Experiment 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare normal policies with/without interactions in different climate change scenarios.\n",
    "\n",
    "- Policies: 11 original Waas policies\n",
    "- Climate change scenarios: Yes\n",
    "- Socioeconomic scenarios: No"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bjafino\\anaconda3\\envs\\py27\\lib\\site-packages\\ema_workbench\\em_framework\\optimization.py:22: ImportWarning: platypus based optimization not available\n",
      "  warnings.warn(\"platypus based optimization not available\", ImportWarning)\n"
     ]
    }
   ],
   "source": [
    "from ema_workbench import load_results\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "results = load_results('S03_22022018_v171.tar.gz')\n",
    "experiments, outcomes = results\n",
    "\n",
    "#experiments df\n",
    "experiments_df = pd.DataFrame(experiments)\n",
    "experiments_df['integrator'] = experiments_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_outcomes = outcomes.copy()\n",
    "\n",
    "'''\n",
    "Outcomes for no interaction simulation runs\n",
    "'''\n",
    "outcomes0 = {k: all_outcomes.get(k, None) for k in ('ATP_0', 'DRFlooded_0', 'StadFldA_0', 'DamAgriKSum_0', 'DamCumFloat_0',\\\n",
    "                                                    'agriSuit_0', 'greeSuit_0', 'natuSuit_0', 'recrSuit_0', 'resiSuit_0',\\\n",
    "                                                    'workSuit_0')}\n",
    "outcomes0_df = pd.DataFrame.from_dict(outcomes0)\n",
    "outcomes0_df['States'] = 'no_interaction'\n",
    "outcomes0_df.columns = [colname[:-1] for colname in outcomes0_df.columns]\n",
    "outcomes0_df['integrator'] = outcomes0_df.index\n",
    "outcomes0_df = pd.merge(outcomes0_df, experiments_df, on='integrator')\n",
    "\n",
    "'''\n",
    "Outcomes for simulation runs with interactions\n",
    "'''\n",
    "outcomesi = {k: all_outcomes.get(k, None) for k in ('ATP_i', 'DRFlooded_i', 'StadFldA_i', 'DamAgriKSum_i', 'DamCumFloat_i',\\\n",
    "                                                    'agriSuit_i', 'greeSuit_i', 'natuSuit_i', 'recrSuit_i', 'resiSuit_i',\\\n",
    "                                                    'workSuit_i')}\n",
    "outcomesi_df = pd.DataFrame.from_dict(outcomesi)\n",
    "outcomesi_df['States'] = 'interaction'\n",
    "outcomesi_df.columns = [colname[:-1] for colname in outcomesi_df.columns]\n",
    "outcomesi_df['integrator'] = outcomesi_df.index\n",
    "outcomesi_df = pd.merge(outcomesi_df, experiments_df, on='integrator')\n",
    "\n",
    "outcomes_df = pd.concat([outcomes0_df, outcomesi_df], join='outer')\n",
    "\n",
    "'''\n",
    "merging the outcomes\n",
    "'''\n",
    "outcomes_df['ClimateChange'] = outcomes_df['ClimScenNo'].apply(lambda x: 'NoCC' if x<11 else 'G' if x<21 else 'Wp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bjafino\\anaconda3\\envs\\py27\\lib\\site-packages\\seaborn\\categorical.py:490: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
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ufwJuBTY0vfdN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n6jXGMMUExCBnhF09j4Az2IiDtT7Dt3zus6HliS23BX4H4U+9O13daVUvp8rtO5+a33NtVpNvV6\nJfAWirH1eooJKoso9pnDKcbinYD/Lv+HbkTsBvwbRXt+Crh7SmmX3GeLc9n3UPS3JGk6GdsOh7Ft\nax+liBlvo+ivJbltlgNfo5ikcocO5X8I/A1wd2CXXHYX4BjgPIoJAp/pUoeVFFeIeSywCFiSn19H\nET+9C/gsxWSEI3OMtZhijG2kaJvXtFjvrXnbTwT2o2i3JcBSikkhN1NMsnh5c8Hc7l/OdbkM+DOK\neHwJ8BCKePvkdh9oCPF8Syml93aJqf80XnMs/mWK7wA3Ac+m+G6wJ3A34DSKY8sHIuLxvdZBksZG\n3TNETCZT7wl4OzMzSJcNsJ6VDGdWegLe2GL57hTBdyPPs1rkuVtp+cN73XZTvr5mpXdZ5/4UM5Zv\nB+7S6zZ73T7FCfNEEajuMAM6b78xy/jopmXvye9/F1jQZttPynmubZdntu3ZlOe1zMxovm+L5Uso\nvgQk4LR+tjGEPlqD/8lnMplMphFNxnM7rMN4bsfyfcdzXdplPjP//ffsFstXlz7zcR3W87Kc50KK\nk+St8jwot/9ttLnqWYf1n5bX/6Y+PuMvc9mzgfktlh9X+oxPbbF8oDgS+GQufx2zu+rGqlxuVYd+\nWdllHS3rTjG55pbcH0e3KbuA4kepROnqHhQn+BPFsWBoY9FkMplMk5Mwtm1eh7HtjuX7im1LcUgC\nnt9i+XyKmK+R58RZtutiZq5AssMV5kqx1UaKSarNy59f2vbVwNIWef4xL7+kj33iqe3KUkxAShST\nPFrVbZ/c3u36vOp4vtF2bfuEYgJLo36Pa7F8ATNX+Lhgtu1nMplMo568goY0XpaWnl9fWy1mbALe\n3/xmSulm4Af55e9pMRM5pXQpcEl+ef+qKjhbKaUrKU5cB/CwCjbxKYrg9gCK/zJo9iyK2cFXAGc2\n3sz3/Ht+fvnPKaWtbdb/NYoZ1vtQBNLD9oz8+KWU0oXNC1NK6yn+GwHg8RGxx7ArMAd9JElSlYzn\nKmY811pKaRvFVTQAHt4h6y9TSqd2WP7C/PjBHPu12taPKSZLLKR1G3XyjR7quIOIuD/Ff5cCvD1/\n3uZ6nUrx35IAJ8yyXt22v4iZWPmdqcerW8yBZ1H8B+75KaXvtsqQx+Jn88vHlRbdmB8Xsv2xS5Kk\nBmPbik1xbHt8fvwD8PHmhTnWe/ss1tdcfgNwVn7ZKe78ckrpkhbvn1F6/pGU0roOee6WY8XZaMTE\nd4uIOzUte1p+/HyruqWUrgM+1GHdVcfzvWjEzT9IKZ3RvDCPp7fll/eNiPtVUAdJqs2CuisgaVai\n7go0+VUqXdauyR/z4/kppdQhz92BvYZesw7yZQqPz+mBFJfC26VF1gOGve2U0hURsZri0nPPoZhh\nXta4ZOCnU0q3l96/D8V9FwFWRcTttNe4LPGBwP8NVuMZ+fJ3jS+o3+mQ9dv5cR7FPbjP7JC33bZq\n6yNJkipmPDcExnPtRcQjgBcAR1B8/lYngzu1yzkd1r2EmXjw7RHxlg7raXzWA1us5wHASRQnw5dT\nfN7mfWO2fXd4ftzKzMn2Vr5N8R+Zh3fI04/DgZ3y804TXOZa4weH+0bE1R3yNW57U+6vS4FfA/cG\n/i8iPkTxQ8MFrSbASJKmkrHtEBjbttSI1VZ36K/vUcR+bX/nyrfAew7wYOCOFBNXm3Vq1/PavP/H\n0vMf9ZBnT4qrmpXrtoTi1i3HAgfnPDuxo/2Bq3KZhcAh+f1OMe9q4M3Nbw4rnh+CRv92Osd8JsXV\nW+bn/BdUUA9JqoUTNKTxcl3p+d7A2roqkrWcYZttnUWeVoFnJfJ9lE9j+5m/mylm+W/Jr/fOdZrt\nzOZe/RfFl56nRMRLU0obc90eSHHfvUaesvI9uDvdW7Gs1ReOQexNERADXNkh3xWl5/vOdiMj0keS\nJFXFeG5AIxIrjGQ8FxHvYvt7XG8DbqBoHyhOji+ic7tc02HZfszcZ37vDvnKtvsMEfFyivuFN9aT\nKO47fVt+vSvFZchn23eNuPO6lNJtHfI1YtVZx6ldlO8B/7shr3sQjXG3KzOTMDr5U3+llLZFxPHA\nV4GDgHfmtDEizgW+AnyiMf4lSVPJ2HZAxrZtNWK1tucgU0qbImIdxcSL7eRJL59i+6umbWX72HgP\niokwndq13RUmthYXEWmfh5nxBE1jKiLuSTEZpjw5ZCPFFcwak10an6tcv/L52U77W7t2GzieH5Je\n+/c6inYYduwuSbXyFifSePll6fmhtdVivL2R4gvPrcDfUcwA3iWltDSltF9KaT9mZnJX9V8AX6YI\nuBdT3IexoTEj/ccppV81lZlfer5fSil6SKsqqj8UJ9J7WdYpXzuj0EeSJFXFeG5woxArjFw8FxGP\nYWZyxgeB+wE7p5T2LrXL+xrZO6yq05URyp/hiB4/w8pSHQ+muOz4POCLFFey2CWltFepjq/qoY6d\n9Bp/9hOnjqNGn324x/5aXi6cUvo5xRU0ngJ8hOJe5bsCx1CMs197yWlJmmrGtoMztu2s35jtBRST\nM7YB/wjcgx1j4y/lvHWcX/w4xeSMNRS3LFmaUlqUUto3123/Ut5o87xT27T7TAPF8xUwdpc0lZyg\nIY2XM5mZQfukThm7aMzebXWpvIY9Blj/oP40uzgiWtYxIvqtX+P+hf+YUnp/Sun3LS6Tt19zoWHK\n9zj8an75HICImM/MjO5PtihWvhxxXSdAr2fmhP2dO+QrL7u2j+3U3keSJFXIeG7mfeO54Wq0yxkp\npZellC5scRuKQdulfJnmfj7DUylOCl8EHJ9S+lFKaXNTnn7r2Ljyxx0iYucO+Rr/pdhPnNrJVaXn\nVVwGul+Ncdf3mEspbU4pfSWldFJK6X4U/yX7YorvB3cGPjF4NSVJY8rYduZ9Y9vhasR2bW8/kmO+\npW0WN9r1oymlt6aULknb36IFajq/GBF3Bh6WX56QUvpSSun6pmzt6raOmfOzy9rk6bRs0Hh+WBr9\n2/Ycc97XGv077NhdkmrlBA1pjKSU/kgxoxngmflSaD2J0jXXKC7lBp1/ZH/oLKs3TDeUnrerY7/1\na6zvp60WRsRyintNttP4gjTozOrGZQGPiYj9KP4D7U4UX/g+2yL/hcDN+fnxLZYPQ8fPlk+e/yK/\nPLrDeo7Jj7cDP5nNNrJB+0iSpJFlPLcd47nh6tYuQXHp6r6llG4AGv892c9naNTx5y1OkDcc0+b9\nbs7PjwuAR3XI11h/u3uF9+t8Zi6XfdwQ19top37H6zn58YiIGMrEkZTSupTSycBr81uHRkS7H0ck\nSRPM2HY7xrbD1YjtHtU0VsoeSRH7tdKtXRdT35gqj6GW9aNNTJzPzzauXLOiwzZaLhtCPN+LXuLX\nRv92Ose8gpn+HXbsLkm1coKGNH7eBGyguKzsVyJi/06ZI2KviPgy288y/3l+XBYRR7Qosy/woiHV\ntx8XU1zWD4pL6W4n30Pw9X2u+6b8+IA2y9/ZpXzji8eefW6/4TsU9wmcDzyLmUsGnp5S2uG+3yml\nrcAp+eXzIuLhnVYeEb3eQ7Csl8/2ufz41Ii4b/PC/OWmcWntb6aUbmrK0ss2Bu0jSZJGnfGc8VwV\n8Vy3dnkxcNdZrrOVj+THoyOi40ndFp+hUcf7tTrRHhGPp/OJ5rZSSr9g5mTzm/J/fTav/wnMnIhv\n9UND31Jxr/ZGrPy6/J+RwzDoeP0kxb44H/iPVu3SEBHzImLP0utOVyKBmX0cOt8aR5I02YxtjW2r\niG0/nx/vAjyvxfrmUYy9drq165uBJbOs07CUz5fuUL+IWELnz9a4NcvTI2KH+D5PnH1xh/KDxPO9\nmM055iMj4rEttrsAeEt+eWFK6cI+6iFJI8sJGtKYSSldTBEgbwYOAX4WEa+NiD/NpI6I+RFxaET8\nI3AZ8OSm1ZwL/C4/XxURh0dhXkSsAFZT4/EhpbSFmdn3b4iIp0fEQoCIuBfFJffaBdfdnJ4f3xQR\nT87BHhFxUER8Bng628+Kb9YIBp8VEbv1WQfyfwx+Jr98AfCX+XmrSwY2vB24lGLm8OkR8aqIuENj\nYUTsERF/HhGfAM7uo1q9fLYPAZcDOwH/ExGPz1+IyPeePgM4iGJ8tvoi0cs2Bu0jSZJGmvGc8RzV\nxHONdnl8RLw5Ihblde4ZEW8A/o3iksiD+jAz90H/ZES8ozwZISJ2i4gVEfHvFJ+1VR0PoZgssHcu\nsygiTqI42TxIHRtXdHgE8KWIOCivf6eIeBYzkzLOBb42wHbaeSNwHcWlmM/J437XXIedI+L+EfGe\niHhOx7VsrzFen9DtB69WUkpXA6/LL58IfDsijmpM1MjHjXtHxKvyto4tFT8+Is6JiJPKJ//z8elx\nzPxg9IOU0o2zrZskaTIY2xrbUkFsm1L6P+C/88sPRcSLIk8ejYi7UEzgOBLY2GYVjXZ9UUT8dam/\n9ouI91H8g9kwYuN+/Ar4fX5+SkQ8qLEgIo6kGO97dSj/7xS3KtkVOCMi/nSVkYg4HPg27a8sAoPF\n871ojMmnRkS7z/HlUh2+EBHPjIid8vYPysuPzMtf06K8JI23lJLJZBrDBBwF/JbiMnaNdBsz96Fr\nvNcIrndqKv84ii9OjXy3UMwETxSzwo9vLGux7ZV52eoO9VuV86zqkGd1zrOyxbIDgCtL9dtMMbs4\nUczCfVRp2YqmsstLy5Y3LTuQ4h6JjeVbgBtLr1/fpV7PbqrTFcAa4Pu9bL9pXfdt6r8bgV269PtB\nwM+ayt1QaptG+m0fY6rrZyvV+4pS3lubtr8JeGq/2xhCH63Jy06sez81mUwmk6lTwnjOeG6I8RzF\nBNrvNY2b60tj6TSKk+gt+71Tm7XIuw/w3ab63pQ/x+3lvmlR9rMtPvvW/Px84OX5+Zo+96u/a6rD\nDRT7VeP1L4BlbcquYcA4EjiM7WPlrbkfynV6Za/7GnAPZvbrbXnsr8npgF7rDvxDqZ0bx5rr2P4Y\nkoBnlcqc2LRsUy5TPj5dCdy73/YymUwm0+QkjG2NbYd/rnJp07o353U3xtFLaRMDUVy94aJS2W1s\nH6t+uNOYaLfepjwt+7vHfj8293V5vN9Sen50p/UDDwfWN5VvvL4BeGpp2X4tyvcdz/fQb48srWMr\nxZVZ1tAU3wP7U0zmKB8vbmD7PvvbKo9bJpPJVFfyChrSmEopnQPcGzgB+DRwCcUJsyUUJwC/D/wT\ncHBK6ZmpmOldLn8GxX+WnUYR+MwH/kDxX1APovhiUJuU0hUUlx/+KMWXHygul/hfwGEppbP6XO/v\ngMOBj1EEh1C022nA41JK/1+X8p+i+K+A71PM0L4TxRepA/qoy4UUXzIavpBS2tSlzOW5/s/Ndb4K\nWAQspLiyxVeB5zMzw3g29enps+V6H0Lx5fdnFIH2zhQzqj8MHJJS+hIt9LKNQftIkqRxYTxnPMcQ\n47k8Ph4LvI3iR4wtFPd9Pg94CfAXDOk2FCml6yjui/3/KK568QeKeHBXir7+H4qJFstbFH8W8EqK\niRK3UYzbCyh+fDiKYowMUrf3UbTvp3K9dqP4ceeHwKuAh6SU1rZfw2BSSj8BDqa4asUPKU6UL6L4\nsWR1rsNn2pVvsb7fAo+m+A/Sayl+qDgwp07/Gdm8nvdQHG/eR9H2myh+uNhAcU/vdwMPa6rbf1OM\n049TXHr+JorL0a+nGFdvpoj9f91rPSRJk8vY1tiW4Z+rXEcRn7wV+DXFj/5bKa6O8ZiU0gc7lL0x\nl30/xeSAbbnsauCElFKnW4BULqV0GsVEhm9QTIRZQDER9uMU4+m7Xcp/H7h/zr82l7+R4rYzh7H9\nlS92uNLZgPF8t8/2PYort32HIn68IzPxaznflRRj51UUcfOtFLH7Hyiu3PKglNIHZrt9SRoHkVKq\nuw6SJEmSJEmSJEmSBhQRLwI+AlyWUrpb3fWRJG3PK2hIkiRJkiRJkiRJYy4idqG4Yh0UVxuRJI0Y\nJ2hIkiRJkiRJkiRJYyAijo+Id0TEfSNiYX5vQUQ8Evhf4D4Ut8r51zrrKUlqref7lUqSJEmSJEmS\nJEmq1X7AG3NKEXEDsBhYmJdvBv4qpXRxTfWTJHXgBA1JkiRJkiRJkiRpPJwG/397dx4mV1Hucfz7\nS3dXrzEAACAASURBVAKEhCWELYBAQARUkEWQRZawuHOVRQGXKwgqeAVF5AIXEQO4gCLiDoIQ4SIi\noniRTUDCvomAoMgiq8hOCAQTCMl7/6g6dqfT53RPT096ZvL7PE8/Z7pPVZ3qZWbqvP2eKpYHJgGr\nA8sBs4EHgauAk5ycYWY2eCkiet0HMzMzMzMzMzMzMzMzMzMzs2FtRK87YGZmZmZmZmZmZmZmZmZm\nZjbcOUHDzMzMzMzMzMzMzMzMzMzMbIA5QcNsmJM0RVJImtLrvtj8JH0yvz8/6nVfhgtJIyT9RdJs\nSev0uj9mZmZmZmZmZtZ9kibmuFpImtjr/gBImpz7M7XXfRlIkiYVr32v+zLUSbo2v5ab9bovw4Wk\nzfNrek2v+2Jm1owTNMzMekTSWOBY4BXg6032T6k7yQxJm7fR5t31dUrKRB9uk/v7PBe0iJhLel1H\nAd/scXfMzMysgaSRkg6WdLukl+vGHTvn/VOH4jik10FqSQ/n4+/djzYWlfR3Sa9IWrWL3VuoOCBs\nZmZmzUjaOSdw7NzrvtjgIGk3YCvg4oi4ucn++jjtPW20t2lDnSlNyuzdx/jwxC481QUqIm4CLgO2\nlrRLr/tjZtbICRpmZr1zKDABOC0i/tFG+U9U7cxZ1m/uw/FfBp5qcZvRh/YGk18CfwXeL2mbXnfG\nzMzM5nES8G1gQ1JCZTHumNXLThkABwJrksanj9XvaLhCde+yBnK5B3K56ZK2rds3IAnIJfVGSjq/\nru6ZktRmXUnaQtJXc8LQU3l2tumSbpP0DUmrlNV3QNjMzGyBmQ3cm2+ze9yXduwMfCVvbSEnaRHg\nuHx3chtV1pW0RYsy+/SxG8/SOj48p49tDhaT8/Y4SaN62REzs0ZO0DAz6wFJi5MC4AAntyj+KBDA\nnrlemSKB4+E2u3FCRExocTuhzbYGlTyLxqn57qG97IuZmZnVSFoS2C/fPRQYXTfuuLSHXVvoSRoP\nHEma3e0bHbbxZuB64PXAM8B2EXF1RZVuJyAX9QScDuxa9/B/Aj9ss4kjgBuALwHbAsuTEpeXBDYG\nDgf+Kun9FW1MzlsHhM3MzAZIRDweEevm2+O97o9ZH+0GrAX8MSJubVH24bwtHT9LGg3sSYojP9pm\nHzZtIz78WOtmBp+cNH0nsDZOijKzQcYJGmZmvbEHsAxwR0Tc3aLsQ8A1wFKkgft8cuJGMQA/s4v9\nHMrOIWV4v0fSar3ujJmZmQGwLrBI/vnHEeE1qwePTwPjgAvbnN1tHpLeBlwNrAw8BmwdEX8qKT5Q\nCciFHwAfzz8fTi3h5DOSjm+j/iLAi6RE6u2BsRGxDDCWNB5/lDQ2P0/SG5s14ICwmZmZmbWwf97+\nbxtlzySNn/eQNKakzK6k8fzVpHiy1V7b/SpLmZktYE7QMBsGJH1U0vWSXsrT7t4s6dOtpvCVtIKk\nfST9WtI9ue7MPCXxafkKuLK6xfTEU/L9vSXdmNt4XtIV9UtLSBol6cA8JfCLudzFkjauOMbGko6S\ndI2kRyTNkvSCpJskHSZpiRbPb6yko/Nzmynp6XzMHfL+lut0K60lfo6kR/Pxp0u6RdKhksZWHb+F\nT+Xtz9ssf0belmVJ7wYsDUxlAAfgSutkhqSpFWVK119vrC9pN0m/z+/NXDWsNS9ptKSDJN0gaVp+\nDx7JU1RvWNXXiHgK+APpf92+fX+2ZmZmNgD+HUyMiKG6lNqwk88bivFpOwHixvrbA1cCy5KmGH97\nRNxbUWXAEpAlfQP4L2Au8OmIOD4ijgC+mNs6VNKXWjRzATAxIj4TEVdFxEyAiJgZEb8GJgEzgUVz\nu2UcEDYzsyFL0tKS9pR0tqS7cryviMv8XO0tVbaNpAslPZtjc/dK+pqkJXIsMSQ93KTev+OOSj4p\n6TpJz9XH8jTvEmwTS/owQtLuki6Q9LikVyQ9k2OUx0lar+zYFc+rtO8l5SflONle+aG9NO+ybyFp\nUl35+R5r0ubUXGZyk33/rq8U/z1R0n2S/lUfr5O0uKT3SzpV0h35dXlF0j/z6/Wedp5fX+X35O35\n9b9J0j8kvZrf36sl7a+0/EezuvO855JeL+knkh7KfX+4SZ0J+Vh3KsV2Z0l6UCn+/aaKfvYrNt3i\nNXgDaaa2AH7RRpWHSIkXpeNnasubnFGyvyvUXky99Peovn7+W3BM/hvzUrPf5fwe/1jS/fnvyIuS\n/pTfm6VadLeIve8gac2+PVMzs4HjBA2zISyfoJxOCvxtSbqiaw6wCXAKrb/8/ybwU2AX0tWMkNYB\nfz3pC+3bJJUN+Or7MYU08NsEEGlmiB2AKyXtJGkx4CLge8B6pL89SwHvAa6V9NaSpm8Djga2Bl5H\nCoIuDWxGWp/vZkkrlPRpBeAW4Kj83EaSroR7D3C5pP2b1aurP0rSqcBVpMDwqqS1LMcCmwLHk16f\n1avaKWl7aaA4ib22zWq/Al4Ctis54SwSNwZ0AN5Nkr5Nel47kj53cxv2rwLcCnwH2IL02s8CViNN\nUX2bpAOpdk3evrt7PTczM7O+KoLYpGTS4rH6gPTU0srN29tV0u8kPZWDuU/l+7u0UXcjpWTPIsg6\nTSkZ9KA8bq2qu67SlxRP1gV2vy9pxTb7PVrS53Lg+dnc9ydzALxyvKIUQD9S0l/VJPm4n3YE1gRe\nAC7pS0VJOwMXA0uQxu9btzkNctcTkJUSLw4HXgX2iIhiyTsi4kRS0HoO8FVJnytrJyLuiIhpFfsf\nIp0nQDo3KOOAsJmZDWVfIM1O+hFSPK/4wnw14MPADVX/T3PMZiqwEymJ8xVgImkpsVtIV/q3IuCX\npGVst8j351bWmLcPy5H+Z58LfIA001cRX9wYOAz4arvt9cOrwFOkuBZ5+1TD7dUBOO5awJ9J7+Wq\nwGsN+/cAfgt8EtiAFHt7DViJ9HpdLGkglj9eDbiO9PpvBqxIel/GA9sAPybFlatmWoMUD7+DlGi8\nAil2Ow9JOwH352O9BVic9BzXIMW/b5f08cZ6Wcex6TYUY//78kVm7SgdPyvNHrw9KX78qw77tKAt\nS3qNv0yada7x84mk3YG/kGYbWYv0Hi8KbER6b+5WyYx2ABHxT+DvpL8d7+py/83MOuYEDbOh7UBq\nA7IfACtExHjSYHYyaZD9gYr6D5FOQjYCloiIpYHFSCddZ+effyZp5Yo2PgDsTroqbKmIWIqUEHEb\n6Uv37wMnkJI3dicFbpfM9/9OuoryuyVtX0EKoq4OLJanFR5Dmq7tXuBNpGmHm/lZ3j+TNNheMtdf\njXRi913SWtJlTiCdnDxFugJv2YhYkjSI3w64HVgH+LWkvv4t3Yr093c26SSipYh4OfdbwN71+3LC\nxnakaZjP72NfeuWtwMGkJKEV8+d2LPlEQ9JI0nNZD5gOfIz0GR1HSiD6Hek1/J6qs/lvztuN+5PV\nbmZmZv02kzSuqv/Suz4g/Xw7jUhaVNIvSOOE9wHLATPy9n2ksdnPVX7F3UGkcep/ksaFs0hjkC1I\nSaG3SFqppO67SWO3j5CCyLNJwesDSGPDNVr0/Q2kAPl3SYHn8cC/clsfAC6R9KOSuuOB64FjgTeS\nxtn1ycefqTp2G4oA8c0R0faXA5L2IgWAFyN9AbNdRDzTZvWuJiBLOoB0bvMysFNEzBeYjogppMSP\nV4CTJJWu4d2G4guWkWUFHBA2M7Mh7knS+GhzYJm6uNia1GJ5J0raqLGipC2Bk0j/Ay8H1skxnbHA\nh0jjn6Pa6MOupKXCDsl9GE/6gvyyVhUljSLNirUN6X//YaTY6bj8PNYgxTP/2kY/+iUiboiICaRE\nEYBzI2JCw+2GATj0d0gJuDuQlmxbihTPLLwA/IQUV1wuIsZExFhSIstXSOPdL0p6f5f79RopMWQP\nYBVS3HdpUsz4E8A/SUkRX2vRzimkL+83jYixEbEE8M5ip9ISfOeT4tGnkMbRi+dyqwM/In3Z/1NJ\nmzRpvz+x6VaKmadv6UOdX5Hiv5MkNZ57fIL0+3ZuRPyrwz4taJNJF3HuSor7LkNKJHoa0gwmpAtT\nFyOdC22QP8NjgPcDT+TyF7aI+xbx4W0H4DmYmXUmInzzzbcheANGA8+Rp/wtKfONvD+AKR0c43e5\n7pFN9k2pa/ujTfavScpoL8ps1aTM9nX7X9fHvq1CCorOBVZr2LdVXbsfa1J3BGnpi6LM3g3718vt\nvgysX3L8JUlrawewcx/7fkyud1eLcsVrPDXff3u+/xCgunJH58d/ku/vXTy3knaL5z2DdLJddVu1\noe7k+j6VtD+p7Ph19QP4dkUbe9SVe1eT/aOAm1q9jqQva4p2tuvP75xvvvnmm2+++db/W9U4oa7M\n1FxmcpN9J+R9c/OYalx+fBlSALf4v39ck7o71e2/AFgjP74oKWHjxbzvemBkQ93XkZJGA7gTeFt+\nfAQpueExUvJJ2RhoXB7DBWkpkK1JQV5IXzJ8gZSsEMDnm9T/dd43i/RFwuj8+Op536ukset8Y9s2\n35dbc91jKspMrHv99gYOojbe/23RpxbHmVI/lgROa/Ze52PNza/5GFqPb/fO5Z8HNm/zc/gi6cuB\nD3Xwei1CGisHcE6Lsmfncr/o9e+fb7755ptvvnXzRrpYLIDTmuy7Iu/7SzHmadi/Xd244uEm+6fU\n7T+wog/145OJDfv2pTZufG8fnldx7CkVZfau6Pvk+vFOX9vO5YrnNKmizNRm46iG+tPpY8y1oZ1D\ncjtXNNk3qWp81s/P1ibUYpejG/bVv+cPk77YL2vnFlqPcb+by1zQxz6WxqbbrP9oPu4X2/ws7J3v\nn5rvH11XRsCD+fEtGz4f833W6j6/ATxDdWz4103qP1zfp5J+l37W6+q/BmxU0cYludz9wJgm+zci\nJREFcEgbn+NHuv1Z9c0333zr9OYZNMyGrneSrrqDFJxu5jhqV3Z14qK83aqizKM0WUolIh4kXS0G\ncG1EXNek7tWkDHZIU8y1LSIeJwXHRZrOrt6H8vZhUkC0se5cqqcv3De3e1FE3FVy/JdIgX3o+9Vw\nxYwk7V5dWBzzelJ29kTSiWyxXvheucjpfezHWNIVC1W30isC+2kuaZmYMnvk7Y0RMd9VERHxGikx\nBWA9SeuXtPM8takvq2aCMTMzs0EuL3/2+Xz3uIg4KiJeAIiIaRHxJeDEvP/gJjNhFGOP64DdIi1T\nQUS8GhFnAR/N+7ckLQFY7wjS1V3PAe+IiFty3bkRcSlpJouxFd3/EmkM9wdS8um1EfFKbmN6RHwH\nKKZWPjJf8Vk877fV9ee/IuKUiJiV6z5CGvveTEpk6DNJxRTBkMbX7TiAdEWmgDNJr2cn5x1n5O1e\neVxb6NMVgBExJSJGRMT4iLipjfJTI2KpiBgVEed10O+DSWNlSEHyKrfn7RYdHMfMzGwwaxo3zDN/\nbZ/vfqsY89SLiKtob9nfaaSZDzqxT95eHBEXd9jGUHdWRPyjH/WL93iLPNvtAhERfyTNojAW2LCi\n6A8iYkazHZI2IC1FNxv4dkUbZ+btjn15ji1i05XyuLc4V+lTfJha/Ld+/LwdaUaYe6PvM7EsR3Vs\neHx51X67NCJub7ZD0jhqMfdvNTsnyHV/ne9+uOI4z+Zt05kSzcx6wQkaZkNXMe3aYxHxQLMCETGd\nNIVzKUkbSPqRpD9LelHS3GItcNI0b5CuGCzzx4iIkn3F+nm3lvRvDrUB0jJN+jZC0kck/Z+kR/Na\n21HXv7eV9G/jvL2mom/X02Rdu6w4sXxPXhO86Y3atMurl7RTplhapa2pvBtMydviJHOHfPx72glG\nNzg6ItTi9nAHfWzHAxHxdMX+4vN9RUWZq0hriNeXn0dOxpme71YtaWNmZmaD326kWbRmkRKRm/kq\nKQF4EeCDxYOS3kKaghjg2DwOnUdEXEhtiuEP19UVteTRk5uNYSLibkrWes71i7Hbt3OiaTMXkGZ1\nWI60HFxhz7x9jCbLfeTncmxJm+1YgVpSbrsB4qJ/TwKfrXhOlQYgAXnASdqKWoL8ORHxhxZVHBA2\nM7MhS9Kakk6QdJukFyTNqYvLFUkPjXG5jUhfWkO6OKvM1Da6cGv0Yfm1Qk523TTfvbCv9YeR61sV\nkLSipKMl3SjpOUmv1b3HxfIvY2gSu+2PvHTh/pJ+L+mfkmY1xH1XyEWr4tJVz6+I744A7q2I716a\ny40Flm3oY6ex6VbGkc5roI/x4Yi4EfgbKR68Q364ONeY71yhDWu0iA1P6qDNdlW9fxtT+ztSFR++\nPG/fUrbMJbXXeJGc+GFm1nOjWhcxs0GqGKQ+3qJcaZZ0Xqf5u9SStYqp74rM9sVJVwpWXQ34UsW+\n1/pQZp4BlKQxpCVWtqt7+FXSgGp2vj8+12vsX/FF/D/LDhoRr0h6FpjQZHcx08IS+dZKX69WHJ23\n811B0Iafkb542FXS0vRxbe5BpCo5A9r4fEfErPwerlhXvpmZpJPI0RVlzMzMbPArEjJvjYgXmxWI\niGmS/khaGq4+gbP4+TWqvyi4nBRora+7BrUrx6q+jP8Dza/celNd/SmS5jYpUyjGnqtTWyu56MvU\niuTja0jPrZNz/Pok1nYDxDeSZoSYAFwk6X1lVy+2YQppacZ9SK9hfxKQB5SkdUlX6S1KmrJ9vzaq\nzRMQLmZ9MTMzG+wk7QKcAyxW9/CLpGTZIP0/XIbyuBxUxOZoHdOE1vGjMstSizU+0mEbw0Hl6ydp\nC1KiTf2X1jOAf5He45Gk5GFI7/OzdIGkFUhfutfPiDsrt18kUi9PillXxaWrnl8R3x1JbeazVv4d\n4+1nbLqV+hhlJ/HhM0izA35C0i3ArqTX7czKWoNP1ftXH+ut+ltRfPcxivR+PNWkzMy6nx0fNrNB\nwTNomA19ZUHaSpLeCJxE+jtwHikQPToilomICRExgTR1L9SyVRekL5EGwDNJa3Kvnvu3bF3/iqB1\nY/+K+61em7LnVVxBeHgbM0x0kkn8XN72OfM8Ip4ALiMlz+xHmu56DnBWX9vqsfmuWi3R7ue7qlzx\nhchzFWXMzMxs8OtrgnJ9UK/4+dlm02y3WbfVscsSo+uXWVue6imEi3P0+gTgthJX6Xys00mA+CfA\nF/PP2wCXSGonsbmZn5HGhoM6AVnS2qQEkuVJs37smJc9bMUBYTMzG3IkLUtKolyM9P9vEjAmIpaO\niBVzXO5DZdXrfq6K17QTb2w3flSlo9jpMFH6+uVZRs4hJWfcAbwXWCoilqx7jzevr9LFfn2HlJzx\nHClJd6WIWDwilq+L+xbJPVXHrfp8FPHdv7UZ322cSbg/selW6sftncxMchbpue8C7E+KE1+a48ZD\nSTd+v+uV/a7XL9Pi+LCZDQpO0DAbuooM01ZTqK1S8vgHSQPVe4A9I6LZlIHNZpdYUIqpnI+JiJMi\n4tEmVwyW9a94bVYu2Y+kxWiYtq7Ok3m7fsn+/iqmju50Db8iWH0saQB+SUQ8WVG+m4oZT6qCy0t3\n4TjFe7hqWQFJo6m9h02n485lRleVMTMzsyGnPwmc/U3+7CTAX7+W9YQ2g8NTunTsdnQUII6IE0nB\nakhTSF8qacm+HnwoJCDn5IyrSMuU3Ads14fxtwPCZmY2FL2XNKvuNOA/IuLqiJjZUKZVXA4qYnMt\n9vXXc9RmOZjYx7oLKvZVpfjieiD7sAUp6WAOsFNEXNIk+bTrseG8DMWu+e4BEXFG47hKUv3MHZ0q\n2lxTUl9nuID+xaYr5Rh8MSNgn+PDefx8KWn8XCx1uCCTmxfE70j935Gq7z+Kfa+R/l41U7zG0yNi\ndkkZM7MFygkaZkPXH/N2VUmvb1ZA0lLMu351veKL7zsjomya5R370b/+Kvp3e7OdkiYCa5XU/VPe\nblvR/tspnwK6WP/uff24ErBKsX7jmh3W/z/Sieai+f6CXJu7GOiWJk4Am3XhOMXne4eKMpOovYe3\nlpSpf43v6WefzMzMrLdaJnBmRZCuPjmzqLt8TtTtpG79/mbKEqPrA86dJAC3TMxukXzcSv1z7VOA\nOCJOAj6X776dDpM06G0CcqW65IyVgftJyRl9uTrRAWEzMxuKivHWvRHxr5IyZXHD26kllk6qOEbV\nvn6JiNeAW/Ld/+hj9YGMfRUx2FYzLlT2IY+33thhHwpF289ERNlMbQMRG16e2hf7TeO+pOTf/s48\nVsR3FyUlAPdVf2LT7ehvfLiIBy9KWhrmwn70pa9afT5HMO+SkZ34E7Xfl6r4cPEZvbNirL1G3jo2\nbGaDhhM0zIauy6kNhr5cUuZQUoCzmel5u76k+U4KJL2HATxRakPRvw1K9h9XUfdXeTtR0kcad+bn\ne0RF/VNJJ5LjgG9VdVLSIh0kcVyTt8vnwXyf5CzrzwPfBr5JWg9xQbkzb1eWtHnjzryG5Ke6cJxf\n5O0Wkt7Z5DijgKPy3bsj4u6SdooT5qci4t4u9MvMzMx6p0jg3CQvhTEfSeOoBQPrEziLuqOoTuIt\nAnz1dR8irTUN865B3Wj7ksfvpnaF3J4lZaoUfd+22bg924by5ONKETGNuisMO6j/feCAfHdL4Pc5\nUbwvepmAXConZ0yllpwxKSL+WVlpfg4Im5nZUFTE5dbOs5POQ9KGwHwxN4CIeJ6U3AjwRUmLNpaR\ntA2wdZf6WuanefteSe/tQ70i9rWppPm+gM7LRu/a+HibijHhuDb7sFvJ/kNIy8/0R/Eeryhpxcad\nkl5HLRG3m16klsAzX9w3x/y+1oXj/JFacsXXJC1fVVhSY6Jyf2LT7Sjiw2/rsP6FpLjwt4GDmsyM\nPZCKz+cuJecne9F61u9KEfECaZY9gP+WNKaxjKQNqP2OnFPRXBEfvro/fTIz6yYnaJgNUXlawWIK\ns70knZTXh0TSUpK+TEpCeKGkiUvz9s3AD4tBqKSxkvYjJTn0cgreon9HSto1D86RtIaknwO7UzJt\nWURcS0pgAThV0t7FlZL55OJs0klg0ysAIuIO4KR8d39J50nasBhwShopaYP8Gv8d2LAvTywi7qF2\nJWRHGfcRcXZEHBIRhy3gK/FuAB7JP0+RtImSEZImkQLY3fjfcj61dRx/KekjeQpEJK2R92+R9x9a\n0Y4H4GZmZsPH+aSpa0cDh5WUOYIUrJ6dywMQEX+mdpXakXna5HnkwH0xdjinrm4Av8x395c033TL\nkt5EWkJwPvkKziLhYC9JW5X0vWirMTh8bt6uRgp2NpYfARxZ1WYb+hUgjogfAp8lBds3JyVptD2t\ncY8TkJuqS84oljXpJDkDPB41M7Oh6fekq9fHA2dLWgVA0qKSds/7G5fDqPcV0rhgPeD/JL0h1x8l\naVfSOK1sOYJuOQu4jjRbxfmS/rsYx+XY3kRJX5B0fEO9C4EZwCKkmNQ6uc4ikj4AXAG83GGfiguM\ntpa0bkW5Yiz6LklHF8mvkpaT9HXS2K8s5tuu60jPQ6TnuXY+xkhJ7yKNg7q+xF5EzKA2u8WJkrbP\n41kkrQdcTEq47vQ1Lo4TwP7AK6Rx9M2SPlj/Rb+kVSR9TNLlQOPnoOPYdJum5u0mzc5NWomI2Tku\nfEhEnN2PfnSi+Hy+EfhJw3cSXwBOppbg3h9fIp3XrQVcJmn9fJwR+dztYlKS+t+BU5o1kF/bYoZx\nj8fNbNBwgobZ0PZdamszfx54WtLzpAHQMaRg7m+bVYyIK6nNUvAZ4DlJ00jZwSeTrvCaPGA9b+1I\n4ClgSdJJ20xJLwAPAh8mDdD+XFH/48DfgDGkKZNfys/vMWAP0lV+z+ays5rU/29qSRofJGVc/0vS\ns7n8HaTXeFU6O1kpBrLv76Butxwi6ckWt1/XV8jL4exHGhyvQ7q6dAbppOkq0qD4s/3tWETMIWVA\n/4W0ZuHZwIz8Hj5Iet3mAp+PiEuatZFP7t6X7/68v30yMzOz3spTL3833z08B6vHQZo5Q9KxpDEc\nwIlNlqEokjq2Bn6Vkz6LYPtHqY3PbgAuaKj7DdKXEMsBl0vaJNeV0mxfl1CS/JsdSwocjiItA3Jw\n/VV8kpaW9G5JPwOubXjeN5NmmAD4saRP1SUfr0Ya82/R4vitTM3bjpeqi4gfkc4rIrdzefH+tFm/\nVwnI85G0FmlsuxJwLx0mZzggbGZmQ1VE3E9tVtldgX/kuNwM0thjBhWzK0TEdcDB+e67gPtyTGcG\nKc73JCmuBs3jcv2Wk2R3IY2tRpOSQJ/O/ZhJmiXtRFJ8q77edOCgfHdz4G+SXsx9vwB4lNqsrn11\nPml5uWWAeyQ9I+nhfKufqXYKtVlIjgJeyDHfp4HDSePaO+mH/DwPyXe3Ae6V9BLpeV5Kisd9oj/H\nqHAQKZa4CnAlKeb6InAXaca6T1GL23YsIm4hLXHzHGlWs/OAFyU9K+ll4B+k2HqzpVz6G5tu5UrS\nZ2Es5TPxLQi3thEf3qO+Qv5e4cx895PAs/n3ahrpd+ondGHJlYi4HfhP4FXSsjd/ljSd9Nm5iDTL\n3WPAf+TEn2Z2IL3GT5NeczOzQcEJGmZDWETMjYiPk5IRbiKdXIwirdG2PyVTDdb5KGlA/GdSNvFI\n0kD4f0hrSJcNbAZcRDxCypb+KVAEQ2eRrqZ7V0R8o0X9J4FNga+SrnibS7ri8mJg+4g4lXSiAU0y\nziNiTkR8AdiYNKi8F5iT60wjZXpPBjaMiOsb67ehyOr9gKSxHdTvhrHAii1u861DHhGXkb7Y+B3p\ntRhJGgwfRwpAd2W98PwlzCakE/ri8z0mH+ss4K0R8b2KJrYlTaf3OIPgKkwzMzPriiNIs1mIFKx+\nLgern6M2i8Q5NFkCMCJ+RxpXBLAz8GDdFwX/CyxFGgt/KCeL1td9lBSIfYU0e9qtOYj8Mmnq3UWo\nfQkxnzzV9ztIgfSxpJkinpY0LQcZXyAleXyc2jIf9fbJdUeTxqZF8vEjpKTWg0gB3k6dTwp8rltc\n4dqJiDiFlMwbpLF4n5I0BpEjSAFfSEkat1cFrSvacUDYzMyGrIg4nDQ2uYUUk1kEeAD4OrARqVJs\nagAADY9JREFUtXhdWf2TSMsnX0yKH40GHibF6jYnjeeg/zNBVPXh2dyHj5HGWsUX4tOA20ixrPmW\nQY6InwLvBf5AWpJjFCm+eDgp3tTR7A55abltSBfNPU6KM66eb6Prys0hXXT0FdIFaK+Sxle/B94R\nESd0cvwm/Tk5H2cqaUw8Kvfr+6SlPe7qxnGaHPc20sxtvyQlYowgJUP/EtgyIs6qqN7XY11OmoHh\nf0izhkwnLTEzlzTD3k9JF4Id2FCvX7HpNvr1KumiQkgx+l5Zjtbx4WZLqO9DumD0DtLfhxGkePke\nEXFgk/IdiYhzSTOAn0JKeF+MFOO/g/T7sV6erbpM8dqesYCXgTEzq6Q005OZ2cIlB57vy3dXi4jH\netCHP5CywveKiDNblbe+kXQ6KdP/KxFxTKvyZmZmNvCUlkS7CiAimq1XjKSppMD30RExuaTMrqSr\ntTYhXYE4jbTO9KkR8ZsWfdgY+EI+xoqkgOI9pIDwjyOi9CpOpaVMvky6ym1p4AlSkParpCl+Wz23\nUaREj91Jia3LkZKAnyAFGS8ELsxfJjTWHUO6yvHDpCsAX8rP+YSIuFLSw6Tg/iciYkrVa1DSt3Nz\nv5q+7pImkq40pdUxJO0LnEr64uU20hcJ0yRNIS3TcnVETOpD3/YmB7DLXttuqutnWyre75+Rvtg6\nPn/JZWZmZpmks0kXl50eEfv2uj9mC5qkNUnx6X8BK0VEv5Z1sXnliyKfIF3wt3ZEPNjjLpmZ/ZsT\nNMxsoVT35f1fI+LNPerDFqQptP8CrB/+g9w1klYlXdUxHVgrIl7scZfMzMzMBjVJ25CW4fg78AaP\nTfvHAWEzM7NyktamNjvY7hFxXo+7ZNYTkk4BPg0cFhHf7HV/hhNJh5FmyTklIvbvdX/MzOp5iRMz\nG5YkrSvpNEnbSFqy4fEzqK2heFxveggRcSPwK9I0bR/qVT+GqSNI04NPdnKGmZmZWWsRcQ1p2uzX\n47FpNxxAWrP8NCdnmJnZwkjSMZIOkLSapBH5sbGS9iDNOjaatHzHBb3sp1mPHUVaXuaQHi6DPexI\nWoI0++AM0lIoZmaDimfQMLNhSdKGwO11D00nrZU5pu6x70XE5xdoxxrkqaL3Bu6PiLN72ZfhIp/0\nH0ZKQjw+Il7rcZfMzMzMhgRJ65OWWrkHeEtEzO1xl4akHBB+iPTF01oR8VSPu2RmZrbASboA+EC+\nO5u0PNs4aheNPg68OyLu7kH3zAYNSTsDGwLnRcRfet2f4UDSesAHgdsj4re97o+ZWSMnaJjZsJRn\nzfg0sCOwDrACMAp4GrgR+ElEXNm7HpqZmZmZDT6S9gYmAqdGxOO97c3Q5ICwmZkZSNoW2APYElgJ\nGA+8DNwH/A74QUQ837sempmZmfWGEzTMzMzMzMzMzMzMzMzMzMzMBtiI1kXMzMzMzMzMzMzMzMzM\nzMzMrD+coGFmZmZmZmZmZmZmZmZmZmY2wJygYWZmZmZmZmZmZmZmZmZmZjbAnKBhZmZmZmZmZmZm\nZmZmZmZmNsCcoGFm1oKkr0oKSYd2sc1Lc5vbd6tNMzMzM1t4lI1R82MhaVIHba4m6RVJD0hatGud\nNTMzM7OFSjfjqZI2z21d042+mZmZ9Zoiotd9MDMbtCS9DrgPmAGsEREvd6ndzYEbgduBTSJibjfa\nNTMzM7Phr2qMKqk4yd8uIqZ20PbJwH7AwRHxnS5018zMzMwWIgMRT5V0KfAuYNeI+E1/2zMzM+sl\nz6BhZlbta8DiwDe7lZwBEBE3AZcBGwEf61a7ZmZmZrZQGJAxavZ1YDZwpKRxXW7bzMzMzIa/gRir\nTs7b4ySN6lKbZmZmPeEEDTOzEpJWAT4KvAqcPgCHODlvu7Z0ipmZmZkNbwM9Ro2IR4GLgfHAJ7vd\nvpmZmZkNXwM1Vs0Xu90JrA3s3K12zczMesEJGmZm5T4FjAQujojnB6D9i4HngTdLevsAtG9mZmZm\nw89Aj1EB/jdvPy1JA3QMMzMzMxt+BnKsWoxR9+tyu2ZmZguUEzTMzJrIgeh9892fl5RZTdJnJV0k\n6T5JL0uaIemvkk6StFrVMSLiVeD8fPfT3eu9mZmZmQ1H7YxRG8pPkPQDSQ9JmiXpSUlnS1q3RdUL\ngZeANwCT+tVpMzMzM1so9GWsKmkzSWdIeiDHVF/MMdXTJb2zpFrR5g6S1uxWv83MzBY0RUSv+2Bm\nNuhIWh/4c767UkQ82aTMVGDbuoemA0tSS36bDuwUEddVHOdjwFnA0xGxYhe6bmZmZmbDVJtj1OIk\nfx/g68AEYCYwB1gi75sF7BIRl1Yc6wpgB9La4Yd15xmYmZmZ2XDV5lh1JHAi8Lm6h18GBIzJ96dH\nxLiSYzwAvB74r4j4cbf6bmZmtiB5Bg0zs+a2ydvHmp1MZHcDhwNvAsbkE4fFgM2AS4GlgXMlLV5x\nnJvzdoU2rmQ0MzMzs4VbO2PUwndIa3+/ExgbEUuSxql3AaNJ49TXVdQvxqnbVpQxMzMzMyu0M1b9\nOrXkjNOBdSJiiYgYC6wI7EyKq5bxGNXMzIY8J2iYmTW3Wd7eWVYgIg6IiOMj4p6ImJkfey0ibgF2\nImWMrwzsVtHG/cCMfHeLrvTczMzMzIarlmPUOosD746IyyNPnZnHqTsCzwNLAf9TUf/2vH2rpEU7\n7K+ZmZmZLTwqx6qS1gYOyXe/GRH7RsR9xf6IeDoifhsRe1YcoxijOo5qZmZDlhM0zMyaWzlvn+mk\nckTMoZbtvVWL4s81HNPMzMzMrJm+jFHPi4h7Gh+MiKeBk/PdPSrqP5u3o4Dl2+6hmZmZmS2sWo1V\n9yJ9J/Uc8JUOj1GMUVfqsL6ZmVnPOUHDzKy5Igj9fFUhSVtLmiLpb5JmSIriBhyai1VNHV1/DAe+\nzczMzKxKW2PU7A9t7FtW0holZeqP4XGqmZmZmbXSaqy6Zd5eHhGzOjxG0fYiksZ12IaZmVlPjep1\nB8zMBqnReftKWQFJx1NLwgCYA0wjrfUNsAQwNt+qzGw4ppmZmZlZMy3HqHUeb3PfCsBDTcrMrPvZ\n41QzMzMza6XVWHVC3j7Sj2N4jGpmZkOeZ9AwM2uuWHZkmWY7Jb2DWnLGj4D1gcUiYnxETIiICcB3\niuItjjW+4ZhmZmZmZs1UjlEbRD+PNb7uZ49TzczMzKyVdseq/RmneoxqZmZDnmfQMDNrrlgrcXzJ\n/j3z9rKI+GxJmQkljzcqjtHOWuJmZmZmtvBqNUatV7XM3ip1Pz9dUqb+GB6nmpmZmVkrrcaqTwDr\nAhP7cYyi7ekRMbsf7ZiZmfWMZ9AwM2vur3m7Zsn+VfP29mY7JQnYvtVBJC0JLJfv3tOXDpqZmZnZ\nQqfVGLXedm3sez4imi1vArBG3j4RES+00zkzMzMzW6i1GqvekLfvkNTp8iTFGNVxVDMzG7KcoGFm\n1tw1ebuBpMWa7J9e7C+pvz/tBc43If0tfg24vk89NDMzM7OFTasxar0PSVqn8UFJywH75bvnVtTf\nrOGYZmZmZmZVWo1VpwBzgGWBozs8RjFGvbrD+mZmZj3nBA0zs+auJyVNLAps2GT/pXn7HklfljQW\nQNI4SUcA36e9dRCLk4o/RcSMfvbZzMzMzIa3VmPUerOASyXtmGd3Q9KmwBWkGdxeAo6rqO/gt5mZ\nmZn1ReVYNSIeAL6V7x4q6TRJbyj2S1pe0h6SftOscUkjgbfmux6jmpnZkOUEDTOzJiLiReCifPf9\nTYqcCVybfz4GeEnS86SkjK+REjh+3MahirZ/3nlvzczMzGxh0MYYtd7BwGjgcmCGpJeAW0gzwL0C\nfDgiHm1WUdLawDrAq8CvutB1MzMzMxvm2hyrHgn8MP+8L3CfpJckvQw8DfyC8qX6dgDG5nJXdqXT\nZmZmPeAEDTOzcqfk7UeKqw4LETEbeCdpOr77gNmASEHvz5BOQuZUNS5pDWALYCYp4cPMzMzMrJXS\nMWqDB4GNSAHwZ0hXMj4NnANsFBEXVdT9aN7+JiKe6Wd/zczMzGzhUTlWjYg5EXEAsBVwNvAosAgp\nMfgvwE+B3UraLsaoZ0TEq13ttZmZ2QKkiOh1H8zMBiVJI0jJF68Hto2Irq6/LekoUoLHGRGxTzfb\nNjMzM7PhaQGMUQXcP1Dtm5mZmdnwNVBj1by89BPAGGDtiHiwG+2amZn1gmfQMDMrERFzgS/nu4d3\ns+18UnEgaXrpo7vZtpmZmZkNXwM5Rs12JwXUL3NyhpmZmZn1xQCOVQ8AlgROc3KGmZkNdZ5Bw8ys\nQr6C8CbgbcDmEXFzl9o9DDgO+FZEHNqNNs3MzMxs4TCAY9QRwF3AusCGEXFXN9o1MzMzs4VHt8eq\nkpYAHgJGA2tFxFP976WZmVnvjOp1B8zMBrOICEn7ATsDy3Wx6ZeBycBJXWzTzMzMzBYCAzhGXRk4\nD3jIyRlmZmZm1okBGKtOBH4I3O7kDDMzGw48g4aZmZmZmZmZmZmZmZmZmZnZABvR6w6YmZmZmZmZ\nmZmZmZmZmZmZDXdO0DAzMzMzMzMzMzMzMzMzMzMbYE7QMDMzMzMzMzMzMzMzMzMzMxtgTtAwMzMz\nMzMzMzMzMzMzMzMzG2BO0DAzMzMzMzMzMzMzMzMzMzMbYE7QMDMzMzMzMzMzMzMzMzMzMxtg/w8a\nFke6Lqd9fwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b5d6c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(1,3, figsize=(30,10))\n",
    "\n",
    "bp0  = sns.boxplot(x=\"StadFldA_\", y=\"ClimateChange\", data=outcomes_df, hue=\"State\", ax=axarr[1], orient='h', \n",
    "                   palette=\"PRGn\", order=['NoCC', 'G', 'Wp'])\n",
    "bp0.set_xlabel('Cumulative area of cities \\nflooded (KM^2)\\n(b)').set_fontsize(25)\n",
    "bp0.set_ylabel(\"\",fontsize=0)\n",
    "bp0  = sns.despine()\n",
    "\n",
    "bp1 = sns.boxplot(x=\"DamAgriKSum_\", y=\"ClimateChange\", data=outcomes_df, hue=\"State\", ax=axarr[2], orient='h', \n",
    "                  palette=\"PRGn\", order=['NoCC', 'G', 'Wp'])\n",
    "bp1.set_xlabel('Cumulative damage to \\nagricultural area (MEuro)\\n(c)').set_fontsize(25)\n",
    "bp1.set_ylabel(\"Climate Change Scenario\",fontsize=30)\n",
    "bp1 = sns.despine()\n",
    "\n",
    "bp2 = sns.boxplot(x=\"DamCumFloat_\", y=\"ClimateChange\", data=outcomes_df, hue=\"State\", ax=axarr[0], orient='h', \n",
    "                  palette=\"PRGn\", order=['NoCC', 'G', 'Wp'])\n",
    "bp2.set_xlabel('Cumulative total \\ndamage (MEuro)\\n(a)').set_fontsize(25)\n",
    "bp2.set_ylabel(\"\",fontsize=0)\n",
    "bp2 = sns.despine()\n",
    "\n",
    "f.subplots_adjust(hspace=10)\n",
    "\n",
    "for x in range(3):\n",
    "        axarr[x].yaxis.tick_right()\n",
    "        axarr[x].yaxis.set_label_position(\"right\")\n",
    "        axarr[x].spines['left'].set_visible(False)\n",
    "        axarr[x].spines['right'].set_visible(True)\n",
    "        axarr[x].xaxis.set_tick_params(labelsize=25)\n",
    "        axarr[x].legend_.remove()\n",
    "        if x<2:\n",
    "            axarr[x].set_yticklabels([])\n",
    "        else:\n",
    "            axarr[x].yaxis.set_tick_params(labelsize=25)\n",
    "            axarr[x].legend(ncol=2, bbox_to_anchor=(-0.25,1.1), fancybox=True, shadow=True)\n",
    "            plt.setp(axarr[x].get_legend().get_texts(), fontsize='30') # for legend text\n",
    "        \n",
    "plt.subplots_adjust(hspace = 0.2)\n",
    "f.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:py27]",
   "language": "python",
   "name": "conda-env-py27-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}