CCLAMP_Network_Diachronic_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dfd325fd",
   "metadata": {},
   "source": [
    "# CCLAMP Diachronic Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9e82ce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from itertools import combinations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98cef93",
   "metadata": {},
   "source": [
    "# Read data\n",
    "mentions_and_authors.txt and metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c776028",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>File</th>\n",
       "      <th>Mentions</th>\n",
       "      <th>Year</th>\n",
       "      <th>Title</th>\n",
       "      <th>Author</th>\n",
       "      <th>DOB</th>\n",
       "      <th>POB</th>\n",
       "      <th>DOD</th>\n",
       "      <th>POD</th>\n",
       "      <th>Link</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GEM_1925_3</td>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>1925</td>\n",
       "      <td>Sint Maartensavond Bij een linoleumsnede van J...</td>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>6 oktober 1897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25 juli 1972</td>\n",
       "      <td>Steyl</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=nic...</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GEM_1925_5</td>\n",
       "      <td>Hendrik Andriessen</td>\n",
       "      <td>1925</td>\n",
       "      <td>Is de nieuwe meerstemmige kerkmuziek in Nederl...</td>\n",
       "      <td>Albert Helman</td>\n",
       "      <td>7 november 1903</td>\n",
       "      <td>Paramaribo</td>\n",
       "      <td>10 juli 1996</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=hel...</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GEM_1925_6</td>\n",
       "      <td>Willem Nieuwenhuis</td>\n",
       "      <td>1925</td>\n",
       "      <td>Dagelijksch brood en dagelijksch leven.</td>\n",
       "      <td>Willem Nieuwenhuis</td>\n",
       "      <td>1886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=nie...</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GEM_1925_7</td>\n",
       "      <td>Karel van den Oever</td>\n",
       "      <td>1925</td>\n",
       "      <td>In Memoriam Fratris</td>\n",
       "      <td>Karel van den Oever</td>\n",
       "      <td>19 november 1879</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>6 oktober 1926</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=oev...</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GEM_1925_9</td>\n",
       "      <td>Wies Moens</td>\n",
       "      <td>1925</td>\n",
       "      <td>Koraal.</td>\n",
       "      <td>Wies Moens</td>\n",
       "      <td>28 januari 1898</td>\n",
       "      <td>Sint-Gillis-Bij-Dendermonde</td>\n",
       "      <td>5 februari 1982</td>\n",
       "      <td>Geleen</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=moe...</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GEM_1925_11</td>\n",
       "      <td>Henri Bruning</td>\n",
       "      <td>1925</td>\n",
       "      <td>Jonge litteratuur</td>\n",
       "      <td>Henk Kuitenbrouwer; Jan Bruna</td>\n",
       "      <td>20 november 1903; 17 augustus 1876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 augustus 1974; 11 maart 1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=kui...</td>\n",
       "      <td>mixed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GEM_1925_11</td>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>1925</td>\n",
       "      <td>Jonge litteratuur</td>\n",
       "      <td>Henk Kuitenbrouwer; Jan Bruna</td>\n",
       "      <td>20 november 1903; 17 augustus 1876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 augustus 1974; 11 maart 1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=kui...</td>\n",
       "      <td>mixed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GEM_1925_11</td>\n",
       "      <td>Albert Kuyle</td>\n",
       "      <td>1925</td>\n",
       "      <td>Jonge litteratuur</td>\n",
       "      <td>Henk Kuitenbrouwer; Jan Bruna</td>\n",
       "      <td>20 november 1903; 17 augustus 1876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 augustus 1974; 11 maart 1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=kui...</td>\n",
       "      <td>mixed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GEM_1925_11</td>\n",
       "      <td>Albe</td>\n",
       "      <td>1925</td>\n",
       "      <td>Jonge litteratuur</td>\n",
       "      <td>Henk Kuitenbrouwer; Jan Bruna</td>\n",
       "      <td>20 november 1903; 17 augustus 1876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 augustus 1974; 11 maart 1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=kui...</td>\n",
       "      <td>mixed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GEM_1925_12</td>\n",
       "      <td>Carel Scharten</td>\n",
       "      <td>1925</td>\n",
       "      <td>Kroniek</td>\n",
       "      <td>Jan Engelman; Willem Maas; Albert Kuyle; Jan B...</td>\n",
       "      <td>7 juni 1900; 28 april 1897; 17 februari 1904; ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20 maart 1972; 6 mei 1950; 4 maart 1958; 11 ma...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=eng...</td>\n",
       "      <td>mixed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          File             Mentions  Year  \\\n",
       "0   GEM_1925_3         Joep Nicolas  1925   \n",
       "1   GEM_1925_5   Hendrik Andriessen  1925   \n",
       "2   GEM_1925_6   Willem Nieuwenhuis  1925   \n",
       "3   GEM_1925_7  Karel van den Oever  1925   \n",
       "4   GEM_1925_9           Wies Moens  1925   \n",
       "5  GEM_1925_11        Henri Bruning  1925   \n",
       "6  GEM_1925_11         Joep Nicolas  1925   \n",
       "7  GEM_1925_11         Albert Kuyle  1925   \n",
       "8  GEM_1925_11                 Albe  1925   \n",
       "9  GEM_1925_12       Carel Scharten  1925   \n",
       "\n",
       "                                               Title  \\\n",
       "0  Sint Maartensavond Bij een linoleumsnede van J...   \n",
       "1  Is de nieuwe meerstemmige kerkmuziek in Nederl...   \n",
       "2            Dagelijksch brood en dagelijksch leven.   \n",
       "3                                In Memoriam Fratris   \n",
       "4                                            Koraal.   \n",
       "5                                  Jonge litteratuur   \n",
       "6                                  Jonge litteratuur   \n",
       "7                                  Jonge litteratuur   \n",
       "8                                  Jonge litteratuur   \n",
       "9                                            Kroniek   \n",
       "\n",
       "                                              Author  \\\n",
       "0                                       Joep Nicolas   \n",
       "1                                      Albert Helman   \n",
       "2                                 Willem Nieuwenhuis   \n",
       "3                                Karel van den Oever   \n",
       "4                                         Wies Moens   \n",
       "5                      Henk Kuitenbrouwer; Jan Bruna   \n",
       "6                      Henk Kuitenbrouwer; Jan Bruna   \n",
       "7                      Henk Kuitenbrouwer; Jan Bruna   \n",
       "8                      Henk Kuitenbrouwer; Jan Bruna   \n",
       "9  Jan Engelman; Willem Maas; Albert Kuyle; Jan B...   \n",
       "\n",
       "                                                 DOB  \\\n",
       "0                                     6 oktober 1897   \n",
       "1                                    7 november 1903   \n",
       "2                                               1886   \n",
       "3                                   19 november 1879   \n",
       "4                                    28 januari 1898   \n",
       "5                 20 november 1903; 17 augustus 1876   \n",
       "6                 20 november 1903; 17 augustus 1876   \n",
       "7                 20 november 1903; 17 augustus 1876   \n",
       "8                 20 november 1903; 17 augustus 1876   \n",
       "9  7 juni 1900; 28 april 1897; 17 februari 1904; ...   \n",
       "\n",
       "                           POB  \\\n",
       "0                          NaN   \n",
       "1                   Paramaribo   \n",
       "2                          NaN   \n",
       "3                    Antwerpen   \n",
       "4  Sint-Gillis-Bij-Dendermonde   \n",
       "5                          NaN   \n",
       "6                          NaN   \n",
       "7                          NaN   \n",
       "8                          NaN   \n",
       "9                          NaN   \n",
       "\n",
       "                                                 DOD        POD  \\\n",
       "0                                       25 juli 1972      Steyl   \n",
       "1                                       10 juli 1996  Amsterdam   \n",
       "2                                               1935        NaN   \n",
       "3                                     6 oktober 1926  Antwerpen   \n",
       "4                                    5 februari 1982     Geleen   \n",
       "5                     3 augustus 1974; 11 maart 1931        NaN   \n",
       "6                     3 augustus 1974; 11 maart 1931        NaN   \n",
       "7                     3 augustus 1974; 11 maart 1931        NaN   \n",
       "8                     3 augustus 1974; 11 maart 1931        NaN   \n",
       "9  20 maart 1972; 6 mei 1950; 4 maart 1958; 11 ma...        NaN   \n",
       "\n",
       "                                                Link Gender  \n",
       "0  https://www.dbnl.org/auteurs/auteur.php?id=nic...   male  \n",
       "1  https://www.dbnl.org/auteurs/auteur.php?id=hel...   male  \n",
       "2  https://www.dbnl.org/auteurs/auteur.php?id=nie...   male  \n",
       "3  https://www.dbnl.org/auteurs/auteur.php?id=oev...   male  \n",
       "4  https://www.dbnl.org/auteurs/auteur.php?id=moe...   male  \n",
       "5  https://www.dbnl.org/auteurs/auteur.php?id=kui...  mixed  \n",
       "6  https://www.dbnl.org/auteurs/auteur.php?id=kui...  mixed  \n",
       "7  https://www.dbnl.org/auteurs/auteur.php?id=kui...  mixed  \n",
       "8  https://www.dbnl.org/auteurs/auteur.php?id=kui...  mixed  \n",
       "9  https://www.dbnl.org/auteurs/auteur.php?id=eng...  mixed  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read mentions\n",
    "mentions = pd.read_csv(\"mentions_and_authors.txt\", sep=\"\\t\", encoding='utf-8')\n",
    "mentions.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "09ddbca7",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File</th>\n",
       "      <th>Mentions</th>\n",
       "      <th>Year</th>\n",
       "      <th>Title</th>\n",
       "      <th>Author</th>\n",
       "      <th>DOB</th>\n",
       "      <th>POB</th>\n",
       "      <th>DOD</th>\n",
       "      <th>POD</th>\n",
       "      <th>Link</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Era</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GEM_1925_3</td>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>1925</td>\n",
       "      <td>Sint Maartensavond Bij een linoleumsnede van J...</td>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>6 oktober 1897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25 juli 1972</td>\n",
       "      <td>Steyl</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=nic...</td>\n",
       "      <td>male</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GEM_1925_5</td>\n",
       "      <td>Hendrik Andriessen</td>\n",
       "      <td>1925</td>\n",
       "      <td>Is de nieuwe meerstemmige kerkmuziek in Nederl...</td>\n",
       "      <td>Albert Helman</td>\n",
       "      <td>7 november 1903</td>\n",
       "      <td>Paramaribo</td>\n",
       "      <td>10 juli 1996</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=hel...</td>\n",
       "      <td>male</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GEM_1925_6</td>\n",
       "      <td>Willem Nieuwenhuis</td>\n",
       "      <td>1925</td>\n",
       "      <td>Dagelijksch brood en dagelijksch leven.</td>\n",
       "      <td>Willem Nieuwenhuis</td>\n",
       "      <td>1886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=nie...</td>\n",
       "      <td>male</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GEM_1925_7</td>\n",
       "      <td>Karel van den Oever</td>\n",
       "      <td>1925</td>\n",
       "      <td>In Memoriam Fratris</td>\n",
       "      <td>Karel van den Oever</td>\n",
       "      <td>19 november 1879</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>6 oktober 1926</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=oev...</td>\n",
       "      <td>male</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GEM_1925_9</td>\n",
       "      <td>Wies Moens</td>\n",
       "      <td>1925</td>\n",
       "      <td>Koraal.</td>\n",
       "      <td>Wies Moens</td>\n",
       "      <td>28 januari 1898</td>\n",
       "      <td>Sint-Gillis-Bij-Dendermonde</td>\n",
       "      <td>5 februari 1982</td>\n",
       "      <td>Geleen</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=moe...</td>\n",
       "      <td>male</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GEM_1925_11</td>\n",
       "      <td>Henri Bruning</td>\n",
       "      <td>1925</td>\n",
       "      <td>Jonge litteratuur</td>\n",
       "      <td>Henk Kuitenbrouwer; Jan Bruna</td>\n",
       "      <td>20 november 1903; 17 augustus 1876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 augustus 1974; 11 maart 1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=kui...</td>\n",
       "      <td>mixed</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GEM_1925_11</td>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>1925</td>\n",
       "      <td>Jonge litteratuur</td>\n",
       "      <td>Henk Kuitenbrouwer; Jan Bruna</td>\n",
       "      <td>20 november 1903; 17 augustus 1876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 augustus 1974; 11 maart 1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=kui...</td>\n",
       "      <td>mixed</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GEM_1925_11</td>\n",
       "      <td>Albert Kuyle</td>\n",
       "      <td>1925</td>\n",
       "      <td>Jonge litteratuur</td>\n",
       "      <td>Henk Kuitenbrouwer; Jan Bruna</td>\n",
       "      <td>20 november 1903; 17 augustus 1876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 augustus 1974; 11 maart 1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=kui...</td>\n",
       "      <td>mixed</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GEM_1925_11</td>\n",
       "      <td>Albe</td>\n",
       "      <td>1925</td>\n",
       "      <td>Jonge litteratuur</td>\n",
       "      <td>Henk Kuitenbrouwer; Jan Bruna</td>\n",
       "      <td>20 november 1903; 17 augustus 1876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 augustus 1974; 11 maart 1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=kui...</td>\n",
       "      <td>mixed</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GEM_1925_12</td>\n",
       "      <td>Carel Scharten</td>\n",
       "      <td>1925</td>\n",
       "      <td>Kroniek</td>\n",
       "      <td>Jan Engelman; Willem Maas; Albert Kuyle; Jan B...</td>\n",
       "      <td>7 juni 1900; 28 april 1897; 17 februari 1904; ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20 maart 1972; 6 mei 1950; 4 maart 1958; 11 ma...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=eng...</td>\n",
       "      <td>mixed</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          File             Mentions  Year  \\\n",
       "0   GEM_1925_3         Joep Nicolas  1925   \n",
       "1   GEM_1925_5   Hendrik Andriessen  1925   \n",
       "2   GEM_1925_6   Willem Nieuwenhuis  1925   \n",
       "3   GEM_1925_7  Karel van den Oever  1925   \n",
       "4   GEM_1925_9           Wies Moens  1925   \n",
       "5  GEM_1925_11        Henri Bruning  1925   \n",
       "6  GEM_1925_11         Joep Nicolas  1925   \n",
       "7  GEM_1925_11         Albert Kuyle  1925   \n",
       "8  GEM_1925_11                 Albe  1925   \n",
       "9  GEM_1925_12       Carel Scharten  1925   \n",
       "\n",
       "                                               Title  \\\n",
       "0  Sint Maartensavond Bij een linoleumsnede van J...   \n",
       "1  Is de nieuwe meerstemmige kerkmuziek in Nederl...   \n",
       "2            Dagelijksch brood en dagelijksch leven.   \n",
       "3                                In Memoriam Fratris   \n",
       "4                                            Koraal.   \n",
       "5                                  Jonge litteratuur   \n",
       "6                                  Jonge litteratuur   \n",
       "7                                  Jonge litteratuur   \n",
       "8                                  Jonge litteratuur   \n",
       "9                                            Kroniek   \n",
       "\n",
       "                                              Author  \\\n",
       "0                                       Joep Nicolas   \n",
       "1                                      Albert Helman   \n",
       "2                                 Willem Nieuwenhuis   \n",
       "3                                Karel van den Oever   \n",
       "4                                         Wies Moens   \n",
       "5                      Henk Kuitenbrouwer; Jan Bruna   \n",
       "6                      Henk Kuitenbrouwer; Jan Bruna   \n",
       "7                      Henk Kuitenbrouwer; Jan Bruna   \n",
       "8                      Henk Kuitenbrouwer; Jan Bruna   \n",
       "9  Jan Engelman; Willem Maas; Albert Kuyle; Jan B...   \n",
       "\n",
       "                                                 DOB  \\\n",
       "0                                     6 oktober 1897   \n",
       "1                                    7 november 1903   \n",
       "2                                               1886   \n",
       "3                                   19 november 1879   \n",
       "4                                    28 januari 1898   \n",
       "5                 20 november 1903; 17 augustus 1876   \n",
       "6                 20 november 1903; 17 augustus 1876   \n",
       "7                 20 november 1903; 17 augustus 1876   \n",
       "8                 20 november 1903; 17 augustus 1876   \n",
       "9  7 juni 1900; 28 april 1897; 17 februari 1904; ...   \n",
       "\n",
       "                           POB  \\\n",
       "0                          NaN   \n",
       "1                   Paramaribo   \n",
       "2                          NaN   \n",
       "3                    Antwerpen   \n",
       "4  Sint-Gillis-Bij-Dendermonde   \n",
       "5                          NaN   \n",
       "6                          NaN   \n",
       "7                          NaN   \n",
       "8                          NaN   \n",
       "9                          NaN   \n",
       "\n",
       "                                                 DOD        POD  \\\n",
       "0                                       25 juli 1972      Steyl   \n",
       "1                                       10 juli 1996  Amsterdam   \n",
       "2                                               1935        NaN   \n",
       "3                                     6 oktober 1926  Antwerpen   \n",
       "4                                    5 februari 1982     Geleen   \n",
       "5                     3 augustus 1974; 11 maart 1931        NaN   \n",
       "6                     3 augustus 1974; 11 maart 1931        NaN   \n",
       "7                     3 augustus 1974; 11 maart 1931        NaN   \n",
       "8                     3 augustus 1974; 11 maart 1931        NaN   \n",
       "9  20 maart 1972; 6 mei 1950; 4 maart 1958; 11 ma...        NaN   \n",
       "\n",
       "                                                Link Gender          Era  \n",
       "0  https://www.dbnl.org/auteurs/auteur.php?id=nic...   male  1900s-1950s  \n",
       "1  https://www.dbnl.org/auteurs/auteur.php?id=hel...   male  1900s-1950s  \n",
       "2  https://www.dbnl.org/auteurs/auteur.php?id=nie...   male  1900s-1950s  \n",
       "3  https://www.dbnl.org/auteurs/auteur.php?id=oev...   male  1900s-1950s  \n",
       "4  https://www.dbnl.org/auteurs/auteur.php?id=moe...   male  1900s-1950s  \n",
       "5  https://www.dbnl.org/auteurs/auteur.php?id=kui...  mixed  1900s-1950s  \n",
       "6  https://www.dbnl.org/auteurs/auteur.php?id=kui...  mixed  1900s-1950s  \n",
       "7  https://www.dbnl.org/auteurs/auteur.php?id=kui...  mixed  1900s-1950s  \n",
       "8  https://www.dbnl.org/auteurs/auteur.php?id=kui...  mixed  1900s-1950s  \n",
       "9  https://www.dbnl.org/auteurs/auteur.php?id=eng...  mixed  1900s-1950s  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add new column with Era to the mentions\n",
    "# Creating custom intervals\n",
    "conditions = [\n",
    "    (mentions['Year'] < 1900),\n",
    "    (mentions['Year'] >= 1900) & (mentions['Year'] <= 1950),\n",
    "    (mentions['Year'] > 1950)\n",
    "]\n",
    "\n",
    "choices = ['Pre-1900s', '1900s-1950s', 'Post-1950s']\n",
    "\n",
    "mentions['Era'] = np.select(conditions, choices, default='Other')\n",
    "\n",
    "mentions.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ed47af32",
   "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>File</th>\n",
       "      <th>Year</th>\n",
       "      <th>Title</th>\n",
       "      <th>Author</th>\n",
       "      <th>DOB</th>\n",
       "      <th>POB</th>\n",
       "      <th>DOD</th>\n",
       "      <th>POD</th>\n",
       "      <th>Link</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GEM_1925_1</td>\n",
       "      <td>1925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GEM_1925_2</td>\n",
       "      <td>1925</td>\n",
       "      <td>Gedachten over architectuur.</td>\n",
       "      <td>Jan Engelman; Willem Maas</td>\n",
       "      <td>7 juni 1900; 28 april 1897</td>\n",
       "      <td>Utrecht; Utrecht</td>\n",
       "      <td>20 maart 1972; 6 mei 1950</td>\n",
       "      <td>Amsterdam; Utrecht</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=eng...</td>\n",
       "      <td>mixed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GEM_1925_3</td>\n",
       "      <td>1925</td>\n",
       "      <td>Sint Maartensavond Bij een linoleumsnede van J...</td>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>6 oktober 1897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25 juli 1972</td>\n",
       "      <td>Steyl</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=nic...</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GEM_1925_4</td>\n",
       "      <td>1925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GEM_1925_5</td>\n",
       "      <td>1925</td>\n",
       "      <td>Is de nieuwe meerstemmige kerkmuziek in Nederl...</td>\n",
       "      <td>Albert Helman</td>\n",
       "      <td>7 november 1903</td>\n",
       "      <td>Paramaribo</td>\n",
       "      <td>10 juli 1996</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=hel...</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GEM_1925_6</td>\n",
       "      <td>1925</td>\n",
       "      <td>Dagelijksch brood en dagelijksch leven.</td>\n",
       "      <td>Willem Nieuwenhuis</td>\n",
       "      <td>1886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=nie...</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GEM_1925_7</td>\n",
       "      <td>1925</td>\n",
       "      <td>In Memoriam Fratris</td>\n",
       "      <td>Karel van den Oever</td>\n",
       "      <td>19 november 1879</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>6 oktober 1926</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=oev...</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GEM_1925_8</td>\n",
       "      <td>1925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GEM_1925_9</td>\n",
       "      <td>1925</td>\n",
       "      <td>Koraal.</td>\n",
       "      <td>Wies Moens</td>\n",
       "      <td>28 januari 1898</td>\n",
       "      <td>Sint-Gillis-Bij-Dendermonde</td>\n",
       "      <td>5 februari 1982</td>\n",
       "      <td>Geleen</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=moe...</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GEM_1925_10</td>\n",
       "      <td>1925</td>\n",
       "      <td>De gemeenschap der volkeren.</td>\n",
       "      <td>Eduard A. Serrarens</td>\n",
       "      <td>27 december 1895</td>\n",
       "      <td>Maastricht</td>\n",
       "      <td>25 november 1955</td>\n",
       "      <td>Roermond</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=ser...</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          File  Year                                              Title  \\\n",
       "0   GEM_1925_1  1925                                                NaN   \n",
       "1   GEM_1925_2  1925                       Gedachten over architectuur.   \n",
       "2   GEM_1925_3  1925  Sint Maartensavond Bij een linoleumsnede van J...   \n",
       "3   GEM_1925_4  1925                                                NaN   \n",
       "4   GEM_1925_5  1925  Is de nieuwe meerstemmige kerkmuziek in Nederl...   \n",
       "5   GEM_1925_6  1925            Dagelijksch brood en dagelijksch leven.   \n",
       "6   GEM_1925_7  1925                                In Memoriam Fratris   \n",
       "7   GEM_1925_8  1925                                                NaN   \n",
       "8   GEM_1925_9  1925                                            Koraal.   \n",
       "9  GEM_1925_10  1925                       De gemeenschap der volkeren.   \n",
       "\n",
       "                      Author                         DOB  \\\n",
       "0                        NaN                         NaN   \n",
       "1  Jan Engelman; Willem Maas  7 juni 1900; 28 april 1897   \n",
       "2               Joep Nicolas              6 oktober 1897   \n",
       "3                        NaN                         NaN   \n",
       "4              Albert Helman             7 november 1903   \n",
       "5         Willem Nieuwenhuis                        1886   \n",
       "6        Karel van den Oever            19 november 1879   \n",
       "7                        NaN                         NaN   \n",
       "8                 Wies Moens             28 januari 1898   \n",
       "9        Eduard A. Serrarens            27 december 1895   \n",
       "\n",
       "                           POB                        DOD                 POD  \\\n",
       "0                          NaN                        NaN                 NaN   \n",
       "1             Utrecht; Utrecht  20 maart 1972; 6 mei 1950  Amsterdam; Utrecht   \n",
       "2                          NaN               25 juli 1972               Steyl   \n",
       "3                          NaN                        NaN                 NaN   \n",
       "4                   Paramaribo               10 juli 1996           Amsterdam   \n",
       "5                          NaN                       1935                 NaN   \n",
       "6                    Antwerpen             6 oktober 1926           Antwerpen   \n",
       "7                          NaN                        NaN                 NaN   \n",
       "8  Sint-Gillis-Bij-Dendermonde            5 februari 1982              Geleen   \n",
       "9                   Maastricht           25 november 1955            Roermond   \n",
       "\n",
       "                                                Link Gender  \n",
       "0                                                NaN    NaN  \n",
       "1  https://www.dbnl.org/auteurs/auteur.php?id=eng...  mixed  \n",
       "2  https://www.dbnl.org/auteurs/auteur.php?id=nic...   male  \n",
       "3                                                NaN    NaN  \n",
       "4  https://www.dbnl.org/auteurs/auteur.php?id=hel...   male  \n",
       "5  https://www.dbnl.org/auteurs/auteur.php?id=nie...   male  \n",
       "6  https://www.dbnl.org/auteurs/auteur.php?id=oev...   male  \n",
       "7                                                NaN    NaN  \n",
       "8  https://www.dbnl.org/auteurs/auteur.php?id=moe...   male  \n",
       "9  https://www.dbnl.org/auteurs/auteur.php?id=ser...   male  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the metadata file\n",
    "metadata = pd.read_csv(\"C-CLAMP_metadata_gender.txt\", sep=\"\\t\", encoding='utf-8')\n",
    "metadata.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4dfcfef3",
   "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>File</th>\n",
       "      <th>Year</th>\n",
       "      <th>Title</th>\n",
       "      <th>Author</th>\n",
       "      <th>DOB</th>\n",
       "      <th>POB</th>\n",
       "      <th>DOD</th>\n",
       "      <th>POD</th>\n",
       "      <th>Link</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Era</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GEM_1925_1</td>\n",
       "      <td>1925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GEM_1925_2</td>\n",
       "      <td>1925</td>\n",
       "      <td>Gedachten over architectuur.</td>\n",
       "      <td>Jan Engelman; Willem Maas</td>\n",
       "      <td>7 juni 1900; 28 april 1897</td>\n",
       "      <td>Utrecht; Utrecht</td>\n",
       "      <td>20 maart 1972; 6 mei 1950</td>\n",
       "      <td>Amsterdam; Utrecht</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=eng...</td>\n",
       "      <td>mixed</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GEM_1925_3</td>\n",
       "      <td>1925</td>\n",
       "      <td>Sint Maartensavond Bij een linoleumsnede van J...</td>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>6 oktober 1897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25 juli 1972</td>\n",
       "      <td>Steyl</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=nic...</td>\n",
       "      <td>male</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GEM_1925_4</td>\n",
       "      <td>1925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GEM_1925_5</td>\n",
       "      <td>1925</td>\n",
       "      <td>Is de nieuwe meerstemmige kerkmuziek in Nederl...</td>\n",
       "      <td>Albert Helman</td>\n",
       "      <td>7 november 1903</td>\n",
       "      <td>Paramaribo</td>\n",
       "      <td>10 juli 1996</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=hel...</td>\n",
       "      <td>male</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GEM_1925_6</td>\n",
       "      <td>1925</td>\n",
       "      <td>Dagelijksch brood en dagelijksch leven.</td>\n",
       "      <td>Willem Nieuwenhuis</td>\n",
       "      <td>1886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=nie...</td>\n",
       "      <td>male</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GEM_1925_7</td>\n",
       "      <td>1925</td>\n",
       "      <td>In Memoriam Fratris</td>\n",
       "      <td>Karel van den Oever</td>\n",
       "      <td>19 november 1879</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>6 oktober 1926</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=oev...</td>\n",
       "      <td>male</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GEM_1925_8</td>\n",
       "      <td>1925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GEM_1925_9</td>\n",
       "      <td>1925</td>\n",
       "      <td>Koraal.</td>\n",
       "      <td>Wies Moens</td>\n",
       "      <td>28 januari 1898</td>\n",
       "      <td>Sint-Gillis-Bij-Dendermonde</td>\n",
       "      <td>5 februari 1982</td>\n",
       "      <td>Geleen</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=moe...</td>\n",
       "      <td>male</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GEM_1925_10</td>\n",
       "      <td>1925</td>\n",
       "      <td>De gemeenschap der volkeren.</td>\n",
       "      <td>Eduard A. Serrarens</td>\n",
       "      <td>27 december 1895</td>\n",
       "      <td>Maastricht</td>\n",
       "      <td>25 november 1955</td>\n",
       "      <td>Roermond</td>\n",
       "      <td>https://www.dbnl.org/auteurs/auteur.php?id=ser...</td>\n",
       "      <td>male</td>\n",
       "      <td>1900s-1950s</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          File  Year                                              Title  \\\n",
       "0   GEM_1925_1  1925                                                NaN   \n",
       "1   GEM_1925_2  1925                       Gedachten over architectuur.   \n",
       "2   GEM_1925_3  1925  Sint Maartensavond Bij een linoleumsnede van J...   \n",
       "3   GEM_1925_4  1925                                                NaN   \n",
       "4   GEM_1925_5  1925  Is de nieuwe meerstemmige kerkmuziek in Nederl...   \n",
       "5   GEM_1925_6  1925            Dagelijksch brood en dagelijksch leven.   \n",
       "6   GEM_1925_7  1925                                In Memoriam Fratris   \n",
       "7   GEM_1925_8  1925                                                NaN   \n",
       "8   GEM_1925_9  1925                                            Koraal.   \n",
       "9  GEM_1925_10  1925                       De gemeenschap der volkeren.   \n",
       "\n",
       "                      Author                         DOB  \\\n",
       "0                        NaN                         NaN   \n",
       "1  Jan Engelman; Willem Maas  7 juni 1900; 28 april 1897   \n",
       "2               Joep Nicolas              6 oktober 1897   \n",
       "3                        NaN                         NaN   \n",
       "4              Albert Helman             7 november 1903   \n",
       "5         Willem Nieuwenhuis                        1886   \n",
       "6        Karel van den Oever            19 november 1879   \n",
       "7                        NaN                         NaN   \n",
       "8                 Wies Moens             28 januari 1898   \n",
       "9        Eduard A. Serrarens            27 december 1895   \n",
       "\n",
       "                           POB                        DOD                 POD  \\\n",
       "0                          NaN                        NaN                 NaN   \n",
       "1             Utrecht; Utrecht  20 maart 1972; 6 mei 1950  Amsterdam; Utrecht   \n",
       "2                          NaN               25 juli 1972               Steyl   \n",
       "3                          NaN                        NaN                 NaN   \n",
       "4                   Paramaribo               10 juli 1996           Amsterdam   \n",
       "5                          NaN                       1935                 NaN   \n",
       "6                    Antwerpen             6 oktober 1926           Antwerpen   \n",
       "7                          NaN                        NaN                 NaN   \n",
       "8  Sint-Gillis-Bij-Dendermonde            5 februari 1982              Geleen   \n",
       "9                   Maastricht           25 november 1955            Roermond   \n",
       "\n",
       "                                                Link Gender          Era  \n",
       "0                                                NaN    NaN  1900s-1950s  \n",
       "1  https://www.dbnl.org/auteurs/auteur.php?id=eng...  mixed  1900s-1950s  \n",
       "2  https://www.dbnl.org/auteurs/auteur.php?id=nic...   male  1900s-1950s  \n",
       "3                                                NaN    NaN  1900s-1950s  \n",
       "4  https://www.dbnl.org/auteurs/auteur.php?id=hel...   male  1900s-1950s  \n",
       "5  https://www.dbnl.org/auteurs/auteur.php?id=nie...   male  1900s-1950s  \n",
       "6  https://www.dbnl.org/auteurs/auteur.php?id=oev...   male  1900s-1950s  \n",
       "7                                                NaN    NaN  1900s-1950s  \n",
       "8  https://www.dbnl.org/auteurs/auteur.php?id=moe...   male  1900s-1950s  \n",
       "9  https://www.dbnl.org/auteurs/auteur.php?id=ser...   male  1900s-1950s  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add Era to metadata\n",
    "# Creating custom intervals\n",
    "conditions = [\n",
    "    (metadata['Year'] < 1900),\n",
    "    (metadata['Year'] >= 1900) & (metadata['Year'] <= 1950),\n",
    "    (metadata['Year'] > 1950)\n",
    "]\n",
    "\n",
    "choices = ['Pre-1900s', '1900s-1950s', 'Post-1950s']\n",
    "\n",
    "metadata['Era'] = np.select(conditions, choices, default='Other')\n",
    "\n",
    "metadata.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd7d93df",
   "metadata": {},
   "source": [
    "# Create subsets per Era"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e70b18dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame for Era 1900s-1950s:\n",
      "                   Mentions                                             Author\n",
      "0              Joep Nicolas                                       Joep Nicolas\n",
      "1        Hendrik Andriessen                                      Albert Helman\n",
      "2        Willem Nieuwenhuis                                 Willem Nieuwenhuis\n",
      "3       Karel van den Oever                                Karel van den Oever\n",
      "4                Wies Moens                                         Wies Moens\n",
      "...                     ...                                                ...\n",
      "138371             Henricus  Eugenius Druwé; Albert Deblaere; R. Leijs; Pau...\n",
      "138372           M. Huybens  Eugenius Druwé; Albert Deblaere; R. Leijs; Pau...\n",
      "138373             R. Leijs  Eugenius Druwé; Albert Deblaere; R. Leijs; Pau...\n",
      "138374                 Léon  Eugenius Druwé; Albert Deblaere; R. Leijs; Pau...\n",
      "138375             Horatius  Eugenius Druwé; Albert Deblaere; R. Leijs; Pau...\n",
      "\n",
      "[50126 rows x 2 columns]\n",
      "\n",
      "\n",
      "DataFrame for Era Pre-1900s:\n",
      "                 Mentions                      Author\n",
      "3086  Willem van den Berg          W.J.C. van Hasselt\n",
      "3087                Plato          W.J.C. van Hasselt\n",
      "3088               Hubert          W.J.C. van Hasselt\n",
      "3089             Henricus          W.J.C. van Hasselt\n",
      "3090       A.C.W. Staring          W.J.C. van Hasselt\n",
      "...                   ...                         ...\n",
      "8881       A.G. van Hamel    R.P.J. Tutein Nolthenius\n",
      "8882       G.A. van Hamel              G.A. van Hamel\n",
      "8883         G.F. Haspels                G.F. Haspels\n",
      "8884                 Léon  Henricus Anastasius Viotta\n",
      "8885       Louis Couperus    R.P.J. Tutein Nolthenius\n",
      "\n",
      "[5800 rows x 2 columns]\n",
      "\n",
      "\n",
      "DataFrame for Era Post-1950s:\n",
      "                      Mentions                               Author\n",
      "19847             Gabriël Smit                         Gabriël Smit\n",
      "19848              Nes Tergast                          Nes Tergast\n",
      "19849                Multatuli                              P. Geyl\n",
      "19850             Jan Engelman                              P. Geyl\n",
      "19851            Jan te Winkel                              P. Geyl\n",
      "...                        ...                                  ...\n",
      "136380  Karel van de Woestijne  Raymond Vervliet; Joris Vlasselaers\n",
      "136381       Emiel Hullebroeck  Raymond Vervliet; Joris Vlasselaers\n",
      "136382          H.P.H. Teesing  Raymond Vervliet; Joris Vlasselaers\n",
      "136383    Lambrecht Lambrechts  Raymond Vervliet; Joris Vlasselaers\n",
      "136384       Joris Vlasselaers  Raymond Vervliet; Joris Vlasselaers\n",
      "\n",
      "[82450 rows x 2 columns]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Era_dataframes = {}\n",
    "\n",
    "# Iterate through Era values and create subsets\n",
    "for Era in mentions['Era'].unique():\n",
    "    subset = mentions[mentions['Era'] == Era][['Mentions', 'Author']]  # Subset based on the Era and keep only Author and Mentions\n",
    "    Era_dataframes[Era] = subset  # Store the subset DataFrame in the dictionary\n",
    "\n",
    "# Print each subset DataFrame\n",
    "for Era, dataframe in Era_dataframes.items():\n",
    "    print(f\"DataFrame for Era {Era}:\")\n",
    "    print(dataframe)\n",
    "    print(\"\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "19eb01b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame for Era 1900s-1950s_metadata:\n",
      "                                                  Author\n",
      "0                                                    NaN\n",
      "1                              Jan Engelman; Willem Maas\n",
      "2                                           Joep Nicolas\n",
      "3                                                    NaN\n",
      "4                                          Albert Helman\n",
      "...                                                  ...\n",
      "52926                                      André Vlerick\n",
      "52927                                      Arthur Thomas\n",
      "52928                                        G. de Vocht\n",
      "52929  Eugenius Druwé; Albert Deblaere; R. Leijs; Pau...\n",
      "52930                                                NaN\n",
      "\n",
      "[21778 rows x 1 columns]\n",
      "\n",
      "\n",
      "DataFrame for Era Pre-1900s_metadata:\n",
      "                          Author\n",
      "2053                         NaN\n",
      "2054                         NaN\n",
      "2055                         NaN\n",
      "2056                         NaN\n",
      "2057                         NaN\n",
      "...                          ...\n",
      "7757                         NaN\n",
      "7758  Henricus Anastasius Viotta\n",
      "7759                         NaN\n",
      "7760    R.P.J. Tutein Nolthenius\n",
      "7761                         NaN\n",
      "\n",
      "[5709 rows x 1 columns]\n",
      "\n",
      "\n",
      "DataFrame for Era Post-1950s_metadata:\n",
      "                                    Author\n",
      "14143                   Johannes Lindeboom\n",
      "14144                         Gabriël Smit\n",
      "14145                                  NaN\n",
      "14146                          Nes Tergast\n",
      "14147                              P. Geyl\n",
      "...                                    ...\n",
      "52167                          H. van Gorp\n",
      "52168                           W. Gobbers\n",
      "52169                     M.H. Schenkeveld\n",
      "52170                           Ada Deprez\n",
      "52171  Raymond Vervliet; Joris Vlasselaers\n",
      "\n",
      "[25444 rows x 1 columns]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Era_dataframes_metadata = {}\n",
    "\n",
    "# Iterate through Era values and create subsets\n",
    "for Era in metadata['Era'].unique():\n",
    "    subset = metadata[metadata['Era'] == Era][['Author']]  # Subset based on the Era'and keep only Author\n",
    "    dataframe_name = f\"{Era}\"  # Creating a dynamic name for the DataFrame\n",
    "    Era_dataframes_metadata[dataframe_name] = subset  # Store the subset DataFrame in the dictionary\n",
    "\n",
    "# Print each subset DataFrame\n",
    "for Era, dataframe in Era_dataframes_metadata.items():\n",
    "    print(f\"DataFrame for Era {Era}_metadata:\")\n",
    "    print(dataframe)\n",
    "    print(\"\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2119c5dd",
   "metadata": {},
   "source": [
    "# Calculate the weights per Era"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b0e490e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_13744\\2190741628.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_filtered['Author Combinations'] = df_filtered.apply(get_author_combinations, axis=1)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_13744\\2190741628.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_filtered['Author Combinations'] = df_filtered.apply(get_author_combinations, axis=1)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_13744\\2190741628.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_filtered['Author Combinations'] = df_filtered.apply(get_author_combinations, axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame for Era 1900s-1950s:\n",
      "                   Author            Coauthor\n",
      "0            Jan Engelman         Willem Maas\n",
      "1      Henk Kuitenbrouwer           Jan Bruna\n",
      "2            Jan Engelman         Willem Maas\n",
      "3            Jan Engelman        Albert Kuyle\n",
      "4            Jan Engelman           Jan Bruna\n",
      "...                   ...                 ...\n",
      "16305            J. Hoing  Hendrik Imberechts\n",
      "16306            J. Hoing            H. Aerts\n",
      "16307       Flor Kielbaey  Hendrik Imberechts\n",
      "16308       Flor Kielbaey            H. Aerts\n",
      "16309  Hendrik Imberechts            H. Aerts\n",
      "\n",
      "[16310 rows x 2 columns]\n",
      "\n",
      "\n",
      "DataFrame for Era Pre-1900s:\n",
      "                                         Author              Coauthor\n",
      "0                    Henry Wadsworth Longfellow        E.J. Potgieter\n",
      "1                               Petrus de Raadt  Jan Gerhardus Ottema\n",
      "2                                     P.J. Veth    W.J.C. van Hasselt\n",
      "3                                E.J. Potgieter    S.J. van den Bergh\n",
      "4                                E.J. Potgieter    W.J.C. van Hasselt\n",
      "...                                         ...                   ...\n",
      "1373  Joan Karel Hendrik de Roo van Alderwerelt      Willem Jan Knoop\n",
      "1374  Joan Karel Hendrik de Roo van Alderwerelt      A.L.W. Seyffardt\n",
      "1375                           Willem Jan Knoop      A.L.W. Seyffardt\n",
      "1376                          P.H. van der Kemp      Herman Muntinghe\n",
      "1377                                   G. Kalff         J.N. van Hall\n",
      "\n",
      "[1378 rows x 2 columns]\n",
      "\n",
      "\n",
      "DataFrame for Era Post-1950s:\n",
      "                          Author             Coauthor\n",
      "0      Ernst Denny Hirsch Ballin        W.J.W. Koster\n",
      "1      Ernst Denny Hirsch Ballin      J.C.H. de Pater\n",
      "2      Ernst Denny Hirsch Ballin  Jan Sebastian Rabie\n",
      "3      Ernst Denny Hirsch Ballin       M.J. Langeveld\n",
      "4                  W.J.W. Koster      J.C.H. de Pater\n",
      "...                          ...                  ...\n",
      "13269             Karel Porteman           Jan Herman\n",
      "13270             Ignace Bossuyt      Piet Couttenier\n",
      "13271             Ignace Bossuyt           Jan Herman\n",
      "13272            Piet Couttenier           Jan Herman\n",
      "13273           Raymond Vervliet    Joris Vlasselaers\n",
      "\n",
      "[13274 rows x 2 columns]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Create coauthor x coauthor pairs\n",
    "\n",
    "Era_coauthors_dataframes = {}\n",
    "\n",
    "# Function to get combinations of authors\n",
    "def get_author_combinations(row):\n",
    "    authors = row['Author'].split('; ')\n",
    "    return list(combinations(authors, 2))  # Change the number inside combinations() for different combinations\n",
    "\n",
    "for Era, dataframe in Era_dataframes_metadata.items():\n",
    "    # Create coauthors_df\n",
    "    coauthors_df = dataframe[['Author']].copy()\n",
    "\n",
    "    # Filter out rows with only 1 author\n",
    "    df_filtered = coauthors_df[coauthors_df['Author'].str.count(';') > 0]\n",
    "    \n",
    "    # Apply the function to each row: this will create a new column 'Author Combinations' with a list of combinations for each row\n",
    "    df_filtered['Author Combinations'] = df_filtered.apply(get_author_combinations, axis=1)\n",
    "\n",
    "    # Assuming df_filtered is your DataFrame with author combinations\n",
    "\n",
    "    # Create an empty list to store the combinations\n",
    "    combinations_list = []\n",
    "\n",
    "    # Iterate through each row and extract combinations\n",
    "    for _, row in df_filtered.iterrows():\n",
    "        combinations_list.extend(row['Author Combinations'])\n",
    "    \n",
    "    dataframe_name = f\"{Era}\"\n",
    "    Era_coauthors_dataframes[dataframe_name] = pd.DataFrame(combinations_list, columns=['Author', 'Coauthor'])\n",
    "    \n",
    "for Era, dataframe in Era_coauthors_dataframes.items():\n",
    "    print(f\"DataFrame for Era {Era}:\")\n",
    "    print(dataframe)\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "167fa05b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1900s-1950s\n",
      "DataFrame for Era 1900s-1950s:\n",
      "                          Author             Mentions\n",
      "0      Ernst Denny Hirsch Ballin        W.J.W. Koster\n",
      "1      Ernst Denny Hirsch Ballin      J.C.H. de Pater\n",
      "2      Ernst Denny Hirsch Ballin  Jan Sebastian Rabie\n",
      "3      Ernst Denny Hirsch Ballin       M.J. Langeveld\n",
      "4                  W.J.W. Koster      J.C.H. de Pater\n",
      "...                          ...                  ...\n",
      "13269             Karel Porteman           Jan Herman\n",
      "13270             Ignace Bossuyt      Piet Couttenier\n",
      "13271             Ignace Bossuyt           Jan Herman\n",
      "13272            Piet Couttenier           Jan Herman\n",
      "13273           Raymond Vervliet    Joris Vlasselaers\n",
      "\n",
      "[13274 rows x 2 columns]\n",
      "\n",
      "\n",
      "Pre-1900s\n",
      "DataFrame for Era Pre-1900s:\n",
      "                          Author             Mentions\n",
      "0      Ernst Denny Hirsch Ballin        W.J.W. Koster\n",
      "1      Ernst Denny Hirsch Ballin      J.C.H. de Pater\n",
      "2      Ernst Denny Hirsch Ballin  Jan Sebastian Rabie\n",
      "3      Ernst Denny Hirsch Ballin       M.J. Langeveld\n",
      "4                  W.J.W. Koster      J.C.H. de Pater\n",
      "...                          ...                  ...\n",
      "13269             Karel Porteman           Jan Herman\n",
      "13270             Ignace Bossuyt      Piet Couttenier\n",
      "13271             Ignace Bossuyt           Jan Herman\n",
      "13272            Piet Couttenier           Jan Herman\n",
      "13273           Raymond Vervliet    Joris Vlasselaers\n",
      "\n",
      "[13274 rows x 2 columns]\n",
      "\n",
      "\n",
      "Post-1950s\n",
      "DataFrame for Era Post-1950s:\n",
      "                          Author             Mentions\n",
      "0      Ernst Denny Hirsch Ballin        W.J.W. Koster\n",
      "1      Ernst Denny Hirsch Ballin      J.C.H. de Pater\n",
      "2      Ernst Denny Hirsch Ballin  Jan Sebastian Rabie\n",
      "3      Ernst Denny Hirsch Ballin       M.J. Langeveld\n",
      "4                  W.J.W. Koster      J.C.H. de Pater\n",
      "...                          ...                  ...\n",
      "13269             Karel Porteman           Jan Herman\n",
      "13270             Ignace Bossuyt      Piet Couttenier\n",
      "13271             Ignace Bossuyt           Jan Herman\n",
      "13272            Piet Couttenier           Jan Herman\n",
      "13273           Raymond Vervliet    Joris Vlasselaers\n",
      "\n",
      "[13274 rows x 2 columns]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create mention x author pairs\n",
    "\n",
    "Era_mentions_authors_dataframes = {}\n",
    "\n",
    "for Era, dataframe in Era_dataframes.items():\n",
    "    # Splitting authors column\n",
    "    authors_split = dataframe['Author'].str.split('; ', expand=True).stack()\n",
    "\n",
    "    # Dropping the original authors column\n",
    "    dataframe.drop('Author', axis=1, inplace=True)\n",
    "\n",
    "    # Restoring the index alignment\n",
    "    authors_split.index = authors_split.index.droplevel(1)\n",
    "\n",
    "    # Combining the DataFrame with split authors and original DataFrame\n",
    "    dataframe = dataframe.join(authors_split.rename('Author'))\n",
    "    \n",
    "    dataframe_name = f\"{Era}\"\n",
    "    Era_mentions_authors_dataframes[dataframe_name] = pd.DataFrame(combinations_list, columns=['Author', 'Mentions'])\n",
    "\n",
    "for Era, dataframe in Era_mentions_authors_dataframes.items():\n",
    "    print(Era)\n",
    "    print(f\"DataFrame for Era {Era}:\")\n",
    "    print(dataframe)\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6fb33a79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformed DataFrame for 1900s-1950s:\n",
      "                          Source               Target\n",
      "0      Ernst Denny Hirsch Ballin        W.J.W. Koster\n",
      "1      Ernst Denny Hirsch Ballin      J.C.H. de Pater\n",
      "2      Ernst Denny Hirsch Ballin  Jan Sebastian Rabie\n",
      "3      Ernst Denny Hirsch Ballin       M.J. Langeveld\n",
      "4                  W.J.W. Koster      J.C.H. de Pater\n",
      "...                          ...                  ...\n",
      "45889         Hendrik Imberechts             J. Hoing\n",
      "45890                   H. Aerts             J. Hoing\n",
      "45891         Hendrik Imberechts        Flor Kielbaey\n",
      "45892                   H. Aerts        Flor Kielbaey\n",
      "45893                   H. Aerts   Hendrik Imberechts\n",
      "\n",
      "[45894 rows x 2 columns]\n",
      "\n",
      "\n",
      "Transformed DataFrame for Pre-1900s:\n",
      "                          Source                                     Target\n",
      "0      Ernst Denny Hirsch Ballin                              W.J.W. Koster\n",
      "1      Ernst Denny Hirsch Ballin                            J.C.H. de Pater\n",
      "2      Ernst Denny Hirsch Ballin                        Jan Sebastian Rabie\n",
      "3      Ernst Denny Hirsch Ballin                             M.J. Langeveld\n",
      "4                  W.J.W. Koster                            J.C.H. de Pater\n",
      "...                          ...                                        ...\n",
      "16025           Willem Jan Knoop  Joan Karel Hendrik de Roo van Alderwerelt\n",
      "16026           A.L.W. Seyffardt  Joan Karel Hendrik de Roo van Alderwerelt\n",
      "16027           A.L.W. Seyffardt                           Willem Jan Knoop\n",
      "16028           Herman Muntinghe                          P.H. van der Kemp\n",
      "16029              J.N. van Hall                                   G. Kalff\n",
      "\n",
      "[16030 rows x 2 columns]\n",
      "\n",
      "\n",
      "Transformed DataFrame for Post-1950s:\n",
      "                          Source               Target\n",
      "0      Ernst Denny Hirsch Ballin        W.J.W. Koster\n",
      "1      Ernst Denny Hirsch Ballin      J.C.H. de Pater\n",
      "2      Ernst Denny Hirsch Ballin  Jan Sebastian Rabie\n",
      "3      Ernst Denny Hirsch Ballin       M.J. Langeveld\n",
      "4                  W.J.W. Koster      J.C.H. de Pater\n",
      "...                          ...                  ...\n",
      "39817                 Jan Herman       Karel Porteman\n",
      "39818            Piet Couttenier       Ignace Bossuyt\n",
      "39819                 Jan Herman       Ignace Bossuyt\n",
      "39820                 Jan Herman      Piet Couttenier\n",
      "39821          Joris Vlasselaers     Raymond Vervliet\n",
      "\n",
      "[39822 rows x 2 columns]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Combine DataFrames based on Era\n",
    "\n",
    "# Initialize a dictionary to hold the transformed DataFrames\n",
    "transformed_dataframes = {}\n",
    "\n",
    "for Era, df_mentions_authors in Era_mentions_authors_dataframes.items():    \n",
    "    if Era in Era_coauthors_dataframes:\n",
    "        df_coauthors = Era_coauthors_dataframes[Era]\n",
    "        \n",
    "        # Transform mentions/authors DataFrame\n",
    "        df_mentions_authors_transformed = df_mentions_authors.rename(columns={'Author': 'Source', 'Mentions': 'Target'})\n",
    "        \n",
    "        # Transform coauthors DataFrame\n",
    "        df_coauthors_transformed1 = df_coauthors.rename(columns={'Author': 'Source', 'Coauthor': 'Target'})\n",
    "        df_coauthors_transformed2 = df_coauthors.rename(columns={'Coauthor': 'Source', 'Author': 'Target'})\n",
    "        \n",
    "        # Combine the three DataFrames\n",
    "        combined_df = pd.concat([\n",
    "            df_mentions_authors_transformed,\n",
    "            df_coauthors_transformed1,\n",
    "            df_coauthors_transformed2\n",
    "        ], ignore_index=True)\n",
    "        \n",
    "        # Store the combined DataFrame in the dictionary\n",
    "        transformed_dataframes[Era] = combined_df\n",
    "    else:\n",
    "        print(f\"No matching coauthor data for Era {Era}\")\n",
    "\n",
    "# Print transformed DataFrames\n",
    "for Era, dataframe in transformed_dataframes.items():\n",
    "    print(f\"Transformed DataFrame for {Era}:\")\n",
    "    print(dataframe)\n",
    "    print(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c1d67378",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edge weights for 1900s-1950s:\n",
      "                      Source                 Target  Weight\n",
      "0             't Haakstertje                A. Ampe       1\n",
      "1             't Haakstertje          A. Darquennes       1\n",
      "2             't Haakstertje  Andreas Nicolaas Metz       1\n",
      "3             't Haakstertje         Antoon Fimmers       1\n",
      "4             't Haakstertje         Arthur Janssen       1\n",
      "...                      ...                    ...     ...\n",
      "28763  Émile O.J.J.G. Lousse               R. Leijs       1\n",
      "28764  Émile O.J.J.G. Lousse    R. Vanalderweireldt       1\n",
      "28765  Émile O.J.J.G. Lousse        R. van Ooteghem       1\n",
      "28766  Émile O.J.J.G. Lousse          René Verbeeck       1\n",
      "28767  Émile O.J.J.G. Lousse         Walter F. Smet       1\n",
      "\n",
      "[28768 rows x 3 columns]\n",
      "\n",
      "\n",
      "Edge weights for Pre-1900s:\n",
      "                                             Source            Target  Weight\n",
      "0                                  A. Agnes Sneller    Agnes Verbiest       3\n",
      "1                                  A. Agnes Sneller    Arie Schippers       1\n",
      "2                                  A. Agnes Sneller         B.J. Hoff       1\n",
      "3                                  A. Agnes Sneller  Dorothé te Riele       1\n",
      "4                                  A. Agnes Sneller          Eric Vos       1\n",
      "...                                             ...               ...     ...\n",
      "12560                              Zoran Djukanovic       Roel Schuyt       1\n",
      "12561        [tijdschrift] Nieuw Vlaams Tijdschrift  Achilles Mussche       1\n",
      "12562  [tijdschrift] Vaderlandsche Letteroefeningen        Jan Hartog       2\n",
      "12563                   anoniem Ridder metter swane      J.F.D. Blöte       1\n",
      "12564                                   Ágnes Roboz         Eva Bodor       1\n",
      "\n",
      "[12565 rows x 3 columns]\n",
      "\n",
      "\n",
      "Edge weights for Post-1950s:\n",
      "                                       Source                  Target  Weight\n",
      "0                                    A. Aerts  M.P.R. van den Broecke       1\n",
      "1                                    A. Aerts        Marijke Elstrodt       1\n",
      "2                            A. Agnes Sneller          Agnes Verbiest       7\n",
      "3                            A. Agnes Sneller            Anneke Neijt       1\n",
      "4                            A. Agnes Sneller          Arie Schippers       2\n",
      "...                                       ...                     ...     ...\n",
      "19525                          Yücel Yesilgöz         Frank Bovenkerk       1\n",
      "19526                      Zbigniew Ogonowski              Tom Eekman       2\n",
      "19527                        Zoran Djukanovic             Roel Schuyt       2\n",
      "19528  [tijdschrift] Nieuw Vlaams Tijdschrift        Achilles Mussche       2\n",
      "19529                             Ágnes Roboz               Eva Bodor       2\n",
      "\n",
      "[19530 rows x 3 columns]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Add weights\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# Dictionary to hold the weights for each DataFrame\n",
    "edge_weights_dict = {}\n",
    "\n",
    "for key, df in transformed_dataframes.items():\n",
    "    # Count occurrences of each (source, target) pair\n",
    "    edge_weights = df.groupby(['Source', 'Target']).size().reset_index(name='Weight')\n",
    "    \n",
    "    # Exclude pairs where source and target are the same\n",
    "    edge_weights = edge_weights[edge_weights['Source'] != edge_weights['Target']]\n",
    "    \n",
    "    # Store the result in the dictionary\n",
    "    edge_weights_dict[key] = edge_weights\n",
    "    \n",
    "    # Print the resulting weights for verification\n",
    "    print(f\"Edge weights for {key}:\")\n",
    "    print(edge_weights)\n",
    "    print(\"\\n\")\n",
    "\n",
    "# The resulting edge_weights_dict now contains the weights for each (source, target) pair excluding self-loops\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f2e052",
   "metadata": {},
   "source": [
    "# Add DOB to source and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0913a9f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final merged DataFrame for 1900s-1950s:\n",
      "                      Source                 Target  Weight  \\\n",
      "0             't Haakstertje                A. Ampe       1   \n",
      "1             't Haakstertje          A. Darquennes       1   \n",
      "2             't Haakstertje  Andreas Nicolaas Metz       1   \n",
      "3             't Haakstertje         Antoon Fimmers       1   \n",
      "4             't Haakstertje         Arthur Janssen       1   \n",
      "...                      ...                    ...     ...   \n",
      "29406  Émile O.J.J.G. Lousse               R. Leijs       1   \n",
      "29407  Émile O.J.J.G. Lousse    R. Vanalderweireldt       1   \n",
      "29408  Émile O.J.J.G. Lousse        R. van Ooteghem       1   \n",
      "29409  Émile O.J.J.G. Lousse          René Verbeeck       1   \n",
      "29410  Émile O.J.J.G. Lousse         Walter F. Smet       1   \n",
      "\n",
      "              Target_DOB Source_DOB  \n",
      "0          17 april 1912  19de eeuw  \n",
      "1      14 september 1909  19de eeuw  \n",
      "2              19de eeuw  19de eeuw  \n",
      "3              19de eeuw  19de eeuw  \n",
      "4                   1886  19de eeuw  \n",
      "...                  ...        ...  \n",
      "29406         20ste eeuw       1905  \n",
      "29407               1903       1905  \n",
      "29408          19de eeuw       1905  \n",
      "29409      18 april 1904       1905  \n",
      "29410               1915       1905  \n",
      "\n",
      "[29411 rows x 5 columns]\n",
      "\n",
      "\n",
      "Final merged DataFrame for Pre-1900s:\n",
      "                                             Source            Target  Weight  \\\n",
      "0                                  A. Agnes Sneller    Agnes Verbiest       3   \n",
      "1                                  A. Agnes Sneller    Arie Schippers       1   \n",
      "2                                  A. Agnes Sneller         B.J. Hoff       1   \n",
      "3                                  A. Agnes Sneller  Dorothé te Riele       1   \n",
      "4                                  A. Agnes Sneller          Eric Vos       1   \n",
      "...                                             ...               ...     ...   \n",
      "12928                              Zoran Djukanovic       Roel Schuyt       1   \n",
      "12929        [tijdschrift] Nieuw Vlaams Tijdschrift  Achilles Mussche       1   \n",
      "12930  [tijdschrift] Vaderlandsche Letteroefeningen        Jan Hartog       2   \n",
      "12931                   anoniem Ridder metter swane      J.F.D. Blöte       1   \n",
      "12932                                   Ágnes Roboz         Eva Bodor       1   \n",
      "\n",
      "             Target_DOB       Source_DOB  \n",
      "0                  1934  19 januari 1940  \n",
      "1                  1947  19 januari 1940  \n",
      "2                  1926  19 januari 1940  \n",
      "3            20ste eeuw  19 januari 1940  \n",
      "4            20ste eeuw  19 januari 1940  \n",
      "...                 ...              ...  \n",
      "12928              1948    18 maart 1955  \n",
      "12929  12 augustus 1896              NaN  \n",
      "12930  14 augustus 1829              NaN  \n",
      "12931               NaN              NaN  \n",
      "12932        20ste eeuw     18 juni 1926  \n",
      "\n",
      "[12933 rows x 5 columns]\n",
      "\n",
      "\n",
      "Final merged DataFrame for Post-1950s:\n",
      "                                       Source                  Target  Weight  \\\n",
      "0                                    A. Aerts  M.P.R. van den Broecke       1   \n",
      "1                                    A. Aerts        Marijke Elstrodt       1   \n",
      "2                            A. Agnes Sneller          Agnes Verbiest       7   \n",
      "3                            A. Agnes Sneller            Anneke Neijt       1   \n",
      "4                            A. Agnes Sneller          Arie Schippers       2   \n",
      "...                                       ...                     ...     ...   \n",
      "20001                          Yücel Yesilgöz         Frank Bovenkerk       1   \n",
      "20002                      Zbigniew Ogonowski              Tom Eekman       2   \n",
      "20003                        Zoran Djukanovic             Roel Schuyt       2   \n",
      "20004  [tijdschrift] Nieuw Vlaams Tijdschrift        Achilles Mussche       2   \n",
      "20005                             Ágnes Roboz               Eva Bodor       2   \n",
      "\n",
      "              Target_DOB       Source_DOB  \n",
      "0            25 mei 1942       20ste eeuw  \n",
      "1             20ste eeuw       20ste eeuw  \n",
      "2                   1934  19 januari 1940  \n",
      "3                   1951  19 januari 1940  \n",
      "4                   1947  19 januari 1940  \n",
      "...                  ...              ...  \n",
      "20001  25 september 1943             1951  \n",
      "20002        20 mei 1923             1924  \n",
      "20003               1948    18 maart 1955  \n",
      "20004   12 augustus 1896              NaN  \n",
      "20005         20ste eeuw     18 juni 1926  \n",
      "\n",
      "[20006 rows x 5 columns]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the authors' DOB data\n",
    "authors_dob_df = pd.read_csv(\"author_DOB_list.txt\", sep=\"\\t\", encoding='utf-8')\n",
    "\n",
    "# Dictionary to store merged DataFrames for each era\n",
    "merged_dataframes = {}\n",
    "\n",
    "# Assuming pairs_df_dict is a dictionary with era names as keys and DataFrames as values\n",
    "for era, pairs_df in edge_weights_dict.items():\n",
    "    # First merge: Get Target_DOB by merging on 'Target' (pairs_df) and 'Author' (authors_dob_df)\n",
    "    merged_pairs_df = pd.merge(pairs_df, authors_dob_df[['Author', 'DOB']], left_on='Target', right_on='Author', how='left')\n",
    "\n",
    "    # Rename the 'DOB' column to 'Target_DOB' for clarity\n",
    "    merged_pairs_df = merged_pairs_df.rename(columns={'DOB': 'Target_DOB'})\n",
    "\n",
    "    # Second merge: Get Source_DOB by merging on 'Source' (pairs_df) and 'Author' (authors_dob_df)\n",
    "    merged_pairs_df = pd.merge(merged_pairs_df, authors_dob_df[['Author', 'DOB']], left_on='Source', right_on='Author', how='left')\n",
    "\n",
    "    # Rename the 'DOB' column from the second merge to 'Source_DOB'\n",
    "    merged_pairs_df = merged_pairs_df.rename(columns={'DOB': 'Source_DOB'})\n",
    "\n",
    "    # Drop the 'Author' columns (from both merges) as they are no longer needed\n",
    "    merged_pairs_df = merged_pairs_df.drop(columns=['Author_x', 'Author_y'])\n",
    "\n",
    "    # Store the merged DataFrame for the current era\n",
    "    merged_dataframes[era] = merged_pairs_df\n",
    "\n",
    "    # Display the final result for the current era\n",
    "    print(f\"Final merged DataFrame for {era}:\")\n",
    "    print(merged_pairs_df)\n",
    "    print(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "176a0351",
   "metadata": {},
   "source": [
    "# Filter by date of birth of the target and source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "92de0a88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered DataFrame for 1900s-1950s (based on both Source_DOB and Target_DOB):\n",
      "                      Source                 Target  Weight  \\\n",
      "0             't Haakstertje                A. Ampe       1   \n",
      "1             't Haakstertje          A. Darquennes       1   \n",
      "2             't Haakstertje  Andreas Nicolaas Metz       1   \n",
      "3             't Haakstertje         Antoon Fimmers       1   \n",
      "4             't Haakstertje         Arthur Janssen       1   \n",
      "...                      ...                    ...     ...   \n",
      "28688  Émile O.J.J.G. Lousse               R. Leijs       1   \n",
      "28689  Émile O.J.J.G. Lousse    R. Vanalderweireldt       1   \n",
      "28690  Émile O.J.J.G. Lousse        R. van Ooteghem       1   \n",
      "28691  Émile O.J.J.G. Lousse          René Verbeeck       1   \n",
      "28692  Émile O.J.J.G. Lousse         Walter F. Smet       1   \n",
      "\n",
      "              Target_DOB Source_DOB  \n",
      "0          17 april 1912  19de eeuw  \n",
      "1      14 september 1909  19de eeuw  \n",
      "2              19de eeuw  19de eeuw  \n",
      "3              19de eeuw  19de eeuw  \n",
      "4                   1886  19de eeuw  \n",
      "...                  ...        ...  \n",
      "28688         20ste eeuw       1905  \n",
      "28689               1903       1905  \n",
      "28690          19de eeuw       1905  \n",
      "28691      18 april 1904       1905  \n",
      "28692               1915       1905  \n",
      "\n",
      "[28693 rows x 5 columns]\n",
      "\n",
      "\n",
      "Filtered DataFrame for Pre-1900s (based on both Source_DOB and Target_DOB):\n",
      "                   Source            Target  Weight   Target_DOB  \\\n",
      "0        A. Agnes Sneller    Agnes Verbiest       3         1934   \n",
      "1        A. Agnes Sneller    Arie Schippers       1         1947   \n",
      "2        A. Agnes Sneller         B.J. Hoff       1         1926   \n",
      "3        A. Agnes Sneller  Dorothé te Riele       1   20ste eeuw   \n",
      "4        A. Agnes Sneller          Eric Vos       1   20ste eeuw   \n",
      "...                   ...               ...     ...          ...   \n",
      "12508       Yves Bonnefoy    Jan H. Mysjkin       1         1955   \n",
      "12509      Yücel Yesilgöz     Ankie Lempens       1   20ste eeuw   \n",
      "12510  Zbigniew Ogonowski        Tom Eekman       1  20 mei 1923   \n",
      "12511    Zoran Djukanovic       Roel Schuyt       1         1948   \n",
      "12512         Ágnes Roboz         Eva Bodor       1   20ste eeuw   \n",
      "\n",
      "            Source_DOB  \n",
      "0      19 januari 1940  \n",
      "1      19 januari 1940  \n",
      "2      19 januari 1940  \n",
      "3      19 januari 1940  \n",
      "4      19 januari 1940  \n",
      "...                ...  \n",
      "12508     24 juni 1923  \n",
      "12509             1951  \n",
      "12510             1924  \n",
      "12511    18 maart 1955  \n",
      "12512     18 juni 1926  \n",
      "\n",
      "[12513 rows x 5 columns]\n",
      "\n",
      "\n",
      "Filtered DataFrame for Post-1950s (based on both Source_DOB and Target_DOB):\n",
      "                   Source                  Target  Weight         Target_DOB  \\\n",
      "0                A. Aerts  M.P.R. van den Broecke       1        25 mei 1942   \n",
      "1                A. Aerts        Marijke Elstrodt       1         20ste eeuw   \n",
      "2        A. Agnes Sneller          Agnes Verbiest       7               1934   \n",
      "3        A. Agnes Sneller            Anneke Neijt       1               1951   \n",
      "4        A. Agnes Sneller          Arie Schippers       2               1947   \n",
      "...                   ...                     ...     ...                ...   \n",
      "19233      Yücel Yesilgöz           Ankie Lempens       2         20ste eeuw   \n",
      "19234      Yücel Yesilgöz         Frank Bovenkerk       1  25 september 1943   \n",
      "19235  Zbigniew Ogonowski              Tom Eekman       2        20 mei 1923   \n",
      "19236    Zoran Djukanovic             Roel Schuyt       2               1948   \n",
      "19237         Ágnes Roboz               Eva Bodor       2         20ste eeuw   \n",
      "\n",
      "            Source_DOB  \n",
      "0           20ste eeuw  \n",
      "1           20ste eeuw  \n",
      "2      19 januari 1940  \n",
      "3      19 januari 1940  \n",
      "4      19 januari 1940  \n",
      "...                ...  \n",
      "19233             1951  \n",
      "19234             1951  \n",
      "19235             1924  \n",
      "19236    18 maart 1955  \n",
      "19237     18 juni 1926  \n",
      "\n",
      "[19238 rows x 5 columns]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "# Initialize a dictionary to hold the filtered DataFrames for each era\n",
    "filtered_dataframes_both_dob = {}\n",
    "\n",
    "# Function to determine if a DOB value is valid\n",
    "def is_valid_dob(dob_value):\n",
    "    if pd.isna(dob_value):\n",
    "        return False\n",
    "    if 'eeuw' in dob_value:\n",
    "        # Extract the century number using a regular expression\n",
    "        century_match = re.search(r'(\\d+)', dob_value)\n",
    "        if century_match:\n",
    "            century = int(century_match.group())\n",
    "            return century >= 18  # Keep rows for 18th century and later\n",
    "    elif 'v.Chr.' in dob_value:\n",
    "        # Handle cases with 'v.Chr.' (before Christ)\n",
    "        return False\n",
    "    else:\n",
    "        # Extract 4-digit years using a regular expression\n",
    "        year_match = re.search(r'(\\d{4})', dob_value)\n",
    "        if year_match:\n",
    "            year = int(year_match.group())\n",
    "            return year >= 1700  # Keep rows for 1700 and later\n",
    "    return False\n",
    "\n",
    "# Iterate over each era's DataFrame\n",
    "for era, merged_pairs_df in merged_dataframes.items():\n",
    "    # Identify rows to drop\n",
    "    rows_to_drop = []\n",
    "\n",
    "    # Iterate over the DataFrame to filter based on both Source_DOB and Target_DOB\n",
    "    for index, row in merged_pairs_df.iterrows():\n",
    "        source_dob_valid = is_valid_dob(row['Source_DOB'])\n",
    "        target_dob_valid = is_valid_dob(row['Target_DOB'])\n",
    "        \n",
    "        if not source_dob_valid or not target_dob_valid:\n",
    "            # Drop rows where either Source_DOB or Target_DOB is not valid\n",
    "            rows_to_drop.append(index)\n",
    "\n",
    "    # Drop identified rows from the DataFrame\n",
    "    filtered_df_both_dob = merged_pairs_df.drop(rows_to_drop).reset_index(drop=True)\n",
    "    \n",
    "    # Store the filtered DataFrame for the current era\n",
    "    filtered_dataframes_both_dob[era] = filtered_df_both_dob\n",
    "\n",
    "    # Display the filtered DataFrame for the current era\n",
    "    print(f\"Filtered DataFrame for {era} (based on both Source_DOB and Target_DOB):\")\n",
    "    print(filtered_df_both_dob)\n",
    "    print(\"\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4895d7a",
   "metadata": {},
   "source": [
    "# Saving the networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a24af244",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved edge weights to CCLAMP_Network_1900s-1950s.txt\n",
      "Saved edge weights to CCLAMP_Network_Pre-1900s.txt\n",
      "Saved edge weights to CCLAMP_Network_Post-1950s.txt\n"
     ]
    }
   ],
   "source": [
    "# Save each DataFrame to a text file\n",
    "for Era, dataframe in filtered_dataframes_both_dob.items():\n",
    "    txt_filename = f\"CCLAMP_Network_{Era}.txt\"\n",
    "    \n",
    "    # Save the DataFrame to a text file with tab separation\n",
    "    dataframe.to_csv(txt_filename, sep='\\t', index=False, encoding='utf-8')\n",
    "    \n",
    "    print(f\"Saved edge weights to {txt_filename}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f6db86",
   "metadata": {},
   "source": [
    "# Draw networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "def56da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import networkx as nx\n",
    "from pyvis.network import Network\n",
    "import community.community_louvain as community_louvain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2dbd025e",
   "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>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Target_DOB</th>\n",
       "      <th>Source_DOB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A. Agnes Sneller</td>\n",
       "      <td>Agnes Verbiest</td>\n",
       "      <td>3</td>\n",
       "      <td>1934</td>\n",
       "      <td>19 januari 1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A. Agnes Sneller</td>\n",
       "      <td>Arie Schippers</td>\n",
       "      <td>1</td>\n",
       "      <td>1947</td>\n",
       "      <td>19 januari 1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A. Agnes Sneller</td>\n",
       "      <td>B.J. Hoff</td>\n",
       "      <td>1</td>\n",
       "      <td>1926</td>\n",
       "      <td>19 januari 1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A. Agnes Sneller</td>\n",
       "      <td>Dorothé te Riele</td>\n",
       "      <td>1</td>\n",
       "      <td>20ste eeuw</td>\n",
       "      <td>19 januari 1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A. Agnes Sneller</td>\n",
       "      <td>Eric Vos</td>\n",
       "      <td>1</td>\n",
       "      <td>20ste eeuw</td>\n",
       "      <td>19 januari 1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A. Agnes Sneller</td>\n",
       "      <td>Frank Jansen</td>\n",
       "      <td>1</td>\n",
       "      <td>1952</td>\n",
       "      <td>19 januari 1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A. Agnes Sneller</td>\n",
       "      <td>Margreet den Buurman</td>\n",
       "      <td>1</td>\n",
       "      <td>31 mei 1953</td>\n",
       "      <td>19 januari 1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A. Agnes Sneller</td>\n",
       "      <td>Marnel Breure</td>\n",
       "      <td>1</td>\n",
       "      <td>1960</td>\n",
       "      <td>19 januari 1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A. Agnes Sneller</td>\n",
       "      <td>R.H. Bremmer Jr.</td>\n",
       "      <td>1</td>\n",
       "      <td>13 augustus 1950</td>\n",
       "      <td>19 januari 1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A. Alberts</td>\n",
       "      <td>Anton Koolhaas</td>\n",
       "      <td>1</td>\n",
       "      <td>16 november 1912</td>\n",
       "      <td>23 augustus 1911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A. Ampe</td>\n",
       "      <td>K.H. Heeroma</td>\n",
       "      <td>1</td>\n",
       "      <td>13 september 1909</td>\n",
       "      <td>17 april 1912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A. Bergsma</td>\n",
       "      <td>J.M.J. Sicking</td>\n",
       "      <td>1</td>\n",
       "      <td>1 maart 1936</td>\n",
       "      <td>20ste eeuw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>A. Bouwman</td>\n",
       "      <td>Evert van den Berg</td>\n",
       "      <td>1</td>\n",
       "      <td>1942</td>\n",
       "      <td>20ste eeuw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>A. Dees</td>\n",
       "      <td>J.A. de Vries</td>\n",
       "      <td>1</td>\n",
       "      <td>1951</td>\n",
       "      <td>4 november 1928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>A. Marja</td>\n",
       "      <td>Evert Werkman</td>\n",
       "      <td>1</td>\n",
       "      <td>1915</td>\n",
       "      <td>8 maart 1917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>A. Mertens</td>\n",
       "      <td>Lidy van Eijsselsteijn</td>\n",
       "      <td>1</td>\n",
       "      <td>3 juni 1904</td>\n",
       "      <td>20ste eeuw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A. Middeldorp</td>\n",
       "      <td>A.L. Sötemann</td>\n",
       "      <td>1</td>\n",
       "      <td>11 augustus 1920</td>\n",
       "      <td>13 december 1921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>A. Middeldorp</td>\n",
       "      <td>Bert Voeten</td>\n",
       "      <td>1</td>\n",
       "      <td>6 juli 1918</td>\n",
       "      <td>13 december 1921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>A. Middeldorp</td>\n",
       "      <td>James Brockway</td>\n",
       "      <td>1</td>\n",
       "      <td>21 oktober 1916</td>\n",
       "      <td>13 december 1921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>A. Middeldorp</td>\n",
       "      <td>Paul Rodenko</td>\n",
       "      <td>1</td>\n",
       "      <td>26 november 1920</td>\n",
       "      <td>13 december 1921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Source                  Target  Weight         Target_DOB  \\\n",
       "0   A. Agnes Sneller          Agnes Verbiest       3               1934   \n",
       "1   A. Agnes Sneller          Arie Schippers       1               1947   \n",
       "2   A. Agnes Sneller               B.J. Hoff       1               1926   \n",
       "3   A. Agnes Sneller        Dorothé te Riele       1         20ste eeuw   \n",
       "4   A. Agnes Sneller                Eric Vos       1         20ste eeuw   \n",
       "5   A. Agnes Sneller            Frank Jansen       1               1952   \n",
       "6   A. Agnes Sneller    Margreet den Buurman       1        31 mei 1953   \n",
       "7   A. Agnes Sneller           Marnel Breure       1               1960   \n",
       "8   A. Agnes Sneller        R.H. Bremmer Jr.       1   13 augustus 1950   \n",
       "9         A. Alberts          Anton Koolhaas       1   16 november 1912   \n",
       "10           A. Ampe            K.H. Heeroma       1  13 september 1909   \n",
       "11        A. Bergsma          J.M.J. Sicking       1       1 maart 1936   \n",
       "12        A. Bouwman      Evert van den Berg       1               1942   \n",
       "13           A. Dees           J.A. de Vries       1               1951   \n",
       "14          A. Marja           Evert Werkman       1               1915   \n",
       "15        A. Mertens  Lidy van Eijsselsteijn       1        3 juni 1904   \n",
       "16     A. Middeldorp           A.L. Sötemann       1   11 augustus 1920   \n",
       "17     A. Middeldorp             Bert Voeten       1        6 juli 1918   \n",
       "18     A. Middeldorp          James Brockway       1    21 oktober 1916   \n",
       "19     A. Middeldorp            Paul Rodenko       1   26 november 1920   \n",
       "\n",
       "          Source_DOB  \n",
       "0    19 januari 1940  \n",
       "1    19 januari 1940  \n",
       "2    19 januari 1940  \n",
       "3    19 januari 1940  \n",
       "4    19 januari 1940  \n",
       "5    19 januari 1940  \n",
       "6    19 januari 1940  \n",
       "7    19 januari 1940  \n",
       "8    19 januari 1940  \n",
       "9   23 augustus 1911  \n",
       "10     17 april 1912  \n",
       "11        20ste eeuw  \n",
       "12        20ste eeuw  \n",
       "13   4 november 1928  \n",
       "14      8 maart 1917  \n",
       "15        20ste eeuw  \n",
       "16  13 december 1921  \n",
       "17  13 december 1921  \n",
       "18  13 december 1921  \n",
       "19  13 december 1921  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the network\n",
    "network = pd.read_csv(\"CCLAMP_Network_Pre-1900s.txt\", sep=\"\\t\", encoding='utf-8')\n",
    "network.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f0d1f912",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph with 2602 nodes and 10599 edges\n"
     ]
    }
   ],
   "source": [
    "import networkx as nx\n",
    "# Create a weighted graph\n",
    "G = nx.from_pandas_edgelist(network, \n",
    "                            source = 'Source', \n",
    "                            target = 'Target', \n",
    "                            edge_attr = 'Weight',\n",
    "                            create_using = nx.Graph())\n",
    "print(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2a00e8ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: When  cdn_resources is 'local' jupyter notebook has issues displaying graphics on chrome/safari. Use cdn_resources='in_line' or cdn_resources='remote' if you have issues viewing graphics in a notebook.\n",
      "CCLAMP.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000px\"\n",
       "            height=\"700px\"\n",
       "            src=\"CCLAMP.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x215c8fc8670>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyvis.network import Network\n",
    "net = Network(notebook = True, width=\"1000px\", height=\"700px\", bgcolor=\"#222222\", font_color=\"white\")\n",
    "\n",
    "node_degree = dict(G.degree)\n",
    "      \n",
    "# setting up node attribute\n",
    "nx.set_node_attributes(G, node_degree, 'size')\n",
    "\n",
    "net.from_nx(G)\n",
    "net.show('CCLAMP.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e362741",
   "metadata": {},
   "source": [
    "# Centrality measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "26826dbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Degree centrality\n",
    "degree_dict = nx.degree_centrality(G)\n",
    "degree_dict\n",
    "\n",
    "degree_df = pd.DataFrame.from_dict(degree_dict, orient='index', columns=['centrality'])\n",
    "# Plot top 30 nodes\n",
    "degree_df.sort_values('centrality', ascending=False)[0:29].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b242170b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Betweenness centrality\n",
    "betweenness_dict = nx.betweenness_centrality(G)\n",
    "betweenness_df = pd.DataFrame.from_dict(betweenness_dict, orient='index', columns=['centrality'])\n",
    "# Plot top 30 nodes\n",
    "betweenness_df.sort_values('centrality', ascending=False)[0:29].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "72a0a6b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DBg2wdu1axMfHY+jQoQDyunyeP3+OkJAQ4ZyoqCgAeYOBX716haioKOjr68Pd3R0AMH36dNSvXx+VK1dGWloali5diqioKKxYsUL0dRljjDFWsikd1PTo0QOpqakICAhAYmIiatSogSNHjsDJyQlAXrK9wrlj8mcxAcCNGzewdetWODk5IS4uDgDw5s0bDBkyBElJSbCwsICHhwfOnj2LunXrir4uY4wxxko2pYMaABg2bBiGDRsm97Hg4GCZfURUbHlLlizBkiVL1LouY58LTwtnjLH/Bl77iTHGGGNagYMaxhhjjGkFDmoYY4wxphU4qGGMMcaYVuCghjHGGGNagYMaxhhjjGkFDmoYY4wxphU4qGGMMcaYVuCghjHGGGNagYMaxhhjjGkFDmoYY4wxphU4qGGMMcaYVuCghjHGGGNaQaVVuhljyuGVvhlj7PPjlhrGGGOMaQVuqWHsP4JbexhjrHjcUsMYY4wxrcBBDWOMMca0Agc1jDHGGNMKHNQwxhhjTCvwQGHGShBFg40BxQOONVEGY4x9DtxSwxhjjDGtwEENY4wxxrQCdz8xxr447sJijH0O3FLDGGOMMa3ALTWMsf8kbu1hjBXGLTWMMcYY0woc1DDGGGNMK3D3E2OsxOIuLMa0C7fUMMYYY0wrcEsNY4ypSVGLD7f2MPZlcEsNY4wxxrQCt9QwxthXgFt7GFMft9QwxhhjTCtwUMMYY4wxrcDdT4wxpiU00YXF3WDsv4xbahhjjDGmFTioYYwxxphWUCmoWblyJVxcXGBoaAhPT0+cO3euyGMTExPh6+uLqlWrQkdHB6NGjZI5Zt26dWjcuDGsrKxgZWWFli1b4urVq1LHTJs2DRKJRGqzt7dXpfqMMcY+I+cJh4vdGPtclB5Ts2PHDowaNQorV65Eo0aNsGbNGvj4+ODevXtwdHSUOT4jIwO2traYNGkSlixZIrfM8PBw/PTTT2jYsCEMDQ0xf/58tG7dGnfv3kX58uWF46pXr46TJ08Kv+vq6ipbfcYYY/8BmljC4mspg305SrfULF68GAMHDsSgQYPg5uaGwMBAODg4YNWqVXKPd3Z2RlBQEPr27QsLCwu5x/z1118YNmwYateujWrVqmHdunXIzc3FqVOnpI7T09ODvb29sNna2ipbfcYYY4xpKaWCmszMTNy4cQOtW7eW2t+6dWtcvHhRY5VKT09HVlYWSpcuLbU/Ojoa5cqVg4uLC3r27IknT54UW05GRgbS0tKkNsYYY4xpJ6WCmpSUFOTk5MDOzk5qv52dHZKSkjRWqQkTJqB8+fJo2bKlsK9evXoICQlBWFgY1q1bh6SkJDRs2BCpqalFljNnzhxYWFgIm4ODg8bqyBhjjLGvi0oDhSUSidTvRCSzT1Xz58/Htm3bsGfPHhgaGgr7fXx80KVLF9SsWRMtW7bE4cN5/ZybN28usqyJEyfi7du3wpaQkKCROjLGGGPs66PUQGEbGxvo6urKtMokJyfLtN6oYuHChZg9ezZOnjyJb775pthjTUxMULNmTURHRxd5jIGBAQwMDNSuF2OMMca+fkq11Ojr68PT0xMnTpyQ2n/ixAk0bNhQrYosWLAAM2bMwLFjx+Dl5aXw+IyMDNy/fx9ly5ZV67qMMcYY0w5KT+n29/dHnz594OXlhQYNGmDt2rWIj4/H0KFDAeR1+Tx//hwhISHCOVFRUQCA9+/f49WrV4iKioK+vj7c3d0B5HU5TZ48GVu3boWzs7PQEmRqagpTU1MAwNixY/HDDz/A0dERycnJmDlzJtLS0tCvXz+1XgDGGGOMaQelg5oePXogNTUVAQEBSExMRI0aNXDkyBE4OTkByEu2Fx8fL3WOh4eH8PONGzewdetWODk5IS4uDkBeMr/MzEx07dpV6rypU6di2rRpAIBnz57hp59+QkpKCmxtbVG/fn1cvnxZuC5jjDHGSjaVFrQcNmwYhg0bJvex4OBgmX1EVGx5+cFNcbZv3y6maowxxhgroXiVbsYYY+wz4qzEXw4HNYwxxthXjgMjcXiVbsYYY4xpBQ5qGGOMMaYVOKhhjDHGmFbgMTWMMcZYCaFobI6YcTmaKONz4ZYaxhhjjGkFbqlhjDHG2Bf1uVp7uKWGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQWVgpqVK1fCxcUFhoaG8PT0xLlz54o8NjExEb6+vqhatSp0dHQwatQoucft3r0b7u7uMDAwgLu7O/bu3avWdRljjDFWsigd1OzYsQOjRo3CpEmTEBkZicaNG8PHxwfx8fFyj8/IyICtrS0mTZqEWrVqyT3m0qVL6NGjB/r06YObN2+iT58+6N69O65cuaLydRljjDFWsigd1CxevBgDBw7EoEGD4ObmhsDAQDg4OGDVqlVyj3d2dkZQUBD69u0LCwsLuccEBgaiVatWmDhxIqpVq4aJEyeiRYsWCAwMVPm6jDHGGCtZlApqMjMzcePGDbRu3Vpqf+vWrXHx4kWVK3Hp0iWZMtu0aSOUqep1MzIykJaWJrUxxhhjTDspFdSkpKQgJycHdnZ2Uvvt7OyQlJSkciWSkpKKLVPV686ZMwcWFhbC5uDgoHIdGWOMMfZ1U2mgsEQikfqdiGT2fY4ylb3uxIkT8fbtW2FLSEhQq46MMcYY+3rpKXOwjY0NdHV1ZVpHkpOTZVpRlGFvb19smape18DAAAYGBirXizHGGGP/HUq11Ojr68PT0xMnTpyQ2n/ixAk0bNhQ5Uo0aNBApszjx48LZX6u6zLGGGNMeyjVUgMA/v7+6NOnD7y8vNCgQQOsXbsW8fHxGDp0KIC8Lp/nz58jJCREOCcqKgoA8P79e7x69QpRUVHQ19eHu7s7AGDkyJFo0qQJ5s2bh44dO2L//v04efIkzp8/L/q6jDHGGCvZlA5qevTogdTUVAQEBCAxMRE1atTAkSNH4OTkBCAv2V7h3DEeHh7Czzdu3MDWrVvh5OSEuLg4AEDDhg2xfft2/PHHH5g8eTIqVqyIHTt2oF69eqKvyxhjjLGSTemgBgCGDRuGYcOGyX0sODhYZh8RKSyza9eu6Nq1q8rXZYwxxljJxms/McYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa2gUlCzcuVKuLi4wNDQEJ6enjh37lyxx585cwaenp4wNDSEq6srVq9eLfW4t7c3JBKJzNa+fXvhmGnTpsk8bm9vr0r1GWOMMaaFlA5qduzYgVGjRmHSpEmIjIxE48aN4ePjg/j4eLnHx8bGol27dmjcuDEiIyPx+++/Y8SIEdi9e7dwzJ49e5CYmChsd+7cga6uLrp16yZVVvXq1aWOu337trLVZ4wxxpiW0lP2hMWLF2PgwIEYNGgQACAwMBBhYWFYtWoV5syZI3P86tWr4ejoiMDAQACAm5sbrl+/joULF6JLly4AgNKlS0uds337dhgbG8sENXp6etw6wxhjjDG5lGqpyczMxI0bN9C6dWup/a1bt8bFixflnnPp0iWZ49u0aYPr168jKytL7jkbNmxAz549YWJiIrU/Ojoa5cqVg4uLC3r27IknT54UW9+MjAykpaVJbYwxxhjTTkoFNSkpKcjJyYGdnZ3Ufjs7OyQlJck9JykpSe7x2dnZSElJkTn+6tWruHPnjtASlK9evXoICQlBWFgY1q1bh6SkJDRs2BCpqalF1nfOnDmwsLAQNgcHB7FPlTHGGGP/MSoNFJZIJFK/E5HMPkXHy9sP5LXS1KhRA3Xr1pXa7+Pjgy5duqBmzZpo2bIlDh8+DADYvHlzkdedOHEi3r59K2wJCQnFPzHGGGOM/WcpNabGxsYGurq6Mq0yycnJMq0x+ezt7eUer6enB2tra6n96enp2L59OwICAhTWxcTEBDVr1kR0dHSRxxgYGMDAwEBhWYwxxhj771OqpUZfXx+enp44ceKE1P4TJ06gYcOGcs9p0KCBzPHHjx+Hl5cXSpUqJbV/586dyMjIQO/evRXWJSMjA/fv30fZsmWVeQqMMcYY01JKdz/5+/tj/fr12LhxI+7fv4/Ro0cjPj4eQ4cOBZDX5dO3b1/h+KFDh+Lp06fw9/fH/fv3sXHjRmzYsAFjx46VKXvDhg3o1KmTTAsOAIwdOxZnzpxBbGwsrly5gq5duyItLQ39+vVT9ikwxhhjTAspPaW7R48eSE1NRUBAABITE1GjRg0cOXIETk5OAIDExESpnDUuLi44cuQIRo8ejRUrVqBcuXJYunSpMJ0736NHj3D+/HkcP35c7nWfPXuGn376CSkpKbC1tUX9+vVx+fJl4bqMMcYYK9mUDmoAYNiwYRg2bJjcx4KDg2X2NW3aFBEREcWWWaVKFWEAsTzbt29Xqo6MMcYYK1l47SfGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGMcYYY1qBgxrGGGOMaQUOahhjjDGmFVQKalauXAkXFxcYGhrC09MT586dK/b4M2fOwNPTE4aGhnB1dcXq1aulHg8ODoZEIpHZPn36pNZ1GWOMMVZyKB3U7NixA6NGjcKkSZMQGRmJxo0bw8fHB/Hx8XKPj42NRbt27dC4cWNERkbi999/x4gRI7B7926p48zNzZGYmCi1GRoaqnxdxhhjjJUsSgc1ixcvxsCBAzFo0CC4ubkhMDAQDg4OWLVqldzjV69eDUdHRwQGBsLNzQ2DBg2Cn58fFi5cKHWcRCKBvb291KbOdQEgIyMDaWlpUhtjjDHGtJNSQU1mZiZu3LiB1q1bS+1v3bo1Ll68KPecS5cuyRzfpk0bXL9+HVlZWcK+9+/fw8nJCRUqVMD333+PyMhIta4LAHPmzIGFhYWwOTg4iH6ujDHGGPtvUSqoSUlJQU5ODuzs7KT229nZISkpSe45SUlJco/Pzs5GSkoKAKBatWoIDg7GgQMHsG3bNhgaGqJRo0aIjo5W+boAMHHiRLx9+1bYEhISlHm6jDHGGPsP0VPlJIlEIvU7EcnsU3R8wf3169dH/fr1hccbNWqEOnXqYNmyZVi6dKnK1zUwMICBgYGCZ8MYY4wxbaBUS42NjQ10dXVlWkeSk5NlWlHy2dvbyz1eT08P1tbW8iulo4Nvv/1WaKlR5bqMMcYYK1mUCmr09fXh6emJEydOSO0/ceIEGjZsKPecBg0ayBx//PhxeHl5oVSpUnLPISJERUWhbNmyKl+XMcYYYyWL0t1P/v7+6NOnD7y8vNCgQQOsXbsW8fHxGDp0KIC8cSzPnz9HSEgIAGDo0KFYvnw5/P39MXjwYFy6dAkbNmzAtm3bhDKnT5+O+vXro3LlykhLS8PSpUsRFRWFFStWiL4uY4wxxko2pYOaHj16IDU1FQEBAUhMTESNGjVw5MgRODk5AQASExOlcse4uLjgyJEjGD16NFasWIFy5cph6dKl6NKli3DMmzdvMGTIECQlJcHCwgIeHh44e/Ys6tatK/q6jDHGGCvZVBooPGzYMAwbNkzuY8HBwTL7mjZtioiIiCLLW7JkCZYsWaLWdRljjDFWsvHaT4wxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTCioFNStXroSLiwsMDQ3h6emJc+fOFXv8mTNn4OnpCUNDQ7i6umL16tVSj69btw6NGzeGlZUVrKys0LJlS1y9elXqmGnTpkEikUht9vb2qlSfMcYYY1pI6aBmx44dGDVqFCZNmoTIyEg0btwYPj4+iI+Pl3t8bGws2rVrh8aNGyMyMhK///47RowYgd27dwvHhIeH46effsLp06dx6dIlODo6onXr1nj+/LlUWdWrV0diYqKw3b59W9nqM8YYY0xL6Sl7wuLFizFw4EAMGjQIABAYGIiwsDCsWrUKc+bMkTl+9erVcHR0RGBgIADAzc0N169fx8KFC9GlSxcAwF9//SV1zrp16xAaGopTp06hb9++/1dZPT1unWGMMcaYXEq11GRmZuLGjRto3bq11P7WrVvj4sWLcs+5dOmSzPFt2rTB9evXkZWVJfec9PR0ZGVloXTp0lL7o6OjUa5cObi4uKBnz5548uRJsfXNyMhAWlqa1MYYY4wx7aRUUJOSkoKcnBzY2dlJ7bezs0NSUpLcc5KSkuQen52djZSUFLnnTJgwAeXLl0fLli2FffXq1UNISAjCwsKwbt06JCUloWHDhkhNTS2yvnPmzIGFhYWwOTg4iH2qjDHGGPuPUWmgsEQikfqdiGT2KTpe3n4AmD9/PrZt24Y9e/bA0NBQ2O/j44MuXbqgZs2aaNmyJQ4fPgwA2Lx5c5HXnThxIt6+fStsCQkJip8cY4wxxv6TlBpTY2NjA11dXZlWmeTkZJnWmHz29vZyj9fT04O1tbXU/oULF2L27Nk4efIkvvnmm2LrYmJigpo1ayI6OrrIYwwMDGBgYFBsOYwxxhjTDkq11Ojr68PT0xMnTpyQ2n/ixAk0bNhQ7jkNGjSQOf748ePw8vJCqVKlhH0LFizAjBkzcOzYMXh5eSmsS0ZGBu7fv4+yZcsq8xQYY4wxpqWU7n7y9/fH+vXrsXHjRty/fx+jR49GfHw8hg4dCiCvy6fgjKWhQ4fi6dOn8Pf3x/3797Fx40Zs2LABY8eOFY6ZP38+/vjjD2zcuBHOzs5ISkpCUlIS3r9/LxwzduxYnDlzBrGxsbhy5Qq6du2KtLQ09OvXT53nzxhjjDEtofSU7h49eiA1NRUBAQFITExEjRo1cOTIETg5OQEAEhMTpXLWuLi44MiRIxg9ejRWrFiBcuXKYenSpcJ0biAvmV9mZia6du0qda2pU6di2rRpAIBnz57hp59+QkpKCmxtbVG/fn1cvnxZuC5jjDHGSjalgxoAGDZsGIYNGyb3seDgYJl9TZs2RURERJHlxcXFKbzm9u3bxVaPMcYYYyUQr/3EGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtAIHNYwxxhjTChzUMMYYY0wrcFDDGGOMMa3AQQ1jjDHGtIJKQc3KlSvh4uICQ0NDeHp64ty5c8Uef+bMGXh6esLQ0BCurq5YvXq1zDG7d++Gu7s7DAwM4O7ujr1796p9XcYYY4yVHEoHNTt27MCoUaMwadIkREZGonHjxvDx8UF8fLzc42NjY9GuXTs0btwYkZGR+P333zFixAjs3r1bOObSpUvo0aMH+vTpg5s3b6JPnz7o3r07rly5ovJ1GWOMMVayKB3ULF68GAMHDsSgQYPg5uaGwMBAODg4YNWqVXKPX716NRwdHREYGAg3NzcMGjQIfn5+WLhwoXBMYGAgWrVqhYkTJ6JatWqYOHEiWrRogcDAQJWvyxhjjLGSRU+ZgzMzM3Hjxg1MmDBBan/r1q1x8eJFuedcunQJrVu3ltrXpk0bbNiwAVlZWShVqhQuXbqE0aNHyxyTH9Socl0AyMjIQEZGhvD727dvAQBpaWnCvtyM9CLPL3xsURSVIaYcLkPzZYgph8tQvhwuQ/lyuAzNlyGmHC5D+XK+1jLyfyei4gsmJTx//pwA0IULF6T2z5o1i6pUqSL3nMqVK9OsWbOk9l24cIEA0IsXL4iIqFSpUvTXX39JHfPXX3+Rvr6+ytclIpo6dSoB4I033njjjTfetGBLSEgoNk5RqqUmn0QikfqdiGT2KTq+8H4xZSp73YkTJ8Lf31/4PTc3F//88w+sra3lnpeWlgYHBwckJCTA3Ny8yHKLo01lfE114TK4jP9KXbgM7S3ja6pLSSuDiPDu3TuUK1eu2LKUCmpsbGygq6uLpKQkqf3Jycmws7OTe469vb3c4/X09GBtbV3sMfllqnJdADAwMICBgYHUPktLy6Kf4P9nbm6u1j+9tpXxNdWFy+Ay/it14TK0t4yvqS4lqQwLCwuFZSg1UFhfXx+enp44ceKE1P4TJ06gYcOGcs9p0KCBzPHHjx+Hl5cXSpUqVewx+WWqcl3GGGOMlSxKdz/5+/ujT58+8PLyQoMGDbB27VrEx8dj6NChAPK6fJ4/f46QkBAAwNChQ7F8+XL4+/tj8ODBuHTpEjZs2IBt27YJZY4cORJNmjTBvHnz0LFjR+zfvx8nT57E+fPnRV+XMcYYYyVcsSNuirBixQpycnIifX19qlOnDp05c0Z4rF+/ftS0aVOp48PDw8nDw4P09fXJ2dmZVq1aJVPmrl27qGrVqlSqVCmqVq0a7d69W6nrasKnT59o6tSp9OnTJy7jK6sLl8Fl/FfqwmVobxlfU124DPkkRIrmRzHGGGOMff147SfGGGOMaQUOahhjjDGmFTioYYwxxphW4KCGsc+IiPD06VN8/Pjx364KY4xpPQ5qGCvgxx9/FNYYCQkJkVo7TBVEhMqVK+PZs2dqlRMQEID0dNm1Uj5+/IiAgACVyszJyUFUVBRev36tVt2YrDdv3ih9TnZ2Nk6ePIk1a9bg3bt3AIAXL17g/fv3Gq5d0eLj4+X+z+fm5iI+Pl7t8tUJ7pV9TT9+/Cj1nnn69CkCAwNx/Phx0WVkZWXB1dUV9+7dU+ramvQ11KGg5s2by/1bpKWloXnz5l++QoWU+NlPf/75J1avXo3Y2FhcunQJTk5OCAwMhIuLCzp27Kjw/JycHAQHB+PUqVNITk5Gbm6u1ON///23wjKOHTsGU1NTfPfddwCAFStWYN26dXB3d8eKFStgZWX1ReqRLzc3F48fP5ZbTpMmTRSeX9RiZhKJBAYGBtDX1xdVjzdv3uDq1aty69G3b1+F53/48AFz584t8jV58uSJzDn6+vp4+vQpypYtC11dXSQmJqJMmTKi6luU6tWrY8OGDahfv77KZRRVl9TUVJQpUwY5OTkKyxg1ahRq1qyJgQMHIicnB02bNsXFixdhbGyMQ4cOwdvbW2EZ5cqVg7e3N7y9vdG0aVNUrVpVVP1v3bol6jgA+Oabb0QfC+QteCvv7+vo6Kjw3JcvX2Ls2LHC/0jh26GY13XevHlwdnZGjx49AADdu3fH7t27YW9vjyNHjqBWrVoKy3j69Cnatm0rBBWPHj2Cq6srRo0ahU+fPmH16tVyz1u6dKnCsvONGDFC4TE6Ojpwc3PDgQMHULFiRWH/y5cvUa5cOVGvx/Dhw7FixQqZ/R8+fED79u0RHh6usAxNvKatW7fGjz/+iKFDh+LNmzeoVq0aSpUqhZSUFCxevBi//PKLwjIAoHz58jh58iTc3NxEHV8Ude5nmqqDJj4ndHR0kJSUJHMvSk5ORvny5ZGVlfVF6lEUldZ+0harVq3ClClTMGrUKMyaNUt4w1paWiIwMFBUUDNy5EgEBwejffv2qFGjRrFrURVl3LhxmDdvHgDg9u3bGDNmDPz9/fH333/D398fmzZt+iL1AIDLly/D19cXT58+lbnBSyQSUTc1S0vLYq9foUIF9O/fH1OnToWOjvzGwoMHD6JXr1748OEDzMzMZNYJExPUDBo0CGfOnEGfPn1QtmxZUa9JtWrVMHHiRDRr1gxEhJ07dxaZtltMHQBg/vz5GDduHFatWoUaNWqIOqcwKmKds5s3b6J06dKiyggNDUXv3r0B5L2+sbGxePDgAUJCQjBp0iRcuHBBYRmLFi3CmTNnsHjxYgwdOhR2dnZo2rSpEOQUddOtXbs2JBJJkSvs5j8m9n8MAKKjo+Hn54eLFy9K7VemnP79+yM+Ph6TJ08W/T9S2Jo1a7BlyxYAeVnOT5w4gaNHj2Lnzp0YN26cqJaBkSNHwsvLCzdv3hSWjwGAzp07Y9CgQUWet2TJElF1lEgkooIaAHBzc0PdunWxc+dOtGjRQtgv9vvv8ePH8ccff2DmzJnCvg8fPqBt27aizgc085pGREQIr09oaCjs7OwQGRmJ3bt3Y8qUKaKDmt9++w3z5s3D+vXroaen2kemuvczTdQBUO9zouAXk3v37kktW5STk4Njx46hfPnyn70eCqmd6eY/zM3Njfbu3UtERKamphQTE0NERLdv3yZra2tRZVhbW9Phw4fVqoeJiQnFxsYSUd7K4l26dCEiohs3bpCdnd0XqwcRUa1atahbt2507949ev36Nb1580ZqE2Pz5s1UoUIF+uOPP+jAgQO0f/9++uOPP8jBwYHWrFlDM2fOJEtLS5nV2wuqXLkyjRw5kj58+KDyc7GwsKDz588rdc6FCxeoXr16ZGNjQzo6OmRhYUGWlpYym5WVlegyLS0tSV9fn3R0dMjQ0JCsrKykNkXnWllZkY6OjvBz/mZubk46Ojo0bNgwUfUwMDAQVrgdPHgwjRw5koiInjx5QmZmZqKfT76kpCTatm0b9erVi/T09EhHR6fIY+Pi4kRvYjVs2JCaNGlCR44cocjISIqKipLaxDA1NaXIyEjR15TH0NCQ4uPjiYhoxIgRNGTIECIievjwIVlaWooqw9ramh48eCDUKf9eFBsbS0ZGRmrVTxk6Ojr08uVLWrx4MRkYGFBQUBAR5f2ti/v7FvTkyRMqV64cLV68mIiI0tLSqEGDBtS4cWN6//69qDI08ZoaGRnR06dPiYioW7duNG3aNCIiio+PV+o17dSpE5mZmVHZsmWpdevW1LlzZ6lNDHXvZ5qoA5F6nxMSiYR0dHRIR0eHJBKJzGZsbEwbNmz47PVQpES31MTGxsLDw0Nmv4GBAT58+CCqDH19fVSqVEmteujr6wt9vydPnhSi9tKlSxfZlfM56gHkffsNDQ1Vq6zNmzdj0aJF6N69u7CvQ4cOqFmzJtasWYNTp07B0dERs2bNwu+//y63jOfPn2PEiBEwNjZWuR5WVlaiWzHyNWzYEJcvXwaQ18z66NEjtbufAgMD1TqXiODn54fp06dLLeimr68PZ2dnNGjQQFRZdnZ2uHfvHsqWLYtjx45h5cqVAID09HTo6uqKrtP79+9x/vx5nDlzBuHh4YiMjETNmjXRtGnTIs9xcnISfv7w4QNMTExEX68oUVFRuHHjBqpVq6ZyGQ4ODqJbIIpiZWWFhIQEODg44NixY0ILBRGJbnXKzc2Ve+yzZ89gZmamVH0yMzMRGxuLihUrKv2tPv+1GD16NKpVq4affvoJt27dwpQpU0SX4eLigrCwMHh7e0NHRwfbt2+HgYEBDh8+LPrvronXtFKlSti3bx86d+6MsLAwjB49GkBeN4kyCy9aWlqiS5cuoo+XR937mSbqAKj3OREbGwsigqurK65evQpbW1upcsuUKSP6PqKpzyu5Pkuo9B/h5uZG+/btIyLpb0dBQUFUp04dUWUsXLiQhg0bRrm5uSrX44cffqA2bdpQQEAAlSpVip49e0ZERGFhYVS5cuUvVg8iombNmtHRo0fVKsPIyIgePXoks//Ro0fCN6QnT54U+22pc+fOtGPHDrXq8eeff1LXrl1V/nYUFxen9uupKeHh4ZSZmalWGVOnTiULCwuqVq0aOTo6CinJN2zYQPXr1xdVRt26dcnQ0JC8vLxo7NixdODAAXr9+rVS9TAxMaEBAwbQuXPnlH0KUry8vNQuIywsjFq3bi20lKpi+PDh5OTkRC1btiRra2t69+4dERFt376dPDw8RJXRvXt3Gjx4MBHl3YuePHlC7969o+bNm1P//v1FlfHhwwfy8/MjXV1d0tXVFe5nv/32G82ZM0dUGRKJhF6+fCn8fvfuXapYsSLVrFlTdEtNvkuXLpGJiQk1b96c0tPTlTpXE6/prl27qFSpUqSjo0MtW7YU9s+ePZvatm2rVH3UpYn7mSZo6nPia65HiW6pGTduHIYPH45Pnz6BiHD16lVs27YNc+bMwfr160WVcf78eZw+fRpHjx5F9erVhZXH8+3Zs0dhGcuXL8ewYcMQGhqKVatWCf2SR48eFd0PrYl6AHl9t2PGjEFSUhJq1qwpU46YQZwVKlTAhg0bMHfuXKn9GzZsgIODA4C8Aa7FDYBu3749xo0bh3v37smtR4cOHRTWY9GiRYiJiYGdnR2cnZ1lyoiIiCj2fCcnJ5w7dw5r1qxBTEwMQkNDUb58efz5559wcXERBnaLERMTg02bNiEmJgZBQUEoU6YMjh07BgcHB1SvXl3uOWlpacI3Sg8PD3z8+LHI2SNivnlOmzYNNWvWRHx8PLp16wYDAwMAeYOQJ0yYIOp5REdHw9jYGK6urnB1dUWlSpVgaWkp6tx827ZtQ3BwMFq0aAEnJyf4+fmhb9++KFeunFLlzJs3D+PHj8fs2bPl/o+IeU169OiB9PR0VKxYEcbGxjJl/PPPPwrLWLJkCZydnZGQkID58+fD1NQUAJCYmIhhw4aJei6LFy9G8+bN4e7ujk+fPsHX1xfR0dGwsbGRWvy3OBMnTsTNmzcRHh4udd9o2bIlpk6dKupv3LRpU6mB/O7u7rh69So6d+5cbIuWh4eH3HERBgYGePHiBRo1aiTsU/S+AzTzmnbt2hXfffcdEhMTpQYWt2jRAp07dxZVRr7s7GyEh4cjJiYGvr6+MDMzw4sXL2Bubi7UrTiauJ9pgqY+J2JiYhAYGIj79+9DIpHAzc0NI0eOlBpc/iXqIU+Jn/20bt06zJw5EwkJCQDyRplPmzYNAwcOFHX+gAEDin1czCBfTdBUPeQN3FV2EOeBAwfQrVs3VKtWDd9++y0kEgmuXbuGBw8eIDQ0FN9//z1WrVqF6OhoLF68WHQ9CtZHTD2mT59e7ONTp04t9vHdu3ejT58+6NWrF/7880/cu3cPrq6uWLlyJQ4dOoQjR44orAMAnDlzBj4+PmjUqBHOnj2L+/fvw9XVFfPnz8fVq1cRGhoq97yCM550dHTkfmiI/btkZWWhdevWWLNmDapUqSKq3kW5desWwsPDcebMGZw7dw46Ojpo2rQpmjVrhqFDh4ouJzU1FSEhIQgODsa9e/fQpk0b+Pn5oUOHDqK6TfL/Rwq/Lsr8r27evLnYx/v166ewDE35+PEjtm/fjhs3biA3Nxd16tRBr169YGRkJOp8Jycn7NixA/Xr14eZmRlu3rwJV1dXPH78GHXq1BHdla0KRe+1ghS97zTt8ePHiImJQZMmTWBkZFTkoPuiqDozrSBN3M9CQ0Oxc+dOxMfHIzMzU+oxMYEioJnPibCwMHTo0AG1a9dGo0aNQES4ePEibt68iYMHD6JVq1ZfpB5FKfFBTb6UlBTk5uaqPX5CVepMo87OzsZff/2FNm3awN7eXq16PH36tNjHC46NUFTO6tWr8fDhQxARqlWrhp9//hnOzs5q1e9L8vDwwOjRo9G3b1+pD4moqCi0bdtWavR/cRo0aIBu3brB399fqpxr166hU6dOeP78udzzzpw5g0aNGkFPTw9nzpwp9hrFjWfJZ2tri4sXL6Jy5cqi6i3GjRs3sHz5cmzZsqXIcSFiLFu2DOPGjUNmZiZsbGwwdOhQTJgwodgxCJp4TTTl0aNHCA8Pl/v+VTQeJSsrC1WrVsWhQ4fg7u6uch2MjY1x584duLq6Sv2f3bx5E02aNMHbt28VlqGJ1AGaEBISUuzjYmYepqamonv37jh9+jQkEgmio6Ph6uqKgQMHwtLSEosWLRJVl06dOsHMzAwbNmyAtbW18LqeOXMGgwYNQnR0tKhy1LF06VJMmjQJ/fr1w7p16zBgwADExMTg2rVrGD58OGbNmvXZ65DPw8MDbdq0kWmJnzBhAo4fPy46wPpsNN6h9R+Snp4uNd4iLi6OlixZQmFhYUqVk5WVRSdOnKDVq1dTWloaERE9f/5c6AdW5NKlS+Ti4iJ3VLnYfmwjIyOlZo58LpmZmeTt7U0PHz7UWJkfP35U6/yMjAxKSEigp0+fSm2KGBkZCWMtCo65iomJIQMDA9HXNzExoSdPnsiUExsbq1Q56vL396f//e9/apURERFBixcvpg4dOpCVlRXp6emRp6cnjRkzhg4dOqRUWYmJiTRv3jyqVq0aGRsbU69evejvv/+mLVu2UI0aNahVq1Zq1VWsx48f06RJk6hnz57CeJKjR4/SnTt3RJ2/du1a0tXVJTs7O6pVqxbVrl1b2MSO/yhXrhzdu3dP5edARNSkSRNaunQpEf3fuByivPEpbdq0EVVG4TE1+Z4/f06GhoZK1UfV9x0Rycw2NDExIYlEQgYGBqJnHvbp04fatGlDCQkJUu+7sLAwcnd3F/08voaZaVWrVqWtW7fK1GHy5Mk0fPhwpcpS9/PKwMBA7pjJhw8fftH7WVFK9Jiajh07SiVnqlu3LvT19ZVKzlS4abJVq1YwMzPD/PnzRTdNDh06FF5eXjh8+LDKuTLq1auHyMhI0S0pxVEnIWGpUqVw584dtfMO5OTkYPbs2Vi9ejVevnwpNPlOnjwZzs7OoroHHz16hIEDB6qcx6Rs2bJ4/PixTOvS+fPn4erqKvq5WFpaIjExES4uLlL7IyMjRed1AIBPnz7h1q1bclsDxPTJZ2ZmYv369Thx4gS8vLxkZqIU1RVY0LfffgsPDw80bdoUgwcPRpMmTZSaSQLk9Zdv2rQJYWFhcHd3x/Dhw9G7d2+psTm1a9eWOzOxsPwxT0+ePMGuXbuUHvNUuGtw1qxZKFOmDG7duoX169cX2TVY0MyZMzFr1iz873//U3hsUTSRh2TOnDlo27Yt7t27h+zsbAQFBeHu3bu4dOmSwlat/CR+EokE69evlxonkpOTg7Nnz4qeZabu+w6A3CzX0dHR+OWXXzBu3DhR9Th+/DjCwsJQoUIFqf2VK1dW2CJdkKoz05YuXYohQ4bA0NBQYZJEU1NTVK9eHfXq1ZP7eHx8PBo2bAgAMDIyEjJO9+nTB/Xr18fy5ctFPRdNfF7Z2toiKipKpsU3Kiqq2J6OOnXq4NSpU7CysipyDFa+/Nfj999/F8ZhilWig5rCyZns7e2VTs6katKsgjQxjXrYsGEYM2YMnj17Bk9PT5kPLLFZWjWRkLBv375yBworY9asWdi8eTPmz5+PwYMHC/tr1qyJJUuWiApqBgwYAD09PRw6dEilYPHnn3/GyJEjsXHjRkgkErx48QKXLl3C2LFjlZri6uvri//973/YtWsXJBIJcnNzceHCBYwdO1Z0Ar9jx46hb9++SElJkXlM7AfFnTt3UKdOHQB5HzyFyxDjn3/+UTqIKWzAgAHo2bMnLly4gG+//VbuMa6urpg0aVKx5RQc8xQRESGk93/37h1mz54taszThAkTMHPmTKFrMF+zZs0QFBQk6vm8fv0a3bp1E3VsUa5cuYJTp07h+PHjqFmzpsz7V8zAyYYNG+LChQtYuHAhKlasiOPHj6NOnTq4dOkSatasWey5+fdBIsLq1aulpubmpw4Q84EHqP++K0rlypUxd+5c9O7dGw8ePFB4/IcPH+R2X6akpAiD5MVo1aoVAgMDsXbtWgB575X3799j6tSpaNeuXZHnLVmyBL169YKhoaHCJIkZGRlITk7G6NGjsWDBApnH7e3tkZqaCicnJzg5OeHy5cuoVauWMM1aLE18Xg0ePBhDhgzBkydP0LBhQ0gkEpw/fx7z5s3DmDFjijyvY8eOwuveqVOnYq+RkZGBU6dOoXfv3goDchn/bkPRv0sTyZk00TSpiWnU8pIh5XdnKTMVUxMJCX/99VcyNzenOnXq0JAhQ2j06NFSmxgVK1akkydPytTj/v37opNvGRsb0/3790UdW5Tff/+djIyMhNfU0NCQ/vjjD6XKyMzMJF9fX+HvkT/NtHfv3pSdnS2qjIoVK9KwYcMoKSlJlafxVVEnoWJBtWvXps2bNxOR9P9IZGSk6KSVmuga9PPzo1WrVilbfSn9+/cvdvtSvL296Z9//lGrDE2874oSEREhOlFku3bthPdqfndcTk4OdevWTUhwKsbz58+pSpUq5ObmRnp6elS/fn2ytramqlWryu2qU9Xx48fJxsZG7mMDBw4UPp9WrVpFRkZG1LJlS7K0tCQ/Pz/R19DE51Vubi4tXryYypcvL9wXy5cvT4GBgRqdov348WOVurNKdEuNJpIzqdo0WTDltCamUcfGxoqqr5hy1E1IqIkWgefPn8ttucrNzRW1tgiQNx1VXsuGMmbNmoVJkybh3r17yM3Nhbu7u6gpnAWVKlUKf/31FwICAhAZGYnc3Fx4eHgoNWA3OTkZ/v7+sLOzU/YpyFBnNkhRs7DyiWkxUvQ3EbNmEwA8fPhQ7iB6c3Nz0YsfaqJrsFKlSpg8eTIuX74s9/0rZnkCTcySbNasGXr37o2uXbtKJWlUxunTp9WuhybedwcOHJD6nYiQmJiI5cuXS00PL86CBQvg7e2N69evIzMzE+PHj8fdu3fxzz//iFoSJF+5cuUQFRWFbdu2ISIiArm5uRg4cKBSM9PE+O677/DHH3/IfWzt2rVCl/PQoUNRunRpnD9/Hj/88INSMw41keRRIpFg9OjRGD16tNANpmyCSDEqVqyIly9fKn1eiZ79FBoaCl9fX+Tk5KBFixbCeiJz5szB2bNncfToUYVl9OjRAxYWFli7di3MzMxw69Yt2NraomPHjnB0dCzyZpX/4VDUy6/KWjia4O7ujjlz5qBjx45SMyiWLl2KzZs348aNG1+kHl5eXhg1ahR69+4tVY/p06fj5MmTOHfunMIy/v77b/zxxx9q5TH5Wvj5+aFRo0aiUw3Io4nZIPv375f6PSsrC5GRkdi8eTOmT58uqn6aCIyAvJvemjVr0LJlS6n/kZCQEMydO1fUqsbjx4/HpUuXsGvXLlSpUgURERF4+fIl+vbti759+4qaflw4ICpIIpHIXThVHnVzoYwYMQK7du3Cmzdv0K5dO/Tp0wft2rVTuICsv78/ZsyYARMTE/j7+xd7rJhxV5p43xWeAi2RSGBra4vmzZtj0aJFKFu2rMIyACApKQkrV64UgpE6depg+PDhos9XhzJjasSuy6UuVT+v5Hn16hUePnwIiUSCqlWrwsbGptjjlRlTo84MqhId1AB5//T5yZny30hXr16Fubm5qIFxL168QLNmzaCrq4vo6Gh4eXkJSbPOnj1b5MApZQaqiR38q25CJCDvG+PkyZOxaNEiDBw4EOvXr0dMTIyQkLBnz56iy1KnReDgwYPo06cPJk6ciICAAEyfPh0PHz5ESEgIDh06JCoXgip5TH788UdR9QOKH+eg6MOhIDEfFOnp6ejWrRtsbW1Vbg3o27cvkpOTsX79eri5uQlBwPHjxzF69GjcvXtXdJ0L27p1K3bs2CET9Mhz8+ZNqd/zA6PFixdj1qxZov8G8+fPx+bNm7Fx40a0atUKR44cwdOnTzF69GhMmTIFv/76q8IysrKy0L9/f2zfvh1EBD09PeTk5MDX1xfBwcFKLR+hDk3kQgHyvomfPHkSW7duxd69e6Grq4uuXbuiV69eRU5xb9asGfbu3QtLS0s0a9asyLIlEonoVZzzjy/o3/iSpil//vmnMCA9f/LEkiVL4OrqWuQ4QxcXF1y/fh3W1tYaC3zT09Pl5qkRO2ZS1c+rgj58+IDffvsNISEhQuuRrq4u+vbti2XLlhWZhmH69OkYN24cjI2N1c4hVpwSH9RogrpJszRBEwmR8qmbkFBT+SHCwsIwe/Zsqdd1ypQpaN26tajzVcljoigpVEHFfasp7sOhILEfFOvXr8fQoUNhZGQEa2trmVV+xdwU7e3tERYWhlq1akm1bMTGxqJmzZp4//69qDrLExMTg2+++UZ0F6U8hw8fxoIFCxAeHi76nEmTJmHJkiX49OkTgLxu0rFjx2LGjBkKzyUixMfHw9bWFklJScK3eWW7BjXhc+RC+fTpEw4ePIhZs2bh9u3bXyyY+JryB6k7Ow6Qnjwxc+ZM3L17F66urggODsbmzZs10mWnyKtXrzBgwIAiew+U+duq+3n1888/4+TJk1JdgefPn8eIESPQqlUrrFq1SnRdPocSF9Ro6pu4Js2ZMwd2dnbw8/OT2r9x40a8evVK1FTRz5EQSdWEhJ+zRaCksre3x4gRIzBhwoRis5MWx8zMDBEREahcubJMEsC2bdsiNTVVpXI/fvyIiRMn4ujRo3j48KFKZQB5swBr166tdGCUnp6u0pin3NxcGBoa4u7du2oHMc+ePcOBAwfkfosW0xJnY2ODCxcuoGrVqlJ/m7i4OLi7uwsL3oqVlJSE7du3Y8uWLYiIiMC3336LK1euKDzvxIkT+O67777oF7J8mu4G01RGcHd3d8yePVsIPPP/Nnfu3IG3t7fa44fE6NWrF+Li4hAYGCi0rL18+RIzZ87EokWL0L59+89eh3w2NjYIDQ2Ft7e31P7Tp0+je/fuePXqlahy3rx5g9DQUMTExGDcuHEoXbo0IiIiYGdnp1Sqi8JK3EDhggPoiAh79+6FhYUFvLy8AORlSH3z5o3o4Gfz5s2wsbER/qnGjx+PtWvXwt3dHdu2bRPVdbRmzRps3bpVZn/16tXRs2dPUUHN/fv3sXPnTpn9fn5+Sq0S/fHjRxARjI2NYWNjg6dPnyIwMBDu7u6iW0g0lR9CU9Rtsv0aZGZmokePHioHNEBeZuqQkBChFSN/evmCBQtEtyxZWVlJtRIREd69ewdjY2Ns2bJFVBmF0/XnDwKdNm2aUsGFn58fgoKCYGZmJrx/gf9rHt+4cWOx5+vo6KBy5cpITU1VK6g5deoUOnToABcXFzx8+BA1atRAXFwciEgYMK+IJgZwpqWlYffu3di6dSvCw8Ph6uoKX19fbN++XXS6iC5duiAjIwOenp5o2rQpvL290ahRI6UHxwPKv+8iIyOFSQCRkZFKX6+wmTNnYvXq1ejbty+2b98u7G/YsCECAgJEl6PO5Amx11GUIuLvv//G/v378e2330JHRwdOTk5o1aoVzM3NMWfOHNFBjSY+r9LT0+VOWChTpozo4PvWrVto2bIlLCwsEBcXh8GDB6N06dLYu3cvnj59qjCjdLHUmXL1Xzd+/HgaNGiQ1LTa7OxsGjJkCI0dO1ZUGVWqVKFTp04REdHFixfJyMiI1qxZQz/88AN17txZVBkGBgbCtNKClMlcW6FCBdq5c6fM/h07dpCDg4OoMoiIWrVqJUxPff36NZUpU4YqVKhAhoaGtHLlSlFlmJqaChknC04bvHr1KpUuXVpUGZaWlmRlZSWzlS5dmsqVK0dNmjShjRs3FltGcnIytW/fnnR0dORuiiQlJVHv3r2pbNmypKurq9T5nTt3Fr2JMWrUKJo1a5aoY4ty9+5dsrW1pbZt25K+vj517dqV3NzcyM7Ojh4/fiyqjODgYKktJCSEjh49qtQ04Pw0AwU3iURCjo6OdOHCBdHl6OjoyJ1S++rVK9LV1RVVxqFDh+i7776j27dvi75uYd9++y1NnjyZiP7v//3du3fUoUMH0e8ZTazSbWhoSGXLlqWRI0fS1atXVXou2dnZdPHiRZozZw61adOGzMzMqFSpUlSvXj3R2ajVfd9piqYygru5udG+fftkygkKCqI6deoUe27+dGcPDw+pTNPKZp02MzMTnouTkxOdP3+eiIiePHmiVFZjTXxeNW/enLp16yaV6T09PZ26detGLVq0EFVGixYtaNy4cUQk/ZpeuHCBnJycRD8feUpcS01BGzduxPnz56UGA+rq6sLf3x8NGzaUmwSpsISEBOFb0L59+9C1a1cMGTIEjRo1kmmeK4qDgwMuXLggM5jswoULolcuVjUhUmGaSEioiRaBKVOmYNasWfDx8UHdunVBRLh27RqOHTuG4cOHIzY2Fr/88guys7OlkvMVNGrUKLx+/RqXL1+W22SrSP/+/REfH4/JkycrnURM1Sm1RcnJycH8+fMRFhaGb775RmagsJjmeHd3d9y6dQurVq2Crq4uPnz4gB9//FGp2SCaWOCx8BgEHR0d2NraolKlSqKy6aalpYGIhFYiQ0ND4bGcnBwcOXJEdJdp7969kZ6ejlq1akFfX1+m20XMKt33798XVtLW09PDx48fYWpqioCAAHTs2FHUe2bJkiVo1qyZWqt079+/Hy1btlSrNU9XVxcNGjRAgwYNMGHCBNy5cwcLFy7EX3/9hWvXrolKqKnu+w6QboUrSGwrHKC5jODjxo3D8OHD8enTJxARrl69im3btgmTJ4rTtm1bnD59Gl5eXvDz80P79u1VGnxetWpVPHz4EM7OzqhduzbWrFkjJERUZiaXJj6vAgMD4ePjgwoVKqBWrVqQSCSIioqCoaEhwsLCRJVx7do1rFmzRmZ/+fLlRa+pVyS1QqL/OEtLSyHRXEF79+4VneDN1taWIiIiiEg6Gdjjx4/JxMREVBlz584la2tr2rhxI8XFxVFcXBxt2LCBrK2tafbs2aLK0FRCJE0kJNREi8CPP/4oN6HZ6tWr6ccffyQioqVLl1KNGjWKLMPe3p6uXLlCRHnfdPLXo9q/fz81atRIYR1MTU0pMjJSVH0/N29v7yK3Zs2afdG6nD17lnr16kUNGjSgZ8+eERFRSEgInTt3TtT5+UkV5Vm2bJnC8+W19BTcdHV1aebMmaLqUrjlqfAmhp2dHd29e5eIiNzd3Wn//v1ERBQVFSX6HkCU9213w4YNNHz4cPrll19o3bp1lJ6eLvp8Tbh37x6tWrWKevToQfb29mRra0udO3emoKAgioqKElWGuu87Is20ws2bN4/c3d3p8uXLZGZmRufOnaMtW7aQra2tqP+zgtauXUuOjo7CvbVChQq0fv16Uee+ePGCZs+eTVWqVCF7e3saP368kABPrC1bttCmTZuIKC8Boa2tLeno6JChoSFt375ddDma+LwiyvtfXbt2Lfn7+9Po0aOV/l8tU6aMUI/C63JVqFBBdDnylOigZvTo0WRlZUULFiygc+fO0blz52jBggVkbW0tOvOtr68v1alThwYOHEjGxsaUkpJCRHlv4OrVq4sqIzc3l8aPH0+GhobCjdnY2JimT5+u0vNKS0sTFipTVs2aNSkoKIji4+PJ3NycLl68SERE169fF52llShvscIpU6ZQ+/btycfHhyZNmkQvXrwQfb6JiQlFR0fL7I+OjhbefI8fPyZjY+Miy1C3ydbNzU1446lL3UXkNOXjx4905coVOnjwIO3fv19qEyM0NJSMjIxo0KBBZGBgINyMVqxYQT4+PqLKsLCwkNs9smTJElHZYsPDw+n06dMkkUhoz549FB4eLmwXL16k58+fi6qHpnTs2JHWrl1LRETjxo2jSpUq0cyZM6lOnTqim+M1wdnZmVxcXIrcxJBIJFSmTBmaPXu26AU9C1Pnfff27Vt68+YNSSQSevz4Mb19+1bY/vnnH9q8eTOVLVtWdF00kRG8oFevXqmVRfjMmTPUv39/MjMzo4YNG6octH748IFu3LhBr169Uuo8dT+vMjMzycXFRQjiVTV48GDq1KkTZWZmCt2tT58+JQ8PDxo5cqRaZZfooCYnJ4fmzZtH5cqVE/7py5UrR/PmzROdvv7169c0fPhw6tChg9RSB1OmTBH9bTHfu3fv6OrVq3T79m369OmTUufmS05OpnPnztH58+eV/ocnItq1a5eQxr/gKsmzZ8+mtm3bqlQnVTg4ONDixYtl9i9evFgYI3Tz5s1iAy0vLy86duwYEeV98PTp04eePXtG48ePJ1dXV4V1CAsLo9atWws3aFXFxcUJK1Hr6uoKgcDIkSPp559/VrnMu3fvUk5Ojuhzjh49Sra2tkUuqSGGJpYm2LhxI9nY2EjdGBcsWEDm5uZ09uxZ0c8nLi5OqecvT+EVpFVZUTomJoZu3rxJRHkfNr/88gvVrFmTOnfuTHFxcUrV5+7du3T06FGVAs7AwECpbcGCBeTr60ulS5emOXPmiCpj5MiR5OHhQfr6+lS3bl0aP348HTlyRKngW533naZa4bKzsyk8PJxSU1Ppw4cPdO3aNbpy5coX/xJRWHp6Om3evJnq1q1LRkZG9Pbt2y96fU18XmliRfm3b99So0aNyNLSknR1dcnBwYFKlSpFTZo0offv36tVdokOagrK/zagrKdPn8q9sebm5oq+KWrC+/fvacCAAaSrqyt8UOnp6ZGfn5/Sa+0kJiZSRESE1PO6cuWKSuu5vH//njZs2EDLly+Xu1x9UdauXUu6urr0ww8/0IwZM2jmzJnUoUMH0tPTE5p9Fy5cSN27dy+yDHWbbC0tLUlfX590dHTI1NRUZtCyWB07dqTevXtTRkaGVCAQHh5OlSpVKvbc4OBgWrJkidS+wYMHCzd5Nzc3io+PF1UPTawfpanBlwsWLKDy5ctTbGwszZ07l8zNzZUaJJzv9evXFBYWRn/++Sdt3rxZahND0YfolxITE0PffPON1JptBeumjuXLlyu9ftTr16/pwIEDNGbMGPLy8iIDAwOqV6+eqHPVed9pshWuqAkYykpJSaFhw4aRm5sbWVtbq3QfuHjxIg0aNIjMzc3Jy8uLVqxYQa9fvxZdh+zsbFq/fj399NNP1KJFC2rWrJnU9iXNmTOH+vXrR1lZWWqXderUKVqwYAHNmzePTpw4oYHalfCBwgWpmjLfxcUFiYmJMgMT//nnH7i4uIhKitSsWbNiB6GKSc7m7++PM2fO4ODBgzIJkcaMGaNUQiR7e3vY29sDyBuU+ffff6Nq1aoKMyzHx8ejT58+iIiIQP369bFhwwa0atVKSBxmZGSEo0ePyl2vp7DBgwfD3d0dy5cvx549e0BEqFatGs6cOYOGDRsCgMIB0L169RJ+9vDwQFxcHB48eABHR0eFKb0BKDUVvjjnz5/HhQsXZNLVOzk54fnz58Weu3r1agwZMkT4/dixY9i0aRNCQkLg5uaGX3/9FdOnT1c4YBHQzPpRmhp8OXbsWKSmpsLLyws5OTk4fvw46tWrp1RdDh48iF69euHDhw8wMzOTSUgoZgX0wlOHC2c3Vtb79++FLKv5xNxbRo4cCRcXF5w8eRKurq64evUqUlNTMWbMGCxcuFDpehTk4+ODiRMnKpUCPzc3F9nZ2cjMzERGRgaysrIQFxcn6lx13nf5ifliY2Ph6Oio1grfNWvWxJMnT4rN5itG7969ERMTg4EDB8LOzk6pOs2fPx+bNm1CamoqevXqhfPnzytcMV2ekSNHIjg4GO3bt0eNGjVUfl2OHTsGU1NTIfHgihUrsG7dOri7u2PFihWwsrJSWIYmVpTP17x5czRv3ly5J6FAiUu+V9DLly8xduxYnDp1CsnJyTLrMIkJSHR0dJCUlCQT1Dx9+hTu7u6iEonlL6SZLysrC1FRUbhz5w769euHoKAghWVoKiFS9+7d0aRJE/z666/4+PEjatWqJeTc2L59O7p06VLsuQkJCRg+fDh27dqFR48eoWLFitiwYQN0dHQwbNgwpKamigrS/m1ZWVkYMmQIJk+erNSHtTz5i8+5u7tLJe86f/48unTpUuyibdbW1ggPDxduhL/88guSk5Oxe/duAEB4eDgGDBggakFTTawfperSBEWtfbNw4UI0adIEdevWFfaJXQenSpUqaNeuHWbPnl1kanZVKZPdODY2Fr/++ivCw8OFzMaAcssC2NjY4O+//8Y333wDCwsLXL16FVWrVsXff/+NMWPGqJW3Zf78+Vi5cqWooGTkyJEIDw/H3bt3Ubp0aTRp0gTe3t7w9vZGjRo1VK6DsjTx4Xv8+HH873//w4wZM+Dp6Snz4Sv2i6yZmRnOnz+PWrVqKf08dHR04OjoiO+//77YNbgUzV60sbFBSEgI2rVrp3QdCqpZsybmzZuHdu3a4fbt2/j222/h7++Pv//+G25ubqICX0VZ14sqQ9H6VwWpsxZWiQ5qfHx8EB8fj19//VXulN2i1vQA/m9tn6CgIAwePFjqppqTk4MrV65AV1dXqdVgC5s2bRrev38v6puasbExbty4ATc3N6n9d+/eRd26dUVnaS2YSn/r1q2YOnUqbt68ic2bN2Pt2rXF3lzt7e1x4MAB1K1bF//884+QJbVBgwYA8tb8adGihegMnLm5uXj8+DGSk5Nlvv2Kae0hIoSGhuL06dNyy1D0jcLS0hIRERFqBzXqLCJnbGyM+/fvC0mxatWqBT8/P4wcORJAXutY1apV8fHjR4X10MT6UYBqSxOI/baszDo4JiYmuH37ttp/H3mUyW6c33I4cuRIud/kxSwLYGVlhRs3bsDV1RUVK1bE+vXr0axZM8TExKBmzZqikpoVXiSQiJCUlIRXr15h5cqVUi1+RenatataQUx0dDRu3bqFOnXqwMXFBYcPH8a8efPw8eNHdOrUCb///ruoVobCH75eXl4YM2aMUh++Bae2F35dlFmD6ttvv8WyZctQv359UccX5O3trfD5ilkqpVy5cggPD0eVKlWUrkNBpqamuHPnDpydnTFt2jTcuXMHoaGhiIiIQLt27dSfTl2Mz3EPkKdEdz+dP38e586dQ+3atZU+N//DnYhw+/ZtqShcX18ftWrVwtixY9WqX+/evVG3bl1RQU2DBg0wdepUhISECHk7Pn78iOnTpwtBhRhv375F6dKlAeR9W+rSpQuMjY3Rvn17jBs3rthzX716JXz4li5dGsbGxlJdHfb29nj9+rWoely+fBm+vr54+vSpTAua2BvSyJEjsXbtWjRr1kzpZmMA6Ny5M/bt26fU4pTyqJODxMnJCTdu3ICTkxNSUlJw9+5dqTVrkpKSROfE2bp1K8LCwmBkZITw8HCZ7hqxQc2sWbMwadIkpZYmENOSpKw2bdrg+vXragU1mshufOvWLdy4cQNVq1ZVuR41atTArVu34Orqinr16mH+/PnQ19fH2rVrRT+/Tp06Sf2en//H29tb1OK8QF7G2cItGmLt3bsX3bt3F1ZhX7t2LYYMGYJmzZrB3Nwc06ZNg56enqgM6bGxsXB3dweQt9zBDz/8gNmzZwsfvmJoak2mlStXYsKECZgyZQpq1Kih1KrjyqxjVpwxY8YgKCgIy5cvV6tLTl9fXwiQT548KXTRli5dWua9oGmf4x4gT4kOahwcHGQ+MMXKf8MMGDAAQUFBKo/JKc6lS5ekEovJo6uri8TERI0kRALyXpNLly6hdOnSOHbsmJBe/PXr1wrrkv8NKJ86b76hQ4fCy8sLhw8fVjrxXb4tW7Zgz549KjfZVqpUCTNmzMDFixflNl8rCgKysrJQqlQplCtXDlFRUVKLyA0cOBC9evVCTExMsYni+vbti+HDh+Pu3bv4+++/Ua1aNXh6egqPX7x4UfQ36j/++AMBAQFqrR+V78WLF0hNTVV6BfasrCxUrVoVhw4dEj60VJUfaN+7d09uy1OHDh0UlmFpaSl3NWkHBwep1PrF+fbbb5GQkKBWUPPHH38IrUIzZ87E999/j8aNG8Pa2ho7duwQVYY6Kxvns7OzQ/fu3eHn5yd6wcd8s2bNwvjx4zFz5kwEBwdj6NChmDt3LkaNGgUAWLt2LZYsWSIqqNHEh6+mFs60tLTE27dvZcZ+KNvio6zCS/X8/fffOHr0KKpXry7zvy52HMt3330Hf39/NGrUCFevXhX+tx49eiSzrE1hMTExmDVrlpD40NHRUWoRXF1dXZw/f17h+0CT9wB5SnT30/Hjx7Fo0SIhO+O/pfA/b/63xevXr2Py5MnF3qwKjun5+PEjtmzZggcPHoCI4O7urvRq4StXrsTIkSNhamoKJycnREREQEdHB8uWLcOePXuK/fajo6ODIUOGCF1xK1asQO/evYWWhPT0dKxbt07UTcDExAQ3b94UvWaNPC4uLjh69Kjob6nyzi+KmCbSrl27YteuXUV+2N+5cwctWrQodkxNbm4upk6dikOHDsHe3h6LFy+W6mLs1q0b2rZtK2qcTOnSpXHt2jVUrFhR4bFF0cQK7OXLl8fJkydlukqVVVxgJvbDpvCK0spmNwbybvZDhw5F79695X6TV3WNsX/++UdmrS0xddm0aRNiYmIQFBSEMmXK4NixY3BwcED16tUVnn/w4EEEBwfj0KFDcHJygp+fH/r27Ssqs7mZmRmioqJQsWJF5ObmQl9fH1FRUULQrczinD/88AOysrLQqFEjzJgxA7GxsShfvjyOHz+OX3/9FY8ePVJYRnR0NPbv34+4uDhIJBK4urqiY8eOSrfs1a1bF3p6emp1L6pC0diVgsQOAo+Pj8ewYcOQkJCAESNGCPeN0aNHIycnp9hxL6NGjYKxsTFmz54NIO/vPWXKFOFL2Y4dO+Do6IjVq1crrIem7gHylOigxsrKCunp6cjOzoaxsbHMzaioFOk//vgjgoODYW5urnDhSzERdOF/3vwba/PmzRUuIlnUQGV1XL9+HQkJCWjVqpXQrXD48GFYWloKM6vkEdN/DIhrFm7evDnGjx+Ptm3biq94IZs3b8axY8ewcePGf2XVYQcHB/j4+GDt2rUyj927dw/NmjVDkyZNsGvXri9Sn9GjR8PW1ha///67ymVoYgX2uXPn4sGDB1i/fr3owOFrlt9VWnAgrkQi+ezf5As7c+YMfHx80KhRI5w9exb379+Hq6sr5s+fj6tXryI0NFR0WampqQgJCUFwcDDu3buHNm3awM/PDx06dCjyb1b4XlRwUDyQNzGjXLlyol6P+Ph4DB8+HPHx8Up/+ALAnDlzMGXKFOTm5qJMmTIgIrx69Qq6urqYPXu2UkMDjI2NERkZqVZLnDaoUaMGli1bJix1U/jve+bMGQwaNEiY7Vqcz3kP+O/fUdSg6pRdCwsL4cNbnTV+8qcbKjPVUp6wsDCF9RDTFJ/Py8tLatVjAKJWgdVU/zEA/PbbbxgzZgySkpLkdi2I+fbbrVs3bNu2DWXKlIGzs7NMGREREaLqkpmZidjYWFSsWFGpN+Dx48fRpEkTlC5dWmrNnPv376N58+Zo1KiR6C4OTdDE+lGaWIFdk1NC5UlNTcWff/4pdHsUR9FqwGKmhfv5+cHDwwPbtm1TauyWoi9EBYl5TSZMmICZM2fC399fas2kZs2aiZpBWZC1tTVGjx6N0aNHY9myZRg3bhyOHDkCGxsbDB06FBMmTJCZcSaRSGS6n1XpNs7Ozsbp06exdu1amXWN8telK87p06fxxx9/YPLkyRg5cqQwU+qff/5BYGAgJkyYgLp164qabADk3Q/V7V78mqjamvf06VOp1utBgwZJfe44Ozvj2bNnourwOe8BJbqlRl1EhPj4eNja2qo0pTR/PEz+N5sePXpg6dKlSuURETM2Qplvi35+fsU+LmYhOU2Q97yU/fab303StWtXuR82isYgpKen47fffsPmzZsB5PU7u7q6YsSIEShXrhwmTJigsA7Xrl1DixYtMHnyZIwbNw4PHjxAs2bNULduXezZs0elxe1UVdxiomJmYAB5384iIiJQuXJlqW9q165dQ9u2bZGamqqwDFWnhBaHiHD8+HFs2LAB+/fvh7m5uag0BoWnBmdlZSE9PR36+vowNjYWtaClql2lmu5eMDU1xe3bt+Hi4iL1t4mLi0O1atWkppsrkpSUhJCQEGzatAnx8fHo3LkzBg4ciBcvXmDu3LkoW7Ysjh8/LnWOjo6O1Be+N2/ewNzcXHgvExHS0tJEvXcLz/pTRo8ePWBpaSl3wUQAGDJkCN69eyd6odBdu3Zh2rRpGDdunEpfsLKzszFr1iz4+fnBwcFB3JOA7Gy24oj9gqZOa56FhQVOnDghlX6hoKtXr6Jly5aixjx9jntAvhLdUlPQx48fkZWVJbVP0eBfIkLlypVx9+5d0TMlCp9f0JEjRzBnzhyly9Fk91Ph2UlZWVm4c+cO3rx5o/EkScXRxEj5w4cPIywsTOkBj/kmTpyImzdvIjw8XKobrGXLlpg6daqooObbb7/Fvn378P333+PDhw9Yt24dvLy8EBoa+kUDGkAzs0E0sQK7ui2TBcXFxWHjxo0IDg7G8+fP0atXLxw+fFh0XeTNxouOjsYvv/yicLZfvubNm6sU1GjydQDyBrQmJibKjAWLjIxE+fLlRZWxZ88ebNq0CWFhYXB3d8fw4cPRu3dvWFpaCsfUrl0bHh4eMudq8vnUq1cPkZGRKgU1V69exZ9//lnk43369BHVApevR48eAKS/8CnzBUtPTw8LFixQeoX7wrPZNEGd1rzq1avj5MmTRQY1YWFhoictaPp/X4pG8hL/R71//56GDx8upPFWJUW6u7s7Xbp0SaXrSyQSqcXRCqadF6uo1Ww1KScnh37++WeaN2/eZ72OplWtWlVYk0cVjo6Owt+24N8mOjpa1MKLBe3du5f09PSoXbt2lJmZqXKd/m2aWIGdSL0FPj99+kRbt26l5s2bk6GhIXXu3Jl27dpFenp6ai+0l+/atWtUtWpVUceuWbOGHBwcaOrUqRQaGqrSuk1E6i96Om7cOPruu+8oMTGRzMzMKDo6ms6fP0+urq40bdo0UWWYm5vTkCFD5C44mi89PV10earauXMnubq60rJly+jixYt08+ZNqa04RkZGlJCQUOTjCQkJZGhoKLoucXFxxW5idOzYUVg64t9kYmIiLB1R8J4WGxurcJmTtWvXkrGxMR06dEjmsQMHDpCxsbGwsKtYBdcqTE5OVurcopTooCZ/PY9du3aRkZERbdy4kWbMmEEVKlSgLVu2iCrj0KFD9N1339Ht27eVvr6Ojo7UHzJ/tVJlFA6MPpcHDx6Qvb39Z73G/v37hQ/8wh8MqnxQHDp0iNq0aaPygpRGRkbCm77gDSAqKorMzc0Vnm9paSm1Royenh6ZmZmpvIaUuj5+/Ejz588nHx8f8vT0JA8PD6lNLHVXYFd3gU9ra2tq3LgxrVmzhv755x9hvyaDmoiICNGBq7wFQpVdKFQTi55mZmaSr6+vsHZU/sK0vXv3Fr1Ar7LrxH0uRb2WYl5TRffEpKQk0X8XTa1KvXr1arK3t6cxY8bQ1q1bVQ581VW+fHlhjbWC97Q9e/aIWuS3Z8+eJJFIyM3NjTp16kSdO3cmNzc30tHRoW7duomuhybXKiysRHc/HTx4ECEhIfD29oafnx8aN26MSpUqwcnJCX/99ZfUGiZF6d27N9LT01GrVi3o6+vLzLIprk+eiNC/f38YGBgAAD59+oShQ4cqNWiqX79+X2RmT0xMDLKzsz/rNTp16iR0pRXX9Cp2TE3+36ZixYpKzW7L9+233+Lw4cP47bffhOsCwLp160QlNNTU2lHF2b9/P96+fSt6QOuJEyfQtWtX1K1bV+U8Qvb29pg+fbrUvoSEBPj5+YkaczVy5Eh4eXnh5s2bsLa2FvZ37twZgwYNUnh+Tk6OMAhV3S68AwcOSP1O/z+dwvLly4ud6VdQ4UzVqlD3NQGAUqVK4a+//kJAQAAiIyORm5sLDw8PpbrGjY2N1Z4Wrgnqdj+vX7++yISQ7969E11OqVKlkJGRoVbOLSBveRNA/mD8ou5npUuXxqNHj2BjY6Nwar+YsV8A4Ovri//9739Cqonc3FxcuHABY8eOFXUP2bZtGzp27Ijt27fj4cOHAPImCUyZMgU9e/YUVQdAs2sVFlaiBwqbmpri7t27cHJyQoUKFbBnzx7UrVsXsbGxqFmzplRioaLkDyItSnH9qGIHCn7W/sdCCmfPzb/JHz58GP369cPy5ctFlfPmzRtcvXpV7vIEyvRnq0Odvw2Ql9iubdu26NWrF4KDg/Hzzz/j7t27uHTpEs6cOSOVBO9zy8jIQHZ2tkzAW61aNURHR4sK8iwsLHDkyBHRH9bKuHnzJurUqSN6naMLFy6gatWqMgNaxeQx+fTpE3bv3o0NGzbg8uXL8PHxQe/evdGjRw9ERUUpldCr8IB0iUQipFNYtGiRzOybz0Xd10RTNDkt/N/i7OwsKggRGzj9WykINm/ejJ49e8LAwADBwcHFPiex43WysrLQv39/bN++HUQEPT095OTkwNfXF8HBwV9snJ+m1iqUp0S31OTfNJycnODu7o6dO3eibt26OHjwoNTAuKJkZWUhPDxc5UUPv2SwIlbhtZ3yc+YsWrRI4cyofOqunpyVlYXWrVtjzZo1aq11ouzAvMIaNmyICxcuYOHChahYsSKOHz+OOnXq4NKlSyqttKuKlJQU9OvXD8ePH0dubi7q1auHLVu2CP9vDx48EF1W+fLlpQYH/ltyc3PlBj/Pnj0TVT9DQ0P06tVLyMi8adMmjBgxQphl0r9/fzRv3lzUDVrVVhZNL86n7msC5LVgBQcHCwv0Fn5uYma3aXJauLpiYmIQGBiI+/fvQyKRwM3NDSNHjlSYPFLsauJiaXr68adPnxRmZwek71/9+/dX6hpF0URrniakp6fLneVbpkwZtQP4Et1Ss2TJEujq6mLEiBE4ffo02rdvj5ycHGRnZ2Px4sXCooHF0dSih9pEE6sn29ra4uLFi2q/2b6GpnR1DB48GAcPHsSIESNgaGiI1atXw8nJCSdOnFC6rKNHj2Lp0qVCGZqkTEuNOgt8FiU3NxdhYWHYsGEDDh48CDMzM9ELp6pC04vzaeI1+fXXXxEcHIz27dvLXVpETI4XTUwLDwgIwNixY2Xe+x8/fsSCBQswZcoUhWWEhYWhQ4cOqF27Nho1agQiwsWLF3Hz5k0cPHgQrVq1UliGpmhi+nFOTg5mz56N1atX4+XLl0J6iMmTJ8PZ2VlhRvBevXrB29sbTZs2VXtRy69BixYtYG1tLbNWYb9+/fDPP//g5MmTqheu1ogcLfP06VPavXs3RUVFiT6nf//+tGjRos9Yqy/v1atXdO3aNbp+/TqlpKQofb6xsbHSs7gK8/f3p//9739qlREeHk5GRkbUsmVL0tfXF+o0b9486tKli1Jl5ebm0qlTp+jQoUNSg1M/NwcHBzp8+LDw+/3790lXV1elGVTJycnk7e1NOjo6ZGpqqtEBy1FRUaIHXz579oyqVKlCbm5upKenR/Xr1ydra2uqWrWqRga9Jycni3pPPnr0iEJDQ4XB+YcOHaLGjRuTl5cXzZw5k3Jzc9Wui1jPnz9X+zWxtraW+l9RhboDSYmKnpGZkpIi+n+kdu3act////vf/5Qa1P61mD59Orm6utKWLVukJiDs2LGD6tevr/D8IUOGUNWqVUkikVDZsmWpZ8+etGrVKrp//77Cc0ePHk3v378Xfi5u+1Ju375N5cuXJ2tra2revDm1aNGCrK2tqXz58nTnzh21yi7RLTUhISHo0aOHMFA3X2ZmJrZv3y5q7MesWbOwcOFCtGjRQqVFD78md+/exS+//IILFy5I7W/atClWrVolOqPmjz/+iJ49e6J79+4q1+W3335DSEgIKlWqBC8vL5nXVUz22wYNGqBbt25CU3rBRHGdOnXC8+fP5Z735s0bjBw5EhEREahfvz4WLVqEdu3a4eLFiwDyWpFOnDih8po+ytDT00NCQoLU2A5VE5O1bNkS8fHxGDhwoNxkhMV11ynKfvvmzRucOXOm2JaawMBA9O3bF6VLl8bHjx+xbds2REREIDc3F3Xq1FF6nTJ1FLeitK6uLsLCwjBz5kxRiy/mUzXzdD51X5Ny5cohPDxcrW/y48ePx6VLl7Br1y5UqVIFERERePnyJfr27Yu+ffuKWjRTR0cHL1++hK2trdT+v//+Gz169BA1XsLQ0BC3b9+Waal99OgRvvnmG6USCWpCdnY2wsPDERMTA19fX5iZmeHFixcwNzdXuEI9kLc47po1a9CiRQupe9GDBw/QoEEDufmS5ElKSkJ4eDjCw8Nx5swZPHr0CGXKlEFiYmKR5zRr1gx79+6FpaVlsUvZiE3AqSmaWKtQnhId1BTO6JsvNTUVZcqUEdWUru6ih5ry4cMHzJ07t8j+dEX1SEpKQo0aNWBra4uhQ4eiWrVqICLcu3cP69atQ2pqKu7cuSMqyd+GDRsQEBCAAQMGqLx6siay36ralD5o0CCcPXsWffv2xaFDh6CjowMiQmBgIHR0dDB+/HiYmpri4MGDCusgT05ODm7fvg0nJyeZjLaF6erqIikpSeoDwtzcHDdv3hTdBZLP2NgYly5dQq1atZSusyYGtVtZWeHjx4/o0KEDBg0ahFatWqk9q0RVXl5eaNOmjbCi9PDhwzF79myZFaXv37+vsCxNZJ7WhEWLFuHJkydYvny5yq+rOgNJ82fovH37Fubm5lJ1yMnJwfv37zF06FCsWLFCYT0cHBywePFidOvWTWr/zp07MXbsWMTHx6v0/FTx9OlTtG3bFvHx8cjIyBD+vqNGjcKnT59ELeBoZGSEBw8ewMnJSepedO/ePdStW1fUpBQg7z5//vx5IbCJiIiAu7u7zFjIL+Hx48eIiYlBkyZNYGRkJCQj/Nep14j03yaRSOQm/ImKivqi+UM0oWfPnlS2bFkaP348LVmyhAIDA6U2RcaPH0916tShjx8/yjyWnp5OderUoQkTJoiqiybydmiCqk3p5cqVo/DwcCLK6yqRSCR0+vRp4fErV66QnZ2d6HqMHDmS1q9fT0RE2dnZ1KhRI5JIJGRiYiJVrjwSiYRq1qwplU9GV1eXqlevrnSOGQ8PD5UTRWrCp0+faMuWLdSiRQvS0dEhBwcHmjJlisp5hNRhamoqJAvMyckhXV1dqVxTsbGxZGRkJKqsESNGkKenJ507d45MTEyE/7P9+/dT7dq1RdfpwYMHNHz4cKE5fvjw4Qq7Fzp37iy1WVhYkIuLC33//fcyjynj8ePHtGvXLtqxYwc9evRI1DnBwcG0adMmkkgkFBQURMHBwcK2detWunjxoujrT58+nSwtLWnu3Ll09uxZOnfuHM2ZM4csLS1pxowZCs/Pysqi4OBgSkxMFH3NonTs2JF69+5NGRkZUveR8PBwqlSpkqgyPD096c8//yQi6XvRtGnT6LvvvlN4/vjx46levXpkaGhIXl5e5O/vT/v376fXr1+Lfh5ZWVky/+eqSElJoRYtWgj38/zn4ufnR/7+/qLKOHXqFA0fPpzat29P33//PY0YMYLOnDmjVr3ylcjZT/lrakgkErRo0UKqqTgnJwexsbFKrw6tbNPzgQMH4OPjg1KlSsnkyihMTMvG0aNHcfjwYZWn6544cQITJkyQOyrfyMgI48aNw/z580Ut46CJvB351Pk2oGpOhpcvXwpN+OXLl4ehoaHUmi2Ojo5KTTkMDQ1F7969AeTNDIuNjcWDBw8QEhKCSZMmyXT3FSSvub9jx46ir13Q3LlzMWbMGMyaNUtuC5qiZUHUZWBgIMxaiouLw6ZNmxASEoJZs2bB29sbgwYNQufOnWW6gz+H/Jl5QF53iZGRkdTAViMjI2RkZIgqa9++fdixYwfq168v9b/p7u6OmJgYUWWEhobip59+gpeXl5AD6fLly6hZsya2bt0q02KRr/BCtp07dxZ1PUUqVqyocJZRYf369RNyWbVs2VJm0VNlTJ48GWZmZli0aBEmTpwIIK97bdq0aaK69PX09PDLL7+IamlT5Pz587hw4QL09fWl9js5ORXZhV3Y1KlT0adPHzx//hy5ubnYs2cPHj58iJCQEBw6dEjh+QsWLICtrS2mTp2Kjh07ws3NTennoaenBycnJ7VXjR89ejT09PQQHx8vVY8ePXpg9OjRWLRoUbHnDx06FGvXroWVlRWqVKkiDAJfvnw5hg0bhmXLlqlVvxLZUjNt2jSaNm0aSSQSGjt2rPD7tGnTaPbs2bR161bKyMgQVdaHDx/Iz8+PdHV1pTKB/vbbbzRnzpwizyuY9VITLRvOzs507949UcfKY2FhQdHR0UU+Hh0dTRYWFkqXK6/lR4yUlBRq3ry5Wt8GVM2wqmj5CmUykhIRGRgYCGnbBw8eTCNHjiQioidPnii93II6Cv5PFdy+dAtaYSdOnCBfX18yNjam0qVLq1xOdnY2RUZGihrIXTibt5mZmVQ2b2X+xupmniYicnFxocmTJ8vsnzJlCrm4uIgqQx2vX7+mlStXCr/7+vpKtfJ07dpVdKuAkZGR6OUDxEhLSxOWjVCGt7c37du3T+3rW1lZCRmFC/59z507R2XKlBFdzrFjx6hJkyZkYmJCRkZG1KhRIwoLCxN1blRUFAUFBVHnzp3JxsaG7OzsqHv37rRy5Uql7vsbN24kHx8fSk1NFX1OYXZ2dsJkmoKvx5MnT8jExKTYc/fs2UP6+vq0adMmqYH4OTk5tGHDBtLX11c7w3KJDGryBQcH06dPn9QqQ1NNz+r6888/qWvXriqnmFa0hlRSUhLp6uqKKis7O5sCAgKoXLlyUoHeH3/8IXTDKNKnTx9q06YNJSQkSL1xwsLCyN3dXVQZ+WJiYpRqSpdIJDRr1iwKCgqioKAgMjQ0pMmTJwu/z5w5U6kgwNHRkcLCwig7O5scHBzo4MGDRER0584dsrS0VOq5qCM8PLzY7d9y6tQp6tWrFxkZGSn1eqjbrVdwGQuJREIWFhbC75aWlqL/xk2aNKGlS5cSkfRSJ8OHD6c2bdqIKsPIyEjul4pHjx6J7gZLT0+Xev/HxcXRkiVLRH1wzp8/n3r16iX8bmpqSl26dKH+/ftT//79qWrVqjR16lRR9fD29qa9e/eKOraw9PR02r9/v9wg5u3bt7R//37R92x11o8qqHv37jR48GAi+r+/77t376h58+bUv39/0eVoUlRUFPXv35/09PSUuhfVrl2bTE1NycDAgKpUqaLSUimmpqbCfbTgvfnq1asKv5T88MMPxQ5jGD9+PHXo0EHks5GvRHY/5WvevDlevXolNJNevXoVW7duhbu7O4YMGSKqDE00PWvCokWLEBMTAzs7Ozg7O8t0LYhZmv7du3dFJoVKS0uTWVW8KLNmzcLmzZsxf/58DB48WNhfs2ZNLFmyRGFOBgA4fvw4wsLCZJqwK1eujKdPn4qqR36+DFdXV6k8QoryZTg6OmLdunXC7/b29jKr/jo6OoqqA5A3yLZ79+5C7pD8HBtXrlxBtWrVRJcjz++//46kpCRRyxM0bdpUrWtp0tOnTxEcHIzg4GAkJCSgSZMmWLduHbp06SK6DHW69TSZ+HLOnDlo27Yt7t27h+zsbAQFBUllnhbD29sb586dk1np+/z582jcuLGoMjp27Igff/wRQ4cOxZs3b1C3bl3o6+sjJSUFixcvFlL1yxMaGirT1Tl//nzhfbN3714EBARg2rRpCusxbNgwjBkzBs+ePZM7I7S4WYNr167FgQMH5Ha5m5ubY+nSpUhISMDw4cMV1iN/de2C3VXKrK6db/HixWjevDnc3d3x6dMn+Pr6Ijo6GjY2Nti2bZuoMlR19uxZNGzYEHp6eoiMjBQGCJ87dw5paWmoXbu26BXpAc2s/N2kSROEhIRgxowZACB07S9YsEBhXSIiIvDHH38U+XiXLl0UzrRUpETPfmrcuDGGDBmCPn36ICkpCVWqVEGNGjXw6NEjjBgxQlSSKGNjY9y5cweurq5So9pv3ryJJk2a4O3bt3LP03RG0sJr8RSmaCpm/tTWoihzI9DE9EUzMzNERESgcuXKMtOx27Zti9TUVIVlaGJ2m6aEhoYiISEB3bp1EwK1zZs3w9LSUuUxMkDekhMJCQk4ffq06HPS09MRHx+PzMxMqf2fe4p6/vIGGzduxJkzZ1C2bFn069cPfn5+KiWvNDQ0xOPHj1GhQgUMGTIExsbGCAwMRGxsLGrVqoW0tLTP8Czku337NhYuXIgbN24I07H/97//ic48vXr1akyZMgXdu3dH/fr1AeSNqdm1axemT5+OcuXKCccWNcbOxsYGZ86cQfXq1bF+/XosW7YMkZGR2L17N6ZMmVLs+BIbGxtcvHhRGEvm5eWFffv2Cf+rT548wTfffCNqlk7hpScA8cFE3bp1MXnyZPzwww9yHz906BACAgJw9epVhfVQ9OVHUUoEdVMQKFqvqaCi1m7Kv4dVrVoV79+/R61ateDt7Q1vb280adLks4+Dk+fevXvw9vaGp6cn/v77b3To0AF3797FP//8gwsXLhQ7FsvQ0BAxMTEoX7683MefP3+OSpUq4ePHj6pXUK12nv84S0tLevDgARERBQUFUcOGDYkor4tDbD+2qk3Pzs7OUpuJiQlJJBKp5nATE5Mv0p9OpLhrQpkuCkNDQ6FPvWDz5N27dxX2ueZr164d/fHHH0IZT548oZycHOrWrZvoxHlFzW47deoU2djYiCpDmyQnJ1P79u1lxtTkb2KFhIRQw4YNqWzZssLfecmSJQrHL1hYWJCBgQF16dKFjhw5Qjk5OWo9n6+lW08TihtXJ3aMnZGRET19+pSIiLp160bTpk0jIqL4+HiFXVhGRkbFzoq5deuW6G6wuLi4YrfiWFpaCs9BnqdPn36xv62lpSUZGBhQt27dKCwsTOlkjAVnfy1atIisrKyoZ8+eQjd2z549ycrKihYvXlxkGfnj+w4ePEhv375V9ylpTGJiIk2ZMoXat29PPj4+NGnSJHrx4oXC84q6J+dTdryiPCW6+ykrK0uYaXHy5EnhG1C1atWKTWZUkKpNzwUXU9u6dStWrlyJDRs2CAnuHj58iMGDB+Pnn38W/XzevHmD0NBQxMTEYNy4cShdujQiIiJgZ2dXZGScT5NdE9WrV8e5c+dkvgnt2rULHh4eospYsGABvL29cf36dWRmZmL8+PFS3waKk/8NSSKRoEqVKkXmy/iSTp06VWQOITFdR/Lk5ubi8OHD2LBhA/bt26fw+FGjRuH169e4fPmykJDr5cuXmDlzpsIZC/lWrVqFKVOmYNSoUZg1a5bwrdvS0hKBgYHFtjpNmTIFffv2hY2NjahrKfI5u/WUlZOTg3379gnrFLm7u6NDhw6iFwjUxIzBSpUqYd++fejcuTPCwsIwevRoAEBycrLCb/Surq6IiIhAjRo15D5+/fp10XmR1FmCIzs7G69evSqye/fVq1fCDCsx/vzzT6xevRqxsbG4dOkSnJycEBgYCBcXF4UtpElJSQgNDcWmTZvg4+OD8uXLY8CAARgwYACcnZ0VXrtgMssuXbogICAAv/76q7BvxIgRWL58OU6ePCn8reSRSCT4/vvvFT/ZImiixagwe3t7hb0DRZk8eXKRy+doYuHWEt39VK9ePTRr1gzt27dH69atcfnyZdSqVQuXL19G165d8ezZM1HlqNv0XLFiRYSGhsp84N+4cQNdu3YVtZrsrVu30LJlS1hYWCAuLg4PHz4U1hZ5+vQpQkJCRNVFEw4ePIg+ffpg4sSJCAgIwPTp06WmL4pdtyUpKQmrVq2Sel2HDx+ucOXkzZs3g4jg5+eHwMBAqWmv+vr6cHZ2FqbNfgnTp09HQEAAvLy85K7Js3fvXqXKi46OxsaNG7F582a8fv0abdq0ERXUlC1bFvv370fdunVhbm6O69evo0qVKjhw4ADmz5+P8+fPKyzD3d0ds2fPRqdOnaS6Be/cuQNvb+/Put6SPJ+rW08Zjx8/Rvv27fHs2TNUrVoVRIRHjx7BwcEBhw8fVnpqdL43b96IWlg3X2hoKHx9fZGTk4PmzZsL64PNmTMHZ8+exdGjR4s8d/Lkydi8eTOuXr0Ke3t7qccSExNRr1499O3bFzNnzhRVF1WDifr166Nz585FZnKeO3cu9u3bh8uXLyusQ+EAPH+YQHBwMDZv3qxUl23BFAQJCQlKpyAwNTVFVFSUzJip6OhoeHh4FNmtp6OjI3StFqe4DOv5SSGBvK73mTNnok2bNsI98NKlSwgLC8PkyZOLDK5u3bpV7PULKq4bu7iMxgUp87eRoVY7z3/c6dOnhVkOAwYMEPZPnDhR6WRV6jAyMqIrV67I7L9y5YroJt8WLVrQuHHjiEi6y+fChQvk5OSksbqKpc70RU0JDw9XaY0kTbO3t6eQkBC1ykhPT6fg4GBq3LixMDU9KCiI3r17J7oMMzMzIdGdk5MTnT9/nojypmKK/T8rqmvx0aNHZGhoqMQz0h4+Pj7Utm1bqWmyKSkp1LZtW2rXrp2oMubOnUvbt28Xfu/atStJJBIqV66cUmvRJSYmUkREhFTX3pUrVxQm8UtLSyM3NzcyMzOjYcOGUWBgIAUFBdEvv/xCZmZmVK1aNdHTqleuXEk2NjY0c+ZMqenumzZtIm9v72LPXbNmDZmYmAhdiQUdOHCATExMaM2aNaLq4ebmJszCKvi/evv2bbK2thZVhjyqpCBwdHSk+fPny+yfP38+OTo6FnmeRCKhhg0bkre3d5Fbs2bNRNf9xx9/pGXLlsnsX7ZsGXXs2LHYehRM/1AwHUThff+2Eh3UEOVNBS2c1yI2NlbphfXu3LkjNV1QmUW5vv/+e/rmm2/o2rVrQr/ttWvXqHbt2vTDDz+IKsPc3FzIkFrwDRwXF0cGBgZKPZevyYcPH+j+/fsqT8fMzs6m0NBQmjFjBs2cOZP27NlTbI6az6F06dLC30ZZV65cocGDB5O5uTl5eXlRYGAgJSUlkZ6enpA7QywvLy86duwYEeVlSe3Tpw89e/aMxo8fL3qxQjc3N2HsTMH/s6CgIKpTp45S9dGEkydP0sSJE2ngwIE0YMAAqe1LMTY2plu3bsnsj4qKEj2GzMXFRch+ffz4cbK0tKSwsDAaOHAgtWrVSuk6JSQk0LNnz5Q6559//qGff/5ZGNOXP8bv559/ViqvibrBRK9evUgikZCbmxt16tSJOnfuTNWqVSMdHR3q2bOn6Hp8rgBclRQEmzZtIh0dHWrXrh3NmDGDZsyYQe3btyddXV3atGlTkecVzpmlLhMTkyJTBxT3v1pwTNTevXupYsWKtHr1auF+vHr1aqpcubLKU/k1qcQHNao6e/YseXl5Cb+bmprKRK4nTpwQVVZycjL5+PiQRCIhfX190tfXJx0dHfLx8RH9D12mTBmKiIgQ6lIwr0uFChWUfHb/PlUHtRYcTBcdHU2VK1cmY2Nj8vDwoNq1a5OxsTFVrVpV5SBDFePHj6eAgACVztXV1aVRo0YJA9rzqRLUbNmyRbiBRkREkK2tLeno6JChoSFt27ZNVBkbN26k8uXL0/bt28nExIS2bdtGM2fOFH7+kqZNm0Y6OjpUt25d6tixI3Xq1ElqEyM7O5vWr19PP/30E7Vo0YKaNWsmtYlhZWUlBCQFnT9/XvRyK4aGhhQfH09EebmvhgwZQkREDx8+FP3BmZOTQ9OnTydzc3PhfWJhYUEBAQFKDcrOzc2lly9f0suXL1VaqVwTwcSOHTuoY8eO5O7uTm5ubtSxY0fasWOHUvXQZAAeFxdH06ZNI2dnZ9LV1aVmzZrRli1blEouevnyZfL19RXuRb6+vnT58uViz1GUP0xZqrYYFfTtt9/KXQ3+8OHD/8oXm8JKdFCTlJREvXv3prJly5Kurq5Ss0HyR7HnMzU1pTNnzlBcXBzFxsbS6NGj6ccff1SqPg8fPqT9+/fTvn376OHDh0qdO3jwYOrUqRNlZmYKs4WePn1KHh4eQgZbRdRZG6RgIjNFmxi+vr7UsGFDunr1KpmYmNDx48fpzz//pKpVq9KhQ4eKPK/gTUAT3QLytGjRQqlZaSNGjCBLS0tq0qQJ/frrrzR69GiprTitWrUiMzMz8vX1paNHjwofMqoENYV9+PCBbty4Qa9evVLqvLVr15Kjo6MQwFeoUEF0UkVN0kS33vDhw8nExIS6d+9OI0eOpFGjRkltYvTp04eqV69Oly9fptzcXMrNzaVLly5RjRo1qF+/fqLKKFu2rBAYValShXbu3ElEeetBic06PWHCBLK1taWVK1fSzZs3KSoqilasWEG2trb0+++/iypDE76W1jx1A/CPHz/Sli1bqHnz5qSrq0sVKlSgSZMmSWUX/9w03VKjaotRQYaGhnKzGN+7d++r6IIu0UFN27Ztyd3dnVauXEl79+6lffv2SW3FqVixotTigIVT6UdERFDZsmU/W90Le/v2LTVq1IgsLS1JV1eXHBwcqFSpUtSkSRN6//696HJcXV2V6sPPp4npiwXZ29sL44zMzMyEIG///v3UqFGjIs8LDw+nrKwsItJMt4A8y5YtE6bLiqFuf3h8fDxNnz6dnJ2dyc7OjkaMGEF6enpqLYtR0OPHj5Xql8/36tUrjd5w9+3bR5s3bxZ9vDrdevmsra3lfutUxuvXr6lDhw4yLa2dOnWiN2/eiCpj+PDh5OTkRC1btiRra2thrNT27dtFZ3otW7as3BTz+/bto3Llyol/Qv+fmZmZSh/gX1NrnjoBuKZTEKhCE1nvC1OlxaggDw8P8vX1lWql+vTpk1Dmv61EBzWmpqYUGRmp0rkFm1iJiHbv3i2TolxfX19UWYXHAqgzNuDUqVO0YMECmjdvnujur4I0sTaIqoPRClJ1UGtQUJDwZtNEt8DX5vjx49SzZ08yNDSkypUr08SJE+nGjRtqlRkVFfVVDPCrWrWqUvVQp1svX9myZZVuFc1XeMxKdHQ0HThwgPbv3y+MW9iyZYuosjIzM2nBggU0YsQIoRuZKC//z7p160SVYWBgIPe5PHjwQKVv0IW/qCnja2nNy6dKAL5o0SKlWzFLgitXrlCZMmXIxsaGWrRoQS1atCAbGxuytbWVO+HlSyvRU7rd3d3x119/ic6dUlCZMmWwc+dOeHt7y308PDwc3bp1E7Wac+GVdbOysnDnzh28efMGzZs3x549e5Sun6o8PDzw+PFjZGVlwcnJSSbFuZjlFlSdvljQt99+K0w97NSpE8zNzTFnzhwsXbpUyMUjj4uLC65fvw5ra2v07dsXERER2LBhA+rWrQsgL4fJ4MGD4enpieDgYLlliEnTraenB3t7e7Rq1arI7Kef0+vXr7FlyxZs3LgRt27dUis78s2bN1GnTp0iy8hf1V4MMf8fmjJy5EiEhITgm2++wTfffCOzNEhx01zzLVq0CE+ePMHy5ctFP8d87u7uuHDhAqysrOQ+vm3bNvTr108mc/PnUq9ePdSrV08mW/lvv/2Ga9euiZoGXVDBKfuqSklJQW5urkxWb/bvyc3NxePHj+XmzGrSpImoMtLT07FlyxY8ePAARAR3d3f4+vrKfF78G0p08r3AwEBMmDABa9asEZVMqaB69eohJCSkyKAmODgY9erVE1WWvDwlubm5GDZsWLE3FE0vtQBoZm0Qa2tr7N27F+PGjZPav2/fPlhbW4sqY9SoUUICxKlTp6JNmzb466+/oK+vX2QwAkgnNVy6dCn69euHBg0aCB942dnZ6NChA4KCgooso2Bem6Lk5uYiOjoa69evx9ixYxEQEFDs8deuXcOuXbvkLk+gStBqZWWF3377Db/99ttnDyQK/k98+vQJK1euhLu7u5Dn4vLly7h79y6GDRv2WetR2K1bt1C7dm0AwJ07d6QeExugnD9/HqdPn8bRo0dRvXp1mcCouL9NmTJl0LZtW/z9998yN/Pt27ejb9++mD9/fpHnHzhwAD4+PihVqhQOHDhQbD2LWhqhoPnz56N9+/Y4efIkGjRoAIlEgosXLyIhIQFHjhxReH5hvXv3VisNf3JyMh4+fCgkwrS1tVW5LLHq1KmDU6dOwcrKSmEwbmpqiurVq+P333+Hg4PDZ6/b1+Dy5cvw9fXF06dPZdbyU2Y9LGNjY9HrIyrDzc0Njx49UutLWoluqbGyskJ6ejqys7NhbGwsc0MrLrvi6dOn0bJlS/j7+2PcuHHCN5Hk5GTMmzcPQUFBOH78OJo3b65y/R4+fAhvb+8isxsXzvD56tUrpKenCwm73rx5A2NjY5QpUwZPnjxRuR7KCg4OxsCBA9G2bVupD75jx45h/fr16N+/v9Jlpqen48GDB3B0dFQ6I+3jx49x//594RtF4RYkdRw+fBi//PIL4uPjizwm/wOudevWOHHiBFq3bo3o6GgkJSWhc+fOGl1cURWKWmoKGjRoEMqWLSssZpdv6tSpSEhIKDY7spgEXvktYKVLl1ZccQ0YMGBAsY8X97d5//49vL29YWlpiaNHjwr3j507d6JXr16YM2cOxo4dW+T5Ojo6SEpKQpkyZeSul5RPmQ+bFy9eYMWKFVLfoIcNGya1dtTnlpaWhuHDh2Pbtm1CS4Curi569OiBFStWiPrS4Ofnh6CgIJiZmUnt//DhA3777bci/8+mT5+OcePGwdjYWGHG24yMDJw6dQqGhoaiFx79r6tduzaqVKmC6dOny00EKuZv8znt27cPb9++lcrGrKwSHdQUzLQoj6IXduXKlRg9ejSys7Nhbm4OiUSCt2/fQk9PD4sWLZJKia2KI0eOoF+/fqK6sBQttdCrVy/R11VnuYV8V65cwdKlS6WCiREjRohuvfocsrOz8enTJ5iammqszDdv3sDPz6/Yb/TffPMNfv75ZwwfPlxo0ndxccHPP/+MsmXLqpxuHBD3zUbRN9b09HRER0eL+uC0sLDA9evXUblyZan90dHR8PLyKnIBV+D/Fk1VdMuRSCSoVasWQkJCikzb/7V49eoVmjRpAnd3d4SGhiI0NBS9evXCjBkzisyK+7VTd0mP7t27IyoqCsuWLZNqMRo5ciS++eYb7Ny5U2EZRS1Gm5KSAnt7e6WWSihOTEwMqlevjk+fPmmkvKJ8+PABc+fOLfJ1FfulMzMzU+75RS0rUZiJiQlu3ryp0S92X5sSHdRoQkJCAkJDQxEdHQ0AqFy5Mrp27apUc6a/v7/U70SExMREHD58GP369cPy5csVlqGJpRaAf3+5hcKvRVGKGy9x5MgRpKamok+fPsK+WbNmYcaMGcjOzkbz5s2xY8eOIsdCaJqJiQnu3r0LZ2dn2NjY4PTp06hZsybu37+P5s2bF7vO2OPHj4u9AYn5ZiM2aFK0kjuQt+bLnDlzZFo4Nm3ahAkTJuDly5dFnqto1WQgr1vv5cuXWLBgAZKTk3Hu3Llij9d0t54qEhIS8N1336FSpUo4f/48pkyZgkmTJok6d/369WjevLla41aKayUsSMwHnyaW9DAxMUFYWBi+++47qf3nzp1D27Zt8eHDhyLPTUtLAxHBysoK0dHRUl1WOTk5OHjwICZMmIAXL14orIdYb9++/ewtFD/99BPOnDmDPn36yH1dR44cWez50dHR8PPzw8WLF6X2k4iVzwtq3rw5xo8fj7Zt2yr3BP5DSvSYGkD9hegcHByKXYxMjMjISKnfdXR0YGtri0WLFsHPz09UGYmJicjKypLZn5OTU+wHTWH+/v7o378/5s+fL9X06+PjA19fX9HlqKrwa3H+/Hl4enrCyMhI2KdovMTChQvRpUsX4feLFy9iypQpCAgIgJubGyZNmoQZM2aIGkiqCaVLl8a7d+8AAOXLl8edO3dQs2ZNvHnzRuECblWqVEH58uXRrFkzYSs4/kvMGCgxwYpYo0aNwi+//IIbN26gfv36APK6Fjdu3IgpU6YUe67YhQ5dXFwwb9481KpVq9jjFHXriRUaGoqdO3fKDYyKG69UsDttwYIF6Nu3Lzr/v/buMyqqqwsD8DtD72AFFSmiKIgRY9fQ7Bq7xihgT1Sw9xJrFGOJSmyoiAgC9hJLCFGKDcQGakSxoBAVOypNKef74eJ+DGXmDkyh7GetWTrl3tnAMJw5Z5+9Bw5E3759Re4T1wtn2rRpyM7OFvkZu7i48P7kDYguQxd8Ri38OyLNHz4fHx/4+/uLfCCQVs2aNUscJBgYGEj8IGFoaCjSjLYogUAgdpAuTU5Nwc9W0oCGMYbDhw8jIiKixFkSPoPnv/76C6dPn0anTp0kPrYko0ePhqqqKk6dOlXioIivKVOmYNasWUhNTYWdnV2xlAtxr1VZ4LPpoPAmjAkTJkBdXV2q56jWg5qHDx+id+/eePbsmUwb0UlLXPOuZ8+e8Vry6dKlC3766Sfs3r0b3377LQQCAa5du4YJEyaga9euvGO5evUqduzYUez2+vXrIzU1lfd5yqro90JPTw/BwcFSfZK9c+eOSNfpw4cPo1u3btynZ01NTUybNk1hg5rvvvsO//zzD+zs7PDDDz9g2rRpCA8Pxz///IMuXbqIPTYqKgpRUVGIjIzE5MmTkZ2djYYNG8LFxYX7I8h3SVAW5s+fD0tLS3h7eyM4OBjA1yUwf39//PDDDzJ7HgsLi2KfSovy8vLCxo0buWU9b29vkWU9Pv744w8sWrQIo0aNwokTJzBmzBg8evQIV69ehaenp9hjW7ZsyS2nFfx78OBBHDp0SGRwIW4wkZaWhpiYGERFRSEiIgKenp7Izs6GmZmZyM9YXE6MQCBAgwYNMHr0aPTt2xeqqmV/W//y5Qs6duxY5uMB4JdffsHMmTMREBDA/RxSU1MxZ84cLF68WOyxERERYIzBxcUFR44cEcmtUldXh5mZmdjvRf/+/bkGk7LY9AB8HXju3LkTzs7OqFu3bpkGFEZGRuXKE4uLi8P169fL3X2+4MNe4Q/LhV/DfAa+KSkp3GsOAGJjYxEcHAwbGxuJycN8fib5+fl49eoVVq5ciYSEBGzbtk3iMSIUtXe8IpJXxVlZePHiBZs8eTLv+hKyaLXAWMVrt1CWWhmamprs6dOn3PU2bdqwNWvWcNefPHnCtLW1ZRajJG/fvmXPnj1jjH0tZb9mzRrWt29fNmPGjGJ9x8T58uULi4qKYsuXL2fOzs5MS0uLCYVC1qRJE3mFrlAF/X340tbW5moZ1axZkyu0ePfuXWZsbMzrHNbW1iw4OJgxJvpaW7x4MfP09BR7bOF+OOIu0vjy5Qs7f/489zPW1tZmKioqYo958eIF++2331jTpk1Z3bp12axZs8pcmLGstX9atmzJ7O3tuYuuri5TU1NjjRo1Yo0aNWJqampMV1eXd3G2J0+elKlFgzwYGRmVu0BjYGAgGzJkiEgtM2m0bt2aXbhwoVwxMCb5NctH586duUreL168YPr6+qxDhw6sZs2abPny5eWOsUBUVBSrW7eu1MdV65maqKgoxMTEiIyga9asid9++63M04TSSEtLg6enJ8LCwqCmpob58+dj8uTJWLZsGdavXw9bW1teiXkAULt2bZw5cwYPHjzgknObNWtW4hSuOP3798eKFSu4ZD6BQIDk5GTMnz9fZEmnIqtXrx4SEhLQsGFDpKenIz4+Hhs3buTuf/v2LbS1tRUWT+HXl1AoxNy5czF37lypz6OmpgYHBwe0adMGHTp0wN9//41du3bh4cOHsgxXaVavXi020bio8izrFUhOTuZmJrS0tLjzubu7o3379mLz2fgup0kjLy8PX758wefPn/H582fk5uYW2+VYlLGxMebNm4d58+bh4sWL2LNnD9q1awcbGxuMGzcO48aNE7u7qrDs7Gzs3LkTZ8+elar2jyxmRYrujrt9+3apj+W7TCKLTQ8GBgblynkCvtZDevToEerWrQtzc/Ni31dJZRnWrFmDuXPnwsvLq8RlI75b70t7zRbkK/F5Td+5c4er+3Xw4EE0b94cly5dQlhYGCZOnChxGZqvVq1alSnloVoPajQ0NLg3scLS09OlXscri4ULF+L8+fMYNWoUQkNDMWPGDISGhiI7Oxt//fUXHB0dpT5n48aNi+1Mkcb69evRu3dv1KlTB1lZWXB0dERqaio6dOiAVatWlfm8ijRkyBBMnz4dCxcuxJkzZ2BsbMzlfwDAtWvXuB1i8vTx40dej5P0hpSdnY3Lly8jIiICkZGRuHr1KiwsLODo6Ijt27eX6XVSEUn7h7E8y3oFjI2N8fbtW5iZmcHMzAwxMTH45ptvkJSUJHGXliwU/GwjIyMRHh6Oa9euwdLSEg4ODpg8eTIcHR2l2o7duXNndO7cGV5eXhg+fDgmTpyIwYMH8176KGvtH1nkbRVdzhOHzzJJ0U0PP/30E2rUqIFjx45Jtelh2bJlWL58Ofz8/ERy+6RR3kFfQQpB0dc1kzJRuKh79+7Bz88Pe/fuxfv373kViszJyeGW+M6ePcvVUGratKnYTQ98LFy4EKmpqfDz84Ourm6ZUgSq9aDm+++/x88//1ys4uzEiRN5Fbsqr9OnT2PPnj3o2rUrPDw8YGVlhSZNmmDTpk1yf+7S6Ovr4+LFiwgPD8eNGzeQn5+PVq1aSZWXU57ti0U/rTHGcO/evWJViMV9Ulu6dCmeP3+OqVOnwtjYGPv27RNJ/A4JCVFIFeCCpMfS8HlDcnR0xNWrV9GoUSM4ODhgypQpcHR0RN26deURslzJapBXYMuWLdxW3AULFkBNTQ0XL17EoEGDJOZuFHBxccHJkyfRqlUrjBs3DjNmzMDhw4dx7do1XpWly8vQ0BB169ZFv379MG3aNDg6Opar+u7ly5fh5+eHQ4cOwdraGlu3buXqVvEhLr9P3grv0Lx58yZmz56NOXPmcLWuoqOj8fvvv4staFiYrDY9DB06FCEhIahTp06ZZlmA8g/6ZPlzycjIwIEDB7B7927ExMTA2dkZq1at4j3wsrW1hY+PD/r06YN//vmHq1n1/Plz3sVVS/Ps2TOkpKSU6xzVekt3WloaRo0ahZMnTxarOOvv71+ubX6jRo1CSkoKwsPDS32Mmpoanj59yn0S09bWRmxsbIWvzSFJebYviqtlIm1Cm7IVLujFGEPv3r3h6+tbbNpb3EyLmpoaTExMMGDAADg5OcHBwUHq4oOFlbcGSXkU/GxLo4yfbX5+PvLz87nk2oMHD+LixYuwsrLCxIkT5T5j265dO8TFxcHa2hpOTk5wdHSEk5OTVH8cXrx4gYCAAOzZswfv37+Hq6srxo0bB1tb2zLH9fDhQzx69AgODg7Q0tKSOHtiZGTEO4FWXFHTAm3btsWyZcvQu3dvkdvPnDmDxYsX4/r16xLPYWBggBs3bqBRo0YiLR+ePn0Ka2tr3rVpfvjhB0RERGDIkCElJgrLcnehPEVHR8PX1xcHDx5E48aN4erqinnz5uHWrVuwsbHhfZ7IyEgMHDgQHz9+xKhRo7j3jYULF+LevXsKbetTkmo9U2NoaIgTJ06UueLs06dPERYWhpycHDg6Ooq8idSvX1/iOnZ+fr7IqF9FRUUpvTNk3W6hPNsX+dbTqQyKDlZUVFTQvn17qdbn09LScOHCBURGRmLNmjUYPnw4mjRpwv3xc3R05F1+XlINEj5WrFiB2bNnF8tJysrKwrp168Supxf+tClukCeJLGd8hEKhyO/pDz/8INNdXJJcuXIFGRkZuHDhAiIiIrB27VruZ1zw85U0e1OwI2jUqFHo168f1NTUkJeXV2zWk08eytu3b7k/4gKBAA8ePIClpSXGjx8PQ0NDkV2Fhcl6dvn27dsl5hJZWFjg7t27vM6hqalZ4mvl/v37UrVsOH36dIl1d6SRl5eHjRs3llo6gM9A78KFC9ixYwceP36MQ4cOoX79+ggMDISFhYXE2GxsbJCZmYkRI0bgypUr3CBm/vz5Un8tTk5OePPmDT5+/CiyRf/nn3+uEL2fqu3upw8fPpTYSj4vL499+PBB4vFRUVFMR0eH60KrpqbG7aLgSyAQsN69e7OBAweygQMHMlVVVda9e3fuesFF3szNzUUuBV+XkZERMzIyYgKBgOno6DALCwve5yvr7ouqrDxdjwt8/PiRnTlzhs2ZM4e1adOGqaurM1tbW17HGhsbc7sWykooFJa4m+7NmzdSd/ou6/dDIBAwoVBY6qXgfj7Mzc3ZL7/8whISEqSOQ14+fvzITp8+zaZPn84MDAwk7n4qeA8q/H0pfJs03w93d3fWo0cPlpKSUmz3o42NTbm/Nr7s7e3ZiBEjWFZWFndbdnY2GzFiBO8dVD/99BMbMGAA+/LlC9PV1WWPHz9mT58+Zfb29mzatGm8Y7G2tmbx8fHSfgkiFi9ezExMTNi6deuYpqYm+/XXX9m4ceNYzZo1mbe3t8TjDx8+zLS0tNj48eOZhoYG93PZunUr69Wrl8Tj1dTUmLu7OwsLCxPZVaaqqsr+/fdfqb4WZ2dn9v79+2K3f/jwgTk7O4s9NjMzk508eZK7Pn/+fDZjxgzuMnv2bJGfeVlUy5maY8eOYd68eYiLiyv2iTM7Oxtt2rTB+vXrxeZdLF68GM7OztixYwe0tLSwYMECzJ07F8OHD+cdR9EqsG5ubtJ9IUWkpaUhNja2xKWFkSNHlnpc4dkRSe0W+Pj111+xZMkS7N27V6G7jKoDHR0d1KhRAzVq1ICRkRFUVVWRkJDA61hZ1CBhpSxDxMfHK6xfk6xmfICvxchCQkKwatUq2Nvbw93dHcOGDeNd50acwkmPfOTn5+Pq1auIjIxEREQELl26hIyMDIk7UmQ5uxkWFoa///6bq0FSoHHjxmIrQn/8+JGbGZM0k8ZnBs3Hxwd9+/aFqakpV4QxPj4eAoEAp06dkng8ILtND7///jvmzp0LHx8fqRsfFwgKCsKuXbvQp08fLF++HMOHD0ejRo3QokULxMTESJwBX7lyJXx8fDBy5Ejs37+fu71jx44Sm+kCX18j/v7+mDRpErKysjB8+HC4urqWabY2MjKyxITi7OxsiRXAAwICcOrUKXz//fcAvubF2dracgnY9+7dQ7169cpX0LZcQ6JKqlu3bmzXrl2l3r97927WvXt3secwMjJit2/f5q6np6czoVAoVd0RWfrzzz+Znp4eEwqFzMDAgBkaGnIXIyMj3uextLTk6tQUdu3aNWZubs7rHC1btmR6enpMV1eXNW/eXKR+Bd9PWVVRwadFaeTl5bErV66wNWvWsJ49e3I/Y1NTUzZy5Ei2Z88e3vUlylqDhDHGvY6EQiH3/4KLvr4+EwqFzMPDQ6pzymLmSlbnuX//PluyZAlr0qQJU1VVZd26dWN79+4t1znd3d2Zk5OT2MfExsayNWvWsF69ejE9PT0mEAiYqakpc3d3Z35+flwdHkXR1dVliYmJ3P8Lvq+xsbGsRo0apR5XeAavtJk0aWaMGGMsIyOD7dixg82YMYNNnz6d7dy5k6Wnp0v9NZ07d46tW7eOrVmzhv3zzz9SH29oaMjV/dLV1RV57fN9b9XW1uZqZxkbG7Pr168zxhh79OgR09fXl3i8lpYW91oo/HN59OgR09DQkOrrOXfuHHN1dWVaWlpMIBCwOXPmsPv370s8Lj4+nsXHxzOBQMAiIiK46/Hx8ezGjRvMy8uLmZmZiT3Hd999x44ePcpdL/q7GxgYyNq3by/V11NUtZypuXPnjtgqhQ4ODvjll1/EniMtLU1knVtHRwfa2tpIS0tTWE+hwmbNmoWxY8fCy8urXLMjsmi3IKtKnpVd0d0z2dnZmDhxYrF1Z3GJdYaGhsjIyICJiQmcnJywYcMGODs7l6nadVlrkABfcyYYYxg7diyWL18ukkSvrq4Oc3NzbpeKNMpa7l3WCjoXL1++HDExMZg0aRLGjBkjdoZTEj5bhtu1ayfys3VyclJqs0EHBwcEBARwO1oEAgHy8/Oxbt06ODs7l3pceHg4N1MXHh4uk5+rtra2xAq1fLi4uMDFxaXMx8siX6hBgwZ48eIFGjZsCCsrK4SFhaFVq1a4evUqtz1aHBMTEzx8+LDYTNHFixelrqFT8P348OEDgoKC4Ofnh/Xr16N58+bF8rAKK9hyLxAISvx+amlpYfPmzWKfOzExUaR2mqampkhOW9u2bSVW8pakWg5q3r9/L7bLa05ODt6/fy/xPHfv3hVpHcAYQ0JCgkjtG3n30ijw7NkzTJ06tdzLPbJot1DRdwNcu3YNmZmZcHBwkOvzFN09V5blxYI/JtIWUSxJWWuQAF+XSgt+Z7p27VpseYIPWQzy5Kmg3PuBAwfw4cMHDBkypFznS0lJwdKlS8UuPyUkJCikZhJf69atg5OTE65du4YvX75g7ty5+Pfff/Hu3TtcunSp1OMcHR255qtOTk4yiSUxMRGRkZElLqeLS0jnW3+G74BVXLNYvgYOHIhz586hXbt2mDZtGoYPH47du3cjOTmZ11LLhAkTMG3aNPj5+UEgEOD58+eIjo7G7Nmzy1zszsDAAB4eHvDw8EBcXJzEZdKC2k2WlpaIjY0VSbZWV1dHnTp1JPZM/PDhg0gbj9evX4vcn5+fj8+fP5fhq/m/armlu6CpYWl/ZAIDA7Fq1Srcu3ev1HNUtK3HgwYNwo8//ljunRuvX7/migEW3ubeo0cP+Pv7l6uGRkXRrFkzJCYmVopt4RWNtrY2EhISylRNt2hn79Ls2bNHqvPq6enh1q1bEivvliQxMRFBQUEIDg7GkydP4OzsDFdXVwwaNEiktklZxMfHo1WrVpXudZaamort27fj+vXrXJ0qT09PiXlGQqFQbPNVaezatQuTJk1CrVq1YGxsLDLoFggEYmvDCIVC6OrqQlVVtdQCigKBQOyOI1nnCBV15coVXLp0CVZWVrxroi1atAgbN27ktqJraGhg9uzZ3KxaZdC4cWP89ttvpVanP3jwIBYuXFiuKunVclCzaNEi7Nu3D7GxscWKmKWmpqJdu3Zwc3MTm0wmLmmuMHmUUi/J7t27sWLFCowZM6bEMtrSFhMsT7sFWWxffPnyJWbPns3VVCn6Mi3PH4rnz58jJydHYT+bqsTZ2RnTpk1T6hJj0RmfkydPwsXFpUwzPkKhEK1bt8aIESPw448/wtjYWGZxVtZBTVlduHCBa74aHR1druarZmZm8PDwwLx586SOw9bWFi9fvoSbmxvGjh1bptlyFRUVvHjxAnXq1Cm1vpI0H1wzMzNlsmkiMzMTd+/eRX5+PmxsbKCrq1vuc5ZFYGAgfHx8kJSUhOjoaJiZmWHjxo2wtLRE//79Sz1u2rRpOHv2LK5fvw5NTU2R+7KystC6dWt07doV3t7eZY6tWg5qPn36hA4dOiA5ORlubm6wtraGQCBAQkICgoKCYGpqipiYmHJ/UlMkcTVxFF3QbMmSJfD19cXMmTOxePFiLFq0CE+ePMHx48exZMkSXrVuevXqheTkZEyePLnEmirifnEkycjIwPXr1+W+/FQRXb16FYcOHSpxsMlnEHDo0CHMnz8fM2bMwLfffltsIKGI5VZZzvgUXeOXpco4qAkNDYWuri5X92Tr1q3YtWsXbGxssHXrVt75gjk5OYiOjkZkZCQiIyMRExODz58/w8rKCvfv35d4vL6+PuLi4srcc+nKlSvw8/PDgQMHYGVlhXHjxsHV1ZX3rEpUVBQ6deoEVVVVREZGil2e5dOmRFdXFwMGDIC7uzu6devGuxdXRbR9+3YsWbIE06dPx6pVq3Dnzh1YWlrC398fe/fuFVv9+OXLl2jZsiXU1dUxefJkNGnSBAKBAPfu3cOWLVuQm5uLmzdvlq9iernSjCuxtLQ0NmnSJFajRg2ulkONGjXYpEmTStyDT/iztLRkp06dYox9zW5/+PAhY4wxb29vNnz4cF7n0NXVZTdv3pRLfHFxcVLXVKkKQkJCmJqaGuvTpw9TV1dn33//PbO2tmYGBgZs9OjRvM5RtP5JwY4WaXe2VAcV5XW2YMECNmbMGF6Pbd68OdeR+tatW0xdXZ0tWLCAtWvXjvdrpLDMzEwWFhbGZs2axe2S42Ps2LFs+/btUj9fSc+/d+9e5uTkxLS1tdmIESNYdnY2r2MfPHhQ7ucvcOTIETZkyBCmpaXF6taty6ZOncpiY2PFHjNw4ECuZlrR2mVFL+7u7uy3335jaWlpMou5NM2aNWPHjh1jjInuXrp9+zarWbOmxOMfP37MevToIVJPSSgUsh49eshkN2S1TBQGviZJbdu2DVu3bsWbN2/AGEPt2rVlthujOudtpKamws7ODsDXTygFnZe///573j15TE1NFdJQsDrx8vLCxo0b4enpCT09PXh7e8PCwgITJkzgXZelKlV8Li9JvaHS0tKkPueXL1+QlJSERo0aiSRUlsd///3Hu59OUlISV232yJEj6Nu3L7y8vHDjxo1iLQtKIqvmq1ZWVli8eDFiYmJKXE7nM9sLfN2RM3LkSJibm2Pp0qXYv38/tmzZwmvHUZMmTWSWIzRo0CAMGjQInz59wuHDhxESEoKOHTvCwsICbm5uJSb7GhgYcH+PJLXs+fz5M3x8fHDp0iX8+eefUsWWlpYmVX+wpKQk2NvbF7tdQ0MDGRkZEo+3sLBAaGgo3r17x+XOWFlZyazOVbVcfpKFgkz/0hw/fhwfPnyQSeY8XxkZGYiKiipxaYHvm4AsWFtbIyAgAO3atcN3332HPn36YP78+Thw4ACmTJmCV69eSTxHWFgYfv/9d+zYsaPMbySlqYzLArKgo6ODf//9F+bm5qhVqxYiIiJgZ2eHhIQEuLi4lLvDbnUjy2WwzMxMTJkyBXv37gXwdVnM0tISU6dORb169cpUzr4satSogYsXL8LGxgadO3fGyJEj8fPPP+PJkydcqf3SFG2+WtDioSxLCeKSvgUCgdimuAWePXuGvXv3Ys+ePcjIyOBybJo2bcorBlnmCJXk7t27cHV1xa1bt2TyXnT37l20adNG7MBizZo1MDc3x7BhwwB8bQty5MgRGBsb48yZM1yhQ3FsbGywevVq9O/fX6Sn1h9//IG9e/fy6sslV+We66mmBAIBa9CggdKKZBV148YNZmxszPT19ZmKigqrXbu21O0NZGXevHls1apVjDHGDh06xFRVVZmVlRVTV1dn8+bN43UOWRS8Kk1FWRZQtAYNGrBbt24xxhhr0aIF19bj8uXLvAqAFQgICGAdO3ZkJiYmXOG/jRs3suPHj8s+6Gpi6tSp7Ntvv2UXLlxgOjo63DT8iRMnWMuWLct17uTkZN7LT3379mU9evRgK1asYGpqauy///5jjH1tk9C4cWOxx6qqqjJTU1M2ZcoUduTIEfb69etyxV1WBw4cYD179mRaWlpswIAB7MSJEyw3N7dc5/zy5QuLiopiy5cvZ87OzkxLS4sJhULWpEkTqc6TlZXFDhw4wPr37880NDSYqakpmzt3brliK5Cbm8vi4uLEPsbCwoJdunSJMcZYWFgYMzQ0ZH///TcbN24c69atG6/n8fPzY/Xr12f79+9nOjo6LCQkhK1cuZL7v7LRTE0ZyXsULy0nJyc0adIE27dvh6GhIeLj46GmpgY3NzdMmzZN4lR5YWVtt1CasmxfLPjEWhpxM2CSpl+TkpIwc+bMajdTM2LECLRu3RozZ87EqlWr4O3tjf79++Off/5Bq1ateCUKlydJsDLIy8vD7du3YWZmptAimmZmZjhw4ADat28v8un34cOHaNWqFe8mniWRZmYyOTkZHh4eSElJwdSpUzFu3DgAwIwZM5CXlye2+W1BY86CNg9xcXFlbr5aHkKhEA0bNoSrq6vYWaKyzF5nZWXh4sWL+Pvvv7Fr1y6kp6fz+r6GhYUhKCgIx48fh4qKCoYMGQJXV1exy3GDBg2Cv78/9PX1Jb5/863tpKWlhcTERJiammLatGnIzs7Gjh07kJiYiHbt2vGqzwZ83XK/cuVKblmzfv36WLZsGfd6USYa1MhAeTP9ZcHQ0BBXrlyBtbU1DA0NER0djWbNmuHKlSsYNWqU2Jo7hZ08eRKurq7IyMiAnp5esfoQfLZjy2r7Ylnx2Vmg6B1hFcG7d++QnZ2NevXqIT8/H+vXr8fFixe5/AU+f8RtbGzg5eWFAQMGiPzxvXPnDte9tzKZPn067OzsMG7cOOTl5cHR0RGXL1+GtrY2Tp06JbNCcpJoa2tzA8TC39f4+Hg4ODhweWlloazl1k+fPuHixYtcfk18fDwaN25crPBjaf777z/8+eefJS6ni6t+bW5uLjE3ku8SlrgcoYIlNj4fXrW1tdGnTx+4urqiT58+xXKESjJmzBj88ccf0NPTk7jUybe2U7169XD48GF07NgR1tbWWLlyJYYOHYr79++jTZs2Ug+e37x5g/z8/IpVv0y5E0VVS1kz/WWhVq1aXP+OJk2asNDQUMYYYwkJCUxLS4v3eRo3bsymTZvGMjIyyhyLjo4Oc3V1ZaGhoSV2QpdWZmYm+/Dhg8iFKIempia35FR450NiYiLT1NRUZmhlUr9+fXb16lXGGGPHjh1j9erVY/fv32eLFi1iHTt2VFgcDg4O7I8//mCMifYI8/T0ZD169CjXuaVZbpVlF/a8vDwWExPDVq9ezbp37860tbV5n+Ps2bNMW1ub2draMlVVVdayZUtmaGjIDAwMJHaClhUHBwempaXFmjdvzjw8PNiBAwdYampqmc5VUd6zPD09mZmZGevatSurWbMm+/TpE2OMsf3791eZvnzVdveTOHzL6Msq018W7O3tce3aNTRp0gTOzs5YsmQJ3rx5g8DAQG4nEh+yaLcQEBCAkJAQDBw4EPr6+hg2bBjc3NzQpk0b3ufIyMjAvHnzcPDgQbx9+7bY/dVtlqU8+H764lPDw8LCAnFxccUKF/7111/crpnK5M2bN1zBvTNnzmDo0KFo0qQJxo0bJ3apRdZWr16Nnj174u7du8jNzYW3tzf+/fdfREdHIyoqSmFxsFIm7j9//gx1dXWxx+bn5+PatWvFuowX7CDaunWr2P5RhS1YsACzZs3CihUroKenhyNHjqBOnTpwdXVFz549pf66yuLy5cswMTGBs7MznJyc4ODggFq1apXpXGWpOiwPGzduhLm5OVJSUrB27VqueN+LFy/g4eHB6xzyLIwqCzSoKYG7u7vE7dhFM/2nTJlS5kx/WfDy8uJ6Tv36668YNWoUJk2aBCsrK6nKzvfo0QPXrl0rc9EroGzbF4uaO3cuIiIisG3bNowcORJbt27Fs2fPsGPHDvz2229ljg34f9POhg0blus8lYWhoaHY6XgmRWXUOXPmwNPTE9nZ2WCMITY2FiEhIVi9ejV8fX1lGbZC1K1bF3fv3oWJiQlCQ0O5RreZmZkS+9jIUseOHXHp0iWsX78ejRo14hoeRkdHS/xQIout5QUDOIFAAF9fX5FKtXl5eTh//rzEXUOybL6akJCAkJAQAICqqiqysrKgq6uLFStWoH///pg0aZLU55RWWloalyO0Zs0aDB8+XOE5Qs7OzryW0s6dO8frfGpqapg9e3ax26dPn847ptGjRyM5ORmLFy8usTCqslFOTQn4lNFXU1ODiYkJBgwYUO5RfEUi63YLBaTdvtiwYUMEBATAyckJ+vr6uHHjBqysrBAYGIiQkBCcOXOmTHEA1a+GUOFP+owx9O7dG76+vsVyAfjOLFbkJEFpLVu2DJs2bYKJiQkyMzORmJgIDQ0N+Pn5YdeuXYiOjlZ2iBLJYmt5wRbqp0+fokGDBiIDuoIu7CtWrEC7du1KPceOHTtk1nzV2NgY4eHhsLGxga2tLVavXo1+/fohPj4enTp1Qnp6ermfQ1rlzREqC3HNLj9+/IiQkBB8/vxZ7HvZn3/+iV69ekFNTU3iJgo+7+96enq4cOEC1xy3wlHi0leFlZ6ezqKioiQ+5q+//mLz5s1jbdu2Zerq6qx58+bM09OTHTp0iL169UpB0X61bNkyrnJveZRUMbZw1UdplGf7oo6ODpe7Ub9+fXblyhXG2NdqlDo6OtJ9UUXExsayyMjIcp2jMiucC1Mer1+/LjH/orI5dOgQ27BhA0tJSeFu8/f3l/sW9aJ5YuIuiuLk5MTevXunsOcrTf/+/dnOnTsZY4zNmTOHWVlZsZUrV7JWrVqxLl26KCWm8uQIyVJOTg7btGkTq127NrOyspK4jVogEHC/p7J4f2/WrBm7ceNGub8OeaFBTQnKUsfk48eP7MyZM2zOnDmsTZs2TF1dndna2sopwuLs7OyYUChk7dq1Y5s3b1b4oKqwv//+m40cOZLp6+szIyMj9tNPP0k9iLCzs+OO6datG5s1axZj7Gurhfr165c7Rnm1YKgMZDGoefnyJTt//jy7cOGCUl9rlVnBHxI+l+rm0aNHLD4+njHGWEZGBps0aRKzs7NjAwcO5D7syFteXh67cuUKW7NmDevZsyfT09NjQqGQmZqaspEjR7I9e/YoLJYC+/btY5aWlszExIRt3bqV5eTkKPT5Gfv6/t69e3el12YrDS0/laAsWyDz8/Nx9epVREREICIiAhcvXkR2drZClzj+/fdfBAUFYf/+/fjvv//QtWtXuLm5YcCAAQrdYl2W7YtFbdy4ESoqKpg6dSoiIiLQp08f5OXlITc3Fxs2bMC0adOkPueHDx8QFBQEX19fxMfHV5vlp6IKbxmW1sePH+Hp6YmQkBCuhpGKigqGDRuGrVu3SiznXhGdO3eOS3osWpfJz89Pbs9beFnwyZMnmD9/PkaPHo0OHToAAKKjo7F3716sXr1arpXJZ86ciV9//RU6OjqYOXOm2MeK20otK3l5ebh48SJatGhR7lpBjx49wp49e/Do0SN4e3ujTp06CA0NhampKWxtbcUeq6+vL5Ij5OTkJFWOkDS1wSTVmQkNDcX8+fORlJSE2bNnY+bMmcWaySqKkZERMjMzkZubC21t7WLv73zKfsgTDWpKwGdQIynTv+AiLi9Hni5duoTg4GAcOnQI2dnZUtUfKG+7hY8fP8o82z85ORnXrl1Do0aNeJXyLiw8PBx+fn44evQozMzMMHjwYAwePLjE/iXVgZ6eHm7duiW2FH1pfvjhB8TFxWHz5s3o0KEDBAIBLl++jGnTpqFFixY4ePCgHCKWn+XLl2PFihVo3bp1iUmPx44dU0gcXbp0wfjx4zF8+HCR24ODg7Fz505ERkbK7bmdnZ1x7NgxGBoawsnJqdTET4FAgPDwcLnFUZimpiYSEhLK9BotEBUVhV69eqFTp044f/48EhISYGlpibVr1yI2NhaHDx8We3x5c4QK5zoxxnDs2DEYGBigdevWAIDr168jLS0NgwYNKjXfKTY2FvPmzUNMTAwmTpyIRYsWlSt3c8WKFWLv57OJozyFURWBBjUl4DOoKe8oXt7i4uKwb98+7N+/H2/fvkVWVhav427evInevXsjMzMTGRkZqFGjBt68eQNtbW3UqVOHV8EqWXjy5Em5ej79999/8Pf3h5+fHzIyMvDDDz/Ax8cH8fHxlXLrcXkU/cR48uRJuLi4FPukx6cqqY6ODv7++2907txZ5PYLFy6gZ8+evBraVSQmJiZYu3Yt3N3dlRqHtrY2l3haWGJiIlq2bCm251JV1KZNG/z222/o0qVLmc/RoUMHDB06FDNnzhSZnbx69SoGDBiAZ8+eyTBi8ebNm4d3797Bx8eHS8LOy8uDh4cH9PX1sW7duhKPEwqF0NLSwoQJE8S+H/Ktjlz0g1xOTg6SkpKgqqqKRo0a4caNG2KPz8nJwc8//4zFixeXa4esPFXLLd18yuhLsm7dOpll+stKUlISgoODERQUhMTERDg4OGDZsmUYOnQo73PMmDEDffv25dotxMTEiLRbUBRLS0t07NgR7u7uGDp0qFQdXHv37o2LFy/i+++/x+bNm9GzZ0+oqKjAx8dHjhFXXEWXhNzc3Mp8rpo1a5a4xGRgYKDQtgKy8uXLF3Ts2FHZYcDU1BQ+Pj74/fffRW7fsWMHTE1NFRJDbm4uNDU1ERcXh+bNmyvkOUuzatUqzJ49G7/++iu+/fbbYgNwPjPBt2/fRnBwcLHba9euXWLtK3ny8/PDxYsXRXaVqaioYObMmejYsWOpg5qGDRtCIBCInTEUCAS8BzU3b94sdtvHjx8xevRoDBw4UOLxampqOHbsGBYvXszr+ZRCifk8SiMuA7ysO32UrX379kwoFLJvvvmGrV27lmtEJy0DAwN279497v93795ljDEWExPDrK2tZRavJNevX2ezZ89mDRo0YBoaGqxfv37s4MGDLDs7W+KxKioqbMaMGSwxMVHkdlVVVfbvv//KK+RqYceOHaxr167s+fPn3G0vXrxg3bt3Zz4+PkqMrGzmzp3LVqxYoeww2OnTp5mmpiaztbVl48aNY+PGjWO2trZMU1OTnT59WmFxWFpaSmyKqAhF34sLLtK8N9evX59r3lg4Of7o0aPM0tJSbrGXxNDQkB07dqzY7ceOHWOGhoYKjaUkt2/fZmZmZrweO3r0aPb777/LN6ByqJYzNUWTAasCZ2dn+Pr6Skx+k0RNTY1bU69bty6Sk5PRrFkzGBgYIDk5WRah8tKqVSu0atUKa9euRWRkJIKDgzFhwgSMHz8egwcPFpvAeeHCBfj5+aF169Zo2rQp3N3dMWzYMIXFXtXY29uL5Fk8ePAAZmZmXPHC5ORkaGho4PXr15gwYYKywiyT7Oxs7Ny5E2fPnkWLFi2KJT0qIjEW+Dq7+ODBA2zfvh0JCQlgjKF///6YOHGiwmZqAOCXX37BggULsG/fPqlmR2VNFo1RR4wYgXnz5uHQoUMQCATIz8/HpUuXMHv27DI15i2PMWPGYOzYsXj48CHat28PAIiJicFvv/3Gu86QPKWlpfHuL2ZlZYVff/0Vly9fLnEWrSyNQmWJcmqIiO7du2P06NEYMWIEJk6ciJs3b2Lq1KkIDAzE+/fvceXKFaXFduPGDYwbN453Ab/MzEzs378ffn5+iI2NRV5eHjZs2ICxY8dCT09PARFXDcuXL+f92KVLl8oxEtkTV7ZfkYmxFYW9vT0ePnzIFR8t+gdLUs5FRZKTk4PRo0dj//79YIxBVVUVeXl5GDFiBPz9/RVaMbqggay3tzdevHgB4Gs+17Rp0zBr1iyFxVK09QdjDC9evEBgYCAcHBy4Ks7iiEve5tsoVJ5oUFOC6lZGv7Br167h06dPcHZ2xuvXrzFq1Ciuk/OePXuk3nlU2LZt2/DmzRteGfYFUlJSEBISguDgYNy+fRsdOnSAq6ur1GXS79+/j927dyMwMBBpaWno1q2bxNwqQqobSQNYZQxaMzIycODAAWRlZaF79+7Fkqklefz4MW7cuIH8/HzY29ujcePGyMrKgpaWlpwiFq9gJ6oy+kEVHZAIhULUrl0bLi4uWLBgQdX4sKfEpa8Kq2nTppUup6YycHFxYRYWFrweu2PHDubg4MBUVFSYjY0NW7VqlUyKPeXm5rJjx46xvn37lvtc1dnnz59ZSkoKe/r0qcilMktJSSlzLlpVkJOTw5YtW8aSk5OVFsPTp0+Zg4MD09XVZV27dmVPnz5lTZo04fJrtLW1JVZ7L+Dh4VHi7enp6czR0VGGUVdf+fn5LD8/X9lhiKBBTQmqcxl9WbVbKK8GDRqw2bNnV+vKvxXR/fv3WefOnYtVvK2MyfWMfa0au3z5cqavr899LQYGBmzFihUsLy9P2eEpnK6urlIrxQ4dOpS1b9+eBQYGsn79+rGmTZuyPn36sNTUVPbq1Ss2ZMgQ5uzszOtcVlZWbNGiRSK3paens86dO7POnTvLI3yxDh06xIYOHcratWvH7O3tRS7KUJ5B/N69e1nz5s2ZhoYG09DQYHZ2diwgIEDGEZYNDWpKUV3/mFaUdgsVbfRPvurYsSNzcHBgZ86cYTdv3mRxcXEil8pm/vz5rHbt2mzbtm0sPj6excXFsa1bt7LatWuzhQsXKjs8hevfvz/bs2eP0p6/bt26XJ+3t2/fMoFAwC5fvszdHxcXx2rWrMnrXI8fP2b16tVjGzZsYIx9bWXToUMH9t1337H09HTZBy+Gt7c309XVZZ6enkxdXZ1NmDCBde3alRkYGPB+neXl5bH79++zCxcusKioKJELX7IYxP/+++9MW1ubzZ07l504cYIdP36czZkzh2lra3Pfa2WiQU0haWlpbOvWrcze3r5SfuqUlTt37rAFCxYwCwsLpqamxnr16sWCgoJYRkYGr+PfvHnD/T85OZktXryYzZ49W6pfPlIxaWtrs4SEBGWHITMmJibsxIkTxW4/fvw4q1evnhIiUi4fHx9mbGzMZs2axYKDg9mJEydELvImFApZamoqd11HR0ekT1lqaqpU7823b99mNWvWZJs2bWLt27dnjo6OCh/QMMaYtbU1Cw4OZoyJbi9fvHgx8/T0lHh8dHQ0s7Cw4GZFy1p+RBaDeHNzc7Z3795it/v7+zNzc3PescgLDWoYY+fOnWOurq5MS0uLNW3alC1atKhCdyGVRnnzgy5evMg8PDxY7dq1mZ6entjH3rp1i5mZmTGhUMisra3ZzZs3Wd26dZmuri7T19dnKioqJdZqIJVH69at2YULF5QdhsxoaGiw+/fvF7v93r17TFNTU67P3bJly2LLEKVdFEXZtbsKd5RmrHjzVWkHNYx9HRDo6OgwFxcXlpmZKbNYpaGlpcU1v6xduzY3q5mYmMhq1Kgh8fhvvvmGDR06lN29e5e9f/+epaWliVz4ksUgXkNDgz148KDY7YmJiUxDQ4N3LPJSLevUACWX0c/JycGRI0eqVBl9Ly8vqfo+FaWjowMtLS2oq6vj06dPYh87d+5c2NnZYd++fdi3bx++//579O7dG76+vgCAKVOm4LfffsOAAQPKHA9RrjVr1mDu3Lnw8vKCnZ1dsbouytjRUR7ffPMNtmzZUmyr65YtW8q104+Pivh7UBFqeC1ZsoRrwPvlyxesWrWKq2ItqV1E0ZpKBTQ0NPD8+XN06tSJu02R29ONjY3x9u1bmJmZwczMDDExMfjmm2+QlJQExmMD8oMHD3D48GFYWVmVK453796hadOmxW5v2rQp70aUVlZWOHjwIBYuXChy+4EDB6TemSYP1XJLd+Ey+q6urlwZfTU1tWrZG6ioktotjBgxAkOHDhXbhblWrVoIDw9HixYtkJ6eDn19fcTGxnIN3O7du4f27dsjLS1NQV8JkTWhUAgAxf5wMMYgEAgqXefzqKgo9OnTBw0bNhRp0JmSkoIzZ87gu+++U3aISpOdnQ1NTU2FPqe4hpqFlVacr6LWVBo/fjxMTU2xdOlS+Pj4YObMmejUqROuXbuGQYMGYffu3WKPd3Fxwdy5c9GzZ89yxdGuXTu0a9eu2CB+ypQpuHr1KmJiYiSe48iRIxg2bBi6du2KTp06QSAQ4OLFizh37hwOHjzIq92CPFXLQY2qqiqmTp2KSZMmiYwsK+OgJisrC//88w+cnZ2L1Rj4+PEjIiMj0aNHD2hoaPA6X4cOHRAbGws7Ozu4urpixIgRqF+/Pq9jhUIhUlNTUadOHQAQaSIHAC9fvkS9evUq3R8+8n9RUVFi73d0dFRQJLLz/PlzbN26Fffu3QNjDDY2NvDw8EC9evWUHZrC5eXlwcvLCz4+Pnj58iUSExNhaWmJxYsXw9zcHOPGjVN2iJVSfn4+8vPzoar6dXHk4MGDXP2viRMnQl1dXezxx44dwy+//II5c+aUOEPaokULXnHIahB//fp1bNy4kat+bWNjg1mzZhVrmKkUylv5Up7Lly+z8ePHM319fda2bVtul09l7A20adMm5uLiUur9Xbp0YZs3b+Z9vgULFrA7d+6UKRaBQCCyW0pXV5c9fvyYu16W9fCS4hszZky5zkHIo0ePKtQOu6I9jopeFGX58uXM0tKS7du3j2lpaXH5LAcOHGDt27dXWBzVCZ+dtqXlOJUl1+nZs2ds4cKFbNCgQWzgwIFs0aJF7NmzZ2WMvuKpljM1BapCGf22bdti8eLF6Nu3b4n3nzp1CitWrEBsbKzU5/bw8MCKFStQq1YtXo8XCoXo1asXNyt08uRJuLi4cKXWP3/+jNDQ0HLN1IwaNQopKSnVrny9Mt26dQvNmzeHUCjErVu3xD6W7ydGZVNRUcGLFy+4WcVhw4bhjz/+QN26dZUSz4kTJ0Su5+Tk4ObNm9i7dy+WL1+usBkSKysr7NixA126dBGZab137x46dOiA9+/fKySOsqpRowYSExNRq1YtGBkZiV3K0tXVha2tLdasWaPw1+2HDx8QFBQEX19fxMfHS3xPfPr0qdj7zczMyhVPSkoKli5dKran3vPnz7FhwwYsWbKkWO7chw8fsHLlSsyePVtpv0MFqvWgprDKWkbfyMgI8fHxpbZ0SE5OxjfffFOmNyN9fX3ExcVxy0eS8G3MtmfPHqljIcpTeFlRKBRCIBCUmNxYmXJqJC2VVhTBwcE4cOBAsUGPvGhpaeHevXswMzMT+Z7cvXsXbdu2RXp6ukLiKKu9e/fixx9/hIaGBvbu3Sv2sZ8/f8aZM2eQkpKC69evKyS+8PBw+Pn54ejRozAzM8PgwYMxePBgpS/bxMfHo1WrVmJ/f2fPno2PHz9i586dJd4/ceJEGBgYYM2aNfIKk5dqu/upKGtra6xduxarV6/GyZMnxY5YK5Lc3Fy8fv261EHN69evkZubW6ZzSzvepcFK1ZSUlITatWtz/yeK065dO/z0008Kez5bW1tcuHCh2Cf/Q4cOKf0PLx+jRo0q8f+l6dWrF7799lt5hiTTnbZ3795FcnIyvnz5InJ7v379ZBlyiUJDQ+Hj41Pq/SNHjsRPP/1Eg5qKRkVFBQMGDKiQ2y1LYmtri7Nnz5b6i/nPP//A1tZWwVGVz6dPn5CYmAhra2vo6urixo0b2LRpE7KysjBgwAC4uroqO8RqpfAfuPJOc1cUAoGg2NIEn103ipSVlYXNmzejQYMGCnvOpUuXwt3dHc+ePUN+fj6OHj2K+/fvIyAgAKdOnVJYHIpiamqKV69eye38hXfabt68mdtpK25wUJLHjx9j4MCBuH37tshMacFrVhEzpElJSWKbPDdo0ABPnjyRexyS0KCmkhs7dixmzpwJW1tbfP/99yL3nTx5EitXrsSGDRvKdG5JdWnk4fz58/j++++Rnp4OIyMjhISEYMiQIahfvz5UVFRw9OhRZGZmKvTTKxEVGBgIHx8fJCUlITo6GmZmZti0aRMsLCzQv39/ZYfHC2MMo0eP5vK/srOzMXHiRC7/q8DRo0cVEk/R/A/GGD59+gRtbW3s27dPITEAQN++fXHgwAF4eXlBIBBgyZIlaNWqFU6ePIlu3bopLI6ykianhm9dlvIICwsrcaettKZNmwYLCwucPXsWlpaWiI2Nxdu3bzFr1iysX79ehhGXTktLC0+ePCl1YPPkyROldT4vjAY1ldzPP/+M8+fPo1+/fmjatCmsra0hEAiQkJCAxMRE/PDDD/j555/FnkOa4nzyLq72yy+/YOjQoVi+fDn27NmDYcOGYfLkyfDy8gIArFy5Elu3bqVBjZJs374dS5YswfTp07Fq1SruE6KhoSE2bdpUaQY1RZcm3NzclBTJV5s2bRK5LhQKUbt2bbRr1w5GRkYKjaVHjx7o0aOHQp+Tr2bNmiExMbHUmYmNGzdymzyKfk+V4cKFC/Dz80Pr1q3RtGlTuLu7Y9iwYVKfJzo6GuHh4ahduzaEQiGEQiE6d+6M1atXY+rUqbh586bY4wcNGiT2fj61w9q1a4fAwEA4ODiUeH9AQADatm0r8TzyRonCVcTBgwcRHByMBw8egDGGJk2aYMSIEfjhhx8kHluQ/MmHvKc5DQ0NERMTg6ZNm+LLly/Q0tLCjRs3uOquDx8+hL29vVJmkQhgY2MDLy8vDBgwQCSR9M6dO3BycsKbN2+UHSKRgU+fPonk1AmFQujq6ioxoq+OHTuGjx8/SsyXyc3NRVBQEHr06AFjY2MFRVe68u60NTIywvXr12FpaYlGjRrB19cXzs7OePToEezs7CRWWpbFJo6IiAh069YN06dPx5w5c7hdTi9fvsTatWvh7e2NsLAwuLi48HouuVHKRnJSoURGRnIXf39/ZmxszObPn881sZs/fz4zMTFh/v7+co9FHr1fiOxoampyPWwK/2wSExPl3iupOsjIyGAJCQksPj5e5CJvN2/eZL179+au6+rqitTJUVFRYbGxsXKPQ5YK91uqSO7du8fmzJnDjI2NmaamJuvbt6/EYzp37sz1zRs+fDjr2bMnu3jxIhs5ciSztbWVc8T/5+PjwzQ0NJhQKGSGhobMyMiICYVCpqGhwbZt26awOMShQQ0R4eLiwnWTLSwoKIg5OjrK/fmFQqFIAT89PT2ZF/AjZdesWTN2/PhxxpjooMbb25u1atVKmaFVaq9evWJ9+vRRWvG9sWPHMi8vL+66rq4uCwoKYpGRkSwiIoK5u7szNzc3uceRmZnJTpw4wT5+/Fjsvg8fPrATJ06w7OxsXudycnKq0A10c3Nz2bFjx3gNakJDQ9mRI0cYY4w9fPiQNWvWjAkEAlarVi129uxZeYcq4r///mMbNmxgHh4ebNKkSWzjxo0sJSVFoTGIQ4OaKq5Lly7MwsKC9+O1tLRYYmJisdvv37/PtLS0ZBlaiQQCAbOzs+O6E6uoqDBbW1vuup2dHQ1qlMjPz4/Vr1+f7d+/n+no6LCQkBC2cuVK7v+kbEaMGME6duzIYmNjmY6ODgsLC2OBgYHM2tqanTp1Su7Pb21tzc6fP89dLzpDGhMTwxo2bCj3OGRZIf3gwYPM0tKSbd68mV2+fFnhs1/y9vbt2wpVFbuioEThKm7gwIFS5TmYmprCx8cHv//+u8jtO3bsgKmpqazDK6Zok7mSEk8HDx4s9zhIycaMGYPc3FzMnTsXmZmZXG8wb29v/Pjjj8oOr9IKDw/HiRMn0KZNGwiFQpiZmaFbt27Q19fH6tWr0adPH7k+f0pKisiulqKVxE1MTPDy5Uu5xgAAQUFBWLx4can3T58+HStWrMDkyZMlnqsgIXfq1KncbQXboStLoUhJCb7A116GxsbG6NatW6mV5asTShQmIs6cOYPBgwejUaNGaN++PQAgJiYGjx49wpEjR9C7d28lR0gqijdv3iA/P5+ryvvs2TPezU+JKH19fdy6dQvm5uYwNzdHUFAQOnXqhKSkJNja2kpMBC2vGjVq4OTJk+jUqVOJ91+6dAl9+/aV+zZoWVZIl3drAUXgk+Cbn5+PV69eISoqCrNnz8aKFSsUEFnFRTM1RETv3r2RmJiI7du3c12L+/fvj4kTJypkpoZUHgWf5FNTU7Fq1Sr4+voiKytLyVFVTtbW1rh//z7Mzc3RsmVL7NixA+bm5vDx8YGJiYncn9/e3h7Hjx8vdVBz9OhRhVQUlmWF9MowaJFEmirtp0+fxqRJk2hQo+wASPmMHTuW1+OkaftgamrK1YWpaBYuXIjU1NRK08aiqkhLS4OnpyfCwsKgpqaG+fPnY/LkyVi2bBnWr18PW1tb+pmUw/Tp0/H8+XMAX5dge/TogaCgIKirq8Pf31/uz+/h4YEff/wR5ubmmDRpEoRCIYCvJRy2bduGzZs3Izg4WO5xyKNCujJbCyhSp06d0Lp1a2WHoXS0/FTJFay/29vbi+3VdOzYsVLvk9R5uTBld2GmLt3K4eHhgZMnT2LYsGEIDQ1FQkICevTogezsbCxduhSOjo7KDrFKyczMxL1799CwYUOR3BZ5mjdvHtatWwc9PT1YWlpCIBDg0aNHSE9Px8yZM7Fu3Tq5x7Bz507MnDkT+/fvL7FC+vDhw7FhwwaJBUWBitFagCgeDWoqOQ8PD+zfvx8NGzbE2LFj4ebmhho1akh1DnGdlwurLMl1RPbMzMywe/dudO3aFY8fP4aVlRWmTp1aIaq2VmaZmZmYM2cOjh8/jpycHHTt2hV//PGHwgYyRcXExCAkJAQPHjwAADRu3BjDhw/n8usUwc3NDcHBwaVWSA8JCeF1nr59+0JFRQW7du0qsbXAd999J+evpHqpKB84aVBTBXz+/BlHjx6Fn58fLl++jD59+mDcuHHo3r07r0rBkhLqCqsK69REempqanj69Cnq1asHANDW1kZsbCyaN2+u5Mgqtzlz5mDbtm1wdXWFpqYmQkJC4OTkhEOHDik7NKUqT4X0ArVq1UJ4eDhatGgBAwMDxMbGwtraGuHh4Zg1a5bE1gJEOgsWLEBqaqpUeUByoYx95ER+njx5wpYtW8YsLS2Zqakp+/Tpk7JDksq5c+dYs2bN2IcPH4rdl5aWxmxsbFhUVJQSIqveihZF1NXVFSmKSMrG0tJSpL7PlStXmKqqKsvNzVViVFWDoaEhV2vH0tKShYeHM8a+Fq9TRM0tohyUKFzFCAQCbikpPz+/TOdQZhfmTZs24aeffiqxcaaBgQEmTJiAjRs3ltpUjcgHq2BdrauKlJQUkWWQtm3bQlVVFc+fP6fdhuXUvHlz3Lp1C5aWlmjXrh3Wrl0LdXV17Ny5E5aWlsoOr0p5//499u3bh927dyMuLk6psQiV+uxEJj5//oyQkBB069YN1tbWuH37NrZs2YLk5GSpm9Bt374dM2fORO/evZGWllasC7O8xcfHo2fPnqXe3717d1y/fl3ucRBRo0aNQp06dWBgYAADAwO4ubmhXr163PWCC5FOXl4e1NXVRW5TVVXlvW25uunatSvvAckvv/zCfbBbuXIlnj59iu+++w5nzpzBH3/8Ic8wq42zZ89i+PDhqFevHtauXVshNgxQTk0lVzhReMyYMXBzc0PNmjXLfD5ld2HW1NTEnTt3YGVlVeL9Dx8+hJ2dHdVDIVWCUChEr169uBkw4OsuHxcXF5FZMJoB+2rLli14+/ZtscrjfL179w5GRka8cg1JyZKTk7Fnzx7s2bMH6enpeP/+PQ4ePFhhKr3T8lMl5+Pjg4YNG8LCwgJRUVGIiooq8XF83xSTkpJKLLKloaGBjIyMcsXKR/369XH79u1SBzW3bt1SSDEyQhRh1KhRxW5zc3NTQiSVA5/2COJIuzOU/N/Bgwfh6+uLS5cuoXfv3vD29kavXr2go6ODZs2aKTs8Dg1qKrmRI0fK9FOHhYUF4uLiiu1y+uuvv2BjYyOz5ylN7969sWTJEvTq1Quampoi92VlZWHp0qXF6lcQUlkpfacIT4oqeimLXkfyKEhKgBEjRmDu3Lk4cuQI9PT0lB1OqWhQU8nJutronDlz4OnpiezsbDDGEBsbi5CQEKxevRq+vr4yfa6S/PLLLzh69CiaNGmCyZMni9Sp2Lp1K/Ly8rBo0SK5x0EI+b9nz54hJSVF7s/DJy8rPz8fDx48gK+vb4m9jvz9/XkVJCXSGTt2LLZt24aoqCi4u7tj2LBhMDIyUnZYxSlz6xWpmHbu3MkaNmzIBAIBEwgErEGDBszX11dhz//kyRPWq1cvJhQKuRiEQiHr1asXS0pKUlgchJCK69SpU8zU1LTY7ZMmTWJGRkbsm2++Yd7e3uzt27dKiK5qyszMZP7+/szBwYFpaGiwfv36MRUVFXb79m1lh8ahRGFSqqJdmBXt/fv3ePjwIRhjaNy4ccX8VEAIUYq0tDSMHTu2xHzB8hYkJZI9ePAAfn5+CAgIQHp6Ovr06YMhQ4bwWkKUJxrUEBHLly+Hm5sbGjVqpOxQCCEKkpWVhXPnznH5agsWLMDnz5+5+1VUVPDrr78Wy3OrDJ4+fQp/f38EBAQgJycHd+/elbrUBSldfn4+Tp8+jd27d+Ovv/4Sed0oA9WpISKOHDmCJk2aoH379tiyZQtev36t7JAIIXIWEBCAHTt2cNe3bNmCy5cv4+bNm7h58yb27duH7du3KzHCspNFQVJSOqFQiL59++L48eMKybuSGI+yAyAVy61bt3Dr1i24uLhgw4YNqF+/Pnr37o3g4GBkZmYqOzxCiBwEBQUV2zUUHByMiIgIREREYN26dTh48KCSopOeLAuSEn5evHiB7OxsZYdBy09EvEuXLiE4OBiHDh1CdnY2Pn78qOyQCCEyZmxsjHPnzsHW1hYAULt2bVy9ehXm5uYAgMTERLRp0wYfPnxQYpT8yLogKeGnWbNmSExM5KrQKwtt6SZi6ejoQEtLC+rq6vj06ZOywyGEyMGHDx+gqvr/PwdFl53z8/OVnivBl6wLkhJ+AgICKsRsPg1qSDFJSUkIDg5GUFAQEhMT4eDggGXLlmHo0KHKDo0QIgcNGjTAnTt3YG1tXeL9t27dQoMGDRQcVdnIuiAp4adNmzZ49uyZssOg5SciqkOHDoiNjYWdnR1cXV0xYsQI1K9fX9lhEULkaNq0aTh79iyuX79eYiXv1q1bo2vXrvD29lZShKQiS01NxapVq+Dr66v0vnw0qCEiFi5cCFdXV25tnRBS9b18+RItW7aEuro6Jk+ejCZNmkAgEODevXvYsmULcnNzcfPmTdStW1fZoRIlSUtLg6enJ8LCwqCmpob58+dj8uTJWLZsGdavXw9bW1vMnDkTw4cPV2qcNKghJfry5QuSkpLQqFEjkbV2QkjVlJSUhEmTJuGff/7h2gsIBAJ069YN27Ztg6WlpZIjJMrk4eGBkydPYtiwYQgNDUVCQgJ69OiB7OxsLF26FI6OjsoOEQANakgRWVlZmDx5Mvbu3Qvg664HS0tLTJ06FfXq1cP8+fOVHCEhRJ7evXuHhw8fAgCsrKyoszUBAJiZmWH37t3o2rUrHj9+DCsrK0ydOhWbNm1SdmgiqE4NETF//nzEx8cjMjJSZG29a9euOHDggBIjI4QoQo0aNdC2bVu0bduWBjSE8/z5c9jY2AAALC0toampifHjxys5quJoXYGIOH78OA4cOID27duL7CCwsbHBo0ePlBgZIYQQZcnPz4eamhp3XUVFBTo6OkqMqGQ0qCEiXr9+XWIDy4yMDNomSQgh1RRjDKNHj4aGhgYAIDs7GxMnTiw2sFF2/R8a1BARbdq0wenTpzFlyhQA4AYyu3btQocOHZQZGiGEECUZNWqUyHU3NzclRSIeDWqIiNWrV6Nnz564e/cucnNz4e3tjX///RfR0dGlVuYkhBBSte3Zs0fZIfBCicJERMeOHXHp0iVkZmaiUaNGCAsLQ926dREdHY1vv/1W2eERQgghpaIt3UTErVu30KJFixLvO378OAYMGKDYgAghhBCeaKaGiOjRowceP35c7PYjR47A1dVVCRERQggh/NCghoiYNGkSunTpghcvXnC3HThwACNHjoS/v7/yAiOEEEIkoOUnUkxBc7sLFy4gNDQU48ePR2BgIAYPHqzs0AghhJBS0aCGlMjd3R1XrlzBs2fPEBwcjP79+ys7JEIIIUQsGtQQ/Pnnn8Vuy8nJwYwZM9C9e3f069ePu73w/wkhhJCKhAY1BEIhv9QqgUCAvLw8OUdDCCGElA0NagghhBBSJdDuJ0IIIYRUCdQmgRRz7tw5nDt3Dq9evUJ+fr7IfX5+fkqKihBCCBGPBjVExPLly7FixQq0bt0aJiYm1JmbEEJIpUE5NUSEiYkJ1q5dC3d3d2WHQgghhEiFcmqIiC9fvqBjx47KDoMQQgiRGg1qiIjx48cjODhY2WEQQgghUqOcGiIiOzsbO3fuxNmzZ9GiRQuoqamJ3L9hwwYlRUYIIYSIRzk1RISzs3Op9wkEAoSHhyswGkIIIYQ/GtQQQgghpEqgnBpSoocPH+Lvv/9GVlYWAIDGvoQQQio6GtQQEW/fvkWXLl3QpEkT9O7dGy9evADwNYF41qxZSo6OEEIIKR0NaoiIGTNmQE1NDcnJydDW1uZuHzZsGEJDQ5UYGSGEECIe7X4iIsLCwvD333+jQYMGIrc3btwYT58+VVJUhBBCiGQ0U0NEZGRkiMzQFHjz5g00NDSUEBEhhBDCDw1qiAgHBwcEBARw1wUCAfLz87Fu3Tqx270JIYQQZaMt3UTE3bt34eTkhG+//Rbh4eHo168f/v33X7x79w6XLl1Co0aNlB0iIYQQUiIa1JBiUlNTsX37dly/fh35+flo1aoVPD09YWJiouzQCCGEkFLRoIYQQgghVQLtfiLFpKWlITY2Fq9evUJ+fr7IfSNHjlRSVIQQQoh4NFNDRJw8eRKurq7IyMiAnp4eBAIBd59AIMC7d++UGB0hhBBSOhrUEBEFlYS9vLxK3NpNCCGEVFQ0qCEidHR0cPv2bVhaWio7FEIIIUQqVKeGiOjRoweuXbum7DAIIYQQqVGiMMGff/7J/b9Pnz6YM2cO7t69Czs7O6ipqYk8tl+/fooOjxBCCOGFlp8IhEJ+E3YCgQB5eXlyjoYQQggpGxrUEEIIIaRKoJwaQgghhFQJNKghIqZOnYo//vij2O1btmzB9OnTFR8QIYQQwhMNaoiII0eOoFOnTsVu79ixIw4fPqyEiAghhBB+aFBDRLx9+xYGBgbFbtfX18ebN2+UEBEhhBDCDw1qiAgrKyuEhoYWu/2vv/6ignyEEEIqNKpTQ0TMnDkTkydPxuvXr+Hi4gIAOHfuHH7//Xds2rRJucERQgghYtCWblLM9u3bsWrVKjx//hwAYG5ujmXLllGHbkIIIRUaDWpIqV6/fg0tLS3o6uoqOxRCCCFEIhrUELGioqKQmZmJ9u3bw8jISNnhEEIIIaWinBoCAFi3bh3S09OxfPlyAABjDL169UJYWBgAoE6dOjh37hxsbW2VGSYhhBBSKtr9RAAAISEhsLGx4a4fPnwY58+fx4ULF/DmzRu0bt2aG/AQQgghFREtPxEAgJGRES5fvoxmzZoBAMaMGYPc3FwEBgYCAGJiYjB06FCkpKQoM0xCCCGkVDRTQwAAOTk50NDQ4K5HR0ejY8eO3PV69epR8T1CCCEVGg1qCICvRffOnz8PAEhOTkZiYiIcHR25+//77z/UrFlTWeERQgghElGiMAEATJo0CZMnT8aFCxcQExODDh06iOTYhIeHw97eXokREkIIIeLRoIYAACZMmABVVVWcOnUKDg4OWLp0qcj9z58/x9ixY5UUHSGEECIZJQoTQgghpEqgnBpCCCGEVAk0qCGEEEJIlUCDGkIIIYRUCTSoIYQQQkiVQIMaQgghhFQJNKghvGzbtg0rVqxQdhiEEEJIqWhLN+GlS5cuSEpKwuPHj5UdCiGEEFIiGtQQQgghpEqg5SfCy+3btzF9+nRlh0EIIYSUigY1pFQfP37Ejh070LZtW3zzzTeIjIxUdkiEEEJIqWhQQ4qJiorCyJEjYWJiAg8PD7i4uCAxMRFxcXHKDo0QQggpFQ1qCADgxYsX8PLygpWVFX788UfUqlULUVFREAqFGDlyJKysrJQdIiGEECIWdekmAAALCwsMHToUW7duRbdu3SAU0niXEEJI5UJ/uQgAwMzMDBcvXsT58+eRmJio7HAIIYQQqdGghgAA7t+/j3379uHFixdo06YNvv32W2zcuBEAIBAIlBwdIYQQIhnVqSHFpKenIyQkBH5+frhy5QocHR0xYsQIDBgwALVr11Z2eIQQQkiJaFBDxEpISMDu3bsRGBiId+/eIScnR9khEUIIISWiQQ3hJScnBydPnsSgQYOUHQohhBBSIhrUEEIIIaRKoERhwkvXrl1haWmp7DAIIYSQUlGdGsLLwIED8ebNG2WHQQghhJSKlp8IIYQQUiXQ8hMhhBBCqgRafiIAgLFjx/J6nJ+fn5wjIYQQQsqGlp8IAEAoFMLMzAz29vYQ95I4duyYAqMihBBC+KNBDQEAeHh4YP/+/WjYsCHGjh0LNzc31KhRQ9lhEUIIIbzRoIZwPn/+jKNHj8LPzw+XL19Gnz59MG7cOHTv3p36PxFCCKnwaFBDSvT06VP4+/sjICAAOTk5uHv3LnR1dZUdFiGEEFIq2v1ESiQQCCAQCMAYQ35+vrLDIYQQQiSiQQ3hfP78GSEhIejWrRusra1x+/ZtbNmyBcnJyTRLQwghpMKjLd0EgGii8JgxY7B//37UrFlT2WERQgghvFFODQHwdUt3w4YNYW9vLzYp+OjRowqMihBCCOGPZmoIAGDkyJG0w4kQQkilRjM1hBBCCKkSKFGYEEIIIVUCDWoIIYQQUiXQoIYQQgghVQINagghhBBSJdCghhBCCCFVAg1qCCGEEFIl0KCGEEIIIVUCDWoIIYQQUiX8Dw6ZDwvPUQhoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Closeness centrality\n",
    "closeness_dict = nx.closeness_centrality(G)\n",
    "closeness_df = pd.DataFrame.from_dict(closeness_dict, orient='index', columns=['centrality'])\n",
    "# Plot top 30 nodes\n",
    "closeness_df.sort_values('centrality', ascending=False)[0:29].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "64a4f3a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save centrality measures\n",
    "nx.set_node_attributes(G, degree_dict, 'degree_centrality')\n",
    "nx.set_node_attributes(G, betweenness_dict, 'betweenness_centrality')\n",
    "nx.set_node_attributes(G, closeness_dict, 'closeness_centrality')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "049e8fd6",
   "metadata": {},
   "source": [
    "# Community detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de1e7ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Willem Maas': 0,\n",
       " 'Jan Engelman': 0,\n",
       " 'Jan Bruna': 0,\n",
       " 'Henk Kuitenbrouwer': 0,\n",
       " 'Albert Kuyle': 0,\n",
       " 'Albert Helman': 0,\n",
       " 'Anton van Duinkerken': 0,\n",
       " 'Sybold van Ravesteyn': 0,\n",
       " 'A. van Domburg': 0,\n",
       " 'A.J.D. van Oosten': 0,\n",
       " 'Ad. Sassen': 0,\n",
       " 'Wouter Paap': 0,\n",
       " 'A.G.W. Severijnen': 0,\n",
       " 'Gerard Wijdeveld': 0,\n",
       " 'Jan Vercammen': 0,\n",
       " 'Carel Scharten': 1,\n",
       " 'Margo Scharten-Antink': 1,\n",
       " 'J.N. van Hall': 1,\n",
       " 'Johan de Meester': 1,\n",
       " 'R.P.J. Tutein Nolthenius': 1,\n",
       " 'H.T. Colenbrander': 1,\n",
       " 'D. van Blom': 1,\n",
       " 'Johan Huizinga': 1,\n",
       " 'M.H. van Campen': 1,\n",
       " 'R.C. Bakhuizen van den Brink': 1,\n",
       " 'H. Marsman': 1,\n",
       " 'J.C. Bloem': 1,\n",
       " 'Arthur J.H. Cornette': 1,\n",
       " 'D.A.M. Binnendijk': 1,\n",
       " 'Martinus Nijhoff': 1,\n",
       " 'N.A. Donkersloot': 1,\n",
       " 'Jan Greshoff': 2,\n",
       " 'H.E.H. van Loon': 1,\n",
       " 'E.J. Dijksterhuis': 1,\n",
       " 'Theun de Vries': 1,\n",
       " 'J.H. Plantenga': 1,\n",
       " 'B.M. Telders': 1,\n",
       " 'Anton van der Boom': 1,\n",
       " 'J.N. Bakhuizen van den Brink': 1,\n",
       " 'F. Bordewijk': 1,\n",
       " 'A.N. Molenaar': 1,\n",
       " 'C.F.A. van Dam': 4,\n",
       " 'J.W.F. Werumeus Buning': 4,\n",
       " 'B.A. van Groningen': 1,\n",
       " 'J.C.H. de Pater': 1,\n",
       " 'Cornelia Serrurier': 1,\n",
       " 'G.P. Frets': 1,\n",
       " 'Marie Ramondt': 1,\n",
       " 'Johanna K. Oudendijk': 1,\n",
       " 'Jan Barents': 1,\n",
       " 'P. Geyl': 1,\n",
       " 'Martin Permys': 1,\n",
       " 'L. van den Ham': 1,\n",
       " 'C.J. Kelk': 1,\n",
       " 'Willem van Maanen': 1,\n",
       " 'Jaap Romijn': 1,\n",
       " 'A. Dupont': 5,\n",
       " 'Jaak Muyldermans': 5,\n",
       " 'Frans Drijvers': 5,\n",
       " 'Jan Emiel Vliebergh': 5,\n",
       " 'Jules Persyn': 2,\n",
       " 'E. Vliebergh': 2,\n",
       " 'M.E. Belpaire': 2,\n",
       " 'August van Cauwelaert': 2,\n",
       " 'J.A. Hallez': 2,\n",
       " 'Gerard Walschap': 2,\n",
       " 'Jac. Weil': 6,\n",
       " 'Frederik van Eeden': 6,\n",
       " 'Jozef Uytterhoeven': 2,\n",
       " 'Louisa Duykers': 2,\n",
       " 'L. de Smet': 2,\n",
       " 'J.A. Goris': 2,\n",
       " 'Marnix Gijsen': 2,\n",
       " 'Raphaël M.J. Kreemers': 2,\n",
       " 'Maurice Roelants': 2,\n",
       " 'Julien Jozef Kuypers': 2,\n",
       " 'Fernand Deschamps': 2,\n",
       " 'Urbain van de Voorde': 7,\n",
       " 'Paul Claudel': 7,\n",
       " 'André Demedts': 8,\n",
       " 'Albert Westerlinck': 8,\n",
       " 'E. du Perron': 9,\n",
       " 'Menno ter Braak': 9,\n",
       " 'Simon Vestdijk': 9,\n",
       " 'Raymond Herreman': 2,\n",
       " 'Raymond Brulez': 2,\n",
       " 'Karel Jonckheere': 2,\n",
       " 'Hendrik van der Wal': 10,\n",
       " 'Jan L. Walch': 10,\n",
       " 'Nico van Suchtelen': 11,\n",
       " 'Johann Wolfgang von Goethe': 11,\n",
       " 'Mea Verwey': 10,\n",
       " 'Frans Coenen': 10,\n",
       " 'C.G.N. de Vooys': 12,\n",
       " 'Jan Koopmans': 12,\n",
       " 'D.C. Tinbergen': 12,\n",
       " 'D.C. Hesseling': 12,\n",
       " 'M. Schönfeld': 12,\n",
       " 'C.B. van Haeringen': 12,\n",
       " 'G.G. Kloeke': 12,\n",
       " 'W. Kramer': 12,\n",
       " 'C.C. de Bruin': 12,\n",
       " 'W.A.P. Smit': 12,\n",
       " 'Fernand Toussaint van Boelaere': 2,\n",
       " 'Albert van Hoogenbemt': 2,\n",
       " 'Herman Teirlinck': 2,\n",
       " 'Hubert Lampo': 2,\n",
       " 'Johan Daisne': 2,\n",
       " 'Piet van Aken': 2,\n",
       " 'Bert Decorte': 2,\n",
       " 'Achilles Mussche': 2,\n",
       " 'Paul Van Keymeulen': 2,\n",
       " 'Cornelis Rijnsdorp': 13,\n",
       " 'Dingeman van der Stoep': 13,\n",
       " 'M. Siesling': 13,\n",
       " 'P.J. Risseeuw': 13,\n",
       " 'C. Ouboter': 13,\n",
       " 'J.C. Schuller': 13,\n",
       " 'P.A. Hekstra': 13,\n",
       " 'C. Vermeer': 13,\n",
       " 'Jacoba M. Vreugdenhil': 13,\n",
       " 'Jaap Das': 13,\n",
       " 'Johan van Hulzen': 13,\n",
       " 'J. van Doorne': 13,\n",
       " 'Jan Elzo Niemeijer': 13,\n",
       " 'Hugo Vandeperre': 14,\n",
       " 'J. Salsmans': 14,\n",
       " 'F. De Raedemaeker': 14,\n",
       " 'Ad. Jansen': 14,\n",
       " 'Alb. Raignier': 14,\n",
       " 'Eugenius Druwé': 14,\n",
       " 'L. Arts': 14,\n",
       " 'K. du Bois de Vroylande': 14,\n",
       " 'Edw. De Schaepdrijver': 14,\n",
       " 'J. Donceel': 14,\n",
       " 'R. van Ooteghem': 14,\n",
       " 'M. Dufraing': 14,\n",
       " 'J.A. van Houtte': 14,\n",
       " 'Emiel Jan Janssen': 14,\n",
       " 'L. Elaut': 14,\n",
       " 'G. De Wolf': 14,\n",
       " 'A. Snoeck': 14,\n",
       " 'H. Bruders': 14,\n",
       " 'Louis H.M. Monden': 14,\n",
       " 'B. De Wint': 14,\n",
       " 'Jan Jozef van Laer': 14,\n",
       " 'L.G. Polspoel': 14,\n",
       " 'A. Darquennes': 14,\n",
       " 'L. Verelst': 14,\n",
       " 'L. Vander Kerken': 14,\n",
       " 'Leo Simoens': 14,\n",
       " 'E.J. Vandenbussche': 14,\n",
       " 'Alb. Block': 14,\n",
       " 'J. De Jaegher': 14,\n",
       " 'Edouard Dhanis': 14,\n",
       " 'P. Ellerbeek': 14,\n",
       " 'W. Talloen': 14,\n",
       " 'Michiel Dierickx': 14,\n",
       " 'L. Baudez': 14,\n",
       " 'Karel van Isacker': 14,\n",
       " 'Walter F. Smet': 14,\n",
       " 'Albert Deblaere': 14,\n",
       " 'P. Goedertier': 14,\n",
       " 'E. de Strycker': 14,\n",
       " 'R. Leijs': 14,\n",
       " 'E. De Cooman': 14,\n",
       " 'Fr. van Baarle': 14,\n",
       " 'Pieter Frans Fransen': 14,\n",
       " 'Marcel Brauns': 14,\n",
       " 'Louis Sterkens': 14,\n",
       " 'A. Ampe': 14,\n",
       " 'Jos. Burvenich': 14,\n",
       " 'J. Noë': 14,\n",
       " 'I. De la Potterie': 14,\n",
       " 'Louis Bruyns': 14,\n",
       " 'W. Luyten': 14,\n",
       " 'B. Boeyckens': 14,\n",
       " 'Jozef Andriessen': 14,\n",
       " 'Paul Maria Jozef de Meester': 14,\n",
       " 'L. Mestdagh': 14,\n",
       " 'A. Boone': 14,\n",
       " 'Homerus': 3,\n",
       " 'P.C. Boutens': 3,\n",
       " 'Miguel de Cervantes Saavedra': 4}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import community.community_louvain as community_louvain\n",
    "\n",
    "communities = community_louvain.best_partition(G)\n",
    "communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "259abc4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: When  cdn_resources is 'local' jupyter notebook has issues displaying graphics on chrome/safari. Use cdn_resources='in_line' or cdn_resources='remote' if you have issues viewing graphics in a notebook.\n",
      "CCLAMP_communities.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000px\"\n",
       "            height=\"700px\"\n",
       "            src=\"CCLAMP_communities.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x13b8e22e050>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set communities as a node attribute\n",
    "nx.set_node_attributes(G, communities, 'group')\n",
    "\n",
    "# draw graph with communities\n",
    "com_net = Network(notebook = True, width=\"1000px\", height=\"700px\", bgcolor='#222222', font_color='white')\n",
    "com_net.from_nx(G)\n",
    "com_net.show(\"CCLAMP_communities.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e93a65c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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