Decision Rules.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nomis_df_one = pd.read_csv(\"./nomis_data/Nomis Solutions_Data Part I.csv\").dropna()\n",
    "nomis_df_two = pd.read_csv(\"./nomis_data/Nomis Solutions Part II.csv\")\n",
    "\n",
    "nomis_df_one.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'7-1-02'"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nomis_df_one['Approve Date'][1].replace(\"/\", \"-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tier</th>\n",
       "      <th>FICO</th>\n",
       "      <th>Approve Date</th>\n",
       "      <th>Term</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Previous Rate</th>\n",
       "      <th>Car  Type</th>\n",
       "      <th>Competition rate</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Cost of Funds</th>\n",
       "      <th>Partner Bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>208072</th>\n",
       "      <td>1.0</td>\n",
       "      <td>777.0</td>\n",
       "      <td>11/16/04</td>\n",
       "      <td>48.0</td>\n",
       "      <td>35000.0</td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.45</td>\n",
       "      <td>2.127</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208073</th>\n",
       "      <td>1.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>11/16/04</td>\n",
       "      <td>60.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.45</td>\n",
       "      <td>2.127</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208074</th>\n",
       "      <td>4.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>11/16/04</td>\n",
       "      <td>60.0</td>\n",
       "      <td>31800.0</td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.85</td>\n",
       "      <td>2.127</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208075</th>\n",
       "      <td>1.0</td>\n",
       "      <td>742.0</td>\n",
       "      <td>11/16/04</td>\n",
       "      <td>60.0</td>\n",
       "      <td>33000.0</td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.45</td>\n",
       "      <td>2.127</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208078</th>\n",
       "      <td>3.0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>11/16/04</td>\n",
       "      <td>60.0</td>\n",
       "      <td>20022.0</td>\n",
       "      <td>8</td>\n",
       "      <td>R</td>\n",
       "      <td>5.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.53</td>\n",
       "      <td>2.127</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Tier   FICO Approve Date  Term   Amount Previous Rate Car  Type  \\\n",
       "208072   1.0  777.0     11/16/04  48.0  35000.0                       N   \n",
       "208073   1.0  790.0     11/16/04  60.0  25000.0                       N   \n",
       "208074   4.0  663.0     11/16/04  60.0  31800.0                       N   \n",
       "208075   1.0  742.0     11/16/04  60.0  33000.0                       N   \n",
       "208078   3.0  685.0     11/16/04  60.0  20022.0             8         R   \n",
       "\n",
       "        Competition rate  Outcome   Rate  Cost of Funds  Partner Bin  \n",
       "208072              4.45      0.0   4.45          2.127          3.0  \n",
       "208073              4.45      0.0   4.45          2.127          3.0  \n",
       "208074              4.45      0.0  10.85          2.127          3.0  \n",
       "208075              4.45      0.0   4.45          2.127          1.0  \n",
       "208078              5.55      0.0   6.53          2.127          3.0  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nomis_df_one.tail()\n",
    "\n",
    "# plt.plot(nomis_df_one['Approve Date'], nomis_df_one['Rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27823    2003-01-01\n",
       "27794    2003-01-01\n",
       "27796    2003-01-01\n",
       "27797    2003-01-01\n",
       "27798    2003-01-01\n",
       "27807    2003-01-01\n",
       "27808    2003-01-01\n",
       "27810    2003-01-01\n",
       "27788    2003-01-01\n",
       "27811    2003-01-01\n",
       "27813    2003-01-01\n",
       "27816    2003-01-01\n",
       "27817    2003-01-01\n",
       "27818    2003-01-01\n",
       "27819    2003-01-01\n",
       "27822    2003-01-01\n",
       "27828    2003-01-01\n",
       "27812    2003-01-01\n",
       "27787    2003-01-01\n",
       "27783    2003-01-01\n",
       "27782    2003-01-01\n",
       "27755    2003-01-01\n",
       "27757    2003-01-01\n",
       "27758    2003-01-01\n",
       "27759    2003-01-01\n",
       "27760    2003-01-01\n",
       "27761    2003-01-01\n",
       "27762    2003-01-01\n",
       "27763    2003-01-01\n",
       "27764    2003-01-01\n",
       "            ...    \n",
       "191812   2004-09-09\n",
       "191813   2004-09-09\n",
       "191814   2004-09-09\n",
       "191815   2004-09-09\n",
       "191816   2004-09-09\n",
       "191817   2004-09-09\n",
       "191818   2004-09-09\n",
       "191819   2004-09-09\n",
       "191820   2004-09-09\n",
       "191821   2004-09-09\n",
       "191822   2004-09-09\n",
       "191823   2004-09-09\n",
       "191824   2004-09-09\n",
       "191825   2004-09-09\n",
       "191826   2004-09-09\n",
       "191827   2004-09-09\n",
       "191828   2004-09-09\n",
       "191829   2004-09-09\n",
       "191830   2004-09-09\n",
       "191831   2004-09-09\n",
       "191832   2004-09-09\n",
       "191834   2004-09-09\n",
       "191835   2004-09-09\n",
       "191836   2004-09-09\n",
       "191837   2004-09-09\n",
       "191838   2004-09-09\n",
       "191839   2004-09-09\n",
       "191840   2004-09-09\n",
       "191842   2004-09-09\n",
       "192019   2004-09-09\n",
       "Name: Approve Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_and_rate = nomis_df_one[['Approve Date', 'Rate']]\n",
    "date_and_rate = date_and_rate.sort_values('Approve Date').dropna()\n",
    "pd.to_datetime(date_and_rate['Approve Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# date_and_rate['month_year'] = date_and_rate['Approve Date'].apply(lambda x: x[0]+\"_\"+x[4:6])\n",
    "# df = date_and_rate.groupby('month_year')\n",
    "# date_and_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# date_and_rate.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# nomis_df_one.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x124f56710>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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1TZgw0guGSpI0LnT9DbTtLqFasNJyD2Whn0f446WdpwMPV38/OEj6g5TKmE4Z19JK66ul\nD3buVZowYQKLFy9h2bLlq95Zq62nZyLTpk2xzjvIOu8867zzrPPOa9V5t7XbJfTXlJUIZ9S+AXUr\nyndN3MiKFUZbdmTFyp7zKIsKXVSda2PKGJS5mflw9aVZO1Xnh7LQ0AOZuaj6OvVNImKj2mJHO1H7\nBtOhWLZsOb29XuCdZJ13nnXeedZ551nn40+7LSw3U7phzo+IkyjfNXIqZYntrwGnVN8L8TnKt62u\nQ1ndEcqCctdVwcd84FPAf2fmA7X0UyKi1dryccpqiWTmfRFxFXBxtdz4qyhrtuzc/lOWJEmjTVvT\nmjPzd5Tlm/8UuJWy8ux5mfnJakn1N1CCiPmUoGLP1peOZeY8yvLfJ1KmJD/Oc9dQOY2yVPfllJlH\nF2bmWbX0d1K+32MepSXnoMy8ra1nK0mSRqW2l+YfxfqeeOL3NiF2yFprTWTdddfBOu8c67zzrPPO\ns847r6rzrg+69csPJUlS4xmwSJKkxjNgkSRJjWfAIkmSGs+ARZIkNZ4BiyRJajwDFkmS1HgGLJIk\nqfEMWCRJUuMZsEiSpMYzYJEkSY1nwCJJkhrPgEWSJDWeAYskSWo8AxZJktR4BiySJKnxDFgkSVLj\nGbBIkqTGM2CRJEmNZ8AiSZIaz4BFkiQ13lrdLoDWnGeeeYa77vpJV/Lu6ZnI7NnbdyVvSdLYY8Ay\nht1110+49nXfZSYzO573AhYw7ZYpbLrp5h3PW5I09hiwjHEzmcksZnW7GJIkrRbHsEiSpMYzYJEk\nSY1nwCJJkhrPgEWSJDWeAYskSWo8AxZJktR4BiySJKnxXIdFa0Qvvdx9990sXryEZcuWdzz/LbbY\nkkmTJnU8X0nSmmHAojViIQvhQFjCko7nvYAFcBVstdU2Hc9bkrRmGLBojXGVXUnSSHEMiyRJajwD\nFkmS1HgGLJIkqfEMWCRJUuMZsEiSpMYzYJEkSY3ntGaNOb30kvmzruXvonWSNPIMWDTmLGQhHAHP\n60LeLlonSWuGAYvGJBetk6SxxTEskiSp8QxYJElS4xmwSJKkxjNgkSRJjWfAIkmSGs+ARZIkNZ4B\niyRJarxhr8MSEVcCizJzTvV4K+BcYEvgp8B7MvP22v77AycD04GrgUMy8/Fa+ieAOZQg6guZeWwt\nbT3g88BrgUeBEzLzkuGWXZIkjS7DamGJiLcCe9YeTwWuBK4HtgbmAldGxJQq/VXA+cCJwPbAusCX\nascfDewP7A3sBxwQEUfVsrwQeAGwHfBR4PyI2HY4ZZckSaNP2wFLRKwLnArcUtv8VuCpzDw2i/cB\nvwX+rkp/L3BpZl6SmT8F3gG8PiI2qdKPAI7PzLmZeT1wLHB4ld9LgTcAB2fmPZl5AXAxcFi7ZZck\nSaPTcFpYTgcuAu6pbdsOuKnffj8Edqj+3h64oZWQmQuBB4DtI2JDYGPgxtqxNwGbRMQGwKuABzLz\nV/3Sd0CSJI0LbQUsEbE7MJsyFqVuQ+ChftsWATOGkL4h0NcvfREwoZY+2LklSdIYN+RBtxExmTKo\n9rDMfDoi6slTgaf7HfI0MHkI6VMBMvOZfmnU0gc795D19IyvSVHj7fk2RU/PRNZaq/N133q9fd07\nxzrvPOu885pS1+3MEvoQMD8zrxkgbSl/HEBMBp4aQvpSgIiYVAtaWvs+tYpj2zJt2pR2DxnVxtvz\nbYpp06aw7rrrdDV/dZZ13nnW+fjTTsDyFmCDiPht9XgyQES8GfgyZbpy3XTg4ervBwdJf5DS/TOd\nMq6lldZXSx/s3EO2ePESli1b3u5ho9bixUu6XYRxafHiJTzxxO87nm9Pz0SmTZsy7q7zbrLOO886\n77xWnXdbOwHLLsDzao9PpQQVx1Zpx/bbf0fgI9Xf84CdKIN1iYiNKWNQ5mbmwxHxQJX+5Wr/2ZSB\ntosiYh5lAO5Gmdkay7JTdc62LFu2nN7e8XOB+8/cHd2+zrqd/3hknXeedT7+DDlg6TdLh6qlpS8z\n742IR4GPR8SZwOeAdwPrAJdVu58LXFcFH/OBTwH/nZkP1NJPiYhWa8vHgdOqfO+LiKuAiyPinymz\nhvYHdh7OE5YkSaPPiIykyczfAm+kBBHzKUHFnpm5pEqfBxxKWTjuJuBxyqq2LacBlwKXA18FLszM\ns2rp7wQWU1pVjgMOyszbRqLskiSp+Ya9NH9mHtTv8Xxgm0H2v4iqS2iAtOXAMdXPQOmPAfsMt6yS\nJGl0a8ZcJUmSpEEYsEiSpMYzYJEkSY1nwCJJkhrPgEWSJDWeAYskSWo8AxZJktR4BiySJKnxDFgk\nSVLjGbBIkqTGM2CRJEmNZ8AiSZIaz4BFkiQ1ngGLJElqPAMWSZLUeAYskiSp8QxYJElS4xmwSJKk\nxjNgkSRJjWfAIkmSGs+ARZIkNZ4BiyRJajwDFkmS1HgGLJIkqfEMWCRJUuMZsEiSpMYzYJEkSY1n\nwCJJkhrPgEWSJDWeAYskSWo8AxZJktR4BiySJKnxDFgkSVLjGbBIkqTGM2CRJEmNZ8AiSZIaz4BF\nkiQ1ngGLJElqPAMWSZLUeAYskiSp8dbqdgGksaSXXjJ/1pW8e3omMnv29l3JW5LWNAMWaQQtZCEc\nAc/rQt4LWMC0W6aw6aabdyF3SVqzDFikETaTmcxiVreLIUljimNYJElS4xmwSJKkxjNgkSRJjWfA\nIkmSGs+ARZIkNZ4BiyRJajwDFkmS1Hhtr8MSES8F/h14NfA48JnMPL1Kmwl8HtgBWAAcmZnfqx27\nB3Am8BJgLnBIZt5XS38fcAzwAuAy4PDMXFqlTQbOAfYFngI+mZlntFt+SZI0+rTVwhIRE4ArgUXA\nK4B3A8dHxFurXb4BPARsA1wMXBERM6pjNwauAL4AbAs8Bny9du79gBOAQ4Ddge2BU2vZnw5sDewK\nHAacGBH7tlN+SZI0OrXbJbQBcAdwWGb+MjO/C3wf2CkidgNeDByaxScorShzqmMPAW7NzE9l5j3A\nQcDMiNi5Sj8CODMzv5OZtwGHAgdHxNoRMRU4GDgiM+/MzG9QgpnDh/3MJUnSqNFWwJKZj2Tm/pn5\ne4CIeDUwG/gBpUXk9lYXTuUmSvcQwHbADbVzLQFuB3aIiInAK4Eba8fOAyYBL69+1qIEQPVzb9dO\n+SVJ0ug07EG3EbGAEoDMBS4HNqR0B9UtAmZUfw+W/kJg7Xp6Zi6jjJGZUR37WGb29jt27YhYf7jP\nQZIkjQ6r8+WH+wLTgXMpA2mnAk/32+dpYHL192DpU2uPB0qfuJI0audfpZ6e8TUparw9XxW+7p3T\nqmvrvHOs885rSl0PO2DJzNsBIuIo4BLKYNp1++02mTKjB2ApfxxcTAaeqNJYSfpTVTkHSqN2/lWa\nNm3KUHcdE8bb81Xh69551nnnWefjT1sBS0T8GbBDNei15W7KWJOHgc36HTK92g7wYPW4f/odlK6f\npdXjn1d59QDrV8dPBF4UERMzc3nt2CWZ+eRQy7948RKWLVu+6h3HiMWLl3S7COqC8Xadd1NPz0Sm\nTZtinXeQdd55rTrvtnZbWF4MXB4Rf56Zj1TbtgX+jzII9l8iYnJmtrprdmLFQNp51WMAqpk/WwEn\nZGZfRNxapbcG5u4IPAPcCUwA/kAZ2HtzlT4buLWdwi9btpze3vFzgfvPPD6Nt+u8CazzzrPOx592\nA5ZbgfnAF6uuoBcDpwAfoQQavwK+FBEnA3tRZv4cWB17AXBMRLwf+BZwInBvZrYClHOA8yLiLsrg\n23OAz9UWjruoSp9DGYh7dO3ckiRpDGt3WvNyYG/g95SWjs8BZ2XmZ6q0vShdNfOBtwH7ZObC6tj7\nKQN15wC3UGYG7VM796XAx4HPAldRZh8dW8v+KOA24FrgbOCDmfl1JEnSmNf2oNuqK+jNK0m7F9ht\nkGOvAmYNkn4qz13dtp62hLLY3EHtlFeSJI1+zZirJEmSNAgDFkmS1HgGLJIkqfEMWCRJUuMZsEiS\npMYzYJEkSY1nwCJJkhrPgEWSJDWeAYskSWo8AxZJktR4BiySJKnxDFgkSVLjGbBIkqTGM2CRJEmN\nZ8AiSZIaz4BFkiQ1ngGLJElqPAMWSZLUeAYskiSp8QxYJElS4xmwSJKkxjNgkSRJjWfAIkmSGs+A\nRZIkNZ4BiyRJajwDFkmS1HgGLJIkqfEMWCRJUuMZsEiSpMYzYJEkSY1nwCJJkhrPgEWSJDWeAYsk\nSWo8AxZJktR4BiySJKnxDFgkSVLjGbBIkqTGM2CRJEmNt1a3CzDWPfPMM9x110+6knfmz3heV3KW\nJGlkGbCsYXfd9ROufd13mcnMjuc9n7nswA4dz1eSpJFmwNIBM5nJLGZ1PN8FLOh4npIkrQmOYZEk\nSY1nwCJJkhrPgEWSJDWeAYskSWo8AxZJktR4BiySJKnxDFgkSVLjGbBIkqTGM2CRJEmN19ZKtxGx\nEfBpYDfgKeCrwHGZ+UxEzAQ+D+wALACOzMzv1Y7dAzgTeAkwFzgkM++rpb8POAZ4AXAZcHhmLq3S\nJgPnAPtW+X4yM88YxvOVJEmjULstLF8D1gZeDbwVeBNwcpX2DeAhYBvgYuCKiJgBEBEbA1cAXwC2\nBR4Dvt46aUTsB5wAHALsDmwPnFrL93Rga2BX4DDgxIjYt82yS5KkUWrILSwREcCrgA0y87Fq2wnA\naRHxXeDFwHZVq8gnIuI1wBzgJEogcmtmfqo67iDgkYjYOTNvAI4AzszM71TphwJXR8T7KUHVwcDr\nMvNO4M6IOBU4HLh89atAkiQ1XTstLI8Ae7aClZo/obSI3N7qwqncBM9+VfB2wA2thMxcAtwO7BAR\nE4FXAjfWjp0HTAJeXv2sRelGqp97uzbKLkmSRrEhByyZ+ZvMvLr1OCImUFo5vg9sSOkOqlsEzKj+\nHiz9hZRupmfTM3MZ8HiVviHwWGb29jt27YhYf6jllyRJo1dbg277OQ3YitI6chTwdL/0p4HJ1d9T\nB0mfWns8UPrElaRRO/+Q9PR0flJUN/LU+OY11zmturbOO8c677ym1PWwApaIOIUy7uTvM/PuiFgK\nrNdvt8mUGT0AS/nj4GIy8ESVxkrSn6rKOFAatfMPybRpU9rZfUR0I0+Nb15znWedd551Pv60HbBE\nxNnAocABmdma6fMgsHm/XacDD9fSpw+Qfgel62dp9fjnVR49wPrV8ROBF0XExMxcXjt2SWY+2U7Z\nFy9ewrJly1e94whavHhJR/OTunGdj1c9PROZNm2Kdd5B1nnnteq829pdh+VE4F3AWzLzilrSPODY\niJicma3ump1YMZB2XvW4dZ6plO6kEzKzLyJurdJbA3N3BJ4B7gQmAH+gDOy9uUqfDdzaTtkBli1b\nTm9vZy9w/6HUad24zsc767zzrPPxp51pzZsBxwMfA26OiA1qydcDvwK+FBEnA3tRxrYcWKVfABxT\nTVP+FnAicG81pRnKonDnRcRdlMG35wCfqy0cd1GVPocyEPfo2rklSdIY185Imr2q/Y+nBBUPUbps\nHqq6avahdNXMB94G7JOZCwEy837KKrVzgFsoM4P2aZ04My8FPg58FriKMoX52FreRwG3AdcCZwMf\nrHVHSZKkMW7ILSyZeQpwyiDpv6Qs2b+y9KuAWYOkn8pzV7etpy0BDqp+JEnSONOMuUqSJEmDMGCR\nJEmNZ8AiSZIaz4BFkiQ1ngGLJElqPAMWSZLUeAYskiSp8QxYJElS4xmwSJKkxjNgkSRJjWfAIkmS\nGs+ARZIkNZ4BiyRJajwDFkmS1HgGLJIkqfEMWCRJUuMZsEiSpMYzYJEkSY1nwCJJkhrPgEWSJDWe\nAYskSWo8AxZJktR4BiySJKnxDFgkSVLjGbBIkqTGM2CRJEmNZ8AiSZIaz4BFkiQ1ngGLJElqPAMW\nSZLUeAYskiSp8QxYJElS4xmwSJKkxjNgkSRJjWfAIkmSGs+ARZIkNZ4BiyRJajwDFkmS1HgGLJIk\nqfEMWCRJUuMZsEiSpMYzYJEkSY1nwCJJkhrPgEWSJDWeAYskSWo8AxZJktR4BiySJKnxDFgkSVLj\nGbBIkqTGM2CRJEmNt9ZwD4yIycB84L2ZeUO1bSbweWAHYAFwZGZ+r3bMHsCZwEuAucAhmXlfLf19\nwDHAC4Bl06D6AAAOfklEQVTLgMMzc2ktv3OAfYGngE9m5hnDLb8kSRo9htXCUgUPXwE275f0deAh\nYBvgYuCKiJhRHbMxcAXwBWBb4LFq/9Y59wNOAA4Bdge2B06tnft0YGtgV+Aw4MSI2Hc45ZckSaNL\n2wFLRGwGzANe3G/77pSWk0Oz+ASlFWVOtcshwK2Z+anMvAc4CJgZETtX6UcAZ2bmdzLzNuBQ4OCI\nWDsipgIHA0dk5p2Z+Q1KMHN4u+WXJEmjz3BaWHYBvk/p9plQ274dcHurC6dyU7VfK/2GVkJmLgFu\nB3aIiInAK4Eba8fOAyYBL69+1qIEQPVzbzeM8kuSpFGm7TEsmXle6++IqCdtSOkOqlsEzBhC+guB\ntevpmbksIh6v0vuAxzKzt9+xa0fE+pn5eLvPQxpreunl7rvvZvHiJSxbtrzj+W+xxZZMmjSp4/lK\nGh+GPeh2AFOBp/ttexqYPIT0qbXHA6VPXEkatfOvUk9P5ydFdSNPjU8LWQgHwhKWdDzvBSyg55qJ\nbL31Nh3Pu5ta/9/+n3eOdd55TanrkQxYlgLr9ds2mTKjp5XeP7iYDDxRpbGS9Kco5Rwojdr5V2na\ntClD3XXEdCNPjV8zmcksZnUl72nTprDuuut0Je9u8/+886zz8WckA5YH+eNZQ9OBh2vp0wdIvwN4\nnBK0TAd+DhARPcD61fETgRdFxMTMXF47dklmPjnUAnajqXzx4s5/2pW6YfHiJTzxxO+7XYyO6umZ\nyLRpU7rWDTceWeed16rzbhvJgGUecGxETM7MVnfNTqwYSDuvegxANfNnK+CEzOyLiFur9NbA3B2B\nZ4A7KYN7/0CZ6nxzlT4buLWdAi5btpze3s5e4P5Dabzoxv9XU4zn594t1vn4M5IBy/XAr4AvRcTJ\nwF6UmT8HVukXAMdExPuBbwEnAve2Fp2jLAp3XkTcRRl8ew7wudrCcRdV6XMoA3GPrp1bkiSNYas7\nkqav9UfVVbM3patmPvA2YJ/MXFil309ZpXYOcAtlZtA+teMvBT4OfBa4ijKF+dhaXkcBtwHXAmcD\nH8zMryNJksa81Wphycyefo/vBXYbZP+rYOUjAjPzVJ67um09bQllsbmDhlVYSZI0ajVjrpIkSdIg\nDFgkSVLjGbBIkqTGM2CRJEmNZ8AiSZIaz4BFkiQ1ngGLJElqPAMWSZLUeAYskiSp8QxYJElS4xmw\nSJKkxjNgkSRJjWfAIkmSGs+ARZIkNZ4BiyRJajwDFkmS1HgGLJIkqfEMWCRJUuMZsEiSpMYzYJEk\nSY1nwCJJkhrPgEWSJDWeAYskSWo8AxZJktR4BiySJKnxDFgkSVLjGbBIkqTGW6vbBZA0+vXSS+bP\nupb/FltsyaRJk7qWv6Q1z4BF0mpbyEI4Ap7XhbwXsACugq222qYLuUvqFAMWSSNiJjOZxaxuF0PS\nGOUYFkmS1HgGLJIkqfEMWCRJUuMZsEiSpMYzYJEkSY1nwCJJkhrPgEWSJDWeAYskSWo8AxZJktR4\n42al23M/eC6P3Pl/9PV1Nt+HnnyQbdm6s5lKkjTGjJuA5dHbHmW37+zS8Xx/Rve+EE6SpLHCLiFJ\nktR4BiySJKnxxk2XkKSxqZdeMrvT9drTM5HZs7fvSt7SeGPAImlUW8hCOAKe14W8F7CAabdMYdNN\nN+9C7tL4YsAiadSbyUxmMavbxZC0BjmGRZIkNZ4BiyRJajwDFkmS1HgGLJIkqfFG1aDbiJgMnAPs\nCzwFfDIzz+huqSSNV730cvfdd7N48RKWLVve8fy32GJLJk2a1PF8pW4YVQELcDqwNbArMBO4KCIW\nZObl3SyUpPFpIQvhQFjCko7nvYAFcBVstdU2Hc9b6oZRE7BExFTgYOB1mXkncGdEnAocDhiwSOoK\np1RLnTGaxrC8nBJgza1tuwnYrjvFkSRJnTJqWliADYHHMrO3tm0RsHZErJ+Zj3epXJLUcd38SgJw\n/Iw6bzQFLFOBp/ttaz2e3OGytGUBC7qS78M83JV8zdu8zXvNu5VbefqIp/klnQ9aHuERZn9mN2bN\n2qzjeU+cOIHnP39tfve7pSxf3tfx/MejiRMnsNtus7tdDCb09Y2OFzwi3gx8OjM3qm2bBdwFrJ+Z\nT3atcJIkaY0aTWNYHgReFBH1Mk8HlhisSJI0to2mgOXHwB+A+ne5zwZu7U5xJElSp4yaLiGAiDgX\neDUwB5gBfAk4MDO/3s1ySZKkNWs0DboFOIqy0u21wG+ADxqsSJI09o2qFhZJkjQ+jaYxLJIkaZwy\nYJEkSY1nwCJJkhrPgEWSJDWeAYskSWq80TatuW0RMZkyFXpf4Cngk5l5RndL1XwRsRHwaWA3Sr19\nFTguM5+JiJnA54EdgAXAkZn5vdqxewBnAi+hfLv2IZl5Xy39fcAxwAuAy4DDM3NplTbuX6+IuBJY\nlJlzqsdbAecCWwI/Bd6TmbfX9t8fOJmy8vPVlPp+vJb+CcraRROBL2TmsbW09Siv5WuBR4ETMvOS\nNfsMmyMiJlGu1f0p3012QWZ+oEqz3teAiJhBqdedgceBszLzrCrNOh9B1f10PvDezLyh2jaTLt2/\nV5X3qoyHFpbTga2BXYHDgBMjYt+ulmh0+BqwNmWhvrcCb6LcKAC+ATwEbANcDFxR3YSIiI2BK4Av\nANsCjwHPrpUTEfsBJwCHALtTVi4+tZbvuH69IuKtwJ61x1OBK4HrKfUyF7gyIqZU6a8CzgdOpNTl\nupQFFVvHH015M94b2A84ICKOqmV5IeXGsx3wUeD8iNh2DT29Jvo08BrKm9jbgEMi4hDrfY26DPgt\npV7fB3w0Iva2zkdWFTx8Bdi8X9LX6d79e6V5D8WYXoel+gd4DHhdZt5YbfsA8JrM3L2rhWuwiAjg\nbmCDzHys2vZW4DTgnZSA5c9qUfX3gBsz86SIOAnYqVW/1c3mEeBNmXlDRFwPXJOZJ1fpr6Z8Ulqf\nEkCP29crItYF7qT8Q9+dmXMiYg7wb5m5aW2/nwMfycyLIuJCYFmtNWYGcD/wksy8PyLuB47PzP+o\n0g8ATs7Ml0TES4FfAJtk5q+q9M8DPa3zjWVVfS8Cds/Mm6pt7wdeBvwQ+ID1PrIi4oXAr4G/zMy7\nq23/Rbnm78A6HxERsRnw5erhXwG7Vfff3SlBQ8fv36vKeyjPa6y3sLyc0u01t7btJkqErZV7BNiz\nFazU/Aklor69dcFVbqI08UGp2xtaCZm5BLgd2KH64spXAjfWjp0HTKK8VuP99ToduAi4p7ZtO0od\n1P2QFfW9Pc+t74XAA8D2EbEhsDHPre+bgE0iYgPgVcADrRt4LX0HxoedgCdbwQpAZp6amf9IqVfr\nfeQtAX4PHBQRa1UfjnakBCvW+cjZBfg+5flNqG3fju7dv1eV9yqN9YBlQ+CxzOytbVsErB0R63ep\nTI2Xmb/JzKtbjyNiAnA45R9gQ8qnobpFlO92YhXpL6R0Mz2bnpnLKP3YMxjHr1f16WM2K7rdWlan\nvjcE+vqlL6LcwGas4tjx4CXAgoh4R0TcExG/jIjjq+vdel8DMvNpyr3k3ZTg5R7gO5n5RazzEZOZ\n52XmMf2CA+ju/Xu1X4OxPuh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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a429a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(nomis_df_one['Amount']).hist(bins=15, color='m')\n",
    "plt.title(\"e-Car Total Loan Amounts in Dollars\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_columns = []\n",
    "\n",
    "for column in nomis_df_one.columns:\n",
    "    new_columns.append(\"_\".join(column.lower().split()))\n",
    "    \n",
    "nomis_df_one.columns = new_columns\n",
    "nomis_df_one.drop('approve_date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# define function to calculate net present value\n",
    "# based on amount of loan, APR, term length, and cost of capital\n",
    "\n",
    "def calculate_NPV(loan_amt, APR, term_in_months, cost_of_capital):\n",
    "    r = APR/12/100\n",
    "    r_c = cost_of_capital/12/100\n",
    "    \n",
    "    numerator = (1+r)**term_in_months\n",
    "    monthly_payment = loan_amt*r*(numerator/(numerator-1))\n",
    "    \n",
    "    PV = monthly_payment*(1-(1+r_c)**(-term_in_months))/r_c\n",
    "    \n",
    "    NPV = PV - loan_amt\n",
    "    \n",
    "    return NPV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# make a dictionary mapping tuple of tier segment, loan amount segment, car type segment, partner bin segment,\n",
    "# and term length segment to overall segment\n",
    "\n",
    "tier_list = [1,2,3,4]\n",
    "amount_list = [1,2,3,4]\n",
    "car_type_list = [1,2,3]\n",
    "partner_bin_list = [1,2,3]\n",
    "term_length_list = [1,2,3,4,5]\n",
    "\n",
    "segment_map = {}\n",
    "\n",
    "segment = 1\n",
    "\n",
    "for tier in tier_list:\n",
    "    for amount in amount_list:\n",
    "        for car_type in car_type_list:\n",
    "            for partner_bin in partner_bin_list:\n",
    "                for term_length in term_length_list:\n",
    "                    segment_map[(tier, amount, car_type, partner_bin, term_length)] = segment\n",
    "                    segment += 1\n",
    "\n",
    "\n",
    "def convert_to_segments(data_row, segment_map):\n",
    "    if data_row['tier'] == 1.0:\n",
    "        tier_seg = 1\n",
    "        \n",
    "    elif data_row['tier'] == 2.0:\n",
    "        tier_seg = 2\n",
    "        \n",
    "    elif data_row['tier'] == 3.0:\n",
    "        tier_seg = 3\n",
    "        \n",
    "    else:\n",
    "        tier_seg = 4\n",
    "    \n",
    "    if data_row['amount'] > 33000:\n",
    "        amount_seg = 1\n",
    "        \n",
    "    elif data_row['amount'] > 25000:\n",
    "        amount_seg = 2\n",
    "        \n",
    "    elif data_row['amount'] > 17800:\n",
    "        amount_seg = 3\n",
    "        \n",
    "    else:\n",
    "        amount_seg = 4\n",
    "        \n",
    "    if data_row['car_type'] == \"N\":\n",
    "        car_type_seg = 1\n",
    "        \n",
    "    elif data_row['car_type'] == \"U\":\n",
    "        car_type_seg = 2\n",
    "        \n",
    "    else:\n",
    "        car_type_seg = 3\n",
    "        \n",
    "    if data_row['partner_bin'] == 1.0:\n",
    "        partner_bin_seg = 1\n",
    "        \n",
    "    elif data_row['partner_bin'] == 2.0:\n",
    "        partner_bin_seg = 2\n",
    "        \n",
    "    else:\n",
    "        partner_bin_seg = 3\n",
    "        \n",
    "    if data_row['term'] == 36.0:\n",
    "        term_seg = 1\n",
    "        \n",
    "    elif data_row['term'] == 48.0:\n",
    "        term_seg = 2\n",
    "        \n",
    "    elif data_row['term'] == 60.0:\n",
    "        term_seg = 3\n",
    "        \n",
    "    elif data_row['term'] == 66.0:\n",
    "        term_seg = 4\n",
    "        \n",
    "    else:\n",
    "        term_seg = 5\n",
    "        \n",
    "    return segment_map[(tier_seg, amount_seg, car_type_seg, partner_bin_seg, term_seg)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# add segment column to dataframe\n",
    "\n",
    "segments = nomis_df_one.apply(lambda x: convert_to_segments(x, segment_map), axis=1)\n",
    "nomis_df_one['segment'] = segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# set cost of capital constant\n",
    "\n",
    "COST_OF_CAPITAL = nomis_df_one['cost_of_funds'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/justw/anaconda/envs/nlp/lib/python3.5/site-packages/ipykernel/__main__.py:28: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "/Users/justw/anaconda/envs/nlp/lib/python3.5/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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ISN5Ldo3JC8BCADPbAJzk7jszVMMMYKe79ya0NQLlZjY5cX2Iu3viG83sSOBcgvUpA9fa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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a49a9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6k1BygreTbh+fxpkYNM6uafP7WfPOfp5cu5O6Ru//QWmpPm6+bjw3TB5HWpws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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1259e2ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UeFhrHwLOBs4NNV0JnNPusRhnounlkY8o0Wjy6FzOnVEAwHt7ynlFG8mJiEgX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\nT3H8V4C3gGN9l1LEMf20wdx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CNLtmizg6UsGI2AAMJ00PnhARz2exXEwa33In\n8CQwk9QKU7F6m1nrOjU1+V45ZmZmlg+3eJiZmVlunHiYmZlZbpx4mJmZWW6ceJiZmVlunHiYmZlZ\nbpx4mJmZWW6ceJiZmVlunHiYmZlZbpx4mJmZWW6ceJiZmVlunHiYmZlZbv4PVUxm2lq6/YcAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12572f320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Lx7sdSUREJGp6OsdjP/BDnA3hAsDHgdAlNwM4K5r+eyTDJYP8nAymjytg054Klm8u4aol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\n4mFmZmalceJhZmZmpXHiYWZmZqX5H7sVHm8mMwtUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12aecb898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nFBIMBKitC/H8wq1+xxERkSiKuPBwzk10zv0KKALuAp4EzgO+A3wJeCgaASX5\ndOuUxZTwXI+3VhezT6MeIiIJo8Urlzrn7gKuAwYDS4FvAo+YWVmDY6qA30UrpCSfWVMKWbC62Bv1\nWLSNT19wqt+RREQkCiJZMv0LeKMZfzSzdSc4Zj3wnxGnkqTXrVMWk0d0563Vxcx/p4iLJ/enIC/L\n71giItJKkZxq+SZwZ+OiwzmX7Zy7HcDMVpjZr6MRUJLXrAZzPV7Quh4iIgmhWSMezrkCoEP44W+B\n5c65fY0OGwv8APhZ9OJJMuue34HJw7vz1ppi5r1TxMWTC+mSl+l3LBERaYXmjnhcBGwFtgABYEn4\nfsPbU3iX14pEzawphQQCHJ/rISIi7VuzCg8zexCYAZwdbroSOKvBbSYwAbgi+hElmXXv3IHJw70r\nXOat2s2BsgqfE4mISGs0e3Kpmb0J4JybCbxlZjVtlkqkgUumFLJwbfgKl4XbuO58rdYvItJeNXeO\nx7eAe83sGDAdmH6irVrM7LvRiyfijXpMGtaDhWuLmffObi6e3J/OuZrrISLSHjV3xOMG4FfAsfD9\nEwnh7eUiElWXTC1k0bpiampDvLBoG588T6MeIiLtUbMKDzMb0NT9aHHOZQC/BmbjFTc/MbOffsRr\nCoE1wEX1p4EkcfXo3IGJw7qzaG0Jb67azUWT+tOtc4ePfqGIiMSViJdMj7J7gXF4E1hvBb7tnJv9\nEa+5H9CKUknkkvAVLjW1usJFRKS9au4cjzq80ygfycxatDmcc64DcBNwvpmtAlY55+4BbsPb/6Wp\n13wCyGnJ50j717NL9vujHit3c8mUQvLzs/2OJSIiLdDcOR430szCIwKjwzkWNmibD9zZ1MHOuS7A\n/wLn451qkSRy2dQBLF63h9q6EM/M38JXBxT4HUlERFqguXM8HmjDDD2BfY0uzy0BMp1zXcxsf6Pj\nfwo8YGbrTnRljSSu7p07MHVkD+a9U8S8VUVce8ERslIDfscSEZFmau6plj8BXzKzw+H7JxIys5ta\nmKEDUNmorf5xRqMc5wBTgJtb+BkfkJISL1Nb2kZ9/xK1n1ecOZAFa7x1PR7+t3HLpcP8jtSmEv37\n2VCy9FX9TCzJ1s/Wau6plgFASoP70VRBowKjweNj9Q3OuUy8CaW3mllVaz4wNzc55qQmaj/z87O5\ncHIhz721hTdX7uSqs4fQv2eu37HaXKJ+P5uSLH1VPxNLsvSztQKhUFtN3Wge59xk4A0g08zqwm0z\ngOfMLKeyTbGMAAAgAElEQVTBcWcCrwFH8faLAcgGyoG/mNmtzfzIUFlZObW1dVHqQfxJSQmSm5tF\nIvez9HAlX/nVW1TX1HHa0G584WOj/I7UZpLh+1kvWfqqfiaWJOtnq89tN3vJ9IbCow/XAiPwTous\nBh6PcBn1lUA1MAlYEG6bhrcRXUNvA0Matb2Hd0XMKy35wNraOmpqEvcvR71E7mdOVhrnTOjLi4u2\nsWT9Ht7bWUphj8Qe9Ujk72djydJX9TOxJEs/W6vFhYdzbhTwL6AjsAHvFMyXgLudcxea2daWvJ+Z\nlTvnHgR+45y7EegDfAW4Pvx53YFDZlYBbG6UBWC3me1raT+k/bt4cn9eX7GT8spannpzC7fPGe13\nJBER+QiRzBS5D1gK9DGz8WY2BugP7AZ+EWGOO4BlwKvh97jbzJ4OP1cEzDnB6/w9TyS+ys1O59Jp\ngwBYvXk/G3eW+pxIREQ+SiSnWiYB483sUH2Dme11zn0FeCuSEGZWjrcHzIf2gTGzExZHLV2sTBLP\n5TMG8+z8zRyrqOGpNzfzn9eO8zuSiIicRCQjHruAXk205wM65SExlZOVxkWT+gPw7vZS1m494HMi\nERE5meau49GvwcP7gD845z6PN8JRC4wHfgvcFfWEIh/hvNP78u/F2zl8rJp/vL6JYZ/OJxDQomIi\nIvGouSMeW4Et4dvPgX7As8B+oBSYCwwGHoh6QpGPkJmeyqwphQBsLT7MUtvrbyARETmh5s7xmNmm\nKURaacaY3ry8ZAf7DlXw5BubGDukgNQEX0VQRKQ9au5eLW+0dRCR1khLDXLFtIH8/rl1lBwsZ/47\nRcwY29vvWCIi0kgk63hkArcAI3l/GfUA3jLnE8zslOjFE2m+icO78+Lb29m59wjPvLWFycN7kJGu\nC59EROJJJGPR/wfcg7dq6afw5nacD3wc+Gf0oom0TDAQ4MoZ3roeh45U8fLSHT4nEhGRxiIpPC4D\nbjCzyXiTTm/GW0DsGSA9etFEWm7kwM64vp0AePHtbRwpr/Y5kYiINBRJ4ZHP+wuFrQXGmVk18ANg\nVrSCiUQi0GDUo7yylucXbvU1j4iIfFAkhcceoFv4/ka8uR7gLR7WIxqhRFpjUO88xp3SFYC5y3ax\n/1CFz4lERKReJIXHi8CvnXPDgXnAtc65CcDnAZ1Ul7gw+8yBBAJQU1vHM/O3+B1HRETCIik8voa3\nIdx0vMmk64DFwBeBb0cvmkjkehVkM21UTwDeWlPEzr1HfE4kIiIQweW0ZlYKXF7/2Dl3MTAGKDaz\noihmE2mVy84YyKK1JVTV1PH4q+9xx8fH+B1JRCTpRbI7bf1aHtfiXVJbCawO30TiRn7HDM4/vR/P\nLtjKmi0HWL15PyMHdvE7lohIUmvxqRbn3ChgM95mcdOBC4E/AKudc4VRTSfSShdO6kdetneV92Ov\nvkdtXZ3PiUREklskczzuA5YCfcxsvJmNwVvHYzfwi2iGE2mtzPRUrjhzIAC79x1l3iqdDRQR8VMk\nhcck4Otmdqi+wcz2Al8BzopWMJFoOWNkT/p0zQHgqXmbKa+s8TmRiEjyiqTw2AX0aqI9H28tD5G4\nEgwG+PjZgwE4fKya5xdu8zmRiEjyatbkUudcvwYP7wP+4Jz7PN4KprXAeOC3wF1RTygSBcMLOzN6\nUBdWbdrPS0t2MGNMLwo6ZfkdS0Qk6TR3xGMrsCV8+znQD3gW2A+UAnPxNot7IOoJRaJkzlmDCQYC\n1NTW8fc3NvkdR0QkKTX3ctqZbZpCJAZ6dslm5tjezF2+k8Xr93DOhEMM7p3ndywRkaTSrMLDzN5o\nqt051xWoDi8qJhL3Lj2jkAVriymvrOGxuRv5xnXjCQYCfscSEUkakUwuxTn3RedcEVAM7HfO7XLO\nfTm60USir2OHdC6ZUgjApt1lLFxT7G8gEZEkE8kCYv8B3AM8AswGrgQeB37onLsxuvFEou+cCX3o\n3rkDAE+8voljFbq8VkQkViIZ8bgd+KqZ3WFmz5jZU2Z2O/BVQKMeEvdSU4J84twhAJQdrdLutSIi\nMRRJ4dEfeLGJ9n8BQ1oXRyQ2RgzowrhTugIwd9lO7V4rIhIjkRQe24AJTbSfBpS0Lo5I7Fx99mDS\nUoPUhUI8/PIGQqGQ35FERBJeJLvT/hb4lXOuM94CYiFgGvBdvMXFRNqFgrwsLp7cn6fnbeHd7aUs\nXr+HicO6+x1LRCShRVJ43Id3uuXnDV5fg1eQfD9KuURi4sKJ/XhrdRF7Syt47NWNjB7chcz0SH4s\nRESkOSI51TIV+BpQgLdh3GSgwMy+aGbac1zalbTUFK455xQASo9U8exbW/0NJCKS4CL5r90/gAvM\nbDmwJMp5RGJuzOACRg3qwjvhfVzOGNWTnl2y/Y4lIpKQIhnx2AtonWlJKNeeM4TUlAC1dSH+pomm\nIiJtJpIRjxeA551zLwAbgfKGT5rZd6MRTCSWuuV34MKJ/Xl2wVbWbT3IwrXFTBnR0+9YIiIJJ5LC\n40q8y2bHh28NhfCubhFpd2ZN6c/i9SWUHCzn0bnvMWJgF3I7pPsdS0QkobS48DCzAW0RRMRvaakp\nfPqCU7nnkRUcKa/msbkbufmS4X7HEhFJKM0uPJxzfYAr8E6tvGhmu9oslYhPTu2fz7RRPZn3ThEL\n15YweXgPRgzs4ncsEZGE0azJpc65M4B38dbw+B2wzjl3XlsGE/HLnLMGk5vtnWJ58N9GZVWtz4lE\nRBJHc69q+R4wF+gN9AD+Dfy0rUKJ+Ck7M41rz/G2Hdp3qIKn52/2OZGISOJobuExDviGmRWZ2R68\nHWqHOuc6tl00Ef+cdmo3Rg/yTrG8tGQHW4vLfE4kIpIYmlt45AD76x+E53dUAZ3bIpSI3wKBANed\n78hITyEUggdefJfaOi3MKyLSWs0tPAJ4l8o2VAOkRDeOSPzonJvJ7DMHArC95Aj/XrzD50QiIu1f\nJCuXiiSNs8f1YWCvXACenreZnXuP+JxIRKR9a8k6Hl9xzh1t8DgN+KJz7kDDg7RyqSSSYDDAjRcN\n5b8fWEJ1TR1/eHYdd316AqkpqtlFRCLR3MJjOzCnUVsRcFmjNq1cKgmnV0E2V04fxCNzN7J9zxH+\n+dYWZp85yO9YIiLtUrMKDzMrbOMcInHt7Al9WLFxL+9uL+X5hdsYPaiAQb21V6KISEtpvFikGYKB\nADddPIysDO8qlz88t04Li4mIRECFh0gzdcnL5NpzTgGg5GA5T7z+ns+JRETaHxUeIi0wZUQPxg4p\nAODV5btYs2X/R7xCREQaUuEh0gKBQIBPX3AqHTukAfDnF97laEW1z6lERNoPFR4iLZSbnc6nzj8V\ngIOHK3nghXcJhRqvryciIk1pyToebcY5lwH8GpgNHAN+YmZNbkLnnLsY+B9gMLAJuNvMno1VVhGA\n8a4rZ4zqyfx3ili2YS+vLt/F2eP7+B1LRCTuxcuIx714G9HNAG4Fvu2cm934IOfcSOAfwB+A0cDv\ngL+H20Vi6hPnnkLvgmwAHnt1ozaSExFpBt8LD+dcB+Am4ItmtsrMngHuAW5r4vBrgblm9isz22xm\nvwZe48OLm4m0uYy0FD57+Qj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/K3YBBc65hll6AOVm1vhauF3h52h0bFEb5hMREZEoiYfC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\nzKw0TjzMzMysNP8DtdqHlgRPsTwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12af93ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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et1zYm/OP6xx4tFPE4+8s5JXP9GhHRORwCKpNm3PuIedcG6A50AjoXHEyrEhd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\niIiIxAwVJiIiIhIz/j+Iip8S98zvegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1259e9cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XEBfFTZdMIik+Gi/wt4Xr2bGvxulYIiLSC1R4SEgYOCCe786dSJTHRVNLG396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\naafnqyVtyK75NPBbYBzwQeAySYsj4iLgq8BhwGr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ae54630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MgTHmtlZ85jhjzOYWju8zxtQbYxoCX/XGmORQ5pUjY9Sgbnzn/DF4PR4O1NRx\n/3Mr2aV9dUREolpcK8/bCrxijFmJs6DaX4wxVS2daK29IYgcfwAmADOAQcBsY8zWQw2mNcaMBV4A\nqpod74uz2Nvgpu9Za/W3WYQ6algPvnn2SP4xZx3lB2r5w7Mr+PlVE+meluh2NBER6QCtbUyuBhpX\nf/UDA4CDoQgQuJsxCzjNWrsKWGWMuQ+4BfhKY2KM+TbOzsab+fIuTqORQKG1Nj8U2SQ8TBvdm+qD\n9cx+27K3vIY/PLuCn109kbSUTm5HExGREGtVY2KtXQZcCGCMyQPOtdbuCVGG7ECOhU2OzQfuOMT5\npwHXAF2Bu5q9NwrYGKJcEkZmHJVJVU0dL3y0maLSKv747Ep+etVRpCTGux1NRERCqLV3TL5grc0C\nMMaMAMYCtcA6a22wDUEfoMRa23Rr2SIg0RjTvXkDZK1tbJC+0cK1RgIpxpgPAQOsAH5grd0UZDYJ\nI2dMG8iBmjrmLsxnR/F+/vT8Kn50+XgSO7X5t7GIiISpNv+JboxJAJ4Bzm9y2G+MmQNcZq2taeMl\nk4Hmn2l8ndDGa40A0oGfARWB/75vjBlpra1s7UV8vuieRd1YXyTWeelJQ6mpree9pTvYsrOcB15c\nzY8uP4qETr6vnBvJdbaF6owusVInxE6tsVZnewXzT83fAVNwGpOPcWb2nAA8CPwK+Hkbr1fNVxuQ\nxtdtHbR6GhDfONjVGHMVsB04B3i2tRdJTU1q4y8bmSK1zu9dNoEGPHywdDt22z4efHkNd86aesg7\nJ5FaZ1upzugSK3VC7NQaK3W2VzCNyRXAt6y1bzQ59poxph54iLY3JgVAhjHGa61tCBzrDVRZa/e1\n5ULW2lqcR0uNr2sCY2Iy23Kd8vIq6usbDn9ihPL5vKSmJkV0ndfOHE51dS0LcnaxOreEux5ZyG2X\nZdMp/ss7J9FQZ2uozugSK3VC7NQaa3W2VzCNSRdgQwvHLdAjiOutxGkmpvHl7sTHA0vaeiFjTC5w\nt7V2duB1CjDsEHkPqb6+gbq66P3N0yjS67z+zBHU1Tfw+frdrNu6lz89t5JbLx5HfNx/P9aJ9Dpb\nS3VGl1ipE2Kn1lips72CeSCUQ8u7CF+K05y0ibW2CpgNPGyMmWSMOR+4HXgAwBjTyxjT2kUr5gK/\nNsZMN8aMBp4EtgFvtjWXhD+f18uN54xi8oieAKzdWsqDL6+htq7e5WQiIhKsYO6Y/Abn0c144DOc\ndU2Ox5lUnmFwAAAgAElEQVROfEWQOW7DeQz0AVAG3GmtfTXwXiFwHU7zcjg/xllf5WmcNU7eB86y\n1vqDzCVhrrE5afD7WWaLydmyl7+9ksPNF4wlLi66B5qJiEQjj9/f9r+zA3c1foYzXdgDrAbuO9RK\nrRHGX1paGdW32+LivKSnpxBNddbVN/Dwa2tZvrEYgPFDM7j1knH0yOgSVXW2JBp/ni1RndEnVmqN\nsTrbvZdeUAtABO5mvHrYE0WOkDifl5vOG81Dr+SwMreElbkl/PWlNfxy1jS3o4mISBvoXrdEjTif\nl++cP4ZxQ7oDsHxjMf/31FLqongUvIhItFFjIlElPs7LzReMZexgpzlZuKaQB19crQGxIiIRQo2J\nRJ34OC+3XPjlnZMVm0r4y0trqKlVcyIiEu7a3JgYY9o9sEWko8XH+fj+JdlMG9MbgLV5e/nz86uo\nqqk7zCdFRMRNwdwxKTDG3GuMMSFPIxJC8XFefnrtZKaN6gWA3b6P+59fyYHq2sN8UkRE3BJMY3I3\nzt44640xC40x3zLGpIY4l0hIxPm83HT+GI4d69w52VxQzv89s5L9VWpORETCUZsbE2vtw9baYwGD\ns4DZz4BCY8zTxphTQh1QpL28Xg/XnzmSGUc5WyblF1Xw+/8sp6zyoMvJRESkuaAHv1prN1lrf4nT\noNyDs9vw28aYrcaY24wxX92HXsQlXo+Ha2YO59RJ/QEoKK7k908vp7SixuVkIiLSVNCNiTFmqjHm\nbzhLxv8SeBmYCfwK+D7wVCgCioSKx+Ph8pOHctbRAwHYtfcA//v0Mkr2VbmcTEREGrV55VdjzC+B\na4ChwFLgF8Az1tryJuccBB4NVUiRUPF4PFw0fQid4ry88mkexfuquffp5dx+2Xj6ZqS4HU9EJOYF\nsyT993DuhvzTWrvuEOesB34SdCqRDnbOsVnEx/l4/sNcSitquPepZfzg0myG9E1zO5qISEwL5lHO\nL4A7mjclxpgUY8wPAay1K6y1D4UioEhHOX3qAK47YwQeD1RW1/GHZ1aSk7fH7VgiIjGtVXdMjDEZ\nQHLg5SPAcmNMSbPTjgJ+B/wpdPFEOtYJ2X1JSYznkdfXUlNbzwMvrObGc0YxZWQvt6OJiMSk1t4x\nORPYCuQBHmBJ4PumX6/gTB8WiSgTTQ9uuzSbxE4+6hv8PPLaWt5ftsPtWCIiMalVjYm1djYwAzg5\ncOhi4KQmXycCk4ALQh9RpOONGJjOT6+cQGpyPH7g6Xc38uqnW/D7/W5HExGJKa0e/Gqt/QTAGHMi\n8Jm1VpuOSFQZ2LsLP796In98biUlZdW8/tlWKqpqueqU4Xi92iJKRORIaO0Yk/8B/mCtPQBMB6Yf\naqsca+3doYsncmT16pbMz6+eyP3Pr6SguJIPlxdQWVXLN88eRZxPm3GLiHS01t4xuR74G3Ag8P2h\n+HH20hGJWOldEvjZVRN44MXV5O4o4/P1u6k4UMvNF4whOTHe7XgiIlGtVY2JtTarpe9FolVKYjy3\nXzaev7+aw+rNe1ifX8q9Ty3nB5dk0z0t0e14IiJRS/emRQ4hId7H9y4ay/TxfQEoKKnkN7OXkr+r\nwuVkIiLRq7VjTBpwHtMclrVWm/dJ1PB5vVx7miEjLZGXPt5CWeVB/vfp5Xzn/NGMG5LhdjwRkajT\n2jEmN9DKxkQk2ng8Hs46ehDd0xJ5fO56amrr+cuLa7h65nBmHJXpdjwRkajS2jEmT3RwDpGwN21U\nb9I7J/DXl9dQWV3H7LctxWVVXDR9CF6PphOLiIRCax/lPA5831pbEfj+UPzW2lmhiSYSfsyAdO64\nZiJ/en4VJWXVzFu0jT1l1cw6axTxcRqyJSLSXq39kzQL8DX5/lBfg0MdUCTc9Omewi+unURWny4A\nfL5+N398dgX7q2pdTiYiEvk8WnL7K/ylpZXU1TW4naPDxMV5SU9PQXW2T83Beh6ds5YVm5z9LHt0\nTeTWi8aR2aNzyH+tr6OfZ3SJlTohdmqNsTrb/Vy71UvSN2WMSQSuBMYANcAa4HktUy+xJKGTj5sv\nGMtzH+Ty7tLtFO+r5rdPLuNb545m/FDN2BERCUabH4obY8YBW4AHcJanPwN4DFhjjBkU0nQiYc7r\n9XDFKcO47owR+Lweqg/W8+CLq5m3OF8bAIqIBCGY0XoPAEuBftbaidba8cBAYCfwYCjDiUSKE7L7\n8qPLx9M5ydmd+IUPN/PYG+uprat3O5qISEQJpjGZBvzMWlvWeMBaWwzcDpwUqmAikcYMSOfOb0wi\ns0cKAAvX7uK+/6ygbH+Ny8lERCJHMI1JAdC3hePpQEn74ohEth5dk7jj6olfjDHZvLOcu/+tZexF\nRFqrteuYDGjy8gHgMWPMzcBnQD0wEXgE+GXIE4pEmKSEOG65aCyvfLKFuQvzKa2o4d6nljHr7FFM\nHtHT7XgiImGttbNytvLfS9J7gDktHHsCeDIUwUQimdfj4aLpQ+ibkcK/3tzAwboG/v5qDlunDuDC\n6YPxebUYm4hIS1rbmJzYoSlEotTRo3vTKz2ZB19eTdn+g8xbvI2tuyr49nmjSU3u5HY8EZGw09q9\ncj7u6CAi0Wpw31R+dd1k/v5qDht3lLE+v5Rf/2sJN18wlsF9U92OJyISVtq8wFpgcbVvAWP5cpl6\nD5AATLLWDg9dPJHokNY5gR9dcRQvfLiZd5dup7Sihv99ehlXnjqc6dl98WgTQBERILhZOX8B7sNZ\n9fVaYChwGnAZ8HrooolElziflytOGca3zx1Np3gvdfV+Zr9l+de8DVrvREQkIJjG5Dzgemvt0TiD\nYm/EWWDtNUAPzUUOY+qoXvzy2kn0Sk8CYP7qQn731HJKyqpcTiYi4r5gGpN0nGnCAGuBCdbaWuB3\nwNmhCiYSzfr16Myd35j8xXon+bsq+PW/lpCzZY/LyURE3BVMY7IbaFyMYRPOWBNwFlfrHUwIY0yC\nMeafxphSY0yBMea2VnzmOGPM5haOX2GMyTXG7DfGvGyM6R5MJpGOlpzorHdy4QmD8QCV1XX86flV\nvPTxZuoboncHUhGRrxNMYzIPeMgYMxr4FLjSGDMJuBnYHmSOPwATgBnAd4G7jDEXHupkY8xY4AWc\nQbdNj0/B2VDwLpyl89Nx1lYRCUtej4ezjxnEDy/L/mKfnbkL8/n9f1awt7za7XgiIkdcMI3Jj3E2\n7JuOM9h1HfA5cCtOQ9AmxphkYBZwq7V2lbX2NZzBtbcc4vxv4zxK2tXC2zcDz1lrn7bW5gDXAGca\nYwa2NZfIkTQmqzu/vmEKw/t3BSB3Rxl3Pf45KzdplwcRiS1tbkystfustedbax+y1vqBs3CWpB9k\nrX02iAzZONOWFzY5Nh+YeojzT8NpOP7cwnvTgE+aZN0BbAscFwlr6V0S+PEV4znnmEFfPNr5y0ur\nefb9TdTV69GOiMSGNq9jAl+sZXIlzpThGmBN4CsYfYASa21dk2NFQKIxpru19r9GA1prLwxk+MYh\nrrWz2bEioF+Q2USOKJ/XywUnDMYM6Mqjc9ZRXnmQd5ZsZ9OOfdx03hh6dE1yO6KISIcKZoG1ccBb\nQBdgI84ia98H7jTGnGGt3drGSybjNDdNNb5OCNG12nQdny+69zFprE91hq9xQzP47Y1Tefi1tazN\n20teoTNr54azRzJlZK//OjeS62wL1Rl9YqXWWKuzvYK5Y/IAsBS4xlpbBmCM6QE8CzwInNPG61Xz\n1cah8fWBEF2rTddJTY2Nf5WqzvCWnp7Cb797HC9+sJH/vLWBAzV1/PWlNZw2rZxvnjuGxIT//r9v\npNbZVqoz+sRKrbFSZ3sF05hMAyY2NiUA1tpiY8ztfLm+SVsUABnGGK+1tvFBem+gylq7L4hrNZ+y\n3BsobMtFysurqI/iZ/o+n5fU1CTVGSFmTuzHwB4pPPRKDqUVNby9KJ+VG4u56bzRDMlMi5o6D0d1\nRp9YqTXW6myvYBqTAqAvzmycptJx1jJpq5VALU7DsyBw7HhgSRDXWgQcB8wGMMb0xxlfsqgtF6mv\nb6CuLnp/8zRSnZFjSN80fn3DFP49bwPLNhZTtPcA9zyxlHOPG8R5x2cB0VFna6jO6BMrtcZKne3V\nqsbEGDOgycsHgMeMMTfj3CGpx5mV8wjwy7YGsNZWGWNmAw8bY27AaSRuB64L/Nq9gDJrbWsWdfg7\n8KExZhHO46Y/A3OstfltzSUSbjonxfPdC8Ywf00h/3lvEzUH63n10zxy8vbyk2smk+g7/DVERMJd\na0eqbAXyAl9/BgYAc4A9wD7gfZzN/J4IMsdtwDLgA5xxKndaa18NvFcIXNqai1hrFwHfxllPZX4g\n3w1BZhIJOx6Ph+PH9eXX109mSGYq4Kx58v37P+STlTvx+/0uJxQRaR9Pa/4gM8ZMb+0FrbUftyuR\n+/ylpZVRfbstLs5LenoKqjOy1Tc0MHdhPq/P30pD4P/HE4f34BtnjKBzUrzL6UIv2n+ejWKlToid\nWmOsTs/hzzzMdVpz0qGajcBsnNogBqmKSDv5vF7OPTaL7KEZ/GPOOnaWVLJsYzG5O8v4xukjvtgg\nUEQkkgQ16dgYc6sxphBnWfg9gY33fhDaaCLSGkMy03jgthmcOCETgLL9B/nLi6v5x5x1VFbXupxO\nRKRt2tyYBPaquQ94BrgQuBh4Hrg3MHhVRI6wxIQ4rj9zJN+/eBxpnTsBsHDtLn752GLttyMiESWY\n6cI/BH5krf1rk2OvGGNygR8Aj4ckmYi0WfbQDH7zzak8+94mPsvZ5dw9eWk1R4/uxRWnDI/KsSci\nEl2CeZQzEJjXwvG3gGHtiyMi7ZWSGM+ss0fx/YvH0fWLuydF3PnYYlZsKnY5nYjI1wumMckHJrVw\nfDLOhnkiEgYa754cO9ZZDLms8iAPvrSGR+esZX+Vxp6ISHgK5lHOI8DfjDHdcBZY8+Os1Ho3zuJr\nIhImkhPjmXXWKCaP6Mm/37KUVtSwaG0R6/L2cvnJw5g6qhceT7tn94mIhEywm/gNxFlorfHzdTgN\ny29DlEtEQmjckAzumZXGsx/kMn91IeUHanl0zjo+y9nFNacZenbV5mIiEh6CeZRzLPBjIANnf5uj\ngQxr7a1NNuETkTCTnBjPDWeO5PbLxtOjayIAa/P28j+PLebNRfnURfHmYiISOYK5Y/IScLq1djnB\nbbQnIi4andWNe2ZNZc6Crby1eBsH6xp48aPNLFpbxDfOMAzpm+Z2RBGJYcHcMSkG9CeXSATrFO/j\noulDuOu6L/fc2VG8n9/NXsZT71iqaupcTigisSqYOyZvAnONMW8Cm4Cqpm9aa+8ORTAR6Xj9enbm\n51dP5OMVBbz48Waqaur5YHkByzcWc/nJw5g8oqcGx4rIERVMY3IxzrTgiYGvpvw4s3NEJEJ4PR5O\nnNCP8cN68Mx7G1lqi9m3/yAPv7aWj1fu5MpTh5OZkeJ2TBGJEW1uTKy1WR0RRETcld4lge9eMJaV\nuSX8592NlJRVsz6/lF89/jknT+zHecdlkZQQzL9lRERar9V/yhhj+gEX4Dy6mWetLeiwVCLimvFD\nMxg1MJ15i7fx5qJ8ausaeGfJdhavK+KSE4dw9OjeerwjIh2mVYNfjTHHARtw1jB5FFhnjJnZkcFE\nxD2d4n2cd1wWv/nmVI4algE4K8c+9sZ67n16OduKKlxOKCLRqrWzcu4B3gcygd7A28D9HRVKRMJD\nj65JfO+icfzw0mx6pTuLsOXuKOPXTyzhyXeslrYXkZBrbWMyAfi5tbbQWrsbZ4fhkcaYLh0XTUTC\nxdjB3bl71lQumj6YTvFe/H74cHkBP3t4IW8t3kZtnRZnE5HQaG1j0hnY0/giML7kINCtI0KJSPiJ\nj/Ny1tGD+N2N05gysicAB2rqeP7DXH752CKWbtiN3+93OaWIRLrWNiYenKnATdUBvtDGEZFw1y01\nkZvOG8MdV09kSF9ncbbifdU89GoO9z69nM07y1xOKCKRLJiVX0VEGNovjTuumchN540mI83Zeyd3\nRxm/nb2MR15fS0lZ1WGuICLyVW1ZlOB2Y0xlk9fxwK3GmL1NT9LKryKxw+PxMGVkL44alsF7y3bw\nxoKtVNXUs3hdEctsMadO7seZ0waSkhjvdlQRiRCtbUy2AZc2O1YInNfsmFZ+FYlB8XE+zpg6kGPH\n9uH1+Xl8tGIndfUNzFu0jY9X7OSMaQM4ZVJ/EuL19FdEvl6rGhNr7aAOziEiUSA1uRNXzzScNKEf\nL360mZW5JRyoqeOlj7fw3tIdnHPsIE7I7kucT0+RRaRl+tNBREKub0YKt148jjuunsjw/l0BZ4G2\np97ZyC/+sYiFa3fRoBk8ItICNSYi0mGG9kvjp1cexQ8vzWZAr86AM4PnH3PW8avHP2dlbommGIvI\nf9GOXCLSoTweD2MHd2d0VjeWbtjNK59soai0ih3FlfzlxdUM6ZvKecdlMTqrm/bgERE1JiJyZHgD\nM3gmDO/BZ2sKef2zrZRW1LB5Zzn3P7+KwYEGZYwaFJGYpsZERI6oOJ+X6eMzOXp0bz5aUcC8xdso\nqzzIlp3l/On5VWT1SeW84wYxdnB3NSgiMUiNiYi4olO8j5lTBjDjqEw+XrmTNxflU1Z5kLzCcv78\nwmqy+nTh3GOzGDdEDYpILFFjIiKu6hTv49TJ/Zk+vi8frwo0KPsPkldYwQMvrmZQ7y6cfcwgJgX2\n5xGR6KbGRETCQqd4H6dO6s/07L58smoncwMNytZdFfz15TX06Z7MpacYsgenux1VRDqQGhMRCSud\n4n2cMilwB2XlTuYt3kZpRQ2Few7wwHMr6JaawGmTB3BCdl8SOmklWZFoo8ZERMJSfJzToMw4KpPF\n64qYt3gbO0sq2VtewzPvb+L1z/I4ZVJ/Tp7Yj85J2otHJFqoMRGRsBbn83Ls2D4cP74vGwsqePad\nDWzZWU5ldR2vzc9j3uJ8pmdncuqkfmR0TXI7roi0kxoTEYkIXo+Ho8f2wWR2Ye2Wvby5KJ+cvL0c\nrG3g3aXbeW/ZdiYM78HMyf0ZmpmmmTwiEUqNiYhEFI/Hw4iB6YwYmE7+rgreXJTPUrsbvx+W2WKW\n2WKy+nRh5uQBTDQ9tGGgSIRRYyIiEWtg7y585/wxlJRV8f6yHXyyaidVNfXkFVbwyOtrSe+SwCkT\n+3HC+L6kJGocikgkUGMiIhEvIy2Jy04axrnHZjF/TSHvLd1O8b5qSitqeOGjzbz2WR7HjunDSRMy\nyezR2e24IvI1wqIxMcYkAA8BFwIHgD9aa+8/xLlHAX8HxgI5wHestcubvL8P6AI0PmD2A12stQc6\nrgIRCQdJCXGcOqk/J0/ox8rcEt75fBsbd5RxsLaBD1cU8OGKAkz/rpw4IZMJw/WYRyQchUVjAvwB\nmADMAAYBs40xW621Lzc9yRiTDMwFngS+AXwHmGuMGWytrTLG9MVpSgYDVY2fU1MiElu8Xg8Thvdg\nwvAebN1VzrtLtrNkw27q6v3Y7fuw2/eRltKJ6eP7Mn18JuldEtyOLCIBrjcmgWZjFnCatXYVsMoY\ncx9wC/Bys9MvBw5Ya38aeP0DY8yZwCXAbGAkUGitzT8y6UUk3A3qncqN54zmspOH8emqnXy0Yid7\nyqspqzzI659t5Y0F+Rw1PIOTJvRjxICums0j4jLXGxMgGyfHwibH5gN3tHDu1MB7TX0GHI3TmIwC\nNnZARhGJcKnJnTjr6EGcMXUgqzfv4YMVO8jZspcGv/+L2Ty9uiVzwrg+HDO2D2kpndyOLBKTwqEx\n6QOUWGvrmhwrAhKNMd2ttXuanZvT7PNFwOjA9yOBFGPMh4ABVgA/sNZu6pjoIhJpvF4P44dlMH5Y\nBkWlB/hoRQHzVxdSWV1H0d4DvPDRZl7+ZAvZQzM4IbsPY7K64/XqLorIkRIOjUkyUNPsWOPr5g9+\nD3Vu43kjgHTgZ0BF4L/vG2NGWmsrQ5ZYRKJCr/RkLjtpGBccP5glG3bz6aqdbNxRRn2Dn+Ubi1m+\nsZj0LgkcN7YPx4/ro5VlRY6AcGhMqvlqA9L4uvmg1UOd23jeaUB842BXY8xVwHbgHODZ1gbyRflI\n/cb6VGd0UJ3tFxfnZfpRmUw/KpOdJZV8vHIn81fvpOJALaUVNcxZsJU3FmxldFY3jsvuy0TTg4T4\njtlAMFZ+nhA7tcZane0VDo1JAZBhjPFaaxsCx3oDVdbafS2c27vZsd5AIYC1thaobXzDWltjjMkD\nMtsSKDU1Nv5VpDqji+oMjfT0FEYP68mNF4zj83W7eGdxPisCK8vm5O0lJ28vSQk+jh2XyUmT+jN6\ncMc86omVnyfETq2xUmd7hUNjshKnmZgGLAgcOx5Y0sK5i4CfNjt2DPAbAGNMLnC3tXZ24HUKMAzY\n0JZA5eVV1Nc3HP7ECOXzeUlNTVKdUUJ1dpxR/dMY1X8cJWXVfLLSGYtSUlZNVU097y3ZxntLtpGR\nlsgxY/tw7Nje9Ome0u5fM1Z+nhA7tcZane3lemMSWH9kNvCwMeYGoB9wO3AdgDGmF1Bmra0GXgTu\nNcb8CXgUuAlIAV4IXG4u8GtjTD5QAtwDbAPebEum+voG6uqi9zdPI9UZXVRnx+ma0olzj83i7GMG\nkbujjAU5hSzZsJuqmnpKyqp5fX4er8/PY0jfVI4e05tJI3qSmty+WT2x8vOE2Kk1VupsL9cbk4Db\ncFZ+/QAoA+601r4aeK8Qp0mZba2tMMacDTwCfAtYDZxhrW1cTO3HwEHgaSANeB84y1rrP1KFiEj0\n8no8DO/fleH9u3LlKcNZmVvCZ2t2kZO3B78fNu8sZ/POcv7z7iZGDkpn6sheTBieQbL26RFpNY/f\nr7+zm/GXllZGdVcbF+clPT0F1RkdVKf7yvbXsGhdEQtydrF99/7/ei/O52Hs4O5MHdWL7KEZhx00\nG851hlqs1BpjdbZ7wFW43DEREYlYaZ0TOG3KAE6bMoCCkko+X1fE4vVF7C6toq7ez4pNJazYVEJC\nvI/xwzKYPKInY7K60amDZvaIRDI1JiIiIZSZkcIFJwzm/OOzyC+qYPG6Ij5fv5vSihpqautZvK6I\nxeuKSIj3MW5IdyaaHowb0p3ETvrjWATUmIiIdAiPx8Og3qkM6p3KJScOJXdHGYvXF7F0w24qDtRS\nU1vPkg27WbJhN/FxXsZkdWOi6cHEET1Jdzu8iIs0xuSrNMYkSqjO6BItdTY0+Nm4fZ+zP8/G3ezb\nf/C/3vd5PWQP78G4rG6MyepGt9REl5J2vGj5mR5OjNWpMSYiIpHE6/UwYmA6Iwamc8Wpw9iys5xl\ndjfLbDElZdXOcvgbdrN8w24ABvbqQvbQ7owflsHAXl20+7FEPTUmIiIu8Xo8DM1MY2hmGpeeOJRt\nRftZvqmYlbklbC9yZvfkF1WQX1TB659tJb1LAtlDupM9NIORA9M1eFaikhoTEZEw4PF4GNi7C0P6\npfGtC7PZsLmYZbaYVbklbNy+j/oGP6UVNXy0cicfrdxJp3gvowd1I3toBtlDupPWufk2YiKRSY2J\niEgY6tUtmZmT+zNzcn8OVNeyZsteVuWWsHrzHg7U1HGwtuGLacgAA3p2ZvTgbowZ1I2h/boSHxfd\nG8ZJ9FJjIiIS5pIT45k6qhdTR/Wirr6B3B1lrMwtYVVuCUWlzsLX23bvZ9vu/cxbtI2EeB9mQFfG\nZHVjzODu9EpP0tgUiRhqTEREIkicz/vF4NnLTx5G4Z5K1mzZS07eHjZu28fBugZqautZvXkPqzfv\nATaRkZbImKxujM7qzsiB6SQn6o9+CV/63SkiEsH6dE+hT/cUZk7uT21dPRu3l5GTt4ecvL0UFFcC\nUFJW/cXYFK/HQ1bfLowYkM7IgekMzUzTIFoJK2pMRESiRHycj9FZ3Rid1Y3LgNKKGnLy9rA2by9r\n8/ZSWV1Hg9/P5oJyNheUM3dhPnE+D4P7pjFyYDojBnRlcN80jU8RV6kxERGJUuldEjh+XF+OH9eX\nhgY/+UUV5OTtZUN+KbkFZdTWNVBX7yz4tnH7Pl77//buPLrOus7j+PsmafalWZqmabrQhS9VoYBH\nqbKD67gxPS4I4hEQR5ZxEI4yjiwKImMpOoqCOo5wGBmO4oyAOjoODC4VeqQijAzwtXRP06RpkiZp\n9uXOH78n6W2Wkra5vTe3n9c5Oc19nuc++f16k5tPfiuQm5PF0vlRUFlUzuKaEnKyFVTk6FEwERE5\nBmRlxThuXinHzSvlPW9ezMDgEJsbOnhpWxsvb2tjU0MHQ8Nx+geHeWlbGy9tawMgb1Y2S+eXsmx+\nGcsXzGZpban29ZGk0neXiMgxaFZONrawHFtYDmdC38AQr+xs5+UoqGzZ1clwPE7fwBAvbm3jxa0h\nqGTFYiyYW8zyujKOr5vNsroyZmsNFZlGCiYiIkLerGxeu7iC1y6uAKCnb5CN9e34jjZeqW9ny64O\nBofiDMfjbGvsZFtjJ49vqAdgzux8ltfNZnldGUvnl1FbWURWlqYny+FRMBERkXEK8nI4aWklJy2t\nBGBgcIitjZ1srG9n4469vLKzna7eQQCa9/bSvLeRp15oBCA/N5vFNSUsqS1jSW0pS2pL1aoiU6Zg\nIiIir2pWTnbUKjIbVi1iOB5nV0s3G+v3snFHOxvr97KnvReA3v4hXt6+l5e37x19fkVpHkvmlY6G\nlUU1JeRo9o9MQMFEREQOWVYsxvyqIuZXFXHOyfOBMD15c0M7mxs62NzQwdbGTvoGhgBo7eijtaOZ\nDd48+vy66iJOWFxJbUUBdXOKqZtTpDVVRMFERESmR3lJHq+3al5v1QAMD8fZuaeLzQ1hjMrmhg52\n7ukiHofheJztTfvYHu2iDCGs1FYVsaimmEVzS1hcU8qC6mLychVWjiUKJiIikhRZWTEWVBezoLqY\ns6NWlZ6+QbY1drJ5VwdbGzvYvruL3a3dQAgr9c37qG/ex+//HMarxGJQU1HI4poSFs4tYUF1MXXV\nxQquaTYAABDvSURBVJQW5qasXpJcCiYiInLUFOTljO71k5OTRXl5Edt3trG5oWN0ts+2pk52R5sT\nxuOwq6WbXS3dPP1/TaP3KSvKpa66mAVzikfDyrzKQi0GlwEUTEREJKVKCnMPmKoM0N07wPamfWxt\n7GR7UydbGztpau0mHp1v7+qnPVpqf0R2Vox5lYWjgWX+nGJqqwqpKM0nS7srzxgKJiIiknYK82eN\ntqyM6OsfYueeLuqb97Fj9z7qd4d/u/vCtOWh4Tj1zV3UN3exnv2tK7mzsphXWURtZRG1VYXUVhVR\nW1XEnLICrbeShhRMRERkRsjLzR5dF2VEPB6nrbMvBJUosOzYvY/G1m7iUfNK/8DwaDdRopzsLOZV\nRkGlsjCEl6oiqssL1CWUQgomIiIyY8ViMSpK86kozWflsqrR4wODQzS29tCwp4uGPV3saumioaWb\nptZuhoZDYhkcGh4NMomys2JUzS5gbnkBNRWFzC0vYG5FIXPLCykvzVO3UJIpmIiISMaZlZM9OiMo\n0eDQMLvbosDSMhJawuDawaFhIHQJNbWGEPO/m1rG3DeL6vICasoLo7AShZaKQkoLZxFTaDliCiYi\nInLMyMnOGh1jkmh4OE5ze89oUGlq7aaprYem1m7au/pHrxsYHGZncxc7m7vG3bsgL5s5swv2f5Tl\nM2d2ATVVRRSXFCS9bplCwURERI55WVkx5paH7ppTlh94rqdvkN1tPTS2dtPUdmBoGdkvKFw3NG7R\nuBGxWFiAbk5ZAVWz86PgMhJi8iktylVrS0TBRERE5CAK8nJYVFPCopqScef29QyEwBKFlrChYQ97\n9vbQ0T0wel08PrIsfx++Y/zXmJWTRUVpPlWleVSU5lMZjZupLM2joiyfipI8ZuUcGyvgKpiIiIgc\npuKCWSybX8ay+WXjzvX2D7KnvZfWzj729Q2xvaGdptZumtt72bO3h/7B4dFrBwaHR8e1TKa0KJfK\n0ryE0JIfDfzNo6Ikj5Ki3IwYmKtgIiIikgT5uTnUzSlm8bxSysuLaGvrYjAKI/F4nI6ufprb97ew\ntHT00drRS0v00T8wfMD9Orr66ejqZ8uuzom+HNlZMc45ZT4Xv/X4pNctmRRMREREjrJYLEZZcR5l\nxXkTtrbE43G6egf3B5X2Xlo7+mjp6B091r6vf3QlXAiziZ55qYmL3rJ8Ro9XUTARERFJM7FYjOKC\nWRQXzGLh3PFjWyBMfW7t7KOto5e2zj7au/o5YWH5jA4loGAiIiIyI+VkZ1E9u4Dq2Zk1FVlr7oqI\niEjaUDARERGRtKFgIiIiImlDwURERETShoKJiIiIpI20mJVjZnnAPcBqoBu4y92/Osm1pwD3AicC\nLwBXuvuzCec/DNwG1AC/Aq5w95aJ7iUiIiLpJV1aTNYCpwLnAFcBt5jZ6rEXmVkh8HPgN9H1TwM/\nN7OC6Pwbge8BtwCrgHLg/uQXX0RERKZDyoNJFDYuBz7l7s+7+6PAGuCaCS6/EOh29xs8uBboBD4Q\nnb8a+KG7P+juLwCXAH9lZouSXxMRERE5UikPJsBKQpfS0wnH1gGnTXDtadG5RL8H3hR9vgr47cgJ\nd68HtkfHRUREJM2lQzCZB+xx98GEY01AvplVTnBtw5hjTUDdFM+LiIhIGkuHwa+FQN+YYyOP86Z4\nbd4Uz09JdnY65LXkGamf6pkZVM/McqzUE46duh5r9TxS6RBMehkfHEYed0/x2u4pnp+KWGlpZu07\nMBnVM7OonpnlWKknHDt1PVbqeaTSIb7tBKrMLLEsNUCPu++d4NqaMcdqgF1TPC8iIiJpLB2CyXPA\nAAcOUD0TeGaCa9cDbx5z7M3sHzi7Hjhj5ISZLSCML1k/XYUVERGR5InF4/FUlwEzuxc4HbiMECTu\nBz7m7o+Y2Vyg3d17zawE2Ag8BHwX+CTwfmCZu/eY2SrgScK04Q3AP0XP/eujXScRERE5dOnQYgJw\nHfBH4H+Au4Gb3P2R6Nwu4IMA7t4JvBs4ixA83gi80917ovPrgb8hLLC2DmghhB0RERGZAdKixURE\nREQE0qfFRERERETBRERERNKHgomIiIikDQUTERERSRsKJiIiIpI20mFJ+rRgZnnAPcBqwhL2d7n7\nV1NbqullZrXAN4BzCXX8EfA5d+9PacGSyMx+DjS5e0ZOGzezXOBrwIcJ+0J9390/n9pSTT8zqwPu\nJSwV0AJ83d2/ntpSTZ/o/WcDcLW7/zY6thj4Z8Lu6VuBT7v7f6eqjNNhknquAu4CTgLqgbXu/i+p\nK+X0mKiuCedKgZcI778PpKJ802WS13QB8B3gbMKK7J9394enek+1mOy3FjgVOAe4CrjFzFantETT\n79+BfMJidhcC7wFuS2mJksjMLgTemepyJNk3gPOBtwIXAVeY2RWpLVJSPAx0En5GrwVuN7P3pbZI\n0yN6Y38IeM2YU48Qdkt/PfAD4CdRQJuRJqpntIDmfxLWsDoZ+AJwt5nN6J/bg7ymI9YwfvuUGWeS\n1zSb8Jr2El7TtcAPzGyy/4tx1GICmFkhcDnwdnd/HnjezNYA1wD/kdLCTRMzM8KCdHPdfU907Gbg\nTuCGVJYtGcysnPDD/4dUlyVZojpeBpzn7n+Mjq0FTiP8pZ0RzGw2oU6Xu/smYJOZ/ZIQyB5NaeGO\nkJmtAP5tguPnAUuAVe7eC/yjmZ1PeL1vPbqlPHKT1RO4ANjl7jdFjzeZ2bmEkP2Lo1W+6XSQuo6c\nPwM4D2g8aoVKgoPU813AfML3bhew0czeQdg+5sWp3FstJsFKQkh7OuHYOsKbYaZoJKySuyfhWAwo\nS1F5km0t8AChuTRTnQHsdfd1IwfcfY27fzyFZUqGHqALuNTMcqKQfTrwbGqLNS3OBp4gdNfEEo6f\nBjwbhZIR66LrZqLJ6vkL4NIJrp/J70uT1RUzm0Xo4rgKmOld6JPV82zgiSiUAODuq939e1O9sVpM\ngnnAHncfTDjWBOSbWaW7t6SoXNPG3duBX408NrMYoUXo8ZQVKkmivzbPBE4Evp3i4iTTEmCrmV0C\n/AOQC9wH3O7uGbOks7v3mdk1wDcJ3TjZwH3ufn9KCzYN3H30+zPkrVHzCN04iZoIe4nNOJPV0923\nA9sTzlUTuplvPprlm04HeU0BbiQEzscnODejHKSeS4AtZnYHcAnQDHzB3afcuqkWk6CQMHAw0cjj\nvKNclqPlTkL/X0YNlIz6PO8FrnL3sa9ppikGjgeuAD4GXA98Cvi7FJYpWVYAjxG6Iz8GvN/MPpzS\nEiXXZO9Jmfp+hJnlE8bBNRA2ac0o0RiLTwCfTnVZkqyY0Ao2m7C33b8CPzazU6d6A7WYBL2M/4Ef\nedx9lMuSdGb2FcIvsA+6e6Z1dXwB2ODuGdcSNIFBoAS4yN3rAcxsEXAlYWftjBCNrbgcqIvC5p+i\nQaA3EgbeZaJeoGLMsTwy8P0IwMyKCMFzGXD6mC6sTPFd4OYx3emZaJDQA3Fl9Pg5MzuTEMo+OZUb\nqMUk2AlUmVni/0cN0OPue1NUpqQws7sJif3ihB2cM8mHgAvMrNPMOoGLgY+YWUeKy5UMu4DekVAS\ncWBBisqTLKcCG8e0gP0JWJSi8hwNOxk/a6OG8JpnFDMrIXQzvwY41903p7hI087MFhIGf96V8N60\nEPh2tKRBJtkF/GXMsUN6X1IwCZ4DBoBVCcfOBJ5JTXGSw8xuIaTWDx3KnPIZ5mzC2JKV0cdjhJkb\nK1NZqCR5mjAOalnCsdcQ1rzIJA3AMjNLbOFdAWxJUXmOhvXAqVHX5IgzouMZIxrr9hNgMXCWu7+c\n2hIlTT2hNehk9r83NQA3AZk2WH098LrotR2xgkN4X1JXDuDuPWb2ACG9XkYYYHY9oS87I0RTu24E\nvgw8Fa0fAIC7N6WsYNPM3XckPo7+Mom7e8b9EnP3jdFfW/eb2VWEAZM3MAOnk76KnxKmfn/PzG4H\nTgA+F31kqt8AOwiv7W3Ae4E3kEHvSZGPE9aOeg/QkfC+1O/ubSkr1TRz92HggJYgMxsEmt0901rB\nHiIErnui5QveDryDMD5sStRist91wB8JC/3cDdyUYV0d7yW83jcSknoDoclt7Mh/mVkuBl4Bfgfc\nD9zt7t9KaYmmmbt3ENYsmUdYl+Yu4NZDmX44Q4zOpIp+kb2P0H2zgbCuxwVjuu1mqjj767qaMNX0\nZ+x/X2ogDILNBAebHZcxM+c48Hu3k7Dg4wrgz8DfEsYzPj/Vm8Xi8Uz6vxEREZGZTC0mIiIikjYU\nTERERCRtKJiIiIhI2lAwERERkbShYCIiIiJpQ8FERERE0oaCiYiIiKQNBRMRERFJGwomIiIikja0\nV46IYGb3AYvc/bxDeM67gE2p3njt1cphZr8GzhpzuB9oImzy+Bl3752uryciR0YtJiJyyKJt3H8K\nVM+AcsSBHwJzCXvP1BB2Yb6DsNv22mn+eiJyBNRiIiKHI4v02IRsquXocffmhMe7CbuJvwG4ELhm\nmr+eiBwmBRMRGcfMtgDfBN5E2La8D3gQ+DSwgLCFexx40sy+6O63mtkKQuvDWUAnYafu6929Kbrn\nk8BfgJXA8cDV7v6QmV0KfAZYDGwBvkPYJTkePe+jwGeBpUAL8HD0uHaichxiVfuAgYR6LwDuBM4F\nygndPQ+6+9+b2aLDqbeIHBp15YjIZG4FngROBK4ntCpcBGwH3kjYrn41sNbM5gG/BRw4FXgXUAo8\nZWYFCfe8HPgacAbwSzP7BLAGuIXQvXIjcAOhmwUzOwn4LnATsBy4FLiEEGTGlWOqFTOz7GisyEeA\nBxJOPQaUAOcTwtOdwGfN7L1HWG8RmSK1mIjIZP7L3b8Vfb7VzK4FTnf3H5jZSLdIm7t3m9nngB3u\nft3Ik83sQqAZ+AD7f/k/5+4/TLjmRuA2d3844euUAfeY2c3AccAwsM3d64F6M3sb0OHu8bHlOEhd\nPmJmH0h4XABsBb7C/hCUH5XzR+6+M7ruG1HdTnT3x46g3iIyRQomIjKZl8Y83gvkTnLtKcDrzKxz\nzPE84ISExxtHPjGzKqAOuMPMbk+4Jiv6OscBvwSeAjZE3Uu/Ah5192cPsS6PErp/sgitHl8HHgfu\ncPdhAHfvNbNvAe83s9OAZcBJhIGu2YdR7xWHWEYRQcFERCbXN8Gx2CTXZhHGVlw5wTV7Ez7vGfMc\ngGuBJya453Z3HwTeYmYrCWNd3gb8zMzud/ePv0r5E3W6+5bo801mtosQTAaJBr6aWSHwO0KoeBi4\nD/gDsO4g951qvUVkijTGREQOx9iZKS8QWgjq3X2zu28G2ggtEydOdAN3302YHbN05DnR894A3A7E\nzOwdZnaTuz/v7mvc/S3AzYSZNBOVY0rc/dfAXcCVUdcQhOBzMnCOu3/R3X8M7CNMMx4JHUdcbxE5\nOLWYiMjh2Bf9e6KZPQfcQ1gT5EEz+xLhF/la4HWEX96TWQN8ycx2AL8gzNi5B/iJuw+Y2QBwS9RV\n8ghQCbwb+P1E5XD3jkOow83ABcB3zOy1QH10/KNm9mNgIfBlwvtk3jTXW0QmoRYTEZnIQVsi3L0V\n+D5h1sqt7r4VOJswo2UdYTZPD3Cuu7cc5D5fBa4DrgZeJMzY+TahawR3fwK4LPp4gRBenDA7aFw5\nDqWC7t4HXEGY/ny7uz8TleVThPE13wd+DTxEaMWZtnqLyORi8bjWChIREZH0oBYTERERSRsKJiIi\nIpI2FExEREQkbSiYiIiISNpQMBEREZG0oWAiIiIiaUPBRERERNKGgomIiIikDQUTERERSRsKJiIi\nIpI2FExEREQkbfw/YzTMKwCbx1gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x157547b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4WGsfAc4Ezok0XQ2c3ebrLGAOcGXsI4qbMtOS+OT5EwE4Vt3Ek6/vdjmRiIj0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fe3cbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AzyIJIjxp9kFgOLAbuMvd797LuWcBNwH7AzOBK9x9ernjowiWM7cC3gF+5e5b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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1257dcac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YN3/bTABScWZbDnrGGuEUJi7Pa2uMOdtam1n7EOVkio9twP9d1ZeXPl7N6q0HWL31AE9P\nXcZdl/amUWwDf4cnIiJBzptHOS8Ba4He1toEa20C0B1YBtwKtAG2AM/4LEo5qaIahHPHuF6c2iMR\ngIzdh3nqraVk5xz1c2QiIhLsvClM7gF+Z61dVTFgrV0H3AY8YK3NAh4CzvZNiOIP4WFubhrTldGD\nUgDYk+s0YtuadcjPkYmISDDzpjBpzH8e4VR2FGji+T4HiPY2KAkMLpeLccM6cNVZnXABh48W88zU\nZazast/foYmISJDypjD5AXjGGNOoYsAY0xh4CpjnGboEsLUPTwLBWWnJ/PaiHoSHuSgsLuXv01by\n06osf4clIiJByJvFr7cBs4FMY4zFKW46AfuAUcaYs4Gngct8FqX43YAuLWgYHcGLH60iv7CE16av\nIzevkNGDUnC5XCe+gYiISDXUeMbEWrsF6ArcgTNDMhenWDHWWgtsAHpYaz/yYZwSALqkJHD/1f1o\nHOfszvnwuy1MmbmBsjI1YhMREd/wqjmFtTYfeMPzVfW9jNoGJYErqUUcD45P4/n3l5O1/yiz03dy\n4FAhv7mgO5ENwvwdnoiI1HPeNFhLBB4HTgMa8J/eJQBYa9v7JjQJVE0bRXH/Nf35x0er2LAjl+Wb\n9vH01HTuHNeLRnGR/g5PRETqMW9mTP4f0B94D8j1bThSX8RFR3Dv5X14/ct1LFybzbbdh3niraXc\ndWlvWjeL9Xd4IiJST3lTmAwHRllrf/B1MFK/RIS7+fX53WjWKIrp8zPYd7CAJ99ayu2X9MS0TfB3\neCIiUg95s104D8j2dSBSP7ldLi4Z2oFrRxncLhdHC0t47r3lLFi729+hiYhIPeRNYTIZ+IMxRisd\n5WfD+rThjnG9iIwIo6S0nFc+W8v0+dsoL9eOHRERqT5vHuU0A64EzjPGbAYKK79prR3ui8Ck/unV\noSl/vLoff5u2goN5RXz43Rb2HSzgmnM6E+b2pgYWEZFQ4+3fFu8AM3B6lmRU+ZIQlpLYkIfGp9HG\nswD2u+W7ePHDVRQUlfg5MhERqQ9qPGNirb2hLgKR4OFsJ+7HPz9ezbqMHFZu3s+fp6Rz57jeJDTU\ndmIRETm+ahUmxphrgfestYWe74+n3Fr7lm9Ck/osJiqCuy/rzaQZ65m3ejfbs/N44q0l3DWuN0kt\n4vwdnoiIBKjqzphMAr4C9ni+P55yQIWJABAe5uamMV1p1iiKz37axoFDhTz59lJ+e2EPenVo6u/w\nREQkAFWrMLHWuo/1va8YYyKBl4CxwFHgOWvt88e5dgxO59mOwGZggrX280rv5wIN+U9H2nKgobX2\nqK/jlhNzuVxcdEZ7mjeOZtKM9RQUlfLCtBVcdVZnRvRP8nd4IiISYGpcZBhjZhtjGh1jvIUxZpmX\ncTwL9AOGAbcAjxhjxh7j/9ET+BB4FegNvAJM84xjjGmNU5S0BxI9X61UlPjfaT1b8fsr+hAbFU55\nOUyZuYGpOgBQRESqqO4ak9FAmuflMOBBY0xelcs6Ae1qGoAxJga4CRhprV0BrDDGPINzYnHVE4qv\nAmZZa//pef2SMeYC4DJgFc6px1k6SDAwmbYJPHhtGi98sILsnHy+XZrJntx8fnNBd6IjvTpPUkRE\ngkx1/zbYBvwD5/FIOXAFUFrp/XKcjrD3eRFDb08c8yuN/Qg8cIxrJ+EcHFhVxQxON5wtzBKgEpvE\n8OC1aT8fALhy836eejuduy7tRZP4KH+HJyIiflbdNSZrcR6PYIzZCgyw1u7zUQytgH3W2sqNLrKB\nKGNMU2vt/kpx2MofNMZ0B0bgrE8BZ8Yk1hgzBzDAMuAua+1GH8UqPlBxAODkr9bz0+rdZO7N47E3\nl3DHuF6ktor3d3giIuJHNV5jYq1NtdbuM8bEGmP6GWN6G2Nq80/dGKp0j630+rhNL4wxzXDWm/xg\nrf3MM9wFSAAmAhcA+cAsY4yOuw0wEeFubhzTlbFD2gNw8EgRT09JZ6nd6+fIRETEn2r8YN8YEw68\nANyI81jFBRwxxvzdWvugFzEU8L8FSMXrYy5aNca0BGbiPEK6tNJbI4GIisWuxpirgR3A+cC71Q0o\nLCy426dX5BcIeV40pD2tmsXyyqdrKCop46WPV3HZiI6MHpSCy+U68Q1+QSDlWZeUZ3AJlTwhdHIN\ntTxry1XTQ9aMMc8C1wITgHlAGHAa8Cfgr9baJ2t4v8HAd0CUtbbMMzYM+MJa+z+duIwxbYDZOGtc\nzrTW/uJJx8aYhcD71trnqhmSton4wfqMAzzx+iJy85zJsnNOSeF3l/QiPMh/I4uIBJna/YsS7w7x\nGw/caK39otLYcmNMFvA3oEaFCbAcKAYG4RQ6AGcAi6te6NnB85Xn+jOt/e95f2PMJmCitXay53Us\nzm6h9TUJ6NChfEpLy2qYRv0RFuYmPj46oPJsGR/JhOvTeP695ezce4RvFmawY/chbrukJw1jjrXe\n+cQCMc+6oDyDS6jkCaGTa6jlWVveFCZROI3NqlqDs76jRqy1+caYycDLxpgbgSTgXuB6+PmxzUFr\nbQHwIJCKs2XZ7XkPIN9aewiYDjxqjMkA9gGPAduBL2sSU2lpGSUlwfvDUyHQ8kyIi+T+q/vzr09X\ns2brAdZl5PDo64u5Y1wvWjfzfplQoOVZV5RncAmVPCF0cg2VPGvLm3nyycBjnm6tABhjXMBdwJte\nxnEPsBTnEc2LON1cP/G8l4XTpwSczrDRwEJgV6Wvv3ne/wMwDZgCLMDJb4y1Vo9n6omYqHDuurTX\nz11h9+Tm88RbS1i1Zf8JPikiIsHAmzUmU3EKhBycAqEYp2trO5xi4OcdNtba4b4K9CQqz8k5EtRV\nbXi4m4SEWAI9z7nLdjJl5gZKy8pxueDy4Z04Oy2p2oti60uetaU8g0uo5Amhk2uI5emXNSaFwDtV\nxr73fIn4zLC+bWjZJIaXPl7FkYIS3p21kZ178xg/0mhRrIhIkKpxYWKtvaEuAhE5lq4pCTx0XRp/\nn7aSrP1H+WFlFtkHjnLL2J7Ee7koVkREApdX/+w0xrQyxjxsjJnqObxvnDHG+Do4EYCWCTE8OD6N\nnu2bArAh8yCPv7mEzL1Vj2sSEZH6zpvThTsCq3F2zYwD4oDLgSXGmFN8Gp2IR0xUOHeO68U5A5IB\n2HewgCfeWsryjb46GUFERAKBNzMmzwEfW2vb85+FrlcCnwN/9lVgIlW53S6uGNGJ68/tQpjbRWFR\nKS9+uJIZCzKo6SJuEREJTN4UJqcCz1ce8BzANxFnd45InRrSuzX3XdmXuOgIyoEP5m7mtenrKC4p\nPeFnRUQksHlTmIQf53PxOG3iRepc5+TGTLgujTbNncZr81bv5s9TlpFzuOp5kCIiUp94U5h8Ddxv\njKn4bLkxpgnwNDDLZ5GJnEDzxtE8cE1/+nRsBsDWrENMnLSYTTsP+jkyERHxljeFyT3AAJyOrNE4\na0sygA7A730XmsiJRUeGc9slPTn/1HYAHDxSxDNT0/l+xS7/BiYiIl6pcWFird0F9AHuB17Gaaz2\nf0APa22Gb8MTOTG3y8XFQ9pz68U9iIwIo6S0nEkz1jP5q/WUBPGBWSIiwcjb9pkDgO3W2tustbcA\n7YHevgtLpOb6mxY8OL4/zRtHAfDtkkwm/Hseh44U+TkyERGpLm/6mFwBzMSZNamQAnxrjLnQV4GJ\neCOpRRwTrhtAt3bOQderN+/nkdcWkbH7sJ8jExGR6vBmxuRB4B5r7bMVA9baS4H7gEd9FZiIt+Ki\nI7j7st6cO6gtAPsPFfDU20tZsHa3nyMTEZET8aYw6QBMP8b4F0Dn2oUj4hthbjdXntWZe67qR0S4\nm6KSMl75bC3vz9lEWZmasYmIBCpvCpPtwNBjjA/G2akjEjDO7J/MQ9elkdAwEoCvFm7nbx+s4EhB\nsZ8jExGRY6nx6cLAv4AXjTEdgIWesQHAXcBjvgpMxFdSW8Xz8PUDeOnjVWzMPMjqrQd4bNISbhvb\nk6QWcf4OT0REKvFmu/ALOAXIjcBnnq+bgAcrrzsRCSSNYhtw35V9Gda3DQB7cvN5/K0lWnciIhJg\nvNoubK19xlrbBmgONALaW2v/4dPIRHwsPMzNtSMN15/bhfAwF0XFzrqTd77dqH4nIiIBosaPcowx\n0cA/gY3W2qc8Y1uNMTOB2621OqxEAtqQ3q1JbhHHPz9exYFDhcxcsoOM7MP87sLuNIqL9Hd4IiIh\nzZsZk+eAIcC8SmP3AGcCT/giKJG6VrHupGuK0+9kw45cHtU5OyIifudNYXIxMN5a+13FgLX2Y5x1\nJlf4KjCRuhYf04B7Lu/Nuac4/U5y84p4eko6s9MzKS/XlmIREX/wpjCJA3KPMb4HaFK7cEROrjC3\nm0vP7MgtF/UgskEYpWXlvP3NBl6bvo6i4lJ/hyciEnK8KUwWAH8wxvz8WWOMC7gbWOyrwEROprQu\nLZhwbRqtmsYAMG/1bp58eyl7c/P9HJmISGjxpjB5ABgHbDbGTDPGTAM24zzG+YMvgxM5mVo3i+Wh\na9Po37k5ANuz85g4aTGrt+z3c2QiIqHDmz4mi4GewLtApOceU4Eu1tqFv/RZkUAXHRnOLRf3YNyw\nDrhccKSghL++v4LPftpKmdadiIjUOW86v2Kt3QbcX3nMGNPSGPOAtfZJXwQm4i8ul4vRg1Jol9iQ\nlz9dQ15+MZ/8sJVNmQf59fndaBjTwN8hiogELa8arFVmjBlhjHkf5wwdtaSXoNGtXRMeuX4Aqa3i\nAVi99QB/emMxm7WlWESkznhVmBhjmhpj7jXGWOAb4AKcxzl9fBmciL81bRTF/df0Y0T/JAByDhfy\n5ynpzFy8Q1uKRUTqQI0e5RhjTgd+C1yCs75kI1AODNX6EglW4WFurj67M52SGvHGjPUUFpXyzqyN\nbMzM5YbRXYmO9OqJqIiIHEO1ZkyMMbcbY1YD3wNnAP8A0qy1BqcwOVx3IYoEhoFdW/LwdWm0aR4L\nwBK7l4mTFrNjT56fIxMRCR7VfZTzgufaC621Kdba+6y16XUYl0hAatXU2VJ8ao9EALJz8nl88hJ+\nWLnLz5GJiASH6hYmT+F0fP3EGLPcGPOAMaZ9HcYlErAiI8K4aUxXzynFbopLynjjy/W8/qW6xYqI\n1Fa1ChNr7YNACjAGsMAEYKMxZjHgAhrWWYQiAcjlcjGkd2seHN+fFo2jAfhxZRaPT15K9oGjfo5O\nRKT+qvauHGttubX2K2vt5UBr4E7P593Ad8aYt4wxp9RRnCIBKSWxIQ9fn0bfTs0AyNybx6OTFrNo\nXbafIxMRqZ+82i5src2x1v7DWtsfZ4vwv4GRwDxfBidSH8RERXDb2J5cPrwjbpeLgqJSXv50DZO/\nWq9HOyIiNVTrBmvW2pXW2juBNsCltQ9JpP5xuVyMHNiWP17djybxkQDMXb6LxycvIWv/ET9HJyJS\nf9S6MKlgrS221n7kq/uJ1EcdkxrxpxsG0qdjxaOdIzw6aTE/rcryc2QiIvWDzwoTEXHERUdw+yU9\nuWJEJ8LcLoqKy3ht+jpe+2ItBUUl/g5PRCSgqTARqQMul4tzBiTzwPj+NG8cBcBPq3fz2JtL1JBN\nROQXVLfz6zPGmATP922NMa66DUskOKS2iueR6weS1qUFAFn7j/L45CXMXb5TZ+2IiBxDdQ/5uB14\nCcgBtgKJwF5fBWGMifTcfyxwFHjOWvv8ca4dAzwOdAQ2AxOstZ9Xev9KnFOOE3EOGPy1tXa/r2IV\nqamYqHB+d2F3vktJYOq3GykuKWPyV5b1GTlcN6qLztoREamkun8ibgM+NsYsx2mo9ndjTP6xLrTW\n3uhFHM8C/YBhQDtgsjFmW9XFtMaYnsCHwL3ADGAUMM0Yk2atXWWMGQi8CtwMrABeBCYB53sRk4jP\nuFwuhvVtQ/vW8bz86Rp2HzjKonV72JZ1mN9e1J12ifH+DlFEJCBUd43JNTgzJSk4h/a1BVKP81Uj\nxpgY4CbgDmvtCmvtp8AzwG3HuPwqYJa19p/W2i3W2peAOcBlnvdvBd6z1k6x1q4GxgOjjTEpNY1L\npC60bek0ZBvc3TlrZ09uPk9MXspXC7dTpkc7IiLVmzGx1i7FecyCMWYrcIEPH4/09sQxv9LYj8AD\nx7h2EtCpFwcsAAAgAElEQVTgGOONPP8dhHOuDwDW2kxjzHbPeIYvghWpragG4fzqvK50TUng7ZmW\nouIy3p+ziTVb93PTed1oHBfp7xBFRPymxg+3rbWpAMaYLkBPoBhYa63d4GUMrYB91trK+yizgShj\nTNPKBZC11lb+oDGmOzACZ31Kxb2qHvOaDSR5GZtInXC5XJzeqxUd2sTz78/WsD07jzXbcnj4tUXc\nOKbrz31QRERCTY0LE89C1XeAiyoNlxtjPgcut9YW1vCWMUDVz1S8Pu4/HY0xzXDWm/xgrf3sBPeq\n0T9Bw8KCexd1RX7K0/+SWzbkkRsGMm3uJmYs2E5efjF/n7aSswckc/mIjjQIDzvhPepDnr6gPINP\nqOQaannWljfbAZ4EBuIUJt/hrFMZgrPQ9E/A/TW8XwH/WzhUvD7mMa3GmJbATJz1LpXb4B/vXjU6\n7jU+Proml9dbyjNw3HJpXwb1asNf30kn93AhMxfvYGPmQX5/TX9Sqrkwtj7k6QvKM/iESq6hkmdt\neVOYXAncbK39otLYp8aYUpxHKjUtTHYCzYwxbmttmWcsEci31uZWvdgY0waYDZQCw6qsddnp+Wxl\niUCN+oEfOpRPaWnZiS+sp8LC3MTHRyvPAJPaIpbHf3UK/+/zNazYtJ9tWYe4+6/fcdXZnRnerw0u\n17HbB9W3PL2lPINPqOQaannWljeFSUNg/THGLdDci/stx1mnMoj/nE58BrC46oWeHTxfea4/01pb\ntZfKAuB0YLLn+mSc9SULahJQaWkZJSXB+8NTQXkGnpjIcO64pBezlmby/pzNFJeU8eaM9azYuI8b\nRnehYcyx1n476lOetaE8g0+o5BoqedaWNw+EVnPsU4QvwylOasRam49TSLxsjEkzxlyE06fkBXAe\n2xhjojyXP4izJfl6wO15r6UxpmKu+1/AeGPMjcaYXsCbwOfWWu3IkXrD5XJxVloyE65Lo3WzWACW\nb9rHI68vYt22A36OTkSkbnkzY/I4zqObPsBPOOs8zsDZTnyll3Hcg/MYaDZwEKeb6yee97JwCpHJ\nnv9HNLCwyuffBG601i4wxvwGp/NrAvA1TrM1kXonuUUcE65L473Zm5i7bCe5eUU8++5yRg5sy8VD\n2hMRHtwL6UQkNLm8Oa/DM6vxR5ztwi5gJfBM1U6t9VR5Ts6RoJ5uCw93k5AQi/KsP9I37OWNL9dx\npMDZVZ/UPI6bz+9GUou4oMrzlyjP4BMquYZYnrU+S8+rQzo8sxmfnPBCEfGJfp2bk9oqntenr2XN\nthwy9+Yx8c3FXDK0A+cOVmNjEQkemgsWqScSGkZy9+V9uOqsTkSEuykpLee92Zt4+u109uYc8+gq\nEZF6R4WJSD3i9iyMffj6AbRtGQfAuowcbn92NvNW7/ZzdCIitafCRKQeatMsloeuTWPM4BRcLjhS\nUMLLn6zm5U9Xc6Sg2N/hiYh4rcaFiTGm1gtbRKT2wsPcXDK0Aw+MT6NlkxgAFq3bw8OvLWKtthWL\nSD3lzYzJTmPMU8YY4/NoRKTGTNvG/P3eYZzRuxUAOYcLefbd5bzz7UaKikv9HJ2ISM14U5hMxDkb\nZ50xZr4x5uZKDc5ExA9ioiL49fndufXiHsRFRwAwc8kOJr65hG27D/k5OhGR6qtxYWKtfdlaexpg\ngFk4/UyyjDFTjDFn+TpAEam+/qYFj900kJ7tmwKwa98RHn9zKZ/8sIWSID6jQ0SCh9eLX621G621\nD+EUKI/hnDb8tTFmmzHmHmPMic9rFxGfaxQXyV2X9mL8SENkRBhl5eV89tM2Hn9zCZl78vwdnojI\nL/K6MDHGnGKM+SdOy/iHgI+Ac4A/AXcCb/siQBGpOZfLxZl92/DoTQPpnNwYgO178nh00mKmz99G\naZlmT0QkMNW486sx5iFgPNARWIJzsN471tpDla4pAl7xVZAi4p0WjaP5w1V9mbUkk2nfOacVf/jd\nFtI37ONX53WlVdNYf4coIvJfvGlJfzvObMhr1tq1x7lmHfAHr6MSEZ9xu1ycPSCZHu2b8Pr0dWze\ndYitWYf40xuLGTukPWenJeN2qwuAiAQGbx7lPAg8ULUoMcbEGmPuBrDWLrPWvuSLAEXEN1o1jeX+\na/ozblgHwsNcFJeUOS3tp6azJ+eov8MTEQGqOWNijGkGxHhe/htIN8bsq3JZX+BJ4K++C09EfMnt\ndjF6UAq9OjTltS/WkZF9mI2ZB3n49UVcdmZHhvVtg9ul2RMR8Z/qzpiMBrYBWwEXsNjzfeWvj3G2\nD4tIgEtqHseD1/bnotNTCXO7KCou4+1vNvDcu8vZk6sDAUXEf6o1Y2KtnWyM2YZTyMwGxgGVe16X\nA3nAKh/HJyJ1JDzMzQWnp9K7YzNenb6WnXuPsC4jh4dfW8glQzowon+S1p6IyElX7cWv1trvAYwx\nZwI/WWtL6iwqETlpUhIb8vB1A/hi3ja+XJBBUXEZ78zayOL1e7hhdBft3BGRk6q6a0weBp611h4F\nhgJDj3dUjrV2ou/CE5GTISLczcVD2tPfNOf1L9exPTuPTTsP8sjri7nw9HaMOqUtYW4dRi4ida+6\nMyY3AP8Ejnq+P55ynLN0RKQeatuyIQ9dm8bXi7bz6Y9bKSl1+p4ssXu5cXRXklvE+TtEEQly1V1j\nknqs70Uk+ISHuRkzuB19OzXnjS+dvicZuw8zcdJixgxO4bxT2xEeptkTEakb+tNFRI6pdTOn78kV\nIzrRINxNaZlz5s6jkxazNUsnFotI3ajuGpMynMc0J2St1eF9IkHC7XZxzoBk+nRsyqQZ61m/PZed\ne4/w+OQljBzQlgvPSCUyQr/lRcR3qrvG5EaqWZiISPBpkRDD76/sy/fLd/H+nE0UFJXy1aLtLN2w\nh2tHdqF7ahN/hygiQaK6a0wm1XEcIhLg3C4Xw/q2oVeHprz5lWXVlv3szS3gufeWM7h7IleM6EjD\nmAb+DlNE6rnqPsp5HbjTWnvY8/3xlFtrb/JNaCISiJrER3HXpb1YuC6bd77dyOGjxcxfs5tVW/Zz\n+fCOnNojEZfa2ouIl6r7KCcVCKv0vYiEMJfLxaBuifRIbcr7czbx48os8vKLeW36Ouav2c21Iw0t\nEmJOfCMRkSpc5eVaOlJFeU7OEUpKyvwdR50JD3eTkBCL8gwOgZDnuowcJn+1nuwc55ydiHA3F56e\nyjkDkn22tTgQ8jwZQiVPCJ1cQyzPWk+XVrslfWXGmCjgKqAHUIhzRs77alMvEpq6piQw8aaBfD4v\ngxkLMiguKWPa3M0sWJPN9ed2oX3reH+HKCL1RI3/KWOM6QVsAV7AaU9/LvAqsMoY086n0YlIvRER\nHsbYIe155IYBdPAUIpl783hi8hKmztxAfqH+3SIiJ+bNHOsLwBIgyVrb31rbB0gBdgEv+jI4Eal/\nkprHcf/4/lxzTmeiGoRRDny7NJOHXl3IUrsHPT4WkV/iTWEyCPijtfZgxYC1di9wLzDcV4GJSP3l\ndrkY3i+JJ349iL6dmgGQc7iQf368mhemrWRPbr6fIxSRQOVNYbITaH2M8QRgX+3CEZFgktAwktsv\n6cXtY3vSJD4SgJWb9zPh1YV8/tNWioN4IaCIeKe6fUzaVnr5AvCqMeZW4CegFOgP/Bt4yOcRiki9\n17dzc7q1a8JnP23lm8U7KC4p4+MftjJ/TTbXnNOZbu3UOVZEHNXdlbON/25J7wI+P8bYJOAtXwQm\nIsElskEYl57ZkcE9Enn7a8uGzIPsPnCUZ99dzindWnLF8I40iov0d5gi4mfVLUzOrNMoRCRkJDWP\n4/+u7se81bt5b/Ym8vKLWbg2m5Wb9zF2SAfO7NsGt1udY0VCVXXPyvmurgMRkdDhcrk4rWcrends\nxkffbea75bvILyxlyswN/Lgqi2tHGlJbqfeJSCiqcYM1T3O1m4Ge/KdNvQuIBNKstZ19F56IBLO4\n6AiuHdWF03q24q2vLdv35JGx+zCPv7mEoX1aM3ZoB+KiI/wdpoicRN7syvk78AxO19drgY7ASOBy\n4DPfhSYioaJDm0ZMuD6NK8/q9HPvk7nLd3H/v+czJz2TsjL1PhEJFd4UJhcCN1hrB+Msiv01ToO1\nTwGdeS4iXglzuzk7LZknfj2IQd1bAnCkoIS3vtnAxEmL2bAj188RisjJ4E1hkoCzTRhgDdDPWlsM\nPAmc56vARCQ0JTSM5Obzu/PHq/uR1DwOgO178nj8zSU8N3UpuYcL/RyhiNQlbw7x2wO0ALYDG3HW\nmryD01wt0ZsgjDGRwEvAWOAo8Jy19vkTfOZ04E1rbYcq47lAQ5x1L+BsaW5orT3qTWwi4h+dkxvz\nyA1pzF22i4+/38LRwhLmLs1kwaosLjgtlbPSknx2crGIBA5vflfPAF4yxnQHfgCuMsakAbcCO7yM\n41mgHzAMuAV4xBgz9ngXG2N6Ah/wn+KjYrw1TlHSHqdISgRaqSgRqZ/C3G5G9E/iyd8MYljfNrhc\nUFBUyvtzNvHI64tYs/WAv0MUER/zZsbkPpxGakOBfwG/ARYBxcB1Nb2ZMSYGuAkYaa1dAawwxjwD\n3AZ8dIzrfwP8BdgMNKrydlcgy1qbUdM4RCRwxcc04MYxXblgaAf++cEKNu88SNb+ozz33nL6d27O\n5cM70qxxtL/DFBEfqHFhYq3NBS6qeG2MGQP0AXZba7O8iKG3J475lcZ+BB44zvUjgfFAY+CRKu91\nAzZ4EYOI1AOdkhOYcH0a3y/bxbS5mzh0tJilG/aycst+Rg5sy+hBbYlq4M2/t0QkUHj1O9jTy+Qq\nnC3DhcAqz5c3WgH7rLUllcaygShjTFNr7f7KF1trx3piONbsTFcg1hgzBzDAMuAua+1GL2MTkQDj\ndrk4vVcr+nVuzqc/bmXW0kyKS8r4Yt42fly5i0uGdmBwj0TcLnWPFamPvGmw1gv4CmctxwacJmt3\nAhOMMedaa7fV8JYxOMVNZRWva3pwRhecXUN/BA57/jvLGNPVWnukujcJC/IFdRX5Kc/gEKp5xsc1\nYPwow5n92zB15gZWbzlAbl4Rr01fx+z0nVx9Tmc6Jzf2Z8heCZVfTwidXEMtz9ryZsbkBWAJMN5a\nexDAGNMceBd4ETi/hvcr4H8LkIrXNV20OhKIqFjsaoy5GmdB7vme+KolPj40nlUrz+ASqnkmJMTy\nZKcWLF2/h1c/Xc3OvXlszTrE428u4Yw+bbh+TDdaNInxU7TeC5VfTwidXEMlz9rypjAZBPSvKEoA\nrLV7jTH38p/+JjWxE2hmjHFba8s8Y4lAvmc9S7V5+qkUV3pdaIzZCrSpyX0OHcqntLTsxBfWU2Fh\nbuLjo5VnkFCejg6JcTz2q4HMXprJx99v4UhBCT8s38mCVVmcO6gt553Wrl6sPwmVX08InVxDLc/a\n8uZ36U6gNbC2yngCTi+TmlqOU0wMAuZ5xs4AFtf0RsaYTcBEa+1kz+tYoBOwvib3KS0to6QkeH94\nKijP4KI8HcP7JTGwa0s+/WErc5btpLi0jM9+2sZ3K3ZxyZAOnNqzfqw/CZVfTwidXEMlz9qqVmFi\njGlb6eULwKvGmFtxZkhKgf7Av4GHahqAtTbfGDMZeNkYcyOQBNwLXO/5f7cEDlprC6pxu+nAo8aY\nDJwi6TGcRnBf1jQuEam/4qIjuPqczgzr14b3Zm9k9ZYDHMwr4vUv1zErPZMrR3Sql+tPREJBdWdM\ntuF0UK3gAj4/xtgk4C0v4rgHp/PrbOAgMMFa+4nnvSycImVyNe5zH1AETMHpcTILGGOt1QlgIiGo\nTbNY7rmsDys37+e92RvJ2n+UjN2H+fOUdPp2asa4YR1o1TTW32GKSCWu8vIT/51tjBla3Rtaa7+r\nVUT+V56TcySop9vCw90kJMSiPIOD8qyektIy5i7byac/buVIgdOdwO1yMbRPay48PZX42MA4gzRU\nfj0hdHINsTxr/Zy0WjMmxys2PLtximu6SFVE5GQLD3NzVloyg3skMn1+Bt8u2UFJaTlzlu1k3prd\njD6lLecMbEtkRJi/QxUJaV5tOjbG3GGMyQJ2A/uNMTuNMXf5NjQREd+LjYrgsjM78uSvBzGoW0sA\nCotK+fiHrdz/7/n8sGIXZWV6+iviLzUuTDxn1TyDc6LwWGAc8D7wlGfxqohIwGvWOJqbL+jOhOvS\n6NLWWQibm1fEGzPW86c3FrFqy36q86hbRHzLm+3CdwO/t9b+o9LYx56tuncBr/skMhGRkyC1VTz3\nXdmXlZv388Hczezad4TMvUf46/sr6NYugcvO7Ejblg39HaZIyPDmUU4KMOMY41/h9AwREalXXC4X\nvTs249EbB3DdKEMjz0LYtdtyePSNxbzy+Rr25Ob7OUqR0ODNjEkGkAZsrjI+AOfwPRGReinM7WZo\nnzac0q0lXy/awVcLt1NYXMqCNdksXreHoX1ac/6p7WgUV9NjvESkurwpTP4N/NMY0wSnwVo5TqfW\niTjN10RE6rWoBuFceHoqw/q05rN52/h++S5Ky8qZnb6TH1dlcc6AZEYNTCEmKvBb3IvUN94e4pcC\n/K3S50twCpYnfBSXiIjfNYqLZPw5hpEDkvnkh60sWJtNUXEZX8zLYE76TsYMbsfwfm1ooC3GIj7j\nTWFyGk6H1QlAF5yOr+ustYd9GZiISKBokRDDzRd0Z9Qpbfno+y2s3LyfIwUlvD9nEzOX7ODC01M5\nrWciYe7gPtZe5GTwpjD5EBhlrU3Hi4P2RETqq7YtG3LXpb2x23P48LstbNp5kJzDhUyasZ4ZC7dz\nyZD29DfNcdWDQwJFApU35f1enHNoRERCkmmbwP3X9OOOS3rRprlz1k72gaO89MlqJr65hJWb96kH\nioiXvJkx+RKYboz5EtgI/NceOmvtRF8EJiISyFwuF306NaNXh6YsXJvNxz9sYd/BAjJ2H+ZvH6yk\nQ+t4LhrSnm4pCZpBEakBbwqTcTjbgvt7viorx9mdIyISEtxuF4N7JDKgawu+W76LL+Zv42BeEZt3\nHeK5d5fTObkxF5+Rimmb4O9QReqFGhcm1trUughERKQ+Cw9zM6J/Emf0asWcZTv5ckEGh48Ws2FH\nLk9PXUa3dglcfEZ7OrTRk3CRX1LtwsQYkwRcjPPoZoa1dmedRSUiUk81iAhj5MC2DO3TmtnpO5mx\nIIMjBSWs3ZbD2m1L6dWhKRedkUq7xHh/hyoSkKpVmBhjTsdpOR/jGTpsjLnUWvtNnUUmIlKPRTUI\nZ/SgFM7s24aZS3bw9aId5BeWsHLzflZu3k/fTs246Iz2JLeI83eoIgGlurtyHgNmAW2AROBr4Pm6\nCkpEJFhER4ZzwWmpPPO7wZx3ajsiGzjN2JZt3Mcjry/ipY9XsWNPnp+jFAkc1X2U0w8YbK3NAjDG\n3A1sN8Y0VGM1EZETi42KYOyQ9pydlsRXC7cza2kmRSVlLLF7WWL30rdTMy44LZUOSVqDIqGtujMm\nccD+ihee9SVFQJO6CEpEJFg1jGnApWd25Onfnco5A5JpEO78Mbxs4z4enbSY599bzobtOX6OUsR/\nqjtj4sLZClxZCaADIkREvNAotgFXjOjE6EEpfL1oO7PTd1JYXMryjftY/sL39OrQlPNObUdH7eKR\nEKOjMUVE/Cg+1plBGXVKW75ZvINZSzMpKCr9eZFs93YJnH9aKp2TG/s7VJGToiaFyb3GmCOVXkcA\ndxhjDlS+SJ1fRURqrmFMAy4Z2oExp7bj+5W7+ez7zRwtLGHNthzWbMuhS9vGXHBaKqZtY3WSlaBW\n3cJkO3BZlbEs4MIqY+r8KiJSC3HREVw9qgtDeyfy9cLtzFy8gyMFJazfnsv67cvo0CaeMYPb0btD\nUxUoEpSqVZhYa9vVcRwiIlJJbFQEF5yWytlpycxamsnXi7ZzpKCEzTsP8fdpK0lqHsvoQSkM6NqC\nMLc357GKBCb9NIuIBLDoyHDOO7Udf7nlVC4f3pHGcQ0AyNx7hFc+X8sDryxg7rKdFJeU+jlSEd/Q\n4lcRkXogqkE4Iwe2ZXi/JOatzmLGgu3syc1nb24Bk7+2fPrj1p9b4UdH6o92qb/00ysiUo9EhLsZ\n2qcNp/dqxZL1e5k+P4PMvXkcPFLE+3M2MX3+Nob3S+KstCQaxjTwd7giNabCRESkHgpzuzmlW0sG\ndm3Bqi37+WJ+BpsyD3KkoITP523j68XbGdK7NecMSKZZo2h/hytSbSpMRETqMZfLRa8OzejVoRkb\nduQyfX4Gq7bsp6i4jG+XZDJ76U7SujTn3FNSSEls6O9wRU5IhYmISJDonNyYzsmNydh9mBkLM1i8\nfg9l5eUsWreHRev20DUlgZED29KzfRNtNZaApcJERCTIpCQ25LcX9uCSofl8s3gHP6zcRVFxGesy\ncliXkUOb5rGMGtiWU7q1JDxMmzMlsOgnUkQkSDVvHM3VZ3fm2VtO4+Ih7YmPdRbD7tx7hNemr+MP\n/5rHjAUZHC0o8XOkIv+hGRMRkSAXFx3B+ae2Y9TAZOavyearhdvZfeAouXlFfDB3M5/P28bQPq05\nq38yTRtF+TtcCXEqTEREQkREeBhDerfm9F6tWLFpH18v3M6GzIMUFJXy9aIdzFycSX/TnLMHJNOh\ndbzWoYhfqDAREQkxbpeLvp2a07dTczbvOshXC7eTvmEvZeXlLF6/h8Xr95DaKp6zBySRZlpoHYqc\nVCpMRERCWIfWjbj14p7syc1n9tJMvl+xi4KiUrZmHeKVz9byQcPNDO/XhqF92hAXHeHvcCUEqDAR\nERFaNI7mihGduPD0VH5clcW3S3awN7eAnMOFfPjdFj7/aRun9kjkrLRkWjeL9Xe4EsRUmIiIyM+i\nI8M5Oy2ZEf2SWLFpHzOX7GD99lyKSsqYu3wXc5fvokdqE84ekEz31Ca4tQ5FfEyFiYiI/A+320Xf\nzs3p27k527MPM3PJDhauzaaktJzVWw+weusBWiZEM7xfEqf1TCQmSo95xDcCojAxxkQCLwFjgaPA\nc9ba50/wmdOBN621HaqMXwk8BiQC3wC/ttbur5PARURCQNuWDblpTDfGDevI3GU7mZOeyaGjxWTn\n5PPOrI18+P1mTu2eyPB+SSS1iPN3uFLPBURhAjwL9AOGAe2AycaYbdbaj451sTGmJ/ABkF9lfCDw\nKnAzsAJ4EZgEnF9HcYuIhIxGsQ248PRURg9KYcn6PcxKz2TLrkMUFf/nMY9JbsyI/kn06dRMu3nE\nK34vTIwxMcBNwEhr7QpghTHmGeA24H8KE2PMb4C/AJuBRlXevhV4z1o7xXPteCDDGJNirc2owzRE\nREJGRLibwT0SGdwjka1Zh5i9NJOF6/ZQUlqG3ZGL3ZFLQsNIhvZpzdDerWkUF+nvkKUeCYRytjdO\ngTS/0tiPwCnHuX4kMB742zHeGwR8X/HCWpsJbPeMi4iIj6W2iuem87rx3K2nMm5YB5rGO0VIzuFC\nPvlhK79/aR6vfLaGjZm5lJeX+zlaqQ/8PmMCtAL2WWsrH9aQDUQZY5pWXR9irR0LYIy57jj32lVl\nLBtI8mG8IiJSRcOYBowelMKogW1ZsWkfs9IzWbsth9KychaszWbB2mzaNI9laO/WnNpDi2Xl+AKh\nMIkBCquMVbyu6fzf8e6leUQRkZOg8m6erP1HmJ2+k59WZVFQVMrOvUeY+u1Gps3dzICuLRjRP5m0\nxjH+DlkCTCAUJgX8b+FQ8fqoj+5Vo/uEBfmCrYr8lGdwUJ7BJZjyTG7ZkOvO7cLlIzqyYE02c9J3\nsjXrEEUlZfy0ajc/rdpNu1bxDOvbmkHdEomJCoS/knwvmH5Nf4mv8guEn4KdQDNjjNtaW+YZS/z/\n7d15dJ1ltcfx70la0jkd0iQdaZumu/OAdMK2UJTBiyh2IaKIS0BcglyvwnK6MiiIXLHoVRTR6xVW\nBVHhOqCIAxWQ0pbSEeiwm060adIkLU3TIU2TNPeP5w09pklJ2pOek9PfZ62s5rznzZu986bn7Dwj\nUO3ulSdxrfwmx/KB0rZcpFevrm38th2T8kwvyjO9pFueH8rL5kMXjmJTcSV/WbKNF1cWc/hIPdtK\nq3i0tIonnitizuRBXDpzGIVDeqflBoLpdk/bSyoUJquBWsIA1cXRsdnAqydxraXALGABgJkNIYwv\nWdqWi1RVVVNff/SdT+ygMjMz6NWrq/JME8ozvaR7nv26d+aa9xYyb/ZwXllXxgurS9iycx81R+r5\n+7Lt/H3Zds7O78ncKYOYOT6frlmp8DZ1atL9njZqzPNUJf2Ou3u1mS0AHjaz6wmFxG3AJwHMLA/Y\n5+6HW3G5HwPPm9lSYDlh5s4f2zpVuL7+KHV16fvL00h5phflmV7SPc/OmRlcMGUQV8wtZMXaUhau\nKGbZ+jKO1B7lzV37efTZDTyxsIgZY/OYM2kgw/J7dvhWlHS/p4mS9MIkcith5dd/APuAO9z999Fz\npYQiZcE7XcTdl0brnNwD9AH+SlhsTUREUlAsFqNgUDZn5/Xk6gsLWbJ2Fy+u3klxxUFqjtTz4uoS\nXlxdwqCc7syaOICZ4/Lp1f2sZIct7SimeeXHadi792BaV7WdOmXQp093lGd6UJ7p5UzJE1rOtaGh\ngc0lVby4aievbijnSNxzmRkxJo3MYdbEAUwY0ZfMjNQfUHqm3NMoz1Nu1kqVFhMREREgtKKMHJTN\nyEHZfOyiUby6oZyXXith884q6o82sHJjBSs3VpDd/SzOm5DPrAkDGNCve7LDlgRRYSIiIimra1Yn\n5kwayJxJAynZfZBFr5ey+I1dVB08wr6DR3h26XaeXbqdkYOymTVxAFNH56bFgNkzme6eiIh0CANz\nunPV3JHMmzOC17fsYdFrpazZtIejDQ1s2rmPTTv38cvnNjJ1dC6zJw6kcHB2hx8weyZSYSIiIh1K\np8wMphT2Z0phf/YdPMKSN3bx0msllO45xJHaY4u35fbuyoxxecwcn09eH60w21GoMBERkQ4ru/tZ\nXFjuFoIAABKhSURBVDp9KJdMG8KW0ioWvVbKK+vKOHyknvLKap5+eRtPv7yNgoG9mDk+n2lj8ujR\nVfv0pDIVJiIi0uHFYjEKBmZTMDCbq99TyEqvYPHaXazb9hYNDbC5pIrNJWGF2YkF/Zg5Lp9JI3Po\n3Cn1Z/WcaVSYiIhIWsnqnMnM8fnMHJ/P3v01vLKujCVrd7Gj/AD1RxtYVbSbVUW76ZbVialjcpk5\nLl/jUVKIChMREUlbfXpmcen0oVw6fSjF5QdYvHYXS9fuovLAEQ7V1L29gFtOdhdmjMvnvPH55PfV\neJRkUmEiIiJnhMG5PbgqdyRXnl/A+u17WfLGLlZ4BTW19ezed5g/Ld7GnxZvY/iAXswYm8e5o3Pp\n07PphvXS3lSYiIjIGSUjI8a4YX0ZN6wv115cz8qiCpas3cXarWE8ytbSKraWVvGrhUXY0N5MG5PH\nu6w/PbtpKfzTQYWJiIicsbLOymTmuHxmjstn34EwHmXpujK27dpPA7BheyUbtlfy2N82MnZ4H6aP\nyWNKYX+6ddHbZ3vRT1ZERATI7pHFxdOGcvG0oZTtPcSy9eUsW1/GzoqDHG1o4I0tb/HGlrfolOlM\nLOjHtDG5TBqZQ1bnzGSHnlZUmIiIiDSR16cbl583jMvPG0ZxxQGWrS9j2bpyyiurqas/+vZ+PVmd\nM5lcmMO0MbmMH95P048TQIWJiIjICQzu34PB/Xvwodkj2LZrfyhS1pezd38NNbX1vLKujFfWldE1\nqxPnjMrhXMtl7LC+KlJOkgoTERGRVojFYgwf0IvhA3rx4bkj2VS8j2Xry1i+oZyqQ7VU19S9vRx+\n16xMJo3M4V2jcpkyKifZoXcoKkxERETaKCMWY9SQ3owa0puPvreQDdsrWbaujJUbKzh4uI7qmnqW\nri1j6doysjpncu7YPCYX9GPcsD50OUtvvSein46IiMgpyMzIODb9+BLDd1SywitY6aElpaa2npfX\nlPDymhI6d8pg/PC+nDs6l0kFOZrd0wz9RERERBKkU+axIuXjF42iqLiSlUW7WeEVvFV1mNq6o28v\nid8pM8bYYX0513KZXJijzQUjKkxERETaQUZGDBvah3Ej+nHLVVNYvraEV9aWscIr2FN1mLr6Bl7b\nvIfXNu8hMyN0DU0uzGFKYQ452V2THX7SqDARERFpZxkZMQoH92Z4fi8+cuFItu3az3IvZ4VXUL63\nmvqjDax/cy/r39zLE88VMTS3R1Sk9GdoXo8zaoNBFSYiIiKnUfzsnivPL2BH+QFWbqxgVdFudpQf\nAGB7+QG2lx/g6Ze30a9XFpML+zOlMIdRQ3rTKTO9pyGrMBEREUmSWCzG0LyeDM3ryRWzR7C7sppV\nm3azamMFG3fs42hDA3uqali4opiFK4rpltWJiSP7MaWwP+OH96VrVvq9jadfRiIiIh1UTu+uXHTu\nEC46dwgHqmt5ffMeVhVV8PqWt6ipredQTd3b05A7ZcYYfXYfphT2Z1JBP/r26pLs8BNChYmIiEgK\n6tG1MzPH5zNzfD61dfWsf3Mvq4p2s7poN/sOHqGu/tj+Pb8Ahub14LKZw5g6OjfZoZ8SFSYiIiIp\nrnOnTCYW5DCxIIdrL2lga0lVNO24gtI9hwDYXnaAR/68XoWJiIiInD4ZsRgFg7IpGJTNlRcUUL73\nEGs27cF3VDJ6aO9kh3fKVJiIiIh0YLl9unHR1G5cNHVIskNJiPSecyQiIiIdigoTERERSRkqTERE\nRCRlqDARERGRlKHCRERERFKGChMRERFJGSpMREREJGWoMBEREZGUocJEREREUoYKExEREUkZKkxE\nREQkZagwERERkZShwkRERERSRkrsLmxmWcBDwDzgEPCAu3+3hXOnAD8GJgBvADe5+8q45yuBnkAs\nOtQA9HT3Q+2XgYiIiCRCqrSYzAfOAS4AbgbuMrN5TU8ys27AM8CL0flLgGfMrGv0/EBCUTICyI8+\nBqgoERER6RiS3mISFRs3AJe4+xpgjZndD9wC/LbJ6VcDh9z9y9Hjz5vZvwEfBhYAY4BSd3/z9EQv\nIiIiiZQKLSaTCAXSkrhji4DpzZw7PXou3svAzOjzscDGRAcoIiIip0cqFCYDgN3uXhd3rAzoYmb9\nmjm3pMmxMmBw9PkYoLuZPW9mJWb2jJkVtkvUIiIiknBJ78oBugE1TY41Ps5q5bmN540G+gBfAfZH\n/y40szHufrC1AWVmpkK91n4a81Oe6UF5ppczJU84c3I90/I8ValQmBzm+AKk8XHTQastndt43iVA\n58bBrmZ2DbADuBz4VSvjifXq1bWVp3ZsyjO9KM/0cqbkCWdOrmdKnqcqFcq3nUCOmcXHkg9Uu3tl\nM+fmNzmWD5QCuHtt/Awcd68BtgKDEh61iIiIJFwqFCargVpgRtyx2cCrzZy7FDivybHziAbOmtkm\nM/tE4xNm1h0oBDYkMmARERFpH0nvynH3ajNbADxsZtcTBrLeBnwSwMzygH3ufhh4CrjPzL4H/BT4\nDNAdeDK63DPAN8zsTWA3cA+wHfjz6ctIRERETlYqtJgA3AqsAP4BPAjc4e6/j54rBa4CcPf9wPuB\nOcByYBrwPnevjs79IqF4eZzQupIBXObuDacpDxERETkFsYYGvWeLiIhIakiVFhMRERERFSYiIiKS\nOlSYiIiISMpQYSIiIiIpQ4WJiIiIpIykr2OSKswsC3gImEdY4v4Bd/9ucqNKLDMbCPwAmEvI8TfA\nV939SFIDa0dm9gxQ5u7XJzuW9mBmZwHfAz5K2Dfq5+7+teRGlXhmNhj4MWGpgD3A9939+8mNKnGi\n15/lwGfd/Z/RsWHA/xB2T98GfMHd/56sGBOhhTxnAA8AE4FiYL67/2/yokyM5nKNe64XsJ7w+rsg\nGfElSgv3dAjwE+B8wortX3P3J1u+yr9Si8kx84FzgAuAm4G7zGxeUiNKvP8DugDvBq4m7CF0T1Ij\nakdmdjXwvmTH0c5+ALwHuAj4GHCjmd2Y3JDaxZOEjTnPAT4P3GtmH0xuSIkRvbA/AYxt8tTvCbup\nvwt4DPhdVKB1SM3lGS2g+WfCGlaTga8DD5pZh/5/e4J72uh+jt9epcNp4Z5mEu7pYcI9nQ88ZmYt\n/SyOoxYTwMy6ATcAl7j7GmCNmd0P3AL8NqnBJYiZGWFBujx33x0duxP4DvDlZMbWHsysD+E//7Jk\nx9JeohyvBy509xXRsfnAdMJf2mnBzHoTcrrB3TcDm83sL4SC7A9JDe4UmdkY4JfNHL8QGAHMiFa9\n/i8zew/hft99eqM8dS3lCVwBlLr7HdHjzWY2l1BkP3u64kukE+Ta+Pws4EJg12kLqh2cIM/LCPvT\nzXD3g0CRmV1K2D5mXWuurRaTYBKhSFsSd2wR4cUwXewirJK7O+5YDMhOUjztbT6wgNBcmq5mAZXu\nvqjxgLvf7+6fSmJM7aEaOAhcZ2adoiL73cDK5IaVEOcDCwndNbG449OBlVFR0mhRdF5H1FKezwLX\nNXN+R35dailXzKwzoYvjZqCjd6G3lOf5wMKoKAHA3ee5+89ae2G1mAQDgN3uXhd3rAzoYmb93H1P\nkuJKGHffB/yt8bGZxQgtQs8lLah2Ev21ORuYADyc5HDa0whgm5ldC/wncBbwCHBvOm3D4O41ZnYL\n8ENCN04m8Ii7P5rUwBLA3d/+/Qz11tsGELpx4pUR9hLrcFrK0923E/Yza3wul9DNfOfpjC+RTnBP\nAW4nFJzPNfNch3KCPEcAW83sPuBaoAL4uru3unVTLSZBN8LAwXiNj7NOcyyny3cI/X9pNVAy6vP8\nMXCzuze9p+mmBzAKuJGw6eVtwOeA/0hiTO1lDPA0oTvyk8CVZvbRpEbUvlp6TUrX1yPMrAthHFwJ\nYZPWtBKNsfg08IVkx9LOehBawXoT9rb7BfCUmZ3T2guoxSQ4zPH/4RsfHzrNsbQ7M/s24Q3sKndP\nt66OrwPL3T3tWoKaUQf0BD7m7sUAZnY2cBPw38kMLJGisRU3AIOjYnNVNAj0dsLAu3R0GOjb5FgW\nafh6BGBm3QmF50jg3U26sNLFT4E7m3Snp6M6Qg/ETdHj1WY2m1CUfaY1F1CLSbATyDGz+J9HPlDt\n7pVJiqldmNmDhIr9mrgdnNPJR4ArzGy/me0HrgE+bmZVSY6rPZQChxuLkogDQ5IUT3s5Byhq0gK2\nCjg7SfGcDjs5ftZGPuGepxUz60noZh4LzHX3LUkOKeHMbChh8OcDca9NQ4GHoyUN0kkpsLHJsTa9\nLqkwCVYDtcCMuGOzgVeTE077MLO7CFXrR9oyp7yDOZ8wtmRS9PE0YebGpGQG1U6WEMZBjYw7Npaw\n5kU6KQFGmll8C+8YYGuS4jkdlgLnRF2TjWZFx9NGNNbtd8AwYI67b0huRO2mmNAaNJljr00lwB1A\nug1WXwqMj+5tozG04XVJXTmAu1eb2QJC9Xo9YYDZbYS+7LQQTe26HfgWsDhaPwAAdy9LWmAJ5u47\n4h9Hf5k0uHvavYm5e1H019ajZnYzYcDkl+mA00nfwR8JU79/Zmb3AqOBr0Yf6epFYAfh3t4DfACY\nShq9JkU+RVg76nKgKu516Yi7701aVAnm7keBf2kJMrM6oMLd060V7AlCwfVQtHzBJcClhPFhraIW\nk2NuBVYQFvp5ELgjzbo6PkC437cTKvUSQpNb05H/0rFcA2wCXgIeBR509x8lNaIEc/cqwpolAwjr\n0jwA3N2W6YcdxNszqaI3sg8Sum+WE9b1uKJJt11H1cCxXOcRppr+iWOvSyWEQbDp4ESz49Jm5hz/\n+ru7n7Dg4xjgdeDfCeMZ17T2YrGGhnT62YiIiEhHphYTERERSRkqTERERCRlqDARERGRlKHCRERE\nRFKGChMRERFJGSpMREREJGWoMBEREZGUocJEREREUoYKExEREUkZ2itHRDCzR4Cz3f3CNnzNZcDm\nZG+89k5xmNkLwJwmh48AZYRNHr/o7ocT9f1E5NSoxURE2izaxv2PQG4HiKMB+DWQR9h7Jp+wC/N9\nhN225yf4+4nIKVCLiYicjAxSYxOy1sZR7e4VcY/LCbuJTwWuBm5J8PcTkZOkwkREjmNmW4EfAjMJ\n25bXAI8DXwCGELZwbwCeN7NvuPvdZjaG0PowB9hP2Kn7Nncvi675PLARmASMAj7r7k+Y2XXAF4Fh\nwFbgJ4Rdkhuir/sE8CWgANgDPBk9HthcHG1MtQaojct7CPAdYC7Qh9Dd87i7f8XMzj6ZvEWkbdSV\nIyItuRt4HpgA3EZoVfgYsB2YRtiufh4w38wGAP8EHDgHuAzoBSw2s65x17wB+B4wC/iLmX0auB+4\ni9C9cjvwZUI3C2Y2EfgpcAdQCFwHXEsoZI6Lo7WJmVlmNFbk48CCuKeeBnoC7yEUT98BvmRmHzjF\nvEWkldRiIiIt+au7/yj6fJuZfR54t7s/ZmaN3SJ73f2QmX0V2OHutzZ+sZldDVQAH+bYm/9qd/91\n3Dm3A/e4+5Nx3ycbeMjM7gSGA0eBN929GCg2s4uBKndvaBrHCXL5uJl9OO5xV2Ab8G2OFUFdojh/\n4+47o/N+EOU2wd2fPoW8RaSVVJiISEvWN3lcCZzVwrlTgPFmtr/J8SxgdNzjosZPzCwHGAzcZ2b3\nxp2TEX2f4cBfgMXA8qh76W/AH9x9ZRtz+QOh+yeD0OrxfeA54D53Pwrg7ofN7EfAlWY2HRgJTCQM\ndM08ibzHtDFGEUGFiYi0rKaZY7EWzs0gjK24qZlzKuM+r27yNQCfBxY2c83t7l4HvNfMJhHGulwM\n/MnMHnX3T71D/PH2u/vW6PPNZlZKKEzqiAa+mlk34CVCUfEk8AiwDFh0guu2Nm8RaSWNMRGRk9F0\nZsobhBaCYnff4u5bgL2ElokJzV3A3csJs2MKGr8m+rqpwL1AzMwuNbM73H2Nu9/v7u8F7iTMpGku\njlZx9xeAB4Cboq4hCIXPZOACd/+Guz8FHCBMM24sOk45bxE5MbWYiMjJOBD9O8HMVgMPEdYEedzM\nvkl4I58PjCe8ebfkfuCbZrYDeJYwY+ch4HfuXmtmtcBdUVfJ74F+wPuBl5uLw92r2pDDncAVwE/M\nbBxQHB3/hJk9BQwFvkV4ncxKcN4i0gK1mIhIc07YEuHubwE/J8xaudvdtwHnE2a0LCLM5qkG5rr7\nnhNc57vArcBngXWEGTsPE7pGcPeFwPXRxxuE4sUJs4OOi6MtCbp7DXAjYfrzve7+ahTL5wjja34O\nvAA8QWjFSVjeItKyWEOD1goSERGR1KAWExEREUkZKkxEREQkZagwERERkZShwkRERERShgoTERER\nSRkqTERERCRlqDARERGRlKHCRERERFKGChMRERFJGSpMREREJGWoMBEREZGU8f8IpQStdDlN0wAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125a48908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-204-e1268d95aa1d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mlogisticModel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0mthis_segment\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnomis_df_one\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnomis_df_one\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'segment'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0msegment\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis_segment\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/justw/anaconda/envs/nlp/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2051\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2052\u001b[0m             \u001b[0;31m# either boolean or fancy integer index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2053\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2054\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2055\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/justw/anaconda/envs/nlp/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2093\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2094\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2095\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2096\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2097\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/justw/anaconda/envs/nlp/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mtake\u001b[0;34m(self, indices, axis, convert, is_copy, **kwargs)\u001b[0m\n\u001b[1;32m   1667\u001b[0m         new_data = self._data.take(indices,\n\u001b[1;32m   1668\u001b[0m                                    \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_block_manager_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1669\u001b[0;31m                                    convert=True, verify=True)\n\u001b[0m\u001b[1;32m   1670\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/justw/anaconda/envs/nlp/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mtake\u001b[0;34m(self, indexer, axis, verify, convert)\u001b[0m\n\u001b[1;32m   3960\u001b[0m         \u001b[0mnew_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3961\u001b[0m         return self.reindex_indexer(new_axis=new_labels, indexer=indexer,\n\u001b[0;32m-> 3962\u001b[0;31m                                     axis=axis, allow_dups=True)\n\u001b[0m\u001b[1;32m   3963\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3964\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlsuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/justw/anaconda/envs/nlp/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mreindex_indexer\u001b[0;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy)\u001b[0m\n\u001b[1;32m   3850\u001b[0m         \u001b[0mnew_axes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3851\u001b[0m         \u001b[0mnew_axes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3852\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_blocks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_axes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3853\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3854\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_slice_take_blocks_ax0\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_or_indexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_tuple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/justw/anaconda/envs/nlp/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, blocks, axes, do_integrity_check, fastpath)\u001b[0m\n\u001b[1;32m   2763\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_consolidate_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2764\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2765\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rebuild_blknos_and_blklocs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2766\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2767\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmake_empty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/justw/anaconda/envs/nlp/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36m_rebuild_blknos_and_blklocs\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2853\u001b[0m             \u001b[0mnew_blklocs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2854\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2855\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnew_blknos\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2856\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Gaps in blk ref_locs\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2857\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# build two dictionarys\n",
    "# keys are segments\n",
    "# values are trained logistic models and the best APR rate\n",
    "\n",
    "logistic_models = {}\n",
    "best_APR = {}\n",
    "\n",
    "possible_APRs = list(np.arange(.1, 15, .1))\n",
    "\n",
    "for segment in segment_map.values():\n",
    "    logisticModel = LogisticRegression()\n",
    "    \n",
    "    this_segment = nomis_df_one[nomis_df_one['segment'] == segment]\n",
    "    \n",
    "    if len(this_segment) == 0:\n",
    "        continue\n",
    "    \n",
    "    avg_segment_loan_amt = float(this_segment['amount'].mean())\n",
    "    \n",
    "    segment_loan_term = float(this_segment['term'].max())\n",
    "    \n",
    "    X = this_segment['rate']\n",
    "    y = this_segment['outcome']\n",
    "    \n",
    "    if y.mean() == 0.0 or y.mean() == 1.0:\n",
    "        continue\n",
    "    \n",
    "    logisticModel.fit(X.reshape(len(X),1), y.reshape(len(y), 1))\n",
    "    \n",
    "    logistic_models[segment] = logisticModel\n",
    "    \n",
    "    profits_per_APR = []\n",
    "    \n",
    "    flag = False\n",
    "    \n",
    "    probabilities = []\n",
    "    \n",
    "    for APR in possible_APRs:\n",
    "        \n",
    "        probability_of_accepting = logisticModel.predict_proba(APR)[0][1]\n",
    "        \n",
    "        probabilities.append(probability_of_accepting)\n",
    "        \n",
    "        APR_NPV = calculate_NPV(avg_segment_loan_amt, APR, segment_loan_term, COST_OF_CAPITAL)\n",
    "        \n",
    "        adjusted_NPV = probability_of_accepting*APR_NPV\n",
    "        \n",
    "        profits_per_APR.append(adjusted_NPV)\n",
    "        \n",
    "        if APR == 2:\n",
    "            flag = True\n",
    "        if APR == 3:\n",
    "            flag = False\n",
    "    \n",
    "    if flag:\n",
    "#         profits_per_APR = [*map(lambda x: x/100, profits_per_APR)]\n",
    "#         print(profits_per_APR)\n",
    "        plt.plot(possible_APRs, probabilities)\n",
    "        plt.ylabel('Probability of Accepting')\n",
    "        plt.xlabel('Interest Rate')\n",
    "        \n",
    "        plt.show()\n",
    "    \n",
    "        \n",
    "    best_segment_APR = possible_APRs[np.argmax(profits_per_APR)]\n",
    "        \n",
    "    best_APR[segment] = best_segment_APR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_our_APR(data_row):\n",
    "    try:\n",
    "        return best_APR[data_row['segment']]\n",
    "    \n",
    "    except KeyError:\n",
    "        return data_row['rate']\n",
    "    \n",
    "def get_their_NPV(data_row):\n",
    "    if data_row['outcome'] == 0.0:\n",
    "        return 0\n",
    "    \n",
    "    return calculate_NPV(data_row['amount'], data_row['rate'], data_row['term'], data_row['cost_of_funds'])\n",
    "\n",
    "def get_our_NPV(data_row, logistic_models):\n",
    "    try:\n",
    "        prob = logistic_models[data_row['segment']].predict_proba(data_row['our_APR'])[0][1]\n",
    "    except:\n",
    "        prob = data_row['outcome']\n",
    "\n",
    "    return calculate_NPV(data_row['amount'], data_row['our_APR'], data_row['term'], data_row['cost_of_funds'])*prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nomis_df_one['our_APR'] = nomis_df_one.apply(get_our_APR, axis=1)\n",
    "nomis_df_one.head()\n",
    "nomis_df_one['their_NPV'] = nomis_df_one.apply(get_their_NPV, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nomis_df_one['our_NPV'] = nomis_df_one.apply(lambda x: get_our_NPV(x, logistic_models), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200939584.683\n",
      "94940078.2757\n"
     ]
    }
   ],
   "source": [
    "our_total_value = nomis_df_one['our_NPV'].sum()\n",
    "their_total_value = nomis_df_one['their_NPV'].sum()\n",
    "\n",
    "print(our_total_value)\n",
    "print(their_total_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tier</th>\n",
       "      <th>fico</th>\n",
       "      <th>term</th>\n",
       "      <th>amount</th>\n",
       "      <th>previous_rate</th>\n",
       "      <th>car_type</th>\n",
       "      <th>competition_rate</th>\n",
       "      <th>outcome</th>\n",
       "      <th>rate</th>\n",
       "      <th>cost_of_funds</th>\n",
       "      <th>partner_bin</th>\n",
       "      <th>segment</th>\n",
       "      <th>our_APR</th>\n",
       "      <th>their_NPV</th>\n",
       "      <th>our_NPV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>207040</th>\n",
       "      <td>1.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>44500.0</td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.39</td>\n",
       "      <td>2.100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.693077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207079</th>\n",
       "      <td>1.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>37105.0</td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>4.39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.39</td>\n",
       "      <td>2.101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1732.016411</td>\n",
       "      <td>46.371034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207511</th>\n",
       "      <td>1.0</td>\n",
       "      <td>779.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>35000.0</td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.39</td>\n",
       "      <td>2.101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.740363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207583</th>\n",
       "      <td>1.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>35999.0</td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.39</td>\n",
       "      <td>2.110</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.404068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207894</th>\n",
       "      <td>1.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>35000.0</td>\n",
       "      <td></td>\n",
       "      <td>N</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.45</td>\n",
       "      <td>2.127</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.098291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        tier   fico  term   amount previous_rate car_type  competition_rate  \\\n",
       "207040   1.0  816.0  48.0  44500.0                      N              4.39   \n",
       "207079   1.0  716.0  48.0  37105.0                      N              4.39   \n",
       "207511   1.0  779.0  48.0  35000.0                      N              4.45   \n",
       "207583   1.0  770.0  48.0  35999.0                      N              4.45   \n",
       "207894   1.0  797.0  48.0  35000.0                      N              4.45   \n",
       "\n",
       "        outcome  rate  cost_of_funds  partner_bin  segment  our_APR  \\\n",
       "207040      0.0  4.39          2.100          1.0        2      2.8   \n",
       "207079      1.0  4.39          2.101          1.0        2      2.8   \n",
       "207511      0.0  4.39          2.101          1.0        2      2.8   \n",
       "207583      0.0  4.39          2.110          1.0        2      2.8   \n",
       "207894      0.0  4.45          2.127          1.0        2      2.8   \n",
       "\n",
       "          their_NPV    our_NPV  \n",
       "207040     0.000000  55.693077  \n",
       "207079  1732.016411  46.371034  \n",
       "207511     0.000000  43.740363  \n",
       "207583     0.000000  44.404068  \n",
       "207894     0.000000  42.098291  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segment = nomis_df_one[nomis_df_one['segment'] == 2]\n",
    "segment.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x158ee2c88>]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b6qZcbfX3zFztSz/nDI8t77ttd2OTS0FGrVb0Lr12fMp7jUMz5a4+r8XOHQmn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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x158a2d198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(segment['rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:nlp]",
   "language": "python",
   "name": "conda-env-nlp-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}