solveig_master/plot.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Angle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>category</th>\n",
       "      <th>angle</th>\n",
       "      <th>count</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>high angle</td>\n",
       "      <td>2</td>\n",
       "      <td>9.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>eye-level</td>\n",
       "      <td>13</td>\n",
       "      <td>59.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>low angle</td>\n",
       "      <td>7</td>\n",
       "      <td>31.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>high angle</td>\n",
       "      <td>5</td>\n",
       "      <td>27.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>eye-level</td>\n",
       "      <td>8</td>\n",
       "      <td>44.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>low angle</td>\n",
       "      <td>5</td>\n",
       "      <td>27.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>casual</td>\n",
       "      <td>high angle</td>\n",
       "      <td>6</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>casual</td>\n",
       "      <td>eye-level</td>\n",
       "      <td>3</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>casual</td>\n",
       "      <td>low angle</td>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       category       angle  count        pct\n",
       "0  white-collar  high angle      2   9.090909\n",
       "1  white-collar   eye-level     13  59.090909\n",
       "2  white-collar   low angle      7  31.818182\n",
       "3   blue-collar  high angle      5  27.777778\n",
       "4   blue-collar   eye-level      8  44.444444\n",
       "5   blue-collar   low angle      5  27.777778\n",
       "6        casual  high angle      6  60.000000\n",
       "7        casual   eye-level      3  30.000000\n",
       "8        casual   low angle      1  10.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'white-collar',\n",
    "        'white-collar',\n",
    "        'white-collar',\n",
    "        'blue-collar',\n",
    "        'blue-collar',\n",
    "        'blue-collar',\n",
    "        'casual',\n",
    "        'casual',\n",
    "        'casual',\n",
    "    ],\n",
    "    'angle': [\n",
    "        'high angle',\n",
    "        'eye-level',\n",
    "        'low angle',\n",
    "        'high angle',\n",
    "        'eye-level',\n",
    "        'low angle',\n",
    "        'high angle',\n",
    "        'eye-level',\n",
    "        'low angle',\n",
    "    ],\n",
    "    'count': [\n",
    "        2,\n",
    "        13,\n",
    "        7,\n",
    "        5,\n",
    "        8,\n",
    "        5,\n",
    "        6,\n",
    "        3,\n",
    "        1,\n",
    "    ],\n",
    "    'pct': [\n",
    "            2/22*100,\n",
    "            13/22*100,\n",
    "            7/22*100,\n",
    "            5/18*100,\n",
    "            8/18*100,\n",
    "            5/18*100,\n",
    "            6/10*100,\n",
    "            3/10*100,\n",
    "            1/10*100,\n",
    "        ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 725.625x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(\n",
    "    data=df, kind=\"bar\",\n",
    "    x=\"category\", y=\"pct\", hue=\"angle\",\n",
    "    palette=\"Blues\", alpha=.6, height=6\n",
    ")\n",
    "g.despine(left=True)\n",
    "sns.set_style(\"ticks\",{'axes.grid' : True})\n",
    "g.set_axis_labels(\"\", \"percent of total images in category (%)\")\n",
    "g.legend.set_title(\"\")\n",
    "g.savefig('plots/angle.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>category</th>\n",
       "      <th>contact</th>\n",
       "      <th>count</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>demand structure</td>\n",
       "      <td>14</td>\n",
       "      <td>63.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>offer structure</td>\n",
       "      <td>8</td>\n",
       "      <td>36.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>demand structure</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>offer structure</td>\n",
       "      <td>18</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>casual</td>\n",
       "      <td>demand structure</td>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>casual</td>\n",
       "      <td>offer structure</td>\n",
       "      <td>9</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       category           contact  count         pct\n",
       "0  white-collar  demand structure     14   63.636364\n",
       "1  white-collar   offer structure      8   36.363636\n",
       "2   blue-collar  demand structure      0    0.000000\n",
       "3   blue-collar   offer structure     18  100.000000\n",
       "4        casual  demand structure      1   10.000000\n",
       "5        casual   offer structure      9   90.000000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'white-collar',\n",
    "        'white-collar',\n",
    "        'blue-collar',\n",
    "        'blue-collar',\n",
    "        'casual',\n",
    "        'casual',\n",
    "    ],\n",
    "    'contact': [\n",
    "        'demand structure',\n",
    "        'offer structure',\n",
    "        'demand structure',\n",
    "        'offer structure',\n",
    "        'demand structure',\n",
    "        'offer structure',\n",
    "    ],\n",
    "    'count': [\n",
    "        14,\n",
    "        8,\n",
    "        0,\n",
    "        18,\n",
    "        1,\n",
    "        9,\n",
    "    ],\n",
    "    'pct': [\n",
    "            14/22*100,\n",
    "            8/22*100,\n",
    "            0/18*100,\n",
    "            18/18*100,\n",
    "            1/10*100,\n",
    "            9/10*100,\n",
    "        ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 773.75x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(\n",
    "    data=df, kind=\"bar\",\n",
    "    x=\"category\", y=\"pct\", hue=\"contact\",\n",
    "    palette=\"Blues\", alpha=.6, height=6\n",
    ")\n",
    "g.despine(left=True)\n",
    "sns.set_style(\"ticks\",{'axes.grid' : True})\n",
    "g.set_axis_labels(\"\", \"percent of total images in category (%)\")\n",
    "g.legend.set_title(\"\")\n",
    "g.savefig('plots/contact.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Point-of-view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>category</th>\n",
       "      <th>poit_of_view</th>\n",
       "      <th>count</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>frontal</td>\n",
       "      <td>16</td>\n",
       "      <td>72.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>oblique</td>\n",
       "      <td>2</td>\n",
       "      <td>9.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>frontal</td>\n",
       "      <td>4</td>\n",
       "      <td>22.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>oblique</td>\n",
       "      <td>14</td>\n",
       "      <td>77.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>casual</td>\n",
       "      <td>frontal</td>\n",
       "      <td>3</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>casual</td>\n",
       "      <td>oblique</td>\n",
       "      <td>7</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       category poit_of_view  count        pct\n",
       "0  white-collar      frontal     16  72.727273\n",
       "1  white-collar      oblique      2   9.090909\n",
       "2   blue-collar      frontal      4  22.222222\n",
       "3   blue-collar      oblique     14  77.777778\n",
       "4        casual      frontal      3  30.000000\n",
       "5        casual      oblique      7  70.000000"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'white-collar',\n",
    "        'white-collar',\n",
    "        'blue-collar',\n",
    "        'blue-collar',\n",
    "        'casual',\n",
    "        'casual',\n",
    "    ],\n",
    "    'poit_of_view': [\n",
    "        'frontal',\n",
    "        'oblique',\n",
    "        'frontal',\n",
    "        'oblique',\n",
    "        'frontal',\n",
    "        'oblique',\n",
    "    ],\n",
    "    'count': [\n",
    "        16,\n",
    "        2,\n",
    "        4,\n",
    "        14,\n",
    "        3,\n",
    "        7,\n",
    "    ],\n",
    "    'pct': [\n",
    "            16/22*100,\n",
    "            2/22*100,\n",
    "            4/18*100,\n",
    "            14/18*100,\n",
    "            3/10*100,\n",
    "            7/10*100,\n",
    "        ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 704.847x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(\n",
    "    data=df, kind=\"bar\",\n",
    "    x=\"category\", y=\"pct\", hue=\"poit_of_view\",\n",
    "    palette=\"Blues\", alpha=.6, height=6\n",
    ")\n",
    "g.despine(left=True)\n",
    "sns.set_style(\"ticks\",{'axes.grid' : True})\n",
    "g.set_axis_labels(\"\", \"percent of total images in category (%)\")\n",
    "g.legend.set_title(\"\")\n",
    "g.savefig('plots/point-of-view.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>category</th>\n",
       "      <th>distance</th>\n",
       "      <th>count</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>long shot</td>\n",
       "      <td>6</td>\n",
       "      <td>27.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>medium shot</td>\n",
       "      <td>16</td>\n",
       "      <td>72.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>white-collar</td>\n",
       "      <td>close-up</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>long shot</td>\n",
       "      <td>15</td>\n",
       "      <td>83.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>medium shot</td>\n",
       "      <td>3</td>\n",
       "      <td>16.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>close-up</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>casual</td>\n",
       "      <td>long shot</td>\n",
       "      <td>3</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>casual</td>\n",
       "      <td>medium shot</td>\n",
       "      <td>5</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>casual</td>\n",
       "      <td>close-up</td>\n",
       "      <td>2</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       category     distance  count        pct\n",
       "0  white-collar    long shot      6  27.272727\n",
       "1  white-collar  medium shot     16  72.727273\n",
       "2  white-collar     close-up      0   0.000000\n",
       "3   blue-collar    long shot     15  83.333333\n",
       "4   blue-collar  medium shot      3  16.666667\n",
       "5   blue-collar     close-up      0   0.000000\n",
       "6        casual    long shot      3  30.000000\n",
       "7        casual  medium shot      5  50.000000\n",
       "8        casual     close-up      2  20.000000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'white-collar',\n",
    "        'white-collar',\n",
    "        'white-collar',\n",
    "        'blue-collar',\n",
    "        'blue-collar',\n",
    "        'blue-collar',\n",
    "        'casual',\n",
    "        'casual',\n",
    "        'casual',\n",
    "    ],\n",
    "    'distance': [\n",
    "        'long shot',\n",
    "        'medium shot',\n",
    "        'close-up',\n",
    "        'long shot',\n",
    "        'medium shot',\n",
    "        'close-up',\n",
    "        'long shot',\n",
    "        'medium shot',\n",
    "        'close-up',\n",
    "    ],\n",
    "    'count': [\n",
    "        6,\n",
    "        16,\n",
    "        0,\n",
    "        15,\n",
    "        3,\n",
    "        0,\n",
    "        3,\n",
    "        5,\n",
    "        2,\n",
    "    ],\n",
    "    'pct': [\n",
    "            6/22*100,\n",
    "            16/22*100,\n",
    "            0/22*100,\n",
    "            15/18*100,\n",
    "            3/18*100,\n",
    "            0/18*100,\n",
    "            3/10*100,\n",
    "            5/10*100,\n",
    "            2/10*100,\n",
    "        ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 742.25x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(\n",
    "    data=df, kind=\"bar\",\n",
    "    x=\"category\", y=\"pct\", hue=\"distance\",\n",
    "    palette=\"Blues\", alpha=.6, height=6\n",
    ")\n",
    "g.despine(left=True)\n",
    "sns.set_style(\"ticks\",{'axes.grid' : True})\n",
    "g.set_axis_labels(\"\", \"percent of total images in category (%)\")\n",
    "g.legend.set_title(\"\")\n",
    "g.savefig('plots/distance.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pie Charts about Subtypes of Processes"
   ]
  },
  {
   "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>category</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>non-transactional\\naction</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speech</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unidirectional\\ntransactional\\naction</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>conversion</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>non-transactional\\nreaction</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bidirectional\\ntransactional\\naction</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>unidirectional\\ntransactional\\nreaction</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bidirectional\\ntransactional\\nreaction</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  category  count\n",
       "0                non-transactional\\naction     26\n",
       "1                                   speech      2\n",
       "2    unidirectional\\ntransactional\\naction     23\n",
       "3                               conversion      2\n",
       "4              non-transactional\\nreaction      6\n",
       "5     bidirectional\\ntransactional\\naction      2\n",
       "6  unidirectional\\ntransactional\\nreaction      5\n",
       "7   bidirectional\\ntransactional\\nreaction      2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'non-transactional\\naction',\n",
    "        'speech',\n",
    "        'unidirectional\\ntransactional\\naction',\n",
    "        'conversion',\n",
    "        'non-transactional\\nreaction',\n",
    "        'bidirectional\\ntransactional\\naction',\n",
    "        'unidirectional\\ntransactional\\nreaction',\n",
    "        'bidirectional\\ntransactional\\nreaction',\n",
    "        \n",
    "        \n",
    "    ],\n",
    "    'count': [\n",
    "        26,\n",
    "        2,\n",
    "        23,\n",
    "        2,\n",
    "        6,\n",
    "        2,\n",
    "        5,\n",
    "        2,\n",
    "    ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define Seaborn color palette to use\n",
    "colors = sns.color_palette('Blues')[0:len(df)]\n",
    "\n",
    "#create pie chart\n",
    "fig = plt.figure(figsize=(6,6))\n",
    "plt.pie(df['count'], labels=df['category'], colors = colors, autopct='%.0f%%', startangle=0)\n",
    "plt.tight_layout()\n",
    "plt.savefig('plots/narrative_processes.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>category</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>classification\\novert\\ntaxonomy</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>analytical\\ntemporal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>analytical\\nexhaustive</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>analytical\\ndistributed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>analytical\\ndisarranged</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>analytical\\ntopological</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>analytical\\nexploded</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>analytical\\ninclusive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>symbolic\\nsuggestive</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>symbolic\\nattributive</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          category  count\n",
       "0  classification\\novert\\ntaxonomy      4\n",
       "1             analytical\\ntemporal      2\n",
       "2           analytical\\nexhaustive     18\n",
       "3          analytical\\ndistributed      1\n",
       "4          analytical\\ndisarranged      6\n",
       "5          analytical\\ntopological      2\n",
       "6             analytical\\nexploded      4\n",
       "7            analytical\\ninclusive      1\n",
       "8             symbolic\\nsuggestive     13\n",
       "9            symbolic\\nattributive      5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'classification\\novert\\ntaxonomy',\n",
    "        'analytical\\ntemporal',\n",
    "        'analytical\\nexhaustive',\n",
    "        'analytical\\ndistributed',\n",
    "        'analytical\\ndisarranged',\n",
    "        'analytical\\ntopological',\n",
    "        'analytical\\nexploded',\n",
    "        'analytical\\ninclusive',\n",
    "        'symbolic\\nsuggestive',\n",
    "        'symbolic\\nattributive',\n",
    "    ],\n",
    "    'count': [\n",
    "        4,\n",
    "        2,\n",
    "        18,\n",
    "        1,\n",
    "        6,\n",
    "        2,\n",
    "        4,\n",
    "        1,\n",
    "        13,\n",
    "        5,\n",
    "    ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wxEioTP0J4RUkiRKiFrI7HJzIKSEps5AjmYXkyULwWiXIoKV5uInm4b6EmfTuDkcIcZUkiRKilrDY7Bw7V8SRs4UkZxXJNJ2XCPU9n1CZCDbKCJUQtYkkUUJ4uJM5xexLy+f3swVYbPLj6s3CTTpahJtoHmEiwEfr7nCEEFcgSZQQHii/1MqBM/nsS8snu9ji7nCEG9Tz19M83ETLCD+MOrW7wxFCVECSKCE8hM3uICmzkH1p+RzLLkJ+MgWAWoH4MBPt6/nTIMjg7nCEEBeQJEoIN8soKGVfWj4H0/Mptsg6J3FpQQYt7er50ybKD4NWRqeEcDdJooRwA4fDwZHMQram5nA6r9Td4YhaRqNSaBFhomP9ACL85O4+IdxFkighapDFZmf/mXy2ncglR9Y6CReIDvChY3QATUN9pZ+fEDVMkighakCR2cbOU7nsPpVLkUzZiWrgp1fTpUEg7aL80ahV7g5HiDpBkighqlF2kYVtJ3LYfyYfq11+1ET189WpuS4mkA71/dFKMiVEtZIkSohqcDq3hK0ncjhythD5ARPuYNSq/kimAtBpJJkSojpIEiWEC2UUlPJL8jmOnitydyhCAGDQqugUHUinaH/0GrmjTwhXkiRKCBfILbbw67FzJKYXyMiT8Eh6jYpO0QF0jg7AR8ojCOESkkQJcQ0KzVY2peSw53Qu0pFF1AY6tUKXBoFcFxMoa6aEuEaSRAlxFUqtdradyGHbiRzpZydqJT+9ht6Ng2kZ4efuUISotSSJEqIKbHYHu07lsvl4tpQqEF6hnr+em+NDifL3cXcoQtQ6kkQJUUmHzxbwc1IWuSVWd4cihMu1jDDRu3EIfnqNu0MRotaQJEqIK8gtsbDmcCbJWXLHnfBuWrVCl5hAujSQ9VJCVIYkUUJcgs3uYPuJHH5LycYihTJFHSLrpYSoHEmihKjAyZxifjqcSWah2d2hCOE2DQJ96N88nECD1t2hCOGRJIkS4gLFFhvrk7PYl5bv7lCE8AhatULPhsF0ig5AUaTBsRAXkiRKiD/sS8tjfXIWxXLXnRAXqR/gw63Nwwg26twdihAeQ5IoUedlF1tY+XsGJ3JK3B2KEB5No1Lo3jCI62ICUcmolBCSRIm6bffpPH5OypSCmUJUQZSfnltbhBPqK6NSom6TJErUSYVmKyt/PytlC4S4SmoFusUFc32DQFQqGZUSdZMkUaLOOXK2kJWHMmTtkxAuEG7SMbBVBCGyVkrUQZJEiTrDYrOzLimLPafz3B2KEF5Fq1bo1yxM6kqJOkeSKFEnnC0o5fuD6WQWWtwdihBeq22UH7fEh6KRaueijpAkSni9nSdzWZ+chVWqjgtR7cJNOv7eKkJKIYg6QZIo4bXMVjs//J7B4bOF7g5FiDpFp1bo1yycFhEmd4ciRLWSJEp4pZxiC9/uOyNtW4Rwo/b1/LkpPhSN3L0nvJQkUcLrpJwr4r8H0imxyt13QrhbuEnHHa0jCZL+e8ILSRIlvMq21BzWH81CvquF8Bw6tYoBLcNpEurr7lCEcClJooRXsNrsrDp0lgPpBe4ORQhRAQW4sUkInWMC3R2KEC4jSZSo9fJLrHy3/wxn8kvdHYoQ4go61Pfn5vhQ6b0nvIIkUaJWO5lTzPID6RSabe4ORQhRSY2CjQxsFYFeI/WkRO0mSZSotfam5bH60Fmkd7AQtU+Yr45720bh56NxdyhCXDVJokSttCklm1+PnXN3GEKIa2DSqbm7bRSRfnp3hyLEVZEkStQqDoeDn5Oz2H4i192hCCFcQKtWGNAygni5c0/UQpJEiVrDbnfw46EMDpyRO/CE8CYK0KdJCNfJnXuilpEkStQKFpud/x5IJzmryN2hCCGqSdfYQHo2CnF3GEJUmiRRwuOVWm18s/cMJ3NL3B2KEKKaXRcTwI1NQt0dhhCVIkmU8GiFZitf7Ukjo0B64AlRV3SsH8DN8SEoUktKeDhJooTHyim2sHTPaXKKre4ORQhRw9rV8+dvTUMlkRIeTZIo4ZGyisws2XVaimgKUYe1jvTj1uZhkkgJjyXlYoXHySm28OVuSaCEqOv2n8nnfwczsNvlvb7wTJJECY+SV2Lly92nKSiVBEoIAYkZBfz3YDo2SaSEB5IkSniMQnNZApVbImughBB/Ony2kOUHzkgiJTyOJFHCIxRbbHy5O43sYou7QxFCeKCkzCL+eyAdWcYrPIkkUcLtSq02lu4+TWahlDEQQlzakcxCfjqc6e4whHCSJEq4ldlm5+s9Z0iXOlBCiErYczqPjdJ8XHgISaKE21htdr7dm8apPKlELoSovN9Sstl1SpqQC/eTJEq4hc3uYNn+dFJzJIESQlTdmsOZHMqQZuTCvSSJEm7x0+GzHD0nzYSFEFfHAfwvMYPU7GJ3h1Lj+vTpw8SJE6/pGCkpKSiKwu7du10SE4CiKCxbtsxlx6sNJIkSNW5Lajb70vLdHYYQopaz2R18u+8M6fml7g7Fow0fPpw777yz3GMxMTGkpaXRunVr9wTlJSSJEjXqyNlCfkmWRaFCCNcw2+x8vTeNHCmPUiVqtZrIyEg0Go27Q6nVJIkSNSY9v5T/JaYjVV6EEK5UaLaxdE8aRR7QKmrlypX06NGDwMBAQkJCGDBgAMnJycCfU2jffvstN954I0ajkXbt2rFp0ybn/llZWQwePJj69etjNBpp06YNX3zxxSXP9/LLL1c4mtS+fXumTp3KtGnTWLBgAcuXL0dRFBRFYf369RVO5x04cIABAwbg7++Pn58fPXv2dMa+bds2+vbtS2hoKAEBAfTu3ZudO3e66FWrvSSJEjWioNTKt/vSsNgkhRJCuF5OscUjqpoXFhby1FNPsX37dtauXYtKpeKuu+7Cbrc7t5kyZQqTJk1i9+7dNG3alMGDB2O1lnVqKCkpoVOnTqxYsYL9+/fzyCOPMHToULZu3Vrh+UaOHEliYiLbtm1zPrZr1y727t3LiBEjmDRpEoMGDaJ///6kpaWRlpZGt27dLjrOqVOn6NWrF3q9nnXr1rFjxw5GjhzpjCs/P59hw4axceNGNm/eTHx8PLfddhv5+XV7aYaM44lqZ7HZ+XbfGfKlH54QohqdyClh7ZFM/tYszG0x3HPPPeU+//TTTwkLC+PgwYOYTCYAJk2axO233w7ASy+9RKtWrUhKSqJ58+bUr1+fSZMmOfcfP348q1atYunSpXTp0uWi80VHR9OvXz/mzZvHddddB8C8efPo3bs3jRo1AsBgMFBaWkpkZOQl437//fcJCAhgyZIlaLVaAJo2bep8/qabbiq3/Zw5cwgMDGTDhg0MGDCg0q+Pt5GRKFGtHA4HKxIzOCMLP4UQNWD36Tx2u7GG1JEjRxg8eDCNGjXC39+fuLg4AFJTU53btG3b1vlxVFQUABkZGQDYbDamT59OmzZtCA4OxmQysWrVqnL7/9WYMWP44osvKCkpwWw2s3jxYkaOHFmluHfv3k3Pnj2dCdRfpaenM2bMGOLj4wkICMDf35+CgoLLxlUXyEiUqFa/Hj3H4bOF7g5DuNCm5YvY/P1iss+cBCAiLp6bh46n+fW9KcrLYfX8WRzevpGcjNP4BgbTqntf/jbiSQwmPwCK8nJY+tpkkndvITQ6lnsnv0b9+FbO4y+b9U+Co2LoNWi0W65P1H5rjmQS4qsjJtBQ4+ceOHAgsbGxzJ07l3r16mG322ndujVm859dGS5MVBRFAXBO97355pvMmjWLmTNn0qZNG3x9fZk4cWK5/Ss6p16v57vvvkOn02GxWLj33nurFLfBcPnXatiwYWRlZTFr1ixiY2PR6/V07dr1snHVBZJEiWpz4Ew+m1Nz3B2GcLGAsEhuHT2Z0Og4HA4HO376ls+mPsaEj5cDkJeVwe2PPUdEbBOy00/z3cyp5GWlM3Ta+wCsW/QBpcWFTPh4OZv/u4hvZkxhwkfLADh+cBepiXv4+7gX3XV5wguoFIUjGYWE+erw0apr7LxZWVkcOnSIuXPn0rNnTwA2btxYpWMkJCRwxx138NBDDwFlydXhw4dp2bLlJffRaDQMGzaMefPmodPpeOCBB8olRTqdDpvt8ssp2rZty4IFC7BYLBWORiUkJPDBBx9w2223AXDixAkyM6WPoUzniWqRVWjmp0Nn3R2GqAYtu91M8xv6EBodR1hMQ/qPehqdwUhq4m4iGzZl6Evv07LbzYTUj6VJx670G/kUiZvWYbOVLVDNOJ5MuxsHEBbTkC4DHiAjtezuH5vVwnfvvMjdT05Hpa65P3zCu/jrNbSN8MNmg72n8rE7am6heVBQECEhIcyZM4ekpCTWrVvHU089VaVjxMfHs3r1an777TcSExN59NFHSU9Pv+J+o0ePZt26daxcufKiqby4uDj27t3LoUOHyMzMxGK5uBzEuHHjyMvL44EHHmD79u0cOXKEhQsXcujQIWdcCxcuJDExkS1btjBkyJArjl7VBZJECZez2OwsP5COxc13yYjqZ7fZ2L3uf5hLioht2aHCbUoK8/ExmlCrywa+oxo3J2nXJmw2K4e3/UpUo2YAbFgyl0btrie6WZsai194l3r+ehoHGXE4yqbIcoutHDpTc8sJVCoVS5YsYceOHbRu3Zonn3ySN998s0rHeOGFF+jYsSP9+vWjT58+REZGXlQosyLx8fF069aN5s2bc/3115d7bsyYMTRr1ozOnTsTFhZGQkLCRfuHhISwbt06CgoK6N27N506dWLu3LnOUalPPvmE7OxsOnbsyNChQ5kwYQLh4eFVujZvpDgcNZimizph5e8Z7JWK5F4t7eghPhh3H1ZzKTqDkcFT3qH5DX0u2q4w9xzvPnonHfreQf9RTwNQXJDPspkvkrJ/B0GR9blr4suoNVrm/d9oxr73Fas+fZsj2zdSv2lr7nn63861VEJcTtNQX3zVavhjjdGFWkWZqBfo44aoao7D4SA+Pp6xY8dWefRLXD1JooRLHUzP538HM9wdhqhmVouZnIzTlBQWsG/Dj2z7YSmPvrOYiLh45zYlhfn8Z/JwDP4BDH/lY9Saiu/6AZjz1EN0v2cYOemnSdy0jhGv/odvZkzB6B/IgMf/ryYuSdRSGpVCq3ATqstMrKgUuL5hICa9dy4DPnv2LEuWLOH555/nxIkTBAUFuTukOkOm84TLZBdZZB1UHaHR6gitH0d009bcOmYyUY1bsPHbBc7nS4sK+OTZkeiNvjz88oeXTaC2/fg1PiZ/WnXvS/LuLbTq0Re1Rkub3rdydPeWmrgcUUudX/90uQQKwO6AfTW8PqomhYeH8/LLLzNnzhxJoGqYd6blosZZ7Q6WHziDWSqS10kOux2bpexW55LCfD55dgQarY5hr3yMVqe/5H4FOVmsXTibx2ct+eM4Nmx/VEi2W63lqjwLcaH6/j5E+Oqc65+upKDURlJGEU0jfKs5sponE0ruIyNRwiXWJ2WSUVC364XUFT/OfZOje7Zy7sxJ0o4e+uPzLbS/+e9lU3jPDMdcUsy9k16ltKiA/HNnyT93FnsFt1h///6/6HXfSALCyiopx7buxM7Vy0g/nsSW/y0hrnXHmr48UQs0C/Ul3FD5BOq84+eKySqU31PCdWQkSlyzw2cL2Hkqz91hiBpSkJPF0tcmk3cuAx9fP6IaNWfk6/No2rkHybs3cyJxDwBvDL253H7PLl5PcGS08/ND234h69Rx7n/+Ledj3e4cyslD+3j/H/cQ07wdtzw8oWYuStQKWpVCq3A/FKqWPF3owOkCujYKRKuWMYRrMW3aNJYtW1augXFdJAvLxTXJLbEwf9tJSq0y7SKEqD4BPppy5QuuRbifjnbR/i6I6mJ9+vShffv2zJw5s1qO7ykkiSojqbi4Jj8mZkgCJYSoVvUDfGgU6JoECiAj38ypnBKXHMvb1PU2LlUlSZS4antO55Eqv4iEENWoeZgvYT46uIYpvIocOlNAkfnyrVCqavjw4WzYsIFZs2ahKAqKopCSksL+/fu59dZbMZlMREREMHTo0HItU/r06cP48eOZOHEiQUFBREREMHfuXAoLCxkxYgR+fn40adKEH3/80bnP+vXrURSFFStW0LZtW3x8fLjhhhvYv39/uZi++eYbWrVqhV6vJy4ujhkzZpR7Pi4ujunTp/Pwww/j7+/PI488AsCzzz5L06ZNMRqNNGrUiKlTp1ZY6byukyRKXJX8Uivrk7PcHYYQwktp1Qrto/wxqDXOJr2uZPuj7IErV7TMmjWLrl27MmbMGNLS0khLS8PPz4+bbrqJDh06sH37dlauXEl6ejqDBg0qt++CBQsIDQ1l69atjB8/nscff5z77ruPbt26sXPnTv72t78xdOhQioqKyu03efJkZsyYwbZt2wgLC2PgwIHOZGfHjh0MGjSIBx54gH379jFt2jSmTp3K/Pnzyx3jrbfeol27duzatYupU6cC4Ofnx/z58zl48CCzZs1i7ty5vPPOOy57rbyFrIkSV+XbfWkkZRZdeUMhhKiiQB8NjYJ8qYm/TvHhRuJCjC473l/XRL3yyiv8+uuvrFq1yrnNyZMniYmJ4dChQzRt2pQ+ffpgs9n49ddfAbDZbAQEBHD33Xfz2WefAXDmzBmioqLYtGkTN9xwA+vXr+fGG29kyZIl3H///QCcO3eO6Oho5s+fz6BBgxgyZAhnz57lp59+cp77mWeeYcWKFRw4cAAoG4nq0KED33333WWv66233mLJkiVs374dkDVR58lIlKiyxPQCSaCEENUiJsCHhoE1k0ABJJ8tcvm03oX27NnDzz//jMlkcv5r3rx52bmTk53btW3b1vmxWq0mJCSENm3+7CMZEREBQEZG+Y4QXbt2dX4cHBxMs2bNSExMBCAxMZHu3buX27579+4cOXIE2wUlRzp37nxR3F9++SXdu3cnMjISk8nECy+8QGpqapWv39tJiQNRJcUWG2uPZF55QyGEqKIWYSb0qpp9b293QGJaAZ1iA6rl+AUFBQwcOJDXX3/9oueioqKcH59v9HueoijlHjs/pVkdBWh9fcsXIN20aRNDhgzhpZdeol+/fgQEBLBkyZKL1lMJSaJEFa09kkmRpfretQkh6h6dWqFl2LXVf7oW54osnMopob4LmhTrdLpyozwdO3bkm2++IS4uDo3G9X9yN2/eTIMGDQDIzs7m8OHDtGjRAoAWLVqQkJBQbvuEhASaNm2KWq2+5DF/++03YmNjmTJlivOx48ePuzx2byDTeaLSjmYVcjC9wN1hCCG8SKBBQ+twf7clUOcdySjE7IJyLXFxcWzZsoWUlBQyMzP5xz/+wblz5xg8eDDbtm0jOTmZVatWMWLEiHLJ1tV6+eWXWbt2Lfv372f48OGEhoZy5513AvD000+zdu1apk+fzuHDh1mwYAGzZ89m0qRJlz1mfHw8qampLFmyhOTkZN59990rrpmqqySJEpVSarXz0yGZxhNCuE5MoA+NAmpu/dPlWGwODmcUXvNxJk2ahFqtpmXLloSFhWE2m0lISMBms/G3v/2NNm3aMHHiRAIDA1G5YOrytdde44knnqBTp06cOXOG77//Hp1OB5SNgi1dupQlS5bQunVrXnzxRV5++WWGDx9+2WP+/e9/58knn2TcuHG0b9+e3377zXnXnihP7s4TlbLm8Flp7SKEcJkW4Sb0iqpayhdci86xAQQZtVfe0M3O352XnZ1NYGCgu8Ops2QkSlzR2YJSdp2WBEoIce30ahXto/zxUak9LoGCskXmdhlbEJUkSZS4op+TsjxiuF0IUbsFGbRlDYRd1L6lOhSabaSeK3Z3GKKWkCTqL/r06cPEiROv6RgpKSkoiuLSImSKorBs2TKXHa+yjmYVkpItv1CEENemQaAPcQHGWvGG7FhmsUsWmVenPn364HA4ZCrPzSSJukbDhw933glxXkxMDGlpabRu3do9QbmI3eHg5yRp7SKEuHoK0DLcRLBe5+5QKs1qd3BUCgqLSpAkqhqo1WoiIyOrpSZITdpzOo+sImk4KYS4OnqNinaR/ug9dP3T5ZzMLqGwGiuZC+9Qq5KolStX0qNHDwIDAwkJCWHAgAHOsvnnp9C+/fZbbrzxRoxGI+3atWPTpk3O/bOyshg8eDD169fHaDTSpk0bvvjii0ue7+WXX65wNKl9+/ZMnTqVadOmsWDBApYvX+7s2L1+/foKp/MOHDjAgAED8Pf3x8/Pj549ezpj37ZtG3379iU0NJSAgAB69+7Nzp07XfSqXZ1Sq52EY+fcGoMQovYKNmhpGWpye/2nq+UAklxQ8kB4t1qVRBUWFvLUU0+xfft21q5di0ql4q677ipXBn/KlClMmjSJ3bt307RpUwYPHozVagWgpKSETp06sWLFCvbv388jjzzC0KFD2bp1a4XnGzlyJImJiWzbts352K5du9i7dy8jRoxg0qRJDBo0iP79+zs7dnfr1u2i45w6dYpevXqh1+tZt24dO3bsYOTIkc648vPzGTZsGBs3bmTz5s3Ex8dz2223kZ+f78qXr0o2Hc+myOLZawKEEJ4pNtBAbIARamkCdV5GvpkcGY0Xl1Gr5pvuueeecp9/+umnhIWFcfDgQUwmE1BW6Oz2228H4KWXXqJVq1YkJSXRvHlz6tevX65S6/jx41m1ahVLly6lS5cuF50vOjqafv36MW/ePK677joA5s2bR+/evWnUqBEABoOB0tJSIiMjLxn3+++/7+w9dL4XUtOmTZ3P33TTTeW2nzNnDoGBgWzYsIEBAwZU+vVxldxiCztO5tb4eYUQtZvCH/WfVJduKVLbHM4opEtcoLvDEB6qVo1EHTlyhMGDB9OoUSP8/f2Ji4sDKNdZ+sJO2OebO57vem2z2Zg+fTpt2rQhODgYk8nEqlWrLtuZesyYMXzxxReUlJRgNptZvHgxI0eOrFLcu3fvpmfPnhc1mDwvPT2dMWPGEB8fT0BAAP7+/hQUFLitY/b6o1nY7LXgFhohhMfQa8rqP3lTAgWQW2wlPa/U3WEID1WrRqIGDhxIbGwsc+fOpV69etjtdlq3bo3ZbHZuc7mu12+++SazZs1i5syZtGnTBl9fXyZOnFhu/4rOqdfr+e6779DpdFgsFu69994qxW0wGC77/LBhw8jKymLWrFnExsai1+vp2rXrZeOqLqdySzgk6wCEEFUQYtQSG2DA4cH1n67FkYxCwvx0qGrZ4nhR/WpNEpWVlcWhQ4eYO3cuPXv2BGDjxo1VOkZCQgJ33HEHDz30EFCWXB0+fJiWLVtech+NRsOwYcOYN28eOp2OBx54oFxS9NeO3RVp27YtCxYswGKxVDgalZCQwAcffMBtt90GwIkTJ8jMdE+ful+PymJyIUTlxQUZCNJrvTaBAii22DmZXUKD4Mu/IRZ1T62ZzgsKCiIkJIQ5c+aQlJTEunXreOqpp6p0jPj4eFavXs1vv/1GYmIijz76KOnp6Vfcb/To0axbt46VK1deNJUXFxfH3r17OXToEJmZmVgsFy9CHDduHHl5eTzwwANs376dI0eOsHDhQg4dOuSMa+HChSQmJrJlyxaGDBlyxdGr6nAyp5jUHCmsKYS4MgVoFe5HkF5HbV9AXhkpWcXYZZmD+Itak0SpVCqWLFnCjh07aN26NU8++SRvvvlmlY7xwgsv0LFjR/r160efPn2IjIy8qFBmReLj4+nWrRvNmzfn+uuvL/fcmDFjaNasGZ07dyYsLIyEhISL9g8JCWHdunUUFBTQu3dvOnXqxNy5c52jUp988gnZ2dl07NiRoUOHMmHCBMLDw6t0ba7wW0p2jZ9TCFH7+Pyx/kmnqjV/Qq5ZqdXOqZwSd4chPIzicNSGIvzu5XA4iI+PZ+zYsVUe/aotTuWWsGjnKXeHIYTwcKFGLTEBBvDi6btL0WtU9GgSJGujhFOtWRPlLmfPnmXJkiWcOXOGESNGuDucavNbiqyFEkJc3vn1T3UxgYI/R6NigmRtlCgjSdQVhIeHExoaypw5cwgKCnJ3ONUiKbOQPSfzCffTUWKTAptCiPIUoFWECa3iXeULqkIBQkw6LLay2Yna1sZGVA9Joq6gLsx2Ltubzu8ZhfyeUUjjUCP1A/WUeHgHcyFEzTBoVDQPM9XZ0SeVAqG+Oow6DYqiYLdDQakNPx/58ykkiarzTuWWsDU1x/l5cmYRyZlFxAYZiA32kZEpIeqwUF8dDfwM1IH3khdRKxDqp8OgUaMo5RfQZxdaMelrX1Nl4XqSRNVxy/elU9Fdu8ezizmeXUx0gA8NQw2UWm0gvzCEqDMaBhsI1Gmpa/mTRqUQZtKh11w6STLbHBSZ7fjq6+70pijj8fen9unTh4kTJwJlNZlmzpzp8nMMHz68XKmDC8/pCVJSUlAUhd27d7v0uOeKzPySfPkF5SdzS/g1OZvj50rQq1Qode5XqhB1i0qB1hF+BOrqRv2n87RqhXoBemICDfhoNVccZcoustZQZMKT1aqRqG3btuHr61upbePi4pg4cWKlkqFZs2a5fO1TSkoKDRs2ZNeuXbRv396lx3aVVYmZWCtZPC4930x6vplQXy3NI0xYHPY6OcQvhDczalU0C61b65/0GhUhvlp06qpNz5VY7JRY7PhoPX4sQlSjWpVEhYWFufR4NpsNRVEICAhw6XFrA4vNzurDVW8tk1loYePRbAINGlpGmrDjqHA6UAhRu4T56oipQ+uffLQqQn11aFSqq17blFtsxUerc3FkojbxqBS6sLCQhx9+GJPJRFRUFDNmzCj3/IXTeQ6Hg2nTptGgQQP0ej316tVjwoQJQNl03PHjx3nyySdRFMX5AzJ//nwCAwP573//S8uWLdHr9aSmpl40nQdgtVoZN24cAQEBhIaGMnXq1HKjVYqisGzZsnL7BAYGMn/+fAAaNmwIQIcOHVAUhT59+ji3+89//kOLFi3w8fGhefPmfPDBB+WOs3XrVjp06ICPjw+dO3dm165dV/NyXlbCsWzySq5+ODqn2Mpvx3LYf7oANQpqWS8lRK3VKNhItJ9PnZisN+pUxAT6EOnng7aKo09/VVBiwybvIus0jxqJmjx5Mhs2bGD58uWEh4fzf//3f+zcubPC6bBvvvmGd955hyVLltCqVSvOnDnDnj17APj2229p164djzzyCGPGjCm3X1FREa+//jr/+c9/CAkJuWR7lQULFjBq1Ci2bt3K9u3beeSRR2jQoMFFx7uUrVu30qVLF9asWUOrVq3Q6crerSxatIgXX3yR2bNn06FDB3bt2sWYMWPw9fVl2LBhFBQUMGDAAPr27cvnn3/OsWPHeOKJJ6rwKlbOysSzLjlOQamNTSk5GLUq2tTzQ60GqY4gRO2gUqBluB9axaPeT1cLk15NkFGLWrn6kae/cgD5JTYCjR71p1TUII/5yhcUFPDJJ5/w+eefc/PNNwNliUx0dHSF26emphIZGcktt9yCVqulQYMGdOnSBYDg4GDUajV+fn5ERkaW289isfDBBx/Qrl27y8YTExPDO++8g6IoNGvWjH379vHOO+9UOok6P/UYEhJSLoZ//vOfzJgxg7vvvhsoG7E6ePAgH3/8McOGDWPx4sXY7XY++eQTfHx8aNWqFSdPnuTxxx+v1Hkr48jZQpIyi1x2PIAii50tx3PRa1S0iTKh16qwyDs0ITyWUaumWaiv169/8vNRE2TQoa6mPn+5xVYCDFLuoK7ymLcfycnJmM3mcg1+g4ODadasWYXb33fffRQXF9OoUSPGjBnDd999h9V65ekpnU5H27Ztr7jdDTfcUO6HomvXrhw5cgSbzVaJq6lYYWEhycnJjBo1CpPJ5Pz3yiuvkJycDEBiYiJt27bFx8en3Lld6adDVV8LVVmlVjvbT+SxNSUXq9WBTiW/WITwNOEmHc29fAF5gEFDbJCBUF+fakugACw2B8UWGX6vqzxmJKqqYmJiOHToEGvWrGH16tWMHTuWN998kw0bNqDVai+5n8FgcMk7BkVRLrqjz2KxXHafgoICAObOnVsuWQRQq2um3kih2cZvx7Kr/TwWu4OdJ/NQK9A6yg9/g5pSm4xMCeFujUOM+Gs1XruAPMioxd9Hg6oGpyhzi6wYdVIzqi7ymJGoxo0bo9Vq2bJli/Ox7OxsDh8+fMl9DAYDAwcO5N1332X9+vVs2rSJffv2AWUjTtcyanRhHACbN28mPj7emeyEhYWRlpbmfP7IkSMUFf05RXZ+DdSFMURERFCvXj2OHj1KkyZNyv07vxC9RYsW7N27l5KSknLndpUNSVmU1uCiJZsD9pzOZ+PRHIpKbOjVHvMtJ0SdolagTYQf/lot3lj/KdhXS2ywgUCDrkYTKIBCsx2rh7xJrK66ghU5f7PWhebMmUNMTAwqlYqZM2cybdq0GinzU111JK/EY0aiTCYTo0aNYvLkyc4F31OmTEF1iWHY+fPnY7PZuP766zEajXz++ecYDAZiY2OBshf0l19+4YEHHkCv1xMaGlqleFJTU3nqqad49NFH2blzJ++99165uwVvuukmZs+eTdeuXbHZbDz77LPlRsDCw8MxGAysXLmS6OhofHx8CAgI4KWXXmLChAkEBATQv39/SktL2b59O9nZ2Tz11FM8+OCDTJkyhTFjxvD888+TkpLCW2+9dRWvaMVWV+NU3uU4HLD/TNlIXPNwX2l2LEQN8tWpaRrifeufzjcFNumuXByzuuUWWwkxXXoWxBvdf//93Hbbbc7P8/LyGDduHG+//Tb33HMPAQEB2O12xo8f77Jzzp8/n4kTJ5KTk1Pu8arUkXQljxoWePPNN+nZsycDBw7klltuoUePHnTq1KnCbQMDA5k7dy7du3enbdu2rFmzhu+//56QkBAAXn75ZVJSUmjcuPFV1Zd6+OGHKS4upkuXLvzjH//giSee4JFHHnE+P2PGDGJiYujZsycPPvggkyZNwmg0Op/XaDS8++67fPzxx9SrV4877rgDgNGjR/Of//yHefPm0aZNG3r37s38+fOdI1Emk4nvv/+effv20aFDB6ZMmcLrr79e5fgrcjijkBM5JVfesJr9nlHIL8nZnCuwyMiUENUswqSjaYh3rX9SKWXrumKDjfjptW5PoADySqx1omH9hQwGQ7k73FNTU7FYLNx+++1ERUVhNBoxmUzOv8vVKSwsrNzf4JqiOOraV70Om7splVW/u2ck6nLigg00CJJmx0K4WpMQIyatBqWC6bslc2fx6TuvcNfQR3j8+X8B8NHrU1n93RL0RiOjnpzKzQPvdW7/y8rlrP7vUqZ/sKjG4v8rtQKhJh0G7cVNgT1BvUBdja2NstvtvPXWW8yZM4cTJ04QERHBo48+ypAhQ8p1y7DZbDzyyCOsW7eOM2fO0KBBA8aOHVuudM769et55plnOHDgAFqtllatWrF48WJiY2PZs2cPEydOZPv27SiKQnx8PB9//DGdO3cuNyo0f/58RowYUS7GY8eOMX/+fJYtW1ZuevHTTz9lxowZJCUlERwczD333MPs2bMBePvtt5k3bx5Hjx4lODiYgQMH8sYbb2AymVi/fj033nhjuXP885//ZNq0aRd1KUlNTWX8+PGsXbsWlUpF//79ee+994iIiABg2rRpLFu2jKeffpqpU6eSnZ3Nrbfeyty5c/Hz86v018FjpvNE9bLaHTWyoPxqpJwrJuWcNDsWwlXUCrSM8ENzicmGQ/t2sWLpZzRq1sr52KafV/Hz/77h1f98xanjR5nxwhN07nEjAUEhFObnMW/Wv3n9k29q6hLK0agUQk06fC7TFNgTFJTYaiyJev7555k7dy7vvPMOPXr0IC0tjd9///2i7ex2O9HR0Xz11VeEhITw22+/8cgjjxAVFcWgQYOwWq3ceeedjBkzhi+++AKz2czWrVudr/OQIUPo0KEDH374IWq1mt27d1d489b9999PTEwMt9xyC1u3biUmJqbCWaAPP/yQp556itdee41bb72V3NxcEhISnM+rVCreffddGjZsyNGjRxk7dizPPPMMH3zwAd26dWPmzJm8+OKLHDp0CCibvanomu+44w5MJhMbNmzAarXyj3/8g/vvv5/169c7t0tOTmbZsmX873//Izs7m0GDBvHaa6/xr3/9q9JfB0mi6ohdJ3PJL736hfY14WRuCSdzS4jw09E03BezzYbDCxfAClGdrrT+qbiwgNeeeYwnX3qbxR+/7Xz8xNHDtO3Snaat29O0dXs+fO0FzpxMJSAohLlvvcSAB0YQXq/iun3VRatWCDPpqtzXzl0KSm2EORzVHmt+fj6zZs1i9uzZDBs2DCi7OatHjx6kpKSU21ar1fLSSy85P2/YsCGbNm1i6dKlDBo0iLy8PHJzcxkwYACNGzcGym5wOi81NZXJkyfTvHlzAOLj4yuMyWAwOKftwsLCLqrReN4rr7zC008/XW4k7LrrrnN+fGG/27i4OF555RUee+wxPvjgA3Q6HQEBASiKcsnjA6xdu5Z9+/Zx7NgxYmJiAPjss89o1aoV27Ztc57Pbrczf/5858jT0KFDWbt2bZWSKM8bDxXV4pfkc+4OodLS8838mpxN8tlidCqVDEoJUUkRfnqaBl9+Afl7rzxLl9596ditd7nHGzVrxZH9u8nPzeHwgT2YS4qp16Ah+3dsJilxL3c+VLlCw66g16ioF6CnfoABvcb9i8Yry+4ou1OvuiUmJlJaWuosTH0l77//Pp06dSIsLAyTycScOXNITU0FyuoxDh8+nH79+jFw4EBmzZpV7s7zp556itGjR3PLLbfw2muvOWsaXo2MjAxOnz592bjXrFnDzTffTP369fHz82Po0KFkZWWVu/v9ShITE4mJiXEmUAAtW7YkMDCQxMRE52NxcXHlpu6ioqLIyMio0jVJElUHFJVa2XkkkygfDXp17fhlBH82Oz50phCNoiB1O4W4tPgQI/WMei5XvuDnH74j6eA+Rj35wkXPde5xEzcNvI/xg/ry1v+NZ9Krs/ExGHn35WeY8M+3+N+SeYy87QYmDrmNlCMXTxu5go9WRXSgD1H+PrUqebpQQUn1j/gbDIZKb7tkyRImTZrEqFGj+Omnn9i9ezcjRozAbDY7t5k3bx6bNm2iW7dufPnllzRt2tRZWmfatGkcOHCA22+/nXXr1tGyZUu+++67aok7JSWFAQMG0LZtW7755ht27NjB+++/D1AuXlf567SkoijY7VVLgiWJqgN+/v0se1Jz+flAOgeTszBYbUQaNGhqye+n882OD0izYyEuolYU2kb6YdJqL7uWMCPtFB++OoXn3vgQnd6nwm0eHvcM81dtY87yX+hxy+0smTuLDl17odFoWPzR27z9+f+49Z6HeOP5f7j0Gow6FTFBrmkK7G6FpTbs1dzyKj4+HoPBwNq1a6+4bUJCAt26dWPs2LF06NCBJk2aVDia1KFDB55//nl+++03WrduzeLFi53PNW3alCeffJKffvqJu+++m3nz5l1V3H5+fsTFxV0y7h07dmC325kxYwY33HADTZs25fTp0+W2qUwNyBYtWnDixAlOnDjhfOzgwYPk5OTQsmXLq4r9UmRNVB2wan+682OLzcGu4zkAGHVqWkX7YzTqOFtswe7h64/ypdmxEOWYdGriQ0xlnXCv4MiBPeRknWXsvX9OpdhtNvZt38TyxZ+wYvepcp0TUo8eYe33X/HhN+tY+e1i2nTuSmBwKL3638GMF56gqLAAo+/Fi3qrwlenJtjXtU2B3c1BWWcIP5/q+/Pq4+PDs88+yzPPPINOp6N79+6cPXuWAwcOXDRVFh8fz2effcaqVato2LAhCxcuZNu2bc6yOseOHWPOnDn8/e9/p169ehw6dIgjR444y/xMnjyZe++9l4YNG3Ly5Em2bdvGPffcc9WxT5s2jccee4zw8HBuvfVW8vPzSUhIYPz48TRp0gSLxcJ7773HwIEDSUhI4KOPPiq3f1xcHAUFBaxdu5Z27dphNBovKm1wyy230KZNG4YMGcLMmTOxWq2MHTuW3r1707lz56uOvSKSRHm5wlIrCUeyKnyuyGxj29GyO/YCjVpa1PdHo9dwtsji0XfHlWt2XM8PvUaRZseizon001PPV1/p9i0duvbi4+W/lHtsxpQJxDSMZ9Do8eUSKIfDwaxpT/Pos9Mx+Jqw2+3O3qS2P/63X0NHiOpuCuxu+SXVm0QBTJ06FY1Gw4svvsjp06eJioriscceu2i7Rx99lF27dnH//fejKAqDBw9m7Nix/PjjjwAYjUZ+//13FixYQFZWFlFRUfzjH//g0UcfxWq1kpWVxcMPP0x6ejqhoaHcfffd5RaqV9WwYcMoKSnhnXfeYdKkSYSGhnLvvWWlNNq1a8fbb7/N66+/zvPPP0+vXr149dVXefjhh537d+vWjccee4z777+frKwsZ4mDCymKwvLlyxk/fjy9evUqV+LA1aROlJdbsSeNZ7/aX6V9wv31NKvnj02lcM7D7+gD0KoUWtfzw1enwizJlKgD4kN9ManV1/xmZ9KwO2jcvLWzTtR5P3y1kO0J63hxZtm0ze97d/Lc6Ht5de5Stv2yll9/+p6532+s8vkCDBoCfbSX7EThTRqG+qCWhZxeT0aivNyFU3mVlZFXSkbeWQAaBBtoGGGiGIU8s2cmVBa7g13nmx3X88PPR43ZQ/pYCeFKGpVCy3AT6mpczpqdmcEXH7/DzMU/OB9r3rYj9w4fywuPPUhgSCiT/z27SscMMmrx12vqRPJ0XmGpDX+D/In1djIS5cUKS630enWDyxoOx0eYqB9iJN/moNCDFyMpCrSKNBFk1FIqVdCFl/DTq2kSXLn1T54i2FeLn15T4w2BPYGvXkVUgN7dYYhqJmmyF9twKNNlCRTAkfQCjqQXoFKgRT1/wgMNnLPYKPWwUR+HA/anSbNj4T2i/PRE+uprRQKlACG+Wkwe0tPOXYrNdhw1UHhTuJckUV5sU1LFC8qvld0BB07lceBUHlq1QqvoAAL99GSV2jxugffvGYX8nlFIk1Aj9QL0kkyJWqdpqC++ajWXq//kCVQKhPrqMOpqZ30nV7M7oMRix1BDbWCEe8h0nhfr99avnMopqbHzlZVMCMBo1HpsyYS4YAMxQT4yzSc8Xtn6Jz/UHvhzdCFPbwrsToFGDaGmi/vMCe8hSZSXOnmumP5vV/3uGVdxlkzQaThb7HklE6IDfWgUYqBEmh0LD+Sv19Ak2IjjMu1b3K22NAV2J51aoUFIxYVNa6s+ffrQvn17Zs6c6e5QPIJM53mpLUfd2ysvp8jCpj/qU4X762ka5YddrfKYkgknc0o4mVNCpJ+e+HCjNDsWHqOev54Io95jE6ja1hTYncw2B1abA00tard1KWazGZ1O5+4wPI6MvXqprW5Ooi6UkVfKxkOZ/HYwg8LsIsJ1Kvw9ZJ3AmfxSaXYsPEbTUF8iDJfvf+cuOrVSK5sCu1thNZWGKS0tZcKECYSHh+Pj40OPHj3Ytm0bdrud6OhoPvzww3Lb79q1C5VKxfHjxwHIyclh9OjRhIWF4e/vz0033cSePXuc20+bNo327dvzn//8h4YNG+Lj48Pw4cPZsGEDs2bNQlEUFEUhJSWlWq6vtpCRKC+19ZjnJFEXOnGumBPnigFoEu5LdJgvBTYHBZbqWaP0+4pPOb3jZ/LTUlDr9AQ3aUubeyfgFxXn3GbPkrc5nvA9Gp2BLg8+wd/vvR+7w4Ed2Lv+B3b+9B3D/z23WuITAsqmxlqFm1B54PtaH42KEJMOnUolU99XoajURkA11It65pln+Oabb1iwYAGxsbG88cYb9OvXj6SkJAYPHszixYt5/PHHndsvWrSI7t27ExsbC8B9992HwWDgxx9/JCAggI8//pibb76Zw4cPExwcDEBSUhLffPMN3377LWq1mtjYWA4fPkzr1q15+eWXAQgLC3P5tdUmkkR5oeSMAs7mu77jtaslZRSSlFGISoHm9fyJCDKQbbZR4sKSCZmHdtLopvsIbtgKu83GgW9ns/Htf9D3la/R6A2c3v0LJzavpOdT71OQnsqvc17Cr2kXQkNDaewHqz59m9FvfuayeIT4K09d/2TUliVP3tTXzh2KLK4vdVBYWMiHH37I/PnzufXWWwGYO3cuq1ev5pNPPmHIkCHMmDGD1NRUGjRogN1uZ8mSJbzwwgsAbNy4ka1bt5KRkYFeX1bL6q233mLZsmV8/fXXPPLII0DZFN5nn31WLlHS6XQYjUYiIyNddj21mee97RHXbOsf/fBqC7sDDp7K4+f96RxIzsLHaiPSoEHrgpYJPZ6aTVyPv+NfvzGBDZrSeeRLFGWdITslEYD8tGOENe9EUMOWxNzQH63Bl8LM0+SX2vh0xsvE9rqH4PB6aOQnRVSD+v4+HpdA+erUNAgyEO7ng0Yl656uleOPUgeulJycjMVioXv37s7HtFotXbp0ITExkfbt29OiRQsWL14MwIYNG8jIyOC+++4DYM+ePRQUFBASEoLJZHL+O3bsGMnJyc5jxsbG1vmRpiuRkSgv5O5F5dfCYnOw+3gOUD0lEyzFZUU4db7+AATExHNsw7eYC/MoPHsKm7kUU3gMmYd3kXP8dzoMfe7PZsdRfui0ClYPq4Ulaqdmob4Y1WqPSaD89GqCjN7bFNid3FEvasiQISxevJjnnnuOxYsX079/f0JCQgAoKCggKiqK9evXX7RfYGCg82NfX98airb2kiTKyzgcDrYdq10jUZdSZLax7Y+EMMCgpWW0P1qdhoyrLJngsNvZ88VbhDRpR0B0EwAiW3ejwQ23sW76UNRaPZ1HTUOjN7Br4at0HvUSyT9/TfKaL9H7BXJ22BRCYprQpp4fRml2LK6SVqXQKtwPxUMWj9elpsDuUmyxE+TC4zVu3BidTkdCQoJzjZPFYmHbtm1MnDgRgAcffJAXXniBHTt28PXXX/PRRx859+/YsSNnzpxBo9EQFxdXpXPrdDpsNs+4y9oTSBLlZX5Pyye32OLuMFwut/jaSybs+vw18k4l0/v5T8o93vLOR2l556POzw8un0N4y+tRqTX8/v0n9H35S9L2/Mq2/7zIzf9cxM7zzY6j/PAzSLNjUXkBPhoaB3nG9F1dbArsLiUuXhfl6+vL448/zuTJkwkODqZBgwa88cYbFBUVMWrUKADi4uLo1q0bo0aNwmaz8fe//925/y233ELXrl258847eeONN2jatCmnT59mxYoV3HXXXXTu3PmS546Li2PLli2kpKRgMpkIDg6u099DdffKvVRtnsqrrL+WTIjQqwi4wlD5rs9f58yejfR65mOMwRGX3C4v7Ripm36g1V2Pc/b37YQ27YDeP4joLn3JOf47luJCAGwO2HM6n4SjORSV2vBRy4+SuLz6AT40CnR/AhXiqyU22ECgQVen//jVJLsDzFbXvtl67bXXuOeeexg6dCgdO3YkKSmJVatWERT055jXkCFD2LNnD3fddRcGg8H5uKIo/PDDD/Tq1YsRI0bQtGlTHnjgAY4fP05ExKV/PwJMmjQJtVpNy5YtCQsLIzU11aXXVdtIxXIv8/hnu/j1cKa7w3CLJhG+RIeWL5ngcDjYvegNTu/8mV7PzsEvosEl93c4HPzy+iPE9x9Kvfa9OPLTIjIP7aTr+BmYi/L5flwfBs5ej87oV+H+0uxYXErzMF983LhIW5oCu1+YSUuAUSZ/vI18Rb2I1WZn53HvWA91NZLSC0lK/7NkQniQDz/N/RcnNv1I1wlvo/UxUpJblmBqDSbUuvLtGFJ++Q69XxD12vcCIKRJOw4u/5is5H2k70vAr16jSyZQIM2OxcW0aoWWYX6o3LT+SaVAiK8OX2kK7HYlVjsB7g5CuJyMRHmRfSdzGfzRVneH4VEO/OtvFT7eaeQ/ievx5xqBktwsfn5lGH3+bx6GoD9v6U387xySVi9B7x9E51EvEdyodaXPHRdsoEGwDyVWSabqorL1T7644zesSoEwkx6DViVNgT2EVq0Q62V99IQkUV7l6+0nmbYs0d1heCyDVk3rmLKSCZklVmpqPXhMoA8NpdlxnRId4EOYQV/j55WmwJ6tYagPahfUvxOeQ6bzvEhyRqG7Q/BoxZaLSyZodBrOXmXJhMo6kVPCCWl2XGc0DzPhU8MLtrXqsuRJL02BPVqJxY6v3jP6hgrXkCTKixxJL3B3CLXGhSUTwvz0NKtX9ZIJVXUmv5Qz+aWE+mppHmHC4rC7ZapHVA/dH+ufarL+k+6P5EmnUXtM3SlxaaVWSaK8jSRRXiQ5Q5Koq3E2v5Szh0oBiA420CjCRCkKudXUfT2z0MLGo9kEGjS0ijRh+6PZsai9Ag0aGgXW3PonaQpcO7myzEGfPn1o3749M2fOdNkxRdVJEuUlcosstaLpsKc7ea6Yk+eKAWgc7ktMWPmSCa6UU2wl4VgOfnoNraNMOBSwy9BUrRMT6EOYj75GEiiDVkWIrw6NSpoC10ZmucnE68htG14iSUahXC45o5D1BzLYeegsqhILUQY1PmrX/+HKL7WyKSWHvafyURyKNDuuRVqEmwjR66ju/MlXpyYmyECEnw9aWfdUa5ltDlxxL9fw4cPZsGEDs2bNQlEUFEUhOTmZUaNG0bBhQwwGA82aNWPWrFnOfUpKSmjVqhWPPPKI87Hk5GT8/Pz49NNPASgtLWXChAmEh4fj4+NDjx492LZtm3P79evXoygKa9eupXPnzhiNRrp168ahQ4fKxffhhx86W9M0a9aMhQsXlnteURQ+/vhjBgwYgNFopEWLFmzatImkpCT69OmDr68v3bp1czZDTklJQaVSsX379nLHmTlzJrGxsdjt7ktO5de1l5D1UNXH7oCDp/L4eX8GB5Kz8LHYiDRo0Lr4Lpsis40tx3PYkZqPw152p5XwTHq1ivZR/tVeQNNPr6ZBkIFwPx8ZffISFhfcFjxr1iy6du3KmDFjSEtLIy0tjejoaKKjo/nqq684ePAgL774Iv/3f//H0qVLAfDx8WHRokUsWLCA5cuXY7PZeOihh+jbty8jR44E4JlnnuGbb75hwYIF7Ny5kyZNmtCvXz/OnSvfCWPKlCnMmDGD7du3o9FonPsDfPfddzzxxBM8/fTT7N+/n0cffZQRI0bw888/lzvG9OnTefjhh9m9ezfNmzfnwQcf5NFHH+X5559n+/btOBwOxo0bB5S1mrnllluYN29euWPMmzeP4cOHu7XyvpQ48BL/+v53vthywt1h1CnVXTJBq1JoU88Pg17lkl+8wjWCDFoaBhqrdfouwEdDoEGaAnujSH8dJp9rX1xemTVR48aN48yZM3z99dfOx958803eeOMNHnjgAb755hv27dtHSEgIhYWFBAUFMX/+fB588EGgrKlxXFwcEydOZPLkyaxfv54bb7yRNWvWcPPNNwPwww8/cPvtt1NcXIyPjw/du3enVatWzJkzx3nOQYMGUVhYyIoVK4CykagXXniB6dOnA7B582a6du3KJ5984kzIlixZwogRIyguLltesXTpUh577DHS0tLQ6/Xs3LmTzp07c/To0So3UXYl+Qn1EjISVfPOl0zYsD+dk6fyCFQgzKDBVX9dLXYHO0/msfloDqVmO7pqmEoUVdMg0Ie4gOpLoIKMWmKDjAT76j0qgerTuTnxEcaL/k17biIA/37xWTo3q0/PDvEs/3pJuX1//O+3PPLQPW6I2jOVVuO6qPfff59OnToRFhaGyWRizpw5F/W2e/rpp2natCmzZ8/m008/JSQkBCib2rNYLHTv3t25rVarpUuXLiQmlq8/2LZtW+fHUVFRAGRkZACQmJhY7hgA3bt3v+wxzvfra9OmTbnHSkpKyMvLA+DOO+9ErVbz3XffATB//nxuvPFGtyZQIAvLvYbcmedeucUWNieVL5ng0KjIKrn2O/zONztWFGgVaSLYqJWWMjVMoWz9k66aqn8HG7X4+2g8trr4Nyt/xW7/83v5cOJBhg8awK0D72btqhV8/+2XzPvyv6QcTeb5Jx+j5423EBwSSn5eLm+/Oo0FX61wY/SexVxNP7tLlixh0qRJzJgxg65du+Ln58ebb77Jli1bym2XkZHB4cOHUavVHDlyhP79+1f5XFqt1vnx+Snmqq5LqugYlzuuTqfj4YcfZt68edx9990sXry43Jovd/HMn1hRJZkFpWQXWdwdhvjD2fxSNh7KJOFABgXZRYTrVATqrn343uGA/WkF/JKcTV6xFb2sQK8Reo2KdpH+6F28/kkBQn21xAUbCTDoPDaBAggJDSMsPNL57+fVP9IgrhFduvUk+cghru/WizbtOzHw7kGYTP6cTE0B4PWXp/DgsDHUi45x7wV4EFeVOdDpdNhsfya2CQkJdOvWjbFjx9KhQweaNGniXJh9oZEjR9KmTRsWLFjAs88+6xwhOr8QPCEhwbmtxWJh27ZttGzZstJxtWjRotwxzsdWlWNcyujRo1mzZg0ffPABVquVu++++5qPea1kJMoLJMlUnse6qGRCqC/5dgeF11gy4ff0Qn5PLyQ+1EiUNDuuNsEGLbEBBnBhIcva3hTYbDbz32+WMOLR8SiKQotWbfhy4afk5mRz4vgxSkqKiW3YmO1bfuPgvt289Lr7Rws8ieWPO/Su9WsfFxfHli1bSElJwWQyER8fz2effcaqVato2LAhCxcuZNu2bTRs2NC5z/vvv8+mTZvYu3cvMTExrFixgiFDhrB582Z8fX15/PHHmTx5MsHBwTRo0IA33niDoqIiRo0aVem4Jk+ezKBBg+jQoQO33HIL33//Pd9++y1r1qy5puuFsgTthhtu4Nlnn2XkyJEYDIZrPua18ty3PqLSkqTdS62QnFHI+oMZ7Dp0FqXYTKSPGh/Ntf0iPZJZxC/J2ZzNM+Ojlh9nV4oNNBAbYMRVCZRKgQg/PQ2CDJj02lqZQAGs+fF78nJzuPuBhwDoeWNf7rj3Ae7u15NnJzzKG+/NwWD05Z/PPMHLb7zL4vlz+Fu3dtw/4CaO/H7QzdF7BlfcKDJp0iTUajUtW7YkLCyMfv36cffdd3P//fdz/fXXk5WVxdixY53b//7770yePJkPPviAmJiykcEPPviAzMxMpk6dCsBrr73GPffcw9ChQ+nYsSNJSUmsWrWKoKCgSsd15513MmvWLN566y1atWrFxx9/zLx58+jTp881XzPAqFGjMJvN5e4IdCe5O88LTFt2kK+3n3J3GOIqaNQKrer7E+zvQ2apDYv92n4cpdnxtTu//kmvck17DrVKIcykw6BRe8XXZMT9f0er1TLn828uuc17b/2LvNxc7nlgKCPuH8j/1m/j59U/8vmnH7Fs9W81GK1nqhegwyjtX67K9OnT+eqrr9i7d6+7QwFkJMorSOPh2stqc7AnNZef96eTlJKNyWEnwqDham/EO5FTwi/J2ZzILkWvUqFUexlI76LXlNV/ckUCpVUrRPnriQk0YNBqvCKBOnUild9+WcegIcMvuU3ykUMs/3oJE597kS2//cp1XXsQEhrGbX+/hwN7d1NQkF9zAXso6zW+WaqLCgoK2L9/P7Nnz2b8+PHuDsdJ1kR5gfS8EneHIFyg2GJj+9FsAPwNGlpFB6DRazhbZKnyH+C0vFLS8i5odmy34ZAGtZcVYixb/+RwXNvrpFMrhJn0aDUqr2sK/M2SzwgJDaNP31srfN7hcDB18nief+k1fH1N2G02rJaym16s1rL/7bbqa/JdW0jdt6obN24cX3zxBXfeeafHTOWBJFFeIbtQeuZ5m7xiK5uOnC+ZoKNZPf+rKplwvtlxkEFLi0hf7NLsuEJxQQaC9NprSqD0GhWhXtwU2G63882Shdw16CE0mor/dCz9fB7BIaHc3O92ADp2uYF33/oXu7Zv5Zd1q2jSrAX+AYE1GLVnkpGoqps/fz7z5893dxgXkSSqlisy2yiuhua4wnOczTdz9lAmANFBBhpFmjCjkGOufEKVXWzhN2l2fBEFaBnuV5b4XKW60hQ44Zd1nD55gnsffLjC5zMz0vlw1ht8+b91zsfadbyOkY9P4JGH7iY4NIw33p1T4b51jSRR3kMWltdyJ88V0//tje4OQ7jBtZRMMOrUtInyQ612UFcby/toVLQIM8FVjj756tQE++pQ/9EAVojK0qoVYkN83B2GcAEZiarlsotkKq+uSs4oJDmjEJUCzaL8iAwykm21UVKJYn7nmx3rNSra1PNDp1Hq1LvjUKOWmADDVSVQfno1QUYdag9qyyJql7r0s+btJImq5bIKJImq6+wOSDydT+Lp/CqXTCi12tmemlunmh2fX/9U1QTK/4+mwJI8iWvlcIDN7kCtkhHM2k6SqFpOFpWLC50vmQC5GLRqWsf4YzTqyCyxcrnc6HyzY7UCber5YdJrMFexF5anU4BWESa0StXKFwQaNAQYdKhkyk64kFWSKK8gSVQtd06SKHEJxRYb2y4omdCyfgBaHw2ZxZZLljuwOWD3qT+bHQcZtZR6QUsZg0ZF8yquf/L0psCidrPaHOjlL3Ct55W/Hfr06cPEiROv6RgpKSkoisLu3btdEhOUdaVetmyZy44HkkSJyskrtrI5KYtf96eTmVFAqEYhxOfSv8HPNzvemJxNfi1vdhzqq6NFqF+lEqjzTYFja0FTYFG7yd2x3kHyYGD48OHk5OSUS3BiYmJIS0sjNDTUfYFVwrlCi7tDELVMRSUTShWF3NKLSyY4gMT0QqilzY4bBhsI1GmvWLddUcqSrdraFFjUPrK23DtIEnUJarWayMhId4dxRTISJa7FyexiTmYXA2UlE6JDfSm4RMmEI5lFHMksomGwgZggH49OplRKWf0n7RVGklQKhJn0GLQqGXUSNUpGorxDjf/WWLlyJT169CAwMJCQkBAGDBhAcnIy8OcU2rfffsuNN96I0WikXbt2bNq0ybl/VlYWgwcPpn79+hiNRtq0acMXX3xxyfO9/PLLtG7d+qLH27dvz9SpU5k2bRoLFixg+fLlKH/Ue1m/fn2F03kHDhxgwIAB+Pv74+fnR8+ePZ2xb9u2jb59+xIaGkpAQAC9e/dm586dLnrVLk0WlgtXSc4oZMPBDHYdOouq2EyUjwYfzcWjMsfOFfNLcjZpOaX4qFV4Wqk5o1ZFu0j/yyZQapVCpL+e2CAjRp2sexI1z8vu26izavw3R2FhIU899RTbt29n7dq1qFQq7rrrLuwXfEdNmTKFSZMmsXv3bpo2bcrgwYOxWq0AlJSU0KlTJ1asWMH+/ft55JFHGDp0KFu3bq3wfCNHjiQxMZFt27Y5H9u1axd79+5lxIgRTJo0iUGDBtG/f3/S0tJIS0ujW7duFx3n1KlT9OrVC71ez7p169ixYwcjR450xpWfn8+wYcPYuHEjmzdvJj4+nttuu438/OpttiklDoSr2R1w8HQ+Px9IZ39SFnqLlUiDBu1f7iQ63+z45B/NjvGAZsdhvjqah1x6/ZM3NgUWtZOMRHmHGp/Ou+eee8p9/umnnxIWFsbBgwcxmUwATJo0idtvL+u99NJLL9GqVSuSkpJo3rw59evXZ9KkSc79x48fz6pVq1i6dCldunS56HzR0dH069ePefPmcd111wEwb948evfuTaNGjQAwGAyUlpZedvru/fffJyAggCVLlqDVagFo2rSp8/mbbrqp3PZz5swhMDCQDRs2MGDAgEq/PlWVXSRrokT1ubBkgo9WRevoAEy+Os5eUDLhfLPjMF8dzSJ83dbsuFGwkQCdpsJUTqdWCDXp0XlhU2BRO8maKO9Q4yNRR44cYfDgwTRq1Ah/f3/i4uIASE1NdW7Ttm1b58dRUVEAZGRkAGCz2Zg+fTpt2rQhODgYk8nEqlWryu3/V2PGjOGLL76gpKQEs9nM4sWLq9wFevfu3fTs2dOZQP1Veno6Y8aMIT4+noCAAPz9/SkoKLhsXNeqoMSKua727BA1rsRiZ/uxbNbvT+fEqVwCFQjz0aD8kbacLTSz8Wg2h9OL0ShKjf1yUSnQOsKPAJ0W/pIg6TUq6gf6UD/AgF6jlgRKeAwZifIONT4SNXDgQGJjY5k7dy716tXDbrfTunVrzOY/p6UuTFTO3ylzfrrvzTffZNasWcycOZM2bdrg6+vLxIkTy+1f0Tn1ej3fffcdOp0Oi8XCvffeW6W4DQbDZZ8fNmwYWVlZzJo1i9jYWPR6PV27dr1sXNcqv8RabccW4nLOl0wACPPT0ayeP3a1mnOl1hptdmzUqmkW6nvR9F1daQosai9ZE+UdajSJysrK4tChQ8ydO5eePXsCsHFj1ZrnJiQkcMcdd/DQQw8BZcnV4cOHadmy5SX30Wg0DBs2jHnz5qHT6XjggQfKJUU6nQ6b7eLbuy/Utm1bFixYgMViqXA0KiEhgQ8++IDbbrsNgBMnTpCZmVmla6sqm4wHCw9wYcmE+kEGGjtLJljZlJLzR7NjE2o1Lm12HG7SEe1n4ML8TJoCi9pCRqK8Q40mUUFBQYSEhDBnzhyioqJITU3lueeeq9Ix4uPj+frrr/ntt98ICgri7bffJj09/bJJFMDo0aNp0aIFUJbwXCguLo5Vq1Zx6NAhQkJCCAgIuGj/cePG8d577/HAAw/w/PPPExAQwObNm+nSpQvNmjUjPj6ehQsX0rlzZ/Ly8pg8efIVR6+ula0OvpUpTN1L5qavKDlzBGvBOWLu/Sf+zbo7n8/7fSPndv6PkjNHsBXn02jUhxgiG5c7xpnVH5GzdzWKVk/ETaMIbH2z87ncxF/I2bua2Pun19g1eZNT2cWc+qNkQqMwX2LCfCm0O9hyPBe9RkXben5oXdDsuHGIEX+txplAnW8KrJLkSdQS8h7YO9TomiiVSsWSJUvYsWMHrVu35sknn+TNN9+s0jFeeOEFOnbsSL9+/ejTpw+RkZHceeedV9wvPj6ebt260bx5c66//vpyz40ZM4ZmzZrRuXNnwsLCLkqyAEJCQli3bh0FBQX07t2bTp06MXfuXOeo1CeffEJ2djYdO3Zk6NChTJgwgfDw8CpdW1V5eZ/YCtnNJfhENCKq37iKn7eUYIxpTcSNoyt8Pv/wJnIP/Ezs4FeJvHkMp1e8g7UoFwBbSSEZ6+cR1X98tcVflxw9W1YyYcehsyjFZoK1KvafymPb8VxsNgdaddWTHbUCbSL88NeWrX/y99HQIMhAqMkHtUzdiVpEBqK8g+LwtCIv1cThcBAfH8/YsWN56qmn3B2OSxxJL+Cu9zZdeUMvdeBff7toJOo8c84Zjrz/8EUjUZmbllJ85ggxd00B4PeZg4gdNB1DvWac/mEmupAYQq+/56LjCdfQqBVa1fcnyN+HHIud5hG+lW527KtT0zSkbP1TWVNgLSqp7yRqKY1KIS7Ux91hiGtUJyqWnz17liVLlnDmzBlGjBjh7nBcRtZEVZ1PeCOyd/2ArTgfc04aDosZXVA9Ck/sp/hMkoxCVbO/lkzQmK2YTDrCAnwIvEyz4wiTjvp+BoIM0hRYCOE56kQSFR4eTmhoKHPmzCEoKMjd4biMJFFVZ2rcmYDWN3F03ngUjY76Ayeh6HxI+/Fd6g+cxLkd/+Pc9uWojf7Uu20iPmFx7g7Za50vmQDgb9DQqn4A0WFGAk06Si/43m4SYiQu0IifXivTdcJrODygOK24dnUiifLWGUu5u+PqhPd6mPBeDzs/z/hlIb4NO6CoNGQmLKbxmI/JT9rCqf++QeNRH7gx0rojr9jKpqQsSMoi1KSjfVwg9UIMtI0OJMrkg6IoFFsufwetELXJ1awJFJ6nTiRRQlxKaWYqufvX0mj0h+TsWYmxQRs0voEEtOjF6f/NwFZahFpvdHeYdYaCA5Ma6hk0tI7y5/DZQr7ec4acYqmJJrxLkEHL3AfauDsMcY1csrCgT58+TJw4ESgrFzBz5kxXHNarrF+/HkVRyMnJcdkxVTK1cU0cDgenf5xF5C2PotYZcNjtOGxlf6wd9j9GPRx1r4yEO0T7a2loVAi0lvLcwJbceX0cKbkllNjsNI800SnGX9rcCa+iku9nr+Dykaht27bh6+vr6sOKCqjr4E+hzVyM+dxp5+fmnDMUn0lGbfBDFxCOtTgPS+5ZrAVl1bTN504AoDEFoTUFlztW9u4f0RgD8GvaFQBjdCvO/rqQolOJFCRtQx8ai9rHVENXVvdE+GrwUzk4lHqOjYcLmDakM3d1bYzd4SA1t4gCc1kia3c4UKsVejYKIjmziFO5pW6OXIhrp6qDv7+9kcuTqLCwMFcf0snhcGCz2dBoyodtNpvR6XTVdl5PpamDP4QlaYdJ+Xyy8/P0NR8DENi2L/UHTib/8GZO/+8t5/Mnv/s3AGE9Hyq3DspakE1mwhc0HDbT+ZixfnNCrr+X1C9fQG0MpP7AP88jXCPYR02IXuH46Ry2HC6rz3VDs3CWPtsXg48OB2Cx2TiSVXjRviU2O/WD9DQIMrDjRC7mulgoTXiNOvjr2ytVeTqvsLCQhx9+GJPJRFRUFDNmzCj3/IXTeQ6Hg2nTptGgQQP0ej316tVjwoQJzm3PV/j28/MjMjKSBx980NloGP6cAvvxxx/p1KkTer2ejRs30qdPH8aNG8fEiRMJDQ2lX79+ALz99tvOfnoxMTGMHTuWgoIC5/Hmz59PYGAgq1atokWLFphMJvr3709aWppzG6vVyoQJEwgMDCQkJIRnn32WYcOGlSvoabfbefXVV2nYsCEGg4F27drx9ddfl3sdfvjhB5o2bYrBYODGG28kJSWlqi/1FdXFdzK+se1oNeWni/6dT3iC2v2twucvTKCgbGSq6biFaP1Cyj0e3vMhmj/1DfGPfYKxfvMauy5v5qdTER+gxd9czK59qazedpzDp3LRa1V8+kQfPh5/IwafsjdBDuycyish75J9IRVsOOgSG0DTMBnxFrWXNMP2DlVOoiZPnsyGDRtYvnw5P/30E+vXr2fnzp0VbvvNN9/wzjvv8PHHH3PkyBGWLVtGmzZ/LqSzWCxMnz6dPXv2sGzZMlJSUhg+fPhFx3nuued47bXXSExMpG3btgAsWLAAnU5HQkICH330UdnFqFS8++67HDhwgAULFrBu3TqeeeaZcscqKirirbfeYuHChfzyyy+kpqYyadIk5/Ovv/46ixYtYt68eSQkJJCXl8eyZcvKHePVV1/ls88+46OPPuLAgQM8+eSTPPTQQ2zYsAEo65t39913M3DgQHbv3s3o0aOr3N6mMuriSJSoHXzUCk0CdYQ6zCQmnmT1thT2ppxzVmke1KMRm2bcQ8f4iHI3eucVW0jJKb7i8c12B4G+Gno2DsLfR+6PEbWPSkqdeYUq/fYpKCjgk08+4fPPP+fmm8v6jS1YsIDo6OgKt09NTSUyMpJbbrkFrVZLgwYN6NKli/P5kSNHOj9u1KgR7777Ltdddx0FBQWYTH+uRXn55Zfp27dvuWPHx8fzxhtvlHvs/OJ2KBsRe+WVV3jsscf44IM/b1O3WCx89NFHNG5cVsV63LhxvPzyy87n33vvPZ5//nnuuusuAGbPns0PP/zgfL60tJR///vfrFmzhq5duzpj37hxIx9//DG9e/fmww8/pHHjxs5RumbNmrFv3z5ef/31S720V0UWlgtPolEpxPprKMovYXfyWX43X1ySIDLQwH+euJHoML+LnrPZ7WQWWcgutlT6nKU2O62ifCkxO9h9Kk8q74haQ94Ee4cqJVHJycmYzeZyveeCg4Np1qxZhdvfd999zJw5k0aNGtG/f39uu+02Bg4c6FzTtGPHDqZNm8aePXvIzs7G/kfrh9TU1HINhTt37nzRsTt16nTRY2vWrOHVV1/l999/Jy8vD6vVSklJCUVFRRiNZbepG41GZwIFEBUV5ZxCzM3NJT09vVyip1ar6dSpkzO2pKQkioqKLkrqzGYzHTp0ACAxMfGi/nznEy5X0kidEeFmCg5i/fXYS0vZk3yWpCLzJbd97r4ODO4dj6OCaQwHDjLySzmVX1LlGGwO0GoVejYO4nBGEWfyZeG58HwmnYygeoNq/SrGxMRw6NAh1qxZw+rVqxk7dixvvvkmGzZswGw2069fP/r168eiRYsICwsjNTWVfv36YTaX/0Vc0d1+f30sJSWFAQMG8Pjjj/Ovf/2L4OBgNm7cyKhRozCbzc4k6nzD4PMURalSMc7za6xWrFhB/fr1yz2n1+srfRxX8Ddor7yRENUg2k+L1mblwLFzrD98+em39g1DmP14T0xG/SVHiootVoqtNjILL52EXUmJzU5siJ64EAM7UnOxSEV/4cFMerW7QxAuUKUkqnHjxmi1WrZs2UKDBg0AyM7O5vDhw/Tu3bvCfQwGAwMHDmTgwIH84x//oHnz5uzbtw+Hw0FWVhavvfYaMTExAGzfvv2qL2THjh3Y7XZmzJiB6o/J5qVLl1bpGAEBAURERLBt2zZ69eoFgM1mY+fOnbRv3x6Ali1botfrSU1NveQ1t2jRgv/+97/lHtu8eXMVr+jKjDo1Bq2KYovUMhLVL8KkwQ87h05ks/FwwRW316pVvPdYd7q2qHfZaTa7w05Gnpm0gmsfQXKg4MDB9Q0DSMs1k5xZdM3HFKI6mPQyEuUNqvRVNJlMjBo1ismTJxMSEkJ4eDhTpkxxJi1/NX/+fGw2G9dffz1Go5HPP/8cg8FAbGwsdrsdnU7He++9x2OPPcb+/fuZPn36VV9IkyZNsFgsvPfeewwcOLDcgvOqGD9+PK+++ipNmjShefPmvPfee2RnZzt7dvn5+TFp0iSefPJJ7HY7PXr0IDc3l4SEBPz9/Rk2bBiPPfYYM2bMYPLkyYwePZodO3Ywf/78q762ywn21XEqp+pTIEJURrCPmhCdQsoFJQkq447r43jxwc6o1erLJlAOh4OsQjMOxUG6C5Ko88w2ByEmLfX8g9h1Ms9Zc0oITyEjUd6hyvcHvPnmm/Ts2ZOBAwdyyy230KNHjwrXJwEEBgYyd+5cunfvTtu2bVmzZg3ff/89ISEhhIWFMX/+fL766itatmzJa6+9xltvvVXhcSqjXbt2vP3227z++uu0bt2aRYsW8eqrr1b5OM8++yyDBw/m4YcfpmvXrphMJvr164ePj49zm+nTpzN16lReffVVWrRoQf/+/VmxYgUNGzYEoEGDBnzzzTcsW7aMdu3a8dFHH/Hvf//7qq/tcoJ86159LFG9/HQqmgZq8TtfkmD7cY6crlwCFeKnZ9kL/Xhp6PWo1Vf+I2Gx2SgotZFeWD3rmErtdtrWN9G23sUL2YVwJxmJ8g6Kw1u787qI3W6nRYsWDBo06JpGyqrL2IW7+OVQprvDELWcj0YhxqQlO7uA3UczsV5FIcsn72jDsL4toJL1bxwOBydyirE57OxKy6vy+arKR60iMb2QswVXv+5KCFd5pGsMf2tefcWpRc2QVPgvjh8/zk8//UTv3r0pLS1l9uzZHDt2jAcffNDdoVUo2CgjUeLqaFQKDfw0FBeWsDup4pIEldEyJpAP/9GbAJPPlTe+QG6JBZvdQWZxzSQ1JTY7jUMNNAoxsP1EHjZZeC7cSEaivIN8Ff9CpVIxf/58Jk2ahMPhoHXr1qxZs4YWLVq4O7QKBZskiRKVp1Kggb8OW0kpe5IzSCqqfE2mv1KrFN4e3Y0+baOrXJ/JZreRXWRBURycyK25NX12AAW6NQzkZHYJx85dubCnENVB1kR5B0mi/iImJoaEhAR3h1Fpwb5S5kBcWYyfFo3Nyv5jmaw/dO1JS/+OMbzy8PVoNJdfOF4Rh8NBen7Z6NO5kqtP4q5Fqc1OqJ+W+oE+7DyRS5Hc4SpqmIxEeQf5KtZysrBcXEqESYOfYudQaja/VqIkQWUE+mqZM74PTaODr/oYRRYrpVY7igLHs903EqQoCma7nfYx/uQVW9mf5prXSIjK8JORKK8gSVQtFyJJlLhAsEFNiLbqJQkq4/HbWvLora3hGtoN2R12Mv4YhcoptXhEmxar3YFRr6ZXkyAOpBWQVeie0TFRt8hIlHeQr2ItJyNRwk+nItKo4kxGPruO/Nnk11Xi6/nz8bg+BPsbruk4DoeDzD/ujFMUBynZnlUIs8RqJz7cSCMb7DiRi6w7F9VFo1IwaGUkyhtIElXLBUsSVSf5aBSiTRqyswvZk5jJvqsoSXAligKvD7+Bfp1iXTJiZLbZKfzjDsB8s9UjkxS7AxQVdG8UyPFzJaRmSyFb4Xr+PvKn11vIV7KWk4XldYdGpRDrr6Uov/iaShJURp829XhjZFd0Wo1LEiiHw8GZvD8SEgWOuXEtVGWU2hxEBuiICfRhx4k8Sqyy8Fy4TqR/zfZZFdVHkqhaTq9V46tXU1gqbS28kStLElSGyUfDx+N60you1KXHzSk2O0eeCi1WrJ44DHURBYvDQacG/mQXWjmYLgvPhWtE+UkS5S0kifICwb46Cks9+529qJpoPy1aF5YkqIwRfZsx4e/tnH0iXcVqt5NTbP3jMwfHznnWWqgrsdgdmAxqejUOYt/pArKLZeG5uDYyEuU9JInyAlEBPpyQooG1XoRJgwk7h1PPsfFwYY2dNzbMxNwJfQgP8nX5sR0OB+l5fyaBJTY7lloxCnWxEpudZpG+WKx2dp7Mc/kCflF3SBLlPSSJ8gJNIkxsPZbt7jDEVajOkgSVMf2h6/j7DY2qrdRAodmK+Y9F7wpw1MPuyKsqu8OBWq3Qo3EQR88WcSq3ehonC+8WKdN5XkOSKC/QJNzk7hBEFZwvSZCWXj0lCSqja/MI3hnTHR+9ttoSKLvDXq7Zb4ndRqmXLNAutdqJDtITG2Rgx8k8r7muiliKCzn43Yec3vUzJXnZBDZoRrsHJxHcsBUAh1d+xuEfPwOg6a3DaNp/qHPfc8n72PX5a9z4wgJUavlzc56MRHkP+a72Ak3CXT8NI1zLR6MQY9JwrhpLElQqDq2aD//Rk45NIqq10KXD4SiXQIGDY7V8FOqvHChYcXBdA3/OFlg4lFFzU7A1aef86eSeSqbz6OkYAsNI3fQDv771OH975WvMBbkcXPYR3Z6YCQ5ImDWRiNY3EBAdj91mZefCV+k4bIokUBcINGikRpQXke9sL9AkQkaiPNGFJQl2HTnL7xb33kH5YO8mTLqnAyqVqtorhZdabRRdUILB4rBT7KX96cx2BwFGDT0bB7H3VD65JdYr71RL2MwlnNqxjq7jZxDWrCMALe98lLQ9v3D0568JiG5CQHQ84S26ABAQ3YT8tBQCouM5vPIzQpt2cI5YiTIyleddJInyAv4GLRH+etLzZH2Gu5WVJNBiKzaz52j1lySojHrBBuZOuJH6oX41cr6yBsN/fi86cHh8XShXKLXZaRnlS4nFwe6TeR7R0uZa2W02HHYbam35P/xqrZ7MI7uJueFW8tNTKcpKw+GAgvRU/Os3oSDjBMc3fs9NL37upsg9l0zleRdJorxE43CTJFFuFOOvRWO1su9ozZUkqIwpgzoyqFcTHLi2bMHlZF9QEwrAhsNZqdzb2Ryg1Sj0bBzEkbNFpNXyn0mtwZfgxm1J/P4/+EU1xCcgmBNbVpGVvA9TeAz+9RrS+u5/8Otb/wCg9T3j8K/XkF/efJzW900g/cAmEpfPQVFraDd4knM0qy6TkSjvIkmUl4iP8OW3pCx3h1GnRPpqMCkODqVm8WsNliSojE5NQnn30Z74GnQ1OiJitdvILb5gOsvh4Hiu949C/VWJzU6DYD2xwQZ2pObW2rIOANeNeZkdn77MD0/3R1GpCYxtTsz1/cg5nghAoxvvpdGN9zq3P57wPVofIyGN2/LT/93NjS8upPhcOls/ep7+b3yPWlu3W1VFyUiUV5Ekyks0ljv0akSwQU2ITuHYqWw2H85zdzgX0apVvP94D65vHlXj00llrV3Kj7zYFQd5XrRGqCocKDhwcH3DQM7klpKUWTsX1pvCY+j93FyspcVYigswBIax5cPn8A2rf9G2pfnZHFw+l97PzeXc0f2YImPxi2iAX0QD7DYrBenHCYiOd8NVeI5Ifx93hyBcSJIoLxEvi8urjZ9OTaRBRdrZPLeVJKiMu7vFMeX+61Crq3/heEUKSi1YLrjr0OFwkJrnOVOb7mK22Qk2aekZEMSuk3kU1NIWTRq9AY3egLkwj/T9m2h93xMXbbN3ydvE/+1BjMERZB87gN32ZwLtsNtw2L3z5oLKUisQHShJlDeRJMpLNA7zRVHw2D/wtY2zJMG5QnYfzGSfB0/HhPnr+c8TNxIbEeC2GOx2O5mF5RfROxSkRcoFSm122tY3UVhqZ8+pfHeHU2ln9v8GDvCLjKUg4wT7ls7CLyqOuB4Dy22XfmAz+WeO03nUSwAENWxFfloKZ/YmUHQuHUWlwi8y1h2X4DFiggzoNSp3hyFcSJIoL2HUa6gX4MOpHHnnf7XOlyQozC9i95FMt5ckqIyn72rL0JubQw0uHP8rh8NBRsHFC6hP5cv34l9Z7aDXqujVJIjfzxSSUa6WlmeyFhWw/5vZFGdnoPP1p16nm2l991hUGq1zG5u5hN2fv8H1j7+KoipLEozBEbQfMpntn76EWqul86iXUOvq9ihMfKjU9PM2isMhYxfeYuzCXfxyKNPdYdQqKgUa+GmxlZrZk3yWPA8oSVAZbWKDeX9sT/x93f9HqdhivWgtlAMHu8943poxT6KibOR4+4k8bB480ilc5/HuDbi5aai7wxAuJCNRXiQ+3CRJVCV5akmCK1GrFGY+0o1eraM9og7RX2tCnZdWUHteU3exAyjQtWEAp7JLOSZNxL1e0zAZifI2kkR5kcbS/uWyIn01+Cp2DqWe87iSBJVxa+cYpj90PRqN2mMSqHNF5ovX4SkO0mvBNJWnMNschPppqR/ow86TeeUqvQvvYdCqqC+Lyr2OJFFepEU9f3eH4HFCDGqCtAoppz2zJEFlBJn0zB3fmyb1g9wdSjlWu73C8gXphbW7wKQ7KIqC2W6nfbQfecU29qfVnoXnonIahxhRKe5buyiqhyRRXqRJuC/BvlrOFdaOdT3VxVmSICOPnR5ckqAyxt7ekkdubY07F45XxMHFNaEAFMXB6VpepdudrHYHRr2KXo2DOHim4KI7HkXtFS9TeV5JkigvoigK1zUMZtX+dHeHUuMMGhXRJnWtKElQGU3rBfDx+N4E+RncHUqF8kssWCt4jc8WyTSeK5TY7DQJM9IwBHaeyMVWu7+dBdBEkiivJAUrvEyXRp415VOdNCqFxgE6otRWjh05zeqtKexIOlur73RSFHhrVFe++r/+HptA2ex2sioaIVEcnMiVBeWuYgcUFXRvFESDIFlLU9vFhxndHUKVxcXFMXPmzGs6xrRp02jfvr3z8+HDh3PnnXde0zE9iYxEeZnrGwW7O4RqVb4kQQZJV1mSwPz7Ciz7vkYT3xd9+wcBKN39BdaUBBSNDl2b+9DEdnVubz2xDevxBHx6THTFZVToprb1eG1EV3RajUcsHK+Iw+Ego4K78QDOlcjUU3UosdmJ9NcRE2hgx8lcSix1u+p3bRRi1BJsrNs9A8+bNWsW3lRZSZIoLxMX6kuEv550L1uXEuOvRW21st8FJQls545iTV6PKiDG+Zj19G5sqZvx6fU0joJ0Srd9ijqyNYreD4elCPP+b/DpPflaL6NCJh8Nc8f3oUVsSLUc35WKLVZKrBX8EVcgNVtu0a82ioLFYadTjD/ZhRYOpte+u0vrMpnK+1NAgPs6K1QHmc7zQl28ZDQqyqSlsUmF9VwOv25PYf3uk2ReYy82h7WE0i1z0HceDro/h9fteadRhTdHHdwQTYMbQGvAXngWAPPepWgb34jK6PokZ1Tf5mx88+5akUDZHXYy8ite85RbavHY0TNvYrE7MBk09GwcRJBRe+UdhEdwVX2or7/+mjZt2mAwGAgJCeGWW25hw4YNaLVazpw5U27biRMn0rNnTwDmz59PYGAg//vf/2jWrBlGo5F7772XoqIiFixYQFxcHEFBQUyYMAGbrXyJjfz8fAYPHoyvry/169fn/fffL/d8amoqd9xxByaTCX9/fwYNGkR6+qXX5f51Os9ut/PGG2/QpEkT9Ho9DRo04F//+tc1vlI1R5IoL1Sbp/RCDGri/TVo8vPZtDOFtTtPcDLTde+6zTsXoo5qhzqiVbnHVYEx2M+l4DAXYstOAZsZlSkCW+Zh7NnH0cT3dVkMAHERJla/MoDxd7QrWwjl4RwOB+cKzRUmSoriICW7qMZjqstKbXaaRRjpHONfG7596rw2UX7XfIy0tDQGDx7MyJEjSUxMZP369dx999106tSJRo0asXDhQue2FouFRYsWMXLkSOdjRUVFvPvuuyxZsoSVK1eyfv167rrrLn744Qd++OEHFi5cyMcff8zXX39d7rxvvvkm7dq1Y9euXTz33HM88cQTrF69GihLgO644w7OnTvHhg0bWL16NUePHuX++++v9HU9//zzvPbaa0ydOpWDBw+yePFiIiIirvHVqjkyneeFrq9li8svLEmw4/C5ajuPNXULtuzjGG7550XPaSLbYI/tSvGal0GtRd9lNGj0lO74DH2X0ViT12E5shZFb0LfaTiqgPpXHce/hnZhwPUNa9XIjcVuJ7+04iKQeWYrtXgtf61ld4BKrdCjURDHsoo5KX0zPZK/j4aGIdd+k0haWhpWq5W7776b2NiyRs5t2rQBYNSoUcybN4/Jk8uWHHz//feUlJQwaNAg5/4Wi4UPP/yQxo0bA3DvvfeycOFC0tPTMZlMtGzZkhtvvJGff/65XBLUvXt3nnvuOQCaNm1KQkIC77zzDn379mXt2rXs27ePY8eOERNTtjzis88+o1WrVmzbto3rrrvusteUn5/PrFmzmD17NsOGDQOgcePG9OjR45pfr5oiI1FeKCrQQEywZ97ZdZ5BoyI+UEuI3czBgydYvS2F/cerL4GyF2VRunsxPtc/iqKueBpE1+pOjLe9jrHfK2jqd8KS+D/UEa1QFDWWg99juOl5NA17Ubp17lXF0L1FBJtn3M3ttSyBcjjsFdaEAkBB2pW4WanNTv1AHd0aBqLXyK90T9O2nh+KC4YL27Vrx80330ybNm247777mDt3LtnZ2UDZFFlSUhKbN28GyqbvBg0ahK/vn9OIRqPRmUABREREEBcXh8lkKvdYRkZGufN27dr1os8TExMBSExMJCYmxplAAbRs2ZLAwEDnNpeTmJhIaWkpN998c2VfBo8jI1Fe6vpGwZw4d8rdYZSjVSnE+mspyC9i95FMEi01197Cnn0cSvMoXjPtzwcdduxnD2NNWovxnrkoyp9/gOx5aVhTN2Ho+xLWY7+iCmuGovdHE9MF8/ZPcViKUbSVS1R9tGo+Ht+Ldo3CXXxVNSOvxHrJshGFFis2L7rTprZyoGB1OOgc609WvoXfM2Thuado76JOEmq1mtWrV/Pbb7/x008/8d577zFlyhS2bNlCw4YNGThwIPPmzaNhw4b8+OOPrF+/vtz+Wm35N4+KolT4mN1ec3d/Ggye/Wa/MiSJ8lJdGgXz9Xb3J1EqBWL9dViLS9mdnMGRYvfcBq8Ob4Hhb9PLPVa67RMUvyh0zW8rl0A5HA5KdyxA124wisYHHHaw/5Hwnf+/konDkD7xTLq7PYqqdo4Q2Ow2zl2yjISDY+dkLZQnsdgc+BvLFp7vPZVPbgVteUTNalffde24FEWhe/fudO/enRdffJHY2Fi+++47nnrqKUaPHs3gwYOJjo6mcePGdO/e3SXnPD+6deHnLVq0AKBFixacOHGCEydOOEejDh48SE5ODi1btrzisePj4zEYDKxdu5bRo0e7JN6aJkmUl3L34vIYPy0am5V9R8/y8yH3l1tQtAaUgOjyD2r0KHoTqr88bj32C4reD0299gCoQuMxH1iOLSsZ25m9KP71UHSXL5wXHeLL3Am9iQq59gWl7uLAQfol7sYDKLbZschiKI9UarPTMsqXUouDXSfzatX0sTdpEOTjsrsot2zZwtq1a/nb3/5GeHg4W7Zs4ezZs86Epl+/fvj7+/PKK6/w8ssvu+ScAAkJCbzxxhvceeedrF69mq+++ooVK1YAcMstt9CmTRuGDBnCzJkzsVqtjB07lt69e9O5c+crHtvHx4dnn32WZ555Bp1OR/fu3Tl79iwHDhxg1KhRLruG6iRJlJcKMeloEu5LUg0O60eZtPhi4/fUc/x6uHZOJzhKcrEkfo/PTVOcj6mDG6Ft1o+Sje+g6P3LFp1fxtQHOnJvjyY4PKzfXVUVm62UVlQTirJOfsfkjjyPZnOARqPQs0kQSRlF0tPQDTpFu64mkr+/P7/88gszZ84kLy+P2NhYZsyYwa233gqASqVi+PDh/Pvf/+bhhx922Xmffvpptm/fzksvvYS/vz9vv/02/fr1A8pGxpYvX8748ePp1asXKpWK/v37895771X6+FOnTkWj0fDiiy9y+vRpoqKieOyxx1wWf3VTHN5UOlSU8+///c7izSeq9RwhBjVBWkg5lUNSWl61nsvTXdckjFmP9cDoU/srE9sddo5fZsF4qd3GwYyCGoxIXBsHalRsP5GLRRrx1ZhXbmtK8wjTlTd0kVGjRnH27Fn++9//1tg56zoZifJi3eNDqiWJOl+S4HRGLjsOZ7v8+LWNXqti9uM96dI00iumTRwOB1mFl2skLGuhah8FGw6ujwskPbeUI5ny9atu/j4amobXTKXy3Nxc9u3bx+LFiyWBqmGSRHmxbo1D8PPRkO+CxaUGjYpok4asc/nsOZjFPlkLA8A93Roy5YHOqFQqr0igAMw2OwWXqAkFYHHYKb7ENJ/wbGabnSCTlp7+Qew+lXfJ2l/i2nWM9kdVQ5VQ77jjDrZu3cpjjz1G376uLQwsLk+SKC+m1ajo0zyM73enXd3+bixJ4OnCAw38Z0IfGoS77s4bT+BwOEi/bGsdB8ekR16tV2q306a+iaJSO7tP5bs7HK/UOabmesT9tZyBqDmSRHm5fq0jqpREqRRo4K/D5uaSBJ5s8j3teejGprV+4XhFcorNXG7JjNXhoNAsybQ3sNpBp1XRq3EQhzIKL3snpqgarVpxaWkD4blqZ/EaUWndm5RN6V1JjJ+WOAPkpWWxftsxft1/mnxJoMpp2zCYX9+4kyE3NvPKBMpqt5NTfJmpX4eD4zkyCuVtSmx24kINXB8bgEblfd/X7tCunj8GrdrdYVTZ+UbF502bNo327dvXyLlqK0mivJxWo+LG5mEVPhdp0tLYpMKSlcOvO1JYv+cUmZedyqmb1CqF9x/vwcKn++Jn1Ls7nGpx5Wk8sCsO8kqleKM3cjjAoUDXhgE0ckGft7qud5Oar9OXkpKCoijs3r273OPDhw/nzjvvrNQx7r//fg4fPuzy2OLi4pg5c2aNnKumyXReHfC31hH8948pvVCDhiAtHDuVzebDdbskQWUMuK4B0x7qgkat9pqF4xUpslgxX2Yez+FwkCoJttcrtTkIMWmpF+DDrpN5MnV7FUw6dY2uh3IVi8WCwWCosVYsNXmu6iQjUXVA9yYhtA33wbe0iO17jrN6+/E6X9PpSoJMer75v368MqwrGnXtG5avCrvDQcYV1sM4FMiW6d06QVEUzHY77aP9aBNVczWOvEXXhkFo1dXzp3XlypX06NGDwMBAQkJCGDBgAMnJyQA0bNgQgA4dOqAoCn369GHatGksWLCA5cuXoygKiqKwfv1656jVl19+Se/evfHx8WHRokWXnGL7+OOPiYmJwWg0MmjQIHJzc53P9enTh4kTJ5bb/s4772T48OHO548fP86TTz7pjAHKT+cdPnwYRVH4/fffyx3nnXfeKdc0ef/+/dx6662YTCYiIiIYOnQomZmZ1/KSXjNJouoArUZFsE7FgeNS06kyJgxsw8+v3UHjeoHuDqXaORwOzhZcuZL1qTxZC1XXWOwODHo1vZoEEeZb+wvI1pTejatvKq+wsJCnnnqK7du3s3btWlQqFXfddRd2u52tW7cCsGbNGtLS0vj222+ZNGkSgwYNon///qSlpZGWlka3bt2cx3vuued44oknSExMdFYh/6ukpCSWLl3K999/z8qVK9m1axdjx46tdMzffvst0dHRvPzyy84Y/qpp06Z07tyZRYsWlXt80aJFPPjggwDk5ORw00030aFDB7Zv387KlStJT09n0KBBlY6lOsh0Xh1xf6/GzF93xN1heLTm0YF89I9eBPrV/iHmyjLbbBRdYcrGgYPMSzYhFt6uxGqnUZiBhiEGdpzIvezdm3VdpJ++WiuU33PPPeU+//TTTwkLC+PgwYOEhZWtfQ0JCSEyMtK5jcFgoLS0tNxj502cOJG77777sucsKSnhs88+o379+gC899573H777cyYMaPCY/5VcHAwarUaPz+/y24/ZMgQZs+ezfTpZY3iDx8+zI4dO/j8888BmD17Nh06dODf//53ueuPiYnh8OHDNG3a9IqxVAcZiaojerSIICa0Zqrn1jaKAm+P7sqXz/WrUwmUw+HgTCX6qaUVyFqous4BoIJujYKIDao7PyNV1bNxULUe/8iRIwwePJhGjRrh7+9PXFwcAKmpqVd1vMo0CW7QoIEzgQLo2rUrdrudQ4cOXdU5L+WBBx4gJSWFzZs3A2WjUB07dqR58+YA7Nmzh59//hmTyeT8d/6581Oa7iAjUXWEoijc36MRby3b5+5QPErfDvX598M3oNVqvHrheEWyi81csfC84iC9QOoHiTKlNjsR/lqig/TsPJFHsUUq11+oT5OQaj3+wIEDiY2NZe7cudSrVw+73U7r1q0xm/+/vTsPi6psHzj+nQFm2BlWWUQQBUTFBbcUFTRNM/25lUmmqIVtbplmZZrW22qZaCtWaL4planVa2mmaUruaxquqZAiKi4sss3M+f1BTqGoLAMDeH+uiytmzjnPuWca5OZZ7qdiP6MODpX/w1qtVnP9FrxFReXvufb29qZ79+4sWbKEu+66iyVLlvDEE0+Yjufk5NCvXz/efPPNG6718fEpf+BmIj1Rd5CYqEa3P+kO4Wxnw5dTezL7kc7Y2Nx5f0voDQau3Kom1N8ycm/fUyXuMCoVRUaFCH9nmnnLxPNrmng5UM+p6kqgZGZmcvjwYV588UXuvvtuwsLCuHTpn3muGk3xvDWDoeTwvEajueG58khNTeXMmTOmx1u3bkWtVhMaGgqAp6dniXlOBoOBAwcOVCiGYcOG8eWXX7Jlyxb+/PNPhg4dajoWERHBwYMHCQwMpHHjxiW+zJEMVpQkUXeQUD8dkWH1LB2GxcX1DuPXtwYS6l/9tVxqAkVROJtdhuRIpXCmDMN94s5UZFRwsC2eeO5mb2PpcCyuaxVOKAdwdXXF3d2dhIQEjh07xvr165k0aZLpuJeXF3Z2dqYJ19dW0AUGBrJ//34OHz7MhQsXyt1LZGtrS2xsLPv27WPTpk2MHz+eIUOGmOY3de/enVWrVrFq1SoOHTrEE088weXLl0u0ERgYyK+//srp06dvuZpu0KBBZGdn88QTT9CtWzd8fX1Nx5566ikuXrxITEwMO3bs4Pjx46xZs4ZRo0ZVKkmsLEmi7jBxvZpYOgSLaVjPiZ9f7cdTfVsUT4S6Q+UW6ikqw+zgC1dlGE/cXr7eSEg9e9r6O3OnFjy3sVIR2bBq50Op1WqSkpLYtWsXzZs35+mnn2b27Nmm49bW1sybN4+PP/4YX19f+vfvD0BcXByhoaG0bdsWT09PkpOTy3Xfxo0bM2jQIPr06cM999xDixYt+OCDD0zHR48eTWxsLCNGjCAqKoqgoCC6detWoo2XX36ZkydP0qhRI9ME+NI4OTnRr18/9u3bx7Bhw0oc8/X1JTk5GYPBwD333EN4eDgTJ05Ep9OhVlsulVEp1w9mijqtSG+kyZNfk3GHbd/xemwH+rQLvOPmPV3PaDRyqiwbCKsU9kgtMVFOWms1Jy/kkXb5zlqMcFegjsndgiwdhrAA6Ym6w9hYq4ntHmzpMKpN12bebJsziHslgQJF4VwZakIBXMyXkgai/Ar0Rnx0GiIb6tBa3zm/XnqEeFg6BGEhd86nXJg80jMUqzre726vtWLxM92Z90QUWo3M1wDI1xvKtJpKpYLUsvRWCVEqFUWKQtsAZ5p41f2yKg10trTyc7Z0GMJCJIm6A/m5O3BvG39Lh1FlhncPIfntQYQ3vPnY+52mzJPJgcsFRdJrJyqtyKDgbG9N10au6Ozq7grY+5p5WToEYUGSRN2hxtTBCeb+Hg6sfvk+nhnUGpWqej7a0W2bEFzP/oavmc9NBOC1GVNpG+pHl9bBfLssqcS1P363nDEPDy6lVfNSFIWLVwsp0+xHlcLJS1erPCZx58g3GAnzdiSivjN1rf9bZ2dd5avyRM0mSVQZlLbBYnld2/Bx7969ZokJigtorly5skLXdgv3Idin7nRBv/RQG/43sy/ebtVbt+ab1Zv47fc/TV8Lv/ofAPf2G8S6Nav4fvmXJH75Hc9Of5VpzzzJxczi5b3ZWVeY8/pMZr4xt8pj1BuNZOXfviYUQHah/vYFOIUoJ4OiYG2toksjV3ydq66WUnXr1cSzyjYbFrWD/N+vAiNHjmTAgAElnvP39yc9PZ3mzZtbJqjrqFQqHukZaukwKu2uUC+2vD2QgZ0aW2QIyt3DE08vb9PXL2t/pEFgEO07deH40cN06NSV8FZt6DdoCI6OzvyVehKAN1+exkOxcfjWr9ph1bJu7QLFc6FOXJS5UKLq5BuM1HfT0jFQh8aqdvdLaaxU9GoiUwbudJJEVRMrKyu8vb2xtq45cwOGdw/G2a52TrrW2qj5dEI0H4/rhp1tzdhhvrCwkO++SeL+mBGoVCrCmoXz+77dXLl8iQP7dpOfn0dAw0bs3PYbf/y+lxFxZd8JvaKyC4rQl7FrKadIj0Eqnogqp8KAQvtAF0I87S0dTIVFNXbH2bbm/HsuLKPWJ1GrV6+mc+fO6HQ63N3d6du3r2kzwmtDaMuXL6dbt27Y29vTsmVLtmzZYro+MzOTmJgY/Pz8sLe3Jzw8nKVLl970fi+//HKpvUmtWrVi+vTpzJw5k0WLFvHtt9+iUqlQqVRs2LCh1OG8gwcP0rdvX5ydnXFycqJLly6m2Hfs2EHPnj3x8PDAxcWFqKgodu/ebaZ3rZiLvaZW9kYN6RzElncG0ya4Xo2aAP3zj9+TdeUyg4Y+DECXbj3pf/9QBvXqwtTxj/HW/ATs7B146dkJvPzWPJYsTOCeTi15sG93jh76w+zxGBUjmbllLVWgcOKizIUS1afQoKBzsKFLI1ectLUrGVEBfWVCuaAOJFG5ublMmjSJnTt3sm7dOtRqNQMHDsRo/Gcp97Rp05g8eTJ79+4lJCSEmJgY9PriOSL5+fm0adOGVatWceDAAcaMGcPw4cPZvn17qfcbPXo0KSkp7Nixw/Tcnj172L9/P6NGjWLy5MkMGTKE3r17k56eTnp6Op06dbqhndOnT9O1a1e0Wi3r169n165djB492hRXdnY2sbGxbN68ma1btxIcHEyfPn3Izs4259vH2L5N0drUjo+Bt86O72fcywtD21m0Qu3NfL1kEV2730M973+2Khg/5UXWbTvAqo07uKdPfz6eN5tOXbthbW3DB+++ydLvfuaBYSOZMu5Rs8aiKAoZZVyNB5BnMFIkk6GEBRQYjDT3daCVn5OlQymzCH9n/FxsLR2GqAFqV/pfisGDS65u+uyzz/D09OSPP/7A0bF4kvHkyZO57777AJg1axbNmjXj2LFjNGnSBD8/PyZPnmy6fty4caxZs4avvvqK9u3b33C/+vXr06tXLxITE2nXrh0AiYmJpnL3AHZ2dhQUFJj2FirN+++/j4uLC0lJSdjYFA+phYSEmI537969xPkJCQnodDo2btxI3759y/z+3E49nT3Dohrz2c9HzNZmVZh6fyseig5BqaHre06npfLbr+t5/7Ob92IeP3qYb5cl8e26LSxb8jntOnbG3cOTPv83mOcnPk5OTjaOjub5RZKvN5BfhppQ10gvlLAkgwIaGzVdG7tyOCOXjOyaveVQv2ayB6koVvP+nC+no0ePEhMTQ1BQEM7OzgQGBgLFO09f06JFC9P3Pj4+AJw7dw4o3nH6lVdeITw8HDc3NxwdHVmzZk2J668XFxfH0qVLyc/Pp7CwkCVLljB69Ohyxb137166dOliSqCul5GRQVxcHMHBwbi4uODs7ExOTs4t46qoif/XvMYW32zV0J1Nbw0gJjq0xiZQAN8kfY67hyfRPe8t9biiKEyfMo7nZ72Bg4MjRoMB/d8bger1xf81mmkTTUUxklGOjYMLjAYKDGVPuISoKvl6I4HuttwVqMOmhv6bFORuR3Of2tNrJqpWre+J6tevHwEBASxYsABfX1+MRiPNmzensPCfv2T+naio/t549tpw3+zZs4mPj2fu3LmEh4fj4ODAxIkTS1xf2j21Wi0rVqxAo9FQVFTE/fffX6647ezsbnk8NjaWzMxM4uPjCQgIQKvV0rFjx1vGVVFB3s7c36khX27+0+xtV5SNlZp5j0XSqalvjZr3VBqj0cg3SYsZOOThmy4c+Oq/ibi5e3B3r+Ie0Yj2dzHv7VfZs3M7v65fQ+PQMJxddJWORVEUMnMLy/GeyVwoUbMoqFBQ6NDQhfTLhRzPrFmfz77SCyX+pVYnUZmZmRw+fJgFCxbQpUsXADZv3lyuNpKTk+nfvz8PP1w8GdhoNHLkyBGaNm1602usra2JjY0lMTERjUbD0KFDSyRFGo0Gw216FVq0aMGiRYsoKioqtTcqOTmZDz74gD59+gCQlpbGhQsXyvXayuPZwS34OvkExhqwOqt/h0BmPNQWKyurGp9AAST/up4zf6Vx/0MjSj1+4VwGH8a/xZf/W296rmVEO0Y/MZ4xDw/CzcOTt+YlmCUWvdFIdkHZe7SKFCN5eumFEjVPoUHBzdEaX50re9KyyCk0T09tZfi6aIls6GrpMEQNUquTKFdXV9zd3UlISMDHx4fU1FSee+65crURHBzMsmXL+O2333B1dWXOnDlkZGTcMokCePTRRwkLCwOKE55/CwwMZM2aNRw+fBh3d3dcXFxuuH7s2LHMnz+foUOH8vzzz+Pi4sLWrVtp3749oaGhBAcHs3jxYtq2bUtWVhZTpky5be9VZYT66RjUMZBlv52osnvcjruTlk8nRBPorbNYDBXRJboHRzNu/teyh1c9Nuw8dMPz4555gXHPvGC2OBTFSHo5hvFA4YTskSdqMJVKRYHBSAs/R3IKjOw/Y96FNeX1UIRvjZ36ICyjVs+JUqvVJCUlsWvXLpo3b87TTz/N7Nmzy9XGiy++SEREBL169SI6Ohpvb+8bCmWWJjg4mE6dOtGkSRM6dOhQ4lhcXByhoaG0bdsWT0/PG5IsAHd3d9avX09OTg5RUVG0adOGBQsWmHqlPv30Uy5dukRERATDhw9n/PjxeHlV7ZLaqfe3RGWhfx+e7h/Outf717oEqibJytdjKMcKO72ikFsD/roX4nb0Cthq1HRt5Iqno2XqwjX2sOeuQOmFEiWpFKUGjN/UQoqiEBwczJNPPsmkSZMsHY7ZjJy7sVp7o5r66/jwqShcHGW5cGUYjEZSy9OrpCgcv3yVrIKybQcjRE2hAlBgZ1pWuf5oqKyZvYNlQrm4Qa0ezrOU8+fPk5SUxNmzZxk1apSlwzGrl2Ii+G77KQqreJ6MSgXvxnWiWwv/WjHvqSZTKF9NKACDSpEEStRKCoAKOjbU8delfE5Ww1ZFLX2dJIESpZIkqgK8vLzw8PAgISEBV9e61b3bsJ4TY3o14b1V5q+gfc09revzauxd2FjXjonjNV1eoYGCciS9iqKQliVzoUTtVmgw4ulkQ32dLbvTrnC1HHXRykMFDGvrVyVti9pPhvPEDS7lFNBi3DdcyjVvOQWdgw0J46IJqe9m1nbvZIpi5NTFvHIlo0YU9p3NqrKYhKhu1moVWVf1HDibY/a2Ixu68nR0Q7O3K+qGWj2xXFQNV0ctUwa1uP2J5fD4vU3Z8MZASaDMSFEULpSrJlSx09ILJeoYvVHB3taKro1ccbc336bq1moVMRG+tz9R3LEkibrOwoUL0el0Zm2ztM2HK0ulUrFy5UqztXe9x+8NI9DLsdLtNPJxZt1r/Xj8vnAstvSvjio0GMgpR00oAEWlcOFqWTclFqJ2yTcYCa5nT7sGLpijEsHdIe54O2sr35Cos2psEhUdHc3EiRMtHYZZ+Pv7k56eTvPmzS0dSplprK14KSaiwterVPDWqLtYPu1e3J3tzRiZgL83GC5XTahiZ7LzqyAaIWoOowIqNUQG6fB3rfiqX1trNQ+08jFjZKIuqrFJVF1iZWWFt7f3TbcEqakeiAyibWOPcl8XHe7LtjmDuadNgEwcr6SP4mczqFdnWgV50aFpAE/EDuHPY0e4kl+E4e8396M3pzP4rmAe6t6Sdd8vK3H9r6u/ZfqTw4ofqBTO5dTsjV2FMJcCg4KPi4ZODXXYWpf/V919zbzQ2ZlvaFDUTTUyiRo5ciQbN24kPj4elUqFSqXi5MmTbNy4kfbt26PVavHx8eG5555Dr/9nmXZ0dDRjx45l7NixuLi44OHhwfTp0/n33PlLly4xYsQIXF1dsbe359577+Xo0aO3jOfDDz+kUaNGaDQaQkNDWbx4cYnjhw4donPnztja2tK0aVN+/vnnEsNtpQ3nHTx4kL59++Ls7IyTkxNdunTh+PHjAOzYsYOePXvi4eGBi4sLUVFR7N69u5LvasW8Orxdmc91tLXmi8l3M/exLmhsalfCWFNt37KJYaMe4+sfNrDw6+8p0hcxckg/zly4DMCWX9bwy/++4fVPvibumZd4d8bTXLmUCUBudhaJ8a8x7sU3AcjILX/PlRC1mwq9otCmgTNh9RzKfJXOzpr+zWWPPHF7NTKJio+Pp2PHjsTFxZGenk56ejo2Njb06dOHdu3asW/fPj788EM+/fRT/vOf/5S4dtGiRVhbW7N9+3bi4+OZM2cOn3zyien4yJEj2blzJ9999x1btmxBURT69OlDUVHp80RWrFjBhAkTeOaZZzhw4ACPPfYYo0aN4pdffgHAYDAwYMAA7O3t2bZtGwkJCUybNu2Wr+/06dN07doVrVbL+vXr2bVrF6NHjzYlhNnZ2cTGxrJ582a2bt1KcHAwffr0ITu7+rc8iAyrR/8OAbc9b2SPEDbNHkSzwPL3XImb+yzpOwYPHU5wk6aENWvBm3M/Jv10Gkf/2AdA2p9HaNE+kpDmreh23yDsHZ04+1cqAAvenkXfoaPw8q0PKoUzFRj+E6IuKDIqONlZ07WRKzq72/+BN6Jdfew1VtUQmajtamR3gYuLCxqNBnt7e7y9vQGYNm0a/v7+vPfee6hUKpo0acKZM2eYOnUqM2bMQK0uzgf9/f159913UalUhIaG8vvvv/Puu+8SFxfH0aNH+e6770hOTqZTp04AfPHFF/j7+7Ny5UoeeOCBG2J5++23GTlyJE8++SQAkyZNYuvWrbz99tt069aNtWvXcvz4cTZs2GCK9dVXX6Vnz543fX3vv/8+Li4uJCUlmbZ5CQkJMR3v3r17ifMTEhLQ6XRs3LiRvn37VvRtrbA3Y9vz897T5JZSnDHA05GE8dHUcy37X3mi4s5dvAiAk0txfbKg0Gb88NXnZF+5TPpfpyjMz8O3QUMO7NrKsZT9jJvxFgDnr8ownhD5BiNNvB0p0hvZ/VcWpRX4ae7jRNdGsopYlE2N7IkqTUpKCh07dkT1rxVekZGR5OTk8Ndff5meu+uuu0qc07FjR44ePYrBYCAlJQVra+sSe925u7sTGhpKSkrKTe8bGRlZ4rnIyEjT+YcPH8bf39+UQAG0b9/+lq9l7969dOnSxZRAXS8jI4O4uDiCg4NxcXHB2dmZnJwcUlNTb9luVanv4cALD7S64fmXh7Xju5fukwSqmuj1el6dPpVmEe1pGFy8+XXbzt3p3u8Bxg3pydsvjGPy6+9ha2fPvJefZfxLb/O/pERG97mLF0b8H2dPHLHwKxDC8oyKgpWVii5Brvi5lFx5Z61WEXeXv4UiE7VRjeyJquvs7OxueTw2NpbMzEzi4+MJCAhAq9XSsWNHCgst15vw1H1NWfLrcQ6mXqJjk3rMiYvETmsjE8eriaIoPD9lAiePHmLOf/9X4tiIsc8yYuyzpseL359N645dsba2ZslHc3jzq3X8tPoHvnpjCuM//ra6QxeiRso3GKnvqqWBmx27Uq9QaFD4v+Ze+OlkH09RdjW2J0qj0WAw/FMDJywszDSH6Zrk5GScnJyoX7++6blt27aVaOfanCIrKyvCwsLQ6/UlzsnMzOTw4cM0bdq01DjCwsJITk4u8VxycrLp/NDQUNLS0sjIyDAd37Fjxy1fW4sWLdi0adNN52ElJyczfvx4+vTpQ7NmzdBqtVy4cOGWbVY1ays18XEd+WxCNB+NjcZOK6tWqtOM5yayef0a3lq4Ak/vmxf/S/3zKOu+/5qR455j3/Zkwtt15LLKnpbRfTh99CAFV81f0VmI2kpBhUFRaB/gQvsGLgxuKSUNRPnU2CQqMDCQbdu2cfLkSS5cuMCTTz5JWloa48aN49ChQ3z77be89NJLTJo0yTQfCiA1NZVJkyZx+PBhli5dyvz585kwYQIAwcHB9O/fn7i4ODZv3sy+fft4+OGH8fPzo3///qXGMWXKFBYuXMiHH37I0aNHmTNnDsuXL2fy5MkA9OzZk0aNGhEbG8v+/ftJTk7mxRdfBCgxrPhvY8eOJSsri6FDh7Jz506OHj3K4sWLOXz4sCnOxYsXk5KSwrZt2xg2bNhte6+qw12hXkS38JXep2qkKAozn3uatT98z+zPluNT/+aT/BVFIX7mMzw29RXsHBwxGo3kFRRXNDcYiuezGY1Vu7G0ELVRoVHh/lY+aCtQCkHc2WrsJ2by5MlYWVnRtGlTPD09KSoq4ocffmD79u20bNmSxx9/nEceecSUsFwzYsQI8vLyaN++PU899RQTJkxgzJgxpuOJiYm0adOGvn370rFjRxRF4Ycffrjp/KQBAwYQHx/P22+/TbNmzfj4449JTEwkOjoaKK4BtXLlSnJycmjXrh2PPvqoaXWerW3p3cLu7u6sX7+enJwcoqKiaNOmDQsWLDDF8Omnn3Lp0iUiIiIYPnw448ePx8vLq7JvqVl4ONhgVWM/NXXPzOcm8u2ypTw/+yPsHBy5eD6Di+czKMi/ceuWH5f9Fxc3dzp26wVA84h2/L59M6f+2MOmZZ/hFdAYO0fn6n4JQtR4Tes5EuQuRYFF+dWpDYijo6Np1aoVc+fOtWgcycnJdO7cmWPHjtGoUSOLxlIVcgsMpF+R1V7VIbhe6f+wT351HvcMjDE9vnThHOOH9mbukh9w9ype5JBdVMRH775J8vJFOOjceXDqW/iHtayWuGuKX5Z8yIFNP3Eu9U9stFoCmkXQJ+5ZPBsEmc75Zs6LHNuVTFbmObR29gQ0i+DeMc/i1aD4Z/dq1mW+emMKx/duw6N+APdPeQO/4Gam61fGv4Sbjz9dhzxa7a9PVJ6djZpH2jeQkgaiQiSJMoMVK1bg6OhIcHAwx44dY8KECbi6urJ58+ZqjaM6nb1SWO5920T5KIrC6St5FBnK/yOqUsG+s1kY6s6Pd4V8OnUULbv1pX5oOEajgTWfvMPZE0d4JnE1GrviBHXb/5Lw9A9CV8+XvKzLrF00jzPHU3juiw2oraz434evcfrIAQZNepWt333Bid93Mv6jlQCc+mMP386bxdj3v0FtJb+Ea6O+Tb1oWs/J0mGIWkoGZswgOzubp556iiZNmjBy5EjatWvHt9/W7VVQHk42WMl+wlUqt1BfoQQKIKdIf8cnUACPvJlI296D8W4Ygm+jMB6Y+iaXz53hryMHTOd06DuUoJbtcfOuj19Ic3qNnsSVc+lcOltcOuXcqeO07NYXT/+GtO87lHOpxTsLGPRFrHh3BoOefkUSqFqqiZejJFCiUupUiYMNGzZY5L4jRoxgxIgRFrm3pVirVXg5a2RYr4oYFSPnK7zPncKfF6+aNZ66Ij+3uOq/vbOu1OOFeVfZuXoZbj7+uHgVr9TyadSEY3u20O6+IRzZsQmfoFAANiYtIKhlB+qHhldL7MK8HDVW3BMiOyyIyqlTw3mi+p3PLuRKngzrmZOiKJzLzudqUcVW0uUZDBw6L6UMrmc0Gln04mPk52TxxLwvSxzb8u1/+eHjtyjMv4qnfxCjXluAu1/xSsi8nGxWzp3ByQO7cPX2Y+DEl7GytiHxhUd5cv7XrPlsDkd3bsYvpDmDn3kNO0fp2agNHmjpQ0M3mUwuKkeSKFEpRkUh7WJBhYedxI3yi/SkV2Kfuz/OZ1NgkFIG11vx7gwOb9/I4/OS0HmWrAeUl5NN7uVMsjLP8etXn5B1IYMn5n+FjUZbalsJkx4mcnAslzPOkLJlPaNe/4Rv3pmGvbOOvk+8UB0vR1RCaz9neoZ4WjoMUQfInChRKWqVCm9njaXDqDMURSEju+IJVIHRIAlUKVbGzyRl63rGzPnvDQkUgJ2jEx71Awlq2Z6HZ77HubQ/Objpp1Lb2vHjMmwdnWkW2ZPje7fRrHNPrKxtCI+6lz/3biv1GlFzuNnbEN3I3dJhiDpCkihRaVobNe6OdWp6ncVcvFqIsaKdeorCCZkLVYKiKKyMn8nBzWsZ885/cfMpw75oigKKgr7oxjlpOZczWbf4PfqPm1F8qtGAQf93IVO9XoqZ1nBqFdwX5oWNFLsTZiK/+YRZ6OysuVpgJK+C83gEFBkMZOXrK3x9IUby9PL+/9vK+JfYu+57Yv/zEVp7B7IvngfA1sEJG60tmWdS2b9hFcFtu+Dg4saV82fZsPQjbLS2NOkQfUN737//Kl0fGI2LZ3EtroDmbdi9diXBbTuz7X9JBDaPqM6XJ8opMtANH2fZG0+Yj8yJEmajNyikXsyveE/KnUxRSLuch77i3VAcuZhLbqFM8v+3qd0bl/r8A8++Sdveg8m6kMGyt1/g9NED5GVn4ejqTsMW7ekxfGyJgpwAh3f8ytrEeJ5872vTVlOF+Xl89eazHNnxK/5NWhIz7V0cXWWoqCYKcrNncAvvm27HJURFSBIlzCon38DZLCl7UF7Z+YVcyC19Q+qy0CtGfs/INmNEQtQdzlprYtvVx85G6nkJ85KBYWFWjrZW6OxllLg8DEZjpRIoFIVTl2/cS08IAVYq+L9m9SSBElVCkihhdu4O1tjZyEerLBRF4VxOxVfjARhUClkFFZ9LJURdFt3YA18XmQclqob8phNmp1Kp8HbRYK2WuQe3k683kF+JyfiKopB6RXqhhChNEy9H2tR3sXQYog6TJEpUCSu1Ch8XDZJG3ZyiGMmoRFFNAFRwOU96oYS4npu9Db1DpaCmqFqSRIkqo7VR4+VsY+kwaiRFUci8WkhlV3WkZUkvlBDX01qrGdjcG421/IoTVUs+YaJKOdlao7OTiebXKzIayc6vXDkCRaWQebUSE9KFqINUf08kd3eQnRRE1ZMkSlQ5d0eZaP5vZhnGA85k55shGiHqlrsbe8jGwqLayG82UeVkonlJ2QX6ShTV/JtK4VyO1OMS4t9a+zkTIRPJRTWSJEpUCyu1Cl+dhjs9jzIYjWRWpibU385WsiyCEHVNoKsddwd7WDoMcYeRJEpUG421Gh/dnbtiT0EhI9sMyY9KId0c7QhRR7jb29C/eT3UsqWLqGaSRIlqZWdjhbfLnTnhM69QT4EZNgg+f1WG8YS4xt7GikEtfNBaS0VyUf0kiRLVzkFrhafTnVX6QFGMnMuufPKjUin8dUUmlAsBoLFS80BLH1zt7qx/T0TNIUmUsAgXO2tc75A99hRF4UJu5WtCAWTmSUkDIQCs1SoGhXtTz0lr6VDEHUySKGEx7o42ONvW/S74IoOBnILK1YSC4vo3qbLRsBCoVNCvaT0auNpZOhRxh5MkSliUp5MN9pq6+zFUFIWzZqgJBXC5wDy9WULUdr1DPQn2dLB0GEJIEiUs61oNKds6WozzSn4RBjNkPiqVwslL0gslRHQjd8J9nC0dhhCAJFGiBlCrVPi6aNBa163lyXqjgUtm2pYlq1BPZetzClHbdWigo30DnaXDEMJEkqhaLjo6mokTJ1aqjZMnT6JSqdi7d69ZYoLiHqaVK1eW+Xy1WoWvTlt3EilFMcvWLlA8/+PERemFEne2Dg10RDVyt3QYQpQgSdQdZuTIkQwYMKDEc/7+/qSnp9O8eXPLBPU3qzqUSOUW6Sk0xzgekFOkx6BIN5S4c90lCZSooSSJElhZWeHt7Y21teVLDtSFRMpopppQxRT+vHjVTG0JUft0DNDRVRIoUUNJElWFVq9eTefOndHpdLi7u9O3b1+OHz8O/DOEtnz5crp164a9vT0tW7Zky5YtpuszMzOJiYnBz88Pe3t7wsPDWbp06U3v9/LLL5fam9SqVSumT5/OzJkzWbRoEd9++y0qlQqVSsWGDRtKHc47ePAgffv2xdnZGScnJ7p06WKKfceOHfTs2RMPDw9cXFyIiopi9+7dZnrXancipSgKF8y4MXCewVD5zYqFqKU6BrjSJUgSKFFzSRJVhXJzc5k0aRI7d+5k3bp1qNVqBg4ciNH4z9Yf06ZNY/Lkyezdu5eQkBBiYmLQ6/UA5Ofn06ZNG1atWsWBAwcYM2YMw4cPZ/v27aXeb/To0aSkpLBjxw7Tc3v27GH//v2MGjWKyZMnM2TIEHr37k16ejrp6el06tTphnZOnz5N165d0Wq1rF+/nl27djF69GhTXNnZ2cTGxrJ582a2bt1KcHAwffr0ITs722zvXW1NpAoNRnILK18T6hqZCyXuVJ0CXekS5GbpMIS4JcuP39RhgwcPLvH4s88+w9PTkz/++ANHR0cAJk+ezH333QfArFmzaNasGceOHaNJkyb4+fkxefJk0/Xjxo1jzZo1fPXVV7Rv3/6G+9WvX59evXqRmJhIu3btAEhMTCQqKoqgoCAA7OzsKCgowNvb+6Zxv//++7i4uJCUlISNTfF2CiEhIabj3bt3L3F+QkICOp2OjRs30rdv3zK/P7djpVbhp9Ny5koh+UWV33OuqhXXhDLfliwFRgMFhpr/uoUwt84N3egU6GrpMIS4LemJqkJHjx4lJiaGoKAgnJ2dCQwMBCA1NdV0TosWLUzf+/j4AHDu3DkADAYDr7zyCuHh4bi5ueHo6MiaNWtKXH+9uLg4li5dSn5+PoWFhSxZsoTRo0eXK+69e/fSpUsXUwJ1vYyMDOLi4ggODsbFxQVnZ2dycnJuGVdFFa/a0+Cgrfkf1ct5heYrQ6DIXChx51Gp4J4QD0mgRK0hPVFVqF+/fgQEBLBgwQJ8fX0xGo00b96cwsJ/5sz8O1FRqYqHrq4N982ePZv4+Hjmzp1LeHg4Dg4OTJw4scT1pd1Tq9WyYsUKNBoNRUVF3H///eWK287u1lspxMbGkpmZSXx8PAEBAWi1Wjp27HjLuCpDrVLh7azhfE4RWXnmGyozJ73ByOU8vdnaK8RIvl56ocSdw0qtol9TL0I8HS0dihBlJklUFcnMzOTw4cMsWLCALl26ALB58+ZytZGcnEz//v15+OGHgeLk6siRIzRt2vSm11hbWxMbG0tiYiIajYahQ4eWSIo0Gg0Gw60TkRYtWrBo0SKKiopK7Y1KTk7mgw8+oE+fPgCkpaVx4cKFcr228lKpVHg5abBWF3Ex13zJijkoikJGtvmG8UCqk4s7i9ZazaBwb/x1sheeqF1q/hhJLeXq6oq7uzsJCQkcO3aM9evXM2nSpHK1ERwczNq1a/ntt99ISUnhscceIyMj47bXPfroo6xfv57Vq1ffMJQXGBjI/v37OXz4MBcuXKCo6MaK2mPHjiUrK4uhQ4eyc+dOjh49yuLFizl8+LAprsWLF5OSksK2bdsYNmzYbXuvzMXNwQYvp9KHGS0lt9B8NaEA9Ipi1snpQtRkDhorYlr7SgIlaiVJoqqIWq0mKSmJXbt20bx5c55++mlmz55drjZefPFFIiIi6NWrF9HR0Xh7e99QKLM0wcHBdOrUiSZNmtChQ4cSx+Li4ggNDaVt27Z4enqSnJx8w/Xu7u6sX7+enJwcoqKiaNOmDQsWLDD1Sn366adcunSJiIgIhg8fzvjx4/Hy8irXa6sMZztrfFw0qGrAwj2jYuS8GUsaoCicuiy9UOLO4Gpnw8MRfng5ai0dihAVolIUKYVc1yiKQnBwME8++WS5e79qk/wiI+mXC8yywW9FKIrCuZx8rhaab+6SASP7z5qvVIQQNVV9F1sGNPfGXmNl6VCEqDCZE1XHnD9/nqSkJM6ePcuoUaMsHU6VsrVR4+eqJf1KIUUWyKQK9AazJlCKopCaJb1Qou5r4eNEzxBPrNQ1oDtZiEqQJKqO8fLywsPDg4SEBFxd6/4yYY21mvquWjKyCs2a0NxO8WRy82wwbGpTpZh1hZ8QNY1KBd0be9CmvoulQxHCLCSJqmPuxNFZK7UKHxcNF3P1XLpaPUnIpatmrAn1t7/MWKhTiJrG1lrN/zWrR6CbvaVDEcJsJIkSdYJKpcLd0QZbGzUZWeZPcP6tyGDgSr55kzVFZSTz6o0rJYWoC9zsbRgU7o2bvcbSoQhhVrI6r45YuHAhOp2u0u2oVCpWrlxZqTZGjhxZplWEVcFBa0V9Vy02VlUz16IqhvEAzlRBm0LUBI3c7Rnexk8SKFEnSU+UqHM01mr8/54nlWvmeVI5BUXmn8SuUjhnzjIJQtQAahV0CXKjvb/OtBuDEHWNJFGiTlKrVXi7aLh0VW+2CucGo5ELueYfcjubI71Qom5x0lrxf8288XOxtXQoQlQpGc6rJkajkddff52GDRtiZ2dHy5YtWbZsGYqi0KNHD3r16mWaFH7x4kXq16/PjBkzANiwYQMqlYpVq1bRokULbG1tueuuuzhw4MAt7/nhhx/SqFEjNBoNoaGhLF68uMTxo0eP0rVrV2xtbWnatClr1669oY20tDSGDBmCTqfDzc2N/v37c/LkSdNxg8HApEmT0Ol0uLu78+yzz9aYye0qlQo3Bxt8dRqsKvlJVxSF81WR7KgU0mUoT9QhQW72jGznLwmUuCNIElVNXn/9dT7//HM++ugjDh48yNNPP83DDz/Mr7/+yqJFi9ixYwfz5s0D4PHHH8fPz8+URF0zZcoU3nnnHXbs2IGnpyf9+vUrddsWgBUrVjBhwgSeeeYZDhw4wGOPPcaoUaP45ZdfgOKkbtCgQWg0GrZt28ZHH33E1KlTS7RRVFREr169cHJyYtOmTSQnJ+Po6Ejv3r1Nmw2/8847LFy4kM8++4zNmzdz8eJFVqxYYe63r1LsNVY0cLPFQVvxj3u+3kBekflLKJy/KsN4om5QqyAqyI3BLbyxs5ECmuLOIBXLq0FBQQFubm78/PPPdOzY0fT8o48+ytWrV1myZAlff/01I0aMYOLEicyfP589e/YQHBwMFPdEdevWjaSkJB588EHgn96qhQsXMmTIEBYuXMjEiRO5fPkyAJGRkTRr1oyEhATT/YYMGUJubi6rVq3ip59+4r777uPUqVP4+voCsHr1au69915WrFjBgAED+O9//8t//vMfUlJSTHMaCgsL0el0rFy5knvuuQdfX1+efvpppkyZAoBer6dhw4a0adOm0hPUq0JWnp7zOUWU51OvKAqnLl7F3D8oKpXC7vQsM7cqRPVzsbXmvjAv6sv+d+IOI3OiqsGxY8e4evUqPXv2LPF8YWEhrVu3BuCBBx5gxYoVvPHGG3z44YemBOrf/p2Aubm5ERoaSkpKSqn3TElJYcyYMSWei4yMJD4+3nTc39/flEBd3z7Avn37OHbsGE5OTiWez8/P5/jx41y5coX09PQS+/NZW1vTtm3bGjOkdz1nO2tTGYQC/e1jVBSFi1cLzJ5AAWTmSUkDUfuF+zjRvbEHWmsZ2BB3HkmiqkFOTg4Aq1atws/Pr8QxrbZ4482rV6+ya9curKysOHr0aLXHWJqcnBzatGnDF198ccMxT09PC0RkHteqnJelOKfeaCQr32D2GFQqZKNhUas5aKzoFepJYw8HS4cihMXInw7VoGnTpmi1WlJTU2ncuHGJL39/fwCeeeYZ1Go1P/74I/PmzWP9+vU3tLN161bT95cuXeLIkSOEhYWVes+wsDCSk5NLPJecnEzTpk1Nx9PS0khPTy+1fYCIiAiOHj2Kl5fXDXG7uLjg4uKCj48P27ZtM12j1+vZtWtXOd+h6netOKefToP1TfbvUjByNqtqJn1fKpC5UKL2CvVyYHR7f0mgxB1PeqKqgZOTE5MnT+bpp5/GaDTSuXNnrly5QnJyMs7Oznh4ePDZZ5+xZcsWIiIimDJlCrGxsezfv7/E/ncvv/wy7u7u1KtXj2nTpuHh4XHTopZTpkxhyJAhtG7dmh49evD999+zfPlyfv75ZwB69OhBSEgIsbGxzJ49m6ysLKZNm1aijWHDhjF79mz69+/Pyy+/TP369Tl16hTLly/n2WefpX79+kyYMIE33niD4OBgmjRpwpw5c0zzsmoDO40VDdzUZOYWcSWvZI9Tdr4efRWUPlepFE5dkl4oUfvYWqvpGeJBWD2n258sxB1AeqKqySuvvML06dN5/fXXCQsLo3fv3qxatYrAwEAeeeQRZs6cSUREBACzZs2iXr16PP744yXaeOONN5gwYQJt2rTh7NmzfP/992g0pVcBHjBgAPHx8bz99ts0a9aMjz/+mMTERKKjowFQq9WsWLGCvLw82rdvz6OPPsqrr75aog17e3t+/fVXGjRowKBBgwgLC+ORRx4hPz8fZ2dnoLgHbfjw4cTGxtKxY0ecnJwYOHCgmd+9qqVWq/B00uDnqkHzd6Vzg9FIZhXUhALIKtRX6bY0QlSFUM/i3idJoIT4h6zOqwWurc67dOmSWbZ2ETenKAoXc4tIOZtTJSUNVCrYd/YK5i56LkRVcbG1pmeIB0HuMnQnxPVkOE+IfymeK6Whtb8zKWdzuWTmTYFzivSSQIlaQa2C9v46Oga6YlPZarVC1FGSRAlRCgetNW0DXEi/ks+RjFwKzZL5KPx58aoZ2hGiatV3seWeUE88HGTTYCFuRYbzhLiNIoOR4+ev8tel/ErVi8oz6Dl0PtdscQlhbg4aK7oGudHc20k2DRaiDCSJEqKMcgr0HM3IreAmxAoHz2ebqUdLCPOyVqto5+9C+wauUjRTiHKQJEqIcsrMKeTIuVxyCspehLPAaOCPczlVGJUQFdPM25GuDd1xspXZHUKUlyRRQlSAoiicuVLA8fNXKdDfZhWfopCSmUP+7c4Tohr562zp1tgDbyetpUMRotaSfltR7aKjo5k4cWKl2jh58iQqlYq9e/eaJSYoXplX1k2TVSoVfjpbIhu50tjT/qZVzwEKUSSBEjWGu70NA8O9iWntJwmUEJUk/beixhs5ciSXL18ukeD4+/uTnp6Oh4eH5QIDrNQqGnrYU9/VlhMX8vjrUt51JQwUTl6SFXnC8tztbegU6EoTL0eZNC6EmUgSJWolKysrvL29LR2GiY2VmpB6DgS623HqYh5pl/IxGBX0ikJuofk3MBairCR5EqLqyHCeKGH16tV07twZnU6Hu7s7ffv25fjx48A/Q2jLly+nW7du2Nvb07JlS7Zs2WK6PjMzk5iYGPz8/LC3tyc8PJylS5fe9H4vv/wyzZs3v+H5Vq1aMX36dGbOnMmiRYv49ttvUalUqFQqNmzYUOpw3sGDB+nbty/Ozs44OTnRpUsXU+w7duygZ8+eeHh44OLiQlRUFLt37zbTu/YPjbWaYC8HujR2paG7Hek5+Wa/hxBl4W5vQ7+mXqatWiSBEsL8JIkSJeTm5jJp0iR27tzJunXrUKvVDBw4EKPxnzk906ZNY/Lkyezdu5eQkBBiYmLQ6/UA5Ofn06ZNG1atWsWBAwcYM2YMw4cPZ/v27aXeb/To0aSkpLBjxw7Tc3v27GH//v2MGjWKyZMnM2TIEHr37k16ejrp6el06tTphnZOnz5N165d0Wq1rF+/nl27djF69GhTXNnZ2cTGxrJ582a2bt1KcHAwffr0ITs725xvn4mNlZrGXg4Mi6hP1yA3HDRWVXIfIa7n7aTl/5rVk+RJiGogw3mihMGDB5d4/Nlnn+Hp6ckff/yBo6MjAJMnT+a+++4DijdLbtasGceOHaNJkyb4+fkxefJk0/Xjxo1jzZo1fPXVV7Rv3/6G+9WvX59evXqRmJhIu3btAEhMTCQqKoqgoCAA7OzsKCgouOXw3fvvv4+LiwtJSUnY2NgAEBISYjrevXv3EucnJCSg0+nYuHEjffv2LfP7U15aazV3BbjStr4Lv5/NZmfaFS7lVc3GxuLOpQIaezjQzt+F+jo7S4cjxB1DeqJECUePHiUmJoagoCCcnZ0JDAwEIDU11XROixYtTN/7+PgAcO7cOQAMBgOvvPIK4eHhuLm54ejoyJo1a0pcf724uDiWLl1Kfn4+hYWFLFmyhNGjR5cr7r1799KlSxdTAnW9jIwM4uLiCA4OxsXFBWdnZ3Jycm4ZlzlZW6lp7efCox38ub+FD0Fu9tVyX1G32VipiPBzIe6uBgwM95YESohqJj1RooR+/foREBDAggUL8PX1xWg00rx5cwoLC03n/DtRuTZUcG24b/bs2cTHxzN37lzCw8NxcHBg4sSJJa4v7Z5arZYVK1ag0WgoKiri/vvvL1fcdna3/uURGxtLZmYm8fHxBAQEoNVq6dix4y3jqgoqlYogd3uC3O25dLWI3aevcOBs9u1rTQnxL85aa1r7OdPS1xlbGxkqFsJSJIkSJpmZmRw+fJgFCxbQpUsXADZv3lyuNpKTk+nfvz8PP/wwUJxcHTlyhKZNm970Gmtra2JjY0lMTESj0TB06NASSZFGo8FguPUKtxYtWrBo0SKKiopK7Y1KTk7mgw8+oE+fPgCkpaVx4cKFcr02c3O1t+HuYA+6NHTjYEY2u/+6QuZVGeoTpVOriofsWvg409DNTuY6CVEDSBIlTFxdXXF3dychIQEfHx9SU1N57rnnytVGcHAwy5Yt47fffsPV1ZU5c+aQkZFxyyQK4NFHHyUsLAwoTnj+LTAwkDVr1nD48GHc3d1xcXG54fqxY8cyf/58hg4dyvPPP4+Liwtbt26lffv2hIaGEhwczOLFi2nbti1ZWVlMmTLltr1X1UVjXTzU19rPhTNX8jlwNptD56TCuSjmZm9DCx9nmnk74qCRf7KFqElkTpQwUavVJCUlsWvXLpo3b87TTz/N7Nmzy9XGiy++SEREBL169SI6Ohpvb28GDBhw2+uCg4Pp1KkTTZo0oUOHDiWOxcXFERoaStu2bfH09LwhyQJwd3dn/fr15OTkEBUVRZs2bViwYIGpV+rTTz/l0qVLREREMHz4cMaPH4+Xl1e5Xlt18HWx5Z5QT56MDOT/mtUjyM0e6XC489hYqWju7cRDrX15tEMD2jfQSQIlRA0ke+eJGkFRFIKDg3nyySeZNGmSpcOpUXIK9PyRkcPBs9mcz63eOVyi+liri+fLNfFypJG7PTZW8jeuEDWdJFHC4s6fP09SUhLPP/88aWlpuLq6WjqkGuvi1UKOnM/lyPlczmYXWDocUUlWahUN3exo4uVIY3cHNNaSOAlRm0gSJSxOpVLh4eFBfHw8Dz30kKXDqTWy8vUcOZ/DkfO5nL6Sj/wg1w42ahUBbnaEeDoS7GGP1lpW1wlRW0kSJUQdkFuo59iFq5y8eJVTl/JkUnoN42JrTZC7PY3c7Wmgs8NahuqEqBMkiRKijlEUhfSsAk5eyuPkxaucycrHKD/l1cpGrcJfZ0dDdzsC3exxt9dYOiQhRBWQJEqUWXR0NK1atWLu3LmVbmvmzJmsXLmyxAbClWHO2OqaQr2R1Mt5nLqUx1+X8zifWyhJlZnZWqvxc7Glvost/jo76jlpsVLLskoh6jpZMyvKbPny5TfdVsXSanJslqaxVtPYw4HGHg4AFBmMpGcVcCYrn9NX8jmTlU9ekQz/lYeT1go/Fzv8dcWJk4eDRopfCnEHkiRKlJmbm5ulQ7ipmhxbTWNjpaaBqx0NXP8pNnrxaiFnsgo4l13A+dxCLuQWklt46yrxdwIVoLOzoZ6TlnqOGryctNRz1GKvqd2TwQMDA5k4cSITJ06scBvX9yaPHDmSy5cvs3LlSrPEKERtILMbRZlFR0eb/tENDAzktddeY/To0Tg5OdGgQQMSEhJKnP/XX38RExODm5sbDg4OtG3blm3btt227WsGDBjAyJEjTY8/+OADgoODsbW1pV69eiX21/v39S+88MINBTsBWrZsycsvv2x6/MknnxAWFoatrS1NmjThgw8+KMe7Ube42Wto7u1E92APHmzly1ORgYyNDOTBlj50b+xOuI8T3k5aNHV0QrRKBTpbaxq62RHh50yPYA+GRfgxoUtD4u5qwP81q0eHAFcautnX+gSqqsTHx7Nw4UJLhyFEtZKeKFFh77zzDq+88govvPACy5Yt44knniAqKorQ0FBT5XA/Pz++++47vL292b17t2mj4vLauXMn48ePZ/HixXTq1ImLFy+yadOmUs8dNmwYr7/+OsePH6dRo0YAHDx4kP379/PNN98A8MUXXzBjxgzee+89WrduzZ49e4iLi8PBwYHY2NiKvSF1jL3GigA3ewLc7Es8n1dk4Eq+nit5RVzOKyr+Pr+Iy3l6sgv06GvghCsVxa/HQWOFo8YaZ1trXO1tcLWzwdXeBp2tjcxhqqTStmMSoq6TJEpUWJ8+fXjyyScBmDp1Ku+++y6//PILoaGhLFmyhPPnz7Njxw7TUFvjxo0rfK/U1FQcHBzo27cvTk5OBAQE0Lp161LPbdasGS1btmTJkiVMnz4dKE6aOnToYIrhpZde4p133mHQoEEANGzYkD/++IOPP/5YkqjbsLOxws7GCm8nbanHC/RGrhYZyCs0kFtk4GqhgbwiA7l//7fIoFBkMKI3Fv+3yKigNygUGYufu7bURaUqTn5AZfpepQK1SoXGSo3GWo3GSoXWWl38+O8vrbUae40VjhorHLTWOGqssNdYoa7Bc5aWLVvGrFmzOHbsGPb29rRu3Zrp06fTo0cP0tLS8Pb2Np07ceJEdu3axaZNm1i4cCETJ07kv//9L8888wxpaWn06dOHzz//nK+//pqXXnqJK1euMHz4cN59912srP7pRcvOziYmJobvvvsOnU7HCy+8wFNPPWU6npqayrhx41i3bh1qtZrevXszf/586tWrV+pruH44z2g08vbbb5OQkEBaWhr16tXjscceY9q0aVXzJgphAZJEiQpr0aKF6XuVSoW3tzfnzp0DYO/evbRu3dpsc5V69uxJQEAAQUFB9O7dm969ezNw4EDs7e1LPX/YsGF89tlnTJ8+HUVRWLp0qWk7mdzcXI4fP84jjzxCXFyc6Rq9Xi9/TZuB1ro4kXG1k4n+ZZGenk5MTAxvvfUWAwcOJDs7m02bNtGmTRuCgoJYvHgxU6ZMAaCoqIgvvviCt956y3T91atXmTdvHklJSWRnZzNo0CAGDhyITqfjhx9+4M8//2Tw4MFERkby4IMPmq6bPXs2L7zwArNmzWLNmjVMmDCBkJAQevbsidFopH///jg6OrJx40b0ej1PPfUUDz74IBs2bCjT63r++edZsGAB7777Lp07dyY9PZ1Dhw6Z9b0TwtIkiRIVdv1qOJVKZRqus7OzK+2Sm1Kr1VxfbaOoqMj0vZOTE7t372bDhg389NNPzJgxg5kzZ7Jjxw50Ot0N7cXExDB16lR2795NXl4eaWlppl8gOTk5ACxYsOCGuVP//ktdiOqQnp6OXq9n0KBBBAQEABAeHg7AI488QmJioimJ+v7778nPz2fIkCGm64uKivjwww9NQ9f3338/ixcvJiMjA0dHR5o2bUq3bt345ZdfSiRRkZGRPPfccwCEhISQnJzMu+++S8+ePVm3bh2///47J06cwN/fH4DPP/+cZs2asWPHDtq1a3fL15SdnU18fDzvvfeeqWe3UaNGdO7c2RxvmRA1Rt2cJSosrkWLFuzdu5eLFy+W6XxPT0/S09NNjw0GAwcOHChxjrW1NT169OCtt95i//79nDx5kvXr15faXv369YmKiuKLL77giy++oGfPnnh5eQFQr149fH19+fPPP2ncuHGJr4YNG1bwFQtRMS1btuTuu+8mPDycBx54gAULFnDp0iWgeIjs2LFjbN26FYCFCxcyZMgQHBwcTNfb29ubEigo/nwHBgbi6OhY4rlrvcTXdOzY8YbHKSkpAKSkpODv729KoACaNm2KTqcznXMrKSkpFBQUcPfdd5f1bRCiVpIkSlSJmJgYvL29GTBgAMnJyfz555988803bNmypdTzu3fvzqpVq1i1ahWHDh3iiSee4PLly6bj//vf/5g3bx579+7l1KlTfP755xiNRkJDQ28aw7Bhw0hKSuLrr79m2LBhJY7NmjWL119/nXnz5nHkyBF+//13EhMTmTNnjllevxBlZWVlxdq1a/nxxx9p2rQp8+fPJzQ0lBMnTuDl5UW/fv1ITEwkIyODH3/8kdGjR5e4vrQe4Vv1EleH8vZEC1FbSRIlqoRGo+Gnn37Cy8uLPn36EB4ezhtvvHHT4bLRo0cTGxvLiBEjiIqKIigoiG7dupmO63Q6li9fTvfu3QkLC+Ojjz5i6dKlNGvW7KYx3H///WRmZnL16lUGDBhQ4tijjz7KJ598QmJiIuHh4URFRbFw4ULpiRIWoVKpiIyMZNasWezZsweNRsOKFSuA4s/ql19+SUJCAo0aNSIyMtIs97zWu/Xvx2FhYQCEhYWRlpZGWlqa6fgff/zB5cuXadq06W3bDg4Oxs7OjnXr1pklViFqKpkTJcrs3xNKT548ecPx67dwCQgIYNmyZaW2NXPmTGbOnGl6bGNjwwcffHDTWk2dO3e+5YTW0o7pdDry8/Nves1DDz3EQw89dNPjQlSHbdu2sW7dOu655x68vLzYtm0b58+fNyU0vXr1wtnZmf/85z8l6pxVVnJyMm+99RYDBgxg7dq1fP3116xatQqAHj16EB4ezrBhw5g7dy56vZ4nn3ySqKgo2rZte9u2bW1tmTp1Ks8++ywajYbIyEjOnz/PwYMHeeSRR8z2GoSwNEmihBDCgpydnfn111+ZO3cuWVlZBAQE8M4773DvvfcCxYsuRo4cyWuvvcaIESPMdt9nnnmGnTt3MmvWLJydnZkzZw69evUCinvGvv32W8aNG0fXrl1LlDgoq+nTp2Ntbc2MGTM4c+YMPj4+PP7442aLX4iaQDYgFkKIGu6RRx7h/PnzfPfdd5YORQjxL9ITJYQQNdSVK1f4/fffWbJkiSRQQtRAkkQJIUQN1b9/f7Zv387jjz9Oz549LR2OEOI6MpwnhBBCCFEBUuJACCGEEKICJIkSQghRJVQqlWlDYiHqIkmihBBCVMrMmTNp1arVDc+np6ebSjUIURfJxHIhhBBVwtvb29IhCFGlpCdKCCEsbNmyZYSHh2NnZ4e7uzs9evQgNzeX6OhoJk6cWOLcAQMGMHLkSNPj9PR07rvvPuzs7GjYsCFLliwhMDCQuXPnms45dOgQnTt3xtbWlqZNm/Lzzz/fMNSWlpbGkCFD0Ol0uLm50b9//xI7E2zYsIH27dvj4OCATqcjMjKSU6dOsXDhQmbNmsW+fftQqVSoVCoWLlwIlBzO69SpE1OnTi3xWs6fP4+NjQ2//vorAAUFBUyePBk/Pz8cHBzo0KHDLXcqEMLSJIkSQggLSk9PJyYmhtGjR5OSksKGDRsYNGgQZV04PWLECM6cOcOGDRv45ptvSEhI4Ny5c6bjBoOBAQMGYG9vz7Zt20hISGDatGkl2igqKqJXr144OTmxadMmkpOTcXR0pHfv3hQWFqLX6xkwYABRUVHs37+fLVu2MGbMGFQqFQ8++CDPPPMMzZo1Iz09nfT0dB588MEb4ry2Ifi/X9eXX36Jr68vXbp0AWDs2LFs2bKFpKQk9u/fzwMPPEDv3r05evRoRd5aIaqcDOcJIYQFpaeno9frGTRoEAEBAQCEh4eX6dpDhw7x888/s2PHDtOedp988gnBwcGmc9auXcvx48fZsGGDaXjt1VdfLVF36ssvv8RoNPLJJ5+gUqkASExMRKfTsWHDBtq2bcuVK1fo27cvjRo1AjDt7Qfg6OiItbX1LYfvhgwZwsSJE9m8ebMpaVqyZAkxMTGoVCpSU1NJTEwkNTUVX19fACZPnszq1atJTEzktddeK9N7IkR1kp4oIYSwoJYtW3L33XcTHh7OAw88wIIFC7h06VKZrj18+DDW1tZERESYnmvcuDGurq4lzvH39y+R4LRv375EO/v27ePYsWM4OTnh6OiIo6Mjbm5u5Ofnc/z4cdzc3Bg5ciS9evWiX79+xMfHk56eXq7X6enpyT333MMXX3wBwIkTJ9iyZQvDhg0D4Pfff8dgMBASEmKKwdHRkY0bN3L8+PFy3UuI6iI9UUIIYUFWVlasXbuW3377jZ9++on58+czbdo0tm3bhlqtvmFYr6ioyOwx5OTk0KZNG1OC82+enp5Acc/U+PHjWb16NV9++SUvvvgia9eu5a677irzfYYNG8b48eOZP38+S5YsITw83NTrlpOTg5WVFbt27cLKyqrEdY6OjpV4dUJUHemJEkIIC1OpVERGRjJr1iz27NmDRqNhxYoVeHp6lujxMRgMHDhwwPQ4NDQUvV7Pnj17TM8dO3asRE9WaGgoaWlpZGRkmJ7bsWNHiftHRERw9OhRvLy8aNy4cYkvFxcX03mtW7fm+eef57fffqN58+YsWbIEAI1Gg8FguO3r7N+/P/n5+axevZolS5aYeqGutW0wGDh37twNMcgqP1FTSRIlhBAWtG3bNl577TV27txJamoqy5cv5/z584SFhdG9e3dWrVrFqlWrOHToEE888QSXL182XdukSRN69OjBmDFj2L59O3v27GHMmDHY2dmZ5jb17NmTRo0aERsby/79+0lOTubFF18EMJ0zbNgwPDw86N+/P5s2beLEiRNs2LCB8ePH89dff3HixAmef/55tmzZwqlTp/jpp584evSoaV5UYGAgJ06cYO/evVy4cIGCgoJSX6uDgwMDBgxg+vTppKSkEBMTYzoWEhLCsGHDGDFiBMuXL+fEiRNs376d119/nVWrVlXFWy9E5SlCCCEs5o8//lB69eqleHp6KlqtVgkJCVHmz5+vKIqiFBYWKk888YTi5uameHl5Ka+//rrSv39/JTY21nT9mTNnlHvvvVfRarVKQECAsmTJEsXLy0v56KOPTOekpKQokZGRikajUZo0aaJ8//33CqCsXr3adE56eroyYsQIxcPDQ9FqtUpQUJASFxenXLlyRTl79qwyYMAAxcfHR9FoNEpAQIAyY8YMxWAwKIqiKPn5+crgwYMVnU6nAEpiYqKiKIoCKCtWrCjxen/44QcFULp27XrDe1FYWKjMmDFDCQwMVGxsbBQfHx9l4MCByv79+830bgthXrIBsRBC1CF//fUX/v7+/Pzzz9x9992lnpOcnEznzp05duyYabWdEKL8JIkSQohabP369eTk5BAeHk56ejrPPvssp0+f5siRI9jY2ACwYsUKHB0dCQ4O5tixY0yYMAFXV1c2b95s4eiFqN1kdZ4QQtRiRUVFvPDCC/z55584OTnRqVMnvvjiC1MCBZCdnc3UqVNJTU3Fw8ODHj168M4771gwaiHqBumJEkIIIYSoAFmdJ4QQQghRAZJECSGEEEJUgCRRQgghhBAVIEmUEEIIIUQFSBIlhBBCCFEBkkQJIYQQQlSAJFFCCCGEEBUgSZQQQgghRAVIEiWEEEIIUQGSRAkhhBBCVIAkUUIIIYQQFSBJlBBCCCFEBUgSJYQQQghRAZJECSGEEEJUgCRRQgghhBAVIEmUEEIIIUQFSBIlhBBCCFEBkkQJIYQQQlSAJFFCCCGEEBUgSZQQQgghRAVIEiWEEEIIUQGSRAkhhBBCVIAkUUIIIYQQFSBJlBBCCCFEBUgSJYQQQghRAZJECSGEEEJUgCRRQgghhBAVIEmUEEIIIUQFSBIlhBBCCFEB/w+wbXbj3j9CiwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define Seaborn color palette to use\n",
    "colors = sns.color_palette('Blues')[0:len(df)]\n",
    "\n",
    "#create pie chart\n",
    "fig = plt.figure(figsize=(6,6))\n",
    "plt.pie(df['count'], labels=df['category'], colors = colors, autopct='%.0f%%', startangle=0)\n",
    "plt.tight_layout()\n",
    "plt.savefig('plots/conceptual_processes.png')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Types of Processes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>category</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>material</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>relational</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>verbal</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>behavioral</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mental</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>existential</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      category  count\n",
       "0     material    244\n",
       "1   relational     52\n",
       "2       verbal     23\n",
       "3   behavioral     17\n",
       "4       mental      9\n",
       "5  existential      3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'material',\n",
    "        'relational',\n",
    "        'verbal',\n",
    "        'behavioral',\n",
    "        'mental',\n",
    "        'existential',\n",
    "    ],\n",
    "    'count': [\n",
    "        244,\n",
    "        52,\n",
    "        23,\n",
    "        17,\n",
    "        9,\n",
    "        3,\n",
    "    ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define Seaborn color palette to use\n",
    "colors = sns.color_palette('Blues', n_colors=len(df))\n",
    "\n",
    "#create pie chart\n",
    "fig = plt.figure(figsize=(6,6))\n",
    "plt.pie(df['count'], labels=df['category'], colors = colors, autopct='%.0f%%', startangle=0)\n",
    "plt.tight_layout()\n",
    "plt.savefig('plots/types_of_processes.png')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Content Analysis: Topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>category</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emissions</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>litter</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>general\\nsustainability</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>shipbreaking</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>materials</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  category  count\n",
       "0                emissions     71\n",
       "1                   litter     14\n",
       "2  general\\nsustainability     12\n",
       "3             shipbreaking      2\n",
       "4                materials      1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'category': [\n",
    "        'emissions',\n",
    "        'litter',\n",
    "        'general\\nsustainability',\n",
    "        'shipbreaking',\n",
    "        'materials',\n",
    "    ],\n",
    "    'count': [\n",
    "        71,\n",
    "        14,\n",
    "        12,\n",
    "        2,\n",
    "        1,\n",
    "    ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define Seaborn color palette to use\n",
    "colors = sns.color_palette('Blues', n_colors=len(df))\n",
    "\n",
    "#create bar chart\n",
    "fig = plt.figure(figsize=(6,6))\n",
    "g = sns.barplot(data=df, x='category', y='count', palette=colors)\n",
    "g.set(xlabel=None)\n",
    "g.set(ylabel='count (n)')\n",
    "sns.despine(left=True)\n",
    "sns.set_style(\"ticks\",{'axes.grid' : True})\n",
    "plt.tight_layout()\n",
    "plt.savefig('plots/content_analysis_topics.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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