sfmc_engagement.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_events = pd.read_csv('sfmc_csv/subscriber_events.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>SubscriberKey</th>\n",
       "      <th>Event</th>\n",
       "      <th>Domain</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>JobID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aaron02@example.org</td>\n",
       "      <td>unsub</td>\n",
       "      <td>example.org</td>\n",
       "      <td>2024-04-25 00:20:48.898632</td>\n",
       "      <td>442411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aaron30@example.org</td>\n",
       "      <td>click</td>\n",
       "      <td>example.org</td>\n",
       "      <td>2024-04-28 10:08:46.575807</td>\n",
       "      <td>355427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aaron30@example.org</td>\n",
       "      <td>unsub</td>\n",
       "      <td>example.org</td>\n",
       "      <td>2024-04-28 10:08:46.575807</td>\n",
       "      <td>964505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aaron85@example.org</td>\n",
       "      <td>open</td>\n",
       "      <td>example.org</td>\n",
       "      <td>2024-04-10 17:35:42.060171</td>\n",
       "      <td>701968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aaron85@example.org</td>\n",
       "      <td>unsub</td>\n",
       "      <td>example.org</td>\n",
       "      <td>2024-04-10 17:35:42.060171</td>\n",
       "      <td>217868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009</th>\n",
       "      <td>zwise@example.net</td>\n",
       "      <td>sent</td>\n",
       "      <td>example.net</td>\n",
       "      <td>2024-04-09 19:00:34.543371</td>\n",
       "      <td>795586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4010</th>\n",
       "      <td>zwise@example.net</td>\n",
       "      <td>click</td>\n",
       "      <td>example.net</td>\n",
       "      <td>2024-04-09 19:02:56.543371</td>\n",
       "      <td>334812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4011</th>\n",
       "      <td>zwise@example.net</td>\n",
       "      <td>open</td>\n",
       "      <td>example.net</td>\n",
       "      <td>2024-04-09 19:56:56.543371</td>\n",
       "      <td>577041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4012</th>\n",
       "      <td>zwise@example.net</td>\n",
       "      <td>open</td>\n",
       "      <td>example.net</td>\n",
       "      <td>2024-04-09 20:25:56.543371</td>\n",
       "      <td>217868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4013</th>\n",
       "      <td>zwise@example.net</td>\n",
       "      <td>sent</td>\n",
       "      <td>example.net</td>\n",
       "      <td>2024-04-29 14:31:15.622476</td>\n",
       "      <td>436466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4014 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            SubscriberKey  Event       Domain                   Timestamp  \\\n",
       "0     aaron02@example.org  unsub  example.org  2024-04-25 00:20:48.898632   \n",
       "1     aaron30@example.org  click  example.org  2024-04-28 10:08:46.575807   \n",
       "2     aaron30@example.org  unsub  example.org  2024-04-28 10:08:46.575807   \n",
       "3     aaron85@example.org   open  example.org  2024-04-10 17:35:42.060171   \n",
       "4     aaron85@example.org  unsub  example.org  2024-04-10 17:35:42.060171   \n",
       "...                   ...    ...          ...                         ...   \n",
       "4009    zwise@example.net   sent  example.net  2024-04-09 19:00:34.543371   \n",
       "4010    zwise@example.net  click  example.net  2024-04-09 19:02:56.543371   \n",
       "4011    zwise@example.net   open  example.net  2024-04-09 19:56:56.543371   \n",
       "4012    zwise@example.net   open  example.net  2024-04-09 20:25:56.543371   \n",
       "4013    zwise@example.net   sent  example.net  2024-04-29 14:31:15.622476   \n",
       "\n",
       "       JobID  \n",
       "0     442411  \n",
       "1     355427  \n",
       "2     964505  \n",
       "3     701968  \n",
       "4     217868  \n",
       "...      ...  \n",
       "4009  795586  \n",
       "4010  334812  \n",
       "4011  577041  \n",
       "4012  217868  \n",
       "4013  436466  \n",
       "\n",
       "[4014 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate scores for each event type in event_data\n",
    "df_events['open_score'] = df_events.apply(lambda row: 2 if row['Event'] == 'open' else 0, axis=1)\n",
    "df_events['click_score'] = df_events.apply(lambda row: 5 if row['Event'] == 'click' else 0, axis=1)\n",
    "df_events['sent_score'] = df_events.apply(lambda row: 1 if row['Event'] == 'sent' else 0, axis=1)\n",
    "df_events['bounce_score'] = df_events.apply(lambda row: -1 if row['Event'] == 'bounce' else 0, axis=1)\n",
    "df_events['unsub_score'] = df_events.apply(lambda row: -2 if row['Event'] == 'unsub' else 0, axis=1)\n",
    "df_events['complaint_score'] = df_events.apply(lambda row: -2 if row['Event'] == 'complaint' else 0, axis=1)\n",
    "\n",
    "df_events['Timestamp'] = pd.to_datetime(df_events['Timestamp'])\n",
    "df_events['week_of_year'] = df_events['Timestamp'].dt.isocalendar().week\n",
    "df_events['start_of_week'] = df_events['Timestamp'].dt.to_period('W').dt.start_of\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "distinct_subscribers = df_events.drop_duplicates('SubscriberKey', keep='last')[['SubscriberKey','Domain']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "distinct_subscribers['open_score'] = distinct_subscribers['SubscriberKey'].map(df_events.groupby('SubscriberKey')['open_score'].sum())\n",
    "distinct_subscribers['click_score'] = distinct_subscribers['SubscriberKey'].map(df_events.groupby('SubscriberKey')['click_score'].sum())\n",
    "distinct_subscribers['sent_score'] = distinct_subscribers['SubscriberKey'].map(df_events.groupby('SubscriberKey')['sent_score'].sum())\n",
    "distinct_subscribers['unsub_score'] = distinct_subscribers['SubscriberKey'].map(df_events.groupby('SubscriberKey')['unsub_score'].sum())\n",
    "distinct_subscribers['bounce_score'] = distinct_subscribers['SubscriberKey'].map(df_events.groupby('SubscriberKey')['bounce_score'].sum())\n",
    "distinct_subscribers['complaint_score'] = distinct_subscribers['SubscriberKey'].map(df_events.groupby('SubscriberKey')['complaint_score'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "distinct_subscribers['eses_score'] =  df_events['sent_score'] + df_events['open_score'] + distinct_subscribers['click_score'] + distinct_subscribers['unsub_score']+distinct_subscribers['bounce_score']+distinct_subscribers['complaint_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average ESES Score: 3.1715145436308925\n",
      "Max ESES Score: 53.0\n",
      "Min ESES Score: -4.0\n",
      "Segment Counts: segments\n",
      "Unengaged             48.445336\n",
      "Less Engaged          48.345035\n",
      "Moderately Engaged     3.109328\n",
      "Highly Engaged         0.100301\n",
      "Name: proportion, dtype: float64\n",
      "Health Score: At-Risk\n"
     ]
    }
   ],
   "source": [
    "# Calculate the average eses_score\n",
    "average_eses_score = distinct_subscribers['eses_score'].mean();\n",
    "\n",
    "# Segment the subscribers based on eses_score\n",
    "distinct_subscribers['segments'] = pd.cut(distinct_subscribers['eses_score'], bins=[-5, -1, 20, 40, 100], labels=['Unengaged', 'Less Engaged', 'Moderately Engaged', 'Highly Engaged'])\n",
    "\n",
    "# Calculate the percentage of subscribers in each segment\n",
    "segment_counts = distinct_subscribers['segments'].value_counts(normalize=True) * 100\n",
    "\n",
    "# Assign a health score to the database\n",
    "if (segment_counts['Highly Engaged'] >= 70) and (segment_counts['Unengaged'] <= 10):\n",
    "\thealth_score = 'Healthy'\n",
    "elif (segment_counts['Highly Engaged'] >= 40) and (segment_counts['Highly Engaged'] < 70) and (segment_counts['Unengaged'] >= 10) and (segment_counts['Unengaged'] < 20):\n",
    "\thealth_score = 'Neutral'\n",
    "else:\n",
    "\thealth_score = 'At-Risk'\n",
    "\n",
    "print('Average ESES Score:', average_eses_score)\n",
    "print('Max ESES Score:', distinct_subscribers['eses_score'].max())\n",
    "print('Min ESES Score:', distinct_subscribers['eses_score'].min())\n",
    "print('Segment Counts:', segment_counts)\n",
    "print('Health Score:', health_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average ESES Score (scaled): 5.983989704963948\n"
     ]
    }
   ],
   "source": [
    "# Scale the average ESES score to a 0-100 range\n",
    "max_eses_score =  distinct_subscribers['eses_score'].max()\n",
    "scaled_score = (average_eses_score / max_eses_score) * 100\n",
    "\n",
    "print('Average ESES Score (scaled):', scaled_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QKSkpiomJ0aJFizRhwgQ9+uij2r59u6pWrZqt/V5/PISEhMjFxcW+DrBr166aMWOG+vTpo9dee03NmzdXp06d9Oijj9qPrVs5LjJUqFDhlrO5uLgoODjYYaxy5cqSZM93u8dbVu9/K5/j7br+uM74AelWj+vGjRv/68lZs2fP1rhx47Rnzx5dvXrVPv7PzzEuLk4lS5aUv7//bWfOyH0rmUuUKKGpU6fqk08+UWxsrJYuXaoPPvhAI0aMUIkSJdSnTx/FxsZK0k2XE5w7d05XrlzR5cuXM/17lTL/G75Rdl9fX0lSmTJlshzP+JxuNdM/f8C92dfWx8fnhvsBbgXFFXeVunXrOqxZ+6f09HTZbDb98ssvcnV1zfT69Zerye6Z1unp6QoICNC8efOyfP36QphVFkmZTsDJytmzZ9WkSRP5+Pho1KhRCgkJkYeHh7Zu3aqhQ4dma/YsPT1dNWrU0Pjx47N8/fr/EeY3Nzc31alTR3Xq1FHlypXVq1cvLViwQCNHjsw0g5YhqxPVbuT6fXh6emr16tWKjo7WTz/9pCVLluirr75Ss2bNtGzZsht+/W4kJ2fwZ+V2j7es3j+3P0cpZ8f1rfjf//6nnj17qkOHDnrllVcUEBAgV1dXjRkzxuFEyduRG5ltNpsqV66sypUrq23btqpUqZLmzZunPn362P89fvjhhw5rcP/Jy8sr08mBOcn+b5/TrWa6nX0COUFxBf5PSEiIDMNQhQoV7LNW2dnHpk2bdPXq1RteczMkJES//vqrHnjggVwrKTcqZCtXrtSpU6e0cOFCNW7c2D7+z6soSNdOHpGunQjWtGlT+3hqaqoOHTqke+65xyF/TEyMmjdvfsP3vZGM99m7d699aUGGvXv32l/PLRk/pBw7dkzS/5/5uf4OXDf79XRsbKzDDN3+/fuVnp7uMBPt4uKi5s2bq3nz5ho/frxGjx6tN954Q9HR0fZfL//bcZEd6enpOnDggMPxum/fPkmy58ut4+3fPsc7zTfffKPg4GAtXLjQ4Ti9fklASEiIli5dqtOnT9/SrGtuCg4Olp+fn/34zFiG4OPjc9O/04CAAHl4eGj//v2ZXstqLCduNdPtut3vHUAG1rgC/6dTp05ydXXV22+/nWlmwDAMnTp16l/30blzZ508eVKTJ0/O9FrGPrt06aK0tDS98847mbZJTU3N1m1NM+4odf3HZsx8/PPzSUlJ0SeffOKwXe3atVW0aFF99tlnSk1NtY/Pmzcv069Bu3Tpor///lufffZZphyXL1/WxYsXb5izdu3aCggI0LRp0xwunfXLL7/ozz//VNu2bf/lM81adHR0lrM5GetQM5Ym+Pj4qFixYlq9erXDdtf/ffzTlClTHJ5n3IkrYy1hVme3Z8xMZXyOt3JcZNc/92kYhiZPnqyCBQuqefPmknLneLuVz/FOk9Wxv2nTJm3YsMFhu86dO8swDL399tuZ9pFbM4SbNm3K8t/Fb7/9plOnTtmPz/DwcIWEhOijjz7K8m5aGZcLc3V1VUREhBYvXqyjR4/aX9+/f79++eWXXMmc4VYz3a7ChQvn6y2c4TyYcQX+T0hIiN59910NGzbMfikob29vHTx4UIsWLdJzzz2nIUOG3HQf3bt315w5czR48GD99ttvatSokS5evKhff/1Vzz//vNq3b68mTZqob9++GjNmjLZv366HHnpIBQsWVGxsrBYsWKCPP/5Yjz766G1lv+++++Tq6qoPPvhA586dk7u7u5o1a6b7779ffn5+6tGjh1588UXZbDbNnTs30/+Q3dzc9NZbb2ngwIFq1qyZunTpokOHDikyMlIhISEOsyNPP/20vv76a/Xr10/R0dF64IEHlJaWpj179ujrr7+2XwM0KwULFtQHH3ygXr16qUmTJnriiSfsl8MqX768Bg0adFufd4aBAwfq0qVL6tixo8LCwpSSkqL169frq6++Uvny5R1OouvTp4/ef/999enTR7Vr19bq1avts5RZOXjwoNq1a6dWrVppw4YN+t///qdu3brp3nvvlXTtbmyrV69W27ZtVa5cOSUmJuqTTz5R6dKl1bBhQ0m3dlxkh4eHh5YsWaIePXqoXr16+uWXX/TTTz/p9ddfty8ByI3j7VY+x/z2zTffZHm3qRYtWigwMFAPP/ywFi5cqI4dO6pt27Y6ePCgpk2bpqpVqzoUsKZNm+rpp5/WpEmTFBsbq1atWik9PV1r1qxR06ZNHU5+y665c+dq3rx56tixo8LDw+Xm5qY///xTn3/+uTw8POzXw3VxcdGMGTPUunVrVatWTb169VKpUqX0999/Kzo6Wj4+Pvrhhx8kXbtm6rJly/TAAw+of//+SktL0+TJk1W9enVt3749x5kz3E6m2xEeHq5ff/1V48ePV8mSJVWhQgX7iWHATeXzVQwAU9zOpZW+/fZbo2HDhkbhwoWNwoULG2FhYcaAAQOMvXv32rdp0qSJUa1atSw//tKlS8Ybb7xhVKhQwShYsKARFBRkPProo0ZcXJzDdtOnTzfCw8MNT09Pw9vb26hRo4bx6quvGkePHrVvU65cOaNt27aZ3qNJkyYOl6gyDMP47LPPjODgYMPV1dXh0ljr1q0z6tevb3h6eholS5a0Xyrqn9tkmDRpklGuXDnD3d3dqFu3rrFu3TojPDzcaNWqlcN2KSkpxgcffGBUq1bNcHd3N/z8/Izw8HDj7bffNs6dO/dvf8XGV199ZdSsWdNwd3c3/P39jSeffNL466+/HLa5na/ZL7/8YjzzzDNGWFiY4eXlZbi5uRkVK1Y0Bg4caBw/ftxh20uXLhm9e/c2fH19DW9vb6NLly5GYmLiDS+H9ccffxiPPvqo4e3tbfj5+RkvvPCCw6W8oqKijPbt2xslS5Y03NzcjJIlSxpPPPGEsW/fvkzve7PjIuOSVx9++GGmz+9Gl8MqXLiwERcXZzz00ENGoUKFjMDAQGPkyJFGWlpapn3k5Hi71c8xKze6HNb1X9fo6OgbXtLtn252Oax/fnx6eroxevRo+/Fcs2ZN48cffzR69OhhlCtXzmGfqampxocffmiEhYUZbm5uRvHixY3WrVsbW7ZssW8jyRgwYMC/fn5Z2bFjh/HKK68YtWrVMvz9/Y0CBQoYJUqUMB577DFj69atmbbftm2b0alTJ6No0aKGu7u7Ua5cOaNLly5GVFSUw3ZRUVFGzZo1DTc3NyMkJMSYMWOG8fLLLxseHh4O22WV/UbHW8bXYcGCBbedKeNrc/1l0zK+5v+8HNeePXuMxo0bG56enoYkLo2FW2YzDFZLA8haenq6ihcvrk6dOmW5NADAnaVDhw7avXu3/WoAgLNhjSsASdduSXr9z7Fz5szR6dOnM93yFYD5Ll++7PA8NjZWP//8M/9e4dSYcQUg6doVCAYNGqTHHntMRYsW1datWzVz5kxVqVJFW7ZsyXTNWgDmKlGihHr27Kng4GAdPnxYU6dOVXJysrZt23bD61EDVsfJWQAkXbt8UpkyZTRp0iT7pYG6d++u999/n9IK3IFatWqlL774QgkJCXJ3d1eDBg00evRoSiucGjOuAAAAsATWuAIAAMASKK4AAACwBKdf45qenq6jR4/K29ubW8wBAADcgQzD0Pnz51WyZEm5uNx4XtXpi+vRo0dVpkwZs2MAAADgXxw5ckSlS5e+4etOX1y9vb0lXfuL8PHxMTkNAAAArpeUlKQyZcrYe9uNOH1xzVge4OPjQ3EFAAC4g/3bsk5OzgIAAIAlmFpc33rrLdlsNodHWFiY/fUrV65owIABKlq0qLy8vNS5c2cdP37cxMQAAAAwi+kzrtWqVdOxY8fsj7Vr19pfGzRokH744QctWLBAq1at0tGjR9WpUycT0wIAAMAspq9xLVCggIKCgjKNnzt3TjNnztT8+fPVrFkzSdKsWbNUpUoVbdy4UfXr18/vqAAAADCR6TOusbGxKlmypIKDg/Xkk08qPj5ekrRlyxZdvXpVERER9m3DwsJUtmxZbdiw4Yb7S05OVlJSksMDAAAA1mdqca1Xr54iIyO1ZMkSTZ06VQcPHlSjRo10/vx5JSQkyM3NTUWKFHH4mMDAQCUkJNxwn2PGjJGvr6/9wTVcAQAAnIOpSwVat25t//M999yjevXqqVy5cvr666/l6emZrX0OGzZMgwcPtj/PuC4YAAAArM30pQL/VKRIEVWuXFn79+9XUFCQUlJSdPbsWYdtjh8/nuWa2Azu7u72a7Zy7VYAAADncUcV1wsXLiguLk4lSpRQeHi4ChYsqKioKPvre/fuVXx8vBo0aGBiSgAAAJjB1KUCQ4YM0SOPPKJy5crp6NGjGjlypFxdXfXEE0/I19dXvXv31uDBg+Xv7y8fHx8NHDhQDRo04IoCAAAAdyFTi+tff/2lJ554QqdOnVLx4sXVsGFDbdy4UcWLF5ckTZgwQS4uLurcubOSk5PVsmVLffLJJ2ZGBgAAgElshmEYZofIS0lJSfL19dW5c+dY7woAAHAHutW+dketcQUAAABuhOIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBJMvQEBpPKv/WR2BMs59H5bsyMAAAATUFyBuwQ/JN0+fkgCgDsLSwUAAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZwxxTX999/XzabTf/5z3/sY1euXNGAAQNUtGhReXl5qXPnzjp+/Lh5IQEAAGCaO6K4bt68WZ9++qnuueceh/FBgwbphx9+0IIFC7Rq1SodPXpUnTp1MiklAAAAzGR6cb1w4YKefPJJffbZZ/Lz87OPnzt3TjNnztT48ePVrFkzhYeHa9asWVq/fr02btx4w/0lJycrKSnJ4QEAAADrM724DhgwQG3btlVERITD+JYtW3T16lWH8bCwMJUtW1YbNmy44f7GjBkjX19f+6NMmTJ5lh0AAAD5x9Ti+uWXX2rr1q0aM2ZMptcSEhLk5uamIkWKOIwHBgYqISHhhvscNmyYzp07Z38cOXIkt2MDAADABAXMeuMjR47opZde0vLly+Xh4ZFr+3V3d5e7u3uu7Q8AAAB3BtNmXLds2aLExETVqlVLBQoUUIECBbRq1SpNmjRJBQoUUGBgoFJSUnT27FmHjzt+/LiCgoLMCQ0AAADTmDbj2rx5c+3cudNhrFevXgoLC9PQoUNVpkwZFSxYUFFRUercubMkae/evYqPj1eDBg3MiAwAAAATmVZcvb29Vb16dYexwoULq2jRovbx3r17a/DgwfL395ePj48GDhyoBg0aqH79+mZEBgAAgIlMK663YsKECXJxcVHnzp2VnJysli1b6pNPPjE7FgAAAExwRxXXlStXOjz38PDQlClTNGXKFHMCAQAA4I5h+nVcAQAAgFtBcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCaYW16lTp+qee+6Rj4+PfHx81KBBA/3yyy/2169cuaIBAwaoaNGi8vLyUufOnXX8+HETEwMAAMAsphbX0qVL6/3339eWLVv0+++/q1mzZmrfvr12794tSRo0aJB++OEHLViwQKtWrdLRo0fVqVMnMyMDAADAJAXMfPNHHnnE4fl7772nqVOnauPGjSpdurRmzpyp+fPnq1mzZpKkWbNmqUqVKtq4caPq169vRmQAAACY5I5Z45qWlqYvv/xSFy9eVIMGDbRlyxZdvXpVERER9m3CwsJUtmxZbdiw4Yb7SU5OVlJSksMDAAAA1md6cd25c6e8vLzk7u6ufv36adGiRapataoSEhLk5uamIkWKOGwfGBiohISEG+5vzJgx8vX1tT/KlCmTx58BAAAA8oPpxTU0NFTbt2/Xpk2b1L9/f/Xo0UN//PFHtvc3bNgwnTt3zv44cuRILqYFAACAWUxd4ypJbm5uqlixoiQpPDxcmzdv1scff6yuXbsqJSVFZ8+edZh1PX78uIKCgm64P3d3d7m7u+d1bAAAAOQz02dcr5eenq7k5GSFh4erYMGCioqKsr+2d+9excfHq0GDBiYmBAAAgBlMnXEdNmyYWrdurbJly+r8+fOaP3++Vq5cqaVLl8rX11e9e/fW4MGD5e/vLx8fHw0cOFANGjTgigIAAAB3IVOLa2Jiorp3765jx47J19dX99xzj5YuXaoWLVpIkiZMmCAXFxd17txZycnJatmypT755BMzIwMAAMAk2S6uZ8+e1TfffKO4uDi98sor8vf319atWxUYGKhSpUrd0j5mzpx509c9PDw0ZcoUTZkyJbsxAQAA4CSyVVx37NihiIgI+fr66tChQ3r22Wfl7++vhQsXKj4+XnPmzMntnAAAALjLZevkrMGDB6tnz56KjY2Vh4eHfbxNmzZavXp1roUDAAAAMmSruG7evFl9+/bNNF6qVKmb3hwAAAAAyK5sFVd3d/csb6W6b98+FS9ePMehAAAAgOtlq7i2a9dOo0aN0tWrVyVJNptN8fHxGjp0qDp37pyrAQEAAAApm8V13LhxunDhggICAnT58mU1adJEFStWlLe3t957773czggAAABk76oCvr6+Wr58udauXasdO3bowoULqlWrliIiInI7HwAAACAphzcgaNiwoRo2bJhbWQAAAIAbylZxnTRpUpbjNptNHh4eqlixoho3bixXV9cchQMAAAAyZKu4TpgwQSdOnNClS5fk5+cnSTpz5owKFSokLy8vJSYmKjg4WNHR0SpTpkyuBgYAAMDdKVsnZ40ePVp16tRRbGysTp06pVOnTmnfvn2qV6+ePv74Y8XHxysoKEiDBg3K7bwAAAC4S2VrxnX48OH69ttvFRISYh+rWLGiPvroI3Xu3FkHDhzQ2LFjuTQWAAAAck22ZlyPHTum1NTUTOOpqan2O2eVLFlS58+fz1k6AAAA4P9kq7g2bdpUffv21bZt2+xj27ZtU//+/dWsWTNJ0s6dO1WhQoXcSQkAAIC7XraK68yZM+Xv76/w8HC5u7vL3d1dtWvXlr+/v2bOnClJ8vLy0rhx43I1LAAAAO5e2VrjGhQUpOXLl2vPnj3at2+fJCk0NFShoaH2bZo2bZo7CQEAAADl8AYEYWFhCgsLy60sAAAAwA1lu7j+9ddf+v777xUfH6+UlBSH18aPH5/jYAAAAMA/Zau4RkVFqV27dgoODtaePXtUvXp1HTp0SIZhqFatWrmdEQAAAMjeyVnDhg3TkCFDtHPnTnl4eOjbb7/VkSNH1KRJEz322GO5nREAAADIXnH9888/1b17d0lSgQIFdPnyZXl5eWnUqFH64IMPcjUgAAAAIGWzuBYuXNi+rrVEiRKKi4uzv3by5MncSQYAAAD8Q7bWuNavX19r165VlSpV1KZNG7388svauXOnFi5cqPr16+d2RgAAACB7xXX8+PG6cOGCJOntt9/WhQsX9NVXX6lSpUpcUQAAAAB5IlvFNTg42P7nwoULa9q0abkWCAAAAMhKtta4BgcH69SpU5nGz54961BqAQAAgNySreJ66NAhpaWlZRpPTk7W33//neNQAAAAwPVua6nA999/b//z0qVL5evra3+elpamqKgolS9fPtfCAQAAABluq7h26NBBkmSz2dSjRw+H1woWLKjy5ctr3LhxuRYOAAAAyHBbxTU9PV2SVKFCBW3evFnFihXLk1AAAADA9bJ1VYGDBw/mdg4AAADgprJVXCUpKipKUVFRSkxMtM/EZvj8889zHAwAAAD4p2wV17ffflujRo1S7dq1VaJECdlsttzOBQAAADjIVnGdNm2aIiMj9fTTT+d2HgAAACBL2bqOa0pKiu6///7czgIAAADcULaKa58+fTR//vzczgIAAADcULaWCly5ckXTp0/Xr7/+qnvuuUcFCxZ0eH38+PG5Eg4AAADIkK3iumPHDt13332SpF27djm8xolaAAAAyAvZKq7R0dG5nQMAAAC4qWytcc2wf/9+LV26VJcvX5YkGYaRK6EAAACA62WruJ46dUrNmzdX5cqV1aZNGx07dkyS1Lt3b7388su5GhAAAACQsllcBw0apIIFCyo+Pl6FChWyj3ft2lVLlizJtXAAAABAhmytcV22bJmWLl2q0qVLO4xXqlRJhw8fzpVgAAAAwD9la8b14sWLDjOtGU6fPi13d/cchwIAAACul63i2qhRI82ZM8f+3GazKT09XWPHjlXTpk1zLRwAAACQIVtLBcaOHavmzZvr999/V0pKil599VXt3r1bp0+f1rp163I7IwAAAJC9Gdfq1atr3759atiwodq3b6+LFy+qU6dO2rZtm0JCQnI7IwAAAJC9GVdJ8vX11RtvvJGbWQAAAIAbytaM66xZs7RgwYJM4wsWLNDs2bNzHAoAAAC4XraK65gxY1SsWLFM4wEBARo9enSOQwEAAADXy1ZxjY+PV4UKFTKNlytXTvHx8TkOBQAAAFwvW8U1ICBAO3bsyDQeExOjokWL5jgUAAAAcL1sFdcnnnhCL774oqKjo5WWlqa0tDStWLFCL730kh5//PHczggAAABk76oC77zzjg4dOqTmzZurQIFru0hPT1f37t1Z4woAAIA8cdvF1TAMJSQkKDIyUu+++662b98uT09P1ahRQ+XKlcuLjAAAAED2imvFihW1e/duVapUSZUqVcqLXAAAAICD217j6uLiokqVKunUqVN5kQcAAADIUrZOznr//ff1yiuvaNeuXbmdBwAAAMhStk7O6t69uy5duqR7771Xbm5u8vT0dHj99OnTuRIOAAAAyJCt4jpx4sRcjgEAAADcXLaKa48ePXI7BwAAAHBT2VrjKklxcXEaPny4nnjiCSUmJkqSfvnlF+3evTvXwgEAAAAZslVcV61apRo1amjTpk1auHChLly4IOnaLV9HjhyZqwEBAAAAKZvF9bXXXtO7776r5cuXy83NzT7erFkzbdy4MdfCAQAAABmyVVx37typjh07ZhoPCAjQyZMncxwKAAAAuF62imuRIkV07NixTOPbtm1TqVKlchwKAAAAuF62iuvjjz+uoUOHKiEhQTabTenp6Vq3bp2GDBmi7t2753ZGAAAAIHvFdfTo0apSpYrKli2rCxcuqGrVqmrcuLHuv/9+DR8+PLczAgAAALd3Hdf09HR9+OGH+v7775WSkqKnn35anTt31oULF1SzZk1VqlQpr3ICAADgLndbxfW9997TW2+9pYiICHl6emr+/PkyDEOff/55XuUDAAAAJN3mUoE5c+bok08+0dKlS7V48WL98MMPmjdvntLT0/MqHwAAACDpNotrfHy82rRpY38eEREhm82mo0eP5nowAAAA4J9uq7impqbKw8PDYaxgwYK6evVqroYCAAAArndba1wNw1DPnj3l7u5uH7ty5Yr69eunwoUL28cWLlyYewkBAAAA3WZx7dGjR6axp556KtfCAAAAADdyW8V11qxZeZUDAAAAuKls3YAAAAAAyG8UVwAAAFgCxRUAAACWQHEFAACAJZhaXMeMGaM6derI29tbAQEB6tChg/bu3euwzZUrVzRgwAAVLVpUXl5e6ty5s44fP25SYgAAAJjF1OK6atUqDRgwQBs3btTy5ct19epVPfTQQ7p48aJ9m0GDBumHH37QggULtGrVKh09elSdOnUyMTUAAADMcFuXw8ptS5YscXgeGRmpgIAAbdmyRY0bN9a5c+c0c+ZMzZ8/X82aNZN07ZJcVapU0caNG1W/fn0zYgMAAMAEd9Qa13PnzkmS/P39JUlbtmzR1atXFRERYd8mLCxMZcuW1YYNG7LcR3JyspKSkhweAAAAsL47primp6frP//5jx544AFVr15dkpSQkCA3NzcVKVLEYdvAwEAlJCRkuZ8xY8bI19fX/ihTpkxeRwcAAEA+uGOK64ABA7Rr1y59+eWXOdrPsGHDdO7cOfvjyJEjuZQQAAAAZjJ1jWuGF154QT/++KNWr16t0qVL28eDgoKUkpKis2fPOsy6Hj9+XEFBQVnuy93dXe7u7nkdGQAAAPnM1BlXwzD0wgsvaNGiRVqxYoUqVKjg8Hp4eLgKFiyoqKgo+9jevXsVHx+vBg0a5HdcAAAAmMjUGdcBAwZo/vz5+u677+Tt7W1ft+rr6ytPT0/5+vqqd+/eGjx4sPz9/eXj46OBAweqQYMGXFEAAADgLmNqcZ06daok6cEHH3QYnzVrlnr27ClJmjBhglxcXNS5c2clJyerZcuW+uSTT/I5KQAAAMxmanE1DONft/Hw8NCUKVM0ZcqUfEgEAACAO9Udc1UBAAAA4GYorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAAS6C4AgAAwBIorgAAALAEiisAAAAsgeIKAAAASzC1uK5evVqPPPKISpYsKZvNpsWLFzu8bhiGRowYoRIlSsjT01MRERGKjY01JywAAABMZWpxvXjxou69915NmTIly9fHjh2rSZMmadq0adq0aZMKFy6sli1b6sqVK/mcFAAAAGYrYOabt27dWq1bt87yNcMwNHHiRA0fPlzt27eXJM2ZM0eBgYFavHixHn/88fyMCgAAAJPdsWtcDx48qISEBEVERNjHfH19Va9ePW3YsOGGH5ecnKykpCSHBwAAAKzvji2uCQkJkqTAwECH8cDAQPtrWRkzZox8fX3tjzJlyuRpTgAAAOSPO7a4ZtewYcN07tw5++PIkSNmRwIAAEAuuGOLa1BQkCTp+PHjDuPHjx+3v5YVd3d3+fj4ODwAAABgfXdsca1QoYKCgoIUFRVlH0tKStKmTZvUoEEDE5MBAADADKZeVeDChQvav3+//fnBgwe1fft2+fv7q2zZsvrPf/6jd999V5UqVVKFChX05ptvqmTJkurQoYN5oQEAAGAKU4vr77//rqZNm9qfDx48WJLUo0cPRUZG6tVXX9XFixf13HPP6ezZs2rYsKGWLFkiDw8PsyIDAADAJKYW1wcffFCGYdzwdZvNplGjRmnUqFH5mAoAAAB3ojt2jSsAAADwTxRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZAcQUAAIAlUFwBAABgCRRXAAAAWALFFQAAAJZQwOwAAADnUv61n8yOYDmH3m9rdgTAEphxBQAAgCVQXAEAAGAJFFcAAABYAsUVAAAAlkBxBQAAgCVQXAEAAGAJFFcAAABYAsUVAAAAlkBxBQAAgCVQXAEAAGAJFFcAAABYAsUVAAAAlkBxBQAAgCVQXAEAAGAJFFcAAABYAsUVAAAAlkBxBQAAgCVQXAEAAGAJFFcAAABYAsUVAAAAlkBxBQAAgCVQXAEAAGAJFFcAAABYAsUVAAAAlmCJ4jplyhSVL19eHh4eqlevnn777TezIwEAACCf3fHF9auvvtLgwYM1cuRIbd26Vffee69atmypxMREs6MBAAAgH93xxXX8+PF69tln1atXL1WtWlXTpk1ToUKF9Pnnn5sdDQAAAPmogNkBbiYlJUVbtmzRsGHD7GMuLi6KiIjQhg0bsvyY5ORkJScn25+fO3dOkpSUlJS3YbMpPfmS2REs5079Wt7pONZuH8da9nCs3T6OteypPnKp2REsZ9fbLc2OkKWMfwOGYdx0uzu6uJ48eVJpaWkKDAx0GA8MDNSePXuy/JgxY8bo7bffzjRepkyZPMmI/Oc70ewEuFtwrCG/cKwhv9zpx9r58+fl6+t7w9fv6OKaHcOGDdPgwYPtz9PT03X69GkVLVpUNpvNxGTWkZSUpDJlyujIkSPy8fExOw6cGMca8gvHGvILx1r2GIah8+fPq2TJkjfd7o4ursWKFZOrq6uOHz/uMH78+HEFBQVl+THu7u5yd3d3GCtSpEheRXRqPj4+/KNDvuBYQ37hWEN+4Vi7fTebac1wR5+c5ebmpvDwcEVFRdnH0tPTFRUVpQYNGpiYDAAAAPntjp5xlaTBgwerR48eql27turWrauJEyfq4sWL6tWrl9nRAAAAkI/u+OLatWtXnThxQiNGjFBCQoLuu+8+LVmyJNMJW8g97u7uGjlyZKYlF0Bu41hDfuFYQ37hWMtbNuPfrjsAAAAA3AHu6DWuAAAAQAaKKwAAACyB4goAAABLoLgCAADAEiiuAAAAsIQ7/nJYAKztn7dg/jfjx4/PwyRwdp06dbrlbRcuXJiHSeDsvv/++1vetl27dnmY5O5DcQWQp7Zt2+bwfOvWrUpNTVVoaKgkad++fXJ1dVV4eLgZ8eBE/nm7SMMwtGjRIvn6+qp27dqSpC1btujs2bO3VXCBrHTo0MHhuc1m0z+vLmqz2ex/TktLy69YdwWK612O2TDktejoaPufx48fL29vb82ePVt+fn6SpDNnzqhXr15q1KiRWRHhJGbNmmX/89ChQ9WlSxdNmzZNrq6ukq4ViOeff577xyPH0tPT7X/+9ddfNXToUI0ePdp+O/oNGzZo+PDhGj16tFkRnRY3ILjLNW3a1OH5zWbDVqxYYUZEOJFSpUpp2bJlqlatmsP4rl279NBDD+no0aMmJYOzKV68uNauXWv/XpZh7969uv/++3Xq1CmTksHZVK9eXdOmTVPDhg0dxtesWaPnnntOf/75p0nJnBMzrnc5ZsOQn5KSknTixIlM4ydOnND58+dNSARnlZqaqj179mQqrnv27HGYLQNyKi4uTkWKFMk07uvrq0OHDuV7HmfHjCvsmA1DXuvevbvWrFmjcePGqW7dupKkTZs26ZVXXlGjRo00e/ZskxPCWQwePFhz5szR66+/7nCsvf/++3r66adZ+oRc07hxY3l4eGju3LkKDAyUJB0/flzdu3fXlStXtGrVKpMTOhdmXGHHbBjy2rRp0zRkyBB169ZNV69elSQVKFBAvXv31ocffmhyOjiTjz76SEFBQRo3bpyOHTsmSSpRooReeeUVvfzyyyangzP5/PPP1bFjR5UtW1ZlypSRJB05ckSVKlXS4sWLzQ3nhJhxhR2zYcgvFy9eVFxcnCQpJCREhQsXNjkRnFlSUpIkcVIW8oxhGFq+fLn27NkjSapSpYoiIiIcri6A3EFxhd2lS5c0ZMgQff7551nOhlEukFv279+vuLg4NW7cWJ6enjIMg2/wyHWpqalauXKl4uLi1K1bN3l7e+vo0aPy8fGRl5eX2fHghK5cuSJ3d3e+n+UhiisyYTYMeeXUqVPq0qWLoqOjZbPZFBsbq+DgYD3zzDPy8/PTuHHjzI4IJ3H48GG1atVK8fHxSk5O1r59+xQcHKyXXnpJycnJmjZtmtkR4STS09P13nvvadq0aTp+/Lj9WHvzzTdVvnx59e7d2+yIToVbviKTY8eO6dixY6pUqZIKFy4sfrZBbhk0aJAKFiyo+Ph4FSpUyD7etWtXLVmyxMRkcDYvvfSSateurTNnzsjT09M+3rFjR0VFRZmYDM7m3XffVWRkpMaOHSs3Nzf7ePXq1TVjxgwTkzknTs6C3Y1mw3r37s1sGHLFsmXLtHTpUpUuXdphvFKlSjp8+LBJqeCM1qxZo/Xr1zsUCUkqX768/v77b5NSwRnNmTNH06dPV/PmzdWvXz/7+L333mtf84rcw4wr7JgNQ167ePGiw7GV4fTp03J3dzchEZxVenp6lrfa/Ouvv+Tt7W1CIjirv//+WxUrVsw0np6ebj9fBLmH4gq7ZcuW6YMPPmA2DHmmUaNGmjNnjv25zWZTenq6xo4dm+kubkBOPPTQQ5o4caL9uc1m04ULFzRy5Ei1adPGvGBwOlWrVtWaNWsyjX/zzTeqWbOmCYmcG0sFYMdsGPLa2LFj1bx5c/3+++9KSUnRq6++qt27d+v06dNat26d2fHgRMaNG6eWLVuqatWqunLlirp166bY2FgVK1ZMX3zxhdnx4ERGjBihHj166O+//1Z6eroWLlyovXv3as6cOfrxxx/Njud0uKoA7Nq0aaPw8HC988478vb21o4dO1SuXDk9/vjjSk9P1zfffGN2RDiBc+fOafLkyYqJidGFCxdUq1YtDRgwQCVKlDA7GpxMamqqvvzyS+3YscN+rD355JMOJ2sBuWHNmjUaNWqUw/e1ESNG6KGHHjI7mtOhuMJu165dat68uWrVqqUVK1aoXbt2DrNhISEhZkcEAAB3MYorHDAbhry0Y8eOLMdtNps8PDxUtmxZlqUgV3z//fdZjmccaxUrVlSFChXyORWAnKK4Asg3Li4u9jvKZHzr+ecdZgoWLKiuXbvq008/lYeHhykZ4RwyjrXr/xeXMWaz2dSwYUMtXrxYfn5+JqWEM/Dz88vyTln//CGpZ8+e6tWrlwnpnA9XFYDdjh07snzs3LlTsbGxSk5ONjsiLG7RokWqVKmSpk+frpiYGMXExGj69OkKDQ3V/PnzNXPmTK1YsULDhw83Oyosbvny5apTp46WL1+uc+fO6dy5c1q+fLnq1aunH3/8UatXr9apU6c0ZMgQs6PC4kaMGCEXFxe1bdtWb7/9tt5++221bdtWLi4uGjBggCpXrqz+/fvrs88+MzuqczCA/2Oz2QwXFxfDxcXFsNlsDs9dXFwMd3d3o3v37sbly5fNjgqLqlOnjrFkyZJM40uWLDHq1KljGIZhLFq0yAgODs7vaHAy1apVM9atW5dpfO3atUbVqlUNwzCM5cuXG2XKlMnvaHAynTp1MqZOnZppfNq0aUanTp0MwzCMSZMmGdWrV8/vaE6JGVfYMRuGvLZz506VK1cu03i5cuW0c+dOSdJ9992nY8eO5Xc0OJm4uDj5+PhkGvfx8dGBAwckXbtG9cmTJ/M7GpzM0qVLFRERkWm8efPmWrp0qaRrV+3JOO6QMxRX2L333nv6+OOP1bt3b9WoUUM1atRQ7969NWHCBI0bN05PPvmk/vvf/2rRokVmR4VFhYWF6f3331dKSop97OrVq3r//fcVFhYm6dpdaAIDA82KCCcRHh6uV155RSdOnLCPnThxQq+++qrq1KkjSYqNjVWZMmXMiggn4e/vrx9++CHT+A8//CB/f39J166Tzh3bcgc3IIAds2HIa1OmTFG7du1UunRp3XPPPZKuHXdpaWn2C3UfOHBAzz//vJkx4QRmzpyp9u3bq3Tp0vZyeuTIEQUHB+u7776TJF24cIHfICHH3nzzTfXv31/R0dGqW7euJGnz5s36+eefNW3aNEnX1lw3adLEzJhOg6sKwK5mzZq69957NX36dLm5uUm6Nhv27LPPKiYmRtu2bdO6dev01FNP6eDBgyanhVWdP39e8+bN0759+yRJoaGh6tatG7MRyHXp6elatmyZw7HWokULubjwy0bkrnXr1mny5Mnau3evpGvH2sCBA3X//febnMz5UFxht379erVr104uLi5ZzobVr19fc+fOVUJCgl555RWT0wIAgLsNxRUOmA1Dfvjjjz8UHx/vsNZVktq1a2dSIjijixcvatWqVVkeay+++KJJqeDMrly5kulYy+okQWQfxRVAvjlw4IA6duyonTt3OlwcPuPi3WlpaWbGgxPZtm2b2rRpo0uXLunixYvy9/fXyZMnVahQIQUEBHCGN3LNpUuX9Oqrr+rrr7/WqVOnMr3O97XcxUIfZPLHH39oyZIl+v777x0eQE699NJLqlChghITE1WoUCHt3r1bq1evVu3atbVy5Uqz48GJDBo0SI888ojOnDkjT09Pbdy4UYcPH1Z4eLg++ugjs+PBibzyyitasWKFpk6dKnd3d82YMUNvv/22SpYsqTlz5pgdz+kw4wo7ZsOQ14oVK6YVK1bonnvuka+vr3777TeFhoZqxYoVevnll7Vt2zazI8JJFClSRJs2bVJoaKiKFCmiDRs2qEqVKtq0aZN69OihPXv2mB0RTqJs2bKaM2eOHnzwQfn4+Gjr1q2qWLGi5s6dqy+++EI///yz2RGdCjOusGM2DHktLS3Nvl66WLFiOnr0qKRrl1zLOBsXyA0FCxa0Xz0gICBA8fHxkiRfX18dOXLEzGhwMqdPn1ZwcLCka+tZT58+LUlq2LChVq9ebWY0p8R1XGG3YcMGrVixQsWKFZOLi4tcXFzUsGFDjRkzRi+++CKzYcix6tWrKyYmRhUqVFC9evU0duxYubm5afr06fZv/EBuqFmzpjZv3qxKlSqpSZMmGjFihE6ePKm5c+eqevXqZseDEwkODtbBgwdVtmxZhYWF6euvv1bdunX1ww8/qEiRImbHczrMuMKO2TDkteHDhys9PV2SNGrUKB08eFCNGjXSzz//rEmTJpmcDs5k9OjRKlGihKRrdwX08/NT//79deLECU2fPt3kdHAmvXr1UkxMjCTptdde05QpU+Th4aFBgwZx6cg8wBpX2DVq1Egvv/yyOnTooG7duunMmTMaPny4pk+fri1btmjXrl1mR4QTOn36tPz8/OxrqQHAyg4fPqwtW7aoYsWK9muiI/dQXGG3dOlSXbx4UZ06ddL+/fv18MMPa9++fSpatKi++uorNWvWzOyIAADgLkZxxU0xG4bc1LFjxyyPJZvNJg8PD1WsWFHdunVTaGioCengTGrWrPmvx1rPnj3VtGlTE9LBmdxomdM/j7XGjRvL1dU1n5M5J4orgHzTs2dPLV68WEWKFFF4eLgkaevWrTp79qweeughxcTE6NChQ4qKitIDDzxgclpY2bBhwzR16lTVqFFDdevWlSRt3rxZO3bsUM+ePfXHH38oKipKCxcuVPv27U1OCyurUKGCTpw4oUuXLsnPz0+SdObMGRUqVEheXl5KTExUcHCwoqOjVaZMGZPTWh/FFXbMhiGvvfbaa0pKStLkyZPtlypKT0/XSy+9JG9vb7333nvq16+fdu/erbVr15qcFlb27LPPqmzZsnrzzTcdxt99910dPnxYn332mUaOHKmffvpJv//+u0kp4Qy++OILTZ8+XTNmzFBISIgkaf/+/erbt6+ee+45PfDAA3r88ccVFBSkb775xuS01kdxhR2zYchrxYsX17p161S5cmWH8X379un+++/XyZMntXPnTjVq1Ehnz541JyScgq+vr/0EmX/av3+/wsPDde7cOe3Zs0d16tTR+fPnTUoJZxASEqJvv/1W9913n8P4tm3b1LlzZx04cEDr169X586ddezYMXNCOhEuhwW7oKAgdevWTQcOHNC3336rb7/9VnFxcXrqqacUEhKiP//8Uz169NDQoUPNjgqLSk1NzfKORXv27LHfmc3Dw4M11cgxDw8PrV+/PtP4+vXr5eHhIenabH/Gn4HsOnbsmFJTUzONp6amKiEhQZJUsmRJfkDKJdyAAHYzZ87UunXr7L/ClSQXFxcNHDhQ999/v0aPHq0XXnhBjRo1MjElrOzpp59W79699frrr6tOnTqSrq07HD16tLp37y5JWrVqlapVq2ZmTDiBgQMHql+/ftqyZYvDsTZjxgy9/vrrkq5dSeX6WTLgdjVt2lR9+/bVjBkzVLNmTUnXZlv79+9vvxrPzp07VaFCBTNjOg2WCsDOz89Ps2fPVrt27RzGv//+e/Xo0UNnzpxRbGys6tatqzNnzpiUElaWlpam999/X5MnT9bx48clSYGBgRo4cKCGDh0qV1dXxcfHy8XFRaVLlzY5Laxu3rx5mjx5sv0GKqGhoRo4cKC6desmSbp8+bJ9DT+QXQkJCXr66acVFRWlggULSro229q8eXPNnTtXgYGBio6O1tWrV/XQQw+ZnNb6KK6we/HFF/XFF19kORvWrVs3ffzxx5oxY4YiIyM5cQY5lpSUJOnavb0BwOr27Nmjffv2Sbr2QxInMucNiivsmA0D4GxSUlKUmJhov9VwhrJly5qUCEBOUFyRJWbDkBeOHz+uIUOGKCoqSomJibr+20/GCVpATsXGxuqZZ57JdIKWYRiy2Wwca8g1aWlpioyMtH9fu/6HpBUrVpiUzDlxchayRGFFXujZs6fi4+P15ptvqkSJElw9AHmmZ8+eKlCggH788UeONeSpl156SZGRkWrbtq2qV6/OsZbHmHGFHbNhyGve3t5as2YNZ3IjzxUuXFhbtmxRWFiY2VHg5IoVK6Y5c+aoTZs2Zke5KzDjCjtmw5DXypQpk+kHIiAvVK1aVSdPnjQ7Bu4Cbm5umW50gbzDjCvsmA1DXlu2bJnGjRunTz/9VOXLlzc7DpzYihUrNHz4cI0ePVo1atSwX6YoA8uhkFvGjRunAwcOaPLkyUz45AOKK+yqVq2qefPm2S+gDOQ2Pz8/Xbp0SampqSpUqFCmMnH69GmTksHZZNxI5foiwclZyG0dO3ZUdHS0/P39Va1atUzf1xYuXGhSMufEUgHYTZw4Ua+99hqzYcgzEydONDsC7hLR0dFmR8BdokiRIurYsaPZMe4azLjCjtkwAABwJ2PGFXbMhiGvfP311+rQoYPc3NwkSX/99ZdKlixp/3XupUuXNHnyZL366qtmxoQTGDt2rAYOHChPT09J0rp161S7dm25u7tLks6fP6+hQ4fqk08+MTMmnEBiYqICAgJu+Hpqaqq2bt2qunXr5mMq58eMK4A85+rqqmPHjtm/yfv4+Gj79u0KDg6WdO1SbCVLlmTdIXKMYw355fpjrUaNGvr5559VpkwZSRxrecXF7AC4s8TFxWn48OF64oknlJiYKEn65ZdftHv3bpOTwcqu//mYn5eRVzjWkF+uP7YOHTqkq1ev3nQb5BzFFXarVq1SjRo1tGnTJi1cuFAXLlyQJMXExGjkyJEmpwMAwFq4PFbuo7jC7rXXXtO7776r5cuX29ciSlKzZs20ceNGE5MBAABwchb+YefOnZo/f36m8YCAAO5AgxxbunSpfH19JUnp6emKiorSrl27JElnz541MRmczYwZM+Tl5SXp2gkykZGRKlasmKRrJ2cBucFms+n8+fPy8PCwXx/4woULSkpKkiT7f5G7ODkLdqVLl9bXX3+t+++/X97e3oqJiVFwcLAWLVqkIUOGKC4uzuyIsKiMqwfcDBeFR24oX778Lf169uDBg/mQBs7MxcXF4VjLKK/XP+f7Wu5ixhV2jz/+uIYOHaoFCxbIZrMpPT1d69at05AhQ9S9e3ez48HC0tPTzY6Au8ShQ4fMjoC7BDe5MAczrrBLSUnRgAEDFBkZqbS0NBUoUEBpaWnq1q2bIiMj5erqanZEAABwF6O4IpP4+Hjt2rVLFy5cUM2aNVWpUiWzIwEAAFBcAQAAYA2scYVdWlqaIiMjFRUVpcTExEzrElesWGFSMgAAAIor/uGll15SZGSk2rZtq+rVq3PhZAAAcEdhqQDsihUrpjlz5qhNmzZmR4GTOnLkiGw2m0qXLi1J+u233zR//nxVrVpVzz33nMnp4EyaNGmi3r1767HHHpOnp6fZceDEZs2apa5du6pQoUJmR7krcOcs2Lm5ualixYpmx4AT69atm/0SMgkJCWrRooV+++03vfHGGxo1apTJ6eBMatasqSFDhigoKEjPPvssd/9DnnnttdcUFBSk3r17a/369WbHcXoUV9i9/PLL+vjjj8UkPPLKrl27VLduXUnS119/rerVq2v9+vWaN2+eIiMjzQ0HpzJx4kQdPXpUs2bNUmJioho3bqyqVavqo48+0vHjx82OByfy999/a/bs2Tp58qQefPBBhYWF6YMPPlBCQoLZ0ZwSSwVg17FjR0VHR8vf31/VqlVTwYIFHV5fuHChScngLLy8vLRr1y6VL19e7dq10wMPPKChQ4cqPj5eoaGhunz5stkR4aQSExM1ffp0vffee0pLS1ObNm304osvqlmzZmZHgxM5fvy4/ve//2n27Nnas2ePWrVqpd69e+uRRx65pTsI4t/xtwi7IkWKqGPHjmrSpImKFSsmX19fhweQU9WqVdO0adO0Zs0aLV++XK1atZIkHT16VEWLFjU5HZzVb7/9ppEjR2rcuHEKCAjQsGHDVKxYMT388MMaMmSI2fHgRAIDA9WwYUM1aNBALi4u2rlzp3r06KGQkBCtXLnS7HhOgRlXAPlm5cqV6tixo5KSktSjRw99/vnnkqTXX39de/bsYVYfuSYxMVFz587VrFmzFBsbq0ceeUR9+vRRy5Yt7VdMWbt2rVq1aqULFy6YnBZWd/z4cfvxduDAAXXo0EG9e/dWRESELl68qFGjRunLL7/U4cOHzY5qeRRXyM/PL8tLX/n6+qpy5coaMmSIWrRoYUIyOKO0tDQlJSXJz8/PPnbo0CEVKlRIAQEBJiaDM3Fzc1NISIieeeYZ9ezZU8WLF8+0TVJSktq3b88955EjjzzyiJYuXarKlSurT58+6t69u/z9/R22SUxMVFBQUKbro+P2cR1XaOLEiVmOnz17Vlu2bNHDDz+sb775Ro888kj+BoPTuXz5sgzDsJfWw4cPa9GiRapSpYpatmxpcjo4k6ioKDVq1Oim2/j4+FBakWMBAQFatWqVGjRocMNtihcvroMHD+ZjKufFjCv+1fjx4/XNN99wmQ/k2EMPPaROnTqpX79+Onv2rMLCwlSwYEGdPHlS48ePV//+/c2OCAC4g1Fc8a/27dun+vXr6/Tp02ZHgcUVK1ZMq1atUrVq1TRjxgz997//1bZt2/Ttt99qxIgR+vPPP82OCAurWbPmLd/xb+vWrXmcBs5s0qRJt7ztiy++mIdJ7j4sFcC/Sk5Olpubm9kx4AQuXbokb29vSdKyZcvUqVMnubi4qH79+py0gBzr0KGD2RFwl5gwYcItbWez2SiuuYziin81c+ZM3XfffWbHgBOoWLGiFi9erI4dO2rp0qUaNGiQpGsnLvj4+JicDlY3cuRIsyPgLsF6VfNQXKHBgwdnOX7u3Dlt3bpV+/bt0+rVq/M5FZzRiBEj1K1bNw0aNEjNmjWzn8ywbNky1axZ0+R0cDZnz57VN998o7i4OL3yyivy9/fX1q1bFRgYqFKlSpkdD0A2sMYVatq0aZbjPj4+Cg0NVf/+/VWhQoV8TgVnlZCQoGPHjunee++130nmt99+k4+Pj8LCwkxOB2exY8cORUREyNfXV4cOHdLevXsVHBys4cOHKz4+XnPmzDE7IpxEWlqaIiMjFRUVpcTExEyXvFqxYoVJyZwTxRVAvtu/f7/i4uLUuHFjeXp6yjCMWz6pBrgVERERqlWrlsaOHStvb2/FxMQoODhY69evV7du3XTo0CGzI8JJvPDCC4qMjFTbtm1VokSJTN/LbnU9LG4NSwUA5JtTp06pS5cuio6Ols1mU2xsrIKDg9W7d2/5+flp3LhxZkeEk9i8ebM+/fTTTOOlSpVSQkKCCYngrL788kt9/fXXatOmjdlR7gouZgcAcPcYNGiQChYsqPj4eBUqVMg+3rVrVy1ZssTEZHA27u7uSkpKyjS+b9++LO+iBWSXm5ubKlasaHaMuwbFFUC+WbZsmT744AOVLl3aYbxSpUpcDgu5ql27dho1apSuXr0q6dplieLj4zV06FB17tzZ5HRwJi+//LI+/vhjsfIyf7BUAEC+uXjxosNMa4bTp0/L3d3dhERwVuPGjdOjjz6qgIAAXb58WU2aNFFCQoIaNGig9957z+x4sLhOnTo5PF+xYoV++eUXVatWTQULFnR4beHChfkZzelRXAHkm0aNGmnOnDl65513JF2bBUtPT9fYsWNveHULIDt8fX21fPlyrVu3TjExMbpw4YJq1aqliIgIs6PBCfj6+jo879ixo0lJ7j5cVQBAvtm1a5eaN2+uWrVqacWKFWrXrp12796t06dPa926dQoJCTE7IpzEnDlz1LVr10wz+SkpKfryyy/VvXt3k5IByAmKK4B8de7cOU2ePNlhFmzAgAEqUaKE2dHgRFxdXXXs2DEFBAQ4jJ86dUoBAQFKS0szKRmAnGCpAIB85evrqzfeeMNh7K+//tJzzz2n6dOnm5QKzuZG1wb+66+/Mv2aF8iJmjVrZnms2Ww2eXh4qGLFiurZsyfLoXIJVxUAYLpTp05p5syZZseAE6hZs6Zq1aolm81mX5aS8bj33nvVqFEj1rkiV7Vq1UoHDhxQ4cKF1bRpUzVt2lReXl6Ki4tTnTp1dOzYMUVEROi7774zO6pTYMYVAOA0OnToIEnavn27WrZsKS8vL/trbm5uKl++PJfDQq46efKkXn75Zb355psO4++++64OHz6sZcuWaeTIkXrnnXfUvn17k1I6D9a4AjBdTEyMatWqxbpD5JrZs2era9eu8vDwMDsKnJyvr6+2bNmS6SYE+/fvV3h4uM6dO6c9e/aoTp06On/+vEkpnQdLBQAATqdHjx6UVuQLDw8PrV+/PtP4+vXr7cdgeno6x2MuYakAgDx3/cW6r3f27Nn8CYK7RlpamiZMmKCvv/5a8fHxSklJcXj99OnTJiWDsxk4cKD69eunLVu2qE6dOpKkzZs3a8aMGXr99dclSUuXLtV9991nYkrnwVIBAHmuV69et7TdrFmz8jgJ7hYjRozQjBkz9PLLL2v48OF64403dOjQIS1evFgjRozQiy++aHZEOJF58+Zp8uTJ2rt3ryQpNDRUAwcOVLdu3SRJly9ftl9lADlDcQUAOJ2QkBBNmjRJbdu2lbe3t7Zv324f27hxo+bPn292RADZwBpXAIDTSUhIUI0aNSRJXl5eOnfunCTp4Ycf1k8//WRmNAA5QHEFADid0qVL69ixY5Kuzb4uW7ZM0rW1h9ffBha4Xf7+/jp58qQkyc/PT/7+/jd8IHdxchYAwOl07NhRUVFRqlevngYOHKinnnpKM2fOVHx8vAYNGmR2PFjchAkT5O3tLUmaOHGiuWHuMqxxBQA4vY0bN2r9+vWqVKmSHnnkEbPjAMgmiisAwKlcvXpVffv21ZtvvqkKFSqYHQdOKikp6Za28/HxyeMkdxeKKwDA6fj6+mr79u0UV+QZFxcX2Wy2G75uGIZsNht3BMxlrHEFADidDh06aPHixaxnRZ6Jjo62/9kwDLVp00YzZsxQqVKlTEzl/CiuAACnU6lSJY0aNUrr1q1TeHi4Chcu7PA6NyBATjVp0sThuaurq+rXr6/g4GCTEt0dWCoAAHA6N1siYLPZdODAgXxMg7uBt7e3YmJiKK55jBlXAIDTOXjwoNkRAOQBbkAAAHBaKSkp2rt3r1JTU82OgrvAzU7WQu5gxhUA4HQuXbqkgQMHavbs2ZKkffv2KTg4WAMHDlSpUqX02muvmZwQVtepUyeH51euXFG/fv0yradeuHBhfsZyesy4AgCczrBhwxQTE6OVK1fKw8PDPh4REaGvvvrKxGRwFr6+vg6Pp556SiVLlsw0jtzFyVkAAKdTrlw5ffXVV6pfv77DSTP79+9XrVq1bvni8QDuLMy4AgCczokTJxQQEJBp/OLFi6xDBCyM4goAcDq1a9fWTz/9ZH+eUVZnzJihBg0amBULQA5xchYAwOmMHj1arVu31h9//KHU1FR9/PHH+uOPP7R+/XqtWrXK7HgAsokZVwCA02nYsKG2b9+u1NRU1ahRQ8uWLVNAQIA2bNig8PBws+MByCZOzgIAAIAlsFQAAOAUbudKAT4+PnmYBEBeYcYVAOAUXFxcbvmKAWlpaXmcBkBeYMYVAOAUoqOj7X8+dOiQXnvtNfXs2dN+FYENGzZo9uzZGjNmjFkRAeQQM64AAKfTvHlz9enTR0888YTD+Pz58zV9+nStXLnSnGAAcoTiCgBwOoUKFVJMTIwqVarkML5v3z7dd999unTpkknJAOQEl8MCADidMmXK6LPPPss0PmPGDJUpU8aERAByAzOuAACn8/PPP6tz586qWLGi6tWrJ0n67bffFBsbq2+//VZt2rQxOSGA7KC4AgCc0l9//aVPPvlEe/bskSRVqVJF/fr1Y8YVsDCKKwAAACyBy2EBAJzS2bNnNXPmTP3555+SpGrVqumZZ56Rr6+vyckAZBczrgAAp/P777+rZcuW8vT0VN26dSVJmzdv1uXLl7Vs2TLVqlXL5IQAsoPiCgBwOo0aNVLFihX12WefqUCBa79cTE1NVZ8+fXTgwAGtXr3a5IQAsoPiCgBwOp6entq2bZvCwsIcxv/44w/Vrl2b67gCFsV1XAEATsfHx0fx8fGZxo8cOSJvb28TEgHIDRRXAIDT6dq1q3r37q2vvvpKR44c0ZEjR/Tll19meRtYANbBVQUAAE7no48+ks1mU/fu3ZWamipJKliwoPr376/333/f5HQAsos1rgAAp3Xp0iXFxcVJkkJCQlSoUCGTEwHICYorAAAALIGlAgAAp/HMM8/c0naff/55HicBkBeYcQUAOA0XFxeVK1dONWvW1M3+97Zo0aJ8TAUgtzDjCgBwGv3799cXX3yhgwcPqlevXnrqqafk7+9vdiwAuYQZVwCAU0lOTtbChQv1+eefa/369Wrbtq169+6thx56SDabzex4AHKA4goAcFqHDx9WZGSk5syZo9TUVO3evVteXl5mxwKQTdyAAADgtFxcXGSz2WQYhtLS0syOAyCHKK4AAKeSnJysL774Qi1atFDlypW1c+dOTZ48WfHx8cy2AhbHyVkAAKfx/PPP68svv1SZMmX0zDPP6IsvvlCxYsXMjgUgl7DGFQDgNFxcXFS2bFnVrFnzpidiLVy4MB9TAcgtzLgCAJxG9+7duXIA4MSYcQUAAIAlcHIWAAAALIHiCgAAAEuguAIAAMASKK4AAACwBIorAAAALIHiCgAAAEuguAJADp04cUL9+/dX2bJl5e7urqCgILVs2VLr1q0zO1quKF++vCZOnGh2DADgBgQAkFOdO3dWSkqKZs+ereDgYB0/flxRUVE6deqU2dEAwKkw4woAOXD27FmtWbNGH3zwgZo2bapy5cqpbt26GjZsmNq1a2ffpk+fPipevLh8fHzUrFkzxcTEOOzn3XffVUBAgLy9vdWnTx+99tpruu++++yv9+zZUx06dNDo0aMVGBioIkWKaNSoUUpNTdUrr7wif39/lS5dWrNmzXLY75EjR9SlSxcVKVJE/v7+at++vQ4dOpRpvx999JFKlCihokWLasCAAbp69aok6cEHH9Thw4c1aNAg2Ww27koFwFQUVwDIAS8vL3l5eWnx4sVKTk7OcpvHHntMiYmJ+uWXX7RlyxbVqlVLzZs31+nTpyVJ8+bN03vvvacPPvhAW7ZsUdmyZTV16tRM+1mxYoWOHj2q1atXa/z48Ro5cqQefvhh+fn5adOmTerXr5/69u2rv/76S5J09epVtWzZUt7e3lqzZo3WrVsnLy8vtWrVSikpKfb9RkdHKy4uTtHR0Zo9e7YiIyMVGRkpSVq4cKFKly6tUaNG6dixYzp27Fgu/w0CwG0wAAA58s033xh+fn6Gh4eHcf/99xvDhg0zYmJiDMMwjDVr1hg+Pj7GlStXHD4mJCTE+PTTTw3DMIx69eoZAwYMcHj9gQceMO6991778x49ehjlypUz0tLS7GOhoaFGo0aN7M9TU1ONwoULG1988YVhGIYxd+5cIzQ01EhPT7dvk5ycbHh6ehpLly512G9qaqp9m8cee8zo2rWr/Xm5cuWMCRMmZOevBgByFTOuAJBDnTt31tGjR/X999+rVatWWrlypWrVqqXIyEjFxMTowoULKlq0qH121svLSwcPHlRcXJwkae/evapbt67DPq9/LknVqlWTi8v//7YdGBioGjVq2J+7urqqaNGiSkxMlCTFxMRo//798vb2tr+vv7+/rly5Yn/vjP26urran5coUcK+DwC4k3ByFgDkAg8PD7Vo0UItWrTQm2++qT59+mjkyJF6/vnnVaJECa1cuTLTxxQpUuS23qNgwYIOz202W5Zj6enpkqQLFy4oPDxc8+bNy7Sv4sWL33S/GfsAgDsJxRUA8kDVqlW1ePFi1apVSwkJCSpQoIDKly+f5bahoaHavHmzunfvbh/bvHlzjjPUqlVLX331lQICAuTj45Pt/bi5uSktLS3HeQAgp1gqAAA5cOrUKTVr1kz/+9//tGPHDh08eFALFizQ2LFj1b59e0VERKhBgwbq0KGDli1bpkOHDmn9+vV644039Pvvv0uSBg4cqJkzZ2r27NmKjY3Vu+++qx07duT4DP4nn3xSxYoVU/v27bVmzRodPHhQK1eu1Isvvmg/getWlC9fXqtXr9bff/+tkydP5igTAOQEM64AkANeXl6qV6+eJkyYoLi4OF29elVlypTRs88+q9dff102m00///yz3njjDfXq1UsnTpxQUFCQGjdurMDAQEnXCuaBAwc0ZMgQXblyRV26dFHPnj3122+/5ShboUKFtHr1ag0dOlSdOnXS+fPnVapUKTVv3vy2ZmBHjRqlvn37KiQkRMnJyTIMI0e5ACC7bAbfgQDgjtOiRQsFBQVp7ty5ZkcBgDsGM64AYLJLly5p2rRpatmypVxdXfXFF1/o119/1fLly82OBgB3FGZcAcBkly9f1iOPPKJt27bpypUrCg0N1fDhw9WpUyezowHAHYXiCgAAAEvgqgIAAACwBIorAAAALIHiCgAAAEuguAIAAMASKK4AAACwBIorAAAALIHiCgAAAEuguAIAAMAS/h+2Fx1OxjEvQgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "top_domains = distinct_subscribers['Domain'].value_counts().head(10)\n",
    "\n",
    "# Plot a chart to display the percentage of health braces per domain\n",
    "plt.figure(figsize=(8, 6))\n",
    "top_domains.plot(kind='bar')\n",
    "plt.title('Top 10 Domains by Subscriber Count')\n",
    "plt.xlabel('Domain')\n",
    "plt.ylabel('Subscriber Count')\n",
    "plt.show()\n",
    "\n",
    "# Plot a chart to display the percentage of health braces per segment\n",
    "plt.figure(figsize=(8, 6))\n",
    "segment_counts.plot(kind='bar')\n",
    "plt.title('Percentage of Subscribers in Each Segment')\n",
    "plt.xlabel('Segment')\n",
    "plt.ylabel('Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekly_stats = df_events.groupby('start_of_week')[['sent_score','open_score','click_score','unsub_score','bounce_score','complaint_score']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(weekly_stats)\n",
    "\n",
    "# Plot a stacked bar chart\n",
    "df.plot(kind='bar', stacked=True, figsize=(8, 6))\n",
    "\n",
    "# Add a title and labels\n",
    "plt.title('Weekly Engagement Score Chart')\n",
    "plt.xlabel('Week')\n",
    "plt.ylabel('Score')\n",
    "\n",
    "# Show the plot\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",
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