{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimating the effect of agricultural conservation practices (CP) on reducing nutrient loss\n",
    "\n",
    "We will use the Measured Annual Nutrient loads from AGricultural Environments (MANAGE) data from the USDA,\n",
    "as published by [Qian and Harmel](https://onlinelibrary.wiley.com/doi/epdf/10.1111/1752-1688.12377), where they used causal inference in order to quantify the effects of water and soil conservation practices in reducing phosphorus loss from agricultural fields.\n",
    "\n",
    "The data is [provided](https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2F1752-1688.12377&file=jawr12377-sup-0001-manage.RData) in `Rdata` format, so we will first have to load it and transform it. \n",
    "We use the `rpy2` package, which is not a part of the requirements for `causallib` and so you will have to install it if reproducing the analysis.\n",
    "\n",
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rpy2.robjects as objects\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = objects.r['url']('https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2F1752-1688.12377&file=jawr12377-sup-0001-manage.RData')\n",
    "try:\n",
    "    objects.r['load'](url)\n",
    "except Exception:  # fallback - if the above doesn't work please download the file directly\n",
    "    objects.r['load']('jawr12377-sup-0001-manage.rdata')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(274, 71)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WatershedID</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Watershed.Years</th>\n",
       "      <th>ShortLU</th>\n",
       "      <th>LandUse</th>\n",
       "      <th>Tillage</th>\n",
       "      <th>CP1</th>\n",
       "      <th>...</th>\n",
       "      <th>PP.Form</th>\n",
       "      <th>MaxTPLoad</th>\n",
       "      <th>MinTPLoad</th>\n",
       "      <th>AvgTPLoad</th>\n",
       "      <th>FormTP</th>\n",
       "      <th>FlowIndicator</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Ref</th>\n",
       "      <th>CPs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RecordID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>4 Yr. Bahiagrass 1</td>\n",
       "      <td>Tifton, GA</td>\n",
       "      <td>GA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1952 - 1960</td>\n",
       "      <td>4</td>\n",
       "      <td>Pasture</td>\n",
       "      <td>1st Yr. Bahiagrass, Clover (4 Yr. Rotation of ...</td>\n",
       "      <td>Pasture</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Surface Runoff</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thomas, A.W., R.L. Carter, and J.R. Carreker. ...</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>4 Yr. Bahiagrass 2</td>\n",
       "      <td>Tifton, GA</td>\n",
       "      <td>GA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1952 - 1960</td>\n",
       "      <td>4</td>\n",
       "      <td>Pasture</td>\n",
       "      <td>2nd Yr. Bahiagrass, Clover (4 Yr. Rotation of ...</td>\n",
       "      <td>Pasture</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Surface Runoff</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thomas, A.W., R.L. Carter, and J.R. Carreker. ...</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>KA-1</td>\n",
       "      <td>Middleton, WI</td>\n",
       "      <td>WI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-2004</td>\n",
       "      <td>2</td>\n",
       "      <td>Alfalfa</td>\n",
       "      <td>Alfalfa</td>\n",
       "      <td>Pasture</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Surface Runoff</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cabot, P.E., K.G. Karthikeyan, P.S. Miller, an...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>KA-2</td>\n",
       "      <td>Middleton, WI</td>\n",
       "      <td>WI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003-2004</td>\n",
       "      <td>2</td>\n",
       "      <td>Alfalfa</td>\n",
       "      <td>Alfalfa</td>\n",
       "      <td>Pasture</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.21</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Surface Runoff</td>\n",
       "      <td>Flow from outside the watershed may have contr...</td>\n",
       "      <td>Cabot, P.E., K.G. Karthikeyan, P.S. Miller, an...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>C7</td>\n",
       "      <td>Chickasha, OK</td>\n",
       "      <td>OK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>July 1972 - June 1973</td>\n",
       "      <td>1</td>\n",
       "      <td>Alfalfa</td>\n",
       "      <td>Alfalfa (Dryland)</td>\n",
       "      <td>Pasture</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Surface Runoff</td>\n",
       "      <td>Estimated precipitation, runoff, and soil loss...</td>\n",
       "      <td>Olness, A., S.J. Smith, E.D. Rhoades, and R.G....</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 WatershedID           City State Location  \\\n",
       "RecordID                                                     \n",
       "151       4 Yr. Bahiagrass 1     Tifton, GA    GA      NaN   \n",
       "152       4 Yr. Bahiagrass 2     Tifton, GA    GA      NaN   \n",
       "260                     KA-1  Middleton, WI    WI      NaN   \n",
       "261                     KA-2  Middleton, WI    WI      NaN   \n",
       "93                        C7  Chickasha, OK    OK      NaN   \n",
       "\n",
       "                           Date  Watershed.Years  ShortLU  \\\n",
       "RecordID                                                    \n",
       "151                 1952 - 1960                4  Pasture   \n",
       "152                 1952 - 1960                4  Pasture   \n",
       "260                   2003-2004                2  Alfalfa   \n",
       "261                   2003-2004                2  Alfalfa   \n",
       "93        July 1972 - June 1973                1  Alfalfa   \n",
       "\n",
       "                                                    LandUse  Tillage  CP1  \\\n",
       "RecordID                                                                    \n",
       "151       1st Yr. Bahiagrass, Clover (4 Yr. Rotation of ...  Pasture  NaN   \n",
       "152       2nd Yr. Bahiagrass, Clover (4 Yr. Rotation of ...  Pasture  NaN   \n",
       "260                                                 Alfalfa  Pasture  NaN   \n",
       "261                                                 Alfalfa  Pasture  NaN   \n",
       "93                                        Alfalfa (Dryland)  Pasture  NaN   \n",
       "\n",
       "          ... PP.Form MaxTPLoad MinTPLoad AvgTPLoad FormTP   FlowIndicator  \\\n",
       "RecordID  ...                                                                \n",
       "151       ...     NaN       NaN       NaN       NaN    NaN  Surface Runoff   \n",
       "152       ...     NaN       NaN       NaN       NaN    NaN  Surface Runoff   \n",
       "260       ...     NaN      0.12      0.12      0.12    NaN  Surface Runoff   \n",
       "261       ...     NaN      2.21      0.03      1.12    NaN  Surface Runoff   \n",
       "93        ...     NaN       NaN       NaN      2.48    NaN  Surface Runoff   \n",
       "\n",
       "                                                   Comments  \\\n",
       "RecordID                                                      \n",
       "151                                                     NaN   \n",
       "152                                                     NaN   \n",
       "260                                                     NaN   \n",
       "261       Flow from outside the watershed may have contr...   \n",
       "93        Estimated precipitation, runoff, and soil loss...   \n",
       "\n",
       "                                                  Reference Ref  CPs  \n",
       "RecordID                                                              \n",
       "151       Thomas, A.W., R.L. Carter, and J.R. Carreker. ...  44    0  \n",
       "152       Thomas, A.W., R.L. Carter, and J.R. Carreker. ...  44    0  \n",
       "260       Cabot, P.E., K.G. Karthikeyan, P.S. Miller, an...   7    0  \n",
       "261       Cabot, P.E., K.G. Karthikeyan, P.S. Miller, an...   7    0  \n",
       "93        Olness, A., S.J. Smith, E.D. Rhoades, and R.G....  29    0  \n",
       "\n",
       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normally we would use pandas to transform the R-datafram into a pandas dataframe, \n",
    "# from rpy2.robjects import pandas2ri\n",
    "# pandas2ri.activate() \n",
    "# but in many versions of pandas this is broken with categorical data as we have here\n",
    "# so instead we will use R to save the DataFrame to csv and load it in pandas\n",
    "objects.r['write.csv'](x=objects.r[\"LoadApr2007\"], file=\"MANAGE.csv\", fileEncoding=\"UTF-8\", **{\"row.names\": False})\n",
    "manage = pd.read_csv(\"MANAGE.csv\", index_col='RecordID')\n",
    "print(manage.shape)\n",
    "manage.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pre-processing\n",
    "We will follow the [procedure used by Qian and Harmel](https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2F1752-1688.12377&file=jawr12377-sup-0002-causalClean.R)\n",
    "to preprocess the data, which includes creating a few log-transforms and cleaning the data.\n",
    "\n",
    "The intervention we are interested in is one of either contour farming, terracing, buffering, or creating a waterway.\n",
    "Together these are referred to as conservation practices, or CP.\n",
    "The outcome of interest is the amount of phosphorus that was measured in runoff water (`AvgTPLoad`). \n",
    "To see why this is a problems see [an explanation from the USGS](https://www.usgs.gov/special-topic/water-science-school/science/phosphorus-and-water) and [from the EPA](https://www.epa.gov/nutrient-policy-data/estimated-total-nitrogen-and-total-phosphorus-loads-and-yields-generated-within).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#manage.fillna(manage.median(axis=0), inplace=True)\n",
    "manage[\"AvgTPLoad\"].clip(0.002, inplace=True)\n",
    "manage[\"AvgTNLoad\"].clip(0.02, inplace=True)\n",
    "manage[\"F.AppMethod1\"].replace(\"Other\", \"Surface Applied\", inplace=True)\n",
    "manage[\"F.AppMethod1\"].replace(\"Other2\", \"Unknown\", inplace=True)\n",
    "\n",
    "manage[\"CFarm\"] = (manage[[\"CP1\", \"CP2\", \"CP3\"]] == \"Contour Farming\").any(axis=1)\n",
    "manage[\"Buffer\"] = ((manage[[\"CP1\", \"CP2\", \"CP3\"]] == \"Filter Strip\").any(axis=1)) | \\\n",
    "                   ((manage[[\"CP1\", \"CP2\", \"CP3\"]] == \"Riparian Buffer\").any(axis=1))\n",
    "manage[\"Terrace\"] = (manage[[\"CP1\", \"CP2\", \"CP3\"]] == \"Terrace\").any(axis=1)\n",
    "manage[\"Waterway\"] = (manage[[\"CP1\", \"CP2\", \"CP3\"]] == \"Waterway\").any(axis=1)\n",
    "manage[\"NumCP\"] = manage[[\"CFarm\", \"Buffer\", \"Terrace\", \"Waterway\"]].sum(axis=1)\n",
    "manage[\"AnyCP\"] = manage[[\"CFarm\", \"Buffer\", \"Terrace\", \"Waterway\"]].any(axis=1)\n",
    "\n",
    "manage.dropna(axis=1, thresh=50, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def boxplot(df:pd.DataFrame, name:str, by=\"AnyCP\", ax=None):\n",
    "    \"\"\"A helper function to plot boxplots in log scale and annotate the median\"\"\"\n",
    "    ax = ax or plt.gca()\n",
    "    _ = df.boxplot(name, by=by, ax=ax)\n",
    "    ax.set_yscale('log')\n",
    "    ax.autoscale()\n",
    "    ax.grid(False)\n",
    "    medians = df[name].groupby(df[by]).median().values\n",
    "    ticks = ax.get_xticklabels()\n",
    "    for median, tick in zip(medians, ticks):\n",
    "        ax.text(x=tick.get_position()[0], y=median, s=median, horizontalalignment=\"center\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VUl5aSplfSnlERheJWadTB4DpQT8Os4uADjPfH2Z0ntg3Mzr/7TX73hwAaBD9OMwihrgDAABAA7iDDgAAAA3Q6OegH3300XXJkiXtLgMAGuW66677Ya31Ue2uY3/04wCwZ3vryxsd0JcsWZJrr7223WUAQKOUUr7T7hoOhH4cAPZsb325Ie4AAADQAI0M6KWUlaWUtZs3b253KQDAQdKPA8ChaWRAr7Wuq7We1dHR0e5SAICDpB8HgEPTyIAOAAAAs42ADgAAAA0goAMAAEADCOgAAADQAAI6AAAANICADgAAAA0goAMAAEADNDKgl1JWllLWbt68ud2lAAAAwJRoZECvta6rtZ7V0dHR7lIAAABgSjQyoAMAAMBsI6ADABPKVDUAODQCOgAwoUxVA4BDI6AzaYaGhtLV1ZU5c+akq6srQ0ND7S4JADhA+nGAqTe33QUwMw0NDaW/vz+Dg4Pp7u7O8PBwent7kyQ9PT1trg4A2Bf9OEB7lFpru2vYq+XLl9drr7223WVwCLq6urJ69eqsWLFiV9v69evT19eXDRs2tLEygOmvlHJdrXV5u+vYH/349KUfB5hce+vLBXQmxZw5c7J169bMmzdvV9v27duzcOHC7Nixo42VAUx/AjqTTT8OMLn21pebg86k6OzszPDw8Li24eHhdHZ2tqkiAOBA6ccB2kNAZ1L09/ent7c369evz/bt27N+/fr09vamv7+/3aUBAPuhHwdoD4vEMSl2LiDT19eXkZGRdHZ2ZmBgwMIyADAN6McB2qORc9BLKSuTrFy6dOmZN910U7vLAYBGMQcdAKa3aTUHvda6rtZ6VkdHR7tLAQAAgCnRyIAOAAAAs42ADgAAAA0goAMAAEADCOgAAADQAAI6AAAANICADgBMqFLKylLK2s2bN7e7FACYVgR0AGBCeVwqABwaAR0AAAAaQEBn0gwNDaWrqytz5sxJV1dXhoaG2l0SAABAYwnoTIqhoaG89rWvzZYtW1JrzZYtW/La175WSAcAANgLAZ1J8cY3vjFz5szJpZdemvvuuy+XXnpp5syZkze+8Y3tLg0AAKCRBHQmxcaNG3P55ZdnxYoVmTdvXlasWJHLL788GzdubHdpAAAAjSSgAwAAQAMI6EyKxYsX51WvelXWr1+f7du3Z/369XnVq16VxYsXt7s0AACARmpkQC+lrCylrN28eXO7S+EQXXTRRbnrrrvynOc8J/Pnz89znvOc3HXXXbnooovaXRoAcAA8jQVg6jUyoNda19Vaz+ro6Gh3KRyif/u3f8t9992XRz/60XnQgx6URz/60bnvvvvyb//2b+0uDQDYj6GhofT392f16tXZunVrVq9enf7+fiEdYJI1MqAz/V1yySX5y7/8y9x2223ZsWNHbrvttvzlX/5lLrnkknaXBgDsx8DAQAYHB8ct9jo4OJiBgYF2lwYwownoTIr77rsvRx111LihcUcddVTuu+++dpcGAOzHyMhIuru7x7V1d3dnZGSkTRUBzA4COpNi7ty5Offcc8cNjTv33HMzd+7cdpcGAOxHZ2dnhoeHx7UNDw+ns7OzTRUBzA4COpPiyCOPzF133ZX//M//zPbt2/Of//mfueuuu3LkkUe2uzQAYD/6+/vT29s77mksvb296e/vb3dpADOa25lMirvuuit/+Id/mDe/+c0599xzs2DBgvzhH/5h3vOe97S7NABgP3p6epIkfX19GRkZSWdnZwYGBna1AzA5BHQmRWdnZ/7H//gf+du//dtdbevXr8/VV1/dxqoAgAPV09MjkANMMUPcmRSGxgEAABwcd9CZFIbGAQAAHBwBnUljaBwAAMCBM8SdSTM0NDTuOehDQ0PtLgkAAKCx3EFnUgwNDaW/vz+Dg4Pp7u7O8PBwent7k8RddQAAgD1wB51JMTAwkFNPPTV9fX1ZuHBh+vr6cuqpp2ZgYKDdpQEAADSSO+hMihtvvDH33HPPA+6g33zzze0uDQAAoJHcQWdSzJ8/P+ecc05WrFiRefPmZcWKFTnnnHMyf/78dpcGAADQSAI6k2Lbtm1ZvXr1uOegr169Otu2bWt3aQAcolLKS0opl5RSPl5KeV676wGAmUZAZ1Icd9xxWbZsWV7wghdk/vz5ecELXpBly5bluOOOa3dpAIxRSrm0lHJ7KWXDbu2nlFK+Vkr5RinlvCSptf5jrfXMJKcneXkbymUKeRoLNIufydlBQGdSrFixIp/85Cdz/vnnZ8uWLTn//PPzyU9+MitWrGh3aQCM974kp4xtKKXMSfLuJC9IclySnlLK2Cusb2m9zwy182ksq1evztatW7N69er09/cLBNAmfiZnDwGdSbF+/fqsWrUql156aR72sIfl0ksvzapVq7J+/fp2lwbAGLXWq5PcuVvzM5N8o9b6rVrrtiRXJHlxGXVhkqtqrV+Z6lqZOgMDAxkcHBy3lszg4KCnsUCb+JmcPUqttd017NXy5cvrtdde2+4yOARz5szJ1q1bM2/evF1t27dvz8KFC7Njx442VgYw/ZVSrqu1Lp/A4y1J8slaa1fr9cuSnFJrfXXr9e8neVaSryd5VZIvJ7m+1rpmD8c6K8lZSXLsscc+/Tvf+c5ElckU0o9Ds/iZnHn21pe7g86k6OzszNvf/vZx82Te/va3p7Ozs92lAbB/ZQ9ttdb6rlrr02utZ+8pnLc2WltrXV5rXf6oRz1qkstksnR2dmZ4eHhc2/DwsH4c2sTP5OwhoDMpVqxYkQsvvDBnnHFGfvKTn+SMM87IhRdeaA46wPSwMcnjxrxenOTWNtVCG/T396e3t3fc01h6e3vT39/f7tJgVvIzOXvMbXcBzExj56C/4Q1vSGdnZ1atWpV//Md/bHdpAOzfl5M8qZTyhCTfS/KKJKe2tySmUk9PT5Kkr68vIyMj6ezszMDAwK52YGr5mZw9pmwOeinliUn6k3TUWl92IPuYgz59mScDMHkmcg56KWUoybOTHJ3kB0neWmsdLKX8ZpJ3JpmT5NJa6wGvRFRKWZlk5dKlS8+86aabJqJMAJhRJmUO+kE+O/Vbtdbewzkf04d5MgDTQ621p9b6mFrrvFrr4lrrYKv9U7XWX6i1/vzBhPPWvutqrWd1dHRMTtEAMEMd7hD39yW5OMnlOxvGPDv15IzOYftyKeUTtdYbD/NcTCP9/f15+ctfnoc+9KH5zne+k8c//vHZsmVL/uZv/qbdpQEAADTSYd1BP5hnpx7OeZjeStnTYsAAQJP19fVl4cKFKaVk4cKF6evra3dJADPeZKzi/tgkt4x5vTHJY0spjyylrElyYinlTXvbuZRyVinl2lLKtXfccccklMdUGBgYyFlnnZWHPvShSZKHPvShOeusszIwcFCjJAGANujr68uaNWty/vnnZ8uWLTn//POzZs0aIR1gkk3GKu57e3bqj5Kcvb+da61rk6xNRheJm+DamCI33nhj7rnnngwODqa7uzvDw8Pp7e3NzTff3O7SAJhkYxaJa3cpHKJLLrkkF154YV73utclya4/3/zmN2f16tXtLA1gRpuMO+ienUrmz5+fc845JytWrMi8efOyYsWKnHPOOZk/f367SwNgklkkbvq77777cvbZ4++rnH322bnvvvvaVBHA7DAZAX3Xs1NLKfMz+uzUT0zCeWiwbdu25W1ve1vmz5+fUkrmz5+ft73tbdm2bVu7SwMA9mPBggVZs2bNuLY1a9ZkwYIFbaoIYHY43MesDSX5UpJfLKVsLKX01lrvT3JOks8kGUnywVrrDQd53JWllLWbN28+nPJoo0c84hG5++67c9RRRyVJjjrqqNx99915xCMe0ebKYHYaGhpKV1dX5syZk66urgwNDbW7JKDBzjzzzKxatSrveMc7cs899+Qd73hHVq1alTPPPLPdpQHMaIc1B73W2rOX9k8l+dRhHHddknXLly/XC0xTP/7xj/OIRzwiQ0NDu+agv+xlL8uPf/zjdpcGs87Q0FD6+/sfsCZEkvT07PGfcWCW2znP/M1vfnPOPffcLFiwIGeffbb55wCTrNTa3HXYli9fXq+99tp2l8EhKKXk0ksvzV/91V9lZGQknZ2dOffcc3PGGWekyf/PwUzU1dWV1atXZ8WKFbva1q9fn76+vmzYsKGNlXGoSinX1VqXt7uOvRmzSNyZN910U7vLAYDG2VtfPhlz0CELFizIpk2bsmHDhuzYsSMbNmzIpk2bzF2DNhgZGUl3d/e4tu7u7oyMjLSpImY6i8QBwKER0JkU5q5Bc3R2dmZ4eHhc2/DwcDo7O9tUEQAAezIZz0E/bJ6fOv2ZuwbN0d/fn97e3gfMQR8YGGh3aQAAjGEOOsAsMDQ0lIGBgV1rQvT391sgbhpr+hz0nfTjALBne+vLG3kHHYCJ1dPTI5ADADScOegAAADQAAI6k2ZoaChdXV2ZM2dOurq6MjQ01O6SAJgCpZSVpZS1mzdvbncpADCtNDKg69inv6GhofT392f16tXZunVrVq9enf7+fiEdYBbwmDUAODSNDOg69ulvYGAgg4ODWbFiRebNm5cVK1ZkcHDQqtEAAAB70ciAzvQ3MjKS7u7ucW3d3d0ZGRlpU0Uwu5lyAgDQfAI6k6KzszPDw8Pj2oaHh9PZ2dmmimD2MuUEAGB6ENCZFP39/ent7c369euzffv2rF+/Pr29venv7293aTDrmHICADA9COhMip6engwMDKSvry8LFy5MX19fBgYGPIcZ2sCUE+BQmBoDMPUaGdCt4j59lFL2+nXqqafmhhtuyE9/+tPccMMNOfXUU/e6LTB5TDlhqunHpz9TYwDao5EB3Sru00etdb9fj1/1yf1uA0weU06Yavrx6c/UGID2mNvuAgCYXDunlvT19WVkZCSdnZ2mnAD7NDIyko0bN6arq2vXvxurVq0yNQZgkgnoALNAT0+PQA4csEWLFqWvry+PeMQjkiRbtmxJX19fFi1a1ObKYGabqKmfRqhOX40c4g4AQPvcc889ufvuu9PX15ef/OQn6evry91335177rmn3aXBjGb6KAI6AADj3HnnnXnDG96QSy+9NA972MNy6aWX5g1veEPuvPPOdpcGMKMJ6ACzgMclAQfrOc95TjZs2JAdO3Zkw4YNec5zntPukgBmPAEdYIbzuCTgYC1evDinnXbauKc/nHbaaVm8eHG7SwOY0RoZ0D0/FWDieFwScLAuuuii7NixI2eccUYWLFiQM844Izt27MhFF13U7tIAZrRGBnTPTwWYOCMjI/nQhz6UhQsXppSShQsX5kMf+pDHJTFpXGif/np6evLyl788t912W2qtue222/Lyl7/c0yAAJlkjAzoAE+fhD3941q5dm/PPPz9btmzJ+eefn7Vr1+bhD394u0tjhnKhffobGhrKlVdemauuuirbtm3LVVddlSuvvNLUGIBJJqADzHA//vGPc+SRR+bEE0/MvHnzcuKJJ+bII4/Mj3/843aXBjTUwMBATj311PT19WXhwoXp6+vLqaeeamoMwCSb2+4CAJhc999/f/7qr/4qfX19GRkZSWdnZ/7qr/4qZ5xxRrtLAxrqxhtvzHe/+91s3bo1P/3pT/P1r38973rXu3L33Xe3uzSAGc0ddIAZbsGCBdm0adO4xyVt2rQpCxYsaHdpQEM96EEPypYtW3LBBReM+/NBD/KrI8Bk8q8swAx35plnZtWqVXnHO96Re+65J+94xzuyatWqnHnmme0uDWioHTt2ZOHChVm9enWOOOKIrF69OgsXLsyOHTvaXRrAjGaIO8AMt3r16iTJm9/85px77rlZsGBBzj777F3tAHsyd+7or4mllHGvAZg87qADzAKrV6/O1q1bU2vN1q1bhXNgv2qtufTSS7N169ZceumlqbW2uySAGa+Rl0JLKSuTrFy6dGm7SwEAmJW2bNmSU089NbfffnuOOeaYbNmypd0lAcx4jbyD7vmpAADtc/zxx+dFL3pRNm3alJ/+9KfZtGlTXvSiF+X4449vd2kAM1ojAzoAAO3T39+fr371q7nqqquybdu2XHXVVfnqV7+a/v7+dpcGMKM1cog7AADt09PTkyTp6+vLyMhIOjs7MzAwsKsdgMnhDjoAMKFKKStLKWs3b97c7lIAYFoR0AGACWUtmelvaGgo/f39u54AsXr16vT392doaKjdpQHMaAI6AADjDAwMZHBwMCtWrMi8efOyYsWKDA4OZmBgoN2lAcxoAjoAAOOMjIxk48aN6erqypw5c9LV1ZWNGzdmZGSk3aUBzGgWiQMAYJxFixZl1apVef/735/u7u4MDw/nla98ZRYtWtTu0gBmNHfQAQB4gFrrPl8DMPHcQQeYIUopE3Icv4QDt956a973vveNe8zaRRddlNNPP73dpQHMaI28g+7xLAAHr9a636/Hr/rkfrcB6OzszOLFi7Nhw4bs2LEjGzZsyOLFi9PZ2dnu0gBmtEYGdI9nAQBon/7+/vT29mb9+vXZvn171q9fn97e3vT397e7NIAZzRB3AADG6enpSZJxQ9wHBgZ2tQMwOQR0AIBZ7EDWr7jhhhty6qmn5tRTT93rNqbIwL6d8PbPZvO92yfkWEvOu/Kw9u948Lx89a3Pm5BamFgCOgDALLa/YL3kvCtz8wUvnKJqYObafO/2xvwsHW7AZ/I0cg46AAAAzDYCOgAAADSAIe7s00TNlTFPBgAAYN8EdPapKXNlzJMBAABmOkPcAQAAoAEEdABgQpVSVpZS1m7evLndpQDAtCKgAwATqta6rtZ6VkdHR7tLAYBpRUAHAACABhDQAQAAoAEEdAAAAGgAAR0AAAAaQEAHAACABmhkQPd4FgAAAGabRgZ0j2cBAABgtmlkQAcAAIDZRkAHAACABhDQAQAAoAEEdAAAAGiAue0uAIADc8LbP5vN924/7OMsOe/Kw9q/48Hz8tW3Pu+w6wAAYDwBHWCa2Hzv9tx8wQvbXcZhB3wAAPbMEHcAAABoAAEdAAAAGkBABwAAgAYQ0AEAAKABBHQAAABoAAEdAAAAGkBABwAAgAYQ0AEAAKABBHQAAABoAAEdAAAAGkBABwAOSCnliaWUwVLKh9tdCwDMRAI6AMxipZRLSym3l1I27NZ+Sinla6WUb5RSzkuSWuu3aq297akUAGY+AR1gmvr0pz+dX/zFX8zSpUtzwQUXPOD9TZs25aUvfWme+tSn5pnPfGY2bPhZ/vrrv/7rHH/88enq6kpPT0+2bt2aJHnb296Wxz72sVm2bFmWLVuWT33qU1P2eWib9yU5ZWxDKWVOkncneUGS45L0lFKOm/rSAGB2mdvuAgA4eDt27Mgf//Ef53Of+1wWL16cZzzjGXnRi16U4477WYY6//zzs2zZsnzsYx/Lf//3f+eP//iP8/nPfz7f+9738q53vSs33nhjHvzgB+d3f/d3c8UVV+T0009Pkvzpn/5pXv/617fpkzHVaq1Xl1KW7Nb8zCTfqLV+K0lKKVckeXGSG/d3vFLKWUnOSpJjjz12YovloJ3w9s9m873bD/s4S8678rD273jwvHz1rc877DoAZjoBHWAauuaaa7J06dI88YlPTJK84hWvyMc//vFxAf3GG2/Mm970piTJk5/85Nx88835wQ9+kCS5//77c++992bevHm55557smjRoqn/EDTZY5PcMub1xiTPKqU8MslAkhNLKW+qtf7F7jvWWtcmWZsky5cvr1NRLHu3+d7tufmCF7a7jMMO+ACzhSHuANPQ9773vTzucY/b9Xrx4sX53ve+N26bE044IR/96EeTjAb673znO9m4cWMe+9jH5vWvf32OPfbYPOYxj0lHR0ee97yf3dm6+OKL89SnPjVnnHFGNm3aNDUfiKYpe2irtdYf1VrPrrX+/J7COQBweKYsoJdSHlpKuayUckkp5ZVTdV6AmajWB96YLGV8pjrvvPOyadOmLFu2LKtXr86JJ56YuXPnZtOmTfn4xz+eb3/727n11luzZcuW/N//+3+TJK95zWvyzW9+M9dff30e85jH5Nxzz52Sz0PjbEzyuDGvFye5tU21AMCscVgB/WBWfk3y20k+XGs9M8mLDue8ALPd4sWLc8stPxuBvHHjxgcMUz/yyCPz3ve+N9dff30uv/zy3HHHHXnCE56Qf/qnf8oTnvCEPOpRj8q8efPy27/92/m3f/u3JMnP/dzPZc6cOXnQgx6UM888M9dcc82Ufi4a48tJnlRKeUIpZX6SVyT5RJtrAoAZ73DvoL8vB77y6+L8bD7bjsM8L8Cs9oxnPCM33XRTvv3tb2fbtm254oor8qIXjb/2edddd2Xbtm1Jkr//+7/PSSedlCOPPDLHHnts/v3f/z333HNPaq35/Oc/n87OziTJbbfdtmv/j33sY+nq6pq6D0VblFKGknwpyS+WUjaWUnprrfcnOSfJZ5KMJPlgrfWGgzjmylLK2s2bN09O0QAwQx3WInEHufLrxoyG9OuzjwsDVn8F2L+5c+fm4osvzvOf//zs2LEjZ5xxRo4//visWbMmSXL22WdnZGQkp512WubMmZPjjjsug4ODSZJnPetZednLXpanPe1pmTt3bk488cScddZZSZI3vvGNuf7661NKyZIlS/Ke97ynbZ+RqVFr7dlL+6eSHNJz9mqt65KsW758+ZmHUxsAzDaTsYr7Hld+TfKuJBeXUl6YZN3edrb6K8CePazzvDzlsvPGtS3oX5AkuSJX5IrLrkgePNr+7svenSRZ+JaFSZKbclNO+sRJP9vxicm8N81Lklyf67P8iuWj7b8x+lVT8+18O8/77AMfi/SwziRp/6rQAAAzzWQE9L2t/LolyR9MwvkAZoWfjFzgcUkAADPYZKzibuVXAAAAOEiTEdCt/AoAs5hF4gAO3l133ZWXvexlefKTn5zOzs586UtfGvf+F7/4xXR0dGTZsmVZtmxZ/uzP/mzXe3/zN3+Trq6uHH/88XnnO985xZUzkQ5riHtr5ddnJzm6lLIxyVtrrYOllJ0rv85JcunBrPzaOu7KJCuXLl16OOUBAG1gkTiAg/fa1742p5xySj784Q9n27Ztueeeex6wza/92q/lk5/85Li2DRs25JJLLsk111yT+fPn55RTTskLX/jCPOlJT5qq0plAh3UHvdbaU2t9TK11Xq11ca11sNX+qVrrL9Raf77WOnAIx11Xaz2ro6PjcMoDAABovB//+Me5+uqr09vbmySZP39+Hv7whx/QviMjI/mlX/qlPOQhD8ncuXPz67/+6/nYxz42idUymSZjiDsAAAAH6Fvf+lYe9ahH5Q/+4A9y4okn5tWvfnW2bNnygO2+9KUv5YQTTsgLXvCC3HDD6CDlrq6uXH311fnRj36Ue+65J5/61Kdyyy23PGBfpgcBHQAAoI3uv//+fOUrX8lrXvOa/Od//mce+tCH5oILLhi3zdOe9rR85zvfyVe/+tX09fXlJS95SZKks7Mzq1atysknn5xTTjklJ5xwQubOnYyHdTEVGhnQLS4DANOXfhzg4CxevDiLFy/Os571rCTJy172snzlK18Zt82RRx6ZI444Iknym7/5m9m+fXt++MMfJkl6e3vzla98JVdffXWOOuoo88+nsUYGdHPQAWD60o8DHJxHP/rRedzjHpevfe1rSZLPf/7zOe6448Zt8/3vfz+11iTJNddck5/+9Kd55CMfmSS5/fbbkyTf/e5389GPfjQ9PT1TWD0TydgHAACANlu9enVe+cpXZtu2bXniE5+Y9773vVmzZk2S5Oyzz86HP/zh/N3f/V3mzp2bBz/4wbniiitSSkmS/M7v/E5+9KMfZd68eXn3u9+dRzziEe38KBwGAZ2D8ulPfzqvfe1rs2PHjrz61a/Oeeed94BtvvjFL+ZP/uRPsn379hx99NH553/+5yTJGWeckU9+8pM55phjsmHDhl3bv+1tb8sll1ySRz3qUUmS888/P7/5m785NR8IAACmwMM6z8tTLnvg787j9I3+8c18Myd94qTkwaOv333Zu5OHJQ9644Py0/w0W7Ilf3jTHyY3tfZ7dTInc/LT/DR/svFPksv2V0uSvPDQPwyTRkDngO3YsSN//Md/nM997nNZvHhxnvGMZ+RFL3rRuOE3d911V/7oj/4on/70p3PsscfuGm6TJKeffnrOOeecnHbaaQ849p/+6Z/m9a9//ZR8DgAAmGo/GbkgN1/QjFC85Lwr210Ce9HIOeg00zXXXJOlS5fmiU98YubPn59XvOIV+fjHPz5umw984AP57d/+7Rx77LFJkmOOOWbXeyeddFKOOuqoKa0ZAABgumhkQLf6azN973vfy+Me97hdrxcvXpzvfe9747b5+te/nk2bNuXZz352nv70p+fyyy8/oGNffPHFeepTn5ozzjgjmzZtmtC6AZha+nEAODSNDOhWf22mnatGjrVzYYqd7r///lx33XW58sor85nPfCZ//ud/nq9//ev7PO5rXvOafPOb38z111+fxzzmMTn33HMntG4AppZ+HAAOjTnoHLDFixfnlltu2fV648aNWbRo0QO2Ofroo/PQhz40D33oQ3PSSSflq1/9an7hF35hr8f9uZ/7uV3fn3nmmfmt3/qtiS8eAACg4Rp5B51mesYznpGbbrop3/72t7Nt27ZcccUVedGLXjRumxe/+MX5l3/5l9x///2555578h//8R/p7Ozc53Fvu+22Xd9/7GMfS1dX16TUDwAA0GTuoHPA5s6dm4svvjjPf/7zs2PHjpxxxhk5/vjjxz2fsbOzM6ecckqe+tSn5kEPelBe/epX7wrcPT09+eIXv5gf/vCHWbx4cd7+9rent7c3b3zjG3P99denlJIlS5bkPe95Tzs/JgAAQFsI6OzTnp7XuKB/QZLkilyRKy67YvzzGZPkmGTOqjlJksEMZvCywdH2U5KjTzk6R+foJMk7886887J3Jr+R5DeSmppv59t53meft4c6Es9qBAAAZrJGBvRSysokK5cuXdruUma9pjyv0bMaAQCAma6Rc9Ct/goAAMBs08iADgBMX56DDgCHRkAHACaUkXAAcGgEdACAWWjr1q155jOfmRNOOCHHH3983vrWtz5gm02bNuWlL31pnvrUp+aZz3xmNmzYsOu9M844I8ccc4zHowJMIAEdAGAWWrBgQb7whS/kq1/9aq6//vp8+tOfzr//+7+P2+b888/PsmXL8l//9V+5/PLL89rXvnbXe6effno+/elPT3XZADOagA4AMAuVUnLEEUckSbZv357t27enlDJumxtvvDHPfe5zkyRPfvKTc/PNN+cHP/hBkuSkk07KUUcdNbVFA8xwjQzoFpcBAJh8O3bsyLJly3LMMcfk5JNPzrOe9axx759wwgn56Ec/miS55ppr8p3vfCcbN25sR6kAs0IjA7rFZQAAJt+cOXNy/fXXZ+PGjbnmmmvGzTFPkvPOOy+bNm3KsmXLsnr16px44omZO3dum6oFmPn8CwsAMMs9/OEPz7Of/ex8+tOfHrfo25FHHpn3vve9SZJaa57whCfkCU94QrvKBJjxBHSAaWTJeVe2u4R0PHheu0sAJsAdd9yRefPm5eEPf3juvffe/NM//VNWrVo1bpu77rorD3nIQzJ//vz8/d//fU466aQceeSRbaoYYOYT0AGmiZsveOFhH2PJeVdOyHFgX0opK5OsXLp0abtLYR9uu+22vOpVr8qOHTvy05/+NL/7u7+b3/qt38qaNWuSJGeffXZGRkZy2mmnZc6cOTnuuOMyODi4a/+enp588YtfzA9/+MMsXrw4b3/729Pb29uujwMwIwjoAMCEqrWuS7Ju+fLlZ7a7ltnuYZ3n5SmXnbf3Df5k9I+Skg/lQ/nQZR9KHjza9u7L3p0kWfiWhUmSm3JTTvrEST/b95Tk6FOOztE5Oknyzrwz77zsnXupI0lcHATYHwEdAGCG+snIBY0YNdOE6TkA00EjV3EHAACA2UZABwAAgAZoZEAvpawspazdvHlzu0sBAACAKdHIgF5rXVdrPaujo6PdpQAAAMCUaGRABwAAgNlGQAcAAIAGENABAACgAQR0AAAAaAABHQAAABpgbrsLAABmllLKyiQrly5d2u5SABplyXlXtruEJEnHg+e1uwT2QkAHACZUrXVdknXLly8/s921ADTFzRe8cEKOs+S8KyfsWDSPIe4AAADQAAI6AAAANICADgAAAA3QyIBeSllZSlm7efPmdpcCAAAAU6KRAb3Wuq7WelZHR0e7SwEAAIAp0ciADgAAALONgA4AAAANIKADAABAAwjoAAAA0AACOgAAADSAgA4AAAANIKADAABAAwjoAAAA0AACOgAAADSAgA4ATKhSyspSytrNmze3uxQAmFYEdABgQtVa19Vaz+ro6Gh3KQAwrQjoAAAA0AACOgAAADSAgA4AAAAN0MiAbnEZAAAAZptGBnSLywAAADDbNDKgAwAAwGwjoAMAAEADCOgAAADQAAI6AAAANICADgAAAA0goAMAAEADCOgAAADQAAI6AAAANICADgAAAA0wt90F0HxLzruy3SWk48Hz2l0CAADApBLQ2aebL3jhYR9jyXlXTshxAAAAZjJD3AEAAKABBHQAAABoAAEdAAAAGkBABwAAgAYQ0AEAAKABBHQAAABoAAEdAAAAGsBz0AGA/SqlPDTJ3ybZluSLtdb3t7kkAJhx3EEHgFmqlHJpKeX2UsqG3dpPKaV8rZTyjVLKea3m307y4VrrmUleNOXFAsAsIKADwOz1viSnjG0opcxJ8u4kL0hyXJKeUspxSRYnuaW12Y4prBEAZg0BHQBmqVrr1Unu3K35mUm+UWv9Vq11W5Irkrw4ycaMhvTE7w8AMCmmrIMtpTyxlDJYSvnwVJ0TADhoj83P7pQno8H8sUk+muR3Sil/l2Td3nYupZxVSrm2lHLtHXfcMbmVAsAMc0AB/SDnqO1R60p87+EUCwBMurKHtlpr3VJr/YNa62v2tUBcrXVtrXV5rXX5ox71qEksEwBmngNdxf19SS5OcvnOhjFz1E7O6NX1L5dSPpFkTpK/2G3/M2qttx92tQDAZNuY5HFjXi9OcmubagGAWeWAAnqt9epSypLdmnfNUUuSUsoVSV5ca/2LJL81oVUCAFPly0meVEp5QpLvJXlFklPbWxIAzA6HMwd9b3PU9qiU8shSypokJ5ZS3rSP7cxdA4ApUEoZSvKlJL9YStlYSumttd6f5Jwkn0kykuSDtdYbDvK4K0spazdv3jzxRQPADHagQ9z3ZI9z1Pa2ca31R0nO3t9Ba61rk6xNkuXLl+/1eADA4am19uyl/VNJPnUYx12XZN3y5cvPPNRjAMBsdDh30M1RAwAAgAlyOAF91xy1Usr8jM5R+8TElAUAAACzy4E+Zm1S5qjt43zmrgEAADCrHOgq7pMyR20f5zN3DQCmqVLKyiQrly5d2u5SSLLkvCvbXUI6Hjyv3SUATAuHs0gcAMADuNDeHDdf8MLDPsaS866ckOMAsH+HMwcdAAAAmCACOgAAADRAIwO6ReIAAACYbRoZ0Gut62qtZ3V0dLS7FAAAAJgSjQzoAMD0ZSQcABwaAR0AmFBGwgHAoRHQAQAAoAEaGdANjQMAAGC2aWRANzQOAACA2aaRAR0AAABmGwEdAJhQpqoBwKER0AGACWWqGgAcGgEdAAAAGqCRAd3QOAAAAGabRgZ0Q+MAAACYbRoZ0AEAAGC2EdABAACgAQR0AGBCWUsGAA6NgA4ATChryQDAoRHQAQAAoAEEdAAAAGiARgZ0c9cAAACYbRoZ0M1dAwAAYLZpZEAHAACA2UZABwAAgAYQ0AEAAKABBHQAAABoAAEdAJhQnsYCAIdGQAcAJpSnsQDAoWlkQHflHQAAgNmmkQHdlXcAAABmm0YGdAAAAJhtBHQAAABoAAEdAAAAGkBABwAAgAYQ0AEAAKABBHQAAABoAAEdAAAAGkBABwAmVCllZSll7ebNm9tdCgBMKwI6ADChaq3raq1ndXR0tLsUAJhWBHQAAABogEYGdEPjAAAAmG0aGdANjQMAAGC2aWRABwAAgNlGQAcAAIAGENABAACgAQR0AAAAaAABHQAAABpAQAcAAIAGENABAACgAQR0AAAAaAABHQAAABpAQAcAAIAGENABAACgAQR0AAAAaAABHQCYUKWUlaWUtZs3b253KQAwrQjoAMCEqrWuq7We1dHR0e5SAGBaaWRAd+UdAACA2aaRAd2VdwAAAGabRgZ0AAAAmG0EdAAAAGgAAR0AAAAaQEAHAACABhDQAQAAoAEEdAAAAGgAAR0AAAAaQEAHAACABhDQAQAAoAEEdAAAAGgAAR0AAAAaQEAHAACABhDQAQAAoAEEdAAAAGgAAR0AAAAaQEAHAACABhDQAQAAoAEEdAAAAGgAAR0AAAAaQEAHAACABhDQAQAAoAEEdADggJRSnlhKGSylfLjdtQDATDRlAb2U8pJSyiWllI+XUp43VecFAJJSyqWllNtLKRt2az+llPK1Uso3Sinn7esYtdZv1Vp7J7dSAJi9DiigT1Cn/o+11jOTnJ7k5YdcMQBwKN6X5JSxDaWUOUneneQFSY5L0lNKOa6U8pRSyid3+zpm6ksGgNll7gFu974kFye5fGfDmE795CQbk3y5lPKJJHOS/MVu+59Ra7299f1bWvsBAFOk1np1KWXJbs3PTPKNWuu3kqSUckWSF9da/yLJb01xiQAw6x3QHfRa69VJ7tyteVenXmvdlmRnp/7/aq2/tdvX7WXUhUmuqrV+ZWI/BgBwCB6b5JYxrze22vaolPLIUsqaJCeWUt60l23OKqVcW0q59o477pjYagFghjvQO+h7sqdO/Vn72L4vyW8k6SilLK21rtnTRqWUs5KclSTHHnvsYZQHMLuUUg5suwv3/X6tdQKqYZrY0/80e/0foNb6oyRn7+uAtda1SdYmyfLly/3PBAAH4XAC+sF26u9K8q79HVTHDnBoBGsOwcYkjxvzenGSW9tUCwDMeoezirtOHQCmty8neVIp5QmllPlJXpHkE22uCQBmrcMJ6Dp1AJgmSilDSb6U5BdLKRtLKb211vuTnJPkM0lGknyw1nrDBJxrZSll7ebNmw/3UAAwqxzoY9amrFNvnU/HDgATqNbaU2t9TK11Xq11ca11sNX+qVrrL9Raf77WOjBB51pXaz2ro6NjIg4HALPGAc1Br7X27KX9U0k+NaEVjR53XZJ1y5cvP3Oijw0AAABNdDhD3AEAAIAJIqADAABAAwjoAMCEspYMAByaRgZ0HTsATF8WiQOAQ9PIgK5jBwAAYLZpZEAHAACA2UZABwAAgAYQ0AGACWUtGQA4NI0M6Dp2AJi+rCUDAIemkQFdxw4AAMBs08iADgAAALONgA4AAAANIKADAABAAwjoAMCEstgrABwaAR0AmFAWewWAQ9PIgO7KOwAAALNNIwO6K+8AAADMNo0M6AAAADDbCOgAAADQAAI6AAAANICADgAAAA0goAMAE8rTWADg0DQyoOvYAWD68jQWADg0jQzoOnYAAABmm0YGdAAAAJhtBHQAAABoAAEdAAAAGkBABwAAgAYQ0AEAAKABBHQAAABoAAEdAJhQpZSVpZS1mzdvbncpADCtNDKg69gBYPqqta6rtZ7V0dHR7lIAYFppZEDXsQMAADDbNDKgAwAAwGwjoAMAAEADCOgAAADQAAI6AAAANICADgAAAA0goAMAAEADCOgAAADQAAI6AAAANICADgAAAA3QyIBeSllZSlm7efPmdpcCABwk/TgAHJpGBvRa67pa61kdHR3tLgUAOEj6cQA4NI0M6AAAADDbCOgAAADQAAI6AAAANICADgAAAA0goAMAAEADCOgAAADQAAI6AAAANICADgAAAA0goAMAAEADCOgAAADQAAI6AAAANICADgAAAA0goAMAAEADCOgAAADQAAI6AAAANMDcdhewJ6WUlUlWLl26tN2lsB+llAPb7sJ9v19rnYBqAGgC/fj0ciB9+f768URfDhPB79Y08g56rXVdrfWsjo6OdpfCftRaJ+QLgJlDPz696MuhOfw80siADgAAALONgA4AAAANIKADAABAAwjoAAAA0AACOgAAADSAgA4AAAANIKADAABAAwjoAAAA0AACOgAAADSAgA4AAAANIKADAABAAwjoAAAA0AACOgAAADSAgA4AAAANIKADAABAAwjoAAAA0AACOgAAADRAqbW2u4a9KqXckeQ77a6Dw3Z0kh+2uwggiZ/HmeLxtdZHtbuI/dGPzxj+3YBm8TM5M+yxL290QGdmKKVcW2td3u46AD+PwMHz7wY0i5/Jmc0QdwAAAGgAAR0AAAAaQEBnKqxtdwHALn4egYPl3w1oFj+TM5g56AAAANAA7qADAABAAwjoAAAA0ABz210A008pZUeS/zem6SW11pv3su3dtdYjpqQwmKVKKY9M8vnWy0cn2ZHkjtbrZ9Zat7WlMKCx9OXQLPpydjIHnYN2MB21Th2mVinlbUnurrX+7zFtc2ut97evKqBp9OXQXPry2c0Qdw5bKeWIUsrnSylfKaX8v1LKi/ewzWNKKVeXUq4vpWwopfxaq/15pZQvtfb9UCnFLwAwAUop7yulvKOUsj7JhaWUt5VSXj/m/Q2llCWt73+vlHJN6+fzPaWUOe2qG2gPfTk0j758dhLQORQPbv3wX19K+ViSrUleWmt9WpIVSf6qlFJ22+fUJJ+ptS5LckKS60spRyd5S5LfaO17bZLXTdmngJnvFzL683Xu3jYopXQmeXmSX239fO5I8sqpKQ9oI305TA/68lnGHHQOxb2tH/4kSSllXpLzSyknJflpkscm+bkk3x+zz5eTXNra9h9rrdeXUn49yXFJ/rX1O8D8JF+amo8As8KHaq079rPNc5M8PcmXWz+HD05y+2QXBrSdvhymB335LCOgMxFemeRRSZ5ea91eSrk5ycKxG9Rar251+i9M8n9KKX+ZZFOSz9Vae6a6YJgltoz5/v6MHzW182e0JLms1vqmKasKaCJ9OTSTvnyWMcSdidCR5PZWh74iyeN336CU8vjWNpckGUzytCT/nuRXSylLW9s8pJTyC1NYN8wmN2f05y6llKcleUKr/fNJXlZKOab13lGtn1dgdtGXQ/PdHH35jOcOOhPh/UnWlVKuTXJ9kv/ewzbPTvKGUsr2JHcnOa3Wekcp5fQkQ6WUBa3t3pLk65NeMcw+H0lyWinl+owOU/16ktRabyylvCXJZ0spD0qyPckfJ/lOuwoF2kJfDs2nL58FPGYNAAAAGsAQdwAAAGgAAR0AAAAaQEAHAACABhDQAQAAoAEEdAAAAGgAAR1mmVLKS0sptZTy5MM4xhGllPeUUr5ZSrmhlHJ1KeVZrfd2lFKuL6VsKKV8qJTykImrHgDQl8PMJaDD7NOTZDjJKw7jGH+f5M4kT6q1Hp/k9CRHt967t9a6rNbalWRbkrMP4zwAwAPpy2GGEtBhFimlHJHkV5P0ptWpl1KeXUr5Yinlw6WU/y6lvL+Mem4p5WNj9j25lPLRUsrPJ3lWkrfUWn+aJLXWb9Var9zDKf8lydJJ/2AAMEvoy2FmE9BhdnlJkk/XWr+e5M5SytNa7Scm+ZMkxyV5YkY7/i8k6SylPKq1zR8keW+S45NcX2vdsa8TlVLmJnlBkv83wZ8BAGazl0RfDjOWgA6zS0+SK1rfX9F6nSTX1Fo3tq6iX59kSa21Jvk/SX6vlPLwJL+c5KoDOMeDSynXJ7k2yXeTDE5Y9QCAvhxmsLntLgCYGqWURyZ5TpKuUkpNMidJTfKpJPeN2XRHfvZvw3uTrEuyNcmHaq33l1JuSHJCKeVBO4fF7ebeWuuySfoYADBr6cth5nMHHWaPlyW5vNb6+Frrklrr45J8O0n33naotd6a5NYkb0nyvlbbNzN6Rf3tpZSSJKWUJ5VSXjzJ9QPAbKcvhxlOQIfZoyfJx3Zr+0iSU/ez3/uT3FJrvXFM26uTPDrJN0op/y/JJRnt/AGAyaMvhxmujE5NAdizUsrFSf6z1mr+GQBMQ/pymD4EdGCvSinXJdmS5ORa63372x4AaBZ9OUwvAjoAAAA0gDnoAAAA0AACOgAAADSAgA4AAAANIKADAABAAwjoAAAA0AACOgAAADSAgA4AAAANIKADAABAAwjoAAAA0AACOtAWpZQvllJe3fr+laWUzx7icU4vpQxPbHUAwJ7s3u+WUu4upTzxEI9VSylLJ646mP4EdGiQVmjdVEpZMAnHvrmU8oNSykPHtL26lPLF/ez3hFLKT0spfzvRNe1Ua31/rfV5k3V8AJhKU9Cf39sKxj8opby3lHLEVNexU631iFrrtybr+DDbCOjQEKWUJUl+LUlN8qJJOs3cJK89yH1OS7IpySsms4MHgJlgivrzlbXWI5I8LckzkrylTXUAE0xAh+Y4Lcm/J3lfkleVUhaUUu4qpXTt3KCU8qjWVfNjWq/fWEq5rZRya+tu+P6Giv1lkteXUh5+kHW9Jcn2JCvHvtE63/8spXyrlPLDUspfllIe1Hrv9FLKv5ZSVpdSNpdS/ruU8tw9nWAPw+WeXEr5XCnlzlLK10opvzvmvUeWUj5RSvlxKeWaJD9/EJ8FACbbVPTnSZJa6/eSXJWkaw9vj6tj7BullPeVUta0+tqflFL+uZTy+DHv77V/393YWluf9X+XUr7buru/ppTy4DHbvmHM5zxjf58PZiMBHZrjtCTvb309P8nDk3w0Sc+YbX43yT/XWm8vpZyS5HVJfiPJ0iS/fgDnuDbJF5O8/kAKKqX8WpLFSa5I8sFWjbt7aZLlGb2K/+IkYzvcZyX5VpKjk7w1yUdLKUft55wPTfK5JB9IckxGP//fllKOb23y7iRbkzymdS4dPABNMhX9eZKklPK4JL+Z5D/3V0cp5ed2e/+VSf48o3309a3txtpX/743Fyb5hSTLMvpZHpvkf7VqPSWjv3+cnORJGf28wG4EdGiAUkp3kscn+WCt9bok30xyakZD6tgOfWdbMtq5v7fWekOt9Z4kbz/A0/2vJH2llEcdwLavSnJVrXVT67wv2Hm1f4wLa6131lq/m+Sdu9V7e5J31lq311r/IcnXkrxwP+f8rSQ311rfW2u9v9b6lSQfSfKyUsqcJL+T5H/VWrfUWjckuewAPgcATLop7M//sZRyV5LhJP+c5PwDrGOsK2utV9da70vSn+SXW4F/p33173v67CXJmUn+tLXfT1p1vWK3z7mh1rolydsO4HPCrCOgQzO8Kslna60/bL3+QKvtC0keXEp5Vmvo2bIkH2ttsyjJLWOOMfb7vWqF2k8mOW9f27WGpP2PtK6o11q/lOS7eWAHP/a832nVtdP3aq11H+/vyeOTPKs1HPCu1i8gr0zy6CSPyug8+t3PCQBNMFX9+UtqrQ+vtT6+1vpHtdZ7D7COsXadp9Z6d5I7M76P3lf/viePSvKQJNeN6b8/3WpPHvg59d+wB3PbXQDMdq0g/LtJ5pRSvt9qXpDRIXFPyejQ8p4kP0jyydYV6SS5LaPDz3cae9V7f96a5CtJ/mof27w0yZEZHV6+utX28IwOmXvnbue9ofX9sUluHfPeY0spZUxIPzbJJ/ZT2y0ZHfZ38u5vtO6g398653+POSYAtFWb+vODqqOUckKt9au7n6e1CvxRGd+H76t/35MfJrk3yfGtufG7uy3jP5v+G/bAHXRov5ck2ZHkuIxeUV+WpDPJv2Q0DH8gycszehf5A2P2+2CSPyildJZSHpLWHK8DUWv9RpJ/SPI/97HZq5JcmtFfKnbW9atJlpVSnjJmuzeUUh7RGhb32tZxdzomyf8spcwrpfyP1uf61H7K+2SSXyil/H5rv3mllGeUUjprrTsyOo/vbaWUh5RSjssD7wgAQDu8JFPcnx9iHTv9Zimlu5QyP6Nz0f+j1jr2Dve++vcHqLX+NMklSf56zOJ3jy2lPL+1yQeTnF5KOa71Od96WJ8SZigBHdrvVRmdk/XdWuv3d34luTijnfh1SbZkdGjYVTt3qrVeleRdSdYn+UaSL7Xeui9JSilvLqVclb37syQPHdtQSrmhlPLKUspjkzw3o/PHvz/m67qMDlcbG4o/3qrx+iRXJhkc895/ZHQhmB8mGUjyslrrj/b1l9G6o/C8jM5ZuzXJ9zO66MzOR7ydk+SIVvv7krx3X8cDgCnSrv589232WUcpZecI2g9kNCTfmeTprRrH2lf/vjerWp/h30spP07yT0l+ccznfGdGh/t/o/UnsJsyfnooMF2VUjqTbEiyoNZ6/xSdsyZ5UuuO/O7vnZ7k1bXW7qmoBQBmgqnoz0sp70uysdb6gOent97fa/8OTC530GEaK6W8tJQyv5TyiIzeZV43VeEcAJgY+nNgJwEdprc/THJHRh+fsiPJa9pbDgBwCPTnQBJD3AEAAKAR3EEHAA5Ia5XpNaWUD5dS3OEDgAkmoAPALFZKubSUcnspZcNu7aeUUr5WSvlGKeW8JKm1jtRaz87oM5aXt6NeAJjJGj3E/eijj65LlixpdxkA0CjXXXfdD2utj5qIY5VSTkpyd5LLa61drbY5Sb6e5OQkG5N8OUlPrfXGUsqLkpyX5OJa6wf2ctgk+nEA2Ju99eVz97RxUyxZsiTXXnttu8sAgEYppXxnoo5Va726lLJkt+ZnJvlGrfVbrfNdkeTFSW6stX4iySdKKVdm9DnKe6UfB4A921tf3uiADgC0xWOT3DLm9cYkzyqlPDvJbydZkORTe9qxlHJWkrOS5Nhjj53UIgFgphHQAYDdlT201VrrF5N8cV871lrXJlmbJMuXL2/uPDoAaCCLxAEAu9uY5HFjXi9OcmubagGAWUNABwB29+UkTyqlPKGUMj/JK5J8os01AcCMJ6ADwCxWShlK8qUkv1hK2VhK6a213p/knCSfSTKS5IO11hvaWScAzAbmoAPALFZr7dlL+6eyl4XgAIDJ4Q46AAAANICADgAAAA0goAMAAEADCOgAAADQAAI6ADChSikrSylrN2/e3O5SAGBambKAXkrpLKWsKaV8uJTymqk6LwAwtWqt62qtZ3V0dLS7FACYVg4roJdSLi2l3F5K2bBb+ymllK+VUr5RSjkvSWqtI7XWs5P8bpLlh3NeAAAAmGkO9w76+5KcMrahlDInybuTvCDJcUl6SinHtd57UZLhJJ8/zPMCAADAjHJYAb3WenWSO3drfmaSb9Rav1Vr3ZbkiiQvbm3/iVrrryR55d6OWUo5q5RybSnl2jvuuONwyqPNhoaG0tXVlTlz5qSrqytDQ0PtLgkAOED6cYCpN3cSjvnYJLeMeb0xybNKKc9O8ttJFiT51N52rrWuTbI2SZYvX14noT6mwNDQUPr7+zM4OJju7u4MDw+nt7c3SdLT09Pm6gCAfdGPA7THZCwSV/bQVmutX6y1/s9a6x/WWt89CeelQQYGBjI4OJgVK1Zk3rx5WbFiRQYHBzMwMNDu0gCA/dCPA7THZAT0jUkeN+b14iS3TsJ5aLCRkZF0d3ePa+vu7s7IyEibKgIADpR+HKA9JiOgfznJk0opTyilzE/yiiSfmITz0GCdnZ0ZHh4e1zY8PJzOzs42VQQAHCj9OEB7HO5j1oaSfCnJL5ZSNpZSemut9yc5J8lnkowk+WCt9YbDL5XppL+/P729vVm/fn22b9+e9evXp7e3N/39/e0uDQDYD/04QHsc1iJxtdY9rhJSa/1U9rEQHDPfzgVk+vr6MjIyks7OzgwMDFhYBgCmAf04QHuUWpu7UPry5cvrtdde2+4yAKBRSinX1VqXt7uOvSmlrEyycunSpWfedNNN7S4HABpnb335ZMxBBwBmsVrrulrrWR0dHe0uBQCmFQEdAAAAGkBABwAAgAYQ0AEAAKABBHQAAABoAAEdAAAAGkBABwAAgAYQ0AEAAKABGhnQSykrSylrN2/e3O5SAAAAYEo0MqDXWtfVWs/q6OhodykAAAAwJRoZ0AEAAGC2EdABAACgAQR0AGBCWUsGAA6NgA4ATChryQDAoRHQAQAAoAEEdAAAAGgAAR0AAAAaQEAHAACABhDQAQAAoAEEdAAAAGgAAZ1JMzQ0lK6ursyZMyddXV0ZGhpqd0kAAACNNbfdBTAzDQ0Npb+/P4ODg+nu7s7w8HB6e3uTJD09PW2uDgAAoHncQWdSDAwMZHBwMCtWrMi8efOyYsWKDA4OZmBgoN2lAQAANJKAzqQYGRlJd3f3uLbu7u6MjIy0qSIAAIBmE9CZFJ2dnRkeHh7XNjw8nM7OzjZVBAAA0GyNDOillJWllLWbN29udykcov7+/vT29mb9+vXZvn171q9fn97e3vT397e7NAAAgEZq5CJxtdZ1SdYtX778zHbXwqHZuRBcX19fRkZG0tnZmYGBAQvEAcwCpZSVSVYuXbq03aUAwLRSaq3trmGvli9fXq+99tp2lwEAjVJKua7WurzddeyPfhwA9mxvfXkjh7gDAADAbCOgAwAAQAMI6AAAANAAAjoAAAA0gIAOAAAADSCgAwAAQAMI6AAAANAAAjoAAAA0gIAOAAAADSCgAwAAQAMI6AAAANAAAjoAAAA0gIAOAAAADSCgAwAAQAMI6AAAANAAAjoAAAA0QCMDeillZSll7ebNm9tdCgBwkPTjAHBoGhnQa63raq1ndXR0tLsUAOAg6ccB4NA0MqADAADAbCOgAwAAQAMI6AAAANAAAjoAAAA0gIAOAAAADSCgAwAAQAMI6AAAANAAAjoAAA8wNDSUrq6uzJkzJ11dXRkaGmp3SQAz3tx2FwAAQLMMDQ2lv78/g4OD6e7uzvDwcHp7e5MkPT09ba4OYOZyB51J48o7AExPAwMDGRwczIoVKzJv3rysWLEig4ODGRgYaHdpADOaO+hMClfeAWD6GhkZSXd397i27u7ujIyMtKkigNnBHXQmhSvvADB9dXZ2Znh4eFzb8PBwOjs721QRwOwgoDMpRkZGsnHjxnFD3Ddu3OjKOwBMA/39/ent7c369euzffv2rF+/Pr29venv7293aQAzmiHuTIpFixZl1apVef/7379riPsrX/nKLFq0qN2lAQD7sXM6Wl9fX0ZGRtLZ2ZmBgQHT1AAmmYDOpKm17vM1ANBcPT09AjnAFDPEnUlx66235qKLLkpfX18WLlyYvr6+XHTRRbn11lvbXRoAAEAjuYPOpOjs7MzixYuzYcOGXW3r16+3uAwAAMBeuIPOpLC4DAAAwMFxB51JYXEZAACAgyOgM2ksLgMwO5VSViZZuXTp0naXAgDTiiHuAMCEqrWuq7We1dHR0e5SAGBaaWRAL6WsLKWs3bx5c7tLAQAAgCnRyIDuyjsAAACzTSMDOgAAAMw2AjoAAAA0gIAOAAAADSCgAwAAQAMI6AAAAA03NDSUrq6uzJkzJ11dXRkaGmp3SUyCue0uAAAAgL0bGhpKf39/BgcH093dneHh4fT29iZJenp62lwdE8kddAAAgAYbGBjI4OBgVqxYkXnz5mXFihUZHBzMwMBAu0tjggnoAAAADTYyMpLu7u5xbd3d3RkZGWlTRUwWAR0AAKDBOjs7Mzw8PK5teHg4nZ2dbaqIySKgAwDwABakgubo7+9Pb29v1q9fn+3bt2f9+vXp7e1Nf39/u0tjglkkDgCAcSxIBc2y8+eur68vIyMj6ezszMDAgJ/HGcgddCaNK+8AMD1ZkAqap6enJxs2bMiOHTuyYcMG4XyGcgedSeHKOwBMXxakAmgPd9CZFK68A8D0ZUEqgPYQ0JkUrrwDwPRlQSqA9jDEnUmx88r7ihUrdrW58g4A04MFqQDaQ0BnUuy88r77HHRD3AFgeujp6RHIAaaYgM6kcOUdAADg4AjoTBpX3gEAAA6cReIAAACgAQR0AAAAaAABHQAAABpAQAcAAIAGENABAACgARoZ0EspK0spazdv3tzuUgCAg6QfB4BD08iAXmtdV2s9q6Ojo92lAAAHST8OMPGGhobS1dWVOXPmpKurK0NDQ+0uiUngOegAAAANNjQ0lP7+/gwODqa7uzvDw8Pp7e1NkvT09LS5OiZSI++gAwAAMGpgYCCDg4NZsWJF5s2blxUrVmRwcDADAwPtLo0JJqADAAA02MjISLq7u8e1dXd3Z2RkpE0VMVkEdAAAgAbr7OzM8PDwuLbh4eF0dna2qSImi4AOAADQYP39/ent7c369euzffv2rF+/Pr29venv7293aUwwi8QBAAA02M6F4Pr6+jIyMpLOzs4MDAxYIG4GEtABAAAarqenRyCfBQxxBwAAgAYQ0AEAAKABBHQAAABoAAEdAAAAGkBAB5gF+vr6snDhwpRSsnDhwvT19bW7JAAAdiOgA8xwfX19WbNmTc4///xs2bIl559/ftasWSOkAwA0jIAOMMNdcsklufDCC/O6170uD3nIQ/K6170uF154YS655JJ2lwYAwBgCOsAMd9999+Xss88e13b22Wfnvvvua1NFAADsiYAOMMMtWLAga9asGde2Zs2aLFiwoE0VAQCwJ3PbXQAAk+vMM8/MqlWrkozeOV+zZk1WrVr1gLvqAAC0l4AOMMOtXr06SfLmN7855557bhYsWJCzzz57VzsAAM1giDvALLB69eps3bo1tdZs3bpVOAf2y+MZAaaegA4AwDgezwjQHgI6AADjeDwjNM/Q0FC6uroyZ86cdHV1ZWhoqN0lMQkEdAAAxvF4RmiWoaGh9Pf375qytnr16vT39wvpM5CADgDAOB7PCM0yMDCQwcHBrFixIvPmzcuKFSsyODiYgYGBdpfGBLOKOwAA43g8IzTLyMhIuru7x7V1d3dnZGSkTRUxWdxBBwBgnNWrV+c5z3lOXv/61+ehD31oXv/61+c5z3mOJ0BAm3R2dmZ4eHhc2/DwcDo7O9tUEZNFQAeYBSwsAxyMoaGh3HTTTfn85z+fbdu25fOf/3xuuukm/3ZAm/T396e3tzfr16/P9u3bs379+vT29qa/v7/dpTHBDHEHmOF2LiwzODiY7u7uDA8Pp7e3N0nS09PT5uqAJho73zXJrvmufX19/t2ANtj5c9fX15eRkZF0dnZmYGDAz+MMVGqt7a5hr5YvX16vvfbadpcBMK11dXVl9erVu37RTpL169enr68vGzZsaGNlHKpSynW11uXtrmN/9OPT15w5c7J169bMmzdvV9v27duzcOHC7Nixo42VAcwMe+vLDXEHmOEsLAMcLPNdAdpDQAeY4fyiDRws810B2sMcdIAZbucv2rvPQffsVGBvzHcFaI9GBvRSysokK5cuXdruUgCmPb9oA4eip6fHvxMAU8wicQAwzVgkDgCmt7315Y28g870UUqZkOM0+UIRAADAVLBIHIel1rrfr8ev+uR+twEAAJjtBHQAAICGGxoaSldXV+bMmZOurq4MDQ21uyQmgSHuAMCEstgrwMQaGhpKf3//A57IksRijjOMO+gAwISqta6rtZ7V0dHR7lIAZoSBgYEMDg5mxYoVmTdvXlasWJHBwUGPTJ2BBHQAAIAGGxkZSXd397i27u7ujIyMtKkiJouADjALmLcGANNXZ2dnhoeHx7UNDw+ns7OzTRUxWQR0gBlu57y11atXZ+vWrVm9enX6+/uFdACYJvr7+9Pb25v169dn+/btWb9+fXp7e9Pf39/u0phgFokDmOHGzltLsmveWl9fn4VlAGAa2Nlf9/X1ZWRkJJ2dnRkYGNCPz0ACOsAMZ94aAEx/PT09AvksYIg7wAxn3hpwKKxdATD1BHSAGc68NeBgWbsCoD0McQeY4cxbAw6WtSsA2sMddIBZoKenJxs2bMiOHTuyYcMGv2AD+zQyMpKNGzeOG+K+ceNGa1cATDJ30AEAGGfRokVZtWpV3v/+96e7uzvDw8N55StfmUWLFrW7NIAZzR10AAAeoNa6z9cATDwBHQCAcW699dZcdNFF6evry8KFC9PX15eLLroot956a7tLA5jRDHEHAGCczs7OLF68OBs2bNjVtn79eo9nBJhk7qADADCOxzMCtIc76AAAjNPT05N/+7d/ywte8ILcd999WbBgQc4880xPgACYZO6gAwAwztDQUK688spcddVV2bZtW6666qpceeWVGRoaandpADOagA4AwDgDAwMZHBzMihUrMm/evKxYsSKDg4MZGBhod2kAM5qADgDAOCMjI9m4cWO6uroyZ86cdHV1ZePGjRkZGWl3aQAzmoAOMAsMDQ2N+0XbMFVgXxYtWpQ3vvGNWb16dbZu3ZrVq1fnjW98YxYtWtTu0gBmNIvEAcxwQ0ND6e/vz+DgYLq7uzM8PJze3t4kseATsFellH2+BmDiuYMOMMOZSwocrFtvvTUXXnhh+vr6snDhwvT19eXCCy/Mrbfe2u7SAGY0d9ABZriRkZF0d3ePa+vu7jaXFNirzs7OLF68OBs2bNjVtn79+nR2draxKoCZzx10gBmus7Mzw8PD49qGh4f9og3sVX9/f3p7e7N+/fps374969evT29vb/r7+9tdGsCM5g46wAy38xft3eegG+IO7M3O9Sn6+voyMjKSzs7ODAwMWLcCYJIJ6AAznF+0gUPR09Pj3wmAKSagA8wCftEGAGg+c9ABAACgAQR0AAAAaAABHQAAABpAQAcAAIAGENABAACgAQR0AAAAaAABHQAAABpAQAcAAIAGaGRAL6WsLKWs3bx5c7tLAQAAgCnRyIBea11Xaz2ro6Oj3aUAAADAlJjb7gIAmBillAk5Tq11Qo4DAMDBaeQddAAOXq11v1+PX/XJ/W4DAEB7COgAAADQAAI6AAAANICADgAAAA0goAMAAEADCOgAADxAX19fFi5cmFJKFi5cmL6+vnaXBDDjCegAAIzT19eXNWvW5Pzzz8+WLVty/vnnZ82aNUI6tNHQ0FC6uroyZ86cdHV1ZWhoqN0lMQk8Bx0AgHEuueSSXHjhhXnd616XJLv+fPOb35zVq1e3szSYlYaGhtLf35/BwcF0d3dneHg4vb29SZKenp42V8dEcgcdAIBx7rvvvpx99tnj2s4+++zcd999baoIZreBgYEMDg5mxYoVmTdvXlasWJHBwcEMDAy0uzQmmIAOAMA4CxYsyJo1a8a1rVmzJgsWLGhTRTC7jYyMpLu7e1xbd3d3RkZG2lQRk0VABwBgnDPPPDOrVq3KO97xjtxzzz15xzvekVWrVuXMM89sd2kwK3V2dmZ4eHhc2/DwcDo7O9tUEZPFHHQAAMbZOc/8zW9+c84999wsWLAgZ599tvnn0Cb9/f3p7e19wBx0Q9xnHgEdAIAH+JVf+ZWsX78+IyMjWbp0aX7lV36l3SXBrLVzIbi+vr6MjIyks7MzAwMDFoibgQR0AADGsWI0NE9PT4+fv1nAHHQAAMYZGBjIqaeemr6+vixcuDB9fX059dRTDacFmGTuoAMAMM6NN96Ye+655wF30G+++eZ2lwYwo7mDDgDAOPPnz88555wz7pnL55xzTubPn9/u0gBmNHfQAQAYZ9u2bbtWcN9pwYIF2bZtWxurApj53EEHAGCchzzkIbnvvvvyiEc8IknyiEc8Ivfdd18e8pCHtLkygJlNQAcAYJwtW7bkYQ97WD7ykY9k27Zt+chHPpKHPexh2bJlS7tLA5jRBHQAAB7gr//6r8et4v7Xf/3X7S4JYMYT0AEAGKeUkuuuuy4bNmzIjh07smHDhlx33XUppbS7NIAZTUAHAGCck08+OX/3d3+XP/qjP8rmzZvzR3/0R/m7v/u7nHzyye0uDWBGs4o7AADjfOYzn8nzn//8rFmzJn/3d3+XUkqe97zn5TOf+Uy7SwOY0QR0AOCAlFJekuSFSY5J8u5a62fbWxGTSRgHmHqGuAPALFZKubSUcnspZcNu7aeUUr5WSvlGKeW8JKm1/mOt9cwkpyd5eRvKZRKUUibkC4DDJ6ADwOz2viSnjG0opcxJ8u4kL0hyXJKeUspxYzZ5S+t9ZoBa6z6/Hr/qk/vdptba7o8BMCMI6AAwi9Var05y527Nz0zyjVrrt2qt25JckeTFZdSFSa6qtX5lqmsFgJlOQAcAdvfYJLeMeb2x1daX5DeSvKyUcvaediylnFVKubaUcu0dd9wx+ZUCwAxikTgAYHd7mlBca63vSvKufe1Ya12bZG2SLF++3LhnADgI7qADALvbmORxY14vTnJrm2oBgFlDQAcAdvflJE8qpTyhlDI/ySuSfKLNNQHAjCegA8AsVkoZSvKlJL9YStlYSumttd6f5Jwkn0kykuSDtdYb2lknAMwG5qADwCxWa+3ZS/unknxqissBgFnNHXQAAABoAAEdAAAAGkBABwAAgAYQ0AEAAKABBHQAYEKVUlaWUtZu3ry53aUAwLQioAMAE6rWuq7WelZHR0e7SwGAaUVABwAAgAYQ0AEAAKABBHQAAABoAAEdAAAAGkBABwAAgAYQ0AEAAKABBHQAAABoAAEdAAAAGmBuuwsAAAAgKaVMyHFqrRNyHKaeO+gAwIQqpawspazdvHlzu0sBmFZqrfv9evyqT+53G6YvAR0AmFC11nW11rM6OjraXQoATCsCOgAAADSAgA4AAAANIKADAABAAwjoAAAA0AACOgAAADRAIwO6x7MAAAAw2zQyoHs8CwAAALNNIwM6AAAAzDYCOgAAADSAgA4AAAANIKADAABAAwjoAMCE8jQWADg0AjoAMKE8jQUADo2ADgAAAA0goAMAAEADCOgAAADQAAI6AAAANICADgAAAA0goAMAAEADCOgAAADQAAI6AAAANICADgAAAA0goAMAAEADCOgAAADQAAI6AAAANICADgBMqFLKylLK2s2bN7e7FACYVgR0AGBC1VrX1VrP6ujoaHcpADCtCOgAAADQAAI6AAAANICADgAAAA0goAMAAEADzG13ATTbCW//bDbfu/2wj7PkvCsPa/+OB8/LV9/6vMOuA6YzP48AADObgM4+bb53e26+4IXtLuOwAwXMBH4eAQBmNkPcAQAAoAEEdAAAAGgAAR0AAAAaQEAHAACABhDQAQAAoAEEdAAAAGgAAR0AAAAaQEAHAACABhDQAQAAoAEEdAAAAGiAue0uAACYWUopK5OsXLp0abtLAWiME97+2Wy+d/uEHGvJeVce1v4dD56Xr771eRNSCxNLQAcAJlStdV2SdcuXLz+z3bUANMXme7fn5gte2O4ykhx+wGfyGOIOAAAADSCgAwAAQAMI6AAAANAAAjoAAAA0gIAOAAAADSCgAwAAQAMI6AAAANAAAjoAAAA0gIAOAAAADSCgAwAAQAMI6AAAANAAAjoAAAA0gIAOAAAADSCgA0xTZ5xxRo455ph0dXXtavv//r//L0996lOzbNmyPO95z8utt96aJPnc5z6Xpz/96XnKU56Spz/96fnCF76wx2PeeeedOfnkk/OkJz0pJ598cjZt2jQlnwUAgGRuuwsA4NCcfvrpOeecc3LaaaftanvDG96QP//zP0+SvOtd78qf/dmfZc2aNTn66KOzbt26LFq0KBs2bMjzn//8fO9733vAMS+44II897nPzXnnnZcLLrggF1xwQS688MIp+0zAxDrh7Z/N5nu3H/Zxlpx35WHt3/HgefnqW5932HUAzHQCOsA0ddJJJ+Xmm28e13bkkUfu+n7Lli0ppSRJTjzxxF3txx9/fLZu3Zr77rsvCxYsGLf/xz/+8Xzxi19MkrzqVa/Ks5/9bAEdprHN927PzRe8sN1lHHbAB5gtBHSAGaa/vz+XX355Ojo6sn79+ge8/5GPfCQnnnjiA8J5kvzgBz/IYx7zmCTJYx7zmNx+++2TXi8AAKPMQQeYYQYGBnLLLbfkla98ZS6++OJx791www1ZtWpV3vOe97SpOgAA9kZAB5ihTj311HzkIx/Z9Xrjxo156Utfmssvvzw///M/v8d9fu7nfi633XZbkuS2227LMcccMyW1AgAgoAPMKDfddNOu7z/xiU/kyU9+cpLkrrvuygtf+ML8xV/8RX71V391r/u/6EUvymWXXZYkueyyy/LiF794cgtmRiqlrCylrN28eXO7SwGAaUVAB5imenp68su//Mv52te+lsWLF2dwcDDnnXdeurq68tSnPjWf/exn8zd/8zdJkosvvjjf+MY38ud//udZtmxZli1btmt++atf/epce+21SZLzzjsvn/vc5/KkJz0pn/vc53Leeee17fMxfdVa19Vaz+ro6Gh3KQAwrVgkDmCaeFjneXnKZWMC8ynJ0accnaNzdJLknXln8qKkpKSm5ubcnFP+6ZTRbR+XPPFvn5gd2bFr9+de9dzRb34t+Y8b/iO5ofXGacnCLMztuT2/vu7X91BHkrR/VWgAgJlGQAeYJn4ycoHHJQEAzGCGuAMAAEADCOgAAADQAAI6AAAANICADgAA0EZbt27NM5/5zJxwwgk5/vjj89a3vnXc+//7f//vlFLywx/+cI/7L1myJE95ylOybNmyLF++fCpKZpJYJA4AAKCNFixYkC984Qs54ogjsn379nR3d+cFL3hBfumXfim33HJLPve5z+XYY4/d5zHWr1+fo48+eooqZrK4gw4AANBGpZQcccQRSZLt27dn+/btKaUkSf70T/80F1100a7XzGwCOgAAQJvt2LEjy5YtyzHHHJOTTz45z3rWs/KJT3wij33sY3PCCSfsc99SSp73vOfl6U9/etauXTtFFTMZDHEHAABoszlz5uT666/PXXfdlZe+9KX5r//6rwwMDOSzn/3sfvf913/91yxatCi33357Tj755Dz5yU/OSSedNAVVM9HcQQcAAGiIhz/84Xn2s5+dj3/84/n2t7+dE044IUuWLMnGjRvztKc9Ld///vcfsM+iRYuSJMccc0xe+tKX5pprrpnqspkgAjoAAEAb3XHHHbnrrruSJPfee2/+6Z/+KSeeeGJuv/323Hzzzbn55puzePHifOUrX8mjH/3ocftu2bIlP/nJT3Z9/9nPfjZdXV1T/RGYIIa4AwAATLKHdZ6Xp1x23h7f23rL1my8ZGPqT2tSk45nduRNP3pT3nTZm3Ztc+vdt+bXrvi1zH3Y3GzftD3fe+/3suR1S7Lt9m357urvJknqjpqOX+rIG37whrzhsjfso5YkeeFEfjwmiIAOAAAwyX4yckFuvmAfofgt+znAq3Z7/Sdjvt97Ft+jJeddeXA7MGUMcQcAAIAGENABAACgAQR0AAAAaAABHQAAABpAQAcAAIAGENABAACgAQR0AAAAaAABHQAAABpAQAcAAIAGENABAACgAQR0AAAAaAABHQAAABpAQAcAJlQpZWUpZe3mzZvbXQoATCsCOgAwoWqt62qtZ3V0dLS7FACYVgR0AAAAaAABHQAAABpAQAcAAIAGENABAACgAQR0AAAAaAABHQAAABpAQAcAAIAGENABAACgAaYsoJdSXlJKuaSU8vFSyvOm6rwAAAAwHRxWQC+lXFpKub2UsmG39lNKKV8rpXyjlHJektRa/7HWemaS05O8/HDOCwAAADPN4d5Bf1+SU8Y2lFLmJHl3khckOS5JTynluDGbvKX1PgAAANByWAG91np1kjt3a35mkm/UWr9Va92W5IokLy6jLkxyVa31K4dzXgAAAJhpJmMO+mOT3DLm9cZWW1+S30jyslLK2XvbuZRyVinl2lLKtXfccccklAcAAADNM3cSjln20FZrre9K8q797Vz//+3db6yeZX0H8O9Pax0IHpgy/ng6jniAHhAtSFeNc5M5CIRQQZrQWuMkZWSGJRoYSZeQxb1glmwYTeYLcIKdIa2AOKhIHXZbYAukdtAIFoVJ69qjiy5KFUT779qLHrtKT8ufnnOeu+f5fF4993Vf1/P87hf38zvfc9/3Oa3dnOTmJDn77LPbBNcGAAAAnTQZV9C3JJm11/Zgkh9MwucAAADAtDEZAf2bSU6uqjdX1cwkC5PcMwmfAwAAANPGwf6btRVJHkpyalVtqaolrbUdSf48ydeTPJHk9tbatw++VAAAAJi+DuoZ9Nbaov2Mfy3J1w7mvQEAAKCfTMYt7gAAAMDLJKADAABABwjoAAB9aPPmzTnnnHMyMjKS008/PZ/5zGeSJNdee21mz56dt73tbbnkkkvyzDPPjLt+9erVOfXUUzM8PJxly5ZNYeUA05eADgDQh2bMmJEbb7wxTzzxRB5++OF89rOfzYYNG3Luuefm8ccfz7e+9a2ccsop+eQnP7nP2p07d+aqq67Kfffdlw0bNmTFihXZsGFDD44CYHoR0AEA+tDxxx+fs846K0ly5JFHZmRkJKOjoznvvPMyY8buvyP8zne+M1u2bNln7dq1azM8PJyTTjopM2fOzMKFC3P33XdPaf0A05GADgDQ5zZt2pRHH3008+bN+43xW265JRdccME+80dHRzNr1qw924ODgxkdHZ30OgGmOwEdAKCPPfvss7n00kvz6U9/Oq9//ev3jF9//fWZMWNGFi9evM+a1to+Y1U1qXUC9IOD+j/oAAAcurZv355LL700ixcvzgc+8IE948uXL89Xv/rVrFmzZtzgPTg4mM2bN+/Z3rJlS0444YQpqRlgOnMFHQCgD7XWsmTJkoyMjOTqq6/eM7569erccMMNueeee3L44YePu3bu3Ll56qmnsnHjxmzbti0rV67M/Pnzp6p0gGmrk1fQq+qiJBcNDw/3uhQAgEPWkSNLc8bypePue+7J57Lxixvz2sHX5qa7bkqSHLvg2Pzwth9m145d+d13/G6S5LC3HJY3feRN2f7T7Rm9dTRDVw8lSXZdvCuz3zU7bVfL0e85OgvXLUzW7a+OJLlwgo8OYPrpZEBvra1Ksurss8/+017XAgBwqPr5E8uyadkBgvH144x96gBv+PGXMXcvQ0vvfWkTAfqcW9wBAACgAwR0AAAA6AABHQAAADpAQAcAAIAOENABAACgAwR0AAAA6AABHQAAADpAQAcAJlRVXVRVN2/durXXpQDAIUVABwAmVGttVWvtyoGBgV6XAgCHFAEdAAAAOkBABwAAgA4Q0AEAAKADBHQAAADoAAEdAAAAOkBABwAAgA4Q0AEAAKADOhnQq+qiqrp569atvS4FAAAApkQnA3prbVVr7cqBgYFelwIAAABTopMBHQAAAPqNgA4AAAAdIKADAABABwjoAAAA0AECOgAAAHSAgA4AAAAdIKADAABABwjoAAAA0AECOgAAAHSAgA4AAAAdIKADAABABwjoAAAA0AEzel0AAC/d0NJ7e11CBg57Ta9LAACYlgR0gEPEpmUXHvR7DC29d0LeBwCAiecWdwAAAOgAAR0AAAA6QEDnFXvmmWeyYMGCzJ49OyMjI3nooYdy2WWXZc6cOZkzZ06GhoYyZ86ccdeuXr06p556aoaHh7Ns2bKpLRwAAKCDOvkMelVdlOSi4eHhXpfCAXzsYx/L+eefnzvvvDPbtm3LL37xi3zpS1/as/+aa67JwMDAPut27tyZq666Kvfff38GBwczd+7czJ8/P6eddtpUlg8AANApnbyC3lpb1Vq7crxwRzf87Gc/ywMPPJAlS5YkSWbOnJmjjjpqz/7WWm6//fYsWrRon7Vr167N8PBwTjrppMycOTMLFy7M3XffPVWlAwAAdFInAzrd9/TTT+eYY47J5ZdfnjPPPDNXXHFFnnvuuT37H3zwwRx77LE5+eST91k7OjqaWbNm7dkeHBzM6OjolNQNAADQVQI6r8iOHTvyyCOP5KMf/WgeffTRvO51r/uNZ8lXrFgx7tXzZPfV9ReqqkmrFQAA4FAgoPOKDA4OZnBwMPPmzUuSLFiwII888kiS3eH9rrvuymWXXbbftZs3b96zvWXLlpxwwgmTXzQAAECHCei8Iscdd1xmzZqV7373u0mSNWvW7Pkjb9/4xjcye/bsDA4Ojrt27ty5eeqpp7Jx48Zs27YtK1euzPz586esdgAAgC7q5F9xpzuOHFmaM5YvHXff8+c+nznnz0nb0TLzmJkZvGIwq5evzpbPbcnhbzk8Zyw/Y8/c7T/dntFbRzN09VCSZNfFuzL7XbPTdrUc/Z6js3DdwmTdgepIkgsn7sAAAGCKDS29t9clJEkGDntNr0tgPwR0DujnTyzLpmUHCMZ/Nc7Yn+xn7sdfsP2pl15HV77MAADglTjgz9Qvw9DSeyfsveget7gDAABABwjoAAAA0AECOgAAAHSAgA4AAAAdIKADAABABwjoAAAA0AECOgAAAHSAgA4AAAAdIKADAABABwjoAAAA0AECOgAAAHSAgA4AAAAdIKADAABABwjoAAAA0AGdDOhVdVFV3bx169ZelwIAAABTopMBvbW2qrV25cDAQK9LAQAAgCnRyYAOAAAA/UZABwAAgA6o1lqva9ivqvpxku/3ug4O2huT/G+viwCSOB+nixNba8f0uogXo49PG743oFuck9PDuL280wGd6aGq1rXWzu51HYDzEXj5fG9Atzgnpze3uAMAAEAHCOgAAADQAQI6U+HmXhcA7OF8BF4u3xvQLc7Jacwz6AAAANABrqADAABAB8zodQEceqpqZ5LH9hq6uLW2aT9zn22tHTElhUGfqqo3JFkztnlckp1Jfjy2/XuttW09KQzoLL0cukUv59fc4s7L9nIataYOU6uqPpHk2dba3+01NqO1tqN3VQFdo5dDd+nl/c0t7hy0qjqiqtZU1SNV9VhVvX+cOcdX1QNVtb6qHq+q94yNn1dVD42tvaOq/AAAE6CqvlBVn6qqf01yQ1V9oqr+Yq/9j1fV0NjrD1XV2rHz86aqenWv6gZ6Qy+H7tHL+5OAzitx2NjJv76qvpLkl0kuaa2dleScJDdWVb1gzQeTfL21NifJ25Osr6o3JrkuyR+PrV2X5OopOwqY/k7J7vPrmv1NqKqRJJcleffY+bkzyeKpKQ/oIb0cDg16eZ/xDDqvxPNjJ3+SpKpek+RvquoPkuxK8qYkxyb5n73WfDPJLWNz/6m1tr6q/jDJaUn+Y+xngJlJHpqaQ4C+cEdrbeeLzHlfknck+ebYeXhYkh9NdmFAz+nlcGjQy/uMgM5EWJzkmCTvaK1tr6pNSX5r7wmttQfGmv6FSb5YVX+b5KdJ7m+tLZrqgqFPPLfX6x35zbumfn2OVpLlrbW/nLKqgC7Sy6Gb9PI+4xZ3JsJAkh+NNfRzkpz4wglVdeLYnM8l+XySs5I8nOTdVTU8NufwqjplCuuGfrIpu8+7VNVZSd48Nr4myYKq+p2xfb89dr4C/UUvh+7bFL182nMFnYlwW5JVVbUuyfok3xlnznuTXFtV25M8m+TDrbUfV9VHkqyoqteOzbsuyZOTXjH0ny8n+XBVrc/u21SfTJLW2oaqui7JP1fVq5JsT3JVku/3qlCgJ/Ry6D69vA/4N2sAAADQAW5xBwAAgA4Q0AEAAKADBHQAAADoAAEdAAAAOkBABwAAgA4Q0KHPVNUlVdWqavZBvMcRVXVTVX2vqr5dVQ9U1byxfTuran1VPV5Vd1TV4RNXPQCgl8P0JaBD/1mU5N+TLDyI9/iHJD9JcnJr7fQkH0nyxrF9z7fW5rTW3ppkW5I/O4jPAQD2pZfDNCWgQx+pqiOSvDvJkow19ap6b1X9W1XdWVXfqarbarf3VdVX9lp7blXdVVVvSTIvyXWttV1J0lp7urV27zgf+WCS4Uk/MADoE3o5TG8COvSXi5Osbq09meQnVXXW2PiZST6e5LQkJ2V34/+XJCNVdczYnMuT3Jrk9CTrW2s7D/RBVTUjyQVJHpvgYwCAfnZx9HKYtgR06C+Lkqwce71ybDtJ1rbWtoz9Fn19kqHWWkvyxSQfqqqjkrwryX0v4TMOq6r1SdYl+e8kn5+w6gEAvRymsRm9LgCYGlX1hiR/lOStVdWSvDpJS/K1JL/aa+rO/P93w61JViX5ZZI7Wms7qurbSd5eVa/69W1xL/B8a23OJB0GAPQtvRymP1fQoX8sSPKPrbUTW2tDrbVZSTYm+f39LWit/SDJD5Jcl+QLY2Pfy+7fqP91VVWSVNXJVfX+Sa4fAPqdXg7TnIAO/WNRkq+8YOzLST74IutuS7K5tbZhr7ErkhyX5L+q6rEkn8vu5g8ATB69HKa52v1oCsD4qurvkzzaWvP8GQAcgvRyOHQI6MB+VdV/JnkuybmttV+92HwAoFv0cji0COgAAADQAZ5BBwAAgA4Q0AEAAKADBHQAAADoAAEdAAAAOkBABwAAgA4Q0AEAAKAD/g+OwVPyKQbwsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2)\n",
    "fig.set_size_inches(14,14)\n",
    "boxplot(manage, \"AvgTPLoad\", ax=axes[0][0])\n",
    "boxplot(manage, \"AvgTNLoad\", ax=axes[0][1])\n",
    "boxplot(manage, \"Avg.N.Applied\", ax=axes[1][0])\n",
    "boxplot(manage, \"Avg.P.Applied\", ax=axes[1][1])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Causal inference\n",
    "The box-plots above show both the outcome and several features are correlated with the treatment, which indicates that there is some bias in the data. We will now use causallib to estimate the effect of treatment by correcting for these biases.\n",
    "\n",
    "As features, we use the amount of phosphorus and nitrogen applied, the total amount of water runoff, the soil volume, the phosphosros application method, and the type of land use (LU)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from causallib.estimation import IPW\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(solver='lbfgs', max_iter=500)\n",
    "ipw = IPW(lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(222, 23)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Runoff</th>\n",
       "      <th>Soil</th>\n",
       "      <th>PApplied</th>\n",
       "      <th>NApplied</th>\n",
       "      <th>Method_Incorporated</th>\n",
       "      <th>Method_Injected</th>\n",
       "      <th>Method_Surface Applied</th>\n",
       "      <th>Method_Unknown</th>\n",
       "      <th>LU_Alfalfa</th>\n",
       "      <th>LU_Corn</th>\n",
       "      <th>...</th>\n",
       "      <th>LU_Other</th>\n",
       "      <th>LU_Pasture</th>\n",
       "      <th>LU_Peanuts</th>\n",
       "      <th>LU_Rotation</th>\n",
       "      <th>LU_Sorghum</th>\n",
       "      <th>LU_Soybeans</th>\n",
       "      <th>Tillage_Conservation</th>\n",
       "      <th>Tillage_Conventional</th>\n",
       "      <th>Tillage_No Till</th>\n",
       "      <th>Tillage_Pasture</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RecordID</th>\n",
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       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>5.010635</td>\n",
       "      <td>5.525453</td>\n",
       "      <td>3.806662</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Runoff      Soil  PApplied  NApplied  Method_Incorporated  \\\n",
       "RecordID                                                                \n",
       "260       0.009950  4.529368  3.288402  4.430817                    0   \n",
       "261       1.615420  4.726502  3.288402  4.430817                    0   \n",
       "93        5.010635  5.525453  3.806662  3.713572                    0   \n",
       "68        3.295837  1.609438  3.157000  4.430817                    0   \n",
       "259       1.308333  6.669498  3.465736  4.430817                    0   \n",
       "\n",
       "          Method_Injected  Method_Surface Applied  Method_Unknown  LU_Alfalfa  \\\n",
       "RecordID                                                                        \n",
       "260                     0                       1               0           1   \n",
       "261                     0                       1               0           1   \n",
       "93                      0                       0               1           1   \n",
       "68                      0                       0               1           1   \n",
       "259                     0                       1               0           0   \n",
       "\n",
       "          LU_Corn  ...  LU_Other  LU_Pasture  LU_Peanuts  LU_Rotation  \\\n",
       "RecordID           ...                                                  \n",
       "260             0  ...         0           0           0            0   \n",
       "261             0  ...         0           0           0            0   \n",
       "93              0  ...         0           0           0            0   \n",
       "68              0  ...         0           0           0            0   \n",
       "259             0  ...         1           0           0            0   \n",
       "\n",
       "          LU_Sorghum  LU_Soybeans  Tillage_Conservation  Tillage_Conventional  \\\n",
       "RecordID                                                                        \n",
       "260                0            0                     0                     0   \n",
       "261                0            0                     0                     0   \n",
       "93                 0            0                     0                     0   \n",
       "68                 0            0                     0                     0   \n",
       "259                0            0                     0                     1   \n",
       "\n",
       "          Tillage_No Till  Tillage_Pasture  \n",
       "RecordID                                    \n",
       "260                     0                1  \n",
       "261                     0                1  \n",
       "93                      0                1  \n",
       "68                      0                1  \n",
       "259                     0                0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.log(manage[\"AvgTPLoad\"])\n",
    "\n",
    "a = manage[\"AnyCP\"]\n",
    "\n",
    "X = pd.DataFrame({\"Runoff\": np.log(manage[\"Avg.Runoff\"]),\n",
    "                  \"Soil\": np.log(manage[\"Avg.SoilL\"] + 1),\n",
    "                  \"PApplied\": np.log(manage[\"Avg.P.Applied\"] + 1),\n",
    "                  \"NApplied\": np.log(manage[\"Avg.N.Applied\"] + 1)})\n",
    "X = pd.concat([X, pd.get_dummies(manage[\"F.AppMethod1\"], prefix=\"Method\"), \n",
    "               pd.get_dummies(manage[\"ShortLU\"], prefix=\"LU\"),\n",
    "               pd.get_dummies(manage[\"Tillage\"], prefix=\"Tillage\")], axis=1)\n",
    "X.fillna(X.median(axis=0), inplace=True)\n",
    "\n",
    "y = y.dropna()  # remove the data where there is no outcome\n",
    "a, X = a[y.index], X.loc[y.index]\n",
    "\n",
    "print(X.shape)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IPW(truncate_eps=None, use_stabilized=False,\n",
       "    learner=LogisticRegression(max_iter=500))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipw.fit(X, a)  # train the propensity model to apply causal inference "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>causal</th>\n",
       "      <th>marginal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnyCP</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>0.695621</td>\n",
       "      <td>0.592249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.515510</td>\n",
       "      <td>0.798039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         causal  marginal\n",
       "AnyCP                    \n",
       "False  0.695621  0.592249\n",
       "True   0.515510  0.798039"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"causal\": np.exp(ipw.estimate_population_outcome(X, a, y)),\n",
    "              \"marginal\": np.exp(y.groupby(a).mean())})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the causal inference identifies that using any CP will reduce the phosphorus load, while the marginal observation (just checking what happened in treated vs. untreated areas) shows the opposite trend. How can this be?\n",
    "## Evaluation and adjustments\n",
    "One way to understand what is occurring is to evaluate the performance of the propensity model and check how consistent it is. Causallib provides the tools to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from causallib.evaluation import PropensityEvaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluator = PropensityEvaluator(ipw)\n",
    "_ = evaluator.evaluate_simple(X, a, y, plots=[\"roc_curve\", \"weight_distribution\", \"calibration\", \n",
    "                                          \"covariate_balance_love\"])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first glance it might seem that while the model has good predictive performance in the ROC curve (top left). However, the fact that the weighted ROC curve is far from the diagonal hints that the model is in fact not very good at balancing the data. This can also be seen in the covariate balancing plot (bottom right), where we see that the `Pasture` variable, which was extremely biased in the data, is still quite biased even after weighting. It would make sense, therefore, to stratify the analysis according to it, or to remove it from the analysis completely.\n",
    "\n",
    "To check whether it is better to stratify or limit our analysis to a specific strata, let's check the overlap between `Pasture` and the treatment. We may as well look at all the categorical features and check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AnyCP</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Method_Incorporated</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Method_Incorporated</th>\n",
       "      <th>0</th>\n",
       "      <td>148</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Method_Injected</th>\n",
       "      <th>0</th>\n",
       "      <td>171</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Method_Surface Applied</th>\n",
       "      <th>0</th>\n",
       "      <td>139</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Method_Unknown</th>\n",
       "      <th>0</th>\n",
       "      <td>82</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LU_Alfalfa</th>\n",
       "      <th>0</th>\n",
       "      <td>175</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LU_Corn</th>\n",
       "      <th>0</th>\n",
       "      <td>156</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LU_Cotton</th>\n",
       "      <th>0</th>\n",
       "      <td>173</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LU_Fallow</th>\n",
       "      <th>0</th>\n",
       "      <td>179</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LU_Oats.Wheat</th>\n",
       "      <th>0</th>\n",
       "      <td>162</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LU_Other</th>\n",
       "      <th>0</th>\n",
       "      <td>165</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LU_Pasture</th>\n",
       "      <th>0</th>\n",
       "      <td>99</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LU_Peanuts</th>\n",
       "      <th>0</th>\n",
       "      <td>176</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LU_Rotation</th>\n",
       "      <th>0</th>\n",
       "      <td>160</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LU_Sorghum</th>\n",
       "      <th>0</th>\n",
       "      <td>175</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LU_Soybeans</th>\n",
       "      <th>0</th>\n",
       "      <td>180</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tillage_Conservation</th>\n",
       "      <th>0</th>\n",
       "      <td>157</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tillage_Conventional</th>\n",
       "      <th>0</th>\n",
       "      <td>120</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tillage_No Till</th>\n",
       "      <th>0</th>\n",
       "      <td>168</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tillage_Pasture</th>\n",
       "      <th>0</th>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "AnyCP                                       False  True \n",
       "                       Method_Incorporated              \n",
       "Method_Incorporated    0                      148     31\n",
       "                       1                       32     11\n",
       "Method_Injected        0                      171     36\n",
       "                       1                        9      6\n",
       "Method_Surface Applied 0                      139     32\n",
       "                       1                       41     10\n",
       "Method_Unknown         0                       82     27\n",
       "                       1                       98     15\n",
       "LU_Alfalfa             0                      175     42\n",
       "                       1                        5      0\n",
       "LU_Corn                0                      156     24\n",
       "                       1                       24     18\n",
       "LU_Cotton              0                      173     42\n",
       "                       1                        7      0\n",
       "LU_Fallow              0                      179     40\n",
       "                       1                        1      2\n",
       "LU_Oats.Wheat          0                      162     39\n",
       "                       1                       18      3\n",
       "LU_Other               0                      165     42\n",
       "                       1                       15      0\n",
       "LU_Pasture             0                       99     41\n",
       "                       1                       81      1\n",
       "LU_Peanuts             0                      176     42\n",
       "                       1                        4      0\n",
       "LU_Rotation            0                      160     30\n",
       "                       1                       20     12\n",
       "LU_Sorghum             0                      175     38\n",
       "                       1                        5      4\n",
       "LU_Soybeans            0                      180     40\n",
       "                       1                        0      2\n",
       "Tillage_Conservation   0                      157     24\n",
       "                       1                       23     18\n",
       "Tillage_Conventional   0                      120     26\n",
       "                       1                       60     16\n",
       "Tillage_No Till        0                      168     35\n",
       "                       1                       12      7\n",
       "Tillage_Pasture        0                       95     41\n",
       "                       1                       85      1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat({col: pd.crosstab(X[col], a) for col in X.select_dtypes('uint8').columns.values}, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there is only one case of pasture land that was in fact treated. We should limit the analysis only to non-pasture land. \n",
    "\n",
    "But it's a bit more than that, since treatment was also never observed for Alfalfa, Cotton, Peanuts, or Other. For Other, the difference is quite large while the others might still be attributed to small number fluctuations. While this could be tested using some sort of statistical test, this is not the focus of this example, and we will simply remove all these samples (and then remove the columns to avoid having all-zero columns)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We are left with 107 samples\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>causal</th>\n",
       "      <th>marginal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>1.450745</td>\n",
       "      <td>1.436078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.698854</td>\n",
       "      <td>0.826860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         causal  marginal\n",
       "False  1.450745  1.436078\n",
       "True   0.698854  0.826860"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X.query('LU_Pasture == 0 & LU_Other == 0 & LU_Cotton ==0 & LU_Alfalfa == 0 & LU_Peanuts ==0 & LU_Soybeans == 0')\n",
    "X = X.drop(columns=['LU_Pasture', 'LU_Other', 'LU_Cotton', 'LU_Alfalfa', 'LU_Peanuts', 'LU_Soybeans'])\n",
    "a, y = a[X.index], y[X.index]\n",
    "print(f'We are left with {len(y)} samples')\n",
    "ipw.fit(X, a)\n",
    "pd.DataFrame({\"causal\": np.exp(ipw.estimate_population_outcome(X, a, y)),\n",
    "              \"marginal\": np.exp(y.groupby(a).mean())})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluator = PropensityEvaluator(ipw)\n",
    "evaluator.evaluate_simple(X, a, y, plots=[\"roc_curve\", \"weight_distribution\", \"calibration\", \n",
    "                                          \"covariate_balance_love\"])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performance evaluation is much better now: \n",
    "- all the features are sufficiently balanced\n",
    "- the model is as calibrated as can be expected given the small sample size that we now have\n",
    "- the propensity distribution doesn't exhibit any areas with only one treatment group dominsating\n",
    "- the ROC curves behave well.\n",
    "\n",
    "Most importantly, after limiting the data the causal analysis agrees with the marginal analysis, and only makes a small adjustment to the causal effect size that is estimated. \n",
    "\n",
    "## Summary\n",
    "In summary, the causal analysis allowed us to focus on where the data allows us to check differences between treatment groups. Indeed, unlike the initial analysis, we now understand that in non-pasture lands the conservation procedures of terracing, buffering, waterway, or contour farming actually improve phosphorus retention in the soil and reduce phosphorus leaking into the watershed when growing crops such as oats, wheat, corn, soybean and sorghum. While we can surmise that this may be true for other crops, we simply cannot answer that from the data.\n",
    "\n",
    "### Acknowledgement\n",
    "We would like to thank Biance Zadrozny, Sr. Manager, Spatiotemporal Modeling\n",
    "IBM Research Brazil, for valuable feedback regarding the validity of the analyis and interpretation of the data and the results."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-causal] *",
   "language": "python",
   "name": "conda-env-.conda-causal-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}