chris-q4-q5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries and Import Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_injury = pd.read_csv('InjuryRecord.csv')\n",
    "df_tracking = pd.read_csv('PlayerTrackData.csv')\n",
    "df_plays = pd.read_csv('PlayList.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean PlayerTrackData."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chris\\AppData\\Local\\Temp\\ipykernel_13932\\1183363209.py:16: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  df_tracking['event'] = df_tracking['event'].fillna(method='ffill')\n"
     ]
    }
   ],
   "source": [
    "in_play = [\"tackle\", \"ball_snap\", \"pass_outcome_incomplete\", \"out_of_bounds\", \"first_contact\", \"handoff\", \n",
    "           \"pass_forward\", \"pass_outcome_caught\", \"touchdown\", \"qb_sack\", \"touchback\", \"kickoff\", \"punt\", \n",
    "           \"pass_outcome_touchdown\", \"pass_arrived\", \"extra_point\", \"field_goal\", \"play_action\", \"kick_received\", \n",
    "           \"fair_catch\", \"punt_downed\", \"run\", \"punt_received\", \"qb_kneel\", \"pass_outcome_interception\", \n",
    "           \"field_goal_missed\", \"fumble\", \"fumble_defense_recovered\", \"qb_spike\", \"extra_point_missed\", \n",
    "           \"fumble_offense_recovered\", \"pass_tipped\", \"lateral\", \"qb_strip_sack\", \"safety\", \"kickoff_land\", \n",
    "           \"snap_direct\", \"kick_recovered\", \"field_goal_blocked\", \"punt_muffed\", \"pass_shovel\", \"extra_point_blocked\", \n",
    "           \"pass_lateral\", \"punt_blocked\", \"run_pass_option\", \"free_kick\", \"punt_fake\", \"end_path\", \"drop_kick\", \n",
    "           \"field_goal_fake\", \"extra_point_fake\", \"xp_fake\"]\n",
    "is_kick = [\"kickoff, kickoff_play\", \"punt\", \"punt_play\", \"free_kick\", \"drop_kick\"]\n",
    "kick_plays = df_tracking[df_tracking.event.isin(is_kick)].PlayKey.tolist()\n",
    "\n",
    "# Add boolean column to track whether a play was injury-causing or not.\n",
    "df_tracking['isInjury'] = df_tracking.PlayKey.isin(df_injury.PlayKey.tolist())\n",
    "# Forward fill the event column so each row tracks the most recent event.\n",
    "df_tracking['event'] = df_tracking['event'].fillna(method='ffill')\n",
    "# Add boolean column to track whether the ball is in play or not.\n",
    "df_tracking['inPlay'] = df_tracking.event.isin(in_play)\n",
    "# Add bolean column to track whether the current play is a kickoff or punt play.\n",
    "df_tracking['isKick'] = df_tracking.PlayKey.isin(kick_plays)\n",
    "# Convert player speed from yd/s to mph.\n",
    "df_tracking['s_mph'] = df_tracking['s'] * 2.04545\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Groupby to calculate total distance traveled and explore player speed data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>PlayKey</th>\n",
       "      <th>dis_sum</th>\n",
       "      <th>s_mph_mean</th>\n",
       "      <th>s_mph_min</th>\n",
       "      <th>s_mph_max</th>\n",
       "      <th>isInjury_last</th>\n",
       "      <th>isKick_last</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26624-1-1</td>\n",
       "      <td>16.83</td>\n",
       "      <td>1.095855</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.013623</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26624-1-10</td>\n",
       "      <td>17.07</td>\n",
       "      <td>1.178802</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.504531</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26624-1-11</td>\n",
       "      <td>31.20</td>\n",
       "      <td>1.627713</td>\n",
       "      <td>0.020455</td>\n",
       "      <td>6.013623</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26624-1-12</td>\n",
       "      <td>14.70</td>\n",
       "      <td>0.715277</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.743174</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26624-1-13</td>\n",
       "      <td>13.26</td>\n",
       "      <td>0.873631</td>\n",
       "      <td>0.020455</td>\n",
       "      <td>3.047721</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266955</th>\n",
       "      <td>47888-9-54</td>\n",
       "      <td>48.70</td>\n",
       "      <td>4.384922</td>\n",
       "      <td>0.184091</td>\n",
       "      <td>9.409070</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266956</th>\n",
       "      <td>47888-9-6</td>\n",
       "      <td>83.64</td>\n",
       "      <td>4.401893</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.181780</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266957</th>\n",
       "      <td>47888-9-7</td>\n",
       "      <td>60.26</td>\n",
       "      <td>3.664050</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.479516</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266958</th>\n",
       "      <td>47888-9-8</td>\n",
       "      <td>67.35</td>\n",
       "      <td>4.405741</td>\n",
       "      <td>0.040909</td>\n",
       "      <td>14.440877</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266959</th>\n",
       "      <td>47888-9-9</td>\n",
       "      <td>60.54</td>\n",
       "      <td>3.691152</td>\n",
       "      <td>0.061364</td>\n",
       "      <td>11.188612</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266960 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           PlayKey  dis_sum  s_mph_mean  s_mph_min  s_mph_max  isInjury_last  \\\n",
       "0        26624-1-1    16.83    1.095855   0.000000   6.013623          False   \n",
       "1       26624-1-10    17.07    1.178802   0.000000   6.504531          False   \n",
       "2       26624-1-11    31.20    1.627713   0.020455   6.013623          False   \n",
       "3       26624-1-12    14.70    0.715277   0.000000   3.743174          False   \n",
       "4       26624-1-13    13.26    0.873631   0.020455   3.047721          False   \n",
       "...            ...      ...         ...        ...        ...            ...   \n",
       "266955  47888-9-54    48.70    4.384922   0.184091   9.409070          False   \n",
       "266956   47888-9-6    83.64    4.401893   0.000000  17.181780          False   \n",
       "266957   47888-9-7    60.26    3.664050   0.000000  13.479516          False   \n",
       "266958   47888-9-8    67.35    4.405741   0.040909  14.440877          False   \n",
       "266959   47888-9-9    60.54    3.691152   0.061364  11.188612          False   \n",
       "\n",
       "        isKick_last  \n",
       "0             False  \n",
       "1             False  \n",
       "2             False  \n",
       "3             False  \n",
       "4             False  \n",
       "...             ...  \n",
       "266955        False  \n",
       "266956        False  \n",
       "266957        False  \n",
       "266958        False  \n",
       "266959        False  \n",
       "\n",
       "[266960 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df_tracking.groupby(['PlayKey']).agg({'dis':'sum','s_mph':['mean','min','max'],'isInjury':'last','isKick':'last'}).reset_index()\n",
    "df1.columns = ['_'.join(col).strip() for col in df1.columns.values]\n",
    "df1.rename(columns={'PlayKey_':'PlayKey'}, inplace=True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using plots to show how distance traveled and player speed differ on injury-causing plays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df1[df1['isInjury_last'] == True]['dis_sum'], fill=True, color='red', label='Injury')\n",
    "sns.kdeplot(df1[df1['isInjury_last'] == False]['dis_sum'], fill=True, color='blue', label='No Injury')\n",
    "\n",
    "plt.axvline(df1[df1['isInjury_last'] == True]['dis_sum'].mean(), color='red', linestyle='--', linewidth=2, label='Injury Mean')\n",
    "plt.axvline(df1[df1['isInjury_last'] == False]['dis_sum'].mean(), color='blue', linestyle='--', linewidth=2, label='No Injury Mean')\n",
    "\n",
    "plt.text(df1[df1['isInjury_last'] == True]['dis_sum'].mean() + 31, plt.ylim()[1] * 0.8, str(round(df1[df1['isInjury_last'] == True]['dis_sum'].mean(),1)) + ' Yds', color='red', \n",
    "         ha='right', va='center', fontsize=10, bbox=dict(facecolor='white', edgecolor='red', boxstyle='round,pad=0.5'))\n",
    "plt.text(df1[df1['isInjury_last'] == False]['dis_sum'].mean(), plt.ylim()[1] * 0.9, str(round(df1[df1['isInjury_last'] == False]['dis_sum'].mean(),1)) + ' Yds', color='blue',\n",
    "         ha='left', va='center', fontsize=10, bbox=dict(facecolor='white', edgecolor='blue', boxstyle='round,pad=0.5'))\n",
    "\n",
    "plt.xlabel('Distance Traveled')\n",
    "plt.ylabel('')\n",
    "plt.title('Players Run 12.4% Further in Injury-Causing Plays')\n",
    "plt.legend(title='')\n",
    "\n",
    "plt.yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xMQwdOpSjR4+W2XWg1Wrp1atXsfcZ8zqr7HN/6tQpDh48WOIbfOEZGYVfexVh6tdF/fr1DZIL3TkPHjxYsQBLUNzvbmdnx8iRI1m4cCHp6em4urqyYsUKnJ2dGTx4MKCOOSn4fwHUfm/dTBTd2BVjLF26lLlz53L8+HFycnJKja0slX3tlPU86pK7wn8D9vb25eq6vOOOO3jjjTfQaDS4urrStGlTvLy8yjzO2MdKNwvp/PnzhISEsGbNGnJychg1apR+nxdffJE//viDjh070rBhQ3r37s2IESPo0qWL0b9HVVSjWgY6duxIr1696N69O02bNi0zEcjLy+Pee+/l559/5sUXX2Tt2rVs2LBBPyin4Kd4rVbLiBEj+Pbbb8nMzGTTpk3ExsYafGLQ7f/uu+8W+YSou7m7uxvEUNyb9KRJkzh58iSzZ8/G2dmZV199laZNmxY7gLE4d999N7169WL48OFs2LABFxcXRo4cafD7FP6HW/AxKU7BPtSClFvjHCorMDAQKDoAEMDX15ecnJxSBxR+++23XLt2jQcffNBge7du3fDw8CgygLOgmJgY5s6dy4IFC7Czs+Orr75iwoQJ9OjRg3HjxtGpUye+/vrrCv5m5XudVea5z8/P59577y3xtadrwdKpbIIIpn9dVPR85X09l/S7jx49mtTUVNauXYuiKKxcuZIHHngAT09PAFatWkVAQIDBDaBJkyYAHDp0qNQ4dZYvX05UVBTh4eEsWrSI9evXs2HDBnr06FHhv9PKvHbM/fetU7duXXr16kXPnj3p1KmTUYmAsY8VwLBhw3BwcNC3Dixfvpz27dvTuHFj/T5NmzblxIkTfP3119x11118++233HXXXUybNs2kv6utqVEtA+V16NAhTp48ydKlSxk9erR++4YNG4rdf/To0cydO5effvqJX3/9FR8fH/r06aO/Pzw8HAAPD48SPyEaKzw8nOeee47nnnuOU6dOERkZydy5c1m+fHm5zuPu7s60adMYO3Ysq1ev1g+kq127drGFiM6fP0+DBg0qFXtFBAYG4u/vbzArQyc2NhZnZ+dSm2B1xaUK/5NUFIW8vLxSiwdNnTqVBx98UD+bIzY2Vp+c6GIrLi5jlfd1VtZzX9IbRHh4OKmpqZV+7VVFtWvXLrYrobzdFS1atKBNmzasWLGC+vXrc+HCBT744AP9/X369Cn2eYuIiKBx48b88MMPLFiwoEjSX9g333xDgwYN+O677wyez8JvSLpP6IX/Vkv6vUz1f6OwkJAQAE6fPs0999yj356bm0tMTAytWrWq1PlLY+xjBVCnTh369evHihUrGDlyJNu2bSsycBbAzc2NoUOHMnToULKzs3n44Yd58803efnll3F2djbb72JNNaploLx02XDB7FdRFBYsWFDs/q1ataJVq1Z88cUXfPvttwwbNsyg2bpdu3aEh4czZ84cfR94QcZMuUpPTzfo3wb1D7xWrVpkZWUZ9XsVNnLkSOrXr2/QLxceHs7OnTvJzs7Wb1u3bh0XL16s0DVMYejQoVy8eNHgn218fDw//PADPXr00Lf05OTkcPz4ca5cuaLfLyIiAqDIJ/gff/yRtLS0IrNAdDZt2sQvv/xiMH3Nz8+P48eP638+duyY0V00xTH2dWbsc+/m5lZsIjdkyBB27NjBb7/9VuS+xMREo6spVkXh4eEcP37c4G/swIEDpbYIlWTUqFH8/vvvzJ8/H29vb4NZDAEBAfTq1cvgpvP666+TkJDAY489Vuxj/fvvv7Nu3Tqg+NfErl27ilTDDAkJQavVFhnv8fHHHxv8bI7/GwW1b98eb29vPv/8c4PfbcWKFSbrKiyJsY+VzqhRozh69CjPP/88Wq1W/wFIJyEhweBnR0dHmjVrhqIoBl0Q1Y20DJSiSZMmhIeHM3XqVC5fvoyHhwfffvttqS/u0aNHM3XqVIAig4rs7Oz44osvuO+++2jevDljx46lXr16XL58mU2bNuHh4cFPP/1UakwnT56kZ8+eDBkyhGbNmmFvb8/333/PtWvXiryojeXg4MCzzz7L888/z/r16+nbty+PPfYY33zzDX379mXIkCGcOXOG5cuX61s3TOnDDz8kMTGR2NhYQJ3qc+nSJUCdmqZrgn355ZdZvXo1gwYNYsqUKXh6evLJJ5+Qk5PDrFmz9Oe7fPkyTZs2ZcyYMfqm9v79+9O8eXNmzJjB+fPnufPOOzl9+jQffvghAQEBjB8/vkhceXl5TJo0ieeff96gz/SRRx7hhRdewMfHh/Pnz3Po0CGDQUnlZezrzNjnvl27dixcuJA33niDhg0b4uvrS48ePXj++ef58ccfeeCBB4iKiqJdu3akpaVx6NAhvvnmG2JiYqhbt26Ffw9bNm7cOObNm0efPn0YP348169f55NPPqF58+b6wX3GGjFiBC+88ALff/89TzzxhNGVDocOHcqhQ4d48803+ffffxk+fLi+AuH69evZuHGjfi77Aw88wHfffcfAgQPp168f586d45NPPqFZs2YGHyQ8PT0ZPHgwH3zwARqNhvDwcNatW1dk/Ic5/m8U5OjoyPTp03n66afp0aMHQ4YMISYmhiVLlhAeHl5ia5UpGPtY6fTr1w9vb2/WrFnDfffdh6+vr8H9vXv3xt/fny5duuDn58exY8f48MMP6devn9EDQKskK8xgsLiSKhAWVtw0naNHjyq9evVS3N3dlbp16yqPP/64cuDAgRKnwF25ckXRarUGFbMK+/fff5WHH35Y8fb2VpycnJSQkBBlyJAhysaNG/X7lFSBLT4+XnnyySeVJk2aKG5uboqnp6dyxx13KKtXry7zcSitqltSUpLi6elpMGVn7ty5Sr169RQnJyelS5cuyj///FPi1KM1a9YYnK+46V0lCQkJKXbaHaCcO3fOYN8zZ84oAwcOVDw8PBQXFxelR48eyu7du4u9duGpkTdu3FAmT56sREREKE5OTkrdunWVYcOGKWfPni02ro8++kipX7++wZRLRVEr0k2ZMkWpW7euEhISoixdurTM37GsCoTGvM6Mfe6vXr2q9OvXT6lVq1aRaV0pKSnKyy+/rDRs2FBxdHRU6tatq3Tu3FmZM2eOviqe7vErz/TVkqYWFv6bM7bKZ0lTC4ub8lXS9MDCr73ly5crDRo0UBwdHZXIyEjlt99+K/HYsn73+++/XwGU7du3l7pfcTZu3Kg89NBDiq+vr2Jvb6/4+Pgo/fv3V3744Qf9Pvn5+cqsWbOUkJAQxcnJSWnTpo2ybt26IvEqiqLExcUpgwYNUlxdXZXatWsrEyZMUA4fPlyh105l/77ff/99fcwdO3ZUtm3bprRr107p27dvmY9LcRUIi1M4xvI8Vjr//e9/DarPFvTpp58qd999t/7/c3h4uPL8889XaEpxVaJRFBOPAKnh4uPjCQgI4LXXXuPVV1+1djhC1DhnzpyhYcOGfPnll2VO+auogQMHcujQIaOqbNZk+fn5+Pj48PDDD/P5559bOxy9yZMns2jRIq5evVqj1qUpjYwZMLElS5aQl5dnMFVFCGE5urEi5uryuHLlCj///LP8jReSmZlZZHbBsmXLuHHjhk0tOZyZmcny5csZNGiQJAIFyJgBE/nzzz85evQob775JgMGDKhQWWAhROUsXryYxYsX4+rqalAnwhTOnTvHtm3b+OKLL3BwcDCoZS9g586dTJ48mcGDB+Pt7c2+fftYtGgRLVq00NdhsKbr16/zxx9/8M0335CQkFDsOhM1mpW7KaqNbt26KQ4ODkr37t315Y2FEJal1WqVpk2bKj///LPJz60bBxEcHFykD12o4wj69++v+Pn5KQ4ODoqfn58yduxY5dq1a9YOTVGU2+MffH19lQ8++MDa4dgcGTMghBBC1HAyZkAIIYSo4SQZEEIIIWo4owYQ5ufnExsbS61atcxaPEIIIYQQpqMoCikpKQQGBpa6Ho9RyUBsbCxBQUEmC04IIYQQlnPx4kXq169f4v1GJQO6EowXL17UL8UpqrYmTeDKFQgIgAJl9iuxoxBCCFuTnJxMUFBQmaWUjUoGdF0DHh4ekgxUE9OnQ2oquLtDqU+p0TsKIYSwVWV18Rs1tTA5ORlPT0+SkpIkGRBCCCGqCGPfv2U2gRBCCFHDSTIghBBC1HCyNkENdeUK5OWBVquODaz8jkKIkuTl5ZGTk2PtMEQ15ODggFarrfR5JBmooTp0gMuXoV49uHTJFDsKIQpTFIWrV6+SmJho7VBENebl5YW/v3+l6gBJMiCEEGaiSwR8fX1xdXWVom3CpBRFIT09nevXrwMQUInWW0kGhBDCDPLy8vSJgLe3t7XDEdWUi4sLoC7R7OvrW+EuAxlAKIQQZqAbI+Dq6mrlSER1p3uNVWZciiQDQghhRtI1IMzNFK8xSQaEEEKIGk7GDAghhKVduADx8Za5Vt26EBxs1kuEhoYyadIkJk2aZNbrCPORZEAIISzpwgVo2hTS0y1zPVdXOHasXAlBVFQUiYmJrF271qj99+zZg5ubWwUDFLZAkgEhhLCk+Hg1Efi//4OQEPNe6/x5mDVLvaYZWwd8fHwqdbyiKOTl5WFvL29J1iJjBoQQwhpCQiAiwrw3EyQb3bt355lnnuGFF16gTp06+Pv7M336dIN9QkNDmT9/PgAxMTFoNBr279+vvz8xMRGNRsPmzZsB2Lx5MxqNhl9//ZV27drh5OTE8uXLsbOz459//jE49/z58wkJCSE/P7/Sv4somaRhVcSWLWrrYseO0KgR2FUyjdu4EXJzocxE3OgdhRDV1dKlS5kyZQq7du1ix44dREVF0aVLF+69995Knfell15izpw5NGjQgNq1a9OrVy+io6Np3769fp/o6GiioqKwq+w/PVEq+Q9fBaxdC4MGgS4x7tABfvoJ/Pwqfs7GjU29oxCiumrVqhXTpk0DoFGjRnz44Yds3Lix0snAjBkzDM7x2GOPMXHiRObNm4eTkxP79u3j0KFD/PDDD5W6jiibpFo2butWGDYMunVTk4JZs+DsWejUCU6etHZ0QoiaoFWrVgY/BwQE6EvgVkbBFgCAAQMGoNVq+f777wFYsmQJ99xzD6GhoZW+liidJAM2LCtLbRFo3hxeegk8PdUk4MMP1VaCTp1g1y5rRymEqO4cHBwMftZoNCX24eua8xVF0W8rqTJe4RkIjo6OjB49mujoaLKzs1m5ciXjxo2rTOjCSJIM2LCff1YHAT/zDDg63t7u7w/vvw+BgdCjB/zyS/nPvXIlfPGF+tU0OwohxO2ZBVeuXNFvKziYsCyPPfYYf/zxBx9//DG5ubk8/PDDpg5RFEPGDNiwZcugSZPiBwR7eMCcOfDGG/DQQ+q+w4eXfc6rV2HdOpgyBVJSwM0N9uwp5YDP0yAtFdwUKG2/as7ZWa3d0qMHREaCVJgVonguLi7ceeedvPXWW4SFhXH9+nVeeeUVo49v2rQpd955Jy+++CLjxo3TL8QjzEuSARsVH69+4p84seR9nJxg+nR4910YORKSk2HChOL3/fNPmDlTHYOg0YC3N/j4gFYLv/9eSiCBY0ABNEBp+1VzGRlw7RpMnQrh4fDYY/Dii5IUiEo4f756XKMYixcvZvz48bRr147GjRvzzjvv0Lt3b6OPHz9+PNu3b5cuAguSZMBGrVoFiqJ+Ei2NVgsvvKAWGZs4Uf30OmiQ4T6//aa2HkRGwmefwYABajJgHMeyd6khcnLUpOrrr+Hll+HSJfjgA0kIRDnVrav+wc6aZZnrubqq1yyHJUuW6L/X1QYoqHBlwqysLNzd3fU/N23alO3btxvsU3AMQffu3Q1+Luzy5cu0bNmSDh06lCtuUXGSDNiopUvVmgJeXmXva2cHTz8NiYkQFaVWOm3WTL1v/341Ebj3XvjmG7U1QVSMgwP06aPeOneG//xHnd756qvWjkxUKcHBanngarA2QXp6Otu2bePatWs0b9680udLTU0lJiaGDz/8kDfeeMMEEQpjSTJggy5fVvvxy/Mmo9HA88/DU0+pb/5796rjCpYsgTp1JBEwtccfh3//hU8/hf/9r/JFoEQNExxs9sWDLOGzzz5j5syZTJo0iU6dOlX6fE899RRfffUVAwYMkC4CC5N/YTZo5071a6GpvWVycYHXX1ebr2fMUKcfrlkDjzwiiYA5DB+uJm47dlg7EiGsY9KkSSQkJDBv3jyTnG/JkiVkZWWxatUqtFqtSc4pjCPJgA3auVOdPljObj4A6tdXBxMuWKBOTYyNVVsKhOl16aI+R6UOwBRCiCpAkgEbtGOHOqWwooYMUfuyX39d/bkatEbaJDs7qFfPcl2/QghhLpIM2JicHLW/XzcAsCIcHeGJJ9TzgNp9IMzD1dVyy9ILIYS5SDJgYw4ehMzMyiUDoI52r19f/b60qW+zZ6sLH9WqBb6+6rTDEydKP3f37uo5C9/69bu9z3ffQe/e6hRGjUad1VASRYH77lP3KzRjqcRrv/VW0fv69VPvK7i6asFYnZ3Vx/Xjj2/fv2RJyTM2jIlHphUKIaoDSQZszM6d6mrBjRpV7jwajfomX5YtW+DJJ9Xrbtigtkz07g1paSUf8913cOXK7dvhw2q9g8GDb++TlgZ33QVvv112DPPnl+9NNShIfRMv6PJldbXlgICi+z/+uBrn0aNqF8qTT8JXXxl/PSGEqO5kaqGN2blTTQQcTVDrJzISbi3+VaL16w1/XrJEbSHYuxfuvrv4Y+rUMfz566/V5vKCycCoUerXmJjSr79/P8ydC//8U/wbeXEeeABWr4Zt29RBfKDWZejdGy5cKLq/q6s6IBPUVoOVK+HHH40r3yyEEDWBJAM2ZscO9U3cFJydy39MUpL6tfAbfmkWLVKXWS60AFmZ0tNhxAj46KPbb9bGcHRUZ0xER99OBpYsgXfeMewiKImLC2Rnly9WIUzpwoVqUXPIIqKiokhMTCxS9VCYliQDNiQ+Hs6cUd8grSE/HyZNUt9gW7Qw7pjdu9VugkWLyn+9yZPVsQ0Vmfo4bhx07apOody7V01iHnig9GQgL0/tHjh4UK0eqJOUBAUqqQphVhcuqFVCLTXw1NVVLXhobEIQFRXF0qVLmT17Ni+99JJ++9q1axk4cGCpZYTLsmTJEiZNmkRiYqLRxyxYsKBS1xTGkWTAhhw8qH6t7HiBinrySfWN/e+/jT9m0SJo2VItnVweP/6o1vn/99/yHafTurX6OH3zDWzapHZL2Jfwav74Y3UV5uxsdWzD5MnqbAudWrVg376ix1nreRDVW3y8mgj83/8VvyKpKZ0/ry6BEB9fvtYBZ2dn3n77bSZMmEDt2rXNF6ARPD09K32OnJwcHBwcTBBN9SUDCG3IyZPqm5Wxfeem9NRT6tLGmzbdnoVQlrQ0dbzA+PHlv96ff6qtIF5e6pu47o180CB1BoAxxo1Tuxi++Ub9viQjR6pjE86dU2OeN8+wfLCdHTRsWPQmhDmFhEBEhHlvFU02evXqhb+/P7Nnzy51v2+//ZbmzZvj5OREaGgoc+fOLdd1pk+fTmRkJF9++SWhoaF4enoybNgwUlJS9PtERUUxYMAA/c+hoaHMnz/f4DyRkZFML9AsqNFoWLhwIQ8++CBubm688cYbNGzYkDlz5hgct3//fjQaDadPny5X3NWRJAM25NQpCAws+ROuOSiKmgh8/736Bh0WZvyxa9ZAVhY8+mj5r/vSS2pLyP79t28A772njgUwxogRcOiQ2qVR2lRMT0/1zb1ePVlDQAhjaLVaZs2axQcffMClS5eK3Wfv3r0MGTKEYcOGcejQIaZPn86rr75qsOKhMc6cOcPatWtZt24d69atY8uWLbxV3Nzhcpo+fToDBw7k0KFDjB8/nnHjxhFd6J9LdHQ0d999Nw0l+5duAlty8qT6hmVJTz6pjq7/4Qe1ufzqVXW7p+ftYkWjR6txFf6QsGhRycsh37ih9o3Gxqo/62oX+Psb3goLDjY+IaldW50yKK1/QpjewIEDiYyMZNq0aSwqZlDQvHnz6NmzJ6/eWlEtIiKCo0eP8u677xIVFWX0dfLz81myZAm1atUCYNSoUWzcuJE333yzUvGPGDGCsWPH6n+OioritddeY/fu3XTs2JGcnBxWrlxZpLWgppLPSTbk5Enjm+hNZeFCdQBd9+5q94TutmrV7X0uXFDfdAs6cUIdW1BSF8GPP0KbNrcLEQ0bpv78ySemjd/Lq/yzGIQQxnn77bdZunQpx44dK3LfsWPH6KKbznNLly5dOHXqFHl5eUZfIzQ0VJ8IAAQEBHD9+vWKB31L+/btDX4ODAykX79+LF68GICffvqJrKwsBhecE12DScuAjcjNhbNn1Up8lmTMIN3Nm4tua9y49GOjotSbJWIpqHClw7L2Ly1OGcAsarq7776bPn368PLLL5fr0355FB7Yp9FoyM/PL3F/Ozu7IrMLcnJyiuznVsynhMcee4xRo0bx3nvvER0dzdChQ3F1da1g5NWLJAM24vx5NSEICrJ2JEIIcdtbb71FZGQkjRs3NtjetGlTtm3bZrBt27ZtREREmHX5YR8fH64UaKpMTk7m3LlzRh17//334+bmxsKFC1m/fj1bt241V5hVjnQT2IiTJ9Wvlh4zIIQQpWnZsiUjR47k/fffN9j+3HPPsXHjRmbOnMnJkydZunQpH374IVOnTjVrPD169ODLL7/kr7/+4tChQ4wZM8bo5EOr1RIVFcXLL79Mo0aN6NSpk1ljrUqkZcBGnDqlVtbz9TXdOXWzEjIzTXdOYSgjA5ycrB2FqIrOn68615gxYwarCg4kAtq2bcvq1at57bXXmDlzJgEBAcyYMcNs3Qk6L7/8MufOneOBBx7A09OTmTNnGt0yADB+/HhmzZplMLhQgEYxorRTcnIynp6eJCUl4eHhYYm4apynnoJff61YJb+SnDqlVtrbtk2t9CdMS1HUAZ9jxqiFXYQoKDMzk3PnzhEWFoZzgdrgtl6B0NYMHz4crVbL8uXLTXK+v/76i549e3Lx4kX8/PxMck5rK+m1Bsa/f0vLgI04eVKtMWBKoaHqdMFffpFkwBwOHlSnTnbtau1IRFUSHKy+OcvaBKXLzc3l5MmT7NixgwkTJlT6fFlZWcTFxTF9+nQGDx5cbRIBU5FkwEacPAmm7r5ycFDXGVi1CmbOLN8ywaJsa9aotQ569rR2JKKqCQ6umm/QlnT48GE6d+7MPffcw8SJEyt9vq+++orx48cTGRnJsmXLTBBh9SIDCG1AVpbadGiOGgO9e8Pp0zBlikyVM6WNG9Wll4cNM81y00IIQ5GRkaSnp/Pzzz+bZH2EqKgo8vLy2Lt3L/VkpHYRkgzYgDNnbvc/m1qbNvDsszB/vlo2ePt2dXVCUTHnz8O776orJHbvrq5zIIQQVZ10E9gA3RoZ5qo+OGCA2mWwZIlaetjPT63n7+kptfqNoSjqrIHLl+HAAXX2wJAh8NlnUGisjhBCVEmSDNiAixfVaYDmXCm0Xz/o21ddonjaNNiyRd3u7FzG4MLt29R+DCcn6NyllB2rN2dnaNcOXnhBbRWQSTVCiOpEkgEbcPky+PiY/1O6VgutW0OPHuraAXl5auvAb7+VclD9oWqAvvXgt+JXLxNCCFG1SSOxDbh8ufiV/8yldWs1ERBCCCFAkgGbYOlkoFUry11LCCGE7ZNkwAZcvqwWBrGU2rXVLgMhhDCF0NBQ5s+fb+0wRCVIMmADLN0yAOrsAiGEKCwqKooBAwaU65g9e/bwn//8xzwBlaF79+5oNBreeuutIvf169cPjUbD9OnTLR9YFSPJgJWlpkJKimVbBuB2MiA1B4QQleXj44Orq2uFj1cUhdzc3AofHxQUxJIlSwy2Xb58mY0bNxIQEFDh89YkkgxYWWys+tXHx7LX1SUDWVmWva4Qomrp3r07zzzzDC+88AJ16tTB39+/yCftgt0EMTExaDQa9u/fr78/MTERjUbD5s2bAdi8eTMajYZff/2Vdu3a4eTkxPLly7Gzs+Off/4xOPf8+fMJCQkhv5RPLg888ADx8fFs27ZNv23p0qX07t0b30JLwWZlZTF16lTq1auHm5sbd9xxhz4ugISEBIYPH069evVwdXWlZcuWfPXVV+V+TKoamVpoZZcvq18t3TKgm8ZYrmQgPx927FBr8SYkyNrIOi4u6trTvXurxQhkEQhRzSxdupQpU6awa9cuduzYQVRUFF26dOHee++t1Hlfeukl5syZQ4MGDahduza9evUiOjqa9u3b6/eJjo4mKioKu1LmXjs6OjJy5Eiio6Pp0kWth7JkyRLeeeedIm/STz31FEePHuXrr78mMDCQ77//nr59+3Lo0CEaNWpEZmYm7dq148UXX8TDw4Off/6ZUaNGER4eTseOHc3+mFiLJANWpksGLD1mQCc728gdk5PVlVUuX4Y6dSAgQH0TrOkURV2LNjYW/vc/CAuDJ59UF4OQpECUZN4842pZt22rFgUp6MEHYd++so+dMkW9mUCrVq2YNm0aAI0aNeLDDz9k48aNlX7jmzFjhsE5HnvsMSZOnMi8efNwcnJi3759HDp0iB9++KHMc40bN46uXbuyYMEC9u7dS1JSEg888IBBMnDhwgWio6O5cOECgbeWiZ06dSrr168nOjqaWbNmUa9ePaZOnao/5umnn+a3335j9erVBsmAuR4Ta5FkwMouXwZ3d8u/r06Zon7IX7dOHbNQq1YJO378Mbz3HmzdCqNHqyvzdO4sdYwLy82FzZvVes9Tp0JcHMyeLQmBKF5y8u1PAqUJCiq6LS7OuGOTk8sfVwlaFZqPHBAQwPXr1yt93oItAAADBgzgySef5Pvvv2fYsGEsWbKEe+65h9DQ0DLP1bp1axo1asQ333zDpk2bGDVqFPb2hm9xhw4dIi8vj4iICIPtWVlZeN/6RJaXl8esWbNYvXo1ly9fJjs7m6ysrCJjIsz1mFiLJANWFhtr+fECoC6X7O8PP/0Ee/aoVQmLtWuXmggsWwYjR1o0xirF3h569VJvrVrB5MkQGAjPPGPtyIQt8vAAY1bOK+6fg4+PcceasGa2Q6HpRxqNpsQ+fF1zvlJgmdScnJxi93VzczP42dHRkdGjRxMdHc3DDz/MypUrWbBggdFxjhs3jo8++oijR4+ye/fuIvenpqai1WrZu3cv2kLzq93d3QF49913WbBgAfPnz6dly5a4ubkxadIksgs1o5bnMakKJBmwMmtMK9QJCVFbJXbuLCEZyMuDRYvg6aclESiPSZPUB/XzzyUZEMWrTBN+4W4DG+NzK4G5cuUKbdq0ATAYTFiWxx57jBYtWvDxxx+Tm5vLww8/bPSxI0aMYOrUqbRu3ZpmzZoVub9Nmzbk5eVx/fp1unbtWuw5tm3bxkMPPcSjjz4KQH5+PidPniz2fNWJtPVa2aVL1ksG7OygSRO1u6BYf/0F166pXQOifIYNU1eFOn7c2pEIYVEuLi7ceeedvPXWWxw7dowtW7bwyiuvGH1806ZNufPOO3nxxRcZPnw4LuXoQ61duzZXrlxh48aNxd4fERHByJEjGT16NN999x3nzp1j9+7dzJ49m59//hlQ+/83bNjA9u3bOXbsGBMmTODatWtGx1BVSTJgZbGxlp9JAHDiBBw5oi5nvGOHOg6uiI0b1SbJO+6weHxVXt++4OioPoZC1DCLFy8mNzeXdu3aMWnSJN54441yHT9+/Hiys7MZN25cua/t5eVVpPuhoOjoaEaPHs1zzz1H48aNGTBgAHv27CE4OBiAV155hbZt29KnTx+6d++Ov79/uYswVUUaRSn2bcBAcnIynp6eJCUl4SFrt5pMfr66MvCTT4KlX2uDB0N8vNqtmJwMp05Bw4aFdnriCfj7bzh0yLLBVReBgTBhgrpmtKhxMjMzOXfuHGFhYTg7O1s7HLMKCAhg5syZPPbYYyY538yZM1mzZg0HDx40yfmqu9Jea8a+f0vLgBXFxamD0K3RMqCjGwNTbFdBZiZUoqpYjefiAhkZ1o5CCLNJT09nw4YNXLt2jebNm1f6fKmpqRw+fJgPP/yQp59+2gQRCmNJMmBF1io4VJBGo5YP2LmzlB1Ks3ChOnrew0O9deoEv/5a+jFHjsCgQRAaqp6/uAVOdPcVvj35ZMnnnT5d3adv36L3vfuuel/37kX312jU2QChoeosgNRU9f6YGPW+4gY/de+uDhQsjUwrFNXcZ599xrBhw5g0aRKdOnWq9Pmeeuop2rVrR/fu3SvURSAqTmYTWJGuFLE1kwGAZs1g+/YKHly/Prz1FjRqpA48WLoUHnoI/v0XSvqkkJ4ODRqofRWTJxe/z5496mwGncOH4d571WNKExAAmzapIzPr17+9ffFiNesprHlz+OMPtYlm2zYYN06N79NPS7+OEIJJkyYxqaykuByWLFlSZI0BYRnSMmBFly+rSwnXrm3dOJo2VYcFVKhFu39/uP9+NRmIiIA337w9X7EkHTqon9SHDVMHTRTHx0cthKC7rVsH4eHQrVvp8ejKAi9denvb9u3qAIl+/Yrub2+vnr9+fRg6VJ1CaeNTt4QQwtQkGbCiq1fBy0tNCKypYUP1Q/jRo5U8UV4efP01pKWp3QWmkp0Ny5ern9qNaXofNw4KfrpYvFh9k3d0LPtYF5dy1GgWQojqQZIBK4qLU5MBawsLU99jKzxw99AhtTXAyQkmToTvv1f7Hkxl7VpITISoKOP2f+ABdYrE1q1qYrJ6tZoglGXvXrWccOEKTJ07q79fwdtff5XzlxBCCNslYwasKC4OPD2tHYX6YbhevUokA40bq4PskpLgm29gzBjYssV0CcGiRXDffepUPWM4OMCjj0J0NJw9q3ZfFKojrqdLZPLy1BaBfv3gww8N91m1Su1LKUgqMgohqhFJBqzo+nXbSAZAHc9X4WTA0fF2kYJ27dTBfwsWmGYQ3vnz6gC/774r33HjxqnFkg4fLr1VoHFjdYyAvb2abBTXlRAUVLQIg6zYKISoRqSbwIri422jmwDUZODAgRIqEZZXfj5kZZngRKif7n19ix/8V5rmzdXb4cMwYkTJ++kSmdBQ48YUCCFENSQtA1YUFweFVvC0mKVL1Td+3Xi8Bg0gIUFdisDfv8CO9mW8RF5+WW3CDw5W10JeuVJdyve3327vM3q02g8xe7b6c3b27dGK2dnqtIr9+9Xm+oKfwPPz1WRgzJiy4yjOn39CTo7tZFxCiHKLiooiMTGRtWvXWjuUak1aBqxEUdQ3X2t1E7i6gpvb7QKDDRqoX8vdVXD9uvpm37gx9OypdhH89ptaE0DnwgW4cuX2z7Gx0KaNertyBebMUb8vXMr0jz/UYytafMTNTRIBIcopKioKjUbDW2+9ZbB97dq1aCpZSGvJkiV4lfNvcsGCBVarPTB9+nQ0Gg19iylk9u6776LRaOhesJBZFSYtA1aSmKjWubGV96qAALUb/OBBdZq+0RYtKnufzZsNfw4NNa4/onfv8vVbTJ+u3kpSuNJhWfuXFmfh30mIasTZ2Zm3336bCRMmUNvKhVA8TfCJKScnBwdd7fVyCggIYNOmTVy6dIn6BQqZLV68WL+4UXUgLQNWEhenfrWVZMDOTm0dkDWJhBC9evXC39+f2bquvRJ8++23NG/eHCcnJ0JDQ5k7d265rjN9+nQiIyP58ssvCQ0NxdPTk2HDhpGSkqLfJyoqymDVwNDQUOYXSuwjIyOZXiCx12g0LFy4kAcffBA3NzfeeOMNGjZsyJw5cwyO279/PxqNhtOnT5cYo6+vL71792ZpgUJm27dvJz4+nn7FjGX64osvaNq0Kc7OzjRp0oSPP/7Y4P4XX3yRiIgIXF1dadCgAa+++io5OTnlekzMQZIBK9ElA9bqJli9Wq3Ls3r17W1hYeogQgP5+ZYMSwhhA7RaLbNmzeKDDz7g0qVLxe6zd+9ehgwZwrBhwzh06BDTp0/n1VdfLXeT/pkzZ1i7di3r1q1j3bp1bNmypUgXRUVMnz6dgQMHcujQIcaPH8+4ceOIjo422Cc6Opq7776bhkWWbDU0btw4g99r8eLFjBw5EsdCg45XrFjBa6+9xptvvsmxY8eYNWsWr776qkEiUatWLZYsWcLRo0dZsGABn3/+Oe+9957Becz1mJRGkgEriY9Xv1qrZWDNGnUQ4Zo1t7c1aADHjqlj7gC1iFBamlXiqxYyMkoutyxqtHnz1ArYZd0efLDosQ8+aNyx8+ZVLsaBAwcSGRnJtBKW4J43bx49e/bk1VdfJSIigqioKJ566inefffdcl0nPz+fJUuW0KJFC7p27cqoUaPYuHFj5YIHRowYwdixY2nQoAHBwcFERUVx4sQJdu/eDahdBytXrjRqQaQHHniA5ORktm7dSlpaGqtXry72uGnTpjF37lwefvhhwsLCePjhh5k8eTKfFphm/corr9C5c2dCQ0Pp378/U6dOZXXBT2WY7zEpjYwZsJK4OHUkfynLS1tcWJg6uP/UqVv1gurUUWsmF5x2IIyTk6OOEPX2tnYkwgYlJ99etbQ0QUFFt8XFGXdscnL54yrs7bffpkePHkydOrXIfceOHeOhhx4y2NalSxfmz59PXl4eWiPrrIeGhlKrVi39zwEBAVy/fr1ygQPtC03VCgwMpF+/fixevJiOHTvy008/kZWVxeCyFj8DHBwcePTRR4mOjubs2bNERETQqlAhs7S0NM6cOcP48eN5/PHH9dtzc3MNxj2sWrWK999/nzNnzpCamkpubi4ehd4IzPWYlEaSASuJi1MTAWuvS1CQbizMyZO3koFu3dTpgAcOQGSkNUOrejZuVGstlLWwkqiRPDzU2bZl8fEpfpsxx5rig8bdd99Nnz59ePnll4kythx4ORUe2KfRaMgvpXvSzs4OpdDA3oJ97jpubm5Ftj322GOMGjWK9957j+joaIYOHYqrbkpVGcaNG8cdd9zB4cOHi20VSL219Pnnn3/OHXfcYXCfLjHasWMHI0eO5PXXX6dPnz54enry9ddfFxlrUd7HxBQkGbASW1mXoKDatdWp/idO3NrQo4da2nfVKkkGymvVKnUlx5LKIIsabcoU9VYRll5U86233iIyMpLGjRsbbG/atCnbtm0z2LZt2zYiIiKMbhWoCB8fH64UmKqcnJzMuXPnjDr2/vvvx83NjYULF7J+/Xq2bt1q9HWbN29O8+bNOXjwICOKKWTm5+dHYGAgZ8+eZWQJ5cq3b99OSEgI//vf//Tbzp8/b3QM5iTJgJXYyroEBWk0arPkyZO3Njg4qFX55syBrl3VpYpF2ZYvVwdkzJwp3SuiymvZsiUjR47k/fffN9j+3HPP0aFDB2bOnMnQoUPZsWMHH374YZHR86bWo0cPlixZQv/+/fHy8uK1114zOvnQarVERUXx8ssv06hRIzqVc3XVP//8k5ycnBJrJbz++us888wzeHp60rdvX7Kysvjnn3+4efMmU6ZMoVGjRly4cIGvv/6aDh068PPPP/P999+XKwZzkQGEVmKLyQCozY/6lgFQg8zLgwED4PXXC90p9BRF7U558UW1CNPYsWp1RiGqgRkzZhRppm7bti2rV6/m66+/pkWLFrz22mvMmDHDbN0JOi+//DLdunXjgQceoF+/fgwYMIDw8HCjjx8/fjzZ2dmMHTu23Nd2c3MrtWjSY489xhdffEF0dDQtW7akW7duLFmyhLCwMAAefPBBJk+ezFNPPUVkZCTbt2/n1VdfLXcc5qBRCne+FCM5ORlPT0+SkpKKDHQQFdO2rfrG+9xz1rn+4MHqjIa6dQ1nFCxdCuvW3Z76SP366mglNzf1U25qqroKYGCgWqWopn/yVRR1xsXFi3DunNrX8vjj6lgLO8m1a7LMzEzOnTtHWFgYzs7O1g6nyho+fDharZbly5eb5Hx//fUXPXv25OLFi/j5+ZnknNZW2mvN2Pdv6Sawkvh4dR0dWxMUpMZ286b6vqbn5aVOM1i/Xq35n5AAmZnWCtO21KmjllPu00ctySwLHglRabm5uZw8eZIdO3YwYcKESp8vKyuLuLg4pk+fzuDBg6tNImAqkgxYSXy8bXYT6KYynTyprgBswMUFBg5Ub0IIYUaHDx+mc+fO3HPPPUycOLHS5/vqq68YP348kZGRLFu2zAQRVi+SDFhBWppaj8aayUBEhLoycOHuL13pbX0y0LatmiEUN8dJCCHMJDIykvT0dJOdLyoqyuzjGaoySQaswBbWJXjzzeK3u7ioSYJ+nKCl5zEJIYSwOBnhZAW2kAyUpl69AtMLhRCVYsQYbSEqxRSvMUkGrMDWk4GgIJlBKERl6arImbKpW4ji6F5jFV2mGaSbwCp0ixTZ4gBCUMcNbNigLlgos+OEqBitVouXl5e+pryrqyuamj4VV5iUoiikp6dz/fp1vLy8KlX5UZIBK4iLA1dX685A+9//IDFRbZ0oPH6gfn11gOPlyxD05INqwD4+Mn5AiHLy9/cHMPsiM6Jm8/Ly0r/WKkqSASuwhXUJTp68XXSoMN2CRSdOQNC+fWpWYMzKKEIIAxqNhoCAAHx9fYtdTEeIynJwcDDJWhCSDFiBrdYY0PH3B3t7tcZQL2sHI0Q1oNVqzbp4jxCVJT3CVnDzpro6oK3SatWE4PRpa0cihBDCEiQZsIIbN2w7GQC1V+DUKWtHIYQQwhIkGbCCmzehVi1rR1E6qTUghBA1hyQDVmDr3QSgJgPnzoGUSxFCiOpPkgErSEy0/ZaB+vUhOxvy8qwdiRBCCHOTZMDC8vIgOdn2kwHdTMLcXOvGIYQQwvwkGbCwpCT1q613E+imF0oyIIQQ1Z/UGbCwmzfVr9ZuGRg8GNLT1UqIxdFqISAANoZN4aF7ksHDw7IBCiGEsBhJBizMVpKBIUPK3qdePfjCYwoPTTd7OEIIIaxIugkszFaSAWPI9EIhhKgZJBmwMF0yYOtjBuD29EIZNyCEENWbJAMWdvOmuixwSX31lpKeDmlp6teS1KsHTjkpXDqaDCkplgtOCCGERcmYAQvTFRyys3IaNmbM7VUL16wpfp/69eEYTanf+taqhZcuWTZIIYQQFiEtAxZmlVLE2dkQGwupqeU6zM/PTPEIIYSwKdIyYGEWK0WsKLB9O6xeDYcPQ/6tUoLhDeHBB4H+gKbUU2i16g2pQiiEENWaJAMWZpFkIDUV3nkH/toKDcLh4YfV/oCUFDh0CN6bB/ZdgdplnkqSASGEqP4kGbAwsycDN27A1Klw7RqMiYLISMP727eHmBh4P1/9OS+X0l4GWq2Z4hRCCGEzZMyAhd24YcYxA2lpaiJw4wY8/XTRREAnNBRq3cpIkpLU/UugSwZk9UIhhKi+JBmwMLMNIMzPh5kz4coVmDBBXVygNJpb4wUUBV5/vcTlCe1vJQNSa0AIIaovSQYszGzJwPffw66dMGqUuqiAsVxd4eBB+OqrYu/WtQzk5pggRiGEEDZJkgELys9Xly82+ZiBCxfg00/h7m7QrFn5jrW3h549YenSYusIaG69QnKkZUAIIaotSQYsKDlZbZU3acuAosCCBeDpCf36VewcvXurqxIuWKCerwDd5EPpJhBCiOpLZhNYkFkWKdq6FfbthcceB0dHow97Y/w5cvM02GsV9bgBA2DxItizBzp21O936I0fWPZFNqnZjiw2YdhCCCFshyQDFmTyRYry8uDzz6FJU2jevFyHNg7KMNzQogWEhsGiRdChg36AYWrjdqS0gJ9+MlHMQgghbI50E1iQyVsG1q+Hy5cq3j1QkEajnufkCdi2zeCuoCBISCh1BqIQQogqTJIBCzJpMpCXB19+Ca0j1RWFTKFhQ7ViYaGZBUFB6teTJ01zGSGEELZFkgELunlT/QDu5maCk23ZAteuQq9eFTp8xxEPNu/3ZMcRD8M7uneHo0fU9QwA7x3raH9uDf1YJ8mAEEJUUzJmwIJMtnyxosDKldC4SYVbBeatqU98kiN1PbNZ0/zo7TuaNwdfP3Vd4xYtiJg3Eaf4y3xmV4+PTsgSxkIIUR1Jy4AFmazg0KFDcOY03HOPCU5WiJ0ddOkCf/+tDhS4RauFEydMfzkhhBDWJ8mABZksGVi7Vv303qiRCU5WjA4d1Hf/X37Rb9Jq4dgx81xOCCGEdUkyYEE3b5pgvMCNG2ptgc6dTdDfUAIXF2jTxmA+ob0WTp2S4kNCCFEdSTJgQSZZvvj339VRiB06mCSmEt15J8Rdhxx1UQKtvfrtuXPmvawQQgjLk2TAgpKTK9kyoCjw66/QsqW6wJA5hYZCXR/IzARuL1h0/Lh5LyuEEMLyJBmwoKSkSiYDJ07AhfPmbxUAtfWhfXvIygLUHglXV0kGhBCiOpJkwIIq3TLw22/g6QUREaYKqXTt2wPqwkUaIDhYkgEhhKiOJBmwoJSUSrTu5+XBpk0QGXm7zd7cvL3VwQK3BAXJjAIhhKiOJBmwkLw8NRmocMvA/v2QlKiO8jcBF6d8XJ3ycHHKL3W/PBd3ctGS5+xGcLCaDBRa5VgIIUQVJxUILSQ1Vf1a4ZaBTZvAu67aVm8Cy142rr1/9+SvYOYMGPMKQQ6QmAhxceDra5IwhBBC2ABpGbCQ5GT1a4VaBvLy4K+/oHVr/dLCFlOnDoSEwtathISom2TcgBBCVC+SDFhIpZKBw4chOQlatTJpTEZr0QL27CHQOwutVpIBIYSobiQZsJCkJPVrhboJ/v5bnUWgW0vY0lq0gMwMHA/vIzBQkgEhhKhuZMyAhehaBspdgVBR1C6C5s1NWn74kx8DSUnXUss1j4kPxpa4X4Mf38MhPZkclyuc/ftvgoI6cfRoibsLIYSogqRlwEJ0yUC5WwbOnoVrV9VP5ya0cZ8Xv+zyZuM+r1L389u3noBda/HLvwrbtxMcpMj0QiGEqGYkGbAQXTeBi0s5D9y1CxwcoWFDk8dULvb2kHiTEOdrXLgAaWnWDUcIIYTpSDJgIbrqg+Vu6d+5Exo3BgcHs8RlNHt7cHYhOGEfoFZGFkIIUT1IMmAhFSpFnJoKR45AkyZmiancGjcm+OQfgFQiFEKI6kSSAQupUDKwdy/k50HTpmaJqdyaNsX95L/4eOdLMiCEENWIJAMWkpRUgcGDu3eDf4Ba+McWNGkCKAR7JEoyIIQQ1YgkAxZS7pYBRYF//rHcCoXG8PSEgECC88/J9EIhhKhGJBmwkKSkcs4kiI2F69dsKxkAiIggOH4fp08r5ORYOxghhBCmIMmAhSQllbNlYO9esNNCeLjZYqqQxo0JSTtCbq6GM2esHYwQQghTkAqEFpKcjH6hH6Ps3ase4OxslnjubJZMSro9tVxzS90voVlX7NOTyHX1VDeEhxOi/R7y1BkFtjLRQQghRMVJMmAhycnlKEWcnw///gt33mm2eJ4bcsmo/U4OecVwg6MjtUM9qXU+nWPHXBk40AzBCSGEsCjpJrCQlJRyzCaIiYGUZGjUyJwhVZgmohEh+TEcO5Jv7VCEEEKYgCQDFpCXp9YPMjoZ2L8ftPbl7FewoEaNCMo/x9G9GdaORAghhAlIMmABKSnqV6O7Cfbvh9BQcHQ0U0SVFBxMiDaWE2cdyJfGASGEqPJkzIAFlGvFwvx8OHDArOMFACbMjeBGij11auXy6XMnS9yv3dwROKYkkF3Lm73PrVQ3arUEB+SQdsmRy5chKMisoQohhDAzaRmwAF0yYNTUwgsXIDkJGjQwa0w3UuyJT3LkRkrp+aBjSgJOSddxTEkw2B4c4QTAsUOlz0YQQghh+yQZsADd8sVGtQwcOqTWFwgNNWdIlebf0gdHsjj2+0VrhyKEEKKSJBmwgHK1DBw6BPXqgZOTWWOqLG1QPYI1lzi246a1QxFCCFFJkgxYQLmSgYMHISzMrPGYhFZLkPtNjp50sHYkQgghKkmSAQtISgKNxohignFxcO1q1UgGgBD/LI4lBiCLFAghRNUmyYAF6FYstCvr0T58WP1q5sGDphIc7kA8dUnYfMjaoQghhKgESQYswOjli48cgbo+4OFh9phMIaSZ+ksd+6HkqYlCCCFsnyQDFmB0MnD0KAQHmz0eU6nvn4sd+RzfFm/tUIQQQlSCJAMWkJRkxLTC7Gw4dcp2SxAXw9FBIdA1kWPHNUgpQiGEqLqkAqEFJCcbkQycPg25ORarLzChfyxZOXY4OZT+Jn6m/yS0ORnkObgUe3+QTwbHzofB8ePQrJk5QhVCCGFmkgxYgFEtA0eOgIMjBAZaJKZe7RKN2u96u/tKvT84RMPO883gr98kGRBCiCpKugksICnJiDEDR4+qxYbsq1Z+FhKYywWCSd+yx9qhCCGEqCBJBizAqG6CY8eq1OBBnWC/TBTsOLUl1tqhCCGEqCBJBiwgJaWMloHERLXYkAUHD1647sS5K85cuF562WOX6zG4XjmDy/WYYu8P8skC4HhsLbhyxdRhCiGEsICq1SZdRaWkgEvx4+9Ux4+rXy24FvBzH4cTn+RIXc9s1kw/WuJ+kR9PwCnpOlmevuyY/luR+z3c8qjjnsXx1CawbRs88og5wxZCCGEG0jJgZvn5kJpaRjfB8ePg5g5161osLlMK8svmuEsbNRkQQghR5UgyYGZpaerXMpOBoCB1AYMqKMg3i2PalpIMCCFEFSXJgJmlpKhfS0wGFEUdPGjBLgJTC/bN4mRmEPn79kNGhrXDEUIIUU6SDJhZmcnA9euQnFS1kwG/TDJyHbmYFwD//GPtcIQQQpSTJANmlpysfi0xGTh5a5GfqpwM+N6aUeAUCdu3WzcYIYQQ5SbJgJnpWgZKnE1w8iR4eIKnp8ViMjXf2tk4OeRz3KerJANCCFEFSTJgZmV2E5w4oVYerKKDBwG0dhDkm8lx11szChTF2iEJIYQoB0kGzEyXDBRbdEhR1GSgfn2LxmQO9X2yOJbTCBIS4MwZa4cjhBCiHCQZMLPkZHW5AQeHYu6Mi6vygwd1gn2zOH7TV/1hxw7rBiOEEKJcpAKhmaWkqF0ExfYCWHHw4CeTT5KvaLDTlN6kv3fycjRKPoqm9Lwx2C+Ta4nOJAa1xGvHDhg1ypThCiGEMCNJBsxMlwwU6/RptfKgFQYPenvmGrVftqePUfvpZhScqNeDO3ZsqXBcQgghLE+6Ccys1GTg1KkqP3hQp/6tBYuOed4Jhw7dLr0ohBDC5kkyYGalJgMnT6rJQDXg4pRPQJ0sjts1g7w82LPH2iEJIYQwknQTmFlycgk1BpKSID7OasnAT9u9yci2w8Uxn/6dE0rcL2D7t2iz08lzdOVK50GlnjPIN4vjSQHq1ImdO6F7dxNHLYQQwhwkGTCzEpOB06fVr1aaVrjsdz/9EsalJQOhv3+mX8K4rGQg2DeTA2fcoUkTNRkQQghRJUg3gZmV2E1w6hQ4OFbZZYuLE+SXxdkrLmRHtFCnF0rxISGEqBIkGTCzlJQSWgbOnFG7CLRai8dkLsG+meTlazjt21ldgOn8eWuHJIQQwgiSDJhZSkoJ1QdPnYLAQIvHY04hfrcWLHKOVDdI8SEhhKgSJBkws+TkYroJsrPh4sVqlwx4uefi4ZrLsfi66lgISQaEEKJKkGTAjBQFUlOL6SaIiYH8vGqXDGg0aiXC4xdcoWlTGUQohBBVhCQDZpSVBbm5xbQMnDkDaKpdMgDq9MJj528lA/v3Q2amtUMSQghRBkkGzKjE5YvPnFFnETg5WTwmcwv2zeTERVeUps0gJwf+/dfaIQkhhCiDJANmVGIycPp0tWwVAHUQYWqGPZc8mqnJjnQVCCGEzZOiQ2aUnKx+NUgGFEVtGejSxSox6QT5ZOHmnEedWqUvWJTuE0KuszvZtbyNOm+wn9otcOyyB0EREZIMCCFEFSDJgBkV2zKQkACpKVZfk2Dek2eM2u/Ak5+V67z+dbJxcsjn6Hk3ejdtCtu3VyQ8IYQQFiTdBGakSwYMZhOcPat+rabdBFo7CPHL5EiMKzRrBpcuQWystcMSQghRCkkGzKjYloEzZ8DJGWrXtkpMlqAmA25qMgCwa5d1AxJCCFEqSQbMqMSWgYAAsKu+D32Iv5oMKHV9wNdXxg0IIYSNkzEDZqSrPmjwvn/mjJoMWNkbXwaTlGaPp1sur4y6UOJ+Tb/8PxzSEslx8+LYqFlGnTvUP5PkNHuuJDgS2LSpVCIUQggbJ8mAGRVZsTAnBy5cgLZtrRaTzoEz7voljEvjdWavfgljY4XemlFwJMaNwGbNYOlStfqSvbzchBDCFlXftmobUCQZuHQJ8nJtomXAnPy91RkFR2Lc1EqE6elw6JC1wxJCCFECSQbMqMjyxefOqV+reTKgtVPrDRyNcYWICLVFQMYNCCGEzZJkwIyKtAycPQtetYspSVj9hPhlcjjGTa1C2LChJANCCGHDJBkwo+TkYloG/P2tFo8lhdxqGVAU1K4CGUQohBA2S5IBM9LNJtCzkZkElhDqn0lSmgNXEhzVZODUKbX6ohBCCJsjyYAZGXQTZGTAtas1pmUgzP/2jAKaN1c3SleBEELYJEkGzMggGYiJUb/WkGTA3zsbZ8c8Dp9zU1tDateWZEAIIWyUJANmZJAMnD0LaGpMMqBbo+DwOTfQaNTSxDJuQAghbJJUgTEjg6mFMTHg4wOOjtYMSe+BOxNIzdTi7pxX6n6xdz6MfWYKuc61yn2NsIBMDp51U39o2hRWrYK8PNBqKxKyEEIIM5FkwExyc9VhAm633gs5exb8/KwaU0Fj+l4zar/zfSdU+BphAZlsPeBFfj7YNW+uZkdHj0LLlhU+pxBCCNOTbgIzKbJiYUxMjeki0GkQkEF6lpazV1ygcWO1RUC6CoQQwuZIMmAmycnqV1fXWz/cSKgx0wp1dDMKDp11U/tLwsMlGRBCCBskyYCZ6FoG3NyocTMJdOp45OLplqMmA6COG9i+3bpBCSGEKELGDJiJQcvAwRiw04Kv8Sv/mdvg6c30qxaumX60xP06Te+jX7Vwx/TfynUNjQYaBNyaUQBqvYEffoD4eKhbtzLhCyGEMCFpGTATXTLg5oZahtjHp0Yu4RsakMnBs+7qDy1aqF+l3oAQQtgUSQbMRJcMuLhQo9YkKKxBQAanL7uQmW2nPgbe3tJVIIQQNkaSATPRjRmo6clAWEAmefkajp13VfsNmjeHbdusHZYQQogCJBkwE90iRdqUREhOqrnJQMEZBaBWItyzB3JyrBiVEEKIgiQZMJPk5Jo9k0DH1TmfAO8sDukGEbZooVZj2r/fqnEJIYS4TZIBM9EvXxwTo84k8PGxdkhW0yAg4/YgwkaN1JLM0lUghBA2Q5IBM9EvUhQTo04prMH1+MMCMm93Ezg6qtUIJRkQQgibIcmAmRi0DNjQmgTWEBaQyZUEJxKSbk2tbN4c/v4bFMW6gQkhhAAkGTAbfTJQg2cS6DQIyADg0LkC9QauXr09nkIIIYRV1bwqOBaSlAS1nLJtdibB/x69QHauBkf70j+dH3v0TTS52Sj2FV96OcgnCwf7fA6ddaN7ZOLt4kPbtkFYWIXPK4QQwjQkGTCT5GTw80pSf7DBZCCyYapR+yU2bF/pa2m1EOqfyUHduAFPTwgNVbsKHn200ucXQghROdJNYCYpKeCadVOdSSB1+NVk4Iz77Q3Nm8PWrdYLSAghhJ4kA2aSnAxuaddr7JoEhTUIyOBIjBv5+bc2tGoFx45BQoJV4xJCCCHJgFkoyq2WgaQrNtlFALD/tDu7j9di/2n3UvfzOv0PtY9vx+v0P5W6XoPATNIytcRcdVY3tGypfpUphkIIYXXykdUMMjMhNxdcb16G5rY5rfDN5cFGLWHcdPn/KryEcUG6GQUHz7rTIDBTTZJ8feGvv+DBByt8XiGEEJUnLQNmoFukyDX9eo2vMaDj7ZGLp1vu7eJDGo06q0DGDQghhNVJMmAGuuWL3UiHgADrBmMjNBpdWWK32xtbt4a9eyHVuJkNQgghzEOSATPQJQOumkyZSVBAg8BMwzEKrVpBXh7s2GG9oIQQQkgyYA76ZKCOMzg4WDcYG9IgMIMzsS6kZdx62YWEgJeXdBUIIYSVSTJgBvpkwMet9B1rmPDADBRFw+FzBcYNtGoFmzdbNS4hhKjpJBkwA90AQrcAD+sGYmPC/DOx0ygcOFOoq2D3bsjIsF5gQghRw0kyYAbJV9OxJwfHQBkvUJCjg0KwX6ZhMtCmDWRny7gBIYSwIkkGzCD5TBxupKEJsM2CQ9bUIKBQMhAaqq5VIF0FQghhNZIMmEHy+Zu4kq6WIhYGwgMzOHTWDUW3WKKdnTrF8M8/rRqXEELUZFKB0AxSYlNw1XqBY8WX/TW30qoOFlSZqoPFCa+XQXK6PTFXnQkLyFQ3RkbCwoWQng6uria9nhBCiLJJy4AZJF/PwNUp19ph2KQGtxKAg2cKzLSIjIScHFmnQAghrESSATNIvpGHq4tS9o41UF3PHDzdcoqOG6hTBzZutFpcQghRk0kyYGrJySRlOuDiJg9tcTQaCC9ciVCjUWcV/PGH9QITQogaTMYMmNrx4yTjiZ+HbT+0S9f7kZqpxd05jzF9r5W4X8j6T7HPTCHXuRbn+04wybUb1c9gx5FCNRjatoU5c+DmTahd2yTXEUIIYRz5+GpqR4+SQi1cvWx38CDAup3efLPFl3U7vUvdL3DndwRtWUHgzu9Mdu2IoHQuXHcmLrFAqea2bUFRZIqhEEJYgSQDpnb0KMl2tXGTSsQlahKUDsA/J2rd3ujvD/XrS1eBEEJYgSQDpnarZcDFOd/akdisAO9sPN1yDZMBgHbt4DfTTmUUQghRNkkGTCz/8FFS811xc86zdig2S6OBiPrp7DleKBlo3x7OnIGzZ60TmBBC1FCSDJhSWhqp5+MBcHWSloHSRASls6dwy0CbNqDVwoYN1glKCCFqKEkGTOn4cZLwBMBVWgZK1Tgog6s3nIiNLzDQ0s0NmjeXrgIhhLAwSQZM6ehRfTJQy0WSgdI0CVYHERbbVfDHH2pFQiGEEBYhyYApHT1KYp1wANwkGShVXc8c6njk8M/JQslAx46QkiKliYUQwoIkGTClI0dI9FaTAXfpJihViYMIGzVSSxP/+qt1AhNCiBpIkgFTOnKEpNqhALjbeMtA6/BU2jdOpnV4aqn7JYa340bjTiSGtzN5DE1D0tl51IP8gmMt7eygQwf45ReTX08IIUTxbLtmblWSkQHnzpEYFoSDfT6ODra9UNEroy4Ytd+xUbPMFkPLsDSifw3g6Hk3WoSl3b7jjjtgxgy4cAGCg812fSGEECppGTCV48dBUUh0CaCWSx4ajbUDsn1NgtOx1+bz10FPwzs6dAB7e1i3zjqBCSFEDSPJgKkcOQJAooOPDB40kotTPo3qZ/D3oULJgLs7tG4NP/5oncCEEKKGkWTAVI4eBV9fErNdZPBgObQMS2Nr4ZYBgDvvhE2bILX0MQ1CCCEqT5IBUzlyBEJDSUy1rxItA1M+CifqrcZM+Si81P1af/QfOrw1iNYf/ccscbRskMalOGcuXHMyvKNzZ8jOht9/N8t1hRBC3CbJgKkcPgwhIWoyUAVaBi7GOXH+mgsX45xK3c817jxu187iGnfeLHG0CFM/+RcZNxAYCOHh8P33ZrmuEEKI2yQZMIX0dDh3Tt8yYOvTCm2Jl3seIX6Z/H24mK6CLl3UcQPZ2ZYPTAghahBJBkzh1kyC28mALFJUHi3C0th6wKvoHV27QnIybN5s6ZCEEKJGkWTAFI4eVb9Ky0CFtGqQytHzbsQlOhjeER4O9erBt99aJzAhhKghJBkwhSNHwM8PxcWVpLSqMYDQlkQ2VMcNbCncOqDRqK0D334LubmWD0wIIWoISQZM4fBhCA0lM9uOnFw7aRkoJ9/aOdSvm8mmf72K3tm9OyQkSFeBEEKYkSQDpnDokL6LAGSRoopo3TCNP4tLBiIi1K6CVassHpMQQtQUkgxUVmoqnD9vmAxIy0C5RTZM4fgFN67ecDS8Q6OBbt3UrgKZVSCEEGYhyUBl6QYPhoVJMlAJbW6NG9i836vonb16wc2b8Ntvlg1KCCFqCFm1sLKOHFE/vYaEkHig6iQDo3tfIyPbDhfH0qdBxvT+D9rsdPIcXc0aj7dnLsF+6riBYT2uG94ZFgYNG8Ly5dC/v1njEEKImkiSgco6fFjt03Z2rlItA/07Jxi135XOg8wcyW2R4anFjxsA6NkTli6FpCTwLKZAkRBCiAqTboLKOnQIQkIASEqzR2un4FzGp21RvMiGqZy+7Mql4kok9+ypjhlYs8bygQkhRDUnyUBl3ZpWCNwqOJSLRmPdkKqqNo1SAIqfYujjA+3bw6JFlg1KCCFqAEkGKuPGDbhyBRo0AKhSpYgTkuyJS3QgIan0niLHpDicEq/hmBRn9pi83PNoGJjOxn21i9/hvvtg5061/LMQQgiTkTEDlXH4sPo1LAygSpUinvheBPFJjtT1zGbN9KMl7tfuvUdxSrpOlqcvO6abfzR/ZKNU/thbG0WhaAtL587qeIEvvoA5c8weixBC1BTSMlAZhw6BvT3Urw9QZZYvtmVtG6VyOd6J05ddit7p6Ai9e0N0NGRmWj44IYSopiQZqIzDhyE4GBzUBXaS0rSyLkEltQ5PxV6bX3JXQf/+avfMN99YNjAhhKjGJBmojIMH9YMHAW6mOFSZbgJb5eqcT5PgdDbu9Sp+h6AgaNcOPvrIonEJIUR1JslARSmK2jJwa/AgSDeBqbRpmMqf+2uTX9JYzAED1IGEe/ZYMiwhhKi2JBmoqEuXIDlZP3gQqtYAQlvWNiKVG8kOHDjjXvwOnTpBYCAsWGDZwIQQopqSZKCiDh5UvxZoGUhK00oyYALNQtNwdsxjwz8ljBvQamHgQHUlw0uXLBucEEJUQ5IMVNShQ+DuDn5+AGRla8jMlmTAFBztFdo0TGX9njol73TffeDiAu+9Z7nAhBCimpJkoKIOHlRbBW5Nhk9KqzrrElQF7RqnsO2QJ2kZJbxE3dzgoYfg008hwbh1FoQQQhRPkoGKOnDAYCZBVVqkqCro0CSF7Fw7thzwKnmnQYMgL0/GDgghRCVJBcKKyMqCEyegTx/9Jl0yUFVmE8z97xny8jRotUqp++3/76do8vJQtFoLRaYK8skioE4Wv+2pw/133ih+Jy8vte7AggUweTLULmGMgRBCiFJJy0BFHD+ufiItMHjwRopaeKiWa9VIBoJ9swgLyCTYN6vU/TJ8Q0kPCCfDN9Qygd2i0UDbiBTW7y5l3ADAsGHqaoZz51omMCGEqIYkGaiIAwfUrwWmFeoW/PF0y7VGRNVShyYpnLzkyvmrxSxprFOnjlp3YP58uHrVUqEJIUS1IslARRw4oM5zd3PTb4pPcsDZMQ9nx9Kb3YXx2jZKRWun8Otu79J3HDFCnW44Y4ZlAhNCiGpGkoGKOHAAwsMNNiUkO+DhVjW6CAD+2OvFzzvr8EdJZX9v8d37KwE7v8N376+WCayAWq55tGqQyo/by0gGatVSE4LPPoMjRywTnBBCVCMygLC8FAX271cHrhUQn+RQpboIPv0pUL+Eca92iSXuF/7TfP0Sxtfb3We5AG/p1DyZRb8EkJpRRg2HgQNh3Tp49lnYsKGY9Y+FEEKURFoGyis2Vp3XXqhlID7JAY8qMniwKuncIomsHDt+31PGTAFHR3jiCdi4Eb77zjLBCSFENSHJQHnpBg82bGiwOT7ZAY8q1DJQVdSrm01YQAY/bq9b9s6dOkHnzvD00+q6EUIIIYwiyUB5HThgUIZYJz7RAQ9XSQbMoVPzZH7a4U1eWQ0vGg088wwkJsJLL1kiNCGEqBYkGSiv/fvVLoJCfdIJyQ54VqEBhFVJl+ZJ3Eh2YPsRz7J39vODxx+HhQvhzz/NH5wQQlQDkgyU1759RboIFEU3m0BaBsyhSXA63h45fP+XEV0FoK5Z0KYNREWprQRCCCFKJclAeSQnw+nTRZKB9Ew7snLsqtRsgqrEzg7uapnIN1t9UIwp42BnBy+8ADdvwoQJGHeQEELUXJIMlIdu8GCjRgab45PUUsTSTWA+3SOTuHjdmd3Hahl3gL8/TJkCq1er9QeEEEKUSJKB8vj3X3UKW0iIweaEZDUZkG4C82nZIJU6Hjms3uxr/EH33KN2GTzzDPzzj/mCE0KIKk6SgfL49191PQJ7w1pNt1sGqk4yUKdWLnU9s6lTq/SYs2t5k+XpS3atMqoAmpnWDu5qmcSaLUZ2Fej8979qt85DD8naBUIIUQKpQFgexQwehNstA1Wpm+DT504atd/e51aaORLjdW+dyI/b6rL7WC3uaJZi3EGOjvD662pBoocegk2bwNXVvIEKIUQVIy0DxsrMhKNHi4wXALVlwNE+H2fHfCsEVnO0Ck+lTq0c1mwpR1cBQN268MYbcPAgPPooZRcsEEKImkWSAWMdPAi5uRARUeQutcZArpTDNzOtHXRtlcjXf/qSX968q3FjePVV+OEHePJJmWEghBAFSDJgrL171bEChdYkgFuLFLlXnfECVVnPtolcjncyrgBRYZ07w9Sp8Omn8OKLkhAIIcQtMmbAWP/8ow4edHQscldCsj21qtgiRXNX1ycl3Z5arrk8N+RSiftFrH4D+/Qkcl09OTnkFQtGWLzmoWn4emXz1UZf7mqZVP4T3HcfpKfDu++qz+XMmbLCoRCixpNkwFj//FNsFwFAXBVcl2DnUQ/9Esal8T76l34JY1tgZwf3tElk9WYfFjx9GnttBT7dDxoEOTnw5pvq+IFZsyQhEELUaNJNYIyMDHXwYAnJgKxLYFn3tLlJfJIjf+7zqvhJhg1TZxi89RY8+yzlH4QghBDVhyQDxihl8CDcGjNQhWoMVHUR9TMI8s3kqz8r2VoxZIhapfDDD2H0aMguvZVECCGqK0kGjLF7Nzg4QIMGxd59I9kBD2kZsBiNBnq0SeS7rT5kZFXyJdy/P7z2mlq2+P771fUnhBCihpFkwBi7d6vFhooZPJiRZUd6llZaBiysV7sbJKfb89N2E1RG7N4d3nlHfZ67dIELFyp/TiGEqEIkGTDGrl3QpEmxd92uPijJgCXV98mmWUgaX27wM80JIyPh/fchIQE6dIAdO0xzXiGEqAIkGSjLzZtw6lSJyYBuXQJZpMjyerW7yfrddfTPQaWFhsJHH6krHnbvDtHRpjmvEELYOEkGyrJnj/q1adNi75bli63nnjY3AVj1p4/pTlq7NsyZA/feC+PGqQsdycBCIUQ1J8lAWXbvBnd3qFev2LsTktRSDdIyYHle7nl0aJLCst/9TXtiBwe1UuGUKfDFF9C1q4wjEEJUa1J0qCw7dqitAnbF501Xbzji5JCPq1PVmqfes20iKenaMisnXmvbF4f0ZHJcPSwUWfn0bn+D15eGcey8K01D0k178v791YGjM2aoYwqWLlW3CSFENSPJQGkURU0GHnywxF1iE5yo65lT5QrYTXww1qj9zj442cyRVE7nFsl4ueew6JcA5jxxxvQXaNpUXcvg7bfV18Ezz6jfOzub/lpCCGEl0k1QmpMn1QGEzZuXuEtsgiN1PXMsGJQoyNFeoVe7myxZ7092jpkyMg8PdQnkJ5+EhQuhY0c4dMg81xJCCCuQZKA027erFW5KGDwIcDneiToekgxY0/133CAh2cE0NQdKotHAI4/Axx9Daiq0b68udpQnA0eFEFWfJAOl2bFDnW7m7l7iLpfjnPCWZMCqwgIyaRaSxhe/BJj/Yg0bwiefwIAB6jLId90Fx4+b/7pCCGFGkgyUZvt2aNas1F2uJDjiUwW7CUbPbkK/l1oyenbx9RN0Os4eyF0v3UXH2QMtFFnF3H9nAr/tqcPZWAv05Ts6qoscLVgAsbHQurXajSBTEIUQVZQkAyW5cQOOHIEWLUrcJSVdS0qGPd5VMBnQlVEuq7a/Nisd+6w0tFkmHqlvYj3b3sTDNZf3v6tvuYu2bAmffaZ2H0yfriYFW7da7vpCCGEikgyUZNs29WurViXuEhuvrlUg3QTW5+yo8GDnBBb94k9iqgUnyTg5weOPqzMOtFro1g1GjYIrVywXgxBCVJIkAyX56y/w8YGAkvuhYxOcAKpkN0F19FCXeDKz7fjiZwuMHSgsPFxd22DqVFi3Dho3VgcYZmVZPhYhhCgnSQZKsnWr2kVQSgEBXctAHQ+pPmgLvD1z6dn2Jgu+rUdOrhUKP9jZQb9+sGyZWs745ZfVMSfff6/WrBBCCBslyUBx0tNh3z61T7gUl+OdcHfJxaWKVR+szoZ0j+NSnDPLfjPRaoYVUasWPP20Wsq4bl14+GF14aN9+6wXkxBClEKSgeLs2AE5OeqAsFLEJjhJF4GNaRCYSffWN3l9WShZ2VYuCxkaqlYrfPttdW2D9u0hKgouX7ZuXEIIUYgkA8XZtEldvS40tNTdYuMdpeCQDRp731Uuxznx2bpAa4ei6thRbSV49ln48UeIiICZMyEjw9qRCSEEIMlA8f78U20VKGFxIp1LcU54y3gBmxPsl8W97W/wxvIQ0jJs5CWu1cJDD6njCfr3Vxc/atJExhMIIWyCjfyntCGpqbBnj7pKXRliE5yo6yUtA7ZoTJ9r3Ei2Z96aIGuHYsjdHSZOhOhoCAxUxxPcfz+cO2ftyIQQNZisWljY339Dbi60aVPqboqiVh+sW0W7CaYMvkRWjgYnh9I/lZ4c/D/scrLId3CyUGSmEeCdzcNd43lrZTDj779CYF0bqw5Yvz7MmqXWs/jwQ3XmyptvqgMPtVprRyeEqGGkZaCwP/5Q6wsElf6J8kayPdm5dlWy+iBAp+bJdI9MolPz5FL3S2h+N3GR95LQ/G4LRWY6o3pfw9Ehn/99EWbtUIqn0ahrGyxeDH36wJQpatGis2etHZkQooaRZKCw336Ddu1KrS8A6rRCQJYvtmHuLnlE9b3K0t/92Xui5MWmrM7VFZ55Bt57T+0uiIyEFSusHZUQogaRZKCgK1fg8GF1ClgZdNUHq2o3QU3xwJ0JhPpn8swHjWx/nF7r1upaB3feCY8+Co89BpmZ1o5KCFEDSDJQ0IYN6td27crcNTbeEY1GqbJTC09cdOFIjCsnLrqUup/7xaN4xBzA/eJRC0VmWlotPDngMtuPePLVRl9rh1M2Nze1cuHzz8OXX6rdCJcuWTsqIUQ1J8lAQb/9ptaU9/Iqc9dLcU7UqZWLfRUd6/XKojCeWhDBK4tK709vuWgybRdE0XLRZAtFZnrtIlK5u1Uiz38SbjtTDUuj0agzDD74QE0E2reHf/6xdlRCiGqsCvxntJC8PPj1V6O6CABOXXYhwFsWoakqJj4YS3ySA28sD7F2KMaLiICPPwZvb3Vg4c8/WzsiIUQ1JcmAzs6dcPMmdO5s1O7HL7hS30eSgaoiwDubkfdeY86qIA6ecbN2OMarUwfmzoW2bW8XLRJCCBOTZEBn3Tq1BHHjxmXuqihw8qIrwb6SDFQlw3tcp75PFo/PaUxenrWjKQdnZ5g+Hfr2hTFjYOFCa0ckhKhmJBnQWbdOrSFvRMGXazcdSU63J0iSgSrFwV7huSEX2X3cgw+/r2ftcMpHq4XnnoNBg+C//1ULFQkhhIlIMgBqkZfDh6FTJ6N2P3FBHYEf5CvTvqqaFmHpPNw1jhc+DWfP8VrWDqd8NBp48kkYOlStVCgtBEIIE5FkAOC778DJSW0ZMMKJi65o7RQCvW2sxK0wysQHY2lUP4OHX2tOfJKDtcMpH40GJkyARx5RWwiWLLF2REKIakCSAYBvvoEOHcCl9Dn3OicuuhLgnYWDva1XsRHFcbBXmDYmhrRMLY9Ma0ZmdhX7M9Bo1ETggQdg/Hg1mRVCiEqoYv8FzeDyZdi1C+42vvb+iYsyk6Cq8/HKYfqYGHYe9WTI683IyS29/LTN0Whg0iR1yuHw4bB5s7UjEkJUYZIMrFkDDg5GjxcAdVqhDB6s+lqFp/H62HP8uqsOY2Y3qVozDEAdVPjyy9CypTrt8OBBa0ckhKiiNIpSdsX25ORkPD09SUpKwsPDwxJxWU779mr3wMyZRu2enaPBte/dPDvoEv07J5g5OPNJz7RDATSAq3N+iftpM9Pg1p55zlVofn45bNnvycwvQxnZ6xqLXzhe9VYQTktTVzxMTVVbuerXt3ZEQggbYez7d81uGTh5EvbuhV69jD7kTKwLefmaKj+TwNU5Hzfn/FITAYA8ZzfynN2rbSIA0C0yiZdHnmf5Bj8en9uY/NIfEtvj5gazZkF+Ptx3HySXviy1EEIUVrOTgZUrwd29XF0EJy66AkjBoWqmZ9tEXhx+gSXr/Zk4L6LqJQTe3mpCEBMDgwdDbq61IxJCVCE1NxnIy4PFi9UBWI6ORh924qIL7s651K4l/2yrm94dbvLCsAt88UsATy1oVPUSgrAweP11+PNPtQ6Bza/ZLISwFfbWDsBqNm6EixfhpZfKddix824E+WahqWKDzwtbvdmH9Ewtrs55DOkeV+J+9Td/iX1mGrnOblzqPsqCEVpH3443yVc0vPt1MBnZdnw+9ST22ir0ptq2LUyeDO++C40aqWMJhBCiDDU3GVi0SP0k1bRpuQ7bftiD5mFpZgrKctZs9iE+yZG6ntmlJgNBm5fjlHSdLE/fGpEMANx/xw0ctApvfxVMcpo9K185ipNjFUoI7r9fnTI7dSqEh6szDYQQohQ1s5sgNlYt1HL//ZTnI/71mw6cuuxKy2qQDIjS3dv+JjPGnmPdDm/ufrYNl+OM70qyCePHQ9euMGIE7Ntn7WiEEDauZiYDn36q1hbo27dch20/4glAC0kGaoTOLZJZ8PQpzl11pu2E9mze72XtkIxnZ6fWIAgJgX791C4xIYQoQc1LBrKy1AVe+vRRZxKUw9+HPPGvk4Vv7RwzBSdsTZPgDD6ZcpJ63ln0mNKaFz5pQFZ2FRkw4uwMb7yhft+vn0w5FEKUqOYlA8uWQXw8DBxY7kP/OuhJs9B0MwQlbFmdWrm8+8QZHu93hfe+qU/7ie04cLqK1F2oU0edcnj2rDrlMEcSWSFEUTUrGcjNhdmz1XUIgoPLdWh6ph3/nnanZViqmYITtkxrB8N7Xmfh5JNkZGnp8EQ73loZXDVKGBeccjhhgkw5FEIUUbOSga+/hnPnYOTIch+653gtcnLtZLxADdewXiYfTz7JI3fH8X9fhNFrauuqMbiwXTt4/nmIjoZp06wdjRDCxtScZCA7G159Fbp0Uedfl9O2w564O+cSFlC1yxCLynO0V/hP/yvMfeIMR2LcaDGuA6v+9LF2WGXr3Rv+8x91HY4PP7R2NEIIG1JzkoFPP4ULF9QpVxWw5aAXzULT0dacR0yUoU2jVL6YeoI2jVIZNrM5g6c142yss7XDKt2wYerYgWeegeXLrR2NEMJG1IyiQ/HxatNonz5q/2k5JSTZ8+c+LyY+GGuG4Kwjon4Gvl45eLmXXlY5pX4TMr38yHGvbaHIqhYPtzxeG32eu1oksfDHQBqP7sj4+6/y5IDLtGxgg11KGg1MnKiucBgVpa7YOWiQtaMSQlhZzVjC+D//ga++gqVL1dHV5fTZTwE88V4Ea14/Qh1Zk0CUIDNbw9q/67Jmsy83Uhxo3ziZif1jGdbjOm4uNrbQQV6eOph2yxZYvbpCs2uEELZPljDW2bwZPv8cxo2rUCIAsOIPP9pGpEgiIErl7KgwrEccq6YdYcbYc2jtFB6f25h6gzvz6uJQElNtqCFOq1WLEnXtqnYbfP21tSMSQliRDf13MoPkZBgzBiIjK1yf/VKcE38d8uSFYVLBTRjHXgtdWyXRtVUSV284svbvusxZFcQH39XjlVEXeObhSzg62MD0Pq0W/vc/tRrniBFw8yY88YS1oxJCWEH1bRlQFHWwYEICvPiiWp61AlZt8sFBq3BXy0TTxidqBP862Ux8MJbl/3eMbpFJvPRZA5pFdWD1Jh/bqFGg1ap/HwMHwn//qyYHVW7tZiFEZVXfloF33oFvvoEZM8Dfv0KnUBRY+ps/dzZLxt3W+nwr6X9fhJGYao+Xey5vPnauxP1afPEsDqk3yXGvzeHHFlgwwurF2zOXyY9cYkCXeD75MZChM5oTHpjBfx+6TP/OCTSqn2G94Ozs4KmnwMdHrVZ48qRaj6Cc5bqFEFVX9UwGVq6El16CRx9V+0Qr6Os/fTl01p15/z1twuBsw8lLLvoljEtT69Jx/RLGovLCAjJ5e8JZTlxw4es/fXnp8wY8t7AhYQEZdG2pdi30bn+DYL8sywam0ajTDuvVUwcW3nmnmkw3aWLZOIQQVlH9koHVq9VxAn36qIMGKygjy44XPm3AXS0SadNIShAL02ocnMG0qPNkZNmx75Q7e0/WYtcxD5Zv8CNf0dA4KJ377kigb4cbdG6RTC1XC/UpdO0K9eurLWrt2sHcuWoJ43Is9S2EqHqqVzLw0UdqMZUePdTSq5X4BzZvTX2u3nBk9uNnTRigEIZcnPLp0iKZLi3UFQVT0rXsO+XOnuMerPzDj/nfBGFnp9AyLJVWDdKICMogzD+D+j5ZBPlmUa9uFk6OJh6MGBYGH3+sru75xBOwapVatCsiwrTXEULYjOqRDKSmwtNPw5Il8MgjalEVrbbCp9t51INZy0MY2DWe+j6lN6MLYUq1XPPo1jqJbq2TUBS4cN2Jw+fcOBLjxr+n3PlxuzdJaQ4Gx/h4ZRPkk0VYQCYR9dNpGpJOu4gUGgelV/zPwMUFpkxRWwrmz4cWLdRE++WXwdu70r+nEMK2VO1kQFHg55/VRODaNXVUdN++lTrl3hPu9H2hFQ3rZTC271UTBSpE+Wk0EOKXRYhfFv3uvKHfnpFlR1yig3pLcuTaTQfiEh2JuerMX4c8uX5TXTjJzTmPyIap+sQgLCATH68cPFxzcXfJw805j1queaVPtOnQARYvVlsHPv4YPvsMnnxSTQwCAsz8CAghLKVqJgOKAhs2wBtvwF9/Qfv26vf16lXqtBv+qc2Q15tRr24Wsx4/i4tT9ZpBIKoHF6d8gv2yShxkmJphx6lLrpy46MLJS66s/bsusQmO5OYVfde3s1Oo7Z6Lf51sgnwzCfbNItQ/kxC/TOr7ZBFYN5s6tezxGDkG+/791aTg/fdhzhx1OuKYMXDvveBYBVZuFEKUqOokA/n5cOAA/PCDusDKmTNqH+abb0KnTpUaH5CTq2HmshDeWB5Cu4gUpo05j5uzJAKianJ3yadNo1SDga95+RCf5EBKuj2pGXZkZGnJzLYjNUNLUpo9N5LtuZ7owOb9Xly94UhyetF/DS6Oebi79qNO7Wzq5l/D7+dT+K85Q12n9/BsGoh7qwY4tWiEc5APrm4a3NzAy0u9eXqCh4da30hHUSA3V62MnJ8P9vbqrYIlQYQQlWB7yYCiqJXQzp+H06fhyBHYtw+2bYMbN9S5z3fdpTZTtmxZqSQgLw++3erD/xaFcTbWhXH3XWVEz2vyz0hUO1o78Kudg1/tHKP2T8u0IyHJgYRkB1IytKRmaEnPVJOI5HQtSam1uejRmYM3OpGcAmkHHEjb72rUue3t1T/zkoouubqiTyRq11ariNeurSYUnp7q/S4uamOEbkxEbu7tm6Ko9zk53T7GxUW9gZp4ZGert6wsyMxUbxkZ6ra8PPXfSuE4dNfVXS8nRz2+cI0mXZKTk3P7Gjk56jY7O/Xm5KTeXF1vn9fFRU2WNBr1lp+vnsvOTv1Zq719vJ2d+jg6Oho+DkJUlFHJgG4to+TkZNNH8P336uppZbGzgzZtoGlT9a9g7171Vg774oMYs2U8F9IKD4DK5W6/f8g5Ec/SE+U6ZZWVlloPcCItNYvoD6+UuF9Yah61gZupeaXuJ2oO11u3AACPWzdAyc0lLzGZnKQMsnAkAxfScNPfMnAhBwfycrXYkY+WPP1N4+hIfl0/cvK1ZGZCUhLExVntVxRCT5ecFfzcqSi3b8YqmMjpEryCt9BQddJOixamjV/3vl3WmoRGrVp46dIlgoKCTBOZEEIIISzq4sWL1K9fv8T7jUoG8vPziY2NpVatWmiqcfGR5ORkgoKCuHjxYtVcqrmGkuetapLnrWqS561qURSFlJQUAgMDsSulD9yobgI7O7tSM4rqxsPDQ17kVZA8b1WTPG9VkzxvVYenp2eZ+8hQOSGEEKKGk2RACCGEqOEkGSjAycmJadOm4eTkZO1QRDnI81Y1yfNWNcnzVj0ZNYBQCCGEENWXtAwIIYQQNZwkA0IIIUQNJ8mAEEIIUcNJMiCEEELUcJIM3PLRRx8RGhqKs7Mzd9xxB7t377Z2SKIU06dPR6PRGNyaNGli7bBEIVu3bqV///4EBgai0WhYu3atwf2KovDaa68REBCAi4sLvXr14tSpU9YJVuiV9bxFRUUV+fvr27evdYIVJiHJALBq1SqmTJnCtGnT2LdvH61bt6ZPnz5cv37d2qGJUjRv3pwrV67ob3///be1QxKFpKWl0bp1az766KNi73/nnXd4//33+eSTT9i1axdubm706dOHzMxMC0cqCirreQPo27evwd/fV199ZcEIhanZ3hLGVjBv3jwef/xxxo4dC8Ann3zCzz//zOLFi3nppZesHJ0oib29Pf7+/tYOQ5Tivvvu47777iv2PkVRmD9/Pq+88goPPfQQAMuWLcPPz4+1a9cybNgwS4YqCijtedNxcnKSv79qpMa3DGRnZ7N371569eql32ZnZ0evXr3YsWOHFSMTZTl16hSBgYE0aNCAkSNHcuHCBWuHJMrh3LlzXL161eBvz9PTkzvuuEP+9qqAzZs34+vrS+PGjXniiSdISEiwdkiiEmp8MhAfH09eXh5+fn4G2/38/Lh69aqVohJlueOOO1iyZAnr169n4cKFnDt3jq5du5KSkmLt0ISRdH9f8rdX9fTt25dly5axceNG3n77bbZs2cJ9991HXl6etUMTFSTdBKJKKtiE2apVK+644w5CQkJYvXo148ePt2JkQlR/BbtwWrZsSatWrQgPD2fz5s307NnTipGJiqrxLQN169ZFq9Vy7do1g+3Xrl2T/rAqxMvLi4iICE6fPm3tUISRdH9f8rdX9TVo0IC6devK318VVuOTAUdHR9q1a8fGjRv12/Lz89m4cSOdOnWyYmSiPFJTUzlz5gwBAQHWDkUYKSwsDH9/f4O/veTkZHbt2iV/e1XMpUuXSEhIkL+/Kky6CYApU6YwZswY2rdvT8eOHZk/fz5paWn62QXC9kydOpX+/fsTEhJCbGws06ZNQ6vVMnz4cGuHJgpITU01+LR47tw59u/fT506dQgODmbSpEm88cYbNGrUiLCwMF599VUCAwMZMGCA9YIWpT5vderU4fXXX2fQoEH4+/tz5swZXnjhBRo2bEifPn2sGLWoFEUoiqIoH3zwgRIcHKw4OjoqHTt2VHbu3GntkEQphg4dqgQEBCiOjo5KvXr1lKFDhyqnT5+2dliikE2bNilAkduYMWMURVGU/Px85dVXX1X8/PwUJycnpWfPnsqJEyesG7Qo9XlLT09Xevfurfj4+CgODg5KSEiI8vjjjytXr161dtiiEmQJYyGEEKKGq/FjBoQQQoiaTpIBIYQQooaTZEAIIYSo4SQZEEIIIWo4SQaEEEKIGk6SASGEEKKGk2RACCGEqOEkGRDCBnXv3p1JkyZZO4wqydjH7u6772blypXmD6gMR48epX79+qSlpVk7FFGDSTIgbMKOHTvQarX069fP2qFYhEaj0d88PT3p0qULf/75p7XDMtrnn39O69atcXd3x8vLizZt2jB79mxrh2W0H3/8kWvXrhmsvhcaGopGo+Hrr78usn/z5s3RaDQsWbKkyP4ajQY3Nzfatm3LmjVr9PdPnz6dyMjIIueKiYlBo9Gwf/9+AJo1a8add97JvHnzTPb7CVFekgwIm7Bo0SKefvpptm7dSmxsrFmvpSgKubm5Zr2GMaKjo7ly5Qrbtm2jbt26PPDAA5w9e9baYellZ2cXu33x4sVMmjSJZ555hv3797Nt2zZeeOEFUlNTLRxhxb3//vuMHTsWOzvDf4FBQUFER0cbbNu5cydXr17Fzc2tyHlmzJjBlStX+Pfff+nQoQNDhw5l+/bt5Y5n7NixLFy40CZel6JmkmRAWF1qaiqrVq3iiSeeoF+/fgafvkaMGMHQoUMN9s/JyaFu3bosW7YMUFeZnD17NmFhYbi4uNC6dWu++eYb/f6bN29Go9Hw66+/0q5dO5ycnPj77785c+YMDz30EH5+fri7u9OhQwf++OMPg2tduXKFfv364eLiQlhYGCtXriQ0NJT58+fr90lMTOSxxx7Dx8cHDw8PevTowYEDB8r8vb28vPD396dFixYsXLiQjIwMNmzYUOy+X375Je3bt6dWrVr4+/szYsQIrl+/DqjJTcOGDZkzZ47BMfv370ej0egXnCkrTt0n2S+++IKwsDCcnZ2LjeXHH39kyJAhjB8/noYNG9K8eXOGDx/Om2++qd8nKiqKAQMG8Prrr+uvN3HiRIMEo6znDeDw4cPcd999uLu74+fnx6hRo4iPj9ffn5aWxujRo3F3dycgIIC5c+eW+bjHxcXx559/0r9//yL3jRw5ki1btnDx4kX9tsWLFzNy5Ejs7Yuu66Z7PiIiIvjoo49wcXHhp59+KjOGwu69915u3LjBli1byn2sEKYgyYCwutWrV9OkSRMaN27Mo48+yuLFi9EtmTFy5Eh++ukng0+dv/32G+np6QwcOBCA2bNns2zZMj755BOOHDnC5MmTefTRR4v8Y33ppZd46623OHbsGK1atSI1NZX777+fjRs38u+//9K3b1/69+/PhQsX9MeMHj2a2NhYNm/ezLfffstnn32mfxPWGTx4MNevX+fXX39l7969tG3blp49e3Ljxg2jHwMXFxeg5E/jOTk5zJw5kwMHDrB27VpiYmKIiooC1C6HcePGFflEGx0dzd13303Dhg2NjvP06dN8++23fPfdd/pm7ML8/f3ZuXMn58+fL/V32rhxI8eOHWPz5s189dVXfPfdd7z++uv6+8t63hITE+nRowdt2rThn3/+Yf369Vy7do0hQ4boz/H888+zZcsWfvjhB37//Xc2b97Mvn37So3r77//xtXVlaZNmxa5z8/Pjz59+rB06VIA0tPTWbVqFePGjSv1nAD29vY4ODiU+ByWxtHRkcjISP76669yHyuESVh1mSQhFEXp3LmzMn/+fEVRFCUnJ0epW7eusmnTJoOfly1bpt9/+PDhytChQxVFUZTMzEzF1dVV2b59u8E5x48frwwfPlxRlNsrsK1du7bMWJo3b6588MEHiqIoyrFjxxRA2bNnj/7+U6dOKYDy3nvvKYqiKH/99Zfi4eGhZGZmGpwnPDxc+fTTT0u8DqB8//33iqIoSlpamvLf//5X0Wq1yoEDBxRFUZRu3bopzz77bInH79mzRwGUlJQURVEU5fLly4pWq1V27dqlKIqiZGdnK3Xr1lWWLFlidJzTpk1THBwclOvXr5f2ECmxsbHKnXfeqQBKRESEMmbMGGXVqlVKXl6efp8xY8YoderUUdLS0vTbFi5cqLi7uyt5eXlGPW8zZ85UevfubXD/xYsXFUA5ceKEkpKSojg6OiqrV6/W35+QkKC4uLiU+ti99957SoMGDYpsDwkJUd577z1l7dq1Snh4uJKfn68sXbpUadOmjaIoiuLp6alER0cX2V9RFCUrK0uZNWuWAijr1q1TFEV9PO3s7BQ3NzeDm6urqwIo//77r8H1Bw4cqERFRZUYtxDmVLTdSwgLOnHiBLt37+b7778H1E9XQ4cOZdGiRXTv3h17e3uGDBnCihUrGDVqFGlpafzwww/6QV6nT58mPT2de++91+C82dnZtGnTxmBb+/btDX5OTU1l+vTp/Pzzz1y5coXc3FwyMjL0LQMnTpzA3t6etm3b6o9p2LAhtWvX1v984MABUlNT8fb2Njh3RkYGZ86cKfV3Hz58OFqtloyMDHx8fFi0aBGtWrUqdt+9e/cyffp0Dhw4wM2bN8nPzwfgwoULNGvWjMDAQPr168fixYvp2LEjP/30E1lZWQwePLhccYaEhODj41Nq3AEBAezYsYPDhw+zdetWtm/fzpgxY/jiiy9Yv369vh++devWuLq66o/r1KkTqampXLx4kdTU1DKftwMHDrBp0ybc3d2LxHDmzBkyMjLIzs7mjjvu0G+vU6cOjRs3LjX+jIyMErtAAPr168eECRPYunUrixcvLrVV4MUXX+SVV14hMzMTd3d33nrrLYNBsI0bN+bHH380OOby5ct07969yLlcXFxIT08vNXYhzEWSAWFVixYtIjc3l8DAQP02RVFwcnLiww8/xNPTk5EjR9KtWzeuX7/Ohg0bcHFxoW/fvgD67oOff/6ZevXqGZzbycnJ4OfCA8CmTp3Khg0bmDNnDg0bNsTFxYVHHnmkXM28qampBAQEsHnz5iL3eXl5lXrse++9R69evfD09Cz1DTgtLY0+ffrQp08fVqxYgY+PDxcuXKBPnz4GsT722GOMGjWK9957j+joaIYOHap/MzY2zuIGyZWkRYsWtGjRgv/+979MnDiRrl27smXLFu65554yjzXmeUtNTaV///68/fbbRY4PCAjQj4Uor7p163Lz5s0S77e3t2fUqFFMmzaNXbt26RPV4jz//PNERUXpxzRoNBqD+x0dHfXdNAXPX5wbN24QHh5ejt9ECNORZEBYTW5uLsuWLWPu3Ln07t3b4L4BAwbw1VdfMXHiRDp37kxQUBCrVq3i119/ZfDgwTg4OADqtCwnJycuXLhAt27dynX9bdu2ERUVpR97kJqaSkxMjP7+xo0bk5uby7///ku7du0AtSWi4BtJ27ZtuXr1Kvb29oSGhpbr+v7+/kXeKIpz/PhxEhISeOuttwgKCgLgn3/+KbLf/fffj5ubGwsXLmT9+vVs3brVJHEao1mzZgAGc+UPHDhARkaGfjzEzp07cXd3JygoiDp16pT5vLVt25Zvv/2W0NDQYt9Aw8PDcXBwYNeuXQQHBwNw8+ZNTp48WeproU2bNly9epWbN28atPIUNG7cOObMmcPQoUNL3AfUxMKY59AYhw8f5pFHHjHJuYQoL0kGhNWsW7eOmzdvMn78eDw9PQ3uGzRoEIsWLWLixImAOqvgk08+4eTJk2zatEm/X61atZg6dSqTJ08mPz+fu+66i6SkJLZt24aHhwdjxowp8fqNGjXiu+++o3///mg0Gl599VV98ztAkyZN6NWrF//5z39YuHAhDg4OPPfcc7i4uOg/Afbq1YtOnToxYMAA3nnnHSIiIoiNjeXnn39m4MCBRbomKiI4OBhHR0c++OADJk6cyOHDh5k5c2aR/bRaLVFRUbz88ss0atSITp066e8zZZxPPPEEgYGB9OjRg/r163PlyhXeeOMNfHx8DK6ZnZ3N+PHjeeWVV4iJiWHatGk89dRT2NnZGfW8Pfnkk3z++ecMHz6cF154gTp16nD69Gm+/vprvvjiC9zd3Rk/fjzPP/883t7e+Pr68r///a/IdMHC2rRpQ926ddm2bRsPPPBAsfs0bdqU+Ph4g24Oc4qJieHy5cv06tXLItcTojCZTSCsZtGiRfpm8sIGDRrEP//8w8GDBwF1VsHRo0epV68eXbp0Mdh35syZvPrqq8yePZumTZvSt29ffv75Z8LCwkq9/rx586hduzadO3emf//+9OnTx2B8AMCyZcvw8/Pj7rvvZuDAgTz++OPUqlVL3+es0Wj45ZdfuPvuuxk7diwREREMGzaM8+fP4+fnV5mHR8/Hx4clS5awZs0amjVrxltvvVVkGqHO+PHjyc7OZuzYsQbbTRlnr1692LlzJ4MHDyYiIoJBgwbh7OzMxo0bDcYk9OzZk0aNGnH33XczdOhQHnzwQaZPn66/v6znLTAwkG3btpGXl0fv3r1p2bIlkyZNwsvLS/+G/+6779K1a1f69+9Pr169uOuuu/StOCXRarWMHTuWFStWlLqft7e3vlXD3L766it69+5NSEiIRa4nRGEaRbk1h0sIUaZLly4RFBTEH3/8Qc+ePa0dThF//fUXPXv25OLFiyZLRioiKiqKxMRE1q5da7UYSnP16lWaN2/Ovn37rP4GnJ2dTaNGjVi5cmWRRFcIS5FuAiFK8eeff5KamkrLli25cuUKL7zwAqGhodx9993WDs1AVlYWcXFxTJ8+ncGDB1s1EagK/P39WbRoERcuXLB6MnDhwgX+7//+TxIBYVWSDAhRipycHP7v//6Ps2fPUqtWLTp37syKFSv0AxhtxVdffcX48eOJjIzUV2YUpRswYIC1QwDU6aqmGoQoREVJN4EQQghRw8kAQiGEEKKGk2RACCGEqOEkGRBCCCFqOEkGhBBCiBpOkgEhhBCihpNkQAghhKjhJBkQQgghajhJBoQQQogaTpIBIYQQoob7f8jPOFRC667PAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df1[df1['isInjury_last'] == True]['s_mph_mean'], fill=True, color='red', label='Injury')\n",
    "sns.kdeplot(df1[df1['isInjury_last'] == False]['s_mph_mean'], fill=True, color='blue', label='No Injury')\n",
    "\n",
    "plt.axvline(df1[df1['isInjury_last'] == True]['s_mph_mean'].mean(), color='red', linestyle='--', linewidth=2, label='Injury Mean')\n",
    "plt.axvline(df1[df1['isInjury_last'] == False]['s_mph_mean'].mean(), color='blue', linestyle='--', linewidth=2, label='No Injury Mean')\n",
    "\n",
    "plt.text(df1[df1['isInjury_last'] == True]['s_mph_mean'].mean() + 3.25, plt.ylim()[1] * 0.8, str(round(df1[df1['isInjury_last'] == True]['s_mph_mean'].mean(),3)) + ' MPH', color='red', \n",
    "         ha='right', va='center', fontsize=10, bbox=dict(facecolor='white', edgecolor='red', boxstyle='round,pad=0.5'))\n",
    "plt.text(df1[df1['isInjury_last'] == False]['s_mph_mean'].mean(), plt.ylim()[1] * 0.9, str(round(df1[df1['isInjury_last'] == False]['s_mph_mean'].mean(),3)) + ' MPH', color='blue',\n",
    "         ha='left', va='center', fontsize=10, bbox=dict(facecolor='white', edgecolor='blue', boxstyle='round,pad=0.5'))\n",
    "\n",
    "plt.xlabel('Average Player Speed (MPH)')\n",
    "plt.ylabel('')\n",
    "plt.title('Players Run 16.8% Faster in Injury-Causing Plays')\n",
    "plt.legend(title='')\n",
    "\n",
    "plt.yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df1[df1['isInjury_last'] == True]['s_mph_max'], fill=True, color='red', label='Injury')\n",
    "sns.kdeplot(df1[df1['isInjury_last'] == False]['s_mph_max'], fill=True, color='blue', label='No Injury')\n",
    "\n",
    "plt.axvline(df1[df1['isInjury_last'] == True]['s_mph_max'].mean(), color='red', linestyle='--', linewidth=2, label='Injury Mean')\n",
    "plt.axvline(df1[df1['isInjury_last'] == False]['s_mph_max'].mean(), color='blue', linestyle='--', linewidth=2, label='No Injury Mean')\n",
    "\n",
    "plt.text(df1[df1['isInjury_last'] == True]['s_mph_max'].mean() + 20, plt.ylim()[1] * 0.8, str(round(df1[df1['isInjury_last'] == True]['s_mph_max'].mean(),3)) + ' MPH', color='red', \n",
    "         ha='right', va='center', fontsize=10, bbox=dict(facecolor='white', edgecolor='red', boxstyle='round,pad=0.5'))\n",
    "plt.text(df1[df1['isInjury_last'] == False]['s_mph_max'].mean(), plt.ylim()[1] * 0.9, str(round(df1[df1['isInjury_last'] == False]['s_mph_max'].mean(),3)) + ' MPH', color='blue',\n",
    "         ha='left', va='center', fontsize=10, bbox=dict(facecolor='white', edgecolor='blue', boxstyle='round,pad=0.5'))\n",
    "\n",
    "plt.xlabel('Max Player Speed (MPH)')\n",
    "plt.ylabel('')\n",
    "plt.title('Player Speed Tops Out 26.3% Higher in Injury-Causing Plays')\n",
    "plt.legend(title='')\n",
    "\n",
    "plt.yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring how player speed differs between PlayTypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.merge(df_plays, df1, on='PlayKey')\n",
    "\n",
    "# Combining different Kickoff/Punt PlayTypes into one category.\n",
    "df2['PlayType'] = df2['PlayType'].replace({\n",
    "    'Kickoff': 'Kickoff',\n",
    "    'Kickoff Returned': 'Kickoff',\n",
    "    'Kickoff Not Returned': 'Kickoff',\n",
    "    'Punt': 'Punt',\n",
    "    'Punt Returned': 'Punt',\n",
    "    'Punt Not Returned': 'Punt'\n",
    "})\n",
    "df2 = df2[df2.PlayType != '0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg Max Speed: 9.916 MPH\n",
      "Avg Speed: 2.716 MPH\n"
     ]
    }
   ],
   "source": [
    "avg_max_spd = df2.s_mph_max.mean()\n",
    "avg_speed = df2.s_mph_mean.mean()\n",
    "print('Avg Max Speed: ' + str(round(avg_max_spd,3)) + ' MPH')\n",
    "print('Avg Speed: ' + str(round(avg_speed,3)) + ' MPH')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>PlayType</th>\n",
       "      <th>dis_sum</th>\n",
       "      <th>s_mph_mean</th>\n",
       "      <th>s_mph_max</th>\n",
       "      <th>Avg Speed (%Diff)</th>\n",
       "      <th>Avg Max Speed (%Diff)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Extra Point</td>\n",
       "      <td>17.603432</td>\n",
       "      <td>1.838916</td>\n",
       "      <td>5.396667</td>\n",
       "      <td>-47.7</td>\n",
       "      <td>-83.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Field Goal</td>\n",
       "      <td>21.483090</td>\n",
       "      <td>1.610662</td>\n",
       "      <td>5.544739</td>\n",
       "      <td>-68.6</td>\n",
       "      <td>-78.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kickoff</td>\n",
       "      <td>58.650190</td>\n",
       "      <td>5.346698</td>\n",
       "      <td>15.461459</td>\n",
       "      <td>49.2</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pass</td>\n",
       "      <td>40.041913</td>\n",
       "      <td>2.718189</td>\n",
       "      <td>10.217862</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Punt</td>\n",
       "      <td>69.793448</td>\n",
       "      <td>4.596921</td>\n",
       "      <td>15.704781</td>\n",
       "      <td>40.9</td>\n",
       "      <td>36.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rush</td>\n",
       "      <td>30.177991</td>\n",
       "      <td>2.218608</td>\n",
       "      <td>8.472922</td>\n",
       "      <td>-22.4</td>\n",
       "      <td>-17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PlayType    dis_sum  s_mph_mean  s_mph_max  Avg Speed (%Diff)  \\\n",
       "0  Extra Point  17.603432    1.838916   5.396667              -47.7   \n",
       "1   Field Goal  21.483090    1.610662   5.544739              -68.6   \n",
       "2      Kickoff  58.650190    5.346698  15.461459               49.2   \n",
       "3         Pass  40.041913    2.718189  10.217862                0.1   \n",
       "4         Punt  69.793448    4.596921  15.704781               40.9   \n",
       "5         Rush  30.177991    2.218608   8.472922              -22.4   \n",
       "\n",
       "   Avg Max Speed (%Diff)  \n",
       "0                  -83.7  \n",
       "1                  -78.8  \n",
       "2                   35.9  \n",
       "3                    3.0  \n",
       "4                   36.9  \n",
       "5                  -17.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_speeds = df2.groupby(['PlayType']).agg({'dis_sum':'mean','s_mph_mean':'mean','s_mph_max':'mean'}).reset_index()\n",
    "df_speeds['Avg Speed (%Diff)'] = round((100 * (1 - (avg_speed/df_speeds['s_mph_mean']))),1)\n",
    "df_speeds['Avg Max Speed (%Diff)'] = round((100 * (1 - (avg_max_spd/df_speeds['s_mph_max']))),1)\n",
    "df_speeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>PlayType</th>\n",
       "      <th>dis_sum</th>\n",
       "      <th>s_mph_mean</th>\n",
       "      <th>s_mph_max</th>\n",
       "      <th>Avg Speed (%Diff)</th>\n",
       "      <th>Avg Max Speed (%Diff)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kickoff</td>\n",
       "      <td>58.650190</td>\n",
       "      <td>5.346698</td>\n",
       "      <td>15.461459</td>\n",
       "      <td>49.2</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Punt</td>\n",
       "      <td>69.793448</td>\n",
       "      <td>4.596921</td>\n",
       "      <td>15.704781</td>\n",
       "      <td>40.9</td>\n",
       "      <td>36.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pass</td>\n",
       "      <td>40.041913</td>\n",
       "      <td>2.718189</td>\n",
       "      <td>10.217862</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rush</td>\n",
       "      <td>30.177991</td>\n",
       "      <td>2.218608</td>\n",
       "      <td>8.472922</td>\n",
       "      <td>-22.4</td>\n",
       "      <td>-17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Extra Point</td>\n",
       "      <td>17.603432</td>\n",
       "      <td>1.838916</td>\n",
       "      <td>5.396667</td>\n",
       "      <td>-47.7</td>\n",
       "      <td>-83.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Field Goal</td>\n",
       "      <td>21.483090</td>\n",
       "      <td>1.610662</td>\n",
       "      <td>5.544739</td>\n",
       "      <td>-68.6</td>\n",
       "      <td>-78.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PlayType    dis_sum  s_mph_mean  s_mph_max  Avg Speed (%Diff)  \\\n",
       "2      Kickoff  58.650190    5.346698  15.461459               49.2   \n",
       "4         Punt  69.793448    4.596921  15.704781               40.9   \n",
       "3         Pass  40.041913    2.718189  10.217862                0.1   \n",
       "5         Rush  30.177991    2.218608   8.472922              -22.4   \n",
       "0  Extra Point  17.603432    1.838916   5.396667              -47.7   \n",
       "1   Field Goal  21.483090    1.610662   5.544739              -68.6   \n",
       "\n",
       "   Avg Max Speed (%Diff)  \n",
       "2                   35.9  \n",
       "4                   36.9  \n",
       "3                    3.0  \n",
       "5                  -17.0  \n",
       "0                  -83.7  \n",
       "1                  -78.8  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_speeds.sort_values(by=['Avg Speed (%Diff)'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using bar plots to show how player speeds are highest on Kickoff and Punt plays. Previous plot showed higher player speeds to be correlated with more injuries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x='Avg Speed (%Diff)', y='PlayType', data=df_speeds.sort_values(by=['Avg Speed (%Diff)'], ascending=False))\n",
    "\n",
    "plt.title('Avg Player Speed (MPH) by Play Type')\n",
    "plt.xlabel('Percentage Difference vs Population Avg')\n",
    "plt.ylabel('')\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate(\n",
    "        format(p.get_width(), '.1f'),\n",
    "        (p.get_width(), p.get_y() + p.get_height() / 2.),\n",
    "        ha='left', va='center',\n",
    "        xytext=(5, 0),  # 5 points horizontal offset\n",
    "        textcoords='offset points'\n",
    "    )\n",
    "ax.set_xlim(-70, 60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x='Avg Max Speed (%Diff)', y='PlayType', data=df_speeds.sort_values(by=['Avg Max Speed (%Diff)'], ascending=False))\n",
    "\n",
    "plt.title('Avg Max Speed (MPH) by Play Type')\n",
    "plt.xlabel('Percentage Difference vs Population Avg')\n",
    "plt.ylabel('')\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate(\n",
    "        format(p.get_width(), '.1f'),\n",
    "        (p.get_width(), p.get_y() + p.get_height() / 2.),\n",
    "        ha='left', va='center',\n",
    "        xytext=(5, 0),  # 5 points horizontal offset\n",
    "        textcoords='offset points'\n",
    "    )\n",
    "ax.set_xlim(-90, 50)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}