FinalProj (2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>THE PRICE OF GAS INFLUENCES THE AUTO MARKET</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src =https://n6-img-fp.akamaized.net/free-vector/petrol-station-design_1454-41.jpg?size=338&ext=jpg>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>MOTIVATION</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "When thinking about what type of final project we wanted to do, we wanted a topic that we can extract years of data from as well as be a topic that applies to current times. What we have selected was the Fuel economy data that contained fuel economy data from as far as 1978 but for this project we'll be focusing on years 2001 - 2017. The reason we used this time range is due to only wanting to use data within the 2000s while having enough data for formulate more accurate findings. With this data, we also wanted to have a correlations with the price of gas and the types of cars that people bought that year. By the end of this tutorial, you'll find out whether gas prices influence the purchase of cars for the current year.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#DEPRSSION DIDN'T WIN THIS TIME AHAHAHAHAHAHAHAH\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn import linear_model\n",
    "import sklearn\n",
    "from statsmodels.formula.api import ols\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (12,13,14,18,19,23,31,55,56,57,58,59,62,67,74,75,78,80,84,85,88,93,96,98,99,100,103,105,106,107,109) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n",
      "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (12,13,14,18,19,23,31,55,56,57,58,59,62,67,74,75,78,80,84,85,88,93,96,97,98,99,100,103,105,106,107,109,113,114,115,118,121,122,125,129,183) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n",
      "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (12,13,14,18,19,23,31,55,56,57,58,59,62,67,74,75,78,80,84,85,88,93,96,97,98,99,100,103,105,106,107,108,109,112,113,118,125) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f577c74a240>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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WRVHcppPp9kQsXsbxx8WJDFx9BTHGvkMwjPNUmfFvUBTlhqYDWZZXAMsURflAluW/AncA\n7R1rPYXRiC2jtRa3sz63zRbbE70SCAQCv9LdUT0XA584/l4NXNrN9xcIBIKgJ9Az/hGyLH8CRAOP\nAxFOrp1DQKKniwcONGIwSAHuom/ExQWHbEMwjFOMse8QDOP0xxgDafjz0Yz9+8AQ4Ks29/PogwI4\ncqR3bpAKBl8iBMc4xRj7DsEwzk74+D2eD5jhVxSlBHjPcbhfluVy4DxZlsMVRakDkoHSQN1fIBAI\nepquyDI3UV1dxc0338Bf/7qIK674pV/6FTDDL8vy74BERVEWy7KcAMQDK4DrgTcdvz8N1P0FAoGg\ns6gqVFToiI+3NwX2+UxXZZmbdvsuW7aUpKRkP4yuhUAu7n4CXCTL8kZgFXA3MB+4zVEWDbwWwPv7\njKpCYaEOtZWnSUWvL8CTPo+qqhQWFqCqvdNFJRAIXGOxQHZ2CJMmGRk/PoJJk4xkZ4dgsfje5lln\nnc2TT2qbPJ1lmS+44EJAk2Xetu1b9uzZ1SzLHBoa1izLDPDdd1sxGiM4/fQzujxGZwLp6jkBXO3i\n1GWBumdXsVjAZAohL89ASYme5GQbWVkNLFo0h4iI1c07+uBanGWZLRYLJtN88vLWUlJSTHJyCllZ\nUzGZFmIwiD1yAkFvx2QKIScntPnYbJbIydECSxYsaPCpza7KMjc2NrJixcs89dRzvPDCc74OzSVB\nL9LmTNObbzZL2Gw6x5sfzqOPno4kFaHT2ZCkItrKMptM88nJWY7ZXITNZsNsLiInZzkmk5BuFgh6\nO6oKeXmuJ2h5eQa6+gDvqyzzm2+u5Oqrr6F/f/9HKgnD78DTm79q1TRUNbxVWZMss6qq5OW51vLO\nzV0j3D4CQS+nokJHSYlrU1haqqeiosMARLd0RZb5v//dwocfvs+MGbezZct/+NvfniE/P9/nvjgj\nDL8DT2/+wYNpmM2tZVubZJkrKsopKXGt5V1cbGbevFlYuuIoFAgEASU+3k5yss3luaQkG/HxvinH\ndFWWefnyV8nJWUlOzkrGj7+AWbPmMnToUN8G2QbhgHbQ9Oabze03jNntEi+8cB/Llt3fXNYkyxwf\nD8nJKZidZJ2deffdtxkw4DQWLOjbSp4CwamK0QhZWZZmn74zWVkWn6N7uirLHEiELLMTc+eGsGJF\nqMtz6ekF7N6didFYB7SWZc7OnktOjnvJodTUNDZu/BZjV+PDuhmxIaZvEAxjhK6N0zmwo7RUT1KS\njawsCyZTA70pPsNfIm3C8DuRn69j4sQIXG0qlqRG9u4dQUZGPZJ0HYcPt47qefDBe3nvvbddtitJ\nEps3f0dGGwG43k4wGAwxxr6DP8bpzzj+QOAvwy98/E7Ex9uJjHT9XZOaWkZiYpnba43GCPR617pC\nSUkpxMeLbF0CQW/HaISMjN5p9P1JL3qI6XmefTaEmhrX34XTpn1IREQtUIsWztnQ7OoxmeazYsXL\nbtvNyppyyrl5BAJB30XM+B14CuccMOAYTzzxaKsyb8I59XqJO+6YjsmR4UsgEAh6A8LwO/AUznni\nRCRlZa0VpL0J57TZrCKOXyAQ9DqE4XfgKZa3KZzTmZZwzgSSk1NcXgdaOKfYwSsQCHoTwvA7MBrh\n0kvdb7TKzZ3aavduff0UwIjRaCQra6rHtvPycsXMXyAIQl58cSl33fUHpk+/lQ0bvqSiopx7753B\nPfdM5y9/mUdDg6YD9PnneUyffit33nkba9Z8DEBu7mquu24q9947g3vvncFrr73it36JxV0npk9v\nZMWKEFyFc5rNqZSWppCR0YAkXUNt7WPN50ymhRw7dsxtOGdpaTEVFeWnXDinQBBsqKpKRUU58fEJ\nXQ7I6KosM8DkyZdx770P+GNorRAzfieSk+2kprrbuq0jNPTfVFd/CyzB+TvTYDDwzDN/c+vyEeGc\nAkHvxmKxkJ09l0mTxjF+/DlMmjSO7Oy5XZJb8Ycsc6AQht+Jpq3brsjKshIWlgG4ngUYjUamTnWl\nQi3COQWC3k4gFHZdyTLX1dV5LcsMsH3798yadR8zZ97Nvn17uzDC1ghXTxtMJs3n5mrrdsfXLnRc\nm0tpaTFJSSlkZU0R4ZwCQS/GU0h2Xl4ujzzyWJcmbk2yzM8/v4ybbrq2ubwjWeaRI0cRFTWQCRMu\nYNeuHSxY8Bh5ebk+98MZYfjbYDBoiRceeaSh01u3DQYDCxY8wyOPPOY3P6FAIAgsnkKyu7o+1yLL\n/PdWssyhoWEeZZlHjhxFWlo6aWnpAGRmjubo0aNYrVaf+tEW4epxQ1e2bhuNRjIyhgijLxCcAngK\nye7K+lxXZZnfeus11q3T0pIXFPxMVFQUkuRaFqaziBm/QCAIappCsl0p7HZlfa6rssyXXXYlTz75\nKKtWfYTVamHevL/4PMa2CHXOthSbCdmyiYbxEyElFdhHWNin5B8exuqf93JZ+pVMOPNcoXbYRxBj\n7Dt0TZa5KW92+/W53pQ3W8gy+5uaGqLHjkJf7fC1RYK9AnROGRfVBkhYDBbC2HHrPqLColy31UcI\nBoMhxth38I8ss//i+AOBkGX2M9FjRyFVV6FD276lKwe9EXS6lp+IUCifDSctJxn12pk93WWBQOBn\ngmV9rsNnGFmWRwNXAumOogPAp4qi7Ahct7qZYnPLTB/gdNyF62MMgdOjYP/Rk+Qf2cfQgeILQCAQ\nnFq4nfHLspwoy3IusBKIBXY7fmKBlbIsr5VlOdHd9acSIVs2tS5wvQ+r5fQw7fe6A58GpkMCgUAQ\nQDzN+P8NmBRFWefqpCzLlwEfABcEomPdScP4ia0LVgN/c19/tWMD3WXpVwasTwKBQBAoPPn4p7gz\n+gCOc55lKU8VUlKxRce0HO8H3Ihpqg2w/yiESmHCzSMQCE5J3Bp+RVGOybLcvL9YluX3ZFn+Rpbl\nz2RZjmmq0x2d7A6qt+3EGh2DHbSfBLCpYLe3/NTWa1E9YYYwdt62r6e7LBAIBD7h1tUjy/L9wHRZ\nllcrimIBBgPzgMuB+cCsjhqXZTkc2AU8CawH3gAkoAy4RVGU+i6PwF9ERlK9t7B1HH9tKtS2juP/\n7MbgieMXCAR9E0+untuBSx1GH+CkoigbABMwycv2s4Fqx99PAMsURZkE/Azc0enedgcpqTTceJNj\n8xbAmZw8eT+p/S/nnrPvF+4dgUBwyuPJ8NcoinLI6fhtAEVRGoHajhqWZXkYMAJokr27GPjE8fdq\n4NLOdlYgEAgEXcdTVE+k84GiKC87HZ7mRdvPAfcCtzmOI5xcO4eADkNBBw40YjD4R5TI38TF9e/p\nLnQLwTBOMca+QzCM0x9j9GT4d8iyfGcbg48sy3OBrzw1KsvyrcAWRVEKZVl2VcXjduImjhzpnXlq\nxRb4voMYY98hGMbZCckGj+c9Gf65wCqHEd/mqDsBqAR+1cF9pwJDZFm+CkgB6oEaWZbDFUWpA5KB\n0g57LxAIBAK/49bwK4pSAfxCluVLgJGAFXhfUZSNHTWqKMpvmv6WZdmEJvMwAbgeeNPxW2x7FQgE\ngh7Ao1aPLMsj0fz5/6soirmL93oMeF2W5buAg8BrXWyv2ykuhi1bJK66CsLD3dUxs2XLJsaPn0hK\nc2SQQCAQ9B7cyjLLsvw/wGxgO3AucJeiKJ93Y996Ro/fBTU1MHaskerqpiAoHdHRVrZtU4mMbKpT\nw9ixo6h2EnuLjo5h27adREZGtm/0FED4TPsGwTBGCI5xdocs8+3AWYqi3ABMBP7cmQ72JTSjL0Gz\naDNUV0uMHWt0qtPa6Gt1qhg7dlQ39lQgEAg6xpPhP6koSi2AoiilQGj3dKl3UVyM00y/NdXVeoqL\nNfdOW6PfUqeK4uKueskEAoHAf3gy/LYOjoOCLVs87yPYskViS1tZ53Z1PJ8XCASC7sTT4u4QWZaf\ncHesKMqjgetW72H8eKsX5yd2UMfzeYFAIOhOPM34V6CFcDb9tD0OClJSIDra9cNOdLSNlBRISUkl\n2lnWuVWdGBHdIxAIehWe4vgf786O9Ga2bVPbR/UMtLD9hXVU5Y9gT3ksn322kyuuaBvVM5Dvv1+F\nqlZSUXHcYwLnriR57u0JogUCQe/Ckyzzl54uVBRlsv+70zsJC4MbbrCwerWB8nI9yVIZ0478m6t/\nP4FdJGHFgF4fQlTUbzFGr0StruG0QfCra46wePEFrF4tYTZbSU5OJSvrKkymhRgM2ktvsVgwmeaT\nl7eWkpJikpNTyMqa2qqOO7pyrUAgCF48WQcbmpDaauB9wHXYShBgMoWQk9MS1FRsSWQZ97WqY7PN\nobr6H83Hxw7BypymI80zZjabyclZDsCCBc842p7fXKbVKWpXx32/fL9WIBAEL54ycF0KXAnUoMks\n/B34BVChKMrB7ulez6OqkJfX0exZBT72us28vDWoqoqqquTlrXVZZ82aT/jpp92oqmuhOk/X5uXl\nur1OIBAIPC3uoiiKWVGUBYqijACeBX4J/CTL8opu6V0voKJCR0mJx5cJLaGY97H6paVmTpyYRUVF\nCSUlxW7qlHDRReO54ILzyM6ei8ViaXW+oqLcw7XFVFSUe90fgUAQXHRk0QCQZXkgcDZwDnAC+CmQ\nnepNxMfbSU7uaAtDIlpmSu9ITYUhQ95myJDlJCeneKxbXKy5h0ym+W36leD22qSkFOLjE7zuj0Ag\nCC7cGn5ZlnWyLF8py/L7wPdAGjBDUZTRiqI822097GGMRsjKsnRUC5jmdZvTpmntDhy4jqysK7y6\npq37xmg0kpU11WXdrKwpIrpHIBC4xZPz2ow2u/9f4J+ABRggy/KFAIqifBP47vUOTKYGQPP1l5bq\nSdWXMrXxQ9bzS/aSiabfs9hRexXaS5cKXA1YSUnJoazMQmqqZvQXO6rq9cU88cRdHD9ey7vvvu2x\nD03um4yMIU79WujoVy6lpcUkJaWQlTWluVwgEAhc4UmdcyXgTh3TrihKwJOl9xZ1ziZUFSoK68i8\nYSzhVWZUwhnOT5hJc66F5vNPBIykpxewdetIjh07SWKiNtNvwmpNo7r6W1QVLrjgPI+aPqmpaWzc\n+K3LmXyg4viF2mHfIBjGCMExTn+pc3qa8T+hKEqBp4tlWc5QFKWww170EYxGOPf6EUiOTVoR1HHd\nPR+x9MUHnWsBpzcfTZu2itjYk8TGtm+vvn4KYMRohClTrmoVmtkWT+4bo9HY6klAIBAIPOHJ8K+U\nZfl1YKWiKK2c3LIsS8AdwO+BiwLYv95FsRm9swrn6bB4yRzoBx9/PI2iojT0ehtWq560tIMcS19F\nyvVzKKiGtCiw2UGvA5sthcaGq6mtbXHJtLht1mI2m5EkPVarlZSUwUyZMlW4bwQCgd/w5OqJAJ4B\nrgM20hKvOBhNlewjYF6TdHMg6G2unpAP3mXAn2a0ZIp/APgboANVDaesLJHTTjvKsWNRDBhwlIfW\nRLHy5zLCjXUk9oejdRAdrue1K3ORYxKw2RLQnhBaaHLbDBgwgOPHjzdH5/jiyumqC0g8OvcNgmGM\nEBzj9Jerx63hb0KW5TjgErTVStC+ANYrinLYu676Tm8z/BSbiT1nZIvhPx3IB5xeYotFYvbsRaxa\ndQ0HDqbCADMM+xgun4NksLLoMrh+hERyfyt2WyoNDVc5Zv7tH758lWTwl5SD+EfqGwTDGCE4xtkd\nPn4AHAb+Xe+71odJScUWHdPs42c/2lpuREuV2bMXsXSpk8//WAZ8qx0ven4WD46HZnFTvRmDQfPr\n19a2l1jwVZJBSDkIBAJPeLWBS9BC9badEBuLHS3kyZ4Atlqw22Hv3tN5442bXV+450ZKc+egKKdT\nVARvvglFRdqpfiGrMBg+Ayqbq3ckyZCfv48PPni3XSSQkHIQCAQd0aGrpyfpda4eB3Fx/Tn8wx5C\ntmyiYfxEavSnMe4XRipPRjlquHrKsqPJHg3B2cDHxkJBAY6k7XoslpEcPbqewsJSxo8/B5ut48Rn\nzkndCwsL3F4nSRKbN3/ndQSQeHTuGwTDGCE4xhnwZOuyLN/qQ7+Ch5RUGm68CVJSGXd+fypPDsQ5\nGXt7dLQ1+gCVlTBkCOh0oNPZ6NdvJ1FRl3RqQdY5qbuQchAIBB3hydVzqyzL62VZPqPbenMKUpyv\nUlnf34sfbkGXAAAgAElEQVSaRbQ1+k1UVra4fQAMhj10VgW7Kam7kHIQCAQd0ZEs88vAZ7IsPybL\ncr/u69apw/+tq/OypmeFi29anbZSVbWJ2trORcquW/cphYUFPPTQfGbMuJvU1DQkSSI1NY0ZM+4W\newEEAgHQQVSPoijvyrK8Gk2rJ1+W5SKncxcGunOnAr+4LBxM3tT0/HJd2Oq0REzMRFJSUjGbi9xd\n0o6HH56DzWYjNVXL9PXVV5uoqqoUKRkFAkErPBp+WZYNwH1oG7YWoEWtC5xIGWokNvQElfUDOqg5\nGIjFlbsnNhYGO6k6WywjMBpTycqa6lHGoS1NC7quMn0JBAJBE54Wdy8BdgAZwLmKovxLUZQNTT/d\n1sNuRFWhsFCHu4hHVVUpLCxoFxL5351WYkOPQ0uQJ7FUMJ0XkWh0qlmIZvxb0OsHsmtXCHY7NFqh\nvmE4x4+/DKiYTAtbuWw60u5viwjfFAgErvAk2bADuFtRlE2+NCzLshFYCcQDYcCTwI/AG4CEJmF5\ni6Io9e7a6K5wTotFy6ubl2egpERPcrKNrCwLJlMDBkP7nbCDBw/m8suz2u2ELc5X+b91dfwivZhR\nc6/hwB9P48ynFOz2tg9WRWg+/wuBVMLPXcH598zg16OsXDc8gkERddhsKdTXT6W2diGq2kBFRTn7\n9+dz8803ej2uzoZvtkWEx/UNgmGMEBzj7I6du7N8NfoOrga2KYryrCzLacA6YBOwTFGUD2RZ/iua\n0Jv3vowA0TaZutkskZMjAbBgQUO7nbAHDhxw6UpJGWrkhqFGoib/CsPcKpLuUkl728yBAxlt7jgY\nTd9Oo+67O7B9doy775gFaAu6klSE0dh0z2fIyBjCgAEDkCQJq9Xq1bhE+KZAIHCFp3DOR7rSsKIo\n7zll6koFioGLgU8cZauBS7tyD3/gKZl6Xp6BqqpO7oStqsRwYDdcA0ZjHdOmeZeEveD/pqGq4e3K\nQ0Nz0XQhICYmluHDR3jVHojwTYFA4BrvFbt8RJblzUAKcBXwhZNr5xBathK3DBxoxGCQAtq//fuh\npMT1udJSidLSGo9JzS2WGuLi4lsKd26FQbZmSbvFi+cAmmzzwYNpaN+17Z/CSosHs3v3cM477/tW\n5Xr9QeLiDgPhQCJbt/6X8ePHs337do/juvzyy3n88UfZuXMro0ePJtZVQgAviIvzZo/CqY0YY98h\nGMbpjzF68vEfRVvcdUlnwjllWR4DvA4kKooS5yg7A3hdUZQJ7q7rDh+/qsKkSUbM5vZfMKmpVj7/\nvJLLLx/nMqzSZVasqkpizzsD3Q6btizu4PjxSO655x+8/fbN2O2utkTY0estjB69gy1bJhAWpqV7\ntNu1H50ObLbBzX7/nTv3cMklF7gdV79+/bDZbFitViRJYvjwEeTmricsLMzr10b4TPsGwTBGCI5x\nBlyyAS0E5S8efjwiy/K5siynAiiKsh3t6eKELMtN/oxkoLSjdgKNp2TqWVkWYmI6uRM2JhZL+kho\n4+F59NEneOut29wYfQAdNls/tm8/l/HjN7eU6kCv1343+f0jIuYzatRoQkPdG/HGxsbmtQCr1cqu\nXTuZMuUSt/UFAkHw4MnVc6SLYZsXAmnAA7IsxwORwKfA9cCbjt+fdqF9v9E2mXpSUktUj3a+dVLz\n1NTU5qgeVxzNXU/U1IsxsAfdNFBjwvn439d43Z+dO0dTWRlNbGy1y/OhobkcPjyHmJgYSkvd+Klc\n8NNPe6iqqiQmxje3j0Ag6Bt4cvW8pijKbb427JjZv4Lm7Q4HHge2obl8woCDwB8URWl010Z3q3Oq\nKlRU6IiPt+NqTbQpo1Vm5lBqaz1H1ugLC4j+xdnowuzsjx3CmWYFm9dLKnbWr5/M5MlfO+6rZfdK\nTCzDaKzDbtfx3XdvcP75t3ql3unMihVvMnXqr1qNx93OXvHo3DcIhjFCcIyz2zJw9SS9Wpa5oxdf\nVYmedB6S2YxKOCPZzQHahnW6w86dd/6TF154gHnznmbVqmsoKkpl8GAz06Z9zKJFc2hosDNypMTB\ng26/N12i1+sZPnwE558/nnXrPvOYoUv8I/UNgmGMEBzjDHgcvyzLZrRtqG3RAXZFUQa7OCdowmjE\ndloUktmMkTqm8TFLebDj6wDQ8fLLd7N16zi2bz+3ufTAgYzm7F5LlszimmtsLF3auW7ZbDZ2797F\n7t27mstEhi6BILjw5HtwHzIi6BhVRX/0aPPhYrSwzg+5hmLSnSq6/2LesWO0469KtACr0YCRf/97\nDNnZYSxefBK7Hf73fxMwm8u73OW1a9fwu9/dRlpauoj/Fwj6MJ5kmQ8CekVRDjb9oMXehzj+FnhA\nX1GO3mnh1YCVJcxCYSQ/cwbT9K922IbNZgHOBhLQ8t0PAqIoKZnMmDEWZs+GxYvhp5/K+emnM/n+\n+++5/35vnyraU1Ji5pe/nMCkSePIzp6LxeI62kkgEJzadCTStkmW5dOcik8HPpVl+Vw3lwkc2OIT\nsLkQVTNSx+kUMNf2vBetTAC205ycHTs4RN9KSiwsXQpz5kBEBAwbto/MzNu4/fbpXeu3zdbs+pk9\ne3aX2hIIBL0TT3H8jwGXK4pyrKlAUZRdwK/QJJoFnjAaqXcT/w9wFgW4XkJpohLY2eFtVq2iWU3U\nYNhDSko40dExneqq+7ZXCXVPgaAP4snw6xyGvhWKouxGC8cUdECtaSHqjLuxpqZhlySskZHNpr6M\nRMBTKOYOWmb67jGboays6ciKwfAh27bt9IvxLyoqYuXKf1FcbO5yWwKBoPfgyfBHejjnnyllX8dg\noHbBM1Rv/Jbqzd9R/V7Ldt5EykjHk0EdjaZe7ZnUVEh0UjyKippDWloSe/d+xZIlg3zvO5rbx2TK\n5pxzRjJsWAY1NTVdak8gEPQOPBn+XbIs/0/bQlmWHwK+DVyX+iBGI7aMIXDeOOx6zZg3hXi6JxYY\n1WHT06bRvNlMp2uReIiJGc3MmYe63ncH1dVVjB3bcX8EAkHvx1M45xzgY1mWbwW2ok0/JwLHAffO\na4FHqnblE5N5BjqbjcXM4RDRvMOtuA7r3AKMR/P1Wx11DICFuLgwbr65jsWL219VVJTMhg0XkZLy\nBVogln+orq7io48+4KKLfklMTCw7d+5g1aoPmTbtekaNGt1xAwKBoFfQ4c5dR3TPSDTLs1NRlG+6\no2Nwiu/c9YC+sIDo88egAx7mSZ5mPp7i+dvG8WvadolAOGZzAikphwGoqQknI6OQysomF89z4Ng/\n4E/0en07qQi9XmLXrnyf5Z97CrHbs+8QDOMUkg09SJc/YKpK9KRxSOYitjOSs9mJZ8PvCRt2u+To\nVzmVlU65AVCAYb73s5Po9RLl5Ue67X7+QBiLvkMwjLM7ZJkFgcIp1HMMu9skZO8sOr76agJFRclO\nM/0mZLozAMtms7Jzp9sUDgKBoJcgDH8P0RzqGRlJGYOQaECL6+/8Q8699y7hm28ucnO2jO40/qtW\nfdht9xIIBL4hDH9P0RTq+d7HxHIMC6Fs5jz603lXyZ49Y7nlljfcnI0C6oC9wGJgL3p9A3fc4XvX\nPTFt2vWBaVggEPgNYfh7GqcQz/F8xx281skGdI4fPZ6fFmTgz4CMzSbxyiu+dLZjRHSPQND7EYbf\nR1QVCgt1+EPRoGpXPja9hB1YxBxm8jzRlNF5t4+3C8Q6pkzpMHumT6xdu5oFCx5r5evfuvW/zJ49\nk61b/xuQewoEgs4hono6icUCzzzTn48+slJSoic5uSVNo8HbBFtuMLy+gqjZM9EB5/Mf/ssEOhft\nY/eyvh14GAis9r5Op8PV52vHjn0kJCQE9N7eIiJB+g7BME4R1dNDmEwhLF0KZrOEzabDbJbIyQnF\nZArpctuWqVeDpLl9HmSRDy14P+OHm3xov3O4m1SMHn1mwO8tEAjcIwx/J1BVLSG7K/LyDF13+8TE\nYhk+AoCbwlfhS4SP94zBGy2gQCHcPgJBzyEMfyeoqNBRUuL6JSst1VNR4esmrBaO5q6nMXMU9iTY\nszMDTcHTtzBP7Rrn69u2UU5PGf/33nuLL774nN/97ka++OLzHumDQBCsCMPfCeJjGkkxVrk8l5Rk\nIz7eDzP0sDCOfrmJyn+sYVhEEXa7xMqVv6FfvzofG2z6MnLVt1jAAvwAzHP8tgMvAEOAGT7es2Ne\nf30FN998A+vWfcbNN9/AoEEDOHDgQMDuJxAIWhCGvxPEPTufa2tcx8tnZVnwa5racy/Evkabjd92\n2wfcc89LPjSio3W4p7snkjHAU47fAPcB+wFf7uk748aJUFCBoDsQht9bVJXQvLUsdoRbplOARCPp\nFHB/5CuYHjracRudpOrKfGx/l7AXwKKn5jDz9udJH1iAln7RkwvHX1Tiu4aQbwi3j0AQeITh9xJ9\nRTn6kuLmpOm7yURhGLvJZEndXYRUlfv/plGxVN10BPXhX2HItLLkvVnsPpLJzwyjlkje4Vp01Pv/\nvs3sILALzO1ZseJldu7c0W4vgEAg8B8ijt9bnBQ122JNTaN647f419fjxNb/Ejv1Updz72weYiFP\nE5iZeSWQgDcpIANFoOWeRex33yEYxini+LsbD8nT67OmBM7ogybr4ObUAp4lcLNy77KABRKbzUpm\n5tAe7YNA0NcQhr8TNClqkp6uJU9PTUOdcTe1poWgqugLC/CLhoMLqnbsc+nVLyKZwPrht9A65r97\nff7QWu75xRf/zpgxw3jxxb+3q6eqKoWFBagBeg8Egr5CQF09siw/C0xCyxf4FFoKxzfQrEgZcIui\nKG6d1L3K1eNEXISk6evEJ0BICBGm+YTmrUVfUowtOYX6rKnal0FXNRxcsfW/DLjnj4QcPIgOeJOb\nuYU3CbxBbsoC9h/gsQDfqz3XX38jH374QbvytWvXcfbZ52IyzScvby0lJcUkJ6eQlTUVk2khhg7e\nA+Ee6DsEwzh7fQYuWZZ/CcxRFGWKLMsxaEHi64FcRVE+kGX5r4BZUZTl7trotYbf6cWPyJ6LMaf9\nENQZd1O7IEBaOBu/Ifb6q9ChzfjTMNN9M/HuzerlDTNm3E2Oi/dgxoy7WdDBeyCMRd8hGMZ5Kvj4\nvwFudPx9FIgALgY+cZStBi4N4P0DjyPE0xWhebkBc/sw6cJmV89gSoj1Y0L1junerF7e8N5777gs\nz8vLFW4fgcAFAfBFaCiKYgVqHYd/BHKBK5xcO4fQMoa7ZeBAIwZDz+nJeCIurj/sPwQlxS7PS6XF\nxFlqIC7e5fkuYzZDaioAhWSQQSGVOKde9PUJwBuFzzK0t+6kj/fwL8eOud5DUVpajMVSQ1wH70Fc\nXP9AdKtXEQxjhOAYpz/GGDDD34Qsy9PQDP/lQL7TqQ4t05EjvXO21vy4ZYgkOjnFdYhnUgrVhkgI\n1KNn6Glw6Dhs/IaIV5ZzKDcBM8k8w4O8yKzA3LOZpqxeCrAGLfrn9gDfs/P07z8AgyHS46OxcA/0\nHYJhnJ1w9Xg8H9CoHlmWrwDmA1mKohwDamRZDnecTgZKA3n/gNOTIZ5NTLqQ2pXvYMkcxWBKWMZs\nuhbe2Zlrm7J63daF+wWOo0ePYOyO90AgOMUImOGXZfk0YBFwlaIo1Y7iL4CmpKzXA58G6v7dRXPS\n9NS09iGe3UizqqcksYe2qp6dkXXw1UVkdlMemExf3jJ16uXNf8+bN5vBgwcxb97sHuyRQNDzBDKq\nZwZgAvY5Fd8G/AttdfAg8AdFURrdtXEqRPU0o6roK8q1EM+enGVWVTLgkgsIKS3lPaaxhD/zW95i\nFv/A5pVnz9ssXu74EngZTdnzl07lk4GvHGU/AtXtL+1m3njjPX7/+18L90AfIRjG2evDOf3BKWX4\nexOvryDWkcIRQCWckezmABleXNxVw++pXTvaQ2Y20L1PRO6w2+29+730A73+8+ongmGcp0I4p6Cn\nuPUPrRw7RuqYxsc91p0Wmj6LC3q0F87cd999Pd0FgaDbEYa/j1K17ptWXv5FzOF+JznpAZTQfh3A\n1o09/KIb7+We5cuXN8tBZGc/THp6AtnZD/dwrwSCwCIMf1/lrDFUHTpO5eKlNAwdytHFfyP70HS+\nnvFPdhjPZcel92FDYgpvAg1M4U1+HTWzGzt4CdqXzXwgmrS0KcDSbry/htVq5ZJLLmDQoAHk5CxD\nVVVycpYxaNAAkRtA0GcRPn4f6Cu+xJhBA9p98+uw0v3zgaYnDU9ZwnqGQ4eO93QXukxf+bx2RDCM\nU/j4BV2mavP37Zw9GxiD76GgvtKUHrIywPfpPMLtI+iLCMMfzJxxhuYOWvEW9RMnUbniLc42/QYb\nEk9xP4kUMYJvu7FDnjNuORQqupU331zBokUPM2xYAosWaV8CnVkLEFLRgt6IcPX4QJ9+pMzfR+zE\nsc0Ol6+YwGT+Q2BdME1vcxXuMn7p9WCzLQLmBLAfvvH22//m0ksvb1VmsVh8lor2N3368+pEMIxT\nxPH3IH39AxaTOgh9fYsAW/f6/c8GtrsoH4Om7N271gCaaLsWkJ0912epaH/T1z+vTQTDOIWPv68S\n4Exe3lC1cx+2UE162Q4UkYArv38ixbj3/3taG/C0dtA245eEJMnAZsfxDy5b/O1vIS7O/ZgCjbPb\n5+abb3Rp9ME7qeiqqko2btxAVVXvWvOoqqtkY/EGqup6V78Enad7nzkF7rFYujeTlyeioqgyHyKu\nupSad/9N2GVXcmhoLRs3wptvhnBj8V85a9vbfD76fh788V4fb9IAhLgoD0Mz7k0Zv0ZhtTonWh+D\n9oWRAzwPPEBi4pXk5Aynf//LaEn30L28+eYKhg0bzqxZnl8Ps/kgFRXlZGQMaXfu5MmTTJlyCT/9\ntAer1YokSQwfPoLc3PWEhfVcDoSTlpNM+egSfqrag9VuRdJJDI8ZQe516wkz9K7cDALvEK4eHwjE\nI2WPZPLqgI7GWVwM55wTiWv3S9Nb5/pcUtJ2SkvHuDnfGew89dT9PPdcNpWVe9A0gbqfGTP+RE7O\nMq/qHjhQ7lI1dPLkiezatbNdeWbmKL78cpPPfevq53Xy+xPZVemiX7Gj+PLXvvfL3whXT6t6wtXT\n6+mpTF5dJCUFoqNd7/bVsoK5d/XU1vpPyC46+giVlYNoLQrXvWzb9l+v6y5a9HS7sqqqSn76aY/L\n+j/9tKfH3D5VdZX8VOWmX1V7hNvnFEUY/l6AvqIcvZtMXvrSYvQV5d3cI+/Ztk0lOtqKs98+lgrW\nDciAS6/HpSzEFddz7Fi6h1Y7t3/g+ecfcjpyJw8dWL7/fqvXdV977V+Ail5fAGhf6vPmzcZqbR/N\nBNru4j17dnfYbiDWBvZU7cZqd9Mvu5U9VR33qwmxRtB7EIa/F2CLT8CWnOL6XFKKJvXcS4mMhL17\nVb7/voZly+r4/rzfUEwCA885j9QrvweTBOnvAA3ab5NEuvwxAzjQQcvef9k9+OCzTkcpaF8YXwIX\ndXBlU9L4icBhr+/XVe68M4Po6HFER5/Dtm1nMGjQAFat+shtfZ1Oz4gRI92eP3nyJJMnTyQzcyjX\nX381mZlDmTx5IidPdj015oiYkUg61+lPJZ3EiBj3/Wrun+Ukk9+fSObKoVz/ydVkrhzK5PcnctLS\nO1J3BiPCx+8DwsfvHdn/mUvOjvZjmrkFlnzmKUzUht0uodN5E0baUV1Prk7nj5cBV/sHAoHzv5zO\nyyUOT9IRHa0N9LSPv7vWCISPv1U94eM/FQh0Ji9VhcJCXbcuF5gmLGTG6LtJtUchWSG9Gu7fAovW\naQ6gqWd8hUtXUNLTDBq4A/dGu6Vufv5g/vjHxR7qFnpRvp3uMvoAr72m/Z471/trmhRENVrcRDt3\n/ps9e9obVfDf2kDudevJjB3VPPOXdBKZsaPIvW69y/rOu5XFGkHvRMz4fSCgMws/Z/KyWMBkCiEv\nz0BJiZ7kZBtZWRZMpoYOo0T9NU61UaVCLSf+93eS8O1Wjp/3C1jbonw5dFAZx0gjjAOYw4cRWwc6\nTqKFe7qLGGrAbtdCCUNDj9PQ4C66SKt/7rl/ISFhIdddF8f06aXY7c6Dfxhov+DaQjRaxrDRaNnD\n3gRu8W7wLjjrrH5s395IVBQcO+btNbPJy3uE006bT2joWlS1iCFDoLIDu/nhh6u57rqr/PI+VtVV\nsqdqNyNiRhITHtvuvKvdyqMnjWHt6Z+0bMto279frWZSSkcuOe8QM/5W9cSM/5TCaMSWMcRv6RtN\nphByckIxmyVsNh1ms0ROTigmk6sY+sBg7Gck47QhGFev5/ih462MPkD+zQuxEUYdmtEHiAvb56Kl\nFqJjW87//vc5HfbhySc3s2YN3HRTDQZD29n9bzq4+gu0L5sfHcfXAekd3tMdDz6oZRu96y7vr/nx\nx9+xYMH3GI3LkSTvjD7gcW2gs8SExzIp5SKXRh/AZJpPTs5yzOYibDYbZnMRa9/+BNa5bs/bNQKB\n/xGGvw+jqpCX53pan5dn6D1Rokv+0Tp+JxwOfHe+x0seer/lfFpaSQc3sJOV9RUAqhqOxdL2NXHe\nKdwavb7pvDNGYFoH93TPjTeGA3DNNZ25KoXc3KGoajhFRd4ZfYC1a1d3un++oKoqeW5Ckvvl99P2\n67VheMwIt18igsAiDH8fpqJCR0mJ67e4tFRPRYX2NNjs/y+uwrBxA/RAzHjVK2+0ePsToVRKxH1G\nMCuKKRHrUqAA/vGPP3to2U50dHnzssCPP47Gbnf1mpTT1vhLElx11d/dtLsYuJ+IiFgkCdLTYfhw\nD91oZg8FBbOpPhbNCy94U7+JHZjNiZSVJfLNN95f9dJL//C67tat/2X27Jls3er9noQmKirKKXET\nkmw7amNovzO9XiMQBB5h+Psw8fF2kpNdG88UfQkxkXVkZ4cw6QIj488P4+Jz7Jiuzydq5DCiJk8E\nP4QDes3V0zSJ6JtvpaHSQHzocdLTi1xWTdMV8Y9vypAeADLh3obnPDY9s3oxfA00QIxlB+gsLmrF\nIkl1fPllf+bNgx9+0NZHHnjgHTetGoAlvPrqaBQFdu+GPXu0iJ2U5sjcwcA7wPmO33bgdBY/9xzn\njqnm3Xc7eE2akYDRJCWVk5hYxoUXensd3HVXx5Ia5eXlDBo0gKlTL+X111cwdeqlDBo0gPJy70Nq\n4+MTSEhIcnkuISGRdX/4hl235/Phr1az6/Z8vvz1JiH30IOIxV0fOJUWkbKzNR9/W2byPNboWP5R\n3X6RcibPs4RZMGYMhz/vxPTSj0REzOWRR4aydOmD7c4196+Jv4FulofQ0DYz+ciocmqPxrerGRtb\nweHD7fdMeAorDQurpa4usvUdbWA0Wqmvd3XNA3Q+xaSmTCpJDVgs2nsZF+edu+fQoeMdfl4HDRrg\n8XpvSUgYiM3mSlJborz8iNft+Mqp9H/pK2JxV+AVpvtKuZ+lzUnW0ylgJs/zBI+yuvoCl9esYhoq\n4bBzZ4+4fQBqaxfyxBP7uf/+V0hPL0CSGklLq2TGDaU8y5yWANBwsF8DX311Lq5CQ7/i3FbtqmHh\nRIe7XtwID6+lpiYcu53mnwMHkvG0F+DkSSOKcnqrMrM5mfp6V9eowMfevgRoM/0xaIqlYLX2a75X\nYSHEeuEebx0G2p6O3Dreun3y8/e5NPoANpuV/HzPi/WC7kUY/j5O2L7dLOEBdpOJwjB2k8kSZnGY\nQZhxndLKTCplJILVisEhFeDtPgD/7RcwUF//FNnZV/HNNzVs3nyMDRtCWfBif44dOkLlP1+l4Zyx\nVL/yNKTrufji7djtEo8//mfi4kp5/PE/Y2uQuHhIa23/srhESipcj7u0NJW9FyVSvhEaGkD3Dtx0\nfcehhmvWXN3q+Jtv3F1ThidJiZtugj/d/WvgELAebd3hBzTFUo2XXprOli0wezZ88gncfbfnvq36\n8D3Yv9+t3tP8+Z4T27z33lueb+Bg3bpPu3S+L9KbJSqEq8cHTqlHyqpKYjOHomujA6MSzkh2c4CM\ndpekU8BuMjFKDZRtz8f096QO9wF0Zb9A11CJjh6LJLVfWLQXgC4TqHOqHRbOyJjdHChxMe60InZV\njiCitra57N2EZH5bbsbTHoG9e4ciy/ubSw4eTCY93dU1KjASXMhVpKdr6wRFm09n+GX5bu5XBrj2\no7tj26BBnFtZibWNzPfWrd8ydeplHV6/du0XnHfeuA7r5efvY+LEsW7Pb9q0jaFDz+xU3ztLb/m/\nDKSMtXD1CLwjJhbL8BHtio3UcVX0f1xeMo1VGKmDUaMw/T3Jq30APbdfwEgry+6EPaL9KePJOn51\numt3y5Sp+YS00cm/qbwEjIfc3j0sTG1l9AHSUksc6qSu+uo6DHTaNG3rxpk79+NenK5zRl8Czj10\nCGw2JHMRxpzlRJjmA3hl9AGvjD7QoVEPtNHvTUz56BJ2Ve5sFrez2q3sqtzJlI8u6eGetSAMfxBw\nNHc9jZmjsEuS5v2WJBozR/HItiuZMaOe1FRrs///fp5nkX4ejZmjUNdv8WofQFf2C3TdNVSJXn/U\n5RldnB5bdDtBCOY9OpZ77llPenoRktRIenoR99yznuzsc1y+VjvOvAJCKmjdkp2wsFrKyhJarQnY\nbEAG7NZngNT+Gp3ur8yYEUJ6Os1hoDNnwlPPgLocfpw1Etez/S2delUkNEdRHr/kKj4izyFZHZqX\ny4tLPEdBOeOt5EN+vuf9Ah2d94a1a1dz7bVTWbv2LQyGDWjJenoXp4pERUAfwmVZzgRWAc8rivIP\nWZZTgTfQPpdlwC2KotQHsg8CICyMo19ugqpKDHt2YxkxEmJiMQALFjTwyCMNVFToiO+nZ0DhUI6O\n2AsxsRw/EkZJievZZ9M+gIwMu1f7BTIyWrfjL9eQwbAbtzo7OhvHfl6N5c1iBry0jON3/Qlu+h0G\nwDQaHnpIxWIpxWAIw2gc19Rgu9cqISaWSn0BJ2rG8v57N3DiRBS///0bzTN9uwUadkDoE6BbpTVz\nYs2InQgAABHASURBVGAd+vsSsB1Phu03w4kUEibnYH56Nwa99oVXVgaJidpMv9EK296FVdzkZqSv\neXwdbvwVPPwJvBsdQ9aiJaT9cS5xtLib1nINYGeP+QyeePoVr1/f4pVnEfMnBcIiPdb7+ut/dnh+\n6NCrPdZxx88//8yECec0H2/atBGAffsgPX0UR4+ux3kdpCfxRsbaXxIVXSFgPn5ZliOANUA+sMNh\n+FcAuYqifCDL8l8Bs6IorpOTInz8PU1ERH+GDbNiNrff1ZqaamXjRhWjUTNikyYZO6znjLsw0xkz\n6lmwwMU2T7dUEhs71BFy2Rq7XaKyMh9wH/7i/XupEh09DklysbegAGi7ltAPRt4DBwa2lIUboGwW\nnBbevomjdWBJAXP1SM5hJ+1n/VuACW579/aF8FtH5G3jiJGE7NmBu/BWWAJ42vSmIUlQXg4D82Ko\nvtKd2J1Gfv5qJk78ndvzmza95bPh9xRuardDY+Mojh7tugqpP6iqqyRz5VCXxl/SSey6Pb9Lu5VP\nBR9/PTAFKHUqu5iWpKirgUsDeH9BFzEaISvL1WYnrbzJmHtbrwn/SknEYrG0X8MAHOX+kgQwUl8/\n1fWpVbRfS2iEaXtd1HXz7yjZIbYOzmI3er2r+c54j737rdN2i3V7Et3fCB3wPx7bamLUKC1kVD+x\nCqo9J7jJyNiHu7TAYWHaeV/oSHLif/8XDIY99Ba3T0x4LMNjXH8ee5VEhd1uD+jPmWeeaTrzzDPv\ndfx9yKn89DPPPHOzp2sbGy12Qc/S2Gi3z5xpt6en2+2SpP2eOVMr96We3W63//yz3a7XO3vGW34k\nSTvfOersdvsYu90u2bWPjuQ4ruv8gNuwd+9e++LFi+179+612+2Ndrt9pt1uT9fuYU2z29+MtdtD\nnAbTr1/zC9A48z77zLX32dOXpNulxyX7hSuS7Rar64+7rQG7fQh2e2Gh/fBhrYn2r09Z6wUDx8/O\n1ssM9ql8ZAeby9dXK//ZDnqXbQF2vR77mDHY6+oc/WvAbt/8RvNrcviw3b5+vfa7iZMnE+3V1djD\nwlq3FRaGvboa+8mTiZ163Wtrtc/BRRdd7LafgP3ii7HbbNjt9vVdeZubOXjwoP2NN96wHzx40Oc2\n6hrr7GP+OcYuPS7ZMWGXHpfsY/45xl7X6PnzWFtba//555/ttbW1Pt/bCY92OeDhnLIsm4BKh6vn\nkKIogxzlZwCvK4ri9vlVuHp6Fudxqqqm/RMfb/coHOpNPV9cQ95RicGwG4tlJN7O9N29l0ePHmXU\nqDOpr2+RrQgNDWPnzn1ERYWg15djsyUAxtZrJ+HGdrLaTbLUCREDSB50sUt3kfXEIKpP7tDac/D4\n47BsmSu56S3ASm7NMPNaYV67tnK5lKl87uI60OxlHe7CShMS4NtvYfBgpysKoLJxNyeNqUyZEs5P\nP0lYrZoraPhwK7m5dRgMzxEV9RQTJmxm+/ZQ4FPgSsaMqWfz5gkcPfowBkPH7qW2az9RUR9RXX2D\n2/offaSJ3e07HEJadAUnjrjRf+6Ampoaxo4dRXV1VXNZdHQM27btJDLS8/qGOzqSsW7ClZx1VtZU\nTKaFGNoseJ0Krh5X1Miy3OThTKa1G0jQizEaISPDs9H3tl5nXUPeE4vFchH+cO+0NfoA9fUnGTXq\nTMCIzTaEZiMdE4tl0kUQE+tSVrtJljrcEOvWXVQvXY+z0Qd47DFwHdo5HljOvyJci6JdltkUTeQK\nO57CSn/zm9ZGH8C2KQaiNaO/a5cBq1UH6LBadezaZWDKlHAMhj87jP65aAses4FMtm8/lwkTNntl\n9KF9WPD/t3fvwVFVdwDHv+wCiwlWIR0RJRPpCL+RxhkFqqMIYn0R1DqK2Crt+JxYpj7HFq06Gq1S\npYOto46jA5WKWLWtjlpDLA8tPpBHqTYK8yNWSokIaIJWdnEhm/SPcxc2m7vJ7pqE7N7fZ8Yxe+4+\n7o8Dv9z9nXPPaW6e1unzL7jA7WImh+1h087hWX2Gn/SkD9Dc3MT48cfm/Z5dLWOd5Lec9RNPPEaN\nN/W2J/R24l8KJHtyGu6ywARQTc2e/VNJw22Ulyeoro5TU5PLwG7PaGjY2CHpJ8XjX3+j5Qei0fuI\nxWaSSFTQ1hYmkaggFptJNOq/01pdXRS/pSjq6qIZp+l+UbuM1atTX8e+161YEaWysoV+oTnA9UAF\nEKKiAq6/HmomDKbtY2jb6670EwvLaJ5cT1MTbNjgfzW9YUOYhgaorz/e93h9/fE0NaU1xmKENn3c\n7o7izGM/Db7v25DWfPTQr9kZz71vGhu3dEj6Sc3NTTQ2Zhrf2L8TWr46W8568eJaYj20dnqPTecU\nkXHAXNyOFXtF5CJgBrBARK4BNtPVHDVTtPr3T5tK2kUJqTdls/xA/jck9ScafYBo9K725aIMxo6F\nHTuizJsHjz8+kGuu2cPVVyeP+k/TBXd/wI4dUZYuhUWLDmbGjF2c4U2lWL58N01NsH79bCKHVdAW\nmsuQQ37G6MOuYy/wefMWBi5/mz1HTYApbnmL9WtCJDLMmk0kYMmS5DcBv+P9WL8+xMSJrdDSQmnN\n7UQWv0rok0ZaU+4o3r59QIZpwUcTDrcye/YfefaF27hj1mYuvNDnTzYE22J1DInk1jcrV3a+7+/K\nlW8zfXrqNNsWSkvdTmihUCOtrSOIx8/xfnnnllI7W85669ZGtm/fxsi0mwq7gy3ZkIcg1viLlV+M\nfWH5ge7UHf3Y1ASVlYN9k3s43MaKFbuYNCnz8Q8+2EVZGZTecQslT3ScwR2rnslntz3Q5dhPPLyR\nkcPHM8Dny8feBGz6dG3Oib+xcQtjx2beCWzdug8ZMWL/+k6lpbdQUuITQ2wm0egDOX12LBZj4sQT\n2LKl47hPeXkFb765ipKUK6JCrfEb0+eNGjWaSMR/bmIkMqigkn53KStzA7l+jjkmwahRnR8vKwNi\nMSIZyhqRxbWUEOty7GdIZDQfNfn3zUfNg3JO+gAjRpQzdGiZ77GhQ8vaJX2IEYlkiCFSS65ln5KS\nEqqq/Md9qqqmtkv63ckSvzE+6us3dkj+yVk9QVVbu5vKyhbCYTfWEA63UVnZQm3t7qyOh7ZvI5Sh\nrBHa2kho+7asxn4GJzayYccg9ia8G7gSsGHHIEYO+TTv2Naure+Q/JOzetqdZ2gboVCGGEKNhELZ\nb16TVFNzH9XVMykvryAcDlNeXkF19UxqavzHfbqDlXryEIQSCAQjzq5ibGjYyJIldZx55pSCvdLv\n7n504wMhxoxpdVfy2R6PxRg68QTCPmWNRHkFzW+u2j8FNotpwTvjG9kWq+PwkikMiYzuljgbG7ew\ncuXbnHTShLQr/X1BZLyDO5GooLl5FZ2N2XQmFouxffs2hg07POOVfneVenp0rR5jCt2oUaMLNuH3\nlLIy3EBtrsdLSohXneNb449XTW0/BdabFtyZIZHReZV2OjNiRHnaQG46dwe3X40/Hp9KvkkfXNmn\nJwZy/VjiN8b0mqhXvogsriW0tZHWI0YQr5q6r70QJKfeRiK1KbN6pmacktsXWaknD0EogUAw4rQY\nD5BYrMMdzt9U78cZy2pKbneyUo8xpnAl73AuaMk7uAuPzeoxxpiAscRvjDEBY4nfGGMCxhK/McYE\njCV+Y4wJGEv8xhgTMJb4jTEmYCzxG2NMwPTpO3eNMcZ0P7viN8aYgLHEb4wxAWOJ3xhjAsYSvzHG\nBIwlfmOMCRhL/MYYEzCW+I0xJmBsIxYfIjIZ+BPwoddUD8wBFgJh4FPgJ6oaF5EZwI1AK/CEqs7v\n/TPOjYhUAi8Bv1XVR0SknCxjE5EBwAKgAkgAV6jqxwcijs74xLgAGAc0eU/5jaq+WsgxAojIHGAi\n7t/yr4E1FF9fpsf4A4qoL0WkBHeOw4BBwK+A9+nBfrQr/sz+rqqTvf+uA+4BHlXVicBHwJUiUgrc\nCZwBTAZuEpGhB+yMs+Cd88PAspTmXGK7FPhCVU8B7sP9Q+xTMsQI8MuUPn21kGMEEJHTgEpVPQmY\nAvyO4utLvxihuPryPGCtqp4KXAw8SA/3oyX+7E0GXvZ+fgX3h38isEZVv1TV3cDbwIQDc3pZiwNT\nga0pbZPJPrbTgRe95y6lb8brF6OfQo4RYAUw3fv5C6CU4utLvxjDPs8r2BhV9TlVneM9LAca6eF+\ntMSf2RgReVlE3hKRM4FSVY17x3YAw4HDgc9SXpNs77NUtcX7S5Mql9j2tatqK9AmIgN79qxzkyFG\ngGtFZLmIPCsi36aAYwRQ1YSqRr2HVwG1FF9f+sWYoMj6EkBE3gGewZVyerQfLfH7awDuBs4HLgPm\n0348JNMO9p3ubF8gco2tUGJeCNyqqt8H3gNqfJ5TkDGKyPm4pHht2qGi6cu0GIuyL1X1ZNz4xdO0\nP89u70dL/D5U9RPv61ebqv4b2AYMEZGDvKcciSsjbMX9tiWtvdDsyiG2fe3eoFI/Vd3Ti+eaF1Vd\npqrveQ9fBo6lCGIUkbOB24EqVf2SIuzL9BiLrS9FZJw3wQIvrv7AVz3Zj5b4fYjIDBH5uffz4bjR\n9ieBad5TpgF1wCrgeyJyqIgMxtXW3jwAp/xNLSX72P7G/prrecDrvXyueRGRv4jId7yHk4EPKPAY\nReQQ4DfAuara7DUXVV/6xViEfTkJuBlARIYBg+nhfrRlmX2IyMG4WtuhwEBc2eefwFO46VabcVOm\n9orIRcAvgDbgYVVddGDOOjsiMg6YCxwF7AU+AWbgpoN1GZuIhIF5wCjcIOrlqrqlt+PoTIYYHwZu\nBWLALlyMOwo1RgARqcaVOTamNF+GO/di6Uu/GJ/ElXyKoi+9K/v5uIHdg3D5Zi1Z5pt8YrTEb4wx\nAWOlHmOMCRhL/MYYEzCW+I0xJmAs8RtjTMBY4jfGmICx1TlNnyUiw4EtwB2qen8vfu7lwBmq+uO0\n9jeAIcDOlOa6fM5NRNqAAaraktZ+AjDb+5wW3M2DN6rqpjw+Yyrwbsocf2MAS/ymb7sMWA9cDvRa\n4u/Czaq6tCfe2LtZ8AVguqqu9NouBV4TkTHpvySycBMwE7DEb9qxxG/6sitxiWuBiJysqu8AiMh/\ngIeAKmAk8FNVXeZdkS8FTgZGA3d5N7gsAN5S1Xne69uAAUAZbt2X/sAhwEOq+lSuJ+nt33Cvtywu\nqZ8nIhcD1+HWT/kMuFpVmzK81bXA08mkD6Cqz4jIYlVt8e7qnI+7szMCzFHVF72li+/H3dA0CLge\nGI9bw36RiFyhqutzjcsUL6vxmz5JRCbhEvJy3B2MV6Q9ZbeqngXci0t0SYNVdSpuQa9ZXXzMEcAj\n3mJf5+LWQe823vort+PKRqcAbwC3dfKS7+I2UmlHVZOlpXvw9onALSD4mHeX+Y3Ag6p6Gu7b0XBV\nfQxXJpphSd+ksyt+01ddBSxQ1TYReRL4h4jcoKox7/gb3v83A6mb32Rq97MVmCUis3BL/ZZlcV5z\nRSS1xj8fNw7h5yTcsrmviQi4q/TOavUJ/NeaTzoReAzAW6KgERDc8iKzvfGBl1T15U7ewxhL/Kbv\nEZFv4Ram+q+IXOg1h722hd7j1Hp36jK0fu371iVJW6f8XqBBVS/xFr36KovT61DjF5FT056T/Iw4\nsFpVz83ifcFt8TkBeD7t/U8EVpMSh6cf0Kaqz4nIa8BZwJ0islpVO/tmYQLOSj2mL7oEV9IYo6rH\nqepxQDUdyz3Z+h9uASxwuxUlE+gw9u+rfCnQKiKRPN//SBHpJ27/1BO99jXACd6gLSIy3VtXPpNH\ngelezR7vNT/EfasYCLwLnO21H4H7NqEicjcQVtXngRtw3zTA7cs6II94TJGzxG/6oqvwShop/ozb\nFe2oPN7v98CPROR14DjgS6/9EeAeEVmCu9pfhiub5Op94F/AOuAPwDsAqroVl4j/KiIrcHG9m+lN\nVPVz4FTgVhFZJyJv4ZbZPd3bjeku4BRvEPsFoFpVd+E2DloiIstwvzxqvLd8DXhFRE7OIyZTxGx1\nTmOMCRi74jfGmICxxG+MMQFjid8YYwLGEr8xxgSMJX5jjAkYS/zGGBMwlviNMSZg/g9dJew2Szqh\nsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5780d84da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_2001 = pd.read_csv(\"2001.csv\")\n",
    "data_2001['year'] = 2001\n",
    "\n",
    "data_2002 = pd.read_csv(\"2002.csv\")\n",
    "data_2002['year'] = 2002\n",
    "\n",
    "data_2005 = pd.read_csv(\"2005.csv\")\n",
    "data_2005['year'] = 2005\n",
    "\n",
    "data_2006 = pd.read_csv(\"2006.csv\")\n",
    "data_2006['year'] = 2006\n",
    "\n",
    "data_2007 = pd.read_csv(\"2007.csv\")\n",
    "data_2007['year'] = 2007\n",
    "\n",
    "data_2008 = pd.read_csv(\"2008.csv\")\n",
    "data_2008['year'] = 2008\n",
    "\n",
    "data_2003 = pd.read_csv(\"2003.csv\")\n",
    "data_2003['year'] = 2003\n",
    "\n",
    "data_2004 = pd.read_csv(\"2004.csv\")\n",
    "data_2004['year'] = 2004\n",
    "\n",
    "data_2009 = pd.read_csv(\"2009.csv\")\n",
    "data_2009['year'] = 2009\n",
    "\n",
    "data_2010 = pd.read_csv(\"2010.csv\")\n",
    "data_2010['year'] = 2010\n",
    "\n",
    "data_2011 = pd.read_csv(\"2011.csv\")\n",
    "data_2011['year'] = 2011\n",
    "\n",
    "data_2012 = pd.read_csv(\"2012.csv\")\n",
    "data_2012['year'] = 2012\n",
    "\n",
    "data_2013 = pd.read_csv(\"2013.csv\")\n",
    "data_2013['year'] = 2013\n",
    "\n",
    "data_2014 = pd.read_csv(\"2014.csv\")\n",
    "data_2014['year'] = 2014\n",
    "\n",
    "data_2015 = pd.read_csv(\"2015.csv\")\n",
    "data_2015['year'] = 2015\n",
    "\n",
    "data_2016 = pd.read_csv(\"2016.csv\")\n",
    "data_2016['year'] = 2016\n",
    "\n",
    "data_2017 = pd.read_csv(\"2017.csv\")\n",
    "data_2017['year'] = 2017\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.xlabel('Annual Fuel Cost')\n",
    "plt.ylabel('CITY (MPG)')\n",
    "\n",
    "cur = data_2001\n",
    "i = cur['fcost']\n",
    "j = cur['cty']\n",
    "plt.scatter(i,j, color = 'red',label = '2001')\n",
    "\n",
    "cur = data_2002\n",
    "i = cur['fcost']\n",
    "j = cur['cty']\n",
    "plt.scatter(i,j, color = 'green',label = '2002')\n",
    "\n",
    "cur = data_2003\n",
    "i = cur['fcost']\n",
    "j = cur['cty']\n",
    "plt.scatter(i,j, color = 'yellow',label = '2003')\n",
    "\n",
    "cur = data_2004\n",
    "i = cur['fcost']\n",
    "j = cur['cty']\n",
    "plt.scatter(i,j, color = 'blue',label = '2004')\n",
    "\n",
    "cur = data_2005\n",
    "i = cur['fcost']\n",
    "j = cur['cty']\n",
    "plt.scatter(i,j, color = 'black',label = '2005')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f577ef126d8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cr9XNdtmrVxybNn2la9ZZWVkhvXolEknQaFnKqTCnzsu3hH7Y8BV/x8FGRtP7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\nizfw9ddXAV0alZhRtYZ/Bkro3Rtee+1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Gt2+fl1/8wmqznJqEKipobTkC9ds57bE/2CIx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f577eb30c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.xlabel('Annual Fuel Cost')\n",
    "plt.ylabel('HIGHWAY (MPG)')\n",
    "\n",
    "cur = data_2001\n",
    "i = cur['fcost']\n",
    "j = cur['hwy']\n",
    "plt.scatter(i,j, color = 'red',label = '2001')\n",
    "\n",
    "cur = data_2002\n",
    "i = cur['fcost']\n",
    "j = cur['hwy']\n",
    "plt.scatter(i,j, color = 'green',label = '2002')\n",
    "\n",
    "cur = data_2003\n",
    "i = cur['fcost']\n",
    "j = cur['hwy']\n",
    "plt.scatter(i,j, color = 'yellow',label = '2003')\n",
    "\n",
    "cur = data_2004\n",
    "i = cur['fcost']\n",
    "j = cur['hwy']\n",
    "plt.scatter(i,j, color = 'blue',label = '2004')\n",
    "\n",
    "cur = data_2005\n",
    "i = cur['fcost']\n",
    "j = cur['hwy']\n",
    "plt.scatter(i,j, color = 'black',label = '2005')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f577ee1fe80>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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94C/lE5g15nJG7fMeCAMz5rCicXwcaDkQlk9fIBAIjDA0+pIkWYHfoW3GWgDc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\n0nfOuRTxpO+ccyniSd8551Lk/8ZpBQ8UWFXHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f578038d2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.xlabel('Annual Fuel Cost')\n",
    "plt.ylabel('CITY (MPG)')\n",
    "\n",
    "cur = data_2006\n",
    "i = cur['ANL FL CST']\n",
    "j = cur['CITY MPG (GUIDE)']\n",
    "plt.scatter(i,j, color = 'red',label = '2006')\n",
    "\n",
    "cur = data_2007\n",
    "i = cur['ANL FL CST']\n",
    "j = cur['CITY MPG (GUIDE)']\n",
    "plt.scatter(i,j, color = 'green',label = '2007')\n",
    "\n",
    "cur = data_2008\n",
    "i = cur['ANL FL CST']\n",
    "j = cur['CITY MPG (GUIDE)']\n",
    "plt.scatter(i,j, color = 'yellow',label = '2008')\n",
    "\n",
    "cur = data_2009\n",
    "i = cur['ANL FL CST']\n",
    "j = cur['CITY MPG (GUIDE)']\n",
    "plt.scatter(i,j, color = 'blue',label = '2009')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f577d91ae10>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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khbpj1bphnb6+/JqaTCoqcrT7qcihpibTp6Tvvv26OhNVVdq/bpMJnngC3nyz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\nDCOHMKdvGIaRQ/w/LQm4zhgUbdUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f57803a0320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.xlabel('Annual Fuel Cost')\n",
    "plt.ylabel('HIGHWAY (MPG)')\n",
    "\n",
    "cur = data_2006\n",
    "i = cur['ANL FL CST']\n",
    "j = cur['HWY MPG (GUIDE)']\n",
    "plt.scatter(i,j, color = 'red',label = '2006')\n",
    "\n",
    "cur = data_2007\n",
    "i = cur['ANL FL CST']\n",
    "j = cur['HWY MPG (GUIDE)']\n",
    "plt.scatter(i,j, color = 'green',label = '2007')\n",
    "\n",
    "cur = data_2008\n",
    "i = cur['ANL FL CST']\n",
    "j = cur['HWY MPG (GUIDE)']\n",
    "plt.scatter(i,j, color = 'yellow',label = '2008')\n",
    "\n",
    "cur = data_2009\n",
    "i = cur['ANL FL CST']\n",
    "j = cur['HWY MPG (GUIDE)']\n",
    "plt.scatter(i,j, color = 'blue',label = '2009')\n",
    "\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f577e8a6358>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YDNQ99Qx1jzyOpPyEVF6O7eyxEBtHfHg48pz7SJn+c7697UbQUGSylZdjLS/D\nmJrWqjw8OJz9tZBXcZDvGvM5NyiVtOAU8r6ArzfXMKz6QUZFrSXlUC4FOwo4UlPL5DR3qWGA5AG5\n7C8sJqyNU9oCgcA3ElUHqapKrt493pmiqiSqvu2276zc95tv/puEhEQuvfQKjh49TFRUFHqNMfqT\nNtVcZVm+BBgF2IHdiqJs7o6BQc+quTb5Le0WC1tv+TWWIvekIaFJyUz813/Qh7ZfP+nHVx9iavqL\nbuVV5ggije5+0pyKQTDtW8Ii/B+wFv7p/k+gzRd8i0E0MasTMYjVq1fx6qsrSE8/GZdskvtuaGgg\nKSmZhx56FJ1O10rue9CgIdx66+2kp2fw+OOP4HA4sNttzJlzPyNHntGuZ/v9HASALMujcMYbPlQU\nJbcrHtjX0IeGkjDpAnI+eMetLmHSlA4Zh/oas0dXkqe4/cG6KzmzG4yDQBDIZLmMwPoQAwWSRIqq\nkmm1NZf7wowZ1zJjxrVu5c89t8yt7MUXV2j28cILL/v8/K7Ao4GQZfn3OHd+/QA8I8vyHYqifNpt\nI+tFDJ89B4CSLZuwlBQTmpBIwqQpzeWeMNvNlNoriNfHYNQbOVFYzKhIbTtrDK7jy+M3MSRiM8kD\ncimsTm/exSQQCPyLAVhY18BDdQ0USzoSVQfitaztcxBnKopSJ8tyCvAaEJAGQjIYkOfcx7DbZ2Mt\nLyMkNs7rysGm2lho28W6qOHkhA0no76AaZV7+WPSaRR8n0FGzDG3ewqr0xly3dMA7C8sJjo5Uawc\nBIJuxggM9jHm0B/xtovJoihKHYCiKAVA2/s++zn60FCMqWltupUW2naxLPUSjpkyUCUDx0wZLEu9\nhGdCD3Cw7krNew7WXUlYhJGwCCMpIwZ3S8xBIBAIvOHNQJyqca2teS1ohdluZm3UcM26dZHDGXzD\ng3yWezc5FYNotOnJqRjEZ7l3C1eSQCDodXhzMQ2RZfkxT9eKoogTXC4aKiupOXqYiCHDKDXWkhum\nbSByjcmcqDrMmbc9wVc517NeV06mI5ah0hl8uFrPhAl20tLMSFIRqpoEwgsqEAh6EG8G4rU2rgMe\nm9XCd3fNovboYVBVkCSqE8PI+M8bHIt3lxhJNxfSWO8gOSkEe9zPAHgV0KuNhEyvwlwYR1yclaNH\nJ2E0xmK1TqeubhEiM6xAIOgJvMl9/6U7B9IX+e6uWdQePniyQFUZUFjHvb/5Hfeucz8uMq3qEJMH\nT8auD26gKlk/AAAgAElEQVRVbtcHY82NBIOOsrI4hgw5QmlpEkajUw29rm6xX+chEAj8Q2fkvgHe\neusNPv10PQaDgT/+cR6nnz6qW8fvbZvrRm83KopycdcPpw9gNiMVF2HB4Vw5aBB9vIxLNz3JT2df\nR6EpnZS6XKZXHuWSolDetxVSmZBAQ1hYq3vsUhCDL9pH9hejmmXAMzLyCQlZR13dA1ikGgpJIVEN\nEY4ngcBP2C2Wdu1UbA8t5b6rqiq59dYbOOec8Vx77a+4+OKpvPzyS6xdu4ZrrvlFs9z34RYvnEeP\nHuHzzz/llVde58iRw3z99Ve9x0DgDEon48zd8C5Q3i0j6q3YbJiy5hOy7hOkvFyKwsMhXTu1qB4d\neW8vpOzrx0mJSqa8opCUI+dyItvOkvIqypOT2XnRRbz1wAOohpM/grS7isn+wvkL4JQBfwtVymNh\n7Ges0U0lhyjSHJVcYTGRVWcTjieBoItQbTYOLX+Bki1fYSkuITQxgYRJFzB89hwkg2//0s488+zm\nD/Tw8AgsFgvff7+TuXMfBGDSpMn85z9vcM01v+DPf17AgQM/tTIQW7du5uKLp2IwGJDl03Cm2Ole\nvLmYpsqynI4zn8NK4DDwJrBaURR3hbp+jilrPsYVJxPgRZvNTiE+DQOh4iA30oKlUSW39Cg3fp/I\n0EMnFV4T8vPJXLkSgJUPPthcnvdSYvPXU6Z8BcADuqd5XndSMzFHF8cKI0DnZYgFAoGTQ8tfaKWW\nYCkqar6W59znU596vZ4wl6fgk09WM2HCRLZt+5bgYKeLOTo6hvJy53u30Whyu7+oqBBJkrj//jnY\n7Tbuvvs+hg8f4dNYfMXbNlcURclVFGWhoigjgb8CFwE/ybIcWAFrs5mQ9WtbFRkcDo9JLVQdNBic\nh22CbTrG5Wtrs4/duJHg+qac1A4KdzkF+STJTlxcBWbC+IirNe9dH6LvkqxXAkGgY7dYKNnylWZd\nyZZNmorNHcFXuW+Hw4GqqjzzzPPcdtsdLF68sFPj8AWvBqIJWZajgbOBsUAN8JM/B9XbkIqLkPLz\nWpXVGww4NFYPADqHjiiLc3EWZTEQa9ZOPBRXVER8nrNfvcNO/IhS5/06B4WFyRRak8l1pGve65Qh\nrmxRYkaSjoIwGwJBh7CWl2EpLtGss5QUYy3Xzu/SHjoj9x0TE8uZZ56NTqfjzDPPoqio+1PoeMtJ\nrZNl+QpZlt8FdgEDgVmKooxRFOWvnu7rj6iJSainSHqH2WyE2Ro125cbG6kMdWZqrQy1UR6m3U5S\nVebOns2NTz5Jyok8Sg/GAzB08CEG/XSU5I8KSa/T1m5KdeQwKmYkUVETMZnmEhMznpiYscTEjMdk\nmocziaJAIGiLkNg4QhMTNOtCExIJifX8Ie6NJrnvv/71OTe5b6BNue/zzpvI9u3fAnD8+DESEhI9\ntvUX3qIvuThXCx8Cf8f5iTNAluUpAIqibPL/8HoJRiPWzOmtYhAGh4P06hoOxsa6Nd+VWtPsYmow\nOKgLsUO9WzN0QEJhIZkrVxJlruDFKqcW02cPXIq+GozUU6fX3rOUfyIFU2wtBO0lKGhvc7lenyO2\nxwoEHaArFZtb8vnnn1JZWcnDD/+5uaxJ7nv16lUkJSWTmXkldru9ldz33XfP4tZbb2fcuHPZtm0r\nd9xxKwD33z/P06P8hsd8ELIs/wutLDlOHIqi3OavQTXRG/JBNNO8i2ktUl4O6PWodjs7ExPZmxBB\nkD2IMmMju1JreOvMYlTX2izYpmPxhqEkmIO1H+SiPDmRZYXLuPqK93hi3CPogLLgGBKvKULVa7io\nGh0cqBqOHHdEsz+7fSAVFdvoyGlskSug/xNo84X2zfnkLiZ3xWZfdzH1JN2RD+IxRVGOertZluXB\niqJkd8VAej0GA3ULF1P30KNIxUWoQUGELX+Bc//xd0aWlfHZIBP3ZNZRF6YSZw6iMtRGg8HhNQbR\nksiSMuYseYXZZz2DzpXQdXfUGFTJw4/IAKtyr+XBuKc1qyUpzyXZMcTXGQsEAUNHFZsDBW8G4l+y\nLL8O/EtRlFYObVmW9cBtwI3ABX4cX+8jOJiwl5cR9vpr2GyNrB88mMqQENDpeH69A7sOJIeOcmMj\nO1NreH9UCeXGxrZXEAkJPDtpPkpYGs/o5mJw2BlTuRu9asOutYIAXhozh2KSWcIDGGidq1ZV01x6\nTgKBoL00KTYLnHgzEJnAYiBHluXNOGMSABnAJGAVMM2/w+t9mLLmY3zVmf3pf4MHU9niRLSEDsnl\nFEswB5N5yBmf2Jla0/y1J8yRkeQOiOZ57kX3MwfPfXM/cQ0VjK7czQ+x49xv0OnI16ezFOce7ee4\nv1W11ToNIfYnEAg6g8ddTIqi1CmKcjdwJs5AdbHrzyrgLEVR5jTliwgYzGZCPlkDgEWSnCuHNjg3\nN4I1cinrh5dTYmzA4SGsY6qubj4T8fbAGziekIoD2Pq/iYwp34XObnMezNPgI/VGauwyDoceu30g\nZvNsl8ifOxW1Zrbl5FNR27QdVmyPFQgE2ngMUvcGelWQGpCyjxLzs7PRORwUGY1sHDhQ8yR1Sxw4\nqAizsT2tmq8GnWDR/4ai1zhiZ5MkHli7lpKMDHA4kFSV08r3s/2zcymviOMF2wyW/OF5kPRu9+od\nDrZWlDOUfI8y4ZYGG/cU/MB3I4ZREJFMSk0h4w/9wOuj7sMYmo2qpqHXX0Np6aMEknpsoAVtA22+\nELBz7pIgdbsOygmcqIlJqCkpAERZLB7f6FuiQ0dsfRCZh2K56Gg0qk77HockURkf77pJh6rXsz9h\nNOdfvoWMmHz+kvwa0ZXah3lSVJVENcQVkNZ2K91T8AMfnnsReZHpqJKBvMh0Vp1zFb/Z/yQ6nYpe\nnwMsxWSa3+acBAJBYCAMREcwGrFO/zkAoapKlNXaodvHFkSg92BTJJuNYI0j/XuixlAWHIPRXs+M\n42s17oRMq81rtKGi1sx3I4Zq1m0fdh5l1dHN1yEh6xDuJoFAAN5PUt/cnQPpK9RlLcJ82ywcQUFc\nmp1NVH09OrVFNla9uwuoiZj6IHQeFJwkYPDu3W7ldsnA22m/RAkfyojGAwzY8S7hJQdIOJ5NelUF\nN1VbmXrIRrnGQbwmDlWcoCAiRbOuICKZ/UUnJYSbtscKBAKBt4Nyn+E87HuHoijaiQ/8TG+LQbTC\nbEZSfkIqL8M8dBjVZWUUf7mRsu++oaFE2xVUEdJIpFWPXsMuO4CypCR2TJ3aWga8xc9Hstm4fskS\nxm3cSGxREeVJSXx/4YWs/OOf0JUYOP2gnXWj6gk9JYRQUWtmakwDeZHuuk7pVTns0p1F3IATgG8H\n7PoygeafDrT5QsDO2b8xCEVRpgL/AP4ry/Kjsiy3fdorkDAaUc8eh23q5QQPHkr5t1spWLvao3EA\niLLqyYnSdkvpgPiiIjJXruT6p1scftPpmv9cv2QJmStXklBQgF5VSSgo4PK33uL6Z5dgz9Cxd6qB\nafvC3PqOCTdy7kFtGz/+8LZm4wBie6xAIDhJW3LfbwNjgGHAIVmWNzX96ZbR9RG8yQW3RIeOd04r\nJDuqHrvHDa+nyoA7Ca6vZ9xG7SR/Ldv/NEKv6W5amnIW13z3BelVOejVRtKrcrh2x8f8e+RDzdtj\n4R6P22MFAkHg4XU/oyzLBmAOzoNxC4FD3TGovoY3ueBTuVpJ4KkpxzlTGswdH4WgFbGILSwk5fBh\njo0e3VwWVVpKbJF2bCC2uJio0lJKMjKwJ8P+/RKTw9RWbUKDDbw86BwqKswcOvwTcpwVNXY0X+z/\nADnOSpQxg/j4RJz6jGaXTIf2llmBQBAYeMtJfQnwArAZGKcoyglPbT3cbwT+BSQCocDjwI/AG4Ae\nKARuUhSlY1uBeiFNcsEWDx/gLZFPmFi+Rsauc3hcvukdDh67/npyRowg6803sYWGUhkfT3lSEgkF\n7prw5QkJzVtk9YU2Jg05CyybgPBTWtpIT/wLaUmfcvP+J9k+LJGCgSNJqSnk3IP7eSsyEpNpHiEh\na5GkPFQ1Dat1umtVEThnIwQCgRNvLqZngdsVRbmjo8bBxVXADkVRLgB+BfwNeAx4SVGUyThTmPpd\nEbY7aJILbgud6z8JHUEOyWNGOh2gV1UGHzhA1g03ANAQFkbdgAGa7c2RkTS4JD9SdXkkRhwmJma0\nWzuTaT5G43J+s/9JVo37JXmRGc1nIj489yJu3PMZRuNy9Pqc5rMRRuNycTZCIAhQvL0W3q8oyhZf\nO1YUpaW4ejqQB1wI/N5V9jEwF1hOP2D4Lb+D6ipytn0D1VVd1m/6wYOEnzhBkMVCVGmpZpsmmY6G\nsDDqY4woJ4Zw/NN4aituRx9+DfEXhbGJY/w8dAcJ1dFsG3aeZj/bh42hrDq6VdAanGcj6uoeRbib\nBILAwts2142Kolzc2QfIsrwVSAOuBD5TFCXBVT4UeENRFI8plWw2u8Ng8HyuoFdgs8HcubB6NRw7\nxn/T0ykPD29TgqO9OIDC9HQS8/KQPOTBtun1PPDJJ80yHThUglQbjZIeyQFqC3mO4Np6bKZgTRlx\nvdrIxsMXM2XE16fWAAqgfdhOIBD0OvyeD6JLUBRloizLZwEraT3oNidw4kTPneht795p04J5rTLN\nnVZWxpbwU33/nSMlVzvtaBPliYmtZDrQ6Wl0GQX1lLYNEUYk1Y4WKTWFjEza51Zut6dRURGOM4Dd\nvwi0PfKBNl8I3Dl3Bd4MxFhv21kVRZnirWNZlscBJYqi5CqK8oNrR1SNLMthiqLUA6lA92fh7krM\nZkLWt5a/GFhfzxaHo8tWEO1h18UXN8cg2oOq0w49jT+8m7hx7uEmcTZCIAhMvBmIbODhTvQ9BRgI\n3CvLciLOLTUbgJk4VxMzXdd9Fqm4CCk/z638igMH2HDaac1GwqHDo8SGrziAmshItlx1FW/Nndvh\n++W83ZgjopqVXc85eJCVZ07EbJ5NSMi6FruYpomzEQJBgOK3GIQsy2HAP3EGqMOAvwA7gNdxbns9\nDtyqKEqjpz56tdQGgNlMzOTx6HNzNKuPh4XxQ0IctaZwnw2EA21fXNM3RgXu/OwzapOTO9Cpo/nv\nlXuuZ2rGXhKifkKny8BszqSubj6SVBYQ5yACzf0QaPOFgJ2z32MQ3h3fbeByI12vUXVpZ/rtVRiN\nWDOnt4pBtGRgfT0Dj+eycuRpPhsIb1thwRk+XjZ1Kjfvc48deO5U1/z3jaPfwtEcxD6G0eicS13d\nYh9GKxAI+hPetJh+050D6avUZS3CPGs29uRUTemME1GS3zXVJWDQDz84LzqaAEqnY33h5FZFQvJb\nIBCA95PUuaD5macDHIqiZPhtVD1Axd69HNu2nUHnjSfmjDNa1LQhO2EwULdwMdYLLyHq+l+0qsqJ\ngM9+Fkpojv8D1pe9+Sb/kmWiSkqoTEjoUND6H413kWnbDPVAGFj05eSHfkG05WcY8Z5LWyAQ9F+8\nuZjO77ZR9CCW0jLuzdnB9gnnUXDBXaTUFDD+mw28OXYypvDH2i07YTt7rDMXhN1OrQEG3wNl4RBn\nt7A0zwGqf43ElHXrOH/9enA4KE9NZedFF7WWDffCb398mZrPjYSGWnlg/NOsTr2anPB0MsJzuVL9\niIcqrsBAqF/HLxAIeh9ec1LLsjxYUZTsFtdhQJqiKN0i2tcdQerf79zAqit+6VY+8+v3eP/8X7mV\nm82zPfrnY04bjL6inPg/QlmLbcivfTOY4Nz2v9F3FetvvJGVDz7ovZGq4njbGYO4d+zfWHrafW5N\n7ra/wSMVV/tjiD1OoAUwA22+ELBz9m8+CJdY3xZZliNbFA8FNrjOOPR5KvbuZfsED7ITo1un4mzC\no3/ebIawMHIinCuHlvx+fDYN6fUgqTi9do7m//yJlmy4Gzod95+eRVlwDO+l/UKzySfSJMyU+2GE\nAoGgN+MtfvoocJmiKM3CQoqi7AV+jlP6u89zbNv2dqfibMJTSk6puAipsIBNg9z7surh1gnZ3HP1\nQSwZx8ksUNBZG9D5eX0UW1joUb+pJc+e9QjxM8soMKVp1ueQTnnw0a4enkAg6OV4MxA6l0FohaIo\n+6B/OKQHnTeelBrtw9yeZCdUNc0VsD6lPDEJNTWNKcc8P69MrzJ1i5noSpURFRVdpJbiGVWvPynB\n4YkWGes8nf42UUdswxA/jFAgEPRmvBkIb4JC/WJrS8wZZzD+m22adeN3b3NTNQUvshOuMxFxFpcn\nSQNJhTiL8+thNv/7RA12O8EWS6f7cZ7hyG6znUAg6F94MxB7ZVn+/amFsiz/CdD+VO2DPJdxDtdu\neK9VKs6ZG95j5aU3wLPgOAoO1ZmS0xmg9iw7UZe1iOxfX+VxZaADCsOd218/PaNrxLS8oXM4SFcU\nRm7axL133snITb5liq0mgmWHNwGftijNJTj4bTp5nlIgEPRivEltJAEf4VRz+A7nod1JQDUwXVGU\nWn8PrlukNmpriRl3BhWqg59GjGLkwX3EVZ1cOTjCwDEomPI1eyCybTkLc3UZFy4fzrEId8XUgSeg\n1gDl4ZBRZ+DJdcPxp5/JrtOhO0Ui3AHMWb+eyowOHGNpIc3x+YFRXCAXIUmVzdWqGktFxR68Lzp7\nJ4G2wyXQ5gsBO+cu+WDxus0VmnczjQLswB5FUXx7DfWB7jAQTVtT20INCaU8t315p7MWT2JZxB63\n8lArWEJOXr+xSkay+S/fhYr2ElEFbuqINEerm9UW0hwnsdtjqajoe26oQPvwCLT5QsDO2b/bXJtQ\nFOVzRVGeVxTlpe40Dt1CXi5SO4wDgM5qgUMH29V2wX2fc2fNaAZVS+jtMKgCbtsJluDW7X4/XUE1\n2Gna+trVeNNx8tXdhE7H375yzxQrSeUId5NA0L/wt0xQz2M2I2UfdZ5TOIXgbzxnVDUHwZFo599N\nhL71hse+WmIIDiVr3ha+Hvs6B16EfcvhomPu7epC4KZrFT45rX0rk67kijff9PneVcnXs+HHC/m/\n919nw48XNpcHB/ucoVYgEPRC2nQx9SSdcjHZbJiy5hOyfi1Sfh5qahrWzOnUZS2CJvmJvFzixo5q\n9aZtk2DupbD6NMiJhIwqmHEAnv4fGFy7k9TUNKzTr2rdlxblZcSdMRyd3U5OBAy8H83X+oRKA8/+\nd3iXJxnyJhVeGRvLt5mZ7ZbjOHmzgwENlVQHRzUXmapr2GMZQbi0Eae6e98h0NwPgTZfCNg5d4+L\nqa9iypqPccVy9Lk56FQVfW4OxhXLMWXNP9koLR01pvWO3bmXwtIJcCwaVMn599IJ8MClzg9bHaDP\nz3PvS4vYOGynjwQgowbiPIT1Vb0NSfWwN7YTeHMxRZeXk7lyJdc//XTHOnU4qA6JbnV2oi5yAKND\nD9LXjINAIPBO/zQQGqlAmwhZv66Vi6hixx7sMbE4gLog+Og07S5Xy63dTQAhH34Aed797pXrPqfx\njNE4JImjSyGuhpMhB4fzOnspXK0oSHa7c8eQo0mGo72xCd8XWuetW0d0YSHRhYVMXLOG6MJC7zd4\nWOXUDYjgq/1fU258lPeCd1McvBoRkxAI+jb90sUkZR8lZsJYdBpv5Q69noqtO1EHn3IyOC+X/K8/\nZFzFw6gaH7h6OygvwtAWZ+ccAJKEbeQoKtd9DqGeD5gb1n5M1K03oMN5DmLTIJhyzLmygJOurc1p\nBhJrIygOr+G271KJthlpeyusJ2dS22h9g6ujo7l3wwYawjW2rXrKt+1wEN5QTW3wgOaiOEo4rJ5D\nQ8V2eusW2EBzPwTafCFg5yxcTJ5okr3QrEtJQ010l8ogLZ3omb8jNVzbTZJeBcmnuIh0gE5VCdq7\nh6hpl3gdk+1nE5xy4DiNwo17ThoHOOna2pVuY/3pJ9iVbmPlOW3rKHUWncafyBMneO6KKzzc4Pn3\nrjYkspXrqUyXyDD9DmJiRnf9wAUCgd/plwaiSfZCC2vmNDBq51k2BhnJHKJ93wwFjB6zZ4Php/1Q\nXua5QYt4xKmYPbi29iZY/Kz36pkBJ0607W5qB2UkkCeFItxNAkHfo38aCFqkAk0fiEOvx54+EPOs\n2c6dR17ImriIWWNmMyhqEHr0DKrU8YdvYMn/2nig3Y5hv/fDZ83xCFq7dgrDITfSvX16Vc9qIk5+\n7z2C6+tJyMkhuL6+1dcdYRMXUB28ms0GSYiGCwR9iH4Zg2iF2YxUXOR0K3lYOWhhitKz9/ghEi1B\nZIwe1XYUQJIo23cYYuPa7nzPbuIuOb+5T3MQDL8TCk5JPxFukVi2ZgT6Nu247zEIbz02oQIWkwkd\nEFJfT3lysnvGOi+xiZHsQeF07BjQA6fb7KyrrO8VksCB5p8OtPlCwM5ZxCDahdHoDEh3wDiA0900\nOHIIxsT09u0jUtX2GQeA0WNaba81NkKBxgqiNlQlJ8ranqe377kdoGVMQg+Y6uow1tWhV1US8vPd\nt8h6ik04HOzXjcGuCwKdDrtOx94gA9Oiuj/DnkAg6Bj930B0llXvt+vdXAew9uN2d9tye+3WZDwu\nALIuziY7qh4bqpcMdO0Zofa9nTEt7c1Yp8VPBr1wNwkEvRxhINpgwIpl7W4b8URW+zsOD6fiQDZl\nu/bx6nXDPTazGWDBZdksndj7gryxxcVElZb6FJuwA/vZy/bKV7njgJnNRWYk6SjOdK4tvxYIBD1F\n/49B+Eiz33LV+8T9/rZ2nURo+rt841Y444x2P+u7Hz9m+tc3eF0IeI9HtCcGod2mM9GL0uRkdl1w\nAWdv3kxsYWGHYhOSasNgt9JgaOH6q3dw3DaMtPBSdLo6VDUdq3W6KwdHB+RAOkCg+acDbb4QsHPu\nHrnvnqRXGAggNmFAh5ZaKlBeUt2h5yW8OKDN9dxT6weTXqPlu/ck7N3RNh3DJkkYNA4jrr/xRlY+\n+KDrsSpIGs/1VG5WcRhby4k7EzUt7oohuxFoHx6BNl8I2DmLIHV3Ub5pOyqtFDK8imDoAJ54HMrL\nMGz+yvv5CBe7f7ELjw9xSXLcsTObqPp6V05TB0gqYUPq0UXa2jGLrk9MpPegH9UqNuEpeO2pPEzH\nI8vvRTEP5Zki598hIW8zYMAE4E0KSo6wcecqCkqOeBhVGQbDV0Db33OBQOAdv64gZFn+KzAZp3/g\nSZyZ6d7AuTGmELhJURSP23R6ywqimVXvM2DFMqpn3cmAxxYQXFDgUS21FXo9ttNHtinHAbD7yTt4\n98B/uOUHSK11l+QA+Cla4vrrQ2lMt1BzQs/Cj4eh96McR0d7tAEPrF9PSUaG1+2vnsqNjTWYg06m\nZA2tMXPQPpD5R5ewacQF5JrSSa/LZcrBTfwl7mLCjVGAhaioSzAY9uOMcOix2UZSWfk5eNlQG2hv\nl4E2XwjYOfduF5MsyxcBDyiKMk2W5Vjge+BzYJ2iKO/JsvwEkKsoynJPffQ6A9GSt98k7g+zO/SR\n23jGaCo3tpEz4dBB4iad47VfcxBE/QkagyDYpmPxhqEkmIO93AH+MBCeUIHf7thBQ1iYZ0PgCQ/t\ngxvqaQh2d6/dtPPfPJNxLVFRkwgKcs/i19g4mspKz9/zQPvwCLT5QsDOude7mDYBv3R9XQmYgAuB\nNa6yj4Gpfny+fzl/SodvaVOOA2D4CBwh3lcZZgM0ZSptMDjYmdq7fvl1wMDdu7u0z4Yg7e/JphGT\nKSjZ6Vo5uOMsF+4mgcAX/LM1BFAUxQ7UuS5/C6wDLm/hUioBkr31ER1txGDwX87mtoiPj/Bc+d9d\nHe5PZ7cT/+8VMHs2ZGQ4C81mKCyE5OSTh/mKXNcWi2Y/uxPB0eL94K0ziwEYmx9BrDkICQc612rB\n+f/2rR68JRjq6OvIpW+/TXRhIVe+/jqf3Hwz26++WrNdcH09UaWlVMbHO1ccHSTPlM6xslWcqbM7\nfVv1QBiUNcSwu3QMY+J3Ex+fDQx23WHG6d1MBpzfb68/535IoM0XAnPOXYHfdzHJsjwDeAi4DDik\nKEqCq3wY8LqiKBM93durXUwa2ejaopV8RUwM1p9fS8jnn3rOeHfoIKHr1mB8ZQX64qLme8vCIGku\n2E+xncE2HVH1BirDbMRXG5hWGskTC6pQG3R8dsNQumsrrNYPzQH8cdUqSmQZAMlm4/qnn2bcF1+0\n3iI7dy5qUJBGB9qup5TaHL6uuJJBJXvQ5UOjWc/do1/gg6RfUB4Vh77QxumKyrpRZmIj5xMSshZJ\nykNV07Bap2M0Pk9pace0pfoyAepuCcQ593oXE7IsXw7MBzIVRakCamVZbnpNTAUK/Pl8v6KRja4t\nWslXVFRg/Ncr3jPeDR+B5Z65WGdc06qfuHoI0ti41GBwUB7WSIPBQX5MI/+Qywgb2kjCWQ10JvGQ\njo5lu9OSEJeAZ669trnN9U8/TebKlSTk57eW71iyxMPQtMdWYExjcMkedAeBOnjg7KdZccZsyuPi\nwaDDnh7E3qkhXPlTDUbjcvT6HHQ6Fb0+B6NxOTC3Q3MTCAIJvxkIWZYjgaeBKxVFqXAVfwbMdH09\nE9jgr+d3By3lMrpqqXNqxjtwV6YtzkjA6sE5aNfBrO8grcI5qKQlUGeFqz47QOt9tFp4S1LqTkfn\nrAPGf/QRwfX1jNu4UbONR/kOL9tl/179fwCY9WF8lKrtyto3LJkyc4xGzWrEiW2BQBt/riCuA+KA\nd2VZ/lKW5S+BRcBvZFneDMQA//bj8/2PSy6j7tbfdVmXUkEeUgt3EmYzUm4OdQ89SsXmbVRs3cl3\nN12Bw9NPToIYMyz8wnlZa4Pwp+Dsd+G7vx3gRLqHxNjdxJWvv05UaSmxRUWa9U3yHR3h6bGPUBYc\nQ2FYMrkm7YRP9mQDu0vHaNTkIknaYxEIAh1/BqlXACs0qi711zN7ivobb8H02itd0ldzxjubDVPW\nfELWr3WLUQybejP6ja+7xSCaeEpjg9WRSnjuW5gil3JHbtek/9T5EL7+5OabqYyP50R0NHHl7nJ9\nlVMjz6MAACAASURBVNHRVMbHd6jPo5EySdcUMapqL3H1BZSYMtwbldgYE6+1syoRVdXIMCgQCPxn\nIAKK0WNwSHp0qr3TXTVlvDMtmIdxxckjIk0xCgAWLmb02/CD1h6wNj6vN8VYuKPD+5W6Zn+TA5p3\nM8VoGAeAmLIy7d1Mns5TuMrs+iB2x5ztlPDQIllPnFShUVFA024mgUDQGiG10UWU7z2EKuk7FY9w\n6HTUhRkZcMYwQt56Q7NNU4xizT0HOasQ9Ha8hxU0mJt5ajzC4bruWAyio7EJHXD///0f4z/6yGuP\n4z/6iOlLl/LihAlMX7rUVdFOQ+QlVnHzrufI3hBLxatGsjfEMm79BnRlFqZ+09JYNSnJzmXAgCTg\nwfY9VyDohwixPg/4ujUu7A+zMb39pk9nlk+drOY7u15PxdadziRIQPm+7fzz7dksSTrU/pd514v/\nlIpQrjoey8cDy/ldQTz6/eGentqhcm+tG4A8WWaIonhsc+r9DuDRf/yDoxM97ohu0diLtAcQajOz\n8suf84tL/te6ncPBgs2vkHX+AiSp1O3W8vL3ce7U7tsE6JbPQJxz75ba6Ar6ooE4NZ1oV2NPH0jF\n5m2tMuTlHdzO2P9N7bCBaMl1dWH8fO0g9wpPN/hQ7gB2jRnD19ddxx/mz+9Qjypw0z7vOb+dHbRD\n2sOLwqxD0g7sqCqUl3dMobc3EqAfloE4595/DiIgccUj/EVTjKIlaSPGE9fJnZrvmOrxR+rSUyka\nNoyJ773X4ft0cNLd1Fm8uKHGrdfeee28RbibBIGFMBB+4NR4RGc+dpvutwPmc89znrQG5/bX7KPN\nZya2//YgcXW0Dit08MGPTdeOTXSl2bjo448Z88MPPt2b+fbbbmVXLFvG8+efzxXL2p/5zxu7zr0Q\ndgHvArvggjWr0OVauPDjVQwY8NoprZukxV9gwIBxwKn1AkHfRriYPNAly9I9uwl75e+Y/rPSZ5fT\nqd8AB2C56mpCftilKdGRd3A73337JmeMns4Nn17HsQiNnVVteIauqwtjenYcaweXsVZv9SGTnWcX\nU8mkGIbX7aPqh+QOO63+M2sWa++5B4BB27fz+K23usUqHn71VY6dd55GDy0beo5TPPD9w/z1wCI2\nR45mSuYPbnGKRzfWcdeZTdLiezn1J+SMV2wCzvI+hh4iQN0tgThnEYPwJ135SxWbFI3UBVtgvWGe\nNZu6ha2zrmUtnsSyCHcJbL0N7FobnD0knVv46WAGV3Ykk53n8uv3HQBg5aiRHbyzdQzijVGj2tVO\nE28xiLed7kHdr+0e2zTYz9KUFm/ZfW+NVwToh2UgzlnEIPoKWi4nreRxnUFLomPBfZ9zZ81oBlXr\n0dthULWeWSdGkOLp34qHX6msi7LJjqzHhooDBzZUsqPqWX6uJ5eU502sPyyLYvU10UgedZ88D23W\n7N+Dw8EVL73ktV2b7iYvMYgLxq/iqaF3e20zdf3j7eheuJsEfR+xgvCAX9469uwmbPUH/H97Zx4f\nVXU+7meWbJMFMpOEhBB2OBgIRTY3UGzRyqJ8FS1WsC61WLQiLVpttYrWrYr7Wn91q0utS11QEImo\n7IKiDQU5QRYTSEjITjLZZu78/rg3yUzmzhACgZCchw+f3Jw599zzzkzue8/7vud9a2fMhKyRcNcd\nJLzyD2hoILKh4Ygin3w2G2Ur10J0NFpCAtaqKn1HtsOBu6qEA3lbSe47nPKlbzCm5Ha0w7UY+SCu\nwUpGZTT5Peqojm7ZkHZbTiKZuU62DS2j104nzsbI5nTjrQeKSGygsRwg0vRiIafggwYLpLySxO75\njSRWVoacallsLPM3bmxuG5mdzZTXX2fZ7NnkTJ4cPhS2oYHIqmJ6ujUqUlKCN+35fFDQgC+9pT6F\n3D6Ij+rPZ3rUEh7d+xtecf6WOQfe4r7Rl7a6QAl2+0Icjk+pqvoNcDduoMhqoZfmOybb9brp03R3\nlFmZmDqSY/qleugBkh6674gUhBYVjc/lwlqwD2w28HrRMjKonzI9IIW4e08uk14fy55Ek0HCKIhe\nVVDUI/wcHLVwZm5P5si0kAoi8/pCflxhoSY3NfTFwhZy9VE4ykbqd+brFJ/fz7+89JKpn+LGJUso\nGzgw6FxrYyOXLV7MmJUrce3f35KC/Oab0ZpSsPt84NMYdmA7y2rO5KSMPOrsIW7tPh8vr65hamYd\nPXuOw27/sfklDzZu8j3Ee7657LM6SNc0ptR7WFTT0KHpDbrpzbI7yqwUREdyrL9UrpSEDrP3tfZP\n3PObFJ442aQYUSgHgBeiPVAX1bbrvfrWMKyH8EG8MXxYiIuFmkRgHy3EFdoyUig/xZz772fKa68F\ntS+bM4fX/mQS4qp54VAhzSF8Fgt4hMf5fVD3ue567qlpCD/mEdBNb5bdUWblg+hKlL738VH3SzQR\n4J/YkcuizIXM/yaS/mV6qo7+ZTB/cxT507L1eFr/CXhh4zNQF6rktQ96V0C8oW8iPRZKMClWARDr\nYf8BCzs/dnD4aT0C+7StV+j20UuXBrS1LwW5tfnclLy8kGnK52RfH9DkXhfD+wfN05Ivi7LgiXwb\nyPdrfZiEhMHAwyEkCkdT6hCV0lxx+KgVRAiO21PHQw+Q8MJzYI8gsrjoqOzI9gFl2atInHYulvqW\nlUNNBOyPg7RqcDSCJSmJAxtz2LT6dd776ikuPOV3jDvrct5/+Xrm+t4Nafm5awXcNRk0K6RUR7B4\n6WBsZp19PpYN+YGx7jSSC2JpfwJAn19R1fByh0z5ERHBylmzms1HKXl5LJ4yBbP1gBe4adkyivsG\nZoltNkm1rorX2iQFOGtL2LqzH6lbatkZN5DB0yVYTYxJmocdFsEgdqFpsVgsNa0jbSkt/QQ4VNoR\nD7GxqoIeqBXEkaBWEJ2Nm2+lavsequ6+/6gOmzjtHKz1dQFV3uIaYXA5xDYaN9KSEpxjsxg35Vru\nW7SFcVOuBYeDsZPnhR17sncofSv144poD6WORtN+dfZG3hrp4T8DSo6iZIePBYhqbNQr2D30EAAV\nyckh/xisxuutuWzxYvOqeMaY+sUsYLFQ5kgma/BuANJqC8ESwjRlsdHbUojFAjZbDVZr8xBYLHrk\nrct13iFljI29TVXQUxwxSkF0Vi66+KjuYLbU17epn7WsFPbmB7SFS+WR5IYhS77mAt21QIPdxzfp\n5k9r36QfpMHuY1W6m9AGtLZI7Ttq702T+WjaE0+E7df69faYpEqiU8iLSeeuIX8Ie60xby8P+7q+\noghnbnITFfVxiNdUBT1F21EKohNTumqjaVJus7b6+ISjdtNMmPV/8PES/Ze9+US+/SYbp3wWlMoj\nqUZP8QFw69i/MH+97s/494gi1maUUmdrAJ+P2IYGkmpKeX5sUfM1bp5svodi4l3b6Tm0FixNr5lh\nIfJXu9pQQPXQ74iroICRX6zkvLeC03j48/O33iKuvJyTNmwgrry83VXxVqWcxZPDWhSEmf9i+9nj\nkfmDePj9Bcj8QQHnl3zkZOVnk2jY8CSDl5YwoqyIwUtLgHwiI9+kqUKe1bo3+OKlwLd7sB78guwi\nmL0nkuyi4G7BKD9Gd0X5IELQqeyW/3mHhOefoWrudXDRxXrbP/5Owt+foura38E118J/3iHpt1eH\nDf1si1HS1+rY/xzN6SLnzZfYlPMe406dTZ+h44POd0+bRM0Pm4mNSiKpzkPJLy4j6vZF1P9pAZOS\n3ggKrz1zn4MLdiSRWVzCedv0G5DFCdXDbSwvHki9LSLoGp5oOPf9L7BFR/PPS04l7cDhZptt3UNX\nMz6sIUfxoj9NWQDNaiV/8GASiotxVlQE9S9zuVi4fLn5HgpjD4bV4zH3X9x0EycVf0dBxEDKnU7I\nh+jlbnYPT2ZK5mq29ByJTyMwHDc1lW9++lMqEqx8eN2taJoTiMRmMxSYG3hfPzwQ0YO0i4rxWv3e\nV4+PjXtr6B9UaNDcj1FTcy8nUq2xTvW3fIxQYa4dzIn4pQoVKtu0pe1oLBe9Thdl23e369x7Lk/g\nidOC229cD48tD57n1716ketyBfXvO3MW4gY9RNSVmsibw4bQ/pDZ9vY+/DBa/xQfhxtSm1RbRElM\nr0Oe++qf/tz6UvBGSx/7rHq8NpOQtEaN4oqagKbY2FsMv0Ugbvc8amr+FtTeWTkR/5aPFOWkVgQR\nyiRV+twLIZ+jDzek1sxH0bbJlbB4ha4M/MNrb1wPD60w5nnjwoB5ji4qYmhpKbENLaaqoaWlDBl3\nqt5hSw61Ni/Pnmturhr/6HY0Iz2I/79Q0oZK/xGKcOlCmlOTG6uG+Nqy5t3b7fVftOXcxFd0xdQc\n+eRXLO+7HsMDVw7+2C2tzE2h/RhRUUtR5qbugVIQXYlhwygtrqLkuRdpGD2WkudepLS4Cse6NWFP\na4gLsi2EJXL92sAG/9TjXjdW9y7wBt5A7Nu2Ytf0lcLWZ0E+pf98bDnYNah8dwkJb/8r4BwrMLao\niGk7d3L+Dz8wbedOxhQVEfeO7i+I+eBdCuNgXQLM/sV21l34I9En1dDrzz/y6q3bGbYGRlur6HXj\n9TT0dtHrxuspHm9Wl/ro05yaXPNy5pvnM/HAl82vtdd/0ZZzI09ttTrIazn8d9/WqT8Ceba85dyQ\nfgzAas3Dav22paEyh5jcO6Eyh02FcNO3kWwqDHcl5dM4UVAKoity0cVUfbKy2V/hnjU7bPeq+QsP\na/iG087QDzweYm+/BefE8TjPOBnXA4NxfTwY59rRONeNJ1beApq+ac6TOVxPAYK+52JQuf4TAJsN\nT+ZwquZeb3I1sPt8xDc2YjfMoU3y1M6YSaIbbIZt6ukINzOz8pj8g5tPdkFGJcT+8np+cuGvmP7G\nUn5y4a/Ynzn0sGRtL8su1W/G/a15LL10JdeXPNn8WkVyMmW9epmeV9arl2lIrf+55SFeL09OpmFD\nq13Yfls3ZuWFd8SvnhfJsGEOqqtB01LRtKQQPX04nVNwRvfBtaInSRsn4Nv7HJnpg5mWFcc/z4lk\nWlYcKcSyv9r/PA+xsbfgdI7H6RyN0zme2NhbINTGSsVxRymI7sC48eGDShcsbLNxxWe1QZ8MAGIX\n3Ybj+Wex5edhudSHdWI11phqLGjY6vJw5D1L7I7b9BNdSXhOyjQd03NSJriS4LobDjkPnyEPAFkj\nmXwFNIbwl14gwfGbGwLabrryY0Ib1A7X5WXe3wfNdStmWD4g1lLL1NGfN2+aa4iJIbLOJNUJEFVb\nG+zchoBznUXmoUeu/fspv2K4f3fwc+GMqtzq94LJ+FstlJXZGDvWATiwWIpNuzbtybAtqcKKhgUY\neMFu3Ufiv2kj2crIuNjm80LtzYiNvc18TorjjlIQ3YTSnFxz/0ROLpSWtHnHtsWnQWkJ7qoS9q55\nH3cEemLWMeb9o4qXNpubKpZ+RuOILHw2I/V5nJXG84ZSscwIqXW78bnMn1oD5tsk08tPssX8QRw0\nuHU1sCM3oHnTX+aw8AJzn0VpyCdZHxa7Bha9r8WqET+wllARUhZg6PdfMd/3KIu5GYDyA7FMXPUR\nceXl+n+T6CeAuIoKrlr1DInFJdDog10+hnyS0+xUyFy1KmwKkbvuvQav14nPZ9yYd7S8vj5xjGkW\n27jyck7auJG+Z30PQFmZlb17nwlf2tvPt5EXk97sIwkiyUK2dHPsfBrKfHU0OXFi1RRHRmoqpcVV\nsGkjjn+/rptpjCdx++ovD3FyCx6Lj79kL2Cp+2v2XVxI30q4ei/c7jK/XVrr9mKt34/mGAjR0VSs\nXAul+4mz3URkv2+wR/yAUzuT+vpp1O7+NdbyED4Cq5Xy9ZshNbW5ac8HT+OdHGKiFtiWAuNXfELd\nkBaz0nsFS9ifqvssJtRFcmFxIu+llJPr8bF46eAQ14bz3t5FTLKHChlNT1HHrvd78N3iVNPuFuDe\njy9g5rBivHWwdPYAKnKjuVb7E5rVSnF6OtYQT/JWn4+peUOY27OINWvv4/yxH/JR+vncWncfi+bM\nod/27SEE1tm3IY2KipU4nUZFOz/9+MrAqwL62uvqWDR7Nn1zc7FqGprFSt6AoSza/RrffruLUeGK\n4vn5NlalnBV2Tpc962Lk//LZsKEYs8WR1boXq3U/mhacYbfthAvJVbQXtYLobowbj3vx4y1mGgL9\nA4fipp/D30s/JL+2AM0KexLh/pFQFuKBTYvugxYVeCON7fsoMUM+xBa5L8DUECP+jpbex3yc9Ay9\nvoUf/Wdc3+x/aI1Ng5FFUHdOYFqKC3uf33y8JrqBhX2LWBPdEDZFSFRKI/EZjUQnaqSe6iY6UaP3\npPBhk/sydFPPp7MHULE9BjQ9d5RN00jLzw9r5Br2k2EMSkviN1MeRWTsZHrMEhbNmcOA7dub92KE\nwjnqPMN/4NQb/FwuV+wKLGK0aPZsBmzfjk3TzUQ2n8aAmO0sGjCHk08+xM3az7dxZvEhHjC+tJOT\nM4DTT18XooMVTTNXtm1Fma86BqUgFGH9A/64I+CD4cFhkrU+eL/a5ASgPmUq2PzrJYQxNcR9Sv0F\n55qPM2UqOALrLriuvIGsEDuBs4rApUXDkECn9Lhz57ZsuPCjwe7jmxCl9qqy3NhjAm/pPQd40Ozm\n2skSGUlW1uXUlVupyDXPkR7OTBTbUMm6vWuaXRz9XLvpm5sb4oxArphQDzior5+lN/jV8Fjfc2zz\ncVx5ecgxM6Jy+X7zmeEv5OcLX+c3rhnRE/XcW1u2jKCkxGnSo5EjMwmpkNyOQikIBaD7Bxg1qsU/\nYLPpRYgslubf80YPJT/OvLb2dZVQ8TlQBD4PeEnB3XceNUMCl/jhwyf3UnvHPNxz5+HN6IfPZsOb\n0U+vZ7HI3FTw4cwVjCrQ91Xg03+OKoC1L0DpluAbYOxrL1HwIKZpzR9YXsTAnolEp6aB1Up0ahp9\nZ87irD+s5anVsKsMGr36z0fXw2+nSvB6W3ZIoyuHie8uYX/9M1TkRoN2ePuVLEDJ+jV8kvf/iDV0\nS4WMxtaGmuYWYN9HHwBQU3MvbvclaLtaXOmPDb+5uW+GlFg1cwVns2gsf9X8M2pmZ8vhY5nho+Ds\nN+m3Ga/XTk7OSNM+8fFXAKtaGvzCpcuq3XyVt4+y6sAbvdsNu3dbqKsrCvudgqaY25Z0JNTmE1nw\nJtS2Y09PN6JDfRBCiBHo2cEelVI+JYTIAF4FbOif2uVSyrZlkVN0LNHR8O23lGzfjX3bVt3s5EqC\n0pLm32MSHKT/azz51XlBp/euhMhX0O9SLitl2Z9DckZQP9380QebLXgMTeuDZkmn5p6/UfPnO7EW\n7W8umxpy2iefwqcnV1H68pPsefdxRK9RxN1yHzX3mIez1sy5itT33sX3V1ifBq+Mgiu+g9MK9Rtp\n5MI/0X/ceOpLS4hyJWGL1kuLzlp5L7UX38Zfz4XseJj/GVTnAkjK7XZ+uHgWSTfdSlzffgCk1l1H\nz37XgNUXQkmETgOSdNoEzrMmk1+5nAGJ0FPUhRknkPTpM4wjOzU1L7DNNoEx6BFVC7Y+xMJT9Z3R\n+UKgWa3YTJSE12fl55ebm/qaGUSzH2LBtof5ZcqEkF09i5uu4WHkyBzTPlFRq4mKWo3PC3UbLieq\n+Eu0+gIWjHyMj/pOY2+/wfQ+WMi43G083GsUD9zrYNkyO/v2WUlPz+TCC5/j4YfnYbcHKlJN64XN\nFo/TOQCrtVRf+awgYAWk2V2UnbEFIg9vP1B3oMNSbQghYoGP0OMocgwF8RKwVEr5thDiPiBfShm8\nl99Apdo4trRF5tvX3MLzOcEfWVO6DIDGEVm6MzoExzuFQ7iUJKXFVUftPEdCAl9eZvggWuFFw2Yy\nmiUyksmf6k/Sr/2QwO+N1CRLZ5qP05pzvtgQ1OZcnoDNuJTlUm9zDo5nJkygR3l5UP/KxkQuXrsM\nlysFq9U8HBcISOHhP24AmkbzxdHw+Q7h6/qaZsf6gtGP8Piw4Kp7537xOZ+efXZQ+403PspjjwVm\nytVTjiQBRor5dwhQDk147S7Kzm5fCpnOyImQaqMemAoU+LVNAj40jpcAoWJQFJ2URaffy9yR88iI\n74cNG/2qI5j/lZ4uw2ez6cph6Wdhx9DNH/Pwevvh89nwevsZyuHYRJyUbt7WHOQKfiG0m7eFPsnt\nxucMzgsF6O3uYDv3roKNnPHibnoOq8ViNa5o1fixZy3XTdlOvcWrp/5oZZ5qYlJKDo+uh7wyOOe1\nVuP4/W+S44y33g+aw5YDOaTsBK+mX2bdx1mgaUS63dQbq6PWeOLLOXiwjNLSXDQtutmC1vq/NlV/\n37bHDTINn9WFspAyuukWYGHFisvx+Wz+Vjm/CwP79EO3LYb3082r7m07eRDRPYLf7w8+mIHbHahE\n9WkZyqEaU+UAYPWUKnOTCR1mYpJSegCPEMK/OdbPpFQMpIUbIzHRgd3etuiajiA5Of64Xft40RaZ\n/37hM7gb3RQeLCQtPg1HpRtycmDkSCKSkgi9D9ifZ9Cdh4XYbGk4HI5wlqSjS/JJ+t3p88/h+eex\nzJ2L5eyzw897ZzFUBD9tA9gqK0j2VENy4KaM5ORxgI/Up96k4vPrKf5XHfnuOi7+XuMX30NataTB\nZ6cgPp7eU6fSs1UCvuTkLE4e5ONvq5z8cWI5U9/drTu+jVDb8twIdr7j5KvebhaeugyGDwma24r/\nfkQZYN8JYyLhqoRtjPmbDV/PG3EVmafs6FHno7Ymn4EDJwC1gEQ3BkwHdgFPA9dj6TkFLoP38z4I\n987R+4p9FG/uDVh48cV/cs45jwCXAF8EdqwFjHyBhTFp5McGmygBCuPT6D20gPxN/QLa8/MzKCxM\nY9CgXc1tAXrLfN+f3g9I9myG5EMHa3Qnjuc+iEMugcrLj1/0gTIxHZoEUqip8FJDFGSN0x9oD/s9\nS0H3GB+H93rEWJL/dbYu86HmbY/Dmd4HW36w78Tbuw9l9riQY2TYp5HRM5mkjyYHfekdeOhRXk7J\nZVeFfO9To+YDdwE0h9oCpJ1ST+r4QvZPAd8fTseHsZHQb6/IOb2n8wAPAPBNA3xjPExHFjxBafQg\nUtzBmV2r4jRiYjP85tMbmOt3rPsbPFo5i9bdxrqy7+DqC0znDlDwSrpx5OOSS6o5cCAKuImkpC8C\nb+AxQCxQo1fd61uTz574AUHjpR0s5EBusDrPyMgnLS0wCZSRXV0nxF4+fWZQYh/dju9v5+RoPdwe\n6yimaiFE0xownUDzk0LReXE4qJ8yzfQlsxDcIA6V7sRvX0prpg5ciDuEacTdABcv15+2rIBrZKBz\nPit5JFaTStvhKv/VZPUlPt4sHDWQRetu4/mcZ/nf3vVEecwf5qI8tcbqAcDHxIlNr5wZbGKyo98V\nAIe3lhn7gk1mAJnf7qSuMvj9njHjAxyOwEy4us/D2J0fh77r3wTN7oIY8xVLd+ZYK4hsYKZxPBP4\n5BhfX6FoNzWL7j2sENzWhE13cgi+/uETauoDfQA19bC7f2A/C8CmjQFt/7tyR5CSsGLj5sVfkH9a\nOmVxXjz4KIvzkn9aOnPufr2lo1FRsHWKd3ejm2W7WvYeaA+mEtVYEzDBqEY3WmpTPLHG5s2B9SZK\nS7ehaa38GqPA3fdyvNH9eOjbW7hu69P0q9qDTWskozKP/9v0GXf0quHqayrJyPBis/nIyPAyd24l\nDz54a+BYmo3S0h3Abrxel95+PvgiW0U4N0UxHQtCZDsGKK2F1QVWSoOzvR83OjKKaQx64dz+6Dth\n9gGzgZeBaOBH4Coppfn2VVQU07FGydxG3O42heCGxCTdSVtZuuth9hffz6CnG7jYpHS1D3D/6ip9\nt3wrthzIYUXBR5zTezpZyS37EQ4eLKNwnyQtXbSsHKqrcY7N0ut/GGhOF2Vfb4G4OHZX7uK010ej\ntdp1mOIaRHrm+dyePgO2juG11yKZM6fBb+VgxipiY1+ipuYqwNig53XrKVqiUimrhe2lxXgjfsvY\n/l/RO95LwUEbOT+OxlW7jN6pkX4fQw4xMe9SWzsT0GVs+YzziYxcS0PDGVALkeVraUg849isHDQP\nsTtuI6r4Y6x1e9Gi+1CfMo2aIfdSp9mZujWG74fa8PYGWwGclOtl6fBaotvpBFAV5ToYdbPsHpyQ\nMm/aSNK0YH8GGLb0j7NDKp62yuscNgCbn3JooqmioLvRzcQQe2Iy4vux+tKvcEQcvaiD/1acweQh\nwU/52Tuy+EnP0CHV0Dk+41h5C448k9DuvvM4pe4J/jc5WBOMyPaw8iftW06cCGGuCoWiIzgCf0ab\n2JsfsHLwp6mioCPCwZSB5j6ZKQOmHlXlUOspYWiKeQjy0JRt1HpKjtq1OgSvm6hi81QgjSUr2S7M\n7+XfD7Udd3OTUhAKxQnIkfgzDkVQxcAQrwfsibHYyIjvx9yR81h0+tHdz1Jev5Xe8eapRtLivJTX\nm9T/7kRY6/djrTNPBVJs1fCkmd+GvWmwrfz43qJVum+F4kQkTPr2I6W5YuAhXrdb7dwz4W/8+ZQ7\nKXLvp5cj9aiuHJpIjBpOwUEbfXsEK4nCahuJUcOP+jWPJlpUKlp0H2x1wea4FM2KvVDDkxEcaWYr\nhMzEEOmKjxFqBaFQnMiYpG8/YvpkoIXYNa45Xc0VBZtwRDgY0GNghygHgBh7ErnF5hvYcoszibGH\nKo3aSbA5qE8xN8dFJP2UYdLcYHhSrhfXobOrdChKQSgUiiDKvt6C1+kKDAdtimI6DgxP+IzsHVnk\nVdpo9EJepY3sHVkMTwif1qWzUDPkXtx95+GN7ocPG97ofs3ZjpcOr2VEtgdbng8afdjyfIzI9rB0\n+PGPd1VRTCHoDJEPxxolc9fnsOXdm0/k+rW6WanP8d9IVuspobx+K4lRw9u8cuhUn7Ff+G5gnRR9\nH8S2ciuZidoRrxyOVhST8kEoFIrQ9Mmg4ZJLj/csmomxJxFjD1/itFNjc+jld01wxcDEmOPrYc89\nTAAAB6hJREFUc2iNMjEpFAqFwhSlIBQKhUJhilIQCoVCoTBFKQiFQqFQmKIUhEKhUChMUQpCoVAo\nFKYoBaFQKBQKU5SCUCgUCoUpnXontUKhUCiOH2oFoVAoFApTlIJQKBQKhSlKQSgUCoXCFKUgFAqF\nQmGKUhAKhUKhMEUpCIVCoVCYohSEQqFQKEzplgWDhBAjgA+AR6WUTwkhMoBXARtQCFwupawXQswG\nFgAa8LyU8gUhRATwMtAP8AJXSSl3HQ852ooQ4kFgIvrnfT+wia4trwN9zr2AaOCvwH/pwjIDCCFi\ngP+hy/sZXVheIcQk4G1gq9G0BXiQLiwzgCHLHwEPcAeQQwfK3O1WEEKIWOBJ9D+gJu4GnpZSTgR+\nAK42+t0BTAYmAb8XQjiBy4AKKeUE4F70G26nRQhxNjBCSnkacB7wGF1YXoPzga+llGcBvwAeoevL\nDHA7UGYcdwd5v5RSTjL+30AXl1kI4QLuBCYA04EZdLDM3U5BAPXAVKDAr20S8KFxvAT9jT0F2CSl\nrJRS1gJrgTOAnwHvGX2zjbbOzCrgEuO4Aoila8uLlPLfUsoHjV8zgL10cZmFEMOATOBjo2kSXVje\nEEyia8s8GciWUh6UUhZKKefSwTJ3OwUhpfQYb5o/sVLKeuO4GEgDUoEDfn2C2qWUGuATQkR27Kzb\nj5TSK6WsMX79NbCULiyvP0KIdcAb6Evtri7zw8Af/H7v6vICZAohPhRCrBFCnEPXl7k/4DBkXi2E\n+BkdLHO3UxBtwHKU2jsVQogZ6Arid61e6pLyAkgpTwcuAF4jcN5dSmYhxK+A9VLK3SG6dCl5DXYA\nd6GbWa4AXiDQp9oVZbYALuAi4ErgJTr4e60UhE614eADSEc3PxWga1xCtRtOH4uUsuEYzvWwEUL8\nHLgNmCKlrKTryzvGCDxASvkd+o3jYBeWeRowQwixAbgG+Atd/DOWUu4zTIk+KeVOYD+Q2JVlBoqA\ndYYVZCdwkA7+XisFoZMNzDSOZwKfAF8B44QQPYUQcej2utXAp7TY9M8HPj/Gcz0shBA9gIeA6VLK\nJgdml5XX4ExgIYAQohcQRxeWWUo5S0o5Tkp5KvAP9CimLisv6NE8QoibjONU9Ii1l+jCMqPP+adC\nCKvhsO7w73W3S/cthBiDbq/tDzQC+4DZ6OFf0cCP6OFfjUKIi4GbAR/wpJTydSGEDf2PcAi6w/tK\nKWX+sZajrQgh5gKLgFy/5ivQZehy8kJzuOcL6A7qGHRTxNfAP+miMjchhFgE7AGW04XlFULEo/uX\negKR6J/xt3RhmQGEENeim4oB7kEPWe8wmbudglAoFApF21AmJoVCoVCYohSEQqFQKExRCkKhUCgU\npigFoVAoFApTlIJQKBQKhSndMpur4sRACJEG5AO3SykfOIbXvRKYLKWc06r9CyARKPdr/qQ9cxNC\n+IAIKaWnVft44D7jOh70DWALwuySDneNqcAGv/0vCsVhoRSEojNzBbANPa3AMVMQh2ChlDK7IwY2\nNnz9B7hESrneaLsMWC6EyGytTNrA74F5tGR4VSgOC6UgFJ2Zq9FvcC8LIU6XUq4DEELsAR4HpgAD\ngN9KKT8znvCzgdOBocCdxgahl4E1Usp/GOf7gAj0vDavov8d9AAel1L+83AnadQmuMdIo4z/9YQQ\nvwBuQM97cwC4RkpZGmKo3wGvNSkHACnlG0KIZVJKj7Er/AX0HbRRwINSyveMlO4PAG70DVPzgbHo\nNUBeF0JcJaXcdrhyKRTKB6HolAghzkS/ca9E3yl6VasutVLKc9F3k873a4+TUk5F3236x0Ncpjfw\nlJTyp+j59R85GnNvwsgHdRu6uWoC8AXw5zCnDEffGRuAlLLJpHU3Rg0E9CR1zxo7ihcAj0gpz0Zf\nbaVJKZ9FN0/NVspB0V7UCkLRWfk18LKU0ieEeAn4Rghxo5TSbbz+hfHzR8Dpd16odjMKgD8KIf6I\nXmHL1YZ5PSyE8PdBvIDuJzHjNPQ0y8uFEKA/9YfzJXjRK4OF4hTgWQApZbEQYi8g0FNO3Gf4Lz6Q\nUn4YZgyFos0oBaHodAghEtATj+UJIS4ymm1G26vG7/72eP+0xWbtzflkWuW/vwfYIaX8pZHU7GAb\nphfkgxBCnNWqT9M16oGNUsrpbRgX9LKZZwBvtRr/FGAjfnIYWACflPLfQojlwLnAHUKIjVLKcCsV\nhaJNKBOTojPyS3RTSqaUcpSUchQwl2AzU1upQk/cB3pVraYbbS9aahpfBmhCiKh2jp8uhLAIvR72\nKUb7JmC84XxGCHGJUZcjFE8Dlxg+BYxzZqGvUiKBDcDPjfbe6KsTKYS4C7BJKd8CbkRfuYBejzii\nHfIoFIBSEIrOya8xTCl+vINeQax/O8Z7EbhUCPE5MAqoNNqfAu4WQqxAXz18hm6uOVz+i148fjPw\nCrAOQEpZgH7D/kgIsQpdrg2hBpFSlgBnAbcKITYLIdagp2X+mVE17E5gguGM/w8wV0pZjV48Z4UQ\n4jN0JbPIGHI5sEQIcXo7ZFIoVDZXhUKhUJijVhAKhUKhMEUpCIVCoVCYohSEQqFQKExRCkKhUCgU\npigFoVAoFApTlIJQKBQKhSlKQSgUCoXClP8PqUBLZe62c7wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f575d3e93c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.xlabel('Annual Fuel Cost')\n",
    "plt.ylabel('CITY (MPG)')\n",
    "\n",
    "cur = data_2010\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['City FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'red',label = '2010')\n",
    "\n",
    "cur = data_2011\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['City FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'green',label = '2011')\n",
    "\n",
    "cur = data_2012\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['City FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'yellow',label = '2012')\n",
    "\n",
    "cur = data_2013\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['City FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'blue',label = '2013')\n",
    "\n",
    "cur = data_2014\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['City FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'orange',label = '2014')\n",
    "\n",
    "cur = data_2015\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['City FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'cyan',label = '2015')\n",
    "\n",
    "cur = data_2016\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['City FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'brown',label = '2016')\n",
    "\n",
    "# cur = data_2017\n",
    "# i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "# j = cur['City FE (Guide) - Conventional Fuel']\n",
    "# plt.scatter(i,j, color = 'pink',label = '2017')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f575d9527f0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UVVVy++1zsdmsPPHEvwPqR28YtE8uS+5CzCsPTsn0hQXe49vOhZU/HSwrbCzwHt93qifB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\n9iomvOgjWMUNvz4BZ18DBRp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XAyiKYgWsIe3VQCUxCefRxwStOR0gNTYiNTaiU1VPkqG3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\nQAgEAoHAFGEgBAKBQGDK/wOihGgjg++58QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f575d3e91d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.xlabel('Annual Fuel Cost')\n",
    "plt.ylabel('HIGHWAY (MPG)')\n",
    "\n",
    "cur = data_2010\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['Hwy FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'red',label = '2010')\n",
    "\n",
    "cur = data_2011\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['Hwy FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'green',label = '2011')\n",
    "\n",
    "cur = data_2012\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['Hwy FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'yellow',label = '2012')\n",
    "\n",
    "cur = data_2013\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['Hwy FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'blue',label = '2013')\n",
    "\n",
    "cur = data_2014\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['Hwy FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'orange',label = '2014')\n",
    "\n",
    "cur = data_2015\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['Hwy FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'cyan',label = '2015')\n",
    "\n",
    "cur = data_2016\n",
    "i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "j = cur['Hwy FE (Guide) - Conventional Fuel']\n",
    "plt.scatter(i,j, color = 'brown',label = '2016')\n",
    "\n",
    "# cur = data_2017\n",
    "# i = cur['Annual Fuel1 Cost - Conventional Fuel']\n",
    "# j = cur['City FE (Guide) - Conventional Fuel']\n",
    "# plt.scatter(i,j, color = 'pink',label = '2017')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f577ed6c908>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bNhQ/4E0bimP4wkB1k0pap35AV+Yk/MHDgh8jXZmTAjuu1eBuwuCXXrM3+LsU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\n1rY33QqJ7uAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f577d07b860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.xlabel('YEAR')\n",
    "plt.ylabel('CITY (MPG)')\n",
    "\n",
    "data_arr_2001_2005 = [data_2001, data_2002, data_2003, data_2004, data_2005] \n",
    "data_arr_2006_2009 = [data_2006, data_2007, data_2008, data_2009] \n",
    "data_arr_2010_2013 = [data_2010, data_2011,data_2012, data_2013] \n",
    "data_arr_2014_2017 = [data_2014, data_2015, data_2016, data_2017]\n",
    "\n",
    "for idx in data_arr_2001_2005:\n",
    "    i = idx['year']\n",
    "    j = idx['cty']\n",
    "    plt.scatter(i,j, color = 'red')\n",
    "\n",
    "for idx in data_arr_2006_2009:\n",
    "        i = idx['year']\n",
    "        j = idx['CITY MPG (GUIDE)']\n",
    "        plt.scatter(i,j, color = 'blue')\n",
    "            \n",
    "for idx in data_arr_2010_2013:\n",
    "    i = idx['year']\n",
    "    j = idx['City FE (Guide) - Conventional Fuel']\n",
    "    plt.scatter(i,j, color = 'orange')    \n",
    "\n",
    "for idx in data_arr_2014_2017:\n",
    "    i = idx['year']\n",
    "    j = idx['City FE (Guide) - Conventional Fuel']\n",
    "    plt.scatter(i,j, color = 'brown')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f577e79f470>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o7oAnZbgpQhR3QPymLex2qy8QAJFIWu9D6LmGRc744lLktPXrGsbfZ6kyo2I5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v0cNUVSJoSIUc2dUPLwu1B6d7T4HHUA9Hzgj9L+PIS2btcfgHFCiBUoEh1PqDnSw9mmvcBXUsoG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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5780e3f668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.xlabel('YEAR')\n",
    "plt.ylabel('CITY (MPG)')\n",
    "\n",
    "data_arr_2001_2005 = [data_2001, data_2002, data_2003, data_2004, data_2005] \n",
    "data_arr_2006_2009 = [data_2006, data_2007, data_2008, data_2009] \n",
    "data_arr_2010_2013 = [data_2010, data_2011,data_2012, data_2013] \n",
    "data_arr_2014_2017 = [data_2014, data_2015, data_2016, data_2017]\n",
    "\n",
    "for idx in data_arr_2001_2005:\n",
    "    i = idx['year']\n",
    "    j = idx['hwy']\n",
    "    plt.scatter(i,j)\n",
    "\n",
    "for idx in data_arr_2006_2009:\n",
    "        i = idx['year']\n",
    "        j = idx['HWY MPG (GUIDE)']\n",
    "        plt.scatter(i,j)\n",
    "            \n",
    "for idx in data_arr_2010_2013:\n",
    "    i = idx['year']\n",
    "    j = idx['Hwy FE (Guide) - Conventional Fuel']\n",
    "    plt.scatter(i,j)    \n",
    "\n",
    "for idx in data_arr_2014_2017:\n",
    "    i = idx['year']\n",
    "    j = idx['Hwy FE (Guide) - Conventional Fuel']\n",
    "    plt.scatter(i,j)\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'P'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-da3644aa7b1e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fl'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hwy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'red'\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;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_arr_2006_2009\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m   3432\u001b[0m                          \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3433\u001b[0m                          \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3434\u001b[0;31m                          edgecolors=edgecolors, data=data, **kwargs)\n\u001b[0m\u001b[1;32m   3435\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3436\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1896\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1897\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1898\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1899\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1900\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)\u001b[0m\n\u001b[1;32m   4061\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ymargin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4062\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4063\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_collection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4064\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4065\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_collection\u001b[0;34m(self, collection, autolim)\u001b[0m\n\u001b[1;32m   1760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mautolim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1762\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_datalim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_datalim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransData\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   1763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1764\u001b[0m         \u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_remove_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mget_datalim\u001b[0;34m(self, transData)\u001b[0m\n\u001b[1;32m    218\u001b[0m             \u001b[0mtransOffset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransOffset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m         \u001b[0moffsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moffsets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    221\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misMaskedArray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moffsets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m             \u001b[0moffsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moffsets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masanyarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \"\"\"\n\u001b[0;32m--> 583\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'P'"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'P'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    330\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2424\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2426\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2428\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, dsu, suppress_composite)\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    884\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    885\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 886\u001b[0;31m         \u001b[0mCollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    287\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_scalarmappable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 289\u001b[0;31m         \u001b[0mtransform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransOffset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_points\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    290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    291\u001b[0m         \u001b[0mgc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_gc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36m_prepare_points\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    260\u001b[0m                 \u001b[0moffsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m         \u001b[0moffsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moffsets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    263\u001b[0m         \u001b[0moffsets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m             \u001b[0;31m# Make it Nx2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masanyarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \"\"\"\n\u001b[0;32m--> 583\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'P'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f577c79aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.xlabel('YEAR')\n",
    "plt.ylabel('HWY (MPG)')\n",
    "\n",
    "data_arr_2001_2005 = [data_2001, data_2002, data_2003, data_2004, data_2005] \n",
    "data_arr_2006_2009 = [data_2006, data_2007, data_2008, data_2009] \n",
    "data_arr_2010_2013 = [data_2010, data_2011,data_2012, data_2013] \n",
    "data_arr_2014_2017 = [data_2014, data_2015, data_2016, data_2017]\n",
    "\n",
    "for idx in data_arr_2001_2005:\n",
    "    i = idx['fl']\n",
    "    j = idx['hwy']\n",
    "    plt.scatter(i,j, color = 'red')\n",
    "\n",
    "for idx in data_arr_2006_2009:\n",
    "        i = idx['year']\n",
    "        j = idx['HWY MPG (GUIDE)']\n",
    "        plt.scatter(i,j, color = 'blue')\n",
    "            \n",
    "for idx in data_arr_2010_2013:\n",
    "    i = idx['year']\n",
    "    j = idx['Hwy FE (Guide) - Conventional Fuel']\n",
    "    plt.scatter(i,j, color = 'orange')    \n",
    "\n",
    "for idx in data_arr_2014_2017:\n",
    "    i = idx['year']\n",
    "    j = idx['Hwy FE (Guide) - Conventional Fuel']\n",
    "    plt.scatter(i,j, color = 'brown')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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