1-Graph.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb488d6b",
   "metadata": {},
   "source": [
    "## Building the graph\n",
    "\n",
    "After getting and parsing the data, we need to understand and create metrics that could lead us to our hypothesis and it's impacts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "12ebbc4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from netpixi.integration.gt import Graph\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "g = Graph(directed=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0f08a11c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/colaboration_comments.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1de1e7b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the source repositories (usually belonging to organizations where users collaborate)\n",
    "data['source'] = list(map(lambda x : x.split(\"https://github.com/\")[-1].split('/')[0],data['URL']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1a6cd505",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop columns which we won't use\n",
    "data = data.drop(['COMMIT_ID','COMMENT_ID','URL'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8baf9157",
   "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>AUTHOR</th>\n",
       "      <th>CREATED_AT</th>\n",
       "      <th>BODY</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>zindlerb</td>\n",
       "      <td>2015-01-01T00:00:08Z</td>\n",
       "      <td>:sparkles: :pray: :sparkles:</td>\n",
       "      <td>selfhub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>msabramo</td>\n",
       "      <td>2015-01-01T00:00:53Z</td>\n",
       "      <td>&gt; Can you remove this empty line?\\r\\n\\r\\nDone.</td>\n",
       "      <td>tsuru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>myclamm</td>\n",
       "      <td>2015-01-01T00:00:55Z</td>\n",
       "      <td>Thanks man. That's from week 4 of Hack Reactor...</td>\n",
       "      <td>GoldenLions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>zindlerb</td>\n",
       "      <td>2015-01-01T00:01:44Z</td>\n",
       "      <td>_.reduce would be more elegant, but your call</td>\n",
       "      <td>selfhub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rwjblue</td>\n",
       "      <td>2015-01-01T00:01:54Z</td>\n",
       "      <td>Tiny nit-pick: can you name this something mea...</td>\n",
       "      <td>emberjs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635297</th>\n",
       "      <td>rings360</td>\n",
       "      <td>2015-02-28T09:59:16Z</td>\n",
       "      <td>Asar Sunny &lt;ring2rings93@gmail.com&gt;</td>\n",
       "      <td>AGV-IIT-KGP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635298</th>\n",
       "      <td>jeffcarp</td>\n",
       "      <td>2015-02-28T09:59:16Z</td>\n",
       "      <td>Thank you for doing this! Would you mind if I ...</td>\n",
       "      <td>veswill3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635299</th>\n",
       "      <td>msabramo</td>\n",
       "      <td>2015-02-28T09:59:42Z</td>\n",
       "      <td>Done.</td>\n",
       "      <td>Robpol86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635300</th>\n",
       "      <td>msabramo</td>\n",
       "      <td>2015-02-28T09:59:52Z</td>\n",
       "      <td>Done.</td>\n",
       "      <td>Robpol86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635301</th>\n",
       "      <td>janno42</td>\n",
       "      <td>2015-02-28T09:59:54Z</td>\n",
       "      <td>before it didn't matter because i was deleting...</td>\n",
       "      <td>janno42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>635302 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          AUTHOR            CREATED_AT  \\\n",
       "0       zindlerb  2015-01-01T00:00:08Z   \n",
       "1       msabramo  2015-01-01T00:00:53Z   \n",
       "2        myclamm  2015-01-01T00:00:55Z   \n",
       "3       zindlerb  2015-01-01T00:01:44Z   \n",
       "4        rwjblue  2015-01-01T00:01:54Z   \n",
       "...          ...                   ...   \n",
       "635297  rings360  2015-02-28T09:59:16Z   \n",
       "635298  jeffcarp  2015-02-28T09:59:16Z   \n",
       "635299  msabramo  2015-02-28T09:59:42Z   \n",
       "635300  msabramo  2015-02-28T09:59:52Z   \n",
       "635301   janno42  2015-02-28T09:59:54Z   \n",
       "\n",
       "                                                     BODY       source  \n",
       "0                           :sparkles: :pray: :sparkles:       selfhub  \n",
       "1          > Can you remove this empty line?\\r\\n\\r\\nDone.        tsuru  \n",
       "2       Thanks man. That's from week 4 of Hack Reactor...  GoldenLions  \n",
       "3           _.reduce would be more elegant, but your call      selfhub  \n",
       "4       Tiny nit-pick: can you name this something mea...      emberjs  \n",
       "...                                                   ...          ...  \n",
       "635297                Asar Sunny <ring2rings93@gmail.com>  AGV-IIT-KGP  \n",
       "635298  Thank you for doing this! Would you mind if I ...     veswill3  \n",
       "635299                                              Done.     Robpol86  \n",
       "635300                                              Done.     Robpol86  \n",
       "635301  before it didn't matter because i was deleting...      janno42  \n",
       "\n",
       "[635302 rows x 4 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First view of data\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a7b5a153",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AUTHOR\n",
       "houndci             25587\n",
       "nrel-bot             2997\n",
       "jirikuncar           2136\n",
       "bors-servo           2123\n",
       "codeschool-kiddo     1843\n",
       "                    ...  \n",
       "kevinzurek              1\n",
       "kevinykchan             1\n",
       "kevinxucs               1\n",
       "Specialkbyte            1\n",
       "0-afflatus              1\n",
       "Name: source, Length: 63722, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking to the total contribuitions for the users in the source repositories\n",
    "total_contributions = data.groupby(['AUTHOR']).count()['source'].sort_values(ascending=False)\n",
    "total_contributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "44a6f378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    63722.000000\n",
       "mean         9.969901\n",
       "std        108.223037\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          2.000000\n",
       "75%          6.000000\n",
       "max      25587.000000\n",
       "Name: source, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As we can see there are many contribuitors who participated much less than the average, we're going to use this to filter \n",
    "# the data: contribuitors who has less contributions than the average will not be considered for the network\n",
    "total_contributions.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fa4ae252",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    41971.00000\n",
       "mean        15.13669\n",
       "std        117.15284\n",
       "min          1.00000\n",
       "25%          1.00000\n",
       "50%          2.00000\n",
       "75%          6.00000\n",
       "max       8167.00000\n",
       "Name: AUTHOR, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "contributions = data.groupby(['source']).count()['AUTHOR'].sort_values(ascending=False)\n",
    "contributions.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b1607f4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.histplot( contributions, bins=100)\n",
    "plt.xlabel(\"Contributions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "90f32c0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mozilla', 'aspnet', 'edx', 'apache', 'rust-lang',\n",
       "       'GoogleCloudPlatform', 'caskdata', 'mozilla-b2g', 'Homebrew', 'NREL',\n",
       "       ...\n",
       "       'criteo-forks', 'raintank', 'runabove', 'leizongmin', 'Zeken',\n",
       "       'garethr', 'TaiwanSparkUserGroup', 'jeffbcross', 'marco-c', 'tomekwi'],\n",
       "      dtype='object', name='source', length=4929)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cleaning the data as explaneid above\n",
    "contributions_mean = contributions.mean()\n",
    "repos_edge = contributions[contributions>contributions_mean]\n",
    "repos_edge.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "88f445b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#filtering\n",
    "data_clean = data[data['source'].isin(repos_edge.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7f4fe276",
   "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>AUTHOR</th>\n",
       "      <th>CREATED_AT</th>\n",
       "      <th>BODY</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>zindlerb</td>\n",
       "      <td>2015-01-01T00:00:08Z</td>\n",
       "      <td>:sparkles: :pray: :sparkles:</td>\n",
       "      <td>selfhub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>msabramo</td>\n",
       "      <td>2015-01-01T00:00:53Z</td>\n",
       "      <td>&gt; Can you remove this empty line?\\r\\n\\r\\nDone.</td>\n",
       "      <td>tsuru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>zindlerb</td>\n",
       "      <td>2015-01-01T00:01:44Z</td>\n",
       "      <td>_.reduce would be more elegant, but your call</td>\n",
       "      <td>selfhub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rwjblue</td>\n",
       "      <td>2015-01-01T00:01:54Z</td>\n",
       "      <td>Tiny nit-pick: can you name this something mea...</td>\n",
       "      <td>emberjs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bradenmacdonald</td>\n",
       "      <td>2015-01-01T00:02:33Z</td>\n",
       "      <td>Yeah, I will just remove that. It is always True.</td>\n",
       "      <td>edx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635295</th>\n",
       "      <td>Zuhayr</td>\n",
       "      <td>2015-02-28T09:57:23Z</td>\n",
       "      <td>Whoops, good call.</td>\n",
       "      <td>Baystation12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635296</th>\n",
       "      <td>XhmikosR</td>\n",
       "      <td>2015-02-28T09:58:15Z</td>\n",
       "      <td>@kasper93: are you sure [this](https://github....</td>\n",
       "      <td>kasper93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635297</th>\n",
       "      <td>rings360</td>\n",
       "      <td>2015-02-28T09:59:16Z</td>\n",
       "      <td>Asar Sunny &lt;ring2rings93@gmail.com&gt;</td>\n",
       "      <td>AGV-IIT-KGP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635299</th>\n",
       "      <td>msabramo</td>\n",
       "      <td>2015-02-28T09:59:42Z</td>\n",
       "      <td>Done.</td>\n",
       "      <td>Robpol86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635300</th>\n",
       "      <td>msabramo</td>\n",
       "      <td>2015-02-28T09:59:52Z</td>\n",
       "      <td>Done.</td>\n",
       "      <td>Robpol86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>523262 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AUTHOR            CREATED_AT  \\\n",
       "0              zindlerb  2015-01-01T00:00:08Z   \n",
       "1              msabramo  2015-01-01T00:00:53Z   \n",
       "3              zindlerb  2015-01-01T00:01:44Z   \n",
       "4               rwjblue  2015-01-01T00:01:54Z   \n",
       "5       bradenmacdonald  2015-01-01T00:02:33Z   \n",
       "...                 ...                   ...   \n",
       "635295           Zuhayr  2015-02-28T09:57:23Z   \n",
       "635296         XhmikosR  2015-02-28T09:58:15Z   \n",
       "635297         rings360  2015-02-28T09:59:16Z   \n",
       "635299         msabramo  2015-02-28T09:59:42Z   \n",
       "635300         msabramo  2015-02-28T09:59:52Z   \n",
       "\n",
       "                                                     BODY        source  \n",
       "0                           :sparkles: :pray: :sparkles:        selfhub  \n",
       "1          > Can you remove this empty line?\\r\\n\\r\\nDone.         tsuru  \n",
       "3           _.reduce would be more elegant, but your call       selfhub  \n",
       "4       Tiny nit-pick: can you name this something mea...       emberjs  \n",
       "5       Yeah, I will just remove that. It is always True.           edx  \n",
       "...                                                   ...           ...  \n",
       "635295                                 Whoops, good call.  Baystation12  \n",
       "635296  @kasper93: are you sure [this](https://github....      kasper93  \n",
       "635297                Asar Sunny <ring2rings93@gmail.com>   AGV-IIT-KGP  \n",
       "635299                                              Done.      Robpol86  \n",
       "635300                                              Done.      Robpol86  \n",
       "\n",
       "[523262 rows x 4 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "af8cef06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4929.00000\n",
       "mean      106.15987\n",
       "std       327.76278\n",
       "min        16.00000\n",
       "25%        22.00000\n",
       "50%        36.00000\n",
       "75%        76.00000\n",
       "max      8167.00000\n",
       "Name: AUTHOR, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the data after filter\n",
    "contributions_clean = data_clean.groupby(['source']).count()['AUTHOR'].sort_values(ascending=False)\n",
    "contributions_clean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "da80f160",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Hx1/1fgEAQPVU7T6HqDq6nM8xuFx8DhEAAFWjRn4OEQAAgL8QiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOX5NRDNnTtX1113nRo1aqTQ0FDdfvvtysnJ8akZMGCAbDabzzJ+/Hifmry8PCUkJCgoKEihoaGaOnWqzp0751OzZcsW9ezZUw6HQ+3atVNycnJVtwcAAGoIvwai9PR0JSYmaseOHUpNTdXZs2c1aNAgnTp1yqdu3LhxOnbsmLnMnz/fHCstLVVCQoJKSkq0fft2vfnmm0pOTtbMmTPNmtzcXCUkJOimm25SVlaWJk2apLFjxyolJeWq9QoAAKqvAH8efMOGDT7rycnJCg0NVWZmpvr3729uDwoKksvluuA+PvzwQ+3fv18bN25UWFiYunfvrieffFLTp0/X7NmzZbfbtXTpUkVFRen555+XJHXq1Enbtm3Tiy++qPj4+KprEAAA1AjV6h6ioqIiSVKTJk18ti9btkzNmjVTly5dlJSUpO+//94cy8jIUExMjMLCwsxt8fHx8nq92rdvn1kTFxfns8/4+HhlZGRccB7FxcXyer0+CwAAqL38eobox8rKyjRp0iT169dPXbp0Mbffc889atWqlSIiIrRnzx5Nnz5dOTk5WrVqlSTJ4/H4hCFJ5rrH47lojdfr1enTp1W/fn2fsblz5+qJJ56o9B4BAED1VG0CUWJiovbu3att27b5bL///vvNn2NiYhQeHq6BAwfq0KFDatu2bZXMJSkpSVOmTDHXvV6vIiMjq+RYAADA/6rFJbOJEydqzZo12rx5s1q0aHHR2t69e0uSDh48KElyuVzKz8/3qSlfL7/v6KdqnE5nhbNDkuRwOOR0On0WAABQe/k1EBmGoYkTJ+q9997Tpk2bFBUV9bOvycrKkiSFh4dLktxut7Kzs1VQUGDWpKamyul0Kjo62qxJS0vz2U9qaqrcbncldQIAAGoyvwaixMREvfXWW1q+fLkaNWokj8cjj8ej06dPS5IOHTqkJ598UpmZmTp8+LDef/99jRo1Sv3791fXrl0lSYMGDVJ0dLRGjhypzz//XCkpKZoxY4YSExPlcDgkSePHj9dXX32ladOm6YsvvtDixYu1cuVKTZ482W+9AwCA6sOvgWjJkiUqKirSgAEDFB4ebi7vvPOOJMlut2vjxo0aNGiQOnbsqIcffljDhg3TBx98YO6jbt26WrNmjerWrSu3263f/e53GjVqlObMmWPWREVFae3atUpNTVW3bt30/PPP67XXXuORewAAIMnPN1UbhnHR8cjISKWnp//sflq1aqV169ZdtGbAgAH67LPPLmt+AADAGqrFTdUAAAD+RCACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWd0WBqE2bNvr2228rbC8sLFSbNm1+8aQAAACupisKRIcPH1ZpaWmF7cXFxfr6669/8aQAAACupoDLKX7//ffNn1NSUhQcHGyul5aWKi0tTa1bt660yQEAAFwNlxWIbr/9dkmSzWbT6NGjfcYCAwPVunVrPf/885U2OQAAgKvhsgJRWVmZJCkqKkq7d+9Ws2bNqmRSAAAAV9NlBaJyubm5lT0PAAAAv7miQCRJaWlpSktLU0FBgXnmqNzrr7/+iycGAABwtVxRIHriiSc0Z84c9erVS+Hh4bLZbJU9LwAAgKvmigLR0qVLlZycrJEjR1b2fAAAAK66K/ocopKSEvXt27ey5wIAAOAXVxSIxo4dq+XLl1f2XAAAAPziii6ZnTlzRq+++qo2btyorl27KjAw0Gf8hRdeqJTJAQAAXA1XFIj27Nmj7t27S5L27t3rM8YN1gAAoKa5okC0efPmyp4HAACA31zRPUQAAAC1yRWdIbrpppsuemls06ZNVzwhAACAq+2KAlH5/UPlzp49q6ysLO3du7fCl74CAABUd1cUiF588cULbp89e7ZOnjz5iyYEAABwtVXqPUS/+93v+B4zAABQ41RqIMrIyFC9evUqc5cAAABV7ooumd1xxx0+64Zh6NixY/rkk0/0+OOPV8rEAAAArpYrOkMUHBzsszRp0kQDBgzQunXrNGvWrEvez9y5c3XdddepUaNGCg0N1e23366cnByfmjNnzigxMVFNmzZVw4YNNWzYMOXn5/vU5OXlKSEhQUFBQQoNDdXUqVN17tw5n5otW7aoZ8+ecjgcateunZKTk6+kdQAAUAtd0RmiN954o1IOnp6ersTERF133XU6d+6c/vd//1eDBg3S/v371aBBA0nS5MmTtXbtWr377rsKDg7WxIkTdccdd+jjjz+WJJWWliohIUEul0vbt2/XsWPHNGrUKAUGBurpp5+WJOXm5iohIUHjx4/XsmXLlJaWprFjxyo8PFzx8fGV0gsAAKi5bIZhGFf64szMTB04cECS1LlzZ/Xo0eMXTeabb75RaGio0tPT1b9/fxUVFal58+Zavny57rzzTknSF198oU6dOikjI0N9+vTR+vXrdeutt+ro0aMKCwuTJC1dulTTp0/XN998I7vdrunTp2vt2rU+XzMyfPhwFRYWasOGDRXmUVxcrOLiYnPd6/UqMjJSRUVFcjqdv6jH823fvl0z/7FXzdrGSJL+cyhbc4Z2Ud++fSv1OAAAWI3X61VwcPAl/f2+oktmBQUFuvnmm3XdddfpwQcf1IMPPqjY2FgNHDhQ33zzzRVNWpKKiookSU2aNJH0Q+A6e/as4uLizJqOHTuqZcuWysjIkPTDjdwxMTFmGJKk+Ph4eb1e7du3z6z58T7Ka8r3cb65c+f6XBKMjIy84p4AAED1d0WB6IEHHtCJEye0b98+HT9+XMePH9fevXvl9Xr14IMPXtFEysrKNGnSJPXr109dunSRJHk8HtntdoWEhPjUhoWFyePxmDU/DkPl4+VjF6vxer06ffp0hbkkJSWpqKjIXI4cOXJFPQEAgJrhiu4h2rBhgzZu3KhOnTqZ26Kjo7Vo0SINGjToiiaSmJiovXv3atu2bVf0+srkcDjkcDj8PQ0AAHCVXNEZorKyMgUGBlbYHhgYqLKyssve38SJE7VmzRpt3rxZLVq0MLe7XC6VlJSosLDQpz4/P18ul8usOf+ps/L1n6txOp2qX7/+Zc8XAADULlcUiG6++WY99NBDOnr0qLnt66+/1uTJkzVw4MBL3o9hGJo4caLee+89bdq0SVFRUT7jsbGxCgwMVFpamrktJydHeXl5crvdkiS3263s7GwVFBSYNampqXI6nYqOjjZrfryP8pryfQAAAGu7okD0yiuvyOv1qnXr1mrbtq3atm2rqKgoeb1evfzyy5e8n8TERL311ltavny5GjVqJI/HI4/HY97XExwcrDFjxmjKlCnavHmzMjMz9fvf/15ut1t9+vSRJA0aNEjR0dEaOXKkPv/8c6WkpGjGjBlKTEw0L3uNHz9eX331laZNm6YvvvhCixcv1sqVKzV58uQraR8AANQyV3QPUWRkpD799FNt3LhRX3zxhSSpU6dOFZ7k+jlLliyRJA0YMMBn+xtvvKF7771X0g9fJFunTh0NGzZMxcXFio+P1+LFi83aunXras2aNZowYYLcbrcaNGig0aNHa86cOWZNVFSU1q5dq8mTJ2vhwoVq0aKFXnvtNT6DCAAASLrMzyHatGmTJk6cqB07dlR4nr+oqEh9+/bV0qVLdeONN1b6RP3pcj7H4HLxOUQAAFSNKvscogULFmjcuHEX3GlwcLD+53/+Ry+88MLlzRYAAMDPLisQff755xo8ePBPjg8aNEiZmZm/eFIAAABX02UFovz8/As+bl8uICDgF31SNQAAgD9cViC65pprfL4P7Hx79uxReHj4L54UAADA1XRZgeiWW27R448/rjNnzlQYO336tGbNmqVbb7210iYHAABwNVzWY/czZszQqlWrdO2112rixInq0KGDpB++gX7RokUqLS3VY489ViUTBQAAqCqXFYjCwsK0fft2TZgwQUlJSSp/Yt9msyk+Pl6LFi2q8CWqAAAA1d1lfzBjq1attG7dOn333Xc6ePCgDMNQ+/bt1bhx46qYHwAAQJW7ok+qlqTGjRvruuuuq8y5AAAA+MUVfZcZAABAbUIgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlufXQLR161bddtttioiIkM1m0+rVq33G7733XtlsNp9l8ODBPjXHjx/XiBEj5HQ6FRISojFjxujkyZM+NXv27NGNN96oevXqKTIyUvPnz6/q1gAAQA3i10B06tQpdevWTYsWLfrJmsGDB+vYsWPm8vbbb/uMjxgxQvv27VNqaqrWrFmjrVu36v777zfHvV6vBg0apFatWikzM1PPPvusZs+erVdffbXK+gIAADVLgD8PPmTIEA0ZMuSiNQ6HQy6X64JjBw4c0IYNG7R792716tVLkvTyyy/rlltu0XPPPaeIiAgtW7ZMJSUlev3112W329W5c2dlZWXphRde8AlOAADAuqr9PURbtmxRaGioOnTooAkTJujbb781xzIyMhQSEmKGIUmKi4tTnTp1tHPnTrOmf//+stvtZk18fLxycnL03XffXfCYxcXF8nq9PgsAAKi9qnUgGjx4sP76178qLS1NzzzzjNLT0zVkyBCVlpZKkjwej0JDQ31eExAQoCZNmsjj8Zg1YWFhPjXl6+U155s7d66Cg4PNJTIysrJbAwAA1YhfL5n9nOHDh5s/x8TEqGvXrmrbtq22bNmigQMHVtlxk5KSNGXKFHPd6/USigAAqMWq9Rmi87Vp00bNmjXTwYMHJUkul0sFBQU+NefOndPx48fN+45cLpfy8/N9asrXf+reJIfDIafT6bMAAIDaq0YFon//+9/69ttvFR4eLklyu90qLCxUZmamWbNp0yaVlZWpd+/eZs3WrVt19uxZsyY1NVUdOnRQ48aNr24DAACgWvJrIDp58qSysrKUlZUlScrNzVVWVpby8vJ08uRJTZ06VTt27NDhw4eVlpamoUOHql27doqPj5ckderUSYMHD9a4ceO0a9cuffzxx5o4caKGDx+uiIgISdI999wju92uMWPGaN++fXrnnXe0cOFCn0tiAADA2vwaiD755BP16NFDPXr0kCRNmTJFPXr00MyZM1W3bl3t2bNHv/nNb3TttddqzJgxio2N1UcffSSHw2HuY9myZerYsaMGDhyoW265RTfccIPPZwwFBwfrww8/VG5urmJjY/Xwww9r5syZPHIPAABMfr2pesCAATIM4yfHU1JSfnYfTZo00fLlyy9a07VrV3300UeXPT8AAGANNeoeIgAAgKpAIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJbn10C0detW3XbbbYqIiJDNZtPq1at9xg3D0MyZMxUeHq769esrLi5OX375pU/N8ePHNWLECDmdToWEhGjMmDE6efKkT82ePXt04403ql69eoqMjNT8+fOrujUAAFCD+DUQnTp1St26ddOiRYsuOD5//ny99NJLWrp0qXbu3KkGDRooPj5eZ86cMWtGjBihffv2KTU1VWvWrNHWrVt1//33m+Ner1eDBg1Sq1atlJmZqWeffVazZ8/Wq6++WuX9AQCAmiHAnwcfMmSIhgwZcsExwzC0YMECzZgxQ0OHDpUk/fWvf1VYWJhWr16t4cOH68CBA9qwYYN2796tXr16SZJefvll3XLLLXruuecUERGhZcuWqaSkRK+//rrsdrs6d+6srKwsvfDCCz7BCQAAWFe1vYcoNzdXHo9HcXFx5rbg4GD17t1bGRkZkqSMjAyFhISYYUiS4uLiVKdOHe3cudOs6d+/v+x2u1kTHx+vnJwcfffddxc8dnFxsbxer88CAABqr2obiDwejyQpLCzMZ3tYWJg55vF4FBoa6jMeEBCgJk2a+NRcaB8/Psb55s6dq+DgYHOJjIz85Q0BAIBqq9oGIn9KSkpSUVGRuRw5csTfUwIAAFWo2gYil8slScrPz/fZnp+fb465XC4VFBT4jJ87d07Hjx/3qbnQPn58jPM5HA45nU6fBQAA1F7VNhBFRUXJ5XIpLS3N3Ob1erVz50653W5JktvtVmFhoTIzM82aTZs2qaysTL179zZrtm7dqrNnz5o1qamp6tChgxo3bnyVugEAANWZXwPRyZMnlZWVpaysLEk/3EidlZWlvLw82Ww2TZo0SX/84x/1/vvvKzs7W6NGjVJERIRuv/12SVKnTp00ePBgjRs3Trt27dLHH3+siRMnavjw4YqIiJAk3XPPPbLb7RozZoz27dund955RwsXLtSUKVP81DUAAKhu/PrY/SeffKKbbrrJXC8PKaNHj1ZycrKmTZumU6dO6f7771dhYaFuuOEGbdiwQfXq1TNfs2zZMk2cOFEDBw5UnTp1NGzYML300kvmeHBwsD788EMlJiYqNjZWzZo108yZM3nkHgAAmGyGYRj+nkR15/V6FRwcrKKiokq/n2j79u2a+Y+9atY2RpL0n0PZmjO0i/r27VupxwEAwGou5+93tb2HCAAA4GohEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMur1oFo9uzZstlsPkvHjh3N8TNnzigxMVFNmzZVw4YNNWzYMOXn5/vsIy8vTwkJCQoKClJoaKimTp2qc+fOXe1WAABANRbg7wn8nM6dO2vjxo3mekDA/5/y5MmTtXbtWr377rsKDg7WxIkTdccdd+jjjz+WJJWWliohIUEul0vbt2/XsWPHNGrUKAUGBurpp5++6r0AAIDqqdoHooCAALlcrgrbi4qK9Je//EXLly/XzTffLEl644031KlTJ+3YsUN9+vTRhx9+qP3792vjxo0KCwtT9+7d9eSTT2r69OmaPXu27Hb7BY9ZXFys4uJic93r9VZNcwAAoFqo1pfMJOnLL79URESE2rRpoxEjRigvL0+SlJmZqbNnzyouLs6s7dixo1q2bKmMjAxJUkZGhmJiYhQWFmbWxMfHy+v1at++fT95zLlz5yo4ONhcIiMjq6g7AABQHVTrQNS7d28lJydrw4YNWrJkiXJzc3XjjTfqxIkT8ng8stvtCgkJ8XlNWFiYPB6PJMnj8fiEofLx8rGfkpSUpKKiInM5cuRI5TYGAACqlWp9yWzIkCHmz127dlXv3r3VqlUrrVy5UvXr16+y4zocDjkcjirbPwAAqF6q9Rmi84WEhOjaa6/VwYMH5XK5VFJSosLCQp+a/Px8854jl8tV4amz8vUL3ZcEAACsqUYFopMnT+rQoUMKDw9XbGysAgMDlZaWZo7n5OQoLy9PbrdbkuR2u5Wdna2CggKzJjU1VU6nU9HR0Vd9/gAAoHqq1pfMHnnkEd12221q1aqVjh49qlmzZqlu3bq6++67FRwcrDFjxmjKlClq0qSJnE6nHnjgAbndbvXp00eSNGjQIEVHR2vkyJGaP3++PB6PZsyYocTExGp7Sazs3FllZ2dX2B4bG1tt5wwAQE1XrQPRv//9b91999369ttv1bx5c91www3asWOHmjdvLkl68cUXVadOHQ0bNkzFxcWKj4/X4sWLzdfXrVtXa9as0YQJE+R2u9WgQQONHj1ac+bM8VdLP8ubn6eXD5+W6yubua3w60N66Q9S3759/TgzAABqr2odiFasWHHR8Xr16mnRokVatGjRT9a0atVK69atq+ypValGrig1axvj72kAAGAZNeoeIgAAgKpAIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJYX4O8J4OeVnTur7Oxsn22xsbFyOBx+mhEAALULgagG8Obn6eXDp+X6yiZJKvz6kF76g9S3b18/zwwAgNqBQFRDNHJFqVnbGH9PAwCAWol7iAAAgOURiAAAgOURiAAAgOURiAAAgOVxU3UNdKHH8CUexQcA4EoRiGqg8x/Dl3gUHwCAX4JAVEPxGD4AAJWHQFRL8GnWAABcOQJRLXH+ZbTjeTn6nwHZionxPYtESAIAoCICUS3y48tohV8f0ssf7uM+IwAALoGlHrtftGiRWrdurXr16ql3797atWuXv6dUpcoDUvkSck1bf08JAIBqyTJniN555x1NmTJFS5cuVe/evbVgwQLFx8crJydHoaGh/p7eVXGh+4xKSkokSXa7/YLr5bjUBgCozSwTiF544QWNGzdOv//97yVJS5cu1dq1a/X666/r0Ucf9fPsro4LPa7/76ytCmjYRK52XS64LlW8H+lCoelSglVV1UgVA1txcbEyMzN9+r+SGgCANVgiEJWUlCgzM1NJSUnmtjp16iguLk4ZGRkV6ouLi1VcXGyuFxUVSZK8Xm+lz+3UqVP69vB+nSs+/cOxjh1WgLdIjsD/fzXz/G2/qKZBY/NYklR67qxsZ0vMbeevS9KJgn/rj6/vV4jrU0nSt4f3q269hgpxtTRrzt92NWu+P+7RA8NuUufOnc2affv26eX/26ygJq5fVAMAuDp69+5d6fss/7ttGMbPFxsW8PXXXxuSjO3bt/tsnzp1qnH99ddXqJ81a5YhiYWFhYWFhaUWLEeOHPnZrGCJM0SXKykpSVOmTDHXy8rKdPz4cTVt2lQ2m+0ir7w8Xq9XkZGROnLkiJxOZ6Xttzqozb1Jtbs/equ5anN/9FZz+bM/wzB04sQJRURE/GytJQJRs2bNVLduXeXn5/tsz8/Pl8vlqlDvcDgq3EcSEhJSZfNzOp218h+BVLt7k2p3f/RWc9Xm/uit5vJXf8HBwZdUZ4nH7u12u2JjY5WWlmZuKysrU1pamtxutx9nBgAAqgNLnCGSpClTpmj06NHq1auXrr/+ei1YsECnTp0ynzoDAADWZZlAdNddd+mbb77RzJkz5fF41L17d23YsEFhYWF+m5PD4dCsWbNq5WPetbk3qXb3R281V23uj95qrprSn80wLuVZNAAAgNrLEvcQAQAAXAyBCAAAWB6BCAAAWB6BCAAAWB6ByI8WLVqk1q1bq169eurdu7d27drl7yn52Lp1q2677TZFRETIZrNp9erVPuOGYWjmzJkKDw9X/fr1FRcXpy+//NKn5vjx4xoxYoScTqdCQkI0ZswYnTx50qdmz549uvHGG1WvXj1FRkZq/vz5Vd2a5s6dq+uuu06NGjVSaGiobr/9duXk5PjUnDlzRomJiWratKkaNmyoYcOGVfhwz7y8PCUkJCgoKEihoaGaOnWqzp0751OzZcsW9ezZUw6HQ+3atVNycnJVt6clS5aoa9eu5gehud1urV+/vlb0dr558+bJZrNp0qRJ5raa2t/s2bNls9l8lo4dO9b4vsp9/fXX+t3vfqemTZuqfv36iomJ0SeffGKO1+TfKa1bt67w3tlsNiUmJkqq2e9daWmpHn/8cUVFRal+/fpq27atnnzySZ/vB6vJ792Pm4AfrFixwrDb7cbrr79u7Nu3zxg3bpwREhJi5Ofn+3tqpnXr1hmPPfaYsWrVKkOS8d577/mMz5s3zwgODjZWr15tfP7558ZvfvMbIyoqyjh9+rRZM3jwYKNbt27Gjh07jI8++sho166dcffdd5vjRUVFRlhYmDFixAhj7969xttvv23Ur1/f+NOf/lSlvcXHxxtvvPGGsXfvXiMrK8u45ZZbjJYtWxonT540a8aPH29ERkYaaWlpxieffGL06dPH6Nu3rzl+7tw5o0uXLkZcXJzx2WefGevWrTOaNWtmJCUlmTVfffWVERQUZEyZMsXYv3+/8fLLLxt169Y1NmzYUKX9vf/++8batWuNf/7zn0ZOTo7xv//7v0ZgYKCxd+/eGt/bj+3atcto3bq10bVrV+Ohhx4yt9fU/mbNmmV07tzZOHbsmLl88803Nb4vwzCM48ePG61atTLuvfdeY+fOncZXX31lpKSkGAcPHjRravLvlIKCAp/3LTU11ZBkbN682TCMmv3ePfXUU0bTpk2NNWvWGLm5uca7775rNGzY0Fi4cKFZU5Pfu3IEIj+5/vrrjcTERHO9tLTUiIiIMObOnevHWf208wNRWVmZ4XK5jGeffdbcVlhYaDgcDuPtt982DMMw9u/fb0gydu/ebdasX7/esNlsxtdff20YhmEsXrzYaNy4sVFcXGzWTJ8+3ejQoUMVd+SroKDAkGSkp6cbhvFDL4GBgca7775r1hw4cMCQZGRkZBiG8UNgrFOnjuHxeMyaJUuWGE6n0+xn2rRpRufOnX2Odddddxnx8fFV3VIFjRs3Nl577bVa09uJEyeM9u3bG6mpqcavfvUrMxDV5P5mzZpldOvW7YJjNbkvw/jh3/UNN9zwk+O17XfKQw89ZLRt29YoKyur8e9dQkKCcd999/lsu+OOO4wRI0YYhlF73jsumflBSUmJMjMzFRcXZ26rU6eO4uLilJGR4ceZXbrc3Fx5PB6fHoKDg9W7d2+zh4yMDIWEhKhXr15mTVxcnOrUqaOdO3eaNf3795fdbjdr4uPjlZOTo+++++4qdSMVFRVJkpo0aSJJyszM1NmzZ33669ixo1q2bOnTX0xMjM+He8bHx8vr9Wrfvn1mzY/3UV5zNd/n0tJSrVixQqdOnZLb7a41vSUmJiohIaHCHGp6f19++aUiIiLUpk0bjRgxQnl5ebWir/fff1+9evXSf/3Xfyk0NFQ9evTQn//8Z3O8Nv1OKSkp0VtvvaX77rtPNputxr93ffv2VVpamv75z39Kkj7//HNt27ZNQ4YMkVR73jsCkR/85z//UWlpaYVPyQ4LC5PH4/HTrC5P+Twv1oPH41FoaKjPeEBAgJo0aeJTc6F9/PgYVa2srEyTJk1Sv3791KVLF/PYdru9wpf6nt/fz839p2q8Xq9Onz5dFe2YsrOz1bBhQzkcDo0fP17vvfeeoqOja0VvK1as0Keffqq5c+dWGKvJ/fXu3VvJycnasGGDlixZotzcXN144406ceJEje5Lkr766istWbJE7du3V0pKiiZMmKAHH3xQb775ps/8asPvlNWrV6uwsFD33nuvedya/N49+uijGj58uDp27KjAwED16NFDkyZN0ogRI3zmV9PfO8t8dQfwUxITE7V3715t27bN31OpVB06dFBWVpaKior097//XaNHj1Z6erq/p/WLHTlyRA899JBSU1NVr149f0+nUpX/H7ckde3aVb1791arVq20cuVK1a9f348z++XKysrUq1cvPf3005KkHj16aO/evVq6dKlGjx7t59lVrr/85S8aMmSIIiIi/D2VSrFy5UotW7ZMy5cvV+fOnZWVlaVJkyYpIiKiVr13nCHyg2bNmqlu3boVnjDIz8+Xy+Xy06wuT/k8L9aDy+VSQUGBz/i5c+d0/Phxn5oL7ePHx6hKEydO1Jo1a7R582a1aNHC3O5yuVRSUqLCwsIKc7ucuf9UjdPprPI/cHa7Xe3atVNsbKzmzp2rbt26aeHChTW+t8zMTBUUFKhnz54KCAhQQECA0tPT9dJLLykgIEBhYWE1ur8fCwkJ0bXXXquDBw/W+PctPDxc0dHRPts6depkXhKsLb9T/vWvf2njxo0aO3asua2mv3dTp041zxLFxMRo5MiRmjx5snmGtra8dwQiP7Db7YqNjVVaWpq5raysTGlpaXK73X6c2aWLioqSy+Xy6cHr9Wrnzp1mD263W4WFhcrMzDRrNm3apLKyMvXu3dus2bp1q86ePWvWpKamqkOHDmrcuHGVzd8wDE2cOFHvvfeeNm3apKioKJ/x2NhYBQYG+vSXk5OjvLw8n/6ys7N9/pGnpqbK6XSav/jdbrfPPspr/PE+l5WVqbi4uMb3NnDgQGVnZysrK8tcevXqpREjRpg/1+T+fuzkyZM6dOiQwsPDa/z71q9fvwofbfHPf/5TrVq1klTzf6eUe+ONNxQaGqqEhARzW01/777//nvVqeMbF+rWrauysjJJtee94ykzP1mxYoXhcDiM5ORkY//+/cb9999vhISE+Dxh4G8nTpwwPvvsM+Ozzz4zJBkvvPCC8dlnnxn/+te/DMP44THLkJAQ4x//+IexZ88eY+jQoRd8zLJHjx7Gzp07jW3bthnt27f3ecyysLDQCAsLM0aOHGns3bvXWLFihREUFFTlj1lOmDDBCA4ONrZs2eLzqOz3339v1owfP95o2bKlsWnTJuOTTz4x3G634Xa7zfHyx2QHDRpkZGVlGRs2bDCaN29+wcdkp06dahw4cMBYtGjRVXlM9tFHHzXS09ON3NxcY8+ePcajjz5q2Gw248MPP6zxvV3Ij58yM4ya29/DDz9sbNmyxcjNzTU+/vhjIy4uzmjWrJlRUFBQo/syjB8+IiEgIMB46qmnjC+//NJYtmyZERQUZLz11ltmTU3+nWIYPzwt3LJlS2P69OkVxmryezd69GjjmmuuMR+7X7VqldGsWTNj2rRpZk1Nf+8Mg8fu/erll182WrZsadjtduP66683duzY4e8p+di8ebMhqcIyevRowzB+eNTy8ccfN8LCwgyHw2EMHDjQyMnJ8dnHt99+a9x9991Gw4YNDafTafz+9783Tpw44VPz+eefGzfccIPhcDiMa665xpg3b16V93ahviQZb7zxhllz+vRp4w9/+IPRuHFjIygoyPjtb39rHDt2zGc/hw8fNoYMGWLUr1/faNasmfHwww8bZ8+e9anZvHmz0b17d8Nutxtt2rTxOUZVue+++4xWrVoZdrvdaN68uTFw4EAzDNX03i7k/EBUU/u76667jPDwcMNutxvXXHONcdddd/l8Tk9N7avcBx98YHTp0sVwOBxGx44djVdffdVnvCb/TjEMw0hJSTEkVZizYdTs987r9RoPPfSQ0bJlS6NevXpGmzZtjMcee8zn8fia/t4ZhmHYDONHHzUJAABgQdxDBAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABKDWmz17trp3726u33vvvbr99tuvyrEA1AwEIgCVzuPx6IEHHlCbNm3kcDgUGRmp2267rcIXU/4SAwYM0KRJky6p9pFHHqnUY5ez2WxavXr1VTkWgKoV4O8JAKhdDh8+rH79+ikkJETPPvusYmJidPbsWaWkpCgxMVFffPHFVZuLYRgqLS1Vw4YN1bBhw6tyzKt5LACVhzNEACrVH/7wB9lsNu3atUvDhg3Ttddeq86dO2vKlCnasWOHJCkvL09Dhw5Vw4YN5XQ69d///d/Kz88391F+2elvf/ubWrdureDgYA0fPlwnTpyQ9MMlr/T0dC1cuFA2m002m02HDx/Wli1bZLPZtH79esXGxsrhcGjbtm0/eRnriSeeUPPmzeV0OjV+/HiVlJSYY61bt9aCBQt86rt3767Zs2eb45L029/+VjabzVw//1hlZWWaM2eOWrRoIYfDoe7du2vDhg3m+OHDh2Wz2bRq1SrddNNNCgoKUrdu3ZSRkWHW/Otf/9Jtt92mxo0bq0GDBurcubPWrVt3uW8NgIsgEAGoNMePH9eGDRuUmJioBg0aVBgPCQlRWVmZhg4dquPHjys9PV2pqan66quvdNddd/nUHjp0SKtXr9aaNWu0Zs0apaena968eZKkhQsXyu12a9y4cTp27JiOHTumyMhI87WPPvqo5s2bpwMHDqhr164XnGtaWpoOHDigLVu26O2339aqVav0xBNPXHKvu3fvliS98cYbOnbsmLl+voULF+r555/Xc889pz179ig+Pl6/+c1v9OWXX/rUPfbYY3rkkUeUlZWla6+9VnfffbfOnTsnSUpMTFRxcbG2bt2q7OxsPfPMM5yFAioZl8wAVJqDBw/KMAx17NjxJ2vS0tKUnZ2t3NxcM8T89a9/VefOnbV7925dd911kn44s5KcnKxGjRpJkkaOHKm0tDQ99dRTCg4Olt1uV1BQkFwuV4VjzJkzR7/+9a8vOle73a7XX39dQUFB6ty5s+bMmaOpU6fqySefVJ06P///is2bN5f0Q8i70BzKPffcc5o+fbqGDx8uSXrmmWe0efNmLViwQIsWLTLrHnnkESUkJEj64cxV586ddfDgQXXs2FF5eXkaNmyYYmJiJElt2rT52fkBuDycIQJQaQzD+NmaAwcOKDIy0ueMTnR0tEJCQnTgwAFzW+vWrc0wJEnh4eEqKCi4pHn06tXrZ2u6deumoKAgc93tduvkyZM6cuTIJR3jUni9Xh09elT9+vXz2d6vXz+fXiX5nMkKDw+XJLPfBx98UH/84x/Vr18/zZo1S3v27Km0OQL4AYEIQKVp3769bDZbpdw4HRgY6LNus9lUVlZ2Sa+90OW6y1WnTp0KAe/s2bO/eL8/5cf92mw2STL7HTt2rL766iuNHDlS2dnZ6tWrl15++eUqmwtgRQQiAJWmSZMmio+P16JFi3Tq1KkK44WFherUqZOOHDnicyZm//79KiwsVHR09CUfy263q7S09Irn+vnnn+v06dPm+o4dO9SwYUPzzFXz5s117Ngxc9zr9So3N9dnH4GBgRedg9PpVEREhD7++GOf7R9//PFl9SpJkZGRGj9+vFatWqWHH35Yf/7zny/r9QAujkAEoFItWrRIpaWluv766/V///d/+vLLL3XgwAG99NJLcrvdiouLU0xMjEaMGKFPP/1Uu3bt0qhRo/SrX/3qki51lWvdurV27typw4cP6z//+c8lnz0qV1JSojFjxmj//v1at26dZs2apYkTJ5r3D918883629/+po8++kjZ2dkaPXq06tatW2EOaWlp8ng8+u677y54nKlTp+qZZ57RO++8o5ycHD366KPKysrSQw89dMlznTRpklJSUpSbm6tPP/1UmzdvVqdOnS6rXwAXx03VACpVmzZt9Omnn+qpp57Sww8/rGPHjql58+aKjY3VkiVLZLPZ9I9//EMPPPCA+vfvrzp16mjw4MGXfQnokUce0ejRoxUdHa3Tp09XOHvzcwYOHKj27durf//+Ki4u1t13320+Ui9JSUlJys3N1a233qrg4GA9+eSTFY7x/PPPa8qUKfrzn/+sa665RocPH65wnAcffFBFRUV6+OGHVVBQoOjoaL3//vtq3779Jc+1tLRUiYmJ+ve//y2n06nBgwfrxRdfvKx+AVyczbiUuyABAABqMS6ZAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAy/t/Pcz17xql79QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.histplot( contributions_clean, bins=100)\n",
    "plt.xlabel(\"Contributions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac21274d",
   "metadata": {},
   "source": [
    "We already have the filter to build the network with a good context to define an edge between two collaborators: \n",
    "\n",
    "**There are an edge between them if they have at least one collaboration in a relevant source**\n",
    "\n",
    "Even so, there many developers who collaborate very little, and we need to adjust our data to maintain only developers with a appropriate average frequency in mass of data to avoid any bias due collators with less interactions than the average of collaborations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "aeb4b680",
   "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>AUTHOR</th>\n",
       "      <th>CREATED_AT</th>\n",
       "      <th>BODY</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>zindlerb</td>\n",
       "      <td>2015-01-01T00:00:08Z</td>\n",
       "      <td>:sparkles: :pray: :sparkles:</td>\n",
       "      <td>selfhub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>msabramo</td>\n",
       "      <td>2015-01-01T00:00:53Z</td>\n",
       "      <td>&gt; Can you remove this empty line?\\r\\n\\r\\nDone.</td>\n",
       "      <td>tsuru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>zindlerb</td>\n",
       "      <td>2015-01-01T00:01:44Z</td>\n",
       "      <td>_.reduce would be more elegant, but your call</td>\n",
       "      <td>selfhub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rwjblue</td>\n",
       "      <td>2015-01-01T00:01:54Z</td>\n",
       "      <td>Tiny nit-pick: can you name this something mea...</td>\n",
       "      <td>emberjs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bradenmacdonald</td>\n",
       "      <td>2015-01-01T00:02:33Z</td>\n",
       "      <td>Yeah, I will just remove that. It is always True.</td>\n",
       "      <td>edx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635292</th>\n",
       "      <td>Zuhayr</td>\n",
       "      <td>2015-02-28T09:55:58Z</td>\n",
       "      <td>Just being picky, but if you made it rig/light...</td>\n",
       "      <td>Baystation12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635295</th>\n",
       "      <td>Zuhayr</td>\n",
       "      <td>2015-02-28T09:57:23Z</td>\n",
       "      <td>Whoops, good call.</td>\n",
       "      <td>Baystation12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635296</th>\n",
       "      <td>XhmikosR</td>\n",
       "      <td>2015-02-28T09:58:15Z</td>\n",
       "      <td>@kasper93: are you sure [this](https://github....</td>\n",
       "      <td>kasper93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635299</th>\n",
       "      <td>msabramo</td>\n",
       "      <td>2015-02-28T09:59:42Z</td>\n",
       "      <td>Done.</td>\n",
       "      <td>Robpol86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635300</th>\n",
       "      <td>msabramo</td>\n",
       "      <td>2015-02-28T09:59:52Z</td>\n",
       "      <td>Done.</td>\n",
       "      <td>Robpol86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>419294 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AUTHOR            CREATED_AT  \\\n",
       "0              zindlerb  2015-01-01T00:00:08Z   \n",
       "1              msabramo  2015-01-01T00:00:53Z   \n",
       "3              zindlerb  2015-01-01T00:01:44Z   \n",
       "4               rwjblue  2015-01-01T00:01:54Z   \n",
       "5       bradenmacdonald  2015-01-01T00:02:33Z   \n",
       "...                 ...                   ...   \n",
       "635292           Zuhayr  2015-02-28T09:55:58Z   \n",
       "635295           Zuhayr  2015-02-28T09:57:23Z   \n",
       "635296         XhmikosR  2015-02-28T09:58:15Z   \n",
       "635299         msabramo  2015-02-28T09:59:42Z   \n",
       "635300         msabramo  2015-02-28T09:59:52Z   \n",
       "\n",
       "                                                     BODY        source  \n",
       "0                           :sparkles: :pray: :sparkles:        selfhub  \n",
       "1          > Can you remove this empty line?\\r\\n\\r\\nDone.         tsuru  \n",
       "3           _.reduce would be more elegant, but your call       selfhub  \n",
       "4       Tiny nit-pick: can you name this something mea...       emberjs  \n",
       "5       Yeah, I will just remove that. It is always True.           edx  \n",
       "...                                                   ...           ...  \n",
       "635292  Just being picky, but if you made it rig/light...  Baystation12  \n",
       "635295                                 Whoops, good call.  Baystation12  \n",
       "635296  @kasper93: are you sure [this](https://github....      kasper93  \n",
       "635299                                              Done.      Robpol86  \n",
       "635300                                              Done.      Robpol86  \n",
       "\n",
       "[419294 rows x 4 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Contributions to define an edge\n",
    "edge_contrib = data_clean.groupby(['AUTHOR']).count()['source'].sort_values(ascending=False)\n",
    "#Average contributions\n",
    "average_contrib =edge_contrib.mean()\n",
    "#Filtering\n",
    "above_average_contrib = edge_contrib[edge_contrib>average_contrib]\n",
    "data_clean = data_clean[data_clean['AUTHOR'].isin(above_average_contrib.index)]\n",
    "authors = data_clean.groupby(['AUTHOR']).count()['source'].sort_values(ascending=False)\n",
    "data_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31fd1211",
   "metadata": {},
   "source": [
    "## Defining focus\n",
    "\n",
    "We now need to define a custom metric to help to define our hypothesis: \n",
    "\n",
    "**As much a developers belong to a community (obtained by cluster coefficient metric), he has focus in the collaborations**\n",
    "\n",
    "That way we need to define the concept of focus. Focus will be understanding in this context by: \n",
    "\n",
    "*Number of developer collaborations considered for edge formation divided per total collaborations in the data, thoughtful by the Number of developer collaborations per the max Number of collaborations of an individual:*\n",
    "\n",
    " $$focus = \\frac{edgeContributions_i}{totalContributions_i}\\cdot\\frac{edgeContributions_i}{maxEdgeContribution}$$\n",
    " \n",
    " This metric is interesting because as define the percentage of relevant contributions to the graph, it takes into account the quantity of the individual contributions of the developer, giving different weights to them, even the  percentage of relevant contributions is similar, deepening the relevance of this metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ed3747af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AUTHOR\n",
       "luca020400    0.000001\n",
       "bmuschko      0.000002\n",
       "aequasi       0.000002\n",
       "kernc         0.000002\n",
       "koriym        0.000002\n",
       "                ...   \n",
       "miabot        0.072338\n",
       "bors-servo    0.086084\n",
       "jirikuncar    0.086125\n",
       "nrel-bot      0.119424\n",
       "houndci       0.963849\n",
       "Name: source, Length: 27920, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focus = (edge_contrib/total_contributions)*(edge_contrib/np.max(edge_contrib))\n",
    "focus = focus.dropna().sort_values(ascending=True)\n",
    "focus"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbd24c01",
   "metadata": {},
   "source": [
    "## Building the Network properly\n",
    "\n",
    "To the define the network we need to enumerate the collaborators who interact in the same repo by repo, and after it set pairs of developers to create an edge in the network. \n",
    "\n",
    "Concurrently we are defining the focus of each developer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fe8bcc4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "source\n",
       "01org                                [dawagner, pozdnyakov, rakuco]\n",
       "0xAX                                                         [0xAX]\n",
       "10gen                                                   [behackett]\n",
       "12factor                                       [fernandes, gwmoura]\n",
       "18F               [afeld, cmc333333, gboone, khandelwal, konklon...\n",
       "                                        ...                        \n",
       "zopefoundation              [davisagli, mgedmin, msabramo, tseaver]\n",
       "zotero            [adam3smith, aurimasv, dstillman, rmzelle, sim...\n",
       "zpp-2014-15                                                [xavrin]\n",
       "zxqfox                                 [andrewblond, eGavr, zxqfox]\n",
       "zyndiecate                                             [zyndiecate]\n",
       "Name: AUTHOR, Length: 3819, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "same_repo_colab = data_clean.groupby('source')['AUTHOR'].apply(lambda x: list(np.unique(x)))\n",
    "same_repo_colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ef5ead84",
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') \n",
    "\n",
    "g.add_vp('focus')\n",
    "g.add_vp('colabs')\n",
    "for index, row in same_repo_colab.iteritems():\n",
    "    pairs = itertools.combinations(row, 2)\n",
    "    for i,pair in enumerate(pairs):\n",
    "        try:\n",
    "            g.add_vertex(pair[0])\n",
    "            u = g.get_vertex(pair[0])\n",
    "            u['focus'] = focus[pair[0]]\n",
    "            u['colabs'] = int(edge_contrib[pair[0]])\n",
    "        except Exception as inst:\n",
    "            pass\n",
    "        try:\n",
    "            g.add_vertex(pair[1])\n",
    "            v = g.get_vertex(pair[1])\n",
    "            v['focus'] = focus[pair[1]]\n",
    "            v['colabs'] = int(edge_contrib[pair[1]])\n",
    "        except Exception as inst:\n",
    "            pass\n",
    "        finally:\n",
    "            try:\n",
    "                g.add_edge(pair[0],pair[1])\n",
    "            except:\n",
    "                pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6335b33d",
   "metadata": {},
   "source": [
    "#### Number of vertex in the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3a42b4ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g's number of vertex: 5097\n"
     ]
    }
   ],
   "source": [
    "n = g.num_vertices()\n",
    "print(\"g's number of vertex:\", n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0535bfd6",
   "metadata": {},
   "source": [
    "#### Number of edges in the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "59d444bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g's number of edges: 54035\n"
     ]
    }
   ],
   "source": [
    "m = g.num_edges()\n",
    "print(\"g's number of edges:\", m)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfad626f",
   "metadata": {},
   "source": [
    "#### Density of the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "fb1d2c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12987156"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if g.is_directed():\n",
    "    max_edges = n * (n - 1)\n",
    "else:\n",
    "    max_edges = n * (n - 1) // 2\n",
    "max_edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d388dcbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density is 0.004160649182931198\n"
     ]
    }
   ],
   "source": [
    "d = m / max_edges\n",
    "d\n",
    "print(f'Density is {d}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e76885fb",
   "metadata": {},
   "source": [
    "### Analyzing the graph\n",
    "\n",
    "After obtained the general data of the graph, we need to create insights by regression details which describes better if our hypothesis will be confirmed or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "75c50ee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from netpixi.integration.gt import *\n",
    "from regression.integration.gt import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7376383e",
   "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>focus</th>\n",
       "      <th>colabs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dawagner</th>\n",
       "      <td>0.000772</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pozdnyakov</th>\n",
       "      <td>0.002841</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rakuco</th>\n",
       "      <td>0.001227</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fernandes</th>\n",
       "      <td>0.001428</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gwmoura</th>\n",
       "      <td>0.000697</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               focus  colabs\n",
       "id                          \n",
       "dawagner    0.000772      20\n",
       "pozdnyakov  0.002841      72\n",
       "rakuco      0.001227      33\n",
       "fernandes   0.001428      38\n",
       "gwmoura     0.000697      19"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = gt_data(g)\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da3d37e7",
   "metadata": {},
   "source": [
    "##### Adding clustering and degree metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "36112486",
   "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>focus</th>\n",
       "      <th>colabs</th>\n",
       "      <th>clustering</th>\n",
       "      <th>degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5097.000000</td>\n",
       "      <td>5097.000000</td>\n",
       "      <td>5097.000000</td>\n",
       "      <td>5097.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.002982</td>\n",
       "      <td>76.116735</td>\n",
       "      <td>0.766684</td>\n",
       "      <td>0.004161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.014120</td>\n",
       "      <td>360.604392</td>\n",
       "      <td>0.364494</td>\n",
       "      <td>0.006215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000318</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.001015</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.623188</td>\n",
       "      <td>0.000589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.001622</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.002965</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.963849</td>\n",
       "      <td>24662.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             focus        colabs   clustering       degree\n",
       "count  5097.000000   5097.000000  5097.000000  5097.000000\n",
       "mean      0.002982     76.116735     0.766684     0.004161\n",
       "std       0.014120    360.604392     0.364494     0.006215\n",
       "min       0.000318     19.000000     0.000000     0.000196\n",
       "25%       0.001015     27.000000     0.623188     0.000589\n",
       "50%       0.001622     42.000000     1.000000     0.001374\n",
       "75%       0.002965     76.000000     1.000000     0.004710\n",
       "max       0.963849  24662.000000     1.000000     0.074961"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from graph_tool import clustering\n",
    "\n",
    "lc = clustering.local_clustering(g)\n",
    "g.add_vp('clustering', lc)\n",
    "dc = gt_total_degree(g)\n",
    "g.add_vp('degree', dc)\n",
    "data = gt_data(g)\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9ab4cebe",
   "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>degree_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5097.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.202668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31.670509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>382.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       degree_count\n",
       "count   5097.000000\n",
       "mean      21.202668\n",
       "std       31.670509\n",
       "min        1.000000\n",
       "25%        3.000000\n",
       "50%        7.000000\n",
       "75%       24.000000\n",
       "max      382.000000"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degrees = []\n",
    "for v in g.all_vertices():\n",
    "    degrees.append(v.total_degree())\n",
    "data['degree_count'] = degrees\n",
    "data['degree_count'].describe().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3e868744",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='degree_count', ylabel='Count'>"
      ]
     },
     "execution_count": 71,
     "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": [
    "sns.histplot(data['degree_count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46acd71c",
   "metadata": {},
   "source": [
    "#### Dropping the outliers (colabs > 1000)\n",
    "\n",
    "As we can see above there collaborators who are much higher than the collaborations, and much of them are bot's accounts (e.g *houndci* and *nrel-bot*). For this, we're removing them for the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "599f26e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "selRows = data[data['colabs'] > 1000].index\n",
    "data = data.drop(selRows,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16439770",
   "metadata": {},
   "source": [
    "### Normalizing focus\n",
    "\n",
    "After removing the outliers we need to normalize the focus metric to properly give insights about the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0974e5b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data['focus']\n",
    "x_norm = (x-np.min(x))/(np.max(x)-np.min(x)) + 1e-10 #For log issues in the next cells\n",
    "data['focus'] = x_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "00cf02c4",
   "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>degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5085.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.004129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.006098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.001374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.004710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.039443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            degree\n",
       "count  5085.000000\n",
       "mean      0.004129\n",
       "std       0.006098\n",
       "min       0.000196\n",
       "25%       0.000589\n",
       "50%       0.001374\n",
       "75%       0.004710\n",
       "max       0.039443"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['degree'].describe().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5f233e7f",
   "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>focus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.085000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.405395e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.514443e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.899041e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.552129e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.197925e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              focus\n",
       "count  5.085000e+03\n",
       "mean   6.405395e-02\n",
       "std    8.514443e-02\n",
       "min    1.000000e-10\n",
       "25%    1.899041e-02\n",
       "50%    3.552129e-02\n",
       "75%    7.197925e-02\n",
       "max    1.000000e+00"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['focus'].describe().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a923183",
   "metadata": {},
   "source": [
    "### Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cb2b21be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='focus', ylabel='Count'>"
      ]
     },
     "execution_count": 76,
     "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": [
    "sns.histplot(data['focus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "72b75297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='degree', ylabel='Count'>"
      ]
     },
     "execution_count": 77,
     "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": [
    "sns.histplot(data['degree'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "82e480fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import regression as reg\n",
    "\n",
    "from scipy import stats\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f04c58a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=data['clustering'], y=np.log(data['focus']));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "812c9170",
   "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>focus</th>\n",
       "      <th>colabs</th>\n",
       "      <th>clustering</th>\n",
       "      <th>degree</th>\n",
       "      <th>degree_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>casey-collab</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>913</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mgbot</th>\n",
       "      <td>9.889521e-01</td>\n",
       "      <td>903</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001570</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linthubot</th>\n",
       "      <td>9.437364e-01</td>\n",
       "      <td>870</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.001177</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fodoj</th>\n",
       "      <td>8.955112e-01</td>\n",
       "      <td>837</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.002747</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whimboo</th>\n",
       "      <td>7.978376e-01</td>\n",
       "      <td>733</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024725</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neobuddy89</th>\n",
       "      <td>3.060061e-03</td>\n",
       "      <td>19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sagikazarmark</th>\n",
       "      <td>2.724912e-03</td>\n",
       "      <td>19</td>\n",
       "      <td>0.805556</td>\n",
       "      <td>0.001766</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>viboes</th>\n",
       "      <td>2.724912e-03</td>\n",
       "      <td>19</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000785</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ThiefMaster</th>\n",
       "      <td>1.150152e-03</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000785</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stoeffel</th>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>19</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.001374</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5085 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      focus  colabs  clustering    degree  degree_count\n",
       "id                                                                     \n",
       "casey-collab   1.000000e+00     913    0.000000  0.000196             1\n",
       "mgbot          9.889521e-01     903    1.000000  0.001570             8\n",
       "linthubot      9.437364e-01     870    0.400000  0.001177             6\n",
       "Fodoj          8.955112e-01     837    0.384615  0.002747            14\n",
       "whimboo        7.978376e-01     733    1.000000  0.024725           126\n",
       "...                     ...     ...         ...       ...           ...\n",
       "neobuddy89     3.060061e-03      19    0.000000  0.000392             2\n",
       "sagikazarmark  2.724912e-03      19    0.805556  0.001766             9\n",
       "viboes         2.724912e-03      19    1.000000  0.000785             4\n",
       "ThiefMaster    1.150152e-03      20    1.000000  0.000785             4\n",
       "stoeffel       1.000000e-10      19    0.571429  0.001374             7\n",
       "\n",
       "[5085 rows x 5 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by=\"focus\",ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c79617dd",
   "metadata": {},
   "source": [
    "### Residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "fb99bdd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = reg.linear(data=data, formula='focus ~ clustering')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a0775e9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>focus</td>      <th>  R-squared:         </th>  <td>   0.001</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.001</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   6.437</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 08 Jun 2023</td> <th>  Prob (F-statistic):</th>   <td>0.0112</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:15:00</td>     <th>  Log-Likelihood:    </th>  <td>  5314.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  5085</td>      <th>  AIC:               </th> <td>-1.063e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  5083</td>      <th>  BIC:               </th> <td>-1.061e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td>    0.0577</td> <td>    0.003</td> <td>   20.754</td> <td> 0.000</td> <td>    0.052</td> <td>    0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clustering</th> <td>    0.0083</td> <td>    0.003</td> <td>    2.537</td> <td> 0.011</td> <td>    0.002</td> <td>    0.015</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4469.747</td> <th>  Durbin-Watson:     </th>  <td>   1.804</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>150922.792</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 4.177</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>28.348</td>  <th>  Cond. No.          </th>  <td>    4.50</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  focus   R-squared:                       0.001\n",
       "Model:                            OLS   Adj. R-squared:                  0.001\n",
       "Method:                 Least Squares   F-statistic:                     6.437\n",
       "Date:                Thu, 08 Jun 2023   Prob (F-statistic):             0.0112\n",
       "Time:                        21:15:00   Log-Likelihood:                 5314.8\n",
       "No. Observations:                5085   AIC:                        -1.063e+04\n",
       "Df Residuals:                    5083   BIC:                        -1.061e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      0.0577      0.003     20.754      0.000       0.052       0.063\n",
       "clustering     0.0083      0.003      2.537      0.011       0.002       0.015\n",
       "==============================================================================\n",
       "Omnibus:                     4469.747   Durbin-Watson:                   1.804\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           150922.792\n",
       "Skew:                           4.177   Prob(JB):                         0.00\n",
       "Kurtosis:                      28.348   Cond. No.                         4.50\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3e13992f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_48361\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_48361_level0_col0\" class=\"col_heading level0 col0\" >variable</th>\n",
       "      <th id=\"T_48361_level0_col1\" class=\"col_heading level0 col1\" >coefficient</th>\n",
       "      <th id=\"T_48361_level0_col2\" class=\"col_heading level0 col2\" >std error</th>\n",
       "      <th id=\"T_48361_level0_col3\" class=\"col_heading level0 col3\" >p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_48361_row0_col0\" class=\"data row0 col0\" >clustering</td>\n",
       "      <td id=\"T_48361_row0_col1\" class=\"data row0 col1\" >0.008306</td>\n",
       "      <td id=\"T_48361_row0_col2\" class=\"data row0 col2\" >0.003274</td>\n",
       "      <td id=\"T_48361_row0_col3\" class=\"data row0 col3\" >0.011206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc199b28a30>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.micro_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6f99a5f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot_residuals()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8c5cea9",
   "metadata": {},
   "source": [
    "#### \"Massaging\" the data\n",
    "\n",
    "As we can see, the distribution of residuals is not good as we want, because the residuals has not a randomic and normal distribution to give the give the necessary confidence to the model. \n",
    "\n",
    "Because of this, we can apply a logaritmic scale to the focus to get a better shape and use the degree as control variable (which means the quantity of neighbors of the vertex)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "2ba3840b",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = reg.linear(data=data, formula='np.log(focus) ~ clustering + degree')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "bb9e641e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>np.log(focus)</td>  <th>  R-squared:         </th> <td>   0.022</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.021</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   56.30</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 08 Jun 2023</td> <th>  Prob (F-statistic):</th> <td>6.54e-25</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:15:03</td>     <th>  Log-Likelihood:    </th> <td> -6964.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  5085</td>      <th>  AIC:               </th> <td>1.393e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  5082</td>      <th>  BIC:               </th> <td>1.395e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td>   -3.4152</td> <td>    0.031</td> <td> -108.792</td> <td> 0.000</td> <td>   -3.477</td> <td>   -3.354</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clustering</th> <td>    0.1250</td> <td>    0.037</td> <td>    3.362</td> <td> 0.001</td> <td>    0.052</td> <td>    0.198</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>degree</th>     <td>   20.7603</td> <td>    2.223</td> <td>    9.341</td> <td> 0.000</td> <td>   16.403</td> <td>   25.117</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2446.109</td> <th>  Durbin-Watson:     </th>  <td>   1.826</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>270547.726</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-1.326</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>38.635</td>  <th>  Cond. No.          </th>  <td>    213.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          np.log(focus)   R-squared:                       0.022\n",
       "Model:                            OLS   Adj. R-squared:                  0.021\n",
       "Method:                 Least Squares   F-statistic:                     56.30\n",
       "Date:                Thu, 08 Jun 2023   Prob (F-statistic):           6.54e-25\n",
       "Time:                        21:15:03   Log-Likelihood:                -6964.1\n",
       "No. Observations:                5085   AIC:                         1.393e+04\n",
       "Df Residuals:                    5082   BIC:                         1.395e+04\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     -3.4152      0.031   -108.792      0.000      -3.477      -3.354\n",
       "clustering     0.1250      0.037      3.362      0.001       0.052       0.198\n",
       "degree        20.7603      2.223      9.341      0.000      16.403      25.117\n",
       "==============================================================================\n",
       "Omnibus:                     2446.109   Durbin-Watson:                   1.826\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           270547.726\n",
       "Skew:                          -1.326   Prob(JB):                         0.00\n",
       "Kurtosis:                      38.635   Cond. No.                         213.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0289d85e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_e88fa\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_e88fa_level0_col0\" class=\"col_heading level0 col0\" >variable</th>\n",
       "      <th id=\"T_e88fa_level0_col1\" class=\"col_heading level0 col1\" >coefficient</th>\n",
       "      <th id=\"T_e88fa_level0_col2\" class=\"col_heading level0 col2\" >std error</th>\n",
       "      <th id=\"T_e88fa_level0_col3\" class=\"col_heading level0 col3\" >p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_e88fa_row0_col0\" class=\"data row0 col0\" >clustering</td>\n",
       "      <td id=\"T_e88fa_row0_col1\" class=\"data row0 col1\" >0.124981</td>\n",
       "      <td id=\"T_e88fa_row0_col2\" class=\"data row0 col2\" >0.037175</td>\n",
       "      <td id=\"T_e88fa_row0_col3\" class=\"data row0 col3\" >0.000780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e88fa_row1_col0\" class=\"data row1 col0\" >degree</td>\n",
       "      <td id=\"T_e88fa_row1_col1\" class=\"data row1 col1\" >20.760311</td>\n",
       "      <td id=\"T_e88fa_row1_col2\" class=\"data row1 col2\" >2.222538</td>\n",
       "      <td id=\"T_e88fa_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc1c32b9390>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.micro_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8f5f4bbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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/xsaNG2E0GtGuXTvcddddmDt3LhSKK1+noqKikJ2djRUrVmDGjBmIiIjA3Llz8cADDzTSOyQiIiI7BjiIiIjomtW5c2fk5ub63e9Pf/oT/vSnP3ndPmHCBEyYMKFWr3nDDTdg48aNtR4jERERNQzW4CAiIiIiIiKigMcABxEREREREREFPAY4iIiIiIiIiCjgMcBBRERERERERAGPAQ4iIiIiIiIiCngMcBARERERERFRwGOAg4iIiIiIiIgCHgMcRERERERERBTwFM09ACIiIiIiopZKJhMAAHK593vDoihBFKWmGhIRecEABxERERERUQ2hQUqIooSQEA0AQKfTet3XKoooKdYzyEHUzBjgICIiIiIiqkGjVkAmE/DvL47jcqkRVqsISXIPYLSNCMIfb+8JmUxggIOomTHAQURERERE5MXFYj0uFBlgsVg9BjiIqOVgkVEiIiIiIiIiCngMcBARERERERFRwGOAg4iIiIiIiIgCHgMcRERERERERBTwGOAgIiIiIiIiooDHAAcRERERERERBTwGOIiIiIiIiIgo4DHAQUREREREREQBjwEOIiIiIiIiIgp4DHAQERERERERUcBjgIOIiIiIiIiIAh4DHEREREREREQU8BjgICIiIiIiIqKAxwAHEREREREREQU8BjiIiIiIiIiIKOAxwEFEREREREREAY8BDiIiIiIiIiIKeAEf4CgoKMDSpUsxduxY9OrVC6NGjfK43wcffIDbbrsNffr0wZgxY/D111838UiJiIiIiIiIqLEEfIAjLy8Pu3btQlRUFGJjYz3u89lnn+GJJ57AiBEjsG7dOqSkpGD27Nn4+eefm3awRERERERERNQoAj7AMXToUOzatQsvvfQSevfu7XGfl156CSNHjsS8efMwcOBAPPXUU+jTpw9eeeWVJh4tERERNaXaZHpOmTIFCQkJbv87deqUy37l5eVYvHgx+vfvj9TUVMydOxcXL150O95PP/2Eu+++G0lJSbj55puxdu1aSJLUaO+RiIiIbBTNPYD6ksl8x2jOnj2LM2fOYMGCBS6PZ2ZmYuXKlTCZTFCpVI05RCIiImom9kzP5ORkiKLoNdBwww03YOHChS6Pde7c2eXnefPm4eTJk1i2bBnUajVWrVqF6dOn46OPPoJCYftKVVBQgKysLAwaNAjz5s1Dbm4unn/+ecjlcmRlZTXOmyQiIiIA10CAw5/8/HwAQHR0tMvjsbGxMJvNOHv2rNelLbWhUDReEoxcLnP5l5oGz3vT4zlvejznzYPnvekNHToUw4cPBwAsWrQIR44c8bifTqdDSkqK1+McOHAA3377LbKzs5Geng7A9t0iMzMTO3bsQGZmJgAgOzsb4eHheOGFF6BSqZCWloaioiK8/vrrmDJlCm+qEBERNaJrPsBRWloKwPbFxZn9Z/v2qyGTCQgPD776wdWSTqdt9NcgdzzvTY/nvOnxnDcPnvem4y/Ts7ZycnKg0+kwaNAgx2MxMTHo2bMncnJyHAGOnJwc3HLLLS6BjMzMTKxZswYHDhzAgAEDGmQ8RERE5O6aD3A0JlGUUFamb7Tjy+Uy6HRalJUZYLWKjfY65IrnvenxnDc9nvPmca2dd51Oe81ko/zwww9ISUmB1WpFcnIyHnnkEfTr18+xPT8/H9HR0RAEweV5MTExjmxRvV6P33//HTExMW77CIKA/Px8BjiIiIga0TUf4AgLCwNgKwwWGRnpeLysrMxl+9WyWBr/C6rVKjbJ65Arnvemx3Pe9HjOmwfPe8vSr18/jB07Ft26dcPFixeRnZ2N+++/H2+//TZSU1MB2L43hIaGuj03LCzMseylvLwcgHvWqEqlglarrVfWKNC4y2LtuIyq5eC1aDz2cyoIglvQ0pnbNgEQ4L6/fT9eq8bH34uWo6Vei2s+wGG/i5Kfn+9yRyU/Px9KpRJdunRprqERERFRCzB37lyXn2+66SaMGjUKr776KtatW9dMo3LVVMti7biMquXgtWg8crkMCoXc+/bqJW72fxVyz/vaJ3i8Vk2H57rlaGnX4poPcHTp0gXdunXD9u3bHUXGAGDbtm1IS0tjsS8iIiJyERQUhCFDhuCLL75wPKbT6XD+/Hm3fUtLSx3ZoPYMD3smh53JZILBYKhX1mhjL4u1u9aWUQUyXovGYz+3tmw6q9f9rKLo8q/FagU8NGKyXx9eq8bH34uWo6mvRW2XxQZ8gMNgMGDXrl0AgHPnzqGiogLbt28HAPTv3x8RERGYM2cOHnvsMXTt2hUDBgzAtm3bcOjQIbzzzjvNOXQiIiIKEDExMdi7dy8kSXJJWz99+jTi4+MB2AIjHTp0cNTkcN5HkiS32hx11ZTLmriMquXgtWg8kiR5bR1t3+76gIfHnPbjtWo6PNctR0u7Fi1rwcxVuHz5Mh555BE88sgj+OGHH/D77787fs7LywMAjBo1Cn/729+wdetWZGVl4aeffsLq1asd62qJiIiI7PR6Pb755hv06dPH8VhGRgZKS0uxd+9ex2OnT5/G0aNHkZGR4bLfV199BbPZ7Hhs27Zt0Ol0/N5BRETUyAI+g6Nz587Izc31u9+ECRMwYcKEJhgRERERtRT+Mj3z8/Oxfv163HLLLejUqRMuXryIN998E4WFhfjHP/7hOE5qairS09OxePFiLFy4EGq1Gi+++CISEhJw6623OvbLysrCp59+ikcffRSTJk3CiRMnkJ2djfnz53NZLBERUSML+AAHERERkTf2TE9n9p83bNiA9u3bw2w248UXX0RJSQm0Wi1SU1Px5JNPIikpyeV5q1atwjPPPIOlS5fCYrEgPT0dS5YsgUJx5etUVFQUsrOzsWLFCsyYMQMRERGYO3cuHnjggcZ/s0RERNc5BjiIiIjomlWbTM/s7OxaHSs0NBTLly/H8uXLfe53ww03YOPGjbUeIxERETWMgK/BQURERERERETEAAcRERERERERBTwGOIiIiIiIiIgo4DHAQUREREREREQBjwEOIiIiIiIiIgp4DHAQERERERERUcBjgIOIiIiIiIiIAh4DHEREREREREQU8BjgICIiIiIiIqKAxwAHEREREREREQU8BjiIiIiIiIiIKOAxwEFEREREREREAY8BDiIiIiIiIiIKeAxwEBEREREREVHAY4CDiIiIiIiIiAIeAxxEREREREREFPAY4CAiIiIiIiKigMcABxEREREREREFPAY4iIiIiIiIiCjgMcBBRERERERERAGPAQ4iIiIiIiIiCngMcBARERERERFRwGOAg4iIiIiIiIgCHgMcRERERERERBTwGOAgIiIiIiIiooDHAAcRERERERERBTwGOIiIiIiIiIgo4DHAQUREREREREQBjwEOIiIiIiIiIgp4DHAQERERERERUcBjgIOIiIiIiIiIAp6iuQdARERE1FgKCgqQnZ2NgwcPIi8vDzExMdi6datje0VFBd58803s2rULZ86cgUqlQlJSEubPn4+EhATHfr/++iuGDRvmdvzk5GRs3LjR5bGffvoJzz77LI4dO4bWrVtj0qRJmD59OgRBaLw3SkRERAxwEBER0bUrLy8Pu3btQnJyMkRRhCRJLtt/++03vP/++7jzzjsxb948VFVV4Y033sDdd9+Njz76CLGxsS77//nPf8aAAQMcPwcHB7tsLygoQFZWFgYNGoR58+YhNzcXzz//PORyObKyshrvjRIREREDHERERHTtGjp0KIYPHw4AWLRoEY4cOeKyvXPnzvjyyy+h1Wodjw0cOBBDhw7Fu+++iyeeeMJl/6ioKKSkpHh9vezsbISHh+OFF16ASqVCWloaioqK8Prrr2PKlClQqVQN9+aIiIjIBWtwEBER0TVLJvP9VScoKMgluAHYsjK6du2Kixcv1vn1cnJyMGzYMJdARmZmJsrKynDgwIE6H4+IiIhqjwEOIiIiIidlZWWOeh01LVu2DD179kRaWhqWLFmCkpISxza9Xo/ff//d7XkxMTEQBAH5+fmNPXQiIqLrGpeoEBERETl57rnnIAgCJk2a5HhMpVJh0qRJSE9Ph06nw8GDB/H666/jyJEj+OCDD6BUKlFeXg4A0Ol0LsdTqVTQarUoLS2t17gUisa/LyWXy1z+pebDa9F47OdUEASfxX/dtgmAAPf97fvxWjU+/l60HC31WjDAQURERFTto48+wsaNG7FixQq0b9/e8Xjbtm2xbNkyx8/9+/dHXFwcZs6ciS+//BKZmZmNOi6ZTEB4eLD/HRuITqf1vxM1CV6LxiOXy6BQyL1vr17iZv9XIfe8r32Cx2vVdHiuW46Wdi0Y4GgBJEGAwWyF3mhBkEYBrVIOoUaVdyIiImpcu3btwtKlSzFr1izccccdfvcfMmQIgoKC8MsvvyAzMxOhoaEA4MjksDOZTDAYDAgLC7vqsYmihLIy/VU/v7bkchl0Oi3KygywWsVGfz3yjtei8djPrdUqwmKxet3PKoou/1qsVsDDV3T79eG1anz8vWg5mvpa6HTaWmWLMMDRzKyCgFc/OoQDJwodj6UmRGLW+KQmvTgMshAR0fXs559/xiOPPIJx48bhkUceuapjBAUFoUOHDm61Nk6fPg1JkjzW9KgLi6XpvszbJn6cPLQEvBaNR5Ikt9bRNbe7PuDhMaf9eK2aDs91y9HSrgUDHM1I8hDcAIADuYV49eNDmHtXcpOMw1eQRe4nyNEUgREGX4iIqDGdPHkSM2fOxMCBA/Hkk0/W+nlff/019Ho9+vTp43gsIyMDX331FRYsWAClUgkA2LZtG3Q6HVJTUxt87ERERHQFAxzNyGC2ugU37A7kFqKyyoo2jTwGf0GW2eOTvAYT6hMYqa2meA0iIrp2GQwG7Nq1CwBw7tw5VFRUYPv27QBsdTQkSUJWVhbUajXuvfdeHDlyxPHckJAQdO/eHQCwYsUKCIKAlJQU6HQ6HDp0CGvWrEFiYiKGDx/ueE5WVhY+/fRTPProo5g0aRJOnDiB7OxszJ8/36V1LBERETU8Bjiakd5o8bPd3Ohj8BdkMZitCPJQtb0+gZHa8vcaD9+ZDKPJwswOIiLy6vLly25LTuw/b9iwAQBw/vx5AMB9993nsl///v3x9ttvAwBiY2Px73//Gxs3boTRaES7du1w1113Ye7cuVAornydioqKQnZ2NlasWIEZM2YgIiICc+fOxQMPPNBYb5GIiIiq1SnAsWnTpqt6kXHjxl3V8651QRrfpz9Io2z0MfgPslgQFOJ+x8lXYOTY6SKYRQkWq1iv4IPBLPoMvpQbzCguM+J4QTG25JxCz+iIJs3sqLl0RqNSoMpsQaWheQIuXMpDROSuc+fOyM3N9bmPv+0AMGHCBEyYMKFWr3nDDTdg48aNtdqXiIiIGk6dAhyLFi2q8wsIgsAAhxcalQLJcZE4mOc+iU+Oi4RG7b1tVUPxF2RRq+TQW0S3ybK3wIhGJceCyX2x5pPDdV5WIspk0FdZYDCaERaqwYXiSp9ju3BZjxUbfkRyXCQWTO6L597Zj1c/PoSZ4/qgrNLkc5Jf32CAp6UzyXGRGDM4Bs+9sx9Gk7XBl9L4GjOX8hARERER0fWuTgGOr776qrHGcV0ymCwYM9hWUd05yGGfKBuqfGdXNAStUo7UhEgcyPUcZNlz6Hds3HnCbbLsLTAyJiMWW3bnuwVt/C1dsQgCXvngIA7mFWLi8HicPleKSbclYGnWAJjMIlRKmSNTw2iytfNSKW1LZ+yvNSYjFltyTkFfZUGFwYyisiqEBivRNjwIcvFKZd/aBgOsEqC3uGeheFs64zyOjTtPNOhyHYsg4DVvYwZqtVyIGR5ERERERHQtq1OAo1OnTo01jutShd6M597ZjzEZsRibEeMykX/unf1YNi2t3q/hbVJrf9xQZcHMO5Kw5pNDLkEO52wEwH2y7CkwolHJMTCxPXpEhWNEWje3oETNmh72MSjkcmRvPoKEqHCMzYhBkFqBIamdsG7zEfxcI0PCnqmREBWB4wXFjm0H8woxNiMGCZP7YsO2Yzh+pghjMmLRIyoc/ztfjnYRQdBWB0T8BQMAoLDEgJc/OOgxoFDlY3mOfRzOx/VWx6S2rDIZ/vnpLxiZHoOpI3tBb7QgWKPApVIj3tp2FJNv7+lzKY/RYoVaIcfPJy8hQqeBySxWB4CMSOneBjIvQY6WGhBpqeOqr5b6vlrquIiIiIiIamKR0WYUrFXCaLJi484THrdr1HIUlhiu+iJ5y1R4aHwSsjcfwb6jF2yvo5Jj5h198MDoROgNZiiVcgAS9h+74HI858m6IEmYNT4Jr35sC4zYl6Zs2HbMa1DCaLI6ano4j+0ffx6C29O6YcvufGzceQITh8cjt6DYLQvkYF4hZAKwcvZgWEQRT63/3mW7Qi7DR1+fRG5BER6f0tdxPOf3/sDoRD9FVUXIZHK8/MEBr0GQSbck+DzvJrNrH2hvdUxqQxIEXC7R44+398C6TUfcMn2mj0uE2Wr1cxQBF0uM2P3zb27P79gmBO1baSABLpNYtUqB9ZsOOz4jQPO2DrYfs9JogcUq4mDeJUfg7FpYiiMKQp0DUE2BS5+IiIiIKJDUO8BRWFiIDz/8EEePHkV5eTlE0XVyJwgC3nrrrfq+zDUpSK1Av57tEN0pDD2iwl0yOE6fK3XcoZ97V3Kdj+2rA8lrHx3ClMyeiOoYhi05pwAAumA11m92n0A7BycA18m6XJIwe3wSjBYr5DI51nxyyCW4Abgv2wjSKAGZDHqjBVMye2HSLRYEaZRoHQZMHB6P+0f1giTBa9DnwIlCjC41YHNOPuZMTHVkmIzJiEWrUDVGpHXD5BE9YLZYMS4jFrcPdM0kqdCboFHJHdkdNZe/VBrMjvPk8fVzC3H/qN5ez7tGJUfbCK3L0hpd8NW3BTSYrQgNVuOfW486Mlycx/zW1qPIGpPo8xhymYD3d57wGDACgIfGV2fweKgncvDkJce1b67Wwd7qndg/m3VdCtTiMhIE/wEo1GF8DfX+mqJTEhERERFRQ6pXgOP48eOYOnUqjEYjoqOjceLECXTv3h1lZWW4cOECunbtivbt2zfUWK85MlHEjHGJ+Dnvksvjka20uKVfF/ztjX3434UKVFZZEaQQ6nRsn+1fTxRi9OAY5BYUY8Hkvjh5rsRj3YyawQnAvfaGIElQKRUwVFn8LttIjotE3tliJERF4I1Pf0HB76V4+qFBWF1de8PuiQcG+HxvJrOIg3mFEARgwvA4dGsf5patYZ+gr3rvJxhNVseEWKtRYMFk9+yOK9vlftvzygTBY90SjUqOpVkD3bJY6jPBN1RZoFErXDJcar5Hs9XqtY5KakIkqsxWj4VsAdu18XTtatY1cQ4I6U1WBKncJ82NMSGuS72T2iwFaokZCSZR8hOA6gNVLX/9G/L9XW0LaSIiIiKi5lKvAMff//53BAUFYdOmTdBoNLjxxhuxePFipKWl4fPPP8eyZcvw/PPPN9RYrz0yGQqLDfj2oPud206RIZg2NhFL134PvdFc5yUO/tq/2oMEAPCn2xLw7y88t8hzrimRmhAJrVIOVNfwqLKKkMtlePWDgxiR1s3n60kSMGZwDE6eK8E3Px3GwbxCLHlggNuyCwAQnCZznrItWoWqoVHJ8fOJQswY1wdrNx32G5yx/zzrziSfwZyH70qGIPietAkCXJbn2GWNScTGr064ZbHUZ4IfGqSC2SL6HPO0MYl4YHQiLqbrIRcEWEQJMgGQALQLD3JkpXjjrZjtwbxC3DEkFgldwz0u96k5aTaLEkYPjsFtAz3XXzFarNAo5HXKLvA1ya5Z70RvtEAbqvZ6fFEmw4UiPW4b2A2jB8c4xlffAEx9syX8BaCqzFaoVP47KjV0gOlqW0gTXa1Nmzahb9++6Ny5s8ftv/76K/bv38/ObERERORVvQIcP/30E6ZNm4aOHTuipKQEACBVf4EeMWIE/vvf/2LlypV455136j3Qa5HJKuGTb056XHrwyTcnkTXGthQiSKOs87H9tX917kBy9/B43+M0i44JrSBJjrvEI9Nj8Gn1xNt5oulJhE6D4nIj+vds7wimtAnTeJzYHS8oRnJcJHILijxmW6TEX1meUGW2ugUU7A7mFeLekb3QIyrcabJtsRUjjQp36chi399QZYZCLvPZvlcuEyCXJDx8ZzL0VRZUGswI0SoBQcArHx70OJarveNtsUqQAJ8TYAkS5j7/jaMOyqc1giF/fyTD52so5N7TA0KCVHj782N+u+JYBQFrPj6EY07FXRO6huOZh9Pxw9Hz+HzPacgEGVZ7yS7w9mn1N8lWOJ1PrUbp8fgPj0+CBODVDw/6XOZS1+vTUNkSxirfNVSMVVaEquR+gykGs+i3vkxdMsH8/Q3xt52orv7yl79g5cqVXgMchw4dwl/+8hcGOIiIiMiren1DFUURbdq0AQDodDrI5XJHoAMAEhIS8NFHH9VrgNcyk8WCETe6Lz1IibctPTBZbEsPgtVySFbRx5Hc+Wv/6tyBRKv2/THoGBmMh+9MhtFkgd5ogdkqIq5rOFo7BSjsQQlPE/HU+Eic/LUEr3x4EIum9gNgy8yQy2Ue28Du+P4Mnn5oEI6dLvKYufDziUJbRkhGLAxGi8+aGsbq7ITIVlosnNoP5ZVmPJW9z2N9EQAo15sRHqrGuCGxADy3760wmBAWrHabMD+R5XtpzdXe8TZ4mOQ7v+dKgxlLswZAlIDte894OF8XfS5hOeAleKJRyRGsVWBsRoxbVxwAiOsSjkqTFTJBQPaWwzh2xnNAKjUhEs88nI7jZ4o8Zni8+vEhr3VmtH6Ce+Ehasdr5BYUecxe+PnkJew5+JvfZS51uT6+siXWbzmC6WP7OH5f/GV2BGt9v8eQIGWtgin+MnUqDWYEhdb+8+frb4hzNlddtbgaKHUU6ONvySQ/51Gv10Mu95/NRERERNevegU4OnfujF9//RUAIJPJ0LlzZ+zduxeZmZkAbBkeoaGh9R/lNUomyPDp7ny3DISfTxRCgG3Jw9yJqZBLInzfx3ZXs8uJXc32rxqVHEEahSNjomag4HKZEVZRxMs16mQkx0UiNT7S8fOWnFNYMLkvANegQGpCJCYMjcdT2baOJyqlzJFp8MaWI64TtvhI/P2RDFwoNuDd7ccxbkgsXvnQe+bChGFxUCoELJzaD5dKDC7b7QENk9nqCGjcPTwercPUWJo1AMcLirF97xmX+iKAbbK5fvMR9IiO8JhZ89w7+/GPP9+EgycvuU3W/d0br2smjrU6cDA2I9bl8VYhKizNGoi3Pz/mFkiYMa4PRqfH4Mjpy45AREynVujZrTVE0f3aPHhHEtZvPuL22vZ6Ims/OeyW9bBwaj9IkoTNObZAxtKsAThwohATh8d7DEgdyC3E2k2HcWOfjngqe5/LsexBpsoqK9p4OAdqpe9sGrVK7ngfj7zwjcfzGKHT1GqZiz0joTYTWG9LZzQqOW4d0A2vfOi5xbCnzA6tUuYSSHAOXkkA5DJZrZaeaNS+J37+ttfk7W+IczZXXbXEGih1Eejjb4mOHz+O48ePO37ev38/rB46Q5WVleG9995DdHR0Uw6PiIiIAky9Ahzp6enYvn075s+fDwCYNGkSVqxYgbNnz0KSJPzwww+4//77G2Sg1yIJkuOLsqcsBIVcQJtWWhQXV17V8e1dTgxmEYUlBoSFqGCxiCgur8LjU/rieEExOrQOwhtbjmDckFiolfF4f+cJt0lz58gQ5BYUuRz7YF4h7h/Vy2XcZouIP93eA/eO7IlLJUa0bx0EhVyGP6/a5ciSOPlrCZZOG4BN35xCXNdwjM2IRUiQCnK5gLJKE0orTOjQOgh33xKPSoPvsI5MEFBpsECtlHusY3L38HhoNHJoVHKXgo32gMeYwTGQywSX51ittmty19A4HDx5ye01b0hoC1GSPHa8GJjY3udk3NdSkJpEmcyRIdIjOsIxAdao5Pjr9DRs+Oyoxwnv2k2HMTWzF06fK3UKRJzC8eqlI84Bm8ulRlisVtw7qhdMFtFl3FljEvHBVyc8Zj0MSe0EqyQ5MjvCQzWYODwePaLCXT47zp8Ni1VCl7YhWDk7HcXlVVAqZC5BJm/ZB0aTBeOGxGJIaid0bhsCi1WCVq2AXCbAKomoMlswKKmjzzu/Ndv2etpuz0iwArWawHpbOjMmI9ZrkMdbHQxBkvDw+CRHm1iNSgFJknDwpK0V7uNT+taq2KdaqfAdDFIqYKvMUnsyABOHxWFqZi8YjBZoNQoYq8xwXsjjMSAEQO/hsUDuysKuMo1j586dWL16NQBb17X3338f77//vsd9dTodnn322aYcHhEREQWYegU4HnzwQYwcORJmsxlKpRL33nsv9Ho9duzYAZlMhlmzZmHmzJkNNdZrjr56ecX4m7tjUFJHrNt8xC24MGdiar0ukiQIkCQJQRoFDEaLY9Jk7ywytG8XrPnkMGK6tMKx00UeJ2aieKWbxvibu6Nvz3YQIECpkGFp1kC3oIg9uKCQCyitqHIENzQqORK7tUabMC0m394TFQYzQoOVOF5QjOzNRxz7pcZHYlR6DLRe7jjbJ85ajQKtQtVY84l7kdGC30sBQYJCJsNfpw2EVq3ApVIjzFarSwbHlMyeWJo1AJdLjegVE4HikipMHB6PkCAlEqLCAQD5v5ViS84p9OwWgVl3JePVDw+6vd7BvEK8v0OGWXcm4fVPXO9425ccletNaF2LlrFWQcCFIr1jIhWslePBO5Lw6keHkBAVDlGUfE54J98uIa5rK6iVcoiS5MgQ8tR69+XHbsKGz9xb0IZolR6zZzQqORKiwrF2k3tL4ZS4SJf97MtV7Nk9r9e4Ts5BJk/ZBZIgwGiyQi4TsPvncy7vOSXe9hkLD7W1N+4ZHeGWjWNnrzfjTfvWQbjnlgQYzCKOFxTh2BnXYJ6nCay3+hM1gzw1j+OtzocoXQk9lFWaoFLKXLKQfLEvrREEyVFPx1OwTxDqOPkWBFwoMeL9nXkej9e+lcZrQMieteX4nU6IxMw7ktzOrV0gdGVhV5nGMXHiRNx0002QJAkTJkzA3LlzkZHhWjdIEARotVp07doVCgVrvxAREZF39fqmEBYWhrCwMMfPgiBg1qxZmDVrVr0Hdj0Iqm5ZWlRmdJswArYvzS9vPOC1PoE/FkHAKx6WltiXBRzMK8SaTw5hTEYsErqE++ykMv6mWPTsFgG1Uo63PrMVnZx0W4LHoMjBvELIZMD9o3q7TAQnZyagTYQWr3x4yOU5KdVLU84VVkIhFxzBhxE3dnOrAeA8cd648wRemDfE7fVbhajw9EOD3Dq0JMdFYtadSXjp/QOI6hCGMYNjIEoSnsreh5T4SHRtF4LwMDVyC4odk1SNSo5pYxPx3JzBKC6vgt5oRlzXcBw7UwSjyeqWpQAANyZ1xOh010wJQRBqVZTRfpf4toHdHI/1TeiA9ZuPICEqHGl9OqDK5HvCa7aI6NuzHd767Jiju423OiWGKgsOn7qEH49dcDmGvVZKTWMyYrFus/tn1b5kyHk/eyaDt6Ur9p/vG9UTWrUSv14sR3mlCUEaBdQqBdZvOoz7x/TGv7847Dax/PmErU3wTTd0dgQ27rw5zmNwoajM6LMezXeHf3c8z1ttlgO5hdCbRQRXF+n0Vp/Cni3i63wHKWoEuQQBl0qM+NZDVtDdw+MRrK1dsc+yShOqzFakJ3d0y9SpMltRVlm7AJvjvYj+iiAnYs0nnjMaIAGPTe6L56vP44HcK39rvAWArqZGTVPWw2BXmcbRtm1btG3bFgCwYcMGxMbGonXr1s08KiIiIgpUvBXSjJRyGbbszsfYjBivXTIO5Baisspap+4HgG2JQ83gBuBeWPFAbiFGp8dApfS3fl+BcxcrsOvAOccx4jq3cgmK1JzUAYBFlNCvZzv8eOwCBvTqiNUexvTziUKs3WSbwG/cecJxZ79tuBYP3uGaEVFzCYDRQ4vT2RNTPbafPZhXiFc/OoTZE1Px9Bu2WhDTxibaJuA5p/C/CxX47tCVSaZzMGX1B1e6o9gnwS9vPIA5E1OxZXc+dnx/Bk9MG4jXP3af8NmfM2dCMiD6Xi5hv0s8enCM43yaLFYM69cVKqUMVqvktyhlsFYJSZIwIq0b2rcOwqTbEhDfJRybdp1yy7QZ2rcLJg6Pw4Ztx12O4S3rwVeGwqGTl5AaH2lbVlPdpWbi8Hik9eng9Tm5BUXQqpV47SP3DidjBsfAYvWdrXL/yN4IC7YVGpUJgtsSjdSESKR0b4Pk7m381qMB3H8/nF0s1qNb2xAIkuS1PkVIkNItCOf8ekP7doHBakWl4cqE3CxKeN+plXHNscy8I9F7AV+nYp9atQJPrNmLMRmxaB2mcexTXG5EUbkRaYkdcKnC5Hhdf0wWK25Pcy+CbD9vJovF+7U5UYg7h8a5BIvsf2u8qWtXlqauh8GuMo2vf//+zT0EIiIiCnD1+kb2l7/8xe8+giBg+fLl9XmZa5a97oH9Lrs3te1+4Hw3U6NW+GwtWrOta0iQ70mzWilHTKcwnC/SO+7malRXPj7eJnUpcZF48M4kALZgRG3GZN/n3pE9cbHYgJ7dIjA6PQaSBESGa12O76muRWsv7Wftx75vVC8Atsm1XCYgqXsbpMRFIkijQGGJwZGd4a2egv3n2dXBjYLfS/H0Q4NQUl7ls5hlpdEMuUzweZfZfpf47IUyj8t/BvRqh/tH9/ZZa0EpF/DbJT0AW/ZCa50GWpXcYx2VNZ8cwvSxiW4BDm9dceRy7yn4W3JO4YV5Q7Bu02FYrJLj8xDbKcxrRoNCbmsv663DybSxiV5fDwAqjWaEBNl+N4wmi1u2Qcc2wZBVn2tbPZorvx/fHvzNLVPD/tqe2h4LgMsyhCs1bq5kEGhUCkwbm+j1c7Pm40OI63olSJSaEIn7Rvby+XmtMosYM9j1d8P+XOdin1qlHD2jI9zqoNivg3Mw0v5cX2SCzOfnf7qfa1OhN+Pz6horW3JOYUxGLMJ1Giya2s+105HJWueuLE1ZD8P57+r/e+hGHMy75NZiuj5dZcjV7t278eGHH+Ls2bMoKytzq68jCAJ27tzZTKMjIiKilq5eAY59+/a5PSaKIgoLC2G1WhEREQGtVlufl7imGaqzD/zVCKhN94OadzO9LTGwcy68GBkeBEmSfE6araIEtUrusnxjqVNbVG/BgJ+rJ3XTxyWiXO+9jaVGJUfrMI1L21iNSoEv9ubiloHdsPJt20T0iQdcW7EeyCt0ZA3Y+Uslt7eWXTC5L9ZtOux2B3jV/Jvw2+VKyOC5bgVwJVByMK8QSx4YgHWbjiDzxm4+W9aWVphgsYoQBAEdWgfDYhWhN5pdUuttk2Q5+vduj1/yi9xatMqr65p4mvDa76yXVZgc58v++N3D4zH+5u54t8YypAO5hbBaJSx5YABah2mgN1oQrFGgqLwKNyREQiaDS4ZCmJ8lDjLBtjRJkAnI3mLLopl8ewIWTe2HTTmuGSSp8ZGYNjYRH3990ut581cUU61SQBCutD6ueb1Wzk5Hm+plA4IkIUghQ1CICpcqTF6vLeBemLRfz3YQJcAqSi5ZEM7HBACIIhKiIlwyfpzZs3McP+cW4u7hvpccGaoseO6d/cgak4g/3ZaAorIqR/DGOVPBU1aJv6Kn9s5HnoiS5DPwIvqZzKuUMhzMK8QdQ2KR0DXcYybIgsl9sWPfGUwbk1ingERT1cPwlCVScxlTfbrKkKv169fj73//O1q3bo2kpCQkJCQ095CIiIgowNQrwPGf//zH4+Nmsxnvv/8+3nrrLbzxxhv1eYlrmr1tqLe75YDty7RWrQQk70sbPN3N9Bc0sW9PTYiEXCbAZPF8l9g+aZbJgPU1ai8cLyhGv57tEN0pDGl9OiCmYxjGZsS43JkFbJO64vIqhAZ5nhxrVHIsnNoPeWdLEKG7klp/9PRl3H5jNNRKGVb9+SaYLbbj2Tt2WKwSIsM1yEjphLWbDjsmdf5SxUOClHh8aj98vuc04rqGY/Rg1/oC2VuOoH/v9ujazneLY0N1IMWeMTL59gSvSxMWTO6LkCAlFr+6B4um9sPrnxxyaQ9sb3WqVSkw684kCILMY6eW+0b2giAAf133vVtXFHsb25VzBrssO3HOiKkZ4ACASqMFX+w941KHo1/Pdph5Rx88MKo3zCMkGKssUMgFCB6WgQBX2squqW4ruzRrgOP9hQQp8e4XuW7tkA+cKMT6LUcwYXgcurUP83jebr6hs2OJU03JcZEAJFQaLJg+LhFLXtvjto+3z4K/z4jz70+/nu1w76hebsuevC2HMBi9B/IA9+BJsJ+xaFUK9OwWgQidBkvX7nVMqmePT3LLGKiZVaJRK3wWPS2tqEKwl78VBg/Lv5wZqyw+/27ZAlRASJAKb39+zGutnofvTIbMz9KtmpqiHoa3LBH7uJ+fmwFAatS6H9ebDRs2YODAgVi7di2Uyrq11SYiIiICGqkGh1KpxOTJk3Hy5En87W9/w9q1axvjZQKeRinDzX/ohIGJ7ZGe3BGVBjOCtUqYrSJWbvgRHSNDMGpQDMwWq88Wo57uZvoLmti3Z41ORIXehDK9GV/sPeOxoOD2vWcwYVicY4Jqv9ueGN0aQ/t2wWsfHfI4oXdO/xcgQKWQOwIiznfqIQjQqORuE/qU+EhMH9cHlXoTikqNCA1WQhAEJHQNhyAIaNNKjXe35+LwqUsYkxHrWMYSpPbdLlOlkKFj6yCf9QXkMgEKP3eAQ6uX9dgnW8FaJd7ZnuuzlsKEYXHYnHPKfbKfW4jXPzmEm27ojJ7dWiN782GP16K43IhWoWrckNAW8V3DEa67knUR3zUcNyS0hQBgUFInbMnJR0mFyWkMvTy+D61ajntuTcDIQdGOwqmhwSq8XKNeSmp8JO4f3RtjM2IgCHD5PCydNgAf7LzSVtZ5Em+x+K6jkTU60Wvh0rWbD+P+Ub3d2tjas1L2H7uA1Pi2OFFQjMX39XdkNxwvKEbB76Velw14KxIK2AIXHdsE45lZgxCsVUIuF7D2E89FgD0th6hL8AQAFAqZz8+rRiXH9HF9HO2W/WUM1MxU8aXSYEawUu1xm7/3odUo8OD4Pi7BRfuYnWubyOWCzxpDRpOlztkWjVUPo+YyP1+fW4y2nWcuS2k4ZWVluO222xjcICIioqvWqFXRevTogc2bNzfmSwQ0OYBJt/Zw6yqSHBeJv828EQpBwJ9fysHi+/pD6+NupKe7mTu+P+O1k8hDdybBYrFCLhcgCLYCosv/+QOefmgQsrcc8TjhLy6vAuC6ph8Acr856beQKWBLd7eIVtw3qhfWbnJ9jdkTkvHtwd/cjvPziUKs/eSwo/iofUmDVZQgCMCpX0uROSgah09dcjnekNROePiuJI/nddadSXj3i+OYdGuCz/oCU0b0hFwmuC1/sUuNj4RcbstmsE+mqkyi14mcrf6FgP69O+Dtz4973OdAbiGmjuiFSyV63DrQc/AlLbEDRFhx78heePUjz++vwliF9Vt+wYqH02E0WXGp1IjVGw94LMiamhAJtVKONz69cp4nDo9HbkGx+4T+RCEGny1BxzbBGJTU0VEEtFNkMIpr1B9xnsRX+rnbbhG9n7cDuYW4fxS8dgY59WsJ+vZs51L81n4uHr7LdxDAU5FQe4vTeS/ucgTnnn7wRp/jq7kcwlfwxDmzwa680uQze+rXwgqEaJWYd88NaNc6CFqVvNZFNP1N9H0VrFXIfQdeFHIZ5r+4C1ljEjF1RC9cLNYjWKtEkEaBp9Z/7wjG1LbNbV34C1BdTT2Mui7zY9eUhtenTx+cPn26uYdBREREAaxRAxzfffcda3D4YPXQxhWwTXJe+fAQZk9IxiP33ADtVdytvHVgN7y19ajHLIA3Nh/BA2MSkRTbBus2H0F813BEdQjD37K/x9MzB6HSaIbeaIFWo8DlUqOtVe3dqQBc1/SPzYjxWaPCXqgxOS4Sh05eQkZKJ6zb4n4nPEKnccto8HScAx66rdw9PB6PT+2HlRt+dIyvb4+2+PViBe4b1QtqpRxlFVVQq23v5a3PjqJzu1BUma1+C5FaRQkThsVDlNwnnhOGxcNiETFjXCLKKqvQv2c7VHmZyDnX+7j7lgSfdTqqzFaolAq8vzPP4+fi5K8l6BPb2tE2tua1Xb/5CLLG9MbBvEKYzCKKyowoLjPi/80a5DbRTI6LxPSxfSCXCS7n2VenlOJyI3b/fM4xCZw4PB6f7TntVijXOYNI66eGjLHK9wS40mBGQlQ4yipNjsyQwhID9h89j/tG9cabW3/xeK5e81NssuZyDq1GieNnivBU9vcuBSQrfNSOAdwnut6CJ566tgC2AOPStXsx/ubuuHdkTwCCbUmQQoYDJy7ihvhIKBRybNx5AveP7o2ySpPPlqiSIMBosUKSBCjkMp+BgLAQNSxVnt9feaUJdwyJRXpKR7TWaa4El8qMiAzTorzShMen9MXxgmL88Mt5RHcKc/xe3jqwG/LOFmPW+CSvvxd2JosIqyDUqfOJrwDV1dTDuJplfuya0vCWLVuG6dOnIzExEaNHj27u4RAREVEAqtc3tNWrV3t8vLy8HD/++COOHj2KGTNm1OclrmkGP11FDFUWqJVyqBRy+Cq26OluZq9utm4KnmoXAMCfRvTAxp0n8POJQhw/U4S/3Nsf4aFqvFqjXWdKfCTm3p2KkvIqpMZHukx+a9YSqMlkFpESb5vUrXx7PwandPQYyPB3HIVc5lJ8tH3rYPTqFoGjZ4rwyTcncd+oXvjLvf1hFUVszsl3K2T5wJhEXC4x4MT/ivFT7kUM798VBj+T6vJKE0KDVVj59o+YPTEV943q5VgKcqnUiJVv/4hl09Ow9LU9+H+zbsS0cYlu3TjsnINC943u5bNOh1Yjhyjarr+nQEjeryUwW0Wfy2vMVhEalRwVBjOeyt6H5LhIdIoMQZswDV6cPwSG6uCVscqMH4/9jqTubV2ug6/r0Se2jUsGiv3zULPryJacU44Clmql3GcmQLDW95+hKpMVBpMV7SOCEBKkhLHKirTEDlArZbCIIg6dvOTxebUpNum8nKPCLOKVD92Lg17NRNdThxW1SoH1mw+7dd9QKWToE9sGsZ1a4a3PXGtVpCZE4qbUzhAlEfeP7o2la75zLDvyVAPEKghYv/mIIwMot6AICyb3dXymnI87684khAapUOwlwBGkVSBYq8SmGkuq7JlUEiQsXbsXPbpF2Or0CLZldAfzCpE1pjdu69cFgiT5zWg5dPIS8s4W17nziadzfLX1MOq6zC81IRKAAL1FbJAaHKJMBn2VBZUGM0K0SmjVijrXJbkWzJs3DxaLBY8//jiWLVuG9u3bQyZz/f0TBAFbtmxpphESERFRS9coAY6wsDB06dIFTz75JCZOnFifl7imVRp83xmuNFjw/s4TeGh8H1uPSi883c1UKf3cNTe5fqEPC1Hhza2/uH3J//lEIQQBGDc4FqMHx7iEWfxN/Nq1DsKgpI4Qqic+lQbPSxX8HUcmE/DU2isde1LjIzEqPQZnfivFiBu7wWwR0TZCi7WfHPa4rMKe7ZBbUIwFk/vCYpUQrFWiVYgKJRUmj4GEkCAlqqrMmFPdCrZmBseciakwVlnw/x4aBKVchlc+PIQZdyR6nBA5B4W0KgW2e6l18sXeM5g2LhG/X9Z7bbubHBeJGxM7+FxeM21MIsZkxDoyf+yPPzS+Dy5c1kOllOHnvEIU/F6Ku4cnoKjM6HIdfF0Pi9V1ImcPhuT9WoKU+EjHRNhosuK5d/ZjTEYsLFYRdw+PdxmL/b3cPTweSh9LIVITIhERpkH2ll88PlerUrgsharJObvCub6Cp8mwt99HfxNdb8shPHVYmTEmEVNG9HQZw+WKKvzx9h5467Ojnut8fHQI947siX9/kYs5E1MdtW0O5NqKtE4f2wdGkwV6owVmq4hbBnbD9r1nHMeyX4exGbbf33bhQdAqZVDKfPxRAaBWKrDaQ4aZPZNq9oRkLJraD7lni6FSyh2ByOMFxagyWRBS/TmqTUaL0WS9qs4nbuf4KgMNnpb5OQfpan72Rg2KwWMv5bjURKlLBoozi4dMPvsSK8V1Vt+jVatWaNWqFaKiopp7KERERBSg6hXgOH7ccy2BlujUqVN4+umnceDAAQQHB2Ps2LGYN28eVKrmW0Pta/27bbsCuQVFtmULKt8BC7kkYea4PvjtUiVMZhEhQVeO7WkCHxasQrsILS4UGTAmIxZW0XchyEm3JGDp2r14bs5gx+P+CpnuPfy7I2V9TEas16UKvo6TEhcJo8nqksFhL3wa3SkMn+7OxwOje+NyqdHr+O1LTgBg+94zmDyiJyr0JiyflY4n1+/FjHFJboEEe1eTj772XPwSAKaPTcTaTVeWhIii5LGWgvMcxWSx+sy+MFUXlPXW3vNgXiGkGsd33y4hqXsbXC41ujxuNFuxonopj/313v8yF/eN7o2H70pGiFaJRVP7ISRIhdkTkrF+8xG3rBSt2vVPhkopQ6sQFfr3ao8BvdvjcokRggDHkpu8s8XISOkIk9mK9JQadTTKjDCZrTBbvXfwmTGuD/71+TGPAaHNOSdx/+je6BEV7vFcAFeyKzy1+6w5MfX2+2if6NZsmXs1yyE8Tcg1KjmsInwu09Ib4xDdydZpxh7Q0ajkuHVAN7zyoWvWlT0AePjUJRhNVhhNVpfP2urHbqoes+8AR20yzFRKOY6dLsK/nbrzJMdFYmjfLnDOOpNLErJGJ+Jiut6t64/9M9acNS08ZeE4B+myxvSGscoCk8WWMeQ8bm/FZmtDlMm8LlN8tXqZ4vWUyfH222839xCIiIgowF0Xi4hLS0tx7733olu3bnj55Zdx4cIFrFixAkajEUuXLm22cWn9dPvQqhVYMLkvqkxWhPoJcABAWaUJT2XbMh1eevQmJMdFOlLUPU3gn5xxI/669jv0iApHVXX3DG+1IUQJmD/pBsCpTaivO5z2O7MalRwJUeFI69MBKi9LFbbknMLSrIEunTkA20Qta2wi3tp61GWpjXOnk407T0CC/zoJFy7rkVtQbHueXIBSIeDNT3/BkvsHYP0W9xoO9q4mPbpFuHV9sZ8TSZJwMK/Qcedfb7S43C13ZIM4TZwlSfCZfTF9bCLahQchqXsbn1kJvhirLFAqZFArBKycMxgHTlzEJ1+fRJXTshz76/WOiYAgCNhz6De88qHr5H1p1kCXehS2z6TrcoMzv5di+ax0rNt02C148Pd5Q7Dn4DloVQqvwS2tWo4glQKnzpVU15/o5WhJeyCvEBariJv7dvUeEDJboVUrsGhqP5drY7+zrlXKIQEe233WnJh6675jNFmxfe8ZPHxnsiNToj7LIWrSqBQ4e6HC5z4VerMjE+hPtyVgS84pjL+5O4rKjBg9OAa3Dezm9vvqLbOltoEEfxlmeqMFG7864RaYOZhXiDWfeJrwS46/T540Z00Lb8tojCYr8s4W47Z+XSATgMef/8bj82uzHMoTX0Gkn6uDSN7a+BIRERGRuzp9o/ztt9+u6kU6dux4Vc9rKO+99x4qKyuxevVqtGrVCgBgtVrx5JNPYubMmWjXrl2zjMtgMmP6uESPnU6mj0uE0WTGlt35eGh8EnzV4LBzniAIAjBmcAyKkjt6nFAfyC3E6x8dwtJpA1FlEiGXw2dtCLVKjhf//RNWPJzucrfdPqG/b2QvXCjSu9yZtbUPHQi90YILl/WI7RiKWXcmuXX/SIiKQJXZip7REdWTVlsGSmQrLdZvPuJWR8S50wlgm2j5W+aiUsocz7tvVC/IZDLcntbNZzaEvavJPz876vGcGKqutIe1n/+ad8sBWyFO+8TZHhTx5GBeIURJAiQJch/LBzR+gl0atQISJFwqNuK5d/ajZ7cIPD6lL4KDXH/dD+YV4t6RPbHm40Me29YCwLOzB+P3S5WO6yqXCbh7WDxu7NMRka206NAmGK95eb69A45ZFPHelyc8ZtgM6N0OD4xJxC/5RXi3RhbA3cPjoVLIvS7p2b73DO4d2QuGKotLZsqCyX2xY98ZTBuTCEGSoLeIPrOT7BNTmSh67b4zfVwiZA20HKKmKrMIwXcyBVRKmWM5ULnejIVT+6FtuBZrPfztsLdorlkXxc5e1NPfH/+QIN9BkGCtwmvWSV27y6QmRCKoOhjVEDU16qo2RUv9BRavJgOlNscMVjZMVou/JVotwY8//lir/fr1893hhoiIiK5fdQpwDB061FFPoS6OHTtW5+c0pJycHKSlpTmCGwAwYsQI/PWvf8WePXswfvz4qzquJEmo8lJYsjZKKkxYu+kQ/vzHP8Aq9oDeYEaQVgm5TMCKt37AzHFJjgwBSeH/vMsEAUlxbXAo7xLMZhGf7TmN8TfF+rxDeOGyHk9l78PjU/pi23dncOSUa8HGg3mFsIoSskb3wiN3p+Lo6cvYf+wiYjuHYURalGPCWWkwOSaZAKBWyvD41H5449OjjmOufuxmvP35L7htYBQm3ZaAqioLNNXdTVb9+78orXS9Y/zc3MFei6QezCvEpNsSHD8fyb+MpO5tPBacTOreBlqNAuNv7o7Pvs2HxSJCgoRPdp3CxGFxPs/p+cuVHrMtrKKE6WN6A7Al+ifGtoHVKiExto3bOdy86yT+9uAgWEUJZRVVGH9zd8R3aeU4dyfOluCzb/NRZRZRWl6FDTmncFd1zQpPqsxWj6+D6nGYzSIEGfDZntPIHBSNj78+CatoW8Lkfizfk/8/3SYispUGhioLBvRqh9JKE8oqTcj5+Rx6RoUjKEiBmE5huH1glNv7OZhXiBFpUSjXm72+Rqe2oXjlg4Nu180eDMoa0xvD+nXBZ9+dcQkc9eneBiNv7AajyYIj+ZddnwcJM8f2gaXKAguAkkqT13MJACXlVZAHX5lEzhibCL3Jgkq9GRqNAkWlRix4KQdRHcMwY0ziVdda8KZUb4JWo3D87taUGNsGR/IvI75LKwCA2WJFUZkRn3xz0uN5s4oSMgdFe8xqSoxtg59yL+LkryWYNa4PtFUWVJmssFjcl0GIkoAbEtqiW0ed2+f1zG9lEK223/OR6TEeP881zysAZI3qjbXiEZf3mRTXBtNG9YbeZMXazUdc3lNSXJtGOefeTB/VG4bbrDBUWaBV24IA9s+Rr6AjqrfX5r8HVlGCsfq8y+S+jymTCyjWW2CoMjvGczVBCasgNNq5lRrw2kyZMqVW3zGa+zsFERERtVyCVIdvJx9//LHLlw9RFLFhwwb89ttvGD16NKKjowEA+fn52Lp1Kzp16oQpU6bgzjvvbPiR10FaWhruvPNOPPbYYy6PDx48GGPHjnV7vLbOX67E9OU7G2KIREREAWfd4uFo3zq4QY71ww8/uD1mtVpx7tw5bNy4EaIo4tFHH0VaWlqDvF6gsVpFFBVVNvrrKBQyhIcHo7i40mPwk5oOr0XjsZ/bVe/+F+cKvS8TTYmPxJ9G9MI/3vsJF4oMsFisHgO7nSJDMO+Pf+C1agL8vWg5mvpaREQEQy73v3S3ThkcNTMdXnvtNVRVVWHHjh0ID3ct9DdnzhxMmjQJly55buHYlMrKyqDT6dweDwsLQ2lpaTOMiIiIiJz179/f67bx48fjj3/8I3744YfrNsBBRERE/tWrqtt7772H++67zy24AQARERGYOHEi3n77bcycObM+L9NitQnTYt3jN1/180uMFix4abfX7c/NHYy3PjuG6WMSEaSs29IgvVnEhs+PYVi/rtCo5KjQm92WQyTGtkF811b4+OuTeObhQfjX9lzEd22FE/8rcVv+MP7m7m6Pq5UyzLvnBnxWY2lLUvc2mDA0Dk+s3ev2fny930VT+2LFhv0AbKn0D97RB2s3Hfa67GT8zbGoMokI1ijw8Ten8FPuRYy/uTv69myHxa/u8fo6z80dDBkEPJW9F//3wAAo5DIcP1OECJ3GkWZfXG5Ej6gILHrlW1SZPUckn5tjq08R1V6HdVuO4N7Mnljwsuf3t2iqrRjri//+CfMn2c7ZYaf31ad7G0wbkwiL1YJzF/Xo0CYYb39+zO29j7+5OwYktsfCl7/1+v6enZ2Ohau/xYqH01FSbnScU+fzn9S9De65NQHnLpZj98HfPC6NSIprgwfH9YGhygpDlRlBGiVECfjnZ7ZlR8/PHYz/e22P1yUKC6f2w5H8y7j5hs54c+tRj0sd+sS0xpM+Ck+unJOOx32816emp2Hpur1IjLUtWVn13k+oMov4+9wM6DRXapWYrBIKS4348OuTrp/V6jR9tdNSgQulRixZ4/rZBa583j/fe8Yt1T9rdCIq9FXQqpUIUskhAjhfpPf4e5cU1wazxvWB/SV/LzFiafXviqclH6HBanzydR7GD43Ds2/9gNJKs8vviifPzklHSVkVLpcaUFJehY1f5bnt88IjGejSLhRl5QaIVve7UbX5++Rr+7Oz06GQyVyugzdlRive/Oyo12VX94/sVavjNAWrBFRW/05o1UoEq+Xws9LEhUwuQBeqRVm5AWaLhPVbf0HX9p6XAXXrqMPHX590O0bNz7cv3j7Pdk/PTEO7ME3t30ANbcK0V/3cupDJZBg5ciTWrFmDRx55pElek4iIiAJPvQIcJSUlMBgMXrcbDAaUlJTU5yUahE6nQ3l5udvjpaWlCAsLu+rjCoL/ddm++EvlsVokPDQ+CXqjCaHquhWaE2RA5o3RbgVGk+Mi8fiUftix7wxuHdANz71jmyTJBQFHTl3C+JtiPX6hToxp7fb42CHdsf37AsekxN6FpVe3CLQKVePF+UNQVmmCKEo4dqYIouhvNdSVc3nk1CWIkoRxQ2Ihlwtuhf9m3pGE3y5V4rWPDmLu3ano3qUVRqVHo1WoBhV63zUXzBYRH3x1As88PBiCIKGk3IQfjl5w6+DSIyoCKfGR2PeLex2Q1IRIHD19CW9uPYalWQNw8myxz/eX/1sZekSFY/zQOGz/vsAluAEAh09ewj8/+wVZoxPRppUGGrXCY2AnMaa1SzcUT5zrAOT/VuYYr1alwPKHBkGjVgCQsP/YBWzbcxqPT+kHucz9HD80PgnZm49g39Er7/+lR29yXG9RlPD4lH7Ysjvf5bNh/4yFBCmxbc9pDO3bBSMHRXvcLz25o9fCk8lxkWgVovb5XkOrazwcOXUJcpmAsUO6I+9sMVoFK11qFWjkMmzOOeU2gT6UdwnZW39x6fjRKtTza9o/7zWvy6G8S3jj01+QEBXu6Aj0wVeuRVXt5+S5d/bjUN4lWCQJquoUO51TYcoqs+j2e7Y0awD2H78Is1XCbWnR2LjzBMr1Zp/n7b/HLjpaNM8Yl4gtu/NdWv6mJkQiPEQFjVoBg14Gi+ShBoeHoIfLdj+/z1UmKwQVIJf5/8+MUinzGNwAbNdWqZTV629tQ5IDUAUpEB50df/5VMhljvMuSCJmjOuDdZuOQCGXObo1/aFHW6Qnd8RT67/3eIySyirogkNqVT/D2+fZeXt9zu1VlOW6aqWlpR7/W05ERERkV68AR3JyMt566y1kZGQgMTHRZdvhw4fx9ttvIykpqV4DbAgxMTHIz893eay8vByFhYWIifHcaaApBDu1D/UkSKuATAboguteRV8myLy2I5UJwMw7kvDO50cdk57i8ioAcHRqqMnT4/a2lYAtuLFgcl9bx4uu4W6tV5PjIpGR0snnmGt2B9EbLPjPj/9D1ujesIyUcLnUCAHA8YJizHvhG/ToFoGFU/vh8KlLjnEsmtoP7VsH+XydEK0SB3ILseaTw5h9VzLe+/KEe5eZ6snp/aN7w2gS3d7LhKHxMJlt585ilbBgcl8EaRVeW+3u+P4MBqd0QohWiX87dQtxec3cQkijgQidBiUVnoM09g4zvmiqW7IGaxTYknMKqfGRuH9kb0CQUFZpgtFkwfGCYpw+V4o5E1Ox8u0fseLhdFisoqPDgkalwLpNh12CGwAcnWMAW3DhX1/kem15++D4PnhqRhogwetn8d9fyDBzXBJe+9i9c8mYwTEwWaw+WylLTt2FDuYVYsKwONzSr4tbIUaD2VqrTiqA924fzp/3mg7mFdq6lmTE4v2d7p8n+8/21q3OHTc0Sjn69WznsR3x6XOlOF5Q7DjGfaN6IaFrOFq30iAxtjXWbjrsMk7nFs3256zffMSlZay9M4hcAMr1JpQZrdAbzW6dNYK0Sp+to4M0vj+HoijVuvWr0elz5W17SAN1E6mtpuo6YrFacc+tCXj782Nu3Zrm3p2Kk7+WoHvnVi7nXy4ILi2OffHXvUarlDdYR6D68tapraysDPv370d2djb69u3bxKMiIiKiQFKvAMfSpUsxZcoUTJgwAcnJyejWrRsA4MyZMzh48CDCwsLwxBNPNMQ46yUjIwOvv/66Sy2O7du3QyaTYdCgQc02Lq1K4fuLp0qB1R8exMN3JdX5C6joox3pgROFMJos+OPtPWGyiPjh6AUoqyd33tqtenrcOegxJiMWW3bn2+5ie5nMqlVyn5PV0BotFrUaBf54e09YrBa8tfWY2wT15xOFEATg5j90wcTh8egRFY5WoRooFTKf51VV/V4P5hXCYLIgt6DI8fyak7hLJQaPLUqfyv4ez84eDABo00qDtz47hofu7IOlWQPx/s4TbhOVx6f0g1IuQ4Wfu956gxkms9XtXNiplDIo5DKf51Eht71/mQA8NTMNSoUcb39+zKUjjX0ivH3vGdw6sBtKK0xoo1M7Jvl6k8UtuAG4BqEsVtHrZ+xgXqEjYKJRK7zu9+OxC5g8oofHc/zcO/vx1Iw03F3dUaZmAOTu4fHYX6PLjkoh83hXuy4tPp1bhh47XeSY4GtUCizNGuD4bBhrdMwwmcXaBUHg2tJZJtgCaW9++gsAOD6Hyd3bYGjfLvjr2u8c+164rHd0K0pP6oBZ45NxoVgPmSA4AlfPvbPfZWwHThTigTG9MSipA4xVVgRrlZDBtmznxXf2e2yLKpckyGXw+nlemjUQchmQEheJnz1c25S4SKhVslpPnv0FQmobKGkoVkHAqx8dcvmb43xuGpIoCtiw7ZjXYPSNSR3xlNMyruS4SAzo3R7HThe5teL1pDYtcFsKX53aJElCSkoKnnzyyToft6CgANnZ2Th48CDy8vIQExODrVu3uu33wQcfYP369fjtt98QHR2N+fPn4+abXZehlpeX45lnnsHOnTthNpsxePBgLFmyBG3btnXZ76effsKzzz6LY8eOoXXr1pg0aRKmT59+VZ3oiIiIqPbq9a2xe/fu+PTTT7F27Vrk5ORg27ZtAICOHTti6tSpmDZtGiIjIxtkoPVxzz334O2338bDDz+MmTNn4sKFC1i5ciXuuecetGvXrtnGJUoiJgyNhyi6T94mDI2HKIko+L0URpMVIV4CD974m9CVVZrw0dcn8fBdSfjT7T0gEwSkJkTieEGxx8mzp8edgx72id3YjBivEzyjyYIxg20TPE93653TpJPjInG51IDNOfl4cHwfn3ffHxidiK9+PIuNO09g4vB4DP1DZ5/n1fn7pd5gwcKp/XCpxHWpVWQrLRZO7QezRfT6fuzZDPaJvsUiYeNXnu/eywRg0q0J0PqZqGnUcoQEKSETBK/XISWujc/zaKyyVL9PAT+fKER4qAYjbuyGYf26ugRvtgBIiApHj6hwVBrNeG9nrmMC5+3zo1ZeCVJVGnx/xkorTAgJUrlkfXhiNFm9nmOtRgGNUo705I4uAZDLpUa0CdPgqx//V+P8KSAJgtukra4TaLkkYc74JJgkYM3Hh9wm+Asm93ULJKiUMq8ZUHYms+h211wQBLy19QhuT+uGLbvzPQbH/u+1PTCarC6/cx3bhmL1hwdxMK8Qi6b2c2nTXNPFYgP+5jRJTk2w/S4cO13kst+B3EJHZoBSLuADb59nGTBrfBLGDonFoOSOaB2mcb02rbQIDVbXevLckrIMJA/BDcD13DRkUMBfMHr0YNcsw4N5hdiwzRZUdg7M+SKXJMwen9QkGSn1sXz5crcAgCAI0Ol06Nq1K7p3735Vx83Ly8OuXbuQnJwMURQ9dkD47LPP8MQTT+DBBx/EwIEDsW3bNsyePRv/+te/kJKS4thv3rx5OHnyJJYtWwa1Wo1Vq1Zh+vTp+Oijj6BQ2P6OFBQUICsrC4MGDcK8efOQm5uL559/HnK5HFlZWVf1HoiIiKh26n1brE2bNli8eDEWL17cEONpFGFhYXjrrbfwt7/9DQ8//DCCg4Nx1113Yf78+c06LqPJiqeyv8eYjFgvGQLpePqhQTAYzHVOz/Y3obNPyi8WG7Dk9e8cS0y+2HvG4+S54PdSzLyjD9ZtOuz44u8c9LBP7HxN8Cr0Zjz3zn6P7/e5d/ZjadZAAHDUDfi/1/agpMIEs0XE0qwBbtkV9sml3mh2jHVLzikMSurg97w6zpNWAVGS8O3B3zxmCLQK9X7eg7W2c2yveSFKEo6f8Z4NolbJYRUln9kXKqUcaz85jLFDumPmHX2w5pPDLvuePleKwSmd8FR2jtfzuHLOYDz+8m48NzcDQ1I749WP3Jd/2CfoYzNioFLKcejkJccEbua4PtAFq6BRyd2yFAQBuOcWW5DIbPFdC8RqFaFUCFAJvj+LWrXn7akJkVArFVi76TCiO4WhdXUhRJkgIFynQWGJAY/cfYMjc6Hgt1J8e/A35J0tdrvTfjUTaAm24EbNia49YLV81iB8f+Q8tuScQkJUBI4XFKNHlHvBZWchQUq3u+YGkxVRHcO8Zj6987ltMptbUOxYrgK4LpnxlnllV/Oe8YHcQogiXJauOG8zmK0QJfhe1mOyQgCw59BvLvVrUuJtgTaD0YygWi6va0lZBrVdztRQS1j8Lc/x9DfVng1Ul8wWQZIQpJBdCYi0sOAG4N6praEMHToUw4cPBwAsWrQIR44ccdvnpZdewsiRIzFv3jwAwMCBA3HixAm88sorWLduHQDgwIED+Pbbb5GdnY30dNt/R6Kjo5GZmYkdO3YgMzMTAJCdnY3w8HC88MILUKlUSEtLQ1FREV5//XVMmTIFKlXTLrciIiK6njRt3m8zio2NxT//+c/mHoYLQ5XV591rY5UV7+44elVLVLRqhdf08eS4SMdEqUJvtr2WyYqXNx7A0mkDYayyYMqInrhvVC+UV5oglwmIDA/CE2v24PEp/TA1sxeKyoyQywQMTGyPtz+/MsHyNdHyV3MkJEiJF+cPweVSoyO4AQCXSgxuKdrOd9BrTpD9nVdDdZHO5LhIqJVyZO/8xePEErDVkfAkOS4SyuoikfZCmMYqCxZM7uvxLvyCyX2hUspx/EyRzyUXgmCbVCbGtsbne/I9Lt2QywQkREV4fH/2JSpGkxV6gxnrd3ivkTEmIxYms4g2rbTYknMKgG0C99ulSnz6bT6WZg3EU9nfuwQ5SiuqoAtRYVBSR7QLD/IZrAkJVsFQZUWwRuFzPwECUuIjXSbJtiBXH1SZLfjx2AXH8hp7IO5TD8VzH7ozCYtW70ZJhcntTvvVTKB9TnSr76znFhRjadZAVJmteHbDj0BGrNf3mpoQifYRQZCJrhPWSoPZ59KWAycKcefQOCR0DXfU1gBcJ77eMq/s58Y5MGLnvGSmJr3R4vdPjsFowaacUy7XDbAtHZMk4CEvvzvetJQsg9osZ1KHqhtsCYu/IIW3v6kS0KLqZzS0kydP4ty5cwCATp06XXX2BmDrwOLL2bNncebMGSxYsMDl8czMTKxcuRImkwkqlQo5OTnQ6XQuS1tjYmLQs2dP5OTkOAIcOTk5uOWWW1wCGZmZmVizZg0OHDiAAQMGXPV7ISIiIt/qFOD4y1/+AkEQ8Le//Q1yuRx/+ctf/D5HEAQsX778qgd4LbNnAHgTpLXVLriaJSpVZgumjUtE9pYjPosQOn95vnVgN7z1mftacMD25X3IDV0gCLY2ofZ9/nhbAm7s0xGtQtVIjfe+xAWwFVP0tab/UqnRY9eAkBqBEecJem5BsUtK85iMWEc9EW+CNLbaJ6MGxcBotvisI2Eyuxe5TI6LxPRxiSiv7taiVNhqYuj8FN2cfVcSeke3RlGZ0W3JhS1gdOVOe3THMLz9+XGPdTBuqL5D7nxs+7jGDI5BWaWtYKxGLff53sZmxCBCp4HBaMHjU/o6Mk1MZtHxmZk2NhGrPzjoeJ4uWI3XPrZllbz06BCf41DKBSjlckCAz/0gSOgZHWErKuoUyBFF0RGAs7PXevF0jtd8fAi3DuyGjTtPuBUOBeo+gfY30TWZRUc2R4/oCBhNVkcXFZkMHgMpNYMbgC3wV+qlqKydQi7D8xtdl8RoVHJHtpDZIiKtTwecPFuC7C1HHPvZP+fOgZGa78GTII0C/poeaTUKt+CGna0GiwSVom71BlpCloH/5UzKBl3CEqSUY/aEZJc21fbfRXtmkCdtw4Na3BKThrBz506sWLHCEdyw69y5MxYtWoRhw4Y1+Gvai5BHR0e7PB4bGwuz2YyzZ88iNjYW+fn5iI6OdltG41zIXK/X4/fff3crYB4TEwNBEJCfn88ABxERUSOqU4Bj3759EAQBoihCLpdj3759fp/DglreCRCQGh/p8S5xarztzjZgqxNR1yUq5ZVmPLvhRzwxbSDuvDkOFXqzyzIGo8k2cRclOJZ/2LuP5BYUuS1NOJBbiNHpMbBYXAtLdu/cCk9l73PpouJtMmu2ij7X9CfGtHZ7H8lxkVB4CFjYO2YkdA13mQT1iArH/uMXvJ/X6iKjD41Pwty/f4Nl09N8nkdDldVjFsVbW48ia0xvLJraD6UVVRgzOAZmq/e19AfzClFlEaGRyxAcpER813BYRQnGKgsUcgEl5VWQJNvyC8D3Uh+1SoFl6z0vwXnunf14ZlY6kuMi/Xa5kCTgu8O/O4JN9kwTey2UA7mFyBqdiNWP3eQICDi/x0qDxe+So9ZhGshkgq27jofzuH3vGcy4o49bZ5nUhEjc2r8rgrWuY/aX6eBcr8BTfYK6TKBre2f9wIlC3D+6N1LjIhGkUdgmrHUIpGhUcv+dcVRyzJmY6vjd1ajkCA1WIbeg2OV8pCZE4sV5Q3ChSI/QYBWCNUrMe/Ebt9/nmu/BmX3JjsEs+swKkfn52643mmtVH6Kl8becSSEXat2RpzasAPYc/M2trfDSrIGQCcAyD0Hf1IRIBCll11z2xq5duzB37lx07NgR8+fPR2xsLADg1KlT2LhxI+bMmYPXX38dGRkZDfq6paWlAOAoQm5n/9m+vaysDKGhoW7PDwsLcyx7sbexrXkslUoFrVbrONbV8vTfw4Ymr85OtP9LzYfXovHYz6kgCD7nKm7bBDi+n3vaj9eq8fH3ouVoqdeiTgGO//znPz5/proRJRGjB8dAAtzWsI8eHANRsk1yg/xkengSrFWipMKE/3t1DxZM7ovP955xmaikJkQia0wi3tp61K27hqcCioDtu7S9nayd/QNtNFkdk125THAscakyWSGTAQW/l8MqSj4nBnfeHOfymP0O/6USo8fniKKEHfvOYNyQK6nLJrOIT74+icen9PV4XqeNSYTBZIFaqYDRZEWQl/oPdkFqhdcJ9dSRvbBiw4946dGb8Nd132PZ9IE+j1WhN+OC0YwOrYNw/EyRS2FGqyhBF6JCaWWVLQjjI2NHguRziYpcJtiKjZp8ZyBE6DSOpSnAlYCUa5DAjDYhKsdE9ULZlesfrFX6LRD6+ieH8KfbEpA1NhGvfeRerHPWnUkorXC9vvZisJdKDGgVqnGZZNemiKddfTtv+Jro1lz2YayyoI1T0EQAah1IkQNoF+F9uU9KXCQulRrx6e58R82MMRmxHjtvHMgtxOufHHYs11nxcDp6Rkd4nawXlRndHrMv2ZEg+cy8cW7R60lTdz5pKP6WM5VUVPl4tufAmjfeCprag74P35nsdv1aYveThvLqq68iISEB//rXvxAUdKXd97BhwzB58mT88Y9/xCuvvNLgAY5AIZMJCA8PbrLX0+m0/neiJsFr0XjkchkUCrn37dVL3Oz/KuSe97V/H+a1ajo81y1HS7sWgfkN9BqhUijw+XdnEN813C09//PvziBrTCKS4yJtNSY8pLb7olHJ0a9nO0R3CnMJONhrarSNCMLaTw67BDcA1+UfNSeurVtp3JYM6JyKCHqa7K768xCUlFUhplOY324aoii5FRN97p39eHxKX4/7y2QCHhidiPOX9Y7JoUppqz+x8m1bsKXmeTVbRJSWmxAWaou0Xy41+Lxbe7nU4Pa4XaXBdi40KjkSoiL8ZkwEaRTQG81Y/OoezJ6YivDq5SHa6uUAi1/dg6dm3ojpY/vgl/zLXie8pRUmP0tUTHjunf1YNi3N+6Q5PhI/HDvvFsSqWZeh5iQ1WKuERiXHmIxYaPy0/b1casSB3ELcN7IX/rr2Ozw+tR+U8t6oNJgRrFXCbBWxdO13WHxff7fr/lT290iIisCMO3pj1p1JjkKp/opp2rc3ROcNbxPdmsu8PJ2nurAC2LD1F0zN7Il3Pnct7JkSF4lZdyXh8ZdttUXuH9ULf0hoC41a7rMd7aUSA24d2A3lepP3yfqdSQjWqtAjKsKWbeGWaeI78+bekb1aTOeThuZrOZO3orh2dfks+CtoajRZWkRdkqaSm5uL+fPnuwQ37IKCgnDHHXfgxRdfbPDXDQsLA2DLvnDu/FZWVuayXafT4fz5827PLy0tdexjz/CwZ3LYmUwmGAwGx35XQxQllJXpr/r5tSWXy6DTaVFWZoDVWrfvHtSweC0aj/3cWq0iLD6Kplurv3/b/7VYrfAU37dfH16rxsffi5ajqa+FTqetVbZIvQIcFRUVKC8vR4cOHRyPXbhwAe+99x5MJhNuu+02JCUl1eclrmkquYA7burusSbF3cPjoZILePiuJJTrjWilqVuqt1UUMW1sIl71dNf8riRH8UZPPBUfTE2IhEImQCaDy/IPq9V3VxCLRcKy9d9Do5Ljubm+77pZrKJLMVH7MTytQU9NiIRWo8CeQ+dw7HSxYxKmC1Y7Jl01J4Ap8bYij+eL9LghoS2S4yLxj/cP4P89NAjrNx9xLRgYH4kH70jCwtW7vY7XXuvDYhUx844+kMs8t3a1vw+NSoEObYIR3SkMT7/hvrwrNSESgiCg0mhCcbkRM8Yluo0rOS4SbVppseAl711UHp/S15adolV4DITUti6Dp0mqVnWljkq/nm1dgg/OY5w+LhFLXtsDAFAp5WjfOgSPrsrxeF5USrnbdbePWS7IbIVZb4nD/aN7QVOLrIqGvMN9ZaIr4kKxHgLgsswLqP9kvsos4ttDv+NI/mXMnpiKqSN7OQJfl0uNMFtFR8FdQ5UFIUFK/FZY6fOYFXpb4VKtWuF1sq6UCQgNUsFSZUaQvVaG03uQJNFr69ox1Rlm94/sDeCXazLDwNtypoZsaVubgqZBClmz1yVpKmq12ucSjtLSUqjV6gZ/XXu9jPz8fJfaGfn5+VAqlejSpYtjv71790KSJJe09dOnTyM+3lY8OigoCB06dHDU5HDeR5Ikt9ocdWWxNN2XedvEj5OHloDXovFIkuSxdbTzdtcHPDzmtB+vVdPhuW45Wtq1qFeAY+nSpfj111+xceNGALaAx913343z589DJpNhw4YNWL9+PQtqeaE3WyCKEganuBecFEUJerMVKzf8iMen9KvzsWWCgNc/PuSx3sWrHx7CvSN7+ny+c6p/ctyVyX7moGhMG3tl4l2h951NYDLbvsAbTVYoFTKftTFqpsunVHfGWL/ZtaVfcpxtgv5/r9om0M6dSzQquSPjo+Zd94nD4lFltmLtpsMY0Ls9HrozCcYqCy6VGDF9XCLMFhGVhisTy+NnihDdKczrRMZssWJp1gAYTVZYzFYEBys9tnZNjovEzDv6QAEJCgE+09+rLCJ+yi3E8TNF+PjrkxiTEYvRg10/G1qVHD2jvS9RsRd6Vcpl2L73DLLG9IYkSTCarKg0mNEqVI3Fr+7xWZfB2yRVEARHHZXJIxJgMJnx4Pg+MJqsLpPyJU5dcGQCfHaOkfko5WCyiHjt40NYOLUfIFnwr8+PY9SgGIiie9Bmxrg+EEURt/Xr0qCTa9tEV0DXtiH1amPqra1opcHsqLFRs4Cq/bNjb9lrqLJAo1b4LaSrUspcumx4nqz7rqGhUsh9ZnBkjemNRa98ixUPp8NiFa+LDAOgYVva+i9oen0lWQ4YMAAbNmzA4MGDkZqa6rLt4MGDePvtt106mDSULl26oFu3bti+fbujnSwAbNu2DWlpaY5uKBkZGXj11Vexd+9e3HjjjQBsgYujR49i2rRpjudlZGTgq6++woIFC6BUKh3H0ul0bu+LiIiIGla9vj3997//xd133+34efPmzbh48SLee+89dO/eHffddx9ee+01Bji8qNRb8PSb+zAmIxYROo3j8YvFBqzffARPzkhD61ZaBKvlkOqY9mO2eK93cTCvEILQy+fz27UOwqKp/RASpES7iCCUlBtRUmHCu1/kuky8Q4JU+GBnrsdJ0I59tjT2F+cPQXmlCWWVVR5rjqQmRGLmHUkQIOGlR29Chd7smCgvXfsdJgyLx9SRvVCpN0FfZXG7g26v/XHvyF64WKSHTBAwa3wyyvQmVJms0KgVACTsP3YBH399Ej2jIxASpMTCl23ZGU8/NAhrPjniNrGccUciWunUkCT38T40Pgm7D55DdIcwXCoxonPbYOQWFKNHt3Dcc0sc7ht15S68scoMtVxw3Hn1mf6ukCH/1xKMG2Irrucps8cqipgwNN5tXPag0va9ZzDzjj4QZMCk2xJw8mwJwnUaBKkVCNdpIJcJPusydGwT7LUThMFkcXyuyivN+Py7M5h0W4LX7ju2miAyyAS4dY65XGqETIDPWiGGKguMJitOnC3GsdNF+PlEIQ6fuuSSvRISpLzSflXeeIUX69PG1Oqh1oJ9QqzVyH12h1m3+bCjY9DxgmIkxrS2Zap4CRbag1yDUzrVK9BgESWfGWZWUUJJhQlllSaXOi3XcoaBXUO1tG3IbJBrwYIFC3DPPffgj3/8I5KSkhxdTU6fPo1Dhw6hdevWeOyxx+p8XIPBgF27dgEAzp07h4qKCmzfvh0A0L9/f0RERGDOnDl47LHH0LVrVwwYMADbtm3DoUOH8M477ziOk5qaivT0dCxevBgLFy6EWq3Giy++iISEBNx6662O/bKysvDpp5/i0UcfxaRJk3DixAlkZ2dj/vz5Lq1jiYiIqOHVK8BRXFyMdu3aOX7+z3/+gz/84Q9ISUkBAIwbNw6rV6+u1wCvZVqNwneRRrUCcyemQi6J8J3I7E5vNPvcXl5p8rqcIjXBdve/c9tg25d2UXRZd+48Znv3lK3f5rt1c3jwjiRYrCLkMgEalRxatQJL1+x11MZQyGVoFaKGRRRx/lIljp4pwo7vz+BPt/dEj24RkAnAzDuScLygGOs352DJAwM8LmWwj6dHVDhWbPgRALD8oRttbTt357sGJ6oLjV64pHdkGLy7/TimjuwJmdDLqSaGhPKKKmjVCsy8o4/tDrXBArVKbgv2lBnx9rbjjuPa61KEh2oQEqSCXCZAIReglAvQhWndMyG8pL8LkoRpYxJxJP8ybuzTEaPTY9xqU6x4OB1PZX+PO27ujvtG9oKhyoLg6la6oiRh/M2xEAQJ/ztfCYVcwPkiPdZuOoyVcwZj7t+/cVwzT1kQs8YnQS5JXidVhhop9T8eu4C8s8V4ImugW/2I1PhIjEqPwa+FlejYOghnL1a4Ha91mAbv7jju9rh9PPZMhbjOrRydVjz9zqx+7KY6da64WlfTxtRbIUl7W9GHxichqXsb791hqgvwJnQNx3Pv7EePqHBsyTmFZ2cPhvjpLx4zp3bsO4Pb+nWp1+TYKoqoMls9BqaqzFbHeuTrLcvAriFa2gqShIfGJ+GVD92XeT10DSzzqasuXbpgy5YtWLNmDXJycrBt2zYAQMeOHTF16lTMmDEDrVu7d9vy5/Lly3jkkUdcHrP/vGHDBgwYMACjRo2CwWDAunXrsHbtWkRHR2P16tVuGRerVq3CM888g6VLl8JisSA9PR1LliyBQnHl9yAqKgrZ2dlYsWIFZsyYgYiICMydOxcPPPBAncdOREREdVOvb6Y6nQ6XLl0CABiNRvz3v//Fgw8+6Ngul8thNHrugEG2Dh0pcZH42UvnhCC1Am1aaVFc7Hu9vScaP4XwLFYRYwbHQCaDxzRruT2F3c+6c6PJiu17z2Bkeoyja4rz3UylXADkAkKUKpgkuHX/sAcGkrq3wR96tEWPqHCEhaqx4bOjbjVCDp285LPGhb1WR3JcJCAAocFK3D+yF4TRAgxGW3DiUqkR//faHjw4Ptnxfu8b1QvZW45g3y9XXs9+HtbXeNzu6QdvdDsPzu9r9WM3uXTVqAu5JCGuSzhmP/+1x+16oy2r4d9f5OKT6myaHlHhjgloZCstzl824IV3/+vIcrF3V7GP1Z71MmFYHFQKWa3vQDtPZu3LmEoqTPi/1/a4LafRBaux5PU9eHxKXzzhKDIqQ6XBgmCtAl3bhUImivjjrT1QUmHynO4vCEhNiPTbPaUunSuamr9CklVm0XFtvBFFCc+9sx8JURE4XlBsy0JSyzF7QjIuFOld2kDv2HcG08Yk1ntyrFbIsW3PaUR3CkPrsCsZZoUlBvzwy3lkjel9XWYZNCRJEJC9+YjHDLjsLUcwowGuYyCxWCxQq9VYvHgxFi9e7La9oqICFovFJZhQG507d0Zubq7f/SZMmIAJEyb43Cc0NBTLly/H8uXLfe53ww03OJbvEhERUdOpV4AjNTUV7777LmJiYrB7925UVVVh2LBhju1nzpxxyfAgVyarBQ95KdI4664kSOLVf7GVCb4LXh4vKMaWnFNYNX9IrdbPC5KEmXck4TUPY709rRuee2c/np872OekvqzSvV6H0WRFbkExErqGQyET8FT2Prw4f4jHAqhbck5hweS+bkEZ564WjnoXMgFnzpdDIRcc79W55kSHNkFY/dhNjvc7Y0wipozoWX0elIgI08BSZca0MYkwWUS3yXe7iKBGTS33lYGjkF+ZDHvKZlg5Ox2bdp1ydMKxF/0sr6xyeZ79vH+6+1StJ8TOgS7njiaexrE0awB6dItA6zAtHv3jH6BWyKBVyhFif151BoBckjD3rmQYLSLKK02un8PqO9wXi713swFadhaBv0KSlXoTQrS+O/BYrCJ6Rke41RmRSxI6hGthCFFBb7QgI6UjtA1Ug0Qhl/lcoiKXCddEMdHmZDBbse/oBew76rng85QRPZskM6mlePrpp7F//35s3brV4/ZJkyZhwIABWLJkSROPjIiIiAJFvWYFjz32GB544AHMmTMHAHD//fcjLi4OAGC1WrF9+3YMHjy4/qO8RlVUWvD3d/dh4dR+UNRon/nEmu/QMTIEcyamXtVFkiBhzOAYCILnOg3PvbMfPaMjoJQJUAm1S7NWCXAriGqvh9EzOsJlUu+poGKQRomla/d67f7x1AxbW1NvE0J75sHKOYMx6RYLKgxmhGiVUChkuFRixONT+uJ4QTH+ufUXjEjrhs/2nPa6BCdErXBMoAHXdHOFQobQIBWKq8ze19qLYoMVGvTE14T9QF6h9+BKfCQO5BXiYF4h7h/dC2l9OuByqRHv7TiO+0b2xtMP3uhyt99ey8RkEb3W3XDmXGDRXszUWxCtqMyIh8YnQSkAoarqSZqX48sFoHPbUBQXV9qqMDvtp5AktG/kgFJj8ltIUqvED0fP+yzA61IXpUadkYZYKuGJyWJFWKjK4xKVsFAVTFYRWj+ZJ+RbrbqotNDMpMawe/dujBs3zuv22267DVu2bGm6AREREVHAqVeAIyoqCtu3b8epU6cQEhKCzp07O7YZDAY88cQT6NGjR70Hea0K0ihwociA74+cR25BsdtE8UKRAS9vPIC5dyXX+dgCBGzfewY9oyNwb2YvFJUZIQhwCUjUeSIuSUju3sbvpN5bQcWH70z22v0jNT4ScrkMM6q7mXhjNFlhFSVUGMwe63HY3Z7WzWt3l7qubfc2gWyoQoOeqFUKr8GDk/8rwZTbbV1wnK9DSnwkRg+Owcq3be1fz1/SY8WGH690aDFbsOT17zy+3oHcQhjM1lrdLba/b6PFiptv6Iy1mw+7fR4evMMW2IAkeewXX1eyRg4oNSZ/hSSD1AocP1OMUekxECXPn1eFj7oojaVCb8byf/6A2RNTEa7TONWnAf7v1T1YfF9/aK+jyXdjqE8XFW9deQLZxYsXfWZ9tm3bFhcueM52ISIiIgLqGeAAAKVS6TGIERIS4tJujdzZJz49osJ9FhisrLIiSFG3O6WCIOH2tG7YsjvfpU5DQtdwPDMrHUEaeXWdjbrxN6n3VVBx3ebDHiepyXG2YpT/95qt7evKOYN9ZgZcLrUFa3wxmUWseu8nR7aIRqWA0WRp8LXtjXH3XBIErN902GuA5p5bE/DxN3mYOS4Jv12qcMmEWfn2le4yNZfhVBoa7m6xIEnQym3BEK+fhwaeazVmQKkx+WsrKhNFTB+XiHWbXGsx2DsYycXm6SsepFGgpMKEp9/wHEhsycuCAsXVdlHx1ZXnav6utxStWrXC6dOnvW6330whIiIi8qbe31ArKirw7rvvYt++fbh8+TKeeuopJCUloaSkBJ988gmGDh2KqKiohhjrNcc+8fmfh+4SzvRGc53TlDUKOXbsO+MyYQKA3P8Vo+D3UswYk3jVk3Ffk3pfBRX3/XIBUzN74uE7k3HeqTAiAEToNFgwuS8EAdh/7DxmjEvE2k3urVunj0vEezuO4+5bfGcGqZQyl7oQS7NcO7C05LXt9nX5B09e8ricR6WU4f5MW5vfT7/N9zo5qrkMpz53i31prCUSzf1aDclfcEYhSZh5Rx/oqyzQGyxo00qBILXC1vq2mbCFaePzF/zyFLzz15WnNkvNWqrBgwfjvffew+jRo9Grl2sr819++QUbN27E7bff3kyjIyIiokBQrwDH+fPnMXnyZJw/fx5RUVHIz89HZaWt40erVq3w3nvv4dy5cywI5oNcktAuPMjnPkEa3wUIPbG3G33140Nu7VsbM6Xfb0FFgwWSBLelEvZuKj2iwtEjqjUulxgxe0IyDNUTviCtAiqFHKJkdUzuvU2+nDuqePrZPs6Wurbdfg69tRDu26MtgquDM3WZHHHC2rz8BWdkoogQpQwhyurtzRjcAK5u8k11V9fMJH9deWq71KwleuSRR7B7925MmDABQ4cORffu3QEAeXl5+PrrrxEREeHW7pWIiIjIWb0CHCtXrkRlZSU2bdqEiIgI3Hija+vM4cOH45tvvqnPS1wXtEqZz4lnsFoOyVr3yU5zpPTXJkvAUxDEeTK/aGo/rNjwI1bOTkebENWVCR8kQLhSYNHbchd7EVVPP9d2nM2pLpkWdbnGnLBSXQXqsqBAU5fMpGu5MGm7du3w0Ucf4e9//zu++uorfPnllwBsS15Hjx6N+fPnszMbERER+VSvWd6ePXtw7733onv37iguLnbb3qVLF/z+++/1eYnrgq+J59yJqZBLInx/pfV97KZM6a9VloAf9mUr/ib6niZfGpUCVWYLnpqRBpNFxKGTlxydQtzG0UInaXXNtKjLNeaEleoqUJcFXasaa6lZS9G2bVs8++yzkCQJRUVFAICIiAgI/govEREREaGeAQ6j0YiIiAiv2+3LVcg/TxPPYLUCbVppUVwcOOexNlkCvibw9uUktQ1CuE2+RBFauQzaEBWsgoC8s8VuwY2Wnq3Q2JkWnLASBa7rZamZIAho3bp1cw+DiIiIAky9AhyxsbH48ccfcc8993jcvnPnTrdCYeRdzYmnPEBvWPnLEnCewB87XeSovSFJQESYBmd+K8Wt/brUeyIfyNkKgTx2Imo8XGpGRERE5F29Ahz33nsvFi1ahISEBIwYMQIAIEkSCgoKsHr1avz88894+eWXG2SgFFj8ZQnIJQlzxifBJAFrPBRCTYxpmDt3gZytEMhjJ6LGwwAoERERkWf1CnCMHTsWv/32G/7xj39g1apVAIBp06ZBkiTIZDLMnz8fw4cPb4hx0jVIgi24cS22OyQiakwMgBIRERG5u+oAh8FgwJ/+9CdMmDABX375JXbs2IGCggKIooiuXbvi1ltvRZcuXRpyrHSNuZbbHRIREREREVHTuuoAh1arxa+//gpBENCxY0fcd999DTgsuh5cy+0OiYiIiIiIqGnV6/b44MGD8e233zbUWOg6c623OyQiIiIiIqKmU68Ax6xZs3DmzBksWLAA+/fvx4ULF1BSUuL2PyJP7O0OPXG0OyQiIiIiIiKqhXrdIh85ciQA4OTJk9i6davX/Y4dO1afl6FrFNsdEhERERERUUOpV4Dj4YcfhiAIDTUWug6x3SERERERXQvkcv/J8aIoQRT5PZeosdQrwDFnzpyGGgddx9jukIiIiIgCVWiQEqIoQafT+t3XKoooKdYzyEHUSFjFkYiIiIiI6Cpp1ArIZAL+/cVxXLhc6XW/thFB+OPtPSGTCQxwEDUSBjiIiIiIiIjq6WKRHucKK5p7GETXtXp1USEiIiIiIiIiagkY4CAiIiIiIiKigMcABxEREREREREFPAY4iIiIiIiIiCjgMcBBRERERERERAGPAQ4iIiIiIiIiCngMcBARERERERFRwGOAg4iIiIiIiIgCHgMcRERERERERBTwGOAgIiIiIiIiooCnaO4BEBERETWnKVOm4IcffvC47YUXXsDIkSO97rNt2zbExsY6fi4vL8czzzyDnTt3wmw2Y/DgwViyZAnatm3baOMnIiIiGwY4iIiI6Lr217/+FRUVFS6PvfXWW9ixYwfS0tIcj91www1YuHChy36dO3d2+XnevHk4efIkli1bBrVajVWrVmH69On46KOPoFDwaxcREVFj4n9piYiI6LrWvXt3t8ceffRRDBo0CBEREY7HdDodUlJSvB7nwIED+Pbbb5GdnY309HQAQHR0NDIzM7Fjxw5kZmY2+NiJiIjoCtbgICIiInLy008/4ddff8Xo0aPr9LycnBzodDoMGjTI8VhMTAx69uyJnJychh4mERER1cAABxEREZGTrVu3IigoCMOGDXN5/IcffkBKSgr69OmDyZMn48cff3TZnp+fj+joaAiC4PJ4TEwM8vPzG33cRERE1zsuUSEiIiKqZrFY8Pnnn2Po0KEICgpyPN6vXz+MHTsW3bp1w8WLF5GdnY37778fb7/9NlJTUwEAZWVlCA0NdTtmWFgYjhw5Uu+xKRSNf19KLpe5/EvNh9ei8djPqSAIbgFJZ27bBECA+/6O/QQPz/GwH6/p1ePvRcvRUq8FAxxERERE1fbs2YOioiKMGjXK5fG5c+e6/HzTTTdh1KhRePXVV7Fu3bpGH5dMJiA8PLjRX8dOp9M22WuRb7wWjUcul0GhkHvfLpO5/KuQe97XeT+fx6ueCPKa1h/PYcvR0q4FAxxERERE1bZu3YpWrVo5ioR6ExQUhCFDhuCLL75wPKbT6XD+/Hm3fUtLSxEWFlavcYmihLIyfb2OURtyuQw6nRZlZQZYrWKjvx55x2vReOzn1moVYbFYve5nFUWXfy1WKyD53s/n8aqvI6/p1ePvRcvR1NdCp9PWKluEAQ4iIiIiAEajETt37sSYMWOgVCrr/PyYmBjs3bsXkiS5pKmfPn0a8fHx9R6fxdJ0X+ZtEz9OHloCXovGI0kSJMlDxMJpu+sDHh5z3s/L9pr78ZrWH89hy9HSrkXLWjBDRERE1Ez+85//QK/X16p7il6vxzfffIM+ffo4HsvIyEBpaSn27t3reOz06dM4evQoMjIyGmXMREREdAUzOIiIiIgAfPrpp+jYsSP+8Ic/uDy+f/9+rF+/Hrfccgs6deqEixcv4s0330RhYSH+8Y9/OPZLTU1Feno6Fi9ejIULF0KtVuPFF19EQkICbr311qZ+O0RERNcdBjiIiIjouldaWordu3fj3nvvdeuCEBkZCbPZjBdffBElJSXQarVITU3Fk08+iaSkJJd9V61ahWeeeQZLly6FxWJBeno6lixZAoWCX7mIiIgaG/9rS0RERNc9X61co6KikJ2dXavjhIaGYvny5Vi+fHlDDo+IiIhqgTU4iIiIiIiIiCjgMcBBRERERERERAGPAQ4iIiIiIiIiCngMcBARERERERFRwGOAg4iIiIiIiIgCHgMcRERERERERBTwGOAgIiIiIiIiooDHAAcRERERERERBTwGOIiIiIiIiIgo4DHAQUREREREREQBjwEOIiIiIiIiIgp4DHAQERERERERUcBjgIOIiIiIiIiIAh4DHEREREREREQU8BjgICIiIiIiIqKAF9ABjj179uDRRx/F8OHDkZCQgKeeesrjfiaTCc8++ywGDRqElJQU3H///cjPz2/i0RIRERERERFRYwnoAMfu3btx/Phx9OvXDzqdzut+Tz/9ND744APMnz8fL7/8MkwmE+677z6Ul5c34WiJiIiIiIiIqLEEdIDj8ccfx2effYZnnnkGoaGhHvc5f/48PvzwQyxYsAB33XUXBg8ejFdeeQXl5eV47733mnjERERERERERNQYAjrAIZP5H/63334LURRx++23Ox5r1aoVBg0ahJycnMYcHhERERERERE1kYAOcNRGfn4+WrdujbCwMJfHY2NjWYeDiIiIiIiI6BqhaO4BNLaysjKPy1d0Oh1KS0vrfXyFovFiRHK5zOVfaho8702P57zp8Zw3D553IiIiosbTogIc5eXluHjxot/9unTpApVK1QQj8k0mExAeHtzor6PTaRv9Ncgdz3vT4zlvejznzYPnnYiIiKjhtagAx/bt27FkyRK/+23btg2xsbG1OqZOp0NFRYXb42VlZW7LVupKFCWUlenrdQxf5HIZdDotysoMsFrFRnsdcsXz3vR4zpsez3nzuNbOu06nZTYKERERtRgtKsAxYcIETJgwoUGPGRMTg0uXLqG0tNQloJGfn4+YmJh6H99iafwvqFar2CSvQ6543psez3nT4zlvHjzvRERERA3vmr/tkp6eDplMhh07djgeKy0txbfffouMjIxmHBkRERERERERNZQWlcFRV+fOncPhw4cBAAaDAf/73/+wfft2AHC0hW3fvj3uuusurFy5EjKZDO3atcOaNWsQGhqKe+65p9nGTkREREREREQNJ6ADHPv27cNf/vIXx8+7d+/G7t27AQC5ubmOx5csWYLg4GD8/e9/R2VlJW644Qa8+eabHrurEBEREREREVHgCegAx/jx4zF+/Hi/+6lUKixcuBALFy5sglERERERERERUVO75mtwEBEREREREdG1jwEOIiIiIiIiIgp4DHAQERERERERUcBjgIOIiIiuax9//DESEhLc/vf888+77PfBBx/gtttuQ58+fTBmzBh8/fXXbscqLy/H4sWL0b9/f6SmpmLu3Lm4ePFiU70VIiKi61pAFxklIiIiaijr16936bDWrl07x///7LPP8MQTT+DBBx/EwIEDsW3bNsyePRv/+te/kJKS4thv3rx5OHnyJJYtWwa1Wo1Vq1Zh+vTp+Oijj6BQ8GsXERFRY+J/aYmIiIgA9O7dGxERER63vfTSSxg5ciTmzZsHABg4cCBOnDiBV155BevWrQMAHDhwAN9++y2ys7ORnp4OAIiOjkZmZiZ27NiBzMzMJnkfRERE1ysuUSEiIiLy4ezZszhz5gxGjBjh8nhmZib27t0Lk8kEAMjJyYFOp8OgQYMc+8TExKBnz57Iyclp0jETERFdj5jBQURERARg1KhRKC4uRseOHTFx4kRMmzYNcrkc+fn5AGzZGM5iY2NhNptx9uxZxMbGIj8/H9HR0RAEwWW/mJgYxzHqQ6Fo/PtScrnM5V9qPrwWjcd+TgVBcPt9dea2TQAEuO/v2E/w8BwP+/GaXj3+XrQcLfVaMMBBRERE17XIyEjMmTMHycnJEAQB//nPf7Bq1SpcuHABS5cuRWlpKQBAp9O5PM/+s317WVmZSw0Pu7CwMBw5cqReY5TJBISHB9frGHWh02mb7LXIN16LxiOXy6BQyL1vl8lc/lXIPe/rvJ/P41VPBHlN64/nsOVoadeCAQ4iIiK6rg0ePBiDBw92/Jyeng61Wo233noLDz74YDOO7ApRlFBWpm/015HLZdDptCgrM8BqFRv99cg7XovGYz+3VqsIi8XqdT+rKLr8a7FaAcn3fj6PV30deU2vHn8vWo6mvhY6nbZW2SIMcBARERHVMGLECLzxxhs4duwYwsLCANhawEZGRjr2KSsrAwDHdp1Oh/Pnz7sdq7S01LFPfVgsTfdl3jbx4+ShJeC1aDySJEGSPEQsnLa7PuDhMef9vGyvuR+vaf3xHLYcLe1atKwFM0REREQtTExMDAC41dHIz8+HUqlEly5dHPudPn3abYJz+vRpxzGIiIio8TDAQURERFTDtm3bIJfL0atXL3Tp0gXdunXD9u3b3fZJS0uDSqUCAGRkZKC0tBR79+517HP69GkcPXoUGRkZTTp+IiKi6xGXqBAREdF1LSsrCwMGDEBCQgIA4KuvvsLGjRsxdepUx5KUOXPm4LHHHkPXrl0xYMAAbNu2DYcOHcI777zjOE5qairS09OxePFiLFy4EGq1Gi+++CISEhJw6623Nst7IyIiup4wwEFERETXtejoaHz00Uc4f/48RFFEt27dsHjxYkyZMsWxz6hRo2AwGLBu3TqsXbsW0dHRWL16NVJTU12OtWrVKjzzzDNYunQpLBYL0tPTsWTJEigU/MpFRETU2PhfWyIiIrquLVmypFb7TZgwARMmTPC5T2hoKJYvX47ly5c3xNCIiIioDliDg4iIiIiIiIgCHgMcRERERERERBTwGOAgIiIiIiIiooDHAAcRERERERERBTwGOIiIiIiIiIgo4DHAQUREREREREQBjwEOIiIiIiIiIgp4DHAQERERERERUcBjgIOIiIiIiIiIAh4DHEREREREREQU8BjgICIiIiIiIqKAxwAHEREREREREQU8BjiIiIiIiIiIKOAxwEFEREREREREAY8BDiIiIiIiIiIKeAxwEBEREREREVHAY4CDiIiIiIiIiAIeAxxEREREREREFPAY4CAiIiIiIiKigMcABxEREREREREFPEVzD4CIiIiIiKghyGQCZDLB5z5yOe/xEl2rGOAgIiIiIqKAJ5MJaBUeBLmMAQyi6xUDHEREREREFPBkMgFymQzvbj+Gi0V6r/sldIvAiBujIQi+Mz2IKPAwwEFERERERNeMi0V6nCus8Lo9MlzbhKMhoqbE/C36/+3dfVhUdd7H8Q8zgoIKaCrms9gtqyaCqxKLUnqbCWmUabr5vEmmaUp5rVimWeoq95oZZiJ4WfZkPpSloXZr5vPWrWXutmkmamIrPqAMCAbMzP2Hy6wTKAMyAwPv13V5yfzOb875nu/vMPzmO+ecAQAAAADA7VHgAAAAAAAAbo8CBwAAAAAAcHsUOAAAAAAAgNujwAEAAAAAANweBQ4AAAAAAOD2KHAAAAAAAAC3V6uyAwAAAKhMW7Zs0aeffqrvv/9eJpNJrVu31siRI/Xoo4/Kw8NDkjRy5Eh9/fXXxZ6bmpqqdu3a2R5nZ2frL3/5i7Zv366CggL16tVLM2fOVJMmTVy2PwAA1FQUOAAAQI321ltvqXnz5oqPj1eDBg20f/9+vfjiizp37pwmTZpk69e1a1dNnz7d7rktWrSwezx16lT99NNPeumll1S7dm299tprio2N1YYNG1SrFtMuAACcib+0AACgRnvzzTfVsGFD2+Pw8HBduXJFq1at0sSJE2UwXL+i19fXVyEhITddz7fffqu9e/dq5cqV6tmzpySpbdu2io6O1ueff67o6Gin7gcAADUd9+AAAAA12o3FjSIdOnRQTk6OcnNzHV7P7t275evrq4iICFtbYGCgOnTooN27d1dIrAAA4OY4gwMAAOA3Dh06pICAANWrV8/W9vXXXyskJERms1ldunTRlClT1L17d9vytLQ0tW3b1nbfjiKBgYFKS0u77Zhq1XL+51JGo8Huf1QexqLsinLl4eFR7PfwRrZlHnKsn61B8lDx/mVdH2NafvxeVB1VdSwocAAAANzg4MGDSk1NtbvfRvfu3RUTE6M2bdro/PnzWrlypcaOHat33nlHoaGhkiSTyaT69esXW5+fn5/+8Y9/3FZMBoOHGjSoe1vrKAtfX2+XbQu3xliUndFoUK1axpsv//dlZ0aD4/0kqZax5L4Or89YdLkbY3q7yGHVUdXGggIHAADAv507d05xcXEKCwvTqFGjbO3PPPOMXb/77rtPAwYM0LJly5ScnOz0uCwWq0wmxy+XKS+j0SBfX2+ZTHkymy1O3x5ujrEou6Kcmc0WFRaab9rPbLHY/ne0nyQVms2S9TbW9+9xZEzLj9+LqsPVY+Hr6+3Q2SIUOAAAAHT9DIzY2Fj5+/srMTHRdnPRkvj4+Ojee+/Vtm3bbG2+vr46d+5csb5ZWVny8/O77fgKC103mb/+BpE3D1UBY1F2VqtVVmsJlYgbll//QY71szWU3L+s62NMbx85rDqq2lhUrQtmAAAAKsG1a9c0fvx4ZWdnKyUlpcRLTUoTGBiokydPFnuDc/LkSQUGBlZUqAAA4CYocAAAgBqtsLBQU6dOVVpamlJSUhQQEFDqc3Jzc/Xll1+qc+fOtrbIyEhlZWXpwIEDtraTJ0/qn//8pyIjI50SOwAA+A8uUQEAADXanDlztHPnTsXHxysnJ0eHDx+2LevYsaOOHDmilJQU3X///WrevLnOnz+vVatW6cKFC1qyZImtb2hoqHr27Knnn39e06dPV+3atbV48WIFBQWpX79+lbBnAADULBQ4AABAjbZv3z5J0oIFC4ot27Fjhxo3bqyCggItXrxYV65ckbe3t0JDQzVnzhwFBwfb9X/ttdf0l7/8RbNmzVJhYaF69uypmTNnqlYtplwAADgbf20BAECN9sUXX5TaZ+XKlQ6tq379+po/f77mz59/u2EBAIAy4h4cAAAAAADA7VHgAAAAAAAAbo9LVAAAAADARYzG0j9jtlisslispfYDYI8CBwAAAAA4WX0fT1ksVvn6epfa12yx6MrlXIocQBlR4AAAAAAAJ6tTu5YMBg99sO2oMi5dvWm/Jg199Hj/DjIYPChwAGVEgQMAAAAAXOR8Zq7OXsip7DCAaombjAIAAAAAALdHgQMAAAAAALg9ty1wmM1mJScna/jw4QoLC1OPHj00cuRIHTx4sFjf/Px8LVy4UBEREQoJCdHYsWOVlpZWCVEDAAAAAABncNsCx7Vr17RixQp16tRJCxcu1F//+lf5+flp1KhROnDggF3fuXPnat26dYqLi1NiYqLy8/M1ZswYZWdnV1L0AAAAAACgIrntTUbr1Kmj7du3y8/Pz9YWERGhAQMG6O2331Z4eLgk6dy5c1q/fr1mz56twYMHS5I6d+6s3r17a82aNYqNja2U+AEAAAAAQMVx2zM4jEajXXGjqC0oKEjnz5+3te3du1cWi0X9+/e3tfn7+ysiIkK7d+92WbwAAAAAAMB53LbAUZLCwkJ99913CgwMtLWlpaXpjjvuKFYMadeuHffhAAAAAACgmnDbS1RKkpKSooyMDI0ZM8bWZjKZVL9+/WJ9fX19lZWVddvbrFXLeTUio9Fg9z9cg7y7Hjl3PXJeOcg7AACA81SpAkd2drbd5SU307JlS3l5edm17du3T4mJiZo4caLuvvtuZ4Vox2DwUIMGdZ2+HV9fb6dvA8WRd9cj565HzisHeQcAAKh4VarAsXXrVs2cObPUfqmpqWrXrp3t8ffff6/JkydrwIABmjRpkl1fX19f5eTkFFuHyWQqdtlKWVksVplMube1jlsxGg3y9fWWyZQns9nitO2Uh9kqXf3VrNxrBarr7SkfL6OMHpUdVcWoynmvrsi565HzylHd8u7r683ZKAAAoMqoUgWOIUOGaMiQIWV6zunTpxUbG6vQ0FDNnTu32PLAwEBdvHhRWVlZdgWNtLQ0u3t1lFdhofMnqGazxSXbcZTZw0PLNhzRtz9esLWFBjXWxEHBMlqtlRhZxapqea8JyLnrkfPKQd4BAAAqnlt/7HL+/Hn96U9/0p133qnXX39dnp6exfr07NlTBoNBn3/+ua0tKytLe/fuVWRkpCvDrRasJRQ3JOnbYxe07KMjsnpUk9M4AAAAAABupUqdwVEW165dU2xsrC5fvqwXXnhBx48fty3z8vJSx44dJUlNmzbV4MGDlZCQIIPBoICAACUlJal+/foaNmxYZYXvtvIKzMWKG0W+PXZBeQVm+TjxxqsAAAAAAJTEbQscFy9e1NGjRyVJEyZMsFvWvHlzffHFF7bHM2fOVN26dbVo0SJdvXpVXbt21apVq0r8dhXcWu61wlKX+9TzumUfAAAAAAAqmtsWOFq0aKFjx4451NfLy0vTp0/X9OnTnRxV9edT59aHTGnLAQAAAABwBq4lQJl4exoVGtS4xGWhQY3l7Wl0cUQAAAAAAFDgQBl5WK2aOCi4WJGj6FtUPKrRt6gAAAAAANwH1xOgzIxWqyYNClZegfn6PTfq1JK3p5HiBgAAAACg0lDgQLl4WK3yqWX4zw1FKW4AAAAAACoRl6gAAAAAAAC3R4EDAAAAAAC4PQocAAAAAADA7VHgAAAAAAAAbo8CBwAAAAAAcHsUOAAAAAAAgNujwAEAAFCBTpw4obFjxyokJEQRERFKSEhQfn5+ZYcFwM0YjQbVqnXrfwaDR2WHCVQptSo7AAAAgOoiKytLo0ePVps2bZSYmKiMjAwtWLBA165d06xZsyo7PABuoL6PpywWq3x9vUvta7ZYdOVyriwWqwsiA6o+ChwAAAAVZM2aNbp69aqWLl0qf39/SZLZbNacOXM0fvx4BQQEVG6AgBszGDxuecaC0Vg9Tk6vU7uWDAYPfbDtqDIuXb1pvyYNffR4/w4yGDwocAD/RoEDAACgguzevVvh4eG24oYkRUVFafbs2dq3b58GDRpUecEBbsxg8JB/Ax8ZDdWjiOGI85m5Onshp7LDANyKh9VqpdxXTlar1enVUqPRILPZ4tRtoDjy7nrk3PXIeeWoTnk3GDzk4cH13zcKDw/Xo48+qmnTptm19+rVSzExMcXaHXW7c46yDJPBYJDFUj2OUXfHWNgzGAzKvVYgy03evtQyGlTHq5ZycvNlvsXvi2ctg3zqeLp9P6PBQ/V8vBw6RqrLOz4Pj//8XlSXfXJXrh4LR+ccnMFxGzw8PGQ0On9iV11Ot3M35N31yLnrkfPKQd6rL5PJJF9f32Ltfn5+ysrKKvd6XTXnKGKoQZ+SV3WMhT2fOp6l9qnn4+XQuqpLv5p4jNTEfa6qqtpYVK1oAAAAAAAAyoECBwAAQAXx9fVVdnZ2sfasrCz5+flVQkQAANQcFDgAAAAqSGBgoNLS0uzasrOzdeHCBQUGBlZSVAAA1AwUOAAAACpIZGSk9u/fL5PJZGvbunWrDAaDIiIiKjEyAACqP75FBQAAoIJkZWXpwQcfVNu2bTV+/HhlZGRowYIFGjhwoGbNmlXZ4QEAUK1R4AAAAKhAJ06c0CuvvKJvv/1WdevWVUxMjOLi4uTl5dg3IgAAgPKhwAEAAAAAANwe9+AAAAAAAABujwIHAAAAAABwexQ4AAAAAACA26PAAQAAAAAA3B4FDgAAAAAA4PYocAAAAAAAALdHgaOKSUlJ0cMPP6xu3bopJCREAwcO1LvvvquyfpvvvHnzFBQUpJdfftlJkVYf5c15WlqaXn75ZUVHR6tLly7q06ePZs+erczMTBdF7r5u5zjPz8/XwoULFRERoZCQEI0dO1ZpaWkuiNq9mc1mJScna/jw4QoLC1OPHj00cuRIHTx40KHn//jjjxo/frzuuecedevWTcOHD9ff/vY3J0ft3m4355J0+PBhjRkzRqGhoeratasee+wx/fDDD06MGjVVWY5XXoedb9++fXruuefUt2/fW87nGAvnOnHihMaOHauQkBBFREQoISFB+fn5lR1WtXf69GnNmjVLMTEx6tixowYMGFBiv3Xr1umBBx5Q586d9dBDD2nnzp0ujrT627JliyZMmKDIyEiFhIQoJiZG69evLzZnr0pjQYGjisnOzlZ0dLT+53/+R8uWLdN9992nuXPnKikpyeF1HDt2TBs2bFC9evWcGGn1Ud6c79+/XwcPHtTQoUO1YsUKTZ48Wbt379bw4cP541eK2znO586dq3Xr1ikuLk6JiYnKz8/XmDFjlJ2d7YLI3de1a9e0YsUKderUSQsXLtRf//pX+fn5adSoUTpw4MAtn5uZmakxY8boypUrmjdvnl599VX5+PgoNjZWx44dc9EeuJ/bybkkHThwQCNHjlSbNm20dOlSLV68WL169VJeXp4LokdNU5bjlddh59uzZ4+OHj2q7t27y9fX96b9GAvnycrK0ujRo1VQUKDExETFxcVp7dq1WrBgQWWHVu0dP35cu3btUuvWrdWuXbsS+3z22Wd68cUXFRUVpeTkZIWEhGjSpEk6fPiwa4Ot5t566y15e3srPj5eb775piIjI/Xiiy/qjTfesPWpcmNhRZX37LPPWvv16+dw/+HDh1uXLFli7d27t3XOnDlOjKz6ciTnmZmZVovFYtd26NAha/v27a1bt251ZnjVkiM5/9e//mXt0KGDdc2aNba2y5cvW0NCQqwrVqxwdohurbCw0HrlypVibf3797eOHz/+ls/dvHmztX379tYzZ87Y2vLy8qydO3e2Ll261CnxVge3k/OCggJr7969rQkJCc4MEbBx9Hjlddg1zGaz7eebzecYC+davny5NSQkxHr58mVb25o1a6wdOnSwnjt3rvICqwFuPP6nT59uffDBB4v16devn/XZZ5+1axs6dKh13LhxTo+vJrl06VKxtpkzZ1q7du1qG6eqNhacweEGGjRooIKCAof6fvrpp0pPT1dsbKyTo6reHMl5gwYN5OHhYdfWsWNHSdL58+edFlt15UjO9+7dK4vFov79+9va/P39FRERod27dzs7RLdmNBrl5+dXrC0oKKjU47VoXOrXr29rq127tjw9Pct8+VxNcjs5379/v86ePatRo0Y5M0TAxtHjlddh1zAYSp+iMxbOtXv3boWHh8vf39/WFhUVJYvFon379lVeYDVAacf/mTNndOrUKUVFRdm1R0dH68CBA5xJXYEaNmxYrK1Dhw7KyclRbm5ulRwLChxVVGFhoXJycvTll19q48aNDk1yc3JylJCQoD//+c/y9vZ2QZTVS3ly/luHDh2SpJueTgd7Zc15Wlqa7rjjjmKT8Hbt2nHNcTkUFhbqu+++U2Bg4C379e7dW40aNdKCBQt0/vx5ZWZmatGiRfLw8FBMTIyLoq0eHM35d999J39/f/3973/XAw88oI4dO+qBBx7Qxo0bXRMooJKPV16Hqw7GwrnS0tKKvVb7+vqqcePG5LeSFeW/bdu2du3t2rVTQUGBzpw5Uxlh1RiHDh1SQECA6tWrVyXHopbLt4hSnT59Wv369bM9njBhgsaMGVPq85YuXarWrVsrOjraidFVT+XN+Y1+/fVXLVy4UB07dlR4eHgFR1j9lCfnJpPJ7iyCIr6+vsrKyqroEKu9lJQUZWRklJp3Pz8/vffeexo/frx69eol6fqnhMnJyWrZsqULIq0+HM35hQsXlJeXp+eff17PPPOM2rVrp82bN2v69Om64447bOMAOFNJxyuvw1UHY+FcJpOpxPuf+Pn5kd9KVpT/345P0WPGx3kOHjyo1NRUTZ8+XVLVHAsKHE6WnZ3t0OUKLVu2lJeXlyTpzjvv1Pr165Wbm6uDBw8qOTlZBoNBzzzzzE2ff/z4cb333ntau3ZthcXurlyV89+aPXu20tPTtWbNmmKXrlR3lZXzmq48eS+yb98+JSYmauLEibr77rtv+fxLly5p0qRJatWqlZ5//nkZjUatXbtWEyZM0HvvvVejzlhyVc6tVqt+/fVXTZs2TSNGjJAkhYeHKy0tTcuXL6fAAYe46nhF6W5nLACgKjh37pzi4uIUFhZWpS+hpcDhZFu3btXMmTNL7Zeammp7k+Dl5aXOnTtLksLCwlSvXj0tXLhQf/zjH9W4ceMSn79gwQL1799fzZs3l8lkkiRZLBYVFBTIZDKpXr16Dl3PWR24Kuc3Wrx4sTZt2qTly5erffv2t7cDbshVOff19VVOTk6xdpPJVOwU3ZqgPHmXpO+//16TJ0/WgAEDNGnSpFKfn5KSoqysLH300Ue2iXd4eLgefPBBLVu2TIsWLSr/TrgZV+W86JOPe+65x649PDxc7733XhmjRk3ljOOV1+HyKe9Y3Apj4Vy+vr4lfhtNVlYW+a1kRfnPzs62mzMWvQdifCqeyWRSbGys/P39lZiYaHtfWRXHggKHkw0ZMkRDhgy5rXV06tRJZrNZZ8+evekbv5MnT2rv3r369NNP7drXrl2rtWvXlukPprtzVc6LvPPOO0pKStKCBQtq7Keqrsp5YGCgLl68WGxyUdJ1sjVBefJ++vRpxcbGKjQ0VHPnznXoOT/99JMCAwPtPlUsuvngzz//XKbtuztX5fy//uu/brrs119/LdP2UXM543jldbh8KuLv5G8xFs4VGBhY7F4b2dnZunDhAvmtZEX5/+2xnpaWJk9PTy6frWDXrl3T+PHjlZ2drQ8//NDu0riqOBY14yN9N/fNN9/Iw8NDLVq0uGmfV199VatXr7b716hRI/Xt21erV69Ws2bNXBix+3Mk55K0efNmzZs3T88++6wefvhh1wRXTTmS8549e8pgMOjzzz+3tWVlZWnv3r2KjIx0RZhu7fz58/rTn/6kO++8U6+//ro8PT0del6zZs104sQJuzfWZrNZR48eVfPmzZ0VbrVQ3pz37NlTnp6e2r9/v137/v371alTJ2eECjh0vPI6XHUwFs4VGRmp/fv32z6Jlq6fiWMwGBQREVGJkaFly5Zq06aNtm7dateempqq8PBwLvOqQIWFhZo6darS0tKUkpKigIAAu+VVcSw4g6MKyc7OVmxsrB566CG1bt1ahYWF+uqrr7R69WoNHTpUjRo1svW9//771axZM7399tuSpJCQkGLrq127tgICAhQWFuaqXXA7t5Pzr7/+WvHx8brnnnvUo0cPHT582Na3adOmatq0qat3xy3cTs6bNm2qwYMHKyEhQQaDQQEBAUpKSlL9+vU1bNiwytolt3Dt2jXFxsbq8uXLeuGFF3T8+HHbMi8vL9tXHEvXv+744Ycf1vz58yVd/+Rx/fr1mjhxooYPHy6j0agPP/xQp0+fdviMhJrodnLeqFEjjRw5UkuWLJGHh4fatWunzz77TIcPH1ZKSorL9wXVn6PHK6/DrnH27Fn9/e9/lyTl5eXp559/tr2BKPpaWMbCuYYNG6Z33nlHTz/9tMaPH6+MjAwlJCRo2LBhxd7koWLl5eVp165dkq7/LuTk5NiO/x49eqhhw4aaPHmypk2bplatWiksLEypqak6cuSI3n333coMvdqZM2eOdu7cqfj4eOXk5Ni93+nYsaO8vLyq3FhQ4KhCateurbZt2+qtt95SRkaG6tSpo1atWmnOnDnFzg4wm82yWCyVE2g1cjs5/+qrr1RQUKADBw7owIEDdn0nTZqkyZMnu2IX3M7tHuczZ85U3bp1tWjRIl29elVdu3bVqlWrSryTPP7j4sWLOnr0qKTr31hzo+bNm+uLL76wPf5t3u+++26lpKRo2bJlmjFjhiwWi+666y6tWLFC3bt3d80OuKHbybkkPffcc/Lx8dHKlSuVmZmpdu3a6Y033lDPnj2dHzxqnLIcr7wOO99XX32lGTNm2B7v2bNHe/bskSQdO3bM1s5YOI+fn5/efvttvfLKK3r66adVt25dDR48WHFxcZUdWrV36dIlTZkyxa6t6PHq1asVFhamAQMGKC8vT8nJyVqxYoXatm2rpUuXKjQ0tDJCrrb27dsn6fr9Hn9rx44datGiRZUbCw+r1WqtlC0DAAAAAABUEO7BAQAAAAAA3B4FDgAAAAAA4PYocAAAAAAAALdHgQMAAAAAALg9ChwAAAAAAMDtUeAAAAAAAABujwIHAAAAAABwexQ4AEiSjhw5omHDhikkJERBQUH64YcflJiYqKCgoMoOTSNHjtTIkSNdvt0+ffooPj7e5dsFAACVw9G//R999JGCgoKUnp7utFjS09MVFBSkjz76yGnbAKqbWpUdAIDKV1BQoKlTp8rLy0szZsxQnTp11KxZsxL7Ll++XHfddZf69u1r1/7NN99o3759Gj16tHx9fV0RNgAAAADYcAYHAP388886e/asnnjiCQ0dOlQxMTHy8/PThAkTdOTIEbu+SUlJ2r59e7F1fPvtt1q6dKlMJpOrwgYAAKhQW7du1SuvvFLZYQAoJwocAJSZmSlJql+/vl17rVq1VLt27coICQAAoFS5ubkVuj4vLy95enpW6DoBuA4FDqCGi4+P14gRIyRJU6ZMUVBQkO1+F7+9B0dQUJByc3P18ccfKygoSEFBQYqPj1diYqISEhIkSf/93/9tW3bjdamffPKJBg0apODgYPXo0UNxcXH617/+VSyeDz/8UH379lVwcLAGDx6sgwcPOrQfAwYMKPE+HRaLRb169dIzzzxja1u5cqWGDRumsLAwBQcHa9CgQdq6dWup27jZPUludh3url279PjjjyskJEShoaF68skndfz4cYf2BwAA2Cv6O/zTTz/pueeeU/fu3fX4449LcmyecerUKU2ePFkRERHq3LmzIiMjFRcXp+zsbFufku7Bcfz4cY0aNUrBwcGKjIzUsmXLZLFYisUXFBSkxMTEYu2/XeeVK1e0cOFCDRw4UKGhoeratavGjRuno0eP3lZ+AHAPDqDGGzp0qAICArR8+XKNHDlSnTt3VqNGjUrsm5CQoJkzZyo4OFiPPfaYJKlVq1by9vbWqVOntHnzZs2YMUMNGjSQJDVs2FCS9Oabb2rJkiWKiorS4MGDlZmZqXfffVfDhw/Xxo0bbffsWLdunWbNmqXQ0FCNHj1aZ86c0YQJE+Tn56c777zzlvsRFRWlpUuX6sKFC2rcuLGt/dChQzp//ryio6NtbatXr1afPn00cOBAFRQU6LPPPtOUKVOUlJSk++67r9y5vNHGjRsVHx+vnj17atq0acrLy9MHH3ygxx9/XB9//LFatGhRIdsBAKCmmTJlilq3bq24uDhZrVaH5hn5+fl64oknlJ+frxEjRqhRo0bKyMjQl19+KZPJVOws1iIXLlzQqFGjZDab9eSTT8rb21tr1669rTNcz5w5o+3bt6t///5q0aKFLl68qA8//FAjRozQZ599poCAgHKvG6jpKHAANVxoaKjy8/O1fPlydevWTf37979p35iYGL300ktq2bKlYmJi7JZ17NhRmzdvVt++fe3evJ89e1aJiYmaOnWqnnrqKVt7v3799Mgjj+j999/XU089pYKCAi1evFgdOnTQ6tWr5eXlJUm666679OKLL5Za4IiOjtbrr7+ubdu22c5IkaTU1FT5+PjYFS62bdumOnXq2B4PHz5cgwYN0qpVqyqkwHH16lXNmzdPQ4YMsbuO95FHHlH//v2VlJTE9b0AAJTT7373Oy1atEjS9XnG/fffX+o848SJE0pPT9eSJUvs5jqTJk265baSk5OVmZmpdevWKTg4WNL1v+f9+vUrd/xBQUHatm2bDIb/nEwfExOjqKgorV+/Xk8//XS51w3UdFyiAsCp/vd//1cWi0VRUVHKzMy0/WvUqJFat26tr776SpL0j3/8Q5cuXdKwYcNsxQ3p+iTiZp+q3Kht27bq0KGDUlNTbW1ms1nbtm1Tnz597AoaN/6clZWl7Oxs/f73v9c///nPithl7d+/XyaTSQ8++KDdPhsMBnXp0sW2zwAAoOyGDRtm+9nReUa9evUkSXv37lVeXp7D29q1a5dCQkJsxQ3p+hmqAwcOLHf8Xl5etuKG2WzW5cuX5ePjo7Zt21bYXASoqTiDA4BTnTp1Slar9aafdNSqdf1l6JdffpEktW7d2m65p6enWrZs6dC2oqOj9eqrryojI0MBAQH6+uuvdenSJUVFRdn127lzp95880398MMPys/Pt7V7eHg4vF+3curUKUnS6NGjS1xeNMkCAABld+OZoo7OM1q2bKmxY8dq1apV2rRpk7p166Y+ffrooYceuuUHKb/88ou6dOlSrL1t27bljt9isWj16tV6//33lZ6eLrPZbFvm7+9f7vUCoMABwMksFos8PDyUnJwso9FYbLmPj0+FbSsqKkqLFi3Sli1bNGbMGG3ZskX169dXZGSkrc/Bgwc1YcIEde/eXbNnz1bjxo3l6empDRs2aPPmzbdc/80KIDdOTCTJarVKun7PkhvvB1KkpDwAAADH3Hj/i7LMM+Lj4/XII49ox44d2rdvn+bOnaukpCStXbtWTZs2dVq8v50nLF++XEuWLNGjjz6qKVOmyM/PTwaDQfPnz7fNIQCUDwUOABXiZm/+W7VqJavVqhYtWtzy045mzZpJkk6fPq3w8HBbe0FBgdLT0/W73/2u1Bhatmyp4OBgbdmyRSNGjNDnn3+uvn372l3ysm3bNtWuXVsrV660a9+wYUOp6y+6GarJZLL9LP3n7JMb45CkO+64Q3/4wx9KXS8AACgfR+cZRYq+6W3ixIn65ptv9Mc//lEffPCB4uLiSuzfrFkznT59ulj7yZMni7X5+fnJZDLZteXn5+vChQt2bdu2bVNYWJjmz59v124ymWw3agdQPtyDA0CZ+Pj4FPvjLUne3t6SZPdVa9L1m3wZjUYtXbq02KcSVqtVly9fliTdfffdatiwodasWWN32cjHH39c4vZuJjo6WocPH9aGDRt0+fLlYpenGI1GeXh42H2akp6erh07dpS67latWkmS/u///s/Wlpubq40bN9r169Wrl+rVq6ekpCQVFBQUW09mZqbD+wMAAG7O0XlGTk6OCgsL7Za3b99eBoPBbt7xW/fee68OHz6sI0eO2NoyMzO1adOmYn1btmxZ7Ovt165dW+wMDqPRWCzWLVu2KCMj4xZ7CsARnMEBoEw6deqkAwcOaNWqVWrSpIlatGihLl26qFOnTpKkxYsXKzo6Wp6enurdu7datWqlqVOnatGiRTp79qz69u2runXrKj09Xdu3b9djjz2mJ554Qp6enpo6dapmzZql0aNHKzo6Wunp6froo48cvgeHdP0ylYULF2rhwoXy9/cvdgbFvffeq1WrVmncuHEaMGCALl26pPfff1+tWrXSsWPHbrnuiIgINWvWTC+88ILS0tJkNBq1YcMGNWjQwO4sjnr16umll17Sn//8Zw0aNEjR0dFq2LChfvnlF+3atUtdu3bVrFmzypB1AABQEkfnGX/729/08ssvq3///mrTpo3MZrM++eQTGY1GPfDAAzdd/7hx4/TJJ59o3LhxGjVqlO1rYps1a1Zs3jBkyBDNnj1bkydP1h/+8AcdPXpUe/fuLXZWxn333ac33nhDM2bMUGhoqH788Udt2rSpTPMdACWjwAGgTOLj4zVr1iy99tprunbtmh555BF16dJFwcHBmjJlitasWaM9e/bIYrFox44d8vHx0ZNPPqk2bdrorbfe0htvvCFJatq0qSIiItSnTx/buocOHSqz2ayVK1cqISFB7du3t323vaOaNm2q0NBQffPNNxoyZIg8PT3tloeHh2vevHlKTk7W/Pnz1aJFC02bNk1nz54ttcDh6emppUuXas6cOVqyZIkaN26s0aNHy9fXVzNmzLDrO3DgQDVp0kQrVqzQypUrlZ+fr4CAAHXr1k2DBg1yeH8AAMCtOTLPCAoKUs+ePbVz505lZGTI29tbQUFBSk5OVkhIyE3X3aRJE61evVpz587VihUr5O/vr2HDhqlJkyZ64YUX7Po+9thjSk9P1/r167Vnzx79/ve/16pVqzRmzBi7fk899ZTy8vK0adMmpaamqmPHjkpKSrJ99S2A8vOwcicbAAAAAADg5rgHBwAAAAAAcHsUOAAAAAAAgNujwAEAAAAAANweBQ4AAAAAAOD2KHAAAAAAAAC3R4EDAAAAAAC4PQocAAAAAADA7VHgAAAAAAAAbo8CBwAAAAAAcHsUOAAAAAAAgNujwAEAAAAAANweBQ4AAAAAAOD2KHAAAAAAAAC39///dGxRrUe37wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1280x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot_residuals()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15462d91",
   "metadata": {},
   "source": [
    "## Saving the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2c0b7e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import netpixi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d382824e",
   "metadata": {},
   "outputs": [],
   "source": [
    "gt_save(g, 'networks/colabs.net.gz')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
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