td>\n",
       "      <td>26.363001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11549</th>\n",
       "      <td>11549</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.403145</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.291120</td>\n",
       "      <td>793223902815015012</td>\n",
       "      <td>0.482</td>\n",
       "      <td>423.016357</td>\n",
       "      <td>0.998</td>\n",
       "      <td>-0.018945</td>\n",
       "      <td>...</td>\n",
       "      <td>11.431400</td>\n",
       "      <td>9.957340</td>\n",
       "      <td>10.196000</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1121.034790</td>\n",
       "      <td>2455.416748</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>26.188999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11550</th>\n",
       "      <td>11550</td>\n",
       "      <td>1.935</td>\n",
       "      <td>0.369275</td>\n",
       "      <td>1.292</td>\n",
       "      <td>0.246565</td>\n",
       "      <td>786232670415015135</td>\n",
       "      <td>1.123</td>\n",
       "      <td>5.410284</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.077989</td>\n",
       "      <td>...</td>\n",
       "      <td>12.512000</td>\n",
       "      <td>20.259399</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>3052.684326</td>\n",
       "      <td>2093.844482</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>26.278999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11551</th>\n",
       "      <td>11551</td>\n",
       "      <td>1.833</td>\n",
       "      <td>0.385895</td>\n",
       "      <td>1.189</td>\n",
       "      <td>0.250316</td>\n",
       "      <td>739458591615010002</td>\n",
       "      <td>0.721</td>\n",
       "      <td>7.560311</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0.096468</td>\n",
       "      <td>...</td>\n",
       "      <td>21.166901</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>20.226500</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>77.071404</td>\n",
       "      <td>2386.187988</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>26.271000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11552</th>\n",
       "      <td>11552</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.316860</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.237499</td>\n",
       "      <td>757172973715015018</td>\n",
       "      <td>1.217</td>\n",
       "      <td>110.132088</td>\n",
       "      <td>0.996</td>\n",
       "      <td>-0.027756</td>\n",
       "      <td>...</td>\n",
       "      <td>11.218000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>15.170200</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>2392.119385</td>\n",
       "      <td>1160.997314</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>26.139000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11553</th>\n",
       "      <td>11553</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.241104</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.210736</td>\n",
       "      <td>760143561415015025</td>\n",
       "      <td>3.879</td>\n",
       "      <td>3.974642</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.077196</td>\n",
       "      <td>...</td>\n",
       "      <td>18.407200</td>\n",
       "      <td>19.270201</td>\n",
       "      <td>12.458000</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>2683.692627</td>\n",
       "      <td>623.401001</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>26.049999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11554</th>\n",
       "      <td>11554</td>\n",
       "      <td>1.448</td>\n",
       "      <td>0.218401</td>\n",
       "      <td>1.078</td>\n",
       "      <td>0.162594</td>\n",
       "      <td>786157411215015113</td>\n",
       "      <td>0.579</td>\n",
       "      <td>6.098668</td>\n",
       "      <td>0.110</td>\n",
       "      <td>-0.078874</td>\n",
       "      <td>...</td>\n",
       "      <td>19.115700</td>\n",
       "      <td>11.614000</td>\n",
       "      <td>14.894300</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>1652.860962</td>\n",
       "      <td>2199.916748</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>26.172001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11555</th>\n",
       "      <td>11555</td>\n",
       "      <td>0.680</td>\n",
       "      <td>0.400921</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.278286</td>\n",
       "      <td>813190542115015037</td>\n",
       "      <td>0.352</td>\n",
       "      <td>141.159576</td>\n",
       "      <td>0.997</td>\n",
       "      <td>-0.018263</td>\n",
       "      <td>...</td>\n",
       "      <td>11.206800</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>11.291000</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1591.399780</td>\n",
       "      <td>2193.085693</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>26.139999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11556 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  aimage   aimagerat  bimage   bimagerat              candid  \\\n",
       "0               0   1.051    0.308211   0.991    0.290616  737414800515015021   \n",
       "1               1   0.911    0.283801   0.877    0.273209  548211733715015007   \n",
       "2               2   0.802    0.586078   0.669    0.488886  527282584015015018   \n",
       "3               3   0.708    0.534324   0.673    0.507909  540383446315015002   \n",
       "4               4   0.927    0.317466   0.881    0.301712  806286202415015004   \n",
       "5               5   0.736    0.281992   0.712    0.272797  669136891615015022   \n",
       "6               6   0.847    0.298239   0.742    0.261268  540292995315015018   \n",
       "7               7   0.587    0.327933   0.527    0.294413  675144872815015062   \n",
       "8               8   0.663    0.453448   0.605    0.413780  671461315015015045   \n",
       "9               9   0.705    0.282000   0.649    0.259600  713456381615015039   \n",
       "10             10   0.810    0.366516   0.741    0.335294  643113934915015008   \n",
       "11             11   0.718    0.396685   0.605    0.334254  606166925615015007   \n",
       "12             12   0.686    0.306250   0.655    0.292411  541251494615015004   \n",
       "13             13   0.721 -999.000000   0.616 -999.000000  463203553015015008   \n",
       "14             14   0.656    0.540779   0.630    0.519345  540454345315015016   \n",
       "15             15   0.881    0.641649   0.847    0.616886  589165214915015018   \n",
       "16             16   0.887    0.329740   0.716    0.266171  548443560315015005   \n",
       "17             17   0.845    0.368996   0.746    0.325764  721509665915015005   \n",
       "18             18   0.859    0.249709   0.715    0.207849  718520154415015009   \n",
       "19             19   0.698    0.338835   0.664    0.322330  540458075815015028   \n",
       "20             20   0.988    0.733216   0.907    0.673104  540313932115015034   \n",
       "21             21   0.956    0.511230   0.847    0.452941  733519053815015013   \n",
       "22             22   0.799    0.340000   0.743    0.316170  540357892415015003   \n",
       "23             23   0.913    0.335662   0.876    0.322059  748489661115015007   \n",
       "24             24   0.718    0.305532   0.631    0.268511  607270240615015009   \n",
       "25             25   0.734    0.313675   0.685    0.292735  472391270115010000   \n",
       "26             26   1.003    0.404435   0.791    0.318952  512243981415015000   \n",
       "27             27   0.974 -999.000000   0.832 -999.000000  472499660415015028   \n",
       "28             28   1.281    0.213856   1.072    0.178965  671528463915015027   \n",
       "29             29   0.932    0.222967   0.723    0.172967  527264303315015010   \n",
       "...           ...     ...         ...     ...         ...                 ...   \n",
       "11526       11526   1.405    0.282129   0.951    0.190964  786208592215015087   \n",
       "11527       11527   0.681    0.430891   0.507    0.320796  741222994915015055   \n",
       "11528       11528   0.618    0.398139   0.489    0.315032  842190841115015005   \n",
       "11529       11529   0.661    0.433582   0.500    0.327974  740466961615015043   \n",
       "11530       11530   0.813    0.154269   0.777    0.147438  760144470815015096   \n",
       "11531       11531   0.556    0.357728   0.411    0.264435  792300843015015009   \n",
       "11532       11532   0.666    0.514808   0.511    0.394995  739201431715015009   \n",
       "11533       11533   0.695    0.439749   0.537    0.339778  741222994915015064   \n",
       "11534       11534   2.528    0.459636   1.655    0.300909  806469363515015035   \n",
       "11535       11535   0.680    0.440981   0.487    0.315820  768171553415015016   \n",
       "11536       11536   0.781    0.392944   0.508    0.255590  755188836315015009   \n",
       "11537       11537   0.492    0.388074   0.358    0.282379  786152535215010011   \n",
       "11538       11538   0.580    0.372926   0.420    0.270050  793242020015015001   \n",
       "11539       11539   0.762    0.571999   0.490    0.367821  818485475815015089   \n",
       "11540       11540   4.179    0.720517   2.833    0.488448  793148771415015031   \n",
       "11541       11541   2.682    0.525882   1.696    0.332549  768548070715015068   \n",
       "11542       11542   0.567    0.406869   0.380    0.272681  854403175715010034   \n",
       "11543       11543   2.789    0.477569   1.793    0.307021  768227390515015080   \n",
       "11544       11544   5.326    0.786706   4.118    0.608272  846395602015015021   \n",
       "11545       11545   0.723    0.395552   0.548    0.299810  741263394215015022   \n",
       "11546       11546   0.592    0.426196   0.431    0.310288  740183801715010002   \n",
       "11547       11547   2.776    0.630909   2.116    0.480909  813140151615015036   \n",
       "11548       11548   2.704    0.494333   1.695    0.309872  754469972515010004   \n",
       "11549       11549   0.547    0.403145   0.395    0.291120  793223902815015012   \n",
       "11550       11550   1.935    0.369275   1.292    0.246565  786232670415015135   \n",
       "11551       11551   1.833    0.385895   1.189    0.250316  739458591615010002   \n",
       "11552       11552   0.543    0.316860   0.407    0.237499  757172973715015018   \n",
       "11553       11553   0.786    0.241104   0.687    0.210736  760143561415015025   \n",
       "11554       11554   1.448    0.218401   1.078    0.162594  786157411215015113   \n",
       "11555       11555   0.680    0.400921   0.472    0.278286  813190542115015037   \n",
       "\n",
       "       chinr      chipsf  classtar  clrcoeff  ...        szmag1      szmag2  \\\n",
       "0      3.024    1.074084     0.953  0.090182  ...     19.752300   18.349100   \n",
       "1      0.895    1.868645     0.921       NaN  ...     18.470301   20.777000   \n",
       "2      2.373    1.259607     0.876       NaN  ...     17.358500 -999.000000   \n",
       "3      0.217    5.922930     0.992       NaN  ...     14.093500   19.026699   \n",
       "4      3.389    3.200528     0.983 -0.080435  ...     20.331200   20.488501   \n",
       "5      7.770    4.304990     0.984  0.078656  ...     20.375401 -999.000000   \n",
       "6      1.325    0.999686     0.978       NaN  ...     15.185000   19.643900   \n",
       "7      7.422    1.742821     0.974  0.095612  ...     18.376801 -999.000000   \n",
       "8      3.011    2.007285     0.915  0.101529  ...     16.375799   18.535400   \n",
       "9      1.809    1.126633     0.876 -0.046420  ...     18.462299   19.901100   \n",
       "10     1.007    7.479780     1.000 -0.056851  ...     20.523600   16.165600   \n",
       "11     2.880    1.123649     0.845 -0.044078  ...     17.462700   21.091801   \n",
       "12     0.434    7.556235     0.857       NaN  ...     15.868200   19.186800   \n",
       "13     1.861    1.230512     0.992       NaN  ...     16.039101 -999.000000   \n",
       "14     0.714    4.039593     0.980       NaN  ...     11.641400   11.633000   \n",
       "15     1.113    0.991862     0.597  0.069088  ...     19.412701   20.025999   \n",
       "16     0.942    0.821699     0.868       NaN  ...     19.489599 -999.000000   \n",
       "17     6.164    0.907599     0.975 -0.020890  ...     17.931299   20.005699   \n",
       "18     3.117    1.446563     0.911 -0.054953  ...     18.396400   16.757799   \n",
       "19     0.250    1.089895     0.980       NaN  ...     16.524099   20.554800   \n",
       "20     0.390   43.569832     0.047       NaN  ...     11.123000   14.061200   \n",
       "21     0.161    1.351089     0.936 -0.052905  ...     18.105101   21.419201   \n",
       "22     0.408    3.322113     0.997       NaN  ...     13.685000   19.652300   \n",
       "23     0.871    3.733582     0.984 -0.063086  ...   -999.000000 -999.000000   \n",
       "24     1.236   10.746737     0.983  0.060617  ...     20.790100   20.289700   \n",
       "25     2.843    1.984797     0.894       NaN  ...     16.710100 -999.000000   \n",
       "26     6.630    1.277926     0.976       NaN  ...     19.279200 -999.000000   \n",
       "27     0.822    1.559985     0.876       NaN  ...     17.132500 -999.000000   \n",
       "28     1.993    1.074707     0.690 -0.044614  ...     18.696600 -999.000000   \n",
       "29     1.193    1.103376     0.696       NaN  ...     19.625500   19.883900   \n",
       "...      ...         ...       ...       ...  ...           ...         ...   \n",
       "11526  0.863   11.402366     0.973  0.118292  ...     15.632100   12.362000   \n",
       "11527  0.313   93.579430     0.997 -0.063208  ...   -999.000000   12.180000   \n",
       "11528  1.019  276.043854     0.997 -0.081304  ...     12.118000   15.060500   \n",
       "11529  0.321  279.181824     0.997 -0.100461  ...     11.258000   20.523701   \n",
       "11530  3.616    2.345076     0.183  0.074892  ...     20.057100   12.343000   \n",
       "11531  0.643   56.792229     0.997  0.107531  ...   -999.000000   12.376000   \n",
       "11532  0.557  363.456329     0.997 -0.090926  ...     12.149000   19.707399   \n",
       "11533  0.968   84.353371     0.997 -0.063208  ...     11.696000   18.039900   \n",
       "11534  0.746    6.540512     0.953  0.084826  ...     16.664301 -999.000000   \n",
       "11535  0.950  250.648529     0.997 -0.029441  ...     11.708000   14.736800   \n",
       "11536  0.707  141.658356     0.996 -0.040612  ...     13.392200   10.873000   \n",
       "11537  0.364  152.949066     0.997 -0.086900  ...     13.216600    9.803000   \n",
       "11538  1.227  239.140793     0.997 -0.024018  ...     11.876000   11.838100   \n",
       "11539  0.713  169.053787     0.996 -0.045754  ...     13.172100 -999.000000   \n",
       "11540  0.453   10.503124     0.082  0.078190  ...     12.628000   20.290100   \n",
       "11541  2.113   13.740769     0.982  0.101218  ...     20.849600   12.163000   \n",
       "11542  1.257  189.841843     0.997 -0.067524  ...   -999.000000 -999.000000   \n",
       "11543  0.684   85.339645     0.959  0.075182  ...     19.907600   14.029000   \n",
       "11544  0.304   10.398323     0.006 -0.081571  ...     13.770300 -999.000000   \n",
       "11545  1.424   76.838898     0.997 -0.037693  ...     10.871000   10.167700   \n",
       "11546  0.300   77.442604     0.019 -0.085127  ...     11.089800   14.460300   \n",
       "11547  0.781  118.523621     0.148  0.094964  ...     17.861799   12.913000   \n",
       "11548  0.517    3.779280     0.719  0.124624  ...     14.930900   21.316799   \n",
       "11549  0.482  423.016357     0.998 -0.018945  ...     11.431400    9.957340   \n",
       "11550  1.123    5.410284     0.981  0.077989  ...     12.512000   20.259399   \n",
       "11551  0.721    7.560311     0.977  0.096468  ...     21.166901 -999.000000   \n",
       "11552  1.217  110.132088     0.996 -0.027756  ...     11.218000 -999.000000   \n",
       "11553  3.879    3.974642     0.950  0.077196  ...     18.407200   19.270201   \n",
       "11554  0.579    6.098668     0.110 -0.078874  ...     19.115700   11.614000   \n",
       "11555  0.352  141.159576     0.997 -0.018263  ...     11.206800 -999.000000   \n",
       "\n",
       "           szmag3  tblid  tooflag         xpos         ypos  zpclrcov  \\\n",
       "0       19.709101     21        0  1102.627808  2502.003174 -0.000003   \n",
       "1       20.725700      7        0  2904.806396   280.736298       NaN   \n",
       "2     -999.000000     18        0  1045.247070  2085.618652       NaN   \n",
       "3       20.882000      2        0   279.002289   381.362091       NaN   \n",
       "4     -999.000000      4        0  1296.672852   861.606018 -0.000052   \n",
       "5       16.879499     22        0  2776.215820  1663.289185 -0.000003   \n",
       "6       19.476601     18        0  1236.580688  2640.000977       NaN   \n",
       "7     -999.000000     62        0   564.192993  1361.725952 -0.000002   \n",
       "8     -999.000000     45        0   397.519012   960.680725 -0.000009   \n",
       "9       18.954800     39        0  1343.400757  2941.009033 -0.000149   \n",
       "10      18.843700      8        0  1495.528931   906.415100 -0.000004   \n",
       "11      17.618099      7        0  2275.073975  1323.382446 -0.000005   \n",
       "12      20.595699      4        0   354.270599   863.774597       NaN   \n",
       "13    -999.000000      8        0  2026.512451  1096.865723       NaN   \n",
       "14    -999.000000     16        0   446.462402  2086.012939       NaN   \n",
       "15      20.939600     18        0  1809.097656  1261.588257 -0.000002   \n",
       "16      18.663799      5        0  2045.520996  2851.742676       NaN   \n",
       "17      19.206400      5        0  1037.806519  2612.273926 -0.000028   \n",
       "18      19.865200      9        0   527.156006   544.836182 -0.000025   \n",
       "19      20.409901     28        0  1185.079468  2668.985596       NaN   \n",
       "20      16.833200     34        0  2426.998291  1134.494385       NaN   \n",
       "21      20.964100     13        0   499.907013   943.767395 -0.000013   \n",
       "22      16.926600      3        0   588.096985   880.659424       NaN   \n",
       "23      19.263901      7        0  2088.093994   414.101807 -0.000056   \n",
       "24    -999.000000      9        0  1971.924072   787.639282 -0.000003   \n",
       "25    -999.000000      0        0  2864.901367   206.689407       NaN   \n",
       "26    -999.000000      0        0   781.290894  1145.643799       NaN   \n",
       "27    -999.000000     28        0   766.579224  2460.427979       NaN   \n",
       "28      21.125401     27        0  1068.085083  2810.945068 -0.000046   \n",
       "29      19.965500     10        0  2239.733887  2014.218140       NaN   \n",
       "...           ...    ...      ...          ...          ...       ...   \n",
       "11526   20.124300     87        0  1228.687256  1893.212402 -0.000006   \n",
       "11527   21.392599     55        0  2861.200928  2029.687378 -0.000005   \n",
       "11528 -999.000000      5        0   713.289978   926.252014 -0.000023   \n",
       "11529 -999.000000     43        0  2010.190552  2875.514648 -0.000018   \n",
       "11530   18.802299     96        0  1564.928345  1099.196899 -0.000004   \n",
       "11531   17.662399      9        0  2885.297607  3036.010254 -0.000006   \n",
       "11532   20.848400      9        0   165.596695  1752.099121 -0.000021   \n",
       "11533   21.608801     64        0  2833.287598  1939.674561 -0.000005   \n",
       "11534   21.000401     35        0  1559.899170   987.594910 -0.000002   \n",
       "11535 -999.000000     16        0   391.118713  2323.190430 -0.000008   \n",
       "11536 -999.000000      9        0   130.946793  2031.903076 -0.000008   \n",
       "11537   11.819100     11        0   378.983612  2884.036865 -0.000007   \n",
       "11538   16.358999      1        0  1830.754028   986.742676 -0.000006   \n",
       "11539 -999.000000     89        0  2573.898682  2569.170410 -0.000003   \n",
       "11540   16.239700     31        0  2220.919189  2042.546753 -0.000004   \n",
       "11541   18.504900     68        0    97.573799  2726.532227 -0.000006   \n",
       "11542   19.938299     34        0  2802.614258  2645.082520 -0.000004   \n",
       "11543   17.732201     80        0  2639.854492  2456.470215 -0.000002   \n",
       "11544   21.148399     21        0   188.390701  2539.527832 -0.000004   \n",
       "11545   14.963800     22        0  2607.035400  2662.958252 -0.000004   \n",
       "11546   13.960500      2        0   304.294312   739.737183 -0.000014   \n",
       "11547   16.619200     36        0  2740.830811  1318.207764 -0.000003   \n",
       "11548   18.669201      4        0  2951.753662  2822.529297 -0.000029   \n",
       "11549   10.196000     12        0  1121.034790  2455.416748 -0.000003   \n",
       "11550 -999.000000    135        0  3052.684326  2093.844482 -0.000003   \n",
       "11551   20.226500      2        0    77.071404  2386.187988 -0.000008   \n",
       "11552   15.170200     18        0  2392.119385  1160.997314 -0.000008   \n",
       "11553   12.458000     25        0  2683.692627   623.401001 -0.000005   \n",
       "11554   14.894300    113        0  1652.860962  2199.916748 -0.000017   \n",
       "11555   11.291000     37        0  1591.399780  2193.085693 -0.000018   \n",
       "\n",
       "           zpmed  label  \n",
       "0      26.278999      1  \n",
       "1            NaN      1  \n",
       "2            NaN      1  \n",
       "3            NaN      1  \n",
       "4      26.166000      1  \n",
       "5      26.191999      1  \n",
       "6            NaN      1  \n",
       "7      26.330999      1  \n",
       "8      26.287001      1  \n",
       "9      23.504000      1  \n",
       "10     26.191000      1  \n",
       "11     26.291000      1  \n",
       "12           NaN      1  \n",
       "13           NaN      1  \n",
       "14           NaN      1  \n",
       "15     26.146999      1  \n",
       "16           NaN      1  \n",
       "17     26.312000      1  \n",
       "18     26.291000      1  \n",
       "19           NaN      1  \n",
       "20           NaN      1  \n",
       "21     26.212000      1  \n",
       "22           NaN      1  \n",
       "23     26.152000      1  \n",
       "24     26.301001      1  \n",
       "25           NaN      1  \n",
       "26           NaN      1  \n",
       "27           NaN      1  \n",
       "28     26.094000      1  \n",
       "29           NaN      1  \n",
       "...          ...    ...  \n",
       "11526  26.416000      0  \n",
       "11527  26.139000      0  \n",
       "11528  26.219999      0  \n",
       "11529  26.158001      0  \n",
       "11530  26.261000      0  \n",
       "11531  26.243999      1  \n",
       "11532  26.156000      1  \n",
       "11533  26.139000      0  \n",
       "11534  26.294001      0  \n",
       "11535  26.237000      0  \n",
       "11536  26.188000      0  \n",
       "11537  26.177999      0  \n",
       "11538  26.163000      0  \n",
       "11539  26.271000      0  \n",
       "11540  26.038000      0  \n",
       "11541  26.233000      0  \n",
       "11542  26.157000      0  \n",
       "11543  26.275000      0  \n",
       "11544  26.200001      0  \n",
       "11545  26.281000      0  \n",
       "11546  26.186001      0  \n",
       "11547  26.200001      0  \n",
       "11548  26.363001      0  \n",
       "11549  26.188999      0  \n",
       "11550  26.278999      0  \n",
       "11551  26.271000      0  \n",
       "11552  26.139000      0  \n",
       "11553  26.049999      0  \n",
       "11554  26.172001      0  \n",
       "11555  26.139999      0  \n",
       "\n",
       "[11556 rows x 103 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11556 entries, 0 to 11555\n",
      "Columns: 103 entries, Unnamed: 0 to label\n",
      "dtypes: float64(79), int64(19), object(5)\n",
      "memory usage: 9.1+ MB\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>aimage</th>\n",
       "      <th>aimagerat</th>\n",
       "      <th>bimage</th>\n",
       "      <th>bimagerat</th>\n",
       "      <th>candid</th>\n",
       "      <th>chinr</th>\n",
       "      <th>chipsf</th>\n",
       "      <th>classtar</th>\n",
       "      <th>clrcoeff</th>\n",
       "      <th>...</th>\n",
       "      <th>szmag1</th>\n",
       "      <th>szmag2</th>\n",
       "      <th>szmag3</th>\n",
       "      <th>tblid</th>\n",
       "      <th>tooflag</th>\n",
       "      <th>xpos</th>\n",
       "      <th>ypos</th>\n",
       "      <th>zpclrcov</th>\n",
       "      <th>zpmed</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11556.00000</td>\n",
       "      <td>11556.000000</td>\n",
       "      <td>11556.000000</td>\n",
       "      <td>11556.000000</td>\n",
       "      <td>11556.000000</td>\n",
       "      <td>1.155600e+04</td>\n",
       "      <td>11556.000000</td>\n",
       "      <td>11556.000000</td>\n",
       "      <td>11556.000000</td>\n",
       "      <td>8448.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11556.000000</td>\n",
       "      <td>11556.000000</td>\n",
       "      <td>11556.000000</td>\n",
       "      <td>11556.000000</td>\n",
       "      <td>11556.0</td>\n",
       "      <td>11556.000000</td>\n",
       "      <td>11556.000000</td>\n",
       "      <td>8448.000000</td>\n",
       "      <td>8448.000000</td>\n",
       "      <td>11556.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5777.50000</td>\n",
       "      <td>1.073267</td>\n",
       "      <td>-8.026193</td>\n",
       "      <td>0.895498</td>\n",
       "      <td>-8.076731</td>\n",
       "      <td>6.393815e+17</td>\n",
       "      <td>-22.500750</td>\n",
       "      <td>17.101113</td>\n",
       "      <td>0.860534</td>\n",
       "      <td>0.027765</td>\n",
       "      <td>...</td>\n",
       "      <td>-141.066174</td>\n",
       "      <td>-237.406817</td>\n",
       "      <td>-260.553024</td>\n",
       "      <td>24.679387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1562.592689</td>\n",
       "      <td>1563.468948</td>\n",
       "      <td>-0.000087</td>\n",
       "      <td>26.162647</td>\n",
       "      <td>0.691589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3336.07419</td>\n",
       "      <td>0.888390</td>\n",
       "      <td>91.182468</td>\n",
       "      <td>0.676887</td>\n",
       "      <td>91.176462</td>\n",
       "      <td>1.074571e+17</td>\n",
       "      <td>156.702592</td>\n",
       "      <td>95.183081</td>\n",
       "      <td>0.232018</td>\n",
       "      <td>0.088743</td>\n",
       "      <td>...</td>\n",
       "      <td>368.660709</td>\n",
       "      <td>441.456463</td>\n",
       "      <td>454.140601</td>\n",
       "      <td>45.581802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>891.664377</td>\n",
       "      <td>872.083664</td>\n",
       "      <td>0.002138</td>\n",
       "      <td>0.280054</td>\n",
       "      <td>0.461857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.289000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>0.254000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>4.444618e+17</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>0.145480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.791248</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.030300</td>\n",
       "      <td>10.019900</td>\n",
       "      <td>-0.127653</td>\n",
       "      <td>21.896999</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2888.75000</td>\n",
       "      <td>0.731000</td>\n",
       "      <td>0.268761</td>\n",
       "      <td>0.654000</td>\n",
       "      <td>0.232750</td>\n",
       "      <td>5.404585e+17</td>\n",
       "      <td>0.733000</td>\n",
       "      <td>1.450537</td>\n",
       "      <td>0.841000</td>\n",
       "      <td>-0.051204</td>\n",
       "      <td>...</td>\n",
       "      <td>12.753225</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>787.948593</td>\n",
       "      <td>809.693359</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>26.132000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5777.50000</td>\n",
       "      <td>0.838000</td>\n",
       "      <td>0.320363</td>\n",
       "      <td>0.752000</td>\n",
       "      <td>0.287891</td>\n",
       "      <td>6.714277e+17</td>\n",
       "      <td>1.416000</td>\n",
       "      <td>2.603290</td>\n",
       "      <td>0.974000</td>\n",
       "      <td>0.073595</td>\n",
       "      <td>...</td>\n",
       "      <td>17.182651</td>\n",
       "      <td>18.185300</td>\n",
       "      <td>18.388551</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1562.246704</td>\n",
       "      <td>1591.725708</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>26.218000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8666.25000</td>\n",
       "      <td>1.075000</td>\n",
       "      <td>0.380548</td>\n",
       "      <td>0.923000</td>\n",
       "      <td>0.337443</td>\n",
       "      <td>7.182429e+17</td>\n",
       "      <td>3.944000</td>\n",
       "      <td>7.096814</td>\n",
       "      <td>0.983000</td>\n",
       "      <td>0.097531</td>\n",
       "      <td>...</td>\n",
       "      <td>19.207199</td>\n",
       "      <td>20.155224</td>\n",
       "      <td>20.324699</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2331.450623</td>\n",
       "      <td>2338.382385</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>26.280001</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11555.00000</td>\n",
       "      <td>23.407000</td>\n",
       "      <td>55.650002</td>\n",
       "      <td>15.154000</td>\n",
       "      <td>47.450001</td>\n",
       "      <td>8.652526e+17</td>\n",
       "      <td>121.080002</td>\n",
       "      <td>7037.184570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.095540</td>\n",
       "      <td>...</td>\n",
       "      <td>22.355900</td>\n",
       "      <td>23.205200</td>\n",
       "      <td>22.111300</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3062.439697</td>\n",
       "      <td>3070.481934</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>26.827999</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0        aimage     aimagerat        bimage     bimagerat  \\\n",
       "count  11556.00000  11556.000000  11556.000000  11556.000000  11556.000000   \n",
       "mean    5777.50000      1.073267     -8.026193      0.895498     -8.076731   \n",
       "std     3336.07419      0.888390     91.182468      0.676887     91.176462   \n",
       "min        0.00000      0.289000   -999.000000      0.254000   -999.000000   \n",
       "25%     2888.75000      0.731000      0.268761      0.654000      0.232750   \n",
       "50%     5777.50000      0.838000      0.320363      0.752000      0.287891   \n",
       "75%     8666.25000      1.075000      0.380548      0.923000      0.337443   \n",
       "max    11555.00000     23.407000     55.650002     15.154000     47.450001   \n",
       "\n",
       "             candid         chinr        chipsf      classtar     clrcoeff  \\\n",
       "count  1.155600e+04  11556.000000  11556.000000  11556.000000  8448.000000   \n",
       "mean   6.393815e+17    -22.500750     17.101113      0.860534     0.027765   \n",
       "std    1.074571e+17    156.702592     95.183081      0.232018     0.088743   \n",
       "min    4.444618e+17   -999.000000      0.145480      0.000000    -0.791248   \n",
       "25%    5.404585e+17      0.733000      1.450537      0.841000    -0.051204   \n",
       "50%    6.714277e+17      1.416000      2.603290      0.974000     0.073595   \n",
       "75%    7.182429e+17      3.944000      7.096814      0.983000     0.097531   \n",
       "max    8.652526e+17    121.080002   7037.184570      1.000000     2.095540   \n",
       "\n",
       "           ...             szmag1        szmag2        szmag3         tblid  \\\n",
       "count      ...       11556.000000  11556.000000  11556.000000  11556.000000   \n",
       "mean       ...        -141.066174   -237.406817   -260.553024     24.679387   \n",
       "std        ...         368.660709    441.456463    454.140601     45.581802   \n",
       "min        ...        -999.000000   -999.000000   -999.000000      0.000000   \n",
       "25%        ...          12.753225   -999.000000   -999.000000      4.000000   \n",
       "50%        ...          17.182651     18.185300     18.388551     11.000000   \n",
       "75%        ...          19.207199     20.155224     20.324699     25.000000   \n",
       "max        ...          22.355900     23.205200     22.111300    871.000000   \n",
       "\n",
       "       tooflag          xpos          ypos     zpclrcov        zpmed  \\\n",
       "count  11556.0  11556.000000  11556.000000  8448.000000  8448.000000   \n",
       "mean       0.0   1562.592689   1563.468948    -0.000087    26.162647   \n",
       "std        0.0    891.664377    872.083664     0.002138     0.280054   \n",
       "min        0.0     11.030300     10.019900    -0.127653    21.896999   \n",
       "25%        0.0    787.948593    809.693359    -0.000017    26.132000   \n",
       "50%        0.0   1562.246704   1591.725708    -0.000008    26.218000   \n",
       "75%        0.0   2331.450623   2338.382385    -0.000004    26.280001   \n",
       "max        0.0   3062.439697   3070.481934    -0.000001    26.827999   \n",
       "\n",
       "              label  \n",
       "count  11556.000000  \n",
       "mean       0.691589  \n",
       "std        0.461857  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        1.000000  \n",
       "75%        1.000000  \n",
       "max        1.000000  \n",
       "\n",
       "[8 rows x 98 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('candidates.csv')\n",
    "display(df)\n",
    "df.info()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_bogus: 3564\n",
      "num_real: 7992\n"
     ]
    }
   ],
   "source": [
    "print(f'num_bogus: {np.sum(df.label == 0)}')\n",
    "print(f'num_real: {np.sum(df.label == 1)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cutout images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_triplet(alert, normalize: bool = False, to_tpu: bool = False):\n",
    "    \"\"\"\n",
    "        Feed in alert packet\n",
    "    \"\"\"\n",
    "    cutout_dict = dict()\n",
    "\n",
    "    for cutout in ('science', 'template', 'difference'):\n",
    "        cutout_data = loads(dumps([alert[f'cutout{cutout.capitalize()}']['stampData']]))[0]\n",
    "\n",
    "        # unzip\n",
    "        with gzip.open(io.BytesIO(cutout_data), 'rb') as f:\n",
    "            with fits.open(io.BytesIO(f.read())) as hdu:\n",
    "                data = hdu[0].data\n",
    "                # replace nans with zeros\n",
    "                cutout_dict[cutout] = np.nan_to_num(data)\n",
    "                # normalize\n",
    "                if normalize:\n",
    "                    cutout_dict[cutout] /= np.linalg.norm(cutout_dict[cutout])\n",
    "\n",
    "        # pad to 63x63 if smaller\n",
    "        shape = cutout_dict[cutout].shape\n",
    "        if shape != (63, 63):\n",
    "            # print(f'Shape of {candid}/{cutout}: {shape}, padding to (63, 63)')\n",
    "            cutout_dict[cutout] = np.pad(cutout_dict[cutout], [(0, 63 - shape[0]), (0, 63 - shape[1])],\n",
    "                                         mode='constant', constant_values=1e-9)\n",
    "\n",
    "    triplet = np.zeros((63, 63, 3))\n",
    "    triplet[:, :, 0] = cutout_dict['science']\n",
    "    triplet[:, :, 1] = cutout_dict['template']\n",
    "    triplet[:, :, 2] = cutout_dict['difference']\n",
    "    \n",
    "    if to_tpu:\n",
    "        # Edge TPUs require additional processing\n",
    "        triplet = np.rint(triplet * 128 + 128).astype(np.uint8).flatten()\n",
    "    \n",
    "    return triplet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now load pre-processed image cutout triplets: [epochal science image, reference image, ZOGY difference image]. The ZTF cutout images are centered on the event candidate and are of size 63x63 pixels (or smaller, if the event is detected near the CCD edge) at a plate scale of 1 \"  per pixel. We perform independent  𝐿2 -normalization of the epochal science, reference, and difference cutouts and stack them to form 63x63x3 triplets that are input into the model. Smaller examples are accordingly padded using a constant pixel value of  10−9 . See function make_triplet above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will use memory mapping as the file is relatively large (1 GB)\n",
    "triplets = np.load('triplets.norm.npy', mmap_mode='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visuals\n",
    "\n",
    "Plot a few triplet examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "candid: 539429711615015014, label: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x200 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "candid: 465413155215015003, label: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x200 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "candid: 684519011015015141, label: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x200 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "candid: 672380100415015070, label: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x200 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "candid: 515175250815015001, label: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x200 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = np.random.randint(0, high=len(df), size=5)\n",
    "for ii in ind:\n",
    "    print(f'candid: {df.loc[ii, \"candid\"]}, label: {df.loc[ii, \"label\"]}')\n",
    "    fig = plt.figure(figsize=(8, 2), dpi=100)\n",
    "    triplet = triplets[ii, :]\n",
    "    ax = fig.add_subplot(131)\n",
    "    ax.axis('off')\n",
    "    ax.imshow(triplet[:, :, 0], origin='lower', cmap=plt.cm.bone)\n",
    "    ax2 = fig.add_subplot(132)\n",
    "    ax2.axis('off')\n",
    "    ax2.imshow(triplet[:, :, 1], origin='lower', cmap=plt.cm.bone)\n",
    "    ax3 = fig.add_subplot(133)\n",
    "    ax3.axis('off')\n",
    "    ax3.imshow(triplet[:, :, 2], origin='lower', cmap=plt.cm.bone)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use 81\\% / 9\\% / 10\\% training/validation/test data split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10400, 63, 63, 3) (1156, 63, 63, 3) (10400,) (1156,)\n"
     ]
    }
   ],
   "source": [
    "test_split = 0.1\n",
    "\n",
    "# set random seed for reproducable results:\n",
    "random_state = 42\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(triplets, df.label, \n",
    "                                                    test_size=test_split, random_state=random_state)\n",
    "print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `braai` architecture\n",
    "\n",
    "We will use a simple custom VGG-like sequential model ($VGG6$; this architecture was first proposed by the Visual Geometry Group of the Department of Engineering Science, University of Oxford, UK). The model has six layers with trainable parameters: four convolutional and two fully-connected. The first two convolutional layers use 16 3x3 pixel filters each while in the second pair, 32 3x3 pixel filters are used. To prevent over-fitting, a dropout rate of 0.25 is applied after each max-pooling layer and a dropout rate of 0.5 is applied after the second fully-connected layer. ReLU activation functions (Rectified Linear Unit --  a function defined as the positive part of its argument) are used for all five hidden trainable layers; a sigmoid activation function is used for the output layer.\n",
    "\n",
    "![](img/fig-braai2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vgg6(input_shape=(63, 63, 3), n_classes: int = 1):\n",
    "    \"\"\"\n",
    "        VGG6\n",
    "    :param input_shape:\n",
    "    :param n_classes:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "\n",
    "    model = tf.keras.models.Sequential(name='VGG6')\n",
    "    # input: 63x63 images with 3 channel -> (63, 63, 3) tensors.\n",
    "    # this applies 16 convolution filters of size 3x3 each.\n",
    "    model.add(tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=input_shape, name='conv1'))\n",
    "    model.add(tf.keras.layers.Conv2D(16, (3, 3), activation='relu', name='conv2'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(tf.keras.layers.Dropout(0.25))\n",
    "\n",
    "    model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu', name='conv3'))\n",
    "    model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu', name='conv4'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(4, 4)))\n",
    "    model.add(tf.keras.layers.Dropout(0.25))\n",
    "\n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "\n",
    "    model.add(tf.keras.layers.Dense(256, activation='relu', name='fc_1'))\n",
    "    model.add(tf.keras.layers.Dropout(0.5))\n",
    "    # output layer\n",
    "    activation = 'sigmoid' if n_classes == 1 else 'softmax'\n",
    "    model.add(tf.keras.layers.Dense(n_classes, activation=activation, name='fc_out'))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training\n",
    "\n",
    "`braai` is implemented using `TensorFlow` software and its high-level `Keras` API. We will use the binary cross-entropy loss function, the Adam optimizer, a batch size of 64, and a 81\\%/9\\%/10\\% training/validation/test data split. The training image data are weighted per class to mitigate the real vs. bogus imbalance in the data sets. To augment the data, the images may be flipped horizontally and/or vertically at random. No random rotations and translations will be added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_report(path: str = './', stamp: str = None, report: dict = dict()):\n",
    "    f_name = os.path.join(path, f'report.{stamp}.json')\n",
    "    with open(f_name, 'w') as f:\n",
    "        json.dump(report, f, indent=2)\n",
    "\n",
    "# make train and test masks:\n",
    "_, _, mask_train, mask_test = train_test_split(df.label, list(range(len(df.label))),\n",
    "                                                   test_size=test_split, random_state=random_state)\n",
    "masks = {'training': mask_train, 'test': mask_test}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "\n",
    "loss = 'binary_crossentropy'\n",
    "optimizer = 'adam'\n",
    "epochs = 100\n",
    "patience = 50\n",
    "# epochs = 10\n",
    "# patience = 5\n",
    "validation_split = 0.1\n",
    "class_weight = True\n",
    "batch_size = 64\n",
    "\n",
    "# halt training if no gain in validation accuracy over patience epochs\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=patience)\n",
    "\n",
    "data_augmentation = {'horizontal_flip': True,\n",
    "                     'vertical_flip': True,\n",
    "                     'rotation_range': 0,\n",
    "                     'fill_mode': 'constant',\n",
    "                     'cval': 1e-9}\n",
    "datagen = tf.keras.preprocessing.image.ImageDataGenerator(horizontal_flip=data_augmentation['horizontal_flip'],\n",
    "                                                          vertical_flip=data_augmentation['vertical_flip'],\n",
    "                                                          rotation_range=data_augmentation['rotation_range'],\n",
    "                                                          fill_mode=data_augmentation['fill_mode'],\n",
    "                                                          cval=data_augmentation['cval'],\n",
    "                                                          validation_split=validation_split)\n",
    "\n",
    "training_generator = datagen.flow(x_train, y_train, batch_size=batch_size, subset='training')\n",
    "validation_generator = datagen.flow(x_train, y_train, batch_size=batch_size, subset='validation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input image shape: (63, 63, 3)\n"
     ]
    }
   ],
   "source": [
    "binary_classification = True if loss == 'binary_crossentropy' else False\n",
    "n_classes = 1 if binary_classification else 2\n",
    "\n",
    "# training data weights\n",
    "if class_weight:\n",
    "    # weight data class depending on number of examples?\n",
    "    if not binary_classification:\n",
    "        num_training_examples_per_class = np.sum(y_train, axis=0)\n",
    "    else:\n",
    "        num_training_examples_per_class = np.array([len(y_train) - np.sum(y_train), np.sum(y_train)])\n",
    "\n",
    "    assert 0 not in num_training_examples_per_class, 'found class without any examples!'\n",
    "\n",
    "    # fewer examples -- larger weight\n",
    "    weights = (1 / num_training_examples_per_class) / np.linalg.norm((1 / num_training_examples_per_class))\n",
    "    normalized_weight = weights / np.max(weights)\n",
    "\n",
    "    class_weight = {i: w for i, w in enumerate(normalized_weight)}\n",
    "\n",
    "else:\n",
    "    class_weight = {i: 1 for i in range(2)}\n",
    "    \n",
    "# image shape:\n",
    "image_shape = x_train.shape[1:]\n",
    "print('Input image shape:', image_shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build and compile the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/mjg/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1 (Conv2D)               (None, 61, 61, 16)        448       \n",
      "_________________________________________________________________\n",
      "conv2 (Conv2D)               (None, 59, 59, 16)        2320      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 29, 29, 16)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 29, 29, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv3 (Conv2D)               (None, 27, 27, 32)        4640      \n",
      "_________________________________________________________________\n",
      "conv4 (Conv2D)               (None, 25, 25, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 6, 6, 32)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 6, 6, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1152)              0         \n",
      "_________________________________________________________________\n",
      "fc_1 (Dense)                 (None, 256)               295168    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "fc_out (Dense)               (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 312,081\n",
      "Trainable params: 312,081\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = vgg6(input_shape=image_shape, n_classes=n_classes)\n",
    "\n",
    "# set up optimizer:\n",
    "if optimizer == 'adam':\n",
    "    optimzr = tf.keras.optimizers.Adam(lr=3e-4, beta_1=0.9, beta_2=0.999,\n",
    "                                       epsilon=None, decay=0.0, amsgrad=False)\n",
    "elif optimizer == 'sgd':\n",
    "    optimzr = tf.keras.optimizers.SGD(lr=0.01, momentum=0.9, decay=1e-6, nesterov=True)\n",
    "else:\n",
    "    print('Could not recognize optimizer, using Adam')\n",
    "    optimzr = tf.keras.optimizers.Adam(lr=3e-4, beta_1=0.9, beta_2=0.999,\n",
    "                                       epsilon=None, decay=0.0, amsgrad=False)\n",
    "\n",
    "model.compile(optimizer=optimzr, loss=loss, metrics=['accuracy'])\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/mjg/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/100\n",
      "17/17 [==============================] - 2s 117ms/step - loss: 5.0967 - acc: 0.6644\n",
      "147/147 [==============================] - 56s 382ms/step - loss: 7.0317 - acc: 0.7044 - val_loss: 5.0967 - val_acc: 0.6644\n",
      "Epoch 2/100\n",
      "17/17 [==============================] - 2s 107ms/step - loss: 5.2632 - acc: 0.6471\n",
      "147/147 [==============================] - 55s 377ms/step - loss: 6.6706 - acc: 0.7202 - val_loss: 5.2632 - val_acc: 0.6471\n",
      "Epoch 3/100\n",
      "17/17 [==============================] - 2s 116ms/step - loss: 4.2607 - acc: 0.6885\n",
      "147/147 [==============================] - 59s 401ms/step - loss: 6.0671 - acc: 0.6902 - val_loss: 4.2607 - val_acc: 0.6885\n",
      "Epoch 4/100\n",
      "17/17 [==============================] - 2s 121ms/step - loss: 1.5977 - acc: 0.8510\n",
      "147/147 [==============================] - 66s 448ms/step - loss: 3.4134 - acc: 0.7395 - val_loss: 1.5977 - val_acc: 0.8510\n",
      "Epoch 5/100\n",
      "17/17 [==============================] - 2s 125ms/step - loss: 0.8910 - acc: 0.8750\n",
      "147/147 [==============================] - 62s 425ms/step - loss: 1.8679 - acc: 0.7984 - val_loss: 0.8910 - val_acc: 0.8750\n",
      "Epoch 6/100\n",
      "17/17 [==============================] - 2s 140ms/step - loss: 0.7961 - acc: 0.8769\n",
      "147/147 [==============================] - 64s 435ms/step - loss: 1.1827 - acc: 0.8462 - val_loss: 0.7961 - val_acc: 0.8769\n",
      "Epoch 7/100\n",
      "17/17 [==============================] - 2s 130ms/step - loss: 0.5037 - acc: 0.9010\n",
      "147/147 [==============================] - 71s 481ms/step - loss: 0.7571 - acc: 0.8696 - val_loss: 0.5037 - val_acc: 0.9010\n",
      "Epoch 8/100\n",
      "17/17 [==============================] - 2s 130ms/step - loss: 0.4019 - acc: 0.9115\n",
      "147/147 [==============================] - 68s 463ms/step - loss: 0.6173 - acc: 0.8814 - val_loss: 0.4019 - val_acc: 0.9115\n",
      "Epoch 9/100\n",
      "17/17 [==============================] - 3s 148ms/step - loss: 0.3690 - acc: 0.9144\n",
      "147/147 [==============================] - 75s 508ms/step - loss: 0.5051 - acc: 0.8863 - val_loss: 0.3690 - val_acc: 0.9144\n",
      "Epoch 10/100\n",
      "17/17 [==============================] - 3s 202ms/step - loss: 0.3258 - acc: 0.9135\n",
      "147/147 [==============================] - 69s 470ms/step - loss: 0.4553 - acc: 0.8908 - val_loss: 0.3258 - val_acc: 0.9135\n",
      "Epoch 11/100\n",
      "17/17 [==============================] - 2s 136ms/step - loss: 0.2868 - acc: 0.9144\n",
      "147/147 [==============================] - 74s 504ms/step - loss: 0.4168 - acc: 0.8965 - val_loss: 0.2868 - val_acc: 0.9144\n",
      "Epoch 12/100\n",
      "17/17 [==============================] - 2s 113ms/step - loss: 0.2968 - acc: 0.9154\n",
      "147/147 [==============================] - 73s 498ms/step - loss: 0.3961 - acc: 0.8943 - val_loss: 0.2968 - val_acc: 0.9154\n",
      "Epoch 13/100\n",
      "17/17 [==============================] - 3s 156ms/step - loss: 0.2827 - acc: 0.9106\n",
      "147/147 [==============================] - 70s 473ms/step - loss: 0.4011 - acc: 0.8949 - val_loss: 0.2827 - val_acc: 0.9106\n",
      "Epoch 14/100\n",
      "17/17 [==============================] - 2s 104ms/step - loss: 0.2990 - acc: 0.9115\n",
      "147/147 [==============================] - 63s 426ms/step - loss: 0.3951 - acc: 0.8987 - val_loss: 0.2990 - val_acc: 0.9115\n",
      "Epoch 15/100\n",
      "17/17 [==============================] - 2s 127ms/step - loss: 0.2799 - acc: 0.9163\n",
      "147/147 [==============================] - 58s 394ms/step - loss: 0.3901 - acc: 0.8965 - val_loss: 0.2799 - val_acc: 0.9163\n",
      "Epoch 16/100\n",
      "17/17 [==============================] - 2s 121ms/step - loss: 0.2920 - acc: 0.9154\n",
      "147/147 [==============================] - 69s 467ms/step - loss: 0.3704 - acc: 0.9001 - val_loss: 0.2920 - val_acc: 0.9154\n",
      "Epoch 17/100\n",
      "17/17 [==============================] - 2s 130ms/step - loss: 0.2979 - acc: 0.9183\n",
      "147/147 [==============================] - 64s 436ms/step - loss: 0.3725 - acc: 0.8979 - val_loss: 0.2979 - val_acc: 0.9183\n",
      "Epoch 18/100\n",
      " 35/147 [======>.......................] - ETA: 48s - loss: 0.3520 - acc: 0.8982"
     ]
    }
   ],
   "source": [
    "run_t_stamp = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
    "model_name = f'braai_{model.name}_{run_t_stamp}'\n",
    "\n",
    "h = model.fit_generator(training_generator,\n",
    "                        steps_per_epoch=len(x_train) // batch_size,\n",
    "                        validation_data=validation_generator,\n",
    "                        validation_steps=(len(x_train)*validation_split) // batch_size,\n",
    "                        class_weight=class_weight,\n",
    "                        epochs=epochs,\n",
    "                        verbose=1, callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model evaluation\n",
    "\n",
    "Let's now evaluate the resulting model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Evaluating on training set to check misclassified samples:')\n",
    "labels_training_pred = model.predict(x_train, batch_size=batch_size, verbose=1)\n",
    "# XOR will show misclassified samples\n",
    "misclassified_train_mask = np.array(list(map(int, df.label[masks['training']]))).flatten() ^ \\\n",
    "                           np.array(list(map(int, np.rint(labels_training_pred)))).flatten()\n",
    "misclassified_train_mask = [ii for ii, mi in enumerate(misclassified_train_mask) if mi == 1]\n",
    "\n",
    "misclassifications_train = {int(c): [int(l), float(p)]\n",
    "                            for c, l, p in zip(df.candid.values[masks['training']][misclassified_train_mask],\n",
    "                                               df.label.values[masks['training']][misclassified_train_mask],\n",
    "                                               labels_training_pred[misclassified_train_mask])}\n",
    "\n",
    "print('Evaluating on test set for loss and accuracy:')\n",
    "preds = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1)\n",
    "test_loss = float(preds[0])\n",
    "test_accuracy = float(preds[1])\n",
    "print(\"Loss = \" + str(test_loss))\n",
    "print(\"Test Accuracy = \" + str(test_accuracy))\n",
    "\n",
    "print('Evaluating on training set to check misclassified samples:')\n",
    "preds = model.predict(x=x_test, batch_size=batch_size, verbose=1)\n",
    "\n",
    "# XOR will show misclassified samples\n",
    "misclassified_test_mask = np.array(list(map(int, df.label[masks['test']]))).flatten() ^ \\\n",
    "                          np.array(list(map(int, np.rint(preds)))).flatten()\n",
    "misclassified_test_mask = [ii for ii, mi in enumerate(misclassified_test_mask) if mi == 1]\n",
    "\n",
    "misclassifications_test = {int(c): [int(l), float(p)]\n",
    "                           for c, l, p in zip(df.candid.values[masks['test']][misclassified_test_mask],\n",
    "                                              df.label.values[masks['test']][misclassified_test_mask],\n",
    "                                              preds[misclassified_test_mask])}\n",
    "\n",
    "# round probs to nearest int (0 or 1)\n",
    "labels_pred = np.rint(preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training vs. validation accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if 'accuracy' in h.history:\n",
    "    train_acc = h.history['accuracy']\n",
    "    val_acc = h.history['val_accuracy']\n",
    "else:\n",
    "    train_acc = h.history['acc']\n",
    "    val_acc = h.history['val_acc']\n",
    "\n",
    "train_loss = h.history['loss']\n",
    "val_loss = h.history['val_loss']\n",
    "\n",
    "fig = plt.figure(figsize=(7, 3), dpi=100)\n",
    "ax = fig.add_subplot(121)\n",
    "ax.plot(train_acc, label='Training', linewidth=1.2)\n",
    "ax.plot(val_acc, label='Validation', linewidth=1.2)\n",
    "ax.set_xlabel('Epoch')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.legend(loc='best')\n",
    "ax.grid(True, linewidth=.3)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(train_loss, label='Training', linewidth=1.2)\n",
    "ax2.plot(val_loss, label='Validation', linewidth=1.2)\n",
    "ax2.set_xlabel('Epoch')\n",
    "ax2.set_ylabel('Loss')\n",
    "ax2.legend(loc='best')\n",
    "ax2.grid(True, linewidth=.3)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confusion matrices with score threshold=0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, classes,\n",
    "                          normalize=False,\n",
    "                          title=None,\n",
    "                          cmap=plt.cm.Blues,\n",
    "                          colorbar=False,\n",
    "                          savefig=False):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if title is not None:\n",
    "        if normalize:\n",
    "            title = 'Normalized confusion matrix'\n",
    "        else:\n",
    "            title = 'Confusion matrix, without normalization'\n",
    "\n",
    "    # Compute confusion matrix\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    # Only use the labels that appear in the data\n",
    "#     print(unique_labels(y_true, y_pred))\n",
    "#     classes = classes[unique_labels(y_true, y_pred)]\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] * 100\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    fig, ax = plt.subplots(dpi=70)\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    if colorbar:\n",
    "        ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=classes, yticklabels=classes,\n",
    "           title=title,\n",
    "           ylabel='True label',\n",
    "           xlabel='Predicted label')\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "#     fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, f'{cm[i, j]:.1f}%' if normalize else f'{cm[i, j]:d}', #format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"white\" if cm[i, j] > thresh else \"black\",\n",
    "                    size=20)\n",
    "    fig.tight_layout()\n",
    "    \n",
    "    if savefig:\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(savefig, dpi=300)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matr = confusion_matrix(y_test, labels_pred)\n",
    "confusion_matr_normalized = confusion_matr.astype('float') / confusion_matr.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "print('Confusion matrix:')\n",
    "print(confusion_matr)\n",
    "\n",
    "print('Normalized confusion matrix:')\n",
    "print(confusion_matr_normalized)\n",
    "\n",
    "# plot:\n",
    "t_set = 'test'\n",
    "plot_confusion_matrix(y_true=df.label.values[masks[t_set]], y_pred=labels_pred, \n",
    "                      classes=['bogus', 'real'],\n",
    "                      normalize=False,\n",
    "                      title=None,\n",
    "                      cmap=plt.cm.Blues)\n",
    "plot_confusion_matrix(y_true=df.label.values[masks[t_set]], y_pred=labels_pred, \n",
    "                      classes=['bogus', 'real'],\n",
    "                      normalize=True,\n",
    "                      title=None,\n",
    "                      cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FNR vs FPR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rbbins = np.arange(-0.0001, 1.0001, 0.0001)\n",
    "h_b, e_b = np.histogram(preds[(df.label[masks['test']] == 0).values], bins=rbbins, density=True)\n",
    "h_b_c = np.cumsum(h_b)\n",
    "h_r, e_r = np.histogram(preds[(df.label[masks['test']] == 1).values], bins=rbbins, density=True)\n",
    "h_r_c = np.cumsum(h_r)\n",
    "\n",
    "# h_b, e_b\n",
    "\n",
    "fig = plt.figure(figsize=(7, 4), dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "rb_thres = np.array(list(range(len(h_b)))) / len(h_b)\n",
    "\n",
    "ax.plot(rb_thres, h_r_c/np.max(h_r_c), \n",
    "        label='False Negative Rate (FNR)', linewidth=1.5)\n",
    "ax.plot(rb_thres, 1 - h_b_c/np.max(h_b_c), \n",
    "        label='False Positive Rate (FPR)', linewidth=1.5)\n",
    "\n",
    "mmce = (h_r_c/np.max(h_r_c) + 1 - h_b_c/np.max(h_b_c))/2\n",
    "ax.plot(rb_thres, mmce, '--',\n",
    "        label='Mean misclassification error', color='gray', linewidth=1.5)\n",
    "\n",
    "ax.set_xlim([-0.05, 1.05])\n",
    "\n",
    "ax.set_xticks(np.arange(0, 1.1, 0.1))\n",
    "ax.set_yticks(np.arange(0, 1.1, 0.1))\n",
    "\n",
    "# vals = ax.get_yticks()\n",
    "# ax.set_yticklabels(['{:,.0%}'.format(x) for x in vals])\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.set_ylim([5e-4, 1])\n",
    "vals = ax.get_yticks()\n",
    "ax.set_yticklabels(['{:,.1%}'.format(x) if x < 0.01 else '{:,.0%}'.format(x) for x in vals])\n",
    "\n",
    "# thresholds:\n",
    "thrs = [0.5, ]\n",
    "# thrs = [0.5, 0.74]\n",
    "for t in thrs:\n",
    "    m_t = rb_thres < t\n",
    "    fnr = np.array(h_r_c/np.max(h_r_c))[m_t][-1]\n",
    "    fpr = np.array(1 - h_b_c/np.max(h_b_c))[m_t][-1]\n",
    "    print(t, fnr*100, fpr*100)\n",
    "    # ax.vlines(t_1, 0, 1.1)\n",
    "    ax.vlines(t, 0, max(fnr, fpr))\n",
    "    ax.text(t - .05, max(fnr, fpr) + 0.01, f' {fnr*100:.1f}% FNR\\n {fpr*100:.1f}% FPR', fontsize=10)\n",
    "\n",
    "ax.set_xlabel('RB score threshold')\n",
    "ax.set_ylabel('Cumulative percentage')\n",
    "ax.legend(loc='lower center')\n",
    "ax.grid(True, which='major', linewidth=.5)\n",
    "ax.grid(True, which='minor', linewidth=.3)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(14, 5), dpi=100)\n",
    "fig.subplots_adjust(bottom=0.09, left=0.05, right=0.70, top=0.98, wspace=0.2, hspace=0.2)\n",
    "lw = 1.6\n",
    "# ROCs\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "# zoomed ROCs\n",
    "ax2 = fig.add_subplot(1, 2, 2)\n",
    "\n",
    "ax.plot([0, 1], [0, 1], color='#333333', lw=lw, linestyle='--')\n",
    "ax.set_xlim([0.0, 1.0])\n",
    "ax.set_ylim([0.0, 1.05])\n",
    "ax.set_xlabel('False Positive Rate (Contamination)')\n",
    "ax.set_ylabel('True Positive Rate (Sensitivity)')\n",
    "# ax.legend(loc=\"lower right\")\n",
    "# ax.legend(loc=\"best\")\n",
    "ax.grid(True, linewidth=.3)\n",
    "\n",
    "# ax2.set_xlim([0.0, .2])\n",
    "# ax2.set_ylim([0.8, 1.0])\n",
    "ax2.set_xlim([0.0, .2])\n",
    "ax2.set_ylim([0.8, 1.005])\n",
    "ax2.set_xlabel('False Positive Rate (Contamination)')\n",
    "ax2.set_ylabel('True Positive Rate (Sensitivity)')\n",
    "# ax.legend(loc=\"lower right\")\n",
    "ax2.grid(True, linewidth=.3)\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(df['label'][masks['test']], preds)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "ax.plot(fpr, tpr, lw=lw)\n",
    "ax2.plot(fpr, tpr, lw=lw, label=f'ROC (area = {roc_auc:.5f})')\n",
    "ax2.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some real data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_triplet(tr):\n",
    "    fig = plt.figure(figsize=(8, 2), dpi=100)\n",
    "    ax = fig.add_subplot(131)\n",
    "    ax.axis('off')\n",
    "    ax.imshow(tr[:, :, 0], origin='upper', cmap=plt.cm.bone)\n",
    "    ax2 = fig.add_subplot(132)\n",
    "    ax2.axis('off')\n",
    "    ax2.imshow(tr[:, :, 1], origin='upper', cmap=plt.cm.bone)\n",
    "    ax3 = fig.add_subplot(133)\n",
    "    ax3.axis('off')\n",
    "    ax3.imshow(tr[:, :, 2], origin='upper', cmap=plt.cm.bone)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('714287740515015072.json', 'r') as f:\n",
    "    al = json.load(f)\n",
    "print(al['candidate']['rb'])\n",
    "tr = make_triplet(al)\n",
    "plot_triplet(tr)\n",
    "model.predict(np.expand_dims(tr, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('893215910715010007.json', 'r') as f:\n",
    "    al = json.load(f)\n",
    "print(al['candidate']['rb'])\n",
    "tr = make_triplet(al)\n",
    "plot_triplet(tr)\n",
    "model.predict(np.expand_dims(tr, axis=0))"
   ]
  }
 ],
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