FetchData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "    \n",
    "from api.RequestOpenFood import QuerryError\n",
    "from api.RequestOpenFood import RequestOpenFood\n",
    "from api.RequestOpenFood import ProductBuilder\n",
    "\n",
    "import api.Chart\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No name found\n",
      "33177\n",
      "Acides gras monoinsaturés\n",
      "Acides gras poly-insaturés\n",
      "Sel\n",
      "Protéines\n",
      "Sucres\n",
      "Glucides\n",
      "Acides gras saturées\n",
      "Matières grasses\n",
      "Énergie (kCal)\n",
      "Énergie\n",
      "False\n"
     ]
    },
    {
     "data": {
      "image/png": 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jiYvwN/XVgJtLRhA+SP7cGNvRL8aR7SzpmtjfD/c4zY3v8OlNvilJR9MSQ3US\nPvt0cMF4GmhmHy+UKQ9vNeBjw0rUSskwCTgu6i7V39fMKgkKv4EPbSw3av6Ez7j9c3isSjxEbWN+\nJN6P7hrG/Ib4Pao2Vmoobgy+XqPOpIWkAdS9MDzcNQmYAVwMnGRmo2P/McBGwPlamidmyTgfSWdJ\nGl2o7wRgVVx08xbgeDN7lUaI0MsPgIclbRAziz6H/wD+VT7I9kH8reYNvDP5E+51Gg/Mp/YbXFKZ\nlYEf4v+vd/H/335m9kahzOvAe/gb8/3A/MIA0J2Af0qaC/w+lpLaPJJeUuSCirDEAbh3ZgawPW5s\nV8PiuA/xGWRDiDEfZnYXcAj+/Xwb9wyMwmcdgg8s/RT+fbmP5QdMp55PN8LMJgMPAJcVDOKNVZa3\nqow7gJMkDZU0EB8LV41rgLNLg4/jxeHAKm1ZhBs1y405M7P/widsPCxPCwH+IvA5ST+RtFbUv6k8\nP1t/3Jh/H5/evhpuRNX6/o4A/myZRLJ9MLNccqnLBTBbkpHQOmjBOvo+5JJLeyz4y8x7eDi7tNwV\n+44AHi8r/xGwcXzuh+chm4SHof4OHFzj2JXwdArTgHHAScD7hf1/Bo4urB8KvIgb1ROAX9W4jr2A\nPxTWzwNuKqz/AB/MPDDWN8MNsmnR9hfwfFrCvZ/3xL0Yj4dzi9d9PTCqUPfvga909P+yuy4phprU\nLZJs6QCZjnoOhJnleJYkaUPkU+qvNrMmz4BspL4ngO/YsskQ2xVJ2wDXmNlOK+qc9UYaQEndkgZQ\nknQPYlzbF/HQ2dr4VPSnzezUmgcmdU2OAUrqlt4wxS2PXouXjkNc0Uvvanl2kiRpOsLHis3Aw2Uv\n46GqJKlKeoCSJEmSJKk70gOUJEmSJEndkQZQUvdIerSj25AkyVLkOl7VknAmSZuQBlDSKJIGSfqd\npHmSxpdywdQof7Jc7G+WXNBw5di+Sqy/KVd9f14FAcQOpM11peRijM9KWijp100o/0O51MNMSX9W\nbYHEJOkSRELLp6IvmCYXQP1UEw7NsRlJu5MGUNIUrsIzs66J5624WtJWlQpG9ukz8BkZw3B5hAti\nd09gIrCLueDmucAdqqLE3MV5G88Pcl1jBSUdDByJJyJcHXiGRjJuJ0lnR65vdR9wOTAIl5q4gAri\nzJUOb8emJQmQBlDSCJJWxTP3nmNmC8zsKTxD72FVDjkcuM7MXjMXJRyF64NhLgA4yswmxfpoPBlY\nxTfCkPMo7sMXAAAgAElEQVS4VNK7ksaFV2WxpE7/vTWze8zsf/FZKY2xIfCkmU0wn5VwC1DRwAQI\nuYvXwlt0paRHJR1drXySdBCb44k+7zDnfTN7yMxeApB0tFwAebqkP3bTF6GkE9Ppf0iSDmdzYJG5\nOniJMcDwKuWHx/5i2SGSBpUXjFTxm1FdQPNYYHfg48Anga/SPV3jtwObSNoswoVHAn+sVDBS7t8J\nnIkrT78O7LiC2pkkzWEs8JGkGyTtERIVAEjaD9ea+yruWX4CuK1jmpnUK2kAJY3RF0/bXmQO1cVK\n+wKzy8qqvLyknrin4warLqB5EHC5mTWEN+nHzWx7V6EBeIqlOl0HAKdUKbsX8JKZ3Wtmi83sCiBz\nCSWdDjObC+wMLAb+G3hX0j0hHnoccJGZjTWzxfizvZ2k9TuuxUm9kQZQ0hjzgP5l2wYAc5tYfgDu\ntVlSPlTAb8HHApxY49xDWVYUtbsKpJ4HfBofI9EbDxs+oqWq7UXK7wnAW+3bvCRpGWb2upkdbWYb\n4N7hocDP8PGBl0uaIWkGMB3vJ9btuNYm9UYaQEljjAV6StqksG1bqoetXo79JbYDppgrwpe4DlgD\n2N/MPqpx7gZgvcJ6dx0jsC1we3i6FpvZjfig0UozwRqA8rfk9SqUS5JORXh6bwS2xidDHGdmq8cy\nyMz6mtkzHdvKpJ5IAyipiZnNB+4GRklaVdLOwD5Un6V0E3CMpK1i3M85uMIxAJKuAbYE9jWzDxo5\n/R3ASZKGxviBM1p5OSsMSSuFB2cl3IDsJWmlKsWfBQ6SNETOYfiMuTcqlB0NbC1p3zjHd4C12uUi\nkqQVSNpC0imS1o319YFvAH8BrgHOLqV7kDRA0oEd19qkHkkDKGkKJwCrAlPx0NXxZvYqeKcmaY6k\n9QDM7E/AxcAj+AyvccD5UXYDfGDzdsAUSXPj2Gp5ha7FxQ1fxPV9RgMfxpiBzs45wHx8sPKh8fl7\nsPw9A36CDxb/BzATOAn3jpWPvcLMpuNjoy4BpuHG5HM0bWpxkqxI5gKfBf4qaS7wNP4sn2pm9+Lj\nfm6XNCu2F3OCdcfJDkknI7XAki5DJE282sw2auN6rasqssd4qreAkWb2WEe3J0mSpKuQHqCk0yKp\nt6Q9I9SzLj5Y+O6ObldHE3mABkjqRXiV8OSJSZIkSRNJAyjpzAjPHDsDD4G9jBtB9c6OeGhxKrA3\nsJ+ZZQgsSZKkGWQILKl7JD1qZl/o6HYkSZIkK440gJIkSZIkqTsyBJYkSZIkSd2RBlBS90h6tKPb\nkCT1QiENhGL9kc4o5hvCyxvH56slfa+xYzoaSTtLerWj29FVSAMoaTWSBkn6naR5ksbXyOtTKn+y\npAZJsyT9KgRAO5IRHXz+JOl2SHpT0vwwdko5v9Y2s0lm1t86//iLJe0zs2+b2Y9WdAMknSfppqaW\nN7MnzWyr9mxTdyINoKQtuApYiKs6fxO4WlLFh1DS7nhG5y/iekCb4DO9kiTpXhiwdxg7/eLv5NZU\nWCObenvQJXODVWMF37suQRpASauQtCqwP3COmS0ws6eAe4HDqhxyOHCdmb0WCu+jgKNq1L+bpNck\nzZR0paRHO6O7PEmSiixnREgaFuGl4u/PppL+Kml2eJMHlpU9WtIE4OHYvoOkp6JfeEHSiEL9R0oa\nFx6ncdU80pI+LenpqONtST+X1LNK2esljYrPIyRNknS6pClx7H6Rs+x1SdMknVU4VpL+U9Ibkt6V\ndHuF6ztc0gRJUyWdHft2B84GDgkP2guF63slru8NSccWzjVC0qTC+nhJZ0gaA8yT1EPSOpJ+G+ca\nJ+nEQvlPS3o2/g8Nkv6r6n+2G5AGUNJaNgcWmdm4wrYxuPJzJYbH/mLZIXLdsGWQNBi4E5eTGAy8\njufASZKka1Me/joMOBJYG/gI+HnZ/s/jsi+7SxoK/B4YZWaDgNOAuyQNjheyy4Hdzaw/8DlcYqYS\nHwH/AayO9yu7Av/exPavDayCq9ufh8v2HAp8Itp6rqRhUfa7wL7ALlF+Ju41L7ITsBnwb8D3JW0R\nskIXAr8JD9onouwUYK+4vqOAyyRtV6ir/N5+HdgTGBj77gNeANYBvoTrLX45yl4O/MzMBuDe+Tua\neD+6JGkAJa2lL1CuWTUH6Fej/OyysqpSfi/gJTO7N1TSr8Af/iRJugb3SJoRS60s7jeb2atmtgA4\nFzhYUsl7ZMB54WF+Hw+zjw4DATN7GNfD2yvKfwRsI6m3mU0p6RaWY2bPm9nfzJkI/DdNHw/4AXCh\nmX0E3A6sgRsO883sFeAVYNsoexzwPTNrMLNFuNf7wIIHzIDzzewDM3sRfyncliqY2R/N7M34/ASu\nl7hLjbZebmbvxL37NLCGmf3IzD6Ken6FG0kAi3Bv3OC4lr818X50SdIASlrLPKB/2bYBuBBiU8oP\nwDuASuWHApPKtr3VgjauUCSNLAz6HN3R7UmSDmQ/M1s9lv1rlCs+5xOAlXGjokTxuR+GG0glw2om\n7kFZx8zmA4cA3wYaJN0naYtKJ5S0WexvkAuy/qjsnLWYXhjEvSD+Ti3sX4C/7JXa+7tSe3HjaBGw\nVqF88cVufuHYSu3eU9JfJE2Pa9+zkXaX37t1y+7dWcCQ2H80sAXwWoQk965Rb5cnDaCktYwFekra\npLBtW1y2ohIvs+zbzXbAFDObWaFsA7B+2bb1KpTrVJjZrYVBn926A0mSRmjqQOLicz4M97BMK2wr\nhnUmATcVDKtB8bxdDGBmD5rZbniY6nU8PFWJq4FXgU3MbCCuq9ceA58nAnuWtXc1M2towrHLhLMk\nrQL8FrgYWDNCgH+kdrvL793/lbVlgJntA2Bm48xspJmtGef4raQ+Tb/UrkUaQEmriDeuu4FRklaV\ntDOwD3BzlUNuAo6RtFWM+zkHuL5K2dHA1pL2lQuifodl35qSJOmalP9gf1PSljGG5wLgzoKHpbzs\nLcA+8gkSPeSiySMkDZU0JPqLVXEvyzw8JFaJfsAcM5svaUvca9Qe/BK4UNIGAJLWlLRvYX8t42UK\nsGEhHLhKLNPMbLGkPYHdmtGWvwFzY2B07+hXh0vaPtp2qKSSN2k2bjwtbkb9XYo0gJK24ARgVdwF\nfAtwfCnurqVJz9YDiLj9xcAjwHhc1PP8SpWa2XTgIOAS/G1wSzzWn8KfSdL5qZXnx8o+3wzcCLyD\n/8CfVK0eM3sL2A+fIfUuHjI7Df896wGcAryN9xmfp7phcxpwqKQ5uJFyezPaX+t6ytcvx2fGPiBp\nNvA08JkmHnsnbiBNl/Scmc3D782dEU77etTdpHaZ2WLgK7jnfTzeZ1/L0mEJewAvxz25DDikOwst\npxZY0mWIt6C3gJFm9lgb1mtm1q1yfiRJkiS1SQ9Q0qkJN/cASb3wGD3AMx3ZpiRJkqTrkwZQ0tnZ\nEQ+TTQX2xmeVdFuXbJIkSbJiyBBYUvdIetTMvtDR7UiSJElWHGkAJUmSJElSd2QILEmSJEmSuiMN\noKTukfrMk2RSn1YpVSdJ0noKqTMqzsyUdJ6kannGkhYgaWdJFSVDOiuS1pAL4X6ypXWkAZQkLFzN\n02UsbLMki5JWkfQrSW+GsvLzkvYo7N8qVJdnREr7ByRtVaO+QXKV7HlyheeKCtdJ0tmQ9Gh8z1du\nSnkzmxRZ1JuaRyhpJWb2pJlV7X/aAklHSHqijerqiSfQPd7Mnm9pPWkAJUn70BNPgb9LKCufC9xR\nygaLJ3w72MxWx3V87mP5RGxFrgIWAmviYpBX1zKYkqQzEIroO+PZhPdtpHiXQNJKHd2GLopoheFa\nvO9m9qGZ7WNmf21Ng9IASpJ2IJSUR5nZpFgfjWde/VSszzaz8VF8JfwHYpNKdUVa//2Bc0IR+yk8\n++thVcr3kHSppHcljZN0gqTFBfXpJFlRHA78BbgBOLK4I6QYLg0v6UxJj0vqJWlY8fsqacPwIs2W\n9CfKhD8l7SDpqajjBUkjCvuOjGdgTvyt6DmNttwYnqqXJZ0uaVJh//iQjxgDzItn7ExJb0TdL0n6\naqH8JtHmWZKmSrqtynlL13qkpInhDT5O0vaSxkR7fl4oL0nnxD2bLOkGSf3L6jpc0oQ479mFY1eR\n9DNJb0t6S9JlJa+cXEqk/HpPjTbMlHSbXIcMSYPlIrIzo72PFY6reE/kUiNXAzvKhaJnxPZHJB1d\nOH4ZL1Fcz79LGovrTiKXTHkgzv2qpIMK5feK/98cSZMknVLpvi/BzHLJpa4XwMDMH4d2O8dauMrz\n5mXbZ+LCjx8CZ1U5djtgXtm2U4B7q5Q/HngJWAcYADyI6yH16Oh7nUt9LcC/gOOAT8b3fM3CviuB\nP+OipQJ2wFXghxW/r7h0xCWxbxdgDi6GCrAuLnmxe6x/KdYH4/I8s4FNY99awFZV2vljXJ6nPzAU\nGANMLOwfDzwf+3rFtgOAteLzQbjuWGn91tLzjEt7fK7KeYfhLz9XRbl/w5Xk745rGIrrge0S5Y/G\nDYFhcX13Fe5Fqa5fRl0fx73GW8T+UXEvB8fyFHBB7BtR4XqfiXs2EFewPzb2XRjt7YG/vO1UOK7W\nPTkCeLzs+h8Bji6sL1MmrudP0YZecc0TccNauLD2u8CWUf6d0r3G+77tan0/840wSdqZiFffAtxg\nZmOL+8zVnAcA38E73Ur0xTv9InNwMcdKHARcbmYNZjYb79yTZIUiF0beALjDfJzGG8DI2CfgKOC7\nZjbZnGfMbFFZHRsA2wPfN7NFZvYEHi4ucSgw2lxjEDN7GNcL3Cv2fwRsI6m3mU2x0CiswEHAj8xs\njpm9A1xRoczlZvaORSJWM7vLzKbE5ztxY6+k8bUIGCZpXTP7wMyernGrDBgV5R4C3gNuM7Pp0ZYn\ngE9E2ZHAT81sgrkQ9VnA1wveXQPOj7pexPuUbQvHXhD1TsdFZyt6kQvXO8XMZuH3fLvCta0DbGRm\nH5l7pGnCPWkpF5rZrLjvXwHGm9lN8Z0ZgxuBJS/QB8BwSf3Mvez/qFVxGkBJl0DSyHCdzpE0uqPb\n01Sio78FF3A9sVIZM1uAv7XdpKVKzEXmsVSssMQAYG6V0w4FJhXWJ1UplyTtyeHAA2Y2M9Zvw9/w\nwcNYvYD/a6SOdYCZ8YyUmFD4PAw4OEJFMyTNBHYC1gkD4RBcDLUhwjZbVDnPUFxnsESlZ6a4nwg1\nvRChoJnAcJaG507Hf1//Jumfko5q5DqnFj4vwL0+xfW+hXYWr38CPt6wOIGjeOz8smMnlh07tEab\nqtVzCZ6d/4EId51ZKtTIPWkpxfs+DNih7P89kqXXfwCuGDAhwms71Kq4ZysbliQrBDO7FXcrdzWu\nwzuAvczsoxrlVsLduyWXfpGxQE9Jm5jZuNi2LfBylboagPUK6xtUKZck7YKk3sDBQA9JDbF5FWCg\npG3wEO1CfNzbP2tU1QAMktSnYARtgIdGwA2Vm8zsuEoHm9mDwINyLcEf4crnn69Q9B38mXmtcI7l\nqitc3wbAfwNfNLO/xLYX8LAMZjYVODa27wQ8JOkxM2vM4GuMd3AjoMQw3CMzBVi/iceWvGDDYluz\nMFekPw04TdLHgEck/Q03iqreEyoPgH4P7/dKrF3plIXPk4BHzWz3Km37O/BV+YDpE4E7qNH/pQco\nSdoJSdcAWwL7mtkHZfv+TdJ28sGU/YGfAjNY2jktId5k7wZGSVo1Qgv7ANVyodwBnCRpqKSBwBlt\nd1VJ0iS+ho9r2wo31reNz08Ch5sP0rge+KmkdeI52EFLp8qXDImJeEjrAkkrF777JW4B9pGLJveQ\nD2YeEd/9IZL2lU8iWIR7Uqu9hNwJnCVpoKR1gRMaub7VcCNsWpz3KGDr0k5JB0Y9ALOi7OLlq1l6\nrU3kNuBk+cDwvrhRd7uZlequVddtwDny/Dlr4DNTm51PSdLekkoTNubi/+fFNHJPcCNtPS2bDuEf\nwP6S+kjaFDimkdP/Hthc0jcl9YzvxPYxMHrliBT0j5fNuVT/fwNpACVJuxBviMficfMphfBdaRbK\nQLxDmoXHyTcC9igZSpLOKgv1nYC/KU3FO/3ja4xnuBZ4AHgR+DswGviw0EkmSXtzOPBrM3vbzKaW\nFuAXwKExZuU03PvzLDAdH6tWHMtSYiQ+QHo6/qN9Y2mHmb0F7AecjQ+GnRD19ojlFOBt3Kv6eTwc\nVolRUW48/uzciYetl5yqWDievUvxgcKT8VDPk4Uinwb+KmkOcA8+1unNKucu94zUWv81brQ8jntc\n5gPfbeKxP8SNydLYoOdwA6opbSqyGe7RmosPpL7SzB5rwj35M+61niypFPK7DDdOJ+MG8S212hHe\np92Ar+Peq3fw780qUeQwYLykWXj/O7LGdaQWWJJIMn/OhJk1522sSyBPwHi1mW3U0W1Jkq6ApOOB\nQ8zsix3dlqT9SA9QknQzIgywp6SVwg1/Hh5CS5KkApLWlvQ5OVsAp5LPTLcnPUBJ3dPdPECS+gCP\nAVvgM0h+D/xHuI+TJCkjQtajgQ3xsPRtwNlm9mFHtitpX9IASuoeqc881wPrPcVsQaVZCEmSJEk3\nIw2gJEmSJEnqjhwDlCRJkiRJ3ZEGUFL3SH3mSTKpz+SObkuSJLWJXC/3F9YHSfprY1l/k6ScNICS\nVhMd0O8kzZOrCFdUXI6yR0j6MHLilHLjVMrMugJZuJoPgl64VqNFm4hceflXctXm2ZKej+noxTIH\nS3ol9r8kab8a9TX5HidJZyC++wslrV62/QW5ynejGcpVpgwPnhXezPYorM/E5Q8uLCQfTJJGSQMo\naQuuwtParwl8E7ha0lY1yj9tZv3NrF/8fXyFtHLF0hPX3dnFzAbgCdzuKHX6kobiCc3+I/afAdyq\nylpg0Px7nCQdjeGJBZcY65K2BvpQO9FeEVGaolnrRGbTzGxXM3u7ZmUFQ2pFEtIMSScjDaCkVUSa\n+f2Bc8xsQSgD30ttleHm1L+bpNfk4npXSnpU0tFtUXd7YmbzzWyUmU2K9dH4j8Gnosh6uMjjA7H/\nD7guzibldTX3Hkca+kslvStpnKQTyt+ik2QFcTNLBVCJzzcWC0jaKzyksyVNkHReYfdj8XdWeIs/\nG17kJwrHbynpAUnTo684pLDveklXSRodmYu/EN7Z/4pzNcT+XlF+sFw0dWbU9xhVqNU3RRuflPRT\nSdOA8yRtLOlhSdMkTZV0i1wGp1TfmZLeiut8VdIXY/unJT0b96dB0n8VjtlB0lPRhhckjSjsOzKe\n/znxN73GZWSHmLSWzYFFBZFO8DTrw2sc84noAF6TdE61H2ZJg/GU9GcCg4HXgR3bqN0rFElr4Snk\nSwKmzwGvSvpKGCxfxT08L1Y4vLn3+Fhgd+DjwCeBr9L0N+4kaUueAfpJ2iKe80NwuYOiR2cecFh4\nQvcGjpe0b+wrhcf7h7f4r7FusOTl4AE8b88auLfpKknFZ+MbwA/MrB8u3fATYFP8+dgUFyD+fpQ9\nFRfcHAwMwSU2lqOJfdNngTeinh/FNV+IC35uhb8EnR/1bY7L3XzKzPrjz++bUc/lwM/i/myCa/2V\nvMi/B0aZ2SBcAuSuMOJWjeN2j/o+h+tuJQXSAEpaS19gTtm2OUC/KuUfA7Y2syHAAXjndHqVsnsB\nL5nZvWa22MyuwAX1uhSSeuKd/g1mNhYgdLluxjvu92P/cQXF6yLNvccHAZebWYOZzca1cpKkoyh5\ngb6Mi/0uo0BuZo+b2cvx+SXgdmBEWR3VQmBfASaY2fXmvAD8Fu9bStxrZs9E/e8D/w842cxmm9l7\n+PNR8o4sAtYBNjKzj8LbWomm9E1vm9lVsf99MxtnZg+b2YdmNh3XwSpd50e4ntXWknqa2UQzGx/7\nPgA2lTQ4PMt/i+3fBEab2Z/i2h7GX6z2KtS5jaTeZjalhnZg3ZIGUNJa5gH9y7YNwJV4l8PM3jSz\nCfH5ZVyE8MAqdQ/F38aKvNXypq54JAk3bt4HTixs/zfgYuDzZrYy8AXgOkkfr1BNs+4xy9+38nuY\nJCuSW3BRyiOBm8p3Rljrz+EVngUch3tzmsIw/Ef+lVhexb0nAwtllnz/Ja2Jiwr/XdIMSTOAP+Je\nHIBLcJHRByS9IenMKudtSt+0zH65Ov1tEeaahd+XNQDCu/sfuEdoiqRbJa0Thx6DZ3V/TT7bbe/C\ntR9cug5JM4GdgHXMbD7ubfs20BBhvS2qXEvdkgZQ0lrGAj0lFceubMvSUE9TqPZ21wCsX7ZtvWbU\n2xm4Du/k9jezjwrbtwUeizdWzOw54K/Av1Woo7n3uIFl71Ojs22SpL0ws4n4+Lc9qayv9T+4Yvq6\nZjYQ+CVL+4TGQreTgGfN7GOxbGVmG5rZKcUmFD5PwxXUh5vZ6rEMjPASZjbPzE4zs02AfYFTSmNx\nymhK31Te9guBxXHugbgHZ0nfZ2a3m9kuuGED4bkNz9FIM1sTf2n6rVzuZhJwU+E6BsXEkovjuAfN\nbDc85PY6cG3FO1jHpAGUtIp407gbGCVpVUk7A/vgbu/lkLSHpCHxeUvgHLzzq8Ro3CW8r1zY8ztA\nm01Vb28kXQNsCexrZh+U7X4W2FnStlH2E8DOVBgD1Nx7jI8ROEnSUEkD8RlmSdKRHA3sWiPEO9PM\nFkn6DO4tKvEubjQsNzkg+D2wWQw6XjmW7at5O8ylD64FfhbeICStK2m3+Lx34UVjLvBhnL+clvRN\n/XBv7lz5dP0loX9Jm0v6oqRV8JDXgtJ5JR2qpbNDZ+OG1WLcg7RPDMbuIRdBHhHP/ZBo26p4WG8e\nHhJLCqQBlLQFJ+Bu5an4Q3l8Kd4saf2YhVB6O/oS8KJ8Rsbv8Xj9RZUqjTj5QbhbehpuTDyHh5M6\nNfLp7scC2+Eu7VLOo2+Aj3sALsDf5mbjAyp/ZGYPxfFnSRpdqLLqPa7AtfjA0BeBv+Od9Ycx7ihJ\nVhRLPCBmNt7Mnq+0D/h34AfxHJwD/KZw3AJ8APFTEeb5zDIncIHfL+P9xNv4+KIfA71qtOtMfHDy\nMxGKegCfaAA+UeGh6J+eAq40s+VmgrWwb7oAnwU6C7gPuKuwr1e0+924hjWBs2LfHsDLkubg44YO\niTFFbwH74QO13wUm4AOhe8RyStyTafhg8m/XaFtdklpgSZchxtO8BYys1Cm1ot5upQZfjjwB49Vm\ntlFHtyVJuiPt1Tcl7Ut6gJJOTbh3B8jzdHwvNj/TkW3q7IQrfM9wza8LnEflsRdJkrSQ7Ju6PmkA\nJZ2dHfFZGVPxHCH7xVTWpDrC3e0z8BDYy7gRlCRJ25F9UxcnQ2BJ3SP1med6YL2nmC1Yu6PbkyRJ\nkrQ/aQAlSZIkSVJ3ZAgsSZIkaTck9ZJ0mqSVO7otSVKkZ0c3IKkvJD0C3Gxmv15R5+wjTV5YM0dH\nr8Xwfr4M1DUZ/pQ0DE9Y2LONUyb8HHjIzBa1YZ1J0mqy0++myBXAn5W0UFJVY0PS9+VK4buWbf+J\nXLX4XUk1taQkfUmuXjxPrnbcoszDcuXmUS05thYLYS3DJ7pXWtz4qVUil+6/LGyzBJtytfFfSXpT\nruD9fKQiKO1fWdKdksbHs/f5KvWsHM/VxAr7TpL0f/HMvSxp0xrtafKzjN+MavUslrRxI8cvX6HZ\nsWZ2R3OPaysk/UHSYR11/qTzkgZQ9+Vt4Ae4FENFojM7kDJxQknH4Wngt8EVk/eRdGyVOgbjCb2+\nB6yOzzr6TaWySVIn9AQmAruExMK5wB1lLwZPAIfikgrVOIMK4r+SvgUcBexpZn1xQdBplSpozrPc\nBKoaR1XO/aak+ZEEtJQI9IoWnrvFmNleZlYta3pSx6QB1E0xs3vM7H/xqdDVuBLvZMtd04cDl4aa\neAPwX7iQYSX2x1WR7w65h/OBbSVtXqX8EiStI2mMpFMbK5skXYVQ7B5lZpNifTQeWvpUrC8ysyvM\n7GkqyywgaSNcEuKisu0Cvo+rmb8e9Y03s1lVmtOcZ7m8DQeEl+ljpU1NOa6AAXuHPlU/M+tvZt9t\nZh2NtXGltqwvqS/SAKpTJB0ELDSz+yvsHg6MKayPiW2VWKZs6Fa9UaN86fwbAo8CV5jZpU1td5J0\nNSSthUssNEcg+ApcCmFh2fb1YtlG0kRJ4ySdX6Oe5jzLxTYfhRtfXzKzV5rR7uWqqlD3EZKekHRJ\nyFuMKwsR9o8Q4juSJkn6QRh+pWOflPRTSdOA8+Q6WJdGiG9chP8XS+oRxzwi6ehC/UfLleOnS/pj\nS0P2SdcnDaA6RFJfXF+n2ttYX1x0r8Sc2NaUsqXy/Wo0YTjwCHCumVUN0SVJV0dST1y77QYzG9vE\nY74G9AgPbjklTb0v48/RrsA3JB1TpbrmPMvgBsvJwKnACDMb35Q2t4DPAK8Cg3E9rWI/cCMuCLox\n8An8Wr9V2P9Z/CVrCN6PHQvsjof4Pgl8lSrhOkn7Af8ZZdbEQ5G3tdE1JV2MNIDqk/OBm0ou+grM\nA/oX1gfEtqaULZWfW+P8I3HdnLtqlEmSLk14LW7BBTJPbOIxqwI/YenLSbkHpaSm/hMzm2tmE4Bf\nAntVqbI5z3KJ03AR0Frjk5rKPeHlmRl/S4baBDP7daiz3wisI1cwHwLsiYf4FprZNOBnwDcKdb5t\nZleZ2eLIvHwQcHmE+WbjoqLVOA64yMzGxky3HwPbSVq/Da416WKkAVSffAn4rqQGSQ3A+vggzdNj\n/8vAtoXy21Hdff9y7AdA0mrAJjXKgxtg04DbSq7tJOmGXAesAexvZh818ZjNgGHAE/Fs3gUMjXDQ\nBsDruHekSK3Byc15lkt17QacK2n/Jra5FvuZ2epmNij+ljw9k5ec0BXfwT1Tw4CVgYaS4QRcg9/H\nEuUvbkPLtlV7sSPqvzzqngFMx6953eZeWNL1SQOomyIXwuwNrAT0lCcjKw0Y3BXYGu8Yt8VngR2L\nD4oGuAk4RdJQuZjmKcD1VU71O2C4pK/JRQHPA/7RiLt/Ef7WthpwcxpBSXdD0jXAlsC+MTmgfP8q\n8cojR8IAABw8SURBVHwC9IpnB+Cf+AvJdviz+S3cWNgWmBTGwu3AGZL6SloPf3bvq9KU5jzL4B6n\nl4E9gF9I2qfJF129vuYwCR/3NLhgOA00s48XypQbfA0sDQ0C1BrTMwk4Luou1d/XzFLEtA5JA6j7\ncg4wHzgTn247n1AsNrOZZja1tAAfArNiADNm9ku8Q/0nPmjyf83s2lLFkl6S9I0oOw04ALgQn3G2\nPfD1Gu2yOO5DfAbZEOC6ghHUrKm2SdLZCE/NsbgRM6UwBbwYxnkdeA/3XtwPzJe0QYR1is/mDGCx\nmb1rS3WLToxj3wGeAm4xsxvi3DtLmlM6SWPPcgVKz+eLwD7Af0vavXx/e2Fmk4EHgMsk9ZOzsark\nSgruAE4KI28gPrO1GtcAZ5dmtsnV3A9sswtIuhSpBZZ0GiTdBTxmZm2aK0RSzW+5W175HNQ3wszS\nE1kFSf2AWcAgM5vTWPk4Zjz+gvMR/pgZ8CBwL/AtM/t8oexHwGb2/9u78/CqqnOP4983BCSBBJII\nMgiJ4qVa9XFCiqAizozKxSuCFS+orXPVK44til7nWOtQJ9QKikzaKyCUqlRpFdRaERwrImMIg0kg\nBBAlee8feyeehBwQknAS9u/zPHnIOXudtdfenLPznrXWXq/7N+G+7iMIwJoD3xDMeZpsZhcCF1V5\nbSPgfuBCggnfjwD3u/s+4fa/EQSJz4WPzyf4YtgxLP+Gu8dOspaIUAAk9ULYPf8RMDBcH6U261YA\nJDuhAGhHzOwSYKS773R9r0QLb6l/wt0PSHRbpH7TEJgknJldRrCC9LO1HfyISM2Y2bvAb4B4t9on\nlJk1NbPe4bzH9gTzEP+c6HZJ/aceINnrKRmq7JySoTZUZpYCzAF+RrBMwGvANe6+s9v9JeIUAImI\niEjk6FuviIiIRI4CIIk8M3s70W0QEZE9SwGQ1IiZZZvZjHBl1VVm9mh5EsI45a8NV6BeHyY8bLwn\n2xtHz9quMEzI+E8z+87MnttJ2WFm9qGZbbAgweV9OzqHIg1FuC7Ru+Hn/VsLkqAek+h2iYACIKm5\nx4G1BJOMjyQIJi6vrmC4oNoNQC+CJek7AaP3TDP3uDzgTioneYwnheAumyyCRI+nEORjEmmwwvV8\npgMPAxkE6SZGE+RGq619NNp5KZHqKQCSmsoBJrn7D+HKtbMIslRXZxjBre5fhkkL7wCGx6vYzE43\nsy/DRIp/NLO3zWxEbR9AXXD3V8Ns3oU/oexT7v6uu28LE1COB3rEK9+Qz4tESmfA3X2yB7a6+5vu\n/qmZ3WZmL5QXDHuSy8p7Ps0sw8yeM7M8Myswsz+Hz/c0sxVmdkOYK618ccN+ZjY//Ey8Y2aHx9R9\no5mtDFfj/sLMeu3Z0yD1lQIgqak/AOeZWUq4Bkdv4C9xyh5KsBx/uQVAazPLqFrQzLKAKQQrtmYR\npA44rjYbXo+dSJyElRE/L9KwfAWUmtnzZnZmmKYiVtVbkGMfv0jQM3oIwWrSD8VsawO0JFjJ+Vdm\ndhRBT+slQCbwFDDNzBqbWWfgCuAYd08HzgCW1sbBScOnAEhq6h8EiVWLgeXAP8Oej+o0J1h6vlwx\nwULMadWU7QN86u5Tw/xIjwBraq/Z9VPYk3MMkBunSCTPizQ87r4ROB4oA54G1pnZq2bWekevM7O2\nBIHKr9292N1L3f0fMUVKgdvCXuetBIHPk+7+YdjT9ALBMFu3sGwT4DAzS3b35e6+pNYPVhokBUCy\n28IEprOAl4FUYF8g08zui/OSEiA95nELgm99G6sp244gc3OslTVqcD1nZmcDdwFnunu8obPInRdp\nuNz93+4+wt07EvQAtyPoNd6R/YHCHeQcW+fuP8Q8zgb+J7wRo9DMisI62rn7YuAa4HaCxLQvhQGW\niAIgqZFMoAPwx/DbWBHwJ4JhsOp8BhwR8/hIYE34uqryw7pj7V/D9tZbYf6ip4B+7v75DopG6rzI\n3sPdvwLGEgRCJQRfmsrFBiUrCL5IxX5ZqlRVlccrgLvcPTP8yXD35u4+KdzvRHc/gSBQAri3psci\newcFQLLb3L0AWAJcGubhaUmQkXlBnJeMAy4ys0PCeT+/JQiYqjODoNt6QFj3lewwnUX9Era5KdAI\nSDazfeLdsWJmJxPMeRjk7v/aSdUN+rxIdJjZz8zsunBuIGbWARgCzCO4RpxoZh3MrAVwU/nr3H01\nwTzCx82spZklm9kJO9jVGIJrUNdwP83MrE/4b2cz62VmTYDvCVJllNXF8UrDowBIauo/CealrCOY\n9Pg9cB0EF7zwzov9Adz9r8D9wFsEgdNigq7p7YTB1X8BDwDfAgcDH1KLt9DWsd8CmwkmK58f/n4r\nbH9ewrLpwEwz2xhum1FdpXvBeZHo2EiwrMP7ZrYRmAssBK539zeBSeHjfxLcLh/rAmAb8CXBHLff\nxNtJ+KXhEuAxMyskuA5dGG7eh6DHZx2wCmgF3FwbBycNn3KBSYMQzjdaCQx19zm1XLe7u9VmnXtK\nXZ4XEZG9mXqApN4K17tpYWb7EPaeAO8lsk31gc6LiEjNKQCS+uw4gmGytUBf4Kzwtteo03kREakh\nDYFJ5JnZ2+5+UqLbISIie44CIBEREYkcDYGJiIhI5CgAksgzSykxS1md6HaISKVlIqq9M7NqIlWp\nuTCX4ztmFm8R272SAiARvmsG39XaYoJm1sTMnjGzpWa2wcw+Cld6jlf+QjPbFl70y9cBOrG22iOS\nSGb2dpiiovFPKe/uK9w93Xc8PyPSczfMrKeZVU2JUxNPArnuHi+R9V4pOdENENkLJRMkhj3B3VeY\nWV9gspkd5u7L47xmrrsr6JG9ipllEyREXQ8MAF5JbItqzswauXtpoptBDYLAqsfg7hfuqPzeSj1A\nIrXM3Te7+x3uviJ8PINg5etjaqP+cB2gL82syMz+GH7DHlEbdYvUsmEEqS+eB/47doOZNTWzB8Oe\n0iIz+3uYMibbzMrMLCkslxO+xzeY2V8Jki7H1tPNzN4N65hvZj1jtv23mS0Oe1UXm9mQ6hoZtmVs\n2FP1mZmNjO1hMbMlZnaDmS0ASswsycxuNLOvw7o/tSCZcXn5TmGb15vZWjObEGe/+5jZC2b2bdj+\n982sVUzbPw/r/9rMfhU+nwrMBNrF9Bi3MbM/mdkdMXVX6iWKcwxtzezlsI2LzeyqmPJmZjeF+15n\nZhMtSHe0w3Y3JAqAROqYme0H/AdBMth4jgovQl+a2W/LL/7V1JUFTCFIsZEF/JtgXSCR+mgYQZ67\nl4AzqvyRfBA4CuhGkFj5Bn7M0xXbu/ESQbqMfYH/5cc0F1iQZ+w14A53zwCuB14xs6wwUHgYOMPd\n04HuwMdx2nk70BHIAU4Dfsn2PSznESR6bunuZcDXQI+w7tHAi+FnHeBO4K/u3pIgWfGjcfZ7IUEa\nnPbhObiUIF8ZBClA+oT1DwceMrMj3X1z2I5V7p4WDhfGm8MY9xjCbdOB+QTJaE8BfmNmp4Vlrybo\ntTsBaAcUAY//hHY3GAqAROqQmSUT/AF4PsyGXZ05wGHu3hoYRJAwcmScsn2AT919qruXufsjBBdK\nkXrFzI4nCComu/tHBAHD0HCbEfxRv9rdV3vgPXf/oUodHYEuwCh3/8Hd/0HlvGHnAzPCPIO4+2yC\n3Hh9wu2lwOFm1tTd17j7F3Ga+18EGeWL3X0V8Eg1ZR5291Xli466+yvuvib8fQqwCOgalv0ByDaz\n9u7+vbvPjbPfHwi+yHQOz8F8dy8J6/yLuy8Nf/8H8DpBMFITscdwLLCvu9/l7qXhvp4hCJIAfg3c\n6u754f/LHcA54ZezuO1uSBQASYNgZkNtJ4lC65vwIv8iQaLSq+KVc/el7r4s/P0zwgtNnOLtgKqT\nH1fWvLUitW4Y8Lq7F4WPJ/Bj782+BIlKv9lJHW2BIneP7V1YFvN7NnBuOHRVaGZFQA+gbdhTMhi4\nDMg3s+lm9rM4+2lH5c9RdROMK33OzGxYOORWFO73UH4cnhtJ8Pf1AzP7xMyGx9nvOOCvwEQzW2lm\n95pZo7D+3mY2z8wKwvp7U2X4bzfEHkM20L7KubsZaB2z/f/KtwOfEwQ++wEvxGt3Q6JJ0NIguPtL\nBF3hDcmzBBesPrsxaTJectZ8gm7pWPtXV1AkUcysKXAukGRm+eHTTYCWZnY48CnwHdAJ+GQHVeUD\nGWaWEhMEdeTHobIVwDh3/3V1L3b3N4A3LMibdxcwBqjuZoNVBJ+jL2P2sV11McfXEXga6OXu88Ln\n5hN+bt19LVA+Z6cH8KaZzXH3SgFfeF24E7gzrPMvwL/NbDzwMsFQ3FR3LzOz/+PH60J1E6A3Aakx\nj9vu6BgIzt037h4vKFwOjCg/vmps127gT3HK1kvqARKpA2b2JHAwMMDdv99J2TPNrHX4+8HAb4FX\n4xSfARxmZgPMrJGZXUnwjUykPhkIbAMOAY4Ifw4B3gGGhbe4/wn4fTgRN8mCyczlt8qXBxLLCYa0\nRptZ43BYrX/Mfl4E+ltwY0CSBZOZe5pZOzNrHX5OUgl6LkoIhsSqMwW42cxahvOKrtjJ8TUjCMK+\nDfc7HDisfKOZnRPWA8EdcGX8GLQRU+4kMzssHFYqCdtZShAsNgG+DYOf3sDpMS9dA2SZWXrMcx8D\nfcwsw8zaAL/ZyTF8AGwMJ0Y3Da8nh5pZl3D7U8DdYYCDmbUyswE7aPd2x1ffKQASqWXhBeNXwJHA\nmpihuyHh9vKF3sp7bk4BFprZRoIJnS8D91RXt7sXEMxXeAD4liDI+pBgmE2kvhgGPOfuee6+tvwH\neAw4P/zDeT1B788/gQLgXn78mxTbUzGUYKJ0AfA7YGz5BndfCZwF3AKsIxgeuz6sJwm4Dsgj+Kyc\nSDAcVp07wnJLCObaTKHyZ6pSj0s4l+hB4D1gNcHw1zsxRY4F3jezYoIvM1eXz+epog3B530DwU0S\nbwEvhvNprgamhMNP5wFTY/b/b4IhxW/CIao2BMNSC4GlwCxgYpV9VT2GMqAfwXVqCUFy5TEEk5sh\nmEA+FXjdzDYAc/lxjlN17W5wi1MqF5hEnpk5gLvHG3aqt8J5RiuBoe4+J9HtEdkbmNmlwGB375Xo\ntkjdUQ+QSAMTdve3COc13Bo+/V4i2yTSkFmwjk53C/wM+B/gz4lul9QtTYIWaXiOI5gQ3pjgzoyz\nym/NFZHd0oRgzksOwZydCcATiWyQ1D0NgUnkmaWUACXuW9okui0iIrJnKAASERGRyNEcIBEREYkc\nBUAiIiISOQqAREREJHIUAImIiEjkKAASERGRyFEAJCIiIpGjAEhEREQiRwGQiIiIRI4CIBEREYkc\nBUAiIiISOQqAREREJHIUAImIiEjkKAASERGRyFEAJCIiIpGjAEhEREQiRwGQiIiIRI4CIBEREYkc\nBUAiIiISOQqAREREJHIUAImIiEjkKAASERGRyFEAJCIiIpGjAEhEREQiRwGQiIiIRI4CIBEREYkc\nBUAiIiISOQqAREREJHIUAImIiEjkKAASERGRyFEAJCIiIpGjAEhEREQiRwGQiIiIRI4CIBEREYkc\nBUAiIiISOQqAREREJHIUAImIiEjkKAASERGRyFEAJCIiIpGjAEhEREQiRwGQiIiIRE5yohuQKMnJ\nyUNSU1MfKikpae3uluj2iEhiJSUllaWlpc3dsGHDqe6+NdHtEZG6Ze6e6DbsccnJyUOaN2/+7MyZ\nM1O6dOlCkyZNEt0kEUmwLVu2cPbZZ3/3/vvvP3/iiSdeOW3atNJEt0lE6k4kh8BSU1MfmjlzZkr3\n7t0V/IgIACkpKTz++ONNS0tLLwJ+NWDAgMaJbpOI1J1IBkAlJSWtu3TpkuhmiEg9k52dzaZNmxoD\nxwGnJ7o9IlJ3IhkAubup50dEqkpOTiacFrAe+I8EN0dE6lAkA6CG7rLLLuOuu+7a7dePHTuWE044\nYbdfP3z4cDIzM+nWrdtu1xHPnDlz6NChQ8XjAw44gL/97W+1vh+RnSgjwjeJiESBAqB6KCcnh9TU\nVNLT08nKyqJ///7k5eVVbH/iiSe49dZba7QPs9278e2dd95h9uzZrFq1ivfee69GbYjnp7Zt+PDh\njBo1quLxDz/8wO23307nzp1JS0vjwAMP5OKLL2b58uUAnHTSSaSkpJCenl7xc9ZZZ21X7z333ENa\nWhrp6emkpKSQnJxMeno6aWlpHH744QAkJSVVlCn/Nzc3t1I9zz//PElJSUyZMmV3T0Vk5OTksN9+\n+7Fly5aK55599ll69eqVwFaJyN5MAVAop00bzKzOfnLatPnJbTEzZsyYQXFxMfn5+bRu3Zqrrrqq\nDo/+p1u6dCk5OTk0bdo00U3ZzqBBg3jttdeYOHEiGzZsYMGCBXTp0oXZs2cDwXl9/PHHKS4urviZ\nOnXqdvXcfPPNbNy4keLiYp588km6d+9OcXExGzdu5JNPPqmoa+HChRXPFxcXc/3111eqZ9y4cWRl\nZTFu3Li6P/jd1KZNTp2+79u0yflJ7TAzysrK+MMf/rDd8yIidUEBUGjZmjU41NnPsjVrdqk95csT\nNGnShHPOOYfPP/+8Yltsz0dBQQH9+/cnIyODrKwsevbsWVFu5cqVDBo0iNatW9OqVSuuvvrqSvWP\nHDmSzMxMOnXqxKxZsyq25efnc9ZZZ5GVlUXnzp155plnAHjuuee45JJLmDdvHunp6YwePTru/idP\nnlzRM5Kenk7Tpk05+eSTAfj++++5/vrryc7Opm3btlx++eVs3VqzZVfefPNNZs+ezbRp0zj66KMr\nemguvfRShg8fvt15rSl332Fdy5Yt4+9//ztPP/00s2bNYu3atbWy39q2Zs0y6u5d72H9P83IkSN5\n8MEHKS4u3m7b3Llz6dq1KxkZGfziF79g3rx5Fdt69erFqFGjOP7440lPT+fMM8+ksLCwYvt7771H\njx49yMjI4KijjmLOnDk/uU0isvdSAFTPbd68mUmTJnHcccdVu/3BBx+kQ4cOFBQUsHbtWu6++24A\nysrK6NevHwcccADLly8nLy+P8847r+J177//PocccggFBQWMHDmSiy66qGLb4MGD6dixI6tXr2bK\nlCnccsstvP3224wYMYInn3yS4447juLiYm677ba4+z/33HMrekby8vI48MADGTp0KAA33ngjX3/9\nNQsXLuTrr78mLy+PO+64o0bnafbs2XTt2pV27drVqJ7aMm7cOLp06cLAgQM55JBDGD9+fKKbVO91\n6dKFk046iQceeKDS80VFRfTr149rrrmGgoICrr32Wvr27UtRUVFFmQkTJjB27FjWrVvH1q1bK4Yj\n8/Ly6NevH6NGjaKoqIjc3FwGDRpEQUHBHj02Eal/FADVU2effTaZmZm0bNmSN998c7vhlXKNGzcm\nPz+fJUuW0KhRI3r06AHABx98QH5+Pvfffz9NmzalSZMmdO/eveJ1OTk5jBgxAjPjwgsvJD8/n7Vr\n17Jy5UrmzZvHfffdR+PGjTniiCO4+OKL4w7jxNt/OXdnyJAhnHzyyVx88cUAjBkzhoceeogWLVrQ\nrFkzbrrpJiZMmFCj81VQUEDbtm13Wu6qq64iMzOTjIwMMjMzue2223Z7n0cffXSlut54442KbS+8\n8ALnn38+AEOHDq3Xw2D1yejRo3nssccqBSgzZsygc+fODB06lKSkJM477zwOPvhgpk+fXlFm+PDh\ndOrUiX322Ydzzz2Xjz/+GIDx48fTt29fzjjjDABOOeUUunTpwsyZM/fsgYlIvaMAqJ6aOnUqhYWF\nbN26lUcffZQTTzyx2mGUkSNH0qlTJ04//XQOOugg7rvvPgBWrFhBdnY2SUnV/xe3iZmTlJKSAkBJ\nSQmrVq0iMzOT1NTUiu3Z2dmVJmHHuuGGG6rdf7lbbrmFTZs28fDDDwOwbt06Nm/ezDHHHENmZiaZ\nmZn07t27xt/Is7KyyM/P32m5Rx99lMLCQoqKiigsLGT06NG7vc/58+dXquu0004D4N1332XJkiUM\nHjwYgCFDhrBw4UIWLly42/uKikMPPZR+/fpxzz33AEEAvWrVKrKzsyuVq/qejH0/p6amUlJSAgRD\nkZMnT654r2VkZPDuu+/+pPeKiOzdFADVU+XzS8yMgQMH0qhRI955553tyjVv3pzc3FwWL17MtGnT\n+P3vf89bb71Fhw4dWL58OWVlZbu033bt2lFYWMimTZsqnlu+fDnt27evtnyzZs2q3T/AxIkTmTRp\nEq+88gqNGjUCYN999yU1NZXPPvuMwsJCCgsLWb9+PRs2bNildlZ16qmn8sEHH7Bq1aoa1bMr4s0B\nGjt2LABHHnkkbdu2pVu3bphZxfOyY7fffjtjxowhLy8PM6N9+/YsXbq0UpkdvSdjdejQgWHDhlW8\n14qKiti4cSM33HBDHbVeRBoKBUANwNSpU1m/fj0///nPt9s2Y8YMFi9eDEBaWhrJyckkJSXRtWtX\n2rZty0033cTmzZvZunUrc+fO3em+9t9/f7p3787NN9/M1q1bWbhwIc8++ywXXHBBteXj7X/+/Plc\nffXVvPrqq2RmZlaUNzMuueQSrrnmGtatWwcE8zRef/31XT4vsU455RROO+00Bg4cyEcffURpaSkl\nJSU89dRTPP/88zWqe1ds3bqVKVOmMGbMGD7++GMWLFjAggULeOSRRxg/fvwuB6RR1KlTJwYPHswj\njzwCQO/evVm0aBETJ06ktLSUSZMm8cUXX9C/f/+d1vXLX/6S6dOn8/rrr1NWVsZ3333HnDlz9mig\nLCL1kwKgeqp///6kp6fTokULfve73zFu3DgOPvjg7cotWrSIU089lbS0NHr06MEVV1xBz549SUpK\nYvr06SxatIiOHTvSoUMHJk+eHHd/sbcbT5gwgSVLltCuXTsGDRrEnXfeGXc9lnj7nzZtGuvXr+f4\n44+vuBusb9++ANx7770cdNBBdOvWjZYtW3L66afz1Vdf7bRdO9v28ssv06dPHwYPHkzLli05/PDD\n+de//sWpp55aUebKK6+suDMtLS2NY489Nm79O2JmHHHEEZXWAbruuut49dVXSU1N5YILLqB169YV\nPyNGjKC0tJRZs2axYsUK0tPTWblyJQAvvfRSxfpCECx0efnll+9Wuxqqqv+Xo0aNYvPmzZgZmZmZ\nvPbaa+Tm5rLvvvuSm5vLjBkzyMjIqPa1sfbff3+mTp3K3XffTatWrcjOziY3N1eBqIhEMxu8mXnV\n485p02aXb1XfFdn77cfS1avrrH6R3dGmTc4u3aq+q/bbL5vVq5fWWf11wczo37//w8DKadOm5e70\nBSLSIGmp95CCE4mihhaciIjUFg2BiYiISOQoABIREZHIUQAkIiIikRPJAMjM/Pvvv090M0Skntm2\nbZsSsIpERCQDoObNm6/98MMPE90MEalnli1bRrNmzWqWmVdEGoRIBkCbN2++tk+fPlvmzp2LeoJE\nBGDLli1cccUVP2RkZHxIcG3clug2iUjdieRt8Nu2bZuQnJxMnz59xhQXFzeL4lpIIlJZUlKSt2rV\nKv+oo456G+gILEpwk0SkDkUyAIIgCBowYMAbwI1ARqLbIyL1RntgHlCz/CwiUq9FciXoWAMGDEgB\n2gBNEt0WEakXNgH506ZNK010Q0Sk7kQ+ABIREZHoieQkaBEREYk2BUAiIiISOQqAREREJHL+Hz8a\n/boC2ALAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f77508e5a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name_search = ['chocolat', 'noix']\n",
    "ingerdient_search = ['lait', 'miel']\n",
    "barcode_search = '7610848493136'\n",
    "avoid_ingredient = 'noisette'\n",
    "\n",
    "\n",
    "# 7610235000329 - henniez bleu\n",
    "# 7610235000558 - henniez vert\n",
    "# 7613033774188 - henniez special\n",
    "\n",
    "\n",
    "    \n",
    "try:\n",
    "    #print('--- Name search:', name_search)\n",
    "    #res = RequestOpenFood.search_name(id_from=0, id_size=5, name=name_search)\n",
    "    #res = ProductBuilder.clean_data(res)\n",
    "    \n",
    "    #print('--- Barcode search', barcode_search)\n",
    "    #res = RequestOpenFood.get_product(barcode=barcode_search)\n",
    "    #res = ProductBuilder.clean_data(res)\n",
    "    \n",
    "    \n",
    "    res = RequestOpenFood.get_product(barcode='76168352')\n",
    "    res = ProductBuilder.clean_data(res)\n",
    "    res2 = RequestOpenFood.get_product(barcode='4000522006890')\n",
    "    res2 = ProductBuilder.clean_data(res2)\n",
    "    data = RequestOpenFood.compare_data(res[0], res2[0])\n",
    "    print(len(data))\n",
    "    \n",
    "    barcode=4000522006890\n",
    "    ingredient='Sel'\n",
    "    res = RequestOpenFood.get_product(barcode=barcode)\n",
    "    res = RequestOpenFood.is_containing_ingredient(res[0], ingredient)\n",
    "    print(res)\n",
    "    \n",
    "except QuerryError as err:\n",
    "    print('Error', err.id_error, err.message)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Énergie': ['30.0', '-'], 'Matières grasses': ['0.0', '-'], 'Calcium': ['-', '104.0'], 'Sodium': ['-', '7.0'], 'Magnesium': ['-', '20.0'], 'Énergie (kCal)': ['7.0', '-'], 'Protéines': ['0.0', '-'], 'Glucides': ['1.7', '-']}\n",
      "['kJ', 'g', 'mg', 'g', 'mg', 'kCal', 'g', 'g']\n"
     ]
    }
   ],
   "source": [
    "print(data)\n",
    "res = RequestOpenFood.get_nutrient(data)\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'BarContainer' object has no attribute 'set_xlabel'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-443-9e9929aa0422>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbarh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mval\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malign\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'center'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'green'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Amount ($)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Frequency\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'BarContainer' object has no attribute 'set_xlabel'"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f00087ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\n",
    "import io\n",
    "from pylab import *\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "frequencies = [6, 16, 75, 160, 244, 260, 145, 73, 16, 4, 1]   # bring some raw data\n",
    "\n",
    "freq_series = pd.Series.from_array(frequencies)   # in my original code I create a series and run on that, so for consistency I create a series from the list.\n",
    "\n",
    "x_labels = [108300.0, 110540.0, 112780.0, 115020.0, 117260.0, 119500.0, 121740.0, 123980.0, 126220.0, 128460.0, 130700.0]\n",
    "\n",
    "# now to plot the figure...\n",
    "plt.figure(figsize=(12, 8))\n",
    "ax = barh(pos,val, align='center', color='green')\n",
    "ax.set_xlabel(\"Amount ($)\")\n",
    "ax.set_ylabel(\"Frequency\")\n",
    "ax.set_xticklabels(x_labels)\n",
    "\n",
    "rects = ax.patches\n",
    "\n",
    "# Now make some labels\n",
    "labels = [\"label%d\" % i for i in range(len(rects))]\n",
    "\n",
    "for rect, label in zip(rects, labels):\n",
    "    height = rect.get_height()\n",
    "    ax.text(rect.get_x() + rect.get_width()/2, height + 5, label, ha='center', va='bottom')\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}